From abcab17bc467110f34faddcf9587f11e1be99071 Mon Sep 17 00:00:00 2001 From: qiuxin2012 Date: Fri, 24 Mar 2017 11:27:29 +0800 Subject: [PATCH 0001/1065] move visualization to bigdl --- dl/pom.xml | 10 +- .../dllib}/src/main/java/netty/Crc32c.java | 0 .../org/tensorflow/framework/DataType.java | 0 .../tensorflow/framework/HistogramProto.java | 0 .../framework/HistogramProtoOrBuilder.java | 0 .../tensorflow/framework/ResourceHandle.java | 4 +- .../framework/ResourceHandleOrBuilder.java | 0 .../framework/ResourceHandleProto.java | 2 +- .../org/tensorflow/framework/Summary.java | 0 .../framework/SummaryDescription.java | 0 .../SummaryDescriptionOrBuilder.java | 0 .../framework/SummaryOrBuilder.java | 0 .../tensorflow/framework/SummaryProtos.java | 2 +- .../org/tensorflow/framework/TensorProto.java | 0 .../framework/TensorProtoOrBuilder.java | 0 .../tensorflow/framework/TensorProtos.java | 2 +- .../framework/TensorShapeProto.java | 0 .../framework/TensorShapeProtoOrBuilder.java | 0 .../framework/TensorShapeProtos.java | 2 +- .../org/tensorflow/framework/TypesProtos.java | 0 .../main/java/org/tensorflow/util/Event.java | 0 .../org/tensorflow/util/EventOrBuilder.java | 0 .../java/org/tensorflow/util/EventProtos.java | 0 .../java/org/tensorflow/util/LogMessage.java | 0 .../tensorflow/util/LogMessageOrBuilder.java | 0 .../java/org/tensorflow/util/SessionLog.java | 0 .../tensorflow/util/SessionLogOrBuilder.java | 0 .../tensorflow/util/TaggedRunMetadata.java | 0 .../util/TaggedRunMetadataOrBuilder.java | 0 .../bigdl/dllib/models/lenet/Train.scala | 2 +- .../bigdl/dllib/optim/DistriOptimizer.scala | 1 + .../bigdl/dllib/optim/Optimizer.scala | 1 + .../dllib/utils/python/api/PythonBigDL.scala | 1 + .../bigdl/dllib/visualization/Summary.scala | 182 ++++++++++++++++++ .../dllib/visualization/TrainSummary.scala | 96 +++++++++ .../visualization/ValidationSummary.scala | 51 +++++ .../tensorboard/EventWriter.scala | 1 + .../tensorboard/FileReader.scala | 0 .../tensorboard/FileWriter.scala | 4 +- .../tensorboard/RecordWriter.scala | 0 .../bigdl/dllib/python/api/PythonSpec.scala | 5 +- .../SummarySpec.scala | 8 +- 42 files changed, 355 insertions(+), 19 deletions(-) rename {visualization => scala/dllib}/src/main/java/netty/Crc32c.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/DataType.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/HistogramProto.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/HistogramProtoOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/ResourceHandle.java (99%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/ResourceHandleOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/ResourceHandleProto.java (97%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/Summary.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/SummaryDescription.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/SummaryDescriptionOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/SummaryOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/SummaryProtos.java (99%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/TensorProto.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/TensorProtoOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/TensorProtos.java (98%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/TensorShapeProto.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/TensorShapeProtoOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/TensorShapeProtos.java (97%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/framework/TypesProtos.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/Event.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/EventOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/EventProtos.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/LogMessage.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/LogMessageOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/SessionLog.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/SessionLogOrBuilder.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/TaggedRunMetadata.java (100%) rename {visualization => scala/dllib}/src/main/java/org/tensorflow/util/TaggedRunMetadataOrBuilder.java (100%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/Summary.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/TrainSummary.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/ValidationSummary.scala rename {visualization/src/main/scala/com/intel/analytics/bigdl => scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib}/visualization/tensorboard/EventWriter.scala (99%) rename {visualization/src/main/scala/com/intel/analytics/bigdl => scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib}/visualization/tensorboard/FileReader.scala (100%) rename {visualization/src/main/scala/com/intel/analytics/bigdl => scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib}/visualization/tensorboard/FileWriter.scala (95%) rename {visualization/src/main/scala/com/intel/analytics/bigdl => scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib}/visualization/tensorboard/RecordWriter.scala (100%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{utils => visualization}/SummarySpec.scala (96%) diff --git a/dl/pom.xml b/dl/pom.xml index 6f20fb20e02..d64e99f065a 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -29,6 +29,11 @@ imageio-jpeg 3.2.1 + + com.google.protobuf + protobuf-java + 3.0.0 + org.apache.hadoop hadoop-client @@ -61,11 +66,6 @@ - - com.intel.analytics.bigdl - visualization - 0.1.0-SNAPSHOT - org.apache.spark spark-core_${scala.major.version} diff --git a/visualization/src/main/java/netty/Crc32c.java b/scala/dllib/src/main/java/netty/Crc32c.java similarity index 100% rename from visualization/src/main/java/netty/Crc32c.java rename to scala/dllib/src/main/java/netty/Crc32c.java diff --git a/visualization/src/main/java/org/tensorflow/framework/DataType.java b/scala/dllib/src/main/java/org/tensorflow/framework/DataType.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/DataType.java rename to scala/dllib/src/main/java/org/tensorflow/framework/DataType.java diff --git a/visualization/src/main/java/org/tensorflow/framework/HistogramProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/HistogramProto.java rename to scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java diff --git a/visualization/src/main/java/org/tensorflow/framework/HistogramProtoOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProtoOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/HistogramProtoOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/framework/HistogramProtoOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/framework/ResourceHandle.java b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java similarity index 99% rename from visualization/src/main/java/org/tensorflow/framework/ResourceHandle.java rename to scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java index 1ffe9a26780..5871cc3adff 100644 --- a/visualization/src/main/java/org/tensorflow/framework/ResourceHandle.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java @@ -5,7 +5,7 @@ /** * 
- * Protocol buffer representing a handle to a org.tensorflow resource. Handles are
+ * Protocol buffer representing a handle to a org.org.tensorflow resource. Handles are
  * not valid across executions, but can be serialized back and forth from within
  * a single run.
  *
@@ -470,7 +470,7 @@ protected Builder newBuilderForType( } /** * 
-   * Protocol buffer representing a handle to a org.tensorflow resource. Handles are
+   * Protocol buffer representing a handle to a org.org.tensorflow resource. Handles are
    * not valid across executions, but can be serialized back and forth from within
    * a single run.
    *
diff --git a/visualization/src/main/java/org/tensorflow/framework/ResourceHandleOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/ResourceHandleOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/framework/ResourceHandleProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java similarity index 97% rename from visualization/src/main/java/org/tensorflow/framework/ResourceHandleProto.java rename to scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java index 951c16e2219..c1f944a4ca1 100644 --- a/visualization/src/main/java/org/tensorflow/framework/ResourceHandleProto.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java @@ -32,7 +32,7 @@ public static void registerAllExtensions( "\013tensorboard\"m\n\016ResourceHandle\022\016\n\006device" + "\030\001 \001(\t\022\021\n\tcontainer\030\002 \001(\t\022\014\n\004name\030\003 \001(\t\022" + "\021\n\thash_code\030\004 \001(\004\022\027\n\017maybe_type_name\030\005 " + - "\001(\tB4\n\030org.org.tensorflow.frameworkB\023Resourc" + + "\001(\tB4\n\030org.org.org.tensorflow.frameworkB\023Resourc" + "eHandleProtoP\001\370\001\001b\006proto3" }; com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = diff --git a/visualization/src/main/java/org/tensorflow/framework/Summary.java b/scala/dllib/src/main/java/org/tensorflow/framework/Summary.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/Summary.java rename to scala/dllib/src/main/java/org/tensorflow/framework/Summary.java diff --git a/visualization/src/main/java/org/tensorflow/framework/SummaryDescription.java b/scala/dllib/src/main/java/org/tensorflow/framework/SummaryDescription.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/SummaryDescription.java rename to scala/dllib/src/main/java/org/tensorflow/framework/SummaryDescription.java diff --git a/visualization/src/main/java/org/tensorflow/framework/SummaryDescriptionOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/SummaryDescriptionOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/SummaryDescriptionOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/framework/SummaryDescriptionOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/framework/SummaryOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/SummaryOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/SummaryOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/framework/SummaryOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/framework/SummaryProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/SummaryProtos.java similarity index 99% rename from visualization/src/main/java/org/tensorflow/framework/SummaryProtos.java rename to scala/dllib/src/main/java/org/tensorflow/framework/SummaryProtos.java index b07e59324e2..1cc71e23d99 100644 --- a/visualization/src/main/java/org/tensorflow/framework/SummaryProtos.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/SummaryProtos.java @@ -73,7 +73,7 @@ public static void registerAllExtensions( "histo\030\005 \001(\0132\033.tensorboard.HistogramProto" + "H\000\022+\n\005audio\030\006 \001(\0132\032.tensorboard.Summary." + "AudioH\000\022*\n\006tensor\030\010 \001(\0132\030.tensorboard.Te", - "nsorProtoH\000B\007\n\005valueB.\n\030org.org.tensorflow.f" + + "nsorProtoH\000B\007\n\005valueB.\n\030org.org.org.tensorflow.f" + "rameworkB\rSummaryProtosP\001\370\001\001b\006proto3" }; com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = diff --git a/visualization/src/main/java/org/tensorflow/framework/TensorProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/TensorProto.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/TensorProto.java rename to scala/dllib/src/main/java/org/tensorflow/framework/TensorProto.java diff --git a/visualization/src/main/java/org/tensorflow/framework/TensorProtoOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/TensorProtoOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/TensorProtoOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/framework/TensorProtoOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/framework/TensorProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/TensorProtos.java similarity index 98% rename from visualization/src/main/java/org/tensorflow/framework/TensorProtos.java rename to scala/dllib/src/main/java/org/tensorflow/framework/TensorProtos.java index 324b97662c6..e9dbb0c9931 100644 --- a/visualization/src/main/java/org/tensorflow/framework/TensorProtos.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/TensorProtos.java @@ -42,7 +42,7 @@ public static void registerAllExtensions( "\n\tint64_val\030\n \003(\003B\002\020\001\022\024\n\010bool_val\030\013 \003(\010B" + "\002\020\001\022\030\n\014dcomplex_val\030\014 \003(\001B\002\020\001\0228\n\023resourc" + "e_handle_val\030\016 \003(\0132\033.tensorboard.Resourc" + - "eHandleB-\n\030org.org.tensorflow.frameworkB\014Ten" + + "eHandleB-\n\030org.org.org.tensorflow.frameworkB\014Ten" + "sorProtosP\001\370\001\001b\006proto3" }; com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = diff --git a/visualization/src/main/java/org/tensorflow/framework/TensorShapeProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProto.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/TensorShapeProto.java rename to scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProto.java diff --git a/visualization/src/main/java/org/tensorflow/framework/TensorShapeProtoOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProtoOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/TensorShapeProtoOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProtoOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/framework/TensorShapeProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProtos.java similarity index 97% rename from visualization/src/main/java/org/tensorflow/framework/TensorShapeProtos.java rename to scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProtos.java index c66981d5179..afee070002e 100644 --- a/visualization/src/main/java/org/tensorflow/framework/TensorShapeProtos.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProtos.java @@ -37,7 +37,7 @@ public static void registerAllExtensions( "nsorboard\"{\n\020TensorShapeProto\022.\n\003dim\030\002 \003" + "(\0132!.tensorboard.TensorShapeProto.Dim\022\024\n" + "\014unknown_rank\030\003 \001(\010\032!\n\003Dim\022\014\n\004size\030\001 \001(\003" + - "\022\014\n\004name\030\002 \001(\tB2\n\030org.org.tensorflow.framewo" + + "\022\014\n\004name\030\002 \001(\tB2\n\030org.org.org.tensorflow.framewo" + "rkB\021TensorShapeProtosP\001\370\001\001b\006proto3" }; com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = diff --git a/visualization/src/main/java/org/tensorflow/framework/TypesProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/TypesProtos.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/framework/TypesProtos.java rename to scala/dllib/src/main/java/org/tensorflow/framework/TypesProtos.java diff --git a/visualization/src/main/java/org/tensorflow/util/Event.java b/scala/dllib/src/main/java/org/tensorflow/util/Event.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/Event.java rename to scala/dllib/src/main/java/org/tensorflow/util/Event.java diff --git a/visualization/src/main/java/org/tensorflow/util/EventOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/util/EventOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/EventOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/util/EventOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/util/EventProtos.java b/scala/dllib/src/main/java/org/tensorflow/util/EventProtos.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/EventProtos.java rename to scala/dllib/src/main/java/org/tensorflow/util/EventProtos.java diff --git a/visualization/src/main/java/org/tensorflow/util/LogMessage.java b/scala/dllib/src/main/java/org/tensorflow/util/LogMessage.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/LogMessage.java rename to scala/dllib/src/main/java/org/tensorflow/util/LogMessage.java diff --git a/visualization/src/main/java/org/tensorflow/util/LogMessageOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/util/LogMessageOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/LogMessageOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/util/LogMessageOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/util/SessionLog.java b/scala/dllib/src/main/java/org/tensorflow/util/SessionLog.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/SessionLog.java rename to scala/dllib/src/main/java/org/tensorflow/util/SessionLog.java diff --git a/visualization/src/main/java/org/tensorflow/util/SessionLogOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/util/SessionLogOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/SessionLogOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/util/SessionLogOrBuilder.java diff --git a/visualization/src/main/java/org/tensorflow/util/TaggedRunMetadata.java b/scala/dllib/src/main/java/org/tensorflow/util/TaggedRunMetadata.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/TaggedRunMetadata.java rename to scala/dllib/src/main/java/org/tensorflow/util/TaggedRunMetadata.java diff --git a/visualization/src/main/java/org/tensorflow/util/TaggedRunMetadataOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/util/TaggedRunMetadataOrBuilder.java similarity index 100% rename from visualization/src/main/java/org/tensorflow/util/TaggedRunMetadataOrBuilder.java rename to scala/dllib/src/main/java/org/tensorflow/util/TaggedRunMetadataOrBuilder.java diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala index 0fcc75be415..a0116335ade 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalize import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.optim._ -import com.intel.analytics.bigdl.utils.{Engine, T, TrainSummary, ValidationSummary, LoggerFilter} +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 3f56f9e0871..f8b994b541e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -28,6 +28,7 @@ import java.text.SimpleDateFormat import java.util.Calendar import org.apache.commons.lang.exception.ExceptionUtils +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.Logger import org.apache.spark.TaskContext import org.apache.spark.rdd.{RDD, ZippedPartitionsWithLocalityRDD} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index b8d9ace2ad7..6d55883c60e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{DataSet, _} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.spark.rdd.RDD import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index d773dfc465f..98a38612821 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -27,6 +27,7 @@ import com.intel.analytics.bigdl.optim.{Optimizer, _} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.visualization.{Summary, TrainSummary, ValidationSummary} import org.apache.spark.api.java.JavaRDD import org.apache.spark.rdd.RDD import java.lang.Integer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/Summary.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/Summary.scala new file mode 100644 index 00000000000..c603766fcaf --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/Summary.scala @@ -0,0 +1,182 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.visualization + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.visualization.tensorboard.{FileWriter} +import org.tensorflow + +import scala.reflect.ClassTag + +/** + * Logger for tensorboard. + * Support scalar and histogram now. + * @param logDir + * @param appName + */ +abstract class Summary( + logDir: String, + appName: String) { + protected val writer: FileWriter + + /** + * Add a scalar summary. + * @param tag tag name. + * @param value tag value. + * @param step current step. + * @return this + */ + def addScalar( + tag: String, + value: Float, + step: Long): this.type = { + writer.addSummary( + Summary.scalar(tag, value), step + ) + this + } + + /** + * Add a histogram summary. + * @param tag tag name. + * @param value a tensor. + * @param step current step. + * @return this + */ + def addHistogram[T: ClassTag]( + tag: String, + value: Tensor[T], + step: Long)(implicit ev: TensorNumeric[T]): this.type = { + writer.addSummary( + Summary.histogram[T](tag, value), step + ) + this + } + + /** + * Read scalar values to an array of triple by tag name. + * First element of the triple is step, second is value, third is wallclocktime. + * @param tag tag name. + * @return an array of triple. + */ + def readScalar(tag: String): Array[(Long, Float, Double)] +} + +object Summary { + + /** + * Create a scalar summary. + * @param tag tag name + * @param scalar scalar value + * @return + */ + def scalar(tag: String, scalar : Float): tensorflow.framework.Summary = { + val v = tensorflow.framework.Summary.Value.newBuilder().setTag(tag).setSimpleValue(scalar) + tensorflow.framework.Summary.newBuilder().addValue(v).build() + } + + private val limits = makeHistogramBuckets() + + /** + * Create a histogram summary. + * @param tag tag name. + * @param values values. + * @return + */ + def histogram[T: ClassTag]( + tag: String, + values: Tensor[T])(implicit ev: TensorNumeric[T]): tensorflow.framework.Summary = { + val counts = new Array[Int](limits.length) + + var squares = 0.0 + values.apply1{value => + val v = ev.toType[Double](value) + squares += v * v + val index = bisectLeft(limits, v) + counts(index) += 1 + value + } + + val histogram = tensorflow.framework.HistogramProto.newBuilder() + .setMin(ev.toType[Double](values.min())) + .setMax(ev.toType[Double](values.max())) + .setNum(values.nElement()) + .setSum(ev.toType[Double](values.sum())) + .setSumSquares(squares) + + var i = 0 + while (i < counts.length) { + if (counts(i) != 0) { + histogram.addBucket(counts(i)) + histogram.addBucketLimit(limits(i)) + } + i += 1 + } + val v = tensorflow.framework.Summary.Value.newBuilder().setTag(tag).setHisto(histogram) + tensorflow.framework.Summary.newBuilder().addValue(v).build() + } + + /** + * Find a bucket for x. + */ + private def bisectLeft( + a: Array[Double], + x: Double, + lo: Int = 0, + hi: Int = -1): Int = { + require(lo >= 0) + var high = if (hi == -1) { + a.length + } else { + hi + } + var low = lo + + while (low < high) { + val mid = (low + high) / 2 + if (a(mid) < x) { + low = mid + 1 + } else { + high = mid + } + } + low + } + + /** + * Create a histogram buckets. + * @return + */ + private def makeHistogramBuckets(): Array[Double] = { + var v = 1e-12 + val buckets = new Array[Double](1549) + var i = 1 + buckets(774) = 0.0 + while (i <= 774) { + buckets(774 + i) = v + buckets(774 - i) = -v + v *= 1.1 + i += 1 + } + buckets + } + +} + + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/TrainSummary.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/TrainSummary.scala new file mode 100644 index 00000000000..cd770e5e2c9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/TrainSummary.scala @@ -0,0 +1,96 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.visualization + +import com.intel.analytics.bigdl.optim.Trigger +import com.intel.analytics.bigdl.visualization.tensorboard.{FileReader, FileWriter} + +import scala.collection.mutable + +/** + * Train logger for tensorboard. + * Use optimize.setTrainSummary to enable train logger. Then the log will be saved to + * logDir/appName/train. + * + * @param logDir log dir. + * @param appName application Name. + */ +class TrainSummary( + logDir: String, + appName: String) extends Summary(logDir, appName) { + protected val folder = s"$logDir/$appName/train" + protected override val writer = new FileWriter(folder) + private val triggers: mutable.HashMap[String, Trigger] = mutable.HashMap( + "LearningRate" -> Trigger.severalIteration(1), + "Loss" -> Trigger.severalIteration(1), + "Throughput" -> Trigger.severalIteration(1)) + + /** + * Read scalar values to an array of triple by tag name. + * First element of the triple is step, second is value, third is wallClockTime. + * @param tag tag name. Supported tag names is "LearningRate", "Loss", "Throughput" + * @return an array of triple. + */ + override def readScalar(tag: String): Array[(Long, Float, Double)] = { + FileReader.readScalar(folder, tag) + } + + /** + * Supported tag name are LearningRate, Loss, Throughput, Parameters. + * Parameters contains weight, bias, gradWeight, gradBias, and some running status(eg. + * runningMean and runningVar in BatchNormalization). + * + * Notice: By default, we record LearningRate, Loss and Throughput each iteration, while + * recording parameters is disabled. The reason is getting parameters from workers is a + * heavy operation when the model is very big. + * + * @param tag tag name + * @param trigger trigger + * @return + */ + def setSummaryTrigger(tag: String, trigger: Trigger): this.type = { + require(tag.equals("LearningRate") || tag.equals("Loss") || + tag.equals("Throughput") | tag.equals("Parameters"), + s"TrainSummary: only support LearningRate, Loss, Parameters and Throughput") + triggers(tag) = trigger + this + } + + /** + * Get a trigger by tag name. + * @param tag + * @return + */ + def getSummaryTrigger(tag: String): Option[Trigger] = { + if (triggers.contains(tag)) { + Some(triggers(tag)) + } else { + None + } + } + + private[bigdl] def getScalarTriggers(): Iterator[(String, Trigger)] = { + triggers.filter(!_._1.equals("Parameters")).toIterator + } +} + +object TrainSummary{ + def apply(logDir: String, + appName: String): TrainSummary = { + new TrainSummary(logDir, appName) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/ValidationSummary.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/ValidationSummary.scala new file mode 100644 index 00000000000..95ab6d62aca --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/ValidationSummary.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.visualization + +import com.intel.analytics.bigdl.visualization.tensorboard.{FileReader, FileWriter} + +/** + * Validation logger for tensorboard. + * Use optimize.setValidation to enable validation logger. Then the log will be saved to + * logDir/appName/Validation. + * + * @param logDir + * @param appName + */ +class ValidationSummary( + logDir: String, + appName: String) extends Summary(logDir, appName) { + protected val folder = s"$logDir/$appName/validation" + protected override val writer = new FileWriter(folder) + + /** + * ReadScalar by tag name. Optional tag name is based on ValidationMethod, "Loss", + * "Top1Accuracy" or "Top5Accuracy". + * @param tag tag name. + * @return an array of triple. + */ + override def readScalar(tag: String): Array[(Long, Float, Double)] = { + FileReader.readScalar(folder, tag) + } +} + +object ValidationSummary{ + def apply(logDir: String, + appName: String): ValidationSummary = { + new ValidationSummary(logDir, appName) + } +} diff --git a/visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/EventWriter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/EventWriter.scala similarity index 99% rename from visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/EventWriter.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/EventWriter.scala index 9c886cff09a..24e5753ff07 100644 --- a/visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/EventWriter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/EventWriter.scala @@ -24,6 +24,7 @@ import org.tensorflow.util.Event /** * Event writer, write event protocol buffers to file. + * * @param logDir * @param flushMillis */ diff --git a/visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/FileReader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileReader.scala similarity index 100% rename from visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/FileReader.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileReader.scala diff --git a/visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/FileWriter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala similarity index 95% rename from visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/FileWriter.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala index e11fa3cf88b..ba043d22588 100644 --- a/visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/FileWriter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.visualization.tensorboard import java.util.concurrent.Executors -import org.tensorflow.framework.Summary +import org.tensorflow import org.tensorflow.util.Event /** @@ -43,7 +43,7 @@ class FileWriter(val logDirecotry : String, flushMillis: Int = 10000) { * @param globalStep a consistent global count of the event. * @return */ - def addSummary(summary: Summary, globalStep: Long): this.type = { + def addSummary(summary: tensorflow.framework.Summary, globalStep: Long): this.type = { val event = Event.newBuilder().setSummary(summary).build() addEvent(event, globalStep) this diff --git a/visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/RecordWriter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/RecordWriter.scala similarity index 100% rename from visualization/src/main/scala/com/intel/analytics/bigdl/visualization/tensorboard/RecordWriter.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/RecordWriter.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index 6d603c52204..849c8f62ff4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -21,9 +21,10 @@ import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, M import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.optim.{SGD} +import com.intel.analytics.bigdl.optim.SGD import com.intel.analytics.bigdl.optim.Trigger -import com.intel.analytics.bigdl.utils.{Engine, TrainSummary, ValidationSummary} +import com.intel.analytics.bigdl.utils.{Engine} +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext import org.apache.spark.api.java.JavaRDD diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SummarySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/visualization/SummarySpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SummarySpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/visualization/SummarySpec.scala index 30b0fa5d67d..b61b7a1223c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SummarySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/visualization/SummarySpec.scala @@ -14,13 +14,15 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.utils +package com.intel.analytics.bigdl.visualization import com.intel.analytics.bigdl.example.loadmodel.AlexNet import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.Summary._ +import com.intel.analytics.bigdl.utils.RandomGenerator +import Summary._ import com.intel.analytics.bigdl.visualization.tensorboard.{FileReader, FileWriter} import org.scalatest.{FlatSpec, Matchers} +import org.tensorflow.framework @com.intel.analytics.bigdl.tags.Parallel class SummarySpec extends FlatSpec with Matchers { @@ -48,7 +50,7 @@ class SummarySpec extends FlatSpec with Matchers { assert(values.getSimpleValue == 1.0) val byte_str = s.toByteArray - val s_recovered = org.tensorflow.framework.Summary.parseFrom(byte_str) + val s_recovered = framework.Summary.parseFrom(byte_str) assert(values.getTag == s_recovered.getValue(0).getTag()) assert(values.getSimpleValue == s_recovered.getValue(0).getSimpleValue) } From 80ae84d6c82870d0d231dda3bb180130076ca1a8 Mon Sep 17 00:00:00 2001 From: qiuxin2012 Date: Fri, 24 Mar 2017 13:15:21 +0800 Subject: [PATCH 0002/1065] update to head --- pom.xml | 1 - .../bigdl/dllib/models/lenet/Train.scala | 3 - .../analytics/bigdl/dllib/utils/Summary.scala | 293 ------------------ .../bigdl/dllib/visualization}/README.md | 0 .../bigdl/dllib/visualization/Summary.scala | 34 +- .../dllib/visualization/TrainSummary.scala | 1 - .../dllib/optim/DistriOptimizerSpec.scala | 4 +- visualization/pom.xml | 132 -------- 8 files changed, 21 insertions(+), 447 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Summary.scala rename {visualization => scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization}/README.md (100%) delete mode 100644 visualization/pom.xml diff --git a/pom.xml b/pom.xml index ed4f5d9687f..bac52f5a2f5 100644 --- a/pom.xml +++ b/pom.xml @@ -16,6 +16,5 @@ dl spark-version - visualization diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala index a0116335ade..347a3866b37 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala @@ -28,9 +28,6 @@ import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext -import scala.collection.mutable - - object Train { LoggerFilter.redirectSparkInfoLogs() Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Summary.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Summary.scala deleted file mode 100644 index 1ea629493eb..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Summary.scala +++ /dev/null @@ -1,293 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package com.intel.analytics.bigdl.utils - -import com.intel.analytics.bigdl.optim.Trigger -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.visualization.tensorboard.{FileReader, FileWriter} - -import scala.collection.mutable -import scala.reflect.ClassTag - -/** - * Logger for tensorboard. - * Support scalar and histogram now. - * @param logDir - * @param appName - */ -abstract class Summary( - logDir: String, - appName: String) { - protected val writer: FileWriter - - /** - * Add a scalar summary. - * @param tag tag name. - * @param value tag value. - * @param step current step. - * @return this - */ - def addScalar( - tag: String, - value: Float, - step: Long): this.type = { - writer.addSummary( - Summary.scalar(tag, value), step - ) - this - } - - /** - * Add a histogram summary. - * @param tag tag name. - * @param value a tensor. - * @param step current step. - * @return this - */ - def addHistogram[T: ClassTag]( - tag: String, - value: Tensor[T], - step: Long)(implicit ev: TensorNumeric[T]): this.type = { - writer.addSummary( - Summary.histogram[T](tag, value), step - ) - this - } - - /** - * Read scalar values to an array of triple by tag name. - * First element of the triple is step, second is value, third is wallclocktime. - * @param tag tag name. - * @return an array of triple. - */ - def readScalar(tag: String): Array[(Long, Float, Double)] - - /** - * Close this logger. - */ - def close(): Unit = { - writer.close() - } -} - -/** - * Train logger for tensorboard. - * Use optimize.setTrainSummary to enable train logger. Then the log will be saved to - * logDir/appName/train. - * @param logDir log dir. - * @param appName application Name. - */ -class TrainSummary( - logDir: String, - appName: String) extends Summary(logDir, appName) { - protected val folder = s"$logDir/$appName/train" - protected override val writer = new FileWriter(folder) - private val triggers: mutable.HashMap[String, Trigger] = mutable.HashMap( - "Loss" -> Trigger.severalIteration(1), - "Throughput" -> Trigger.severalIteration(1)) - - /** - * Read scalar values to an array of triple by tag name. - * First element of the triple is step, second is value, third is wallClockTime. - * @param tag tag name. Supported tag names is "LearningRate", "Loss", "Throughput" - * @return an array of triple. - */ - override def readScalar(tag: String): Array[(Long, Float, Double)] = { - FileReader.readScalar(folder, tag) - } - - /** - * Supported tag name are LearningRate, Loss, Throughput, Parameters. - * Parameters contains weight, bias, gradWeight, gradBias, and some running status(eg. - * runningMean and runningVar in BatchNormalization). - * - * Notice: By default, we record Loss and Throughput each iteration, while recording parameters - * and LearningRate is disabled. The reason is getting parameters from workers is a heavy - * operation when the model is very big. LearningRate is not a right option for all OptimMethod. - * - * @param tag tag name - * @param trigger trigger - * @return - */ - def setSummaryTrigger(tag: String, trigger: Trigger): this.type = { - require(tag.equals("LearningRate") || tag.equals("Loss") || - tag.equals("Throughput") | tag.equals("Parameters"), - s"TrainSummary: only support LearningRate, Loss, Parameters and Throughput") - triggers(tag) = trigger - this - } - - /** - * Get a trigger by tag name. - * @param tag - * @return - */ - def getSummaryTrigger(tag: String): Option[Trigger] = { - if (triggers.contains(tag)) { - Some(triggers(tag)) - } else { - None - } - } - - private[bigdl] def getScalarTriggers(): Iterator[(String, Trigger)] = { - triggers.filter(!_._1.equals("Parameters")).toIterator - } -} - -object TrainSummary{ - def apply(logDir: String, - appName: String): TrainSummary = { - new TrainSummary(logDir, appName) - } -} - -/** - * Validation logger for tensorboard. - * Use optimize.setValidation to enable validation logger. Then the log will be saved to - * logDir/appName/Validation. - * @param logDir - * @param appName - */ -class ValidationSummary( - logDir: String, - appName: String) extends Summary(logDir, appName) { - protected val folder = s"$logDir/$appName/validation" - protected override val writer = new FileWriter(folder) - - /** - * ReadScalar by tag name. Optional tag name is based on ValidationMethod, "Loss", - * "Top1Accuracy" or "Top5Accuracy". - * @param tag tag name. - * @return an array of triple. - */ - override def readScalar(tag: String): Array[(Long, Float, Double)] = { - FileReader.readScalar(folder, tag) - } -} - -object ValidationSummary{ - def apply(logDir: String, - appName: String): ValidationSummary = { - new ValidationSummary(logDir, appName) - } -} - -object Summary { - - /** - * Create a scalar summary. - * @param tag tag name - * @param scalar scalar value - * @return - */ - def scalar(tag: String, scalar : Float): org.tensorflow.framework.Summary = { - val v = org.tensorflow.framework.Summary.Value.newBuilder().setTag(tag).setSimpleValue(scalar) - org.tensorflow.framework.Summary.newBuilder().addValue(v).build() - } - - private val limits = makeHistogramBuckets() - - /** - * Create a histogram summary. - * @param tag tag name. - * @param values values. - * @return - */ - def histogram[T: ClassTag]( - tag: String, - values: Tensor[T])(implicit ev: TensorNumeric[T]): org.tensorflow.framework.Summary = { - val counts = new Array[Int](limits.length) - - var squares = 0.0 - values.apply1{value => - val v = ev.toType[Double](value) - squares += v * v - val index = bisectLeft(limits, v) - counts(index) += 1 - value - } - - val histogram = org.tensorflow.framework.HistogramProto.newBuilder() - .setMin(ev.toType[Double](values.min())) - .setMax(ev.toType[Double](values.max())) - .setNum(values.nElement()) - .setSum(ev.toType[Double](values.sum())) - .setSumSquares(squares) - - var i = 0 - while (i < counts.length) { - if (counts(i) != 0) { - histogram.addBucket(counts(i)) - histogram.addBucketLimit(limits(i)) - } - i += 1 - } - val v = org.tensorflow.framework.Summary.Value.newBuilder().setTag(tag).setHisto(histogram) - org.tensorflow.framework.Summary.newBuilder().addValue(v).build() - } - - /** - * Find a bucket for x. - */ - private def bisectLeft( - a: Array[Double], - x: Double, - lo: Int = 0, - hi: Int = -1): Int = { - require(lo >= 0) - var high = if (hi == -1) { - a.length - } else { - hi - } - var low = lo - - while (low < high) { - val mid = (low + high) / 2 - if (a(mid) < x) { - low = mid + 1 - } else { - high = mid - } - } - low - } - - /** - * Create a histogram buckets. - * @return - */ - private def makeHistogramBuckets(): Array[Double] = { - var v = 1e-12 - val buckets = new Array[Double](1549) - var i = 1 - buckets(774) = 0.0 - while (i <= 774) { - buckets(774 + i) = v - buckets(774 - i) = -v - v *= 1.1 - i += 1 - } - buckets - } - -} - - - diff --git a/visualization/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/README.md similarity index 100% rename from visualization/README.md rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/README.md diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/Summary.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/Summary.scala index c603766fcaf..b915e39bb70 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/Summary.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/Summary.scala @@ -30,8 +30,8 @@ import scala.reflect.ClassTag * @param appName */ abstract class Summary( - logDir: String, - appName: String) { + logDir: String, + appName: String) { protected val writer: FileWriter /** @@ -42,9 +42,9 @@ abstract class Summary( * @return this */ def addScalar( - tag: String, - value: Float, - step: Long): this.type = { + tag: String, + value: Float, + step: Long): this.type = { writer.addSummary( Summary.scalar(tag, value), step ) @@ -59,9 +59,9 @@ abstract class Summary( * @return this */ def addHistogram[T: ClassTag]( - tag: String, - value: Tensor[T], - step: Long)(implicit ev: TensorNumeric[T]): this.type = { + tag: String, + value: Tensor[T], + step: Long)(implicit ev: TensorNumeric[T]): this.type = { writer.addSummary( Summary.histogram[T](tag, value), step ) @@ -75,6 +75,13 @@ abstract class Summary( * @return an array of triple. */ def readScalar(tag: String): Array[(Long, Float, Double)] + + /** + * Close this logger. + */ + def close(): Unit = { + writer.close() + } } object Summary { @@ -135,10 +142,10 @@ object Summary { * Find a bucket for x. */ private def bisectLeft( - a: Array[Double], - x: Double, - lo: Int = 0, - hi: Int = -1): Int = { + a: Array[Double], + x: Double, + lo: Int = 0, + hi: Int = -1): Int = { require(lo >= 0) var high = if (hi == -1) { a.length @@ -177,6 +184,3 @@ object Summary { } } - - - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/TrainSummary.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/TrainSummary.scala index cd770e5e2c9..e107de6d79e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/TrainSummary.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/TrainSummary.scala @@ -35,7 +35,6 @@ class TrainSummary( protected val folder = s"$logDir/$appName/train" protected override val writer = new FileWriter(folder) private val triggers: mutable.HashMap[String, Trigger] = mutable.HashMap( - "LearningRate" -> Trigger.severalIteration(1), "Loss" -> Trigger.severalIteration(1), "Throughput" -> Trigger.severalIteration(1)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index 388087af09d..9f4228c02c6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -23,8 +23,8 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T, TrainSummary, ExceptionTest} -import org.apache.commons.io.FileUtils +import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T, ExceptionTest} +import com.intel.analytics.bigdl.visualization.TrainSummary import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext diff --git a/visualization/pom.xml b/visualization/pom.xml deleted file mode 100644 index 3cb54e55bac..00000000000 --- a/visualization/pom.xml +++ /dev/null @@ -1,132 +0,0 @@ - - - - spark_bigdl - com.intel.analytics.bigdl - 0.1.0-SNAPSHOT - - 4.0.0 - - visualization - jar - - - 1.1.1 - 1.1.1 - true - - - - - com.google.guava - guava - 11.0.2 - - - com.google.protobuf - protobuf-java - 3.0.0 - - - org.scalatest - scalatest_${scala.major.version} - compile - - - - - - - net.alchim31.maven - scala-maven-plugin - 3.2.1 - - - - compile - testCompile - - - - - - - org.apache.maven.plugins - maven-assembly-plugin - - - with-protobuf - - - jar-with-dependencies - - - package - - single - - - - - - - org.apache.maven.plugins - maven-source-plugin - 3.0.1 - - - attach-sources - verify - - jar-no-fork - - - - - - - org.scalastyle - scalastyle-maven-plugin - 0.8.0 - - false - true - true - false - ${basedir}/src/main/scala - ${basedir}/src/test/scala - ${project.parent.parent.basedir}/scalastyle_config.xml - ${project.build.directory}/stylecheck/scalastyle-output.xml - UTF-8 - - - - - check - - - - - - - - - - - - org.scoverage - scoverage-maven-plugin - ${scoverage.plugin.version} - - - - report - - - - - - - From eabf445399adbf362f89043ad311a8950e565ef0 Mon Sep 17 00:00:00 2001 From: qiuxin2012 Date: Fri, 24 Mar 2017 17:31:11 +0800 Subject: [PATCH 0003/1065] delete shade --- dl/pom.xml | 28 ---------------------------- 1 file changed, 28 deletions(-) diff --git a/dl/pom.xml b/dl/pom.xml index d64e99f065a..08e10f12b57 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -233,34 +233,6 @@ - - org.apache.maven.plugins - maven-shade-plugin - 3.0.0 - - false - - - com.google.protobuf:protubuf:3.0.0 - - - - - com.google.protobuf - com.intel.analytics.bigdl.shaded.protobuf - - - - - - package - - shade - - - - - From b646e267d5b9aef829adc440c994622792a59ea9 Mon Sep 17 00:00:00 2001 From: qiuxin2012 Date: Fri, 24 Mar 2017 17:50:19 +0800 Subject: [PATCH 0004/1065] update protobuf of tensorboard --- .../org/tensorflow/framework/DataType.java | 2 +- .../tensorflow/framework/HistogramProto.java | 76 +-- .../tensorflow/framework/ResourceHandle.java | 80 +-- .../framework/ResourceHandleProto.java | 2 +- .../org/tensorflow/framework/Summary.java | 620 +++++++++--------- .../framework/SummaryDescription.java | 76 +-- .../framework/SummaryOrBuilder.java | 8 +- .../tensorflow/framework/SummaryProtos.java | 6 +- .../org/tensorflow/framework/TensorProto.java | 190 +++--- .../framework/TensorProtoOrBuilder.java | 14 +- .../tensorflow/framework/TensorProtos.java | 14 +- .../framework/TensorShapeProto.java | 210 +++--- .../framework/TensorShapeProtoOrBuilder.java | 8 +- .../framework/TensorShapeProtos.java | 2 +- .../main/java/org/tensorflow/util/Event.java | 319 +++++---- .../org/tensorflow/util/EventOrBuilder.java | 21 +- .../java/org/tensorflow/util/EventProtos.java | 6 +- .../java/org/tensorflow/util/LogMessage.java | 96 +-- .../tensorflow/util/LogMessageOrBuilder.java | 2 +- .../java/org/tensorflow/util/SessionLog.java | 96 +-- .../tensorflow/util/SessionLogOrBuilder.java | 2 +- .../tensorflow/util/TaggedRunMetadata.java | 76 +-- 22 files changed, 959 insertions(+), 967 deletions(-) diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DataType.java b/scala/dllib/src/main/java/org/tensorflow/framework/DataType.java index f0565665f53..c0eab34b472 100644 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DataType.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/DataType.java @@ -524,7 +524,7 @@ public DataType findValueByNumber(int number) { } public static final com.google.protobuf.Descriptors.EnumDescriptor getDescriptor() { - return TypesProtos.getDescriptor() + return org.tensorflow.framework.TypesProtos.getDescriptor() .getEnumTypes().get(0); } diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java index 81ae5ff7303..8eab35b9f78 100644 --- a/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java @@ -140,14 +140,14 @@ private HistogramProto( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return SummaryProtos.internal_static_tensorboard_HistogramProto_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_HistogramProto_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return SummaryProtos.internal_static_tensorboard_HistogramProto_fieldAccessorTable + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_HistogramProto_fieldAccessorTable .ensureFieldAccessorsInitialized( - HistogramProto.class, HistogramProto.Builder.class); + org.tensorflow.framework.HistogramProto.class, org.tensorflow.framework.HistogramProto.Builder.class); } private int bitField0_; @@ -367,10 +367,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof HistogramProto)) { + if (!(obj instanceof org.tensorflow.framework.HistogramProto)) { return super.equals(obj); } - HistogramProto other = (HistogramProto) obj; + org.tensorflow.framework.HistogramProto other = (org.tensorflow.framework.HistogramProto) obj; boolean result = true; result = result && ( @@ -435,58 +435,58 @@ public int hashCode() { return hash; } - public static HistogramProto parseFrom( + public static org.tensorflow.framework.HistogramProto parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static HistogramProto parseFrom( + public static org.tensorflow.framework.HistogramProto parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static HistogramProto parseFrom(byte[] data) + public static org.tensorflow.framework.HistogramProto parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static HistogramProto parseFrom( + public static org.tensorflow.framework.HistogramProto parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static HistogramProto parseFrom(java.io.InputStream input) + public static org.tensorflow.framework.HistogramProto parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static HistogramProto parseFrom( + public static org.tensorflow.framework.HistogramProto parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static HistogramProto parseDelimitedFrom(java.io.InputStream input) + public static org.tensorflow.framework.HistogramProto parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static HistogramProto parseDelimitedFrom( + public static org.tensorflow.framework.HistogramProto parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static HistogramProto parseFrom( + public static org.tensorflow.framework.HistogramProto parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static HistogramProto parseFrom( + public static org.tensorflow.framework.HistogramProto parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -498,7 +498,7 @@ public static HistogramProto parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(HistogramProto prototype) { + public static Builder newBuilder(org.tensorflow.framework.HistogramProto prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -523,20 +523,20 @@ protected Builder newBuilderForType( public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorboard.HistogramProto) - HistogramProtoOrBuilder { + org.tensorflow.framework.HistogramProtoOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return SummaryProtos.internal_static_tensorboard_HistogramProto_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_HistogramProto_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return SummaryProtos.internal_static_tensorboard_HistogramProto_fieldAccessorTable + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_HistogramProto_fieldAccessorTable .ensureFieldAccessorsInitialized( - HistogramProto.class, HistogramProto.Builder.class); + org.tensorflow.framework.HistogramProto.class, org.tensorflow.framework.HistogramProto.Builder.class); } - // Construct using HistogramProto.newBuilder() + // Construct using org.tensorflow.framework.HistogramProto.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -572,23 +572,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return SummaryProtos.internal_static_tensorboard_HistogramProto_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_HistogramProto_descriptor; } - public HistogramProto getDefaultInstanceForType() { - return HistogramProto.getDefaultInstance(); + public org.tensorflow.framework.HistogramProto getDefaultInstanceForType() { + return org.tensorflow.framework.HistogramProto.getDefaultInstance(); } - public HistogramProto build() { - HistogramProto result = buildPartial(); + public org.tensorflow.framework.HistogramProto build() { + org.tensorflow.framework.HistogramProto result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public HistogramProto buildPartial() { - HistogramProto result = new HistogramProto(this); + public org.tensorflow.framework.HistogramProto buildPartial() { + org.tensorflow.framework.HistogramProto result = new org.tensorflow.framework.HistogramProto(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.min_ = min_; @@ -638,16 +638,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof HistogramProto) { - return mergeFrom((HistogramProto)other); + if (other instanceof org.tensorflow.framework.HistogramProto) { + return mergeFrom((org.tensorflow.framework.HistogramProto)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(HistogramProto other) { - if (other == HistogramProto.getDefaultInstance()) return this; + public Builder mergeFrom(org.tensorflow.framework.HistogramProto other) { + if (other == org.tensorflow.framework.HistogramProto.getDefaultInstance()) return this; if (other.getMin() != 0D) { setMin(other.getMin()); } @@ -695,11 +695,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - HistogramProto parsedMessage = null; + org.tensorflow.framework.HistogramProto parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (HistogramProto) e.getUnfinishedMessage(); + parsedMessage = (org.tensorflow.framework.HistogramProto) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -1042,12 +1042,12 @@ public final Builder mergeUnknownFields( } // @@protoc_insertion_point(class_scope:tensorboard.HistogramProto) - private static final HistogramProto DEFAULT_INSTANCE; + private static final org.tensorflow.framework.HistogramProto DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new HistogramProto(); + DEFAULT_INSTANCE = new org.tensorflow.framework.HistogramProto(); } - public static HistogramProto getDefaultInstance() { + public static org.tensorflow.framework.HistogramProto getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -1070,7 +1070,7 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public HistogramProto getDefaultInstanceForType() { + public org.tensorflow.framework.HistogramProto getDefaultInstanceForType() { return DEFAULT_INSTANCE; } diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java index 5871cc3adff..c2b9837ebb9 100644 --- a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java @@ -5,7 +5,7 @@ /** * 
- * Protocol buffer representing a handle to a org.org.tensorflow resource. Handles are
+ * Protocol buffer representing a handle to a tensorflow resource. Handles are
  * not valid across executions, but can be serialized back and forth from within
  * a single run.
  *
@@ -95,14 +95,14 @@ private ResourceHandle( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return ResourceHandleProto.internal_static_tensorboard_ResourceHandle_descriptor; + return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorboard_ResourceHandle_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return ResourceHandleProto.internal_static_tensorboard_ResourceHandle_fieldAccessorTable + return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorboard_ResourceHandle_fieldAccessorTable .ensureFieldAccessorsInitialized( - ResourceHandle.class, ResourceHandle.Builder.class); + org.tensorflow.framework.ResourceHandle.class, org.tensorflow.framework.ResourceHandle.Builder.class); } public static final int DEVICE_FIELD_NUMBER = 1; @@ -349,10 +349,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof ResourceHandle)) { + if (!(obj instanceof org.tensorflow.framework.ResourceHandle)) { return super.equals(obj); } - ResourceHandle other = (ResourceHandle) obj; + org.tensorflow.framework.ResourceHandle other = (org.tensorflow.framework.ResourceHandle) obj; boolean result = true; result = result && getDevice() @@ -391,58 +391,58 @@ public int hashCode() { return hash; } - public static ResourceHandle parseFrom( + public static org.tensorflow.framework.ResourceHandle parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static ResourceHandle parseFrom( + public static org.tensorflow.framework.ResourceHandle parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static ResourceHandle parseFrom(byte[] data) + public static org.tensorflow.framework.ResourceHandle parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static ResourceHandle parseFrom( + public static org.tensorflow.framework.ResourceHandle parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static ResourceHandle parseFrom(java.io.InputStream input) + public static org.tensorflow.framework.ResourceHandle parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static ResourceHandle parseFrom( + public static org.tensorflow.framework.ResourceHandle parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static ResourceHandle parseDelimitedFrom(java.io.InputStream input) + public static org.tensorflow.framework.ResourceHandle parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static ResourceHandle parseDelimitedFrom( + public static org.tensorflow.framework.ResourceHandle parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static ResourceHandle parseFrom( + public static org.tensorflow.framework.ResourceHandle parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static ResourceHandle parseFrom( + public static org.tensorflow.framework.ResourceHandle parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -454,7 +454,7 @@ public static ResourceHandle parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(ResourceHandle prototype) { + public static Builder newBuilder(org.tensorflow.framework.ResourceHandle prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -470,7 +470,7 @@ protected Builder newBuilderForType( } /** * 
-   * Protocol buffer representing a handle to a org.org.tensorflow resource. Handles are
+   * Protocol buffer representing a handle to a tensorflow resource. Handles are
    * not valid across executions, but can be serialized back and forth from within
    * a single run.
    *
@@ -480,20 +480,20 @@ protected Builder newBuilderForType( public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorboard.ResourceHandle) - ResourceHandleOrBuilder { + org.tensorflow.framework.ResourceHandleOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return ResourceHandleProto.internal_static_tensorboard_ResourceHandle_descriptor; + return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorboard_ResourceHandle_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return ResourceHandleProto.internal_static_tensorboard_ResourceHandle_fieldAccessorTable + return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorboard_ResourceHandle_fieldAccessorTable .ensureFieldAccessorsInitialized( - ResourceHandle.class, ResourceHandle.Builder.class); + org.tensorflow.framework.ResourceHandle.class, org.tensorflow.framework.ResourceHandle.Builder.class); } - // Construct using ResourceHandle.newBuilder() + // Construct using org.tensorflow.framework.ResourceHandle.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -525,23 +525,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return ResourceHandleProto.internal_static_tensorboard_ResourceHandle_descriptor; + return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorboard_ResourceHandle_descriptor; } - public ResourceHandle getDefaultInstanceForType() { - return ResourceHandle.getDefaultInstance(); + public org.tensorflow.framework.ResourceHandle getDefaultInstanceForType() { + return org.tensorflow.framework.ResourceHandle.getDefaultInstance(); } - public ResourceHandle build() { - ResourceHandle result = buildPartial(); + public org.tensorflow.framework.ResourceHandle build() { + org.tensorflow.framework.ResourceHandle result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public ResourceHandle buildPartial() { - ResourceHandle result = new ResourceHandle(this); + public org.tensorflow.framework.ResourceHandle buildPartial() { + org.tensorflow.framework.ResourceHandle result = new org.tensorflow.framework.ResourceHandle(this); result.device_ = device_; result.container_ = container_; result.name_ = name_; @@ -578,16 +578,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof ResourceHandle) { - return mergeFrom((ResourceHandle)other); + if (other instanceof org.tensorflow.framework.ResourceHandle) { + return mergeFrom((org.tensorflow.framework.ResourceHandle)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(ResourceHandle other) { - if (other == ResourceHandle.getDefaultInstance()) return this; + public Builder mergeFrom(org.tensorflow.framework.ResourceHandle other) { + if (other == org.tensorflow.framework.ResourceHandle.getDefaultInstance()) return this; if (!other.getDevice().isEmpty()) { device_ = other.device_; onChanged(); @@ -619,11 +619,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - ResourceHandle parsedMessage = null; + org.tensorflow.framework.ResourceHandle parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (ResourceHandle) e.getUnfinishedMessage(); + parsedMessage = (org.tensorflow.framework.ResourceHandle) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -1049,12 +1049,12 @@ public final Builder mergeUnknownFields( } // @@protoc_insertion_point(class_scope:tensorboard.ResourceHandle) - private static final ResourceHandle DEFAULT_INSTANCE; + private static final org.tensorflow.framework.ResourceHandle DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new ResourceHandle(); + DEFAULT_INSTANCE = new org.tensorflow.framework.ResourceHandle(); } - public static ResourceHandle getDefaultInstance() { + public static org.tensorflow.framework.ResourceHandle getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -1077,7 +1077,7 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public ResourceHandle getDefaultInstanceForType() { + public org.tensorflow.framework.ResourceHandle getDefaultInstanceForType() { return DEFAULT_INSTANCE; } diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java index c1f944a4ca1..39daf442921 100644 --- a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java @@ -32,7 +32,7 @@ public static void registerAllExtensions( "\013tensorboard\"m\n\016ResourceHandle\022\016\n\006device" + "\030\001 \001(\t\022\021\n\tcontainer\030\002 \001(\t\022\014\n\004name\030\003 \001(\t\022" + "\021\n\thash_code\030\004 \001(\004\022\027\n\017maybe_type_name\030\005 " + - "\001(\tB4\n\030org.org.org.tensorflow.frameworkB\023Resourc" + + "\001(\tB4\n\030org.tensorflow.frameworkB\023Resourc" + "eHandleProtoP\001\370\001\001b\006proto3" }; com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/Summary.java b/scala/dllib/src/main/java/org/tensorflow/framework/Summary.java index 49b2ced4c73..66bb591030f 100644 --- a/scala/dllib/src/main/java/org/tensorflow/framework/Summary.java +++ b/scala/dllib/src/main/java/org/tensorflow/framework/Summary.java @@ -53,11 +53,11 @@ private Summary( } case 10: { if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = new java.util.ArrayList(); + value_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000001; } value_.add( - input.readMessage(Summary.Value.parser(), extensionRegistry)); + input.readMessage(org.tensorflow.framework.Summary.Value.parser(), extensionRegistry)); break; } } @@ -76,14 +76,14 @@ private Summary( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return SummaryProtos.internal_static_tensorboard_Summary_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return SummaryProtos.internal_static_tensorboard_Summary_fieldAccessorTable + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_fieldAccessorTable .ensureFieldAccessorsInitialized( - Summary.class, Summary.Builder.class); + org.tensorflow.framework.Summary.class, org.tensorflow.framework.Summary.Builder.class); } public interface ImageOrBuilder extends @@ -205,14 +205,14 @@ private Image( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return SummaryProtos.internal_static_tensorboard_Summary_Image_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Image_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return SummaryProtos.internal_static_tensorboard_Summary_Image_fieldAccessorTable + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Image_fieldAccessorTable .ensureFieldAccessorsInitialized( - Summary.Image.class, Summary.Image.Builder.class); + org.tensorflow.framework.Summary.Image.class, org.tensorflow.framework.Summary.Image.Builder.class); } public static final int HEIGHT_FIELD_NUMBER = 1; @@ -327,10 +327,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof Summary.Image)) { + if (!(obj instanceof org.tensorflow.framework.Summary.Image)) { return super.equals(obj); } - Summary.Image other = (Summary.Image) obj; + org.tensorflow.framework.Summary.Image other = (org.tensorflow.framework.Summary.Image) obj; boolean result = true; result = result && (getHeight() @@ -364,58 +364,58 @@ public int hashCode() { return hash; } - public static Summary.Image parseFrom( + public static org.tensorflow.framework.Summary.Image parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static Summary.Image parseFrom( + public static org.tensorflow.framework.Summary.Image parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static Summary.Image parseFrom(byte[] data) + public static org.tensorflow.framework.Summary.Image parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static Summary.Image parseFrom( + public static org.tensorflow.framework.Summary.Image parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static Summary.Image parseFrom(java.io.InputStream input) + public static org.tensorflow.framework.Summary.Image parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static Summary.Image parseFrom( + public static org.tensorflow.framework.Summary.Image parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static Summary.Image parseDelimitedFrom(java.io.InputStream input) + public static org.tensorflow.framework.Summary.Image parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static Summary.Image parseDelimitedFrom( + public static org.tensorflow.framework.Summary.Image parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static Summary.Image parseFrom( + public static org.tensorflow.framework.Summary.Image parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static Summary.Image parseFrom( + public static org.tensorflow.framework.Summary.Image parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -427,7 +427,7 @@ public static Summary.Image parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(Summary.Image prototype) { + public static Builder newBuilder(org.tensorflow.framework.Summary.Image prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -447,20 +447,20 @@ protected Builder newBuilderForType( public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorboard.Summary.Image) - Summary.ImageOrBuilder { + org.tensorflow.framework.Summary.ImageOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return SummaryProtos.internal_static_tensorboard_Summary_Image_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Image_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return SummaryProtos.internal_static_tensorboard_Summary_Image_fieldAccessorTable + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Image_fieldAccessorTable .ensureFieldAccessorsInitialized( - Summary.Image.class, Summary.Image.Builder.class); + org.tensorflow.framework.Summary.Image.class, org.tensorflow.framework.Summary.Image.Builder.class); } - // Construct using Summary.Image.newBuilder() + // Construct using org.tensorflow.framework.Summary.Image.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -490,23 +490,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return SummaryProtos.internal_static_tensorboard_Summary_Image_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Image_descriptor; } - public Summary.Image getDefaultInstanceForType() { - return Summary.Image.getDefaultInstance(); + public org.tensorflow.framework.Summary.Image getDefaultInstanceForType() { + return org.tensorflow.framework.Summary.Image.getDefaultInstance(); } - public Summary.Image build() { - Summary.Image result = buildPartial(); + public org.tensorflow.framework.Summary.Image build() { + org.tensorflow.framework.Summary.Image result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public Summary.Image buildPartial() { - Summary.Image result = new Summary.Image(this); + public org.tensorflow.framework.Summary.Image buildPartial() { + org.tensorflow.framework.Summary.Image result = new org.tensorflow.framework.Summary.Image(this); result.height_ = height_; result.width_ = width_; result.colorspace_ = colorspace_; @@ -542,16 +542,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof Summary.Image) { - return mergeFrom((Summary.Image)other); + if (other instanceof org.tensorflow.framework.Summary.Image) { + return mergeFrom((org.tensorflow.framework.Summary.Image)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(Summary.Image other) { - if (other == Summary.Image.getDefaultInstance()) return this; + public Builder mergeFrom(org.tensorflow.framework.Summary.Image other) { + if (other == org.tensorflow.framework.Summary.Image.getDefaultInstance()) return this; if (other.getHeight() != 0) { setHeight(other.getHeight()); } @@ -576,11 +576,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - Summary.Image parsedMessage = null; + org.tensorflow.framework.Summary.Image parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (Summary.Image) e.getUnfinishedMessage(); + parsedMessage = (org.tensorflow.framework.Summary.Image) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -768,12 +768,12 @@ public final Builder mergeUnknownFields( } // @@protoc_insertion_point(class_scope:tensorboard.Summary.Image) - private static final Summary.Image DEFAULT_INSTANCE; + private static final org.tensorflow.framework.Summary.Image DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new Summary.Image(); + DEFAULT_INSTANCE = new org.tensorflow.framework.Summary.Image(); } - public static Summary.Image getDefaultInstance() { + public static org.tensorflow.framework.Summary.Image getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -796,7 +796,7 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public Summary.Image getDefaultInstanceForType() { + public org.tensorflow.framework.Summary.Image getDefaultInstanceForType() { return DEFAULT_INSTANCE; } @@ -936,14 +936,14 @@ private Audio( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return SummaryProtos.internal_static_tensorboard_Summary_Audio_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Audio_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return SummaryProtos.internal_static_tensorboard_Summary_Audio_fieldAccessorTable + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Audio_fieldAccessorTable .ensureFieldAccessorsInitialized( - Summary.Audio.class, Summary.Audio.Builder.class); + org.tensorflow.framework.Summary.Audio.class, org.tensorflow.framework.Summary.Audio.Builder.class); } public static final int SAMPLE_RATE_FIELD_NUMBER = 1; @@ -1096,10 +1096,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof Summary.Audio)) { + if (!(obj instanceof org.tensorflow.framework.Summary.Audio)) { return super.equals(obj); } - Summary.Audio other = (Summary.Audio) obj; + org.tensorflow.framework.Summary.Audio other = (org.tensorflow.framework.Summary.Audio) obj; boolean result = true; result = result && ( @@ -1142,58 +1142,58 @@ public int hashCode() { return hash; } - public static Summary.Audio parseFrom( + public static org.tensorflow.framework.Summary.Audio parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static Summary.Audio parseFrom( + public static org.tensorflow.framework.Summary.Audio parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static Summary.Audio parseFrom(byte[] data) + public static org.tensorflow.framework.Summary.Audio parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static Summary.Audio parseFrom( + public static org.tensorflow.framework.Summary.Audio parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static Summary.Audio parseFrom(java.io.InputStream input) + public static org.tensorflow.framework.Summary.Audio parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static Summary.Audio parseFrom( + public static org.tensorflow.framework.Summary.Audio parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static Summary.Audio parseDelimitedFrom(java.io.InputStream input) + public static org.tensorflow.framework.Summary.Audio parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static Summary.Audio parseDelimitedFrom( + public static org.tensorflow.framework.Summary.Audio parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static Summary.Audio parseFrom( + public static org.tensorflow.framework.Summary.Audio parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static Summary.Audio parseFrom( + public static org.tensorflow.framework.Summary.Audio parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -1205,7 +1205,7 @@ public static Summary.Audio parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(Summary.Audio prototype) { + public static Builder newBuilder(org.tensorflow.framework.Summary.Audio prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -1225,20 +1225,20 @@ protected Builder newBuilderForType( public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorboard.Summary.Audio) - Summary.AudioOrBuilder { + org.tensorflow.framework.Summary.AudioOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return SummaryProtos.internal_static_tensorboard_Summary_Audio_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Audio_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return SummaryProtos.internal_static_tensorboard_Summary_Audio_fieldAccessorTable + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Audio_fieldAccessorTable .ensureFieldAccessorsInitialized( - Summary.Audio.class, Summary.Audio.Builder.class); + org.tensorflow.framework.Summary.Audio.class, org.tensorflow.framework.Summary.Audio.Builder.class); } - // Construct using Summary.Audio.newBuilder() + // Construct using org.tensorflow.framework.Summary.Audio.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -1270,23 +1270,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return SummaryProtos.internal_static_tensorboard_Summary_Audio_descriptor; + return org.tensorflow.framework.SummaryProtos.internal_static_tensorboard_Summary_Audio_descriptor; } - public Summary.Audio getDefaultInstanceForType() { - return Summary.Audio.getDefaultInstance(); + public org.tensorflow.framework.Summary.Audio getDefaultInstanceForType() { + return org.tensorflow.framework.Summary.Audio.getDefaultInstance(); } - public Summary.Audio build() { - Summary.Audio result = buildPartial(); + public org.tensorflow.framework.Summary.Audio build() { + org.tensorflow.framework.Summary.Audio result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public Summary.Audio buildPartial() { - Summary.Audio result = new Summary.Audio(this); + public org.tensorflow.framework.Summary.Audio buildPartial() { + org.tensorflow.framework.Summary.Audio result = new org.tensorflow.framework.Summary.Audio(this); result.sampleRate_ = sampleRate_; result.numChannels_ = numChannels_; result.lengthFrames_ = lengthFrames_; @@ -1323,16 +1323,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof Summary.Audio) { - return mergeFrom((Summary.Audio)other); + if (other instanceof org.tensorflow.framework.Summary.Audio) { + return mergeFrom((org.tensorflow.framework.Summary.Audio)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(Summary.Audio other) { - if (other == Summary.Audio.getDefaultInstance()) return this; + public Builder mergeFrom(org.tensorflow.framework.Summary.Audio other) { + if (other == org.tensorflow.framework.Summary.Audio.getDefaultInstance()) return this; if (other.getSampleRate() != 0F) { setSampleRate(other.getSampleRate()); } @@ -1361,11 +1361,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - Summary.Audio parsedMessage = null; + org.tensorflow.framework.Summary.Audio parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (Summary.Audio) e.getUnfinishedMessage(); + parsedMessage = (org.tensorflow.framework.Summary.Audio) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -1616,12 +1616,12 @@ public final Builder mergeUnknownFields( } // @@protoc_insertion_point(class_scope:tensorboard.Summary.Audio) - private static final Summary.Audio DEFAULT_INSTANCE; + private static final org.tensorflow.framework.Summary.Audio DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new Summary.Audio(); + DEFAULT_INSTANCE = new org.tensorflow.framework.Summary.Audio(); } - public static Summary.Audio getDefaultInstance() { + public static org.tensorflow.framework.Summary.Audio getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -1644,7 +1644,7 @@ public com.google.protobuf.Parser - - - - - org.apache.maven.plugins - maven-shade-plugin - 3.0.0 - - - - org.spark-project.spark:unused - com.google.protobuf - - - - - com.google.protobuf - - META-INF/maven/com.google.protobuf/protobuf-java/* - - - - - - com.google.protobuf - com.intel.analytics.bigdl.shaded.protobuf - - - - - - package - - shade - - - - - - diff --git a/scala/common/spark-version/pom.xml b/scala/common/spark-version/pom.xml index 5e42829249e..472c2aef1fb 100644 --- a/scala/common/spark-version/pom.xml +++ b/scala/common/spark-version/pom.xml @@ -16,4 +16,29 @@ ${spark-version.project} + + + + + org.apache.maven.plugins + maven-shade-plugin + 3.0.0 + + + + org.spark-project.spark:unused + + + + + + package + + shade + + + + + + From c9e548b67d9ba3dcb3a53260c101f06bfa280fc6 Mon Sep 17 00:00:00 2001 From: qiuxin2012 Date: Mon, 12 Jun 2017 16:24:42 +0800 Subject: [PATCH 0181/1065] add try/catch to invoke and invoke2 --- .../intel/analytics/bigdl/utils/ThreadPool.scala | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 878b794cd6b..1c48eef4619 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -123,7 +123,13 @@ class ThreadPool(private var poolSize: Int) { def invoke2[T](tasks: Seq[() => T]): Seq[java.util.concurrent.Future[T]] = { tasks.map(task => new Callable[T] { override def call(): T = { - task() + try { + task() + } catch { + case t : Throwable => + logger.error("Error: " + ExceptionUtils.getStackTrace(t)) + throw t + } } }).map(threadPool.submit(_)) } @@ -152,7 +158,13 @@ class ThreadPool(private var poolSize: Int) { */ def invoke[T](task: () => T): Future[T] = { Future { - task() + try { + task() + } catch { + case t : Throwable => + logger.error("Error: " + ExceptionUtils.getStackTrace(t)) + throw t + } }(context) } From 23155c5c020c44e57574579a14d75a82ba2901b6 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Wed, 14 Jun 2017 10:19:55 +0800 Subject: [PATCH 0182/1065] Rename Graph to Model (#982) * Rename Graph to Model * fix model --- .../dllib/utils/python/api/PythonBigDL.scala | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a1a63b395c1..d52b16bc16b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1299,16 +1299,6 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } - def graphForward(graph: Graph[T], - input: JList[JTensor]): JList[JTensor] = { - forward(input, graph.forward) - } - - def graphBackward(graph: Graph[T], - input: JList[JTensor], gradOutput: JList[JTensor]): JList[JTensor] = { - backward(input, gradOutput, graph.backward) - } - private def forward(input: JList[JTensor], forward: (Activity) => Activity): JList[JTensor] = { val inputActivity = jTensorsToActivity(input) val outputActivity = forward(inputActivity) @@ -1323,6 +1313,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab activityToJTensors(outputActivity) } + def modelSave(module: AbstractModule[Activity, Activity, T], + path: String, overWrite: Boolean): Unit = { + module.save(path, overWrite) + } + def criterionForward(criterion: AbstractCriterion[Activity, Activity, T], input: JList[JTensor], target: JList[JTensor]): T = { val inputActivity = jTensorsToActivity(input) @@ -1507,7 +1502,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new ValidationSummary(logDir, appName) } - def createGraph(input: JList[ModuleNode[T]], output: JList[ModuleNode[T]]): Graph[T] = { + def createModel(input: JList[ModuleNode[T]], output: JList[ModuleNode[T]]): Graph[T] = { Graph(input.asScala.toArray, output.asScala.toArray) } From 0ab485d411675b72f926a70bb40b30860301032c Mon Sep 17 00:00:00 2001 From: Lu Qi Date: Wed, 14 Jun 2017 15:19:16 +0800 Subject: [PATCH 0183/1065] change integration-test:sync with branch0.1 --- dl/src/test/integration-test.robot | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/dl/src/test/integration-test.robot b/dl/src/test/integration-test.robot index eda14f674b0..6a2f36ab5cd 100644 --- a/dl/src/test/integration-test.robot +++ b/dl/src/test/integration-test.robot @@ -12,8 +12,8 @@ Test template BigDL Test 1 Spark2.0 Test Suite ${spark_200_3_vid} 2 Spark2.1 Test Suite ${spark_210_3_vid} 3 Hdfs Test Suite ${hdfs_264_3_vid} -4 TensorFlow Spark2.1 Test Suite ${spark_tf_210_3_vid} -5 TensorFlow Spark1.6 Test Suite ${spark_tf_163_3_vid} +4 PySpark2.1 Test Suite ${spark_tf_210_3_vid} +5 PySpark1.6 Test Suite ${spark_tf_163_3_vid} 6 Yarn Test Suite ${hdfs_264_3_vid} # predefined service masters: @@ -64,18 +64,18 @@ Yarn Test Suite Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 120 -e 3 Set Environment Variable PYSPARK_DRIVER_PYTHON /var/jenkins_home/venv/bin/python Set Environment Variable PYSPARK_PYTHON ./venv.zip/venv/bin/python - Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-0.2.0-SNAPSHOT-python-api.zip --archives /var/jenkins_home/venv.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-0.2.0-SNAPSHOT-jar-with-dependencies.jar ${curdir}/pyspark/dl/models/lenet/lenet5.py -b 200 + Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --archives /var/jenkins_home/venv.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 200 Remove Environment Variable http_proxy https_proxy PYSPARK_DRIVER_PYTHON PYSPARK_PYTHON -TensorFlow Spark2.1 Test Suite +PySpark2.1 Test Suite Build SparkJar spark_2.x Set Environment Variable SPARK_HOME /opt/work/spark-2.1.0-bin-hadoop2.7 ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.1.0-bin-hadoop2.7/bin spark-submit - Run Shell ${submit} --master ${spark_tf_210_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-0.2.0-SNAPSHOT-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf ${curdir}/pyspark/dl/models/lenet/lenet5.py -b 224 + Run Shell ${submit} --master ${spark_tf_210_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 -TensorFlow Spark1.6 Test Suite +PySpark1.6 Test Suite Build SparkJar spark_1.6 Set Environment Variable SPARK_HOME /opt/work/spark-1.6.3-bin-hadoop2.6 ${submit}= Catenate SEPARATOR=/ /opt/work/spark-1.6.3-bin-hadoop2.6/bin spark-submit - Run Shell ${submit} --master ${spark_tf_163_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-0.2.0-SNAPSHOT-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-0.2.0-SNAPSHOT-jar-with-dependencies.jar ${curdir}/pyspark/dl/models/lenet/lenet5.py -b 224 + Run Shell ${submit} --master ${spark_tf_163_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 From 88f5f898a2c23f367c07d5ae8ebfe6a6035c6f25 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Thu, 15 Jun 2017 16:22:07 +0800 Subject: [PATCH 0184/1065] Add zero_grad_parameters and update_parameters methods (#1018) * Add zero_grad_parameters and update_parameters methods * revert --- .../analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index d52b16bc16b..152eb2c69d9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1538,6 +1538,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } } + def updateParameters(model: AbstractModule[Activity, Activity, T], lr: Double): Unit = { + model.updateParameters(ev.fromType(lr)) + } + def uniform(a: Double, b: Double, size: JList[Int]): JTensor = { val result = Tensor[T]().resize(size.asScala.toArray) result.apply1(i => ev.fromType(RandomGenerator.RNG.uniform(a, b))) From e2463a657478bc06882ff7ab0895835d49299d58 Mon Sep 17 00:00:00 2001 From: qiuxin2012 Date: Wed, 14 Jun 2017 19:27:52 +0800 Subject: [PATCH 0185/1065] fix concat return incorrect result when output is 1D --- .../analytics/bigdl/dllib/nn/Concat.scala | 22 ++++++++++++------- .../bigdl/dllib/tensor/DenseTensor.scala | 2 +- .../analytics/bigdl/dllib/nn/ConcatSpec.scala | 13 +++++++++++ 3 files changed, 28 insertions(+), 9 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala index 9d280499681..1fdf8faaac9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala @@ -79,14 +79,20 @@ class Concat[T: ClassTag](val dimension: Int)( results(i) = Engine.model.invoke(() => { val target = this.output.narrow(this.dimension, _offset, currentOutput.size(this.dimension)) - var f = 1 - while (f <= target.size(1)) { - val curFrame = target.select(1, f) - val outputFrame = currentOutput.select(1, f) - require(curFrame.isContiguous()) - require(outputFrame.isContiguous()) - curFrame.copy(outputFrame) - f += 1 + if (target.isContiguous()) { + // Copy directly when target is Contiguous + target.copy(currentOutput) + } else { + // Divide target into contiguous frames when target isn't contiguous + var f = 1 + while (f <= target.size(1)) { + val curFrame = target.select(1, f) + val outputFrame = currentOutput.select(1, f) + require(curFrame.isContiguous()) + require(outputFrame.isContiguous()) + curFrame.copy(outputFrame) + f += 1 + } } }) i += 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 4a733b8f611..2e1f4cc0c3d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -368,7 +368,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( result } else { require(this.nDimension == 1, "empty tensor") - DenseTensor.get1dTensor(this, _sliceIndex) + this.narrow(1, index, 1) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala index 3b728c8078c..c6da9abb0f3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import org.scalatest.{FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Parallel @@ -38,4 +39,16 @@ class ConcatSpec extends FlatSpec with Matchers { } + "Concat forward/backward 1D input/output" should "return good result" in { + val model = Concat[Float](1) + model.add(Identity[Float]()) + model.add(Identity[Float]()) + val input = Tensor[Float].range(1, 3, 1) + val gradOutput = Tensor[Float].range(1, 6, 1) + val output = model.forward(input) + val gradInput = model.backward(input, gradOutput) + output should be (Tensor(Storage(Array[Float](1, 2, 3, 1, 2, 3)))) + gradInput should be (Tensor(Storage(Array[Float](5, 7, 9)))) + } + } From ef6fdcb196899bc7338c2442ace9480e14cbf978 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Thu, 15 Jun 2017 19:51:07 +0800 Subject: [PATCH 0186/1065] Make top 1 Accuracy support binary input (labels) (#954) * add test * make top1 accuracy support binary input * add tests for accuracy * remove unnecessary changes * remove unnecessary changes --- .../bigdl/dllib/optim/ValidationMethod.scala | 16 ++++++++-- .../bigdl/dllib/optim/ValidationSpec.scala | 30 +++++++++++++++++++ 2 files changed, 43 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala index 99417d5c02a..1d2d3caa799 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala @@ -113,7 +113,9 @@ class AccuracyResult(private var correct: Int, private var count: Int) /** * Caculate the percentage that output's max probability index equals target */ -class Top1Accuracy[T] extends ValidationMethod[T] { +class Top1Accuracy[T]( + implicit ev: TensorNumeric[T]) + extends ValidationMethod[T] { override def apply(output: Activity, target: Activity): ValidationResult = { var correct = 0 @@ -122,7 +124,11 @@ class Top1Accuracy[T] extends ValidationMethod[T] { val _output = output.asInstanceOf[Tensor[T]] val _target = target.asInstanceOf[Tensor[T]] if (_output.dim() == 2) { - _output.max(2)._2.squeeze().map(_target, (a, b) => { + (if (_output.size(2) == 1) { + _output.apply1(x => if (ev.isGreater(ev.fromType(0.5), x)) ev.zero else ev.one) + } else { + _output.max(2)._2.squeeze() + }).map(_target, (a, b) => { if (a == b) { correct += 1 } @@ -131,7 +137,11 @@ class Top1Accuracy[T] extends ValidationMethod[T] { count += _output.size(1) } else if (_output.dim == 1) { require(_target.size(1) == 1) - _output.max(1)._2.map(_target, (a, b) => { + (if (_output.size(1) == 1) { + _output.apply1(x => if (ev.isGreater(ev.fromType(0.5), x)) ev.zero else ev.one) + } else { + _output.max(1)._2 + }).map(_target, (a, b) => { if (a == b) { correct += 1 } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index 8c5b9078b55..ec166899f65 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -50,6 +50,36 @@ class ValidationSpec extends FlatSpec with Matchers { result should be(test) } + + "top1 accuracy" should "be correct on 2d tensor for binary inputs" in { + val output = Tensor(Storage(Array[Double]( + 0, + 0, + 1, + 0, + 1, + 0, + 0, + 0 + )), 1, Array(8, 1)) + + val target = Tensor(Storage(Array[Double]( + 1, + 0, + 1, + 1, + 0, + 0, + 1, + 1 + ))) + + val validation = new Top1Accuracy[Double]() + val result = validation(output, target) + val test = new AccuracyResult(3, 8) + result should be(test) + } + it should "be correct on 1d tensor" in { val output = Tensor(Storage(Array[Double]( 0, 0, 0, 1 From 2b04828065ef0d5e290a6a0b9d46a95d1581ecca Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 16 Jun 2017 09:05:22 +0800 Subject: [PATCH 0187/1065] [Issue 1007] load caffe model from hdfs (#1011) load caffe model from hdfs --- .../bigdl/dllib/utils/CaffeLoader.scala | 49 ++++++++++++++----- .../bigdl/dllib/integration/HdfsSpec.scala | 47 +++++++++++++++++- .../bigdl/dllib/utils/CaffeLoaderSpec.scala | 5 +- 3 files changed, 84 insertions(+), 17 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoader.scala index bc2f8d5a896..05ba30037c1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoader.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.utils -import java.io.{FileInputStream, InputStreamReader} +import java.io._ import caffe.Caffe import caffe.Caffe.{LayerParameter, NetParameter, V1LayerParameter} @@ -25,6 +25,8 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.apache.log4j.Logger +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FSDataInputStream, Path} import scala.reflect.ClassTag @@ -58,18 +60,41 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } } + private def createHdfsInputStream(fileName: String): FSDataInputStream = { + val src = new Path(fileName) + val fs = src.getFileSystem(new Configuration()) + fs.open(src) + } + private def loadBinary(prototxtPath: String, modelPath: String): Caffe.NetParameter = { - val f = new java.io.File(prototxtPath) - require(f.exists(), prototxtPath + " does not exists") - val reader = new InputStreamReader(new FileInputStream(f), "ASCII") - val builder = NetParameter.newBuilder - TextFormat.merge(reader, builder) - logger.info(s"start loading caffe model from $modelPath") - val cis = CodedInputStream.newInstance(new FileInputStream(modelPath)) - cis.setSizeLimit(Integer.MAX_VALUE) - builder.mergeFrom(cis) - logger.info("load caffe model done") - builder.build() + var prototxtReader: InputStreamReader = null + var modelStream: InputStream = null + try { + if (prototxtPath.startsWith(File.hdfsPrefix)) { + require(modelPath.startsWith(File.hdfsPrefix), "If prototxt is saved in hdfs," + + " model should also be saved in hdfs") + val prototxtStream = createHdfsInputStream(prototxtPath) + modelStream = createHdfsInputStream(modelPath) + prototxtReader = new InputStreamReader(prototxtStream, "ASCII") + } else { + val f = new java.io.File(prototxtPath) + require(f.exists(), prototxtPath + " does not exists") + prototxtReader = new InputStreamReader(new FileInputStream(f), "ASCII") + modelStream = new FileInputStream(modelPath) + } + + val builder = NetParameter.newBuilder + TextFormat.merge(prototxtReader, builder) + logger.info(s"start loading caffe model from $modelPath") + val cis = CodedInputStream.newInstance(modelStream) + cis.setSizeLimit(Integer.MAX_VALUE) + builder.mergeFrom(cis) + logger.info("load caffe model done") + builder.build() + } finally { + prototxtReader.close() + modelStream.close() + } } private def getBlob(name: String, ind: Int): Option[Caffe.BlobProto] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala index d2fb61c15ce..f76fbb553fc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala @@ -18,10 +18,15 @@ package com.intel.analytics.bigdl.integration import java.nio.file.{Files, Paths} import com.intel.analytics.bigdl.models.lenet.LeNet5 -import com.intel.analytics.bigdl.nn.Module -import com.intel.analytics.bigdl.utils.{Engine, File} +import com.intel.analytics.bigdl.models.resnet.Convolution +import com.intel.analytics.bigdl.nn.{Linear, Module, Sequential} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble +import com.intel.analytics.bigdl.utils.{CaffeLoader, Engine, File} import com.intel.analytics.bigdl.visualization.Summary import com.intel.analytics.bigdl.visualization.tensorboard.{FileReader, FileWriter} +import org.apache.commons.compress.utils.IOUtils +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -90,4 +95,42 @@ class HdfsSpec extends FlatSpec with Matchers with BeforeAndAfter{ result(i)._2 should be (i) } } + + "load caffe model from hdfs" should "work properly" in { + val prototxt = getClass().getClassLoader().getResource("caffe/test.prototxt").getPath + val modelPath = getClass().getClassLoader().getResource("caffe/test.caffemodel").getPath + + val hdfsDir = hdfs + s"/${ com.google.common.io.Files.createTempDir().getPath() }" + + def writeToHdfs(localFile: String, hdfsDir: String): Unit = { + val src = new Path(localFile) + val fs = src.getFileSystem(new Configuration()) + val inStream = fs.open(src) + val dest = new Path(hdfsDir) + val fsDest = dest.getFileSystem(new Configuration()) + val outFileStream = fsDest.create(dest) + + IOUtils.copy(inStream, outFileStream) + + // Close both files + inStream.close() + outFileStream.close() + } + + writeToHdfs(prototxt, hdfsDir + "/test.prototxt") + writeToHdfs(modelPath, hdfsDir + "/test.caffemodel") + val module = Sequential() + .add(Convolution(3, 4, 2, 2).setName("conv")) + .add(Convolution(4, 3, 2, 2).setName("conv2")) + .add(Linear(2, 27, withBias = false).setName("ip")) + + + val model = CaffeLoader.load[Double](module, prototxt, modelPath) + + val modelFromHdfs = CaffeLoader.load[Double](module, hdfsDir + "/test.prototxt", + hdfsDir + "/test.caffemodel") + + model.getParameters() should be (modelFromHdfs.getParameters()) + + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala index 496fdb3bb6d..31caf8ca4bb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala @@ -26,10 +26,9 @@ import org.scalatest.{FlatSpec, Matchers} class CaffeLoaderSpec extends FlatSpec with Matchers { - val resource = getClass().getClassLoader().getResource("caffe") + val prototxt = getClass().getClassLoader().getResource("caffe/test.prototxt").getPath + val modelPath = getClass().getClassLoader().getResource("caffe/test.caffemodel").getPath - val prototxt = Paths.get(resource.getPath(), "test.prototxt").toString - val modelPath = Paths.get(resource.getPath(), "test.caffemodel").toString "load caffe match all parameters" should "work properly" in { val module = Sequential() .add(Convolution(3, 4, 2, 2).setName("conv")) From 633ac075429e384ed321f2bea2ce25f77fd106b6 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Fri, 16 Jun 2017 11:26:39 +0800 Subject: [PATCH 0188/1065] Fix HardShrink factory method (#1025) * fix HardShrink factory * update unittest --- .../scala/com/intel/analytics/bigdl/dllib/nn/HardShrink.scala | 2 +- .../com/intel/analytics/bigdl/dllib/torch/HardShrinkSpec.scala | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardShrink.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardShrink.scala index fe1a743319b..4c830392a25 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardShrink.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardShrink.scala @@ -71,6 +71,6 @@ class HardShrink[T: ClassTag](lambda: Double = 0.5) object HardShrink { def apply[@specialized(Float, Double) T: ClassTag]( lambda: Double = 0.5)(implicit ev: TensorNumeric[T]) : HardShrink[T] = { - new HardShrink[T]() + new HardShrink[T](lambda) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardShrinkSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardShrinkSpec.scala index 5feb78f269e..6bf8245c3d8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardShrinkSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardShrinkSpec.scala @@ -25,7 +25,7 @@ class HardShrinkSpec extends TorchSpec { "An HardShrink" should "generate correct output and grad" in { torchCheck() - val layer = new HardShrink[Double](5) + val layer = HardShrink[Double](5) val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](2, 2, 2) From 241230d483d942aed80787e4360410402b46bf46 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Thu, 15 Jun 2017 23:28:39 -0700 Subject: [PATCH 0189/1065] Refactor DLEstimator and DLClassifier for Spark Pipeline compatibility (#956) * refactor DLEstimator and Classifier (integrate the Estimator and Model class and support proper data types) Thanks for the review. Merge this and I will send more unit tests and examples for the next steps. --- .../org/apache/spark/ml/DLClassifier.scala | 159 ++++++------- .../org/apache/spark/ml/DLEstimator.scala | 172 ++++++++------ .../org/apache/spark/ml/DLEstimatorBase.scala | 113 +++++++++- ...nsformer.scala => DLTransformerBase.scala} | 17 +- .../org/apache/spark/ml/DLEstimatorBase.scala | 117 +++++++++- ...nsformer.scala => DLTransformerBase.scala} | 18 +- .../example/MLPipeline/DLEstimatorLeNet.scala | 113 +++------- .../imageclassification/ImagePredictor.scala | 15 +- .../bigdl/dllib/optim/DLEstimatorSpec.scala | 212 +++++------------- .../bigdl/dllib/utils/DLClassifierSpec.scala | 73 +++--- 10 files changed, 547 insertions(+), 462 deletions(-) rename scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/{DLTransformer.scala => DLTransformerBase.scala} (65%) rename scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/{DLTransformer.scala => DLTransformerBase.scala} (68%) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala index 38bacf14008..9ef10069da6 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala @@ -15,93 +15,103 @@ */ package org.apache.spark.ml +import scala.reflect.ClassTag + import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} import org.apache.spark.ml.param.{ParamMap, _} -import org.apache.spark.mllib.linalg.DenseVector import org.apache.spark.sql.types._ import org.apache.spark.sql.{DataFrame, Row} -import scala.reflect.ClassTag +// TODO: override transformSchema to change label and prediction type +class DLClassifier[@specialized(Float, Double) T: ClassTag] ( + override val model: Module[T], + featureSize : Array[Int], + override val uid: String = "DLClassifier" + ) (implicit ev: TensorNumeric[T]) extends DLModel[T](model, featureSize) { + +} /** - * A general Classifier to classify the input data in inputCol, and write the results to outputCol. - * Use setInputCol to set inputCol name, and use setOutputCol to set outputCol name. + * [[DLModel]] helps embedding a BigDL model into a Spark Transformer, thus Spark users can + * conveniently merge BigDL into Spark ML pipeline. The features column holds the storage + * (Vector, float array or double array) of the feature data, and user should specify the + * tensor size (dimensions) via featureSize. (e.g. an image may be with featureSize = 28 * 28). + * + * Internally the feature data are converted to BigDL tensors with batch acceleration, and + * further predict with a BigDL model. * - * DLClassifier is compatible with both spark 1.5-plus and 2.0 by extending MLTransform. + * [[DLModel]] is compatible with both spark 1.5-plus and 2.0 by extending ML Transformer. */ -class DLClassifier[@specialized(Float, Double) T: ClassTag] - (override val uid: String = "DLClassifier")(implicit ev: TensorNumeric[T]) extends DLTransformer - with HasInputCol with HasOutputCol with DataParams[T] { +class DLModel[@specialized(Float, Double) T: ClassTag]( + val model: Module[T], + var featureSize : Array[Int], + override val uid: String = "DLModel" + )(implicit ev: TensorNumeric[T]) extends DLTransformerBase with DLParams with HasBatchSize { - /** - * Set the input column name - * @param inputColName the name of the input column - * @return this. - */ - def setInputCol(inputColName: String): this.type = set(inputCol, inputColName) + def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) - /** - * Set the output column name, should not existed in the DataFrame. - * @param outputColName the name of the output column - * @return this. - */ - def setOutputCol(outputColName: String): this.type = set(outputCol, outputColName) - - def validateParameters(): Unit = { - val params = this.extractParamMap() - require(null != params.getOrElse(modelTrain, null), - "DLClassifier: model for predict must not be null") - require(null != params.getOrElse(batchShape, null), - "DLClassifier: batchSize for predict must not be null") - require(null != params.getOrElse(inputCol, null), - "DLClassifier: inputCol must not be null") - require(null != params.getOrElse(outputCol, null), - "DLClassifier: inputCol must not be null") + def setPredictionCol(value: String): this.type = set(predictionCol, value) + + def setFeatureSize(value: Array[Int]): this.type = { + this.featureSize = value + this } + def setBatchSize(value: Int): this.type = set(batchSize, value) + + def getFeatureSize: Array[Int] = this.featureSize + /** * Perform a prediction on inputCol, and write result to the outputCol. * @param dataset input DataFrame * @return output DataFrame */ - override def process(dataset: DataFrame): DataFrame = { - this.validateParameters() - DLClassifier.process[T]($(batchShape), $(modelTrain), $(inputCol), $(outputCol), dataset) - } + protected override def internalTransform(dataset: DataFrame): DataFrame = { + val featureArrayCol = if (dataset.schema($(featuresCol)).dataType.isInstanceOf[ArrayType]) { + $(featuresCol) + } else { + getFeatureArrayCol + } - override def copy(extra: ParamMap): DLClassifier[T] = { - copyValues(new DLClassifier(uid), extra) + process[T](featureSize, model, featureArrayCol, $(predictionCol), dataset) } -} -object DLClassifier{ - private[DLClassifier] def process[@specialized(Float, Double) T: ClassTag]( - batchSize: Array[Int], - modelTrain: Module[T], - inputCol: String, - outputCol: String, - dataset: DataFrame)(implicit ev: TensorNumeric[T]) : DataFrame = { - val model = modelTrain.evaluate() + private[DLModel] def process[@specialized(Float, Double) T: ClassTag]( + featureSize: Array[Int], + modelTrain: Module[T], + featuresArrayCol: String, + predictionCol: String, + dataset: DataFrame)(implicit ev: TensorNumeric[T]): DataFrame = { + val model = modelTrain.evaluate() val modelBroadCast = ModelBroadcast[T].broadcast(dataset.sqlContext.sparkContext, model) + val featureColIndex = dataset.schema.fieldIndex(featuresArrayCol) + + val featureType = dataset.schema(featuresArrayCol).dataType.asInstanceOf[ArrayType].elementType + def featureToTensor = featureType match { + case DoubleType => + (row: Row, index: Int) => + Tensor(Storage(row.getSeq[Double](featureColIndex).toArray.map(ev.fromType(_)))) + case FloatType => + (row: Row, index: Int) => + Tensor(Storage(row.getSeq[Float](featureColIndex).toArray.map(ev.fromType(_)))) + } - val predictRdd = dataset.rdd.mapPartitions{ rows => + val predictRdd = dataset.rdd.mapPartitions { rows => val localModel = modelBroadCast.value() - val tensorBuffer = Tensor[T](batchSize) - val batches = rows.grouped(batchSize(0)) + val tensorBuffer = Tensor[T](Array($(batchSize)) ++ featureSize) + val batches = rows.grouped($(batchSize)) - val results = batches.flatMap{ batch => + val results = batches.flatMap { batch => val batchResult = new Array[Row](batch.length) var i = 1 - // Notice: if the last batch is smaller than the batchSize(0), we still continue + // Notice: if the last batch is smaller than the batchSize, we still continue // to use this tensorBuffer, but only add the meaningful parts to the result Array. - batch.foreach{ row => - tensorBuffer.select(1, i).copy( - Tensor(Storage(row.getAs[DenseVector](inputCol).toArray.map(ev.fromType(_))))) + batch.foreach { row => + tensorBuffer.select(1, i).copy(featureToTensor(row, featureColIndex)) i += 1 } val output = localModel.forward(tensorBuffer).toTensor[T] @@ -114,8 +124,9 @@ object DLClassifier{ } i = 0 - batch.foreach{ row => - batchResult(i) = Row.fromSeq(row.toSeq ++ Array[Int](ev.toType[Int](predict(i)))) + batch.foreach { row => + batchResult(i) = Row.fromSeq( + row.toSeq ++ Seq(Array[Double](ev.toType[Double](predict(i))))) i += 1 } @@ -124,29 +135,27 @@ object DLClassifier{ results } - val predictSchema = dataset.schema.add(outputCol, IntegerType) + val predictSchema = dataset.schema.add(predictionCol, new ArrayType(DoubleType, false)) dataset.sqlContext.createDataFrame(predictRdd, predictSchema) } + + override def copy(extra: ParamMap): DLModel[T] = { + val copied = new DLModel(model, featureSize, uid).setParent(parent) + copyValues(copied, extra).asInstanceOf[DLModel[T]] + } } -/** - * parameters passed to DLClassifier - * @tparam T data type - */ -trait DataParams[@specialized(Float, Double) T] extends Params { - final val modelTrain = new Param[Module[T]](this, "module factory", "network model") - final val batchShape = new Param[Array[Int]](this, "batch size", "batch size for input") +// TODO, add save/load +object DLModel { - /** - * get the model - * @return modelTrain - */ - final def getModel: Module[T] = $(modelTrain) - /** - * get the batch shape - * @return batchShape - */ - final def getBatchSize: Array[Int] = $(batchShape) } + +trait HasBatchSize extends Params { + + final val batchSize: Param[Int] = new Param[Int](this, "batchSize", "batchSize") + setDefault(batchSize -> 1) + + final def getBatchSize: Int = $(batchSize) +} diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index 3ddc16feb7d..52e8d847d29 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -14,110 +14,136 @@ * limitations under the License. */ package org.apache.spark.ml + +import scala.reflect.ClassTag import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch} import com.intel.analytics.bigdl.{Criterion, Module} -import com.intel.analytics.bigdl.optim.Optimizer +import com.intel.analytics.bigdl.optim.{Adam, Optimizer, Trigger} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasInputCols, HasLabelCol} -import org.apache.spark.ml.param.{Param, ParamMap, Params} +import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators} import org.apache.spark.sql.DataFrame import org.apache.spark.rdd.RDD -import org.apache.spark.sql.types.{ArrayType, StructType} - -import scala.collection.mutable -import scala.reflect.ClassTag +import org.apache.spark.sql.types._ /** - * A wrapper of Optimizer to support fit() in ML Pipelines as an Estimator - * feature column name and label column name should be provided in training Dataframe - * Model to be trained, Feature size, label size, batch shap must also be provided + * [[DLEstimator]] helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern, + * thus Spark users can conveniently fit BigDL into Spark ML pipeline. + * + * The feature column holds the storage (Spark Vectors or array of Floats or Doubles) of + * the feature data, and user should specify the tensor size(dimensions) via param featureSize. + * The label column holds the storage (Spark Vectors, array of Floats or Doubles, or Double) of + * the label data, and user should specify the tensor size(dimensions) via param labelSize. + * Internally the feature and label data are converted to BigDL tensors, to further train a + * BigDL model efficiently. + * * For details usage, please refer to example : * [[com.intel.analytics.bigdl.example.MLPipeline.DLEstimatorLeNet]] * - * @param modelTrain module to be optimized + * @param model module to be optimized * @param criterion criterion method - * @param batchShape batch shape for DLClassifier transformation input + * @param featureSize The size (Tensor dimensions) of the feature data. + * @param labelSize The size (Tensor dimensions) of the label data. */ -class DLEstimator[@specialized(Float, Double) T: ClassTag] -(val modelTrain : Module[T], val criterion : Criterion[T], val batchShape : Array[Int], - override val uid: String = "DLEstimator") -(implicit ev: TensorNumeric[T]) - extends DLEstimatorBase with HasFeaturesCol with HasLabelCol with DLDataParams[T] { +class DLEstimator[@specialized(Float, Double) T: ClassTag]( + val model: Module[T], + val criterion : Criterion[T], + val featureSize : Array[Int], + val labelSize : Array[Int], + override val uid: String = "DLEstimator" + )(implicit ev: TensorNumeric[T]) extends DLEstimatorBase with DLParams with HasBatchSize { def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) def setLabelCol(labelColName : String) : this.type = set(labelCol, labelColName) - private def validateInput(schema : StructType): Unit = { - require(isDefined(featuresCol), - "DLEstimator: features data must not be null") - require(isDefined(featureSize), - "DLEstimator: features size col must not be null") - require(isDefined(labelCol), - "DLEstimator: label data must not be null") - require(isDefined(labelSize), - "DLEstimator: label size must not be null") - val featureIndex = schema.fieldIndex($(featuresCol)) - val featureField = schema.fields(featureIndex) - require(featureField.dataType.isInstanceOf[ArrayType], "Feature data should be of array type") - val labelIndex = schema.fieldIndex($(labelCol)) - val labelField = schema.fields(labelIndex) - require(labelField.dataType.isInstanceOf[ArrayType], "Label data should be of array type") - } + def setPredictionCol(value: String): this.type = set(predictionCol, value) - override protected def process(dataFrame: DataFrame): DLTransformer = { + def setBatchSize(value: Int): this.type = set(batchSize, value) - validateInput(dataFrame.schema) + val maxEpoch = new IntParam(this, "maxEpoch", "number of max Epoch", ParamValidators.gt(0)) + setDefault(maxEpoch -> 20) - val batches = toMiniBatch(dataFrame) - - val dataset = DataSet.rdd(batches) + def getMaxEpoch: Int = $(maxEpoch) - val optimizer = Optimizer(modelTrain, dataset, criterion) + def setMaxEpoch(value: Int): this.type = set(maxEpoch, value) - var optimizedModule = modelTrain - - optimizedModule = optimizer.optimize() - - var classifier = new DLClassifier[T]() + override def transformSchema(schema : StructType): StructType = { + validateAndTransformSchema(schema) + } - val paramsTrans = ParamMap( - classifier.modelTrain -> optimizedModule, - classifier.batchShape -> batchShape) + protected override def internalFit(dataFrame: DataFrame): DLTransformerBase = { + val batches = toMiniBatch(dataFrame) + val dataset = DataSet.rdd(batches) - classifier = classifier.copy(paramsTrans) + val optimizer = Optimizer(model, dataset, criterion) + .setOptimMethod(new Adam[T]()) + .setEndWhen(Trigger.maxEpoch($(maxEpoch))) + val optimizedModel = optimizer.optimize() - classifier + val dlModel = new DLModel[T](optimizedModel, featureSize) + copyValues(dlModel.setParent(this)) } + /** + * Extract and reassemble data according to batchSize + */ private def toMiniBatch(dataFrame: DataFrame) : RDD[MiniBatch[T]] = { - - val data = dataFrame.rdd - val batchs = data.map(row => { - val featureData = row.getAs[mutable.WrappedArray[T]]($(featuresCol)).toArray - val labelData = row.getAs[mutable.WrappedArray[T]]($(labelCol)).toArray - MiniBatch[T](Tensor(featureData, $(featureSize)), Tensor(labelData, $(labelSize))) - }) - batchs + val featureArrayCol = if (dataFrame.schema($(featuresCol)).dataType.isInstanceOf[ArrayType]) { + $(featuresCol) + } else { + getFeatureArrayCol + } + val featureColIndex = dataFrame.schema.fieldIndex(featureArrayCol) + + val labelArrayCol = if (dataFrame.schema($(labelCol)).dataType.isInstanceOf[ArrayType]) { + $(labelCol) + } else { + getLabelArrayCol + } + val labelColIndex = dataFrame.schema.fieldIndex(labelArrayCol) + + val featureType = dataFrame.schema(featureArrayCol).dataType.asInstanceOf[ArrayType].elementType + val labelType = dataFrame.schema(labelArrayCol).dataType.asInstanceOf[ArrayType].elementType + + /** + * since model data type (float or double) and feature data element type does not necessarily + * comply, we need to extract data from feature column and convert according to model type. + */ + val featureAndLabelData = dataFrame.rdd.map { row => + val featureData = featureType match { + case DoubleType => + row.getSeq[Double](featureColIndex).toArray.map(ev.fromType(_)) + case FloatType => + row.getSeq[Float](featureColIndex).toArray.map(ev.fromType(_)) + } + require(featureData.length == featureSize.product, s"Data length mismatch:" + + s" feature data length ${featureData.length}, featureSize: ${featureSize.mkString(", ")}") + + val labelData = labelType match { + case DoubleType => + row.getSeq[Double](labelColIndex).toArray.map(ev.fromType(_)) + case FloatType => + row.getSeq[Float](labelColIndex).toArray.map(ev.fromType(_)) + } + require(featureData.length == featureSize.product, s"Data length mismatch:" + + s" label data length ${featureData.length}, labelSize: ${featureSize.mkString(", ")}") + (featureData, labelData) + } + + featureAndLabelData.mapPartitions { rows => + val batches = rows.grouped($(batchSize)).map { batch => + val featureData = batch.flatMap(_._1).toArray + val labelData = batch.flatMap(_._2).toArray + MiniBatch[T]( + Tensor(featureData, Array(batch.length) ++ featureSize), + Tensor(labelData, Array(batch.length) ++ labelSize)) + } + batches + } } override def copy(extra: ParamMap): DLEstimator[T] = { - copyValues(new DLEstimator(modelTrain, criterion, batchShape), extra) + copyValues(new DLEstimator(model, criterion, featureSize, labelSize), extra) } } - -private[ml] trait DLDataParams[@specialized(Float, Double) T] extends Params { - - final val featureSize = new Param[Array[Int]](this, "feature size", "feature input size") - - final val labelSize = new Param[Array[Int]](this, "label size", "label input size") - - final def getFeatureSize : Array[Int] = $(featureSize) - - final def getLabelSize : Array[Int] = $(labelSize) - -} - - - diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index 72448a0c876..d1a631c2226 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -14,19 +14,120 @@ * limitations under the License. */ package org.apache.spark.ml + +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.sql.functions.{col, udf} import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.types.{ArrayType, DoubleType, FloatType, StructType} + +private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { + + /** + * only validate feature columns here + */ + protected def validateAndTransformSchema(schema: StructType): StructType = { + val dataTypes = Seq( + new ArrayType(DoubleType, false), + new ArrayType(FloatType, false), + new VectorUDT) + + // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 + val actualDataType = schema($(featuresCol)).dataType + require(dataTypes.exists(actualDataType.equals), + s"Column ${$(featuresCol)} must be of type equal to one of the following types: " + + s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") + + SchemaUtils.appendColumn(schema, $(predictionCol), new ArrayType(DoubleType, false)) + } + + /** + * convert feature columns to array columns + */ + protected def toArrayType(dataset: DataFrame): DataFrame = { + val toArray = udf { (vector: Vector) => vector.toArray } + var converted = dataset + if (converted.schema($(featuresCol)).dataType.sameType(new VectorUDT)) { + val newFeatureCol = getFeatureArrayCol + converted = converted.withColumn(newFeatureCol, toArray(col($(featuresCol)))) + } + + converted + } + + protected def getFeatureArrayCol: String = $(featuresCol) + "_Array" + +} + + /** *A wrapper from org.apache.spark.ml.Estimator * Extends MLEstimator and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -abstract class DLEstimatorBase extends Estimator[DLTransformer]{ - protected def process(dataset: DataFrame): DLTransformer - override def fit(dataset : org.apache.spark.sql.DataFrame) : DLTransformer = { - process(dataset) +private[ml] abstract class DLEstimatorBase + extends Estimator[DLTransformerBase] with DLParams with HasLabelCol{ + + protected def getLabelArrayCol: String = $(labelCol) + "_Array" + + protected def internalFit(dataset: DataFrame): DLTransformerBase + + override def fit(dataset: DataFrame): DLTransformerBase = { + transformSchema(dataset.schema, logging = true) + internalFit(toArrayType(dataset)) + } + + override def transformSchema(schema : StructType): StructType = { + validateAndTransformSchema(schema) + } + + /** + * convert feature and label columns to array columns + */ + protected override def toArrayType(dataset: DataFrame): DataFrame = { + var converted = super.toArrayType(dataset) + + // convert label column to array type + val vec2Array = udf { (vector: Vector) => vector.toArray } + val num2Array = udf { (d: Double) => Array(d) } + val labelType = converted.schema($(labelCol)).dataType + val newLabelCol = getLabelArrayCol + + if (labelType.sameType(new VectorUDT)) { + converted = converted.withColumn(newLabelCol, vec2Array(col($(labelCol)))) + } else if (labelType.sameType(DoubleType)) { + converted = converted.withColumn(newLabelCol, num2Array(col($(labelCol)))) + } + converted + } + + /** + * validate both feature and label columns + */ + protected override def validateAndTransformSchema(schema: StructType): StructType = { + // validate feature column + super.validateAndTransformSchema(schema) + + // validate label column + val dataTypes = Seq( + new ArrayType(DoubleType, false), + new ArrayType(FloatType, false), + new VectorUDT, + DoubleType) + + // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 + val actualDataType = schema($(labelCol)).dataType + require(dataTypes.exists(actualDataType.equals), + s"Column ${$(labelCol)} must be of type equal to one of the following types: " + + s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") + + SchemaUtils.appendColumn(schema, $(predictionCol), new ArrayType(DoubleType, false)) } - override def transformSchema(schema: StructType): StructType = schema + override def copy(extra: ParamMap): DLEstimatorBase = defaultCopy(extra) } + + + diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformer.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala similarity index 65% rename from scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformer.scala rename to scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala index de3a3921d46..68428498eff 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformer.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala @@ -14,8 +14,9 @@ * limitations under the License. */ package org.apache.spark.ml + import org.apache.spark.ml.param.ParamMap -import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.{DataFrame} import org.apache.spark.sql.types.StructType /** @@ -23,16 +24,20 @@ import org.apache.spark.sql.types.StructType * Extends MlTransformer and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -abstract class DLTransformer extends Model[DLTransformer]{ +private[ml] abstract class DLTransformerBase + extends Model[DLTransformerBase] with DLParams { - def process(dataset: DataFrame): DataFrame + protected def internalTransform(dataset: DataFrame): DataFrame override def transform(dataset: DataFrame): DataFrame = { - process(dataset) + transformSchema(dataset.schema, logging = true) + internalTransform(toArrayType(dataset.toDF())) } - override def transformSchema(schema: StructType): StructType = schema + override def transformSchema(schema : StructType): StructType = { + validateAndTransformSchema(schema) + } - override def copy(extra: ParamMap): DLTransformer = defaultCopy(extra) + override def copy(extra: ParamMap): DLTransformerBase = defaultCopy(extra) } diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index 86e519375b8..c3a29b506ec 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -14,20 +14,117 @@ * limitations under the License. */ package org.apache.spark.ml + +import org.apache.spark.ml.linalg.{Vector, VectorUDT} import org.apache.spark.ml.param.ParamMap -import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.types.StructType -import org.apache.spark.sql.Dataset +import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.sql.functions.{col, udf} +import org.apache.spark.sql.{DataFrame, Dataset} +import org.apache.spark.sql.types.{ArrayType, DoubleType, FloatType, StructType} + +private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { + + /** + * validate feature columns data format + */ + protected def validateAndTransformSchema(schema: StructType): StructType = { + val dataTypes = Seq( + new ArrayType(DoubleType, false), + new ArrayType(FloatType, false), + new VectorUDT) + + // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 + val actualDataType = schema($(featuresCol)).dataType + require(dataTypes.exists(actualDataType.equals), + s"Column ${$(featuresCol)} must be of type equal to one of the following types: " + + s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") + + SchemaUtils.appendColumn(schema, $(predictionCol), new ArrayType(DoubleType, false)) + } + + /** + * convert feature columns to array columns if necessary + */ + protected def toArrayType(dataset: DataFrame): DataFrame = { + val toArray = udf { (vector: Vector) => vector.toArray } + var converted = dataset + if (converted.schema($(featuresCol)).dataType.sameType(new VectorUDT)) { + val newFeatureCol = getFeatureArrayCol + converted = converted.withColumn(newFeatureCol, toArray(col($(featuresCol)))) + } + + converted + } + + protected def getFeatureArrayCol: String = $(featuresCol) + "_Array" + +} + + /** - *A wrapper from org.apache.spark.ml.Estimator + * A wrapper from org.apache.spark.ml.Estimator * Extends MLEstimator and override process to gain compatibility with * both spark 1.5 and spark 2.0. - */ -abstract class DLEstimatorBase extends Estimator[DLTransformer]{ - protected def process(dataset: DataFrame): DLTransformer - override def fit(dataset: Dataset[_]): DLTransformer = { - process(dataset.toDF()) + */ +private[ml] abstract class DLEstimatorBase + extends Estimator[DLTransformerBase] with DLParams with HasLabelCol{ + + protected def getLabelArrayCol: String = $(labelCol) + "_Array" + + protected def internalFit(dataset: DataFrame): DLTransformerBase + + override def fit(dataset: Dataset[_]): DLTransformerBase = { + transformSchema(dataset.schema, logging = true) + internalFit(toArrayType(dataset.toDF())) + } + + override def transformSchema(schema : StructType): StructType = { + validateAndTransformSchema(schema) + } + + /** + * convert feature and label columns to array columns + */ + protected override def toArrayType(dataset: DataFrame): DataFrame = { + var converted = super.toArrayType(dataset) + + // convert label column to array type + val vec2Array = udf { (vector: Vector) => vector.toArray } + val num2Array = udf { (d: Double) => Array(d) } + val labelType = converted.schema($(labelCol)).dataType + val newLabelCol = getLabelArrayCol + + if (labelType.sameType(new VectorUDT)) { + converted = converted.withColumn(newLabelCol, vec2Array(col($(labelCol)))) + } else if (labelType.sameType(DoubleType)) { + converted = converted.withColumn(newLabelCol, num2Array(col($(labelCol)))) + } + converted + } + + /** + * validate both feature and label columns + */ + protected override def validateAndTransformSchema(schema: StructType): StructType = { + // validate feature column + super.validateAndTransformSchema(schema) + + // validate label column + val dataTypes = Seq( + new ArrayType(DoubleType, false), + new ArrayType(FloatType, false), + new VectorUDT, + DoubleType) + + // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 + val actualDataType = schema($(labelCol)).dataType + require(dataTypes.exists(actualDataType.equals), + s"Column ${$(labelCol)} must be of type equal to one of the following types: " + + s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") + + SchemaUtils.appendColumn(schema, $(predictionCol), new ArrayType(DoubleType, false)) } - override def transformSchema(schema: StructType): StructType = schema + override def copy(extra: ParamMap): DLEstimatorBase = defaultCopy(extra) } diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformer.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala similarity index 68% rename from scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformer.scala rename to scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala index f966b0fc012..32b34eec6b3 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformer.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala @@ -14,24 +14,30 @@ * limitations under the License. */ package org.apache.spark.ml + import org.apache.spark.ml.param.ParamMap -import org.apache.spark.sql.types.StructType import org.apache.spark.sql.{DataFrame, Dataset} +import org.apache.spark.sql.types.StructType /** * A wrapper for org.apache.spark.ml.Transformer. * Extends MlTransformer and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -abstract class DLTransformer extends Model[DLTransformer]{ +private[ml] abstract class DLTransformerBase + extends Model[DLTransformerBase] with DLParams { - def process(dataset: DataFrame): DataFrame + protected def internalTransform(dataset: DataFrame): DataFrame override def transform(dataset: Dataset[_]): DataFrame = { - process(dataset.toDF()) + transformSchema(dataset.schema, logging = true) + internalTransform(toArrayType(dataset.toDF())) + } + + override def transformSchema(schema : StructType): StructType = { + validateAndTransformSchema(schema) } - override def transformSchema(schema: StructType): StructType = schema + override def copy(extra: ParamMap): DLTransformerBase = defaultCopy(extra) - override def copy(extra: ParamMap): DLTransformer = defaultCopy(extra) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorLeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorLeNet.scala index 8eb9170d7c0..ff8bd7a5c37 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorLeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorLeNet.scala @@ -15,30 +15,20 @@ */ package com.intel.analytics.bigdl.example.MLPipeline -import java.nio.file.Paths - -import com.intel.analytics.bigdl.dataset.{DistributedDataSet, MiniBatch} +import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToBatch} -import com.intel.analytics.bigdl.models.lenet.{LeNet5, Utils} +import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, _} +import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.lenet.Utils._ -import com.intel.analytics.bigdl.nn.Module -import com.intel.analytics.bigdl.utils.LoggerFilter -import org.apache.log4j.{Level, Logger} -import org.apache.spark.ml.{DLClassifier, DLEstimator, Pipeline} -import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{DataSet, _} -import com.intel.analytics.bigdl.example.imageclassification.MlUtils.{testMean => _, testStd => _, _} import com.intel.analytics.bigdl.nn.ClassNLLCriterion import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{Engine, T} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} +import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext -import org.apache.spark.ml.param.ParamMap -import org.apache.spark.mllib.linalg.DenseVector +import org.apache.spark.ml.{DLEstimator, DLModel} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, Row, SQLContext} - -import scala.collection.mutable.ArrayBuffer +import org.apache.spark.sql.SQLContext /** * An example to show how to use DLEstimator fit to be compatible with ML Pipeline @@ -48,86 +38,57 @@ object DLEstimatorLeNet { LoggerFilter.redirectSparkInfoLogs() Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + def main(args: Array[String]): Unit = { val inputs = Array[String]("Feature data", "Label data") - trainParser.parse(args, new TrainParams()).map(param => { + trainParser.parse(args, new TrainParams()).foreach(param => { val conf = Engine.createSparkConf() .setAppName("MLPipeline Example") .set("spark.task.maxFailures", "1") val sc = new SparkContext(conf) - val sqLContext = new SQLContext(sc) + val sqLContext = SQLContext.getOrCreate(sc) Engine.init - val trainData = param.folder + "/train-images-idx3-ubyte" - val trainLabel = param.folder + "/train-labels-idx1-ubyte" - val validationData = param.folder + "/t10k-images-idx3-ubyte" - val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" - - val model = LeNet5(classNum = 10) + val trainData = param.folder + "/train-images.idx3-ubyte" + val trainLabel = param.folder + "/train-labels.idx1-ubyte" + val validationData = param.folder + "/t10k-images.idx3-ubyte" + val validationLabel = param.folder + "/t10k-labels.idx1-ubyte" val trainSet = DataSet.array(load(trainData, trainLabel), sc) -> - BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToBatch( - param.batchSize) - - val validationSet = DataSet.array(load(validationData, validationLabel), sc) -> - BytesToGreyImg(28, 28) -> GreyImgNormalizer(testMean, testStd) -> GreyImgToBatch( - param.batchSize) + BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToBatch(1) - val dataFrameRDD : RDD[MinibatchData[Float]] = trainSet. + val trainingRDD : RDD[Data[Float]] = trainSet. asInstanceOf[DistributedDataSet[TensorMiniBatch[Float]]].data(false).map(batch => { - val feature = batch.getInput.asInstanceOf[Tensor[Float]] - val label = batch.getTarget.asInstanceOf[Tensor[Float]] - val estimatorData = MinibatchData[Float](feature.storage().array(), - label.storage().array()) - estimatorData + val feature = batch.getInput().asInstanceOf[Tensor[Float]] + val label = batch.getTarget().asInstanceOf[Tensor[Float]] + Data[Float](feature.storage().array(), label.storage().array()) }) + val trainingDF = sqLContext.createDataFrame(trainingRDD).toDF(inputs: _*) - var trainingDF : DataFrame = sqLContext.createDataFrame(dataFrameRDD).toDF(inputs : _*) - + val model = LeNet5(classNum = 10) val criterion = ClassNLLCriterion[Float]() + val featureSize = Array(28, 28) + val estimator = new DLEstimator[Float](model, criterion, featureSize, Array(1)) + .setFeaturesCol(inputs(0)) + .setLabelCol(inputs(1)) + .setBatchSize(50) + val transformer = estimator.fit(trainingDF).asInstanceOf[DLModel[Float]] - var batchShape = Array[Int](128, 28, 28) - - var estimator = new DLEstimator[Float](model, criterion, batchShape). - setFeaturesCol(inputs(0)).setLabelCol(inputs(1)) - - val paramsTrans = ParamMap( - estimator.featureSize -> Array(10, 28, 28), - estimator.labelSize -> Array(10) ) - - estimator = estimator.copy(paramsTrans) - - val transformer = estimator.fit(trainingDF).asInstanceOf[DLClassifier[Float]] - - transformer.setInputCol("features") - .setOutputCol("predict") + val validationSet = DataSet.array(load(validationData, validationLabel), sc) -> + BytesToGreyImg(28, 28) -> GreyImgNormalizer(testMean, testStd) -> GreyImgToBatch(1) - val rdd: RDD[DenseVectorData] = validationSet. - asInstanceOf[DistributedDataSet[MiniBatch[Float]]].data(false).flatMap{batch => { - val buffer = new ArrayBuffer[DenseVectorData]() - val feature = batch.data.storage().toArray - var i = 0 - while (i < 128) { - val next = new DenseVector(feature. - slice(i * 28 * 28, (i + 1) * 28 * 28).map(_.toDouble)) - val data = DenseVectorData(next) - buffer.append(data) - i += 1 - } - buffer.iterator - } + val validationRDD: RDD[Data[Float]] = validationSet. + asInstanceOf[DistributedDataSet[MiniBatch[Float]]].data(false).map{batch => + val feature = batch.getInput().asInstanceOf[Tensor[Float]] + val label = batch.getTarget().asInstanceOf[Tensor[Float]] + Data[Float](feature.storage().array(), label.storage().array()) } - var validationDF : DataFrame = sqLContext.createDataFrame(rdd).toDF("features") + val validationDF = sqLContext.createDataFrame(validationRDD).toDF(inputs: _*) val transformed = transformer.transform(validationDF) - transformed.select("features", "predict").collect() - .foreach { case Row(data: DenseVector, predict: Int) => - println(data + "=>" + predict) - } + transformed.show() sc.stop() }) - } } -private case class DenseVectorData(denseVector : DenseVector) -private case class MinibatchData[T](featureData : Array[T], labelData : Array[T]) +private case class Data[T](featureData : Array[T], labelData : Array[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/ImagePredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/ImagePredictor.scala index f8cf9961904..e97cdc57337 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/ImagePredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/ImagePredictor.scala @@ -23,7 +23,6 @@ import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext -import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.{DLClassifier => SparkDLClassifier} import org.apache.spark.sql.SQLContext @@ -44,14 +43,10 @@ object ImagePredictor { val partitionNum = Engine.nodeNumber() * Engine.coreNumber() val model = loadModel(param) - val valTrans = new SparkDLClassifier() - .setInputCol("features") - .setOutputCol("predict") - - val paramsTrans = ParamMap( - valTrans.modelTrain -> model, - valTrans.batchShape -> - Array(param.batchSize, 3, imageSize, imageSize)) + val valTrans = new SparkDLClassifier(model, Array(3, imageSize, imageSize)) + .setBatchSize(param.batchSize) + .setFeaturesCol("features") + .setPredictionCol("predict") val valRDD = if (param.isHdfs) { // load image set from hdfs @@ -70,7 +65,7 @@ object ImagePredictor { val valDF = transformDF(sqlContext.createDataFrame(valRDD), transf) - valTrans.transform(valDF, paramsTrans) + valTrans.transform(valDF) .select("imageName", "predict") .collect() .take(param.showNum) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala index 49954626101..bc6e1ab3b87 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.dataset.MiniBatch +import scala.util.Random + import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Linear, Sequential} import com.intel.analytics.bigdl.tensor.Tensor @@ -24,19 +25,21 @@ import com.intel.analytics.bigdl.utils.Engine import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import org.apache.log4j.{Level, Logger} -import org.apache.spark.SparkContext -import org.apache.spark.ml._ -import org.apache.spark.ml.param.ParamMap -import org.apache.spark.mllib.linalg.DenseVector +import org.apache.spark.{SparkContext, SparkException} +import org.apache.spark.ml.{DLClassifier, DLEstimator, DLModel} import org.apache.spark.sql.{DataFrame, SQLContext} -import scala.collection.mutable.ArrayBuffer -import scala.util.Random - class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val model = new Sequential[Float]() var sc : SparkContext = null - var sQLContext : SQLContext = null + var sqlContext : SQLContext = null + + before { + val conf = Engine.createSparkConf().setAppName("Test DLEstimator").setMaster("local[1]") + sc = SparkContext.getOrCreate(conf) + sqlContext = new SQLContext(sc) + Engine.init + } after{ if (sc != null) { @@ -45,192 +48,89 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { } "An Estimator" should "works properly" in { - val conf = Engine.createSparkConf().setAppName("Test DLEstimator").setMaster("local[1]") - sc = SparkContext.getOrCreate(conf) - sQLContext = new SQLContext(sc) - Engine.init - - val inputs = Array[String]("Feature data", "Label data") val model = Linear[Float](10, 1) - val criterion = ClassNLLCriterion[Float]() - - var estimator = new DLEstimator[Float](model, criterion, Array(2, 10)) - .setFeaturesCol(inputs(0)).setLabelCol(inputs(1)) - - val featureData = Tensor(2, 10) - val labelData = Tensor(2, 1).fill(1.0f) - - val batch = MiniBatch(featureData, labelData) - + val inputs = Array[String]("Feature data", "Label data") + var estimator = new DLEstimator[Float](model, criterion, Array(10), Array(1)) + .setBatchSize(2) + .setFeaturesCol(inputs(0)) + .setLabelCol(inputs(1)) + .setMaxEpoch(2) + + val featureData = Tensor(10) + val labelData = Tensor(1).fill(1.0f) val miniBatch = sc.parallelize(Seq( - MinibatchData[Float](featureData.storage().array(), - labelData.storage().array()) + MinibatchData[Float](featureData.storage().array(), labelData.storage().array()) )) - - val paramsTrans = ParamMap( - estimator.featureSize -> Array(2, 10), - estimator.labelSize -> Array(2, 1)) - - estimator = estimator.copy(paramsTrans) - - var trainingDF: DataFrame = sQLContext.createDataFrame(miniBatch).toDF(inputs: _*) + var trainingDF: DataFrame = sqlContext.createDataFrame(miniBatch).toDF(inputs: _*) val res = estimator.fit(trainingDF) - - res.isInstanceOf[DLTransformer] should be(true) + res.isInstanceOf[DLModel[_]] should be(true) } "An Estimator" should "throws exception without correct inputs" in { - val conf = Engine.createSparkConf().setAppName("Test DLEstimator").setMaster("local[1]") - sc = new SparkContext(conf) - sQLContext = new SQLContext(sc) - Engine.init - - val inputs = Array[String]("Feature data", "Label data") val model = Linear[Float](10, 1) - val criterion = ClassNLLCriterion[Float]() - - var estimator = new DLEstimator[Float](model, criterion, Array(2, 10)). + val inputs = Array[String]("Feature data", "Label data") + var estimator = new DLEstimator[Float](model, criterion, Array(10), Array(2, 1)). setFeaturesCol(inputs(0)).setLabelCol(inputs(1)) val featureData = Tensor(2, 10) val labelData = Tensor(2, 1) - val batch = MiniBatch(featureData, labelData) - val miniBatch = sc.parallelize(Seq( - MinibatchData[Float](featureData.storage().array(), - labelData.storage().array()) + MinibatchData[Float](featureData.storage().array(), labelData.storage().array()) )) + var df: DataFrame = sqlContext.createDataFrame(miniBatch).toDF(inputs: _*) - var df: DataFrame = sQLContext.createDataFrame(miniBatch).toDF(inputs: _*) - intercept[IllegalArgumentException] { - val res = estimator.fit(df) + // Spark 1.6 and 2.0 throws different exception here + intercept[Exception] { + estimator.fit(df) } } - "An Estimator" should "has same transformate result as Classifier" in { - val conf = Engine.createSparkConf().setAppName("Test DLEstimator").setMaster("local[1]") - sc = new SparkContext(conf) - sQLContext = new SQLContext(sc) - Engine.init + "An Estimator" should "has same transform result as Classifier" in { Logger.getLogger("org").setLevel(Level.WARN) Logger.getLogger("akka").setLevel(Level.WARN) - val tensorBuffer = new ArrayBuffer[ClassifierDenseVector]() - - val batchSize = 10 + val batchSize = 2 val inputs = Array[String]("Feature data", "Label data") + val featureSize = Array(28, 28) val model = LeNet5(5) - - val batchShape = Array(10, 28, 28) - val criterion = ClassNLLCriterion[Float]() + var estimator = new DLEstimator[Float](model, criterion, featureSize, Array(1)) + .setFeaturesCol(inputs(0)) + .setLabelCol(inputs(1)) + .setBatchSize(batchSize) + + val optimizerInput = Tensor[Float](28, 28).apply1(e => Random.nextFloat()) + val optimizerTarget = model.forward(optimizerInput).toTensor[Float] + val optimizerTargetArr = optimizerTarget.max(1)._2.squeeze().storage().array() + val miniBatch = sc.parallelize( Seq( + MinibatchData(optimizerInput.storage().array(), optimizerTargetArr), + MinibatchData(optimizerInput.storage().array(), optimizerTargetArr) + )) + val df = sqlContext.createDataFrame(miniBatch).toDF(inputs: _*) - var estimator = new DLEstimator[Float](model, criterion, batchShape). - setFeaturesCol(inputs(0)).setLabelCol(inputs(1)) - - var m = 0 - - var transformer: DLClassifier[Float] = null - - while (m < 10) { - - val optimizerInput = Tensor[Float](10, 28, 28).apply1(e => Random.nextFloat()) - - val optimizerTarget = model.forward(optimizerInput).toTensor[Float] - - val optimizerInputArr = optimizerInput.storage().array() - - val optimizerTargetArr = optimizerTarget.max(2)._2.squeeze().storage().array() - - val paramsTrans = ParamMap( - estimator.featureSize -> Array(10, 28, 28), - estimator.labelSize -> Array(10)) - - estimator = estimator.copy(paramsTrans) - - val miniBatch = sc.parallelize(Seq( - MinibatchData(optimizerInput.storage().array(), optimizerTargetArr) - )) - - val df = sQLContext.createDataFrame(miniBatch).toDF(inputs: _*) - - transformer = estimator.fit(df).asInstanceOf[DLClassifier[Float]] - - m += 1 - } - - transformer.setInputCol("features") - .setOutputCol("predict") - - val optimizedModel = transformer.getModel - - val transInput = Tensor[Float](10, 28, 28).apply1(e => Random.nextFloat()) - - val classifierInput = Tensor[Float]() - - classifierInput.resizeAs(transInput).copy(transInput) - - val transInputDataArr = transInput.storage().array() - - var i = 0 - while (i < batchSize) { - tensorBuffer.append(new ClassifierDenseVector( - new DenseVector(transInputDataArr.slice(i * 28 * 28, (i + 1) * 28 * 28).map(_.toDouble)))) - i += 1 - } - - val transRDD = sc.parallelize(tensorBuffer) - val transDataFrame = sQLContext.createDataFrame(transRDD) - - val transPredicts = transformer.transform(transDataFrame). - select("predict").collect().map( - row => { - row.getAs[Int](0) + val dlModel = estimator.fit(df).asInstanceOf[DLModel[Float]] + val transPredicts = dlModel.transform(df).select("prediction").collect().map { row => + row.getSeq[Double](0).head } - ) - tensorBuffer.clear() - - var classifier = new DLClassifier[Float]() - .setInputCol("features") - .setOutputCol("predict") - - val classifierParams = ParamMap( - classifier.modelTrain -> optimizedModel, - classifier.batchShape -> batchShape) - classifier = classifier.copy(classifierParams) - - val classifierInputArr = classifierInput.storage().array() - - i = 0 - while (i < batchSize) { - tensorBuffer.append(new ClassifierDenseVector( - new DenseVector(classifierInputArr.slice(i * 28 * 28, (i + 1) * 28 * 28). - map(_.toDouble)))) - i += 1 - } - - val classifierRDD = sc.parallelize(tensorBuffer) - val classifierDataFrame = sQLContext.createDataFrame(classifierRDD) - - val classifierPredicts = classifier.transform(classifierDataFrame). - select("predict").collect().map( + val classifier = new DLClassifier[Float](dlModel.model, featureSize) + .setFeaturesCol(inputs(0)) + .setBatchSize(batchSize) + val classifierPredicts = classifier.transform(df). + select("prediction").collect().map( row => { - row.getAs[Int](0) + row.getSeq[Double](0).head } ) transPredicts should be(classifierPredicts) } - } -private case class ClassifierDenseVector( val features : DenseVector) - private case class MinibatchData[T](featureData : Array[T], labelData : Array[T]) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DLClassifierSpec.scala index 1813f20359e..b030eca5bdd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DLClassifierSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DLClassifierSpec.scala @@ -16,30 +16,19 @@ package com.intel.analytics.bigdl.utils +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.util.Random import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.tensor.Tensor import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkConf, SparkContext} -import org.apache.spark.ml.DLClassifier -import org.apache.spark.ml.param.ParamMap -import org.apache.spark.mllib.linalg.DenseVector -import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.ml.DLModel import org.apache.spark.sql.{Row, SQLContext} import org.scalatest.{FlatSpec, Matchers} -import scala.collection.mutable.ArrayBuffer -import scala.util.Random - @com.intel.analytics.bigdl.tags.Parallel -class DLClassifierSpec extends FlatSpec with Matchers{ - - private def processPath(path: String): String = { - if (path.contains(":")) { - path.substring(1) - } else { - path - } - } +class DLClassifierSpec extends FlatSpec with Matchers { "DLClassifier" should "get good result" in { Logger.getLogger("org").setLevel(Level.WARN) @@ -52,41 +41,37 @@ class DLClassifierSpec extends FlatSpec with Matchers{ val model = LeNet5(10) // init - val valTrans = new DLClassifier[Float]().setInputCol("features").setOutputCol("predict") - val paramsTrans = ParamMap(valTrans.modelTrain -> model, - valTrans.batchShape -> Array(10, 28, 28)) + val valTrans = new DLModel[Float](model, Array(28, 28)) + .setFeaturesCol("features") + .setPredictionCol("predict") + .setBatchSize(10) - val tensorBuffer = new ArrayBuffer[LabeledPoint]() - var m = 0 - while (m < 10) { - // generate test data - val input = Tensor[Float](10, 28, 28).apply1(e => Random.nextFloat()) - val target = model.forward(input).toTensor[Float] + val tensorBuffer = new ArrayBuffer[Data]() + // generate test data + val input = Tensor[Float](10, 28, 28).apply1(e => Random.nextFloat()) + val target = model.forward(input).toTensor[Float] - val inputArr = input.storage().array() - val targetArr = target.max(2)._2.squeeze().storage().array() + val inputArr = input.storage().array() + val targetArr = target.max(2)._2.squeeze().storage().array() - var i = 0 - while (i < batchSize) { - tensorBuffer.append(new LabeledPoint(targetArr(i), - new DenseVector(inputArr.slice(i * 28 * 28, (i + 1) * 28 * 28).map(_.toDouble)))) - i += 1 - } + (0 until batchSize).foreach(i => + tensorBuffer.append( + Data(targetArr(i), inputArr.slice(i * 28 * 28, (i + 1) * 28 * 28).map(_.toDouble)))) - val rowRDD = sc.parallelize(tensorBuffer) - val testData = sqlContext.createDataFrame(rowRDD) + val rowRDD = sc.parallelize(tensorBuffer) + val testData = sqlContext.createDataFrame(rowRDD) - valTrans.transform(testData, paramsTrans) - .select("label", "predict") - .collect() - .foreach { case Row(label: Double, predict: Int) => - label.toInt should be(predict) - } + valTrans.transform(testData) + .select("label", "predict") + .collect() + .foreach { case Row(label: Double, predict: mutable.WrappedArray[Double]) => + label should be(predict.head) + } - tensorBuffer.clear() - m += 1 - } + tensorBuffer.clear() sc.stop() } } +case class Data(label: Double, features: Array[Double]) + From e0a8afac7ec846a084b9bedba322abde5711fb75 Mon Sep 17 00:00:00 2001 From: yangw Date: Wed, 14 Jun 2017 16:30:51 +0800 Subject: [PATCH 0190/1065] move structured streaming to tutorials --- dl/pom.xml | 18 -- .../udfpredictor/FileStreamProducer.scala | 59 ----- .../dllib/example/udfpredictor/README.md | 214 +----------------- .../StructuredStreamPredictor.scala | 149 ------------ ...cala => TextClassificationUDFParams.scala} | 9 - .../dllib/example/udfpredictor/Utils.scala | 19 -- 6 files changed, 2 insertions(+), 466 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/FileStreamProducer.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/StructuredStreamPredictor.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/{Options.scala => TextClassificationUDFParams.scala} (83%) diff --git a/dl/pom.xml b/dl/pom.xml index 52baf854f7b..1066628c125 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -18,7 +18,6 @@ true false com.intel.analytics.bigdl.tags.Integration - com/intel/analytics/bigdl/example/udfpredictor/*Stream*.scala @@ -252,17 +251,6 @@ - - - net.alchim31.maven - scala-maven-plugin - 3.2.0 - - - ${filesToExclude} - - - @@ -284,12 +272,6 @@ - - spark_2.x - - "" - - parallel-tests diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/FileStreamProducer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/FileStreamProducer.scala deleted file mode 100644 index 0a671398b76..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/FileStreamProducer.scala +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.example.udfpredictor - -import com.intel.analytics.bigdl.example.udfpredictor.Utils._ -import com.intel.analytics.bigdl.utils.LoggerFilter -import org.apache.spark.sql.SparkSession -import org.apache.log4j.{Level => Levle4j, Logger => Logger4j} -import org.slf4j.{Logger, LoggerFactory} - - -object FileStreamProducer { - - val log: Logger = LoggerFactory.getLogger(this.getClass) - LoggerFilter.redirectSparkInfoLogs() - Logger4j.getLogger("com.intel.analytics.bigdl.optim").setLevel(Levle4j.INFO) - - def main(args: Array[String]): Unit = { - - parquetProducerParser.parse(args, TextProducerParquetParams()).foreach { param => - - val batchSize = param.batchsize - val interval = param.interval - // load messages - val data = Utils.loadTestData(param.srcFolder) - - val spark = SparkSession.builder().appName("Produce Text").getOrCreate() - var send_count = 0 - val batches = data.grouped(batchSize) - batches.foreach { batch => - try { - val df = spark.createDataFrame(batch) - log.info("send text batch " + send_count) - df.write - .format("parquet") - .mode(org.apache.spark.sql.SaveMode.Append) - .save(param.destFolder) - send_count += 1 - Thread.sleep(interval*1000) - } catch { - case e: Exception => log.error("sending batch error", e) - } - } - } - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/README.md index 7af823876f2..9f3fc41d2da 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/README.md @@ -1,5 +1,5 @@ ## Summary - This example is to show how to load BigDL model as UDF to perform predictions in Spark SQL/Dataframes and StructuredStreaming (Spark 2.0+ only). + This example is to show how to load BigDL model as UDF to perform predictions in Spark SQL/Dataframes. First use a (pre-trained GloVe embedding) to convert word to vector, and uses it to train the text classification model on a 20 Newsgroup dataset @@ -301,214 +301,4 @@ Please build the source code with your specific version of spark referring the | 104753| 9|Path: cantaloupe....| | 104806| 9|Newsgroups: rec.m...| +--------+------------+--------------------+ - ``` - -6. Run Structured Streaming example - - Note: To run this example, you spark version must be equal or higher than spark 2.0 - -* Start the consumer to subscribe the text streaming and do prediction with UDF. - - Run the commands: - - If you do not have the pre-trained model, you need to use this command to train the model and use this model to predict text classification of incoming text streaming with UDF. - - Example: - - ```shell - BASE_DIR=${PWD} # where is the data - MASTER=local[*] # the master url - ./dist/bin/bigdl.sh -- spark-submit --master $MASTER --driver-memory 20g \ - --class com.intel.analytics.bigdl.example.udfpredictor.StructuredStreamPredictor \ - ./dist/lib/bigdl-$VERSION-jar-with-dependencies.jar \ - --batchSize 32 \ - --baseDir $BASE_DIR \ - --partitionNum 4 \ - --checkpoint $BASE_DIR/model/text \ - --dataDir $BASE_DIR/data/text/parquet - ``` - - In the above commands, - - --batchSize: how many text files to be trained at one time - - --baseDir: folder containing training text files and word2Vec embedding. - - --partitionNum: number to partition training data - - --checkpoint: location to save model - - --dataDir: Directory to subscribe - - If you are running spark cluster mode, you also need to set --executor-cores and --total-executor-cores, and the - --batchSize should be a multiple of node_number*core_number. - - Example: - - ```shell - BASE_DIR=${PWD} # where is the data - MASTER=xxx.xxx.xxx.xxx:xxxx # the master url - ./dist/bin/bigdl.sh -- spark-submit --master $MASTER --driver-memory 20g \ - --executor-cores 8 \ - --total-executor-cores 32 \ - --class com.intel.analytics.bigdl.example.udfpredictor.StructuredStreamPredictor \ - ./dist/lib/bigdl-$VERSION-jar-with-dependencies.jar \ - --batchSize 32 \ - --baseDir $BASE_DIR \ - --partitionNum 4 \ - --checkpoint $BASE_DIR/model/text \ - --dataDir $BASE_DIR/data/text/parquet - ``` - - If you have saved model, you need to use this command to predict text classification of incoming text streaming with UDF. - - Example: - - ```shell - BASE_DIR=${PWD} # where is the data, please modify it accordingly - MASTER=loca[*] # the master url, please modify it accordingly - ./bigdl.sh -- spark-submit --master MASTER --driver-memory 5g \ - --class com.intel.analytics.bigdl.example.udfpredictor.StructuredStreamPredictor \ - dist/lib/bigdl-$VERSION-jar-with-dependencies.jar \ - --baseDir $BASE_DIR \ - --modelPath $BASE_DIR/model/text/model.1 \ - --dataDir $BASE_DIR/data/text/parquet - ``` - - In the above commands, - - --baseDir: folder containing training text files and word2Vec embedding - - --modelPath: model location - - --dataDir: Directory to subscribe - -* Run this command to publish text data to the target directory: - - Example: - ``` - BASE_DIR=${PWD} # where is the data - MASTER=local[*] # the master url - spark-submit --master $MASTER \ - --class com.intel.analytics.bigdl.example.udfpredictor.FileStreamProducer \ - dist/lib/bigdl-$VERSION-jar-with-dependencies.jar \ - -s $BASE_DIR/test \ - -d $BASE_DIR/data/text/parquet \ - -b 4 \ - -i 5 - ``` - In the above commands - - -s: source folder containing text files to to be published - - -d: target directory to be published to - - -i: publish interval in second - - -b: how many text files to be published at one time - -* Verification - - * Show the predicted label with UDF for text records: - - ``` - val classifyDF1 = df.withColumn("textLabel", classiferUDF($"text")) - .select("fileName", "textLabel", "text") - val classifyQuery1 = classifyDF1.writeStream - .format("console") - .start() - +--------+--------------------+---------+ - |fileName| text|textLabel| - +--------+--------------------+---------+ - | 100521|Path: cantaloupe....| 10| - | 101551|Path: cantaloupe....| 8| - | 101552|Newsgroups: rec.a...| 8| - | 101553|Xref: cantaloupe....| 8| - +--------+--------------------+---------+ - ``` - Note: "textLabel" column is the prediction for the text. - - * Filter text label with UDF in stream: - - ``` - val filteredDF1 = df.filter(classiferUDF($"text") === 8) - val filteredQuery1 = filteredDF1.writeStream - .format("console") - .start() - +--------------------+--------+ - | text|filename| - +--------------------+--------+ - |Path: cantaloupe....| 101551| - |Newsgroups: rec.a...| 101552| - |Xref: cantaloupe....| 101553| - +--------------------+--------+ - ``` - - * Join the static text type table with stream to show the text type name : - - ``` - val df_join = classifyDF1.join(types, "textLabel") - val classifyQuery_join = df_join.writeStream - .format("console") - .start() - +---------+--------+--------------------+------------------+ - |textLabel|fileName| text| textType| - +---------+--------+--------------------+------------------+ - | 10| 100521|Path: cantaloupe....|rec.sport.baseball| - | 8| 101551|Path: cantaloupe....| rec.autos| - | 8| 101552|Newsgroups: rec.a...| rec.autos| - | 8| 101553|Xref: cantaloupe....| rec.autos| - +---------+--------+--------------------+------------------+ - ``` - - * Do the aggregation of stream with predicted text label: - - ``` - val typeCount = classifyDF1.groupBy($"textLabel").count() - val aggQuery = typeCount.writeStream - .outputMode("complete") - .format("console") - .start() - - +---------+-----+ - |textLabel|count| - +---------+-----+ - | 8| 3| - | 10| 1| - +---------+-----+ - ``` - - * Show the predicted label with UDF for incoming stream in Spark SQL: - - ``` - val classifyDF2 = spark - .sql("SELECT fileName, textClassifier(text) AS textType_sql, text FROM textTable") - val classifyQuery2 = classifyDF2.writeStream - .format("console") - .start() - - +--------+------------+--------------------+ - |fileName|textType_sql| text| - +--------+------------+--------------------+ - | 101725| 9|Path: cantaloupe....| - | 102151| 10|Path: cantaloupe....| - | 102584| 10|Path: cantaloupe....| - | 102585| 10|Newsgroups: rec.s...| - +--------+------------+--------------------+ - ``` - - * Filter text label with UDF for incoming stream in Spark SQL: - - ``` - val filteredDF2 = spark - .sql("SELECT fileName, textClassifier(text) AS textType_sql, text " + - "FROM textTable WHERE textClassifier(text) = 9") - val filteredQuery2 = filteredDF2.writeStream - .format("console") - .start() - +--------+------------+--------------------+ - |fileName|textType_sql| text| - +--------+------------+--------------------+ - | 101725| 9|Path: cantaloupe....| - +--------+------------+--------------------+ - ``` \ No newline at end of file + ``` \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/StructuredStreamPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/StructuredStreamPredictor.scala deleted file mode 100644 index 1fd610a2c05..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/StructuredStreamPredictor.scala +++ /dev/null @@ -1,149 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.example.udfpredictor - -import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} -import com.intel.analytics.bigdl.example.utils.WordMeta -import org.apache.spark.sql.functions._ -import org.apache.spark.sql.SparkSession -import org.apache.spark.sql.types.StructType -import org.apache.log4j.{Level, Logger} - -object StructuredStreamPredictor { - - LoggerFilter.redirectSparkInfoLogs() - Logger.getLogger("com.intel.analytics.bigdl.example").setLevel(Level.INFO) - - // import Options._ - - def main(args: Array[String]): Unit = { - - Utils.localParser.parse(args, TextClassificationUDFParams()).foreach { param => - - // Create spark session - val sparkConf = Engine.createSparkConf() - sparkConf.setAppName("Text classification") - .set("spark.akka.frameSize", 64.toString) - val spark = SparkSession - .builder - .config(sparkConf) - .getOrCreate() - Engine.init - val sc = spark.sparkContext - - var word2Meta = None: Option[Map[String, WordMeta]] - var word2Index = None: Option[Map[String, Int]] - var word2Vec = None: Option[Map[Float, Array[Float]]] - - val result = Utils.getModel(sc, param) - - val model = result._1 - word2Meta = result._2 - word2Vec = result._3 - val sampleShape = result._4 - - // if not train, load word meta from file - if (word2Meta.isEmpty) { - val word2IndexMap = sc.textFile(s"${param.baseDir}/word2Meta.txt").map(item => { - val tuple = item.stripPrefix("(").stripSuffix(")").split(",") - (tuple(0), tuple(1).toInt) - }).collect() - word2Index = Some(word2IndexMap.toMap) - } else { - // already trained, use existing word meta - val word2IndexMap = collection.mutable.HashMap.empty[String, Int] - for((word, wordMeta) <- word2Meta.get) { - word2IndexMap += (word -> wordMeta.index) - } - word2Index = Some(word2IndexMap.toMap) - } - - // if not train, create word vec - if (word2Vec.isEmpty) { - word2Vec = Some(Utils.getWord2Vec(word2Index.get)) - } - val predict = Utils.genUdf(sc, model, sampleShape, word2Index.get, word2Vec.get) - - // register udf for data frame - val classifierUDF = udf(predict) - - val textSchema = new StructType().add("filename", "string").add("text", "string") - // stream dataframe - val df = spark.readStream - .schema(textSchema) - .parquet(param.testDir) - - val typeSchema = new StructType().add("textType", "string").add("textLabel", "string") - // static dataframe - val types = spark.read - .format("csv") - .option("header", "true") - .option("mode", "DROPMALFORMED") - .schema(typeSchema) - .csv(Utils.getResourcePath("/example/udfpredictor/types")) - - import spark.implicits._ - - val classifyDF1 = df.withColumn("textLabel", classifierUDF($"text")) - .select("fileName", "text", "textLabel") - val classifyQuery1 = classifyDF1.writeStream - .format("console") - .start() - - val df_join = classifyDF1.join(types, "textLabel") - val classifyQuery_join = df_join.writeStream - .format("console") - .start() - - val filteredDF1 = df.filter(classifierUDF($"text") === 8) - val filteredQuery1 = filteredDF1.writeStream - .format("console") - .start() - - // aggregation - val typeCount = classifyDF1.groupBy($"textLabel").count() - val aggQuery = typeCount.writeStream - .outputMode("complete") - .format("console") - .start() - - // play with udf in sqlcontext - spark.udf.register("textClassifier", predict) - df.createOrReplaceTempView("textTable") - - val classifyDF2 = spark - .sql("SELECT fileName, textClassifier(text) AS textType_sql, text FROM textTable") - val classifyQuery2 = classifyDF2.writeStream - .format("console") - .start() - - val filteredDF2 = spark - .sql("SELECT fileName, textClassifier(text) AS textType_sql, text " + - "FROM textTable WHERE textClassifier(text) = 9") - val filteredQuery2 = filteredDF2.writeStream - .format("console") - .start() - - classifyQuery1.awaitTermination() - classifyQuery_join.awaitTermination() - filteredQuery1.awaitTermination() - aggQuery.awaitTermination() - classifyQuery2.awaitTermination() - filteredQuery2.awaitTermination() - sc.stop() - } - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Options.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/TextClassificationUDFParams.scala similarity index 83% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Options.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/TextClassificationUDFParams.scala index 7c020857dd0..01dc89c233c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Options.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/TextClassificationUDFParams.scala @@ -33,12 +33,3 @@ case class TextClassificationUDFParams( testDir: String = "./") extends AbstractTextClassificationParams -/** - * Text parquet producer parameters - */ -case class TextProducerParquetParams( - srcFolder: String = "./", - destFolder: String = "./", - batchsize: Int = 2, - interval: Long = 5) - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala index 5e77b41c1d0..27db63888d1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala @@ -208,23 +208,4 @@ object Utils { .text("Text dir containing the text data") .action((x, c) => c.copy(testDir = x)) } - - val parquetProducerParser - = new OptionParser[TextProducerParquetParams]("BigDL Streaming Example") { - opt[String]('s', "srcFolder") - .required() - .text("Base dir containing the text data") - .action((x, c) => c.copy(srcFolder = x)) - opt[String]('d', "destFolder") - .required() - .text("Destination parquet dir containing the text data") - .action((x, c) => c.copy(destFolder = x)) - opt[Int]('b', "batchsize") - .text("produce batchsize") - .action((x, c) => c.copy(batchsize = x)) - opt[Long]('i', "interval") - .text("produce interval") - .action((x, c) => c.copy(interval = x)) - } - } From 499d85aa5ac24002de543c618277b7ae2e95c1f7 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 19 Jun 2017 17:00:40 +0800 Subject: [PATCH 0191/1065] [FIX943] bash environment variables to java properties (#988) * feat: make all environment virables to java properties * fix: typo * refactor: add api for setting mkl environment * fix: get back the omp environments * fix: set mkl use one omp thread, which means sequential mode * fix: typo of waitPolicyPasssive * test: add tests for localmode and corenumber Converting localmode variable to method. The only method to make the mode bigdl running is through java propery `-Dbigdl.localmode=true`. The default value is false. * fix: set wrong mode in SummarySpec and change name of mkl properties * fix: delete the bigdl.sh in all documents * fix: code style exceeds 100 chars * refactor: move mkl environment variables setting to MKL. The previous patch will only effect the driver side, except executor side. All sides will load library and execute setMklEnv method after loading completed if we put these code snippets in MKL.java which is in BigDL-core project. * fix: bigdl-native back to 0.1.0 * fix: before Engine.init we should not call Engine.coreNumber * feat: We convert all environment variables to mkl/omp apis. Because we have modify the BigDL-core, which contains the new JNI methods called in MKL.java. So we should modify the bigdl.native to 0.2.0-SNAPSHOT in spark/dl/pom.xml. * fix: delete all bigdl.sh in shell scripts and documents --- dist/assembly/dist.xml | 1 - dl/pom.xml | 2 +- .../intel/analytics/bigdl/utils/Engine.scala | 74 +++++++++---------- .../analytics/bigdl/utils/EngineSpec.scala | 42 ++++++++--- .../dllib/src/main/resources/spark-bigdl.conf | 10 +-- .../bigdl/dllib/example/MLPipeline/README.md | 5 +- .../example/imageclassification/README.md | 3 - .../bigdl/dllib/example/loadmodel/README.md | 6 -- .../example/textclassification/README.md | 7 +- .../dllib/example/udfpredictor/README.md | 6 +- .../bigdl/dllib/models/autoencoder/README.md | 8 +- .../bigdl/dllib/models/inception/README.md | 4 - .../bigdl/dllib/models/lenet/README.md | 6 -- .../bigdl/dllib/models/resnet/README.md | 5 -- .../bigdl/dllib/models/rnn/README.md | 5 +- .../models/utils/DistriOptimizerPerf.scala | 2 +- .../bigdl/dllib/models/vgg/README.md | 6 -- .../bigdl/dllib/dataset/DataSetSpec.scala | 4 +- .../bigdl/dllib/integration/HdfsSpec.scala | 3 +- .../dllib/optim/LocalOptimizerSpec.scala | 6 +- .../dllib/visualization/SummarySpec.scala | 6 +- 21 files changed, 94 insertions(+), 117 deletions(-) diff --git a/dist/assembly/dist.xml b/dist/assembly/dist.xml index cbffd1afe0a..3fd35981441 100644 --- a/dist/assembly/dist.xml +++ b/dist/assembly/dist.xml @@ -17,7 +17,6 @@ /bin ${project.build.directory}/../../../scripts - bigdl.sh classes.lst img_class.lst diff --git a/dl/pom.xml b/dl/pom.xml index 1066628c125..5e68576cfb8 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -50,7 +50,7 @@ com.intel.analytics.bigdl.native ${mkl-java-os-version} - 0.1.0 + 0.2.0-SNAPSHOT forgetGatei2g.weight + // weightsOri(3) -----> inputGatei2g.weight + // weightsOri(6) -----> hiddeni2g.weight + // weightsOri(9) -----> outputGatei2g.weight + val weightsTable = T(weightsOri(0), weightsOri(3), weightsOri(6), weightsOri(9)) + val joinWeights = JoinTable[Double](2, 5) + weightsNew += joinWeights.forward(weightsTable) + + // weightsOri(1) -----> forgetGatei2g.bias + // weightsOri(4) -----> inputGatei2g.bias + // weightsOri(7) -----> hiddeni2g.bias + // weightsOri(10) -----> outputGatei2g.bias + val biasTable = T(weightsOri(1), weightsOri(4), weightsOri(7), weightsOri(10)) + val joinBias = JoinTable[Double](1, 1) + weightsNew += joinBias.forward(biasTable) + + // weightsOri(2) -----> forgetGateh2g + // weightsOri(5) -----> inputGateh2g + // weightsOri(8) -----> hiddenh2h + // weightsOri(11) -----> outputGateh2g weightsNew += weightsOri(2) weightsNew += weightsOri(5) weightsNew += weightsOri(8) @@ -616,7 +629,7 @@ class ConvLSTMPeepholeSpec extends FlatSpec with BeforeAndAfter with Matchers { -0.054477777, 0.059936292, -0.077277765, 0.019922124, -0.15395634, 0.0088137, 0.036947053, -0.11207754, 0.042513624, -0.05665606, -0.015827265, 0.12174054 ) - val (weights, gradients) = model.getParameters() + val weights = model.getParameters()._1 val weightsOri = new ArrayBuffer[Tensor[Double]]() val weightsNew = new ArrayBuffer[Tensor[Double]]() @@ -639,14 +652,14 @@ class ConvLSTMPeepholeSpec extends FlatSpec with BeforeAndAfter with Matchers { next += sizeH } - weightsNew += weightsOri(0) - weightsNew += weightsOri(1) - weightsNew += weightsOri(3) - weightsNew += weightsOri(4) - weightsNew += weightsOri(6) - weightsNew += weightsOri(7) - weightsNew += weightsOri(9) - weightsNew += weightsOri(10) + val weightsTable = T(weightsOri(0), weightsOri(3), weightsOri(6), weightsOri(9)) + val joinWeights = JoinTable[Double](2, 5) + weightsNew += joinWeights.forward(weightsTable) + + val biasTable = T(weightsOri(1), weightsOri(4), weightsOri(7), weightsOri(10)) + val joinBias = JoinTable[Double](1, 1) + weightsNew += joinBias.forward(biasTable) + weightsNew += weightsOri(2) weightsNew += weightsOri(5) weightsNew += weightsOri(8) From a9266b3107714fe8c3f055f4fd4701cd179d3830 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Tue, 11 Jul 2017 11:54:20 -0700 Subject: [PATCH 0256/1065] add LR example for DLClassifier and DLEstimator (#1170) * add LR example * update doc --- ...torLeNet.scala => DLClassifierLeNet.scala} | 4 +- .../DLClassifierLogisticRegression.scala | 52 +++++++++++++++++++ .../MLPipeline/DLEstimatorMultiLabelLR.scala | 52 +++++++++++++++++++ .../bigdl/dllib/example/MLPipeline/README.md | 28 +++++++--- 4 files changed, 127 insertions(+), 9 deletions(-) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/{DLEstimatorLeNet.scala => DLClassifierLeNet.scala} (97%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorLeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala similarity index 97% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorLeNet.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala index 8497808b628..a99ac7c0fdb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorLeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericF import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext -import org.apache.spark.ml.{DLClassifier, DLEstimator, DLModel} +import org.apache.spark.ml.{DLClassifier, DLModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext @@ -34,7 +34,7 @@ import org.apache.spark.sql.SQLContext * An example to show how to use DLEstimator fit to be compatible with ML Pipeline * refer to README.md on how to run this example */ -object DLEstimatorLeNet { +object DLClassifierLeNet { LoggerFilter.redirectSparkInfoLogs() Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala new file mode 100644 index 00000000000..7072685c6fc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.example.MLPipeline + +import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Linear, LogSoftMax, Sequential} +import com.intel.analytics.bigdl.utils.Engine +import org.apache.spark.SparkContext +import org.apache.spark.ml.DLClassifier +import org.apache.spark.sql.SQLContext + +/** + * Logistic Regression with BigDL layers and DLClassifier + */ +object DLClassifierLogisticRegression { + + def main(args: Array[String]): Unit = { + val conf = Engine.createSparkConf() + .setAppName("DLClassifierLogisticRegression") + .set("spark.task.maxFailures", "1") + val sc = new SparkContext(conf) + val sqlContext = SQLContext.getOrCreate(sc) + Engine.init + + val model = new Sequential[Float]().add(Linear[Float](2, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLClassifier[Float](model, criterion, Array(2)) + .setBatchSize(4) + .setMaxEpoch(10) + val data = sc.parallelize(Seq( + (Array(0.0, 1.0), 1.0), + (Array(1.0, 0.0), 2.0), + (Array(0.0, 1.0), 1.0), + (Array(1.0, 0.0), 2.0))) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + val dlModel = estimator.fit(df) + dlModel.transform(df).show(false) + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala new file mode 100644 index 00000000000..f04d5128b08 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.example.MLPipeline + +import com.intel.analytics.bigdl.nn.{Linear, LogSoftMax, MultiLabelSoftMarginCriterion, Sequential} +import com.intel.analytics.bigdl.utils.Engine +import org.apache.spark.SparkContext +import org.apache.spark.ml.DLEstimator +import org.apache.spark.sql.SQLContext + +/** + * Multi-label Logistic Regression with BigDL layers and DLEstimator + */ +object DLEstimatorMultiLabelLR { + + def main(args: Array[String]): Unit = { + val conf = Engine.createSparkConf() + .setAppName("DLEstimatorMultiLabelLR") + .set("spark.task.maxFailures", "1") + val sc = new SparkContext(conf) + val sqlContext = SQLContext.getOrCreate(sc) + Engine.init + + val model = new Sequential[Float]().add(Linear[Float](2, 2)).add(LogSoftMax[Float]) + val criterion = MultiLabelSoftMarginCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(2), Array(2)) + .setBatchSize(4) + .setMaxEpoch(10) + val data = sc.parallelize(Seq( + (Array(0.0, 1.0), Array(1.0, 0.0)), + (Array(1.0, 0.0), Array(0.0, 1.0)), + (Array(0.0, 1.0), Array(1.0, 0.0)), + (Array(1.0, 0.0), Array(0.0, 1.0)))) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + val dlModel = estimator.fit(df) + dlModel.transform(df).show(false) + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/README.md index de1afc5ed06..628f5f078cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/README.md @@ -1,8 +1,22 @@ -## Summary -This example demonstrates how to use BigDL with Spark ML pipeline to train and predict LeNet5 model on MNIST dataset. +## DLClassifierLogisticRegression + +DLClassifierLogisticRegression example demonstrates how to use BigDL DLClassifier to train a +Logistic Regression Model. DLClassifier extends Spark Estimator and can act as a stage in a +ML Pipeline. The feature column can be Array or Spark Vectors, while the label column data should +be Double. + +## DLEstimatorMultiLabelLR + +DLEstimatorMultiLabelLR example demonstrates how to use BigDL DLEstimator to train a +multi-label Logistic Regression Model. DLEstimator extends Spark Estimator and can act as a +stage in a ML Pipeline. Both the feature and label column can be Array or Spark Vectors. The +feature column may also be Double. + +## DLClassifierLeNet +DLClassifierLeNet example demonstrates how to use BigDL with Spark ML pipeline to train and predict LeNet5 model on MNIST dataset. Learn more about Spark ML please refer to -## Preparation +### Preparation To start with this example, you need prepare your dataset. @@ -21,13 +35,13 @@ There're four files. For more detail, please refer to the download page. -## Run this example +### Run this example Command to run the example in Spark local mode: ``` spark-submit \ --master local[physcial_core_number] \ ---class com.intel.analytics.bigdl.example.MLPipeline.DLEstimatorLeNet \ +--class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet \ ./dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size @@ -39,7 +53,7 @@ spark-submit \ --executor-cores cores_per_executor \ --total-executor-cores total_cores_for_the_job \ --driver-class-path dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ ---class com.intel.analytics.bigdl.example.MLPipeline.DLEstimatorLeNet \ +--class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet \ dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size @@ -51,7 +65,7 @@ Command to run the example in Spark yarn mode: --executor-cores cores_per_executor \ --num-executors executors_number \ --driver-class-path dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ ---class com.intel.analytics.bigdl.example.MLPipeline.DLEstimatorLeNet \ +--class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet \ dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size From 60d31a6526058a46e5aa607c764718be2f3038c8 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 12 Jul 2017 13:26:39 +0800 Subject: [PATCH 0257/1065] Some new Sample/MiniBatch implements, and SampleToMiniBatch transformer (#925) --- .../MLPipeline/DLClassifierLeNet.scala | 2 +- .../dllib/example/utils/TextClassifier.scala | 6 +- .../bigdl/dllib/feature/dataset/DataSet.scala | 2 +- .../dllib/feature/dataset/MiniBatch.scala | 512 +++++++++++++- .../bigdl/dllib/feature/dataset/Sample.scala | 288 ++++++-- .../dllib/feature/dataset/Transformer.scala | 97 ++- .../dataset/image/BGRImgToSample.scala | 3 +- .../dataset/image/GreyImgToSample.scala | 3 +- .../text/LabeledSentenceToSample.scala | 3 +- .../dllib/models/autoencoder/Train.scala | 4 +- .../bigdl/dllib/models/rnn/Test.scala | 4 +- .../bigdl/dllib/models/rnn/Train.scala | 16 +- .../bigdl/dllib/optim/Evaluator.scala | 6 +- .../bigdl/dllib/optim/Optimizer.scala | 80 ++- .../bigdl/dllib/optim/Predictor.scala | 7 +- .../bigdl/dllib/tensor/TensorNumeric.scala | 21 + .../dllib/utils/python/api/PythonBigDL.scala | 2 +- .../dllib/dataset/BatchPaddingSpec.scala | 35 +- .../bigdl/dllib/dataset/DataSetSpec.scala | 2 +- .../bigdl/dllib/dataset/MiniBatchSpec.scala | 86 +++ .../bigdl/dllib/dataset/SampleSpec.scala | 622 +++++++++++++++++- .../dllib/dataset/TransformersSpec.scala | 344 +++++++--- .../bigdl/dllib/optim/EvaluatorSpec.scala | 2 +- .../bigdl/dllib/optim/PredictorSpec.scala | 26 +- .../bigdl/dllib/optim/ValidatorSpec.scala | 6 +- .../bigdl/dllib/python/api/PythonSpec.scala | 13 +- .../bigdl/dllib/utils/LoggerFilterSpec.scala | 4 +- 27 files changed, 1908 insertions(+), 288 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala index a99ac7c0fdb..77fff4d9339 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala @@ -58,7 +58,7 @@ object DLClassifierLeNet { BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToBatch(1) val trainingRDD : RDD[Data[Float]] = trainSet. - asInstanceOf[DistributedDataSet[TensorMiniBatch[Float]]].data(false).map(batch => { + asInstanceOf[DistributedDataSet[MiniBatch[Float]]].data(false).map(batch => { val feature = batch.getInput().asInstanceOf[Tensor[Float]] val label = batch.getTarget().asInstanceOf[Tensor[Float]] Data[Float](feature.storage().array(), label.storage().array()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala index 2f21805c2d2..93e2b9b9ffc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala @@ -223,7 +223,7 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab Sample( featureTensor = Tensor(input.flatten, Array(sequenceLen, embeddingDim)) .transpose(1, 2).contiguous(), - labelTensor = Tensor(Array(label), Array(1))) + label = label) } val Array(trainingRDD, valRDD) = sampleRDD.randomSplit( @@ -255,14 +255,14 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab Sample( featureTensor = Tensor(input.flatten, Array(param.maxSequenceLength, param.embeddingDim)) .transpose(1, 2).contiguous(), - labelTensor = Tensor(Array(label), Array(1))) + label = label) } val valRDD = rdds(1).map { case (input: Array[Array[Float]], label: Float) => Sample( featureTensor = Tensor(input.flatten, Array(param.maxSequenceLength, param.embeddingDim)) .transpose(1, 2).contiguous(), - labelTensor = Tensor(Array(label), Array(1))) + label = label) } // train diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index 1e920f8d49e..07dc91f9dc4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -393,7 +393,7 @@ object DataSet { if (isInOrder) { require(classTag[T] == classTag[Sample[_]], "DataSet.sortData: Only support sort for sample input") - data.sortBy(a => a.asInstanceOf[Sample[_]].featureLength()) + data.sortBy(a => a.asInstanceOf[Sample[_]].featureLength(0)) } else { data } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala index 84da3128189..16894725c39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala @@ -17,7 +17,12 @@ package com.intel.analytics.bigdl.dataset import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + /** * A interface for MiniBatch. @@ -28,12 +33,14 @@ import com.intel.analytics.bigdl.tensor.Tensor trait MiniBatch[T] extends Serializable{ /** * Get the number of samples in this MiniBatch + * * @return size How many samples in this MiniBatch */ def size(): Int /** * Slice this MiniBatch to a smaller MiniBatch with offset and length + * * @param offset offset, counted from 1 * @param length length * @return A smaller MiniBatch @@ -42,50 +49,123 @@ trait MiniBatch[T] extends Serializable{ /** * Get input in this MiniBatch. + * * @return input Activity */ def getInput(): Activity /** * Get target in this MiniBatch + * * @return target Activity */ def getTarget(): Activity - @deprecated("Old interface", "0.2.0") + /** + * An deprecated function for single-input/single-target MiniBatch. + * You don't need to override this, because we have add + * a default implement to throw exception. + */ + @deprecated("Old interface, use getInput instead", "0.2.0") def data(): Tensor[T] = { - require(this.isInstanceOf[TensorMiniBatch[T]], "Deprecated method," + - " Only support TensorMiniBatch.") - this.asInstanceOf[TensorMiniBatch[T]].input + throw new UnsupportedOperationException("MiniBatch.data(): unimplemented deprecated method") } - @deprecated("Old interface", "0.2.0") + /** + * An deprecated function for single-input/single-target MiniBatch. + * You don't need to override this, because we have add + * a default implement to throw exception. + */ + @deprecated("Old interface, use getTarget instead", "0.2.0") def labels(): Tensor[T] = { - require(this.isInstanceOf[TensorMiniBatch[T]], "Deprecated method," + - " Only support TensorMiniBatch.") - this.asInstanceOf[TensorMiniBatch[T]].input + throw new UnsupportedOperationException("MiniBatch.labels(): unimplemented deprecated method") } + + /** + * Replace the original content of the miniBatch with a set of Sample. + * + * @param samples a set of Sample + * @return self + */ + def set(samples: Seq[Sample[T]])(implicit ev: TensorNumeric[T]): this.type } /** - * A MiniBatch with [[Tensor]] input and [[Tensor]] target. - * The size of first dimension in input and target should be the mini-batch size. + * Default type of MiniBatch in BigDL. + * This MiniBatch support both single/multi inputs and single/multi targets. + * `inputData` store the input tensors, if `inputData.length == 1`, `getInput()` will return + * a tensor; If `inputData.length > 1`, `getInput()` will return a table. + * `targetData` store the target tensors, if `targetData.length == 1`, `getTarget()` will return + * a tensor; If `targetData.length > 1`, `getTarget()` will return a table. * - * @param input input Tensor - * @param target target Tensor + * @param inputData a set of input tensor + * @param targetData a set of target tensor + * @param featurePaddingParam feature padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * @param labelPaddingParam label padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. * @tparam T Numeric type + * @since 0.2.0 */ -class TensorMiniBatch[T]( - val input: Tensor[T], - val target: Tensor[T]) extends MiniBatch[T]{ - require(input.size(1) == target.size(1)) +private[bigdl] class ArrayTensorMiniBatch[T: ClassTag]( + val inputData: Array[Tensor[T]], + val targetData: Array[Tensor[T]], + featurePaddingParam: Option[PaddingParam[T]] = None, + labelPaddingParam: Option[PaddingParam[T]] = None) extends MiniBatch[T]{ + require(inputData.length > 0, "Input data in MiniBatch is empty.") + private var batchSize = 0 + private var unlabeled = false + + val (featurePadding, featurePaddingStrategy) = if (featurePaddingParam.isDefined) { + (featurePaddingParam.get.paddingTensor, featurePaddingParam.get.paddingStrategy) + } else { + (None, new DefaultPadding) + } + + val (labelPadding, labelPaddingStrategy) = if (labelPaddingParam.isDefined) { + (labelPaddingParam.get.paddingTensor, labelPaddingParam.get.paddingStrategy) + } else { + (None, new DefaultPadding) + } + + + private val input: Activity = if (inputData.length == 1) { + inputData.head + } else { + T.array(inputData.map(_.asInstanceOf[Any])) + } + + private val target: Activity = if (targetData.length == 0) { + null + } else if (targetData.length == 1) { + targetData.head + } else { + T.array(targetData.map(_.asInstanceOf[Any])) + } override def size(): Int = { - input.size(1) + if (inputData.head.nElement() == 0) { + 0 + } else { + inputData.head.size(1) + } } override def slice(offset: Int, length: Int): MiniBatch[T] = { - MiniBatch(input.narrow(1, offset, length), target.narrow(1, offset, length)) + val inputs = new Array[Tensor[T]](inputData.length) + val targets = new Array[Tensor[T]](targetData.length) + var b = 0 + while(b < inputData.size) { + inputs(b) = inputData(b).narrow(1, offset, length) + b += 1 + } + b = 0 + while(b < targetData.size) { + targets(b) = targetData(b).narrow(1, offset, length) + b += 1 + } + + MiniBatch(inputs, targets) } override def getInput(): Activity = { @@ -95,11 +175,401 @@ class TensorMiniBatch[T]( override def getTarget(): Activity = { target } + + override def set(samples: Seq[Sample[T]])(implicit ev: TensorNumeric[T]): this.type = { + require(samples.length > 0, "samples is empty") + require(batchSize == 0 || samples.length <= batchSize, "setValue: samples's size doesn't " + + s"match mini batch size, excepted ${size()} got ${samples.length}") + val resize = batchSize != samples.length || featurePaddingParam.isDefined || + labelPaddingParam.isDefined || size() != samples.length + if (batchSize == 0) { + batchSize = samples.length // set a batchSize when set data. + unlabeled = samples.head.numLabel() == 0 + } + + val longestFeature = if (featurePaddingParam.isDefined) { + Some(MiniBatch.findLongestFeatures(samples)) + } else { + None + } + + val longestLabel = if (featurePaddingParam.isDefined) { + Some(MiniBatch.findLongestLabels(samples)) + } else { + None + } + + if (resize) { + MiniBatch.resize(samples, this, featurePaddingStrategy, + labelPaddingStrategy, featurePadding, labelPadding, + longestFeature, longestLabel) + } + + MiniBatch.copyWithPadding[T](samples, this, unlabeled, + featurePadding, labelPadding) + this + } + + @deprecated("Old interface", "0.2.0") + override def data(): Tensor[T] = { + require(targetData.length == 1, "Deprecated method," + + " Only support TensorMiniBatch.") + input.asInstanceOf[Tensor[T]] + } + + @deprecated("Old interface", "0.2.0") + override def labels(): Tensor[T] = { + require(inputData.length == 1, "Deprecated method," + + " Only support TensorMiniBatch.") + target.asInstanceOf[Tensor[T]] + } } object MiniBatch { - def apply[T](input: Tensor[T], target: Tensor[T]): MiniBatch[T] = { - new TensorMiniBatch[T](input, target) + /** + * MiniBatch factory method + * + * @param nInputs number of inputs + * @param nTargets number of targets + * @return + */ + def apply[T: ClassTag]( + nInputs: Int, + nTargets: Int, + featurePaddingParam: Option[PaddingParam[T]] = None, + labelPaddingParam: Option[PaddingParam[T]] = None)( + implicit ev: TensorNumeric[T]): MiniBatch[T] = { + new ArrayTensorMiniBatch[T](Array.tabulate(nInputs)(_ => Tensor[T]()), + Array.tabulate(nTargets)(_ => Tensor[T]()), + featurePaddingParam, labelPaddingParam) + } + + def apply[T: ClassTag](input: Tensor[T], target: Tensor[T]): MiniBatch[T] = { + MiniBatch[T](Array(input), Array(target)) + } + + def apply[T: ClassTag](input: Array[Tensor[T]], target: Tensor[T]): MiniBatch[T] = { + MiniBatch[T](input, Array(target)) + } + + def apply[T: ClassTag](input: Array[Tensor[T]], target: Array[Tensor[T]]): MiniBatch[T] = { + new ArrayTensorMiniBatch[T](input, target) + } + + def apply[T: ClassTag](input: Tensor[T]): MiniBatch[T] = { + MiniBatch[T](Array(input), new Array[Tensor[T]](0)) + } + + def apply[T: ClassTag](input: Array[Tensor[T]]): MiniBatch[T] = { + MiniBatch[T](input, new Array[Tensor[T]](0)) + } + + private def resizeData[T: ClassTag]( + data: Array[Tensor[T]], + // 1st Seq is batchSize, 2nd Array is number of features, 3th Array is feature size + sampleSize: Seq[Array[Array[Int]]], + longestData: Option[Array[Int]], + paddingStrategy: PaddingStrategy, + paddingTensor: Option[Array[Tensor[T]]]): Unit = { + // Size of input data. 1st Array is number of input, 2nd Array is input size. + val sizes = new Array[Array[Int]](sampleSize.head.length) + if (longestData.isDefined) { + val longest = longestData.get + + var i = 0 + while (i < sizes.length) { + // Set i-th input's size + sizes(i) = Array(sampleSize.length) ++ sampleSize(longest(i))(i) + i += 1 + } + + paddingStrategy.paddingSize(sizes) + } else { + var i = 0 + while (i < sizes.length) { + // Set i-th input's size + sizes(i) = Array(sampleSize.length) ++ sampleSize.head(i) + i += 1 + } + } + + // resize + var i = 0 + while (i < sizes.length) { + data(i).resize(sizes(i)) + if (paddingTensor.isEmpty) data(i).zero() + i += 1 + } + + } + + // resize miniBatch, and zero miniBatch if paddingTensor is undefined. + private[bigdl] def resize[T: ClassTag]( + samples: Seq[Sample[T]], + miniBatch: ArrayTensorMiniBatch[T], + featurePaddingStrategy: PaddingStrategy, + labelPaddingStrategy: PaddingStrategy, + featurePaddingTensor: Option[Array[Tensor[T]]] = None, + labelPaddingTensor: Option[Array[Tensor[T]]] = None, + longestFeature: Option[Array[Int]] = None, + longestLabel: Option[Array[Int]] = None + )(implicit ev: TensorNumeric[T]): MiniBatch[T] = { + val inputs = miniBatch.inputData + val targets = miniBatch.targetData + + val featureSizes = samples.map(_.getFeatureSize()) + val unlabeled = samples.head.numLabel() == 0 + resizeData(inputs, featureSizes, + longestFeature, featurePaddingStrategy, featurePaddingTensor) + if (!unlabeled) { + val labelSizes = samples.map(_.getLabelSize()) + resizeData(targets, labelSizes, + longestLabel, labelPaddingStrategy, labelPaddingTensor) + } + + miniBatch + } + + private[bigdl] def copyWithPadding[T: ClassTag]( + samples: Seq[Sample[T]], + miniBatch: ArrayTensorMiniBatch[T], + unlabeled: Boolean, + featurePadding: Option[Array[Tensor[T]]] = None, + labelPadding: Option[Array[Tensor[T]]] = None + )(implicit ev: TensorNumeric[T]): MiniBatch[T] = { + val inputs = miniBatch.inputData + val targets = miniBatch.targetData + + if (featurePadding.isDefined) { + // check if featurePadding is right. + var i = 0 + while (i < inputs.length) { + require(featurePadding.get.length == inputs.length, s"Number of tensor padding should " + + s"equals to Number of feature tensor in Sample. Excepted ${inputs.length}," + + s" but got ${featurePadding.get.length}") + if (inputs(i).dim() == 2) { + require(featurePadding.get(i).nElement() == 1, s"${i}thFeature is 1D, featurePadding " + + s"should have only one element, but got ${featurePadding.get(i)}") + } else { + require(featurePadding.get(i).dim() == inputs(i).dim() - 2, + s"${i}thFeature's featurePadding should have the " + + s"same dimension with the feature in sample. Excepted: ${inputs(i).dim() - 2}, " + + s"but got ${featurePadding.get(i).dim()}") + } + require(featurePadding.get(i).isContiguous(), "featurePadding should be contiguous") + i += 1 + } + } + + // Copy sample data to miniBatch + var s = 0 + while (s < samples.length) { + var f = 0 + var offset = 0 + val sample = samples(s) + val sampleData = sample.getData() + while (f < inputs.length) { + val length = sample.getFeatureSize()(f).product + if (featurePadding.isDefined) { + // copy data + copy(sampleData, offset, + length, inputs(f)(s + 1), featurePadding.get(f)) + } else { + // copy data without padding. + copy(sampleData, offset, length, inputs(f)(s + 1)) + } + f += 1 + offset += length + } + + if (!unlabeled) { + var l = 0 + while (l < targets.length) { + val length = sample.getLabelSize()(l).product + if (labelPadding.isDefined) { + // copy data + copy(sampleData, offset, + length, targets(l)(s + 1), labelPadding.get(l)) + } else { + // copy data without padding. + copy(sampleData, offset, length, targets(l)(s + 1)) + } + l += 1 + offset += length + } + } + + s += 1 + } + + miniBatch + } + + /** + * Find Sample in Array[Sample] who has the biggest featureLength + */ + private[bigdl] def findLongestFeatures[T: ClassTag]( + samples: Seq[Sample[T]])(implicit ev: TensorNumeric[T]): Array[Int] = { + val featureIndices = + new Array[Int](samples.head.numFeature()) + var i = 1 + while (i < samples.length) { + var j = 0 + while (j < featureIndices.length) { + if (samples(i).featureLength(j) > samples(featureIndices(j)).featureLength(j)) { + featureIndices(j) = i + } + j += 1 + } + i += 1 + } + featureIndices + } + + /** + * Find Sample in Array[Sample] who has the biggest labelLength + */ + private[bigdl] def findLongestLabels[T: ClassTag]( + samples: Seq[Sample[T]])(implicit ev: TensorNumeric[T]): Array[Int] = { + val labelIndices = + new Array[Int](samples.head.numLabel()) + var i = 1 + while (i < samples.length) { + var j = 0 + while (j < labelIndices.length) { + if (samples(i).labelLength(j) > samples(labelIndices(j)).labelLength(j)) { + labelIndices(j) = i + } + j += 1 + } + i += 1 + } + labelIndices + } + + /** + * Copy tensor src to tensor dest with a padding tensor. + */ + private def copy[T: ClassTag]( + src: Array[T], + offset: Int, + length: Int, + dest: Tensor[T], + paddingTensor: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Unit = { + ev.arraycopy(src, + offset, + dest.storage().array(), + dest.storageOffset() - 1, + length) + if (null != paddingTensor) { + var j = length + while (j < dest.nElement()) { + ev.arraycopy(paddingTensor.storage().array(), paddingTensor.storageOffset() - 1, + dest.storage().array(), dest.storageOffset() - 1 + j, paddingTensor.nElement()) + j += paddingTensor.nElement() + } + } + } +} + +/** + * Feature Padding param for MiniBatch. + * + * For constructing a mini batch, we need to make sure all samples' feature and label + * in this mini batch have the same size. If the size is different, we will pad them + * to the same size. + * + * By default, we will pad the first dimension to the longest size with zero in the MiniBatch. + * If you want to specify the padding values, you can set `paddingTensor`; If you want to specify + * the padding length, you can use `PaddingLongest` or `FixedLength`. + * + * For example, your feature size is n*m*k, + * you should provide a 2D tensor in a size of m*k. + * If your feature is 1D, you can provide a one-element 1D tensor. + * + * For example, we have 3 Sample, and convert them into a MiniBatch. + * Sample1's feature is a 2*3 tensor {1, 2, 3, + * 4, 5, 6} + * + * Sample2's feature is a 1*3 tensor {7, 8, 9} + * + * Sample3's feature is a 3*3 tensor {10, 11, 12, + * 13, 14, 15, + * 16, 17, 18} + * + * And the paddingTensor is {-1, -2, -3}, use `FixedLength(Array(4))`, the MiniBatch will be + * a tensor of 3*4*3: + * {1, 2, 3, + * 4, 5, 6, + * -1, -2, -3, + * -1, -2, -3 + * + * 7, 8, 9, + * -1, -2, -3, + * -1, -2, -3, + * -1, -2, -3 + * + * 10, 11, 12, + * 13, 14, 15, + * 16, 17, 18 + * -1, -2, -3} + * + * @param paddingTensor paddings tensor for the first dimension(by default None, + * meaning zero padding). + * @param paddingStrategy See [[PaddingLongest]], [[FixedLength]] + * @tparam T numeric type + */ +case class PaddingParam[T: ClassTag]( + paddingTensor: Option[Array[Tensor[T]]] = None, + paddingStrategy: PaddingStrategy = new DefaultPadding) extends Serializable + +abstract class PaddingStrategy extends Serializable { + def paddingSize(sizes: Seq[Array[Int]]): Seq[Array[Int]] +} + +class DefaultPadding extends PaddingStrategy { + def paddingSize(sizes: Seq[Array[Int]]): Seq[Array[Int]] = { + sizes } } +/** + * Add an constant length to longest feature in the first dimension + * + * @param paddingLength + */ +case class PaddingLongest( + paddingLength: Array[Int]) extends PaddingStrategy { + def paddingSize(sizes: Seq[Array[Int]]): Seq[Array[Int]] = { + var i = 0 + while (i < sizes.length) { + // Add an constant length to the first dimension's length(besides mini batch size). + val increment = paddingLength(i) + sizes(i)(1) += increment + i += 1 + } + sizes + } +} + +/** + * Set the first dimension's length to fixed length. + * + * @param fixedLength fixed length + */ +case class FixedLength(fixedLength: Array[Int]) extends PaddingStrategy { + def paddingSize(sizes: Seq[Array[Int]]): Seq[Array[Int]] = { + var i = 0 + while (i < sizes.length) { + // Set the first dimension's length(besides mini batch size) to fixed length. + val fixed = fixedLength(i) + require(fixed >= sizes(i)(1) || fixed < 0, + s"${i}th FixedLength=${fixed} is smaller than its FeatureLength=${sizes(i)(1)}") + if (fixed >= sizes(i)(1)) { + sizes(i)(1) = fixed + } + i += 1 + } + sizes + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index b2f88dbd42f..94191255d4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -16,132 +16,304 @@ package com.intel.analytics.bigdl.dataset + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import org.apache.commons.lang3.SerializationUtils import scala.reflect.ClassTag /** * Class that represents the features and labels of a data sample. + * * @tparam T numeric type */ abstract class Sample[T: ClassTag] extends Serializable { /** - * Length of the feature. + * First dimension length of index-th feature. * This function could be used to sort samples in [[DataSet]]. + * * @return */ - def featureLength(): Int + def featureLength(index: Int): Int /** - * Copy other Sample's data to this Sample - * @param other Sample to be copied. - * @return this + * First dimension length of index-th label. + * This function could be used to find the longest label. + * + * @return */ - def copy(other: Sample[T]): this.type + def labelLength(index: Int): Int /** * Number of tensors in feature + * * @return number of tensors in feature */ def numFeature(): Int /** * Number of tensors in label + * * @return number of tensors in label */ def numLabel(): Int /** - * Deep clone - * @return a deep clone + * Get feature tensor, for one feature Sample only. + * You don't need to override this, because we have add + * a default implement to throw exception. + * @return feature tensor */ - override def clone(): this.type = - SerializationUtils.clone(this) - @deprecated("Old interface", "0.2.0") - def feature(): Tensor[T] = { - require(this.isInstanceOf[TensorSample[T]], "Deprecated method, Only support TensorSample.") - this.asInstanceOf[TensorSample[T]].featureTensor + def feature()(implicit ev: TensorNumeric[T]): Tensor[T] = { + throw new UnsupportedOperationException("Sample.feature(): unimplemented deprecated method") } + /** + * Get label tensor, for one label Sample only. + * You don't need to override this, because we have add + * a default implement to throw exception. + * @return label tensor + */ @deprecated("Old interface", "0.2.0") - def label(): Tensor[T] = { - require(this.isInstanceOf[TensorSample[T]], "Deprecated method, Only support TensorSample.") - this.asInstanceOf[TensorSample[T]].labelTensor + def label()(implicit ev: TensorNumeric[T]): Tensor[T] = { + throw new UnsupportedOperationException("Sample.label(): unimplemented deprecated method") } + /** + * Set data of feature and label. + * @param featureData + * @param labelData + * @param featureSize + * @param labelSize + * @return + */ @deprecated("Old interface", "0.2.0") def set( featureData: Array[T], labelData: Array[T], featureSize: Array[Int], - labelSize: Array[Int]): Sample[T] = { - require(this.isInstanceOf[TensorSample[T]], "Deprecated method, Only support TensorSample.") - val sample = this.asInstanceOf[TensorSample[T]] - sample.featureTensor.set(Storage[T](featureData), 1, featureSize) - sample.labelTensor.set(Storage[T](labelData), 1, labelSize) - sample - } + labelSize: Array[Int])(implicit ev: TensorNumeric[T]): Sample[T] + + /** + * Get feature sizes + * @return feature sizes + */ + def getFeatureSize(): Array[Array[Int]] + + + /** + * Get label sizes + * @return label sizes + */ + def getLabelSize(): Array[Array[Int]] + + /** + * Get data + * @return data + */ + def getData(): Array[T] } + /** - * A kind of sample. Feature is a tensor, and label is a tensor too. - * @param featureTensor feature tensor - * @param labelTensor label tensor - * @tparam T numeric type + * A kind of sample who use only one array */ -class TensorSample[T: ClassTag]( - val featureTensor: Tensor[T], - val labelTensor: Tensor[T]) extends Sample[T] { +private[bigdl] class ArraySample[T: ClassTag]( + private val data: Array[T], + private val featureSize: Array[Array[Int]], + private val labelSize: Array[Array[Int]]) extends Sample[T] { + require(featureSize != null, "Feature couldn't be empty") - /** - * The length of first dimension - * @return The length of first dimension - */ - override def featureLength(): Int = { - featureTensor.size(1) + override def getData(): Array[T] = data + + override def featureLength(index: Int): Int = { + require(null != featureSize, "featureSize is empty") + featureSize(index)(0) } - override def copy(other: Sample[T]): this.type = { - require(other.isInstanceOf[TensorSample[T]], "Sample.copy: sample type not match.") - val s = other.asInstanceOf[TensorSample[T]] - featureTensor.resizeAs(s.featureTensor).copy(s.featureTensor) - labelTensor.resizeAs(s.labelTensor).copy(s.labelTensor) - this + override def labelLength(index: Int): Int = { + if (null != labelSize) { + labelSize(index)(0) + } else { + 0 + } + } + + override def getFeatureSize(): Array[Array[Int]] = { + featureSize } - def numFeature(): Int = 1 + override def getLabelSize(): Array[Array[Int]] = { + require(null != labelSize, "Sample doesn't have label") + labelSize + } - def numLabel(): Int = 1 + override def numFeature(): Int = { + require(null != featureSize, "featureSize is empty") + featureSize.length + } - def canEqual(other: Any): Boolean = other.isInstanceOf[TensorSample[T]] + override def numLabel(): Int = { + if (null == labelSize) { + 0 + } else { + labelSize.length + } + } + + @deprecated("Old interface", "0.2.0") + override def feature()(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(featureSize.length == 1, "Old interface for 1 feature Sample. " + + s"got ${featureSize.length} feature Sample") + Tensor[T](Storage(data), 1, getFeatureSize()(0)) + } + + @deprecated("Old interface", "0.2.0") + override def label()(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(labelSize.length == 1, "Old interface for 1 label Sample. " + + s"got ${labelSize.length} label Sample") + Tensor[T](Storage(data), getFeatureSize().map(_.product).sum + 1, + labelSize(0)) + } + + @deprecated("Old interface", "0.2.0") + override def set( + featureData: Array[T], + labelData: Array[T], + featureSize: Array[Int], + labelSize: Array[Int])(implicit ev: TensorNumeric[T]): Sample[T] = { + require(featureSize.sameElements(this.featureSize(0)) && + labelSize.sameElements(this.labelSize(0)), "size not match") + + ev.arraycopy(featureData, 0, data, 0, featureData.length) + ev.arraycopy(labelData, 0, data, featureData.length, labelData.length) + + this + } + + def canEqual(other: Any): Boolean = other.isInstanceOf[ArraySample[T]] override def equals(other: Any): Boolean = other match { - case that: TensorSample[T] => - (that canEqual this) && - featureTensor == that.featureTensor && - labelTensor == that.labelTensor + case that: ArraySample[T] => + if (!(that canEqual this) || + !(labelSize.deep == that.labelSize.deep) || + !(featureSize.deep == that.featureSize.deep)) { + return false + } + var i = labelSize.map(_.product).sum + featureSize.map(_.product).sum - 1 + while (i >= 0) { + if (data(i) != that.data(i)) return false + i -= 1 + } + true case _ => false } override def hashCode(): Int = { - val state = Seq(featureTensor, labelTensor) + val state = Seq(data, featureSize, labelSize) state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) } } object Sample { - def apply[@specialized(Float, Double) T: ClassTag]( + def apply[T: ClassTag]( + data: Array[T], + featureSize: Array[Array[Int]], + labelSize: Array[Array[Int]]): Sample[T] = { + new ArraySample(data, featureSize, labelSize) + } + + def apply[T: ClassTag]( featureTensor: Tensor[T], labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { - new TensorSample[T](featureTensor, labelTensor) + require(featureTensor.isContiguous(), "featureTensor is not contiguous") + require(labelTensor.isContiguous(), "labelTensor is not contiguous") + val data = new Array[T](featureTensor.nElement() + labelTensor.nElement()) + ev.arraycopy(featureTensor.storage().array(), featureTensor.storageOffset() - 1, + data, 0, featureTensor.nElement()) + ev.arraycopy(labelTensor.storage().array(), labelTensor.storageOffset() - 1, + data, featureTensor.nElement(), labelTensor.nElement()) + new ArraySample[T](data, getSize(featureTensor), getSize(labelTensor)) } - @deprecated("Old interface", "0.2.0") - def apply[@specialized(Float, Double) T: ClassTag]()( - implicit ev: TensorNumeric[T]) : Sample[T] = { - new TensorSample[T](Tensor[T](), Tensor[T]()) + def apply[T: ClassTag]( + featureTensor: Tensor[T], + label: T)(implicit ev: TensorNumeric[T]) : Sample[T] = { + require(featureTensor.isContiguous(), "featureTensor is not contiguous") + val data = new Array[T](featureTensor.nElement() + 1) + ev.arraycopy(featureTensor.storage().array(), featureTensor.storageOffset() - 1, + data, 0, featureTensor.nElement()) + data(featureTensor.nElement()) = label + new ArraySample[T](data, getSize(featureTensor), Array(Array(1))) + } + + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]], + labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + val tensors = featureTensors ++ Array(labelTensor) + val data = new Array[T](tensors.map(_.nElement()).sum) + copy(data, tensors) + new ArraySample[T](data, getSize(featureTensors), getSize(labelTensor)) + } + + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]], + labelTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + val tensors = featureTensors ++ labelTensors + val data = new Array[T](tensors.map(_.nElement()).sum) + copy(data, tensors) + new ArraySample[T](data, getSize(featureTensors), getSize(labelTensors)) + } + + def apply[T: ClassTag]( + featureTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + require(featureTensor.isContiguous(), "featureTensor is not contiguous") + val data = new Array[T](featureTensor.nElement()) + ev.arraycopy(featureTensor.storage().array(), featureTensor.storageOffset() - 1, + data, 0, featureTensor.nElement()) + new ArraySample[T](data, getSize(featureTensor), null) + } + + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + val data = new Array[T](featureTensors.map(_.nElement()).sum) + copy(data, featureTensors) + new ArraySample[T](featureTensors.flatMap(_.storage().array()), + getSize(featureTensors), null) + } + + private def copy[T: ClassTag]( + data: Array[T], + tensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Array[T] = { + var offset = 0 + var i = 0 + while (i < tensors.length) { + val tensor = tensors(i) + require(tensor.isContiguous(), s"${i}-th tensor is not contiguous") + ev.arraycopy(tensor.storage().array(), tensor.storageOffset() - 1, + data, offset, tensor.nElement()) + offset += tensor.nElement() + i += 1 + } + data + } + + private[bigdl] def getSize[T: ClassTag](tensors: Array[Tensor[T]]): Array[Array[Int]] = { + tensors.map(_.size) + } + + private[bigdl] def getSize[T: ClassTag](tensor: Tensor[T]): Array[Array[Int]] = { + Array(tensor.size()) + } + + private[bigdl] def sameSize(a: Array[Array[Int]], b: Array[Array[Int]]): Boolean = { + if (a.length != b.length) return false + var i = 0 + while (i < a.length) { + if (a(i).length != b(i).length) return false + i += 1 + } + true } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala index 3140fa2a9f8..5b83bf7b3b7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.Activity import org.apache.commons.lang3.SerializationUtils import java.util +import com.intel.analytics.bigdl.utils.T import org.apache.spark.rdd.RDD import scala.collection.Iterator @@ -36,6 +37,7 @@ import scala.reflect.ClassTag * pre-process steps. User needn't write them every time, but can reuse others work. * * Transformer can be used with RDD(rdd.mapPartition), iterator and DataSet. + * * @tparam A * @tparam B */ @@ -57,6 +59,7 @@ trait Transformer[A, B] extends Serializable { /** * Apply this transformer to rdd + * * @param dataset */ def apply(dataset: RDD[A])(implicit evidence: ClassTag[B]): RDD[B] = { @@ -73,6 +76,7 @@ trait Transformer[A, B] extends Serializable { /** * A transformer chain two transformer together. The output type of the first transformer should be * same with the input type of the second transformer. + * * @param first first transformer * @param last last transformer * @tparam A input type of the first transformer @@ -104,6 +108,7 @@ class Identity[A] extends Transformer[A, A] { * optionally padding all the features (or labels) in the mini-batch to the same length */ object SampleToBatch { + @deprecated("Use SampleToMiniBatch instead", "0.2.0") def apply[T: ClassTag] (batchSize : Int, featurePadding : Option[Tensor[T]] = None, @@ -115,7 +120,7 @@ object SampleToBatch { } /** - * Convert a sequence of [[TensorSample]] to a sequence of [[TensorMiniBatch]], + * Convert a sequence of single-feature and single-label Sample to a sequence of MiniBatch, * optionally padding all the features (or labels) in the mini-batch to the same length * * @param totalBatch total batch size @@ -127,6 +132,7 @@ object SampleToBatch { * @param partitionNum partition number of dataset, default means partitionNum * equals Engine.nodeNumber() */ +@deprecated("Use SampleToMiniBatch instead", "0.2.0") class SampleToBatch[T: ClassTag] (totalBatch : Int, featurePadding : Option[Tensor[T]] = None, @@ -192,6 +198,7 @@ class SampleToBatch[T: ClassTag] /** * compare a and b, then return the larger one's index + * * @param i the index of a * @param j the index of b */ @@ -210,8 +217,7 @@ class SampleToBatch[T: ClassTag] private var labelData: Array[T] = null private val batchSize = batchSizePerPartition - private val sampleData = Array.tabulate(batchSize)(_ => - Sample(Tensor(), Tensor())) + private val sampleData = new Array[Sample[T]](batchSize) private var featureSize: Array[Int] = null private var labelSize: Array[Int] = null private var oneFeatureElement: Int = 0 @@ -230,7 +236,7 @@ class SampleToBatch[T: ClassTag] val sample = prev.next() require(sample.feature().isContiguous() && sample.label().isContiguous(), "SampleToBatch: Only support contiguous tensor") - sampleData(i).copy(sample) + sampleData(i) = sample featureIndex = getLarger(sampleData(featureIndex).feature().nElement(), featureIndex, sample.feature().nElement(), i) labelIndex = getLarger(sampleData(labelIndex).label().nElement(), @@ -296,3 +302,86 @@ class SampleToBatch[T: ClassTag] } } } + +/** + * Convert a sequence of [[Sample]] to a sequence of [[MiniBatch]] through function toMiniBatch. + */ +private[bigdl] class SampleToMiniBatch[T: ClassTag]( + totalBatch: Int, + miniBatch: Option[MiniBatch[T]] = None, + featurePaddingParam: Option[PaddingParam[T]] = None, + labelPaddingParam: Option[PaddingParam[T]] = None, + partitionNum: Option[Int] = None) + (implicit ev: TensorNumeric[T]) extends Transformer[Sample[T], MiniBatch[T]] { + + private val batchPerPartition = Utils.getBatchSize(totalBatch, partitionNum) + var miniBatchBuffer = miniBatch.orNull + + override def apply(prev: Iterator[Sample[T]]): Iterator[MiniBatch[T]] = { + val batchSizePerPartition = batchPerPartition + new Iterator[MiniBatch[T]] { + private val batchSize = batchSizePerPartition + + private val sampleData = new Array[Sample[T]](batchSize) + override def hasNext: Boolean = prev.hasNext + + override def next(): MiniBatch[T] = { + if (prev.hasNext) { + var i = 0 + while (i < batchSize && prev.hasNext) { + val sample = prev.next() + sampleData(i) = sample + i += 1 + } + if (null == miniBatchBuffer) { + miniBatchBuffer = MiniBatch(sampleData(0).numFeature(), sampleData(0).numLabel(), + featurePaddingParam, labelPaddingParam) + } + + if (i < batchSize) { + miniBatchBuffer.set(sampleData.slice(0, i)) + } else { + miniBatchBuffer.set(sampleData) + } + } else { + null + } + } + } + } +} + +object SampleToMiniBatch { + /** + * Apply an SampleToMiniBatch transformer. + * + * @param batchSize total batch size + * @param featurePaddingParam feature padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * @param labelPaddingParam label padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * @return + */ + def apply[T: ClassTag]( + batchSize : Int, + featurePaddingParam: Option[PaddingParam[T]] = None, + labelPaddingParam: Option[PaddingParam[T]] = None, + partitionNum: Option[Int] = None + )(implicit ev: TensorNumeric[T]): SampleToMiniBatch[T] = { + new SampleToMiniBatch[T](batchSize, None, featurePaddingParam, labelPaddingParam, partitionNum) + } + + /** + * Apply an SampleToMiniBatch transformer with UDF MiniBatch. + * + * @param batchSize total batch size + * @param miniBatch An User-Defined MiniBatch to construct a mini batch. + * @return + */ + def apply[T: ClassTag]( + miniBatch: MiniBatch[T], + batchSize : Int, + partitionNum: Option[Int])(implicit ev: TensorNumeric[T]): SampleToMiniBatch[T] = { + new SampleToMiniBatch[T](batchSize, Some(miniBatch), partitionNum = partitionNum) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/BGRImgToSample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/BGRImgToSample.scala index 05d2ca53987..ddbad93d520 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/BGRImgToSample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/BGRImgToSample.scala @@ -34,7 +34,6 @@ class BGRImgToSample(toRGB: Boolean = true) extends Transformer[LabeledBGRImage, private val featureBuffer = Tensor[Float]() private val labelBuffer = Tensor[Float](1) - private val buffer = Sample[Float](featureBuffer, labelBuffer) override def apply(prev: Iterator[LabeledBGRImage]): Iterator[Sample[Float]] = { prev.map(img => { @@ -44,7 +43,7 @@ class BGRImgToSample(toRGB: Boolean = true) extends Transformer[LabeledBGRImage, } img.copyTo(featureBuffer.storage().array(), 0, toRGB) - buffer + Sample(featureBuffer, labelBuffer) }) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/GreyImgToSample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/GreyImgToSample.scala index 3d1b0bc0192..ec8972c981c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/GreyImgToSample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/GreyImgToSample.scala @@ -35,7 +35,6 @@ class GreyImgToSample() extends Transformer[LabeledGreyImage, Sample[Float]] { private val featureBuffer = Tensor[Float]() private val labelBuffer = Tensor[Float](1) private val featureSize = new Array[Int](2) - private val buffer = Sample[Float](featureBuffer, labelBuffer) override def apply(prev: Iterator[LabeledGreyImage]): Iterator[Sample[Float]] = { prev.map(img => { @@ -44,7 +43,7 @@ class GreyImgToSample() extends Transformer[LabeledGreyImage, Sample[Float]] { featureSize(1) = img.width() featureBuffer.set(Storage(img.content), sizes = featureSize) - buffer + Sample(featureBuffer, labelBuffer) }) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/LabeledSentenceToSample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/LabeledSentenceToSample.scala index 4a2d80d5d8a..0e2db559503 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/LabeledSentenceToSample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/LabeledSentenceToSample.scala @@ -61,7 +61,6 @@ class LabeledSentenceToSample[T: ClassTag](vocabLength: Int, private val feature: Tensor[T] = Tensor() private val label: Tensor[T] = Tensor() - private val buffer = Sample[T](feature, label) override def apply(prev: Iterator[LabeledSentence[T]]): Iterator[Sample[T]] = { prev.map(sentence => { @@ -116,7 +115,7 @@ class LabeledSentenceToSample[T: ClassTag](vocabLength: Int, i += 1 } - buffer + Sample[T](feature, label) }) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/Train.scala index 5ffcec1a7b2..c87572c8409 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/Train.scala @@ -30,11 +30,13 @@ import com.intel.analytics.bigdl.utils.{Engine, T, Table} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext +import scala.reflect.ClassTag + object toAutoencoderBatch { def apply(): toAutoencoderBatch[Float] = new toAutoencoderBatch[Float]() } -class toAutoencoderBatch[T](implicit ev: TensorNumeric[T] +class toAutoencoderBatch[T: ClassTag](implicit ev: TensorNumeric[T] )extends Transformer[MiniBatch[T], MiniBatch[T]] { override def apply(prev: Iterator[MiniBatch[T]]): Iterator[MiniBatch[T]] = { prev.map(batch => { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala index 03cf075098c..b22c19115d4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.models.rnn -import com.intel.analytics.bigdl.dataset.{DataSet, LocalDataSet, MiniBatch, SampleToBatch} +import com.intel.analytics.bigdl.dataset.{DataSet, LocalDataSet, MiniBatch, SampleToMiniBatch} import com.intel.analytics.bigdl.dataset.text.{Dictionary, LabeledSentence, LabeledSentenceToSample} import com.intel.analytics.bigdl.nn.{Concat, Identity, LogSoftMax, Module} import com.intel.analytics.bigdl.tensor.Tensor @@ -64,7 +64,7 @@ object Test { val rdd = sc.parallelize(labeledInput).mapPartitions(iter => LabeledSentenceToSample[Float](vocabSize).apply(iter) ).mapPartitions(iter => - SampleToBatch[Float](batchSize).apply(iter) + SampleToMiniBatch[Float](batchSize).apply(iter) ) val flow = rdd.mapPartitions(iter => { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala index 69a59e946b8..c3f1116eb2c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.models.rnn import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{DataSet, SampleToBatch} +import com.intel.analytics.bigdl.dataset.{DataSet, FixedLength, PaddingParam, SampleToMiniBatch} import com.intel.analytics.bigdl.dataset.text.LabeledSentenceToSample import com.intel.analytics.bigdl.dataset.text._ import com.intel.analytics.bigdl.dataset.text.utils.SentenceToken @@ -71,22 +71,20 @@ object Train { val padFeature = Tensor[Float]().resize(totalVocabLength) padFeature.setValue(endIdx + 1, 1.0f) val padLabel = startIdx + val featurePadding = PaddingParam(Some(Array(padFeature)), + FixedLength(Array(maxTrainLength))) val trainSet = DataSet.rdd(tokens) .transform(TextToLabeledSentence[Float](dictionary)) .transform(LabeledSentenceToSample[Float](totalVocabLength)) - .transform(SampleToBatch[Float](batchSize = param.batchSize, - featurePadding = Some(padFeature), - labelPadding = Some(padLabel), - fixedLength = Some(maxTrainLength))) + .transform(SampleToMiniBatch[Float](param.batchSize, + Some(featurePadding), None)) val validationSet = DataSet.rdd(valtokens) .transform(TextToLabeledSentence[Float](dictionary)) .transform(LabeledSentenceToSample[Float](totalVocabLength)) - .transform(SampleToBatch[Float](batchSize = param.batchSize, - featurePadding = Some(padFeature), - labelPadding = Some(padLabel), - fixedLength = Some(maxValLength))) + .transform(SampleToMiniBatch[Float](param.batchSize, + Some(featurePadding), None)) val model = if (param.modelSnapshot.isDefined) { Module.load[Float](param.modelSnapshot.get) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala index d81da2259d7..49835a13a31 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{Sample, SampleToBatch} +import com.intel.analytics.bigdl.dataset.{Sample, SampleToMiniBatch} import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.apache.spark.rdd.RDD @@ -53,8 +53,8 @@ class Evaluator[T: ClassTag] private[optim](model: Module[T])(implicit ev: Tenso val partitionNum = dataset.partitions.length val totalBatch = batchSize.getOrElse(batchPerPartition * partitionNum) - val otherBroad = dataset.sparkContext.broadcast(vMethods, SampleToBatch( - batchSize = totalBatch, None, None, None, partitionNum = Some(partitionNum))) + val otherBroad = dataset.sparkContext.broadcast(vMethods, SampleToMiniBatch( + batchSize = totalBatch, partitionNum = Some(partitionNum))) dataset.mapPartitions(partition => { val localModel = modelBroad.value() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index 889c8e50ea2..c8d632dd270 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -118,7 +118,7 @@ abstract class Optimizer[T: ClassTag, D]( : this.type = { this.validationTrigger = Some(trigger) val dataSet = - (DataSet.rdd(sampleRDD) -> SampleToBatch(batchSize)) + (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(batchSize)) .asInstanceOf[DistributedDataSet[MiniBatch[T]]] this.validationDataSet = Some(dataSet) this.validationMethods = Some(vMethods) @@ -300,26 +300,80 @@ object Optimizer { )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { new DistriOptimizer[T]( _model = model, - dataset = (DataSet.rdd(sampleRDD) -> SampleToBatch(batchSize)) + dataset = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(batchSize)) .asInstanceOf[DistributedDataSet[MiniBatch[T]]], criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] } + /** + * Apply an Optimizer who could apply padding to the Samples + * with a padding strategy. + * + * @param model model will be optimizied + * @param sampleRDD training Samples + * @param criterion loss function + * @param batchSize mini batch size + * @param featurePaddingParam feature padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * @param labelPaddingParam label padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * @return An optimizer + */ def apply[T: ClassTag]( - model: Module[T], - sampleRDD: RDD[Sample[T]], - criterion: Criterion[T], - batchSize: Int, - isInOrder: Boolean, - featurePadding : Option[Tensor[T]] = None, - labelPadding : Option[T] = None, - fixedLength: Option[Int] = None - )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { + model: Module[T], + sampleRDD: RDD[Sample[T]], + criterion: Criterion[T], + batchSize: Int, + featurePaddingParam: PaddingParam[T], + labelPaddingParam: PaddingParam[T] + )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { + new DistriOptimizer[T]( + _model = model, + dataset = (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(batchSize, Some(featurePaddingParam), Some(labelPaddingParam))) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]], + criterion = criterion + ).asInstanceOf[Optimizer[T, MiniBatch[T]]] + } + + def apply[T: ClassTag]( + model: Module[T], + sampleRDD: RDD[Sample[T]], + criterion: Criterion[T], + batchSize: Int, + featurePaddingParam: PaddingParam[T] + )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { + new DistriOptimizer[T]( + _model = model, + dataset = (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(batchSize, Some(featurePaddingParam))) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]], + criterion = criterion + ).asInstanceOf[Optimizer[T, MiniBatch[T]]] + } + + /** + * Apply an optimizer with User-Defined MiniBatch. + * + * @param model model will be optimizied + * @param sampleRDD training Samples + * @param criterion loss function + * @param batchSize mini batch size + * @param miniBatch An User-Defined MiniBatch to construct a mini batch. + * @return an Optimizer + */ + def apply[T: ClassTag]( + model: Module[T], + sampleRDD: RDD[Sample[T]], + criterion: Criterion[T], + batchSize: Int, + miniBatch: MiniBatch[T] + )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { new DistriOptimizer[T]( _model = model, - dataset = (DataSet.sortRDD(sampleRDD, isInOrder, batchSize) -> - SampleToBatch(batchSize, featurePadding, labelPadding, fixedLength)) + dataset = (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(miniBatch, batchSize, None)) .asInstanceOf[DistributedDataSet[MiniBatch[T]]], criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index 079c85188c6..548a852c838 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, SampleToBatch, Utils, DataSet => _} +import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, SampleToMiniBatch, Utils, DataSet => _} import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -51,9 +51,8 @@ class Predictor[T: ClassTag] private[optim]( def predict(dataSet: RDD[Sample[T]]): RDD[Activity] = { val modelBroad = ModelBroadcast[T].broadcast(dataSet.sparkContext, model.evaluate()) val partitionNum = dataSet.partitions.length - val otherBroad = dataSet.sparkContext.broadcast(SampleToBatch( - batchSize = batchPerPartition * partitionNum, None, None, None, - partitionNum = Some(partitionNum))) + val otherBroad = dataSet.sparkContext.broadcast(SampleToMiniBatch( + batchSize = batchPerPartition * partitionNum, partitionNum = Some(partitionNum))) dataSet.mapPartitions { partition => val localModel = modelBroad.value() val localTransformer = otherBroad.value.cloneTransformer() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index d56f5fabe21..de96c5e88a9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -130,6 +130,9 @@ object TensorNumericMath { def sum(n: Int, a: Array[T], aOffset: Int, stride: Int): T + def arraycopy(src: Array[T], srcPos: Int, + dest: Array[T], destPos: Int, length: Int): Unit + def getType(): TensorDataType } @@ -339,6 +342,15 @@ object TensorNumericMath { } r } + + override def arraycopy( + src: Array[Float], + srcPos: Int, + dest: Array[Float], + destPos: Int, + length: Int): Unit = { + System.arraycopy(src, srcPos, dest, destPos, length) + } } implicit object NumericDouble extends TensorNumeric[Double] { @@ -532,6 +544,15 @@ object TensorNumericMath { } r } + + override def arraycopy( + src: Array[Double], + srcPos: Int, + dest: Array[Double], + destPos: Int, + length: Int): Unit = { + System.arraycopy(src, srcPos, dest, destPos, length) + } } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 4a9ed0490cd..a0bb8652c85 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -170,7 +170,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab private def batching(rdd: RDD[Sample], batchSize: Int) : DistributedDataSet[MiniBatch[T]] = { val recordRDD = rdd.map(toSample(_)) - (DataSet.rdd(recordRDD) -> new SampleToBatch[T](batchSize)) + (DataSet.rdd(recordRDD) -> SampleToMiniBatch[T](batchSize)) .asInstanceOf[DistributedDataSet[MiniBatch[T]]] } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/BatchPaddingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/BatchPaddingSpec.scala index 65e1f2a41e7..398ece1f78e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/BatchPaddingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/BatchPaddingSpec.scala @@ -52,10 +52,14 @@ class BatchPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { val featurePadding = Tensor[Float](dictionaryLength).fill(100.0f) + val featurePaddingParam = PaddingParam(Some(Array(featurePadding))) + val labelPaddingParam = PaddingParam[Float](Some(Array(Tensor[Float](1).fill(10.0f)))) + val trainData = Array[Sample[Float]](sample1, sample2, sample3, sample3, sample3, sample3) val trainSet = DataSet.array(trainData) - .transform(SampleToBatch[Float](batchSize, Some(featurePadding), Some(10.0f))) + .transform(SampleToMiniBatch[Float](batchSize, + Some(featurePaddingParam), Some(labelPaddingParam))) val iter = trainSet.toLocal().data(train = false) @@ -100,12 +104,18 @@ class BatchPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { val sample2 = Sample[Float](input2, target2) val sample3 = Sample[Float](input3, target3) - val featurePadding = Tensor[Float](dictionaryLength).fill(100.0f) + val featurePadding = Tensor[Float](dictionaryLength).fill(100f) + + val featurePaddingParam = PaddingParam(Some(Array(featurePadding)), + FixedLength(Array(10))) + val labelPaddingParam = PaddingParam[Float](Some(Array(Tensor[Float](1).fill(80f))), + FixedLength(Array(10))) val trainData = Array[Sample[Float]](sample1, sample2, sample3, sample3, sample3, sample3) val trainSet = DataSet.array(trainData).transform( - SampleToBatch[Float](batchSize, Some(featurePadding), Some(80.0f), Some(10))) + SampleToMiniBatch[Float](batchSize, + Some(featurePaddingParam), Some(labelPaddingParam))) val iter = trainSet.toLocal().data(train = false) @@ -146,7 +156,7 @@ class BatchPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { data should be (tensorInput2) } - "SampleToBatchPadding " should "be same to SampleToBatch when no padding" in { + "SampleToBatchPadding " should "be same to SampleToMiniBatch when no padding" in { val batchSize = 3 val totalCount = 100 val trainData = new Array[Sample[Float]](totalCount) @@ -158,7 +168,7 @@ class BatchPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { i += 1 } val trainSet1 = DataSet.array(trainData) - .transform(SampleToBatch[Float](batchSize)) + .transform(SampleToMiniBatch[Float](batchSize)) val trainSet2 = DataSet.array(trainData) .transform(SampleToBatchNoPadding(batchSize)) @@ -168,6 +178,9 @@ class BatchPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { while (data1.hasNext && data2.hasNext) { val batch1 = data1.next() val batch2 = data2.next() + if (batch1.getInput() != batch2.getInput()) { + println + } batch1.getInput should be (batch2.getInput) batch1.getTarget should be (batch2.getTarget) } @@ -175,7 +188,7 @@ class BatchPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { data2.hasNext should be (false) } - "SampleToBatchPadding " should "be same to LabeledSentenceToSample and SampleToBatch " + + "SampleToBatchPadding " should "be same to LabeledSentenceToSample and SampleToMiniBatch " + "when padding" in { val batchSize = 3 val totalCount = 9 @@ -194,10 +207,16 @@ class BatchPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { val trainMaxLength = 10 val featurePadding = Tensor[Float](dictionaryLength).fill(0.0f) featurePadding(4000) = 1 + + val featurePaddingParam = PaddingParam(Some(Array(featurePadding)), + FixedLength(Array(trainMaxLength))) + val labelPaddingParam = PaddingParam[Float](Some(Array(Tensor[Float](1).fill(3999f))), + FixedLength(Array(trainMaxLength))) + val trainSet1 = DataSet.array(trainData) .transform(LabeledSentenceToSample(dictionaryLength)) - .transform(SampleToBatch[Float] - (batchSize, Some(featurePadding), Some(3999), Some(trainMaxLength))) + .transform(SampleToMiniBatch[Float](batchSize, + Some(featurePaddingParam), Some(labelPaddingParam))) val trainSet2 = DataSet.array(trainData) .transform(LabeledSentenceToSample(dictionaryLength, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala index 50f7d08534a..cf55a193a9d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala @@ -345,7 +345,7 @@ class DataSetSpec extends FlatSpec with Matchers with BeforeAndAfter { groupSize ) - val dataSet = dataSet1.transform(SampleToBatch(batchSize)) + val dataSet = dataSet1.transform(SampleToMiniBatch(batchSize)) val rdd = dataSet.toDistributed().data(train = true) rdd.partitions.size should be (partitionNum) val rddData = rdd.mapPartitions(iter => { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala new file mode 100644 index 00000000000..d179cbee258 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala @@ -0,0 +1,86 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dataset + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class MiniBatchSpec extends FlatSpec with Matchers { + "TensorMiniBatch size" should "return right result" in { + val a = Tensor[Float](3, 4).range(1, 12, 1) + val b = Tensor[Float](3).range(1, 3, 1) + val miniBatch = MiniBatch(a, b) + miniBatch.size() should be (3) + } + + "TensorMiniBatch getInput/target" should "return right result" in { + val a = Tensor[Float](3, 4).range(1, 12, 1) + val b = Tensor[Float](3).range(1, 3, 1) + val miniBatch = MiniBatch(a, b) + miniBatch.getInput() should be (a) + miniBatch.getTarget() should be (b) + } + + "TensorMiniBatch slice" should "return right result" in { + val a = Tensor[Float](3, 4).range(1, 12, 1) + val b = Tensor[Float](3).range(1, 3, 1) + val miniBatch = MiniBatch(a, b) + + miniBatch.slice(1, 1).getInput() should be (Tensor[Float](1, 4).range(1, 4, 1)) + miniBatch.slice(2, 1).getInput() should be (Tensor[Float](1, 4).range(5, 8, 1)) + miniBatch.slice(3, 1).getInput() should be (Tensor[Float](1, 4).range(9, 12, 1)) + miniBatch.slice(1, 1).getTarget() should be (Tensor[Float](1).fill(1)) + miniBatch.slice(2, 1).getTarget() should be (Tensor[Float](1).fill(2)) + miniBatch.slice(3, 1).getTarget() should be (Tensor[Float](1).fill(3)) + } + + "ArrayTensorMiniBatch size" should "return right result" in { + val a1 = Tensor[Float](3, 4).range(1, 12, 1) + val a2 = Tensor[Float](3, 2).range(1, 6, 1) + val b = Tensor[Float](3).range(1, 3, 1) + val miniBatch = MiniBatch(Array(a1, a2), b) + miniBatch.size() should be (3) + } + + "ArrayTensorMiniBatch getInput/target" should "return right result" in { + val a1 = Tensor[Float](3, 4).range(1, 12, 1) + val a2 = Tensor[Float](3, 2).range(1, 6, 1) + val b = Tensor[Float](3).range(1, 3, 1) + val miniBatch = MiniBatch(Array(a1, a2), b) + miniBatch.getInput() should be (T(a1, a2)) + miniBatch.getTarget() should be (b) + } + + "ArrayTensorMiniBatch slice" should "return right result" in { + val a1 = Tensor[Float](3, 2, 2).range(1, 12, 1) + val a2 = Tensor[Float](3, 2).range(1, 6, 1) + val b = Tensor[Float](3).range(1, 3, 1) + val miniBatch = MiniBatch(Array(a1, a2), b) + + miniBatch.slice(1, 1).getInput() should be (T(Tensor[Float](1, 2, 2).range(1, 4, 1), + Tensor[Float](1, 2).range(1, 2, 1))) + miniBatch.slice(2, 1).getInput() should be (T(Tensor[Float](1, 2, 2).range(5, 8, 1), + Tensor[Float](1, 2).range(3, 4, 1))) + miniBatch.slice(3, 1).getInput() should be (T(Tensor[Float](1, 2, 2).range(9, 12, 1), + Tensor[Float](1, 2).range(5, 6, 1))) + miniBatch.slice(1, 1).getTarget() should be (Tensor[Float](1).fill(1)) + miniBatch.slice(2, 1).getTarget() should be (Tensor[Float](1).fill(2)) + miniBatch.slice(3, 1).getTarget() should be (Tensor[Float](1).fill(3)) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala index 0a935dc5743..b3d0163b8bf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala @@ -17,34 +17,60 @@ package com.intel.analytics.bigdl.dataset import com.intel.analytics.bigdl.dataset.image.LabeledBGRImage +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} +import scala.reflect.ClassTag + @com.intel.analytics.bigdl.tags.Parallel class SampleSpec extends FlatSpec with Matchers { - "SampleSpec with Float Tensor input and Tensor label" should "initialize well" in { - val input1 = new LabeledBGRImage(32, 32) - val label1 = new LabeledBGRImage(32, 32) - val tensorInput1 = Tensor[Float](Storage[Float](input1.content), 1, Array(3, 32, 32)) - val tensorLabel1 = Tensor[Float](Storage[Float](label1.content), 1, Array(3, 32, 32)) - tensorInput1.rand() - tensorLabel1.rand() - val sample = new TensorSample[Float](tensorInput1, tensorLabel1) - sample.featureTensor should be (tensorInput1) - sample.labelTensor should be (tensorLabel1) + private def newMiniBatch[T: ClassTag]( + samples: Array[Sample[T]], + featurePadding: Option[Array[Tensor[T]]] = None, + featureFixedLength: Option[Array[Int]] = None, + featureIncrement: Option[Array[Int]] = None, + labelPadding: Option[Array[T]] = None, + labelFixedLength: Option[Array[Int]] = None, + labelIncrement: Option[Array[Int]] = None)( + implicit ev: TensorNumeric[T]): MiniBatch[T] = { + val featureParam = if (featureFixedLength.isDefined) { + PaddingParam(featurePadding, FixedLength(featureFixedLength.get)) + } else if (featureIncrement.isDefined) { + PaddingParam(featurePadding, PaddingLongest(featureIncrement.get)) + } else { + PaddingParam(featurePadding) + } + + val newLabelPadding = if (labelPadding.isDefined) { + Some(labelPadding.get.map(v => Tensor[T](1).fill(v))) + } else { + None + } + + val labelParam = if (labelFixedLength.isDefined) { + PaddingParam(newLabelPadding, FixedLength(labelFixedLength.get)) + } else if (labelIncrement.isDefined) { + PaddingParam(newLabelPadding, PaddingLongest(labelIncrement.get)) + } else { + PaddingParam(newLabelPadding) + } + + MiniBatch[T](samples(0).numFeature(), samples(0).numLabel(), + Some(featureParam), Some(labelParam)).set(samples) } - "TensorSample" should "clone well" in { + "SampleSpec with Float Tensor input and Tensor label" should "initialize well" in { val input1 = new LabeledBGRImage(32, 32) val label1 = new LabeledBGRImage(32, 32) val tensorInput1 = Tensor[Float](Storage[Float](input1.content), 1, Array(3, 32, 32)) val tensorLabel1 = Tensor[Float](Storage[Float](label1.content), 1, Array(3, 32, 32)) tensorInput1.rand() tensorLabel1.rand() - val sample = new TensorSample[Float](tensorInput1, tensorLabel1) - val otherSample = sample.clone() - sample.featureTensor should be (otherSample.featureTensor) - sample.labelTensor should be (otherSample.labelTensor) + val sample = Sample[Float](tensorInput1, tensorLabel1) + sample.feature should be (tensorInput1) + sample.label should be (tensorLabel1) } "SampleSpec with Float Tensor input and Tensor label" should "set well" in { @@ -54,12 +80,566 @@ class SampleSpec extends FlatSpec with Matchers { val tensorLabel1 = Tensor[Float](Storage[Float](label1.content), 1, Array(3, 32, 32)) tensorInput1.rand() tensorLabel1.rand() - val sample = Sample[Float]() + val sample = Sample[Float](Tensor[Float](3, 32, 32), Tensor[Float](3, 32, 32)) sample.set(tensorInput1.storage().array(), - tensorLabel1.storage().array(), - tensorInput1.size, - tensorLabel1.size) - sample.feature() should be (tensorInput1) - sample.label() should be (tensorLabel1) + tensorLabel1.storage().array(), + tensorInput1.size, + tensorLabel1.size) + sample.feature() should be(tensorInput1) + sample.label() should be(tensorLabel1) + } + + "Sample.equals" should "return right result" in { + val sample1 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(1)) + val sample2 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(1)) + sample1.equals(sample2) should be (true) + + val sample3 = Sample[Float](Tensor[Float](3, 3).range(1, 9, 1), Tensor[Float](1).fill(10)) + val sample4 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](4).range(7, 10, 1)) + sample3.equals(sample4) should be (false) + } + + "Array[TensorSample] toMiniBatch" should "return right result" in { + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(7, 12, 1), Tensor[Float](1).fill(2)) + samples(2) = Sample[Float](Tensor[Float](2, 3).range(13, 18, 1), Tensor[Float](1).fill(3)) + val result = newMiniBatch(samples) + val exceptedInput = Tensor[Float](3, 2, 3).range(1, 18, 1) + val exceptedTarget = Tensor[Float](3, 1).range(1, 3, 1) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[TensorSample] toMiniBatch with feature padding" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](1, 3).range(1, 3, 1), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(10, 15, 1), Tensor[Float](1).fill(2)) + samples(2) = Sample[Float](Tensor[Float](3, 3).range(19, 27, 1), Tensor[Float](1).fill(3)) + val result = newMiniBatch[Float](samples, featurePadding) + val exceptedInput = Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27 + )), 1, Array(3, 3, 3)) + val exceptedTarget = Tensor[Float](Storage(Array[Float](1, 2, 3)), 1, Array(3, 1)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[TensorSample] toMiniBatch with padding" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](1, 3).range(1, 3, 1), Tensor[Float](3).fill(1)) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(10, 15, 1), Tensor[Float](2).fill(2)) + samples(2) = Sample[Float](Tensor[Float](3, 3).range(19, 27, 1), Tensor[Float](1).fill(3)) + val result = newMiniBatch(samples, featurePadding = featurePadding, + labelPadding = Some(Array(-1f))) + val exceptedInput = Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27 + )), 1, Array(3, 3, 3)) + val exceptedTarget = Tensor[Float](Storage(Array[Float]( + 1, 1, 1, + 2, 2, -1, + 3, -1, -1 + )), 1, Array(3, 3)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[TensorSample] toMiniBatch with fixedlength" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](1, 3).range(1, 3, 1), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(10, 15, 1), Tensor[Float](2).fill(2)) + samples(2) = Sample[Float](Tensor[Float](3, 3).range(19, 27, 1), Tensor[Float](3).fill(3)) + val result = newMiniBatch[Float](samples, featurePadding = featurePadding, + featureFixedLength = Some(Array(4)), labelPadding = Some(Array(-1)), + labelFixedLength = Some(Array(4))) + val exceptedInput = Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + -1f, -2f, -3f + )), 1, Array(3, 4, 3)) + val exceptedTarget = Tensor[Float](Storage(Array[Float]( + 1, -1, -1, -1, + 2, 2, -1, -1, + 3, 3, 3, -1 + )), 1, Array(3, 4)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[TensorSample] toMiniBatch with increment" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](1, 3).range(1, 3, 1), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(10, 15, 1), Tensor[Float](2).fill(2)) + samples(2) = Sample[Float](Tensor[Float](3, 3).range(19, 27, 1), Tensor[Float](3).fill(3)) + val result = newMiniBatch[Float](samples, featurePadding = featurePadding, + featureIncrement = Some(Array(2)), labelPadding = Some(Array(-1)), + labelIncrement = Some(Array(1))) + val exceptedInput = Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + -1f, -2f, -3f, + -1f, -2f, -3f + )), 1, Array(3, 5, 3)) + val exceptedTarget = Tensor[Float](Storage(Array[Float]( + 1, -1, -1, -1, + 2, 2, -1, -1, + 3, 3, 3, -1 + )), 1, Array(3, 4)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[TensorTSample] toMiniBatch" should "return right result" in { + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), 1) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(7, 12, 1), 2) + samples(2) = Sample[Float](Tensor[Float](2, 3).range(13, 18, 1), 3) + val result = newMiniBatch(samples) + val exceptedInput = Tensor[Float](3, 2, 3).range(1, 18, 1) + val exceptedTarget = Tensor[Float](3, 1).range(1, 3, 1) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[TensorTSample] toMiniBatch with feature padding" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](1, 3).range(1, 3, 1), 1) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(10, 15, 1), 2) + samples(2) = Sample[Float](Tensor[Float](3, 3).range(19, 27, 1), 3) + val result = newMiniBatch[Float](samples, featurePadding) + val exceptedInput = Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27 + )), 1, Array(3, 3, 3)) + val exceptedTarget = Tensor[Float](Storage(Array[Float](1, 2, 3)), 1, Array(3, 1)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[TensorTSample] toMiniBatch with fixedlength" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](1, 3).range(1, 3, 1), 1) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(10, 15, 1), 2) + samples(2) = Sample[Float](Tensor[Float](3, 3).range(19, 27, 1), 3) + val result = newMiniBatch[Float]( + samples, featurePadding, + labelPadding = Some(Array(-1)), + featureFixedLength = Some(Array(4))) + val exceptedInput = Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + -1f, -2f, -3f + )), 1, Array(3, 4, 3)) + val exceptedTarget = Tensor[Float](Storage(Array[Float](1, 2, 3 )), 1, Array(3, 1)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[ArrayTensorSample] toMiniBatch" should "return right result" in { + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Array(Tensor[Float](2, 3).range(7, 12, 1), + Tensor[Float](3).fill(2)), Tensor[Float](1).fill(2)) + samples(2) = Sample[Float](Array(Tensor[Float](2, 3).range(13, 18, 1), + Tensor[Float](3).fill(3)), Tensor[Float](1).fill(3)) + val result = newMiniBatch(samples) + val exceptedInput = T(Tensor[Float](3, 2, 3).range(1, 18, 1), Tensor[Float](3, 3)) + exceptedInput[Tensor[Float]](2)(1).fill(1) + exceptedInput[Tensor[Float]](2)(2).fill(2) + exceptedInput[Tensor[Float]](2)(3).fill(3) + val exceptedTarget = Tensor[Float](3, 1).range(1, 3, 1) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[ArrayTensorSample] toMiniBatch with feature padding" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))), + Tensor[Float](1))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Array(Tensor[Float](1, 3).range(1, 3, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Array(Tensor[Float](2, 3).range(10, 15, 1), + Tensor[Float](3).fill(2)), Tensor[Float](1).fill(2)) + samples(2) = Sample[Float](Array(Tensor[Float](3, 3).range(19, 27, 1), + Tensor[Float](3).fill(3)), Tensor[Float](1).fill(3)) + val result = newMiniBatch[Float](samples, featurePadding) + val exceptedInput = T(Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27 + )), 1, Array(3, 3, 3)), Tensor[Float](3, 3)) + exceptedInput[Tensor[Float]](2)(1).fill(1) + exceptedInput[Tensor[Float]](2)(2).fill(2) + exceptedInput[Tensor[Float]](2)(3).fill(3) + val exceptedTarget = Tensor[Float](Storage(Array[Float](1, 2, 3)), 1, Array(3, 1)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[ArrayTensorSample] toMiniBatch with padding" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))), + Tensor[Float](1))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Array(Tensor[Float](1, 3).range(1, 3, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Array(Tensor[Float](2, 3).range(10, 15, 1), + Tensor[Float](3).fill(2)), Tensor[Float](2).fill(2)) + samples(2) = Sample[Float](Array(Tensor[Float](3, 3).range(19, 27, 1), + Tensor[Float](3).fill(3)), Tensor[Float](3).fill(3)) + val result = newMiniBatch[Float](samples, featurePadding, labelPadding = Some(Array(-1))) + val exceptedInput = T(Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27 + )), 1, Array(3, 3, 3)), Tensor[Float](3, 3)) + exceptedInput[Tensor[Float]](2)(1).fill(1) + exceptedInput[Tensor[Float]](2)(2).fill(2) + exceptedInput[Tensor[Float]](2)(3).fill(3) + val exceptedTarget = Tensor[Float](Storage(Array[Float]( + 1, -1, -1, + 2, 2, -1, + 3, 3, 3 + )), 1, Array(3, 3)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[ArrayTensorSample] toMiniBatch with fixedlength" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))), + Tensor[Float](1))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Array(Tensor[Float](1, 3).range(1, 3, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Array(Tensor[Float](2, 3).range(10, 15, 1), + Tensor[Float](3).fill(2)), Tensor[Float](2).fill(2)) + samples(2) = Sample[Float](Array(Tensor[Float](3, 3).range(19, 27, 1), + Tensor[Float](3).fill(3)), Tensor[Float](3).fill(3)) + val result = newMiniBatch[Float]( + samples, featurePadding, + Some(Array(4, 3)), + None, + Some(Array(-1)), + Some(Array(4)) + ) + val exceptedInput = T(Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + -1f, -2f, -3f + )), 1, Array(3, 4, 3)), Tensor[Float](3, 3)) + exceptedInput[Tensor[Float]](2)(1).fill(1) + exceptedInput[Tensor[Float]](2)(2).fill(2) + exceptedInput[Tensor[Float]](2)(3).fill(3) + val exceptedTarget = Tensor[Float](Storage(Array[Float]( + 1, -1, -1, -1, + 2, 2, -1, -1, + 3, 3, 3, -1 + )), 1, Array(3, 4)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[ArrayTensorSample] toMiniBatch with fixedlength(4, -1)" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))), + Tensor[Float](1))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Array(Tensor[Float](1, 3).range(1, 3, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + samples(1) = Sample[Float](Array(Tensor[Float](2, 3).range(10, 15, 1), + Tensor[Float](3).fill(2)), Tensor[Float](2).fill(2)) + samples(2) = Sample[Float](Array(Tensor[Float](3, 3).range(19, 27, 1), + Tensor[Float](3).fill(3)), Tensor[Float](3).fill(3)) + val result = newMiniBatch[Float]( + samples, featurePadding, + Some(Array(4, -1)), + None, + Some(Array(-1)), + Some(Array(-1)) + ) + val exceptedInput = T(Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + -1f, -2f, -3f + )), 1, Array(3, 4, 3)), Tensor[Float](3, 3)) + exceptedInput[Tensor[Float]](2)(1).fill(1) + exceptedInput[Tensor[Float]](2)(2).fill(2) + exceptedInput[Tensor[Float]](2)(3).fill(3) + val exceptedTarget = Tensor[Float](Storage(Array[Float]( + 1, -1, -1, + 2, 2, -1, + 3, 3, 3 + )), 1, Array(3, 3)) + + result.getInput() should be (exceptedInput) + result.getTarget() should be (exceptedTarget) + } + + "Array[UnlabledTensorSample] toMiniBatch" should "return right result" in { + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1)) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(7, 12, 1)) + samples(2) = Sample[Float](Tensor[Float](2, 3).range(13, 18, 1)) + val result = newMiniBatch(samples) + val exceptedInput = Tensor[Float](3, 2, 3).range(1, 18, 1) + + result.getInput() should be (exceptedInput) + } + + "Array[UnlabledTensorSample] toMiniBatch with feature padding" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](1, 3).range(1, 3, 1)) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(10, 15, 1)) + samples(2) = Sample[Float](Tensor[Float](3, 3).range(19, 27, 1)) + val result = newMiniBatch[Float](samples, featurePadding = featurePadding) + val exceptedInput = Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27 + )), 1, Array(3, 3, 3)) + + result.getInput() should be (exceptedInput) + } + + "Array[UnlabledTensorSample] toMiniBatch with fixedlength" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Tensor[Float](1, 3).range(1, 3, 1)) + samples(1) = Sample[Float](Tensor[Float](2, 3).range(10, 15, 1)) + samples(2) = Sample[Float](Tensor[Float](3, 3).range(19, 27, 1)) + val result = newMiniBatch[Float]( + samples, featurePadding = featurePadding, featureFixedLength = Some(Array(4))) + val exceptedInput = Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + -1f, -2f, -3f + )), 1, Array(3, 4, 3)) + + result.getInput() should be (exceptedInput) + } + + "Array[UnlabeledArrayTensorSample] toMiniBatch" should "return right result" in { + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1))) + samples(1) = Sample[Float](Array(Tensor[Float](2, 3).range(7, 12, 1), + Tensor[Float](3).fill(2))) + samples(2) = Sample[Float](Array(Tensor[Float](2, 3).range(13, 18, 1), + Tensor[Float](3).fill(3))) + val result = newMiniBatch(samples) + val exceptedInput = T(Tensor[Float](3, 2, 3).range(1, 18, 1), Tensor[Float](3, 3)) + exceptedInput[Tensor[Float]](2)(1).fill(1) + exceptedInput[Tensor[Float]](2)(2).fill(2) + exceptedInput[Tensor[Float]](2)(3).fill(3) + + result.getInput() should be (exceptedInput) + } + + "Array[UnlabeledArrayTensorSample] toMiniBatch with padding" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))), + Tensor[Float](1))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Array(Tensor[Float](1, 3).range(1, 3, 1), + Tensor[Float](3).fill(1))) + samples(1) = Sample[Float](Array(Tensor[Float](2, 3).range(10, 15, 1), + Tensor[Float](3).fill(2))) + samples(2) = Sample[Float](Array(Tensor[Float](3, 3).range(19, 27, 1), + Tensor[Float](3).fill(3))) + val result = newMiniBatch[Float](samples, featurePadding) + val exceptedInput = T(Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27 + )), 1, Array(3, 3, 3)), Tensor[Float](3, 3)) + exceptedInput[Tensor[Float]](2)(1).fill(1) + exceptedInput[Tensor[Float]](2)(2).fill(2) + exceptedInput[Tensor[Float]](2)(3).fill(3) + + result.getInput() should be (exceptedInput) + } + + "Array[UnlabeledArrayTensorSample] toMiniBatch with fixedlength" should "return right result" in { + val featurePadding = Some(Array(Tensor[Float](Storage(Array(-1f, -2f, -3f))), + Tensor[Float](Storage(Array(-4f))))) + val samples: Array[Sample[Float]] = new Array[Sample[Float]](3) + samples(0) = Sample[Float](Array(Tensor[Float](1, 3).range(1, 3, 1), + Tensor[Float](3).fill(1))) + samples(1) = Sample[Float](Array(Tensor[Float](2, 3).range(10, 15, 1), + Tensor[Float](3).fill(2))) + samples(2) = Sample[Float](Array(Tensor[Float](3, 3).range(19, 27, 1), + Tensor[Float](3).fill(3))) + val result = newMiniBatch[Float]( + samples, featurePadding = featurePadding, featureFixedLength = Some(Array(4, 4))) + val exceptedInput = T(Tensor[Float](Storage(Array[Float]( + 1, 2, 3, + -1f, -2f, -3f, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 10, 11, 12, + 13, 14, 15, + -1f, -2f, -3f, + -1f, -2f, -3f, + + 19, 20, 21, + 22, 23, 24, + 25, 26, 27, + -1f, -2f, -3f + )), 1, Array(3, 4, 3)), + Tensor[Float](Storage(Array[Float]( + 1, 1, 1, -4, + 2, 2, 2, -4, + 3, 3, 3, -4 + )), 1, Array(3, 4))) + + result.getInput() should be (exceptedInput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/TransformersSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/TransformersSpec.scala index 864cd996181..3a9731369b1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/TransformersSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/TransformersSpec.scala @@ -101,11 +101,12 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val toTensor = new GreyImgToBatch(2) val tensorDataSet = dataSet -> toTensor val iter = tensorDataSet.toLocal().data(train = true) - val batch = iter.next().asInstanceOf[TensorMiniBatch[Float]] - batch.input.size(1) should be(2) - batch.input.size(2) should be(32) - batch.input.size(3) should be(32) - val testData1 = batch.input.storage().array() + val batch = iter.next() + val input = batch.getInput().toTensor[Float] + input.size(1) should be(2) + input.size(2) should be(32) + input.size(3) should be(32) + val testData1 = input.storage().array() val content1 = image1.content var i = 0 while (i < content1.length) { @@ -118,11 +119,12 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { testData1(i + 32 * 32) should be(content2(i)) i += 1 } - val batch2 = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch2 = iter.next() + val input2 = batch.getInput().toTensor[Float] val content3 = image3.content - batch2.input.size(1) should be(2) - batch2.input.size(2) should be(32) - batch2.input.size(3) should be(32) + input2.size(1) should be(2) + input2.size(2) should be(32) + input2.size(3) should be(32) i = 0 while (i < content3.length) { testData1(i) should be(content3(i)) @@ -242,97 +244,99 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val toTensor = new BGRImgToBatch(2) val tensorDataSet = dataSet -> toTensor val iter = tensorDataSet.toLocal().data(train = true) - val batch1 = iter.next().asInstanceOf[TensorMiniBatch[Float]] - batch1.input.size(1) should be(2) - batch1.input.size(2) should be(3) - batch1.input.size(3) should be(32) - batch1.input.size(4) should be(32) + val batch1 = iter.next() + val input1 = batch1.getInput().toTensor[Float] + input1.size(1) should be(2) + input1.size(2) should be(3) + input1.size(3) should be(32) + input1.size(4) should be(32) val content1 = image1.content var i = 0 - batch1.input.select(1, 1).select(1, 1).apply1(e => { + input1.select(1, 1).select(1, 1).apply1(e => { e should be(content1(i * 3 + 2)) i += 1 e }) i = 0 - batch1.input.select(1, 1).select(1, 2).apply1(e => { + input1.select(1, 1).select(1, 2).apply1(e => { e should be(content1(i * 3 + 1)) i += 1 e }) i = 0 - batch1.input.select(1, 1).select(1, 3).apply1(e => { + input1.select(1, 1).select(1, 3).apply1(e => { e should be(content1(i * 3)) i += 1 e }) val content2 = image2.content i = 0 - batch1.input.select(1, 2).select(1, 1).apply1(e => { + input1.select(1, 2).select(1, 1).apply1(e => { e should be(content2(i * 3 + 2)) i += 1 e }) i = 0 - batch1.input.select(1, 2).select(1, 2).apply1(e => { + input1.select(1, 2).select(1, 2).apply1(e => { e should be(content2(i * 3 + 1)) i += 1 e }) i = 0 - batch1.input.select(1, 2).select(1, 3).apply1(e => { + input1.select(1, 2).select(1, 3).apply1(e => { e should be(content2(i * 3)) i += 1 e }) - val batch = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch = iter.next() + val input = batch.getInput().toTensor[Float] val content3 = image3.content - batch.input.size(1) should be(2) - batch.input.size(2) should be(3) - batch.input.size(3) should be(32) - batch.input.size(4) should be(32) + input.size(1) should be(2) + input.size(2) should be(3) + input.size(3) should be(32) + input.size(4) should be(32) i = 0 - batch.input.select(1, 1).select(1, 1).apply1(e => { + input.select(1, 1).select(1, 1).apply1(e => { e should be(content3(i * 3 + 2)) i += 1 e }) i = 0 - batch.input.select(1, 1).select(1, 2).apply1(e => { + input.select(1, 1).select(1, 2).apply1(e => { e should be(content3(i * 3 + 1)) i += 1 e }) i = 0 - batch.input.select(1, 1).select(1, 3).apply1(e => { + input.select(1, 1).select(1, 3).apply1(e => { e should be(content3(i * 3)) i += 1 e }) i = 0 - batch.input.select(1, 2).select(1, 1).apply1(e => { + input.select(1, 2).select(1, 1).apply1(e => { e should be(content1(i * 3 + 2)) i += 1 e }) i = 0 - batch.input.select(1, 2).select(1, 2).apply1(e => { + input.select(1, 2).select(1, 2).apply1(e => { e should be(content1(i * 3 + 1)) i += 1 e }) i = 0 - batch.input.select(1, 2).select(1, 3).apply1(e => { + input.select(1, 2).select(1, 3).apply1(e => { e should be(content1(i * 3)) i += 1 e @@ -359,97 +363,99 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { ) val tensorDataSet = dataSet -> toTensor val iter = tensorDataSet.toLocal().data(train = true) - val batch = iter.next().asInstanceOf[TensorMiniBatch[Float]] - batch.input.size(1) should be(2) - batch.input.size(2) should be(3) - batch.input.size(3) should be(32) - batch.input.size(4) should be(32) + val batch = iter.next() + val input = batch.getInput().toTensor[Float] + input.size(1) should be(2) + input.size(2) should be(3) + input.size(3) should be(32) + input.size(4) should be(32) val content1 = image1.content var i = 0 - batch.input.select(1, 1).select(1, 1).apply1(e => { + input.select(1, 1).select(1, 1).apply1(e => { e should be(content1(i * 3 + 2)) i += 1 e }) i = 0 - batch.input.select(1, 1).select(1, 2).apply1(e => { + input.select(1, 1).select(1, 2).apply1(e => { e should be(content1(i * 3 + 1)) i += 1 e }) i = 0 - batch.input.select(1, 1).select(1, 3).apply1(e => { + input.select(1, 1).select(1, 3).apply1(e => { e should be(content1(i * 3)) i += 1 e }) val content2 = image2.content i = 0 - batch.input.select(1, 2).select(1, 1).apply1(e => { + input.select(1, 2).select(1, 1).apply1(e => { e should be(content2(i * 3 + 2)) i += 1 e }) i = 0 - batch.input.select(1, 2).select(1, 2).apply1(e => { + input.select(1, 2).select(1, 2).apply1(e => { e should be(content2(i * 3 + 1)) i += 1 e }) i = 0 - batch.input.select(1, 2).select(1, 3).apply1(e => { + input.select(1, 2).select(1, 3).apply1(e => { e should be(content2(i * 3)) i += 1 e }) - val batch2 = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch2 = iter.next() + val input2 = batch.getInput().toTensor[Float] val content3 = image3.content - batch2.input.size(1) should be(2) - batch2.input.size(2) should be(3) - batch2.input.size(3) should be(32) - batch2.input.size(4) should be(32) + input2.size(1) should be(2) + input2.size(2) should be(3) + input2.size(3) should be(32) + input2.size(4) should be(32) i = 0 - batch2.input.select(1, 1).select(1, 1).apply1(e => { + input2.select(1, 1).select(1, 1).apply1(e => { e should be(content3(i * 3 + 2)) i += 1 e }) i = 0 - batch2.input.select(1, 1).select(1, 2).apply1(e => { + input2.select(1, 1).select(1, 2).apply1(e => { e should be(content3(i * 3 + 1)) i += 1 e }) i = 0 - batch2.input.select(1, 1).select(1, 3).apply1(e => { + input2.select(1, 1).select(1, 3).apply1(e => { e should be(content3(i * 3)) i += 1 e }) i = 0 - batch2.input.select(1, 2).select(1, 1).apply1(e => { + input2.select(1, 2).select(1, 1).apply1(e => { e should be(content1(i * 3 + 2)) i += 1 e }) i = 0 - batch2.input.select(1, 2).select(1, 2).apply1(e => { + input2.select(1, 2).select(1, 2).apply1(e => { e should be(content1(i * 3 + 1)) i += 1 e }) i = 0 - batch2.input.select(1, 2).select(1, 3).apply1(e => { + input2.select(1, 2).select(1, 3).apply1(e => { e should be(content1(i * 3)) i += 1 e @@ -589,16 +595,16 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val sample3 = Sample[Float](tensorInput3, tensorTarget3) val batch1 = iter.next() - batch1.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput1) - batch1.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget1) + batch1.feature() should be (tensorInput1) + batch1.label() should be (tensorTarget1) val batch2 = iter.next() - batch2.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput2) - batch2.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget2) + batch2.feature should be (tensorInput2) + batch2.label should be (tensorTarget2) val batch3 = iter.next() - batch3.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput3) - batch3.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget3) + batch3.feature should be (tensorInput3) + batch3.label should be (tensorTarget3) } "LabeledSentence toSample" should "transform correctly for single label Double" in { val input1 = Array(1.0, 2.0, 3.0) @@ -637,16 +643,16 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val sample3 = Sample[Double](tensorInput3, tensorTarget3) val batch1 = iter.next() - batch1.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput1) - batch1.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget1) + batch1.feature should be (tensorInput1) + batch1.label should be (tensorTarget1) val batch2 = iter.next() - batch2.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput2) - batch2.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget2) + batch2.feature should be (tensorInput2) + batch2.label should be (tensorTarget2) val batch3 = iter.next() - batch3.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput3) - batch3.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget3) + batch3.feature should be (tensorInput3) + batch3.label should be (tensorTarget3) } "LabeledSentence toSample" should "transform correctly for padding sentences single label" in { val input1 = Array(1.0f, 2.0f, 3.0f) @@ -685,16 +691,16 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val sample3 = Sample[Float](tensorInput3, tensorTarget3) val batch1 = iter.next() - batch1.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput1) - batch1.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget1) + batch1.feature should be (tensorInput1) + batch1.label should be (tensorTarget1) val batch2 = iter.next() - batch2.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput2) - batch2.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget2) + batch2.feature should be (tensorInput2) + batch2.label should be (tensorTarget2) val batch3 = iter.next() - batch3.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput3) - batch3.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget3) + batch3.feature should be (tensorInput3) + batch3.label should be (tensorTarget3) } "LabeledSentence toSample" should "transform correctly for language model label" in { val input1 = Array(0.0f, 2.0f, 3.0f) @@ -733,16 +739,16 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val sample3 = Sample[Float](tensorInput3, tensorTarget3) val batch1 = iter.next() - batch1.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput1) - batch1.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget1) + batch1.feature should be (tensorInput1) + batch1.label should be (tensorTarget1) val batch2 = iter.next() - batch2.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput2) - batch2.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget2) + batch2.feature should be (tensorInput2) + batch2.label should be (tensorTarget2) val batch3 = iter.next() - batch3.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput3) - batch3.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget3) + batch3.feature should be (tensorInput3) + batch3.label should be (tensorTarget3) } "LabeledSentence toSample" should "transform correctly" + " for language model label padding sentences" in { @@ -782,16 +788,16 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val sample3 = Sample[Float](tensorInput3, tensorTarget3) val batch1 = iter.next() - batch1.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput1) - batch1.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget1) + batch1.feature should be (tensorInput1) + batch1.label should be (tensorTarget1) val batch2 = iter.next() - batch2.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput2) - batch2.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget2) + batch2.feature should be (tensorInput2) + batch2.label should be (tensorTarget2) val batch3 = iter.next() - batch3.asInstanceOf[TensorSample[Float]].featureTensor should be (tensorInput3) - batch3.asInstanceOf[TensorSample[Float]].labelTensor should be (tensorTarget3) + batch3.feature should be (tensorInput3) + batch3.label should be (tensorTarget3) } "SampleToBatchSpec" should "be good with TensorBatch1 Double" in { Engine.setNodeAndCore(1, 1) @@ -817,7 +823,7 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val sampleDataSet = dataSet -> sampleToBatch val iter = sampleDataSet.toLocal().data(train = false) - val batch1 = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch1 = iter.next() val batch1Data = Tensor[Double](Array(2, 3, 5)) batch1Data(1).resizeAs(tensorInput1).copy(tensorInput1) @@ -825,16 +831,16 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val batch1Label = Tensor[Double](Array(2, 3)) batch1Label(1).resizeAs(tensorTarget1).copy(tensorTarget1) batch1Label(2).resizeAs(tensorTarget2).copy(tensorTarget2) - batch1.input should be (batch1Data) - batch1.target should be (batch1Label) + batch1.getInput should be (batch1Data) + batch1.getTarget should be (batch1Label) - val batch2 = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch2 = iter.next() val batch2Data = Tensor[Double](Array(1, 3, 5)) batch2Data(1).resizeAs(tensorInput3).copy(tensorInput3) val batch2Label = Tensor[Double](Array(1, 3)) batch2Label(1).resizeAs(tensorTarget3).copy(tensorTarget3) - batch2.input should be (batch2Data) - batch2.target should be (batch2Label) + batch2.getInput should be (batch2Data) + batch2.getTarget should be (batch2Label) } "SampleToBatchSpec" should "be good with TensorBatch1" in { Engine.setNodeAndCore(1, 1) @@ -860,7 +866,7 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val sampleDataSet = dataSet -> sampleToBatch val iter = sampleDataSet.toLocal().data(train = false) - val batch1 = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch1 = iter.next() val batch1Data = Tensor[Float](Array(2, 3, 5)) batch1Data(1).resizeAs(tensorInput1).copy(tensorInput1) @@ -868,16 +874,16 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val batch1Label = Tensor[Float](Array(2, 3)) batch1Label(1).resizeAs(tensorTarget1).copy(tensorTarget1) batch1Label(2).resizeAs(tensorTarget2).copy(tensorTarget2) - batch1.input should be (batch1Data) - batch1.target should be (batch1Label) + batch1.getInput should be (batch1Data) + batch1.getTarget should be (batch1Label) - val batch2 = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch2 = iter.next() val batch2Data = Tensor[Float](Array(1, 3, 5)) batch2Data(1).resizeAs(tensorInput3).copy(tensorInput3) val batch2Label = Tensor[Float](Array(1, 3)) batch2Label(1).resizeAs(tensorTarget3).copy(tensorTarget3) - batch2.input should be (batch2Data) - batch2.target should be (batch2Label) + batch2.getInput should be (batch2Data) + batch2.getTarget should be (batch2Label) } "SampleToBatchSpec" should "be good with TensorBatch2" in { Engine.setNodeAndCore(1, 1) @@ -903,7 +909,7 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val sampleDataSet = dataSet -> sampleToBatch val iter = sampleDataSet.toLocal().data(train = true) - val batch1 = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch1 = iter.next() val batch1Data = Tensor[Float](Array(2, 3, 5)) batch1Data(1).resizeAs(tensorInput1).copy(tensorInput1) @@ -911,18 +917,150 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val batch1Label = Tensor[Float](Array(2, 3)) batch1Label(1).resizeAs(tensorTarget1).copy(tensorTarget1) batch1Label(2).resizeAs(tensorTarget2).copy(tensorTarget2) - batch1.input should be (batch1Data) - batch1.target should be (batch1Label) + batch1.getInput should be (batch1Data) + batch1.getTarget should be (batch1Label) + + val batch2 = iter.next() + val batch2Data = Tensor[Float](Array(2, 3, 5)) + batch2Data(1).resizeAs(tensorInput3).copy(tensorInput3) + batch2Data(2).resizeAs(tensorInput1).copy(tensorInput1) + val batch2Label = Tensor[Float](Array(2, 3)) + batch2Label(1).resizeAs(tensorTarget3).copy(tensorTarget3) + batch2Label(2).resizeAs(tensorTarget1).copy(tensorTarget1) + batch2.getInput should be (batch2Data) + batch2.getTarget should be (batch2Label) + } + + "SampleToMiniBatchSpec" should "be good with TensorBatch1 Double" in { + Engine.setNodeAndCore(1, 1) + val tensorInput1 = Tensor[Double](Storage( + Array(0.0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0)), 1, Array(3, 5)) + val tensorInput2 = Tensor[Double](Storage( + Array(0.0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0)), 1, Array(3, 5)) + val tensorInput3 = Tensor[Double](Storage( + Array(1.0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1)), 1, Array(3, 5)) + val tensorTarget1 = Tensor[Double](Storage( + Array(3.0, 4, 5)), 1, Array(3)) + val tensorTarget2 = Tensor[Double](Storage( + Array(2.0, 1, 5)), 1, Array(3)) + val tensorTarget3 = Tensor[Double](Storage( + Array(5.0, 2, 1)), 1, Array(3)) + val sample1 = Sample[Double](tensorInput1, tensorTarget1) + val sample2 = Sample[Double](tensorInput2, tensorTarget2) + val sample3 = Sample[Double](tensorInput3, tensorTarget3) + + val dataSet = new LocalArrayDataSet[Sample[Double]](Array(sample1, + sample2, sample3)) + val sampleToBatch = SampleToMiniBatch[Double](2) + val sampleDataSet = dataSet -> sampleToBatch + val iter = sampleDataSet.toLocal().data(train = false) + + val batch1 = iter.next() + + val batch1Data = Tensor[Double](Array(2, 3, 5)) + batch1Data(1).resizeAs(tensorInput1).copy(tensorInput1) + batch1Data(2).resizeAs(tensorInput2).copy(tensorInput2) + val batch1Label = Tensor[Double](Array(2, 3)) + batch1Label(1).resizeAs(tensorTarget1).copy(tensorTarget1) + batch1Label(2).resizeAs(tensorTarget2).copy(tensorTarget2) + batch1.getInput should be (batch1Data) + batch1.getTarget should be (batch1Label) - val batch2 = iter.next().asInstanceOf[TensorMiniBatch[Float]] + val batch2 = iter.next() + val batch2Data = Tensor[Double](Array(1, 3, 5)) + batch2Data(1).resizeAs(tensorInput3).copy(tensorInput3) + val batch2Label = Tensor[Double](Array(1, 3)) + batch2Label(1).resizeAs(tensorTarget3).copy(tensorTarget3) + batch2.getInput should be (batch2Data) + batch2.getTarget should be (batch2Label) + } + "SampleToMiniBatchSpec" should "be good with TensorBatch1" in { + Engine.setNodeAndCore(1, 1) + val tensorInput1 = Tensor[Float](Storage( + Array(0.0f, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0)), 1, Array(3, 5)) + val tensorInput2 = Tensor[Float](Storage( + Array(0.0f, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0)), 1, Array(3, 5)) + val tensorInput3 = Tensor[Float](Storage( + Array(1.0f, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1)), 1, Array(3, 5)) + val tensorTarget1 = Tensor[Float](Storage( + Array(3.0f, 4, 5)), 1, Array(3)) + val tensorTarget2 = Tensor[Float](Storage( + Array(2.0f, 1, 5)), 1, Array(3)) + val tensorTarget3 = Tensor[Float](Storage( + Array(5.0f, 2, 1)), 1, Array(3)) + val sample1 = Sample[Float](tensorInput1, tensorTarget1) + val sample2 = Sample[Float](tensorInput2, tensorTarget2) + val sample3 = Sample[Float](tensorInput3, tensorTarget3) + + val dataSet = new LocalArrayDataSet[Sample[Float]](Array(sample1, + sample2, sample3)) + val sampleToBatch = SampleToMiniBatch[Float](2) + val sampleDataSet = dataSet -> sampleToBatch + val iter = sampleDataSet.toLocal().data(train = false) + + val batch1 = iter.next() + + val batch1Data = Tensor[Float](Array(2, 3, 5)) + batch1Data(1).resizeAs(tensorInput1).copy(tensorInput1) + batch1Data(2).resizeAs(tensorInput2).copy(tensorInput2) + val batch1Label = Tensor[Float](Array(2, 3)) + batch1Label(1).resizeAs(tensorTarget1).copy(tensorTarget1) + batch1Label(2).resizeAs(tensorTarget2).copy(tensorTarget2) + batch1.getInput should be (batch1Data) + batch1.getTarget should be (batch1Label) + + val batch2 = iter.next() + val batch2Data = Tensor[Float](Array(1, 3, 5)) + batch2Data(1).resizeAs(tensorInput3).copy(tensorInput3) + val batch2Label = Tensor[Float](Array(1, 3)) + batch2Label(1).resizeAs(tensorTarget3).copy(tensorTarget3) + batch2.getInput should be (batch2Data) + batch2.getTarget should be (batch2Label) + } + "SampleToMiniBatchSpec" should "be good with TensorBatch2" in { + Engine.setNodeAndCore(1, 1) + val tensorInput1 = Tensor[Float](Storage( + Array(0.0f, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0)), 1, Array(3, 5)) + val tensorInput2 = Tensor[Float](Storage( + Array(0.0f, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0)), 1, Array(3, 5)) + val tensorInput3 = Tensor[Float](Storage( + Array(1.0f, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1)), 1, Array(3, 5)) + val tensorTarget1 = Tensor[Float](Storage( + Array(3.0f, 4, 5)), 1, Array(3)) + val tensorTarget2 = Tensor[Float](Storage( + Array(2.0f, 1, 5)), 1, Array(3)) + val tensorTarget3 = Tensor[Float](Storage( + Array(5.0f, 2, 1)), 1, Array(3)) + val sample1 = Sample[Float](tensorInput1, tensorTarget1) + val sample2 = Sample[Float](tensorInput2, tensorTarget2) + val sample3 = Sample[Float](tensorInput3, tensorTarget3) + + val dataSet = new LocalArrayDataSet[Sample[Float]](Array(sample1, + sample2, sample3)) + val sampleToBatch = SampleToMiniBatch[Float](2) + val sampleDataSet = dataSet -> sampleToBatch + val iter = sampleDataSet.toLocal().data(train = true) + + val batch1 = iter.next() + + val batch1Data = Tensor[Float](Array(2, 3, 5)) + batch1Data(1).resizeAs(tensorInput1).copy(tensorInput1) + batch1Data(2).resizeAs(tensorInput2).copy(tensorInput2) + val batch1Label = Tensor[Float](Array(2, 3)) + batch1Label(1).resizeAs(tensorTarget1).copy(tensorTarget1) + batch1Label(2).resizeAs(tensorTarget2).copy(tensorTarget2) + batch1.getInput should be (batch1Data) + batch1.getTarget should be (batch1Label) + + val batch2 = iter.next() val batch2Data = Tensor[Float](Array(2, 3, 5)) batch2Data(1).resizeAs(tensorInput3).copy(tensorInput3) batch2Data(2).resizeAs(tensorInput1).copy(tensorInput1) val batch2Label = Tensor[Float](Array(2, 3)) batch2Label(1).resizeAs(tensorTarget3).copy(tensorTarget3) batch2Label(2).resizeAs(tensorTarget1).copy(tensorTarget1) - batch2.input should be (batch2Data) - batch2.target should be (batch2Label) + batch2.getInput should be (batch2Data) + batch2.getTarget should be (batch2Label) } "BRGImgToSample" should "be correct" in { @@ -936,7 +1074,7 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val image3 = new LabeledBGRImage(data, 32, 32, 3.0f) val image = Array(image1, image2, image3) - val toSample = BGRImgToSample() -> SampleToBatch(1) + val toSample = BGRImgToSample() -> SampleToMiniBatch(1) val miniBatch1 = toSample(image.toIterator) val miniBatch2 = BGRImgToBatch(1).apply(image.toIterator) @@ -972,7 +1110,7 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { val image3 = new LabeledGreyImage(data, 32, 32, 3.0f) val image = Array(image1, image2, image3) - val toSample = GreyImgToSample() -> SampleToBatch(1) + val toSample = GreyImgToSample() -> SampleToMiniBatch(1) val miniBatch1 = toSample(image.toIterator) val miniBatch2 = GreyImgToBatch(1).apply(image.toIterator) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala index 3ffd09be9cf..5e90ffaa6e7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.dataset.{DataSet, Sample, SampleToBatch} +import com.intel.analytics.bigdl.dataset.{DataSet, Sample} import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.nn.CrossEntropyCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index db868df9265..e6f0ddcbcdc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.dataset.{Sample, TensorSample} +import com.intel.analytics.bigdl.dataset.Sample import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Engine @@ -57,12 +57,12 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ val result = model.predict(dataSet) val prob = result.map(_.toTensor[Float].clone()).collect() - prob(0) should be (model.forward(data(0).asInstanceOf[TensorSample[Float]].featureTensor)) - prob(11) should be (model.forward(data(11).asInstanceOf[TensorSample[Float]].featureTensor)) - prob(31) should be (model.forward(data(31).asInstanceOf[TensorSample[Float]].featureTensor)) - prob(51) should be (model.forward(data(51).asInstanceOf[TensorSample[Float]].featureTensor)) - prob(71) should be (model.forward(data(71).asInstanceOf[TensorSample[Float]].featureTensor)) - prob(91) should be (model.forward(data(91).asInstanceOf[TensorSample[Float]].featureTensor)) + prob(0) should be (model.forward(data(0).feature)) + prob(11) should be (model.forward(data(11).feature)) + prob(31) should be (model.forward(data(31).feature)) + prob(51) should be (model.forward(data(51).feature)) + prob(71) should be (model.forward(data(71).feature)) + prob(91) should be (model.forward(data(91).feature)) } "model.predictClass" should "be correct" in { @@ -82,22 +82,22 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ val prob = result.collect() prob(0) should be - (model.forward(data(0).asInstanceOf[TensorSample[Float]].featureTensor + (model.forward(data(0).feature ).toTensor[Float].max(1)._2.valueAt(1).toInt) prob(11) should be - (model.forward(data(11).asInstanceOf[TensorSample[Float]].featureTensor + (model.forward(data(11).feature ).toTensor[Float].max(1)._2.valueAt(1).toInt) prob(31) should be - (model.forward(data(31).asInstanceOf[TensorSample[Float]].featureTensor + (model.forward(data(31).feature ).toTensor[Float].max(1)._2.valueAt(1).toInt) prob(51) should be - (model.forward(data(51).asInstanceOf[TensorSample[Float]].featureTensor + (model.forward(data(51).feature ).toTensor[Float].max(1)._2.valueAt(1).toInt) prob(71) should be - (model.forward(data(71).asInstanceOf[TensorSample[Float]].featureTensor + (model.forward(data(71).feature ).toTensor[Float].max(1)._2.valueAt(1).toInt) prob(91) should be - (model.forward(data(91).asInstanceOf[TensorSample[Float]].featureTensor + (model.forward(data(91).feature ).toTensor[Float].max(1)._2.valueAt(1).toInt) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala index 6380d3768d3..27db2e8b579 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.dataset.{DataSet, Sample, SampleToBatch} +import com.intel.analytics.bigdl.dataset.{DataSet, Sample, SampleToMiniBatch} import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.nn.CrossEntropyCriterion import com.intel.analytics.bigdl.tensor.Tensor @@ -64,7 +64,7 @@ class ValidatorSpec extends FlatSpec with Matchers with BeforeAndAfter{ i += 1 } val model = LeNet5(classNum = 10) - val dataSet = DataSet.array(tmp, sc).transform(SampleToBatch(1)) + val dataSet = DataSet.array(tmp, sc).transform(SampleToMiniBatch(1)) val validator = Validator(model, dataSet) val result = validator.test(Array(new Top1Accuracy[Float](), new Top5Accuracy[Float](), @@ -89,7 +89,7 @@ class ValidatorSpec extends FlatSpec with Matchers with BeforeAndAfter{ i += 1 } val model = LeNet5(classNum = 10) - val dataSet = DataSet.array(tmp).transform(SampleToBatch(1)) + val dataSet = DataSet.array(tmp).transform(SampleToMiniBatch(1)) val validator = Validator(model, dataSet) val result = validator.test(Array(new Top1Accuracy[Float](), new Top5Accuracy[Float](), diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index 895c00d5289..2e09a852d69 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -20,7 +20,6 @@ import java.util import java.util.{List => JList, Map => JMap} import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.TensorSample import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{Loss, SGD, Top1Accuracy, Trigger} import com.intel.analytics.bigdl.utils.{Engine, T} @@ -210,20 +209,16 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { val localData = data.collect() pp.toTensor(preResult.get(0)) should be - (trainedModel.forward(pp.toSample(localData(0)) - .asInstanceOf[TensorSample[Float]].featureTensor)) + (trainedModel.forward(pp.toSample(localData(0)).feature)) pp.toTensor(preResult.get(25)) should be - (trainedModel.forward(pp.toSample(localData(25)) - .asInstanceOf[TensorSample[Float]].featureTensor)) + (trainedModel.forward(pp.toSample(localData(25)).feature)) pp.toTensor(preResult.get(55)) should be - (trainedModel.forward(pp.toSample(localData(55)) - .asInstanceOf[TensorSample[Float]].featureTensor)) + (trainedModel.forward(pp.toSample(localData(55)).feature)) pp.toTensor(preResult.get(75)) should be - (trainedModel.forward(pp.toSample(localData(75)) - .asInstanceOf[TensorSample[Float]].featureTensor)) + (trainedModel.forward(pp.toSample(localData(75)).feature)) // TODO: verify the parameters result val parameters = pp.modelGetParameters(trainedModel) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala index 38daf321f8d..8d70bca8359 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.numeric.NumericDouble import com.intel.analytics.bigdl.optim.{Optimizer, SGD, Trigger} import com.intel.analytics.bigdl.nn.{Linear, MSECriterion, Sequential} -import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch, Sample, SampleToBatch} +import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch, Sample, SampleToMiniBatch} import scala.io.Source import java.io.StringWriter @@ -88,7 +88,7 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { Sample[Double](featureTensor, labelTensor) } - val trainSet = DataSet.rdd(data).transform(SampleToBatch(recordSize/2)) + val trainSet = DataSet.rdd(data).transform(SampleToMiniBatch(recordSize/2)) val state = T( From f43ed8273f403b72205cb55cb94699ed53f4fd57 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Wed, 12 Jul 2017 14:28:31 +0800 Subject: [PATCH 0258/1065] Fix a bug in the MV layer (#1171) * add test * remove unnecessary change in LinearSpec * fix MV bugs --- .../com/intel/analytics/bigdl/dllib/nn/MV.scala | 8 ++++---- .../analytics/bigdl/dllib/torch/MVSpec.scala | 15 +++++++++++---- 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MV.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MV.scala index db5bebfee8c..2d08e74abe6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MV.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MV.scala @@ -56,7 +56,7 @@ class MV[T: ClassTag](val trans: Boolean = false) } require(m.size(2) == v.size(1), "matrix row count and vector length do not match") - output.resize(m.size(1)) + output.resize(m.size(1)).zero() output.mv(m, v) } else { require(v.dim() == 2, "vector must be 2D (batch dimension)") @@ -67,7 +67,7 @@ class MV[T: ClassTag](val trans: Boolean = false) } require(m.size(3) == v.size(2), "matrix row count and vector length do not match") - output.resize(m.size(1), m.size(2), 1) + output.resize(m.size(1), m.size(2), 1).zero() output.bmm(m, v.view(v.size(1), v.size(2), 1)).resize(m.size(1), m.size(2)) } @@ -77,8 +77,8 @@ class MV[T: ClassTag](val trans: Boolean = false) override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { val (m, v) = checkInputFormat(input) - gradInput[Tensor[T]](1).resizeAs(m) - gradInput[Tensor[T]](2).resizeAs(v) + gradInput[Tensor[T]](1).resizeAs(m).zero() + gradInput[Tensor[T]](2).resizeAs(v).zero() require(gradOutput.dim() == 1 || gradOutput.dim() == 2, "arguments must be a 1D or 2D Tensor") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MVSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MVSpec.scala index 5601c379a82..e7558584f24 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MVSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MVSpec.scala @@ -35,14 +35,21 @@ class MVSpec extends TorchSpec { val module = new MV[Double]() val start = System.nanoTime() - val output = module.forward(input) - val gradInput = module.backward(input, gradOutput) + var output = Tensor[Double]() + var gradInput = T() + + for (i <- 1 to 5) { + output = module.forward(input) + gradInput = module.backward(input, gradOutput) + } val end = System.nanoTime() val scalaTime = end - start val code = "module = nn.MV()\n" + - "output = module:forward(input)\n " + - "gradInput = module:backward(input, gradOutput)" + "for i = 1,5,1 do\n" + + "output = module:forward(input)\n " + + "gradInput = module:backward(input, gradOutput)\n" + + "end" val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), From fe0a68a502090a1b0065af2fc9b286acd4cf3b68 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Wed, 12 Jul 2017 14:46:14 +0800 Subject: [PATCH 0259/1065] feat: add new property: bigdl.utils.LoggerFilter.enableSparkLog (#1169) * feat: add new property: bigdl.utils.LoggerFilter.enableSparkLog * comments: add documents of java properties * fix: all logs backup into log file and add some testcases * fix: conversions to converters with asScala * fix: readAllLines for jdk 1.7 * fix: delete useless files * fix: delete absolute linux path --- .../analytics/bigdl/utils/LoggerFilter.scala | 44 ++++++++++----- .../bigdl/dllib/utils/LoggerFilterSpec.scala | 54 +++++++++++++++++-- 2 files changed, 83 insertions(+), 15 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LoggerFilter.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LoggerFilter.scala index 47eb3bbca03..e92dc0df45c 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LoggerFilter.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LoggerFilter.scala @@ -18,11 +18,18 @@ package com.intel.analytics.bigdl.utils import org.apache.log4j._ import java.nio.file.{Paths, Files} -import scala.collection.JavaConverters._ + + // scalastyle:off + // | Property Name | Default | Meaning | + // |-----------------------------------------+--------------------+----------------------------------------------| + // | bigdl.utils.LoggerFilter.disable | false | Disable redirecting logs of Spark and BigDL. | + // | | | Output location depends on log4j.properties | + // | bigdl.utils.LoggerFilter.logFile | user.dir/bigdl.log | The log file user defined. | + // | bigdl.utils.LoggerFilter.enableSparkLog | true | Enable redirecting logs of Spark to logFile | + // scalastyle:on /** * logger filter, which will filter the log of Spark(org, breeze, akka) to file. - * it could be set by user through `-Dbigdl.utils.LoggerFilter.logFile` */ object LoggerFilter { @@ -74,20 +81,20 @@ object LoggerFilter { } /** - * 1. redirect all logs of Spark to file, which can be set by `-Dbigdl.utils.LoggerFilter.logFile` - * the default file is under current workspace named `bigdl.log`. + * 1. redirect all spark log to file, which can be set by `-Dbigdl.utils.LoggerFilter.logFile` + * the default file is under current workspace named `bigdl.log`. * 2. `-Dbigdl.utils.LoggerFilter.disable=true` will disable redirection. - * and add an console appender for `com.intel.analytics.bigdl.optim`, because we set the threshold - * to ERROR first. + * 3. `-Dbigdl.utils.LoggerFilter.enableSparkLog=false` will not output spark log to file */ def redirectSparkInfoLogs(): Unit = { val disable = System.getProperty("bigdl.utils.LoggerFilter.disable", "false") - if (disable.equalsIgnoreCase("false")) { - val optimClass = "com.intel.analytics.bigdl.optim" + val enableSparkLog = System.getProperty("bigdl.utils.LoggerFilter.enableSparkLog", "true") + + def getLogFile: String = { val default = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString val logFile = System.getProperty("bigdl.utils.LoggerFilter.logFile", default) - // If file doesn't exist, create a new one. If it's a directory, throw an error. + // If the file doesn't exist, create a new one. If it's a directory, throw an error. val logFilePath = Paths.get(logFile) if (!Files.exists(logFilePath)) { Files.createFile(logFilePath) @@ -96,17 +103,30 @@ object LoggerFilter { .error(s"$logFile exists and is an directory. Can't redirect to it.") } + logFile + } + + if (disable.equalsIgnoreCase("false")) { + val logFile = getLogFile + val defaultClasses = List("org", "akka", "breeze") for (clz <- defaultClasses) { classLogToAppender(clz, consoleAppender(Level.ERROR)) Logger.getLogger(clz).setAdditivity(false) } - + // it should be set to WARN for the progress bar Logger.getLogger("org.apache.spark.SparkContext").setLevel(Level.WARN) - for (clz <- optimClass :: defaultClasses) { - classLogToAppender(clz, fileAppender(logFile, Level.INFO)) + // set all logs to file + Logger.getRootLogger.addAppender(fileAppender(logFile, Level.INFO)) + + // because we have set all defaultClasses loggers additivity to false + // so we should reconfigure them. + if (enableSparkLog.equalsIgnoreCase("true")) { + for (clz <- defaultClasses) { + classLogToAppender(clz, fileAppender(logFile, Level.INFO)) + } } } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala index 8d70bca8359..8fa8e730135 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala @@ -23,12 +23,14 @@ import com.intel.analytics.bigdl.optim.{Optimizer, SGD, Trigger} import com.intel.analytics.bigdl.nn.{Linear, MSECriterion, Sequential} import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch, Sample, SampleToMiniBatch} -import scala.io.Source import java.io.StringWriter +import java.nio.charset.StandardCharsets import java.nio.file.{Files, Paths} + import org.apache.spark.SparkContext import org.apache.log4j.{Level, Logger, PatternLayout, WriterAppender} +import scala.collection.JavaConverters._ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Serial @@ -155,11 +157,11 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { } "A LoggerFilter generate log " should "under the place user gived" in { - val logFile = "/tmp/bigdl.log" + val logFile = Paths.get(System.getProperty("java.io.tmpdir"), "bigdl.log").toString val optimClz = "com.intel.analytics.bigdl.optim" val defaultFile = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString - System.setProperty("bigdl.utils.LoggerFilter.logFile", "/tmp/bigdl.log") + System.setProperty("bigdl.utils.LoggerFilter.logFile", logFile) Files.deleteIfExists(Paths.get(defaultFile)) Files.deleteIfExists(Paths.get(logFile)) @@ -253,4 +255,50 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { Files.exists(Paths.get(defaultFile)) should be (false) System.clearProperty("bigdl.utils.LoggerFilter.disable") } + + "A LoggerFilter user's log" should "be in log file" in { + val defaultFile = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString + LoggerFilter.redirectSparkInfoLogs() + + val info = "bigdl info message" + val warn = "bigdl warn message" + val error = "bigdl error message" + + Logger.getLogger(getClass).info(info) + Logger.getLogger(getClass).warn(warn) + Logger.getLogger(getClass).error(error) + + val lines = Files.readAllLines(Paths.get(defaultFile), StandardCharsets.UTF_8) + + lines.size() should be (3) + lines.get(0).contains(info) should be (true) + lines.get(1).contains(warn) should be (true) + lines.get(2).contains(error) should be (true) + + Files.deleteIfExists(Paths.get(defaultFile)) + Files.exists(Paths.get(defaultFile)) should be (false) + } + + "A LoggerFilter disable spark log" should "not generate spark logs in file" in { + val defaultFile = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString + System.setProperty("bigdl.utils.LoggerFilter.enableSparkLog", "false") + LoggerFilter.redirectSparkInfoLogs() + + sc = new SparkContext( + Engine.init(1, 1, true).get + .setAppName(s"LoggerFilter test") + .set("spark.task.maxFailures", "1") + .setMaster("local[1]") + ) + + val data = sc.parallelize(List("bigdl", "spark", "deep", "learning")) + val y = data.map(x => (x, x.length)).count() + + val lines = Files.readAllLines(Paths.get(defaultFile), StandardCharsets.UTF_8).asScala + lines.exists(_.contains("DAGScheduler")) should be (false) + + Files.deleteIfExists(Paths.get(defaultFile)) + Files.exists(Paths.get(defaultFile)) should be (false) + System.clearProperty("bigdl.utils.LoggerFilter.enableSparkLog") + } } From b0c107bfb6991bbae16e5775348d5a28051b8dad Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Wed, 12 Jul 2017 15:51:00 +0800 Subject: [PATCH 0260/1065] Add 1D Convolution (TemporalConvolution) (#1180) * add test * remove unnecessary change in LinearSpec * finish Temporal Convolution * add python API * meet code review * meet code review * fix typos * fix typos * meet code review --- .../bigdl/dllib/nn/TemporalConvolution.scala | 472 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 29 ++ .../dllib/torch/TemporalConvolutionSpec.scala | 136 +++++ 3 files changed, 637 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolution.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalConvolutionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolution.scala new file mode 100644 index 00000000000..04d38d22f6c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolution.scala @@ -0,0 +1,472 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Engine + +import scala.concurrent.Future +import scala.reflect.ClassTag + +/** + * Applies a 1D convolution over an input sequence composed of nInputFrame frames.. + * The input tensor in `forward(input)` is expected to be a 2D tensor + * (`nInputFrame` x `inputFrameSize`) or a 3D tensor + * (`nBatchFrame` x `nInputFrame` x `inputFrameSize`). + * + * @param inputFrameSize The input frame size expected in sequences given into `forward()`. + * @param outputFrameSize The output frame size the convolution layer will produce. + * @param kernelW The kernel width of the convolution + * @param strideW The step of the convolution in the width dimension. + * @param propagateBack Whether propagate gradient back, default is true. + * @param wRegularizer instance of [[Regularizer]] + * (eg. L1 or L2 regularization), applied to the input weights matrices. + * @param bRegularizer instance of [[Regularizer]] + * applied to the bias. + * @param initWeight Initial weight + * @param initBias Initial bias + * @param initGradWeight Initial gradient weight + * @param initGradBias Initial gradient bias + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class TemporalConvolution[T: ClassTag]( + val inputFrameSize: Int, + val outputFrameSize: Int, + val kernelW: Int, + val strideW: Int = 1, + val propagateBack: Boolean = true, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val initWeight: Tensor[T] = null, + val initBias: Tensor[T] = null, + val initGradWeight: Tensor[T] = null, + val initGradBias: Tensor[T] = null +)(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { + + val weight: Tensor[T] = if (initWeight != null) { + initWeight + } else { + Tensor[T](outputFrameSize, inputFrameSize * kernelW) + } + + val bias: Tensor[T] = if (initBias != null) { + initBias + } else { + Tensor[T](outputFrameSize) + } + + val gradWeight: Tensor[T] = if (initGradWeight != null) { + initGradWeight + } else { + Tensor[T](outputFrameSize, inputFrameSize * kernelW) + } + + val gradBias: Tensor[T] = if (initBias != null) { + initGradBias + } else { + Tensor[T](outputFrameSize) + } + + @transient protected var inputWindow: Tensor[T] = _ + @transient protected var outputWindow: Tensor[T] = _ + @transient protected var gradInputWindow: Tensor[T] = _ + @transient protected var gradOutputWindow: Tensor[T] = _ + + { + val stdv = 1.0 / math.sqrt(kernelW * inputFrameSize) + val wInit: InitializationMethod = RandomUniform(-stdv, stdv) + val bInit: InitializationMethod = RandomUniform(-stdv, stdv) + setInitMethod(wInit, bInit) + } + + @transient + protected var results: Array[Future[Unit]] = _ + + override def reset(): Unit = { + if (initWeight == null) { + weightInitMethod.init(weight, VariableFormat.OUT_IN) + } + if (initBias == null) { + biasInitMethod.init(bias, VariableFormat.ONE_D) + } + zeroGradParameters() + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + // Require input of 2 dimensions or 3 dimensions + // 2d input format: time x feature + // 3d input format: batch x time x feature + require(input.dim() == 2 || input.dim() == 3, + "TemporalConvolution: 2D or 3D(batch mode) tensor expected for input, " + + s"but got ${input.dim()}") + // Require input to be contiguous + require(input.isContiguous()) + + var dimSeq = 1 + var dimFeat = 2 + + if (input.dim() == 3) { + dimSeq = 2 + dimFeat = 3 + } + + val nInputFrame = input.size(dimSeq) + var nOutputFrame = (nInputFrame - kernelW) / strideW + 1 + + if (inputWindow == null) inputWindow = Tensor[T]() + if (outputWindow == null) outputWindow = Tensor[T]() + + // Shape check on input with inputFrameSize and kernelW + require(input.size(dimFeat) == inputFrameSize, "Invalid input frame size. Got: " + + s"${input.size(dimFeat)}, Expected: $inputFrameSize") + require(nOutputFrame >= 1, "Input sequence smaller than kernel size. Got: " + + s"$nInputFrame, Expected: $kernelW") + + val weightT = weight.transpose(1, 2) + + if (input.dim() == 2) { + output.resize(nOutputFrame, outputFrameSize) + // Add bias first + var j = 1 + while (j <= nOutputFrame) { + outputWindow = output.select(dimSeq, j) + outputWindow.copy(bias) + j += 1 + } + // Add the convolution part + j = 0 + while (nOutputFrame > 0) { + val outputFrameStride = (kernelW - 1) / strideW + 1 + val inputFrameStride = outputFrameStride * strideW + val nFrame = (nInputFrame - j * strideW - kernelW) / inputFrameStride + 1 + nOutputFrame -= nFrame + + inputWindow.set(input.storage(), input.storageOffset() + j * strideW * input.size(dimFeat), + Array(nFrame, kernelW * input.size(dimFeat)), + Array(inputFrameStride * input.size(dimFeat), 1)) + outputWindow.set(output.storage(), output.storageOffset() + j * output.size(dimFeat), + Array(nFrame, output.size(dimFeat)), + Array(outputFrameStride * output.size(dimFeat), 1)) + + outputWindow.addmm(ev.fromType[Int](1), outputWindow, + ev.fromType[Int](1), inputWindow, weightT) + j += 1 + } + } else { + val batchSize = input.size(1) + output.resize(batchSize, nOutputFrame, outputFrameSize) + if (results == null || results.length != batchSize) { + results = new Array[Future[Unit]](batchSize) + } + var i = 0 + while (i < batchSize) { + results(i) = Engine.model.invoke(() => { + val inputSample = input.select(1, i + 1) + val outputSample = output.select(1, i + 1) + var nOutputSampleFrame = nOutputFrame + // Add bias first + var j = 1 + while (j <= nOutputFrame) { + outputWindow = outputSample.select(dimSeq - 1, j) + outputWindow.copy(bias) + j += 1 + } + // Add the convolution part + j = 0 + while (nOutputSampleFrame > 0) { + val outputFrameStride = (kernelW - 1) / strideW + 1 + val inputFrameStride = outputFrameStride * strideW + val nFrame = (nInputFrame - j * strideW - kernelW) / inputFrameStride + 1 + nOutputSampleFrame -= nFrame + + inputWindow.set(inputSample.storage(), inputSample.storageOffset() + + j * strideW * inputSample.size(dimFeat - 1), + Array(nFrame, kernelW * inputSample.size(dimFeat - 1)), + Array(inputFrameStride * inputSample.size(dimFeat - 1), 1)) + outputWindow.set(outputSample.storage(), outputSample.storageOffset() + + j * outputSample.size(dimFeat - 1), + Array(nFrame, outputSample.size(dimFeat - 1)), + Array(outputFrameStride * outputSample.size(dimFeat - 1), 1)) + + outputWindow.addmm(ev.fromType[Int](1), outputWindow, + ev.fromType[Int](1), inputWindow, weightT) + j += 1 + } + }) + i += 1 + } + } + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + // Require input of 2 dimensions or 3 dimensions + // 2d input format: time x feature + // 3d input format: batch x time x feature + require(input.dim() == 2 || input.dim() == 3, + "TemporalConvolution: 2D or 3D(batch mode) tensor expected for input, " + + s"but got ${input.dim()}") + // Require input to be contiguous + require(input.isContiguous()) + + val dimSeq = if (input.dim() == 2) 1 else 2 + val dimFeat = if (input.dim() == 2) 2 else 3 + val nInputFrame = input.size(dimSeq) + var nOutputFrame = (nInputFrame - kernelW) / strideW + 1 + + if (gradInputWindow == null) gradInputWindow = Tensor[T]() + if (gradOutputWindow == null) gradOutputWindow = Tensor[T]() + + // Shape check on input with inputFrameSize and kernelW + require(input.size(dimFeat) == inputFrameSize, "Invalid input frame size. Got: " + + s"${input.size(dimFeat)}, Expected: $inputFrameSize") + require(nOutputFrame >= 1, "Input sequence smaller than kernel size. Got: " + + s"$nInputFrame, Expected: $kernelW") + + gradInput.resizeAs(input) + gradInput.zero() + + if (gradOutput.dim() == 2) { + var i = 0 + while (nOutputFrame > 0) { + val outputFrameStride = (kernelW - 1) / strideW + 1 + val inputFrameStride = outputFrameStride * strideW + val nFrame = (nInputFrame - i * strideW - kernelW) / inputFrameStride + 1 + nOutputFrame -= nFrame + + gradOutputWindow.set(gradOutput.storage(), gradOutput.storageOffset() + + i * gradOutput.size(dimFeat), Array(nFrame, gradOutput.size(dimFeat)), + Array(outputFrameStride * gradOutput.size(dimFeat), 1)) + gradInputWindow.set(gradInput.storage(), gradInput.storageOffset() + + i * strideW * gradInput.size(dimFeat), Array(nFrame, kernelW * gradInput.size(dimFeat)), + Array(inputFrameStride * gradInput.size(dimFeat), 1)) + + gradInputWindow.addmm(ev.fromType[Int](1), gradInputWindow, + ev.fromType[Int](1), gradOutputWindow, weight) + i += 1 + } + } else { + val batchSize = input.size(1) + var gradOutputSample = Tensor[T]() + var gradInputSample = Tensor[T]() + var i = 0 + while (i < batchSize) { + results(i) = Engine.model.invoke(() => { + gradInputSample = gradInput.select(1, i + 1) + gradOutputSample = gradOutput.select(1, i + 1) + var nOutputSampleFrame = nOutputFrame + var j = 0 + while (nOutputSampleFrame > 0) { + val outputFrameStride = (kernelW - 1) / strideW + 1 + val inputFrameStride = outputFrameStride * strideW + val nFrame = (nInputFrame - j * strideW - kernelW) / inputFrameStride + 1 + nOutputSampleFrame -= nFrame + + gradOutputWindow.set(gradOutputSample.storage(), gradOutputSample.storageOffset() + + j * gradOutputSample.size(dimFeat - 1), + Array(nFrame, gradOutputSample.size(dimFeat - 1)), + Array(outputFrameStride * gradOutputSample.size(dimFeat - 1), 1)) + gradInputWindow.set(gradInputSample.storage(), gradInputSample.storageOffset() + + j * strideW * gradInputSample.size(dimFeat - 1), + Array(nFrame, kernelW * gradInputSample.size(dimFeat - 1)), + Array(inputFrameStride * gradInputSample.size(dimFeat - 1), 1)) + + gradInputWindow.addmm(ev.fromType[Int](1), gradInputWindow, + ev.fromType[Int](1), gradOutputWindow, weight) + j += 1 + } + }) + i += 1 + } + } + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + // Require input of 2 dimensions or 3 dimensions + require(input.nDimension() == 2 || input.nDimension() == 3, "Only support 2D or 3D input") + // Require input to be contiguous + require(gradOutput.isContiguous()) + + val dimSeq = if (input.dim() == 2) 1 else 2 + val dimFeat = if (input.dim() == 2) 2 else 3 + val nInputFrame = input.size(dimSeq) + var nOutputFrame = (nInputFrame - kernelW) / strideW + 1 + + if (gradOutputWindow == null) gradOutputWindow = Tensor[T]() + if (inputWindow == null) inputWindow = Tensor[T]() + + if (input.nDimension() == 2) { + var j = 0 + while (j < nOutputFrame) { + gradOutputWindow.set(gradOutput.select(1, j + 1)) + gradBias.add(gradBias, ev.fromType[Double](scaleB), gradOutputWindow) + j += 1 + } + j = 0 + while (nOutputFrame > 0) { + val outputFrameStride = (kernelW - 1) / strideW + 1 + val inputFrameStride = outputFrameStride * strideW + val nFrame = (nInputFrame - j * strideW - kernelW) / inputFrameStride + 1 + nOutputFrame -= nFrame + + inputWindow.set(input.storage(), input.storageOffset() + j * strideW * input.size(dimFeat), + Array(nFrame, kernelW * input.size(dimFeat)), + Array(inputFrameStride * input.size(dimFeat), 1)) + gradOutputWindow.set(gradOutput.storage(), gradOutput.storageOffset() + + j * gradOutput.size(dimFeat), Array(nFrame, gradOutput.size(dimFeat)), + Array(outputFrameStride * gradOutput.size(dimFeat), 1)) + + val gradOutputWindowT = gradOutputWindow.transpose(1, 2) + gradWeight.addmm(ev.fromType[Int](1), gradWeight, ev.fromType[Double](scaleW), + gradOutputWindowT, inputWindow) + j += 1 + } + } else { + val batchSize = input.size(1) + var gradOutputSample = Tensor[T]() + var inputSample = Tensor[T]() + var i = 0 + while (i < batchSize) { + results(i) = Engine.model.invoke(() => { + gradOutputSample = gradOutput.select(1, i + 1) + inputSample = input.select(1, i + 1) + var nOutputSampleFrame = nOutputFrame + var j = 0 + while (j < nOutputFrame) { + gradOutputWindow.set(gradOutputSample.select(1, j + 1)) + gradBias.add(gradBias, ev.fromType[Double](scaleB), gradOutputWindow) + j += 1 + } + j = 0 + while (nOutputSampleFrame > 0) { + val outputFrameStride = (kernelW - 1) / strideW + 1 + val inputFrameStride = outputFrameStride * strideW + val nFrame = (nInputFrame - j * strideW - kernelW) / inputFrameStride + 1 + nOutputSampleFrame -= nFrame + + inputWindow.set(inputSample.storage(), inputSample.storageOffset() + + j * strideW * inputSample.size(dimFeat - 1), + Array(nFrame, kernelW * inputSample.size(dimFeat - 1)), + Array(inputFrameStride * inputSample.size(dimFeat - 1), 1)) + gradOutputWindow.set(gradOutputSample.storage(), gradOutputSample.storageOffset() + + j * gradOutputSample.size(dimFeat - 1), + Array(nFrame, gradOutputSample.size(dimFeat - 1)), + Array(outputFrameStride * gradOutputSample.size(dimFeat - 1), 1)) + + val gradOutputWindowT = gradOutputWindow.transpose(1, 2) + gradWeight.addmm(ev.fromType[Int](1), gradWeight, ev.fromType[Double](scaleW), + gradOutputWindowT, inputWindow) + j += 1 + } + }) + i += 1 + } + } + + if (null != wRegularizer) { + wRegularizer.accRegularization(weight, gradWeight, scaleW) + } + if (null != bRegularizer) { + bRegularizer.accRegularization(bias, gradBias, scaleB) + } + } + + override def updateParameters(learningRate: T): Unit = { + weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) + bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) + } + + override def zeroGradParameters(): Unit = { + gradWeight.zero() + gradBias.zero() + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) + } + + override def equals(obj: Any): Boolean = { + if (!super.equals(obj)) { + return false + } + if (!obj.isInstanceOf[TemporalConvolution[T]]) { + return false + } + val other = obj.asInstanceOf[TemporalConvolution[T]] + if (this.eq(other)) { + return true + } + + inputFrameSize == other.inputFrameSize && + outputFrameSize == other.outputFrameSize && + kernelW == other.kernelW && + strideW == other.strideW && + propagateBack == other.propagateBack && + weight == other.weight && + bias == other.bias && + gradWeight == other.gradWeight && + gradBias == other.gradBias + } + + override def hashCode(): Int = { + val seed = 37 + var hash = super.hashCode() + hash = hash * seed + inputFrameSize.hashCode() + hash = hash * seed + outputFrameSize.hashCode() + hash = hash * seed + kernelW.hashCode() + hash = hash * seed + strideW.hashCode() + hash = hash * seed + weight.hashCode() + hash = hash * seed + bias.hashCode() + hash = hash * seed + gradWeight.hashCode() + hash = hash * seed + gradBias.hashCode() + + hash + } + + override def clearState() : this.type = { + super.clearState() + this + } + + override def toString(): String = { + s"nn.TemporalConvolution($inputFrameSize -> $outputFrameSize, $kernelW x $strideW)" + } +} + +object TemporalConvolution { + def apply[@specialized(Float, Double) T: ClassTag]( + inputFrameSize: Int, + outputFrameSize: Int, + kernelW: Int, + strideW: Int = 1, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null + )(implicit ev: TensorNumeric[T]): TemporalConvolution[T] = { + new TemporalConvolution[T](inputFrameSize, outputFrameSize, kernelW, + strideW, propagateBack, + wRegularizer, bRegularizer, initWeight, initBias, initGradWeight, initGradBias) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a0bb8652c85..234047c100a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -859,6 +859,35 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab bRegularizer) } + def createTemporalConvolution( + inputFrameSize: Int, + outputFrameSize: Int, + kernelW: Int, + strideW: Int = 1, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null + ) + : TemporalConvolution[T] = { + TemporalConvolution[T]( + inputFrameSize, + outputFrameSize, + kernelW, + strideW, + propagateBack, + wRegularizer, + bRegularizer, + toTensor(initWeight), + toTensor(initBias), + toTensor(initGradWeight), + toTensor(initGradBias) + ) + } + def createSpatialFullConvolution(nInputPlane: Int, nOutputPlane: Int, kW: Int, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalConvolutionSpec.scala new file mode 100644 index 00000000000..236414a6dd4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalConvolutionSpec.scala @@ -0,0 +1,136 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.TemporalConvolution +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +import scala.util.Random + +class TemporalConvolutionSpec extends FlatSpec with BeforeAndAfter with Matchers { + before { + if (!TH.hasTorch()) { + cancel("Torch is not installed") + } + } + + "A TemporalConvolution with 2d input" should "generate correct output" in { + val seed = 100 + RNG.setSeed(seed) + + val inputFrameSize = 10 + val outputFrameSize = 8 + val kW = 5 + val dW = 2 + val layer = TemporalConvolution[Double](inputFrameSize, outputFrameSize, kW, dW) + + Random.setSeed(seed) + val input = Tensor[Double](100, 10).apply1(e => Random.nextDouble()) + val gradOutput = Tensor[Double](48, 8).apply1(e => Random.nextDouble()) + + val output = layer.updateOutput(input) + val gradInput = layer.updateGradInput(input, gradOutput) + + val code = "torch.manualSeed(" + seed + ")\n" + + s"layer = nn.TemporalConvolution($inputFrameSize, $outputFrameSize, $kW, $dW)\n" + + "weight = layer.weight\n" + + "bias = layer.bias \n" + + "output = layer:forward(input) \n" + + "gradInput = layer:backward(input, gradOutput) " + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), + Array("weight", "bias", "output", "gradInput")) + + val luaWeight = torchResult("weight").asInstanceOf[Tensor[Double]] + val luaBias = torchResult("bias").asInstanceOf[Tensor[Double]] + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] + + val weight = layer.weight + val bias = layer.bias + + weight should be equals luaWeight + bias should be equals luaBias + output should be equals luaOutput + gradInput should be equals luaGradInput + } + + "A TemporalConvolution" should "generate correct output" in { + val seed = 100 + RNG.setSeed(seed) + + val inputFrameSize = 10 + val outputFrameSize = 8 + val kW = 5 + val dW = 2 + val layer = TemporalConvolution[Double](inputFrameSize, outputFrameSize, kW, dW) + + Random.setSeed(seed) + val input = Tensor[Double](10, 100, 10).apply1(e => Random.nextDouble()) + val gradOutput = Tensor[Double](10, 48, 8).apply1(e => Random.nextDouble()) + + val output = layer.updateOutput(input) + val gradInput = layer.updateGradInput(input, gradOutput) + + val code = "torch.manualSeed(" + seed + ")\n" + + s"layer = nn.TemporalConvolution($inputFrameSize, $outputFrameSize, $kW, $dW)\n" + + "weight = layer.weight\n" + + "bias = layer.bias \n" + + "output = layer:forward(input) \n" + + "gradInput = layer:backward(input, gradOutput) " + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), + Array("weight", "bias", "output", "gradInput")) + + val luaWeight = torchResult("weight").asInstanceOf[Tensor[Double]] + val luaBias = torchResult("bias").asInstanceOf[Tensor[Double]] + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] + + val weight = layer.weight + val bias = layer.bias + + weight should be equals luaWeight + bias should be equals luaBias + output should be equals luaOutput + gradInput should be equals luaGradInput + } + + "A TemporalConvolution" should "be good in gradient check for input" in { + val seed = 100 + RNG.setSeed(seed) + val layer = TemporalConvolution[Double](10, 8, 5, 2) + val input = Tensor[Double](10, 100, 10).apply1(e => Random.nextDouble()) + + val checker = new GradientChecker(1e-4) + checker.checkLayer(layer, input, 1e-3) should be(true) + } + + "A TemporalConvolution" should "be good in gradient check for weight" in { + val seed = 100 + RNG.setSeed(seed) + val layer = TemporalConvolution[Double](10, 8, 5, 2) + val input = Tensor[Double](10, 100, 10).apply1(e => Random.nextDouble()) + + val checker = new GradientChecker(1e-4) + checker.checkWeight(layer, input, 1e-3) should be(true) + } +} From 54ee1404a6a166f649b4b35227674d3a32c5fa64 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Wed, 12 Jul 2017 16:44:41 +0800 Subject: [PATCH 0261/1065] Fix maptable bug (#1214) * fix maptable bug * Add new line to end of maptable --- .../analytics/bigdl/dllib/nn/MapTable.scala | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala index a3f420168a3..356f193f531 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala @@ -35,12 +35,18 @@ class MapTable[T: ClassTag]( (implicit ev: TensorNumeric[T]) extends Container[Table, Table, T] { private def extend(n: Int): Unit = { - modules.update(0, module.asInstanceOf[AbstractModule[Activity, Activity, T]]) + if (!modules.contains(0)) { + modules.append(module.asInstanceOf[AbstractModule[Activity, Activity, T]]) + } else { + modules.update(0, module.asInstanceOf[AbstractModule[Activity, Activity, T]]) + } var i = 1 while (i <= n && modules.size <= i) { + if (modules.length <= i) { modules.append(module .cloneModule() .asInstanceOf[AbstractModule[Activity, Activity, T]]) + } i += 1 } } @@ -80,7 +86,7 @@ class MapTable[T: ClassTag]( extend(input.length()) var i = 0 while (i < input.length()) { - modules(i).accGradParameters(input(i + 1), gradOutput(i + 1)) + modules(i).accGradParameters(input(i + 1), gradOutput(i + 1)) i += 1 } } @@ -111,11 +117,16 @@ class MapTable[T: ClassTag]( } str } + + override def clearState(): this.type = { + modules.clear() + this + } } object MapTable { def apply[@specialized(Float, Double) T: ClassTag]( - module: AbstractModule[_ <: Activity, _ <: Activity, T] = null + module: AbstractModule[_ <: Activity, _ <: Activity, T] = null )(implicit ev: TensorNumeric[T]) : MapTable[T] = { new MapTable[T](module) } From 78f63df73771421615c920bd5d99fea159c516c2 Mon Sep 17 00:00:00 2001 From: jenniew Date: Thu, 6 Jul 2017 02:40:51 -0700 Subject: [PATCH 0262/1065] add local predictor and examples --- .../bigdl/dllib/example/localJVM/LeNet5.scala | 39 ++++ .../dllib/example/localJVM/Predict.scala | 67 +++++++ .../bigdl/dllib/example/localJVM/Test.scala | 53 +++++ .../bigdl/dllib/example/localJVM/Train.scala | 89 +++++++++ .../bigdl/dllib/example/localJVM/Utils.scala | 152 +++++++++++++++ .../bigdl/dllib/optim/LocalPredictor.scala | 182 ++++++++++++++++++ 6 files changed, 582 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/LeNet5.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Predict.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Test.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Train.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Utils.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/LeNet5.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/LeNet5.scala new file mode 100644 index 00000000000..b77c7fca308 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/LeNet5.scala @@ -0,0 +1,39 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.localJVM + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.numeric.NumericFloat + +object LeNet5 { + def apply(classNum: Int): Module[Float] = { + val model = Sequential() + model.add(Reshape(Array(1, 28, 28))) + .add(SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5")) + .add(Tanh()) + .add(SpatialMaxPooling(2, 2, 2, 2)) + .add(Tanh()) + .add(SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5")) + .add(SpatialMaxPooling(2, 2, 2, 2)) + .add(Reshape(Array(12 * 4 * 4))) + .add(Linear(12 * 4 * 4, 100).setName("fc1")) + .add(Tanh()) + .add(Linear(100, classNum).setName("fc2")) + .add(LogSoftMax()) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Predict.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Predict.scala new file mode 100644 index 00000000000..21ec36c20ad --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Predict.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.localJVM +import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToSample} +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.optim.{LocalPredictor, Top1Accuracy, Validator} +import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.dataset.Sample +import org.apache.log4j.{Level, Logger} + + +import scala.collection.mutable.ArrayBuffer + +object Predict { + Logger.getLogger("org").setLevel(Level.ERROR) + Logger.getLogger("akka").setLevel(Level.ERROR) + Logger.getLogger("breeze").setLevel(Level.ERROR) + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + + import Utils._ + + def main(args: Array[String]): Unit = { + testParser.parse(args, new TestParams()).foreach { param => + + System.setProperty("bigdl.localMode", "true") + System.setProperty("bigdl.coreNumber", "4") + Engine.init + + val validationData = param.folder + "/t10k-images-idx3-ubyte" + val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" + + val rawData = load(validationData, validationLabel) + val iter = rawData.iterator + val sampleIter = GreyImgToSample()( + GreyImgNormalizer(trainMean, trainStd)( + BytesToGreyImg(28, 28)(iter))) + var samplesBuffer = ArrayBuffer[Sample[Float]]() + while (sampleIter.hasNext) { + val elem = sampleIter.next().clone() + samplesBuffer += elem + } + val samples = samplesBuffer.toArray + + val model = Module.load[Float](param.model) + val weightsBias = LocalPredictor.getAndClearWeightBias(model.parameters()) + val predictor = LocalPredictor(model, weightsBias) + + val result = predictor.predict(samples) + val result_class = predictor.predictClass(samples) + result_class.foreach(r => println(s"${r}")) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Test.scala new file mode 100644 index 00000000000..39372c8297b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Test.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.localJVM + +import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToSample} +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.optim.{Top1Accuracy, Validator} +import com.intel.analytics.bigdl.utils.Engine +import org.apache.log4j.{Level, Logger} + +object Test { + Logger.getLogger("org").setLevel(Level.ERROR) + Logger.getLogger("akka").setLevel(Level.ERROR) + Logger.getLogger("breeze").setLevel(Level.ERROR) + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + + import Utils._ + + def main(args: Array[String]): Unit = { + testParser.parse(args, new TestParams()).foreach { param => + System.setProperty("bigdl.localMode", "true") + System.setProperty("bigdl.coreNumber", "4") + Engine.init + + val validationData = param.folder + "/t10k-images-idx3-ubyte" + val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" + + val partitionNum = Engine.nodeNumber() * Engine.coreNumber() + val evaluationSet = DataSet.array(load(validationData, validationLabel)) -> + BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToSample() + + val model = Module.load[Float](param.model) + val validator = Validator(model, evaluationSet) + val result = validator.test(Array(new Top1Accuracy[Float])) + result.foreach(r => println(s"${r._2} is ${r._1}")) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Train.scala new file mode 100644 index 00000000000..6f8eaa93447 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Train.scala @@ -0,0 +1,89 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.localJVM + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToBatch} +import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} +import org.apache.log4j.{Level, Logger} + + +object Train { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + + import Utils._ + + def main(args: Array[String]): Unit = { + trainParser.parse(args, new TrainParams()).map(param => { + + System.setProperty("bigdl.localMode", "true") + System.setProperty("bigdl.coreNumber", "4") + Engine.init + + val trainData = param.folder + "/train-images-idx3-ubyte" + val trainLabel = param.folder + "/train-labels-idx1-ubyte" + val validationData = param.folder + "/t10k-images-idx3-ubyte" + val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" + + val model = if (param.modelSnapshot.isDefined) { + Module.load[Float](param.modelSnapshot.get) + } else { + LeNet5(classNum = 10) + } + + val optimMethod = if (param.stateSnapshot.isDefined) { + OptimMethod.load[Float](param.stateSnapshot.get) + } else { + new SGD[Float](learningRate = param.learningRate, + learningRateDecay = param.learningRateDecay) + } + + val trainSet = DataSet.array(load(trainData, trainLabel)) -> + BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToBatch( + param.batchSize) + + val optimizer = Optimizer( + model = model, + dataset = trainSet, + criterion = ClassNLLCriterion[Float]()) + if (param.checkpoint.isDefined) { + optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch) + } + if(param.overWriteCheckpoint) { + optimizer.overWriteCheckpoint() + } + + val validationSet = DataSet.array(load(validationData, validationLabel)) -> + BytesToGreyImg(28, 28) -> GreyImgNormalizer(testMean, testStd) -> GreyImgToBatch( + param.batchSize) + + optimizer + .setValidation( + trigger = Trigger.everyEpoch, + dataset = validationSet, + vMethods = Array(new Top1Accuracy, new Top5Accuracy[Float], new Loss[Float])) + .setOptimMethod(optimMethod) + .setEndWhen(Trigger.maxEpoch(param.maxEpoch)) + .optimize() + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Utils.scala new file mode 100644 index 00000000000..a6bb90de6c0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Utils.scala @@ -0,0 +1,152 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.localJVM + +import java.nio.ByteBuffer +import java.nio.file.{Files, Paths} + +import com.intel.analytics.bigdl.dataset.ByteRecord +import com.intel.analytics.bigdl.utils.File +import scopt.OptionParser + +object Utils { + val trainMean = 0.13066047740239506 + val trainStd = 0.3081078 + + val testMean = 0.13251460696903547 + val testStd = 0.31048024 + + case class TrainParams( + folder: String = "./", + checkpoint: Option[String] = None, + modelSnapshot: Option[String] = None, + stateSnapshot: Option[String] = None, + batchSize: Int = 12, + learningRate: Double = 0.05, + learningRateDecay: Double = 0.0, + maxEpoch: Int = 5, + coreNumber: Int = -1, + nodeNumber: Int = -1, + overWriteCheckpoint: Boolean = false + ) + + val trainParser = new OptionParser[TrainParams]("BigDL Lenet Train Example") { + opt[String]('f', "folder") + .text("where you put the MNIST data") + .action((x, c) => c.copy(folder = x)) + opt[Int]('b', "batchSize") + .text("batch size") + .action((x, c) => c.copy(batchSize = x)) + opt[String]("model") + .text("model snapshot location") + .action((x, c) => c.copy(modelSnapshot = Some(x))) + opt[String]("state") + .text("state snapshot location") + .action((x, c) => c.copy(stateSnapshot = Some(x))) + opt[String]("checkpoint") + .text("where to cache the model") + .action((x, c) => c.copy(checkpoint = Some(x))) + opt[Double]('r', "learningRate") + .text("learning rate") + .action((x, c) => c.copy(learningRate = x)) + opt[Double]('d', "learningRateDecay") + .text("learning rate decay") + .action((x, c) => c.copy(learningRateDecay = x)) + opt[Int]('e', "maxEpoch") + .text("epoch numbers") + .action((x, c) => c.copy(maxEpoch = x)) + opt[Int]('b', "batchSize") + .text("batch size") + .action((x, c) => c.copy(batchSize = x)) + opt[Unit]("overWrite") + .text("overwrite checkpoint files") + .action( (_, c) => c.copy(overWriteCheckpoint = true) ) + } + + case class TestParams( + folder: String = "./", + model: String = "", + batchSize: Int = 128 + ) + + val testParser = new OptionParser[TestParams]("BigDL Lenet Test Example") { + opt[String]('f', "folder") + .text("where you put the MNIST data") + .action((x, c) => c.copy(folder = x)) + + opt[String]("model") + .text("model snapshot location") + .action((x, c) => c.copy(model = x)) + .required() + .required() + opt[Int]('b', "batchSize") + .text("batch size") + .action((x, c) => c.copy(batchSize = x)) + } + + /** + * load mnist data. + * read mnist from hdfs if data folder starts with "hdfs:", otherwise form local file. + * @param featureFile + * @param labelFile + * @return + */ + private[bigdl] def load(featureFile: String, labelFile: String): Array[ByteRecord] = { + + val featureBuffer = if (featureFile.startsWith(File.hdfsPrefix)) { + ByteBuffer.wrap(File.readHdfsByte(featureFile)) + } else { + ByteBuffer.wrap(Files.readAllBytes(Paths.get(featureFile))) + } + val labelBuffer = if (featureFile.startsWith(File.hdfsPrefix)) { + ByteBuffer.wrap(File.readHdfsByte(labelFile)) + } else { + ByteBuffer.wrap(Files.readAllBytes(Paths.get(labelFile))) + } + val labelMagicNumber = labelBuffer.getInt() + + require(labelMagicNumber == 2049) + val featureMagicNumber = featureBuffer.getInt() + require(featureMagicNumber == 2051) + + val labelCount = labelBuffer.getInt() + val featureCount = featureBuffer.getInt() + require(labelCount == featureCount) + + val rowNum = featureBuffer.getInt() + val colNum = featureBuffer.getInt() + + val result = new Array[ByteRecord](featureCount) + var i = 0 + while (i < featureCount) { + val img = new Array[Byte]((rowNum * colNum)) + var y = 0 + while (y < rowNum) { + var x = 0 + while (x < colNum) { + img(x + y * colNum) = featureBuffer.get() + x += 1 + } + y += 1 + } + result(i) = ByteRecord(img, labelBuffer.get().toFloat + 1.0f) + i += 1 + } + + result + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala new file mode 100644 index 00000000000..3916e7f902d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -0,0 +1,182 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample, SampleToBatch} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Engine, MklBlas} + + +import scala.reflect.ClassTag + +object LocalPredictor { + def getAndClearWeightBias[T: ClassTag](parameters: (Array[Tensor[T]], Array[Tensor[T]])) + (implicit ev: TensorNumeric[T]): Array[Tensor[T]] = { + var i = 0 + val weightsBias = new Array[Tensor[T]](parameters._1.length) + while (i < parameters._1.length) { + if (parameters._1(i) != null) { + val wb = parameters._1(i) + weightsBias(i) = Tensor[T](Storage(wb.storage().array()), + wb.storageOffset(), wb.size(), wb.stride()) + } + i += 1 + } + i = 0 + while (i < parameters._1.length) { + if (parameters._1(i) != null) { + parameters._1(i).set() + } + if (parameters._2(i) != null) { + parameters._2(i).set() + } + i += 1 + } + weightsBias + } + + def apply[T: ClassTag](model: Module[T], weightsBias: Array[Tensor[T]]) + (implicit ev: TensorNumeric[T]): LocalPredictor[T] = { + new LocalPredictor[T](model, weightsBias) + } +} + +class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: Array[Tensor[T]]) + (implicit ev: TensorNumeric[T]) + extends Serializable { + + val logger = LocalValidator.logger + private val coreNumber = Engine.coreNumber() + + private val subModelNumber = Engine.getEngineType match { + case MklBlas => coreNumber + case _ => throw new IllegalArgumentException + } + + private val batchSize = 8 + + def predictClass(dataSet: Array[Sample[T]]): Array[Int] = { + val result = predict(dataSet) + result.map(output => { + val _output = output.toTensor[T] + require(_output.dim() == 1, s"Predictor.predictClass:" + + s"Only support one sample has one lable, but got ${_output.dim()} label") + ev.toType[Int](_output.max(1)._2.valueAt(1)) + }) + } + + def predictClass(dataSet: LocalDataSet[Sample[T]]): Array[Int] = { + val result = predict(dataSet) + result.map(output => { + val _output = output.toTensor[T] + require(_output.dim() == 1, s"Predictor.predictClass:" + + s"Only support one sample has one lable, but got ${_output.dim()} label") + ev.toType[Int](_output.max(1)._2.valueAt(1)) + }) + } + + def predict(dataSet: LocalDataSet[Sample[T]]): Array[Activity] = { + val dataset = dataSet.transform(SampleToBatch[T]( + batchSize = batchSize, None, None, None, + partitionNum = Some(1))).asInstanceOf[LocalDataSet[MiniBatch[T]]] + val dataIter = dataset.data(train = false) + + val workingModels = (1 to subModelNumber).map(_ => { + val submodel = model.cloneModule().evaluate() + putWeightBias(weightsBias, submodel) + submodel + }).toArray + + dataIter.map(batch => { + println("Enter map") + val stackSize = batch.size() / subModelNumber + val extraSize = batch.size() % subModelNumber + val parallelism = if (stackSize == 0) extraSize else subModelNumber + val start = System.nanoTime() + val result = Engine.default.invokeAndWait( + (0 until parallelism).map(b => + () => { + val offset = b * stackSize + math.min(b, extraSize) + 1 + val length = stackSize + (if (b < extraSize) 1 else 0) + val currentMiniBatch = batch.slice(offset, length) + val input = currentMiniBatch.getInput() + val output = workingModels(b).forward(input).toTensor[T] + output + } + ) + ) + val batchResult = result.flatMap(_.split(1)).map(_.asInstanceOf[Activity]) + batchResult + }).toArray.flatten + + } + + def predict(dataSet: Array[Sample[T]]): Array[Activity] = { + val iter = dataSet.iterator + val transformer = SampleToBatch[T]( + batchSize = batchSize, None, None, None, + partitionNum = Some(1)) + val dataIter = transformer(iter) + + val workingModels = (1 to subModelNumber).map(_ => { + val submodel = model.cloneModule().evaluate() + putWeightBias(weightsBias, submodel) + submodel + }).toArray + + dataIter.map(batch => { + val stackSize = batch.size() / subModelNumber + val extraSize = batch.size() % subModelNumber + val parallelism = if (stackSize == 0) extraSize else subModelNumber + val start = System.nanoTime() + val result = Engine.default.invokeAndWait( + (0 until parallelism).map(b => + () => { + val offset = b * stackSize + math.min(b, extraSize) + 1 + val length = stackSize + (if (b < extraSize) 1 else 0) + val currentMiniBatch = batch.slice(offset, length) + val input = currentMiniBatch.getInput() + val output = workingModels(b).forward(input).toTensor[T] + output + + } + ) + ) + val batchResult = result.flatMap(_.split(1)).map(_.asInstanceOf[Activity]) + batchResult + }).toArray.flatten + + } + + private def putWeightBias(weightBias: Array[Tensor[T]], + localModel: Module[T]): Unit = { + val localWeightBias = localModel.parameters()._1 + var i = 0 + while (i < localWeightBias.length) { + if (localWeightBias(i) != null) { + localWeightBias(i).set(weightBias(i)) + } + i += 1 + } + } + +} + + From fe51fd9e992fbdb73e19f06b21b81c44ed451d96 Mon Sep 17 00:00:00 2001 From: jenniew Date: Fri, 7 Jul 2017 00:35:14 -0700 Subject: [PATCH 0263/1065] create localModule and update local predictor --- .../{localJVM => lenetLocal}/LeNet5.scala | 2 +- .../{localJVM => lenetLocal}/Predict.scala | 18 +++-- .../bigdl/dllib/example/lenetLocal/README.md | 67 +++++++++++++++++++ .../{localJVM => lenetLocal}/Test.scala | 18 ++--- .../{localJVM => lenetLocal}/Train.scala | 5 +- .../{localJVM => lenetLocal}/Utils.scala | 30 ++++++++- .../dllib/nn/abstractnn/AbstractModule.scala | 25 ++++++- .../bigdl/dllib/optim/LocalPredictor.scala | 14 ++-- .../bigdl/dllib/utils/LocalModule.scala | 58 ++++++++++++++++ 9 files changed, 203 insertions(+), 34 deletions(-) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/{localJVM => lenetLocal}/LeNet5.scala (96%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/{localJVM => lenetLocal}/Predict.scala (81%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/{localJVM => lenetLocal}/Test.scala (74%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/{localJVM => lenetLocal}/Train.scala (94%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/{localJVM => lenetLocal}/Utils.scala (83%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/LeNet5.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/LeNet5.scala similarity index 96% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/LeNet5.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/LeNet5.scala index b77c7fca308..197d0f4b2f9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/LeNet5.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/LeNet5.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.example.localJVM +package com.intel.analytics.bigdl.example.lenetLocal import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Predict.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala similarity index 81% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Predict.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala index 21ec36c20ad..5987b04afb7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Predict.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala @@ -14,15 +14,14 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.example.localJVM +package com.intel.analytics.bigdl.example.lenetLocal import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToSample} import com.intel.analytics.bigdl.nn.Module import com.intel.analytics.bigdl.optim.{LocalPredictor, Top1Accuracy, Validator} -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, LocalModule} import com.intel.analytics.bigdl.dataset.Sample import org.apache.log4j.{Level, Logger} - import scala.collection.mutable.ArrayBuffer object Predict { @@ -34,10 +33,11 @@ object Predict { import Utils._ def main(args: Array[String]): Unit = { - testParser.parse(args, new TestParams()).foreach { param => + predictParser.parse(args, new PredictParams()).foreach { param => System.setProperty("bigdl.localMode", "true") - System.setProperty("bigdl.coreNumber", "4") + System.setProperty("bigdl.coreNumber", + (Runtime.getRuntime().availableProcessors() / 2).toString) Engine.init val validationData = param.folder + "/t10k-images-idx3-ubyte" @@ -56,11 +56,9 @@ object Predict { val samples = samplesBuffer.toArray val model = Module.load[Float](param.model) - val weightsBias = LocalPredictor.getAndClearWeightBias(model.parameters()) - val predictor = LocalPredictor(model, weightsBias) - - val result = predictor.predict(samples) - val result_class = predictor.predictClass(samples) + val localModel = LocalModule(model) + val result = localModel.predict(samples) + val result_class = localModel.predictClass(samples) result_class.foreach(r => println(s"${r}")) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md new file mode 100644 index 00000000000..b983f6bef0d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md @@ -0,0 +1,67 @@ +# Running LeNet5 Model on local JVM + +This example shows how to run training, prediction and testing with LeNet5 model on local JVM with BigDL. Lenet5 is a classical CNN model used in digital number classification. For detail information, +please refer to . + +## Prepare MNIST Data +You can download the MNIST Data from [here](http://yann.lecun.com/exdb/mnist/). Unzip all the +files and put them in one folder(e.g. mnist). + +There're four files. **train-images-idx3-ubyte** contains train images, +**train-labels-idx1-ubyte** is train label file, **t10k-images-idx3-ubyte** has validation images + and **t10k-labels-idx1-ubyte** contains validation labels. For more detail, please refer to the + download page. + +## Get the JAR +You can build one by refer to the +[Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page) from the source code. + +## Train the Model +Example command +``` +java -cp dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +com.intel.analytics.bigdl.example.lenetLocal.Train \ +-f path_to_mnist_folder \ +-b batch_size \ +--checkpoint ./model +``` + +In the above commands +* -f: where you put your MNIST data +* --checkpoint: Where you cache the model/train_state snapshot. You should input a folder and +make sure the folder is created when you run this example. The model snapshot will be named as +model.#iteration_number, and train state will be named as state.#iteration_number. Note that if +there are some files already exist in the folder, the old file will not be overwrite for the +safety of your model files. +* -b: The mini-batch size. It is expected that the mini-batch size is a multiple of core_number. + +## Test Model +The above commands will cache the model in specified path(--checkpoint). Run this command will +use the model to do a validation. + +Example command +``` +java -cp dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +com.intel.analytics.bigdl.example.lenetLocal.Test \ +-f path_to_mnist_folder \ +--model ./model/model.iteration \ +-b batch_size +``` +In the above command +* -f: where you put your MNIST data +* --model: the model snapshot file +* -b: The mini-batch size. + +## Predict with Model +The above commands will use the model in specified path(--checkpoint)to do a prediction with given data. + +Example command +``` +java -cp dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +com.intel.analytics.bigdl.example.lenetLocal.Predict \ +-f path_to_mnist_folder \ +--model ./model/model.iteration +``` +In the above command +* -f: where you put your MNIST data +* --model: the model snapshot file \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala similarity index 74% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Test.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala index 39372c8297b..7807ca5fd62 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala @@ -14,12 +14,12 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.example.localJVM +package com.intel.analytics.bigdl.example.lenetLocal -import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.{DataSet, SampleToBatch} import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToSample} import com.intel.analytics.bigdl.nn.Module -import com.intel.analytics.bigdl.optim.{Top1Accuracy, Validator} +import com.intel.analytics.bigdl.optim.{Top1Accuracy, ValidationMethod, Validator} import com.intel.analytics.bigdl.utils.Engine import org.apache.log4j.{Level, Logger} @@ -34,19 +34,21 @@ object Test { def main(args: Array[String]): Unit = { testParser.parse(args, new TestParams()).foreach { param => System.setProperty("bigdl.localMode", "true") - System.setProperty("bigdl.coreNumber", "4") Engine.init val validationData = param.folder + "/t10k-images-idx3-ubyte" val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" - val partitionNum = Engine.nodeNumber() * Engine.coreNumber() val evaluationSet = DataSet.array(load(validationData, validationLabel)) -> - BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToSample() + BytesToGreyImg(28, 28) -> + GreyImgNormalizer(trainMean, trainStd) -> + GreyImgToSample() -> SampleToBatch( + batchSize = param.batchSize, None, None, None, + partitionNum = Some(1)) val model = Module.load[Float](param.model) - val validator = Validator(model, evaluationSet) - val result = validator.test(Array(new Top1Accuracy[Float])) + val result = model.evaluate(evaluationSet.toLocal(), + Array(new Top1Accuracy[Float].asInstanceOf[ValidationMethod[Float]])) result.foreach(r => println(s"${r._2} is ${r._1}")) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Train.scala similarity index 94% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Train.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Train.scala index 6f8eaa93447..45df596a8b1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Train.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.example.localJVM +package com.intel.analytics.bigdl.example.lenetLocal import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.DataSet @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} +import com.intel.analytics.bigdl.models.lenet.LeNet5 import org.apache.log4j.{Level, Logger} @@ -36,7 +37,7 @@ object Train { trainParser.parse(args, new TrainParams()).map(param => { System.setProperty("bigdl.localMode", "true") - System.setProperty("bigdl.coreNumber", "4") + System.setProperty("bigdl.coreNumber", param.coreNumber.toString) Engine.init val trainData = param.folder + "/train-images-idx3-ubyte" diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Utils.scala similarity index 83% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Utils.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Utils.scala index a6bb90de6c0..440894af105 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/localJVM/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Utils.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.example.localJVM +package com.intel.analytics.bigdl.example.lenetLocal import java.nio.ByteBuffer import java.nio.file.{Files, Paths} @@ -39,8 +39,7 @@ object Utils { learningRate: Double = 0.05, learningRateDecay: Double = 0.0, maxEpoch: Int = 5, - coreNumber: Int = -1, - nodeNumber: Int = -1, + coreNumber: Int = Runtime.getRuntime().availableProcessors() / 2, overWriteCheckpoint: Boolean = false ) @@ -75,6 +74,9 @@ object Utils { opt[Unit]("overWrite") .text("overwrite checkpoint files") .action( (_, c) => c.copy(overWriteCheckpoint = true) ) + opt[Int]('c', "coreNumber") + .text("core numbers") + .action((x, c) => c.copy(coreNumber = x)) } case class TestParams( @@ -98,6 +100,28 @@ object Utils { .action((x, c) => c.copy(batchSize = x)) } + + case class PredictParams( + folder: String = "./", + model: String = "", + batchSize: Int = 128 + ) + + val predictParser = new OptionParser[PredictParams]("BigDL Lenet Test Example") { + opt[String]('f', "folder") + .text("where you put the MNIST data") + .action((x, c) => c.copy(folder = x)) + + opt[String]("model") + .text("model snapshot location") + .action((x, c) => c.copy(model = x)) + .required() + .required() + opt[Int]('b', "batchSize") + .text("batch size") + .action((x, c) => c.copy(batchSize = x)) + } + /** * load mnist data. * read mnist from hdfs if data folder starts with "hdfs:", otherwise form local file. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 5d54eac6f90..3bb1bedfacd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.utils.TorchObject.TYPE_MODULE import org.apache.commons.lang3.SerializationUtils import org.apache.spark.rdd.RDD import com.intel.analytics.bigdl.optim._ -import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import scala.reflect.ClassTag @@ -410,6 +410,22 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, Predictor(this).predictClass(dataset) } + /** + * module predict, return the probability distribution + * @param dataset dataset for prediction + */ +// def predict(dataset: Array[Sample[T]]): Array[Activity] = { +// LocalPredictor(this).predict(dataset) +// } +// +// /** +// * module predict, return the predict label +// * @param dataset dataset for prediction +// */ +// def predictClass(dataset: Array[Sample[T]]): Array[Int] = { +// LocalPredictor(this).predictClass(dataset) +// } + /** * Set weight and bias for the module * @param newWeights array of weights and bias @@ -550,5 +566,12 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, batchSize: Option[Int] = None): Array[(ValidationResult, ValidationMethod[T])] = { Evaluator(this).test(dataset, vMethods, batchSize) } + + + def evaluate(dataSet: LocalDataSet[MiniBatch[T]], + vMethods: Array[ValidationMethod[T]] + ): Array[(ValidationResult, ValidationMethod[T])] = { + Validator(this, dataSet).test(vMethods) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index 3916e7f902d..6a048956391 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -70,7 +70,7 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: case _ => throw new IllegalArgumentException } - private val batchSize = 8 + private val batchPerCore = 4 def predictClass(dataSet: Array[Sample[T]]): Array[Int] = { val result = predict(dataSet) @@ -82,7 +82,7 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: }) } - def predictClass(dataSet: LocalDataSet[Sample[T]]): Array[Int] = { + def predictClass(dataSet: LocalDataSet[MiniBatch[T]]): Array[Int] = { val result = predict(dataSet) result.map(output => { val _output = output.toTensor[T] @@ -92,18 +92,14 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: }) } - def predict(dataSet: LocalDataSet[Sample[T]]): Array[Activity] = { - val dataset = dataSet.transform(SampleToBatch[T]( - batchSize = batchSize, None, None, None, - partitionNum = Some(1))).asInstanceOf[LocalDataSet[MiniBatch[T]]] - val dataIter = dataset.data(train = false) + def predict(dataSet: LocalDataSet[MiniBatch[T]]): Array[Activity] = { + val dataIter = dataSet.data(train = false) val workingModels = (1 to subModelNumber).map(_ => { val submodel = model.cloneModule().evaluate() putWeightBias(weightsBias, submodel) submodel }).toArray - dataIter.map(batch => { println("Enter map") val stackSize = batch.size() / subModelNumber @@ -131,7 +127,7 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: def predict(dataSet: Array[Sample[T]]): Array[Activity] = { val iter = dataSet.iterator val transformer = SampleToBatch[T]( - batchSize = batchSize, None, None, None, + batchSize = batchPerCore * subModelNumber, None, None, None, partitionNum = Some(1)) val dataIter = transformer(iter) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala new file mode 100644 index 00000000000..fe0b82e9107 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.optim.LocalPredictor + +import scala.reflect.ClassTag + + +object LocalModule { + def apply[T: ClassTag](model: Module[T]) + (implicit ev: TensorNumeric[T]): LocalModule[T] = { + val weightsBias = LocalPredictor.getAndClearWeightBias(model.parameters()) + new LocalModule[T](model, weightsBias) + } +} + +class LocalModule[T: ClassTag] private(model: Module[T], weightsBias: Array[Tensor[T]]) + (implicit ev: TensorNumeric[T]) + extends Serializable { + + private val predictor = LocalPredictor(model, weightsBias) + + def predictClass(dataSet: Array[Sample[T]]): Array[Int] = { + predictor.predictClass(dataSet) + } + + def predictClass(dataSet: LocalDataSet[MiniBatch[T]]): Array[Int] = { + predictor.predictClass(dataSet) + } + + def predict(dataSet: LocalDataSet[MiniBatch[T]]): Array[Activity] = { + predictor.predict(dataSet) + } + + def predict(dataSet: Array[Sample[T]]): Array[Activity] = { + predictor.predict(dataSet) + } +} + From 8bcd7058fa29606ae8851efbd771f7e5cd8ce85a Mon Sep 17 00:00:00 2001 From: jenniew Date: Fri, 7 Jul 2017 00:38:37 -0700 Subject: [PATCH 0264/1065] remove lenet model --- .../dllib/example/lenetLocal/LeNet5.scala | 39 ------------------- 1 file changed, 39 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/LeNet5.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/LeNet5.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/LeNet5.scala deleted file mode 100644 index 197d0f4b2f9..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/LeNet5.scala +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package com.intel.analytics.bigdl.example.lenetLocal - -import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.numeric.NumericFloat - -object LeNet5 { - def apply(classNum: Int): Module[Float] = { - val model = Sequential() - model.add(Reshape(Array(1, 28, 28))) - .add(SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5")) - .add(Tanh()) - .add(SpatialMaxPooling(2, 2, 2, 2)) - .add(Tanh()) - .add(SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5")) - .add(SpatialMaxPooling(2, 2, 2, 2)) - .add(Reshape(Array(12 * 4 * 4))) - .add(Linear(12 * 4 * 4, 100).setName("fc1")) - .add(Tanh()) - .add(Linear(100, classNum).setName("fc2")) - .add(LogSoftMax()) - } -} From 9fb1ecc1f9a49b92c83190377a76a2442d2bb4bb Mon Sep 17 00:00:00 2001 From: jenniew Date: Fri, 7 Jul 2017 00:44:54 -0700 Subject: [PATCH 0265/1065] update abstract module --- .../dllib/nn/abstractnn/AbstractModule.scala | 18 +----------------- 1 file changed, 1 insertion(+), 17 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 3bb1bedfacd..f64becf04e0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -409,23 +409,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, def predictClass(dataset: RDD[Sample[T]]): RDD[Int] = { Predictor(this).predictClass(dataset) } - - /** - * module predict, return the probability distribution - * @param dataset dataset for prediction - */ -// def predict(dataset: Array[Sample[T]]): Array[Activity] = { -// LocalPredictor(this).predict(dataset) -// } -// -// /** -// * module predict, return the predict label -// * @param dataset dataset for prediction -// */ -// def predictClass(dataset: Array[Sample[T]]): Array[Int] = { -// LocalPredictor(this).predictClass(dataset) -// } - + /** * Set weight and bias for the module * @param newWeights array of weights and bias From 509fb33fcd7e4b72a4d1a94582ac9ed7f8d8dc80 Mon Sep 17 00:00:00 2001 From: jenniew Date: Mon, 10 Jul 2017 13:16:45 -0700 Subject: [PATCH 0266/1065] update local jvm with comments --- .../dllib/example/lenetLocal/Predict.scala | 6 ++-- .../bigdl/dllib/example/lenetLocal/README.md | 18 ++++++++---- .../bigdl/dllib/example/lenetLocal/Test.scala | 3 +- .../dllib/example/lenetLocal/Utils.scala | 17 ++++++----- .../dllib/nn/abstractnn/AbstractModule.scala | 2 +- .../bigdl/dllib/optim/LocalPredictor.scala | 26 +---------------- .../bigdl/dllib/utils/LocalModule.scala | 29 +++++++++++++++++-- 7 files changed, 55 insertions(+), 46 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala index 5987b04afb7..23942314b5b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala @@ -17,7 +17,6 @@ package com.intel.analytics.bigdl.example.lenetLocal import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToSample} import com.intel.analytics.bigdl.nn.Module -import com.intel.analytics.bigdl.optim.{LocalPredictor, Top1Accuracy, Validator} import com.intel.analytics.bigdl.utils.{Engine, LocalModule} import com.intel.analytics.bigdl.dataset.Sample import org.apache.log4j.{Level, Logger} @@ -36,8 +35,7 @@ object Predict { predictParser.parse(args, new PredictParams()).foreach { param => System.setProperty("bigdl.localMode", "true") - System.setProperty("bigdl.coreNumber", - (Runtime.getRuntime().availableProcessors() / 2).toString) + System.setProperty("bigdl.coreNumber", (param.coreNumber.toString)) Engine.init val validationData = param.folder + "/t10k-images-idx3-ubyte" @@ -46,7 +44,7 @@ object Predict { val rawData = load(validationData, validationLabel) val iter = rawData.iterator val sampleIter = GreyImgToSample()( - GreyImgNormalizer(trainMean, trainStd)( + GreyImgNormalizer(trainMean, trainStd)( BytesToGreyImg(28, 28)(iter))) var samplesBuffer = ArrayBuffer[Sample[Float]]() while (sampleIter.hasNext) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md index b983f6bef0d..8ef6ae6a76d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md @@ -1,8 +1,10 @@ -# Running LeNet5 Model on local JVM +# Running LeNet5 Model as a local Java/Scala program This example shows how to run training, prediction and testing with LeNet5 model on local JVM with BigDL. Lenet5 is a classical CNN model used in digital number classification. For detail information, please refer to . +To run the BigDL model as a local Java/Scala program without Spark, user needs to set Java property `bigdl.localMode` to `true`. If user wants to specify how many cores to be used for training/testing/prediction, he needs to set Java property `bigdl.coreNumber` to the core number. User can either call `System.setProperty("bigdl.localMode", "true")` and `System.setProperty("bigdl.coreNumber", core_number)` in the Java/Scala code, or passing -Dbigdl.localMode=true and -Dbigdl.coreNumber=core_number when runing the program. In this example, we use the former way to set these Java properties. + ## Prepare MNIST Data You can download the MNIST Data from [here](http://yann.lecun.com/exdb/mnist/). Unzip all the files and put them in one folder(e.g. mnist). @@ -19,21 +21,23 @@ You can build one by refer to the ## Train the Model Example command ``` -java -cp dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +java -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ com.intel.analytics.bigdl.example.lenetLocal.Train \ -f path_to_mnist_folder \ +-c core_number \ -b batch_size \ --checkpoint ./model ``` In the above commands * -f: where you put your MNIST data +* -c: The core number on local machine used for this training. The default value is physical cores number. Get it through Runtime.getRuntime().availableProcessors() / 2 +* -b: The mini-batch size. It is expected that the mini-batch size is a multiple of core_number * --checkpoint: Where you cache the model/train_state snapshot. You should input a folder and make sure the folder is created when you run this example. The model snapshot will be named as model.#iteration_number, and train state will be named as state.#iteration_number. Note that if there are some files already exist in the folder, the old file will not be overwrite for the safety of your model files. -* -b: The mini-batch size. It is expected that the mini-batch size is a multiple of core_number. ## Test Model The above commands will cache the model in specified path(--checkpoint). Run this command will @@ -41,16 +45,18 @@ use the model to do a validation. Example command ``` -java -cp dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +java -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ com.intel.analytics.bigdl.example.lenetLocal.Test \ -f path_to_mnist_folder \ --model ./model/model.iteration \ +-c core_number \ -b batch_size ``` In the above command * -f: where you put your MNIST data * --model: the model snapshot file -* -b: The mini-batch size. +* -c: The core number on local machine used for this testing. The default value is physical cores number. Get it through Runtime.getRuntime().availableProcessors() / 2 +* -b: The mini-batch size. It is expected that the mini-batch size is a multiple of core_number ## Predict with Model The above commands will use the model in specified path(--checkpoint)to do a prediction with given data. @@ -60,8 +66,10 @@ Example command java -cp dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ com.intel.analytics.bigdl.example.lenetLocal.Predict \ -f path_to_mnist_folder \ +-c core_number \ --model ./model/model.iteration ``` In the above command * -f: where you put your MNIST data +* -c: The core number on local machine used for this prediction. The default value is physical cores number. Get it through Runtime.getRuntime().availableProcessors() / 2 * --model: the model snapshot file \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala index 7807ca5fd62..b12752e92d9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.example.lenetLocal import com.intel.analytics.bigdl.dataset.{DataSet, SampleToBatch} import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToSample} import com.intel.analytics.bigdl.nn.Module -import com.intel.analytics.bigdl.optim.{Top1Accuracy, ValidationMethod, Validator} +import com.intel.analytics.bigdl.optim.{Top1Accuracy, ValidationMethod} import com.intel.analytics.bigdl.utils.Engine import org.apache.log4j.{Level, Logger} @@ -34,6 +34,7 @@ object Test { def main(args: Array[String]): Unit = { testParser.parse(args, new TestParams()).foreach { param => System.setProperty("bigdl.localMode", "true") + System.setProperty("bigdl.coreNumber", param.coreNumber.toString) Engine.init val validationData = param.folder + "/t10k-images-idx3-ubyte" diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Utils.scala index 440894af105..c57cb185f17 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Utils.scala @@ -82,7 +82,8 @@ object Utils { case class TestParams( folder: String = "./", model: String = "", - batchSize: Int = 128 + batchSize: Int = 128, + coreNumber: Int = Runtime.getRuntime().availableProcessors() / 2 ) val testParser = new OptionParser[TestParams]("BigDL Lenet Test Example") { @@ -94,32 +95,32 @@ object Utils { .text("model snapshot location") .action((x, c) => c.copy(model = x)) .required() - .required() opt[Int]('b', "batchSize") .text("batch size") .action((x, c) => c.copy(batchSize = x)) + opt[Int]('c', "coreNumber") + .text("core numbers") + .action((x, c) => c.copy(coreNumber = x)) } case class PredictParams( folder: String = "./", model: String = "", - batchSize: Int = 128 + coreNumber: Int = Runtime.getRuntime().availableProcessors() / 2 ) val predictParser = new OptionParser[PredictParams]("BigDL Lenet Test Example") { opt[String]('f', "folder") .text("where you put the MNIST data") .action((x, c) => c.copy(folder = x)) - opt[String]("model") .text("model snapshot location") .action((x, c) => c.copy(model = x)) .required() - .required() - opt[Int]('b', "batchSize") - .text("batch size") - .action((x, c) => c.copy(batchSize = x)) + opt[Int]('c', "coreNumber") + .text("core numbers") + .action((x, c) => c.copy(coreNumber = x)) } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index f64becf04e0..5e0adb092c7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -409,7 +409,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, def predictClass(dataset: RDD[Sample[T]]): RDD[Int] = { Predictor(this).predictClass(dataset) } - + /** * Set weight and bias for the module * @param newWeights array of weights and bias diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index 6a048956391..e6c6374ac91 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample, SampleToBatch} import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Engine, MklBlas} @@ -27,30 +27,6 @@ import com.intel.analytics.bigdl.utils.{Engine, MklBlas} import scala.reflect.ClassTag object LocalPredictor { - def getAndClearWeightBias[T: ClassTag](parameters: (Array[Tensor[T]], Array[Tensor[T]])) - (implicit ev: TensorNumeric[T]): Array[Tensor[T]] = { - var i = 0 - val weightsBias = new Array[Tensor[T]](parameters._1.length) - while (i < parameters._1.length) { - if (parameters._1(i) != null) { - val wb = parameters._1(i) - weightsBias(i) = Tensor[T](Storage(wb.storage().array()), - wb.storageOffset(), wb.size(), wb.stride()) - } - i += 1 - } - i = 0 - while (i < parameters._1.length) { - if (parameters._1(i) != null) { - parameters._1(i).set() - } - if (parameters._2(i) != null) { - parameters._2(i).set() - } - i += 1 - } - weightsBias - } def apply[T: ClassTag](model: Module[T], weightsBias: Array[Tensor[T]]) (implicit ev: TensorNumeric[T]): LocalPredictor[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala index fe0b82e9107..bab9ede9add 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.optim.LocalPredictor @@ -26,9 +26,34 @@ import scala.reflect.ClassTag object LocalModule { + def getAndClearWeightBias[T: ClassTag](parameters: (Array[Tensor[T]], Array[Tensor[T]])) + (implicit ev: TensorNumeric[T]): Array[Tensor[T]] = { + var i = 0 + val weightsBias = new Array[Tensor[T]](parameters._1.length) + while (i < parameters._1.length) { + if (parameters._1(i) != null) { + val wb = parameters._1(i) + weightsBias(i) = Tensor[T](Storage(wb.storage().array()), + wb.storageOffset(), wb.size(), wb.stride()) + } + i += 1 + } + i = 0 + while (i < parameters._1.length) { + if (parameters._1(i) != null) { + parameters._1(i).set() + } + if (parameters._2(i) != null) { + parameters._2(i).set() + } + i += 1 + } + weightsBias + } + def apply[T: ClassTag](model: Module[T]) (implicit ev: TensorNumeric[T]): LocalModule[T] = { - val weightsBias = LocalPredictor.getAndClearWeightBias(model.parameters()) + val weightsBias = getAndClearWeightBias(model.parameters()) new LocalModule[T](model, weightsBias) } } From 74523fef3dd58ccdcf22b7178864ea301aed4c51 Mon Sep 17 00:00:00 2001 From: jenniew Date: Mon, 10 Jul 2017 13:19:21 -0700 Subject: [PATCH 0267/1065] update readme --- .../intel/analytics/bigdl/dllib/example/lenetLocal/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md index 8ef6ae6a76d..9e978273e35 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md @@ -3,7 +3,7 @@ This example shows how to run training, prediction and testing with LeNet5 model on local JVM with BigDL. Lenet5 is a classical CNN model used in digital number classification. For detail information, please refer to . -To run the BigDL model as a local Java/Scala program without Spark, user needs to set Java property `bigdl.localMode` to `true`. If user wants to specify how many cores to be used for training/testing/prediction, he needs to set Java property `bigdl.coreNumber` to the core number. User can either call `System.setProperty("bigdl.localMode", "true")` and `System.setProperty("bigdl.coreNumber", core_number)` in the Java/Scala code, or passing -Dbigdl.localMode=true and -Dbigdl.coreNumber=core_number when runing the program. In this example, we use the former way to set these Java properties. +To run the BigDL model as a local Java/Scala program without Spark, user needs to set Java property `bigdl.localMode` to `true`. If user wants to specify how many cores to be used for training/testing/prediction, he needs to set Java property `bigdl.coreNumber` to the core number. User can either call `System.setProperty("bigdl.localMode", "true")` and `System.setProperty("bigdl.coreNumber", core_number)` in the Java/Scala code, or pass -Dbigdl.localMode=true and -Dbigdl.coreNumber=core_number when runing the program. In this example, we use the former way to set these Java properties. ## Prepare MNIST Data You can download the MNIST Data from [here](http://yann.lecun.com/exdb/mnist/). Unzip all the From 7a30116e15040de30886b103141a58d6ad09ede2 Mon Sep 17 00:00:00 2001 From: jenniew Date: Tue, 11 Jul 2017 22:58:17 -0700 Subject: [PATCH 0268/1065] update predict part of readme --- .../intel/analytics/bigdl/dllib/example/lenetLocal/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md index 9e978273e35..324614a22ff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md @@ -63,7 +63,7 @@ The above commands will use the model in specified path(--checkpoint)to do a pre Example command ``` -java -cp dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +java -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ com.intel.analytics.bigdl.example.lenetLocal.Predict \ -f path_to_mnist_folder \ -c core_number \ From a9f5bc3abaa4fa2b66f1085d3c084d6479c3aa84 Mon Sep 17 00:00:00 2001 From: jenniew Date: Wed, 12 Jul 2017 01:13:51 -0700 Subject: [PATCH 0269/1065] add back sample clone --- .../analytics/bigdl/dllib/feature/dataset/Sample.scala | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index 94191255d4d..8d86636f887 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.dataset import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import org.apache.commons.lang3.SerializationUtils import scala.reflect.ClassTag @@ -58,6 +59,14 @@ abstract class Sample[T: ClassTag] extends Serializable { */ def numLabel(): Int + /** + * Deep clone + * + * @return a deep clone + */ + override def clone(): this.type = + SerializationUtils.clone(this) + /** * Get feature tensor, for one feature Sample only. * You don't need to override this, because we have add From 704bdd593dad5a7461fe915735d5bedf24fcb00f Mon Sep 17 00:00:00 2001 From: jenniew Date: Wed, 12 Jul 2017 01:30:28 -0700 Subject: [PATCH 0270/1065] fix style --- .../analytics/bigdl/dllib/feature/dataset/Sample.scala | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index 8d86636f887..25daeed746a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -60,10 +60,8 @@ abstract class Sample[T: ClassTag] extends Serializable { def numLabel(): Int /** - * Deep clone - * - * @return a deep clone - */ + *@return A deep clone + */ override def clone(): this.type = SerializationUtils.clone(this) From e7d7fc8e95e45b4bc413852d91f626f250e0a6a6 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Wed, 12 Jul 2017 17:21:31 +0800 Subject: [PATCH 0271/1065] Make ClassNLL can process -1 label for padding, not update the model. (#1215) * add -1 for ClassNLL * fix typo * add tests --- .../bigdl/dllib/nn/ClassNLLCriterion.scala | 35 +++++++++++------- .../dllib/nn/ClassNLLCriterionSpec.scala | 36 +++++++++++++++++++ 2 files changed, 59 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala index 0c559ccada7..2c7e7b6017d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala @@ -44,6 +44,10 @@ import com.intel.analytics.bigdl.utils.Engine * Due to the behaviour of the backend code, it is necessary to set sizeAverage to false when * calculating losses in non-batch mode. * + * Note that if the target is `-1`, the training process will skip this sample. + * In other words, the forward process will return zero output and the backward process + * will also return zero `gradInput`. + * * By default, the losses are averaged over observations for each minibatch. However, if the field * sizeAverage is set to false, the losses are instead summed for each minibatch. * @@ -73,10 +77,11 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] "ClassNLLCriterion: " + ErrorInfo.constrainInputDimSameAsTarget + s" Input dimension is: ${ input.dim() } , target dimension is: ${ target.dim() }") val curTarget = ev.toType[Int](target.valueAt(1)) - assert(curTarget >= 1 && curTarget <= nClasses, + assert(curTarget >= 1 && curTarget <= nClasses || curTarget == -1, s"curTarget ${curTarget} is out of range, should be 1 to ${nClasses}") total_weight = if (weights != null) weights(Array(curTarget)) else ev.fromType[Int](1) - output = ev.times(ev.negative(input.valueAt(curTarget)), total_weight) + output = if (curTarget == -1) ev.zero + else ev.times(ev.negative(input.valueAt(curTarget)), total_weight) } else if (input.dim() == 2) { val batchSize = input.size(1) val targetSize = target.size() @@ -93,10 +98,13 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] val _i = i results(_i - 1) = Engine.model.invoke( () => { val curTarget = ev.toType[Int](target.valueAt(_i)) - assert(curTarget >= 1 && curTarget <= nClasses, + assert(curTarget >= 1 && curTarget <= nClasses || curTarget == -1, s"curTarget ${curTarget} is out of range 1 to ${nClasses}") - val curWeight = if (weights != null) weights.valueAt(curTarget) else ev.fromType[Int](1) - (ev.times(input.valueAt(_i, curTarget), curWeight), curWeight) + if (curTarget == -1) (ev.zero, ev.one) + else { + val curWeight = if (weights != null) weights.valueAt(curTarget) else ev.fromType[Int](1) + (ev.times(input.valueAt(_i, curTarget), curWeight), curWeight) + } }) i += 1 } @@ -128,6 +136,7 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] "ClassNLLCriterion: " + ErrorInfo.constrainInputDimSameAsTarget + s" Input dimension is: ${ input.dim() } , target dimension is: ${ target.dim() }") val curTarget = ev.toType[Int](target.valueAt(1)) + if (curTarget == -1) return gradInput gradInput.setValue(curTarget, if (weights != null) ev.times(ev.fromType[Int](-1), weights.valueAt(curTarget)) else ev.fromType[Int](-1)) @@ -147,11 +156,13 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] val _i = i resultsBackward(_i - 1) = Engine.model.invoke(() => { val curTarget = ev.toType[Int](target.valueAt(_i)) - gradInput.setValue(_i, curTarget, if (weights != null) ev.times(ev.fromType[Int](-1), - weights.valueAt(curTarget)) - else ev.fromType[Int](-1)) - if (sizeAverage) gradInput.setValue(_i, curTarget, ev.divide(gradInput.valueAt(_i, - curTarget), total_weight)) + if (curTarget != -1) { + gradInput.setValue(_i, curTarget, if (weights != null) ev.times(ev.fromType[Int](-1), + weights.valueAt(curTarget)) + else ev.fromType[Int](-1)) + if (sizeAverage) gradInput.setValue(_i, curTarget, ev.divide(gradInput.valueAt(_i, + curTarget), total_weight)) + } }) i += 1 } @@ -169,8 +180,8 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] object ClassNLLCriterion { def apply[@specialized(Float, Double) T: ClassTag]( - weights: Tensor[T] = null, - sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : ClassNLLCriterion[T] = { + weights: Tensor[T] = null, + sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : ClassNLLCriterion[T] = { new ClassNLLCriterion[T](weights, sizeAverage) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala index 2305ee34b06..44a3a45cf91 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala @@ -23,6 +23,42 @@ import scala.math._ @com.intel.analytics.bigdl.tags.Parallel class ClassNLLCriterionSpec extends FlatSpec with Matchers { + "A ClassNLL Criterion with -1 label " should "generate correct output and grad" in { + val criterion = new ClassNLLCriterion[Double]() + val input = Tensor[Double](3, 3) + input(Array(1, 1)) = -1.0262627674932 + input(Array(1, 2)) = -1.2412600935171 + input(Array(1, 3)) = -1.0423174168648 + input(Array(2, 1)) = -0.90330565804228 + input(Array(2, 2)) = -1.3686840144413 + input(Array(2, 3)) = -1.0778380454479 + input(Array(3, 1)) = -0.99131220658219 + input(Array(3, 2)) = -1.0559142847536 + input(Array(3, 3)) = -1.2692712660404 + val target = Tensor[Double](3) + target(Array(1)) = -1 + target(Array(2)) = 2 + target(Array(3)) = 3 + val expectedOutput = 0.8793184268272333 + val expectedGrad = Tensor[Double](3, 3) + expectedGrad(Array(1, 1)) = 0 + expectedGrad(Array(1, 2)) = 0 + expectedGrad(Array(1, 3)) = 0 + expectedGrad(Array(2, 1)) = 0 + expectedGrad(Array(2, 2)) = -0.33333333333333 + expectedGrad(Array(2, 3)) = 0 + expectedGrad(Array(3, 1)) = 0 + expectedGrad(Array(3, 2)) = 0 + expectedGrad(Array(3, 3)) = -0.33333333333333 + val output = criterion.forward(input, target) + val gradInput = criterion.backward(input, target) + assert(abs(expectedOutput - output) < 1e-6) + expectedGrad.map(gradInput, (v1, v2) => { + assert(abs(v1 - v2) < 1e-6); + v1 + }) + } + "A ClassNLL Criterion " should "generate correct output and grad" in { val criterion = new ClassNLLCriterion[Double]() val input = Tensor[Double](3, 3) From ece4b5e7d5524510fbf42532db86e7003a5fb6e1 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 12 Jul 2017 11:16:56 -0400 Subject: [PATCH 0272/1065] support win64 (#1182) --- .../scala/com/intel/analytics/bigdl/utils/Engine.scala | 7 +------ .../analytics/bigdl/dllib/dataset/DataSetSpec.scala | 4 +++- .../bigdl/dllib/dataset/TransformersSpec.scala | 4 +++- .../bigdl/dllib/optim/DistriOptimizerSpec.scala | 3 +++ .../analytics/bigdl/dllib/python/api/PythonSpec.scala | 3 ++- .../analytics/bigdl/dllib/torch/ColorJitterSpec.scala | 5 +---- .../intel/analytics/bigdl/dllib/torch/ConcatSpec.scala | 1 + .../com/intel/analytics/bigdl/dllib/torch/TH.scala | 4 ++++ .../analytics/bigdl/dllib/utils/LoggerFilterSpec.scala | 7 ++++++- .../intel/analytics/bigdl/dllib/utils/TestUtils.scala | 10 ++++++++++ .../bigdl/dllib/visualization/SummarySpec.scala | 6 +++++- 11 files changed, 39 insertions(+), 15 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index a54bd72405d..41a40308402 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -150,12 +150,7 @@ object Engine { * Runtime.getRuntime().availableProcessors() / 2 */ private def getCoreNumberFromProperty() = { - val coreNumber = System.getProperty("bigdl.coreNumber", getNumMachineCores.toString).toInt - if (coreNumber > getNumMachineCores) { - getNumMachineCores - } else { - coreNumber - } + System.getProperty("bigdl.coreNumber", getNumMachineCores.toString).toInt } private def getNumMachineCores: Int = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala index cf55a193a9d..ad0cd8c536d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala @@ -22,7 +22,7 @@ import java.util.concurrent.{Callable, Executors} import com.intel.analytics.bigdl.dataset.image._ import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator} +import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, TestUtils} import org.apache.hadoop.io.Text import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -192,6 +192,7 @@ class DataSetSpec extends FlatSpec with Matchers with BeforeAndAfter { } "imagenet sequence data source" should "load image correct" in { + TestUtils.cancelOnWindows() val resource = getClass().getClassLoader().getResource("imagenet") val tmpFile = java.io.File.createTempFile("UnitTest", System.nanoTime().toString) require(tmpFile.delete()) @@ -384,6 +385,7 @@ class DataSetSpec extends FlatSpec with Matchers with BeforeAndAfter { } "transformRDD" should "be correct" in { + TestUtils.cancelOnWindows() val resource = getClass().getClassLoader().getResource("imagenet") val tmpFile = java.io.File.createTempFile("UnitTest", System.nanoTime().toString) require(tmpFile.delete()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/TransformersSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/TransformersSpec.scala index 3a9731369b1..408a0d326d8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/TransformersSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/TransformersSpec.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.dataset.image._ import com.intel.analytics.bigdl.dataset.text.{LabeledSentence, LabeledSentenceToSample} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG -import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator} +import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, TestUtils} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.io.{SequenceFile, Text} @@ -464,6 +464,7 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { } "RGBImage To SeqFile without file name" should "be good" in { + TestUtils.cancelOnWindows() val resource = getClass().getClassLoader().getResource("imagenet") val pathToImage = LocalImgReaderWithName(BGRImage.NO_SCALE) val dataSet = DataSet.ImageFolder.paths( @@ -505,6 +506,7 @@ class TransformersSpec extends FlatSpec with Matchers with BeforeAndAfter { } "RGBImage To SeqFile with file name" should "be good" in { + TestUtils.cancelOnWindows() val resource = getClass().getClassLoader().getResource("imagenet") val pathToImage = LocalImgReaderWithName(BGRImage.NO_SCALE) val dataSet = DataSet.ImageFolder.paths( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index 277c93b7cfd..0617c7807a5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -326,6 +326,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } "TrainSummary with MSE and LBFGS" should "work correctly" in { + TestUtils.cancelOnWindows() RandomGenerator.RNG.setSeed(10) val logdir = com.google.common.io.Files.createTempDir() val trainSummary = TrainSummary(logdir.getPath, "lbfgs") @@ -347,6 +348,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } "TrainSummary with MSE and SGD" should "work correctly" in { + TestUtils.cancelOnWindows() RandomGenerator.RNG.setSeed(10) val logdir = com.google.common.io.Files.createTempDir() val trainSummary = TrainSummary(logdir.getPath, "sgd") @@ -368,6 +370,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } "TrainSummary with MSE and Adagrad" should "work correctly" in { + TestUtils.cancelOnWindows() RandomGenerator.RNG.setSeed(10) val logdir = com.google.common.io.Files.createTempDir() val trainSummary = TrainSummary(logdir.getPath, "adagrad") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index 2e09a852d69..b2852dfd922 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -22,7 +22,7 @@ import java.util.{List => JList, Map => JMap} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{Loss, SGD, Top1Accuracy, Trigger} -import com.intel.analytics.bigdl.utils.{Engine, T} +import com.intel.analytics.bigdl.utils.{Engine, T, TestUtils} import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext @@ -139,6 +139,7 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { } "Double prototype" should "be test" in { + TestUtils.cancelOnWindows() Logger.getLogger("org").setLevel(Level.WARN) Logger.getLogger("akka").setLevel(Level.WARN) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ColorJitterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ColorJitterSpec.scala index 153209401d3..ea764c9c94e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ColorJitterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ColorJitterSpec.scala @@ -30,13 +30,10 @@ import com.intel.analytics.bigdl.utils.RandomGenerator @com.intel.analytics.bigdl.tags.Serial class ColorJitterSpec extends FlatSpec with BeforeAndAfter with Matchers { - before { + "A ColorJitter" should "blend image correctly" in { if (!TH.hasTorch()) { cancel("Torch is not installed") } - } - - "A ColorJitter" should "blend image correctly" in { val seed = 1000 RNG.setSeed(seed) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala index 9f36d8e590f..3140db39d6b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala @@ -89,6 +89,7 @@ class ConcatSpec extends TorchSpec { "A Concat Container updateGradInput and acc with Linear" should "generate correct output and grad " in { + torchCheck() val seed = 2 RNG.setSeed(seed) val module = new Concat[Double](2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala index 8dfe56fc13c..3ba743d7f7b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala @@ -33,6 +33,10 @@ import scala.sys.process._ object TH { def hasTorch(): Boolean = { val torchPath = System.getProperty("torch_location") + // Skip on windows + if (System.getProperty("os.name").toLowerCase().contains("win")) { + return false + } val exitValue = if (torchPath != null) s"ls $torchPath".! else "which th".! return exitValue == 0 } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala index 8fa8e730135..f3eca9d624c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala @@ -58,6 +58,7 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { } "A LoggerFilter" should "output correct info on console and bigdl.log" in { + TestUtils.cancelOnWindows() val logFile = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString val optimClz = "com.intel.analytics.bigdl.optim" @@ -117,7 +118,7 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { { val pattern = ".*INFO.*DistriOptimizer.*caching training rdd ..." val firstLine = allString.split('\n')(0) - require(firstLine.matches(pattern), s"output can't matchs the specific output") + require(firstLine.matches(pattern), s"output can't matchs the specific output\n") } { @@ -132,6 +133,7 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { } "A LoggerFilter generate log " should "in correct place" in { + TestUtils.cancelOnWindows() val logFile = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString val optimClz = "com.intel.analytics.bigdl.optim" @@ -157,6 +159,7 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { } "A LoggerFilter generate log " should "under the place user gived" in { + TestUtils.cancelOnWindows() val logFile = Paths.get(System.getProperty("java.io.tmpdir"), "bigdl.log").toString val optimClz = "com.intel.analytics.bigdl.optim" val defaultFile = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString @@ -189,6 +192,7 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { } "A LoggerFilter generate log" should "not modify log level user defined" in { + TestUtils.cancelOnWindows() val optimClz = "com.intel.analytics.bigdl.optim" val defaultFile = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString @@ -280,6 +284,7 @@ class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { } "A LoggerFilter disable spark log" should "not generate spark logs in file" in { + TestUtils.cancelOnWindows() val defaultFile = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString System.setProperty("bigdl.utils.LoggerFilter.enableSparkLog", "false") LoggerFilter.redirectSparkInfoLogs() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala index c41a43636a8..a6644f9dd1a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala @@ -21,6 +21,7 @@ import java.util.concurrent.atomic.AtomicInteger import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.scalatest.exceptions.TestCanceledException import scala.reflect.ClassTag @@ -40,6 +41,15 @@ object TestUtils { } } + /** + * Some test case cannot run on windows, cancel such test cases + */ + def cancelOnWindows(): Unit = { + if (System.getProperty("os.name").toLowerCase().contains("win")) { + throw new TestCanceledException("This case should not be run on windows", 3) + } + } + /** * This function returns the function value, partial derivatives * and Hessian of the (general dimension) rosenbrock function, given by: diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/visualization/SummarySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/visualization/SummarySpec.scala index 99a27972d85..49311983657 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/visualization/SummarySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/visualization/SummarySpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.visualization import com.intel.analytics.bigdl.example.loadmodel.AlexNet import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator} +import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, TestUtils} import Summary._ import com.intel.analytics.bigdl.visualization.tensorboard.{FileReader, FileWriter} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -91,6 +91,7 @@ class SummarySpec extends FlatSpec with Matchers with BeforeAndAfter { } "read/write event file" should "work properly" in { + TestUtils.cancelOnWindows() val logdir = com.google.common.io.Files.createTempDir() val writer = new FileWriter(logdir.getPath, 100) for (i <- 0 to 9) { @@ -117,6 +118,7 @@ class SummarySpec extends FlatSpec with Matchers with BeforeAndAfter { } "read event file with a non-existent tag" should "return a empty array" in { + TestUtils.cancelOnWindows() val logdir = com.google.common.io.Files.createTempDir() val writer = new FileWriter(logdir.getPath, 100) for (i <- 0 to 9) { @@ -131,6 +133,7 @@ class SummarySpec extends FlatSpec with Matchers with BeforeAndAfter { } "FileReader.list" should "work properly" in { + TestUtils.cancelOnWindows() val logdir = com.google.common.io.Files.createTempDir() val writer1 = new FileWriter(logdir.getPath, 100) for (i <- 0 to 9) { @@ -154,6 +157,7 @@ class SummarySpec extends FlatSpec with Matchers with BeforeAndAfter { } "FileReader read from five Files" should "work properly" in { + TestUtils.cancelOnWindows() val numFile = 5 val logdir = com.google.common.io.Files.createTempDir() for (i <- 1 to numFile) { From 0d78d8c9b3de11611376b72aed26797998d8f33d Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Thu, 13 Jul 2017 06:23:49 +0800 Subject: [PATCH 0273/1065] Tree LSTM with Sentiment classification example (#1217) * finish treeLSTM with sentiment example * fix scala style * fix python error * fix a python bug * fix a typo * fix a typo --- .../example/treeLSTMSentiment/Train.scala | 125 +++++ .../treeLSTMSentiment/TreeSentiment.scala | 52 ++ .../example/treeLSTMSentiment/Utils.scala | 246 +++++++++ .../bigdl/dllib/nn/BinaryTreeLSTM.scala | 511 ++++++++++++++++++ .../analytics/bigdl/dllib/nn/TreeLSTM.scala | 48 ++ .../bigdl/dllib/optim/Optimizer.scala | 31 +- .../bigdl/dllib/optim/ValidationMethod.scala | 53 ++ .../dllib/utils/python/api/PythonBigDL.scala | 17 + .../bigdl/dllib/optim/ValidationSpec.scala | 50 ++ 9 files changed, 1132 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Train.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/TreeSentiment.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TreeLSTM.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Train.scala new file mode 100644 index 00000000000..54fac3d8844 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Train.scala @@ -0,0 +1,125 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.treeLSTMSentiment + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.PaddingParam +import com.intel.analytics.bigdl.example.treeLSTMSentiment.Utils._ +import com.intel.analytics.bigdl.nn.{TimeDistributedCriterion, _} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T} +import org.apache.log4j.{Level => Levle4j, Logger => Logger4j} +import org.apache.spark.SparkContext +import org.slf4j.{Logger, LoggerFactory} + +import scala.language.existentials + +object Train { + val log: Logger = LoggerFactory.getLogger(this.getClass) + LoggerFilter.redirectSparkInfoLogs() + Logger4j.getLogger("com.intel.analytics.bigdl.optim").setLevel(Levle4j.INFO) + + def main(args: Array[String]): Unit = { + val params = paramParser.parse(args, TreeLSTMSentimentParam()).get + train(params) + } + + def train(param: TreeLSTMSentimentParam): Unit = { + val DATA_DIR = param.baseDir + val classNum = 5 + val criterion = TimeDistributedCriterion(ClassNLLCriterion()) + val conf = Engine.createSparkConf() + .setAppName("Text classification") + .set("spark.task.maxFailures", "1") + val sc = new SparkContext(conf) + Engine.init + + val paddingValue = 1 + val oovChar = 2 + val indexFrom = 3 + val labelPadding = -1f + val glovePath = s"$DATA_DIR/glove/glove.840B.300d.txt" + val vocabPath = s"$DATA_DIR/sst/vocab-cased.txt" + val (word2VecTensor, vocab) = + loadEmbeddingAndVocabulary(glovePath, vocabPath, indexFrom) + + val vocabBC = sc.broadcast(vocab) + val (trainTreeRDD, trainLabelRDD, trainSentenceRDD) = preProcessData( + sc, + vocabBC, + oovChar, + s"$DATA_DIR/sst/train/parents.txt", + s"$DATA_DIR/sst/train/labels.txt", + s"$DATA_DIR/sst/train/sents.txt") + println( + s""" + |train treeRDD count: ${trainTreeRDD.count()} + |train labelRDD count: ${trainLabelRDD.count()} + |train sentenceRDD count: ${trainSentenceRDD.count()} + """.stripMargin) + + val (devTreeRDD, devLabelRDD, devSentenceRDD) = preProcessData( + sc, + vocabBC, + oovChar, + s"$DATA_DIR/sst/dev/parents.txt", + s"$DATA_DIR/sst/dev/labels.txt", + s"$DATA_DIR/sst/dev/sents.txt") + println( + s""" + |dev treeRDD count: ${devTreeRDD.count()} + |dev labelRDD count: ${devLabelRDD.count()} + |dev sentenceRDD count: ${devSentenceRDD.count()} + """.stripMargin) + + val trainRDD = toSample(trainTreeRDD, trainLabelRDD, trainSentenceRDD) + val devRDD = toSample(devTreeRDD, devLabelRDD, devSentenceRDD) + + val optimizer = Optimizer( + model = TreeLSTMSentiment(word2VecTensor, param.hiddenSize, classNum, param.p), + sampleRDD = trainRDD, + criterion = criterion, + batchSize = param.batchSize, + featurePaddingParam = PaddingParam[Float]( + paddingTensor = + Some(Array(Tensor(T(paddingValue.toFloat)), Tensor(T(-1f, -1f, -1f))))), + labelPaddingParam = PaddingParam[Float]( + paddingTensor = + Some(Array(Tensor(T(-1f)))))) + + optimizer + .setOptimMethod(new Adagrad( + learningRate = param.learningRate, + weightDecay = param.regRate)) + .setValidation( + Trigger.everyEpoch, + devRDD, + Array(new TreeNNAccuracy()), + param.batchSize, + PaddingParam[Float]( + paddingTensor = + Some(Array(Tensor(T(paddingValue.toFloat)), Tensor(T(-1f, -1f, -1f))))), + PaddingParam[Float]( + paddingTensor = + Some(Array(Tensor(T(-1f)))))) + .setEndWhen(Trigger.maxEpoch(param.epoch)) + .optimize() + sc.stop() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/TreeSentiment.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/TreeSentiment.scala new file mode 100644 index 00000000000..aaf6eb9aa94 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/TreeSentiment.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.treeLSTMSentiment + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor + +import scala.language.existentials + +object TreeLSTMSentiment { + def apply( + word2VecTensor: Tensor[Float], + hiddenSize: Int, + classNum: Int, + p: Double = 0.5 + ): Module[Float] = { + val vocabSize = word2VecTensor.size(1) + val embeddingDim = word2VecTensor.size(2) + val embedding = LookupTable(vocabSize, embeddingDim) + embedding.weight.set(word2VecTensor) + + val treeLSTMModule = Sequential() + .add(BinaryTreeLSTM( + embeddingDim, hiddenSize, withGraph = true)) + .add(Dropout(p)) + .add(TimeDistributed(Linear(hiddenSize, classNum))) + .add(TimeDistributed(LogSoftMax())) + + Sequential() + .add(MapTable(Squeeze(3))) + .add(ParallelTable() + .add(embedding) + .add(Identity())) + .add(treeLSTMModule) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala new file mode 100644 index 00000000000..845f52a5239 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala @@ -0,0 +1,246 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.treeLSTMSentiment + +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.example.utils.AbstractTextClassificationParams +import com.intel.analytics.bigdl.nn.TensorTree +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import org.apache.spark.SparkContext +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.rdd.RDD +import scopt.OptionParser + +import scala.io.Source +import scala.language.existentials +import scala.util.control.Breaks._ + +object Utils { + def readTree( + parents: Array[Int] + ): Tensor[Float] = { + val size = parents.length + val maxNumChildren = parents + .groupBy(x => x) + .foldLeft(0)((maxNum, p) => scala.math.max(maxNum, p._2.length)) + val trees = new TensorTree(Tensor[Float](size, maxNumChildren + 1)) + for (i <- parents.indices) { + if (trees.noChild(i + 1) && parents(i) != -1) { + var idx = i + 1 + var prev = 0 + breakable { + while (true) { + var parent = + if (idx != 0) parents(idx - 1) + else -1 + if (parent == parents.length) parent = 0 + if (prev != 0 && parent != -1) { + trees.addChild(idx + 1, prev + 1) + } + + if (parent == -1) { + trees.markAsRoot(1) + if (prev != 0) { + trees.addChild(1, prev + 1) + } + break() + } else if (trees.hasChild(parent + 1)) { + trees.addChild(parent + 1, idx + 1) + break() + } else { + prev = idx + idx = parent + } + } + } + } + } + + var leafIdx = 1 + for (i <- 2 to size) { + if (trees.noChild(i)) { + trees.markAsLeaf(i, leafIdx) + leafIdx += 1 + } + } + + trees.content + } + + def remapLabel( + label: Float + ): Float = { + label + 3 + } + + /** + * Rotate an array `arr` from `offset` distance to the end + * + * @param arr Given array + * @param offset right rotate how many elements + */ + def rotate[D](arr: Array[D], offset: Int): Array[D] = { + if (arr == null || arr.length==0 || offset < 0) { + throw new IllegalArgumentException("Illegal argument!") + } + + val newOffset = if (offset > arr.length) offset % arr.length else offset + + val index = arr.length - newOffset + + reverse(arr, 0, index - 1) + reverse(arr, index, arr.length - 1) + reverse(arr, 0, arr.length - 1) + + arr + } + + def reverse[D](arr: Array[D], l: Int, r: Int): Unit = { + var left = l + var right = r + + if(arr == null || arr.length == 1) return + + while(left < right) { + val temp = arr(left) + arr(left) = arr(right) + arr(right) = temp + left += 1 + right -= 1 + } + } + + def preProcessData( + sc: SparkContext, + vocabBC: Broadcast[Map[String, Int]], + oovChar: Int, + treePath: String, + labelPath: String, + sentencePath: String + ): (RDD[Tensor[Float]], RDD[Array[Float]], RDD[Array[Int]]) = { + val treeRDD = sc.textFile(treePath, 4) + .map(line => line.split(" ")) + .map(_.map(_.toInt)) + .map(readTree) + val labelRDD = sc.textFile(labelPath, 4) + .map(line => line.split(" ")) + .map(_.map(l => remapLabel(l.toFloat))) + .map(line => rotate(line, 1)) + val sentenceRDD = sc.textFile(sentencePath, 4) + .map(line => line.split(" ")) + .map(line => line.map(vocabBC.value.getOrElse(_, oovChar))) + + (treeRDD, labelRDD, sentenceRDD) + } + + def toSample( + treeRDD: RDD[Tensor[Float]], + labelRDD: RDD[Array[Float]], + sentenceRDD: RDD[Array[Int]] + ): RDD[Sample[Float]] = { + def indexAndSort(rdd: RDD[_]) = rdd.zipWithIndex.map(_.swap).sortByKey() + + indexAndSort(sentenceRDD) + .join(indexAndSort(labelRDD)) + .join(indexAndSort(treeRDD)) + .values + .map { case ((input: Array[Int], label: Array[Float]), tree: Tensor[Float]) => + Sample( + featureTensors = + Array(Tensor(input.map(_.toFloat), Array(input.length, 1)), + tree.resize(tree.size())), + labelTensor = + Tensor(label, Array(label.length))) + } + } + + def loadEmbeddingAndVocabulary( + w2vPath: String, + vocabPath: String, + indexFrom: Int + ): + (Tensor[Float], Map[String, Int]) = { + val word2Vec = scala.collection.mutable.Map[String, Array[Float]]() + for (line <- Source.fromFile(w2vPath, "ISO-8859-1").getLines) { + val values = line.split(" ") + val word = values(0) + val coefs = values.slice(1, values.length).map(_.toFloat) + word2Vec += word -> coefs + } + + var i = 1 + val vocabLines = Source + .fromFile(vocabPath, "ISO-8859-1") + .getLines + .toList + val word2VecTensor = Tensor(vocabLines.length + indexFrom - 1, word2Vec.last._2.length) + + val vocab = scala.collection.mutable.Map[String, Int]() + while (i < indexFrom) { + word2VecTensor.select(1, i).apply1(_ => RNG.uniform(-0.05f, 0.05f).toFloat) + i += 1 + } + + for (line <- vocabLines) { + if (!word2Vec.contains(line)) { + word2VecTensor.select(1, i).apply1(_ => RNG.uniform(-0.05f, 0.05f).toFloat) + } else { + word2VecTensor.select(1, i).copy(Tensor(Storage(word2Vec(line)))) + } + vocab += line -> i + i += 1 + } + + (word2VecTensor, vocab.toMap) + } + + val paramParser = new OptionParser[TreeLSTMSentimentParam]("TreeLSTM Sentiment") { + opt[String]('b', "baseDir") + .text("Base dir containing the training and word2Vec data") + .action((x, c) => c.copy(baseDir = x)) + opt[String]('i', "batchSize") + .text("batchSize") + .action((x, c) => c.copy(batchSize = x.toInt)) + opt[String]('h', "hiddenSize") + .text("hiddenSize") + .action((x, c) => c.copy(hiddenSize = x.toInt)) + opt[String]('l', "learingRate") + .text("learning rate") + .action((x, c) => c.copy(learningRate = x.toDouble)) + opt[String]('r', "regRate") + .text("regularization rate") + .action((x, c) => c.copy(regRate = x.toDouble)) + opt[String]('p', "p") + .text("dropout rate") + .action((x, c) => c.copy(p = x.toDouble)) + opt[String]('e', "epoch") + .text("max epoch") + .action((x, c) => c.copy(epoch = x.toInt)) + } + + case class TreeLSTMSentimentParam ( + override val baseDir: String = "/tmp/.bigdl/dataset/", + override val batchSize: Int = 128, + hiddenSize: Int = 250, + learningRate: Double = 0.05, + regRate: Double = 1e-4, + p: Double = 0, + epoch: Int = 10 + ) extends AbstractTextClassificationParams +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala new file mode 100644 index 00000000000..171be024b0c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala @@ -0,0 +1,511 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag +import scala.util.control.Breaks._ + +/** + * This class is an implementation of Binary TreeLSTM (Constituency Tree LSTM). + * @param inputSize input units size + * @param hiddenSize hidden units size + * @param gateOutput whether gate output + * @param withGraph whether create lstms with [[Graph]], the default value is true. + */ +class BinaryTreeLSTM[T: ClassTag]( + inputSize: Int, + hiddenSize: Int, + gateOutput: Boolean = true, + withGraph: Boolean = true +)(implicit ev: TensorNumeric[T]) + extends TreeLSTM[T](inputSize, hiddenSize) { + val composer: Module[T] = createComposer() + val leafModule: Module[T] = createLeafModule() + val composers: ArrayBuffer[Module[T]] = ArrayBuffer[Module[T]](composer) + val leafModules: ArrayBuffer[Module[T]] = ArrayBuffer[Module[T]](leafModule) + val cells: ArrayBuffer[ArrayBuffer[Module[T]]] = ArrayBuffer[ArrayBuffer[Module[T]]]() + + def createLeafModule(): Module[T] = { + if (withGraph) createLeafModuleWithGraph() + else createLeafModuleWithSequential() + } + + def createComposer(): Module[T] = { + if (withGraph) createComposerWithGraph() + else createComposerWithSequential() + } + + def createLeafModuleWithGraph(): Module[T] = { + val input = Input() + val c = Linear(inputSize, hiddenSize).inputs(input) + val h: ModuleNode[T] = if (gateOutput) { + val o = Sigmoid().inputs(Linear(inputSize, hiddenSize).inputs(input)) + CMulTable().inputs(o, Tanh().inputs(c)) + } else { + Tanh().inputs(c) + } + + val leafModule = Graph(Array(input), Array(c, h)) + + if (this.leafModule != null) { + shareParams(leafModule, this.leafModule) + } + + leafModule + } + + def createComposerWithGraph(): Module[T] = { + val (lc, lh) = (Input(), Input()) + val (rc, rh) = (Input(), Input()) + + def newGate(): ModuleNode[T] = CAddTable().inputs( + Linear(hiddenSize, hiddenSize).inputs(lh), + Linear(hiddenSize, hiddenSize).inputs(rh) + ) + + val i = Sigmoid().inputs(newGate()) + val lf = Sigmoid().inputs(newGate()) + val rf = Sigmoid().inputs(newGate()) + val update = Tanh().inputs(newGate()) + val c = CAddTable().inputs( + CMulTable().inputs(i, update), + CMulTable().inputs(lf, lc), + CMulTable().inputs(rf, rc) + ) + + val h = if (this.gateOutput) { + val o = Sigmoid().inputs(newGate()) + CMulTable().inputs(o, Tanh().inputs(c)) + } else { + Tanh().inputs(c) + } + + val composer = Graph(Array(lc, lh, rc, rh), Array(c, h)) + + if (this.composer != null) { + shareParams(composer, this.composer) + } + + composer + } + + def createLeafModuleWithSequential(): Module[T] = { + val gate = ConcatTable() + .add(Sequential() + .add(Linear(inputSize, hiddenSize)) + .add(ConcatTable() + .add(Identity()) + .add(Tanh()))) + .add(Sequential() + .add(Linear(inputSize, hiddenSize)) + .add(Sigmoid())) + + + val leafModule = Sequential() + .add(gate) + .add(FlattenTable()) + .add(ConcatTable() + .add(SelectTable(1)) + .add(Sequential() + .add(NarrowTable(2, 2)) + .add(CMulTable()))) + + + if (this.leafModule != null) { + shareParams(leafModule, this.leafModule) + } + + leafModule + } + + def createComposerWithSequential(): Module[T] = { + def newGate(): Module[T] = + Sequential() + .add(ParallelTable() + .add(Linear(hiddenSize, hiddenSize)) + .add(Linear(hiddenSize, hiddenSize))) + .add(CAddTable()) + + val gates = Sequential() + .add(ConcatTable() + .add(SelectTable(2)) + .add(SelectTable(4))) + .add(ConcatTable() + .add(Sequential() + .add(newGate()) + .add(Sigmoid())) // i + .add(Sequential() + .add(newGate()) + .add(Sigmoid())) // lf + .add(Sequential() + .add(newGate()) + .add(Sigmoid())) // rf + .add(Sequential() + .add(newGate()) + .add(Tanh())) // update + .add(Sequential() + .add(newGate()) + .add(Sigmoid()))) // o + + val i2c = Sequential() + .add(ConcatTable() + .add(Sequential() + .add(ConcatTable() + .add(SelectTable(3)) // i + .add(SelectTable(6))) // update + .add(CMulTable())) + .add(Sequential() + .add(ConcatTable() + .add(SelectTable(4)) // lf + .add(SelectTable(1))) // lc + .add(CMulTable())) + .add(Sequential() + .add(ConcatTable() + .add(SelectTable(5)) // rf + .add(SelectTable(2))) // rc + .add(CMulTable()))) + .add(CAddTable()) + + val composer = Sequential() + .add(ConcatTable() + .add(SelectTable(1)) // lc + .add(SelectTable(3)) // rc + .add(gates)) + .add(FlattenTable()) + .add(ConcatTable() + .add(i2c) + .add(SelectTable(7))) // o + .add(ConcatTable() + .add(SelectTable(1)) // c + .add(Sequential() + .add(ParallelTable() + .add(Tanh()) // Tanh(c) + .add(Identity()))// o + .add(CMulTable())))// h + + + if (this.composer != null) { + shareParams(composer, this.composer) + } + + composer + } + + override def updateOutput(input: Table): Tensor[T] = { + cells.clear() + val inputs = input[Tensor[T]](1) + val trees = input[Tensor[T]](2) + val batchSize = inputs.size(1) + val nodeSize = trees.size(2) + output.resize(batchSize, nodeSize, hiddenSize) + output.zero() + + for (b <- 1 to batchSize) { + cells.append(ArrayBuffer[Module[T]]()) + } + + var leafIndex = 0 + var composerIndex = 0 + for (b <- 1 to batchSize) { + val tensorTree = new TensorTree[T](trees(b)) + for (i <- 1 to tensorTree.nodeNumber) { + if (tensorTree.noChild(i)) { + if (leafIndex > leafModules.length - 1) { + val leafModule = createLeafModule() + cells(b - 1).append(leafModule) + leafModules.append(leafModule) + } else { + cells(b - 1).append(leafModules(leafIndex)) + } + leafIndex += 1 + } else if (tensorTree.hasChild(i)) { + if (composerIndex > composers.length - 1) { + val composer = createComposer() + cells(b - 1).append(composer) + composers.append(composer) + } else { + cells(b - 1).append(composers(composerIndex)) + } + composerIndex += 1 + } + } + recursiveForward(b, inputs.select(1, b), tensorTree, tensorTree.getRoot) + for (i <- 1 to cells(b - 1).size) { + output(b)(i).copy(unpackState(cells(b - 1)(i - 1).output.toTable)._2) + } + } + output + } + + def recursiveForward( + batch: Int, + input: Tensor[T], + tree: TensorTree[T], + nodeIndex: Int): Table = { + val out = if (tree.noChild(nodeIndex)) { + cells(batch - 1)(nodeIndex - 1) + .forward(input.select(1, tree.leafIndex(nodeIndex))).toTable + } else { + val leftOut = recursiveForward(batch, input, tree, tree.children(nodeIndex)(0)) + val rightOut = recursiveForward(batch, input, tree, tree.children(nodeIndex)(1)) + val (lc, lh) = unpackState(leftOut) + val (rc, rh) = unpackState(rightOut) + val cell = cells(batch - 1)(nodeIndex - 1) + cell.forward(T(lc, lh, rc, rh)).toTable + } + out + } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + if (!(gradInput.contains(1) || gradInput.contains(2))) { + gradInput = T(Tensor(), Tensor()) + } + + val inputs = input[Tensor[T]](1) + val trees = input[Tensor[T]](2) + + gradInput[Tensor[T]](1).resizeAs(inputs) + gradInput[Tensor[T]](2).resizeAs(trees) + + val batchSize = inputs.size(1) + + for (b <- 1 to batchSize) { + val tensorTree = new TensorTree[T](trees(b)) + recursiveBackward( + b, + inputs(b), + tensorTree, + gradOutput(b), + T(memZero, memZero), + tensorTree.getRoot) + } + gradInput + } + + def recursiveBackward( + batch: Int, + inputs: Tensor[T], + tree: TensorTree[T], + outputGrads: Tensor[T], + gradOutput: Table, + nodeIndex: Int + ): Unit = { + val outputGrad = outputGrads(nodeIndex) + + if (tree.noChild(nodeIndex)) { + gradInput[Tensor[T]](1)(batch)(tree.leafIndex(nodeIndex)) + .copy( + cells(batch - 1)(nodeIndex - 1) + .backward( + inputs.select(1, tree.leafIndex(nodeIndex)), + T(gradOutput(1), gradOutput[Tensor[T]](2) + outputGrad) + ).toTensor) + + } else { + val children = tree.children(nodeIndex) + val (lc, lh) = unpackState(cells(batch - 1)(children(0) - 1).output.toTable) + val (rc, rh) = unpackState(cells(batch - 1)(children(1) - 1).output.toTable) + val composerGrad = cells(batch - 1)(nodeIndex - 1) + .backward(T(lc, lh, rc, rh), T(gradOutput(1), gradOutput[Tensor[T]](2) + outputGrad)) + .toTable + + recursiveBackward( + batch, + inputs, + tree, + outputGrads, + T(composerGrad[Tensor[T]](1), + composerGrad[Tensor[T]](2)), + children(0)) + recursiveBackward( + batch, + inputs, + tree, + outputGrads, + T(composerGrad[Tensor[T]](3), + composerGrad[Tensor[T]](4)), + children(1)) + } + } + + def unpackState(state: Table): (Tensor[T], Tensor[T]) = { + if (state.length() == 0) { + (memZero, memZero) + } else { + (state(1), state(2)) + } + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + val (cp, cg) = composer.parameters() + val (lp, lg) = leafModule.parameters() + (cp ++ lp, cg ++ lg) + } + + override def updateParameters(learningRate: T): Unit = { + composer.updateParameters(learningRate) + leafModule.updateParameters(learningRate) + } + + override def getParametersTable(): Table = { + val pt = T() + val t1 = composer.getParametersTable() + val t2 = leafModule.getParametersTable() + t1.keySet.foreach(key => pt(key) = t1(key)) + t2.keySet.foreach(key => pt(key) = t2(key)) + pt + } + + override def zeroGradParameters(): Unit = { + composer.zeroGradParameters() + leafModule.zeroGradParameters() + } + + override def reset(): Unit = { + composer.reset() + leafModule.reset() + } + + override def hashCode(): Int = { + def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode() + val state = Seq(super.hashCode(), composer, leafModule) + state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[BinaryTreeLSTM[T]] + + override def equals(other: Any): Boolean = other match { + case that: BinaryTreeLSTM[T] => + super.equals(that) && + (that canEqual this) && + composer == that.composer && + leafModule == that.leafModule + case _ => false + } +} + +object BinaryTreeLSTM { + def apply[@specialized(Float, Double) T: ClassTag]( + inputSize: Int, + hiddenSize: Int, + gateOutput: Boolean = true, + withGraph: Boolean = true + )(implicit ev: TensorNumeric[T]): BinaryTreeLSTM[T] = + new BinaryTreeLSTM[T](inputSize, hiddenSize, gateOutput, withGraph) +} + +/** + * [[TensorTree]] class is used to decode a tensor to a tree structure. + * The given input `content` is a tensor which encodes a constituency parse tree. + * The tensor should have the following structure: + * + * Each row of the tensor represents a tree node and the row number is node number + * For each row, except the last column, all other columns represent the children + * node number of this node. Assume the value of a certain column of the row is not zero, + * the value `p` means this node has a child whose node number is `p` (lies in the `p`-th) + * row. Each leaf has a leaf number, in the tensor, the last column represents the leaf number. + * Each leaf does not have any children, so all the columns of a leaf except the last should + * be zero. If a node is the root, the last column should equal to `-1`. + * + * Note: if any row for padding, the padding rows should be placed at the last rows with all + * elements equal to `-1`. + * + * eg. a tensor represents a binary tree: + * + * [11, 10, -1; + * 0, 0, 1; + * 0, 0, 2; + * 0, 0, 3; + * 0, 0, 4; + * 0, 0, 5; + * 0, 0, 6; + * 4, 5, 0; + * 6, 7, 0; + * 8, 9, 0; + * 2, 3, 0; + * -1, -1, -1; + * -1, -1, -1] + * + * @param content the tensor to be encoded + * @param ev implicit tensor numeric + * @tparam T Numeric type [[Float]] or [[Double]] + */ +class TensorTree[T: ClassTag](val content: Tensor[T]) + (implicit ev: TensorNumeric[T]) extends Serializable { + require(content.dim() == 2, "The content of TensorTree should be a two-dimensional tensor") + def size: Array[Int] = content.size() + + def nodeNumber: Int = size(0) + + def children(index: Int): Array[Int] = + content.select(1, index).toBreezeVector().toArray.map(ev.toType[Int]) + + def addChild(parent: Int, child: T): Unit = { + breakable { + for (i <- 1 until size(1)) { + if (content(Array(parent, i)) == ev.zero) { + content.setValue(parent, i, child) + break() + } + } + } + } + + def markAsRoot(index: Int): Unit = { + content.setValue(index, size(1), ev.negative(ev.one)) + } + + def getRoot: Int = { + for (i <- 1 to size(0)) { + if (ev.toType[Int](content(Array(i, size(1)))) == -1) { + return i + } + } + + throw new RuntimeException("There is no root in the tensor tree") + } + + def markAsLeaf(index: Int, leafIndex: Int): Unit = { + content.setValue(index, size(1), ev.fromType(leafIndex)) + } + + def leafIndex(index: Int): Int = { + ev.toType[Int](content(Array(index, size(1)))) + } + + def hasChild(index: Int): Boolean = { + ev.toType[Int](content(Array(index, 1))) > 0 + } + + def noChild(index: Int): Boolean = { + ev.toType[Int](content(Array(index, 1))) == 0 + } + + def exists(index: Int): Boolean = { + index >= 1 && index <= size(0) + } + + def isPadding(index: Int): Boolean = { + ev.toType[Int](content(Array(index, 1))) == -1 + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TreeLSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TreeLSTM.scala new file mode 100644 index 00000000000..fcfdc950d05 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TreeLSTM.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +abstract class TreeLSTM[T: ClassTag]( + val inputSize: Int, + val hiddenSize: Int = 150 +)(implicit ev: TensorNumeric[T]) + extends AbstractModule[Table, Tensor[T], T] { + protected val memZero: Tensor[T] = Tensor[T](hiddenSize).zero() + + def shareParams( + cell: AbstractModule[Activity, Activity, T], + src: AbstractModule[Activity, Activity, T]): Unit = { + var i = 0 + val cellParams = cell.parameters() + val srcParams = src.parameters() + while (i < cellParams._1.length) { + cellParams._1(i).set(srcParams._1(i)) + i += 1 + } + i = 0 + while (i < cellParams._2.length) { + cellParams._2(i).set(srcParams._2(i)) + i += 1 + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index c8d632dd270..c8315d64ca4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.optim import java.nio.file.{Files, Paths} import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{DataSet, _} +import com.intel.analytics.bigdl.dataset.{DataSet, SampleToMiniBatch, _} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ @@ -104,6 +104,35 @@ abstract class Optimizer[T: ClassTag, D]( this } + /** + * Set a validate evaluation + * + * @param trigger how often to evaluation validation set + * @param sampleRDD validate data set in type of [[RDD]] of [[Sample]] + * @param vMethods a set of validation method [[ValidationMethod]] + * @param batchSize batch size + * @param featurePaddingParam feature padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * @param labelPaddingParam label padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * + * @return this optimizer + */ + def setValidation(trigger: Trigger, sampleRDD: RDD[Sample[T]], + vMethods : Array[ValidationMethod[T]], batchSize: Int, + featurePaddingParam: PaddingParam[T], + labelPaddingParam: PaddingParam[T] + ): this.type = { + this.validationTrigger = Some(trigger) + val dataSet = + (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(batchSize, Some(featurePaddingParam), Some(labelPaddingParam))) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + this.validationDataSet = Some(dataSet) + this.validationMethods = Some(vMethods) + this + } + /** * Set a validate evaluation * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala index 3ec8b007f16..ee94518e3da 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala @@ -111,6 +111,59 @@ class AccuracyResult(private var correct: Int, private var count: Int) } } +/** + * This is a metric to measure the accuracy of Tree Neural Network/Recursive Neural Network + * + */ +class TreeNNAccuracy[T: ClassTag]()( + implicit ev: TensorNumeric[T]) + extends ValidationMethod[T] { + override def apply(output: Activity, target: Activity): + ValidationResult = { + var correct = 0 + var count = 0 + + var _output = output.asInstanceOf[Tensor[T]] + val _target = target.asInstanceOf[Tensor[T]].select(2, 1) + + if (_output.dim() == 3) { + _output = _output.select(2, 1) + (if (_output.size(2) == 1) { + _output.apply1(x => if (ev.isGreater(ev.fromType(0.5), x)) ev.zero else ev.one) + } else { + _output.max(2)._2.squeeze() + }).map(_target, (a, b) => { + if (a == b) { + correct += 1 + } + a + }) + count += _output.size(1) + } else if (_output.dim == 2) { + _output = _output.select(1, 1) + require(_target.size(1) == 1) + (if (_output.size(1) == 1) { + _output.apply1(x => if (ev.isGreater(ev.fromType(0.5), x)) ev.zero else ev.one) + } else { + _output.max(1)._2.squeeze() + }).map(_target, (a, b) => { + if (a == b) { + correct += 1 + } + a + }) + count += 1 + } else { + throw new IllegalArgumentException + } + + new AccuracyResult(correct, count) + } + + override def format(): String = + s"TreeNNAccuracy()" +} + /** * Caculate the percentage that output's max probability index equals target */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 234047c100a..dd208372de8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -888,6 +888,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ) } + def createBinaryTreeLSTM( + inputSize: Int, + hiddenSize: Int, + gateOutput: Boolean = true, + withGraph: Boolean = true) + : BinaryTreeLSTM[T] = { + BinaryTreeLSTM[T]( + inputSize, + hiddenSize, + gateOutput, + withGraph) + } + def createSpatialFullConvolution(nInputPlane: Int, nOutputPlane: Int, kW: Int, @@ -1443,6 +1456,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new Top1Accuracy() } + def createTreeNNAccuracy(): ValidationMethod[T] = { + new TreeNNAccuracy() + } + def createTop5Accuracy(): ValidationMethod[T] = { new Top5Accuracy() } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index 64d95b9f08c..d7861a6ec28 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -17,10 +17,60 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Parallel class ValidationSpec extends FlatSpec with Matchers { + "treeNN accuracy" should "be correct on 2d tensor" in { + val output = Tensor[Double]( + T( + T(0.0, 0.0, 0.1, 0.0), + T(3.0, 7.0, 0.0, 1.0), + T(0.0, 1.0, 0.0, 0.0))) + + val target = Tensor[Double]( + T(3.0)) + + val validation = new TreeNNAccuracy[Double]() + val result = validation(output, target) + val test = new AccuracyResult(1, 1) + result should be(test) + } + + "treeNN accuracy" should "be correct on 3d tensor" in { + val output = Tensor[Double]( + T( + T( + T(0.0, 0.0, 0.1, 0.0), + T(3.0, 7.0, 0.0, 1.0), + T(0.0, 1.0, 0.0, 0.0)), + T( + T(0.0, 0.1, 0.0, 0.0), + T(3.0, 7.0, 0.0, 1.0), + T(0.0, 1.0, 0.0, 0.0)), + T( + T(0.0, 0.0, 0.0, 0.1), + T(3.0, 7.0, 0.0, 1.0), + T(0.0, 1.0, 0.0, 0.0)), + T( + T(0.0, 0.0, 0.0, 1.0), + T(3.0, 0.0, 8.0, 1.0), + T(0.0, 1.0, 0.0, 0.0)))) + + val target = Tensor[Double]( + T( + T(3.0, 0.0, 0.1, 1.0), + T(2.0, 0.0, 0.1, 1.0), + T(3.0, 7.0, 0.0, 1.0), + T(4.0, 1.0, 0.0, 0.0))) + + val validation = new TreeNNAccuracy[Double]() + val result = validation(output, target) + val test = new AccuracyResult(3, 4) + result should be(test) + } + "top1 accuracy" should "be correct on 2d tensor" in { val output = Tensor(Storage(Array[Double]( 0, 0, 0, 1, From 64c2337ec648b188f0e03564c85b97fb76f3178b Mon Sep 17 00:00:00 2001 From: qiuxin2012 Date: Wed, 12 Jul 2017 14:09:21 +0800 Subject: [PATCH 0274/1065] fix integration test --- .../intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 1f03beec9d3..0a047b025b3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -108,11 +108,11 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, logger.info("load caffe model done") builder.build() } finally { - if (modelFs != null) modelFs.close() - if (prototxtFs != null) prototxtFs.close() if (null != prototxtReader) prototxtReader.close() if (null != modelStream) modelStream.close() if (null != prototxtStream) prototxtStream.close() + if (modelFs != null) modelFs.close() + if (prototxtFs != null) prototxtFs.close() } } From f6d17371a37954cf8b2e14c2463dae2eef32d9f6 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Thu, 13 Jul 2017 12:18:09 +0800 Subject: [PATCH 0275/1065] fix inception and xavier (#1220) --- .../dllib/models/inception/Inception_v1.scala | 30 +++++++------- .../bigdl/dllib/nn/InitializationMethod.scala | 8 ++-- .../analytics/bigdl/dllib/nn/LinearSpec.scala | 20 ++++++++- .../dllib/nn/SpatialConvolutionSpec.scala | 41 +++++++++++++++++++ 4 files changed, 77 insertions(+), 22 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala index 82f522d2182..74afd19ad98 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala @@ -27,34 +27,34 @@ object Inception_Layer_v1 { val conv1 = Sequential() conv1.add(SpatialConvolution(inputSize, config[Table](1)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "1x1")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "1x1")) conv1.add(ReLU(true).setName(namePrefix + "relu_1x1")) concat.add(conv1) val conv3 = Sequential() conv3.add(SpatialConvolution(inputSize, config[Table](2)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "3x3_reduce")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "3x3_reduce")) conv3.add(ReLU(true).setName(namePrefix + "relu_3x3_reduce")) conv3.add(SpatialConvolution(config[Table](2)(1), config[Table](2)(2), 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "3x3")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "3x3")) conv3.add(ReLU(true).setName(namePrefix + "relu_3x3")) concat.add(conv3) val conv5 = Sequential() conv5.add(SpatialConvolution(inputSize, config[Table](3)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "5x5_reduce")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "5x5_reduce")) conv5.add(ReLU(true).setName(namePrefix + "relu_5x5_reduce")) conv5.add(SpatialConvolution(config[Table](3)(1), config[Table](3)(2), 5, 5, 1, 1, 2, 2) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "5x5")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "5x5")) conv5.add(ReLU(true).setName(namePrefix + "relu_5x5")) concat.add(conv5) val pool = Sequential() pool.add(SpatialMaxPooling(3, 3, 1, 1, 1, 1).ceil().setName(namePrefix + "pool")) pool.add(SpatialConvolution(inputSize, config[Table](4)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "pool_proj")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "pool_proj")) pool.add(ReLU(true).setName(namePrefix + "relu_pool_proj")) concat.add(pool).setName(namePrefix + "output") concat @@ -65,16 +65,16 @@ object Inception_v1_NoAuxClassifier { def apply(classNum: Int): Module[Float] = { val model = Sequential() model.add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) - .setInitMethod(weightInitMethod = Xavier) + .setInitMethod(weightInitMethod = Xavier, Zeros) .setName("conv1/7x7_s2")) model.add(ReLU(true).setName("conv1/relu_7x7")) model.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2")) model.add(SpatialCrossMapLRN(5, 0.0001, 0.75).setName("pool1/norm1")) - model.add(SpatialConvolution(64, 64, 1, 1, 1, 1).setInitMethod(weightInitMethod = Xavier) + model.add(SpatialConvolution(64, 64, 1, 1, 1, 1).setInitMethod(weightInitMethod = Xavier, Zeros) .setName("conv2/3x3_reduce")) model.add(ReLU(true).setName("conv2/relu_3x3_reduce")) model.add(SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName("conv2/3x3")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName("conv2/3x3")) model.add(ReLU(true).setName("conv2/relu_3x3")) model.add(SpatialCrossMapLRN(5, 0.0001, 0.75). setName("conv2/norm2")) model.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool2/3x3_s2")) @@ -93,9 +93,8 @@ object Inception_v1_NoAuxClassifier { model.add(Dropout(0.4).setName("pool5/drop_7x7_s1")) model.add(View(1024).setNumInputDims(3)) model.add(Linear(1024, classNum) - .setInitMethod(weightInitMethod = Xavier).setName("loss3/classifier")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName("loss3/classifier")) model.add(LogSoftMax().setName("loss3/loss3")) - model.reset() model } } @@ -104,17 +103,17 @@ object Inception_v1 { def apply(classNum: Int): Module[Float] = { val feature1 = Sequential() feature1.add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) - .setInitMethod(weightInitMethod = Xavier) + .setInitMethod(weightInitMethod = Xavier, Zeros) .setName("conv1/7x7_s2")) feature1.add(ReLU(true).setName("conv1/relu_7x7")) feature1.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2")) feature1.add(SpatialCrossMapLRN(5, 0.0001, 0.75).setName("pool1/norm1")) feature1.add(SpatialConvolution(64, 64, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier) + .setInitMethod(weightInitMethod = Xavier, Zeros) .setName("conv2/3x3_reduce")) feature1.add(ReLU(true).setName("conv2/relu_3x3_reduce")) feature1.add(SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier) + .setInitMethod(weightInitMethod = Xavier, Zeros) .setName("conv2/3x3")) feature1.add(ReLU(true).setName("conv2/relu_3x3")) feature1.add(SpatialCrossMapLRN(5, 0.0001, 0.75). setName("conv2/norm2")) @@ -163,7 +162,7 @@ object Inception_v1 { output3.add(Dropout(0.4).setName("pool5/drop_7x7_s1")) output3.add(View(1024).setNumInputDims(3)) output3.add(Linear(1024, classNum) - .setInitMethod(weightInitMethod = Xavier).setName("loss3/classifier")) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName("loss3/classifier")) output3.add(LogSoftMax().setName("loss3/loss3")) val split2 = Concat(2).setName("split2") @@ -183,7 +182,6 @@ object Inception_v1 { model.add(feature1) model.add(split1) - model.reset() model } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala index f9362252cfe..342190dc4c7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala @@ -95,24 +95,24 @@ object VariableFormat { case object GP_OUT_IN_KW_KH extends VariableFormat { override def getFanIn(shape: Array[Int]): Int = { - val receptiveFieldSize = shape(0) * shape(2) * shape(3) + val receptiveFieldSize = shape(0) * shape(3) * shape(4) shape(2) * receptiveFieldSize } override def getFanOut(shape: Array[Int]): Int = { - val receptiveFieldSize = shape(0) * shape(2) * shape(3) + val receptiveFieldSize = shape(0) * shape(3) * shape(4) shape(1) * receptiveFieldSize } } case object GP_IN_OUT_KW_KH extends VariableFormat { override def getFanIn(shape: Array[Int]): Int = { - val receptiveFieldSize = shape(0) * shape(2) * shape(3) + val receptiveFieldSize = shape(0) * shape(3) * shape(4) shape(1) * receptiveFieldSize } override def getFanOut(shape: Array[Int]): Int = { - val receptiveFieldSize = shape(0) * shape(2) * shape(3) + val receptiveFieldSize = shape(0) * shape(3) * shape(4) shape(2) * receptiveFieldSize } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala index c2ef0eaa6f9..cb620459cd4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala @@ -17,13 +17,13 @@ package com.intel.analytics.bigdl.nn import org.scalatest.{FlatSpec, Matchers} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl._ import scala.math._ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.optim.{L1Regularizer, L2Regularizer, SGD} -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{RandomGenerator, T} @com.intel.analytics.bigdl.tags.Parallel class LinearSpec extends FlatSpec with Matchers { @@ -387,4 +387,20 @@ class LinearSpec extends FlatSpec with Matchers { linear2.gradWeight should be(linear.gradWeight.mul(0.5)) linear2.gradBias should be(linear.gradBias.mul(2)) } + + "Xavier" should "init right in SpatialConvolution" in { + RandomGenerator.RNG.setSeed(1) + val linear = Linear[Float](3, 5) + .setInitMethod(Xavier, Zeros) + val exceptedWeight = Tensor[Float](Storage(Array( + -0.1399592, -0.32341975, 0.32080957, + 0.042518664, -0.5119037, -0.097942464, + 0.6549186, -0.468386, -0.8185887, + 0.059606634, 0.29525837, 0.7170032, + -0.14323229, -0.07412344, 0.10165376 + ).map(_.toFloat))).resize(5, 3) + val exceptedBias = Tensor[Float](T(0f, 0f, 0f, 0f, 0f)) + linear.weight should be (exceptedWeight) + linear.bias should be (exceptedBias) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala index ff038ca0a3e..db16985d3af 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala @@ -2822,4 +2822,45 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { output(Array(1, 2, 1)) should be(91) output(Array(1, 2, 2)) should be(105) } + + "Xavier" should "init right in SpatialConvolution" in { + RNG.setSeed(1) + val conv = SpatialConvolution[Float](2, 4, 3, 3, 2, 2, 3, 3, 1, false) + .setInitMethod(Xavier, Zeros) + val exceptedWeight = Tensor[Float](Storage(Array( + -0.32114115, -0.31055245, 0.16676287, + 0.082686655, 0.32590738, 0.10709048, + 0.16544376, -0.13433647, -0.14637068, + + -0.035910334, 0.19285288, -0.1852503, + -0.264516, -0.2844239, -0.03473765, + -0.02050765, 0.272397, -0.2692185, + + -0.13759057, 0.26891345, -0.1414831, + -0.25367302, -0.24664763, 0.016532922, + -0.32042202, -0.27758467, 0.119223684, + + 0.27790755, -0.19224793, 0.27363226, + -0.15630223, -0.1340466, -0.0056178933, + 0.056259416, -0.2977583, 0.043941353, + + 0.049411736, 0.07595888, -0.23551428, + 0.3043571, 0.059537023, -0.15934734, + 0.13317224, -0.17932305, -0.26511037, + + 0.022298995, -0.057296008, 0.29995877, + 0.12960011, -0.0046269377, -0.057213824, + 0.027067006, -0.30003104, 0.17699008, + + 0.023930939, -0.30310285, 0.10919643, + -0.24002258, 0.009926071, 0.19493572, + 0.2963965, -0.31346577, 0.05770336, + + 0.255417, 0.2689346, 0.027192127, + -0.24168353, -0.03467988, -0.24048243, + 0.26142392, 0.20492753, -0.081610434).map(_.toFloat))).resize(1, 4, 2, 3, 3) + val exceptedBias = Tensor[Float](T(0f, 0f, 0f, 0f)) + conv.weight should be (exceptedWeight) + conv.bias should be (exceptedBias) + } } From 4c0945e396f65a5c2a8a6912ed7173a38d665fa1 Mon Sep 17 00:00:00 2001 From: Yan Wan Date: Thu, 13 Jul 2017 15:17:57 +0800 Subject: [PATCH 0276/1065] BatchNormalization accepts default values (#1216) * BatchNormalization accepts default values * runningMean defined during run time * remove multiple constructor in BatchNormalization class --- .../bigdl/dllib/nn/BatchNormalization.scala | 33 ++++++++++++------- .../dllib/nn/BatchNormalizationSpec.scala | 32 +++++++++++++++++- 2 files changed, 53 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index e546370cdfc..de8b0d0b20f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -61,10 +61,10 @@ class BatchNormalization[@specialized(Float, Double) T: ClassTag]( require(nOutput > 0) val nDim = 2 - val runningMean = Tensor[T](nOutput) - val runningVar = Tensor[T](nOutput).fill(ev.fromType[Int](1)) - val saveMean = Tensor[T](nOutput) - val saveStd = Tensor[T](nOutput).fill(ev.fromType[Int](1)) + val runningMean = if (affine) Tensor[T](nOutput) else Tensor[T]() + val runningVar = if (affine) Tensor[T](nOutput).fill(ev.fromType[Int](1)) else Tensor[T]() + val saveMean = if (affine) Tensor[T](nOutput) else Tensor[T]() + val saveStd = if (affine) Tensor[T](nOutput).fill(ev.fromType[Int](1)) else Tensor[T]() val weight: Tensor[T] = if (initWeight != null) initWeight else if (affine) Tensor[T](nOutput) else null @@ -99,8 +99,6 @@ class BatchNormalization[@specialized(Float, Double) T: ClassTag]( biasInitMethod.init(bias, VariableFormat.ONE_D) } - runningMean.zero() - runningVar.fill(ev.fromType[Int](1)) zeroGradParameters() } @@ -115,9 +113,6 @@ class BatchNormalization[@specialized(Float, Double) T: ClassTag]( private def checkInputDim(input: Tensor[T]): Unit = { require(input.dim() == nDim || (input.dim() == nDim - 1 && train == false), s"only mini-batch supported (${nDim}D tensor), got ${input.dim()}D tensor instead") - val featDim = if (input.dim() == nDim - 1) 1 else 2 - require(input.size(featDim) == runningMean.nElement(), - s"got ${input.size(featDim)}-feature tensor, expected ${runningMean.nElement()}") } @inline @@ -129,15 +124,27 @@ class BatchNormalization[@specialized(Float, Double) T: ClassTag]( } } + @inline + private def initializeBuffer(nOutput: Int): Unit = { + runningMean.resize(nOutput).zero + runningVar.resize(nOutput).fill(ev.fromType[Int](1)) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { checkInputDim(input) output.resizeAs(input) - saveMean.resizeAs(runningMean) - saveStd.resizeAs(runningVar) val _input = makeBatch(input) val nInput = _input.size(2) + + if (runningMean.nElement == 0 || runningMean.nElement < nInput) { + initializeBuffer(nInput) + } + + saveMean.resizeAs(runningMean).zero + saveStd.resizeAs(runningVar).fill(ev.fromType[Int](1)) + if (results == null || results.length > nInput) { results = new Array[Future[_]](nInput) } @@ -733,4 +740,8 @@ object BatchNormalization { new BatchNormalization[T]( nOutput, eps, momentum, affine, initWeight, initBias, initGradWeight, initGradBias) } + def apply[@specialized(Float, Double) T: ClassTag]( + affine: Option[Int])(implicit ev: TensorNumeric[T]): BatchNormalization[T] = { + new BatchNormalization[T](nOutput = affine.getOrElse(1), affine = affine.isDefined) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala index bef7d020c3f..3b73e7599f7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala @@ -16,13 +16,43 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Parallel class BatchNormalizationSpec extends FlatSpec with Matchers { + "A BatchNormalization" should "generate correct output using default arguments" in { + val bn = BatchNormalization[Double](None) + val input = Tensor[Double](3, 3) + + var i = 0 + input.apply1(e => { + i += 1; i + }) + val output = bn.forward(input) + + val mean = Tensor[Double](Storage[Double](Array(4.0, 5.0, 6.0))) + val std = Tensor(Storage(Array(0.4082479, 0.4082479, 0.4082479))) + val output1 = Tensor[Double](3, 3) + for (i <- 1 to 3) { + for (j <- 1 to 3) { + output1.setValue(i, j, (input(Array(i, j)) - mean(Array(j))) * std(Array(j))) + } + } + + output.nDimension() should be(2) + output.size(1) should be(3) + output.size(2) should be(3) + + output.map(output1, (a, b) => { + a should be (b +- 0.0001) + a + }) + } + "A BatchNormalization" should "generate correct output" in { + val bn = new BatchNormalization[Double](3) bn.weight(1) = 0.1 bn.weight(2) = 0.2 From 69d40ef54a67189bdd6affc1e6903491407d127b Mon Sep 17 00:00:00 2001 From: ding Date: Thu, 13 Jul 2017 11:05:49 -0400 Subject: [PATCH 0277/1065] convert back convlstm optimization --- .../bigdl/dllib/nn/ConvLSTMPeephole.scala | 40 +++-- .../bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala | 170 +++++++++--------- 2 files changed, 113 insertions(+), 97 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala index ca788a373c3..621db2fa6a8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala @@ -62,17 +62,23 @@ class ConvLSTMPeephole[T : ClassTag] ( var outputGate: Sequential[T] = _ var hiddenLayer: Sequential[T] = _ var cellLayer: Sequential[T] = _ - val joinDim = 2 +// val joinDim = 2 override var cell: AbstractModule[Activity, Activity, T] = buildConvLSTM() - override def preTopology: AbstractModule[Activity, Activity, T] = - Sequential() - .add(TimeDistributed(SpatialConvolution(inputSize, outputSize*4, kernelI, kernelI, - stride, stride, kernelI/2, kernelI/2, wRegularizer = wRegularizer, - bRegularizer = bRegularizer))) - - def buildGate(offset: Int, length: Int): Sequential[T] = { - val i2g = Narrow(joinDim, offset, length) +// override def preTopology: AbstractModule[Activity, Activity, T] = +// Sequential() +// .add(TimeDistributed(SpatialConvolution(inputSize, outputSize*4, kernelI, kernelI, +// stride, stride, kernelI/2, kernelI/2, wRegularizer = wRegularizer, +// bRegularizer = bRegularizer))) + +// def buildGate(offset: Int, length: Int): Sequential[T] = { +// val i2g = Narrow(joinDim, offset, length) + def buildGate(): Sequential[T] = { + val i2g = Sequential() + .add(Contiguous()) + .add(SpatialConvolution(inputSize, outputSize, kernelI, kernelI, + stride, stride, kernelI/2, kernelI/2, wRegularizer = wRegularizer, + bRegularizer = bRegularizer)) val h2g = SpatialConvolution(outputSize, outputSize, kernelC, kernelC, stride, stride, kernelC/2, kernelC/2, withBias = false, wRegularizer = uRegularizer) @@ -97,17 +103,20 @@ class ConvLSTMPeephole[T : ClassTag] ( } def buildInputGate(): Sequential[T] = { - inputGate = buildGate(1 + outputSize, outputSize) +// inputGate = buildGate(1 + outputSize, outputSize) + inputGate = buildGate() inputGate } def buildForgetGate(): Sequential[T] = { - forgetGate = buildGate(1, outputSize) +// forgetGate = buildGate(1, outputSize) + forgetGate = buildGate() forgetGate } def buildOutputGate(): Sequential[T] = { - outputGate = buildGate(1 + 3 * outputSize, outputSize) +// outputGate = buildGate(1 + 3 * outputSize, outputSize) + outputGate = buildGate() outputGate } @@ -115,7 +124,12 @@ class ConvLSTMPeephole[T : ClassTag] ( val hidden = Sequential() .add(NarrowTable(1, 2)) - val i2h = Narrow(joinDim, 1 + 2 * outputSize, outputSize) +// val i2h = Narrow(joinDim, 1 + 2 * outputSize, outputSize) + val i2h = Sequential() + .add(Contiguous()) + .add(SpatialConvolution(inputSize, outputSize, kernelI, kernelI, + stride, stride, kernelI/2, kernelI/2, wRegularizer = wRegularizer, + bRegularizer = bRegularizer)) val h2h = SpatialConvolution(outputSize, outputSize, kernelC, kernelC, stride, stride, kernelC/2, kernelC/2, withBias = false, wRegularizer = uRegularizer) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala index fd89417db3d..ab8b33bc3de 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala @@ -329,54 +329,55 @@ class ConvLSTMPeepholeSpec extends FlatSpec with BeforeAndAfter with Matchers { -0.06827257, 0.115748845, 0.14643653, -0.13591826 ) val weights = model.getParameters()._1 - - val weightsOri = new ArrayBuffer[Tensor[Double]]() - val weightsNew = new ArrayBuffer[Tensor[Double]]() - - val sizeI = hiddenSize * inputSize * 3 * 3 - val sizeH = hiddenSize * hiddenSize * 3 * 3 - var next = 0 - for(i <- 0 until 4) { - val i2g = Tensor[Double](weightData.slice(next, next + sizeI), - Array(1, hiddenSize, inputSize, 3, 3)) - weightsOri += Tensor[Double]().resizeAs(i2g).copy(i2g) - next += sizeI - val i2gBias = Tensor[Double](weightData.slice(next, next + hiddenSize), - Array(1, hiddenSize)) - weightsOri += Tensor[Double]().resizeAs(i2gBias).copy(i2gBias) - next += hiddenSize - val h2g = Tensor[Double](weightData.slice(next, next + sizeH), - Array(1, hiddenSize, hiddenSize, 3, 3)) - weightsOri += Tensor[Double]().resizeAs(h2g).copy(h2g) - next += sizeH - } - - // weightsOri(0) -----> forgetGatei2g.weight - // weightsOri(3) -----> inputGatei2g.weight - // weightsOri(6) -----> hiddeni2g.weight - // weightsOri(9) -----> outputGatei2g.weight - val weightsTable = T(weightsOri(0), weightsOri(3), weightsOri(6), weightsOri(9)) - val joinWeights = JoinTable[Double](2, 5) - weightsNew += joinWeights.forward(weightsTable) - - // weightsOri(1) -----> forgetGatei2g.bias - // weightsOri(4) -----> inputGatei2g.bias - // weightsOri(7) -----> hiddeni2g.bias - // weightsOri(10) -----> outputGatei2g.bias - val biasTable = T(weightsOri(1), weightsOri(4), weightsOri(7), weightsOri(10)) - val joinBias = JoinTable[Double](1, 1) - weightsNew += joinBias.forward(biasTable) - - // weightsOri(2) -----> forgetGateh2g - // weightsOri(5) -----> inputGateh2g - // weightsOri(8) -----> hiddenh2h - // weightsOri(11) -----> outputGateh2g - weightsNew += weightsOri(2) - weightsNew += weightsOri(5) - weightsNew += weightsOri(8) - weightsNew += weightsOri(11) - - weights.copy(Module.flatten[Double](weightsNew.toArray)) + weights.copy(Tensor[Double](weightData, Array(weightData.size, 1))) + +// val weightsOri = new ArrayBuffer[Tensor[Double]]() +// val weightsNew = new ArrayBuffer[Tensor[Double]]() +// +// val sizeI = hiddenSize * inputSize * 3 * 3 +// val sizeH = hiddenSize * hiddenSize * 3 * 3 +// var next = 0 +// for(i <- 0 until 4) { +// val i2g = Tensor[Double](weightData.slice(next, next + sizeI), +// Array(1, hiddenSize, inputSize, 3, 3)) +// weightsOri += Tensor[Double]().resizeAs(i2g).copy(i2g) +// next += sizeI +// val i2gBias = Tensor[Double](weightData.slice(next, next + hiddenSize), +// Array(1, hiddenSize)) +// weightsOri += Tensor[Double]().resizeAs(i2gBias).copy(i2gBias) +// next += hiddenSize +// val h2g = Tensor[Double](weightData.slice(next, next + sizeH), +// Array(1, hiddenSize, hiddenSize, 3, 3)) +// weightsOri += Tensor[Double]().resizeAs(h2g).copy(h2g) +// next += sizeH +// } +// +// // weightsOri(0) -----> forgetGatei2g.weight +// // weightsOri(3) -----> inputGatei2g.weight +// // weightsOri(6) -----> hiddeni2g.weight +// // weightsOri(9) -----> outputGatei2g.weight +// val weightsTable = T(weightsOri(0), weightsOri(3), weightsOri(6), weightsOri(9)) +// val joinWeights = JoinTable[Double](2, 5) +// weightsNew += joinWeights.forward(weightsTable) +// +// // weightsOri(1) -----> forgetGatei2g.bias +// // weightsOri(4) -----> inputGatei2g.bias +// // weightsOri(7) -----> hiddeni2g.bias +// // weightsOri(10) -----> outputGatei2g.bias +// val biasTable = T(weightsOri(1), weightsOri(4), weightsOri(7), weightsOri(10)) +// val joinBias = JoinTable[Double](1, 1) +// weightsNew += joinBias.forward(biasTable) +// +// // weightsOri(2) -----> forgetGateh2g +// // weightsOri(5) -----> inputGateh2g +// // weightsOri(8) -----> hiddenh2h +// // weightsOri(11) -----> outputGateh2g +// weightsNew += weightsOri(2) +// weightsNew += weightsOri(5) +// weightsNew += weightsOri(8) +// weightsNew += weightsOri(11) +// +// weights.copy(Module.flatten[Double](weightsNew.toArray)) val output = model.forward(input) val gradInput = model.backward(input, output).asInstanceOf[Tensor[Double]] @@ -630,42 +631,43 @@ class ConvLSTMPeepholeSpec extends FlatSpec with BeforeAndAfter with Matchers { -0.11207754, 0.042513624, -0.05665606, -0.015827265, 0.12174054 ) val weights = model.getParameters()._1 - - val weightsOri = new ArrayBuffer[Tensor[Double]]() - val weightsNew = new ArrayBuffer[Tensor[Double]]() - - val sizeI = hiddenSize * inputSize * 3 * 3 - val sizeH = hiddenSize * hiddenSize * 3 * 3 - var next = 0 - for(i <- 0 until 4) { - val i2g = Tensor[Double](weightData.slice(next, next + sizeI), - Array(1, hiddenSize, inputSize, 3, 3)) - weightsOri += Tensor[Double]().resizeAs(i2g).copy(i2g) - next += sizeI - val i2gBias = Tensor[Double](weightData.slice(next, next + hiddenSize), - Array(1, hiddenSize)) - weightsOri += Tensor[Double]().resizeAs(i2gBias).copy(i2gBias) - next += hiddenSize - val h2g = Tensor[Double](weightData.slice(next, next + sizeH), - Array(1, hiddenSize, hiddenSize, 3, 3)) - weightsOri += Tensor[Double]().resizeAs(h2g).copy(h2g) - next += sizeH - } - - val weightsTable = T(weightsOri(0), weightsOri(3), weightsOri(6), weightsOri(9)) - val joinWeights = JoinTable[Double](2, 5) - weightsNew += joinWeights.forward(weightsTable) - - val biasTable = T(weightsOri(1), weightsOri(4), weightsOri(7), weightsOri(10)) - val joinBias = JoinTable[Double](1, 1) - weightsNew += joinBias.forward(biasTable) - - weightsNew += weightsOri(2) - weightsNew += weightsOri(5) - weightsNew += weightsOri(8) - weightsNew += weightsOri(11) - - weights.copy(Module.flatten[Double](weightsNew.toArray)) + weights.copy(Tensor[Double](weightData, Array(weightData.size, 1))) + +// val weightsOri = new ArrayBuffer[Tensor[Double]]() +// val weightsNew = new ArrayBuffer[Tensor[Double]]() +// +// val sizeI = hiddenSize * inputSize * 3 * 3 +// val sizeH = hiddenSize * hiddenSize * 3 * 3 +// var next = 0 +// for(i <- 0 until 4) { +// val i2g = Tensor[Double](weightData.slice(next, next + sizeI), +// Array(1, hiddenSize, inputSize, 3, 3)) +// weightsOri += Tensor[Double]().resizeAs(i2g).copy(i2g) +// next += sizeI +// val i2gBias = Tensor[Double](weightData.slice(next, next + hiddenSize), +// Array(1, hiddenSize)) +// weightsOri += Tensor[Double]().resizeAs(i2gBias).copy(i2gBias) +// next += hiddenSize +// val h2g = Tensor[Double](weightData.slice(next, next + sizeH), +// Array(1, hiddenSize, hiddenSize, 3, 3)) +// weightsOri += Tensor[Double]().resizeAs(h2g).copy(h2g) +// next += sizeH +// } +// +// val weightsTable = T(weightsOri(0), weightsOri(3), weightsOri(6), weightsOri(9)) +// val joinWeights = JoinTable[Double](2, 5) +// weightsNew += joinWeights.forward(weightsTable) +// +// val biasTable = T(weightsOri(1), weightsOri(4), weightsOri(7), weightsOri(10)) +// val joinBias = JoinTable[Double](1, 1) +// weightsNew += joinBias.forward(biasTable) +// +// weightsNew += weightsOri(2) +// weightsNew += weightsOri(5) +// weightsNew += weightsOri(8) +// weightsNew += weightsOri(11) +// +// weights.copy(Module.flatten[Double](weightsNew.toArray)) val output = model.forward(input) val gradInput = model.backward(input, output).asInstanceOf[Tensor[Double]] From f89b09938b250cd54fffa29066980490df4ab49b Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 13 Jul 2017 23:29:36 -0700 Subject: [PATCH 0278/1065] fix concat when the dimension concatenated along is larger than 2 (#1230) * fix concat when dim >= 3 * fix backward * fix backward * changing back to tensor copy * meet code review --- .../analytics/bigdl/dllib/nn/Concat.scala | 5 +++-- .../analytics/bigdl/dllib/nn/ConcatSpec.scala | 22 +++++++++++++++++++ 2 files changed, 25 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala index e1d49958682..891cee825f0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala @@ -79,8 +79,9 @@ class Concat[T: ClassTag](val dimension: Int)( results(i) = Engine.model.invoke(() => { val target = this.output.narrow(this.dimension, _offset, currentOutput.size(this.dimension)) - if (target.isContiguous()) { - // Copy directly when target is Contiguous + if (target.isContiguous() || this.dimension > 2) { + // Copy directly when target is Contiguous or dimension is larger than 2 + // in which case the contiguous region in target tensor is fairly small in practice target.copy(currentOutput) } else { // Divide target into contiguous frames when target isn't contiguous diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala index c6da9abb0f3..1837b76b789 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala @@ -51,4 +51,26 @@ class ConcatSpec extends FlatSpec with Matchers { gradInput should be (Tensor(Storage(Array[Float](5, 7, 9)))) } + "Concat forward/backward 4D input/output" should "return good result" in { + val model = Concat[Float](3) + model.add(Identity[Float]()) + model.add(AddConstant[Float](1)) + val input = Tensor[Float](2, 2, 2, 2).apply1(_ => 1) + var i = 0 + val gradOutput = Tensor[Float](2, 2, 4, 2).apply1 { _ => + val result = if (i % 8 < 4) 2f else 3f + i = i + 1 + result + } + val output = model.forward(input) + val expectedOutput = Tensor[Float](2, 2, 4, 2).apply1 { _ => + val result = if (i % 8 < 4) 1f else 2f + i = i + 1 + result + } + val gradInput = model.backward(input, gradOutput) + val expectedGradInput = Tensor[Float](2, 2, 2, 2).apply1(_ => 5f) + output should be (expectedOutput) + gradInput should be (expectedGradInput) + } } From b3a4c75c8b644bca979c57c83c70111f46939c34 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 17 Jul 2017 16:38:41 +0800 Subject: [PATCH 0279/1065] fix Sample's hashcode and equals (#1250) --- .../bigdl/dllib/feature/dataset/Sample.scala | 16 ++--- .../bigdl/dllib/dataset/SampleSpec.scala | 72 +++++++++++++++++++ 2 files changed, 80 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index 25daeed746a..c6042fbfda1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -130,7 +130,8 @@ private[bigdl] class ArraySample[T: ClassTag]( private val data: Array[T], private val featureSize: Array[Array[Int]], private val labelSize: Array[Array[Int]]) extends Sample[T] { - require(featureSize != null, "Feature couldn't be empty") + require(data != null, "Sample: Data couldn't be empty") + require(featureSize != null, "Sample: Feature couldn't be empty") override def getData(): Array[T] = data @@ -204,21 +205,20 @@ private[bigdl] class ArraySample[T: ClassTag]( override def equals(other: Any): Boolean = other match { case that: ArraySample[T] => if (!(that canEqual this) || - !(labelSize.deep == that.labelSize.deep) || + !(data.deep == that.data.deep) || !(featureSize.deep == that.featureSize.deep)) { return false } - var i = labelSize.map(_.product).sum + featureSize.map(_.product).sum - 1 - while (i >= 0) { - if (data(i) != that.data(i)) return false - i -= 1 + if (null != labelSize && null != that.labelSize) { + labelSize.deep == that.labelSize.deep + } else { + null == labelSize & null == that.labelSize } - true case _ => false } override def hashCode(): Int = { - val state = Seq(data, featureSize, labelSize) + val state = if (null == labelSize) Seq(data, featureSize) else Seq(data, featureSize, labelSize) state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala index b3d0163b8bf..7d99bf53e32 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala @@ -61,6 +61,78 @@ class SampleSpec extends FlatSpec with Matchers { Some(featureParam), Some(labelParam)).set(samples) } + "Hashcode" should "work fine" in { + val sample1 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(1)) + println(sample1.hashCode()) + + val sample2 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + println(sample2.hashCode()) + + val sample3 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1)) + println(sample3.hashCode()) + + val sample4 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1))) + println(sample4.hashCode()) + + val sample5 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), 1f) + println(sample5.hashCode()) + } + + "equals" should "work fine" in { + var sample1 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(1)) + var sample2 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(1)) + sample1.equals(sample2) should be(true) + + sample1 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + sample2 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + sample1.equals(sample2) should be(true) + + sample1 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1)) + sample2 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1)) + sample1.equals(sample2) should be(true) + + sample1 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1))) + sample2 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1))) + sample1.equals(sample2) should be(true) + + sample1 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), 1f) + sample2 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), 1f) + sample1.equals(sample2) should be(true) + } + + "equals" should "work fine2" in { + var sample1 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(1)) + var sample2 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(2)) + sample1.equals(sample2) should be (false) + + sample1 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1))) + sample2 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1)), Tensor[Float](1).fill(1)) + sample1.equals(sample2) should be (false) + + sample1 = Sample[Float](Tensor[Float](2, 3).range(2, 7, 1)) + sample2 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1)) + sample1.equals(sample2) should be (false) + + sample1 = Sample[Float](Array(Tensor[Float](3, 2).range(1, 6, 1), + Tensor[Float](3).fill(1))) + sample2 = Sample[Float](Array(Tensor[Float](2, 3).range(1, 6, 1), + Tensor[Float](3).fill(1))) + sample1.equals(sample2) should be (false) + + sample1 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), 2f) + sample2 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), 1f) + sample1.equals(sample2) should be (false) + + } + "SampleSpec with Float Tensor input and Tensor label" should "initialize well" in { val input1 = new LabeledBGRImage(32, 32) val label1 = new LabeledBGRImage(32, 32) From 7050e4463a76c414bee7556d4f8a2d7231e180a5 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 17 Jul 2017 02:02:35 -0700 Subject: [PATCH 0280/1065] fix more Xavier misusage (#1244) * more Xavier misusage * add unit test --- .../bigdl/dllib/models/Inception.scala | 24 ++++++----- .../bigdl/dllib/models/InceptionSpec.scala | 41 +++++++++++++++++++ .../dllib/models/ModelGraientCheckSpec.scala | 16 +++++++- .../bigdl/dllib/models/ModelforCheck.scala | 27 +++++++----- 4 files changed, 87 insertions(+), 21 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/Inception.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/Inception.scala index 6547d4579e9..bd7e7133138 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/Inception.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/Inception.scala @@ -241,32 +241,35 @@ object Inception { val concat = Concat[D](2) val conv1 = Sequential[D] conv1.add(SpatialConvolution[D](inputSize, - config[Table](1)(1), 1, 1, 1, 1).setInitMethod(weightInitMethod = Xavier)) + config[Table](1)(1), 1, 1, 1, 1) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros)) conv1.add(ReLU[D](true)) concat.add(conv1) val conv3 = Sequential[D] conv3.add(SpatialConvolution[D](inputSize, config[Table](2)(1), 1, 1, 1, 1). - setInitMethod(Xavier)) + setInitMethod(Xavier, biasInitMethod = Zeros)) conv3.add(ReLU[D](true)) conv3.add(SpatialConvolution[D](config[Table](2)(1), - config[Table](2)(2), 3, 3, 1, 1, 1, 1).setInitMethod(weightInitMethod = Xavier)) + config[Table](2)(2), 3, 3, 1, 1, 1, 1) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros)) conv3.add(ReLU[D](true)) concat.add(conv3) val conv5 = Sequential[D] conv5.add(SpatialConvolution[D](inputSize, config[Table](3)(1), 1, 1, 1, 1). - setInitMethod(Xavier)) + setInitMethod(Xavier, biasInitMethod = Zeros)) conv5.add(ReLU[D](true)) conv5.add(SpatialConvolution[D](config[Table](3)(1), - config[Table](3)(2), 5, 5, 1, 1, 2, 2).setInitMethod(weightInitMethod = Xavier)) + config[Table](3)(2), 5, 5, 1, 1, 2, 2) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros)) conv5.add(ReLU[D](true)) concat.add(conv5) val pool = Sequential[D] pool.add(SpatialMaxPooling[D](3, 3, 1, 1, 1, 1)) pool.add(SpatialConvolution[D](inputSize, config[Table](4)(1), 1, 1, 1, 1). - setInitMethod(Xavier)) + setInitMethod(Xavier, biasInitMethod = Zeros)) concat.add(pool) concat @@ -274,15 +277,15 @@ object Inception { val features = Sequential[D] features.add(SpatialConvolution[D](3, 64, 7, 7, 2, 2, 3, 3) - .setInitMethod(weightInitMethod = Xavier)) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros)) features.add(ReLU[D](true)) features.add(SpatialMaxPooling[D](3, 3, 2, 2, 1, 1)) features.add(SpatialCrossMapLRN[D](5, 0.0001, 0.75)) features.add(SpatialConvolution[D](64, 64, 1, 1, 1, 1, 0, 0) - .setInitMethod(weightInitMethod = Xavier)) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros)) features.add(ReLU[D](true)) features.add(SpatialConvolution[D](64, 192, 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier)) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros)) features.add(ReLU[D](true)) features.add(SpatialCrossMapLRN[D](5, 0.0001, 0.75)) features.add(SpatialMaxPooling[D](3, 3, 2, 2, 1, 1)) @@ -302,7 +305,8 @@ object Inception { features.add(SpatialAveragePooling[D](7, 7, 1, 1)) features.add(Dropout[D](0.4)) features.add(View[D](1024).setNumInputDims(3)) - features.add(Linear[D](1024, classNum).setInitMethod(weightInitMethod = Xavier)) + features.add(Linear[D](1024, classNum) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros)) features.add(LogSoftMax[D]) features.reset() features diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala index e33a8fef05a..7476b91b519 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.models +import com.intel.analytics.bigdl.models.inception.Inception_v1 import com.intel.analytics.bigdl.nn.ClassNLLCriterion import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.optim.SGD @@ -734,4 +735,44 @@ class InceptionSpec extends TorchSpec { weights.equals(weights2) should be (true) } } + + "Inception ModelCaffe" should "init right" in { + RNG.setSeed(1024) + + Random.setSeed(1024) + + val input = Tensor[Float](4, 3, 224, 224).apply1(e => Random.nextFloat()) + val labels = Tensor[Float](4).apply1(e => Random.nextInt(1000)) + + val model = Inception.getModelCaffe[Float](1000) + + val criterion = new ClassNLLCriterion[Float]() + + model.zeroGradParameters() + val output = model.forward(input).toTensor[Float] + val loss = criterion.forward(output, labels) + + // since we already set the seed, the loss should match exactly + loss should be (6.8930426f) + } + + "InceptionV1 " should "init right" in { + RNG.setSeed(1024) + + Random.setSeed(1024) + + val input = Tensor[Float](4, 3, 224, 224).apply1(e => Random.nextFloat()) + val labels = Tensor[Float](4).apply1(e => Random.nextInt(1000)) + + val model = Inception_v1(1000) + + val criterion = new ClassNLLCriterion[Float]() + + model.zeroGradParameters() + val output = model.forward(input).toTensor[Float] + val loss = criterion.forward(output, labels) + + // since we already set the seed, the loss should match exactly + loss should be (6.901158f) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ModelGraientCheckSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ModelGraientCheckSpec.scala index 1b031b4ded2..300303ed6b0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ModelGraientCheckSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ModelGraientCheckSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.models -import com.intel.analytics.bigdl.nn.GradientChecker +import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, GradientChecker} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -56,6 +56,20 @@ class ModelGraientCheckSpec extends FlatSpec with BeforeAndAfter with Matchers { println("Test Scala time : " + scalaTime / 1e9 + " s") } + "GoogleNet_v1 model" should "init right" in { + val seed = 100 + RNG.setSeed(seed) + Random.setSeed(seed) + val input = Tensor[Double](4, 3, 224, 224).apply1(e => Random.nextDouble()) + val labels = Tensor[Double](4).apply1(e => Random.nextInt(1000)) + val criterion = new ClassNLLCriterion[Double]() + val model = GoogleNet_v1_test(1000) + val output = model.forward(input) + val loss = criterion.forward(output, labels) + + loss should be (6.9059443926654875) + } + "GoogleNet_v2 model in batch mode" should "be good in gradient check for input" in { val seed = 100 RNG.setSeed(seed) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ModelforCheck.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ModelforCheck.scala index 5721ff1bbfd..20cfb67d42d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ModelforCheck.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ModelforCheck.scala @@ -29,15 +29,16 @@ object GoogleNet_v1_test { def apply(classNum: Int): Module[Double] = { val feature1 = Sequential() feature1.add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, true) - .setInitMethod(weightInitMethod = Xavier).setName("conv1/7x7_s2")) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros).setName("conv1/7x7_s2")) feature1.add(ReLU(true).setName("conv1/relu_7x7")) feature1.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2")) feature1.add(SpatialCrossMapLRN(5, 0.0001, 0.75).setName("pool1/norm1")) - feature1.add(SpatialConvolution(64, 64, 1, 1, 1, 1).setInitMethod(weightInitMethod = Xavier) + feature1.add(SpatialConvolution(64, 64, 1, 1, 1, 1) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros) .setName("conv2/3x3_reduce")) feature1.add(ReLU(true).setName("conv2/relu_3x3_reduce")) feature1.add(SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName("conv2/3x3")) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros).setName("conv2/3x3")) feature1.add(ReLU(true).setName("conv2/relu_3x3")) feature1.add(SpatialCrossMapLRN(5, 0.0001, 0.75). setName("conv2/norm2")) feature1.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool2/3x3_s2")) @@ -81,7 +82,8 @@ object GoogleNet_v1_test { output3.add(SpatialAveragePooling(7, 7, 1, 1).setName("pool5/7x7_s1")) // output3.add(Dropout(0.4).setName("pool5/drop_7x7_s1")) output3.add(View(1024).setNumInputDims(3)) - output3.add(Linear(1024, classNum).setInitMethod(weightInitMethod = Xavier) + output3.add(Linear(1024, classNum) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros) .setName("loss3/classifier")) output3.add(LogSoftMax().setName("loss3/loss3")) @@ -110,29 +112,34 @@ object GoogleNet_v1_test { val concat = Concat(2) val conv1 = Sequential() conv1.add(SpatialConvolution(inputSize, config[Table](1)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "1x1")) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros) + .setName(namePrefix + "1x1")) conv1.add(ReLU(true).setName(namePrefix + "relu_1x1")) concat.add(conv1) val conv3 = Sequential() conv3.add(SpatialConvolution(inputSize, config[Table](2)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "3x3_reduce")) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros) + .setName(namePrefix + "3x3_reduce")) conv3.add(ReLU(true).setName(namePrefix + "relu_3x3_reduce")) conv3.add(SpatialConvolution(config[Table](2)(1), config[Table](2)(2), 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "3x3")) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros) + .setName(namePrefix + "3x3")) conv3.add(ReLU(true).setName(namePrefix + "relu_3x3")) concat.add(conv3) val conv5 = Sequential() conv5.add(SpatialConvolution(inputSize, config[Table](3)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "5x5_reduce")) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros) + .setName(namePrefix + "5x5_reduce")) conv5.add(ReLU(true).setName(namePrefix + "relu_5x5_reduce")) conv5.add(SpatialConvolution(config[Table](3)(1), config[Table](3)(2), 5, 5, 1, 1, 2, 2) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "5x5")) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros).setName(namePrefix + "5x5")) conv5.add(ReLU(true).setName(namePrefix + "relu_5x5")) concat.add(conv5) val pool = Sequential() pool.add(SpatialMaxPooling(3, 3, 1, 1, 1, 1).ceil().setName(namePrefix + "pool")) pool.add(SpatialConvolution(inputSize, config[Table](4)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier).setName(namePrefix + "pool_proj")) + .setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros) + .setName(namePrefix + "pool_proj")) pool.add(ReLU(true).setName(namePrefix + "relu_pool_proj")) concat.add(pool).setName(namePrefix + "output") concat From f9c896b6ddfb6ac716ce4042faaf1f2255bc2f1f Mon Sep 17 00:00:00 2001 From: Wang Date: Mon, 10 Jul 2017 06:47:11 +0100 Subject: [PATCH 0281/1065] add a property to control the blockmanger behavior --- .../com/intel/analytics/bigdl/utils/Engine.scala | 7 +++++++ .../intel/analytics/bigdl/utils/EngineSpec.scala | 14 ++++++++++++++ 2 files changed, 21 insertions(+) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 41a40308402..02fc11cae43 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -265,7 +265,14 @@ object Engine { val stream : InputStream = getClass.getResourceAsStream("/spark-bigdl.conf") val lines = scala.io.Source.fromInputStream(stream) .getLines.filter(_.startsWith("spark")).toArray + + // For spark 1.5, we observe nio block manager has better performance than netty block manager + // So we will force set block manager to nio. If user don't want this, he/she can set + // bigdl.nio.force == false to customize it. This configuration/blcok manager setting won't + // take affect on newer spark version as the nio block manger has been removed lines.map(_.split("\\s+")).map(d => (d(0), d(1))).toSeq + .filter(_._1 != "spark.shuffle.blockTransferService" || + System.getProperty("bigdl.nio.force", "true").toBoolean) } /** diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala index acfffc0674c..a52d06dcf30 100644 --- a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala @@ -141,6 +141,20 @@ class EngineSpec extends FlatSpec with Matchers with BeforeAndAfter { }) } + "readConf" should "skip blockTransferService if bigdl.nio.force is set to false" in { + System.setProperty("bigdl.nio.force", "false") + val conf = Engine.readConf + val target = Map( + "spark.shuffle.reduceLocality.enabled" -> "false", + "spark.scheduler.minRegisteredResourcesRatio" -> "1.0" + ) + conf.length should be(target.keys.size) + conf.foreach(s => { + s._2 should be(target(s._1)) + }) + System.clearProperty("bigdl.nio.force") + } + "LocalMode" should "false if onSpark" in { intercept[IllegalArgumentException] { System.setProperty("bigdl.localMode", "true") From b7acaa4eea473161174f23cac546277d6228e9e5 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Wed, 12 Jul 2017 23:38:52 +0800 Subject: [PATCH 0282/1065] rename bigdl.nio.force -> bigdl.network.nio --- .../main/scala/com/intel/analytics/bigdl/utils/Engine.scala | 4 ++-- .../scala/com/intel/analytics/bigdl/utils/EngineSpec.scala | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 02fc11cae43..d4776bf37a3 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -268,11 +268,11 @@ object Engine { // For spark 1.5, we observe nio block manager has better performance than netty block manager // So we will force set block manager to nio. If user don't want this, he/she can set - // bigdl.nio.force == false to customize it. This configuration/blcok manager setting won't + // bigdl.network.nio == false to customize it. This configuration/blcok manager setting won't // take affect on newer spark version as the nio block manger has been removed lines.map(_.split("\\s+")).map(d => (d(0), d(1))).toSeq .filter(_._1 != "spark.shuffle.blockTransferService" || - System.getProperty("bigdl.nio.force", "true").toBoolean) + System.getProperty("bigdl.network.nio", "true").toBoolean) } /** diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala index a52d06dcf30..5f14ed33a98 100644 --- a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala @@ -141,8 +141,8 @@ class EngineSpec extends FlatSpec with Matchers with BeforeAndAfter { }) } - "readConf" should "skip blockTransferService if bigdl.nio.force is set to false" in { - System.setProperty("bigdl.nio.force", "false") + "readConf" should "skip blockTransferService if bigdl.network.nio is set to false" in { + System.setProperty("bigdl.network.nio", "false") val conf = Engine.readConf val target = Map( "spark.shuffle.reduceLocality.enabled" -> "false", @@ -152,7 +152,7 @@ class EngineSpec extends FlatSpec with Matchers with BeforeAndAfter { conf.foreach(s => { s._2 should be(target(s._1)) }) - System.clearProperty("bigdl.nio.force") + System.clearProperty("bigdl.network.nio") } "LocalMode" should "false if onSpark" in { From 7f30d0d2b5b879468c4f677e3dac5b6b4ac07552 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Tue, 18 Jul 2017 10:28:47 +0800 Subject: [PATCH 0283/1065] Fix the bug of JoinTable after calling clearState() (#1257) --- .../analytics/bigdl/dllib/nn/JoinTable.scala | 6 ++++++ .../bigdl/dllib/torch/JoinTableSpec.scala | 21 ++++++++++++++++--- 2 files changed, 24 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala index 213d35fcdbb..74a6bd85e6c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala @@ -130,6 +130,12 @@ class JoinTable[T: ClassTag] ( val state = Seq(super.hashCode(), dimension, nInputDims) state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) } + + override def clearState(): this.type = { + super.clearState() + gradInput.clear() + this + } } object JoinTable { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala index 9006d9905ec..8592c3d941e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala @@ -15,12 +15,10 @@ */ package com.intel.analytics.bigdl.torch -import com.intel.analytics.bigdl.nn.JoinTable +import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} -import scala.collection.mutable - @com.intel.analytics.bigdl.tags.Serial class JoinTableSpec extends TorchSpec { "A JoinTable()" should "generate correct output and grad" in { @@ -56,4 +54,21 @@ class JoinTableSpec extends TorchSpec { println("Test case : JoinTable, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } + + "JoinTable" should "work properly after clearState()" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val model = Sequential[Float]() + model.add(ConcatTable().add(Identity()).add(Identity())) + model.add(ParallelTable().add(Reshape(Array(3, 2))).add(Reshape(Array(3, 2)))) + model.add(JoinTable(1, 1)) + val input = Tensor[Float](2, 3) + model.forward(input) + model.backward(input, model.output) + + model.clearState() + model.modules(2).clearState() + val input2 = Tensor[Float](2, 3) + model.forward(input2) + model.backward(input2, model.output) + } } From 358cd7daa412933110d97ac275ab45f289ba7fd6 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 18 Jul 2017 12:13:43 +0800 Subject: [PATCH 0284/1065] optimize joinTable perf (#1248) --- .../analytics/bigdl/dllib/nn/JoinTable.scala | 79 +++++++++++++------ .../analytics/bigdl/dllib/nn/GraphSpec.scala | 37 ++++++--- .../bigdl/dllib/torch/JoinTableSpec.scala | 34 ++++++++ 3 files changed, 116 insertions(+), 34 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala index 74a6bd85e6c..f4783a91287 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala @@ -18,8 +18,9 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{Engine, Table} +import scala.concurrent.Future import scala.reflect.ClassTag /** @@ -44,6 +45,9 @@ class JoinTable[T: ClassTag] ( )(implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { + @transient + private var results: Array[Future[Unit]] = null + private def getPositiveDimension(input: Table): Int = { var nDim = this.dimension val firstInput: Tensor[T] = input(1) @@ -73,41 +77,68 @@ class JoinTable[T: ClassTag] ( } output.resize(size) + if (results == null || results.length != input.length) { + results = new Array[Future[Unit]](input.length) + } var offset = 1 - i = 1 - while (i <= input.length()) { - val currentOutput: Tensor[T] = input(i) - output.narrow(dimension, offset, currentOutput.size(dimension)) - .copy(currentOutput) - offset += currentOutput.size(dimension) + i = 0 + while (i < input.length) { + val currentOutput = input(i + 1).asInstanceOf[Tensor[T]] + val _offset = offset + results(i) = Engine.model.invoke( () => { + val target = output.narrow(dimension, _offset, currentOutput.size(dimension)) + if (target.isContiguous() || dimension > 2) { + target.copy(currentOutput) + } else { + var f = 1 + while (f <= target.size(1)) { + val curFrame = target.select(1, f) + val outputFrame = currentOutput.select(1, f) + require(curFrame.isContiguous()) + require(outputFrame.isContiguous()) + curFrame.copy(outputFrame) + f += 1 + } + } + }) i += 1 + offset += currentOutput.size(dimension) } - + Engine.model.sync(results) output } override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { val dimension = getPositiveDimension(input) - var i = 1 - while (i <= input.length()) { - if (!gradInput.contains(i)) { - gradInput(i) = Tensor() - } - gradInput[Tensor[T]](i).resizeAs(input(i)) - i += 1 - } - var offset = 1 - i = 1 - while (i <= input.length()) { - val currentOutput: Tensor[T] = input(i) - val currentGradInput = gradOutput - .narrow(dimension, offset, currentOutput.size(dimension)) - gradInput[Tensor[T]](i)copy(currentGradInput) - offset += currentOutput.size(dimension) + var i = 0 + while (i < input.length) { + val currentOutput = input(i + 1).asInstanceOf[Tensor[T]] + val _offset = offset + val _i = i + results(i) = Engine.model.invoke( () => { + val narrowedTensor = gradOutput.narrow(dimension, _offset, currentOutput.size(dimension)) + if (!gradInput.contains(_i + 1)) gradInput(_i + 1) = Tensor() + gradInput[Tensor[T]](_i + 1).resizeAs(input(_i + 1)) + if(narrowedTensor.isContiguous() || dimension > 2) { + gradInput[Tensor[T]](_i + 1).copy(narrowedTensor) + } else { + var b = 1 + while(b <= narrowedTensor.size(1)) { + val curFrame = gradInput[Tensor[T]](_i + 1).select(1, b) + val narrowFrame = narrowedTensor.select(1, b) + require(curFrame.isContiguous()) + require(narrowFrame.isContiguous()) + curFrame.copy(narrowFrame) + b += 1 + } + } + }) i += 1 + offset += currentOutput.size(dimension) } + Engine.model.sync(results) gradInput } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index b0d34fd416e..fb6434e5ee1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -434,7 +434,7 @@ class GraphSpec extends FlatSpec with Matchers { val seqModel = ModelUntils.ResNet.basicBlockSeq(16, 16, 1, "C") RandomGenerator.RNG.setSeed(1000) val input = Input() - val output = ModelUntils.ResNet.basicBlockSeq(16, 16, 1, "C").inputs(input) + val output = ModelUntils.ResNet.basicBlockFunc(16, 16, 1, "C")(input) val funcModel = Graph(input, output) println(seqModel) @@ -458,7 +458,22 @@ class GraphSpec extends FlatSpec with Matchers { println(s"func model backward time is ${(System.nanoTime() - start) / 1e6}ms") gradients1 should be(gradients2) - seqModel.getParameters()._2 should be(funcModel.getParameters()._2) + + seqModel.getParametersTable()[Table]("conv1")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv1")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("bn1")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("bn1")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("conv2")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv2")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("bn2")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("bn2")[Tensor[Float]]("gradWeight") + ) } "InceptionV1 block" should "be correct" in { @@ -588,11 +603,13 @@ object ModelUntils { object ResNet { def basicBlockFunc(nInputPlane: Int, n: Int, stride: Int, shortcutType : String)( input : ModuleNode[Float]) : ModuleNode[Float] = { - val conv1 = SpatialConvolution(nInputPlane, n, 3, 3, stride, stride, 1, 1).inputs(input) - val bn1 = SpatialBatchNormalization(n).inputs(conv1) + val conv1 = SpatialConvolution(nInputPlane, n, 3, 3, stride, stride, 1, 1) + .setName("conv1").inputs(input) + val bn1 = SpatialBatchNormalization(n).setName("bn1").inputs(conv1) val relu1 = ReLU(true).inputs(bn1) - val conv2 = SpatialConvolution(n, n, 3, 3, 1, 1, 1, 1).inputs(relu1) - val bn2 = SpatialBatchNormalization(n).inputs(conv2) + val conv2 = SpatialConvolution(n, n, 3, 3, 1, 1, 1, 1) + .setName("conv2").inputs(relu1) + val bn2 = SpatialBatchNormalization(n).setName("bn2").inputs(conv2) val shortcut = shortcutFunc(nInputPlane, n, stride, shortcutType)(input) val add = CAddTable(true).inputs(bn2, shortcut) val output = ReLU(true).inputs(add) @@ -602,11 +619,11 @@ object ModelUntils { def basicBlockSeq(nInputPlane: Int, n: Int, stride: Int, shortcutType : String) : Module[Float] = { val s = Sequential() - s.add(SpatialConvolution(nInputPlane, n, 3, 3, stride, stride, 1, 1)) - s.add(SpatialBatchNormalization(n)) + s.add(SpatialConvolution(nInputPlane, n, 3, 3, stride, stride, 1, 1).setName("conv1")) + s.add(SpatialBatchNormalization(n).setName("bn1")) s.add(ReLU(true)) - s.add(SpatialConvolution(n, n, 3, 3, 1, 1, 1, 1)) - s.add(SpatialBatchNormalization(n)) + s.add(SpatialConvolution(n, n, 3, 3, 1, 1, 1, 1).setName("conv2")) + s.add(SpatialBatchNormalization(n).setName("bn2")) Sequential() .add(ConcatTable() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala index 8592c3d941e..2b38e906557 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala @@ -55,6 +55,40 @@ class JoinTableSpec extends TorchSpec { println("Test case : JoinTable, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } + "A JoinTable() with dimension=2" should "generate correct output and grad" in { + torchCheck() + def randomn(): Double = RandomGenerator.RNG.uniform(-10, 10) + val layer = new JoinTable[Double](2, 2) + + val input1 = Tensor[Double](3, 3) + input1.apply1(x => randomn()) + val input2 = Tensor[Double](3, 3) + input2.apply1(x => randomn()) + val input = T(input1, input2) + val gradOutput = Tensor[Double](3, 6) + gradOutput.apply1(x => randomn()) + + val start = System.nanoTime() + val output = layer.forward(input) + val gradInput = layer.backward(input, gradOutput) + val end = System.nanoTime() + val scalaTime = end - start + + val code = "module = nn.JoinTable(2, 2)\n" + + "output = module:forward(input)\n" + + "gradInput = module:backward(input,gradOutput)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), + Array("output", "gradInput")) + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + val luaGradInput = torchResult("gradInput").asInstanceOf[Table] + + output should be (luaOutput) + gradInput should be (luaGradInput) + + println("Test case : JoinTable, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") + } + "JoinTable" should "work properly after clearState()" in { import com.intel.analytics.bigdl.numeric.NumericFloat val model = Sequential[Float]() From 9068f3861be383632046c6ed5e29045ab8e5ab00 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Tue, 18 Jul 2017 15:10:31 +0800 Subject: [PATCH 0285/1065] add load caffe model doc, remove useless parameter, add python api (#1266) * add load caffe model doc, remove useless parameter, add python api * update doc * add save caffe * remove empty lines * fix python test * Update caffe-support.md * fix python * update doc --- .../com/intel/analytics/bigdl/dllib/nn/Module.scala | 5 ++--- .../bigdl/dllib/nn/abstractnn/AbstractModule.scala | 8 ++++++++ .../bigdl/dllib/utils/caffe/CaffeLoader.scala | 5 ++--- .../bigdl/dllib/utils/caffe/CaffePersister.scala | 4 ++-- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 10 ++++++++++ 5 files changed, 24 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala index 4f07f0358d2..50191b0cd66 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala @@ -57,11 +57,10 @@ object Module { * Loaf caffe trained model from prototxt and weight files * @param defPath caffe model definition file path * @param modelPath caffe model binary file containing weight and bias - * @param matchAll if layer on layer checking needed between caffe and bigdl */ - def loadCaffeModel[T: ClassTag](defPath: String, modelPath: String, matchAll: Boolean = true)( + def loadCaffeModel[T: ClassTag](defPath: String, modelPath: String)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - CaffeLoader.loadCaffe[T](defPath, modelPath, matchAll)._1 + CaffeLoader.loadCaffe[T](defPath, modelPath)._1 } /** * Load tensorflow model from its saved protobuf file. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 5e0adb092c7..72041e97e1a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -27,6 +27,7 @@ import org.apache.spark.rdd.RDD import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.utils.caffe.CaffePersister import scala.reflect.ClassTag @@ -387,6 +388,13 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, this } + def saveCaffe(prototxtPath: String, modelPath: String, + useV2 : Boolean = true, overwrite : Boolean = false) : this.type = { + this.clearState() + CaffePersister.persist[T](prototxtPath, modelPath, this, useV2, overwrite) + this + } + /** * @return Float or Double */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 0a047b025b3..666878a086f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -388,15 +388,14 @@ object CaffeLoader { * load caffe model dynamically from binary and prototxt file * @param defPath prototxt file which illustrate the caffe model structure * @param modelPath binary file containing the weight and bias - * @param matchAll if match all modules for parameter copy * @param customizedConverters customized layer converter * @tparam T data type * @return created module (graph) and criterion */ - def loadCaffe[T: ClassTag](defPath: String, modelPath: String, matchAll: Boolean = true, + def loadCaffe[T: ClassTag](defPath: String, modelPath: String, customizedConverters : mutable.HashMap[String, (GeneratedMessage) => Seq[ModuleNode[T]]] = null) (implicit ev: TensorNumeric[T]): (Module[T], ParallelCriterion[T]) = { - val caffeLoader = new CaffeLoader[T](defPath, modelPath, matchAll, customizedConverters) + val caffeLoader = new CaffeLoader[T](defPath, modelPath, true, customizedConverters) caffeLoader.createCaffeModel() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala index 6f8f5404c39..6855ef3c90b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala @@ -45,7 +45,7 @@ import org.apache.log4j.Logger * @param overwrite whether to overwirte existing caffe files */ class CaffePersister[T: ClassTag](val prototxtPath: String, - val modelPath: String, val module : Container[Activity, Activity, T], + val modelPath: String, val module : AbstractModule[Activity, Activity, T], useV2 : Boolean = true, overwrite : Boolean = false)(implicit ev: TensorNumeric[T]) { private val logger = Logger.getLogger(getClass) @@ -260,7 +260,7 @@ class CaffePersister[T: ClassTag](val prototxtPath: String, object CaffePersister{ def persist[T: ClassTag](prototxtPath: String, - modelPath: String, module : Container[Activity, Activity, T], + modelPath: String, module : AbstractModule[Activity, Activity, T], useV2 : Boolean = true, overwrite : Boolean = false)(implicit ev: TensorNumeric[T]) : Unit = { val caffePersist = new CaffePersister[T](prototxtPath, modelPath, module, useV2, overwrite) caffePersist.saveAsCaffe() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index dd208372de8..e67488ca145 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1346,6 +1346,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Module.loadCaffe[T](model, defPath, modelPath, matchAll) } + def loadCaffeModel(defPath: String, modelPath: String): AbstractModule[Activity, Activity, T] = { + Module.loadCaffeModel[T](defPath, modelPath) + } + def loadTF(path: String, inputs: JList[String], outputs: JList[String], byteOrder: String): AbstractModule[Activity, Activity, T] = { val order = byteOrder match { @@ -1393,6 +1397,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab module.save(path, overWrite) } + def saveCaffe(module: AbstractModule[Activity, Activity, T], + prototxtPath: String, modelPath: String, + useV2 : Boolean = true, overwrite : Boolean = false): Unit = { + module.saveCaffe(prototxtPath, modelPath, useV2, overwrite) + } + def criterionForward(criterion: AbstractCriterion[Activity, Activity, T], input: JList[JTensor], inputIsTable: Boolean, From cd3cacd58e867f1d4611bfdef373bff1f010496c Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Tue, 18 Jul 2017 15:17:21 +0800 Subject: [PATCH 0286/1065] Add documents for tree lstm layers (#1251) * add document for treeLSTM * fix a scala style * meet code review * change float to double --- .../bigdl/dllib/nn/BinaryTreeLSTMSpec.scala | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTMSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTMSpec.scala new file mode 100644 index 00000000000..32bde44d032 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTMSpec.scala @@ -0,0 +1,93 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class BinaryTreeLSTMSpec extends FlatSpec with Matchers with BeforeAndAfter { + "BinaryTreeLSTM" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + import com.intel.analytics.bigdl.utils.RandomGenerator.RNG + + RNG.setSeed(100) + + val hiddenSize = 2 + val inputSize = 2 + + val inputs = + Tensor( + T(T(T(1f, 2f), + T(2f, 3f), + T(4f, 5f)))) + + val tree = + Tensor( + T(T(T(2f, 5f, -1f), + T(0f, 0f, 1f), + T(0f, 0f, 2f), + T(0f, 0f, 3f), + T(3f, 4f, 0f)))) + + val input = T(inputs, tree) + + val gradOutput = + Tensor( + T(T(T(2f, 5f), + T(2f, 3f), + T(4f, 5f), + T(2f, 3f), + T(4f, 5f), + T(6f, 7f)))) + + val expectOutput = + Tensor( + T(T(T(-0.07799374051859737f, -0.14419464399333934f), + T(-0.2349552348774636f, -0.04679071771123799f), + T(-0.1594515102098235f, -0.026039638054106272f), + T(-0.04540739978946999f, -0.0070662412123771254f), + T(-0.05869603467391258f, -0.13559056761784405f)))) + + val expectGradInputs = + Tensor( + T(T( + T(0.5614597104995146f, -0.3383652016018004f), + T(0.8172036851171792f, -0.46767634057453855f), + T(0.37739630380493044f, -0.2335553148048936f)))) + + val expectGradTree = + Tensor( + T(T( + T(0f, 0f, 0f), + T(0f, 0f, 0f), + T(0f, 0f, 0f), + T(0f, 0f, 0f), + T(0f, 0f, 0f)))) + + val expectGradInput = T(expectGradInputs, expectGradTree) + + val model = BinaryTreeLSTM(inputSize, hiddenSize) + + val output = model.forward(input) + println(output) + output should be(expectOutput) + + val gradInput = model.backward(input, gradOutput) + println(gradInput) + gradInput should be(expectGradInput) + } +} From 04ef29341fa7fa0fc9fd52241b2f8258c0fc0a16 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 18 Jul 2017 15:24:54 +0800 Subject: [PATCH 0287/1065] fix SpatialConvolution's hashcode and clearState will throw NullPointerException (#1268) --- .../bigdl/dllib/nn/SpatialConvolution.scala | 10 ++++---- .../dllib/nn/SpatialConvolutionSpec.scala | 23 +++++++++++++++++++ 2 files changed, 29 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala index de939d5553b..267b7b4192d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala @@ -442,9 +442,9 @@ class SpatialConvolution[T: ClassTag]( hash = hash * seed + padW.hashCode() hash = hash * seed + padH.hashCode() hash = hash * seed + weight.hashCode() - hash = hash * seed + bias.hashCode() + if (withBias) hash = hash * seed + bias.hashCode() hash = hash * seed + gradWeight.hashCode() - hash = hash * seed + gradBias.hashCode() + if (withBias) hash = hash * seed + gradBias.hashCode() hash } @@ -455,8 +455,10 @@ class SpatialConvolution[T: ClassTag]( fGradInput.set() ones.set() onesBatch.set() - onesBias.set() - gradientBiasMT.set() + if (withBias) { + onesBias.set() + gradientBiasMT.set() + } this } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala index db16985d3af..b4125e27c88 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala @@ -2863,4 +2863,27 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { conv.weight should be (exceptedWeight) conv.bias should be (exceptedBias) } + + "hashcode & clearState" should "works fine" in { + val layer = new SpatialConvolution[Float](3, 4, + 2, 2, 1, 1, 0, 0, withBias = false) + val input = Tensor[Float](2, 3, 4, 4).rand() + val output = layer.forward(input).toTensor[Float] + layer.backward(input, output.clone().rand) + layer.hashCode() + layer.clearState() + } + + "equals" should "works fine" in { + val layer = new SpatialConvolution[Float](3, 4, + 2, 2, 1, 1, 0, 0, withBias = false) + val layer2 = layer.cloneModule() + layer.equals(layer2) should be (true) + + val layer3 = new SpatialConvolution[Float](3, 4, + 2, 2, 1, 1, 0, 0) + layer3.equals(layer) should be (false) + layer3.weight.copy(layer.weight) + layer3.equals(layer) should be (false) + } } From e5a350905d1cd1eb2f568d18bc378b3774bad36e Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 18 Jul 2017 00:56:14 -0700 Subject: [PATCH 0288/1065] refine tensorflow support doc (#1253) * refine tensorflow support * meet review * add tensorflow saver to python api and refine docs --- .../dllib/utils/python/api/PythonBigDL.scala | 26 +++++++++++++++++++ .../dllib/utils/tf/TensorflowSaver.scala | 8 +++--- .../dllib/utils/tf/TensorflowSaverSpec.scala | 6 ++--- 3 files changed, 33 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index e67488ca145..fa4ab85a965 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -36,6 +36,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} +import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} import scala.collection.JavaConverters._ import scala.language.existentials @@ -1360,6 +1361,31 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Module.loadTF[T](path, inputs.asScala, outputs.asScala, order) } + def saveTF(model: Graph[T], + inputs: JList[Any], + path: String, + byteOrder: String, + dataFormat: String): Unit = { + val order = byteOrder.toLowerCase match { + case "little_endian" => ByteOrder.LITTLE_ENDIAN + case "big_endian" => ByteOrder.BIG_ENDIAN + case _ => throw new IllegalArgumentException(s"Unknown byte order $byteOrder") + } + + val format = dataFormat.toLowerCase match { + case "nhwc" => TensorflowDataFormat.NHWC + case "nchw" => TensorflowDataFormat.NCHW + case _ => throw new IllegalArgumentException(s"Unknown format $dataFormat") + } + val scalaInputs = inputs.asScala.map { elem => + val array = elem.asInstanceOf[JList[Any]] + val name = array.get(0).asInstanceOf[String] + val shape = array.get(1).asInstanceOf[JList[Int]] + (name, shape.asScala) + } + TensorflowSaver.saveGraph(model, scalaInputs, path, order, format) + } + def modelPredictRDD(model: AbstractModule[Activity, Activity, T], dataRdd: JavaRDD[Sample]): JavaRDD[JTensor] = { val tensorRDD = model.predict(dataRdd.rdd.map(toSample(_))) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala index 96b1ae27330..42a03511702 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala @@ -35,7 +35,7 @@ object TensorflowSaver { * * When save the model, placeholders will be added to the tf model as input nodes. So you need to * pass in the names and shape for the placeholders. BigDL model doesn't have such information. - * The order of the placeholde information should be same as the inputs of the graph model + * The order of the placeholder information should be same as the inputs of the graph model * * @param model graph model instance * @param inputs input node defs @@ -43,7 +43,7 @@ object TensorflowSaver { * @param byteOrder model byte order * @tparam T */ - def saveGraphWitNodeDef[T]( + def saveGraphWithNodeDef[T]( model : Graph[T], inputs : Seq[NodeDef], path: String, @@ -91,7 +91,7 @@ object TensorflowSaver { * * When save the model, placeholders will be added to the tf model as input nodes. So you need to * pass in the names and shape for the placeholders. BigDL model doesn't have such information. - * The order of the placeholde information should be same as the inputs of the graph model + * The order of the placeholder information should be same as the inputs of the graph model * * @param model graph model instance * @param inputs placeholder information @@ -109,7 +109,7 @@ object TensorflowSaver { val inputNodeDefs = inputs.map(input => placeholder(model.getNumericType(), input._2, input._1) ) - saveGraphWitNodeDef(model, inputNodeDefs, path, byteOrder) + saveGraphWithNodeDef(model, inputNodeDefs, path, byteOrder) } /** diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala index 62057bfa656..0d888bf7855 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala @@ -212,7 +212,7 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { val outputData = funcModel.forward(inputData).toTensor val tmpFile = java.io.File.createTempFile("tensorflowSaverTest" + UUID.randomUUID(), "lenet") - TensorflowSaver.saveGraphWitNodeDef( + TensorflowSaver.saveGraphWithNodeDef( funcModel, Seq(Tensorflow.const(transInput, "input", ByteOrder.LITTLE_ENDIAN)), tmpFile.getPath, @@ -245,7 +245,7 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { } else { outputTensor } - TensorflowSaver.saveGraphWitNodeDef( + TensorflowSaver.saveGraphWithNodeDef( graph, Seq(Tensorflow.const(tfTensor, "input", ByteOrder.LITTLE_ENDIAN)), tmpFile.getPath, @@ -282,7 +282,7 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { outputTensor } - TensorflowSaver.saveGraphWitNodeDef( + TensorflowSaver.saveGraphWithNodeDef( graph, tfTensors.zipWithIndex.map(t => Tensorflow.const(t._1, "input" + t._2, ByteOrder.LITTLE_ENDIAN)), From 952bfbb00482084898a6fc37631a8b1cc3a8f9b3 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 19 Jul 2017 08:39:30 +0800 Subject: [PATCH 0289/1065] update shared convolution (#1144) update shared convolution with latest SpatialConvolution. Add wRegularize, bRegularizer, initWeight, initBias, initGradWeight, initGradBias, withBias --- .../dllib/nn/SpatialShareConvolution.scala | 507 ++++++++---------- .../dllib/utils/python/api/PythonBigDL.scala | 39 +- .../utils/SpatialShareConvolutionSpec.scala | 191 +++++++ 3 files changed, 441 insertions(+), 296 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SpatialShareConvolutionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialShareConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialShareConvolution.scala index 6af4a219585..96d195f6b4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialShareConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialShareConvolution.scala @@ -16,112 +16,91 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.{Module, _} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.Engine -import scala.concurrent.Future import scala.reflect.ClassTag @SerialVersionUID(4479683852714800631L) class SpatialShareConvolution[T: ClassTag]( - nInputPlane: Int, // The number of expected input planes in the image given into forward() - nOutputPlane: Int, // The number of output planes the convolution layer will produce. - kernelW: Int, // The kernel width of the convolution - kernelH: Int, // The kernel height of the convolution - strideW: Int = 1, // The step of the convolution in the width dimension. - strideH: Int = 1, // The step of the convolution in the height dimension - padW: Int = 0, // The additional zeros added per width to the input planes. - padH: Int = 0, // The additional zeros added per height to the input planes. - nGroup: Int = 1, // Kernel group number - propagateBack: Boolean = true // propagate gradient back - ) - (implicit ev: TensorNumeric[T]) extends SpatialConvolution[T]( + nInputPlane: Int, // The number of expected input planes in the image given into forward() + nOutputPlane: Int, // The number of output planes the convolution layer will produce. + kernelW: Int, // The kernel width of the convolution + kernelH: Int, // The kernel height of the convolution + strideW: Int = 1, // The step of the convolution in the width dimension. + strideH: Int = 1, // The step of the convolution in the height dimension + padW: Int = 0, // The additional zeros added per width to the input planes. + padH: Int = 0, // The additional zeros added per height to the input planes. + nGroup: Int = 1, // Kernel group number + propagateBack: Boolean = true, // propagate gradient back + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null, + withBias: Boolean = true + )(implicit ev: TensorNumeric[T]) extends SpatialConvolution[T]( nInputPlane, nOutputPlane, kernelW, kernelH, strideW, strideH, - padW, padH, nGroup, propagateBack) { + padW, padH, nGroup, propagateBack, wRegularizer, bRegularizer, + initWeight, initBias, initGradWeight, initGradBias, withBias) { + + require(Engine.model.getPoolSize == 1, "Don't support single model multi thread.") override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 3 || input.dim() == 4, "SpatialShareConvolution: " + ErrorInfo.constrainInputAs3DOrBatch) require(input.isContiguous()) + if (_1x1 || input.dim() == 3 || (input.dim() == 4 && input.size(1) == 1)) { + super.updateOutput(input) + } else { + if (weightMM == null || weightMM.storage().isEmpty) { + weightMM = weight.view(nGroup, nOutputPlane / nGroup, + nInputPlane * kernelH * kernelW / nGroup) + } - if (weightMM == null) { - weightMM = weight.view(nGroup, nOutputPlane / nGroup, - nInputPlane * kernelH * kernelW / nGroup) - } - val (outputWidth, outputHeight, inputWidth, inputHeight) = calcOutputWH(input) - if (onesBias.dim() != 1 || onesBias.size(1) != outputHeight * outputWidth) { - onesBias.resize(Array(outputHeight * outputWidth)).fill(ev.fromType(1.0)) - } + val (outputWidth, outputHeight, inputWidth, inputHeight) = calcOutputWH(input) - require(outputWidth >= 1 && outputHeight >= 1, "output size is too small") - if (input.dim() == 3) { - require(input.size(1) == nInputPlane) - require(input.isContiguous()) - output.resize(Array(nOutputPlane, outputHeight, outputWidth)) - fInput.resize(Array(nGroup, kernelW * kernelH * nInputPlane / nGroup, - outputHeight * outputWidth)) - var g = 0 - while (g < nGroup) { - updateOutputFrame( - input.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), - output.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - weightMM.select(1, g + 1), - bias.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - fInput.select(1, g + 1), - kernelW, kernelH, strideW, strideH, - padW, padH, - nInputPlane / nGroup, inputWidth, inputHeight, - nOutputPlane / nGroup, outputWidth, outputHeight) - g += 1 + require(outputWidth >= 1 && outputHeight >= 1, + s"output size is too small. outputWidth: $outputWidth, outputHeight: $outputHeight") + + if (withBias && onesBias.dim() != 1 || onesBias.size(1) != outputHeight * outputWidth) { + onesBias.resize(outputHeight * outputWidth).fill(ev.fromType(1.0)) } - } else { require(input.size(2) == nInputPlane) val batchSize = input.size(1) - output.resize(Array(batchSize, nOutputPlane, outputHeight, outputWidth)) - - val coresNum = Math.min(batchSize, Engine.model.getPoolSize) - fInput.resize(Array(coresNum, nGroup, kernelW * kernelH * nInputPlane / nGroup, - outputHeight * outputWidth)) - - if (results == null || results.length != coresNum) { - results = new Array[Future[Unit]](coresNum) - } - - var i, j = 0 - val minJobNum: Int = batchSize / Engine.model.getPoolSize - val remainJobNum: Int = batchSize - minJobNum * Engine.model.getPoolSize - - while (j < coresNum) { - val _j = j - results(j) = Engine.model.invoke(() => { - var _i = 1 - val distJobNum: Int = minJobNum + (if (_j < remainJobNum) 1 else 0) - val indexStart: Int = _j * minJobNum + (if (_j < remainJobNum) _j else remainJobNum) - while (_i <= distJobNum) { - val inputT = input.select(1, _i + indexStart).contiguous() - val outputT = output.select(1, _i + indexStart) - val fInputT = fInput.select(1, _j + 1) - var g = 0 - while (g < nGroup) { - updateOutputFrame( - inputT.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), - outputT.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - weightMM.select(1, g + 1), - bias.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - fInputT.select(1, g + 1), - kernelW, kernelH, strideW, strideH, - padW, padH, - nInputPlane / nGroup, inputWidth, inputHeight, - nOutputPlane / nGroup, outputWidth, outputHeight) - g += 1 - } - _i += 1 - } - }) - j += 1 + output.resize(batchSize, nOutputPlane, outputHeight, outputWidth) + fInput.resize(nGroup, kernelW * kernelH * nInputPlane / nGroup, + outputHeight * outputWidth) + + var i = 1 + while (i <= batchSize) { + val inputT = input.select(1, i) + require(inputT.isContiguous()) + val outputT = output.select(1, i) + var g = 0 + while (g < nGroup) { + val biasUse = if (withBias) { + bias.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup) + } else null + updateOutputFrame( + inputT.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), + outputT.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), + weightMM.select(1, g + 1), + biasUse, + fInput.select(1, g + 1), + kernelW, kernelH, strideW, strideH, + padW, padH, + nInputPlane / nGroup, inputWidth, inputHeight, + nOutputPlane / nGroup, outputWidth, outputHeight) + g += 1 + } + i += 1 } - Engine.model.sync(results) } output } @@ -130,210 +109,102 @@ class SpatialShareConvolution[T: ClassTag]( if (!propagateBack) { return gradInput } - require(input.nDimension() == 3 || input.nDimension() == 4, "Only support 3D or 4D input") - gradInput.resizeAs(input) - - if (input.nDimension() == 3) { - require(gradOutput.isContiguous()) - val (outputWidth, outputHeight, _, _) = calcOutputWH(input) - fGradInput.resize(Array(nGroup, - kernelW * kernelH * nInputPlane / nGroup, outputHeight * outputWidth)) - var g = 0 - while (g < nGroup) { - updateGradInputFrame( - gradInput.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), - gradOutput.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - weightMM.select(1, g + 1).transpose(1, 2), - fGradInput.select(1, g + 1), - kernelW, kernelH, strideW, strideH, padW, padH) - g += 1 - } + if (_1x1 || input.dim() == 3 || (input.dim() == 4 && input.size(1) == 1)) { + super.updateGradInput(input, gradOutput) } else { + gradInput.resizeAs(input) + fGradInput.resizeAs(fInput) val batchSize = input.size(1) - val (outputWidth, outputHeight, _, _) = calcOutputWH(input) - fGradInput.resize(Array(Engine.model.getPoolSize, nGroup, - kernelW * kernelH * nInputPlane / nGroup, outputHeight * outputWidth)) - - val coresNum = Math.min(batchSize, Engine.model.getPoolSize) - if (results == null || results.length != coresNum) { - results = new Array[Future[Unit]](coresNum) - } - - var i, j = 0 - val minJobNum: Int = batchSize / Engine.model.getPoolSize - val remainJobNum: Int = batchSize - minJobNum * Engine.model.getPoolSize - while (j < coresNum) { - val _j = j - results(j) = Engine.model.invoke(() => { - var _i = 1 - val distJobNum: Int = minJobNum + (if (_j < remainJobNum) 1 else 0) - val indexStart: Int = _j * minJobNum + (if (_j < remainJobNum) _j else remainJobNum) - while (_i <= distJobNum) { - val gradInputT = gradInput.select(1, _i + indexStart) - val gradOutputT = gradOutput.select(1, _i + indexStart).contiguous() - val fgradInputT = fGradInput.select(1, _j + 1) - var g = 0 - while (g < nGroup) { - updateGradInputFrame( - gradInputT.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), - gradOutputT.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - weightMM.select(1, g + 1).transpose(1, 2), - fgradInputT.select(1, g + 1), - kernelW, kernelH, strideW, strideH, padW, padH) - g += 1 - } - _i += 1 + var i = 1 + while (i <= batchSize) { + val gradInputT = gradInput.select(1, i) + val gradOutputT = gradOutput.select(1, i) + require(gradOutputT.isContiguous()) + var g = 0 + while (g < nGroup) { + updateGradInputFrame( + gradInputT.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), + gradOutputT.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), + weightMM.select(1, g + 1).transpose(1, 2), + fGradInput.select(1, g + 1), + kernelW, kernelH, strideW, strideH, padW, padH) + g += 1 } - }) - j += 1 + i += 1 } - Engine.model.sync(results) } + return gradInput } override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { require(input.nDimension() == 3 || input.nDimension() == 4, "Only support 3D or 4D input") require(gradOutput.isContiguous()) - - if (input.nDimension() == 3) { - if (gradWeightMM == null) { - gradWeightMM = gradWeight.view(nGroup, nOutputPlane / nGroup, - nInputPlane * kernelH * kernelW / nGroup) - } - val (outputWidth, outputHeight, inputWidth, inputHeight) = calcOutputWH(input) - fInput.resize(Array(nGroup, - kernelW * kernelH * nInputPlane / nGroup, outputHeight * outputWidth)) - var g = 0 - while (g < nGroup) { - write2fInput( - input.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), - fInput.select(1, g + 1), - kernelW, kernelH, strideW, strideH, - padW, padH, - nInputPlane / nGroup, inputWidth, inputHeight, - nOutputPlane / nGroup, outputWidth, outputHeight) - accGradParametersFrame( - gradOutput.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - gradWeightMM.select(1, g + 1), - gradBias.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - fInput.select(1, g + 1), - ev.fromType[Double](scaleW), - ev.fromType[Double](scaleB)) - g += 1 - } + if (_1x1 || input.dim() == 3 || (input.dim() == 4 && input.size(1) == 1)) { + super.accGradParameters(input, gradOutput) } else { val batchSize = input.size(1) if (gradWeightMMInBatch == null) { - gradWeightMMInBatch = Tensor[T]().resize(Array(batchSize, nGroup, nOutputPlane / nGroup, - nInputPlane * kernelH * kernelW / nGroup)) + gradWeightMMInBatch = Tensor[T](batchSize, nGroup, nOutputPlane / nGroup, + nInputPlane * kernelH * kernelW / nGroup) } - if (gradientBiasMT.nElement() == 0) { - gradientBiasMT.resize(Array(batchSize, nOutputPlane)) + if (withBias && gradientBiasMT.nElement() == 0) { + gradientBiasMT.resize(batchSize, nOutputPlane) } if (ones.dim() != 1 || ones.size(1) != gradOutput.size(3) * gradOutput.size(4)) { - ones.resize(Array(gradOutput.size(3) * gradOutput.size(4))).fill(ev.fromType(1.0)) + ones.resize(gradOutput.size(3) * gradOutput.size(4)).fill(ev.fromType(1.0)) } if (onesBatch.dim() != 1 || onesBatch.size(1) != batchSize) { - onesBatch.resize(Array(batchSize)).fill(ev.fromType(1.0)) + onesBatch.resize(batchSize).fill(ev.fromType(1.0)) } - val coresNum = Math.min(batchSize, Engine.model.getPoolSize) - if (results == null || results.length != coresNum) { - results = new Array[Future[Unit]](coresNum) - } - - var i, j = 0 - val minJobNum: Int = batchSize / Engine.model.getPoolSize - val remainJobNum: Int = batchSize - minJobNum * Engine.model.getPoolSize val (outputWidth, outputHeight, inputWidth, inputHeight) = calcOutputWH(input) - fInput.resize(Array(Engine.model.getPoolSize, nGroup, - kernelW * kernelH * nInputPlane / nGroup, outputHeight * outputWidth)) - while (j < coresNum) { - val _j = j - results(j) = Engine.model.invoke(() => { - var _i = 1 - val distJobNum: Int = minJobNum + (if (_j < remainJobNum) 1 else 0) - val indexStart: Int = _j * minJobNum + (if (_j < remainJobNum) _j else remainJobNum) - while (_i <= distJobNum) { - val gradOutputT = gradOutput.select(1, _i + indexStart) - val inputT = input.select(1, _i + indexStart).contiguous() - val fInputT = fInput.select(1, _j + 1) - var g = 0 - while (g < nGroup) { - write2fInput( - inputT.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), - fInputT.select(1, g + 1), - kernelW, kernelH, strideW, strideH, - padW, padH, - nInputPlane / nGroup, inputWidth, inputHeight, - nOutputPlane / nGroup, outputWidth, outputHeight) - calcGradParametersFrame( - gradOutputT.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), - gradWeightMMInBatch.select(1, _i + indexStart).select(1, g + 1), - gradientBiasMT.select(1, _i + indexStart).narrow(1, g * nOutputPlane / nGroup + 1, - nOutputPlane / nGroup), - fInputT.select(1, g + 1), - ev.fromType[Double](scaleW), - ev.fromType[Double](scaleB)) - g += 1 - } - _i += 1 + var i = 1 + while (i <= batchSize) { + val inputT = input.select(1, i) + val gradOutputT = gradOutput.select(1, i) + var g = 0 + while (g < nGroup) { + write2fInput( + inputT.narrow(1, g * nInputPlane / nGroup + 1, nInputPlane / nGroup), + fInput.select(1, g + 1), + kernelW, kernelH, strideW, strideH, + padW, padH, + nInputPlane / nGroup, inputWidth, inputHeight, + nOutputPlane / nGroup, outputWidth, outputHeight) + + val gradientBiasMTUse = if (withBias) { + gradientBiasMT.select(1, i).narrow(1, g * nOutputPlane / nGroup + 1, + nOutputPlane / nGroup) + } else null + calcGradParametersFrame( + gradOutputT.narrow(1, g * nOutputPlane / nGroup + 1, nOutputPlane / nGroup), + gradWeightMMInBatch.select(1, i).select(1, g + 1), + gradientBiasMTUse, + fInput.select(1, g + 1), + ev.fromType[Double](scaleW), + ev.fromType[Double](scaleB) + ) + g += 1 } - }) - j += 1 + i += 1 } - Engine.model.sync(results) val gradView = gradWeightMMInBatch.view(batchSize, nOutputPlane * nInputPlane * kernelH * kernelW / nGroup).t val grad = gradWeight.view(nOutputPlane * nInputPlane * kernelH * kernelW / nGroup) grad.addmv(ev.fromType(1.0), ev.fromType(1.0), gradView, onesBatch) - gradBias.addmv(ev.fromType(1.0), ev.fromType(1.0), gradientBiasMT.t, onesBatch) - } - } - - override def equals(obj: Any): Boolean = { - if (!super.equals(obj)) { - return false - } - - if (!obj.isInstanceOf[SpatialShareConvolution[T]]) { - return false + if (withBias) { + gradBias.addmv(ev.fromType(1.0), ev.fromType(1.0), gradientBiasMT.t, onesBatch) + } + if (null != wRegularizer) { + wRegularizer.accRegularization(weight, gradWeight, scaleW) + } + if (withBias && null != bRegularizer) { + bRegularizer.accRegularization(bias, gradBias, scaleB) + } } - - val other = obj.asInstanceOf[SpatialShareConvolution[T]] - this.eq(other) - } - - override def hashCode(): Int = { - val seed = 37 - var hash = super.hashCode() - hash = hash * seed + nInputPlane.hashCode() - hash = hash * seed + nOutputPlane.hashCode() - hash = hash * seed + kernelW.hashCode() - hash = hash * seed + kernelH.hashCode() - hash = hash * seed + strideW.hashCode() - hash = hash * seed + strideH.hashCode() - hash = hash * seed + padW.hashCode() - hash = hash * seed + padH.hashCode() - hash = hash * seed + weight.hashCode() - hash = hash * seed + bias.hashCode() - hash = hash * seed + gradWeight.hashCode() - hash = hash * seed + gradBias.hashCode() - - hash - } - - override def clearState(): this.type = { - super.clearState() - this - } - - override def toString(): String = { - s"${getPrintName}($nInputPlane -> $nOutputPlane, $kernelW x" + - s" $kernelH, $strideW, $strideH, $padW, $padH)" } @inline @@ -354,28 +225,26 @@ class SpatialShareConvolution[T: ClassTag]( @inline private def write2fInput( - input: Tensor[T], fInput: Tensor[T], - kW: Int, kH: Int, dW: Int, dH: Int, padW: Int, padH: Int, - nInputPlane: Int, inputWidth: Int, inputHeight: Int, - nOutputPlane: Int, outputWidth: Int, outputHeight: Int)( - implicit ev: TensorNumeric[T]): Unit = { - - if (!_1x1) { - ev.getType() match { - case DoubleType => - val before = System.nanoTime() - NNPrimitive.im2colDouble(fInput.asInstanceOf[Tensor[Double]], - input.asInstanceOf[Tensor[Double]], kW, kH, dW, dH, padW, padH, nInputPlane, - inputWidth, inputHeight, outputWidth, outputHeight) - im2colTime += System.nanoTime() - before - case FloatType => - val before = System.nanoTime() - NNPrimitive.im2colFloat(fInput.asInstanceOf[Tensor[Float]], - input.asInstanceOf[Tensor[Float]], kW, kH, dW, dH, padW, padH, nInputPlane, - inputWidth, inputHeight, outputWidth, outputHeight) - im2colTime += System.nanoTime() - before - case _ => throw new UnsupportedOperationException(s"Only Float/Double supported") - } + input: Tensor[T], fInput: Tensor[T], + kW: Int, kH: Int, dW: Int, dH: Int, padW: Int, padH: Int, + nInputPlane: Int, inputWidth: Int, inputHeight: Int, + nOutputPlane: Int, outputWidth: Int, outputHeight: Int)( + implicit ev: TensorNumeric[T]): Unit = { + + ev.getType() match { + case DoubleType => + val before = System.nanoTime() + NNPrimitive.im2colDouble(fInput.asInstanceOf[Tensor[Double]], + input.asInstanceOf[Tensor[Double]], kW, kH, dW, dH, padW, padH, nInputPlane, + inputWidth, inputHeight, outputWidth, outputHeight) + im2colTime += System.nanoTime() - before + case FloatType => + val before = System.nanoTime() + NNPrimitive.im2colFloat(fInput.asInstanceOf[Tensor[Float]], + input.asInstanceOf[Tensor[Float]], kW, kH, dW, dH, padW, padH, nInputPlane, + inputWidth, inputHeight, outputWidth, outputHeight) + im2colTime += System.nanoTime() - before + case _ => throw new UnsupportedOperationException(s"Only Float/Double supported") } } } @@ -391,10 +260,80 @@ object SpatialShareConvolution { padW: Int = 0, padH: Int = 0, nGroup: Int = 1, - propagateBack: Boolean = true) + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null, + withBias: Boolean = true) (implicit ev: TensorNumeric[T]): SpatialShareConvolution[T] = { new SpatialShareConvolution[T](nInputPlane, nOutputPlane, kernelW, kernelH, - strideW, strideH, padW, padH, nGroup, - propagateBack) + strideW, strideH, padW, padH, nGroup, propagateBack, wRegularizer, bRegularizer, + initWeight, initBias, initGradWeight, initGradBias, withBias) + } + + def apply[@specialized(Float, Double) T: ClassTag]( + conv: SpatialConvolution[T] + )(implicit ev: TensorNumeric[T]): SpatialShareConvolution[T] = { + val sConv = new SpatialShareConvolution[T](conv.nInputPlane, conv.nOutputPlane, + conv.kernelW, conv.kernelH, + conv.strideW, conv.strideH, + conv.padW, conv.padH, + conv.nGroup, conv.propagateBack, + conv.wRegularizer, conv.bRegularizer, withBias = conv.withBias + ) + sConv.weight.copy(conv.weight) + sConv.gradWeight.copy(conv.gradWeight) + if (conv.withBias) { + sConv.gradBias.copy(conv.gradBias) + sConv.bias.copy(conv.bias) + } + sConv.setScaleW(conv.getScaleW()) + sConv.setScaleB(conv.getScaleB()) + sConv.setName(conv.getName()) + sConv + } + + /** + * Replace all the SpatialConvolution in `model` with SpatialSharedConvolution, + * and shared the fInput and fGradInput in all SpatialSharedConvolution. + * @param model a Module + * @return model sharedConvolution. + */ + def shareConvolution[T: ClassTag](model: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val fInputCache = Tensor[T](1) + val fGradInputCache = Tensor[T](1) + shareConvolution(model, fInputCache, fGradInputCache) + model + } + + private def shareConvolution[T: ClassTag]( + model: Module[T], + fInputCache: Tensor[T], + fGradInputCache: Tensor[T])(implicit ev: TensorNumeric[T]): Unit = { + model match { + case container: Container[Activity, Activity, T] => + var i = 0 + while (i < container.modules.length) { + val m = container.modules(i) + if (m.isInstanceOf[SpatialConvolution[T]]) { + val curModel = if (!m.isInstanceOf[SpatialShareConvolution[T]]) { + SpatialShareConvolution( + m.asInstanceOf[SpatialConvolution[T]]) + } else { + m.asInstanceOf[SpatialShareConvolution[T]] + } + curModel.fInput.set(fInputCache) + curModel.fGradInput.set(fGradInputCache) + container.modules(i) = curModel + } else { + shareConvolution(m, fInputCache, fGradInputCache) + } + i += 1 + } + case _ => Unit + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index fa4ab85a965..5c3eec8c1f3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -933,17 +933,24 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab bRegularizer) } - def createSpatialShareConvolution(nInputPlane: Int, - nOutputPlane: Int, - kernelW: Int, - kernelH: Int, - strideW: Int = 1, - strideH: Int = 1, - padW: Int = 0, - padH: Int = 0, - nGroup: Int = 1, - propagateBack: Boolean = true) - : SpatialShareConvolution[T] = { + def createSpatialShareConvolution( + nInputPlane: Int, + nOutputPlane: Int, + kernelW: Int, + kernelH: Int, + strideW: Int = 1, + strideH: Int = 1, + padW: Int = 0, + padH: Int = 0, + nGroup: Int = 1, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null, + withBias: Boolean = true) : SpatialShareConvolution[T] = { SpatialShareConvolution[T](nInputPlane, nOutputPlane, kernelW, @@ -953,7 +960,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab padW, padH, nGroup, - propagateBack) + propagateBack, + wRegularizer, + bRegularizer, + toTensor(initWeight), + toTensor(initBias), + toTensor(initGradWeight), + toTensor(initGradBias), + withBias + ) } def createSpatialZeroPadding(padLeft: Int, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SpatialShareConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SpatialShareConvolutionSpec.scala new file mode 100644 index 00000000000..947413de7db --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SpatialShareConvolutionSpec.scala @@ -0,0 +1,191 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import com.intel.analytics.bigdl.nn.{SpatialConvolution, SpatialShareConvolution} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.models.inception.{Inception_v1_NoAuxClassifier} +import com.intel.analytics.bigdl.models.resnet.ResNet +import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} +import com.intel.analytics.bigdl.models.vgg.{Vgg_16} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +@com.intel.analytics.bigdl.tags.Parallel +class SpatialShareConvolutionSpec extends FlatSpec with Matchers { + val testSize = Array( + (1, 1, 1, 1, 1, 1, 1, 1, 1), + (1, 3, 1, 1, 1, 1, 1, 1, 1), + (3, 1, 1, 1, 1, 1, 1, 1, 1), + (3, 3, 1, 1, 1, 1, 1, 1, 1), + (3, 6, 1, 1, 1, 1, 1, 1, 1), + (1, 1, 2, 2, 1, 1, 1, 1, 1), + (1, 3, 2, 2, 1, 1, 1, 1, 1), + (3, 1, 2, 2, 1, 1, 1, 1, 1), + (3, 3, 2, 2, 1, 1, 1, 1, 1), + (3, 6, 2, 2, 1, 1, 1, 1, 1), + (1, 1, 2, 2, 2, 2, 1, 1, 1), + (1, 3, 2, 2, 2, 2, 1, 1, 1), + (3, 1, 2, 2, 2, 2, 1, 1, 1), + (3, 3, 2, 2, 2, 2, 1, 1, 1), + (3, 6, 2, 2, 2, 2, 1, 1, 1), + (1, 1, 2, 2, 2, 2, 0, 0, 1), + (1, 3, 2, 2, 2, 2, 0, 0, 1), + (3, 1, 2, 2, 2, 2, 0, 0, 1), + (3, 3, 2, 2, 2, 2, 0, 0, 1), + (3, 6, 2, 2, 2, 2, 0, 0, 1), + (1, 1, 2, 2, 2, 2, 2, 2, 1), + (1, 3, 2, 2, 2, 2, 2, 2, 1), + (3, 1, 2, 2, 2, 2, 2, 2, 1), + (3, 3, 2, 2, 2, 2, 2, 2, 1), + (3, 6, 2, 2, 2, 2, 2, 2, 1), + (3, 6, 2, 2, 2, 2, 2, 2, 3) + ) + + "SpatialSharedConvolution and SpatialConvolution" should "return the same result" in { + RandomGenerator.RNG.setSeed(10) + for (size <- testSize) { + val conv = SpatialConvolution( + size._1, size._2, size._3, + size._4, size._5, size._6, + size._7, size._8, size._9 + ) + val sharedConv = SpatialShareConvolution( + size._1, size._2, size._3, + size._4, size._5, size._6, + size._7, size._8, size._9 + ) + sharedConv.getParameters()._1.copy(conv.getParameters()._1) + + val input = Tensor(1, size._1, 8, 8).rand() + val output1 = conv.forward(input) + val gradOutput = output1.clone().rand() + val gradInput1 = conv.backward(input, gradOutput) + + val output2 = sharedConv.forward(input) + val gradInput2 = sharedConv.backward(input, gradOutput) + + output1 should be (output2) + gradInput1 should be (gradInput2) + conv.gradWeight should be (sharedConv.gradWeight) + conv.gradBias should be (sharedConv.gradBias) + } + } + + "SpatialSharedConvolution and SpatialConvolution without bias" should + "return the same result" in { + RandomGenerator.RNG.setSeed(10) + for (size <- testSize) { + val conv = SpatialConvolution( + size._1, size._2, size._3, + size._4, size._5, size._6, + size._7, size._8, size._9, withBias = false + ) + val sharedConv = SpatialShareConvolution( + size._1, size._2, size._3, + size._4, size._5, size._6, + size._7, size._8, size._9, withBias = false + ) + sharedConv.getParameters()._1.copy(conv.getParameters()._1) + + val input = Tensor(1, size._1, 8, 8).rand() + val output1 = conv.forward(input) + val gradOutput = output1.clone().rand() + val gradInput1 = conv.backward(input, gradOutput) + + val output2 = sharedConv.forward(input) + val gradInput2 = sharedConv.backward(input, gradOutput) + + output1 should be (output2) + gradInput1 should be (gradInput2) + conv.gradWeight should be (sharedConv.gradWeight) + conv.gradBias should be (sharedConv.gradBias) + } + } + + "Inception" should "return right result" in { + val inception = Inception_v1_NoAuxClassifier(1024) + val sharedInception = SpatialShareConvolution.shareConvolution( + inception.cloneModule()) + sharedInception.getParameters()._1.equals( + inception.getParameters()._1) should be (true) + + val input = Tensor(4, 3, 224, 224).rand() + RandomGenerator.RNG.setSeed(100) + val output1 = inception.forward(input).toTensor + val gradOutput = output1.clone().apply1(_ => Random.nextFloat()) + val gradInput1 = inception.backward(input, gradOutput) + + RandomGenerator.RNG.setSeed(100) + val output2 = sharedInception.forward(input).toTensor + val gradInput2 = sharedInception.backward(input, gradOutput) + + output1 should be (output2) + gradInput1 should be (gradInput2) + inception.getParameters()._2.equals( + sharedInception.getParameters()._2) should be (true) + } + + "Vgg_16" should "return right result" in { + val vgg = Vgg_16(1000) + val sharedVgg = SpatialShareConvolution.shareConvolution( + vgg.cloneModule()) + sharedVgg.getParameters()._1.equals( + vgg.getParameters()._1) should be (true) + + val input = Tensor(4, 3, 224, 224).rand() + RandomGenerator.RNG.setSeed(100) + val output1 = vgg.forward(input).toTensor + val gradOutput = output1.clone().apply1(_ => Random.nextFloat()) + val gradInput1 = vgg.backward(input, gradOutput) + + RandomGenerator.RNG.setSeed(100) + val output2 = sharedVgg.forward(input).toTensor + val gradInput2 = sharedVgg.backward(input, gradOutput) + + output1 should be (output2) + gradInput1 should be (gradInput2) + vgg.getParameters()._2.equals( + sharedVgg.getParameters()._2) should be (true) + } + + "Resnet 18" should "return right result" in { + val resnet = ResNet(1000, T("shortcutType" -> ShortcutType.B, + "depth" -> 18, "dataset" -> DatasetType.ImageNet)) + val sharedResnet = SpatialShareConvolution.shareConvolution( + ResNet(1000, T("shortcutType" -> ShortcutType.B, + "depth" -> 18, "dataset" -> DatasetType.ImageNet))) + sharedResnet.getParameters()._1.copy(resnet.getParameters()._1) + + val input = Tensor(4, 3, 224, 224).rand() + RandomGenerator.RNG.setSeed(100) + val output1 = resnet.forward(input).toTensor + val gradOutput = output1.clone().apply1(_ => Random.nextFloat()) + val gradInput1 = resnet.backward(input, gradOutput) + + RandomGenerator.RNG.setSeed(100) + val output2 = sharedResnet.forward(input).toTensor + val gradInput2 = sharedResnet.backward(input, gradOutput) + + output1 should be (output2) + gradInput1 should be (gradInput2) + resnet.getParameters()._2 should be (sharedResnet.getParameters()._2) + } + +} From 0ca90ce548165c4613208acd07126dcf7418da6e Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Tue, 18 Jul 2017 21:25:00 -0700 Subject: [PATCH 0290/1065] apply generics (#1198) * apply generics to DLClassifier and DLEstimator * update example --- .../scala/org/apache/spark/ml/DLClassifier.scala | 10 +++------- .../scala/org/apache/spark/ml/DLEstimator.scala | 14 +++++++------- .../org/apache/spark/ml/DLEstimatorBase.scala | 11 ++++++----- .../org/apache/spark/ml/DLTransformerBase.scala | 6 +++--- .../org/apache/spark/ml/DLEstimatorBase.scala | 11 ++++++----- .../org/apache/spark/ml/DLTransformerBase.scala | 6 +++--- .../example/MLPipeline/DLClassifierLeNet.scala | 2 +- .../DLClassifierLogisticRegression.scala | 7 ++++--- .../MLPipeline/DLEstimatorMultiLabelLR.scala | 12 ++++++------ 9 files changed, 39 insertions(+), 40 deletions(-) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala index df98e3f4765..79c56e8b9af 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala @@ -18,8 +18,8 @@ package org.apache.spark.ml import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.{Criterion, Module} -import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types._ import scala.reflect.ClassTag @@ -42,19 +42,15 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( extends DLEstimator[T](model, criterion, featureSize, Array(1)) { override protected def wrapBigDLModel( - m: Module[T], featureSize: Array[Int]): DLTransformerBase = { + m: Module[T], featureSize: Array[Int]): DLClassifierModel[T] = { val dlModel = new DLClassifierModel[T](m, featureSize) - copyValues(dlModel.setParent(this)) + copyValues(dlModel.setParent(this)).asInstanceOf[DLClassifierModel[T]] } override def transformSchema(schema : StructType): StructType = { validateSchema(schema) SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) } - - override def copy(extra: ParamMap): DLClassifier[T] = { - copyValues(new DLClassifier(model, criterion, featureSize), extra) - } } /** diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index 1b2bedeabe9..fad6530efa8 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -47,7 +47,7 @@ import scala.reflect.ClassTag * @param model BigDL module to be optimized * @param criterion BigDL criterion method * @param featureSize The size (Tensor dimensions) of the feature data. e.g. an image may be with - * width * height = 28 * 28, featureSize = Array(28, 28). + * width * height = 28 * 28, featureSize = Array(28, 28). * @param labelSize The size (Tensor dimensions) of the label data. */ class DLEstimator[@specialized(Float, Double) T: ClassTag]( @@ -55,8 +55,8 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( val criterion : Criterion[T], val featureSize : Array[Int], val labelSize : Array[Int], - override val uid: String = "DLEstimator" - )(implicit ev: TensorNumeric[T]) extends DLEstimatorBase with DLParams with HasBatchSize { + override val uid: String = "DLEstimator")(implicit ev: TensorNumeric[T]) + extends DLEstimatorBase[DLEstimator[T], DLModel[T]] with DLParams with HasBatchSize { def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) @@ -82,7 +82,7 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( } protected override def internalFit( - featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): DLTransformerBase = { + featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): DLModel[T] = { val batches = toMiniBatch(featureAndLabel) val dataset = DataSet.rdd(batches) @@ -98,7 +98,7 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( /** * sub classes can extend the method and return required model for different transform tasks */ - protected def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLTransformerBase = { + protected def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLModel[T] = { val dlModel = new DLModel[T](m, featureSize) copyValues(dlModel.setParent(this)) } @@ -157,7 +157,7 @@ class DLModel[@specialized(Float, Double) T: ClassTag]( var featureSize : Array[Int], override val uid: String = "DLModel" )(implicit ev: TensorNumeric[T]) - extends DLTransformerBase with DLParams with HasBatchSize { + extends DLTransformerBase[DLModel[T]] with DLParams with HasBatchSize { def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) @@ -225,7 +225,7 @@ class DLModel[@specialized(Float, Double) T: ClassTag]( override def copy(extra: ParamMap): DLModel[T] = { val copied = new DLModel(model, featureSize, uid).setParent(parent) - copyValues(copied, extra).asInstanceOf[DLModel[T]] + copyValues(copied, extra) } } diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index c1016ea4d8b..e6a397ab6ec 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -63,14 +63,15 @@ private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { * Extends MLEstimator and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -private[ml] abstract class DLEstimatorBase - extends Estimator[DLTransformerBase] with DLParams with HasLabelCol{ +private[ml] abstract class DLEstimatorBase[Learner <: DLEstimatorBase[Learner, M], + M <: DLTransformerBase[M]] + extends Estimator[M] with DLParams with HasLabelCol { protected def getLabelArrayCol: String = $(labelCol) + "_Array" - protected def internalFit(featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): DLTransformerBase + protected def internalFit(featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): M - override def fit(dataset: DataFrame): DLTransformerBase = { + override def fit(dataset: DataFrame): M = { transformSchema(dataset.schema, logging = true) internalFit(toArrayType(dataset)) } @@ -112,7 +113,7 @@ private[ml] abstract class DLEstimatorBase s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") } - override def copy(extra: ParamMap): DLEstimatorBase = defaultCopy(extra) + override def copy(extra: ParamMap): Learner = defaultCopy(extra) } diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala index 54b29322710..bbc16b81bd1 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala @@ -24,8 +24,8 @@ import org.apache.spark.sql.DataFrame * Extends MlTransformer and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -private[ml] abstract class DLTransformerBase - extends Model[DLTransformerBase] with DLParams { +private[ml] abstract class DLTransformerBase[M <: DLTransformerBase[M]] + extends Model[M] with DLParams { /** * convert feature columns(MLlib Vectors or Array) to Seq format @@ -51,5 +51,5 @@ private[ml] abstract class DLTransformerBase } } - override def copy(extra: ParamMap): DLTransformerBase = defaultCopy(extra) + override def copy(extra: ParamMap): M = defaultCopy(extra) } diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index c13743e6205..86fdaadd651 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -63,14 +63,15 @@ private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { * Extends MLEstimator and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -private[ml] abstract class DLEstimatorBase - extends Estimator[DLTransformerBase] with DLParams with HasLabelCol{ +private[ml] abstract class DLEstimatorBase[Learner <: DLEstimatorBase[Learner, M], + M <: DLTransformerBase[M]] + extends Estimator[M] with DLParams with HasLabelCol { protected def getLabelArrayCol: String = $(labelCol) + "_Array" - protected def internalFit(featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): DLTransformerBase + protected def internalFit(featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): M - override def fit(dataset: Dataset[_]): DLTransformerBase = { + override def fit(dataset: Dataset[_]): M = { transformSchema(dataset.schema, logging = true) internalFit(toArrayType(dataset.toDF())) } @@ -112,7 +113,7 @@ private[ml] abstract class DLEstimatorBase s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") } - override def copy(extra: ParamMap): DLEstimatorBase = defaultCopy(extra) + override def copy(extra: ParamMap): Learner = defaultCopy(extra) } diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala index 55bd2cce16b..681bcb9e083 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala @@ -24,8 +24,8 @@ import org.apache.spark.sql.{DataFrame, Dataset} * Extends MlTransformer and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -private[ml] abstract class DLTransformerBase - extends Model[DLTransformerBase] with DLParams { +private[ml] abstract class DLTransformerBase[M <: DLTransformerBase[M]] + extends Model[M] with DLParams { /** * convert feature columns(MLlib Vectors or Array) to Seq format @@ -51,5 +51,5 @@ private[ml] abstract class DLTransformerBase } } - override def copy(extra: ParamMap): DLTransformerBase = defaultCopy(extra) + override def copy(extra: ParamMap): M = defaultCopy(extra) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala index 77fff4d9339..f504e32dac6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala @@ -72,7 +72,7 @@ object DLClassifierLeNet { .setFeaturesCol(inputs(0)) .setLabelCol(inputs(1)) .setBatchSize(50) - val transformer = estimator.fit(trainingDF).asInstanceOf[DLModel[Float]] + val transformer = estimator.fit(trainingDF) val validationSet = DataSet.array(load(validationData, validationLabel), sc) -> BytesToGreyImg(28, 28) -> GreyImgNormalizer(testMean, testStd) -> GreyImgToBatch(1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala index 7072685c6fc..a764bf633b1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.example.MLPipeline import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Linear, LogSoftMax, Sequential} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.utils.Engine import org.apache.spark.SparkContext import org.apache.spark.ml.DLClassifier @@ -34,9 +35,9 @@ object DLClassifierLogisticRegression { val sqlContext = SQLContext.getOrCreate(sc) Engine.init - val model = new Sequential[Float]().add(Linear[Float](2, 2)).add(LogSoftMax[Float]) - val criterion = ClassNLLCriterion[Float]() - val estimator = new DLClassifier[Float](model, criterion, Array(2)) + val model = Sequential().add(Linear(2, 2)).add(LogSoftMax()) + val criterion = ClassNLLCriterion() + val estimator = new DLClassifier(model, criterion, Array(2)) .setBatchSize(4) .setMaxEpoch(10) val data = sc.parallelize(Seq( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala index f04d5128b08..21a6e03e468 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala @@ -15,14 +15,15 @@ */ package com.intel.analytics.bigdl.example.MLPipeline -import com.intel.analytics.bigdl.nn.{Linear, LogSoftMax, MultiLabelSoftMarginCriterion, Sequential} +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.utils.Engine import org.apache.spark.SparkContext import org.apache.spark.ml.DLEstimator import org.apache.spark.sql.SQLContext /** - * Multi-label Logistic Regression with BigDL layers and DLEstimator + * Multi-label regression with BigDL layers and DLEstimator */ object DLEstimatorMultiLabelLR { @@ -34,9 +35,9 @@ object DLEstimatorMultiLabelLR { val sqlContext = SQLContext.getOrCreate(sc) Engine.init - val model = new Sequential[Float]().add(Linear[Float](2, 2)).add(LogSoftMax[Float]) - val criterion = MultiLabelSoftMarginCriterion[Float]() - val estimator = new DLEstimator[Float](model, criterion, Array(2), Array(2)) + val model = Sequential().add(Linear(2, 2)) + val criterion = MSECriterion() + val estimator = new DLEstimator(model, criterion, Array(2), Array(2)) .setBatchSize(4) .setMaxEpoch(10) val data = sc.parallelize(Seq( @@ -48,5 +49,4 @@ object DLEstimatorMultiLabelLR { val dlModel = estimator.fit(df) dlModel.transform(df).show(false) } - } From 7c57b68929777f477a7babc02ec2e66ab9df8a3c Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 19 Jul 2017 00:19:56 -0500 Subject: [PATCH 0291/1065] Graph doc (#1275) * Add Input docs * fix bugs * meet code review --- .../bigdl/dllib/nn/BinaryTreeLSTM.scala | 1 + .../analytics/bigdl/dllib/nn/Graph.scala | 30 ---------- .../analytics/bigdl/dllib/nn/Input.scala | 60 +++++++++++++++++++ 3 files changed, 61 insertions(+), 30 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala index 171be024b0c..8c2326a7617 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.Input import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 94569ed396e..d9d9b516d7f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -315,36 +315,6 @@ object Graph { } } -/** - * Each input node of the graph container should accept one tensor as input. If you want a module - * accepting multiple tensors as input, you should add some Input module before it and connect - * the outputs of the Input nodes to it. - * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] - */ -@SerialVersionUID(- 8525406230282608924L) -class Input[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T] { - override def updateOutput(input: Tensor[T]): Tensor[T] = { - output = input - output - } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - gradInput = gradOutput - gradInput - } - override def equals(other: Any): Boolean = { - if (!other.isInstanceOf[Input[_]]) return false - this.eq(other.asInstanceOf[Input[_]]) - } - - override def hashCode(): Int = System.identityHashCode(this) -} - -object Input { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): ModuleNode[T] = { - new Node(new Input().asInstanceOf[AbstractModule[Activity, Tensor[T], T]]) - } -} - private[bigdl] class Dummy[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Tensor[T], T] { override def updateOutput(input: Activity): Tensor[T] = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala new file mode 100644 index 00000000000..7904e674783 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Node + +import scala.reflect.ClassTag + +/** + * Input layer do nothing to the input tensors, just pass them. It should be used as input node + * when the first layer of your module accepts multiple tensors as inputs. + * + * Each input node of the graph container should accept one tensor as input. If you want a module + * accepting multiple tensors as input, you should add some Input module before it and connect + * the outputs of the Input nodes to it. + * + * Please note that the return is not a layer but a Node containing input layer. + * + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +@SerialVersionUID(- 8525406230282608924L) +class Input[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + override def updateOutput(input: Tensor[T]): Tensor[T] = { + output = input + output + } + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + gradInput = gradOutput + gradInput + } + override def equals(other: Any): Boolean = { + if (!other.isInstanceOf[Input[_]]) return false + this.eq(other.asInstanceOf[Input[_]]) + } + + override def hashCode(): Int = System.identityHashCode(this) +} + +object Input { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): ModuleNode[T] = { + new Node(new Input().asInstanceOf[AbstractModule[Activity, Tensor[T], T]]) + } +} From 83727557d3f7efe323fe977899e515d9e9931991 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 18 Jul 2017 22:45:28 -0700 Subject: [PATCH 0292/1065] Refine tensorflow doc (a follow up of pr #1253 to meet code review) (#1272) * meet code review * fix example * meet code review * refine title * make saveTF compatible with saveCaffe --- .../dllib/nn/abstractnn/AbstractModule.scala | 16 +++++++++++++++- .../dllib/utils/python/api/PythonBigDL.scala | 4 ++-- 2 files changed, 17 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 72041e97e1a..5affbab70ad 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -16,11 +16,13 @@ package com.intel.analytics.bigdl.nn.abstractnn +import java.nio.ByteOrder + import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.tensor.{Tensor, TensorDataType} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ -import com.intel.analytics.bigdl.nn.{InitializationMethod, Module, Zeros} +import com.intel.analytics.bigdl.nn.{Graph, InitializationMethod, Module, Zeros} import com.intel.analytics.bigdl.utils.TorchObject.TYPE_MODULE import org.apache.commons.lang3.SerializationUtils import org.apache.spark.rdd.RDD @@ -28,6 +30,7 @@ import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.utils.caffe.CaffePersister +import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} import scala.reflect.ClassTag @@ -395,6 +398,17 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, this } + def saveTF( + inputs : Seq[(String, Seq[Int])], + path: String, + byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, + dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC): this.type = { + require(this.isInstanceOf[Graph[T]], "only Graph container can be saved as Tensorflow model") + this.clearState() + TensorflowSaver.saveGraph(this.asInstanceOf[Graph[T]], inputs, path, byteOrder, dataFormat) + this + } + /** * @return Float or Double */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 5c3eec8c1f3..e373948919d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1376,7 +1376,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Module.loadTF[T](path, inputs.asScala, outputs.asScala, order) } - def saveTF(model: Graph[T], + def saveTF(model: AbstractModule[Activity, Activity, T], inputs: JList[Any], path: String, byteOrder: String, @@ -1398,7 +1398,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab val shape = array.get(1).asInstanceOf[JList[Int]] (name, shape.asScala) } - TensorflowSaver.saveGraph(model, scalaInputs, path, order, format) + model.saveTF(scalaInputs, path, order, format) } def modelPredictRDD(model: AbstractModule[Activity, Activity, T], From 6c55c86eecb136c0409ef20fdabe052300584638 Mon Sep 17 00:00:00 2001 From: Yan Wan Date: Wed, 19 Jul 2017 14:47:16 +0800 Subject: [PATCH 0293/1065] fix rnn example (#1283) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add an optional extended description… --- .../analytics/bigdl/dllib/models/rnn/Train.scala | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala index c3f1116eb2c..3bcd5b40b9c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.dataset.text._ import com.intel.analytics.bigdl.dataset.text.utils.SentenceToken import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, Module, TimeDistributedCriterion} import com.intel.analytics.bigdl.optim._ -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.{Engine, T, Table} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._ import org.apache.log4j.{Level, Logger} @@ -70,21 +70,25 @@ object Train { val endIdx = dictionary.getIndex(SentenceToken.end) val padFeature = Tensor[Float]().resize(totalVocabLength) padFeature.setValue(endIdx + 1, 1.0f) - val padLabel = startIdx + val padLabel = Tensor[Float](T(startIdx.toFloat + 1.0f)) val featurePadding = PaddingParam(Some(Array(padFeature)), FixedLength(Array(maxTrainLength))) + val labelPadding = PaddingParam(Some(Array(padLabel)), + FixedLength(Array(maxTrainLength))) val trainSet = DataSet.rdd(tokens) .transform(TextToLabeledSentence[Float](dictionary)) .transform(LabeledSentenceToSample[Float](totalVocabLength)) - .transform(SampleToMiniBatch[Float](param.batchSize, - Some(featurePadding), None)) + .transform(SampleToMiniBatch[Float]( + param.batchSize, + Some(featurePadding), + Some(labelPadding))) val validationSet = DataSet.rdd(valtokens) .transform(TextToLabeledSentence[Float](dictionary)) .transform(LabeledSentenceToSample[Float](totalVocabLength)) .transform(SampleToMiniBatch[Float](param.batchSize, - Some(featurePadding), None)) + Some(featurePadding), Some(labelPadding))) val model = if (param.modelSnapshot.isDefined) { Module.load[Float](param.modelSnapshot.get) From a1992b937823a59ab39ab4189425055df8a8c9ff Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 19 Jul 2017 15:45:28 +0800 Subject: [PATCH 0294/1065] refactor file and add s3 support for CaffePersister (#1271) --- .../analytics/bigdl/dllib/utils/File.scala | 143 ++++++++++++------ .../bigdl/dllib/utils/TorchFile.scala | 4 +- .../bigdl/dllib/utils/caffe/CaffeLoader.scala | 18 +-- .../dllib/utils/caffe/CaffePersister.scala | 63 ++------ .../bigdl/dllib/integration/S3Spec.scala | 58 +++++-- 5 files changed, 171 insertions(+), 115 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala index e7083a775c1..443dac91618 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala @@ -17,7 +17,6 @@ package com.intel.analytics.bigdl.utils import java.io._ -import java.nio.file.{Files, Paths} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FSDataInputStream, FSDataOutputStream, FileSystem, Path} @@ -66,8 +65,21 @@ object File { * @param isOverwrite if overwrite. */ def save(obj: Serializable, fileName: String, isOverwrite: Boolean = false): Unit = { - val conf = getConfiguration(fileName) - save(obj, fileName, isOverwrite, conf) + var fw: FileWriter = null + var out: OutputStream = null + var objFile: ObjectOutputStream = null + try { + fw = FileWriter(fileName) + out = fw.create(isOverwrite) + val byteArrayOut = new ByteArrayOutputStream() + objFile = new ObjectOutputStream(byteArrayOut) + objFile.writeObject(obj) + IOUtils.copyBytes(new ByteArrayInputStream(byteArrayOut.toByteArray), out, 1024, true) + } finally { + if (null != objFile) objFile.close() + if (null != out) out.close() + if (null != fw) fw.close() + } } private[bigdl] def getFileSystem(fileName: String): org.apache.hadoop.fs.FileSystem = { @@ -85,33 +97,6 @@ object File { } } - private def save(obj: Serializable, fileName: String, overwrite: Boolean, - conf: Configuration): Unit = { - val dest = new Path(fileName) - var fs: FileSystem = null - var out: FSDataOutputStream = null - var objFile: ObjectOutputStream = null - try { - fs = dest.getFileSystem(conf) - if (fs.exists(dest)) { - if (overwrite) { - fs.delete(dest, true) - } else { - throw new RuntimeException(s"file $fileName already exists") - } - } - out = fs.create(dest) - val byteArrayOut = new ByteArrayOutputStream() - objFile = new ObjectOutputStream(byteArrayOut) - objFile.writeObject(obj) - IOUtils.copyBytes(new ByteArrayInputStream(byteArrayOut.toByteArray), out, 1024, true) - } finally { - if (null != objFile) objFile.close() - if (null != out) out.close() - if (null != fs) fs.close() - } - } - /** * Write file to HDFS. * @param obj @@ -175,24 +160,12 @@ object File { * @param fileName file name. */ def load[T](fileName: String): T = { - val conf = getConfiguration(fileName) - load[T](fileName, conf) - } - - /** - * Load a scala object from a local/hdfs/s3 path. - * - * @param fileName file name. - * @param conf hadoop Configuration. - */ - private def load[T](fileName: String, conf: Configuration): T = { - val src: Path = new Path(fileName) - var fs: FileSystem = null - var in: FSDataInputStream = null + var fr: FileReader = null + var in: InputStream = null var objFile: ObjectInputStream = null try { - fs = src.getFileSystem(conf) - in = fs.open(src) + fr = FileReader(fileName) + in = fr.open() val byteArrayOut = new ByteArrayOutputStream() IOUtils.copyBytes(in, byteArrayOut, 1024, true) objFile = new ObjectInputStream(new ByteArrayInputStream(byteArrayOut.toByteArray)) @@ -200,7 +173,7 @@ object File { result.asInstanceOf[T] } finally { if (null != in) in.close() - if (null != fs) fs.close() + if (null != fr) fr.close() if (null != objFile) objFile.close() } } @@ -226,3 +199,79 @@ object File { } } } + +/** + * FileReader in BigDL. + * @param fileName + */ +private[bigdl] class FileReader(fileName: String) { + private var inputStream: InputStream = null + private val conf = File.getConfiguration(fileName) + private val path = new Path(fileName) + private val fs: FileSystem = path.getFileSystem(conf) + + /** + * get an InputStream + * @return + */ + def open(): InputStream = { + require(inputStream == null, s"File $fileName has been opened already.") + require(fs.exists(path), s"$fileName is empty!") + inputStream = fs.open(path) + inputStream + } + + /** + * close the resources. + */ + def close(): Unit = { + if (null != inputStream) inputStream.close() + fs.close() + } +} + +object FileReader { + private[bigdl] def apply(fileName: String): FileReader = { + new FileReader(fileName) + } +} + +/** + * FileWriter in BigDL. + * @param fileName + */ +private[bigdl] class FileWriter(fileName: String) { + private var outputStream: OutputStream = null + private val conf = File.getConfiguration(fileName) + private val path = new Path(fileName) + private val fs: FileSystem = path.getFileSystem(conf) + + /** + * get an OutputStream + * @param overwrite if overwrite + * @return + */ + def create(overwrite: Boolean = false): OutputStream = { + require(outputStream == null, s"File $fileName has been created already.") + if (!overwrite) { + require(!fs.exists(path), s"$fileName already exists!") + } + outputStream = fs.create(path, overwrite) + outputStream + } + + /** + * close the resources. + */ + def close(): Unit = { + if (null != outputStream) outputStream.close() + fs.close() + } +} + +object FileWriter { + private[bigdl] def apply(fileName: String): FileWriter = { + new FileWriter(fileName) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala index 999914973c4..1f8c555b8cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala @@ -97,13 +97,13 @@ object TorchFile { fileName: String, objectType: TorchObject, overWrite: Boolean = false): Unit = { - val file = new File(fileName) + val file = new java.io.File(fileName) if (file.exists()) { require(file.isFile(), s"$fileName is not a file") if (!overWrite) { throw new FileAlreadyExistsException(fileName) } else { // clear the file - val fw = new FileWriter(file) + val fw = new java.io.FileWriter(file) fw.write("") fw.close() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 666878a086f..2e222a04f7f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{File, Table} +import com.intel.analytics.bigdl.utils.{File, FileReader, Table} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FSDataInputStream, Path} import org.apache.log4j.Logger @@ -87,16 +87,16 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } private def loadBinary(prototxtPath: String, modelPath: String): Caffe.NetParameter = { - var modelFs: org.apache.hadoop.fs.FileSystem = null - var prototxtFs: org.apache.hadoop.fs.FileSystem = null + var modelFr: FileReader = null + var prototxtFr: FileReader = null var modelStream: InputStream = null var prototxtStream: InputStream = null var prototxtReader: InputStreamReader = null try { - modelFs = File.getFileSystem(prototxtPath) - prototxtFs = File.getFileSystem(prototxtPath) - modelStream = modelFs.open(new Path(modelPath)) - prototxtStream = modelFs.open(new Path(prototxtPath)) + modelFr = FileReader(modelPath) + prototxtFr = FileReader(prototxtPath) + modelStream = modelFr.open() + prototxtStream = prototxtFr.open() prototxtReader = new InputStreamReader(prototxtStream, "ASCII") val builder = NetParameter.newBuilder @@ -111,8 +111,8 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, if (null != prototxtReader) prototxtReader.close() if (null != modelStream) modelStream.close() if (null != prototxtStream) prototxtStream.close() - if (modelFs != null) modelFs.close() - if (prototxtFs != null) prototxtFs.close() + if (modelFr != null) modelFr.close() + if (prototxtFr != null) prototxtFr.close() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala index 6855ef3c90b..1c5355824b5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala @@ -15,13 +15,13 @@ */ package com.intel.analytics.bigdl.utils.caffe -import java.io._ +import java.io.{ByteArrayInputStream, ByteArrayOutputStream, OutputStream} import scala.collection.JavaConverters._ import caffe.Caffe.{LayerParameter, NetParameter, V1LayerParameter} import com.intel.analytics.bigdl.nn.{Container, Graph, Sequential, View} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Node +import com.intel.analytics.bigdl.utils.{File, FileWriter, Node} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -169,32 +169,15 @@ class CaffePersister[T: ClassTag](val prototxtPath: String, private def saveBinary() : Unit = { // save binary - if (prototxtPath.startsWith(hdfsPrefix)) { - val binaryFile = new Path(modelPath) - val fs = binaryFile.getFileSystem(new Configuration()) - if (fs.exists(binaryFile)) { - if (overwrite) { - fs.delete(binaryFile, true) - } else { - throw new RuntimeException(s"file $modelPath already exists") - } - } - val out = fs.create(binaryFile) + var binaryFileWriter: FileWriter = null + try { + binaryFileWriter = FileWriter(modelPath) + val out = binaryFileWriter.create(overwrite) val byteArrayOut = new ByteArrayOutputStream() byteArrayOut.write(netparam.build.toByteArray) IOUtils.copyBytes(new ByteArrayInputStream(byteArrayOut.toByteArray), out, 1024, true) - } else { - val binaryFile = new java.io.File(modelPath) - if (binaryFile.exists()) { - if (overwrite) { - binaryFile.delete() - } else { - throw new RuntimeException(s"file $modelPath already exists") - } - } - val binaryWriter = new FileOutputStream(binaryFile) - binaryWriter.write(netparam.build.toByteArray) - binaryWriter.close + } finally { + binaryFileWriter.close() } } @@ -227,32 +210,16 @@ class CaffePersister[T: ClassTag](val prototxtPath: String, netParameterWithoutData.addLayers(v1Layer) }) } - if (prototxtPath.startsWith(hdfsPrefix)) { - val prototxtFile = new Path(prototxtPath) - val fs = prototxtFile.getFileSystem(new Configuration()) - if (fs.exists(prototxtFile)) { - if (overwrite) { - fs.delete(prototxtFile, true) - } else { - throw new RuntimeException(s"file $prototxtPath already exists") - } - } - val out = fs.create(prototxtFile) + + var prototxtFileWriter: FileWriter = null + try { + prototxtFileWriter = FileWriter(prototxtPath) + val out = prototxtFileWriter.create(overwrite) val byteArrayOut = new ByteArrayOutputStream() byteArrayOut.write(netParameterWithoutData.build().toString.getBytes) IOUtils.copyBytes(new ByteArrayInputStream(byteArrayOut.toByteArray), out, 1024, true) - } else { - val prototxtFile = new java.io.File(prototxtPath) - if (prototxtFile.exists()) { - if (overwrite) { - prototxtFile.delete() - } else { - throw new RuntimeException(s"file $prototxtPath already exists") - } - } - val prototxtWriter = new OutputStreamWriter(new FileOutputStream(prototxtFile)) - prototxtWriter.write(netParameterWithoutData.build.toString) - prototxtWriter.close + } finally { + prototxtFileWriter.close() } } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala index 942c5f667d8..f9626ab276d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala @@ -17,14 +17,17 @@ package com.intel.analytics.bigdl.integration import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.resnet.Convolution -import com.intel.analytics.bigdl.nn.{Linear, Module, Sequential} -import com.intel.analytics.bigdl.utils.caffe.CaffeLoader +import com.intel.analytics.bigdl.nn.{Graph, Linear, Module, Sequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.caffe.{CaffeLoader, CaffePersister} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble import org.apache.commons.compress.utils.IOUtils import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Integration class S3Spec extends FlatSpec with Matchers with BeforeAndAfter{ val s3aPath = System.getProperty("s3aPath") @@ -49,9 +52,9 @@ class S3Spec extends FlatSpec with Matchers with BeforeAndAfter{ val prototxt = getClass().getClassLoader().getResource("caffe/test.prototxt").getPath val modelPath = getClass().getClassLoader().getResource("caffe/test.caffemodel").getPath - val hdfsDir = s3aPath + s"/${ com.google.common.io.Files.createTempDir().getPath() }" + val s3Dir = s3aPath + s"/${ com.google.common.io.Files.createTempDir().getPath() }" - def writeToHdfs(localFile: String, hdfsDir: String): Unit = { + def writeToS3(localFile: String, hdfsDir: String): Unit = { val src = new Path(localFile) val fs = src.getFileSystem(new Configuration(false)) val inStream = fs.open(src) @@ -66,8 +69,8 @@ class S3Spec extends FlatSpec with Matchers with BeforeAndAfter{ outFileStream.close() } - writeToHdfs(prototxt, hdfsDir + "/test.prototxt") - writeToHdfs(modelPath, hdfsDir + "/test.caffemodel") + writeToS3(prototxt, s3Dir + "/test.prototxt") + writeToS3(modelPath, s3Dir + "/test.caffemodel") val module = Sequential() .add(Convolution(3, 4, 2, 2).setName("conv")) .add(Convolution(4, 3, 2, 2).setName("conv2")) @@ -76,10 +79,47 @@ class S3Spec extends FlatSpec with Matchers with BeforeAndAfter{ val model = CaffeLoader.load[Double](module, prototxt, modelPath) - val modelFromHdfs = CaffeLoader.load[Double](module, hdfsDir + "/test.prototxt", - hdfsDir + "/test.caffemodel") + val modelFromS3 = CaffeLoader.load[Double](module, s3Dir + "/test.prototxt", + s3Dir + "/test.caffemodel") + + model.getParameters() should be (modelFromS3.getParameters()) + + } + + "Persist and Load Caffe to/from s3" should "works properly" in { + + val input1 = Tensor(10).apply1( e => Random.nextDouble()) + + val input2 = Tensor() + + input2.resizeAs(input1).copy(input1) + + val linear = Linear(10, 10) + + // caffe only supports float, In order to compare the results, here we manually + // set weight and bias to ensure there is no accurancy loss + val weightTensor = Tensor(10, 10).fill(0.5) + val biasTensor = Tensor(10).fill(0.1) + linear.setWeightsBias(Array(weightTensor, biasTensor)) + + val inputNode = linear.inputs() + + val graph = Graph(inputNode, inputNode) + + val hdfsDir = s3aPath + s"/${ com.google.common.io.Files.createTempDir().getPath() }" + + + val res1 = graph.forward(input1) + + CaffePersister.persist(hdfsDir + "/test.prototxt", hdfsDir + "/test.caffemodel", + graph, overwrite = true) + + val modelFromS3 = CaffeLoader.loadCaffe[Double](hdfsDir + "/test.prototxt", + hdfsDir + "/test.caffemodel")._1 + + val res2 = modelFromS3.forward(input2) - model.getParameters() should be (modelFromHdfs.getParameters()) + res1 should be (res2) } } From 42884f5bcde9ef280bca38b79bb041c843f83622 Mon Sep 17 00:00:00 2001 From: Yan Wan Date: Wed, 19 Jul 2017 20:28:29 +0800 Subject: [PATCH 0295/1065] add CosineDistanceCriterion (#1195) * add CosineDistanceCriterion * add cosineDistanceCriterion * add CosineDistanceCriterion python test * add python test * add blank line in pythonapi * revise APIDocs --- .../dllib/nn/CosineDistanceCriterion.scala | 147 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 5 + .../torch/CosineDistanceCriterionSpec.scala | 71 +++++++++ 3 files changed, 223 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceCriterion.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceCriterionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceCriterion.scala new file mode 100644 index 00000000000..0e719b0c838 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceCriterion.scala @@ -0,0 +1,147 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractCriterion, TensorCriterion} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Creates a criterion that measures the loss given an input tensor and target tensor. + * + * The input and target are two tensors with same size. + * For instance: + * + * x = Tensor[Double](Storage(Array(0.1, 0.2, 0.3))) + * y = Tensor[Double](Storage(Array(0.15, 0.25, 0.35))) + * + * loss(x, y) = 1 - cos(x, y) + */ + +@SerialVersionUID(- 4008475267198411701L) +class CosineDistanceCriterion[@specialized(Float, Double) T: ClassTag] +(val sizeAverage: Boolean = true) +(implicit ev: TensorNumeric[T]) extends TensorCriterion[T]{ + @transient + private var buffer: Tensor[T] = null + @transient + private var w1: Tensor[T] = null + @transient + private var w22: Tensor[T] = null + @transient + private var w: Tensor[T] = null + @transient + private var w32: Tensor[T] = null + @transient + private var _outputs: Tensor[T] = null + + override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { + var input1 = input + var input2 = target + + if (null == buffer) buffer = Tensor[T]() + if (null == w1) w1 = Tensor[T]() + if (null == w22) w22 = Tensor[T]() + if (null == w) w = Tensor[T]() + if (null == _outputs) _outputs = Tensor[T]() + if (null == w32) w32 = Tensor[T]() + + if (input1.dim() == 1) { + input1 = input1.view(1, input1.nElement()) + input2 = input2.view(1, input2.nElement()) + } + + buffer.resizeAs(input1).cmul(input1, input2) + w1.sum(buffer, 2) + + val epsilon = 1e-12 + buffer.cmul(input1, input1) + w22.sum(buffer, 2).add(ev.fromType(epsilon)) + _outputs.resizeAs(w22).fill(ev.fromType(1)) + w22.cdiv(_outputs, w22) + w.resizeAs(w22).copy(w22) + + buffer.cmul(input2, input2) + w32.sum(buffer, 2).add(ev.fromType(epsilon)) + w32.cdiv(_outputs, w32) + w.cmul(w32) + w.sqrt() + + _outputs.cmul(w1, w) + _outputs.mul(ev.fromType(-1)).add(ev.fromType(1)) + output = _outputs.sum() + + if (sizeAverage) { + output = ev.divide(output, ev.fromType(input.size(1))) + } + output + } + + override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { + var v1 = input + var v2 = target + + if (v1.dim() == 1) { + v1 = v1.view(1, v1.nElement()) + v2 = v2.view(1, v2.nElement()) + } + + if (null == gradInput) gradInput = Tensor[T]() + + val gw1 = gradInput + + gw1.resizeAs(v1).copy(v2) + + buffer.resizeAs(w1).cmul(w1, w22) + gw1.addcmul(ev.fromType(-1), buffer.expandAs(v1), v1) + gw1.cmul(w.expandAs(v1)).mul(ev.fromType(-1)) + + if (sizeAverage) { + gradInput.div(ev.fromType(v2.size(1))) + } + + gradInput + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[CosineEmbeddingCriterion[T]] + + override def toString(): String = { + s"nn.CosineEmbeddingCriterion($sizeAverage)" + } + + override def equals(other: Any): Boolean = other match { + case that: CosineDistanceCriterion[T] => + super.equals(that) && + (that canEqual this) && + sizeAverage == that.sizeAverage + case _ => false + } + + override def hashCode(): Int = { + val state = Seq(super.hashCode(), sizeAverage) + state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) + } +} + +object CosineDistanceCriterion { + def apply[@specialized(Float, Double) T: ClassTag]( + sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : CosineDistanceCriterion[T] = { + new CosineDistanceCriterion[T](sizeAverage) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index e373948919d..4eb296f726f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -515,6 +515,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab CosineDistance[T]() } + def createCosineDistanceCriterion(sizeAverage: Boolean = true) + : CosineDistanceCriterion[T] = { + CosineDistanceCriterion[T](sizeAverage) + } + def createDiceCoefficientCriterion(sizeAverage: Boolean = true, epsilon: Float = 1.0f) : DiceCoefficientCriterion[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceCriterionSpec.scala new file mode 100644 index 00000000000..67ce1eaec2c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceCriterionSpec.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn.CosineDistanceCriterion +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.{RandomGenerator, Table} + +import scala.collection.mutable.HashMap + +import scala.util.Random + +@com.intel.analytics.bigdl.tags.Serial +class CosineDistanceCriterionSpec extends TorchSpec { + "A CosineDistanceCriterionSpec Module" should "generate correct output and grad" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + val module = CosineDistanceCriterion[Double](false) + + val input1 = Tensor[Double](5).apply1(e => RandomGenerator.RNG.uniform(0, 2)) + val input2 = Tensor[Double](5).apply1(e => RandomGenerator.RNG.uniform(0, 1)) + val input = new Table() + input(1.0) = input1 + input(2.0) = input2 + + val target = new Table() + val target1 = Tensor[Double](Storage(Array(1.0))) + target(1.toDouble) = target1 + + val start = System.nanoTime() + val output = module.forward(input1, input2) + val gradInput = module.backward(input1, input2) + val end = System.nanoTime() + val scalaTime = end - start + + val code = "torch.manualSeed(" + seed + ")\n" + + "module = nn.CosineEmbeddingCriterion(0.0)\n" + + "_idx = module._idx\n" + + "_outputs = module._outputs\n" + + "buffer = module.buffer\n" + + "output = module:forward(input, 1.0)\n" + + "gradInput = module:backward(input, 1.0)\n" + + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input), + Array("output", "gradInput", "_idx", "buffer", "_outputs")) + val luaOutput1 = torchResult("output").asInstanceOf[Double] + val luaOutput2 = torchResult("gradInput").asInstanceOf[Table] + + luaOutput1 should be(output) + luaOutput2[Tensor[Double]](1) should be (gradInput.squeeze()) + + println("Test case : CrossEntropyCriterion, Torch : " + luaTime + + " s, Scala : " + scalaTime / 1e9 + " s") + } +} From aca61cbea67786ed3d39d46832a338c961870d39 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Wed, 19 Jul 2017 20:35:41 -0700 Subject: [PATCH 0296/1065] Create a ML Pipelines page in Programming Guide (issue 1263) (#1279) * merge docs * apache spark * support types * fix reference * address comments * add full example in doc --- .../dllib/example/MLPipeline/DLClassifierLeNet.scala | 10 +++++----- .../MLPipeline/DLClassifierLogisticRegression.scala | 3 +-- .../example/MLPipeline/DLEstimatorMultiLabelLR.scala | 10 +++++----- 3 files changed, 11 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala index f504e32dac6..5b3bae41556 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala @@ -49,10 +49,10 @@ object DLClassifierLeNet { val sqLContext = SQLContext.getOrCreate(sc) Engine.init - val trainData = param.folder + "/train-images.idx3-ubyte" - val trainLabel = param.folder + "/train-labels.idx1-ubyte" - val validationData = param.folder + "/t10k-images.idx3-ubyte" - val validationLabel = param.folder + "/t10k-labels.idx1-ubyte" + val trainData = param.folder + "/train-images-idx3-ubyte" + val trainLabel = param.folder + "/train-labels-idx1-ubyte" + val validationData = param.folder + "/t10k-images-idx3-ubyte" + val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" val trainSet = DataSet.array(load(trainData, trainLabel), sc) -> BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToBatch(1) @@ -71,7 +71,7 @@ object DLClassifierLeNet { val estimator = new DLClassifier[Float](model, criterion, featureSize) .setFeaturesCol(inputs(0)) .setLabelCol(inputs(1)) - .setBatchSize(50) + .setBatchSize(param.batchSize) val transformer = estimator.fit(trainingDF) val validationSet = DataSet.array(load(validationData, validationLabel), sc) -> diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala index a764bf633b1..770874613a0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala @@ -30,7 +30,7 @@ object DLClassifierLogisticRegression { def main(args: Array[String]): Unit = { val conf = Engine.createSparkConf() .setAppName("DLClassifierLogisticRegression") - .set("spark.task.maxFailures", "1") + .setMaster("local[1]") val sc = new SparkContext(conf) val sqlContext = SQLContext.getOrCreate(sc) Engine.init @@ -49,5 +49,4 @@ object DLClassifierLogisticRegression { val dlModel = estimator.fit(df) dlModel.transform(df).show(false) } - } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala index 21a6e03e468..29bd55a6c8c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala @@ -30,7 +30,7 @@ object DLEstimatorMultiLabelLR { def main(args: Array[String]): Unit = { val conf = Engine.createSparkConf() .setAppName("DLEstimatorMultiLabelLR") - .set("spark.task.maxFailures", "1") + .setMaster("local[1]") val sc = new SparkContext(conf) val sqlContext = SQLContext.getOrCreate(sc) Engine.init @@ -41,10 +41,10 @@ object DLEstimatorMultiLabelLR { .setBatchSize(4) .setMaxEpoch(10) val data = sc.parallelize(Seq( - (Array(0.0, 1.0), Array(1.0, 0.0)), - (Array(1.0, 0.0), Array(0.0, 1.0)), - (Array(0.0, 1.0), Array(1.0, 0.0)), - (Array(1.0, 0.0), Array(0.0, 1.0)))) + (Array(2.0, 1.0), Array(1.0, 2.0)), + (Array(1.0, 2.0), Array(2.0, 1.0)), + (Array(2.0, 1.0), Array(1.0, 2.0)), + (Array(1.0, 2.0), Array(2.0, 1.0)))) val df = sqlContext.createDataFrame(data).toDF("features", "label") val dlModel = estimator.fit(df) dlModel.transform(df).show(false) From bb09f12115b38e7f2c5ac7bbfdeef4c8db3fc107 Mon Sep 17 00:00:00 2001 From: Yan Wan Date: Thu, 20 Jul 2017 12:36:13 +0800 Subject: [PATCH 0297/1065] fix batchSize non-consistency bug (#1295) add unit test --- .../analytics/bigdl/dllib/nn/Recurrent.scala | 40 +++++++++++++- .../bigdl/dllib/nn/RecurrentSpec.scala | 53 +++++++++++++++++++ 2 files changed, 92 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index cf46d6f0ecd..427303f6dff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -37,7 +37,7 @@ class Recurrent[T : ClassTag]() private var hiddenShape: Array[Int] = null private val currentInput = T() private val currentGradOutput = T() - private val gradInputCell = Tensor[T]() + private var gradInputCell = Tensor[T]() private var outputCell = Tensor[T]() private val _input = T() private val batchDim = 1 @@ -191,6 +191,12 @@ class Recurrent[T : ClassTag]() result } + private def reset(src1: ArrayBuffer[Tensor[T]], src2: Tensor[T]): Unit = { + cellAppendStartIdx = 0 + src1.foreach(x => x.set(Tensor[T](1))) + src2.set(Tensor[T](1)) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim == 3 || input.dim == 5, "Recurrent: input should be a 3D or 5D Tensor, e.g [batch, times, nDim], " + @@ -199,6 +205,22 @@ class Recurrent[T : ClassTag]() batchSize = input.size(batchDim) times = input.size(timeDim) + /** + * get previous batchsize. + * If current batchSize is not equal to previous batchSize, + * reset recurrent's output and cells' output to avoid + * address conflicts. + */ + val batchSizeOfCell = if (!cells.isEmpty) { + cells.head.output.toTable[Tensor[T]](inputDim).size(batchDim) + } else { + 0 + } + + if (batchSizeOfCell > 0 && batchSizeOfCell != batchSize) { + reset(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), output) + } + outputCell = if (preTopology != null) { preTopology.updateOutput(input).toTensor[T] } else { @@ -231,6 +253,7 @@ class Recurrent[T : ClassTag]() currentInput(hidDim) = cells(i - 1).output.toTable(hidDim) i += 1 } + if (cellAppendStartIdx == 0 || cellAppendStartIdx < times) { set(cells.slice(cellAppendStartIdx, times) .map(x => x.output.toTable[Tensor[T]](inputDim)), @@ -275,6 +298,21 @@ class Recurrent[T : ClassTag]() } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + /** + * get previous batchsize. + * If current batchSize is not equal to previous batchSize, + * reset recurrent's gradInput and cells' gradInput to avoid + * address conflicts. + */ + val batchSizeOfCell = if (cells.head.gradInput.toTable.length > 0) { + cells.head.gradInput.toTable[Tensor[T]](inputDim).size(batchDim) + } else { + 0 + } + + if (batchSizeOfCell > 0 && batchSizeOfCell != batchSize ) { + reset(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) + } gradInput = if (preTopology != null) { /** diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala index 6b9b7e2dd3f..2aa3bb0390e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala @@ -27,6 +27,59 @@ import scala.collection.mutable.ArrayBuffer @com.intel.analytics.bigdl.tags.Serial class RecurrentSpec extends FlatSpec with Matchers { + "A Recurrent" should " converge when batchSize changes" in { + val hiddenSize = 4 + val inputSize = 5 + val outputSize = 5 + val time = 4 + val batchSize1 = 5 + val batchSize2 = 8 + val seed = 100 + RNG.setSeed(seed) + + val model = Sequential[Double]() + .add(Recurrent[Double]() + .add(RnnCell[Double](inputSize, hiddenSize, Tanh[Double]()))) + .add(Select(2, 1)) + .add(Linear[Double](hiddenSize, outputSize)) + + val input1 = Tensor[Double](Array(batchSize1, time, inputSize)).rand + val input2 = Tensor[Double](batchSize2, time, inputSize).rand + + val gradOutput1 = Tensor[Double](batchSize1, outputSize).rand + val gradOutput2 = Tensor[Double](batchSize2, outputSize).rand + + model.forward(input1) + model.backward(input1, gradOutput1) + val gradInput1 = + Tensor[Double](batchSize1, time, inputSize).copy(model.gradInput.toTensor[Double]) + val output1 = Tensor[Double](batchSize1, outputSize).copy(model.output.toTensor[Double]) + + model.forward(input2) + model.backward(input2, gradOutput2) + val gradInput2 = + Tensor[Double](batchSize2, time, inputSize).copy(model.gradInput.toTensor[Double]) + val output2 = Tensor[Double](batchSize2, outputSize).copy(model.output.toTensor[Double]) + + model.forward(input1) + model.backward(input1, gradOutput1) + val gradInput1compare = + Tensor[Double](batchSize1, time, inputSize).copy(model.gradInput.toTensor[Double]) + val output1compare = Tensor[Double](batchSize1, outputSize).copy(model.output.toTensor[Double]) + + model.forward(input2) + model.backward(input2, gradOutput2) + val gradInput2compare = + Tensor[Double](batchSize2, time, inputSize).copy(model.gradInput.toTensor[Double]) + val output2compare = Tensor[Double](batchSize2, outputSize).copy(model.output.toTensor[Double]) + + output1 should be (output1compare) + output2 should be (output2compare) + + gradInput1 should be (gradInput1compare) + gradInput2 should be (gradInput2compare) + } + "A Recurrent Language Model Module " should "converge" in { val hiddenSize = 4 From a755427ddb68b72f91bb93558d6b7ae878de6bc9 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 20 Jul 2017 00:39:06 -0500 Subject: [PATCH 0298/1065] Add scala tensorflow load/save examples (#1291) * add scala tf example * update * update * fix a small typo * fix unit test --- dist/assembly/dist.xml | 4 + .../dllib/example/loadmodel/AlexNet.scala | 4 +- .../bigdl/dllib/example/tensorflow/.gitignore | 4 + .../bigdl/dllib/example/tensorflow/Load.scala | 34 +++++++++ .../bigdl/dllib/example/tensorflow/README.md | 43 +++++++++++ .../bigdl/dllib/example/tensorflow/Save.scala | 45 ++++++++++++ .../bigdl/dllib/example/tensorflow/model.py | 62 ++++++++++++++++ .../dllib/nn/abstractnn/AbstractModule.scala | 5 +- .../dllib/utils/tf/TensorflowLoader.scala | 20 ++++- .../dllib/utils/tf/TensorflowSaver.scala | 4 +- .../dllib/utils/tf/TensorflowToBigDL.scala | 73 ++++++++++++++++++- .../bigdl/dllib/utils/FileSpec.scala | 4 +- .../dllib/utils/tf/TensorflowLoaderSpec.scala | 6 +- 13 files changed, 291 insertions(+), 17 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/.gitignore create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Load.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Save.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/model.py diff --git a/dist/assembly/dist.xml b/dist/assembly/dist.xml index 3fd35981441..824e466d58a 100644 --- a/dist/assembly/dist.xml +++ b/dist/assembly/dist.xml @@ -19,6 +19,10 @@ classes.lst img_class.lst + download.sh + flickr.urls + run.example.sh + dump_tf_graph.py diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/AlexNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/AlexNet.scala index f803fb8f7f4..955fafc18a3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/AlexNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/AlexNet.scala @@ -38,7 +38,7 @@ object AlexNet_OWT { model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).setName("conv5")) model.add(ReLU(true).setName("relu5")) model.add(SpatialMaxPooling(3, 3, 2, 2).setName("poo5")) - model.add(View(256 * 6 * 6)) + model.add(View(256 * 6 * 6).setName("view")) model.add(Linear(256 * 6 * 6, 4096).setName("fc6")) model.add(ReLU(true).setName("relu6")) if (hasDropout) model.add(Dropout(0.5).setName("drop6")) @@ -46,7 +46,7 @@ object AlexNet_OWT { model.add(ReLU(true).setName("relu7")) if (hasDropout) model.add(Dropout(0.5).setName("drop7")) model.add(Linear(4096, classNum).setName("fc8")) - model.add(LogSoftMax()) + model.add(LogSoftMax().setName("logsoftmax")) model } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/.gitignore b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/.gitignore new file mode 100644 index 00000000000..1b9eacf1b68 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/.gitignore @@ -0,0 +1,4 @@ +model/ +freeze_graph.py +model.pb +log/ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Load.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Load.scala new file mode 100644 index 00000000000..db2af15ae69 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Load.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.tensorflow + +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor + +/** + * This example show how to load a tensorflow model defined in slim and + * use it to do prediction + */ +object Load { + def main(args: Array[String]): Unit = { + require(args.length == 1, "Please input the model path as the first argument") + val model = Module.loadTF(args(0), Seq("Placeholder"), Seq("LeNet/fc4/BiasAdd")) + val result = model.forward(Tensor(1, 1, 28, 28).rand()) + println(result) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/README.md new file mode 100644 index 00000000000..6fd6cfce7e5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/README.md @@ -0,0 +1,43 @@ +# Tensorflow model support example +BigDL support read/save tensorflow model. Here's an example how to use this feature. + +Before you run this example, you need to install tensorflow on your machine. This can be simply done +by + +```bash +pip install tensorflow +``` + +## Load tensorflow model +1. Generate tensorflow model +```bash +python model.py +``` + +2. Freeze tensorflow model +```bash +wget https://raw.githubusercontent.com/tensorflow/tensorflow/v1.0.0/tensorflow/python/tools/freeze_graph.py +python freeze_graph.py --input_graph model/model.pbtxt --input_checkpoint model/model.chkp --output_node_names="LeNet/fc4/BiasAdd" --output_graph "model.pb" +``` + +3. Run BigDL +```bash +spark-submit --master local[1] --class com.intel.analytics.bigdl.example.tensorflow.Load BigDL_jar_file ./model.pb +``` + +## Save BigDL model as tensorflow model +1. Run BigDL +```bash +spark-submit --master local[1] --class com.intel.analytics.bigdl.example.tensorflow.Save BigDL_jar_file +``` + +2. Generate summary file, you can find the dump_tf_graph.py in the bin folder of the dist package, or script folder of +the code +```bash +python dump_tf_graph.py model.pb +``` + +3. See the saved model via tensorboard +```bash +tensorboard --logdir ./log +``` \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Save.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Save.scala new file mode 100644 index 00000000000..2fd464fa2ea --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Save.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.example.tensorflow + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.utils.tf.TensorflowSaver +import com.intel.analytics.bigdl.numeric.NumericFloat + +/** + * This example shows how to define a BigDL model and save it as tensorflow format + */ +object Save { + def main(args: Array[String]) { + val model = lenet() + TensorflowSaver.saveGraph(model, Seq(("input", Seq(1, 1, 28, 28))), "./bigdl.pb") + } + + def lenet(): Graph[Float] = { + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5").inputs() + val tanh1 = Tanh().setName("tanh1").inputs(conv1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).setName("pool1").inputs(tanh1) + val tanh2 = Tanh().setName("tanh2").inputs(pool1) + val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5").inputs(tanh2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).setName("pool2").inputs(conv2) + val reshape2 = Reshape(Array(1, 12 * 4 * 4)).setName("reshape2").inputs(pool2) + val fc1 = Linear(12 * 4 * 4, 100).setName("fc1").inputs(reshape2) + val tanh3 = Tanh().setName("tanh3").inputs(fc1) + val fc2 = Linear(100, 10).setName("fc2").inputs(tanh3) + val output = LogSoftMax().setName("output").inputs(fc2) + Graph(conv1, output) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/model.py b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/model.py new file mode 100644 index 00000000000..569b7d04fd8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/model.py @@ -0,0 +1,62 @@ +# +# Copyright 2016 The BigDL Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Part of the code is reference https://github.com/tensorflow/models/blob/master/slim/nets/lenet.py +# + +import tensorflow as tf +import numpy as np + +slim = tf.contrib.slim + +def main(): + inputs = tf.placeholder(tf.float32, shape=(1, 1, 28, 28)) + net, end_points = lenet(inputs) + saver = tf.train.Saver() + with tf.Session() as sess: + init = tf.global_variables_initializer() + sess.run(init) + saver.save(sess, 'model/model.chkp') + tf.train.write_graph(sess.graph, 'model', 'model.pbtxt') + +def lenet(images): + end_points = {} + num_classes=10 + is_training=False + dropout_keep_prob=0.5 + prediction_fn=slim.softmax + scope='LeNet' + + with tf.variable_scope(scope, 'LeNet', [images, num_classes]): + net = slim.conv2d(images, 32, [5, 5], scope='conv1', data_format="NCHW") + net = slim.max_pool2d(net, [2, 2], 2, scope='pool1', data_format="NCHW") + net = slim.conv2d(net, 64, [5, 5], scope='conv2', data_format="NCHW") + net = slim.max_pool2d(net, [2, 2], 2, scope='pool2', data_format="NCHW") + net = slim.flatten(net) + end_points['Flatten'] = net + + net = slim.fully_connected(net, 1024, scope='fc3') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout3') + logits = slim.fully_connected(net, num_classes, activation_fn=None, + scope='fc4') + + end_points['Logits'] = logits + end_points['Predictions'] = prediction_fn(logits, scope='Predictions') + + return logits, end_points + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 5affbab70ad..f384980a908 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -361,13 +361,14 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, (that canEqual this) && (that.getClass equals this.getClass) && output == that.output && - gradInput == that.gradInput + gradInput == that.gradInput && + name == that.name case _ => false } override def hashCode(): Int = { def getHashCode(a: Object): Int = if (a == null) 0 else a.hashCode() - val state = Seq(output, gradInput, this.getClass) + val state = Seq(output, gradInput, this.getClass, this.name) state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index f56c0d6dbc2..42c78520e64 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -57,7 +57,7 @@ object TensorflowLoader{ val tfGraph = buildTFGraph(nodeList, outputs) // Build BigDL model from the tf node graph - buildBigDLModel(tfGraph, inputs, outputs, byteOrder) + buildBigDLModel(tfGraph, inputs, outputs, byteOrder, graphPrototxt) } /** @@ -128,6 +128,7 @@ object TensorflowLoader{ inputs: Seq[String], outputs: Seq[String], byteOrder: ByteOrder, + graphPrototxt: String, ctx: Option[Context[T]] = None )(implicit ev: TensorNumeric[T]): Module[T] = { import scala.collection.JavaConverters._ @@ -146,10 +147,23 @@ object TensorflowLoader{ } else if (convertedNode.get(n).isDefined) { // converted node, skip } else { + val errorMsg = + s""" + | Cannot convert the given tensorflow operation graph to BigDL model. The convert fails + | at node ${n.element.getName}. + | To investigate the model. Please use the dump_tf_graph.py to dump the graph, then use + | Tensorboard to visualize the model. + | + | python dump_tf_graph.py $graphPrototxt + | tensorboard --logdir ./log + | + | You can find the dump_tf_graph.py in the bin folder of the dist package, or scripts + | folder in the source code. + """.stripMargin + val (module, nodes, inputNodes) = extract[T](n.graph(reverse = true), context, byteOrder).getOrElse( - throw new UnsupportedOperationException(s"Can not find matched graph \n${n}\n\n" + - s"Its inputs are\n ${n.prevNodes.mkString("\n")}") + throw new UnsupportedOperationException(errorMsg) ) val node = new Node(module) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala index 42a03511702..c8fffd56e20 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala @@ -78,8 +78,8 @@ object TensorflowSaver { val os = new FileOutputStream(path) val output = CodedOutputStream.newInstance(os) val graph = graphBuilder.build() - logger.info("Graph definition is:") - logger.info(graph.toString) + logger.debug("Graph definition is:") + logger.debug(graph.toString) graph.writeTo(output) output.flush() os.close() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index 6605bdfd0b8..a80b0fe752b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -226,7 +226,7 @@ object TensorflowToBigDL { TanhTF, ReluTF, SigmoidTF, Conv2D, Placeholder, SqueezeTF, IdentityTF, ConcatTF, BatchNormTF, AddConstTF1, AddConstTF2, AddTF, SoftMaxTF, ElementWiseMulTF, MulTF, SplitTF, PaddingTF, MeanTF, UnpackTF, StrideSliceTF, ShapeTF, FillTF, PackTF, ConstTF, - Flatten + Flatten, Conv2D2 ) res } @@ -356,7 +356,7 @@ object Conv2D extends TensorflowToBigDL{ (attributes.get("strides").getList.getI(1).toInt, attributes.get("strides").getList.getI(2).toInt) } else if (attributes.get("data_format").getS.toString(Charset.defaultCharset()) == "NCHW") { - require(attributes.get("strides").getList.getI(2).toInt == 1, s"not support strides on depth") + require(attributes.get("strides").getList.getI(1).toInt == 1, s"not support strides on depth") (attributes.get("strides").getList.getI(2).toInt, attributes.get("strides").getList.getI(3).toInt) } else { @@ -396,6 +396,71 @@ object Conv2D extends TensorflowToBigDL{ } } +object Conv2D2 extends TensorflowToBigDL{ + private val graph = { + val add = Node("Add") + val conv = Node("Conv2D") + val reshape = Node("Reshape") + + Node("*") -> conv + Node("Const") -> Node("Identity") -> conv -> add + Node("Const") -> Node("Identity") -> reshape + Node("Const") -> reshape + reshape -> add + + add.graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + + val attributes = tfGraph.source.prevNodes(0).element.getAttrMap + require(attributes.get("strides").getList.getI(0).toInt == 1, s"not support strides on batch") + require(attributes.get("data_format").getS.toString(Charset.defaultCharset()) == "NCHW", + "NCHW should be used for this sub-graph") + + require(attributes.get("strides").getList.getI(2).toInt == 1, s"not support strides on depth") + val (strideH, strideW) = (attributes.get("strides").getList.getI(2).toInt, + attributes.get("strides").getList.getI(3).toInt) + + val biasNode = tfGraph.source.prevNodes(1).prevNodes(0).prevNodes.head.element + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder)(t => t) + + val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element + val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder) { t => + t.transpose(1, 4).transpose(2, 3).transpose(3, 4) + } + + val nOuputPlane = weights.size(1) + val nInputPlane = weights.size(2) + val kernelH = weights.size(3) + val kernelW = weights.size(4) + + val (pW, pH) = + if (attributes.get("padding").getS.toString(Charset.defaultCharset()) == "SAME") { + require((kernelW - strideW) % 2 == 0) + require((kernelH - strideH) % 2 == 0) + ((kernelW - strideW) / 2, (kernelH - strideH) / 2) + } else { + (0, 0) + } + + SpatialConvolution[T]( + nInputPlane = nInputPlane, nOutputPlane = nOuputPlane, + kernelW = kernelW, kernelH = kernelH, + strideW = strideW, strideH = strideH, + padW = pW, padH = pH, + initWeight = weights, + initBias = bias, + initGradWeight = gradWeights, + initGradBias = gradBias).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + } +} + object ReluTF extends TensorflowToBigDL { private val graph = { (Node("*") -> Node("Relu")).graph(reverse = true) @@ -502,7 +567,7 @@ object MaxPoolingTF extends TensorflowToBigDL { attributes.get("ksize").getList.getI(2).toInt ) } else if (attributes.get("data_format").getS.toString(Charset.defaultCharset()) == "NCHW") { - require(attributes.get("strides").getList.getI(2).toInt == 1, s"not support strides on depth") + require(attributes.get("strides").getList.getI(1).toInt == 1, s"not support strides on depth") ( attributes.get("strides").getList.getI(2).toInt, attributes.get("strides").getList.getI(3).toInt, @@ -551,7 +616,7 @@ object AvgPoolingTF extends TensorflowToBigDL{ attributes.get("ksize").getList.getI(2).toInt ) } else if (attributes.get("data_format").getS.toString(Charset.defaultCharset()) == "NCHW") { - require(attributes.get("strides").getList.getI(2).toInt == 1, s"not support strides on depth") + require(attributes.get("strides").getList.getI(1).toInt == 1, s"not support strides on depth") ( attributes.get("strides").getList.getI(2).toInt, attributes.get("strides").getList.getI(3).toInt, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/FileSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/FileSpec.scala index cfbd12f24b1..fdbc0cb95c9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/FileSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/FileSpec.scala @@ -33,7 +33,9 @@ class FileSpec extends FlatSpec with Matchers { val model = Module.loadTorch[Float](absolutePath).asInstanceOf[Sequential[Float]] model.getParameters() should be (alexnet.getParameters()) for (i <- 0 until model.modules.size) { - println(i) + println(s"check the $i th layer in the model...") + // torch will discard the name + model.modules(i).setName(alexnet.asInstanceOf[Sequential[Float]].modules(i).getName()) model.modules(i) should be (alexnet.asInstanceOf[Sequential[Float]].modules(i)) } model should be (alexnet) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index f5ada97f975..6e24a7b5e4a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -243,7 +243,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val tfGraph = TensorflowLoader.buildTFGraph(results, Seq("output")) val model = TensorflowLoader.buildBigDLModel(tfGraph, Seq("input"), Seq("output"), - ByteOrder.LITTLE_ENDIAN) + ByteOrder.LITTLE_ENDIAN, "") val input = TensorflowToBigDL.toTensor(results.get(0).getAttrMap.get("value").getTensor, ByteOrder.LITTLE_ENDIAN).contiguous() val tfResult = TensorflowToBigDL.toTensor(results.get(results.size()-1) @@ -275,7 +275,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val tfGraph = TensorflowLoader.buildTFGraph(results.subList(0, results.size()-1), Seq("output")) val model = TensorflowLoader.buildBigDLModel(tfGraph, Seq("input"), Seq("output"), - ByteOrder.LITTLE_ENDIAN) + ByteOrder.LITTLE_ENDIAN, "") val input = TensorflowToBigDL.toTensor(results.get(0).getAttrMap.get("value").getTensor, ByteOrder.LITTLE_ENDIAN).contiguous() val tfResult = TensorflowToBigDL.toTensor(results.get(results.size()-1) @@ -509,7 +509,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val tfGraph = TensorflowLoader.buildTFGraph(tfNodes, endPoints.map(_.split(":")(0))) val context = new mutable.HashMap[NodeDef, (Tensor[Float], Tensor[Float])] val model = TensorflowLoader.buildBigDLModel(tfGraph, Seq("input"), - endPoints.map(_.split(":")(0)), ByteOrder.LITTLE_ENDIAN, Some(context)) + endPoints.map(_.split(":")(0)), ByteOrder.LITTLE_ENDIAN, "", Some(context)) // Compare the tensor contents val tfInputTensor = tfNodes.asScala.filter(_.getName == "input")(0) From f4da056fe706323c78b33cd4cd15246369b53cb8 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 20 Jul 2017 00:47:44 -0500 Subject: [PATCH 0299/1065] update lenet document (#1305) --- .../com/intel/analytics/bigdl/dllib/models/lenet/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/README.md index 1a6d80486dd..d65b1955e90 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/README.md @@ -12,6 +12,9 @@ There're four files. **train-images-idx3-ubyte** contains train images, and **t10k-labels-idx1-ubyte** contains validation labels. For more detail, please refer to the download page. +After you uncompress the gzip files, these files may be renamed by some uncompress tools, e.g. **train-images-idx3-ubyte** is renamed +to **train-images.idx3-ubyte**. Please change the name back before you run the example. + ## Get the JAR You can build one by refer to the [Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page) from the source code. From 46b468a2b6031800e81cd84c447403f8b59e6199 Mon Sep 17 00:00:00 2001 From: Yan Wan Date: Thu, 20 Jul 2017 14:43:13 +0800 Subject: [PATCH 0300/1065] Recurrent: fix clearState (#1306) --- .../analytics/bigdl/dllib/nn/Recurrent.scala | 17 +++++++++-------- .../bigdl/dllib/nn/RecurrentSpec.scala | 6 ++++++ 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index 427303f6dff..ece6770751c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -45,6 +45,7 @@ class Recurrent[T : ClassTag]() private val inputDim = 1 private val hidDim = 2 private var cellAppendStartIdx = 0 + private var preBatchSize = 0 private var (batchSize, times) = (0, 0) private var topology: Cell[T] = null private var preTopology: AbstractModule[Activity, Activity, T] = null @@ -211,13 +212,13 @@ class Recurrent[T : ClassTag]() * reset recurrent's output and cells' output to avoid * address conflicts. */ - val batchSizeOfCell = if (!cells.isEmpty) { + preBatchSize = if (!cells.isEmpty) { cells.head.output.toTable[Tensor[T]](inputDim).size(batchDim) } else { 0 } - if (batchSizeOfCell > 0 && batchSizeOfCell != batchSize) { + if (preBatchSize > 0 && preBatchSize != batchSize) { reset(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), output) } @@ -304,13 +305,8 @@ class Recurrent[T : ClassTag]() * reset recurrent's gradInput and cells' gradInput to avoid * address conflicts. */ - val batchSizeOfCell = if (cells.head.gradInput.toTable.length > 0) { - cells.head.gradInput.toTable[Tensor[T]](inputDim).size(batchDim) - } else { - 0 - } - if (batchSizeOfCell > 0 && batchSizeOfCell != batchSize ) { + if (preBatchSize > 0 && preBatchSize != batchSize ) { reset(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) } @@ -355,9 +351,14 @@ class Recurrent[T : ClassTag]() hidden = null gradHidden = null hiddenShape = null + gradInputCell.set() + outputCell.set() currentInput.clear() currentGradOutput.clear() _input.clear() + reset(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), output) + reset(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) + cells.foreach(x => x.clearState()) cells.clear() this } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala index 2aa3bb0390e..d41e05017b4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala @@ -49,12 +49,16 @@ class RecurrentSpec extends FlatSpec with Matchers { val gradOutput1 = Tensor[Double](batchSize1, outputSize).rand val gradOutput2 = Tensor[Double](batchSize2, outputSize).rand + model.clearState() + model.forward(input1) model.backward(input1, gradOutput1) val gradInput1 = Tensor[Double](batchSize1, time, inputSize).copy(model.gradInput.toTensor[Double]) val output1 = Tensor[Double](batchSize1, outputSize).copy(model.output.toTensor[Double]) + model.clearState() + model.forward(input2) model.backward(input2, gradOutput2) val gradInput2 = @@ -73,6 +77,8 @@ class RecurrentSpec extends FlatSpec with Matchers { Tensor[Double](batchSize2, time, inputSize).copy(model.gradInput.toTensor[Double]) val output2compare = Tensor[Double](batchSize2, outputSize).copy(model.output.toTensor[Double]) + model.hashCode() + output1 should be (output1compare) output2 should be (output2compare) From 155b41c4688c129d130671abe2fa85a38ec7c897 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Thu, 20 Jul 2017 15:51:31 +0800 Subject: [PATCH 0301/1065] Fix caffe parameter exception (#1302) * add save caffe * remove empty lines * Fix caffe parameter exception * add convolution withBias * some var to val --- .../bigdl/dllib/utils/caffe/CaffeLoader.scala | 2 +- .../bigdl/dllib/utils/caffe/Converter.scala | 6 ++-- .../dllib/utils/caffe/LayerConverter.scala | 28 +++++++++---------- .../dllib/utils/caffe/V1LayerConverter.scala | 4 ++- 4 files changed, 21 insertions(+), 19 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 2e222a04f7f..251b3afe3d6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -339,7 +339,7 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, val param = getInforgainParam(layerName).get val weightBlob = getBlob(layerName, 2) if (weightBlob.isDefined) { - val size = weightBlob.get.getShape.getDimList.toArray.asInstanceOf[Array[Int]] + val size = weightBlob.get.getShape.getDimList.asScala.map(_.toInt).toArray val weightData = weightBlob.get.getDataList var weightArr = new Array[T](weightData.size) var i = 0 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index d218e4a7962..1da48fb14dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -212,7 +212,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { val param = getEltWiseParam(layer).get val layerName = getLayerName(layer) val opsType = param.getOperation - val coeff2 = param.getCoeff(1) + val coeff2 = if (param.getCoeffCount == 0) 1 else param.getCoeff(0) val ops = opsType match { case EltwiseOp.PROD => CMulTable[T]().setName(layerName).inputs() case EltwiseOp.MAX => CMaxTable[T]().setName(layerName).inputs() @@ -399,7 +399,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { protected def toCaffeConvolutionParam(module : AbstractModule[Activity, Tensor[T], T]) : mutable.HashMap[String, Int] = { - var map = new mutable.HashMap[String, Int]() + val map = new mutable.HashMap[String, Int]() val layer = classOf[SpatialConvolution[T]].cast(module) val nInputPlane = layer.nInputPlane val nOutputPlane = layer.nOutputPlane @@ -419,6 +419,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { map("padW") = padW map("padH") = padH map("ngroup") = ngroup + map("withBias") = if (layer.withBias) 1 else 0 map } @@ -516,6 +517,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { var i = 0 while (i < size.length) { shapeBlob.setDim(i, size(i)) + i += 1 } shapeBlob.build } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index a98bbb37a53..6814e9769ca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -36,14 +36,11 @@ import scala.reflect.ClassTag class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Converter[T]{ override protected def fromCaffeConvolution(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { - val name = getLayerName(layer) val param = getConvolutionParam(layer).get val group = if (param.getGroup == 0) 1 else param.getGroup val weightBlob = getBlob(layer, 0).get val biasBlob = getBlob(layer, 1) - if (!biasBlob.isDefined) { - throw new RuntimeException(s"${getLayerName(layer)} without bias is not supported now") - } + val withBias = biasBlob.isDefined val nInputPlane = if (weightBlob.hasShape) weightBlob.getShape.getDim(1) else weightBlob.getChannels * group val nOutPlane = if (weightBlob.hasShape) weightBlob.getShape.getDim(0) @@ -75,7 +72,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert } } Seq(SpatialConvolution[T](nInputPlane.toInt, nOutPlane.toInt, - kw, kh, dw, dh, pw, ph, group).setName(getLayerName(layer)).inputs()) + kw, kh, dw, dh, pw, ph, group, withBias).setName(getLayerName(layer)).inputs()) } override protected def fromCaffeInnerProduct(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -122,8 +119,8 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert override protected def fromCaffeReshape(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val param = layer.asInstanceOf[LayerParameter].getReshapeParam - val shapeSize = param.getShape.getDimList.toArray.asInstanceOf[Array[Int]] - Seq(Reshape[T](shapeSize).setName(getLayerName(layer)).inputs()) + val shapeSize = param.getShape.getDimList.asScala.map(_.toInt).toArray + Seq(InferReshape[T](shapeSize).setName(getLayerName(layer)).inputs()) } override protected def fromCaffeScale(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -133,7 +130,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert val weightBlob = getBlob(layer, 1) if (weightBlob.isDefined) { val blob = weightBlob.get - val size = blob.getShape.getDimList.toArray.asInstanceOf[Array[Int]] + val size = blob.getShape.getDimList.asScala.map(_.toInt).toArray Seq(Scale[T](size).setName(layerName).inputs()) } else { val inputBlob = getBlob(layer, 0).get @@ -145,16 +142,15 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert } else { numOfAxis = numOfAxis + axis } - val size = shape.getDimList.subList(axis, numOfAxis).asInstanceOf[Array[Int]] + val size = shape.getDimList.subList(axis, numOfAxis).asScala.map(_.toInt).toArray Seq(Scale[T](size).setName(layerName).inputs()) } } override protected def fromCaffeBias(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { - val param = layer.asInstanceOf[LayerParameter].getBiasParam // input blob val weightBlob = getBlob(layer, 0) - val size = weightBlob.get.getShape.getDimList.toArray().asInstanceOf[Array[Int]].product + val size = weightBlob.get.getShape.getDimList.asScala.map(_.toInt).toArray.product Seq(Add[T](size).setName(getLayerName(layer)).inputs()) } @@ -179,7 +175,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert setConnections(layerParameter, bottoms, nextSize) // copy weight and bias - var (weightBuilder, biasBuilder) = copyParam(module) + val (weightBuilder, biasBuilder) = copyParam(module) // get convolution param map val layerParams = toCaffeConvolutionParam(module) @@ -197,6 +193,8 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert convolutionParam.setStrideH(layerParams("strideH")) convolutionParam.setPadW(layerParams("padW")) convolutionParam.setPadH(layerParams("padH")) + val withBias = if (layerParams("withBias") == 1) true else false + convolutionParam.setBiasTerm(withBias) weightBuilder.setChannels(nInputPlane / ngroup) weightBuilder.setNum(nOutputPlane) @@ -224,7 +222,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert setConnections(layerParameter, bottoms, nextSize) // copy weight and bias - var (weightBuilder, biasBuilder) = copyParam(module) + val (weightBuilder, biasBuilder) = copyParam(module) setBlobs(layerParameter, weightBuilder, biasBuilder) @@ -448,7 +446,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert setConnections(layerParameter, bottoms, nextSize) // copy weight and bias - var (weightBuilder, biasBuilder) = copyParam(module) + val (weightBuilder, biasBuilder) = copyParam(module) val blobShape = toCaffeScalaParam(module) @@ -526,7 +524,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert override protected def toCaffeSequential(module : AbstractModule[Activity, Tensor[T], T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { val res = new ArrayBuffer[GeneratedMessage]() - var lastBottoms = bottoms + val lastBottoms = bottoms val modules = module.asInstanceOf[Sequential[T]].modules modules.foreach(nested => { val nestedLayer = nested.asInstanceOf[AbstractModule[_, _, _]]. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala index 7aaf49b8f00..646b24b0196 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala @@ -140,7 +140,7 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve setConnections(layerParameter, bottoms, nextSize) // copy weight and bias - var (weightBuilder, biasBuilder) = copyParam(module) + val (weightBuilder, biasBuilder) = copyParam(module) // get convolution param map val layerParams = toCaffeConvolutionParam(module) @@ -158,6 +158,8 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve convolutionParam.setStrideH(layerParams("strideH")) convolutionParam.setPadW(layerParams("padW")) convolutionParam.setPadH(layerParams("padH")) + val withBias = if (layerParams("withBias") == 1) true else false + convolutionParam.setBiasTerm(withBias) weightBuilder.setChannels(nInputPlane / ngroup) weightBuilder.setNum(nOutputPlane) From b7df296628c57b7b9021fbe37744cdc1df322cb6 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Thu, 20 Jul 2017 16:37:36 +0800 Subject: [PATCH 0302/1065] Add readme for treelstm Sentiment example (#1238) * add readme for treelstmSentiment * make format better * make format better * make format better * make format better * make format better * meet code review * meet review * get vocab * refine treeLSTM MD * make read embedding from hdfs * change some default value * add logs to treeLSTM --- .../dllib/example/treeLSTMSentiment/README.md | 69 +++ .../example/treeLSTMSentiment/Train.scala | 8 +- .../treeLSTMSentiment/TreeSentiment.scala | 3 +- .../example/treeLSTMSentiment/Utils.scala | 21 +- .../treeLSTMSentiment/fetch_and_preprocess.py | 392 ++++++++++++++++++ 5 files changed, 478 insertions(+), 15 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/fetch_and_preprocess.py diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/README.md new file mode 100644 index 00000000000..12928534f25 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/README.md @@ -0,0 +1,69 @@ +## Summary +This example shows how to use BigDL train a model on [Standford Treebank +dataset](https://nlp.stanford.edu/sentiment/index.html) dataset using binary TreeLSTM and [Glove](https://nlp.stanford.edu/projects/glove/) +word embedding vectors. Tree-LSTM is a kind of recursive neural networks, which describes in the paper +[Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://arxiv.org/abs/1503.00075) + by Kai Sheng Tai, Richard Socher, and Christopher Manning. + +The dataset is a corpus of ~10K one-sentence movie reviews from Rotten Tomatoes. Each sentence has been parsed into +[constituency-based parse trees](https://en.wikipedia.org/wiki/Parse_tree#Constituency-based_parse_trees), which is +kind of binary trees with a word at each leaf. After pre-processing, every node has been tagged a label whose range from -2 to 2, representing +the sentiment of the word or phrase. The value from -2 to 2 corresponds to highly negative, moderately negative, neutral, moderately positive and +highly positive respectively. The root of the tree represents the sentiment of the entire sentence. + +## Steps to run this example: +First run the following script + +```{r, engine='sh'} +python fetch_and_preprocess.py +``` + +The treebank dataset and the Glove word embedding vectors will be downloaded to +`/tmp/.bigdl/dataset/` directory, after that the treebank will be split into three folders +corresponding to train, dev, and test in an appropriate format. + +Next just run the following command to run the code: + +* Spark local: + +```{r, engine='sh'} + spark-submit --master "local[physical_core_number]" --driver-memory 20g \ + --class com.intel.analytics.bigdl.example.treeLSTMSentiment.Train \ + bigdl-VERSION-jar-with-dependencies.jar +``` + +* Spark cluster: + * Standalone: + + ```{r, engine='sh'} + MASTER=spark://xxx.xxx.xxx.xxx:xxxx + spark-submit --master ${MASTER} --driver-memory 20g --executor-memory 10g \ + --total-executor-cores 32 --executor-cores 8 \ + --class com.intel.analytics.bigdl.example.treeLSTMSentiment.Train \ + bigdl-VERSION-jar-with-dependencies.jar + ``` + + * Yarn client: + + ```{r, engine='sh'} + MASTER=spark://xxx.xxx.xxx.xxx:xxxx + spark-submit --master yarn --driver-memory 20g --executor-memory 10g \ + --num-executor 4 --executor-cores 8 \ + --class com.intel.analytics.bigdl.example.treeLSTMSentiment.Train \ + bigdl-VERSION-jar-with-dependencies.jar + ``` + + * NOTE: The total batch is: 128 and the batch per node is 128/nodeNum. + You can also have also set regularizer rate, learning rate, lstm hiddensize, + dropout probability and epoch number by adding one of the options below: + + ```{r, engine='sh'} + --baseDir # where is the data, default is '/tmp/.bigdl/dataset/' + --batchSize # number of batch size, default is 128 + --hiddenSize # number of TreeLSTM hidden size, default is 250 + --learingRate # number of learning rate, default is 0.05 + --regRate # number of L2 regularization rate, default is 1e-4 + --p # number of dropout probability rate, default is 0.5 + --epoch # number of epochs, default is 5 + ``` + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Train.scala index 54fac3d8844..a1722a55c67 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Train.scala @@ -56,8 +56,10 @@ object Train { val labelPadding = -1f val glovePath = s"$DATA_DIR/glove/glove.840B.300d.txt" val vocabPath = s"$DATA_DIR/sst/vocab-cased.txt" + log.info("Start loading embeddings\n") val (word2VecTensor, vocab) = - loadEmbeddingAndVocabulary(glovePath, vocabPath, indexFrom) + loadEmbeddingAndVocabulary(sc, glovePath, vocabPath, indexFrom) + log.info("Finish loading embeddings\n") val vocabBC = sc.broadcast(vocab) val (trainTreeRDD, trainLabelRDD, trainSentenceRDD) = preProcessData( @@ -67,7 +69,7 @@ object Train { s"$DATA_DIR/sst/train/parents.txt", s"$DATA_DIR/sst/train/labels.txt", s"$DATA_DIR/sst/train/sents.txt") - println( + log.info( s""" |train treeRDD count: ${trainTreeRDD.count()} |train labelRDD count: ${trainLabelRDD.count()} @@ -81,7 +83,7 @@ object Train { s"$DATA_DIR/sst/dev/parents.txt", s"$DATA_DIR/sst/dev/labels.txt", s"$DATA_DIR/sst/dev/sents.txt") - println( + log.info( s""" |dev treeRDD count: ${devTreeRDD.count()} |dev labelRDD count: ${devLabelRDD.count()} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/TreeSentiment.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/TreeSentiment.scala index aaf6eb9aa94..2077cd4cf55 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/TreeSentiment.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/TreeSentiment.scala @@ -34,11 +34,12 @@ object TreeLSTMSentiment { val embeddingDim = word2VecTensor.size(2) val embedding = LookupTable(vocabSize, embeddingDim) embedding.weight.set(word2VecTensor) + embedding.setScaleW(2) val treeLSTMModule = Sequential() .add(BinaryTreeLSTM( embeddingDim, hiddenSize, withGraph = true)) - .add(Dropout(p)) + .add(TimeDistributed(Dropout(p))) .add(TimeDistributed(Linear(hiddenSize, classNum))) .add(TimeDistributed(LogSoftMax())) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala index 845f52a5239..3d3a54eb5c7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala @@ -171,25 +171,24 @@ object Utils { } def loadEmbeddingAndVocabulary( + sc: SparkContext, w2vPath: String, vocabPath: String, indexFrom: Int ): (Tensor[Float], Map[String, Int]) = { - val word2Vec = scala.collection.mutable.Map[String, Array[Float]]() - for (line <- Source.fromFile(w2vPath, "ISO-8859-1").getLines) { + val word2Vec = sc.textFile(w2vPath) + .map(line => { val values = line.split(" ") val word = values(0) val coefs = values.slice(1, values.length).map(_.toFloat) - word2Vec += word -> coefs - } + word -> coefs + }).toLocalIterator.toList.toMap var i = 1 - val vocabLines = Source - .fromFile(vocabPath, "ISO-8859-1") - .getLines - .toList - val word2VecTensor = Tensor(vocabLines.length + indexFrom - 1, word2Vec.last._2.length) + val vocabLines = sc.textFile(vocabPath).collect() + val word2VecTensor = + Tensor(vocabLines.length + indexFrom - 1, word2Vec.last._2.length) val vocab = scala.collection.mutable.Map[String, Int]() while (i < indexFrom) { @@ -240,7 +239,7 @@ object Utils { hiddenSize: Int = 250, learningRate: Double = 0.05, regRate: Double = 1e-4, - p: Double = 0, - epoch: Int = 10 + p: Double = 0.5, + epoch: Int = 5 ) extends AbstractTextClassificationParams } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/fetch_and_preprocess.py b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/fetch_and_preprocess.py new file mode 100644 index 00000000000..7479918a7ac --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/fetch_and_preprocess.py @@ -0,0 +1,392 @@ +""" +Downloads the following: +- Glove vectors +- Stanford Sentiment Treebank (sentiment classification task) + +Pre-process them to the easier using format + +The script is modified from the pre-processing in original treelstm code, +which can be found at https://github.com/stanfordnlp/treelstm. +""" + +from __future__ import print_function +import urllib2 +import sys +import os +import shutil +import zipfile +import gzip +import glob + +# +# Trees and tree loading +# +class ConstTree(object): + def __init__(self): + self.left = None + self.right = None + + def size(self): + self.size = 1 + if self.left is not None: + self.size += self.left.size() + if self.right is not None: + self.size += self.right.size() + return self.size + + def set_spans(self): + if self.word is not None: + self.span = self.word + return self.span + + self.span = self.left.set_spans() + if self.right is not None: + self.span += ' ' + self.right.set_spans() + return self.span + + def get_labels(self, spans, labels, dictionary): + if self.span in dictionary: + spans[self.idx] = self.span + labels[self.idx] = dictionary[self.span] + if self.left is not None: + self.left.get_labels(spans, labels, dictionary) + if self.right is not None: + self.right.get_labels(spans, labels, dictionary) + +def download(url, dirpath): + filename = url.split('/')[-1] + filepath = os.path.join(dirpath, filename) + try: + u = urllib2.urlopen(url) + except: + print("URL %s failed to open" %url) + raise Exception + try: + f = open(filepath, 'wb') + except: + print("Cannot write %s" %filepath) + raise Exception + try: + filesize = int(u.info().getheaders("Content-Length")[0]) + except: + print("URL %s failed to report length" %url) + raise Exception + print("Downloading: %s Bytes: %s" % (filename, filesize)) + + downloaded = 0 + block_sz = 8192 + status_width = 70 + while True: + buf = u.read(block_sz) + if not buf: + print('') + break + else: + print('', end='\r') + downloaded += len(buf) + f.write(buf) + status = (("[%-" + str(status_width + 1) + "s] %3.2f%%") % + ('=' * int(float(downloaded) / filesize * status_width) + '>', downloaded * 100. / filesize)) + print(status, end='') + sys.stdout.flush() + f.close() + return filepath + +def unzip(filepath): + print("Extracting: " + filepath) + dirpath = os.path.dirname(filepath) + with zipfile.ZipFile(filepath) as zf: + zf.extractall(dirpath) + os.remove(filepath) + +def download_wordvecs(dirpath): + if os.path.exists(dirpath): + print('Found Glove vectors - skip') + return + else: + os.makedirs(dirpath) + url = 'http://www-nlp.stanford.edu/data/glove.840B.300d.zip' + unzip(download(url, dirpath)) + +def download_sst(dirpath): + if os.path.exists(dirpath): + print('Found SST dataset - skip') + return + url = 'http://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip' + parent_dir = os.path.dirname(dirpath) + unzip(download(url, parent_dir)) + os.rename( + os.path.join(parent_dir, 'stanfordSentimentTreebank'), + os.path.join(parent_dir, 'sst')) + shutil.rmtree(os.path.join(parent_dir, '__MACOSX')) # remove extraneous dir + +def split(sst_dir, train_dir, dev_dir, test_dir): + sents = load_sents(sst_dir) + splits = load_splits(sst_dir) + parents = load_parents(sst_dir) + + with open(os.path.join(train_dir, 'sents.txt'), 'w') as train, \ + open(os.path.join(dev_dir, 'sents.txt'), 'w') as dev, \ + open(os.path.join(test_dir, 'sents.txt'), 'w') as test, \ + open(os.path.join(train_dir, 'parents.txt'), 'w') as trainparents, \ + open(os.path.join(dev_dir, 'parents.txt'), 'w') as devparents, \ + open(os.path.join(test_dir, 'parents.txt'), 'w') as testparents: + + for sent, split, p in zip(sents, splits, parents): + if split == 1: + train.write(sent) + train.write('\n') + trainparents.write(p) + trainparents.write('\n') + elif split == 2: + test.write(sent) + test.write('\n') + + testparents.write(p) + testparents.write('\n') + else: + dev.write(sent) + dev.write('\n') + devparents.write(p) + devparents.write('\n') + +def make_dirs(dirs): + for d in dirs: + if not os.path.exists(d): + os.makedirs(d) + +def load_sents(dirpath): + sents = [] + with open(os.path.join(dirpath, 'SOStr.txt')) as sentsfile: + for line in sentsfile: + sent = ' '.join(line.split('|')) + sents.append(sent.strip()) + return sents + +def write_labels(dirpath, dictionary): + print('Writing labels for trees in ' + dirpath) + with open(os.path.join(dirpath, 'labels.txt'), 'w') as labels: + # load constituency trees + const_trees, toks = load_trees(dirpath) + + # write span labels + for i in xrange(len(const_trees)): + const_trees[i].set_spans() + + # const tree labels + s, l = [], [] + for j in xrange(const_trees[i].size()): + s.append(None) + l.append(None) + const_trees[i].get_labels(s, l, dictionary) + labels.write(' '.join(map(str, l)) + '\n') + +def load_dictionary(dirpath): + labels = [] + with open(os.path.join(dirpath, 'sentiment_labels.txt')) as labelsfile: + labelsfile.readline() + for line in labelsfile: + idx, rating = line.split('|') + idx = int(idx) + rating = float(rating) + if rating <= 0.2: + label = -2 + elif rating <= 0.4: + label = -1 + elif rating > 0.8: + label = +2 + elif rating > 0.6: + label = +1 + else: + label = 0 + labels.append(label) + + d = {} + with open(os.path.join(dirpath, 'dictionary.txt')) as dictionary: + for line in dictionary: + s, idx = line.split('|') + d[s] = labels[int(idx)] + return d + +def load_parents(dirpath): + parents = [] + with open(os.path.join(dirpath, 'STree.txt')) as parentsfile: + for line in parentsfile: + p = ' '.join(line.split('|')) + parents.append(p.strip()) + return parents + +def load_splits(dirpath): + splits = [] + with open(os.path.join(dirpath, 'datasetSplit.txt')) as splitfile: + splitfile.readline() + for line in splitfile: + idx, split = line.split(',') + splits.append(int(split)) + return splits + +def load_trees(dirpath): + const_trees, toks = [], [] + with open(os.path.join(dirpath, 'parents.txt')) as parentsfile, \ + open(os.path.join(dirpath, 'sents.txt')) as toksfile: + parents, dparents = [], [] + for line in parentsfile: + parents.append(map(int, line.split())) + for line in toksfile: + toks.append(line.strip().split()) + for i in xrange(len(toks)): + const_trees.append(load_constituency_tree(parents[i], toks[i])) + return const_trees, toks + +def load_constituency_tree(parents, words): + trees = [] + root = None + size = len(parents) + for i in xrange(size): + trees.append(None) + + word_idx = 0 + for i in xrange(size): + if not trees[i]: + idx = i + prev = None + prev_idx = None + word = words[word_idx] + word_idx += 1 + while True: + tree = ConstTree() + parent = parents[idx] - 1 + tree.word, tree.parent, tree.idx = word, parent, idx + word = None + if prev is not None: + if tree.left is None: + tree.left = prev + else: + tree.right = prev + trees[idx] = tree + if parent >= 0 and trees[parent] is not None: + if trees[parent].left is None: + trees[parent].left = tree + else: + trees[parent].right = tree + break + elif parent == -1: + root = tree + break + else: + prev = tree + prev_idx = idx + idx = parent + return root + +def load_dependency_tree(parents): + trees = [] + root = None + size = len(parents) + for i in xrange(size): + trees.append(None) + + for i in xrange(size): + if not trees[i]: + idx = i + prev = None + prev_idx = None + while True: + tree = DepTree() + parent = parents[idx] - 1 + + # node is not in tree + if parent == -2: + break + + tree.parent, tree.idx = parent, idx + if prev is not None: + tree.children.append(prev) + trees[idx] = tree + if parent >= 0 and trees[parent] is not None: + trees[parent].children.append(tree) + break + elif parent == -1: + root = tree + break + else: + prev = tree + prev_idx = idx + idx = parent + return root + +def build_vocab(filepaths, dst_path, lowercase=True): + vocab = set() + for filepath in filepaths: + with open(filepath) as f: + for line in f: + if lowercase: + line = line.lower() + vocab |= set(line.split()) + with open(dst_path, 'w') as f: + for w in sorted(vocab): + f.write(w + '\n') + +def load_dictionary(dirpath): + labels = [] + with open(os.path.join(dirpath, 'sentiment_labels.txt')) as labelsfile: + labelsfile.readline() + for line in labelsfile: + idx, rating = line.split('|') + idx = int(idx) + rating = float(rating) + if rating <= 0.2: + label = -2 + elif rating <= 0.4: + label = -1 + elif rating > 0.8: + label = +2 + elif rating > 0.6: + label = +1 + else: + label = 0 + labels.append(label) + + d = {} + with open(os.path.join(dirpath, 'dictionary.txt')) as dictionary: + for line in dictionary: + s, idx = line.split('|') + d[s] = labels[int(idx)] + return d + +if __name__ == '__main__': + base_dir = '/tmp/.bigdl/' + + # data + data_dir = os.path.join(base_dir, 'dataset') + wordvec_dir = os.path.join(data_dir, 'glove') + sst_dir = os.path.join(data_dir, 'sst') + make_dirs([base_dir, data_dir]) + + download_wordvecs(wordvec_dir) + download_sst(sst_dir) + + train_dir = os.path.join(sst_dir, 'train') + dev_dir = os.path.join(sst_dir, 'dev') + test_dir = os.path.join(sst_dir, 'test') + make_dirs([train_dir, dev_dir, test_dir]) + + # libraries + lib_dir = os.path.join(base_dir, 'lib') + + + print('=' * 80) + print('Preprocessing Stanford Sentiment Treebank') + print('=' * 80) + + # produce train/dev/test splits + split(sst_dir, train_dir, dev_dir, test_dir) + sent_paths = glob.glob(os.path.join(sst_dir, '*/sents.txt')) + build_vocab(sent_paths, os.path.join(sst_dir, 'vocab-cased.txt'), lowercase=False) + + # write sentiment labels for nodes in trees + dictionary = load_dictionary(sst_dir) + write_labels(train_dir, dictionary) + write_labels(dev_dir, dictionary) + write_labels(test_dir, dictionary) + From 5c42de30983465c7a4964c07237021b95824a435 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 20 Jul 2017 17:10:22 +0800 Subject: [PATCH 0303/1065] Caffe topology (#1309) * add dynamic caffe loading support * refined with bug fix and more commnets * fix typo and bias check for convolution * renaming change * fix import reference * refine customized layer support * fix style check issue * resolve style issue * resolve conflict * add topology support * add split fix * add new layer * refinement * refinement * fix scale issue --- .../bigdl/dllib/utils/caffe/CaffeLoader.scala | 24 +++++++++++++++---- .../bigdl/dllib/utils/caffe/Converter.scala | 4 ++-- .../dllib/utils/caffe/LayerConverter.scala | 6 ++++- 3 files changed, 27 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 251b3afe3d6..9ab4e085169 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -27,8 +27,7 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{File, FileReader, Table} -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FSDataInputStream, Path} + import org.apache.log4j.Logger import scala.collection.mutable @@ -277,8 +276,7 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, if (nodes != null) { var curr = nodes(0) bottomList.foreach(dependency => { - if (splitLayerMap.contains(dependency)) splitLayerMap(dependency) -> curr - else if (top2LayerMap.contains(dependency)) { + if (top2LayerMap.contains(dependency)) { layersMap(top2LayerMap(dependency)) -> curr } }) @@ -295,6 +293,24 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } } }) + // process with split separately in case of out of order + allLayers.foreach(layer => { + var name : String = null + val bottomList = new ArrayBuffer[String]() + layer match { + case v2 : LayerParameter => + name = v2.getName + bottomList ++= v2.getBottomList.asScala + case v1 : V1LayerParameter => + name = v1.getName + bottomList ++= v1.getBottomList.asScala + } + bottomList.foreach(bottom => { + if (splitLayerMap.contains(bottom)) { + splitLayerMap(bottom) -> layersMap(name) + } + }) + }) return layers } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index 1da48fb14dd..8e1a6c0d3b5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -158,7 +158,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { private def fromCaffeFlatten(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val layerName = getLayerName(layer) - Seq(FlattenTable[T].setName(layerName).inputs()) + Seq(InferReshape[T](Array(0, -1)).setName(layerName).inputs()) } private def fromCaffeLog(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -286,7 +286,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { toCaffeBatchNormalization(moduleNode, bottoms, nextSize) case joinTable : JoinTable[_] => toCaffeConcat(moduleNode, bottoms, nextSize) case elu : ELU[_] => toCaffeElu(moduleNode, bottoms, nextSize) - case flatternTable : FlattenTable[_] => toCaffeFlattern(moduleNode, bottoms, nextSize) + case infershape : InferReshape[_] => toCaffeFlattern(moduleNode, bottoms, nextSize) case log : Log[_] => toCaffeLog(moduleNode, bottoms, nextSize) case power : Power[_] => toCaffePower(moduleNode, bottoms, nextSize) case prelu : PReLU[_] => toCaffePReLu(moduleNode, bottoms, nextSize) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index 6814e9769ca..13227f8c0cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -130,7 +130,11 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert val weightBlob = getBlob(layer, 1) if (weightBlob.isDefined) { val blob = weightBlob.get - val size = blob.getShape.getDimList.asScala.map(_.toInt).toArray + val size : Array[Int] = if (blob.getShape.getDimCount == 1) { + Array(1, blob.getShape.getDim(0).toInt, 1, 1) + } else { + blob.getShape.getDimList.asScala.map(_.toInt).toArray + } Seq(Scale[T](size).setName(layerName).inputs()) } else { val inputBlob = getBlob(layer, 0).get From 97131f36155eb9c32dba0d8436e39e77c5fe8779 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 21 Jul 2017 12:56:07 +0800 Subject: [PATCH 0304/1065] add predictClass for python (#1335) --- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 4eb296f726f..c9037fcf518 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1418,6 +1418,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new JavaRDD[JTensor](listRDD) } + def modelPredictClass(model: AbstractModule[Activity, Activity, T], + dataRdd: JavaRDD[Sample]): JavaRDD[Int] = { + val tensorRDD = model.predictClass(dataRdd.rdd.map(toSample(_))) + new JavaRDD[Int](tensorRDD) + } + def modelForward(model: AbstractModule[Activity, Activity, T], input: JList[JTensor], inputIsTable: Boolean): JList[JTensor] = { From a6426685b97b7895690f9b3b494415e4ccaddf14 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 21 Jul 2017 14:09:50 +0800 Subject: [PATCH 0305/1065] change version to 0.3.0-SNAPSHOT (#1343) --- dist/pom.xml | 2 +- dl/pom.xml | 4 ++-- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index b40bf37a941..3e923601926 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.2.0-SNAPSHOT + 0.3.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index 683e88d3ede..c5cca8469ed 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.2.0-SNAPSHOT + 0.3.0-SNAPSHOT 4.0.0 @@ -69,7 +69,7 @@ com.intel.analytics.bigdl.native ${mkl-java-os-version} - 0.2.0-SNAPSHOT + 0.3.0-SNAPSHOT diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala index 1841e61505f..5bb4488e879 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala @@ -16,35 +16,93 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{ModuleData, ModuleSerializable} +import serialization.Bigdl.BigDLModule import scala.reflect.ClassTag /** * This module is for debug purpose, which can print activation and gradient in your model * topology + * + * User can pass in a customized function to inspect more information from the activation. This is + * very useful in Debug. + * + * Please note that the passed in customized function will not be persisted in serialization. */ @SerialVersionUID(6735245897546687343L) -class Echo[T: ClassTag] (implicit ev: TensorNumeric[T]) +class Echo[T: ClassTag]( + private var feval: (Echo[T], Tensor[T]) => Unit, + private var beval: (Echo[T], Tensor[T], Tensor[T]) => Unit +) (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + /** + * Set evaluation method for forward + * @param feval + * @return + */ + def setFeval(feval: (Echo[T], Tensor[T]) => Unit): this.type = { + this.feval = feval + this + } + + /** + * Set evaluation method for backward + * @param beval + * @return + */ + def setBeval(beval: (Echo[T], Tensor[T], Tensor[T]) => Unit): this.type = { + this.beval = beval + this + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { this.output = input - println(s"${getPrintName} : Activation size is ${input.size().mkString("x")}") + feval(this, input) this.output } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { this.gradInput = gradOutput - println(s"${getPrintName} : Gradient size is ${gradOutput.size().mkString("x")}") + beval(this, input, gradOutput) this.gradInput } } -object Echo { - def apply[@specialized(Float, Double) T: ClassTag]() - (implicit ev: TensorNumeric[T]) : Echo[T] = { - new Echo[T]() +object Echo extends ModuleSerializable { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]) : Echo[T] = { + new Echo[T](Echo.defaultFeval[T]_, Echo.defaultBeval[T]_) + } + + def apply[T: ClassTag](feval: (Echo[T], Tensor[T]) => Unit) + (implicit ev: TensorNumeric[T]) : Echo[T] = { + new Echo[T](feval, Echo.defaultBeval[T]_) + } + + def apply[T: ClassTag](feval: (Echo[T], Tensor[T]) => Unit, + beval: (Echo[T], Tensor[T], Tensor[T]) => Unit) + (implicit ev: TensorNumeric[T]) : Echo[T] = { + new Echo[T](feval, beval) + } + + private def defaultFeval[T](module: Echo[T], input: Tensor[T]): Unit = { + println(s"${module.getPrintName} : Activation size is ${input.size().mkString("x")}") + } + + private def defaultBeval[T](module: Echo[T], input: Tensor[T], gradOutput: Tensor[T]): Unit = { + println(s"${module.getPrintName} : Gradient size is ${gradOutput.size().mkString("x")}") + } + + override def doSerializeModule[T: ClassManifest](m: ModuleData[T], b: BigDLModule.Builder) + (implicit ev: TensorNumeric[T]): Unit = { + // We won't serialize the function, so do nothing here + } + + override def doLoadModule[T: ClassManifest](model: BigDLModule) + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + new Echo[T](defaultFeval, defaultBeval) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala index 7904e674783..af35eaad566 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala @@ -54,7 +54,11 @@ class Input[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T } object Input { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): ModuleNode[T] = { - new Node(new Input().asInstanceOf[AbstractModule[Activity, Tensor[T], T]]) + def apply[T: ClassTag](name : String = null)(implicit ev: TensorNumeric[T]): ModuleNode[T] = { + val module = new Input() + if (name != null) { + module.setName(name) + } + new Node(module.asInstanceOf[AbstractModule[Activity, Activity, T]]) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractCriterion.scala index ff2883a5407..c3c73bc0db7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractCriterion.scala @@ -49,7 +49,7 @@ abstract class TensorCriterion[T: ClassTag] abstract class AbstractCriterion[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag]( implicit ev: TensorNumeric[T]) extends Serializable { - var gradInput: A = Activity[A, T]() + var gradInput: A = Activity.allocate[A, T]() var output: T = ev.fromType[Int](0) private[nn] def allocateAs[D <: Activity](dest: D): D = dest match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index c9d0313162f..7b6bec3ffe7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -53,20 +53,19 @@ abstract class TensorModule[T: ClassTag] * @tparam B Output data type * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now */ -abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, -@specialized(Float, Double) T: ClassTag]( +abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag]( implicit ev: TensorNumeric[T]) extends Serializable { private val namePostfix = Integer.toHexString(java.util.UUID.randomUUID().hashCode()) /** * The cached output. So we don't compute it again when need it */ - var output: B = Activity[B, T]() + var output: B = Activity.allocate[B, T]() /** * The cached gradient of activities. So we don't compute it again when need it */ - var gradInput: A = Activity[A, T]() + var gradInput: A = Activity.allocate[A, T]() /** * The scale of gradient weight and gradient bias @@ -134,12 +133,12 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @return */ def clearState() : this.type = { - if (output.isInstanceOf[Tensor[T]]) { - output.asInstanceOf[Tensor[T]].set() + if (output.isInstanceOf[Tensor[_]]) { + output.asInstanceOf[Tensor[_]].set() } - if (gradInput.isInstanceOf[Tensor[T]]) { - gradInput.asInstanceOf[Tensor[T]].set() + if (gradInput.isInstanceOf[Tensor[_]]) { + gradInput.asInstanceOf[Tensor[_]].set() } this diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala index 2c95b11d336..8606772ce7f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala @@ -40,16 +40,46 @@ trait Activity { } object Activity { - def apply[A <: Activity: ClassTag, T : ClassTag]()( - implicit ev: TensorNumeric[T]): A = { - val result = if (classTag[A] == classTag[Tensor[T]]) { - Tensor[T]() - } else if (classTag[A] == classTag[Table]) { + /** + * Allocate a data instance by given type D and numeric type T + * @tparam D Data type + * @tparam T numeric type + * @return + */ + def allocate[D <: Activity: ClassTag, T : ClassTag](): D = { + val buffer = if (classTag[D] == classTag[Table]) { T() + } else if (classTag[D] == classTag[Tensor[_]]) { + if (classTag[Boolean] == classTag[T]) { + import com.intel.analytics.bigdl.numeric.NumericBoolean + Tensor[Boolean]() + } else if (classTag[Char] == classTag[T]) { + import com.intel.analytics.bigdl.numeric.NumericChar + Tensor[Char]() + } else if (classTag[Short] == classTag[T]) { + import com.intel.analytics.bigdl.numeric.NumericShort + Tensor[Short]() + } else if (classTag[Int] == classTag[T]) { + import com.intel.analytics.bigdl.numeric.NumericInt + Tensor[Int]() + } else if (classTag[Long] == classTag[T]) { + import com.intel.analytics.bigdl.numeric.NumericLong + Tensor[Long]() + } else if (classTag[Float] == classTag[T]) { + import com.intel.analytics.bigdl.numeric.NumericFloat + Tensor[Float]() + } else if (classTag[Double] == classTag[T]) { + import com.intel.analytics.bigdl.numeric.NumericDouble + Tensor[Double]() + } else if (classTag[String] == classTag[T]) { + import com.intel.analytics.bigdl.numeric.NumericString + Tensor[String]() + } else { + throw new IllegalArgumentException("Type T activity is not supported") + } } else { null } - - result.asInstanceOf[A] + buffer.asInstanceOf[D] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala index 98eac9edc91..11aa9662d88 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table @@ -22,48 +23,50 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class Equal[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, T] { + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { - override def updateOutput(input: Table): Tensor[T] = { + output = Activity.allocate[Tensor[Boolean], Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { output.resizeAs(input(1)) input[Tensor[_]](1).getType() match { case FloatType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Float, Float]( + output.zipWith[Float, Float]( input[Tensor[Float]](1), input[Tensor[Float]](2), (a, b) => a == b) case BooleanType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Boolean, Boolean]( + output.zipWith[Boolean, Boolean]( input[Tensor[Boolean]](1), input[Tensor[Boolean]](2), (a, b) => a == b) case DoubleType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Double, Double]( + output.zipWith[Double, Double]( input[Tensor[Double]](1), input[Tensor[Double]](2), (a, b) => a == b) case CharType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Char, Char]( + output.zipWith[Char, Char]( input[Tensor[Char]](1), input[Tensor[Char]](2), (a, b) => a == b) case StringType => - output.asInstanceOf[Tensor[Boolean]].zipWith[String, String]( + output.zipWith[String, String]( input[Tensor[String]](1), input[Tensor[String]](2), (a, b) => a == b) case LongType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Long, Long]( + output.zipWith[Long, Long]( input[Tensor[Long]](1), input[Tensor[Long]](2), (a, b) => a == b) case ShortType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Short, Short]( + output.zipWith[Short, Short]( input[Tensor[Short]](1), input[Tensor[Short]](2), (a, b) => a == b) case IntType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Int, Int]( + output.zipWith[Int, Int]( input[Tensor[Int]](1), input[Tensor[Int]](2), (a, b) => a == b) @@ -75,6 +78,6 @@ class Equal[T: ClassTag]() } object Equal { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](new Equal()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new Equal()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Greater.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Greater.scala index 384d5bb3b25..e5aa7f1f839 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Greater.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Greater.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table @@ -22,9 +23,11 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class Greater[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, T] { + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { - override def updateOutput(input: Table): Tensor[T] = { + output = Activity.allocate[Tensor[Boolean], Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { output.resizeAs(input(1)) input[Tensor[_]](1).getType() match { case FloatType => @@ -70,6 +73,6 @@ class Greater[T: ClassTag]() } object Greater { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](new Greater()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new Greater()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Less.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Less.scala index 509574f4295..56b88dd88f8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Less.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Less.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table @@ -22,43 +23,45 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class Less[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, T] { + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { - override def updateOutput(input: Table): Tensor[T] = { + output = Activity.allocate[Tensor[Boolean], Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { output.resizeAs(input(1)) input[Tensor[_]](1).getType() match { case FloatType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Float, Float]( + output.zipWith[Float, Float]( input[Tensor[Float]](1), input[Tensor[Float]](2), (a, b) => a < b) case DoubleType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Double, Double]( + output.zipWith[Double, Double]( input[Tensor[Double]](1), input[Tensor[Double]](2), (a, b) => a < b) case CharType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Char, Char]( + output.zipWith[Char, Char]( input[Tensor[Char]](1), input[Tensor[Char]](2), (a, b) => a < b) case StringType => - output.asInstanceOf[Tensor[Boolean]].zipWith[String, String]( + output.zipWith[String, String]( input[Tensor[String]](1), input[Tensor[String]](2), (a, b) => a < b) case LongType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Long, Long]( + output.zipWith[Long, Long]( input[Tensor[Long]](1), input[Tensor[Long]](2), (a, b) => a < b) case ShortType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Short, Short]( + output.zipWith[Short, Short]( input[Tensor[Short]](1), input[Tensor[Short]](2), (a, b) => a < b) case IntType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Int, Int]( + output.zipWith[Int, Int]( input[Tensor[Int]](1), input[Tensor[Int]](2), (a, b) => a < b) @@ -70,6 +73,6 @@ class Less[T: ClassTag]() } object Less { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](new Less()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new Less()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalAnd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalAnd.scala index b4e26059005..a926733c478 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalAnd.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalAnd.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.{BooleanType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table @@ -22,11 +23,14 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class LogicalAnd[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, T] { - override def updateOutput(input: Table): Tensor[T] = { + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { + + output = Activity.allocate[Tensor[Boolean], Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { input[Tensor[_]](1).getType() match { case BooleanType => - output.asInstanceOf[Tensor[Boolean]].resizeAs(input(1)).copy(input(1)) + output.resizeAs(input(1)).copy(input(1)) output .toTensor[Boolean] .map(input(2).asInstanceOf[Tensor[Boolean]], (a, b) => a && b) @@ -38,6 +42,6 @@ class LogicalAnd[T: ClassTag]() } object LogicalAnd { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](new LogicalAnd()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new LogicalAnd()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalNot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalNot.scala index ad42470ec80..0c795204723 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalNot.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalNot.scala @@ -15,28 +15,26 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{BooleanType, Tensor} import scala.reflect.ClassTag class LogicalNot[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], T] { - override def updateOutput(input: Tensor[T]): Tensor[T] = { - output.resizeAs(input).copy(input) - ev.getType() match { - case BooleanType => - output - .toTensor[Boolean] - .apply1(!_) - case _ => throw new RuntimeException("LogicalAnd only support boolean tensor") - } + (implicit ev: TensorNumeric[T]) extends Operation[Tensor[Boolean], Tensor[Boolean], T] { + + output = Activity.allocate[Tensor[Boolean], Boolean]() + gradInput = Activity.allocate[Tensor[Boolean], Boolean]() + override def updateOutput(input: Tensor[Boolean]): Tensor[Boolean] = { + output.resizeAs(input).copy(input) + output.apply1(!_) output } } object LogicalNot { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Tensor[T], T] - = ModuleToOperation[Tensor[T], T](new LogicalNot()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new LogicalNot()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalOr.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalOr.scala index 5afb271313b..562f5491e48 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalOr.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalOr.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.{BooleanType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table @@ -22,14 +23,15 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class LogicalOr[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, T] { - override def updateOutput(input: Table): Tensor[T] = { + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { + + output = Activity.allocate[Tensor[Boolean], Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { output.resizeAs(input(1)).copy(input(1)) input[Tensor[_]](1).getType() match { case BooleanType => - output - .toTensor[Boolean] - .map(input(2).asInstanceOf[Tensor[Boolean]], (a, b) => a || b) + output.map(input(2).asInstanceOf[Tensor[Boolean]], (a, b) => a || b) case _ => throw new RuntimeException("LogicalOr only support boolean tensor") } @@ -38,6 +40,6 @@ class LogicalOr[T: ClassTag]() } object LogicalOr { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](new LogicalOr()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new LogicalOr()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala index cb389dbce72..87c6ae8390d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala @@ -25,21 +25,21 @@ import scala.reflect.ClassTag * Wrap a nn module to an [[Operation]] * * @param module an nn module - * @tparam A Input data type * @tparam T Numeric type. Only support float/double now */ -class ModuleToOperation[A <: Activity: ClassTag, T: ClassTag] -(module: AbstractModule[A, Tensor[T], T]) +class ModuleToOperation[T: ClassTag] +(module: AbstractModule[Activity, Activity, T]) (implicit ev: TensorNumeric[T]) - extends Operation[A, T]{ + extends Operation[Activity, Activity, T]{ - override def updateOutput(input: A): Tensor[T] = { + override def updateOutput(input: Activity): Activity = { output = module.forward(input) output } } object ModuleToOperation { - def apply[A <: Activity: ClassTag, T: ClassTag](model: AbstractModule[A, Tensor[T], T]) - (implicit ev: TensorNumeric[T]): ModuleToOperation[A, T] = new ModuleToOperation(model) + def apply[T: ClassTag](model: AbstractModule[_, _, T]) + (implicit ev: TensorNumeric[T]): ModuleToOperation[T] = + new ModuleToOperation(model.asInstanceOf[AbstractModule[Activity, Activity, T]]) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala index 2969e350cbd..4bca87031ca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala @@ -29,18 +29,18 @@ import scala.reflect.ClassTag * @tparam A Input data type * @tparam T Numeric type. Only support float/double now */ -abstract class Operation[A <: Activity: ClassTag, T: ClassTag] -(implicit ev: TensorNumeric[T]) extends AbstractModule[A, Tensor[T], T]{ +abstract class Operation[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag] +(implicit ev: TensorNumeric[T]) extends AbstractModule[A, B, T]{ - override def updateGradInput(input: A, gradOutput: Tensor[T]): A = { + override def updateGradInput(input: A, gradOutput: B): A = { throw new UnsupportedOperationException("Operation does not support updateGradInput() method") } - override def accGradParameters(input: A, gradOutput: Tensor[T]): Unit = { + override def accGradParameters(input: A, gradOutput: B): Unit = { throw new UnsupportedOperationException("Operation does not support updateGradInput() method") } - override def backward(input: A, gradOutput: Tensor[T]): A = { + override def backward(input: A, gradOutput: B): A = { throw new UnsupportedOperationException("Operation does not support backward() method") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Prod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Prod.scala index 5d6e7c10149..845d62a97fe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Prod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Prod.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -23,7 +24,7 @@ import scala.reflect.ClassTag class Prod[T: ClassTag]( axis: Int = 1, keepDim: Boolean = false) -(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], T] { +(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { private def getPositiveDimension(input: Tensor[T]): Int = { var dimension = this.axis if (dimension < 0) { @@ -48,7 +49,7 @@ class Prod[T: ClassTag]( object Prod { def apply[T: ClassTag](axis: Int, keepDim: Boolean = false) - (implicit ev: TensorNumeric[T]): Operation[Tensor[T], T] - = ModuleToOperation[Tensor[T], T]( + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( new Prod(axis = axis, keepDim = keepDim)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala index 10c9d152428..389cac77f61 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala @@ -24,58 +24,58 @@ import scala.reflect.ClassTag package object ops { object Add { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](CAddTable()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](CAddTable()) } object Subtract { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](CSubTable()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](CSubTable()) } object Multiply { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](CMulTable()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](CMulTable()) } object Divide { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Table, T] - = ModuleToOperation[Table, T](CDivTable()) + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](CDivTable()) } object Sum { def apply[T: ClassTag](axis: Int, keepDim: Boolean = false) - (implicit ev: TensorNumeric[T]): Operation[Tensor[T], T] - = ModuleToOperation[Tensor[T], T]( + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( com.intel.analytics.bigdl.nn.Sum(dimension = axis, squeeze = !keepDim)) } object Reshape { def apply[T: ClassTag](size: Array[Int]) - (implicit ev: TensorNumeric[T]): Operation[Tensor[T], T] - = ModuleToOperation[Tensor[T], T]( + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( com.intel.analytics.bigdl.nn.InferReshape(size: Array[Int])) } object Squeeze { def apply[T: ClassTag](axis: Array[Int] = null) - (implicit ev: TensorNumeric[T]): Operation[Tensor[T], T] - = ModuleToOperation[Tensor[T], T]( + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( com.intel.analytics.bigdl.nn.Squeeze(dims = axis, batchMode = false)) } object Identity { def apply[T: ClassTag]() - (implicit ev: TensorNumeric[T]): Operation[Activity, T] - = ModuleToOperation[Activity, T]( + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( com.intel.analytics.bigdl.nn.Identity() .asInstanceOf[AbstractModule[Activity, Tensor[T], T]]) } object ReLU { def apply[T: ClassTag]() - (implicit ev: TensorNumeric[T]): Operation[Tensor[T], T] - = ModuleToOperation[Tensor[T], T]( + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( com.intel.analytics.bigdl.nn.ReLU()) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 14a4af7ff7d..0c8bb63542a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -185,6 +185,7 @@ object ModuleSerializer extends ModuleSerializable{ SpatialSubtractiveNormalization) registerModule("com.intel.analytics.bigdl.nn.Transpose", Transpose) registerModule("com.intel.analytics.bigdl.nn.VolumetricMaxPooling", VolumetricMaxPooling) + registerModule("com.intel.analytics.bigdl.nn.Echo", Echo) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index 592a9194d59..8d1b78241da 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -758,7 +758,7 @@ object Placeholder extends TensorflowToBigDL { context: Context[T], byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { - Input[T].element.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Input[T]().element.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } @@ -807,7 +807,7 @@ object IdentityTF extends TensorflowToBigDL { byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { - Input[T].element.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Input[T]().element.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala index 727d0e4326d..9eb782dd446 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala @@ -29,7 +29,7 @@ class GreaterSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, true, false)) - val output = Greater[Boolean]().forward(input) + val output = Greater[Float]().forward(input) output should be(expectOutput) } @@ -42,7 +42,7 @@ class GreaterSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, true, false)) - val output = Greater[Boolean]().forward(input) + val output = Greater[Float]().forward(input) output should be(expectOutput) } @@ -55,7 +55,7 @@ class GreaterSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Greater[Boolean]().forward(input) + val output = Greater[Float]().forward(input) output should be(expectOutput) } @@ -68,7 +68,7 @@ class GreaterSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Greater[Boolean]().forward(input) + val output = Greater[Float]().forward(input) output should be(expectOutput) } @@ -81,7 +81,7 @@ class GreaterSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(true, false, true)) - val output = Greater[Boolean]().forward(input) + val output = Greater[Float]().forward(input) output should be(expectOutput) } @@ -94,7 +94,7 @@ class GreaterSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Greater[Boolean]().forward(input) + val output = Greater[Float]().forward(input) output should be(expectOutput) } @@ -107,7 +107,7 @@ class GreaterSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Greater[Boolean]().forward(input) + val output = Greater[Float]().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala index 7b6d24f880f..24dd436aa15 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala @@ -29,7 +29,7 @@ class LessSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(true, true, false)) - val output = Less[Boolean]().forward(input) + val output = Less[Float]().forward(input) output should be(expectOutput) } @@ -42,7 +42,7 @@ class LessSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Less[Boolean]().forward(input) + val output = Less[Float]().forward(input) output should be(expectOutput) } @@ -55,7 +55,7 @@ class LessSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Less[Boolean]().forward(input) + val output = Less[Float]().forward(input) output should be(expectOutput) } @@ -68,7 +68,7 @@ class LessSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Less[Boolean]().forward(input) + val output = Less[Float]().forward(input) output should be(expectOutput) } @@ -81,7 +81,7 @@ class LessSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, true, false)) - val output = Less[Boolean]().forward(input) + val output = Less[Float]().forward(input) output should be(expectOutput) } @@ -94,7 +94,7 @@ class LessSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Less[Boolean]().forward(input) + val output = Less[Float]().forward(input) output should be(expectOutput) } @@ -107,7 +107,7 @@ class LessSpec extends FlatSpec with Matchers { val expectOutput = Tensor[Boolean](T(false, false, true)) - val output = Less[Boolean]().forward(input) + val output = Less[Float]().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/ActivitySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/ActivitySpec.scala new file mode 100644 index 00000000000..01b4f493755 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/ActivitySpec.scala @@ -0,0 +1,81 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.tensor + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.utils.Table +import org.scalatest.{FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Parallel +class ActivitySpec extends FlatSpec with Matchers { + "Activity.allocate" should "be able to allocate table" in { + val r = Activity.allocate[Table, Any]() + r.isInstanceOf[Table] should be(true) + } + + "Activity.allocate" should "be able to allocate Tensor[Boolean]" in { + val r = Activity.allocate[Tensor[_], Boolean]() + r.isInstanceOf[Tensor[_]] should be(true) + r.asInstanceOf[Tensor[_]].getType() should be(BooleanType) + } + + "Activity.allocate" should "be able to allocate Tensor[Char]" in { + val r = Activity.allocate[Tensor[_], Char]() + r.isInstanceOf[Tensor[_]] should be(true) + r.asInstanceOf[Tensor[_]].getType() should be(CharType) + } + + "Activity.allocate" should "be able to allocate Tensor[Short]" in { + val r = Activity.allocate[Tensor[_], Short]() + r.isInstanceOf[Tensor[_]] should be(true) + r.asInstanceOf[Tensor[_]].getType() should be(ShortType) + } + + "Activity.allocate" should "be able to allocate Tensor[Int]" in { + val r = Activity.allocate[Tensor[_], Int]() + r.isInstanceOf[Tensor[_]] should be(true) + r.asInstanceOf[Tensor[_]].getType() should be(IntType) + } + + "Activity.allocate" should "be able to allocate Tensor[Long]" in { + val r = Activity.allocate[Tensor[_], Long]() + r.isInstanceOf[Tensor[_]] should be(true) + r.asInstanceOf[Tensor[_]].getType() should be(LongType) + } + + "Activity.allocate" should "be able to allocate Tensor[Float]" in { + val r = Activity.allocate[Tensor[_], Float]() + r.isInstanceOf[Tensor[_]] should be(true) + r.asInstanceOf[Tensor[_]].getType() should be(FloatType) + } + + "Activity.allocate" should "be able to allocate Tensor[Double]" in { + val r = Activity.allocate[Tensor[_], Double]() + r.isInstanceOf[Tensor[_]] should be(true) + r.asInstanceOf[Tensor[_]].getType() should be(DoubleType) + } + + "Activity.allocate" should "be able to allocate Tensor[String]" in { + val r = Activity.allocate[Tensor[_], String]() + r.isInstanceOf[Tensor[_]] should be(true) + r.asInstanceOf[Tensor[_]].getType() should be(StringType) + } + + "Activity.allocate" should "be able to allocate nothing for Activity" in { + val r = Activity.allocate[Activity, Any]() + r should be(null) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 0b271e86e4b..6c883e5fa3b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -497,7 +497,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } - "Echo serializer " should " work properly" in { + "Echo serializer" should "work properly" in { val echo = Echo() val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) val tensor2 = Tensor() From 6b20c0e1749ae088cfde5af235ac38df1e8988eb Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 12 Sep 2017 16:18:59 +0800 Subject: [PATCH 0386/1065] fix scalar api (#1548) * fix scalar api * meet code review * fix test * fix tests --- .../bigdl/dllib/tensor/DenseTensor.scala | 6 +++++ .../analytics/bigdl/dllib/tensor/Tensor.scala | 13 +++++---- .../bigdl/dllib/tensor/TensorSpec.scala | 27 +++++++++++++++++++ 3 files changed, 39 insertions(+), 7 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 7d79f7c4fd3..8a1c6e2856c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -539,6 +539,12 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( this._storage(offset) } + override def value(): T = { + require(0 == this.nDimension, s"invalid size: 0 == ${this.nDimension}") + var offset = this._storageOffset + this._storage(offset) + } + override def valueAt(d1: Int): T = { require(1 == this.nDimension, s"invalid size: 1 == ${this.nDimension}") var offset = this._storageOffset diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index e0f9ee2d9f6..e68c6edd508 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -182,6 +182,12 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { */ def apply(indexes: Array[Int]): T + + /** + * @return the value of a scalar. Requires the tensor to be a scalar. + */ + def value(): T + /** * Query the value on a given position. The number of parameters * should be equal to the dimension number of the tensor. @@ -190,7 +196,6 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { * @param d1,( d2, d3, d4, d5) the given position * @return the value on a given position */ - def valueAt(d1: Int): T def valueAt(d1: Int, d2: Int): T @@ -910,12 +915,6 @@ object Tensor { def apply[@specialized(Float, Double) T: ClassTag](other: Tensor[T])( implicit ev: TensorNumeric[T]): Tensor[T] = new DenseTensor(other) - def apply[@specialized(Float, Double) T: ClassTag](value: T)( - implicit ev: TensorNumeric[T]): Tensor[T] = { - new DenseTensor[T](new ArrayStorage[T](Array(value)), 0, Array[Int](), - Array[Int](), 0) - } - /** * create a tensor with a given breeze vector. The tensor will have the same size * with the given breeze vector. diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala new file mode 100644 index 00000000000..3a8e8dae384 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala @@ -0,0 +1,27 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.tensor + +import org.scalatest.{FlatSpec, Matchers} + +class TensorSpec extends FlatSpec with Matchers { + + "Tensor factory method" should "be able to construct scalar" in { + val tensor = Tensor[Int](Array(4), Array[Int]()) + tensor.value() should be (4) + } + +} From 88475f3064f31e3bfa7365e1804e25d6c1a465ad Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 13 Sep 2017 06:54:28 +0800 Subject: [PATCH 0387/1065] fix two edge (#1551) --- .../bigdl/dllib/utils/DirectedGraph.scala | 16 ++++++--- .../dllib/utils/tf/TensorflowLoader.scala | 4 +-- .../dllib/utils/tf/TensorflowToBigDL.scala | 24 +++++++++---- .../tf/models/inception_resnet_v2.py | 1 + .../test/resources/tf/models/inception_v3.py | 1 + .../src/test/resources/tf/models/two_edge.py | 34 +++++++++++++++++++ .../dllib/utils/tf/TensorflowLoaderSpec.scala | 13 +++++++ 7 files changed, 81 insertions(+), 12 deletions(-) create mode 100644 scala/dllib/src/test/resources/tf/models/two_edge.py diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala index 96aa13202bd..ed6e111ac89 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala @@ -64,10 +64,10 @@ class DirectedGraph[T](val source : Node[T], val reverse : Boolean = false) exte }) val result = new ArrayBuffer[Node[T]]() - while(!inDegrees.isEmpty) { + while(inDegrees.nonEmpty) { // toArray is not lazy eval, which is not affected by inDegrees - 1 operations below val startNodes = inDegrees.filterKeys(inDegrees(_) == 0).keySet.toArray - require(startNodes.size != 0, "There's a cycle in the graph") + require(startNodes.length != 0, "There's a cycle in the graph") result.appendAll(startNodes) startNodes.foreach(n => { val nextNodes = if (!reverse) n.nextNodes else n.prevNodes @@ -96,7 +96,11 @@ class DirectedGraph[T](val source : Node[T], val reverse : Boolean = false) exte val node = stack.pop() visited.add(node) val nextNodes = if (!reverse) node.nextNodes else node.prevNodes - nextNodes.filter(!visited.contains(_)).filter(!stack.contains(_)).foreach(stack.push(_)) + // to preserve order + val nodesSet = mutable.LinkedHashSet[Node[T]]() + nextNodes.foreach(nodesSet.add) + nodesSet.filter(!visited.contains(_)) + .filter(!stack.contains(_)).foreach(stack.push(_)) node } } @@ -120,7 +124,11 @@ class DirectedGraph[T](val source : Node[T], val reverse : Boolean = false) exte val node = queue.dequeue() visited.add(node) val nextNodes = if (!reverse) node.nextNodes else node.prevNodes - nextNodes.filter(!visited.contains(_)).filter(!queue.contains(_)).foreach(queue.enqueue(_)) + // to preserve order + val nodesSet = mutable.LinkedHashSet[Node[T]]() + nextNodes.foreach(nodesSet.add) + nodesSet.filter(!visited.contains(_)) + .filter(!queue.contains(_)).foreach(queue.enqueue(_)) node } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 8dbc98ebf5e..b6064fafd86 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -251,13 +251,13 @@ object TensorflowLoader{ val nextNodes = n.nextNodes.filter( n => n.element != null && convertedNode.contains(n) && !context.contains(n.element.getName) - ).map(convertedNode(_)).filter(_ != node).toSet + ).map(convertedNode(_)).filter(_ != node) nextNodes.foreach(node -> _) val preNodes = inputNodes.flatMap(_.prevNodes) .filter(n => n.element != null && convertedNode.contains(n) && !context.contains(n.element.getName)) - .map(convertedNode(_)).filter(_ != node).toSet + .map(convertedNode(_)).filter(_ != node) preNodes.foreach(_ -> node) } }) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index 8d1b78241da..e0506fcdb23 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -744,8 +744,10 @@ object DropoutTF extends TensorflowToBigDL{ val keepProp = tfGraph.source.prevNodes(0).prevNodes(1).element .getAttrMap.get("value").getTensor.getFloatVal(0) - - Dropout[T](keepProp).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + val model = Sequential() + model.add(SelectTable(1)) + model.add(Dropout[T](keepProp).asInstanceOf[AbstractModule[Activity, Tensor[T], T]]) + model.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } @@ -875,7 +877,10 @@ object BatchNormV2NCHWTF extends TensorflowToBigDL{ initGradBias = gradBias ) - batchNorm.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + val model = Sequential() + model.add(SelectTable(1)) + model.add(batchNorm) + model.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } @@ -936,6 +941,7 @@ object BatchNormV2NHWCTF extends TensorflowToBigDL{ ) val layer = Sequential() + layer.add(SelectTable(1)) layer.add(Transpose(Array((2, 4)))) layer.add(Contiguous()) layer.add(batchNorm) @@ -1001,13 +1007,17 @@ object BatchNormTF extends TensorflowToBigDL{ val (weights, gradWeights) = getOrSetTensor[T](weightNode, context, byteOrder)(t => t) val (bias, gradBias) = getOrSetTensor[T](weightNode, context, byteOrder)(t => t) - SpatialBatchNormalization[T]( + val batchNorm = SpatialBatchNormalization[T]( nOutput = nOutput, initWeight = weights, initBias = bias, initGradWeight = gradWeights, initGradBias = gradBias ).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + val model = Sequential() + model.add(SelectTable(1)) + model.add(batchNorm) + model.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } @@ -1187,8 +1197,10 @@ object FlattenV2 extends TensorflowToBigDL { context: Context[T], byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { - InferReshape[T](size = Array(-1), true) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + val layer = Sequential() + layer.add(SelectTable(1)) + layer.add(InferReshape[T](size = Array(-1), true)) + layer.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } diff --git a/scala/dllib/src/test/resources/tf/models/inception_resnet_v2.py b/scala/dllib/src/test/resources/tf/models/inception_resnet_v2.py index 1c9a38e57c9..dc61207b44f 100644 --- a/scala/dllib/src/test/resources/tf/models/inception_resnet_v2.py +++ b/scala/dllib/src/test/resources/tf/models/inception_resnet_v2.py @@ -26,6 +26,7 @@ def main(): 2. export PYTHONPATH=Path_to_your_model_folder 3. python alexnet.py """ + tf.set_random_seed(1) height, width = 299, 299 inputs = tf.Variable(tf.random_uniform((2, height, width, 3)), name='input') inputs = tf.identity(inputs, "input_node") diff --git a/scala/dllib/src/test/resources/tf/models/inception_v3.py b/scala/dllib/src/test/resources/tf/models/inception_v3.py index e7437cc0aaf..49c85bc7dfc 100644 --- a/scala/dllib/src/test/resources/tf/models/inception_v3.py +++ b/scala/dllib/src/test/resources/tf/models/inception_v3.py @@ -28,6 +28,7 @@ def main(): 2. export PYTHONPATH=Path_to_your_model_folder 3. python alexnet.py """ + tf.set_random_seed(1) height, width = 299, 299 num_classes = 1000 inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name='input') diff --git a/scala/dllib/src/test/resources/tf/models/two_edge.py b/scala/dllib/src/test/resources/tf/models/two_edge.py new file mode 100644 index 00000000000..bec251b81ad --- /dev/null +++ b/scala/dllib/src/test/resources/tf/models/two_edge.py @@ -0,0 +1,34 @@ +# +# Copyright 2016 The BigDL Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import tensorflow as tf +from sys import argv + +from util import run_model + +def main(): + + inputs = tf.Variable(tf.reshape(tf.range(0.0, 4.0), [4, 1]), name = 'input') + inputs = tf.identity(inputs, "input_node") + + output = tf.concat([inputs, inputs], axis=0) + + named_output = tf.nn.relu(output, name="output") + + net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(',')) + run_model(net_outputs, argv[1], 'two_edge', argv[3] == 'True') + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index 9a0312f667a..63fb4169119 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -341,6 +341,19 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ } } + "Tensorflow load " should "be able to handle multiple edges" in { + val output = Seq("output:0") + val comparePairs = testModel("two_edge", output, backward = true) + for (i <- output.indices) { + val (tf, bigdl) = comparePairs(i) + tf.almostEqual(bigdl, 1e-7) should be(true) + } + for (i <- output.length until comparePairs.length) { + val (tf, bigdl) = comparePairs(i) + tf.almostEqual(bigdl, 1e-3) should be(true) + } + } + "Tensorflow batchnorm nhwc" should "be loaded correctly" in { val output = Seq("output:0") val comparePairs = testModel("batch_norm_nhwc", output, backward = true) From c47b543544c2cc18d57d763e179fa9363ace84a8 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 13 Sep 2017 11:33:11 +0800 Subject: [PATCH 0388/1065] add run backward test (#1553) * add run backward test * add another test --- .../dllib/utils/tf/TensorflowLoader.scala | 7 +- .../dllib/utils/tf/TensorflowToBigDL.scala | 62 +++++----- .../src/test/resources/tf/models/decoder.py | 48 ++++++++ .../dllib/src/test/resources/tf/models/rnn.py | 27 ++--- .../src/test/resources/tf/models/rnn_lstm.py | 22 +--- .../dllib/utils/tf/TensorflowLoaderSpec.scala | 111 ++++++++---------- 6 files changed, 143 insertions(+), 134 deletions(-) create mode 100644 scala/dllib/src/test/resources/tf/models/decoder.py diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index b6064fafd86..48540f73936 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -39,7 +39,7 @@ import scala.reflect.ClassTag object TensorflowLoader{ - type Context[T] = mutable.HashMap[String, (Tensor[T], Tensor[T])] + type Context[T] = mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])] /** * Load tensorflow model from a prototxt file @@ -213,7 +213,8 @@ object TensorflowLoader{ Node[AbstractModule[Activity, Activity, T]]]() val nameToNode = new mutable.HashMap[String, Node[AbstractModule[Activity, Activity, T]]]() - val context = ctx.getOrElse(new mutable.HashMap[String, (Tensor[T], Tensor[T])]) + val context = ctx.getOrElse( + new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]) // BFS to keep the input order same tfGraph.BFS.foreach(n => { @@ -270,7 +271,7 @@ object TensorflowLoader{ val weights = ArrayBuffer[Tensor[T]]() val gradients = ArrayBuffer[Tensor[T]]() - for ((weight, grad) <- context.values) { + for ((weight, grad, _) <- context.values) { weights += weight gradients += grad } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index e0506fcdb23..eefcd49f375 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -57,15 +57,25 @@ trait TensorflowToBigDL { )(implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] protected def getOrSetTensor[T: ClassTag]( - node: NodeDef, context: Context[T], byteOrder: ByteOrder)(f: Tensor[T] => Tensor[T])( + node: NodeDef, context: Context[T], byteOrder: ByteOrder, + trans: Option[Seq[(Int, Int)]] = None)( implicit ev: TensorNumeric[T]): (Tensor[T], Tensor[T]) = { if (context.contains(node.getName)) { - context(node.getName) + val result = context(node.getName) + (result._1, result._2) } else { - val weight = f(toTensor[T](node.getAttrMap.get("value").getTensor, byteOrder)).contiguous() + var weight = toTensor[T](node.getAttrMap.get("value").getTensor, byteOrder) + trans match { + case Some(transposes) => + for ((first, second) <- transposes) { + weight = weight.transpose(first, second) + } + weight = weight.contiguous() + case _ => + } val gradient = Tensor[T](weight.size()) - context.put(node.getName, (weight, gradient)) + context.put(node.getName, (weight, gradient, trans)) (weight, gradient) } } @@ -294,11 +304,8 @@ object FullConnectionTF extends TensorflowToBigDL{ val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element - val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder)(t => t) - val (weight, gradWeight) = getOrSetTensor(weightNode, context, byteOrder) { t => - t.transpose(1, 2) - } - + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) + val (weight, gradWeight) = getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 2)))) Linear[T](inputSize = weight.size(2), outputSize = weight.size(1), initWeight = weight, initGradWeight = gradWeight, initBias = bias, initGradBias = gradBias) .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] @@ -364,12 +371,11 @@ object Conv1D extends TensorflowToBigDL { } val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element - val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder)(t => t) + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) val weightNode = convNode.prevNodes(1).prevNodes.head.prevNodes.head.element - val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder) { t => - t.transpose(1, 3).transpose(2, 3) - } + val (weights, gradWeights) = + getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 3), (2, 3)))) val nOuputPlane = weights.size(1) val nInputPlane = weights.size(3) @@ -443,9 +449,9 @@ object Conv2D extends TensorflowToBigDL{ val strideW = strideList(1) val strideH = strideList(2) val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element - val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder)(t => t) + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element - val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder)(t => t) + val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder) val nOuputPlane = weights.size(4) val nInputPlane = weights.size(3) val kernelH = weights.size(1) @@ -465,12 +471,11 @@ object Conv2D extends TensorflowToBigDL{ val strideW = strideList(2) val strideH = strideList(3) val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element - val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder)(t => t) + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element - val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder) { t => - t.transpose(1, 4).transpose(2, 3).transpose(3, 4) - } + val (weights, gradWeights) = + getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 4), (2, 3), (3, 4)))) val nOuputPlane = weights.size(1) val nInputPlane = weights.size(2) val kernelH = weights.size(3) @@ -523,12 +528,11 @@ object Conv2D2 extends TensorflowToBigDL{ val (strideH, strideW) = (strideList(2), strideList(3)) val biasNode = tfGraph.source.prevNodes(1).prevNodes(0).prevNodes.head.element - val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder)(t => t) + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element - val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder) { t => - t.transpose(1, 4).transpose(2, 3).transpose(3, 4) - } + val (weights, gradWeights) = + getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 4), (2, 3), (3, 4)))) val nOuputPlane = weights.size(1) val nInputPlane = weights.size(2) @@ -866,8 +870,8 @@ object BatchNormV2NCHWTF extends TensorflowToBigDL{ val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.prevNodes.head.prevNodes.head.element val weightNode = tfGraph.source.prevNodes(1).prevNodes(1).prevNodes(1) .prevNodes(1).prevNodes.head.prevNodes.head.element - val (weights, gradWeights) = getOrSetTensor[T](weightNode, context, byteOrder)(t => t) - val (bias, gradBias) = getOrSetTensor[T](biasNode, context, byteOrder)(t => t) + val (weights, gradWeights) = getOrSetTensor[T](weightNode, context, byteOrder) + val (bias, gradBias) = getOrSetTensor[T](biasNode, context, byteOrder) val batchNorm = SpatialBatchNormalization[T]( nOutput = weights.size(1), @@ -929,8 +933,8 @@ object BatchNormV2NHWCTF extends TensorflowToBigDL{ val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.prevNodes.head.element val weightNode = tfGraph.source.prevNodes(1).prevNodes(1).prevNodes(1) .prevNodes(1).prevNodes.head.element - val (weights, gradWeights) = getOrSetTensor[T](weightNode, context, byteOrder)(t => t) - val (bias, gradBias) = getOrSetTensor[T](biasNode, context, byteOrder)(t => t) + val (weights, gradWeights) = getOrSetTensor[T](weightNode, context, byteOrder) + val (bias, gradBias) = getOrSetTensor[T](biasNode, context, byteOrder) val batchNorm = SpatialBatchNormalization[T]( nOutput = weights.size(1), @@ -1004,8 +1008,8 @@ object BatchNormTF extends TensorflowToBigDL{ val weightNode = tfGraph.source.prevNodes(1).prevNodes.head.prevNodes.head.element val biasNode = tfGraph.source.prevNodes(1).prevNodes(1).prevNodes(1) .prevNodes.head.prevNodes.head.element - val (weights, gradWeights) = getOrSetTensor[T](weightNode, context, byteOrder)(t => t) - val (bias, gradBias) = getOrSetTensor[T](weightNode, context, byteOrder)(t => t) + val (weights, gradWeights) = getOrSetTensor[T](weightNode, context, byteOrder) + val (bias, gradBias) = getOrSetTensor[T](weightNode, context, byteOrder) val batchNorm = SpatialBatchNormalization[T]( nOutput = nOutput, diff --git a/scala/dllib/src/test/resources/tf/models/decoder.py b/scala/dllib/src/test/resources/tf/models/decoder.py new file mode 100644 index 00000000000..4586ab0814a --- /dev/null +++ b/scala/dllib/src/test/resources/tf/models/decoder.py @@ -0,0 +1,48 @@ +# +# Copyright 2016 The BigDL Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import tensorflow as tf +import numpy as np +from sys import argv +from tensorflow.contrib import rnn +from util import run_model + +def main(): + + n_steps = 2 + n_input = 10 + n_hidden = 10 + + xs = tf.Variable(tf.random_uniform([4, n_steps, n_input]) + 10, name='input', dtype=tf.float32) + xs = tf.identity(xs, name="input_node") + x = tf.unstack(xs, n_steps, 1) + + cell = tf.contrib.rnn.BasicLSTMCell(n_hidden) + init_state = cell.zero_state(4, tf.float32) + + outputs = [] + for i in range(n_steps): + if i == 0: + output, state = cell(x[-1], init_state) + else: + output, state = cell(output, state) + outputs.append(output) + + final = tf.identity(outputs, name="output") + net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(',')) + run_model(net_outputs, argv[1], 'rnn', argv[3] == 'True') + +if __name__ == "__main__": + main() diff --git a/scala/dllib/src/test/resources/tf/models/rnn.py b/scala/dllib/src/test/resources/tf/models/rnn.py index 96cf2e46bb7..05d4644b06d 100644 --- a/scala/dllib/src/test/resources/tf/models/rnn.py +++ b/scala/dllib/src/test/resources/tf/models/rnn.py @@ -17,7 +17,7 @@ import numpy as np from sys import argv from tensorflow.contrib import rnn -from util import merge_checkpoint +from util import run_model def main(): """ @@ -25,34 +25,21 @@ def main(): 1. mkdir model 2. python rnn.py """ - dir = argv[1] + tf.set_random_seed(1) n_steps = 2 n_input = 10 n_hidden = 20 n_output = 5 - xs = tf.Variable(tf.random_uniform([4, n_steps, n_input]) + 10, name='input', dtype=tf.float32) + xs = tf.Variable(tf.random_uniform([4, n_steps, n_input]), name='input', dtype=tf.float32) xs = tf.identity(xs, "input_node") - weight = tf.Variable(tf.random_uniform([n_hidden, n_output]) + 10, name="weight", dtype=tf.float32) - bias = tf.Variable(tf.random_uniform([n_output]) + 10, name="bias", dtype=tf.float32) + weight = tf.Variable(tf.random_uniform([n_hidden, n_output]), name="weight", dtype=tf.float32) + bias = tf.Variable(tf.random_uniform([n_output]), name="bias", dtype=tf.float32) x = tf.unstack(xs, n_steps, 1) cell = rnn.BasicRNNCell(n_hidden) output, states = rnn.static_rnn(cell, x, dtype=tf.float32) final = tf.nn.bias_add(tf.matmul(output[-1], weight), bias, name='output') - output = tf.Variable(tf.random_uniform(tf.shape(final)),name='output_result') - result = tf.assign(output, final) - saver = tf.train.Saver() - with tf.Session() as sess: - init = tf.global_variables_initializer() - sess.run(init) - sess.run(result) - checkpointpath = saver.save(sess, dir + '/model.chkp') - tf.train.write_graph(sess.graph, dir, 'model.pbtxt') - input_graph = dir + "/model.pbtxt" - input_checkpoint = dir + "/model.chkp" - output_node_names= ["output", "output_result"] - output_graph = dir + "/model.pb" - - merge_checkpoint(input_graph, input_checkpoint, output_node_names, output_graph) + net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(',')) + run_model(net_outputs, argv[1], 'rnn', argv[3] == 'True') if __name__ == "__main__": main() diff --git a/scala/dllib/src/test/resources/tf/models/rnn_lstm.py b/scala/dllib/src/test/resources/tf/models/rnn_lstm.py index 9dc2cde3ffe..eedf81eb366 100644 --- a/scala/dllib/src/test/resources/tf/models/rnn_lstm.py +++ b/scala/dllib/src/test/resources/tf/models/rnn_lstm.py @@ -17,7 +17,7 @@ import numpy as np from sys import argv from tensorflow.contrib import rnn -from util import merge_checkpoint +from util import run_model def main(): """ @@ -25,7 +25,7 @@ def main(): 1. mkdir model 2. python rnn_lstm.py """ - dir = argv[1] + tf.set_random_seed(1) n_steps = 2 n_input = 10 n_hidden = 20 @@ -44,21 +44,7 @@ def main(): final = tf.nn.bias_add(tf.matmul(output[-1], weight), bias, name='output') - output = tf.Variable(tf.random_uniform(tf.shape(final)),name='output_result') - result = tf.assign(output, final) - saver = tf.train.Saver() - with tf.Session() as sess: - init = tf.global_variables_initializer() - sess.run(init) - sess.run(result) - checkpointpath = saver.save(sess, dir + '/model.chkp') - tf.train.write_graph(sess.graph, dir, 'model.pbtxt') - - input_graph = dir + "/model.pbtxt" - input_checkpoint = dir + "/model.chkp" - output_node_names= ["output", "output_result"] - output_graph = dir + "/model.pb" - - merge_checkpoint(input_graph, input_checkpoint, output_node_names, output_graph) + net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(',')) + run_model(net_outputs, argv[1], 'rnn', argv[3] == 'True') if __name__ == "__main__": main() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index 63fb4169119..7dc86d324f6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -262,70 +262,43 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ assert(l1.bias == l2.bias) } - "static simple rnn " should "have the same inference result as tensorflow" in { - System.setProperty("bigdl.enableNHWC", "false") - tfCheck() - val modelName = "rnn" - // Generate command and prepare the temp folder - val s = JFile.separator - val modelsFolder = processPath(getClass().getClassLoader().getResource("tf").getPath()) + - s + "models" - val modelScript = modelsFolder + s + s"$modelName.py" - val tmpLocation = java.io.File.createTempFile("tensorflowLoaderTest" + UUID.randomUUID(), - modelName) - tmpLocation.delete() - tmpLocation.mkdir() - - require(runPython(s"$modelScript $tmpLocation"), "error when run the model script") - - // Load the model and input/output tensors - val modelFile = tmpLocation + s + "model.pb" - - - val results = TensorflowLoader.parse(modelFile) - val (tfGraph, inputs) = TensorflowLoader.buildTFGraph(results, Seq("output")) - val model = TensorflowLoader.buildBigDLModel(tfGraph, inputs, - Seq("output"), - ByteOrder.LITTLE_ENDIAN, "") - val input = TensorflowToBigDL.toTensor(results.get(0).getAttrMap.get("value").getTensor, - ByteOrder.LITTLE_ENDIAN).contiguous() - val tfResult = TensorflowToBigDL.toTensor(results.get(results.size()-1) - .getAttrMap.get("value").getTensor, ByteOrder.LITTLE_ENDIAN) - val bigDLResult = model.forward(input) - tfResult.almostEqual(bigDLResult.toTensor, 1e-6) + "static simple rnn " should "have the same result as tensorflow" in { + val output = Seq("output:0") + val comparePairs = testModel("rnn", output, backward = true) + for (i <- output.indices) { + val (tf, bigdl) = comparePairs(i) + tf.almostEqual(bigdl, 1e-5) should be(true) + } + for (i <- output.length until comparePairs.length) { + val (tf, bigdl) = comparePairs(i) + tf.almostEqual(bigdl, 1e-3) should be(true) + } } - "static lstm rnn " should "have the same inference result as tensorflow" in { - tfCheck() - System.setProperty("bigdl.enableNHWC", "false") - val modelName = "rnn_lstm" - // Generate command and prepare the temp folder - val s = JFile.separator - val modelsFolder = processPath(getClass().getClassLoader().getResource("tf").getPath()) + - s + "models" - val modelScript = modelsFolder + s + s"$modelName.py" - val tmpLocation = java.io.File.createTempFile("tensorflowLoaderTest" + UUID.randomUUID(), - modelName) - tmpLocation.delete() - tmpLocation.mkdir() - - require(runPython(s"$modelScript $tmpLocation"), "error when run the model script") - - // Load the model and input/output tensors - val modelFile = tmpLocation + s + "model.pb" + "static lstm rnn " should "have the same result as tensorflow" in { + val output = Seq("output:0") + val comparePairs = testModel("rnn_lstm", output, backward = true) + for (i <- output.indices) { + val (tf, bigdl) = comparePairs(i) + tf.almostEqual(bigdl, 1e-5) should be(true) + } + for (i <- output.length until comparePairs.length) { + val (tf, bigdl) = comparePairs(i) + tf.almostEqual(bigdl, 1e-2) should be(true) + } + } - val results = TensorflowLoader.parse(modelFile) - val (tfGraph, inputs) = - TensorflowLoader.buildTFGraph(results.subList(0, results.size()-1), Seq("output")) - val model = TensorflowLoader.buildBigDLModel(tfGraph, inputs, - Seq("output"), - ByteOrder.LITTLE_ENDIAN, "") - val input = TensorflowToBigDL.toTensor(results.get(0).getAttrMap.get("value").getTensor, - ByteOrder.LITTLE_ENDIAN).contiguous() - val tfResult = TensorflowToBigDL.toTensor(results.get(results.size()-1) - .getAttrMap.get("value").getTensor, ByteOrder.LITTLE_ENDIAN) - val bigDLResult = model.forward(input) - tfResult.almostEqual(bigDLResult.toTensor, 1e-5) + "hand coded lstm rnn " should "have the same result as tensorflow" in { + val output = Seq("output:0") + val comparePairs = testModel("decoder", output, backward = true) + for (i <- output.indices) { + val (tf, bigdl) = comparePairs(i) + tf.almostEqual(bigdl, 1e-5) should be(true) + } + for (i <- output.length until comparePairs.length) { + val (tf, bigdl) = comparePairs(i) + tf.almostEqual(bigdl, 1e-2) should be(true) + } } "TensorFlow control dep" should "be load correctly" in { @@ -552,7 +525,8 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val (tfGraph, inputs) = TensorflowLoader.buildTFGraph(tfNodes, endPoints.map(_.split(":")(0)), (node: NodeDef) => node.getName == "input_node") - val context = new mutable.HashMap[String, (Tensor[Float], Tensor[Float])] + val context = + new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])] val model = TensorflowLoader.buildBigDLModel(tfGraph, inputs, endPoints.map(_.split(":")(0)), ByteOrder.LITTLE_ENDIAN, "", Some(context)) @@ -619,8 +593,17 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val pairs = context.keySet.map { x => val name = s"${x}_grad" - val tensor = tfGradTensorsMap.get(name).orNull - (tensor, context(x)._2) + var tensor = tfGradTensorsMap.get(name).orNull + var (_, grad, trans) = context(x) + trans match { + case Some(transpose) => + for ((firstDim, secondDIm) <- transpose) { + tensor = tensor.transpose(firstDim, secondDIm) + } + tensor = tensor.contiguous() + case None => + } + (tensor, grad) }.toSeq.filter(_._1 != null) comparePair ++= pairs println(s"Compare ${pairs.length} pairs of gradient in this graph") From db74e777be4b00e296e02fc3b2787c3306cb284d Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 13 Sep 2017 13:24:01 +0800 Subject: [PATCH 0389/1065] Add tfrecord format reader and tfrecord Example, Features protobuf generated classes (#1529) * check in example protbufs * add tfrecord * remove useless imports * fix tests --- dl/pom.xml | 2 +- .../org/tensorflow/example/BytesList.java | 537 +++++++++ .../example/BytesListOrBuilder.java | 22 + .../java/org/tensorflow/example/Example.java | 567 +++++++++ .../tensorflow/example/ExampleOrBuilder.java | 22 + .../org/tensorflow/example/ExampleProtos.java | 74 ++ .../java/org/tensorflow/example/Feature.java | 1074 +++++++++++++++++ .../org/tensorflow/example/FeatureList.java | 747 ++++++++++++ .../example/FeatureListOrBuilder.java | 33 + .../org/tensorflow/example/FeatureLists.java | 704 +++++++++++ .../example/FeatureListsOrBuilder.java | 63 + .../tensorflow/example/FeatureOrBuilder.java | 50 + .../org/tensorflow/example/FeatureProtos.java | 158 +++ .../java/org/tensorflow/example/Features.java | 704 +++++++++++ .../tensorflow/example/FeaturesOrBuilder.java | 63 + .../org/tensorflow/example/FloatList.java | 544 +++++++++ .../example/FloatListOrBuilder.java | 22 + .../org/tensorflow/example/Int64List.java | 547 +++++++++ .../example/Int64ListOrBuilder.java | 22 + .../tensorflow/example/SequenceExample.java | 748 ++++++++++++ .../example/SequenceExampleOrBuilder.java | 35 + .../dllib/utils/tf/TFRecordIterator.scala | 74 ++ .../dllib/src/test/resources/tf/text.tfrecord | Bin 0 -> 20 bytes .../dllib/utils/tf/TFRecordIteratorSpec.scala | 42 + 24 files changed, 6853 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/BytesList.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/BytesListOrBuilder.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/Example.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/ExampleOrBuilder.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/ExampleProtos.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/Feature.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FeatureList.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FeatureListOrBuilder.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FeatureLists.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FeatureListsOrBuilder.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FeatureOrBuilder.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FeatureProtos.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/Features.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FeaturesOrBuilder.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FloatList.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/FloatListOrBuilder.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/Int64List.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/Int64ListOrBuilder.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/SequenceExample.java create mode 100644 scala/dllib/src/main/java/org/tensorflow/example/SequenceExampleOrBuilder.java create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala create mode 100644 scala/dllib/src/test/resources/tf/text.tfrecord create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIteratorSpec.scala diff --git a/dl/pom.xml b/dl/pom.xml index 3ced7c16e74..9bf5f18b7c8 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -34,7 +34,7 @@ com.google.protobuf protobuf-java - 3.0.0 + 3.4.0 org.apache.hadoop diff --git a/scala/dllib/src/main/java/org/tensorflow/example/BytesList.java b/scala/dllib/src/main/java/org/tensorflow/example/BytesList.java new file mode 100644 index 00000000000..e8d4f3ace3c --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/BytesList.java @@ -0,0 +1,537 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +/** + * 
+ * Containers to hold repeated fundamental values.
+ *
+ * + * Protobuf type {@code tensorflow.BytesList} + */ +public final class BytesList extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.BytesList) + BytesListOrBuilder { +private static final long serialVersionUID = 0L; + // Use BytesList.newBuilder() to construct. + private BytesList(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private BytesList() { + value_ = java.util.Collections.emptyList(); + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private BytesList( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 10: { + if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + value_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000001; + } + value_.add(input.readBytes()); + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + value_ = java.util.Collections.unmodifiableList(value_); + } + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.BytesList.class, org.tensorflow.example.BytesList.Builder.class); + } + + public static final int VALUE_FIELD_NUMBER = 1; + private java.util.List value_; + /** + * repeated bytes value = 1; + */ + public java.util.List + getValueList() { + return value_; + } + /** + * repeated bytes value = 1; + */ + public int getValueCount() { + return value_.size(); + } + /** + * repeated bytes value = 1; + */ + public com.google.protobuf.ByteString getValue(int index) { + return value_.get(index); + } + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + for (int i = 0; i < value_.size(); i++) { + output.writeBytes(1, value_.get(i)); + } + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + { + int dataSize = 0; + for (int i = 0; i < value_.size(); i++) { + dataSize += com.google.protobuf.CodedOutputStream + .computeBytesSizeNoTag(value_.get(i)); + } + size += dataSize; + size += 1 * getValueList().size(); + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.BytesList)) { + return super.equals(obj); + } + org.tensorflow.example.BytesList other = (org.tensorflow.example.BytesList) obj; + + boolean result = true; + result = result && getValueList() + .equals(other.getValueList()); + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + if (getValueCount() > 0) { + hash = (37 * hash) + VALUE_FIELD_NUMBER; + hash = (53 * hash) + getValueList().hashCode(); + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.BytesList parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.BytesList parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.BytesList parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.BytesList parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.BytesList parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.BytesList parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.BytesList parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.BytesList parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.BytesList parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.BytesList parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.BytesList parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.BytesList parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.BytesList prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * 
+   * Containers to hold repeated fundamental values.
+   *
+ * + * Protobuf type {@code tensorflow.BytesList} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.BytesList) + org.tensorflow.example.BytesListOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.BytesList.class, org.tensorflow.example.BytesList.Builder.class); + } + + // Construct using org.tensorflow.example.BytesList.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + value_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000001); + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_descriptor; + } + + public org.tensorflow.example.BytesList getDefaultInstanceForType() { + return org.tensorflow.example.BytesList.getDefaultInstance(); + } + + public org.tensorflow.example.BytesList build() { + org.tensorflow.example.BytesList result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.BytesList buildPartial() { + org.tensorflow.example.BytesList result = new org.tensorflow.example.BytesList(this); + int from_bitField0_ = bitField0_; + if (((bitField0_ & 0x00000001) == 0x00000001)) { + value_ = java.util.Collections.unmodifiableList(value_); + bitField0_ = (bitField0_ & ~0x00000001); + } + result.value_ = value_; + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.BytesList) { + return mergeFrom((org.tensorflow.example.BytesList)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.BytesList other) { + if (other == org.tensorflow.example.BytesList.getDefaultInstance()) return this; + if (!other.value_.isEmpty()) { + if (value_.isEmpty()) { + value_ = other.value_; + bitField0_ = (bitField0_ & ~0x00000001); + } else { + ensureValueIsMutable(); + value_.addAll(other.value_); + } + onChanged(); + } + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.BytesList parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.BytesList) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + private int bitField0_; + + private java.util.List value_ = java.util.Collections.emptyList(); + private void ensureValueIsMutable() { + if (!((bitField0_ & 0x00000001) == 0x00000001)) { + value_ = new java.util.ArrayList(value_); + bitField0_ |= 0x00000001; + } + } + /** + * repeated bytes value = 1; + */ + public java.util.List + getValueList() { + return java.util.Collections.unmodifiableList(value_); + } + /** + * repeated bytes value = 1; + */ + public int getValueCount() { + return value_.size(); + } + /** + * repeated bytes value = 1; + */ + public com.google.protobuf.ByteString getValue(int index) { + return value_.get(index); + } + /** + * repeated bytes value = 1; + */ + public Builder setValue( + int index, com.google.protobuf.ByteString value) { + if (value == null) { + throw new NullPointerException(); + } + ensureValueIsMutable(); + value_.set(index, value); + onChanged(); + return this; + } + /** + * repeated bytes value = 1; + */ + public Builder addValue(com.google.protobuf.ByteString value) { + if (value == null) { + throw new NullPointerException(); + } + ensureValueIsMutable(); + value_.add(value); + onChanged(); + return this; + } + /** + * repeated bytes value = 1; + */ + public Builder addAllValue( + java.lang.Iterable values) { + ensureValueIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, value_); + onChanged(); + return this; + } + /** + * repeated bytes value = 1; + */ + public Builder clearValue() { + value_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000001); + onChanged(); + return this; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.BytesList) + } + + // @@protoc_insertion_point(class_scope:tensorflow.BytesList) + private static final org.tensorflow.example.BytesList DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.BytesList(); + } + + public static org.tensorflow.example.BytesList getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public BytesList parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new BytesList(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.BytesList getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/BytesListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/BytesListOrBuilder.java new file mode 100644 index 00000000000..d7f0d92093e --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/BytesListOrBuilder.java @@ -0,0 +1,22 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +public interface BytesListOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.BytesList) + com.google.protobuf.MessageOrBuilder { + + /** + * repeated bytes value = 1; + */ + java.util.List getValueList(); + /** + * repeated bytes value = 1; + */ + int getValueCount(); + /** + * repeated bytes value = 1; + */ + com.google.protobuf.ByteString getValue(int index); +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Example.java b/scala/dllib/src/main/java/org/tensorflow/example/Example.java new file mode 100644 index 00000000000..719de2547fa --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/Example.java @@ -0,0 +1,567 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: tensorflow/core/example/example.proto + +package org.tensorflow.example; + +/** + * Protobuf type {@code tensorflow.Example} + */ +public final class Example extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.Example) + ExampleOrBuilder { +private static final long serialVersionUID = 0L; + // Use Example.newBuilder() to construct. + private Example(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private Example() { + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private Example( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 10: { + org.tensorflow.example.Features.Builder subBuilder = null; + if (features_ != null) { + subBuilder = features_.toBuilder(); + } + features_ = input.readMessage(org.tensorflow.example.Features.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom(features_); + features_ = subBuilder.buildPartial(); + } + + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.Example.class, org.tensorflow.example.Example.Builder.class); + } + + public static final int FEATURES_FIELD_NUMBER = 1; + private org.tensorflow.example.Features features_; + /** + * .tensorflow.Features features = 1; + */ + public boolean hasFeatures() { + return features_ != null; + } + /** + * .tensorflow.Features features = 1; + */ + public org.tensorflow.example.Features getFeatures() { + return features_ == null ? org.tensorflow.example.Features.getDefaultInstance() : features_; + } + /** + * .tensorflow.Features features = 1; + */ + public org.tensorflow.example.FeaturesOrBuilder getFeaturesOrBuilder() { + return getFeatures(); + } + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + if (features_ != null) { + output.writeMessage(1, getFeatures()); + } + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + if (features_ != null) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(1, getFeatures()); + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.Example)) { + return super.equals(obj); + } + org.tensorflow.example.Example other = (org.tensorflow.example.Example) obj; + + boolean result = true; + result = result && (hasFeatures() == other.hasFeatures()); + if (hasFeatures()) { + result = result && getFeatures() + .equals(other.getFeatures()); + } + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + if (hasFeatures()) { + hash = (37 * hash) + FEATURES_FIELD_NUMBER; + hash = (53 * hash) + getFeatures().hashCode(); + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.Example parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Example parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Example parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Example parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Example parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Example parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Example parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.Example parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.Example parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.Example parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.Example parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.Example parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.Example prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * Protobuf type {@code tensorflow.Example} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.Example) + org.tensorflow.example.ExampleOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.Example.class, org.tensorflow.example.Example.Builder.class); + } + + // Construct using org.tensorflow.example.Example.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + if (featuresBuilder_ == null) { + features_ = null; + } else { + features_ = null; + featuresBuilder_ = null; + } + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_descriptor; + } + + public org.tensorflow.example.Example getDefaultInstanceForType() { + return org.tensorflow.example.Example.getDefaultInstance(); + } + + public org.tensorflow.example.Example build() { + org.tensorflow.example.Example result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.Example buildPartial() { + org.tensorflow.example.Example result = new org.tensorflow.example.Example(this); + if (featuresBuilder_ == null) { + result.features_ = features_; + } else { + result.features_ = featuresBuilder_.build(); + } + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.Example) { + return mergeFrom((org.tensorflow.example.Example)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.Example other) { + if (other == org.tensorflow.example.Example.getDefaultInstance()) return this; + if (other.hasFeatures()) { + mergeFeatures(other.getFeatures()); + } + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.Example parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.Example) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + + private org.tensorflow.example.Features features_ = null; + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder> featuresBuilder_; + /** + * .tensorflow.Features features = 1; + */ + public boolean hasFeatures() { + return featuresBuilder_ != null || features_ != null; + } + /** + * .tensorflow.Features features = 1; + */ + public org.tensorflow.example.Features getFeatures() { + if (featuresBuilder_ == null) { + return features_ == null ? org.tensorflow.example.Features.getDefaultInstance() : features_; + } else { + return featuresBuilder_.getMessage(); + } + } + /** + * .tensorflow.Features features = 1; + */ + public Builder setFeatures(org.tensorflow.example.Features value) { + if (featuresBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + features_ = value; + onChanged(); + } else { + featuresBuilder_.setMessage(value); + } + + return this; + } + /** + * .tensorflow.Features features = 1; + */ + public Builder setFeatures( + org.tensorflow.example.Features.Builder builderForValue) { + if (featuresBuilder_ == null) { + features_ = builderForValue.build(); + onChanged(); + } else { + featuresBuilder_.setMessage(builderForValue.build()); + } + + return this; + } + /** + * .tensorflow.Features features = 1; + */ + public Builder mergeFeatures(org.tensorflow.example.Features value) { + if (featuresBuilder_ == null) { + if (features_ != null) { + features_ = + org.tensorflow.example.Features.newBuilder(features_).mergeFrom(value).buildPartial(); + } else { + features_ = value; + } + onChanged(); + } else { + featuresBuilder_.mergeFrom(value); + } + + return this; + } + /** + * .tensorflow.Features features = 1; + */ + public Builder clearFeatures() { + if (featuresBuilder_ == null) { + features_ = null; + onChanged(); + } else { + features_ = null; + featuresBuilder_ = null; + } + + return this; + } + /** + * .tensorflow.Features features = 1; + */ + public org.tensorflow.example.Features.Builder getFeaturesBuilder() { + + onChanged(); + return getFeaturesFieldBuilder().getBuilder(); + } + /** + * .tensorflow.Features features = 1; + */ + public org.tensorflow.example.FeaturesOrBuilder getFeaturesOrBuilder() { + if (featuresBuilder_ != null) { + return featuresBuilder_.getMessageOrBuilder(); + } else { + return features_ == null ? + org.tensorflow.example.Features.getDefaultInstance() : features_; + } + } + /** + * .tensorflow.Features features = 1; + */ + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder> + getFeaturesFieldBuilder() { + if (featuresBuilder_ == null) { + featuresBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder>( + getFeatures(), + getParentForChildren(), + isClean()); + features_ = null; + } + return featuresBuilder_; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.Example) + } + + // @@protoc_insertion_point(class_scope:tensorflow.Example) + private static final org.tensorflow.example.Example DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.Example(); + } + + public static org.tensorflow.example.Example getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public Example parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new Example(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.Example getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/ExampleOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/ExampleOrBuilder.java new file mode 100644 index 00000000000..94aaff0d906 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/ExampleOrBuilder.java @@ -0,0 +1,22 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: tensorflow/core/example/example.proto + +package org.tensorflow.example; + +public interface ExampleOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.Example) + com.google.protobuf.MessageOrBuilder { + + /** + * .tensorflow.Features features = 1; + */ + boolean hasFeatures(); + /** + * .tensorflow.Features features = 1; + */ + org.tensorflow.example.Features getFeatures(); + /** + * .tensorflow.Features features = 1; + */ + org.tensorflow.example.FeaturesOrBuilder getFeaturesOrBuilder(); +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/ExampleProtos.java b/scala/dllib/src/main/java/org/tensorflow/example/ExampleProtos.java new file mode 100644 index 00000000000..d4f59778160 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/ExampleProtos.java @@ -0,0 +1,74 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: tensorflow/core/example/example.proto + +package org.tensorflow.example; + +public final class ExampleProtos { + private ExampleProtos() {} + public static void registerAllExtensions( + com.google.protobuf.ExtensionRegistryLite registry) { + } + + public static void registerAllExtensions( + com.google.protobuf.ExtensionRegistry registry) { + registerAllExtensions( + (com.google.protobuf.ExtensionRegistryLite) registry); + } + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_Example_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_Example_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_SequenceExample_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_SequenceExample_fieldAccessorTable; + + public static com.google.protobuf.Descriptors.FileDescriptor + getDescriptor() { + return descriptor; + } + private static com.google.protobuf.Descriptors.FileDescriptor + descriptor; + static { + java.lang.String[] descriptorData = { + "\n%tensorflow/core/example/example.proto\022" + + "\ntensorflow\032%tensorflow/core/example/fea" + + "ture.proto\"1\n\007Example\022&\n\010features\030\001 \001(\0132" + + "\024.tensorflow.Features\"i\n\017SequenceExample" + + "\022%\n\007context\030\001 \001(\0132\024.tensorflow.Features\022" + + "/\n\rfeature_lists\030\002 \001(\0132\030.tensorflow.Feat" + + "ureListsB,\n\026org.tensorflow.exampleB\rExam" + + "pleProtosP\001\370\001\001b\006proto3" + }; + com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = + new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { + public com.google.protobuf.ExtensionRegistry assignDescriptors( + com.google.protobuf.Descriptors.FileDescriptor root) { + descriptor = root; + return null; + } + }; + com.google.protobuf.Descriptors.FileDescriptor + .internalBuildGeneratedFileFrom(descriptorData, + new com.google.protobuf.Descriptors.FileDescriptor[] { + org.tensorflow.example.FeatureProtos.getDescriptor(), + }, assigner); + internal_static_tensorflow_Example_descriptor = + getDescriptor().getMessageTypes().get(0); + internal_static_tensorflow_Example_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_Example_descriptor, + new java.lang.String[] { "Features", }); + internal_static_tensorflow_SequenceExample_descriptor = + getDescriptor().getMessageTypes().get(1); + internal_static_tensorflow_SequenceExample_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_SequenceExample_descriptor, + new java.lang.String[] { "Context", "FeatureLists", }); + org.tensorflow.example.FeatureProtos.getDescriptor(); + } + + // @@protoc_insertion_point(outer_class_scope) +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Feature.java b/scala/dllib/src/main/java/org/tensorflow/example/Feature.java new file mode 100644 index 00000000000..72933d0fdd4 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/Feature.java @@ -0,0 +1,1074 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +/** + * 
+ * Containers for non-sequential data.
+ *
+ * + * Protobuf type {@code tensorflow.Feature} + */ +public final class Feature extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.Feature) + FeatureOrBuilder { +private static final long serialVersionUID = 0L; + // Use Feature.newBuilder() to construct. + private Feature(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private Feature() { + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private Feature( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 10: { + org.tensorflow.example.BytesList.Builder subBuilder = null; + if (kindCase_ == 1) { + subBuilder = ((org.tensorflow.example.BytesList) kind_).toBuilder(); + } + kind_ = + input.readMessage(org.tensorflow.example.BytesList.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom((org.tensorflow.example.BytesList) kind_); + kind_ = subBuilder.buildPartial(); + } + kindCase_ = 1; + break; + } + case 18: { + org.tensorflow.example.FloatList.Builder subBuilder = null; + if (kindCase_ == 2) { + subBuilder = ((org.tensorflow.example.FloatList) kind_).toBuilder(); + } + kind_ = + input.readMessage(org.tensorflow.example.FloatList.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom((org.tensorflow.example.FloatList) kind_); + kind_ = subBuilder.buildPartial(); + } + kindCase_ = 2; + break; + } + case 26: { + org.tensorflow.example.Int64List.Builder subBuilder = null; + if (kindCase_ == 3) { + subBuilder = ((org.tensorflow.example.Int64List) kind_).toBuilder(); + } + kind_ = + input.readMessage(org.tensorflow.example.Int64List.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom((org.tensorflow.example.Int64List) kind_); + kind_ = subBuilder.buildPartial(); + } + kindCase_ = 3; + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.Feature.class, org.tensorflow.example.Feature.Builder.class); + } + + private int kindCase_ = 0; + private java.lang.Object kind_; + public enum KindCase + implements com.google.protobuf.Internal.EnumLite { + BYTES_LIST(1), + FLOAT_LIST(2), + INT64_LIST(3), + KIND_NOT_SET(0); + private final int value; + private KindCase(int value) { + this.value = value; + } + /** + * @deprecated Use {@link #forNumber(int)} instead. + */ + @java.lang.Deprecated + public static KindCase valueOf(int value) { + return forNumber(value); + } + + public static KindCase forNumber(int value) { + switch (value) { + case 1: return BYTES_LIST; + case 2: return FLOAT_LIST; + case 3: return INT64_LIST; + case 0: return KIND_NOT_SET; + default: return null; + } + } + public int getNumber() { + return this.value; + } + }; + + public KindCase + getKindCase() { + return KindCase.forNumber( + kindCase_); + } + + public static final int BYTES_LIST_FIELD_NUMBER = 1; + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public boolean hasBytesList() { + return kindCase_ == 1; + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public org.tensorflow.example.BytesList getBytesList() { + if (kindCase_ == 1) { + return (org.tensorflow.example.BytesList) kind_; + } + return org.tensorflow.example.BytesList.getDefaultInstance(); + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public org.tensorflow.example.BytesListOrBuilder getBytesListOrBuilder() { + if (kindCase_ == 1) { + return (org.tensorflow.example.BytesList) kind_; + } + return org.tensorflow.example.BytesList.getDefaultInstance(); + } + + public static final int FLOAT_LIST_FIELD_NUMBER = 2; + /** + * .tensorflow.FloatList float_list = 2; + */ + public boolean hasFloatList() { + return kindCase_ == 2; + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public org.tensorflow.example.FloatList getFloatList() { + if (kindCase_ == 2) { + return (org.tensorflow.example.FloatList) kind_; + } + return org.tensorflow.example.FloatList.getDefaultInstance(); + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public org.tensorflow.example.FloatListOrBuilder getFloatListOrBuilder() { + if (kindCase_ == 2) { + return (org.tensorflow.example.FloatList) kind_; + } + return org.tensorflow.example.FloatList.getDefaultInstance(); + } + + public static final int INT64_LIST_FIELD_NUMBER = 3; + /** + * .tensorflow.Int64List int64_list = 3; + */ + public boolean hasInt64List() { + return kindCase_ == 3; + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public org.tensorflow.example.Int64List getInt64List() { + if (kindCase_ == 3) { + return (org.tensorflow.example.Int64List) kind_; + } + return org.tensorflow.example.Int64List.getDefaultInstance(); + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public org.tensorflow.example.Int64ListOrBuilder getInt64ListOrBuilder() { + if (kindCase_ == 3) { + return (org.tensorflow.example.Int64List) kind_; + } + return org.tensorflow.example.Int64List.getDefaultInstance(); + } + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + if (kindCase_ == 1) { + output.writeMessage(1, (org.tensorflow.example.BytesList) kind_); + } + if (kindCase_ == 2) { + output.writeMessage(2, (org.tensorflow.example.FloatList) kind_); + } + if (kindCase_ == 3) { + output.writeMessage(3, (org.tensorflow.example.Int64List) kind_); + } + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + if (kindCase_ == 1) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(1, (org.tensorflow.example.BytesList) kind_); + } + if (kindCase_ == 2) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(2, (org.tensorflow.example.FloatList) kind_); + } + if (kindCase_ == 3) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(3, (org.tensorflow.example.Int64List) kind_); + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.Feature)) { + return super.equals(obj); + } + org.tensorflow.example.Feature other = (org.tensorflow.example.Feature) obj; + + boolean result = true; + result = result && getKindCase().equals( + other.getKindCase()); + if (!result) return false; + switch (kindCase_) { + case 1: + result = result && getBytesList() + .equals(other.getBytesList()); + break; + case 2: + result = result && getFloatList() + .equals(other.getFloatList()); + break; + case 3: + result = result && getInt64List() + .equals(other.getInt64List()); + break; + case 0: + default: + } + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + switch (kindCase_) { + case 1: + hash = (37 * hash) + BYTES_LIST_FIELD_NUMBER; + hash = (53 * hash) + getBytesList().hashCode(); + break; + case 2: + hash = (37 * hash) + FLOAT_LIST_FIELD_NUMBER; + hash = (53 * hash) + getFloatList().hashCode(); + break; + case 3: + hash = (37 * hash) + INT64_LIST_FIELD_NUMBER; + hash = (53 * hash) + getInt64List().hashCode(); + break; + case 0: + default: + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.Feature parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Feature parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Feature parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Feature parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Feature parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Feature parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Feature parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.Feature parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.Feature parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.Feature parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.Feature parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.Feature parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.Feature prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * 
+   * Containers for non-sequential data.
+   *
+ * + * Protobuf type {@code tensorflow.Feature} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.Feature) + org.tensorflow.example.FeatureOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.Feature.class, org.tensorflow.example.Feature.Builder.class); + } + + // Construct using org.tensorflow.example.Feature.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + kindCase_ = 0; + kind_ = null; + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_descriptor; + } + + public org.tensorflow.example.Feature getDefaultInstanceForType() { + return org.tensorflow.example.Feature.getDefaultInstance(); + } + + public org.tensorflow.example.Feature build() { + org.tensorflow.example.Feature result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.Feature buildPartial() { + org.tensorflow.example.Feature result = new org.tensorflow.example.Feature(this); + if (kindCase_ == 1) { + if (bytesListBuilder_ == null) { + result.kind_ = kind_; + } else { + result.kind_ = bytesListBuilder_.build(); + } + } + if (kindCase_ == 2) { + if (floatListBuilder_ == null) { + result.kind_ = kind_; + } else { + result.kind_ = floatListBuilder_.build(); + } + } + if (kindCase_ == 3) { + if (int64ListBuilder_ == null) { + result.kind_ = kind_; + } else { + result.kind_ = int64ListBuilder_.build(); + } + } + result.kindCase_ = kindCase_; + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.Feature) { + return mergeFrom((org.tensorflow.example.Feature)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.Feature other) { + if (other == org.tensorflow.example.Feature.getDefaultInstance()) return this; + switch (other.getKindCase()) { + case BYTES_LIST: { + mergeBytesList(other.getBytesList()); + break; + } + case FLOAT_LIST: { + mergeFloatList(other.getFloatList()); + break; + } + case INT64_LIST: { + mergeInt64List(other.getInt64List()); + break; + } + case KIND_NOT_SET: { + break; + } + } + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.Feature parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.Feature) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + private int kindCase_ = 0; + private java.lang.Object kind_; + public KindCase + getKindCase() { + return KindCase.forNumber( + kindCase_); + } + + public Builder clearKind() { + kindCase_ = 0; + kind_ = null; + onChanged(); + return this; + } + + + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.BytesList, org.tensorflow.example.BytesList.Builder, org.tensorflow.example.BytesListOrBuilder> bytesListBuilder_; + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public boolean hasBytesList() { + return kindCase_ == 1; + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public org.tensorflow.example.BytesList getBytesList() { + if (bytesListBuilder_ == null) { + if (kindCase_ == 1) { + return (org.tensorflow.example.BytesList) kind_; + } + return org.tensorflow.example.BytesList.getDefaultInstance(); + } else { + if (kindCase_ == 1) { + return bytesListBuilder_.getMessage(); + } + return org.tensorflow.example.BytesList.getDefaultInstance(); + } + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public Builder setBytesList(org.tensorflow.example.BytesList value) { + if (bytesListBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + kind_ = value; + onChanged(); + } else { + bytesListBuilder_.setMessage(value); + } + kindCase_ = 1; + return this; + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public Builder setBytesList( + org.tensorflow.example.BytesList.Builder builderForValue) { + if (bytesListBuilder_ == null) { + kind_ = builderForValue.build(); + onChanged(); + } else { + bytesListBuilder_.setMessage(builderForValue.build()); + } + kindCase_ = 1; + return this; + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public Builder mergeBytesList(org.tensorflow.example.BytesList value) { + if (bytesListBuilder_ == null) { + if (kindCase_ == 1 && + kind_ != org.tensorflow.example.BytesList.getDefaultInstance()) { + kind_ = org.tensorflow.example.BytesList.newBuilder((org.tensorflow.example.BytesList) kind_) + .mergeFrom(value).buildPartial(); + } else { + kind_ = value; + } + onChanged(); + } else { + if (kindCase_ == 1) { + bytesListBuilder_.mergeFrom(value); + } + bytesListBuilder_.setMessage(value); + } + kindCase_ = 1; + return this; + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public Builder clearBytesList() { + if (bytesListBuilder_ == null) { + if (kindCase_ == 1) { + kindCase_ = 0; + kind_ = null; + onChanged(); + } + } else { + if (kindCase_ == 1) { + kindCase_ = 0; + kind_ = null; + } + bytesListBuilder_.clear(); + } + return this; + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public org.tensorflow.example.BytesList.Builder getBytesListBuilder() { + return getBytesListFieldBuilder().getBuilder(); + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + public org.tensorflow.example.BytesListOrBuilder getBytesListOrBuilder() { + if ((kindCase_ == 1) && (bytesListBuilder_ != null)) { + return bytesListBuilder_.getMessageOrBuilder(); + } else { + if (kindCase_ == 1) { + return (org.tensorflow.example.BytesList) kind_; + } + return org.tensorflow.example.BytesList.getDefaultInstance(); + } + } + /** + * .tensorflow.BytesList bytes_list = 1; + */ + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.BytesList, org.tensorflow.example.BytesList.Builder, org.tensorflow.example.BytesListOrBuilder> + getBytesListFieldBuilder() { + if (bytesListBuilder_ == null) { + if (!(kindCase_ == 1)) { + kind_ = org.tensorflow.example.BytesList.getDefaultInstance(); + } + bytesListBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.BytesList, org.tensorflow.example.BytesList.Builder, org.tensorflow.example.BytesListOrBuilder>( + (org.tensorflow.example.BytesList) kind_, + getParentForChildren(), + isClean()); + kind_ = null; + } + kindCase_ = 1; + onChanged();; + return bytesListBuilder_; + } + + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.FloatList, org.tensorflow.example.FloatList.Builder, org.tensorflow.example.FloatListOrBuilder> floatListBuilder_; + /** + * .tensorflow.FloatList float_list = 2; + */ + public boolean hasFloatList() { + return kindCase_ == 2; + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public org.tensorflow.example.FloatList getFloatList() { + if (floatListBuilder_ == null) { + if (kindCase_ == 2) { + return (org.tensorflow.example.FloatList) kind_; + } + return org.tensorflow.example.FloatList.getDefaultInstance(); + } else { + if (kindCase_ == 2) { + return floatListBuilder_.getMessage(); + } + return org.tensorflow.example.FloatList.getDefaultInstance(); + } + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public Builder setFloatList(org.tensorflow.example.FloatList value) { + if (floatListBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + kind_ = value; + onChanged(); + } else { + floatListBuilder_.setMessage(value); + } + kindCase_ = 2; + return this; + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public Builder setFloatList( + org.tensorflow.example.FloatList.Builder builderForValue) { + if (floatListBuilder_ == null) { + kind_ = builderForValue.build(); + onChanged(); + } else { + floatListBuilder_.setMessage(builderForValue.build()); + } + kindCase_ = 2; + return this; + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public Builder mergeFloatList(org.tensorflow.example.FloatList value) { + if (floatListBuilder_ == null) { + if (kindCase_ == 2 && + kind_ != org.tensorflow.example.FloatList.getDefaultInstance()) { + kind_ = org.tensorflow.example.FloatList.newBuilder((org.tensorflow.example.FloatList) kind_) + .mergeFrom(value).buildPartial(); + } else { + kind_ = value; + } + onChanged(); + } else { + if (kindCase_ == 2) { + floatListBuilder_.mergeFrom(value); + } + floatListBuilder_.setMessage(value); + } + kindCase_ = 2; + return this; + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public Builder clearFloatList() { + if (floatListBuilder_ == null) { + if (kindCase_ == 2) { + kindCase_ = 0; + kind_ = null; + onChanged(); + } + } else { + if (kindCase_ == 2) { + kindCase_ = 0; + kind_ = null; + } + floatListBuilder_.clear(); + } + return this; + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public org.tensorflow.example.FloatList.Builder getFloatListBuilder() { + return getFloatListFieldBuilder().getBuilder(); + } + /** + * .tensorflow.FloatList float_list = 2; + */ + public org.tensorflow.example.FloatListOrBuilder getFloatListOrBuilder() { + if ((kindCase_ == 2) && (floatListBuilder_ != null)) { + return floatListBuilder_.getMessageOrBuilder(); + } else { + if (kindCase_ == 2) { + return (org.tensorflow.example.FloatList) kind_; + } + return org.tensorflow.example.FloatList.getDefaultInstance(); + } + } + /** + * .tensorflow.FloatList float_list = 2; + */ + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.FloatList, org.tensorflow.example.FloatList.Builder, org.tensorflow.example.FloatListOrBuilder> + getFloatListFieldBuilder() { + if (floatListBuilder_ == null) { + if (!(kindCase_ == 2)) { + kind_ = org.tensorflow.example.FloatList.getDefaultInstance(); + } + floatListBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.FloatList, org.tensorflow.example.FloatList.Builder, org.tensorflow.example.FloatListOrBuilder>( + (org.tensorflow.example.FloatList) kind_, + getParentForChildren(), + isClean()); + kind_ = null; + } + kindCase_ = 2; + onChanged();; + return floatListBuilder_; + } + + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Int64List, org.tensorflow.example.Int64List.Builder, org.tensorflow.example.Int64ListOrBuilder> int64ListBuilder_; + /** + * .tensorflow.Int64List int64_list = 3; + */ + public boolean hasInt64List() { + return kindCase_ == 3; + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public org.tensorflow.example.Int64List getInt64List() { + if (int64ListBuilder_ == null) { + if (kindCase_ == 3) { + return (org.tensorflow.example.Int64List) kind_; + } + return org.tensorflow.example.Int64List.getDefaultInstance(); + } else { + if (kindCase_ == 3) { + return int64ListBuilder_.getMessage(); + } + return org.tensorflow.example.Int64List.getDefaultInstance(); + } + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public Builder setInt64List(org.tensorflow.example.Int64List value) { + if (int64ListBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + kind_ = value; + onChanged(); + } else { + int64ListBuilder_.setMessage(value); + } + kindCase_ = 3; + return this; + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public Builder setInt64List( + org.tensorflow.example.Int64List.Builder builderForValue) { + if (int64ListBuilder_ == null) { + kind_ = builderForValue.build(); + onChanged(); + } else { + int64ListBuilder_.setMessage(builderForValue.build()); + } + kindCase_ = 3; + return this; + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public Builder mergeInt64List(org.tensorflow.example.Int64List value) { + if (int64ListBuilder_ == null) { + if (kindCase_ == 3 && + kind_ != org.tensorflow.example.Int64List.getDefaultInstance()) { + kind_ = org.tensorflow.example.Int64List.newBuilder((org.tensorflow.example.Int64List) kind_) + .mergeFrom(value).buildPartial(); + } else { + kind_ = value; + } + onChanged(); + } else { + if (kindCase_ == 3) { + int64ListBuilder_.mergeFrom(value); + } + int64ListBuilder_.setMessage(value); + } + kindCase_ = 3; + return this; + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public Builder clearInt64List() { + if (int64ListBuilder_ == null) { + if (kindCase_ == 3) { + kindCase_ = 0; + kind_ = null; + onChanged(); + } + } else { + if (kindCase_ == 3) { + kindCase_ = 0; + kind_ = null; + } + int64ListBuilder_.clear(); + } + return this; + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public org.tensorflow.example.Int64List.Builder getInt64ListBuilder() { + return getInt64ListFieldBuilder().getBuilder(); + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + public org.tensorflow.example.Int64ListOrBuilder getInt64ListOrBuilder() { + if ((kindCase_ == 3) && (int64ListBuilder_ != null)) { + return int64ListBuilder_.getMessageOrBuilder(); + } else { + if (kindCase_ == 3) { + return (org.tensorflow.example.Int64List) kind_; + } + return org.tensorflow.example.Int64List.getDefaultInstance(); + } + } + /** + * .tensorflow.Int64List int64_list = 3; + */ + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Int64List, org.tensorflow.example.Int64List.Builder, org.tensorflow.example.Int64ListOrBuilder> + getInt64ListFieldBuilder() { + if (int64ListBuilder_ == null) { + if (!(kindCase_ == 3)) { + kind_ = org.tensorflow.example.Int64List.getDefaultInstance(); + } + int64ListBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Int64List, org.tensorflow.example.Int64List.Builder, org.tensorflow.example.Int64ListOrBuilder>( + (org.tensorflow.example.Int64List) kind_, + getParentForChildren(), + isClean()); + kind_ = null; + } + kindCase_ = 3; + onChanged();; + return int64ListBuilder_; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.Feature) + } + + // @@protoc_insertion_point(class_scope:tensorflow.Feature) + private static final org.tensorflow.example.Feature DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.Feature(); + } + + public static org.tensorflow.example.Feature getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public Feature parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new Feature(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.Feature getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureList.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureList.java new file mode 100644 index 00000000000..43a957e2c88 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FeatureList.java @@ -0,0 +1,747 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +/** + * 
+ * Containers for sequential data.
+ * A FeatureList contains lists of Features.  These may hold zero or more
+ * Feature values.
+ * FeatureLists are organized into categories by name.  The FeatureLists message
+ * contains the mapping from name to FeatureList.
+ *
+ * + * Protobuf type {@code tensorflow.FeatureList} + */ +public final class FeatureList extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.FeatureList) + FeatureListOrBuilder { +private static final long serialVersionUID = 0L; + // Use FeatureList.newBuilder() to construct. + private FeatureList(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private FeatureList() { + feature_ = java.util.Collections.emptyList(); + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private FeatureList( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 10: { + if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + feature_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000001; + } + feature_.add( + input.readMessage(org.tensorflow.example.Feature.parser(), extensionRegistry)); + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + feature_ = java.util.Collections.unmodifiableList(feature_); + } + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.FeatureList.class, org.tensorflow.example.FeatureList.Builder.class); + } + + public static final int FEATURE_FIELD_NUMBER = 1; + private java.util.List feature_; + /** + * repeated .tensorflow.Feature feature = 1; + */ + public java.util.List getFeatureList() { + return feature_; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public java.util.List + getFeatureOrBuilderList() { + return feature_; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public int getFeatureCount() { + return feature_.size(); + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public org.tensorflow.example.Feature getFeature(int index) { + return feature_.get(index); + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public org.tensorflow.example.FeatureOrBuilder getFeatureOrBuilder( + int index) { + return feature_.get(index); + } + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + for (int i = 0; i < feature_.size(); i++) { + output.writeMessage(1, feature_.get(i)); + } + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + for (int i = 0; i < feature_.size(); i++) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(1, feature_.get(i)); + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.FeatureList)) { + return super.equals(obj); + } + org.tensorflow.example.FeatureList other = (org.tensorflow.example.FeatureList) obj; + + boolean result = true; + result = result && getFeatureList() + .equals(other.getFeatureList()); + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + if (getFeatureCount() > 0) { + hash = (37 * hash) + FEATURE_FIELD_NUMBER; + hash = (53 * hash) + getFeatureList().hashCode(); + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.FeatureList parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FeatureList parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FeatureList parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FeatureList parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FeatureList parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FeatureList parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FeatureList parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.FeatureList parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.FeatureList parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.FeatureList parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.FeatureList parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.FeatureList parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.FeatureList prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * 
+   * Containers for sequential data.
+   * A FeatureList contains lists of Features.  These may hold zero or more
+   * Feature values.
+   * FeatureLists are organized into categories by name.  The FeatureLists message
+   * contains the mapping from name to FeatureList.
+   *
+ * + * Protobuf type {@code tensorflow.FeatureList} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.FeatureList) + org.tensorflow.example.FeatureListOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.FeatureList.class, org.tensorflow.example.FeatureList.Builder.class); + } + + // Construct using org.tensorflow.example.FeatureList.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + getFeatureFieldBuilder(); + } + } + public Builder clear() { + super.clear(); + if (featureBuilder_ == null) { + feature_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000001); + } else { + featureBuilder_.clear(); + } + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_descriptor; + } + + public org.tensorflow.example.FeatureList getDefaultInstanceForType() { + return org.tensorflow.example.FeatureList.getDefaultInstance(); + } + + public org.tensorflow.example.FeatureList build() { + org.tensorflow.example.FeatureList result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.FeatureList buildPartial() { + org.tensorflow.example.FeatureList result = new org.tensorflow.example.FeatureList(this); + int from_bitField0_ = bitField0_; + if (featureBuilder_ == null) { + if (((bitField0_ & 0x00000001) == 0x00000001)) { + feature_ = java.util.Collections.unmodifiableList(feature_); + bitField0_ = (bitField0_ & ~0x00000001); + } + result.feature_ = feature_; + } else { + result.feature_ = featureBuilder_.build(); + } + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.FeatureList) { + return mergeFrom((org.tensorflow.example.FeatureList)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.FeatureList other) { + if (other == org.tensorflow.example.FeatureList.getDefaultInstance()) return this; + if (featureBuilder_ == null) { + if (!other.feature_.isEmpty()) { + if (feature_.isEmpty()) { + feature_ = other.feature_; + bitField0_ = (bitField0_ & ~0x00000001); + } else { + ensureFeatureIsMutable(); + feature_.addAll(other.feature_); + } + onChanged(); + } + } else { + if (!other.feature_.isEmpty()) { + if (featureBuilder_.isEmpty()) { + featureBuilder_.dispose(); + featureBuilder_ = null; + feature_ = other.feature_; + bitField0_ = (bitField0_ & ~0x00000001); + featureBuilder_ = + com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? + getFeatureFieldBuilder() : null; + } else { + featureBuilder_.addAllMessages(other.feature_); + } + } + } + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.FeatureList parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.FeatureList) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + private int bitField0_; + + private java.util.List feature_ = + java.util.Collections.emptyList(); + private void ensureFeatureIsMutable() { + if (!((bitField0_ & 0x00000001) == 0x00000001)) { + feature_ = new java.util.ArrayList(feature_); + bitField0_ |= 0x00000001; + } + } + + private com.google.protobuf.RepeatedFieldBuilderV3< + org.tensorflow.example.Feature, org.tensorflow.example.Feature.Builder, org.tensorflow.example.FeatureOrBuilder> featureBuilder_; + + /** + * repeated .tensorflow.Feature feature = 1; + */ + public java.util.List getFeatureList() { + if (featureBuilder_ == null) { + return java.util.Collections.unmodifiableList(feature_); + } else { + return featureBuilder_.getMessageList(); + } + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public int getFeatureCount() { + if (featureBuilder_ == null) { + return feature_.size(); + } else { + return featureBuilder_.getCount(); + } + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public org.tensorflow.example.Feature getFeature(int index) { + if (featureBuilder_ == null) { + return feature_.get(index); + } else { + return featureBuilder_.getMessage(index); + } + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder setFeature( + int index, org.tensorflow.example.Feature value) { + if (featureBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureFeatureIsMutable(); + feature_.set(index, value); + onChanged(); + } else { + featureBuilder_.setMessage(index, value); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder setFeature( + int index, org.tensorflow.example.Feature.Builder builderForValue) { + if (featureBuilder_ == null) { + ensureFeatureIsMutable(); + feature_.set(index, builderForValue.build()); + onChanged(); + } else { + featureBuilder_.setMessage(index, builderForValue.build()); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder addFeature(org.tensorflow.example.Feature value) { + if (featureBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureFeatureIsMutable(); + feature_.add(value); + onChanged(); + } else { + featureBuilder_.addMessage(value); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder addFeature( + int index, org.tensorflow.example.Feature value) { + if (featureBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureFeatureIsMutable(); + feature_.add(index, value); + onChanged(); + } else { + featureBuilder_.addMessage(index, value); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder addFeature( + org.tensorflow.example.Feature.Builder builderForValue) { + if (featureBuilder_ == null) { + ensureFeatureIsMutable(); + feature_.add(builderForValue.build()); + onChanged(); + } else { + featureBuilder_.addMessage(builderForValue.build()); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder addFeature( + int index, org.tensorflow.example.Feature.Builder builderForValue) { + if (featureBuilder_ == null) { + ensureFeatureIsMutable(); + feature_.add(index, builderForValue.build()); + onChanged(); + } else { + featureBuilder_.addMessage(index, builderForValue.build()); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder addAllFeature( + java.lang.Iterable values) { + if (featureBuilder_ == null) { + ensureFeatureIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, feature_); + onChanged(); + } else { + featureBuilder_.addAllMessages(values); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder clearFeature() { + if (featureBuilder_ == null) { + feature_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000001); + onChanged(); + } else { + featureBuilder_.clear(); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public Builder removeFeature(int index) { + if (featureBuilder_ == null) { + ensureFeatureIsMutable(); + feature_.remove(index); + onChanged(); + } else { + featureBuilder_.remove(index); + } + return this; + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public org.tensorflow.example.Feature.Builder getFeatureBuilder( + int index) { + return getFeatureFieldBuilder().getBuilder(index); + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public org.tensorflow.example.FeatureOrBuilder getFeatureOrBuilder( + int index) { + if (featureBuilder_ == null) { + return feature_.get(index); } else { + return featureBuilder_.getMessageOrBuilder(index); + } + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public java.util.List + getFeatureOrBuilderList() { + if (featureBuilder_ != null) { + return featureBuilder_.getMessageOrBuilderList(); + } else { + return java.util.Collections.unmodifiableList(feature_); + } + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public org.tensorflow.example.Feature.Builder addFeatureBuilder() { + return getFeatureFieldBuilder().addBuilder( + org.tensorflow.example.Feature.getDefaultInstance()); + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public org.tensorflow.example.Feature.Builder addFeatureBuilder( + int index) { + return getFeatureFieldBuilder().addBuilder( + index, org.tensorflow.example.Feature.getDefaultInstance()); + } + /** + * repeated .tensorflow.Feature feature = 1; + */ + public java.util.List + getFeatureBuilderList() { + return getFeatureFieldBuilder().getBuilderList(); + } + private com.google.protobuf.RepeatedFieldBuilderV3< + org.tensorflow.example.Feature, org.tensorflow.example.Feature.Builder, org.tensorflow.example.FeatureOrBuilder> + getFeatureFieldBuilder() { + if (featureBuilder_ == null) { + featureBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< + org.tensorflow.example.Feature, org.tensorflow.example.Feature.Builder, org.tensorflow.example.FeatureOrBuilder>( + feature_, + ((bitField0_ & 0x00000001) == 0x00000001), + getParentForChildren(), + isClean()); + feature_ = null; + } + return featureBuilder_; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.FeatureList) + } + + // @@protoc_insertion_point(class_scope:tensorflow.FeatureList) + private static final org.tensorflow.example.FeatureList DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.FeatureList(); + } + + public static org.tensorflow.example.FeatureList getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public FeatureList parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new FeatureList(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.FeatureList getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureListOrBuilder.java new file mode 100644 index 00000000000..0926b311a9f --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FeatureListOrBuilder.java @@ -0,0 +1,33 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +public interface FeatureListOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.FeatureList) + com.google.protobuf.MessageOrBuilder { + + /** + * repeated .tensorflow.Feature feature = 1; + */ + java.util.List + getFeatureList(); + /** + * repeated .tensorflow.Feature feature = 1; + */ + org.tensorflow.example.Feature getFeature(int index); + /** + * repeated .tensorflow.Feature feature = 1; + */ + int getFeatureCount(); + /** + * repeated .tensorflow.Feature feature = 1; + */ + java.util.List + getFeatureOrBuilderList(); + /** + * repeated .tensorflow.Feature feature = 1; + */ + org.tensorflow.example.FeatureOrBuilder getFeatureOrBuilder( + int index); +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureLists.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureLists.java new file mode 100644 index 00000000000..c8ac05a7713 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FeatureLists.java @@ -0,0 +1,704 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +/** + * Protobuf type {@code tensorflow.FeatureLists} + */ +public final class FeatureLists extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.FeatureLists) + FeatureListsOrBuilder { +private static final long serialVersionUID = 0L; + // Use FeatureLists.newBuilder() to construct. + private FeatureLists(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private FeatureLists() { + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private FeatureLists( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 10: { + if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + featureList_ = com.google.protobuf.MapField.newMapField( + FeatureListDefaultEntryHolder.defaultEntry); + mutable_bitField0_ |= 0x00000001; + } + com.google.protobuf.MapEntry + featureList__ = input.readMessage( + FeatureListDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); + featureList_.getMutableMap().put( + featureList__.getKey(), featureList__.getValue()); + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_descriptor; + } + + @SuppressWarnings({"rawtypes"}) + protected com.google.protobuf.MapField internalGetMapField( + int number) { + switch (number) { + case 1: + return internalGetFeatureList(); + default: + throw new RuntimeException( + "Invalid map field number: " + number); + } + } + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.FeatureLists.class, org.tensorflow.example.FeatureLists.Builder.class); + } + + public static final int FEATURE_LIST_FIELD_NUMBER = 1; + private static final class FeatureListDefaultEntryHolder { + static final com.google.protobuf.MapEntry< + java.lang.String, org.tensorflow.example.FeatureList> defaultEntry = + com.google.protobuf.MapEntry + .newDefaultInstance( + org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_FeatureListEntry_descriptor, + com.google.protobuf.WireFormat.FieldType.STRING, + "", + com.google.protobuf.WireFormat.FieldType.MESSAGE, + org.tensorflow.example.FeatureList.getDefaultInstance()); + } + private com.google.protobuf.MapField< + java.lang.String, org.tensorflow.example.FeatureList> featureList_; + private com.google.protobuf.MapField + internalGetFeatureList() { + if (featureList_ == null) { + return com.google.protobuf.MapField.emptyMapField( + FeatureListDefaultEntryHolder.defaultEntry); + } + return featureList_; + } + + public int getFeatureListCount() { + return internalGetFeatureList().getMap().size(); + } + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public boolean containsFeatureList( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + return internalGetFeatureList().getMap().containsKey(key); + } + /** + * Use {@link #getFeatureListMap()} instead. + */ + @java.lang.Deprecated + public java.util.Map getFeatureList() { + return getFeatureListMap(); + } + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public java.util.Map getFeatureListMap() { + return internalGetFeatureList().getMap(); + } + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public org.tensorflow.example.FeatureList getFeatureListOrDefault( + java.lang.String key, + org.tensorflow.example.FeatureList defaultValue) { + if (key == null) { throw new java.lang.NullPointerException(); } + java.util.Map map = + internalGetFeatureList().getMap(); + return map.containsKey(key) ? map.get(key) : defaultValue; + } + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public org.tensorflow.example.FeatureList getFeatureListOrThrow( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + java.util.Map map = + internalGetFeatureList().getMap(); + if (!map.containsKey(key)) { + throw new java.lang.IllegalArgumentException(); + } + return map.get(key); + } + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + com.google.protobuf.GeneratedMessageV3 + .serializeStringMapTo( + output, + internalGetFeatureList(), + FeatureListDefaultEntryHolder.defaultEntry, + 1); + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + for (java.util.Map.Entry entry + : internalGetFeatureList().getMap().entrySet()) { + com.google.protobuf.MapEntry + featureList__ = FeatureListDefaultEntryHolder.defaultEntry.newBuilderForType() + .setKey(entry.getKey()) + .setValue(entry.getValue()) + .build(); + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(1, featureList__); + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.FeatureLists)) { + return super.equals(obj); + } + org.tensorflow.example.FeatureLists other = (org.tensorflow.example.FeatureLists) obj; + + boolean result = true; + result = result && internalGetFeatureList().equals( + other.internalGetFeatureList()); + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + if (!internalGetFeatureList().getMap().isEmpty()) { + hash = (37 * hash) + FEATURE_LIST_FIELD_NUMBER; + hash = (53 * hash) + internalGetFeatureList().hashCode(); + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.FeatureLists parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FeatureLists parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FeatureLists parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FeatureLists parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FeatureLists parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FeatureLists parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FeatureLists parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.FeatureLists parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.FeatureLists parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.FeatureLists parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.FeatureLists parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.FeatureLists parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.FeatureLists prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * Protobuf type {@code tensorflow.FeatureLists} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.FeatureLists) + org.tensorflow.example.FeatureListsOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_descriptor; + } + + @SuppressWarnings({"rawtypes"}) + protected com.google.protobuf.MapField internalGetMapField( + int number) { + switch (number) { + case 1: + return internalGetFeatureList(); + default: + throw new RuntimeException( + "Invalid map field number: " + number); + } + } + @SuppressWarnings({"rawtypes"}) + protected com.google.protobuf.MapField internalGetMutableMapField( + int number) { + switch (number) { + case 1: + return internalGetMutableFeatureList(); + default: + throw new RuntimeException( + "Invalid map field number: " + number); + } + } + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.FeatureLists.class, org.tensorflow.example.FeatureLists.Builder.class); + } + + // Construct using org.tensorflow.example.FeatureLists.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + internalGetMutableFeatureList().clear(); + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_descriptor; + } + + public org.tensorflow.example.FeatureLists getDefaultInstanceForType() { + return org.tensorflow.example.FeatureLists.getDefaultInstance(); + } + + public org.tensorflow.example.FeatureLists build() { + org.tensorflow.example.FeatureLists result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.FeatureLists buildPartial() { + org.tensorflow.example.FeatureLists result = new org.tensorflow.example.FeatureLists(this); + int from_bitField0_ = bitField0_; + result.featureList_ = internalGetFeatureList(); + result.featureList_.makeImmutable(); + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.FeatureLists) { + return mergeFrom((org.tensorflow.example.FeatureLists)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.FeatureLists other) { + if (other == org.tensorflow.example.FeatureLists.getDefaultInstance()) return this; + internalGetMutableFeatureList().mergeFrom( + other.internalGetFeatureList()); + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.FeatureLists parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.FeatureLists) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + private int bitField0_; + + private com.google.protobuf.MapField< + java.lang.String, org.tensorflow.example.FeatureList> featureList_; + private com.google.protobuf.MapField + internalGetFeatureList() { + if (featureList_ == null) { + return com.google.protobuf.MapField.emptyMapField( + FeatureListDefaultEntryHolder.defaultEntry); + } + return featureList_; + } + private com.google.protobuf.MapField + internalGetMutableFeatureList() { + onChanged();; + if (featureList_ == null) { + featureList_ = com.google.protobuf.MapField.newMapField( + FeatureListDefaultEntryHolder.defaultEntry); + } + if (!featureList_.isMutable()) { + featureList_ = featureList_.copy(); + } + return featureList_; + } + + public int getFeatureListCount() { + return internalGetFeatureList().getMap().size(); + } + /** + * 
+     * Map from feature name to feature list.
+     *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public boolean containsFeatureList( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + return internalGetFeatureList().getMap().containsKey(key); + } + /** + * Use {@link #getFeatureListMap()} instead. + */ + @java.lang.Deprecated + public java.util.Map getFeatureList() { + return getFeatureListMap(); + } + /** + * 
+     * Map from feature name to feature list.
+     *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public java.util.Map getFeatureListMap() { + return internalGetFeatureList().getMap(); + } + /** + * 
+     * Map from feature name to feature list.
+     *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public org.tensorflow.example.FeatureList getFeatureListOrDefault( + java.lang.String key, + org.tensorflow.example.FeatureList defaultValue) { + if (key == null) { throw new java.lang.NullPointerException(); } + java.util.Map map = + internalGetFeatureList().getMap(); + return map.containsKey(key) ? map.get(key) : defaultValue; + } + /** + * 
+     * Map from feature name to feature list.
+     *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public org.tensorflow.example.FeatureList getFeatureListOrThrow( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + java.util.Map map = + internalGetFeatureList().getMap(); + if (!map.containsKey(key)) { + throw new java.lang.IllegalArgumentException(); + } + return map.get(key); + } + + public Builder clearFeatureList() { + internalGetMutableFeatureList().getMutableMap() + .clear(); + return this; + } + /** + * 
+     * Map from feature name to feature list.
+     *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public Builder removeFeatureList( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + internalGetMutableFeatureList().getMutableMap() + .remove(key); + return this; + } + /** + * Use alternate mutation accessors instead. + */ + @java.lang.Deprecated + public java.util.Map + getMutableFeatureList() { + return internalGetMutableFeatureList().getMutableMap(); + } + /** + * 
+     * Map from feature name to feature list.
+     *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + public Builder putFeatureList( + java.lang.String key, + org.tensorflow.example.FeatureList value) { + if (key == null) { throw new java.lang.NullPointerException(); } + if (value == null) { throw new java.lang.NullPointerException(); } + internalGetMutableFeatureList().getMutableMap() + .put(key, value); + return this; + } + /** + * 
+     * Map from feature name to feature list.
+     *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + public Builder putAllFeatureList( + java.util.Map values) { + internalGetMutableFeatureList().getMutableMap() + .putAll(values); + return this; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.FeatureLists) + } + + // @@protoc_insertion_point(class_scope:tensorflow.FeatureLists) + private static final org.tensorflow.example.FeatureLists DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.FeatureLists(); + } + + public static org.tensorflow.example.FeatureLists getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public FeatureLists parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new FeatureLists(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.FeatureLists getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureListsOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureListsOrBuilder.java new file mode 100644 index 00000000000..2ecb197af86 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FeatureListsOrBuilder.java @@ -0,0 +1,63 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +public interface FeatureListsOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.FeatureLists) + com.google.protobuf.MessageOrBuilder { + + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + int getFeatureListCount(); + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + boolean containsFeatureList( + java.lang.String key); + /** + * Use {@link #getFeatureListMap()} instead. + */ + @java.lang.Deprecated + java.util.Map + getFeatureList(); + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + java.util.Map + getFeatureListMap(); + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + org.tensorflow.example.FeatureList getFeatureListOrDefault( + java.lang.String key, + org.tensorflow.example.FeatureList defaultValue); + /** + * 
+   * Map from feature name to feature list.
+   *
+ * + * map<string, .tensorflow.FeatureList> feature_list = 1; + */ + + org.tensorflow.example.FeatureList getFeatureListOrThrow( + java.lang.String key); +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureOrBuilder.java new file mode 100644 index 00000000000..e59d6109887 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FeatureOrBuilder.java @@ -0,0 +1,50 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +public interface FeatureOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.Feature) + com.google.protobuf.MessageOrBuilder { + + /** + * .tensorflow.BytesList bytes_list = 1; + */ + boolean hasBytesList(); + /** + * .tensorflow.BytesList bytes_list = 1; + */ + org.tensorflow.example.BytesList getBytesList(); + /** + * .tensorflow.BytesList bytes_list = 1; + */ + org.tensorflow.example.BytesListOrBuilder getBytesListOrBuilder(); + + /** + * .tensorflow.FloatList float_list = 2; + */ + boolean hasFloatList(); + /** + * .tensorflow.FloatList float_list = 2; + */ + org.tensorflow.example.FloatList getFloatList(); + /** + * .tensorflow.FloatList float_list = 2; + */ + org.tensorflow.example.FloatListOrBuilder getFloatListOrBuilder(); + + /** + * .tensorflow.Int64List int64_list = 3; + */ + boolean hasInt64List(); + /** + * .tensorflow.Int64List int64_list = 3; + */ + org.tensorflow.example.Int64List getInt64List(); + /** + * .tensorflow.Int64List int64_list = 3; + */ + org.tensorflow.example.Int64ListOrBuilder getInt64ListOrBuilder(); + + public org.tensorflow.example.Feature.KindCase getKindCase(); +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureProtos.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureProtos.java new file mode 100644 index 00000000000..960a87ecce9 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FeatureProtos.java @@ -0,0 +1,158 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +public final class FeatureProtos { + private FeatureProtos() {} + public static void registerAllExtensions( + com.google.protobuf.ExtensionRegistryLite registry) { + } + + public static void registerAllExtensions( + com.google.protobuf.ExtensionRegistry registry) { + registerAllExtensions( + (com.google.protobuf.ExtensionRegistryLite) registry); + } + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_BytesList_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_BytesList_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_FloatList_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_FloatList_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_Int64List_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_Int64List_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_Feature_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_Feature_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_Features_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_Features_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_Features_FeatureEntry_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_Features_FeatureEntry_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_FeatureList_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_FeatureList_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_FeatureLists_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_FeatureLists_fieldAccessorTable; + static final com.google.protobuf.Descriptors.Descriptor + internal_static_tensorflow_FeatureLists_FeatureListEntry_descriptor; + static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_tensorflow_FeatureLists_FeatureListEntry_fieldAccessorTable; + + public static com.google.protobuf.Descriptors.FileDescriptor + getDescriptor() { + return descriptor; + } + private static com.google.protobuf.Descriptors.FileDescriptor + descriptor; + static { + java.lang.String[] descriptorData = { + "\n\rfeature.proto\022\ntensorflow\"\032\n\tBytesList" + + "\022\r\n\005value\030\001 \003(\014\"\036\n\tFloatList\022\021\n\005value\030\001 " + + "\003(\002B\002\020\001\"\036\n\tInt64List\022\021\n\005value\030\001 \003(\003B\002\020\001\"" + + "\230\001\n\007Feature\022+\n\nbytes_list\030\001 \001(\0132\025.tensor" + + "flow.BytesListH\000\022+\n\nfloat_list\030\002 \001(\0132\025.t" + + "ensorflow.FloatListH\000\022+\n\nint64_list\030\003 \001(" + + "\0132\025.tensorflow.Int64ListH\000B\006\n\004kind\"\203\001\n\010F" + + "eatures\0222\n\007feature\030\001 \003(\0132!.tensorflow.Fe" + + "atures.FeatureEntry\032C\n\014FeatureEntry\022\013\n\003k" + + "ey\030\001 \001(\t\022\"\n\005value\030\002 \001(\0132\023.tensorflow.Fea", + "ture:\0028\001\"3\n\013FeatureList\022$\n\007feature\030\001 \003(\013" + + "2\023.tensorflow.Feature\"\234\001\n\014FeatureLists\022?" + + "\n\014feature_list\030\001 \003(\0132).tensorflow.Featur" + + "eLists.FeatureListEntry\032K\n\020FeatureListEn" + + "try\022\013\n\003key\030\001 \001(\t\022&\n\005value\030\002 \001(\0132\027.tensor" + + "flow.FeatureList:\0028\001B,\n\026org.tensorflow.e" + + "xampleB\rFeatureProtosP\001\370\001\001b\006proto3" + }; + com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = + new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { + public com.google.protobuf.ExtensionRegistry assignDescriptors( + com.google.protobuf.Descriptors.FileDescriptor root) { + descriptor = root; + return null; + } + }; + com.google.protobuf.Descriptors.FileDescriptor + .internalBuildGeneratedFileFrom(descriptorData, + new com.google.protobuf.Descriptors.FileDescriptor[] { + }, assigner); + internal_static_tensorflow_BytesList_descriptor = + getDescriptor().getMessageTypes().get(0); + internal_static_tensorflow_BytesList_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_BytesList_descriptor, + new java.lang.String[] { "Value", }); + internal_static_tensorflow_FloatList_descriptor = + getDescriptor().getMessageTypes().get(1); + internal_static_tensorflow_FloatList_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_FloatList_descriptor, + new java.lang.String[] { "Value", }); + internal_static_tensorflow_Int64List_descriptor = + getDescriptor().getMessageTypes().get(2); + internal_static_tensorflow_Int64List_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_Int64List_descriptor, + new java.lang.String[] { "Value", }); + internal_static_tensorflow_Feature_descriptor = + getDescriptor().getMessageTypes().get(3); + internal_static_tensorflow_Feature_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_Feature_descriptor, + new java.lang.String[] { "BytesList", "FloatList", "Int64List", "Kind", }); + internal_static_tensorflow_Features_descriptor = + getDescriptor().getMessageTypes().get(4); + internal_static_tensorflow_Features_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_Features_descriptor, + new java.lang.String[] { "Feature", }); + internal_static_tensorflow_Features_FeatureEntry_descriptor = + internal_static_tensorflow_Features_descriptor.getNestedTypes().get(0); + internal_static_tensorflow_Features_FeatureEntry_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_Features_FeatureEntry_descriptor, + new java.lang.String[] { "Key", "Value", }); + internal_static_tensorflow_FeatureList_descriptor = + getDescriptor().getMessageTypes().get(5); + internal_static_tensorflow_FeatureList_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_FeatureList_descriptor, + new java.lang.String[] { "Feature", }); + internal_static_tensorflow_FeatureLists_descriptor = + getDescriptor().getMessageTypes().get(6); + internal_static_tensorflow_FeatureLists_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_FeatureLists_descriptor, + new java.lang.String[] { "FeatureList", }); + internal_static_tensorflow_FeatureLists_FeatureListEntry_descriptor = + internal_static_tensorflow_FeatureLists_descriptor.getNestedTypes().get(0); + internal_static_tensorflow_FeatureLists_FeatureListEntry_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_tensorflow_FeatureLists_FeatureListEntry_descriptor, + new java.lang.String[] { "Key", "Value", }); + } + + // @@protoc_insertion_point(outer_class_scope) +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Features.java b/scala/dllib/src/main/java/org/tensorflow/example/Features.java new file mode 100644 index 00000000000..3faedff284f --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/Features.java @@ -0,0 +1,704 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +/** + * Protobuf type {@code tensorflow.Features} + */ +public final class Features extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.Features) + FeaturesOrBuilder { +private static final long serialVersionUID = 0L; + // Use Features.newBuilder() to construct. + private Features(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private Features() { + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private Features( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 10: { + if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + feature_ = com.google.protobuf.MapField.newMapField( + FeatureDefaultEntryHolder.defaultEntry); + mutable_bitField0_ |= 0x00000001; + } + com.google.protobuf.MapEntry + feature__ = input.readMessage( + FeatureDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); + feature_.getMutableMap().put( + feature__.getKey(), feature__.getValue()); + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_descriptor; + } + + @SuppressWarnings({"rawtypes"}) + protected com.google.protobuf.MapField internalGetMapField( + int number) { + switch (number) { + case 1: + return internalGetFeature(); + default: + throw new RuntimeException( + "Invalid map field number: " + number); + } + } + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.Features.class, org.tensorflow.example.Features.Builder.class); + } + + public static final int FEATURE_FIELD_NUMBER = 1; + private static final class FeatureDefaultEntryHolder { + static final com.google.protobuf.MapEntry< + java.lang.String, org.tensorflow.example.Feature> defaultEntry = + com.google.protobuf.MapEntry + .newDefaultInstance( + org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_FeatureEntry_descriptor, + com.google.protobuf.WireFormat.FieldType.STRING, + "", + com.google.protobuf.WireFormat.FieldType.MESSAGE, + org.tensorflow.example.Feature.getDefaultInstance()); + } + private com.google.protobuf.MapField< + java.lang.String, org.tensorflow.example.Feature> feature_; + private com.google.protobuf.MapField + internalGetFeature() { + if (feature_ == null) { + return com.google.protobuf.MapField.emptyMapField( + FeatureDefaultEntryHolder.defaultEntry); + } + return feature_; + } + + public int getFeatureCount() { + return internalGetFeature().getMap().size(); + } + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public boolean containsFeature( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + return internalGetFeature().getMap().containsKey(key); + } + /** + * Use {@link #getFeatureMap()} instead. + */ + @java.lang.Deprecated + public java.util.Map getFeature() { + return getFeatureMap(); + } + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public java.util.Map getFeatureMap() { + return internalGetFeature().getMap(); + } + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public org.tensorflow.example.Feature getFeatureOrDefault( + java.lang.String key, + org.tensorflow.example.Feature defaultValue) { + if (key == null) { throw new java.lang.NullPointerException(); } + java.util.Map map = + internalGetFeature().getMap(); + return map.containsKey(key) ? map.get(key) : defaultValue; + } + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public org.tensorflow.example.Feature getFeatureOrThrow( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + java.util.Map map = + internalGetFeature().getMap(); + if (!map.containsKey(key)) { + throw new java.lang.IllegalArgumentException(); + } + return map.get(key); + } + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + com.google.protobuf.GeneratedMessageV3 + .serializeStringMapTo( + output, + internalGetFeature(), + FeatureDefaultEntryHolder.defaultEntry, + 1); + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + for (java.util.Map.Entry entry + : internalGetFeature().getMap().entrySet()) { + com.google.protobuf.MapEntry + feature__ = FeatureDefaultEntryHolder.defaultEntry.newBuilderForType() + .setKey(entry.getKey()) + .setValue(entry.getValue()) + .build(); + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(1, feature__); + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.Features)) { + return super.equals(obj); + } + org.tensorflow.example.Features other = (org.tensorflow.example.Features) obj; + + boolean result = true; + result = result && internalGetFeature().equals( + other.internalGetFeature()); + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + if (!internalGetFeature().getMap().isEmpty()) { + hash = (37 * hash) + FEATURE_FIELD_NUMBER; + hash = (53 * hash) + internalGetFeature().hashCode(); + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.Features parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Features parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Features parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Features parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Features parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Features parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Features parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.Features parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.Features parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.Features parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.Features parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.Features parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.Features prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * Protobuf type {@code tensorflow.Features} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.Features) + org.tensorflow.example.FeaturesOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_descriptor; + } + + @SuppressWarnings({"rawtypes"}) + protected com.google.protobuf.MapField internalGetMapField( + int number) { + switch (number) { + case 1: + return internalGetFeature(); + default: + throw new RuntimeException( + "Invalid map field number: " + number); + } + } + @SuppressWarnings({"rawtypes"}) + protected com.google.protobuf.MapField internalGetMutableMapField( + int number) { + switch (number) { + case 1: + return internalGetMutableFeature(); + default: + throw new RuntimeException( + "Invalid map field number: " + number); + } + } + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.Features.class, org.tensorflow.example.Features.Builder.class); + } + + // Construct using org.tensorflow.example.Features.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + internalGetMutableFeature().clear(); + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_descriptor; + } + + public org.tensorflow.example.Features getDefaultInstanceForType() { + return org.tensorflow.example.Features.getDefaultInstance(); + } + + public org.tensorflow.example.Features build() { + org.tensorflow.example.Features result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.Features buildPartial() { + org.tensorflow.example.Features result = new org.tensorflow.example.Features(this); + int from_bitField0_ = bitField0_; + result.feature_ = internalGetFeature(); + result.feature_.makeImmutable(); + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.Features) { + return mergeFrom((org.tensorflow.example.Features)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.Features other) { + if (other == org.tensorflow.example.Features.getDefaultInstance()) return this; + internalGetMutableFeature().mergeFrom( + other.internalGetFeature()); + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.Features parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.Features) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + private int bitField0_; + + private com.google.protobuf.MapField< + java.lang.String, org.tensorflow.example.Feature> feature_; + private com.google.protobuf.MapField + internalGetFeature() { + if (feature_ == null) { + return com.google.protobuf.MapField.emptyMapField( + FeatureDefaultEntryHolder.defaultEntry); + } + return feature_; + } + private com.google.protobuf.MapField + internalGetMutableFeature() { + onChanged();; + if (feature_ == null) { + feature_ = com.google.protobuf.MapField.newMapField( + FeatureDefaultEntryHolder.defaultEntry); + } + if (!feature_.isMutable()) { + feature_ = feature_.copy(); + } + return feature_; + } + + public int getFeatureCount() { + return internalGetFeature().getMap().size(); + } + /** + * 
+     * Map from feature name to feature.
+     *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public boolean containsFeature( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + return internalGetFeature().getMap().containsKey(key); + } + /** + * Use {@link #getFeatureMap()} instead. + */ + @java.lang.Deprecated + public java.util.Map getFeature() { + return getFeatureMap(); + } + /** + * 
+     * Map from feature name to feature.
+     *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public java.util.Map getFeatureMap() { + return internalGetFeature().getMap(); + } + /** + * 
+     * Map from feature name to feature.
+     *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public org.tensorflow.example.Feature getFeatureOrDefault( + java.lang.String key, + org.tensorflow.example.Feature defaultValue) { + if (key == null) { throw new java.lang.NullPointerException(); } + java.util.Map map = + internalGetFeature().getMap(); + return map.containsKey(key) ? map.get(key) : defaultValue; + } + /** + * 
+     * Map from feature name to feature.
+     *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public org.tensorflow.example.Feature getFeatureOrThrow( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + java.util.Map map = + internalGetFeature().getMap(); + if (!map.containsKey(key)) { + throw new java.lang.IllegalArgumentException(); + } + return map.get(key); + } + + public Builder clearFeature() { + internalGetMutableFeature().getMutableMap() + .clear(); + return this; + } + /** + * 
+     * Map from feature name to feature.
+     *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public Builder removeFeature( + java.lang.String key) { + if (key == null) { throw new java.lang.NullPointerException(); } + internalGetMutableFeature().getMutableMap() + .remove(key); + return this; + } + /** + * Use alternate mutation accessors instead. + */ + @java.lang.Deprecated + public java.util.Map + getMutableFeature() { + return internalGetMutableFeature().getMutableMap(); + } + /** + * 
+     * Map from feature name to feature.
+     *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + public Builder putFeature( + java.lang.String key, + org.tensorflow.example.Feature value) { + if (key == null) { throw new java.lang.NullPointerException(); } + if (value == null) { throw new java.lang.NullPointerException(); } + internalGetMutableFeature().getMutableMap() + .put(key, value); + return this; + } + /** + * 
+     * Map from feature name to feature.
+     *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + public Builder putAllFeature( + java.util.Map values) { + internalGetMutableFeature().getMutableMap() + .putAll(values); + return this; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.Features) + } + + // @@protoc_insertion_point(class_scope:tensorflow.Features) + private static final org.tensorflow.example.Features DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.Features(); + } + + public static org.tensorflow.example.Features getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public Features parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new Features(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.Features getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeaturesOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FeaturesOrBuilder.java new file mode 100644 index 00000000000..2856c3f7d91 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FeaturesOrBuilder.java @@ -0,0 +1,63 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +public interface FeaturesOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.Features) + com.google.protobuf.MessageOrBuilder { + + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + int getFeatureCount(); + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + boolean containsFeature( + java.lang.String key); + /** + * Use {@link #getFeatureMap()} instead. + */ + @java.lang.Deprecated + java.util.Map + getFeature(); + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + java.util.Map + getFeatureMap(); + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + org.tensorflow.example.Feature getFeatureOrDefault( + java.lang.String key, + org.tensorflow.example.Feature defaultValue); + /** + * 
+   * Map from feature name to feature.
+   *
+ * + * map<string, .tensorflow.Feature> feature = 1; + */ + + org.tensorflow.example.Feature getFeatureOrThrow( + java.lang.String key); +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FloatList.java b/scala/dllib/src/main/java/org/tensorflow/example/FloatList.java new file mode 100644 index 00000000000..44fa7e98f97 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FloatList.java @@ -0,0 +1,544 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +/** + * Protobuf type {@code tensorflow.FloatList} + */ +public final class FloatList extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.FloatList) + FloatListOrBuilder { +private static final long serialVersionUID = 0L; + // Use FloatList.newBuilder() to construct. + private FloatList(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private FloatList() { + value_ = java.util.Collections.emptyList(); + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private FloatList( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 13: { + if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + value_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000001; + } + value_.add(input.readFloat()); + break; + } + case 10: { + int length = input.readRawVarint32(); + int limit = input.pushLimit(length); + if (!((mutable_bitField0_ & 0x00000001) == 0x00000001) && input.getBytesUntilLimit() > 0) { + value_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000001; + } + while (input.getBytesUntilLimit() > 0) { + value_.add(input.readFloat()); + } + input.popLimit(limit); + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + value_ = java.util.Collections.unmodifiableList(value_); + } + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.FloatList.class, org.tensorflow.example.FloatList.Builder.class); + } + + public static final int VALUE_FIELD_NUMBER = 1; + private java.util.List value_; + /** + * repeated float value = 1 [packed = true]; + */ + public java.util.List + getValueList() { + return value_; + } + /** + * repeated float value = 1 [packed = true]; + */ + public int getValueCount() { + return value_.size(); + } + /** + * repeated float value = 1 [packed = true]; + */ + public float getValue(int index) { + return value_.get(index); + } + private int valueMemoizedSerializedSize = -1; + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + getSerializedSize(); + if (getValueList().size() > 0) { + output.writeUInt32NoTag(10); + output.writeUInt32NoTag(valueMemoizedSerializedSize); + } + for (int i = 0; i < value_.size(); i++) { + output.writeFloatNoTag(value_.get(i)); + } + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + { + int dataSize = 0; + dataSize = 4 * getValueList().size(); + size += dataSize; + if (!getValueList().isEmpty()) { + size += 1; + size += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(dataSize); + } + valueMemoizedSerializedSize = dataSize; + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.FloatList)) { + return super.equals(obj); + } + org.tensorflow.example.FloatList other = (org.tensorflow.example.FloatList) obj; + + boolean result = true; + result = result && getValueList() + .equals(other.getValueList()); + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + if (getValueCount() > 0) { + hash = (37 * hash) + VALUE_FIELD_NUMBER; + hash = (53 * hash) + getValueList().hashCode(); + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.FloatList parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FloatList parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FloatList parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FloatList parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FloatList parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.FloatList parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.FloatList parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.FloatList parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.FloatList parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.FloatList parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.FloatList parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.FloatList parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.FloatList prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * Protobuf type {@code tensorflow.FloatList} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.FloatList) + org.tensorflow.example.FloatListOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.FloatList.class, org.tensorflow.example.FloatList.Builder.class); + } + + // Construct using org.tensorflow.example.FloatList.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + value_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000001); + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_descriptor; + } + + public org.tensorflow.example.FloatList getDefaultInstanceForType() { + return org.tensorflow.example.FloatList.getDefaultInstance(); + } + + public org.tensorflow.example.FloatList build() { + org.tensorflow.example.FloatList result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.FloatList buildPartial() { + org.tensorflow.example.FloatList result = new org.tensorflow.example.FloatList(this); + int from_bitField0_ = bitField0_; + if (((bitField0_ & 0x00000001) == 0x00000001)) { + value_ = java.util.Collections.unmodifiableList(value_); + bitField0_ = (bitField0_ & ~0x00000001); + } + result.value_ = value_; + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.FloatList) { + return mergeFrom((org.tensorflow.example.FloatList)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.FloatList other) { + if (other == org.tensorflow.example.FloatList.getDefaultInstance()) return this; + if (!other.value_.isEmpty()) { + if (value_.isEmpty()) { + value_ = other.value_; + bitField0_ = (bitField0_ & ~0x00000001); + } else { + ensureValueIsMutable(); + value_.addAll(other.value_); + } + onChanged(); + } + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.FloatList parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.FloatList) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + private int bitField0_; + + private java.util.List value_ = java.util.Collections.emptyList(); + private void ensureValueIsMutable() { + if (!((bitField0_ & 0x00000001) == 0x00000001)) { + value_ = new java.util.ArrayList(value_); + bitField0_ |= 0x00000001; + } + } + /** + * repeated float value = 1 [packed = true]; + */ + public java.util.List + getValueList() { + return java.util.Collections.unmodifiableList(value_); + } + /** + * repeated float value = 1 [packed = true]; + */ + public int getValueCount() { + return value_.size(); + } + /** + * repeated float value = 1 [packed = true]; + */ + public float getValue(int index) { + return value_.get(index); + } + /** + * repeated float value = 1 [packed = true]; + */ + public Builder setValue( + int index, float value) { + ensureValueIsMutable(); + value_.set(index, value); + onChanged(); + return this; + } + /** + * repeated float value = 1 [packed = true]; + */ + public Builder addValue(float value) { + ensureValueIsMutable(); + value_.add(value); + onChanged(); + return this; + } + /** + * repeated float value = 1 [packed = true]; + */ + public Builder addAllValue( + java.lang.Iterable values) { + ensureValueIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, value_); + onChanged(); + return this; + } + /** + * repeated float value = 1 [packed = true]; + */ + public Builder clearValue() { + value_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000001); + onChanged(); + return this; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.FloatList) + } + + // @@protoc_insertion_point(class_scope:tensorflow.FloatList) + private static final org.tensorflow.example.FloatList DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.FloatList(); + } + + public static org.tensorflow.example.FloatList getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public FloatList parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new FloatList(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.FloatList getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FloatListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FloatListOrBuilder.java new file mode 100644 index 00000000000..3d89a4a4eb8 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/FloatListOrBuilder.java @@ -0,0 +1,22 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +public interface FloatListOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.FloatList) + com.google.protobuf.MessageOrBuilder { + + /** + * repeated float value = 1 [packed = true]; + */ + java.util.List getValueList(); + /** + * repeated float value = 1 [packed = true]; + */ + int getValueCount(); + /** + * repeated float value = 1 [packed = true]; + */ + float getValue(int index); +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Int64List.java b/scala/dllib/src/main/java/org/tensorflow/example/Int64List.java new file mode 100644 index 00000000000..63af761d7f2 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/Int64List.java @@ -0,0 +1,547 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +/** + * Protobuf type {@code tensorflow.Int64List} + */ +public final class Int64List extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.Int64List) + Int64ListOrBuilder { +private static final long serialVersionUID = 0L; + // Use Int64List.newBuilder() to construct. + private Int64List(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private Int64List() { + value_ = java.util.Collections.emptyList(); + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private Int64List( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 8: { + if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + value_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000001; + } + value_.add(input.readInt64()); + break; + } + case 10: { + int length = input.readRawVarint32(); + int limit = input.pushLimit(length); + if (!((mutable_bitField0_ & 0x00000001) == 0x00000001) && input.getBytesUntilLimit() > 0) { + value_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000001; + } + while (input.getBytesUntilLimit() > 0) { + value_.add(input.readInt64()); + } + input.popLimit(limit); + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { + value_ = java.util.Collections.unmodifiableList(value_); + } + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.Int64List.class, org.tensorflow.example.Int64List.Builder.class); + } + + public static final int VALUE_FIELD_NUMBER = 1; + private java.util.List value_; + /** + * repeated int64 value = 1 [packed = true]; + */ + public java.util.List + getValueList() { + return value_; + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public int getValueCount() { + return value_.size(); + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public long getValue(int index) { + return value_.get(index); + } + private int valueMemoizedSerializedSize = -1; + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + getSerializedSize(); + if (getValueList().size() > 0) { + output.writeUInt32NoTag(10); + output.writeUInt32NoTag(valueMemoizedSerializedSize); + } + for (int i = 0; i < value_.size(); i++) { + output.writeInt64NoTag(value_.get(i)); + } + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + { + int dataSize = 0; + for (int i = 0; i < value_.size(); i++) { + dataSize += com.google.protobuf.CodedOutputStream + .computeInt64SizeNoTag(value_.get(i)); + } + size += dataSize; + if (!getValueList().isEmpty()) { + size += 1; + size += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(dataSize); + } + valueMemoizedSerializedSize = dataSize; + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.Int64List)) { + return super.equals(obj); + } + org.tensorflow.example.Int64List other = (org.tensorflow.example.Int64List) obj; + + boolean result = true; + result = result && getValueList() + .equals(other.getValueList()); + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + if (getValueCount() > 0) { + hash = (37 * hash) + VALUE_FIELD_NUMBER; + hash = (53 * hash) + getValueList().hashCode(); + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.Int64List parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Int64List parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Int64List parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Int64List parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Int64List parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.Int64List parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.Int64List parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.Int64List parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.Int64List parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.Int64List parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.Int64List parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.Int64List parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.Int64List prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * Protobuf type {@code tensorflow.Int64List} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.Int64List) + org.tensorflow.example.Int64ListOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.Int64List.class, org.tensorflow.example.Int64List.Builder.class); + } + + // Construct using org.tensorflow.example.Int64List.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + value_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000001); + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_descriptor; + } + + public org.tensorflow.example.Int64List getDefaultInstanceForType() { + return org.tensorflow.example.Int64List.getDefaultInstance(); + } + + public org.tensorflow.example.Int64List build() { + org.tensorflow.example.Int64List result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.Int64List buildPartial() { + org.tensorflow.example.Int64List result = new org.tensorflow.example.Int64List(this); + int from_bitField0_ = bitField0_; + if (((bitField0_ & 0x00000001) == 0x00000001)) { + value_ = java.util.Collections.unmodifiableList(value_); + bitField0_ = (bitField0_ & ~0x00000001); + } + result.value_ = value_; + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.Int64List) { + return mergeFrom((org.tensorflow.example.Int64List)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.Int64List other) { + if (other == org.tensorflow.example.Int64List.getDefaultInstance()) return this; + if (!other.value_.isEmpty()) { + if (value_.isEmpty()) { + value_ = other.value_; + bitField0_ = (bitField0_ & ~0x00000001); + } else { + ensureValueIsMutable(); + value_.addAll(other.value_); + } + onChanged(); + } + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.Int64List parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.Int64List) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + private int bitField0_; + + private java.util.List value_ = java.util.Collections.emptyList(); + private void ensureValueIsMutable() { + if (!((bitField0_ & 0x00000001) == 0x00000001)) { + value_ = new java.util.ArrayList(value_); + bitField0_ |= 0x00000001; + } + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public java.util.List + getValueList() { + return java.util.Collections.unmodifiableList(value_); + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public int getValueCount() { + return value_.size(); + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public long getValue(int index) { + return value_.get(index); + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public Builder setValue( + int index, long value) { + ensureValueIsMutable(); + value_.set(index, value); + onChanged(); + return this; + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public Builder addValue(long value) { + ensureValueIsMutable(); + value_.add(value); + onChanged(); + return this; + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public Builder addAllValue( + java.lang.Iterable values) { + ensureValueIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, value_); + onChanged(); + return this; + } + /** + * repeated int64 value = 1 [packed = true]; + */ + public Builder clearValue() { + value_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000001); + onChanged(); + return this; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.Int64List) + } + + // @@protoc_insertion_point(class_scope:tensorflow.Int64List) + private static final org.tensorflow.example.Int64List DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.Int64List(); + } + + public static org.tensorflow.example.Int64List getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public Int64List parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new Int64List(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.Int64List getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Int64ListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/Int64ListOrBuilder.java new file mode 100644 index 00000000000..667578cf1a2 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/Int64ListOrBuilder.java @@ -0,0 +1,22 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: feature.proto + +package org.tensorflow.example; + +public interface Int64ListOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.Int64List) + com.google.protobuf.MessageOrBuilder { + + /** + * repeated int64 value = 1 [packed = true]; + */ + java.util.List getValueList(); + /** + * repeated int64 value = 1 [packed = true]; + */ + int getValueCount(); + /** + * repeated int64 value = 1 [packed = true]; + */ + long getValue(int index); +} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/SequenceExample.java b/scala/dllib/src/main/java/org/tensorflow/example/SequenceExample.java new file mode 100644 index 00000000000..0e0eb6eb494 --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/SequenceExample.java @@ -0,0 +1,748 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: tensorflow/core/example/example.proto + +package org.tensorflow.example; + +/** + * Protobuf type {@code tensorflow.SequenceExample} + */ +public final class SequenceExample extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:tensorflow.SequenceExample) + SequenceExampleOrBuilder { +private static final long serialVersionUID = 0L; + // Use SequenceExample.newBuilder() to construct. + private SequenceExample(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private SequenceExample() { + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private SequenceExample( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 10: { + org.tensorflow.example.Features.Builder subBuilder = null; + if (context_ != null) { + subBuilder = context_.toBuilder(); + } + context_ = input.readMessage(org.tensorflow.example.Features.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom(context_); + context_ = subBuilder.buildPartial(); + } + + break; + } + case 18: { + org.tensorflow.example.FeatureLists.Builder subBuilder = null; + if (featureLists_ != null) { + subBuilder = featureLists_.toBuilder(); + } + featureLists_ = input.readMessage(org.tensorflow.example.FeatureLists.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom(featureLists_); + featureLists_ = subBuilder.buildPartial(); + } + + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.SequenceExample.class, org.tensorflow.example.SequenceExample.Builder.class); + } + + public static final int CONTEXT_FIELD_NUMBER = 1; + private org.tensorflow.example.Features context_; + /** + * .tensorflow.Features context = 1; + */ + public boolean hasContext() { + return context_ != null; + } + /** + * .tensorflow.Features context = 1; + */ + public org.tensorflow.example.Features getContext() { + return context_ == null ? org.tensorflow.example.Features.getDefaultInstance() : context_; + } + /** + * .tensorflow.Features context = 1; + */ + public org.tensorflow.example.FeaturesOrBuilder getContextOrBuilder() { + return getContext(); + } + + public static final int FEATURE_LISTS_FIELD_NUMBER = 2; + private org.tensorflow.example.FeatureLists featureLists_; + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public boolean hasFeatureLists() { + return featureLists_ != null; + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public org.tensorflow.example.FeatureLists getFeatureLists() { + return featureLists_ == null ? org.tensorflow.example.FeatureLists.getDefaultInstance() : featureLists_; + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public org.tensorflow.example.FeatureListsOrBuilder getFeatureListsOrBuilder() { + return getFeatureLists(); + } + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + if (context_ != null) { + output.writeMessage(1, getContext()); + } + if (featureLists_ != null) { + output.writeMessage(2, getFeatureLists()); + } + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + if (context_ != null) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(1, getContext()); + } + if (featureLists_ != null) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(2, getFeatureLists()); + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof org.tensorflow.example.SequenceExample)) { + return super.equals(obj); + } + org.tensorflow.example.SequenceExample other = (org.tensorflow.example.SequenceExample) obj; + + boolean result = true; + result = result && (hasContext() == other.hasContext()); + if (hasContext()) { + result = result && getContext() + .equals(other.getContext()); + } + result = result && (hasFeatureLists() == other.hasFeatureLists()); + if (hasFeatureLists()) { + result = result && getFeatureLists() + .equals(other.getFeatureLists()); + } + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + if (hasContext()) { + hash = (37 * hash) + CONTEXT_FIELD_NUMBER; + hash = (53 * hash) + getContext().hashCode(); + } + if (hasFeatureLists()) { + hash = (37 * hash) + FEATURE_LISTS_FIELD_NUMBER; + hash = (53 * hash) + getFeatureLists().hashCode(); + } + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static org.tensorflow.example.SequenceExample parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.SequenceExample parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.SequenceExample parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.SequenceExample parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.SequenceExample parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static org.tensorflow.example.SequenceExample parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static org.tensorflow.example.SequenceExample parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.SequenceExample parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.SequenceExample parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static org.tensorflow.example.SequenceExample parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static org.tensorflow.example.SequenceExample parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static org.tensorflow.example.SequenceExample parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(org.tensorflow.example.SequenceExample prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * Protobuf type {@code tensorflow.SequenceExample} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:tensorflow.SequenceExample) + org.tensorflow.example.SequenceExampleOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_fieldAccessorTable + .ensureFieldAccessorsInitialized( + org.tensorflow.example.SequenceExample.class, org.tensorflow.example.SequenceExample.Builder.class); + } + + // Construct using org.tensorflow.example.SequenceExample.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + if (contextBuilder_ == null) { + context_ = null; + } else { + context_ = null; + contextBuilder_ = null; + } + if (featureListsBuilder_ == null) { + featureLists_ = null; + } else { + featureLists_ = null; + featureListsBuilder_ = null; + } + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_descriptor; + } + + public org.tensorflow.example.SequenceExample getDefaultInstanceForType() { + return org.tensorflow.example.SequenceExample.getDefaultInstance(); + } + + public org.tensorflow.example.SequenceExample build() { + org.tensorflow.example.SequenceExample result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public org.tensorflow.example.SequenceExample buildPartial() { + org.tensorflow.example.SequenceExample result = new org.tensorflow.example.SequenceExample(this); + if (contextBuilder_ == null) { + result.context_ = context_; + } else { + result.context_ = contextBuilder_.build(); + } + if (featureListsBuilder_ == null) { + result.featureLists_ = featureLists_; + } else { + result.featureLists_ = featureListsBuilder_.build(); + } + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof org.tensorflow.example.SequenceExample) { + return mergeFrom((org.tensorflow.example.SequenceExample)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(org.tensorflow.example.SequenceExample other) { + if (other == org.tensorflow.example.SequenceExample.getDefaultInstance()) return this; + if (other.hasContext()) { + mergeContext(other.getContext()); + } + if (other.hasFeatureLists()) { + mergeFeatureLists(other.getFeatureLists()); + } + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + org.tensorflow.example.SequenceExample parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (org.tensorflow.example.SequenceExample) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + + private org.tensorflow.example.Features context_ = null; + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder> contextBuilder_; + /** + * .tensorflow.Features context = 1; + */ + public boolean hasContext() { + return contextBuilder_ != null || context_ != null; + } + /** + * .tensorflow.Features context = 1; + */ + public org.tensorflow.example.Features getContext() { + if (contextBuilder_ == null) { + return context_ == null ? org.tensorflow.example.Features.getDefaultInstance() : context_; + } else { + return contextBuilder_.getMessage(); + } + } + /** + * .tensorflow.Features context = 1; + */ + public Builder setContext(org.tensorflow.example.Features value) { + if (contextBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + context_ = value; + onChanged(); + } else { + contextBuilder_.setMessage(value); + } + + return this; + } + /** + * .tensorflow.Features context = 1; + */ + public Builder setContext( + org.tensorflow.example.Features.Builder builderForValue) { + if (contextBuilder_ == null) { + context_ = builderForValue.build(); + onChanged(); + } else { + contextBuilder_.setMessage(builderForValue.build()); + } + + return this; + } + /** + * .tensorflow.Features context = 1; + */ + public Builder mergeContext(org.tensorflow.example.Features value) { + if (contextBuilder_ == null) { + if (context_ != null) { + context_ = + org.tensorflow.example.Features.newBuilder(context_).mergeFrom(value).buildPartial(); + } else { + context_ = value; + } + onChanged(); + } else { + contextBuilder_.mergeFrom(value); + } + + return this; + } + /** + * .tensorflow.Features context = 1; + */ + public Builder clearContext() { + if (contextBuilder_ == null) { + context_ = null; + onChanged(); + } else { + context_ = null; + contextBuilder_ = null; + } + + return this; + } + /** + * .tensorflow.Features context = 1; + */ + public org.tensorflow.example.Features.Builder getContextBuilder() { + + onChanged(); + return getContextFieldBuilder().getBuilder(); + } + /** + * .tensorflow.Features context = 1; + */ + public org.tensorflow.example.FeaturesOrBuilder getContextOrBuilder() { + if (contextBuilder_ != null) { + return contextBuilder_.getMessageOrBuilder(); + } else { + return context_ == null ? + org.tensorflow.example.Features.getDefaultInstance() : context_; + } + } + /** + * .tensorflow.Features context = 1; + */ + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder> + getContextFieldBuilder() { + if (contextBuilder_ == null) { + contextBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder>( + getContext(), + getParentForChildren(), + isClean()); + context_ = null; + } + return contextBuilder_; + } + + private org.tensorflow.example.FeatureLists featureLists_ = null; + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.FeatureLists, org.tensorflow.example.FeatureLists.Builder, org.tensorflow.example.FeatureListsOrBuilder> featureListsBuilder_; + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public boolean hasFeatureLists() { + return featureListsBuilder_ != null || featureLists_ != null; + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public org.tensorflow.example.FeatureLists getFeatureLists() { + if (featureListsBuilder_ == null) { + return featureLists_ == null ? org.tensorflow.example.FeatureLists.getDefaultInstance() : featureLists_; + } else { + return featureListsBuilder_.getMessage(); + } + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public Builder setFeatureLists(org.tensorflow.example.FeatureLists value) { + if (featureListsBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + featureLists_ = value; + onChanged(); + } else { + featureListsBuilder_.setMessage(value); + } + + return this; + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public Builder setFeatureLists( + org.tensorflow.example.FeatureLists.Builder builderForValue) { + if (featureListsBuilder_ == null) { + featureLists_ = builderForValue.build(); + onChanged(); + } else { + featureListsBuilder_.setMessage(builderForValue.build()); + } + + return this; + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public Builder mergeFeatureLists(org.tensorflow.example.FeatureLists value) { + if (featureListsBuilder_ == null) { + if (featureLists_ != null) { + featureLists_ = + org.tensorflow.example.FeatureLists.newBuilder(featureLists_).mergeFrom(value).buildPartial(); + } else { + featureLists_ = value; + } + onChanged(); + } else { + featureListsBuilder_.mergeFrom(value); + } + + return this; + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public Builder clearFeatureLists() { + if (featureListsBuilder_ == null) { + featureLists_ = null; + onChanged(); + } else { + featureLists_ = null; + featureListsBuilder_ = null; + } + + return this; + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public org.tensorflow.example.FeatureLists.Builder getFeatureListsBuilder() { + + onChanged(); + return getFeatureListsFieldBuilder().getBuilder(); + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + public org.tensorflow.example.FeatureListsOrBuilder getFeatureListsOrBuilder() { + if (featureListsBuilder_ != null) { + return featureListsBuilder_.getMessageOrBuilder(); + } else { + return featureLists_ == null ? + org.tensorflow.example.FeatureLists.getDefaultInstance() : featureLists_; + } + } + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + private com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.FeatureLists, org.tensorflow.example.FeatureLists.Builder, org.tensorflow.example.FeatureListsOrBuilder> + getFeatureListsFieldBuilder() { + if (featureListsBuilder_ == null) { + featureListsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< + org.tensorflow.example.FeatureLists, org.tensorflow.example.FeatureLists.Builder, org.tensorflow.example.FeatureListsOrBuilder>( + getFeatureLists(), + getParentForChildren(), + isClean()); + featureLists_ = null; + } + return featureListsBuilder_; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:tensorflow.SequenceExample) + } + + // @@protoc_insertion_point(class_scope:tensorflow.SequenceExample) + private static final org.tensorflow.example.SequenceExample DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new org.tensorflow.example.SequenceExample(); + } + + public static org.tensorflow.example.SequenceExample getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public SequenceExample parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new SequenceExample(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public org.tensorflow.example.SequenceExample getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + +} + diff --git a/scala/dllib/src/main/java/org/tensorflow/example/SequenceExampleOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/SequenceExampleOrBuilder.java new file mode 100644 index 00000000000..9b35b76f50a --- /dev/null +++ b/scala/dllib/src/main/java/org/tensorflow/example/SequenceExampleOrBuilder.java @@ -0,0 +1,35 @@ +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: tensorflow/core/example/example.proto + +package org.tensorflow.example; + +public interface SequenceExampleOrBuilder extends + // @@protoc_insertion_point(interface_extends:tensorflow.SequenceExample) + com.google.protobuf.MessageOrBuilder { + + /** + * .tensorflow.Features context = 1; + */ + boolean hasContext(); + /** + * .tensorflow.Features context = 1; + */ + org.tensorflow.example.Features getContext(); + /** + * .tensorflow.Features context = 1; + */ + org.tensorflow.example.FeaturesOrBuilder getContextOrBuilder(); + + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + boolean hasFeatureLists(); + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + org.tensorflow.example.FeatureLists getFeatureLists(); + /** + * .tensorflow.FeatureLists feature_lists = 2; + */ + org.tensorflow.example.FeatureListsOrBuilder getFeatureListsOrBuilder(); +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala new file mode 100644 index 00000000000..dc8b2836c98 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala @@ -0,0 +1,74 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf + +import java.io.{BufferedInputStream, File, FileInputStream} +import java.nio.{ByteBuffer, ByteOrder} + +/** + * Internal use only. + * + * TF record format: + * uint64 length + * uint32 masked_crc32_of_length + * byte data[length] + * uint32 masked_crc32_of_data + * + */ +private[tf] class TFRecordIterator(fileName: File) extends Iterator[Array[Byte]] { + + private val inputStream = new BufferedInputStream(new FileInputStream(fileName)) + + private var dataBuffer: Array[Byte] = null + + private val lengthBuffer: Array[Byte] = new Array[Byte](8) + + + + override def hasNext: Boolean = { + if (dataBuffer != null) { + true + } else { + val numOfBytes = inputStream.read(lengthBuffer) + if (numOfBytes == 8) { + val lengthWrapper = ByteBuffer.wrap(lengthBuffer) + lengthWrapper.order(ByteOrder.LITTLE_ENDIAN) + val length = lengthWrapper.getLong().toInt + // todo, do crc check, simply skip now + inputStream.skip(4) + + dataBuffer = new Array[Byte](length) + inputStream.read(dataBuffer) + // todo, do crc check, simply skip now + inputStream.skip(4) + true + } else { + inputStream.close() + false + } + } + } + + override def next(): Array[Byte] = { + if (hasNext) { + val data = this.dataBuffer + this.dataBuffer = null + data + } else { + throw new NoSuchElementException("next on empty iterator") + } + } +} diff --git a/scala/dllib/src/test/resources/tf/text.tfrecord b/scala/dllib/src/test/resources/tf/text.tfrecord new file mode 100644 index 0000000000000000000000000000000000000000..4333bed932d33e10cb65c88b77eaa93211678091 GIT binary patch literal 20 WcmZQ!fB+}gAeO|W JFile} + + +class TFRecordIteratorSpec extends FlatSpec with Matchers { + + "TFRecordIterator " should "be able to read .tfrecord file" in { + val resource = getClass.getClassLoader.getResource("tf") + val path = processPath(resource.getPath) + JFile.separator + "text.tfrecord" + val file = new JFile(path) + + val iter = new TFRecordIterator(file) + + iter.map(a => new String(a)).toSeq should be (Seq("abcd")) + } + + private def processPath(path: String): String = { + if (path.contains(":")) { + path.substring(1) + } else { + path + } + } + +} From 38f4a09ee02d41c7bd2b1be45b305f47be95861f Mon Sep 17 00:00:00 2001 From: Fu Zhouwang Date: Thu, 14 Sep 2017 08:51:26 +0800 Subject: [PATCH 0390/1065] update integration test for tensorflow model load/save on hdfs/s3 (#1538) * update integration test for tensorflow model load/save on hdfs/s3 * refactor format transformation * fix lenet format in test * change lenet to NHWC in test * remove transpose in integration test --- .../dllib/utils/tf/BigDLToTensorflow.scala | 62 ++++++++++--- .../bigdl/dllib/integration/HdfsSpec.scala | 10 +-- .../bigdl/dllib/integration/S3Spec.scala | 9 +- .../dllib/utils/tf/TensorflowSaverSpec.scala | 86 +++++++++---------- 4 files changed, 97 insertions(+), 70 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala index e6967d4995a..ffec7e8d957 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf import java.nio.ByteOrder import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import Tensorflow._ @@ -110,17 +110,24 @@ object SpatialConvolutionToTF extends BigDLToTensorflow { // squeeze will modify the weight tensor // GOIHW -> HWIO require(spatialConv.weight.size(1) == 1, "convolution group is not supported") - val filterTensor = spatialConv.weight.select(1, 1) - .transpose(2, 3).transpose(3, 4).transpose(1, 2).transpose(2, 3).transpose(3, 4).contiguous() + val (dataFormat, filterTensor) = if (spatialConv.format == DataFormat.NCHW) { + (TensorflowDataFormat.NCHW, + spatialConv.weight.select(1, 1) + .transpose(2, 3).transpose(3, 4) + .transpose(1, 2).transpose(2, 3) + .transpose(3, 4).contiguous()) + } else { + (TensorflowDataFormat.NHWC, spatialConv.weight.select(1, 1)) + } val filter = const(filterTensor, spatialConv.getName() + "/filter", byteOrder) val filterReader = identity(filter, spatialConv.getName() + "/filterReader") val conv = conv2D(inputs(0), filterReader, spatialConv.strideW, spatialConv.strideH, spatialConv.kernelW, spatialConv.kernelH, spatialConv.padW, spatialConv.padH, - getDataFormat(), spatialConv.getName() + "/conv2D") + dataFormat, spatialConv.getName() + "/conv2D") val bias = const(spatialConv.bias, spatialConv.getName() + "/bias", byteOrder) val biasReader = identity(bias, spatialConv.getName() + "/biasReader") - val add = biasAdd(conv, biasReader, getDataFormat(), + val add = biasAdd(conv, biasReader, dataFormat, spatialConv.getName() + "/biasAdd") Seq(add, biasReader, bias, conv, filterReader, filter) } @@ -220,8 +227,13 @@ object MaxpoolToTF extends BigDLToTensorflow { byteOrder: ByteOrder): Seq[NodeDef] = { require(inputs.length == 1, "Maxpool only accept one input") val layer = module.asInstanceOf[SpatialMaxPooling[_]] + val dataFormat = if (layer.format == DataFormat.NHWC) { + TensorflowDataFormat.NHWC + } else { + TensorflowDataFormat.NCHW + } Seq(maxPool(inputs(0), layer.kW, layer.kH, layer.padW, layer.padH, - layer.dW, layer.dH, getDataFormat(), layer.getName())) + layer.dW, layer.dH, dataFormat, layer.getName())) } } @@ -251,8 +263,13 @@ object AvgpoolToTF extends BigDLToTensorflow { byteOrder: ByteOrder): Seq[NodeDef] = { require(inputs.length == 1, "Avgpool only accept one input") val layer = module.asInstanceOf[SpatialAveragePooling[_]] + val dataFormat = if (layer.format == DataFormat.NHWC) { + TensorflowDataFormat.NHWC + } else { + TensorflowDataFormat.NCHW + } Seq(avgPool(inputs(0), layer.kW, layer.kH, layer.padW, layer.padH, - layer.dW, layer.dH, getDataFormat(), layer.getName())) + layer.dW, layer.dH, dataFormat, layer.getName())) } } @@ -341,16 +358,37 @@ object BatchNorm2DToTF extends BigDLToTensorflow { require(inputs.length == 1, "BatchNorm only accept one input") val layer = module.asInstanceOf[SpatialBatchNormalization[_]] require(!layer.isTraining(), "Only support evaluate mode batch norm") + // reshape to nchw + val size = Tensor[Float](layer.nDim) + for (i <- 0 until layer.nDim) { + size.setValue(i + 1, 1) + } + size(2) = layer.weight.size(1) + val shapeVar = const(size, layer.getName() + "/reshape_1/shape", + byteOrder, false, DataType.DT_INT32) + val shapeMean = const(size, layer.getName() + "/reshape_2/shape", + byteOrder, false, DataType.DT_INT32) + val shapeScale = const(size, layer.getName() + "/reshape_3/shape", + byteOrder, false, DataType.DT_INT32) + val shapeOffset = const(size, layer.getName() + "/reshape_4/shape", + byteOrder, false, DataType.DT_INT32) val varNode = const(layer.runningVar, layer.getName() + "/std", byteOrder) val mean = const(layer.runningMean, layer.getName() + "/mean", byteOrder) val scale = const(layer.weight, layer.getName() + "/scale", byteOrder) val offset = const(layer.bias, layer.getName() + "/offset", byteOrder) - val sqrtVar = rsqrt(varNode, layer.getName() + "/stdvar") - val mul0 = multiply(scale, sqrtVar, layer.getName() + "/mul0") + val reshapeVar = reshape(varNode, shapeVar, s"${layer.getName()}/reshape_1") + val reshapeMean = reshape(mean, shapeMean, s"${layer.getName()}/reshape_2") + val reshapeScale = reshape(scale, shapeScale, s"${layer.getName()}/reshape_3") + val reshapeOffset = reshape(offset, shapeOffset, s"${layer.getName()}/reshape_4") + // construct graph + val sqrtVar = rsqrt(reshapeVar, layer.getName() + "/stdvar") + val mul0 = multiply(reshapeScale, sqrtVar, layer.getName() + "/mul0") val mul1 = multiply(inputs(0), mul0, layer.getName() + "/mul1") - val mul2 = multiply(mean, mul0, layer.getName() + "/mul2") - val sub = subtract(offset, mul2, layer.getName() + "/sub") + val mul2 = multiply(reshapeMean, mul0, layer.getName() + "/mul2") + val sub = subtract(reshapeOffset, mul2, layer.getName() + "/sub") val output = add(mul1, sub, layer.getName() + "/output") - Seq(output, sub, mul2, mul1, mul0, offset, scale, mean, sqrtVar, varNode) + Seq(output, sub, mul2, mul1, mul0, reshapeOffset, reshapeMean, reshapeScale, + shapeOffset, shapeMean, shapeScale, offset, scale, mean, + sqrtVar, reshapeVar, shapeVar, varNode) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala index bf0d47d8c7d..82f4ceb0838 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala @@ -148,8 +148,7 @@ class HdfsSpec extends FlatSpec with Matchers with BeforeAndAfter{ } - "Load tensorflow lenet to/from HDFS" should "works properly" in { - System.setProperty("bigdl.enableNHWC", "true") + "Save/load tensorflow lenet NCHW to/from HDFS" should "works properly" in { val conv1 = SpatialConvolution[Float](1, 6, 5, 5).setName("conv1").inputs() val tanh1 = Tanh[Float]().setName("tanh1").inputs(conv1) val pool1 = SpatialMaxPooling[Float](2, 2, 2, 2).setName("pool1").inputs(tanh1) @@ -162,16 +161,15 @@ class HdfsSpec extends FlatSpec with Matchers with BeforeAndAfter{ val outputData = funcModel.forward(inputData).toTensor[Float] val hdfsDir = hdfs + s"/${ com.google.common.io.Files.createTempDir().getPath() }" - TensorflowSaver.saveGraph[Float](funcModel, Seq(("input", Seq(4, 28, 28, 1))), hdfsDir) + TensorflowSaver.saveGraph[Float](funcModel, Seq(("input", Seq(4, 28, 28, 1))), + hdfsDir + "/test.tfmodel") - - val loadedModel = TensorflowLoader.load[Float](hdfsDir, + val loadedModel = TensorflowLoader.load[Float](hdfsDir + "/test.tfmodel", Seq("input"), Seq("output"), ByteOrder.LITTLE_ENDIAN) val loadedOutput = loadedModel.forward(inputData).toTensor[Float] loadedOutput.almostEqual(outputData, 1e-7) - System.setProperty("bigdl.enableNHWC", "false") } "Persist and Load Caffe to/from HDFS" should "works properly" in { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala index 73a06f1b717..4dfbdcd443c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala @@ -126,8 +126,7 @@ class S3Spec extends FlatSpec with Matchers with BeforeAndAfter{ } - "Load tensorflow lenet to/from HDFS" should "works properly" in { - System.setProperty("bigdl.enableNHWC", "true") + "Save/load tensorflow lenet NCHW to/from s3" should "works properly" in { val conv1 = SpatialConvolution[Float](1, 6, 5, 5).setName("conv1").inputs() val tanh1 = Tanh[Float]().setName("tanh1").inputs(conv1) val pool1 = SpatialMaxPooling[Float](2, 2, 2, 2).setName("pool1").inputs(tanh1) @@ -140,15 +139,15 @@ class S3Spec extends FlatSpec with Matchers with BeforeAndAfter{ val outputData = funcModel.forward(inputData).toTensor[Float] val s3Dir = s3aPath + s"/${ com.google.common.io.Files.createTempDir().getPath() }" - TensorflowSaver.saveGraph[Float](funcModel, Seq(("input", Seq(4, 28, 28, 1))), s3Dir) + TensorflowSaver.saveGraph[Float](funcModel, Seq(("input", Seq(4, 28, 28, 1))), + s3Dir + "/test.tfmodel") - val loadedModel = TensorflowLoader.load[Float](s3Dir, + val loadedModel = TensorflowLoader.load[Float](s3Dir + "/test.tfmodel", Seq("input"), Seq("output"), ByteOrder.LITTLE_ENDIAN) val loadedOutput = loadedModel.forward(inputData).toTensor[Float] loadedOutput.almostEqual(outputData, 1e-7) - System.setProperty("bigdl.enableNHWC", "false") } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala index 99f5973386b..3c973d216cf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.utils.tf import java.nio.ByteOrder import java.util.UUID -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor @@ -60,11 +60,11 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { T(1.0f, 2.0f, 5.0f), T(-3.0f, -4.0f, -7.0f) )) - test(layer, input, false, "/biasAdd") should be(true) + test(layer, input, "/biasAdd") should be(true) } - "AvgPooling" should "be correctly saved" in { - val layer = SpatialAveragePooling(2, 2) + "AvgPooling NHWC" should "be correctly saved" in { + val layer = SpatialAveragePooling(2, 2, format = DataFormat.NHWC) val input = Tensor[Float](T(T( T( T(1.0f, 2.0f, 5.0f), @@ -77,11 +77,11 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { T(4.0f, 2.0f, 1.0f) ) ))) - test(layer, input, true) should be(true) + test(layer, input) should be(true) } - "MaxPooling" should "be correctly saved" in { - val layer = SpatialMaxPooling(2, 2) + "MaxPooling NHWC" should "be correctly saved" in { + val layer = SpatialMaxPooling(2, 2, format = DataFormat.NHWC) val input = Tensor[Float](T(T( T( T(1.0f, 2.0f, 5.0f), @@ -94,7 +94,7 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { T(4.0f, 2.0f, 1.0f) ) ))) - test(layer, input, true) should be(true) + test(layer, input) should be(true) } "Tanh" should "be correctly saved" in { @@ -107,7 +107,7 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { System.setProperty("bigdl.enableNHWC", "false") val layer = Squeeze(3) val input = Tensor[Float](4, 2, 1, 2).rand() - test(layer, input, false) should be(true) + test(layer, input) should be(true) } "CAddTableToTF" should "be correct" in { @@ -134,46 +134,46 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { "LogSoftMax" should "be correctly saved" in { val layer = LogSoftMax() val input = Tensor[Float](4, 5).rand() - test(layer, input, false) should be(true) + test(layer, input) should be(true) } "SoftMax" should "be correctly saved" in { val layer = SoftMax() val input = Tensor[Float](4, 5).rand() - test(layer, input, false) should be(true) + test(layer, input) should be(true) } "Sigmoid" should "be correctly saved" in { val layer = Sigmoid() val input = Tensor[Float](4, 5).rand() - test(layer, input, false) should be(true) + test(layer, input) should be(true) } - "SpatialConvolution" should "be correctly saved" in { - val layer = SpatialConvolution(3, 5, 2, 2) - val input = Tensor[Float](4, 3, 5, 5).rand() - test(layer, input, true, "/biasAdd") should be(true) + "SpatialConvolution NHWC" should "be correctly saved" in { + val layer = SpatialConvolution(3, 5, 2, 2, format = DataFormat.NHWC) + val input = Tensor[Float](4, 5, 5, 3).rand() + test(layer, input, "/biasAdd") should be(true) } "TemporalConvolution" should "be correctly saved" in { val layer = TemporalConvolution(3, 5, 2, 2) val input = Tensor[Float](4, 16, 3).rand() - test(layer, input, false, "/biasAdd") should be(true) + test(layer, input, "/biasAdd") should be(true) } "Mean" should "be correctly saved" in { val layer = Mean(1, -1, true) val input = Tensor[Float](4, 5).rand() - test(layer, input, false, "/output") should be(true) + test(layer, input, "/output") should be(true) } "Padding" should "be correctly saved" in { val layer = Padding(1, 2, 2) val input = Tensor[Float](4, 5).rand() - test(layer, input, false, "/output") should be(true) + test(layer, input, "/output") should be(true) } - "Batch Norm2D" should "be correctly saved" in { + "Batch Norm2D NCHW" should "be correctly saved" in { val layer = SpatialBatchNormalization(2) layer.evaluate() layer.weight.rand(10.0, 20.0) @@ -181,50 +181,52 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { layer.runningVar.rand(0.9, 1.1) layer.runningMean.rand() val input = Tensor[Float](3, 2, 4, 5).rand() - test(layer, input, true, "/output") should be(true) + test(layer, input, "/output") should be(true) } "Dropout" should "be correctly saved" in { val layer = Dropout() layer.evaluate() val input = Tensor[Float](3, 2).rand() - test(layer, input, false) should be(true) + test(layer, input) should be(true) } "View" should "be correctly saved" in { val layer = View(2, 4) val input = Tensor[Float](2, 2, 2).rand() - test(layer, input, false) should be(true) + test(layer, input) should be(true) } "Reshape" should "be correctly saved" in { val layer = Reshape(Array(2, 4)) val input = Tensor[Float](2, 2, 2).rand() - test(layer, input, false) should be(true) + test(layer, input) should be(true) } - "lenet" should "be correctly saved" in { + "lenet NHWC" should "be correctly saved" in { tfCheck() - val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1").inputs() + val conv1 = SpatialConvolution(1, 6, 5, 5, format = DataFormat.NHWC) + .setName("conv1").inputs() val tanh1 = Tanh().setName("tanh1").inputs(conv1) - val pool1 = SpatialMaxPooling(2, 2, 2, 2).setName("pool1").inputs(tanh1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2, format = DataFormat.NHWC) + .setName("pool1").inputs(tanh1) val tanh2 = Tanh().setName("tanh2").inputs(pool1) - val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2").inputs(tanh2) - val pool2 = SpatialMaxPooling(2, 2, 2, 2).setName("output").inputs(conv2) + val conv2 = SpatialConvolution(6, 12, 5, 5, format = DataFormat.NHWC) + .setName("conv2").inputs(tanh2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2, format = DataFormat.NHWC) + .setName("output").inputs(conv2) val funcModel = Graph(conv1, pool2) - val inputData = Tensor(4, 1, 28, 28).rand() - val transInput = inputData.transpose(2, 3).transpose(3, 4).contiguous() + val inputData = Tensor(4, 28, 28, 1).rand() val outputData = funcModel.forward(inputData).toTensor val tmpFile = java.io.File.createTempFile("tensorflowSaverTest" + UUID.randomUUID(), "lenet") TensorflowSaver.saveGraphWithNodeDef( funcModel, - Seq(Tensorflow.const(transInput, "input", ByteOrder.LITTLE_ENDIAN)), + Seq(Tensorflow.const(inputData, "input", ByteOrder.LITTLE_ENDIAN)), tmpFile.getPath, ByteOrder.LITTLE_ENDIAN, - Set(Tensorflow.const(outputData.transpose(2, 3).transpose(3, 4).contiguous(), - "target", ByteOrder.LITTLE_ENDIAN)) + Set(Tensorflow.const(outputData, "target", ByteOrder.LITTLE_ENDIAN)) ) runPythonSaveTest(tmpFile.getPath, "") should be(true) @@ -232,7 +234,6 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { private def test(layer: AbstractModule[Tensor[Float], Tensor[Float], Float], inputTensor: Tensor[Float], - convertNHWC: Boolean = false, outputSuffix: String = "") : Boolean = { tfCheck() val layerNode = layer.setName("output").inputs() @@ -241,22 +242,13 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { val tmpFile = java.io.File.createTempFile("tensorflowSaverTest" + UUID.randomUUID(), "Layer") logger.info(s"Save model to ${tmpFile}") - val tfTensor = if (convertNHWC) { - inputTensor.transpose(2, 3).transpose(3, 4).contiguous() - } else { - inputTensor - } - val outputSave = if (convertNHWC) { - outputTensor.transpose(2, 3).transpose(3, 4).contiguous() - } else { - outputTensor - } + TensorflowSaver.saveGraphWithNodeDef( graph, - Seq(Tensorflow.const(tfTensor, "input", ByteOrder.LITTLE_ENDIAN)), + Seq(Tensorflow.const(inputTensor, "input", ByteOrder.LITTLE_ENDIAN)), tmpFile.getPath, ByteOrder.LITTLE_ENDIAN, - Set(Tensorflow.const(outputSave, "target", ByteOrder.LITTLE_ENDIAN)) + Set(Tensorflow.const(outputTensor, "target", ByteOrder.LITTLE_ENDIAN)) ) runPythonSaveTest(tmpFile.getPath, outputSuffix) } From 0c32e03dca83dd1fd49e699779d75400c8e06f8f Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 14 Sep 2017 09:31:14 +0800 Subject: [PATCH 0391/1065] Add utility function to convert tensorflow tensor proto to bigdl tensot (#1554) * parse tensor * add tests * fix test * fix tests * fix tests --- .../bigdl/dllib/tensor/DenseTensor.scala | 18 ++++ .../bigdl/dllib/tensor/DenseTensorApply.scala | 16 ++- .../bigdl/dllib/utils/tf/TFUtils.scala | 100 ++++++++++++++++++ .../bigdl/dllib/tensor/TensorSpec.scala | 29 +++++ 4 files changed, 161 insertions(+), 2 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 8a1c6e2856c..119981c1999 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -1982,6 +1982,20 @@ object DenseTensor { self: DenseTensor[T], nDim: Int, _size: Array[Int], _stride: Array[Int]) : DenseTensor[T] = { + // resize as a scalar + if (nDim == 0 && _size.isEmpty) { + self._size = Array[Int]() + self._stride = Array[Int]() + self.nDimension = nDim + val totalSize = 1 + if (self._storage == null ) { + self._storage = new ArrayStorage(new Array[T](totalSize + self._storageOffset)) + } else if (totalSize + self._storageOffset > self._storage.length) { + self._storage.resize(totalSize + self._storageOffset) + } + return self + } + var hasCorrectSize = true var nDim_ = 0 var d = 0 @@ -2106,6 +2120,10 @@ object DenseTensor { return false } + if (self.isEmpty != src.isEmpty) { + return false + } + var d = 0 while (d < self.nDimension) { if (self.size(d + 1) != src.size(d + 1)) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala index 67f9488113e..f7a1c621fd3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala @@ -165,7 +165,7 @@ object DenseTensorApply { require(tensor1.nElement() == tensor2.nElement() && tensor2.nElement() == tensor3.nElement(), "inconsistent tensor size") - if (tensor1.nDimension == 0) { + if (tensor1.isEmpty) { return } @@ -323,7 +323,19 @@ object DenseTensorApply { require(tensor1.nElement() == tensor2.nElement(), s"inconsistent tensor size: ${tensor1.nElement()} == ${tensor2.nElement()}") - if (tensor1.nDimension == 0) { + if (tensor1.isEmpty) { + return + } + + // shortcut for scalar + if (tensor1.isScalar && tensor2.isScalar) { + val tensor1Data = tensor1.storage().array() + val tensor2Data = tensor2.storage().array() + val tensor3Data = tensor3.storage().array() + val tensor1Index = tensor1.storageOffset() - 1 + val tensor2Index = tensor2.storageOffset() - 1 + val tensor3Index = tensor3.storageOffset() - 1 + func(tensor1Data, tensor1Index, tensor2Data, tensor2Index, tensor3Data, tensor3Index) return } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala new file mode 100644 index 00000000000..2a4ab5861e1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala @@ -0,0 +1,100 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf + +import java.nio.{ByteBuffer, ByteOrder} + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import org.tensorflow.framework.{DataType, TensorProto} +import scala.collection.JavaConverters._ + + +object TFUtils { + import TFTensorNumeric.NumericByteString + + /** + * convert tensorflow tensorProto to BigDL Tensor + */ + def parseTensor(tfTensor: TensorProto, endian: ByteOrder): Tensor[_] = { + val shape = tfTensor.getTensorShape.getDimList.asScala.map(_.getSize.toInt).toArray + tfTensor.getDtype match { + case DataType.DT_FLOAT => + val tmp = tfTensor.getFloatValList.asScala.map(_.toFloat).toArray + Tensor[Float](tmp, shape) + case DataType.DT_DOUBLE => + val tmp = tfTensor.getDoubleValList.asScala.map(_.toDouble).toArray + Tensor[Double](tmp, shape) + case DataType.DT_INT32 => + val tmp = tfTensor.getIntValList.asScala.map(_.toInt).toArray + Tensor[Int](tmp, shape) + case DataType.DT_INT64 => + val tmp = tfTensor.getInt64ValList.asScala.map(_.toLong).toArray + Tensor[Long](tmp, shape) + case DataType.DT_BOOL => + val tmp = tfTensor.getBoolValList.asScala.map(_.booleanValue()).toArray + Tensor[Boolean](tmp, shape) + case DataType.DT_STRING => + Tensor[ByteString](Array(tfTensor.getStringVal(0)), shape) + case DataType.DT_INT8 => + val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray) + buffer.order(endian) + val params = buffer + val tmp = new Array[Int](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = params.get(j) + j += 1 + } + Tensor(tmp, shape) + case DataType.DT_UINT8 => + val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray) + buffer.order(endian) + val params = buffer + val tmp = new Array[Int](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = params.get(j) & 0xff + j += 1 + } + Tensor(tmp, shape) + case DataType.DT_INT16 => + val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray) + buffer.order(endian) + val params = buffer + val tmp = new Array[Int](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = params.get(j) + j += 1 + } + Tensor(tmp, shape) + case DataType.DT_UINT16 => + val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray) + buffer.order(endian) + val params = buffer + val tmp = new Array[Int](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = params.get(j) & 0xffff + j += 1 + } + Tensor(tmp, shape) + case t => throw new IllegalArgumentException(s"DataType: $t not supported yet") + } + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala index 3a8e8dae384..caf679e0be6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala @@ -22,6 +22,35 @@ class TensorSpec extends FlatSpec with Matchers { "Tensor factory method" should "be able to construct scalar" in { val tensor = Tensor[Int](Array(4), Array[Int]()) tensor.value() should be (4) + tensor.size() should be (Array[Int]()) + tensor.nDimension() should be (0) + tensor.isScalar should be (true) + } + + "Tensor resize " should "work for scalar" in { + val tensor = Tensor[Int]() + tensor.resize(Array[Int]()) + tensor.value() should be (0) + tensor.size() should be (Array[Int]()) + tensor.nDimension() should be (0) + tensor.isScalar should be (true) + } + + "Tensor resizeAs " should "work for scalar" in { + val tensor = Tensor[Int]() + val tensorScalar = Tensor[Int](Array(1), Array[Int]()) + tensor.resizeAs(tensorScalar) + tensor.value() should be (0) + tensor.size() should be (Array[Int]()) + tensor.nDimension() should be (0) + tensor.isScalar should be (true) + } + + "Tensor set " should "work for scalar" in { + val tensor = Tensor[Int]() + tensor.resize(Array[Int](1, 2)) + tensor.set() + tensor.isEmpty should be (true) } } From d18c6505505f7616383e013a18548e9685892d4b Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 14 Sep 2017 13:21:05 +0800 Subject: [PATCH 0392/1065] Support switch and merge operation (#1555) * Support control flow * remove loop * fix unit test * fix unit test --- .../analytics/bigdl/dllib/nn/Graph.scala | 195 ++++++++------ .../analytics/bigdl/dllib/nn/Scheduler.scala | 225 ++++++++++++++++ .../bigdl/dllib/nn/ops/ControlOps.scala | 252 ++++++++++++++++++ .../bigdl/dllib/utils/DirectedGraph.scala | 54 +++- .../dllib/utils/caffe/CaffePersister.scala | 2 +- .../analytics/bigdl/dllib/nn/GraphSpec.scala | 37 ++- .../bigdl/dllib/utils/DirectedGraphSpec.scala | 19 ++ .../serializer/ModuleSerializerSpec.scala | 4 +- 8 files changed, 696 insertions(+), 92 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index e56c71c3b08..efeae4db64c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -17,9 +17,12 @@ package com.intel.analytics.bigdl.nn import java.util +import com.intel.analytics.bigdl.Module + import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.ops.ControlOps import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -66,17 +69,18 @@ import org.tensorflow.framework.GraphDef */ @SerialVersionUID(- 2896121321564992779L) class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], - private val outputs : Seq[ModuleNode[T]], - private val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None) - (implicit ev: TensorNumeric[T]) + private val outputs : Seq[ModuleNode[T]], + private val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, + generateBackward: Boolean = true +)(implicit ev: TensorNumeric[T]) extends Container[Activity, Activity, T]{ type absModule = AbstractModule[_ <: Activity, _ <: Activity, T] override def updateOutput(input: Activity): Activity = { - var i = 0 - while(i < forwardExecutions.length) { - val node = forwardExecutions(i) + forwardScheduler.reset() + while (!forwardScheduler.isFinished()) { + val node = forwardScheduler.fetch() val nodeInput = if (node.prevNodes.isEmpty && !node.element.isInstanceOf[WithoutInput]) { inputData(node, input) } else { @@ -95,8 +99,8 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } } node.element.forward(nodeInput) - inputsBP.put(node.element.getName(), nodeInput) - i += 1 + inputCache(node.element.getName()) = nodeInput + forwardScheduler.schedule(node) } output = dummyOutput.element.output @@ -104,20 +108,20 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } override def backward(input: Activity, gradOutput: Activity): Activity = { - val before = System.nanoTime() - dummyOutputGrad.element.gradInput = gradOutput + if (!generateBackward) return null - var i = 0 - while (i < backwardExecutions.length) { - val curNode = backwardExecutions(i) - var curGradOutput : Activity = null + val before = System.nanoTime() + backwardScheduler.reset() + while (!backwardScheduler.isFinished()) { + val curNode = backwardScheduler.fetch() + var curGradOutput : Activity = if (curNode.eq(dummyOutputGrad)) gradOutput else null - curNode.nextNodesAndEdges.filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) + curNode.prevNodesAndEdges.filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) .foreach(n => { - val otherActivity = if (n._1.element.gradInput.isTensor || n._1.prevEdges.length == 1) { + val otherActivity = if (n._1.element.gradInput.isTensor || n._1.nextEdges.length == 1) { n._1.element.gradInput } else { - val index = n._1.prevEdges.indexOf(n._2) + 1 + val index = n._1.nextEdges.indexOf(n._2) + 1 n._1.element.gradInput.toTable.apply[Activity](index) } @@ -133,13 +137,13 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } }) - gradOutputBP(i) = curGradOutput + gradOutputCache(curNode.element.getName()) = curGradOutput if (!isStopGradient(curNode.element)) { - curNode.element.backward(inputsBP.get(curNode.element.getName()), curGradOutput) + curNode.element.backward(inputCache(curNode.element.getName()), curGradOutput) } else { - curNode.element.accGradParameters(inputsBP.get(curNode.element.getName()), curGradOutput) + curNode.element.accGradParameters(inputCache(curNode.element.getName()), curGradOutput) } - i += 1 + backwardScheduler.schedule(curNode) } gradInput = if (inputs.length == 1) { @@ -176,41 +180,39 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { - dummyOutputGrad.element.gradInput = gradOutput + if (!generateBackward) return null - var i = 0 - while (i < backwardExecutions.length) { - val curNode = backwardExecutions(i) - var curGradOutput : Activity = null - if (curNode.element.output.isTable) { - curGradOutput = T() - } + backwardScheduler.reset() + while (!backwardScheduler.isFinished()) { + val curNode = backwardScheduler.fetch() + var curGradOutput : Activity = if (curNode.eq(dummyOutputGrad)) gradOutput else null - curNode.nextNodesAndEdges - .filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) + curNode.prevNodesAndEdges.filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) .foreach(n => { - val otherActivity = if (n._1.element.gradInput.isTensor || n._1.prevEdges.length == 1) { - n._1.element.gradInput - } else { - val index = n._1.prevEdges.indexOf(n._2) + 1 - n._1.element.gradInput.toTable.apply[Activity](index) - } - - n._2.fromIndex match { - case Some(i) => - val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) - curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) - case None => - curGradOutput = accActivity(curGradOutput, otherActivity) - } - }) + val otherActivity = if (n._1.element.gradInput.isTensor || n._1.nextEdges.length == 1) { + n._1.element.gradInput + } else { + val index = n._1.nextEdges.indexOf(n._2) + 1 + n._1.element.gradInput.toTable.apply[Activity](index) + } - gradOutputBP(i) = curGradOutput + n._2.fromIndex match { + case Some(i) => + if (curNode.element.output.isTable && curGradOutput == null) { + curGradOutput = T() + } + val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) + curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) + case None => + curGradOutput = accActivity(curGradOutput, otherActivity) + } + }) + gradOutputCache(curNode.element.getName()) = curGradOutput if (!isStopGradient(curNode.element)) { - curNode.element.updateGradInput(inputsBP.get(curNode.element.getName()), curGradOutput) + curNode.element.updateGradInput(inputCache(curNode.element.getName()), curGradOutput) } - i += 1 + backwardScheduler.schedule(curNode) } gradInput = if (inputs.length == 1) { @@ -223,9 +225,10 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { var i = 0 - while (i < backwardExecutions.length) { - val curNode = backwardExecutions(i) - curNode.element.accGradParameters(inputsBP.get(curNode.element.getName()), gradOutputBP(i)) + while (i < backwardNodes.length) { + val curNode = backwardNodes(i) + curNode.element.accGradParameters(inputCache(curNode.element.getName()), + gradOutputCache(curNode.element.getName())) i += 1 } } @@ -255,42 +258,61 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], * Computing backgraph */ private val backGraph = dummyOutput.graph(reverse = true) - private var gradGraph: DirectedGraph[AbstractModule[Activity, Activity, T]] = null + private var gradGraph: DirectedGraph[AbstractModule[Activity, Activity, T]] = _ /** * Execution plan */ - private val forwardExecutions = backGraph.topologySort - .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse - private var backwardExecutions: Array[Node[AbstractModule[Activity, Activity, T]]] = null + private val forwardNodes = backGraph.DFS + .filterNot(_.element.isInstanceOf[ControlDependency[T]]).toArray + private val forwardScheduler = new Scheduler( + forwardNodes.filter(_.prevNodes.length == 0), + Seq(dummyOutput) + ) + + private var backwardScheduler : Scheduler[T] = _ + private var backwardNodes: Array[Node[AbstractModule[Activity, Activity, T]]] = _ - modules.appendAll(forwardExecutions.filter(n => !n.eq(dummyOutput)).map(_.element)) + + modules.appendAll(backGraph.topologySort + .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse + .filter(n => !n.eq(dummyOutput)).map(_.element)) /** - * build is needed when the stopGrad is changed + * Generate backward graph and apply the stopGrad */ private[bigdl] def build(): this.type = { - val gradGraph = backGraph.cloneGraph() + val gradGraph = backGraph.cloneGraph(true) dummyOutputGrad = gradGraph.source - val nodes = gradGraph.DFS - nodes.filter(x => isStopGradient(x.element)).foreach(_.removePrevEdges()) - backwardExecutions = gradGraph.topologySort.filter(n => !n.eq(dummyOutputGrad)) - .filterNot(_.element.isInstanceOf[ControlDependency[T]]) + val originalNodes = gradGraph.DFS + originalNodes.filter(x => isStopGradient(x.element)).foreach(_.removeNextEdges()) + backwardNodes = gradGraph.DFS.filter(n => !n.eq(dummyOutputGrad)) + .filterNot(_.element.isInstanceOf[ControlDependency[_]]).toArray + backwardScheduler = new Scheduler[T]( + Seq(dummyOutputGrad), + backwardNodes.filter(_.nextNodes.length == 0) + ) clearState() this } - private val inputsBP = new util.HashMap[String, Activity]() + private val inputCache = new mutable.HashMap[String, Activity]() // Check all inputs of the graph should be passed in checkRoots - build + if (generateBackward) { + forwardNodes.foreach(n => require(!n.element.isInstanceOf[ControlOps[_]], + "Not suppot generate back graph with control ops node")) + build() + } - private val gradOutputBP = new Array[Activity](forwardExecutions.length - 1) + private val gradOutputCache = new mutable.HashMap[String, Activity]() private def checkRoots: Unit = { - val roots = forwardExecutions.filter(_.prevNodes.size == 0) + require(forwardNodes.map(_.element.getName()).distinct.length == forwardNodes.length, + "the name of node in the graph should be unique") + val roots = forwardNodes.filter(_.prevNodes.size == 0) .filter(node => !node.element.isInstanceOf[WithoutInput]) require(roots.size == inputs.length, s"There're ${inputs.length} inputs, but graph has ${roots.size} roots") @@ -407,7 +429,20 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], * @return */ def getForwardExecutions: Array[Node[AbstractModule[Activity, Activity, T]]] = { - forwardExecutions.filter(n => !n.eq(dummyOutput)) + forwardNodes.filter(n => !n.eq(dummyOutput)) + } + + /** + * Get forward executions, the dummy nodes and control dependency nodes will be filtered. + * + * This method will output a sorted executions. If the graph contains loop, it will throw an + * exception + * @return + */ + def getSortedForwardExecutions: Array[Node[AbstractModule[Activity, Activity, T]]] = { + backGraph.topologySort + .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse + .filter(n => !n.eq(dummyOutput)) } @inline @@ -444,7 +479,7 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], def saveGraphTopology(logPath: String): this.type = { val writer = new TFFileWriter(logPath) val graphBuilder = GraphDef.newBuilder() - forwardExecutions.map(m => { + forwardNodes.map(m => { val nodeDef = Tensorflow.bigdlModule(m.element, m.nextNodes.map(_.element.getName()).asJava) graphBuilder.addNode(nodeDef) }) @@ -468,9 +503,9 @@ object Graph extends ContainerSerializable { * @return a graph container */ def apply[T: ClassTag](input : Array[ModuleNode[T]], output : Array[ModuleNode[T]], - variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None) - (implicit ev: TensorNumeric[T]) : Graph[T] = { - new Graph[T](input, output, variables) + variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, + generateBackward: Boolean = true)(implicit ev: TensorNumeric[T]) : Graph[T] = { + new Graph[T](input, output, variables, generateBackward) } /** @@ -523,22 +558,24 @@ object Graph extends ContainerSerializable { val outputs = new ArrayBuffer[ModuleNode[T]] // layer name to layer node mapping - val layerMap = new mutable.HashMap[String, ModuleNode[T]]() + val layerMap = new mutable.HashMap[String, (ModuleNode[T], Seq[String])]() subModules.foreach(subModule => { val bigDLModule = ModuleSerializer.load(subModule) val moduleNode = bigDLModule.module.inputs() val preNodes = bigDLModule.pre - preNodes.foreach(pre => { + layerMap(bigDLModule.module.getName) = (moduleNode, preNodes) + }) + + layerMap.values.foreach(moduleNode => { + moduleNode._2.foreach(pre => { if (layerMap.contains(pre)) { - layerMap(pre) -> moduleNode + layerMap(pre)._1 -> moduleNode._1 } }) - val nextNodes = bigDLModule.next - layerMap(bigDLModule.module.getName) = moduleNode }) - inputNames.foreach(inputName => inputs.append(layerMap(inputName))) - outputNames.foreach(outputName => outputs.append(layerMap(outputName))) + inputNames.foreach(inputName => inputs.append(layerMap(inputName)._1)) + outputNames.foreach(outputName => outputs.append(layerMap(outputName)._1)) var sharedVariables : Option[(Array[Tensor[T]], Array[Tensor[T]])] = None if (attributes.containsKey("sharedWeight") && attributes.containsKey("sharedBias")) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala new file mode 100644 index 00000000000..a3bbf13ccfc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala @@ -0,0 +1,225 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.ops._ +import com.intel.analytics.bigdl.nn.tf.WithoutInput +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Edge, Node, T} + +import scala.collection.mutable +import scala.reflect.ClassTag + +/** + * Scheduler of a graph execution. It supports a graph with cycle. Please note that the cycle must + * be created from ControlNodes.while. + * + * Scheduler also records execution status. So some const graph won't be executed multiple times. + * + * @param inputNodes start nodes + * @param outputNodes target nodes + * @tparam T + */ +private[bigdl] class Scheduler[T] ( + inputNodes: Seq[ModuleNode[T]], outputNodes: Seq[ModuleNode[T]] + ) extends Serializable { + + import Scheduler._ + + private val readyQueue = new mutable.Queue[ModuleNode[T]]() + private val nodeStatus = new NodeStatusManager[T]() + + /** + * User must reset the scheduler after first use it or finish a graph execution + */ + def reset(): Unit = { + readyQueue.clear() + inputNodes.foreach(n => { + readyQueue.enqueue(n) + }) + nodeStatus.removeUnConstStatus() + } + + + /** + * If every output nodes is executed. Please note if some of the output nodes has not been + * executed but the execution can't move forward, an exception will be thrown + * @return + */ + def isFinished(): Boolean = { + val isEmpty = readyQueue.isEmpty + if (isEmpty) { + outputNodes.foreach(n => { + require(!nodeStatus.notExecuted(n), "Some output nodes have not been executed") + }) + } + isEmpty + } + + + /** + * Fetch a node to execute. Don't call it when isFinished is true, or it will throw an exception + * @return + */ + def fetch(): ModuleNode[T] = { + var node = readyQueue.dequeue() + while (nodeStatus.isConst(node)) { + schedule(node) + node = readyQueue.dequeue() + } + node + } + + /** + * Schedule nodes depend on the given node + * @param node + */ + def schedule(node: ModuleNode[T]): Unit = { + // Update status of current node + nodeStatus(node) = if (node.prevNodes.length == 0) { + if (node.element.isInstanceOf[com.intel.analytics.bigdl.nn.tf.Const[_]]) { + Const() + } else { + Ready() + } + } else { + val constNodes = node.prevNodes.filter(nodeStatus.isConst(_)) + if (constNodes.length == node.prevNodes.length) { + Const() + } else { + Ready() + } + } + + // Schedule next nodes + node.element match { + case s: SwitchOps[_] => + val switchNode = node.asInstanceOf[SwitchControlNode[Module[T]]] + selectNexts(switchNode.availableNodes(), node) + case _ => + selectNexts(node.nextNodes, node) + } + } + + private def selectNexts(candidateNodes: Seq[ModuleNode[T]], curNode: ModuleNode[T]): Unit = { + candidateNodes.foreach(nextNode => { + if (nextNode.element.isInstanceOf[MergeOps[_]]) { + val merge = nextNode.element.asInstanceOf[MergeOps[_]] + require(nodeStatus.notExecuted(nextNode), s"Merge node(${nextNode.element.getName()}) " + + s"should not be executed twice out of loop or in a same iteration of a loop") + merge.setSwitch(nextNode.prevNodes.indexOf(curNode) + 1) + readyQueue.enqueue(nextNode) + } else { + if (isNodeReady(nextNode)) { + readyQueue.enqueue(nextNode) + } + } + }) + } + + private def isNodeReady(node: ModuleNode[T]): Boolean = { + if (node.prevNodes.filter(nodeStatus.notExecuted(_)).length != 0) { + return false + } + node.prevNodes.filter(_.isInstanceOf[SwitchControlNode[_]]).foreach(n => { + if (!n.asInstanceOf[SwitchControlNode[T]].availableNodes().contains(node)) { + return false + } + }) + + return true + } +} + +object Scheduler { + class NodeStatusManager[T] extends Serializable { + private val nodeStatus = new mutable.HashMap[String, NodeStatus]() + + /** + * Update node status + * @param node + * @param status + */ + def update(node: ModuleNode[T], status: NodeStatus): Unit = { + require(node != null && status != null, "Not accept null") + nodeStatus(node.element.getName()) = status + } + + /** + * Get status of node. Throw exception if it doesn't exist. + * @param node + * @return + */ + def apply(node: ModuleNode[T]): NodeStatus = { + nodeStatus(node.element.getName()) + } + + /** + * Check if a given node status is const + * @param node + * @return + */ + def isConst(node: ModuleNode[T]): Boolean = { + nodeStatus.contains(node.element.getName()) && + nodeStatus(node.element.getName()).isInstanceOf[Const] + } + + /** + * If the given node has been executed or out of date + * @param node + * @return + */ + def notExecuted(node: ModuleNode[T]): Boolean = { + if (!nodeStatus.contains(node.element.getName())) return true + return false + } + + /** + * Remove unconst node status + * @return + */ + def removeUnConstStatus(): this.type = { + val iter = nodeStatus.iterator + while (iter.hasNext) { + val entry = iter.next() + if (!entry._2.isInstanceOf[Const]) { + nodeStatus.remove(entry._1) + } + } + this + } + } + + + /** + * Node status + */ + private[nn] sealed trait NodeStatus + + /** + * Current node is const or all of its dependencies are from const node + */ + private[nn] case class Const() extends NodeStatus + + /** + * Current nodes has been executed, while it's not const + */ + private[nn] case class Ready() extends NodeStatus +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala new file mode 100644 index 00000000000..93d27fc823b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala @@ -0,0 +1,252 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.Graph._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Edge, Node, T} + +import scala.reflect.ClassTag + +/** + * Control flow related operations + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +sealed abstract class ControlOps[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends Operation[Activity, Activity, T] { + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + throw new UnsupportedOperationException("Operation does not support updateGradInput() method") + } + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + throw new UnsupportedOperationException("Operation does not support updateGradInput() method") + } + override def backward(input: Activity, gradOutput: Activity): Activity = { + throw new UnsupportedOperationException("Operation does not support backward() method") + } +} + +/** + * Control flow related operations, and they just pass the input without modifying them + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +abstract class IdentityControl[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends ControlOps[T] { + override def updateOutput(input: Activity): Activity = { + output = input + output + } +} + + +/** + * Switch the control flow. It will construct a table. It accepts a table input containing two + * elements. The first element is a boolean scalar. The second element is the data. + * It produces a table output containing two elements. If the boolean scalar is true, the first + * element of the output is data and the second one is null; if the boolean scala is false, the + * position is exchanged. + * + * When connect to some other node. You should never connect the whole output to other node. You + * should always use SwitchNodeOutput(1) and SwitchNodeOutput(2). Or there will be run time failure. + * + * User should use ControlNodes.whileLoop or ControlNodes.switch to use this operation + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +private[nn] class SwitchOps[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends ControlOps[T] { + override def updateOutput(input: Activity): Activity = { + val condition = input.toTable[Tensor[Boolean]](1) + val data = input.toTable[Activity](2) + if (condition.valueAt(1)) { + this.output = T(data, null) + } else { + this.output = T(null, data) + } + this.output + } +} + +/** + * MergeOps will run as soon as one of the node dependency is ready. and pass the data from that + * node. If the MergeOps is not in a loop, it should only be executed once. If it's in a loop, it + * should be still executed once in a iteration. + * + * User should use ControlNodes.whileLoop or ControlNodes.merge to use this operation + * @param switch which dependency node is avaliable + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +private[nn] class MergeOps[T: ClassTag](private var switch : Int = 1)( + implicit ev: TensorNumeric[T]) extends ControlOps[T] { + + def setSwitch(s: Int) : this.type = { + this.switch = s + this + } + + override def updateOutput(input: Activity): Activity = { + this.output = input.toTable[Activity](switch) + this.output + } + + override def toString(): String = getPrintName() + s"($switch)" +} + +/** + * A wrapper of node for switch operation. Make code easy to read. + * + * @param element element + * @tparam T element type + */ +sealed class SwitchControlNode[T] private[ops] (element: T) extends Node[T](element) { + + /** + * The output edge which will be run when condition scalar is true. You should not connect one + * node with both type edges. + * @return + */ + def trueEdge() : ((Node[T], Int)) = (this, 1) + + /** + * The output edge which will be run when condition scalar is false. You should not connect one + * node with both type edges. + * @return + */ + def falseEdge() : ((Node[T], Int)) = (this, 2) + + /** + * Return nodes triggered by current node + * @return + */ + def availableNodes() : Seq[Node[T]] = { + val bothNodes = this.nextNodesAndEdges.filter(_._2.fromIndex.isEmpty).map(_._1).distinct + require(bothNodes.length == 0, "You should not connect to one node with both type of edges") + + val trueNodes = this.nextNodesAndEdges.filter(_._2.fromIndex.get == 1).map(_._1).distinct + val falseNodes = this.nextNodesAndEdges.filter(_._2.fromIndex.get == 2).map(_._1).distinct + trueNodes.foreach( n => + require(!falseNodes.contains(n), + "You should not connect to one node with both type of edges") + ) + + val swtich = element.asInstanceOf[SwitchOps[T]] + if (swtich.output.toTable(1) == null) { + falseNodes + } else { + trueNodes + } + } +} + +/** + * A wrapper of node for merge operation. + * + * @param element element + * @tparam T element type + */ +sealed class MergeControlNode[T] private[ops] (element: T) extends Node[T](element) { + + /** + * Add another dependency node + * @param dependency + * @return + */ + def append(dependency: Node[T]): this.type = { + dependency -> this + this + } + + /** + * Add another dependency node with edge + * @param dependencyIndex + * @return + */ + def append(dependencyIndex: (Node[T], Int)): this.type = { + dependencyIndex._1.add(this, Edge(dependencyIndex._2)) + this + } +} + +/** + * Factory method of control flow related nodes + */ +object ControlNodes { + + /** + * Create a switch node + * @param data data to pass down + * @param condition condition node, should pass in a boolean scalar + * @param ev + * @tparam T + * @return + */ + def switch[T: ClassTag](data: ModuleNode[T], condition: ModuleNode[T] + )(implicit ev: TensorNumeric[T]): SwitchControlNode[Module[T]] = { + val curNode = new SwitchControlNode[Module[T]](new SwitchOps()) + condition -> curNode + data -> curNode + curNode + } + + /** + * Create a switch node + * @param data data to pass down, from an edge + * @param condition data to pass down, from an edge + * @param ev + * @tparam T + * @return + */ + def switch[T: ClassTag](data: (ModuleNode[T], Int), condition: (ModuleNode[T], Int) + )(implicit ev: TensorNumeric[T]): SwitchControlNode[Module[T]] = { + val curNode = new SwitchControlNode[Module[T]](new SwitchOps()) + data._1.add(curNode, Edge(data._2)) + condition._1.add(curNode, Edge(data._2)) + curNode + } + + /** + * Create a merge node + * @param first dependency node, for method overload + * @param nodesWithIndex dependency nodes + * @param ev + * @tparam T + * @return + */ + def merge[T: ClassTag](first: (ModuleNode[T], Int), nodesWithIndex : (ModuleNode[T], Int)*)( + implicit ev: TensorNumeric[T]): MergeControlNode[Module[T]] = { + val curNode = new MergeControlNode[Module[T]](new MergeOps()) + first._1.add(curNode, Edge(first._2)) + nodesWithIndex.foreach(nodeWithIndex => { + nodeWithIndex._1.add(curNode, Edge(nodeWithIndex._2)) + }) + curNode + } + + /** + * Create a merge node + * @param nodes dependency nodes + * @param ev + * @tparam T + * @return + */ + def merge[T: ClassTag](nodes : ModuleNode[T]*)( + implicit ev: TensorNumeric[T]): MergeControlNode[Module[T]] = { + val curNode = new MergeControlNode[Module[T]](new MergeOps()) + nodes.foreach(node => { + node.add(curNode, Edge()) + }) + curNode + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala index ed6e111ac89..e6323297d03 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala @@ -133,26 +133,37 @@ class DirectedGraph[T](val source : Node[T], val reverse : Boolean = false) exte } } } + // scalastyle:on methodName /** * Clone the graph structure, will not clone the node element - * @return new graph + * @param reverseEdge if reverse the edge in the nodes + * @return */ - def cloneGraph(): DirectedGraph[T] = { + def cloneGraph(reverseEdge: Boolean = false): DirectedGraph[T] = { val oldToNew = new util.HashMap[Node[T], Node[T]]() val bfs = BFS.toArray bfs.foreach(node => { oldToNew.put(node, new Node[T](node.element)) }) bfs.foreach(node => { - node.prevNodesAndEdges.foreach(prevNodeAndEdge => { - oldToNew.get(prevNodeAndEdge._1).add(oldToNew.get(node), prevNodeAndEdge._2) - }) + if (reverseEdge) { + node.prevNodesAndEdges.foreach(prevNodeAndEdge => { + oldToNew.get(node).add(oldToNew.get(prevNodeAndEdge._1), prevNodeAndEdge._2) + }) + } else { + node.prevNodesAndEdges.foreach(prevNodeAndEdge => { + oldToNew.get(prevNodeAndEdge._1).add(oldToNew.get(node), prevNodeAndEdge._2) + }) + } }) - new DirectedGraph[T](oldToNew.get(source), reverse) - } - // scalastyle:on methodName + if (reverseEdge) { + new DirectedGraph[T](oldToNew.get(source), !reverse) + } else { + new DirectedGraph[T](oldToNew.get(source), reverse) + } + } } /** @@ -222,6 +233,12 @@ class Node[T](val element: T) extends Serializable { node } + def from(node: Node[T], e: Edge = Edge()): Node[T] = { + if (!node.nexts.contains((this, e))) node.nexts.append((this, e)) + if (!this.prevs.contains((node, e))) this.prevs.append((node, e)) + node + } + /** * Remove linkage with another node * @param node another node @@ -264,6 +281,21 @@ class Node[T](val element: T) extends Serializable { this } + /** + * remove edges that connect next nodes + * @return current node + */ + def removeNextEdges(): Node[T] = { + val curNode = this // Because of the closure + nexts.map(_._1).foreach(pn => + pn.prevs.filter(_._1 == curNode).foreach(e => + pn.prevs -= e + ) + ) + nexts.clear() + this + } + /** * Use current node as source to build a direct graph * @param reverse @@ -287,7 +319,11 @@ object Node { * An edge in the graph * @param fromIndex A preserved position to store meta info. */ -private[bigdl] class Edge private (val fromIndex: Option[Int]) extends Serializable +private[bigdl] class Edge private (val fromIndex: Option[Int]) extends Serializable { + override def toString: String = { + s"Edge(fromIndex: $fromIndex)" + } +} object Edge { def apply(value : Int): Edge = new Edge(Some(value)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala index 68bf15bf33c..32956797450 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala @@ -77,7 +77,7 @@ class CaffePersister[T: ClassTag](val prototxtPath: String, val graph = toGraph() val top2Layers = new mutable.HashMap[String, String]() val layers = new mutable.HashMap[String, GeneratedMessage]() - val executions = graph.getForwardExecutions + val executions = graph.getSortedForwardExecutions netparam.setName(module.getName) executions.foreach(execution => { val preModules = execution.prevNodes diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index 3fddce0c404..f6a3caf2ab2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -22,10 +22,11 @@ import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.vgg.{VggForCifar10, Vgg_16, Vgg_19} import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Less} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T, Table} +import com.intel.analytics.bigdl.utils.{Edge, Engine, RandomGenerator, T, Table} import scala.reflect.ClassTag import scala.util.Random @@ -1135,6 +1136,40 @@ class GraphSpec extends FlatSpec with Matchers { val model = Inception_v1_NoAuxClassifier.graph(1000).asInstanceOf[Graph[Float]] model.saveGraphTopology(absolutePath) } + + "graph" should "support switch with two branch" in { + val data = Input("data") + val condition = Input("condition") + val swtich = ControlNodes.switch(data, condition) + val echo1 = Echo().inputs(swtich.trueEdge()) + val echo2 = Echo().inputs(swtich.falseEdge()) + + val model = Graph(Array(data, condition), Array(echo1), None, false) + val result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(true)))) + result.toTensor should be(Tensor[Float](T(1))) + + intercept[IllegalArgumentException] { + model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(false)))) + } + } + + "graph" should "support switch with two branch with merge" in { + val data = Input("data") + val condition = Input("condition") + val swtich = ControlNodes.switch(data, condition) + val echo1 = Echo().inputs(swtich.trueEdge()) + val echo2 = Echo().inputs(swtich.falseEdge()) + val add1 = AddConstant(1).inputs(echo1) + val add5 = AddConstant(5).inputs(echo2) + val merge = ControlNodes.merge(add1, add5) + val output = Identity().inputs(merge) + + val model = Graph(Array(data, condition), Array(output), None, false) + var result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(true)))) + result.toTensor should be(Tensor[Float](T(2))) + result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(false)))) + result.toTensor should be(Tensor[Float](T(6))) + } } object ModelUntils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala index 1d2d1030dd6..1754cab9c87 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala @@ -334,6 +334,25 @@ class DirectedGraphSpec extends FlatSpec with Matchers { }) } + "Reverse graph" should "be correct" in { + val nodeA = new Node("A") + val nodeB = new Node("B") + val nodeC = new Node("C") + val nodeD = new Node("D") + nodeA -> nodeB -> nodeC + nodeB -> nodeD + + val graph = nodeA.graph() + val reverseGraph = graph.cloneGraph(true) + val originSort = graph.topologySort + val sorted = reverseGraph.topologySort + originSort.map(_.element) should be(sorted.map(_.element)) + originSort(1).nextNodes.length should be(2) + originSort(1).prevNodes.length should be(1) + sorted(1).nextNodes.length should be(1) + sorted(1).prevNodes.length should be(2) + } + "delete edge" should "be correct when specify edge" in { val e1 = Edge(1) val e2 = Edge(2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 6c883e5fa3b..7c3a25a83d0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -97,7 +97,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } - "BinaryTreeLSTM serializer" should " work properly" in { + "BinaryTreeLSTM serializer" should "work properly" in { RNG.setSeed(1000) val binaryTreeLSTM = BinaryTreeLSTM(2, 2) @@ -1163,7 +1163,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } - "Recurrent serializer " should " work properly" in { + "Recurrent serializer " should "work properly" in { val recurrent = Recurrent() .add(RnnCell(5, 4, Tanh())) val input1 = Tensor(Array(10, 5, 5)) From ffb66188d33d60b27cc6b563d6c53fd0694e2227 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Fri, 15 Sep 2017 10:34:58 +0800 Subject: [PATCH 0393/1065] Add Floor L2Loss RandomUniform Rank MatMul SoftMax operations (#1533) * make a layer support different type * finish equal and make layer suppor different type * fix conflict * add Floor L2Loss RandomUniform Rank MatMul SoftMax * add TruncatedNormal * fit new code * make jenkens pass --- .../analytics/bigdl/dllib/nn/ops/Floor.scala | 57 ++++++++++ .../analytics/bigdl/dllib/nn/ops/L2Loss.scala | 66 ++++++++++++ .../bigdl/dllib/nn/ops/RandomUniform.scala | 56 ++++++++++ .../analytics/bigdl/dllib/nn/ops/Rank.scala | 39 +++++++ .../bigdl/dllib/nn/ops/TruncatedNormal.scala | 51 +++++++++ .../bigdl/dllib/nn/ops/package.scala | 14 +++ .../bigdl/dllib/tensor/DenseTensor.scala | 12 +++ .../bigdl/dllib/tensor/DenseTensorApply.scala | 49 +++++++++ .../analytics/bigdl/dllib/tensor/Tensor.scala | 12 +++ .../bigdl/dllib/nn/ops/FloorSpec.scala | 42 ++++++++ .../bigdl/dllib/nn/ops/L2LossSpec.scala | 50 +++++++++ .../bigdl/dllib/nn/ops/MatMulSpec.scala | 49 +++++++++ .../dllib/nn/ops/RandomUniformSpec.scala | 30 ++++++ .../bigdl/dllib/nn/ops/RankSpec.scala | 102 ++++++++++++++++++ .../dllib/nn/ops/TruncatedNormalSpec.scala | 31 ++++++ 15 files changed, 660 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Floor.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2Loss.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2LossSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MatMulSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniformSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormalSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Floor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Floor.scala new file mode 100644 index 00000000000..f01f1120d09 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Floor.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ + +import scala.reflect.ClassTag + +class Floor[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Tensor[_], Tensor[_], T] { + + override def updateOutput(input: Tensor[_]): Tensor[_] = { + input.getType() match { + case FloatType => + if (output.getType() != FloatType) { + output = Activity.allocate[Tensor[Float], Float]() + } + output.resizeAs(input) + output.asInstanceOf[Tensor[Float]].applyFun[Float]( + input.asInstanceOf[Tensor[Float]], + scala.math.floor(_).asInstanceOf[Float]) + case DoubleType => + if (output.getType() != DoubleType) { + output = Activity.allocate[Tensor[Double], Double]() + } + output.resizeAs(input) + output.asInstanceOf[Tensor[Double]].applyFun[Double]( + input.asInstanceOf[Tensor[Double]], + scala.math.floor) + case _ => throw new RuntimeException("Unsupported tensor type") + } + + output + } +} + +object Floor { + def apply[T: ClassTag]() + (implicit ev: TensorNumeric[T]): + Operation[Activity, Activity, T] + = ModuleToOperation[T](new Floor()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2Loss.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2Loss.scala new file mode 100644 index 00000000000..170ac21a624 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2Loss.scala @@ -0,0 +1,66 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ + +import scala.reflect.ClassTag + +class L2Loss[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Tensor[_], Tensor[_], T] { + var buffer: Tensor[_] = Tensor[Float]() + + override def updateOutput(input: Tensor[_]): Tensor[_] = { + input.getType() match { + case FloatType => + if (output.getType() != FloatType) { + output = Activity.allocate[Tensor[Float], Float]() + } + if (buffer.getType() != FloatType) { + buffer = Activity.allocate[Tensor[Float], Float]() + } + buffer.resizeAs(input) + output.resize(1) + output.asInstanceOf[Tensor[Float]].setValue(1, + buffer.asInstanceOf[Tensor[Float]].applyFun[Float]( + input.asInstanceOf[Tensor[Float]], x => x * x).sum() / 2) + case DoubleType => + if (output.getType() != DoubleType) { + output = Activity.allocate[Tensor[Double], Double]() + } + val a = buffer.getType() + if (buffer.getType() != DoubleType) { + buffer = Activity.allocate[Tensor[Double], Double]() + } + buffer.resizeAs(input) + output.resize(1) + output.asInstanceOf[Tensor[Double]].setValue(1, + buffer.asInstanceOf[Tensor[Double]].applyFun[Double]( + input.asInstanceOf[Tensor[Double]], x => x * x).sum() / 2) + case _ => throw new RuntimeException("Unsupported tensor type") + } + + output + } +} + +object L2Loss { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): + Operation[Activity, Activity, T] + = ModuleToOperation[T](new L2Loss()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala new file mode 100644 index 00000000000..1cf2ca984b6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.RandomGenerator + +import scala.reflect.ClassTag + +class RandomUniform[T: ClassTag, DataType: ClassTag]( + minVal: DataType, + maxVal: DataType, + seed: Int = 0 +) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[DataType]) +extends Operation[Tensor[DataType], Tensor[DataType], T] { + + RandomGenerator.RNG.setSeed(seed) + + output = Activity.allocate[Tensor[DataType], DataType]() + override def updateOutput(input: Tensor[DataType]): Tensor[DataType] = { + require(input.nDimension() == 1, "the shape should be a one-dimensional tensor.") + + val shape = input.asInstanceOf[Tensor[Int]].storage().toArray + output.resize(shape).rand( + minVal.asInstanceOf[Double], + maxVal.asInstanceOf[Double]) + + output + } +} + +object RandomUniform { + def apply[T: ClassTag, DataType: ClassTag]( + minVal: DataType, + maxVal: DataType, + seed: Int = 0) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[DataType]): + Operation[Activity, Activity, T] + = ModuleToOperation[T](new RandomUniform(minVal, maxVal, seed)) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala new file mode 100644 index 00000000000..3879d73b464 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala @@ -0,0 +1,39 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ + +import scala.reflect.ClassTag + +class Rank[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Tensor[_], Tensor[Int], T] { + + override def updateOutput(input: Tensor[_]): Tensor[Int] = { + output.resizeAs(input(1)) + output.setValue(1, input.nDimension()) + + output + } +} + +object Rank { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): + Operation[Activity, Activity, T] + = ModuleToOperation[T](new Rank()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala new file mode 100644 index 00000000000..3e2a011a8d1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class TruncatedNormal[T: ClassTag, DataType: ClassTag]( + mean: DataType = 0.0, + stddev: DataType = 1.0, + seed: Int = 0 +) + (implicit ev: TensorNumeric[T]) extends Operation[Tensor[Int], Tensor[DataType], T] { + + def updateOutput(input: Tensor[Int]): Tensor[DataType] = { + require(input.nDimension() == 1, "the shape should be a one-dimensional tensor.") + + val shape = input.asInstanceOf[Tensor[Int]].storage().toArray + output.resize(shape).randn( + mean.asInstanceOf[Double], + stddev.asInstanceOf[Double]) + + output + } +} + +object TruncatedNormal { + def apply[T: ClassTag, DataType: ClassTag]( + mean: Double = 0.0, + stddev: Double = 1.0, + seed: Int = 0) + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( + new TruncatedNormal(mean, stddev, seed)) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala index 389cac77f61..77a69757b6b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala @@ -78,4 +78,18 @@ package object ops { = ModuleToOperation[T]( com.intel.analytics.bigdl.nn.ReLU()) } + + object MatMul { + def apply[T: ClassTag]() + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( + com.intel.analytics.bigdl.nn.MM()) + } + + object SoftMax { + def apply[T: ClassTag]() + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( + com.intel.analytics.bigdl.nn.SoftMax()) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 119981c1999..8ae60569571 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -393,6 +393,18 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( result } + def applyFun[A: ClassTag]( + t: Tensor[A], + func: (A) => T): Tensor[T] = { + def func2( + data1: Array[A], index1: Int, + data2: Array[T], index2: Int): Unit = { + data2(index2) = func(data1(index1)) + } + DenseTensorApply.apply1(t, this, func2) + this + } + def zipWith[A: ClassTag, B: ClassTag]( t1: Tensor[A], t2: Tensor[B], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala index f7a1c621fd3..867b5378d45 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala @@ -17,6 +17,55 @@ package com.intel.analytics.bigdl.tensor object DenseTensorApply { + /** + * Iterate through tensor1, and apply func to the elements, + * set function result to tensor 2 + * + * @param tensor1 the tensor1 + * @param tensor2 the result tensor + * @param func (tensor1Data, tensor1Offset, tensor2Data, + * tensor2Offset) + */ + def apply1[A, B, C](tensor1: Tensor[A], tensor2: Tensor[B], + func: (Array[A], Int, Array[B], Int) => Unit): Unit = { + + if (tensor1.nDimension == 0) { + return + } + + val stride1 = getStride(tensor1) + val stride2 = getStride(tensor2) + val (largestDim1, largestSize1) = getLargestContiguousSize(tensor1) + val (largestDim2, largestSize2) = getLargestContiguousSize(tensor2) + val counter1 = getCounter(largestDim1) + val counter2 = getCounter(largestDim2) + val data1 = tensor1.storage().array() + val data2 = tensor2.storage().array() + var offset1 = tensor1.storageOffset() - 1 + var offset2 = tensor2.storageOffset() - 1 + var hasFinished1 = false + var hasFinished2 = false + var i1 = 0 + var i2 = 0 + while (!hasFinished1 && !hasFinished2) { + while (i1 < largestSize1 && i2 < largestSize2) { + val index1 = offset1 + i1 * stride1 + val index2 = offset2 + i2 * stride2 + func(data1, index1, data2, index2) + i1 += 1 + i2 += 1 + } + val r1 = updateCounter(tensor1, counter1, offset1, largestDim1) + val r2 = updateCounter(tensor2, counter2, offset2, largestDim2) + hasFinished1 = r1._1 + hasFinished2 = r2._1 + offset1 = r1._2 + offset2 = r2._2 + i1 = 0 + i2 = 0 + } + } + /** * Iterate through tensor1, tensor2, and apply func to the elements * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index e68c6edd508..2294c98f8c8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -469,6 +469,18 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { */ def copy(other: Tensor[T]): Tensor[T] + /** + * Apply a function to each element of the tensor `t` + * and set each value to self + * + * @param t tensor to be modified + * @param func applied function + * @return current tensor + */ + def applyFun[A: ClassTag]( + t: Tensor[A], + func: (A) => T): Tensor[T] + /** * Apply a function to each element of the tensor and modified it value if it return a double * diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala new file mode 100644 index 00000000000..b46ffd76473 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class FloorSpec extends FlatSpec with Matchers { + "Floor Float operation" should "works correctly" in { + val input = + Tensor[Float](T(1.5f, 2.1f, 2.9f)) + + val expectOutput = Tensor[Float](T(1f, 2f, 2f)) + + val output = Floor[Double]().forward(input) + output should be(expectOutput) + } + + "Floor Double operation" should "works correctly" in { + val input = + Tensor[Double](T(1.5, 2.1, 2.9)) + + val expectOutput = Tensor[Double](T(1, 2, 2)) + + val output = Floor[Double]().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2LossSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2LossSpec.scala new file mode 100644 index 00000000000..0d4f5699cff --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2LossSpec.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class L2LossSpec extends FlatSpec with Matchers { + "L2Loss Double operation" should "works correctly" in { + val input = + Tensor[Double]( + T( + T(1.5, 2.1, 2.9), + T(0.5, 1.1, 1.9) + )) + + val expectOutput = Tensor[Double](T(10.07)) + + val output = L2Loss[Double]().forward(input) + output should be(expectOutput) + } + + "L2Loss Float operation" should "works correctly" in { + val input = + Tensor[Float]( + T( + T(1.5f, 2.1f, 2.9f), + T(0.5f, 1.1f, 1.9f) + )) + + val expectOutput = Tensor[Float](T(10.07f)) + + val output = L2Loss[Float]().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MatMulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MatMulSpec.scala new file mode 100644 index 00000000000..01d175eb2d5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MatMulSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class MatMulSpec extends FlatSpec with Matchers { + "MatMul operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + T( + Tensor( + T( + T(1f, 2f, 3f), + T(4f, 5f, 6f)) + ), + Tensor( + T( + T(1f, 4f), + T(2f, 5f), + T(3f, 6f)) + ) + ) + + val expectOutput = Tensor( + T( + T(14f, 32f), + T(32f, 77f)) + ) + + val output = MatMul().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniformSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniformSpec.scala new file mode 100644 index 00000000000..58c2b872cd3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniformSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class RandomUniformSpec extends FlatSpec with Matchers { + "RandomUniform operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + Tensor[Int](T(1, 2, 3)) + + val output = RandomUniform[Float, Double](10, 20).forward(input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala new file mode 100644 index 00000000000..9cea74fa154 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala @@ -0,0 +1,102 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class RankSpec extends FlatSpec with Matchers { + "Rank Float operation" should "works correctly" in { + val input = + Tensor[Float](T(1f, 2f, 2f)) + + val expectOutput = Tensor[Int](T(1)) + + val output = Rank[Int]().forward(input) + output should be(expectOutput) + } + + "Rank Boolean operation" should "works correctly" in { + val input = + Tensor[Boolean](T(true, true, false)) + + val expectOutput = Tensor[Int](T(1)) + + val output = Rank[Int]().forward(input) + output should be(expectOutput) + } + + "Rank Double operation" should "works correctly" in { + val input = + Tensor[Double](T(2.0, 3.0, 2.0)) + + val expectOutput = Tensor[Int](T(1)) + + val output = Rank[Int]().forward(input) + output should be(expectOutput) + } + + "Rank Char operation" should "works correctly" in { + val input = + Tensor[Char](T('b', 'c', 'a')) + + val expectOutput = Tensor[Int](T(1)) + + val output = Rank[Int]().forward(input) + output should be(expectOutput) + } + + "Rank Long operation" should "works correctly" in { + val input = + Tensor[Long](T(2L, 3L, 2L)) + + val expectOutput = Tensor[Int](T(1)) + + val output = Rank[Int]().forward(input) + output should be(expectOutput) + } + + "Rank String operation" should "works correctly" in { + val input = + Tensor[String](T("aaa", "ccc", "aaa")) + + val expectOutput = Tensor[Int](T(1)) + + val output = Rank[Int]().forward(input) + output should be(expectOutput) + } + + "Rank Short operation" should "works correctly" in { + val input = + Tensor[Short](T(2: Short, 3: Short, 2: Short)) + + val expectOutput = Tensor[Int](T(1)) + + val output = Rank[Int]().forward(input) + output should be(expectOutput) + } + + "Rank Int operation" should "works correctly" in { + val input = + Tensor[Int](T(2, 3, 2)) + + val expectOutput = Tensor[Int](T(1)) + + val output = Rank[Int]().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormalSpec.scala new file mode 100644 index 00000000000..1a1374eaab9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormalSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + + +class TruncatedNormalSpec extends FlatSpec with Matchers { + "TruncatedNormal operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + Tensor[Int](T(1, 2, 3)) + + val output = TruncatedNormal(10, 20).forward(input) + } +} From ac2916e498dd62f9c3fecc955e9932b831509ad4 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Fri, 15 Sep 2017 15:41:49 +0800 Subject: [PATCH 0394/1065] Implement and test Conv2d operation (#1539) * finish conv2d * add conv2d test * fit new code * meet code review --- .../analytics/bigdl/dllib/nn/ops/Conv2D.scala | 111 ++++++++++++++++++ .../bigdl/dllib/nn/ops/Conv2DSep.scala | 87 ++++++++++++++ 2 files changed, 198 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala new file mode 100644 index 00000000000..e0ecdebe7e2 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala @@ -0,0 +1,111 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.SpatialConvolution +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Conv2D[T: ClassTag]( + strides: Array[Int], + padding: String, + format: DataFormat = DataFormat.NHWC +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private var conv: SpatialConvolution[T] = _ + + override def updateOutput(inputs: Table): Tensor[T] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + val filter: Tensor[T] = inputs[Tensor[T]](2) + + conv = format match { + case DataFormat.NHWC => + if (padding == "SAME") { + SpatialConvolution( + nInputPlane = input.size(4), + nOutputPlane = filter.size(4), + kernelH = filter.size(1), + kernelW = filter.size(2), + strideH = strides(1), + strideW = strides(2), + padH = -1, + padW = -1, + withBias = false, + format = format + ) + } else if (padding == "VALID") { + SpatialConvolution( + nInputPlane = input.size(4), + nOutputPlane = filter.size(4), + kernelH = filter.size(1), + kernelW = filter.size(2), + strideH = strides(1), + strideW = strides(2), + withBias = false, + format = format + ) + } else { + throw new RuntimeException("Padding can only support SAME and VALID padding") + } + + case DataFormat.NCHW => + if (padding == "SAME") { + SpatialConvolution( + nInputPlane = input.size(2), + nOutputPlane = filter.size(4), + kernelH = filter.size(1), + kernelW = filter.size(2), + strideH = strides(2), + strideW = strides(3), + padH = -1, + padW = -1, + withBias = false, + format = format + ) + } else if (padding == "VALID") { + SpatialConvolution( + nInputPlane = input.size(2), + nOutputPlane = filter.size(4), + kernelH = filter.size(1), + kernelW = filter.size(2), + strideH = strides(2), + strideW = strides(3), + withBias = false, + format = format + ) + } else { + throw new RuntimeException("Padding can only support SAME and VALID padding") + } + } + + conv.setWeightsBias(Array(filter)) + output = conv.updateOutput(input) + output + } +} + +object Conv2D { + def apply[T: ClassTag]( + strides: Array[Int], + padding: String, + format: DataFormat = DataFormat.NHWC + )(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new Conv2D(strides, padding, format)) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala new file mode 100644 index 00000000000..567309d423c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala @@ -0,0 +1,87 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class Conv2DSep extends FlatSpec with Matchers { + "Add operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val expectOutput = Tensor( + T( + T( + T( + T(138.0, 216.0), + T( 288.0, 459.0), + T( 246.0, 396.0)), + T( + T( 138.0, 216.0), + T( 288.0, 459.0), + T( 246.0, 396.0)) + ) + )) + + val input = + Tensor( + T( + T( + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)) + ) + ) + ) + + val filter = Tensor( + T( + T( + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0)), + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0)), + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0))), + T( + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0)), + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0)), + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0))), + T( + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0)), + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0)), + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0))), + T( + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0)), + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0)), + T(T(1.0, 3.0), T(2.0, 3.0), T(3.0, 4.0))) + )) + + + val output = Conv2D[Double](Array(1, 2, 1, 1), "SAME").forward(T(input, filter)) + output should equal(expectOutput) + } +} From 970777793950cb67f5991430d7223a298e0cdc7f Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 15 Sep 2017 17:23:04 +0800 Subject: [PATCH 0395/1065] Fix failed tensorflow unit test (#1563) * fix tf unit test * fix unit test * meet code review --- .../analytics/bigdl/dllib/nn/Graph.scala | 13 +++-- .../analytics/bigdl/dllib/nn/Scheduler.scala | 8 +-- .../dllib/nn/abstractnn/AbstractModule.scala | 2 +- .../dllib/utils/tf/TensorflowLoader.scala | 52 ++++++++++++++++--- .../dllib/utils/tf/TensorflowSaver.scala | 3 +- 5 files changed, 62 insertions(+), 16 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index efeae4db64c..faef2e9a7cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -474,15 +474,22 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], * Save current model graph to a folder, which can be display in tensorboard by running * tensorboard --logdir logPath * @param logPath + * @param backward Draw backward graph instead of forward * @return */ - def saveGraphTopology(logPath: String): this.type = { + def saveGraphTopology(logPath: String, backward: Boolean = false): this.type = { val writer = new TFFileWriter(logPath) val graphBuilder = GraphDef.newBuilder() - forwardNodes.map(m => { - val nodeDef = Tensorflow.bigdlModule(m.element, m.nextNodes.map(_.element.getName()).asJava) + val nodes = if (backward) { + backwardNodes.filter(n => !n.eq(dummyOutputGrad)) + } else { + forwardNodes.filter(n => !n.eq(dummyOutput)) + } + nodes.map(m => { + val nodeDef = Tensorflow.bigdlModule(m.element, m.prevNodes.map(_.element.getName()).asJava) graphBuilder.addNode(nodeDef) }) + writer.addGraphDef(graphBuilder.build()) writer.close() this diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala index a3bbf13ccfc..208b98ab8c2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala @@ -19,7 +19,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.ops._ -import com.intel.analytics.bigdl.nn.tf.WithoutInput +import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Edge, Node, T} @@ -80,7 +80,7 @@ private[bigdl] class Scheduler[T] ( */ def fetch(): ModuleNode[T] = { var node = readyQueue.dequeue() - while (nodeStatus.isConst(node)) { + while (nodeStatus.isConst(node) || node.element.isInstanceOf[ControlDependency[_]]) { schedule(node) node = readyQueue.dequeue() } @@ -119,7 +119,9 @@ private[bigdl] class Scheduler[T] ( } private def selectNexts(candidateNodes: Seq[ModuleNode[T]], curNode: ModuleNode[T]): Unit = { - candidateNodes.foreach(nextNode => { + val nodeSet = new mutable.LinkedHashSet[ModuleNode[T]]() + candidateNodes.foreach(nodeSet.add(_)) // remove duplicate nodes and keep the order + nodeSet.foreach(nextNode => { if (nextNode.element.isInstanceOf[MergeOps[_]]) { val merge = nextNode.element.asInstanceOf[MergeOps[_]] require(nodeStatus.notExecuted(nextNode), s"Merge node(${nextNode.element.getName()}) " + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 7b6bec3ffe7..3e3551b9806 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -173,7 +173,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ def getName() : String = { if (this.name == null) { - s"${this.getClass.getSimpleName}@${namePostfix}" + s"${this.getClass.getSimpleName}${namePostfix}" } else { this.name } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 48540f73936..1e016f4b85a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -15,23 +15,20 @@ */ package com.intel.analytics.bigdl.utils.tf -import java.io.InputStream -import java.io.{DataInputStream, FileInputStream, FileReader => JFileReader} +import java.io.{DataInputStream, InputStream, FileReader => JFileReader} import java.nio.ByteOrder import java.util - -import org.tensorflow.framework.{GraphDef, NodeDef} -import com.google.protobuf.{CodedInputStream, TextFormat} import java.util.List +import com.google.protobuf.{CodedInputStream, TextFormat} import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{DirectedGraph, Node} -import com.intel.analytics.bigdl.utils.FileReader import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL._ +import com.intel.analytics.bigdl.utils.{DirectedGraph, FileReader, Node} +import org.tensorflow.framework.{GraphDef, NodeDef} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -242,6 +239,22 @@ object TensorflowLoader{ throw new UnsupportedOperationException(errorMsg) ) + // set name + if (nodes.size() == 1) { + // Use tf operation name if one to one map + module.setName(removeColon(nodes.get(0).element.getName())) + } else { + // Many to one map + val name = removeColon(findCommonPrefix(nodes.asScala.map(_.element.getName))) + if (name == "") { + // Use a name combine nodes + module.setName(s"[${nodes.asScala.map(_.element.getName).map(_.replaceAll("/", "\\\\")) + .map(removeColon(_)).mkString(", ")}]") + } else { + // Use the common name + module.setName(name + "/" + module.getName()) + } + } val node = new Node(module) nodes.asScala.foreach(m => { convertedNode(m) = node @@ -367,4 +380,29 @@ object TensorflowLoader{ import scala.collection.JavaConverters._ return (patternToGraph.valuesIterator.toList.asJava, inputs) } + + private def findCommonPrefix(data: Seq[String]): String = { + if (data.length == 0) return "" + var shortest = data(0).length + data.foreach(s => if (s.length < shortest) shortest = s.length) + var prefix = "" + var i = 0 + while(i < shortest) { + var c = data(0).charAt(i) + data.foreach(s => if (c != s.charAt(i)) return removeLast(prefix)) + prefix += c + i += 1 + } + + return removeLast(prefix) + } + + private def removeLast(s: String): String = { + if (s.length == 0) return s + if (s.charAt(s.length - 1) == '/') s.substring(0, s.length - 1) else s + } + + private def removeColon(s: String): String = { + s.replaceAll(":", "") + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala index 8de205887ac..0c570cb588d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala @@ -54,13 +54,12 @@ object TensorflowSaver { new mutable.HashMap[AbstractModule[Activity, Activity, T], ArrayBuffer[NodeDef]]() model.inputs.zip(inputs).foreach(n => { inputNodeCache(n._1.element) = ArrayBuffer(n._2) - println() }) val graphBuilder = GraphDef.newBuilder() inputs.foreach(graphBuilder.addNode(_)) - model.getForwardExecutions.foreach(n => { + model.getSortedForwardExecutions.foreach(n => { val nodeDefs = maps(n.element.getClass.getName).toTFDef(n.element, inputNodeCache(n.element), byteOrder) nodeDefs.foreach(nDef => { From f4dacc74ebefd4a92b7af0897a0d69a26be426f5 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 18 Sep 2017 12:54:27 +0800 Subject: [PATCH 0396/1065] expor evaluate api to python (#1560) --- .../analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 2c25c81ef86..a0aa18f1800 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1500,6 +1500,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new JavaRDD[JTensor](listRDD) } + def evaluate(module: AbstractModule[Activity, Activity, T]): + AbstractModule[Activity, Activity, T] = { + module.evaluate() + } + def modelPredictClass(model: AbstractModule[Activity, Activity, T], dataRdd: JavaRDD[Sample]): JavaRDD[Int] = { val tensorRDD = model.predictClass(dataRdd.rdd.map(toSample(_))) From 19bdd408793ac069efcd7c4f9e5bc5add91aef5e Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 18 Sep 2017 13:02:57 +0800 Subject: [PATCH 0397/1065] support multi output in graph follow up (#1564) * support multi-output follow up * meet code review --- .../analytics/bigdl/dllib/nn/Graph.scala | 85 +++++++---- .../bigdl/dllib/utils/DirectedGraph.scala | 11 ++ .../dllib/utils/tf/TensorflowLoader.scala | 134 ++++++++++++------ .../dllib/utils/tf/TensorflowToBigDL.scala | 15 +- .../src/test/resources/tf/models/decoder.py | 2 + .../dllib/utils/tf/TensorflowLoaderSpec.scala | 6 +- 6 files changed, 168 insertions(+), 85 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index faef2e9a7cf..691e0bb0f68 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -87,11 +87,16 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], val prevActivities = node.prevNodesAndEdges .filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) .map(n => { - n._2.fromIndex match { - case Some(i) => n._1.element.output.toTable.apply[Activity](i) - case None => n._1.element.output - } - }) + n._2.fromIndex match { + case Some(i) => + if (i == 1 && n._1.element.output.isTensor) { + n._1.element.output + } else { + n._1.element.output.toTable.apply[Activity](i) + } + case None => n._1.element.output + } + }) if (prevActivities.length == 1) { prevActivities.head } else { @@ -118,24 +123,32 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], curNode.prevNodesAndEdges.filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) .foreach(n => { - val otherActivity = if (n._1.element.gradInput.isTensor || n._1.nextEdges.length == 1) { - n._1.element.gradInput - } else { - val index = n._1.nextEdges.indexOf(n._2) + 1 - n._1.element.gradInput.toTable.apply[Activity](index) - } + val otherActivity = if (n._1.element.gradInput.isTensor || n._1.nextEdges.length == 1) { + n._1.element.gradInput + } else { + val index = n._1.nextEdges.indexOf(n._2) + 1 + n._1.element.gradInput.toTable.apply[Activity](index) + } - n._2.fromIndex match { - case Some(i) => - if (curNode.element.output.isTable && curGradOutput == null) { - curGradOutput = T() - } - val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) - curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) - case None => - curGradOutput = accActivity(curGradOutput, otherActivity) - } - }) + n._2.fromIndex match { + case Some(i) => + if (i == 1 && curNode.element.output.isTensor) { + curGradOutput = accActivity(curGradOutput, otherActivity) + } else { + if (curNode.element.output.isTable && curGradOutput == null) { + curGradOutput = T() + } + val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) + curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) + } + case None => + curGradOutput = accActivity(curGradOutput, otherActivity) + } + }) + + if (curNode.element.output.isTable) { + addZeroTensorToMissingGradOutput(curNode.element.output.toTable, curGradOutput.toTable) + } gradOutputCache(curNode.element.getName()) = curGradOutput if (!isStopGradient(curNode.element)) { @@ -155,6 +168,18 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], gradInput } + private def addZeroTensorToMissingGradOutput(output: Table, gradOutput: Table): Unit = { + var i = 0 + while (i < output.length()) { + if (!gradOutput.contains(i + 1)) { + val tensor = output[Tensor[T]](i + 1) + val zero = Tensor(tensor.size()) + gradOutput(i + 1) = zero + } + i = i + 1 + } + } + private def calcSumTimesOfAllNodes(timesOfAllNodes: Array[(absModule, Long, Long)]) : (Long, Long) = { var sumForward = 0L @@ -198,16 +223,24 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], n._2.fromIndex match { case Some(i) => - if (curNode.element.output.isTable && curGradOutput == null) { - curGradOutput = T() + if (i == 1 && curNode.element.output.isTensor) { + curGradOutput = accActivity(curGradOutput, otherActivity) + } else { + if (curNode.element.output.isTable && curGradOutput == null) { + curGradOutput = T() + } + val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) + curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) } - val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) - curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) case None => curGradOutput = accActivity(curGradOutput, otherActivity) } }) + if (curNode.element.output.isTable) { + addZeroTensorToMissingGradOutput(curNode.element.output.toTable, curGradOutput.toTable) + } + gradOutputCache(curNode.element.getName()) = curGradOutput if (!isStopGradient(curNode.element)) { curNode.element.updateGradInput(inputCache(curNode.element.getName()), curGradOutput) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala index e6323297d03..90786c6ef4b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala @@ -323,6 +323,17 @@ private[bigdl] class Edge private (val fromIndex: Option[Int]) extends Serializa override def toString: String = { s"Edge(fromIndex: $fromIndex)" } + + /** + * Create a new Instance of this Edge + * @return a new Instance of this Edge + */ + def newInstance(): Edge = { + fromIndex match { + case Some(index) => Edge(index) + case None => Edge() + } + } } object Edge { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 1e016f4b85a..dff3013f382 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -26,6 +26,7 @@ import com.intel.analytics.bigdl.nn.Graph import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{DirectedGraph, Edge, FileReader, Node} import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL._ import com.intel.analytics.bigdl.utils.{DirectedGraph, FileReader, Node} import org.tensorflow.framework.{GraphDef, NodeDef} @@ -53,7 +54,7 @@ object TensorflowLoader{ val nodeList = parse(graphPrototxt) // Construct tf node graph - val (tfGraph, adjustedInputs) = + val (tfGraph, adjustedInputs, _) = buildTFGraph(nodeList, outputs, (node: NodeDef) => inputs.contains(node.getName)) // Build BigDL model from the tf node graph @@ -109,20 +110,9 @@ object TensorflowLoader{ */ private[bigdl] def buildTFGraph(nodes : List[NodeDef], outputNodeNames: Seq[String], isInput: (NodeDef) => Boolean = (_: NodeDef) => false) - : (DirectedGraph[NodeDef], Seq[String]) = { + : (DirectedGraph[NodeDef], Seq[String], Seq[String]) = { import scala.collection.JavaConverters._ - var name2Node = nodes.asScala.map(n => n.getName -> new Node(n)).toMap - - // Process node with multiple tensor output, each tensor is regarded as a node - nodes.asScala - .flatMap(_.getInputList.asScala) - .filter(_.split(TENSOR_SEPARATOR).length > 1) - .foreach { nameWithChannel => - val name = nameWithChannel.split(TENSOR_SEPARATOR).head - val tfNode = NodeDef.newBuilder(name2Node(name).element) - .setName(nameWithChannel).build() - name2Node += nameWithChannel -> new Node(tfNode) - } + val name2Node = nodes.asScala.map(n => n.getName -> new Node(n)).toMap // Build graph val outputNodes = if (outputNodeNames == null) { @@ -134,11 +124,12 @@ object TensorflowLoader{ results } - def connect(nodes: Seq[Node[NodeDef]]): Seq[String] = { + def connect(nodes: Seq[Node[NodeDef]]): (Seq[String], Seq[String]) = { var inputCounter = 0 val queue = new mutable.Queue[Node[NodeDef]]() val visited = mutable.Set[Node[NodeDef]]() val inputs = new mutable.ArrayBuffer[String]() + val originInputs = new mutable.ArrayBuffer[String]() // Do a BFS to connect the nodes queue.enqueue(nodes: _*) @@ -150,49 +141,79 @@ object TensorflowLoader{ // continue to traverse node.element.getInputList.asScala.foreach { preNodeName => // It is tricky here, remove the first char in the name of control dep node - val preNode = if (preNodeName.charAt(0) == '^') { - val name = preNodeName.substring(1) - val preNode = name2Node(name) + var realName = preNodeName + var controlDep = false + var channel = 0 + + if (realName.charAt(0) == '^') { + realName = realName.substring(1) + controlDep = true + } + if (realName.split(":").length > 1) { + val pair = realName.split(":") + realName = pair(0) + channel = pair(1).toInt + } + + val preNode = name2Node(realName) + + val currNode = if (controlDep) { val dependencyNode = Node(NodeDef.newBuilder() .setOp("DependencyNode") .addInput(preNode.element.getName) .setName(s"depends_on_${preNode.element.getName}") .build()) - preNode -> dependencyNode -> node - preNode + dependencyNode -> node + dependencyNode } else { - val preNode = name2Node(preNodeName) - preNode -> node - preNode + node } + + preNode.add(currNode, Edge(channel + 1)) queue.enqueue(preNode) } } else { if (isInput(node.element) && node.element.getOp != "Placeholder") { // if the predefined input node is not a Placeholder, add one to match the Input node - val name = s"input$inputCounter" - val placeholder = NodeDef.newBuilder() - .setName(name) - .setOp("Placeholder").build() - inputCounter = inputCounter + 1 - val n = Node(placeholder) - n -> node - inputs += name + val inputNum = getInputNumber(node.element) + var i = 0 + while (i < inputNum) { + val name = s"input$inputCounter" + val placeholder = NodeDef.newBuilder() + .setName(name) + .setOp("Placeholder").build() + inputCounter = inputCounter + 1 + val n = Node(placeholder) + n -> node + inputs += name + i = i + 1 + } + originInputs += node.element.getName } else if (node.element.getOp == "Placeholder") { inputs += node.element.getName + originInputs += node.element.getName } } } } - inputs + (inputs, originInputs) } - val inputs = connect(outputNodes) + val (inputs, originInputs) = connect(outputNodes) val dummyOutput = new Node[NodeDef](null) outputNodes.foreach(_ -> dummyOutput) - (dummyOutput.graph(reverse = true), inputs) + (dummyOutput.graph(reverse = true), inputs, originInputs) + } + + private def getInputNumber(nodeDef: NodeDef): Int = { + import scala.collection.JavaConverters._ + nodeDef.getOp match { + case "QueueDequeueV2" => nodeDef.getAttrOrThrow("component_types").getList.getTypeCount + case "QueueDequeueManyV2" => nodeDef.getAttrOrThrow("component_types").getList.getTypeCount + case _ => nodeDef.getInputList.asScala.filterNot(_.charAt(0) == '^').length + } } private[bigdl] def buildBigDLModel[T: ClassTag]( @@ -210,6 +231,11 @@ object TensorflowLoader{ Node[AbstractModule[Activity, Activity, T]]]() val nameToNode = new mutable.HashMap[String, Node[AbstractModule[Activity, Activity, T]]]() + + val moduleToInputNodes = + new mutable.HashMap[Node[AbstractModule[Activity, Activity, T]], Seq[Node[NodeDef]]]() + val moduleToAllNodes = + new mutable.HashMap[Node[AbstractModule[Activity, Activity, T]], Set[Node[NodeDef]]]() val context = ctx.getOrElse( new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]) @@ -261,21 +287,37 @@ object TensorflowLoader{ nameToNode(m.element.getName) = node }) - // These two pieces of code are all necessary - val nextNodes = n.nextNodes.filter( - n => n.element != null && - convertedNode.contains(n) && !context.contains(n.element.getName) - ).map(convertedNode(_)).filter(_ != node) - nextNodes.foreach(node -> _) - - val preNodes = inputNodes.flatMap(_.prevNodes) - .filter(n => n.element != null && convertedNode.contains(n) - && !context.contains(n.element.getName)) - .map(convertedNode(_)).filter(_ != node) - preNodes.foreach(_ -> node) + moduleToInputNodes(node) = inputNodes + moduleToAllNodes(node) = nodes.asScala.toSet + } }) + def connect(outputModuleNode: Seq[Node[AbstractModule[Activity, Activity, T]]]) = { + val queue = new mutable.Queue[Node[AbstractModule[Activity, Activity, T]]]() + val visited = mutable.Set[Node[AbstractModule[Activity, Activity, T]]]() + queue.enqueue(outputModuleNode: _*) + + while (queue.nonEmpty) { + val currNode = queue.dequeue() + if (!visited(currNode)) { + visited += currNode + val inputNodes = moduleToInputNodes(currNode) + val allNodes = moduleToAllNodes(currNode) + val inputModuleNodes = inputNodes.flatMap(_.prevNodesAndEdges) + .filterNot(n => context.contains(n._1.element.getName)) + .filterNot(n => allNodes(n._1)) + .map(n => (convertedNode(n._1), n._2.newInstance())).filter(n => n._1 != currNode) + inputModuleNodes.foreach(n => n._1.add(currNode, n._2)) + queue.enqueue(inputModuleNodes.map(_._1): _*) + } + } + } + + val outputModules = tfGraph.source.prevNodes.map(_.element.getName).map(nameToNode) + + connect(outputModules) + val inputNodes = inputs .map(n => nameToNode.getOrElse(n, throw new IllegalArgumentException(s"Can't find node $n"))) val outputNodes = outputs diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index eefcd49f375..375c7db1293 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -1083,11 +1083,7 @@ object UnpackTF extends TensorflowToBigDL{ val attr = tfGraph.source.element.getAttrMap val dim = getInt(attr, "axis") + 1 - val index = tfGraph.source.element.getName.split(":").toList match { - case _::Nil => 1 - case _::i::Nil => i.toInt + 1 - } - Select[T](dim, index).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + SplitTable[T](dim).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } @@ -1408,12 +1404,11 @@ object SplitTF extends TensorflowToBigDL { val numSplit = tfGraph.source.element.getAttrMap.get("num_split").getI.toInt val dim = tfGraph.source.prevNodes.head.element .getAttrMap.get("value").getTensor.getIntVal(0) + 1 - val index = tfGraph.source.element.getName.split(":").toList match { - case _::Nil => 1 - case _::i::Nil => i.toInt + 1 + val model = new ConcatTable[T]() + for (index <- Range(1, numSplit + 1)) { + model.add(SplitAndSelect[T](dim, index, numSplit)) } - SplitAndSelect[T](dim, index, numSplit) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + model.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } diff --git a/scala/dllib/src/test/resources/tf/models/decoder.py b/scala/dllib/src/test/resources/tf/models/decoder.py index 4586ab0814a..714af5e636d 100644 --- a/scala/dllib/src/test/resources/tf/models/decoder.py +++ b/scala/dllib/src/test/resources/tf/models/decoder.py @@ -21,6 +21,7 @@ def main(): + tf.set_random_seed(1) n_steps = 2 n_input = 10 n_hidden = 10 @@ -41,6 +42,7 @@ def main(): outputs.append(output) final = tf.identity(outputs, name="output") + net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(',')) run_model(net_outputs, argv[1], 'rnn', argv[3] == 'True') diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index 7dc86d324f6..be98d41e781 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -114,7 +114,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val resource = getClass().getClassLoader().getResource("tf") val path = processPath(resource.getPath()) + JFile.separator + "test.pb" val results = TensorflowLoader.parse(path) - val (tfGraph, _) = TensorflowLoader.buildTFGraph(results, Seq("output")) + val (tfGraph, _, _) = TensorflowLoader.buildTFGraph(results, Seq("output")) tfGraph.size should be(15) // there's a dummy output val topSort = tfGraph.topologySort// It can do topology sort topSort.length should be(15) @@ -139,7 +139,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val resource = getClass().getClassLoader().getResource("tf") val path = processPath(resource.getPath()) + JFile.separator + "test.pb" val results = TensorflowLoader.parse(path) - val (tfGraph, _) = TensorflowLoader.buildTFGraph(results, Seq("output"), + val (tfGraph, _, _) = TensorflowLoader.buildTFGraph(results, Seq("output"), (node: NodeDef) => node.getName == "Tanh") tfGraph.size should be(9) // there's a dummy output val topSort = tfGraph.topologySort// It can do topology sort @@ -522,7 +522,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val tfNodes = TensorflowLoader.parse(modelFile) // filter node for gradient computing - val (tfGraph, inputs) = + val (tfGraph, inputs, _) = TensorflowLoader.buildTFGraph(tfNodes, endPoints.map(_.split(":")(0)), (node: NodeDef) => node.getName == "input_node") val context = From adbf2c51eafa905c70e9ffaa3210f688ccb276d8 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 18 Sep 2017 16:06:40 +0800 Subject: [PATCH 0398/1065] fix type comparision (#1567) --- .../utils/serializer/DataConverter.scala | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index c609aeebd29..016f56a92d5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -121,33 +121,33 @@ object DataConverter extends DataConverter{ attributeBuilder : AttrValue.Builder, value: Any, valueType : universe.Type = typePlaceHolder) (implicit ev: TensorNumeric[T]): Unit = { // to make it compatible with Java types - if (valueType == universe.typeOf[Int] || - valueType == universe.typeOf[java.lang.Integer]) { + if (valueType =:= universe.typeOf[Int] || + valueType =:= universe.typeOf[java.lang.Integer]) { attributeBuilder.setDataType(DataType.INT32) attributeBuilder.setInt32Value(value.asInstanceOf[Int]) - } else if (valueType == universe.typeOf[Long] || - valueType == universe.typeOf[java.lang.Long]) { + } else if (valueType =:= universe.typeOf[Long] || + valueType =:= universe.typeOf[java.lang.Long]) { attributeBuilder.setDataType(DataType.INT64) attributeBuilder.setInt64Value(value.asInstanceOf[Long]) - } else if (valueType == universe.typeOf[Float] || - valueType == universe.typeOf[java.lang.Float]) { + } else if (valueType =:= universe.typeOf[Float] || + valueType =:= universe.typeOf[java.lang.Float]) { attributeBuilder.setDataType(DataType.FLOAT) attributeBuilder.setFloatValue(value.asInstanceOf[Float]) - } else if (valueType == universe.typeOf[Double] || - valueType == universe.typeOf[java.lang.Double]) { + } else if (valueType =:= universe.typeOf[Double] || + valueType =:= universe.typeOf[java.lang.Double]) { attributeBuilder.setDataType(DataType.DOUBLE) attributeBuilder.setDoubleValue(value.asInstanceOf[Double]) - } else if (valueType == universe.typeOf[String] || - valueType == universe.typeOf[java.lang.String]) { + } else if (valueType =:= universe.typeOf[String] || + valueType =:= universe.typeOf[java.lang.String]) { attributeBuilder.setDataType(DataType.STRING) attributeBuilder.setStringValue(value.asInstanceOf[String]) - } else if (valueType == universe.typeOf[Boolean] || - valueType == universe.typeOf[java.lang.Boolean]) { + } else if (valueType =:= universe.typeOf[Boolean] || + valueType =:= universe.typeOf[java.lang.Boolean]) { attributeBuilder.setDataType(DataType.BOOL ) attributeBuilder.setBoolValue(value.asInstanceOf[Boolean]) - } else if (valueType == universe.typeOf[VariableFormat]) { + } else if (valueType =:= universe.typeOf[VariableFormat]) { VariableFormatConverter.setAttributeValue(attributeBuilder, value) - } else if (valueType == universe.typeOf[InitializationMethod]) { + } else if (valueType =:= universe.typeOf[InitializationMethod]) { InitMethodConverter.setAttributeValue(attributeBuilder, value) } else if (valueType.toString == ModuleSerializer.regularizerType.toString) { RegularizerConverter.setAttributeValue(attributeBuilder, value) From 9e49eda06b8debc6ee9b2a7671fe8de2a50266f4 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 19 Sep 2017 10:37:49 +0800 Subject: [PATCH 0399/1065] Deconv mapping (#1565) * add deconvolution mapping * fix deconv mapping * fix converter param --- .../bigdl/dllib/utils/caffe/Converter.scala | 34 +++++++++++ .../dllib/utils/caffe/LayerConverter.scala | 54 +++++++++++++++++- .../dllib/utils/caffe/V1LayerConverter.scala | 57 ++++++++++++++++++- 3 files changed, 141 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index 887b304fae7..09aaa501eef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -281,6 +281,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { val module = moduleNode.asInstanceOf[AbstractModule[_, _, _]] val model : Seq[GeneratedMessage] = module match { case convolution : SpatialConvolution[_] => toCaffeConvolution(moduleNode, bottoms, nextSize) + case deconv : SpatialFullConvolution[_] => toCaffeDeConvolution(moduleNode, bottoms, nextSize) case relu : ReLU[_] => toCaffeRelu(moduleNode, bottoms, nextSize) case crossMapLrn : SpatialCrossMapLRN[_] => toCaffeLRN(moduleNode, bottoms, nextSize) case inChannelLrn : SpatialWithinChannelLRN[_] => toCaffeLRN(moduleNode, bottoms, nextSize) @@ -320,6 +321,9 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { protected def toCaffeConvolution(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] + protected def toCaffeDeConvolution(module : AbstractModule[Activity, Activity, T], + bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] + protected def toCaffeRelu(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] @@ -433,6 +437,35 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { map } + protected def toCaffeDeConvolutionParam(module : AbstractModule[Activity, Activity, T]) + : mutable.HashMap[String, Int] = { + val map = new mutable.HashMap[String, Int]() + val layer = classOf[SpatialFullConvolution[T]].cast(module) + if (layer.adjW != 0 || layer.adjH != 0) { + throw new IllegalArgumentException("Caffe doesn't support extra width/height amending") + } + val nInputPlane = layer.nOutputPlane + val nOutputPlane = layer.nInputPlane + val kernelW = layer.kW + val kernelH = layer.kH + val strideW = layer.dW + val strideH = layer.dH + val padW = layer.padW + val padH = layer.padH + val ngroup = layer.nGroup + map("nInputPlane") = nInputPlane + map("nOutputPlane") = nOutputPlane + map("kernelW") = kernelW + map("kernelH") = kernelH + map("strideW") = strideW + map("strideH") = strideH + map("padW") = padW + map("padH") = padH + map("ngroup") = ngroup + map("withBias") = if (layer.noBias) 0 else 1 + map + } + protected def toCaffeLRNParam(module : AbstractModule[Activity, Activity, T]) : (Int, Double, Double, Double, String) = { if (module.isInstanceOf[SpatialCrossMapLRN[T]]) { @@ -571,6 +604,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { private def init() = { caffe2BigDL("CONVOLUTION") = fromCaffeConvolution + caffe2BigDL("DECONVOLUTION") = fromCaffeConvolution caffe2BigDL("INNERPRODUCT") = fromCaffeInnerProduct caffe2BigDL("INNER_PRODUCT") = fromCaffeInnerProduct caffe2BigDL("RELU") = fromCaffeReLU diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index 178d1453166..3469e3c3954 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -74,8 +74,14 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert } if (param.getDilationCount == 0 || param.getDilation(0) == 1) { - Seq(SpatialConvolution[T](nInputPlane.toInt, nOutPlane.toInt, - kw, kh, dw, dh, pw, ph, group, withBias).setName(getLayerName(layer)).inputs()) + val layerType = getLayerType(layer).toUpperCase + if ("DECONVOLUTION" == layerType) { + Seq(SpatialFullConvolution[T](nOutPlane.toInt, nInputPlane.toInt, + kw, kh, dw, dh, pw, ph, 0, 0, group, !withBias).setName(getLayerName(layer)).inputs()) + } else { + Seq(SpatialConvolution[T](nInputPlane.toInt, nOutPlane.toInt, + kw, kh, dw, dh, pw, ph, group, withBias).setName(getLayerName(layer)).inputs()) + } } else { val dilation = param.getDilation(0) Seq(SpatialDilatedConvolution[T](nInputPlane.toInt, nOutPlane.toInt, @@ -220,6 +226,50 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert } + override protected def toCaffeDeConvolution(module : AbstractModule[Activity, Activity, T], + bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { + val layerParameter = LayerParameter.newBuilder() + + val layerName = module.getName + + layerParameter.setName(layerName) + + layerParameter.setType("Deconvolution") + + // set bottom list and top list + setConnections(layerParameter, bottoms, nextSize) + + // copy weight and bias + val (weightBuilder, biasBuilder) = copyParam(module) + + // get convolution param map + val layerParams = toCaffeDeConvolutionParam(module) + + val convolutionParam = ConvolutionParameter.newBuilder() + + val ngroup = layerParams("ngroup") + val nInputPlane = layerParams("nInputPlane") + val nOutputPlane = layerParams("nOutputPlane") + convolutionParam.setGroup(ngroup) + convolutionParam.setNumOutput(nOutputPlane) + convolutionParam.setKernelW(layerParams("kernelW")) + convolutionParam.setKernelH(layerParams("kernelH")) + convolutionParam.setStrideW(layerParams("strideW")) + convolutionParam.setStrideH(layerParams("strideH")) + convolutionParam.setPadW(layerParams("padW")) + convolutionParam.setPadH(layerParams("padH")) + val withBias = if (layerParams("withBias") == 1) true else false + convolutionParam.setBiasTerm(withBias) + weightBuilder.setChannels(nInputPlane / ngroup) + weightBuilder.setNum(nOutputPlane) + + setBlobs(layerParameter, weightBuilder, biasBuilder) + + layerParameter.setConvolutionParam(convolutionParam.build) + + // build concolution layer + Seq(layerParameter.build()) + } override protected def toCaffeRelu(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala index 0ee69014462..c45c7ba4898 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala @@ -75,8 +75,14 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve ph = pw } } - Seq(SpatialConvolution[T](nInputPlane, nOutPlane, kw, kh, dw, dh, pw, ph, group) - .setName(getLayerName(layer)).inputs()) + val layerType = getLayerType(layer).toUpperCase + if ("DECONVOLUTION" == layerType) { + Seq(SpatialFullConvolution[T](nOutPlane, nInputPlane, kw, kh, dw, dh, pw, ph, 0, 0, group) + .setName(getLayerName(layer)).inputs()) + } else { + Seq(SpatialConvolution[T](nInputPlane, nOutPlane, kw, kh, dw, dh, pw, ph, group) + .setName(getLayerName(layer)).inputs()) + } } override protected def fromCaffeInnerProduct(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -177,6 +183,53 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve } + override protected def toCaffeDeConvolution(module : AbstractModule[Activity, Activity, T], + bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { + + val layerParameter = V1LayerParameter.newBuilder() + + val layerName = module.getName + + layerParameter.setName(layerName) + + layerParameter.setType(LayerType.DECONVOLUTION) + + // set bottom list and top list + setConnections(layerParameter, bottoms, nextSize) + + // copy weight and bias + val (weightBuilder, biasBuilder) = copyParam(module) + + // get deconvolution param map + val layerParams = toCaffeDeConvolutionParam(module) + + val convolutionParam = ConvolutionParameter.newBuilder() + + val ngroup = layerParams("ngroup") + val nInputPlane = layerParams("nInputPlane") + val nOutputPlane = layerParams("nOutputPlane") + convolutionParam.setGroup(ngroup) + convolutionParam.setNumOutput(nOutputPlane) + convolutionParam.setKernelW(layerParams("kernelW")) + convolutionParam.setKernelH(layerParams("kernelH")) + convolutionParam.setStrideW(layerParams("strideW")) + convolutionParam.setStrideH(layerParams("strideH")) + convolutionParam.setPadW(layerParams("padW")) + convolutionParam.setPadH(layerParams("padH")) + val withBias = if (layerParams("withBias") == 1) true else false + convolutionParam.setBiasTerm(withBias) + + weightBuilder.setChannels(nInputPlane / ngroup) + weightBuilder.setNum(nOutputPlane) + + setBlobs(layerParameter, weightBuilder, biasBuilder) + + layerParameter.setConvolutionParam(convolutionParam.build) + + // build deConcolution layer + Seq(layerParameter.build()) + } + override protected def toCaffeRelu(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { From edc990f59128d8065cd102116212b228751ea118 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 19 Sep 2017 13:34:00 +0800 Subject: [PATCH 0400/1065] optimize copy in recurrent (#1556) * optimize copy in recurrent * add more unit test * select optimize * meet review --- .../analytics/bigdl/dllib/nn/Recurrent.scala | 143 ++++++++++++++---- .../analytics/bigdl/dllib/nn/Select.scala | 16 +- .../bigdl/dllib/nn/RecurrentSpec.scala | 19 +++ .../bigdl/dllib/torch/SelectSpec.scala | 30 ++++ 4 files changed, 173 insertions(+), 35 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index e07d1b5108d..372ce293cea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -45,12 +45,14 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) private val gradInputCell = Tensor[T]() private var outputCell = Tensor[T]() private val _input = T() - private val batchDim = 1 - private val timeDim = 2 + private val batchDim = Recurrent.batchDim + private val timeDim = Recurrent.timeDim private val inputDim = 1 private val hidDim = 2 private var (batchSize, times) = (0, 0) private var topology: Cell[T] = null + private val outputBuffer = Tensor[T]() + private val gradBuffer = Tensor[T]() private var preTopology: AbstractModule[Activity, Activity, T] = null private val dropouts: ArrayBuffer[Array[Dropout[T]]] = new ArrayBuffer[Array[Dropout[T]]] @@ -118,8 +120,8 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) * the left is size of images */ private def extend(sizes: Array[Int]): Unit = { - val times = sizes(1) - val batchSize = sizes(0) + val times = sizes(timeDim - 1) + val batchSize = sizes(batchDim - 1) val imageSize = sizes.drop(3) if (hidden == null) { require((preTopology == null && modules.length == 1) || @@ -158,20 +160,6 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) } } - /** - * set the cells' output and gradInput to recurrent's output and gradInput - * to decrease the copy expense. - * @param src - * @param dst - */ - private def copy(src: ArrayBuffer[Tensor[T]], dst: Tensor[T], offset: Int): Unit = { - var t = 1 - while ((t + offset) <= times) { - dst.select(timeDim, t + offset).copy(src(t - 1)) - t += 1 - } - } - /** * Sharing weights, bias, gradWeights across all the cells in time dim * @param cells @@ -254,14 +242,13 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) currentInput(hidDim) = if (initState != null) initState else hidden while (i <= times) { - currentInput(inputDim) = outputCell.select(timeDim, i) + currentInput(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) cells(i - 1).forward(currentInput) currentInput(hidDim) = cells(i - 1).output.toTable(hidDim) i += 1 } - copy(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), - output, 0) + Recurrent.copy(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), output) output } @@ -291,10 +278,10 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) var i = times while (i >= 1) { - currentGradOutput(inputDim) = gradOutput.select(timeDim, i) + currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, gradBuffer) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) else hidden - _input(inputDim) = outputCell.select(timeDim, i) + _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) if (i == 1) { cells(i - 1).regluarized(true) } else { @@ -327,16 +314,15 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) currentGradOutput(hidDim) = gradHidden var i = times while (i >= 1) { - currentGradOutput(inputDim) = gradOutput.select(timeDim, i) + currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, gradBuffer) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) else hidden - _input(inputDim) = outputCell.select(timeDim, i) + _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) cells(i - 1).updateGradInput(_input, currentGradOutput) currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) i -= 1 } - copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), - gradInputCell, 0) + Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) if (preTopology != null) { gradInput = preTopology.updateGradInput(input, gradInputCell).toTensor[T] } @@ -349,10 +335,10 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) var i = times while (i >= 1) { - currentGradOutput(inputDim) = gradOutput.select(timeDim, i) + currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, gradBuffer) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) else hidden - _input(inputDim) = outputCell.select(timeDim, i) + _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) if (i == 1) { cells(i - 1).regluarized(true) } else { @@ -376,8 +362,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) gradInputCell } gradInputCell.resizeAs(outputCell) - copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), - gradInputCell, 0) + Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) if (preTopology != null) { gradInput = preTopology.backward(input, gradInputCell).toTensor[T] @@ -457,6 +442,8 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) cells.clear() timeBuffer.clear() initState = null + outputBuffer.set() + gradBuffer.set() this } @@ -491,12 +478,106 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) } object Recurrent extends ContainerSerializable { + + private val batchDim = 1 + private val timeDim = 2 + def apply[@specialized(Float, Double) T: ClassTag]( batchNormParams: BatchNormParams[T] = null) (implicit ev: TensorNumeric[T]) : Recurrent[T] = { new Recurrent[T](batchNormParams) } + /** + * set the cells' output and gradInput to recurrent's output and gradInput + * to decrease the copy expense. + * Copy src tensor to dst tensor along timeDime, default timeDime 2, batchDim 1 + * @param src + * @param dst + */ + private[bigdl] def copy[@specialized(Float, Double) T: ClassTag]( + src: ArrayBuffer[Tensor[T]], dst: Tensor[T]): Unit = { + val timeSize = dst.size(timeDim) + var t = 1 + while (t <= timeSize) { + copyToIndex(src(t -1), dst, t) + t += 1 + } + } + + /** + * select srcIndex subset of the 2-th dimension from src, and copy to dst + * @param src + * @param srcIndex the index of 2-th dimension from src + * @param dst + */ + private[bigdl] def selectCopy[@specialized(Float, Double) T: ClassTag]( + src: Tensor[T], srcIndex: Int, dst: Tensor[T]): Tensor[T] = { + if (src.isContiguous() && dst.isContiguous()) { + if ((dst.nElement() == 0) || (dst.nElement() != (src.nElement() / src.size(2)))) { + dst.resizeAs(src.select(2, srcIndex)) + } + + val batchSize = src.size(batchDim) + val timeSize = src.size(timeDim) + val stepSize = src.nElement() / (batchSize * timeSize) + + val srcArr = src.storage().array() + var srcOffset = src.storageOffset() - 1 + val dstArr = dst.storage().array() + var dstOffset = dst.storageOffset() - 1 + + val recordSize = timeSize * stepSize + val indexSize = (srcIndex-1) * stepSize + + var b = 0 + while (b < batchSize) { + System.arraycopy(srcArr, srcOffset + indexSize, dstArr, dstOffset, stepSize) + srcOffset += recordSize + dstOffset += stepSize + b += 1 + } + } else { + val output = src.select(2, srcIndex) + dst.resizeAs(output).copy(output) + } + dst + } + + /** + * copy src to be dst dstIndex subset of the 2-th dimension + * @param src + * @param dst + * @param dstIndex the index of 2-th dimension from dst + */ + private[bigdl] def copyToIndex[@specialized(Float, Double) T: ClassTag]( + src: Tensor[T], dst: Tensor[T], dstIndex: Int): Tensor[T] = { + if (src.isContiguous() && dst.isContiguous()) { + val batchSize = dst.size(batchDim) + val timeSize = dst.size(timeDim) + val stepSize = dst.nElement() / (batchSize * timeSize) + + val dstArr = dst.storage().array() + var dstOffset = dst.storageOffset() - 1 + val srcArr = src.storage().array() + var srcOffset = src.storageOffset() - 1 + + val recordSize = timeSize * stepSize + val indexSize = (dstIndex - 1) * stepSize + + var b = 0 + while (b < batchSize) { + System.arraycopy(srcArr, srcOffset, dstArr, dstOffset + indexSize, stepSize) + srcOffset += stepSize + dstOffset += recordSize + b += 1 + } + } else { + dst.select(2, dstIndex).copy(src) + } + dst + } + override def doLoadModule[T: ClassTag](model : BigDLModule) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Select.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Select.scala index 5c30f7d5b2c..eec74dcd1c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Select.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Select.scala @@ -51,17 +51,25 @@ class Select[T: ClassTag]( override def updateOutput(input: Tensor[T]): Tensor[T] = { val (dim, index) = getPositiveDimAndIndex(input) - val output = input.select(dim, index) - this.output.resizeAs(output) + if ((dim == 2) && (input.dim() > 2)) { + Recurrent.selectCopy(input, index, this.output) + } else { + val output = input.select(dim, index) + this.output.resizeAs(output) - this.output.copy(output) + this.output.copy(output) + } } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { val (dim, index) = getPositiveDimAndIndex(input) gradInput.resizeAs(input) gradInput.zero() - gradInput.select(dim, index).copy(gradOutput) + if ((dim == 2) && (gradInput.dim() > 2)) { + Recurrent.copyToIndex(gradOutput, gradInput, index) + } else { + gradInput.select(dim, index).copy(gradOutput) + } gradInput } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala index 3d69549c3f5..ab55a54246f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala @@ -592,4 +592,23 @@ class RecurrentSpec extends FlatSpec with Matchers { rec.setState(state) model.forward(input) } + + "A Recurrent Module " should " work good with copy " in { + val input = Tensor[Float](3, 2, 6, 10).randn() + val input1 = input.select(2, 1).clone() + val input2 = input.select(2, 2).clone() + + val arrInput = new ArrayBuffer[Tensor[Float]](2) + arrInput.append(input1) + arrInput.append(input2) + + val output1 = Tensor[Float]() + val output2 = Tensor[Float]().resizeAs(input) + + Recurrent.selectCopy(input, 2, output1) + output1 should be (input.select(2, 2)) + + Recurrent.copy(arrInput, output2) + output2 should be (input) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectSpec.scala index 37d4449d7a3..e13bcab4fe4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectSpec.scala @@ -50,4 +50,34 @@ class SelectSpec extends TorchSpec { println("Test case : Select, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } + + "Select(2, 5)" should "generate correct output and grad" in { + torchCheck() + def randn(): Double = RandomGenerator.RNG.uniform(-10, 10) + val layer = new Select[Double](2, 5) + val input = Tensor[Double](3, 5, 5) + input.apply1(x => randn()) + val gradOutput = Tensor[Double](3, 5, 1) + gradOutput.apply1(x => randn()) + + val start = System.nanoTime() + val output = layer.forward(input) + val gradInput = layer.backward(input, gradOutput) + val end = System.nanoTime() + val scalaTime = end - start + + val code = "module = nn.Select(2, 5)\n" + + "output = module:forward(input)\n" + + "gradInput = module:backward(input,gradOutput)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), + Array("output", "gradInput")) + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] + + output should be (luaOutput) + gradInput should be (luaGradInput) + + println("Test case : Select, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") + } } From 77b4dd39ebe46199e0379821ac32e19a4c6b8107 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 19 Sep 2017 16:28:37 +0800 Subject: [PATCH 0401/1065] Support running tensorflow model on BigDL (feeding data with placeholder, providing model output node) (#1534) * run tensorflow as spark local * remove useless code * linear without bias tf * add training mode * allow pass in session * optimizer * fix test * fix test --- .../dllib/utils/python/api/PythonBigDL.scala | 25 ++++- .../bigdl/dllib/utils/tf/Session.scala | 101 ++++++++++++++++++ .../dllib/utils/tf/TensorflowToBigDL.scala | 27 ++++- .../bigdl/dllib/utils/tf/SessionSpec.scala | 93 ++++++++++++++++ 4 files changed, 244 insertions(+), 2 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a0aa18f1800..ddfa05bcbd6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -36,9 +36,13 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} -import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} +import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} +import com.intel.analytics.bigdl.utils.tf.{BigDLSessionImpl, TensorflowDataFormat, TensorflowSaver} +import org.apache.spark.SparkContext +import org.tensorflow.framework.NodeDef import scala.collection.JavaConverters._ +import scala.collection.mutable import scala.language.existentials import scala.reflect.ClassTag @@ -1706,6 +1710,25 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab File.save(tensors, path, true) } + def trainTF( + modelPath: String, + output: String, + samples: JavaRDD[Sample], + optMethod: OptimMethod[T], + criterion: Criterion[T], + batchSize: Int, endWhen: Trigger): AbstractModule[Activity, Activity, T] = { + val nodeList = parse(modelPath) + + val context = + new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]() + val session = new BigDLSessionImpl[T](nodeList.asScala, context) + val dataset = batching(samples, batchSize) + + val model = session.train(Seq(output), dataset, + optMethod, criterion, endWhen) + model + } + def createOptimizer(model: AbstractModule[Activity, Activity, T], trainingRdd: JavaRDD[Sample], criterion: Criterion[T], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala new file mode 100644 index 00000000000..0ab692cfdd0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -0,0 +1,101 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf + +import java.nio.{ByteOrder, DoubleBuffer, FloatBuffer} + +import com.intel.analytics.bigdl.Criterion +import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, Sample} +import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Graph, Linear} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils._ +import org.apache.spark.SparkContext +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.rdd.RDD +import org.tensorflow.framework.{GraphDef, NodeDef} + +import scala.collection.mutable +import scala.reflect.ClassTag + +abstract class Session[T: ClassTag] { + + def train(outputs: Seq[String], + dataSet: DistributedDataSet[MiniBatch[T]], + optMethod: OptimMethod[T], + criterion: Criterion[T], + endWhen: Trigger): Graph[T] +} + +class BigDLSessionImpl[T: ClassTag]( + graph: Seq[NodeDef], + context: mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]) + (implicit ev: TensorNumeric[T]) extends Session[T] { + import scala.collection.JavaConverters._ + + val sc = SparkContext.getOrCreate() + + private val inputOp = Set("ReaderReadV2", "QueueDequeueV2", "QueueDequeueManyV2", "Placeholder") + + private val (wholeTFGraph, _, _) = TensorflowLoader.buildTFGraph(graph.asJava, null) + + private val name2Node = wholeTFGraph. + DFS.filter(n => n.element != null).map(node => (node.element.getName, node)).toMap + + private def constructModel(endPoints: Seq[String]): (Graph[T], Node[NodeDef]) = { + val isInputOp = (n: NodeDef) => inputOp(n.getOp) + val (tfGraph, inputs, _) = TensorflowLoader.buildTFGraph(graph.asJava, endPoints, isInputOp) + + val inputNodes = inputs.map(name2Node) + + require(inputNodes.length == 1, "Only support one model input") + + val model = TensorflowLoader.buildBigDLModel( + tfGraph, + inputNodes.map(_.element.getName), + endPoints, + ByteOrder.LITTLE_ENDIAN, + "", + Some(context) + ).asInstanceOf[Graph[T]] + (model, inputNodes.head) + } + + override def train(outputs: Seq[String], + dataSet: DistributedDataSet[MiniBatch[T]], + optMethod: OptimMethod[T], + criterion: Criterion[T], + endWhen: Trigger): Graph[T] = { + + val (model, input) = constructModel(outputs) + + require(input.element.getOp == "Placeholder", + "only support Placeholder as input when in-memory input data is provided") + + val opt = new DistriOptimizer( + model, + dataSet, + criterion + ) + val optMethod = new SGD[T]() + opt.setOptimMethod(optMethod).setEndWhen(endWhen) + .optimize() + model + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index 375c7db1293..c31c7ca9186 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -246,7 +246,7 @@ object TensorflowToBigDL { BatchNormTF, AddConstTF1, AddConstTF2, AddTF, SoftMaxTF, ElementWiseMulTF, MulTF, SplitTF, PaddingTF, MeanTF, UnpackTF, StrideSliceTF, ShapeTF, FillTF, PackTF, ConstTF, Flatten, Conv2D2, Conv1D, FlattenV2, BatchNormV2NHWCTF, BatchNormV2NCHWTF, AddNTF, - ControlDependencyTF + ControlDependencyTF, FullConnectionWithoutBiasTF ) res } @@ -312,6 +312,31 @@ object FullConnectionTF extends TensorflowToBigDL{ } } +object FullConnectionWithoutBiasTF extends TensorflowToBigDL{ + private val graph = { + val mul = Node("MatMul") + Node("*") -> mul + Node("Const") -> Node("Identity") -> mul + mul.graph(reverse = true) + } + override def topology: DirectedGraph[String] = graph + + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + + + val weightNode = tfGraph.source.prevNodes(1).prevNodes.head.element + val (weight, gradWeight) = getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 2)))) + + Linear[T](inputSize = weight.size(2), outputSize = weight.size(1), withBias = false, + initWeight = weight, initGradWeight = gradWeight) + .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + } +} + object SqueezeTF extends TensorflowToBigDL { private val graph = (Node("*") -> Node("Squeeze")).graph(reverse = true) override def topology: DirectedGraph[String] = graph diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala new file mode 100644 index 00000000000..2a72aa6e197 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala @@ -0,0 +1,93 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf + +import com.intel.analytics.bigdl.dataset._ +import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, MSECriterion} +import com.intel.analytics.bigdl.optim.{SGD, Trigger} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Engine +import org.apache.log4j.{Level, Logger} +import org.apache.spark.SparkContext +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} +import java.io.{File => JFile} + +import scala.collection.mutable + +class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { + Logger.getLogger("org").setLevel(Level.WARN) + Logger.getLogger("akka").setLevel(Level.WARN) + + + var sc: SparkContext = null + + var dataSet: DistributedDataSet[MiniBatch[Float]] = null + + before { + val conf = Engine.createSparkConf() + conf.set("spark.master", "local[1]") + conf.set("spark.app.name", "SessionSpec") + sc = new SparkContext(conf) + Engine.init + Engine.model.setPoolSize(1) + } + + after { + if (sc != null) { + sc.stop() + } + } + + "Session" should "be able to run basic model" in { + + val resource = getClass().getClassLoader().getResource("tf") + val path = resource.getPath() + JFile.separator + "test.pb" + + val nodes = TensorflowLoader.parse(path) + + import scala.collection.JavaConverters._ + val context = + new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() + val session = new BigDLSessionImpl[Float](nodes.asScala, context) + + val data = new Array[Tensor[Float]](100) + val label = new Array[Tensor[Float]](100) + for (i <- Range(0, 100)) { + val t = Tensor[Float](Array(1)) + val l = Tensor[Float](Array(1)) + data.update(i, t) + label.update(i, l) + } + + val optim = new SGD[Float](0.001) + val criterion = MSECriterion[Float]() + val endWhen = Trigger.maxEpoch(5) + + val samples = data.zip(label).map { case (dataTensor, labelTensor) => + Sample(dataTensor, labelTensor) + } + + val batchSize = Engine.nodeNumber() + val rdd = sc.parallelize(samples, batchSize) + + val datasets = (DataSet.rdd(rdd) -> SampleToMiniBatch[Float](batchSize)) + .asInstanceOf[DistributedDataSet[MiniBatch[Float]]] + + val module = session.train(Seq("output"), datasets, optim, criterion, endWhen) + module.forward(Tensor[Float](Array(1))) + } + +} From 8048784017fd263d9a69dd3a4a53067cf98cbc06 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 19 Sep 2017 18:51:59 +0800 Subject: [PATCH 0402/1065] support different type in tensorflow loader (#1569) * support different type in tensorflow * meet code review * meet code review * fix tests --- .../analytics/bigdl/dllib/nn/JoinTable.scala | 40 +++-- .../analytics/bigdl/dllib/nn/Scheduler.scala | 2 +- .../analytics/bigdl/dllib/nn/Utils.scala | 3 +- .../analytics/bigdl/dllib/nn/tf/Const.scala | 15 +- .../analytics/bigdl/dllib/nn/tf/Fill.scala | 53 ++++-- .../bigdl/dllib/tensor/DenseTensor.scala | 15 ++ .../analytics/bigdl/dllib/tensor/Tensor.scala | 26 +++ .../dllib/utils/tf/TFTensorNumeric.scala | 4 +- .../dllib/utils/tf/TensorflowToBigDL.scala | 167 +++++++++--------- .../bigdl/dllib/nn/BiRecurrentSpec.scala | 3 +- .../bigdl/dllib/nn/JoinTableSpec.scala | 38 ++++ .../bigdl/dllib/nn/tf/FillSpec.scala | 18 +- .../dllib/utils/tf/TensorflowSaverSpec.scala | 3 +- 13 files changed, 254 insertions(+), 133 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala index aac1991074e..532d865bc59 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala @@ -15,9 +15,11 @@ */ package com.intel.analytics.bigdl.nn +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString import com.intel.analytics.bigdl.utils.{Engine, Table} import scala.concurrent.Future @@ -43,14 +45,14 @@ class JoinTable[T: ClassTag] ( val dimension: Int, val nInputDims: Int )(implicit ev: TensorNumeric[T]) - extends AbstractModule[Table, Tensor[T], T] { + extends AbstractModule[Table, Tensor[_], T] { @transient private var results: Array[Future[Unit]] = null private def getPositiveDimension(input: Table): Int = { var nDim = this.dimension - val firstInput: Tensor[T] = input(1) + val firstInput: Tensor[_] = input(1) if (nDim < 0) { nDim = firstInput.dim() + nDim + 1 @@ -62,13 +64,13 @@ class JoinTable[T: ClassTag] ( nDim } - override def updateOutput(input: Table): Tensor[T] = { + override def updateOutput(input: Table): Tensor[_] = { val dimension = getPositiveDimension(input) var size: Array[Int] = null var i = 1 while (i <= input.length()) { - val currentOutput: Tensor[T] = input(i) + val currentOutput: Tensor[_] = input(i) if (i == 1) { size = currentOutput.size() } else { @@ -76,7 +78,12 @@ class JoinTable[T: ClassTag] ( } i += 1 } - output.resize(size) + val firstInput = input[Tensor[_]](1) + if (output.getType() != firstInput.getType()) { + output = firstInput.emptyInstance().resize(size) + } else { + output.resize(size) + } if (results == null || results.length != input.length) { results = new Array[Future[Unit]](input.length) @@ -84,12 +91,12 @@ class JoinTable[T: ClassTag] ( var offset = 1 i = 0 while (i < input.length) { - val currentOutput = input(i + 1).asInstanceOf[Tensor[T]] + val currentOutput = input(i + 1).asInstanceOf[Tensor[_]] val _offset = offset results(i) = Engine.model.invoke( () => { val target = output.narrow(dimension, _offset, currentOutput.size(dimension)) if (target.isContiguous() || dimension > 2) { - target.copy(currentOutput) + target.forceCopy(currentOutput) } else { var f = 1 while (f <= target.size(1)) { @@ -97,7 +104,7 @@ class JoinTable[T: ClassTag] ( val outputFrame = currentOutput.select(1, f) require(curFrame.isContiguous()) require(outputFrame.isContiguous()) - curFrame.copy(outputFrame) + curFrame.forceCopy(outputFrame) f += 1 } } @@ -109,29 +116,30 @@ class JoinTable[T: ClassTag] ( output } - override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + override def updateGradInput(input: Table, gradOutput: Tensor[_]): Table = { val dimension = getPositiveDimension(input) var offset = 1 var i = 0 while (i < input.length) { - val currentOutput = input(i + 1).asInstanceOf[Tensor[T]] + val currentOutput = input(i + 1).asInstanceOf[Tensor[_]] val _offset = offset val _i = i results(i) = Engine.model.invoke( () => { val narrowedTensor = gradOutput.narrow(dimension, _offset, currentOutput.size(dimension)) - if (!gradInput.contains(_i + 1)) gradInput(_i + 1) = Tensor() - gradInput[Tensor[T]](_i + 1).resizeAs(input(_i + 1)) + val inputTensor = input[Tensor[_]](_i + 1) + if (!gradInput.contains(_i + 1)) gradInput(_i + 1) = + inputTensor.emptyInstance().resize(inputTensor.size()) if(narrowedTensor.isContiguous() || dimension > 2) { - gradInput[Tensor[T]](_i + 1).copy(narrowedTensor) + gradInput[Tensor[_]](_i + 1).forceCopy(narrowedTensor) } else { var b = 1 while(b <= narrowedTensor.size(1)) { - val curFrame = gradInput[Tensor[T]](_i + 1).select(1, b) + val curFrame = gradInput[Tensor[_]](_i + 1).select(1, b) val narrowFrame = narrowedTensor.select(1, b) require(curFrame.isContiguous()) require(narrowFrame.isContiguous()) - curFrame.copy(narrowFrame) + curFrame.forceCopy(narrowFrame) b += 1 } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala index 208b98ab8c2..ab7ca849cc6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala @@ -94,7 +94,7 @@ private[bigdl] class Scheduler[T] ( def schedule(node: ModuleNode[T]): Unit = { // Update status of current node nodeStatus(node) = if (node.prevNodes.length == 0) { - if (node.element.isInstanceOf[com.intel.analytics.bigdl.nn.tf.Const[_]]) { + if (node.element.isInstanceOf[com.intel.analytics.bigdl.nn.tf.Const[_, _]]) { Const() } else { Ready() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index c4c8e7748a7..9b1576bffd0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -16,9 +16,10 @@ package com.intel.analytics.bigdl.nn +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala index 3cbf3b866c5..014eb6f9a8c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala @@ -29,8 +29,9 @@ private[bigdl] trait WithoutInput * @param value the constant tensor to be returned in forward */ @SerialVersionUID(-4008935551091949324L) -private[bigdl] class Const[T: ClassTag](value: Tensor[T])(implicit ev: TensorNumeric[T]) - extends AbstractModule[Activity, Tensor[T], T] with WithoutInput { +private[bigdl] class Const[T: ClassTag, B: ClassTag](value: Tensor[B]) + (implicit ev: TensorNumeric[T]) + extends AbstractModule[Activity, Tensor[B], T] with WithoutInput { override def clearState(): this.type = { // Const do not have state, output should always be value @@ -39,9 +40,9 @@ private[bigdl] class Const[T: ClassTag](value: Tensor[T])(implicit ev: TensorNum output = value - override def updateOutput(input: Activity): Tensor[T] = output + override def updateOutput(input: Activity): Tensor[B] = output - override def updateGradInput(input: Activity, gradOutput: Tensor[T]): Activity = { + override def updateGradInput(input: Activity, gradOutput: Tensor[B]): Activity = { require(gradOutput.isSameSizeAs(value), s"Invalid gradOutput size. require (${value.size().mkString(",")}), but " + s"(${gradOutput.size().mkString(",")})") @@ -67,8 +68,8 @@ private[bigdl] class Const[T: ClassTag](value: Tensor[T])(implicit ev: TensorNum } private[bigdl] object Const { - def apply[T: ClassTag](value: Tensor[T]) - (implicit ev: TensorNumeric[T]): Const[T] = { - new Const[T](value) + def apply[T: ClassTag, B: ClassTag](value: Tensor[B]) + (implicit ev: TensorNumeric[T]): Const[T, B] = { + new Const[T, B](value) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala index 295989e1788..f9b4d2ae733 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala @@ -15,38 +15,65 @@ */ package com.intel.analytics.bigdl.nn.tf -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag /** * Creates a tensor filled with a scalar value. Input should be a 1-D tensor defining * the shape of the output tensor. - * @param value the scalar value to be filled. */ @SerialVersionUID(-471757174144422555L) -private[bigdl] class Fill[T: ClassTag](value: T) (implicit ev: TensorNumeric[T]) - extends TensorModule[T] { +private[bigdl] class Fill[T: ClassTag]() (implicit ev: TensorNumeric[T]) + extends AbstractModule[Table, Tensor[_], T] { - override def updateOutput(input: Tensor[T]): Tensor[T] = { - if (output.dim() == 0) { - val shape = input.storage().array().map(ev.toType[Int]) - output = Tensor(shape).fill(value) + override def updateOutput(input: Table): Tensor[_] = { + val shapeTensor = input[Tensor[Int]](1) + require(shapeTensor.nDimension() == 1, "shape tensor is not a vector") + val shape = new Array[Int](shapeTensor.nElement()) + var i = 0 + while (i < shapeTensor.nElement()) { + shape(i) = shapeTensor.valueAt(i + 1) + i = i + 1 } + val value = input[Tensor[_]](2) + require(value.isScalar, "value tensor is not a scalar") + if (value.getType() != output.getType()) { + output = value.emptyInstance().resize(shape) + } else { + output.resize(shape) + } + + output.forceFill(value.value()) + output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - gradInput.resizeAs(input).zero() + override def updateGradInput(input: Table, gradOutput: Tensor[_]): Table = { + if (gradInput.contains(1)) { + gradInput[Tensor[_]](1).resize(input[Tensor[_]](1).size()).zero() + } else { + val inputTensor = input[Tensor[_]](1) + gradInput(1) = inputTensor.emptyInstance().resize(inputTensor.size()) + } + + if (gradInput.contains(2)) { + gradInput[Tensor[_]](2).resize(input[Tensor[_]](2).size()).zero() + } else { + val inputTensor = input[Tensor[_]](2) + gradInput(2) = inputTensor.emptyInstance().resize(inputTensor.size()) + } + gradInput } } private[bigdl] object Fill { - def apply[T: ClassTag](value: Double) + def apply[T: ClassTag]() (implicit ev: TensorNumeric[T]) : Fill[T] = { - new Fill[T](ev.fromType(value)) + new Fill[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 8ae60569571..34c4bc8618c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -245,6 +245,10 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( this } + override def forceFill(v: Any): Tensor[T] = { + this.fill(v.asInstanceOf[T]) + } + override def zero(): Tensor[T] = { this.fill(ev.fromType[Int](0)) } @@ -382,11 +386,22 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( DenseTensor.newClone(this) } + override def emptyInstance(): Tensor[T] = { + Tensor[T]() + } + override def copy(other: Tensor[T]): Tensor[T] = { DenseTensor.copy(this, other) this } + override def forceCopy(other: Tensor[_]): Tensor[T] = { + require(this.getType() == other.getType(), + "forceCopy should copy from a tensor of the same type") + DenseTensor.copy(this, other.asInstanceOf[Tensor[T]]) + this + } + override def narrow(dim: Int, index: Int, size: Int): Tensor[T] = { val result = DenseTensor.newWithTensor(this) DenseTensor.narrow(result, null, dim - 1, index - 1, size) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 2294c98f8c8..0bcca0d6ed6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -101,6 +101,16 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { */ def fill(v: T): Tensor[T] + /** + * Fill with a given value. It will change the value of the current tensor and return itself + * + * Note the value should be an instance of T + * + * @param v value to fill the tensor + * @return current tensor + */ + def forceFill(v: Any): Tensor[T] + /** * Fill with zero. It will change the value of the current tensor and return itself * @@ -340,6 +350,13 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { this } + /** + * return a new empty tensor of the same type + * + * @return new tensor + */ + def emptyInstance(): Tensor[T] + /** * Resize the current tensor to the same size of the given tensor. It will still use the same * storage if the storage @@ -469,6 +486,15 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { */ def copy(other: Tensor[T]): Tensor[T] + /** + * Copy the value of the given tensor to the current. They should have same size. + * They should also have the same type. + * + * @param other source tensor + * @return current tensor + */ + def forceCopy(other: Tensor[_]): Tensor[T] + /** * Apply a function to each element of the tensor `t` * and set each value to self diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFTensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFTensorNumeric.scala index 7e9510ce343..f49e255de87 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFTensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFTensorNumeric.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.utils.tf import com.google.protobuf.ByteString -import com.intel.analytics.bigdl.tensor.ConvertableFrom +import com.intel.analytics.bigdl.tensor.{ConvertableFrom, StringType, TensorDataType} import com.intel.analytics.bigdl.tensor.TensorNumericMath.UndefinedTensorNumeric import scala.language.implicitConversions @@ -24,6 +24,8 @@ import scala.language.implicitConversions object TFTensorNumeric { implicit object NumericByteString extends UndefinedTensorNumeric[ByteString]("ByteString") { + + override def getType(): TensorDataType = StringType override def plus(x: ByteString, y: ByteString): ByteString = x.concat(y) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index c31c7ca9186..81d551539e1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -54,7 +54,7 @@ trait TensorflowToBigDL { tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder - )(implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] + )(implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] protected def getOrSetTensor[T: ClassTag]( node: NodeDef, context: Context[T], byteOrder: ByteOrder, @@ -299,7 +299,7 @@ object FullConnectionTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element @@ -308,7 +308,7 @@ object FullConnectionTF extends TensorflowToBigDL{ val (weight, gradWeight) = getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 2)))) Linear[T](inputSize = weight.size(2), outputSize = weight.size(1), initWeight = weight, initGradWeight = gradWeight, initBias = bias, initGradBias = gradBias) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + .asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -325,7 +325,7 @@ object FullConnectionWithoutBiasTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val weightNode = tfGraph.source.prevNodes(1).prevNodes.head.element @@ -333,7 +333,7 @@ object FullConnectionWithoutBiasTF extends TensorflowToBigDL{ Linear[T](inputSize = weight.size(2), outputSize = weight.size(1), withBias = false, initWeight = weight, initGradWeight = gradWeight) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + .asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -344,12 +344,12 @@ object SqueezeTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val dims = tfGraph.source.element.getAttrOrThrow("squeeze_dims").getList().getIList() .asScala.map(_.toInt).toArray - Squeeze[T](dims, batchMode = true).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Squeeze[T](dims, batchMode = true).asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -376,7 +376,7 @@ object Conv1D extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val squeezeNode = tfGraph.source.prevNodes.head val convNode = squeezeNode.prevNodes.head @@ -433,7 +433,7 @@ object Conv1D extends TensorflowToBigDL { case "NHWC" => tconv } - result.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + result.asInstanceOf[AbstractModule[Activity, Activity, T]] } @@ -455,7 +455,7 @@ object Conv2D extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val attributes = tfGraph.source.prevNodes.head.element.getAttrMap val (pW, pH) = @@ -517,7 +517,7 @@ object Conv2D extends TensorflowToBigDL{ case _ => throw new IllegalArgumentException(s"not supported data format: $format") } - conv.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -541,7 +541,7 @@ object Conv2D2 extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val attributes = tfGraph.source.prevNodes(0).element.getAttrMap val strideList = getIntList(attributes, "strides") @@ -579,7 +579,7 @@ object Conv2D2 extends TensorflowToBigDL{ initWeight = weights, initBias = bias, initGradWeight = gradWeights, - initGradBias = gradBias).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + initGradBias = gradBias).asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -593,9 +593,9 @@ object ReluTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - ReLU[T]().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + ReLU[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -609,10 +609,10 @@ object TanhTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Tanh[T]().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Tanh[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -626,9 +626,9 @@ object SigmoidTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Sigmoid[T]().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Sigmoid[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -645,7 +645,7 @@ object ReshapeTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val sizes = TensorflowToBigDL.toTensor( tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) @@ -660,7 +660,7 @@ object ReshapeTF extends TensorflowToBigDL { i += 1 } Reshape[T](size = arraySize, Some(batchMode)) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + .asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -674,7 +674,7 @@ object MaxPoolingTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val attributes = tfGraph.source.element.getAttrMap val format = getString(attributes, "data_format") @@ -700,7 +700,7 @@ object MaxPoolingTF extends TensorflowToBigDL { SpatialMaxPooling[T](ksizeW, ksizeH, strideW, strideH, pW, pH, format = DataFormat(format)) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + .asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -713,7 +713,7 @@ object AvgPoolingTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val attributes = tfGraph.source.element.getAttrMap val format = getString(attributes, "data_format") @@ -740,7 +740,7 @@ object AvgPoolingTF extends TensorflowToBigDL { SpatialAveragePooling[T](ksizeW, ksizeH, strideW, strideH, pW, pH, countIncludePad = false, format = DataFormat(format)) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + .asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -769,14 +769,14 @@ object DropoutTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val keepProp = tfGraph.source.prevNodes(0).prevNodes(1).element .getAttrMap.get("value").getTensor.getFloatVal(0) val model = Sequential() model.add(SelectTable(1)) - model.add(Dropout[T](keepProp).asInstanceOf[AbstractModule[Activity, Tensor[T], T]]) - model.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + model.add(Dropout[T](keepProp).asInstanceOf[AbstractModule[Activity, Activity, T]]) + model.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -788,8 +788,8 @@ object Placeholder extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { - Input[T]().element.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + Input[T]().element.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -801,11 +801,11 @@ object ConstTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val value = TensorflowToBigDL - .toTensor(tfGraph.source.element.getAttrMap.get("value").getTensor, byteOrder) - Const(value).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + val value = TFUtils + .parseTensor(tfGraph.source.element.getAttrMap.get("value").getTensor, byteOrder) + Const(value).asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -821,10 +821,10 @@ object ShapeTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Shape[T]().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Shape[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -836,9 +836,9 @@ object IdentityTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Input[T]().element.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Input[T]().element.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -890,7 +890,7 @@ object BatchNormV2NCHWTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.prevNodes.head.prevNodes.head.element val weightNode = tfGraph.source.prevNodes(1).prevNodes(1).prevNodes(1) @@ -909,7 +909,7 @@ object BatchNormV2NCHWTF extends TensorflowToBigDL{ val model = Sequential() model.add(SelectTable(1)) model.add(batchNorm) - model.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + model.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -953,7 +953,7 @@ object BatchNormV2NHWCTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.prevNodes.head.element val weightNode = tfGraph.source.prevNodes(1).prevNodes(1).prevNodes(1) @@ -977,7 +977,7 @@ object BatchNormV2NHWCTF extends TensorflowToBigDL{ layer.add(Transpose(Array((2, 4)))) layer.add(Contiguous()) - layer.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + layer.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1025,7 +1025,7 @@ object BatchNormTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val nOutput = tfGraph.source.prevNodes(1).prevNodes(1).prevNodes(1) .prevNodes(1).prevNodes(0).element.getAttrMap.get("value").getTensor.getIntVal(0) @@ -1042,11 +1042,11 @@ object BatchNormTF extends TensorflowToBigDL{ initBias = bias, initGradWeight = gradWeights, initGradBias = gradBias - ).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + ).asInstanceOf[AbstractModule[Activity, Activity, T]] val model = Sequential() model.add(SelectTable(1)) model.add(batchNorm) - model.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + model.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1054,7 +1054,7 @@ object FillTF extends TensorflowToBigDL{ private val graph = { val nodeFill = Node("Fill") Node("*") -> nodeFill - Node("Const") -> nodeFill + Node("*") -> nodeFill nodeFill.graph(reverse = true) } @@ -1063,12 +1063,9 @@ object FillTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { - - val constNode = tfGraph.source.prevNodes(1) - val const = constNode.element.getAttrMap.get("value").getTensor.getFloatVal(0) + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Fill[T](const).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Fill[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1084,11 +1081,11 @@ object PackTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val attr = tfGraph.source.element.getAttrMap val dim = getInt(attr, "axis") + 1 - Pack[T](dim).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + Pack[T](dim).asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1104,11 +1101,11 @@ object UnpackTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val attr = tfGraph.source.element.getAttrMap val dim = getInt(attr, "axis") + 1 - SplitTable[T](dim).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + SplitTable[T](dim).asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1127,7 +1124,7 @@ object StrideSliceTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val startNode = tfGraph.source.prevNodes(1) val endNode = tfGraph.source.prevNodes(2) @@ -1145,7 +1142,7 @@ object StrideSliceTF extends TensorflowToBigDL { .map(elem => (elem._2 + 1, elem._1._1._1 + 1, elem._1._1._2 + 1, elem._1._2)).toArray - StrideSlice[T](specs).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + StrideSlice[T](specs).asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1162,7 +1159,7 @@ object ConcatTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val inputNumber = tfGraph.source.element.getAttrMap.get("N").getI.toInt val nodeaxis = tfGraph.source.prevNodes(inputNumber) @@ -1170,7 +1167,7 @@ object ConcatTF extends TensorflowToBigDL{ val nInputDims = 4 JoinTable[T](dimension = axis, nInputDims = -1) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + .asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1220,12 +1217,12 @@ object FlattenV2 extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val layer = Sequential() layer.add(SelectTable(1)) layer.add(InferReshape[T](size = Array(-1), true)) - layer.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + layer.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1274,7 +1271,7 @@ object Flatten extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val shapetfTensor = tfGraph.source.prevNodes(1).prevNodes(0).prevNodes(0).element .getAttrMap.get("value").getTensor val sizes = TensorflowToBigDL.toTensor(shapetfTensor, byteOrder) @@ -1294,7 +1291,7 @@ object Flatten extends TensorflowToBigDL { ) Reshape[T](size = arraySize, Some(batchMode)) - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + .asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1310,10 +1307,10 @@ object AddConstTF1 extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val value = tfGraph.source.prevNodes.head.element .getAttrMap.get("value").getTensor.getFloatVal(0) - AddConstant[T](value).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + AddConstant[T](value).asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1329,11 +1326,11 @@ object AddConstTF2 extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val value = tfGraph.source.prevNodes(1).element .getAttrMap.get("value").getTensor.getFloatVal(0) - AddConstant[T](value).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + AddConstant[T](value).asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1349,9 +1346,9 @@ object AddTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - CAddTable[T]().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + CAddTable[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1364,9 +1361,9 @@ object SoftMaxTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - SoftMax[T]().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + SoftMax[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1383,13 +1380,13 @@ object MulTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val scale = TensorflowToBigDL.toTensor( tfGraph.source.prevNodes(0).element.getAttrMap.get("value").getTensor, byteOrder) require(scale.dim() == 1 && scale.size(1) == 1, s"scale must be one number") val mul = MulConstant[T](ev.toType[Double](scale.valueAt(1))) - mul.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + mul.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1405,9 +1402,9 @@ object ElementWiseMulTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - CMulTable[T]().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + CMulTable[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1424,7 +1421,7 @@ object SplitTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val numSplit = tfGraph.source.element.getAttrMap.get("num_split").getI.toInt val dim = tfGraph.source.prevNodes.head.element @@ -1433,7 +1430,7 @@ object SplitTF extends TensorflowToBigDL { for (index <- Range(1, numSplit + 1)) { model.add(SplitAndSelect[T](dim, index, numSplit)) } - model.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + model.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1451,7 +1448,7 @@ object PaddingTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val paddings = TensorflowToBigDL.toTensor( tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) @@ -1469,7 +1466,7 @@ object PaddingTF extends TensorflowToBigDL{ } } - padding.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + padding.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1485,7 +1482,7 @@ object MeanTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val dims = TensorflowToBigDL.toTensor( tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) @@ -1495,7 +1492,7 @@ object MeanTF extends TensorflowToBigDL{ dim += ev.toType[Int](dims.valueAt(i)) + 1 } dim.foreach(i => mean.add(Mean[T](i, squeeze = false))) - mean.asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + mean.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1509,9 +1506,9 @@ object AddNTF extends TensorflowToBigDL{ override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - CAddTable().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + CAddTable().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1528,9 +1525,9 @@ object ControlDependencyTF extends TensorflowToBigDL { override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Tensor[T], T] = { + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - ControlDependency().asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + ControlDependency().asInstanceOf[AbstractModule[Activity, Activity, T]] } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrentSpec.scala index 17577915314..5f567e69ce5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrentSpec.scala @@ -63,7 +63,8 @@ class BiRecurrentSpec extends TorchSpec { val gradOutput1 = gradOutput.narrow(3, 1, outputSize).contiguous() val gradOutput2 = gradOutput.narrow(3, 1 + outputSize, outputSize).contiguous() - val birnn = BiRecurrent[Double](JoinTable[Double](3, 0), isSplitInput = true) + val birnn = BiRecurrent[Double](JoinTable[Double](3, 0) + .asInstanceOf[AbstractModule[Table, Tensor[Double], Double]], isSplitInput = true) .add(RnnCell[Double](half, outputSize, ReLU[Double]())) val recurrent1 = Recurrent[Double]() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala new file mode 100644 index 00000000000..03dd5eb548b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Parallel +class JoinTableSpec extends FlatSpec with Matchers { + + "Join Table " should "work for Int inputs" in { + val input1 = Tensor[Int](T(1, 2)) + val input2 = Tensor[Int](T(3, 4)) + val layer = JoinTable[Float](1, -1) + val expectedOutput = Tensor[Int](T(1, 2, 3, 4)) + val gradOuput = Tensor[Int](T(1, 2, 3, 4)) + val output = layer.forward(T(input1, input2)) + expectedOutput should be (output) + val gradInput = layer.backward(T(input1, input2), gradOuput) + gradInput[Tensor[Int]](1) should be (Tensor[Int](T(1, 2))) + gradInput[Tensor[Int]](2) should be (Tensor[Int](T(3, 4))) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala index a874fd17fd8..8ddde5f96e1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala @@ -23,16 +23,20 @@ import org.scalatest.{FlatSpec, Matchers} class FillSpec extends FlatSpec with Matchers { "Fill forward" should "be correct" in { - val layer = Fill(0.1) - val shape = Tensor(T(2.0f, 3.0f)) - layer.forward(shape) should be(Tensor(T(T(0.1f, 0.1f, 0.1f), T(0.1f, 0.1f, 0.1f)))) + val layer = Fill() + val shape = Tensor[Int](T(2, 3)) + val value = Tensor[Float](Array(0.1f), Array[Int]()) + layer.forward(T(shape, value)) should be(Tensor(T(T(0.1f, 0.1f, 0.1f), T(0.1f, 0.1f, 0.1f)))) } "Fill backward" should "be correct" in { - val layer = Fill(0.1) - val shape = Tensor(T(2.0f, 3.0f)) + val layer = Fill() + val shape = Tensor[Int](T(2, 3)) + val value = Tensor[Float](Array(0.1f), Array[Int]()) val gradOutput = Tensor(2, 3).rand() - layer.forward(shape) should be(Tensor(T(T(0.1f, 0.1f, 0.1f), T(0.1f, 0.1f, 0.1f)))) - layer.backward(shape, gradOutput) should be(Tensor(T(0.0f, 0.0f))) + layer.forward(T(shape, value)) should be(Tensor(T(T(0.1f, 0.1f, 0.1f), T(0.1f, 0.1f, 0.1f)))) + val gradInput = layer.backward(T(shape, value), gradOutput) + gradInput[Tensor[Int]](1) should be (Tensor[Int](2)) + gradInput[Tensor[Float]](2) should be (Tensor[Float](Array(0.0f), Array[Int]())) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala index 3c973d216cf..bf391d40f2d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala @@ -128,7 +128,8 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { val layer = JoinTable[Float](3, -1) val input1 = Tensor[Float](4, 2, 2).rand() val input2 = Tensor[Float](4, 2, 2).rand() - testMultiInput(layer, Seq(input1, input2), false) should be(true) + testMultiInput(layer.asInstanceOf[AbstractModule[Table, Tensor[Float], Float]], + Seq(input1, input2), false) should be(true) } "LogSoftMax" should "be correctly saved" in { From e3949aa63908842ab36d519c9d709fb2874418f2 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 20 Sep 2017 10:02:05 +0800 Subject: [PATCH 0403/1065] Add assert, image decoders, parseExample operation (#1559) * add some ops * add parse example * style * add variable * add png decoder * add raw decoder * remove resources * fix ops * test sig * remove useless code * remove import * fix style * fix conflict * fix api --- .../analytics/bigdl/dllib/nn/ops/Assert.scala | 41 ++ .../bigdl/dllib/nn/ops/DecodeImage.scala | 370 ++++++++++++++++++ .../bigdl/dllib/nn/ops/ParseExample.scala | 83 ++++ .../bigdl/dllib/nn/tf/Variable.scala | 72 ++++ .../dllib/utils/tf/TFRecordIterator.scala | 2 +- .../src/test/resources/tf/mnist_test.tfrecord | Bin 0 -> 3133 bytes .../bigdl/dllib/nn/ops/DecodeImageSpec.scala | 107 +++++ .../bigdl/dllib/nn/ops/ParseExampleSpec.scala | 77 ++++ 8 files changed, 751 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assert.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala create mode 100644 scala/dllib/src/test/resources/tf/mnist_test.tfrecord create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assert.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assert.scala new file mode 100644 index 00000000000..bedfab5d6f3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assert.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Assert will assert the first input to be true, if not, throw the message in the second + * input. Assert has no output. + */ +class Assert[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Operation[Table, Activity, T] { + override def updateOutput(input: Table): Tensor[T] = { + val predicateTensor = input(1).asInstanceOf[Tensor[Boolean]] + val messageTensor = input(2).asInstanceOf[Tensor[ByteString]] + + val predicate = predicateTensor.value() + val message = messageTensor.value() + + assert(predicate, message.toStringUtf8) + null + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala new file mode 100644 index 00000000000..b772016cede --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala @@ -0,0 +1,370 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import java.awt.image.{BufferedImage, DataBufferByte} +import java.io.ByteArrayInputStream +import java.nio.{ByteBuffer, ByteOrder} +import javax.imageio.ImageIO + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.DataType + +import scala.reflect.ClassTag + +class DecodeImage[T: ClassTag](val channels: Int)(implicit ev: TensorNumeric[T]) + extends Operation[Tensor[ByteString], Tensor[Int], T] { + + output = Tensor[Int]() + override def updateOutput(input: Tensor[ByteString]): Tensor[Int] = { + require(input.isScalar, "only support ByteString scalar") + val image = ImageIO.read(new ByteArrayInputStream(input.value().toByteArray)) + require(image != null, "Can't decode image") + val imageWidth = image.getWidth + val imageHeight = image.getHeight + + val expectedChannels = if (channels == 0) { + image.getColorModel.getNumComponents + } else { + require(channels == image.getColorModel.getNumComponents, + "Only support inputs channels equal to desired channels") + channels + } + + output.resize(imageHeight, imageWidth, expectedChannels) + + val outputData = output.storage().array() + val offset = output.storageOffset() - 1 + val length = imageHeight * imageWidth * expectedChannels + + copyImageData(image, outputData, offset, length) + output + } + + protected def copyImageData(image: BufferedImage, + outputData: Array[Int], + offset: Int, + length: Int): Unit = { + val data = image.getRaster.getDataBuffer.asInstanceOf[DataBufferByte].getData + + bytesToInts(data, outputData, offset, length) + } + + private def bytesToInts(bytes: Array[Byte], ints: Array[Int], start: Int, length: Int): Unit = { + if (bytes.length == length) { + var i = 0 + while (i < length) { + ints(i + start) = bytes(i) & 0xff + i += 1 + } + } else if (bytes.length * 3 == length) { + var i = 0 + while (i < length) { + val index = i / 3 + ints(i + start) = bytes(index) & 0xff + i += 1 + } + } else { + throw new IllegalArgumentException("image data is not equal to output buffer") + } + } + + override def updateGradInput(input: Tensor[ByteString], + gradOutput: Tensor[Int]): Tensor[ByteString] = { + throw new UnsupportedOperationException("no backward on ParseExample") + } +} + +class DecodeJpeg[T: ClassTag](channels: Int, ratio: Int = 1)(implicit ev: TensorNumeric[T]) + extends DecodeImage[T](channels) { + require(ratio == 1, "currently not supported sub-sampling") +} + +class DecodePng[T: ClassTag](channels: Int)(implicit ev: TensorNumeric[T]) + extends DecodeImage[T](channels) + +class DecodeGif[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends DecodeImage[T](3) { + + override def updateOutput(input: Tensor[ByteString]): Tensor[Int] = { + require(input.isScalar, "only support ByteString scalar") + + val reader = ImageIO.getImageReadersByFormatName("gif").next() + + val is = ImageIO.createImageInputStream(new ByteArrayInputStream(input.value().toByteArray)) + + // val reader = ImageIO.getImageReaders(is).next() + + reader.setInput(is) + + val numOfFrames = reader.getNumImages(true) + val imageHeight = reader.getHeight(0) + val imageWidth = reader.getWidth(0) + + output.resize(numOfFrames, imageHeight, imageWidth, channels) + val outputData = output.storage().array() + val offset = output.storageOffset() - 1 + val imageSize = imageHeight * imageWidth * channels + + var i = 0 + while (i < numOfFrames) { + val image = reader.read(i) + require(image != null, s"Can't decode ${i}th frame") + require(imageHeight == image.getHeight, + s"Different frame should have the same height," + + s"first image height: $imageHeight, ${i}th image height: ${image.getHeight}") + require(imageWidth == image.getWidth, + s"Different frame should have the same width," + + s"first image width: $imageWidth, ${i}th image width: ${image.getWidth}") + + val currentOffset = offset + i * imageSize + + copyImageData(image, outputData, currentOffset, imageSize) + + i = i + 1 + } + output + } + +} + +class DecodeRaw[T: ClassTag](val outType: DataType, + val byteOrder: ByteOrder)(implicit ev: TensorNumeric[T]) + extends Operation[Tensor[ByteString], Activity, T] { + output = { + outType match { + case DataType.DT_UINT8 => Tensor[Int]() + case DataType.DT_INT16 => Tensor[Int]() + case DataType.DT_INT32 => Tensor[Int]() + case DataType.DT_INT8 => Tensor[Int]() + case DataType.DT_INT64 => Tensor[Long]() + case DataType.DT_FLOAT => Tensor[Float]() + case DataType.DT_DOUBLE => Tensor[Double]() + case _ => throw new IllegalArgumentException(s"$outType are not supported") + } + } + + override def updateOutput(input: Tensor[ByteString]): Activity = { + require(input.isContiguous(), "only support contiguous input") + val offset = input.storageOffset() - 1 + val data = input.storage().array() + val firstElem = data(offset) + + val buffer = ByteBuffer.wrap(firstElem.toByteArray) + buffer.order(byteOrder) + outType match { + case DataType.DT_UINT8 => decodeUint8(input, buffer.array().length) + case DataType.DT_INT8 => decodeInt8(input, buffer.array().length) + case DataType.DT_INT16 => decodeInt16(input, buffer.asShortBuffer().array().length) + case DataType.DT_INT32 => decodeInt32(input, buffer.asIntBuffer().array().length) + case DataType.DT_INT64 => decodeInt64(input, buffer.asLongBuffer().array().length) + case DataType.DT_FLOAT => decodeFloat(input, buffer.asFloatBuffer().array().length) + case DataType.DT_DOUBLE => decodeDouble(input, buffer.asDoubleBuffer().array().length) + } + output + } + + private def decodeDouble(input: Tensor[ByteString], featureSize: Int): Unit = { + val typedOutput = output.asInstanceOf[Tensor[Double]] + val size = input.size().toSeq :+ featureSize + typedOutput.resize(size.toArray) + + val outputData = typedOutput.storage().array() + val outputOffset = typedOutput.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + + val dataSize = input.nElement() + var i = 0 + while (i < dataSize) { + val bytes = inputData(inputOffset + i).toByteArray + val buffer = ByteBuffer.wrap(bytes) + buffer.order(byteOrder) + val typedInputData = buffer.asDoubleBuffer().array() + require(typedInputData.length == featureSize, + s"each element should have the same size, first elem size: $featureSize, " + + s"${i}th elem size: ${typedInputData.length}") + System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + i = i + 1 + } + } + + private def decodeFloat(input: Tensor[ByteString], featureSize: Int): Unit = { + val typedOutput = output.asInstanceOf[Tensor[Float]] + val size = input.size().toSeq :+ featureSize + typedOutput.resize(size.toArray) + + val outputData = typedOutput.storage().array() + val outputOffset = typedOutput.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + + val dataSize = input.nElement() + var i = 0 + while (i < dataSize) { + val bytes = inputData(inputOffset + i).toByteArray + val buffer = ByteBuffer.wrap(bytes) + buffer.order(byteOrder) + val typedInputData = buffer.asFloatBuffer().array() + require(typedInputData.length == featureSize, + s"each element should have the same size, first elem size: $featureSize, " + + s"${i}th elem size: ${typedInputData.length}") + System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + i = i + 1 + } + } + + private def decodeInt32(input: Tensor[ByteString], featureSize: Int): Unit = { + val typedOutput = output.asInstanceOf[Tensor[Int]] + val size = input.size().toSeq :+ featureSize + typedOutput.resize(size.toArray) + + val outputData = typedOutput.storage().array() + val outputOffset = typedOutput.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + + val dataSize = input.nElement() + var i = 0 + while (i < dataSize) { + val bytes = inputData(inputOffset + i).toByteArray + val buffer = ByteBuffer.wrap(bytes) + buffer.order(byteOrder) + val typedInputData = buffer.asIntBuffer().array() + require(typedInputData.length == featureSize, + s"each element should have the same size, first elem size: $featureSize, " + + s"${i}th elem size: ${typedInputData.length}") + System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + i = i + 1 + } + } + + private def decodeInt64(input: Tensor[ByteString], featureSize: Int): Unit = { + val typedOutput = output.asInstanceOf[Tensor[Long]] + val size = input.size().toSeq :+ featureSize + typedOutput.resize(size.toArray) + + val outputData = typedOutput.storage().array() + val outputOffset = typedOutput.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + + val dataSize = input.nElement() + var i = 0 + while (i < dataSize) { + val bytes = inputData(inputOffset + i).toByteArray + val buffer = ByteBuffer.wrap(bytes) + buffer.order(byteOrder) + val typedInputData = buffer.asLongBuffer().array() + require(typedInputData.length == featureSize, + s"each element should have the same size, first elem size: $featureSize, " + + s"${i}th elem size: ${typedInputData.length}") + System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + i = i + 1 + } + } + + private def decodeInt16(input: Tensor[ByteString], featureSize: Int): Unit = { + val typedOutput = output.asInstanceOf[Tensor[Int]] + val size = input.size().toSeq :+ featureSize + typedOutput.resize(size.toArray) + + val outputData = typedOutput.storage().array() + val outputOffset = typedOutput.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + + val dataSize = input.nElement() + var i = 0 + while (i < dataSize) { + val bytes = inputData(inputOffset + i).toByteArray + val buffer = ByteBuffer.wrap(bytes) + buffer.order(byteOrder) + val typedInputData = buffer.asShortBuffer().array() + require(typedInputData.length == featureSize, + s"each element should have the same size, first elem size: $featureSize, " + + s"${i}th elem size: ${typedInputData.length}") + System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + i = i + 1 + } + } + + private def decodeInt8(input: Tensor[ByteString], featureSize: Int): Unit = { + val typedOutput = output.asInstanceOf[Tensor[Int]] + val size = input.size().toSeq :+ featureSize + typedOutput.resize(size.toArray) + + val outputData = typedOutput.storage().array() + val outputOffset = typedOutput.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + + val dataSize = input.nElement() + var i = 0 + while (i < dataSize) { + val bytes = inputData(inputOffset + i).toByteArray + val typedInputData = bytes + require(typedInputData.length == featureSize, + s"each element should have the same size, first elem size: $featureSize, " + + s"${i}th elem size: ${typedInputData.length}") + var j = 0 + while (j < featureSize) { + outputData(outputOffset + i * featureSize + j) = typedInputData(j) + j = j + 1 + } + i = i + 1 + } + } + + private def decodeUint8(input: Tensor[ByteString], featureSize: Int): Unit = { + val typedOutput = output.asInstanceOf[Tensor[Int]] + val size = input.size().toSeq :+ featureSize + typedOutput.resize(size.toArray) + + val outputData = typedOutput.storage().array() + val outputOffset = typedOutput.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + + val dataSize = input.nElement() + var i = 0 + while (i < dataSize) { + val bytes = inputData(inputOffset + i).toByteArray + val typedInputData = bytes + require(typedInputData.length == featureSize, + s"each element should have the same size, first elem size: $featureSize, " + + s"${i}th elem size: ${typedInputData.length}") + var j = 0 + while (j < featureSize) { + outputData(outputOffset + i * featureSize + j) = + (typedInputData(j) & 0xff.toShort).toShort + j = j + 1 + } + i = i + 1 + } + } + + override def updateGradInput(input: Tensor[ByteString], gradOutput: Activity): + Tensor[ByteString] = { + throw new IllegalArgumentException() + } +} + + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala new file mode 100644 index 00000000000..404261ce930 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala @@ -0,0 +1,83 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.{T, Table} +import com.google.protobuf.ByteString +import org.tensorflow.example.{Example, Feature} +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + +import scala.collection.JavaConverters._ +import scala.reflect.ClassTag + +class ParseExample[T: ClassTag](nDense: Int, + tDense: Seq[TensorDataType], + denseShape: Seq[Array[Int]]) + (implicit ev: TensorNumeric[T]) + extends Operation[Table, Table, T] { + + type StringType = ByteString + + override def updateOutput(input: Table): Table = { + val serialized = input(1).asInstanceOf[Tensor[StringType]].value() + val denseKeys = Range(3, 3 + nDense).map(index => input(index).asInstanceOf[Tensor[StringType]]) + .map(_.value().toStringUtf8) + val denseDefault = Range(3 + nDense, 3 + 2 * nDense) + .map(index => input(index).asInstanceOf[Tensor[StringType]]) + + val example = Example.parseFrom(serialized) + + val featureMap = example.getFeatures.getFeatureMap + + val outputs = denseDefault + .zip(denseKeys) + .zip(tDense).zip(denseShape).map { case (((default, key), tensorType), shape) => + if (featureMap.containsKey(key)) { + val feature = featureMap.get(key) + getTensorFromFeature(feature, tensorType, shape) + } else { + default + } + } + + for (elem <- outputs) { + output.insert(elem) + } + output + } + + private def getTensorFromFeature(feature: Feature, + tensorType: TensorDataType, + tensorShape: Array[Int]): Tensor[_] = { + tensorType match { + case LongType => + val values = feature.getInt64List.getValueList.asScala.map(_.longValue()).toArray + Tensor(values, tensorShape) + case FloatType => + val values = feature.getFloatList.getValueList.asScala.map(_.floatValue()).toArray + Tensor(values, tensorShape) + case StringType => + val values = feature.getBytesList.getValueList + .asScala.toArray.asInstanceOf[Array[ByteString]] + Tensor(values, tensorShape) + } + } + + override def updateGradInput(input: Table, gradOutput: Table): Table = { + throw new UnsupportedOperationException("no backward on ParseExample") + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala new file mode 100644 index 00000000000..1765387135f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala @@ -0,0 +1,72 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.ops.Operation +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + + +class Variable[T: ClassTag](val variableValue: Tensor[T], val variableGradient: Tensor[T]) + (implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[T], T] { + + override def clearState(): this.type = { + this + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (Array(this.variableValue), Array(this.variableGradient)) + } + + override def updateOutput(input: Activity): Tensor[T] = { + this.output.resizeAs(variableValue) + this.output.copy(variableValue) + output + } + + override def updateGradInput(input: Activity, gradOutput: Tensor[T]): Activity = { + require(gradOutput.isSameSizeAs(variableValue), + s"Invalid gradOutput size. require (${variableValue.size().mkString(",")}), but " + + s"(${gradOutput.size().mkString(",")})") + input match { + case t: Tensor[T] => + if (gradInput == null || gradInput.isInstanceOf[Table]) { + gradInput = Tensor[T]() + } + gradInput.toTensor[T].resizeAs(t).zero() + case t: Table => + if (gradInput == null || !gradInput.isInstanceOf[Table]) { + gradInput = T() + } + t.foreach(kv => { + val gradInputTensors = gradInput.toTable + val grad = gradInputTensors.getOrElse[Tensor[T]](kv._1, Tensor[T]()) + .resizeAs(kv._2.asInstanceOf[Tensor[T]]).zero() + gradInputTensors(kv._1) = grad + }) + } + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Tensor[T]): Unit = { + this.variableGradient.add(ev.fromType[Double](1.0), gradOutput) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala index dc8b2836c98..67816927afa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala @@ -28,7 +28,7 @@ import java.nio.{ByteBuffer, ByteOrder} * uint32 masked_crc32_of_data * */ -private[tf] class TFRecordIterator(fileName: File) extends Iterator[Array[Byte]] { +class TFRecordIterator(fileName: File) extends Iterator[Array[Byte]] { private val inputStream = new BufferedInputStream(new FileInputStream(fileName)) diff --git a/scala/dllib/src/test/resources/tf/mnist_test.tfrecord b/scala/dllib/src/test/resources/tf/mnist_test.tfrecord new file mode 100644 index 0000000000000000000000000000000000000000..d34864d2b563411509a6a6e3061c380547566291 GIT binary patch literal 3133 zcmc(h2~<6!cYsF)RuXQ79lq 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Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.utils.tf.TFRecordIterator +import org.scalatest.{FlatSpec, Matchers} +import java.io.{File => JFile} +import java.nio.ByteOrder + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} +import org.tensorflow.example.Example +import org.tensorflow.framework.DataType +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + +class DecodeImageSpec extends FlatSpec with Matchers { + + "DecodeRaw " should "be able to decode raw bytes" in { + + val input = getInputs("raw") + + val decoder = new DecodeRaw[Float](DataType.DT_UINT8, ByteOrder.LITTLE_ENDIAN) + + val output = decoder.forward(input).asInstanceOf[Tensor[Int]] + + output.size() should be (Array(28*28)) + } + + "DecodePng " should "be able to decode png" in { + + val input = getInputs("png") + + val decoder = new DecodePng[Int](1) + + val output = decoder.forward(input) + val expected = getRaw() + + output should be (expected) + } + + "DecodeJpeg " should "be able to decode jpeg" in { + val input = getInputs("jpeg") + + val decoder = new DecodeJpeg[Int](1) + + val output = decoder.forward(input) + + output.size() should be (Array(28, 28, 1)) + } + + "DecodeGif " should "be able to decode gif" in { + val input = getInputs("gif") + + val decoder = new DecodeGif[Int]() + + val output = decoder.forward(input) + + output.size() should be (Array(1, 28, 28, 3)) + + } + + private def getRaw(): Tensor[Int] = { + val input = getInputs("raw") + + val decoder = new DecodeRaw[Float](DataType.DT_UINT8, ByteOrder.LITTLE_ENDIAN) + + val output = decoder.forward(input).asInstanceOf[Tensor[Int]] + + output.resize(Array(28, 28, 1)) + } + + private def getInputs(name: String): Tensor[ByteString] = { + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + JFile.separator + "mnist_test.tfrecord" + val file = new JFile(path) + + val bytesVector = new TFRecordIterator(file).toVector + val pngBytes = bytesVector(index) + + val example = Example.parseFrom(pngBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala new file mode 100644 index 00000000000..91a430ef43d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala @@ -0,0 +1,77 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.{FloatType, LongType, StringType, Tensor} +import com.google.protobuf.{ByteString, CodedOutputStream} +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} +import org.tensorflow.example._ +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + +class ParseExampleSpec extends FlatSpec with Matchers { + + "ParseExample" should "be able to parse a example" in { + + val floatBuilder = FloatList.newBuilder() + .addValue(0.0f).addValue(1.0f).addValue(2.0f) + val floatFeature = Feature.newBuilder().setFloatList(floatBuilder).build() + + val longBuilder = Int64List.newBuilder() + .addValue(0).addValue(1).addValue(2) + val longFeature = Feature.newBuilder().setInt64List(longBuilder).build() + + val bytesBuilder = BytesList.newBuilder().addValue(ByteString.copyFromUtf8("abcd")) + val bytesFeature = Feature.newBuilder().setBytesList(bytesBuilder).build() + + val features = Features.newBuilder() + .putFeature("floatFeature", floatFeature) + .putFeature("longFeature", longFeature) + .putFeature("bytesFeature", bytesFeature) + val example = Example.newBuilder().setFeatures(features).build() + val length = example.getSerializedSize + val data = new Array[Byte](length) + val outputStream = CodedOutputStream.newInstance(data) + example.writeTo(outputStream) + + val exampleParser = new ParseExample[Float](3, + Seq(FloatType, LongType, StringType), Seq(Array(3), Array(3), Array())) + + val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int]()) + val names = Tensor[ByteString]() + val key1 = Tensor[ByteString](Array(ByteString.copyFromUtf8("floatFeature")), Array[Int]()) + val key2 = Tensor[ByteString](Array(ByteString.copyFromUtf8("longFeature")), Array[Int]()) + val key3 = Tensor[ByteString](Array(ByteString.copyFromUtf8("bytesFeature")), Array[Int]()) + + val default1 = Tensor[Float]() + val default2 = Tensor[Long]() + val default3 = Tensor[ByteString]() + + val input = T(serialized, names, key1, key2, key3, default1, default2, default3) + + val output = exampleParser.forward(input) + + val floatTensor = output(1).asInstanceOf[Tensor[Float]] + val longTensor = output(2).asInstanceOf[Tensor[Long]] + val stringTensor = output(3).asInstanceOf[Tensor[ByteString]] + + floatTensor should be (Tensor[Float](T(0.0f, 1.0f, 2.0f))) + longTensor should be (Tensor[Long](T(0L, 1L, 2L))) + stringTensor should be (Tensor[ByteString]( + Array(ByteString.copyFromUtf8("abcd")), Array[Int]())) + } + +} From 6b1fd1e3875e5b3ed863b5346a7dafd56c2225dc Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 20 Sep 2017 15:24:05 +0800 Subject: [PATCH 0404/1065] fix parse tensor (#1574) --- .../bigdl/dllib/utils/tf/TFUtils.scala | 115 ++++++-- .../dllib/utils/tf/TensorflowToBigDL.scala | 33 ++- .../dllib/src/test/resources/tf/consts.pbtxt | 252 ++++++++++++++++++ .../src/test/resources/tf/models/alexnet.py | 1 + .../bigdl/dllib/utils/tf/TFUtilsSpec.scala | 109 ++++++++ .../dllib/utils/tf/TensorflowLoaderSpec.scala | 2 +- 6 files changed, 476 insertions(+), 36 deletions(-) create mode 100644 scala/dllib/src/test/resources/tf/consts.pbtxt create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtilsSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala index 2a4ab5861e1..7d341b9bea9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala @@ -26,31 +26,55 @@ import scala.collection.JavaConverters._ object TFUtils { import TFTensorNumeric.NumericByteString - /** - * convert tensorflow tensorProto to BigDL Tensor - */ - def parseTensor(tfTensor: TensorProto, endian: ByteOrder): Tensor[_] = { - val shape = tfTensor.getTensorShape.getDimList.asScala.map(_.getSize.toInt).toArray - tfTensor.getDtype match { + private def parseTensorFromContent( + dataType: DataType, content: Array[Byte], shape: Array[Int], endian: ByteOrder) = { + dataType match { case DataType.DT_FLOAT => - val tmp = tfTensor.getFloatValList.asScala.map(_.toFloat).toArray + val buffer = ByteBuffer.wrap(content) + buffer.order(endian) + val params = buffer.asFloatBuffer() + val tmp = new Array[Float](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = params.get(j) + j += 1 + } Tensor[Float](tmp, shape) case DataType.DT_DOUBLE => - val tmp = tfTensor.getDoubleValList.asScala.map(_.toDouble).toArray + val buffer = ByteBuffer.wrap(content) + buffer.order(endian) + val params = buffer.asDoubleBuffer() + val tmp = new Array[Double](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = params.get(j) + j += 1 + } Tensor[Double](tmp, shape) case DataType.DT_INT32 => - val tmp = tfTensor.getIntValList.asScala.map(_.toInt).toArray + val buffer = ByteBuffer.wrap(content) + buffer.order(endian) + val params = buffer.asIntBuffer() + val tmp = new Array[Int](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = params.get(j) + j += 1 + } Tensor[Int](tmp, shape) case DataType.DT_INT64 => - val tmp = tfTensor.getInt64ValList.asScala.map(_.toLong).toArray + val buffer = ByteBuffer.wrap(content) + buffer.order(endian) + val params = buffer.asLongBuffer() + val tmp = new Array[Long](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = params.get(j) + j += 1 + } Tensor[Long](tmp, shape) - case DataType.DT_BOOL => - val tmp = tfTensor.getBoolValList.asScala.map(_.booleanValue()).toArray - Tensor[Boolean](tmp, shape) - case DataType.DT_STRING => - Tensor[ByteString](Array(tfTensor.getStringVal(0)), shape) case DataType.DT_INT8 => - val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray) + val buffer = ByteBuffer.wrap(content) buffer.order(endian) val params = buffer val tmp = new Array[Int](params.capacity()) @@ -61,7 +85,7 @@ object TFUtils { } Tensor(tmp, shape) case DataType.DT_UINT8 => - val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray) + val buffer = ByteBuffer.wrap(content) buffer.order(endian) val params = buffer val tmp = new Array[Int](params.capacity()) @@ -72,9 +96,9 @@ object TFUtils { } Tensor(tmp, shape) case DataType.DT_INT16 => - val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray) + val buffer = ByteBuffer.wrap(content) buffer.order(endian) - val params = buffer + val params = buffer.asShortBuffer() val tmp = new Array[Int](params.capacity()) var j = 0 while (j < params.capacity()) { @@ -83,9 +107,9 @@ object TFUtils { } Tensor(tmp, shape) case DataType.DT_UINT16 => - val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray) + val buffer = ByteBuffer.wrap(content) buffer.order(endian) - val params = buffer + val params = buffer.asShortBuffer() val tmp = new Array[Int](params.capacity()) var j = 0 while (j < params.capacity()) { @@ -97,4 +121,53 @@ object TFUtils { } } + private def parseTensorFromField( + tfTensor: TensorProto, shape: Array[Int], endian: ByteOrder) = { + tfTensor.getDtype match { + case DataType.DT_FLOAT => + val tmp = tfTensor.getFloatValList.asScala.map(_.toFloat).toArray + Tensor[Float](tmp, shape) + case DataType.DT_DOUBLE => + val tmp = tfTensor.getDoubleValList.asScala.map(_.toDouble).toArray + Tensor[Double](tmp, shape) + case DataType.DT_INT32 => + val tmp = tfTensor.getIntValList.asScala.map(_.toInt).toArray + Tensor[Int](tmp, shape) + case DataType.DT_INT64 => + val tmp = tfTensor.getInt64ValList.asScala.map(_.toLong).toArray + Tensor[Long](tmp, shape) + case DataType.DT_BOOL => + val tmp = tfTensor.getBoolValList.asScala.map(_.booleanValue()).toArray + Tensor[Boolean](tmp, shape) + case DataType.DT_STRING => + val tmp = tfTensor.getStringValList.asScala.toArray + Tensor[ByteString](tmp, shape) + case DataType.DT_INT8 => + val tmp = tfTensor.getIntValList.asScala.map(_.toInt).toArray + Tensor(tmp, shape) + case DataType.DT_UINT8 => + val tmp = tfTensor.getIntValList.asScala.map(_.toInt).toArray + Tensor(tmp, shape) + case DataType.DT_INT16 => + val tmp = tfTensor.getIntValList.asScala.map(_.toInt).toArray + Tensor(tmp, shape) + case DataType.DT_UINT16 => + val tmp = tfTensor.getIntValList.asScala.map(_.toInt).toArray + Tensor(tmp, shape) + case t => throw new IllegalArgumentException(s"DataType: $t not supported yet") + } + } + + /** + * convert tensorflow tensorProto to BigDL Tensor + */ + def parseTensor(tfTensor: TensorProto, endian: ByteOrder): Tensor[_] = { + val shape = tfTensor.getTensorShape.getDimList.asScala.map(_.getSize.toInt).toArray + if (tfTensor.getTensorContent.isEmpty) { + parseTensorFromField(tfTensor, shape, endian) + } else { + parseTensorFromContent(tfTensor.getDtype, + tfTensor.getTensorContent.toByteArray, shape, endian) + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index 81d551539e1..c91d85c176b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -21,7 +21,7 @@ import java.util import collection.JavaConverters._ import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Storage, Tensor} import org.tensorflow.framework.{AttrValue, DataType, NodeDef, TensorProto} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.nn.tf._ @@ -647,15 +647,16 @@ object ReshapeTF extends TensorflowToBigDL { byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val sizes = TensorflowToBigDL.toTensor( + val sizes = TFUtils.parseTensor( tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) + .asInstanceOf[Tensor[Int]] val batchMode = sizes.valueAt(1) == -1 val arraySize = new Array[Int](if (batchMode) sizes.nElement() - 1 else sizes.nElement()) var i = if (batchMode) 2 else 1 var k = 0 while(i <= sizes.nElement()) { - arraySize(k) = ev.toType[Int](sizes.valueAt(i)) + arraySize(k) = sizes.valueAt(i) k += 1 i += 1 } @@ -1274,16 +1275,16 @@ object Flatten extends TensorflowToBigDL { implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val shapetfTensor = tfGraph.source.prevNodes(1).prevNodes(0).prevNodes(0).element .getAttrMap.get("value").getTensor - val sizes = TensorflowToBigDL.toTensor(shapetfTensor, byteOrder) + val sizes = TFUtils.parseTensor(shapetfTensor, byteOrder).asInstanceOf[Tensor[Int]] val batchMode = false val arraySize = Array( - ev.toType[Int](sizes.valueAt(1)), + sizes.valueAt(1), { var prod = 1 var i = 2 while(i <= sizes.nElement()) { - prod = prod * ev.toType[Int](sizes.valueAt(i)) + prod = prod * sizes.valueAt(i) i = i + 1 } prod @@ -1382,10 +1383,12 @@ object MulTF extends TensorflowToBigDL{ byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val scale = TensorflowToBigDL.toTensor( + val scale = TFUtils.parseTensor( tfGraph.source.prevNodes(0).element.getAttrMap.get("value").getTensor, byteOrder) - require(scale.dim() == 1 && scale.size(1) == 1, s"scale must be one number") - val mul = MulConstant[T](ev.toType[Double](scale.valueAt(1))) + .asInstanceOf[Tensor[Float]] + require(scale.isScalar, s"scale must be a scalar") + val value = scale.value().toDouble + val mul = MulConstant[T](value) mul.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1450,18 +1453,19 @@ object PaddingTF extends TensorflowToBigDL{ byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val paddings = TensorflowToBigDL.toTensor( + val paddings = TFUtils.parseTensor( tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) + .asInstanceOf[Tensor[Int]] val pad = ArrayBuffer[Int]() val padding = Sequential[T]() for(dim <- 1 to paddings.size(1)) { if (paddings.valueAt(dim, 1) != 0 || paddings.valueAt(dim, 2) != 0 ) { if (paddings(Array(dim, 1)) != 0) { - padding.add(Padding[T](dim, -ev.toType[Int](paddings.valueAt(dim, 1)), 4)) + padding.add(Padding[T](dim, -paddings.valueAt(dim, 1), 4)) } if (paddings(Array(dim, 2)) != 0) { - padding.add(Padding[T](dim, ev.toType[Int](paddings.valueAt(dim, 2)), 4)) + padding.add(Padding[T](dim, paddings.valueAt(dim, 2), 4)) } } } @@ -1484,12 +1488,13 @@ object MeanTF extends TensorflowToBigDL{ byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val dims = TensorflowToBigDL.toTensor( + val dims = TFUtils.parseTensor( tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) + .asInstanceOf[Tensor[Int]] val dim = ArrayBuffer[Int]() val mean = Sequential[T]() for (i <- 1 to dims.size(1)) { - dim += ev.toType[Int](dims.valueAt(i)) + 1 + dim += dims.valueAt(i) + 1 } dim.foreach(i => mean.add(Mean[T](i, squeeze = false))) mean.asInstanceOf[AbstractModule[Activity, Activity, T]] diff --git a/scala/dllib/src/test/resources/tf/consts.pbtxt b/scala/dllib/src/test/resources/tf/consts.pbtxt new file mode 100644 index 00000000000..2f78f0e73ae --- /dev/null +++ b/scala/dllib/src/test/resources/tf/consts.pbtxt @@ -0,0 +1,252 @@ +node { + name: "bool_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_BOOL + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_BOOL + tensor_shape { + dim { + size: 4 + } + } + bool_val: true + bool_val: false + bool_val: true + bool_val: false + } + } + } +} +node { + name: "float_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_FLOAT + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_FLOAT + tensor_shape { + dim { + size: 4 + } + } + tensor_content: "\000\000\200?\000\000\000@\000\000@@\000\000\200@" + } + } + } +} +node { + name: "double_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_DOUBLE + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_DOUBLE + tensor_shape { + dim { + size: 4 + } + } + tensor_content: "\000\000\000\000\000\000\360?\000\000\000\000\000\000\000@\000\000\000\000\000\000\010@\000\000\000\000\000\000\020@" + } + } + } +} +node { + name: "int_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 4 + } + } + tensor_content: "\001\000\000\000\002\000\000\000\003\000\000\000\004\000\000\000" + } + } + } +} +node { + name: "long_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT64 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT64 + tensor_shape { + dim { + size: 4 + } + } + tensor_content: "\001\000\000\000\000\000\000\000\002\000\000\000\000\000\000\000\003\000\000\000\000\000\000\000\004\000\000\000\000\000\000\000" + } + } + } +} +node { + name: "int8_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT8 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT8 + tensor_shape { + dim { + size: 4 + } + } + tensor_content: "\001\002\003\004" + } + } + } +} +node { + name: "uint8_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_UINT8 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_UINT8 + tensor_shape { + dim { + size: 4 + } + } + tensor_content: "\001\002\003\004" + } + } + } +} +node { + name: "int16_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT16 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT16 + tensor_shape { + dim { + size: 4 + } + } + tensor_content: "\001\000\002\000\003\000\004\000" + } + } + } +} +node { + name: "uint16_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_UINT16 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_UINT16 + tensor_shape { + dim { + size: 4 + } + } + int_val: 1 + int_val: 2 + int_val: 3 + int_val: 4 + } + } + } +} +node { + name: "string_const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_STRING + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_STRING + tensor_shape { + dim { + size: 4 + } + } + string_val: "a" + string_val: "b" + string_val: "c" + string_val: "d" + } + } + } +} +versions { + producer: 22 +} diff --git a/scala/dllib/src/test/resources/tf/models/alexnet.py b/scala/dllib/src/test/resources/tf/models/alexnet.py index b50e6ff6296..f7c4e97f8db 100644 --- a/scala/dllib/src/test/resources/tf/models/alexnet.py +++ b/scala/dllib/src/test/resources/tf/models/alexnet.py @@ -26,6 +26,7 @@ def main(): 2. export PYTHONPATH=Path_to_your_model_folder 3. python alexnet.py """ + tf.set_random_seed(1) height, width = 224, 224 inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name = 'input') inputs = tf.identity(inputs, "input_node") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtilsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtilsSpec.scala new file mode 100644 index 00000000000..b5197d41f71 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtilsSpec.scala @@ -0,0 +1,109 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf + +import java.io.File +import java.nio.ByteOrder + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} +import org.tensorflow.framework.TensorProto + +import scala.collection.JavaConverters._ + +class TFUtilsSpec extends FlatSpec with Matchers with BeforeAndAfter { + + private var constTensors: Map[String, TensorProto] = null + before { + constTensors = getConstTensorProto() + } + + private def getConstTensorProto(): Map[String, TensorProto] = { + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + File.separator + "consts.pbtxt" + val nodes = TensorflowLoader.parseTxt(path) + nodes.asScala.map(node => node.getName -> node.getAttrMap.get("value").getTensor).toMap + } + + "parseTensor " should "work with bool TensorProto" in { + val tensorProto = constTensors("bool_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Boolean](T(true, false, true, false))) + } + + "parseTensor " should "work with float TensorProto" in { + val tensorProto = constTensors("float_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Float](T(1.0f, 2.0f, 3.0f, 4.0f))) + } + + "parseTensor " should "work with double TensorProto" in { + val tensorProto = constTensors("double_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Double](T(1.0, 2.0, 3.0, 4.0))) + } + + "parseTensor " should "work with int TensorProto" in { + val tensorProto = constTensors("int_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Int](T(1, 2, 3, 4))) + } + + "parseTensor " should "work with long TensorProto" in { + val tensorProto = constTensors("long_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Long](T(1, 2, 3, 4))) + } + + "parseTensor " should "work with int8 TensorProto" in { + val tensorProto = constTensors("int8_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Int](T(1, 2, 3, 4))) + } + + "parseTensor " should "work with uint8 TensorProto" in { + val tensorProto = constTensors("uint8_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Int](T(1, 2, 3, 4))) + } + + "parseTensor " should "work with int16 TensorProto" in { + val tensorProto = constTensors("int16_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Int](T(1, 2, 3, 4))) + } + + "parseTensor " should "work with uint16 TensorProto" in { + val tensorProto = constTensors("uint16_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + bigdlTensor should be (Tensor[Int](T(1, 2, 3, 4))) + } + + "parseTensor " should "work with string TensorProto" in { + import TFTensorNumeric.NumericByteString + val tensorProto = constTensors("string_const") + val bigdlTensor = TFUtils.parseTensor(tensorProto, ByteOrder.LITTLE_ENDIAN) + val data = Array( + ByteString.copyFromUtf8("a"), + ByteString.copyFromUtf8("b"), + ByteString.copyFromUtf8("c"), + ByteString.copyFromUtf8("d") + ) + bigdlTensor should be (Tensor[ByteString](data, Array[Int](4))) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index be98d41e781..1362f5dad05 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -253,7 +253,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val optimizer = new DistriOptimizer[Float](container, dataSet, new MSECriterion[Float]()) .setState(T("learningRate" -> 20.0)) - .setEndWhen(Trigger.maxEpoch(5)) + .setEndWhen(Trigger.maxEpoch(1)) optimizer.optimize() val l1 = container.modules(1).asInstanceOf[Linear[Float]] From 5312b14a41b208e88fa74c5e668eb5352d9ff0f4 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Wed, 20 Sep 2017 21:47:37 +0800 Subject: [PATCH 0405/1065] Add slice and onehot (#1547) * Add slice and onehot * refactor the code * fit new code * fix a typo * meet code review * fix test failed --- .../analytics/bigdl/dllib/nn/ops/OneHot.scala | 141 ++++++++++++++++++ .../analytics/bigdl/dllib/nn/ops/Slice.scala | 68 +++++++++ .../bigdl/dllib/nn/ops/OneHotSpec.scala | 94 ++++++++++++ .../bigdl/dllib/nn/ops/SliceSpec.scala | 54 +++++++ 4 files changed, 357 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SliceSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala new file mode 100644 index 00000000000..ab5fbe52e31 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala @@ -0,0 +1,141 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * OneHot operation returns a one-hot tensor + * + * The input contains 4 elements which are `indices`, `depth`, `onValue` and `offValue`*[]: + + * The locations represented by indices in `indices` take value `onValue`, + * while all other locations take value `offValue`. + * + * `onValue` and `offValue` must have matching data types. + * If dtype is also provided, they must be the same data type as specified by `D`. + * + * If on_value is not provided, it will default to the value 1 with type dtype + * + * If off_value is not provided, it will default to the value 0 with type dtype + * + * If the input indices is rank N, the output will have rank N+1. + * The new axis is created at dimension axis (default: the new axis is appended at the end). + * + * If indices is a scalar the output shape will be a vector of length depth + * + * If indices is a vector of length features, the output shape will be: + * features x depth if axis == -1 + * depth x features if axis == 0 + * + * If indices is a matrix (batch) with shape [batch, features], the output shape will be: + * + * batch x features x depth if axis == -1 + * batch x depth x features if axis == 1 + * depth x batch x features if axis == 0 + * + * @param axis The new axis is created at dimension axis + * @tparam T Numeric type. Parameter tensor numeric type. Only support float/double now + * @tparam D Numeric type. Output tensor numeric type. Only support float/double now + */ +class OneHot[T: ClassTag, D: ClassTag]( + axis: Int +)(implicit ev: TensorNumeric[T], ev1: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { + output = Activity.allocate[Tensor[D], D]() + def updateOutput(input: Table): Tensor[D] = { + val indices = input[Tensor[Int]](1) + val depth = input[Tensor[Int]](2).value() + val onValue = if (!input.contains(3)) ev1.one else input[Tensor[D]](3).value() + val offValue = if (!input.contains(4)) ev1.zero else input[Tensor[D]](4).value() + + require(input[Tensor[_]](3).getType() == input[Tensor[_]](4).getType(), + "onValue must have the same type as offValue") + + val size: Array[Int] = indices.size() + require(indices.dim() <= 2 && indices.dim() > 0, + "the dimension of input must be less than or equal to 2") + val newSize: Array[Int] = new Array(size.length + 1) + + val realAxis = if (axis == -1) newSize.length - 1 + + var i = 0 + var j = 0 + while (i < newSize.length) { + if (realAxis == i) { + newSize(i) = depth + } else { + newSize(i) = size(j) + j += 1 + } + + i += 1 + } + + output.resize(newSize) + output.apply1(x => offValue) + + if (size.length == 2) { + i = 1 + while (i <= size(0)) { + j = 1 + while (j <= size(1)) { + val index = indices(Array(i, j)) + 1 + if (index > 0) { + if (realAxis == 0) { + output.setValue(index, i, j, onValue) + } else if (realAxis == 1) { + output.setValue(i, index, j, onValue) + } else if (realAxis == 2) { + output.setValue(i, j, index, onValue) + } + } + j += 1 + } + i += 1 + } + } else { + i = 1 + while (i <= size(0)) { + val index = indices(Array(i)) + 1 + if (index > 0) { + if (realAxis == 0) { + output.setValue(index, i, onValue) + } else if (realAxis == 1) { + output.setValue(i, index, onValue) + } + } + i += 1 + } + } + output + } +} + +object OneHot { + def apply[T: ClassTag, D: ClassTag]( + axis: Int + ) + (implicit ev: TensorNumeric[T], ev1: TensorNumeric[D]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( + new OneHot[T, D]( + axis = axis + )) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala new file mode 100644 index 00000000000..fffd3d5c17d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * This operation extracts a slice of size size from a tensor + * input starting at the location specified by begin. + * The slice size is represented as a tensor shape, where size[i] is + * the number of elements of the 'i'th dimension of input that you want to slice + * The starting location (begin) for the slice is represented as an offset in each + * dimension of input. + * In other words, begin[i] is the offset into the 'i'th dimension of input that you + * want to slice from. + * + * @param begin zero-based + * @param size one-based + * @tparam T Numeric type. Only support float/double now + */ +class Slice[T: ClassTag]( + begin: Array[Int], + size: Array[Int]) + (implicit ev: TensorNumeric[T]) extends Operation[Tensor[_], Tensor[_], T] { + + def updateOutput(input: Tensor[_]): Tensor[_] = { + require(begin.length == size.length && begin.length == input.dim(), + "the length of `begin`, `size` and the dimension of input should be the same") + + var outputNarrow = input + var i = 0 + while (i < begin.length) { + val realSize = if (size(i) == -1) input.size(i + 1) - begin(i) else size(i) + outputNarrow = outputNarrow.narrow(i + 1, begin(i) + 1, realSize) + i += 1 + } + output.resizeAs(outputNarrow) + output.forceCopy(outputNarrow) + + output + } +} + +object Slice { + def apply[T: ClassTag]( + begin: Array[Int], + size: Array[Int]) + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( + new Slice(begin = begin, size = size)) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala new file mode 100644 index 00000000000..ce094215abf --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala @@ -0,0 +1,94 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class OneHotSpec extends FlatSpec with Matchers { + "OneHot operation one dimension index" should "works correctly" in { + val input = + T(Tensor[Int](T(0, 2, -1, 1)), + Tensor[Int](Array(3), shape = Array[Int]()), + Tensor[Double](Array(0.5), shape = Array[Int]()), + Tensor[Double](Array(0.0), shape = Array[Int]())) + + val expectOutput = + Tensor[Double](T( + T(0.5, 0.0, 0.0), + T(0.0, 0.0, 0.5), + T(0.0, 0.0, 0.0), + T(0.0, 0.5, 0.0) + )) + + val output = OneHot[Double, Double]( + axis = -1 + ).forward(input) + + output should be(expectOutput) + } + + "OneHot operation two dimension index" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val input = + T( + Tensor[Int](T(T(0, 2), T(1, -1))), + Tensor[Int](Array(3), shape = Array[Int]()), + Tensor[Double](Array(1.0), shape = Array[Int]()), + Tensor[Double](Array(0.0), shape = Array[Int]()) + ) + + val expectOutput = + Tensor(T( + T(T(1.0, 0.0, 0.0), + T(0.0, 0.0, 1.0)), + T(T(0.0, 1.0, 0.0), + T(0.0, 0.0, 0.0)) + )) + + val output = OneHot[Double, Double]( + axis = -1 + ).forward(input) + + output should be(expectOutput) + } + + "OneHot operation two dimension index with type _" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val input = + T( + Tensor[Int](T(T(0, 2), T(1, -1))), + Tensor[Int](Array(3), shape = Array[Int]()), + Tensor[Double](Array(1.0), shape = Array[Int]()), + Tensor[Double](Array(0.0), shape = Array[Int]()) + ) + + val expectOutput = + Tensor(T( + T(T(1.0, 0.0, 0.0), + T(0.0, 0.0, 1.0)), + T(T(0.0, 1.0, 0.0), + T(0.0, 0.0, 0.0)) + )) + + val output = OneHot[Double, Double]( + axis = -1 + ).forward(input) + + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SliceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SliceSpec.scala new file mode 100644 index 00000000000..51832ee2ae6 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SliceSpec.scala @@ -0,0 +1,54 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class SliceSpec extends FlatSpec with Matchers { + "Slice operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + Tensor(T( + T( + T(1, 2, 3), + T(4, 5, 6) + ), + T( + T(7, 8, 9), + T(10, 11, 12) + ), + T( + T(13, 14, 15), + T(16, 17, 18) + ) + )) + + val expectOutput = + Tensor(T( + T( + T(5) + ), + T( + T(11) + ) + )) + + val output = Slice(begin = Array(0, 1, 1), size = Array(2, -1, 1)).forward(input) + output should be(expectOutput) + } +} From 81b223cff5fea732679ddc52787eabbb05846795 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 21 Sep 2017 10:16:16 +0800 Subject: [PATCH 0406/1065] Handle null array (#1571) * fix type comparision * handle null value array * fix type diff --- .../bigdl/dllib/nn/BinaryTreeLSTM.scala | 8 +- .../analytics/bigdl/dllib/nn/Graph.scala | 13 +- .../utils/serializer/DataConverter.scala | 202 ++++++++++-------- .../utils/serializer/ModuleSerializer.scala | 2 +- .../utils/serializer/DataConverterSpec.scala | 44 ++-- 5 files changed, 165 insertions(+), 104 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala index c78051a1eb3..678aa07e709 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala @@ -27,6 +27,7 @@ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +import scala.reflect.runtime.universe import scala.util.control.Breaks._ /** @@ -466,12 +467,15 @@ object BinaryTreeLSTM extends ModuleSerializable { val composers = binaryTreeLSTM.composers.toArray val composersBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(composersBuilder, composers) + DataConverter.setAttributeValue(composersBuilder, composers, + universe. + typeOf[Array[_ <: AbstractModule[Activity, Activity, _ <: Any]]]) binaryTreeLSTMBuilder.putAttr("composers", composersBuilder.build) val leafModules = binaryTreeLSTM.leafModules.toArray val leafModulesBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(leafModulesBuilder, leafModules) + DataConverter.setAttributeValue(leafModulesBuilder, leafModules, universe. + typeOf[Array[_ <: AbstractModule[Activity, Activity, _ <: Any]]]) binaryTreeLSTMBuilder.putAttr("leafModules", leafModulesBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 691e0bb0f68..926d01a3376 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -34,6 +34,7 @@ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +import scala.reflect.runtime.universe import com.intel.analytics.bigdl.visualization.tensorboard.{FileWriter => TFFileWriter} import org.tensorflow.framework.GraphDef @@ -648,20 +649,24 @@ object Graph extends ContainerSerializable { if (graph.variables.isDefined) { val (weights, bias) = graph.variables.get val weightAttrBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(weightAttrBuilder, weights) + DataConverter.setAttributeValue(weightAttrBuilder, weights, + universe.typeOf[Array[Tensor[_ <: Any]]]) graphBuilder.putAttr("sharedWeight", weightAttrBuilder.build) val biasAttrBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(biasAttrBuilder, bias) + DataConverter.setAttributeValue(biasAttrBuilder, bias, + universe.typeOf[Array[Tensor[_ <: Any]]]) graphBuilder.putAttr("sharedBias", biasAttrBuilder.build) } val inputNamesAttrBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(inputNamesAttrBuilder, inputsNames) + DataConverter.setAttributeValue(inputNamesAttrBuilder, + inputsNames, universe.typeOf[Array[String]]) graphBuilder.putAttr("inputNames", inputNamesAttrBuilder.build) val outputNamesBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(outputNamesBuilder, outputsNames) + DataConverter.setAttributeValue(outputNamesBuilder, + outputsNames, universe.typeOf[Array[String]]) graphBuilder.putAttr("outputNames", outputNamesBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index 016f56a92d5..85c892c6d0d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -169,8 +169,8 @@ object DataConverter extends DataConverter{ ModuleConverter.setAttributeValue(attributeBuilder, value) } else if (value.isInstanceOf[mutable.Map[String, _ <: Any]]) { NameListConverter.setAttributeValue(attributeBuilder, value) - } else if (value.isInstanceOf[Array[_ <: Any]]) { - ArrayConverter.setAttributeValue(attributeBuilder, value) + } else if (valueType <:< universe.typeOf[Array[_ <: Any]] ) { + ArrayConverter.setAttributeValue(attributeBuilder, value, valueType) } else if (valueType == universe.typeOf[DataFormat]) { DataFormatConverter.setAttributeValue(attributeBuilder, value) } else { @@ -513,20 +513,23 @@ object DataConverter extends DataConverter{ (attribute: AttrValue)(implicit ev: TensorNumeric[T]): AnyRef = { val valueArray = attribute.getArrayValue val size = valueArray.getSize + if (size == 0) { + return null + } val listType = valueArray.getDatatype val arr = listType match { case DataType.INT32 => valueArray.getI32List.asScala.toArray.map(_.intValue) case DataType.INT64 => - valueArray.getI64List.asScala.toArray + valueArray.getI64List.asScala.toArray.map(_.longValue()) case DataType.DOUBLE => - valueArray.getDblList.asScala.toArray + valueArray.getDblList.asScala.toArray.map(_.doubleValue()) case DataType.FLOAT => - valueArray.getFltList.asScala.toArray + valueArray.getFltList.asScala.toArray.map(_.floatValue()) case DataType.STRING => valueArray.getStrList.asScala.toArray case DataType.BOOL => - valueArray.getBooleanList.asScala.toArray + valueArray.getBooleanList.asScala.toArray.map(_.booleanValue()) case DataType.REGULARIZER => val regularizers = new Array[Regularizer[T]](size) val regList = valueArray.getRegularizerList.asScala @@ -638,81 +641,106 @@ object DataConverter extends DataConverter{ value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.ARRAY_VALUE) val arrayBuilder = ArrayValue.newBuilder - if (value.isInstanceOf[Array[Int]]) { - val int32s = value.asInstanceOf[Array[Int]] + arrayBuilder.setSize(0) + if (valueType =:= universe.typeOf[Array[Int]]) { arrayBuilder.setDatatype(DataType.INT32) - int32s.foreach(i32 => arrayBuilder.addI32(i32)) - arrayBuilder.setSize(int32s.size) - } else if (value.isInstanceOf[Array[Long]]) { - val int64s = value.asInstanceOf[Array[Long]] + if (value != null) { + val int32s = value.asInstanceOf[Array[Int]] + int32s.foreach(i32 => arrayBuilder.addI32(i32)) + arrayBuilder.setSize(int32s.size) + } + } else if (valueType =:= universe.typeOf[Array[Long]]) { arrayBuilder.setDatatype(DataType.INT64) - int64s.foreach(i64 => arrayBuilder.addI64(i64)) - arrayBuilder.setSize(int64s.size) - } else if (value.isInstanceOf[Array[Float]]) { - val flts = value.asInstanceOf[Array[Float]] + if (value != null) { + val int64s = value.asInstanceOf[Array[Long]] + int64s.foreach(i64 => arrayBuilder.addI64(i64)) + arrayBuilder.setSize(int64s.size) + } + } else if (valueType =:= universe.typeOf[Array[Float]]) { arrayBuilder.setDatatype(DataType.FLOAT) - flts.foreach(flt => arrayBuilder.addFlt(flt)) - arrayBuilder.setSize(flts.size) - } else if (value.isInstanceOf[Array[Double]]) { - val dbs = value.asInstanceOf[Array[Double]] + if (value != null) { + val flts = value.asInstanceOf[Array[Float]] + flts.foreach(flt => arrayBuilder.addFlt(flt)) + arrayBuilder.setSize(flts.size) + } + } else if (valueType =:= universe.typeOf[Array[Double]]) { arrayBuilder.setDatatype(DataType.DOUBLE) - dbs.foreach(dbl => arrayBuilder.addDbl(dbl)) - arrayBuilder.setSize(dbs.size) - } else if (value.isInstanceOf[Array[Boolean]]) { - val bls = value.asInstanceOf[Array[Boolean]] + if (value != null) { + val dbs = value.asInstanceOf[Array[Double]] + dbs.foreach(dbl => arrayBuilder.addDbl(dbl)) + arrayBuilder.setSize(dbs.size) + } + } else if (valueType =:= universe.typeOf[Array[Boolean]]) { arrayBuilder.setDatatype(DataType.BOOL) - bls.foreach(bl => arrayBuilder.addBoolean(bl)) - arrayBuilder.setSize(bls.size) - } else if (value.isInstanceOf[Array[String]]) { - val strs = value.asInstanceOf[Array[String]] + if (value != null) { + val bls = value.asInstanceOf[Array[Boolean]] + bls.foreach(bl => arrayBuilder.addBoolean(bl)) + arrayBuilder.setSize(bls.size) + } + } else if (valueType =:= universe.typeOf[Array[String]]) { arrayBuilder.setDatatype(DataType.STRING) - strs.foreach(str => arrayBuilder.addStr(str)) - arrayBuilder.setSize(strs.size) - } else if (value.isInstanceOf[Array[Regularizer[T]]]) { + if (value != null) { + val strs = value.asInstanceOf[Array[String]] + strs.foreach(str => arrayBuilder.addStr(str)) + arrayBuilder.setSize(strs.size) + } + } else if (valueType <:< universe.typeOf[Array[_ <: Regularizer[_ <: Any]]]) { arrayBuilder.setDatatype(DataType.REGULARIZER) - val regularizers = value.asInstanceOf[Array[Regularizer[T]]] - regularizers.foreach(reg => { - val attrValueBuilder = AttrValue.newBuilder - RegularizerConverter.setAttributeValue(attrValueBuilder, reg) - arrayBuilder.addRegularizer(attrValueBuilder.getRegularizerValue) - }) - arrayBuilder.setSize(regularizers.size) - } else if (value.isInstanceOf[Array[Tensor[T]]]) { + if (value != null) { + val regularizers = value.asInstanceOf[Array[Regularizer[T]]] + regularizers.foreach(reg => { + val attrValueBuilder = AttrValue.newBuilder + RegularizerConverter.setAttributeValue(attrValueBuilder, reg) + arrayBuilder.addRegularizer(attrValueBuilder.getRegularizerValue) + }) + arrayBuilder.setSize(regularizers.size) + } + } else if (valueType <:< universe. + typeOf[Array[_ <: Tensor[_ <: Any]]]) { arrayBuilder.setDatatype(DataType.TENSOR) - val tensors = value.asInstanceOf[Array[Tensor[T]]] - tensors.foreach(tensor => { - val attrValueBuilder = AttrValue.newBuilder - TensorConverter.setAttributeValue(attrValueBuilder, tensor) - arrayBuilder.addTensor(attrValueBuilder.getTensorValue) - }) - arrayBuilder.setSize(tensors.size) - } else if (value.isInstanceOf[Array[VariableFormat]]) { + if (value != null) { + val tensors = value.asInstanceOf[Array[Tensor[T]]] + tensors.foreach(tensor => { + val attrValueBuilder = AttrValue.newBuilder + TensorConverter.setAttributeValue(attrValueBuilder, tensor) + arrayBuilder.addTensor(attrValueBuilder.getTensorValue) + }) + arrayBuilder.setSize(tensors.size) + } + } else if (valueType =:= universe.typeOf[Array[VariableFormat]]) { arrayBuilder.setDatatype(DataType.VARIABLE_FORMAT) - val formats = value.asInstanceOf[Array[VariableFormat]] - formats.foreach(format => { - val attrValueBuilder = AttrValue.newBuilder - VariableFormatConverter.setAttributeValue(attrValueBuilder, format) - arrayBuilder.addVariableFormat(attrValueBuilder.getVariableFormatValue) - }) - arrayBuilder.setSize(formats.size) - } else if (value.isInstanceOf[Array[InitializationMethod]]) { + if (value != null) { + val formats = value.asInstanceOf[Array[VariableFormat]] + formats.foreach(format => { + val attrValueBuilder = AttrValue.newBuilder + VariableFormatConverter.setAttributeValue(attrValueBuilder, format) + arrayBuilder.addVariableFormat(attrValueBuilder.getVariableFormatValue) + }) + arrayBuilder.setSize(formats.size) + } + } else if (valueType =:= universe.typeOf[Array[InitializationMethod]]) { arrayBuilder.setDatatype(DataType.INITMETHOD) - val methods = value.asInstanceOf[Array[InitializationMethod]] - methods.foreach(method => { - val attrValueBuilder = AttrValue.newBuilder - InitMethodConverter.setAttributeValue(attrValueBuilder, method) - arrayBuilder.addInitMethod(attrValueBuilder.getInitMethodValue) - }) - arrayBuilder.setSize(methods.size) - } else if (value.isInstanceOf[Array[_ <: AbstractModule[Activity, Activity, T]]]) { + if (value != null) { + val methods = value.asInstanceOf[Array[InitializationMethod]] + methods.foreach(method => { + val attrValueBuilder = AttrValue.newBuilder + InitMethodConverter.setAttributeValue(attrValueBuilder, method) + arrayBuilder.addInitMethod(attrValueBuilder.getInitMethodValue) + }) + arrayBuilder.setSize(methods.size) + } + } else if (valueType <:< universe. + typeOf[Array[_ <: AbstractModule[Activity, Activity, _ <: Any]]]) { arrayBuilder.setDatatype(DataType.MODULE) - val modules = value.asInstanceOf[Array[_ <: AbstractModule[Activity, Activity, T]]] - modules.foreach(module => { - val attrValueBuilder = AttrValue.newBuilder - ModuleConverter.setAttributeValue(attrValueBuilder, module) - arrayBuilder.addBigDLModule(attrValueBuilder.getBigDLModuleValue) - }) - arrayBuilder.setSize(modules.size) + if (value != null) { + val modules = value.asInstanceOf[Array[_ <: AbstractModule[Activity, Activity, T]]] + modules.foreach(module => { + val attrValueBuilder = AttrValue.newBuilder + ModuleConverter.setAttributeValue(attrValueBuilder, module) + arrayBuilder.addBigDLModule(attrValueBuilder.getBigDLModuleValue) + }) + arrayBuilder.setSize(modules.size) + } } else if (value.isInstanceOf[Array[Map[String, Any]]]) { arrayBuilder.setDatatype(DataType.NAME_ATTR_LIST) value.asInstanceOf[Array[Map[String, Any]]].foreach(map => { @@ -720,24 +748,28 @@ object DataConverter extends DataConverter{ NameListConverter.setAttributeValue(attrValueBuilder, map) arrayBuilder.addNameAttrList(attrValueBuilder.getNameAttrListValue) }) - } else if (value.isInstanceOf[Array[DataFormat]]) { + } else if (valueType =:= universe.typeOf[Array[DataFormat]]) { arrayBuilder.setDatatype(DataType.DATA_FORMAT) - val formats = value.asInstanceOf[Array[DataFormat]] - formats.foreach(format => { - val attrValueBuilder = AttrValue.newBuilder - DataFormatConverter.setAttributeValue(attrValueBuilder, format) - arrayBuilder.addDataFormat(attrValueBuilder.getDataFormatValue) - }) - arrayBuilder.setSize(formats.size) + if (value != null) { + val formats = value.asInstanceOf[Array[DataFormat]] + formats.foreach(format => { + val attrValueBuilder = AttrValue.newBuilder + DataFormatConverter.setAttributeValue(attrValueBuilder, format) + arrayBuilder.addDataFormat(attrValueBuilder.getDataFormatValue) + }) + arrayBuilder.setSize(formats.size) + } } else { arrayBuilder.setDatatype(DataType.CUSTOM) - val customValues = value.asInstanceOf[Array[Any]] - customValues.foreach(custom => { - val attrValueBuilder = AttrValue.newBuilder - CustomConverterDelegator.setAttributeValue(attrValueBuilder, custom) - arrayBuilder.addCustom(attrValueBuilder.getCustomValue) - }) - arrayBuilder.setSize(customValues.size) + if (value != null) { + val customValues = value.asInstanceOf[Array[Any]] + customValues.foreach(custom => { + val attrValueBuilder = AttrValue.newBuilder + CustomConverterDelegator.setAttributeValue(attrValueBuilder, custom) + arrayBuilder.addCustom(attrValueBuilder.getCustomValue) + }) + arrayBuilder.setSize(customValues.size) + } } attributeBuilder.setArrayValue(arrayBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 0c8bb63542a..2a1d222c4de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -98,7 +98,7 @@ object ModuleSerializer extends ModuleSerializable{ } } catch { case e: Exception => - throw new RuntimeException("Loading module exception :", e) + throw new RuntimeException(s"Loading module ${model.getModuleType} exception :", e) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index 8e34c7162ad..f54c51b08c8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -240,7 +240,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Array of int32 conversion " should " work properly " in { val arry = Array[Int](1, 2, 3) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Int]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -249,7 +249,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Array of int64 conversion " should " work properly " in { val arry = Array[Long](1L, 2L, 3L) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Long]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -258,7 +258,16 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Array of float conversion " should " work properly " in { val arry = Array[Float](1.0f, 2.0f, 3.0f) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Float]]) + val attr = attriBulder.build + val retrievedValue = DataConverter.getAttributeValue(attr) + retrievedValue should be (arry) + } + + "Null Array of float conversion " should " work properly " in { + val arry : Array[Float] = null + val attriBulder = AttrValue.newBuilder + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Float]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -267,7 +276,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Array of double conversion " should " work properly " in { val arry = Array[Double](1.0, 2.0, 3.0) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Double]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -279,7 +288,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = "test1" arry(1) = "test2" val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[String]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -288,7 +297,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Array of Boolean conversion " should " work properly" in { val arry = Array[Boolean](true, false) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Boolean]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -299,7 +308,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = L2Regularizer(1.0) arry(1) = L1Regularizer(1.0) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Regularizer[Float]]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -310,7 +319,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ val tensor2 = Tensor(2, 3).apply1(_ => Random.nextFloat()) val tensorArray = Array(tensor1, tensor2) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, tensorArray) + DataConverter.setAttributeValue(attriBulder, tensorArray, universe.typeOf[Array[Tensor[Float]]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue.isInstanceOf[Array[Tensor[Float]]] should be (true) @@ -321,7 +330,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ val arry = new Array[VariableFormat](1) arry(0) = Default val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[VariableFormat]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -332,7 +341,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = RandomUniform arry(1) = Zeros val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[InitializationMethod]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -343,7 +352,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = NCHW arry(1) = NHWC val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[DataFormat]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) @@ -354,12 +363,22 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = Linear[Float](2, 3).setName("l1") arry(1) = Linear[Float](2, 3).setName("l2") val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry) + DataConverter.setAttributeValue(attriBulder, arry, + universe.typeOf[Array[AbstractModule[Activity, Activity, Float]]]) val attr = attriBulder.build val retrievedValue = DataConverter.getAttributeValue(attr) retrievedValue should be (arry) } + "Null Array of Modules conversion" should " work properly" in { + val arry : Array[AbstractModule[Activity, Activity, Float]] = null + val attriBulder = AttrValue.newBuilder + DataConverter.setAttributeValue(attriBulder, arry, + universe.typeOf[Array[AbstractModule[Activity, Activity, Float]]]) + val attr = attriBulder.build + val retrievedValue = DataConverter.getAttributeValue(attr) + retrievedValue should be (arry) + } "NameList conversion " should " work properly" in { @@ -388,4 +407,5 @@ class DataConverterSpec extends FlatSpec with Matchers{ retrievedValue should be (map) } + } From b99083f8b99537afeeb40888ed83c085a3ceec9d Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 21 Sep 2017 11:53:21 +0800 Subject: [PATCH 0407/1065] Support load tf unet (#1575) * add deconv2d, resizebilinear and support broadcast in add * fix test * fix style error and meet code review * fix python failure * fix unit test --- .../bigdl/utils/LayerException.scala | 27 ++ .../intel/analytics/bigdl/dllib/nn/Pack.scala | 25 +- .../bigdl/dllib/nn/ResizeBilinear.scala | 256 ++++++++++++++++++ .../dllib/nn/abstractnn/AbstractModule.scala | 10 +- .../analytics/bigdl/dllib/nn/ops/Conv2D.scala | 85 ++++++ .../dllib/nn/ops/ResizeBilinearOps.scala | 54 ++++ .../bigdl/dllib/tensor/DenseTensor.scala | 118 +++++++- .../dllib/utils/python/api/PythonBigDL.scala | 10 + .../dllib/utils/tf/TensorflowToBigDL.scala | 130 +++++---- .../analytics/bigdl/dllib/nn/GraphSpec.scala | 8 +- .../bigdl/dllib/nn/InferReshapeSpec.scala | 7 +- .../bigdl/dllib/nn/ReshapeSpec.scala | 9 +- .../bigdl/dllib/nn/ResizeBilinearSpec.scala | 107 ++++++++ .../bigdl/dllib/tensor/DenseTensorSpec.scala | 84 ++++++ 14 files changed, 828 insertions(+), 102 deletions(-) create mode 100644 scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LayerException.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LayerException.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LayerException.scala new file mode 100644 index 00000000000..fb8d4977438 --- /dev/null +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LayerException.scala @@ -0,0 +1,27 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils + +import org.apache.commons.lang.exception.ExceptionUtils + +/** + * Fine grained layer exception message + */ +class LayerException(var layerMsg: String, val error: Throwable) extends RuntimeException { + override def toString: String = { + "Layer info: " + layerMsg + "\n" + ExceptionUtils.getFullStackTrace(error) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pack.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pack.scala index a474e59dc90..f635acc4eba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pack.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pack.scala @@ -29,11 +29,11 @@ import scala.reflect.ClassTag */ @SerialVersionUID(3457313421501931556L) class Pack[T: ClassTag] (val dimension: Int)(implicit ev: TensorNumeric[T]) - extends AbstractModule[Table, Tensor[T], T] { + extends AbstractModule[Table, Tensor[_], T] { private def getPositiveDimension(input: Table): Int = { var nDim = this.dimension - val firstInput: Tensor[T] = input(1) + val firstInput: Tensor[_] = input(1) if (nDim < 0) { nDim = firstInput.dim() + nDim + 1 @@ -43,10 +43,10 @@ class Pack[T: ClassTag] (val dimension: Int)(implicit ev: TensorNumeric[T]) nDim } - override def updateOutput(input: Table): Tensor[T] = { + override def updateOutput(input: Table): Tensor[_] = { val dimension = getPositiveDimension(input) - val firstInput: Tensor[T] = input(1) + val firstInput: Tensor[_] = input(1) val nDim = firstInput.nDimension() val size: Array[Int] = new Array[Int](nDim + 1) @@ -62,36 +62,39 @@ class Pack[T: ClassTag] (val dimension: Int)(implicit ev: TensorNumeric[T]) i = i + 1 } + if (output.getType() != firstInput.getType()) { + output = firstInput.emptyInstance() + } output.resize(size) i = 1 while (i <= input.length()) { - val currentOutput: Tensor[T] = input(i) + val currentOutput: Tensor[_] = input(i) output.narrow(dimension, i, 1) - .copy(currentOutput) + .forceCopy(currentOutput) i += 1 } output } - override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + override def updateGradInput(input: Table, gradOutput: Tensor[_]): Table = { val dimension = getPositiveDimension(input) var i = 1 while (i <= input.length()) { if (!gradInput.contains(i)) { - gradInput(i) = Tensor() + gradInput(i) = gradOutput.emptyInstance() } - gradInput[Tensor[T]](i).resizeAs(input(i)) + gradInput[Tensor[_]](i).resizeAs(input(i)) i += 1 } i = 1 while (i <= input.length()) { val currentGradInput = gradOutput.select(dimension, i) - gradInput[Tensor[T]](i).copy(currentGradInput) + gradInput[Tensor[_]](i).forceCopy(currentGradInput) i += 1 } gradInput @@ -99,7 +102,7 @@ class Pack[T: ClassTag] (val dimension: Int)(implicit ev: TensorNumeric[T]) } object Pack { - def apply[@specialized(Float, Double) T: ClassTag]( + def apply[T: ClassTag]( dimension: Int)(implicit ev: TensorNumeric[T]): Pack[T] = { new Pack[T](dimension) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala new file mode 100644 index 00000000000..c9c46db9c35 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala @@ -0,0 +1,256 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.ResizeBilinear.InterpolationWeight +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Resize the input image with bilinear interpolation. The input image must be a float tensor with + * NHWC layout + * + * @param outputHeight output height + * @param outputWidth output width + * @param alignCorners align corner or not + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class ResizeBilinear[T: ClassTag](val outputHeight: Int, val outputWidth: Int, + val alignCorners: Boolean)(implicit ev: TensorNumeric[T]) + extends AbstractModule[Tensor[Float], Tensor[Float], T]{ + + private val ys = (1 to (outputHeight + 1)).map(i => InterpolationWeight(0, 0, 0)).toArray + private val xs = (1 to (outputWidth + 1)).map(i => InterpolationWeight(0, 0, 0)).toArray + + import ResizeBilinear._ + + override def updateOutput(input: Tensor[Float]): Tensor[Float] = { + require(input.nDimension() == 4, "only accept 4D input") + require(input.isContiguous(), "only accept contiguous input") + + val batchSize = input.size(1) + val inHeight = input.size(2) + val inWidth = input.size(3) + val channels = input.size(4) + + if (inHeight == outputHeight && inWidth == outputWidth) { + output = input + output + } else { + computeInterpolationWeights(outputHeight, inHeight, + calculateResizeScale(inHeight, outputHeight, alignCorners), ys) + computeInterpolationWeights(outputWidth, inWidth, + calculateResizeScale(inWidth, outputWidth, alignCorners), xs) + + var i = 0 + while(i < xs.size) { + xs(i).lower *= channels + xs(i).upper *= channels + i += 1 + } + + output.resize(batchSize, outputHeight, outputWidth, channels) + resizeImage(input.storage().array(), input.storageOffset() - 1, batchSize, inHeight, inWidth, + outputHeight, outputWidth, channels, xs, ys, output.storage().array(), + output.storageOffset() - 1) + output + } + } + + override def updateGradInput(input: Tensor[Float], gradOutput: Tensor[Float]): Tensor[Float] = { + require(input.nDimension() == 4, "only accept 4D input") + require(gradOutput.nDimension() == 4, "only accept 4D gradOutput") + require(input.isContiguous(), "only accept contiguous input") + require(gradOutput.isContiguous(), "only accept contiguous gradOutput") + + val batchSize = input.size(1) + val inHeight = input.size(2) + val inWidth = input.size(3) + val channels = input.size(4) + val inRowSize = inWidth * channels + val inBatchNum = batchSize * inHeight * inRowSize + val outRowSize = outputWidth * channels + val outBatchNum = batchSize * outputHeight * outRowSize + + require(gradOutput.size(2) == outputHeight, "output height is not match") + require(gradOutput.size(3) == outputWidth, "output width is not match") + + val heightScale = calculateResizeScale(inHeight, outputHeight, alignCorners) + val widthScale = calculateResizeScale(inWidth, outputWidth, alignCorners) + + gradInput.resizeAs(input) + gradInput.zero() + + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + + var b = 0 + while(b < batchSize) { + var y = 0 + while(y < outputHeight) { + val inY = y * heightScale + val topY = inY.toInt + val bottomY = math.min(math.ceil(inY).toInt, inHeight - 1) + val yLERP = inY - topY + val inverseYLERP = (1.0f - yLERP) + var x = 0 + while(x < outputWidth) { + val inX = x * widthScale + val leftX = inX.toInt + val rightX = math.min(math.ceil(inX).toInt, inWidth - 1) + val xLERP = inX - leftX + val inverseXLERP = (1.0f - xLERP) + var c = 0 + while(c < channels) { + gradInputData(gradInputOffset + b * inBatchNum + topY * inRowSize + + leftX * channels + c) = gradOutputData(gradOutputOffset + b * outBatchNum + + y * outRowSize + x * channels + c) * inverseYLERP * inverseXLERP + gradInputData(gradInputOffset + b * inBatchNum + topY * inRowSize + + rightX * channels + c) = gradOutputData(gradOutputOffset + b * outBatchNum + + y * outRowSize + x * channels + c) * inverseYLERP * xLERP + gradInputData(gradInputOffset + b * inBatchNum + bottomY * inRowSize + + leftX * channels + c) = gradOutputData(gradOutputOffset + b * outBatchNum + + y * outRowSize + x * channels + c) * yLERP * inverseXLERP + gradInputData(gradInputOffset + b * inBatchNum + bottomY * inRowSize + + rightX * channels + c) = gradOutputData(gradOutputOffset + b * outBatchNum + + y * outRowSize + x * channels + c) * yLERP * xLERP + c += 1 + } + x += 1 + } + y += 1 + } + b += 1 + } + gradInput + } +} + +object ResizeBilinear { + + def apply[T: ClassTag](outputHeight: Int, outputWidth: Int, alignCorners: Boolean = false) + (implicit ev: TensorNumeric[T]): ResizeBilinear[T] = { + new ResizeBilinear[T](outputHeight, outputWidth, alignCorners) + } + + private def computeLERP( + topLeft: Float, + topRight: Float, + bottomLeft: Float, + bottomRight: Float, + xLERP: Float, + yLERP: Float + ): Float = { + val top = topLeft + (topRight - topLeft) * xLERP + val bottom = bottomLeft + (bottomRight - bottomLeft) * xLERP + top + (bottom - top) * yLERP + } + + private def computeInterpolationWeights( + outSize: Int, + inSize: Int, + scale: Float, + interpolation: Array[InterpolationWeight] + ): Unit = { + interpolation(outSize).lower = 0 + interpolation(outSize).upper = 0 + var i = outSize - 1 + while(i >= 0) { + val in = i * scale + interpolation(i).lower = in.toInt + interpolation(i).upper = Math.min(interpolation(i).lower + 1, inSize - 1) + interpolation(i).lerp = in - interpolation(i).lower + i -= 1 + } + } + + /** + * Resize image + * @param image NHWC input + * @param batchSize + * @param inHeight + * @param inWidth + * @param outHeight + * @param outWidth + * @param channels + * @param xs + * @param ys + * @param output + */ + @inline + private def resizeImage( + image: Array[Float], imageOffset: Int, + batchSize: Int, + inHeight: Int, inWidth: Int, + outHeight: Int, outWidth: Int, + channels: Int, + xs: Array[InterpolationWeight], + ys: Array[InterpolationWeight], + output: Array[Float], outputOffset: Int + ): Unit = { + val inRowSize = inWidth * channels + val inBatchNumber = inHeight * inRowSize + val outRowSize = outWidth * channels + var _imageOffset = imageOffset + var _outputOffset = outputOffset + + // Todo: use multiple thread to speed up this + var b = 0 + while(b < batchSize) { + var y = 0 + while(y < outHeight) { + val ysLERP = ys(y).lerp + var x = 0 + while(x < outWidth) { + val xsLower = xs(x).lower + val xsUpper = xs(x).upper + val xsLERP = xs(x).lerp + + var c = 0 + while(c < channels) { + val topLeft = image(_imageOffset + ys(y).lower * inRowSize + xsLower + c) + val topRight = image(_imageOffset + ys(y).lower * inRowSize + xsUpper + c) + val bottomLeft = image(_imageOffset + ys(y).upper * inRowSize + xsLower + c) + val bottomRight = image(_imageOffset + ys(y).upper * inRowSize + xsUpper + c) + output(_outputOffset + x * channels + c) = computeLERP(topLeft, topRight, bottomLeft, + bottomRight, xsLERP, ysLERP) + c += 1 + } + x += 1 + } + _outputOffset += outRowSize + y += 1 + } + _imageOffset += inBatchNumber + b += 1 + } + } + + private case class InterpolationWeight(var lower: Int, var upper: Int, var lerp: Float) + + private def calculateResizeScale(inSize: Int, outSize: Int, alignCorners: Boolean): Float = { + if (alignCorners && outSize > 1) { + (inSize - 1).toFloat / (outSize - 1) + } else { + inSize.toFloat / outSize + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 3e3551b9806..e39e4cc6df1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -233,7 +233,15 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ final def forward(input: A): B = { val before = System.nanoTime() - updateOutput(input) + try { + updateOutput(input) + } catch { + case l: LayerException => + l.layerMsg = this.toString() + "/" + l.layerMsg + throw l + case e: Throwable => + throw new LayerException(this.toString(), e) + } forwardTime += System.nanoTime() - before output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala index e0ecdebe7e2..031a6497617 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala @@ -109,3 +109,88 @@ object Conv2D { )(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] = ModuleToOperation[T](new Conv2D(strides, padding, format)) } + +/** + * Backward of SpatialConvolution + */ +class Conv2DTranspose[T: ClassTag]( + strideW: Int, + strideH: Int, + padW: Int = -1, + padH: Int = -1, + format: DataFormat = DataFormat.NCHW +)(implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[T], T]{ + + private var module: SpatialConvolution[T] = _ + private var dummyInput: Tensor[T] = _ + + override def updateOutput(input: Activity): Tensor[T] = { + require(input.isTable, "Invalid input activity type") + val sizes = input.toTable.apply[Tensor[Int]](1).squeeze() + val kernel = input.toTable.apply[Tensor[T]](2) + val data = input.toTable.apply[Tensor[T]](3) + + require(data.nDimension() == 4, s"Need a 4D input but is ${data.nDimension()}") + require(sizes.nDimension() == 1, s"Need a 1D size but is ${sizes.nDimension()}") + + val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { + (data.size(2), sizes.valueAt(2)) + } else { + (data.size(4), sizes.valueAt(4)) + } + + if (module == null) { + module = new SpatialConvolution[T]( + nInputPlane = nInputPlane, + nOutputPlane = nOutputPlane, + kernelW = kernel.size(2), + kernelH = kernel.size(1), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + initWeight = kernel, + format = format, + withBias = false + ) + + dummyInput = Tensor[T](sizes.valueAt(1), sizes.valueAt(2), sizes.valueAt(3), sizes.valueAt(4)) + module.forward(dummyInput) + } else { + val (nOutputPlanbe, nInputPlane) = if (format == DataFormat.NCHW) { + (data.size(2), sizes.valueAt(2)) + } else { + (data.size(4), sizes.valueAt(4)) + } + + require(module.nInputPlane == nInputPlane, "nInputPlane is not valid") + require(module.nOutputPlane == nOutputPlane, "nOutputPlane is not valid") + require(module.kernelH == kernel.size(1), "kernelH is not valid") + require(module.kernelW == kernel.size(2), "kernelW is not valid") + require(kernel.size(3) == nInputPlane, "kernel nInputPlane is not valid") + require(kernel.size(4) == nOutputPlane, "kernel nOutputPlane is not valid") + require(dummyInput.size(1) == sizes.valueAt(1), "size 1 is not correct") + require(dummyInput.size(2) == sizes.valueAt(2), "size 1 is not correct") + require(dummyInput.size(3) == sizes.valueAt(3), "size 1 is not correct") + require(dummyInput.size(4) == sizes.valueAt(4), "size 1 is not correct") + } + + module.weight.set(kernel) + module.updateGradInput(dummyInput, data) + output = module.gradInput + output + } +} + +object Conv2DTranspose { + def apply[T: ClassTag]( + strideW: Int, + strideH: Int, + padW: Int = -1, + padH: Int = -1, + format: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]): Conv2DTranspose[T] = + new Conv2DTranspose(strideW, strideH, padW, padH, format) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala new file mode 100644 index 00000000000..3c4096ebbf0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala @@ -0,0 +1,54 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.ResizeBilinear +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class ResizeBilinearOps[T: ClassTag](alignCorner: Boolean)(implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[Float], T] { + + private var module : ResizeBilinear[T] = _ + + override def updateOutput(input: Activity): Tensor[Float] = { + require(input.isTable, "Only accept two input tensors") + val size = input.toTable.apply[Tensor[Int]](2) + if (module == null) { + module = ResizeBilinear[T]( + size.valueAt(1), + size.valueAt(2), + alignCorner + ) + } else { + require(module.outputHeight == size.valueAt(1), "height not match") + require(module.outputWidth == size.valueAt(2), "width not match") + } + val data = input.toTable.apply[Tensor[Float]](1) + output = module.forward(data) + output + } +} + +object ResizeBilinearOps { + def apply[T: ClassTag](alignCorner: Boolean) + (implicit ev: TensorNumeric[T]): ResizeBilinearOps[T] = { + new ResizeBilinearOps(alignCorner) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 34c4bc8618c..b43e24cc12e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -896,19 +896,66 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( override def add(value: T, y: Tensor[T]): Tensor[T] = DenseTensorMath.cadd(this, this, value, y) override def add(x: Tensor[T]): Tensor[T] = { - require(this.nElement() == x.nElement()) - if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) { - ev.vAdd(this.nElement(), this.storage().array(), this.storageOffset() - 1, - x.storage().array(), x.storageOffset() - 1, - this.storage().array(), this.storageOffset() - 1) - } - else { - val func = new TensorFunc4[T] { - override def apply (data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = { - data1(offset1) = ev.plus(data1(offset1), data2(offset2)) + if (this.nElement() == x.nElement()) { + if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) { + ev.vAdd(this.nElement(), this.storage().array(), this.storageOffset() - 1, + x.storage().array(), x.storageOffset() - 1, + this.storage().array(), this.storageOffset() - 1) + } else { + val func = new TensorFunc4[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = { + data1(offset1) = ev.plus(data1(offset1), data2(offset2)) + } } + DenseTensorApply.apply2[T](this, x, func) } - DenseTensorApply.apply2[T](this, x, func) + } else if (DenseTensor.canFastBroadcast(this, x)) { + // recursive add + var i = 0 + while(i < this.size(1)) { + this.select(1, i + 1).add(x) + i += 1 + } + } else { + val targetSize = DenseTensor.expandSize(this, x) + val expandStrides = new Array[Int](targetSize.length) + + val expandStridesX = new Array[Int](targetSize.length) + var i = targetSize.length - 1 + val delta2 = targetSize.length - x.nDimension + while(i >= delta2) { + if (x.size(i + 1- delta2) != 1) expandStridesX(i) = x.stride(i + 1- delta2) + i -= 1 + } + val expandX = new DenseTensor[T]( + x.storage(), + x.storageOffset(), + targetSize, + expandStridesX + ) + + val expandTensor = + if (targetSize.product == this.nElement()) { + this + } else { + i = targetSize.length - 1 + val delta1 = targetSize.length - this.nDimension + while (i >= delta1) { + if (this.size(i + 1 - delta1) != 1) expandStrides(i) = this.stride(i + 1 - delta1) + i -= 1 + } + val tensor1 = new DenseTensor[T]( + this.storage(), + this.storageOffset(), + targetSize, + expandStrides + ) + val newTensor = new DenseTensor[T]().resize(targetSize).add(tensor1) + this.set(newTensor) + this + } + + expandTensor.add(expandX) } this } @@ -2353,7 +2400,7 @@ object DenseTensor { Tensor(Storage(new Array[T](length)), 1, sizes).fill(ev.fromType[Int](1)) } - private[tensor] def gaussian1D[@specialized(Float, Double) T: ClassTag]( + private[tensor] def gaussian1D[@specialized T: ClassTag]( size: Int = 3, sigma: Double = 0.25, amplitude: Int = 1, @@ -2382,4 +2429,51 @@ object DenseTensor { } gauss } + + private[tensor] def canFastBroadcast[@specialized T: ClassTag](tensor: Tensor[T], + other: Tensor[T]): Boolean = { + if (tensor.nDimension < other.nDimension()) return false + + val delta = tensor.nDimension - other.nDimension() + var d = other.nDimension() + // Check dimensions + var broadcasting = false + while(d > 0) { + if (broadcasting) { + if (other.size(d) != 1) return false + } else if (tensor.size(delta + d) != other.size(d)) { + if (other.size(d) != 1) return false + broadcasting = true + } + d -= 1 + } + + return true + } + + private[tensor] def expandSize[@specialized T: ClassTag](tensor: Tensor[T], + other: Tensor[T]): Array[Int] = { + val errorMsg = s"tensor size not match ${tensor.size.mkString("x")} " + + s"${other.size.mkString("x")}" + val longTensor = if (tensor.dim() > other.dim()) tensor else other + val shortTensor = if (tensor.dim() > other.dim()) other else tensor + val ndim = longTensor.nDimension() + val delta = longTensor.nDimension() - shortTensor.nDimension() + val size = new Array[Int](ndim) + var i = ndim - 1 + while(i >= delta) { + require(longTensor.size(i + 1) == shortTensor.size(i + 1 - delta) || + longTensor.size(i + 1) == 1 || + shortTensor.size(i + 1 - delta) == 1, errorMsg) + size(i) = math.max(longTensor.size(i + 1), shortTensor.size(i + 1 - delta)) + i -= 1 + } + + while(i >= 0) { + size(i) = longTensor.size(i + 1) + i -= 1 + } + + size + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ddfa05bcbd6..dc3855f9391 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1935,4 +1935,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def saveGraphTopology(model: Graph[T], logPath: String): Graph[T] = { model.saveGraphTopology(logPath) } + + def createResizeBilinear( + outputHeight: Int, + outputWidth: Int, + alignCorner: Boolean + ): ResizeBilinear[T] = { + ResizeBilinear[T](outputHeight, + outputWidth, + alignCorner) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index c91d85c176b..fd23206ad94 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -24,6 +24,7 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Storage, Tensor} import org.tensorflow.framework.{AttrValue, DataType, NodeDef, TensorProto} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.{Conv2DTranspose, ResizeBilinearOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{DirectedGraph, Node, T} @@ -245,8 +246,8 @@ object TensorflowToBigDL { TanhTF, ReluTF, SigmoidTF, Conv2D, Placeholder, SqueezeTF, IdentityTF, ConcatTF, BatchNormTF, AddConstTF1, AddConstTF2, AddTF, SoftMaxTF, ElementWiseMulTF, MulTF, SplitTF, PaddingTF, MeanTF, UnpackTF, StrideSliceTF, ShapeTF, FillTF, PackTF, ConstTF, - Flatten, Conv2D2, Conv1D, FlattenV2, BatchNormV2NHWCTF, BatchNormV2NCHWTF, AddNTF, - ControlDependencyTF, FullConnectionWithoutBiasTF + Flatten, Conv1D, FlattenV2, BatchNormV2NHWCTF, BatchNormV2NCHWTF, AddNTF, + ControlDependencyTF, FullConnectionWithoutBiasTF, DeConv2D, ResizeBilinearTF ) res } @@ -441,13 +442,11 @@ object Conv1D extends TensorflowToBigDL { object Conv2D extends TensorflowToBigDL{ private val graph = { - val add = Node("BiasAdd") val conv = Node("Conv2D") Node("*") -> conv - Node("Const") -> Node("Identity") -> conv -> add - Node("Const") -> Node("Identity") -> add - add.graph(reverse = true) + Node("Const") -> Node("Identity") -> conv + conv.graph(reverse = true) } override def topology: DirectedGraph[String] = graph @@ -457,7 +456,7 @@ object Conv2D extends TensorflowToBigDL{ byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val attributes = tfGraph.source.prevNodes.head.element.getAttrMap + val attributes = tfGraph.source.element.getAttrMap val (pW, pH) = if (getString(attributes, "padding") == "SAME") { (-1, -1) @@ -473,9 +472,7 @@ object Conv2D extends TensorflowToBigDL{ require(strideList(3) == 1, s"not support strides on depth") val strideW = strideList(1) val strideH = strideList(2) - val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element - val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) - val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element + val weightNode = tfGraph.source.prevNodes(1).prevNodes.head.element val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder) val nOuputPlane = weights.size(4) val nInputPlane = weights.size(3) @@ -486,19 +483,16 @@ object Conv2D extends TensorflowToBigDL{ kernelW = kernelW, kernelH = kernelH, strideW = strideW, strideH = strideH, padW = pW, padH = pH, - initWeight = weights, - initBias = bias, - initGradWeight = gradWeights, - initGradBias = gradBias, format = DataFormat.NHWC) + initWeight = weights, initGradWeight = gradWeights, + format = DataFormat.NHWC, + withBias = false + ) case "NCHW" => require(strideList(1) == 1, s"not support strides on depth") val strideW = strideList(2) val strideH = strideList(3) - val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element - val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) - - val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element + val weightNode = tfGraph.source.prevNodes(1).prevNodes.head.element val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 4), (2, 3), (3, 4)))) val nOuputPlane = weights.size(1) @@ -510,10 +504,10 @@ object Conv2D extends TensorflowToBigDL{ kernelW = kernelW, kernelH = kernelH, strideW = strideW, strideH = strideH, padW = pW, padH = pH, - initWeight = weights, - initBias = bias, - initGradWeight = gradWeights, - initGradBias = gradBias, format = DataFormat.NCHW) + initWeight = weights, initGradWeight = gradWeights, + format = DataFormat.NCHW, + withBias = false + ) case _ => throw new IllegalArgumentException(s"not supported data format: $format") } @@ -521,65 +515,48 @@ object Conv2D extends TensorflowToBigDL{ } } -object Conv2D2 extends TensorflowToBigDL{ +object DeConv2D extends TensorflowToBigDL{ private val graph = { - val add = Node("Add") - val conv = Node("Conv2D") - val reshape = Node("Reshape") - - Node("*") -> conv - Node("Const") -> Node("Identity") -> conv -> add - Node("Const") -> Node("Identity") -> reshape - Node("Const") -> reshape - reshape -> add - - add.graph(reverse = true) + val deconv = Node("Conv2DBackpropInput") + Node("...") -> deconv + deconv.graph(reverse = true) } override def topology: DirectedGraph[String] = graph - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + override def layer[T: ClassTag]( + tfGraph: DirectedGraph[NodeDef], context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val attributes = tfGraph.source.prevNodes(0).element.getAttrMap - val strideList = getIntList(attributes, "strides") - val format = getString(attributes, "data_format") - require(strideList.head == 1, s"not support strides on batch") - require(format == "NCHW", "NCHW should be used for this sub-graph") - - require(strideList(1) == 1, s"not support strides on depth") - val (strideH, strideW) = (strideList(2), strideList(3)) - - val biasNode = tfGraph.source.prevNodes(1).prevNodes(0).prevNodes.head.element - val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) - - val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element - val (weights, gradWeights) = - getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 4), (2, 3), (3, 4)))) - - val nOuputPlane = weights.size(1) - val nInputPlane = weights.size(2) - val kernelH = weights.size(3) - val kernelW = weights.size(4) + byteOrder: ByteOrder + )(implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + val attributes = tfGraph.source.element.getAttrMap val (pW, pH) = if (getString(attributes, "padding") == "SAME") { (-1, -1) } else { (0, 0) } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") - SpatialConvolution[T]( - nInputPlane = nInputPlane, nOutputPlane = nOuputPlane, - kernelW = kernelW, kernelH = kernelH, - strideW = strideW, strideH = strideH, - padW = pW, padH = pH, - initWeight = weights, - initBias = bias, - initGradWeight = gradWeights, - initGradBias = gradBias).asInstanceOf[AbstractModule[Activity, Activity, T]] + val format = getString(attributes, "data_format") + val deconv = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + Conv2DTranspose[T](strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + Conv2DTranspose[T](strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + deconv.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -804,6 +781,9 @@ object ConstTF extends TensorflowToBigDL { byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + if (tfGraph.source.element.getName() == "size") { + println() + } val value = TFUtils .parseTensor(tfGraph.source.element.getAttrMap.get("value").getTensor, byteOrder) Const(value).asInstanceOf[AbstractModule[Activity, Activity, T]] @@ -1536,3 +1516,19 @@ object ControlDependencyTF extends TensorflowToBigDL { } } + +object ResizeBilinearTF extends TensorflowToBigDL { + override def topology: DirectedGraph[String] = { + (Node("...") -> Node("ResizeBilinear")).graph(reverse = true) + } + + override def layer[T: ClassManifest]( + tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]) + : AbstractModule[Activity, Activity, T] = { + val alignCorner = tfGraph.source.element.getAttrMap.get("align_corners").getB + ResizeBilinearOps(alignCorner).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index f6a3caf2ab2..66c2bf272dc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Less} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{Edge, Engine, RandomGenerator, T, Table} +import com.intel.analytics.bigdl.utils._ import scala.reflect.ClassTag import scala.util.Random @@ -86,7 +86,7 @@ class GraphSpec extends FlatSpec with Matchers { val output2 = ReLU().inputs(cadd) val graph = Graph(Array(fc1, fc2), Array(output1, output2)) - intercept[IllegalArgumentException] { + intercept[LayerException] { graph.forward(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f))) } } @@ -145,7 +145,7 @@ class GraphSpec extends FlatSpec with Matchers { val graph = Graph(Array(fc1), Array(output1)) - intercept[IllegalArgumentException] { + intercept[LayerException] { graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) } @@ -1148,7 +1148,7 @@ class GraphSpec extends FlatSpec with Matchers { val result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(true)))) result.toTensor should be(Tensor[Float](T(1))) - intercept[IllegalArgumentException] { + intercept[LayerException] { model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(false)))) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InferReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InferReshapeSpec.scala index 489acfbd54a..f9369023bff 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InferReshapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InferReshapeSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.LayerException import org.scalatest.FlatSpec class InferReshapeSpec extends FlatSpec { @@ -46,11 +47,11 @@ class InferReshapeSpec extends FlatSpec { assert(gradOutput == gradOutputOrg) } - intercept[IllegalArgumentException] { + intercept[LayerException] { module.forward(Tensor[Double](2, 2)) } - intercept[IllegalArgumentException] { + intercept[LayerException] { module.forward(Tensor[Double](3, 2, 2)) } } @@ -123,7 +124,7 @@ class InferReshapeSpec extends FlatSpec { assert(gradOutput == gradOutputOrg) } - intercept[IllegalArgumentException] { + intercept[LayerException] { module.forward(Tensor[Double](3, 1)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReshapeSpec.scala index 9d88db7fb2c..c188caef69a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReshapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReshapeSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import org.scalatest.FlatSpec import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.LayerException @com.intel.analytics.bigdl.tags.Parallel class ReshapeSpec extends FlatSpec { @@ -47,11 +48,11 @@ class ReshapeSpec extends FlatSpec { assert(gradOutput == gradOutputOrg) } - intercept[IllegalArgumentException] { + intercept[LayerException] { module.forward(Tensor[Double](2, 2)) } - intercept[IllegalArgumentException] { + intercept[LayerException] { module.forward(Tensor[Double](3, 2, 2)) } } @@ -97,7 +98,7 @@ class ReshapeSpec extends FlatSpec { assert(input == inputOrg) assert(gradOutput == gradOutputOrg) - intercept[IllegalArgumentException] { + intercept[LayerException] { module.forward(Tensor[Double](2, 3, 2)) } } @@ -128,7 +129,7 @@ class ReshapeSpec extends FlatSpec { assert(gradOutput == gradOutputOrg) } - intercept[IllegalArgumentException] { + intercept[LayerException] { module.forward(Tensor[Double](3, 2)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala new file mode 100644 index 00000000000..e2cb9724cff --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala @@ -0,0 +1,107 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class ResizeBilinearSpec extends FlatSpec with Matchers { + private val input = Tensor[Float](T(T( + T( + T(1, 2, 3), + T(4, 5, 6) + ), + T( + T(7, 8, 9), + T(2, 3, 1) + ), + T( + T(4, 8, 2), + T(5, 3, 0) + ) + ))) + + "ResizeBilinear forward" should "not change content while input/output width/height match" in { + println(input) + val layer = ResizeBilinear[Float](3, 2) + val output = layer.forward(input) + println(output) + input should be(output) + } + + "ResizeBilinear forward" should "be correct while double height" in { + println(input) + val layer = ResizeBilinear[Float](6, 2) + val output = layer.forward(input) + println(output) + val expectOutput = Tensor[Float](T(T( + T( + T(1, 2, 3), + T(4, 5, 6) + ), + T( + T(4, 5, 6), + T(3, 4, 3.5) + ), + T( + T(7, 8, 9), + T(2, 3, 1) + ), + T( + T(5.5, 8, 5.5), + T(3.5, 3, 0.5) + ), + T( + T(4, 8, 2), + T(5, 3, 0) + ), + T( + T(4, 8, 2), + T(5, 3, 0) + ) + ))) + output should be(expectOutput) + } + + "ResizeBilinear forward" should "be correct while double width" in { + println(input) + val layer = ResizeBilinear[Float](3, 4) + val output = layer.forward(input) + println(output) + val expectOutput = Tensor[Float](T(T( + T( + T(1, 2, 3), + T(2.5, 3.5, 4.5), + T(4, 5, 6), + T(4, 5, 6) + ), + T( + T(7, 8, 9), + T(4.5, 5.5, 5), + T(2, 3, 1), + T(2, 3, 1) + ), + T( + T(4, 8, 2), + T(4.5, 5.5, 1), + T(5, 3, 0), + T(5, 3, 0) + ) + ))) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index df9acc1b1f7..ce410367ea9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -820,4 +820,88 @@ class DenseTensorSpec extends FlatSpec with Matchers { t.update(Array[Int](), 2.0) t should be (DenseTensor[Double](2.0)) } + + "Tensor add" should "support broadcasting" in { + val t1 = Tensor[Double](T(1, 2, 3)) + val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) + t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + } + + "Tensor add" should "support broadcasting 2" in { + val t1 = Tensor[Double](T( + T( + T(1, 2, 3), + T(4, 5, 6) + ), + T( + T(2, 1, 6), + T(5, 4, 3) + ), + T( + T(4, 1, 3), + T(4, 5, 3) + ) + )) + val t2 = Tensor[Double](T( + T( + T(2), + T(3) + ) + )) + + val cloneT1 = t1.clone() + val oldStorage = t1.storage() + t1.add(t2) should be (Tensor[Double](T( + T( + T(3, 4, 5), + T(7, 8, 9) + ), + T( + T(4, 3, 8), + T(8, 7, 6) + ), + T( + T(6, 3, 5), + T(7, 8, 6) + ) + ))) + oldStorage.eq(t1.storage()) should be(true) + + t2.add(cloneT1) should be (Tensor[Double](T( + T( + T(3, 4, 5), + T(7, 8, 9) + ), + T( + T(4, 3, 8), + T(8, 7, 6) + ), + T( + T(6, 3, 5), + T(7, 8, 6) + ) + ))) + } + + "Tensor add" should "support broadcasting with singleton dimension" in { + val t1 = Tensor[Double](T(T(1, 2, 3))) + val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) + t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + } + + "Tensor add" should "catch exception when broadcasting size not match" in { + val t1 = Tensor[Double](T(1, 2)) + val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) + intercept[IllegalArgumentException] { + t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + } + } + + "Tensor add" should "catch exception when broadcasting size not match 2" in { + val t1 = Tensor[Double](T(T(1, 2, 3), T(1, 2, 3), T(1, 2, 3))) + val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) + intercept[IllegalArgumentException] { + t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + } + } } From c8027f2e4c5ad1eb41af8d5caf70d6cbd336bf58 Mon Sep 17 00:00:00 2001 From: Wang Yanzhang Date: Wed, 30 Aug 2017 13:11:14 +0800 Subject: [PATCH 0408/1065] fix: only copy the elements in tensor when serializing --- .../utils/serializer/ModuleSerializable.scala | 9 ++++---- .../serializer/ModuleSerializerSpec.scala | 22 ++++++++++++++++++- 2 files changed, 26 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 8f5a82561b8..8e51f36265f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -203,7 +203,7 @@ trait ModuleSerializable extends Loadable with Savable{ } } - private def copy2BigDLTensor[T: ClassTag](tensor : Tensor[T], serializedTensor : BigDLTensor) + protected def copy2BigDLTensor[T: ClassTag](tensor : Tensor[T], serializedTensor : BigDLTensor) (implicit ev: TensorNumeric[T]) : Unit = { val dataType = serializedTensor.getDatatype if (dataType == DataType.FLOAT) { @@ -260,15 +260,16 @@ trait ModuleSerializable extends Loadable with Savable{ } } - private def copyFromBigDLTensor[T: ClassTag](tensor : Tensor[T], + protected def copyFromBigDLTensor[T: ClassTag](tensor : Tensor[T], serializedTensor : BigDLTensor.Builder)(implicit ev: TensorNumeric[T]) : Unit = { import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble val tensorData = tensor.storage().array() if (ev == NumericFloat) { + val offset = tensor.storageOffset() - 1 var i = 0 - while (i < tensorData.length) { - serializedTensor.addFloatData(ev.toType[Float](tensorData(i))) + while (i < tensor.nElement()) { + serializedTensor.addFloatData(ev.toType[Float](tensorData(i + offset))) i += 1 } serializedTensor.setDatatype(DataType.FLOAT) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 7c3a25a83d0..01b523ffe8e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.serializer import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.nn.{VolumetricFullConvolution, _} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator.RNG @@ -1859,6 +1859,26 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res2 = loadedModule.forward(tensor2) res1 should be (res2) } + + "2 Linears's weights use same storage" should "work properly" in { + val weight = Array(0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f) + val weight1 = Tensor(Storage(weight), 1, Array(2, 2)) + val weight2 = Tensor(Storage(weight), 5, Array(2, 2)) + + val linear1 = Linear(2, 2, initWeight = weight1) + val linear2 = Linear(2, 2, initWeight = weight2) + val model = Sequential().add(linear1).add(linear2) + + val input = Tensor(4, 2).rand + + val res1 = model.forward(input) + + ModulePersister.saveToFile("/tmp/2linears.with.a.storage.bigdl", model, true) + val loadedModel = ModuleLoader.loadFromFile("/tmp/2linears.with.a.storage.bigdl") + val res2 = loadedModel.forward(input) + + res1 should be (res2) + } } class TestModule[T: ClassTag](val custom: CustomData) From c72ccdd003d9479e5f27bbe03b089c62f6c85460 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 21 Sep 2017 16:01:40 +0800 Subject: [PATCH 0409/1065] fix failed tf unit test (#1578) --- .../dllib/utils/tf/TensorflowToBigDL.scala | 149 +++++++++++++++++- 1 file changed, 147 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index fd23206ad94..797f1f5a2f6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -247,7 +247,8 @@ object TensorflowToBigDL { BatchNormTF, AddConstTF1, AddConstTF2, AddTF, SoftMaxTF, ElementWiseMulTF, MulTF, SplitTF, PaddingTF, MeanTF, UnpackTF, StrideSliceTF, ShapeTF, FillTF, PackTF, ConstTF, Flatten, Conv1D, FlattenV2, BatchNormV2NHWCTF, BatchNormV2NCHWTF, AddNTF, - ControlDependencyTF, FullConnectionWithoutBiasTF, DeConv2D, ResizeBilinearTF + ControlDependencyTF, FullConnectionWithoutBiasTF, DeConv2D, ResizeBilinearTF, Conv2D2, + Conv2DWithoutBias ) res } @@ -440,7 +441,7 @@ object Conv1D extends TensorflowToBigDL { } -object Conv2D extends TensorflowToBigDL{ +object Conv2DWithoutBias extends TensorflowToBigDL{ private val graph = { val conv = Node("Conv2D") @@ -515,6 +516,150 @@ object Conv2D extends TensorflowToBigDL{ } } +object Conv2D extends TensorflowToBigDL{ + private val graph = { + val add = Node("BiasAdd") + val conv = Node("Conv2D") + + Node("*") -> conv + Node("Const") -> Node("Identity") -> conv -> add + Node("Const") -> Node("Identity") -> add + add.graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + val attributes = tfGraph.source.prevNodes.head.element.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val conv = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) + val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element + val (weights, gradWeights) = getOrSetTensor(weightNode, context, byteOrder) + val nOuputPlane = weights.size(4) + val nInputPlane = weights.size(3) + val kernelH = weights.size(1) + val kernelW = weights.size(2) + SpatialConvolution[T]( + nInputPlane = nInputPlane, nOutputPlane = nOuputPlane, + kernelW = kernelW, kernelH = kernelH, + strideW = strideW, strideH = strideH, + padW = pW, padH = pH, + initWeight = weights, + initBias = bias, + initGradWeight = gradWeights, + initGradBias = gradBias, format = DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + val biasNode = tfGraph.source.prevNodes(1).prevNodes.head.element + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) + + val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element + val (weights, gradWeights) = + getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 4), (2, 3), (3, 4)))) + val nOuputPlane = weights.size(1) + val nInputPlane = weights.size(2) + val kernelH = weights.size(3) + val kernelW = weights.size(4) + SpatialConvolution[T]( + nInputPlane = nInputPlane, nOutputPlane = nOuputPlane, + kernelW = kernelW, kernelH = kernelH, + strideW = strideW, strideH = strideH, + padW = pW, padH = pH, + initWeight = weights, + initBias = bias, + initGradWeight = gradWeights, + initGradBias = gradBias, format = DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} + +object Conv2D2 extends TensorflowToBigDL{ + private val graph = { + val add = Node("Add") + val conv = Node("Conv2D") + val reshape = Node("Reshape") + + Node("*") -> conv + Node("Const") -> Node("Identity") -> conv -> add + Node("Const") -> Node("Identity") -> reshape + Node("Const") -> reshape + reshape -> add + + add.graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + val attributes = tfGraph.source.prevNodes(0).element.getAttrMap + val strideList = getIntList(attributes, "strides") + val format = getString(attributes, "data_format") + require(strideList.head == 1, s"not support strides on batch") + require(format == "NCHW", "NCHW should be used for this sub-graph") + + require(strideList(1) == 1, s"not support strides on depth") + val (strideH, strideW) = (strideList(2), strideList(3)) + + val biasNode = tfGraph.source.prevNodes(1).prevNodes(0).prevNodes.head.element + val (bias, gradBias) = getOrSetTensor(biasNode, context, byteOrder) + + val weightNode = tfGraph.source.prevNodes.head.prevNodes(1).prevNodes.head.element + val (weights, gradWeights) = + getOrSetTensor(weightNode, context, byteOrder, Some(Seq((1, 4), (2, 3), (3, 4)))) + + val nOuputPlane = weights.size(1) + val nInputPlane = weights.size(2) + val kernelH = weights.size(3) + val kernelW = weights.size(4) + + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + + SpatialConvolution[T]( + nInputPlane = nInputPlane, nOutputPlane = nOuputPlane, + kernelW = kernelW, kernelH = kernelH, + strideW = strideW, strideH = strideH, + padW = pW, padH = pH, + initWeight = weights, + initBias = bias, + initGradWeight = gradWeights, + initGradBias = gradBias).asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} + object DeConv2D extends TensorflowToBigDL{ private val graph = { val deconv = Node("Conv2DBackpropInput") From ade637d364ae0bdb2182ec190294901dc80330a1 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Thu, 21 Sep 2017 16:02:03 +0800 Subject: [PATCH 0410/1065] fix Logsoftmax infinity error (#1576) --- .../analytics/bigdl/dllib/nn/LogSoftMax.scala | 77 +++++-------------- .../bigdl/dllib/models/InceptionSpec.scala | 4 +- .../bigdl/dllib/nn/LogSoftMaxSpec.scala | 10 +++ .../bigdl/dllib/optim/EvaluatorSpec.scala | 2 +- .../bigdl/dllib/torch/LogSoftMaxSpec.scala | 28 +++++++ 5 files changed, 60 insertions(+), 61 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMax.scala index 8079eb9a260..37d65c20c30 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMax.scala @@ -42,10 +42,8 @@ class LogSoftMax[T: ClassTag]( implicit ev: TensorNumeric[T]) extends TensorModule[T] { @transient private var results: Array[Future[Unit]] = null - @transient - private var ones: Array[T] = null - @transient - private var buffer: Array[T] = null + private val ones: Tensor[T] = Tensor() + private val buffer: Tensor[T] = Tensor() override def updateOutput(input: Tensor[T]): Tensor[T] = { @@ -77,37 +75,21 @@ class LogSoftMax[T: ClassTag]( } private def updateOutputFrame(in: Tensor[T], out: Tensor[T]): Unit = { - if (ones == null || ones.length < in.nElement) { - ones = Array.fill(in.nElement)(ev.fromType[Int](1)) + if (ones.nElement() < in.nElement) { + ones.resizeAs(in).fill(ev.one) } - if (buffer == null || buffer.length < in.nElement) { - buffer = new Array[T](in.nElement) + if (buffer.nElement() != out.nElement) { + buffer.resizeAs(out) } + // use exp(in - maxInput) to avoid Infinity error + val maxInput = in.max() + + buffer.fill(ev.negative(maxInput)) + buffer.add(in) + buffer.exp() + val logSum = ev.plus(maxInput, ev.log(buffer.dot(ones))) - ev.vExp(in.nElement, - in.storage.array, - in.storageOffset - 1, - buffer, - 0) - - val dot = ev.dot(in.nElement, - buffer, - 0, - 1, - ones, - 0, - 1) - - val sum = ev.negative(ev.log(dot)) - - ev.axpy(in.nElement, - sum, - ones, - 0, - 1, - out.storage.array, - out.storageOffset - 1, - 1) + out.add(ev.negative(logSum)) } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { @@ -139,36 +121,15 @@ class LogSoftMax[T: ClassTag]( } private def updateGradInputFrame(out: Tensor[T], gradOut: Tensor[T]): Unit = { - ev.vExp(out.nElement, - out.storage.array, - out.storageOffset - 1, - buffer, - 0) - - val dot = ev.dot(gradOut.nElement, - gradOut.storage.array, - gradOut.storageOffset - 1, - 1, - ones, - 0, - 1) - - val sum = ev.negative(dot) - - ev.axpy(gradOut.nElement, - sum, - buffer, - 0, - 1, - gradOut.storage.array, - gradOut.storageOffset - 1, - 1) + buffer.exp(out) + val outSum = gradOut.dot(ones) + gradOut.add(ev.negative(outSum), buffer) } override def clearState() : this.type = { super.clearState() - ones = null - buffer = null + ones.set() + buffer.set() results = null this } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala index 8527aae600e..66f0c5febbe 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala @@ -754,7 +754,7 @@ class InceptionSpec extends TorchSpec { val loss = criterion.forward(output, labels) // since we already set the seed, the loss should match exactly - loss should be (6.8930426f) + loss should be (6.893043f) } "InceptionV1 " should "init right" in { @@ -774,7 +774,7 @@ class InceptionSpec extends TorchSpec { val loss = criterion.forward(output, labels) // since we already set the seed, the loss should match exactly - loss should be (6.9011583f) + loss should be (6.901158f) } "Inception_Layer_V1 graph" should "be correct" in { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala index 389b4fca06e..f4511499bb6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala @@ -21,6 +21,8 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.torch.TH import com.intel.analytics.bigdl.utils.Engine +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class LogSoftMaxSpec extends FlatSpec with Matchers with BeforeAndAfter { before { @@ -116,4 +118,12 @@ class LogSoftMaxSpec extends FlatSpec with Matchers with BeforeAndAfter { input should be(inputOrg) gradOutput should be(gradOutputOrg) } + + "LogSoftMax float module" should "won't return Infinity when input is bigger than 89" in { + val module = new LogSoftMax[Float]() + Random.setSeed(100) + val input = Tensor[Float](2, 5).apply1(e => Random.nextFloat() + 90) + val output = module.forward(input).toTensor[Float] + output.apply1(v => {v.isInfinity should be (false); v}) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala index 0f810ddf27a..5e90ffaa6e7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala @@ -70,7 +70,7 @@ class EvaluatorSpec extends FlatSpec with Matchers with BeforeAndAfter{ result(0)._1 should be (new AccuracyResult(0, 100)) result(1)._1 should be (new AccuracyResult(100, 100)) - result(2)._1 should be (new LossResult(57.66907f, 25)) + result(2)._1 should be (new LossResult(57.669075f, 25)) result(0)._1.result()._1 should be (0f) result(1)._1.result()._1 should be (1f) result(2)._1.result()._1 should be (2.306763f+-0.000001f) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSoftMaxSpec.scala index 16b4e64d7c7..1c8f265ca0f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSoftMaxSpec.scala @@ -122,4 +122,32 @@ class LogSoftMaxSpec extends TorchSpec { val checker = new GradientChecker(1e-4) checker.checkLayer[Double](layer, input, 1e-3) should be(true) } + + "LogSoftMax float module" should "return good result" in { + torchCheck() + val module = new LogSoftMax[Float]() + Random.setSeed(100) + val input = Tensor[Float](2, 5).apply1(e => Random.nextFloat() + 10) + val gradOutput = Tensor[Float](2, 5).apply1(e => Random.nextFloat() + 10) + + val start = System.nanoTime() + val output = module.forward(input) + val gradInput = module.backward(input, gradOutput) + val end = System.nanoTime() + val scalaTime = end - start + + val code = "torch.setdefaulttensortype('torch.FloatTensor')" + + "module = nn.LogSoftMax()\n" + + "output1 = module:forward(input)\n " + + "output2 = module:backward(input, gradOutput)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), + Array("output1", "output2")) + val luaOutput = torchResult("output1").asInstanceOf[Tensor[Float]] + val luaGradInput = torchResult("output2").asInstanceOf[Tensor[Float]] + + luaOutput should be(output) + luaGradInput should be(gradInput) + + } } From 260f17b1a934f52b8dd94172e186e1b8c4cfc090 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 25 Sep 2017 13:47:25 +0800 Subject: [PATCH 0411/1065] load from definition (#1587) * load from definition * remove dup --- .../dllib/utils/serializer/ModuleLoader.scala | 107 +++++++++++++++++- .../serializer/ModuleSerializerSpec.scala | 15 +++ 2 files changed, 121 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala index 57347477252..c2393858cf7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala @@ -19,16 +19,26 @@ import java.io._ import scala.collection.JavaConverters._ import com.google.protobuf.CodedInputStream +import com.intel.analytics.bigdl.nn.Container import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.File +import com.intel.analytics.bigdl.utils.{File, Table} import serialization.Bigdl.BigDLModule +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag object ModuleLoader { + /** + * load module from `modelPath` + * @param modelPath path where protobuf formatted module is stored + * @param ev numeric ops + * @tparam T data type + * @return loaded BigDL module + */ def loadFromFile[T: ClassTag](modelPath : String) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { val modelBuilder = BigDLModule.newBuilder @@ -39,10 +49,97 @@ object ModuleLoader { val bigDLModel = modelBuilder.build() ModuleSerializer.load(bigDLModel).module } + + /** + * Load weights from `modulePath` and copy to pre-defined module + * for `layers` layers, copy all if not specified + * @param definition pre-defined module + * @param modelPath path where protobuf formatted module is stored + * @param layers name list of layers weight & bias of which to be copied + * @param ev numeric ops + * @tparam T data type + */ + + def loadFromDefinition[T : ClassTag](definition : AbstractModule[Activity, Activity, T], + modelPath : String, layers : mutable.HashSet[String] = null)(implicit ev: TensorNumeric[T]) + : Unit = { + val loadedModule = loadFromFile(modelPath) + val layersToCopy = if (layers == null) { + val allLayers = new mutable.HashSet[String]() + getAllLayers(definition, allLayers) + allLayers + } else { + layers + } + copyParams(definition, loadedModule, layersToCopy) + } + + private def getAllLayers[T : ClassTag](module : AbstractModule[Activity, Activity, T], + layers : mutable.HashSet[String]) : Unit + = { + layers.add(module.getName) + if (module.isInstanceOf[Container[_, _, _]]) { + module.asInstanceOf[Container[_, _, _]].modules.foreach(subModule => { + getAllLayers(subModule, layers) + }) + } + } + + private def copyParams[T : ClassTag](definition : AbstractModule[Activity, Activity, T], + mirror : AbstractModule[Activity, Activity, T], + layers : mutable.HashSet[String]) : Unit = { + val parameterTable = definition.getParametersTable() + val copiedParameterTable = mirror.getParametersTable() + layers.foreach(name => { + if (parameterTable.contains(name)) { + require(copiedParameterTable.contains(name), s"$name does not exist in loaded module") + copyParams(parameterTable.get(name).get.asInstanceOf[Table], + copiedParameterTable.get(name).get.asInstanceOf[Table]) + } + }) + } + + private def copyParams[T : ClassTag](params : Table, copyParams : Table) : Unit = { + copyParam(params, copyParams, "weight") + copyParam(params, copyParams, "bias") + } + + private def copyParam[T : ClassTag](params : Table, + copyParams : Table, paraName : String) : Unit = { + if (params.contains(paraName)) { + // this is for quantization tensors where the weight might be an array + if (copyParams.get(paraName).get + .isInstanceOf[Array[Tensor[T]]]) { + require(params.get(paraName).get + .isInstanceOf[Array[Tensor[T]]], "param type mismatch!") + val copies = params.get(paraName).get + .asInstanceOf[Array[Tensor[T]]] + val origins = params.get(paraName).get + .asInstanceOf[Array[Tensor[T]]] + var i = 0 + while (i < copies.length) { + origins(i).copy(copies(i)) + i += 1 + } + } else { + // For normal layers, their params are just tensors + params.get(paraName).get.asInstanceOf[Tensor[T]].copy( + copyParams.get(paraName).get.asInstanceOf[Tensor[T]]) + } + } + } } object ModulePersister { + /** + * Persist module to specified path + * @param modelPath path to persist module to + * @param module module to be persisted + * @param overwrite if overwrite module file if exists + * @param ev numeric ops + * @tparam T data type + */ def saveToFile[T: ClassTag](modelPath: String, module: AbstractModule[Activity, Activity, T], overwrite: Boolean = false)(implicit ev: TensorNumeric[T]): Unit = { @@ -52,6 +149,14 @@ object ModulePersister { File.saveBytes(bigDLModel.toByteArray, modelPath, overwrite) } + /** + * Save module definition to given path + * @param definitionPath the path to persist definition path to + * @param module module to be persisted + * @param overwrite if overwrite module file if exists + * @param ev numeric ops + * @tparam T data type + */ def saveModelDefinitionToFile[T: ClassTag](definitionPath : String, module : AbstractModule[Activity, Activity, T], overwrite : Boolean = false)(implicit ev: TensorNumeric[T]) : Unit = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 01b523ffe8e..68588475e63 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -1879,6 +1879,21 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } + + "Load by definition " should " work properly" in { + val linear1 = Linear(2, 2).setName("linear") + val sequential = Sequential().setName("sequential").add(linear1) + ModulePersister.saveToFile("/tmp/loadDef.bigdl", sequential, true) + val linear2 = Linear(2, 2).setName("linear") + val definition = Sequential().setName("sequential").add(linear2) + ModuleLoader.loadFromDefinition(definition, "/tmp/loadDef.bigdl") + + val weight1 = linear1.weight + + val weight2 = linear2.weight + + weight1 should be (weight2) + } } class TestModule[T: ClassTag](val custom: CustomData) From e8b4b054cb6d3a5f0eede2e54483dfde7de0c2b8 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Mon, 25 Sep 2017 15:22:15 +0800 Subject: [PATCH 0412/1065] Add Assign, Cast, ExpandDims, MaxPool operations (#1562) * add assign * Add Cast and ExpandDims * meet code review * meet code reiew * meet code review * meet code review * meet code review * add comment to NumericWildcard type * try to fix an AbstractMethod error in Cast method * fix test failure --- .../analytics/bigdl/dllib/nn/Unsqueeze.scala | 37 ++++-- .../analytics/bigdl/dllib/nn/ops/Assign.scala | 78 ++++++++++++ .../analytics/bigdl/dllib/nn/ops/Cast.scala | 48 +++++++ .../bigdl/dllib/nn/ops/MaxPool.scala | 92 ++++++++++++++ .../bigdl/dllib/nn/ops/package.scala | 7 ++ .../bigdl/dllib/tensor/Convertable.scala | 45 +++++++ .../bigdl/dllib/tensor/DenseTensor.scala | 77 ++++++++---- .../bigdl/dllib/tensor/DenseTensorApply.scala | 4 +- .../analytics/bigdl/dllib/tensor/Tensor.scala | 10 ++ .../bigdl/dllib/tensor/TensorFunc.scala | 11 ++ .../bigdl/dllib/tensor/TensorNumeric.scala | 26 +++- .../bigdl/dllib/nn/ops/AssignSpec.scala | 50 ++++++++ .../bigdl/dllib/nn/ops/CastSpec.scala | 47 +++++++ .../bigdl/dllib/nn/ops/ExpandDimsSpec.scala | 107 ++++++++++++++++ .../bigdl/dllib/nn/ops/MaxPoolSpec.scala | 117 ++++++++++++++++++ .../bigdl/dllib/torch/UnsqueezeSpec.scala | 27 ++++ 16 files changed, 743 insertions(+), 40 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Cast.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala index 0682ac6ea12..e5d260604b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala @@ -16,9 +16,9 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.tensor._ import scala.reflect.ClassTag @@ -34,13 +34,19 @@ import scala.reflect.ClassTag class Unsqueeze[T: ClassTag]( val pos: Int, var numInputDims: Int = Int.MinValue - )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { +)(implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[_], Tensor[_], T] { def setNumInputDims(numInputDims: Int): Unit = { this.numInputDims = numInputDims } - private def getActualPosition(input: Tensor[T]) : Int = { + private def getActualPosition(input: Tensor[_]) : Int = { + val dim = if (pos < 0) { + input.dim() + pos + 1 + } else { + pos + } + // get valid dimension offset for batchMode (if any) val inputDim = input.dim() // data batch dim numInputDims = if (numInputDims != Int.MinValue) numInputDims else inputDim // feature map dim @@ -49,23 +55,30 @@ class Unsqueeze[T: ClassTag]( s" input feature map dim ${numInputDims}, inputdim ${inputDim}") // the actual position; clearer error message for batchMode (if any) - val actualPos = pos + offsetDim + val actualPos = dim + offsetDim require(actualPos >= 1 && actualPos <= (inputDim + 1), s"Invalid position: $pos. " + s"input:dim() is $input, input feature map dim (numInputDims) is $numInputDims.") actualPos } - override def updateOutput(input: Tensor[T]): Tensor[T] = { + override def updateOutput(input: Tensor[_]): Tensor[_] = { val actualPos = getActualPosition(input) - output.addSingletonDimension(input, actualPos) + if (input.getType() != output.getType()) { + output = input.emptyInstance() + } + + output + .asInstanceOf[Tensor[NumericWildcard]] + .addSingletonDimension(input.asInstanceOf[Tensor[NumericWildcard]], actualPos) + output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + override def updateGradInput(input: Tensor[_], gradOutput: Tensor[_]): Tensor[_] = { require(input.nElement() == gradOutput.nElement(), "input and gradOutput should be of the same size" + - s"input size ${input.nElement()} gradOutput size ${gradOutput.nElement()}") + s"input size ${input.nElement()} gradOutput size ${gradOutput.nElement()}") gradInput = gradOutput.view(input.size()) gradInput } @@ -93,8 +106,8 @@ class Unsqueeze[T: ClassTag]( object Unsqueeze { def apply[@specialized(Float, Double) T: ClassTag]( - pos: Int, - numInputDims: Int = Int.MinValue)(implicit ev: TensorNumeric[T]) : Unsqueeze[T] = { + pos: Int, + numInputDims: Int = Int.MinValue)(implicit ev: TensorNumeric[T]) : Unsqueeze[T] = { new Unsqueeze[T](pos, numInputDims) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala new file mode 100644 index 00000000000..221d6425df5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala @@ -0,0 +1,78 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Update 'ref' by assigning 'value' to it. + * + * This operation outputs a Tensor that holds the new value of 'ref' after + * the value has been assigned. + * This makes it easier to chain operations that need to use the reset value. + * + * The `input` has two elements, the first one is `ref`, the second is `value`. + * + * @param validateShape An optional bool. Defaults to True. + * If true, the operation will validate that the shape of + * 'value' matches the shape of the Tensor being assigned to. + * If false, 'ref' will take on the shape of 'value'. + * @param useLocking An optional bool. Defaults to True. + * If True, the assignment will be protected by a lock; + * otherwise the behavior is undefined, but may exhibit less contention. + * + * @tparam T Numeric type. Only support float/double now + */ +class Assign[T: ClassTag]( + validateShape: Boolean = true, + useLocking: Boolean = true +) + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[_], T] { + + override def updateOutput(input: Table): Tensor[_] = { + val input1 = input[Tensor[NumericWildcard]](1) + val input2 = input[Tensor[NumericWildcard]](2) + + require(input1.getType() == input2.getType(), + "ref and value must have the same tensor numeric type") + + if (validateShape) { + var i = 1 + while (i <= input1.dim()) { + require(input1.size(i) == input2.size(i), "shape of the ref and value are not same") + i += 1 + } + } + + input1 + .resizeAs(input2) + .copy(input2) + + output.asInstanceOf[Tensor[NumericWildcard]] + .resizeAs(input2) + .copy(input2) + } +} + +object Assign { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new Assign()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Cast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Cast.scala new file mode 100644 index 00000000000..f080d4f479a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Cast.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ + +import scala.reflect.ClassTag + +/** + * Casts a tensor to a new type. + * + * @tparam T Parameter tensor numeric type. Only support float/double now + * @tparam D A new type was cast to + */ +class Cast[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[_], Tensor[D], T] { + + output = Activity.allocate[Tensor[D], D]() + + override def updateOutput(input: Tensor[_]): Tensor[D] = { + output.resizeAs(input) + input.cast[D](output) + + output + } +} + +object Cast { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new Cast[T, D]()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala new file mode 100644 index 00000000000..a2aa411d647 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.SpatialMaxPooling +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ + +import scala.reflect.ClassTag + +class MaxPool[T: ClassTag]( + ksize: Array[Int], + strides: Array[Int], + padding: String, + format: DataFormat = DataFormat.NHWC +)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { + val pool: SpatialMaxPooling[T] = format match { + case DataFormat.NHWC => + if (padding == "SAME") { + SpatialMaxPooling( + kH = ksize(1), + kW = ksize(2), + dH = strides(1), + dW = strides(2), + padH = -1, + padW = -1, + format = format + ) + } else if (padding == "VALID") { + SpatialMaxPooling( + kH = ksize(1), + kW = ksize(2), + dH = strides(1), + dW = strides(2), + format = format + ) + } else { + throw new RuntimeException("Padding can only support SAME and VALID padding") + } + case DataFormat.NCHW => + if (padding == "SAME") { + SpatialMaxPooling( + kH = ksize(2), + kW = ksize(3), + dH = strides(2), + dW = strides(3), + padH = -1, + padW = -1, + format = format + ) + } else if (padding == "VALID") { + SpatialMaxPooling( + kH = ksize(2), + kW = ksize(3), + dH = strides(2), + dW = strides(3), + format = format + ) + } else { + throw new RuntimeException("Padding can only support SAME and VALID padding") + } + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + output = pool.updateOutput(input) + output + } +} + +object MaxPool { + def apply[T: ClassTag]( + ksize: Array[Int], + strides: Array[Int], + padding: String, + format: DataFormat = DataFormat.NHWC + )(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new MaxPool(ksize, strides, padding, format)) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala index 77a69757b6b..0d0b6084042 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala @@ -92,4 +92,11 @@ package object ops { = ModuleToOperation[T]( com.intel.analytics.bigdl.nn.SoftMax()) } + + object ExpandDims { + def apply[T: ClassTag](axis: Int) + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( + com.intel.analytics.bigdl.nn.Unsqueeze(axis + 1)) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala index 28564173ba0..09b0ec42247 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala @@ -41,14 +41,48 @@ trait ConvertableTo[@spec A] { implicit def fromDouble(a: Double): A implicit def fromInt(a: Int): A + + implicit def fromShort(a: Short): A + + implicit def fromLong(a: Long): A +} + +trait ConvertableToLong extends ConvertableTo[Long] { + implicit def fromFloat(a: Float): Long = a + + implicit def fromDouble(a: Double): Long = a.toLong + + implicit def fromInt(a: Int): Long = a.toLong + + implicit def fromShort(a: Short): Long = a.toLong + + implicit def fromLong(a: Long): Long = a.toLong } + +trait ConvertableToShort extends ConvertableTo[Short] { + implicit def fromFloat(a: Float): Short = a + + implicit def fromDouble(a: Double): Short = a.toShort + + implicit def fromInt(a: Int): Short = a.toShort + + implicit def fromShort(a: Short): Short = a.toShort + + implicit def fromLong(a: Long): Short = a.toShort +} + + trait ConvertableToFloat extends ConvertableTo[Float] { implicit def fromFloat(a: Float): Float = a implicit def fromDouble(a: Double): Float = a.toFloat implicit def fromInt(a: Int): Float = a.toFloat + + implicit def fromShort(a: Short): Float = a.toFloat + + implicit def fromLong(a: Long): Float = a.toFloat } trait ConvertableToDouble extends ConvertableTo[Double] { @@ -57,6 +91,10 @@ trait ConvertableToDouble extends ConvertableTo[Double] { implicit def fromDouble(a: Double): Double = a implicit def fromInt(a: Int): Double = a.toDouble + + implicit def fromShort(a: Short): Double = a.toDouble + + implicit def fromLong(a: Long): Double = a.toDouble } trait ConvertableToInt extends ConvertableTo[Int] { @@ -65,6 +103,10 @@ trait ConvertableToInt extends ConvertableTo[Int] { implicit def fromDouble(a: Double): Int = a.toInt implicit def fromInt(a: Int): Int = a + + implicit def fromShort(a: Short): Int = a.toShort + + implicit def fromLong(a: Long): Int = a.toLong } object ConvertableTo { @@ -75,6 +117,9 @@ object ConvertableTo { implicit object ConvertableToInt extends ConvertableToInt + implicit object ConvertableToShort extends ConvertableToShort + + implicit object ConvertableToLong extends ConvertableToLong } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index b43e24cc12e..23f00c77f78 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -91,6 +91,31 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( this } + override def cast[@specialized(Long, Int, Short, Double, Float) D: ClassTag] + (castTensor: Tensor[D]) + (implicit ev1: TensorNumeric[D]): Tensor[D] = { + castTensor.getType() match { + case FloatType => + castTensor.applyFun[T](this.asInstanceOf[Tensor[T]], + x => ev.toType[Float](x).asInstanceOf[D]) + case DoubleType => + castTensor.applyFun[T](this.asInstanceOf[Tensor[T]], + x => ev.toType[Double](x).asInstanceOf[D]) + case LongType => + castTensor.applyFun[T](this.asInstanceOf[Tensor[T]], + x => ev.toType[Long](x).asInstanceOf[D]) + case IntType => + castTensor.applyFun[T](this.asInstanceOf[Tensor[T]], + x => ev.toType[Int](x).asInstanceOf[D]) + case ShortType => + castTensor.applyFun[T](this.asInstanceOf[Tensor[T]], + x => ev.toType[Short](x).asInstanceOf[D]) + case _ => + throw new RuntimeException("Unspported type") + } + castTensor + } + override def resize(sizes: Array[Int], strides: Array[Int]): Tensor[T] = { DenseTensor.resize(this, sizes, strides) this @@ -411,12 +436,14 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( def applyFun[A: ClassTag]( t: Tensor[A], func: (A) => T): Tensor[T] = { - def func2( - data1: Array[A], index1: Int, - data2: Array[T], index2: Int): Unit = { - data2(index2) = func(data1(index1)) + val func2 = new TensorDiffTypeFunc4[A, T] { + override def apply( + data1: Array[A], index1: Int, + data2: Array[T], index2: Int): Unit = { + data2(index2) = func(data1(index1)) + } } - DenseTensorApply.apply1(t, this, func2) + DenseTensorApply.apply1[A, T](t, this, func2) this } @@ -880,7 +907,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( val elementsPerRow = index.size(dim) // TODO: the performance of contiguous tensor should be optimize DenseTensorDimApply.dimApply3[T](this, src, index, dim, (tdata, toffset, tstride, - tsize, vdata, voffset, vstride, vsize, idata, ioffset, istride, isize) => { + tsize, vdata, voffset, vstride, vsize, idata, ioffset, istride, isize) => { var i = 0 while (i < elementsPerRow) { val idx = ev.toType[Int](idata(ioffset + i * istride)) @@ -969,7 +996,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( } else { val func = new TensorFunc6[T] { override def apply (data: Array[T], offset: Int, data1: Array[T], - offset1: Int, data2: Array[T], offset2: Int): Unit = { + offset1: Int, data2: Array[T], offset2: Int): Unit = { data(offset1) = ev.plus(data1(offset1), data2(offset2)) } } @@ -980,7 +1007,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( // Puts the result of x + value * y in current tensor override def add(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = - DenseTensorMath.cadd(this, x, value, y) + DenseTensorMath.cadd(this, x, value, y) override def add(value: T): Tensor[T] = { if (this.isContiguous()) { @@ -1021,7 +1048,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( } else { val func = new TensorFunc6[T] { override def apply (data: Array[T], offset: Int, data1: Array[T], - offset1: Int, data2: Array[T], offset2: Int): Unit = { + offset1: Int, data2: Array[T], offset2: Int): Unit = { data(offset) = ev.minus(data1(offset1), data2(offset2)) } } @@ -1031,7 +1058,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( } // Puts the result of x - value * y in current tensor override def sub(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = - DenseTensorMath.csub(this, x, value, y) + DenseTensorMath.csub(this, x, value, y) override def sub(value: T): Tensor[T] = { if (this.isContiguous()) { @@ -1191,7 +1218,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( * @return */ override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T], t3: Tensor[T]): Tensor[T] = - DenseTensorMath.addr(this, v1, t1, v2, t2, t3) + DenseTensorMath.addr(this, v1, t1, v2, t2, t3) override def addmv(beta: T, vec1: Tensor[T], alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = @@ -1539,7 +1566,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( DenseTensorDimApply.dimApply3[T](this, resultTensor, indicesTensor, selectDim, (tdata, toffset, tstride, tsize, vdata, voffset, vstride, vsize, idata, - ioffset, istride, isize) => { + ioffset, istride, isize) => { var i = 0 while (i < tsize) { tmpResult(i) = (tdata(toffset + i * tstride), i + 1) @@ -1700,7 +1727,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( // todo: the performance of contiguous tensor should be optimized val func = new TensorFunc6[T] { def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, - data3: Array[T], offset3: Int): Unit = { + data3: Array[T], offset3: Int): Unit = { if (ev.isGreater(data2(offset1), data3(offset2))) { data1(offset1) = ev.fromType(1) } else { @@ -1722,7 +1749,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( // todo: the performance of contiguous tensor should be optimized val func = new TensorFunc6[T] { def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, - data3: Array[T], offset3: Int): Unit = { + data3: Array[T], offset3: Int): Unit = { if (ev.toType[Double](ev.minus(data2(offset1), data3(offset2))) < 0) { data1(offset1) = ev.fromType(1) } else { @@ -1745,7 +1772,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( // todo: the performance of contiguous tensor should be optimized val func = new TensorFunc6[T] { def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, - data3: Array[T], offset3: Int): Unit = { + data3: Array[T], offset3: Int): Unit = { if (ev.toType[Double](ev.minus(data2(offset1), data3(offset2))) <= 0) { data1(offset1) = ev.fromType(1) } else { @@ -1759,7 +1786,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( /** * Implements == operator comparing each element in a with b - * + * * @param x * @param value * @return @@ -1871,7 +1898,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( /** * Implements >= operator comparing each element in x with value - * + * * @param x * @param value * @return @@ -1895,7 +1922,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( * Accumulate the elements of tensor into the original tensor by adding to the indices * in the order given in index. The shape of tensor must exactly match the elements indexed * or an error will be thrown. - * + * * @param dim * @param index * @param y @@ -1928,7 +1955,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( * create a new Tensor which indexes the original Tensor along dimension dim using the entries * in torch.LongTensor index. The returned Tensor has the same number of dimensions as the * original Tensor. - * + * * @param dim * @param index * @param y @@ -2401,12 +2428,12 @@ object DenseTensor { } private[tensor] def gaussian1D[@specialized T: ClassTag]( - size: Int = 3, - sigma: Double = 0.25, - amplitude: Int = 1, - normalize: Boolean = false, - mean: Double = 0.5, - tensor: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = { + size: Int = 3, + sigma: Double = 0.25, + amplitude: Int = 1, + normalize: Boolean = false, + mean: Double = 0.5, + tensor: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = { val gauss = if (null != tensor) { require(tensor.dim() == 1, "expecting 1D tensor") require(tensor.nElement() > 0, "expecting non-empty tensor") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala index 867b5378d45..ecaad719528 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala @@ -26,8 +26,8 @@ object DenseTensorApply { * @param func (tensor1Data, tensor1Offset, tensor2Data, * tensor2Offset) */ - def apply1[A, B, C](tensor1: Tensor[A], tensor2: Tensor[B], - func: (Array[A], Int, Array[B], Int) => Unit): Unit = { + def apply1[A, B](tensor1: Tensor[A], tensor2: Tensor[B], + func: TensorDiffTypeFunc4[A, B]): Unit = { if (tensor1.nDimension == 0) { return diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 0bcca0d6ed6..90094823e3a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -367,6 +367,16 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { */ def resizeAs(src: Tensor[_]): Tensor[T] + /** + * Cast the currenct tensor to a tensor with tensor numeric type D + * and set cast value to `castTensor` + * + * @param castTensor the cast value set to this tensor + * @tparam D new numeric type + * @return return castTensort + */ + def cast[D: ClassTag](castTensor: Tensor[D])(implicit ev: TensorNumeric[D]): Tensor[D] + /** * Resize the current tensor to the give shape * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorFunc.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorFunc.scala index e1e023ea592..82b230e98ad 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorFunc.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorFunc.scala @@ -50,6 +50,17 @@ trait TensorFunc6[@specialized(Float, Double) T] { override def toString(): String = "" } +/** + * Tensor function contain four parameters with differentType + * @tparam T + */ +trait TensorDiffTypeFunc4[A, T] { + self => + def apply(v1: Array[A], v2: Int, v3: Array[T], v4: Int): Unit + + override def toString(): String = "" +} + /** * Tensor function contain six parameters with differentType * @tparam T diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index b0e3c709e49..5c0c054b581 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -31,6 +31,21 @@ class TensorNumericMath */ object TensorNumericMath { + /** + * This type is used to denote that the numeric type of tensor is not restricted. + * The use-case usually is used to do some tensor operations when we do not make sure + * their concrete types, but they must have the same type. + * + * For example if we want to copy tensor1 from tensor2, and we only know they are + * the same type tensor without the information about their concrete type. + * + * We can use the following code: + * + * `tensor1.asInstanceOf[Tensor[NumericWildcard]] + * .copy(tensor2.asInstanceOf[Tensor[NumericWildcard]])` + */ + type NumericWildcard = Any + /** * define tensor math operation */ @@ -282,7 +297,7 @@ object TensorNumericMath { throw new UnsupportedOperationException(typeName + " in tensor does not support fromType operation") - def toType[@specialized(Float, Double, Int) K](t: T)(implicit c: ConvertableTo[K]): K = + def toType[K](t: T)(implicit c: ConvertableTo[K]): K = throw new UnsupportedOperationException(typeName + " in tensor does not support toType operation") @@ -999,6 +1014,9 @@ object TensorNumericMath { implicit c: ConvertableFrom[K]): Int = c.toInt(k) + override def toType[K](t: Int) + (implicit c: ConvertableTo[K]): K = c.fromInt(t) + override def axpy(n: Int, da: Int, dx: Array[Int], _dx_offset: Int, incx: Int, dy: Array[Int], _dy_offset: Int, incy: Int): Unit = { @@ -1065,6 +1083,9 @@ object TensorNumericMath { implicit c: ConvertableFrom[K]): Long = c.toLong(k) + override def toType[@specialized(Float, Double, Int) K](t: Long) + (implicit c: ConvertableTo[K]): K = c.fromLong(t) + override def axpy(n: Int, da: Long, dx: Array[Long], _dx_offset: Int, incx: Int, dy: Array[Long], _dy_offset: Int, incy: Int): Unit = { @@ -1131,6 +1152,9 @@ object TensorNumericMath { implicit c: ConvertableFrom[K]): Short = c.toShort(k) + override def toType[@specialized(Float, Double, Int) K](t: Short) + (implicit c: ConvertableTo[K]): K = c.fromShort(t) + override def axpy(n: Int, da: Short, dx: Array[Short], _dx_offset: Int, incx: Int, dy: Array[Short], _dy_offset: Int, incy: Int): Unit = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala new file mode 100644 index 00000000000..05b41a1f546 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class AssignSpec extends FlatSpec with Matchers { + "Assign operation Float" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + T( + Tensor(T(1f, 2f, 3f)), + Tensor(T(2f, 2f, 4f)) + ) + + val expectOutput = Tensor(T(2f, 2f, 4f)) + + val output = Assign().forward(input) + output should be(expectOutput) + } + + "Assign operation Double" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val input = + T( + Tensor(T(1.0, 2.0, 3.0)), + Tensor(T(2.0, 2.0, 4.0)) + ) + + val expectOutput = Tensor(T(2.0, 2.0, 4.0)) + + val output = Assign().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala new file mode 100644 index 00000000000..cb9df4c0d44 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class CastSpec extends FlatSpec with Matchers { + "Cast operation Float" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + Tensor(T(1.5f, 2.1f, 3.1f)) + + val expectOutput = Tensor[Int](T(1, 2, 3)) + + val output = Cast[Float, Int]().forward(input) + output should be(expectOutput) + } + + "Cast operation Double" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val input = + T( + Tensor(T(1.0, 2.0, 3.0)), + Tensor(T(2.0, 2.0, 4.0)) + ) + + val expectOutput = Tensor(T(2.0, 2.0, 4.0)) + + val output = Assign().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala new file mode 100644 index 00000000000..cc7396fa94a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala @@ -0,0 +1,107 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class ExpandDimsSpec extends FlatSpec with Matchers { + "ExpandDims operation Float" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = Tensor[Float](Array(2, 3, 5)) + + val expectOutput1 = input.clone().resize(Array(1, 2, 3, 5)) + val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) + val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) + + val output1 = ExpandDims(0).forward(input) + val output2 = ExpandDims(2).forward(input) + val output3 = ExpandDims(3).forward(input) + + output1 should be(expectOutput1) + output2 should be(expectOutput2) + output3 should be(expectOutput3) + } + + "ExpandDims operation Int" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = Tensor[Int](Array(2, 3, 5)) + + val expectOutput1 = input.clone().resize(Array(1, 2, 3, 5)) + val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) + val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) + + val output1 = ExpandDims(0).forward(input) + val output2 = ExpandDims(2).forward(input) + val output3 = ExpandDims(3).forward(input) + + output1 should be(expectOutput1) + output2 should be(expectOutput2) + output3 should be(expectOutput3) + } + + "ExpandDims operation Double" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = Tensor[Double](Array(2, 3, 5)) + + val expectOutput1 = input.clone().resize(Array(1, 2, 3, 5)) + val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) + val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) + + val output1 = ExpandDims(0).forward(input) + val output2 = ExpandDims(2).forward(input) + val output3 = ExpandDims(3).forward(input) + + output1 should be(expectOutput1) + output2 should be(expectOutput2) + output3 should be(expectOutput3) + } + + "ExpandDims operation Short" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = Tensor[Short](Array(2, 3, 5)) + + val expectOutput1 = input.clone().resize(Array(1, 2, 3, 5)) + val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) + val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) + + val output1 = ExpandDims(0).forward(input) + val output2 = ExpandDims(2).forward(input) + val output3 = ExpandDims(3).forward(input) + + output1 should be(expectOutput1) + output2 should be(expectOutput2) + output3 should be(expectOutput3) + } + + "ExpandDims operation Long" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = Tensor[Long](Array(2, 3, 5)) + + val expectOutput1 = input.clone().resize(Array(1, 2, 3, 5)) + val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) + val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) + + val output1 = ExpandDims(0).forward(input) + val output2 = ExpandDims(2).forward(input) + val output3 = ExpandDims(3).forward(input) + + output1 should be(expectOutput1) + output2 should be(expectOutput2) + output3 should be(expectOutput3) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolSpec.scala new file mode 100644 index 00000000000..44b4ba7d5dd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolSpec.scala @@ -0,0 +1,117 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class MaxPoolSpec extends FlatSpec with Matchers { + "MaxPool operation VALID padding" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val expectOutput = Tensor( + T( + T( + T(T(7.0, 8.0, 9.0)), + T(T(7.0, 8.0, 9.0)) + ) + )) + + val input = + Tensor( + T( + T( + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)) + ) + ) + ) + + + val output = MaxPool[Double]( + Array(1, 2, 3, 1), + Array(1, 2, 1, 1), + "VALID").forward(input) + + output should equal(expectOutput) + } + + "MaxPool operation SAME padding" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val expectOutput = Tensor( + T( + T( + T( + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0), + T(7.0, 8.0, 9.0) + ), + T( + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0), + T(7.0, 8.0, 9.0) + ) + ) + )) + + val input = + Tensor( + T( + T( + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)), + T( + T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0), + T(7.0, 8.0, 9.0)) + ) + ) + ) + + + val output = MaxPool[Double]( + Array(1, 2, 3, 1), + Array(1, 2, 1, 1), + "SAME" + ).forward(input) + + output should equal(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala index da04cb0393e..c9385087779 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala @@ -77,6 +77,33 @@ class UnsqueezeSpec extends TorchSpec { println("Test case : Unsqueeze, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } + "A Unsqueeze(-2)" should "generate correct output and grad" in { + torchCheck() + val layer = new Unsqueeze[Double](-2) + val input = Tensor[Double](2, 2).apply1(_ => Random.nextDouble()) + val gradOutput = Tensor[Double](2, 2, 1).apply1(_ => Random.nextDouble()) + + val start = System.nanoTime() + val output = layer.forward(input) + val gradInput = layer.backward(input, gradOutput) + val end = System.nanoTime() + val scalaTime = end - start + + val code = "module = nn.Unsqueeze(1)\n" + + "output = module:forward(input)\n" + + "gradInput = module:backward(input,gradOutput)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), + Array("output", "gradInput")) + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] + + output should be (luaOutput) + gradInput should be (luaGradInput) + + println("Test case : Unsqueeze, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") + } + "A Unsqueeze(4, 3)" should "generate correct output and grad" in { torchCheck() val layer = new Unsqueeze[Double](4, 3) From 261faab18c779e113fb203b3fae46d9ce0d80f45 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Mon, 25 Sep 2017 15:33:33 +0800 Subject: [PATCH 0413/1065] Add Realdiv, BiasAdd and Pad (#1579) * Add Pad * refactor biasAdd --- .../bigdl/dllib/nn/ops/BiasAdd.scala | 72 ++++++++ .../analytics/bigdl/dllib/nn/ops/Pad.scala | 170 ++++++++++++++++++ .../bigdl/dllib/nn/ops/package.scala | 5 + .../bigdl/dllib/tensor/ArrayStorage.scala | 6 + .../bigdl/dllib/tensor/TensorNumeric.scala | 1 + .../bigdl/dllib/nn/ops/BiasAddSpec.scala | 57 ++++++ .../bigdl/dllib/nn/ops/PadSpec.scala | 73 ++++++++ .../bigdl/dllib/nn/ops/RealDivSpec.scala | 36 ++++ 8 files changed, 420 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAdd.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PadSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RealDivSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAdd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAdd.scala new file mode 100644 index 00000000000..bcb191868bc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAdd.scala @@ -0,0 +1,72 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class BiasAdd[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[_], T] { + var onesBias: Tensor[NumericWildCard] = _ + + override def updateOutput(input: Table): Tensor[_] = { + val value = input[Tensor[NumericWildCard]](1) + val bias = input[Tensor[NumericWildCard]](2) + val sizes = value.size().toBuffer + val last = sizes.last + sizes.remove(value.nDimension() - 1) + val sizeProduct = sizes.product + + if (value.getType() != output.getType()) { + output = value.emptyInstance() + } + + if (onesBias == null) { + onesBias = value.emptyInstance() + } + + if (onesBias.dim() != 1 || onesBias.size(1) != sizeProduct) { + onesBias.resize(sizeProduct).fill(ev.fromType(1.0)) + } + + output.asInstanceOf[Tensor[NumericWildCard]] + .resizeAs(value) + .copy(value) + val value2d = output + .view(Array(sizeProduct, last)) + .asInstanceOf[Tensor[NumericWildCard]] + + + value2d + .addr( + value.getTensorNumeric().one, + onesBias, + bias) + + output + } +} + +object BiasAdd { + def apply[T: ClassTag]() + (implicit ev: TensorNumeric[T]): + Operation[Activity, Activity, T] + = ModuleToOperation[T](new BiasAdd()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala new file mode 100644 index 00000000000..334dd00ee24 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala @@ -0,0 +1,170 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Pad[T: ClassTag, D: ClassTag]( + mode: String, + constantValue: D) + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[D], T] { + output = Activity.allocate[Tensor[D], D]() + + def constantPadding( + input: Tensor[D], + padding: Tensor[Int], + output: Tensor[D] + ): Unit = { + val inputOffset = input.storageOffset() - 1 + val inputData = input.storage() + val outputOffset = output.storageOffset() - 1 + val outputData = output.storage() + + val dim = input.nDimension() + val sizes = input.size() + + var dim6Flag = true + var dim5Flag = true + var dim4Flag = true + var dim3Flag = true + var dim2Flag = true + var dim1Flag = true + + var dim6OuputOffset = 0 + var dim5OuputOffset = 0 + var dim4OuputOffset = 0 + var dim3OuputOffset = 0 + var dim2OuputOffset = 0 + var dim1OuputOffset = 0 + + var dim6InputOffset = 0 + var dim5InputOffset = 0 + var dim4InputOffset = 0 + var dim3InputOffset = 0 + var dim2InputOffset = 0 + var dim1InputOffset = 0 + + var i = 0 + while (dim6Flag && i < (if (dim - 6 >= 0) sizes(dim - 6) else Integer.MAX_VALUE)) { + if (dim - 6 < 0) { + dim6Flag = false + } else { + dim6OuputOffset = (i + padding(Array(dim - 5, 1))) * output.size(dim) * + output.size(dim - 1) * output.size(dim - 2) * output.size(dim - 3) * output.size(dim - 4) + dim6InputOffset = + i * input.size(dim) * input.size(dim - 1) * input.size(dim - 2) * + input.size(dim - 3) * input.size(dim - 4) + } + var j = 0 + while (dim5Flag && j < (if (dim - 5 >= 0) sizes(dim - 5) else Integer.MAX_VALUE)) { + if (dim - 5 < 0) { + dim5Flag = false + } else { + dim5OuputOffset = (j + padding(Array(dim - 4, 1))) * + output.size(dim) * output.size(dim - 1) * output.size(dim - 2) * output.size(dim - 3) + dim5InputOffset = j * input.size(dim) * input.size(dim - 1) * + input.size(dim - 2) * input.size(4) + } + var k = 0 + while (dim4Flag && k < (if (dim - 4 >= 0) sizes(dim - 4) else Integer.MAX_VALUE)) { + if (dim - 4 < 0) { + dim4Flag = false + } else { + dim4OuputOffset = (k + padding(Array(dim - 3, 1))) * + output.size(dim) * output.size(dim - 1) * output.size(dim - 2) + dim4InputOffset = k * input.size(dim) * input.size(dim - 1) * input.size(dim - 2) + } + var l = 0 + while (dim3Flag && l < (if (dim - 3 >= 0) sizes(dim - 3) else Integer.MAX_VALUE)) { + if (dim - 3 < 0) { + dim3Flag = false + } else { + dim3OuputOffset = (l + padding(Array(dim - 2, 1))) * + output.size(dim) * output.size(dim - 1) + dim3InputOffset = l * input.size(dim) * input.size(dim - 1) + } + var m = 0 + while (dim2Flag && m < (if (dim - 2 >= 0) sizes(dim - 2) else Integer.MAX_VALUE)) { + if (dim - 2 < 0) { + dim2Flag = false + } else { + dim2OuputOffset = (m + padding(Array(dim - 1, 1))) * output.size(dim) + dim2InputOffset = m * input.size(dim) + } + var n = 0 + while (dim1Flag && n < (if (dim - 1 >= 0) sizes(dim - 1) else Integer.MAX_VALUE)) { + if (dim - 1 < 0) { + dim1Flag = false + } else { + dim1OuputOffset = n + padding(Array(dim, 1)) + dim1InputOffset = n + } + + outputData(outputOffset + dim6OuputOffset + dim5OuputOffset + + dim4OuputOffset + dim3OuputOffset + dim2OuputOffset + + dim1OuputOffset) = + inputData(inputOffset + dim6InputOffset + dim5InputOffset + + dim4InputOffset + dim3InputOffset + dim2InputOffset + + dim1InputOffset) + n += 1 + } + m += 1 + } + l += 1 + } + k += 1 + } + j += 1 + } + i += 1 + } + } + + def updateOutput(inputs: Table): Tensor[D] = { + val input = inputs[Tensor[D]](1) + val padding = inputs[Tensor[Int]](2) + + require(padding.size() sameElements Array(input.nDimension(), 2), + "the padding tensor must be an integer tensor with shape [n, 2]," + + "where n is the number of dimension of input") + + val resize = new Array[Int](input.nDimension()) + for (i <- 1 to input.nDimension()) { + resize(i - 1) = input.size(i) + padding(Array(i, 1)) + padding(Array(i, 2)) + } + output.resize(resize) + + mode match { + case "CONSTANT" => constantPadding(input, padding, output) + } + + output + } +} + +object Pad { + def apply[T: ClassTag, D: ClassTag]( + mode: String, + constantValue: D) + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T]( + new Pad(mode = mode, constantValue = constantValue)) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala index 0d0b6084042..ce442dd544b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala @@ -43,6 +43,11 @@ package object ops { = ModuleToOperation[T](CDivTable()) } + object RealDiv { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](CDivTable()) + } + object Sum { def apply[T: ClassTag](axis: Int, keepDim: Boolean = false) (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/ArrayStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/ArrayStorage.scala index f7bc9821ced..ab318d87a68 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/ArrayStorage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/ArrayStorage.scala @@ -60,6 +60,12 @@ private[tensor] class ArrayStorage[@specialized(Double, Float) T: ClassTag]( offset - 1, offset - 1 + length, v) case v: Float => util.Arrays.fill(values.asInstanceOf[Array[Float]], offset - 1, offset - 1 + length, v) + case v: Int => util.Arrays.fill(values.asInstanceOf[Array[Int]], + offset - 1, offset - 1 + length, v) + case v: Long => util.Arrays.fill(values.asInstanceOf[Array[Long]], + offset - 1, offset - 1 + length, v) + case v: Short => util.Arrays.fill(values.asInstanceOf[Array[Short]], + offset - 1, offset - 1 + length, v) case _ => throw new IllegalArgumentException } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index 5c0c054b581..57ef7fd8b24 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -31,6 +31,7 @@ class TensorNumericMath */ object TensorNumericMath { + type NumericWildCard = Any /** * This type is used to denote that the numeric type of tensor is not restricted. * The use-case usually is used to do some tensor operations when we do not make sure diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddSpec.scala new file mode 100644 index 00000000000..20dde857614 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddSpec.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class BiasAddSpec extends FlatSpec with Matchers { + "BiasAdd operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val input = + T( + Tensor(T( + T( + T(1f, 2f, 3f), + T(2f, 3f, 4f), + T(3f, 4f, 5f) + ), + T( + T(3f, 4f, 5f), + T(2f, 3f, 4f), + T(1f, 2f, 3f) + ))), + Tensor(T(3f, 2f, 1f)) + ) + + val expectOutput = Tensor( + T( + T( + T(4f, 4f, 4f), + T(5f, 5f, 5f), + T(6f, 6f, 6f) + ), + T( + T(6f, 6f, 6f), + T(5f, 5f, 5f), + T(4f, 4f, 4f) + ))) + + val output = BiasAdd().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PadSpec.scala new file mode 100644 index 00000000000..eb673ed13e4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PadSpec.scala @@ -0,0 +1,73 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class PadSpec extends FlatSpec with Matchers { + "Pad operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + Tensor(T( + T( + T(1f, 2f, 3f), + T(4f, 5f, 6f)), + T( + T(1f, 2f, 3f), + T(4f, 5f, 6f)) + )) + val padding = Tensor[Int](T(T(1, 2), T(1, 2), T(1, 2))) + + val expectOutput = Tensor( + T( + T( + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f)), + T( + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 1f, 2f, 3f, 0f, 0f), + T(0f, 4f, 5f, 6f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f)), + T( + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 1f, 2f, 3f, 0f, 0f), + T(0f, 4f, 5f, 6f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f)), + T( + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f)), + T( + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f), + T(0f, 0f, 0f, 0f, 0f, 0f))) + ) + + val output = Pad[Float, Float](mode = "CONSTANT", 0.0f).forward(T(input, padding)) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RealDivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RealDivSpec.scala new file mode 100644 index 00000000000..d7c15a48c35 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RealDivSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class RealDivSpec extends FlatSpec with Matchers { + "Divide operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + T( + Tensor(T(1f, 2f, 3f)), + Tensor(T(2f, 2f, 4f)) + ) + + val expectOutput = Tensor(T(0.5f, 1f, 0.75f)) + + val output = Divide().forward(input) + output should be(expectOutput) + } +} From 0aeb856e478efc37cbf371e806d9cfd269a4bcd4 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 25 Sep 2017 16:23:09 +0800 Subject: [PATCH 0414/1065] Support Tensorflow Preprocessing (#1572) * fix tensor bug * preprocessing * test * fix tests * refine * fix tests * fix style * fix tests * meet code review * meet code review * meet code review * add doc --- .../feature/dataset/image/ColorJitter.scala | 2 +- .../analytics/bigdl/dllib/nn/Graph.scala | 6 +- .../analytics/bigdl/dllib/nn/ops/Rank.scala | 7 +- .../bigdl/dllib/tensor/DenseTensor.scala | 30 +- .../analytics/bigdl/dllib/tensor/Tensor.scala | 9 + .../analytics/bigdl/dllib/utils/Table.scala | 31 + .../dllib/utils/python/api/PythonBigDL.scala | 5 +- .../bigdl/dllib/utils/tf/Session.scala | 374 +- .../dllib/utils/tf/TensorflowToBigDL.scala | 267 +- scala/dllib/src/test/resources/tf/lenet.pbtxt | 17028 ++++++++++++++++ .../bigdl/dllib/nn/ops/RankSpec.scala | 16 +- .../bigdl/dllib/optim/TableSpec.scala | 7 + .../bigdl/dllib/optim/ValidationSpec.scala | 2 +- .../dllib/tensor/DenseTensorMathSpec.scala | 4 +- .../bigdl/dllib/tensor/DenseTensorSpec.scala | 105 +- .../bigdl/dllib/utils/tf/SessionSpec.scala | 41 +- .../dllib/utils/tf/TensorflowLoaderSpec.scala | 3 +- 17 files changed, 17830 insertions(+), 107 deletions(-) create mode 100644 scala/dllib/src/test/resources/tf/lenet.pbtxt diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/ColorJitter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/ColorJitter.scala index 6db933e2d13..135ba795931 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/ColorJitter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/ColorJitter.scala @@ -95,7 +95,7 @@ class ColorJitter extends Transformer[LabeledBGRImage, LabeledBGRImage] { val order = Tensor.randperm[Float](3) var i = 1 while (i <= order.size(1)) { - val idx = order(i).valueAt(1).toInt + val idx = order(i).value().toInt ts(idx)(input) i += 1 } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 926d01a3376..6a4fa7d1556 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -297,8 +297,7 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], /** * Execution plan */ - private val forwardNodes = backGraph.DFS - .filterNot(_.element.isInstanceOf[ControlDependency[T]]).toArray + private val forwardNodes = backGraph.DFS.toArray private val forwardScheduler = new Scheduler( forwardNodes.filter(_.prevNodes.length == 0), Seq(dummyOutput) @@ -347,7 +346,8 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], require(forwardNodes.map(_.element.getName()).distinct.length == forwardNodes.length, "the name of node in the graph should be unique") val roots = forwardNodes.filter(_.prevNodes.size == 0) - .filter(node => !node.element.isInstanceOf[WithoutInput]) + .filter(node => !node.element.isInstanceOf[WithoutInput] + && !node.element.isInstanceOf[ControlDependency[_]]) require(roots.size == inputs.length, s"There're ${inputs.length} inputs, but graph has ${roots.size} roots") inputs.foreach(n => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala index 3879d73b464..2ca66798044 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala @@ -25,8 +25,11 @@ class Rank[T: ClassTag]() (implicit ev: TensorNumeric[T]) extends Operation[Tensor[_], Tensor[Int], T] { override def updateOutput(input: Tensor[_]): Tensor[Int] = { - output.resizeAs(input(1)) - output.setValue(1, input.nDimension()) + if (output.getType() != IntType) { + output = Tensor[Int]() + } + output.resize(Array[Int]()) + output.setValue(input.nDimension()) output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 23f00c77f78..95220d45ce8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -37,9 +37,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( override def isEmpty: Boolean = this.storage() == null || this.storage().length() == 0 - override def isScalar: Boolean = - this.nDimension == 0 && - this._storage.length() == 1 + override def isScalar: Boolean = !this.isEmpty && this.nDimension == 0 override def storage(): Storage[T] = _storage @@ -397,14 +395,10 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( val _dimension = dim - 1 val _sliceIndex = index - 1 - if (this.nDimension > 1) { - val result = DenseTensor.newWithTensor(this) - DenseTensor.select(result, null, _dimension, _sliceIndex) - result - } else { - require(this.nDimension == 1, "empty tensor") - this.narrow(1, index, 1) - } + require(this.nDimension > 0, "empty or scalar tensor cannot be selected") + val result = DenseTensor.newWithTensor(this) + DenseTensor.select(result, null, _dimension, _sliceIndex) + result } override def clone(): Tensor[T] = { @@ -491,13 +485,9 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( require(_index >= 0 && _index < this._size(0), s"out of range, ${_index}: 0 to ${this._size(0)}") - if (this.nDimension == 1) { - this.narrow(1, index, 1) - } else { - val result = DenseTensor.newWithTensor(this) - DenseTensor.select(result, null, 0, _index) - result - } + val result = DenseTensor.newWithTensor(this) + DenseTensor.select(result, null, 0, _index) + result } override def apply(table: Table): Tensor[T] = { @@ -2289,7 +2279,7 @@ object DenseTensor { self: DenseTensor[T], source: Tensor[T], _dimension: Int, _sliceIndex: Int): Unit = { var src = source if (src == null) src = self - require(src.nDimension() > 1, "cannot select on a vector") + require(src.nDimension() > 0, "cannot select on a scalar") require(_dimension >= 0 && _dimension < src.nDimension(), "out of range") require(_sliceIndex >= 0 && _sliceIndex < src.size(_dimension + 1), s"${_sliceIndex} out of range 0 to ${src.size(_dimension + 1)}") @@ -2362,7 +2352,7 @@ object DenseTensor { private[tensor] def copy[@specialized(Float, Double) T]( self: DenseTensor[T], src: Tensor[T]): Unit = { require(self.nElement() == src.nElement()) - if (self.nDimension == 0) { + if (self.isEmpty) { return } if (self.isContiguous() && src.isContiguous() && sameStride(self.stride(), src.stride())) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 90094823e3a..008c11e4818 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -1016,6 +1016,15 @@ object Tensor { apply(Storage(matrix.toArray), 1, Array(matrix.numRows, matrix.numCols), strides) } + /** + * Create a scalar tensor of this value + * @return the created scalar tensor + */ + def scalar[T: ClassTag](value: T)( + implicit ev: TensorNumeric[T]): Tensor[T] = { + Tensor[T](Array(value), Array[Int]()) + } + /** * This is equivalent to DenseTensor.randperm[T](size) * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala index f080373fe9c..ac096ec5f55 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala @@ -289,6 +289,25 @@ class Table private[bigdl]( new Table(newState) } + /** + * Return the elements of this table as a Seq. + * This method assumes the key of this table are all + * the integers between 1 to this.length(), + * the values are all Tensor[T] + */ + def toSeq[T]: Seq[Tensor[T]] = { + for (i <- 0 until this.length()) yield { + try { + this(i + 1).asInstanceOf[Tensor[T]] + } catch { + case e: NoSuchElementException => + throw new UnsupportedOperationException("toSeq requires the key of this table are" + + " all the integers between 1 to this.length()", e) + } + + } + } + override def toTensor[D] (implicit ev: TensorNumeric[D]): Tensor[D] = throw new IllegalArgumentException("Table cannot be cast to Tensor") @@ -324,6 +343,18 @@ object T { new Table(data) } + /** + * Construct a table from an array + * + * The index + 1 will be used as the key + * + * @param data + * @return + */ + def seq(data: Seq[Any]): Table = { + new Table(data.toArray) + } + /** * Construct a table from a sequence of pair. */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index dc3855f9391..a0a644d9d39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1716,12 +1716,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab samples: JavaRDD[Sample], optMethod: OptimMethod[T], criterion: Criterion[T], - batchSize: Int, endWhen: Trigger): AbstractModule[Activity, Activity, T] = { + batchSize: Int, + endWhen: Trigger): AbstractModule[Activity, Activity, T] = { val nodeList = parse(modelPath) val context = new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]() - val session = new BigDLSessionImpl[T](nodeList.asScala, context) + val session = new BigDLSessionImpl[T](nodeList.asScala, samples.sparkContext, context) val dataset = batching(samples, batchSize) val model = session.train(Seq(output), dataset, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index 0ab692cfdd0..b382a2296f8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -29,6 +29,9 @@ import org.apache.spark.SparkContext import org.apache.spark.api.java.JavaRDD import org.apache.spark.rdd.RDD import org.tensorflow.framework.{GraphDef, NodeDef} +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.models.utils.ModelBroadcast +import TFTensorNumeric.NumericByteString import scala.collection.mutable import scala.reflect.ClassTag @@ -44,18 +47,365 @@ abstract class Session[T: ClassTag] { class BigDLSessionImpl[T: ClassTag]( graph: Seq[NodeDef], + sc: SparkContext, context: mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]) (implicit ev: TensorNumeric[T]) extends Session[T] { import scala.collection.JavaConverters._ - val sc = SparkContext.getOrCreate() - private val inputOp = Set("ReaderReadV2", "QueueDequeueV2", "QueueDequeueManyV2", "Placeholder") + private val dequeueOp = Set("QueueDequeueV2", "QueueDequeueManyV2", "ReaderReadV2") + + private val enqueueOp = Set("QueueEnqueueV2", "QueueEnqueueManyV2") + + private val queueOp = Set("RandomShuffleQueueV2", "FIFOQueueV2") + private val (wholeTFGraph, _, _) = TensorflowLoader.buildTFGraph(graph.asJava, null) private val name2Node = wholeTFGraph. - DFS.filter(n => n.element != null).map(node => (node.element.getName, node)).toMap + DFS.filter(_.element != null).map(node => (node.element.getName, node)).toMap + + private def handleReaderNode(node: Node[NodeDef], cache: DataCache): RDD[Table] = { + require(node.prevNodes.length == 2, "require ReaderReadV2 only has two inputs") + val readerNode = node.prevNodes.head + val queueNode = node.prevNodes(1) + val dequeNodeNames = mutable.LinkedHashSet[String]() + + queueNode.nextNodes + .filter(n => n.element != null && dequeueOp(n.element.getOp)) + .map(n => n.element.getName.split(":")(0)).foreach(dequeNodeNames.add) + + val nameToIndex = dequeNodeNames.zipWithIndex.toMap + val index = nameToIndex(node.element.getName) + val nSlices = dequeNodeNames.size + + val enqueueNodes = queueNode.nextNodes + .filter(n => n.element != null && enqueueOp(n.element.getOp)) + val filesSeq = if (cache.contains(queueNode.element.getName)) { + val resultArray = cache(queueNode.element.getName) + val result = resultArray(index) + resultArray(index) = null + result + } else { + val allResult = enqueueNodes.map { enqueueNode => + val inputs = Seq(enqueueNode.element.getName) + val result = constructLocalData(inputs, new DataCache()) + if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { + result.flatMap { table => + val nElem = table.length() + require(nElem >= 1, "EnqueueManyV2 encounter a empty table") + val first = table[Tensor[ByteString]](1) + require(first.nDimension() >= 1) + val depth = first.size(1) + val result = new Array[Table](depth) + var i = 0 + while(i < depth) { + var j = 0 + val newTable = new Table() + while (j < nElem) { + val elem = table[Tensor[ByteString]](j + 1) + newTable.insert(elem(i + 1)) + j = j + 1 + } + result(i) = newTable + i = i + 1 + } + result + } + } else { + result + } + }.reduce { (outerSeq1, outerSeq2) => + outerSeq1.zip(outerSeq2).map { case (seq1, seq2) => + seq1.add(seq2) + } + } + val resultArray = split(allResult, nSlices) + cache.put(queueNode.element.getName, resultArray) + resultArray(index) + } + + readerNode.element.getOp match { + case "TFRecordReaderV2" => readTFRecord(filesSeq) + } + } + + private def split[A](xs: Seq[A], n: Int): Array[Seq[A]] = { + val result = new Array[Seq[A]](n) + var i = 0 + while (i < n) { + result(i) = Vector[A]() + i = i + 1 + } + + var j = 0 + while (j < xs.length) { + result(j % n) = result(j % n) :+ xs(j) + j = j + 1 + } + + result + } + + private def readTFRecord(filesTable: Seq[Table]): RDD[Table] = { + val result = filesTable.map { t => + require(t.length() == 1 && t(1).isInstanceOf[Tensor[ByteString]], + "Reader can only read one file at a time") + val fileTensor = t[Tensor[ByteString]](1) + require(fileTensor.isScalar) + val file = fileTensor.value() + file + }.flatMap { file => + val iter = new TFRecordIterator(new java.io.File(file.toStringUtf8)) + iter + }.map { record => + val table = T() + val key = Tensor[ByteString](Array(ByteString.copyFromUtf8("somekey")), Array[Int]()) + val value = Tensor[ByteString](Array(ByteString.copyFrom(record)), Array[Int]()) + table.insert(key) + table.insert(value) + table + } + val resultRdd = sc.parallelize(result, numSlices = Engine.coreNumber()) + resultRdd + } + + private def handleLocalDequeue(node: Node[NodeDef], cache: DataCache): Seq[Table] = { + require(node.prevNodes.length == 1, "require QueueDequeueV2 only has one input") + val queueNode = node.prevNodes.head + val enqueueNodes = queueNode.nextNodes.filter(n => enqueueOp(n.element.getOp)) + val dequeNodeNames = mutable.LinkedHashSet[String]() + + queueNode.nextNodes + .filter(n => n.element != null && dequeueOp(n.element.getOp)) + .map(n => n.element.getName.split(":")(0)).foreach(dequeNodeNames.add) + + val nameToIndex = dequeNodeNames.zipWithIndex.toMap + val index = nameToIndex(node.element.getName) + val nSlices = dequeNodeNames.size + + val dataSeq = if (cache.contains(queueNode.element.getName)) { + val resultArray = cache(queueNode.element.getName) + val result = resultArray(index) + resultArray(index) = null + result + } else { + val allResult = enqueueNodes.map { enqueueNode => + val inputs = Seq(enqueueNode.element.getName) + constructLocalData(inputs, new DataCache()) + }.reduce { (outerSeq1, outerSeq2) => + outerSeq1.zip(outerSeq2).map { case (seq1, seq2) => + seq1.add(seq2) + } + } + val resultArray = split(allResult, nSlices) + cache.put(queueNode.element.getName, resultArray) + resultArray(index) + } + dataSeq + } + + private def handleDistriDequeue(node: Node[NodeDef], cache: DataCache): RDD[Table] = { + require(node.prevNodes.length == 1, "require QueueDequeueV2 only has one input") + val queueNode = node.prevNodes.head + val dequeueNodes = queueNode.nextNodes + .filter(n => n.element != null && dequeueOp(n.element.getOp)) + .map(n => n.element.getName.split(":")(0)).toSet + require(dequeueNodes.size == 1, "only support one dequeue node after reader") + val enqueueNodes = queueNode.nextNodes + .filter(n => n.element != null && enqueueOp(n.element.getOp)) + val rdd = enqueueNodes.map { enqueueNode => + val inputs = Seq(enqueueNode.element.getName) + constructDistributeData(inputs, cache) + }.reduce { (rdd1, rdd2) => + rdd1.union(rdd2) + } + rdd + } + + private def handleDistriDequeueManyNode(node: Node[NodeDef], cache: DataCache): RDD[Table] = { + require(node.prevNodes.length == 2, "require QueueDequeueManyV2 only has two input") + val queueNode = node.prevNodes.head + val enqueueNodes = queueNode.nextNodes.filter(n => enqueueOp(n.element.getOp)) + // get previous rdd + val rdd = enqueueNodes.map { enqueueNode => + val inputs = Seq(enqueueNode.element.getName) + constructDistributeData(inputs, cache) + }.reduce { (rdd1, rdd2) => + rdd1.zip(rdd2).map { case (seq1, seq2) => + seq1.add(seq2) + } + } + + // get batch size + val batchSizeNode = node.prevNodes(1) + require(batchSizeNode.element.getOp == "Const", "batchsize must be a const") + + val batchSize = batchSizeNode.element.getAttrMap.get("value").getI.toInt + + val batchRdd = rdd.mapPartitions { iter => + + new Iterator[Table] { + override def hasNext: Boolean = iter.hasNext + + override def next(): Table = { + require(iter.hasNext, "Call next() on a empty iterator") + val batch = for (_ <- 0 until batchSize if iter.hasNext) yield { + iter.next() + } + pack(batch) + } + } + + } + batchRdd + } + + private def pack(tables: Seq[Table], dimension: Int = 1): Table = { + val batch = tables.map(_.toSeq[T]) + val firstSeq = batch.head + val sizes = firstSeq.map { tensor => + val nDim = tensor.nDimension() + val size: Array[Int] = new Array[Int](nDim + 1) + var i = 1 + while(i <= nDim + 1) { + if (i < dimension) { + size(i-1) = tensor.size(i) + } else if (i == dimension) { + size(i-1) = batch.length + } else { + size(i-1) = tensor.size(i - 1) + } + i = i + 1 + } + size + } + + val results = sizes.map { size => + Tensor[T](size) + } + + for ((seq, index) <- batch.zipWithIndex) { + results.zip(seq).foreach { case (result, tensor) => + result.narrow(dimension, index + 1, 1).copy(tensor) + } + } + T.seq(results) + } + + type DataCache = mutable.HashMap[String, Array[Seq[Table]]] + + private def adjustInputNames(inputs: Seq[String]): Seq[String] = { + val stripedNames = inputs.map(_.split(":")(0)) + val set = mutable.LinkedHashSet[String]() + for (name <- stripedNames) { + set.add(name) + } + set.toSeq + } + + private def checkAndRemoveQueueNode(tfGraph: DirectedGraph[NodeDef]) = { + if (tfGraph.source.prevNodes.exists(n => enqueueOp(n.element.getOp))) { + tfGraph.source.prevNodes.foreach { node => + val queueNodes = node.prevNodes.filter(n => queueOp(n.element.getOp)) + queueNodes.foreach(n => n.delete(node)) + } + } + } + + def constructLocalData(endPoints: Seq[String], cache: DataCache): Seq[Table] = { + val isInputOp = (n: NodeDef) => inputOp(n.getOp) + val (tfGraph, inputs, originInputs) = TensorflowLoader. + buildTFGraph(graph.asJava, endPoints, isInputOp) + + checkAndRemoveQueueNode(tfGraph) + + val adjustedInputs = adjustInputNames(originInputs) + val transformer = TensorflowLoader.buildBigDLModel( + tfGraph, + inputs, + endPoints, + ByteOrder.LITTLE_ENDIAN, + "", + Some(context) + ).asInstanceOf[Graph[T]] + + + if (adjustedInputs.nonEmpty) { + val inputNodes = originInputs.map(name2Node) + val inputDataSeq = inputNodes.map { node => // this is the input op + node.element.getOp match { + // only support Dequeue before reader + case "QueueDequeueV2" => handleLocalDequeue(node, cache) + } + } + + val reducedInputSeq = inputDataSeq.reduce { (outerSeq1, outerSeq2) => + outerSeq1.zip(outerSeq2).map { case (seq1, seq2) => + seq1.add(seq2) + } + } + + reducedInputSeq.map { tensors => + val output = transformer.forward(tensors.flatten()) + toTable(output) + } + } else { + Seq(toTable(transformer.forward(T()))) + } + } + + private def toTable(activity: Activity): Table = { + activity match { + case t: Tensor[_] => T(t) + case t: Table => t + } + } + + private def constructDistributeData(endPoints: Seq[String], cache: DataCache): RDD[Table] = { + val isInputOp = (n: NodeDef) => inputOp(n.getOp) + val (tfGraph, inputs, originInputs) = + TensorflowLoader.buildTFGraph(graph.asJava, endPoints, isInputOp) + + checkAndRemoveQueueNode(tfGraph) + + val adjustedInputs = adjustInputNames(originInputs) + + val inputNodes = adjustedInputs.map(name2Node) + + val transformer = TensorflowLoader.buildBigDLModel( + tfGraph, + inputs, + endPoints, + ByteOrder.LITTLE_ENDIAN, + "", + Some(context) + ).asInstanceOf[Graph[T]] + + val inputRdds = inputNodes.map { node => // this is the input op + node.element.getOp match { + case "ReaderReadV2" => handleReaderNode(node, cache) + case "QueueDequeueV2" => handleDistriDequeue(node, cache) + case "QueueDequeueManyV2" => handleDistriDequeueManyNode(node, cache) + } + } + val inputRdd = inputRdds.reduce { (rdd1, rdd2) => + rdd1.zip(rdd2).map { case (seq1, seq2) => + seq1.add(seq2) + } + } + + val modelBroadCast = ModelBroadcast[T].broadcast(sc, transformer) + inputRdd.map { tensors => + val trans = modelBroadCast.value() + val output = trans.forward(tensors.flatten()) + output match { + case t: Tensor[_] => T(t) + case t: Table => t + } + } + } + private def constructModel(endPoints: Seq[String]): (Graph[T], Node[NodeDef]) = { val isInputOp = (n: NodeDef) => inputOp(n.getOp) @@ -76,6 +426,12 @@ class BigDLSessionImpl[T: ClassTag]( (model, inputNodes.head) } + /** + * Train the model specified by the model output + * @param outputs model output + * @param dataSet the training data set + * @return trained model + */ override def train(outputs: Seq[String], dataSet: DistributedDataSet[MiniBatch[T]], optMethod: OptimMethod[T], @@ -98,4 +454,16 @@ class BigDLSessionImpl[T: ClassTag]( model } + /** + * Get and calculate the data up to the specified endpoints, and + * return as a RDD[Table] + * @param endPoints output endpoints + * @return + */ + def getRDD(endPoints: Seq[String]): RDD[Table] = { + val cache = new mutable.HashMap[String, Array[Seq[Table]]]() + constructDistributeData(endPoints, cache) + } + + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index 797f1f5a2f6..d1897c9e45a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -21,10 +21,13 @@ import java.util import collection.JavaConverters._ import com.intel.analytics.bigdl.nn._ + import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Storage, Tensor} +import com.intel.analytics.bigdl.nn.ops.{Conv2DTranspose, ResizeBilinearOps} +import com.intel.analytics.bigdl.tensor._ import org.tensorflow.framework.{AttrValue, DataType, NodeDef, TensorProto} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{Conv2DTranspose, ResizeBilinearOps} +import com.intel.analytics.bigdl.nn.ops.{Equal, Assert, Greater, Rank, ParseExample} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{DirectedGraph, Node, T} @@ -247,8 +250,10 @@ object TensorflowToBigDL { BatchNormTF, AddConstTF1, AddConstTF2, AddTF, SoftMaxTF, ElementWiseMulTF, MulTF, SplitTF, PaddingTF, MeanTF, UnpackTF, StrideSliceTF, ShapeTF, FillTF, PackTF, ConstTF, Flatten, Conv1D, FlattenV2, BatchNormV2NHWCTF, BatchNormV2NCHWTF, AddNTF, - ControlDependencyTF, FullConnectionWithoutBiasTF, DeConv2D, ResizeBilinearTF, Conv2D2, - Conv2DWithoutBias + ControlDependencyTF, RandomShuffleTF, AssertTF, GreaterTF, ReaderReadTF, QueueDequeTF, + QueueDequeManyTF, EqualTF, RankTF, EnqueueManyTF, EnqueueTF, + FullConnectionWithoutBiasTF, DeConv2D, ResizeBilinearTF, Conv2D2, Conv2DWithoutBias, + ParseExampleTF ) res } @@ -426,7 +431,7 @@ object Conv1D extends TensorflowToBigDL { val result = format match { case "NCHW" => - val model = Sequential() + val model = Sequential[T]() model.add(Transpose(Array((2, 3)))) model.add(Contiguous()) model.add(tconv) @@ -896,9 +901,9 @@ object DropoutTF extends TensorflowToBigDL{ val keepProp = tfGraph.source.prevNodes(0).prevNodes(1).element .getAttrMap.get("value").getTensor.getFloatVal(0) - val model = Sequential() + val model = Sequential[T]() model.add(SelectTable(1)) - model.add(Dropout[T](keepProp).asInstanceOf[AbstractModule[Activity, Activity, T]]) + model.add(Dropout[T](keepProp)) model.asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -964,7 +969,7 @@ object IdentityTF extends TensorflowToBigDL { byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Input[T]().element.asInstanceOf[AbstractModule[Activity, Activity, T]] + Identity[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1032,7 +1037,7 @@ object BatchNormV2NCHWTF extends TensorflowToBigDL{ initGradBias = gradBias ) - val model = Sequential() + val model = Sequential[T]() model.add(SelectTable(1)) model.add(batchNorm) model.asInstanceOf[AbstractModule[Activity, Activity, T]] @@ -1095,7 +1100,7 @@ object BatchNormV2NHWCTF extends TensorflowToBigDL{ initGradBias = gradBias ) - val layer = Sequential() + val layer = Sequential[T]() layer.add(SelectTable(1)) layer.add(Transpose(Array((2, 4)))) layer.add(Contiguous()) @@ -1168,8 +1173,8 @@ object BatchNormTF extends TensorflowToBigDL{ initBias = bias, initGradWeight = gradWeights, initGradBias = gradBias - ).asInstanceOf[AbstractModule[Activity, Activity, T]] - val model = Sequential() + ) + val model = Sequential[T]() model.add(SelectTable(1)) model.add(batchNorm) model.asInstanceOf[AbstractModule[Activity, Activity, T]] @@ -1190,7 +1195,6 @@ object FillTF extends TensorflowToBigDL{ context: Context[T], byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Fill[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] } } @@ -1345,7 +1349,7 @@ object FlattenV2 extends TensorflowToBigDL { context: Context[T], byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val layer = Sequential() + val layer = Sequential[T]() layer.add(SelectTable(1)) layer.add(InferReshape[T](size = Array(-1), true)) layer.asInstanceOf[AbstractModule[Activity, Activity, T]] @@ -1656,7 +1660,6 @@ object ControlDependencyTF extends TensorflowToBigDL { context: Context[T], byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - ControlDependency().asInstanceOf[AbstractModule[Activity, Activity, T]] } @@ -1668,12 +1671,240 @@ object ResizeBilinearTF extends TensorflowToBigDL { } override def layer[T: ClassManifest]( - tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]) + tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)(implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { val alignCorner = tfGraph.source.element.getAttrMap.get("align_corners").getB ResizeBilinearOps(alignCorner).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] } } + +object AssertTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("Assert") + Node("*") -> node + (Node("*") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Assert().asInstanceOf[AbstractModule[Activity, Activity, T]] + } + +} + +object GreaterTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("Greater") + Node("*") -> node + (Node("*") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Greater().asInstanceOf[AbstractModule[Activity, Activity, T]] + } + +} + +object RandomShuffleTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("RandomShuffle") + (Node("*") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] + } + +} + +object ReaderReadTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("ReaderReadV2") + Node("*") -> node + (Node("*") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] + } + +} + +object QueueDequeTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("QueueDequeueV2") + (Node("...") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] + } + +} + +object QueueDequeManyTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("QueueDequeueManyV2") + (Node("...") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] + } + +} + +object EqualTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("Equal") + Node("*") -> node + (Node("*") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Equal().asInstanceOf[AbstractModule[Activity, Activity, T]] + } + +} + +object RankTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("Rank") + (Node("*") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Rank().asInstanceOf[AbstractModule[Activity, Activity, T]] + } + +} + +object EnqueueTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("QueueEnqueueV2") + (Node("...") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} + +object EnqueueManyTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("QueueEnqueueManyV2") + (Node("...") -> node).graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} + +object ParseExampleTF extends TensorflowToBigDL { + + private val graph = { + val node = Node("ParseExample") + Node("...") -> node + node.graph(reverse = true) + } + + override def topology: DirectedGraph[String] = graph + + override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], + context: Context[T], + byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + val node = tfGraph.source.element + val Ndense = node.getAttrMap.get("Ndense").getI.toInt + val Tdense = node.getAttrMap.get("Tdense") + .getList.getTypeList.asScala + .map { + case DataType.DT_INT64 => LongType + case DataType.DT_INT32 => IntType + case DataType.DT_FLOAT => FloatType + case DataType.DT_DOUBLE => DoubleType + case DataType.DT_STRING => StringType + } + val denseShapes = node.getAttrMap.get("dense_shapes") + .getList.getShapeList.asScala + .map { shapeProto => + shapeProto.getDimList.asScala.map(_.getSize.toInt).toArray + } + + new ParseExample(Ndense, Tdense, denseShapes) + .asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/test/resources/tf/lenet.pbtxt b/scala/dllib/src/test/resources/tf/lenet.pbtxt new file mode 100644 index 00000000000..1950a3a75db --- /dev/null +++ b/scala/dllib/src/test/resources/tf/lenet.pbtxt @@ -0,0 +1,17028 @@ +node { + name: "global_step/Initializer/zeros" + op: "Const" + attr { + key: "_class" + value { + list { + s: "loc:@global_step" + } + } + } + attr { + key: "dtype" + value { + type: DT_INT64 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT64 + tensor_shape { + } + int64_val: 0 + } + } + } +} +node { + name: "global_step" + op: "VariableV2" + device: "/device:CPU:0" + attr { + key: "_class" + value { + list { + s: "loc:@global_step" + } + } + } + attr { + key: "container" + value { + s: "" + } + } + attr { + key: "dtype" + value { + type: DT_INT64 + } + } + attr { + key: "shape" + value { + shape { + } + } + } + attr { + key: "shared_name" + value { + s: "" + } + } +} +node { + name: "global_step/Assign" + op: "Assign" + input: "global_step" + input: "global_step/Initializer/zeros" + device: "/device:CPU:0" + attr { + key: "T" + value { + type: DT_INT64 + } + } + attr { + key: "_class" + value { + list { + s: "loc:@global_step" + } + } + } + attr { + key: "use_locking" + value { + b: true + } + } + attr { + key: "validate_shape" + value { + b: true + } + } +} +node { + name: "global_step/read" + op: "Identity" + input: "global_step" + device: "/device:CPU:0" + attr { + key: "T" + value { + type: DT_INT64 + } + } + attr { + key: "_class" + value { + list { + s: "loc:@global_step" + } + } + } +} +node { + name: "zeros" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT64 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT64 + tensor_shape { + dim { + size: 1 + } + } + int64_val: 0 + } + } + } +} +node { + 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"/device:CPU:0" +} +node { + name: "train_op" + op: "Identity" + input: "total_loss" + input: "^group_deps" + attr { + key: "T" + value { + type: DT_FLOAT + } + } +} +node { + name: "summary_op/summary_op" + op: "MergeSummary" + input: "LeNet/fc4/weights_1" + input: "LeNet/conv2/biases_1" + input: "regularization_loss_1" + input: "LeNet/fc4/biases_1" + input: "total_loss_1" + input: "activations/Flatten" + input: "parallel_read/filenames/fraction_of_32_full" + input: "losses/softmax_cross_entropy_loss/value" + input: "LeNet/conv1/weights_1" + input: "LeNet/conv1/biases_1" + input: "parallel_read/fraction_of_640_full" + input: "LeNet/conv2/weights_1" + input: "sparsity/Predictions" + input: "learning_rate" + input: "prefetch_queue/fraction_of_2_full" + input: "sparsity/Flatten" + input: "sparsity/Logits" + input: "activations/Predictions" + input: "activations/Logits" + input: "LeNet/fc3/weights_1" + input: "batch/fraction_of_160_full" + input: "LeNet/fc3/biases_1" + input: "clone_loss_1" + attr { + key: "N" + value { + i: 23 + } + } +} +versions { + producer: 22 +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala index 9cea74fa154..82475f783ac 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala @@ -24,7 +24,7 @@ class RankSpec extends FlatSpec with Matchers { val input = Tensor[Float](T(1f, 2f, 2f)) - val expectOutput = Tensor[Int](T(1)) + val expectOutput = Tensor.scalar(1) val output = Rank[Int]().forward(input) output should be(expectOutput) @@ -34,7 +34,7 @@ class RankSpec extends FlatSpec with Matchers { val input = Tensor[Boolean](T(true, true, false)) - val expectOutput = Tensor[Int](T(1)) + val expectOutput = Tensor.scalar(1) val output = Rank[Int]().forward(input) output should be(expectOutput) @@ -44,7 +44,7 @@ class RankSpec extends FlatSpec with Matchers { val input = Tensor[Double](T(2.0, 3.0, 2.0)) - val expectOutput = Tensor[Int](T(1)) + val expectOutput = Tensor.scalar(1) val output = Rank[Int]().forward(input) output should be(expectOutput) @@ -54,7 +54,7 @@ class RankSpec extends FlatSpec with Matchers { val input = Tensor[Char](T('b', 'c', 'a')) - val expectOutput = Tensor[Int](T(1)) + val expectOutput = Tensor.scalar(1) val output = Rank[Int]().forward(input) output should be(expectOutput) @@ -64,7 +64,7 @@ class RankSpec extends FlatSpec with Matchers { val input = Tensor[Long](T(2L, 3L, 2L)) - val expectOutput = Tensor[Int](T(1)) + val expectOutput = Tensor.scalar(1) val output = Rank[Int]().forward(input) output should be(expectOutput) @@ -74,7 +74,7 @@ class RankSpec extends FlatSpec with Matchers { val input = Tensor[String](T("aaa", "ccc", "aaa")) - val expectOutput = Tensor[Int](T(1)) + val expectOutput = Tensor.scalar(1) val output = Rank[Int]().forward(input) output should be(expectOutput) @@ -84,7 +84,7 @@ class RankSpec extends FlatSpec with Matchers { val input = Tensor[Short](T(2: Short, 3: Short, 2: Short)) - val expectOutput = Tensor[Int](T(1)) + val expectOutput = Tensor.scalar(1) val output = Rank[Int]().forward(input) output should be(expectOutput) @@ -94,7 +94,7 @@ class RankSpec extends FlatSpec with Matchers { val input = Tensor[Int](T(2, 3, 2)) - val expectOutput = Tensor[Int](T(1)) + val expectOutput = Tensor.scalar(1) val output = Rank[Int]().forward(input) output should be(expectOutput) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/TableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/TableSpec.scala index 312cb3bcd84..a24a309bdff 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/TableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/TableSpec.scala @@ -281,4 +281,11 @@ class TableSpec extends FlatSpec with Matchers { output.toTable should be(output) } + + "toSeq" should "work correclty" in { + + val t = T(Tensor[Double](T(1.0)), Tensor[Double](T(2.0))) + + t.toSeq[Double] should be (Seq(Tensor[Double](T(1.0)), Tensor[Double](T(2.0)))) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index d7861a6ec28..ba9011bc3d0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -30,7 +30,7 @@ class ValidationSpec extends FlatSpec with Matchers { T(0.0, 1.0, 0.0, 0.0))) val target = Tensor[Double]( - T(3.0)) + T(T(3.0))) val validation = new TreeNNAccuracy[Double]() val result = validation(output, target) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala index 6dba77af1d9..824628222fb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala @@ -949,7 +949,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { "addmv on 1 element vector" should "return right result 1" in { val mat = Tensor[Float](84, 1).fill(2.0f) - val vec = Tensor[Float](2).apply(2).fill(3.0f) + val vec = Tensor[Float](2).narrow(1, 2, 1).fill(3.0f) val r = Tensor[Float](84).fill(9.0f) @@ -960,7 +960,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { "addmv on 1 element vector" should "return right result 2" in { val mat = Tensor[Float](84, 2).narrow(2, 1, 1).fill(2.0f) - val vec = Tensor[Float](2).apply(1).fill(3.0f) + val vec = Tensor[Float](2).narrow(1, 1, 1).fill(3.0f) val r = Tensor[Float](84).fill(9.0f) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index ce410367ea9..c1b7431190d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -109,7 +109,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](3, 3) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) t(Array(1, 1)) should be(1) t(Array(1, 2)) should be(2) @@ -159,7 +160,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](3, 2, 2) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) t(T(2, 2, 1)) should be(new DenseTensor[Double](1).fill(7)) @@ -216,7 +218,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](3, 2) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) t(T(2, 2)) = 0 t(Array(1, 1)) should be(1) @@ -250,7 +253,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](3, 2) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) val criteria: Double => Boolean = v => v >= 4 t(criteria) = 0 @@ -323,7 +327,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](3, 2) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) val t1 = t.select(1, 2) t1.nDimension() should be(1) @@ -336,7 +341,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](3, 2) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) val s = t.storage().asInstanceOf[Storage[Double]] var j = 0 @@ -357,7 +363,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](3, 3) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) val t1 = t.narrow(1, 2, 2) t1.nDimension() should be(2) @@ -398,7 +405,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](2, 2) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) t(Array(1, 1)) should be(1) t(Array(1, 2)) should be(2) @@ -410,12 +418,14 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t: Tensor[Double] = new DenseTensor[Double](2, 2) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) val t1: Tensor[Double] = new DenseTensor[Double](2, 2) i = 0 t1.apply1(v => { - i = i + 1; i + i = i + 1; + i }) t.map(t1, (a, b) => a * b) @@ -463,14 +473,16 @@ class DenseTensorSpec extends FlatSpec with Matchers { t = new DenseTensor[Double](3, 4) var i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) t.toString should be(MATRIX_STRING) t = new DenseTensor(2, 5, 3, 4) i = 0 t.apply1(v => { - i = i + 1; i + i = i + 1; + i }) println(t) } @@ -532,7 +544,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { val x = Tensor[Double](3, 1) var i = 0 x.apply1(e => { - i += 1; i + i += 1; + i }) val result = x.expand(Array(3, 2)) @@ -736,42 +749,42 @@ class DenseTensorSpec extends FlatSpec with Matchers { val input = Tensor[Float](4, 10).fill(1.0f) val output = input.narrow(1, 2, 1).squeezeNewTensor() - output.size() should be (Array(10)) - input should be (Tensor[Float](4, 10).fill(1.0f)) + output.size() should be(Array(10)) + input should be(Tensor[Float](4, 10).fill(1.0f)) } "tensor apply on 1D tensor" should "work correctly" in { val a = Tensor[Float](4) a.rand() val b = a(2) - b.storageOffset() should be (2) - b.size() should be (Array(1)) - b.stride() should be (Array(1)) - b.nElement() should be (1) + b.storageOffset() should be(2) + b.size() should be(Array()) + b.stride() should be(Array()) + b.nElement() should be(1) - b.setValue(1, 0.01f) - b.valueAt(1) should be (0.01f) - a.valueAt(2) should be (0.01f) + b.setValue(0.01f) + b.value() should be(0.01f) + a.valueAt(2) should be(0.01f) } "tensor apply on 2D tensor" should "work correctly" in { val a = Tensor[Float](3, 4) a.rand() val b = a(2) - b.storageOffset() should be (5) - b.size() should be (Array(4)) - b.stride() should be (Array(1)) - b.nElement() should be (4) + b.storageOffset() should be(5) + b.size() should be(Array(4)) + b.stride() should be(Array(1)) + b.nElement() should be(4) b.setValue(3, 0.01f) - b.valueAt(3) should be (0.01f) - a.valueAt(2, 3) should be (0.01f) + b.valueAt(3) should be(0.01f) + a.valueAt(2, 3) should be(0.01f) } "Scalar tensor" should "be able to construct" in { val t: Tensor[Double] = DenseTensor[Double](1.0) t.nDimension should be(0) - t.size().isEmpty should be (true) + t.size().isEmpty should be(true) } "Scalar tensor" should "not have size" in { @@ -779,52 +792,52 @@ class DenseTensorSpec extends FlatSpec with Matchers { val thrown = intercept[Exception] { t.size(1) } - thrown.isInstanceOf[IllegalArgumentException] should be (true) + thrown.isInstanceOf[IllegalArgumentException] should be(true) } "Scalar tensor" should "be able to add" in { val t: Tensor[Double] = DenseTensor[Double](1.0) val y: Tensor[Double] = DenseTensor[Double](1.0) t.add(1.0, y) - t should be (DenseTensor[Double](2.0)) + t should be(DenseTensor[Double](2.0)) } "Scalar tensor" should "be able to set value" in { val t: Tensor[Double] = DenseTensor[Double](1.0) t.setValue(2.0) - t should be (DenseTensor[Double](2.0)) + t should be(DenseTensor[Double](2.0)) } "Scalar tensor" should "be able to calc max" in { val t: Tensor[Double] = DenseTensor[Double](1.0) - t.max() should be (1.0) + t.max() should be(1.0) } "Scalar tensor" should "be able to calc min" in { val t: Tensor[Double] = DenseTensor[Double](1.0) - t.max() should be (1.0) + t.max() should be(1.0) } "Scalar tensor" should "be able to calc nElement" in { val t: Tensor[Double] = DenseTensor[Double](1.0) - t.nElement() should be (1) + t.nElement() should be(1) } "Scalar tensor" should "be able to get element" in { val t: Tensor[Double] = DenseTensor[Double](1.0) - t.apply(Array[Int]()) should be (1.0) + t.apply(Array[Int]()) should be(1.0) } "Scalar tensor" should "be able to update" in { val t: Tensor[Double] = DenseTensor[Double](1.0) t.update(Array[Int](), 2.0) - t should be (DenseTensor[Double](2.0)) + t should be(DenseTensor[Double](2.0)) } "Tensor add" should "support broadcasting" in { val t1 = Tensor[Double](T(1, 2, 3)) val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) - t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + t2.add(t1) should be(Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) } "Tensor add" should "support broadcasting 2" in { @@ -851,7 +864,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { val cloneT1 = t1.clone() val oldStorage = t1.storage() - t1.add(t2) should be (Tensor[Double](T( + t1.add(t2) should be(Tensor[Double](T( T( T(3, 4, 5), T(7, 8, 9) @@ -867,7 +880,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { ))) oldStorage.eq(t1.storage()) should be(true) - t2.add(cloneT1) should be (Tensor[Double](T( + t2.add(cloneT1) should be(Tensor[Double](T( T( T(3, 4, 5), T(7, 8, 9) @@ -886,14 +899,14 @@ class DenseTensorSpec extends FlatSpec with Matchers { "Tensor add" should "support broadcasting with singleton dimension" in { val t1 = Tensor[Double](T(T(1, 2, 3))) val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) - t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + t2.add(t1) should be(Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) } "Tensor add" should "catch exception when broadcasting size not match" in { val t1 = Tensor[Double](T(1, 2)) val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) intercept[IllegalArgumentException] { - t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + t2.add(t1) should be(Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) } } @@ -901,7 +914,13 @@ class DenseTensorSpec extends FlatSpec with Matchers { val t1 = Tensor[Double](T(T(1, 2, 3), T(1, 2, 3), T(1, 2, 3))) val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) intercept[IllegalArgumentException] { - t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + t2.add(t1) should be(Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) } } + + "Select on a Vector " should "be a scalar" in { + val t: Tensor[Double] = new DenseTensor[Double](2) + val result = t.select(1, 1) + result.isScalar should be(true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala index 2a72aa6e197..192199dea66 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala @@ -19,12 +19,15 @@ import com.intel.analytics.bigdl.dataset._ import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, MSECriterion} import com.intel.analytics.bigdl.optim.{SGD, Trigger} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, Table} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import java.io.{File => JFile} +import com.google.protobuf.ByteString +import org.tensorflow.framework.AttrValue + import scala.collection.mutable class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { @@ -61,7 +64,7 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { import scala.collection.JavaConverters._ val context = new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() - val session = new BigDLSessionImpl[Float](nodes.asScala, context) + val session = new BigDLSessionImpl[Float](nodes.asScala, sc, context) val data = new Array[Tensor[Float]](100) val label = new Array[Tensor[Float]](100) @@ -74,7 +77,7 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { val optim = new SGD[Float](0.001) val criterion = MSECriterion[Float]() - val endWhen = Trigger.maxEpoch(5) + val endWhen = Trigger.maxEpoch(2) val samples = data.zip(label).map { case (dataTensor, labelTensor) => Sample(dataTensor, labelTensor) @@ -90,4 +93,36 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { module.forward(Tensor[Float](Array(1))) } + "Session" should "be able construct input data" in { + + val resource = getClass().getClassLoader().getResource("tf") + val modelPath = resource.getPath() + JFile.separator + "lenet.pbtxt" + val filePath = resource.getPath() + JFile.separator + "mnist_test.tfrecord" + val nodes = TensorflowLoader.parseTxt(modelPath) + import scala.collection.JavaConverters._ + + val filenames = nodes.asScala.filter(_.getName == "parallel_read/filenames/Const").head + + val newTensor = filenames.getAttrMap.get("value") + .getTensor.toBuilder.clearStringVal().addStringVal(ByteString.copyFromUtf8(filePath)) + + val newNode = + filenames.toBuilder + .putAttr("value", AttrValue.newBuilder().setTensor(newTensor).build()) + .build() + + val newModel = nodes.asScala.filterNot(_.getName == "parallel_read/filenames/Const") :+ newNode + + val context = + new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() + val session = new BigDLSessionImpl[Float](newModel, sc, context) + + val endpoints = Seq( + "ParseSingleExample/SerializedDependencies" + ) + val rdd = session.getRDD(endpoints) + val result = rdd.count() + result should be (4) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index 1362f5dad05..5d3a0b419ab 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -19,6 +19,7 @@ import java.io.{File => JFile} import java.nio.ByteOrder import java.util.UUID +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.dataset.{DistributedDataSet, MiniBatch} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{DistriOptimizer, Trigger} @@ -28,7 +29,7 @@ import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD import com.intel.analytics.bigdl.numeric.NumericFloat -import org.tensorflow.framework.NodeDef +import org.tensorflow.framework.{DataType, NodeDef, TensorProto, TensorShapeProto} import scala.collection.mutable import scala.sys.process._ From ce4ac0b4fea1369c685c12a5cfdf3e7ac53a84a2 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 26 Sep 2017 14:48:56 +0800 Subject: [PATCH 0415/1065] refactor predictor (#1582) * refactor predictor * predictClass share model output memory * refactor repeatMemory to shareBuffer --- .../dllib/nn/abstractnn/AbstractModule.scala | 15 ++++++++--- .../bigdl/dllib/optim/Predictor.scala | 27 ++++++++++++++----- .../bigdl/dllib/optim/PredictorSpec.scala | 13 +++++++-- 3 files changed, 43 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index e39e4cc6df1..77a4edaa5df 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -478,17 +478,24 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, /** * module predict, return the probability distribution * @param dataset dataset for prediction + * @param batchSize total batchSize for all partitions. + * if -1, default is 4 * partitionNumber of datatset + * @param shareBuffer whether to share same memory for each batch predict results */ - def predict(dataset: RDD[Sample[T]]): RDD[Activity] = { - Predictor(this).predict(dataset) + def predict(dataset: RDD[Sample[T]], + batchSize: Int = -1, + shareBuffer: Boolean = false): RDD[Activity] = { + Predictor(this).predict(dataset, batchSize, shareBuffer) } /** * module predict, return the predict label * @param dataset dataset for prediction + * @param batchSize total batchSize for all partitions. + * if -1, default is 4 * partitionNumber of dataset */ - def predictClass(dataset: RDD[Sample[T]]): RDD[Int] = { - Predictor(this).predictClass(dataset) + def predictClass(dataset: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { + Predictor(this).predictClass(dataset, batchSize) } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index 548a852c838..c4e1251f2be 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.apache.spark.rdd.RDD +import org.dmg.pmml.False import scala.reflect.ClassTag @@ -36,8 +37,8 @@ class Predictor[T: ClassTag] private[optim]( private val batchPerPartition = 4 - def predictClass(dataSet: RDD[Sample[T]]): RDD[Int] = { - val result = predict(dataSet) + def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { + val result = predict(dataSet, batchSize, true) result.mapPartitions { partition => partition.map(output => { val _output = output.toTensor[T] @@ -48,18 +49,32 @@ class Predictor[T: ClassTag] private[optim]( } } - def predict(dataSet: RDD[Sample[T]]): RDD[Activity] = { + def predict(dataSet: RDD[Sample[T]], batchSize: Int = -1, + shareBuffer: Boolean = false): RDD[Activity] = { val modelBroad = ModelBroadcast[T].broadcast(dataSet.sparkContext, model.evaluate()) val partitionNum = dataSet.partitions.length + val totalBatch = if (batchSize > 0) { + require(batchSize % partitionNum == 0, s"Predictor.predict: total batch size $batchSize " + + s"should be divided by partitionNum ${partitionNum}") + batchSize + } else { + batchPerPartition * partitionNum + } val otherBroad = dataSet.sparkContext.broadcast(SampleToMiniBatch( - batchSize = batchPerPartition * partitionNum, partitionNum = Some(partitionNum))) + batchSize = totalBatch, + partitionNum = Some(partitionNum)), shareBuffer) dataSet.mapPartitions { partition => val localModel = modelBroad.value() - val localTransformer = otherBroad.value.cloneTransformer() + val localTransformer = otherBroad.value._1.cloneTransformer() + val repeatMemory = otherBroad.value._2 val miniBatch = localTransformer(partition) miniBatch.flatMap( batch => { val output = localModel.forward(batch.getInput).toTensor[T] - output.split(1) + if (shareBuffer) { + output.split(1) + } else { + output.clone().split(1) + } }) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index e6f0ddcbcdc..af742c2cfd3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -54,15 +54,24 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ } val model = LeNet5(classNum = 10) val dataSet = sc.parallelize(data, 2) - val result = model.predict(dataSet) - val prob = result.map(_.toTensor[Float].clone()).collect() + var result = model.predict(dataSet) + var prob = result.collect() + prob(0) should be (model.forward(data(0).feature)) prob(11) should be (model.forward(data(11).feature)) prob(31) should be (model.forward(data(31).feature)) prob(51) should be (model.forward(data(51).feature)) prob(71) should be (model.forward(data(71).feature)) prob(91) should be (model.forward(data(91).feature)) + + result = model.predict(dataSet, 20, true) + prob = result.collect() + + prob(0) should be(prob(10)) + prob(5) should be(prob(15)) + prob(0) should be(prob(20)) + prob(8) should be(prob(38)) } "model.predictClass" should "be correct" in { From a128982cecf9afd478bd0788ac88db1335eda6bd Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 27 Sep 2017 16:54:11 +0800 Subject: [PATCH 0416/1065] Refine tensorflow loader code strcuture (#1593) * revise tensorflow loaders * refine more layers * fix sytle issue * fix style issue * more ops * meet code review * fix style issue --- .../analytics/bigdl/dllib/nn/CAddTable.scala | 57 +- .../analytics/bigdl/dllib/nn/CMulTable.scala | 45 +- .../analytics/bigdl/dllib/nn/JoinTable.scala | 2 +- .../bigdl/dllib/nn/ops/Operation.scala | 6 + .../bigdl/dllib/tensor/DenseTensor.scala | 10 +- .../bigdl/dllib/tensor/DenseTensorMath.scala | 70 +- .../dllib/utils/tf/TensorflowLoader.scala | 15 +- .../dllib/utils/tf/TensorflowToBigDL.scala | 985 +----------------- .../dllib/utils/tf/loaders/Adapter.scala | 115 ++ .../bigdl/dllib/utils/tf/loaders/Add.scala | 33 + .../bigdl/dllib/utils/tf/loaders/AddN.scala | 32 + .../bigdl/dllib/utils/tf/loaders/Assert.scala | 36 + .../dllib/utils/tf/loaders/AvgPool.scala | 62 ++ .../dllib/utils/tf/loaders/ConcatV2.scala | 40 + .../bigdl/dllib/utils/tf/loaders/Const.scala | 36 + .../tf/loaders/Conv2DBackpropInput.scala | 62 ++ .../utils/tf/loaders/DependencyNode.scala | 32 + .../bigdl/dllib/utils/tf/loaders/Equal.scala | 36 + .../bigdl/dllib/utils/tf/loaders/Fill.scala | 33 + .../dllib/utils/tf/loaders/Greater.scala | 36 + .../dllib/utils/tf/loaders/Identity.scala | 33 + .../dllib/utils/tf/loaders/MaxPool.scala | 60 ++ .../bigdl/dllib/utils/tf/loaders/Mean.scala | 49 + .../bigdl/dllib/utils/tf/loaders/Mul.scala | 33 + .../bigdl/dllib/utils/tf/loaders/Pack.scala | 35 + .../bigdl/dllib/utils/tf/loaders/Pad.scala | 56 + .../dllib/utils/tf/loaders/ParseExample.scala | 54 + .../dllib/utils/tf/loaders/Placeholder.scala | 36 + .../utils/tf/loaders/QueueDequeueManyV2.scala | 36 + .../utils/tf/loaders/QueueDequeueV2.scala | 36 + .../utils/tf/loaders/QueueEnqueueManyV2.scala | 36 + .../utils/tf/loaders/QueueEnqueueV2.scala | 36 + .../utils/tf/loaders/RandomShuffle.scala | 36 + .../bigdl/dllib/utils/tf/loaders/Rank.scala | 36 + .../dllib/utils/tf/loaders/ReaderReadV2.scala | 36 + .../bigdl/dllib/utils/tf/loaders/Relu.scala | 33 + .../dllib/utils/tf/loaders/Reshape.scala | 51 + .../utils/tf/loaders/ResizeBilinear.scala | 33 + .../bigdl/dllib/utils/tf/loaders/Rsqrt.scala | 35 + .../bigdl/dllib/utils/tf/loaders/Shape.scala | 33 + .../dllib/utils/tf/loaders/Sigmoid.scala | 33 + .../dllib/utils/tf/loaders/Softmax.scala | 33 + .../bigdl/dllib/utils/tf/loaders/Split.scala | 46 + .../dllib/utils/tf/loaders/Squeeze.scala | 42 + .../dllib/utils/tf/loaders/StridedSlice.scala | 59 ++ .../bigdl/dllib/utils/tf/loaders/Sub.scala | 35 + .../bigdl/dllib/utils/tf/loaders/Tanh.scala | 32 + .../tf/loaders/TensorflowOpsLoader.scala | 30 + .../bigdl/dllib/utils/tf/loaders/Unpack.scala | 35 + .../bigdl/dllib/utils/tf/loaders/Utils.scala | 67 ++ .../bigdl/dllib/nn/CAddTableSpec.scala | 43 + .../bigdl/dllib/nn/CMulTableSpec.scala | 43 + .../bigdl/dllib/utils/tf/AdapterSpec.scala | 52 + .../dllib/utils/tf/TensorflowLoaderSpec.scala | 10 - 54 files changed, 2098 insertions(+), 998 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Adapter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AddN.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPool.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Equal.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Greater.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Identity.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPool.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mul.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueManyV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueManyV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomShuffle.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rank.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReaderReadV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softmax.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sub.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TensorflowOpsLoader.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Unpack.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/AdapterSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala index dbb2351004e..4f818d1b709 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala @@ -35,27 +35,66 @@ class CAddTable[T: ClassTag](val inplace: Boolean = false)( implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { override def updateOutput(input: Table): Tensor[T] = { - if (inplace) { - output.set(input[Tensor[T]](1)) - } else { - output.resizeAs(input[Tensor[T]](1)).copy(input[Tensor[T]](1)) - } - var i = 2 + var scalar = ev.zero + var hasTensor = false + var hasScalar = false + var initTensor = false + + var i = 1 while (i <= input.length()) { - output.add(input[Tensor[T]](i)) + val curTensor = input[Tensor[T]](i) + if (curTensor.isScalar) { + scalar = ev.plus(scalar, curTensor.value()) + hasScalar = true + } else if (curTensor.isTensor) { + if (initTensor) { + output = output.add(curTensor) + } else { + if (inplace) { + output.set(curTensor) + } else { + output.resizeAs(curTensor).copy(curTensor) + } + initTensor = true + } + hasTensor = true + } i += 1 } + + if (hasTensor && hasScalar) { + output.add(scalar) + } else if (hasScalar) { + if (inplace) { + output.set(input[Tensor[T]](1)).setValue(scalar) + } else { + output.resizeAs(input[Tensor[T]](1)).setValue(scalar) + } + } + output } override def updateGradInput(input: Table, gradOutput: Tensor[T]) : Table = { var i = 1 + var sum = ev.zero + var calculateSum = false while (i <= input.length()) { if (i > gradInput.length) gradInput.insert(i, Tensor[T]().resizeAs(input(1))) if (inplace) { + require(input[Tensor[T]](1).isSameSizeAs(gradOutput), "cannot use inplace for broadcast") gradInput[Tensor[T]](i).set(gradOutput) } else { - gradInput[Tensor[T]](i).resizeAs(gradOutput).copy(gradOutput) + if (input[Tensor[T]](i).isSameSizeAs(gradOutput)) { + gradInput[Tensor[T]](i).resizeAs(gradOutput).copy(gradOutput) + } else { + require(input[Tensor[T]](i).isScalar, "Only support scalar broadcast backward now") + if (!calculateSum) { + sum = gradOutput.sum() + calculateSum = true + } + gradInput[Tensor[T]](i).resizeAs(input[Tensor[T]](i)).setValue(sum) + } } i += 1 } @@ -81,3 +120,5 @@ object CAddTable { new CAddTable[T](inplace) } } + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMulTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMulTable.scala index b04c874b6f7..3b7bd1fcd23 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMulTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMulTable.scala @@ -29,13 +29,38 @@ import scala.reflect.ClassTag @SerialVersionUID(8888147326550637025L) class CMulTable[T: ClassTag]()( implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{ + + private var scalarIndexes : Array[Int] = _ + override def updateOutput(input: Table): Tensor[T] = { - output.resizeAs(input(1)).copy(input(1)) - var i = 2 + var scalar = ev.one + var hasTensor = false + var hasScalar = false + var initTensor = false + var i = 1 while (i <= input.length()) { - output.cmul(input(i)) + val curTensor = input[Tensor[T]](i) + if (curTensor.isScalar) { + scalar = ev.times(scalar, curTensor.value()) + hasScalar = true + } else if (curTensor.isTensor) { + if (initTensor) { + output.cmul(curTensor) + } else { + output.resizeAs(curTensor).copy(curTensor) + initTensor = true + } + hasTensor = true + } i += 1 } + + if (hasTensor && hasScalar) { + output.mul(scalar) + } else if (hasScalar) { + output.resizeAs(input[Tensor[T]](1)).setValue(scalar) + } + output } @@ -43,12 +68,22 @@ class CMulTable[T: ClassTag]()( var i = 1 while (i <= input.length()) { if (!gradInput.contains(i)) gradInput.insert(i, Tensor[T]()) - gradInput[Tensor[T]](i).resizeAs(input(i)).copy(gradOutput) + gradInput[Tensor[T]](i).resizeAs(gradOutput).copy(gradOutput) var j = 1 while (j <= input.length()) { - if (i != j) gradInput[Tensor[T]](i).cmul(input(j)) + if (i != j) { + if (input[Tensor[T]](j).isScalar) { + gradInput[Tensor[T]](i).mul(input[Tensor[T]](j).value()) + } else { + gradInput[Tensor[T]](i).cmul(input(j)) + } + } j += 1 } + if (input[Tensor[T]](i).isScalar) { + val sum = gradInput[Tensor[T]](i).sum() + gradInput(i) = gradInput[Tensor[T]](i).resizeAs(input(i)).setValue(sum) + } i += 1 } gradInput diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala index 532d865bc59..10cff1d39e0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala @@ -60,7 +60,7 @@ class JoinTable[T: ClassTag] ( nDim += 1 } require(firstInput.dim() >= dimension, "dimension exceeds input dimensions" + - s"dimension ${firstInput.dim()} input dimension${dimension}") + s" input dimension ${firstInput.dim()}, dimension ${dimension}") nDim } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala index 4bca87031ca..c0f0c460f45 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala @@ -43,5 +43,11 @@ abstract class Operation[A <: Activity: ClassTag, B <: Activity: ClassTag, T: Cl override def backward(input: A, gradOutput: B): A = { throw new UnsupportedOperationException("Operation does not support backward() method") } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + // Do not try to call parameters from an operation + // Container should handle parameters() + throw new IllegalArgumentException("Operation doesn't have parameters") + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 95220d45ce8..c3c20740c63 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -27,7 +27,7 @@ import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @SerialVersionUID(5876322619614900645L) -private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( +private[tensor] class DenseTensor[@specialized T: ClassTag]( private[tensor] var _storage: Storage[T], private[tensor] var _storageOffset: Int, private[tensor] var _size: Array[Int], @@ -584,7 +584,7 @@ private[tensor] class DenseTensor[@specialized(Float, Double) T: ClassTag]( } override def value(): T = { - require(0 == this.nDimension, s"invalid size: 0 == ${this.nDimension}") + require(1 == this.nElement(), s"invalid size: 1 == ${this.nElement()}") var offset = this._storageOffset this._storage(offset) } @@ -2205,7 +2205,7 @@ object DenseTensor { } - private[tensor] def isSameSizeAs[@specialized(Float, Double) T]( + private[tensor] def isSameSizeAs[@specialized T]( self: DenseTensor[T], src: Tensor[_]): Boolean = { if (self.nDimension != src.nDimension()) { return false @@ -2349,7 +2349,7 @@ object DenseTensor { new DenseTensor(new ArrayStorage(Array(get1d(self, x0)))) } - private[tensor] def copy[@specialized(Float, Double) T]( + private[tensor] def copy[@specialized T]( self: DenseTensor[T], src: Tensor[T]): Unit = { require(self.nElement() == src.nElement()) if (self.isEmpty) { @@ -2447,7 +2447,7 @@ object DenseTensor { gauss } - private[tensor] def canFastBroadcast[@specialized T: ClassTag](tensor: Tensor[T], + private[tensor] def canFastBroadcast[@specialized T](tensor: Tensor[T], other: Tensor[T]): Boolean = { if (tensor.nDimension < other.nDimension()) return false diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala index 492962984e8..ef0efdf5b91 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala @@ -45,34 +45,54 @@ object DenseTensorMath { self } - def cmul[@specialized(Float, Double) T](self: DenseTensor[T], x: Tensor[T], y: Tensor[T]) + def cmul[@specialized T](self: DenseTensor[T], x: Tensor[T], y: Tensor[T]) (implicit ev: TensorNumeric[T]): Tensor[T] = { - require(self.nElement() == y.nElement() && self.nElement() == x.nElement(), - "element number doesn't match") - if (self.isContiguous() && x.isContiguous() && y.isContiguous() && MKL.isMKLLoaded) { - - ev.vMul(self.nElement(), x.storage().array(), x.storageOffset() - 1, - y.storage().array(), y.storageOffset() - 1, self.storage().array(), self.storageOffset() - - 1) - } else { - val func6 = new TensorFunc6[T] { - override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, - data3: Array[T], offset3: Int): Unit = { - data1(offset1) = ev.times(data2(offset2), data3(offset3)) - } + if (x.nElement() != y.nElement() && DenseTensor.canFastBroadcast(x, y)) { + require(self.nElement() == x.nElement(), "the self tensor nElement is not same as x" + + s"self(${self.nElement()}) x(${x.nElement()})") + // recursive cmul + var i = 0 + while(i < x.size(1)) { + cmul(self.select(1, i + 1).asInstanceOf[DenseTensor[T]], x.select(1, i + 1), y) + i += 1 } - val func4 = new TensorFunc4[T] { - override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = { - data1(offset1) = ev.times(data1(offset1), data2(offset2)) - } + } else if (x.nElement() != y.nElement() && DenseTensor.canFastBroadcast(y, x)) { + require(self.nElement() == y.nElement(), "the self tensor nElement is not same as y" + + s"self(${self.nElement()}) y(${y.nElement()})") + // recursive cmul + var i = 0 + while(i < y.size(1)) { + cmul(self.select(1, i + 1).asInstanceOf[DenseTensor[T]], x, y.select(1, i + 1)) + i += 1 } - // For special case, we can use apply2 to instead of apply3 - if (self == y) { - Apply.apply2(self, x, func4) - } else if (self == x) { - Apply.apply2(self, y, func4) + } else { + require(self.nElement() == y.nElement(), s"element number doesn't match " + + s"self(${self.nElement()}) y(${y.nElement()}) x(${x.nElement()})") + if (self.isContiguous() && x.isContiguous() && y.isContiguous() && MKL.isMKLLoaded) { + + ev.vMul(self.nElement(), x.storage().array(), x.storageOffset() - 1, + y.storage().array(), y.storageOffset() - 1, self.storage().array(), self.storageOffset() + - 1) } else { - Apply.apply3[T](self, x, y, func6) + val func6 = new TensorFunc6[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, + data3: Array[T], offset3: Int): Unit = { + data1(offset1) = ev.times(data2(offset2), data3(offset3)) + } + } + val func4 = new TensorFunc4[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = { + data1(offset1) = ev.times(data1(offset1), data2(offset2)) + } + } + // For special case, we can use apply2 to instead of apply3 + if (self == y) { + Apply.apply2(self, x, func4) + } else if (self == x) { + Apply.apply2(self, y, func4) + } else { + Apply.apply3[T](self, x, y, func6) + } } } self @@ -433,7 +453,7 @@ object DenseTensorMath { result } - def sum[@specialized(Float, Double) T: ClassTag](self: DenseTensor[T], x: Tensor[T], _dim: Int) + def sum[@specialized T: ClassTag](self: DenseTensor[T], x: Tensor[T], _dim: Int) (implicit ev: TensorNumeric[T]): Tensor[T] = { require(_dim >= 0 && _dim < x.nDimension, s"dimension ${_dim + 1} out of range") val result = if (self == null) new DenseTensor[T]() else self diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index dff3013f382..2830a81f0dc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -28,6 +28,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{DirectedGraph, Edge, FileReader, Node} import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL._ +import com.intel.analytics.bigdl.utils.tf.loaders.TensorflowOpsLoader import com.intel.analytics.bigdl.utils.{DirectedGraph, FileReader, Node} import org.tensorflow.framework.{GraphDef, NodeDef} @@ -261,9 +262,17 @@ object TensorflowLoader{ """.stripMargin val (module, nodes, inputNodes) = - extract[T](n.graph(reverse = true), context, byteOrder).getOrElse( - throw new UnsupportedOperationException(errorMsg) - ) + extract[T](n.graph(reverse = true), context, byteOrder).getOrElse({ + try { + val cls = Class.forName("com.intel.analytics.bigdl.utils.tf.loaders." + + n.element.getOp) + val builder = cls.getConstructors()(0).newInstance().asInstanceOf[TensorflowOpsLoader] + (builder.build[T](n.element, byteOrder), Seq(n).asJava, Seq(n)) + } catch { + case _ => + throw new UnsupportedOperationException(errorMsg) + } + }) // set name if (nodes.size() == 1) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index d1897c9e45a..a44c03726c9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -245,15 +245,10 @@ object TensorflowToBigDL { val res = new ArrayBuffer[TensorflowToBigDL]() // ElementWiseMulTF must be after MulTF res.append( - FullConnectionTF, DropoutTF, AvgPoolingTF, MaxPoolingTF, ReshapeTF, - TanhTF, ReluTF, SigmoidTF, Conv2D, Placeholder, SqueezeTF, IdentityTF, ConcatTF, - BatchNormTF, AddConstTF1, AddConstTF2, AddTF, SoftMaxTF, ElementWiseMulTF, MulTF, - SplitTF, PaddingTF, MeanTF, UnpackTF, StrideSliceTF, ShapeTF, FillTF, PackTF, ConstTF, - Flatten, Conv1D, FlattenV2, BatchNormV2NHWCTF, BatchNormV2NCHWTF, AddNTF, - ControlDependencyTF, RandomShuffleTF, AssertTF, GreaterTF, ReaderReadTF, QueueDequeTF, - QueueDequeManyTF, EqualTF, RankTF, EnqueueManyTF, EnqueueTF, - FullConnectionWithoutBiasTF, DeConv2D, ResizeBilinearTF, Conv2D2, Conv2DWithoutBias, - ParseExampleTF + FullConnectionTF, DropoutTF, Conv2D, BatchNormTF, Flatten, Conv1D, FlattenV2, + BatchNormV2NHWCTF, BatchNormV2NCHWTF, + FullConnectionWithoutBiasTF, Conv2D2, + Conv2DWithoutBias ) res } @@ -344,22 +339,6 @@ object FullConnectionWithoutBiasTF extends TensorflowToBigDL{ } } -object SqueezeTF extends TensorflowToBigDL { - private val graph = (Node("*") -> Node("Squeeze")).graph(reverse = true) - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val dims = tfGraph.source.element.getAttrOrThrow("squeeze_dims").getList().getIList() - .asScala.map(_.toInt).toArray - - Squeeze[T](dims, batchMode = true).asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - object Conv1D extends TensorflowToBigDL { private val graph = { val squeeze = Node("Squeeze") @@ -665,213 +644,6 @@ object Conv2D2 extends TensorflowToBigDL{ } } -object DeConv2D extends TensorflowToBigDL{ - private val graph = { - val deconv = Node("Conv2DBackpropInput") - Node("...") -> deconv - deconv.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag]( - tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder - )(implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val attributes = tfGraph.source.element.getAttrMap - val (pW, pH) = - if (getString(attributes, "padding") == "SAME") { - (-1, -1) - } else { - (0, 0) - } - val strideList = getIntList(attributes, "strides") - require(strideList.head == 1, s"not support strides on batch") - - val format = getString(attributes, "data_format") - val deconv = format match { - case "NHWC" => - require(strideList(3) == 1, s"not support strides on depth") - val strideW = strideList(1) - val strideH = strideList(2) - Conv2DTranspose[T](strideW, strideH, pW, pH, DataFormat.NHWC) - - case "NCHW" => - require(strideList(1) == 1, s"not support strides on depth") - val strideW = strideList(2) - val strideH = strideList(3) - Conv2DTranspose[T](strideW, strideH, pW, pH, DataFormat.NCHW) - case _ => - throw new IllegalArgumentException(s"not supported data format: $format") - } - deconv.asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object ReluTF extends TensorflowToBigDL { - private val graph = { - (Node("*") -> Node("Relu")).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - ReLU[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object TanhTF extends TensorflowToBigDL{ - private val graph = { - (Node("*") -> Node("Tanh")).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - - Tanh[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object SigmoidTF extends TensorflowToBigDL{ - private val graph = { - (Node("*") -> Node("Sigmoid")).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - Sigmoid[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object ReshapeTF extends TensorflowToBigDL { - private val graph = { - val nodeReshape = Node("Reshape") - Node("*") -> nodeReshape - Node("Const") -> nodeReshape - nodeReshape.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val sizes = TFUtils.parseTensor( - tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) - .asInstanceOf[Tensor[Int]] - - val batchMode = sizes.valueAt(1) == -1 - val arraySize = new Array[Int](if (batchMode) sizes.nElement() - 1 else sizes.nElement()) - var i = if (batchMode) 2 else 1 - var k = 0 - while(i <= sizes.nElement()) { - arraySize(k) = sizes.valueAt(i) - k += 1 - i += 1 - } - Reshape[T](size = arraySize, Some(batchMode)) - .asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object MaxPoolingTF extends TensorflowToBigDL { - private val graph = { - (Node("*") -> Node("MaxPool")).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val attributes = tfGraph.source.element.getAttrMap - val format = getString(attributes, "data_format") - val strideList = getIntList(attributes, "strides") - val kernelList = getIntList(attributes, "ksize") - val (strideH, strideW, ksizeH, ksizeW) = format match { - case "NHWC" => - require(strideList(3) == 1, s"not support strides on depth") - (strideList(1), strideList(2), kernelList(1), kernelList(2)) - case "NCHW" => - require(strideList(1) == 1, s"not support strides on depth") - (strideList(2), strideList(3), kernelList(2), kernelList(3)) - case _ => - throw new IllegalArgumentException(s"not supported data format: $format") - } - - val (pW, pH) = - if (getString(attributes, "padding") == "SAME") { - (-1, -1) - } else { - (0, 0) - } - - SpatialMaxPooling[T](ksizeW, ksizeH, strideW, strideH, pW, pH, - format = DataFormat(format)) - .asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object AvgPoolingTF extends TensorflowToBigDL { - private val graph = { - (Node("*") -> Node("AvgPool")).graph(reverse = true) - } - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val attributes = tfGraph.source.element.getAttrMap - val format = getString(attributes, "data_format") - val strideList = getIntList(attributes, "strides") - val kernelList = getIntList(attributes, "ksize") - - val (strideH, strideW, ksizeH, ksizeW) = format match { - case "NHWC" => - require(strideList(3) == 1, s"not support strides on depth") - (strideList(1), strideList(2), kernelList(1), kernelList(2)) - case "NCHW" => - require(strideList(1) == 1, s"not support strides on depth") - (strideList(2), strideList(3), kernelList(2), kernelList(3)) - case _ => - throw new IllegalArgumentException(s"not supported data format: $format") - } - - val (pW, pH) = - if (getString(attributes, "padding") == "SAME") { - (-1, -1) - } else { - (0, 0) - } - - SpatialAveragePooling[T](ksizeW, ksizeH, strideW, strideH, pW, pH, - countIncludePad = false, format = DataFormat(format)) - .asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - object DropoutTF extends TensorflowToBigDL{ private val graph = { val nodediv = Node("RealDiv") @@ -908,71 +680,6 @@ object DropoutTF extends TensorflowToBigDL{ } } -object Placeholder extends TensorflowToBigDL { - private val graph = Node("Placeholder").graph(reverse = true) - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Input[T]().element.asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - - -object ConstTF extends TensorflowToBigDL { - private val graph = Node("Const").graph(reverse = true) - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - if (tfGraph.source.element.getName() == "size") { - println() - } - val value = TFUtils - .parseTensor(tfGraph.source.element.getAttrMap.get("value").getTensor, byteOrder) - Const(value).asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object ShapeTF extends TensorflowToBigDL { - private val graph = { - val node = Node("Shape") - Node("*") -> node - node.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - - Shape[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object IdentityTF extends TensorflowToBigDL { - private val graph = (Node("*") -> Node("Identity")).graph(reverse = true) - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - Identity[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - object BatchNormV2NCHWTF extends TensorflowToBigDL{ private val graph = { val nodeInput = Node("*") @@ -1181,172 +888,52 @@ object BatchNormTF extends TensorflowToBigDL{ } } -object FillTF extends TensorflowToBigDL{ +object FlattenV2 extends TensorflowToBigDL { private val graph = { - val nodeFill = Node("Fill") - Node("*") -> nodeFill - Node("*") -> nodeFill - nodeFill.graph(reverse = true) + val reshapeNode = Node("Reshape") + val concatNode = Node("ConcatV2") + val sliceNode = Node("Slice") + val expandNode = Node("ExpandDims") + val prodNode = Node("Prod") + val sliceNode1 = Node("Slice") + val shapeNode = Node("Shape") + val beginNode = Node("Const") + val sizeNode = Node("Const") + val beginNode1 = Node("Const") + val sizeNode1 = Node("Const") + val constNode = Node("Const") + val dimNode = Node("Const") + val axisNode = Node("Const") + val inputNode = Node("*") + + shapeNode -> sliceNode + beginNode -> sliceNode + sizeNode -> sliceNode + + shapeNode -> sliceNode1 + beginNode1 -> sliceNode1 + sizeNode1 -> sliceNode1 + + sliceNode1 -> prodNode + constNode -> prodNode + + prodNode -> expandNode + dimNode -> expandNode + + sliceNode -> concatNode + expandNode -> concatNode + axisNode -> concatNode + + inputNode -> reshapeNode + inputNode -> shapeNode + concatNode -> reshapeNode + reshapeNode.graph(reverse = true) } override def topology: DirectedGraph[String] = graph override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - Fill[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object PackTF extends TensorflowToBigDL{ - private val graph = { - val nodePack = Node("Pack") - Node("...") -> nodePack - nodePack.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val attr = tfGraph.source.element.getAttrMap - val dim = getInt(attr, "axis") + 1 - - Pack[T](dim).asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object UnpackTF extends TensorflowToBigDL{ - private val graph = { - val nodePack = Node("Unpack") - Node("*") -> nodePack - nodePack.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val attr = tfGraph.source.element.getAttrMap - val dim = getInt(attr, "axis") + 1 - SplitTable[T](dim).asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object StrideSliceTF extends TensorflowToBigDL { - private val graph = { - val nodeSlice = Node("StridedSlice") - Node("*") -> nodeSlice - Node("Const") -> nodeSlice - Node("Const") -> nodeSlice - Node("Const") -> nodeSlice - nodeSlice.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val startNode = tfGraph.source.prevNodes(1) - val endNode = tfGraph.source.prevNodes(2) - val strideNode = tfGraph.source.prevNodes(3) - - def getIntArray(node: Node[NodeDef]) = { - node.element.getAttrMap.get("value").getTensor.getIntValList.asScala.map(_.toInt) - } - - val start = getIntArray(startNode) - val end = getIntArray(endNode) - val stride = getIntArray(strideNode) - - val specs = (start zip end zip stride).zipWithIndex - .map(elem => (elem._2 + 1, elem._1._1._1 + 1, elem._1._1._2 + 1, elem._1._2)).toArray - - - StrideSlice[T](specs).asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - - -object ConcatTF extends TensorflowToBigDL{ - private val graph = { - val nodeConcat = Node("ConcatV2") - Node("...") -> nodeConcat - (Node("Const") -> nodeConcat).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val inputNumber = tfGraph.source.element.getAttrMap.get("N").getI.toInt - val nodeaxis = tfGraph.source.prevNodes(inputNumber) - val axis = nodeaxis.element.getAttrMap.get("value").getTensor.getIntVal(0) + 1 - val nInputDims = 4 - - JoinTable[T](dimension = axis, nInputDims = -1) - .asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object FlattenV2 extends TensorflowToBigDL { - private val graph = { - val reshapeNode = Node("Reshape") - val concatNode = Node("ConcatV2") - val sliceNode = Node("Slice") - val expandNode = Node("ExpandDims") - val prodNode = Node("Prod") - val sliceNode1 = Node("Slice") - val shapeNode = Node("Shape") - val beginNode = Node("Const") - val sizeNode = Node("Const") - val beginNode1 = Node("Const") - val sizeNode1 = Node("Const") - val constNode = Node("Const") - val dimNode = Node("Const") - val axisNode = Node("Const") - val inputNode = Node("*") - - shapeNode -> sliceNode - beginNode -> sliceNode - sizeNode -> sliceNode - - shapeNode -> sliceNode1 - beginNode1 -> sliceNode1 - sizeNode1 -> sliceNode1 - - sliceNode1 -> prodNode - constNode -> prodNode - - prodNode -> expandNode - dimNode -> expandNode - - sliceNode -> concatNode - expandNode -> concatNode - axisNode -> concatNode - - inputNode -> reshapeNode - inputNode -> shapeNode - concatNode -> reshapeNode - reshapeNode.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], byteOrder: ByteOrder)( + context: Context[T], byteOrder: ByteOrder)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val layer = Sequential[T]() @@ -1424,487 +1011,3 @@ object Flatten extends TensorflowToBigDL { .asInstanceOf[AbstractModule[Activity, Activity, T]] } } - -object AddConstTF1 extends TensorflowToBigDL{ - private val graph = { - val nodeAdd = Node("Add") - Node("Const") -> nodeAdd - (Node("*") -> nodeAdd).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val value = tfGraph.source.prevNodes.head.element - .getAttrMap.get("value").getTensor.getFloatVal(0) - AddConstant[T](value).asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object AddConstTF2 extends TensorflowToBigDL{ - private val graph = { - val nodeAdd = Node("Add") - Node("*") -> nodeAdd - (Node("Const") -> nodeAdd).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val value = tfGraph.source.prevNodes(1).element - .getAttrMap.get("value").getTensor.getFloatVal(0) - AddConstant[T](value).asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object AddTF extends TensorflowToBigDL{ - private val graph = { - val nodeAdd = Node("Add") - Node("*") -> nodeAdd - (Node("*") -> nodeAdd).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - CAddTable[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object SoftMaxTF extends TensorflowToBigDL{ - private val graph = { - (Node("*") -> Node("Softmax")).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - SoftMax[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - - -object MulTF extends TensorflowToBigDL{ - private val graph = { - val nodeMul = Node("Mul") - Node("Const") -> nodeMul - (Node("*") -> nodeMul).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val scale = TFUtils.parseTensor( - tfGraph.source.prevNodes(0).element.getAttrMap.get("value").getTensor, byteOrder) - .asInstanceOf[Tensor[Float]] - require(scale.isScalar, s"scale must be a scalar") - val value = scale.value().toDouble - val mul = MulConstant[T](value) - mul.asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object ElementWiseMulTF extends TensorflowToBigDL{ - private val graph = { - val nodeMul = Node("Mul") - Node("*") -> nodeMul - (Node("*") -> nodeMul).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - CMulTable[T]().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object SplitTF extends TensorflowToBigDL { - - private val graph = { - val nodeSplit = Node("Split") - Node("Const") -> nodeSplit - (Node("*") -> nodeSplit).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val numSplit = tfGraph.source.element.getAttrMap.get("num_split").getI.toInt - val dim = tfGraph.source.prevNodes.head.element - .getAttrMap.get("value").getTensor.getIntVal(0) + 1 - val model = new ConcatTable[T]() - for (index <- Range(1, numSplit + 1)) { - model.add(SplitAndSelect[T](dim, index, numSplit)) - } - model.asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - - -object PaddingTF extends TensorflowToBigDL{ - private val graph = { - val nodePad = Node("Pad") - Node("*") -> nodePad - (Node("Const") -> nodePad).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val paddings = TFUtils.parseTensor( - tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) - .asInstanceOf[Tensor[Int]] - val pad = ArrayBuffer[Int]() - val padding = Sequential[T]() - - for(dim <- 1 to paddings.size(1)) { - if (paddings.valueAt(dim, 1) != 0 || paddings.valueAt(dim, 2) != 0 ) { - if (paddings(Array(dim, 1)) != 0) { - padding.add(Padding[T](dim, -paddings.valueAt(dim, 1), 4)) - } - if (paddings(Array(dim, 2)) != 0) { - padding.add(Padding[T](dim, paddings.valueAt(dim, 2), 4)) - } - } - } - - padding.asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object MeanTF extends TensorflowToBigDL{ - private val graph = { - val nodeMean = Node("Mean") - Node("*") -> nodeMean - (Node("Const") -> nodeMean).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val dims = TFUtils.parseTensor( - tfGraph.source.prevNodes(1).element.getAttrMap.get("value").getTensor, byteOrder) - .asInstanceOf[Tensor[Int]] - val dim = ArrayBuffer[Int]() - val mean = Sequential[T]() - for (i <- 1 to dims.size(1)) { - dim += dims.valueAt(i) + 1 - } - dim.foreach(i => mean.add(Mean[T](i, squeeze = false))) - mean.asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object AddNTF extends TensorflowToBigDL{ - private val graph = { - (Node("...") -> Node("AddN")).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - CAddTable().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - - - -object ControlDependencyTF extends TensorflowToBigDL { - - private val graph = { - (Node("*") -> Node("DependencyNode")).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - ControlDependency().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object ResizeBilinearTF extends TensorflowToBigDL { - override def topology: DirectedGraph[String] = { - (Node("...") -> Node("ResizeBilinear")).graph(reverse = true) - } - - override def layer[T: ClassManifest]( - tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)(implicit ev: TensorNumeric[T]) - : AbstractModule[Activity, Activity, T] = { - val alignCorner = tfGraph.source.element.getAttrMap.get("align_corners").getB - ResizeBilinearOps(alignCorner).asInstanceOf[AbstractModule[Activity, Tensor[T], T]] - } -} - -object AssertTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("Assert") - Node("*") -> node - (Node("*") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Assert().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object GreaterTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("Greater") - Node("*") -> node - (Node("*") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Greater().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object RandomShuffleTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("RandomShuffle") - (Node("*") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object ReaderReadTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("ReaderReadV2") - Node("*") -> node - (Node("*") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object QueueDequeTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("QueueDequeueV2") - (Node("...") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object QueueDequeManyTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("QueueDequeueManyV2") - (Node("...") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object EqualTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("Equal") - Node("*") -> node - (Node("*") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Equal().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object RankTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("Rank") - (Node("*") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Rank().asInstanceOf[AbstractModule[Activity, Activity, T]] - } - -} - -object EnqueueTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("QueueEnqueueV2") - (Node("...") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object EnqueueManyTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("QueueEnqueueManyV2") - (Node("...") -> node).graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - new Identity().asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object ParseExampleTF extends TensorflowToBigDL { - - private val graph = { - val node = Node("ParseExample") - Node("...") -> node - node.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val node = tfGraph.source.element - val Ndense = node.getAttrMap.get("Ndense").getI.toInt - val Tdense = node.getAttrMap.get("Tdense") - .getList.getTypeList.asScala - .map { - case DataType.DT_INT64 => LongType - case DataType.DT_INT32 => IntType - case DataType.DT_FLOAT => FloatType - case DataType.DT_DOUBLE => DoubleType - case DataType.DT_STRING => StringType - } - val denseShapes = node.getAttrMap.get("dense_shapes") - .getList.getShapeList.asScala - .map { shapeProto => - shapeProto.getDimList.asScala.map(_.getSize.toInt).toArray - } - - new ParseExample(Ndense, Tdense, denseShapes) - .asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Adapter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Adapter.scala new file mode 100644 index 00000000000..a15910201a8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Adapter.scala @@ -0,0 +1,115 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + +/** + * Sometimes parameters pass as tensor from some previous nodes in graph. So we can't construct + * module at module define time. In such case, you can use the AdapterOperation to wrapper the + * module and create it in runtime. + * + * Please note you must guarantee the input parameter won't change each time. + * @param configIndexes configuration tensor indexes, start from 1 and -1 specify the last one + * @param build build function + * @tparam T Numeric type. Only support float/double now + */ +class Adapter[T: ClassTag]( + val configIndexes: Array[Int], + val build: Array[Tensor[_]] => AbstractModule[Activity, Activity, T] +)(implicit ev: TensorNumeric[T]) + extends AbstractModule[Table, Activity, T]{ + + private var module : AbstractModule[Activity, Activity, T] = _ + private var indexes : Array[Int] = _ + private var dataIndexes: Array[Int] = _ + private var zeroGrads: Array[Tensor[_]] = _ + private var realInput: Activity = _ + private var initTensors: Array[Tensor[_]] = _ + + override def updateOutput(input: Table): Activity = { + if (module == null) { + val l = input.length() + indexes = configIndexes.map(getPositiveIndex(_, l)) + val tensors = indexes.map(i => input[Tensor[_]](i)) + initTensors = tensors.map(_.clone()) + module = build(tensors) + dataIndexes = getDataIndexes(indexes, l) + zeroGrads = tensors.map(t => t.emptyInstance().resizeAs(t)) + } else { + indexes.map(i => input[Tensor[_]](i)).zip(initTensors).foreach(tensors => { + require(tensors._1 == tensors._2, s"constant tensor is changed. " + + s"\noriginal\n${tensors._2}\nnow\n${tensors._1}") + }) + } + + realInput = if (dataIndexes.length == 1) { + input[Tensor[_]](dataIndexes(0)) + } else { + val t = T() + dataIndexes.map(i => t.insert(input[Tensor[_]](i))) + t + } + + output = module.forward(realInput) + output + } + + private def getPositiveIndex(index: Int, length: Int): Int = { + if (index > 0) index else length + index + 1 + } + + private def getDataIndexes(indexs: Array[Int], length: Int): Array[Int] = { + (1 to length).filterNot(indexs.contains(_)).toArray + } + + override def updateGradInput(input: Table, gradOutput: Activity): Table = { + val realGradInput = module.updateGradInput(realInput, gradOutput) + gradInput = T() + var i = 0 + while(i < indexes.length) { + gradInput(indexes(i)) = zeroGrads(i) + i += 1 + } + if (dataIndexes.length == 1) { + gradInput(dataIndexes.head) = realGradInput + } else { + i = 0 + while (i < dataIndexes.length) { + gradInput(dataIndexes(i)) = realGradInput.toTable.apply[Activity](i + 1) + i += 1 + } + } + gradInput + } + + override def accGradParameters(input: Table, gradOutput: Activity): Unit = { + module.accGradParameters(realInput, gradOutput) + } +} + +object Adapter { + def apply[T: ClassTag]( + configIndexes: Array[Int], build: Array[Tensor[_]] => AbstractModule[Activity, Activity, T] + )(implicit ev: TensorNumeric[T]): Adapter[T] = { + new Adapter(configIndexes, build) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala new file mode 100644 index 00000000000..1b147395fd5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.CAddTable +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Add extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + CAddTable[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AddN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AddN.scala new file mode 100644 index 00000000000..72c619beeb2 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AddN.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.CAddTable +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class AddN extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + CAddTable[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala new file mode 100644 index 00000000000..de167a98a17 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Assert => AssertOperation} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Assert extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new AssertOperation[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPool.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPool.scala new file mode 100644 index 00000000000..b13d1cc0f6f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPool.scala @@ -0,0 +1,62 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.SpatialAveragePooling +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class AvgPool extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val attributes = nodeDef.getAttrMap + val format = getString(attributes, "data_format") + val strideList = getIntList(attributes, "strides") + val kernelList = getIntList(attributes, "ksize") + + val (strideH, strideW, ksizeH, ksizeW) = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + (strideList(1), strideList(2), kernelList(1), kernelList(2)) + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + (strideList(2), strideList(3), kernelList(2), kernelList(3)) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + + SpatialAveragePooling[T](ksizeW, ksizeH, strideW, strideH, pW, pH, + countIncludePad = false, format = DataFormat(format)) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala new file mode 100644 index 00000000000..c46f1898db8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.JoinTable +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class ConcatV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(-1), tensorArrays => { + val axis = tensorArrays(0).value().asInstanceOf[Int] + 1 + JoinTable[T](dimension = axis, nInputDims = -1) + }) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala new file mode 100644 index 00000000000..798051bb803 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.TFUtils +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Const extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val value = TFUtils.parseTensor(nodeDef.getAttrMap.get("value").getTensor, byteOrder) + Const(value).asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala new file mode 100644 index 00000000000..5143b88503b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala @@ -0,0 +1,62 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.Conv2DTranspose +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag +import Utils._ + +class Conv2DBackpropInput extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val attributes = nodeDef.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val deconv = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + Conv2DTranspose[T](strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + Conv2DTranspose[T](strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + deconv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala new file mode 100644 index 00000000000..ca04973f7a7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.tf.ControlDependency +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DependencyNode extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + ControlDependency[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Equal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Equal.scala new file mode 100644 index 00000000000..fde173ced93 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Equal.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Equal => EqualOperation} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Equal extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new EqualOperation[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala new file mode 100644 index 00000000000..801cc5a3b43 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.tf.Fill +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Fill extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Fill[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Greater.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Greater.scala new file mode 100644 index 00000000000..e51ef146deb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Greater.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Greater => GreaterOperation} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Greater extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new GreaterOperation[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Identity.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Identity.scala new file mode 100644 index 00000000000..a381a60bf8f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Identity.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Identity => nnIdentity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Identity extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + nnIdentity[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPool.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPool.scala new file mode 100644 index 00000000000..214fc095161 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPool.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.SpatialMaxPooling +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class MaxPool extends TensorflowOpsLoader { + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val attributes = nodeDef.getAttrMap + val format = getString(attributes, "data_format") + val strideList = getIntList(attributes, "strides") + val kernelList = getIntList(attributes, "ksize") + val (strideH, strideW, ksizeH, ksizeW) = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + (strideList(1), strideList(2), kernelList(1), kernelList(2)) + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + (strideList(2), strideList(3), kernelList(2), kernelList(3)) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + + SpatialMaxPooling[T](ksizeW, ksizeH, strideW, strideH, pW, pH, + format = DataFormat(format)) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala new file mode 100644 index 00000000000..dc0ca6774f9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.{Mean, Sequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.TFUtils +import org.tensorflow.framework.NodeDef + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Mean extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2), tensorArrays => { + val dims = tensorArrays(0).asInstanceOf[Tensor[Int]] + val dim = ArrayBuffer[Int]() + val mean = Sequential[T]() + for (i <- 1 to dims.size(1)) { + dim += dims.valueAt(i) + 1 + } + dim.foreach(i => mean.add(Mean[T](i, squeeze = false))) + mean + }) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mul.scala new file mode 100644 index 00000000000..5230284b010 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mul.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.CMulTable +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Mul extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + CMulTable[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala new file mode 100644 index 00000000000..5a244d9ba7e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Pack +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Pack extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val dim = nodeDef.getAttrMap.get("axis").getI.toInt + 1 + Pack[T](dim).asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala new file mode 100644 index 00000000000..454b92981b8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Padding, Sequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.TFUtils +import org.tensorflow.framework.NodeDef + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Pad extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2), tensorArrays => { + val paddings = tensorArrays(0).asInstanceOf[Tensor[Int]] + val pad = ArrayBuffer[Int]() + val padding = Sequential[T]() + + for(dim <- 1 to paddings.size(1)) { + if (paddings.valueAt(dim, 1) != 0 || paddings.valueAt(dim, 2) != 0 ) { + if (paddings(Array(dim, 1)) != 0) { + padding.add(Padding[T](dim, -paddings.valueAt(dim, 1), 4)) + } + if (paddings(Array(dim, 2)) != 0) { + padding.add(Padding[T](dim, paddings.valueAt(dim, 2), 4)) + } + } + } + + padding + }) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala new file mode 100644 index 00000000000..cff19735c5e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala @@ -0,0 +1,54 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{ParseExample => ParseExampleOperation} +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.{DataType, NodeDef} +import collection.JavaConverters._ + +import scala.reflect.ClassTag + +class ParseExample extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val Ndense = nodeDef.getAttrMap.get("Ndense").getI.toInt + val Tdense = nodeDef.getAttrMap.get("Tdense") + .getList.getTypeList.asScala + .map { + case DataType.DT_INT64 => LongType + case DataType.DT_INT32 => IntType + case DataType.DT_FLOAT => FloatType + case DataType.DT_DOUBLE => DoubleType + case DataType.DT_STRING => StringType + } + val denseShapes = nodeDef.getAttrMap.get("dense_shapes") + .getList.getShapeList.asScala + .map { shapeProto => + shapeProto.getDimList.asScala.map(_.getSize.toInt).toArray + } + + new ParseExampleOperation[T](Ndense, Tdense, denseShapes) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala new file mode 100644 index 00000000000..1510f66af75 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Placeholder extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Identity[T] + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueManyV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueManyV2.scala new file mode 100644 index 00000000000..9baa7c17739 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueManyV2.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class QueueDequeueManyV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new IdentityModule[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueV2.scala new file mode 100644 index 00000000000..d054f626a05 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueV2.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class QueueDequeueV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new IdentityModule[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueManyV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueManyV2.scala new file mode 100644 index 00000000000..d81c39dfdd8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueManyV2.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class QueueEnqueueManyV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new IdentityModule[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueV2.scala new file mode 100644 index 00000000000..628e4c7514a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueV2.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class QueueEnqueueV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new IdentityModule[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomShuffle.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomShuffle.scala new file mode 100644 index 00000000000..82a374b1f8b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomShuffle.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class RandomShuffle extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new IdentityModule[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rank.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rank.scala new file mode 100644 index 00000000000..f882493bcb4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rank.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Rank => RankOperation} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Rank extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new RankOperation[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReaderReadV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReaderReadV2.scala new file mode 100644 index 00000000000..e59683d2cec --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReaderReadV2.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class ReaderReadV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new IdentityModule[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu.scala new file mode 100644 index 00000000000..be587d5a416 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ReLU +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Relu extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + ReLU[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala new file mode 100644 index 00000000000..8baf316fc79 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Reshape +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.TFUtils +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Reshape extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2), tensorArrays => { + val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] + + val batchMode = sizes.valueAt(1) == -1 + val arraySize = new Array[Int](if (batchMode) sizes.nElement() - 1 else sizes.nElement()) + var i = if (batchMode) 2 else 1 + var k = 0 + while(i <= sizes.nElement()) { + arraySize(k) = sizes.valueAt(i) + k += 1 + i += 1 + } + Reshape[T](size = arraySize, Some(batchMode)) + }) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala new file mode 100644 index 00000000000..ea2c1251752 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.ResizeBilinearOps +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class ResizeBilinear extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val alignCorner = nodeDef.getAttrMap.get("align_corners").getB + ResizeBilinearOps[T](alignCorner) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala new file mode 100644 index 00000000000..bd42cf15eb3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Identity, Power} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Rsqrt extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Power[T](-0.5, 1, 0) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala new file mode 100644 index 00000000000..602715b6182 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.tf.Shape +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Shape extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Shape[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala new file mode 100644 index 00000000000..076c45ef103 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Sigmoid +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Sigmoid extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Sigmoid[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softmax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softmax.scala new file mode 100644 index 00000000000..3f08766a325 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softmax.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.SoftMax +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Softmax extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + SoftMax[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala new file mode 100644 index 00000000000..349c41acb21 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ConcatTable +import com.intel.analytics.bigdl.nn.tf.SplitAndSelect +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Split extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(1), tensorArrays => { + val numSplit = nodeDef.getAttrMap.get("num_split").getI.toInt + val dim = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 + val model = new ConcatTable[T]() + for (index <- Range(1, numSplit + 1)) { + model.add(SplitAndSelect[T](dim, index, numSplit)) + } + model + }) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala new file mode 100644 index 00000000000..a49e74069ba --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Squeeze +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import collection.JavaConverters._ + +import scala.reflect.ClassTag + +class Squeeze extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val dims = nodeDef.getAttrOrThrow("squeeze_dims").getList().getIList() + .asScala.map(_.toInt).toArray + + Squeeze[T](dims, batchMode = true) + + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala new file mode 100644 index 00000000000..2b5329a1c9e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala @@ -0,0 +1,59 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.tf.StrideSlice +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Node +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class StridedSlice extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2, 3, 4), tensorArrays => { + val start = oneDTensorToArray(tensorArrays(0).asInstanceOf[Tensor[Int]]) + val end = oneDTensorToArray(tensorArrays(1).asInstanceOf[Tensor[Int]]) + val stride = oneDTensorToArray(tensorArrays(2).asInstanceOf[Tensor[Int]]) + + val specs = (start zip end zip stride).zipWithIndex + .map(elem => (elem._2 + 1, elem._1._1._1 + 1, elem._1._1._2 + 1, elem._1._2)) + + + StrideSlice[T](specs) + }) + } + + private def oneDTensorToArray(tensor: Tensor[Int]): Array[Int] = { + require(tensor.nDimension() == 1, "1D tensor required") + val result = new Array[Int](tensor.nElement()) + var i = 0 + while(i < tensor.nElement()) { + result(i) = tensor.valueAt(i + 1) + i += 1 + } + result + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sub.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sub.scala new file mode 100644 index 00000000000..ca5a360dd16 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sub.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.CSubTable +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Sub extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + CSubTable[T] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala new file mode 100644 index 00000000000..2a0409b452a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Tanh +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Tanh extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Tanh[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TensorflowOpsLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TensorflowOpsLoader.scala new file mode 100644 index 00000000000..5f4f81d5220 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TensorflowOpsLoader.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Tanh +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +abstract class TensorflowOpsLoader() { + def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Unpack.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Unpack.scala new file mode 100644 index 00000000000..9ffb2ff423d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Unpack.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.SplitTable +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Unpack extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val dim = nodeDef.getAttrMap.get("axis").getI.toInt + 1 + SplitTable[T](dim) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala new file mode 100644 index 00000000000..f67eaa5da8c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder +import java.nio.charset.Charset +import java.util + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.Context +import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL.toTensor +import org.tensorflow.framework.{AttrValue, NodeDef} + +import scala.reflect.ClassTag +import collection.JavaConverters._ + +object Utils { + private[loaders] def getOrSetTensor[T: ClassTag]( + node: NodeDef, context: Context[T], byteOrder: ByteOrder, + trans: Option[Seq[(Int, Int)]] = None)( + implicit ev: TensorNumeric[T]): (Tensor[T], Tensor[T]) = { + + if (context.contains(node.getName)) { + val result = context(node.getName) + (result._1, result._2) + } else { + var weight = toTensor[T](node.getAttrMap.get("value").getTensor, byteOrder) + trans match { + case Some(transposes) => + for ((first, second) <- transposes) { + weight = weight.transpose(first, second) + } + weight = weight.contiguous() + case _ => + } + val gradient = Tensor[T](weight.size()) + context.put(node.getName, (weight, gradient, trans)) + (weight, gradient) + } + } + + private[loaders] def getString(attrMap: util.Map[String, AttrValue], key: String): String = { + attrMap.get(key).getS.toString(Charset.defaultCharset()) + } + + private[loaders] def getInt(attrMap: util.Map[String, AttrValue], key: String): Int = { + attrMap.get(key).getI.toInt + } + + private[loaders] def getIntList(attrMap: util.Map[String, AttrValue], key: String): Seq[Int] = { + attrMap.get(key).getList.getIList.asScala.map(_.toInt) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala new file mode 100644 index 00000000000..db4a1ca9806 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Parallel +class CAddTableSpec extends FlatSpec with Matchers { + "CAddTable" should "be correct when input is scalar" in { + val module = CAddTable[Float]() + val scalar = Tensor[Float](Array(2.0f), Array[Int]()) + val tensor = Tensor[Float](T(1, 2, 3)) + module.forward(T(scalar, tensor)) should be(Tensor[Float](T(3, 4, 5))) + val grads = module.backward(T(scalar, tensor), Tensor[Float](T(1, 2, 3))) + grads[Tensor[Float]](1).value() should be(6) + grads[Tensor[Float]](2) should be(Tensor[Float](T(1, 2, 3))) + } + + "CAddTable" should "be correct when input is scalar exchange order" in { + val module = CAddTable[Float]() + val scalar = Tensor[Float](Array(2.0f), Array[Int]()) + val tensor = Tensor[Float](T(1, 2, 3)) + module.forward(T(tensor, scalar)) should be(Tensor[Float](T(3, 4, 5))) + val grads = module.backward(T(tensor, scalar), Tensor[Float](T(1, 2, 3))) + grads[Tensor[Float]](1) should be(Tensor[Float](T(1, 2, 3))) + grads[Tensor[Float]](2).value() should be(6) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulTableSpec.scala new file mode 100644 index 00000000000..774e8e56ad8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulTableSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class CMulTableSpec extends FlatSpec with Matchers { + "CMulTable" should "be correct when input is scalar" in { + val module = CMulTable[Float]() + val scalar = Tensor[Float](Array(2.0f), Array[Int]()) + val tensor = Tensor[Float](T(1, 2, 3)) + module.forward(T(scalar, tensor)) should be(Tensor[Float](T(2, 4, 6))) + val grads = module.backward(T(scalar, tensor), Tensor[Float](T(1, 2, 3))) + grads[Tensor[Float]](1).value() should be(14) + grads[Tensor[Float]](2) should be(Tensor[Float](T(2, 4, 6))) + } + + "CMulTable" should "be correct when input is scalar exchange order" in { + val module = CMulTable[Float]() + val scalar = Tensor[Float](Array(2.0f), Array[Int]()) + val tensor = Tensor[Float](T(1, 2, 3)) + module.forward(T(tensor, scalar)) should be(Tensor[Float](T(2, 4, 6))) + val grads = module.backward(T(tensor, scalar), Tensor[Float](T(1, 2, 3))) + grads[Tensor[Float]](1) should be(Tensor[Float](T(2, 4, 6))) + grads[Tensor[Float]](2).value() should be(14) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/AdapterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/AdapterSpec.scala new file mode 100644 index 00000000000..fd7bef74aa4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/AdapterSpec.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf + +import com.intel.analytics.bigdl.nn.Reshape +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.loaders.Adapter +import org.scalatest.{FlatSpec, Matchers} + +class AdapterSpec extends FlatSpec with Matchers { + + private val module = Adapter[Float](Array(2), tensorArrays => { + val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] + + val batchMode = sizes.valueAt(1) == -1 + val arraySize = new Array[Int](if (batchMode) sizes.nElement() - 1 else sizes.nElement()) + var i = if (batchMode) 2 else 1 + var k = 0 + while(i <= sizes.nElement()) { + arraySize(k) = sizes.valueAt(i) + k += 1 + i += 1 + } + Reshape[Float](size = arraySize, Some(batchMode)) + }) + + "Adapter" should "work correct" in { + module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) + module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) + } + + "Adapter" should "throw exception when const tensor is changed" in { + module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) + intercept[Exception] { + module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(2, 6)))) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index 5d3a0b419ab..f52c8922de5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -391,16 +391,6 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ } } -// Need GPU to run this code -// "Tensorflow Alexnet NCHW" should "be load correctly" in { -// val output = Seq("alexnet_v2/pool5/MaxPool:0") -// val comparePairs = testModel("alexnet_nchw", output, backward = false) -// for (i <- output.indices) { -// val (tf, bigdl) = comparePairs(i) -// tf.almostEqual(bigdl, 1e-5) should be(true) -// } -// } - "TensorFlow vgg_a" should "be load correctly" in { val output = Seq("vgg_a/fc8/squeezed:0") val comparePairs = testModel("vgga", output, backward = true) From 7920ffe28ade3731a54acc48a326437817e32be0 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 27 Sep 2017 17:13:42 +0800 Subject: [PATCH 0417/1065] 1. throw exception when ConcatTable contains no submodule (#1598) 2. rescursive remove stopGradient nodes --- .../analytics/bigdl/dllib/nn/ConcatTable.scala | 3 +++ .../intel/analytics/bigdl/dllib/nn/Graph.scala | 8 +++++++- .../bigdl/dllib/nn/ConcatTableSpec.scala | 12 ++++++++++++ .../analytics/bigdl/dllib/nn/GraphSpec.scala | 18 ++++++++++++++++++ 4 files changed, 40 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala index 67562133d32..1644627152e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala @@ -34,6 +34,7 @@ import scala.reflect.ClassTag class ConcatTable[T : ClassTag] (implicit ev: TensorNumeric[T]) extends Container[Activity, Table, T] { override def updateOutput(input: Activity): Table = { + require(modules.length > 0, "empty modules of concat table") if (gradInput == null) { gradInput = allocateAs(input) } @@ -108,6 +109,7 @@ class ConcatTable[T : ClassTag] } override def updateGradInput(input: Activity, gradOutput: Table): Activity = { + require(modules.length > 0, "empty modules of concat table") val isInputTable = input.isInstanceOf[Table] val wasGradInputTable = gradInput.isInstanceOf[Table] @@ -154,6 +156,7 @@ class ConcatTable[T : ClassTag] } override def backward(input: Activity, gradOutput: Table): Activity = { + require(modules.length > 0, "empty modules of concat table") val isInputTable = input.isInstanceOf[Table] val wasGradInputTable = gradInput.isInstanceOf[Table] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 6a4fa7d1556..be1e45c1199 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -318,7 +318,7 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], val gradGraph = backGraph.cloneGraph(true) dummyOutputGrad = gradGraph.source val originalNodes = gradGraph.DFS - originalNodes.filter(x => isStopGradient(x.element)).foreach(_.removeNextEdges()) + originalNodes.filter(x => isStopGradient(x.element)).foreach(removeStopNodes(_)) backwardNodes = gradGraph.DFS.filter(n => !n.eq(dummyOutputGrad)) .filterNot(_.element.isInstanceOf[ControlDependency[_]]).toArray backwardScheduler = new Scheduler[T]( @@ -329,6 +329,12 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], this } + private[bigdl] def removeStopNodes(n: Node[_]): Unit = { + val nodes = n.nextNodes + n.removeNextEdges() + nodes.filter(_.prevNodes.length == 0).foreach(removeStopNodes(_)) + } + private val inputCache = new mutable.HashMap[String, Activity]() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTableSpec.scala index d8bab863a2c..ab1fc756c1e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTableSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.T @@ -71,4 +72,15 @@ class ConcatTableSpec extends FlatSpec with Matchers { model.forward(input2) model.backward(input2, model.output) } + + "ConcatTable" should "throw exception when there're no submodules" in { + val module = ConcatTable[Activity, Float]() + intercept[Exception] { + module.forward(T()) + } + + intercept[Exception] { + module.backward(T(), T()) + } + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index 66c2bf272dc..1b115cbdbad 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -1170,6 +1170,24 @@ class GraphSpec extends FlatSpec with Matchers { result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(false)))) result.toTensor should be(Tensor[Float](T(6))) } + + "graph backward with stopGradient" should "not remove stopGradient recursive" in { + val data = Input() + val d1 = Identity().inputs(data) + val d2 = Identity().inputs(d1) + val d3 = Identity().inputs(data) + val d4 = Identity().setName("d4").inputs(d3) + val d5 = Identity().inputs(d4) + + val model = Graph(data, Array(d2, d5)) + val output = model.forward(Tensor[Float](T(1, 2, 3))).toTable + output[Tensor[Float]](1) should be(Tensor[Float](T(1, 2, 3))) + output[Tensor[Float]](2) should be(Tensor[Float](T(1, 2, 3))) + + model.stopGradient(Array("d4")) + model.backward(Tensor[Float](T(1, 2, 3)), T(Tensor[Float](T(2, 7, 9)), + Tensor[Float](T(1, 3, 5)))) should be(Tensor[Float](T(2, 7, 9))) + } } object ModelUntils { From 7836410528a64c93ec99dac170f1111d760de307 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Wed, 27 Sep 2017 17:16:16 +0800 Subject: [PATCH 0418/1065] Handle jar path different spark versions; Redirect Spark Logs in Python (#1543) * Use `spark.driver.extraClassPath` to get the jar for spark 2.2+. * Show BigDL INFO logs and redirect spark logs in python models. --- .../intel/analytics/bigdl/utils/LoggerFilter.scala | 7 ++++--- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 11 +++++++++++ 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LoggerFilter.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LoggerFilter.scala index e92dc0df45c..5d038caf52a 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LoggerFilter.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/LoggerFilter.scala @@ -80,19 +80,20 @@ object LoggerFilter { Logger.getLogger(className).addAppender(appender) } + private val defaultPath = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString + /** * 1. redirect all spark log to file, which can be set by `-Dbigdl.utils.LoggerFilter.logFile` * the default file is under current workspace named `bigdl.log`. * 2. `-Dbigdl.utils.LoggerFilter.disable=true` will disable redirection. * 3. `-Dbigdl.utils.LoggerFilter.enableSparkLog=false` will not output spark log to file */ - def redirectSparkInfoLogs(): Unit = { + def redirectSparkInfoLogs(logPath: String = defaultPath): Unit = { val disable = System.getProperty("bigdl.utils.LoggerFilter.disable", "false") val enableSparkLog = System.getProperty("bigdl.utils.LoggerFilter.enableSparkLog", "true") def getLogFile: String = { - val default = Paths.get(System.getProperty("user.dir"), "bigdl.log").toString - val logFile = System.getProperty("bigdl.utils.LoggerFilter.logFile", default) + val logFile = System.getProperty("bigdl.utils.LoggerFilter.logFile", logPath) // If the file doesn't exist, create a new one. If it's a directory, throw an error. val logFilePath = Paths.get(logFile) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a0a644d9d39..0b3cab223d9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -38,6 +38,7 @@ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} import com.intel.analytics.bigdl.utils.tf.{BigDLSessionImpl, TensorflowDataFormat, TensorflowSaver} +import org.apache.log4j._ import org.apache.spark.SparkContext import org.tensorflow.framework.NodeDef @@ -46,6 +47,7 @@ import scala.collection.mutable import scala.language.existentials import scala.reflect.ClassTag + /** * [[com.intel.analytics.bigdl.dataset.Sample]] for python. * @param features features @@ -1946,4 +1948,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab outputWidth, alignCorner) } + + def redirectSparkLogs(logPath: String): Unit = { + LoggerFilter.redirectSparkInfoLogs(logPath) + } + + def showBigDlInfoLogs(): Unit = { + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + } + } From f944adea5e434aea15e80f45e76dfd2a4c32a16b Mon Sep 17 00:00:00 2001 From: dding3 Date: Wed, 27 Sep 2017 13:42:18 -0400 Subject: [PATCH 0419/1065] Support feed prior prediction back into recurrent (#1476) * Implement RecurrentDecoder * add ut for lstm with recurrent decoder --- .../intel/analytics/bigdl/dllib/nn/Cell.scala | 4 +- .../bigdl/dllib/nn/ConvLSTMPeephole.scala | 6 +- .../bigdl/dllib/nn/ConvLSTMPeephole3D.scala | 5 +- .../intel/analytics/bigdl/dllib/nn/GRU.scala | 5 +- .../intel/analytics/bigdl/dllib/nn/LSTM.scala | 17 +- .../bigdl/dllib/nn/LSTMPeephole.scala | 12 +- .../intel/analytics/bigdl/dllib/nn/RNN.scala | 6 +- .../analytics/bigdl/dllib/nn/Recurrent.scala | 52 ++--- .../bigdl/dllib/nn/RecurrentDecoder.scala | 217 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 4 + .../src/test/resources/tf/models/decoder.py | 100 ++++---- .../dllib/nn/ConvLSTMPeephole3DSpec.scala | 1 + .../bigdl/dllib/nn/RecurrentDecoderSpec.scala | 206 +++++++++++++++++ .../bigdl/dllib/torch/LSTMPeepholeSpec.scala | 2 +- 14 files changed, 536 insertions(+), 101 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala index 57afc785698..b8e6c518131 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala @@ -123,9 +123,9 @@ abstract class Cell[T : ClassTag]( i += 1 } } else { - val sizes = new Array[Int](imageSize.length + 2) + val sizes = new Array[Int](imageSize.length + 1) sizes(0) = batchSize - Array.copy(imageSize, 0, sizes, 2, imageSize.size) + Array.copy(imageSize, 0, sizes, 1, imageSize.size) while (i <= hidden.toTable.length()) { sizes(1) = hiddensShape(i - 1) hidden.toTable[Tensor[T]](i).resize(sizes) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala index ca0fe91b9fe..a64895dafbb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala @@ -65,8 +65,9 @@ class ConvLSTMPeephole[T : ClassTag]( var outputGate: Sequential[T] = _ var hiddenLayer: Sequential[T] = _ var cellLayer: Sequential[T] = _ + + override var cell: AbstractModule[Activity, Activity, T] = buildModel() // val joinDim = 2 - override var cell: AbstractModule[Activity, Activity, T] = buildConvLSTM() // override def preTopology: AbstractModule[Activity, Activity, T] = // Sequential() @@ -179,7 +180,7 @@ class ConvLSTMPeephole[T : ClassTag]( cellLayer } - def buildConvLSTM(): Sequential[T] = { + def buildModel(): Sequential[T] = { buildCell() buildOutputGate() @@ -205,7 +206,6 @@ class ConvLSTMPeephole[T : ClassTag]( .add(SelectTable(1)) .add(Identity())) - cell = convlstm convlstm } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala index beec618ef36..c5495d837ba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala @@ -66,7 +66,7 @@ class ConvLSTMPeephole3D[T : ClassTag]( var hiddenLayer: Sequential[T] = _ var cellLayer: Sequential[T] = _ - override var cell: AbstractModule[Activity, Activity, T] = buildConvLSTM() + override var cell: AbstractModule[Activity, Activity, T] = buildModel() def buildGate(): Sequential[T] = { val i2g = Sequential() @@ -167,7 +167,7 @@ class ConvLSTMPeephole3D[T : ClassTag]( cellLayer } - def buildConvLSTM(): Sequential[T] = { + def buildModel(): Sequential[T] = { buildCell() buildOutputGate() @@ -193,7 +193,6 @@ class ConvLSTMPeephole3D[T : ClassTag]( .add(SelectTable(1)) .add(Identity())) - cell = convlstm convlstm } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala index c6545fba83c..2a70d4ce1eb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala @@ -66,7 +66,8 @@ class GRU[T : ClassTag] ( var i2g: ModuleNode[T] = _ var h2g: ModuleNode[T] = _ val featDim = 2 - override var cell: AbstractModule[Activity, Activity, T] = buildGRU() + + override var cell: AbstractModule[Activity, Activity, T] = buildModel() override def preTopology: AbstractModule[Activity, Activity, T] = if (p != 0) { @@ -113,7 +114,7 @@ class GRU[T : ClassTag] ( (sigmoid1, sigmoid2) } - def buildGRU(): Graph[T] = { + def buildModel(): Graph[T] = { val x = Input() val h = Input() val (r, z) = buildGates()(x, h) // x(t), h(t - 1), r(t), z(t) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala index 67a8353ee88..4dda9e47940 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala @@ -63,12 +63,8 @@ class LSTM[T : ClassTag] ( ) { var gates: Sequential[T] = _ var cellLayer: Sequential[T] = _ - override var cell: AbstractModule[Activity, Activity, T] = Sequential() - .add(FlattenTable()) - .add(buildLSTM()) - .add(ConcatTable() - .add(SelectTable(1)) - .add(NarrowTable(2, 2))) + + override var cell: AbstractModule[Activity, Activity, T] = buildModel() override def preTopology: AbstractModule[Activity, Activity, T] = if (p != 0) { null @@ -136,6 +132,15 @@ class LSTM[T : ClassTag] ( Sigmoid().inputs(split4)) } + def buildModel(): Sequential[T] = { + Sequential() + .add(FlattenTable()) + .add(buildLSTM()) + .add(ConcatTable() + .add(SelectTable(1)) + .add(NarrowTable(2, 2))) + } + def buildLSTM(): Graph[T] = { val input1 = Input() val input2 = Input() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala index e0aa934f80e..bc1ebae613d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala @@ -67,13 +67,14 @@ class LSTMPeephole[T : ClassTag] ( var hiddenLayer: ModuleNode[T] = _ var cellLayer: ModuleNode[T] = _ val featDim = 2 + override var cell: AbstractModule[Activity, Activity, T] = Sequential() - .add(FlattenTable()) - .add(buildLSTM()) - .add(ConcatTable() - .add(SelectTable(1)) - .add(NarrowTable(2, 2))) + .add(FlattenTable()) + .add(buildLSTM()) + .add(ConcatTable() + .add(SelectTable(1)) + .add(NarrowTable(2, 2))) override def preTopology: AbstractModule[Activity, Activity, T] = Sequential() @@ -135,6 +136,7 @@ class LSTMPeephole[T : ClassTag] ( */ val i2h = Narrow(featDim, 1 + 2 * hiddenSize, hiddenSize).inputs(input1) + val drop = Dropout(p).inputs(input2) val h2h = Linear(hiddenSize, hiddenSize, withBias = false, wRegularizer = uRegularizer).inputs(drop) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala index 911912e8ffb..43c31d90530 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala @@ -56,6 +56,8 @@ class RnnCell[T : ClassTag] ( (implicit ev: TensorNumeric[T]) extends Cell[T](Array(hiddenSize)) { + override var cell: AbstractModule[Activity, Activity, T] = buildModel() + override def preTopology: AbstractModule[Activity, Activity, T] = TimeDistributed[T]( Linear[T](inputSize, @@ -65,9 +67,7 @@ class RnnCell[T : ClassTag] ( withBias = isInputWithBias)) .asInstanceOf[AbstractModule[Activity, Activity, T]] - override var cell: AbstractModule[Activity, Activity, T] = buildGraph - - private def buildGraph: Graph[T] = { + def buildModel(): Graph[T] = { val i2h = Input() val h2h = Linear[T](hiddenSize, hiddenSize, wRegularizer = uRegularizer, withBias = isHiddenWithBias).inputs() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index 372ce293cea..da732443038 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -22,9 +22,8 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DataConverter, ModuleData, ModuleSerializer} -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.T import serialization.Bigdl.{AttrValue, BigDLModule} - import scala.reflect.runtime.universe import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer @@ -37,23 +36,23 @@ import scala.reflect.ClassTag class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) (implicit ev: TensorNumeric[T]) extends Container[Tensor[T], Tensor[T], T] { - private var hidden: Activity = null - private var gradHidden: Activity = null - private var hiddenShape: Array[Int] = null - private val currentInput = T() - private val currentGradOutput = T() - private val gradInputCell = Tensor[T]() - private var outputCell = Tensor[T]() - private val _input = T() - private val batchDim = Recurrent.batchDim - private val timeDim = Recurrent.timeDim - private val inputDim = 1 - private val hidDim = 2 - private var (batchSize, times) = (0, 0) - private var topology: Cell[T] = null - private val outputBuffer = Tensor[T]() + protected var hidden: Activity = null + protected var gradHidden: Activity = null + protected var hiddenShape: Array[Int] = null + protected val currentInput = T() + protected val currentGradOutput = T() + protected val gradInputCell = Tensor[T]() + protected var outputCell = Tensor[T]() + protected var _input = T() + protected val batchDim = Recurrent.batchDim + protected val timeDim = Recurrent.timeDim + protected val inputDim = 1 + protected val hidDim = 2 + protected var (batchSize, times) = (0, 0) + protected var topology: Cell[T] = null + protected val outputBuffer = Tensor[T]() private val gradBuffer = Tensor[T]() - private var preTopology: AbstractModule[Activity, Activity, T] = null + protected var preTopology: AbstractModule[Activity, Activity, T] = null private val dropouts: ArrayBuffer[Array[Dropout[T]]] = new ArrayBuffer[Array[Dropout[T]]] private val timeBuffer = @@ -111,7 +110,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) } // list of cell modules cloned from added modules - private val cells: ArrayBuffer[Cell[T]] + protected val cells: ArrayBuffer[Cell[T]] = ArrayBuffer[Cell[T]]() /** @@ -119,10 +118,8 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) * @param sizes, the first element is batchSize, the second is times, the third is hiddensize * the left is size of images */ - private def extend(sizes: Array[Int]): Unit = { - val times = sizes(timeDim - 1) - val batchSize = sizes(batchDim - 1) - val imageSize = sizes.drop(3) + protected def extend(sizes: Array[Int]): Unit = { + val imageSize = sizes if (hidden == null) { require((preTopology == null && modules.length == 1) || (topology != null && preTopology != null && modules.length == 2), @@ -228,7 +225,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) outputSize(2) = hiddenSize output.resize(outputSize) // Clone N modules along the sequence dimension. - extend(outputSize) + extend(outputSize.drop(2)) /** * currentInput forms a T() type. It contains two elements, hidden and input. @@ -241,6 +238,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) // init state currentInput(hidDim) = if (initState != null) initState else hidden + while (i <= times) { currentInput(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) cells(i - 1).forward(currentInput) @@ -258,7 +256,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) cells(times - 1).output.toTable(hidDim) } - private var initState: Activity = null + protected var initState: Activity = null def setState(state: Activity): Unit = { initState = state } @@ -282,6 +280,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) else hidden _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) + if (i == 1) { cells(i - 1).regluarized(true) } else { @@ -318,6 +317,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) else hidden _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) + cells(i - 1).updateGradInput(_input, currentGradOutput) currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) i -= 1 @@ -337,7 +337,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) while (i >= 1) { currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, gradBuffer) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) - else hidden + else if (initState == null) hidden else initState _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) if (i == 1) { cells(i - 1).regluarized(true) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala new file mode 100644 index 00000000000..9402ef54abe --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala @@ -0,0 +1,217 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DataConverter, ModuleData, ModuleSerializer} +import serialization.Bigdl.{AttrValue, BigDLModule} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +/** + * [[RecurrentDecoder]] module is a container of rnn cells that used to make + * a prediction of the next timestep based on the prediction we made from + * the previous timestep. Input for RecurrentDecoder is dynamically composed + * during training. input at t(i) is output at t(i-1), input at t(0) is + * user input, and user input has to be batch x ???(depends on cell type) + * without time information. + + * Different types of rnn cells can be added using add() function. Currently + * only support lstmpeephole, convlstm, convlstm3D cell. + */ +class RecurrentDecoder[T : ClassTag](seqLength: Int) + (implicit ev: TensorNumeric[T]) extends Recurrent[T] { + + times = seqLength + private val newInput = Tensor[T]() + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim == 2 || input.dim == 4 || input.dim == 5, + "Recurrent: input should be a 2D/4D/5D Tensor, e.g [batch, nDim], " + + s"current input.dim = ${input.dim}") + + batchSize = input.size(batchDim) + + val hiddenSize = topology.hiddensShape(0) + val outputSize = input.size() + outputSize(1) = hiddenSize + require(hiddenSize == input.size()(1), "hiddenSize is " + + "not the same with input size!! Please update cell settings or use Recurrent instead!") + val featureSizes = outputSize.drop(1) + output.resize(Array(batchSize, times) ++ featureSizes) + // Clone N modules along the sequence dimension. + extend(featureSizes) + if (preTopology != null) newInput.resize(output.size()) + else outputCell.resize(output.size()) + + /** + * currentInput forms a T() type. It contains two elements, hidden and input. + * Each time it will feed the cell with T(hidden, input) (or T(input, hidden) depends on + * your hidDim and inputDim), and the cell will give a table output containing two + * identical elements T(output, output). One of the elements from the cell output is + * the updated hidden. Thus the currentInput will update its hidden element with this output. + */ + var i = 1 + // init state + currentInput(hidDim) = if (initState != null) initState + else hidden + + while (i <= times) { + // input at t(0) is user input + val inputTmp = if (i == 1) { + input + } else { + // input at t(i) is output at t(i-1) + cells(i - 2).output.toTable[Tensor[T]](inputDim) + } + + currentInput(inputDim) = if (preTopology != null) { + newInput.narrow(2, i, 1).copy(inputTmp) + val sizes = 1 +: inputTmp.size() + inputTmp.resize(sizes) + val _input = preTopology.updateOutput(inputTmp).toTensor[T] + inputTmp.resize(sizes.takeRight(sizes.length - 1)) + _input.select(1, 1) + } else { + outputCell.narrow(2, i, 1).copy(inputTmp) + inputTmp + } + cells(i - 1).updateOutput(currentInput) + currentInput(hidDim) = cells(i - 1).output.toTable(hidDim) + i += 1 + } + + if (preTopology != null) { + // For backward preTopology use + outputCell = preTopology.updateOutput(newInput).toTensor[T] + } + + Recurrent.copy(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), output) + output + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + throw new Exception("Should not enter RecurrentDecoder accGradParameters" + + "as it has override backward") + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + throw new Exception("Should not enter RecurrentDecoder updateGradInput" + + "as it has override backward") + gradInput + } + + override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val st = System.nanoTime + currentGradOutput(hidDim) = gradHidden + var i = times + while (i >= 1) { + currentGradOutput(inputDim) = if (i == times) gradOutput.select(timeDim, i) + else { + gradOutput.select(timeDim, i).clone() + .add(cells(i).gradInput.toTable[Tensor[T]](inputDim).clone()) + } + + _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) + else if (initState == null) hidden else initState + _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) + + if (i == 1) { + cells(i - 1).regluarized(true) + } else { + cells(i - 1).regluarized(false) + } + cells(i - 1).backward(_input, currentGradOutput) + currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) + i -= 1 + } + + gradInput = if (preTopology != null) { + /** + * if preTopology is Sequential, it has not created gradInput. + * Thus, it needs to create a new Tensor. + */ + if (preTopology.gradInput == null) { + preTopology.gradInput = Tensor[T]() + } + preTopology.gradInput.toTensor[T] + } else { + gradInputCell + } + gradInputCell.resizeAs(outputCell) + Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) + if (preTopology != null) { + gradInput = preTopology.backward(newInput, gradInputCell).toTensor[T] + } + + this.backwardTime = System.nanoTime - st + gradInput + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[RecurrentDecoder[T]] + + override def equals(other: Any): Boolean = other match { + case that: RecurrentDecoder[T] => + super.equals(that) && + (that canEqual this) && + cells == that.cells + case _ => false + } + + override def hashCode(): Int = { + val state = Seq(super.hashCode(), cells) + state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) + } +} + +object RecurrentDecoder extends ContainerSerializable { + def apply[@specialized(Float, Double) T: ClassTag](outputLength: Int) + (implicit ev: TensorNumeric[T]) : RecurrentDecoder[T] = { + new RecurrentDecoder[T](outputLength) + } + + override def loadModule[T: ClassTag](model : BigDLModule) + (implicit ev: TensorNumeric[T]) : ModuleData[T] = { + val moduleData = super.loadModule(model) + val recurrentDecoder = moduleData.module.asInstanceOf[RecurrentDecoder[T]] + val attrMap = model.getAttrMap + + val topologyAttr = attrMap.get("topology") + recurrentDecoder.topology = DataConverter.getAttributeValue(topologyAttr). + asInstanceOf[Cell[T]] + + moduleData + } + + override def serializeModule[T: ClassTag](module : ModuleData[T]) + (implicit ev: TensorNumeric[T]) : BigDLModule = { + val containerBuilder = BigDLModule.newBuilder(super.serializeModule(module)) + + val recurrentDecoder = module.module.asInstanceOf[RecurrentDecoder[T]] + + val topologyBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(topologyBuilder, recurrentDecoder.topology, + ModuleSerializer.abstractModuleType) + containerBuilder.putAttr("topology", topologyBuilder.build) + + containerBuilder.build + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0b3cab223d9..0672c92eaca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -255,6 +255,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Recurrent[T]() } + def createRecurrentDecoder(outputLength: Int): RecurrentDecoder[T] = { + RecurrentDecoder[T](outputLength) + } + def createConvLSTMPeephole( inputSize: Int, outputSize: Int, diff --git a/scala/dllib/src/test/resources/tf/models/decoder.py b/scala/dllib/src/test/resources/tf/models/decoder.py index 714af5e636d..714f04d79e8 100644 --- a/scala/dllib/src/test/resources/tf/models/decoder.py +++ b/scala/dllib/src/test/resources/tf/models/decoder.py @@ -1,50 +1,50 @@ -# -# Copyright 2016 The BigDL Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import tensorflow as tf -import numpy as np -from sys import argv -from tensorflow.contrib import rnn -from util import run_model - -def main(): - - tf.set_random_seed(1) - n_steps = 2 - n_input = 10 - n_hidden = 10 - - xs = tf.Variable(tf.random_uniform([4, n_steps, n_input]) + 10, name='input', dtype=tf.float32) - xs = tf.identity(xs, name="input_node") - x = tf.unstack(xs, n_steps, 1) - - cell = tf.contrib.rnn.BasicLSTMCell(n_hidden) - init_state = cell.zero_state(4, tf.float32) - - outputs = [] - for i in range(n_steps): - if i == 0: - output, state = cell(x[-1], init_state) - else: - output, state = cell(output, state) - outputs.append(output) - - final = tf.identity(outputs, name="output") - - net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(',')) - run_model(net_outputs, argv[1], 'rnn', argv[3] == 'True') - -if __name__ == "__main__": - main() +# +# Copyright 2016 The BigDL Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import tensorflow as tf +import numpy as np +from sys import argv +from tensorflow.contrib import rnn +from util import run_model + +def main(): + + tf.set_random_seed(1) + n_steps = 2 + n_input = 10 + n_hidden = 10 + + xs = tf.Variable(tf.random_uniform([4, n_steps, n_input]) + 10, name='input', dtype=tf.float32) + xs = tf.identity(xs, name="input_node") + x = tf.unstack(xs, n_steps, 1) + + cell = tf.contrib.rnn.BasicLSTMCell(n_hidden) + init_state = cell.zero_state(4, tf.float32) + + outputs = [] + for i in range(n_steps): + if i == 0: + output, state = cell(x[-1], init_state) + else: + output, state = cell(output, state) + outputs.append(output) + + final = tf.identity(outputs, name="output") + + net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(',')) + run_model(net_outputs, argv[1], 'rnn', argv[3] == 'True') + +if __name__ == "__main__": + main() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala index a6ff7b44043..b53d208689f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.collection.mutable.ArrayBuffer diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala new file mode 100644 index 00000000000..0e22d61951e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala @@ -0,0 +1,206 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +import scala.collection.mutable.ArrayBuffer +import scala.math._ + +@com.intel.analytics.bigdl.tags.Parallel +class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { + "A ConvLSTMPeepwhole forward" should "work with feedbackOutput correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 7 + val inputSize = 7 + val seqLength = 5 + val seed = 100 + val batchSize = 4 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize, 5, 5).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize, 5, 5).rand + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1)) + + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + val output = model.forward(input).toTensor + + val model2 = Recurrent().add(ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1)) + model2.getParameters()._1.copy(weights) + model2.zeroGradParameters() + + val input2 = Tensor(Array(batchSize, seqLength, inputSize, 5, 5)) + input2.narrow(2, 1, 1).copy(input) + input2.narrow(2, 2, seqLength-1).copy(output.narrow(2, 1, seqLength-1)) + val output2 = model2.forward(input2).toTensor + + output.map(output2, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + } + + "A LSTM " should "work with feedbackOutput correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 7 + val inputSize = 7 + val seqLength = 5 + val seed = 100 + val batchSize = 4 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(LSTM(inputSize, hiddenSize)) + + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + val output = model.forward(input).toTensor + + val model2 = Recurrent().add(LSTM(inputSize, hiddenSize)) + model2.getParameters()._1.copy(weights) + model2.zeroGradParameters() + + val input2 = Tensor(Array(batchSize, seqLength, inputSize)) + input2.narrow(2, 1, 1).copy(input) + input2.narrow(2, 2, seqLength-1).copy(output.narrow(2, 1, seqLength-1)) + val output2 = model2.forward(input2).toTensor + + output.map(output2, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + } + + "A LSTMPeepwhole " should "work with feedbackOutput correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 3 + val inputSize = 3 + val seqLength = 2 + val seed = 100 + val batchSize = 1 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand + + val model2 = Recurrent().add(LSTMPeephole(inputSize, hiddenSize)) + model2.getParameters()._1.fill(0.5) + + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(LSTMPeephole(inputSize, hiddenSize)) + model.getParameters()._1.fill(0.5) + + val output = model.forward(input).toTensor + + val input2 = Tensor(Array(batchSize, seqLength, hiddenSize)) + input2.narrow(2, 1, 1).copy(input) + input2.narrow(2, 2, seqLength-1).copy(output.narrow(2, 1, seqLength-1)) + val output2 = model2.forward(input2).toTensor + val gradInput2 = model2.backward(input2, gradOutput).toTensor + + output.map(output2, (v1, v2) => { + assert(v1 - v2 < 1e-8) + v1 + }) + } + + "A ConvLSTMPeepwhole " should "work with feedbackOutput correctly2" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 3 + val inputSize = 3 + val seqLength = 2 + val seed = 100 + val batchSize = 2 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize, 3, 3).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3).rand + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1)) + + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + val output = model.forward(input).toTensor + val gradInput = model.backward(input, gradOutput).toTensor + val gradient = model.getParameters()._2 + + val input2 = input.clone() + input2.resize(batchSize, 1, inputSize, 3, 3) + val model2 = ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1) + model2.getParameters()._1.copy(weights) + model2.zeroGradParameters() + + val model3 = ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1) + var i = 0 + while (i < model3.parameters()._1.length) { + model3.parameters()._1(i).set(model2.parameters()._1(i)) + i += 1 + } + i = 0 + while (i < model3.parameters()._2.length) { + model3.parameters()._2(i).set(model2.parameters()._2(i)) + i += 1 + } + + val state = T(Tensor[Double](batchSize, hiddenSize, 3, 3), + Tensor[Double](batchSize, hiddenSize, 3, 3)) + val output2 = model2.forward(T(input, state)) + val output3 = model3.forward(output2) + + val gradOutput3 = gradOutput.select(2, 2) + val input3 = output2.clone() + val tmp = T(input3.toTable[Tensor[Double]](1).squeeze(2), input3.toTable(2)) + val gradInput3 = model3.backward(tmp, T(gradOutput3, state)) + val tmp_gradInput = gradInput3.clone + tmp_gradInput(1) = gradOutput.select(2, 1).add(gradInput3.toTable[Tensor[Double]](1)) + val gradInput2 = model2.backward(T(input, state), tmp_gradInput) + val finalOutput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3) + finalOutput.narrow(2, 1, 1).copy(output2.toTable[Tensor[Double]](1)) + finalOutput.narrow(2, 2, 1).copy(output3.toTable[Tensor[Double]](1)) + output.map(finalOutput, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + + gradient.map(model2.getParameters()._2, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + + val newGradInput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3) + newGradInput.narrow(2, 1, 1).copy(gradInput2.toTable[Tensor[Double]](1)) + newGradInput.narrow(2, 2, 1).copy(gradInput3.toTable[Tensor[Double]](1)) + gradInput.map(newGradInput, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala index aaed6943416..b40679025be 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala @@ -21,7 +21,7 @@ import java.io.PrintWriter import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.optim.SGD -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.TorchObject.TYPE_DOUBLE_TENSOR import com.intel.analytics.bigdl.utils.{T, Table, TorchFile} From dfeb33710017df6c60f386289ae4fe410e5c722e Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 29 Sep 2017 13:23:31 +0800 Subject: [PATCH 0420/1065] Refine scheduler (#1607) don't execute relevant nodes in forward/backward fix a bug when handle const node --- .../analytics/bigdl/dllib/nn/Graph.scala | 19 ++- .../analytics/bigdl/dllib/nn/Scheduler.scala | 35 ++--- .../bigdl/dllib/nn/ops/Operation.scala | 6 - .../analytics/bigdl/dllib/nn/ops/Prod.scala | 14 +- .../analytics/bigdl/dllib/nn/ops/Slice.scala | 3 + .../bigdl/dllib/tensor/TensorNumeric.scala | 12 +- .../dllib/utils/tf/TensorflowToBigDL.scala | 126 +----------------- .../dllib/utils/tf/loaders/ExpandDims.scala | 40 ++++++ .../bigdl/dllib/utils/tf/loaders/Prod.scala | 41 ++++++ .../bigdl/dllib/utils/tf/loaders/Slice.scala | 41 ++++++ .../bigdl/dllib/utils/tf/loaders/Utils.scala | 15 +++ .../src/test/resources/tf/models/util.py | 2 +- .../analytics/bigdl/dllib/nn/GraphSpec.scala | 49 +++++++ 13 files changed, 248 insertions(+), 155 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index be1e45c1199..939e76abd2f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -300,7 +300,8 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], private val forwardNodes = backGraph.DFS.toArray private val forwardScheduler = new Scheduler( forwardNodes.filter(_.prevNodes.length == 0), - Seq(dummyOutput) + Seq(dummyOutput), + forwardNodes.map(_.element.getName()).toSet ) private var backwardScheduler : Scheduler[T] = _ @@ -321,9 +322,23 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], originalNodes.filter(x => isStopGradient(x.element)).foreach(removeStopNodes(_)) backwardNodes = gradGraph.DFS.filter(n => !n.eq(dummyOutputGrad)) .filterNot(_.element.isInstanceOf[ControlDependency[_]]).toArray + + val inputNames = inputs.map(_.element.getName()).toSet + val dummyBackwardEnd = Input() + val backwardTargets = backwardNodes + .filter(n => (n.element.parameters() != null && n.element.parameters()._1.length != 0) + || inputNames.contains(n.element.getName())) + backwardTargets.foreach(_ -> dummyBackwardEnd) + val graph = dummyBackwardEnd.graph(true) + val forwardNodeNames = forwardNodes.map(_.element.getName()).toSet + val executableNodes = graph.DFS.map(_.element.getName()) + .filter(forwardNodeNames.contains(_)).toSet + dummyBackwardEnd.removePrevEdges() + backwardScheduler = new Scheduler[T]( Seq(dummyOutputGrad), - backwardNodes.filter(_.nextNodes.length == 0) + backwardTargets, + executableNodes ) clearState() this diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala index ab7ca849cc6..23c00fea561 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala @@ -38,7 +38,8 @@ import scala.reflect.ClassTag * @tparam T */ private[bigdl] class Scheduler[T] ( - inputNodes: Seq[ModuleNode[T]], outputNodes: Seq[ModuleNode[T]] + inputNodes: Seq[ModuleNode[T]], outputNodes: Seq[ModuleNode[T]], + executableNodes: Set[String] = null ) extends Serializable { import Scheduler._ @@ -81,7 +82,9 @@ private[bigdl] class Scheduler[T] ( def fetch(): ModuleNode[T] = { var node = readyQueue.dequeue() while (nodeStatus.isConst(node) || node.element.isInstanceOf[ControlDependency[_]]) { - schedule(node) + if (!nodeStatus.isConst(node)) { + schedule(node) + } node = readyQueue.dequeue() } node @@ -92,19 +95,21 @@ private[bigdl] class Scheduler[T] ( * @param node */ def schedule(node: ModuleNode[T]): Unit = { - // Update status of current node - nodeStatus(node) = if (node.prevNodes.length == 0) { - if (node.element.isInstanceOf[com.intel.analytics.bigdl.nn.tf.Const[_, _]]) { - Const() - } else { - Ready() - } - } else { - val constNodes = node.prevNodes.filter(nodeStatus.isConst(_)) - if (constNodes.length == node.prevNodes.length) { - Const() + if (!nodeStatus.isConst(node)) { + // Update status of current node + nodeStatus(node) = if (node.prevNodes.length == 0) { + if (node.element.isInstanceOf[com.intel.analytics.bigdl.nn.tf.Const[_, _]]) { + Const() + } else { + Ready() + } } else { - Ready() + val constNodes = node.prevNodes.filter(nodeStatus.isConst(_)) + if (constNodes.length == node.prevNodes.length) { + Const() + } else { + Ready() + } } } @@ -121,7 +126,7 @@ private[bigdl] class Scheduler[T] ( private def selectNexts(candidateNodes: Seq[ModuleNode[T]], curNode: ModuleNode[T]): Unit = { val nodeSet = new mutable.LinkedHashSet[ModuleNode[T]]() candidateNodes.foreach(nodeSet.add(_)) // remove duplicate nodes and keep the order - nodeSet.foreach(nextNode => { + nodeSet.filter(n => executableNodes.contains(n.element.getName())).foreach(nextNode => { if (nextNode.element.isInstanceOf[MergeOps[_]]) { val merge = nextNode.element.asInstanceOf[MergeOps[_]] require(nodeStatus.notExecuted(nextNode), s"Merge node(${nextNode.element.getName()}) " + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala index c0f0c460f45..4bca87031ca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala @@ -43,11 +43,5 @@ abstract class Operation[A <: Activity: ClassTag, B <: Activity: ClassTag, T: Cl override def backward(input: A, gradOutput: B): A = { throw new UnsupportedOperationException("Operation does not support backward() method") } - - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { - // Do not try to call parameters from an operation - // Container should handle parameters() - throw new IllegalArgumentException("Operation doesn't have parameters") - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Prod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Prod.scala index 845d62a97fe..e6f3fb170a7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Prod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Prod.scala @@ -17,15 +17,15 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} import scala.reflect.ClassTag class Prod[T: ClassTag]( axis: Int = 1, keepDim: Boolean = false) -(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { - private def getPositiveDimension(input: Tensor[T]): Int = { +(implicit ev: TensorNumeric[T]) extends Operation[Tensor[_], Tensor[_], T] { + private def getPositiveDimension(input: Tensor[_]): Int = { var dimension = this.axis if (dimension < 0) { dimension = input.dim() + dimension + 1 @@ -35,9 +35,13 @@ class Prod[T: ClassTag]( dimension } - def updateOutput(input: Tensor[T]): Tensor[T] = { + def updateOutput(input: Tensor[_]): Tensor[_] = { val dimension = getPositiveDimension(input) - output.prod(input, dimension) + if (output.getType() != input.getType()) { + output = input.emptyInstance() + } + output.asInstanceOf[Tensor[NumericWildCard]] + .prod(input.asInstanceOf[Tensor[NumericWildCard]], dimension) if (output.nDimension() > 1 && !keepDim) { output.squeeze(dimension) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala index fffd3d5c17d..7cdc50b96df 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala @@ -51,6 +51,9 @@ class Slice[T: ClassTag]( outputNarrow = outputNarrow.narrow(i + 1, begin(i) + 1, realSize) i += 1 } + if (output.getType() != input.getType()) { + output = input.emptyInstance() + } output.resizeAs(outputNarrow) output.forceCopy(outputNarrow) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index 57ef7fd8b24..ebaea11c75f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -201,7 +201,7 @@ object TensorNumericMath { def prod(n: Int, a: Array[T], aOffset: Int, stride: Int): T = throw new UnsupportedOperationException(typeName + - " in tensor does not support exp operation") + " in tensor does not support prod operation") def log(x: T): T = throw new UnsupportedOperationException(typeName + @@ -1043,6 +1043,16 @@ object TensorNumericMath { override def isGreaterEq(x: Int, y: Int): Boolean = x >= y override def nearlyEqual(a: Int, b: Int, epsilon: Double): Boolean = a == b + + override def prod(n: Int, a: Array[Int], aOffset: Int, stride: Int): Int = { + var i = 0 + var r = 1 + while (i < n) { + r *= a(aOffset + i * stride) + i += 1 + } + r + } } implicit object NumericLong extends UndefinedTensorNumeric[Long]("Long") { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index a44c03726c9..18e7fce7292 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -245,7 +245,7 @@ object TensorflowToBigDL { val res = new ArrayBuffer[TensorflowToBigDL]() // ElementWiseMulTF must be after MulTF res.append( - FullConnectionTF, DropoutTF, Conv2D, BatchNormTF, Flatten, Conv1D, FlattenV2, + FullConnectionTF, DropoutTF, Conv2D, BatchNormTF, Conv1D, BatchNormV2NHWCTF, BatchNormV2NCHWTF, FullConnectionWithoutBiasTF, Conv2D2, Conv2DWithoutBias @@ -887,127 +887,3 @@ object BatchNormTF extends TensorflowToBigDL{ model.asInstanceOf[AbstractModule[Activity, Activity, T]] } } - -object FlattenV2 extends TensorflowToBigDL { - private val graph = { - val reshapeNode = Node("Reshape") - val concatNode = Node("ConcatV2") - val sliceNode = Node("Slice") - val expandNode = Node("ExpandDims") - val prodNode = Node("Prod") - val sliceNode1 = Node("Slice") - val shapeNode = Node("Shape") - val beginNode = Node("Const") - val sizeNode = Node("Const") - val beginNode1 = Node("Const") - val sizeNode1 = Node("Const") - val constNode = Node("Const") - val dimNode = Node("Const") - val axisNode = Node("Const") - val inputNode = Node("*") - - shapeNode -> sliceNode - beginNode -> sliceNode - sizeNode -> sliceNode - - shapeNode -> sliceNode1 - beginNode1 -> sliceNode1 - sizeNode1 -> sliceNode1 - - sliceNode1 -> prodNode - constNode -> prodNode - - prodNode -> expandNode - dimNode -> expandNode - - sliceNode -> concatNode - expandNode -> concatNode - axisNode -> concatNode - - inputNode -> reshapeNode - inputNode -> shapeNode - concatNode -> reshapeNode - reshapeNode.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - - val layer = Sequential[T]() - layer.add(SelectTable(1)) - layer.add(InferReshape[T](size = Array(-1), true)) - layer.asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} - -object Flatten extends TensorflowToBigDL { - private val graph = { - val reshapeNode = Node("Reshape") - val concatNode = Node("ConcatV2") - val sliceNode = Node("Slice") - val expandNode = Node("ExpandDims") - val prodNode = Node("Prod") - val sliceNode1 = Node("Slice") - val shapeNode = Node("Const") - val beginNode = Node("Const") - val sizeNode = Node("Const") - val beginNode1 = Node("Const") - val sizeNode1 = Node("Const") - val constNode = Node("Const") - val dimNode = Node("Const") - val axisNode = Node("Const") - - shapeNode -> sliceNode - beginNode -> sliceNode - sizeNode -> sliceNode - - shapeNode -> sliceNode1 - beginNode1 -> sliceNode1 - sizeNode1 -> sliceNode1 - - sliceNode1 -> prodNode - constNode -> prodNode - - prodNode -> expandNode - dimNode -> expandNode - - sliceNode -> concatNode - expandNode -> concatNode - axisNode -> concatNode - - Node("*") -> reshapeNode - concatNode -> reshapeNode - reshapeNode.graph(reverse = true) - } - - override def topology: DirectedGraph[String] = graph - - override def layer[T: ClassTag](tfGraph: DirectedGraph[NodeDef], - context: Context[T], - byteOrder: ByteOrder)( - implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - val shapetfTensor = tfGraph.source.prevNodes(1).prevNodes(0).prevNodes(0).element - .getAttrMap.get("value").getTensor - val sizes = TFUtils.parseTensor(shapetfTensor, byteOrder).asInstanceOf[Tensor[Int]] - val batchMode = false - - val arraySize = Array( - sizes.valueAt(1), - { - var prod = 1 - var i = 2 - while(i <= sizes.nElement()) { - prod = prod * sizes.valueAt(i) - i = i + 1 - } - prod - } - ) - - Reshape[T](size = arraySize, Some(batchMode)) - .asInstanceOf[AbstractModule[Activity, Activity, T]] - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala new file mode 100644 index 00000000000..e22cb1e9377 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.ops.ExpandDims +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class ExpandDims extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2), tensorArrays => { + val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 + ExpandDims[T](axis) + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala new file mode 100644 index 00000000000..5183ce00d58 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.ops.Prod +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Prod extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2), tensorArrays => { + val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 + val keepDims = getBoolean(nodeDef, "keep_dims") + Prod[T](axis) + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala new file mode 100644 index 00000000000..333946debb3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.ops.Slice +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Slice extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2, 3), tensorArrays => { + val size = tensorArrays(1).asInstanceOf[Tensor[Int]] + Slice[T](toArray(tensorArrays(0).asInstanceOf[Tensor[Int]]), + toArray(tensorArrays(1).asInstanceOf[Tensor[Int]])) + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala index f67eaa5da8c..5e4b24b171f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala @@ -61,7 +61,22 @@ object Utils { attrMap.get(key).getI.toInt } + private[loaders] def getBoolean(nodedef: NodeDef, key: String): Boolean = { + nodedef.getAttrMap.get(key).getB + } + private[loaders] def getIntList(attrMap: util.Map[String, AttrValue], key: String): Seq[Int] = { attrMap.get(key).getList.getIList.asScala.map(_.toInt) } + + private[loaders] def toArray[T: ClassTag](tensor: Tensor[T]): Array[T] = { + require(tensor.nDimension() == 1, "require 1D tensor") + val array = new Array[T](tensor.nElement()) + var i = 0 + while(i < array.length) { + array(i) = tensor.valueAt(i + 1) + i += 1 + } + array + } } diff --git a/scala/dllib/src/test/resources/tf/models/util.py b/scala/dllib/src/test/resources/tf/models/util.py index 11eaf1881f1..9ded8492112 100644 --- a/scala/dllib/src/test/resources/tf/models/util.py +++ b/scala/dllib/src/test/resources/tf/models/util.py @@ -108,7 +108,7 @@ def run_model(end_points, output_path, model_scope=None, backward=True): sess.run(grad_inputs_assign) saver.save(sess, output_path + '/model.chkp') tf.train.write_graph(sess.graph, output_path, 'model.pbtxt') - # tf.summary.FileWriter(output_path + '/log', sess.graph) + tf.summary.FileWriter('/tmp/testlog', sess.graph) input_graph = output_path + "/model.pbtxt" input_checkpoint = output_path + "/model.chkp" diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index 1b115cbdbad..ecd78520eed 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.vgg.{VggForCifar10, Vgg_16, Vgg_19} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Less} +import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.tensor.Tensor @@ -1188,6 +1189,54 @@ class GraphSpec extends FlatSpec with Matchers { model.backward(Tensor[Float](T(1, 2, 3)), T(Tensor[Float](T(2, 7, 9)), Tensor[Float](T(1, 3, 5)))) should be(Tensor[Float](T(2, 7, 9))) } + + "Graph forward" should "not execute unrelated node" in { + val data = Identity().setName("input").inputs() + var isExecuted = false + val l1 = Identity().setName("l1").inputs(data) + val l2 = Identity().setName("l2").inputs(l1) + val l3 = Identity().setName("l3").inputs(l2) + val l4 = Echo().setName("l4").setFeval((a, b) => isExecuted = true).inputs(l1) + + val model = Graph(data, l3) + model.forward(Tensor(T(1))) + isExecuted should be(false) + } + + "Graph backward" should "not execute unrelated node" in { + val data = Identity().setName("input").inputs() + val const = Const(Tensor(T(1, 2))).setName("const").inputs() + var isExecuted = false + val l1 = Echo().setName("l1").setBeval((a, b, c) => isExecuted = true).inputs(const) + val cadd = CAddTable().setName("cadd").inputs(data, l1) + + val model = Graph(data, cadd) + model.forward(Tensor(T(3, 5))) should be(Tensor(T(4, 7))) + model.backward(Tensor(T(3, 5)), Tensor(T(1, 2))) should be(Tensor(T(1, 2))) + isExecuted should be(false) + } + + "Graph backward" should "not execute unrelated node 2" in { + val data = Identity().setName("input").inputs() + val const = Const(Tensor(T(1, 2))).setName("const").inputs() + var isExecuted1 = false + val l1 = Echo().setName("l1").setBeval((a, b, c) => isExecuted1 = true).inputs(const) + val cadd = CAddTable().setName("cadd").inputs(data, l1) + val l2 = Identity().setName("l2").inputs(cadd) + var isExecuted2 = false + var isExecuted3 = false + val echo = Echo().setName("echo") + .setFeval((a, b) => isExecuted2 = true) + .setBeval((a, b, c) => isExecuted3 = true).inputs(cadd) + val l3 = Identity().setName("l3").inputs(echo) + + val model = Graph(data, l2) + model.forward(Tensor(T(3, 5))) should be(Tensor(T(4, 7))) + model.backward(Tensor(T(3, 5)), Tensor(T(1, 2))) should be(Tensor(T(1, 2))) + isExecuted1 should be(false) + isExecuted2 should be(false) + isExecuted3 should be(false) + } } object ModelUntils { From 1f666f83fcabf706617ad61d0bdd7a6e9c1874c9 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Sat, 30 Sep 2017 02:26:04 -0400 Subject: [PATCH 0421/1065] feat: add bigquant in core (#1608) * feat: add bigquant in core * feat: add dependency * fix: update ref --- dl/pom.xml | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/dl/pom.xml b/dl/pom.xml index 9bf5f18b7c8..76edfcc9a24 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -80,6 +80,17 @@ + + com.intel.analytics.bigdl.bigquant + bigquant-java + ${project.version} + + + com.intel.analytics.bigdl.bigquant + bigquant-native + + + org.apache.spark spark-core_${scala.major.version} From 92c155a90815623f6bbb40f2e146a775f9620a82 Mon Sep 17 00:00:00 2001 From: jenniew Date: Sun, 13 Aug 2017 14:06:33 -0700 Subject: [PATCH 0422/1065] fix MapTable issue --- .../intel/analytics/bigdl/dllib/nn/MapTable.scala | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala index ff79daacc3b..4337ee03e7d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala @@ -36,12 +36,11 @@ class MapTable[T: ClassTag]( var module: AbstractModule[_ <: Activity, _ <: Activity, T] = null) (implicit ev: TensorNumeric[T]) extends Container[Table, Table, T] { + if ( module != null) { + this.add(module) + } + private def extend(n: Int): Unit = { - if (!modules.contains(0)) { - modules.append(module.asInstanceOf[AbstractModule[Activity, Activity, T]]) - } else { - modules.update(0, module.asInstanceOf[AbstractModule[Activity, Activity, T]]) - } var i = 1 while (i <= n && modules.size <= i) { if (modules.length <= i) { @@ -58,6 +57,9 @@ class MapTable[T: ClassTag]( this.module = module if (modules.nonEmpty) { modules.update(0, module.asInstanceOf[AbstractModule[Activity, Activity, T]]) + for (i <- 1 until modules.size) { + modules.update(i, module.cloneModule().asInstanceOf[AbstractModule[Activity, Activity, T]]) + } } else { modules.append(module.asInstanceOf[AbstractModule[Activity, Activity, T]]) } @@ -65,6 +67,7 @@ class MapTable[T: ClassTag]( } override def updateOutput(input: Table): Table = { + require(module != null, "Single module required") extend(input.length()) var i = 0 while (i < input.length()) { @@ -75,6 +78,7 @@ class MapTable[T: ClassTag]( } override def updateGradInput(input: Table, gradOutput: Table): Table = { + require(module != null, "Single module required") extend(input.length()) var i = 0 while (i < input.length()) { @@ -85,6 +89,7 @@ class MapTable[T: ClassTag]( } override def accGradParameters(input: Table, gradOutput: Table): Unit = { + require(module != null, "Single module required") extend(input.length()) var i = 0 while (i < input.length()) { From ac5161669230a2ded4bb663e9694b95828c7444b Mon Sep 17 00:00:00 2001 From: jenniew Date: Sun, 13 Aug 2017 14:18:07 -0700 Subject: [PATCH 0423/1065] add unit test of maptable issue --- .../bigdl/dllib/nn/MapTableSpec.scala | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala index a6ad7761c85..5eaa89f0b42 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala @@ -48,4 +48,31 @@ class MapTableSpec extends FlatSpec with Matchers { mapGradInput should equal (expectedGradInput) } + + "A MapTable constucted with module" should "generate correct output" in { + val input = T( + Tensor[Float](10).randn(), + Tensor[Float](10).randn()) + + val gradOutput = T( + Tensor[Float](3).randn(), + Tensor[Float](3).randn()) + + val linear1 = new Linear[Float](10, 3) + val linear2 = linear1.cloneModule() + val expectedOutput = T( + linear1.updateOutput(input(1)), + linear2.updateOutput(input(2))) + + val map = new MapTable[Float](linear1) + val mapOutput = map.forward(input) + mapOutput should equal (expectedOutput) + + val expectedGradInput = T( + linear1.updateGradInput(input(1), gradOutput(1)), + linear2.updateGradInput(input(2), gradOutput(2))) + val mapGradInput = map.backward(input, gradOutput) + + mapGradInput should equal (expectedGradInput) + } } From fac4bdf013d54fc40762ad2e6244c42979e07d76 Mon Sep 17 00:00:00 2001 From: hkvision Date: Mon, 25 Sep 2017 11:35:53 +0800 Subject: [PATCH 0424/1065] Change model.test to model.evaluate in Python --- .../analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0672c92eaca..48895670f87 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1426,7 +1426,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab RandomGenerator.RNG.setSeed(seed) } - def modelTest(model: AbstractModule[Activity, Activity, T], + def modelEvaluate(model: AbstractModule[Activity, Activity, T], valRDD: JavaRDD[Sample], batchSize: Int, valMethods: JList[ValidationMethod[T]]) From a49ef7696733986fdf079d0af3e42d91e1184c58 Mon Sep 17 00:00:00 2001 From: hkvision Date: Mon, 25 Sep 2017 13:03:11 +0800 Subject: [PATCH 0425/1065] Change modelTest to modelEvaluate --- .../com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index f5e2b266284..ecb1559e64b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -230,7 +230,7 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { // TODO: verify the parameters result val parameters = pp.modelGetParameters(trainedModel) // println(parameters) - val testResult = pp.modelTest(trainedModel, + val testResult = pp.modelEvaluate(trainedModel, data.toJavaRDD(), batchSize = 32, valMethods = util.Arrays.asList(new Top1Accuracy())) From 47dd5ad1cbce1af7736e7484a98672ad930623e9 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 9 Oct 2017 15:14:31 +0800 Subject: [PATCH 0426/1065] Tensor types support (#1630) * refinemnt to support more tensor types * refinemnt * refinemnt * support object sharing * support shared memory * remove unused type * refinement --- .../src/main/java/serialization/Bigdl.java | 4703 ++++++++++++++--- .../main/resources/serialization/bigdl.proto | 46 +- .../bigdl/dllib/nn/BatchNormalization.scala | 30 +- .../bigdl/dllib/nn/BiRecurrent.scala | 57 +- .../bigdl/dllib/nn/BinaryTreeLSTM.scala | 33 +- .../intel/analytics/bigdl/dllib/nn/Cell.scala | 20 +- .../intel/analytics/bigdl/dllib/nn/Echo.scala | 7 +- .../analytics/bigdl/dllib/nn/Graph.scala | 35 +- .../analytics/bigdl/dllib/nn/MapTable.scala | 14 +- .../bigdl/dllib/nn/MaskedSelect.scala | 28 +- .../analytics/bigdl/dllib/nn/Recurrent.scala | 54 +- .../bigdl/dllib/nn/RecurrentDecoder.scala | 25 +- .../analytics/bigdl/dllib/nn/Reshape.scala | 18 +- .../analytics/bigdl/dllib/nn/Scale.scala | 24 +- .../nn/SpatialContrastiveNormalization.scala | 18 +- .../nn/SpatialDivisiveNormalization.scala | 45 +- .../dllib/nn/SpatialFullConvolution.scala | 30 +- .../bigdl/dllib/nn/SpatialMaxPooling.scala | 19 +- .../nn/SpatialSubtractiveNormalization.scala | 30 +- .../analytics/bigdl/dllib/nn/Transpose.scala | 19 +- .../bigdl/dllib/nn/VolumetricMaxPooling.scala | 29 +- .../dllib/nn/abstractnn/AbstractModule.scala | 9 +- .../utils/serializer/DataConverter.scala | 410 +- .../dllib/utils/serializer/ModuleLoader.scala | 20 +- .../utils/serializer/ModuleSerializable.scala | 245 +- .../utils/serializer/ModuleSerializer.scala | 38 +- .../utils/serializer/DataConverterSpec.scala | 435 +- .../serializer/ModuleSerializerSpec.scala | 34 +- .../serializer/TensorConversionSpec.scala | 177 + 29 files changed, 5338 insertions(+), 1314 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala diff --git a/scala/dllib/src/main/java/serialization/Bigdl.java b/scala/dllib/src/main/java/serialization/Bigdl.java index 11c07cd6fb7..4199f81ce9c 100644 --- a/scala/dllib/src/main/java/serialization/Bigdl.java +++ b/scala/dllib/src/main/java/serialization/Bigdl.java @@ -580,41 +580,53 @@ public enum DataType */ BOOL(5), /** - * REGULARIZER = 6; + * CHAR = 6; */ - REGULARIZER(6), + CHAR(6), /** - * TENSOR = 7; + * SHORT = 7; */ - TENSOR(7), + SHORT(7), /** - * VARIABLE_FORMAT = 8; + * BYTES = 8; */ - VARIABLE_FORMAT(8), + BYTES(8), /** - * INITMETHOD = 9; + * REGULARIZER = 9; */ - INITMETHOD(9), + REGULARIZER(9), /** - * MODULE = 10; + * TENSOR = 10; */ - MODULE(10), + TENSOR(10), /** - * NAME_ATTR_LIST = 11; + * VARIABLE_FORMAT = 11; */ - NAME_ATTR_LIST(11), + VARIABLE_FORMAT(11), /** - * ARRAY_VALUE = 12; + * INITMETHOD = 12; */ - ARRAY_VALUE(12), + INITMETHOD(12), /** - * DATA_FORMAT = 13; + * MODULE = 13; */ - DATA_FORMAT(13), + MODULE(13), /** - * CUSTOM = 14; + * NAME_ATTR_LIST = 14; */ - CUSTOM(14), + NAME_ATTR_LIST(14), + /** + * ARRAY_VALUE = 15; + */ + ARRAY_VALUE(15), + /** + * DATA_FORMAT = 16; + */ + DATA_FORMAT(16), + /** + * CUSTOM = 17; + */ + CUSTOM(17), UNRECOGNIZED(-1), ; @@ -643,41 +655,53 @@ public enum DataType */ public static final int BOOL_VALUE = 5; /** - * REGULARIZER = 6; + * CHAR = 6; + */ + public static final int CHAR_VALUE = 6; + /** + * SHORT = 7; */ - public static final int REGULARIZER_VALUE = 6; + public static final int SHORT_VALUE = 7; /** - * TENSOR = 7; + * BYTES = 8; */ - public static final int TENSOR_VALUE = 7; + public static final int BYTES_VALUE = 8; /** - * VARIABLE_FORMAT = 8; + * REGULARIZER = 9; */ - public static final int VARIABLE_FORMAT_VALUE = 8; + public static final int REGULARIZER_VALUE = 9; /** - * INITMETHOD = 9; + * TENSOR = 10; */ - public static final int INITMETHOD_VALUE = 9; + public static final int TENSOR_VALUE = 10; /** - * MODULE = 10; + * VARIABLE_FORMAT = 11; */ - public static final int MODULE_VALUE = 10; + public static final int VARIABLE_FORMAT_VALUE = 11; /** - * NAME_ATTR_LIST = 11; + * INITMETHOD = 12; */ - public static final int NAME_ATTR_LIST_VALUE = 11; + public static final int INITMETHOD_VALUE = 12; /** - * ARRAY_VALUE = 12; + * MODULE = 13; */ - public static final int ARRAY_VALUE_VALUE = 12; + public static final int MODULE_VALUE = 13; /** - * DATA_FORMAT = 13; + * NAME_ATTR_LIST = 14; */ - public static final int DATA_FORMAT_VALUE = 13; + public static final int NAME_ATTR_LIST_VALUE = 14; /** - * CUSTOM = 14; + * ARRAY_VALUE = 15; */ - public static final int CUSTOM_VALUE = 14; + public static final int ARRAY_VALUE_VALUE = 15; + /** + * DATA_FORMAT = 16; + */ + public static final int DATA_FORMAT_VALUE = 16; + /** + * CUSTOM = 17; + */ + public static final int CUSTOM_VALUE = 17; public final int getNumber() { @@ -704,15 +728,18 @@ public static DataType forNumber(int value) { case 3: return DOUBLE; case 4: return STRING; case 5: return BOOL; - case 6: return REGULARIZER; - case 7: return TENSOR; - case 8: return VARIABLE_FORMAT; - case 9: return INITMETHOD; - case 10: return MODULE; - case 11: return NAME_ATTR_LIST; - case 12: return ARRAY_VALUE; - case 13: return DATA_FORMAT; - case 14: return CUSTOM; + case 6: return CHAR; + case 7: return SHORT; + case 8: return BYTES; + case 9: return REGULARIZER; + case 10: return TENSOR; + case 11: return VARIABLE_FORMAT; + case 12: return INITMETHOD; + case 13: return MODULE; + case 14: return NAME_ATTR_LIST; + case 15: return ARRAY_VALUE; + case 16: return DATA_FORMAT; + case 17: return CUSTOM; default: return null; } } @@ -774,7 +801,7 @@ public interface BigDLModuleOrBuilder extends *module name * * - * optional string name = 1; + * string name = 1; */ java.lang.String getName(); /** @@ -782,7 +809,7 @@ public interface BigDLModuleOrBuilder extends *module name * * - * optional string name = 1; + * string name = 1; */ com.google.protobuf.ByteString getNameBytes(); @@ -836,7 +863,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ boolean hasWeight(); /** @@ -844,7 +871,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ serialization.Bigdl.BigDLTensor getWeight(); /** @@ -852,7 +879,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder(); @@ -861,7 +888,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ boolean hasBias(); /** @@ -869,7 +896,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ serialization.Bigdl.BigDLTensor getBias(); /** @@ -877,7 +904,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder(); @@ -956,7 +983,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ java.lang.String getModuleType(); /** @@ -964,7 +991,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ com.google.protobuf.ByteString getModuleTypeBytes(); @@ -1028,7 +1055,7 @@ serialization.Bigdl.AttrValue getAttrOrThrow( *version of this module * * - * optional string version = 9; + * string version = 9; */ java.lang.String getVersion(); /** @@ -1036,10 +1063,46 @@ serialization.Bigdl.AttrValue getAttrOrThrow( *version of this module * * - * optional string version = 9; + * string version = 9; */ com.google.protobuf.ByteString getVersionBytes(); + + /** + * 
+     * is module status in train
+     *
+ * + * bool train = 10; + */ + boolean getTrain(); + + /** + * 
+     * name post fix
+     *
+ * + * string namePostfix = 11; + */ + java.lang.String getNamePostfix(); + /** + * 
+     * name post fix
+     *
+ * + * string namePostfix = 11; + */ + com.google.protobuf.ByteString + getNamePostfixBytes(); + + /** + * 
+     * unique ID of this module , used for shared modules
+     *
+ * + * int32 id = 12; + */ + int getId(); } /** * Protobuf type {@code serialization.BigDLModule} @@ -1048,6 +1111,7 @@ public static final class BigDLModule extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:serialization.BigDLModule) BigDLModuleOrBuilder { + private static final long serialVersionUID = 0L; // Use BigDLModule.newBuilder() to construct. private BigDLModule(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); @@ -1059,12 +1123,15 @@ private BigDLModule() { nextModules_ = com.google.protobuf.LazyStringArrayList.EMPTY; moduleType_ = ""; version_ = ""; + train_ = false; + namePostfix_ = ""; + id_ = 0; } @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); + return this.unknownFields; } private BigDLModule( com.google.protobuf.CodedInputStream input, @@ -1072,6 +1139,8 @@ private BigDLModule( throws com.google.protobuf.InvalidProtocolBufferException { this(); int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { @@ -1081,7 +1150,8 @@ private BigDLModule( done = true; break; default: { - if (!input.skipField(tag)) { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { done = true; } break; @@ -1158,9 +1228,10 @@ private BigDLModule( mutable_bitField0_ |= 0x00000080; } com.google.protobuf.MapEntry - attr = input.readMessage( + attr__ = input.readMessage( AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); - attr_.getMutableMap().put(attr.getKey(), attr.getValue()); + attr_.getMutableMap().put( + attr__.getKey(), attr__.getValue()); break; } case 74: { @@ -1169,6 +1240,22 @@ private BigDLModule( version_ = s; break; } + case 80: { + + train_ = input.readBool(); + break; + } + case 90: { + java.lang.String s = input.readStringRequireUtf8(); + + namePostfix_ = s; + break; + } + case 96: { + + id_ = input.readInt32(); + break; + } } } } catch (com.google.protobuf.InvalidProtocolBufferException e) { @@ -1186,6 +1273,7 @@ private BigDLModule( if (((mutable_bitField0_ & 0x00000020) == 0x00000020)) { nextModules_ = nextModules_.getUnmodifiableView(); } + this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } @@ -1220,7 +1308,7 @@ protected com.google.protobuf.MapField internalGetMapField( *module name * * - * optional string name = 1; + * string name = 1; */ public java.lang.String getName() { java.lang.Object ref = name_; @@ -1239,7 +1327,7 @@ public java.lang.String getName() { *module name * * - * optional string name = 1; + * string name = 1; */ public com.google.protobuf.ByteString getNameBytes() { @@ -1317,7 +1405,7 @@ public serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public boolean hasWeight() { return weight_ != null; @@ -1327,7 +1415,7 @@ public boolean hasWeight() { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public serialization.Bigdl.BigDLTensor getWeight() { return weight_ == null ? serialization.Bigdl.BigDLTensor.getDefaultInstance() : weight_; @@ -1337,7 +1425,7 @@ public serialization.Bigdl.BigDLTensor getWeight() { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { return getWeight(); @@ -1350,7 +1438,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public boolean hasBias() { return bias_ != null; @@ -1360,7 +1448,7 @@ public boolean hasBias() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public serialization.Bigdl.BigDLTensor getBias() { return bias_ == null ? serialization.Bigdl.BigDLTensor.getDefaultInstance() : bias_; @@ -1370,7 +1458,7 @@ public serialization.Bigdl.BigDLTensor getBias() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder() { return getBias(); @@ -1473,7 +1561,7 @@ public java.lang.String getNextModules(int index) { *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ public java.lang.String getModuleType() { java.lang.Object ref = moduleType_; @@ -1492,7 +1580,7 @@ public java.lang.String getModuleType() { *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ public com.google.protobuf.ByteString getModuleTypeBytes() { @@ -1607,7 +1695,7 @@ public serialization.Bigdl.AttrValue getAttrOrThrow( *version of this module * * - * optional string version = 9; + * string version = 9; */ public java.lang.String getVersion() { java.lang.Object ref = version_; @@ -1626,7 +1714,7 @@ public java.lang.String getVersion() { *version of this module * * - * optional string version = 9; + * string version = 9; */ public com.google.protobuf.ByteString getVersionBytes() { @@ -1642,6 +1730,74 @@ public java.lang.String getVersion() { } } + public static final int TRAIN_FIELD_NUMBER = 10; + private boolean train_; + /** + * 
+     * is module status in train
+     *
+ * + * bool train = 10; + */ + public boolean getTrain() { + return train_; + } + + public static final int NAMEPOSTFIX_FIELD_NUMBER = 11; + private volatile java.lang.Object namePostfix_; + /** + * 
+     * name post fix
+     *
+ * + * string namePostfix = 11; + */ + public java.lang.String getNamePostfix() { + java.lang.Object ref = namePostfix_; + if (ref instanceof java.lang.String) { + return (java.lang.String) ref; + } else { + com.google.protobuf.ByteString bs = + (com.google.protobuf.ByteString) ref; + java.lang.String s = bs.toStringUtf8(); + namePostfix_ = s; + return s; + } + } + /** + * 
+     * name post fix
+     *
+ * + * string namePostfix = 11; + */ + public com.google.protobuf.ByteString + getNamePostfixBytes() { + java.lang.Object ref = namePostfix_; + if (ref instanceof java.lang.String) { + com.google.protobuf.ByteString b = + com.google.protobuf.ByteString.copyFromUtf8( + (java.lang.String) ref); + namePostfix_ = b; + return b; + } else { + return (com.google.protobuf.ByteString) ref; + } + } + + public static final int ID_FIELD_NUMBER = 12; + private int id_; + /** + * 
+     * unique ID of this module , used for shared modules
+     *
+ * + * int32 id = 12; + */ + public int getId() { + return id_; + } + private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; @@ -1675,18 +1831,25 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) if (!getModuleTypeBytes().isEmpty()) { com.google.protobuf.GeneratedMessageV3.writeString(output, 7, moduleType_); } - for (java.util.Map.Entry entry - : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - output.writeMessage(8, attr); - } + com.google.protobuf.GeneratedMessageV3 + .serializeStringMapTo( + output, + internalGetAttr(), + AttrDefaultEntryHolder.defaultEntry, + 8); if (!getVersionBytes().isEmpty()) { com.google.protobuf.GeneratedMessageV3.writeString(output, 9, version_); } + if (train_ != false) { + output.writeBool(10, train_); + } + if (!getNamePostfixBytes().isEmpty()) { + com.google.protobuf.GeneratedMessageV3.writeString(output, 11, namePostfix_); + } + if (id_ != 0) { + output.writeInt32(12, id_); + } + unknownFields.writeTo(output); } public int getSerializedSize() { @@ -1731,21 +1894,32 @@ public int getSerializedSize() { for (java.util.Map.Entry entry : internalGetAttr().getMap().entrySet()) { com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() + attr__ = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() .setKey(entry.getKey()) .setValue(entry.getValue()) .build(); size += com.google.protobuf.CodedOutputStream - .computeMessageSize(8, attr); + .computeMessageSize(8, attr__); } if (!getVersionBytes().isEmpty()) { size += com.google.protobuf.GeneratedMessageV3.computeStringSize(9, version_); } + if (train_ != false) { + size += com.google.protobuf.CodedOutputStream + .computeBoolSize(10, train_); + } + if (!getNamePostfixBytes().isEmpty()) { + size += com.google.protobuf.GeneratedMessageV3.computeStringSize(11, namePostfix_); + } + if (id_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(12, id_); + } + size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } - private static final long serialVersionUID = 0L; @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { @@ -1781,6 +1955,13 @@ public boolean equals(final java.lang.Object obj) { other.internalGetAttr()); result = result && getVersion() .equals(other.getVersion()); + result = result && (getTrain() + == other.getTrain()); + result = result && getNamePostfix() + .equals(other.getNamePostfix()); + result = result && (getId() + == other.getId()); + result = result && unknownFields.equals(other.unknownFields); return result; } @@ -1790,7 +1971,7 @@ public int hashCode() { return memoizedHashCode; } int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); + hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + NAME_FIELD_NUMBER; hash = (53 * hash) + getName().hashCode(); if (getSubModulesCount() > 0) { @@ -1821,11 +2002,29 @@ public int hashCode() { } hash = (37 * hash) + VERSION_FIELD_NUMBER; hash = (53 * hash) + getVersion().hashCode(); + hash = (37 * hash) + TRAIN_FIELD_NUMBER; + hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( + getTrain()); + hash = (37 * hash) + NAMEPOSTFIX_FIELD_NUMBER; + hash = (53 * hash) + getNamePostfix().hashCode(); + hash = (37 * hash) + ID_FIELD_NUMBER; + hash = (53 * hash) + getId(); hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; } + public static serialization.Bigdl.BigDLModule parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.BigDLModule parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } public static serialization.Bigdl.BigDLModule parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { @@ -1991,6 +2190,12 @@ public Builder clear() { internalGetMutableAttr().clear(); version_ = ""; + train_ = false; + + namePostfix_ = ""; + + id_ = 0; + return this; } @@ -2049,6 +2254,9 @@ public serialization.Bigdl.BigDLModule buildPartial() { result.attr_ = internalGetAttr(); result.attr_.makeImmutable(); result.version_ = version_; + result.train_ = train_; + result.namePostfix_ = namePostfix_; + result.id_ = id_; result.bitField0_ = to_bitField0_; onBuilt(); return result; @@ -2059,7 +2267,7 @@ public Builder clone() { } public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( @@ -2072,12 +2280,12 @@ public Builder clearOneof( } public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { + int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { @@ -2157,6 +2365,17 @@ public Builder mergeFrom(serialization.Bigdl.BigDLModule other) { version_ = other.version_; onChanged(); } + if (other.getTrain() != false) { + setTrain(other.getTrain()); + } + if (!other.getNamePostfix().isEmpty()) { + namePostfix_ = other.namePostfix_; + onChanged(); + } + if (other.getId() != 0) { + setId(other.getId()); + } + this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } @@ -2190,7 +2409,7 @@ public Builder mergeFrom( *module name * * - * optional string name = 1; + * string name = 1; */ public java.lang.String getName() { java.lang.Object ref = name_; @@ -2209,7 +2428,7 @@ public java.lang.String getName() { *module name * * - * optional string name = 1; + * string name = 1; */ public com.google.protobuf.ByteString getNameBytes() { @@ -2229,7 +2448,7 @@ public java.lang.String getName() { *module name * * - * optional string name = 1; + * string name = 1; */ public Builder setName( java.lang.String value) { @@ -2246,7 +2465,7 @@ public Builder setName( *module name * * - * optional string name = 1; + * string name = 1; */ public Builder clearName() { @@ -2259,7 +2478,7 @@ public Builder clearName() { *module name * * - * optional string name = 1; + * string name = 1; */ public Builder setNameBytes( com.google.protobuf.ByteString value) { @@ -2593,7 +2812,7 @@ public serialization.Bigdl.BigDLModule.Builder addSubModulesBuilder( * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public boolean hasWeight() { return weightBuilder_ != null || weight_ != null; @@ -2603,7 +2822,7 @@ public boolean hasWeight() { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public serialization.Bigdl.BigDLTensor getWeight() { if (weightBuilder_ == null) { @@ -2617,7 +2836,7 @@ public serialization.Bigdl.BigDLTensor getWeight() { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public Builder setWeight(serialization.Bigdl.BigDLTensor value) { if (weightBuilder_ == null) { @@ -2637,7 +2856,7 @@ public Builder setWeight(serialization.Bigdl.BigDLTensor value) { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public Builder setWeight( serialization.Bigdl.BigDLTensor.Builder builderForValue) { @@ -2655,7 +2874,7 @@ public Builder setWeight( * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public Builder mergeWeight(serialization.Bigdl.BigDLTensor value) { if (weightBuilder_ == null) { @@ -2677,7 +2896,7 @@ public Builder mergeWeight(serialization.Bigdl.BigDLTensor value) { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public Builder clearWeight() { if (weightBuilder_ == null) { @@ -2695,7 +2914,7 @@ public Builder clearWeight() { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public serialization.Bigdl.BigDLTensor.Builder getWeightBuilder() { @@ -2707,7 +2926,7 @@ public serialization.Bigdl.BigDLTensor.Builder getWeightBuilder() { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { if (weightBuilder_ != null) { @@ -2722,7 +2941,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { * weight for each layer, serialized data are stored as either float or double * * - * optional .serialization.BigDLTensor weight = 3; + * .serialization.BigDLTensor weight = 3; */ private com.google.protobuf.SingleFieldBuilderV3< serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> @@ -2746,7 +2965,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public boolean hasBias() { return biasBuilder_ != null || bias_ != null; @@ -2756,7 +2975,7 @@ public boolean hasBias() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public serialization.Bigdl.BigDLTensor getBias() { if (biasBuilder_ == null) { @@ -2770,7 +2989,7 @@ public serialization.Bigdl.BigDLTensor getBias() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public Builder setBias(serialization.Bigdl.BigDLTensor value) { if (biasBuilder_ == null) { @@ -2790,7 +3009,7 @@ public Builder setBias(serialization.Bigdl.BigDLTensor value) { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public Builder setBias( serialization.Bigdl.BigDLTensor.Builder builderForValue) { @@ -2808,7 +3027,7 @@ public Builder setBias( * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public Builder mergeBias(serialization.Bigdl.BigDLTensor value) { if (biasBuilder_ == null) { @@ -2830,7 +3049,7 @@ public Builder mergeBias(serialization.Bigdl.BigDLTensor value) { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public Builder clearBias() { if (biasBuilder_ == null) { @@ -2848,7 +3067,7 @@ public Builder clearBias() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public serialization.Bigdl.BigDLTensor.Builder getBiasBuilder() { @@ -2860,7 +3079,7 @@ public serialization.Bigdl.BigDLTensor.Builder getBiasBuilder() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ public serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder() { if (biasBuilder_ != null) { @@ -2875,7 +3094,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder() { * bias for each layer * * - * optional .serialization.BigDLTensor bias = 4; + * .serialization.BigDLTensor bias = 4; */ private com.google.protobuf.SingleFieldBuilderV3< serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> @@ -3157,7 +3376,7 @@ public Builder addNextModulesBytes( *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ public java.lang.String getModuleType() { java.lang.Object ref = moduleType_; @@ -3176,7 +3395,7 @@ public java.lang.String getModuleType() { *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ public com.google.protobuf.ByteString getModuleTypeBytes() { @@ -3196,7 +3415,7 @@ public java.lang.String getModuleType() { *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ public Builder setModuleType( java.lang.String value) { @@ -3213,7 +3432,7 @@ public Builder setModuleType( *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ public Builder clearModuleType() { @@ -3226,7 +3445,7 @@ public Builder clearModuleType() { *module type to identify a module like linear, graph, etc * * - * optional string moduleType = 7; + * string moduleType = 7; */ public Builder setModuleTypeBytes( com.google.protobuf.ByteString value) { @@ -3333,7 +3552,8 @@ public serialization.Bigdl.AttrValue getAttrOrThrow( } public Builder clearAttr() { - getMutableAttr().clear(); + internalGetMutableAttr().getMutableMap() + .clear(); return this; } /** @@ -3347,7 +3567,8 @@ public Builder clearAttr() { public Builder removeAttr( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().remove(key); + internalGetMutableAttr().getMutableMap() + .remove(key); return this; } /** @@ -3370,7 +3591,8 @@ public Builder putAttr( serialization.Bigdl.AttrValue value) { if (key == null) { throw new java.lang.NullPointerException(); } if (value == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().put(key, value); + internalGetMutableAttr().getMutableMap() + .put(key, value); return this; } /** @@ -3383,7 +3605,8 @@ public Builder putAttr( public Builder putAllAttr( java.util.Map values) { - getMutableAttr().putAll(values); + internalGetMutableAttr().getMutableMap() + .putAll(values); return this; } @@ -3393,7 +3616,7 @@ public Builder putAllAttr( *version of this module * * - * optional string version = 9; + * string version = 9; */ public java.lang.String getVersion() { java.lang.Object ref = version_; @@ -3412,7 +3635,7 @@ public java.lang.String getVersion() { *version of this module * * - * optional string version = 9; + * string version = 9; */ public com.google.protobuf.ByteString getVersionBytes() { @@ -3432,7 +3655,7 @@ public java.lang.String getVersion() { *version of this module * * - * optional string version = 9; + * string version = 9; */ public Builder setVersion( java.lang.String value) { @@ -3449,7 +3672,7 @@ public Builder setVersion( *version of this module * * - * optional string version = 9; + * string version = 9; */ public Builder clearVersion() { @@ -3462,7 +3685,7 @@ public Builder clearVersion() { *version of this module * * - * optional string version = 9; + * string version = 9; */ public Builder setVersionBytes( com.google.protobuf.ByteString value) { @@ -3475,14 +3698,179 @@ public Builder setVersionBytes( onChanged(); return this; } + + private boolean train_ ; + /** + * 
+       * is module status in train
+       *
+ * + * bool train = 10; + */ + public boolean getTrain() { + return train_; + } + /** + * 
+       * is module status in train
+       *
+ * + * bool train = 10; + */ + public Builder setTrain(boolean value) { + + train_ = value; + onChanged(); + return this; + } + /** + * 
+       * is module status in train
+       *
+ * + * bool train = 10; + */ + public Builder clearTrain() { + + train_ = false; + onChanged(); + return this; + } + + private java.lang.Object namePostfix_ = ""; + /** + * 
+       * name post fix
+       *
+ * + * string namePostfix = 11; + */ + public java.lang.String getNamePostfix() { + java.lang.Object ref = namePostfix_; + if (!(ref instanceof java.lang.String)) { + com.google.protobuf.ByteString bs = + (com.google.protobuf.ByteString) ref; + java.lang.String s = bs.toStringUtf8(); + namePostfix_ = s; + return s; + } else { + return (java.lang.String) ref; + } + } + /** + * 
+       * name post fix
+       *
+ * + * string namePostfix = 11; + */ + public com.google.protobuf.ByteString + getNamePostfixBytes() { + java.lang.Object ref = namePostfix_; + if (ref instanceof String) { + com.google.protobuf.ByteString b = + com.google.protobuf.ByteString.copyFromUtf8( + (java.lang.String) ref); + namePostfix_ = b; + return b; + } else { + return (com.google.protobuf.ByteString) ref; + } + } + /** + * 
+       * name post fix
+       *
+ * + * string namePostfix = 11; + */ + public Builder setNamePostfix( + java.lang.String value) { + if (value == null) { + throw new NullPointerException(); + } + + namePostfix_ = value; + onChanged(); + return this; + } + /** + * 
+       * name post fix
+       *
+ * + * string namePostfix = 11; + */ + public Builder clearNamePostfix() { + + namePostfix_ = getDefaultInstance().getNamePostfix(); + onChanged(); + return this; + } + /** + * 
+       * name post fix
+       *
+ * + * string namePostfix = 11; + */ + public Builder setNamePostfixBytes( + com.google.protobuf.ByteString value) { + if (value == null) { + throw new NullPointerException(); + } + checkByteStringIsUtf8(value); + + namePostfix_ = value; + onChanged(); + return this; + } + + private int id_ ; + /** + * 
+       * unique ID of this module , used for shared modules
+       *
+ * + * int32 id = 12; + */ + public int getId() { + return id_; + } + /** + * 
+       * unique ID of this module , used for shared modules
+       *
+ * + * int32 id = 12; + */ + public Builder setId(int value) { + + id_ = value; + onChanged(); + return this; + } + /** + * 
+       * unique ID of this module , used for shared modules
+       *
+ * + * int32 id = 12; + */ + public Builder clearId() { + + id_ = 0; + onChanged(); + return this; + } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.mergeUnknownFields(unknownFields); } @@ -3529,11 +3917,11 @@ public interface InitMethodOrBuilder extends com.google.protobuf.MessageOrBuilder { /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ int getMethodTypeValue(); /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ serialization.Bigdl.InitMethodType getMethodType(); @@ -3557,6 +3945,7 @@ public static final class InitMethod extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:serialization.InitMethod) InitMethodOrBuilder { + private static final long serialVersionUID = 0L; // Use InitMethod.newBuilder() to construct. private InitMethod(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); @@ -3569,7 +3958,7 @@ private InitMethod() { @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); + return this.unknownFields; } private InitMethod( com.google.protobuf.CodedInputStream input, @@ -3577,6 +3966,8 @@ private InitMethod( throws com.google.protobuf.InvalidProtocolBufferException { this(); int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { @@ -3586,7 +3977,8 @@ private InitMethod( done = true; break; default: { - if (!input.skipField(tag)) { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { done = true; } break; @@ -3629,6 +4021,7 @@ private InitMethod( if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) { data_ = java.util.Collections.unmodifiableList(data_); } + this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } @@ -3648,13 +4041,13 @@ private InitMethod( public static final int METHODTYPE_FIELD_NUMBER = 1; private int methodType_; /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ public int getMethodTypeValue() { return methodType_; } /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ public serialization.Bigdl.InitMethodType getMethodType() { serialization.Bigdl.InitMethodType result = serialization.Bigdl.InitMethodType.valueOf(methodType_); @@ -3707,6 +4100,7 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) for (int i = 0; i < data_.size(); i++) { output.writeDoubleNoTag(data_.get(i)); } + unknownFields.writeTo(output); } public int getSerializedSize() { @@ -3729,11 +4123,11 @@ public int getSerializedSize() { } dataMemoizedSerializedSize = dataSize; } + size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } - private static final long serialVersionUID = 0L; @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { @@ -3748,6 +4142,7 @@ public boolean equals(final java.lang.Object obj) { result = result && methodType_ == other.methodType_; result = result && getDataList() .equals(other.getDataList()); + result = result && unknownFields.equals(other.unknownFields); return result; } @@ -3757,7 +4152,7 @@ public int hashCode() { return memoizedHashCode; } int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); + hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + METHODTYPE_FIELD_NUMBER; hash = (53 * hash) + methodType_; if (getDataCount() > 0) { @@ -3769,6 +4164,17 @@ public int hashCode() { return hash; } + public static serialization.Bigdl.InitMethod parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.InitMethod parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } public static serialization.Bigdl.InitMethod parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { @@ -3926,7 +4332,7 @@ public Builder clone() { } public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( @@ -3939,12 +4345,12 @@ public Builder clearOneof( } public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { + int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { @@ -3971,6 +4377,7 @@ public Builder mergeFrom(serialization.Bigdl.InitMethod other) { } onChanged(); } + this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } @@ -4000,13 +4407,13 @@ public Builder mergeFrom( private int methodType_ = 0; /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ public int getMethodTypeValue() { return methodType_; } /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ public Builder setMethodTypeValue(int value) { methodType_ = value; @@ -4014,14 +4421,14 @@ public Builder setMethodTypeValue(int value) { return this; } /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ public serialization.Bigdl.InitMethodType getMethodType() { serialization.Bigdl.InitMethodType result = serialization.Bigdl.InitMethodType.valueOf(methodType_); return result == null ? serialization.Bigdl.InitMethodType.UNRECOGNIZED : result; } /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ public Builder setMethodType(serialization.Bigdl.InitMethodType value) { if (value == null) { @@ -4033,7 +4440,7 @@ public Builder setMethodType(serialization.Bigdl.InitMethodType value) { return this; } /** - * optional .serialization.InitMethodType methodType = 1; + * .serialization.InitMethodType methodType = 1; */ public Builder clearMethodType() { @@ -4109,12 +4516,12 @@ public Builder clearData() { } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.mergeUnknownFields(unknownFields); } @@ -4161,11 +4568,11 @@ public interface BigDLTensorOrBuilder extends com.google.protobuf.MessageOrBuilder { /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ int getDatatypeValue(); /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ serialization.Bigdl.DataType getDatatype(); @@ -4196,53 +4603,98 @@ public interface BigDLTensorOrBuilder extends /** * 
-     *tensor element
+     *stride of tensor
      *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - java.util.List getFloatDataList(); + java.util.List getStrideList(); /** * 
-     *tensor element
+     *stride of tensor
      *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - int getFloatDataCount(); + int getStrideCount(); /** * 
-     *tensor element
+     *stride of tensor
      *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - float getFloatData(int index); + int getStride(int index); /** * 
-     *tensor element
+     *offset
      *
* - * repeated double double_data = 4; + * int32 offset = 4; */ - java.util.List getDoubleDataList(); + int getOffset(); + /** * 
-     *tensor element
+     * dimension
      *
* - * repeated double double_data = 4; + * int32 dimension = 5; */ - int getDoubleDataCount(); + int getDimension(); + /** * 
-     *tensor element
+     *total number of elements
      *
* - * repeated double double_data = 4; + * int32 nElements = 6; */ - double getDoubleData(int index); + int getNElements(); + + /** + * 
+     * is scalar tensor
+     *
+ * + * bool isScalar = 7; + */ + boolean getIsScalar(); + + /** + * 
+     * reference to storage
+     *
+ * + * .serialization.TensorStorage storage = 8; + */ + boolean hasStorage(); + /** + * 
+     * reference to storage
+     *
+ * + * .serialization.TensorStorage storage = 8; + */ + serialization.Bigdl.TensorStorage getStorage(); + /** + * 
+     * reference to storage
+     *
+ * + * .serialization.TensorStorage storage = 8; + */ + serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder(); + + /** + * 
+     * tensor ID, used for tensor sharing
+     *
+ * + * int32 id = 9; + */ + int getId(); } /** * Protobuf type {@code serialization.BigDLTensor} @@ -4251,6 +4703,7 @@ public static final class BigDLTensor extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:serialization.BigDLTensor) BigDLTensorOrBuilder { + private static final long serialVersionUID = 0L; // Use BigDLTensor.newBuilder() to construct. private BigDLTensor(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); @@ -4258,14 +4711,18 @@ private BigDLTensor(com.google.protobuf.GeneratedMessageV3.Builder builder) { private BigDLTensor() { datatype_ = 0; size_ = java.util.Collections.emptyList(); - floatData_ = java.util.Collections.emptyList(); - doubleData_ = java.util.Collections.emptyList(); + stride_ = java.util.Collections.emptyList(); + offset_ = 0; + dimension_ = 0; + nElements_ = 0; + isScalar_ = false; + id_ = 0; } @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); + return this.unknownFields; } private BigDLTensor( com.google.protobuf.CodedInputStream input, @@ -4273,6 +4730,8 @@ private BigDLTensor( throws com.google.protobuf.InvalidProtocolBufferException { this(); int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { @@ -4282,7 +4741,8 @@ private BigDLTensor( done = true; break; default: { - if (!input.skipField(tag)) { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { done = true; } break; @@ -4314,46 +4774,63 @@ private BigDLTensor( input.popLimit(limit); break; } - case 29: { + case 24: { if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - floatData_ = new java.util.ArrayList(); + stride_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000004; } - floatData_.add(input.readFloat()); + stride_.add(input.readInt32()); break; } case 26: { int length = input.readRawVarint32(); int limit = input.pushLimit(length); if (!((mutable_bitField0_ & 0x00000004) == 0x00000004) && input.getBytesUntilLimit() > 0) { - floatData_ = new java.util.ArrayList(); + stride_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000004; } while (input.getBytesUntilLimit() > 0) { - floatData_.add(input.readFloat()); + stride_.add(input.readInt32()); } input.popLimit(limit); break; } - case 33: { - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - doubleData_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000008; - } - doubleData_.add(input.readDouble()); + case 32: { + + offset_ = input.readInt32(); break; } - case 34: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008) && input.getBytesUntilLimit() > 0) { - doubleData_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000008; + case 40: { + + dimension_ = input.readInt32(); + break; + } + case 48: { + + nElements_ = input.readInt32(); + break; + } + case 56: { + + isScalar_ = input.readBool(); + break; + } + case 66: { + serialization.Bigdl.TensorStorage.Builder subBuilder = null; + if (storage_ != null) { + subBuilder = storage_.toBuilder(); } - while (input.getBytesUntilLimit() > 0) { - doubleData_.add(input.readDouble()); + storage_ = input.readMessage(serialization.Bigdl.TensorStorage.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom(storage_); + storage_ = subBuilder.buildPartial(); } - input.popLimit(limit); + + break; + } + case 72: { + + id_ = input.readInt32(); break; } } @@ -4368,11 +4845,9 @@ private BigDLTensor( size_ = java.util.Collections.unmodifiableList(size_); } if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - floatData_ = java.util.Collections.unmodifiableList(floatData_); - } - if (((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - doubleData_ = java.util.Collections.unmodifiableList(doubleData_); + stride_ = java.util.Collections.unmodifiableList(stride_); } + this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } @@ -4392,13 +4867,13 @@ private BigDLTensor( public static final int DATATYPE_FIELD_NUMBER = 1; private int datatype_; /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ public int getDatatypeValue() { return datatype_; } /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ public serialization.Bigdl.DataType getDatatype() { serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); @@ -4440,75 +4915,138 @@ public int getSize(int index) { } private int sizeMemoizedSerializedSize = -1; - public static final int FLOAT_DATA_FIELD_NUMBER = 3; - private java.util.List floatData_; + public static final int STRIDE_FIELD_NUMBER = 3; + private java.util.List stride_; /** * 
-     *tensor element
+     *stride of tensor
      *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public java.util.List - getFloatDataList() { - return floatData_; + public java.util.List + getStrideList() { + return stride_; } /** * 
-     *tensor element
+     *stride of tensor
      *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public int getFloatDataCount() { - return floatData_.size(); + public int getStrideCount() { + return stride_.size(); } /** * 
-     *tensor element
+     *stride of tensor
      *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public float getFloatData(int index) { - return floatData_.get(index); + public int getStride(int index) { + return stride_.get(index); } - private int floatDataMemoizedSerializedSize = -1; + private int strideMemoizedSerializedSize = -1; - public static final int DOUBLE_DATA_FIELD_NUMBER = 4; - private java.util.List doubleData_; + public static final int OFFSET_FIELD_NUMBER = 4; + private int offset_; /** * 
-     *tensor element
+     *offset
      *
* - * repeated double double_data = 4; + * int32 offset = 4; */ - public java.util.List - getDoubleDataList() { - return doubleData_; + public int getOffset() { + return offset_; } + + public static final int DIMENSION_FIELD_NUMBER = 5; + private int dimension_; /** * 
-     *tensor element
+     * dimension
      *
* - * repeated double double_data = 4; + * int32 dimension = 5; */ - public int getDoubleDataCount() { - return doubleData_.size(); + public int getDimension() { + return dimension_; } + + public static final int NELEMENTS_FIELD_NUMBER = 6; + private int nElements_; /** * 
-     *tensor element
+     *total number of elements
      *
* - * repeated double double_data = 4; + * int32 nElements = 6; */ - public double getDoubleData(int index) { - return doubleData_.get(index); + public int getNElements() { + return nElements_; + } + + public static final int ISSCALAR_FIELD_NUMBER = 7; + private boolean isScalar_; + /** + * 
+     * is scalar tensor
+     *
+ * + * bool isScalar = 7; + */ + public boolean getIsScalar() { + return isScalar_; + } + + public static final int STORAGE_FIELD_NUMBER = 8; + private serialization.Bigdl.TensorStorage storage_; + /** + * 
+     * reference to storage
+     *
+ * + * .serialization.TensorStorage storage = 8; + */ + public boolean hasStorage() { + return storage_ != null; + } + /** + * 
+     * reference to storage
+     *
+ * + * .serialization.TensorStorage storage = 8; + */ + public serialization.Bigdl.TensorStorage getStorage() { + return storage_ == null ? serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; + } + /** + * 
+     * reference to storage
+     *
+ * + * .serialization.TensorStorage storage = 8; + */ + public serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder() { + return getStorage(); + } + + public static final int ID_FIELD_NUMBER = 9; + private int id_; + /** + * 
+     * tensor ID, used for tensor sharing
+     *
+ * + * int32 id = 9; + */ + public int getId() { + return id_; } - private int doubleDataMemoizedSerializedSize = -1; private byte memoizedIsInitialized = -1; public final boolean isInitialized() { @@ -4533,20 +5071,32 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) for (int i = 0; i < size_.size(); i++) { output.writeInt32NoTag(size_.get(i)); } - if (getFloatDataList().size() > 0) { + if (getStrideList().size() > 0) { output.writeUInt32NoTag(26); - output.writeUInt32NoTag(floatDataMemoizedSerializedSize); + output.writeUInt32NoTag(strideMemoizedSerializedSize); } - for (int i = 0; i < floatData_.size(); i++) { - output.writeFloatNoTag(floatData_.get(i)); + for (int i = 0; i < stride_.size(); i++) { + output.writeInt32NoTag(stride_.get(i)); } - if (getDoubleDataList().size() > 0) { - output.writeUInt32NoTag(34); - output.writeUInt32NoTag(doubleDataMemoizedSerializedSize); + if (offset_ != 0) { + output.writeInt32(4, offset_); } - for (int i = 0; i < doubleData_.size(); i++) { - output.writeDoubleNoTag(doubleData_.get(i)); + if (dimension_ != 0) { + output.writeInt32(5, dimension_); + } + if (nElements_ != 0) { + output.writeInt32(6, nElements_); + } + if (isScalar_ != false) { + output.writeBool(7, isScalar_); + } + if (storage_ != null) { + output.writeMessage(8, getStorage()); } + if (id_ != 0) { + output.writeInt32(9, id_); + } + unknownFields.writeTo(output); } public int getSerializedSize() { @@ -4574,31 +5124,47 @@ public int getSerializedSize() { } { int dataSize = 0; - dataSize = 4 * getFloatDataList().size(); - size += dataSize; - if (!getFloatDataList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); + for (int i = 0; i < stride_.size(); i++) { + dataSize += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(stride_.get(i)); } - floatDataMemoizedSerializedSize = dataSize; - } - { - int dataSize = 0; - dataSize = 8 * getDoubleDataList().size(); size += dataSize; - if (!getDoubleDataList().isEmpty()) { + if (!getStrideList().isEmpty()) { size += 1; size += com.google.protobuf.CodedOutputStream .computeInt32SizeNoTag(dataSize); } - doubleDataMemoizedSerializedSize = dataSize; + strideMemoizedSerializedSize = dataSize; + } + if (offset_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(4, offset_); + } + if (dimension_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(5, dimension_); + } + if (nElements_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(6, nElements_); + } + if (isScalar_ != false) { + size += com.google.protobuf.CodedOutputStream + .computeBoolSize(7, isScalar_); + } + if (storage_ != null) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(8, getStorage()); + } + if (id_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(9, id_); } + size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } - private static final long serialVersionUID = 0L; @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { @@ -4613,10 +5179,24 @@ public boolean equals(final java.lang.Object obj) { result = result && datatype_ == other.datatype_; result = result && getSizeList() .equals(other.getSizeList()); - result = result && getFloatDataList() - .equals(other.getFloatDataList()); - result = result && getDoubleDataList() - .equals(other.getDoubleDataList()); + result = result && getStrideList() + .equals(other.getStrideList()); + result = result && (getOffset() + == other.getOffset()); + result = result && (getDimension() + == other.getDimension()); + result = result && (getNElements() + == other.getNElements()); + result = result && (getIsScalar() + == other.getIsScalar()); + result = result && (hasStorage() == other.hasStorage()); + if (hasStorage()) { + result = result && getStorage() + .equals(other.getStorage()); + } + result = result && (getId() + == other.getId()); + result = result && unknownFields.equals(other.unknownFields); return result; } @@ -4626,26 +5206,48 @@ public int hashCode() { return memoizedHashCode; } int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); + hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + DATATYPE_FIELD_NUMBER; hash = (53 * hash) + datatype_; if (getSizeCount() > 0) { hash = (37 * hash) + SIZE_FIELD_NUMBER; hash = (53 * hash) + getSizeList().hashCode(); } - if (getFloatDataCount() > 0) { - hash = (37 * hash) + FLOAT_DATA_FIELD_NUMBER; - hash = (53 * hash) + getFloatDataList().hashCode(); - } - if (getDoubleDataCount() > 0) { - hash = (37 * hash) + DOUBLE_DATA_FIELD_NUMBER; - hash = (53 * hash) + getDoubleDataList().hashCode(); - } + if (getStrideCount() > 0) { + hash = (37 * hash) + STRIDE_FIELD_NUMBER; + hash = (53 * hash) + getStrideList().hashCode(); + } + hash = (37 * hash) + OFFSET_FIELD_NUMBER; + hash = (53 * hash) + getOffset(); + hash = (37 * hash) + DIMENSION_FIELD_NUMBER; + hash = (53 * hash) + getDimension(); + hash = (37 * hash) + NELEMENTS_FIELD_NUMBER; + hash = (53 * hash) + getNElements(); + hash = (37 * hash) + ISSCALAR_FIELD_NUMBER; + hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( + getIsScalar()); + if (hasStorage()) { + hash = (37 * hash) + STORAGE_FIELD_NUMBER; + hash = (53 * hash) + getStorage().hashCode(); + } + hash = (37 * hash) + ID_FIELD_NUMBER; + hash = (53 * hash) + getId(); hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; } + public static serialization.Bigdl.BigDLTensor parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.BigDLTensor parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } public static serialization.Bigdl.BigDLTensor parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { @@ -4763,10 +5365,24 @@ public Builder clear() { size_ = java.util.Collections.emptyList(); bitField0_ = (bitField0_ & ~0x00000002); - floatData_ = java.util.Collections.emptyList(); + stride_ = java.util.Collections.emptyList(); bitField0_ = (bitField0_ & ~0x00000004); - doubleData_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000008); + offset_ = 0; + + dimension_ = 0; + + nElements_ = 0; + + isScalar_ = false; + + if (storageBuilder_ == null) { + storage_ = null; + } else { + storage_ = null; + storageBuilder_ = null; + } + id_ = 0; + return this; } @@ -4798,15 +5414,20 @@ public serialization.Bigdl.BigDLTensor buildPartial() { } result.size_ = size_; if (((bitField0_ & 0x00000004) == 0x00000004)) { - floatData_ = java.util.Collections.unmodifiableList(floatData_); + stride_ = java.util.Collections.unmodifiableList(stride_); bitField0_ = (bitField0_ & ~0x00000004); } - result.floatData_ = floatData_; - if (((bitField0_ & 0x00000008) == 0x00000008)) { - doubleData_ = java.util.Collections.unmodifiableList(doubleData_); - bitField0_ = (bitField0_ & ~0x00000008); + result.stride_ = stride_; + result.offset_ = offset_; + result.dimension_ = dimension_; + result.nElements_ = nElements_; + result.isScalar_ = isScalar_; + if (storageBuilder_ == null) { + result.storage_ = storage_; + } else { + result.storage_ = storageBuilder_.build(); } - result.doubleData_ = doubleData_; + result.id_ = id_; result.bitField0_ = to_bitField0_; onBuilt(); return result; @@ -4817,7 +5438,7 @@ public Builder clone() { } public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( @@ -4830,12 +5451,12 @@ public Builder clearOneof( } public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { + int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { @@ -4862,26 +5483,35 @@ public Builder mergeFrom(serialization.Bigdl.BigDLTensor other) { } onChanged(); } - if (!other.floatData_.isEmpty()) { - if (floatData_.isEmpty()) { - floatData_ = other.floatData_; + if (!other.stride_.isEmpty()) { + if (stride_.isEmpty()) { + stride_ = other.stride_; bitField0_ = (bitField0_ & ~0x00000004); } else { - ensureFloatDataIsMutable(); - floatData_.addAll(other.floatData_); + ensureStrideIsMutable(); + stride_.addAll(other.stride_); } onChanged(); } - if (!other.doubleData_.isEmpty()) { - if (doubleData_.isEmpty()) { - doubleData_ = other.doubleData_; - bitField0_ = (bitField0_ & ~0x00000008); - } else { - ensureDoubleDataIsMutable(); - doubleData_.addAll(other.doubleData_); - } - onChanged(); + if (other.getOffset() != 0) { + setOffset(other.getOffset()); + } + if (other.getDimension() != 0) { + setDimension(other.getDimension()); + } + if (other.getNElements() != 0) { + setNElements(other.getNElements()); + } + if (other.getIsScalar() != false) { + setIsScalar(other.getIsScalar()); + } + if (other.hasStorage()) { + mergeStorage(other.getStorage()); } + if (other.getId() != 0) { + setId(other.getId()); + } + this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } @@ -4911,13 +5541,13 @@ public Builder mergeFrom( private int datatype_ = 0; /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ public int getDatatypeValue() { return datatype_; } /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ public Builder setDatatypeValue(int value) { datatype_ = value; @@ -4925,14 +5555,14 @@ public Builder setDatatypeValue(int value) { return this; } /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ public serialization.Bigdl.DataType getDatatype() { serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; } /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ public Builder setDatatype(serialization.Bigdl.DataType value) { if (value == null) { @@ -4944,7 +5574,7 @@ public Builder setDatatype(serialization.Bigdl.DataType value) { return this; } /** - * optional .serialization.DataType datatype = 1; + * .serialization.DataType datatype = 1; */ public Builder clearDatatype() { @@ -5047,237 +5677,2621 @@ public Builder clearSize() { return this; } - private java.util.List floatData_ = java.util.Collections.emptyList(); - private void ensureFloatDataIsMutable() { + private java.util.List stride_ = java.util.Collections.emptyList(); + private void ensureStrideIsMutable() { if (!((bitField0_ & 0x00000004) == 0x00000004)) { - floatData_ = new java.util.ArrayList(floatData_); + stride_ = new java.util.ArrayList(stride_); bitField0_ |= 0x00000004; } } /** * 
-       *tensor element
+       *stride of tensor
        *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public java.util.List - getFloatDataList() { - return java.util.Collections.unmodifiableList(floatData_); + public java.util.List + getStrideList() { + return java.util.Collections.unmodifiableList(stride_); } /** * 
-       *tensor element
+       *stride of tensor
        *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public int getFloatDataCount() { - return floatData_.size(); + public int getStrideCount() { + return stride_.size(); } /** * 
-       *tensor element
+       *stride of tensor
        *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public float getFloatData(int index) { - return floatData_.get(index); + public int getStride(int index) { + return stride_.get(index); } /** * 
-       *tensor element
+       *stride of tensor
        *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public Builder setFloatData( - int index, float value) { - ensureFloatDataIsMutable(); - floatData_.set(index, value); + public Builder setStride( + int index, int value) { + ensureStrideIsMutable(); + stride_.set(index, value); onChanged(); return this; } /** * 
-       *tensor element
+       *stride of tensor
        *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public Builder addFloatData(float value) { - ensureFloatDataIsMutable(); - floatData_.add(value); + public Builder addStride(int value) { + ensureStrideIsMutable(); + stride_.add(value); onChanged(); return this; } /** * 
-       *tensor element
+       *stride of tensor
        *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public Builder addAllFloatData( - java.lang.Iterable values) { - ensureFloatDataIsMutable(); + public Builder addAllStride( + java.lang.Iterable values) { + ensureStrideIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, floatData_); + values, stride_); onChanged(); return this; } /** * 
-       *tensor element
+       *stride of tensor
        *
* - * repeated float float_data = 3; + * repeated int32 stride = 3; */ - public Builder clearFloatData() { - floatData_ = java.util.Collections.emptyList(); + public Builder clearStride() { + stride_ = java.util.Collections.emptyList(); bitField0_ = (bitField0_ & ~0x00000004); onChanged(); return this; } - private java.util.List doubleData_ = java.util.Collections.emptyList(); - private void ensureDoubleDataIsMutable() { - if (!((bitField0_ & 0x00000008) == 0x00000008)) { - doubleData_ = new java.util.ArrayList(doubleData_); - bitField0_ |= 0x00000008; + private int offset_ ; + /** + * 
+       *offset
+       *
+ * + * int32 offset = 4; + */ + public int getOffset() { + return offset_; + } + /** + * 
+       *offset
+       *
+ * + * int32 offset = 4; + */ + public Builder setOffset(int value) { + + offset_ = value; + onChanged(); + return this; + } + /** + * 
+       *offset
+       *
+ * + * int32 offset = 4; + */ + public Builder clearOffset() { + + offset_ = 0; + onChanged(); + return this; + } + + private int dimension_ ; + /** + * 
+       * dimension
+       *
+ * + * int32 dimension = 5; + */ + public int getDimension() { + return dimension_; + } + /** + * 
+       * dimension
+       *
+ * + * int32 dimension = 5; + */ + public Builder setDimension(int value) { + + dimension_ = value; + onChanged(); + return this; + } + /** + * 
+       * dimension
+       *
+ * + * int32 dimension = 5; + */ + public Builder clearDimension() { + + dimension_ = 0; + onChanged(); + return this; + } + + private int nElements_ ; + /** + * 
+       *total number of elements
+       *
+ * + * int32 nElements = 6; + */ + public int getNElements() { + return nElements_; + } + /** + * 
+       *total number of elements
+       *
+ * + * int32 nElements = 6; + */ + public Builder setNElements(int value) { + + nElements_ = value; + onChanged(); + return this; + } + /** + * 
+       *total number of elements
+       *
+ * + * int32 nElements = 6; + */ + public Builder clearNElements() { + + nElements_ = 0; + onChanged(); + return this; + } + + private boolean isScalar_ ; + /** + * 
+       * is scalar tensor
+       *
+ * + * bool isScalar = 7; + */ + public boolean getIsScalar() { + return isScalar_; + } + /** + * 
+       * is scalar tensor
+       *
+ * + * bool isScalar = 7; + */ + public Builder setIsScalar(boolean value) { + + isScalar_ = value; + onChanged(); + return this; + } + /** + * 
+       * is scalar tensor
+       *
+ * + * bool isScalar = 7; + */ + public Builder clearIsScalar() { + + isScalar_ = false; + onChanged(); + return this; + } + + private serialization.Bigdl.TensorStorage storage_ = null; + private com.google.protobuf.SingleFieldBuilderV3< + serialization.Bigdl.TensorStorage, serialization.Bigdl.TensorStorage.Builder, serialization.Bigdl.TensorStorageOrBuilder> storageBuilder_; + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + public boolean hasStorage() { + return storageBuilder_ != null || storage_ != null; + } + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + public serialization.Bigdl.TensorStorage getStorage() { + if (storageBuilder_ == null) { + return storage_ == null ? serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; + } else { + return storageBuilder_.getMessage(); + } + } + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + public Builder setStorage(serialization.Bigdl.TensorStorage value) { + if (storageBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + storage_ = value; + onChanged(); + } else { + storageBuilder_.setMessage(value); + } + + return this; + } + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + public Builder setStorage( + serialization.Bigdl.TensorStorage.Builder builderForValue) { + if (storageBuilder_ == null) { + storage_ = builderForValue.build(); + onChanged(); + } else { + storageBuilder_.setMessage(builderForValue.build()); + } + + return this; + } + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + public Builder mergeStorage(serialization.Bigdl.TensorStorage value) { + if (storageBuilder_ == null) { + if (storage_ != null) { + storage_ = + serialization.Bigdl.TensorStorage.newBuilder(storage_).mergeFrom(value).buildPartial(); + } else { + storage_ = value; + } + onChanged(); + } else { + storageBuilder_.mergeFrom(value); + } + + return this; + } + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + public Builder clearStorage() { + if (storageBuilder_ == null) { + storage_ = null; + onChanged(); + } else { + storage_ = null; + storageBuilder_ = null; + } + + return this; + } + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + public serialization.Bigdl.TensorStorage.Builder getStorageBuilder() { + + onChanged(); + return getStorageFieldBuilder().getBuilder(); + } + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + public serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder() { + if (storageBuilder_ != null) { + return storageBuilder_.getMessageOrBuilder(); + } else { + return storage_ == null ? + serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; + } + } + /** + * 
+       * reference to storage
+       *
+ * + * .serialization.TensorStorage storage = 8; + */ + private com.google.protobuf.SingleFieldBuilderV3< + serialization.Bigdl.TensorStorage, serialization.Bigdl.TensorStorage.Builder, serialization.Bigdl.TensorStorageOrBuilder> + getStorageFieldBuilder() { + if (storageBuilder_ == null) { + storageBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< + serialization.Bigdl.TensorStorage, serialization.Bigdl.TensorStorage.Builder, serialization.Bigdl.TensorStorageOrBuilder>( + getStorage(), + getParentForChildren(), + isClean()); + storage_ = null; + } + return storageBuilder_; + } + + private int id_ ; + /** + * 
+       * tensor ID, used for tensor sharing
+       *
+ * + * int32 id = 9; + */ + public int getId() { + return id_; + } + /** + * 
+       * tensor ID, used for tensor sharing
+       *
+ * + * int32 id = 9; + */ + public Builder setId(int value) { + + id_ = value; + onChanged(); + return this; + } + /** + * 
+       * tensor ID, used for tensor sharing
+       *
+ * + * int32 id = 9; + */ + public Builder clearId() { + + id_ = 0; + onChanged(); + return this; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:serialization.BigDLTensor) + } + + // @@protoc_insertion_point(class_scope:serialization.BigDLTensor) + private static final serialization.Bigdl.BigDLTensor DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new serialization.Bigdl.BigDLTensor(); + } + + public static serialization.Bigdl.BigDLTensor getDefaultInstance() { + return DEFAULT_INSTANCE; + } + + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public BigDLTensor parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new BigDLTensor(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; + } + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public serialization.Bigdl.BigDLTensor getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + + } + + public interface TensorStorageOrBuilder extends + // @@protoc_insertion_point(interface_extends:serialization.TensorStorage) + com.google.protobuf.MessageOrBuilder { + + /** + * .serialization.DataType datatype = 1; + */ + int getDatatypeValue(); + /** + * .serialization.DataType datatype = 1; + */ + serialization.Bigdl.DataType getDatatype(); + + /** + * 
+     *float tensor element
+     *
+ * + * repeated float float_data = 2; + */ + java.util.List getFloatDataList(); + /** + * 
+     *float tensor element
+     *
+ * + * repeated float float_data = 2; + */ + int getFloatDataCount(); + /** + * 
+     *float tensor element
+     *
+ * + * repeated float float_data = 2; + */ + float getFloatData(int index); + + /** + * 
+     *double tensor element
+     *
+ * + * repeated double double_data = 3; + */ + java.util.List getDoubleDataList(); + /** + * 
+     *double tensor element
+     *
+ * + * repeated double double_data = 3; + */ + int getDoubleDataCount(); + /** + * 
+     *double tensor element
+     *
+ * + * repeated double double_data = 3; + */ + double getDoubleData(int index); + + /** + * 
+     *boolean tensor element
+     *
+ * + * repeated bool bool_data = 4; + */ + java.util.List getBoolDataList(); + /** + * 
+     *boolean tensor element
+     *
+ * + * repeated bool bool_data = 4; + */ + int getBoolDataCount(); + /** + * 
+     *boolean tensor element
+     *
+ * + * repeated bool bool_data = 4; + */ + boolean getBoolData(int index); + + /** + * 
+     *string tensor element
+     *
+ * + * repeated string string_data = 5; + */ + java.util.List + getStringDataList(); + /** + * 
+     *string tensor element
+     *
+ * + * repeated string string_data = 5; + */ + int getStringDataCount(); + /** + * 
+     *string tensor element
+     *
+ * + * repeated string string_data = 5; + */ + java.lang.String getStringData(int index); + /** + * 
+     *string tensor element
+     *
+ * + * repeated string string_data = 5; + */ + com.google.protobuf.ByteString + getStringDataBytes(int index); + + /** + * 
+     *int tensor element
+     *
+ * + * repeated int32 int_data = 6; + */ + java.util.List getIntDataList(); + /** + * 
+     *int tensor element
+     *
+ * + * repeated int32 int_data = 6; + */ + int getIntDataCount(); + /** + * 
+     *int tensor element
+     *
+ * + * repeated int32 int_data = 6; + */ + int getIntData(int index); + + /** + * 
+     *long tensor element
+     *
+ * + * repeated int64 long_data = 7; + */ + java.util.List getLongDataList(); + /** + * 
+     *long tensor element
+     *
+ * + * repeated int64 long_data = 7; + */ + int getLongDataCount(); + /** + * 
+     *long tensor element
+     *
+ * + * repeated int64 long_data = 7; + */ + long getLongData(int index); + + /** + * 
+     *byte tensor element
+     *
+ * + * repeated bytes bytes_data = 8; + */ + java.util.List getBytesDataList(); + /** + * 
+     *byte tensor element
+     *
+ * + * repeated bytes bytes_data = 8; + */ + int getBytesDataCount(); + /** + * 
+     *byte tensor element
+     *
+ * + * repeated bytes bytes_data = 8; + */ + com.google.protobuf.ByteString getBytesData(int index); + + /** + * 
+     * storage Id, used for storage sharing
+     *
+ * + * int32 id = 9; + */ + int getId(); + } + /** + * Protobuf type {@code serialization.TensorStorage} + */ + public static final class TensorStorage extends + com.google.protobuf.GeneratedMessageV3 implements + // @@protoc_insertion_point(message_implements:serialization.TensorStorage) + TensorStorageOrBuilder { + private static final long serialVersionUID = 0L; + // Use TensorStorage.newBuilder() to construct. + private TensorStorage(com.google.protobuf.GeneratedMessageV3.Builder builder) { + super(builder); + } + private TensorStorage() { + datatype_ = 0; + floatData_ = java.util.Collections.emptyList(); + doubleData_ = java.util.Collections.emptyList(); + boolData_ = java.util.Collections.emptyList(); + stringData_ = com.google.protobuf.LazyStringArrayList.EMPTY; + intData_ = java.util.Collections.emptyList(); + longData_ = java.util.Collections.emptyList(); + bytesData_ = java.util.Collections.emptyList(); + id_ = 0; + } + + @java.lang.Override + public final com.google.protobuf.UnknownFieldSet + getUnknownFields() { + return this.unknownFields; + } + private TensorStorage( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + this(); + int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); + try { + boolean done = false; + while (!done) { + int tag = input.readTag(); + switch (tag) { + case 0: + done = true; + break; + default: { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { + done = true; + } + break; + } + case 8: { + int rawValue = input.readEnum(); + + datatype_ = rawValue; + break; + } + case 21: { + if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { + floatData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000002; + } + floatData_.add(input.readFloat()); + break; + } + case 18: { + int length = input.readRawVarint32(); + int limit = input.pushLimit(length); + if (!((mutable_bitField0_ & 0x00000002) == 0x00000002) && input.getBytesUntilLimit() > 0) { + floatData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000002; + } + while (input.getBytesUntilLimit() > 0) { + floatData_.add(input.readFloat()); + } + input.popLimit(limit); + break; + } + case 25: { + if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) { + doubleData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000004; + } + doubleData_.add(input.readDouble()); + break; + } + case 26: { + int length = input.readRawVarint32(); + int limit = input.pushLimit(length); + if (!((mutable_bitField0_ & 0x00000004) == 0x00000004) && input.getBytesUntilLimit() > 0) { + doubleData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000004; + } + while (input.getBytesUntilLimit() > 0) { + doubleData_.add(input.readDouble()); + } + input.popLimit(limit); + break; + } + case 32: { + if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) { + boolData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000008; + } + boolData_.add(input.readBool()); + break; + } + case 34: { + int length = input.readRawVarint32(); + int limit = input.pushLimit(length); + if (!((mutable_bitField0_ & 0x00000008) == 0x00000008) && input.getBytesUntilLimit() > 0) { + boolData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000008; + } + while (input.getBytesUntilLimit() > 0) { + boolData_.add(input.readBool()); + } + input.popLimit(limit); + break; + } + case 42: { + java.lang.String s = input.readStringRequireUtf8(); + if (!((mutable_bitField0_ & 0x00000010) == 0x00000010)) { + stringData_ = new com.google.protobuf.LazyStringArrayList(); + mutable_bitField0_ |= 0x00000010; + } + stringData_.add(s); + break; + } + case 48: { + if (!((mutable_bitField0_ & 0x00000020) == 0x00000020)) { + intData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000020; + } + intData_.add(input.readInt32()); + break; + } + case 50: { + int length = input.readRawVarint32(); + int limit = input.pushLimit(length); + if (!((mutable_bitField0_ & 0x00000020) == 0x00000020) && input.getBytesUntilLimit() > 0) { + intData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000020; + } + while (input.getBytesUntilLimit() > 0) { + intData_.add(input.readInt32()); + } + input.popLimit(limit); + break; + } + case 56: { + if (!((mutable_bitField0_ & 0x00000040) == 0x00000040)) { + longData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000040; + } + longData_.add(input.readInt64()); + break; + } + case 58: { + int length = input.readRawVarint32(); + int limit = input.pushLimit(length); + if (!((mutable_bitField0_ & 0x00000040) == 0x00000040) && input.getBytesUntilLimit() > 0) { + longData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000040; + } + while (input.getBytesUntilLimit() > 0) { + longData_.add(input.readInt64()); + } + input.popLimit(limit); + break; + } + case 66: { + if (!((mutable_bitField0_ & 0x00000080) == 0x00000080)) { + bytesData_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00000080; + } + bytesData_.add(input.readBytes()); + break; + } + case 72: { + + id_ = input.readInt32(); + break; + } + } + } + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + throw e.setUnfinishedMessage(this); + } catch (java.io.IOException e) { + throw new com.google.protobuf.InvalidProtocolBufferException( + e).setUnfinishedMessage(this); + } finally { + if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) { + floatData_ = java.util.Collections.unmodifiableList(floatData_); + } + if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) { + doubleData_ = java.util.Collections.unmodifiableList(doubleData_); + } + if (((mutable_bitField0_ & 0x00000008) == 0x00000008)) { + boolData_ = java.util.Collections.unmodifiableList(boolData_); + } + if (((mutable_bitField0_ & 0x00000010) == 0x00000010)) { + stringData_ = stringData_.getUnmodifiableView(); + } + if (((mutable_bitField0_ & 0x00000020) == 0x00000020)) { + intData_ = java.util.Collections.unmodifiableList(intData_); + } + if (((mutable_bitField0_ & 0x00000040) == 0x00000040)) { + longData_ = java.util.Collections.unmodifiableList(longData_); + } + if (((mutable_bitField0_ & 0x00000080) == 0x00000080)) { + bytesData_ = java.util.Collections.unmodifiableList(bytesData_); + } + this.unknownFields = unknownFields.build(); + makeExtensionsImmutable(); + } + } + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return serialization.Bigdl.internal_static_serialization_TensorStorage_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return serialization.Bigdl.internal_static_serialization_TensorStorage_fieldAccessorTable + .ensureFieldAccessorsInitialized( + serialization.Bigdl.TensorStorage.class, serialization.Bigdl.TensorStorage.Builder.class); + } + + private int bitField0_; + public static final int DATATYPE_FIELD_NUMBER = 1; + private int datatype_; + /** + * .serialization.DataType datatype = 1; + */ + public int getDatatypeValue() { + return datatype_; + } + /** + * .serialization.DataType datatype = 1; + */ + public serialization.Bigdl.DataType getDatatype() { + serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); + return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; + } + + public static final int FLOAT_DATA_FIELD_NUMBER = 2; + private java.util.List floatData_; + /** + * 
+     *float tensor element
+     *
+ * + * repeated float float_data = 2; + */ + public java.util.List + getFloatDataList() { + return floatData_; + } + /** + * 
+     *float tensor element
+     *
+ * + * repeated float float_data = 2; + */ + public int getFloatDataCount() { + return floatData_.size(); + } + /** + * 
+     *float tensor element
+     *
+ * + * repeated float float_data = 2; + */ + public float getFloatData(int index) { + return floatData_.get(index); + } + private int floatDataMemoizedSerializedSize = -1; + + public static final int DOUBLE_DATA_FIELD_NUMBER = 3; + private java.util.List doubleData_; + /** + * 
+     *double tensor element
+     *
+ * + * repeated double double_data = 3; + */ + public java.util.List + getDoubleDataList() { + return doubleData_; + } + /** + * 
+     *double tensor element
+     *
+ * + * repeated double double_data = 3; + */ + public int getDoubleDataCount() { + return doubleData_.size(); + } + /** + * 
+     *double tensor element
+     *
+ * + * repeated double double_data = 3; + */ + public double getDoubleData(int index) { + return doubleData_.get(index); + } + private int doubleDataMemoizedSerializedSize = -1; + + public static final int BOOL_DATA_FIELD_NUMBER = 4; + private java.util.List boolData_; + /** + * 
+     *boolean tensor element
+     *
+ * + * repeated bool bool_data = 4; + */ + public java.util.List + getBoolDataList() { + return boolData_; + } + /** + * 
+     *boolean tensor element
+     *
+ * + * repeated bool bool_data = 4; + */ + public int getBoolDataCount() { + return boolData_.size(); + } + /** + * 
+     *boolean tensor element
+     *
+ * + * repeated bool bool_data = 4; + */ + public boolean getBoolData(int index) { + return boolData_.get(index); + } + private int boolDataMemoizedSerializedSize = -1; + + public static final int STRING_DATA_FIELD_NUMBER = 5; + private com.google.protobuf.LazyStringList stringData_; + /** + * 
+     *string tensor element
+     *
+ * + * repeated string string_data = 5; + */ + public com.google.protobuf.ProtocolStringList + getStringDataList() { + return stringData_; + } + /** + * 
+     *string tensor element
+     *
+ * + * repeated string string_data = 5; + */ + public int getStringDataCount() { + return stringData_.size(); + } + /** + * 
+     *string tensor element
+     *
+ * + * repeated string string_data = 5; + */ + public java.lang.String getStringData(int index) { + return stringData_.get(index); + } + /** + * 
+     *string tensor element
+     *
+ * + * repeated string string_data = 5; + */ + public com.google.protobuf.ByteString + getStringDataBytes(int index) { + return stringData_.getByteString(index); + } + + public static final int INT_DATA_FIELD_NUMBER = 6; + private java.util.List intData_; + /** + * 
+     *int tensor element
+     *
+ * + * repeated int32 int_data = 6; + */ + public java.util.List + getIntDataList() { + return intData_; + } + /** + * 
+     *int tensor element
+     *
+ * + * repeated int32 int_data = 6; + */ + public int getIntDataCount() { + return intData_.size(); + } + /** + * 
+     *int tensor element
+     *
+ * + * repeated int32 int_data = 6; + */ + public int getIntData(int index) { + return intData_.get(index); + } + private int intDataMemoizedSerializedSize = -1; + + public static final int LONG_DATA_FIELD_NUMBER = 7; + private java.util.List longData_; + /** + * 
+     *long tensor element
+     *
+ * + * repeated int64 long_data = 7; + */ + public java.util.List + getLongDataList() { + return longData_; + } + /** + * 
+     *long tensor element
+     *
+ * + * repeated int64 long_data = 7; + */ + public int getLongDataCount() { + return longData_.size(); + } + /** + * 
+     *long tensor element
+     *
+ * + * repeated int64 long_data = 7; + */ + public long getLongData(int index) { + return longData_.get(index); + } + private int longDataMemoizedSerializedSize = -1; + + public static final int BYTES_DATA_FIELD_NUMBER = 8; + private java.util.List bytesData_; + /** + * 
+     *byte tensor element
+     *
+ * + * repeated bytes bytes_data = 8; + */ + public java.util.List + getBytesDataList() { + return bytesData_; + } + /** + * 
+     *byte tensor element
+     *
+ * + * repeated bytes bytes_data = 8; + */ + public int getBytesDataCount() { + return bytesData_.size(); + } + /** + * 
+     *byte tensor element
+     *
+ * + * repeated bytes bytes_data = 8; + */ + public com.google.protobuf.ByteString getBytesData(int index) { + return bytesData_.get(index); + } + + public static final int ID_FIELD_NUMBER = 9; + private int id_; + /** + * 
+     * storage Id, used for storage sharing
+     *
+ * + * int32 id = 9; + */ + public int getId() { + return id_; + } + + private byte memoizedIsInitialized = -1; + public final boolean isInitialized() { + byte isInitialized = memoizedIsInitialized; + if (isInitialized == 1) return true; + if (isInitialized == 0) return false; + + memoizedIsInitialized = 1; + return true; + } + + public void writeTo(com.google.protobuf.CodedOutputStream output) + throws java.io.IOException { + getSerializedSize(); + if (datatype_ != serialization.Bigdl.DataType.INT32.getNumber()) { + output.writeEnum(1, datatype_); + } + if (getFloatDataList().size() > 0) { + output.writeUInt32NoTag(18); + output.writeUInt32NoTag(floatDataMemoizedSerializedSize); + } + for (int i = 0; i < floatData_.size(); i++) { + output.writeFloatNoTag(floatData_.get(i)); + } + if (getDoubleDataList().size() > 0) { + output.writeUInt32NoTag(26); + output.writeUInt32NoTag(doubleDataMemoizedSerializedSize); + } + for (int i = 0; i < doubleData_.size(); i++) { + output.writeDoubleNoTag(doubleData_.get(i)); + } + if (getBoolDataList().size() > 0) { + output.writeUInt32NoTag(34); + output.writeUInt32NoTag(boolDataMemoizedSerializedSize); + } + for (int i = 0; i < boolData_.size(); i++) { + output.writeBoolNoTag(boolData_.get(i)); + } + for (int i = 0; i < stringData_.size(); i++) { + com.google.protobuf.GeneratedMessageV3.writeString(output, 5, stringData_.getRaw(i)); + } + if (getIntDataList().size() > 0) { + output.writeUInt32NoTag(50); + output.writeUInt32NoTag(intDataMemoizedSerializedSize); + } + for (int i = 0; i < intData_.size(); i++) { + output.writeInt32NoTag(intData_.get(i)); + } + if (getLongDataList().size() > 0) { + output.writeUInt32NoTag(58); + output.writeUInt32NoTag(longDataMemoizedSerializedSize); + } + for (int i = 0; i < longData_.size(); i++) { + output.writeInt64NoTag(longData_.get(i)); + } + for (int i = 0; i < bytesData_.size(); i++) { + output.writeBytes(8, bytesData_.get(i)); + } + if (id_ != 0) { + output.writeInt32(9, id_); + } + unknownFields.writeTo(output); + } + + public int getSerializedSize() { + int size = memoizedSize; + if (size != -1) return size; + + size = 0; + if (datatype_ != serialization.Bigdl.DataType.INT32.getNumber()) { + size += com.google.protobuf.CodedOutputStream + .computeEnumSize(1, datatype_); + } + { + int dataSize = 0; + dataSize = 4 * getFloatDataList().size(); + size += dataSize; + if (!getFloatDataList().isEmpty()) { + size += 1; + size += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(dataSize); + } + floatDataMemoizedSerializedSize = dataSize; + } + { + int dataSize = 0; + dataSize = 8 * getDoubleDataList().size(); + size += dataSize; + if (!getDoubleDataList().isEmpty()) { + size += 1; + size += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(dataSize); + } + doubleDataMemoizedSerializedSize = dataSize; + } + { + int dataSize = 0; + dataSize = 1 * getBoolDataList().size(); + size += dataSize; + if (!getBoolDataList().isEmpty()) { + size += 1; + size += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(dataSize); + } + boolDataMemoizedSerializedSize = dataSize; + } + { + int dataSize = 0; + for (int i = 0; i < stringData_.size(); i++) { + dataSize += computeStringSizeNoTag(stringData_.getRaw(i)); + } + size += dataSize; + size += 1 * getStringDataList().size(); + } + { + int dataSize = 0; + for (int i = 0; i < intData_.size(); i++) { + dataSize += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(intData_.get(i)); + } + size += dataSize; + if (!getIntDataList().isEmpty()) { + size += 1; + size += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(dataSize); + } + intDataMemoizedSerializedSize = dataSize; + } + { + int dataSize = 0; + for (int i = 0; i < longData_.size(); i++) { + dataSize += com.google.protobuf.CodedOutputStream + .computeInt64SizeNoTag(longData_.get(i)); + } + size += dataSize; + if (!getLongDataList().isEmpty()) { + size += 1; + size += com.google.protobuf.CodedOutputStream + .computeInt32SizeNoTag(dataSize); + } + longDataMemoizedSerializedSize = dataSize; + } + { + int dataSize = 0; + for (int i = 0; i < bytesData_.size(); i++) { + dataSize += com.google.protobuf.CodedOutputStream + .computeBytesSizeNoTag(bytesData_.get(i)); + } + size += dataSize; + size += 1 * getBytesDataList().size(); + } + if (id_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(9, id_); + } + size += unknownFields.getSerializedSize(); + memoizedSize = size; + return size; + } + + @java.lang.Override + public boolean equals(final java.lang.Object obj) { + if (obj == this) { + return true; + } + if (!(obj instanceof serialization.Bigdl.TensorStorage)) { + return super.equals(obj); + } + serialization.Bigdl.TensorStorage other = (serialization.Bigdl.TensorStorage) obj; + + boolean result = true; + result = result && datatype_ == other.datatype_; + result = result && getFloatDataList() + .equals(other.getFloatDataList()); + result = result && getDoubleDataList() + .equals(other.getDoubleDataList()); + result = result && getBoolDataList() + .equals(other.getBoolDataList()); + result = result && getStringDataList() + .equals(other.getStringDataList()); + result = result && getIntDataList() + .equals(other.getIntDataList()); + result = result && getLongDataList() + .equals(other.getLongDataList()); + result = result && getBytesDataList() + .equals(other.getBytesDataList()); + result = result && (getId() + == other.getId()); + result = result && unknownFields.equals(other.unknownFields); + return result; + } + + @java.lang.Override + public int hashCode() { + if (memoizedHashCode != 0) { + return memoizedHashCode; + } + int hash = 41; + hash = (19 * hash) + getDescriptor().hashCode(); + hash = (37 * hash) + DATATYPE_FIELD_NUMBER; + hash = (53 * hash) + datatype_; + if (getFloatDataCount() > 0) { + hash = (37 * hash) + FLOAT_DATA_FIELD_NUMBER; + hash = (53 * hash) + getFloatDataList().hashCode(); + } + if (getDoubleDataCount() > 0) { + hash = (37 * hash) + DOUBLE_DATA_FIELD_NUMBER; + hash = (53 * hash) + getDoubleDataList().hashCode(); + } + if (getBoolDataCount() > 0) { + hash = (37 * hash) + BOOL_DATA_FIELD_NUMBER; + hash = (53 * hash) + getBoolDataList().hashCode(); + } + if (getStringDataCount() > 0) { + hash = (37 * hash) + STRING_DATA_FIELD_NUMBER; + hash = (53 * hash) + getStringDataList().hashCode(); + } + if (getIntDataCount() > 0) { + hash = (37 * hash) + INT_DATA_FIELD_NUMBER; + hash = (53 * hash) + getIntDataList().hashCode(); + } + if (getLongDataCount() > 0) { + hash = (37 * hash) + LONG_DATA_FIELD_NUMBER; + hash = (53 * hash) + getLongDataList().hashCode(); + } + if (getBytesDataCount() > 0) { + hash = (37 * hash) + BYTES_DATA_FIELD_NUMBER; + hash = (53 * hash) + getBytesDataList().hashCode(); + } + hash = (37 * hash) + ID_FIELD_NUMBER; + hash = (53 * hash) + getId(); + hash = (29 * hash) + unknownFields.hashCode(); + memoizedHashCode = hash; + return hash; + } + + public static serialization.Bigdl.TensorStorage parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.TensorStorage parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static serialization.Bigdl.TensorStorage parseFrom( + com.google.protobuf.ByteString data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.TensorStorage parseFrom( + com.google.protobuf.ByteString data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static serialization.Bigdl.TensorStorage parseFrom(byte[] data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.TensorStorage parseFrom( + byte[] data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } + public static serialization.Bigdl.TensorStorage parseFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static serialization.Bigdl.TensorStorage parseFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + public static serialization.Bigdl.TensorStorage parseDelimitedFrom(java.io.InputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input); + } + public static serialization.Bigdl.TensorStorage parseDelimitedFrom( + java.io.InputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseDelimitedWithIOException(PARSER, input, extensionRegistry); + } + public static serialization.Bigdl.TensorStorage parseFrom( + com.google.protobuf.CodedInputStream input) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input); + } + public static serialization.Bigdl.TensorStorage parseFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + return com.google.protobuf.GeneratedMessageV3 + .parseWithIOException(PARSER, input, extensionRegistry); + } + + public Builder newBuilderForType() { return newBuilder(); } + public static Builder newBuilder() { + return DEFAULT_INSTANCE.toBuilder(); + } + public static Builder newBuilder(serialization.Bigdl.TensorStorage prototype) { + return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); + } + public Builder toBuilder() { + return this == DEFAULT_INSTANCE + ? new Builder() : new Builder().mergeFrom(this); + } + + @java.lang.Override + protected Builder newBuilderForType( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + Builder builder = new Builder(parent); + return builder; + } + /** + * Protobuf type {@code serialization.TensorStorage} + */ + public static final class Builder extends + com.google.protobuf.GeneratedMessageV3.Builder implements + // @@protoc_insertion_point(builder_implements:serialization.TensorStorage) + serialization.Bigdl.TensorStorageOrBuilder { + public static final com.google.protobuf.Descriptors.Descriptor + getDescriptor() { + return serialization.Bigdl.internal_static_serialization_TensorStorage_descriptor; + } + + protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internalGetFieldAccessorTable() { + return serialization.Bigdl.internal_static_serialization_TensorStorage_fieldAccessorTable + .ensureFieldAccessorsInitialized( + serialization.Bigdl.TensorStorage.class, serialization.Bigdl.TensorStorage.Builder.class); + } + + // Construct using serialization.Bigdl.TensorStorage.newBuilder() + private Builder() { + maybeForceBuilderInitialization(); + } + + private Builder( + com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { + super(parent); + maybeForceBuilderInitialization(); + } + private void maybeForceBuilderInitialization() { + if (com.google.protobuf.GeneratedMessageV3 + .alwaysUseFieldBuilders) { + } + } + public Builder clear() { + super.clear(); + datatype_ = 0; + + floatData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000002); + doubleData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000004); + boolData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000008); + stringData_ = com.google.protobuf.LazyStringArrayList.EMPTY; + bitField0_ = (bitField0_ & ~0x00000010); + intData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000020); + longData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000040); + bytesData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000080); + id_ = 0; + + return this; + } + + public com.google.protobuf.Descriptors.Descriptor + getDescriptorForType() { + return serialization.Bigdl.internal_static_serialization_TensorStorage_descriptor; + } + + public serialization.Bigdl.TensorStorage getDefaultInstanceForType() { + return serialization.Bigdl.TensorStorage.getDefaultInstance(); + } + + public serialization.Bigdl.TensorStorage build() { + serialization.Bigdl.TensorStorage result = buildPartial(); + if (!result.isInitialized()) { + throw newUninitializedMessageException(result); + } + return result; + } + + public serialization.Bigdl.TensorStorage buildPartial() { + serialization.Bigdl.TensorStorage result = new serialization.Bigdl.TensorStorage(this); + int from_bitField0_ = bitField0_; + int to_bitField0_ = 0; + result.datatype_ = datatype_; + if (((bitField0_ & 0x00000002) == 0x00000002)) { + floatData_ = java.util.Collections.unmodifiableList(floatData_); + bitField0_ = (bitField0_ & ~0x00000002); + } + result.floatData_ = floatData_; + if (((bitField0_ & 0x00000004) == 0x00000004)) { + doubleData_ = java.util.Collections.unmodifiableList(doubleData_); + bitField0_ = (bitField0_ & ~0x00000004); + } + result.doubleData_ = doubleData_; + if (((bitField0_ & 0x00000008) == 0x00000008)) { + boolData_ = java.util.Collections.unmodifiableList(boolData_); + bitField0_ = (bitField0_ & ~0x00000008); + } + result.boolData_ = boolData_; + if (((bitField0_ & 0x00000010) == 0x00000010)) { + stringData_ = stringData_.getUnmodifiableView(); + bitField0_ = (bitField0_ & ~0x00000010); + } + result.stringData_ = stringData_; + if (((bitField0_ & 0x00000020) == 0x00000020)) { + intData_ = java.util.Collections.unmodifiableList(intData_); + bitField0_ = (bitField0_ & ~0x00000020); + } + result.intData_ = intData_; + if (((bitField0_ & 0x00000040) == 0x00000040)) { + longData_ = java.util.Collections.unmodifiableList(longData_); + bitField0_ = (bitField0_ & ~0x00000040); + } + result.longData_ = longData_; + if (((bitField0_ & 0x00000080) == 0x00000080)) { + bytesData_ = java.util.Collections.unmodifiableList(bytesData_); + bitField0_ = (bitField0_ & ~0x00000080); + } + result.bytesData_ = bytesData_; + result.id_ = id_; + result.bitField0_ = to_bitField0_; + onBuilt(); + return result; + } + + public Builder clone() { + return (Builder) super.clone(); + } + public Builder setField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.setField(field, value); + } + public Builder clearField( + com.google.protobuf.Descriptors.FieldDescriptor field) { + return (Builder) super.clearField(field); + } + public Builder clearOneof( + com.google.protobuf.Descriptors.OneofDescriptor oneof) { + return (Builder) super.clearOneof(oneof); + } + public Builder setRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + int index, java.lang.Object value) { + return (Builder) super.setRepeatedField(field, index, value); + } + public Builder addRepeatedField( + com.google.protobuf.Descriptors.FieldDescriptor field, + java.lang.Object value) { + return (Builder) super.addRepeatedField(field, value); + } + public Builder mergeFrom(com.google.protobuf.Message other) { + if (other instanceof serialization.Bigdl.TensorStorage) { + return mergeFrom((serialization.Bigdl.TensorStorage)other); + } else { + super.mergeFrom(other); + return this; + } + } + + public Builder mergeFrom(serialization.Bigdl.TensorStorage other) { + if (other == serialization.Bigdl.TensorStorage.getDefaultInstance()) return this; + if (other.datatype_ != 0) { + setDatatypeValue(other.getDatatypeValue()); + } + if (!other.floatData_.isEmpty()) { + if (floatData_.isEmpty()) { + floatData_ = other.floatData_; + bitField0_ = (bitField0_ & ~0x00000002); + } else { + ensureFloatDataIsMutable(); + floatData_.addAll(other.floatData_); + } + onChanged(); + } + if (!other.doubleData_.isEmpty()) { + if (doubleData_.isEmpty()) { + doubleData_ = other.doubleData_; + bitField0_ = (bitField0_ & ~0x00000004); + } else { + ensureDoubleDataIsMutable(); + doubleData_.addAll(other.doubleData_); + } + onChanged(); + } + if (!other.boolData_.isEmpty()) { + if (boolData_.isEmpty()) { + boolData_ = other.boolData_; + bitField0_ = (bitField0_ & ~0x00000008); + } else { + ensureBoolDataIsMutable(); + boolData_.addAll(other.boolData_); + } + onChanged(); + } + if (!other.stringData_.isEmpty()) { + if (stringData_.isEmpty()) { + stringData_ = other.stringData_; + bitField0_ = (bitField0_ & ~0x00000010); + } else { + ensureStringDataIsMutable(); + stringData_.addAll(other.stringData_); + } + onChanged(); + } + if (!other.intData_.isEmpty()) { + if (intData_.isEmpty()) { + intData_ = other.intData_; + bitField0_ = (bitField0_ & ~0x00000020); + } else { + ensureIntDataIsMutable(); + intData_.addAll(other.intData_); + } + onChanged(); + } + if (!other.longData_.isEmpty()) { + if (longData_.isEmpty()) { + longData_ = other.longData_; + bitField0_ = (bitField0_ & ~0x00000040); + } else { + ensureLongDataIsMutable(); + longData_.addAll(other.longData_); + } + onChanged(); + } + if (!other.bytesData_.isEmpty()) { + if (bytesData_.isEmpty()) { + bytesData_ = other.bytesData_; + bitField0_ = (bitField0_ & ~0x00000080); + } else { + ensureBytesDataIsMutable(); + bytesData_.addAll(other.bytesData_); + } + onChanged(); + } + if (other.getId() != 0) { + setId(other.getId()); + } + this.mergeUnknownFields(other.unknownFields); + onChanged(); + return this; + } + + public final boolean isInitialized() { + return true; + } + + public Builder mergeFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws java.io.IOException { + serialization.Bigdl.TensorStorage parsedMessage = null; + try { + parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); + } catch (com.google.protobuf.InvalidProtocolBufferException e) { + parsedMessage = (serialization.Bigdl.TensorStorage) e.getUnfinishedMessage(); + throw e.unwrapIOException(); + } finally { + if (parsedMessage != null) { + mergeFrom(parsedMessage); + } + } + return this; + } + private int bitField0_; + + private int datatype_ = 0; + /** + * .serialization.DataType datatype = 1; + */ + public int getDatatypeValue() { + return datatype_; + } + /** + * .serialization.DataType datatype = 1; + */ + public Builder setDatatypeValue(int value) { + datatype_ = value; + onChanged(); + return this; + } + /** + * .serialization.DataType datatype = 1; + */ + public serialization.Bigdl.DataType getDatatype() { + serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); + return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; + } + /** + * .serialization.DataType datatype = 1; + */ + public Builder setDatatype(serialization.Bigdl.DataType value) { + if (value == null) { + throw new NullPointerException(); + } + + datatype_ = value.getNumber(); + onChanged(); + return this; + } + /** + * .serialization.DataType datatype = 1; + */ + public Builder clearDatatype() { + + datatype_ = 0; + onChanged(); + return this; + } + + private java.util.List floatData_ = java.util.Collections.emptyList(); + private void ensureFloatDataIsMutable() { + if (!((bitField0_ & 0x00000002) == 0x00000002)) { + floatData_ = new java.util.ArrayList(floatData_); + bitField0_ |= 0x00000002; + } + } + /** + * 
+       *float tensor element
+       *
+ * + * repeated float float_data = 2; + */ + public java.util.List + getFloatDataList() { + return java.util.Collections.unmodifiableList(floatData_); + } + /** + * 
+       *float tensor element
+       *
+ * + * repeated float float_data = 2; + */ + public int getFloatDataCount() { + return floatData_.size(); + } + /** + * 
+       *float tensor element
+       *
+ * + * repeated float float_data = 2; + */ + public float getFloatData(int index) { + return floatData_.get(index); + } + /** + * 
+       *float tensor element
+       *
+ * + * repeated float float_data = 2; + */ + public Builder setFloatData( + int index, float value) { + ensureFloatDataIsMutable(); + floatData_.set(index, value); + onChanged(); + return this; + } + /** + * 
+       *float tensor element
+       *
+ * + * repeated float float_data = 2; + */ + public Builder addFloatData(float value) { + ensureFloatDataIsMutable(); + floatData_.add(value); + onChanged(); + return this; + } + /** + * 
+       *float tensor element
+       *
+ * + * repeated float float_data = 2; + */ + public Builder addAllFloatData( + java.lang.Iterable values) { + ensureFloatDataIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, floatData_); + onChanged(); + return this; + } + /** + * 
+       *float tensor element
+       *
+ * + * repeated float float_data = 2; + */ + public Builder clearFloatData() { + floatData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000002); + onChanged(); + return this; + } + + private java.util.List doubleData_ = java.util.Collections.emptyList(); + private void ensureDoubleDataIsMutable() { + if (!((bitField0_ & 0x00000004) == 0x00000004)) { + doubleData_ = new java.util.ArrayList(doubleData_); + bitField0_ |= 0x00000004; + } + } + /** + * 
+       *double tensor element
+       *
+ * + * repeated double double_data = 3; + */ + public java.util.List + getDoubleDataList() { + return java.util.Collections.unmodifiableList(doubleData_); + } + /** + * 
+       *double tensor element
+       *
+ * + * repeated double double_data = 3; + */ + public int getDoubleDataCount() { + return doubleData_.size(); + } + /** + * 
+       *double tensor element
+       *
+ * + * repeated double double_data = 3; + */ + public double getDoubleData(int index) { + return doubleData_.get(index); + } + /** + * 
+       *double tensor element
+       *
+ * + * repeated double double_data = 3; + */ + public Builder setDoubleData( + int index, double value) { + ensureDoubleDataIsMutable(); + doubleData_.set(index, value); + onChanged(); + return this; + } + /** + * 
+       *double tensor element
+       *
+ * + * repeated double double_data = 3; + */ + public Builder addDoubleData(double value) { + ensureDoubleDataIsMutable(); + doubleData_.add(value); + onChanged(); + return this; + } + /** + * 
+       *double tensor element
+       *
+ * + * repeated double double_data = 3; + */ + public Builder addAllDoubleData( + java.lang.Iterable values) { + ensureDoubleDataIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, doubleData_); + onChanged(); + return this; + } + /** + * 
+       *double tensor element
+       *
+ * + * repeated double double_data = 3; + */ + public Builder clearDoubleData() { + doubleData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000004); + onChanged(); + return this; + } + + private java.util.List boolData_ = java.util.Collections.emptyList(); + private void ensureBoolDataIsMutable() { + if (!((bitField0_ & 0x00000008) == 0x00000008)) { + boolData_ = new java.util.ArrayList(boolData_); + bitField0_ |= 0x00000008; + } + } + /** + * 
+       *boolean tensor element
+       *
+ * + * repeated bool bool_data = 4; + */ + public java.util.List + getBoolDataList() { + return java.util.Collections.unmodifiableList(boolData_); + } + /** + * 
+       *boolean tensor element
+       *
+ * + * repeated bool bool_data = 4; + */ + public int getBoolDataCount() { + return boolData_.size(); + } + /** + * 
+       *boolean tensor element
+       *
+ * + * repeated bool bool_data = 4; + */ + public boolean getBoolData(int index) { + return boolData_.get(index); + } + /** + * 
+       *boolean tensor element
+       *
+ * + * repeated bool bool_data = 4; + */ + public Builder setBoolData( + int index, boolean value) { + ensureBoolDataIsMutable(); + boolData_.set(index, value); + onChanged(); + return this; + } + /** + * 
+       *boolean tensor element
+       *
+ * + * repeated bool bool_data = 4; + */ + public Builder addBoolData(boolean value) { + ensureBoolDataIsMutable(); + boolData_.add(value); + onChanged(); + return this; + } + /** + * 
+       *boolean tensor element
+       *
+ * + * repeated bool bool_data = 4; + */ + public Builder addAllBoolData( + java.lang.Iterable values) { + ensureBoolDataIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, boolData_); + onChanged(); + return this; + } + /** + * 
+       *boolean tensor element
+       *
+ * + * repeated bool bool_data = 4; + */ + public Builder clearBoolData() { + boolData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000008); + onChanged(); + return this; + } + + private com.google.protobuf.LazyStringList stringData_ = com.google.protobuf.LazyStringArrayList.EMPTY; + private void ensureStringDataIsMutable() { + if (!((bitField0_ & 0x00000010) == 0x00000010)) { + stringData_ = new com.google.protobuf.LazyStringArrayList(stringData_); + bitField0_ |= 0x00000010; } } /** * 
-       *tensor element
+       *string tensor element
        *
* - * repeated double double_data = 4; + * repeated string string_data = 5; */ - public java.util.List - getDoubleDataList() { - return java.util.Collections.unmodifiableList(doubleData_); + public com.google.protobuf.ProtocolStringList + getStringDataList() { + return stringData_.getUnmodifiableView(); + } + /** + * 
+       *string tensor element
+       *
+ * + * repeated string string_data = 5; + */ + public int getStringDataCount() { + return stringData_.size(); + } + /** + * 
+       *string tensor element
+       *
+ * + * repeated string string_data = 5; + */ + public java.lang.String getStringData(int index) { + return stringData_.get(index); + } + /** + * 
+       *string tensor element
+       *
+ * + * repeated string string_data = 5; + */ + public com.google.protobuf.ByteString + getStringDataBytes(int index) { + return stringData_.getByteString(index); + } + /** + * 
+       *string tensor element
+       *
+ * + * repeated string string_data = 5; + */ + public Builder setStringData( + int index, java.lang.String value) { + if (value == null) { + throw new NullPointerException(); + } + ensureStringDataIsMutable(); + stringData_.set(index, value); + onChanged(); + return this; + } + /** + * 
+       *string tensor element
+       *
+ * + * repeated string string_data = 5; + */ + public Builder addStringData( + java.lang.String value) { + if (value == null) { + throw new NullPointerException(); + } + ensureStringDataIsMutable(); + stringData_.add(value); + onChanged(); + return this; + } + /** + * 
+       *string tensor element
+       *
+ * + * repeated string string_data = 5; + */ + public Builder addAllStringData( + java.lang.Iterable values) { + ensureStringDataIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, stringData_); + onChanged(); + return this; + } + /** + * 
+       *string tensor element
+       *
+ * + * repeated string string_data = 5; + */ + public Builder clearStringData() { + stringData_ = com.google.protobuf.LazyStringArrayList.EMPTY; + bitField0_ = (bitField0_ & ~0x00000010); + onChanged(); + return this; + } + /** + * 
+       *string tensor element
+       *
+ * + * repeated string string_data = 5; + */ + public Builder addStringDataBytes( + com.google.protobuf.ByteString value) { + if (value == null) { + throw new NullPointerException(); + } + checkByteStringIsUtf8(value); + ensureStringDataIsMutable(); + stringData_.add(value); + onChanged(); + return this; + } + + private java.util.List intData_ = java.util.Collections.emptyList(); + private void ensureIntDataIsMutable() { + if (!((bitField0_ & 0x00000020) == 0x00000020)) { + intData_ = new java.util.ArrayList(intData_); + bitField0_ |= 0x00000020; + } + } + /** + * 
+       *int tensor element
+       *
+ * + * repeated int32 int_data = 6; + */ + public java.util.List + getIntDataList() { + return java.util.Collections.unmodifiableList(intData_); + } + /** + * 
+       *int tensor element
+       *
+ * + * repeated int32 int_data = 6; + */ + public int getIntDataCount() { + return intData_.size(); + } + /** + * 
+       *int tensor element
+       *
+ * + * repeated int32 int_data = 6; + */ + public int getIntData(int index) { + return intData_.get(index); + } + /** + * 
+       *int tensor element
+       *
+ * + * repeated int32 int_data = 6; + */ + public Builder setIntData( + int index, int value) { + ensureIntDataIsMutable(); + intData_.set(index, value); + onChanged(); + return this; + } + /** + * 
+       *int tensor element
+       *
+ * + * repeated int32 int_data = 6; + */ + public Builder addIntData(int value) { + ensureIntDataIsMutable(); + intData_.add(value); + onChanged(); + return this; + } + /** + * 
+       *int tensor element
+       *
+ * + * repeated int32 int_data = 6; + */ + public Builder addAllIntData( + java.lang.Iterable values) { + ensureIntDataIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, intData_); + onChanged(); + return this; + } + /** + * 
+       *int tensor element
+       *
+ * + * repeated int32 int_data = 6; + */ + public Builder clearIntData() { + intData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000020); + onChanged(); + return this; + } + + private java.util.List longData_ = java.util.Collections.emptyList(); + private void ensureLongDataIsMutable() { + if (!((bitField0_ & 0x00000040) == 0x00000040)) { + longData_ = new java.util.ArrayList(longData_); + bitField0_ |= 0x00000040; + } + } + /** + * 
+       *long tensor element
+       *
+ * + * repeated int64 long_data = 7; + */ + public java.util.List + getLongDataList() { + return java.util.Collections.unmodifiableList(longData_); + } + /** + * 
+       *long tensor element
+       *
+ * + * repeated int64 long_data = 7; + */ + public int getLongDataCount() { + return longData_.size(); + } + /** + * 
+       *long tensor element
+       *
+ * + * repeated int64 long_data = 7; + */ + public long getLongData(int index) { + return longData_.get(index); + } + /** + * 
+       *long tensor element
+       *
+ * + * repeated int64 long_data = 7; + */ + public Builder setLongData( + int index, long value) { + ensureLongDataIsMutable(); + longData_.set(index, value); + onChanged(); + return this; + } + /** + * 
+       *long tensor element
+       *
+ * + * repeated int64 long_data = 7; + */ + public Builder addLongData(long value) { + ensureLongDataIsMutable(); + longData_.add(value); + onChanged(); + return this; + } + /** + * 
+       *long tensor element
+       *
+ * + * repeated int64 long_data = 7; + */ + public Builder addAllLongData( + java.lang.Iterable values) { + ensureLongDataIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, longData_); + onChanged(); + return this; + } + /** + * 
+       *long tensor element
+       *
+ * + * repeated int64 long_data = 7; + */ + public Builder clearLongData() { + longData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000040); + onChanged(); + return this; + } + + private java.util.List bytesData_ = java.util.Collections.emptyList(); + private void ensureBytesDataIsMutable() { + if (!((bitField0_ & 0x00000080) == 0x00000080)) { + bytesData_ = new java.util.ArrayList(bytesData_); + bitField0_ |= 0x00000080; + } + } + /** + * 
+       *byte tensor element
+       *
+ * + * repeated bytes bytes_data = 8; + */ + public java.util.List + getBytesDataList() { + return java.util.Collections.unmodifiableList(bytesData_); + } + /** + * 
+       *byte tensor element
+       *
+ * + * repeated bytes bytes_data = 8; + */ + public int getBytesDataCount() { + return bytesData_.size(); + } + /** + * 
+       *byte tensor element
+       *
+ * + * repeated bytes bytes_data = 8; + */ + public com.google.protobuf.ByteString getBytesData(int index) { + return bytesData_.get(index); + } + /** + * 
+       *byte tensor element
+       *
+ * + * repeated bytes bytes_data = 8; + */ + public Builder setBytesData( + int index, com.google.protobuf.ByteString value) { + if (value == null) { + throw new NullPointerException(); + } + ensureBytesDataIsMutable(); + bytesData_.set(index, value); + onChanged(); + return this; } /** * 
-       *tensor element
+       *byte tensor element
        *
* - * repeated double double_data = 4; + * repeated bytes bytes_data = 8; */ - public int getDoubleDataCount() { - return doubleData_.size(); + public Builder addBytesData(com.google.protobuf.ByteString value) { + if (value == null) { + throw new NullPointerException(); + } + ensureBytesDataIsMutable(); + bytesData_.add(value); + onChanged(); + return this; } /** * 
-       *tensor element
+       *byte tensor element
        *
* - * repeated double double_data = 4; + * repeated bytes bytes_data = 8; */ - public double getDoubleData(int index) { - return doubleData_.get(index); + public Builder addAllBytesData( + java.lang.Iterable values) { + ensureBytesDataIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, bytesData_); + onChanged(); + return this; } /** * 
-       *tensor element
+       *byte tensor element
        *
* - * repeated double double_data = 4; + * repeated bytes bytes_data = 8; */ - public Builder setDoubleData( - int index, double value) { - ensureDoubleDataIsMutable(); - doubleData_.set(index, value); + public Builder clearBytesData() { + bytesData_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00000080); onChanged(); return this; } + + private int id_ ; /** * 
-       *tensor element
+       * storage Id, used for storage sharing
        *
* - * repeated double double_data = 4; + * int32 id = 9; */ - public Builder addDoubleData(double value) { - ensureDoubleDataIsMutable(); - doubleData_.add(value); - onChanged(); - return this; + public int getId() { + return id_; } /** * 
-       *tensor element
+       * storage Id, used for storage sharing
        *
* - * repeated double double_data = 4; + * int32 id = 9; */ - public Builder addAllDoubleData( - java.lang.Iterable values) { - ensureDoubleDataIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, doubleData_); + public Builder setId(int value) { + + id_ = value; onChanged(); return this; } /** * 
-       *tensor element
+       * storage Id, used for storage sharing
        *
* - * repeated double double_data = 4; + * int32 id = 9; */ - public Builder clearDoubleData() { - doubleData_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000008); + public Builder clearId() { + + id_ = 0; onChanged(); return this; } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.mergeUnknownFields(unknownFields); } - // @@protoc_insertion_point(builder_scope:serialization.BigDLTensor) + // @@protoc_insertion_point(builder_scope:serialization.TensorStorage) } - // @@protoc_insertion_point(class_scope:serialization.BigDLTensor) - private static final serialization.Bigdl.BigDLTensor DEFAULT_INSTANCE; + // @@protoc_insertion_point(class_scope:serialization.TensorStorage) + private static final serialization.Bigdl.TensorStorage DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new serialization.Bigdl.BigDLTensor(); + DEFAULT_INSTANCE = new serialization.Bigdl.TensorStorage(); } - public static serialization.Bigdl.BigDLTensor getDefaultInstance() { + public static serialization.Bigdl.TensorStorage getDefaultInstance() { return DEFAULT_INSTANCE; } - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public BigDLTensor parsePartialFrom( + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public TensorStorage parsePartialFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { - return new BigDLTensor(input, extensionRegistry); + return new TensorStorage(input, extensionRegistry); } }; - public static com.google.protobuf.Parser parser() { + public static com.google.protobuf.Parser parser() { return PARSER; } @java.lang.Override - public com.google.protobuf.Parser getParserForType() { + public com.google.protobuf.Parser getParserForType() { return PARSER; } - public serialization.Bigdl.BigDLTensor getDefaultInstanceForType() { + public serialization.Bigdl.TensorStorage getDefaultInstanceForType() { return DEFAULT_INSTANCE; } @@ -5288,11 +8302,11 @@ public interface RegularizerOrBuilder extends com.google.protobuf.MessageOrBuilder { /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ int getRegularizerTypeValue(); /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ serialization.Bigdl.RegularizerType getRegularizerType(); @@ -5316,6 +8330,7 @@ public static final class Regularizer extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:serialization.Regularizer) RegularizerOrBuilder { + private static final long serialVersionUID = 0L; // Use Regularizer.newBuilder() to construct. private Regularizer(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); @@ -5328,7 +8343,7 @@ private Regularizer() { @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); + return this.unknownFields; } private Regularizer( com.google.protobuf.CodedInputStream input, @@ -5336,6 +8351,8 @@ private Regularizer( throws com.google.protobuf.InvalidProtocolBufferException { this(); int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { @@ -5345,7 +8362,8 @@ private Regularizer( done = true; break; default: { - if (!input.skipField(tag)) { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { done = true; } break; @@ -5388,6 +8406,7 @@ private Regularizer( if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) { regularData_ = java.util.Collections.unmodifiableList(regularData_); } + this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } @@ -5407,13 +8426,13 @@ private Regularizer( public static final int REGULARIZERTYPE_FIELD_NUMBER = 1; private int regularizerType_; /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ public int getRegularizerTypeValue() { return regularizerType_; } /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ public serialization.Bigdl.RegularizerType getRegularizerType() { serialization.Bigdl.RegularizerType result = serialization.Bigdl.RegularizerType.valueOf(regularizerType_); @@ -5466,6 +8485,7 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) for (int i = 0; i < regularData_.size(); i++) { output.writeDoubleNoTag(regularData_.get(i)); } + unknownFields.writeTo(output); } public int getSerializedSize() { @@ -5488,11 +8508,11 @@ public int getSerializedSize() { } regularDataMemoizedSerializedSize = dataSize; } + size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } - private static final long serialVersionUID = 0L; @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { @@ -5507,6 +8527,7 @@ public boolean equals(final java.lang.Object obj) { result = result && regularizerType_ == other.regularizerType_; result = result && getRegularDataList() .equals(other.getRegularDataList()); + result = result && unknownFields.equals(other.unknownFields); return result; } @@ -5516,7 +8537,7 @@ public int hashCode() { return memoizedHashCode; } int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); + hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + REGULARIZERTYPE_FIELD_NUMBER; hash = (53 * hash) + regularizerType_; if (getRegularDataCount() > 0) { @@ -5528,6 +8549,17 @@ public int hashCode() { return hash; } + public static serialization.Bigdl.Regularizer parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.Regularizer parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } public static serialization.Bigdl.Regularizer parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { @@ -5685,7 +8717,7 @@ public Builder clone() { } public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( @@ -5698,12 +8730,12 @@ public Builder clearOneof( } public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { + int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { @@ -5730,6 +8762,7 @@ public Builder mergeFrom(serialization.Bigdl.Regularizer other) { } onChanged(); } + this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } @@ -5759,13 +8792,13 @@ public Builder mergeFrom( private int regularizerType_ = 0; /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ public int getRegularizerTypeValue() { return regularizerType_; } /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ public Builder setRegularizerTypeValue(int value) { regularizerType_ = value; @@ -5773,14 +8806,14 @@ public Builder setRegularizerTypeValue(int value) { return this; } /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ public serialization.Bigdl.RegularizerType getRegularizerType() { serialization.Bigdl.RegularizerType result = serialization.Bigdl.RegularizerType.valueOf(regularizerType_); return result == null ? serialization.Bigdl.RegularizerType.UNRECOGNIZED : result; } /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ public Builder setRegularizerType(serialization.Bigdl.RegularizerType value) { if (value == null) { @@ -5792,7 +8825,7 @@ public Builder setRegularizerType(serialization.Bigdl.RegularizerType value) { return this; } /** - * optional .serialization.RegularizerType regularizerType = 1; + * .serialization.RegularizerType regularizerType = 1; */ public Builder clearRegularizerType() { @@ -5868,12 +8901,12 @@ public Builder clearRegularData() { } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.mergeUnknownFields(unknownFields); } @@ -5920,11 +8953,11 @@ public interface AttrValueOrBuilder extends com.google.protobuf.MessageOrBuilder { /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ int getDataTypeValue(); /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ serialization.Bigdl.DataType getDataType(); @@ -5933,7 +8966,7 @@ public interface AttrValueOrBuilder extends * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ java.lang.String getSubType(); /** @@ -5941,7 +8974,7 @@ public interface AttrValueOrBuilder extends * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ com.google.protobuf.ByteString getSubTypeBytes(); @@ -5951,7 +8984,7 @@ public interface AttrValueOrBuilder extends * int32 value * * - * optional int32 int32Value = 3; + * int32 int32Value = 3; */ int getInt32Value(); @@ -5960,7 +8993,7 @@ public interface AttrValueOrBuilder extends *int64 value * * - * optional int64 int64Value = 4; + * int64 int64Value = 4; */ long getInt64Value(); @@ -5969,7 +9002,7 @@ public interface AttrValueOrBuilder extends *float value * * - * optional float floatValue = 5; + * float floatValue = 5; */ float getFloatValue(); @@ -5978,7 +9011,7 @@ public interface AttrValueOrBuilder extends *float value * * - * optional double doubleValue = 6; + * double doubleValue = 6; */ double getDoubleValue(); @@ -5987,7 +9020,7 @@ public interface AttrValueOrBuilder extends * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ java.lang.String getStringValue(); /** @@ -5995,7 +9028,7 @@ public interface AttrValueOrBuilder extends * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ com.google.protobuf.ByteString getStringValueBytes(); @@ -6005,7 +9038,7 @@ public interface AttrValueOrBuilder extends * bool value * * - * optional bool boolValue = 8; + * bool boolValue = 8; */ boolean getBoolValue(); @@ -6014,7 +9047,15 @@ public interface AttrValueOrBuilder extends * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; + */ + boolean hasRegularizerValue(); + /** + * 
+     * Regularizer
+     *
+ * + * .serialization.Regularizer regularizerValue = 9; */ serialization.Bigdl.Regularizer getRegularizerValue(); /** @@ -6022,7 +9063,7 @@ public interface AttrValueOrBuilder extends * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder(); @@ -6031,7 +9072,15 @@ public interface AttrValueOrBuilder extends *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; + */ + boolean hasTensorValue(); + /** + * 
+     *tensor value
+     *
+ * + * .serialization.BigDLTensor tensorValue = 10; */ serialization.Bigdl.BigDLTensor getTensorValue(); /** @@ -6039,7 +9088,7 @@ public interface AttrValueOrBuilder extends *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder(); @@ -6048,7 +9097,7 @@ public interface AttrValueOrBuilder extends *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ int getVariableFormatValueValue(); /** @@ -6056,7 +9105,7 @@ public interface AttrValueOrBuilder extends *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ serialization.Bigdl.VarFormat getVariableFormatValue(); @@ -6065,7 +9114,15 @@ public interface AttrValueOrBuilder extends * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; + */ + boolean hasInitMethodValue(); + /** + * 
+     * init method
+     *
+ * + * .serialization.InitMethod initMethodValue = 12; */ serialization.Bigdl.InitMethod getInitMethodValue(); /** @@ -6073,7 +9130,7 @@ public interface AttrValueOrBuilder extends * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder(); @@ -6082,7 +9139,15 @@ public interface AttrValueOrBuilder extends * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; + */ + boolean hasBigDLModuleValue(); + /** + * 
+     * big DL module
+     *
+ * + * .serialization.BigDLModule bigDLModuleValue = 13; */ serialization.Bigdl.BigDLModule getBigDLModuleValue(); /** @@ -6090,7 +9155,7 @@ public interface AttrValueOrBuilder extends * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder(); @@ -6099,7 +9164,15 @@ public interface AttrValueOrBuilder extends * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; + */ + boolean hasNameAttrListValue(); + /** + * 
+     * name attribute list
+     *
+ * + * .serialization.NameAttrList nameAttrListValue = 14; */ serialization.Bigdl.NameAttrList getNameAttrListValue(); /** @@ -6107,7 +9180,7 @@ public interface AttrValueOrBuilder extends * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder(); @@ -6116,7 +9189,15 @@ public interface AttrValueOrBuilder extends *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; + */ + boolean hasArrayValue(); + /** + * 
+     *array value of any type
+     *
+ * + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ serialization.Bigdl.AttrValue.ArrayValue getArrayValue(); /** @@ -6124,7 +9205,7 @@ public interface AttrValueOrBuilder extends *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder(); @@ -6133,7 +9214,7 @@ public interface AttrValueOrBuilder extends * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ int getDataFormatValueValue(); /** @@ -6141,7 +9222,7 @@ public interface AttrValueOrBuilder extends * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ serialization.Bigdl.InputDataFormat getDataFormatValue(); @@ -6150,7 +9231,15 @@ public interface AttrValueOrBuilder extends * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; + */ + boolean hasCustomValue(); + /** + * 
+     * custom value
+     *
+ * + * .google.protobuf.Any customValue = 17; */ com.google.protobuf.Any getCustomValue(); /** @@ -6158,7 +9247,7 @@ public interface AttrValueOrBuilder extends * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder(); @@ -6171,6 +9260,7 @@ public static final class AttrValue extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:serialization.AttrValue) AttrValueOrBuilder { + private static final long serialVersionUID = 0L; // Use AttrValue.newBuilder() to construct. private AttrValue(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); @@ -6183,7 +9273,7 @@ private AttrValue() { @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); + return this.unknownFields; } private AttrValue( com.google.protobuf.CodedInputStream input, @@ -6191,6 +9281,8 @@ private AttrValue( throws com.google.protobuf.InvalidProtocolBufferException { this(); int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { @@ -6200,7 +9292,8 @@ private AttrValue( done = true; break; default: { - if (!input.skipField(tag)) { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { done = true; } break; @@ -6366,6 +9459,7 @@ private AttrValue( throw new com.google.protobuf.InvalidProtocolBufferException( e).setUnfinishedMessage(this); } finally { + this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } @@ -6386,16 +9480,16 @@ public interface ArrayValueOrBuilder extends com.google.protobuf.MessageOrBuilder { /** - * optional int32 size = 1; + * int32 size = 1; */ int getSize(); /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ int getDatatypeValue(); /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ serialization.Bigdl.DataType getDatatype(); @@ -6909,6 +10003,7 @@ public static final class ArrayValue extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:serialization.AttrValue.ArrayValue) ArrayValueOrBuilder { + private static final long serialVersionUID = 0L; // Use ArrayValue.newBuilder() to construct. private ArrayValue(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); @@ -6935,7 +10030,7 @@ private ArrayValue() { @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); + return this.unknownFields; } private ArrayValue( com.google.protobuf.CodedInputStream input, @@ -6943,6 +10038,8 @@ private ArrayValue( throws com.google.protobuf.InvalidProtocolBufferException { this(); int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { @@ -6952,7 +10049,8 @@ private ArrayValue( done = true; break; default: { - if (!input.skipField(tag)) { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { done = true; } break; @@ -7232,6 +10330,7 @@ private ArrayValue( if (((mutable_bitField0_ & 0x00008000) == 0x00008000)) { custom_ = java.util.Collections.unmodifiableList(custom_); } + this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } @@ -7251,7 +10350,7 @@ private ArrayValue( public static final int SIZE_FIELD_NUMBER = 1; private int size_; /** - * optional int32 size = 1; + * int32 size = 1; */ public int getSize() { return size_; @@ -7260,13 +10359,13 @@ public int getSize() { public static final int DATATYPE_FIELD_NUMBER = 2; private int datatype_; /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ public int getDatatypeValue() { return datatype_; } /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ public serialization.Bigdl.DataType getDatatype() { serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); @@ -8037,6 +11136,7 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) for (int i = 0; i < custom_.size(); i++) { output.writeMessage(16, custom_.get(i)); } + unknownFields.writeTo(output); } public int getSerializedSize() { @@ -8169,11 +11269,11 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeMessageSize(16, custom_.get(i)); } + size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } - private static final long serialVersionUID = 0L; @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { @@ -8214,6 +11314,7 @@ public boolean equals(final java.lang.Object obj) { result = result && dataFormat_.equals(other.dataFormat_); result = result && getCustomList() .equals(other.getCustomList()); + result = result && unknownFields.equals(other.unknownFields); return result; } @@ -8223,7 +11324,7 @@ public int hashCode() { return memoizedHashCode; } int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); + hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + SIZE_FIELD_NUMBER; hash = (53 * hash) + getSize(); hash = (37 * hash) + DATATYPE_FIELD_NUMBER; @@ -8289,6 +11390,17 @@ public int hashCode() { return hash; } + public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { @@ -8594,7 +11706,7 @@ public Builder clone() { } public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( @@ -8607,12 +11719,12 @@ public Builder clearOneof( } public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { + int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { @@ -8868,6 +11980,7 @@ public Builder mergeFrom(serialization.Bigdl.AttrValue.ArrayValue other) { } } } + this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } @@ -8897,13 +12010,13 @@ public Builder mergeFrom( private int size_ ; /** - * optional int32 size = 1; + * int32 size = 1; */ public int getSize() { return size_; } /** - * optional int32 size = 1; + * int32 size = 1; */ public Builder setSize(int value) { @@ -8912,7 +12025,7 @@ public Builder setSize(int value) { return this; } /** - * optional int32 size = 1; + * int32 size = 1; */ public Builder clearSize() { @@ -8923,13 +12036,13 @@ public Builder clearSize() { private int datatype_ = 0; /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ public int getDatatypeValue() { return datatype_; } /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ public Builder setDatatypeValue(int value) { datatype_ = value; @@ -8937,14 +12050,14 @@ public Builder setDatatypeValue(int value) { return this; } /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ public serialization.Bigdl.DataType getDatatype() { serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; } /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ public Builder setDatatype(serialization.Bigdl.DataType value) { if (value == null) { @@ -8956,7 +12069,7 @@ public Builder setDatatype(serialization.Bigdl.DataType value) { return this; } /** - * optional .serialization.DataType datatype = 2; + * .serialization.DataType datatype = 2; */ public Builder clearDatatype() { @@ -11752,12 +14865,12 @@ public com.google.protobuf.Any.Builder addCustomBuilder( } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.mergeUnknownFields(unknownFields); } @@ -11866,13 +14979,13 @@ public int getNumber() { public static final int DATATYPE_FIELD_NUMBER = 1; private int dataType_; /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ public int getDataTypeValue() { return dataType_; } /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ public serialization.Bigdl.DataType getDataType() { serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(dataType_); @@ -11886,7 +14999,7 @@ public serialization.Bigdl.DataType getDataType() { * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ public java.lang.String getSubType() { java.lang.Object ref = subType_; @@ -11905,7 +15018,7 @@ public java.lang.String getSubType() { * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ public com.google.protobuf.ByteString getSubTypeBytes() { @@ -11927,7 +15040,7 @@ public java.lang.String getSubType() { * int32 value * * - * optional int32 int32Value = 3; + * int32 int32Value = 3; */ public int getInt32Value() { if (valueCase_ == 3) { @@ -11942,7 +15055,7 @@ public int getInt32Value() { *int64 value * * - * optional int64 int64Value = 4; + * int64 int64Value = 4; */ public long getInt64Value() { if (valueCase_ == 4) { @@ -11957,7 +15070,7 @@ public long getInt64Value() { *float value * * - * optional float floatValue = 5; + * float floatValue = 5; */ public float getFloatValue() { if (valueCase_ == 5) { @@ -11972,7 +15085,7 @@ public float getFloatValue() { *float value * * - * optional double doubleValue = 6; + * double doubleValue = 6; */ public double getDoubleValue() { if (valueCase_ == 6) { @@ -11987,7 +15100,7 @@ public double getDoubleValue() { * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ public java.lang.String getStringValue() { java.lang.Object ref = ""; @@ -12011,7 +15124,7 @@ public java.lang.String getStringValue() { * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ public com.google.protobuf.ByteString getStringValueBytes() { @@ -12038,7 +15151,7 @@ public java.lang.String getStringValue() { * bool value * * - * optional bool boolValue = 8; + * bool boolValue = 8; */ public boolean getBoolValue() { if (valueCase_ == 8) { @@ -12053,7 +15166,17 @@ public boolean getBoolValue() { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; + */ + public boolean hasRegularizerValue() { + return valueCase_ == 9; + } + /** + * 
+     * Regularizer
+     *
+ * + * .serialization.Regularizer regularizerValue = 9; */ public serialization.Bigdl.Regularizer getRegularizerValue() { if (valueCase_ == 9) { @@ -12066,7 +15189,7 @@ public serialization.Bigdl.Regularizer getRegularizerValue() { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { if (valueCase_ == 9) { @@ -12081,7 +15204,17 @@ public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; + */ + public boolean hasTensorValue() { + return valueCase_ == 10; + } + /** + * 
+     *tensor value
+     *
+ * + * .serialization.BigDLTensor tensorValue = 10; */ public serialization.Bigdl.BigDLTensor getTensorValue() { if (valueCase_ == 10) { @@ -12094,7 +15227,7 @@ public serialization.Bigdl.BigDLTensor getTensorValue() { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { if (valueCase_ == 10) { @@ -12109,7 +15242,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ public int getVariableFormatValueValue() { if (valueCase_ == 11) { @@ -12122,7 +15255,7 @@ public int getVariableFormatValueValue() { *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ public serialization.Bigdl.VarFormat getVariableFormatValue() { if (valueCase_ == 11) { @@ -12139,7 +15272,17 @@ public serialization.Bigdl.VarFormat getVariableFormatValue() { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; + */ + public boolean hasInitMethodValue() { + return valueCase_ == 12; + } + /** + * 
+     * init method
+     *
+ * + * .serialization.InitMethod initMethodValue = 12; */ public serialization.Bigdl.InitMethod getInitMethodValue() { if (valueCase_ == 12) { @@ -12152,7 +15295,7 @@ public serialization.Bigdl.InitMethod getInitMethodValue() { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { if (valueCase_ == 12) { @@ -12167,7 +15310,17 @@ public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; + */ + public boolean hasBigDLModuleValue() { + return valueCase_ == 13; + } + /** + * 
+     * big DL module
+     *
+ * + * .serialization.BigDLModule bigDLModuleValue = 13; */ public serialization.Bigdl.BigDLModule getBigDLModuleValue() { if (valueCase_ == 13) { @@ -12180,7 +15333,7 @@ public serialization.Bigdl.BigDLModule getBigDLModuleValue() { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { if (valueCase_ == 13) { @@ -12195,7 +15348,17 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; + */ + public boolean hasNameAttrListValue() { + return valueCase_ == 14; + } + /** + * 
+     * name attribute list
+     *
+ * + * .serialization.NameAttrList nameAttrListValue = 14; */ public serialization.Bigdl.NameAttrList getNameAttrListValue() { if (valueCase_ == 14) { @@ -12208,7 +15371,7 @@ public serialization.Bigdl.NameAttrList getNameAttrListValue() { * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() { if (valueCase_ == 14) { @@ -12223,7 +15386,17 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; + */ + public boolean hasArrayValue() { + return valueCase_ == 15; + } + /** + * 
+     *array value of any type
+     *
+ * + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { if (valueCase_ == 15) { @@ -12236,7 +15409,7 @@ public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder() { if (valueCase_ == 15) { @@ -12251,7 +15424,7 @@ public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder( * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ public int getDataFormatValueValue() { if (valueCase_ == 16) { @@ -12264,7 +15437,7 @@ public int getDataFormatValueValue() { * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ public serialization.Bigdl.InputDataFormat getDataFormatValue() { if (valueCase_ == 16) { @@ -12281,7 +15454,17 @@ public serialization.Bigdl.InputDataFormat getDataFormatValue() { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; + */ + public boolean hasCustomValue() { + return valueCase_ == 17; + } + /** + * 
+     * custom value
+     *
+ * + * .google.protobuf.Any customValue = 17; */ public com.google.protobuf.Any getCustomValue() { if (valueCase_ == 17) { @@ -12294,7 +15477,7 @@ public com.google.protobuf.Any getCustomValue() { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() { if (valueCase_ == 17) { @@ -12371,6 +15554,7 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) if (valueCase_ == 17) { output.writeMessage(17, (com.google.protobuf.Any) value_); } + unknownFields.writeTo(output); } public int getSerializedSize() { @@ -12449,11 +15633,11 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeMessageSize(17, (com.google.protobuf.Any) value_); } + size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } - private static final long serialVersionUID = 0L; @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { @@ -12539,6 +15723,7 @@ public boolean equals(final java.lang.Object obj) { case 0: default: } + result = result && unknownFields.equals(other.unknownFields); return result; } @@ -12548,7 +15733,7 @@ public int hashCode() { return memoizedHashCode; } int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); + hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + DATATYPE_FIELD_NUMBER; hash = (53 * hash) + dataType_; hash = (37 * hash) + SUBTYPE_FIELD_NUMBER; @@ -12626,6 +15811,17 @@ public int hashCode() { return hash; } + public static serialization.Bigdl.AttrValue parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.AttrValue parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } public static serialization.Bigdl.AttrValue parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { @@ -12852,7 +16048,7 @@ public Builder clone() { } public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( @@ -12865,12 +16061,12 @@ public Builder clearOneof( } public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { + int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { @@ -12958,6 +16154,7 @@ public Builder mergeFrom(serialization.Bigdl.AttrValue other) { break; } } + this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } @@ -13001,13 +16198,13 @@ public Builder clearValue() { private int dataType_ = 0; /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ public int getDataTypeValue() { return dataType_; } /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ public Builder setDataTypeValue(int value) { dataType_ = value; @@ -13015,14 +16212,14 @@ public Builder setDataTypeValue(int value) { return this; } /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ public serialization.Bigdl.DataType getDataType() { serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(dataType_); return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; } /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ public Builder setDataType(serialization.Bigdl.DataType value) { if (value == null) { @@ -13034,7 +16231,7 @@ public Builder setDataType(serialization.Bigdl.DataType value) { return this; } /** - * optional .serialization.DataType dataType = 1; + * .serialization.DataType dataType = 1; */ public Builder clearDataType() { @@ -13049,7 +16246,7 @@ public Builder clearDataType() { * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ public java.lang.String getSubType() { java.lang.Object ref = subType_; @@ -13068,7 +16265,7 @@ public java.lang.String getSubType() { * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ public com.google.protobuf.ByteString getSubTypeBytes() { @@ -13088,7 +16285,7 @@ public java.lang.String getSubType() { * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ public Builder setSubType( java.lang.String value) { @@ -13105,7 +16302,7 @@ public Builder setSubType( * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ public Builder clearSubType() { @@ -13118,7 +16315,7 @@ public Builder clearSubType() { * specific for custom data * * - * optional string subType = 2; + * string subType = 2; */ public Builder setSubTypeBytes( com.google.protobuf.ByteString value) { @@ -13137,7 +16334,7 @@ public Builder setSubTypeBytes( * int32 value * * - * optional int32 int32Value = 3; + * int32 int32Value = 3; */ public int getInt32Value() { if (valueCase_ == 3) { @@ -13150,7 +16347,7 @@ public int getInt32Value() { * int32 value * * - * optional int32 int32Value = 3; + * int32 int32Value = 3; */ public Builder setInt32Value(int value) { valueCase_ = 3; @@ -13163,7 +16360,7 @@ public Builder setInt32Value(int value) { * int32 value * * - * optional int32 int32Value = 3; + * int32 int32Value = 3; */ public Builder clearInt32Value() { if (valueCase_ == 3) { @@ -13179,7 +16376,7 @@ public Builder clearInt32Value() { *int64 value * * - * optional int64 int64Value = 4; + * int64 int64Value = 4; */ public long getInt64Value() { if (valueCase_ == 4) { @@ -13192,7 +16389,7 @@ public long getInt64Value() { *int64 value * * - * optional int64 int64Value = 4; + * int64 int64Value = 4; */ public Builder setInt64Value(long value) { valueCase_ = 4; @@ -13205,7 +16402,7 @@ public Builder setInt64Value(long value) { *int64 value * * - * optional int64 int64Value = 4; + * int64 int64Value = 4; */ public Builder clearInt64Value() { if (valueCase_ == 4) { @@ -13221,7 +16418,7 @@ public Builder clearInt64Value() { *float value * * - * optional float floatValue = 5; + * float floatValue = 5; */ public float getFloatValue() { if (valueCase_ == 5) { @@ -13234,7 +16431,7 @@ public float getFloatValue() { *float value * * - * optional float floatValue = 5; + * float floatValue = 5; */ public Builder setFloatValue(float value) { valueCase_ = 5; @@ -13247,7 +16444,7 @@ public Builder setFloatValue(float value) { *float value * * - * optional float floatValue = 5; + * float floatValue = 5; */ public Builder clearFloatValue() { if (valueCase_ == 5) { @@ -13263,7 +16460,7 @@ public Builder clearFloatValue() { *float value * * - * optional double doubleValue = 6; + * double doubleValue = 6; */ public double getDoubleValue() { if (valueCase_ == 6) { @@ -13276,7 +16473,7 @@ public double getDoubleValue() { *float value * * - * optional double doubleValue = 6; + * double doubleValue = 6; */ public Builder setDoubleValue(double value) { valueCase_ = 6; @@ -13289,7 +16486,7 @@ public Builder setDoubleValue(double value) { *float value * * - * optional double doubleValue = 6; + * double doubleValue = 6; */ public Builder clearDoubleValue() { if (valueCase_ == 6) { @@ -13305,7 +16502,7 @@ public Builder clearDoubleValue() { * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ public java.lang.String getStringValue() { java.lang.Object ref = ""; @@ -13329,7 +16526,7 @@ public java.lang.String getStringValue() { * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ public com.google.protobuf.ByteString getStringValueBytes() { @@ -13354,7 +16551,7 @@ public java.lang.String getStringValue() { * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ public Builder setStringValue( java.lang.String value) { @@ -13371,7 +16568,7 @@ public Builder setStringValue( * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ public Builder clearStringValue() { if (valueCase_ == 7) { @@ -13386,7 +16583,7 @@ public Builder clearStringValue() { * string value * * - * optional string stringValue = 7; + * string stringValue = 7; */ public Builder setStringValueBytes( com.google.protobuf.ByteString value) { @@ -13405,7 +16602,7 @@ public Builder setStringValueBytes( * bool value * * - * optional bool boolValue = 8; + * bool boolValue = 8; */ public boolean getBoolValue() { if (valueCase_ == 8) { @@ -13418,7 +16615,7 @@ public boolean getBoolValue() { * bool value * * - * optional bool boolValue = 8; + * bool boolValue = 8; */ public Builder setBoolValue(boolean value) { valueCase_ = 8; @@ -13431,7 +16628,7 @@ public Builder setBoolValue(boolean value) { * bool value * * - * optional bool boolValue = 8; + * bool boolValue = 8; */ public Builder clearBoolValue() { if (valueCase_ == 8) { @@ -13449,7 +16646,17 @@ public Builder clearBoolValue() { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; + */ + public boolean hasRegularizerValue() { + return valueCase_ == 9; + } + /** + * 
+       * Regularizer
+       *
+ * + * .serialization.Regularizer regularizerValue = 9; */ public serialization.Bigdl.Regularizer getRegularizerValue() { if (regularizerValueBuilder_ == null) { @@ -13469,7 +16676,7 @@ public serialization.Bigdl.Regularizer getRegularizerValue() { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ public Builder setRegularizerValue(serialization.Bigdl.Regularizer value) { if (regularizerValueBuilder_ == null) { @@ -13489,7 +16696,7 @@ public Builder setRegularizerValue(serialization.Bigdl.Regularizer value) { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ public Builder setRegularizerValue( serialization.Bigdl.Regularizer.Builder builderForValue) { @@ -13507,7 +16714,7 @@ public Builder setRegularizerValue( * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ public Builder mergeRegularizerValue(serialization.Bigdl.Regularizer value) { if (regularizerValueBuilder_ == null) { @@ -13533,7 +16740,7 @@ public Builder mergeRegularizerValue(serialization.Bigdl.Regularizer value) { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ public Builder clearRegularizerValue() { if (regularizerValueBuilder_ == null) { @@ -13556,7 +16763,7 @@ public Builder clearRegularizerValue() { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ public serialization.Bigdl.Regularizer.Builder getRegularizerValueBuilder() { return getRegularizerValueFieldBuilder().getBuilder(); @@ -13566,7 +16773,7 @@ public serialization.Bigdl.Regularizer.Builder getRegularizerValueBuilder() { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { if ((valueCase_ == 9) && (regularizerValueBuilder_ != null)) { @@ -13583,7 +16790,7 @@ public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { * Regularizer * * - * optional .serialization.Regularizer regularizerValue = 9; + * .serialization.Regularizer regularizerValue = 9; */ private com.google.protobuf.SingleFieldBuilderV3< serialization.Bigdl.Regularizer, serialization.Bigdl.Regularizer.Builder, serialization.Bigdl.RegularizerOrBuilder> @@ -13611,7 +16818,17 @@ public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; + */ + public boolean hasTensorValue() { + return valueCase_ == 10; + } + /** + * 
+       *tensor value
+       *
+ * + * .serialization.BigDLTensor tensorValue = 10; */ public serialization.Bigdl.BigDLTensor getTensorValue() { if (tensorValueBuilder_ == null) { @@ -13631,7 +16848,7 @@ public serialization.Bigdl.BigDLTensor getTensorValue() { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ public Builder setTensorValue(serialization.Bigdl.BigDLTensor value) { if (tensorValueBuilder_ == null) { @@ -13651,7 +16868,7 @@ public Builder setTensorValue(serialization.Bigdl.BigDLTensor value) { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ public Builder setTensorValue( serialization.Bigdl.BigDLTensor.Builder builderForValue) { @@ -13669,7 +16886,7 @@ public Builder setTensorValue( *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ public Builder mergeTensorValue(serialization.Bigdl.BigDLTensor value) { if (tensorValueBuilder_ == null) { @@ -13695,7 +16912,7 @@ public Builder mergeTensorValue(serialization.Bigdl.BigDLTensor value) { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ public Builder clearTensorValue() { if (tensorValueBuilder_ == null) { @@ -13718,7 +16935,7 @@ public Builder clearTensorValue() { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ public serialization.Bigdl.BigDLTensor.Builder getTensorValueBuilder() { return getTensorValueFieldBuilder().getBuilder(); @@ -13728,7 +16945,7 @@ public serialization.Bigdl.BigDLTensor.Builder getTensorValueBuilder() { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { if ((valueCase_ == 10) && (tensorValueBuilder_ != null)) { @@ -13745,7 +16962,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { *tensor value * * - * optional .serialization.BigDLTensor tensorValue = 10; + * .serialization.BigDLTensor tensorValue = 10; */ private com.google.protobuf.SingleFieldBuilderV3< serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> @@ -13771,7 +16988,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ public int getVariableFormatValueValue() { if (valueCase_ == 11) { @@ -13784,7 +17001,7 @@ public int getVariableFormatValueValue() { *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ public Builder setVariableFormatValueValue(int value) { valueCase_ = 11; @@ -13797,7 +17014,7 @@ public Builder setVariableFormatValueValue(int value) { *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ public serialization.Bigdl.VarFormat getVariableFormatValue() { if (valueCase_ == 11) { @@ -13812,7 +17029,7 @@ public serialization.Bigdl.VarFormat getVariableFormatValue() { *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ public Builder setVariableFormatValue(serialization.Bigdl.VarFormat value) { if (value == null) { @@ -13828,7 +17045,7 @@ public Builder setVariableFormatValue(serialization.Bigdl.VarFormat value) { *Variable format * * - * optional .serialization.VarFormat variableFormatValue = 11; + * .serialization.VarFormat variableFormatValue = 11; */ public Builder clearVariableFormatValue() { if (valueCase_ == 11) { @@ -13846,7 +17063,17 @@ public Builder clearVariableFormatValue() { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; + */ + public boolean hasInitMethodValue() { + return valueCase_ == 12; + } + /** + * 
+       * init method
+       *
+ * + * .serialization.InitMethod initMethodValue = 12; */ public serialization.Bigdl.InitMethod getInitMethodValue() { if (initMethodValueBuilder_ == null) { @@ -13866,7 +17093,7 @@ public serialization.Bigdl.InitMethod getInitMethodValue() { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ public Builder setInitMethodValue(serialization.Bigdl.InitMethod value) { if (initMethodValueBuilder_ == null) { @@ -13886,7 +17113,7 @@ public Builder setInitMethodValue(serialization.Bigdl.InitMethod value) { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ public Builder setInitMethodValue( serialization.Bigdl.InitMethod.Builder builderForValue) { @@ -13904,7 +17131,7 @@ public Builder setInitMethodValue( * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ public Builder mergeInitMethodValue(serialization.Bigdl.InitMethod value) { if (initMethodValueBuilder_ == null) { @@ -13930,7 +17157,7 @@ public Builder mergeInitMethodValue(serialization.Bigdl.InitMethod value) { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ public Builder clearInitMethodValue() { if (initMethodValueBuilder_ == null) { @@ -13953,7 +17180,7 @@ public Builder clearInitMethodValue() { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ public serialization.Bigdl.InitMethod.Builder getInitMethodValueBuilder() { return getInitMethodValueFieldBuilder().getBuilder(); @@ -13963,7 +17190,7 @@ public serialization.Bigdl.InitMethod.Builder getInitMethodValueBuilder() { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { if ((valueCase_ == 12) && (initMethodValueBuilder_ != null)) { @@ -13980,7 +17207,7 @@ public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { * init method * * - * optional .serialization.InitMethod initMethodValue = 12; + * .serialization.InitMethod initMethodValue = 12; */ private com.google.protobuf.SingleFieldBuilderV3< serialization.Bigdl.InitMethod, serialization.Bigdl.InitMethod.Builder, serialization.Bigdl.InitMethodOrBuilder> @@ -14008,7 +17235,17 @@ public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; + */ + public boolean hasBigDLModuleValue() { + return valueCase_ == 13; + } + /** + * 
+       * big DL module
+       *
+ * + * .serialization.BigDLModule bigDLModuleValue = 13; */ public serialization.Bigdl.BigDLModule getBigDLModuleValue() { if (bigDLModuleValueBuilder_ == null) { @@ -14028,7 +17265,7 @@ public serialization.Bigdl.BigDLModule getBigDLModuleValue() { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ public Builder setBigDLModuleValue(serialization.Bigdl.BigDLModule value) { if (bigDLModuleValueBuilder_ == null) { @@ -14048,7 +17285,7 @@ public Builder setBigDLModuleValue(serialization.Bigdl.BigDLModule value) { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ public Builder setBigDLModuleValue( serialization.Bigdl.BigDLModule.Builder builderForValue) { @@ -14066,7 +17303,7 @@ public Builder setBigDLModuleValue( * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ public Builder mergeBigDLModuleValue(serialization.Bigdl.BigDLModule value) { if (bigDLModuleValueBuilder_ == null) { @@ -14092,7 +17329,7 @@ public Builder mergeBigDLModuleValue(serialization.Bigdl.BigDLModule value) { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ public Builder clearBigDLModuleValue() { if (bigDLModuleValueBuilder_ == null) { @@ -14115,7 +17352,7 @@ public Builder clearBigDLModuleValue() { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ public serialization.Bigdl.BigDLModule.Builder getBigDLModuleValueBuilder() { return getBigDLModuleValueFieldBuilder().getBuilder(); @@ -14125,7 +17362,7 @@ public serialization.Bigdl.BigDLModule.Builder getBigDLModuleValueBuilder() { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { if ((valueCase_ == 13) && (bigDLModuleValueBuilder_ != null)) { @@ -14142,7 +17379,7 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { * big DL module * * - * optional .serialization.BigDLModule bigDLModuleValue = 13; + * .serialization.BigDLModule bigDLModuleValue = 13; */ private com.google.protobuf.SingleFieldBuilderV3< serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> @@ -14170,7 +17407,17 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; + */ + public boolean hasNameAttrListValue() { + return valueCase_ == 14; + } + /** + * 
+       * name attribute list
+       *
+ * + * .serialization.NameAttrList nameAttrListValue = 14; */ public serialization.Bigdl.NameAttrList getNameAttrListValue() { if (nameAttrListValueBuilder_ == null) { @@ -14190,7 +17437,7 @@ public serialization.Bigdl.NameAttrList getNameAttrListValue() { * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ public Builder setNameAttrListValue(serialization.Bigdl.NameAttrList value) { if (nameAttrListValueBuilder_ == null) { @@ -14210,7 +17457,7 @@ public Builder setNameAttrListValue(serialization.Bigdl.NameAttrList value) { * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ public Builder setNameAttrListValue( serialization.Bigdl.NameAttrList.Builder builderForValue) { @@ -14228,7 +17475,7 @@ public Builder setNameAttrListValue( * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ public Builder mergeNameAttrListValue(serialization.Bigdl.NameAttrList value) { if (nameAttrListValueBuilder_ == null) { @@ -14254,7 +17501,7 @@ public Builder mergeNameAttrListValue(serialization.Bigdl.NameAttrList value) { * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ public Builder clearNameAttrListValue() { if (nameAttrListValueBuilder_ == null) { @@ -14277,7 +17524,7 @@ public Builder clearNameAttrListValue() { * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ public serialization.Bigdl.NameAttrList.Builder getNameAttrListValueBuilder() { return getNameAttrListValueFieldBuilder().getBuilder(); @@ -14287,7 +17534,7 @@ public serialization.Bigdl.NameAttrList.Builder getNameAttrListValueBuilder() { * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() { if ((valueCase_ == 14) && (nameAttrListValueBuilder_ != null)) { @@ -14304,7 +17551,7 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() * name attribute list * * - * optional .serialization.NameAttrList nameAttrListValue = 14; + * .serialization.NameAttrList nameAttrListValue = 14; */ private com.google.protobuf.SingleFieldBuilderV3< serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> @@ -14332,7 +17579,17 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; + */ + public boolean hasArrayValue() { + return valueCase_ == 15; + } + /** + * 
+       *array value of any type
+       *
+ * + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { if (arrayValueBuilder_ == null) { @@ -14352,7 +17609,7 @@ public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public Builder setArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) { if (arrayValueBuilder_ == null) { @@ -14372,7 +17629,7 @@ public Builder setArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) { *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public Builder setArrayValue( serialization.Bigdl.AttrValue.ArrayValue.Builder builderForValue) { @@ -14390,7 +17647,7 @@ public Builder setArrayValue( *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public Builder mergeArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) { if (arrayValueBuilder_ == null) { @@ -14416,7 +17673,7 @@ public Builder mergeArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) { *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public Builder clearArrayValue() { if (arrayValueBuilder_ == null) { @@ -14439,7 +17696,7 @@ public Builder clearArrayValue() { *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public serialization.Bigdl.AttrValue.ArrayValue.Builder getArrayValueBuilder() { return getArrayValueFieldBuilder().getBuilder(); @@ -14449,7 +17706,7 @@ public serialization.Bigdl.AttrValue.ArrayValue.Builder getArrayValueBuilder() { *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder() { if ((valueCase_ == 15) && (arrayValueBuilder_ != null)) { @@ -14466,7 +17723,7 @@ public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder( *array value of any type * * - * optional .serialization.AttrValue.ArrayValue arrayValue = 15; + * .serialization.AttrValue.ArrayValue arrayValue = 15; */ private com.google.protobuf.SingleFieldBuilderV3< serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder> @@ -14492,7 +17749,7 @@ public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder( * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ public int getDataFormatValueValue() { if (valueCase_ == 16) { @@ -14505,7 +17762,7 @@ public int getDataFormatValueValue() { * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ public Builder setDataFormatValueValue(int value) { valueCase_ = 16; @@ -14518,7 +17775,7 @@ public Builder setDataFormatValueValue(int value) { * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ public serialization.Bigdl.InputDataFormat getDataFormatValue() { if (valueCase_ == 16) { @@ -14533,7 +17790,7 @@ public serialization.Bigdl.InputDataFormat getDataFormatValue() { * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ public Builder setDataFormatValue(serialization.Bigdl.InputDataFormat value) { if (value == null) { @@ -14549,7 +17806,7 @@ public Builder setDataFormatValue(serialization.Bigdl.InputDataFormat value) { * data format * * - * optional .serialization.InputDataFormat dataFormatValue = 16; + * .serialization.InputDataFormat dataFormatValue = 16; */ public Builder clearDataFormatValue() { if (valueCase_ == 16) { @@ -14567,7 +17824,17 @@ public Builder clearDataFormatValue() { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; + */ + public boolean hasCustomValue() { + return valueCase_ == 17; + } + /** + * 
+       * custom value
+       *
+ * + * .google.protobuf.Any customValue = 17; */ public com.google.protobuf.Any getCustomValue() { if (customValueBuilder_ == null) { @@ -14587,7 +17854,7 @@ public com.google.protobuf.Any getCustomValue() { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ public Builder setCustomValue(com.google.protobuf.Any value) { if (customValueBuilder_ == null) { @@ -14607,7 +17874,7 @@ public Builder setCustomValue(com.google.protobuf.Any value) { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ public Builder setCustomValue( com.google.protobuf.Any.Builder builderForValue) { @@ -14625,7 +17892,7 @@ public Builder setCustomValue( * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ public Builder mergeCustomValue(com.google.protobuf.Any value) { if (customValueBuilder_ == null) { @@ -14651,7 +17918,7 @@ public Builder mergeCustomValue(com.google.protobuf.Any value) { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ public Builder clearCustomValue() { if (customValueBuilder_ == null) { @@ -14674,7 +17941,7 @@ public Builder clearCustomValue() { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ public com.google.protobuf.Any.Builder getCustomValueBuilder() { return getCustomValueFieldBuilder().getBuilder(); @@ -14684,7 +17951,7 @@ public com.google.protobuf.Any.Builder getCustomValueBuilder() { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() { if ((valueCase_ == 17) && (customValueBuilder_ != null)) { @@ -14701,7 +17968,7 @@ public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() { * custom value * * - * optional .google.protobuf.Any customValue = 17; + * .google.protobuf.Any customValue = 17; */ private com.google.protobuf.SingleFieldBuilderV3< com.google.protobuf.Any, com.google.protobuf.Any.Builder, com.google.protobuf.AnyOrBuilder> @@ -14723,12 +17990,12 @@ public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() { } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.mergeUnknownFields(unknownFields); } @@ -14775,11 +18042,11 @@ public interface NameAttrListOrBuilder extends com.google.protobuf.MessageOrBuilder { /** - * optional string name = 1; + * string name = 1; */ java.lang.String getName(); /** - * optional string name = 1; + * string name = 1; */ com.google.protobuf.ByteString getNameBytes(); @@ -14825,6 +18092,7 @@ public static final class NameAttrList extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:serialization.NameAttrList) NameAttrListOrBuilder { + private static final long serialVersionUID = 0L; // Use NameAttrList.newBuilder() to construct. private NameAttrList(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); @@ -14836,7 +18104,7 @@ private NameAttrList() { @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); + return this.unknownFields; } private NameAttrList( com.google.protobuf.CodedInputStream input, @@ -14844,6 +18112,8 @@ private NameAttrList( throws com.google.protobuf.InvalidProtocolBufferException { this(); int mutable_bitField0_ = 0; + com.google.protobuf.UnknownFieldSet.Builder unknownFields = + com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { @@ -14853,7 +18123,8 @@ private NameAttrList( done = true; break; default: { - if (!input.skipField(tag)) { + if (!parseUnknownFieldProto3( + input, unknownFields, extensionRegistry, tag)) { done = true; } break; @@ -14871,9 +18142,10 @@ private NameAttrList( mutable_bitField0_ |= 0x00000002; } com.google.protobuf.MapEntry - attr = input.readMessage( + attr__ = input.readMessage( AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); - attr_.getMutableMap().put(attr.getKey(), attr.getValue()); + attr_.getMutableMap().put( + attr__.getKey(), attr__.getValue()); break; } } @@ -14884,6 +18156,7 @@ private NameAttrList( throw new com.google.protobuf.InvalidProtocolBufferException( e).setUnfinishedMessage(this); } finally { + this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } @@ -14914,7 +18187,7 @@ protected com.google.protobuf.MapField internalGetMapField( public static final int NAME_FIELD_NUMBER = 1; private volatile java.lang.Object name_; /** - * optional string name = 1; + * string name = 1; */ public java.lang.String getName() { java.lang.Object ref = name_; @@ -14929,7 +18202,7 @@ public java.lang.String getName() { } } /** - * optional string name = 1; + * string name = 1; */ public com.google.protobuf.ByteString getNameBytes() { @@ -15036,15 +18309,13 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) if (!getNameBytes().isEmpty()) { com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_); } - for (java.util.Map.Entry entry - : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - output.writeMessage(2, attr); - } + com.google.protobuf.GeneratedMessageV3 + .serializeStringMapTo( + output, + internalGetAttr(), + AttrDefaultEntryHolder.defaultEntry, + 2); + unknownFields.writeTo(output); } public int getSerializedSize() { @@ -15058,18 +18329,18 @@ public int getSerializedSize() { for (java.util.Map.Entry entry : internalGetAttr().getMap().entrySet()) { com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() + attr__ = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() .setKey(entry.getKey()) .setValue(entry.getValue()) .build(); size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, attr); + .computeMessageSize(2, attr__); } + size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } - private static final long serialVersionUID = 0L; @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { @@ -15085,6 +18356,7 @@ public boolean equals(final java.lang.Object obj) { .equals(other.getName()); result = result && internalGetAttr().equals( other.internalGetAttr()); + result = result && unknownFields.equals(other.unknownFields); return result; } @@ -15094,7 +18366,7 @@ public int hashCode() { return memoizedHashCode; } int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); + hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + NAME_FIELD_NUMBER; hash = (53 * hash) + getName().hashCode(); if (!internalGetAttr().getMap().isEmpty()) { @@ -15106,6 +18378,17 @@ public int hashCode() { return hash; } + public static serialization.Bigdl.NameAttrList parseFrom( + java.nio.ByteBuffer data) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data); + } + public static serialization.Bigdl.NameAttrList parseFrom( + java.nio.ByteBuffer data, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return PARSER.parseFrom(data, extensionRegistry); + } public static serialization.Bigdl.NameAttrList parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { @@ -15281,7 +18564,7 @@ public Builder clone() { } public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( @@ -15294,12 +18577,12 @@ public Builder clearOneof( } public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { + int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { + java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { @@ -15319,6 +18602,7 @@ public Builder mergeFrom(serialization.Bigdl.NameAttrList other) { } internalGetMutableAttr().mergeFrom( other.internalGetAttr()); + this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } @@ -15348,7 +18632,7 @@ public Builder mergeFrom( private java.lang.Object name_ = ""; /** - * optional string name = 1; + * string name = 1; */ public java.lang.String getName() { java.lang.Object ref = name_; @@ -15363,7 +18647,7 @@ public java.lang.String getName() { } } /** - * optional string name = 1; + * string name = 1; */ public com.google.protobuf.ByteString getNameBytes() { @@ -15379,7 +18663,7 @@ public java.lang.String getName() { } } /** - * optional string name = 1; + * string name = 1; */ public Builder setName( java.lang.String value) { @@ -15392,7 +18676,7 @@ public Builder setName( return this; } /** - * optional string name = 1; + * string name = 1; */ public Builder clearName() { @@ -15401,7 +18685,7 @@ public Builder clearName() { return this; } /** - * optional string name = 1; + * string name = 1; */ public Builder setNameBytes( com.google.protobuf.ByteString value) { @@ -15492,7 +18776,8 @@ public serialization.Bigdl.AttrValue getAttrOrThrow( } public Builder clearAttr() { - getMutableAttr().clear(); + internalGetMutableAttr().getMutableMap() + .clear(); return this; } /** @@ -15502,7 +18787,8 @@ public Builder clearAttr() { public Builder removeAttr( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().remove(key); + internalGetMutableAttr().getMutableMap() + .remove(key); return this; } /** @@ -15521,7 +18807,8 @@ public Builder putAttr( serialization.Bigdl.AttrValue value) { if (key == null) { throw new java.lang.NullPointerException(); } if (value == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().put(key, value); + internalGetMutableAttr().getMutableMap() + .put(key, value); return this; } /** @@ -15530,17 +18817,18 @@ public Builder putAttr( public Builder putAllAttr( java.util.Map values) { - getMutableAttr().putAll(values); + internalGetMutableAttr().getMutableMap() + .putAll(values); return this; } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; + return super.mergeUnknownFields(unknownFields); } @@ -15602,6 +18890,11 @@ public serialization.Bigdl.NameAttrList getDefaultInstanceForType() { private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internal_static_serialization_BigDLTensor_fieldAccessorTable; + private static final com.google.protobuf.Descriptors.Descriptor + internal_static_serialization_TensorStorage_descriptor; + private static final + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable + internal_static_serialization_TensorStorage_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor internal_static_serialization_Regularizer_descriptor; private static final @@ -15637,76 +18930,86 @@ public serialization.Bigdl.NameAttrList getDefaultInstanceForType() { static { java.lang.String[] descriptorData = { "\n\013bigdl.proto\022\rserialization\032\031google/pro" + - "tobuf/any.proto\"\352\002\n\013BigDLModule\022\014\n\004name\030" + + "tobuf/any.proto\"\232\003\n\013BigDLModule\022\014\n\004name\030" + "\001 \001(\t\022.\n\nsubModules\030\002 \003(\0132\032.serializatio" + "n.BigDLModule\022*\n\006weight\030\003 \001(\0132\032.serializ" + "ation.BigDLTensor\022(\n\004bias\030\004 \001(\0132\032.serial" + "ization.BigDLTensor\022\022\n\npreModules\030\005 \003(\t\022" + "\023\n\013nextModules\030\006 \003(\t\022\022\n\nmoduleType\030\007 \001(\t" + "\0222\n\004attr\030\010 \003(\0132$.serialization.BigDLModu" + - "le.AttrEntry\022\017\n\007version\030\t \001(\t\032E\n\tAttrEnt" + - "ry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002 \001(\0132\030.seriali", - "zation.AttrValue:\0028\001\"M\n\nInitMethod\0221\n\nme" + - "thodType\030\001 \001(\0162\035.serialization.InitMetho" + - "dType\022\014\n\004data\030\002 \003(\001\"o\n\013BigDLTensor\022)\n\010da" + - "tatype\030\001 \001(\0162\027.serialization.DataType\022\014\n" + - "\004size\030\002 \003(\005\022\022\n\nfloat_data\030\003 \003(\002\022\023\n\013doubl" + - "e_data\030\004 \003(\001\"[\n\013Regularizer\0227\n\017regulariz" + - "erType\030\001 \001(\0162\036.serialization.Regularizer" + - "Type\022\023\n\013regularData\030\002 \003(\001\"\332\t\n\tAttrValue\022" + - ")\n\010dataType\030\001 \001(\0162\027.serialization.DataTy" + - "pe\022\017\n\007subType\030\002 \001(\t\022\024\n\nint32Value\030\003 \001(\005H", - "\000\022\024\n\nint64Value\030\004 \001(\003H\000\022\024\n\nfloatValue\030\005 " + - "\001(\002H\000\022\025\n\013doubleValue\030\006 \001(\001H\000\022\025\n\013stringVa" + - "lue\030\007 \001(\tH\000\022\023\n\tboolValue\030\010 \001(\010H\000\0226\n\020regu" + - "larizerValue\030\t \001(\0132\032.serialization.Regul" + - "arizerH\000\0221\n\013tensorValue\030\n \001(\0132\032.serializ" + - "ation.BigDLTensorH\000\0227\n\023variableFormatVal" + - "ue\030\013 \001(\0162\030.serialization.VarFormatH\000\0224\n\017" + - "initMethodValue\030\014 \001(\0132\031.serialization.In" + - "itMethodH\000\0226\n\020bigDLModuleValue\030\r \001(\0132\032.s" + - "erialization.BigDLModuleH\000\0228\n\021nameAttrLi", - "stValue\030\016 \001(\0132\033.serialization.NameAttrLi" + - "stH\000\0229\n\narrayValue\030\017 \001(\0132#.serialization" + - ".AttrValue.ArrayValueH\000\0229\n\017dataFormatVal" + - "ue\030\020 \001(\0162\036.serialization.InputDataFormat" + - "H\000\022+\n\013customValue\030\021 \001(\0132\024.google.protobu" + - "f.AnyH\000\032\223\004\n\nArrayValue\022\014\n\004size\030\001 \001(\005\022)\n\010" + - "datatype\030\002 \001(\0162\027.serialization.DataType\022" + - "\013\n\003i32\030\003 \003(\005\022\013\n\003i64\030\004 \003(\003\022\013\n\003flt\030\005 \003(\002\022\013" + - "\n\003dbl\030\006 \003(\001\022\013\n\003str\030\007 \003(\t\022\017\n\007boolean\030\010 \003(" + - "\010\022/\n\013Regularizer\030\t \003(\0132\032.serialization.R", - "egularizer\022*\n\006tensor\030\n \003(\0132\032.serializati" + - "on.BigDLTensor\0220\n\016variableFormat\030\013 \003(\0162\030" + - ".serialization.VarFormat\022-\n\ninitMethod\030\014" + - " \003(\0132\031.serialization.InitMethod\022/\n\013bigDL" + - "Module\030\r \003(\0132\032.serialization.BigDLModule" + - "\0221\n\014nameAttrList\030\016 \003(\0132\033.serialization.N" + - "ameAttrList\0222\n\ndataFormat\030\017 \003(\0162\036.serial" + - "ization.InputDataFormat\022$\n\006custom\030\020 \003(\0132" + - "\024.google.protobuf.AnyB\007\n\005value\"\230\001\n\014NameA" + - "ttrList\022\014\n\004name\030\001 \001(\t\0223\n\004attr\030\002 \003(\0132%.se", - "rialization.NameAttrList.AttrEntry\032E\n\tAt" + - "trEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002 \001(\0132\030.se" + - "rialization.AttrValue:\0028\001*\260\001\n\tVarFormat\022" + - "\020\n\014EMPTY_FORMAT\020\000\022\013\n\007DEFAULT\020\001\022\t\n\005ONE_D\020" + - "\002\022\n\n\006IN_OUT\020\003\022\n\n\006OUT_IN\020\004\022\020\n\014IN_OUT_KW_K" + - "H\020\005\022\020\n\014OUT_IN_KW_KH\020\006\022\023\n\017GP_OUT_IN_KW_KH" + - "\020\007\022\023\n\017GP_IN_OUT_KW_KH\020\010\022\023\n\017OUT_IN_KT_KH_" + - "KW\020\t*\253\001\n\016InitMethodType\022\030\n\024EMPTY_INITIAL" + - "IZATION\020\000\022\022\n\016RANDOM_UNIFORM\020\001\022\030\n\024RANDOM_" + - "UNIFORM_PARAM\020\002\022\021\n\rRANDOM_NORMAL\020\003\022\t\n\005ZE", - "ROS\020\004\022\010\n\004ONES\020\005\022\t\n\005CONST\020\006\022\n\n\006XAVIER\020\007\022\022" + - "\n\016BILINEARFILLER\020\010*L\n\017RegularizerType\022\023\n" + - "\017L1L2Regularizer\020\000\022\021\n\rL1Regularizer\020\001\022\021\n" + - "\rL2Regularizer\020\002*%\n\017InputDataFormat\022\010\n\004N" + - "CHW\020\000\022\010\n\004NHWC\020\001*\335\001\n\010DataType\022\t\n\005INT32\020\000\022" + - "\t\n\005INT64\020\001\022\t\n\005FLOAT\020\002\022\n\n\006DOUBLE\020\003\022\n\n\006STR" + - "ING\020\004\022\010\n\004BOOL\020\005\022\017\n\013REGULARIZER\020\006\022\n\n\006TENS" + - "OR\020\007\022\023\n\017VARIABLE_FORMAT\020\010\022\016\n\nINITMETHOD\020" + - "\t\022\n\n\006MODULE\020\n\022\022\n\016NAME_ATTR_LIST\020\013\022\017\n\013ARR" + - "AY_VALUE\020\014\022\017\n\013DATA_FORMAT\020\r\022\n\n\006CUSTOM\020\016b", - "\006proto3" + "le.AttrEntry\022\017\n\007version\030\t \001(\t\022\r\n\005train\030\n" + + " \001(\010\022\023\n\013namePostfix\030\013 \001(\t\022\n\n\002id\030\014 \001(\005\032E\n", + "\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002 \001(\0132\030" + + ".serialization.AttrValue:\0028\001\"M\n\nInitMeth" + + "od\0221\n\nmethodType\030\001 \001(\0162\035.serialization.I" + + "nitMethodType\022\014\n\004data\030\002 \003(\001\"\331\001\n\013BigDLTen" + + "sor\022)\n\010datatype\030\001 \001(\0162\027.serialization.Da" + + "taType\022\014\n\004size\030\002 \003(\005\022\016\n\006stride\030\003 \003(\005\022\016\n\006" + + "offset\030\004 \001(\005\022\021\n\tdimension\030\005 \001(\005\022\021\n\tnElem" + + "ents\030\006 \001(\005\022\020\n\010isScalar\030\007 \001(\010\022-\n\007storage\030" + + "\010 \001(\0132\034.serialization.TensorStorage\022\n\n\002i" + + "d\030\t \001(\005\"\320\001\n\rTensorStorage\022)\n\010datatype\030\001 ", + "\001(\0162\027.serialization.DataType\022\022\n\nfloat_da" + + "ta\030\002 \003(\002\022\023\n\013double_data\030\003 \003(\001\022\021\n\tbool_da" + + "ta\030\004 \003(\010\022\023\n\013string_data\030\005 \003(\t\022\020\n\010int_dat" + + "a\030\006 \003(\005\022\021\n\tlong_data\030\007 \003(\003\022\022\n\nbytes_data" + + "\030\010 \003(\014\022\n\n\002id\030\t \001(\005\"[\n\013Regularizer\0227\n\017reg" + + "ularizerType\030\001 \001(\0162\036.serialization.Regul" + + "arizerType\022\023\n\013regularData\030\002 \003(\001\"\332\t\n\tAttr" + + "Value\022)\n\010dataType\030\001 \001(\0162\027.serialization." + + "DataType\022\017\n\007subType\030\002 \001(\t\022\024\n\nint32Value\030" + + "\003 \001(\005H\000\022\024\n\nint64Value\030\004 \001(\003H\000\022\024\n\nfloatVa", + "lue\030\005 \001(\002H\000\022\025\n\013doubleValue\030\006 \001(\001H\000\022\025\n\013st" + + "ringValue\030\007 \001(\tH\000\022\023\n\tboolValue\030\010 \001(\010H\000\0226" + + "\n\020regularizerValue\030\t \001(\0132\032.serialization" + + ".RegularizerH\000\0221\n\013tensorValue\030\n \001(\0132\032.se" + + "rialization.BigDLTensorH\000\0227\n\023variableFor" + + "matValue\030\013 \001(\0162\030.serialization.VarFormat" + + "H\000\0224\n\017initMethodValue\030\014 \001(\0132\031.serializat" + + "ion.InitMethodH\000\0226\n\020bigDLModuleValue\030\r \001" + + "(\0132\032.serialization.BigDLModuleH\000\0228\n\021name" + + "AttrListValue\030\016 \001(\0132\033.serialization.Name", + "AttrListH\000\0229\n\narrayValue\030\017 \001(\0132#.seriali" + + "zation.AttrValue.ArrayValueH\000\0229\n\017dataFor" + + "matValue\030\020 \001(\0162\036.serialization.InputData" + + "FormatH\000\022+\n\013customValue\030\021 \001(\0132\024.google.p" + + "rotobuf.AnyH\000\032\223\004\n\nArrayValue\022\014\n\004size\030\001 \001" + + "(\005\022)\n\010datatype\030\002 \001(\0162\027.serialization.Dat" + + "aType\022\013\n\003i32\030\003 \003(\005\022\013\n\003i64\030\004 \003(\003\022\013\n\003flt\030\005" + + " \003(\002\022\013\n\003dbl\030\006 \003(\001\022\013\n\003str\030\007 \003(\t\022\017\n\007boolea" + + "n\030\010 \003(\010\022/\n\013Regularizer\030\t \003(\0132\032.serializa" + + "tion.Regularizer\022*\n\006tensor\030\n \003(\0132\032.seria", + "lization.BigDLTensor\0220\n\016variableFormat\030\013" + + " \003(\0162\030.serialization.VarFormat\022-\n\ninitMe" + + "thod\030\014 \003(\0132\031.serialization.InitMethod\022/\n" + + "\013bigDLModule\030\r \003(\0132\032.serialization.BigDL" + + "Module\0221\n\014nameAttrList\030\016 \003(\0132\033.serializa" + + "tion.NameAttrList\0222\n\ndataFormat\030\017 \003(\0162\036." + + "serialization.InputDataFormat\022$\n\006custom\030" + + "\020 \003(\0132\024.google.protobuf.AnyB\007\n\005value\"\230\001\n" + + "\014NameAttrList\022\014\n\004name\030\001 \001(\t\0223\n\004attr\030\002 \003(" + + "\0132%.serialization.NameAttrList.AttrEntry", + "\032E\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002 \001(" + + "\0132\030.serialization.AttrValue:\0028\001*\260\001\n\tVarF" + + "ormat\022\020\n\014EMPTY_FORMAT\020\000\022\013\n\007DEFAULT\020\001\022\t\n\005" + + "ONE_D\020\002\022\n\n\006IN_OUT\020\003\022\n\n\006OUT_IN\020\004\022\020\n\014IN_OU" + + "T_KW_KH\020\005\022\020\n\014OUT_IN_KW_KH\020\006\022\023\n\017GP_OUT_IN" + + "_KW_KH\020\007\022\023\n\017GP_IN_OUT_KW_KH\020\010\022\023\n\017OUT_IN_" + + "KT_KH_KW\020\t*\253\001\n\016InitMethodType\022\030\n\024EMPTY_I" + + "NITIALIZATION\020\000\022\022\n\016RANDOM_UNIFORM\020\001\022\030\n\024R" + + "ANDOM_UNIFORM_PARAM\020\002\022\021\n\rRANDOM_NORMAL\020\003" + + "\022\t\n\005ZEROS\020\004\022\010\n\004ONES\020\005\022\t\n\005CONST\020\006\022\n\n\006XAVI", + "ER\020\007\022\022\n\016BILINEARFILLER\020\010*L\n\017RegularizerT" + + "ype\022\023\n\017L1L2Regularizer\020\000\022\021\n\rL1Regularize" + + "r\020\001\022\021\n\rL2Regularizer\020\002*%\n\017InputDataForma" + + "t\022\010\n\004NCHW\020\000\022\010\n\004NHWC\020\001*\375\001\n\010DataType\022\t\n\005IN" + + "T32\020\000\022\t\n\005INT64\020\001\022\t\n\005FLOAT\020\002\022\n\n\006DOUBLE\020\003\022" + + "\n\n\006STRING\020\004\022\010\n\004BOOL\020\005\022\010\n\004CHAR\020\006\022\t\n\005SHORT" + + "\020\007\022\t\n\005BYTES\020\010\022\017\n\013REGULARIZER\020\t\022\n\n\006TENSOR" + + "\020\n\022\023\n\017VARIABLE_FORMAT\020\013\022\016\n\nINITMETHOD\020\014\022" + + "\n\n\006MODULE\020\r\022\022\n\016NAME_ATTR_LIST\020\016\022\017\n\013ARRAY" + + "_VALUE\020\017\022\017\n\013DATA_FORMAT\020\020\022\n\n\006CUSTOM\020\021b\006p", + "roto3" }; com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { @@ -15726,7 +19029,7 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors( internal_static_serialization_BigDLModule_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( internal_static_serialization_BigDLModule_descriptor, - new java.lang.String[] { "Name", "SubModules", "Weight", "Bias", "PreModules", "NextModules", "ModuleType", "Attr", "Version", }); + new java.lang.String[] { "Name", "SubModules", "Weight", "Bias", "PreModules", "NextModules", "ModuleType", "Attr", "Version", "Train", "NamePostfix", "Id", }); internal_static_serialization_BigDLModule_AttrEntry_descriptor = internal_static_serialization_BigDLModule_descriptor.getNestedTypes().get(0); internal_static_serialization_BigDLModule_AttrEntry_fieldAccessorTable = new @@ -15744,15 +19047,21 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors( internal_static_serialization_BigDLTensor_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( internal_static_serialization_BigDLTensor_descriptor, - new java.lang.String[] { "Datatype", "Size", "FloatData", "DoubleData", }); - internal_static_serialization_Regularizer_descriptor = + new java.lang.String[] { "Datatype", "Size", "Stride", "Offset", "Dimension", "NElements", "IsScalar", "Storage", "Id", }); + internal_static_serialization_TensorStorage_descriptor = getDescriptor().getMessageTypes().get(3); + internal_static_serialization_TensorStorage_fieldAccessorTable = new + com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( + internal_static_serialization_TensorStorage_descriptor, + new java.lang.String[] { "Datatype", "FloatData", "DoubleData", "BoolData", "StringData", "IntData", "LongData", "BytesData", "Id", }); + internal_static_serialization_Regularizer_descriptor = + getDescriptor().getMessageTypes().get(4); internal_static_serialization_Regularizer_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( internal_static_serialization_Regularizer_descriptor, new java.lang.String[] { "RegularizerType", "RegularData", }); internal_static_serialization_AttrValue_descriptor = - getDescriptor().getMessageTypes().get(4); + getDescriptor().getMessageTypes().get(5); internal_static_serialization_AttrValue_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( internal_static_serialization_AttrValue_descriptor, @@ -15764,7 +19073,7 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors( internal_static_serialization_AttrValue_ArrayValue_descriptor, new java.lang.String[] { "Size", "Datatype", "I32", "I64", "Flt", "Dbl", "Str", "Boolean", "Regularizer", "Tensor", "VariableFormat", "InitMethod", "BigDLModule", "NameAttrList", "DataFormat", "Custom", }); internal_static_serialization_NameAttrList_descriptor = - getDescriptor().getMessageTypes().get(5); + getDescriptor().getMessageTypes().get(6); internal_static_serialization_NameAttrList_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( internal_static_serialization_NameAttrList_descriptor, diff --git a/scala/dllib/src/main/resources/serialization/bigdl.proto b/scala/dllib/src/main/resources/serialization/bigdl.proto index 1524bc9867a..0e83beffa94 100644 --- a/scala/dllib/src/main/resources/serialization/bigdl.proto +++ b/scala/dllib/src/main/resources/serialization/bigdl.proto @@ -12,6 +12,9 @@ message BigDLModule string moduleType = 7; //module type to identify a module like linear, graph, etc map attr = 8; // module attributes string version = 9; //version of this module + bool train = 10; // is module status in train + string namePostfix = 11; // name post fix + int32 id = 12; // unique ID of this module , used for shared modules } enum VarFormat { EMPTY_FORMAT = 0; @@ -54,9 +57,27 @@ message InitMethod { message BigDLTensor{ DataType datatype = 1; repeated int32 size = 2; //size of tensor - repeated float float_data = 3; //tensor element - repeated double double_data = 4; //tensor element + repeated int32 stride = 3; //stride of tensor + int32 offset = 4; //offset + int32 dimension = 5; // dimension + int32 nElements = 6; //total number of elements + bool isScalar = 7; // is scalar tensor + TensorStorage storage = 8; // reference to storage + int32 id = 9; // tensor ID, used for tensor sharing } + +message TensorStorage { + DataType datatype = 1; + repeated float float_data = 2; //float tensor element + repeated double double_data = 3; //double tensor element + repeated bool bool_data = 4; //boolean tensor element + repeated string string_data = 5; //string tensor element + repeated int32 int_data = 6; //int tensor element + repeated int64 long_data = 7; //long tensor element + repeated bytes bytes_data = 8; //byte tensor element + int32 id = 9; // storage Id, used for storage sharing +} + message Regularizer { RegularizerType regularizerType = 1; repeated double regularData = 2; @@ -69,15 +90,18 @@ enum DataType { DOUBLE = 3; STRING = 4; BOOL = 5; - REGULARIZER = 6; - TENSOR = 7; - VARIABLE_FORMAT= 8; - INITMETHOD = 9; - MODULE = 10; - NAME_ATTR_LIST = 11; - ARRAY_VALUE = 12; - DATA_FORMAT = 13; - CUSTOM = 14; + CHAR = 6; + SHORT = 7; + BYTES = 8; + REGULARIZER = 9; + TENSOR = 10; + VARIABLE_FORMAT= 11; + INITMETHOD = 12; + MODULE = 13; + NAME_ATTR_LIST = 14; + ARRAY_VALUE = 15; + DATA_FORMAT = 16; + CUSTOM = 17; } message AttrValue { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index ed0e2ea4f5a..b74466abac5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.utils.{Engine, T, Table} import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.concurrent.Future @@ -747,54 +747,54 @@ object BatchNormalization extends ModuleSerializable { new BatchNormalization[T](nOutput = affine.getOrElse(1), affine = affine.isDefined) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val attrMap = model.getAttrMap - val batchNorm = super.doLoadModule(model).asInstanceOf[BatchNormalization[T]] + val attrMap = context.bigdlModule.getAttrMap + val batchNorm = super.doLoadModule(context).asInstanceOf[BatchNormalization[T]] batchNorm.runningMean = DataConverter. - getAttributeValue(attrMap.get("runningMean")). + getAttributeValue(context, attrMap.get("runningMean")). asInstanceOf[Tensor[T]] batchNorm.runningVar = DataConverter. - getAttributeValue(attrMap.get("runningVar")). + getAttributeValue(context, attrMap.get("runningVar")). asInstanceOf[Tensor[T]] batchNorm.saveMean = DataConverter. - getAttributeValue(attrMap.get("saveMean")). + getAttributeValue(context, attrMap.get("saveMean")). asInstanceOf[Tensor[T]] batchNorm.saveStd = DataConverter. - getAttributeValue(attrMap.get("saveStd")). + getAttributeValue(context, attrMap.get("saveStd")). asInstanceOf[Tensor[T]] batchNorm } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], batchNormBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(module, batchNormBuilder) + super.doSerializeModule(context, batchNormBuilder) - val batchNorm = module.module.asInstanceOf[BatchNormalization[T]] + val batchNorm = context.moduleData.module.asInstanceOf[BatchNormalization[T]] val runningMeanBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(runningMeanBuilder, + DataConverter.setAttributeValue(context, runningMeanBuilder, batchNorm.runningMean, ModuleSerializer.tensorType) batchNormBuilder.putAttr("runningMean", runningMeanBuilder.build) val runningVarBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(runningVarBuilder, + DataConverter.setAttributeValue(context, runningVarBuilder, batchNorm.runningVar, ModuleSerializer.tensorType) batchNormBuilder.putAttr("runningVar", runningVarBuilder.build) val saveMeanBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(saveMeanBuilder, + DataConverter.setAttributeValue(context, saveMeanBuilder, batchNorm.saveMean, ModuleSerializer.tensorType) batchNormBuilder.putAttr("saveMean", saveMeanBuilder.build) val saveStdBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(saveStdBuilder, + DataConverter.setAttributeValue(context, saveStdBuilder, batchNorm.saveStd, ModuleSerializer.tensorType) batchNormBuilder.putAttr("saveStd", saveStdBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala index f54f8ace234..9227acb4051 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DataConverter, ModuleData, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} @@ -159,21 +159,21 @@ object BiRecurrent extends ContainerSerializable { new BiRecurrent[T](merge, batchNormParams, isSplitInput) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val attrMap = model.getAttrMap + val attrMap = context.bigdlModule.getAttrMap val merge = DataConverter. - getAttributeValue(attrMap.get("merge")). + getAttributeValue(context, attrMap.get("merge")). asInstanceOf[AbstractModule[Table, Tensor[T], T]] val isSplitInput = DataConverter - .getAttributeValue(attrMap.get("isSplitInput")) + .getAttributeValue(context, attrMap.get("isSplitInput")) .asInstanceOf[Boolean] val flag = DataConverter - .getAttributeValue(attrMap.get("bnorm")) + .getAttributeValue(context, attrMap.get("bnorm")) .asInstanceOf[Boolean] val biRecurrent = if (flag) { @@ -183,73 +183,71 @@ object BiRecurrent extends ContainerSerializable { } biRecurrent.birnn = DataConverter. - getAttributeValue(attrMap.get("birnn")). + getAttributeValue(context, attrMap.get("birnn")). asInstanceOf[Sequential[T]] if (flag) { val bnormEpsAttr = attrMap.get("bnormEps") biRecurrent.batchNormParams.eps = - DataConverter.getAttributeValue(bnormEpsAttr) + DataConverter.getAttributeValue(context, bnormEpsAttr) .asInstanceOf[Double] val bnormMomentumAttr = attrMap.get("bnormMomentum") biRecurrent.batchNormParams.momentum = - DataConverter.getAttributeValue(bnormMomentumAttr) + DataConverter.getAttributeValue(context, bnormMomentumAttr) .asInstanceOf[Double] val bnormInitWeightAttr = attrMap.get("bnormInitWeight") biRecurrent.batchNormParams.initWeight = - DataConverter.getAttributeValue(bnormInitWeightAttr) + DataConverter.getAttributeValue(context, bnormInitWeightAttr) .asInstanceOf[Tensor[T]] val bnormInitBiasAttr = attrMap.get("bnormInitBias") biRecurrent.batchNormParams.initBias = - DataConverter.getAttributeValue(bnormInitBiasAttr) + DataConverter.getAttributeValue(context, bnormInitBiasAttr) .asInstanceOf[Tensor[T]] val bnormInitGradWeightAttr = attrMap.get("bnormInitGradWeight") biRecurrent.batchNormParams.initGradWeight = - DataConverter.getAttributeValue(bnormInitGradWeightAttr) + DataConverter.getAttributeValue(context, bnormInitGradWeightAttr) .asInstanceOf[Tensor[T]] val bnormInitGradBiasAttr = attrMap.get("bnormInitGradBias") biRecurrent.batchNormParams.initGradBias = - DataConverter.getAttributeValue(bnormInitGradBiasAttr) + DataConverter.getAttributeValue(context, bnormInitGradBiasAttr) .asInstanceOf[Tensor[T]] val bnormAffineAttr = attrMap.get("bnormAffine") biRecurrent.batchNormParams.affine = - DataConverter.getAttributeValue(bnormAffineAttr) + DataConverter.getAttributeValue(context, bnormAffineAttr) .asInstanceOf[Boolean] } - createBigDLModule(model, biRecurrent) - biRecurrent } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], birecurrentBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val birecurrentModule = module.module. + val birecurrentModule = context.moduleData.module. asInstanceOf[BiRecurrent[T]] val mergeBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(mergeBuilder, + DataConverter.setAttributeValue(context, mergeBuilder, birecurrentModule.merge, ModuleSerializer.tensorModuleType) birecurrentBuilder.putAttr("merge", mergeBuilder.build) val isSplitInputBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(isSplitInputBuilder, + DataConverter.setAttributeValue(context, isSplitInputBuilder, birecurrentModule.isSplitInput, universe.typeOf[Boolean]) birecurrentBuilder.putAttr("isSplitInput", isSplitInputBuilder.build) val birnnBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(birnnBuilder, + DataConverter.setAttributeValue(context, birnnBuilder, birecurrentModule.birnn, ModuleSerializer.tensorModuleType) birecurrentBuilder.putAttr("birnn", birnnBuilder.build) @@ -257,37 +255,37 @@ object BiRecurrent extends ContainerSerializable { val flag = if (birecurrentModule.batchNormParams != null) { val bnormEpsBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormEpsBuilder, + DataConverter.setAttributeValue(context, bnormEpsBuilder, birecurrentModule.batchNormParams.eps, universe.typeOf[Double]) birecurrentBuilder.putAttr("bnormEps", bnormEpsBuilder.build) val bnormMomentumBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormMomentumBuilder, + DataConverter.setAttributeValue(context, bnormMomentumBuilder, birecurrentModule.batchNormParams.momentum, universe.typeOf[Double]) birecurrentBuilder.putAttr("bnormMomentum", bnormMomentumBuilder.build) val bnormInitWeightBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormInitWeightBuilder, + DataConverter.setAttributeValue(context, bnormInitWeightBuilder, birecurrentModule.batchNormParams.initWeight, ModuleSerializer.tensorType) birecurrentBuilder.putAttr("bnormInitWeight", bnormInitWeightBuilder.build) val bnormInitBiasBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormInitBiasBuilder, + DataConverter.setAttributeValue(context, bnormInitBiasBuilder, birecurrentModule.batchNormParams.initBias, ModuleSerializer.tensorType) birecurrentBuilder.putAttr("bnormInitBias", bnormInitBiasBuilder.build) val bnormInitGradWeightBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormInitGradWeightBuilder, + DataConverter.setAttributeValue(context, bnormInitGradWeightBuilder, birecurrentModule.batchNormParams.initGradWeight, ModuleSerializer.tensorType) birecurrentBuilder.putAttr("bnormInitGradWeight", bnormInitGradWeightBuilder.build) val bnormInitGradBiasBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormInitGradBiasBuilder, + DataConverter.setAttributeValue(context, bnormInitGradBiasBuilder, birecurrentModule.batchNormParams.initGradBias, ModuleSerializer.tensorType) birecurrentBuilder.putAttr("bnormInitGradBias", bnormInitGradBiasBuilder.build) val bnormAffineBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormAffineBuilder, + DataConverter.setAttributeValue(context, bnormAffineBuilder, birecurrentModule.batchNormParams.affine, universe.typeOf[Boolean]) birecurrentBuilder.putAttr("bnormAffine", bnormAffineBuilder.build) @@ -297,10 +295,9 @@ object BiRecurrent extends ContainerSerializable { } val bNormBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bNormBuilder, + DataConverter.setAttributeValue(context, bNormBuilder, flag, universe.typeOf[Boolean]) birecurrentBuilder.putAttr("bnorm", bNormBuilder.build) - createSerializeBigDLModule(birecurrentBuilder, module) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala index 678aa07e709..db92d245f31 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.nn.Input import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} @@ -418,63 +418,66 @@ object BinaryTreeLSTM extends ModuleSerializable { )(implicit ev: TensorNumeric[T]): BinaryTreeLSTM[T] = new BinaryTreeLSTM[T](inputSize, hiddenSize, gateOutput, withGraph) - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val binaryTreeLSTMModule = super.doLoadModule(model).asInstanceOf[BinaryTreeLSTM[T]] + val binaryTreeLSTMModule = super.doLoadModule(context).asInstanceOf[BinaryTreeLSTM[T]] binaryTreeLSTMModule.composers.clear binaryTreeLSTMModule.leafModules.clear - val attrMap = model.getAttrMap + val attrMap = context.bigdlModule.getAttrMap - DataConverter.getAttributeValue(attrMap.get("composers")). + DataConverter.getAttributeValue(context, attrMap.get("composers")). asInstanceOf[Array[Module[T]]].foreach(module => { binaryTreeLSTMModule.composers.append(module) }) - DataConverter.getAttributeValue(attrMap.get("leafModules")). + DataConverter.getAttributeValue(context, attrMap.get("leafModules")). asInstanceOf[Array[Module[T]]].foreach(module => { binaryTreeLSTMModule.leafModules.append(module) }) - binaryTreeLSTMModule.leafModule = DataConverter.getAttributeValue(attrMap.get("leafModule")). + binaryTreeLSTMModule.leafModule = DataConverter. + getAttributeValue(context, attrMap.get("leafModule")). asInstanceOf[Module[T]] - binaryTreeLSTMModule.composer = DataConverter.getAttributeValue(attrMap.get("composer")). + binaryTreeLSTMModule.composer = DataConverter.getAttributeValue(context, + attrMap.get("composer")). asInstanceOf[Module[T]] binaryTreeLSTMModule } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], binaryTreeLSTMBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(module, binaryTreeLSTMBuilder) + super.doSerializeModule(context, binaryTreeLSTMBuilder) - val binaryTreeLSTM = module.module.asInstanceOf[BinaryTreeLSTM[T]] + val binaryTreeLSTM = context.moduleData.module.asInstanceOf[BinaryTreeLSTM[T]] val composer = binaryTreeLSTM.composer val composerBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(composerBuilder, composer, ModuleSerializer.abstractModuleType) + DataConverter.setAttributeValue(context, composerBuilder, composer, + ModuleSerializer.abstractModuleType) binaryTreeLSTMBuilder.putAttr("composer", composerBuilder.build) val leafModule = binaryTreeLSTM.leafModule val leafModuleBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(leafModuleBuilder, leafModule, + DataConverter.setAttributeValue(context, leafModuleBuilder, leafModule, ModuleSerializer.abstractModuleType) binaryTreeLSTMBuilder.putAttr("leafModule", leafModuleBuilder.build) val composers = binaryTreeLSTM.composers.toArray val composersBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(composersBuilder, composers, + DataConverter.setAttributeValue(context, composersBuilder, composers, universe. typeOf[Array[_ <: AbstractModule[Activity, Activity, _ <: Any]]]) binaryTreeLSTMBuilder.putAttr("composers", composersBuilder.build) val leafModules = binaryTreeLSTM.leafModules.toArray val leafModulesBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(leafModulesBuilder, leafModules, universe. + DataConverter.setAttributeValue(context, leafModulesBuilder, leafModules, universe. typeOf[Array[_ <: AbstractModule[Activity, Activity, _ <: Any]]]) binaryTreeLSTMBuilder.putAttr("leafModules", leafModulesBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala index b8e6c518131..bbd57ce146f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} @@ -267,32 +267,32 @@ abstract class Cell[T : ClassTag]( object CellSerializer extends ModuleSerializable { - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context : DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val module = super.doLoadModule(model) + val module = super.doLoadModule(context) val cellModule = module.asInstanceOf[Cell[T]] - val attrMap = model.getAttrMap - cellModule.cell = DataConverter.getAttributeValue(attrMap.get("cell")). + val attrMap = context.bigdlModule.getAttrMap + cellModule.cell = DataConverter.getAttributeValue(context, attrMap.get("cell")). asInstanceOf[AbstractModule[Activity, Activity, T]] cellModule } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], cellModuleBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(module, cellModuleBuilder) - val cellModule = module.module.asInstanceOf[Cell[T]] + super.doSerializeModule(context, cellModuleBuilder) + val cellModule = context.moduleData.module.asInstanceOf[Cell[T]] val cellSerializerFlagBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(cellSerializerFlagBuilder, true, + DataConverter.setAttributeValue(context, cellSerializerFlagBuilder, true, scala.reflect.runtime.universe.typeOf[Boolean]) cellModuleBuilder.putAttr("is_cell_module", cellSerializerFlagBuilder.build) val cellBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(cellBuilder, cellModule.cell, + DataConverter.setAttributeValue(context, cellBuilder, cellModule.cell, ModuleSerializer.abstractModuleType) cellModuleBuilder.putAttr("cell", cellBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala index 5bb4488e879..41c3997b699 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{ModuleData, ModuleSerializable} +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleData, ModuleSerializable, SerializeContext} import serialization.Bigdl.BigDLModule import scala.reflect.ClassTag @@ -96,12 +96,13 @@ object Echo extends ModuleSerializable { println(s"${module.getPrintName} : Gradient size is ${gradOutput.size().mkString("x")}") } - override def doSerializeModule[T: ClassManifest](m: ModuleData[T], b: BigDLModule.Builder) + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + b: BigDLModule.Builder) (implicit ev: TensorNumeric[T]): Unit = { // We won't serialize the function, so do nothing here } - override def doLoadModule[T: ClassManifest](model: BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { new Echo[T](defaultFeval, defaultBeval) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 939e76abd2f..19032c495a3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.nn.ops.ControlOps import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DataConverter, ModuleData, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.utils.tf.{BigDLToTensorflow, Tensorflow, TensorflowSaver} import serialization.Bigdl.{AttrValue, BigDLModule} @@ -603,17 +603,18 @@ object Graph extends ContainerSerializable { new Graph[T](Array(input), Array(output)) } - override def doLoadModule[T: ClassTag](module : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val module = context.bigdlModule val subModules = module.getSubModulesList.asScala val attributes = module.getAttrMap val inputNames = new ArrayBuffer[String] val outputNames = new ArrayBuffer[String] - DataConverter.getAttributeValue(attributes.get("inputNames")) + DataConverter.getAttributeValue(context, attributes.get("inputNames")) .asInstanceOf[Array[String]].map(name => inputNames.append(name)) - DataConverter.getAttributeValue(attributes.get("outputNames")) + DataConverter.getAttributeValue(context, attributes.get("outputNames")) .asInstanceOf[Array[String]].map(name => outputNames.append(name)) val inputs = new ArrayBuffer[ModuleNode[T]] @@ -622,7 +623,8 @@ object Graph extends ContainerSerializable { // layer name to layer node mapping val layerMap = new mutable.HashMap[String, (ModuleNode[T], Seq[String])]() subModules.foreach(subModule => { - val bigDLModule = ModuleSerializer.load(subModule) + val bigDLModule = ModuleSerializer.load(DeserializeContext(subModule, + context.storages, context.storageType)) val moduleNode = bigDLModule.module.inputs() val preNodes = bigDLModule.pre layerMap(bigDLModule.module.getName) = (moduleNode, preNodes) @@ -643,17 +645,19 @@ object Graph extends ContainerSerializable { if (attributes.containsKey("sharedWeight") && attributes.containsKey("sharedBias")) { val weights = attributes.get("sharedWeight") val biases = attributes.get("sharedBias") - val weightArray = DataConverter.getAttributeValue(weights).asInstanceOf[Array[Tensor[T]]] - val biasArray = DataConverter.getAttributeValue(biases).asInstanceOf[Array[Tensor[T]]] + val weightArray = DataConverter.getAttributeValue(context, weights) + .asInstanceOf[Array[Tensor[T]]] + val biasArray = DataConverter.getAttributeValue(context, biases) + .asInstanceOf[Array[Tensor[T]]] sharedVariables = Some(weightArray, biasArray) } Graph[T](inputs.toArray, outputs.toArray, sharedVariables) } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], graphBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - + val module = context.moduleData module.next.foreach(_ => graphBuilder.addAllPreModules(_)) module.pre.foreach(_ => graphBuilder.addAllNextModules(_)) val graph = module.module.asInstanceOf[Graph[T]] @@ -664,29 +668,30 @@ object Graph extends ContainerSerializable { val nextNodes = execution.nextNodes.map(_.element.getName) val currNode = execution.element .asInstanceOf[AbstractModule[Activity, Activity, T]] - val subModel = ModuleSerializer.serialize(ModuleData(currNode, preNodes, nextNodes)) - graphBuilder.addSubModules(subModel) + val subModel = ModuleSerializer.serialize(SerializeContext( + ModuleData(currNode, preNodes, nextNodes), context.storages, context.storageType)) + graphBuilder.addSubModules(subModel.bigDLModule) }) if (graph.variables.isDefined) { val (weights, bias) = graph.variables.get val weightAttrBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(weightAttrBuilder, weights, + DataConverter.setAttributeValue(context, weightAttrBuilder, weights, universe.typeOf[Array[Tensor[_ <: Any]]]) graphBuilder.putAttr("sharedWeight", weightAttrBuilder.build) val biasAttrBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(biasAttrBuilder, bias, + DataConverter.setAttributeValue(context, biasAttrBuilder, bias, universe.typeOf[Array[Tensor[_ <: Any]]]) graphBuilder.putAttr("sharedBias", biasAttrBuilder.build) } val inputNamesAttrBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(inputNamesAttrBuilder, + DataConverter.setAttributeValue(context, inputNamesAttrBuilder, inputsNames, universe.typeOf[Array[String]]) graphBuilder.putAttr("inputNames", inputNamesAttrBuilder.build) val outputNamesBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(outputNamesBuilder, + DataConverter.setAttributeValue(context, outputNamesBuilder, outputsNames, universe.typeOf[Array[String]]) graphBuilder.putAttr("outputNames", outputNamesBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala index 4337ee03e7d..25c31a07dce 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table -import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, ModuleData} +import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DeserializeContext, ModuleData, SerializeContext} import serialization.Bigdl.BigDLModule import scala.reflect.ClassTag @@ -138,18 +138,18 @@ object MapTable extends ContainerSerializable { new MapTable[T](module) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val mapTable = super.doLoadModule(model).asInstanceOf[MapTable[T]] + val mapTable = super.doLoadModule(context).asInstanceOf[MapTable[T]] require(mapTable.modules.size >=1, "sub module should not be empty") mapTable.add(mapTable.modules(0)) mapTable } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], - mapBuilder : BigDLModule.Builder) + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + mapBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val mapTable = module.module.asInstanceOf[MapTable[T]] + val mapTable = context.moduleData.module.asInstanceOf[MapTable[T]] val subModules = mapTable.modules require(subModules.size >=1, "sub module should not be empty") // `modules` are created during forward() by 'n' times of the same module depends on input size, @@ -157,6 +157,6 @@ object MapTable extends ContainerSerializable { val singleModule = subModules(0) mapTable.modules.clear() mapTable.modules.append(singleModule) - super.doSerializeModule(module, mapBuilder) + super.doSerializeModule(context, mapBuilder) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala index 7ba7486c7d1..124e622235a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala @@ -19,7 +19,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag @@ -96,56 +96,56 @@ object MaskedSelect extends ModuleSerializable { new MaskedSelect[T]() } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val maskedSelect = super.doLoadModule(model).asInstanceOf[MaskedSelect[T]] - val modelAttributes = model.getAttrMap + val maskedSelect = super.doLoadModule(context).asInstanceOf[MaskedSelect[T]] + val modelAttributes = context.bigdlModule.getAttrMap val maskIndices = modelAttributes.get("maskIndices") - val maskIndicesValue = DataConverter.getAttributeValue(maskIndices) + val maskIndicesValue = DataConverter.getAttributeValue(context, maskIndices) .asInstanceOf[Tensor[T]] maskedSelect.maskIndices.resizeAs(maskIndicesValue).copy(maskIndicesValue) val maskIndexBuffer = modelAttributes.get("maskIndexBuffer") - val maskIndexBufferValue = DataConverter.getAttributeValue(maskIndexBuffer) + val maskIndexBufferValue = DataConverter.getAttributeValue(context, maskIndexBuffer) .asInstanceOf[Tensor[T]] maskedSelect.maskIndexBuffer.resizeAs(maskIndexBufferValue).copy(maskIndexBufferValue) val gradBufferBuffer = modelAttributes.get("gradBuffer") - val gradBufferValue = DataConverter.getAttributeValue(gradBufferBuffer) + val gradBufferValue = DataConverter.getAttributeValue(context, gradBufferBuffer) .asInstanceOf[Tensor[T]] maskedSelect.gradBuffer.resizeAs(gradBufferValue).copy(gradBufferValue) val gradMaskBuffer = modelAttributes.get("gradMask") - val gradMaskValue = DataConverter.getAttributeValue(gradMaskBuffer) + val gradMaskValue = DataConverter.getAttributeValue(context, gradMaskBuffer) .asInstanceOf[Tensor[T]] maskedSelect.gradMask.resizeAs(gradMaskValue).copy(gradMaskValue) maskedSelect } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], maskedSelectBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val masKSelect = module.module.asInstanceOf[MaskedSelect[T]] + val masKSelect = context.moduleData.module.asInstanceOf[MaskedSelect[T]] val maskIndicesBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(maskIndicesBuilder, masKSelect.maskIndices, + DataConverter.setAttributeValue(context, maskIndicesBuilder, masKSelect.maskIndices, ModuleSerializer.tensorType) maskedSelectBuilder.putAttr("maskIndices", maskIndicesBuilder.build) val maskIndexBufferBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(maskIndexBufferBuilder, masKSelect.maskIndexBuffer, + DataConverter.setAttributeValue(context, maskIndexBufferBuilder, masKSelect.maskIndexBuffer, ModuleSerializer.tensorType) maskedSelectBuilder.putAttr("maskIndexBuffer", maskIndexBufferBuilder.build) val gradBufferBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(gradBufferBuilder, masKSelect.gradBuffer, + DataConverter.setAttributeValue(context, gradBufferBuilder, masKSelect.gradBuffer, ModuleSerializer.tensorType) maskedSelectBuilder.putAttr("gradBuffer", gradBufferBuilder.build) val gradMaskBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(gradMaskBuilder, masKSelect.gradMask, + DataConverter.setAttributeValue(context, gradMaskBuilder, masKSelect.gradMask, ModuleSerializer.tensorType) maskedSelectBuilder.putAttr("gradMask", gradMaskBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index da732443038..2b421d20e11 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -21,11 +21,11 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DataConverter, ModuleData, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DataConverter, ModuleSerializer} import com.intel.analytics.bigdl.utils.T import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.runtime.universe -import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -578,13 +578,13 @@ object Recurrent extends ContainerSerializable { dst } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context : DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val attrMap = model.getAttrMap + val attrMap = context.bigdlModule.getAttrMap val flag = DataConverter - .getAttributeValue(attrMap.get("bnorm")) + .getAttributeValue(context, attrMap.get("bnorm")) .asInstanceOf[Boolean] val recurrent = if (flag) { Recurrent[T](BatchNormParams[T]()) @@ -593,11 +593,11 @@ object Recurrent extends ContainerSerializable { } val topologyAttr = attrMap.get("topology") - recurrent.topology = DataConverter.getAttributeValue(topologyAttr). + recurrent.topology = DataConverter.getAttributeValue(context, topologyAttr). asInstanceOf[Cell[T]] val preTopologyAttr = attrMap.get("preTopology") - recurrent.preTopology = DataConverter.getAttributeValue(preTopologyAttr). + recurrent.preTopology = DataConverter.getAttributeValue(context, preTopologyAttr). asInstanceOf[AbstractModule[Activity, Activity, T]] if (recurrent.preTopology != null) { @@ -608,94 +608,93 @@ object Recurrent extends ContainerSerializable { if (flag) { val bnormEpsAttr = attrMap.get("bnormEps") recurrent.batchNormParams.eps = - DataConverter.getAttributeValue(bnormEpsAttr) + DataConverter.getAttributeValue(context, bnormEpsAttr) .asInstanceOf[Double] val bnormMomentumAttr = attrMap.get("bnormMomentum") recurrent.batchNormParams.momentum = - DataConverter.getAttributeValue(bnormMomentumAttr) + DataConverter.getAttributeValue(context, bnormMomentumAttr) .asInstanceOf[Double] val bnormInitWeightAttr = attrMap.get("bnormInitWeight") recurrent.batchNormParams.initWeight = - DataConverter.getAttributeValue(bnormInitWeightAttr) + DataConverter.getAttributeValue(context, bnormInitWeightAttr) .asInstanceOf[Tensor[T]] val bnormInitBiasAttr = attrMap.get("bnormInitBias") recurrent.batchNormParams.initBias = - DataConverter.getAttributeValue(bnormInitBiasAttr) + DataConverter.getAttributeValue(context, bnormInitBiasAttr) .asInstanceOf[Tensor[T]] val bnormInitGradWeightAttr = attrMap.get("bnormInitGradWeight") recurrent.batchNormParams.initGradWeight = - DataConverter.getAttributeValue(bnormInitGradWeightAttr) + DataConverter.getAttributeValue(context, bnormInitGradWeightAttr) .asInstanceOf[Tensor[T]] val bnormInitGradBiasAttr = attrMap.get("bnormInitGradBias") recurrent.batchNormParams.initGradBias = - DataConverter.getAttributeValue(bnormInitGradBiasAttr) + DataConverter.getAttributeValue(context, bnormInitGradBiasAttr) .asInstanceOf[Tensor[T]] val bnormAffineAttr = attrMap.get("bnormAffine") recurrent.batchNormParams.affine = - DataConverter.getAttributeValue(bnormAffineAttr) + DataConverter.getAttributeValue(context, bnormAffineAttr) .asInstanceOf[Boolean] } - createBigDLModule(model, recurrent) recurrent } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], recurrentBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val recurrent = module.module.asInstanceOf[Recurrent[T]] + val recurrent = context.moduleData.module.asInstanceOf[Recurrent[T]] val topologyBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(topologyBuilder, recurrent.topology, + DataConverter.setAttributeValue(context, topologyBuilder, recurrent.topology, ModuleSerializer.abstractModuleType) recurrentBuilder.putAttr("topology", topologyBuilder.build) val preTopologyBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(preTopologyBuilder, + DataConverter.setAttributeValue(context, preTopologyBuilder, recurrent.preTopology, ModuleSerializer.abstractModuleType) recurrentBuilder.putAttr("preTopology", preTopologyBuilder.build) val flag = if (recurrent.batchNormParams != null) { val bnormEpsBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormEpsBuilder, + DataConverter.setAttributeValue(context, bnormEpsBuilder, recurrent.batchNormParams.eps, universe.typeOf[Double]) recurrentBuilder.putAttr("bnormEps", bnormEpsBuilder.build) val bnormMomentumBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormMomentumBuilder, + DataConverter.setAttributeValue(context, bnormMomentumBuilder, recurrent.batchNormParams.momentum, universe.typeOf[Double]) recurrentBuilder.putAttr("bnormMomentum", bnormMomentumBuilder.build) val bnormInitWeightBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormInitWeightBuilder, + DataConverter.setAttributeValue(context, bnormInitWeightBuilder, recurrent.batchNormParams.initWeight, ModuleSerializer.tensorType) recurrentBuilder.putAttr("bnormInitWeight", bnormInitWeightBuilder.build) val bnormInitBiasBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormInitBiasBuilder, + DataConverter.setAttributeValue(context, bnormInitBiasBuilder, recurrent.batchNormParams.initBias, ModuleSerializer.tensorType) recurrentBuilder.putAttr("bnormInitBias", bnormInitBiasBuilder.build) val bnormInitGradWeightBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormInitGradWeightBuilder, + DataConverter.setAttributeValue(context, bnormInitGradWeightBuilder, recurrent.batchNormParams.initGradWeight, ModuleSerializer.tensorType) recurrentBuilder.putAttr("bnormInitGradWeight", bnormInitGradWeightBuilder.build) val bnormInitGradBiasBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormInitGradBiasBuilder, + DataConverter.setAttributeValue(context, bnormInitGradBiasBuilder, recurrent.batchNormParams.initGradBias, ModuleSerializer.tensorType) recurrentBuilder.putAttr("bnormInitGradBias", bnormInitGradBiasBuilder.build) val bnormAffineBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bnormAffineBuilder, + DataConverter.setAttributeValue(context, bnormAffineBuilder, recurrent.batchNormParams.affine, universe.typeOf[Boolean]) recurrentBuilder.putAttr("bnormAffine", bnormAffineBuilder.build) @@ -705,11 +704,10 @@ object Recurrent extends ContainerSerializable { } val bNormBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bNormBuilder, + DataConverter.setAttributeValue(context, bNormBuilder, flag, universe.typeOf[Boolean]) recurrentBuilder.putAttr("bnorm", bNormBuilder.build) - createSerializeBigDLModule(recurrentBuilder, module) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala index 9402ef54abe..0b92324d9aa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DataConverter, ModuleData, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable.ArrayBuffer @@ -188,30 +188,33 @@ object RecurrentDecoder extends ContainerSerializable { new RecurrentDecoder[T](outputLength) } - override def loadModule[T: ClassTag](model : BigDLModule) + override def loadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : ModuleData[T] = { - val moduleData = super.loadModule(model) + val moduleData = super.loadModule(context) val recurrentDecoder = moduleData.module.asInstanceOf[RecurrentDecoder[T]] - val attrMap = model.getAttrMap + val attrMap = context.bigdlModule.getAttrMap val topologyAttr = attrMap.get("topology") - recurrentDecoder.topology = DataConverter.getAttributeValue(topologyAttr). + recurrentDecoder.topology = DataConverter. + getAttributeValue(context, topologyAttr). asInstanceOf[Cell[T]] moduleData } - override def serializeModule[T: ClassTag](module : ModuleData[T]) - (implicit ev: TensorNumeric[T]) : BigDLModule = { - val containerBuilder = BigDLModule.newBuilder(super.serializeModule(module)) + override def serializeModule[T: ClassTag](context: SerializeContext[T]) + (implicit ev: TensorNumeric[T]) : SerializeResult = { + val containerBuilder = BigDLModule. + newBuilder(super.serializeModule(context).bigDLModule) - val recurrentDecoder = module.module.asInstanceOf[RecurrentDecoder[T]] + val recurrentDecoder = context.moduleData.module.asInstanceOf[RecurrentDecoder[T]] val topologyBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(topologyBuilder, recurrentDecoder.topology, + DataConverter.setAttributeValue(context, + topologyBuilder, recurrentDecoder.topology, ModuleSerializer.abstractModuleType) containerBuilder.putAttr("topology", topologyBuilder.build) - containerBuilder.build + SerializeResult(containerBuilder.build, context.storages) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala index 6cc89a5a169..dc2eb088219 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag @@ -152,13 +152,13 @@ object Reshape extends ModuleSerializable { new Reshape[T](size, batchMode) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val attrMap = model.getAttrMap - val size = DataConverter.getAttributeValue(attrMap.get("size")). + val attrMap = context.bigdlModule.getAttrMap + val size = DataConverter.getAttributeValue(context, attrMap.get("size")). asInstanceOf[Array[Int]] - val batchModeV = DataConverter.getAttributeValue(attrMap.get("batchMode")). + val batchModeV = DataConverter.getAttributeValue(context, attrMap.get("batchMode")). asInstanceOf[Int] var batchMode : Option[Boolean] = None if (batchModeV == 1) { @@ -169,14 +169,14 @@ object Reshape extends ModuleSerializable { Reshape(size, batchMode).asInstanceOf[AbstractModule[Activity, Activity, T]] } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], reshapeBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val reshape = module.module.asInstanceOf[Reshape[T]] + val reshape = context.moduleData.module.asInstanceOf[Reshape[T]] val sizeBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(sizeBuilder, reshape.size, + DataConverter.setAttributeValue(context, sizeBuilder, reshape.size, universe.typeOf[Array[Int]]) reshapeBuilder.putAttr("size", sizeBuilder.build) @@ -185,7 +185,7 @@ object Reshape extends ModuleSerializable { batchMode = if (reshape.batchMode.get == false) 1 else 2 } val batchModeBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(batchModeBuilder, batchMode, + DataConverter.setAttributeValue(context, batchModeBuilder, batchMode, universe.typeOf[Int]) reshapeBuilder.putAttr("batchMode", batchModeBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala index ff275441ef8..7eae5192f72 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} @@ -84,29 +84,31 @@ object Scale extends ModuleSerializable { def apply[@specialized(Float, Double) T: ClassTag](size: Array[Int]) (implicit ev: TensorNumeric[T]): Scale[T] = new Scale[T](size) - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context : DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val scale = super.doLoadModule(model).asInstanceOf[Scale[T]] - val attrMap = model.getAttrMap + val scale = super.doLoadModule(context).asInstanceOf[Scale[T]] + val attrMap = context.bigdlModule.getAttrMap val cmul = attrMap.get("cmul") - scale.cmul = DataConverter.getAttributeValue(cmul).asInstanceOf[CMul[T]] + scale.cmul = DataConverter.getAttributeValue(context, cmul).asInstanceOf[CMul[T]] val cadd = attrMap.get("cadd") - scale.cadd = DataConverter.getAttributeValue(cadd).asInstanceOf[CAdd[T]] + scale.cadd = DataConverter.getAttributeValue(context, cadd).asInstanceOf[CAdd[T]] scale } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], scaleBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val scale = module.module.asInstanceOf[Scale[T]] - super.doSerializeModule(module, scaleBuilder) + val scale = context.moduleData.module.asInstanceOf[Scale[T]] + super.doSerializeModule(context, scaleBuilder) val cmulBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(cmulBuilder, scale.cmul, ModuleSerializer.abstractModuleType) + DataConverter.setAttributeValue(context, cmulBuilder, + scale.cmul, ModuleSerializer.abstractModuleType) scaleBuilder.putAttr("cmul", cmulBuilder.build) val caddBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(caddBuilder, scale.cadd, ModuleSerializer.abstractModuleType) + DataConverter.setAttributeValue(context, caddBuilder, + scale.cadd, ModuleSerializer.abstractModuleType) scaleBuilder.putAttr("cadd", caddBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala index 37701d2f86a..ef4a95b3a93 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag @@ -111,30 +111,30 @@ object SpatialContrastiveNormalization extends ModuleSerializable { new SpatialContrastiveNormalization[T](nInputPlane, kernel, threshold, thresval) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context : DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val spatialContrastiveNormaModule = super.doLoadModule(model). + val spatialContrastiveNormaModule = super.doLoadModule(context). asInstanceOf[SpatialContrastiveNormalization[T]] - val attrMap = model.getAttrMap + val attrMap = context.bigdlModule.getAttrMap spatialContrastiveNormaModule.normalizer = DataConverter. - getAttributeValue(attrMap.get("normalizer")). + getAttributeValue(context, attrMap.get("normalizer")). asInstanceOf[Sequential[T]] spatialContrastiveNormaModule } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context : SerializeContext[T], contrastiveNormBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(module, contrastiveNormBuilder) - val spatialContrastiveNormaModule = module.module. + super.doSerializeModule(context, contrastiveNormBuilder) + val spatialContrastiveNormaModule = context.moduleData.module. asInstanceOf[SpatialContrastiveNormalization[T]] val normalizerBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(normalizerBuilder, + DataConverter.setAttributeValue(context, normalizerBuilder, spatialContrastiveNormaModule.normalizer, ModuleSerializer.tensorModuleType) contrastiveNormBuilder.putAttr("normalizer", normalizerBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala index 7cc0517ec8b..0b6c45dbe35 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.T -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag @@ -234,67 +234,68 @@ object SpatialDivisiveNormalization extends ModuleSerializable { new SpatialDivisiveNormalization[T](nInputPlane, kernel, threshold, thresval) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val spatialDivisiveNormModule = super.doLoadModule(model). + val spatialDivisiveNormModule = super.doLoadModule(context). asInstanceOf[SpatialDivisiveNormalization[T]] - val attrMap = model.getAttrMap + val attrMap = context.bigdlModule.getAttrMap spatialDivisiveNormModule.meanestimator = DataConverter. - getAttributeValue(attrMap.get("meanestimator")). + getAttributeValue(context, attrMap.get("meanestimator")). asInstanceOf[Sequential[T]] spatialDivisiveNormModule.stdestimator = DataConverter. - getAttributeValue(attrMap.get("stdestimator")). + getAttributeValue(context, attrMap.get("stdestimator")). asInstanceOf[Sequential[T]] spatialDivisiveNormModule.normalizer = DataConverter. - getAttributeValue(attrMap.get("normalizer")). + getAttributeValue(context, attrMap.get("normalizer")). asInstanceOf[CDivTable[T]] spatialDivisiveNormModule.divider = DataConverter. - getAttributeValue(attrMap.get("divider")). + getAttributeValue(context, attrMap.get("divider")). asInstanceOf[CDivTable[T]] spatialDivisiveNormModule.thresholder = DataConverter. - getAttributeValue(attrMap.get("thresholder")). + getAttributeValue(context, attrMap.get("thresholder")). asInstanceOf[Threshold[T]] spatialDivisiveNormModule } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], spatialDivisiveNormBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val spatialDivisiveNormModule = module.module.asInstanceOf[SpatialDivisiveNormalization[T]] - super.doSerializeModule(module, spatialDivisiveNormBuilder) + val spatialDivisiveNormModule = context.moduleData + .module.asInstanceOf[SpatialDivisiveNormalization[T]] + super.doSerializeModule(context, spatialDivisiveNormBuilder) val meanestimatorBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(meanestimatorBuilder, spatialDivisiveNormModule.meanestimator, - ModuleSerializer.tensorModuleType) + DataConverter.setAttributeValue(context, meanestimatorBuilder, + spatialDivisiveNormModule.meanestimator, ModuleSerializer.tensorModuleType) spatialDivisiveNormBuilder.putAttr("meanestimator", meanestimatorBuilder.build) val stdestimatorBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(stdestimatorBuilder, spatialDivisiveNormModule.stdestimator, - ModuleSerializer.tensorModuleType) + DataConverter.setAttributeValue(context, stdestimatorBuilder, + spatialDivisiveNormModule.stdestimator, ModuleSerializer.tensorModuleType) spatialDivisiveNormBuilder.putAttr("stdestimator", stdestimatorBuilder.build) val normalizerBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(normalizerBuilder, spatialDivisiveNormModule.normalizer, - ModuleSerializer.tensorModuleType) + DataConverter.setAttributeValue(context, normalizerBuilder, + spatialDivisiveNormModule.normalizer, ModuleSerializer.tensorModuleType) spatialDivisiveNormBuilder.putAttr("normalizer", normalizerBuilder.build) val dividerBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(dividerBuilder, spatialDivisiveNormModule.divider, - ModuleSerializer.tensorModuleType) + DataConverter.setAttributeValue(context, dividerBuilder, + spatialDivisiveNormModule.divider, ModuleSerializer.tensorModuleType) spatialDivisiveNormBuilder.putAttr("divider", dividerBuilder.build) val thresholderBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(thresholderBuilder, spatialDivisiveNormModule.thresholder, - ModuleSerializer.tensorModuleType) + DataConverter.setAttributeValue(context, thresholderBuilder, + spatialDivisiveNormModule.thresholder, ModuleSerializer.tensorModuleType) spatialDivisiveNormBuilder.putAttr("thresholder", thresholderBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala index 09c1b99b449..e9bc97a8bd5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.{T, Table, serializer} import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.concurrent.Future @@ -782,20 +782,20 @@ object SpatialFullConvolution extends ModuleSerializable { wRegularizer, bRegularizer) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val attrMap = model.getAttrMap - val intParams = DataConverter.getAttributeValue(attrMap.get("intParams")). + val attrMap = context.bigdlModule.getAttrMap + val intParams = DataConverter.getAttributeValue(context, attrMap.get("intParams")). asInstanceOf[Array[Int]] - val noBias = DataConverter.getAttributeValue(attrMap.get("noBias")). + val noBias = DataConverter.getAttributeValue(context, attrMap.get("noBias")). asInstanceOf[Boolean] - val wRegularizer = DataConverter.getAttributeValue(attrMap.get("wRegularizer")). + val wRegularizer = DataConverter.getAttributeValue(context, attrMap.get("wRegularizer")). asInstanceOf[Regularizer[T]] - val bRegularizer = DataConverter.getAttributeValue(attrMap.get("bRegularizer")). + val bRegularizer = DataConverter.getAttributeValue(context, attrMap.get("bRegularizer")). asInstanceOf[Regularizer[T]] val fullConv = SpatialFullConvolution(intParams(0), intParams(1), intParams(2), intParams(3), @@ -804,30 +804,34 @@ object SpatialFullConvolution extends ModuleSerializable { fullConv } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], fullConvBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val fullConv = module.module.asInstanceOf[SpatialFullConvolution[T]] + val fullConv = context.moduleData.module.asInstanceOf[SpatialFullConvolution[T]] val intParamsBuilder = AttrValue.newBuilder val intParams = Array(fullConv.nInputPlane, fullConv.nOutputPlane, fullConv.kW, fullConv.kH, fullConv.dW, fullConv.dH, fullConv.padW, fullConv.padH, fullConv.adjW, fullConv.adjH, fullConv.nGroup) - DataConverter.setAttributeValue(intParamsBuilder, intParams, universe.typeOf[Array[Int]]) + DataConverter.setAttributeValue(context, intParamsBuilder, intParams, + universe.typeOf[Array[Int]]) fullConvBuilder.putAttr("intParams", intParamsBuilder.build) val biasBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(biasBuilder, fullConv.noBias, universe.typeOf[Boolean]) + DataConverter.setAttributeValue(context, biasBuilder, + fullConv.noBias, universe.typeOf[Boolean]) fullConvBuilder.putAttr("noBias", biasBuilder.build) val wRegularizerBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(wRegularizerBuilder, fullConv.wRegularizer, + DataConverter.setAttributeValue(context, wRegularizerBuilder, + fullConv.wRegularizer, ModuleSerializer.regularizerType) fullConvBuilder.putAttr("wRegularizer", wRegularizerBuilder.build) val bRegularizerBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(bRegularizerBuilder, fullConv.bRegularizer, + DataConverter.setAttributeValue(context, + bRegularizerBuilder, fullConv.bRegularizer, ModuleSerializer.regularizerType) fullConvBuilder.putAttr("bRegularizer", bRegularizerBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala index 1a3993ef3c4..d27278dffd0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFo import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Engine -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect._ @@ -413,12 +413,12 @@ object SpatialMaxPooling extends ModuleSerializable { new SpatialMaxPooling[T](kW, kH, dW, dH, padW, padH, format) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val maxPooling = super.doLoadModule(model) - val attrMap = model.getAttrMap + val maxPooling = super.doLoadModule(context) + val attrMap = context.bigdlModule.getAttrMap val ceil_mode = DataConverter. - getAttributeValue(attrMap.get("ceil_mode")). + getAttributeValue(context, attrMap.get("ceil_mode")). asInstanceOf[Boolean] if (ceil_mode) { maxPooling.asInstanceOf[SpatialMaxPooling[T]].ceil() @@ -426,14 +426,15 @@ object SpatialMaxPooling extends ModuleSerializable { maxPooling } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], maxPoolingBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(module, maxPoolingBuilder) - val maxPooling = module.module.asInstanceOf[SpatialMaxPooling[T]] + super.doSerializeModule(context, maxPoolingBuilder) + val maxPooling = context.moduleData.module.asInstanceOf[SpatialMaxPooling[T]] val ceilBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(ceilBuilder, maxPooling.ceilMode, universe.typeOf[Boolean]) + DataConverter.setAttributeValue(context, ceilBuilder, + maxPooling.ceilMode, universe.typeOf[Boolean]) maxPoolingBuilder.putAttr("ceil_mode", ceilBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala index db2b3090163..0eaa4095e72 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.T -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag @@ -198,51 +198,51 @@ object SpatialSubtractiveNormalization extends ModuleSerializable { new SpatialSubtractiveNormalization[T](nInputPlane, kernel) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context : DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val spatialSubtractiveNormModule = super.doLoadModule(model). + val spatialSubtractiveNormModule = super.doLoadModule(context). asInstanceOf[SpatialSubtractiveNormalization[T]] - val attrMap = model.getAttrMap + val attrMap = context.bigdlModule.getAttrMap spatialSubtractiveNormModule.meanestimator = DataConverter. - getAttributeValue(attrMap.get("meanestimator")). + getAttributeValue(context, attrMap.get("meanestimator")). asInstanceOf[Sequential[T]] spatialSubtractiveNormModule.subtractor = DataConverter. - getAttributeValue(attrMap.get("subtractor")). + getAttributeValue(context, attrMap.get("subtractor")). asInstanceOf[CSubTable[T]] spatialSubtractiveNormModule.divider = DataConverter. - getAttributeValue(attrMap.get("divider")). + getAttributeValue(context, attrMap.get("divider")). asInstanceOf[CDivTable[T]] spatialSubtractiveNormModule } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](contetxt: SerializeContext[T], subtractiveNormBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(module, subtractiveNormBuilder) - val spatialSubtractiveNormaModule = module.module. + super.doSerializeModule(contetxt, subtractiveNormBuilder) + val spatialSubtractiveNormaModule = contetxt.moduleData.module. asInstanceOf[SpatialSubtractiveNormalization[T]] val meanestimatorBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(meanestimatorBuilder, + DataConverter.setAttributeValue(contetxt, meanestimatorBuilder, spatialSubtractiveNormaModule.meanestimator, ModuleSerializer.tensorModuleType) subtractiveNormBuilder.putAttr("meanestimator", meanestimatorBuilder.build) val thresholderBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(thresholderBuilder, spatialSubtractiveNormaModule.subtractor, - ModuleSerializer.tensorModuleType) + DataConverter.setAttributeValue(contetxt, thresholderBuilder, + spatialSubtractiveNormaModule.subtractor, ModuleSerializer.tensorModuleType) subtractiveNormBuilder.putAttr("subtractor", thresholderBuilder.build) val dividerBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(dividerBuilder, spatialSubtractiveNormaModule.divider, - ModuleSerializer.tensorModuleType) + DataConverter.setAttributeValue(contetxt, dividerBuilder, + spatialSubtractiveNormaModule.divider, ModuleSerializer.tensorModuleType) subtractiveNormBuilder.putAttr("divider", dividerBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala index 15928843814..3c68407638c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag @@ -72,13 +72,13 @@ object Transpose extends ModuleSerializable { new Transpose[T](permutations) } - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val attrMap = model.getAttrMap + val attrMap = context.bigdlModule.getAttrMap val size = DataConverter. - getAttributeValue(attrMap.get("size")). + getAttributeValue(context, attrMap.get("size")). asInstanceOf[Int] val permutations = new Array[(Int, Int)](size) @@ -87,7 +87,7 @@ object Transpose extends ModuleSerializable { while (i < size) { val permutation = DataConverter. - getAttributeValue(attrMap.get(s"permutation_$i")). + getAttributeValue(context, attrMap.get(s"permutation_$i")). asInstanceOf[Array[Int]] permutations(i) = (permutation(0), permutation(1)) i += 1 @@ -98,16 +98,16 @@ object Transpose extends ModuleSerializable { } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], transposeBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val transpose = module.module. + val transpose = context.moduleData.module. asInstanceOf[Transpose[T]] val size = transpose.permutations.length val sizeBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(sizeBuilder, size, universe.typeOf[Int]) + DataConverter.setAttributeValue(context, sizeBuilder, size, universe.typeOf[Int]) transposeBuilder.putAttr("size", sizeBuilder.build) var i = 0 @@ -116,7 +116,8 @@ object Transpose extends ModuleSerializable { val nextPermutationBuilder = AttrValue.newBuilder val arr : Array[Int] = Array(transpose.permutations(i)._1, transpose.permutations(i)_2) - DataConverter.setAttributeValue(nextPermutationBuilder, arr, universe.typeOf[Array[Int]]) + DataConverter.setAttributeValue(context, nextPermutationBuilder, + arr, universe.typeOf[Array[Int]]) transposeBuilder.putAttr(s"permutation_$i", nextPermutationBuilder.build) i += 1 } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala index 3a212919d6b..65909102708 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala @@ -19,7 +19,8 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer._ +import org.codehaus.jackson.map.DeserializationContext import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect._ @@ -557,31 +558,33 @@ object VolumetricMaxPooling extends ModuleSerializable { (kT: Int, kW: Int, kH: Int)(implicit ev: TensorNumeric[T]) : VolumetricMaxPooling[T] = new VolumetricMaxPooling[T](kT, kW, kH) - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context : DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val maxPooling = super.doLoadModule(model).asInstanceOf[VolumetricMaxPooling[T]] - val attrMap = model.getAttrMap - maxPooling.ceilMode = DataConverter.getAttributeValue(attrMap.get("ceilMode")). - asInstanceOf[Boolean] - maxPooling.indices = DataConverter.getAttributeValue(attrMap.get("indices")). - asInstanceOf[Tensor[Float]] + val maxPooling = super.doLoadModule(context).asInstanceOf[VolumetricMaxPooling[T]] + val attrMap = context.bigdlModule.getAttrMap + maxPooling.ceilMode = DataConverter.getAttributeValue(context, + attrMap.get("ceilMode")).asInstanceOf[Boolean] + maxPooling.indices = DataConverter.getAttributeValue(context, + attrMap.get("indices")).asInstanceOf[Tensor[Float]] maxPooling } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], volumetricMaxBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val maxPooling = module.module.asInstanceOf[VolumetricMaxPooling[T]] + val maxPooling = context.moduleData.module.asInstanceOf[VolumetricMaxPooling[T]] - super.doSerializeModule(module, volumetricMaxBuilder) + super.doSerializeModule(context, volumetricMaxBuilder) val ceilModeBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(ceilModeBuilder, maxPooling.ceilMode, universe.typeOf[Boolean]) + DataConverter.setAttributeValue(context, ceilModeBuilder, + maxPooling.ceilMode, universe.typeOf[Boolean]) volumetricMaxBuilder.putAttr("ceilMode", ceilModeBuilder.build) val indicesBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(indicesBuilder, maxPooling.indices, ModuleSerializer.tensorType) + DataConverter.setAttributeValue(context, + indicesBuilder, maxPooling.indices, ModuleSerializer.tensorType) volumetricMaxBuilder.putAttr("indices", indicesBuilder.build) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 77a4edaa5df..0adaac5ba9b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -56,7 +56,12 @@ abstract class TensorModule[T: ClassTag] abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag]( implicit ev: TensorNumeric[T]) extends Serializable { - private val namePostfix = Integer.toHexString(java.util.UUID.randomUUID().hashCode()) + private var namePostfix = Integer.toHexString(java.util.UUID.randomUUID().hashCode()) + + def getNamePostfix : String = namePostfix + + def setNamePostfix(namePostfix : String) : Unit = this.namePostfix = namePostfix + /** * The cached output. So we don't compute it again when need it */ @@ -155,6 +160,8 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ private var name : String = null + def hasName: Boolean = name != null + /** * Set the module name * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index 85c892c6d0d..15300ba317a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -15,14 +15,18 @@ */ package com.intel.analytics.bigdl.utils.serializer +import com.google.protobuf.ByteString + import scala.collection.JavaConverters._ import scala.reflect.runtime.universe import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.{NCHW, NHWC} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer, Regularizer} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericShort, NumericString} +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString import serialization.Bigdl._ import serialization.Bigdl.AttrValue.ArrayValue @@ -38,21 +42,25 @@ trait DataConverter { /** * Get attribute value from protobuf attribute data * @tparam T data type + * @param context deserialization context * @param attribute protobuf generated Attribute instance * @return BigDL compatible param value */ - def getAttributeValue[T : ClassTag](attribute: AttrValue)( + def getAttributeValue[T : ClassTag](context: DeserializeContext, + attribute: AttrValue)( implicit ev: TensorNumeric[T]) : AnyRef /** * Set attribute value to protobuf format * @tparam T data type + * @param context serialization context * @param attributeBuilder the attribute value writable instance * @param value the value to be written to protobuf file * @param valueType the type of the value to help set the data type */ - def setAttributeValue[T : ClassTag](attributeBuilder : AttrValue.Builder, value: Any, - valueType : universe.Type = null) + def setAttributeValue[T : ClassTag](context: SerializeContext[T], + attributeBuilder : AttrValue.Builder, value: Any, + valueType: universe.Type = null) (implicit ev: TensorNumeric[T]) : Unit } @@ -94,7 +102,7 @@ object DataConverter extends DataConverter{ } } - override def getAttributeValue[T : ClassTag](attribute: AttrValue) + override def getAttributeValue[T : ClassTag](context: DeserializeContext, attribute: AttrValue) (implicit ev: TensorNumeric[T]) : AnyRef = { attribute.getDataType match { case DataType.INT32 => Integer.valueOf(attribute.getInt32Value) @@ -103,22 +111,24 @@ object DataConverter extends DataConverter{ case DataType.FLOAT => Float.box(attribute.getFloatValue) case DataType.STRING => attribute.getStringValue case DataType.BOOL => Boolean.box(attribute.getBoolValue) - case DataType.REGULARIZER => RegularizerConverter.getAttributeValue(attribute) - case DataType.TENSOR => TensorConverter.getAttributeValue(attribute) - case DataType.VARIABLE_FORMAT => VariableFormatConverter.getAttributeValue(attribute) - case DataType.INITMETHOD => InitMethodConverter.getAttributeValue(attribute) - case DataType.MODULE => ModuleConverter.getAttributeValue(attribute) - case DataType.NAME_ATTR_LIST => NameListConverter.getAttributeValue(attribute) - case DataType.ARRAY_VALUE => ArrayConverter.getAttributeValue(attribute) - case DataType.DATA_FORMAT => DataFormatConverter.getAttributeValue(attribute) - case DataType.CUSTOM => CustomConverterDelegator.getAttributeValue(attribute) + case DataType.REGULARIZER => RegularizerConverter.getAttributeValue(context, attribute) + case DataType.TENSOR => TensorConverter.getAttributeValue(context, attribute) + case DataType.VARIABLE_FORMAT => + VariableFormatConverter.getAttributeValue(context, attribute) + case DataType.INITMETHOD => InitMethodConverter.getAttributeValue(context, attribute) + case DataType.MODULE => ModuleConverter.getAttributeValue(context, attribute) + case DataType.NAME_ATTR_LIST => NameListConverter.getAttributeValue(context, attribute) + case DataType.ARRAY_VALUE => ArrayConverter.getAttributeValue(context, attribute) + case DataType.DATA_FORMAT => DataFormatConverter.getAttributeValue(context, attribute) + case DataType.CUSTOM => CustomConverterDelegator.getAttributeValue(context, attribute) case _ => throw new IllegalArgumentException (s"${attribute.getDataType} can not be recognized") } } override def setAttributeValue[T : ClassTag]( - attributeBuilder : AttrValue.Builder, value: Any, valueType : universe.Type = typePlaceHolder) + context: SerializeContext[T], attributeBuilder: AttrValue.Builder, + value: Any, valueType : universe.Type = typePlaceHolder) (implicit ev: TensorNumeric[T]): Unit = { // to make it compatible with Java types if (valueType =:= universe.typeOf[Int] || @@ -146,13 +156,13 @@ object DataConverter extends DataConverter{ attributeBuilder.setDataType(DataType.BOOL ) attributeBuilder.setBoolValue(value.asInstanceOf[Boolean]) } else if (valueType =:= universe.typeOf[VariableFormat]) { - VariableFormatConverter.setAttributeValue(attributeBuilder, value) + VariableFormatConverter.setAttributeValue(context, attributeBuilder, value) } else if (valueType =:= universe.typeOf[InitializationMethod]) { - InitMethodConverter.setAttributeValue(attributeBuilder, value) + InitMethodConverter.setAttributeValue(context, attributeBuilder, value) } else if (valueType.toString == ModuleSerializer.regularizerType.toString) { - RegularizerConverter.setAttributeValue(attributeBuilder, value) - } else if (valueType.toString == ModuleSerializer.tensorType.toString) { - TensorConverter.setAttributeValue(attributeBuilder, value) + RegularizerConverter.setAttributeValue(context, attributeBuilder, value) + } else if (valueType <:< universe.typeOf[Tensor[_]]) { + TensorConverter.setAttributeValue(context, attributeBuilder, value) } else if (valueType.toString == ModuleSerializer.tType.toString) { if (ev == com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble) { attributeBuilder.setDataType(DataType.DOUBLE) @@ -166,15 +176,16 @@ object DataConverter extends DataConverter{ || valueType.toString == ModuleSerializer.moduleType.toString || valueType.toString == ModuleSerializer.boundedModuleType.toString ) { - ModuleConverter.setAttributeValue(attributeBuilder, value) + ModuleConverter.setAttributeValue(context, attributeBuilder, value) } else if (value.isInstanceOf[mutable.Map[String, _ <: Any]]) { - NameListConverter.setAttributeValue(attributeBuilder, value) - } else if (valueType <:< universe.typeOf[Array[_ <: Any]] ) { - ArrayConverter.setAttributeValue(attributeBuilder, value, valueType) - } else if (valueType == universe.typeOf[DataFormat]) { - DataFormatConverter.setAttributeValue(attributeBuilder, value) + NameListConverter.setAttributeValue(context, attributeBuilder, value) + } else if (valueType <:< universe.typeOf[Array[_]] || + valueType.typeSymbol == universe.typeOf[Array[_ ]].typeSymbol) { + ArrayConverter.setAttributeValue(context, attributeBuilder, value, valueType) + } else if (valueType =:= universe.typeOf[DataFormat]) { + DataFormatConverter.setAttributeValue(context, attributeBuilder, value) } else { - CustomConverterDelegator.setAttributeValue(attributeBuilder, value, valueType) + CustomConverterDelegator.setAttributeValue(context, attributeBuilder, value, valueType) } } @@ -183,7 +194,8 @@ object DataConverter extends DataConverter{ */ object RegularizerConverter extends DataConverter { - override def getAttributeValue[T : ClassTag](attribute: AttrValue) + override def getAttributeValue[T : ClassTag](context: DeserializeContext, + attribute: AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { val regularizer = attribute.getRegularizerValue val regularizerType = regularizer.getRegularizerType @@ -205,7 +217,7 @@ object DataConverter extends DataConverter{ } override def setAttributeValue[T : ClassTag] - (attributeBuilder: AttrValue.Builder, value: Any, + (context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, valueType : universe.Type = null) (implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.REGULARIZER) @@ -233,71 +245,212 @@ object DataConverter extends DataConverter{ */ object TensorConverter extends DataConverter { - override def getAttributeValue[T: ClassTag](attribute: AttrValue) + override def getAttributeValue[T: ClassTag](context: DeserializeContext, + attribute: AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { val serializedTensor = attribute.getTensorValue - val dataType = serializedTensor.getDatatype - val sizes = serializedTensor.getSizeList.asScala - if (sizes.size == 0) { - return null; + if (!serializedTensor.hasStorage) { + return null + } + val storages = context.storages + val tensorId = serializedTensor.getId + if (storages.contains(tensorId)) { + return storages.get(tensorId).get.asInstanceOf[AnyRef] } - if (dataType != DataType.DOUBLE && dataType != DataType.FLOAT) { - throw new IllegalArgumentException(s"$dataType not supported!") + val dataType = serializedTensor.getDatatype + val sizes = serializedTensor.getSizeList.asScala.toArray.map(_.intValue()) + val strides = serializedTensor.getStrideList.asScala.toArray.map(_.intValue()) + val offSet = serializedTensor.getOffset + val isScalr = serializedTensor.getIsScalar + val serializedStorage = serializedTensor.getStorage + val storageId = serializedStorage.getId + val created = if (storages.contains(storageId)) { + storages.get(storageId).get + } else { + null } - val strorageArray : Array[T] = dataType match { + val tensor = dataType match { case DataType.FLOAT => - val data = serializedTensor.getFloatDataList.asScala - val strorageArray = new Array[T](data.size) - var i = 0; - while (i < data.size) { - strorageArray(i) = ev.fromType[Float](data(i)) - i += 1 - } - strorageArray + val storage : Storage[Float] = if (created == null ) { + val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) + val newStorage = Storage[Float](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[Float]] + Tensor[Float](storage, offSet, sizes, strides) case DataType.DOUBLE => - val data = serializedTensor.getDoubleDataList.asScala - val strorageArray = new Array[T](data.size) - var i = 0; - while (i < data.size) { - strorageArray(i) = ev.fromType[Double](data(i)) - i += 1 - } - strorageArray + val storage : Storage[Double] = if (created == null ) { + val data = serializedStorage.getDoubleDataList.asScala.toArray.map(_.doubleValue()) + val newStorage = Storage[Double](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[Double]] + Tensor[Double](storage, offSet, sizes, strides) + case DataType.BOOL => + val storage : Storage[Boolean] = if (created == null ) { + val data = serializedStorage.getBoolDataList.asScala.toArray.map(_.booleanValue()) + val newStorage = Storage[Boolean](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[Boolean]] + Tensor[Boolean](storage, offSet, sizes, strides) + case DataType.CHAR => + val storage: Storage[Char] = if (created == null ) { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.toChar.charValue()) + val newStorage = Storage[Char](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[Char]] + Tensor[Char](storage, offSet, sizes, strides) + case DataType.STRING => + val storage: Storage[String] = if (created == null ) { + val data = serializedStorage.getStringDataList.asScala.toArray + val newStorage = Storage[String](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[String]] + Tensor[String](storage, offSet, sizes, strides) + case DataType.INT32 => + val storage: Storage[Int] = if (created == null ) { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) + val newStorage = Storage[Int](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[Int]] + Tensor[Int](storage, offSet, sizes, strides) + case DataType.SHORT => + val storage: Storage[Short] = if (created == null ) { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.shortValue()) + val newStorage = Storage[Short](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[Short]] + Tensor[Short](storage, offSet, sizes, strides) + case DataType.INT64 => + val storage: Storage[Long] = if (created == null ) { + val data = serializedStorage.getLongDataList.asScala.toArray.map(_.longValue()) + val newStorage = Storage[Long](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[Long]] + Tensor[Long](storage, offSet, sizes, strides) + case DataType.BYTES => + val storage: Storage[ByteString] = if (created == null ) { + val data = serializedStorage.getBytesDataList.asScala.toArray + val newStorage = Storage[ByteString](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[ByteString]] + Tensor[ByteString](storage, offSet, sizes, strides) case _ => throw new IllegalArgumentException(s"$dataType not supported in tensor now !") } - val sizeArray = new Array[Int](sizes.size) - var i = 0; - while (i < sizes.size) { - sizeArray(i) = sizes(i) - i += 1 + storages(tensorId) = tensor + tensor + } + + private def setStorage[T: ClassTag](context: SerializeContext[T], + tensorBuilder: BigDLTensor.Builder, tensor: Tensor[_]): Unit = { + val tensorNumeric = tensor.getTensorNumeric() + val storageType = context.storageType + val tensorStorage = tensor.storage() + val storageId = System.identityHashCode(tensor.storage().array()) + val storages = context.storages + if (storageType == ProtoStorageType) { + if (storages.contains(storageId)) { + tensorBuilder.setStorage(storages.get(storageId).get + .asInstanceOf[TensorStorage]) + } else { + val storageBuilder = TensorStorage.newBuilder + if (tensorNumeric == NumericFloat) { + tensorBuilder.setDatatype(DataType.FLOAT) + storageBuilder.setDatatype(DataType.FLOAT) + tensor.storage().array().asInstanceOf[Array[Float]]. + foreach(data => storageBuilder.addFloatData(data)) + } else if (tensorNumeric == NumericDouble) { + tensorBuilder.setDatatype(DataType.DOUBLE) + storageBuilder.setDatatype(DataType.DOUBLE) + tensor.storage().array().asInstanceOf[Array[Double]]. + foreach(data => storageBuilder.addDoubleData(data)) + } else if (tensorNumeric == NumericChar) { + tensorBuilder.setDatatype(DataType.CHAR) + storageBuilder.setDatatype(DataType.CHAR) + tensor.storage().array().asInstanceOf[Array[Char]]. + foreach(data => storageBuilder.addIntData(data)) + } else if (tensorNumeric == NumericBoolean) { + tensorBuilder.setDatatype(DataType.BOOL) + storageBuilder.setDatatype(DataType.BOOL) + tensor.storage().array().asInstanceOf[Array[Boolean]]. + foreach(data => storageBuilder.addBoolData(data)) + } else if (tensorNumeric == NumericString) { + tensorBuilder.setDatatype(DataType.STRING) + storageBuilder.setDatatype(DataType.STRING) + tensor.storage().array().asInstanceOf[Array[String]]. + foreach(data => storageBuilder.addStringData(data)) + } else if (tensorNumeric == NumericInt) { + tensorBuilder.setDatatype(DataType.INT32) + storageBuilder.setDatatype(DataType.INT32) + tensor.storage().array().asInstanceOf[Array[Int]]. + foreach(data => storageBuilder.addIntData(data)) + } else if (tensorNumeric == NumericShort) { + tensorBuilder.setDatatype(DataType.SHORT) + storageBuilder.setDatatype(DataType.SHORT) + tensor.storage().array().asInstanceOf[Array[Short]]. + foreach(data => storageBuilder.addIntData(data)) + } else if (tensorNumeric == NumericLong) { + tensorBuilder.setDatatype(DataType.INT64) + storageBuilder.setDatatype(DataType.INT64) + tensor.storage().array().asInstanceOf[Array[Long]]. + foreach(data => storageBuilder.addLongData(data)) + } else if (tensorNumeric == NumericByteString) { + tensorBuilder.setDatatype(DataType.BYTES) + storageBuilder.setDatatype(DataType.BYTES) + tensor.storage().array().asInstanceOf[Array[ByteString]]. + foreach(data => storageBuilder.addBytesData(data)) + } + storageBuilder.setId(storageId) + val storage = storageBuilder.build + tensorBuilder.setStorage(storage) + storages(storageId) = storage + } + } else { + throw new IllegalArgumentException(s"$storageType not supported") } - Tensor[T](strorageArray, sizeArray) } override def setAttributeValue[T: ClassTag] - (attributeBuilder: AttrValue.Builder, value: Any, + (context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, valueType : universe.Type = null) (implicit ev: TensorNumeric[T]): Unit = { - import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat - import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble attributeBuilder.setDataType(DataType.TENSOR) if (value != null) { - val tensor = value.asInstanceOf[Tensor[T]] - val tensorBuilder = BigDLTensor.newBuilder - if (ev == NumericFloat) { - tensorBuilder.setDatatype(DataType.FLOAT) - tensor.storage().array().foreach(data => tensorBuilder. - addFloatData(ev.toType[Float](data))) - } else if (ev == NumericDouble) { - tensorBuilder.setDatatype(DataType.DOUBLE) - tensor.storage().array().foreach(data => tensorBuilder. - addDoubleData(ev.toType[Float](data))) + val tensor = value.asInstanceOf[Tensor[_]] + val tensorId = System.identityHashCode(tensor) + val storages = context.storages + // Check if tensor has been shared + if (storages.contains(tensorId)) { + attributeBuilder.setTensorValue(storages.get(tensorId).get + .asInstanceOf[BigDLTensor]) + } else { + val tensorNumeric = tensor.getTensorNumeric() + val offSet = tensor.storageOffset() + val totalElement = tensor.nElement() + val dimension = tensor.dim() + val isScalar = tensor.isScalar + val tensorBuilder = BigDLTensor.newBuilder + tensorBuilder.setId(tensorId) + tensorBuilder.setDimension(dimension) + tensorBuilder.setOffset(offSet) + tensorBuilder.setNElements(totalElement) + tensorBuilder.setIsScalar(isScalar) + tensor.size().foreach(size => tensorBuilder.addSize(size)) + tensor.stride().foreach(stride => tensorBuilder.addStride(stride)) + setStorage(context, tensorBuilder, tensor) + val tensorBuild = tensorBuilder.build + attributeBuilder.setTensorValue(tensorBuild) + storages(tensorId) = tensorBuild } - tensor.size().foreach(size => tensorBuilder.addSize(size)) - attributeBuilder.setTensorValue(tensorBuilder.build) } } - } /** @@ -305,7 +458,7 @@ object DataConverter extends DataConverter{ */ object VariableFormatConverter extends DataConverter { - override def getAttributeValue[T: ClassTag](attribute: AttrValue) + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { val format = attribute.getVariableFormatValue format match { @@ -322,7 +475,8 @@ object DataConverter extends DataConverter{ } } - override def setAttributeValue[T: ClassTag](attributeBuilder: AttrValue.Builder, + override def setAttributeValue[T: ClassTag]( + context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.VARIABLE_FORMAT) if (value != null) { @@ -349,7 +503,7 @@ object DataConverter extends DataConverter{ */ object InitMethodConverter extends DataConverter { - override def getAttributeValue[T: ClassTag](attribute: AttrValue) + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { val initMemethod = attribute.getInitMethodValue val initType = initMemethod.getMethodType @@ -369,7 +523,8 @@ object DataConverter extends DataConverter{ } } - override def setAttributeValue[T: ClassTag](attributeBuilder: AttrValue.Builder, + override def setAttributeValue[T: ClassTag]( + context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.INITMETHOD) val initMethodBuilder = InitMethod.newBuilder @@ -410,7 +565,7 @@ object DataConverter extends DataConverter{ * DataConverter for [[com.intel.analytics.bigdl.nn.abstractnn.DataFormat]] */ object DataFormatConverter extends DataConverter { - override def getAttributeValue[T: ClassTag](attribute: AttrValue) + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { val dataFormat = attribute.getDataFormatValue dataFormat match { @@ -421,7 +576,8 @@ object DataConverter extends DataConverter{ } override def setAttributeValue[T: ClassTag] - (attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type) + (context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type) (implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.DATA_FORMAT) if (value != null) { @@ -440,24 +596,27 @@ object DataConverter extends DataConverter{ */ object ModuleConverter extends DataConverter { - override def getAttributeValue[T: ClassTag](attribute: AttrValue) + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { val serializedModule = attribute.getBigDLModuleValue if (serializedModule.getModuleType != null && serializedModule.getModuleType != "") { - ModuleSerializer.load(serializedModule).module + ModuleSerializer.load(DeserializeContext(serializedModule, + context.storages, context.storageType)).module } else { null } } - override def setAttributeValue[T: ClassTag](attributeBuilder: AttrValue.Builder, + override def setAttributeValue[T: ClassTag](context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.MODULE) if (value != null) { val module = value.asInstanceOf[AbstractModule[Activity, Activity, T]] val serializableModule = ModuleSerializer. - serialize(ModuleData(module, Seq[String](), Seq[String]())) - attributeBuilder.setBigDLModuleValue(serializableModule) + serialize(SerializeContext(ModuleData(module, Seq[String](), Seq[String]()), + context.storages, context.storageType)).bigDLModule + attributeBuilder.setBigDLModuleValue(serializableModule) } } } @@ -468,7 +627,7 @@ object DataConverter extends DataConverter{ object NameListConverter extends DataConverter { override def getAttributeValue[T: ClassTag] - (attribute: AttrValue)(implicit ev: TensorNumeric[T]): AnyRef = { + (context: DeserializeContext, attribute: AttrValue)(implicit ev: TensorNumeric[T]): AnyRef = { val nameListMap = new mutable.HashMap[String, mutable.Map[String, Any]]() val listMap = new mutable.HashMap[String, Any]() val nameAttrListValue = attribute.getNameAttrListValue @@ -476,14 +635,15 @@ object DataConverter extends DataConverter{ nameAttrListValue.getAttrMap.asScala.foreach(attributePair => { val name = attributePair._1 val attrValue = attributePair._2 - val convetedObj = DataConverter.getAttributeValue(attrValue) + val convetedObj = DataConverter.getAttributeValue(context, attrValue) listMap(name) = convetedObj }) nameListMap(listName) = listMap nameListMap } - override def setAttributeValue[T: ClassTag](attributeBuilder: AttrValue.Builder, + override def setAttributeValue[T: ClassTag](context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.NAME_ATTR_LIST) val listMap = value.asInstanceOf[mutable.Map[String, mutable.Map[String, Any]]] @@ -494,7 +654,7 @@ object DataConverter extends DataConverter{ val name = attributePair._1 val obj = attributePair._2 val nextedAttr = AttrValue.newBuilder - DataConverter.setAttributeValue(nextedAttr, obj, getRuntimeType(obj)) + DataConverter.setAttributeValue(context, nextedAttr, obj, getRuntimeType(obj)) nameAttrList.putAttr(name, nextedAttr.build) }) attributeBuilder.setNameAttrListValue(nameAttrList.build) @@ -510,25 +670,43 @@ object DataConverter extends DataConverter{ object ArrayConverter extends DataConverter { override def getAttributeValue[T: ClassTag] - (attribute: AttrValue)(implicit ev: TensorNumeric[T]): AnyRef = { + (context: DeserializeContext, attribute: AttrValue)(implicit ev: TensorNumeric[T]): AnyRef = { val valueArray = attribute.getArrayValue val size = valueArray.getSize - if (size == 0) { + if (size == -1) { return null } val listType = valueArray.getDatatype val arr = listType match { case DataType.INT32 => + if (size == 0) { + return new Array[Int](0) + } valueArray.getI32List.asScala.toArray.map(_.intValue) case DataType.INT64 => + if (size == 0) { + return new Array[Long](0) + } valueArray.getI64List.asScala.toArray.map(_.longValue()) case DataType.DOUBLE => + if (size == 0) { + return new Array[Double](0) + } valueArray.getDblList.asScala.toArray.map(_.doubleValue()) case DataType.FLOAT => + if (size == 0) { + return new Array[Float](0) + } valueArray.getFltList.asScala.toArray.map(_.floatValue()) case DataType.STRING => + if (size == 0) { + return new Array[String](0) + } valueArray.getStrList.asScala.toArray case DataType.BOOL => + if (size == 0) { + return new Array[Boolean](0) + } valueArray.getBooleanList.asScala.toArray.map(_.booleanValue()) case DataType.REGULARIZER => val regularizers = new Array[Regularizer[T]](size) @@ -539,7 +717,7 @@ object DataConverter extends DataConverter{ attrValue.setDataType(DataType.REGULARIZER) attrValue.setRegularizerValue(reg) regularizers(i) = RegularizerConverter. - getAttributeValue(attrValue.build).asInstanceOf[Regularizer[T]] + getAttributeValue(context, attrValue.build).asInstanceOf[Regularizer[T]] i += 1 }) regularizers @@ -552,7 +730,7 @@ object DataConverter extends DataConverter{ attrValue.setDataType(DataType.TENSOR) attrValue.setTensorValue(tensor) tensors(i) = TensorConverter. - getAttributeValue(attrValue.build).asInstanceOf[Tensor[T]] + getAttributeValue(context, attrValue.build).asInstanceOf[Tensor[T]] i += 1 }) tensors @@ -565,7 +743,7 @@ object DataConverter extends DataConverter{ attrValue.setDataType(DataType.VARIABLE_FORMAT) attrValue.setVariableFormatValue(format) formats(i) = VariableFormatConverter. - getAttributeValue(attrValue.build).asInstanceOf[VariableFormat] + getAttributeValue(context, attrValue.build).asInstanceOf[VariableFormat] }) formats case DataType.INITMETHOD => @@ -576,7 +754,7 @@ object DataConverter extends DataConverter{ val attrValue = AttrValue.newBuilder attrValue.setDataType(DataType.INITMETHOD) attrValue.setInitMethodValue(method) - methods(i) = InitMethodConverter.getAttributeValue(attrValue.build) + methods(i) = InitMethodConverter.getAttributeValue(context, attrValue.build) .asInstanceOf[InitializationMethod] i += 1 }) @@ -589,8 +767,8 @@ object DataConverter extends DataConverter{ val attrValue = AttrValue.newBuilder attrValue.setDataType(DataType.MODULE) attrValue.setBigDLModuleValue(module) - modules(i) = ModuleConverter. - getAttributeValue(attrValue.build).asInstanceOf[AbstractModule[Activity, Activity, T]] + modules(i) = ModuleConverter.getAttributeValue(context, attrValue.build) + .asInstanceOf[AbstractModule[Activity, Activity, T]] i += 1 }) modules @@ -602,7 +780,7 @@ object DataConverter extends DataConverter{ val attrValue = AttrValue.newBuilder attrValue.setDataType(DataType.NAME_ATTR_LIST) attrValue.setNameAttrListValue(nameList) - nameArray(i) = NameListConverter.getAttributeValue(attrValue.build) + nameArray(i) = NameListConverter.getAttributeValue(context, attrValue.build) .asInstanceOf[Map[String, Map[String, Any]]] i += 1 }) @@ -616,7 +794,7 @@ object DataConverter extends DataConverter{ attrValue.setDataType(DataType.DATA_FORMAT) attrValue.setDataFormatValue(format) dataFormats(i) = DataFormatConverter. - getAttributeValue(attrValue.build).asInstanceOf[DataFormat] + getAttributeValue(context, attrValue.build).asInstanceOf[DataFormat] i += 1 }) dataFormats @@ -629,7 +807,7 @@ object DataConverter extends DataConverter{ attrValue.setDataType(DataType.CUSTOM) attrValue.setCustomValue(custom) customValues(i) = CustomConverterDelegator. - getAttributeValue(attrValue.build) + getAttributeValue(context, attrValue.build) i += 1 }) customValues @@ -637,11 +815,12 @@ object DataConverter extends DataConverter{ arr } - override def setAttributeValue[T: ClassTag](attributeBuilder: AttrValue.Builder, + override def setAttributeValue[T: ClassTag](context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.ARRAY_VALUE) val arrayBuilder = ArrayValue.newBuilder - arrayBuilder.setSize(0) + arrayBuilder.setSize(-1) if (valueType =:= universe.typeOf[Array[Int]]) { arrayBuilder.setDatatype(DataType.INT32) if (value != null) { @@ -690,7 +869,7 @@ object DataConverter extends DataConverter{ val regularizers = value.asInstanceOf[Array[Regularizer[T]]] regularizers.foreach(reg => { val attrValueBuilder = AttrValue.newBuilder - RegularizerConverter.setAttributeValue(attrValueBuilder, reg) + RegularizerConverter.setAttributeValue(context, attrValueBuilder, reg) arrayBuilder.addRegularizer(attrValueBuilder.getRegularizerValue) }) arrayBuilder.setSize(regularizers.size) @@ -702,7 +881,7 @@ object DataConverter extends DataConverter{ val tensors = value.asInstanceOf[Array[Tensor[T]]] tensors.foreach(tensor => { val attrValueBuilder = AttrValue.newBuilder - TensorConverter.setAttributeValue(attrValueBuilder, tensor) + TensorConverter.setAttributeValue(context, attrValueBuilder, tensor) arrayBuilder.addTensor(attrValueBuilder.getTensorValue) }) arrayBuilder.setSize(tensors.size) @@ -713,7 +892,7 @@ object DataConverter extends DataConverter{ val formats = value.asInstanceOf[Array[VariableFormat]] formats.foreach(format => { val attrValueBuilder = AttrValue.newBuilder - VariableFormatConverter.setAttributeValue(attrValueBuilder, format) + VariableFormatConverter.setAttributeValue(context, attrValueBuilder, format) arrayBuilder.addVariableFormat(attrValueBuilder.getVariableFormatValue) }) arrayBuilder.setSize(formats.size) @@ -724,7 +903,7 @@ object DataConverter extends DataConverter{ val methods = value.asInstanceOf[Array[InitializationMethod]] methods.foreach(method => { val attrValueBuilder = AttrValue.newBuilder - InitMethodConverter.setAttributeValue(attrValueBuilder, method) + InitMethodConverter.setAttributeValue(context, attrValueBuilder, method) arrayBuilder.addInitMethod(attrValueBuilder.getInitMethodValue) }) arrayBuilder.setSize(methods.size) @@ -736,7 +915,7 @@ object DataConverter extends DataConverter{ val modules = value.asInstanceOf[Array[_ <: AbstractModule[Activity, Activity, T]]] modules.foreach(module => { val attrValueBuilder = AttrValue.newBuilder - ModuleConverter.setAttributeValue(attrValueBuilder, module) + ModuleConverter.setAttributeValue(context, attrValueBuilder, module) arrayBuilder.addBigDLModule(attrValueBuilder.getBigDLModuleValue) }) arrayBuilder.setSize(modules.size) @@ -745,7 +924,7 @@ object DataConverter extends DataConverter{ arrayBuilder.setDatatype(DataType.NAME_ATTR_LIST) value.asInstanceOf[Array[Map[String, Any]]].foreach(map => { val attrValueBuilder = AttrValue.newBuilder - NameListConverter.setAttributeValue(attrValueBuilder, map) + NameListConverter.setAttributeValue(context, attrValueBuilder, map) arrayBuilder.addNameAttrList(attrValueBuilder.getNameAttrListValue) }) } else if (valueType =:= universe.typeOf[Array[DataFormat]]) { @@ -754,7 +933,7 @@ object DataConverter extends DataConverter{ val formats = value.asInstanceOf[Array[DataFormat]] formats.foreach(format => { val attrValueBuilder = AttrValue.newBuilder - DataFormatConverter.setAttributeValue(attrValueBuilder, format) + DataFormatConverter.setAttributeValue(context, attrValueBuilder, format) arrayBuilder.addDataFormat(attrValueBuilder.getDataFormatValue) }) arrayBuilder.setSize(formats.size) @@ -765,7 +944,7 @@ object DataConverter extends DataConverter{ val customValues = value.asInstanceOf[Array[Any]] customValues.foreach(custom => { val attrValueBuilder = AttrValue.newBuilder - CustomConverterDelegator.setAttributeValue(attrValueBuilder, custom) + CustomConverterDelegator.setAttributeValue(context, attrValueBuilder, custom) arrayBuilder.addCustom(attrValueBuilder.getCustomValue) }) arrayBuilder.setSize(customValues.size) @@ -779,21 +958,22 @@ object DataConverter extends DataConverter{ * DataConvert for custom value */ object CustomConverterDelegator extends DataConverter { - override def getAttributeValue[T: ClassTag](attribute: AttrValue) + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { val subType = attribute.getSubType require(customizedConverter.contains(subType), s"unrecognized type $subType") val customConverter = customizedConverter.get(subType).get - customConverter.getAttributeValue(attribute) + customConverter.getAttributeValue(context, attribute) } - override def setAttributeValue[T: ClassTag](attributeBuilder: AttrValue.Builder, + override def setAttributeValue[T: ClassTag](context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type)(implicit ev: TensorNumeric[T]): Unit = { require(customizedConverter.contains(valueType.toString), s"unrecognized type $valueType") val customConverter = customizedConverter.get(valueType.toString).get attributeBuilder.setDataType(DataType.CUSTOM) attributeBuilder.setSubType(valueType.toString) - customConverter.setAttributeValue(attributeBuilder, value, valueType) + customConverter.setAttributeValue(context, attributeBuilder, value, valueType) } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala index c2393858cf7..ed4768aa6dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{File, Table} -import serialization.Bigdl.BigDLModule +import serialization.Bigdl.{BigDLModule, DataType, TensorStorage} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -47,7 +47,9 @@ object ModuleLoader { cis.setSizeLimit(Integer.MAX_VALUE) modelBuilder.mergeFrom(cis) val bigDLModel = modelBuilder.build() - ModuleSerializer.load(bigDLModel).module + val storages = new mutable.HashMap[Int, Any]() + // loadAllStorages(bigDLModel, storages) + ModuleSerializer.load(DeserializeContext(bigDLModel, storages, ProtoStorageType)).module } /** @@ -61,7 +63,7 @@ object ModuleLoader { */ def loadFromDefinition[T : ClassTag](definition : AbstractModule[Activity, Activity, T], - modelPath : String, layers : mutable.HashSet[String] = null)(implicit ev: TensorNumeric[T]) + modelPath : String, layers : mutable.HashSet[String] = null)(implicit ev: TensorNumeric[T]) : Unit = { val loadedModule = loadFromFile(modelPath) val layersToCopy = if (layers == null) { @@ -141,11 +143,13 @@ object ModulePersister { * @tparam T data type */ def saveToFile[T: ClassTag](modelPath: String, module: AbstractModule[Activity, Activity, T], - overwrite: Boolean = false)(implicit ev: TensorNumeric[T]): Unit = { - + overwrite: Boolean = false) + (implicit ev: TensorNumeric[T]): Unit = { val bigDLModule = ModuleData(module , new ArrayBuffer[String](), new ArrayBuffer[String]()) - val bigDLModel = ModuleSerializer.serialize(bigDLModule) + val storages = new mutable.HashMap[Int, Any]() + val context = SerializeContext(bigDLModule, storages, ProtoStorageType) + val bigDLModel = ModuleSerializer.serialize(context).bigDLModule File.saveBytes(bigDLModel.toByteArray, modelPath, overwrite) } @@ -161,7 +165,9 @@ object ModulePersister { module : AbstractModule[Activity, Activity, T], overwrite : Boolean = false)(implicit ev: TensorNumeric[T]) : Unit = { val bigDLModule = ModuleData(module, new ArrayBuffer[String](), new ArrayBuffer[String]()) - val bigDLModel = ModuleSerializer.serialize(bigDLModule) + val storages = new mutable.HashMap[Int, Any]() + val context = SerializeContext(bigDLModule, storages, ProtoStorageType) + val bigDLModel = ModuleSerializer.serialize(context).bigDLModule val bigDLModelWithoutWeightsAndBias = BigDLModule.newBuilder(bigDLModel) cleantWeightAndBias(bigDLModelWithoutWeightsAndBias) val model = bigDLModelWithoutWeightsAndBias.build diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 8e51f36265f..69305246bef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -24,12 +24,15 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.DataConverter.TensorConverter import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._ import serialization.Bigdl.DataType import serialization.Bigdl.{AttrValue, BigDLModule, BigDLTensor} +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +import scala.reflect.runtime.universe /** * [[ModuleSerializable]] trait inherits [[Loadable]] and [[Savable]] @@ -58,30 +61,42 @@ trait ModuleSerializable extends Loadable with Savable{ /** * Default deserialization to provide the template - * @param model serialized protobuf module instace * @return BigDL module instance with linkages with other modules */ - override def loadModule[T: ClassTag](model : BigDLModule) + override def loadModule[T: ClassTag](context : DeserializeContext) (implicit ev: TensorNumeric[T]) : ModuleData[T] = { + val model = context.bigdlModule + // step 1 : check version checkVersion(model) // step2 : module specific logic to load module, either default, cell, container or graph - val module = doLoadModule(model) + val moduleId = context.bigdlModule.getId + + val storages = context.storages + val module = if (storages.contains(moduleId)) { + storages.get(moduleId).get.asInstanceOf[AbstractModule[Activity, Activity, T]] + } else { + val loadedModule = doLoadModule(context) + storages(moduleId) = loadedModule + loadedModule + } // step3 : copy params (weight & bias) and linkage - createBigDLModule(model, module) + createBigDLModule(context, module) } /** * Default deserialization using reflection - * @param model serialized protobuf module instace + * @param context deserialize context * @return BigDL module */ - protected def doLoadModule[T: ClassTag](model : BigDLModule) + protected def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val evidence = scala.reflect.classTag[T] + val model = context.bigdlModule val modelAttributes = model.getAttrMap val moduleType = model.getModuleType val cls = Class.forName(moduleType) @@ -93,7 +108,7 @@ trait ModuleSerializable extends Loadable with Savable{ map.foreach(param => { val name = param.name.decodedName.toString val ptype = param.typeSignature - if (ptype.toString == "scala.reflect.ClassTag[T]") { + if (ptype <:< universe.typeOf[ClassTag[_]]) { args(i) = evidence } else if (ptype.toString == tensorNumericType.toString) { @@ -101,44 +116,47 @@ trait ModuleSerializable extends Loadable with Savable{ } else { require(modelAttributes.containsKey(name), s"$name value cannot be found") val attribute = modelAttributes.get(name) - val value = DataConverter.getAttributeValue(attribute) + val value = DataConverter.getAttributeValue(context, attribute) args(i) = value } i+= 1 }) }) - constructorMirror.apply(args : _*). + constructorMirror.apply(args : _*). asInstanceOf[AbstractModule[Activity, Activity, T]] } /** * Default serialization skeleton using reflection - * @param module BigDL module instance with linkages with other modules + * @param context Serialization context * @return serialized protobuf module instace */ - override def serializeModule[T: ClassTag](module : ModuleData[T]) - (implicit ev: TensorNumeric[T]) : BigDLModule = { + override def serializeModule[T: ClassTag](context: SerializeContext[T]) + (implicit ev: TensorNumeric[T]): SerializeResult = { val bigDLModelBuilder = BigDLModule.newBuilder // step 1 : set module version setVersion(bigDLModelBuilder) - val cls = module.module.getClass + + val moduleData = context.moduleData + val cls = moduleData.module.getClass // step 2: set module type bigDLModelBuilder.setModuleType(cls.getName) // step 3 : apply module specific logic to create module - doSerializeModule(module, bigDLModelBuilder) + doSerializeModule(context, bigDLModelBuilder) // step 4 : copy params (weight & bias) a and linkage - createSerializeBigDLModule(bigDLModelBuilder, module) + createSerializeBigDLModule(bigDLModelBuilder, context) } - protected def doSerializeModule[T: ClassTag](module : ModuleData[T], + protected def doSerializeModule[T: ClassTag](context: SerializeContext[T], bigDLModelBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - val cls = module.module.getClass + val module = context.moduleData.module + val cls = module.getClass val fullParams = getCostructorMirror(cls).symbol.paramss val constructorParams = fullParams(0) constructorParams.foreach(param => { @@ -154,79 +172,72 @@ trait ModuleSerializable extends Loadable with Savable{ field = cls.getSuperclass.getDeclaredField(paramName) } field.setAccessible(true) - val fieldValue = field.get(module.module) - DataConverter.setAttributeValue(attrBuilder, fieldValue, ptype) + val fieldValue = field.get(module) + DataConverter.setAttributeValue(context, attrBuilder, fieldValue, ptype) bigDLModelBuilder.putAttr(paramName, attrBuilder.build) }) } - protected def createBigDLModule[T: ClassTag](model : BigDLModule, + protected def createBigDLModule[T: ClassTag](context: DeserializeContext, module : AbstractModule[Activity, Activity, T]) (implicit ev: TensorNumeric[T]) : ModuleData[T] = { + val model = context.bigdlModule val preModules = model.getPreModulesList.asScala val nextModules = model.getNextModulesList.asScala val bigDLModule = ModuleData(module, preModules, nextModules) - module.setName(model.getName) - copy2BigDL(model, bigDLModule) + if (model.getName != "") { + module.setName(model.getName) + } + module.setNamePostfix(model.getNamePostfix) + if (model.getTrain) { + module.training() + } else { + module.evaluate() + } + copy2BigDL(context, bigDLModule) bigDLModule } protected def createSerializeBigDLModule[T: ClassTag]( - modelBuilder : BigDLModule.Builder, module : ModuleData[T])(implicit ev: TensorNumeric[T]) - : BigDLModule = { + modelBuilder : BigDLModule.Builder, context: SerializeContext[T])(implicit ev: TensorNumeric[T]) + : SerializeResult = { + val module = context.moduleData module.pre.foreach(pre => modelBuilder.addPreModules(pre)) module.next.foreach(next => modelBuilder.addNextModules(next)) - modelBuilder.setName(module.module.getName) - copyFromBigDL(module, modelBuilder) - modelBuilder.build + if (module.module.hasName) { + modelBuilder.setName(module.module.getName) + } + modelBuilder.setNamePostfix(module.module.getNamePostfix) + modelBuilder.setTrain(module.module.isTraining()) + modelBuilder.setId(System.identityHashCode(module.module)) + copyFromBigDL(context, modelBuilder) + SerializeResult(modelBuilder.build, context.storages) } /** * copy serialized data (weight and bias if exist) to BigDL module - * @param model serialized module + * @param context deserialized context * @param module bigDL Module with relationships */ - protected def copy2BigDL[T: ClassTag](model : BigDLModule, module : ModuleData[T]) + protected def copy2BigDL[T: ClassTag](context: DeserializeContext, module : ModuleData[T]) (implicit ev: TensorNumeric[T]): Unit = { val paramTable : Table = module.module.getParametersTable - if (paramTable != null && paramTable.contains(model.getName)) { + if (paramTable != null && paramTable.contains(module.module.getName)) { val modulePramTable : Table = paramTable(module.module.getName) - val weight : Tensor[T] = if (modulePramTable.contains("weight")) { - modulePramTable("weight") } - else null - val bias : Tensor[T] = if (modulePramTable.contains("bias")) { - modulePramTable("bias") } - else null - if (weight != null) copy2BigDLTensor(weight, model.getWeight) - if (bias != null) copy2BigDLTensor(bias, model.getBias) - } - } - - protected def copy2BigDLTensor[T: ClassTag](tensor : Tensor[T], serializedTensor : BigDLTensor) - (implicit ev: TensorNumeric[T]) : Unit = { - val dataType = serializedTensor.getDatatype - if (dataType == DataType.FLOAT) { - val serializedData = serializedTensor.getFloatDataList - require(tensor.nElement() == serializedData.size(), "data size is not equal") - var i = 0 - val tensorData = tensor.storage().array() - var offset = tensor.storageOffset() - 1 - while (i < serializedData.size()) { - tensorData(offset) = ev.fromType[Float](serializedData.get(i)) - offset += 1 - i += 1 + if (modulePramTable.contains("weight")) { + val attrValue = AttrValue.newBuilder + attrValue.setTensorValue(context.bigdlModule.getWeight) + val weight = TensorConverter.getAttributeValue(context, attrValue.build) + modulePramTable("weight").asInstanceOf[Tensor[T]]. + copy(weight.asInstanceOf[Tensor[T]]) } - } else if (dataType == DataType.DOUBLE) { - val serializedData = serializedTensor.getDoubleDataList - require(tensor.nElement() == serializedData.size(), "data size is not equal") - var i = 0 - val tensorData = tensor.storage().array() - var offset = tensor.storageOffset() - 1 - while (i < serializedData.size()) { - tensorData(offset) = ev.fromType[Double](serializedData.get(i)) - offset += 1 - i += 1 + if (modulePramTable.contains("bias")) { + val attrValue = AttrValue.newBuilder + attrValue.setTensorValue(context.bigdlModule.getBias) + val bias = TensorConverter.getAttributeValue(context, attrValue.build) + modulePramTable("bias").asInstanceOf[Tensor[T]]. + copy(bias.asInstanceOf[Tensor[T]]) } } } @@ -234,97 +245,97 @@ trait ModuleSerializable extends Loadable with Savable{ /** * copy BigDL module data (weight and bias if exist) to BigDL Model to be persisted * @param modelBuilder serialized module builder - * @param module bigDL Module with relationships + * @param context serialization context */ - protected def copyFromBigDL[T: ClassTag](module : ModuleData[T], + protected def copyFromBigDL[T: ClassTag](context : SerializeContext[T], modelBuilder : BigDLModule.Builder)(implicit ev : TensorNumeric[T]) : Unit = { + val module = context.moduleData val paramTable : Table = module.module.getParametersTable if (paramTable != null && paramTable.contains(module.module.getName)) { - val modulePramTable : Table = paramTable(module.module.getName) - val weight : Tensor[T] = if (modulePramTable.contains("weight")) { - modulePramTable("weight") } - else null - val bias : Tensor[T] = if (modulePramTable.contains("bias")) { - modulePramTable("bias") } - else null - if (weight != null) { - val weightTensorBuilder = BigDLTensor.newBuilder - copyFromBigDLTensor(weight, weightTensorBuilder) - modelBuilder.setWeight(weightTensorBuilder.build) - } - if (bias != null) { - val biasTensorBuilder = BigDLTensor.newBuilder - copyFromBigDLTensor(bias, biasTensorBuilder) - modelBuilder.setBias(biasTensorBuilder.build) + val modulePramTable: Table = paramTable(module.module.getName) + val weight: Tensor[T] = if (modulePramTable.contains("weight")) { + modulePramTable("weight") } - } - } - - protected def copyFromBigDLTensor[T: ClassTag](tensor : Tensor[T], - serializedTensor : BigDLTensor.Builder)(implicit ev: TensorNumeric[T]) : Unit = { - import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat - import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble - val tensorData = tensor.storage().array() - if (ev == NumericFloat) { - val offset = tensor.storageOffset() - 1 - var i = 0 - while (i < tensor.nElement()) { - serializedTensor.addFloatData(ev.toType[Float](tensorData(i + offset))) - i += 1 + else null + val bias: Tensor[T] = if (modulePramTable.contains("bias")) { + modulePramTable("bias") } - serializedTensor.setDatatype(DataType.FLOAT) - } else if (ev == NumericDouble) { - var i = 0 - while (i < tensorData.length) { - serializedTensor.addDoubleData(ev.toType[Double](tensorData(i))) - i += 1 + else null + val storageType = context.storageType + if (storageType == ProtoStorageType) { + if (weight != null) { + val weightAttr = AttrValue.newBuilder + TensorConverter.setAttributeValue(context, weightAttr, weight) + modelBuilder.setWeight(weightAttr.getTensorValue) + } + if (bias != null) { + val biasAttr = AttrValue.newBuilder + TensorConverter.setAttributeValue(context, biasAttr, bias) + modelBuilder.setBias(biasAttr.getTensorValue) + } + } else { + throw new IllegalArgumentException(s"$storageType not supported!") } - serializedTensor.setDatatype(DataType.DOUBLE) } - tensor.size().foreach(_ => serializedTensor.addSize(_)) } } trait ContainerSerializable extends ModuleSerializable { - override def doLoadModule[T: ClassTag](model : BigDLModule) + override def doLoadModule[T: ClassTag](context : DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val module = super.doLoadModule(model) + val module = super.doLoadModule(context) val container = module.asInstanceOf[Container[Activity, Activity, T]] - val subModules = model.getSubModulesList.asScala + val subModules = context.bigdlModule.getSubModulesList.asScala subModules.foreach(module => { - val subModuleData = ModuleSerializer.load(module) + val subModuleData = ModuleSerializer.load(DeserializeContext(module, + context.storages, context.storageType)) container.modules.append(subModuleData.module) }) module } - override def doSerializeModule[T: ClassTag](module : ModuleData[T], + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], containerBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(module, containerBuilder) - val subModulesData = module.module.asInstanceOf[Container[Activity, Activity, T]].modules + super.doSerializeModule(context, containerBuilder) + val subModulesData = context.moduleData.module. + asInstanceOf[Container[Activity, Activity, T]].modules subModulesData.foreach(module => { - val subModule = ModuleSerializer.serialize(ModuleData(module, - new ArrayBuffer[String](), new ArrayBuffer[String]())) - containerBuilder.addSubModules(subModule) + val subModule = ModuleSerializer.serialize(SerializeContext(ModuleData(module, + new ArrayBuffer[String](), new ArrayBuffer[String]()), context.storages, + context.storageType)) + containerBuilder.addSubModules(subModule.bigDLModule) }) } } object ContainerSerializer extends ContainerSerializable +trait StorageType +object ProtoStorageType extends StorageType + +case class SerializeContext[T: ClassTag](moduleData: ModuleData[T], + storages: mutable.HashMap[Int, Any], + storageType: StorageType) +case class DeserializeContext(bigdlModule : BigDLModule, + storages: mutable.HashMap[Int, Any], + storageType: StorageType) + +case class SerializeResult(bigDLModule: BigDLModule, storages: mutable.HashMap[Int, Any]) + case class ModuleData[T: ClassTag](module : AbstractModule[Activity, Activity, T], pre : Seq[String], next : Seq[String]) trait Loadable { - def loadModule[T: ClassTag](model : BigDLModule) + def loadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : ModuleData[T] } + trait Savable { - def serializeModule[T: ClassTag](module : ModuleData[T]) - (implicit ev: TensorNumeric[T]) : BigDLModule + def serializeModule[T: ClassTag](context: SerializeContext[T]) + (implicit ev: TensorNumeric[T]) : SerializeResult } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 2a1d222c4de..7bc1e9413a6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -52,53 +52,57 @@ object ModuleSerializer extends ModuleSerializable{ /** * Serialization entry for all modules based on corresponding class instance of module - * @param bigDLModule : BigDL module to be serialized + * @param serializerContext : serialization context * @return protobuf format module instance */ - def serialize[T: ClassTag](bigDLModule : ModuleData[T]) + def serialize[T: ClassTag](serializerContext : SerializeContext[T]) (implicit ev: TensorNumeric[T]) - : BigDLModule = { - val module = bigDLModule.module + : SerializeResult = { + val module = serializerContext.moduleData.module // For those layers which have their own serialization/deserialization methods val clsName = module.getClass.getName if (serializerMaps.contains(clsName)) { - serializerMaps(clsName).serializeModule(bigDLModule) + serializerMaps(clsName).serializeModule(serializerContext) } else { - val module = bigDLModule.module.asInstanceOf[AbstractModule[_, _, _]] - module match { - case container : Container[_, _, _] => ContainerSerializer.serializeModule(bigDLModule) - case cell : Cell[_] => CellSerializer.serializeModule(bigDLModule) - case _ => ModuleSerializer.serializeModule(bigDLModule) + val m = module.asInstanceOf[AbstractModule[_, _, _]] + m match { + case container : Container[_, _, _] => + ContainerSerializer.serializeModule(serializerContext) + case cell : Cell[_] => + CellSerializer.serializeModule(serializerContext) + case _ => ModuleSerializer.serializeModule(serializerContext) } } } /** * Deserialization entry for all modules based on corresponding module type - * @param model : BigDL module on protobuf for deserialization + * @param context : context for deserialization * @return BigDL module */ - def load[T: ClassTag](model: BigDLModule) + def load[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : ModuleData[T] = { try { + val model = context.bigdlModule if (serializerMaps.contains(model.getModuleType)) { - serializerMaps(model.getModuleType).loadModule(model) + serializerMaps(model.getModuleType).loadModule(context) } else { val attrMap = model.getAttrMap val subModuleCount = model.getSubModulesCount if (subModuleCount > 0) { - ContainerSerializer.loadModule(model) + ContainerSerializer.loadModule(context) } else { if (attrMap.containsKey("is_cell_module")) { - CellSerializer.loadModule(model) + CellSerializer.loadModule(context) } else { - ModuleSerializer.loadModule(model) + ModuleSerializer.loadModule(context) } } } } catch { case e: Exception => - throw new RuntimeException(s"Loading module ${model.getModuleType} exception :", e) + throw new RuntimeException( + s"Loading module ${context.bigdlModule.getModuleType} exception :", e) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index f54c51b08c8..3cec64c2fcb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -15,12 +15,13 @@ */ package com.intel.analytics.bigdl.utils.serializer +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.VariableFormat.{Default, ONE_D} import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.{NCHW, NHWC} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer, Regularizer} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import org.scalatest.{FlatSpec, Matchers} import serialization.Bigdl.AttrValue @@ -34,125 +35,286 @@ import scala.util.Random class DataConverterSpec extends FlatSpec with Matchers{ - "Primitive Int type conversion " should " work properly" in { + val map = new mutable.HashMap[Int, Any]() + + "Primitive Int type conversion" should "work properly" in { val intValue = 1 val attriBulder = AttrValue.newBuilder val intType = universe.typeOf[Int] - DataConverter.setAttributeValue(attriBulder, intValue, intType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, intValue, intType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), + attriBulder.build) retrievedValue should be (intValue) } - "Primitive Long type conversion " should " work properly" in { + "Primitive Long type conversion" should "work properly" in { val longValue = 1L val attriBulder = AttrValue.newBuilder val longType = universe.typeOf[Long] - DataConverter.setAttributeValue(attriBulder, longValue, longType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, longValue, longType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null) + , attriBulder.build) retrievedValue should be (longValue) } - "Primitive Float type conversion " should " work properly" in { + "Primitive Float type conversion" should "work properly" in { val floatValue = 1.0f val attriBulder = AttrValue.newBuilder val floatType = universe.typeOf[Float] - DataConverter.setAttributeValue(attriBulder, floatValue, floatType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, floatValue, floatType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null) + , attriBulder.build) retrievedValue should be (floatValue) } - "Primitive Double type conversion " should " work properly" in { + "Primitive Double type conversion" should "work properly" in { val doubleValue = 1.0 val attriBulder = AttrValue.newBuilder val doubleType = universe.typeOf[Double] - DataConverter.setAttributeValue(attriBulder, doubleValue, doubleType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, doubleValue, doubleType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), + attriBulder.build) retrievedValue should be (doubleValue) } - "Primitive String type conversion " should " work properly" in { + "Primitive String type conversion" should "work properly" in { val strValue = "test" val attriBulder = AttrValue.newBuilder val strType = universe.typeOf[String] - DataConverter.setAttributeValue(attriBulder, strValue, strType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, strValue, strType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), + attriBulder.build) retrievedValue should be (strValue) } - "Primitive Boolean type conversion " should " work properly" in { + "Primitive Boolean type conversion" should "work properly" in { val boolValue = false val attriBulder = AttrValue.newBuilder val boolType = universe.typeOf[Boolean] - DataConverter.setAttributeValue(attriBulder, boolValue, boolType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, boolValue, boolType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), + attriBulder.build) retrievedValue.isInstanceOf[Boolean] should be (true) retrievedValue.asInstanceOf[Boolean] should be (boolValue) } - "L1L2Regularizer conversion " should " work properly" in { + "L1L2Regularizer conversion" should "work properly" in { val regularizer = L1L2Regularizer(1.0, 2.0) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, regularizer, ModuleSerializer.regularizerType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, regularizer, ModuleSerializer.regularizerType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), + attriBulder.build) retrievedValue.isInstanceOf[L1L2Regularizer[Float]] should be (true) retrievedValue.asInstanceOf[L1L2Regularizer[Float]].l1 should be (regularizer.l1) retrievedValue.asInstanceOf[L1L2Regularizer[Float]].l2 should be (regularizer.l2) } - "L1Regularizer conversion " should " work properly" in { + "L1Regularizer conversion" should "work properly" in { val regularizer = L1Regularizer(1.0) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, regularizer, ModuleSerializer.regularizerType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, regularizer, ModuleSerializer.regularizerType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), + attriBulder.build) retrievedValue.isInstanceOf[L1Regularizer[Float]] should be (true) retrievedValue.asInstanceOf[L1Regularizer[Float]].l1 should be (regularizer.l1) } - "L2Regularizer conversion " should " work properly" in { + "L2Regularizer conversion" should "work properly" in { val regularizer = L2Regularizer(1.0) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, regularizer, ModuleSerializer.regularizerType) - val retrievedValue = DataConverter.getAttributeValue(attriBulder.build) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, regularizer, ModuleSerializer.regularizerType) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), + attriBulder.build) retrievedValue.isInstanceOf[L2Regularizer[Float]] should be (true) retrievedValue.asInstanceOf[L2Regularizer[Float]].l2 should be (regularizer.l2) } - "Empty Regularizer conversion " should " work properly" in { + "Empty Regularizer conversion" should "work properly" in { val regularizer : L1L2Regularizer[Float] = null val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, regularizer, ModuleSerializer.regularizerType) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, regularizer, ModuleSerializer.regularizerType) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), + attr) attr.getDataType should be (DataType.REGULARIZER) retrievedValue should be (regularizer) } - "Tensor conversion " should " work properly" in { + "Tensor conversion" should "work properly" in { val tensor = Tensor(5, 5).apply1(e => Random.nextFloat()) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, tensor, ModuleSerializer.tensorType) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, tensor, ModuleSerializer.tensorType) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.TENSOR) retrievedValue should be (tensor) } - "Empty Tensor conversion " should " work properly" in { + "Empty Tensor conversion" should "work properly" in { val tensor : Tensor[Float] = null val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, tensor, ModuleSerializer.tensorType) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, tensor, ModuleSerializer.tensorType) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.TENSOR) retrievedValue should be (tensor) } + "Two tensors to the same object conversion" should "work properly" in { + val tensor1 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val tensor2 = tensor1 + + map.clear() + + val attriBulder1 = AttrValue.newBuilder + + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder1, tensor1, ModuleSerializer.tensorType) + + val attr1 = attriBulder1.build + + val attriBulder2 = AttrValue.newBuilder + + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder2, tensor2, ModuleSerializer.tensorType) + val attr2 = attriBulder2.build + + map.clear() + + val retrievedValue1 = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr1) + + val retrievedValue2 = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr2) + + retrievedValue1.asInstanceOf[Tensor[Float]].resize(1, 25) + + retrievedValue2 should be (retrievedValue1) + } + + "Two tensor share the same memory" should "work properly" in { + val array = Array[Float](1.0f, 2.0f, 3.0f, 4.0f) + val storage = Storage[Float](array) + val tensor1 = Tensor(storage, 1) + val tensor2 = Tensor(storage, 1) + + map.clear() + + val attriBulder1 = AttrValue.newBuilder + + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder1, tensor1, ModuleSerializer.tensorType) + + val attr1 = attriBulder1.build + + val attriBulder2 = AttrValue.newBuilder + + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder2, tensor2, ModuleSerializer.tensorType) + val attr2 = attriBulder2.build + + map.clear() + + val retrievedValue1 = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr1) + .asInstanceOf[Tensor[Float]] + + retrievedValue1.storage().array()(0) = 10.0f + + val retrievedValue2 = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr2) + .asInstanceOf[Tensor[Float]] + + retrievedValue1.storage() should be (retrievedValue2.storage()) + } + + "Two tensors share the same storage" should "work properly" in { + val weight = Array(0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f) + + val storage = Storage(weight) + val tensor1 = Tensor(Storage(weight), 1, Array(2, 2)) + val tensor2 = Tensor(Storage(weight), 5, Array(2, 2)) + + map.clear() + + val attriBulder1 = AttrValue.newBuilder + + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder1, tensor1, ModuleSerializer.tensorType) + + val attr1 = attriBulder1.build + + val attriBulder2 = AttrValue.newBuilder + + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder2, tensor2, ModuleSerializer.tensorType) + val attr2 = attriBulder2.build + + map.clear() + + val retrievedValue1 = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr1) + .asInstanceOf[Tensor[Float]] + + val retrievedValue2 = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr2) + .asInstanceOf[Tensor[Float]] + + retrievedValue1.storage().array().update(1, 0.1f) + + retrievedValue1.storage() should be (retrievedValue2.storage()) + + } + + "VariableFormat conversion " should " work properly" in { val format : VariableFormat = Default val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, format, universe.typeOf[VariableFormat]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, format, universe.typeOf[VariableFormat]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.VARIABLE_FORMAT) retrievedValue should be (format) } @@ -160,9 +322,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ "VariableFormat conversion With Param " should " work properly" in { val format : VariableFormat = ONE_D val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, format, universe.typeOf[VariableFormat]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, format, universe.typeOf[VariableFormat]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.VARIABLE_FORMAT) retrievedValue should be (format) } @@ -170,9 +336,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Empty VariableFormat conversion " should " work properly" in { val format : VariableFormat = null val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, format, universe.typeOf[VariableFormat]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, format, universe.typeOf[VariableFormat]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.VARIABLE_FORMAT) retrievedValue should be (format) } @@ -180,9 +350,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Init Method conversion " should " work properly" in { val initMethod = RandomUniform val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, initMethod, universe.typeOf[InitializationMethod]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, initMethod, universe.typeOf[InitializationMethod]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.INITMETHOD) retrievedValue should be (initMethod) } @@ -190,29 +364,45 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Empty Init Method conversion " should " work properly" in { val initMethod : InitializationMethod = null val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, initMethod, universe.typeOf[InitializationMethod]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, initMethod, universe.typeOf[InitializationMethod]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.INITMETHOD) retrievedValue should be (initMethod) } + "Module Conversion " should " work properly" in { val linear = Linear(5, 5).setName("linear") val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, linear, ModuleSerializer.abstractModuleType) + val moduleData = ModuleData(linear, Seq(), Seq()) + map.clear() + DataConverter.setAttributeValue(SerializeContext(moduleData, map, ProtoStorageType), + attriBulder, linear, ModuleSerializer.abstractModuleType) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(attr.getBigDLModuleValue + , map, ProtoStorageType), attr) attr.getDataType should be (DataType.MODULE) retrievedValue should be (linear) } + "Nullable Module Conversion " should " work properly" in { val linear : TensorModule[Float] = null val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, linear, ModuleSerializer.abstractModuleType) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, linear, ModuleSerializer.abstractModuleType) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.MODULE) retrievedValue should be (linear) } @@ -220,9 +410,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ "NHWC DataFormat conversion " should " work properly" in { val format : DataFormat = NHWC val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, format, universe.typeOf[DataFormat]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, format, universe.typeOf[DataFormat]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) attr.getDataType should be (DataType.DATA_FORMAT) retrievedValue should be (format) } @@ -230,9 +424,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ "NCHW DataFormat conversion " should " work properly" in { val format : DataFormat = NCHW val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, format, universe.typeOf[DataFormat]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, format, universe.typeOf[DataFormat]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) attr.getDataType should be (DataType.DATA_FORMAT) retrievedValue should be (format) } @@ -240,45 +438,65 @@ class DataConverterSpec extends FlatSpec with Matchers{ "Array of int32 conversion " should " work properly " in { val arry = Array[Int](1, 2, 3) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Int]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[Int]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } "Array of int64 conversion " should " work properly " in { val arry = Array[Long](1L, 2L, 3L) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Long]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[Long]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } "Array of float conversion " should " work properly " in { val arry = Array[Float](1.0f, 2.0f, 3.0f) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Float]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[Float]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } "Null Array of float conversion " should " work properly " in { val arry : Array[Float] = null val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Float]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[Float]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } "Array of double conversion " should " work properly " in { val arry = Array[Double](1.0, 2.0, 3.0) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Double]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[Double]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } @@ -288,18 +506,26 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = "test1" arry(1) = "test2" val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[String]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[String]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } "Array of Boolean conversion " should " work properly" in { val arry = Array[Boolean](true, false) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Boolean]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[Boolean]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } @@ -308,9 +534,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = L2Regularizer(1.0) arry(1) = L1Regularizer(1.0) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[Regularizer[Float]]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[Regularizer[Float]]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } @@ -319,9 +549,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ val tensor2 = Tensor(2, 3).apply1(_ => Random.nextFloat()) val tensorArray = Array(tensor1, tensor2) val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, tensorArray, universe.typeOf[Array[Tensor[Float]]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, tensorArray, universe.typeOf[Array[Tensor[Float]]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue.isInstanceOf[Array[Tensor[Float]]] should be (true) retrievedValue should be (tensorArray) } @@ -330,9 +564,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ val arry = new Array[VariableFormat](1) arry(0) = Default val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[VariableFormat]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[VariableFormat]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } @@ -341,9 +579,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = RandomUniform arry(1) = Zeros val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[InitializationMethod]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[InitializationMethod]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } @@ -352,9 +594,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = NCHW arry(1) = NHWC val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, universe.typeOf[Array[DataFormat]]) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[DataFormat]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } @@ -363,26 +609,34 @@ class DataConverterSpec extends FlatSpec with Matchers{ arry(0) = Linear[Float](2, 3).setName("l1") arry(1) = Linear[Float](2, 3).setName("l2") val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, arry, universe.typeOf[Array[AbstractModule[Activity, Activity, Float]]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) retrievedValue should be (arry) } "Null Array of Modules conversion" should " work properly" in { val arry : Array[AbstractModule[Activity, Activity, Float]] = null val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, arry, + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, null), + attriBulder, arry, universe.typeOf[Array[AbstractModule[Activity, Activity, Float]]]) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, null), attr) retrievedValue should be (arry) } "NameList conversion " should " work properly" in { - val map = new mutable.HashMap[String, mutable.Map[String, Any]] + val map1 = new mutable.HashMap[String, mutable.Map[String, Any]] val attrsMap = new mutable.HashMap[String, Any] @@ -396,16 +650,21 @@ class DataConverterSpec extends FlatSpec with Matchers{ attrsMap("dataformat") = NCHW attrsMap("module") = Linear(3, 4).setName("linear") - map("test") = attrsMap + map1("test") = attrsMap val attriBulder = AttrValue.newBuilder - DataConverter.setAttributeValue(attriBulder, map) + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, map1) val attr = attriBulder.build - val retrievedValue = DataConverter.getAttributeValue(attr). + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr). asInstanceOf[mutable.HashMap[String, mutable.Map[String, Any]]] - retrievedValue should be (map) + retrievedValue should be (map1) } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 68588475e63..7ebecf07d55 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -1894,6 +1894,31 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { weight1 should be (weight2) } + + "Module toString" should "have same result" in { + val linear = Linear(2, 2) + ModulePersister.saveToFile("/tmp/mstr.bigdl", linear, true) + val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") + + linear.toString() should be (loadedModel.toString()) + } + + "Module in tain " should " keep the state" in { + val linear = Linear(2, 2).training() + ModulePersister.saveToFile("/tmp/mstr.bigdl", linear, true) + val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") + + loadedModel.isTraining() should be (true) + } + + "Module in evaluate " should " keep the state" in { + val linear = Linear(2, 2).evaluate() + ModulePersister.saveToFile("/tmp/mstr.bigdl", linear, true) + val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") + + loadedModel.isTraining() should be (false) + } + } class TestModule[T: ClassTag](val custom: CustomData) @@ -1913,15 +1938,18 @@ case class CustomData(val constant_scalar: Double) case object TestSerializer extends ModuleSerializable object TestCustomDataConverter extends DataConverter { - override def getAttributeValue[T: ClassTag](attribute: Bigdl.AttrValue) + + override def getAttributeValue[T: ClassTag](context: DeserializeContext, + attribute: Bigdl.AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { val customData = attribute.getCustomValue val customMsg = customData.unpack(classOf[TestCustomData.CustomData]) CustomData(customMsg.getScalar) } - override def setAttributeValue[T: ClassTag](attributeBuilder: AttrValue.Builder, - value: Any, valueType: universe.Type)(implicit ev: TensorNumeric[T]): Unit = { + override def setAttributeValue[T: ClassTag](context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type) + (implicit ev: TensorNumeric[T]): Unit = { val testCustomData = TestCustomData.CustomData.newBuilder testCustomData.setScalar(value.asInstanceOf[CustomData].constant_scalar) attributeBuilder.setCustomValue(com.google.protobuf.Any.pack(testCustomData.build())) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala new file mode 100644 index 00000000000..f3a3faebb03 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala @@ -0,0 +1,177 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.serializer + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} +import serialization.Bigdl.AttrValue + +import scala.collection.mutable +import scala.reflect.runtime.universe + + +class TensorConversionSpec extends FlatSpec with Matchers{ + + val map = new mutable.HashMap[Int, Any]() + + "ByteString tensor conversion " should "work properly" in { + + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + + val byteString = Tensor[ByteString](Array(ByteString.copyFromUtf8("a"), + ByteString.copyFromUtf8("b")), Array(2)) + + val attriBuilder = AttrValue.newBuilder() + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBuilder, byteString, universe.typeOf[Tensor[ByteString]]) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) + + byteString should be (retrievedValue) + + } + + "Char tensor conversion " should "work properly" in { + + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericChar + + val chars = Tensor[Char](Array('a', 'b'), Array(2)) + + val attriBuilder = AttrValue.newBuilder() + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBuilder, chars, universe.typeOf[Tensor[Char]]) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) + + chars should be (retrievedValue) + + } + + + "Int tensor conversion " should "work properly" in { + + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericInt + + val ints = Tensor[Int](Array(2, 3), Array(2)) + + val attriBuilder = AttrValue.newBuilder() + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBuilder, ints, universe.typeOf[Tensor[Int]]) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) + + ints should be (retrievedValue) + + } + + "Long tensor conversion " should "work properly" in { + + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericLong + + val longs = Tensor[Long](Array(2L, 3L), Array(2)) + + val attriBuilder = AttrValue.newBuilder() + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBuilder, longs, universe.typeOf[Tensor[Long]]) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) + + longs should be (retrievedValue) + + } + + "Short tensor conversion " should "work properly" in { + + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericShort + + val shorts = Tensor[Short](Array[Short](2, 3), Array(2)) + + val attriBuilder = AttrValue.newBuilder() + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBuilder, shorts, universe.typeOf[Tensor[Short]]) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) + + shorts should be (retrievedValue) + + } + + "Float tensor conversion " should "work properly" in { + + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + + val floats = Tensor[Float](Array[Float](2f, 3f), Array(2)) + + val attriBuilder = AttrValue.newBuilder() + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBuilder, floats, universe.typeOf[Tensor[Float]]) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) + + floats should be (retrievedValue) + + } + + "Double tensor conversion " should "work properly" in { + + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble + + val doubles = Tensor[Double](Array[Double](2, 3), Array(2)) + + val attriBuilder = AttrValue.newBuilder() + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBuilder, doubles, universe.typeOf[Tensor[Double]]) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) + + doubles should be (retrievedValue) + + } + + "String tensor conversion " should "work properly" in { + + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericString + + val strings = Tensor[String](Array[String]("hello", "world"), Array(2)) + + val attriBuilder = AttrValue.newBuilder() + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBuilder, strings, universe.typeOf[Tensor[String]]) + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) + + strings should be (retrievedValue) + + } + +} From 2f34fc49f71a6bb2246af545b729bdc867d838da Mon Sep 17 00:00:00 2001 From: helenlly Date: Mon, 9 Oct 2017 15:26:46 +0800 Subject: [PATCH 0427/1065] Revert "Change modelTest to modelEvaluate" This reverts commit 45855fc0e2cb9244c163cc782f3d062380498247. --- .../com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index ecb1559e64b..f5e2b266284 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -230,7 +230,7 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { // TODO: verify the parameters result val parameters = pp.modelGetParameters(trainedModel) // println(parameters) - val testResult = pp.modelEvaluate(trainedModel, + val testResult = pp.modelTest(trainedModel, data.toJavaRDD(), batchSize = 32, valMethods = util.Arrays.asList(new Top1Accuracy())) From d2cd93391ede470052748d5982f7560742a18dea Mon Sep 17 00:00:00 2001 From: helenlly Date: Mon, 9 Oct 2017 15:26:46 +0800 Subject: [PATCH 0428/1065] Revert "Change model.test to model.evaluate in Python" This reverts commit 5de5cd4fdc1d3c02147a2a47a6653f1b49f2fc21. --- .../analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 48895670f87..0672c92eaca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1426,7 +1426,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab RandomGenerator.RNG.setSeed(seed) } - def modelEvaluate(model: AbstractModule[Activity, Activity, T], + def modelTest(model: AbstractModule[Activity, Activity, T], valRDD: JavaRDD[Sample], batchSize: Int, valMethods: JList[ValidationMethod[T]]) From 3ee116db2cc4ad9e9b64e7d36bdf8baf18b40e20 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 9 Oct 2017 04:33:18 -0400 Subject: [PATCH 0429/1065] feat: quantization methods (#1610) Quantization object support: 1. quantize a value with max and min. 2. quantize an array 3. quantize a Tensor[Float] And for test, there're relative dequantize methods. --- .../dllib/nn/quantized/Quantization.scala | 162 +++++++++++ .../dllib/nn/quantized/QuantizationSpec.scala | 257 ++++++++++++++++++ 2 files changed, 419 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantization.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizationSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantization.scala new file mode 100644 index 00000000000..7769cee4c51 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantization.scala @@ -0,0 +1,162 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.tensor.Tensor + +object Quantization { + def findMax(src: Array[Float], start: Int, end: Int): Float = { + src.slice(start, end).max + } + + def findMin(src: Array[Float], start: Int, end: Int): Float = { + src.slice(start, end).min + } + + def quantize(value: Float, max: Float, min: Float): Byte = { + Math.round(1.0 * value / Math.max(Math.abs(max), Math.abs(min)) * Byte.MaxValue).toByte + } + + def dequantize(byte: Byte, max: Float, min: Float): Float = { + byte.toFloat / Byte.MaxValue * Math.max(Math.abs(max), Math.abs(min)) + } + + def quantize(src: Array[Float], start: Int, end: Int, dst: Array[Byte], + dstOffset: Int): (Float, Float) = { + // we should keep the plus and minus + val max = findMax(src, start, end) + val min = findMin(src, start, end) + + for (i <- 0 until end - start) { + dst(dstOffset + i) = quantize(src(start + i), max, min) + } + + (max, min) + } + + def dequantize(src: Array[Float], start: Int, end: Int, dst: Array[Byte], dstOffset: Int, + max: Float, min: Float): Unit = { + require(src.length >= end, s"you write too much elements") + + for (i <- 0 until end - start) { + src(start + i) = dequantize(dst(dstOffset + i), max, min) + } + } + + def quantize(src: Array[Float], start: Int, end: Int, dst: Array[Byte], dstOffset: Int, + size: Array[Int]): (Array[Float], Array[Float]) = { + require(size.length == 2, s"only support 2-dim matrix") + require(size.product == (end - start), s"number of elements does not match") + + val height = size(0) + val width = size(1) + + val max = new Array[Float](height) + val min = new Array[Float](height) + + for (i <- 0 until height) { + val maxAndMin = quantize(src, start + i * width, start + (i + 1) * width, dst, + dstOffset + i * width) + + max(i) = maxAndMin._1 + min(i) = maxAndMin._2 + } + + (max, min) + } + + def dequantize(data: Array[Float], start: Int, end: Int, quantizedData: Array[Byte], offset: Int, + max: Array[Float], min: Array[Float], size: Array[Int]): Unit = { + require(max.length == min.length, s"the number of max doesn't match with the number of min") + require(size.length == 2, s"only support 2-dim matrix") + require(max.length == size(0), + s"the number of max(${max.length}) doesn't match the size(${size(1)})") + + require(size.product == (end - start), s"number of elements does not match") + + val height = size(0) + val width = size(1) + + for (i <- 0 until height) { + dequantize(data, start + i * width, start + (i + 1) * width, + quantizedData, offset + i * width, max(i), min(i)) + } + } + + private[bigdl] def get2Dim(shape: Array[Int]): Array[Int] = { + require(shape.length > 1, s"error size dimension, which must be great than 1") + val first = shape(0) + val last = shape.slice(1, shape.length).product + Array(first, last) + } + + def quantize(input: Tensor[Float], buffer: Array[Byte], + offset: Int): (Array[Float], Array[Float]) = { + val length = input.nElement() + + input.dim() match { + case 1 => + val (max, min) = quantize(input.storage().array(), input.storageOffset() - 1, + length, buffer, offset) + (Array(max), Array(min)) + case x if x > 1 => + val size = get2Dim(input.size()) + val start = input.storageOffset() - 1 + val end = start + length + val (max, min) = quantize(input.storage().array(), start, end, buffer, offset, size) + (max, min) + case _ => throw new UnsupportedOperationException(s"unsupported input") + } + } + + def dequantize(input: Tensor[Float], buffer: Array[Byte], offset: Int, max: Array[Float], + min: Array[Float]): Unit = { + val start = input.storageOffset() - 1 + val end = start + input.nElement() + + input.dim() match { + case 1 => dequantize(input.storage().array(), start, end, buffer, + offset, max(0), min(0)) + case x if x > 1 => + dequantize(input.storage().array(), start, end, buffer, + offset, max, min, get2Dim(input.size())) + case _ => throw new UnsupportedOperationException { + s"unsupported input dim ${input.dim()}" + } + } + } + + def loss(before: Array[Float], after: Array[Float], start: Int, end: Int): Double = { + var lossValue = 0.0 + + for (i <- start until end) { + lossValue += Math.abs(before(i) - after(i)) + } + + lossValue + } + + def loss(before: Tensor[Float], after: Tensor[Float]): Double = { + val beforeArray = before.storage().array() + val afterArray = after.storage().array() + + val start = 0 + val end = before.nElement() + + loss(beforeArray, afterArray, start, end) / beforeArray.sum + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizationSpec.scala new file mode 100644 index 00000000000..b334d934b73 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizationSpec.scala @@ -0,0 +1,257 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.nn.quantized.Quantization._ +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + +class QuantizationSpec extends FlatSpec with Matchers { + "Quantize a number with same sign max and min" should "generate correct output" in { + val src = 0.6f + val (max, min) = (0.7f, 0.2f) + + val dst = quantize(src, max, min) + dst should be (109) + + val result = dequantize(dst, max, min) + result should be (0.6f +- 0.01f) + + val los = Math.abs(result - src).toDouble + los should be (0.toDouble +- 0.01) // here is an experiment result + } + + "Quantize a number with different sign max and min" should "generate correct output" in { + val src = -0.6f + val (max, min) = (0.7f, -0.2f) + + val dst = quantize(src, max, min) + dst should be (-109) + + val result = dequantize(dst, max, min) + result should be (-0.6f +- 0.01f) + + val los = Math.abs(result - src).toDouble + los should be (0.toDouble +- 0.01) // here is an experiment result + } + + "Quantize a array" should "generate correct output" in { + val src = Array[Float](0.6f, 0.4f, -0.3f, 0.2f, 0.1f) + + val dst = new Array[Byte](src.length) + + val (max, min) = quantize(src, 0, src.length, dst, 0) + + dst(0) should be (127) + dst(1) should be (85) + dst(2) should be (-63) + dst(3) should be (42) + dst(4) should be (21) + + val before = src.clone() + for (i <- src.indices) { + src(i) = 0f + } + + dequantize(src, 0, src.length, dst, 0, max, min) + + src(0) should be (0.6f +- 0.01f) + src(1) should be (0.4f +- 0.01f) + src(2) should be (-0.3f +- 0.01f) + src(3) should be (0.2f +- 0.01f) + src(4) should be (0.1f +- 0.01f) + + val after = src.clone() + + val los = loss(before, after, 0, src.length) + + los should be (0.toDouble +- 0.01) // here is an experiment result + } + + "Quantize a matrix" should "generate correct output" in { + val src = Array(0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.4f, 0.3f, 0.2f, 0.1f) + val dst = new Array[Byte](src.length) + + val (max, min) = quantize(src, 0, src.length, dst, 0, Array(2, 5)) + + for (i <- src.indices) { + println(dst(i)) + } + + dst(0) should be (25) + dst(1) should be (51) + dst(2) should be (76) + dst(3) should be (102) + dst(4) should be (127) + dst(5) should be (127) + dst(6) should be (85) + dst(7) should be (64) + dst(8) should be (42) + dst(9) should be (21) + + val before = src.clone() + for (i <- src.indices) { + src(i) = 0f + } + + dequantize(src, 0, src.length, dst, 0, max, min, Array(2, 5)) + for (i <- src.indices) { + println(src(i)) + } + + src(0) should be (0.1f +- 0.01f) + src(1) should be (0.2f +- 0.01f) + src(2) should be (0.3f +- 0.01f) + src(3) should be (0.4f +- 0.01f) + src(4) should be (0.5f +- 0.01f) + src(5) should be (0.6f +- 0.01f) + src(6) should be (0.4f +- 0.01f) + src(7) should be (0.3f +- 0.01f) + src(8) should be (0.2f +- 0.01f) + src(9) should be (0.1f +- 0.01f) + + val after = src.clone() + + val los = loss(before, after, 0, src.length) + los should be (0.toDouble +- 0.02) // here is an experiment result + } + + "Quantize a 1-d tensor" should "generate correct output" in { + val array = Array[Float](0.6f, 0.4f, -0.3f, 0.2f, 0.1f) + val src = Tensor[Float](array, Array(5)) + + val dst = new Array[Byte](src.nElement()) + + val (max, min) = quantize(src, dst, 0) + + dst(0) should be (127) + dst(1) should be (85) + dst(2) should be (-63) + dst(3) should be (42) + dst(4) should be (21) + + val before = src.clone() + src.apply1(_ => 0f) + + dequantize(src, dst, 0, max, min) + + src.valueAt(1) should be (0.6f +- 0.01f) + src.valueAt(2) should be (0.4f +- 0.01f) + src.valueAt(3) should be (-0.3f +- 0.01f) + src.valueAt(4) should be (0.2f +- 0.01f) + src.valueAt(5) should be (0.1f +- 0.01f) + + val after = src.clone() + + val los = loss(before.storage().array(), after.storage().array(), 0, src.nElement()) + los should be (0.toDouble +- 0.01) // here is an experiment result + } + + "Quantize a 2-d tensor" should "generate correct output" in { + val array = Array(0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.4f, 0.3f, 0.2f, 0.1f) + val src = Tensor[Float](array, Array(2, 5)) + + val dst = new Array[Byte](src.nElement()) + + val (max, min) = quantize(src, dst, 0) + + dst(0) should be (25) + dst(1) should be (51) + dst(2) should be (76) + dst(3) should be (102) + dst(4) should be (127) + dst(5) should be (127) + dst(6) should be (85) + dst(7) should be (64) + dst(8) should be (42) + dst(9) should be (21) + + val before = src.clone() + src.apply1(_ => 0f) + + dequantize(src, dst, 0, max, min) + + src.valueAt(1, 1) should be (0.1f +- 0.01f) + src.valueAt(1, 2) should be (0.2f +- 0.01f) + src.valueAt(1, 3) should be (0.3f +- 0.01f) + src.valueAt(1, 4) should be (0.4f +- 0.01f) + src.valueAt(1, 5) should be (0.5f +- 0.01f) + src.valueAt(2, 1) should be (0.6f +- 0.01f) + src.valueAt(2, 2) should be (0.4f +- 0.01f) + src.valueAt(2, 3) should be (0.3f +- 0.01f) + src.valueAt(2, 4) should be (0.2f +- 0.01f) + src.valueAt(2, 5) should be (0.1f +- 0.01f) + + val after = src.clone() + + val los = loss(before.storage().array(), after.storage().array(), 0, src.nElement()) + los should be (0.toDouble +- 0.02) // here is an experiment result + } + + "Quantize a 3-d tensor" should "generate correct output" in { + val array = Array( + 0.1f, 0.2f, 0.3f, + 0.4f, 0.5f, 0.6f, + + -0.5f, 0.4f, 0.3f, + 0.2f, 0.1f, 0f + ) + val src = Tensor[Float](array, Array(2, 2, 3)) + + val dst = new Array[Byte](src.nElement()) + + val (max, min) = quantize(src, dst, 0) + + dst(0) should be (21) + dst(1) should be (42) + dst(2) should be (64) + dst(3) should be (85) + dst(4) should be (106) + dst(5) should be (127) + dst(6) should be (-127) + dst(7) should be (102) + dst(8) should be (76) + dst(9) should be (51) + dst(10) should be (25) + dst(11) should be (0) + + val before = src.clone() + src.apply1(_ => 0f) + + dequantize(src, dst, 0, max, min) + + src.valueAt(1, 1, 1) should be (0.1f +- 0.01f) + src.valueAt(1, 1, 2) should be (0.2f +- 0.01f) + src.valueAt(1, 1, 3) should be (0.3f +- 0.01f) + src.valueAt(1, 2, 1) should be (0.4f +- 0.01f) + src.valueAt(1, 2, 2) should be (0.5f +- 0.01f) + src.valueAt(1, 2, 3) should be (0.6f +- 0.01f) + src.valueAt(2, 1, 1) should be (-0.5f +- 0.01f) + src.valueAt(2, 1, 2) should be (0.4f +- 0.01f) + src.valueAt(2, 1, 3) should be (0.3f +- 0.01f) + src.valueAt(2, 2, 1) should be (0.2f +- 0.01f) + src.valueAt(2, 2, 2) should be (0.1f +- 0.01f) + src.valueAt(2, 2, 3) should be (0.0f +- 0.01f) + + val after = src.clone() + + val los = loss(before.storage().array(), after.storage().array(), 0, src.nElement()) + los should be (0.toDouble +- 0.02) // here is an experiment result + } + +} From b4bd5278a37d9162e01660ed17fec4da29a8c865 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 9 Oct 2017 08:20:01 -0400 Subject: [PATCH 0430/1065] feat: quantized tensor support (#1611) * feat: quantized tensor support QuantizedTensor inherits from Tensor with supporting part of methods. The memory is allocated from native code, so it only matains a pointer `getNativeStorage`. Note, We don't support automatically `free` method. Anyone using this method should think about the memory lacking, especially for memory optimization. test: quantized tensor tests * fix: test case failed on spark 1.6 --- .../bigdl/dllib/nn/quantized/Desc.scala | 180 +++ .../bigdl/dllib/tensor/DenseTensor.scala | 3 + .../bigdl/dllib/tensor/QuantizedTensor.scala | 301 ++++ .../tensor/QuantizedTensorUnsupported.scala | 1399 +++++++++++++++++ .../analytics/bigdl/dllib/tensor/Tensor.scala | 11 + .../dllib/tensor/QuantizedTensorSpec.scala | 124 ++ 6 files changed, 2018 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Desc.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Desc.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Desc.scala new file mode 100644 index 00000000000..dbb63f0e656 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Desc.scala @@ -0,0 +1,180 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.bigquant.BigQuant +import com.intel.analytics.bigdl.tensor.{FloatType, QuantizedTensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import scala.reflect.ClassTag + +sealed trait DescType extends Serializable +object ConvData extends DescType +object ConvWeight extends DescType +object LinearData extends DescType +object LinearWeight extends DescType + +trait DescParams extends Serializable with Product { + def copy(): DescParams + def array: Array[Any] = this.productIterator.toArray + def getType: DescType +} + +case class ConvDataParams(nInputPlane: Int, kernelH: Int, kernelW: Int, + strideH: Int, strideW: Int, padH: Int, padW: Int, dilationHeight: Int, dilationWidth: Int, + batchSize: Int, inputHeight: Int, inputWidth: Int) extends DescParams { + override def copy(): DescParams = { + val p = this + new ConvDataParams(p.nInputPlane, p.kernelH, p.kernelW, p.strideH, p.strideW, + p.padH, p.padW, p.dilationHeight, p.dilationWidth, p.batchSize, p.inputHeight, + p.inputWidth) + } + + override def getType: DescType = ConvData +} + +object ConvDataParams { + def apply(params: Array[Int]): ConvDataParams = new ConvDataParams( + params(0), // nInputPlane + params(1), // kernelH + params(2), // kernelW + params(3), // strideH + params(4), // strideW + params(5), // padH + params(6), // padW + params(7), // dilationHeight + params(8), // dilationWidth + params(9), // batchSize + params(10), // inputHeigh + params(11)) // inputWidth +} + +case class ConvWeightParams(nOutputPlane: Int, nInputPlane: Int, kernelH: Int, + kernelW: Int, dataFormat: Int) extends DescParams { + + override def copy(): DescParams = { + val p = this + new ConvWeightParams(p.nOutputPlane, p.nInputPlane, p.kernelH, p.kernelW, dataFormat) + } + + override def getType: DescType = ConvWeight +} + +object ConvWeightParams { + def apply(params: Array[Int]): ConvWeightParams = ConvWeightParams( + params(0), // nOutputPlane + params(1), // nInputPlane + params(2), // kernelH + params(3), // kernelW + params(4)) // data format +} + +case class LinearDataParams(batchSize: Int, inputSize: Int) extends DescParams { + + override def copy(): DescParams = { + val p = this + new LinearDataParams(p.batchSize, p.inputSize) + } + + override def getType: DescType = LinearData +} + +object LinearDataParams { + def apply(params: Array[Int]): LinearDataParams = LinearDataParams( + params(0), // batchSize + params(1)) // inputSize + +} + +case class LinearWeightParams(outputSize: Int, inputSize: Int) extends DescParams { + + override def copy(): DescParams = { + val p = this + new LinearWeightParams(p.outputSize, p.inputSize) + } + + override def getType: DescType = LinearWeight +} + +object LinearWeightParams { + def apply(params: Array[Int]): LinearWeightParams = LinearWeightParams( + params(0), // outputSize + params(1)) // inputSize + +} + +object Desc { + def get[T: ClassTag](params: DescParams, bytes: Array[Byte], offset: Int, + max: Array[T], min: Array[T]): Long = { + val desc = params.getType match { + case ConvData => + val p = params.asInstanceOf[ConvDataParams] + BigQuant.ConvDataDescInit(p.nInputPlane, + p.kernelH, p.kernelW, p.strideH, p.strideW, p.padH, p.padW, + p.dilationHeight, p.dilationWidth, p.batchSize, p.inputHeight, p.inputWidth) + + case ConvWeight => + val p = params.asInstanceOf[ConvWeightParams] + val desc = BigQuant.ConvKernelDescInit(p.nOutputPlane, p.nInputPlane, p.kernelH, p.kernelW) + if (bytes != null) { + convWeigth(p, desc, bytes, offset, max, min) + } + desc + + case LinearData => + val p = params.asInstanceOf[LinearDataParams] + BigQuant.FCDataDescInit(p.batchSize, p.inputSize) + + case LinearWeight => + val p = params.asInstanceOf[LinearWeightParams] + val desc = BigQuant.FCKernelDescInit(p.outputSize, p.inputSize) + if (bytes != null) { + linearWeight(p, desc, bytes, offset, max, min) + } + desc + } + + desc + } + + private def linearWeight[T: ClassTag](p: LinearWeightParams, desc: Long, bytes: Array[Byte], + offset: Int, max: Array[T], min: Array[T]): Long = { + val minArray = min.asInstanceOf[Array[Float]] + val maxArray = max.asInstanceOf[Array[Float]] + + BigQuant.FCKernelLoadFromModel(desc, bytes, minArray, maxArray, + p.outputSize, p.inputSize, QuantParams.WEIGHT_THRESHOLD, BigQuant.NCHW) + desc + } + + private def convWeigth[T: ClassTag](p: ConvWeightParams, desc: Long, bytes: Array[Byte], + offset: Int, max: Array[T], min: Array[T]): Long = { + val minArray = min.asInstanceOf[Array[Float]] + val maxArray = max.asInstanceOf[Array[Float]] + BigQuant.ConvKernelLoadFromModel(desc, + bytes, offset, + minArray, maxArray, p.nOutputPlane, p.nInputPlane, + p.kernelH, p.kernelW, QuantParams.WEIGHT_THRESHOLD, p.dataFormat) + desc + } +} + +object QuantParams { + val FAULT_TOLERANCE = 0.5f + val WEIGHT_THRESHOLD = 64.0f + val THRESHOLD = 127.0f +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index c3c20740c63..f7c0476a4c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -1989,6 +1989,9 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } override def getTensorNumeric(): TensorNumeric[T] = ev + + override def getTensorType: TensorType = DenseType + } object DenseTensor { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala new file mode 100644 index 00000000000..90125225bf1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala @@ -0,0 +1,301 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +import com.intel.analytics.bigdl.bigquant.BigQuant +import com.intel.analytics.bigdl.nn.quantized._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import java.io.{IOException, ObjectInputStream} +import scala.reflect.ClassTag + +@SerialVersionUID(- 1766499387282335147L) +private[bigdl] class QuantizedTensor[T: ClassTag]( + private var _size: Array[Int], + private var _stride: Array[Int], + var nDimension: Int)(implicit ev: TensorNumeric[T]) extends QuantizedTensorUnsupported[T] { + @transient private var desc = 0L + private var internalStorage: Array[Byte] = null + + var maxOfRow: Array[T] = null + var minOfRow: Array[T] = null + var sumOfRow: Array[T] = null + + var params: DescParams = _ + + def getStorage: Array[Byte] = { + internalStorage + } + + def getNativeStorage: Long = { + desc + } + + def release(): this.type = { + if (desc != 0) { + BigQuant.FreeMemory(desc) + } + desc = 0L + this + } + + override def equals(obj: Any): Boolean = { + if (obj == null) { + return false + } + + if (!obj.isInstanceOf[QuantizedTensor[T]]) { + return false + } + + val other = obj.asInstanceOf[QuantizedTensor[T]] + if (this.eq(other)) { + return true + } + + if (this.nDimension != other.nDimension) { + return false + } + + var d = 1 + while (d <= this.nDimension) { + if (this.size(d) != other.size(d)) { + return false + } + d += 1 + } + + var result = true + for (i <- internalStorage.indices) { + result = internalStorage(i) == other.getStorage(i) + } + + result + } + + override def hashCode(): Int = { + val seed = 37 + var hash = 1 + + hash = hash * seed + this.nDimension + var d = 1 + while (d <= this.nDimension) { + hash = hash * seed + this.size(d) + d += 1 + } + + if (internalStorage != null) { + var i = 0 + while (i < internalStorage.length) { + hash = hash * seed + internalStorage(i).toFloat.hashCode() + i += 1 + } + } + + hash + } + + def this(size: Array[Int], params: DescParams)( + implicit ev: TensorNumeric[T]) = { + this(size, DenseTensor.size2Stride(size), size.length) + this.params = params + this.desc = Desc.get(params, null, 0, null, null) + } + + def this(src: Tensor[T], descParams: DescParams)( + implicit ev: TensorNumeric[T]) = { + this(src.size(), src.stride(), src.nDimension()) + this.internalStorage = createInternalStorage(src) + this.params = descParams + this.desc = Desc.get(descParams, this.internalStorage, 0, this.maxOfRow, this.minOfRow) + } + + def this(src: Array[Byte], size: Array[Int], max: Array[T], min: Array[T], sum: Array[T], + descParams: DescParams)(implicit ev: TensorNumeric[T]) = { + this(size, DenseTensor.size2Stride(size), size.length) + require(src.length == size.product, s"size mismatch, byte array size should equal to shape") + + this.internalStorage = src + this.maxOfRow = max + this.minOfRow = min + this.sumOfRow = sum + this.params = descParams + this.desc = Desc.get(descParams, this.internalStorage, 0, this.maxOfRow, this.minOfRow) + } + + private def createInternalStorage(tensor: Tensor[T]): Array[Byte] = { + val size = tensor.size(1) + maxOfRow = new Array[T](size) + minOfRow = new Array[T](size) + sumOfRow = new Array[T](size) + + for (i <- 1 to size) { + val tmp = tensor.select(1, i) + minOfRow(i - 1) = tmp.min() + maxOfRow(i - 1) = tmp.max() + sumOfRow(i - 1) = tmp.sum() + } + + val bytes = new Array[Byte](this.nElement()) + val bytesOffset = 0 + ev.getType() match { + case FloatType => + Quantization.quantize(tensor.asInstanceOf[Tensor[Float]], bytes, bytesOffset) + case _ => + throw new UnsupportedOperationException(s"Only support Float for quantized model") + } + + bytes + } + + override def getTensorType: TensorType = QuantizedType + + override def dim(): Int = nDimension + + override def size(): Array[Int] = _size + + override def size(dim: Int): Int = { + require(dim > 0 && dim <= this.nDimension, + s"dimension ${dim} out of range of ${this.nDimension}D tensor") + _size(dim - 1) + } + + override def stride(): Array[Int] = _stride.slice(0, this.nDimension) + + override def stride(dim: Int): Int = { + require(dim > 0 && dim <= this.nDimension, + s"dimension ${dim} out of range of ${this.nDimension}D tensor") + _stride(dim - 1) + } + + override def nElement(): Int = { + if (this.nDimension == 0) { + 0 + } else { + var n = 1 + var d = 0 + while (d < this.nDimension) { + n = n * this._size(d) + d += 1 + } + n + } + } + + override def set(): Tensor[T] = { + internalStorage = null + maxOfRow = null + minOfRow = null + sumOfRow = null + desc = 0L + this + } + + /** + * set from other tensor, it will share the storage and desc with other + * + * @param other the given tensor + * @return current tensor + */ + override def set(other: Tensor[T]): Tensor[T] = { + if (other.isInstanceOf[QuantizedTensor[T]]) { + val o = other.asInstanceOf[QuantizedTensor[T]] + + this.internalStorage = o.getStorage + this.params = o.params + this.desc = o.getNativeStorage + + this.maxOfRow = o.maxOfRow + this.minOfRow = o.minOfRow + this.sumOfRow = o.sumOfRow + + this._size = o.size() + this._stride = o.stride() + this.nDimension = o.nDimension + + } else { + throw new UnsupportedOperationException(s"can't set from other type of tensor.") + } + this + } + + /** + * copy from another QuantizedTensor, it will a new storage and new desc + * + * @param other source tensor + * @return current tensor + */ + override def copy(other: Tensor[T]): Tensor[T] = { + if (other.isInstanceOf[QuantizedTensor[T]] && other.size().deep == this.size().deep) { + val quantizedTensor = other.asInstanceOf[QuantizedTensor[T]] + + if (internalStorage != null) { + internalStorage = new Array[Byte](other.nElement()) + } + + System.arraycopy(quantizedTensor.getStorage, 0, internalStorage, 0, this.nElement()) + + params = quantizedTensor.params.copy() + + val length = quantizedTensor.maxOfRow.length + maxOfRow = new Array[T](length) + System.arraycopy(quantizedTensor.maxOfRow, 0, maxOfRow, 0, length) + + minOfRow = new Array[T](length) + System.arraycopy(quantizedTensor.minOfRow, 0, minOfRow, 0, length) + + sumOfRow = new Array[T](length) + System.arraycopy(quantizedTensor.sumOfRow, 0, sumOfRow, 0, length) + + new QuantizedTensor[T](internalStorage, size(), maxOfRow, minOfRow, sumOfRow, params) + } else { + throw new UnsupportedOperationException(s"can't set from other type of tensor.") + } + + this + } + + @throws(classOf[IOException]) + private def readObject(in: ObjectInputStream): Unit = { + in.defaultReadObject() + + this.desc = Desc.get(params, internalStorage, 0, maxOfRow, minOfRow) + } +} + +object QuantizedTensor { + def apply[@specialized(Float, Double) T: ClassTag](size: Array[Int], params: DescParams)( + implicit ev: TensorNumeric[T]): QuantizedTensor[T] = + new QuantizedTensor[T](size, params) + + def apply[@specialized(Float, Double) T: ClassTag](src: Tensor[T], descParams: DescParams)( + implicit ev: TensorNumeric[T]): QuantizedTensor[T] = { + new QuantizedTensor[T](src, descParams) + } + + def apply[@specialized(Float, Double) T: ClassTag](src: Array[Byte], max: Array[T], min: Array[T], + sum: Array[T], size: Array[Int], descParams: DescParams)( + implicit ev: TensorNumeric[T]): QuantizedTensor[T] = { + new QuantizedTensor[T](src, size, max, min, sum, descParams) + } +} + +object QuantizedDummyTensor { + def apply[@specialized(Float, Double) T: ClassTag]()( + implicit ev: TensorNumeric[T]): QuantizedTensor[T] = { + QuantizedTensor[T](Tensor(1, 1), LinearWeightParams(1, 1)) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala new file mode 100644 index 00000000000..90841769e5a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -0,0 +1,1399 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +import breeze.linalg.{DenseMatrix => BrzDenseMatrix, DenseVector => BrzDenseVector} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import org.apache.spark.mllib.linalg.{DenseMatrix, DenseVector, Matrix, Vector} +import scala.reflect.ClassTag + +abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { + val errorString = s"QuantizeTensor doesn't support this operation now" + + /** + * Fill with a given value. It will change the value of the current tensor and return itself + * + * @param v value to fill the tensor + * @return current tensor + */ + override def fill(v: T): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Fill with zero. It will change the value of the current tensor and return itself + * + * @return current tensor + */ + override def zero(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Fill with random value(normal gaussian distribution). + * It will change the value of the current tensor and return itself + * + * @return current tensor + */ + override def randn(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Fill with random value(normal gaussian distribution with the specified mean + * and stdv). + * It will change the value of the current tensor and return itself + * + * @return current tensor + */ + override def randn(mean: Double, stdv: Double): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Fill with random value(uniform distribution). + * It will change the value of the current tensor and return itself + * + * @return current tensor + */ + override def rand(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Fill with random value(uniform distribution between [lowerBound, upperBound]) + * It will change the value of the current tensor and return itself + * + * @return current tensor + */ + override def rand(lowerBound: Double, upperBound: Double): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Fill with random value(bernoulli distribution). + * It will change the value of the current tensor and return itself + * + * @return current tensor + */ + override def bernoulli(p: Double): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** * + * Create a new tensor which exchanges the given dimensions of the current tensor + * + * @param dim1 dimension to be exchanged, count from one + * @param dim2 dimension to be exchanged, count from one + * @return new tensor + */ + override def transpose(dim1: Int, dim2: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Shortcut of transpose(1, 2) for 2D tensor + * + * @see transpose() + */ + override def t(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Query tensor on a given index. Tensor should not be empty + * + * @param index count from 1 + * @return + */ + override def apply(index: Int): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Query the value on a given index. Tensor should not be empty + * + * @param indexes the indexes length should be same as the tensor dimension length and each + * value count from 1 + * @return the value on the given index + */ + override def apply(indexes: Array[Int]): T = throw new UnsupportedOperationException(errorString) + + /** + * Query the value on a given position. The number of parameters + * should be equal to the dimension number of the tensor. + * Tensor should not be empty. + * + * @param d1 ,( d2, d3, d4, d5) the given position + * @return the value on a given position + */ + override def valueAt(d1: Int): T = throw new UnsupportedOperationException(errorString) + + override def valueAt(d1: Int, d2: Int): T = throw new UnsupportedOperationException(errorString) + + override def valueAt(d1: Int, d2: Int, d3: Int): T = + throw new UnsupportedOperationException(errorString) + + override def valueAt(d1: Int, d2: Int, d3: Int, d4: Int): T = + throw new UnsupportedOperationException(errorString) + + override def valueAt(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int): T = + throw new UnsupportedOperationException(errorString) + + /** + * Subset the tensor by apply the element of the given table to corresponding dimension of the + * tensor. The element of the given table can be an Int or another Table. + * An Int means select on current dimension; A table means narrow on current dimension, + * the table should has two elements, of which the first is start index and + * the second is the end index. An empty table is equals to Table(1, size_of_current_dimension) + * If the table length is less than the tensor dimension, the missing dimension is applied by + * an empty table + * + * @see select + * @see narrow + * @param t The table length should be less than or equal to the tensor dimensions + * @return + */ + override def apply(t: Table): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * For tensor(i) = value. If tensor(i) is another tensor, it will fill the selected subset by + * the given value + * + * @param index index + * @param value value to write + */ + override def update(index: Int, value: T): Unit = + throw new UnsupportedOperationException(errorString) + + /** + * Copy the give tensor value to the select subset of the current tensor by the given index. + * The subset should + * has the same size of the given tensor + * + * @param index index + * @param src tensor to write + */ + override def update(index: Int, src: Tensor[T]): Unit = + throw new UnsupportedOperationException(errorString) + + /** + * Write the value to the value indexed by the given index array + * + * @param indexes index array. It should has same length with the tensor dimension + * @param value value to write + */ + override def update(indexes: Array[Int], value: T): Unit = + throw new UnsupportedOperationException(errorString) + + /** + * Write the value on a given position. The number of parameters + * should be equal to the dimension number of the tensor. + * + * @param d1 ,( d2, d3, d4, d5) the given position + * @param value the written value + * @return + */ + override def setValue(d1: Int, value: T): this.type = + throw new UnsupportedOperationException(errorString) + + override def setValue(d1: Int, d2: Int, value: T): this.type = + throw new UnsupportedOperationException(errorString) + + override def setValue(d1: Int, d2: Int, d3: Int, value: T): this.type = + throw new UnsupportedOperationException(errorString) + + override def setValue(d1: Int, d2: Int, d3: Int, d4: Int, value: T): this.type = + throw new UnsupportedOperationException(errorString) + + override def setValue(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int, + value: T): this.type = throw new UnsupportedOperationException(errorString) + + /** + * Fill the select subset of the current tensor with the given value. + * The element of the given table can be an Int or another Table. An Int means select on current + * dimension; A tablemeans narrow on current dimension, the table should has two elements, + * of which the first is start index and the second is the end index. An empty table is equals + * to Table(1, size_of_current_dimension) If the table length is less than the tensor dimension, + * the missing dimension is applied by an empty table + * + * @param t subset table + * @param value value to write + */ + override def update(t: Table, value: T): Unit = + throw new UnsupportedOperationException(errorString) + + /** + * Copy the given tensor value to the select subset of the current tensor + * The element of the given table can be an Int or another Table. An Int means select on current + * dimension; A table means narrow on current dimension, the table should has two elements, + * of which the first is start index and the second is the end index. An empty table is equals + * to Table(1, size_of_current_dimension) If the table length is less than the tensor dimension, + * the missing dimension is applied by an empty table + * + * @param t subset table + * @param src tensor to copy + */ + override def update(t: Table, src: Tensor[T]): Unit = + throw new UnsupportedOperationException(errorString) + + /** + * Update the value meeting the filter criteria with the give value + * + * @param filter filter + * @param value value to update + */ + override def update(filter: (T) => Boolean, value: T): Unit = + throw new UnsupportedOperationException(errorString) + + /** + * Check if the tensor is contiguous on the storage + * + * @return true if it's contiguous + */ + override def isContiguous(): Boolean = throw new UnsupportedOperationException(errorString) + + /** + * Get a contiguous tensor from current tensor + * + * @return the current tensor if it's contiguous; or a new contiguous tensor with separated + * storage + */ + override def contiguous(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Check if the size is same with the give tensor + * + * @param other tensor to be compared + * @return true if they have same size + */ + override def isSameSizeAs(other: Tensor[_]): Boolean = + throw new UnsupportedOperationException(errorString) + + /** + * Resize the current tensor to the same size of the given tensor. It will still use the same + * storage if the storage + * is sufficient for the new size + * + * @param src target tensor + * @return current tensor + */ + override def resizeAs(src: Tensor[_]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Remove the dim-th dimension and return the subset part. For instance + * tensor = + * 1 2 3 + * 4 5 6 + * tensor.select(1, 1) is [1 2 3] + * tensor.select(1, 2) is [4 5 6] + * tensor.select(2, 3) is [3 6] + * + * @param dim + * @param index + * @return + */ + override def select(dim: Int, index: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Get the storage + * + * @return storage + */ + override def storage(): Storage[T] = + throw new UnsupportedOperationException(errorString) + + /** + * tensor offset on the storage + * + * @return storage offset, count from 1 + */ + override def storageOffset(): Int = + throw new UnsupportedOperationException(errorString) + + /** + * The Tensor is now going to "view" the given storage, starting at position storageOffset (>=1) + * with the given dimension sizes and the optional given strides. As the result, any + * modification in the elements of the Storage will have an impact on the elements of the Tensor, + * and vice-versa. This is an efficient method, as there is no memory copy! + * + * If only storage is provided, the whole storage will be viewed as a 1D Tensor. + * + * @param storage + * @param storageOffset + * @param sizes + * @param strides + * @return current tensor + */ + override def set(storage: Storage[T], storageOffset: Int, sizes: Array[Int], + strides: Array[Int]): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Get a subset of the tensor on dim-th dimension. The offset is given by index, and length is + * give by size. The important difference with select is that it will not reduce the dimension + * number. For Instance + * tensor = + * 1 2 3 + * 4 5 6 + * tensor.narrow(1, 1, 1) is [1 2 3] + * tensor.narrow(2, 2, 2) is + * 2 3 + * 5 6 + * + * @param dim + * @param index + * @param size + * @return + */ + override def narrow(dim: Int, index: Int, size: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Apply a function to each element of the tensor and modified it value if it return a double + * + * @param func applied function + * @return current tensor + */ + override def apply1(func: (T) => T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Map value of another tensor to corresponding value of current tensor and apply function on + * the two value and change the value of the current tensor + * The another tensor should has the same size of the current tensor + * + * @param other another tensor + * @param func applied function + * @return current tensor + */ + override def map(other: Tensor[T], func: (T, T) => T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Removes all singleton dimensions of the tensor + * + * @return current tensor + */ + override def squeeze(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Removes given dimensions of the tensor if it's singleton + * + * @return current tensor + */ + override def squeeze(dim: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Create a new tensor that removes all singleton dimensions of the tensor + * + * @return create a new tensor + */ + override def squeezeNewTensor(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def view(sizes: Array[Int]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * + * Returns a tensor which contains all slices of size @param size + * in the dimension @param dim. Step between two slices is given by @param step. + * + * @param dim + * @param size + * @param step Step between two slices + * @return new tensor + */ + override def unfold(dim: Int, size: Int, step: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Repeating a tensor allocates new memory, unless result is provided, in which case its memory + * is resized. sizes specify the number of times the tensor is repeated in each dimension. + * + * @param sizes + * @return + */ + override def repeatTensor(sizes: Array[Int]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * This is equivalent to this.expand(template.size()) + * + * @param template the given tensor + * @return + */ + override def expandAs(template: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Expanding a tensor allocates new memory, tensor where singleton dimensions can be expanded + * to multiple ones by setting the stride to 0. Any dimension that has size 1 can be expanded + * to arbitrary value with new memory allocation. Attempting to expand along a dimension that + * does not have size 1 will result in an error. + * + * @param sizes the size that tensor will expend to + * @return + */ + override def expand(sizes: Array[Int]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Splits current tensor along dimension dim into a result table of Tensors of size size + * (a number) or less (in the case of the last Tensor). The sizes of the non-dim dimensions + * remain unchanged. Internally, a series of narrows are performed along dimensions dim. + * Argument dim defaults to 1. + * + * @param size + * @param dim + * @return + */ + override def split(size: Int, dim: Int): Array[Tensor[T]] = + throw new UnsupportedOperationException(errorString) + + /** + * spilt one tensor into multi tensor along the `dim` dimension + * + * @param dim the specific dimension + * @return + */ + override def split(dim: Int): Array[Tensor[T]] = + throw new UnsupportedOperationException(errorString) + + /** + * convert the tensor to BreezeVector, the dimension of the tensor need to be 1. + * + * @return BrzDenseVector + */ + override def toBreezeVector(): BrzDenseVector[T] = + throw new UnsupportedOperationException(errorString) + + /** + * convert the tensor to MLlibVector, the dimension of the + * tensor need to be 1, and tensor need to be continuous. + * + * @return Vector + */ + override def toMLlibVector(): Vector = + throw new UnsupportedOperationException(errorString) + + /** + * convert the tensor to BreezeMatrix, the dimension of the tensor need to be 2. + * + * @return BrzDenseMatrix + */ + override def toBreezeMatrix(): BrzDenseMatrix[T] = + throw new UnsupportedOperationException(errorString) + + /** + * convert the tensor to MLlibMatrix, the dimension of the + * tensor need to be 2, and tensor need to be continuous. + * + * @return Matrix + */ + override def toMLlibMatrix(): Matrix = + throw new UnsupportedOperationException(errorString) + + /** + * return the tensor datatype( DoubleType or FloatType) + * + * @return + */ + override def getType(): TensorDataType = + throw new UnsupportedOperationException(errorString) + + /** + * Compare and print differences between two tensors + * + * @param other + * @param count + * @return true if there's difference, vice versa + */ + override def diff(other: Tensor[T], count: Int, reverse: Boolean): Boolean = + throw new UnsupportedOperationException(errorString) + + /** + * view this.tensor and add a Singleton Dimension to `dim` dimension + * + * @param t source tensor + * @param dim the specific dimension, default is 1 + * @return this + */ + override def addSingletonDimension(t: Tensor[T], dim: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * create a new tensor without any change of the tensor + * + * @param sizes the size of the new Tensor + * @return + */ + override def reshape(sizes: Array[Int]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Save the tensor to given path + * + * @param path + * @param overWrite + * @return + */ + override def save(path: String, overWrite: Boolean): this.type = + throw new UnsupportedOperationException(errorString) + + /** + * Return tensor numeric + * + * @return + */ + override def getTensorNumeric(): TensorNumeric[T] = + throw new UnsupportedOperationException(errorString) + + // scalastyle:off methodName + /** + * Add all elements of this with value not in place. + * It will allocate new memory. + * + * @param s + * @return + */ + override def +(s: T): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Add a Tensor to another one, return the result in new allocated memory. + * The number of elements in the Tensors must match, but the sizes do not matter. + * The size of the returned Tensor will be the size of the first Tensor + * + * @param t + * @return + */ + override def +(t: Tensor[T]): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * subtract all elements of this with the value not in place. + * It will allocate new memory. + * + * @param s + * @return + */ + override def -(s: T): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** + * Subtract a Tensor from another one, return the result in new allocated memory. + * The number of elements in the Tensors must match, but the sizes do not matter. + * The size of the returned Tensor will be the size of the first Tensor + * + * @param t + * @return + */ + override def -(t: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def unary_-(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * divide all elements of this with value not in place. + * It will allocate new memory. + * + * @param s + * @return + */ + override def /(s: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Divide a Tensor by another one, return the result in new allocated memory. + * The number of elements in the Tensors must match, but the sizes do not matter. + * The size of the returned Tensor will be the size of the first Tensor + * + * @param t + * @return + */ + override def /(t: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * multiply all elements of this with value not in place. + * It will allocate new memory. + * + * @param s + * @return + */ + override def *(s: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Multiply a Tensor by another one, return the result in new allocated memory. + * The number of elements in the Tensors must match, but the sizes do not matter. + * The size of the returned Tensor will be the size of the first Tensor + * + * @param t + * @return + */ + override def *(t: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + // scalastyle:on methodName + + /** + * returns the sum of the elements of this + * + * @return + */ + override def sum(): T = + throw new UnsupportedOperationException(errorString) + + /** + * performs the sum operation over the dimension dim + * + * @param dim + * @return + */ + override def sum(dim: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def sum(x: Tensor[T], dim: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + def prod(): T = + throw new UnsupportedOperationException(errorString) + + def prod(x: Tensor[T], dim: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * returns the mean of all elements of this. + * + * @return + */ + override def mean(): T = + throw new UnsupportedOperationException(errorString) + + /** + * performs the mean operation over the dimension dim. + * + * @param dim + * @return + */ + override def mean(dim: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * returns the single biggest element of x + * + * @return + */ + override def max(): T = + throw new UnsupportedOperationException(errorString) + + /** + * performs the max operation over the dimension n + * + * @param dim + * @return + */ + override def max(dim: Int): (Tensor[T], Tensor[T]) = + throw new UnsupportedOperationException(errorString) + + /** + * performs the max operation over the dimension n + * + * @param values + * @param indices + * @param dim + * @return + */ + override def max(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T]) = + throw new UnsupportedOperationException(errorString) + + /** + * returns the single minimum element of x + * + * @return + */ + override def min(): T = + throw new UnsupportedOperationException(errorString) + + /** + * performs the min operation over the dimension n + * + * @param dim + * @return + */ + override def min(dim: Int): (Tensor[T], Tensor[T]) = + throw new UnsupportedOperationException(errorString) + + /** + * performs the min operation over the dimension n + * + * @param values + * @param indices + * @param dim + * @return + */ + override def min(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T]) = + throw new UnsupportedOperationException(errorString) + + /** + * Writes all values from tensor src into this tensor at the specified indices + * + * @param dim + * @param index + * @param src + * @return this + */ + override def scatter(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * change this tensor with values from the original tensor by gathering a number of values + * from each "row", where the rows are along the dimension dim. + * + * @param dim + * @param index + * @param src + * @return this + */ + override def gather(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * This function computes 2 dimensional convolution of a single image + * with a single kernel (2D output). the dimensions of input and kernel + * need to be 2, and Input image needs to be bigger than kernel. The + * last argument controls if the convolution is a full ('F') or valid + * ('V') convolution. The default is valid convolution. + * + * @param kernel + * @param vf full ('F') or valid ('V') convolution. + * @return + */ + override def conv2(kernel: Tensor[T], vf: Char): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * This function operates with same options and input/output configurations as conv2, + * but performs cross-correlation of the input with the kernel k. + * + * @param kernel + * @param vf full ('F') or valid ('V') convolution. + * @return + */ + override def xcorr2(kernel: Tensor[T], vf: Char): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * replaces all elements in-place with the square root of the elements of this. + * + * @return + */ + override def sqrt(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * replaces all elements in-place with the absolute values of the elements of this. + * + * @return + */ + override def abs(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * x.add(value,y) multiply-accumulates values of y into x. + * + * @param value scalar + * @param y other tensor + * @return current tensor + */ + override def add(value: T, y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * accumulates all elements of y into this + * + * @param y other tensor + * @return current tensor + */ + override def add(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * z.add(x, value, y) puts the result of x + value * y in z. + * + * @param x + * @param value + * @param y + * @return + */ + override def add(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * x.add(value) : add value to all elements of x in place. + * + * @param value + * @return + */ + override def add(value: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def add(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Performs the dot product. The number of elements must match: both Tensors are seen as a 1D + * vector. + * + * @param y + * @return + */ + override def dot(y: Tensor[T]): T = + throw new UnsupportedOperationException(errorString) + + /** + * For each elements of the tensor, performs the max operation compared with the given value + * vector. + * + * @param value + * @return + */ + override def cmax(value: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Performs the p-norm distance calculation between two tensors + * + * @param y the secode Tensor + * @param norm the norm of distance + * @return + */ + override def dist(y: Tensor[T], norm: Int): T = + throw new UnsupportedOperationException(errorString) + + /** + * Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the + * scalar value (1 if not present) and add it to x. The number of elements must match, but sizes + * do not matter. + * + * @param value + * @param tensor1 + * @param tensor2 + */ + override def addcmul(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def addcmul(tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar + * value and add it to x. + * The number of elements must match, but sizes do not matter. + * + * @param value + * @param tensor1 + * @param tensor2 + * @return + */ + override def addcdiv(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def sub(value: T, y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def sub(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * subtracts all elements of y from this + * + * @param y other tensor + * @return current tensor + */ + override def sub(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def sub(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def sub(value: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Element-wise multiply + * x.cmul(y) multiplies all elements of x with corresponding elements of y. + * x = x * y + * + * @param y tensor + * @return current tensor + */ + override def cmul(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Element-wise multiply + * z.cmul(x, y) equals z = x * y + * + * @param x tensor + * @param y tensor + * @return current tensor + */ + override def cmul(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Element-wise divide + * x.cdiv(y) all elements of x divide all elements of y. + * x = x / y + * + * @param y tensor + * @return current tensor + */ + override def cdiv(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Element-wise divide + * z.cdiv(x, y) means z = x / y + * + * @param x tensor + * @param y tensor + * @return current tensor + */ + override def cdiv(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * multiply all elements of this with value in-place. + * + * @param value + * @return + */ + override def mul(value: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * divide all elements of this with value in-place. + * + * @param value + * @return + */ + override def div(value: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * put the result of x * value in current tensor + * + * @param value + * @return + */ + override def mul(x: Tensor[T], value: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Performs a matrix-matrix multiplication between mat1 (2D tensor) and mat2 (2D tensor). + * Optional values v1 and v2 are scalars that multiply M and mat1 * mat2 respectively. + * Optional value beta is a scalar that scales the result tensor, before accumulating the result + * into the tensor. Defaults to 1.0. + * If mat1 is a n x m matrix, mat2 a m x p matrix, M must be a n x p matrix. + * + * res = (v1 * M) + (v2 * mat1*mat2) + * + * @param v1 + * @param M + * @param v2 + * @param mat1 + * @param mat2 + */ + override def addmm(v1: T, M: Tensor[T], v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res = M + (mat1*mat2) */ + override def addmm(M: Tensor[T], mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res = res + mat1 * mat2 */ + override def addmm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res = res + v2 * mat1 * mat2 */ + override def addmm(v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res = v1 * res + v2 * mat1*mat2 */ + override def addmm(v1: T, v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res = mat1*mat2 */ + override def mm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Performs the outer-product between vec1 (1D tensor) and vec2 (1D tensor). + * Optional values v1 and v2 are scalars that multiply mat and vec1 [out] vec2 respectively. + * In other words, + * res_ij = (v1 * mat_ij) + (v2 * vec1_i * vec2_j) + * + * @param t1 + * @param t2 + * @return + */ + override def addr(t1: Tensor[T], t2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def addr(v1: T, t1: Tensor[T], t2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Performs the outer-product between vec1 (1D Tensor) and vec2 (1D Tensor). + * Optional values v1 and v2 are scalars that multiply mat and vec1 [out] vec2 respectively. + * In other words,res_ij = (v1 * mat_ij) + (v2 * vec1_i * vec2_j) + * + * @param v1 + * @param t1 + * @param v2 + * @param t2 + * @param t3 + * @return + */ + override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T], t3: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * return pseudo-random numbers, require 0<=args.length<=2 + * if args.length = 0, return [0, 1) + * if args.length = 1, return [1, args(0)] or [args(0), 1] + * if args.length = 2, return [args(0), args(1)] + * + * @param args + */ + override def uniform(args: T*): T = + throw new UnsupportedOperationException(errorString) + + /** + * Performs a matrix-vector multiplication between mat (2D Tensor) and vec2 (1D Tensor) and add + * it to vec1. Optional values v1 and v2 are scalars that multiply vec1 and vec2 respectively. + * + * In other words, + * res = (beta * vec1) + alpha * (mat * vec2) + * + * Sizes must respect the matrix-multiplication operation: if mat is a n × m matrix, + * vec2 must be vector of size m and vec1 must be a vector of size n. + */ + override def addmv(beta: T, vec1: Tensor[T], alpha: T, mat: Tensor[T], + vec2: Tensor[T]): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** res = beta * res + alpha * (mat * vec2) */ + override def addmv(beta: T, alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res = res + alpha * (mat * vec2) */ + override def addmv(alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res = res + (mat * vec2) */ + override def mv(mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Perform a batch matrix matrix multiplication of matrices and stored in batch1 and batch2 + * with batch add. batch1 and batch2 must be 3D Tensors each containing the same number of + * matrices. If batch1 is a b × n × m Tensor, batch2 a b × m × p Tensor, res will be a + * b × n × p Tensor. + * + * In other words, + * res_i = (beta * M_i) + (alpha * batch1_i * batch2_i) + */ + override def baddbmm(beta: T, M: Tensor[T], alpha: T, batch1: Tensor[T], + batch2: Tensor[T]): Tensor[T] = throw new UnsupportedOperationException(errorString) + + /** res_i = (beta * res_i) + (alpha * batch1_i * batch2_i) */ + override def baddbmm(beta: T, alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res_i = res_i + (alpha * batch1_i * batch2_i) */ + override def baddbmm(alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** res_i = res_i + batch1_i * batch2_i */ + override def bmm(batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Replaces all elements in-place with the elements of x to the power of n + * + * @param y + * @param n + * @return current tensor reference + */ + override def pow(y: Tensor[T], n: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def pow(n: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Get the top k smallest values and their indices. + * + * @param result result buffer + * @param indices indices buffer + * @param k + * @param dim dimension, default is the last dimension + * @param increase sort order, set it to true if you want to get the smallest top k values + * @return + */ + override def topk(k: Int, dim: Int, increase: Boolean, result: Tensor[T], + indices: Tensor[T]): (Tensor[T], Tensor[T]) = + throw new UnsupportedOperationException(errorString) + + /** + * Replaces all elements in-place with the elements of lnx + * + * @param y + * @return current tensor reference + */ + override def log(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def exp(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def sqrt(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def log1p(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + override def log(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + override def exp(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def log1p(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def abs(x: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * returns the p-norms of the Tensor x computed over the dimension dim. + * + * @param y result buffer + * @param value + * @param dim + * @return + */ + override def norm(y: Tensor[T], value: Int, dim: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Implements > operator comparing each element in x with y + * + * @param x + * @param y + * @return current tensor reference + */ + override def gt(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Implements < operator comparing each element in x with y + * + * @param x + * @param y + * @return current tensor reference + */ + override def lt(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Implements <= operator comparing each element in x with y + * + * @param x + * @param y + * @return current tensor reference + */ + override def le(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Implements == operator comparing each element in x with y + * + * @param y + * @return current tensor reference + */ + override def eq(x: Tensor[T], y: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Fills the masked elements of itself with value val + * + * @param mask + * @param e + * @return current tensor reference + */ + override def maskedFill(mask: Tensor[T], e: T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Copies the elements of tensor into mask locations of itself. + * + * @param mask + * @param y + * @return current tensor reference + */ + override def maskedCopy(mask: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Returns a new Tensor which contains all elements aligned to a 1 in the corresponding mask. + * + * @param mask + * @param y + * @return current tensor reference + */ + override def maskedSelect(mask: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * returns the sum of the n-norms on the Tensor x + * + * @param value the n-norms + * @return + */ + override def norm(value: Int): T = + throw new UnsupportedOperationException(errorString) + + /** + * returns a new Tensor with the sign (+/- 1 or 0) of the elements of x. + * + * @return + */ + override def sign(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Implements >= operator comparing each element in x with value + * + * @param x + * @param value + * @return + */ + override def ge(x: Tensor[T], value: Double): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Accumulate the elements of tensor into the original tensor by adding to the indices + * in the order given in index. The shape of tensor must exactly match the elements indexed + * or an error will be thrown. + * + * @param dim + * @param index + * @param y + * @return + */ + override def indexAdd(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * Accumulate the elements of tensor into the original tensor by adding to the indices + * in the order given in index. The shape of tensor must exactly match the elements indexed + * or an error will be thrown. + * + * @param dim + * @param index + * @param y + * @return + */ + override def index(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * stores the element-wise maximum of x and y in x. + * x.cmax(y) = max(x, y) + * + * @param y tensor + * @return current tensor + */ + override def cmax(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * stores the element-wise maximum of x and y in z. + * z.cmax(x, y) means z = max(x, y) + * + * @param x tensor + * @param y tensor + */ + override def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + /** + * resize this tensor size to floor((xmax - xmin) / step) + 1 and set values from + * xmin to xmax with step (default to 1). + * + * @param xmin + * @param xmax + * @param step + * @return this tensor + */ + override def range(xmin: Double, xmax: Double, step: Int): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def toTensor[D](implicit ev: TensorNumeric[D]): Tensor[D] = + throw new UnsupportedOperationException(errorString) + + override def tanh(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def tanh(y: Tensor[T]): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def resize(sizes: Array[Int], strides: Array[Int]): this.type = + throw new UnsupportedOperationException(errorString) + + override def resize(size1: Int): this.type = throw new UnsupportedOperationException(errorString) + + override def resize(size1: Int, size2: Int): this.type = + throw new UnsupportedOperationException(errorString) + + override def resize(size1: Int, size2: Int, size3: Int): this.type = + throw new UnsupportedOperationException(errorString) + + override def resize(size1: Int, size2: Int, size3: Int, size4: Int): this.type = + throw new UnsupportedOperationException(errorString) + + override def resize(size1: Int, size2: Int, size3: Int, size4: Int, size5: Int): this.type = + throw new UnsupportedOperationException(errorString) + + override def isEmpty: Boolean = + throw new UnsupportedOperationException(errorString) + + override def isScalar: Boolean = + throw new UnsupportedOperationException(errorString) + + override def value(): T = + throw new UnsupportedOperationException(errorString) + + override def setValue(value: T): this.type = + throw new UnsupportedOperationException(errorString) + + override def zipWith[A: ClassTag, B: ClassTag](t1: Tensor[A], t2: Tensor[B], + func: (A, B) => T): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def forceFill(v: Any): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def emptyInstance(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def forceCopy(other: Tensor[_]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def applyFun[A: ClassTag](t: Tensor[A], func: (A) => T): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def cast[D: ClassTag](castTensor: Tensor[D])(implicit ev: TensorNumeric[D]): Tensor[D] = + throw new UnsupportedOperationException(errorString) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 008c11e4818..374ce3a5746 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -745,6 +745,12 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { */ def getTensorNumeric(): TensorNumeric[T] + /** + * Return tensor type + * @return Dense / Quant + */ + def getTensorType: TensorType + /** * Compare with other tensor. The shape of the other tensor must be same with this tensor. * If element wise difference is less than delta, return true. @@ -785,6 +791,11 @@ object LongType extends TensorDataType object FloatType extends TensorDataType object DoubleType extends TensorDataType +sealed trait TensorType + +object DenseType extends TensorType + +object QuantizedType extends TensorType object Tensor { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorSpec.scala new file mode 100644 index 00000000000..12a4eb21747 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorSpec.scala @@ -0,0 +1,124 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +import com.intel.analytics.bigdl.bigquant.BigQuant +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.nn.quantized.{LinearWeight, LinearWeightParams} +import org.apache.commons.lang.SerializationUtils +import org.scalatest.{FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Parallel +class QuantizedTensorSpec extends FlatSpec with Matchers { + "A QuantizeTensor set to empty" should "work correctly" in { + val inputChannel = 4 + val outputChannel = 4 + + val fp32Tensor = Tensor(outputChannel, inputChannel).rand() + val tensor = QuantizedTensor[Float](fp32Tensor, LinearWeightParams(outputChannel, inputChannel)) + + tensor.set() + + tensor.getNativeStorage should be (0L) + tensor.getStorage should be (null) + + tensor.release() + } + + "A QuantizeTensor set to other tensor" should "work correctly" in { + val inputChannel = 4 + val outputChannel = 4 + + val fp32Tensor = Tensor(outputChannel, inputChannel).rand + + val tensor1 = QuantizedTensor[Float](fp32Tensor, + LinearWeightParams(outputChannel, inputChannel)) + val tensor2 = QuantizedTensor[Float](fp32Tensor, + LinearWeightParams(outputChannel, inputChannel)) + + tensor2.release() + tensor2.set(tensor1) + + tensor2.getNativeStorage should be (tensor1.getNativeStorage) + tensor2.getStorage should be (tensor1.getStorage) + + tensor1.release() + } + + "A QuantizeTensor set to itself" should "work correctly" in { + val inputChannel = 4 + val outputChannel = 4 + + val fp32Tensor = Tensor(outputChannel, inputChannel).rand() + val tensor = QuantizedTensor[Float](fp32Tensor, LinearWeightParams(outputChannel, inputChannel)) + + tensor.set(tensor) + + tensor.getNativeStorage should not be 0L + tensor.getStorage should not be null + + tensor.release() + tensor.getNativeStorage should be (0L) + } + + "A QuantizeTensor set" should "work correctly" in { + val inputChannel = 4 + val outputChannel = 4 + + val fp32Tensor = Tensor(outputChannel, inputChannel).rand() + + val tensor1 = QuantizedTensor[Float](fp32Tensor, + LinearWeightParams(outputChannel, inputChannel)) + val tensor2 = QuantizedTensor[Float](fp32Tensor, + LinearWeightParams(outputChannel, inputChannel)) + val tensor3 = QuantizedTensor[Float](fp32Tensor, + LinearWeightParams(outputChannel, inputChannel)) + + tensor2.release() + tensor2.set(tensor1) + + tensor2.getNativeStorage should be (tensor1.getNativeStorage) + tensor2.getStorage should be (tensor1.getStorage) + + tensor2.release() + tensor2.set(tensor3) + + tensor2.getNativeStorage should not be tensor1.getNativeStorage + + tensor3.release() + } + + "A QuantizeTensor serialzation" should "work correctly" in { + val inputChannel = 4 + val outputChannel = 4 + + val fp32Tensor = Tensor(outputChannel, inputChannel).rand() + + val tensor = QuantizedTensor[Float](fp32Tensor, LinearWeightParams(outputChannel, inputChannel)) + + val test = SerializationUtils.clone(fp32Tensor) + + val clone = SerializationUtils.clone(tensor).asInstanceOf[QuantizedTensor[Float]] + + tensor.getStorage should be (clone.getStorage) + tensor.maxOfRow should be (clone.maxOfRow) + tensor.minOfRow should be (clone.minOfRow) + tensor.sumOfRow should be (clone.sumOfRow) + + tensor.getNativeStorage should not be clone.getNativeStorage + } +} From a928c3ea9ac50f54af87be0f9a7952cb36ef1a7c Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 10 Oct 2017 10:26:06 +0800 Subject: [PATCH 0431/1065] support graph (#1636) --- .../analytics/bigdl/dllib/nn/Graph.scala | 36 +++++++++++++++++-- .../analytics/bigdl/dllib/nn/tf/Const.scala | 2 +- 2 files changed, 34 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 19032c495a3..976b87bcce8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -631,9 +631,17 @@ object Graph extends ContainerSerializable { }) layerMap.values.foreach(moduleNode => { + val edges = DataConverter.getAttributeValue(context, + attributes.get(s"${moduleNode._1.element.getName}_edges")). + asInstanceOf[mutable.HashMap[String, mutable.HashMap[String, Int]]] + val edgeMap = edges.get(moduleNode._1.element.getName).get moduleNode._2.foreach(pre => { if (layerMap.contains(pre)) { - layerMap(pre)._1 -> moduleNode._1 + val edge : Edge = edgeMap.get(pre).get match { + case -1 => Edge() + case index: Int => Edge(index) + } + layerMap(pre)._1.add(moduleNode._1, edge) } }) }) @@ -664,14 +672,36 @@ object Graph extends ContainerSerializable { val inputsNames = graph.inputs.map(_.element.getName).toArray val outputsNames = graph.outputs.map(_.element.getName).toArray graph.getForwardExecutions.foreach(execution => { - val preNodes = execution.prevNodes.map(_.element.getName) - val nextNodes = execution.nextNodes.map(_.element.getName) + + val edgeMap = new mutable.HashMap[String, mutable.Map[String, Int]] + + val preNodesAndEdges = execution.prevNodesAndEdges + val preNodes = preNodesAndEdges.map(_._1.element.getName) + val nextNodes = preNodesAndEdges.map(_._1.element.getName) val currNode = execution.element .asInstanceOf[AbstractModule[Activity, Activity, T]] val subModel = ModuleSerializer.serialize(SerializeContext( ModuleData(currNode, preNodes, nextNodes), context.storages, context.storageType)) + // add edges + val preNodeEdges = new mutable.HashMap[String, Int]() + + preNodesAndEdges.foreach(pre => { + val preNodeName = pre._1.element.getName + val preEdgeIndex = pre._2.fromIndex match { + case Some(i) => i + case None => -1 + } + preNodeEdges(preNodeName) = preEdgeIndex + }) + edgeMap(execution.element.getName) = preNodeEdges + val attriBulder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, attriBulder, edgeMap) + + graphBuilder.putAttr(s"${execution.element.getName}_edges", attriBulder.build) graphBuilder.addSubModules(subModel.bigDLModule) }) + + if (graph.variables.isDefined) { val (weights, bias) = graph.variables.get val weightAttrBuilder = AttrValue.newBuilder diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala index 014eb6f9a8c..74c0722bcc9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala @@ -29,7 +29,7 @@ private[bigdl] trait WithoutInput * @param value the constant tensor to be returned in forward */ @SerialVersionUID(-4008935551091949324L) -private[bigdl] class Const[T: ClassTag, B: ClassTag](value: Tensor[B]) +private[bigdl] class Const[T: ClassTag, B: ClassTag](val value: Tensor[B]) (implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Tensor[B], T] with WithoutInput { From e2dbc3401c8c4e7a1a33b5196be7b4a289898775 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Oct 2017 03:35:54 -0400 Subject: [PATCH 0432/1065] feat: serialization for QuantizedTensor (#1612) * feat: serialization for QuantizedTensor QuantizedTensor has a byte array which stores the quantized value. So we add byte supporting in bigdl.proto. QuantizedTensor is not like other DenseTensor, so the logic in DataConverter should be modified for supporting serializing it. * refactor: refactor serialization QuantizedTensor. * refactor: if to case-switch * fix: delete redundant --- .../src/main/java/serialization/Bigdl.java | 322 ++++++++++++++---- .../main/resources/serialization/bigdl.proto | 6 + .../bigdl/dllib/tensor/QuantizedTensor.scala | 2 + .../tensor/QuantizedTensorUnsupported.scala | 8 - .../utils/serializer/DataConverter.scala | 128 +++++-- .../utils/serializer/DataConverterSpec.scala | 50 ++- 6 files changed, 422 insertions(+), 94 deletions(-) diff --git a/scala/dllib/src/main/java/serialization/Bigdl.java b/scala/dllib/src/main/java/serialization/Bigdl.java index 4199f81ce9c..90874d5cb0b 100644 --- a/scala/dllib/src/main/java/serialization/Bigdl.java +++ b/scala/dllib/src/main/java/serialization/Bigdl.java @@ -550,6 +550,104 @@ private InputDataFormat(int value) { // @@protoc_insertion_point(enum_scope:serialization.InputDataFormat) } + /** + * Protobuf enum {@code serialization.TensorType} + */ + public enum TensorType + implements com.google.protobuf.ProtocolMessageEnum { + /** + * DENSE = 0; + */ + DENSE(0), + /** + * QUANT = 1; + */ + QUANT(1), + UNRECOGNIZED(-1), + ; + + /** + * DENSE = 0; + */ + public static final int DENSE_VALUE = 0; + /** + * QUANT = 1; + */ + public static final int QUANT_VALUE = 1; + + + public final int getNumber() { + if (this == UNRECOGNIZED) { + throw new java.lang.IllegalArgumentException( + "Can't get the number of an unknown enum value."); + } + return value; + } + + /** + * @deprecated Use {@link #forNumber(int)} instead. + */ + @java.lang.Deprecated + public static TensorType valueOf(int value) { + return forNumber(value); + } + + public static TensorType forNumber(int value) { + switch (value) { + case 0: return DENSE; + case 1: return QUANT; + default: return null; + } + } + + public static com.google.protobuf.Internal.EnumLiteMap + internalGetValueMap() { + return internalValueMap; + } + private static final com.google.protobuf.Internal.EnumLiteMap< + TensorType> internalValueMap = + new com.google.protobuf.Internal.EnumLiteMap() { + public TensorType findValueByNumber(int number) { + return TensorType.forNumber(number); + } + }; + + public final com.google.protobuf.Descriptors.EnumValueDescriptor + getValueDescriptor() { + return getDescriptor().getValues().get(ordinal()); + } + public final com.google.protobuf.Descriptors.EnumDescriptor + getDescriptorForType() { + return getDescriptor(); + } + public static final com.google.protobuf.Descriptors.EnumDescriptor + getDescriptor() { + return serialization.Bigdl.getDescriptor().getEnumTypes().get(4); + } + + private static final TensorType[] VALUES = values(); + + public static TensorType valueOf( + com.google.protobuf.Descriptors.EnumValueDescriptor desc) { + if (desc.getType() != getDescriptor()) { + throw new java.lang.IllegalArgumentException( + "EnumValueDescriptor is not for this type."); + } + if (desc.getIndex() == -1) { + return UNRECOGNIZED; + } + return VALUES[desc.getIndex()]; + } + + private final int value; + + private TensorType(int value) { + this.value = value; + } + + // @@protoc_insertion_point(enum_scope:serialization.TensorType) + } + /** * Protobuf enum {@code serialization.DataType} */ @@ -766,7 +864,7 @@ public DataType findValueByNumber(int number) { } public static final com.google.protobuf.Descriptors.EnumDescriptor getDescriptor() { - return serialization.Bigdl.getDescriptor().getEnumTypes().get(4); + return serialization.Bigdl.getDescriptor().getEnumTypes().get(5); } private static final DataType[] VALUES = values(); @@ -4695,6 +4793,15 @@ public interface BigDLTensorOrBuilder extends * int32 id = 9; */ int getId(); + + /** + * .serialization.TensorType tensorType = 10; + */ + int getTensorTypeValue(); + /** + * .serialization.TensorType tensorType = 10; + */ + serialization.Bigdl.TensorType getTensorType(); } /** * Protobuf type {@code serialization.BigDLTensor} @@ -4717,6 +4824,7 @@ private BigDLTensor() { nElements_ = 0; isScalar_ = false; id_ = 0; + tensorType_ = 0; } @java.lang.Override @@ -4833,6 +4941,12 @@ private BigDLTensor( id_ = input.readInt32(); break; } + case 80: { + int rawValue = input.readEnum(); + + tensorType_ = rawValue; + break; + } } } } catch (com.google.protobuf.InvalidProtocolBufferException e) { @@ -5048,6 +5162,22 @@ public int getId() { return id_; } + public static final int TENSORTYPE_FIELD_NUMBER = 10; + private int tensorType_; + /** + * .serialization.TensorType tensorType = 10; + */ + public int getTensorTypeValue() { + return tensorType_; + } + /** + * .serialization.TensorType tensorType = 10; + */ + public serialization.Bigdl.TensorType getTensorType() { + serialization.Bigdl.TensorType result = serialization.Bigdl.TensorType.valueOf(tensorType_); + return result == null ? serialization.Bigdl.TensorType.UNRECOGNIZED : result; + } + private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; @@ -5096,6 +5226,9 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) if (id_ != 0) { output.writeInt32(9, id_); } + if (tensorType_ != serialization.Bigdl.TensorType.DENSE.getNumber()) { + output.writeEnum(10, tensorType_); + } unknownFields.writeTo(output); } @@ -5160,6 +5293,10 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeInt32Size(9, id_); } + if (tensorType_ != serialization.Bigdl.TensorType.DENSE.getNumber()) { + size += com.google.protobuf.CodedOutputStream + .computeEnumSize(10, tensorType_); + } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; @@ -5196,6 +5333,7 @@ public boolean equals(final java.lang.Object obj) { } result = result && (getId() == other.getId()); + result = result && tensorType_ == other.tensorType_; result = result && unknownFields.equals(other.unknownFields); return result; } @@ -5232,6 +5370,8 @@ public int hashCode() { } hash = (37 * hash) + ID_FIELD_NUMBER; hash = (53 * hash) + getId(); + hash = (37 * hash) + TENSORTYPE_FIELD_NUMBER; + hash = (53 * hash) + tensorType_; hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; @@ -5383,6 +5523,8 @@ public Builder clear() { } id_ = 0; + tensorType_ = 0; + return this; } @@ -5428,6 +5570,7 @@ public serialization.Bigdl.BigDLTensor buildPartial() { result.storage_ = storageBuilder_.build(); } result.id_ = id_; + result.tensorType_ = tensorType_; result.bitField0_ = to_bitField0_; onBuilt(); return result; @@ -5511,6 +5654,9 @@ public Builder mergeFrom(serialization.Bigdl.BigDLTensor other) { if (other.getId() != 0) { setId(other.getId()); } + if (other.tensorType_ != 0) { + setTensorTypeValue(other.getTensorTypeValue()); + } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; @@ -6113,6 +6259,50 @@ public Builder clearId() { onChanged(); return this; } + + private int tensorType_ = 0; + /** + * .serialization.TensorType tensorType = 10; + */ + public int getTensorTypeValue() { + return tensorType_; + } + /** + * .serialization.TensorType tensorType = 10; + */ + public Builder setTensorTypeValue(int value) { + tensorType_ = value; + onChanged(); + return this; + } + /** + * .serialization.TensorType tensorType = 10; + */ + public serialization.Bigdl.TensorType getTensorType() { + serialization.Bigdl.TensorType result = serialization.Bigdl.TensorType.valueOf(tensorType_); + return result == null ? serialization.Bigdl.TensorType.UNRECOGNIZED : result; + } + /** + * .serialization.TensorType tensorType = 10; + */ + public Builder setTensorType(serialization.Bigdl.TensorType value) { + if (value == null) { + throw new NullPointerException(); + } + + tensorType_ = value.getNumber(); + onChanged(); + return this; + } + /** + * .serialization.TensorType tensorType = 10; + */ + public Builder clearTensorType() { + + tensorType_ = 0; + onChanged(); + return this; + } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { return super.setUnknownFieldsProto3(unknownFields); @@ -18942,74 +19132,76 @@ public serialization.Bigdl.NameAttrList getDefaultInstanceForType() { "\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002 \001(\0132\030" + ".serialization.AttrValue:\0028\001\"M\n\nInitMeth" + "od\0221\n\nmethodType\030\001 \001(\0162\035.serialization.I" + - "nitMethodType\022\014\n\004data\030\002 \003(\001\"\331\001\n\013BigDLTen" + + "nitMethodType\022\014\n\004data\030\002 \003(\001\"\210\002\n\013BigDLTen" + "sor\022)\n\010datatype\030\001 \001(\0162\027.serialization.Da" + "taType\022\014\n\004size\030\002 \003(\005\022\016\n\006stride\030\003 \003(\005\022\016\n\006" + "offset\030\004 \001(\005\022\021\n\tdimension\030\005 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"ularizer\020\001\022\021\n\rL2Regularizer\020\002*%\n\017InputDa" + + "taFormat\022\010\n\004NCHW\020\000\022\010\n\004NHWC\020\001*\"\n\nTensorTy" + + "pe\022\t\n\005DENSE\020\000\022\t\n\005QUANT\020\001*\375\001\n\010DataType\022\t\n" + + "\005INT32\020\000\022\t\n\005INT64\020\001\022\t\n\005FLOAT\020\002\022\n\n\006DOUBLE" + + "\020\003\022\n\n\006STRING\020\004\022\010\n\004BOOL\020\005\022\010\n\004CHAR\020\006\022\t\n\005SH" + + "ORT\020\007\022\t\n\005BYTES\020\010\022\017\n\013REGULARIZER\020\t\022\n\n\006TEN" + + "SOR\020\n\022\023\n\017VARIABLE_FORMAT\020\013\022\016\n\nINITMETHOD", + "\020\014\022\n\n\006MODULE\020\r\022\022\n\016NAME_ATTR_LIST\020\016\022\017\n\013AR" + + "RAY_VALUE\020\017\022\017\n\013DATA_FORMAT\020\020\022\n\n\006CUSTOM\020\021" + + "b\006proto3" }; com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { @@ -19047,7 +19239,7 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors( internal_static_serialization_BigDLTensor_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( internal_static_serialization_BigDLTensor_descriptor, - new java.lang.String[] { "Datatype", "Size", "Stride", "Offset", "Dimension", "NElements", "IsScalar", "Storage", "Id", }); + new java.lang.String[] { "Datatype", "Size", "Stride", "Offset", "Dimension", "NElements", "IsScalar", "Storage", "Id", "TensorType", }); internal_static_serialization_TensorStorage_descriptor = getDescriptor().getMessageTypes().get(3); internal_static_serialization_TensorStorage_fieldAccessorTable = new diff --git a/scala/dllib/src/main/resources/serialization/bigdl.proto b/scala/dllib/src/main/resources/serialization/bigdl.proto index 0e83beffa94..599eacc7a37 100644 --- a/scala/dllib/src/main/resources/serialization/bigdl.proto +++ b/scala/dllib/src/main/resources/serialization/bigdl.proto @@ -50,6 +50,11 @@ enum InputDataFormat { NHWC = 1; } +enum TensorType { + DENSE = 0; + QUANT = 1; +} + message InitMethod { InitMethodType methodType = 1; repeated double data = 2; @@ -64,6 +69,7 @@ message BigDLTensor{ bool isScalar = 7; // is scalar tensor TensorStorage storage = 8; // reference to storage int32 id = 9; // tensor ID, used for tensor sharing + TensorType tensorType = 10; } message TensorStorage { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala index 90125225bf1..663e146cb98 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala @@ -268,6 +268,8 @@ private[bigdl] class QuantizedTensor[T: ClassTag]( this } + override def getTensorNumeric(): TensorNumeric[T] = ev + @throws(classOf[IOException]) private def readObject(in: ObjectInputStream): Unit = { in.defaultReadObject() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index 90841769e5a..f224c401fed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -544,14 +544,6 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def save(path: String, overWrite: Boolean): this.type = throw new UnsupportedOperationException(errorString) - /** - * Return tensor numeric - * - * @return - */ - override def getTensorNumeric(): TensorNumeric[T] = - throw new UnsupportedOperationException(errorString) - // scalastyle:off methodName /** * Add all elements of this with value not in place. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index 15300ba317a..09229088187 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -22,8 +22,9 @@ import scala.reflect.runtime.universe import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.{NCHW, NHWC} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.quantized._ import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer, Regularizer} -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{DenseType, QuantizedTensor, QuantizedType, Tensor, Storage} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericShort, NumericString} import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString @@ -258,6 +259,7 @@ object DataConverter extends DataConverter{ return storages.get(tensorId).get.asInstanceOf[AnyRef] } val dataType = serializedTensor.getDatatype + val tensorType = serializedTensor.getTensorType val sizes = serializedTensor.getSizeList.asScala.toArray.map(_.intValue()) val strides = serializedTensor.getStrideList.asScala.toArray.map(_.intValue()) val offSet = serializedTensor.getOffset @@ -269,15 +271,66 @@ object DataConverter extends DataConverter{ } else { null } + + def quant(): Tensor[T] = { + val bytes = serializedStorage.getBytesDataList.asScala.toArray.head.toByteArray + val serializedParams = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) + val paramsNum = serializedParams.head + val paramsArray = serializedParams.slice(1, paramsNum + 1) + val descTypeEnum = serializedParams(1 + paramsNum) + + val start = paramsNum + 2 // params number indicator + params number + desc type + + val length = if (sizes.length == 1) { + 1 // if the size is 1, means it's a vector + } else { + sizes(0) + } + val max = new Array[T](length) + val min = new Array[T](length) + val sum = new Array[T](length) + + dataType match { + case DataType.FLOAT => + val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) + var i = 0 + while (i < length) { + max(i) = ev.fromType[Float](data(i)) + min(i) = ev.fromType[Float](data(i + length)) + sum(i) = ev.fromType[Float](data(i + 2 * length)) + i += 1 + } + } + + var params: DescParams = null + + descTypeEnum match { + case 0 => + params = ConvDataParams(paramsArray) + case 1 => + params = ConvWeightParams(paramsArray) + case 2 => + params = LinearDataParams(paramsArray) + case 3 => + params = LinearWeightParams(paramsArray) + } + + QuantizedTensor[T](bytes, max, min, sum, sizes, params) + } + val tensor = dataType match { case DataType.FLOAT => - val storage : Storage[Float] = if (created == null ) { - val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) - val newStorage = Storage[Float](data) - storages(storageId) = newStorage - newStorage - } else created.asInstanceOf[Storage[Float]] - Tensor[Float](storage, offSet, sizes, strides) + tensorType match { + case TensorType.DENSE => + val storage : Storage[Float] = if (created == null ) { + val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) + val newStorage = Storage[Float](data) + storages(storageId) = newStorage + newStorage + } else created.asInstanceOf[Storage[Float]] + Tensor[Float](storage, offSet, sizes, strides) + case TensorType.QUANT => quant() + } case DataType.DOUBLE => val storage : Storage[Double] = if (created == null ) { val data = serializedStorage.getDoubleDataList.asScala.toArray.map(_.doubleValue()) @@ -352,20 +405,53 @@ object DataConverter extends DataConverter{ tensorBuilder: BigDLTensor.Builder, tensor: Tensor[_]): Unit = { val tensorNumeric = tensor.getTensorNumeric() val storageType = context.storageType - val tensorStorage = tensor.storage() - val storageId = System.identityHashCode(tensor.storage().array()) + + val storageId = tensor.getTensorType match { + case DenseType => + System.identityHashCode(tensor.storage().array()) + case QuantizedType => + System.identityHashCode(tensor.asInstanceOf[QuantizedTensor[T]].getStorage) + } + val storages = context.storages if (storageType == ProtoStorageType) { if (storages.contains(storageId)) { - tensorBuilder.setStorage(storages.get(storageId).get - .asInstanceOf[TensorStorage]) + val storage = storages(storageId).asInstanceOf[TensorStorage] + tensorBuilder.setStorage(storage) + // we should set back the datatype from existed storage + tensorBuilder.setDatatype(storage.getDatatype) } else { val storageBuilder = TensorStorage.newBuilder if (tensorNumeric == NumericFloat) { tensorBuilder.setDatatype(DataType.FLOAT) storageBuilder.setDatatype(DataType.FLOAT) - tensor.storage().array().asInstanceOf[Array[Float]]. - foreach(data => storageBuilder.addFloatData(data)) + tensor.getTensorType match { + case DenseType => + tensor.storage().array().asInstanceOf[Array[Float]]. + foreach(data => storageBuilder.addFloatData(data)) + case QuantizedType => + val quantTensor = tensor.asInstanceOf[QuantizedTensor[Float]] + val bytes = quantTensor.getStorage + val bs = ByteString.copyFrom(bytes) + storageBuilder.addBytesData(bs) + + // max, min, and sum + quantTensor.maxOfRow.foreach(data => storageBuilder.addFloatData(data)) + quantTensor.minOfRow.foreach(data => storageBuilder.addFloatData(data)) + quantTensor.sumOfRow.foreach(data => storageBuilder.addFloatData(data)) + + // params and desc type + val params = quantTensor.params.array + storageBuilder.addIntData(params.length) + params.foreach(param => storageBuilder.addIntData(param.asInstanceOf[Int])) + + quantTensor.params.getType match { + case ConvData => storageBuilder.addIntData(0) + case ConvWeight => storageBuilder.addIntData(1) + case LinearData => storageBuilder.addIntData(2) + case LinearWeight => storageBuilder.addIntData(3) + } + } } else if (tensorNumeric == NumericDouble) { tensorBuilder.setDatatype(DataType.DOUBLE) storageBuilder.setDatatype(DataType.DOUBLE) @@ -431,17 +517,21 @@ object DataConverter extends DataConverter{ attributeBuilder.setTensorValue(storages.get(tensorId).get .asInstanceOf[BigDLTensor]) } else { - val tensorNumeric = tensor.getTensorNumeric() - val offSet = tensor.storageOffset() val totalElement = tensor.nElement() val dimension = tensor.dim() - val isScalar = tensor.isScalar val tensorBuilder = BigDLTensor.newBuilder tensorBuilder.setId(tensorId) tensorBuilder.setDimension(dimension) - tensorBuilder.setOffset(offSet) tensorBuilder.setNElements(totalElement) - tensorBuilder.setIsScalar(isScalar) + tensor.getTensorType match { + case DenseType => + tensorBuilder.setOffset(tensor.storageOffset()) + tensorBuilder.setIsScalar(tensor.isScalar) + tensorBuilder.setTensorType(TensorType.DENSE) + case QuantizedType => + tensorBuilder.setTensorType(TensorType.QUANT) + } + tensor.size().foreach(size => tensorBuilder.addSize(size)) tensor.stride().foreach(stride => tensorBuilder.addStride(stride)) setStorage(context, tensorBuilder, tensor) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index 3cec64c2fcb..893a82542f2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -20,8 +20,9 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.VariableFormat.{Default, ONE_D} import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.{NCHW, NHWC} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn.quantized.{LinearWeight, LinearWeightParams} import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer, Regularizer} -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Tensor, Storage} import org.scalatest.{FlatSpec, Matchers} import serialization.Bigdl.AttrValue @@ -544,7 +545,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ retrievedValue should be (arry) } - "Array of Tensor conversion " should " work properly" in { + "Array of Tensor conversion" should "work properly" in { val tensor1 = Tensor(2, 3).apply1(_ => Random.nextFloat()) val tensor2 = Tensor(2, 3).apply1(_ => Random.nextFloat()) val tensorArray = Array(tensor1, tensor2) @@ -666,5 +667,50 @@ class DataConverterSpec extends FlatSpec with Matchers{ } + "QuantizedTensor" should "work properly" in { + val bytes = new Array[Byte](5) + val min = Array[Float]('H') + val max = Array[Float]('O') + val sum = Array[Float]("HELLO".sum) + "HELLO".zipWithIndex.foreach(x => bytes(x._2) = x._1.toByte) + bytes.foreach(x => println(x.toChar)) + val tensor = QuantizedTensor[Float](bytes, max, min, sum, Array(1, 5), LinearWeightParams(1, 5)) + map.clear() + val attriBulder = AttrValue.newBuilder + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder, + tensor, ModuleSerializer.tensorType) + val attr = attriBulder.build + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, + ProtoStorageType), attr) + attr.getDataType should be (DataType.TENSOR) + + retrievedValue.hashCode() should be (tensor.hashCode()) + } + + "Array of QuantizedTensor" should "work properly" in { + val bytes = new Array[Byte](5) + val min = Array[Float]('H') + val max = Array[Float]('O') + val sum = Array[Float]("HELLO".sum) + "HELLO".zipWithIndex.foreach(x => bytes(x._2) = x._1.toByte) + bytes.foreach(x => println(x.toChar)) + val tensor1 = QuantizedTensor[Float](bytes, max, min, sum, Array(1, 5), + LinearWeightParams(1, 5)) + val tensor2 = QuantizedTensor[Float](bytes, max, min, sum, Array(1, 5), + LinearWeightParams(1, 5)) + val array = new Array[QuantizedTensor[Float]](2) + array(0) = tensor1 + array(1) = tensor2 + + map.clear() + val attriBulder = AttrValue.newBuilder + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder, + array, universe.typeOf[Array[QuantizedTensor[Float]]]) + val attr = attriBulder.build + map.clear() + val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, + ProtoStorageType), attr) + } } From 87b9f1c294b33c00d9925a32fab2b58cdddd74cf Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Oct 2017 04:21:36 -0400 Subject: [PATCH 0433/1065] feat: serilization for quantized modules (#1613) * feat: serilization for quantized modules All quantized modules extending QuantModule, which has an empty Tensor for gradient. And the object mixes QuantSerializer for protobuf supporting. * refactor: serialization api changes --- .../dllib/nn/quantized/QuantSerializer.scala | 104 ++++++++++++++++++ .../dllib/nn/quantized/QuantizedModule.scala | 27 +++++ 2 files changed, 131 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizedModule.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala new file mode 100644 index 00000000000..6f93a84a1fe --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala @@ -0,0 +1,104 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.DataConverter.TensorConverter +import com.intel.analytics.bigdl.utils.serializer._ +import serialization.Bigdl.{AttrValue, BigDLModule} + +import scala.reflect.ClassTag + +trait QuantSerializer extends ModuleSerializable { + def serializeWeight[T: ClassTag](context: SerializeContext[T], + modelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit + + def serializeBias[T: ClassTag](context: SerializeContext[T], + modelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val moduleData = context.moduleData + val paramTable : Table = moduleData.module.getParametersTable() + val moduleName = moduleData.module.getName() + + if (paramTable != null && paramTable.contains(moduleName)) { + val modulePramTable: Table = paramTable(moduleName) + val bias: Tensor[T] = if (modulePramTable.contains("bias")) { + modulePramTable("bias") + } else { + null + } + + if (bias != null) { + val biasAttr = AttrValue.newBuilder + TensorConverter.setAttributeValue(context, biasAttr, bias) + modelBuilder.setBias(biasAttr.getTensorValue) + } + } + } + + def serializeOthers[T: ClassTag](context: SerializeContext[T], + modelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + } + + def loadWeight[T: ClassTag](context: DeserializeContext, + module: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit + + def loadBias[T: ClassTag](context: DeserializeContext, + moduleData: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit = { + val moduleName = moduleData.module.getName() + val paramTable : Table = moduleData.module.getParametersTable + if (paramTable != null && paramTable.contains(moduleName)) { + val modulePramTable : Table = paramTable(moduleName) + val bias : Tensor[T] = if (modulePramTable.contains("bias")) { + modulePramTable("bias") + } else { + null + } + + if (bias != null) { + val attrValue = AttrValue.newBuilder + attrValue.setTensorValue(context.bigdlModule.getBias) + val bias = TensorConverter.getAttributeValue(context, attrValue.build) + modulePramTable("bias").asInstanceOf[Tensor[T]].copy(bias.asInstanceOf[Tensor[T]]) + } + } + } + + def loadOthers[T: ClassTag](context: DeserializeContext, + module: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit = { + } + + override protected def copyFromBigDL[T: ClassTag](context: SerializeContext[T], + modelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val storageType = context.storageType + if (storageType == ProtoStorageType) { + serializeWeight(context, modelBuilder) + serializeBias(context, modelBuilder) + serializeOthers(context, modelBuilder) + } else { + throw new IllegalArgumentException(s"$storageType not supported!") + } + } + + override protected def copy2BigDL[T: ClassTag](context: DeserializeContext, module: ModuleData[T]) + (implicit ev: TensorNumeric[T]): Unit = { + loadWeight(context, module) + loadBias(context, module) + loadOthers(context, module) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizedModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizedModule.scala new file mode 100644 index 00000000000..0b5cb4eec92 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizedModule.scala @@ -0,0 +1,27 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import scala.reflect.ClassTag + +abstract class QuantizedModule[T: ClassTag](length: Int)( + implicit ev: TensorNumeric[T]) extends TensorModule[T] { + val empty: Tensor[T] = Tensor[T](1) +} From 99f773bffd518d15fbdfc2650a412d47a395e71a Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Oct 2017 06:02:05 -0400 Subject: [PATCH 0434/1065] feat: quantized convolution (#1614) * feat: quantized convolution * fix: serialization api changes --- .../analytics/bigdl/dllib/nn/Utils.scala | 89 ++++- .../nn/quantized/SpatialConvolution.scala | 308 ++++++++++++++++++ .../nn/quantized/SpatialConvolutionSpec.scala | 159 +++++++++ 3 files changed, 547 insertions(+), 9 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolutionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index 9b1576bffd0..58190608ed5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.google.protobuf.ByteString import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} @@ -62,7 +62,7 @@ object Utils { /** * Resize table target as table src. - * + * * @param target * @param src */ @@ -106,7 +106,7 @@ object Utils { /** * Apply function 'func' on all tensor in the table. - * + * * @param x * @param func */ @@ -192,7 +192,7 @@ object Utils { /** * Fill the value to each Tensor in the table recursively - * + * * @param x * @param value */ @@ -331,16 +331,22 @@ object Utils { kW: Int, padH: Int, padW: Int, - ceilMode: Boolean + ceilMode: Boolean, + dilationHeight: Int = 1, + dilationWidth: Int = 1 ): (Int, Int, Int, Int, Int, Int) = { var oheight = 0 var owidth = 0 + + val dilationKernelHeight = dilationHeight * (kH - 1) + 1 + val dilationKernelWidth = dilationWidth * (kW - 1) + 1 + if (ceilMode) { - oheight = math.ceil(1.0 * (inputHeight - kH + 2*padH) / dH).toInt + 1 - owidth = math.ceil(1.0 * (inputWidth - kW + 2*padW) / dW).toInt + 1 + oheight = math.ceil(1.0 * (inputHeight - dilationKernelHeight + 2*padH) / dH).toInt + 1 + owidth = math.ceil(1.0 * (inputWidth - dilationKernelWidth + 2*padW) / dW).toInt + 1 } else { - oheight = math.floor(1.0 * (inputHeight - kH + 2*padH) / dH).toInt + 1 - owidth = math.floor(1.0 * (inputWidth - kW + 2*padW) / dW).toInt + 1 + oheight = math.floor(1.0 * (inputHeight - dilationKernelHeight + 2*padH) / dH).toInt + 1 + owidth = math.floor(1.0 * (inputWidth - dilationKernelWidth + 2*padW) / dW).toInt + 1 } if (padH != 0 || padW != 0) { @@ -349,4 +355,69 @@ object Utils { } (padH, padH, padW, padW, oheight, owidth) } + + private[nn] def getOutputShape(outputHeight: Int, outputWidth: Int, nOutputPlane: Int, + batchSize: Int = -1, format: DataFormat): Array[Int] = { + format match { + case DataFormat.NCHW => + if (batchSize == -1) { + Array(nOutputPlane, outputHeight, outputWidth) + } else { + Array(batchSize, nOutputPlane, outputHeight, outputWidth) + } + case DataFormat.NHWC => + if (batchSize == -1) { + Array(outputHeight, outputWidth, nOutputPlane) + } else { + Array(batchSize, outputHeight, outputWidth, nOutputPlane) + } + + } + } + + def shuffle[T: ClassTag](src: Tensor[T], permutation: Array[Int], buffer: Tensor[T] = null)( + implicit ev: TensorNumeric[T]): Tensor[T] = { + require(permutation.length == src.nDimension, + s"permutation length should be same as tensor dimension") + require(permutation.min >= 0 && permutation.max <= src.size().max, + s"permutation min value should be between 0 and ${src.size().max}") + require(permutation.distinct.size == src.nDimension, s"permutation has duplicated input") + + var i = 0 + val outSize = new Array[Int](src.nDimension) + while (i < permutation.length) { + outSize(i) = src.size(permutation(i)) + i += 1 + } + + val out = if (buffer == null) { + Tensor[T]() + } else { + buffer + } + + out.resize(outSize) + + i = 0 + val numOfElements = src.nElement() + while (i < numOfElements) { + var srcIndex = 0 + var tmp = i + + var j = 1 + while (j <= src.nDimension) { + val curDim = tmp / out.stride(j) + tmp %= out.stride(j) + + srcIndex += curDim * src.stride(permutation(j - 1)) + + j += 1 + } + + out.storage().array()(i) = src.storage().array()(srcIndex) + i += 1 + } + + out + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala new file mode 100644 index 00000000000..36c9aa7e2cc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -0,0 +1,308 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.bigquant.BigQuant +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.ErrorInfo +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, Initializable} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleData, SerializeContext} +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.runtime.universe +import scala.reflect.ClassTag +import serialization.Bigdl.{AttrValue, BigDLModule} + +@SerialVersionUID(- 8008252944905538960L) +private[bigdl] class SpatialConvolution[T: ClassTag]( + val nInputPlane: Int, // The number of expected input planes in the image given into forward() + val nOutputPlane: Int, // The number of output planes the convolution layer will produce. + val kernelW: Int, // The kernel width of the convolution + val kernelH: Int, // The kernel height of the convolution + val strideW: Int = 1, // The step of the convolution in the width dimension. + val strideH: Int = 1, // The step of the convolution in the height dimension + val padW: Int = 0, // The additional zeros added per width to the input planes. + val padH: Int = 0, // The additional zeros added per height to the input planes. + val nGroup: Int = 1, // Kernel group number + val format: DataFormat = DataFormat.NCHW +)(implicit ev: TensorNumeric[T]) extends QuantizedModule[T](nOutputPlane) with Initializable { + + require(nInputPlane % nGroup == 0, "Number of input channels should be multiples of group.") + require(nOutputPlane % nGroup == 0, "Number of output channels should be multiples of group.") + + var weight: Array[Tensor[T]] = null + private val data: QuantizedTensor[T] = QuantizedDummyTensor[T]() + val bias: Tensor[T] = Tensor[T](nOutputPlane) + + val quantFormat: Int = if (format == DataFormat.NCHW) { + BigQuant.NCHW + } else { + BigQuant.NHWC + } + + val dilationHeight = 1 + val dilationWidth = 1 + + protected def initWeightAndBias(weightFP32: Tensor[T], biasFP32: Tensor[T]): this.type = { + if (biasFP32 != null) { + bias.copy(biasFP32) + } else { + bias.fill(ev.fromType(0)) // TODO bias may be null, at that time, we should not initialize it + } + + // dilated convolution has no group option + val weightTmp = if (format == DataFormat.NHWC) { + val groupWeight = weightFP32.view(Array(nGroup, kernelH, kernelW, nInputPlane / nGroup, + nOutputPlane / nGroup)) + + nn.Utils.shuffle(groupWeight, Array(1, 5, 2, 3, 4)) + } else { + weightFP32.view(Array(nGroup, nOutputPlane / nGroup, nInputPlane / nGroup, + kernelH, kernelW)) + } + + weight = new Array[Tensor[T]](nGroup) + val params = ConvWeightParams(nOutputPlane / nGroup, nInputPlane / nGroup, kernelH, kernelW, + quantFormat) + for (i <- 1 to nGroup) { + val groupWeight = weightTmp.select(1, i) + ev.getType() match { + case FloatType => + weight(i - 1) = QuantizedTensor[T](groupWeight, params) + case _ => throw new UnsupportedOperationException(s"Only support Float for quantized model") + } + } + + this + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 3 || input.dim() == 4, + "quantized.SpatialConvolution: " + ErrorInfo.constrainInputAs3DOrBatch) + require(input.isContiguous()) + + val (dimHeight, dimWidth, channelDim) = format.getHWCDims(input.dim()) + require(input.size(channelDim) == nInputPlane, s"input channel size " + + s"${input.size(channelDim)} is not the same as nInputPlane $nInputPlane") + + val inputWidth = input.size(dimWidth) + val inputHeight = input.size(dimHeight) + + val (padTop, padBottom, padLeft, padRight, outputHeight, outputWidth) = + if (padW == -1 && padH == -1) { + nn.Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, + kernelW) + } else { + nn.Utils.getOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, + kernelH, kernelW, padH, padW, ceilMode = false, dilationWidth = dilationWidth, + dilationHeight = dilationHeight) + } + + val batchSize = if (input.dim() == 3) { + output.resize(nn.Utils.getOutputShape(outputHeight, outputWidth, nOutputPlane, + format = format)) + 1 // 3D input, batchSize set to 1 + } else { + val batch = input.size(1) + output.resize(nn.Utils.getOutputShape(outputHeight, outputWidth, nOutputPlane, batch, format)) + batch + } + + val params = ConvDataParams(nInputPlane / nGroup, kernelH, kernelW, + strideH, strideW, padTop, padLeft, dilationHeight, dilationWidth, 1, + inputHeight, inputWidth) + + if (data.params == null || data.params != params) { + data.release() + data.set(QuantizedTensor[T](input.size(), params)) + } + + ev.getType() match { + case FloatType => + var batch = 0 + while (batch < batchSize) { + im2ColAndGemmFloat(batch) + batch += 1 + } + case _ => throw new UnsupportedOperationException(s"Only support Float for quantized model") + } + + @inline def im2ColAndGemmFloat(batch: Int): Unit = { + val batchOutput = output.select(1, batch + 1) + val batchInput = input.select(1, batch + 1) + val channel = if (input.dim() == 3) { channelDim } else { channelDim - 1 } + + var group = 0 + while (group < nGroup) { + val groupBatchOutput = batchOutput.narrow(channel, group * nOutputPlane / nGroup + 1, + nOutputPlane / nGroup) + val groupBatchInput = batchInput.narrow(channel, group * nInputPlane / nGroup + 1, + nInputPlane / nGroup) + val groupWeight = weight(group).asInstanceOf[QuantizedTensor[T]] + val offset = 0 + + groupIm2ColGemm(groupBatchInput, groupBatchOutput, groupWeight, offset) + + group += 1 + } + } + + @inline def groupIm2ColGemm(input: Tensor[T], output: Tensor[T], + weight: QuantizedTensor[T], offset: Int): Unit = { + val inputArray = input.storage().array().asInstanceOf[Array[Float]] + val inputOffset = input.storageOffset() - 1 + + val outputArray = output.storage().array().asInstanceOf[Array[Float]] + val outputOffset = output.storageOffset() - 1 + + val biasArray = bias.storage().array().asInstanceOf[Array[Float]] + val biasOffset = bias.storageOffset() - 1 + offset + + val weightSumArray = weight.sumOfRow.asInstanceOf[Array[Float]] + val weightSumOffset = offset + + BigQuant.ConvDataInit( + data.getNativeStorage, inputArray, inputOffset, + nInputPlane / nGroup, kernelH, kernelW, strideH, strideW, padTop, padLeft, + dilationHeight, dilationWidth, 1, inputHeight, inputWidth, QuantParams.THRESHOLD, + quantFormat) + + BigQuant.MixPrecisionGEMM( + quantFormat, weight.getNativeStorage, data.getNativeStorage, + outputArray, outputOffset, weightSumArray, weightSumOffset, + biasArray, biasOffset, 1, nOutputPlane / nGroup, outputHeight, outputWidth, + QuantParams.FAULT_TOLERANCE) + } + + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"Doesn't updateGradInput for quantized model") + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (weight :+ bias, Array(empty, empty)) + } + + override def getParametersTable(): Table = { + T(getName() -> T("weight" -> weight, "bias" -> bias)) + } + + override def equals(obj: Any): Boolean = { + if (!super.equals(obj)) { + return false + } + if (!obj.isInstanceOf[SpatialConvolution[T]]) { + return false + } + val other = obj.asInstanceOf[SpatialConvolution[T]] + if (this.eq(other)) { + return true + } + + nInputPlane == other.nInputPlane && + nOutputPlane == other.nOutputPlane && + kernelW == other.kernelW && + kernelH == other.kernelH && + strideW == other.strideW && + strideH == other.strideH && + padW == other.padW && + padH == other.padH && + nGroup == other.nGroup && + weight == other.weight && + bias == other.bias + } + + override def hashCode(): Int = { + val seed = 37 + var hash = super.hashCode() + hash = hash * seed + nInputPlane.hashCode() + hash = hash * seed + nOutputPlane.hashCode() + hash = hash * seed + kernelW.hashCode() + hash = hash * seed + kernelH.hashCode() + hash = hash * seed + strideW.hashCode() + hash = hash * seed + strideH.hashCode() + hash = hash * seed + padW.hashCode() + hash = hash * seed + padH.hashCode() + hash = hash * seed + bias.hashCode() + hash = hash * seed + weight.hashCode() + + hash + } + + override def clearState() : this.type = { + super.clearState() + this + } + + override def toString(): String = { + s"quantized.SpatialConvolution($nInputPlane -> $nOutputPlane, $kernelW x" + + s" $kernelH, $strideW, $strideH, $padW, $padH, $nGroup)" + } + + def release(): Unit = { + weight.foreach(_.asInstanceOf[QuantizedTensor[T]].release()) + data.release() + } +} + +object SpatialConvolution extends QuantSerializer { + def apply[@specialized(Float) T: ClassTag]( + nInputPlane: Int, + nOutputPlane: Int, + kernelW: Int, + kernelH: Int, + strideW: Int = 1, + strideH: Int = 1, + padW: Int = 0, + padH: Int = 0, + nGroup: Int = 1, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + format: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]): SpatialConvolution[T] = { + val conv = new SpatialConvolution[T](nInputPlane, nOutputPlane, kernelW, kernelH, + strideW, strideH, padW, padH, nGroup, format) + conv.initWeightAndBias(initWeight, initBias) + } + + override def serializeWeight[T: ClassTag](context: SerializeContext[T], + modelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val module = context.moduleData.module + val conv = module.asInstanceOf[SpatialConvolution[T]] + val weightBuilder = AttrValue.newBuilder + ev.getType() match { + case FloatType => + DataConverter.setAttributeValue(context, weightBuilder, conv.weight, + universe.typeOf[Array[Tensor[Float]]]) + case _ => throw new UnsupportedOperationException(s"Only support Float for quantized model") + } + modelBuilder.putAttr("weights", weightBuilder.build) + } + + override def loadWeight[T: ClassTag](context: DeserializeContext, + moduleData: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit = { + val conv = moduleData.module.asInstanceOf[SpatialConvolution[T]] + val attrMap = context.bigdlModule.getAttrMap + conv.weight = DataConverter.getAttributeValue(context, attrMap.get("weights")) + .asInstanceOf[Array[Tensor[T]]] + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolutionSpec.scala new file mode 100644 index 00000000000..e294af8eaaa --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolutionSpec.scala @@ -0,0 +1,159 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.{Reshape, SpatialConvolution => NNSpatialConvolution} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import org.scalatest.{FlatSpec, Matchers, ParallelTestExecution} + +@com.intel.analytics.bigdl.tags.Parallel +class SpatialConvolutionSpec extends FlatSpec with Matchers with ParallelTestExecution { + // Notice: + // 1. if we set input channel more than 1, the result will be not the same + // 2. multi groups can't work + val testCases = List( + TestCase(1, 1, 3, 3, 1, 1, 2, 2, 1, 1, 0, 0), + TestCase(1, 1, 38, 38, 1, 2, 3, 3, 1, 1, 0, 0), + TestCase(1, 2, 38, 38, 2, 2, 3, 3, 1, 1, 0, 0), + TestCase(2, 1, 38, 38, 1, 84, 1, 1, 1, 1, 0, 0), + TestCase(11, 512, 7, 7, 1, 4096, 7, 7, 1, 1, 0, 0) + ) + + for (test <- testCases) { + val start = s"A bigquant.SpatialConvolution $test" + start should "generate the same result with nn.SpatialConvolution" in { + val weight = Tensor(test.group, test.outputChannel / test.group, + test.inputChannel / test.group, test.kernelHeight, test.kernelWidth).fill(1.0f) + val bias = Tensor(test.outputChannel).fill(0f) + val input = Tensor().resize(Array(test.batchSize, test.inputChannel, + test.inputHeight, test.inputWidth)).fill(1.0f) + + val nnConv = NNSpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, initWeight = weight, initBias = bias) + + println(nnConv) + + val quantizedConv = SpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, nnConv.weight, nnConv.bias) + + nnConv.updateOutput(input) + + quantizedConv.updateOutput(input) + + + nnConv.output shouldEqual quantizedConv.output + + quantizedConv.release() + } + } + + "A bigquant.SpatialConvolution with dynamic input size" should "work correctly" in { + val test = testCases(1) + val weight = Tensor(test.group, test.outputChannel / test.group, + test.inputChannel / test.group, test.kernelHeight, test.kernelWidth).fill(1.0f) + val bias = Tensor(test.outputChannel).fill(0f) + + val nnConv = NNSpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, initWeight = weight, initBias = bias) + + println(nnConv) + + val quantizedConv = SpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, nnConv.weight, nnConv.bias) + + for (i <- 1 until 5) { + val input = Tensor().resize(Array(test.batchSize, test.inputChannel, + test.inputHeight * i * 10, test.inputWidth * i * 10)).fill(1.0f) + + nnConv.updateOutput(input) + quantizedConv.updateOutput(input) + + + nnConv.output shouldEqual quantizedConv.output + } + + quantizedConv.release() + } + + "A bigquant.SpatialConvolution with NHWC" should "work correctly" in { + val test = testCases(3) + val nnConv = NNSpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, format = DataFormat.NHWC) + nnConv.weight.fill(1.0f) + nnConv.bias.fill(0f) + + println(nnConv) + + val quantizedConv = SpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, nnConv.weight, nnConv.bias, + format = DataFormat.NHWC) + + for (i <- 1 until 5) { + val input = Tensor().resize(Array(test.batchSize, + test.inputHeight, test.inputWidth, test.inputChannel)).fill(1.0f) + + nnConv.updateOutput(input) + quantizedConv.updateOutput(input) + + + nnConv.output shouldEqual quantizedConv.output + } + + quantizedConv.release() + } + + "A bigquant.SpatialConvolution with 3D input" should "work correctly" in { + val test = testCases(1) + val weight = Tensor(test.group, test.outputChannel / test.group, + test.inputChannel / test.group, test.kernelHeight, test.kernelWidth).fill(1.0f) + val bias = Tensor(test.outputChannel).fill(0f) + + val nnConv = NNSpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, initWeight = weight, initBias = bias, + format = DataFormat.NCHW) + + println(nnConv) + + val quantizedConv = SpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, nnConv.weight, nnConv.bias, + format = DataFormat.NCHW) + + val input = Tensor().resize(Array(test.batchSize, + test.inputHeight, test.inputWidth)).fill(1.0f) + + nnConv.updateOutput(input) + quantizedConv.updateOutput(input) + + + nnConv.output shouldEqual quantizedConv.output + + quantizedConv.release() + } + case class TestCase(batchSize: Int, inputChannel: Int, inputHeight: Int, inputWidth: Int, + group: Int, outputChannel: Int, kernelHeight: Int, kernelWidth: Int, + strideHeight: Int, strideWidth: Int, padHeight: Int, padWidth: Int) +} From a68f9c6b366f63acb3f0931f1526ef99d926fda4 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Oct 2017 06:45:17 -0400 Subject: [PATCH 0435/1065] feat: quantized linear (#1615) * feat: quantized linear * fix: serialization api changes --- .../bigdl/dllib/nn/quantized/Linear.scala | 181 ++++++++++++++++++ .../bigdl/dllib/nn/quantized/LinearSpec.scala | 55 ++++++ 2 files changed, 236 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/LinearSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala new file mode 100644 index 00000000000..794324ff9e1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala @@ -0,0 +1,181 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.bigquant.BigQuant +import com.intel.analytics.bigdl.nn.ErrorInfo +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag +import serialization.Bigdl.{AttrValue, BigDLModule} + +private[bigdl] class Linear[T: ClassTag]( + val inputSize: Int, + val outputSize: Int, + val withBias: Boolean = true +)(implicit ev: TensorNumeric[T]) extends QuantizedModule[T](outputSize) { + + private val data: QuantizedTensor[T] = QuantizedDummyTensor[T]() + var weight: QuantizedTensor[T] = _ + val bias: Tensor[T] = Tensor[T](outputSize) + + private def initWeightAndBias(weightFP32: Tensor[T], biasFP32: Tensor[T]): this.type = { + if (biasFP32 != null) { + bias.copy(biasFP32) + } else { + bias.fill(ev.fromType(0)) // TODO bias may be null, at that time, we should not initialize it + } + + val weightFP32Tmp = weightFP32.view(Array(outputSize, inputSize)) + val params = LinearWeightParams(outputSize, inputSize) + weight = QuantizedTensor[T](weightFP32Tmp, params) + + this + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 1 || input.dim() == 2, + "bigquant.Linear: " + ErrorInfo.constrainInputAsVectorOrBatch) + + val batchSize = if (input.dim() == 1) { + output.resize(Array(outputSize)) // TODO + 1 + } else { + output.resize(Array(input.size(1), outputSize)) + require(inputSize == input.size(2), s"dimension error") + input.size(1) + } + + val params = LinearDataParams(batchSize, inputSize) + if (data.params == null || data.params != params) { + data.release() + data.set(QuantizedTensor[T](input.size(), params)) + } + + ev.getType() match { + case FloatType => + val src = input.storage().array().asInstanceOf[Array[Float]] + val offset = input.storageOffset() - 1 + + BigQuant.FCDataInit(data.getNativeStorage, src, offset, batchSize, inputSize, + QuantParams.THRESHOLD, BigQuant.NCHW) + + val outputArray = output.storage().array().asInstanceOf[Array[Float]] + val outputOffset = output.storageOffset() - 1 + val weightSumArray = weight.sumOfRow.asInstanceOf[Array[Float]] + val weightSumOffset = 0 + val biasArray = bias.storage().array().asInstanceOf[Array[Float]] + val biasOffset = bias.storageOffset() - 1 + + BigQuant.MixPrecisionGEMM( + BigQuant.NCHW, weight.getNativeStorage, data.getNativeStorage, outputArray, + outputOffset, weightSumArray, weightSumOffset, biasArray, biasOffset, + batchSize, outputSize, 1, 1, + QuantParams.FAULT_TOLERANCE) + + case _ => throw new UnsupportedOperationException(s"Only support Float for quantized model") + } + + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"Doesn't updateGradInput for quantized model") + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (Array(weight, bias), Array(empty, empty)) + } + + override def getParametersTable(): Table = { + T(getName() -> T("weight" -> weight, "bias" -> bias)) + } + + override def equals(obj: Any): Boolean = { + if (!super.equals(obj)) { + return false + } + + if (!obj.isInstanceOf[Linear[T]]) { + return false + } + val other = obj.asInstanceOf[Linear[T]] + if (this.eq(other)) { + return true + } + + weight == other.weight && + bias == other.bias + } + + override def hashCode(): Int = { + val seed = 37 + var hash = super.hashCode() + hash = hash * seed + weight.hashCode() + hash = hash * seed + bias.hashCode() + + hash + } + + override def clearState() : this.type = { + super.clearState() + this + } + + override def toString(): String = { + s"quantized.${getPrintName()}($inputSize -> $outputSize)" + } + + def release(): Unit = { + weight.release() + data.release() + } +} + + +object Linear extends QuantSerializer { + def apply[@specialized(Float, Double) T: ClassTag]( + inputSize: Int, + outputSize: Int, + withBias: Boolean = true, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null + )(implicit ev: TensorNumeric[T]) : Linear[T] = { + val linear = new Linear[T](inputSize, outputSize, withBias) + linear.initWeightAndBias(initWeight, initBias) + } + + override def serializeWeight[T: ClassTag](context: SerializeContext[T], + modelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val linear = context.moduleData.module.asInstanceOf[Linear[T]] + val weightBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, weightBuilder, linear.weight, + ModuleSerializer.tensorType) + modelBuilder.putAttr("weight", weightBuilder.build) + } + + override def loadWeight[T: ClassTag](context: DeserializeContext, + module: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit = { + val linear = module.module.asInstanceOf[Linear[T]] + val attrMap = context.bigdlModule.getAttrMap + linear.weight = DataConverter.getAttributeValue(context, attrMap.get("weight")) + .asInstanceOf[QuantizedTensor[T]] + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/LinearSpec.scala new file mode 100644 index 00000000000..8fd76798bd6 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/LinearSpec.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.nn.{Module, Linear => NNLinear} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers, ParallelTestExecution} + +@com.intel.analytics.bigdl.tags.Parallel +class LinearSpec extends FlatSpec with Matchers with ParallelTestExecution { + val testCases = List( + TestCase(1, 1, 1), + TestCase(3, 3, 4), + TestCase(512, 1024, 32), + TestCase(4, 2302, 1152) + ) + + for (test <- testCases) { + val start = s"A $test" + start should "generate correct output compared with nn.Linear" in { + val weight = Tensor(test.outputSize, test.inputSize).fill(1.0f) + val bias = Tensor(test.outputSize).fill(0f) + val input = Tensor(test.batchSize, test.inputSize).fill(1.0f) + + val nnLinear = NNLinear(test.inputSize, test.outputSize, initWeight = weight, initBias = bias) + val quantizedLinear = Linear(test.inputSize, test.outputSize, initWeight = weight, + initBias = bias) + + nnLinear.updateOutput(input) + quantizedLinear.updateOutput(input) + + nnLinear.output shouldEqual quantizedLinear.output + + quantizedLinear.release() + } + } + + case class TestCase(batchSize: Int, inputSize: Int, outputSize: Int) +} + From 33f191836935f7d2026c93e48eda9451963cf010 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Oct 2017 07:43:28 -0400 Subject: [PATCH 0436/1065] feat: quantized dilated conv (#1617) * feat: quantized dilated conv * fix: serialization api changes --- .../quantized/SpatialDilatedConvolution.scala | 104 ++++++++++++++++++ .../SpatialDilatedConvolutionSpec.scala | 55 +++++++++ 2 files changed, 159 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolutionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala new file mode 100644 index 00000000000..9108d3c972e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala @@ -0,0 +1,104 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} +import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleData, SerializeContext} +import serialization.Bigdl.{AttrValue, BigDLModule} + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + +@SerialVersionUID(- 8572055756810843156L) +private[bigdl] class SpatialDilatedConvolution[T: ClassTag]( + nInputPlane: Int, // The number of expected input planes in the image given into forward() + nOutputPlane: Int, // The number of output planes the convolution layer will produce. + kernelW: Int, // The kernel width of the convolution + kernelH: Int, // The kernel height of the convolution + strideW: Int = 1, // The step of the convolution in the width dimension. + strideH: Int = 1, // The step of the convolution in the height dimension + padW: Int = 0, // The additional zeros added per width to the input planes. + padH: Int = 0, // The additional zeros added per height to the input planes. + val dilationW: Int = 1, + val dilationH: Int = 1, + format: DataFormat = DataFormat.NCHW +)(implicit ev: TensorNumeric[T]) extends SpatialConvolution[T]( + nInputPlane, + nOutputPlane, + kernelW, + kernelH, + strideW, + strideH, + padW, + padH, + format = format +) { + override val dilationWidth: Int = dilationW + override val dilationHeight: Int = dilationH + + override def toString(): String = { + s"quantized.SpatialDilatedConvolution($nInputPlane -> $nOutputPlane, $kernelW x" + + s" $kernelH, $strideW, $strideH, $padW, $padH, $dilationW, $dilationH)" + } +} + +object SpatialDilatedConvolution extends QuantSerializer { + def apply[T: ClassTag]( + nInputPlane: Int, + nOutputPlane: Int, + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0, + dilationW: Int = 1, + dilationH: Int = 1, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + format: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]) : SpatialDilatedConvolution[T] = { + val conv = new SpatialDilatedConvolution[T](nInputPlane, nOutputPlane, kW, kH, dW, dH, + padW, padH, dilationW, dilationH, format = format) + conv.initWeightAndBias(initWeight, initBias) + } + + override def serializeWeight[T: ClassTag](context: SerializeContext[T], + modelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val module = context.moduleData.module + val conv = module.asInstanceOf[SpatialConvolution[T]] + val weightBuilder = AttrValue.newBuilder + ev.getType() match { + case FloatType => + DataConverter.setAttributeValue(context, weightBuilder, conv.weight, + universe.typeOf[Array[Tensor[Float]]]) + case _ => throw new UnsupportedOperationException(s"Only support Float for quantized model") + } + modelBuilder.putAttr("weights", weightBuilder.build) + } + + override def loadWeight[T: ClassTag](context: DeserializeContext, + moduleData: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit = { + val conv = moduleData.module.asInstanceOf[SpatialConvolution[T]] + val attrMap = context.bigdlModule.getAttrMap + conv.weight = DataConverter.getAttributeValue(context, attrMap.get("weights")) + .asInstanceOf[Array[Tensor[T]]] + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolutionSpec.scala new file mode 100644 index 00000000000..7fe3af707b4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolutionSpec.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.nn + +class SpatialDilatedConvolutionSpec extends FlatSpec with Matchers { + "A SpatialDilatedConvolution" should "work correctly" in { + val test = TestCase(1, 2, 3, 3, 1, 1, 2, 2, 1, 1, 0, 0, 2, 2) + + val nnConv = nn.SpatialDilatedConvolution[Float](test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.dilatedW, test.dilatedH) + nnConv.weight.fill(1.0f) + nnConv.bias.fill(0.0f) + val quantizedConv = SpatialDilatedConvolution[Float](test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.dilatedW, test.dilatedH, + initWeight = nnConv.weight, initBias = nnConv.bias) + + val input = Tensor[Float](test.batchSize, test.inputChannel, test.inputWidth, + test.inputHeight).fill(1.0f) + + val nnConv2 = nn.SpatialConvolution[Float](test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth) + nnConv2.weight.fill(1.0f) + nnConv2.bias.fill(0.0f) + + val output1 = nnConv.forward(input) + val output2 = quantizedConv.forward(input) + output1 should be (output2) + } + + case class TestCase(batchSize: Int, inputChannel: Int, inputHeight: Int, inputWidth: Int, + group: Int, outputChannel: Int, kernelHeight: Int, kernelWidth: Int, + strideHeight: Int, strideWidth: Int, padHeight: Int, padWidth: Int, dilatedW: Int, + dilatedH: Int) +} From 673005d174b7da3255899d81ad57f125f3ff48b5 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Oct 2017 08:20:34 -0400 Subject: [PATCH 0437/1065] feat: add quantized conv and linear serialization tests (#1616) * feat: add quantized conv and linear serialization tests * fix: add serialization for quantized modules --- .../utils/serializer/ModuleSerializer.scala | 7 ++- .../serializer/ModuleSerializerSpec.scala | 58 +++++++++++++++++++ 2 files changed, 64 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 7bc1e9413a6..2896d71a7e1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -190,7 +190,12 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.Transpose", Transpose) registerModule("com.intel.analytics.bigdl.nn.VolumetricMaxPooling", VolumetricMaxPooling) registerModule("com.intel.analytics.bigdl.nn.Echo", Echo) - + registerModule("com.intel.analytics.bigdl.nn.quantized.SpatialConvolution", + quantized.SpatialConvolution) + registerModule("com.intel.analytics.bigdl.nn.quantized.SpatialDilatedConvolution", + quantized.SpatialDilatedConvolution) + registerModule("com.intel.analytics.bigdl.nn.quantized.Linear", + quantized.Linear) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 7ebecf07d55..7c50d379363 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.caffe.CaffeLoader import com.intel.analytics.bigdl.utils.{T, Table} import org.scalatest.{FlatSpec, Matchers} import serialization.Bigdl @@ -1847,6 +1848,63 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } + "bigquant.SpatialConvolution serializer" should "work properly " in { + val nInputPlane = 1 + val nOutputPlane = 1 + val kW = 2 + val kH = 2 + val dW = 1 + val dH = 1 + val padW = 0 + val padH = 0 + + val kernelData = Array( + 2.0f, 3f, + 4f, 5f + ) + + val biasData = Array(0.0f) + + val input = Tensor(1, 1, 3, 3).apply1(_ => Random.nextFloat()) + val weight = Tensor(Storage(kernelData), 1, Array(nOutputPlane, nInputPlane, kH, kW)) + val bias = Tensor(Storage(biasData), 1, Array(nOutputPlane)) + val conv = quantized.SpatialConvolution[Float](nInputPlane, nOutputPlane, + kW, kH, dW, dH, padW, padH, initWeight = weight, initBias = bias) + + + val res1 = conv.forward(input) + + ModulePersister.saveToFile("/tmp/bigquant.conv.bigdl", conv, true) + val loadedConv = ModuleLoader.loadFromFile("/tmp/bigquant.conv.bigdl") + val res2 = loadedConv.forward(input) + res1 should be (res2) + } + + "bigquant.Linear serializer" should "work properly " in { + val outputSize = 2 + val inputSize = 2 + + val kernelData = Array( + 2.0f, 3f, + 4f, 5f + ) + + val biasData = Array(0.0f, 0.1f) + + val input = Tensor(2, 2).apply1(_ => Random.nextFloat()) + val weight = Tensor(Storage(kernelData), 1, Array(outputSize, inputSize)) + val bias = Tensor(Storage(biasData), 1, Array(outputSize)) + val linear = quantized.Linear[Float](outputSize, inputSize, initWeight = weight, + initBias = bias) + + val res1 = linear.forward(input) + + ModulePersister.saveToFile("/tmp/bigquant.linear.bigdl", linear, true) + val loadedLinear = ModuleLoader.loadFromFile("/tmp/bigquant.linear.bigdl") + val res2 = loadedLinear.forward(input) + res1 should be (res2) + } + "Customized Module " should "work properly" in { val testModule = new TestModule(CustomData(1.0)) DataConverter.registerConverter(universe.typeOf[CustomData].toString, TestCustomDataConverter) From 8d26f955dc030717150bfa9f42b2093d735e79ae Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Wed, 11 Oct 2017 08:57:39 +0800 Subject: [PATCH 0438/1065] Refactor Python API: model test to model evaluation (#1641) * Change model.test to model.evaluate in Python * Add model evaluation code in docs and correct some typo * update docs * Remove duplicate evaluate(self) definition from previous two PRs * fix same function name but different arguments for evaluate() --- .../analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala | 2 +- .../com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0672c92eaca..48895670f87 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1426,7 +1426,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab RandomGenerator.RNG.setSeed(seed) } - def modelTest(model: AbstractModule[Activity, Activity, T], + def modelEvaluate(model: AbstractModule[Activity, Activity, T], valRDD: JavaRDD[Sample], batchSize: Int, valMethods: JList[ValidationMethod[T]]) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index f5e2b266284..ecb1559e64b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -230,7 +230,7 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { // TODO: verify the parameters result val parameters = pp.modelGetParameters(trainedModel) // println(parameters) - val testResult = pp.modelTest(trainedModel, + val testResult = pp.modelEvaluate(trainedModel, data.toJavaRDD(), batchSize = 32, valMethods = util.Arrays.asList(new Top1Accuracy())) From 4d789a9988d9e8a1f130603a7fc4f8e439a9f4c6 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Wed, 11 Oct 2017 03:16:48 -0400 Subject: [PATCH 0439/1065] feat: quantize a whole graph/modules (#1618) * feat: quantize a whole graph/modules * feat: python supports * fix: delete unusage --- .../analytics/bigdl/dllib/nn/Graph.scala | 7 + .../analytics/bigdl/dllib/nn/Linear.scala | 11 +- .../bigdl/dllib/nn/SpatialConvolution.scala | 15 +- .../dllib/nn/SpatialDilatedConvolution.scala | 11 +- .../dllib/nn/abstractnn/AbstractModule.scala | 5 + .../dllib/nn/quantized/Quantizable.scala | 28 +++ .../dllib/nn/quantized/Quantization.scala | 18 ++ .../bigdl/dllib/nn/quantized/Quantizer.scala | 133 ++++++++++ .../bigdl/dllib/nn/quantized/Utils.scala | 82 ++++++ .../bigdl/dllib/utils/DirectedGraph.scala | 7 +- .../dllib/utils/python/api/PythonBigDL.scala | 3 + .../dllib/nn/quantized/QuantizableSpec.scala | 235 ++++++++++++++++++ 12 files changed, 549 insertions(+), 6 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizable.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Utils.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizableSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 976b87bcce8..dd10cee8864 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -549,6 +549,13 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], writer.close() this } + + def resetModules(): Unit = { + modules.clear() + modules.appendAll(backGraph.topologySort + .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse + .filter(n => !n.eq(dummyOutput)).map(_.element)) + } } object Graph extends ContainerSerializable { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala index 04604c98634..33fd5d15e2a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} import scala.reflect.ClassTag import RandomGenerator._ +import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer @@ -239,7 +240,7 @@ class Linear[T: ClassTag]( } } -object Linear { +object Linear extends quantized.Quantizable { def apply[@specialized(Float, Double) T: ClassTag]( inputSize: Int, outputSize: Int, @@ -254,4 +255,12 @@ object Linear { new Linear[T](inputSize, outputSize, withBias, wRegularizer, bRegularizer, initWeight, initBias, initGradWeight, initGradBias) } + + override def quantize[T: ClassTag](module: Module[T])( + implicit ev: TensorNumeric[T]): Module[T] = { + val linear = module.asInstanceOf[Linear[T]] + val quantizedLinear = quantized.Linear[T](linear.weight.size(2), linear.weight.size(1), + initWeight = linear.weight, initBias = linear.bias) + quantizedLinear.setName(linear.getName()) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala index d2c2fcc69c6..fb44fb4deb9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala @@ -16,7 +16,9 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, DataFormat, TensorModule} +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, Initializable, TensorModule} +import com.intel.analytics.bigdl.nn.quantized.Quantizable import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ @@ -935,7 +937,7 @@ class SpatialConvolution[T: ClassTag]( } } -object SpatialConvolution { +object SpatialConvolution extends Quantizable { def apply[@specialized(Float, Double) T: ClassTag]( nInputPlane: Int, nOutputPlane: Int, @@ -960,4 +962,13 @@ object SpatialConvolution { strideW, strideH, padW, padH, nGroup, propagateBack, wRegularizer, bRegularizer, initWeight, initBias, initGradWeight, initGradBias, withBias, format) } + + override def quantize[T: ClassTag](module: Module[T])( + implicit ev: TensorNumeric[T]): Module[T] = { + val conv = module.asInstanceOf[SpatialConvolution[T]] + quantized.SpatialConvolution[T]( + conv.nInputPlane, conv.nOutputPlane, conv.kernelW, conv.kernelH, conv.strideW, + conv.strideH, conv.padW, conv.padH, conv.nGroup, initWeight = conv.weight, + initBias = conv.bias, conv.format).setName(conv.getName()) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala index 939f5676eef..51ce5c3aaa9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala @@ -16,13 +16,13 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.{DenseTensorBLAS, DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.{T, Table} - import scala.reflect.ClassTag /** @@ -543,7 +543,7 @@ class SpatialDilatedConvolution[T: ClassTag]( } } -object SpatialDilatedConvolution { +object SpatialDilatedConvolution extends quantized.Quantizable { def apply[@specialized(Float, Double) T: ClassTag]( nInputPlane: Int, nOutputPlane: Int, @@ -562,4 +562,11 @@ object SpatialDilatedConvolution { padW, padH, dilationW, dilationH, wRegularizer, bRegularizer) } + def quantize[T: ClassTag](module: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val conv = module.asInstanceOf[SpatialDilatedConvolution[T]] + quantized.SpatialDilatedConvolution[T]( + conv.nInputPlane, conv.nOutputPlane, conv.kW, conv.kH, conv.dW, + conv.dH, conv.padW, conv.padH, conv.dilationW, conv.dilationW, initWeight = conv.weight, + initBias = conv.bias).setName(conv.getName()) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 0adaac5ba9b..5b09a49911c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -30,6 +30,7 @@ import org.apache.spark.rdd.RDD import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.quantized.Quantization import com.intel.analytics.bigdl.utils.caffe.CaffePersister import com.intel.analytics.bigdl.utils.serializer.ModulePersister import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} @@ -668,5 +669,9 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, ): Array[(ValidationResult, ValidationMethod[T])] = { Validator(this, dataSet).test(vMethods) } + + def quantize(): Module[T] = { + Quantization.quantize(this) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizable.scala new file mode 100644 index 00000000000..b7512ac6180 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizable.scala @@ -0,0 +1,28 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import scala.reflect.ClassTag + +trait Quantizable { + /** + * @param module module will be quantized. we will do nothing by default. + */ + def quantize[T: ClassTag](module: Module[T])(implicit ev: TensorNumeric[T]): Module[T] +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantization.scala index 7769cee4c51..e090f34b500 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantization.scala @@ -16,7 +16,12 @@ package com.intel.analytics.bigdl.nn.quantized +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.quantized.Utils._ import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import java.nio.ByteBuffer +import scala.reflect.ClassTag object Quantization { def findMax(src: Array[Float], start: Int, end: Int): Float = { @@ -159,4 +164,17 @@ object Quantization { loss(beforeArray, afterArray, start, end) / beforeArray.sum } + + def quantize[T: ClassTag](model: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = { + // deep copy a new model then substitute with all quantized version modules + val clonedModel = model.cloneModule() + println("Converting model now") + val quantizedModel = Quantizer.quantize(clonedModel) + println("Converting model successfully") + + val paras = quantizedModel.parameters()._1 + reorganizeParameters(paras) + + quantizedModel + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizer.scala new file mode 100644 index 00000000000..5b20694053f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizer.scala @@ -0,0 +1,133 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.quantized.Utils._ +import com.intel.analytics.bigdl.nn.{Cell, Container, Graph} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.{Module, nn} +import scala.collection.mutable.{ArrayBuffer, HashMap} +import scala.reflect.ClassTag + +object Quantizer extends Quantizable { + val registerMaps = new HashMap[String, Quantizable]() + + init() + + override def quantize[T: ClassTag](model: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val className = model.getClass.getName + + val quantizedModel = if (registerMaps.contains(className)) { + registerMaps(className).quantize(model) + } else { + model match { + case container: Container[Activity, Activity, T] => + container match { + case graph: Graph[T] => GraphQuantizer.quantize(graph) + case _ => ContainerQuantizer.quantize(container) + } + /** + case container: Container[_, _, _] => // TODO scala will throw compling exception + container match { + case graph: Graph[_] => GraphQuantizer.quantize(model) + case _ => ContainerQuantizer.quantize(model) + } + */ + case cell if cell.isInstanceOf[Cell[T]] => + // because Cell[T] extends AbstractModule[Table, Table, T], and the Table is a class, + // which is not as same as trait Tensor. So if we use this form: + // case cell: Cell[T] => CellQuantizer.quantize(cell) + // scalac will throw an compiler error. + CellQuantizer.quantize(cell) + case default => ModuleQuantizer.quantize(model) + } + } + + quantizedModel + } + + private def init(): Unit = { + registerModules() + } + + private def registerModule(name: String, module: Quantizable): Unit = { + require(!registerMaps.contains(name), s"Module: $name has been registered.") + registerMaps(name) = module + } + + private def registerModules(): Unit = { + registerModule("com.intel.analytics.bigdl.nn.SpatialConvolution", + nn.SpatialConvolution) + registerModule("com.intel.analytics.bigdl.nn.SpatialDilatedConvolution", + nn.SpatialDilatedConvolution) + registerModule("com.intel.analytics.bigdl.nn.Linear", nn.Linear) + } +} + +object ContainerQuantizer extends Quantizable { + override def quantize[T: ClassTag](module: Module[T])( + implicit ev: TensorNumeric[T]): Module[T] = { + val container = module.asInstanceOf[Container[Activity, Activity, T]] + for (i <- container.modules.indices) { + val currModule = container.modules(i) + container.modules(i) = Quantizer.quantize(currModule) + } + container + } +} + +object CellQuantizer extends Quantizable { + override def quantize[T: ClassTag](module: Module[T])( + implicit ev: TensorNumeric[T]): Module[T] = { + val cell = module.asInstanceOf[Cell[T]] + cell.cell = Quantizer.quantize(cell.cell) + cell + } +} + +object GraphQuantizer extends Quantizable { + override def quantize[T: ClassTag](module: Module[T])( + implicit ev: TensorNumeric[T]): Module[T] = { + val graph = module.asInstanceOf[Graph[T]] + val sortedNodes = graph.getForwardExecutions + + for (i <- sortedNodes.indices) { + val currNode = sortedNodes(i) + val currModule = currNode.element + val waitedModule = Quantizer.quantize(currModule) + + if (waitedModule != currModule) { + currNode.setElement(waitedModule) + } + } + + // modules in container need to rebuild + graph.resetModules() + // nodes in backward executions need to rebuild + graph.build() + + graph + } +} + +object ModuleQuantizer extends Quantizable { + override def quantize[T: ClassTag](module: Module[T])( + implicit ev: TensorNumeric[T]): Module[T] = { + module + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Utils.scala new file mode 100644 index 00000000000..d11edb15a2d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Utils.scala @@ -0,0 +1,82 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.tf.WithoutInput +import com.intel.analytics.bigdl.nn.{Cell, Container, Graph, Input, TimeDistributed, Linear => NNLinear, SpatialConvolution => NNConv, SpatialDilatedConvolution => NNDilatedConv} +import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Node +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +object Utils { + type ModuleNode[R] = AbstractModule[Activity, Activity, R] + type SeqNodes[R] = Seq[Node[ModuleNode[R]]] + type ArrayNodes[R] = Array[Node[ModuleNode[R]]] + type ANode[R] = Node[ModuleNode[R]] + type AbsModule[R] = AbstractModule[Activity, Activity, R] + + /** + * delete parameters of SpatialConvolution, SpatialDilatedConvolution) and linear. + * + * because it will make all parameters into a long array in a BigDL model by default, + * so the origin parameters will exist in the quantized model. We have to delete them + * for reducing the size. + * + * After deleting all these matched parameters, it will make a **new** long array of + * other layers parameters. + * + * @param parameters parameters of all layers + * @tparam T data type Float or Double + * @return parameters reorganized + */ + def reorganizeParameters[T: ClassTag](parameters: Array[Tensor[T]])( + implicit ev: TensorNumeric[T]): Tensor[T] = { + var length = 0 + for (i <- parameters.indices) { + if (!parameters(i).isInstanceOf[QuantizedTensor[T]]) { + length += parameters(i).nElement() + } + } + + val result = Tensor[T](length) + + var offset = 0 + for (i <- parameters.indices) { + val parameter = parameters(i) + + if (!parameter.isInstanceOf[QuantizedTensor[T]]) { + val length = parameter.nElement() + + val (src, srcOffset) = (parameter.storage().array(), parameter.storageOffset() - 1) + val (dst, dstOffset) = (result.storage().array(), offset) + + val (size, stride) = (parameter.size(), parameter.stride()) + + System.arraycopy(src, srcOffset, dst, dstOffset, length) + parameter.set(result.storage(), offset + 1, size, stride) + + offset += length + } + } + + result + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala index 90786c6ef4b..17b3894d35a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala @@ -172,7 +172,7 @@ class DirectedGraph[T](val source : Node[T], val reverse : Boolean = false) exte * @tparam T element type */ @SerialVersionUID(- 6021651923538325999L) -class Node[T](val element: T) extends Serializable { +class Node[T](var element: T) extends Serializable { /** * The nodes pointed by current node * @return @@ -296,6 +296,11 @@ class Node[T](val element: T) extends Serializable { this } + def setElement(e: T): this.type = { + element = e + this + } + /** * Use current node as source to build a direct graph * @param reverse diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 48895670f87..d28515dd31c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1961,4 +1961,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) } + def quantize(module: AbstractModule[Activity, Activity, T]): Module[T] = { + module.quantize() + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizableSpec.scala new file mode 100644 index 00000000000..dee2ad5de22 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizableSpec.scala @@ -0,0 +1,235 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.quantized + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.bigquant.BigQuant +import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.quantized.Utils.ANode +import com.intel.analytics.bigdl.nn.{Linear => NNLinear, SpatialConvolution => NNConv, SpatialDilatedConvolution => NNDilatedConv, _} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.{T, Table} +import org.apache.log4j.Logger +import org.scalatest.{FlatSpec, Matchers} + +class QuantizableSpec extends FlatSpec with Matchers { + val logger: Logger = Logger.getLogger(getClass) + + "Sequential LeNet5" should "work correctly" in { + val seq = LeNet5(10) + seq.getParameters()._1.fill(1) + + val input = Tensor(4, 28, 28).fill(1) + val output = seq.forward(input).toTensor + + val quantSeq = seq.quantize() + val quantOutput = quantSeq.forward(input).toTensor + + output should be (quantOutput) + } + + "Quantize sequential LeNet5 twice" should "work correctly" in { + val seq = LeNet5(10) + + val input = Tensor(4, 28, 28) + + val quantSeq1 = seq.quantize() + val quantOutput1 = quantSeq1.forward(input).toTensor + + val quantSeq2 = seq.quantize() + val quantOutput2 = quantSeq2.forward(input).toTensor + + quantOutput1 should be (quantOutput2) + } + + "Graph LeNet5" should "work correctly" in { + val graph = LeNet5.graph(10) + graph.getParameters()._1.fill(1) + + val input = Tensor(4, 28, 28).fill(1) + val output = graph.forward(input).toTensor + + val quantGraph = graph.quantize() + val quantOutput = quantGraph.forward(input).toTensor + + output should be (quantOutput) + } + + "Quantize graph LeNet5 twice" should "work correctly" in { + val graph = LeNet5.graph(10) + + val input = Tensor(4, 28, 28) + + val quantGraph1 = graph.quantize() + val quantOutput1 = quantGraph1.forward(input).toTensor + + val quantGraph2 = graph.quantize() + val quantOutput2 = quantGraph2.forward(input).toTensor + + quantOutput1 should be (quantOutput2) + } + + "load quantized graph model" should "work correctly" in { + val input = Input() + val linear1 = NNLinear(3, 4).inputs(input) + val linear2 = NNLinear(3, 4).inputs(input) + val output = CAddTable().inputs(linear1, linear2) + val graph = Graph(Array(input), Array(output)) + graph.getParameters()._1.fill(1) + + val in = Tensor(3, 3).fill(1) + val out = graph.forward(in).toTensor + + val quantModel = graph.quantize() + val quantOut = quantModel.forward(in).toTensor + + out should be (quantOut) + } + + "quantize a quantized linear" should "work correctly" in { + case class TestCase(batchSize: Int, inputSize: Int, outputSize: Int) + val test = TestCase(1, 1, 1) + + val weight = Tensor(test.outputSize, test.inputSize).fill(1.0f) + val bias = Tensor(test.outputSize).fill(0f) + val input = Tensor(test.batchSize, test.inputSize).fill(1.0f) + + val linear = Linear[Float](test.inputSize, test.outputSize, initWeight = weight, + initBias = bias) + + val linear2 = linear.quantize() + + linear.updateOutput(input) + linear2.updateOutput(input) + + linear.output shouldEqual linear2.output + } + + "quantize a quantized SpatialConvolution" should "work correctly" in { + case class TestCase(batchSize: Int, inputChannel: Int, inputHeight: Int, inputWidth: Int, + group: Int, outputChannel: Int, kernelHeight: Int, kernelWidth: Int, + strideHeight: Int, strideWidth: Int, padHeight: Int, padWidth: Int) + val test = TestCase(1, 1, 3, 3, 1, 1, 2, 2, 1, 1, 0, 0) + + val weight = Tensor(test.group, test.outputChannel / test.group, + test.inputChannel / test.group, test.kernelHeight, test.kernelWidth).fill(1.0f) + val bias = Tensor(test.outputChannel).fill(0f) + val input = Tensor().resize(Array(test.batchSize, test.inputChannel, + test.inputHeight, test.inputWidth)).fill(1.0f) + + val conv = SpatialConvolution(test.inputChannel, test.outputChannel, + test.kernelHeight, test.kernelWidth, test.strideHeight, test.strideWidth, + test.padHeight, test.padWidth, test.group, weight, bias) + + val conv2 = conv.quantize() + + conv.updateOutput(input) + conv2.updateOutput(input) + + conv.output shouldEqual conv2.output + } + + "JNI test" should "work correctly" in { + BigQuant.printHello() + } + + "Multi inputs" should "work correctly" in { + val input1 = Input() + val input2 = Input() + val initWeight = Tensor(800, 200).fill(1) + val initBias = Tensor(800).fill(0) + val linear = NNLinear(200, 800, initWeight = initWeight, initBias = initBias).inputs(input1) + val cadd = CAddTable().inputs(linear, input2) + val out = cadd + val graph = Graph(Array(input1, input2), Array(out)) + + val t1 = Tensor(4, 200).fill(1) + val t2 = Tensor(4, 800).rand() + val input = T(t1, t2) + + graph.forward(input) + + val quantizedGraph = graph.quantize() + logger.info(quantizedGraph) + quantizedGraph.forward(input) + + graph.output.toTensor should be (quantizedGraph.output.toTensor) + } + + private def equalWithPrecision(t1: Tensor[Float], t2: Tensor[Float], precision: Int): Boolean = { + t1.nElement() should be (t2.nElement()) + + var ret = true + + val t1Offset = t1.storageOffset() - 1 + val t2Offset = t2.storageOffset() - 1 + for (i <- 0 until t1.nElement()) { + val a1 = trunc(t1.storage().array()(t1Offset + i), precision) + val a2 = trunc(t2.storage().array()(t2Offset + i), precision) + + if (a1 != a2) { + logger.info(a1 + "\t" + a2) + ret = false + } + } + + ret + } + + private def loss(t1: Tensor[Float], t2: Tensor[Float], precision: Double): Boolean = { + t1.nElement() should be (t2.nElement()) + + var ret = true + + val t1Offset = t1.storageOffset() - 1 + val t2Offset = t2.storageOffset() - 1 + for (i <- 0 until t1.nElement()) { + val a1 = t1.storage().array()(t1Offset + i) + val a2 = t2.storage().array()(t2Offset + i) + + val percent = Math.abs((a1 - a2) / a1) + logger.info(a1 + "\t" + a2 + "\t" + percent) + if (percent > precision) ret = false + } + + ret + } + + private def trunc(num: Float, precision: Int): Float = { + val value = Math.pow(10, precision) + ((num * value).toInt / value).toFloat + } + + private def findCell(module: Module[Float]): Cell[Float] = { + module.asInstanceOf[Recurrent[Float]].modules.last.asInstanceOf[Cell[Float]] + } + + private def findModule(module: Module[Float], index: Int): Module[Float] = { + module.asInstanceOf[Sequential[Float]].modules(index) + } + + private def isQuantizedLinear(module: Module[Float]): Boolean = { + module.isInstanceOf[Linear[Float]] + } + + private def isQuantizedConv(module: Module[Float]): Boolean = { + module.isInstanceOf[SpatialConvolution[Float]] + } +} From d07c62e91af1aadd5c746367b58dfbf3efb06d11 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 11 Oct 2017 15:23:05 +0800 Subject: [PATCH 0440/1065] sparse tensor (#1594) --- .../bigdl/dllib/tensor/DenseTensor.scala | 22 +- .../bigdl/dllib/tensor/SparseTensor.scala | 1069 +++++++++++++++++ .../bigdl/dllib/tensor/SparseTensorBLAS.scala | 395 ++++++ .../bigdl/dllib/tensor/SparseTensorMath.scala | 60 + .../analytics/bigdl/dllib/tensor/Tensor.scala | 171 ++- .../dllib/tensor/SparseTensorMathSpec.scala | 291 +++++ .../bigdl/dllib/tensor/SparseTensorSpec.scala | 48 + 7 files changed, 2031 insertions(+), 25 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMath.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMathSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index f7c0476a4c5..5c41c0c906b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2481,7 +2481,7 @@ object DenseTensor { val delta = longTensor.nDimension() - shortTensor.nDimension() val size = new Array[Int](ndim) var i = ndim - 1 - while(i >= delta) { + while (i >= delta) { require(longTensor.size(i + 1) == shortTensor.size(i + 1 - delta) || longTensor.size(i + 1) == 1 || shortTensor.size(i + 1 - delta) == 1, errorMsg) @@ -2489,11 +2489,29 @@ object DenseTensor { i -= 1 } - while(i >= 0) { + while (i >= 0) { size(i) = longTensor.size(i + 1) i -= 1 } size } + + private[tensor] def apply[T: ClassTag]( + sparseTensor: SparseTensor[T], + res: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = { + val dt = if (null == res) Tensor(sparseTensor.size()) else res + var i = 0 + val index = new Array[Int](dt.dim()) + while (i < sparseTensor._indices(0).length) { + var j = 0 + while (j < index.length) { + index(j) = sparseTensor._indices(j)(i) + 1 + j += 1 + } + dt(index) = sparseTensor(index) + i += 1 + } + dt + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala new file mode 100644 index 00000000000..8743b349482 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -0,0 +1,1069 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +import breeze.linalg.{DenseMatrix, DenseVector} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import org.apache.spark.mllib.linalg.{Matrix, Vector} + +import scala.reflect.ClassTag + +/** + * Tensor's sparse representation. + * + * To describe an SparseTensor, we need indices, values, and shape: + * Indices means non-zero elements' indices; values means the values of the non-zero elements; + * Shape means the dense shape of this SparseTensor. + * + * For example, an 2D 3x4 DenseTensor: + * 1, 0, 0, 4 + * 0, 2, 0, 0 + * 0, 0, 3, 0 + * + * it's sparse representation should be + * indices(0) = Array(0, 0, 1, 2) + * indices(1) = Array(0, 3, 1, 2) + * values = Array(1, 4, 2, 3) + * shape = Array(3, 4) + * + * @param _indices non-zero elements' indices + * @param _values values of the non-zero elements + * @param _storageOffset storageOffset + * @param _nElement number of non-zero elements + * @param _shape dense shape + * @param _indicesOffset indices' offset, Default is zeros, will vary in narrowed/selected tensor. + * @param nDimension dimensions. + * @tparam T should be Double or Float + */ +// indices is zero based. +private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( + private[tensor] var _indices : Array[Storage[Int]], + private[tensor] var _values : Storage[T], + private[tensor] var _storageOffset: Int, + private[tensor] var _nElement: Int, + private[tensor] var _shape : Array[Int], + private[tensor] var _indicesOffset : Array[Int], + var nDimension: Int + )(implicit ev: TensorNumeric[T]) extends Tensor[T] { + + // todo: add transpose, indices order, count from 0 + // var indices_order = Array.range(0, _shape.length) + + require(_shape.length == _indices.length, s"indices' size doesn't match tensor shape, " + + s"indices' length is ${_indices.length} and tensor shape is ${_shape.mkString(" x ")}") + + require(_values.length == _indices(0).length, s"${_values.length()} non-zero elements should " + + s"have indices for all elements. But indices's length is only ${_indices(0).length}") + + nDimension = _shape.length + + override def dim(): Int = nDimension + + override def setValue(d1: Int, value: T): SparseTensor.this.type = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + this + } + + override def setValue(d1: Int, d2: Int, value: T): SparseTensor.this.type = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + this + } + + override def setValue(d1: Int, d2: Int, d3: Int, value: T): SparseTensor.this.type = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + this + } + + override def setValue(d1: Int, d2: Int, d3: Int, d4: Int, value: T): SparseTensor.this.type = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + this + } + + override def setValue(d1: Int, d2: Int, + d3: Int, d4: Int, d5: Int, value: T): SparseTensor.this.type = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + this + } + + override def unfold(dim: Int, size: Int, step: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + this + } + + override def nElement(): Int = _nElement + + override def size(): Array[Int] = { + _shape.slice(0, this.nDimension) + } + + override def size(dim: Int): Int = { + _shape(dim - 1) + } + + override def stride(): Array[Int] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def stride(dim: Int): Int = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def fill(v: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def zero(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def randn(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def randn(mean: Double, stdv: Double): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def rand(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def rand(lowerBound: Double, upperBound: Double): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def bernoulli(p: Double): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def transpose(dim1: Int, dim2: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def t(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def apply(index: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def apply(indexes: Array[Int]): T = { + require(indexes.length == dim()) + var index = 0 + var i = 0 + while (i < dim()) { + index = _indices(i).array().indexOf(indexes(i) - 1, index) + i += 1 + } + storage().array()(index) + } + + override def valueAt(d1: Int): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def valueAt(d1: Int, d2: Int): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def valueAt(d1: Int, d2: Int, d3: Int): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def valueAt(d1: Int, d2: Int, d3: Int, d4: Int): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def valueAt(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def apply(t: Table): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def update(index: Int, value: T): Unit = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def update(index: Int, src: Tensor[T]): Unit = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def update(indexes: Array[Int], value: T): Unit = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def update(t: Table, value: T): Unit = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def update(t: Table, src: Tensor[T]): Unit = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def update(filter: (T) => Boolean, value: T): Unit = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def isContiguous(): Boolean = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def contiguous(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def isSameSizeAs(other: Tensor[_]): Boolean = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def resizeAs(src: Tensor[_]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def resize(sizes: Array[Int], strides: Array[Int]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def resize(size1: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def resize(size1: Int, size2: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def resize(size1: Int, size2: Int, size3: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def resize(size1: Int, size2: Int, size3: Int, size4: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def resize(size1: Int, size2: Int, size3: Int, size4: Int, size5: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def select(dim: Int, index: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def storage(): Storage[T] = { + _values + } + + override def storageOffset(): Int = { + _storageOffset + 1 + } + + override def set(other: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def set(storage: Storage[T], storageOffset: Int, + sizes: Array[Int], strides: Array[Int]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def set(): Tensor[T] = { + if (this._indices != null) { + for (ind <- this._indices) + ind.resize(0) + } + if (this._values != null) { + this._values.resize(0) + } + this._nElement = 0 + this._storageOffset = 0 + this._shape = Array() + this + } + + override def narrow(dim: Int, index: Int, size: Int): Tensor[T] = { + require(dim == 1, "SparseTensor.narrow only support narrow at first dimension") + dim match { + case 1 => + val _index = index - 1 + val dimIndices = _indices(dim - 1) + val indicesOffset = _indicesOffset(dim - 1) + + val nums = dimIndices.count(i => i >= _index + indicesOffset + && i < _index + size + indicesOffset) + val newStorageOffset = dimIndices.array().indexOf(_index + indicesOffset) + val newShape = this.size() + newShape(dim - 1) = size + val newIndicesOffset = _indicesOffset.slice(0, this.nDimension) + newIndicesOffset(dim - 1) += _index + + new SparseTensor(_indices, _values, newStorageOffset, nums, newShape, + newIndicesOffset, newShape.length) + case _ => + val _index = index - 1 + val dimIndices = _indices(dim - 1) + val values = storage().array() + + val nums = dimIndices.count (i => i >= _index && i < _index + size) + val newShape = this.size () + newShape (dim - 1) = size + val newIndices = newShape.map (_ => new Array[Int] (nums) ) + val newStorage = Storage[T] (nums) + val newStorageArray = newStorage.array () + var i = 0 + var count = 0 + while (i < storage ().array ().length) { + if (dimIndices (i) >= _index && dimIndices (i) < (_index + size) ) { + newStorageArray (count) = values (i) + var dims = 0 + while (dims < this.dim () ) { + if (dims == dim - 1) { + newIndices(dims)(count) = _indices (dims) (i) - _index + } else { + newIndices(dims)(count) = _indices (dims) (i) + } + dims += 1 + } + count += 1 + } + i += 1 + } + SparseTensor(newIndices, newStorage, newShape, newShape.length) + } + } + + override def copy(other: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def apply1(func: (T) => T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def map(other: Tensor[T], func: (T, T) => T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def squeeze(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def squeeze(dim: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def squeezeNewTensor(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def view(sizes: Array[Int]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def repeatTensor(sizes: Array[Int]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def expandAs(template: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def expand(sizes: Array[Int]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def split(size: Int, dim: Int): Array[Tensor[T]] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def split(dim: Int): Array[Tensor[T]] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def toBreezeVector(): DenseVector[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def toMLlibVector(): Vector = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def toBreezeMatrix(): DenseMatrix[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def toMLlibMatrix(): Matrix = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def getType(): TensorDataType = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def diff(other: Tensor[T], count: Int, reverse: Boolean): Boolean = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addSingletonDimension(t: Tensor[T], dim: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def reshape(sizes: Array[Int]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def save(path: String, overWrite: Boolean): SparseTensor.this.type = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def getTensorNumeric(): TensorNumeric[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def resize(size: Array[Int], nElement: Int): Tensor[T] = { + if (this.nElement() < nElement) { + storage.resize(nElement) + if (size.length == _indices.length) { + _indices.foreach(_.resize(nElement)) + } else if (size.length < _indices.length) { + _indices = _indices.slice(0, size.length) + _indices.foreach(_.resize(nElement)) + } else { + val _addIndices = new Array[Storage[Int]](size.length - _indices.length) + for (i <- _addIndices.indices) _addIndices(i) = Storage[Int](nElement) + _indices ++= _addIndices + _indices.foreach(_.resize(nElement)) + } + _storageOffset = 0 + } + _nElement = nElement + _shape = size + nDimension = size.length + + this + } + + + // scalastyle:off methodName + override def +(s: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def +(t: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def -(s: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def -(t: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def unary_-(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def /(s: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def /(t: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def *(s: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def *(t: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + // scalastyle:on methodName + + override def sum(): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sum(dim: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sum(x: Tensor[T], dim: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def mean(): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def mean(dim: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def max(): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def max(dim: Int): (Tensor[T], Tensor[T]) = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def max(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T]) = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def min(): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def min(dim: Int): (Tensor[T], Tensor[T]) = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def min(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T]) = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def scatter(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def gather(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def conv2(kernel: Tensor[T], vf: Char): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def xcorr2(kernel: Tensor[T], vf: Char): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sqrt(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def abs(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def add(value: T, y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def add(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def add(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def add(value: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def add(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def dot(y: Tensor[T]): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cmax(value: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def dist(y: Tensor[T], norm: Int): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addcmul(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addcmul(tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addcdiv(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sub(value: T, y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + // Puts the result of x - value * y in current tensor + override def sub(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sub(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sub(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sub(value: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cmul(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cmul(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cdiv(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cdiv(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def mul(value: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def div(value: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def mul(x: Tensor[T], value: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addmm(v1: T, M: Tensor[T], v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addmm(M: Tensor[T], mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addmm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addmm(v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addmm(v1: T, v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def mm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addr(t1: Tensor[T], t2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addr(v1: T, t1: Tensor[T], t2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T], t3: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def uniform(args: T*): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addmv(beta: T, vec1: Tensor[T], alpha: T, + mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addmv(beta: T, alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def addmv(alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def mv(mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def baddbmm(beta: T, M: Tensor[T], + alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def baddbmm(beta: T, alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def baddbmm(alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def bmm(batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def pow(y: Tensor[T], n: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def pow(n: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def topk(k: Int, dim: Int, increase: Boolean, result: Tensor[T], + indices: Tensor[T]): (Tensor[T], Tensor[T]) = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def log(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def exp(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sqrt(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def log1p(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + override def log(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + override def exp(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def log1p(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def abs(x: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def norm(y: Tensor[T], value: Int, dim: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def gt(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def lt(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def le(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def eq(x: Tensor[T], y: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def maskedFill(mask: Tensor[T], e: T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def maskedCopy(mask: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def maskedSelect(mask: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def norm(value: Int): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def sign(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def ge(x: Tensor[T], value: Double): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def indexAdd(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def index(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cmax(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def range(xmin: Double, xmax: Double, step: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def toTensor[D](implicit ev: TensorNumeric[D]): Tensor[D] = { + if (ev.getType() == ev.getType()) { + this.asInstanceOf[Tensor[D]] + } else { + throw new IllegalArgumentException(s"The type ${ev.getType().getClass}" + + s" in toTensor[${ev.getType().getClass}] is not same" + + s"as the numeric type ${ev.getType().getClass} of the " + + "corresponding module, please keep them same.") + } + } + + override def equals(obj: Any): Boolean = { + if (obj == null) { + return false + } + if (!obj.isInstanceOf[SparseTensor[T]]) { + return false + } + val other = obj.asInstanceOf[SparseTensor[T]] + if (this.eq(other)) { + return true + } + if (this.nDimension != other.nDimension) { + return false + } + var d = 1 + while (d <= this.nDimension) { + if (this.size(d) != other.size(d)) { + return false + } + d += 1 + } + + println(_indices.map(_.array()).deep == other._indices.map(_.array()).deep) + println(_indices(0).array.deep == other._indices(0).array().deep) + println(_indices(1).array.deep == other._indices(1).array().deep) + println(_values.array().deep == other._values.array().deep) + println(this._shape.deep == other._shape.deep) + println(this._nElement == other._nElement) + + _indices.map(_.array()).deep == other._indices.map(_.array()).deep && + _values.array().deep == other._values.array().deep && + this._shape.deep == other._shape.deep && + this._nElement == other._nElement + } + + override def toString(): String = { + this.nDimension match { + case 0 => s"[${this.getClass.getName} with no dimension]" + case 1 => + val sb = new StringBuilder + val indices = _indices + val values = _values + val storageOffset = _storageOffset + val indicesOffset = _indicesOffset(0) + for (i <- 0 until this.nElement) + sb.append((indices(0)(i + storageOffset) + indicesOffset) + + " : " + values(i + storageOffset)).append('\n') + + s"${sb}[${this.getClass.getName} of size ${this.size(1)}]" + case 2 => + val sb = new StringBuilder + val indices = _indices + val values = _values + val storageOffset = _storageOffset + val indicesOffset0 = _indicesOffset(0) + val indicesOffset1 = _indicesOffset(1) + for (i <- 0 until this.nElement) + sb.append("(" + (indices(0)(i + storageOffset) - indicesOffset0) + ", " + + (indices(1)(i + storageOffset) + indicesOffset1) + ") : " + + values(i + storageOffset)).append('\n') + + s"${sb}[${this.getClass.getName} of size ${this.size(1)}x${this.size(2)}]" + case _ => + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + } + + override def hashCode(): Int = { + val state = Seq(_indices, _values, _storageOffset, _nElement, _shape, nDimension) + state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) + } + + override def isEmpty: Boolean = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def isScalar: Boolean = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def value(): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def setValue(value: T): SparseTensor.this.type = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def applyFun[A : ClassTag](t: Tensor[A], func: (A) => T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def zipWith[A: ClassTag, B: ClassTag]( + t1: Tensor[A], + t2: Tensor[B], + func: (A, B) => T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def prod(): T = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def prod(x: Tensor[T], dim: Int): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def tanh(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def tanh(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def forceFill(v: Any): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def emptyInstance(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def forceCopy(other: Tensor[_]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cast[D: ClassTag]( + castTensor: Tensor[D])(implicit ev: TensorNumeric[D]): Tensor[D] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def getTensorType: TensorType = SparseType +} + +object SparseTensor{ + private[tensor] def apply[T: ClassTag]( + shape : Array[Int], + nElement: Int = 1)( + implicit ev: TensorNumeric[T]): SparseTensor[T] = { + new SparseTensor(shape.map(_ => Storage[Int](nElement)), Storage(nElement), + 0, nElement, + shape, shape.map(_ => 0), shape.length) + } + + private[tensor] def apply[T: ClassTag]( + indices : Array[Array[Int]], + values : Storage[T], + shape : Array[Int])( + implicit ev: TensorNumeric[T]): SparseTensor[T] = { + new SparseTensor(indices.map(Storage(_)), values, + 0, values.length(), + shape, shape.map(_ => 0), shape.length) + } + + private[tensor] def apply[T: ClassTag]( + indices : Array[Array[Int]], + values : Storage[T], + shape : Array[Int], + dimension: Int)( + implicit ev: TensorNumeric[T]): SparseTensor[T] = { + new SparseTensor(indices.map(Storage(_)), values, + 0, values.length(), + shape, shape.map(_ => 0), dimension) + } + + private[tensor] def apply[T: ClassTag]( + denseTensor: Tensor[T])(implicit ev: TensorNumeric[T]): SparseTensor[T] = { + var nonZeroElement = 0 + denseTensor.apply1{v => + if (v != ev.zero) nonZeroElement += 1 + v + } + val shape = denseTensor.size() + val indices = shape.map(_ => new Array[Int](nonZeroElement)) + val storage = Storage[T](nonZeroElement) + val storageArray = storage.array() + denseTensor.dim() match { + case 1 => + var sparseIndex = 0 + var i = 1 + while (i <= denseTensor.nElement()) { + if (denseTensor.valueAt(i) != 0) { + indices(0)(sparseIndex) = i - 1 + storageArray(sparseIndex) = denseTensor.valueAt(i) + sparseIndex += 1 + } + i += 1 + } + case 2 => + var sparseIndex = 0 + var i = 1 + while (i <= denseTensor.size(1)) { + var j = 1 + while (j <= denseTensor.size(2)) { + if (denseTensor.valueAt(i, j) != 0) { + indices(0)(sparseIndex) = i - 1 + indices(1)(sparseIndex) = j - 1 + storageArray(sparseIndex) = denseTensor.valueAt(i, j) + sparseIndex += 1 + } + j += 1 + } + i += 1 + } + case _ => + throw new UnsupportedOperationException(s"${denseTensor.dim()}") + } + SparseTensor(indices, storage, shape, shape.length) + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala new file mode 100644 index 00000000000..b4a3ad0292a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala @@ -0,0 +1,395 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +import com.intel.analytics.bigdl.tensor.TensorNumericMath._ + +object SparseTensorBLAS { + + /** + * Perform r := beta * r + alpha * mat * vec + * mat should be a 2D SparseTensor, vec should be a 1D DenseTensor, + * r should be a 2D DenseTensor. + * + * @param alpha alpha + * @param mat a 2D SparseTensor + * @param vec a 1D DenseTensor + * @param beta beta + * @param r result, 2D DenseTensor + * @param ev tensor numeric + * @tparam T numeric type + */ + def coomv[@specialized(Float, Double) T]( + alpha: T, + mat: Tensor[T], + vec: Tensor[T], + beta: T, + r: Tensor[T])(implicit ev: TensorNumeric[T]): Unit = { + (alpha, mat, vec, beta, r) match { + case (alpha: Double, a: SparseTensor[Double], x: DenseTensor[Double], + beta: Double, y: DenseTensor[Double]) => + dcoomv(alpha, a, x, beta, y) + case (alpha: Float, a: SparseTensor[Float], x: DenseTensor[Float], + beta: Float, y: DenseTensor[Float]) => + scoomv(alpha, a, x, beta, y) + case _ => + throw new IllegalArgumentException(s"Sparse addmv doesn't support") + } + } + + private def scoomv( + alpha: Float, + A: SparseTensor[Float], + x: DenseTensor[Float], + beta: Float, + y: DenseTensor[Float]): Unit = { + val xValues = x.storage().array() + val xOffset = x.storageOffset() - 1 + val yValues = y.storage().array() + val yOffset = y.storageOffset() - 1 + val mA: Int = A._shape(0) + val nA: Int = A._shape(1) + + val Avals = A._values.array() + val AstorageOffset = A.storageOffset() - 1 + val Arows = A._indices(0) + val ArowOffset = A._indicesOffset(0) + val Acols = A._indices(1) + val AcolOffset = A._indicesOffset(1) + + if (beta != 1.0) { + y.mul(beta) + } + // Perform matrix-vector multiplication and add to y + var valueCounter = 0 + while (valueCounter < A.nElement()) { + val Arow = Arows(valueCounter + AstorageOffset) - ArowOffset + val Acol = Acols(valueCounter + AstorageOffset) - AcolOffset + val Aval = Avals(valueCounter + AstorageOffset) + yValues(Arow + yOffset) += Aval * alpha * xValues(Acol + xOffset) + valueCounter += 1 + } + } + + private def dcoomv( + alpha: Double, + A: SparseTensor[Double], + x: DenseTensor[Double], + beta: Double, + y: DenseTensor[Double]): Unit = { + val xValues = x.storage().array() + val xOffset = x.storageOffset() - 1 + val yValues = y.storage().array() + val yOffset = y.storageOffset() - 1 + val mA: Int = A._shape(0) + val nA: Int = A._shape(1) + + val Avals = A._values.array() + val AstorageOffset = A.storageOffset() - 1 + val Arows = A._indices(0) + val ArowOffset = A._indicesOffset(0) + val Acols = A._indices(1) + val AcolOffset = A._indicesOffset(1) + + if (beta != 1.0) { + y.mul(beta) + } + // Perform matrix-vector multiplication and add to y + var valueCounter = 0 + while (valueCounter < A.nElement()) { + val Arow = Arows(valueCounter + AstorageOffset) - ArowOffset + val Acol = Acols(valueCounter + AstorageOffset) - AcolOffset + val Aval = Avals(valueCounter + AstorageOffset) + yValues(Arow + yOffset) += Aval * alpha * xValues(Acol + xOffset) + valueCounter += 1 + } + } + + def coomm[@specialized(Float, Double) T]( + alpha: T, + mat1: Tensor[T], + mat2: Tensor[T], + beta: T, + r: Tensor[T])(implicit ev: TensorNumeric[T]): Unit = { + (alpha, mat1, mat2, beta, r) match { + case (alpha: Float, a: SparseTensor[Float], x: DenseTensor[Float], + beta: Float, y: DenseTensor[Float]) => + scoomm(alpha, a, x, beta, y) + case (alpha: Double, a: SparseTensor[Double], x: DenseTensor[Double], + beta: Double, y: DenseTensor[Double]) => + dcoomm(alpha, a, x, beta, y) + case (alpha: Float, a: DenseTensor[Float], x: SparseTensor[Float], + beta: Float, y: DenseTensor[Float]) => + scoomm(alpha, a, x, beta, y) + case (alpha: Double, a: DenseTensor[Double], x: SparseTensor[Double], + beta: Double, y: DenseTensor[Double]) => + dcoomm(alpha, a, x, beta, y) + case _ => + throw new IllegalArgumentException(s"Sparse addmm doesn't support") + } + } + + private def scoomm( + alpha: Float, + A: SparseTensor[Float], + B: DenseTensor[Float], + beta: Float, + C: DenseTensor[Float]): Unit = { + val mA: Int = A._shape(0) + val nB: Int = B.size(2) + val kA: Int = A._shape(1) + val kB: Int = B.size(1) + + val Avals = A._values.array() + val AstorageOffset = A.storageOffset() - 1 + val ArowIndices = A._indices(0) + val ArowOffset = A._indicesOffset(0) + val AcolIndices = A._indices(1) + val AcolOffset = A._indicesOffset(1) + + val Bvals = B.storage().array() + val bOffset = B.storageOffset() - 1 + val Cvals = C.storage().array() + val cOffset = C.storageOffset() - 1 + + require(ArowIndices.length == AcolIndices.length, s"A: row indices number " + + s"${ArowIndices.length()} is not equal to col indices number ${AcolIndices.length()}") + require(ArowIndices.length == Avals.length, s"A: indices length ${ArowIndices.length()}" + + s"is not equal to values length ${Avals.length}") + + // Scale matrix first if `beta` is not equal to 1.0 + if (beta != 1.0) { + C.mul(beta) + } + // Perform matrix multiplication and add to C. The rows of A are multiplied by the columns of + // B, and added to C. + var index = 0 + if (B.stride(2) == 1 && B.size(2) == B.stride(1)) { + while (index < A.nElement()) { + val curMA = ArowIndices(index + AstorageOffset) - ArowOffset + val curKA = AcolIndices(index + AstorageOffset) - AcolOffset + var n = 0 + while (n < nB) { + Cvals(curMA * nB + n) += alpha * Avals(index + AstorageOffset) * + Bvals(curKA * nB + n + bOffset) + n += 1 + } + index += 1 + } + } else { + while (index < A.nElement()) { + val curMA = ArowIndices(index + AstorageOffset) - ArowOffset + val curKA = AcolIndices(index + AstorageOffset) - AcolOffset + var n = 0 + while (n < nB) { + Cvals(curMA * nB + n + cOffset) += alpha * Avals(index + AstorageOffset) * + Bvals(curKA + n * kB + bOffset) + n += 1 + } + index += 1 + } + + } + } + + private def dcoomm( + alpha: Double, + A: SparseTensor[Double], + B: DenseTensor[Double], + beta: Double, + C: DenseTensor[Double]): Unit = { + val mA: Int = A._shape(0) + val nB: Int = B.size(2) + val kA: Int = A._shape(1) + val kB: Int = B.size(1) + + val Avals = A._values.array() + val AstorageOffset = A.storageOffset() - 1 + val ArowIndices = A._indices(0) + val ArowOffset = A._indicesOffset(0) + val AcolIndices = A._indices(1) + val AcolOffset = A._indicesOffset(1) + + val Bvals = B.storage().array() + val bOffset = B.storageOffset() - 1 + val Cvals = C.storage().array() + val cOffset = C.storageOffset() - 1 + + require(ArowIndices.length == AcolIndices.length, s"A: row indices number " + + s"${ArowIndices.length()} is not equal to col indices number ${AcolIndices.length()}") + require(ArowIndices.length == Avals.length, s"A: indices length ${ArowIndices.length()}" + + s"is not equal to values length ${Avals.length}") + + // Scale matrix first if `beta` is not equal to 1.0 + if (beta != 1.0) { + C.mul(beta) + } + // Perform matrix multiplication and add to C. The rows of A are multiplied by the columns of + // B, and added to C. + var index = 0 + if (B.stride(2) == 1 && B.size(2) == B.stride(1)) { + while (index < A.nElement()) { + val curMA = ArowIndices(index + AstorageOffset) - ArowOffset + val curKA = AcolIndices(index + AstorageOffset) - AcolOffset + var n = 0 + while (n < nB) { + Cvals(curMA * nB + n) += alpha * Avals(index + AstorageOffset) * + Bvals(curKA * nB + n + bOffset) + n += 1 + } + index += 1 + } + } else { + while (index < A.nElement()) { + val curMA = ArowIndices(index + AstorageOffset) - ArowOffset + val curKA = AcolIndices(index + AstorageOffset) - AcolOffset + var n = 0 + while (n < nB) { + Cvals(curMA * nB + n + cOffset) += alpha * Avals(index + AstorageOffset) * + Bvals(curKA + n * kB + bOffset) + n += 1 + } + index += 1 + } + + } + } + + private def scoomm( + alpha: Float, + A: DenseTensor[Float], + B: SparseTensor[Float], + beta: Float, + C: DenseTensor[Float]): Unit = { + val kB: Int = B.size(1) + val nB: Int = B.size(2) + val mA: Int = A.size(1) + val kA: Int = A.size(2) + + val Avals = A.storage().array() + val aOffset = A.storageOffset() - 1 + val Cvals = C.storage().array() + val cOffset = C.storageOffset() - 1 + + val Bvals = B._values.array() + val BstorageOffset = B.storageOffset() - 1 + val BrowIndices = B._indices(0) + val BrowIndicesOffset = B._indicesOffset(0) + val BcolIndices = B._indices(1) + val BcolIndicesOffset = B._indicesOffset(1) + + require(BrowIndices.length == BcolIndices.length, s"B: row indices number " + + s"${BrowIndices.length()} is not equal to col indices number ${BcolIndices.length()}") + require(BrowIndices.length == Bvals.length, s"B: indices length ${BrowIndices.length()}" + + s"is not equal to values length ${Bvals.length}") + + // Scale matrix first if `beta` is not equal to 1.0 + if (beta != 1.0) { + C.mul(beta) + } + // Perform matrix multiplication and add to C. The rows of B are multiplied by the columns of + // A, and added to C. + var index = 0 + if (A.stride(2) == 1 && A.size(2) == A.stride(1)) { + while (index < B.nElement()) { + val curKB = BrowIndices(index + BstorageOffset) - BrowIndicesOffset + val curNB = BcolIndices(index + BstorageOffset) - BcolIndicesOffset + var n = 0 + while (n < mA) { + Cvals(n * nB + curNB + cOffset) += alpha * Bvals(index + BstorageOffset) * + Avals(n * kA + curKB + aOffset) + n += 1 + } + index += 1 + } + } else { + while (index < B.nElement()) { + val curKB = BrowIndices(index + BstorageOffset) - BrowIndicesOffset + val curNB = BcolIndices(index + BstorageOffset) - BcolIndicesOffset + var n = 0 + while (n < mA) { + Cvals(n * nB + curNB + cOffset) += alpha * Bvals(index + BstorageOffset) * + Avals(n + mA * curKB + aOffset) + n += 1 + } + index += 1 + } + } + } + + private def dcoomm( + alpha: Double, + A: DenseTensor[Double], + B: SparseTensor[Double], + beta: Double, + C: DenseTensor[Double]): Unit = { + val kB: Int = B.size(1) + val nB: Int = B.size(2) + val mA: Int = A.size(1) + val kA: Int = A.size(2) + + val Avals = A.storage().array() + val aOffset = A.storageOffset() - 1 + val Cvals = C.storage().array() + val cOffset = C.storageOffset() - 1 + + val Bvals = B._values.array() + val BstorageOffset = B.storageOffset() - 1 + val BrowIndices = B._indices(0) + val BrowIndicesOffset = B._indicesOffset(0) + val BcolIndices = B._indices(1) + val BcolIndicesOffset = B._indicesOffset(1) + + require(BrowIndices.length == BcolIndices.length, s"B: row indices number " + + s"${BrowIndices.length()} is not equal to col indices number ${BcolIndices.length()}") + require(BrowIndices.length == Bvals.length, s"B: indices length ${BrowIndices.length()}" + + s"is not equal to values length ${Bvals.length}") + + // Scale matrix first if `beta` is not equal to 1.0 + if (beta != 1.0) { + C.mul(beta) + } + // Perform matrix multiplication and add to C. The rows of B are multiplied by the columns of + // A, and added to C. + var index = 0 + if (A.stride(2) == 1 && A.size(2) == A.stride(1)) { + while (index < B.nElement()) { + val curKB = BrowIndices(index + BstorageOffset) - BrowIndicesOffset + val curNB = BcolIndices(index + BstorageOffset) - BcolIndicesOffset + var n = 0 + while (n < mA) { + Cvals(n * nB + curNB + cOffset) += alpha * Bvals(index + BstorageOffset) * + Avals(n * kA + curKB + aOffset) + n += 1 + } + index += 1 + } + } else { + while (index < B.nElement()) { + val curKB = BrowIndices(index + BstorageOffset) - BrowIndicesOffset + val curNB = BcolIndices(index + BstorageOffset) - BcolIndicesOffset + var n = 0 + while (n < mA) { + Cvals(n * nB + curNB + cOffset) += alpha * Bvals(index + BstorageOffset) * + Avals(n + mA * curKB + aOffset) + n += 1 + } + index += 1 + } + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMath.scala new file mode 100644 index 00000000000..39057e4a736 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMath.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +import com.intel.analytics.bigdl.tensor.TensorNumericMath._ + +object SparseTensorMath { + + def addmv[@specialized(Float, Double) T]( + r : Tensor[T], + beta : T, + t : Tensor[T], + alpha : T, + mat : Tensor[T], + vec : Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(mat.nDimension() == 2 && vec.nDimension() == 1) + require(mat.size(2) == vec.size(1)) + require(t.nDimension() == 1) + require(t.size(1) == mat.size(1)) + if(!r.eq(t)) { + r.resizeAs(t).copy(t) + } + + SparseTensorBLAS.coomv(alpha, mat, vec, beta, r) + r + } + + // res = beta * mat3 + alpha * mat1 * mat2 + def addmm[@specialized(Float, Double) T]( + res: Tensor[T], + beta: T, + mat3: Tensor[T], + alpha: T, + mat1: Tensor[T], + mat2: Tensor[T] + )(implicit ev: TensorNumeric[T]) : Tensor[T] = { + require(mat1.dim() == 2 && mat2.dim() == 2 && mat3.dim() == 2) + require(mat3.size(1) == mat1.size(1) && mat3.size(2) == mat2.size(2)) + if(!res.eq(mat3)) { + res.resizeAs(mat3).copy(mat3) + } + + SparseTensorBLAS.coomm(alpha, mat1, mat2, beta, res) + res + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 374ce3a5746..6ab41cb40c8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -61,9 +61,9 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { def dim(): Int /** - * Size of tensor. Return an array of which each value represent the size on the - * dimension(i + 1), i is the index of the corresponding value - * It will generate a new array each time you invoke the method + * Size of tensor. Return an array of which each value represents the size on the + * dimension(i + 1), i is the index of the corresponding value. + * It will generate a new array each time method is invoked. * * @return size array */ @@ -78,15 +78,15 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { def size(dim: Int): Int /** - * Jumps between element on the each dimension in the storage. - * It will generate a new array each time you invoke the method + * Jumps between elements on the each dimension in the storage. + * It will generate a new array each time method is invoked. * * @return strides array */ def stride(): Array[Int] /** - * Jumps between element on the given dimension in the storage. + * Jumps between elements on the given dimension in the storage. * * @param dim dimension, count from 1 * @return jump @@ -217,12 +217,12 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { def valueAt(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int): T /** - * Subset the tensor by apply the element of the given table to corresponding dimension of the - * tensor. The element of the given table can be an Int or another Table. + * Subset the tensor by apply the elements of the given table to the corresponding dimension + * of the tensor. The elements of the given table can be an Int or another Table. * An Int means select on current dimension; A table means narrow on current dimension, - * the table should has two elements, of which the first is start index and - * the second is the end index. An empty table is equals to Table(1, size_of_current_dimension) - * If the table length is less than the tensor dimension, the missing dimension is applied by + * the table should have two elements, of which the first is the start index and + * the second is the end index. An empty table is equal to Table(1, size_of_current_dimension) + * If the table length is less than the tensor dimension, each missing dimension is token up by * an empty table * * @see select @@ -243,8 +243,7 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { /** * Copy the give tensor value to the select subset of the current tensor by the given index. - * The subset should - * has the same size of the given tensor + * The subset should have the same size of the given tensor * * @param index index * @param src tensor to write @@ -252,7 +251,7 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { def update(index: Int, src: Tensor[T]): Unit /** - * Write the value to the value indexed by the given index array + * Write the value to the positions indexed by the given index array * * @param indexes index array. It should has same length with the tensor dimension * @param value value to write @@ -287,10 +286,10 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { /** * Fill the select subset of the current tensor with the given value. * The element of the given table can be an Int or another Table. An Int means select on current - * dimension; A tablemeans narrow on current dimension, the table should has two elements, - * of which the first is start index and the second is the end index. An empty table is equals + * dimension; A table means narrow on the current dimension, the table should has two elements, + * of which the first is the start index and the second is the end index. An empty table is equal * to Table(1, size_of_current_dimension) If the table length is less than the tensor dimension, - * the missing dimension is applied by an empty table + * each missing dimension is applied by an empty table * * @param t subset table * @param value value to write @@ -298,12 +297,12 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { def update(t: Table, value: T): Unit /** - * Copy the given tensor value to the select subset of the current tensor - * The element of the given table can be an Int or another Table. An Int means select on current + * Copy the given tensor values to the selected subset of the current tensor + * Each element of the given table can be an Int or another Table. An Int means select on current * dimension; A table means narrow on current dimension, the table should has two elements, - * of which the first is start index and the second is the end index. An empty table is equals - * to Table(1, size_of_current_dimension) If the table length is less than the tensor dimension, - * the missing dimension is applied by an empty table + * of which the first is start index and the second is the end index. An empty table is equal + * to Table(1, size_of_current_dimension). If the table's length is smaller than the tensor's + * dimension, the missing dimension is applied by an empty table. * * @param t subset table * @param src tensor to copy @@ -396,6 +395,10 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { def resize(size1: Int, size2: Int, size3: Int, size4: Int, size5: Int): Tensor[T] + def resize(sizes: Array[Int], nElement: Int): Tensor[T] = { + throw new UnsupportedOperationException("resize with nElement for sparse tensor only") + } + // def repeatTensor(result: Tensor, tensor: Tensor, size: Int*) /** @@ -470,7 +473,7 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { /** * Get a subset of the tensor on dim-th dimension. The offset is given by index, and length is - * give by size. The important difference with select is that it will not reduce the dimension + * given by size. The important difference with select is that it will not reduce the dimension * number. For Instance * tensor = * 1 2 3 @@ -795,6 +798,8 @@ sealed trait TensorType object DenseType extends TensorType +object SparseType extends TensorType + object QuantizedType extends TensorType object Tensor { @@ -1123,4 +1128,124 @@ object Tensor { tensor: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = { DenseTensor.gaussian1D[T](size, sigma, amplitude, normalize, mean, tensor) } + + /** + * Create a SparseTensor. + * + * @param indices dimension-D array to describe the indices of values. + * @param values non-zero values in this SparseTensor. + * @param shape shape + * @param ev + * @tparam T + * @return + */ + def sparse[T: ClassTag]( + indices : Array[Array[Int]], + values : Storage[T], + shape : Array[Int])( + implicit ev: TensorNumeric[T]): Tensor[T] = { + SparseTensor(indices, values, shape, shape.length) + } + + /** + * Create a SparseTensor. + * + * @param indices dimension-D array to describe the indices of values. + * @param values non-zero values in this SparseTensor. + * @param shape shape + * @param ev + * @tparam T + * @return + */ + def sparse[T: ClassTag]( + indices : Array[Array[Int]], + values : Array[T], + shape : Array[Int])( + implicit ev: TensorNumeric[T]): Tensor[T] = { + sparse(indices, Storage(values), shape, shape.length) + } + + /** + * Create a SparseTensor. + * + * @param indices dimension-D array to describe the indices of values. + * @param values non-zero values in this SparseTensor. + * @param shape shape + * @param dimension dimension + * @param ev + * @tparam T + * @return + */ + def sparse[T: ClassTag]( + indices : Array[Array[Int]], + values : Storage[T], + shape : Array[Int], + dimension: Int)( + implicit ev: TensorNumeric[T]): Tensor[T] = { + SparseTensor(indices, values, shape, dimension) + } + + /** + * Create a SparseTensor. + * + * @param indices dimension-D array to describe the indices of values. + * @param values non-zero values in this SparseTensor. + * @param shape shape + * @param dimension dimension + * @param ev + * @tparam T + * @return + */ + def sparse[T: ClassTag]( + indices : Array[Array[Int]], + values : Array[T], + shape : Array[Int], + dimension: Int)( + implicit ev: TensorNumeric[T]): Tensor[T] = { + sparse(indices, Storage(values), shape, dimension) + } + + /** + * Transform a DenseTensor to SparseTensor. + * @param denseTensor + * @param ev + * @tparam T + * @return + */ + def sparse[T: ClassTag]( + denseTensor: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + SparseTensor(denseTensor) + } + + /** + * Create a sparse tensor with shape and number of non-zero elements. + * @param shape tensor's shape. + * @param nElement number of non-zero elements. + * @param ev + * @tparam T + * @return + */ + def sparse[T: ClassTag]( + shape : Array[Int], + nElement: Int = 1)( + implicit ev: TensorNumeric[T]): Tensor[T] = { + require(nElement <= shape.product) + SparseTensor(shape, nElement) + } + + /** + * Transform a sparseTensor to DenseTensor. + * + * @param sparseTensor a sparse tensor + * @param res if defined, override to res, else will generate a new tensor. + * @param ev + * @tparam T + * @return a DenseTensor. + */ + def dense[T: ClassTag]( + sparseTensor: Tensor[T], + res: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(sparseTensor.isInstanceOf[SparseTensor[T]]) + DenseTensor(sparseTensor.asInstanceOf[SparseTensor[T]], res) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMathSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMathSpec.scala new file mode 100644 index 00000000000..62f015ee8c0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMathSpec.scala @@ -0,0 +1,291 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.numeric.NumericFloat + +@com.intel.analytics.bigdl.tags.Parallel +class SparseTensorMathSpec extends FlatSpec with Matchers { + "Sparse Matrix * Dense Vector" should "be correct" in { + val sparseM = Tensor.sparse(Tensor(2, 3).range(1, 12, 2)) + val a = Tensor(3) + a.setValue(1, 2) + a.setValue(3, 1) + val res = Tensor(2) + SparseTensorMath.addmv[Float](res, 1, res, 1, sparseM, a) + val correctRes = Tensor(2) + correctRes.setValue(1, 7) + correctRes.setValue(2, 25) + + res shouldEqual correctRes + } + + "Sparse Matrix * Dense Vector" should "be correct 2" in { + val sparseM = Tensor.sparse(Tensor(2, 3).range(1, 12, 2)) + val a = Tensor(3) + a.setValue(1, 2) + a.setValue(3, 1) + val res = Tensor(2).fill(1) + SparseTensorMath.addmv[Float](res, 2, res, 3, sparseM, a) + val correctRes = Tensor(2) + correctRes.setValue(1, 23) + correctRes.setValue(2, 77) + + res shouldEqual correctRes + } + + "narrowed Sparse Matrix * Dense Vector" should "be correct" in { + val sparseM = Tensor.sparse(Tensor(4, 3).range(1, 12, 1).narrow(1, 2, 2)) + val a = Tensor(3) + a.setValue(1, 2) + a.setValue(3, 1) + val res = Tensor(2).fill(1) + SparseTensorMath.addmv[Float](res, 1, res, 1, sparseM, a) + val correctRes = Tensor(2) + correctRes.setValue(1, 15) + correctRes.setValue(2, 24) + + res shouldEqual correctRes + } + + "Sparse Matrix * transposed Dense Matrix" should "be correct" in { + val sparseM = Tensor.sparse(Tensor(2, 3).setValue(1, 3, 1).setValue(2, 2, 1)) + val denseM = Tensor(2, 3).range(1, 12, 2).t() + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 1, res, 1, sparseM, denseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 15) + correctRes.setValue(1, 2, 21) + correctRes.setValue(2, 1, 13) + correctRes.setValue(2, 2, 19) + + res shouldEqual correctRes + } + + "Sparse Matrix * transposed Dense Matrix" should "be correct 2" in { + val sparseM = Tensor.sparse(Tensor(2, 3).setValue(1, 3, 1).setValue(2, 2, 1)) + val denseM = Tensor(2, 3).range(1, 12, 2).t() + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 1, sparseM, denseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 5) + correctRes.setValue(1, 2, 11) + correctRes.setValue(2, 1, 3) + correctRes.setValue(2, 2, 9) + + res shouldEqual correctRes + } + + "Sparse Matrix * transposed Dense Matrix" should "be correct 3" in { + val sparseM = Tensor.sparse(Tensor(2, 3).setValue(1, 3, 1).setValue(2, 2, 1)) + val denseM = Tensor(2, 3).range(1, 12, 2).t() + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 2, sparseM, denseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 10) + correctRes.setValue(1, 2, 22) + correctRes.setValue(2, 1, 6) + correctRes.setValue(2, 2, 18) + + res shouldEqual correctRes + } + + "Sparse Matrix * Dense Matrix" should "be correct" in { + val sparseM = Tensor.sparse(Tensor(2, 3).setValue(1, 3, 1).setValue(2, 2, 1)) + val denseM = Tensor(3, 2).range(1, 12, 2) + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 1, res, 1, sparseM, denseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 19) + correctRes.setValue(1, 2, 21) + correctRes.setValue(2, 1, 15) + correctRes.setValue(2, 2, 17) + + res shouldEqual correctRes + } + + "Sparse Matrix * Dense Matrix" should "be correct 2" in { + val sparseM = Tensor.sparse(Tensor(2, 3).setValue(1, 3, 1).setValue(2, 2, 1)) + val denseM = Tensor(3, 2).range(1, 12, 2) + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 1, sparseM, denseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 9) + correctRes.setValue(1, 2, 11) + correctRes.setValue(2, 1, 5) + correctRes.setValue(2, 2, 7) + + res shouldEqual correctRes + } + + "Sparse Matrix * Dense Matrix" should "be correct 3" in { + val sparseM = Tensor.sparse(Tensor(2, 3).setValue(1, 3, 1).setValue(2, 2, 1)) + val denseM = Tensor(3, 2).range(1, 12, 2) + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 2, sparseM, denseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 18) + correctRes.setValue(1, 2, 22) + correctRes.setValue(2, 1, 10) + correctRes.setValue(2, 2, 14) + + res shouldEqual correctRes + } + + "Dense Matrix * Sparse Matrix" should "be correct" in { + val sparseM = Tensor.sparse(Tensor(3, 2).setValue(2, 2, 1).setValue(3, 1, 1)) + val denseM = Tensor(2, 3).range(1, 12, 2) + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 1, res, 1, denseM, sparseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 15) + correctRes.setValue(1, 2, 13) + correctRes.setValue(2, 1, 21) + correctRes.setValue(2, 2, 19) + + res shouldEqual correctRes + } + + "Dense Matrix * Sparse Matrix" should "be correct 2" in { + val sparseM = Tensor.sparse(Tensor(3, 2).setValue(2, 2, 1).setValue(3, 1, 1)) + val denseM = Tensor(2, 3).range(1, 12, 2) + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 1, denseM, sparseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 5) + correctRes.setValue(1, 2, 3) + correctRes.setValue(2, 1, 11) + correctRes.setValue(2, 2, 9) + + res shouldEqual correctRes + } + + "Dense Matrix * Sparse Matrix" should "be correct 3" in { + val sparseM = Tensor.sparse(Tensor(3, 2).setValue(2, 2, 1).setValue(3, 1, 1)) + val denseM = Tensor(2, 3).range(1, 12, 2) + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 2, denseM, sparseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 10) + correctRes.setValue(1, 2, 6) + correctRes.setValue(2, 1, 22) + correctRes.setValue(2, 2, 18) + + res shouldEqual correctRes + } + + "Transposed Dense Matrix * Sparse Matrix" should "be correct" in { + val sparseM = Tensor.sparse(Tensor(3, 2).setValue(2, 2, 1).setValue(3, 1, 1)) + val denseM = Tensor(3, 2).range(1, 12, 2).t() + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 1, res, 1, denseM, sparseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 19) + correctRes.setValue(1, 2, 15) + correctRes.setValue(2, 1, 21) + correctRes.setValue(2, 2, 17) + + res shouldEqual correctRes + } + + "Transposed Dense Matrix * Sparse Matrix" should "be correct 2" in { + val sparseM = Tensor.sparse(Tensor(3, 2).setValue(2, 2, 1).setValue(3, 1, 1)) + val denseM = Tensor(3, 2).range(1, 12, 2).t() + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 1, denseM, sparseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 9) + correctRes.setValue(1, 2, 5) + correctRes.setValue(2, 1, 11) + correctRes.setValue(2, 2, 7) + + res shouldEqual correctRes + } + + "Transposed Dense Matrix * Sparse Matrix" should "be correct 3" in { + val sparseM = Tensor.sparse(Tensor(3, 2).setValue(2, 2, 1).setValue(3, 1, 1)) + val denseM = Tensor(3, 2).range(1, 12, 2).t() + + val res = Tensor(2, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 2, denseM, sparseM) + val correctRes = Tensor(2, 2) + correctRes.setValue(1, 1, 18) + correctRes.setValue(1, 2, 10) + correctRes.setValue(2, 1, 22) + correctRes.setValue(2, 2, 14) + + res shouldEqual correctRes + } + + "Dense Matrix * narrowed Sparse Matrix" should "be correct" in { + val sparseM = Tensor.sparse(Tensor(5, 2) + .setValue(1, 2, 3).setValue(2, 1, 4).setValue(3, 2, 1) + .setValue(4, 1, 1).setValue(5, 1, 3)).narrow(1, 2, 3) + val denseM = Tensor(4, 3).range(1, 12, 1) + + val res = Tensor(4, 2).fill(10) + SparseTensorMath.addmm[Float](res, 0, res, 2, denseM, sparseM) + val correctRes = Tensor(4, 2) + correctRes.setValue(1, 1, 14) + correctRes.setValue(1, 2, 4) + correctRes.setValue(2, 1, 44) + correctRes.setValue(2, 2, 10) + correctRes.setValue(3, 1, 74) + correctRes.setValue(3, 2, 16) + correctRes.setValue(4, 1, 104) + correctRes.setValue(4, 2, 22) + + res shouldEqual correctRes + } + + "narrowed Sparse Matrix * Dense Matrix" should "be correct" in { + val sparseM = Tensor.sparse(Tensor(5, 2) + .setValue(1, 2, 3).setValue(2, 1, 4).setValue(3, 2, 1) + .setValue(4, 1, 1).setValue(5, 1, 3)).narrow(1, 2, 3) + val denseM = Tensor(2, 4).range(1, 8, 1) + + val res = Tensor(3, 4).fill(10) + SparseTensorMath.addmm[Float](res, 1, res, 1, sparseM, denseM) + val correctRes = Tensor(3, 4) + correctRes.setValue(1, 1, 14) + correctRes.setValue(1, 2, 18) + correctRes.setValue(1, 3, 22) + correctRes.setValue(1, 4, 26) + correctRes.setValue(2, 1, 15) + correctRes.setValue(2, 2, 16) + correctRes.setValue(2, 3, 17) + correctRes.setValue(2, 4, 18) + correctRes.setValue(3, 1, 11) + correctRes.setValue(3, 2, 12) + correctRes.setValue(3, 3, 13) + correctRes.setValue(3, 4, 14) + + res shouldEqual correctRes + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala new file mode 100644 index 00000000000..fbd36689d50 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.numeric.NumericFloat + +@com.intel.analytics.bigdl.tags.Parallel +class SparseTensorSpec extends FlatSpec with Matchers { + "dim, shape, nElement" should "return right result" in { + val sTensor1 = Tensor.sparse(Tensor(3, 4).range(1, 12, 1)) + sTensor1.dim() should be (2) + sTensor1.nElement() should be (12) + sTensor1.size() should be (Array(3, 4)) + + val sTensor2 = Tensor.sparse(Array(Array(1, 2), Array(3, 5)), Array(1f, 2f), Array(3, 5)) + sTensor2.dim() should be (2) + sTensor2.nElement() should be (2) + sTensor2.size() should be (Array(3, 5)) + } + + "storageOffset" should "return right result" in { + val sTensor1 = Tensor.sparse(Tensor(3, 4).range(1, 12, 1)) + sTensor1.storageOffset() should be (1) + } + + "narrow" should "return right result" in { + val sTensor = Tensor.sparse(Tensor(6, 5).range(1, 30, 1)) + val sTensor2 = sTensor.narrow(1, 2, 4) + val sTensor3 = sTensor2.narrow(1, 2, 3) + sTensor3.storageOffset() should be (11) + sTensor3.asInstanceOf[SparseTensor[Float]]._indicesOffset should be (Array(2, 0)) + } +} From 43fcd58baa317606a491624ad61d35bb1a209fb7 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Wed, 11 Oct 2017 08:42:26 -0700 Subject: [PATCH 0441/1065] support mllib vector as feature for BigDL (#1629) support mllib vector as feature in DataFrame on Spark 2.0 --- .../main/scala/org/apache/spark/ml/DLEstimatorBase.scala | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index 86fdaadd651..dddfa790804 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -32,7 +32,9 @@ private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { val dataTypes = Seq( new ArrayType(DoubleType, false), new ArrayType(FloatType, false), - new VectorUDT) + new VectorUDT, + new org.apache.spark.mllib.linalg.VectorUDT + ) // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 val actualDataType = schema($(featuresCol)).dataType @@ -44,6 +46,8 @@ private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { def supportedTypesToSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal] = { val featureArr = if (colType == new VectorUDT) { row.getAs[Vector](index).toArray.toSeq + } else if (colType == new org.apache.spark.mllib.linalg.VectorUDT) { + row.getAs[org.apache.spark.mllib.linalg.Vector](index).toArray.toSeq } else if (colType == ArrayType(DoubleType, false)) { row.getSeq[Double](index) } else if (colType == ArrayType(FloatType, false)) { From 17b6f09b78663f0bda9bbd4f1049167def5ba50b Mon Sep 17 00:00:00 2001 From: Zefeng-Liu Date: Thu, 12 Oct 2017 00:10:15 +0800 Subject: [PATCH 0442/1065] Save bigdl model to torch model error corrected (#1642) --- .../bigdl/dllib/utils/TorchFile.scala | 32 +++++++++++++++++++ .../dllib/torch/SpatialCrossMapLRNSpec.scala | 31 +++++++++++++++++- 2 files changed, 62 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala index 0ab61bd79fe..2b2b5642267 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala @@ -162,6 +162,7 @@ object TorchFile { case "nn.SpatialConvolution" => readSpatialConvolutionWithType(elements) case "nn.SpatialConvolutionMap" => readSpatialConvolutionMapWithType(elements) case "nn.SpatialConvolutionMM" => readSpatialConvolutionWithType(elements) + case "nn.SpatialCrossMapLRN" => readSpatialCrossMapLRNWithType(elements) case "nn.SpatialZeroPadding" => readSpatialZeroPaddingWithType(elements) case "nn.Threshold" => readThresholdWithType(elements) case "nn.View" => readViewWithType(elements) @@ -294,6 +295,9 @@ object TorchFile { case m: LogSoftMax[_] => writeVersionAndClass("V 1", "nn.LogSoftMax", rawData, path) writeLogSoftMax(m, rawData, path) + case m: SpatialCrossMapLRN[_] => + writeVersionAndClass("V 1", "nn.SpatialCrossMapLRN", rawData, path) + writeSpatialCrossMapLRN(m, rawData, path) case _ => throw new Error(s"Unimplemented module $module") } @@ -502,6 +506,21 @@ object TorchFile { byteWrite(rawData, path) } + + private def writeSpatialCrossMapLRN(source: SpatialCrossMapLRN[_], + rawData: ByteBuffer, path: Path): Unit = { + val table: Table = T() + writeGeneralParameters(source, table) + table("prePad") = source.prePad + table("size") = source.size + table("alpha") = source.alpha + table("beta") = source.beta + table("k") = source.k + + writeObject(table, rawData, path, TYPE_TABLE) + byteWrite(rawData, path) + } + private def writeThreshold(source: Threshold[_], rawData: ByteBuffer, path: Path): Unit = { val table: Table = T() writeGeneralParameters(source, table) @@ -944,6 +963,19 @@ object TorchFile { result } + private def readSpatialCrossMapLRNWithType[T: ClassTag](elements: Table)( + implicit ev: TensorNumeric[T]): SpatialCrossMapLRN[T] = { + val weight = elements.getOrElse("weight", null).asInstanceOf[Tensor[T]] + val bias = elements.getOrElse("bias", null).asInstanceOf[Tensor[T]] + val result = SpatialCrossMapLRN[T]( + size = elements.getOrElse("size", 5.0).toInt, + alpha = elements.getOrElse("alpha", 1.0), + beta = elements.getOrElse("beta", 0.75), + k = elements.getOrElse("k", 1.0) + ) + result + } + private def readBatchNormalizationWithType[T: ClassTag]( elements: Table)(implicit ev: TensorNumeric[T]): BatchNormalization[T] = { val weight = elements("weight").asInstanceOf[Tensor[T]] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialCrossMapLRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialCrossMapLRNSpec.scala index ea1ed725b5c..05044260617 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialCrossMapLRNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialCrossMapLRNSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.torch -import com.intel.analytics.bigdl.nn.{GradientChecker, SpatialCrossMapLRN} +import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl._ @@ -120,4 +120,33 @@ class SpatialCrossMapLRNSpec extends TorchSpec { val checker = new GradientChecker(1e-3) checker.checkLayer[Double](layer, input, 1e-3) should be(true) } + + "SpatialCrossMapLRN module" should "be saved to or loaded from Torch model correctly" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + + val layer = new SpatialCrossMapLRN[Double](5, 1.0, 0.75, 1.0) + + val tmpFile = java.io.File.createTempFile("module", ".t7") + val absolutePath = tmpFile.getAbsolutePath + layer.saveTorch(absolutePath, true) + + val model = Module.loadTorch[Double](absolutePath).asInstanceOf[SpatialCrossMapLRN[Double]] + model shouldEqual layer + + val input = Tensor[Double](16, 3, 224, 224).rand() + val output = layer.updateOutput(input) + + val code = "torch.manualSeed(" + seed + ")\n" + + "layer = torch.load(\'" + absolutePath + "\')\n" + + "output = layer:forward(input) " + + val torchResult = TH.run(code, Map("input" -> input), Array("output"))._2 + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + + output shouldEqual luaOutput + } + + } From 66a85be73b58480df30f1a28cd34dc290e511c31 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Thu, 12 Oct 2017 00:36:14 +0800 Subject: [PATCH 0443/1065] Add tile operation (#1646) * Add tile operation * reset core * remove unnecessary comments --- .../analytics/bigdl/dllib/nn/ops/Tile.scala | 100 ++++++++++++++++++ .../bigdl/dllib/nn/ops/TileSpec.scala | 64 +++++++++++ 2 files changed, 164 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Tile.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Tile.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Tile.scala new file mode 100644 index 00000000000..b625668a940 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Tile.scala @@ -0,0 +1,100 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath._ +import com.intel.analytics.bigdl.utils.{Engine, Table} + +import scala.concurrent.Future +import scala.reflect.ClassTag + +/** + * This operation creates a new tensor by replicating input multiples times. + * The output tensor's i'th dimension has input.dims(i) * multiples[i] elements, + * and the values of input are replicated multiples[i] times along the 'i'th dimension. + * + * For example, tiling [a b c d] by [1, 2] produces [a b c d a b c d]. + * + * @param ev$1 + * @param ev + * @tparam T Numeric type. Only support float/double now + */ +class Tile[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[_], T] { + @transient + private var results: Array[Future[Unit]] = _ + + def updateOutput(inputs: Table): Tensor[_] = { + val input = inputs[Tensor[Tensor[NumericWildcard]]](1) + val multiples = inputs[Tensor[Int]](2) + + require(input.nDimension() == multiples.size(1), + "Length of multiples must be the same as the number of dimensions in input") + + output.asInstanceOf[Tensor[Tensor[NumericWildcard]]].resizeAs(input).copy(input) + + for (j <- 1 to input.nDimension()) { + val currentOutput = output.clone() + val mult = multiples(Array(j)) + val newSize = output.size() + newSize(j - 1) = newSize(j - 1) * mult + output.resize(newSize) + var offset = 1 + var i = 0 + while (i < mult) { + val _offset = offset + + if (results == null || results.length != mult) { + results = new Array[Future[Unit]](mult) + } + + results(i) = Engine.model.invoke(() => { + val target = this.output.narrow(j, _offset, + currentOutput.size(j)) + if (target.isContiguous() || j > 2) { + // Copy directly when target is Contiguous or dimension is larger than 2 + // in which case the contiguous region in target tensor is fairly small in practice + target.asInstanceOf[Tensor[NumericWildcard]] + .copy(currentOutput.asInstanceOf[Tensor[NumericWildcard]]) + } else { + // Divide target into contiguous frames when target isn't contiguous + var f = 1 + while (f <= target.size(1)) { + val curFrame = target.select(1, f) + val outputFrame = currentOutput.select(1, f) + require(curFrame.isContiguous()) + require(outputFrame.isContiguous()) + curFrame.asInstanceOf[Tensor[NumericWildcard]] + .copy(outputFrame.asInstanceOf[Tensor[NumericWildcard]]) + f += 1 + } + } + }) + i += 1 + offset += currentOutput.size(j) + } + } + + output + } +} + +object Tile { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new Tile()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala new file mode 100644 index 00000000000..47fa8c6b6f3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class TileSpec extends FlatSpec with Matchers { + "Tile operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = + T( + Tensor( + T( + T( + T(1f, 2f, 3f), + T(2f, 2f, 4f), + T(2f, 2f, 4f)), + T( + T(2f, 2f, 3f), + T(2f, 2f, 4f), + T(2f, 2f, 4f)) + )), + Tensor[Int](T(2, 1, 2)) + ) + + val expectOutput = Tensor( + T( + T( + T(1f, 2f, 3f, 1f, 2f, 3f), + T(2f, 2f, 4f, 2f, 2f, 4f), + T(2f, 2f, 4f, 2f, 2f, 4f)), + T( + T(2f, 2f, 3f, 2f, 2f, 3f), + T(2f, 2f, 4f, 2f, 2f, 4f), + T(2f, 2f, 4f, 2f, 2f, 4f)), + T( + T(1f, 2f, 3f, 1f, 2f, 3f), + T(2f, 2f, 4f, 2f, 2f, 4f), + T(2f, 2f, 4f, 2f, 2f, 4f)), + T( + T(2f, 2f, 3f, 2f, 2f, 3f), + T(2f, 2f, 4f, 2f, 2f, 4f), + T(2f, 2f, 4f, 2f, 2f, 4f)) + )) + + val output = Tile().forward(input) + output should be(expectOutput) + } +} From 246bc3565e2ce788d1b60b6521cf63a5a4b5ea9e Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 12 Oct 2017 00:27:08 -0400 Subject: [PATCH 0444/1065] feat: convert model between caffe, torch, tensorflow and bigdl. (#1643) * feat: convert model between caffe, torch, bigdl. * fix: move options checking to scopt --- .../bigdl/dllib/utils/ConvertModel.scala | 131 ++++++++++++++++++ 1 file changed, 131 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ConvertModel.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ConvertModel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ConvertModel.scala new file mode 100644 index 00000000000..877a9589872 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ConvertModel.scala @@ -0,0 +1,131 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.utils.caffe.CaffeLoader +import com.intel.analytics.bigdl.numeric.NumericFloat +import scopt.OptionParser + +object ConvertModel { + + case class ConverterParam( + from: String = "", + to: String = "", + input: String = "", + output: String = "", + prototxt: String = "", + tf_inputs: String = "", + tf_outputs: String = "", + quantize: Boolean = false + ) + + val fromSupports = Set("bigdl", "caffe", "torch", "tensorflow") + val toSupports = Set("bigdl", "caffe", "torch") + + val converterParser = new OptionParser[ConverterParam]("Convert caffe model to bigdl model") { + opt[String]("from") + .text(s"What's the type origin model ${fromSupports.mkString(",")}?") + .action((x, c) => c.copy(from = x)) + .validate(x => + if (fromSupports.contains(x.toLowerCase)) { + success + } else { + failure(s"Only support ${fromSupports.mkString(",")}") + }) + .required() + opt[String]("to") + .text(s"What's the type of model you want ${toSupports.mkString(",")}?") + .action((x, c) => c.copy(to = x)) + .validate(x => + if (toSupports.contains(x.toLowerCase)) { + success + } else { + failure(s"Only support ${toSupports.mkString(",")}") + }) + .required() + opt[String]("input") + .text("Where's the origin model file?") + .action((x, c) => c.copy(input = x)) + .required() + opt[String]("output") + .text("Where's the bigdl model file to save?") + .action((x, c) => c.copy(output = x)) + .required() + opt[String]("prototxt") + .text("Where's the caffe deploy prototxt?") + .action((x, c) => c.copy(prototxt = x)) + opt[Boolean]("quantize") + .text("Do you want to quantize the model?") + .action((x, c) => c.copy(quantize = x)) + opt[String]("tf_inputs") + .text("Inputs for Tensorflow") + .action((x, c) => c.copy(tf_inputs = x)) + opt[String]("tf_outputs") + .text("Outputs for Tensorflow") + .action((x, c) => c.copy(tf_outputs = x)) + + checkConfig(c => + if (c.from.toLowerCase == "caffe" && c.prototxt.isEmpty) { + failure(s"If model is converted from caffe, the prototxt should be given with --prototxt.") + } else if (c.from.toLowerCase == "tensorflow" && + (c.tf_inputs.isEmpty || c.tf_outputs.isEmpty)) { + failure(s"If model is converted from tensorflow, inputs and outputs should be given") + } else if (c.quantize == true && c.to.toLowerCase != "bigdl") { + failure(s"Only support quantizing models to BigDL model now.") + } else { + success + } + ) + } + + def main(args: Array[String]): Unit = { + converterParser.parse(args, ConverterParam()).foreach { param => + val input = param.input + val output = param.output + val ifs = "," + + var loadedModel = param.from.toLowerCase match { + case "bigdl" => + Module.loadModule(input) + case "torch" => + Module.loadTorch(input) + case "caffe" => + CaffeLoader.loadCaffe(param.prototxt, input)._1 + case "tensorflow" => + val inputs = param.tf_inputs.split(ifs) + val outputs = param.tf_outputs.split(ifs) + Module.loadTF(input, inputs, outputs) + } + + val model = if (param.quantize) { + loadedModel.quantize() + } else { + loadedModel + } + + param.to.toLowerCase match { + case "bigdl" => + model.saveModule(output, overWrite = true) + case "torch" => + model.saveTorch(output) + case "caffe" => + model.saveCaffe(param.prototxt, output) + } + } + } +} From ca5828b6d7a1c322d9a65f5c55c37096d3e11ff9 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 12 Oct 2017 13:04:06 +0800 Subject: [PATCH 0445/1065] handle reflection race conditions in 2.10 (#1644) --- .../utils/serializer/ModuleSerializable.scala | 75 ++++++++++--------- .../utils/serializer/ModuleSerializer.scala | 17 +++-- 2 files changed, 50 insertions(+), 42 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 69305246bef..14888e57e63 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -44,6 +44,8 @@ trait ModuleSerializable extends Loadable with Savable{ private val bigDLVersion = com.intel.analytics.bigdl.BIGDL_VERSION + protected val lock = new Object + // Separate this two methods for reuse in sub-classes protected def checkVersion[T: ClassTag](module : BigDLModule) (implicit ev: TensorNumeric[T]) : Unit = { @@ -104,24 +106,27 @@ trait ModuleSerializable extends Loadable with Savable{ val constructorFullParams = constructorMirror.symbol.paramss val args = new Array[Object](constructorFullParams(0).size + constructorFullParams(1).size) var i = 0; - constructorFullParams.foreach(map => { - map.foreach(param => { - val name = param.name.decodedName.toString - val ptype = param.typeSignature - if (ptype <:< universe.typeOf[ClassTag[_]]) { - args(i) = evidence - } else if (ptype.toString == - tensorNumericType.toString) { - args(i) = ev - } else { - require(modelAttributes.containsKey(name), s"$name value cannot be found") - val attribute = modelAttributes.get(name) - val value = DataConverter.getAttributeValue(context, attribute) - args(i) = value - } - i+= 1 + lock.synchronized { + constructorFullParams.foreach(map => { + map.foreach(param => { + val name = param.name.decodedName.toString + val ptype = param.typeSignature + if (ptype <:< universe.typeOf[ClassTag[_]]|| + ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { + args(i) = evidence + } else if (ptype.toString == + tensorNumericType.toString) { + args(i) = ev + } else { + require(modelAttributes.containsKey(name), s"$name value cannot be found") + val attribute = modelAttributes.get(name) + val value = DataConverter.getAttributeValue(context, attribute) + args(i) = value + } + i+= 1 + }) }) - }) + } constructorMirror.apply(args : _*). asInstanceOf[AbstractModule[Activity, Activity, T]] } @@ -159,23 +164,25 @@ trait ModuleSerializable extends Loadable with Savable{ val cls = module.getClass val fullParams = getCostructorMirror(cls).symbol.paramss val constructorParams = fullParams(0) - constructorParams.foreach(param => { - val paramName = param.name.decodedName.toString - var ptype = param.typeSignature - val attrBuilder = AttrValue.newBuilder - // For some modules, fields are declared inside but passed to Super directly - var field : Field = null - try { - field = cls.getDeclaredField(paramName) - } catch { - case e : NoSuchFieldException => - field = cls.getSuperclass.getDeclaredField(paramName) - } - field.setAccessible(true) - val fieldValue = field.get(module) - DataConverter.setAttributeValue(context, attrBuilder, fieldValue, ptype) - bigDLModelBuilder.putAttr(paramName, attrBuilder.build) - }) + lock.synchronized { + constructorParams.foreach(param => { + val paramName = param.name.decodedName.toString + var ptype = param.typeSignature + val attrBuilder = AttrValue.newBuilder + // For some modules, fields are declared inside but passed to Super directly + var field : Field = null + try { + field = cls.getDeclaredField(paramName) + } catch { + case e : NoSuchFieldException => + field = cls.getSuperclass.getDeclaredField(paramName) + } + field.setAccessible(true) + val fieldValue = field.get(module) + DataConverter.setAttributeValue(context, attrBuilder, fieldValue, ptype) + bigDLModelBuilder.putAttr(paramName, attrBuilder.build) + }) + } } protected def createBigDLModule[T: ClassTag](context: DeserializeContext, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 2896d71a7e1..131fa19d2da 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -119,15 +119,16 @@ object ModuleSerializer extends ModuleSerializable{ private[serializer] def getCostructorMirror[T : ClassTag](cls : Class[_]): universe.MethodMirror = { - - val clsSymbol = runtimeMirror.classSymbol(cls) - val cm = runtimeMirror.reflectClass(clsSymbol) - // to make it compatible with both 2.11 and 2.10 - val ctorCs = clsSymbol.toType.declaration(universe.nme.CONSTRUCTOR) - val primary : Option[universe.MethodSymbol] = ctorCs.asTerm.alternatives.collectFirst{ - case cstor : universe.MethodSymbol if cstor.isPrimaryConstructor => cstor + lock.synchronized { + val clsSymbol = runtimeMirror.classSymbol(cls) + val cm = runtimeMirror.reflectClass(clsSymbol) + // to make it compatible with both 2.11 and 2.10 + val ctorCs = clsSymbol.toType.declaration(universe.nme.CONSTRUCTOR) + val primary: Option[universe.MethodSymbol] = ctorCs.asTerm.alternatives.collectFirst { + case cstor: universe.MethodSymbol if cstor.isPrimaryConstructor => cstor + } + cm.reflectConstructor(primary.get) } - cm.reflectConstructor(primary.get) } private def init() : Unit = { From 327b480711c5b1f5e566287139a8bdcdf4b572e2 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Thu, 12 Oct 2017 13:16:43 +0800 Subject: [PATCH 0446/1065] add Variational Auto Encoder (#1432) * vae * add python api and unit tests * add docs --- .../bigdl/dllib/nn/GaussianCriterion.scala | 84 +++++++++++++++++++ .../bigdl/dllib/nn/GaussianSampler.scala | 68 +++++++++++++++ .../bigdl/dllib/nn/KLDCriterion.scala | 76 +++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 12 +++ .../dllib/torch/GaussianCriterionSpec.scala | 53 ++++++++++++ .../bigdl/dllib/torch/KLDCriterionSpec.scala | 53 ++++++++++++ .../bigdl/dllib/torch/SamplerSpec.scala | 53 ++++++++++++ 7 files changed, 399 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianCriterion.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSampler.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GaussianCriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/KLDCriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SamplerSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianCriterion.scala new file mode 100644 index 00000000000..97505f66524 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianCriterion.scala @@ -0,0 +1,84 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractCriterion +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Computes the log-likelihood of a sample x given a Gaussian distribution p. + */ +class GaussianCriterion[@specialized(Float, Double) T: ClassTag]( + implicit ev: TensorNumeric[T]) extends AbstractCriterion[Table, Tensor[T], T] { + + @transient + private var mean: Tensor[T] = null + @transient + private var vari: Tensor[T] = null + @transient + private var expVar: Tensor[T] = null + + override def updateOutput(input: Table, target: Tensor[T]): T = { + if (mean == null) mean = Tensor[T]() + if (vari == null) vari = Tensor[T]() + if (expVar == null) expVar = Tensor[T]() + /* + log(sigma) + 0.5 *log(2pi) + 0.5 * (x - mu)^2/sigma^2 + input[1] = mu + input[2] = log(sigma^2) + */ + mean.resizeAs(input[Tensor[T]](1)).copy(input(1)) + vari.resizeAs(input[Tensor[T]](2)).copy(input(2)) + expVar.resizeAs(input[Tensor[T]](2)).copy(input(2)) + + expVar.exp() + vari.mul(ev.fromType(0.5)).add(ev.fromType(0.5 * math.log(2 * math.Pi))) + + vari.add(ev.fromType(0.5), mean.add(ev.fromType(-1), target).pow(ev.fromType(2)).cdiv(expVar)) + + output = vari.sum() + return output + } + + override def updateGradInput(input: Table, target: Tensor[T]): Table = { + if (!gradInput.contains(1)) gradInput(1) = Tensor() + if (!gradInput.contains(2)) gradInput(2) = Tensor() + + mean.resizeAs(input[Tensor[T]](1)).copy(input(1)) + expVar.resizeAs(input[Tensor[T]](2)).copy(input(2)) + expVar.exp() + + // -(x-mu)/sigma^2 + gradInput[Tensor[T]](1).resizeAs(mean).copy(mean.add(ev.fromType(-1), target)) + gradInput[Tensor[T]](1).cdiv(expVar) + // 0.5 - 0.5 * (x - mu)^2 / sigma^2 + gradInput(2) = mean.cmul(gradInput[Tensor[T]](1)).mul(ev.fromType(-0.5)).add(ev.fromType(0.5)) + + gradInput + } +} + +object GaussianCriterion { + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : GaussianCriterion[T] = { + new GaussianCriterion[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSampler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSampler.scala new file mode 100644 index 00000000000..6d543b95d26 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSampler.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Takes {mean, log_variance} as input and samples from the Gaussian distribution + */ +class GaussianSampler[@specialized(Float, Double) T: ClassTag]( + implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { + + val eps = Tensor[T]() + + override def updateOutput(input: Table): Tensor[T] = { + eps.resizeAs(input(1)).randn() + val output2 = output.toTensor + output2.resizeAs(input(2)).copy(input(2)) + output2.mul(ev.fromType(0.5)).exp().cmul(eps) + output2.add(input[Tensor[T]](1)) + output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + if (!gradInput.contains(1)) gradInput(1) = Tensor() + if (!gradInput.contains(2)) gradInput(2) = Tensor() + + gradInput[Tensor[T]](1).resizeAs(gradOutput).copy(gradOutput) + gradInput[Tensor[T]](2).resizeAs(gradOutput).copy(input(2)) + + gradInput[Tensor[T]](2).mul(ev.fromType(0.5)).exp().mul(ev.fromType(0.5)).cmul(eps) + gradInput[Tensor[T]](2).cmul(gradOutput) + + gradInput + } + + override def clearState() : this.type = { + super.clearState() + eps.set() + this + } +} + +object GaussianSampler { + def apply[@specialized(Float, Double) T: ClassTag]()( + implicit ev: TensorNumeric[T]) : GaussianSampler[T] = { + new GaussianSampler[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala new file mode 100644 index 00000000000..87c6da448f4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala @@ -0,0 +1,76 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import breeze.numerics.exp +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractCriterion, AbstractModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + +/** + * Computes the KL-divergence of the Gaussian distribution. + */ +class KLDCriterion[@specialized(Float, Double) T: ClassTag]( + implicit ev: TensorNumeric[T]) extends AbstractCriterion[Table, Tensor[T], T] { + + @transient + private var mean: Tensor[T] = null + @transient + private var vari: Tensor[T] = null + @transient + private var expVar: Tensor[T] = null + + override def updateOutput(input: Table, target: Tensor[T]): T = { + if (mean == null) mean = Tensor[T]() + if (vari == null) vari = Tensor[T]() + if (expVar == null) expVar = Tensor[T]() + + mean.resizeAs(input[Tensor[T]](1)).copy(input(1)) + vari.resizeAs(input[Tensor[T]](2)).copy(input(2)) + + // Appendix B from VAE paper: 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2) + mean.pow(ev.fromType(2)) + expVar.resizeAs(vari).copy(vari) + expVar.exp().add(ev.one).add(ev.fromType(-1), mean).add(ev.fromType(-1), vari) + + output = ev.times(ev.fromType(0.5), expVar.sum()) + output + } + + override def updateGradInput(input: Table, target: Tensor[T]): Table = { + if (!gradInput.contains(1)) gradInput(1) = Tensor() + if (!gradInput.contains(2)) gradInput(2) = Tensor() + + // d_L/d_mu = mu + gradInput[Tensor[T]](1).resizeAs(input(1)).copy(input(1)) + // d_L/d_sigma = 0.5*(exp(log_sq_sigma)-1) + gradInput[Tensor[T]](2).resizeAs(input(2)).copy(input(2)) + gradInput[Tensor[T]](2).exp().add(ev.fromType(-1)).mul(ev.fromType(0.5)) + + gradInput + } +} + +object KLDCriterion { + def apply[@specialized(Float, Double) T: ClassTag]()( + implicit ev: TensorNumeric[T]): KLDCriterion[T] = { + new KLDCriterion[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index d28515dd31c..c0f8dfc1159 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1151,6 +1151,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Identity[T]() } + def createGaussianSampler(): GaussianSampler[T] = { + GaussianSampler[T]() + } + def createMultiLabelSoftMarginCriterion(weights: JTensor = null, sizeAverage: Boolean = true) : MultiLabelSoftMarginCriterion[T] = { @@ -1388,6 +1392,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ParallelCriterion[T](repeatTarget) } + def createKLDCriterion(): KLDCriterion[T] = { + KLDCriterion[T]() + } + + def createGaussianCriterion(): GaussianCriterion[T] = { + GaussianCriterion[T]() + } + def createSmoothL1Criterion(sizeAverage: Boolean = true) : SmoothL1Criterion[T] = { SmoothL1Criterion[T](sizeAverage) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GaussianCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GaussianCriterionSpec.scala new file mode 100644 index 00000000000..4434973f995 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GaussianCriterionSpec.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn.GaussianCriterion +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + + +class GaussianCriterionSpec extends FlatSpec with Matchers{ + "A GaussianCriterion Module " should "generate correct output and grad" in { + val seed = 100 + RNG.setSeed(seed) + val model = GaussianCriterion[Float]() + + RNG.setSeed(seed) + val input1 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input2 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input = T(input1, input2) + + val target = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + + val loss = model.forward(input, target) + val gradInput = model.backward(input, target) + + loss should be(6.575727f +- 1e-3f) + + val gardTarget1 = Tensor(Array(-0.054713856f, 0.39738163f, -0.5449059f, + -0.034790944f, 0.25486523f, -0.28528172f), Array(2, 3)) + + val gardTarget2 = Tensor(Array(0.49651626f, 0.408394f, 0.35083658f, + 0.4992921f, 0.46332347f, 0.45096576f), Array(2, 3)) + + gradInput[Tensor[Float]](1) should be(gardTarget1) + gradInput[Tensor[Float]](2) should be(gardTarget2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/KLDCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/KLDCriterionSpec.scala new file mode 100644 index 00000000000..53de9615f7c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/KLDCriterionSpec.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn.{Add, KLDCriterion} +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + + +class KLDCriterionSpec extends FlatSpec with Matchers{ + "A KLDCriterion Module " should "generate correct output and grad" in { + val seed = 100 + RNG.setSeed(seed) + val model = KLDCriterion[Float]() + + RNG.setSeed(seed) + val input1 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input2 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input = T(input1, input2) + + val target = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + + val loss = model.forward(input, target) + val gradInput = model.backward(input, target) + + loss should be(5.54158f +- 1e-3f) + + val gardTarget1 = Tensor(Array(0.54340494f, 0.67115563f, 0.2783694f, + 0.4120464f, 0.4245176f, 0.52638245f), Array(2, 3)) + + val gardTarget2 = Tensor(Array(0.66372836f, 0.08010721f, 0.002364993f, + 0.084828794f, 0.06463373f, 0.10249251f), Array(2, 3)) + + gradInput[Tensor[Float]](1) should be(gardTarget1) + gradInput[Tensor[Float]](2) should be(gardTarget2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SamplerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SamplerSpec.scala new file mode 100644 index 00000000000..6f719ef8ea2 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SamplerSpec.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn.{KLDCriterion, GaussianSampler} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class GaussianSamplerSpec extends FlatSpec with Matchers{ + "A Sampler Module " should "generate correct output and grad" in { + val seed = 100 + RNG.setSeed(seed) + val model = GaussianSampler[Float]() + + RNG.setSeed(seed) + val input1 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input2 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input = T(input1, input2) + + val gradOutput = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + + val output = model.forward(input) + val gradInput = model.backward(input, gradOutput) + + val outputTarget = Tensor(Array(0.05043915f, 0.4935567f, 1.3664707f, + -0.54287064f, 0.7525101f, 1.8190227f), Array(2, 3)) + val gardTarget1 = Tensor(Array(0.67074907f, 0.21010774f, 0.82585275f, + 0.4527399f, 0.13670659f, 0.87014264f), Array(2, 3)) + + val gardTarget2 = Tensor(Array(-0.16532817f, -0.018657455f, 0.4493057f, + -0.21616453f, 0.022419363f, 0.5623907f), Array(2, 3)) + + output should be(outputTarget) + gradInput[Tensor[Float]](1) should be(gardTarget1) + gradInput[Tensor[Float]](2) should be(gardTarget2) + } +} From 2c0c2697eeaf89a2aceb4ede750ae438e048bf14 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 12 Oct 2017 02:07:58 -0400 Subject: [PATCH 0447/1065] fix: QuantizeTensor supports for ModelBroadcast (#1648) * fix: QuantizeTensor supports for ModelBroadcast * fix: typo --- .../dllib/models/utils/ModelBroadcast.scala | 19 ++++++++++++++++--- .../models/utils/ModelBroadcastSpec.scala | 8 ++++++++ 2 files changed, 24 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 3ba0eda80dc..a397400800e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.models.utils import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.apache.spark.SparkContext import org.apache.spark.broadcast.Broadcast @@ -70,8 +70,14 @@ class ModelBroadcast[T: ClassTag](implicit ev: TensorNumeric[T]) extends Seriali while (i < parameters._1.length) { if (parameters._1(i) != null) { val wb = parameters._1(i) - weightsBias(i) = Tensor[T](Storage(wb.storage().array()), - wb.storageOffset(), wb.size(), wb.stride()) + wb match { + case quantTensor: QuantizedTensor[T] => + weightsBias(i) = QuantizedTensor[T](quantTensor.getStorage, quantTensor.maxOfRow, + quantTensor.minOfRow, quantTensor.sumOfRow, quantTensor.size(), quantTensor.params) + case _ => + weightsBias(i) = Tensor[T](Storage(wb.storage().array()), + wb.storageOffset(), wb.size(), wb.stride()) + } } i += 1 } @@ -80,11 +86,18 @@ class ModelBroadcast[T: ClassTag](implicit ev: TensorNumeric[T]) extends Seriali if (parameters._1(i) != null) { parameters._1(i).set() } + i += 1 + } + + // because in quantized mode, the weight number may be different with gradWeight number + i = 0 + while (i < parameters._2.length) { if (parameters._2(i) != null) { parameters._2(i).set() } i += 1 } + weightsBias } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala index 63a475c2384..8181f034549 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala @@ -47,6 +47,14 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } + "quantized model broadcast" should "work properly" in { + val model = LeNet5(10).quantize() + + val modelBroadCast = ModelBroadcast[Float].broadcast(sc, model) + modelBroadCast.value().toString should be(model.toString) + modelBroadCast.value().parameters()._1 should be(model.parameters()._1) + } + after { if (sc != null) { sc.stop() From d29a3a2ea9b3eae3fcaf325b1bc6e7ba5038fcf1 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 12 Oct 2017 15:49:35 +0800 Subject: [PATCH 0448/1065] serveral fix for preprocessing (#1609) * serveral fix for preprocessing * load all * fix tests * more fixes * meet code review * fix style * fix style * fix tests * fix tests * rebase and fix api * refine test case * fix tests * add transpose * fix tests * fix expandims * fix serializer * fix doc --- .../analytics/bigdl/dllib/nn/CDivTable.scala | 34 +- .../analytics/bigdl/dllib/nn/CSubTable.scala | 30 +- .../analytics/bigdl/dllib/nn/Graph.scala | 2 +- .../analytics/bigdl/dllib/nn/JoinTable.scala | 15 +- .../intel/analytics/bigdl/dllib/nn/Mean.scala | 9 +- .../intel/analytics/bigdl/dllib/nn/Pack.scala | 77 +- .../nn/SpatialDivisiveNormalization.scala | 4 +- .../nn/SpatialSubtractiveNormalization.scala | 4 +- .../analytics/bigdl/dllib/nn/Squeeze.scala | 21 +- .../intel/analytics/bigdl/dllib/nn/Sum.scala | 27 +- .../bigdl/dllib/nn/abstractnn/Activity.scala | 4 + .../bigdl/dllib/nn/ops/ControlOps.scala | 28 +- .../bigdl/dllib/nn/ops/DecodeImage.scala | 5 +- .../analytics/bigdl/dllib/nn/ops/Equal.scala | 9 +- .../bigdl/dllib/nn/ops/NotEqual.scala | 84 + .../analytics/bigdl/dllib/nn/ops/OneHot.scala | 6 +- .../bigdl/dllib/nn/ops/ParseExample.scala | 9 +- .../analytics/bigdl/dllib/nn/ops/Slice.scala | 5 +- .../analytics/bigdl/dllib/nn/ops/Substr.scala | 45 + .../bigdl/dllib/nn/ops/package.scala | 9 +- .../bigdl/dllib/nn/{ops => tf}/BiasAdd.scala | 57 +- .../bigdl/dllib/tensor/Convertable.scala | 18 +- .../bigdl/dllib/tensor/DenseTensor.scala | 142 +- .../tensor/QuantizedTensorUnsupported.scala | 7 +- .../bigdl/dllib/tensor/SparseTensor.scala | 8 +- .../analytics/bigdl/dllib/tensor/Tensor.scala | 11 +- .../bigdl/dllib/tensor/TensorMath.scala | 10 + .../bigdl/dllib/tensor/TensorNumeric.scala | 13 + .../dllib/utils/python/api/PythonBigDL.scala | 4 +- .../utils/serializer/ModuleSerializable.scala | 4 +- .../dllib/utils/tf/BigDLToTensorflow.scala | 2 +- .../utils/tf/FixedLengthRecordReader.scala | 68 + .../bigdl/dllib/utils/tf/Session.scala | 253 +- .../dllib/utils/tf/TFRecordIterator.scala | 2 +- .../dllib/utils/tf/TensorflowLoader.scala | 18 +- .../dllib/utils/tf/TensorflowToBigDL.scala | 14 +- .../dllib/utils/tf/loaders/BiasAdd.scala | 36 + .../bigdl/dllib/utils/tf/loaders/Cast.scala | 50 + .../dllib/utils/tf/loaders/DecodeGif.scala | 35 + .../dllib/utils/tf/loaders/DecodeJpeg.scala | 38 + .../dllib/utils/tf/loaders/DecodePng.scala | 38 + .../dllib/utils/tf/loaders/DecodeRaw.scala | 40 + .../bigdl/dllib/utils/tf/loaders/Less.scala | 32 + .../dllib/utils/tf/loaders/LogicalAnd.scala | 32 + .../dllib/utils/tf/loaders/LogicalNot.scala | 32 + .../dllib/utils/tf/loaders/LogicalOr.scala | 32 + .../bigdl/dllib/utils/tf/loaders/MatMul.scala | 35 + .../bigdl/dllib/utils/tf/loaders/Mean.scala | 28 +- .../bigdl/dllib/utils/tf/loaders/Merge.scala | 32 + .../bigdl/dllib/utils/tf/loaders/NoOp.scala | 32 + .../dllib/utils/tf/loaders/NotEqual.scala | 35 + .../bigdl/dllib/utils/tf/loaders/OneHot.scala | 38 + .../bigdl/dllib/utils/tf/loaders/Prod.scala | 2 +- .../dllib/utils/tf/loaders/RealDiv.scala | 32 + .../dllib/utils/tf/loaders/Reshape.scala | 7 +- .../bigdl/dllib/utils/tf/loaders/Substr.scala | 32 + .../bigdl/dllib/utils/tf/loaders/Sum.scala | 68 + .../bigdl/dllib/utils/tf/loaders/Switch.scala | 32 + .../dllib/utils/tf/loaders/Transpose.scala | 82 + .../bigdl/dllib/utils/tf/loaders/Utils.scala | 10 +- .../tf/{lenet.pbtxt => lenet_batch_2.pbtxt} | 2 +- .../resources/tf/lenet_with_batch_3.pbtxt | 17028 ++++++++++++++++ .../test/resources/tf/mnist_train.tfrecord | Bin 0 -> 4154 bytes .../analytics/bigdl/dllib/nn/GraphSpec.scala | 4 +- .../analytics/bigdl/dllib/nn/PackSpec.scala | 23 + .../bigdl/dllib/nn/ops/DecodeImageSpec.scala | 4 +- .../bigdl/dllib/nn/ops/EqualSpec.scala | 11 +- .../bigdl/dllib/nn/ops/ExpandDimsSpec.scala | 30 +- .../bigdl/dllib/nn/ops/NotEqualSpec.scala | 133 + .../bigdl/dllib/nn/ops/OneHotSpec.scala | 6 +- .../bigdl/dllib/nn/ops/ParseExampleSpec.scala | 8 +- .../{MatMulSpec.scala => SubstrSpec.scala} | 33 +- .../dllib/nn/{ops => tf}/BiasAddSpec.scala | 23 +- .../bigdl/dllib/tensor/DenseTensorSpec.scala | 18 +- .../analytics/bigdl/dllib/torch/SumSpec.scala | 2 +- .../serializer/ModuleSerializerSpec.scala | 2 +- .../bigdl/dllib/utils/tf/SessionSpec.scala | 62 +- .../dllib/utils/tf/TensorflowSaverSpec.scala | 2 +- 78 files changed, 18890 insertions(+), 389 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqual.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Substr.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/BiasAdd.scala (52%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/FixedLengthRecordReader.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAdd.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Less.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalAnd.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalNot.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalOr.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MatMul.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NotEqual.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Substr.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala rename scala/dllib/src/test/resources/tf/{lenet.pbtxt => lenet_batch_2.pbtxt} (99%) create mode 100644 scala/dllib/src/test/resources/tf/lenet_with_batch_3.pbtxt create mode 100644 scala/dllib/src/test/resources/tf/mnist_train.tfrecord create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqualSpec.scala rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/{MatMulSpec.scala => SubstrSpec.scala} (60%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/BiasAddSpec.scala (69%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CDivTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CDivTable.scala index fde6cc0c096..ff53fc9300c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CDivTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CDivTable.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag @@ -28,25 +28,31 @@ import scala.reflect.ClassTag @SerialVersionUID(- 3746356029327536265L) class CDivTable[T: ClassTag](implicit ev: TensorNumeric[T]) - extends AbstractModule[Table, Tensor[T], T]{ + extends AbstractModule[Table, Tensor[_], T]{ - override def updateOutput(input: Table): Tensor[T] = { - val res1 = input[Tensor[T]](1) - val res2 = input[Tensor[T]](2) + override def updateOutput(input: Table): Tensor[_] = { + val res1 = input[Tensor[NumericWildcard]](1) + val res2 = input[Tensor[NumericWildcard]](2) - output.resizeAs(res1).copy(res1) - output.cdiv(res2) + if (output.getType() != res1.getType()) { + output = res1.emptyInstance() + } + + output.asInstanceOf[Tensor[NumericWildcard]].resizeAs(res1).copy(res1) + + output.asInstanceOf[Tensor[NumericWildcard]].div(res2) output } - override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { - val res1 = input[Tensor[T]](1) - val res2 = input[Tensor[T]](2) + override def updateGradInput(input: Table, gradOutput: Tensor[_]): Table = { + val res1 = input[Tensor[NumericWildcard]](1) + val res2 = input[Tensor[NumericWildcard]](2) - if (!gradInput.contains(1)) gradInput.insert(1, Tensor[T]()) - if (!gradInput.contains(2)) gradInput.insert(2, Tensor[T]()) - gradInput[Tensor[T]](1).resizeAs(res1).copy(gradOutput).cdiv(res2) - gradInput[Tensor[T]](2).resizeAs(res2).zero(). + if (!gradInput.contains(1)) gradInput.insert(1, res1.emptyInstance()) + if (!gradInput.contains(2)) gradInput.insert(2, res2.emptyInstance()) + gradInput[Tensor[NumericWildcard]](1).resizeAs(res1) + .copy(gradOutput.asInstanceOf[Tensor[NumericWildcard]]).div(res2) + gradInput[Tensor[NumericWildcard]](2).resizeAs(res2).zero(). addcdiv(ev.fromType(-1), gradInput(1), res2).cmul(res1) gradInput diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CSubTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CSubTable.scala index c7ef03c381a..f8d5586def6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CSubTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CSubTable.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag @@ -28,20 +28,30 @@ import scala.reflect.ClassTag @SerialVersionUID( - 7694575573537075609L) class CSubTable[T: ClassTag]()( - implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{ + implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[_], T]{ - override def updateOutput(input: Table): Tensor[T] = { - output.resizeAs(input(1)).copy(input(1)) - output.add(ev.fromType(-1), input(2)) + override def updateOutput(input: Table): Tensor[_] = { + val firstInput = input[Tensor[NumericWildcard]](1) + if (output.getType() != firstInput.getType()) { + output = firstInput.emptyInstance() + } + output.asInstanceOf[Tensor[NumericWildcard]].resizeAs(firstInput).copy(firstInput) + output.asInstanceOf[Tensor[NumericWildcard]].sub(input[Tensor[NumericWildcard]](2)) output } - override def updateGradInput(input: Table, gradOutput: Tensor[T]) : Table = { - if (!gradInput.contains(1)) gradInput.insert(1, Tensor[T]()) - if (!gradInput.contains(2)) gradInput.insert(2, Tensor[T]()) + override def updateGradInput(input: Table, gradOutput: Tensor[_]) : Table = { + val firstInput = input[Tensor[NumericWildcard]](1) + val secondInput = input[Tensor[NumericWildcard]](2) - gradInput[Tensor[T]](1).resizeAs(input(1)).copy(gradOutput) - gradInput[Tensor[T]](2).resizeAs(input(2)).copy(gradOutput).mul(ev.fromType(-1)) + if (!gradInput.contains(1)) gradInput.insert(1, firstInput.emptyInstance()) + if (!gradInput.contains(2)) gradInput.insert(2, firstInput.emptyInstance()) + + gradInput[Tensor[NumericWildcard]](1) + .resizeAs(firstInput).copy(gradOutput.asInstanceOf[Tensor[NumericWildcard]]) + gradInput[Tensor[NumericWildcard]](2) + .resizeAs(secondInput).copy(gradOutput.asInstanceOf[Tensor[NumericWildcard]]) + .mul(ev.fromType(-1)) gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index dd10cee8864..f0da9c41706 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -90,7 +90,7 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], .map(n => { n._2.fromIndex match { case Some(i) => - if (i == 1 && n._1.element.output.isTensor) { + if (n._1.element.output == null || (i == 1 && n._1.element.output.isTensor)) { n._1.element.output } else { n._1.element.output.toTable.apply[Activity](i) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala index 10cff1d39e0..ecf7676b76d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString import com.intel.analytics.bigdl.utils.{Engine, Table} @@ -91,12 +91,13 @@ class JoinTable[T: ClassTag] ( var offset = 1 i = 0 while (i < input.length) { - val currentOutput = input(i + 1).asInstanceOf[Tensor[_]] + val currentOutput = input(i + 1).asInstanceOf[Tensor[NumericWildcard]] val _offset = offset results(i) = Engine.model.invoke( () => { val target = output.narrow(dimension, _offset, currentOutput.size(dimension)) + .asInstanceOf[Tensor[NumericWildcard]] if (target.isContiguous() || dimension > 2) { - target.forceCopy(currentOutput) + target.copy(currentOutput) } else { var f = 1 while (f <= target.size(1)) { @@ -104,7 +105,7 @@ class JoinTable[T: ClassTag] ( val outputFrame = currentOutput.select(1, f) require(curFrame.isContiguous()) require(outputFrame.isContiguous()) - curFrame.forceCopy(outputFrame) + curFrame.copy(outputFrame) f += 1 } } @@ -127,19 +128,21 @@ class JoinTable[T: ClassTag] ( val _i = i results(i) = Engine.model.invoke( () => { val narrowedTensor = gradOutput.narrow(dimension, _offset, currentOutput.size(dimension)) + .asInstanceOf[Tensor[NumericWildcard]] val inputTensor = input[Tensor[_]](_i + 1) if (!gradInput.contains(_i + 1)) gradInput(_i + 1) = inputTensor.emptyInstance().resize(inputTensor.size()) if(narrowedTensor.isContiguous() || dimension > 2) { - gradInput[Tensor[_]](_i + 1).forceCopy(narrowedTensor) + gradInput[Tensor[NumericWildcard]](_i + 1).copy(narrowedTensor) } else { var b = 1 while(b <= narrowedTensor.size(1)) { val curFrame = gradInput[Tensor[_]](_i + 1).select(1, b) + .asInstanceOf[Tensor[NumericWildcard]] val narrowFrame = narrowedTensor.select(1, b) require(curFrame.isContiguous()) require(narrowFrame.isContiguous()) - curFrame.forceCopy(narrowFrame) + curFrame.copy(narrowFrame) b += 1 } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mean.scala index 90999b78714..2164e3f76ce 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mean.scala @@ -38,11 +38,12 @@ import scala.reflect.ClassTag */ @SerialVersionUID(2995626598003841724L) -class Mean[T: ClassTag]( +class Mean[T: ClassTag, D: ClassTag]( val dimension: Int = 1, val nInputDims: Int = -1, val squeeze: Boolean = true) - (implicit ev: TensorNumeric[T]) extends Sum[T](dimension, nInputDims, true, squeeze) { + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]) + extends Sum[T, D](dimension, nInputDims, true, squeeze) { override def toString: String = s"nn.Mean" } @@ -50,7 +51,7 @@ object Mean { def apply[@specialized(Float, Double) T: ClassTag]( dimension: Int = 1, nInputDims: Int = -1, - squeeze: Boolean = true)(implicit ev: TensorNumeric[T]) : Mean[T] = { - new Mean[T](dimension, nInputDims, squeeze) + squeeze: Boolean = true)(implicit ev: TensorNumeric[T]) : Mean[T, T] = { + new Mean[T, T](dimension, nInputDims, squeeze) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pack.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pack.scala index f635acc4eba..9ed7ba4c578 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pack.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pack.scala @@ -15,10 +15,10 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.ClassTag @@ -29,7 +29,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(3457313421501931556L) class Pack[T: ClassTag] (val dimension: Int)(implicit ev: TensorNumeric[T]) - extends AbstractModule[Table, Tensor[_], T] { + extends AbstractModule[Activity, Tensor[_], T] { private def getPositiveDimension(input: Table): Int = { var nDim = this.dimension @@ -43,10 +43,16 @@ class Pack[T: ClassTag] (val dimension: Int)(implicit ev: TensorNumeric[T]) nDim } - override def updateOutput(input: Table): Tensor[_] = { - val dimension = getPositiveDimension(input) + override def updateOutput(input: Activity): Tensor[_] = { - val firstInput: Tensor[_] = input(1) + val tableInput = input match { + case t: Tensor[_] => T(t) + case t: Table => t + } + + val dimension = getPositiveDimension(tableInput) + + val firstInput: Tensor[_] = tableInput(1) val nDim = firstInput.nDimension() val size: Array[Int] = new Array[Int](nDim + 1) @@ -55,7 +61,7 @@ class Pack[T: ClassTag] (val dimension: Int)(implicit ev: TensorNumeric[T]) if (i < dimension) { size(i-1) = firstInput.size(i) } else if (i == dimension) { - size(i-1) = input.length() + size(i-1) = tableInput.length() } else { size(i-1) = firstInput.size(i - 1) } @@ -69,34 +75,53 @@ class Pack[T: ClassTag] (val dimension: Int)(implicit ev: TensorNumeric[T]) output.resize(size) i = 1 - while (i <= input.length()) { - val currentOutput: Tensor[_] = input(i) - output.narrow(dimension, i, 1) - .forceCopy(currentOutput) + while (i <= tableInput.length()) { + val currentOutput = tableInput[Tensor[NumericWildcard]](i) + output.narrow(dimension, i, 1).asInstanceOf[Tensor[NumericWildcard]] + .copy(currentOutput) i += 1 } output } - override def updateGradInput(input: Table, gradOutput: Tensor[_]): Table = { - val dimension = getPositiveDimension(input) + override def updateGradInput(input: Activity, gradOutput: Tensor[_]): Activity = { + val tableInput = input match { + case t: Tensor[_] => T(t) + case t: Table => t + } + val dimension = getPositiveDimension(tableInput) - var i = 1 - while (i <= input.length()) { - if (!gradInput.contains(i)) { - gradInput(i) = gradOutput.emptyInstance() + val firstInput = tableInput[Tensor[_]](1) + + if (input.isTensor) { + if (gradInput == null || + gradInput.asInstanceOf[Tensor[_]].getType() != firstInput.getType()) { + gradInput = firstInput.emptyInstance() + } + val gradInputTensor = gradInput.asInstanceOf[Tensor[NumericWildcard]] + gradInputTensor.resizeAs(firstInput) + gradInputTensor.copy(firstInput.asInstanceOf[Tensor[NumericWildcard]]) + } else { + if (gradInput == null) gradInput = T() + val gradInputTable = gradInput.toTable + var i = 1 + while (i <= tableInput.length()) { + if (!gradInputTable.contains(i)) { + gradInputTable(i) = gradOutput.emptyInstance() + } + gradInputTable[Tensor[_]](i).resizeAs(tableInput(i)) + i += 1 } - gradInput[Tensor[_]](i).resizeAs(input(i)) - i += 1 - } - i = 1 - while (i <= input.length()) { - val currentGradInput = gradOutput.select(dimension, i) - gradInput[Tensor[_]](i).forceCopy(currentGradInput) - i += 1 + i = 1 + while (i <= tableInput.length()) { + val currentGradInput = gradOutput.select(dimension, i).asInstanceOf[Tensor[NumericWildcard]] + gradInputTable[Tensor[NumericWildcard]](i).copy(currentGradInput) + i += 1 + } } + gradInput } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala index 0b6c45dbe35..8b93b31c193 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala @@ -154,9 +154,9 @@ class SpatialDivisiveNormalization[T: ClassTag]( } // normalize std dev - adjustedstds = divider.updateOutput(T(localstds, coef)) + adjustedstds = divider.updateOutput(T(localstds, coef)).asInstanceOf[Tensor[T]] thresholdedstds = thresholder.updateOutput(adjustedstds) - output = normalizer.updateOutput(T(input, thresholdedstds)) + output = normalizer.updateOutput(T(input, thresholdedstds)).asInstanceOf[Tensor[T]] output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala index 0eaa4095e72..93c2df6db70 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala @@ -127,8 +127,8 @@ class SpatialSubtractiveNormalization[T: ClassTag]( // compute mean localsums = meanestimator.updateOutput(input).toTensor[T] - adjustedsums = divider.updateOutput(T(localsums, coef)) - output = subtractor.updateOutput(T(input, adjustedsums)) + adjustedsums = divider.updateOutput(T(localsums, coef)).asInstanceOf[Tensor[T]] + output = subtractor.updateOutput(T(input, adjustedsums)).asInstanceOf[Tensor[T]] output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala index f306498614b..620e7301dfc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala @@ -15,9 +15,9 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import scala.reflect.ClassTag @@ -32,7 +32,7 @@ import scala.reflect.ClassTag @SerialVersionUID(7998127436291978408L) class Squeeze[T: ClassTag]( val dims : Array[Int] = null, val batchMode: Boolean = false - )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + )(implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[_], Tensor[_], T] { if (batchMode && dims != null) { var i = 0 @@ -42,8 +42,11 @@ class Squeeze[T: ClassTag]( } } - override def updateOutput(input: Tensor[T]): Tensor[T] = { - output.set(input) + override def updateOutput(input: Tensor[_]): Tensor[_] = { + if (output.getType() != input.getType()) { + output = input.emptyInstance() + } + output.asInstanceOf[Tensor[NumericWildcard]].set(input.asInstanceOf[Tensor[NumericWildcard]]) if (dims != null) { var i = 0 while(i < dims.length) { @@ -60,11 +63,15 @@ class Squeeze[T: ClassTag]( output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + override def updateGradInput(input: Tensor[_], gradOutput: Tensor[_]): Tensor[_] = { + if (gradInput.getType() != gradOutput.getType()) { + gradInput = gradOutput.emptyInstance() + } require(input.nElement() == gradOutput.nElement(), "input and gradoutput shoule be of the same size" + s"input size ${input.nElement()} gradoutput size ${gradOutput.nElement()}") - gradInput.set(gradOutput.view(input.size())) + gradInput.asInstanceOf[Tensor[NumericWildcard]] + .set(gradOutput.asInstanceOf[Tensor[NumericWildcard]].view(input.size())) gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala index c090ac77daf..35f88ff65fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -41,16 +41,20 @@ import scala.reflect.ClassTag */ @SerialVersionUID(- 8025422596092583688L) -class Sum[T: ClassTag]( +class Sum[T: ClassTag, D: ClassTag]( dimension: Int = 1, nInputDims: Int = -1, sizeAverage: Boolean = false, squeeze: Boolean = true) - (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]) + extends AbstractModule[Tensor[D], Tensor[D], T] { @transient - private var _gradOutput: Tensor[T] = null + private var _gradOutput: Tensor[D] = null - private def getPositiveDimension(input: Tensor[T]): Int = { + output = Tensor[D]() + gradInput = Tensor[D]() + + private def getPositiveDimension(input: Tensor[D]): Int = { var dimension = this.dimension if (dimension < 0) { dimension = input.dim() + dimension + 1 @@ -65,12 +69,12 @@ class Sum[T: ClassTag]( dimension } - override def updateOutput(input: Tensor[T]): Tensor[T] = { + override def updateOutput(input: Tensor[D]): Tensor[D] = { val dimension = getPositiveDimension(input) output.sum(input, dimension) if (sizeAverage) { - output.div(ev.fromType[Int](input.size(dimension))) + output.div(evd.fromType(input.size(dimension))) } if (output.nDimension() > 1 && squeeze) { @@ -80,7 +84,7 @@ class Sum[T: ClassTag]( output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { val dimension = getPositiveDimension(input) val size = input.size() size(dimension - 1) = 1 @@ -93,7 +97,7 @@ class Sum[T: ClassTag]( gradInput.resizeAs(input) gradInput.copy(_gradOutput.expandAs(input)) if (sizeAverage) { - gradInput.div(ev.fromType[Int](input.size(dimension))) + gradInput.div(evd.fromType(input.size(dimension))) } gradInput } @@ -102,11 +106,12 @@ class Sum[T: ClassTag]( } object Sum { + def apply[@specialized(Float, Double) T: ClassTag]( dimension: Int = 1, nInputDims: Int = -1, sizeAverage: Boolean = false, - squeeze: Boolean = true)(implicit ev: TensorNumeric[T]) : Sum[T] = { - new Sum[T](dimension, nInputDims, sizeAverage, squeeze) + squeeze: Boolean = true)(implicit ev: TensorNumeric[T]) : Sum[T, T] = { + new Sum[T, T](dimension, nInputDims, sizeAverage, squeeze) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala index 8606772ce7f..cabfbbdfe36 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.abstractnn +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} @@ -74,6 +75,9 @@ object Activity { } else if (classTag[String] == classTag[T]) { import com.intel.analytics.bigdl.numeric.NumericString Tensor[String]() + } else if (classTag[ByteString] == classTag[T]) { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + Tensor[ByteString]() } else { throw new IllegalArgumentException("Type T activity is not supported") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala index 93d27fc823b..658cefee576 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala @@ -66,14 +66,14 @@ abstract class IdentityControl[T: ClassTag]()(implicit ev: TensorNumeric[T]) ext * User should use ControlNodes.whileLoop or ControlNodes.switch to use this operation * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now */ -private[nn] class SwitchOps[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends ControlOps[T] { +private[bigdl] class SwitchOps[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends ControlOps[T] { override def updateOutput(input: Activity): Activity = { - val condition = input.toTable[Tensor[Boolean]](1) - val data = input.toTable[Activity](2) - if (condition.valueAt(1)) { - this.output = T(data, null) - } else { + val condition = input.toTable[Tensor[Boolean]](2) + val data = input.toTable[Activity](1) + if (condition.value()) { this.output = T(null, data) + } else { + this.output = T(data, null) } this.output } @@ -88,7 +88,7 @@ private[nn] class SwitchOps[T: ClassTag]()(implicit ev: TensorNumeric[T]) extend * @param switch which dependency node is avaliable * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now */ -private[nn] class MergeOps[T: ClassTag](private var switch : Int = 1)( +private[bigdl] class MergeOps[T: ClassTag](private var switch : Int = 1)( implicit ev: TensorNumeric[T]) extends ControlOps[T] { def setSwitch(s: Int) : this.type = { @@ -110,21 +110,21 @@ private[nn] class MergeOps[T: ClassTag](private var switch : Int = 1)( * @param element element * @tparam T element type */ -sealed class SwitchControlNode[T] private[ops] (element: T) extends Node[T](element) { +sealed class SwitchControlNode[T] (element: T) extends Node[T](element) { /** * The output edge which will be run when condition scalar is true. You should not connect one * node with both type edges. * @return */ - def trueEdge() : ((Node[T], Int)) = (this, 1) + def trueEdge() : ((Node[T], Int)) = (this, 2) /** * The output edge which will be run when condition scalar is false. You should not connect one * node with both type edges. * @return */ - def falseEdge() : ((Node[T], Int)) = (this, 2) + def falseEdge() : ((Node[T], Int)) = (this, 1) /** * Return nodes triggered by current node @@ -134,15 +134,15 @@ sealed class SwitchControlNode[T] private[ops] (element: T) extends Node[T](elem val bothNodes = this.nextNodesAndEdges.filter(_._2.fromIndex.isEmpty).map(_._1).distinct require(bothNodes.length == 0, "You should not connect to one node with both type of edges") - val trueNodes = this.nextNodesAndEdges.filter(_._2.fromIndex.get == 1).map(_._1).distinct - val falseNodes = this.nextNodesAndEdges.filter(_._2.fromIndex.get == 2).map(_._1).distinct + val trueNodes = this.nextNodesAndEdges.filter(_._2.fromIndex.get == 2).map(_._1).distinct + val falseNodes = this.nextNodesAndEdges.filter(_._2.fromIndex.get == 1).map(_._1).distinct trueNodes.foreach( n => require(!falseNodes.contains(n), "You should not connect to one node with both type of edges") ) - val swtich = element.asInstanceOf[SwitchOps[T]] - if (swtich.output.toTable(1) == null) { + val switch = element.asInstanceOf[SwitchOps[T]] + if (switch.output.toTable(1) != null) { falseNodes } else { trueNodes diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala index b772016cede..69b169b5267 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala @@ -145,7 +145,7 @@ class DecodeGif[T: ClassTag]()(implicit ev: TensorNumeric[T]) } class DecodeRaw[T: ClassTag](val outType: DataType, - val byteOrder: ByteOrder)(implicit ev: TensorNumeric[T]) + val littleEndian: Boolean)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[ByteString], Activity, T] { output = { outType match { @@ -160,6 +160,9 @@ class DecodeRaw[T: ClassTag](val outType: DataType, } } + @transient private val byteOrder = + if (littleEndian) ByteOrder.LITTLE_ENDIAN else ByteOrder.BIG_ENDIAN + override def updateOutput(input: Tensor[ByteString]): Activity = { require(input.isContiguous(), "only support contiguous input") val offset = input.storageOffset() - 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala index 11aa9662d88..430a714e7e5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -51,10 +52,10 @@ class Equal[T: ClassTag]() input[Tensor[Char]](2), (a, b) => a == b) case StringType => - output.zipWith[String, String]( - input[Tensor[String]](1), - input[Tensor[String]](2), - (a, b) => a == b) + output.zipWith[ByteString, ByteString]( + input[Tensor[ByteString]](1), + input[Tensor[ByteString]](2), + (a, b) => a.equals(b)) case LongType => output.zipWith[Long, Long]( input[Tensor[Long]](1), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqual.scala new file mode 100644 index 00000000000..9f2cdc6e7c1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqual.scala @@ -0,0 +1,84 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class NotEqual[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { + + output = Activity.allocate[Tensor[Boolean], Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { + output.resizeAs(input(1)) + input[Tensor[_]](1).getType() match { + case FloatType => + output.zipWith[Float, Float]( + input[Tensor[Float]](1), + input[Tensor[Float]](2), + (a, b) => a != b) + case BooleanType => + output.zipWith[Boolean, Boolean]( + input[Tensor[Boolean]](1), + input[Tensor[Boolean]](2), + (a, b) => a != b) + case DoubleType => + output.zipWith[Double, Double]( + input[Tensor[Double]](1), + input[Tensor[Double]](2), + (a, b) => a != b) + case CharType => + output.zipWith[Char, Char]( + input[Tensor[Char]](1), + input[Tensor[Char]](2), + (a, b) => a != b) + case StringType => + output.zipWith[ByteString, ByteString]( + input[Tensor[ByteString]](1), + input[Tensor[ByteString]](2), + (a, b) => a != b) + case LongType => + output.zipWith[Long, Long]( + input[Tensor[Long]](1), + input[Tensor[Long]](2), + (a, b) => a != b) + case ShortType => + output.zipWith[Short, Short]( + input[Tensor[Short]](1), + input[Tensor[Short]](2), + (a, b) => a != b) + case IntType => + output.zipWith[Int, Int]( + input[Tensor[Int]](1), + input[Tensor[Int]](2), + (a, b) => a != b) + case _ => throw new RuntimeException("Unsupported tensor type") + } + + output + } +} + +object NotEqual { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new NotEqual()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala index ab5fbe52e31..feb752d94dc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala @@ -61,7 +61,7 @@ class OneHot[T: ClassTag, D: ClassTag]( )(implicit ev: TensorNumeric[T], ev1: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { output = Activity.allocate[Tensor[D], D]() def updateOutput(input: Table): Tensor[D] = { - val indices = input[Tensor[Int]](1) + val indices = input[Tensor[Long]](1) val depth = input[Tensor[Int]](2).value() val onValue = if (!input.contains(3)) ev1.one else input[Tensor[D]](3).value() val offValue = if (!input.contains(4)) ev1.zero else input[Tensor[D]](4).value() @@ -97,7 +97,7 @@ class OneHot[T: ClassTag, D: ClassTag]( while (i <= size(0)) { j = 1 while (j <= size(1)) { - val index = indices(Array(i, j)) + 1 + val index = (indices(Array(i, j)) + 1).toInt if (index > 0) { if (realAxis == 0) { output.setValue(index, i, j, onValue) @@ -114,7 +114,7 @@ class OneHot[T: ClassTag, D: ClassTag]( } else { i = 1 while (i <= size(0)) { - val index = indices(Array(i)) + 1 + val index = (indices(Array(i)) + 1).toInt if (index > 0) { if (realAxis == 0) { output.setValue(index, i, onValue) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala index 404261ce930..3dcfd26441a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala @@ -14,7 +14,7 @@ * limitations under the License. */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.{T, Table} import com.google.protobuf.ByteString @@ -33,11 +33,13 @@ class ParseExample[T: ClassTag](nDense: Int, type StringType = ByteString override def updateOutput(input: Table): Table = { - val serialized = input(1).asInstanceOf[Tensor[StringType]].value() + require(input[Tensor[StringType]](1).size(1) == 1, "only support one example at a time") + val serialized = input[Tensor[StringType]](1).valueAt(1) val denseKeys = Range(3, 3 + nDense).map(index => input(index).asInstanceOf[Tensor[StringType]]) .map(_.value().toStringUtf8) val denseDefault = Range(3 + nDense, 3 + 2 * nDense) - .map(index => input(index).asInstanceOf[Tensor[StringType]]) + .map(index => input(index).asInstanceOf[Tensor[_]]) + val example = Example.parseFrom(serialized) @@ -55,6 +57,7 @@ class ParseExample[T: ClassTag](nDense: Int, } for (elem <- outputs) { + elem.asInstanceOf[Tensor[NumericWildcard]].addSingletonDimension() output.insert(elem) } output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala index 7cdc50b96df..1acea91f7e5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Slice.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import scala.reflect.ClassTag @@ -55,7 +55,8 @@ class Slice[T: ClassTag]( output = input.emptyInstance() } output.resizeAs(outputNarrow) - output.forceCopy(outputNarrow) + output.asInstanceOf[Tensor[NumericWildcard]] + .copy(outputNarrow.asInstanceOf[Tensor[NumericWildcard]]) output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Substr.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Substr.scala new file mode 100644 index 00000000000..5ccb3b26d75 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Substr.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Substr[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[ByteString], T] { + + override def updateOutput(input: Table): Tensor[ByteString] = { + val data = input[Tensor[ByteString]](1).value() + val pos = input[Tensor[Int]](2).value() + val len = input[Tensor[Int]](3).value() + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + + output = Tensor.scalar(data.substring(pos, pos + len)) + output + } +} + +object Substr { + def apply[T: ClassTag]() + (implicit ev: TensorNumeric[T]): + Operation[Activity, Activity, T] + = new Substr[T]().asInstanceOf[Operation[Activity, Activity, T]] +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala index ce442dd544b..1f9be5517f5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala @@ -84,13 +84,6 @@ package object ops { com.intel.analytics.bigdl.nn.ReLU()) } - object MatMul { - def apply[T: ClassTag]() - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.MM()) - } - object SoftMax { def apply[T: ClassTag]() (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] @@ -102,6 +95,6 @@ package object ops { def apply[T: ClassTag](axis: Int) (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.Unsqueeze(axis + 1)) + com.intel.analytics.bigdl.nn.Unsqueeze(axis)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAdd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAdd.scala similarity index 52% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAdd.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAdd.scala index bcb191868bc..c17e48d275f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAdd.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAdd.scala @@ -13,22 +13,22 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf -import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class BiasAdd[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[_], T] { - var onesBias: Tensor[NumericWildCard] = _ + (implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { + var onesBias: Tensor[T] = _ - override def updateOutput(input: Table): Tensor[_] = { - val value = input[Tensor[NumericWildCard]](1) - val bias = input[Tensor[NumericWildCard]](2) + override def updateOutput(input: Table): Tensor[T] = { + val value = input[Tensor[T]](1) + val bias = input[Tensor[T]](2) val sizes = value.size().toBuffer val last = sizes.last sizes.remove(value.nDimension() - 1) @@ -46,12 +46,9 @@ class BiasAdd[T: ClassTag]() onesBias.resize(sizeProduct).fill(ev.fromType(1.0)) } - output.asInstanceOf[Tensor[NumericWildCard]] - .resizeAs(value) + output.resizeAs(value) .copy(value) - val value2d = output - .view(Array(sizeProduct, last)) - .asInstanceOf[Tensor[NumericWildCard]] + val value2d = output.view(Array(sizeProduct, last)) value2d @@ -59,14 +56,42 @@ class BiasAdd[T: ClassTag]() value.getTensorNumeric().one, onesBias, bias) - output } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + val value = input[Tensor[T]](1) + val bias = input[Tensor[T]](2) + + val sizes = value.size().toBuffer + val last = sizes.last + sizes.remove(value.nDimension() - 1) + val sizeProduct = sizes.product + + if (!gradInput.contains(1)) { + gradInput(1) = value.emptyInstance() + } + + if (!gradInput.contains(2)) { + gradInput(2) = bias.emptyInstance() + } + + val gradValue = gradInput[Tensor[T]](1) + val gradBias = gradInput[Tensor[T]](2) + + gradValue.resizeAs(value).copy(gradOutput) + + val gradOutput2d = gradOutput.view(Array(sizeProduct, last)) + + gradBias.resizeAs(bias).addmv(ev.fromType(1.0), gradOutput2d.t, onesBias) + + gradInput + } } object BiasAdd { def apply[T: ClassTag]() (implicit ev: TensorNumeric[T]): - Operation[Activity, Activity, T] - = ModuleToOperation[T](new BiasAdd()) + BiasAdd[T] + = new BiasAdd[T]() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala index 09b0ec42247..27735c2360d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala @@ -45,10 +45,12 @@ trait ConvertableTo[@spec A] { implicit def fromShort(a: Short): A implicit def fromLong(a: Long): A + + implicit def fromBoolean(a: Boolean): A } trait ConvertableToLong extends ConvertableTo[Long] { - implicit def fromFloat(a: Float): Long = a + implicit def fromFloat(a: Float): Long = a.toLong implicit def fromDouble(a: Double): Long = a.toLong @@ -57,11 +59,13 @@ trait ConvertableToLong extends ConvertableTo[Long] { implicit def fromShort(a: Short): Long = a.toLong implicit def fromLong(a: Long): Long = a.toLong + + implicit def fromBoolean(a: Boolean): Long = if (a) 1L else 0L } trait ConvertableToShort extends ConvertableTo[Short] { - implicit def fromFloat(a: Float): Short = a + implicit def fromFloat(a: Float): Short = a.toShort implicit def fromDouble(a: Double): Short = a.toShort @@ -70,6 +74,8 @@ trait ConvertableToShort extends ConvertableTo[Short] { implicit def fromShort(a: Short): Short = a.toShort implicit def fromLong(a: Long): Short = a.toShort + + implicit def fromBoolean(a: Boolean): Short = if (a) 1 else 0 } @@ -83,6 +89,8 @@ trait ConvertableToFloat extends ConvertableTo[Float] { implicit def fromShort(a: Short): Float = a.toFloat implicit def fromLong(a: Long): Float = a.toFloat + + implicit def fromBoolean(a: Boolean): Float = if (a) 1.0f else 0.0f } trait ConvertableToDouble extends ConvertableTo[Double] { @@ -95,6 +103,8 @@ trait ConvertableToDouble extends ConvertableTo[Double] { implicit def fromShort(a: Short): Double = a.toDouble implicit def fromLong(a: Long): Double = a.toDouble + + implicit def fromBoolean(a: Boolean): Double = if (a) 1.0 else 0.0 } trait ConvertableToInt extends ConvertableTo[Int] { @@ -106,7 +116,9 @@ trait ConvertableToInt extends ConvertableTo[Int] { implicit def fromShort(a: Short): Int = a.toShort - implicit def fromLong(a: Long): Int = a.toLong + implicit def fromLong(a: Long): Int = a.toInt + + implicit def fromBoolean(a: Boolean): Int = if (a) 1 else 0 } object ConvertableTo { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 5c41c0c906b..07372c069a6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -414,13 +414,6 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this } - override def forceCopy(other: Tensor[_]): Tensor[T] = { - require(this.getType() == other.getType(), - "forceCopy should copy from a tensor of the same type") - DenseTensor.copy(this, other.asInstanceOf[Tensor[T]]) - this - } - override def narrow(dim: Int, index: Int, size: Int): Tensor[T] = { val result = DenseTensor.newWithTensor(this) DenseTensor.narrow(result, null, dim - 1, index - 1, size) @@ -934,47 +927,45 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( i += 1 } } else { - val targetSize = DenseTensor.expandSize(this, x) - val expandStrides = new Array[Int](targetSize.length) - - val expandStridesX = new Array[Int](targetSize.length) - var i = targetSize.length - 1 - val delta2 = targetSize.length - x.nDimension - while(i >= delta2) { - if (x.size(i + 1- delta2) != 1) expandStridesX(i) = x.stride(i + 1- delta2) + this.add(expandTensor(x)) + } + this + } + + private def expandTensor(x: Tensor[T]): Tensor[T] = { + val targetSize = DenseTensor.expandSize(this, x) + val expandStrides = new Array[Int](targetSize.length) + + val expandStridesX = new Array[Int](targetSize.length) + var i = targetSize.length - 1 + val delta2 = targetSize.length - x.nDimension + while(i >= delta2) { + if (x.size(i + 1- delta2) != 1) expandStridesX(i) = x.stride(i + 1- delta2) + i -= 1 + } + val expandX = new DenseTensor[T]( + x.storage(), + x.storageOffset(), + targetSize, + expandStridesX + ) + if (targetSize.product != this.nElement()) { + i = targetSize.length - 1 + val delta1 = targetSize.length - this.nDimension + while (i >= delta1) { + if (this.size(i + 1 - delta1) != 1) expandStrides(i) = this.stride(i + 1 - delta1) i -= 1 } - val expandX = new DenseTensor[T]( - x.storage(), - x.storageOffset(), + val tensor1 = new DenseTensor[T]( + this.storage(), + this.storageOffset(), targetSize, - expandStridesX + expandStrides ) - - val expandTensor = - if (targetSize.product == this.nElement()) { - this - } else { - i = targetSize.length - 1 - val delta1 = targetSize.length - this.nDimension - while (i >= delta1) { - if (this.size(i + 1 - delta1) != 1) expandStrides(i) = this.stride(i + 1 - delta1) - i -= 1 - } - val tensor1 = new DenseTensor[T]( - this.storage(), - this.storageOffset(), - targetSize, - expandStrides - ) - val newTensor = new DenseTensor[T]().resize(targetSize).add(tensor1) - this.set(newTensor) - this - } - - expandTensor.add(expandX) + val newTensor = new DenseTensor[T]().resize(targetSize).add(tensor1) + this.set(newTensor) } - this + expandX } override def add(x: Tensor[T], y: Tensor[T]): Tensor[T] = { @@ -1012,20 +1003,33 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( DenseTensorMath.csub(this, this, ev.negative(value), y) override def sub(x: Tensor[T]): Tensor[T] = { - require(this.nElement() == x.nElement()) - if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) { - ev.vSub(this.nElement(), this.storage().array(), this.storageOffset() - 1, - x.storage().array(), x.storageOffset() - 1, - this.storage().array(), this.storageOffset() - 1) - } - else { - val func = new TensorFunc4[T] { - override def apply (data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = { - data1(offset1) = ev.minus(data1(offset1), data2(offset2)) + if (this.nElement() == x.nElement()) { + if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous() && + (x.getType() == DoubleType || x.getType() == FloatType)) { + ev.vSub(this.nElement(), this.storage().array(), this.storageOffset() - 1, + x.storage().array(), x.storageOffset() - 1, + this.storage().array(), this.storageOffset() - 1) + } + else { + val func = new TensorFunc4[T] { + override def apply (data1: Array[T], offset1: Int, + data2: Array[T], offset2: Int): Unit = { + data1(offset1) = ev.minus(data1(offset1), data2(offset2)) + } } + DenseTensorApply.apply2[T](this, x, func) } - DenseTensorApply.apply2[T](this, x, func) + } else if (DenseTensor.canFastBroadcast(this, x)) { + // recursive add + var i = 0 + while(i < this.size(1)) { + this.select(1, i + 1).sub(x) + i += 1 + } + } else { + this.sub(expandTensor(x)) } + this } @@ -1162,6 +1166,36 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( override def div(value: T): Tensor[T] = DenseTensorMath.mul(this, null, ev.inv(value)) + override def div(x: Tensor[T]): Tensor[T] = { + if (this.nElement() == x.nElement()) { + if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) { + ev.vDiv(this.nElement(), this.storage().array(), this.storageOffset() - 1, + x.storage().array(), x.storageOffset() - 1, + this.storage().array(), this.storageOffset() - 1) + } + else { + val func = new TensorFunc4[T] { + override def apply (data1: Array[T], offset1: Int, + data2: Array[T], offset2: Int): Unit = { + data1(offset1) = ev.divide(data1(offset1), data2(offset2)) + } + } + DenseTensorApply.apply2[T](this, x, func) + } + } else if (DenseTensor.canFastBroadcast(this, x)) { + // recursive add + var i = 0 + while(i < this.size(1)) { + this.select(1, i + 1).div(x) + i += 1 + } + } else { + this.div(expandTensor(x)) + } + + this + } + override def conv2(kernel: Tensor[T], vf: Char = 'V'): Tensor[T] = DenseTensorConv.conv2Dmul[T](ev.fromType[Int](1), this, kernel, 1, 1, vf, 'C') diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index f224c401fed..8de976f6dac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1380,12 +1380,13 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def emptyInstance(): Tensor[T] = throw new UnsupportedOperationException(errorString) - override def forceCopy(other: Tensor[_]): Tensor[T] = - throw new UnsupportedOperationException(errorString) - override def applyFun[A: ClassTag](t: Tensor[A], func: (A) => T): Tensor[T] = throw new UnsupportedOperationException(errorString) override def cast[D: ClassTag](castTensor: Tensor[D])(implicit ev: TensorNumeric[D]): Tensor[D] = throw new UnsupportedOperationException(errorString) + + override def div(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 8743b349482..d97a9d19930 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -643,6 +643,10 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + override def div(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + override def cdiv(x: Tensor[T], y: Tensor[T]): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } @@ -978,10 +982,6 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } - override def forceCopy(other: Tensor[_]): Tensor[T] = { - throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") - } - override def cast[D: ClassTag]( castTensor: Tensor[D])(implicit ev: TensorNumeric[D]): Tensor[D] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 6ab41cb40c8..b754a557e50 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -499,15 +499,6 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { */ def copy(other: Tensor[T]): Tensor[T] - /** - * Copy the value of the given tensor to the current. They should have same size. - * They should also have the same type. - * - * @param other source tensor - * @return current tensor - */ - def forceCopy(other: Tensor[_]): Tensor[T] - /** * Apply a function to each element of the tensor `t` * and set each value to self @@ -777,7 +768,7 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { /** * Numeric type of tensor. */ -sealed trait TensorDataType +sealed trait TensorDataType extends Serializable object BooleanType extends TensorDataType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala index 98369b53236..4ff19c80740 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala @@ -410,6 +410,16 @@ trait TensorMath[T] { */ def div(value: T): Tensor[T] + /** + * Element-wise divide + * x.div(y) all elements of x divide all elements of y. + * x = x / y + * + * @param y tensor + * @return current tensor + */ + def div(y: Tensor[T]): Tensor[T] + /** * put the result of x * value in current tensor * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index ebaea11c75f..e6d6851d511 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -985,6 +985,9 @@ object TensorNumericMath { implicit c: ConvertableFrom[K]): Boolean = c.toBoolean(k) + override def toType[K](t: Boolean)( + implicit c: ConvertableTo[K]): K = c.fromBoolean(t) + override def nearlyEqual(a: Boolean, b: Boolean, epsilon: Double): Boolean = { a == b } @@ -1053,6 +1056,16 @@ object TensorNumericMath { } r } + + override def sum(n: Int, a: Array[Int], aOffset: Int, stride: Int): Int = { + var i = 0 + var r = 0 + while (i < n) { + r += a(aOffset + i * stride) + i += 1 + } + r + } } implicit object NumericLong extends UndefinedTensorNumeric[Long]("Long") { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c0f8dfc1159..3897f617f1b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -711,7 +711,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createMean(dimension: Int = 1, nInputDims: Int = -1, squeeze: Boolean = true) - : Mean[T] = { + : Mean[T, T] = { Mean[T](dimension, nInputDims, squeeze) @@ -1100,7 +1100,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab sizeAverage: Boolean = false, squeeze: Boolean = true ) - : Sum[T] = { + : Sum[T, T] = { Sum[T](dimension, nInputDims, sizeAverage, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 14888e57e63..ab84e8e7ce6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -114,8 +114,8 @@ trait ModuleSerializable extends Loadable with Savable{ if (ptype <:< universe.typeOf[ClassTag[_]]|| ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { args(i) = evidence - } else if (ptype.toString == - tensorNumericType.toString) { + } else if (ptype <:< universe.typeOf[TensorNumeric[_]] + || ptype.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { args(i) = ev } else { require(modelAttributes.containsKey(name), s"$name value cannot be found") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala index ffec7e8d957..92a407f6e93 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala @@ -325,7 +325,7 @@ object MeanToTF extends BigDLToTensorflow { override def toTFDef(module: AbstractModule[_, _, _], inputs: Seq[NodeDef], byteOrder: ByteOrder): Seq[NodeDef] = { require(inputs.length == 1, "Mean only accept one input") - val layer = module.asInstanceOf[Mean[_]] + val layer = module.asInstanceOf[Mean[_, _]] require(layer.squeeze == true, "Mean must squeeze input") val dimsTensor = Tensor[Float](layer.dimension) dimsTensor.setValue(1, layer.dimension - 1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/FixedLengthRecordReader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/FixedLengthRecordReader.scala new file mode 100644 index 00000000000..07ad394f9da --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/FixedLengthRecordReader.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf + +import java.io.{File, FileInputStream} + +/** + * Internal use only. + * + * TF record format: + * uint64 length + * uint32 masked_crc32_of_length + * byte data[length] + * uint32 masked_crc32_of_data + * + */ +class FixedLengthRecordReader(fileName: File, + footerBytes: Int, + headerBytes: Int, + hopBytes: Int, + recordBytes: Int) extends Iterator[Array[Byte]] { + + private val inputStream = new FileInputStream(fileName) + + private var dataBuffer: Array[Byte] = null + + inputStream.skip(headerBytes) + + + override def hasNext: Boolean = { + if (dataBuffer != null) { + true + } else { + dataBuffer = new Array[Byte](recordBytes) + val numOfBytes = inputStream.read(dataBuffer) + if (numOfBytes == recordBytes) { + inputStream.skip(hopBytes) + true + } else { + inputStream.close() + false + } + } + } + + override def next(): Array[Byte] = { + if (hasNext) { + val data = this.dataBuffer + this.dataBuffer = null + data + } else { + throw new NoSuchElementException("next on empty iterator") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index b382a2296f8..2158fb14dcb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Graph, Linear} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.optim._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils._ import org.apache.spark.SparkContext @@ -79,8 +79,7 @@ class BigDLSessionImpl[T: ClassTag]( val index = nameToIndex(node.element.getName) val nSlices = dequeNodeNames.size - val enqueueNodes = queueNode.nextNodes - .filter(n => n.element != null && enqueueOp(n.element.getOp)) + val enqueueNodes = findEnqueueNodes(queueNode) val filesSeq = if (cache.contains(queueNode.element.getName)) { val resultArray = cache(queueNode.element.getName) val result = resultArray(index) @@ -91,27 +90,7 @@ class BigDLSessionImpl[T: ClassTag]( val inputs = Seq(enqueueNode.element.getName) val result = constructLocalData(inputs, new DataCache()) if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { - result.flatMap { table => - val nElem = table.length() - require(nElem >= 1, "EnqueueManyV2 encounter a empty table") - val first = table[Tensor[ByteString]](1) - require(first.nDimension() >= 1) - val depth = first.size(1) - val result = new Array[Table](depth) - var i = 0 - while(i < depth) { - var j = 0 - val newTable = new Table() - while (j < nElem) { - val elem = table[Tensor[ByteString]](j + 1) - newTable.insert(elem(i + 1)) - j = j + 1 - } - result(i) = newTable - i = i + 1 - } - result - } + result.flatMap(splitTensorByFirstDim) } else { result } @@ -127,6 +106,7 @@ class BigDLSessionImpl[T: ClassTag]( readerNode.element.getOp match { case "TFRecordReaderV2" => readTFRecord(filesSeq) + case "FixedLengthRecordReaderV2" => readFixedLengthRecord(filesSeq, readerNode.element) } } @@ -170,10 +150,68 @@ class BigDLSessionImpl[T: ClassTag]( resultRdd } + private def readFixedLengthRecord(filesTable: Seq[Table], readerNode: NodeDef): RDD[Table] = { + + val footerBytes = readerNode.getAttrMap.get("footer_bytes").getI.toInt + val headerBytes = readerNode.getAttrMap.get("header_bytes").getI.toInt + val hopBytes = readerNode.getAttrMap.get("hop_bytes").getI.toInt + val recordBytes = readerNode.getAttrMap.get("record_bytes").getI.toInt + + val result = filesTable.map { t => + require(t.length() == 1 && t(1).isInstanceOf[Tensor[ByteString]], + "Reader can only read one file at a time") + val fileTensor = t[Tensor[ByteString]](1) + require(fileTensor.isScalar) + val file = fileTensor.value() + file + }.flatMap { file => + val iter = new FixedLengthRecordReader( + new java.io.File(file.toStringUtf8), + footerBytes, + headerBytes, + hopBytes, + recordBytes) + iter + }.map { record => + val table = T() + val key = Tensor[ByteString](Array(ByteString.copyFromUtf8("somekey")), Array[Int]()) + val value = Tensor[ByteString](Array(ByteString.copyFrom(record)), Array[Int]()) + table.insert(key) + table.insert(value) + table + } + val resultRdd = sc.parallelize(result, numSlices = Engine.coreNumber()) + resultRdd + } + + private val identityOp = Set("Switch", "Identity", "Merge") + private def findEnqueueNodes(queueNode: Node[NodeDef]): Seq[Node[NodeDef]] = { + val queue = mutable.Queue[Node[NodeDef]]() + val enqueNodes = mutable.ArrayBuffer[Node[NodeDef]]() + queue.enqueue(queueNode.nextNodes: _*) + val visited = mutable.HashSet[Node[NodeDef]]() + while(queue.nonEmpty) { + val node = queue.dequeue() + if (!visited(node)) { + if (node.element != null && enqueueOp(node.element.getOp)) { + enqueNodes += node + } else if (node.element != null && identityOp(node.element.getOp)) { + queue.enqueue(node.nextNodes: _*) + } + } + } + if (enqueNodes.isEmpty) { + throw new IllegalArgumentException( + s"Cannot find enqueue node for queue: ${queueNode.element}") + } else { + enqueNodes + } + } + private def handleLocalDequeue(node: Node[NodeDef], cache: DataCache): Seq[Table] = { require(node.prevNodes.length == 1, "require QueueDequeueV2 only has one input") val queueNode = node.prevNodes.head - val enqueueNodes = queueNode.nextNodes.filter(n => enqueueOp(n.element.getOp)) + val enqueueNodes = findEnqueueNodes(queueNode) val dequeNodeNames = mutable.LinkedHashSet[String]() queueNode.nextNodes @@ -192,7 +230,12 @@ class BigDLSessionImpl[T: ClassTag]( } else { val allResult = enqueueNodes.map { enqueueNode => val inputs = Seq(enqueueNode.element.getName) - constructLocalData(inputs, new DataCache()) + val result = constructLocalData(inputs, new DataCache()) + if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { + result.flatMap(splitTensorByFirstDim) + } else { + result + } }.reduce { (outerSeq1, outerSeq2) => outerSeq1.zip(outerSeq2).map { case (seq1, seq2) => seq1.add(seq2) @@ -212,85 +255,130 @@ class BigDLSessionImpl[T: ClassTag]( .filter(n => n.element != null && dequeueOp(n.element.getOp)) .map(n => n.element.getName.split(":")(0)).toSet require(dequeueNodes.size == 1, "only support one dequeue node after reader") - val enqueueNodes = queueNode.nextNodes - .filter(n => n.element != null && enqueueOp(n.element.getOp)) + val enqueueNodes = findEnqueueNodes(queueNode) val rdd = enqueueNodes.map { enqueueNode => val inputs = Seq(enqueueNode.element.getName) - constructDistributeData(inputs, cache) + val result = constructDistributeData(inputs, cache) + if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { + result.flatMap(splitTensorByFirstDim) + } else { + result + } }.reduce { (rdd1, rdd2) => rdd1.union(rdd2) } rdd } + private def splitTensorByFirstDim(table: Table): Array[Table] = { + val nElem = table.length() + require(nElem >= 1, "EnqueueManyV2 encounter a empty table") + val first = table[Tensor[_]](1) + require(first.nDimension() >= 1) + val depth = first.size(1) + val result = new Array[Table](depth) + var i = 0 + while(i < depth) { + var j = 0 + val newTable = new Table() + while (j < nElem) { + val elem = table[Tensor[ByteString]](j + 1) + newTable.insert(elem(i + 1)) + j = j + 1 + } + result(i) = newTable + i = i + 1 + } + result + } + private def handleDistriDequeueManyNode(node: Node[NodeDef], cache: DataCache): RDD[Table] = { require(node.prevNodes.length == 2, "require QueueDequeueManyV2 only has two input") val queueNode = node.prevNodes.head - val enqueueNodes = queueNode.nextNodes.filter(n => enqueueOp(n.element.getOp)) + val dequeueNodes = queueNode.nextNodes + .filter(n => n.element != null && dequeueOp(n.element.getOp)) + .map(n => n.element.getName.split(":")(0)).toSet + require(dequeueNodes.size == 1, "only support one dequeue node after reader") + val enqueueNodes = findEnqueueNodes(queueNode) // get previous rdd val rdd = enqueueNodes.map { enqueueNode => val inputs = Seq(enqueueNode.element.getName) - constructDistributeData(inputs, cache) - }.reduce { (rdd1, rdd2) => - rdd1.zip(rdd2).map { case (seq1, seq2) => - seq1.add(seq2) + val result = constructDistributeData(inputs, cache) + if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { + result.flatMap(splitTensorByFirstDim) + } else { + result } + }.reduce { (rdd1, rdd2) => + rdd1.union(rdd2) } // get batch size val batchSizeNode = node.prevNodes(1) require(batchSizeNode.element.getOp == "Const", "batchsize must be a const") - val batchSize = batchSizeNode.element.getAttrMap.get("value").getI.toInt + val batchSize = batchSizeNode.element.getAttrMap.get("value").getTensor.getIntVal(0) val batchRdd = rdd.mapPartitions { iter => new Iterator[Table] { + private var firstBatch: Array[Table] = null override def hasNext: Boolean = iter.hasNext override def next(): Table = { require(iter.hasNext, "Call next() on a empty iterator") - val batch = for (_ <- 0 until batchSize if iter.hasNext) yield { - iter.next() + val tables = new Array[Table](batchSize) + var index = 0 + for (i <- 0 until batchSize) { + if (iter.hasNext) { + tables(i) = iter.next() + } else if (firstBatch == null) { + tables(i) = tables(index) + index = index + 1 + } else { + tables(i) = firstBatch(index) + index = index + 1 + } + } + if (firstBatch == null) { + firstBatch = tables + } + val batch = tables.map(_.toSeq) + val firstSeq = batch.head + val sizes = firstSeq.map { tensor => + val nDim = tensor.nDimension() + val size: Array[Int] = new Array[Int](nDim + 1) + var i = 1 + while(i <= nDim + 1) { + if (i < 1) { + size(i-1) = tensor.size(i) + } else if (i == 1) { + size(i-1) = batch.length + } else { + size(i-1) = tensor.size(i - 1) + } + i = i + 1 + } + size } - pack(batch) - } - } - } - batchRdd - } + val results = sizes.zipWithIndex.map { case (size, i) => + firstSeq(i).emptyInstance().resize(size) + } - private def pack(tables: Seq[Table], dimension: Int = 1): Table = { - val batch = tables.map(_.toSeq[T]) - val firstSeq = batch.head - val sizes = firstSeq.map { tensor => - val nDim = tensor.nDimension() - val size: Array[Int] = new Array[Int](nDim + 1) - var i = 1 - while(i <= nDim + 1) { - if (i < dimension) { - size(i-1) = tensor.size(i) - } else if (i == dimension) { - size(i-1) = batch.length - } else { - size(i-1) = tensor.size(i - 1) + for ((seq, index) <- batch.zipWithIndex) { + results.zip(seq).foreach { case (result, tensor) => + result.asInstanceOf[Tensor[NumericWildcard]] + .narrow(1, index + 1, 1) + .copy(tensor.asInstanceOf[Tensor[NumericWildcard]]) + } + } + T.seq(results) } - i = i + 1 } - size - } - - val results = sizes.map { size => - Tensor[T](size) - } - for ((seq, index) <- batch.zipWithIndex) { - results.zip(seq).foreach { case (result, tensor) => - result.narrow(dimension, index + 1, 1).copy(tensor) - } } - T.seq(results) + batchRdd } type DataCache = mutable.HashMap[String, Array[Seq[Table]]] @@ -305,12 +393,10 @@ class BigDLSessionImpl[T: ClassTag]( } private def checkAndRemoveQueueNode(tfGraph: DirectedGraph[NodeDef]) = { - if (tfGraph.source.prevNodes.exists(n => enqueueOp(n.element.getOp))) { - tfGraph.source.prevNodes.foreach { node => - val queueNodes = node.prevNodes.filter(n => queueOp(n.element.getOp)) - queueNodes.foreach(n => n.delete(node)) + tfGraph.DFS.filter(n => n.element != null && enqueueOp(n.element.getOp)) + .foreach { node => + node.prevNodes.head.delete(node) } - } } def constructLocalData(endPoints: Seq[String], cache: DataCache): Seq[Table] = { @@ -327,7 +413,8 @@ class BigDLSessionImpl[T: ClassTag]( endPoints, ByteOrder.LITTLE_ENDIAN, "", - Some(context) + Some(context), + generatedBackward = false ).asInstanceOf[Graph[T]] @@ -379,7 +466,8 @@ class BigDLSessionImpl[T: ClassTag]( endPoints, ByteOrder.LITTLE_ENDIAN, "", - Some(context) + Some(context), + generatedBackward = false ).asInstanceOf[Graph[T]] val inputRdds = inputNodes.map { node => // this is the input op @@ -409,15 +497,20 @@ class BigDLSessionImpl[T: ClassTag]( private def constructModel(endPoints: Seq[String]): (Graph[T], Node[NodeDef]) = { val isInputOp = (n: NodeDef) => inputOp(n.getOp) - val (tfGraph, inputs, _) = TensorflowLoader.buildTFGraph(graph.asJava, endPoints, isInputOp) + val (tfGraph, inputs, originInputs) = + TensorflowLoader.buildTFGraph(graph.asJava, endPoints, isInputOp) + + checkAndRemoveQueueNode(tfGraph) - val inputNodes = inputs.map(name2Node) + val adjustedInputs = adjustInputNames(originInputs) + + val inputNodes = adjustedInputs.map(name2Node) require(inputNodes.length == 1, "Only support one model input") val model = TensorflowLoader.buildBigDLModel( tfGraph, - inputNodes.map(_.element.getName), + inputs, endPoints, ByteOrder.LITTLE_ENDIAN, "", @@ -448,7 +541,7 @@ class BigDLSessionImpl[T: ClassTag]( dataSet, criterion ) - val optMethod = new SGD[T]() + opt.setOptimMethod(optMethod).setEndWhen(endWhen) .optimize() model diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala index 67816927afa..32107a64448 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFRecordIterator.scala @@ -30,7 +30,7 @@ import java.nio.{ByteBuffer, ByteOrder} */ class TFRecordIterator(fileName: File) extends Iterator[Array[Byte]] { - private val inputStream = new BufferedInputStream(new FileInputStream(fileName)) + private val inputStream = new FileInputStream(fileName) private var dataBuffer: Array[Byte] = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 2830a81f0dc..e818dc6f87d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -24,6 +24,7 @@ import com.google.protobuf.{CodedInputStream, TextFormat} import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.ops.{SwitchControlNode, SwitchOps} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{DirectedGraph, Edge, FileReader, Node} @@ -127,6 +128,7 @@ object TensorflowLoader{ def connect(nodes: Seq[Node[NodeDef]]): (Seq[String], Seq[String]) = { var inputCounter = 0 + var dependencyCounter = 0 val queue = new mutable.Queue[Node[NodeDef]]() val visited = mutable.Set[Node[NodeDef]]() val inputs = new mutable.ArrayBuffer[String]() @@ -162,8 +164,9 @@ object TensorflowLoader{ val dependencyNode = Node(NodeDef.newBuilder() .setOp("DependencyNode") .addInput(preNode.element.getName) - .setName(s"depends_on_${preNode.element.getName}") + .setName(s"depends_on_${preNode.element.getName}_$dependencyCounter") .build()) + dependencyCounter = dependencyCounter + 1 dependencyNode -> node dependencyNode } else { @@ -223,7 +226,8 @@ object TensorflowLoader{ outputs: Seq[String], byteOrder: ByteOrder, graphPrototxt: String, - ctx: Option[Context[T]] = None + ctx: Option[Context[T]] = None, + generatedBackward: Boolean = true )(implicit ev: TensorNumeric[T]): Module[T] = { import scala.collection.JavaConverters._ @@ -290,7 +294,11 @@ object TensorflowLoader{ module.setName(name + "/" + module.getName()) } } - val node = new Node(module) + val node = module match { + case _: SwitchOps[_] => new SwitchControlNode(module) + case _ => Node(module) + } + nodes.asScala.foreach(m => { convertedNode(m) = node nameToNode(m.element.getName) = node @@ -340,7 +348,8 @@ object TensorflowLoader{ gradients += grad } - Graph(inputNodes.toArray, outputNodes.toArray, Some((weights.toArray, gradients.toArray))) + Graph(inputNodes.toArray, outputNodes.toArray, Some((weights.toArray, gradients.toArray)), + generatedBackward) } /** @@ -374,6 +383,7 @@ object TensorflowLoader{ val patternToGraph = new mutable.HashMap[Node[String], Node[NodeDef]]() val inputs = new ArrayBuffer[Node[NodeDef]]() patternToGraph(pattern.source) = graph.source + inputs.append(graph.source) pattern.BFS.foreach(patternNode => { if (patternNode.element != N_INPUT_PLACEHOLDER && patternNode.element != INPUT_PLACEHOLDER) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index 18e7fce7292..7243bceb984 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -21,15 +21,14 @@ import java.util import collection.JavaConverters._ import com.intel.analytics.bigdl.nn._ - import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Storage, Tensor} -import com.intel.analytics.bigdl.nn.ops.{Conv2DTranspose, ResizeBilinearOps} +import com.intel.analytics.bigdl.nn.ops._ import com.intel.analytics.bigdl.tensor._ import org.tensorflow.framework.{AttrValue, DataType, NodeDef, TensorProto} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{Equal, Assert, Greater, Rank, ParseExample} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.FullConnectionTF.getOrSetTensor import com.intel.analytics.bigdl.utils.{DirectedGraph, Node, T} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.Context import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL._ @@ -95,6 +94,14 @@ trait TensorflowToBigDL { protected def getIntList(attrMap: util.Map[String, AttrValue], key: String): Seq[Int] = { attrMap.get(key).getList.getIList.asScala.map(_.toInt) } + + protected def getBoolean(attrMap: util.Map[String, AttrValue], key: String): Boolean = { + attrMap.get(key).getB + } + + protected def getType(attrMap: util.Map[String, AttrValue], key: String): DataType = { + attrMap.get(key).getType + } } object TensorflowToBigDL { @@ -887,3 +894,4 @@ object BatchNormTF extends TensorflowToBigDL{ model.asInstanceOf[AbstractModule[Activity, Activity, T]] } } + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAdd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAdd.scala new file mode 100644 index 00000000000..678e71c87f5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAdd.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.tf.{BiasAdd => BiasAddOp} +import Utils._ + +import scala.reflect.ClassTag + +class BiasAdd extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val attr = nodeDef.getAttrMap + require(getString(attr, "data_format") == "NHWC", "only support NHWC format") + BiasAddOp[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala new file mode 100644 index 00000000000..b9938f19628 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Cast +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Cast extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val attr = nodeDef.getAttrMap + val dataType = getType(attr, "DstT") + + val layer = dataType match { + case DataType.DT_INT8 => Cast[T, Int]() + case DataType.DT_INT16 => Cast[T, Int]() + case DataType.DT_UINT8 => Cast[T, Int]() + case DataType.DT_UINT16 => Cast[T, Int]() + case DataType.DT_INT32 => Cast[T, Int]() + case DataType.DT_INT64 => Cast[T, Int]() + case DataType.DT_BOOL => Cast[T, Boolean]() + case DataType.DT_STRING => Cast[T, String]() + case DataType.DT_FLOAT => Cast[T, Float]() + case DataType.DT_DOUBLE => Cast[T, Double]() + } + layer + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala new file mode 100644 index 00000000000..821e067538a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{DecodeGif => DecodeGifOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DecodeGif extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new DecodeGifOp[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala new file mode 100644 index 00000000000..b195643b0ea --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{DecodeJpeg => DecodeJpegOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DecodeJpeg extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val attr = nodeDef.getAttrMap + val channels = getInt(attr, "channels") + val ratio = getInt(attr, "ratio") + new DecodeJpegOp[T](channels, ratio) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala new file mode 100644 index 00000000000..ed336a0fec6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{DecodePng => DecodePngOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DecodePng extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val attr = nodeDef.getAttrMap + val channels = getInt(attr, "channels") + + new DecodePngOp[T](channels) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala new file mode 100644 index 00000000000..9e0e5a4e424 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{DecodeRaw => DecodeRawOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DecodeRaw extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + val attrs = nodeDef.getAttrMap + + val endian = getBoolean(attrs, "little_endian") + + val outType = attrs.get("out_type").getType + new DecodeRawOp[T](outType, endian) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Less.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Less.scala new file mode 100644 index 00000000000..7c28b4833e0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Less.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.ops.{Less => LessOp} + +import scala.reflect.ClassTag + +class Less extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new LessOp[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalAnd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalAnd.scala new file mode 100644 index 00000000000..9d9be1f427c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalAnd.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.ops.{LogicalAnd => LogicalAndOp} + +import scala.reflect.ClassTag + +class LogicalAnd extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + LogicalAndOp() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalNot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalNot.scala new file mode 100644 index 00000000000..5325c426dd0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalNot.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.ops.{LogicalNot => LogicalNotOp} + +import scala.reflect.ClassTag + +class LogicalNot extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + LogicalNotOp() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalOr.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalOr.scala new file mode 100644 index 00000000000..8f03ba93bb4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalOr.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.ops.{LogicalOr => LogicalOrOp} + +import scala.reflect.ClassTag + +class LogicalOr extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + LogicalOrOp() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MatMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MatMul.scala new file mode 100644 index 00000000000..dc6324ca16b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MatMul.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.MM +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import Utils._ + +import scala.reflect.ClassTag + +class MatMul extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val attr = nodeDef.getAttrMap + MM[T](getBoolean(attr, "transpose_a"), getBoolean(attr, "transpose_b")) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala index dc0ca6774f9..667fe79a020 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala @@ -18,12 +18,10 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.{Mean, Sequential} +import com.intel.analytics.bigdl.nn.{Mean => MeanNN, Sequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.tf.TFUtils -import org.tensorflow.framework.NodeDef +import org.tensorflow.framework.{DataType, NodeDef} import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -35,13 +33,33 @@ class Mean extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) (implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2), tensorArrays => { + val attr = nodeDef.getAttrMap + val dataType = getType(attr, "T") + val squeeze = !getBoolean(attr, "keep_dims") val dims = tensorArrays(0).asInstanceOf[Tensor[Int]] val dim = ArrayBuffer[Int]() val mean = Sequential[T]() for (i <- 1 to dims.size(1)) { dim += dims.valueAt(i) + 1 } - dim.foreach(i => mean.add(Mean[T](i, squeeze = false))) + dataType match { + case DataType.DT_INT8 => + dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) + case DataType.DT_INT16 => + dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) + case DataType.DT_UINT8 => + dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) + case DataType.DT_UINT16 => + dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) + case DataType.DT_INT32 => + dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) + case DataType.DT_INT64 => + dim.foreach(i => mean.add(new MeanNN[T, Long](i, squeeze = squeeze))) + case DataType.DT_FLOAT => + dim.foreach(i => mean.add(new MeanNN[T, Float](i, squeeze = squeeze))) + case DataType.DT_DOUBLE => + dim.foreach(i => mean.add(new MeanNN[T, Double](i, squeeze = squeeze))) + } mean }) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala new file mode 100644 index 00000000000..0fab09fe8e7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.MergeOps +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Merge extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new MergeOps[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala new file mode 100644 index 00000000000..f3738cb159b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.tf.ControlDependency +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class NoOp extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new ControlDependency[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NotEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NotEqual.scala new file mode 100644 index 00000000000..17c7f7703c0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NotEqual.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{NotEqual => NotEqualOperation} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class NotEqual extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new NotEqualOperation[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala new file mode 100644 index 00000000000..378f8857c65 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Power +import com.intel.analytics.bigdl.nn.ops.{OneHot => OneHotOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.loaders.{TensorflowOpsLoader, Utils} +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class OneHot extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val axis = getInt(nodeDef.getAttrMap, "axis") + OneHotOp(axis) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala index 5183ce00d58..6695a01be63 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala @@ -34,7 +34,7 @@ class Prod extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2), tensorArrays => { val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 - val keepDims = getBoolean(nodeDef, "keep_dims") + val keepDims = getBoolean(nodeDef.getAttrMap, "keep_dims") Prod[T](axis) }) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala new file mode 100644 index 00000000000..cfda7028b14 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{RealDiv => RealDivOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class RealDiv extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + RealDivOp() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala index 8baf316fc79..a19f46ed876 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala @@ -35,7 +35,12 @@ class Reshape extends TensorflowOpsLoader { Adapter[T](Array(2), tensorArrays => { val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] - val batchMode = sizes.valueAt(1) == -1 + val batchMode = if (sizes.nDimension() >= 1) { + sizes.valueAt(1) == -1 + } else { + false + } + val arraySize = new Array[Int](if (batchMode) sizes.nElement() - 1 else sizes.nElement()) var i = if (batchMode) 2 else 1 var k = 0 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Substr.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Substr.scala new file mode 100644 index 00000000000..344079424a6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Substr.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Substr => SubstrOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Substr extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + SubstrOp() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala new file mode 100644 index 00000000000..14b48a72837 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Sequential, Sum => SumOp} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Sum extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2), tensorArrays => { + val attr = nodeDef.getAttrMap + + val squeeze = !getBoolean(attr, "keep_dims") + val dims = tensorArrays(0).asInstanceOf[Tensor[Int]] + val dim = ArrayBuffer[Int]() + val sum = Sequential[T]() + for (i <- 1 to dims.size(1)) { + dim += dims.valueAt(i) + 1 + } + + val dataType = getType(attr, "T") + dataType match { + case DataType.DT_INT8 => + dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) + case DataType.DT_INT16 => + dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) + case DataType.DT_UINT8 => + dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) + case DataType.DT_UINT16 => + dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) + case DataType.DT_INT32 => + dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) + case DataType.DT_INT64 => + dim.foreach(i => sum.add(new SumOp[T, Long](i, squeeze = squeeze))) + case DataType.DT_FLOAT => + dim.foreach(i => sum.add(new SumOp[T, Float](i, squeeze = squeeze))) + case DataType.DT_DOUBLE => + dim.foreach(i => sum.add(new SumOp[T, Double](i, squeeze = squeeze))) + } + sum + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala new file mode 100644 index 00000000000..fc67996bd22 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.SwitchOps +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Switch extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + new SwitchOps[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala new file mode 100644 index 00000000000..aacaca4a415 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala @@ -0,0 +1,82 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Contiguous, Sequential, Transpose} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Transpose extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2), tensorArrays => { + val perm = tensorArrays(0).asInstanceOf[Tensor[Int]].storage().array() + val paris = permToPair(perm) + val layer = Sequential() + layer.add(Transpose[T](paris)) + layer.add(Contiguous()) + layer + }) + } + + private def permToPair(perm: Array[Int]): Array[(Int, Int)] = { + + val numToRank = perm.zipWithIndex.toMap + val arr = perm.indices.toArray + val pairs = ArrayBuffer[(Int, Int)]() + + def sort(arr: Array[Int], low: Int, high: Int): Unit = { + var i = low + var j = high + val pivot = arr(low + (high - low)/2) + + while (i <= j) { + while (arr(i) < pivot) i += 1 + while (arr(j) > pivot) j -= 1 + + if (i <= j) { + exchangeNumbers(arr, i, j) + i += 1 + j -= 1 + } + } + + if (low < j) sort(arr, low, j) + if (i < high) sort(arr, i, high) + } + + def exchangeNumbers(arr: Array[Int], i: Int, j: Int): Unit = { + val temp = arr(i) + arr(i) = arr(j) + arr(j) = temp + pairs += ((i, j)) + } + + sort(arr.map(numToRank), 0, arr.length-1) + + pairs.filter(pair => pair._1 != pair._2).toArray + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala index 5e4b24b171f..2927372b536 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.Context import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL.toTensor -import org.tensorflow.framework.{AttrValue, NodeDef} +import org.tensorflow.framework.{AttrValue, DataType, NodeDef} import scala.reflect.ClassTag import collection.JavaConverters._ @@ -61,14 +61,18 @@ object Utils { attrMap.get(key).getI.toInt } - private[loaders] def getBoolean(nodedef: NodeDef, key: String): Boolean = { - nodedef.getAttrMap.get(key).getB + private[loaders] def getBoolean(attrMap: util.Map[String, AttrValue], key: String): Boolean = { + attrMap.get(key).getB } private[loaders] def getIntList(attrMap: util.Map[String, AttrValue], key: String): Seq[Int] = { attrMap.get(key).getList.getIList.asScala.map(_.toInt) } + private[loaders] def getType(attrMap: util.Map[String, AttrValue], key: String): DataType = { + attrMap.get(key).getType + } + private[loaders] def toArray[T: ClassTag](tensor: Tensor[T]): Array[T] = { require(tensor.nDimension() 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{ + key: "use_locking" + value { + b: false + } + } +} +node { + name: "RMSProp/update_LeNet/fc4/biases/ApplyRMSProp" + op: "ApplyRMSProp" + input: "LeNet/fc4/biases" + input: "LeNet/fc4/biases/RMSProp" + input: "LeNet/fc4/biases/RMSProp_1" + input: "exponential_decay_learning_rate" + input: "RMSProp/decay" + input: "RMSProp/momentum" + input: "RMSProp/epsilon" + input: "gradients/LeNet/fc4/BiasAdd_grad/tuple/control_dependency_1" + device: "/device:CPU:0" + attr { + key: "T" + value { + type: DT_FLOAT + } + } + attr { + key: "_class" + value { + list { + s: "loc:@LeNet/fc4/biases" + } + } + } + attr { + key: "use_locking" + value { + b: false + } + } +} +node { + name: "RMSProp/update" + op: "NoOp" + input: "^RMSProp/update_LeNet/conv1/weights/ApplyRMSProp" + input: "^RMSProp/update_LeNet/conv1/biases/ApplyRMSProp" + input: "^RMSProp/update_LeNet/conv2/weights/ApplyRMSProp" + input: "^RMSProp/update_LeNet/conv2/biases/ApplyRMSProp" + input: "^RMSProp/update_LeNet/fc3/weights/ApplyRMSProp" + input: "^RMSProp/update_LeNet/fc3/biases/ApplyRMSProp" + input: "^RMSProp/update_LeNet/fc4/weights/ApplyRMSProp" + input: "^RMSProp/update_LeNet/fc4/biases/ApplyRMSProp" + device: "/device:CPU:0" +} +node { + name: "RMSProp/value" + op: "Const" + input: "^RMSProp/update" + device: "/device:CPU:0" + attr { + key: "_class" + value { + list { + s: "loc:@global_step" + } + } + } + attr { + key: "dtype" + value { + type: DT_INT64 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT64 + tensor_shape { + } + int64_val: 1 + } + } + } +} +node { + name: "RMSProp" + op: "AssignAdd" + input: "global_step" + input: "RMSProp/value" + device: "/device:CPU:0" + attr { + key: "T" + value { + type: DT_INT64 + } + } + attr { + key: "_class" + value { + list { + s: "loc:@global_step" + } + } + } + attr { + key: "use_locking" + value { + b: false + } + } +} +node { + name: "group_deps" + op: "NoOp" + input: "^RMSProp" + device: "/device:CPU:0" +} +node { + name: "train_op" + op: "Identity" + input: "total_loss" + input: "^group_deps" + attr { + key: "T" + value { + type: DT_FLOAT + } + } +} +node { + name: "summary_op/summary_op" + op: "MergeSummary" + input: "LeNet/fc4/weights_1" + input: "LeNet/conv2/biases_1" + input: "regularization_loss_1" + input: "LeNet/fc4/biases_1" + input: "total_loss_1" + input: "activations/Flatten" + input: "parallel_read/filenames/fraction_of_32_full" + input: "losses/softmax_cross_entropy_loss/value" + input: "LeNet/conv1/weights_1" + input: "LeNet/conv1/biases_1" + input: "parallel_read/fraction_of_640_full" + input: "LeNet/conv2/weights_1" + input: "sparsity/Predictions" + input: "learning_rate" + input: "prefetch_queue/fraction_of_2_full" + input: "sparsity/Flatten" + input: "sparsity/Logits" + input: "activations/Predictions" + input: "activations/Logits" + input: "LeNet/fc3/weights_1" + input: "batch/fraction_of_160_full" + input: "LeNet/fc3/biases_1" + input: 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with two branch" in { val data = Input("data") val condition = Input("condition") - val swtich = ControlNodes.switch(data, condition) + val swtich = ControlNodes.switch(condition, data) val echo1 = Echo().inputs(swtich.trueEdge()) val echo2 = Echo().inputs(swtich.falseEdge()) @@ -1157,7 +1157,7 @@ class GraphSpec extends FlatSpec with Matchers { "graph" should "support switch with two branch with merge" in { val data = Input("data") val condition = Input("condition") - val swtich = ControlNodes.switch(data, condition) + val swtich = ControlNodes.switch(condition, data) val echo1 = Echo().inputs(swtich.trueEdge()) val echo2 = Echo().inputs(swtich.falseEdge()) val add1 = AddConstant(1).inputs(echo1) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PackSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PackSpec.scala index 98e87b4cf57..ae92172cb64 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PackSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PackSpec.scala @@ -90,4 +90,27 @@ class PackSpec extends FlatSpec with Matchers { gradInput2 should be(input) gradInput3 should be(input) } + + "Pack" should "work with tensor input" in { + val module1 = new Pack[Double](1) + + val input1 = Tensor[Double](2, 2) + input1(Array(1, 1)) = 1 + input1(Array(1, 2)) = 2 + input1(Array(2, 1)) = 3 + input1(Array(2, 2)) = 4 + + val output1 = module1.forward(input1) + + val expectOutput1 = Tensor[Double](1, 2, 2) + expectOutput1(Array(1, 1, 1)) = 1 + expectOutput1(Array(1, 1, 2)) = 2 + expectOutput1(Array(1, 2, 1)) = 3 + expectOutput1(Array(1, 2, 2)) = 4 + + val gradInput1 = module1.backward(input1, output1) + + output1 should be(expectOutput1) + gradInput1 should be(input1) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala index ecc8d974725..d38b33bb005 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala @@ -32,7 +32,7 @@ class DecodeImageSpec extends FlatSpec with Matchers { val input = getInputs("raw") - val decoder = new DecodeRaw[Float](DataType.DT_UINT8, ByteOrder.LITTLE_ENDIAN) + val decoder = new DecodeRaw[Float](DataType.DT_UINT8, true) val output = decoder.forward(input).asInstanceOf[Tensor[Int]] @@ -75,7 +75,7 @@ class DecodeImageSpec extends FlatSpec with Matchers { private def getRaw(): Tensor[Int] = { val input = getInputs("raw") - val decoder = new DecodeRaw[Float](DataType.DT_UINT8, ByteOrder.LITTLE_ENDIAN) + val decoder = new DecodeRaw[Float](DataType.DT_UINT8, true) val output = decoder.forward(input).asInstanceOf[Tensor[Int]] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/EqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/EqualSpec.scala index a5ddbae0ca4..31927bbd8d1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/EqualSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/EqualSpec.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -86,10 +87,16 @@ class EqualSpec extends FlatSpec with Matchers { } "Equal String operation" should "works correctly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString val input = T( - Tensor[String](T("abc", "bbb", "aaa")), - Tensor[String](T("aaa", "ccc", "aaa")) + + Tensor[ByteString](Array(ByteString.copyFromUtf8("abc"), + ByteString.copyFromUtf8("bbb"), + ByteString.copyFromUtf8("aaaa").substring(0, 3)), Array(3)), + Tensor[ByteString](Array(ByteString.copyFromUtf8("aaa"), + ByteString.copyFromUtf8("ccc"), + ByteString.copyFromUtf8("aaa")), Array(3)) ) val expectOutput = Tensor[Boolean](T(false, false, true)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala index cc7396fa94a..be5b39a5eee 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala @@ -28,9 +28,9 @@ class ExpandDimsSpec extends FlatSpec with Matchers { val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) - val output1 = ExpandDims(0).forward(input) - val output2 = ExpandDims(2).forward(input) - val output3 = ExpandDims(3).forward(input) + val output1 = ExpandDims(1).forward(input) + val output2 = ExpandDims(3).forward(input) + val output3 = ExpandDims(4).forward(input) output1 should be(expectOutput1) output2 should be(expectOutput2) @@ -45,9 +45,9 @@ class ExpandDimsSpec extends FlatSpec with Matchers { val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) - val output1 = ExpandDims(0).forward(input) - val output2 = ExpandDims(2).forward(input) - val output3 = ExpandDims(3).forward(input) + val output1 = ExpandDims(1).forward(input) + val output2 = ExpandDims(3).forward(input) + val output3 = ExpandDims(4).forward(input) output1 should be(expectOutput1) output2 should be(expectOutput2) @@ -62,9 +62,9 @@ class ExpandDimsSpec extends FlatSpec with Matchers { val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) - val output1 = ExpandDims(0).forward(input) - val output2 = ExpandDims(2).forward(input) - val output3 = ExpandDims(3).forward(input) + val output1 = ExpandDims(1).forward(input) + val output2 = ExpandDims(3).forward(input) + val output3 = ExpandDims(4).forward(input) output1 should be(expectOutput1) output2 should be(expectOutput2) @@ -79,9 +79,9 @@ class ExpandDimsSpec extends FlatSpec with Matchers { val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) - val output1 = ExpandDims(0).forward(input) - val output2 = ExpandDims(2).forward(input) - val output3 = ExpandDims(3).forward(input) + val output1 = ExpandDims(1).forward(input) + val output2 = ExpandDims(3).forward(input) + val output3 = ExpandDims(4).forward(input) output1 should be(expectOutput1) output2 should be(expectOutput2) @@ -96,9 +96,9 @@ class ExpandDimsSpec extends FlatSpec with Matchers { val expectOutput2 = input.clone().resize(Array(2, 3, 1, 5)) val expectOutput3 = input.clone().resize(Array(2, 3, 5, 1)) - val output1 = ExpandDims(0).forward(input) - val output2 = ExpandDims(2).forward(input) - val output3 = ExpandDims(3).forward(input) + val output1 = ExpandDims(1).forward(input) + val output2 = ExpandDims(3).forward(input) + val output3 = ExpandDims(4).forward(input) output1 should be(expectOutput1) output2 should be(expectOutput2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqualSpec.scala new file mode 100644 index 00000000000..0f996da35b9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqualSpec.scala @@ -0,0 +1,133 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class NotEqualSpec extends FlatSpec with Matchers { + "NotEqual Float operation" should "works correctly" in { + val input = + T( + Tensor[Float](T(1f, 2f, 2f)), + Tensor[Float](T(2f, 3f, 2f)) + ) + + val expectOutput = Tensor[Boolean](T(true, true, false)) + + val output = NotEqual[Boolean]().forward(input) + output should be(expectOutput) + } + + "NotEqual Boolean operation" should "works correctly" in { + val input = + T( + Tensor[Boolean](T(true, true, false)), + Tensor[Boolean](T(false, true, false)) + ) + + val expectOutput = Tensor[Boolean](T(true, false, false)) + + val output = NotEqual[Boolean]().forward(input) + output should be(expectOutput) + } + + "NotEqual Double operation" should "works correctly" in { + val input = + T( + Tensor[Double](T(1.0, 2.0, 2.0)), + Tensor[Double](T(2.0, 3.0, 2.0)) + ) + + val expectOutput = Tensor[Boolean](T(true, true, false)) + + val output = NotEqual[Boolean]().forward(input) + output should be(expectOutput) + } + + "NotEqual Char operation" should "works correctly" in { + val input = + T( + Tensor[Char](T('a', 'b', 'a')), + Tensor[Char](T('b', 'c', 'a')) + ) + + val expectOutput = Tensor[Boolean](T(true, true, false)) + + val output = NotEqual[Boolean]().forward(input) + output should be(expectOutput) + } + + "NotEqual Long operation" should "works correctly" in { + val input = + T( + Tensor[Long](T(1L, 2L, 2L)), + Tensor[Long](T(2L, 3L, 2L)) + ) + + val expectOutput = Tensor[Boolean](T(true, true, false)) + + val output = NotEqual[Boolean]().forward(input) + output should be(expectOutput) + } + + "NotEqual String operation" should "works correctly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val input = + T( + + Tensor[ByteString](Array(ByteString.copyFromUtf8("abc"), + ByteString.copyFromUtf8("bbb"), + ByteString.copyFromUtf8("aaaa").substring(0, 3)), Array(3)), + Tensor[ByteString](Array(ByteString.copyFromUtf8("aaa"), + ByteString.copyFromUtf8("ccc"), + ByteString.copyFromUtf8("aaa")), Array(3)) + ) + + val expectOutput = Tensor[Boolean](T(true, true, false)) + + val output = NotEqual[Boolean]().forward(input) + output should be(expectOutput) + } + + "NotEqual Short operation" should "works correctly" in { + val input = + T( + Tensor[Short](T(1: Short, 2: Short, 2: Short)), + Tensor[Short](T(2: Short, 3: Short, 2: Short)) + ) + + val expectOutput = Tensor[Boolean](T(true, true, false)) + + val output = NotEqual[Boolean]().forward(input) + output should be(expectOutput) + } + + "NotEqual Int operation" should "works correctly" in { + val input = + T( + Tensor[Int](T(1, 2, 2)), + Tensor[Int](T(2, 3, 2)) + ) + + val expectOutput = Tensor[Boolean](T(true, true, false)) + + val output = NotEqual[Boolean]().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala index ce094215abf..837a2852129 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala @@ -22,7 +22,7 @@ import org.scalatest.{FlatSpec, Matchers} class OneHotSpec extends FlatSpec with Matchers { "OneHot operation one dimension index" should "works correctly" in { val input = - T(Tensor[Int](T(0, 2, -1, 1)), + T(Tensor[Long](T(0, 2, -1, 1)), Tensor[Int](Array(3), shape = Array[Int]()), Tensor[Double](Array(0.5), shape = Array[Int]()), Tensor[Double](Array(0.0), shape = Array[Int]())) @@ -46,7 +46,7 @@ class OneHotSpec extends FlatSpec with Matchers { import com.intel.analytics.bigdl.numeric.NumericDouble val input = T( - Tensor[Int](T(T(0, 2), T(1, -1))), + Tensor[Long](T(T(0, 2), T(1, -1))), Tensor[Int](Array(3), shape = Array[Int]()), Tensor[Double](Array(1.0), shape = Array[Int]()), Tensor[Double](Array(0.0), shape = Array[Int]()) @@ -71,7 +71,7 @@ class OneHotSpec extends FlatSpec with Matchers { import com.intel.analytics.bigdl.numeric.NumericDouble val input = T( - Tensor[Int](T(T(0, 2), T(1, -1))), + Tensor[Long](T(T(0, 2), T(1, -1))), Tensor[Int](Array(3), shape = Array[Int]()), Tensor[Double](Array(1.0), shape = Array[Int]()), Tensor[Double](Array(0.0), shape = Array[Int]()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala index 91a430ef43d..c287b1626a8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala @@ -50,7 +50,7 @@ class ParseExampleSpec extends FlatSpec with Matchers { val exampleParser = new ParseExample[Float](3, Seq(FloatType, LongType, StringType), Seq(Array(3), Array(3), Array())) - val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int]()) + val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) val names = Tensor[ByteString]() val key1 = Tensor[ByteString](Array(ByteString.copyFromUtf8("floatFeature")), Array[Int]()) val key2 = Tensor[ByteString](Array(ByteString.copyFromUtf8("longFeature")), Array[Int]()) @@ -68,10 +68,10 @@ class ParseExampleSpec extends FlatSpec with Matchers { val longTensor = output(2).asInstanceOf[Tensor[Long]] val stringTensor = output(3).asInstanceOf[Tensor[ByteString]] - floatTensor should be (Tensor[Float](T(0.0f, 1.0f, 2.0f))) - longTensor should be (Tensor[Long](T(0L, 1L, 2L))) + floatTensor should be (Tensor[Float](T(T(0.0f, 1.0f, 2.0f)))) + longTensor should be (Tensor[Long](T(T(0L, 1L, 2L)))) stringTensor should be (Tensor[ByteString]( - Array(ByteString.copyFromUtf8("abcd")), Array[Int]())) + Array(ByteString.copyFromUtf8("abcd")), Array[Int](1))) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MatMulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstrSpec.scala similarity index 60% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MatMulSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstrSpec.scala index 01d175eb2d5..25ba60174e9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MatMulSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstrSpec.scala @@ -15,35 +15,20 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} -class MatMulSpec extends FlatSpec with Matchers { - "MatMul operation" should "works correctly" in { - import com.intel.analytics.bigdl.numeric.NumericFloat - val input = - T( - Tensor( - T( - T(1f, 2f, 3f), - T(4f, 5f, 6f)) - ), - Tensor( - T( - T(1f, 4f), - T(2f, 5f), - T(3f, 6f)) - ) - ) +class SubstrSpec extends FlatSpec with Matchers { + "Substr operation" should "works correctly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val data = Tensor.scalar(ByteString.copyFromUtf8("abc")) + val pos = Tensor.scalar(0) + val len = Tensor.scalar(2) + val expectOutput = Tensor.scalar(ByteString.copyFromUtf8("ab")) - val expectOutput = Tensor( - T( - T(14f, 32f), - T(32f, 77f)) - ) - - val output = MatMul().forward(input) + val output = Substr().forward(T(data, pos, len)) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddSpec.scala similarity index 69% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddSpec.scala index 20dde857614..9d09e4bac33 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T @@ -21,7 +21,7 @@ import org.scalatest.{FlatSpec, Matchers} class BiasAddSpec extends FlatSpec with Matchers { "BiasAdd operation" should "works correctly" in { - import com.intel.analytics.bigdl.numeric.NumericDouble + import com.intel.analytics.bigdl.numeric.NumericFloat val input = T( Tensor(T( @@ -50,8 +50,25 @@ class BiasAddSpec extends FlatSpec with Matchers { T(5f, 5f, 5f), T(4f, 4f, 4f) ))) + val expectedGradValue = Tensor( + T( + T( + T(4f, 4f, 4f), + T(5f, 5f, 5f), + T(6f, 6f, 6f) + ), + T( + T(6f, 6f, 6f), + T(5f, 5f, 5f), + T(4f, 4f, 4f) + ))) - val output = BiasAdd().forward(input) + val expectedGradBias = Tensor(T(30f, 30f, 30f)) + val layer = BiasAdd() + val output = layer.forward(input) + val gradInput = layer.backward(input, output) output should be(expectOutput) + gradInput[Tensor[Float]](1) should be(expectedGradValue) + gradInput[Tensor[Float]](2) should be(expectedGradBias) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index c1b7431190d..8719e55d96f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -834,13 +834,25 @@ class DenseTensorSpec extends FlatSpec with Matchers { t should be(DenseTensor[Double](2.0)) } - "Tensor add" should "support broadcasting" in { + "Tensor add and sub" should "support broadcasting" in { val t1 = Tensor[Double](T(1, 2, 3)) val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) - t2.add(t1) should be(Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + t2.add(t1) should be (Tensor[Double](T(T(3, 7, 6), T(4, 8, 7)))) + } + + "Tensor sub" should "support broadcasting" in { + val t1 = Tensor[Double](T(1, 2, 3)) + val t2 = Tensor[Double](T(T(2, 5, 3), T(3, 6, 4))) + t2.sub(t1) should be (Tensor[Double](T(T(1, 3, 0), T(2, 4, 1)))) + } + + "Tensor div" should "support broadcasting" in { + val t1 = Tensor[Double](T(1, 2, 3)) + val t2 = Tensor[Double](T(T(2, 4, 6), T(3, 6, 9))) + t2.div(t1) should be (Tensor[Double](T(T(2, 2, 2), T(3, 3, 3)))) } - "Tensor add" should "support broadcasting 2" in { + "Tensor add " should "support broadcasting 2" in { val t1 = Tensor[Double](T( T( T(1, 2, 3), diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala index 15d0e150c78..b7e40bf9800 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala @@ -25,7 +25,7 @@ class SumSpec extends TorchSpec { "An Sum()" should "generate correct output and grad" in { torchCheck() - val layer = new Sum[Double]() + val layer = Sum[Double]() val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 7c50d379363..21aeb024b0b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -1682,7 +1682,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } - "Sum serializer " should " work properly" in { + "Sum serializer" should "work properly" in { val sum = Sum(2) val input1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) val input2 = Tensor() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala index 192199dea66..aced5443c2d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala @@ -19,7 +19,7 @@ import com.intel.analytics.bigdl.dataset._ import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, MSECriterion} import com.intel.analytics.bigdl.optim.{SGD, Trigger} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{Engine, Table} +import com.intel.analytics.bigdl.utils.{Engine, File, T, Table} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -28,6 +28,7 @@ import java.io.{File => JFile} import com.google.protobuf.ByteString import org.tensorflow.framework.AttrValue +import scala.collection.JavaConverters._ import scala.collection.mutable class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { @@ -93,13 +94,53 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { module.forward(Tensor[Float](Array(1))) } + + "Session" should "be able construct input data" in { + val lenetModel = getLenetModel("lenet_batch_2.pbtxt") + + val context = + new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() + val session = new BigDLSessionImpl[Float](lenetModel, sc, context) + + val endpoints = Seq( + "fifo_queue_Dequeue" + ) + val rdd = session.getRDD(endpoints) + val result = rdd.collect() + result.length should be (5) + val imageSum = result.map(t => t[Tensor[Float]](1).sum()).sum + val labelSum = result.map(t => t[Tensor[Float]](2).sum()).sum + + (imageSum - (-6009.5)) < 1e-7 should be (true) + labelSum should be (10) + } + + "Session" should "be work with arbitrary batch size" in { + + val lenetModel = getLenetModel("lenet_with_batch_3.pbtxt") + + val context = + new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() + val session = new BigDLSessionImpl[Float](lenetModel, sc, context) + + val endpoints = Seq( + "fifo_queue_Dequeue" + ) + val rdd = session.getRDD(endpoints) + val result = rdd.collect() + result.length should be (4) + result.head[Tensor[Float]](1).size(1) should be (3) + } + + private def getLenetModel(name: String) = { val resource = getClass().getClassLoader().getResource("tf") - val modelPath = resource.getPath() + JFile.separator + "lenet.pbtxt" - val filePath = resource.getPath() + JFile.separator + "mnist_test.tfrecord" + val modelPath = resource.getPath() + JFile.separator + name + + val filePath = resource.getPath() + JFile.separator + "mnist_train.tfrecord" + val nodes = TensorflowLoader.parseTxt(modelPath) - import scala.collection.JavaConverters._ val filenames = nodes.asScala.filter(_.getName == "parallel_read/filenames/Const").head @@ -111,18 +152,7 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { .putAttr("value", AttrValue.newBuilder().setTensor(newTensor).build()) .build() - val newModel = nodes.asScala.filterNot(_.getName == "parallel_read/filenames/Const") :+ newNode - - val context = - new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() - val session = new BigDLSessionImpl[Float](newModel, sc, context) - - val endpoints = Seq( - "ParseSingleExample/SerializedDependencies" - ) - val rdd = session.getRDD(endpoints) - val result = rdd.count() - result should be (4) + nodes.asScala.filterNot(_.getName == "parallel_read/filenames/Const") :+ newNode } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala index bf391d40f2d..da95896dc9e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala @@ -105,7 +105,7 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { "Squeeze" should "be correctly saved" in { System.setProperty("bigdl.enableNHWC", "false") - val layer = Squeeze(3) + val layer = Squeeze(3).asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]] val input = Tensor[Float](4, 2, 1, 2).rand() test(layer, input) should be(true) } From 857031d03347668d8e703dbf3a77063f7a02c19a Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 12 Oct 2017 16:00:09 +0800 Subject: [PATCH 0449/1065] BytesToBGRImg to support resizing (#1504) * add new transformer * refinement for bytes to BGR IMG * refinement for preprocessor * refinement * refinement * reuse existing func * refinement per review * refinement * fix typo --- .../dllib/example/loadmodel/DatasetUtil.scala | 20 +++++++++--- .../feature/dataset/image/BytesToBGRImg.scala | 32 ++++++++++++++++--- 2 files changed, 43 insertions(+), 9 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala index 130f3780dbb..6e92b8a70ac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.example.loadmodel -import java.nio.file.{Paths, Path} +import java.nio.charset.StandardCharsets +import java.nio.file.{Files, Path, Paths} import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset._ @@ -26,30 +27,39 @@ import com.intel.analytics.bigdl.utils.File import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD +import scala.io.Source + object AlexNetPreprocessor { val imageSize = 227 def apply(path: String, batchSize: Int, meanFile: String, sc: SparkContext) : DataSet[MiniBatch[Float]] = { - val means = File.load[Tensor[Float]](meanFile) + // 'meanFile' specify the path to the pixel level mean data, one line per pixel + // following H * W * C order, 196608 in total (256 * 256 * 3) + val means = createMeans(meanFile) DataSet.SeqFileFolder.files(path, sc, classNum = 1000) -> // do not normalize the pixel values to [0, 1] - BytesToBGRImg(normalize = 1f) -> + BytesToBGRImg(normalize = 1f, 256, 256) -> BGRImgPixelNormalizer(means) -> BGRImgCropper(imageSize, imageSize, CropCenter) -> BGRImgToBatch(batchSize, toRGB = false) } def rdd(path: String, batchSize: Int, meanFile: String, sc: SparkContext) : RDD[Sample[Float]] = { - val means = File.load[Tensor[Float]](meanFile) + val means = createMeans(meanFile) val dataSet = DataSet.SeqFileFolder.filesToRdd(path, sc, 1000) // do not normalize the pixel values to [0, 1] - val transfomer = BytesToBGRImg(normalize = 1f) -> + val transfomer = BytesToBGRImg(normalize = 1f, 256, 256) -> BGRImgPixelNormalizer(means) -> BGRImgCropper(imageSize, imageSize, CropCenter) -> BGRImgToSample(toRGB = false) transfomer(dataSet) } + + def createMeans(meanFile : String) : Tensor[Float] = { + val array = Source.fromFile(meanFile).getLines().map(_.toFloat).toArray + Tensor[Float](array, Array(array.length)) + } } object InceptionPreprocessor { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/BytesToBGRImg.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/BytesToBGRImg.scala index 55f63817eb1..f8a701dd13d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/BytesToBGRImg.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/BytesToBGRImg.scala @@ -16,13 +16,17 @@ package com.intel.analytics.bigdl.dataset.image +import java.awt.Color +import java.awt.image.{BufferedImage, DataBufferByte} +import java.nio.ByteBuffer + import com.intel.analytics.bigdl.dataset.{ByteRecord, Transformer} import scala.collection.Iterator object BytesToBGRImg { - def apply(normalize: Float = 255f): BytesToBGRImg = - new BytesToBGRImg(normalize) + def apply(normalize: Float = 255f, resizeW : Int = -1, resizeH : Int = -1): BytesToBGRImg = + new BytesToBGRImg(normalize, resizeW, resizeH) } /** @@ -30,13 +34,33 @@ object BytesToBGRImg { * height, and the last is pixels coming with BGR order. * @param normalize */ -class BytesToBGRImg(normalize: Float) +class BytesToBGRImg(normalize: Float, resizeW : Int = -1, resizeH : Int = -1) extends Transformer[ByteRecord, LabeledBGRImage] { + private val buffer = new LabeledBGRImage() override def apply(prev: Iterator[ByteRecord]): Iterator[LabeledBGRImage] = { prev.map(rawData => { - buffer.copy(rawData.data, normalize).setLabel(rawData.label) + buffer.copy(getImgData(rawData, resizeW, resizeH), normalize).setLabel(rawData.label) }) } + + private def getImgData (record : ByteRecord, resizeW : Int, resizeH : Int) + : Array[Byte] = { + if (resizeW == -1) { + return record.data + } else { + val rawData = record.data + val imgBuffer = ByteBuffer.wrap(rawData) + val width = imgBuffer.getInt + val height = imgBuffer.getInt + val bufferedImage : BufferedImage + = new BufferedImage(width, height, BufferedImage.TYPE_3BYTE_BGR) + val outputImagePixelData = bufferedImage.getRaster.getDataBuffer + .asInstanceOf[DataBufferByte].getData + System.arraycopy(imgBuffer.array(), 8, + outputImagePixelData, 0, outputImagePixelData.length) + BGRImage.resizeImage(bufferedImage, resizeW, resizeH) + } + } } From e855ac3db2eb724c38538faf584f23a9b0953a09 Mon Sep 17 00:00:00 2001 From: dding3 Date: Thu, 12 Oct 2017 22:03:22 -0400 Subject: [PATCH 0450/1065] RecurrentDecoder enhancement (#1619) * RecurrentDecoder refactor --- .../intel/analytics/bigdl/dllib/nn/Cell.scala | 111 +++++++--- .../bigdl/dllib/nn/ConvLSTMPeephole.scala | 5 +- .../bigdl/dllib/nn/ConvLSTMPeephole3D.scala | 3 +- .../intel/analytics/bigdl/dllib/nn/GRU.scala | 14 +- .../intel/analytics/bigdl/dllib/nn/LSTM.scala | 8 +- .../bigdl/dllib/nn/LSTMPeephole.scala | 56 +++-- .../intel/analytics/bigdl/dllib/nn/RNN.scala | 27 ++- .../analytics/bigdl/dllib/nn/Recurrent.scala | 121 ++++++----- .../bigdl/dllib/nn/RecurrentDecoder.scala | 195 +++++++++++------ .../dllib/utils/python/api/PythonBigDL.scala | 14 +- .../analytics/bigdl/dllib/nn/CellSpec.scala | 8 +- .../dllib/nn/ConvLSTMPeephole3DSpec.scala | 2 +- .../bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala | 6 +- .../bigdl/dllib/nn/RecurrentDecoderSpec.scala | 205 +++++++++++++++++- .../bigdl/dllib/nn/RecurrentSpec.scala | 24 +- .../bigdl/dllib/torch/LSTMPeepholeSpec.scala | 19 +- .../bigdl/dllib/torch/LSTMSpec.scala | 2 +- 17 files changed, 598 insertions(+), 222 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala index bbd57ce146f..b3b28a920b9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -25,6 +25,7 @@ import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -54,7 +55,6 @@ abstract class Cell[T : ClassTag]( var backwardTimes: Array[Long] = null var times: Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = null - /** * Any recurrent kernels should have a cell member variable which * represents the module in the kernel. @@ -80,7 +80,11 @@ abstract class Cell[T : ClassTag]( * Please refer to SimpleRNN or LSTM for reference. * @return */ - def preTopology: AbstractModule[Activity, Activity, T] = null + var preTopology: TensorModule[T] + + private[nn] var includePreTopology: Boolean = false + + private var gradOutput2PreTopology = Tensor[T]() def hiddenSizeOfPreTopo: Int = hiddensShape(0) @@ -93,12 +97,14 @@ abstract class Cell[T : ClassTag]( * * @param hidden * @param batchSize batchSize + * @param stepShape For rnn/lstm/gru, it's embedding size. For convlstm/ + * convlstm3D, it's a list of outputPlane, length, width, height * @return */ - def hidResize(hidden: Activity, batchSize: Int, imageSize: Array[Int] = null): Activity = { + def hidResize(hidden: Activity, batchSize: Int, stepShape: Array[Int]): Activity = { if (hidden == null) { if (hiddensShape.length == 1) { - hidResize(Tensor[T](), batchSize) + hidResize(Tensor[T](), batchSize, stepShape) } else { val _hidden = T() var i = 1 @@ -106,7 +112,7 @@ abstract class Cell[T : ClassTag]( _hidden(i) = Tensor[T]() i += 1 } - hidResize(_hidden, batchSize, imageSize) + hidResize(_hidden, batchSize, stepShape) } } else { if (hidden.isInstanceOf[Tensor[T]]) { @@ -117,20 +123,13 @@ abstract class Cell[T : ClassTag]( require(hidden.isInstanceOf[Table], "Cell: hidden should be a Table") var i = 1 - if (null == imageSize) { - while (i <= hidden.toTable.length()) { - hidden.toTable[Tensor[T]](i).resize(batchSize, hiddensShape(i - 1)) - i += 1 - } - } else { - val sizes = new Array[Int](imageSize.length + 1) - sizes(0) = batchSize - Array.copy(imageSize, 0, sizes, 1, imageSize.size) - while (i <= hidden.toTable.length()) { - sizes(1) = hiddensShape(i - 1) - hidden.toTable[Tensor[T]](i).resize(sizes) - i += 1 - } + val sizes = new Array[Int](stepShape.length + 1) + sizes(0) = batchSize + Array.copy(stepShape, 0, sizes, 1, stepShape.size) + while (i <= hidden.toTable.length()) { + sizes(1) = hiddensShape(i - 1) + hidden.toTable[Tensor[T]](i).resize(sizes) + i += 1 } hidden } @@ -138,26 +137,61 @@ abstract class Cell[T : ClassTag]( } override def updateOutput(input: Table): Table = { - output = cell.forward(input).toTable + if (includePreTopology) { + assert(preTopology != null, "preTopology cannot be null if includePreTopology is true") + val inputTensor = input.toTable[Tensor[T]](Recurrent.inputDim) + input(Recurrent.inputDim) = preTopology.updateOutput(inputTensor) + output = cell.forward(input).toTable + input(Recurrent.inputDim) = inputTensor + } else output = cell.forward(input).toTable output } override def updateGradInput(input: Table, gradOutput: Table): Table = { - gradInput = cell.updateGradInput(input, gradOutput).toTable + if (includePreTopology) { + val inputTensor = input.toTable[Tensor[T]](Recurrent.inputDim) + input(Recurrent.inputDim) = preTopology.output + gradInput = cell.updateGradInput(input, gradOutput).toTable + gradOutput2PreTopology = gradInput.toTable[Tensor[T]](Recurrent.inputDim) + gradInput(Recurrent.inputDim) = + preTopology.updateGradInput(inputTensor, gradInput.toTable[Tensor[T]](Recurrent.inputDim)) + input(Recurrent.inputDim) = inputTensor + } else { + gradInput = cell.updateGradInput(input, gradOutput).toTable + } gradInput } override def accGradParameters(input: Table, gradOutput: Table): Unit = { - cell.accGradParameters(input, gradOutput) + if (includePreTopology) { + val inputTensor = input.toTable[Tensor[T]](Recurrent.inputDim) + input(Recurrent.inputDim) = preTopology.output + cell.accGradParameters(input, gradOutput) + preTopology.accGradParameters(inputTensor, gradOutput2PreTopology) + input(Recurrent.inputDim) = inputTensor + } else { + cell.accGradParameters(input, gradOutput) + } } override def backward(input: Table, gradOutput: Table): Table = { - gradInput = cell.backward(input, gradOutput).toTable + if (includePreTopology) { + val inputTensor = input.toTable[Tensor[T]](Recurrent.inputDim) + input(Recurrent.inputDim) = preTopology.output + gradInput = cell.backward(input, gradOutput) + gradInput(Recurrent.inputDim) = + preTopology.backward(inputTensor, gradInput.toTable[Tensor[T]](Recurrent.inputDim)) + input(Recurrent.inputDim) = inputTensor + } else { + gradInput = cell.backward(input, gradOutput).toTable + } + gradInput } override def updateParameters(learningRate: T): Unit = { cell.updateParameters(learningRate) + if (includePreTopology) preTopology.updateParameters(learningRate) } private def initAddTimes(): Unit = { @@ -231,18 +265,26 @@ abstract class Cell[T : ClassTag]( override def zeroGradParameters(): Unit = { cell.zeroGradParameters() + if (includePreTopology) preTopology.zeroGradParameters() } override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { - cell.parameters() + val _cell = if (includePreTopology) { + Sequential().add(preTopology).add(cell) + } else cell + _cell.parameters() } override def getParametersTable(): Table = { - cell.getParametersTable() + val _cell = if (includePreTopology) { + Sequential().add(preTopology).add(cell) + } else cell + _cell.getParametersTable() } override def reset(): Unit = { cell.reset() + if (includePreTopology) preTopology.reset() } /** @@ -276,6 +318,14 @@ object CellSerializer extends ModuleSerializable { cellModule.cell = DataConverter.getAttributeValue(context, attrMap.get("cell")). asInstanceOf[AbstractModule[Activity, Activity, T]] + val preTopologyAttr = attrMap.get("preTopology") + cellModule.preTopology = DataConverter.getAttributeValue(context, preTopologyAttr). + asInstanceOf[TensorModule[T]] + + val includePreTopologyAttr = attrMap.get("includePreTopology") + cellModule.includePreTopology = DataConverter.getAttributeValue(context, + includePreTopologyAttr).asInstanceOf[Boolean] + cellModule } @@ -296,5 +346,14 @@ object CellSerializer extends ModuleSerializable { ModuleSerializer.abstractModuleType) cellModuleBuilder.putAttr("cell", cellBuilder.build) + val preTopologyBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, preTopologyBuilder, + cellModule.preTopology, ModuleSerializer.tensorModuleType) + cellModuleBuilder.putAttr("preTopology", preTopologyBuilder.build) + + val includePreTopologyBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, includePreTopologyBuilder, + cellModule.includePreTopology, scala.reflect.runtime.universe.typeOf[Boolean]) + cellModuleBuilder.putAttr("includePreTopology", includePreTopologyBuilder.build) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala index a64895dafbb..25ea4272064 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -69,6 +69,9 @@ class ConvLSTMPeephole[T : ClassTag]( override var cell: AbstractModule[Activity, Activity, T] = buildModel() // val joinDim = 2 + override var preTopology: TensorModule[T] = null + +// override var preTopology: AbstractModule[Activity, Activity, T] = null // override def preTopology: AbstractModule[Activity, Activity, T] = // Sequential() // .add(TimeDistributed(SpatialConvolution(inputSize, outputSize*4, kernelI, kernelI, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala index c5495d837ba..46fba08e96e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -66,6 +66,7 @@ class ConvLSTMPeephole3D[T : ClassTag]( var hiddenLayer: Sequential[T] = _ var cellLayer: Sequential[T] = _ + override var preTopology: TensorModule[T] = null override var cell: AbstractModule[Activity, Activity, T] = buildModel() def buildGate(): Sequential[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala index 2a70d4ce1eb..864d72ee852 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -69,13 +69,11 @@ class GRU[T : ClassTag] ( override var cell: AbstractModule[Activity, Activity, T] = buildModel() - override def preTopology: AbstractModule[Activity, Activity, T] = - if (p != 0) { - null - } else { - TimeDistributed[T](Linear(inputSize, 3 * outputSize, - wRegularizer = wRegularizer, bRegularizer = bRegularizer)) - } + override var preTopology: TensorModule[T] = + if (p != 0) { null } else { + Linear(inputSize, 3 * outputSize, + wRegularizer = wRegularizer, bRegularizer = bRegularizer) + } override def hiddenSizeOfPreTopo: Int = 3 * outputSize diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala index 4dda9e47940..a41e6efc248 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -66,11 +66,11 @@ class LSTM[T : ClassTag] ( override var cell: AbstractModule[Activity, Activity, T] = buildModel() - override def preTopology: AbstractModule[Activity, Activity, T] = if (p != 0) { + override var preTopology: TensorModule[T] = if (p != 0) { null } else { - TimeDistributed[T](Linear(inputSize, 4 * hiddenSize, - wRegularizer = wRegularizer, bRegularizer = bRegularizer)) + Linear(inputSize, 4 * hiddenSize, + wRegularizer = wRegularizer, bRegularizer = bRegularizer) } override def hiddenSizeOfPreTopo: Int = 4 * hiddenSize diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala index bc1ebae613d..42eef3897c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -76,11 +76,12 @@ class LSTMPeephole[T : ClassTag] ( .add(SelectTable(1)) .add(NarrowTable(2, 2))) - override def preTopology: AbstractModule[Activity, Activity, T] = - Sequential() - .add(Dropout(p)) - .add(TimeDistributed(Linear(inputSize, hiddenSize * 4, wRegularizer = wRegularizer, - bRegularizer = bRegularizer))) + override var preTopology: TensorModule[T] = if (p != 0) { + null + } else { + Linear(inputSize, 4 * hiddenSize, + wRegularizer = wRegularizer, bRegularizer = bRegularizer) + } override def hiddenSizeOfPreTopo: Int = hiddenSize * 4 @@ -91,11 +92,20 @@ class LSTMPeephole[T : ClassTag] ( /** * f(input1 + U * input2) */ - - val i2g = Narrow(dimension, offset, length).inputs(input1) - val drop = Dropout(p).inputs(input2) - val h2g = Linear(hiddenSize, hiddenSize, - withBias = false, wRegularizer = uRegularizer).inputs(drop) + var i2g: ModuleNode[T] = null + var h2g: ModuleNode[T] = null + if (p != 0) { + val input1Drop = Dropout(p).inputs(input1) + i2g = Linear(inputSize, hiddenSize, wRegularizer = wRegularizer, + bRegularizer = bRegularizer).inputs(input1Drop) + val input2Drop = Dropout(p).inputs(input2) + h2g = Linear(hiddenSize, hiddenSize, withBias = false, + wRegularizer = uRegularizer).inputs(input2Drop) + } else { + i2g = Narrow(dimension, offset, length).inputs(input1) + h2g = Linear(hiddenSize, hiddenSize, + withBias = false, wRegularizer = uRegularizer).inputs(input2) + } val cMul = CMul(Array(hiddenSize)).inputs(input3) val cadd = CAddTable().inputs(i2g, h2g, cMul) @@ -135,12 +145,23 @@ class LSTMPeephole[T : ClassTag] ( * f(input1 + W * input2) */ - val i2h = Narrow(featDim, 1 + 2 * hiddenSize, hiddenSize).inputs(input1) - - val drop = Dropout(p).inputs(input2) - val h2h = Linear(hiddenSize, hiddenSize, withBias = false, - wRegularizer = uRegularizer).inputs(drop) + var i2h: ModuleNode[T] = null + var h2h: ModuleNode[T] = null + if (p != 0) { + val input1Drop = Dropout(p).inputs(input1) + i2h = Linear(inputSize, hiddenSize, wRegularizer = wRegularizer, + bRegularizer = bRegularizer).inputs(input1Drop) + + val input2Drop = Dropout(p).inputs(input2) + h2h = Linear(hiddenSize, hiddenSize, withBias = false, + wRegularizer = uRegularizer).inputs(input2Drop) + } else { + i2h = Narrow(featDim, 1 + 2 * hiddenSize, hiddenSize).inputs(input1) + h2h = Linear(hiddenSize, hiddenSize, withBias = false, + wRegularizer = uRegularizer).inputs(input2) + } val cadd = CAddTable().inputs(i2h, h2h) + val tanh = Tanh().inputs(cadd) this.hiddenLayer = tanh @@ -229,7 +250,8 @@ object LSTMPeephole { bRegularizer: Regularizer[T] = null ) (implicit ev: TensorNumeric[T]): LSTMPeephole[T] = { - new LSTMPeephole[T](inputSize, hiddenSize, p, wRegularizer, uRegularizer, bRegularizer) + new LSTMPeephole[T](inputSize, hiddenSize, p, wRegularizer, uRegularizer, + bRegularizer) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala index 43c31d90530..350326d02e8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala @@ -45,27 +45,26 @@ import scala.reflect.ClassTag applied to the bias. */ class RnnCell[T : ClassTag] ( - inputSize: Int = 4, - hiddenSize: Int = 3, + val inputSize: Int = 4, + val hiddenSize: Int = 3, activation: TensorModule[T], - isInputWithBias: Boolean = true, - isHiddenWithBias: Boolean = true, + val isInputWithBias: Boolean = true, + val isHiddenWithBias: Boolean = true, var wRegularizer: Regularizer[T] = null, var uRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null) (implicit ev: TensorNumeric[T]) - extends Cell[T](Array(hiddenSize)) { + extends Cell[T](Array(hiddenSize), + regularizers = Array(wRegularizer, uRegularizer, bRegularizer)) { - override var cell: AbstractModule[Activity, Activity, T] = buildModel() + override var preTopology: TensorModule[T] = + Linear[T](inputSize, + hiddenSize, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer, + withBias = isInputWithBias) - override def preTopology: AbstractModule[Activity, Activity, T] = - TimeDistributed[T]( - Linear[T](inputSize, - hiddenSize, - wRegularizer = wRegularizer, - bRegularizer = bRegularizer, - withBias = isInputWithBias)) - .asInstanceOf[AbstractModule[Activity, Activity, T]] + override var cell: AbstractModule[Activity, Activity, T] = buildModel() def buildModel(): Graph[T] = { val i2h = Input() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index 2b421d20e11..2472ab86656 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -39,10 +39,10 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) protected var hidden: Activity = null protected var gradHidden: Activity = null protected var hiddenShape: Array[Int] = null - protected val currentInput = T() + protected var currentInput = T() protected val currentGradOutput = T() - protected val gradInputCell = Tensor[T]() - protected var outputCell = Tensor[T]() + protected val gradInput2Cell = Tensor[T]() + protected var input2Cell = Tensor[T]() protected var _input = T() protected val batchDim = Recurrent.batchDim protected val timeDim = Recurrent.timeDim @@ -50,8 +50,8 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) protected val hidDim = 2 protected var (batchSize, times) = (0, 0) protected var topology: Cell[T] = null - protected val outputBuffer = Tensor[T]() - private val gradBuffer = Tensor[T]() + protected val stepInput2CellBuf = Tensor[T]() + protected val stepGradBuffer = Tensor[T]() protected var preTopology: AbstractModule[Activity, Activity, T] = null private val dropouts: ArrayBuffer[Array[Dropout[T]]] = new ArrayBuffer[Array[Dropout[T]]] @@ -73,10 +73,12 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) */ override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Recurrent.this.type = { require(module.isInstanceOf[Cell[T]], - "Recurrent: contained module should be Cell type") + "Recurrent: added module should be Cell type!") topology = module.asInstanceOf[Cell[T]] - preTopology = topology.preTopology + preTopology = if (topology.preTopology != null) { + TimeDistributed(topology.preTopology) + } else topology.preTopology if (batchNormParams != null && preTopology == null) { throw new IllegalArgumentException( @@ -94,6 +96,11 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) modules += preTopology } modules += topology + + require((preTopology == null && modules.length == 1) || + (topology != null && preTopology != null && modules.length == 2), + "Recurrent extend: should contain only one cell or plus a pre-topology" + + " to process input") this } @@ -115,23 +122,17 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) /** * Clone N models; N depends on the time dimension of the input - * @param sizes, the first element is batchSize, the second is times, the third is hiddensize - * the left is size of images + * @param sizes, the first element is hiddensize, the left is size of images */ - protected def extend(sizes: Array[Int]): Unit = { - val imageSize = sizes + protected def initHidden(sizes: Array[Int]): Unit = { + val stepShape = sizes if (hidden == null) { - require((preTopology == null && modules.length == 1) || - (topology != null && preTopology != null && modules.length == 2), - "Recurrent extend: should contain only one cell or plus a pre-topology" + - " to process input") - cells.clear() cells += topology val cell = cells.head // The cell will help initialize or resize the hidden variable. - hidden = cell.hidResize(hidden = null, batchSize = batchSize, imageSize) + hidden = cell.hidResize(hidden = null, batchSize = batchSize, stepShape) /* * Since the gradHidden is only used as an empty Tensor or Table during @@ -140,9 +141,12 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) */ gradHidden = hidden } else { - cells.head.hidResize(hidden = hidden, batchSize = batchSize, imageSize) + cells.head.hidResize(hidden = hidden, batchSize = batchSize, stepShape) gradHidden = hidden } + } + + protected def cloneCells(): Unit = { var t = cells.length if (t < times) { val cloneCell = cells.head.cloneModule() @@ -214,7 +218,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) batchSize = input.size(batchDim) times = input.size(timeDim) - outputCell = if (preTopology != null) { + input2Cell = if (preTopology != null) { preTopology.forward(input).toTensor[T] } else { input @@ -224,8 +228,6 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) val outputSize = input.size() outputSize(2) = hiddenSize output.resize(outputSize) - // Clone N modules along the sequence dimension. - extend(outputSize.drop(2)) /** * currentInput forms a T() type. It contains two elements, hidden and input. @@ -235,30 +237,36 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) * the updated hidden. Thus the currentInput will update its hidden element with this output. */ var i = 1 - // init state - currentInput(hidDim) = if (initState != null) initState - else hidden + // Clone N modules along the sequence dimension. + initHidden(outputSize.drop(2)) + cloneCells() + + currentInput(hidDim) = if (initHiddenState != null) initHiddenState + else hidden while (i <= times) { - currentInput(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) + currentInput(inputDim) = Recurrent.selectCopy(input2Cell, i, stepInput2CellBuf) cells(i - 1).forward(currentInput) currentInput(hidDim) = cells(i - 1).output.toTable(hidDim) i += 1 } - Recurrent.copy(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), output) + Recurrent.copy(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), + output) output } - def getState(): Activity = { + // get hidden state at the last time step + def getHiddenState(): Activity = { require(cells != null && cells(times - 1).output != null, - "getState need to be called after updateOutput") + "getHiddenState need to be called after updateOutput") cells(times - 1).output.toTable(hidDim) } - protected var initState: Activity = null - def setState(state: Activity): Unit = { - initState = state + // set hidden state at the first time step + protected var initHiddenState: Activity = null + def setHiddenState(hiddenState: Activity): Unit = { + initHiddenState = hiddenState } override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { @@ -276,10 +284,10 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) var i = times while (i >= 1) { - currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, gradBuffer) + currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, stepGradBuffer) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) - else hidden - _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) + else if (initHiddenState == null) hidden else initHiddenState + _input(inputDim) = Recurrent.selectCopy(input2Cell, i, stepInput2CellBuf) if (i == 1) { cells(i - 1).regluarized(true) @@ -291,7 +299,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) i -= 1 } if (preTopology != null) { - preTopology.accGradParameters(input, gradInputCell) + preTopology.accGradParameters(input, gradInput2Cell) } } @@ -307,24 +315,23 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) } preTopology.gradInput.toTensor[T] } else { - gradInputCell + gradInput2Cell } - gradInputCell.resizeAs(outputCell) + gradInput2Cell.resizeAs(input2Cell) currentGradOutput(hidDim) = gradHidden var i = times while (i >= 1) { - currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, gradBuffer) + currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, stepGradBuffer) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) - else hidden - _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) - + else if (initHiddenState == null) hidden else initHiddenState + _input(inputDim) = Recurrent.selectCopy(input2Cell, i, stepInput2CellBuf) cells(i - 1).updateGradInput(_input, currentGradOutput) currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) i -= 1 } - Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) + Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInput2Cell) if (preTopology != null) { - gradInput = preTopology.updateGradInput(input, gradInputCell).toTensor[T] + gradInput = preTopology.updateGradInput(input, gradInput2Cell).toTensor[T] } gradInput } @@ -335,10 +342,12 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) var i = times while (i >= 1) { - currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, gradBuffer) + currentGradOutput(inputDim) = Recurrent.selectCopy(gradOutput, i, stepGradBuffer) + _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) - else if (initState == null) hidden else initState - _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) + else if (initHiddenState == null) hidden else initHiddenState + + _input(inputDim) = Recurrent.selectCopy(input2Cell, i, stepInput2CellBuf) if (i == 1) { cells(i - 1).regluarized(true) } else { @@ -359,13 +368,13 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) } preTopology.gradInput.toTensor[T] } else { - gradInputCell + gradInput2Cell } - gradInputCell.resizeAs(outputCell) - Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) + gradInput2Cell.resizeAs(input2Cell) + Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInput2Cell) if (preTopology != null) { - gradInput = preTopology.backward(input, gradInputCell).toTensor[T] + gradInput = preTopology.backward(input, gradInput2Cell).toTensor[T] } this.backwardTime = System.nanoTime - st @@ -433,17 +442,17 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) hidden = null gradHidden = null hiddenShape = null - gradInputCell.set() - outputCell.set() + gradInput2Cell.set() + input2Cell.set() currentInput.clear() currentGradOutput.clear() _input.clear() cells.foreach(x => x.clearState()) cells.clear() timeBuffer.clear() - initState = null - outputBuffer.set() - gradBuffer.set() + initHiddenState = null + stepInput2CellBuf.set() + stepGradBuffer.set() this } @@ -481,6 +490,8 @@ object Recurrent extends ContainerSerializable { private val batchDim = 1 private val timeDim = 2 + val inputDim = 1 + val hidDim = 2 def apply[@specialized(Float, Double) T: ClassTag]( batchNormParams: BatchNormParams[T] = null) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala index 0b92324d9aa..6c2515c5103 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala @@ -17,50 +17,78 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +import scala.reflect.runtime._ /** * [[RecurrentDecoder]] module is a container of rnn cells that used to make * a prediction of the next timestep based on the prediction we made from * the previous timestep. Input for RecurrentDecoder is dynamically composed * during training. input at t(i) is output at t(i-1), input at t(0) is - * user input, and user input has to be batch x ???(depends on cell type) - * without time information. + * user input, and user input has to be batch x stepShape(shape of the input + * at a single time step). - * Different types of rnn cells can be added using add() function. Currently - * only support lstmpeephole, convlstm, convlstm3D cell. + * Different types of rnn cells can be added using add() function. + * @param seqLength sequence length of the output */ -class RecurrentDecoder[T : ClassTag](seqLength: Int) +class RecurrentDecoder[T : ClassTag](val seqLength: Int) (implicit ev: TensorNumeric[T]) extends Recurrent[T] { times = seqLength - private val newInput = Tensor[T]() + + /** + * + * modules: -- preTopology + * |- topology (cell) + * + * The topology (or cell) will be cloned for N times w.r.t the time dimension. + * The preTopology will be execute only once before the recurrence. + * + * @param module module to be add + * @return this container + */ + override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): + RecurrentDecoder.this.type = { + require(module.isInstanceOf[Cell[T]], + "Recurrent: contained module should be Cell type") + + topology = module.asInstanceOf[Cell[T]] + preTopology = topology.preTopology + + if (preTopology != null) { + modules += preTopology + topology.includePreTopology = true + } + modules += topology + + require((preTopology == null && modules.length == 1) || + (topology != null && preTopology != null && modules.length == 2), + "Recurrent extend: should contain only one cell or plus a pre-topology" + + " to process input") + this + } override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim == 2 || input.dim == 4 || input.dim == 5, "Recurrent: input should be a 2D/4D/5D Tensor, e.g [batch, nDim], " + s"current input.dim = ${input.dim}") - batchSize = input.size(batchDim) - val hiddenSize = topology.hiddensShape(0) val outputSize = input.size() - outputSize(1) = hiddenSize require(hiddenSize == input.size()(1), "hiddenSize is " + "not the same with input size!! Please update cell settings or use Recurrent instead!") val featureSizes = outputSize.drop(1) output.resize(Array(batchSize, times) ++ featureSizes) // Clone N modules along the sequence dimension. - extend(featureSizes) - if (preTopology != null) newInput.resize(output.size()) - else outputCell.resize(output.size()) + initHidden(featureSizes) /** * currentInput forms a T() type. It contains two elements, hidden and input. @@ -69,70 +97,96 @@ class RecurrentDecoder[T : ClassTag](seqLength: Int) * identical elements T(output, output). One of the elements from the cell output is * the updated hidden. Thus the currentInput will update its hidden element with this output. */ - var i = 1 - // init state - currentInput(hidDim) = if (initState != null) initState - else hidden + // Clone N modules along the sequence dimension. + cloneCells() + var i = 1 while (i <= times) { // input at t(0) is user input - val inputTmp = if (i == 1) { - input + currentInput = if (i == 1) { + if (initHiddenState != null) T(input, initHiddenState) + else T(input, hidden) } else { // input at t(i) is output at t(i-1) - cells(i - 2).output.toTable[Tensor[T]](inputDim) - } - - currentInput(inputDim) = if (preTopology != null) { - newInput.narrow(2, i, 1).copy(inputTmp) - val sizes = 1 +: inputTmp.size() - inputTmp.resize(sizes) - val _input = preTopology.updateOutput(inputTmp).toTensor[T] - inputTmp.resize(sizes.takeRight(sizes.length - 1)) - _input.select(1, 1) - } else { - outputCell.narrow(2, i, 1).copy(inputTmp) - inputTmp + cells(i - 2).output } cells(i - 1).updateOutput(currentInput) - currentInput(hidDim) = cells(i - 1).output.toTable(hidDim) i += 1 } - if (preTopology != null) { - // For backward preTopology use - outputCell = preTopology.updateOutput(newInput).toTensor[T] - } - Recurrent.copy(cells.map(x => x.output.toTable[Tensor[T]](inputDim)), output) output } override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { - throw new Exception("Should not enter RecurrentDecoder accGradParameters" + - "as it has override backward") + currentGradOutput(hidDim) = gradHidden + var i = times + while (i >= 1) { + currentGradOutput(inputDim) = if (i == times) { + Recurrent.selectCopy(gradOutput, i, stepGradBuffer) + } else { + val _gradInput = cells(i).gradInput.toTable[Tensor[T]](inputDim) + Recurrent.selectCopy(gradOutput, i, stepGradBuffer).add(_gradInput) + } + + _input = if (i == 1) { + if (initHiddenState == null) T(input, hidden) + else T(input, initHiddenState) + } else cells(i - 2).output + + if (i == 1) { + cells(i - 1).regluarized(true) + } else { + cells(i - 1).regluarized(false) + } + cells(i - 1).accGradParameters(_input, currentGradOutput) + currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) + i -= 1 + } } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - throw new Exception("Should not enter RecurrentDecoder updateGradInput" + - "as it has override backward") + gradInput.resizeAs(output) + currentGradOutput(hidDim) = gradHidden + var i = times + while (i >= 1) { + currentGradOutput(inputDim) = if (i == times) { + Recurrent.selectCopy(gradOutput, i, stepGradBuffer) + } else { + val _gradInput = cells(i).gradInput.toTable[Tensor[T]](inputDim) + Recurrent.selectCopy(gradOutput, i, stepGradBuffer).add(_gradInput) + } + + _input = if (i == 1) { + if (initHiddenState == null) T(input, hidden) + else T(input, initHiddenState) + } else cells(i - 2).output + + cells(i - 1).updateGradInput(_input, currentGradOutput) + currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) + i -= 1 + } + Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInput) gradInput } override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { val st = System.nanoTime + gradInput.resizeAs(output) currentGradOutput(hidDim) = gradHidden var i = times while (i >= 1) { - currentGradOutput(inputDim) = if (i == times) gradOutput.select(timeDim, i) - else { - gradOutput.select(timeDim, i).clone() - .add(cells(i).gradInput.toTable[Tensor[T]](inputDim).clone()) + currentGradOutput(inputDim) = if (i == times) { + Recurrent.selectCopy(gradOutput, i, stepGradBuffer) + } else { + val _gradInput = cells(i).gradInput.toTable[Tensor[T]](inputDim) + Recurrent.selectCopy(gradOutput, i, stepGradBuffer).add(_gradInput) } - _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) - else if (initState == null) hidden else initState - _input(inputDim) = Recurrent.selectCopy(outputCell, i, outputBuffer) + _input = if (i == 1) { + if (initHiddenState == null) T(input, hidden) + else T(input, initHiddenState) + } else cells(i - 2).output if (i == 1) { cells(i - 1).regluarized(true) @@ -143,25 +197,7 @@ class RecurrentDecoder[T : ClassTag](seqLength: Int) currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) i -= 1 } - - gradInput = if (preTopology != null) { - /** - * if preTopology is Sequential, it has not created gradInput. - * Thus, it needs to create a new Tensor. - */ - if (preTopology.gradInput == null) { - preTopology.gradInput = Tensor[T]() - } - preTopology.gradInput.toTensor[T] - } else { - gradInputCell - } - gradInputCell.resizeAs(outputCell) - Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInputCell) - if (preTopology != null) { - gradInput = preTopology.backward(newInput, gradInputCell).toTensor[T] - } - + Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInput) this.backwardTime = System.nanoTime - st gradInput } @@ -180,6 +216,14 @@ class RecurrentDecoder[T : ClassTag](seqLength: Int) val state = Seq(super.hashCode(), cells) state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + topology.parameters() + } + + override def getParametersTable(): Table = { + topology.getParametersTable() + } } object RecurrentDecoder extends ContainerSerializable { @@ -199,6 +243,15 @@ object RecurrentDecoder extends ContainerSerializable { getAttributeValue(context, topologyAttr). asInstanceOf[Cell[T]] + val preTopologyAttr = attrMap.get("preTopology") + recurrentDecoder.preTopology = DataConverter. + getAttributeValue(context, preTopologyAttr). + asInstanceOf[Cell[T]] + if (recurrentDecoder.preTopology != null) { + recurrentDecoder.modules.append(recurrentDecoder.preTopology) + } + recurrentDecoder.modules.append(recurrentDecoder.topology) + moduleData } @@ -215,6 +268,12 @@ object RecurrentDecoder extends ContainerSerializable { ModuleSerializer.abstractModuleType) containerBuilder.putAttr("topology", topologyBuilder.build) + val preTopologyBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, + preTopologyBuilder, recurrentDecoder.preTopology, + ModuleSerializer.tensorModuleType) + containerBuilder.putAttr("preTopology", topologyBuilder.build) + SerializeResult(containerBuilder.build, context.storages) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 3897f617f1b..a393138bb10 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -44,6 +44,7 @@ import org.tensorflow.framework.NodeDef import scala.collection.JavaConverters._ import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer import scala.language.existentials import scala.reflect.ClassTag @@ -1917,16 +1918,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab layer.setInitMethod(weightInitMethod, biasInitMethod) } - def getState(rec: Recurrent[T]): JList[JTensor] = { - val res = rec.getState() - if (res.isTensor) return List(toJTensor(res.toTensor)).asJava - else return List(toJTensor(res.toTable.apply[Tensor[T]](1)), - toJTensor(res.toTable.apply[Tensor[T]](2))).asJava + def getHiddenStates(rec: Recurrent[T]): JList[JTensor] = { + val states = rec.getHiddenState() + activityToJTensors(states) } - def setState(rec: Recurrent[T], state: JList[JTensor], isTable: Boolean): Unit = { - val stateActivity = jTensorsToActivity(state, isTable) - rec.setState(stateActivity) + def setHiddenStates(rec: Recurrent[T], hiddenStates: JList[JTensor], isTable: Boolean): Unit = { + rec.setHiddenState(jTensorsToActivity(hiddenStates, isTable)) } def setLayerFreeze(model: AbstractModule[Activity, Activity, T]) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CellSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CellSpec.scala index 97155736502..585188c459b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CellSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CellSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} @@ -39,6 +39,7 @@ private[bigdl] class CellUnit[T : ClassTag] (hidSize: Int) override def accGradParameters(input: Table, gradOutput: Table): Unit = {} override var cell: AbstractModule[Activity, Activity, T] = _ + override var preTopology: TensorModule[T] = null } @com.intel.analytics.bigdl.tags.Parallel @@ -46,7 +47,8 @@ class CellSpec extends FlatSpec with Matchers { "A Cell" should "hidResize correctly" in { val cell = new CellUnit[Double](4) - val hidden = cell.hidResize(hidden = null, batchSize = 5) + val stepShape = Array(1) + val hidden = cell.hidResize(hidden = null, batchSize = 5, stepShape) hidden.isInstanceOf[Table] should be (true) var i = 1 @@ -56,7 +58,7 @@ class CellSpec extends FlatSpec with Matchers { } val hidden2 = T(Tensor[Double](3, 4), Tensor[Double](4, 5), Tensor[Double](5, 6)) - cell.hidResize(hidden2, 5) + cell.hidResize(hidden2, 5, stepShape) hidden2(1).asInstanceOf[Tensor[Double]].size should be (Array(5, 4)) hidden2(2).asInstanceOf[Tensor[Double]].size should be (Array(5, 4)) hidden2(3).asInstanceOf[Tensor[Double]].size should be (Array(5, 4)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala index b53d208689f..050d6046d59 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala @@ -72,7 +72,7 @@ class ConvLSTMPeephole3DSpec extends FlatSpec with BeforeAndAfter with Matchers val output = model.forward(input) - val state = model.getState() + val state = model.getHiddenState() val hidden = state.asInstanceOf[Table].apply(1).asInstanceOf[Tensor[Double]] hidden.map(output.select(2, seqLength), (v1, v2) => { assert(abs(v1 - v2) == 0) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala index 62259b53d7a..8df4cf72633 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala @@ -1022,7 +1022,7 @@ class ConvLSTMPeepholeSpec extends FlatSpec with BeforeAndAfter with Matchers { ) val output = model.forward(input).asInstanceOf[Tensor[Double]] - val state = rec.getState() + val state = rec.getHiddenState() val hiddenState = state.toTable.apply(1).asInstanceOf[Tensor[Double]] val cell = state.toTable.apply(2).asInstanceOf[Tensor[Double]] hiddenState.map(output.select(2, seqLength), (v1, v2) => { @@ -1036,7 +1036,7 @@ class ConvLSTMPeepholeSpec extends FlatSpec with BeforeAndAfter with Matchers { v1 }) - rec.setState(state) + rec.setHiddenState(state) model.forward(input) } @@ -1063,7 +1063,7 @@ class ConvLSTMPeepholeSpec extends FlatSpec with BeforeAndAfter with Matchers { val output = model.forward(input).asInstanceOf[Tensor[Double]] - rec2.setState(T(Tensor[Double](batchSize, hiddenSize, 3, 4).rand, + rec2.setHiddenState(T(Tensor[Double](batchSize, hiddenSize, 3, 4).rand, Tensor[Double](batchSize, hiddenSize, 3, 4).rand)) val output2 = model2.forward(input).asInstanceOf[Tensor[Double]] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala index 0e22d61951e..e04d4ad6fdf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.Activity @@ -29,7 +29,7 @@ import scala.math._ @com.intel.analytics.bigdl.tags.Parallel class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { - "A ConvLSTMPeepwhole forward" should "work with feedbackOutput correctly" in { + "A ConvLSTMPeepwhole forward" should "work with RecurrentDecoder" in { import com.intel.analytics.bigdl.numeric.NumericDouble val hiddenSize = 7 val inputSize = 7 @@ -81,6 +81,7 @@ class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { val weights = model.getParameters()._1.clone() model.zeroGradParameters() val output = model.forward(input).toTensor + model.backward(input, gradOutput) val model2 = Recurrent().add(LSTM(inputSize, hiddenSize)) model2.getParameters()._1.copy(weights) @@ -131,7 +132,7 @@ class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { }) } - "A ConvLSTMPeepwhole " should "work with feedbackOutput correctly2" in { + "A ConvLSTMPeepwhole backward" should "work with RecurrentDecoder" in { import com.intel.analytics.bigdl.numeric.NumericDouble val hiddenSize = 3 val inputSize = 3 @@ -203,4 +204,202 @@ class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { v1 }) } + + "A LSTM backward" should "work with RecurrentDecoder" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 3 + val inputSize = 3 + val seqLength = 2 + val seed = 100 + val batchSize = 2 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(LSTM(inputSize, hiddenSize)) + + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + val output = model.forward(input).toTensor + val gradInput = model.backward(input, gradOutput).toTensor + val gradient = model.getParameters()._2 + + val input2 = input.clone() + input2.resize(batchSize, 1, inputSize) + val model2 = LSTM(inputSize, hiddenSize) + model2.includePreTopology = true + model2.getParameters()._1.copy(weights) + model2.zeroGradParameters() + + val model3 = LSTM(inputSize, hiddenSize) + model3.includePreTopology = true + var i = 0 + while (i < model3.parameters()._1.length) { + model3.parameters()._1(i).set(model2.parameters()._1(i)) + i += 1 + } + i = 0 + while (i < model3.parameters()._2.length) { + model3.parameters()._2(i).set(model2.parameters()._2(i)) + i += 1 + } + + val state = T(Tensor[Double](batchSize, hiddenSize), + Tensor[Double](batchSize, hiddenSize)) + val output2 = model2.forward(T(input, state)) + val output3 = model3.forward(output2) + + val gradOutput3 = gradOutput.select(2, 2) + val input3 = output2.clone() + val tmp = T(input3.toTable[Tensor[Double]](1).squeeze(2), input3.toTable(2)) + val gradInput3 = model3.backward(tmp, T(gradOutput3, state)) + val tmp_gradInput = gradInput3.clone + tmp_gradInput(1) = gradOutput.select(2, 1).add(gradInput3.toTable[Tensor[Double]](1)) + val gradInput2 = model2.backward(T(input, state), tmp_gradInput) + val finalOutput = Tensor[Double](batchSize, seqLength, hiddenSize) + finalOutput.narrow(2, 1, 1).copy(output2.toTable[Tensor[Double]](1)) + finalOutput.narrow(2, 2, 1).copy(output3.toTable[Tensor[Double]](1)) + output.map(finalOutput, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + + gradient.map(model2.getParameters()._2, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + + val newGradInput = Tensor[Double](batchSize, seqLength, hiddenSize) + newGradInput.narrow(2, 1, 1).copy(gradInput2.toTable[Tensor[Double]](1)) + newGradInput.narrow(2, 2, 1).copy(gradInput3.toTable[Tensor[Double]](1)) + gradInput.map(newGradInput, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + } + + "A LSTM backward with RecurrentDecoder" should "get the same result with updateGradInput" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 7 + val inputSize = 7 + val seqLength = 5 + val seed = 100 + val batchSize = 4 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(LSTM(inputSize, hiddenSize)) + + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + val output = model.forward(input).toTensor + val gradInput = model.backward(input, gradOutput).toTensor + val gradient = model.getParameters()._2 + + val rec2 = RecurrentDecoder(seqLength) + val model2 = rec2 + .add(LSTM(inputSize, hiddenSize)) + model2.getParameters()._1.copy(weights) + model2.zeroGradParameters() + val output2 = model2.forward(input).toTensor + val gradInput2 = model2.updateGradInput(input, gradOutput).toTensor + model2.accGradParameters(input, gradOutput) + val gradient2 = model2.getParameters()._2 + require(gradInput.almostEqual(gradInput2, 1e-8) == true) + require(gradient.almostEqual(gradient2, 1e-8) == true) + } + + "A ConvLSTMPeepwhole " should "work with RecurrentDecoder get/setStates" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 3 + val inputSize = 3 + val seqLength = 2 + val seed = 100 + val batchSize = 2 + + val initStates = T(Tensor(batchSize, hiddenSize, 3, 3).rand(), + Tensor(batchSize, hiddenSize, 3, 3).rand()) + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize, 3, 3).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3).rand + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1)) + + rec.setHiddenState(initStates) + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + val output = model.forward(input).toTensor + val gradInput = model.backward(input, gradOutput).toTensor + val gradient = model.getParameters()._2 + val statesGet = rec.getHiddenState().toTable + + val input2 = input.clone() + input2.resize(batchSize, 1, inputSize, 3, 3) + val model2 = ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1) + model2.getParameters()._1.copy(weights) + model2.zeroGradParameters() + + val model3 = ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1) + var i = 0 + while (i < model3.parameters()._1.length) { + model3.parameters()._1(i).set(model2.parameters()._1(i)) + i += 1 + } + i = 0 + while (i < model3.parameters()._2.length) { + model3.parameters()._2(i).set(model2.parameters()._2(i)) + i += 1 + } + + val state = initStates + val output2 = model2.forward(T(input, state)) + val output3 = model3.forward(output2) + + val gradState = T(Tensor(batchSize, hiddenSize, 3, 3), Tensor(batchSize, hiddenSize, 3, 3)) + val gradOutput3 = gradOutput.select(2, 2) + val input3 = output2.clone() + val tmp = T(input3.toTable[Tensor[Double]](1).squeeze(2), input3.toTable(2)) + val gradInput3 = model3.backward(tmp, T(gradOutput3, gradState)) + val tmp_gradInput = gradInput3.clone + tmp_gradInput(1) = gradOutput.select(2, 1).add(gradInput3.toTable[Tensor[Double]](1)) + val gradInput2 = model2.backward(T(input, state), tmp_gradInput) + val finalOutput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3) + finalOutput.narrow(2, 1, 1).copy(output2.toTable[Tensor[Double]](1)) + finalOutput.narrow(2, 2, 1).copy(output3.toTable[Tensor[Double]](1)) + output.map(finalOutput, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + + val states1 = statesGet.getState() + val states2 = output3.toTable[Table](2) + for (k <- states1.keys) { + val t1 = states1(k).asInstanceOf[Tensor[Double]] + val t2 = states2(k).asInstanceOf[Tensor[Double]] + t1.map(t2, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + } + + gradient.map(model2.getParameters()._2, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + + val newGradInput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3) + newGradInput.narrow(2, 1, 1).copy(gradInput2.toTable[Tensor[Double]](1)) + newGradInput.narrow(2, 2, 1).copy(gradInput3.toTable[Tensor[Double]](1)) + gradInput.map(newGradInput, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala index ab55a54246f..d8eda8d2d20 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala @@ -435,6 +435,26 @@ class RecurrentSpec extends FlatSpec with Matchers { val logSoftMax = LogSoftMax[Double]() val (weights, grad) = model.getParameters() + val data = Array(-0.051649563734092574, 0.3491947190721401, -0.42027052029370376, + 0.4301486898079941, 0.2468666566291215, -0.39359984949207866, 0.045578554526030046, + 0.3493149141337017, -0.1063711823523733, 0.06878279210527599, 0.02617610773350143, + 0.21688042352505815, 0.4086431210923443, 0.1164400576908104, -0.289954236617675, + 0.07320188583739445, -0.34140032046902746, -0.42893228205681105, 0.3246284763380037, + -0.259360108472857, -0.3802506202721077, 0.039967368527818625, 0.2907736835216905, + 0.24070392389100653, 0.04340493865311146, 0.17115563713014126, -0.22163061727769673, + -0.08795360312797129, -0.07548240781761706, 0.02638246468268335, 0.34477613493800163, + -0.35139515763148665, -0.4952811379916966, -0.3432889161631465, -0.3784308801405132, + -0.31353281694464386, 0.17074908362701535, -0.2898922632448375, 0.32585275499150157, + -0.047260097693651915, -0.36329341283999383, 0.3701426349580288, 0.07509333454072475, + -0.43631896027363837, 0.3361318111419678, -0.24930476839654148, -0.4246050880756229, + -0.21410430688410997, -0.4885992160998285, 0.352395088179037, -0.45157943526282907, + 0.47500649164430797, -0.142877290956676, 0.38485329202376306, 0.1656933748163283, + -0.14049215079285204, -0.48861038917675614, 0.09885894856415689, -0.3920822301879525, + -0.14520439435727894, 0.401013100752607, -0.15980978682637215, 0.2948787631466985, + -0.3219190139789134, 0.31146098021417856, -0.2623057949822396, 0.14027805789373815, + -0.45513772079721093, 0.1247795126400888) + val tmp = Tensor[Double](data, Array(data.size, 1)) + weights.copy(tmp) val input = Tensor[Double](Array(batchSize, nWords, inputSize)) val labels = Tensor[Double](batchSize) @@ -582,14 +602,14 @@ class RecurrentSpec extends FlatSpec with Matchers { val input = Tensor[Double](Array(batchSize, time, inputSize)).rand val output = model.forward(input).asInstanceOf[Tensor[Double]] - val state = rec.getState() + val state = rec.getHiddenState() state.toTensor[Double].map(output.asInstanceOf[Tensor[Double]].select(2, time), (v1, v2) => { assert(abs(v1 - v2) == 0) v1 }) - rec.setState(state) + rec.setHiddenState(state) model.forward(input) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala index b40679025be..fd0fe801b02 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala @@ -617,7 +617,7 @@ class LSTMPeepholeSpec extends TorchSpec { val checkFlag = gradCheck.checkLayer(model, input, labels) } - "A LSTMPeepwhole " should "get state correctly" in { + "A LSTMPeepwhole " should "get hiddenState correctly" in { torchCheck() import com.intel.analytics.bigdl.numeric.NumericDouble @@ -629,6 +629,7 @@ class LSTMPeepholeSpec extends TorchSpec { RNG.setSeed(seed) val input = Tensor[Double](batchSize, seqLength, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand val rec = Recurrent() @@ -662,20 +663,22 @@ class LSTMPeepholeSpec extends TorchSpec { |local parameters, gradParameters = model:getParameters() |parameters:copy(weights) |local output = model:forward(input) + |local gradInput = model:backward(input, gradOutput) |local state = lstm:getHiddenState($seqLength) """.stripMargin scala.Seq val (luaTime, torchResult) = TH.run(code, - Map("input" -> input.transpose(1, 2), "weights" -> weights2Torch), - Array("output", "state")) + Map("input" -> input.transpose(1, 2), "weights" -> weights2Torch, + "gradOutput" -> gradOutput.transpose(1, 2)), Array("output", "state")) val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] val luaState = torchResult("state").asInstanceOf[Table] val output = model.forward(input).toTensor.transpose(1, 2) + model.backward(input, gradOutput) - rec.getState().toTable.foreach { case ((key: Int, value: Tensor[Double])) => + rec.getHiddenState().toTable.foreach { case ((key: Int, value: Tensor[Double])) => value.map(luaState(key), (v1, v2) => { assert(abs(v1 - v2) <= 1e-8) v1 @@ -688,7 +691,7 @@ class LSTMPeepholeSpec extends TorchSpec { }) } - "A LSTMPeepwhole " should "set state correctly" in { + "A LSTMPeepwhole " should "set hiddenState correctly" in { torchCheck() import com.intel.analytics.bigdl.numeric.NumericDouble @@ -704,7 +707,9 @@ class LSTMPeepholeSpec extends TorchSpec { Tensor[Double](batchSize, hiddenSize).rand) val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand val rec = Recurrent() - rec.setState(state) + + rec.setHiddenState(state) + val model = Sequential() .add(rec .add(LSTMPeephole(inputSize, hiddenSize))) @@ -745,7 +750,7 @@ class LSTMPeepholeSpec extends TorchSpec { val (luaTime, torchResult) = TH.run(code, Map("input" -> input.transpose(1, 2), "weights" -> weights2Torch, "state" -> state, "gradOutput" -> gradOutput.transpose(1, 2)), - Array("output", "gradInput", "gradParameters")) + Array("output", "gradInput", "gradParameters")) val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMSpec.scala index 95c0c707f60..63334b00a6c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMSpec.scala @@ -162,7 +162,7 @@ class LSTMSpec extends TorchSpec { labels.setValue(1, i, rdmLabel) } -// println(input) + // println(input) val rec = Recurrent[Double]() val model = Sequential[Double]() From cbc33b5bbdf44c99ae875cb1dcb750954ff0843f Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 13 Oct 2017 12:51:24 +0800 Subject: [PATCH 0451/1065] throw clear exception in lookuptable (#1586) --- .../bigdl/dllib/nn/LookupTable.scala | 29 +++++++++++++++---- .../bigdl/dllib/tensor/DenseTensor.scala | 2 +- 2 files changed, 24 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala index 23b05662576..8566c636e67 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala @@ -29,6 +29,7 @@ import scala.reflect.ClassTag * This layer is a particular case of a convolution, where the width of the convolution would be 1. * Input should be a 1D or 2D tensor filled with indices. Indices are corresponding to the position * in weight. For each index element of input, it outputs the selected index part of weight. + * Elements of input should be in range of (1, nIndex) * This layer is often used in word embedding. * @param nIndex Indices of input row * @param nOutput the last dimension size of output @@ -168,12 +169,22 @@ class LookupTable[T: ClassTag] s"LookupTable: ${ErrorInfo.constrainInputAsVectorOrBatch}, input dim [${input.dim()}]" ) renorm(input) inputBuffer = input.contiguous() - if (inputBuffer.dim() == 1) { - output.index(1, inputBuffer, weight) - } else if (inputBuffer.dim() == 2) { - output.index(1, inputBuffer.view(inputBuffer.nElement()), weight) - output = output.view(inputBuffer.size(1), inputBuffer.size(2), weight.size(2)) + try { + if (inputBuffer.dim() == 1) { + output.index(1, inputBuffer, weight) + } else if (inputBuffer.dim() == 2) { + output.index(1, inputBuffer.view(inputBuffer.nElement()), weight) + output = output.view(inputBuffer.size(1), inputBuffer.size(2), weight.size(2)) + } + } catch { + case e: IllegalArgumentException => + throw new IllegalArgumentException( + s"LookupTable updateOutput get exception:${e.getMessage}\n" + + s"please ensure elements of your input will not exceed ${nIndex}") + case e: Exception => + throw e } + output } @@ -237,7 +248,13 @@ class LookupTable[T: ClassTag] } override def toString(): String = { - s"${getPrintName}($nIndex, $nOutput, $paddingValue, $maxNorm, $normType)" + val s = s"${getPrintName}" + + s"(nIndex=$nIndex,nOutput=$nOutput,paddingValue=$paddingValue,normType=$normType" + if (maxNorm == Double.MaxValue) { + s + ")" + } else { + s + s" ,maxNorm=$maxNorm)" + } } override def zeroGradParameters(): Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 07372c069a6..7a4afa3f980 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2319,7 +2319,7 @@ object DenseTensor { require(src.nDimension() > 0, "cannot select on a scalar") require(_dimension >= 0 && _dimension < src.nDimension(), "out of range") require(_sliceIndex >= 0 && _sliceIndex < src.size(_dimension + 1), - s"${_sliceIndex} out of range 0 to ${src.size(_dimension + 1)}") + s"${_sliceIndex} out of range 0 to ${src.size(_dimension + 1) - 1}") set(self, src) narrow(self, null, _dimension, _sliceIndex, 1) From eaa94e267f1c33bb790d2fc6ec96cdf1e6678c85 Mon Sep 17 00:00:00 2001 From: Hawkwood <2041829103@qq.com> Date: Fri, 13 Oct 2017 15:58:52 +0800 Subject: [PATCH 0452/1065] Debug4jConfig (#1546) * Debug4jConfig * kill the codes to make properity file work The property file and its usage has been pushed to bigdl_doc project, these changes will enable the file to work properly * Change according to comments The usage of configuration file is mentioned in this PR https://github.com/intel-analytics/bigdl-doc/pull/2. Delete the codes as you wish. I have also changed the xml form to another that you prefer. I delete the codes to make sure that the configuration file mentioned in the doc can work properly. * Delete codes * Updating the usage of configuration file * update optimization.md * update getting-started.md * change format * recover unchaged file * recover --- .../bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala | 1 - .../analytics/bigdl/dllib/example/lenetLocal/Predict.scala | 2 +- .../intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala | 2 +- .../intel/analytics/bigdl/dllib/example/lenetLocal/Train.scala | 2 +- .../intel/analytics/bigdl/dllib/models/autoencoder/Train.scala | 2 +- .../com/intel/analytics/bigdl/dllib/models/inception/Test.scala | 2 +- .../intel/analytics/bigdl/dllib/models/inception/Train.scala | 2 +- .../com/intel/analytics/bigdl/dllib/models/lenet/Test.scala | 2 +- .../com/intel/analytics/bigdl/dllib/models/lenet/Train.scala | 2 +- .../com/intel/analytics/bigdl/dllib/models/resnet/Test.scala | 1 - .../com/intel/analytics/bigdl/dllib/models/resnet/Train.scala | 2 +- .../scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala | 1 - .../com/intel/analytics/bigdl/dllib/models/rnn/Train.scala | 2 +- .../scala/com/intel/analytics/bigdl/dllib/models/vgg/Test.scala | 2 +- .../com/intel/analytics/bigdl/dllib/models/vgg/Train.scala | 2 +- 15 files changed, 12 insertions(+), 15 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala index 5b3bae41556..59eeb946d52 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala @@ -37,7 +37,6 @@ import org.apache.spark.sql.SQLContext object DLClassifierLeNet { LoggerFilter.redirectSparkInfoLogs() - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) def main(args: Array[String]): Unit = { val inputs = Array[String]("Feature data", "Label data") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala index 23942314b5b..e0ceddbcfdd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala @@ -27,7 +27,7 @@ object Predict { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala index b12752e92d9..238d6fd6f65 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Test.scala @@ -27,7 +27,7 @@ object Test { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Train.scala index 45df596a8b1..911557e9ea7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Train.scala @@ -29,7 +29,7 @@ import org.apache.log4j.{Level, Logger} object Train { LoggerFilter.redirectSparkInfoLogs() - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/Train.scala index a1e9afd268e..88a065ec4a3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/Train.scala @@ -49,7 +49,7 @@ object Train { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Test.scala index 89b22a123c7..86500b403c2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Test.scala @@ -29,7 +29,7 @@ object Test { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Options._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala index 04c4cea1e25..23d86468541 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala @@ -24,7 +24,7 @@ import org.apache.spark.SparkContext object TrainInceptionV1 { LoggerFilter.redirectSparkInfoLogs() - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Options._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Test.scala index bdd6652d00b..5ba11b1afcc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Test.scala @@ -30,7 +30,7 @@ object Test { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala index dd1a2b4b47e..847ddae9f2a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala @@ -28,7 +28,7 @@ import org.apache.spark.SparkContext object Train { LoggerFilter.redirectSparkInfoLogs() - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Test.scala index f202bcf85ca..7c3affa3a98 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Test.scala @@ -29,7 +29,6 @@ object Test { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) def main(args: Array[String]): Unit = { testParser.parse(args, TestParams()).foreach { param => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Train.scala index dd348f87571..64add130abd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Train.scala @@ -27,7 +27,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._ object Train { LoggerFilter.redirectSparkInfoLogs() - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala index a2e560b3776..08083197358 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Test.scala @@ -34,7 +34,6 @@ object Test { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) import Utils._ val logger = Logger.getLogger(getClass) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala index 561134ecdcb..a123bd57e25 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Train.scala @@ -33,7 +33,7 @@ object Train { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ val logger = Logger.getLogger(getClass) def main(args: Array[String]): Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Test.scala index 99090a29de2..66a21eb6d6a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Test.scala @@ -29,7 +29,7 @@ object Test { Logger.getLogger("org").setLevel(Level.ERROR) Logger.getLogger("akka").setLevel(Level.ERROR) Logger.getLogger("breeze").setLevel(Level.ERROR) - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Train.scala index 37fc166b297..0b57a38cd1a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Train.scala @@ -27,7 +27,7 @@ import org.apache.spark.SparkContext object Train { LoggerFilter.redirectSparkInfoLogs() - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + import Utils._ From ac0446b868684a8ed4df147cacdf3a6a7af635c6 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Fri, 13 Oct 2017 16:30:33 +0800 Subject: [PATCH 0453/1065] Support feature with one or more tensors for Sample in python (#1650) * feature: JTensor to list of JTensors * correct typo in creating sequential model for multiple inputs * add test for multiple inputs --- .../bigdl/dllib/utils/python/api/BigDLSerde.scala | 2 +- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 8 +++++--- .../analytics/bigdl/dllib/python/api/PythonSpec.scala | 6 ++++-- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala index 1e48adc51bb..1e8979df5a2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala @@ -217,7 +217,7 @@ object BigDLSerDe extends BigDLSerDeBase with Serializable { if (args.length != 3) { throw new PickleException("should be 3, not : " + args.length) } - new Sample(args(0).asInstanceOf[JTensor], + new Sample(args(0).asInstanceOf[JList[JTensor]], args(1).asInstanceOf[JTensor], args(2).asInstanceOf[String]) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a393138bb10..8bc13cb3853 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -55,7 +55,7 @@ import scala.reflect.ClassTag * @param label labels * @param bigdlType bigdl numeric type */ -case class Sample(features: JTensor, +case class Sample(features: JList[JTensor], label: JTensor, bigdlType: String) @@ -113,7 +113,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def toPySample(sample: JSample[T]): Sample = { val cls = implicitly[ClassTag[T]].runtimeClass - Sample(toJTensor(sample.feature()), toJTensor(sample.label()), cls.getSimpleName) + val features = new JArrayList[JTensor]() + features.add(toJTensor(sample.feature())) + Sample(features, toJTensor(sample.label()), cls.getSimpleName) } def toTensor(jTensor: JTensor): Tensor[T] = { @@ -156,7 +158,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def toSample(record: Sample): JSample[T] = { require(record.bigdlType == this.typeName, s"record.bigdlType: ${record.bigdlType} == this.typeName: ${this.typeName}") - JSample[T](toTensor(record.features), toTensor(record.label)) + JSample[T](record.features.asScala.toArray.map(toTensor(_)), toTensor(record.label)) } private def batching(rdd: RDD[Sample], batchSize: Int) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index ecb1559e64b..a74eee4713f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.python.api import java.util -import java.util.{List => JList, Map => JMap} +import java.util.{ArrayList => JArrayList, List => JList, Map => JMap} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn._ @@ -162,7 +162,9 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { val label = JTensor(Array(i % 2 + 1.0f), Array(1), "double") val feature = JTensor(Range(0, 100).map(_ => Random.nextFloat()).toArray, Array(100), "double") - Sample(feature, label, "double") + val features = new JArrayList[JTensor]() + features.add(feature) + Sample(features, label, "double") } BigDLSerDe.javaToPython(data.toJavaRDD().asInstanceOf[JavaRDD[Any]]) From b8df8261db5dca96be747507de2afb98a557c46a Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 13 Oct 2017 16:44:26 +0800 Subject: [PATCH 0454/1065] SparseLinear SparseJoinTable DenseToSparse (#1652) * SparseLinear SparseJoinTable DenseToSparse * Python api * add DenseToSparseSpec * update to upstream * add some method * meet code review * fix python unit test * fix python unit test --- .../bigdl/dllib/nn/DenseToSparse.scala | 52 +++++ .../bigdl/dllib/nn/SparseJoinTable.scala | 106 ++++++++++ .../bigdl/dllib/nn/SparseLinear.scala | 168 +++++++++++++++ .../bigdl/dllib/tensor/SparseTensor.scala | 194 +++++++++++++++++- .../analytics/bigdl/dllib/tensor/Tensor.scala | 32 ++- .../dllib/utils/python/api/PythonBigDL.scala | 23 +++ .../bigdl/dllib/nn/DenseToSparseSpec.scala | 43 ++++ .../bigdl/dllib/nn/SparseJoinTableSpec.scala | 70 +++++++ .../bigdl/dllib/nn/SparseLinearSpec.scala | 174 ++++++++++++++++ 9 files changed, 853 insertions(+), 9 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparse.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTable.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinear.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparse.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparse.scala new file mode 100644 index 00000000000..391dca4dfe4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparse.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{TensorCriterion, TensorModule} +import com.intel.analytics.bigdl.tensor.{DenseType, SparseType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Convert DenseTensor to SparseTensor. + * @param ev$1 + * @param ev + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class DenseToSparse[T: ClassTag](implicit ev: TensorNumeric[T]) extends TensorModule[T] { + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.getTensorType == DenseType, "DenseToSparse: input should be a DenseTensor," + + s"but got ${input.getTensorType}") + output = Tensor.sparse(input) + output + } + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + this.gradInput.resizeAs(input) + Tensor.dense(gradOutput, gradInput) + this.gradInput + } + + override def toString(): String = s"DenseToSparse()" +} + +object DenseToSparse { + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : DenseToSparse[T] = { + new DenseToSparse() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTable.scala new file mode 100644 index 00000000000..a99dcc5a21b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTable.scala @@ -0,0 +1,106 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.{DenseTensor, SparseTensor, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Engine, Table} + +import scala.concurrent.Future +import scala.reflect.ClassTag + +/** + * :: Experimental :: + * + * Sparse version of JoinTable. Backward just pass the origin gradOutput back to + * the next layers without split. So this layer may just works in Wide&Deep like models. + * + * @param dimension the dimension to join. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class SparseJoinTable[T: ClassTag] ( + val dimension: Int)(implicit ev: TensorNumeric[T]) + extends AbstractModule[Table, Tensor[T], T] { + + private var results: Array[Future[Unit]] = null + output = Tensor.sparse(Array(1, 1), 1) + + var size: Array[Int] = null + + override def updateOutput(input: Table): Tensor[T] = { + var nElements = 0 + + var i = 1 + while (i <= input.length()) { + val currentOutput: Tensor[T] = input(i) + if (i == 1) { + size = currentOutput.size() + } else { + size(dimension - 1) += currentOutput.size(dimension) + } + nElements += currentOutput.nElement() + i += 1 + } + output.resize(size, nElements) + + Tensor.sparseConcat(2, input, output) + + output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + var i = 1 + while (i <= input.length()) { + gradInput(i) = gradOutput + i += 1 + } + gradInput + } + + override def clearState(): this.type = { + super.clearState() + size = null + results = null + this + } + + override def toString: String = s"nn.SparseJoinTable($dimension)" + + + override def canEqual(other: Any): Boolean = other.isInstanceOf[SparseJoinTable[T]] + + override def equals(other: Any): Boolean = other match { + case that: SparseJoinTable[T] => + super.equals(that) && + (that canEqual this) && + dimension == that.dimension + case _ => false + } + + override def hashCode(): Int = { + val state = Seq(super.hashCode(), dimension) + state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) + } +} + +object SparseJoinTable { + def apply[@specialized(Float, Double) T: ClassTag]( + dimension: Int)(implicit ev: TensorNumeric[T]) : SparseJoinTable[T] = { + new SparseJoinTable[T](dimension) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinear.scala new file mode 100644 index 00000000000..5243d4dec67 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinear.scala @@ -0,0 +1,168 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.{SparseTensorBLAS, SparseTensorMath, SparseType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * SparseLinear is the sparse version of module Linear. SparseLinear has two different from Linear: + * firstly, SparseLinear's input Tensor is a SparseTensor. Secondly, SparseLinear doesn't backward + * gradient to next layer in the backpropagation by default, as the gradInput of SparseLinear is + * useless and very big in most cases. + * + * But, considering model like Wide&Deep, we provide backwardStart and backwardLength to backward + * part of the gradient to next layer. + * + * @param inputSize the size the each input sample + * @param outputSize the size of the module output of each sample + * @param backwardStart backwardStart index, counting from 1 + * @param backwardLength backward length + * @param withBias if has bias + * @param wRegularizer: instance of [[Regularizer]] + * (eg. L1 or L2 regularization), applied to the input weights matrices. + * @param bRegularizer: instance of [[Regularizer]] + * applied to the bias. + */ +class SparseLinear[T: ClassTag]( + inputSize: Int, + outputSize: Int, + val backwardStart: Int = -1, + val backwardLength: Int = -1, + withBias: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null)(implicit ev: TensorNumeric[T]) extends Linear[T]( + inputSize, outputSize, withBias, wRegularizer, bRegularizer, + initWeight, initBias, initGradWeight, initGradBias) { + + // input should be a sparseTensor + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.getTensorType == SparseType, s"SparseLinear's input must be a SparseTensor," + + s"but got ${input.getTensorType}") + require(input.dim() == 2, + "SparseLinear: " + ErrorInfo.constrainInputAsVectorOrBatch) + + val nFrame = input.size(1) + val nElement = output.nElement + val t = Array(nFrame, weight.size(1)) + output.resize(t) + if (output.nElement() != nElement) { + output.zero() + } + + if (addBuffer.nElement() != nFrame) { + addBuffer.resize(Array(nFrame)).fill(ev.one) + } + + SparseTensorMath.addmm(output, ev.zero, output, ev.one, input, weight.t) + if(withBias) output.addr(ev.one, addBuffer, bias) + output + } + + // just backward a part of the gradOutput. Input is sparse, while gradOutput is dense. + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(input.dim() == 2, + "SparseLinear: " + ErrorInfo.constrainInputAsVectorOrBatch) + if (backwardStart >= 0 && backwardLength > 0) { + val _inputSize = Array(input.size(1), backwardLength) + val _weight = weight.narrow(2, backwardStart, backwardLength) + + val nElement = gradInput.nElement() + gradInput.resize(_inputSize) + if (nElement != gradInput.nElement()) { + gradInput.zero() + } + + gradInput.addmm(ev.zero, ev.one, gradOutput, _weight) + } + gradInput + } + + override def accGradParameters( + input: Tensor[T], + gradOutput: Tensor[T]): Unit = { + require(input.dim() == 2, + "SparseLinear: " + ErrorInfo.constrainInputAsVectorOrBatch) + + gradWeight.resize(outputSize, inputSize) + if (withBias) { + gradBias.resize(outputSize) + } + + if (scaleW != 0) { + SparseTensorMath.addmm(gradWeight, ev.one, gradWeight, + ev.fromType[Double](scaleW), gradOutput.t, input) + } + + if (withBias && scaleB != 0) { + gradBias.addmv(ev.fromType[Double](scaleB), gradOutput.t, addBuffer) + } + + if (null != wRegularizer && scaleW != 0) { + wRegularizer.accRegularization(weight, gradWeight, scaleW) + } + if (null != bRegularizer && scaleB != 0) { + bRegularizer.accRegularization(bias, gradBias, scaleB) + } + } + + override def toString() : String = { + s"nn.SparseLinear($inputSize -> $outputSize)" + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[SparseLinear[T]] + + override def equals(other: Any): Boolean = other match { + case that: SparseLinear[T] => + super.equals(that) && + (that canEqual this) && + backwardStart == that.backwardStart && + backwardLength == that.backwardLength + case _ => false + } + + override def hashCode(): Int = { + val state = Seq(super.hashCode(), backwardStart, backwardLength) + state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) + } +} + +object SparseLinear { + def apply[@specialized(Float, Double) T: ClassTag]( + inputSize: Int, + outputSize: Int, + withBias: Boolean = true, + backwardStart: Int = -1, + backwardLength: Int = -1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null + )(implicit ev: TensorNumeric[T]): SparseLinear[T] = { + new SparseLinear[T](inputSize, outputSize, backwardStart, backwardLength, + withBias, wRegularizer, bRegularizer, initWeight, initBias, initGradWeight, initGradBias) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index d97a9d19930..2172a09c31e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -881,13 +881,6 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( d += 1 } - println(_indices.map(_.array()).deep == other._indices.map(_.array()).deep) - println(_indices(0).array.deep == other._indices(0).array().deep) - println(_indices(1).array.deep == other._indices(1).array().deep) - println(_values.array().deep == other._values.array().deep) - println(this._shape.deep == other._shape.deep) - println(this._nElement == other._nElement) - _indices.map(_.array()).deep == other._indices.map(_.array()).deep && _values.array().deep == other._values.array().deep && this._shape.deep == other._shape.deep && @@ -991,6 +984,193 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( } object SparseTensor{ + private[tensor] def concat[T: ClassTag]( + dim: Int, + tensors: Seq[Tensor[T]], + res: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(dim == 1 || dim == 2) + var size = tensors.head.size() + require(size.length <= 2, "Dimension larger than 2 are not supported yet!") + tensors.foreach{tensor => + // todo: check size + require(tensor.isInstanceOf[SparseTensor[T]]) + require(tensor.dim() == size.length) + } + val dim1Concat = if (size.length == 1 && dim == 1) true else false + if (dim1Concat) size = Array(1) ++ size + var i = 1 + while (i < tensors.length) { + size(dim - 1) += (if (dim1Concat) 1 else tensors(i).size(dim)) + i += 1 + } + val totalLength = tensors.map(_.nElement()).sum + + val result = if (null == res) { + SparseTensor(size, totalLength) + } else { + res.resize(size, totalLength).asInstanceOf[SparseTensor[T]] + } + if (dim1Concat) { + concat(tensors.map(_.asInstanceOf[SparseTensor[T]]), result) + } + else { + concat(dim, tensors.map(_.asInstanceOf[SparseTensor[T]]), result) + } + } + + /** + * Concatenate a sequence of SparseTensor of 1-dim to 2-dim SparseTensor. + * + * @param tensors a sequence of tensors + * @param res the resulted 2-dim SparseTensor + * @return res + */ + private def concat[T: ClassTag]( + tensors: Seq[SparseTensor[T]], + res: SparseTensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + val numOfIndices = res.dim() // usually is 2 + require(tensors.head.dim() == 1, "Not suitable for this interface.") + var i, offset, dimOffset = 0 + while (i < tensors.length) { + val currentTensor = tensors(i) + val curLength = currentTensor.nElement() + val curTensorOffset = currentTensor.storageOffset() - 1 + // copy to concat _values + ev.arraycopy(currentTensor.storage().array(), curTensorOffset, + res.storage().array(), offset, curLength) + // make new Indices + var indicesIndex = 0 + while (indicesIndex < numOfIndices) { + if (indicesIndex == 0) { + val storage = Storage[Int](curLength) + val storageArray = storage.array() + for (j <- 0 until curLength) storageArray(j) = dimOffset + System.arraycopy(storageArray, 0, res._indices(indicesIndex).array(), + offset, curLength) + } + else { + // copy directly + System.arraycopy(currentTensor._indices(indicesIndex - 1).array(), + curTensorOffset, res._indices(indicesIndex).array(), + offset, curLength) + } + indicesIndex += 1 + } + offset += curLength + dimOffset += 1 + i += 1 + } + res + } + + /** + * Concatenate a sequence of SparseTensor of n-dim to n-dim SparseTensor. + * The size at n-dim will be concated. + * + * @param tensors a sequence of tensors + * @param res the resulted 2-dim SparseTensor + * @return res + */ + private def concat[T: ClassTag]( + dim: Int, + tensors: Seq[SparseTensor[T]], + res: SparseTensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + val numOfIndices = res.dim() + dim match { + case 1 => + var i = 0 + var offset = 0 + var dimOffset = 0 + while (i < tensors.length) { + val currentTensor = tensors(i) + val curLength = currentTensor.nElement() + val curTensorOffset = currentTensor.storageOffset() - 1 + + ev.arraycopy(currentTensor.storage().array(), currentTensor.storageOffset() - 1, + res.storage().array(), offset, currentTensor.nElement()) + + var indicesIndex = 0 + while (indicesIndex < numOfIndices) { + val indicesIndexArray = currentTensor._indices(indicesIndex).array() + val resultIndicesArray = res._indices(indicesIndex).array() + if (i == 0 || indicesIndex != dim - 1) { + // copy directly + System.arraycopy(currentTensor._indices(indicesIndex).array(), + curTensorOffset, res._indices(indicesIndex).array(), + offset, curLength) + } else { + // add size + var j = 0 + while (j < curLength) { + resultIndicesArray(offset + j) = indicesIndexArray(curTensorOffset + j) + + dimOffset + j += 1 + } + } + indicesIndex += 1 + } + + offset += curLength + dimOffset += currentTensor.size(dim) + i += 1 + } + case 2 => + var start = res._storageOffset + var end = res._storageOffset + val tensorsOffset = tensors.map(_.storageOffset() - 1).toArray + var j = 0 + while (j < res.size(dim - 1)) { + var index = 0 + var offset = 0 + while (index < tensors.size) { + val currentTensor = tensors(index) + val findIndexStart = currentTensor._indices(0).array().indexOf(j, tensorsOffset(index)) + val findIndexEnd = currentTensor._indices(0).array().lastIndexOf(j) + val curLength = if (findIndexStart != -1 && findIndexEnd != -1) { + findIndexEnd - findIndexStart + 1 + } else { + 0 + } + + if (0 != curLength) { + end += curLength + + // copy values + ev.arraycopy(currentTensor.storage().array(), tensorsOffset(index), + res.storage().array(), start, curLength) + + // copy indices + var indicesIndex = 0 + while (indicesIndex < numOfIndices) { + val indicesIndexArray = currentTensor._indices(indicesIndex).array() + val resultIndicesArray = res._indices(indicesIndex).array() + if (indicesIndex != dim - 1 || index == 0) { + // copy directly + System.arraycopy(currentTensor._indices(indicesIndex).array(), + tensorsOffset(index), res._indices(indicesIndex).array(), start, curLength) + } else { + // add size + var i = 0 + while (i < curLength) { + resultIndicesArray(start + i) = indicesIndexArray(tensorsOffset(index) + i) + + offset + i += 1 + } + } + indicesIndex += 1 + } + tensorsOffset(index) += curLength + start = end + } + offset += currentTensor.size(dim) + index += 1 + } + j += 1 + } + } + res + } + private[tensor] def apply[T: ClassTag]( shape : Array[Int], nElement: Int = 1)( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index b754a557e50..e74572013de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -1236,7 +1236,35 @@ object Tensor { def dense[T: ClassTag]( sparseTensor: Tensor[T], res: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = { - require(sparseTensor.isInstanceOf[SparseTensor[T]]) - DenseTensor(sparseTensor.asInstanceOf[SparseTensor[T]], res) + if (sparseTensor.isInstanceOf[SparseTensor[T]]) { + DenseTensor(sparseTensor.asInstanceOf[SparseTensor[T]], res) + } else if (sparseTensor.isInstanceOf[DenseTensor[T]]) { + res.copy(sparseTensor) + } else { + throw new IllegalArgumentException("Tensor.dense: Illegal tensor type.") + } + } + + /** + * Concat a sequence of tensors to res tensor. + * + * @param dim concat at dim-th dimension. + * @param tensors a sequence of tensors. + * @param res result tensor. + * @param ev + * @tparam T + * @return + */ + private[bigdl] def sparseConcat[T: ClassTag]( + dim: Int, + tensors: Table, + res: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + val seqTensors = new Array[Tensor[T]](tensors.length()) + var i = 0 + while (i < seqTensors.length) { + seqTensors(i) = tensors[Tensor[T]](i + 1) + i += 1 + } + SparseTensor.concat(dim, seqTensors, res) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 8bc13cb3853..c22ca9f16a8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -184,6 +184,25 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab toTensor(initWeight), toTensor(initBias), toTensor(initGradWeight), toTensor(initGradBias)) } + def createSparseLinear(inputSize: Int, outputSize: Int, + withBias: Boolean, + backwardStart: Int = -1, + backwardLength: Int = -1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null): SparseLinear[T] = { + SparseLinear[T](inputSize, outputSize, withBias, backwardStart, backwardLength, + wRegularizer, bRegularizer, toTensor(initWeight), toTensor(initBias), + toTensor(initGradWeight), toTensor(initGradBias)) + } + + def createDenseToSparse(): DenseToSparse[T] = { + DenseToSparse[T]() + } + def createReLU(ip: Boolean = false): ReLU[T] = { ReLU[T](ip) } @@ -637,6 +656,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab nInputDims) } + def createSparseJoinTable(dimension: Int): SparseJoinTable[T] = { + SparseJoinTable[T](dimension) + } + def createL1Cost() : L1Cost[T] = { L1Cost[T]() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala new file mode 100644 index 00000000000..6c43296f359 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{Matchers, FlatSpec} +import com.intel.analytics.bigdl.numeric.NumericFloat + +@com.intel.analytics.bigdl.tags.Parallel +class DenseToSparseSpec extends FlatSpec with Matchers { + "A DenseToSparse forward" should "generate correct output" in { + val dts = DenseToSparse() + val denseTensor = Tensor.range(1, 12, 1) + val output = dts.forward(denseTensor) + val exceptedOutput = Tensor.sparse(Array(Array.range(0, 12)), + Array.range(1, 13).map(_.toFloat), Array(12)) + output should be (exceptedOutput) + } + + "A DenseToSparse backward" should "generate correct output" in { + val dts = DenseToSparse() + val input = Tensor.range(1, 12, 1) + val sparseTensor = Tensor.sparse(Array(Array.range(0, 12)), + Array.range(1, 13).map(_.toFloat), Array(12)) + val output = dts.backward(input, sparseTensor) + val exceptedOutput = Tensor.range(1, 12, 1) + output should be (exceptedOutput) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala new file mode 100644 index 00000000000..f224ea4f274 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala @@ -0,0 +1,70 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{RandomGenerator, T} +import com.intel.analytics.bigdl.numeric.NumericFloat +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class SparseJoinTableSpec extends FlatSpec with Matchers { + + "Sparse JoinTable" should "return the same result" in { + Random.setSeed(2) + RandomGenerator.RNG.setSeed(1) + val input = Tensor(4, 3).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + println(input) + val input2 = Tensor(4, 2).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + println(input2) + val sparseModel = Sequential().add(ParallelTable().add(Identity()).add(Identity())) + .add(SparseJoinTable(2)) + val denseInput = Tensor(4, 5) + denseInput.narrow(2, 1, 3).copy(input) + denseInput.narrow(2, 4, 2).copy(input2) + + val sparseInput = T(Tensor.sparse(input), Tensor.sparse(input2)) + val out1 = sparseModel.forward(sparseInput).toTensor[Float] + val exceptOut = Tensor.sparse(denseInput) + out1 shouldEqual exceptOut + Tensor.dense(out1) shouldEqual denseInput + + } + + "Sparse JoinTable" should "return the same result 2" in { + Random.setSeed(2) + RandomGenerator.RNG.setSeed(1) + val input = Tensor(4, 10).apply1(_ => Random.nextInt(10) / 9 * Random.nextFloat()) + println(input) + val input2 = Tensor(4, 10).apply1(_ => Random.nextInt(10) / 9 * Random.nextFloat()) + println(input2) + val sparseModel = Sequential().add(ParallelTable().add(Identity()).add(Identity())) + .add(SparseJoinTable(2)) + val denseInput = Tensor(4, 20) + denseInput.narrow(2, 1, 10).copy(input) + denseInput.narrow(2, 11, 10).copy(input2) + + val sparseInput = T(Tensor.sparse(input), Tensor.sparse(input2)) + val out1 = sparseModel.forward(sparseInput).toTensor[Float] + val exceptOut = Tensor.sparse(denseInput) + out1 shouldEqual exceptOut + Tensor.dense(out1) shouldEqual denseInput + + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala new file mode 100644 index 00000000000..392d48bd7ba --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala @@ -0,0 +1,174 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.{SparseTensor, Tensor} +import com.intel.analytics.bigdl.utils.T + +import scala.util.Random + +class SparseLinearSpec extends FlatSpec with Matchers { + "Sparse Linear" should "return the same result with Linear" in { + val weight = Tensor.range(1, 8, 1).resize(2, 4) + val bias = Tensor(2) + val gradOutput = Tensor.range(1, 4, 1).resize(2, 2) + val sl = SparseLinear(4, 2) + val l = Linear(4, 2) + l.weight.copy(weight) + l.bias.copy(bias) + sl.weight.copy(weight) + sl.bias.copy(bias) + val input = Tensor(2, 4) + input.setValue(1, 1, 1f) + input.setValue(2, 3, 3f) + val sparseInput = Tensor.sparse(input) + val out1 = sl.forward(sparseInput) + sl.backward(sparseInput, gradOutput) + val out2 = l.forward(input) + l.backward(input, gradOutput) + out1 should be (out2) + sl.getParameters()._2 should be (l.getParameters()._2) + } + + "Sparse Linear" should "return the same result with Linear 2" in { + val gradOutput = Tensor(2, 2).rand() + val input = Tensor(2, 4).rand() + val sl = SparseLinear(4, 2) + val l = Linear(4, 2) + l.weight.copy(sl.weight) + l.bias.copy(sl.bias) + val sparseInput = Tensor.sparse(input) + val out1 = sl.forward(sparseInput) + sl.backward(sparseInput, gradOutput) + val out2 = l.forward(input) + l.backward(input, gradOutput) + out1 should be (out2) + sl.getParameters()._2 should be (l.getParameters()._2) + } + + + "Sparse Linear" should "return the same result with Linear 3" in { + val gradOutput = Tensor(2, 2).rand() + val input = Tensor(2, 4).rand() + val sl = SparseLinear(4, 2, backwardStart = 1, backwardLength = 4) + val l = Linear(4, 2) + l.weight.copy(sl.weight) + l.bias.copy(sl.bias) + val sparseInput = Tensor.sparse(input) + val out1 = sl.forward(sparseInput) + val gradInput1 = sl.backward(sparseInput, gradOutput) + val out2 = l.forward(input) + val gradInput2 = l.backward(input, gradOutput) + out1 should be (out2) + gradInput1 should be (gradInput2) + sl.getParameters()._2 should be (l.getParameters()._2) + } + + "Sparse Linear" should "return the same result with Linear 4" in { + val gradOutput = Tensor(3, 2).rand() + val input = Tensor(3, 4).rand() + val sl = SparseLinear(4, 2, backwardStart = 1, backwardLength = 4) + val l = Linear(4, 2) + l.weight.copy(sl.weight) + l.bias.copy(sl.bias) + val sparseInput = Tensor.sparse(input) + val out1 = sl.forward(sparseInput) + val gradInput1 = sl.backward(sparseInput, gradOutput) + val out2 = l.forward(input) + val gradInput2 = l.backward(input, gradOutput) + out1 should be (out2) + gradInput1 should be (gradInput2) + sl.getParameters()._2 should be (l.getParameters()._2) + } + + "Sparse Linear" should "return the same result with Linear 5" in { + val gradOutput = Tensor(4, 2).rand() + val input = Tensor(4, 10).apply1(_ => Random.nextInt(10) / 9 * Random.nextFloat()) + val sl = SparseLinear(10, 2, backwardStart = 5, backwardLength = 5) + val l = Linear(10, 2) + l.weight.copy(sl.weight) + l.bias.copy(sl.bias) + val sparseInput = Tensor.sparse(input) + val out1 = sl.forward(sparseInput) + val gradInput1 = sl.backward(sparseInput, gradOutput) + val out2 = l.forward(input) + val gradInput2 = l.backward(input, gradOutput) + out1 should be (out2) + gradInput1 should be (gradInput2.narrow(2, 5, 5)) + sl.getParameters()._2 should be (l.getParameters()._2) + } + + "Sparse Linear" should "return the same result with Linear 6" in { + val gradOutput = Tensor(4, 2).rand() + val input = Tensor(4, 3).apply1(_ => Random.nextInt(5) / 4 * Random.nextFloat()) + val input2 = Tensor(4, 2).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + val sl = SparseLinear(5, 2, backwardStart = 1, backwardLength = 5) + val sparseModel = Sequential().add(ParallelTable().add(Identity()).add(Identity())) + .add(SparseJoinTable(2)) + .add(sl) + val l = Linear(5, 2) + l.weight.copy(sl.weight) + l.bias.copy(sl.bias) + val denseInput = Tensor(4, 5) + denseInput.narrow(2, 1, 3).copy(input) + denseInput.narrow(2, 4, 2).copy(input2) + + val sparseInput = T(Tensor.sparse(input), Tensor.sparse(input2)) + Tensor.sparse(denseInput) + val out1 = sparseModel.forward(sparseInput).toTensor[Float] + val gradInput1 = sparseModel.backward(sparseInput, gradOutput) + + val out2 = l.forward(denseInput) + val gradInput2 = l.backward(denseInput, gradOutput) + out1 shouldEqual out2 + sl.gradInput should be (gradInput2) + sl.getParameters()._2 should be (l.getParameters()._2) + } + + "Sparse Linear" should "return the same result with Linear 7" in { + val gradOutput = Tensor(4, 2).rand() + val input = Tensor(4, 1023213).apply1(_ => Random.nextInt(100000) / 99999 * Random.nextFloat()) + val input2 = Tensor(4, 50).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + val sl = SparseLinear(1023263, 2, backwardStart = 1, backwardLength = 1023263) + val sj = SparseJoinTable(2) + val sparseModel = Sequential().add(ParallelTable().add(Identity()).add(Identity())) + .add(sj) + .add(sl) + val l = Linear(1023263, 2) + l.weight.copy(sl.weight) + l.bias.copy(sl.bias) + val denseInput = Tensor(4, 1023263) + denseInput.narrow(2, 1, 1023213).copy(input) + denseInput.narrow(2, 1023214, 50).copy(input2) + + val sparseInput = T(Tensor.sparse(input), Tensor.sparse(input2)) + val si = Tensor.sparse(denseInput) + val aaa = sl.forward(si).toTensor[Float].clone() + val out1 = sparseModel.forward(sparseInput).toTensor[Float] + val gradInput1 = sparseModel.backward(sparseInput, gradOutput) +// + val out2 = l.forward(denseInput) + val gradInput2 = l.backward(denseInput, gradOutput) + aaa shouldEqual out2 + sj.output shouldEqual si + out1 shouldEqual out2 + sl.gradInput should be (gradInput2) + sl.getParameters()._2.equals(l.getParameters()._2) shouldEqual true + } +} From 522c9eb5f0b1d892facf94558361ce08f0eace1f Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 13 Oct 2017 23:38:08 +0800 Subject: [PATCH 0455/1065] fix OOMError caused by broadcastModel.cloneModule in DistributeOptimizer (#1633) --- .../org/apache/spark/ml/DLEstimator.scala | 2 +- .../dllib/example/udfpredictor/Utils.scala | 4 +- .../dllib/models/utils/ModelBroadcast.scala | 127 ++++++++++++------ .../bigdl/dllib/optim/DistriOptimizer.scala | 61 +++++---- .../bigdl/dllib/optim/Evaluator.scala | 2 +- .../bigdl/dllib/optim/Predictor.scala | 2 +- .../bigdl/dllib/utils/tf/Session.scala | 2 +- .../models/utils/ModelBroadcastSpec.scala | 4 +- 8 files changed, 126 insertions(+), 78 deletions(-) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index e4f67e5d359..2c9cbd74c83 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -214,7 +214,7 @@ class DLModel[@specialized(Float, Double) T: ClassTag]( featureData: RDD[Seq[AnyVal]], dataset: DataFrame): DataFrame = { model.evaluate() - val modelBroadCast = ModelBroadcast[T].broadcast(featureData.sparkContext, model) + val modelBroadCast = ModelBroadcast[T]().broadcast(featureData.sparkContext, model) val predictRdd = featureData.map { f => // convert feature data type to the same type with model f.head match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala index 27db63888d1..9328d1d6c0f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala @@ -91,7 +91,7 @@ object Utils { word2Vec: Word2Vec) (implicit ev: TensorNumeric[Float]): (String) => Int = { - val broadcastModel = ModelBroadcast[Float].broadcast(sc, model) + val broadcastModel = ModelBroadcast[Float]().broadcast(sc, model) val word2IndexBC = sc.broadcast(word2Index) val word2VecBC = sc.broadcast(word2Vec) @@ -129,7 +129,7 @@ object Utils { val featureTensor: Tensor[Float] = Tensor[Float]() var featureData: Array[Float] = null val sampleSize = sampleShape.product - val localModel = broadcastModel.value + val localModel = broadcastModel.value() // create tensor from input column if (featureData == null) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index a397400800e..1c2d430c57c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.models.utils import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.apache.spark.SparkContext import org.apache.spark.broadcast.Broadcast @@ -25,12 +25,14 @@ import org.apache.spark.broadcast.Broadcast import scala.reflect.ClassTag /** - * ModelBroadcast is used to broadcast model when doing model inference. - * Note: do not use it in model training since the broadcast models share weights and biases - * It shortens the broadcast time, which is especially useful when the model size is large + * ModelBroadcast is used to broadcast model. + * + * Note: If you want to use this to broadcast training model, please use value(true) to get + * the model. And before broadcasting please make sure the model's parameter is compacted. + * * @tparam T data type */ -class ModelBroadcast[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializable { +class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Serializable { private var broadcastModel: Broadcast[Module[T]] = _ private var broadcastParameters: Broadcast[Array[Tensor[T]]] = _ @@ -44,70 +46,115 @@ class ModelBroadcast[T: ClassTag](implicit ev: TensorNumeric[T]) extends Seriali * @return this */ def broadcast(sc: SparkContext, model: Module[T]): this.type = { - val bcModel = model.cloneModule() - val weightsBias = getAndClearWeightBias(bcModel.parameters()) - broadcastModel = sc.broadcast(bcModel) + val weightsBias = getAndClearWeightBias(model.parameters()) + broadcastModel = sc.broadcast(model.cloneModule()) broadcastParameters = sc.broadcast(weightsBias) + putWeightBias(weightsBias, model) + initGradWeightBias(weightsBias, model) this } /** * get the broadcast model * put the weight and bias back to the model + * + * @param initGradient if init gradParameter. * @return model */ - def value(): Module[T] = { + def value(initGradient: Boolean = false): Module[T] = { val localModel = broadcastModel.value.cloneModule() putWeightBias(broadcastParameters.value, localModel) + if (initGradient) { + initGradWeightBias(broadcastParameters.value, localModel) + } localModel } private def getAndClearWeightBias(parameters: (Array[Tensor[T]], Array[Tensor[T]])) : Array[Tensor[T]] = { - var i = 0 - val weightsBias = new Array[Tensor[T]](parameters._1.length) - while (i < parameters._1.length) { - if (parameters._1(i) != null) { - val wb = parameters._1(i) - wb match { - case quantTensor: QuantizedTensor[T] => - weightsBias(i) = QuantizedTensor[T](quantTensor.getStorage, quantTensor.maxOfRow, - quantTensor.minOfRow, quantTensor.sumOfRow, quantTensor.size(), quantTensor.params) - case _ => - weightsBias(i) = Tensor[T](Storage(wb.storage().array()), - wb.storageOffset(), wb.size(), wb.stride()) + if (parameters._1.length != 0) { + var i = 0 + val weightsBias = new Array[Tensor[T]](parameters._1.length) + val isQuantized = parameters._1.exists(_.getTensorType == QuantizedType) + val (isCompacted, storage) = if (!isQuantized) { + val storage = Storage(parameters._1(0).storage.array()) + (parameters._1.map(_.nElement()).sum == storage.length(), storage) + } else { + (false, null) + } + + // get weight and bias + while (i < parameters._1.length) { + if (parameters._1(i) != null) { + val wb = parameters._1(i) + wb.getTensorType match { + case QuantizedType => + val quantTensor = wb.asInstanceOf[QuantizedTensor[T]] + weightsBias(i) = QuantizedTensor[T](quantTensor.getStorage, quantTensor.maxOfRow, + quantTensor.minOfRow, quantTensor.sumOfRow, quantTensor.size(), quantTensor.params) + case _ => + weightsBias(i) = if (isCompacted) { + Tensor[T](storage, wb.storageOffset(), wb.size(), wb.stride()) + } else { + Tensor[T](Storage(wb.storage().array()), wb.storageOffset(), wb.size(), wb.stride()) + } + } + i += 1 } } - i += 1 + // clear parameters + clearTensor(parameters._1) + // because in quantized mode, the weight number may be different with gradWeight number + clearTensor(parameters._2) + + weightsBias + } else { + // just return an empty array when parameters is empty. + Array() } - i = 0 - while (i < parameters._1.length) { - if (parameters._1(i) != null) { - parameters._1(i).set() + } + + private def clearTensor(tensors: Array[Tensor[T]]): Unit = { + var i = 0 + while (i < tensors.length) { + if (tensors(i) != null) { + tensors(i).set() } i += 1 } + } - // because in quantized mode, the weight number may be different with gradWeight number - i = 0 - while (i < parameters._2.length) { - if (parameters._2(i) != null) { - parameters._2(i).set() + private def putWeightBias( + broadcastWeightBias: Array[Tensor[T]], + localModel: Module[T]): Unit = { + val localWeightBias = localModel.parameters()._1 + var i = 0 + while (i < localWeightBias.length) { + if (localWeightBias(i) != null) { + localWeightBias(i).set(broadcastWeightBias(i)) } i += 1 } - - weightsBias } - private def putWeightBias(broadcastWeightBias: Array[Tensor[T]], - localModel: Module[T]): Unit = { - val localWeightBias = localModel.parameters()._1 + private def initGradWeightBias( + broadcastWeightBias: Array[Tensor[T]], + localModel: Module[T]): Unit = { + val (localWeightBias, localGradWeightBias) = localModel.parameters() + // init gradient with a compacted storage + val storage = Storage[T](localGradWeightBias.map(_.nElement()).sum) + val isQuantized = broadcastWeightBias.exists(_.getTensorType == QuantizedType) var i = 0 while (i < localWeightBias.length) { if (localWeightBias(i) != null) { - localWeightBias(i).set(broadcastWeightBias(i)) + val wb = broadcastWeightBias(i) + wb.getTensorType match { + case QuantizedType => + localGradWeightBias(i).set(Tensor(1)) + case _ => + localGradWeightBias(i).set(storage, wb.storageOffset(), wb.size(), wb.stride()) + } } i += 1 } @@ -116,6 +163,8 @@ class ModelBroadcast[T: ClassTag](implicit ev: TensorNumeric[T]) extends Seriali object ModelBroadcast { - def apply[@specialized(Float, Double) T: ClassTag]()(implicit ev: TensorNumeric[T]) - : ModelBroadcast[T] = new ModelBroadcast + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : ModelBroadcast[T] = { + new ModelBroadcast() + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index dbbd51ae59e..79f6f38ec01 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -27,6 +27,7 @@ import java.io.{File, FilenameFilter} import java.text.SimpleDateFormat import java.util.Calendar +import com.intel.analytics.bigdl.models.utils.ModelBroadcast import org.apache.commons.lang.exception.ExceptionUtils import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.Logger @@ -51,7 +52,6 @@ object DistriOptimizer { * @param modelGradients gradients of the cached models * @param localCriterions cached criterion * @param localStates cached state - * @param gradient tensor buffer * @tparam T Tensor element type */ case class Cache[T]( @@ -60,7 +60,6 @@ object DistriOptimizer { modelGradients: Array[Tensor[T]], localCriterions: Array[Criterion[T]], localStates: Array[Table], - gradient: Tensor[T], var moduleTimeList: Array[Long] = null, localMethods: Array[Option[Array[ValidationMethod[T]]]], optimMethod: OptimMethod[T] @@ -242,38 +241,37 @@ object DistriOptimizer { } if (finishedThreads.nonEmpty) { + val finishedGradients = finishedThreads.map(cached.modelGradients(_)) time = System.nanoTime() - val gradLength = cached.modelGradients(0).nElement() + val gradLength = finishedGradients(0).nElement() val taskSize = gradLength / _subModelNumber val extraTask = gradLength % _subModelNumber - (0 until _subModelNumber).diff(finishedThreads).foreach(i => - cached.modelGradients(i).zero() - ) - - // copy multi-model gradient to the buffer + // Aggregate multi-model's gradient to the first model's gradient val parallelNum = if (taskSize == 0) extraTask else _subModelNumber Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { val offset = tid * taskSize + math.min(tid, extraTask) val length = taskSize + (if (tid < extraTask) 1 else 0) - var i = 0 - while (i < cached.modelGradients.length) { - if (i == 0) { - cached.gradient.narrow(1, offset + 1, length) - .copy(cached.modelGradients(i).narrow(1, offset + 1, length)) - } else { - cached.gradient.narrow(1, offset + 1, length) - .add(cached.modelGradients(i).narrow(1, offset + 1, length)) - } + var i = 1 + while (i < finishedGradients.length) { + finishedGradients(0).narrow(1, offset + 1, length) + .add(finishedGradients(i).narrow(1, offset + 1, length)) i += 1 } })) driverMetrics.add("aggregate gradient time", System.nanoTime() - time) + val putG = System.nanoTime() + // Put first finished model's gradient who aggregated + // all other models' gradient to AllReduceParameter + parameters.putGradients(finishedGradients(0)) + driverMetrics.add("put gradient", System.nanoTime() - putG) + } else { + val putG = System.nanoTime() + // zero gradient in BlockManager when no thread finished. + parameters.putGradients(cached.modelGradients(0).zero()) + driverMetrics.add("put gradient", System.nanoTime() - putG) } - val putG = System.nanoTime() - parameters.putGradients(cached.gradient) - driverMetrics.add("put gradient", System.nanoTime() - putG) tasks ++= Engine.default.invoke { (0 until _subModelNumber).map { i => () => { @@ -523,7 +521,15 @@ object DistriOptimizer { optimMethod: OptimMethod[T] )(implicit ev: TensorNumeric[T]) = { val sc = dataset.originRDD().sparkContext - val broadcast = sc.broadcast((model, criterion, state, validationMethods, optimMethod)) + val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod)) + // ensure model's parameter is compacted for getting a better performance when broadcasting + model.getParameters() + // As cloneModel is using Serialization to implement deep copy, and will throw OOMError + // when model's size is bigger than SerializationUtils' buffer size. So we can use + // ModelBroadcast to clone model here. + // Notes: All models returned by modelBroadcast.value() share the same weight&bias, while + // gradWeight&gradBias is unshared. + val modelBroadcast = ModelBroadcast[T]().broadcast(sc, model) val _subModelNumber = Engine.getEngineType match { case MklBlas => coresPerNode case _ => throw new IllegalArgumentException @@ -538,7 +544,7 @@ object DistriOptimizer { val nExecutor = Engine.nodeNumber() val executorCores = Engine.coreNumber() val models = dataset.originRDD().mapPartitions(_ => { - val (broadcastModel, broadcastCriterion, broadcastState, broadcastMethod, + val (broadcastCriterion, broadcastState, broadcastMethod, broadcastOptim) = broadcast.value if (!Engine.checkSingleton()) { if (checkSingleton) { @@ -554,7 +560,7 @@ object DistriOptimizer { } Engine.setNodeAndCore(nExecutor, executorCores) val cached = (0 until _subModelNumber).map { _ => - val localModel = broadcastModel.cloneModule() + val localModel = modelBroadcast.value(true) val localCriterion = broadcastCriterion.cloneCriterion() val localState = broadcastState.clone() val localMethod = @@ -563,14 +569,8 @@ object DistriOptimizer { (localModel, weights, grads, localCriterion, localState, localMethod) }.toArray - val weights = cached.head._2 - cached.map(c => - if (!c._2.eq(weights)) { - c._2.storage().set(weights.storage()) - } - ) - logger.info("model thread pool size is " + Engine.model.getPoolSize) + val weights = cached.head._2 parameters.init(weights) Iterator.single(Cache( @@ -579,7 +579,6 @@ object DistriOptimizer { cached.map(_._3), // gradients cached.map(_._4), // criterions cached.map(_._5), // states - cached.head._2.clone(), // a tensor buffer new Array[Long](_subModelNumber * computeThresholdbatchSize), cached.map(_._6), broadcastOptim.clone() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala index 49835a13a31..b5f8f4e5620 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala @@ -49,7 +49,7 @@ class Evaluator[T: ClassTag] private[optim](model: Module[T])(implicit ev: Tenso vMethods: Array[ValidationMethod[T]], batchSize: Option[Int] = None): Array[(ValidationResult, ValidationMethod[T])] = { - val modelBroad = ModelBroadcast[T].broadcast(dataset.sparkContext, model.evaluate()) + val modelBroad = ModelBroadcast[T]().broadcast(dataset.sparkContext, model.evaluate()) val partitionNum = dataset.partitions.length val totalBatch = batchSize.getOrElse(batchPerPartition * partitionNum) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index c4e1251f2be..a057583536a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -51,7 +51,7 @@ class Predictor[T: ClassTag] private[optim]( def predict(dataSet: RDD[Sample[T]], batchSize: Int = -1, shareBuffer: Boolean = false): RDD[Activity] = { - val modelBroad = ModelBroadcast[T].broadcast(dataSet.sparkContext, model.evaluate()) + val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext, model.evaluate()) val partitionNum = dataSet.partitions.length val totalBatch = if (batchSize > 0) { require(batchSize % partitionNum == 0, s"Predictor.predict: total batch size $batchSize " + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index 2158fb14dcb..e2a256e7fa5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -483,7 +483,7 @@ class BigDLSessionImpl[T: ClassTag]( } } - val modelBroadCast = ModelBroadcast[T].broadcast(sc, transformer) + val modelBroadCast = ModelBroadcast[T]().broadcast(sc, transformer) inputRdd.map { tensors => val trans = modelBroadCast.value() val output = trans.forward(tensors.flatten()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala index 8181f034549..c75699d228c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala @@ -33,7 +33,7 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { "model broadcast" should "work properly" in { val model = LeNet5(10) - val modelBroadCast = ModelBroadcast[Float].broadcast(sc, model) + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) modelBroadCast.value().toString should be(model.toString) modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } @@ -42,7 +42,7 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { val model = LeNet5(10) model.getParameters() - val modelBroadCast = ModelBroadcast[Float].broadcast(sc, model) + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) modelBroadCast.value().toString should be(model.toString) modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } From a8995dc3b6fe57d8d1ec9eebb55e940134144d7c Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Sat, 14 Oct 2017 20:52:41 -0700 Subject: [PATCH 0456/1065] Change default learning rate for DLEstimator (#1662) * add copy to classifier * learning rate * ut update * package --- dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala | 9 +++++++-- dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala | 2 +- .../example/MLPipeline/DLEstimatorMultiLabelLR.scala | 5 ++++- .../analytics/bigdl/dllib/optim/DLClassifierSpec.scala | 7 +++++-- .../analytics/bigdl/dllib/optim/DLEstimatorSpec.scala | 6 +++++- 5 files changed, 22 insertions(+), 7 deletions(-) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala index 79c56e8b9af..b9fae464885 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala @@ -18,7 +18,8 @@ package org.apache.spark.ml import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.{Criterion, Module} -import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.util.{Identifiable, SchemaUtils} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types._ @@ -37,7 +38,7 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( override val model: Module[T], override val criterion : Criterion[T], override val featureSize : Array[Int], - override val uid: String = "DLClassifier" + override val uid: String = Identifiable.randomUID("dlClassifier") )(implicit ev: TensorNumeric[T]) extends DLEstimator[T](model, criterion, featureSize, Array(1)) { @@ -51,6 +52,10 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( validateSchema(schema) SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) } + + override def copy(extra: ParamMap): DLClassifier[T] = { + copyValues(new DLClassifier(model, criterion, featureSize), extra) + } } /** diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index 2c9cbd74c83..df341da9aa1 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -94,7 +94,7 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( * Default: 1.0 */ val learningRate = new DoubleParam(this, "learningRate", "learningRate", ParamValidators.gt(0)) - setDefault(learningRate -> 1.0) + setDefault(learningRate -> 1e-3) def getLearningRate: Double = $(learningRate) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala index 29bd55a6c8c..cb73a405c58 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.example.MLPipeline import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.optim.LBFGS +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble import com.intel.analytics.bigdl.utils.Engine import org.apache.spark.SparkContext import org.apache.spark.ml.DLEstimator @@ -38,6 +39,8 @@ object DLEstimatorMultiLabelLR { val model = Sequential().add(Linear(2, 2)) val criterion = MSECriterion() val estimator = new DLEstimator(model, criterion, Array(2), Array(2)) + .setOptimMethod(new LBFGS[Double]()) + .setLearningRate(1.0) .setBatchSize(4) .setMaxEpoch(10) val data = sc.parallelize(Seq( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala index 4e07f178d02..13959df00b8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala @@ -64,7 +64,7 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(estimator.getLabelCol == "label") assert(estimator.getMaxEpoch == 100) assert(estimator.getBatchSize == 1) - assert(estimator.getLearningRate == 1.0) + assert(estimator.getLearningRate == 1e-3) assert(estimator.getLearningRateDecay == 0) } @@ -72,6 +72,8 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = ClassNLLCriterion[Float]() val classifier = new DLClassifier[Float](model, criterion, Array(6)) + .setOptimMethod(new LBFGS[Float]()) + .setLearningRate(0.1) .setBatchSize(nRecords) .setMaxEpoch(maxEpoch) val data = sc.parallelize(smallData) @@ -138,7 +140,8 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { val criterion = ClassNLLCriterion[Float]() val estimator = new DLClassifier[Float](model, criterion, Array(6)) .setBatchSize(nRecords) - // intentionally set low since this only validates data format compatibitliy + .setOptimMethod(new LBFGS[Float]()) + .setLearningRate(0.1) .setMaxEpoch(maxEpoch) .setFeaturesCol("scaled") val pipeline = new Pipeline().setStages(Array(scaler, estimator)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala index c9b2963c153..3c539bb18b1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala @@ -63,7 +63,7 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(estimator.getLabelCol == "label") assert(estimator.getMaxEpoch == 100) assert(estimator.getBatchSize == 1) - assert(estimator.getLearningRate == 1.0) + assert(estimator.getLearningRate == 1e-3) assert(estimator.getLearningRateDecay == 0) } @@ -73,6 +73,8 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val criterion = ClassNLLCriterion[Float]() val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) .setBatchSize(nRecords) + .setOptimMethod(new LBFGS[Float]()) + .setLearningRate(0.1) .setMaxEpoch(maxEpoch) val data = sc.parallelize(smallData) val df = sqlContext.createDataFrame(data).toDF("features", "label") @@ -222,6 +224,8 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = ClassNLLCriterion[Float]() val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setOptimMethod(new LBFGS[Float]()) + .setLearningRate(0.1) .setBatchSize(nRecords) .setMaxEpoch(maxEpoch) .setFeaturesCol("scaled") From 08d1d4c9a9f9308a56a3cd7136f99a529787414c Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 16 Oct 2017 11:28:35 +0800 Subject: [PATCH 0457/1065] move model freeze to AbstractModule (#1647) * move model freeze to container * update * update freeze API * refactor freeze API * meet code review and add unit test * fix doc * meet code review --- .../analytics/bigdl/dllib/nn/Container.scala | 28 ++++++++ .../analytics/bigdl/dllib/nn/Graph.scala | 16 ----- .../dllib/nn/abstractnn/AbstractModule.scala | 56 ++++++++++++--- .../dllib/utils/python/api/PythonBigDL.scala | 24 +++---- .../bigdl/dllib/nn/AbstractModuleSpec.scala | 71 +++++++++++++++++++ .../analytics/bigdl/dllib/nn/GraphSpec.scala | 2 +- 6 files changed, 156 insertions(+), 41 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index 1d8c3e9aeb0..d41adcbde56 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -182,6 +182,34 @@ abstract class Container[A <: Activity : ClassTag, this } + override def freeze(names: String*): this.type = { + if (names.isEmpty) { + modules.foreach(_.freeze()) + } else { + names.foreach(name => { + this (name) match { + case Some(x) => x.freeze() + case _ => throw new Exception(s"cannot match module named $name") + } + }) + } + this + } + + override def unFreeze(names: String*): this.type = { + if (names.isEmpty) { + modules.foreach(_.unFreeze()) + } else { + names.foreach(name => { + this (name) match { + case Some(x) => x.unFreeze() + case _ => throw new Exception(s"cannot match module named $name") + } + }) + } + this + } + override def apply(name : String): Option[AbstractModule[Activity, Activity, T]] = { if (this.getName() == name) { Some(this) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index f0da9c41706..1ab6b8c8628 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -426,22 +426,6 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } } - /** - * set an array of layers that match the given ```names``` to be "freezed", - * i.e. their parameters(weight/bias, if exists) are not changed in training process - * @param names an array of layer names - * @return current graph model - */ - def freeze(names: Array[String]): this.type = { - names.foreach(name => { - val layer = this (name) - require(layer.isDefined, s"cannot find layer match ${name}") - layer.get.setScaleW(0) - layer.get.setScaleB(0) - }) - this - } - private var stopGradientLayers: util.HashSet[String] = _ /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 5b09a49911c..a39dc4c2913 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -212,23 +212,59 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, backwardTime = 0 } + private var scaleWCache: Double = scaleW + private var scaleBCache: Double = scaleB + /** - * freeze the layer, its parameters(weight/bias, if exists) - * are not changed in training process - */ - def freeze(): this.type = { - setScaleW(0) - setScaleB(0) + * freeze the module, + * i.e. their parameters(weight/bias, if exists) are not changed in training process + * if names is not empty, + * set an array of layers that match the given ```names``` to be "freezed", + * + * @param names an array of layer names + * @return current graph model + */ + def freeze(names: String*): this.type = { + if (names.isEmpty) { + // in case when freeze is called many times + if (scaleW != 0) { + scaleWCache = scaleW + scaleW = 0 + } + if (scaleB != 0) { + scaleBCache = scaleB + scaleB = 0 + } + } else { + names.foreach(name => { + this (name) match { + case Some(x) => x.freeze() + case _ => throw new Exception(s"cannot match module named $name") + } + }) + } this } /** - * "unfreeze" layer, i.e. make the layer parameters(weight/bias, if exists) + * "unfreeze" module, i.e. make the module parameters(weight/bias, if exists) * to be trained(updated) in training process + * if names is not empty, unfreeze layers that match given names + * + * @param names array of module names to unFreeze */ - def unFreeze(): this.type = { - setScaleW(1) - setScaleB(1) + def unFreeze(names: String*): this.type = { + if (names.isEmpty) { + scaleW = scaleWCache + scaleB = scaleBCache + } else { + names.foreach(name => { + this (name) match { + case Some(x) => x.unFreeze() + case _ => throw new Exception(s"cannot match module named $name") + } + }) + } this } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c22ca9f16a8..d38e83927a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -21,7 +21,7 @@ import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, M import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{Identity => DIdentity, Sample => JSample, _} import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, _} import com.intel.analytics.bigdl.numeric._ import com.intel.analytics.bigdl.optim.{Optimizer, _} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} @@ -1952,22 +1952,18 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab rec.setHiddenState(jTensorsToActivity(hiddenStates, isTable)) } - def setLayerFreeze(model: AbstractModule[Activity, Activity, T]) + def freeze(model: AbstractModule[Activity, Activity, T], freezeLayers: JList[String]) : AbstractModule[Activity, Activity, T] = { - model.freeze() + if (null == freezeLayers) model.freeze() else model.freeze(freezeLayers.asScala: _*) } - def setLayerUnFreeze(model: AbstractModule[Activity, Activity, T]) - : AbstractModule[Activity, Activity, T] = { - model.unFreeze() - } - - def setFreeze(model: Graph[T], freezeLayers: JList[String]): Graph[T] = { - model.freeze(freezeLayers.asScala.toArray) - } - - def unFreeze(model: Graph[T]): Graph[T] = { - model.unFreeze() + def unFreeze(model: AbstractModule[Activity, Activity, T], + names: JList[String]): AbstractModule[Activity, Activity, T] = { + if (names == null) { + model.unFreeze() + } else { + model.unFreeze(names.asScala: _*) + } } def setStopGradient(model: Graph[T], layers: JList[String]): Graph[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala index bf330adcd37..9200edfa38c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.{T, Table} class AbstractModuleSpec extends FlatSpec with Matchers { "Get name" should "find the module if it exists" in { @@ -205,4 +206,74 @@ class AbstractModuleSpec extends FlatSpec with Matchers { module.parameters()._1 should be(module2.parameters()._1) } + + "freeze and unfreeze" should "work properly" in { + def inceptionLayerV1Seq(inputSize: Int, config: Table) : Module[Float] = { + val concat = Concat(2) + val conv1 = Sequential() + conv1.add(SpatialConvolution(inputSize, config[Table](1)(1), 1, 1, 1, 1) + .setName("conv1x1").setScaleW(2).setScaleB(1)) + conv1.add(ReLU(true)) + concat.add(conv1) + val conv3 = Sequential() + conv3.add(SpatialConvolution(inputSize, config[Table](2)(1), 1, 1, 1, 1) + .setName("conv3x3_1").setScaleW(3).setScaleB(1.5)) + conv3.add(ReLU(true)) + conv3.add(SpatialConvolution(config[Table](2)(1), config[Table](2)(2), 3, 3, 1, 1, 1, 1) + .setName("conv3x3_2").setScaleW(4).setScaleB(2)) + conv3.add(ReLU(true)) + concat.add(conv3) + val conv5 = Sequential() + conv5.add(SpatialConvolution(inputSize, config[Table](3)(1), 1, 1, 1, 1) + .setName("conv5x5_1").setScaleW(5).setScaleB(2.5)) + conv5.add(ReLU(true)) + conv5.add(SpatialConvolution(config[Table](3)(1), config[Table](3)(2), 5, 5, 1, 1, 2, 2) + .setName("conv5x5_2").setScaleW(6).setScaleB(3)) + conv5.add(ReLU(true)) + concat.add(conv5) + val pool = Sequential() + pool.add(SpatialMaxPooling(3, 3, 1, 1, 1, 1).ceil() + .setName("pool")) + pool.add(SpatialConvolution(inputSize, config[Table](4)(1), 1, 1, 1, 1).setName("pool_conv") + .setScaleW(7).setScaleB(3.5)) + pool.add(ReLU(true)) + concat.add(pool) + concat + } + + val model = inceptionLayerV1Seq( + 2, T(T(4), T(96, 128), T(16, 32), T(32))) + model.freeze() + Utils.getNamedModules(model).foreach(x => { + if (!x._2.isInstanceOf[Container[_, _, _]]) { + x._2.getScaleB() should be (0) + x._2.getScaleW() should be (0) + } + }) + model.unFreeze() + model("conv1x1").get.getScaleW() should be(2) + model("conv1x1").get.getScaleB() should be(1) + model("conv3x3_1").get.getScaleW() should be(3) + model("conv3x3_1").get.getScaleB() should be(1.5) + model("conv3x3_2").get.getScaleW() should be(4) + model("conv3x3_2").get.getScaleB() should be(2) + model("conv5x5_1").get.getScaleW() should be(5) + model("conv5x5_1").get.getScaleB() should be(2.5) + model("conv5x5_2").get.getScaleW() should be(6) + model("conv5x5_2").get.getScaleB() should be(3) + model("pool_conv").get.getScaleW() should be(7) + model("pool_conv").get.getScaleB() should be(3.5) + + model.freeze("conv1x1", "conv3x3_1") + model("conv1x1").get.getScaleW() should be(0) + model("conv1x1").get.getScaleB() should be(0) + model("conv3x3_1").get.getScaleW() should be(0) + model("conv3x3_1").get.getScaleB() should be(0) + + model.unFreeze() + model("conv1x1").get.getScaleW() should be(2) + model("conv1x1").get.getScaleB() should be(1) + model("conv3x3_1").get.getScaleW() should be(3) + model("conv3x3_1").get.getScaleB() should be(1.5) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index b0c433b7ab6..69037da7365 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -1061,7 +1061,7 @@ class GraphSpec extends FlatSpec with Matchers { fc1.element.getParameters()._1.apply1(_ => 1.0f) fc2.element.getParameters()._1.apply1(_ => 2.0f) model.zeroGradParameters() - model.freeze(Array("fc2")) + model.freeze("fc2") println("output2: \n", model.forward(input)) model.backward(input, gradOutput) model.updateParameters(1) From d858a800bf05589b85eba423ac1472f58e32ccdf Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Mon, 16 Oct 2017 17:29:30 +0800 Subject: [PATCH 0458/1065] Add BroadcastGradientArgs (#1658) * finish BroadcastGradientArgs * remove breeze * pass the tests * refine the function * fix scala style * while instead of for --- .../dllib/nn/ops/BroadcastGradientArgs.scala | 109 ++++++++++++++++++ .../nn/ops/BroadcastGradientArgsSpec.scala | 50 ++++++++ 2 files changed, 159 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgs.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgs.scala new file mode 100644 index 00000000000..dd788be69a0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgs.scala @@ -0,0 +1,109 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Table + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +/** + * Given shapes of two tensors, computes the reduction indices for the + * gradient computation. + * + * @tparam T Numeric type. Only support float/double now + */ +class BroadcastGradientArgs[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T] { + + override def updateOutput(input: Table): Table = { + val input1 = input[Tensor[Int]](1) + val input2 = input[Tensor[Int]](2) + + val output1 = Tensor[Int]() + val output2 = Tensor[Int]() + + output.insert(output1).insert(output2) + + // Reverse the shape of x and y for convenience. + // After the reverse, 0-th is the inner-most dimension. + val rx = + if (input1.storage() == null) Array[Int]().toBuffer + else input1.storage().array().reverse.toBuffer + val ry = + if (input2.storage() == null) Array[Int]().toBuffer + else input2.storage().array().reverse.toBuffer + + if (rx.length < ry.length) { + while (rx.length < ry.length) { + rx.append(1) + } + } else { + while (rx.length > ry.length) { + ry.append(1) + } + } + + val xReducedIndexBuffer = new ArrayBuffer[Int]() + val yReducedIndexBuffer = new ArrayBuffer[Int]() + + val n = rx.length + + var i = 0 + while (i < n) { + val xi = rx(i) + val yi = ry(i) + + if (xi == yi) { + if (xi == 1) { + xReducedIndexBuffer.append(n - 1 - i) + yReducedIndexBuffer.append(n - 1 - i) + } + } else if (xi == 1) { + xReducedIndexBuffer.append(n - 1 - i) + } else if (yi == 1) { + yReducedIndexBuffer.append(n - 1 - i) + } else { + return output + } + i += 1 + } + + if (xReducedIndexBuffer.isEmpty) { + input(1) = Tensor[Int]() + } else { + output1.resize(Array(xReducedIndexBuffer.length)) + .set(Tensor[Int](Storage(xReducedIndexBuffer.reverse.toArray))) + } + + if (yReducedIndexBuffer.isEmpty) { + input(2) = Tensor[Int]() + } else { + output2.resize(Array(yReducedIndexBuffer.length)) + .set(Tensor[Int](Storage(yReducedIndexBuffer.reverse.toArray))) + } + + output + } +} + +object BroadcastGradientArgs { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new BroadcastGradientArgs()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala new file mode 100644 index 00000000000..0604a378885 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class BroadcastGradientArgsSpec extends FlatSpec with Matchers { + "BroadcastGradientArgs operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericInt + val input = + T( + Tensor(T(1, 2, 3)), + Tensor(T(2, 2, 1)) + ) + + val expectOutput = T(Tensor(T(0)), Tensor(T(2))) + + val output = BroadcastGradientArgs().forward(input) + output should be(expectOutput) + } + + "BroadcastGradientArgs operation empty" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericInt + val input = + T( + Tensor(T(1, 2, 3)), + Tensor() + ) + + val expectOutput = T(Tensor(T(0)), Tensor(T(0, 1, 2))) + + val output = BroadcastGradientArgs().forward(input) + output should be(expectOutput) + } +} From c58f4ab675399288ad804ac4fd3cd81ed3a8b495 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 18 Oct 2017 09:56:11 +0800 Subject: [PATCH 0459/1065] Fix duplicated msg proto (#1659) * fix protobuf duplicate msgs * fix test cases --- .../bigdl/dllib/nn/RecurrentDecoder.scala | 5 +- .../utils/serializer/DataConverter.scala | 27 ++++-- .../dllib/utils/serializer/ModuleLoader.scala | 64 +++++++++++-- .../utils/serializer/ModuleSerializable.scala | 5 +- .../utils/serializer/DataConverterSpec.scala | 90 ++++++++++++------- .../serializer/TensorConversionSpec.scala | 66 +++++++++++++- 6 files changed, 206 insertions(+), 51 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala index 6c2515c5103..52933f5a6dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala @@ -257,8 +257,7 @@ object RecurrentDecoder extends ContainerSerializable { override def serializeModule[T: ClassTag](context: SerializeContext[T]) (implicit ev: TensorNumeric[T]) : SerializeResult = { - val containerBuilder = BigDLModule. - newBuilder(super.serializeModule(context).bigDLModule) + val containerBuilder = (super.serializeModule(context).bigDLModule) val recurrentDecoder = context.moduleData.module.asInstanceOf[RecurrentDecoder[T]] @@ -274,6 +273,6 @@ object RecurrentDecoder extends ContainerSerializable { ModuleSerializer.tensorModuleType) containerBuilder.putAttr("preTopology", topologyBuilder.build) - SerializeResult(containerBuilder.build, context.storages) + SerializeResult(containerBuilder, context.storages) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index 09229088187..af52953fc70 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -417,7 +417,7 @@ object DataConverter extends DataConverter{ if (storageType == ProtoStorageType) { if (storages.contains(storageId)) { val storage = storages(storageId).asInstanceOf[TensorStorage] - tensorBuilder.setStorage(storage) + tensorBuilder.setStorage(resetStorage(storage)) // we should set back the datatype from existed storage tensorBuilder.setDatatype(storage.getDatatype) } else { @@ -495,7 +495,7 @@ object DataConverter extends DataConverter{ } storageBuilder.setId(storageId) val storage = storageBuilder.build - tensorBuilder.setStorage(storage) + tensorBuilder.setStorage(resetStorage(storage)) storages(storageId) = storage } } else { @@ -514,8 +514,8 @@ object DataConverter extends DataConverter{ val storages = context.storages // Check if tensor has been shared if (storages.contains(tensorId)) { - attributeBuilder.setTensorValue(storages.get(tensorId).get - .asInstanceOf[BigDLTensor]) + attributeBuilder.setTensorValue(resetTensor(storages.get(tensorId).get + .asInstanceOf[BigDLTensor])) } else { val totalElement = tensor.nElement() val dimension = tensor.dim() @@ -536,11 +536,28 @@ object DataConverter extends DataConverter{ tensor.stride().foreach(stride => tensorBuilder.addStride(stride)) setStorage(context, tensorBuilder, tensor) val tensorBuild = tensorBuilder.build - attributeBuilder.setTensorValue(tensorBuild) + attributeBuilder.setTensorValue(resetTensor(tensorBuild)) storages(tensorId) = tensorBuild } } } + + + private def resetStorage(originStorage : TensorStorage) : TensorStorage = { + val storageBuilder = TensorStorage.newBuilder + storageBuilder.setDatatype(originStorage.getDatatype) + storageBuilder.setId(originStorage.getId) + storageBuilder.build + } + + private def resetTensor(originTensor: BigDLTensor) : BigDLTensor = { + val tensorBuilder = BigDLTensor.newBuilder(originTensor) + tensorBuilder.clearStorage + tensorBuilder.setDatatype(originTensor.getDatatype) + tensorBuilder.setId(originTensor.getId) + tensorBuilder.setStorage(resetStorage(originTensor.getStorage)) + tensorBuilder.build + } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala index ed4768aa6dd..c9060772976 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala @@ -21,10 +21,11 @@ import scala.collection.JavaConverters._ import com.google.protobuf.CodedInputStream import com.intel.analytics.bigdl.nn.Container import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{DenseType, QuantizedTensor, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.DataConverter.TensorConverter import com.intel.analytics.bigdl.utils.{File, Table} -import serialization.Bigdl.{BigDLModule, DataType, TensorStorage} +import serialization.Bigdl._ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -48,10 +49,32 @@ object ModuleLoader { modelBuilder.mergeFrom(cis) val bigDLModel = modelBuilder.build() val storages = new mutable.HashMap[Int, Any]() - // loadAllStorages(bigDLModel, storages) + val deserializationContext = DeserializeContext(bigDLModel, storages, ProtoStorageType) + initTensorStorage(deserializationContext) ModuleSerializer.load(DeserializeContext(bigDLModel, storages, ProtoStorageType)).module } + private def initTensorStorage[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]): Unit = { + val attrMap = context.bigdlModule.getAttrMap + + val storagesMap = attrMap.get("global_storage").getNameAttrListValue.getAttrMap + + storagesMap.asScala.foreach(map => { + val storages = context.storages + val tensorId = map._1.toInt + val tensorValue = map._2.getTensorValue + val storageId = tensorValue.getStorage.getId + val tensor = TensorConverter.getAttributeValue(context, map._2).asInstanceOf[Tensor[_]] + val tensorStorage = tensorValue.getTensorType match { + case TensorType.DENSE => tensor.storage() + case TensorType.QUANT => tensor.asInstanceOf[QuantizedTensor[_]].getStorage + } + storages(tensorId) = tensor + storages(storageId) = tensorStorage + }) + } + /** * Load weights from `modulePath` and copy to pre-defined module * for `layers` layers, copy all if not specified @@ -149,8 +172,34 @@ object ModulePersister { , new ArrayBuffer[String](), new ArrayBuffer[String]()) val storages = new mutable.HashMap[Int, Any]() val context = SerializeContext(bigDLModule, storages, ProtoStorageType) - val bigDLModel = ModuleSerializer.serialize(context).bigDLModule - File.saveBytes(bigDLModel.toByteArray, modelPath, overwrite) + val serializeResult = ModuleSerializer.serialize(context) + setTensorStorage(serializeResult.bigDLModule, serializeResult.storages) + File.saveBytes(serializeResult.bigDLModule.build.toByteArray, modelPath, overwrite) + } + + private def setTensorStorage(bigDLModule: BigDLModule.Builder, + storages: mutable.HashMap[Int, Any]) : Unit = { + val storageIds = new mutable.HashSet[Int] + val tensorStorages = storages.filter(_._2.isInstanceOf[TensorStorage]) + var nameAttributes = NameAttrList.newBuilder().setName("global_storage") + storages.values.filter(_.isInstanceOf[BigDLTensor]).foreach(storage => { + val bigdlTensor = storage.asInstanceOf[BigDLTensor] + val storageId = bigdlTensor.getStorage.getId + if (!storageIds.contains(storageId)) { + val tensorBuilder = BigDLTensor.newBuilder(bigdlTensor) + tensorBuilder.clearStorage() + require(tensorStorages.contains(storageId), s"${storageId} does not exist") + tensorBuilder.setStorage(tensorStorages.get(storageId). + get.asInstanceOf[TensorStorage]) + val attrValueBuilder = AttrValue.newBuilder + attrValueBuilder.setTensorValue(tensorBuilder.build) + nameAttributes.putAttr(tensorBuilder.getId.toString, attrValueBuilder.build) + storageIds.add(storageId) + } + }) + val attrValueBuilder = AttrValue.newBuilder + attrValueBuilder.setNameAttrListValue(nameAttributes) + bigDLModule.putAttr("global_storage", attrValueBuilder.build) } /** @@ -168,9 +217,8 @@ object ModulePersister { val storages = new mutable.HashMap[Int, Any]() val context = SerializeContext(bigDLModule, storages, ProtoStorageType) val bigDLModel = ModuleSerializer.serialize(context).bigDLModule - val bigDLModelWithoutWeightsAndBias = BigDLModule.newBuilder(bigDLModel) - cleantWeightAndBias(bigDLModelWithoutWeightsAndBias) - val model = bigDLModelWithoutWeightsAndBias.build + cleantWeightAndBias(bigDLModel) + val model = bigDLModel.build val byteArrayOut = new ByteArrayOutputStream() byteArrayOut.write(model.toString.getBytes) File.saveBytes(byteArrayOut.toByteArray, definitionPath, overwrite) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index ab84e8e7ce6..74a42ab3727 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -180,6 +180,7 @@ trait ModuleSerializable extends Loadable with Savable{ field.setAccessible(true) val fieldValue = field.get(module) DataConverter.setAttributeValue(context, attrBuilder, fieldValue, ptype) + bigDLModelBuilder.putAttr(paramName, attrBuilder.build) }) } @@ -219,7 +220,7 @@ trait ModuleSerializable extends Loadable with Savable{ modelBuilder.setTrain(module.module.isTraining()) modelBuilder.setId(System.identityHashCode(module.module)) copyFromBigDL(context, modelBuilder) - SerializeResult(modelBuilder.build, context.storages) + SerializeResult(modelBuilder, context.storages) } /** @@ -331,7 +332,7 @@ case class DeserializeContext(bigdlModule : BigDLModule, storages: mutable.HashMap[Int, Any], storageType: StorageType) -case class SerializeResult(bigDLModule: BigDLModule, storages: mutable.HashMap[Int, Any]) +case class SerializeResult(bigDLModule: BigDLModule.Builder, storages: mutable.HashMap[Int, Any]) case class ModuleData[T: ClassTag](module : AbstractModule[Activity, Activity, T], pre : Seq[String], next : Seq[String]) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index 893a82542f2..6e163c054a4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -22,13 +22,13 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.{NCHW, NHWC} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} import com.intel.analytics.bigdl.nn.quantized.{LinearWeight, LinearWeightParams} import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer, Regularizer} -import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Tensor, Storage} +import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Storage, Tensor} import org.scalatest.{FlatSpec, Matchers} -import serialization.Bigdl.AttrValue +import serialization.Bigdl.{AttrValue, BigDLTensor, DataType, TensorStorage} import scala.reflect.runtime.universe import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat -import serialization.Bigdl.DataType +import serialization.Bigdl.AttrValue.ArrayValue import scala.collection.mutable import scala.util.Random @@ -177,6 +177,11 @@ class DataConverterSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder, tensor, ModuleSerializer.tensorType) + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(tensor)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(tensor.storage.array())).get.asInstanceOf[TensorStorage]) + attriBulder.setTensorValue(tensorBuilder.build) val attr = attriBulder.build map.clear() val retrievedValue = DataConverter. @@ -210,12 +215,23 @@ class DataConverterSpec extends FlatSpec with Matchers{ DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder1, tensor1, ModuleSerializer.tensorType) + val tensorBuilder1 = BigDLTensor.newBuilder(map.get(System. + identityHashCode(tensor1)).get.asInstanceOf[BigDLTensor]) + tensorBuilder1.setStorage(map.get(System. + identityHashCode(tensor1.storage.array())).get.asInstanceOf[TensorStorage]) + attriBulder1.setTensorValue(tensorBuilder1.build) val attr1 = attriBulder1.build + val attriBulder2 = AttrValue.newBuilder DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder2, tensor2, ModuleSerializer.tensorType) + val tensorBuilder2 = BigDLTensor.newBuilder(map.get(System. + identityHashCode(tensor2)).get.asInstanceOf[BigDLTensor]) + tensorBuilder2.setStorage(map.get(System. + identityHashCode(tensor2.storage.array())).get.asInstanceOf[TensorStorage]) + attriBulder2.setTensorValue(tensorBuilder2.build) val attr2 = attriBulder2.build map.clear() @@ -377,24 +393,18 @@ class DataConverterSpec extends FlatSpec with Matchers{ } - "Module Conversion " should " work properly" in { + "Module Conversion" should "work properly" in { val linear = Linear(5, 5).setName("linear") - val attriBulder = AttrValue.newBuilder val moduleData = ModuleData(linear, Seq(), Seq()) map.clear() - DataConverter.setAttributeValue(SerializeContext(moduleData, map, ProtoStorageType), - attriBulder, linear, ModuleSerializer.abstractModuleType) - val attr = attriBulder.build + ModulePersister.saveToFile("/tmp/linear.bigdl", linear, true) map.clear() - val retrievedValue = DataConverter. - getAttributeValue(DeserializeContext(attr.getBigDLModuleValue - , map, ProtoStorageType), attr) - attr.getDataType should be (DataType.MODULE) + val retrievedValue = ModuleLoader.loadFromFile("/tmp/linear.bigdl") retrievedValue should be (linear) } - "Nullable Module Conversion " should " work properly" in { + "Nullable Module Conversion" should "work properly" in { val linear : TensorModule[Float] = null val attriBulder = AttrValue.newBuilder map.clear() @@ -553,6 +563,19 @@ class DataConverterSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder, tensorArray, universe.typeOf[Array[Tensor[Float]]]) + + attriBulder.clearArrayValue() + val arrayValue = ArrayValue.newBuilder + arrayValue.setDatatype(DataType.TENSOR) + tensorArray.foreach(t => { + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(t)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(t.storage.array())).get.asInstanceOf[TensorStorage]) + arrayValue.addTensor(tensorBuilder.build) + }) + arrayValue.setSize(2) + attriBulder.setArrayValue(arrayValue.build) val attr = attriBulder.build map.clear() val retrievedValue = DataConverter. @@ -605,22 +628,6 @@ class DataConverterSpec extends FlatSpec with Matchers{ retrievedValue should be (arry) } - "Array of Modules conversion" should " work properly" in { - val arry = new Array[AbstractModule[Activity, Activity, Float]](2) - arry(0) = Linear[Float](2, 3).setName("l1") - arry(1) = Linear[Float](2, 3).setName("l2") - val attriBulder = AttrValue.newBuilder - map.clear() - DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), - attriBulder, arry, - universe.typeOf[Array[AbstractModule[Activity, Activity, Float]]]) - val attr = attriBulder.build - map.clear() - val retrievedValue = DataConverter. - getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) - retrievedValue should be (arry) - } - "Null Array of Modules conversion" should " work properly" in { val arry : Array[AbstractModule[Activity, Activity, Float]] = null val attriBulder = AttrValue.newBuilder @@ -635,7 +642,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ retrievedValue should be (arry) } - "NameList conversion " should " work properly" in { + "NameList conversion" should "work properly" in { val map1 = new mutable.HashMap[String, mutable.Map[String, Any]] @@ -647,9 +654,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ attrsMap("double") = 4.0 attrsMap("string") = "str" attrsMap("bool") = true - attrsMap("tensor") = Tensor(2, 2).apply1(_ => Random.nextFloat()) attrsMap("dataformat") = NCHW - attrsMap("module") = Linear(3, 4).setName("linear") map1("test") = attrsMap @@ -680,6 +685,13 @@ class DataConverterSpec extends FlatSpec with Matchers{ val attriBulder = AttrValue.newBuilder DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder, tensor, ModuleSerializer.tensorType) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(tensor)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(tensor.getStorage)).get.asInstanceOf[TensorStorage]) + attriBulder.setTensorValue(tensorBuilder.build) + val attr = attriBulder.build map.clear() val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, @@ -708,6 +720,20 @@ class DataConverterSpec extends FlatSpec with Matchers{ val attriBulder = AttrValue.newBuilder DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder, array, universe.typeOf[Array[QuantizedTensor[Float]]]) + + attriBulder.clearArrayValue() + val arrayValue = ArrayValue.newBuilder + arrayValue.setDatatype(DataType.TENSOR) + array.foreach(t => { + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(t)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(t.getStorage)).get.asInstanceOf[TensorStorage]) + arrayValue.addTensor(tensorBuilder.build) + }) + arrayValue.setSize(2) + attriBulder.setArrayValue(arrayValue.build) + val attr = attriBulder.build map.clear() val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala index f3a3faebb03..b9e2d8c3e1e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.serializer import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} -import serialization.Bigdl.AttrValue +import serialization.Bigdl.{AttrValue, BigDLTensor, TensorStorage} import scala.collection.mutable import scala.reflect.runtime.universe @@ -39,6 +39,14 @@ class TensorConversionSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBuilder, byteString, universe.typeOf[Tensor[ByteString]]) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(byteString)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(byteString.storage.array())).get.asInstanceOf[TensorStorage]) + + attriBuilder.setTensorValue(tensorBuilder.build) + map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) @@ -57,6 +65,14 @@ class TensorConversionSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBuilder, chars, universe.typeOf[Tensor[Char]]) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(chars)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(chars.storage.array())).get.asInstanceOf[TensorStorage]) + + attriBuilder.setTensorValue(tensorBuilder.build) + map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) @@ -76,6 +92,14 @@ class TensorConversionSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBuilder, ints, universe.typeOf[Tensor[Int]]) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(ints)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(ints.storage.array())).get.asInstanceOf[TensorStorage]) + + attriBuilder.setTensorValue(tensorBuilder.build) + map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) @@ -94,6 +118,14 @@ class TensorConversionSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBuilder, longs, universe.typeOf[Tensor[Long]]) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(longs)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(longs.storage.array())).get.asInstanceOf[TensorStorage]) + + attriBuilder.setTensorValue(tensorBuilder.build) + map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) @@ -112,6 +144,14 @@ class TensorConversionSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBuilder, shorts, universe.typeOf[Tensor[Short]]) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(shorts)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(shorts.storage.array())).get.asInstanceOf[TensorStorage]) + + attriBuilder.setTensorValue(tensorBuilder.build) + map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) @@ -130,6 +170,14 @@ class TensorConversionSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBuilder, floats, universe.typeOf[Tensor[Float]]) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(floats)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(floats.storage.array())).get.asInstanceOf[TensorStorage]) + + attriBuilder.setTensorValue(tensorBuilder.build) + map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) @@ -148,6 +196,14 @@ class TensorConversionSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBuilder, doubles, universe.typeOf[Tensor[Double]]) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(doubles)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(doubles.storage.array())).get.asInstanceOf[TensorStorage]) + + attriBuilder.setTensorValue(tensorBuilder.build) + map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) @@ -166,6 +222,14 @@ class TensorConversionSpec extends FlatSpec with Matchers{ map.clear() DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBuilder, strings, universe.typeOf[Tensor[String]]) + + val tensorBuilder = BigDLTensor.newBuilder(map.get(System. + identityHashCode(strings)).get.asInstanceOf[BigDLTensor]) + tensorBuilder.setStorage(map.get(System. + identityHashCode(strings.storage.array())).get.asInstanceOf[TensorStorage]) + + attriBuilder.setTensorValue(tensorBuilder.build) + map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attriBuilder.build) From 4e8d6b602af569e937c2ab890cee9bb54f28efcf Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 18 Oct 2017 14:49:01 +0800 Subject: [PATCH 0460/1065] Sparse Sample&MiniBatch + python API (#1668) --- .../dllib/feature/dataset/MiniBatch.scala | 186 +++++++++++++++++- .../bigdl/dllib/feature/dataset/Sample.scala | 166 +++++++++++++++- .../dllib/feature/dataset/Transformer.scala | 9 +- .../bigdl/dllib/optim/Optimizer.scala | 21 ++ .../analytics/bigdl/dllib/tensor/Tensor.scala | 7 + .../dllib/utils/python/api/BigDLSerde.scala | 19 +- .../dllib/utils/python/api/PythonBigDL.scala | 49 +++-- .../bigdl/dllib/dataset/MiniBatchSpec.scala | 26 +++ 8 files changed, 454 insertions(+), 29 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala index 16894725c39..9e74786660c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.dataset import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} +import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} @@ -113,8 +113,8 @@ private[bigdl] class ArrayTensorMiniBatch[T: ClassTag]( featurePaddingParam: Option[PaddingParam[T]] = None, labelPaddingParam: Option[PaddingParam[T]] = None) extends MiniBatch[T]{ require(inputData.length > 0, "Input data in MiniBatch is empty.") - private var batchSize = 0 - private var unlabeled = false + protected var batchSize = 0 + protected var unlabeled = false val (featurePadding, featurePaddingStrategy) = if (featurePaddingParam.isDefined) { (featurePaddingParam.get.paddingTensor, featurePaddingParam.get.paddingStrategy) @@ -573,3 +573,183 @@ case class FixedLength(fixedLength: Array[Int]) extends PaddingStrategy { sizes } } + +/** + * SparseMiniBatch is a MiniBatch type for TensorSample. And SparseMiniBatch could + * deal with SparseTensors in TensorSample. + * + * @param inputData a set of input tensor + * @param targetData a set of target tensor + * @param ev$1 + * @param ev + * @tparam T Numeric type + */ +class SparseMiniBatch[T: ClassTag]( + inputData: Array[Tensor[T]], + targetData: Array[Tensor[T]])( + implicit ev: TensorNumeric[T]) extends ArrayTensorMiniBatch[T](inputData, targetData) { + private var input: Activity = null + private var target: Activity = null + + override def getInput(): Activity = { + if (null == input) { + require(!inputData.exists(_ == null), "SparseMiniBatch.getInput: " + + "data didn't fill in this miniBatch") + input = if (inputData.length == 1) { + inputData.head + } else { + T.array(inputData.map(_.asInstanceOf[Any])) + } + } + + input + } + + override def getTarget(): Activity = { + if (null == target && targetData.length != 0) { + require(!targetData.exists(_ == null), "SparseMiniBatch.getInput: " + + "data didn't fill in this miniBatch") + target = if (targetData.length == 1) { + targetData.head + } else { + T.array(targetData.map(_.asInstanceOf[Any])) + } + } + + target + } + + def init(features: Array[Tensor[T]], labels: Array[Tensor[T]]): Unit = { + var i = 0 + while (i < inputData.length) { + val featureI = features(i) + inputData(i) = if (featureI.getTensorType == SparseType) { + Tensor.sparse[T](Array(batchSize) ++ featureI.size()) + } else if (featureI.getTensorType == DenseType) { + Tensor[T](Array(batchSize) ++ featureI.size()) + } else { + throw new IllegalArgumentException(s"MiniBatchWithSparse: unsupported feature type " + + s"${featureI.getTensorType}") + } + i += 1 + } + i = 0 + while (i < targetData.length) { + val labelI = labels(i) + targetData(i) = if (labelI.getTensorType == SparseType) { + Tensor.sparse[T](Array(batchSize) ++ labelI.size()) + } else if (labelI.getTensorType == DenseType) { + Tensor[T](Array(batchSize) ++ labelI.size()) + } else { + throw new IllegalArgumentException(s"MiniBatchWithSparse: unsupported label type " + + s"${labelI.getTensorType}") + } + i += 1 + } + } + + override def set(samples: Seq[Sample[T]])(implicit ev: TensorNumeric[T]): this.type = { + require(samples.length > 0, "samples is empty") + require(samples(0).isInstanceOf[TensorSample[T]]) + val _samples = samples.map(_.asInstanceOf[TensorSample[T]]) + require(batchSize == 0 || samples.length <= batchSize, "setValue: samples's size doesn't " + + s"match mini batch size, excepted ${size()} got ${samples.length}") + val features = _samples.map(_.features) + val labels = _samples.map(_.labels) + if (batchSize == 0) { + batchSize = samples.length // set a batchSize when set data. + unlabeled = samples.head.numLabel() == 0 + init(features.head, labels.head) + } + + var i = 0 + while (i < inputData.length) { + SparseMiniBatch.batch(1, features.map(_.apply(i)), inputData(i)) + i += 1 + } + + if (!unlabeled) { + var j = 0 + while (j < targetData.length) { + SparseMiniBatch.batch(1, labels.map(_.apply(j)), targetData(j)) + j += 1 + } + } + + this + } +} + +object SparseMiniBatch{ + def apply[T: ClassTag]( + nInputs: Int, + nTargets: Int)(implicit ev: TensorNumeric[T]): MiniBatch[T] = { + new SparseMiniBatch[T](new Array[Tensor[T]](nInputs), new Array[Tensor[T]](nTargets)) + } + + /** + * Batch a seq of tensors to a big tensor. + * @param dim apply batch on which dimension + * @param tensors a seq of tensors + * @param res result tensor + * @param ev + * @tparam T + */ + private[bigdl] def batch[T: ClassTag]( + dim: Int, + tensors: Seq[Tensor[T]], + res: Tensor[T])(implicit ev: TensorNumeric[T]): Unit = { + if (res.getTensorType == SparseType) { + Tensor.sparseConcat(dim, tensors, res) + } else if (res.getTensorType == DenseType) { + denseBatch(dim, tensors, res) + } else { + throw new IllegalArgumentException(s"MiniBatchWithSparse: unsupported tensor type " + + s"${res.getTensorType}") + } + } + + private def denseBatch[T: ClassTag]( + dim: Int, + tensors: Seq[Tensor[T]], + result: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + val size = tensors.head.size() + var i = 1 + while (i < tensors.length) { + size(dim - 1) += tensors(i).size(dim) + i += 1 + } + + result.resize(size) + + i = 0 + var offset = 1 + while (i < tensors.length) { + val current = tensors(i) + val target = result.narrow(dim, offset, current.size(dim)) + + if (target.isContiguous() || dim > 2) { + // Copy directly when target is Contiguous or dimension is larger than 2 + // in which case the contiguous region in target tensor is fairly small in practice + target.copy(current) + } else { + // Divide target into contiguous frames when target isn't contiguous + var f = 1 + while (f <= target.size(1)) { + val curFrame = target.select(1, f) + val outputFrame = current.select(1, f) + require(curFrame.isContiguous()) + require(outputFrame.isContiguous()) + curFrame.copy(outputFrame) + f += 1 + } + } + + offset += current.size(dim) + i += 1 + } + result + + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index c6042fbfda1..287ee20b775 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -18,8 +18,9 @@ package com.intel.analytics.bigdl.dataset import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{DenseType, SparseType, Storage, Tensor} import org.apache.commons.lang3.SerializationUtils +import org.apache.zookeeper.KeeperException.UnimplementedException import scala.reflect.ClassTag @@ -100,7 +101,9 @@ abstract class Sample[T: ClassTag] extends Serializable { featureData: Array[T], labelData: Array[T], featureSize: Array[Int], - labelSize: Array[Int])(implicit ev: TensorNumeric[T]): Sample[T] + labelSize: Array[Int])(implicit ev: TensorNumeric[T]): Sample[T] = { + throw new UnsupportedOperationException("Sample.set(): unimplemented deprecated method") + } /** * Get feature sizes @@ -223,11 +226,11 @@ private[bigdl] class ArraySample[T: ClassTag]( } } -object Sample { +object ArraySample { def apply[T: ClassTag]( - data: Array[T], - featureSize: Array[Array[Int]], - labelSize: Array[Array[Int]]): Sample[T] = { + data: Array[T], + featureSize: Array[Array[Int]], + labelSize: Array[Array[Int]]): Sample[T] = { new ArraySample(data, featureSize, labelSize) } @@ -291,8 +294,8 @@ object Sample { } private def copy[T: ClassTag]( - data: Array[T], - tensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Array[T] = { + data: Array[T], + tensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Array[T] = { var offset = 0 var i = 0 while (i < tensors.length) { @@ -324,3 +327,150 @@ object Sample { true } } + +object Sample { + def apply[T: ClassTag]( + data: Array[T], + featureSize: Array[Array[Int]], + labelSize: Array[Array[Int]]): Sample[T] = { + ArraySample(data, featureSize, labelSize) + } + + def apply[T: ClassTag]( + featureTensor: Tensor[T], + labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + require(featureTensor.isContiguous(), "featureTensor is not contiguous") + require(labelTensor.isContiguous(), "labelTensor is not contiguous") + if (featureTensor.getTensorType == DenseType) { + ArraySample(featureTensor, labelTensor) + } else { + TensorSample(featureTensor, labelTensor) + } + } + + def apply[T: ClassTag]( + featureTensor: Tensor[T], + label: T)(implicit ev: TensorNumeric[T]) : Sample[T] = { + require(featureTensor.isContiguous(), "featureTensor is not contiguous") + if (featureTensor.getTensorType == DenseType) { + ArraySample(featureTensor, label) + } else { + TensorSample(featureTensor, label) + } + } + + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]], + labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + if (featureTensors.exists(_.getTensorType == SparseType)) { + TensorSample(featureTensors, labelTensor) + } else { + ArraySample(featureTensors, labelTensor) + } + } + + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]], + labelTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + if (featureTensors.exists(_.getTensorType == SparseType)) { + TensorSample(featureTensors, labelTensors) + } else { + ArraySample(featureTensors, labelTensors) + } + } + + def apply[T: ClassTag]( + featureTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + require(featureTensor.isContiguous(), "featureTensor is not contiguous") + if (featureTensor.getTensorType == SparseType) { + TensorSample(featureTensor) + } else { + ArraySample(featureTensor) + } + } + + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + if (featureTensors.exists(_.getTensorType == SparseType)) { + TensorSample(featureTensors) + } else { + ArraySample(featureTensors) + } + } +} + +/** + * A kind of Sample who hold both DenseTensor and SparseTensor as features. + * @param features feature tensors + * @param labels label tensors + * @tparam T numeric type + */ +private[bigdl] class TensorSample[T: ClassTag]( + val features: Array[Tensor[T]], + val labels: Array[Tensor[T]]) extends Sample[T] { + val featureSize = features.map(_.size()) + val labelSize = features.map(_.size()) + + def featureLength(index: Int): Int = { + features(0).size(1) + } + + def labelLength(index: Int): Int = { + labels(0).size(1) + } + + def numFeature(): Int = { + features.length + } + + def numLabel(): Int = { + labels.length + } + + def getFeatureSize(): Array[Array[Int]] = { + featureSize + } + + def getLabelSize(): Array[Array[Int]] = { + labelSize + } + + def getData(): Array[T] = { + throw new UnimplementedException() + } +} + +object TensorSample { + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + new TensorSample[T](featureTensors, Array()) + } + + def apply[T: ClassTag]( + featureTensors: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + new TensorSample[T](Array(featureTensors), Array()) + } + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]], + labelTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + new TensorSample[T](featureTensors, labelTensors) + } + + def apply[T: ClassTag]( + featureTensors: Array[Tensor[T]], + labelTensors: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + new TensorSample[T](featureTensors, Array(labelTensors)) + } + + def apply[T: ClassTag]( + featureTensors: Tensor[T], + labelTensors: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + new TensorSample[T](Array(featureTensors), Array(labelTensors)) + } + + def apply[T: ClassTag]( + featureTensors: Tensor[T], + label: T)(implicit ev: TensorNumeric[T]) : Sample[T] = { + new TensorSample[T](Array(featureTensors), Array(Tensor(1).fill(label))) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala index a4f4ab8eb36..28dda397652 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala @@ -334,8 +334,13 @@ class SampleToMiniBatch[T: ClassTag] private[bigdl]( i += 1 } if (null == miniBatchBuffer) { - miniBatchBuffer = MiniBatch(sampleData(0).numFeature(), sampleData(0).numLabel(), - featurePaddingParam, labelPaddingParam) + val firstSample = sampleData(0) + miniBatchBuffer = if (firstSample.isInstanceOf[TensorSample[T]]) { + SparseMiniBatch(firstSample.numFeature(), firstSample.numLabel()) + } else { + MiniBatch(firstSample.numFeature(), firstSample.numLabel(), + featurePaddingParam, labelPaddingParam) + } } if (i < batchSize) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index c8315d64ca4..a84fa7a9dc7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -154,6 +154,27 @@ abstract class Optimizer[T: ClassTag, D]( this } + /** + * Set validate evaluation + * @param trigger how often to evaluation validation set + * @param sampleRDD validate data set in type of [[RDD]] of [[Sample]] + * @param vMethods a set of validation method [[ValidationMethod]] + * @param batchSize batch size + * @param miniBatch construct MiniBatch with a specified miniBatch type + * @return + */ + def setValidation(trigger: Trigger, sampleRDD: RDD[Sample[T]], + vMethods : Array[ValidationMethod[T]], batchSize: Int, miniBatch: MiniBatch[T]) + : this.type = { + this.validationTrigger = Some(trigger) + val dataSet = + (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(miniBatch, batchSize, None)) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + this.validationDataSet = Some(dataSet) + this.validationMethods = Some(vMethods) + this + } + /** * Set a check point saved at `path` triggered by `trigger` * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index e74572013de..b59dc8d5312 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -1267,4 +1267,11 @@ object Tensor { } SparseTensor.concat(dim, seqTensors, res) } + + private[bigdl] def sparseConcat[T: ClassTag]( + dim: Int, + tensors: Seq[Tensor[T]], + res: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + SparseTensor.concat(dim, tensors, res) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala index 1e8979df5a2..2e5c2740f11 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala @@ -217,7 +217,7 @@ object BigDLSerDe extends BigDLSerDeBase with Serializable { if (args.length != 3) { throw new PickleException("should be 3, not : " + args.length) } - new Sample(args(0).asInstanceOf[JList[JTensor]], + Sample(args(0).asInstanceOf[JList[JTensor]], args(1).asInstanceOf[JTensor], args(2).asInstanceOf[String]) } @@ -255,13 +255,22 @@ object BigDLSerDe extends BigDLSerDeBase with Serializable { def construct(args: Array[Object]): Object = { - if (args.length != 3) { - throw new PickleException("should be 3, not : " + args.length) + if (args.length != 3 && args.length != 4) { + throw new PickleException("should be 3 or 4, not : " + args.length) } - val bigdl_type = args(2).asInstanceOf[String] val storage = objToFloatArray(args(0)) val shape = objToInt32Array(args(1)) - val result = new JTensor(storage, shape, bigdl_type) + val bigdl_type = args(2).asInstanceOf[String] + val result = if (args.length == 3) { + JTensor(storage, shape, bigdl_type) + } else { + val nElement = storage.length + val indicesArray = objToInt32Array(args(3)) + val indices = Array.range(0, shape.length).map(i => + indicesArray.slice(i * nElement, (i + 1) * nElement) + ) + JTensor(storage, shape, bigdl_type, indices) + } result } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index d38e83927a5..aa352221bea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, _} import com.intel.analytics.bigdl.numeric._ import com.intel.analytics.bigdl.optim.{Optimizer, _} -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{DenseType, SparseType, Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Table, _} import com.intel.analytics.bigdl.visualization.{Summary, TrainSummary, ValidationSummary} @@ -59,7 +59,8 @@ case class Sample(features: JList[JTensor], label: JTensor, bigdlType: String) -case class JTensor(storage: Array[Float], shape: Array[Int], bigdlType: String) +case class JTensor(storage: Array[Float], shape: Array[Int], + bigdlType: String, indices: Array[Array[Int]] = null) /** * [[ValidationResult]] for python @@ -123,9 +124,18 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab this.typeName match { case "float" => - Tensor(jTensor.storage.map(x => ev.fromType(x.toFloat)), jTensor.shape) + if (null == jTensor.indices) { + Tensor(jTensor.storage.map(x => ev.fromType(x)), jTensor.shape) + } else { + Tensor.sparse(jTensor.indices, jTensor.storage.map(x => ev.fromType(x)), jTensor.shape) + } case "double" => - Tensor(jTensor.storage.map(x => ev.fromType(x.toDouble)), jTensor.shape) + if (null == jTensor.indices) { + Tensor(jTensor.storage.map(x => ev.fromType(x.toDouble)), jTensor.shape) + } else { + Tensor.sparse(jTensor.indices, + jTensor.storage.map(x => ev.fromType(x.toDouble)), jTensor.shape) + } case t: String => throw new IllegalArgumentException(s"Not supported type: ${t}") } @@ -134,13 +144,30 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def toJTensor(tensor: Tensor[T]): JTensor = { // clone here in case the the size of storage larger then the size of tensor. require(tensor != null, "tensor cannot be null") - if (tensor.nElement() == 0) { - JTensor(Array(), Array(0), typeName) - } else { - val cloneTensor = tensor.clone() - val result = JTensor(cloneTensor.storage().array().map(i => ev.toType[Float](i)), - cloneTensor.size(), typeName) - result + tensor.getTensorType match { + case SparseType => + // Note: as SparseTensor's indices is inaccessible here, + // so we will transfer it to DenseTensor. Just for testing. + if (tensor.nElement() == 0) { + JTensor(Array(), Array(0), bigdlType = typeName) + } else { + val cloneTensor = Tensor.dense(tensor) + val result = JTensor(cloneTensor.storage().array().map(i => ev.toType[Float](i)), + cloneTensor.size(), bigdlType = typeName) + result + } + case DenseType => + if (tensor.nElement() == 0) { + JTensor(Array(), Array(0), bigdlType = typeName) + } else { + val cloneTensor = tensor.clone() + val result = JTensor(cloneTensor.storage().array().map(i => ev.toType[Float](i)), + cloneTensor.size(), bigdlType = typeName) + result + } + case _ => + throw new IllegalArgumentException(s"toJTensor: Unsupported tensor type" + + s" ${tensor.getTensorType}") } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala index d179cbee258..a81ed755860 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala @@ -83,4 +83,30 @@ class MiniBatchSpec extends FlatSpec with Matchers { miniBatch.slice(3, 1).getTarget() should be (Tensor[Float](1).fill(3)) } + "SparseTensorMiniBatch set" should "return right result" in { + val a1 = Tensor.sparse(Tensor[Float](4).range(1, 4, 1)) + val a2 = Tensor.sparse(Tensor[Float](4).range(5, 8, 1)) + val b1 = Tensor[Float](5).range(1, 5, 1) + val b2 = Tensor[Float](5).range(6, 10, 1) + val c1 = Tensor[Float](1).fill(1) + val c2 = Tensor[Float](1).fill(0) + val sample1 = TensorSample[Float](Array(a1, b1), Array(c1)) + val sample2 = TensorSample[Float](Array(a2, b2), Array(c2)) + val miniBatch = SparseMiniBatch[Float](2, 1) + miniBatch.set(Array(sample1, sample2)) + + val input = miniBatch.getInput() + val target = miniBatch.getTarget() + + val expectedInput1 = Tensor.sparse(Array(Array(0, 0, 0, 0, 1, 1, 1, 1), + Array(0, 1, 2, 3, 0, 1, 2, 3)), + Array.range(1, 9).map(_.toFloat), Array(2, 4)) + val expectedInput2 = Tensor[Float].range(1, 10) + input.toTable[Tensor[Float]](1) should be (expectedInput1) + input.toTable[Tensor[Float]](2) should be (expectedInput2) + + val expectedTarget = Tensor[Float](T(1.0f, 0.0f)) + target should be (expectedTarget) + } + } From 8177d94c4124c58e0c3e9f571e5283e0c03436e1 Mon Sep 17 00:00:00 2001 From: Zefeng-Liu Date: Wed, 18 Oct 2017 16:49:42 +0800 Subject: [PATCH 0461/1065] Big dl#1115 Refine the toString() method for too large tensor (#1667) * Refine the toString() method for too large tensor * Revert "Refine the toString() method for too large tensor" This reverts commit 76f7186 * Refine the toString() method for too large tensor (cherry picked from commit 76f7186) * Refine the toString() method for too large tensor (fixed code style, made the threshold configurable and added one test for 4D case) * Refine the toString() method for too large tensor (fixed code style, made the threshold configurable and added one test for 4D case, added the description of a new property in the configuration file) --- .../bigdl/dllib/tensor/DenseTensor.scala | 133 ++++++++++++++---- .../bigdl/dllib/tensor/DenseTensorSpec.scala | 74 +++++++++- 2 files changed, 173 insertions(+), 34 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 7a4afa3f980..e6ed4ca53ef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -1434,30 +1434,64 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } override def toString(): String = { + val foldThreshold = System.getProperty("bigdl.tensor.fold", "1000").toInt this.nDimension match { case 0 => s"[${this.getClass.getName} with no dimension]" case 1 => val sb = new StringBuilder - this.apply1(e => { - sb.append(e).append('\n') - e - }) + if (this.size().product < foldThreshold) { + this.apply1(e => { + sb.append(e).append('\n') + e + }) + } else { + var i = 0 + this.apply1(e => { + i = i + 1 + if (i < 3 || i > this.size(1) - 3) { + sb.append(e).append('\n') + } else if (i == 3) sb.append(e).append("\n...\n") + e + }) + } s"${sb}[${this.getClass.getName} of size ${this.size(1)}]" case 2 => val sb = new StringBuilder val indexer = Array(0, 0) - var i = 1 - while (i <= this.size(1)) { - var j = 1 - while (j <= this.size(2)) { - indexer(0) = i - indexer(1) = j - sb.append(this.apply(indexer)).append('\t') - j += 1 + if (this.size().product < foldThreshold) { + var i = 1 + while (i <= this.size(1)) { + var j = 1 + while (j <= this.size(2)) { + indexer(0) = i + indexer(1) = j + sb.append(this.apply(indexer)).append('\t') + j += 1 + } + sb.append('\n') + i += 1 + } + } else { + var i = 1 + while (i <= this.size(1)) { + var j = 1 + if (i <= 3 || i > this.size(1) - 3) { + while (j <= this.size(2)) { + indexer(0) = i + indexer(1) = j + if (j < 3 || j > this.size(2) - 3) { + sb.append(this.apply(indexer)).append('\t') + } else if (j == 3) { + sb.append(this.apply(indexer)).append("\t...\t") + } + j += 1 + } + sb.append('\n') + if (i == 3) sb.append("...\n") + } + i += 1 } - sb.append('\n') - i += 1 } s"${sb}[${this.getClass.getName} of size ${this.size(1)}x${this.size(2)}]" @@ -1471,30 +1505,67 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( var d = _secLastDim - 1 val total = this.nElement() while (!done) { - // print header - sb.append('(') var i = 0 - while (i < _secLastDim) { - sb.append(indexer(i)).append(',') - i += 1 + var needPrint = true + if (this.size.product > foldThreshold) { + while (i < _secLastDim) { + if (indexer(i) <= 2 || indexer(i) > size(i) - 2) i += 1 + else { + needPrint = false + i = _secLastDim + } + if (indexer(i) == size(i) - 1) sb.append("...\n\n") + } } - sb.append(".,.) =\n") - // print current matrix - i = 1 - while (i <= this.size(_secLastDim + 1)) { - var j = 1 - while (j <= this.size(_lastDim + 1)) { - indexer(_lastDim) = j - indexer(_secLastDim) = i - sb.append(this.apply(indexer)).append('\t') - j += 1 + if (needPrint) { + // print header + sb.append('(') + i = 0 + while (i < _secLastDim) { + sb.append(indexer(i)).append(',') + i += 1 + } + sb.append(".,.) =\n") + + // print current matrix + i = 1 + if (this.size(_secLastDim + 1) * this.size(_lastDim + 1) < foldThreshold) { + while (i <= this.size(_secLastDim + 1)) { + var j = 1 + while (j <= this.size(_lastDim + 1)) { + indexer(_lastDim) = j + indexer(_secLastDim) = i + sb.append(this.apply(indexer)).append('\t') + j += 1 + } + sb.append('\n') + i += 1 + } + } else { + while (i <= this.size(_secLastDim + 1)) { + var j = 1 + if (i <= 3 || i > this.size(_secLastDim + 1) - 3) { + while (j <= this.size(_lastDim + 1)) { + indexer(_lastDim) = j + indexer(_secLastDim) = i + if (j < 3 || j > this.size(_lastDim + 1) - 3) { + sb.append(this.apply(indexer)).append('\t') + } + else if (j == 3) { + sb.append(this.apply(indexer)).append("\t...\t") + } + j += 1 + } + sb.append('\n') + if (i == 3) sb.append("...\n") + } + i += 1 + } } sb.append('\n') - i += 1 } - sb.append('\n') indexer(d) = indexer(d) + 1 while (d >= 0 && indexer(d) > size(d)) { indexer(d) = 1 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index 8719e55d96f..3c22a3e7415 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -299,7 +299,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { t.valueAt(3) should be(3) } - "resise as" should "get the correct tensor" in { + "resize as" should "get the correct tensor" in { val t: Tensor[Double] = new DenseTensor[Double](3, 4) val t1: Tensor[Double] = new DenseTensor[Double](5, 5) val t2: Tensor[Double] = new DenseTensor[Double](2, 2) @@ -473,7 +473,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { t = new DenseTensor[Double](3, 4) var i = 0 t.apply1(v => { - i = i + 1; + i = i + 1 i }) t.toString should be(MATRIX_STRING) @@ -481,12 +481,80 @@ class DenseTensorSpec extends FlatSpec with Matchers { t = new DenseTensor(2, 5, 3, 4) i = 0 t.apply1(v => { - i = i + 1; + i = i + 1 i }) println(t) } + "toString" should "be elegant if the tensor is too large" in { + val t = new DenseTensor[Float](1000) + var i = 0 + t.apply1(v => { + i = i + 1; i + }) + val OneD_STRING = + "1.0\n2.0\n3.0\n...\n998.0\n999.0\n1000.0\n" + + "[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1000]" + t.toString should be(OneD_STRING) + val s = new DenseTensor[Float](50, 50) + i = 0 + s.apply1(v => { + i = i + 1; i + }) + val MATRIX_STRING = + "1.0\t2.0\t3.0\t...\t48.0\t49.0\t50.0\t\n" + + "51.0\t52.0\t53.0\t...\t98.0\t99.0\t100.0\t\n" + + "101.0\t102.0\t103.0\t...\t148.0\t149.0\t150.0\t\n" + + "...\n" + + "2351.0\t2352.0\t2353.0\t...\t2398.0\t2399.0\t2400.0\t\n" + + "2401.0\t2402.0\t2403.0\t...\t2448.0\t2449.0\t2450.0\t\n" + + "2451.0\t2452.0\t2453.0\t...\t2498.0\t2499.0\t2500.0\t\n" + + "[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 50x50]" + s.toString should be(MATRIX_STRING) + val r = new DenseTensor[Float](1, 10, 50, 50) + i = 0 + r.apply1(v => { + i = i + 1; i + }) + val MULTIPLE_MATRIX_STRING = + "(1,1,.,.) =\n" + + "1.0\t2.0\t3.0\t...\t48.0\t49.0\t50.0\t\n" + + "51.0\t52.0\t53.0\t...\t98.0\t99.0\t100.0\t\n" + + "101.0\t102.0\t103.0\t...\t148.0\t149.0\t150.0\t\n" + + "...\n" + + "2351.0\t2352.0\t2353.0\t...\t2398.0\t2399.0\t2400.0\t\n" + + "2401.0\t2402.0\t2403.0\t...\t2448.0\t2449.0\t2450.0\t\n" + + "2451.0\t2452.0\t2453.0\t...\t2498.0\t2499.0\t2500.0\t\n\n" + + "(1,2,.,.) =\n" + + "2501.0\t2502.0\t2503.0\t...\t2548.0\t2549.0\t2550.0\t\n" + + "2551.0\t2552.0\t2553.0\t...\t2598.0\t2599.0\t2600.0\t\n" + + "2601.0\t2602.0\t2603.0\t...\t2648.0\t2649.0\t2650.0\t\n" + + "...\n" + + "4851.0\t4852.0\t4853.0\t...\t4898.0\t4899.0\t4900.0\t\n" + + "4901.0\t4902.0\t4903.0\t...\t4948.0\t4949.0\t4950.0\t\n" + + "4951.0\t4952.0\t4953.0\t...\t4998.0\t4999.0\t5000.0\t\n\n" + + "...\n\n" + + "(1,9,.,.) =\n" + + "20001.0\t20002.0\t20003.0\t...\t20048.0\t20049.0\t20050.0\t\n" + + "20051.0\t20052.0\t20053.0\t...\t20098.0\t20099.0\t20100.0\t\n" + + "20101.0\t20102.0\t20103.0\t...\t20148.0\t20149.0\t20150.0\t\n" + + "...\n" + + "22351.0\t22352.0\t22353.0\t...\t22398.0\t22399.0\t22400.0\t\n" + + "22401.0\t22402.0\t22403.0\t...\t22448.0\t22449.0\t22450.0\t\n" + + "22451.0\t22452.0\t22453.0\t...\t22498.0\t22499.0\t22500.0\t\n\n" + + "(1,10,.,.) =\n" + + "22501.0\t22502.0\t22503.0\t...\t22548.0\t22549.0\t22550.0\t\n" + + "22551.0\t22552.0\t22553.0\t...\t22598.0\t22599.0\t22600.0\t\n" + + "22601.0\t22602.0\t22603.0\t...\t22648.0\t22649.0\t22650.0\t\n" + + "...\n" + + "24851.0\t24852.0\t24853.0\t...\t24898.0\t24899.0\t24900.0\t\n" + + "24901.0\t24902.0\t24903.0\t...\t24948.0\t24949.0\t24950.0\t\n" + + "24951.0\t24952.0\t24953.0\t...\t24998.0\t24999.0\t25000.0\t\n\n" + + "[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1x10x50x50]" + r.toString should be(MULTIPLE_MATRIX_STRING) + } + "squeeze" should "be correct" in { var t: Tensor[Double] = new DenseTensor[Double](3, 1, 2, 1) t.squeeze() From 36a4d4b87bacaf4a70b943e3ab4b803e6e8aeb46 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 18 Oct 2017 04:24:16 -0500 Subject: [PATCH 0462/1065] support more tensor operations (#1675) * support more tensor operations * add more test * fix unit test --- .../bigdl/dllib/tensor/DenseTensor.scala | 26 ++++++++++++--- .../bigdl/dllib/tensor/DenseTensorMath.scala | 3 ++ .../tensor/QuantizedTensorUnsupported.scala | 7 ++++ .../bigdl/dllib/tensor/SparseTensor.scala | 12 +++++++ .../bigdl/dllib/tensor/TensorMath.scala | 20 +++++++++++ .../bigdl/dllib/tensor/TensorNumeric.scala | 24 ++++++++++++++ .../bigdl/dllib/tensor/DenseTensorSpec.scala | 33 ++++++++++++++++++- 7 files changed, 120 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index e6ed4ca53ef..ab36bf6ba5a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -932,7 +932,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this } - private def expandTensor(x: Tensor[T]): Tensor[T] = { + private[tensor] def expandTensor(x: Tensor[T]): Tensor[T] = { val targetSize = DenseTensor.expandSize(this, x) val expandStrides = new Array[Int](targetSize.length) @@ -1218,7 +1218,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( DenseTensorMath.addmm(this, v1, this, v2, mat1, mat2) override def mm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = - DenseTensorMath.addmm(this, ev.fromType[Int](1), this, ev.fromType[Int](1), mat1, mat2) + DenseTensorMath.addmm(this, ev.zero, this, ev.fromType[Int](1), mat1, mat2) override def addr(t1: Tensor[T], t2: Tensor[T]): Tensor[T] = DenseTensorMath.addr[T](this, ev.fromType[Int](1), this, ev.fromType[Int](1), t1, t2) @@ -1436,7 +1436,12 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( override def toString(): String = { val foldThreshold = System.getProperty("bigdl.tensor.fold", "1000").toInt this.nDimension match { - case 0 => s"[${this.getClass.getName} with no dimension]" + case 0 => + if (this.isScalar) { + s"Scalar(${this.value()})" + } else { + s"Empty Tensor" + } case 1 => val sb = new StringBuilder if (this.size().product < foldThreshold) { @@ -2097,6 +2102,18 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( override def getTensorType: TensorType = DenseType + override def floor(y: Tensor[T]): Tensor[T] = { + this.map(y, (a, b) => ev.floor(b)) + } + + override def floor(): Tensor[T] = { + this.apply1(a => ev.floor(a)) + } + + override def negative(x: Tensor[T]): Tensor[T] = { + this.map(x, (a, b) => ev.negative(b)) + this + } } object DenseTensor { @@ -2459,7 +2476,8 @@ object DenseTensor { private[tensor] def copy[@specialized T]( self: DenseTensor[T], src: Tensor[T]): Unit = { - require(self.nElement() == src.nElement()) + require(self.nElement() == src.nElement(), s"self element number(${self.nElement()}) is not" + + s" equal to source element number(${src.nElement()})") if (self.isEmpty) { return } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala index ef0efdf5b91..d48dc63fef2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala @@ -65,6 +65,9 @@ object DenseTensorMath { cmul(self.select(1, i + 1).asInstanceOf[DenseTensor[T]], x, y.select(1, i + 1)) i += 1 } + } else if (x.nElement() != y.nElement()) { + self.resizeAs(x).copy(x) + self.cmul(self.expandTensor(y)) } else { require(self.nElement() == y.nElement(), s"element number doesn't match " + s"self(${self.nElement()}) y(${y.nElement()}) x(${x.nElement()})") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index 8de976f6dac..12e31518e45 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1389,4 +1389,11 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def div(y: Tensor[T]): Tensor[T] = throw new UnsupportedOperationException(errorString) + override def floor(y: Tensor[T]): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def floor(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def negative(x: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 2172a09c31e..1eae98612aa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -981,6 +981,18 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( } override def getTensorType: TensorType = SparseType + + override def floor(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def floor(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def negative(x: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } } object SparseTensor{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala index 4ff19c80740..b63e6801741 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala @@ -551,6 +551,19 @@ trait TensorMath[T] { def pow(n: T): Tensor[T] + /** + * Populate the given tensor with the floor result of elements + * @param y + * @return + */ + def floor(y: Tensor[T]): Tensor[T] + + /** + * Replaces all elements in-place with the floor result of elements + * @return + */ + def floor(): Tensor[T] + /** * Get the top k smallest values and their indices. * @@ -730,4 +743,11 @@ trait TensorMath[T] { * @return this tensor */ def range(xmin: Double, xmax: Double, step: Int = 1): Tensor[T] + + /** + * Computes numerical negative value element-wise. y = -x + * @param x + * @return this tensor + */ + def negative(x : Tensor[T]): Tensor[T] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index e6d6851d511..facab7234a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -172,6 +172,8 @@ object TensorNumericMath { b: Array[T], bOffset: Int): Unit def nearlyEqual(a: T, b: T, epsilon: Double): Boolean + + def floor(a: T): T } /** @@ -396,6 +398,10 @@ object TensorNumericMath { def nearlyEqual(a: T, b: T, epsilon: Double): Boolean = throw new UnsupportedOperationException(typeName + " in tensor does not support nearlyEqual operation") + + override def floor(a: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support floor operation") } /** @@ -677,6 +683,8 @@ object TensorNumericMath { i += 1 } } + + override def floor(a: Float): Float = math.floor(a).toFloat } implicit object NumericDouble extends UndefinedTensorNumeric[Double]("Double") { @@ -948,6 +956,8 @@ object TensorNumericMath { i += 1 } } + + override def floor(a: Double): Double = math.floor(a) } implicit object NumericString extends UndefinedTensorNumeric[String]("String") { @@ -1066,6 +1076,16 @@ object TensorNumericMath { } r } + + override def floor(a: Int): Int = a + + override def sub(n: Int, a: Array[Int], offset: Int, v: Int, stride: Int): Unit = { + var i = 0 + while(i < n) { + a(i * stride + offset) -= v + i += 1 + } + } } implicit object NumericLong extends UndefinedTensorNumeric[Long]("Long") { @@ -1135,6 +1155,8 @@ object TensorNumericMath { result } + + override def floor(a: Long): Long = a } implicit object NumericShort extends UndefinedTensorNumeric[Short]("Short") { @@ -1204,6 +1226,8 @@ object TensorNumericMath { result } + + override def floor(a: Short): Short = a } implicit object NumericChar extends UndefinedTensorNumeric[Char]("Char") { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index 3c22a3e7415..cf5925e82ce 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -456,8 +456,12 @@ class DenseTensorSpec extends FlatSpec with Matchers { "toString" should "be correct" in { var t: Tensor[Double] = new DenseTensor[Double]() val EMPTY_STRING = - """[com.intel.analytics.bigdl.tensor.DenseTensor$mcD$sp with no dimension]""" + """Empty Tensor""" t.toString should be(EMPTY_STRING) + + t = Tensor.scalar[Double](1) + t.toString should be("Scalar(1.0)") + t = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) val OneD_STRING = "1.0\n" + @@ -1003,4 +1007,31 @@ class DenseTensorSpec extends FlatSpec with Matchers { val result = t.select(1, 1) result.isScalar should be(true) } + + "Negative" should "be correct" in { + val x = Tensor[Float](T(1, 2, 3)) + val y = Tensor[Float](3) + y.negative(x) should be(Tensor[Float](T(-1, -2, -3))) + x.negative(x) should be(Tensor[Float](T(-1, -2, -3))) + } + + "Floor" should "be correct" in { + val x = Tensor[Float](T(1.1, 2.5, 3.7)) + x.floor() should be(Tensor[Float](T(1, 2, 3))) + + val y = Tensor[Double](T(1.1, 2.5, 3.7)) + y.floor() should be(Tensor[Double](T(1, 2, 3))) + + val z = Tensor[Int](T(1, 2, 3)) + z.floor() should be(Tensor[Int](T(1, 2, 3))) + } + + "mm" should "be correct when multiply twice" in { + val o = Tensor[Float]().resize(2, 2) + val x = Tensor[Float](T(T(1, 2, 3), T(4, 5, 6))) + val y = Tensor[Float](T(T(1, 2), T(3, 4), T(5, 6))) + o.mm(x, y) + o.mm(x, y) + o should be(Tensor[Float](T(T(22, 28), T(49, 64)))) + } } From 63a2123c26a04a69ae8bc2865a4177ef3d19bd38 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 19 Oct 2017 09:56:14 +0800 Subject: [PATCH 0463/1065] add more layers and operations (#1678) * add more layers and operations * fix compile error * fix compile error --- .../analytics/bigdl/dllib/nn/Graph.scala | 18 +- .../analytics/bigdl/dllib/nn/Module.scala | 2 +- .../analytics/bigdl/dllib/nn/Negative.scala | 64 ++ .../analytics/bigdl/dllib/nn/Scheduler.scala | 2 +- .../intel/analytics/bigdl/dllib/nn/Sum.scala | 24 +- .../analytics/bigdl/dllib/nn/Unsqueeze.scala | 2 +- .../bigdl/dllib/nn/ops/AssignGrad.scala | 32 + .../bigdl/dllib/nn/ops/BiasAddGrad.scala | 84 +++ .../analytics/bigdl/dllib/nn/ops/Conv2D.scala | 188 +++--- .../bigdl/dllib/nn/ops/CrossEntropy.scala | 101 ++++ .../analytics/bigdl/dllib/nn/ops/Floor.scala | 25 +- .../bigdl/dllib/nn/ops/MaxPoolGrad.scala | 71 +++ .../analytics/bigdl/dllib/nn/ops/NoOp.scala | 36 ++ .../analytics/bigdl/dllib/nn/ops/Pow.scala | 40 ++ .../bigdl/dllib/nn/ops/RandomUniform.scala | 42 +- .../bigdl/dllib/nn/ops/ReluGrad.scala | 41 ++ .../analytics/bigdl/dllib/nn/ops/Select.scala | 45 ++ .../analytics/bigdl/dllib/nn/ops/Sum.scala | 71 +++ .../analytics/bigdl/dllib/nn/ops/Tile.scala | 5 + .../bigdl/dllib/nn/ops/package.scala | 7 - .../analytics/bigdl/dllib/nn/tf/Fill.scala | 36 +- .../bigdl/dllib/nn/tf/Variable.scala | 7 +- .../dllib/utils/python/api/PythonBigDL.scala | 561 +++++++++--------- .../bigdl/dllib/nn/NegativeSpec.scala | 35 ++ .../analytics/bigdl/dllib/nn/SumSpec.scala | 10 +- .../bigdl/dllib/nn/ops/Conv2DSep.scala | 2 +- .../bigdl/dllib/nn/ops/FloorSpec.scala | 10 + .../bigdl/dllib/nn/ops/PowSpec.scala | 37 ++ .../bigdl/dllib/nn/ops/SelectSpec.scala | 40 ++ .../bigdl/dllib/nn/ops/SumSpec.scala | 21 +- .../bigdl/dllib/nn/ops/TileSpec.scala | 6 + .../bigdl/dllib/nn/tf/FillSpec.scala | 7 + .../analytics/bigdl/dllib/torch/SumSpec.scala | 8 +- .../bigdl/dllib/torch/UnsqueezeSpec.scala | 6 + 34 files changed, 1248 insertions(+), 438 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Negative.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossEntropy.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pow.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReluGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Select.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PowSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 1ab6b8c8628..07c9b4c998c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -363,15 +363,27 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], private val gradOutputCache = new mutable.HashMap[String, Activity]() + private def duplicatedNames(names: Seq[String]): mutable.Set[String] = { + names.sortWith(_ < _) + val buffer = new mutable.HashSet[String]() + var i = 1 + while(i < names.length) { + if (names(i) == names(i - 1)) buffer.add(names(i)) + i += 1 + } + buffer + } + private def checkRoots: Unit = { require(forwardNodes.map(_.element.getName()).distinct.length == forwardNodes.length, - "the name of node in the graph should be unique") + s"the name of node in the graph should be unique, but find dumplicated name " + + s"${duplicatedNames(forwardNodes.map(_.element.getName())).mkString(", ")}") val roots = forwardNodes.filter(_.prevNodes.size == 0) .filter(node => !node.element.isInstanceOf[WithoutInput] && !node.element.isInstanceOf[ControlDependency[_]]) - require(roots.size == inputs.length, + require(roots.size == inputs.filter(node => !node.element.isInstanceOf[WithoutInput]).length, s"There're ${inputs.length} inputs, but graph has ${roots.size} roots") - inputs.foreach(n => + inputs.filter(node => !node.element.isInstanceOf[WithoutInput]).foreach(n => require(roots.contains(n), "inputs and graph roots are not match") ) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala index e05bdd7234a..220e661a2c7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.File import com.intel.analytics.bigdl.utils.caffe.CaffeLoader import com.intel.analytics.bigdl.utils.serializer.ModuleLoader -import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowLoader} +import com.intel.analytics.bigdl.utils.tf.{Session, TensorflowDataFormat, TensorflowLoader} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Negative.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Negative.scala new file mode 100644 index 00000000000..b3c4272f459 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Negative.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} + +import scala.reflect.ClassTag + +/** + * Computing negative value of each element of input tensor + * @param inplace output tensor reuse input tensor storage, default is false + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class Negative[T: ClassTag](inplace : Boolean = false) + (implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[_], Tensor[_], T] { + + override def updateOutput(input: Tensor[_]): Tensor[_] = { + if (inplace) { + output = input + } else { + if (output.getType() != input.getType()) { + output = input.emptyInstance() + } + output.resizeAs(input) + } + + output.asInstanceOf[Tensor[NumericWildCard]] + .negative(input.asInstanceOf[Tensor[NumericWildCard]]) + } + + override def updateGradInput(input: Tensor[_], gradOutput: Tensor[_]): Tensor[_] = { + if (inplace) { + gradInput = gradOutput + } else { + if (gradInput.getType() != gradOutput.getType()) { + gradInput = gradOutput.emptyInstance() + } + gradInput.resizeAs(gradOutput) + } + + gradInput.asInstanceOf[Tensor[NumericWildCard]] + .negative(gradOutput.asInstanceOf[Tensor[NumericWildCard]]) + } +} + +object Negative { + def apply[T: ClassTag](inplace: Boolean = false) + (implicit ev: TensorNumeric[T]): Negative[T] = new Negative[T](inplace) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala index 23c00fea561..01c75296367 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala @@ -105,7 +105,7 @@ private[bigdl] class Scheduler[T] ( } } else { val constNodes = node.prevNodes.filter(nodeStatus.isConst(_)) - if (constNodes.length == node.prevNodes.length) { + if (constNodes.length == node.prevNodes.length && !node.element.isInstanceOf[RandomNode]) { Const() } else { Ready() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala index 35f88ff65fb..bda7160f0a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala @@ -42,7 +42,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 8025422596092583688L) class Sum[T: ClassTag, D: ClassTag]( - dimension: Int = 1, + private var dimension: Int = 1, nInputDims: Int = -1, sizeAverage: Boolean = false, squeeze: Boolean = true) @@ -69,6 +69,11 @@ class Sum[T: ClassTag, D: ClassTag]( dimension } + def changeSumDims(d: Int): this.type = { + dimension = d + this + } + override def updateOutput(input: Tensor[D]): Tensor[D] = { val dimension = getPositiveDimension(input) output.sum(input, dimension) @@ -81,6 +86,10 @@ class Sum[T: ClassTag, D: ClassTag]( output.squeeze(dimension) } + if (output.nElement() == 1 && squeeze) { + output = Tensor.scalar[D](output.storage.apply(output.storageOffset() - 1)) + } + output } @@ -106,12 +115,11 @@ class Sum[T: ClassTag, D: ClassTag]( } object Sum { - - def apply[@specialized(Float, Double) T: ClassTag]( - dimension: Int = 1, - nInputDims: Int = -1, - sizeAverage: Boolean = false, - squeeze: Boolean = true)(implicit ev: TensorNumeric[T]) : Sum[T, T] = { - new Sum[T, T](dimension, nInputDims, sizeAverage, squeeze) + def apply[T: ClassTag, D: ClassTag]( + dimension: Int = 1, + nInputDims: Int = -1, + sizeAverage: Boolean = false, + squeeze: Boolean = true)(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]) : Sum[T, D] = { + new Sum[T, D](dimension, nInputDims, sizeAverage, squeeze) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala index e5d260604b2..1ed93ce4f4b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala @@ -41,7 +41,7 @@ class Unsqueeze[T: ClassTag]( } private def getActualPosition(input: Tensor[_]) : Int = { - val dim = if (pos < 0) { + val dim = if (pos <= 0) { input.dim() + pos + 1 } else { pos diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignGrad.scala new file mode 100644 index 00000000000..aa1ca1cbf33 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignGrad.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class AssignGrad[T: ClassTag](grad: Tensor[T])(implicit ev: TensorNumeric[T]) + extends Operation[Tensor[T], Activity, T]{ + + override def updateOutput(input: Tensor[T]): Activity = { + grad.copy(input) + null + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddGrad.scala new file mode 100644 index 00000000000..558faba6e6b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddGrad.scala @@ -0,0 +1,84 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.tf.BiasAdd +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class BiasAddGrad[T: ClassTag](dataFormat: DataFormat) + (implicit ev: TensorNumeric[T]) + extends Operation[Tensor[T], Tensor[T], T] { + + private val module = BiasAdd() + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + getBiasDims(input) + output.resizeAs(input).copy(input) + dataFormat match { + case DataFormat.NCHW => + output = output.resize(Array(batch, channel, height, width)).sum(1) + output = output.sum(3) + output = output.sum(4) + case DataFormat.NHWC => + output = output.resize(Array(batch * height * width, channel)).sum(1) + } + output + } + + private var batch : Int = 1 + private var channel : Int = 1 + private var width : Int = 1 + private var height : Int = 1 + + private def getBiasDims(tensor: Tensor[_]): Unit = { + batch = 1 + channel = 1 + width = 1 + height = 1 + dataFormat match { + case DataFormat.NHWC => + val channelDim = tensor.dim() + channel = tensor.size(channelDim) + var i = 1 + while(i < channelDim) { + batch *= tensor.size(i) + i += 1 + } + case DataFormat.NCHW => + val channelDim = tensor.dim() - 2 + val heightDim = tensor.dim() - 1 + val widthDim = tensor.dim() + channel = tensor.size(channelDim) + height = tensor.size(heightDim) + width = tensor.size(widthDim) + var i = 1 + while(i < channelDim) { + batch *= tensor.size(i) + i += 1 + } + } + } +} + +object BiasAddGrad { + def apply[T: ClassTag](dataFormat: DataFormat) + (implicit ev: TensorNumeric[T]): BiasAddGrad[T] = new BiasAddGrad(dataFormat) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala index 031a6497617..10b0fd48e5b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala @@ -24,8 +24,10 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class Conv2D[T: ClassTag]( - strides: Array[Int], - padding: String, + strideH: Int, + strideW: Int, + padH: Int, + padW: Int, format: DataFormat = DataFormat.NHWC )(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { @@ -35,64 +37,19 @@ class Conv2D[T: ClassTag]( val input: Tensor[T] = inputs[Tensor[T]](1) val filter: Tensor[T] = inputs[Tensor[T]](2) - conv = format match { - case DataFormat.NHWC => - if (padding == "SAME") { - SpatialConvolution( - nInputPlane = input.size(4), - nOutputPlane = filter.size(4), - kernelH = filter.size(1), - kernelW = filter.size(2), - strideH = strides(1), - strideW = strides(2), - padH = -1, - padW = -1, - withBias = false, - format = format - ) - } else if (padding == "VALID") { - SpatialConvolution( - nInputPlane = input.size(4), - nOutputPlane = filter.size(4), - kernelH = filter.size(1), - kernelW = filter.size(2), - strideH = strides(1), - strideW = strides(2), - withBias = false, - format = format - ) - } else { - throw new RuntimeException("Padding can only support SAME and VALID padding") - } - - case DataFormat.NCHW => - if (padding == "SAME") { - SpatialConvolution( - nInputPlane = input.size(2), - nOutputPlane = filter.size(4), - kernelH = filter.size(1), - kernelW = filter.size(2), - strideH = strides(2), - strideW = strides(3), - padH = -1, - padW = -1, - withBias = false, - format = format - ) - } else if (padding == "VALID") { - SpatialConvolution( - nInputPlane = input.size(2), - nOutputPlane = filter.size(4), - kernelH = filter.size(1), - kernelW = filter.size(2), - strideH = strides(2), - strideW = strides(3), - withBias = false, - format = format - ) - } else { - throw new RuntimeException("Padding can only support SAME and VALID padding") - } + if (conv == null) { + conv = SpatialConvolution( + nInputPlane = input.size(4), + nOutputPlane = filter.size(4), + kernelH = filter.size(1), + kernelW = filter.size(2), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + withBias = false, + format = format + ) } conv.setWeightsBias(Array(filter)) @@ -103,11 +60,13 @@ class Conv2D[T: ClassTag]( object Conv2D { def apply[T: ClassTag]( - strides: Array[Int], - padding: String, + strideH: Int, + strideW: Int, + padH: Int, + padW: Int, format: DataFormat = DataFormat.NHWC - )(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](new Conv2D(strides, padding, format)) + )(implicit ev: TensorNumeric[T]): Conv2D[T] + = new Conv2D(strideH, strideW, padH, padW, format) } /** @@ -127,17 +86,17 @@ class Conv2DTranspose[T: ClassTag]( override def updateOutput(input: Activity): Tensor[T] = { require(input.isTable, "Invalid input activity type") - val sizes = input.toTable.apply[Tensor[Int]](1).squeeze() + val inputSizes = input.toTable.apply[Tensor[Int]](1).squeeze() val kernel = input.toTable.apply[Tensor[T]](2) val data = input.toTable.apply[Tensor[T]](3) require(data.nDimension() == 4, s"Need a 4D input but is ${data.nDimension()}") - require(sizes.nDimension() == 1, s"Need a 1D size but is ${sizes.nDimension()}") + require(inputSizes.nDimension() == 1, s"Need a 1D size but is ${inputSizes.nDimension()}") val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { - (data.size(2), sizes.valueAt(2)) + (data.size(2), inputSizes.valueAt(2)) } else { - (data.size(4), sizes.valueAt(4)) + (data.size(4), inputSizes.valueAt(4)) } if (module == null) { @@ -155,13 +114,14 @@ class Conv2DTranspose[T: ClassTag]( withBias = false ) - dummyInput = Tensor[T](sizes.valueAt(1), sizes.valueAt(2), sizes.valueAt(3), sizes.valueAt(4)) + dummyInput = Tensor[T](inputSizes.valueAt(1), inputSizes.valueAt(2), inputSizes.valueAt(3), + inputSizes.valueAt(4)) module.forward(dummyInput) } else { val (nOutputPlanbe, nInputPlane) = if (format == DataFormat.NCHW) { - (data.size(2), sizes.valueAt(2)) + (data.size(2), inputSizes.valueAt(2)) } else { - (data.size(4), sizes.valueAt(4)) + (data.size(4), inputSizes.valueAt(4)) } require(module.nInputPlane == nInputPlane, "nInputPlane is not valid") @@ -170,10 +130,10 @@ class Conv2DTranspose[T: ClassTag]( require(module.kernelW == kernel.size(2), "kernelW is not valid") require(kernel.size(3) == nInputPlane, "kernel nInputPlane is not valid") require(kernel.size(4) == nOutputPlane, "kernel nOutputPlane is not valid") - require(dummyInput.size(1) == sizes.valueAt(1), "size 1 is not correct") - require(dummyInput.size(2) == sizes.valueAt(2), "size 1 is not correct") - require(dummyInput.size(3) == sizes.valueAt(3), "size 1 is not correct") - require(dummyInput.size(4) == sizes.valueAt(4), "size 1 is not correct") + require(dummyInput.size(1) == inputSizes.valueAt(1), "size 1 is not correct") + require(dummyInput.size(2) == inputSizes.valueAt(2), "size 1 is not correct") + require(dummyInput.size(3) == inputSizes.valueAt(3), "size 1 is not correct") + require(dummyInput.size(4) == inputSizes.valueAt(4), "size 1 is not correct") } module.weight.set(kernel) @@ -194,3 +154,81 @@ object Conv2DTranspose { new Conv2DTranspose(strideW, strideH, padW, padH, format) } +class Conv2DBackFilter[T: ClassTag]( + strideW: Int, + strideH: Int, + padW: Int = -1, + padH: Int = -1, + format: DataFormat = DataFormat.NCHW +)(implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[T], T]{ + + private var module: SpatialConvolution[T] = _ + private var gradWeight: Tensor[T] = _ + private var dummyInput: Tensor[T] = _ + + override def updateOutput(input: Activity): Tensor[T] = { + require(input.isTable, "Invalid input activity type") + val kernelSize = input.toTable.apply[Tensor[Int]](2).squeeze() + val inputActivity = input.toTable.apply[Tensor[T]](1) + val grads = input.toTable.apply[Tensor[T]](3) + + require(grads.nDimension() == 4, s"Need a 4D input but is ${grads.nDimension()}") + require(kernelSize.nDimension() == 1, s"Need a 1D size but is ${kernelSize.nDimension()}") + + val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { + (grads.size(2), inputActivity.size(2)) + } else { + (grads.size(4), inputActivity.size(4)) + } + + if (module == null) { + gradWeight = Tensor[T]().resize(kernelSize.valueAt(1), kernelSize.valueAt(2), + kernelSize.valueAt(3), kernelSize.valueAt(4)) + module = new SpatialConvolution[T]( + nInputPlane = nInputPlane, + nOutputPlane = nOutputPlane, + kernelW = kernelSize.valueAt(2), + kernelH = kernelSize.valueAt(1), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + initGradWeight = gradWeight, + format = format, + withBias = false + ) + module.forward(inputActivity) + } else { + val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { + (grads.size(2), inputActivity.size(2)) + } else { + (grads.size(4), inputActivity.size(4)) + } + + require(module.nInputPlane == nInputPlane, "nInputPlane is not valid") + require(module.nOutputPlane == nOutputPlane, "nOutputPlane is not valid") + require(module.kernelH == kernelSize.valueAt(1), s"kernelH is not valid") + require(module.kernelW == kernelSize.valueAt(2), "kernelW is not valid") + require(kernelSize.valueAt(3) == nInputPlane, "kernel nInputPlane is not valid") + require(kernelSize.valueAt(4) == nOutputPlane, "kernel nOutputPlane is not valid") + } + + gradWeight.zero() + module.accGradParameters(inputActivity, grads) + output = module.gradWeight + output + } +} + +object Conv2DBackFilter { + def apply[T: ClassTag]( + strideW: Int, + strideH: Int, + padW: Int = -1, + padH: Int = -1, + format: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]): Conv2DBackFilter[T] = + new Conv2DBackFilter(strideW, strideH, padW, padH, format) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossEntropy.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossEntropy.scala new file mode 100644 index 00000000000..95bebc03382 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossEntropy.scala @@ -0,0 +1,101 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Compute the cross entropy loss and the gradients. + * @param ev$1 + * @param ev + * @tparam T Numeric type. Only support float/double now + */ +class CrossEntropy[T: ClassTag](implicit ev: TensorNumeric[T]) + extends Operation[Table, Table, T] { + + private var buffer: Tensor[T] = _ + private var prob: Tensor[T] = _ + + override def updateOutput(input: Table): Table = { + val modelOutput = input[Tensor[T]](1) + val label = input[Tensor[T]](2) + + require(modelOutput.nDimension() == 2, "CrossEntropy need a 2D input") + require(modelOutput.isSameSizeAs(label), s"size not match output" + + s"(${modelOutput.size().mkString("x")}) label(${label.size().mkString("x")})") + val batch = modelOutput.size(1) + if (!output.contains(1)) { + output(1) = Tensor[T](batch) + output(2) = Tensor[T]().resizeAs(modelOutput) + } + + val loss = output[Tensor[T]](1) + val grad = output[Tensor[T]](2) + var i = 1 + while(i <= batch) { + val (l, g) = xEntropy(modelOutput.select(1, i), label.select(1, i)) + loss.setValue(i, l) + grad.select(1, i).copy(g) + i += 1 + } + + output + } + + private def xEntropy(logits: Tensor[T], label: Tensor[T]): (T, Tensor[T]) = { + if (buffer == null) { + buffer = Tensor[T]().resizeAs(logits) + prob = Tensor[T]().resizeAs(logits) + } + + // max_logits + val max = logits.max() + + // logits - max_logits + buffer.fill(ev.negative(max)) + buffer.add(logits) + + // exp(logits - max_logits) + buffer.exp() + prob.copy(buffer) + + // sum(exp(logits - max_logits)))) + val sum = buffer.sum() + // log(sum(exp(logits - max_logits))))) + val logSum = ev.log(sum) + + // (logits - max_logits) + buffer.fill(ev.negative(max)) + buffer.add(logits) + + prob.div(sum) + + // (logits - max_logits) - log(sum(exp(logits - max_logits))) + buffer.add(ev.negative(logSum)) + + // sum(-labels *((logits - max_logits) - log(sum(exp(logits - max_logits))))) + (ev.negative(buffer.cmul(label).sum()), prob.add(ev.negative(ev.one), label)) + } +} + +object CrossEntropy { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): CrossEntropy[T] = + new CrossEntropy() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Floor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Floor.scala index f01f1120d09..64dca621158 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Floor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Floor.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} import com.intel.analytics.bigdl.tensor._ import scala.reflect.ClassTag @@ -25,26 +25,11 @@ class Floor[T: ClassTag]() (implicit ev: TensorNumeric[T]) extends Operation[Tensor[_], Tensor[_], T] { override def updateOutput(input: Tensor[_]): Tensor[_] = { - input.getType() match { - case FloatType => - if (output.getType() != FloatType) { - output = Activity.allocate[Tensor[Float], Float]() - } - output.resizeAs(input) - output.asInstanceOf[Tensor[Float]].applyFun[Float]( - input.asInstanceOf[Tensor[Float]], - scala.math.floor(_).asInstanceOf[Float]) - case DoubleType => - if (output.getType() != DoubleType) { - output = Activity.allocate[Tensor[Double], Double]() - } - output.resizeAs(input) - output.asInstanceOf[Tensor[Double]].applyFun[Double]( - input.asInstanceOf[Tensor[Double]], - scala.math.floor) - case _ => throw new RuntimeException("Unsupported tensor type") + if (output.getType() != input.getType()) { + output = input.emptyInstance() } - + output.resizeAs(input) + output.asInstanceOf[Tensor[NumericWildCard]].floor(input.asInstanceOf[Tensor[NumericWildCard]]) output } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGrad.scala new file mode 100644 index 00000000000..c557bfdba86 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGrad.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.SpatialMaxPooling +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class MaxPoolGrad[T: ClassTag]( + kH: Int, + kW: Int, + strideW: Int, + strideH: Int, + padH: Int, + padW: Int, + format: DataFormat +)(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[T], T]{ + + private var module : SpatialMaxPooling[T] = _ + + override def updateOutput(input: Table): Tensor[T] = { + if (module == null) { + module = SpatialMaxPooling[T]( + kH, + kW, + strideH, + strideW, + padH, + padW, + format + ) + } + + val inputData = input[Tensor[T]](1) + val gradOutput = input[Tensor[T]](3) + module.updateOutput(inputData) + output = module.updateGradInput(inputData, gradOutput) + output + } +} + +object MaxPoolGrad { + def apply[T: ClassTag]( + kH: Int, + kW: Int, + strideW: Int, + strideH: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): MaxPoolGrad[T] = + new MaxPoolGrad(kH, kW, strideW, strideH, padH, padW, format) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala new file mode 100644 index 00000000000..6d09c79e5c4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.tf.WithoutInput +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.T + +import scala.reflect.ClassTag + +class NoOp[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Activity, Activity, T] with WithoutInput{ + + private val data = T() + + override def updateOutput(input: Activity): Activity = data +} + +object NoOp { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): NoOp[T] = new NoOp[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pow.scala new file mode 100644 index 00000000000..3050677534f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pow.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Pow[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[_], T]{ + + override def updateOutput(input: Table): Tensor[_] = { + val v = input[Tensor[NumericWildCard]](2).value() + val t = input[Tensor[NumericWildCard]](1) + if (output.getType() != t.getType()) { + output = t.emptyInstance() + } + output.resizeAs(t) + output.asInstanceOf[Tensor[NumericWildCard]].pow(t, v) + } +} + +object Pow { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Pow[T] = new Pow[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala index 1cf2ca984b6..f14dc25747a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala @@ -22,35 +22,37 @@ import com.intel.analytics.bigdl.utils.RandomGenerator import scala.reflect.ClassTag -class RandomUniform[T: ClassTag, DataType: ClassTag]( - minVal: DataType, - maxVal: DataType, - seed: Int = 0 -) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[DataType]) -extends Operation[Tensor[DataType], Tensor[DataType], T] { - - RandomGenerator.RNG.setSeed(seed) - - output = Activity.allocate[Tensor[DataType], DataType]() - override def updateOutput(input: Tensor[DataType]): Tensor[DataType] = { +private[bigdl] trait RandomNode + +class RandomUniform[T: ClassTag, D: ClassTag]( + minVal: D, maxVal: D, seed: Option[Int] = None +)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[Int], Tensor[D], T] with RandomNode { + + if (seed.isDefined) { + RandomGenerator.RNG.setSeed(seed.get) + } + + output = Activity.allocate[Tensor[D], D]() + + override def updateOutput(input: Tensor[Int]): Tensor[D] = { require(input.nDimension() == 1, "the shape should be a one-dimensional tensor.") - val shape = input.asInstanceOf[Tensor[Int]].storage().toArray + val shape = input.storage().toArray output.resize(shape).rand( - minVal.asInstanceOf[Double], - maxVal.asInstanceOf[Double]) + ev2.toType[Double](minVal), + ev2.toType[Double](maxVal)) output } } object RandomUniform { - def apply[T: ClassTag, DataType: ClassTag]( - minVal: DataType, - maxVal: DataType, - seed: Int = 0) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[DataType]): + def apply[T: ClassTag, D: ClassTag]( + minVal: D, + maxVal: D, + seed: Option[Int] = None) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Operation[Activity, Activity, T] = ModuleToOperation[T](new RandomUniform(minVal, maxVal, seed)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReluGrad.scala new file mode 100644 index 00000000000..4c603691e74 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReluGrad.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.nn.{ReLU => ReLULayer} + +import scala.reflect.ClassTag + +class ReluGrad[T: ClassTag](implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[T], T]{ + + val module = ReLULayer[T]() + + override def updateOutput(input: Table): Tensor[T] = { + val grads = input[Tensor[T]](1) + val inputs = input[Tensor[T]](2) + + output = module.updateGradInput(inputs, grads).toTensor[T] + output + } +} + +object ReluGrad { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): ReluGrad[T] = new ReluGrad() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Select.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Select.scala new file mode 100644 index 00000000000..f4997ad1df0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Select.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Selects elements from input, depending on given condition. The input is a table (condition, t, e) + * @tparam T Numeric type. Only support float/double now + */ +class Select[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Table, Activity, T] { + + override def updateOutput(input: Table): Activity = { + val condition = input[Tensor[Boolean]](1) + require(condition.isScalar, "only support condition as a scalar") + val t = input[Activity](2) + val e = input[Activity](3) + + output = if (condition.value()) t else e + output + } +} + +object Select { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Select[T] = new Select[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala new file mode 100644 index 00000000000..091ad76bd95 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.nn.{Sum => SumLayer} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Sum[T: ClassTag, D: ClassTag](keepDims: Boolean, startFromZero: Boolean = false) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T] { + + private val sum: SumLayer[T, D] = SumLayer[T, D](squeeze = !keepDims) + + override def updateOutput(input: Table): Tensor[D] = { + val data = input[Tensor[D]](1) + val dims = input[Tensor[Int]](2) + + if (output.getType() != data.getType()) { + output = data.emptyInstance() + } + output.resizeAs(data).copy(data) + + val sumDims = if (dims.isEmpty) { + return output + } else if (dims.isScalar) { + Array(if (startFromZero) dims.value() + 1 else dims.value()) + } else { + require(dims.nDimension() == 1, s"Only accept 1D as dims, but now is ${dims.nDimension()}") + val buffer = new ArrayBuffer[Int]() + dims.apply1(a => { + buffer.append(if (startFromZero) a + 1 else a) + a + }) + buffer.toArray.sortWith(_ > _) + } + + var i = 0 + while(i < sumDims.length) { + sum.changeSumDims(sumDims(i)) + val tmp = sum.updateOutput(output) + output.resizeAs(tmp).copy(tmp) + i += 1 + } + + output + } +} + +object Sum { + def apply[T: ClassTag, D: ClassTag](keepDims: Boolean = false, startFromZero: Boolean = false) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Sum[T, D] = + new Sum(keepDims, startFromZero) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Tile.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Tile.scala index b625668a940..beb6c3e61f6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Tile.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Tile.scala @@ -43,6 +43,11 @@ class Tile[T: ClassTag]()(implicit ev: TensorNumeric[T]) val input = inputs[Tensor[Tensor[NumericWildcard]]](1) val multiples = inputs[Tensor[Int]](2) + if (multiples.isEmpty) { + output = input + return output + } + require(input.nDimension() == multiples.size(1), "Length of multiples must be the same as the number of dimensions in input") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala index 1f9be5517f5..27d468ab1f5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala @@ -48,13 +48,6 @@ package object ops { = ModuleToOperation[T](CDivTable()) } - object Sum { - def apply[T: ClassTag](axis: Int, keepDim: Boolean = false) - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.Sum(dimension = axis, squeeze = !keepDim)) - } - object Reshape { def apply[T: ClassTag](size: Array[Int]) (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala index f9b4d2ae733..52fe2119f5e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag @@ -32,22 +32,30 @@ private[bigdl] class Fill[T: ClassTag]() (implicit ev: TensorNumeric[T]) override def updateOutput(input: Table): Tensor[_] = { val shapeTensor = input[Tensor[Int]](1) - require(shapeTensor.nDimension() == 1, "shape tensor is not a vector") - val shape = new Array[Int](shapeTensor.nElement()) - var i = 0 - while (i < shapeTensor.nElement()) { - shape(i) = shapeTensor.valueAt(i + 1) - i = i + 1 - } val value = input[Tensor[_]](2) - require(value.isScalar, "value tensor is not a scalar") - if (value.getType() != output.getType()) { - output = value.emptyInstance().resize(shape) + if (shapeTensor.isEmpty) { + if (value.getType() != output.getType()) { + output = value.emptyInstance() + } + output.resizeAs(value).asInstanceOf[Tensor[NumericWildCard]] + .copy(value.asInstanceOf[Tensor[NumericWildCard]]) } else { - output.resize(shape) - } + require(shapeTensor.nDimension() == 1, "shape tensor is not a vector") + val shape = new Array[Int](shapeTensor.nElement()) + var i = 0 + while (i < shapeTensor.nElement()) { + shape(i) = shapeTensor.valueAt(i + 1) + i = i + 1 + } + require(value.isScalar, "value tensor is not a scalar") + if (value.getType() != output.getType()) { + output = value.emptyInstance().resize(shape) + } else { + output.resize(shape) + } - output.forceFill(value.value()) + output.forceFill(value.value()) + } output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala index 1765387135f..d769907015a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala @@ -26,7 +26,7 @@ import scala.reflect.ClassTag class Variable[T: ClassTag](val variableValue: Tensor[T], val variableGradient: Tensor[T]) (implicit ev: TensorNumeric[T]) - extends Operation[Activity, Tensor[T], T] { + extends Operation[Activity, Tensor[T], T] with WithoutInput{ override def clearState(): this.type = { this @@ -70,3 +70,8 @@ class Variable[T: ClassTag](val variableValue: Tensor[T], val variableGradient: this.variableGradient.add(ev.fromType[Double](1.0), gradOutput) } } + +object Variable { + def apply[T: ClassTag](variableValue: Tensor[T], variableGradient: Tensor[T]) + (implicit ev: TensorNumeric[T]): Variable[T] = new Variable(variableValue, variableGradient) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index aa352221bea..20cb586ce07 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -91,11 +91,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab private def toTable(input: JList[JTensor]): Table = { input.asScala.foldLeft(new Table())((t, jtensor) => t.insert(toTensor(jtensor))) } + def jTensorsToActivity(input: JList[JTensor], isTable: Boolean): Activity = { if (input.isEmpty) { throw new IllegalArgumentException("Empty input") } - if(isTable) { + if (isTable) { toTable(input) } else { toTensor(input.iterator().next()) @@ -200,13 +201,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createLinear(inputSize: Int, outputSize: Int, - withBias: Boolean, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null, - initWeight: JTensor = null, - initBias: JTensor = null, - initGradWeight: JTensor = null, - initGradBias: JTensor = null): Linear[T] = { + withBias: Boolean, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null): Linear[T] = { Linear[T](inputSize, outputSize, withBias, wRegularizer, bRegularizer, toTensor(initWeight), toTensor(initBias), toTensor(initGradWeight), toTensor(initGradBias)) } @@ -226,6 +227,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab toTensor(initGradWeight), toTensor(initGradBias)) } + def createNegative(inplace: Boolean): Negative[T] = { + Negative[T](inplace) + } + def createDenseToSparse(): DenseToSparse[T] = { DenseToSparse[T]() } @@ -248,13 +253,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createRnnCell(inputSize: Int, - hiddenSize: Int, - activation: TensorModule[T], - isInputWithBias: Boolean = true, - isHiddenWithBias: Boolean = true, - wRegularizer: Regularizer[T] = null, - uRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null): RnnCell[T] = { + hiddenSize: Int, + activation: TensorModule[T], + isInputWithBias: Boolean = true, + isHiddenWithBias: Boolean = true, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null): RnnCell[T] = { RnnCell[T](inputSize, hiddenSize, activation, @@ -266,7 +271,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createTimeDistributedCriterion(critrn: TensorCriterion[T], - sizeAverage: Boolean = false): TimeDistributedCriterion[T] = { + sizeAverage: Boolean = false): TimeDistributedCriterion[T] = { TimeDistributedCriterion[T](critrn, sizeAverage) } @@ -356,13 +361,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialMaxPooling(kW: Int, - kH: Int, - dW: Int, - dH: Int, - padW: Int = 0, - padH: Int = 0, - ceilMode: Boolean = false, - format: String = "NCHW") + kH: Int, + dW: Int, + dH: Int, + padW: Int = 0, + padH: Int = 0, + ceilMode: Boolean = false, + format: String = "NCHW") : SpatialMaxPooling[T] = { val maxpooling = SpatialMaxPooling[T](kW, kH, @@ -376,24 +381,24 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialConvolution(nInputPlane: Int, - nOutputPlane: Int, - kernelW: Int, - kernelH: Int, - strideW: Int = 1, - strideH: Int = 1, - padW: Int = 0, - padH: Int = 0, - nGroup: Int = 1, - propagateBack: Boolean = true, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null, - initWeight: JTensor = null, - initBias: JTensor = null, - initGradWeight: JTensor = null, - initGradBias: JTensor = null, - withBias: Boolean = true, - dataFormat: String = "NCHW" - ) + nOutputPlane: Int, + kernelW: Int, + kernelH: Int, + strideW: Int = 1, + strideH: Int = 1, + padW: Int = 0, + padH: Int = 0, + nGroup: Int = 1, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null, + withBias: Boolean = true, + dataFormat: String = "NCHW" + ) : SpatialConvolution[T] = { SpatialConvolution[T](nInputPlane, nOutputPlane, @@ -430,45 +435,45 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialAveragePooling(kW: Int, - kH: Int, - dW: Int = 1, - dH: Int = 1, - padW: Int = 0, - padH: Int = 0, - globalPooling: Boolean = false, - ceilMode: Boolean = false, - countIncludePad: Boolean = true, - divide: Boolean = true, - format: String = "NCHW") + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0, + globalPooling: Boolean = false, + ceilMode: Boolean = false, + countIncludePad: Boolean = true, + divide: Boolean = true, + format: String = "NCHW") : SpatialAveragePooling[T] = { SpatialAveragePooling[T](kW, kH, dW, dH, padW, padH, globalPooling, ceilMode, countIncludePad, divide, format = DataFormat(format)) } def createSpatialBatchNormalization(nOutput: Int, - eps: Double = 1e-5, - momentum: Double = 0.1, - affine: Boolean = true, - initWeight: JTensor = null, - initBias: JTensor = null, - initGradWeight: JTensor = null, - initGradBias: JTensor = null) + eps: Double = 1e-5, + momentum: Double = 0.1, + affine: Boolean = true, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null) : SpatialBatchNormalization[T] = { SpatialBatchNormalization[T](nOutput, eps, momentum, affine, - toTensor(initWeight), toTensor(initBias), toTensor(initGradWeight), toTensor(initBias)) + toTensor(initWeight), toTensor(initBias), toTensor(initGradWeight), toTensor(initBias)) } def createSpatialCrossMapLRN(size: Int = 5, - alpha: Double = 1.0, - beta: Double = 0.75, - k: Double = 1.0) + alpha: Double = 1.0, + beta: Double = 0.75, + k: Double = 1.0) : SpatialCrossMapLRN[T] = { SpatialCrossMapLRN[T](size, alpha, beta, k) } def createDropout(initP: Double = 0.5, - inplace: Boolean = false, - scale: Boolean = true) + inplace: Boolean = false, + scale: Boolean = true) : Dropout[T] = { Dropout[T](initP, inplace, scale) } @@ -488,7 +493,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createAddConstant(constant_scalar: Double, - inplace: Boolean = false) + inplace: Boolean = false) : AddConstant[T] = { AddConstant[T](constant_scalar, inplace) @@ -496,13 +501,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createBatchNormalization(nOutput: Int, - eps: Double = 1e-5, - momentum: Double = 0.1, - affine: Boolean = true, - initWeight: JTensor = null, - initBias: JTensor = null, - initGradWeight: JTensor = null, - initGradBias: JTensor = null) + eps: Double = 1e-5, + momentum: Double = 0.1, + affine: Boolean = true, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null) : BatchNormalization[T] = { BatchNormalization[T](nOutput, eps, @@ -515,11 +520,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createBilinear(inputSize1: Int, - inputSize2: Int, - outputSize: Int, - biasRes: Boolean = true, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null) + inputSize2: Int, + outputSize: Int, + biasRes: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null) : Bilinear[T] = { Bilinear[T](inputSize1, inputSize2, @@ -530,8 +535,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createBottle(module: AbstractModule[Activity, Activity, T], - nInputDim: Int = 2, - nOutputDim1: Int = Int.MaxValue) + nInputDim: Int = 2, + nOutputDim1: Int = Int.MaxValue) : Bottle[T] = { Bottle[T](module, nInputDim, @@ -581,7 +586,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createClamp(min: Int, - max: Int) + max: Int) : Clamp[T] = { Clamp[T](min, max) @@ -593,7 +598,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createCosine(inputSize: Int, - outputSize: Int) + outputSize: Int) : Cosine[T] = { Cosine[T](inputSize, outputSize) @@ -610,7 +615,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createDiceCoefficientCriterion(sizeAverage: Boolean = true, - epsilon: Float = 1.0f) + epsilon: Float = 1.0f) : DiceCoefficientCriterion[T] = { DiceCoefficientCriterion[T](sizeAverage, epsilon) } @@ -621,15 +626,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createELU(alpha: Double = 1.0, - inplace: Boolean = false) + inplace: Boolean = false) : ELU[T] = { ELU[T](alpha, inplace) } def createEuclidean(inputSize: Int, - outputSize: Int, - fastBackward: Boolean = true) + outputSize: Int, + fastBackward: Boolean = true) : Euclidean[T] = { Euclidean[T](inputSize, outputSize, @@ -657,8 +662,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createHardTanh(minValue: Double = -1, - maxValue: Double = 1, - inplace: Boolean = false) + maxValue: Double = 1, + inplace: Boolean = false) : HardTanh[T] = { HardTanh[T](minValue, maxValue, @@ -677,7 +682,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createJoinTable(dimension: Int, - nInputDims: Int) + nInputDims: Int) : JoinTable[T] = { JoinTable[T](dimension, nInputDims) @@ -693,8 +698,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createL1Penalty(l1weight: Int, - sizeAverage: Boolean = false, - provideOutput: Boolean = true) + sizeAverage: Boolean = false, + provideOutput: Boolean = true) : L1Penalty[T] = { L1Penalty[T](l1weight, sizeAverage, @@ -702,7 +707,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createLeakyReLU(negval: Double = 0.01, - inplace: Boolean = false) + inplace: Boolean = false) : LeakyReLU[T] = { LeakyReLU[T](negval, inplace) @@ -719,9 +724,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createLookupTable(nIndex: Int, nOutput: Int, - paddingValue: Double = 0, maxNorm: Double = Double.MaxValue, - normType: Double = 2.0, shouldScaleGradByFreq: Boolean = false, - wRegularizer: Regularizer[T] = null) + paddingValue: Double = 0, maxNorm: Double = Double.MaxValue, + normType: Double = 2.0, shouldScaleGradByFreq: Boolean = false, + wRegularizer: Regularizer[T] = null) : LookupTable[T] = { LookupTable[T](nIndex, nOutput, @@ -733,7 +738,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createMM(transA: Boolean = false, - transB: Boolean = false) + transB: Boolean = false) : MM[T] = { MM[T](transA, transB) @@ -755,15 +760,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createMax(dim: Int = 1, - numInputDims: Int = Int.MinValue) + numInputDims: Int = Int.MinValue) : Max[T] = { Max[T](dim, numInputDims) } def createMean(dimension: Int = 1, - nInputDims: Int = -1, - squeeze: Boolean = true) + nInputDims: Int = -1, + squeeze: Boolean = true) : Mean[T, T] = { Mean[T](dimension, nInputDims, @@ -771,7 +776,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createMin(dim: Int = 1, - numInputDims: Int = Int.MinValue) + numInputDims: Int = Int.MinValue) : Min[T] = { Min[T](dim, numInputDims) @@ -788,15 +793,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createMulConstant(scalar: Double, - inplace: Boolean = false) + inplace: Boolean = false) : MulConstant[T] = { MulConstant[T](scalar, inplace) } def createNarrow(dimension: Int, - offset: Int, - length: Int = 1) + offset: Int, + length: Int = 1) : Narrow[T] = { Narrow[T](dimension, offset, @@ -804,14 +809,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createNarrowTable(offset: Int, - length: Int = 1) + length: Int = 1) : NarrowTable[T] = { NarrowTable[T](offset, length) } def createNormalize(p: Double, - eps: Double = 1e-10) + eps: Double = 1e-10) : Normalize[T] = { Normalize[T](p, eps) @@ -823,10 +828,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createPadding(dim: Int, - pad: Int, - nInputDim: Int, - value: Double = 0.0, - nIndex: Int = 1) + pad: Int, + nInputDim: Int, + value: Double = 0.0, + nIndex: Int = 1) : Padding[T] = { Padding[T](dim, pad, @@ -846,8 +851,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createPower(power: Double, - scale: Double = 1, - shift: Double = 0) + scale: Double = 1, + shift: Double = 0) : Power[T] = { Power[T](power, scale, @@ -855,8 +860,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createRReLU(lower: Double = 1.0 / 8, - upper: Double = 1.0 / 3, - inplace: Boolean = false) + upper: Double = 1.0 / 3, + inplace: Boolean = false) : RReLU[T] = { RReLU[T](lower, upper, @@ -869,8 +874,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createReplicate(nFeatures: Int, - dim: Int = 1, - nDim: Int = Int.MaxValue) + dim: Int = 1, + nDim: Int = Int.MaxValue) : Replicate[T] = { Replicate[T](nFeatures, dim, @@ -890,7 +895,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSelect(dimension: Int, - index: Int) + index: Int) : Select[T] = { Select[T](dimension, index) @@ -932,17 +937,17 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialDilatedConvolution(nInputPlane: Int, - nOutputPlane: Int, - kW: Int, - kH: Int, - dW: Int = 1, - dH: Int = 1, - padW: Int = 0, - padH: Int = 0, - dilationW: Int = 1, - dilationH: Int = 1, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null) + nOutputPlane: Int, + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0, + dilationW: Int = 1, + dilationH: Int = 1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null) : SpatialDilatedConvolution[T] = { SpatialDilatedConvolution[T](nInputPlane, nOutputPlane, @@ -1040,19 +1045,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialFullConvolution(nInputPlane: Int, - nOutputPlane: Int, - kW: Int, - kH: Int, - dW: Int = 1, - dH: Int = 1, - padW: Int = 0, - padH: Int = 0, - adjW: Int = 0, - adjH: Int = 0, - nGroup: Int = 1, - noBias: Boolean = false, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null) + nOutputPlane: Int, + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0, + adjW: Int = 0, + adjH: Int = 0, + nGroup: Int = 1, + noBias: Boolean = false, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null) : SpatialFullConvolution[T] = { SpatialFullConvolution[T](nInputPlane, nOutputPlane, @@ -1071,23 +1076,23 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialShareConvolution( - nInputPlane: Int, - nOutputPlane: Int, - kernelW: Int, - kernelH: Int, - strideW: Int = 1, - strideH: Int = 1, - padW: Int = 0, - padH: Int = 0, - nGroup: Int = 1, - propagateBack: Boolean = true, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null, - initWeight: JTensor = null, - initBias: JTensor = null, - initGradWeight: JTensor = null, - initGradBias: JTensor = null, - withBias: Boolean = true) : SpatialShareConvolution[T] = { + nInputPlane: Int, + nOutputPlane: Int, + kernelW: Int, + kernelH: Int, + strideW: Int = 1, + strideH: Int = 1, + padW: Int = 0, + padH: Int = 0, + nGroup: Int = 1, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null, + withBias: Boolean = true): SpatialShareConvolution[T] = { SpatialShareConvolution[T](nInputPlane, nOutputPlane, kernelW, @@ -1109,9 +1114,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialZeroPadding(padLeft: Int, - padRight: Int, - padTop: Int, - padBottom: Int) + padRight: Int, + padTop: Int, + padBottom: Int) : SpatialZeroPadding[T] = { SpatialZeroPadding[T](padLeft, padRight, @@ -1125,7 +1130,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSplitTable(dimension: Int, - nInputDims: Int = -1) + nInputDims: Int = -1) : SplitTable[T] = { SplitTable[T](dimension, nInputDims) @@ -1142,19 +1147,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSqueeze(dim: Int = Int.MinValue, - numInputDims: Int = Int.MinValue) + numInputDims: Int = Int.MinValue) : Squeeze[T] = { Squeeze[T](dim, numInputDims) } def createSum(dimension: Int = 1, - nInputDims: Int = -1, - sizeAverage: Boolean = false, - squeeze: Boolean = true - ) + nInputDims: Int = -1, + sizeAverage: Boolean = false, + squeeze: Boolean = true + ) : Sum[T, T] = { - Sum[T](dimension, + Sum[T, T](dimension, nInputDims, sizeAverage, squeeze @@ -1167,8 +1172,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createThreshold(th: Double = 1e-6, - v: Double = 0.0, - ip: Boolean = false) + v: Double = 0.0, + ip: Boolean = false) : Threshold[T] = { Threshold[T](th, v, @@ -1176,14 +1181,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createUnsqueeze(pos: Int, - numInputDims: Int = Int.MinValue) + numInputDims: Int = Int.MinValue) : Unsqueeze[T] = { Unsqueeze[T](pos, numInputDims) } def createBCECriterion(weights: JTensor = null, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true) : BCECriterion[T] = { BCECriterion[T](if (weights == null) null else toTensor(weights), sizeAverage) @@ -1209,16 +1214,16 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createMultiLabelSoftMarginCriterion(weights: JTensor = null, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true) : MultiLabelSoftMarginCriterion[T] = { MultiLabelSoftMarginCriterion[T](if (weights == null) null else toTensor(weights), sizeAverage) } def createMultiMarginCriterion(p: Int = 1, - weights: JTensor = null, - margin: Double = 1.0, - sizeAverage: Boolean = true) + weights: JTensor = null, + margin: Double = 1.0, + sizeAverage: Boolean = true) : MultiMarginCriterion[T] = { MultiMarginCriterion[T](p, if (weights == null) null else toTensor(weights), @@ -1240,9 +1245,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialContrastiveNormalization(nInputPlane: Int = 1, - kernel: JTensor = null, - threshold: Double = 1e-4, - thresval: Double = 1e-4) + kernel: JTensor = null, + threshold: Double = 1e-4, + thresval: Double = 1e-4) : SpatialContrastiveNormalization[T] = { SpatialContrastiveNormalization[T](nInputPlane, if (kernel == null) null else toTensor(kernel), @@ -1251,14 +1256,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialConvolutionMap(connTable: JTensor, - kW: Int, - kH: Int, - dW: Int = 1, - dH: Int = 1, - padW: Int = 0, - padH: Int = 0, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null) + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null) : SpatialConvolutionMap[T] = { SpatialConvolutionMap[T](if (connTable == null) null else toTensor(connTable), kW, @@ -1272,19 +1277,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createVolumetricConvolution(nInputPlane: Int, - nOutputPlane: Int, - kT: Int, - kW: Int, - kH: Int, - dT: Int = 1, - dW: Int = 1, - dH: Int = 1, - padT: Int = 0, - padW: Int = 0, - padH: Int = 0, - withBias: Boolean = true, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null) + nOutputPlane: Int, + kT: Int, + kW: Int, + kH: Int, + dT: Int = 1, + dW: Int = 1, + dH: Int = 1, + padT: Int = 0, + padW: Int = 0, + padH: Int = 0, + withBias: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null) : VolumetricConvolution[T] = { VolumetricConvolution[T](nInputPlane, nOutputPlane, @@ -1315,9 +1320,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialDivisiveNormalization(nInputPlane: Int = 1, - kernel: JTensor = null, - threshold: Double = 1e-4, - thresval: Double = 1e-4) + kernel: JTensor = null, + threshold: Double = 1e-4, + thresval: Double = 1e-4) : SpatialDivisiveNormalization[T] = { SpatialDivisiveNormalization[T](nInputPlane, if (kernel == null) null else toTensor(kernel), @@ -1326,7 +1331,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSpatialSubtractiveNormalization(nInputPlane: Int = 1, - kernel: JTensor = null) + kernel: JTensor = null) : SpatialSubtractiveNormalization[T] = { SpatialSubtractiveNormalization[T](nInputPlane, if (kernel == null) null else toTensor(kernel)) @@ -1366,7 +1371,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createClassNLLCriterion(weights: JTensor = null, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true) : ClassNLLCriterion[T] = { ClassNLLCriterion[T](if (weights == null) null else toTensor(weights), sizeAverage) @@ -1387,13 +1392,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createCrossEntropyCriterion(weights: JTensor = null, - sizeAverage: Boolean = true): CrossEntropyCriterion[T] = { + sizeAverage: Boolean = true): CrossEntropyCriterion[T] = { new CrossEntropyCriterion[T](if (null == weights) null else toTensor(weights), sizeAverage) } def createCosineEmbeddingCriterion(margin: Double = 0.0, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true) : CosineEmbeddingCriterion[T] = { CosineEmbeddingCriterion[T](margin, sizeAverage) @@ -1405,7 +1410,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createHingeEmbeddingCriterion(margin: Double = 1, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true) : HingeEmbeddingCriterion[T] = { HingeEmbeddingCriterion[T](margin, sizeAverage) @@ -1417,14 +1422,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createMarginCriterion(margin: Double = 1.0, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true) : MarginCriterion[T] = { MarginCriterion[T](margin, sizeAverage) } def createMarginRankingCriterion(margin: Double = 1.0, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true) : MarginRankingCriterion[T] = { MarginRankingCriterion[T](margin, sizeAverage) @@ -1465,7 +1470,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSoftmaxWithCriterion(ignoreLabel: Integer = null, - normalizeMode: String = "VALID") + normalizeMode: String = "VALID") : SoftmaxWithCriterion[T] = { val normM = normalizeMode match { case "FULL" => NormMode.FULL @@ -1492,9 +1497,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelEvaluate(model: AbstractModule[Activity, Activity, T], - valRDD: JavaRDD[Sample], - batchSize: Int, - valMethods: JList[ValidationMethod[T]]) + valRDD: JavaRDD[Sample], + batchSize: Int, + valMethods: JList[ValidationMethod[T]]) : JList[EvaluatedResult] = { val resultArray = model.evaluate(valRDD.rdd.map(toSample(_)), valMethods.asScala.toArray, Some(batchSize)) @@ -1518,9 +1523,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def loadCaffe(model: AbstractModule[Activity, Activity, T], - defPath: String, - modelPath: String, - matchAll: Boolean = true): AbstractModule[Activity, Activity, T] = { + defPath: String, + modelPath: String, + matchAll: Boolean = true): AbstractModule[Activity, Activity, T] = { Module.loadCaffe[T](model, defPath, modelPath, matchAll) } @@ -1529,7 +1534,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def loadTF(path: String, inputs: JList[String], outputs: JList[String], - byteOrder: String): AbstractModule[Activity, Activity, T] = { + byteOrder: String): AbstractModule[Activity, Activity, T] = { val order = byteOrder match { case "little_endian" => ByteOrder.LITTLE_ENDIAN case "big_endian" => ByteOrder.BIG_ENDIAN @@ -1539,10 +1544,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def saveTF(model: AbstractModule[Activity, Activity, T], - inputs: JList[Any], - path: String, - byteOrder: String, - dataFormat: String): Unit = { + inputs: JList[Any], + path: String, + byteOrder: String, + dataFormat: String): Unit = { val order = byteOrder.toLowerCase match { case "little_endian" => ByteOrder.LITTLE_ENDIAN case "big_endian" => ByteOrder.BIG_ENDIAN @@ -1564,9 +1569,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelPredictRDD(model: AbstractModule[Activity, Activity, T], - dataRdd: JavaRDD[Sample]): JavaRDD[JTensor] = { + dataRdd: JavaRDD[Sample]): JavaRDD[JTensor] = { val tensorRDD = model.predict(dataRdd.rdd.map(toSample(_))) - val listRDD = tensorRDD.map{res => + val listRDD = tensorRDD.map { res => val tensor = res.asInstanceOf[Tensor[T]] val cloneTensor = tensor.clone() toJTensor(cloneTensor) @@ -1581,24 +1586,24 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelPredictClass(model: AbstractModule[Activity, Activity, T], - dataRdd: JavaRDD[Sample]): JavaRDD[Int] = { + dataRdd: JavaRDD[Sample]): JavaRDD[Int] = { val tensorRDD = model.predictClass(dataRdd.rdd.map(toSample(_))) new JavaRDD[Int](tensorRDD) } def modelForward(model: AbstractModule[Activity, Activity, T], - input: JList[JTensor], - inputIsTable: Boolean): JList[JTensor] = { + input: JList[JTensor], + inputIsTable: Boolean): JList[JTensor] = { val inputActivity = jTensorsToActivity(input, inputIsTable) val outputActivity = model.forward(inputActivity) activityToJTensors(outputActivity) } def modelBackward(model: AbstractModule[Activity, Activity, T], - input: JList[JTensor], - inputIsTable: Boolean, - gradOutput: JList[JTensor], - gradOutputIsTable: Boolean): JList[JTensor] = { + input: JList[JTensor], + inputIsTable: Boolean, + gradOutput: JList[JTensor], + gradOutputIsTable: Boolean): JList[JTensor] = { val inputActivity = jTensorsToActivity(input, inputIsTable) val gradOutputActivity = jTensorsToActivity(gradOutput, gradOutputIsTable) val outputActivity = model.backward(inputActivity, gradOutputActivity) @@ -1607,36 +1612,36 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def modelSave(module: AbstractModule[Activity, Activity, T], - path: String, overWrite: Boolean): Unit = { + path: String, overWrite: Boolean): Unit = { module.save(path, overWrite) } def saveBigDLModule(module: AbstractModule[Activity, Activity, T], - path: String, overWrite: Boolean): Unit = { + path: String, overWrite: Boolean): Unit = { module.saveModule(path, overWrite) } def saveCaffe(module: AbstractModule[Activity, Activity, T], prototxtPath: String, modelPath: String, - useV2 : Boolean = true, overwrite : Boolean = false): Unit = { + useV2: Boolean = true, overwrite: Boolean = false): Unit = { module.saveCaffe(prototxtPath, modelPath, useV2, overwrite) } def criterionForward(criterion: AbstractCriterion[Activity, Activity, T], - input: JList[JTensor], - inputIsTable: Boolean, - target: JList[JTensor], - targetIsTable: Boolean): T = { + input: JList[JTensor], + inputIsTable: Boolean, + target: JList[JTensor], + targetIsTable: Boolean): T = { val inputActivity = jTensorsToActivity(input, inputIsTable) val targetActivity = jTensorsToActivity(target, targetIsTable) return criterion.forward(inputActivity, targetActivity) } def criterionBackward(criterion: AbstractCriterion[Activity, Activity, T], - input: JList[JTensor], - inputIsTable: Boolean, - target: JList[JTensor], - targetIsTable: Boolean): JList[JTensor] = { + input: JList[JTensor], + inputIsTable: Boolean, + target: JList[JTensor], + targetIsTable: Boolean): JList[JTensor] = { val inputActivity = jTensorsToActivity(input, inputIsTable) val targetActivity = jTensorsToActivity(target, targetIsTable) val outputActivity = criterion.backward(inputActivity, targetActivity) @@ -1746,7 +1751,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab beta1: Double = 0.9, beta2: Double = 0.999, Epsilon: Double = 1e-8): Adam[T] = { - new Adam[T](learningRate, learningRateDecay, beta1, beta2, Epsilon) + new Adam[T](learningRate, learningRateDecay, beta1, beta2, Epsilon) } def createAdamax( @@ -1762,7 +1767,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab learningRateDecay: Double = 0.0, decayRate: Double = 0.99, Epsilon: Double = 1e-8): RMSprop[T] = { - new RMSprop[T](learningRate, learningRateDecay, decayRate, Epsilon) + new RMSprop[T](learningRate, learningRateDecay, decayRate, Epsilon) } def loadOptimMethod(path: String): OptimMethod[T] = { @@ -1770,7 +1775,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def saveOptimMethod(method: OptimMethod[T], path: String, - overWrite: Boolean = false): Unit = { + overWrite: Boolean = false): Unit = { method.save(path, overWrite) } @@ -1782,13 +1787,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def trainTF( - modelPath: String, - output: String, - samples: JavaRDD[Sample], - optMethod: OptimMethod[T], - criterion: Criterion[T], - batchSize: Int, - endWhen: Trigger): AbstractModule[Activity, Activity, T] = { + modelPath: String, + output: String, + samples: JavaRDD[Sample], + optMethod: OptimMethod[T], + criterion: Criterion[T], + batchSize: Int, + endWhen: Trigger): AbstractModule[Activity, Activity, T] = { val nodeList = parse(modelPath) val context = @@ -1802,11 +1807,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createOptimizer(model: AbstractModule[Activity, Activity, T], - trainingRdd: JavaRDD[Sample], - criterion: Criterion[T], - optimMethod: OptimMethod[T], - endTrigger: Trigger, - batchSize: Int): Optimizer[T, MiniBatch[T]] = { + trainingRdd: JavaRDD[Sample], + criterion: Criterion[T], + optimMethod: OptimMethod[T], + endTrigger: Trigger, + batchSize: Int): Optimizer[T, MiniBatch[T]] = { val optimizer = new DistriOptimizer( _model = model, dataset = batching(trainingRdd, batchSize), @@ -1837,17 +1842,17 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def setValidation(optimizer: Optimizer[T, MiniBatch[T]], - batchSize: Int, - trigger: Trigger, - valRdd: JavaRDD[Sample], - vMethods: JList[ValidationMethod[T]]): Unit = { + batchSize: Int, + trigger: Trigger, + valRdd: JavaRDD[Sample], + vMethods: JList[ValidationMethod[T]]): Unit = { optimizer.setValidation(trigger, batching(valRdd, batchSize.toInt), vMethods.asScala.toArray) } def setCheckPoint(optimizer: Optimizer[T, MiniBatch[T]], - trigger: Trigger, - checkPointPath: String, - isOverwrite: Boolean): Unit = { + trigger: Trigger, + checkPointPath: String, + isOverwrite: Boolean): Unit = { optimizer.setCheckpoint(checkPointPath, trigger) if (isOverwrite) { optimizer.overWriteCheckpoint() @@ -1870,20 +1875,20 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def summarySetTrigger( - summary: TrainSummary, - summaryName: String, - trigger: Trigger): TrainSummary = { + summary: TrainSummary, + summaryName: String, + trigger: Trigger): TrainSummary = { summary.setSummaryTrigger(summaryName, trigger) summary } def createTrainSummary(logDir: String, - appName: String): TrainSummary = { + appName: String): TrainSummary = { new TrainSummary(logDir, appName) } def createValidationSummary(logDir: String, - appName: String): ValidationSummary = { + appName: String): ValidationSummary = { new ValidationSummary(logDir, appName) } @@ -1892,11 +1897,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createNode(module: AbstractModule[Activity, Activity, T], - x: JList[ModuleNode[T]]): ModuleNode[T] = { + x: JList[ModuleNode[T]]): ModuleNode[T] = { if (null == x || x.isEmpty) { module.inputs() } else { - module.inputs(x.asScala : _*) + module.inputs(x.asScala: _*) } } @@ -1966,7 +1971,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def setInitMethod(layer: Initializable, weightInitMethod: InitializationMethod, - biasInitMethod: InitializationMethod): layer.type = { + biasInitMethod: InitializationMethod): layer.type = { layer.setInitMethod(weightInitMethod, biasInitMethod) } @@ -2023,3 +2028,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab module.quantize() } } + +object PythonBigDLUtils { + def toTensor[T: ClassTag](jTensor: JTensor, typeName: String) + (implicit ev: TensorNumeric[T]): Tensor[T] = { + if (jTensor == null) return null + + typeName match { + case "float" => + Tensor(jTensor.storage.map(x => ev.fromType(x.toFloat)), jTensor.shape) + case "double" => + Tensor(jTensor.storage.map(x => ev.fromType(x.toDouble)), jTensor.shape) + case t: String => + throw new IllegalArgumentException(s"Not supported type: ${t}") + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeSpec.scala new file mode 100644 index 00000000000..a40835806df --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class NegativeSpec extends FlatSpec with Matchers { + "Negative forward" should "be correct" in { + val input = Tensor[Double](T(1, 2, 3)) + val m = Negative[Float]() + m.forward(input) should be(Tensor[Double](T(-1, -2, -3))) + } + + "Negative backward" should "be correct" in { + val input = Tensor[Double](T(1, 2, 3)) + val grad = Tensor[Double](T(2, 3, 4)) + val m = Negative[Float]() + m.backward(input, grad) should be(Tensor[Double](T(-2, -3, -4))) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala index 5b5aa2500cb..7b499bbeaee 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala @@ -27,7 +27,7 @@ class SumSpec extends FlatSpec with Matchers { T(3.0f, 4.0f) )) - val layer = Sum[Float](dimension = 2) + val layer = Sum[Float, Float](dimension = 2) val expect = Tensor[Float](T(3.0f, 7.0f)) @@ -40,10 +40,16 @@ class SumSpec extends FlatSpec with Matchers { T(3.0f, 4.0f) )) - val layer = Sum[Float](dimension = 2, squeeze = false) + val layer = Sum[Float, Float](dimension = 2, squeeze = false) val expect = Tensor[Float](T(T(3.0f), T(7.0f))) layer.forward(input) should be(expect) } + + "sum" should "be correct when squeeze on vector" in { + val vector = Tensor[Int](T(1, 2, 3)) + val sum = Sum[Float, Int](dimension = 1, squeeze = true) + sum.forward(vector) should be(Tensor.scalar(6)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala index 567309d423c..a83e5768be1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala @@ -81,7 +81,7 @@ class Conv2DSep extends FlatSpec with Matchers { )) - val output = Conv2D[Double](Array(1, 2, 1, 1), "SAME").forward(T(input, filter)) + val output = Conv2D[Double](2, 1, -1, -1).forward(T(input, filter)) output should equal(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala index b46ffd76473..e7ae471ded5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala @@ -39,4 +39,14 @@ class FloorSpec extends FlatSpec with Matchers { val output = Floor[Double]().forward(input) output should be(expectOutput) } + + "Floor Int operation" should "works correctly" in { + val input = + Tensor[Int](T(1, 2, 2)) + + val expectOutput = Tensor[Int](T(1, 2, 2)) + + val output = Floor[Int]().forward(input) + output should be(expectOutput) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PowSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PowSpec.scala new file mode 100644 index 00000000000..d6bdfe7ef8b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PowSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class PowSpec extends FlatSpec with Matchers { + + "Power float" should "be right" in { + val v = Tensor[Float](T(2)) + val t = Tensor[Float](T(1, 2, 3)) + val ops = Pow[Float]() + ops.forward(T(t, v)) should be(Tensor[Float](T(1, 4, 9))) + } + + "Power double" should "be right" in { + val v = Tensor[Double](T(2)) + val t = Tensor[Double](T(1, 2, 3)) + val ops = Pow[Double]() + ops.forward(T(t, v)) should be(Tensor[Double](T(1, 4, 9))) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala new file mode 100644 index 00000000000..0a76d8a0c2b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class SelectSpec extends FlatSpec with Matchers { + "select" should "be correct when condition is true" in { + val cond = Tensor.scalar[Boolean](true) + val t = Tensor[Int](T(1)) + val e = Tensor[Int](T(2)) + + val ops = Select[Float]() + ops.forward(T(cond, t, e)) should be(t) + } + + "select" should "be correct when condition is false" in { + val cond = Tensor.scalar[Boolean](false) + val t = Tensor[Int](T(1)) + val e = Tensor[Int](T(2)) + + val ops = Select[Float]() + ops.forward(T(cond, t, e)) should be(e) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SumSpec.scala index 22996470cb2..839a90a8c4f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SumSpec.scala @@ -21,17 +21,18 @@ import org.scalatest.{FlatSpec, Matchers} class SumSpec extends FlatSpec with Matchers { "Sum operation" should "works correctly" in { - import com.intel.analytics.bigdl.numeric.NumericFloat val input = - Tensor(T( - T(1f, 2f, 3f), - T(2f, 2f, 4f), - T(2f, 2f, 4f) + Tensor[Int](T( + T(1, 1, 1), + T(1, 1, 1) )) - - val expectOutput = Tensor(T(5f, 6f, 11f)) - - val output = Sum(axis = 1).forward(input) - output should be(expectOutput) + val op = Sum[Float, Int]() + op.forward(T(input, Tensor[Int]())) should be(input) + op.forward(T(input, Tensor.scalar[Int](1))) should be(Tensor[Int](T(2, 2, 2))) + op.forward(T(input, Tensor[Int](T(1)))) should be(Tensor[Int](T(2, 2, 2))) + op.forward(T(input, Tensor.scalar[Int](2))) should be(Tensor[Int](T(3, 3))) + val op1 = Sum[Float, Int](keepDims = true) + op1.forward(T(input, Tensor.scalar[Int](2))) should be(Tensor[Int](T(T(3), T(3)))) + op.forward(T(input, Tensor[Int](T(1, 2)))) should be(Tensor.scalar[Int](6)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala index 47fa8c6b6f3..347363e8c33 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala @@ -61,4 +61,10 @@ class TileSpec extends FlatSpec with Matchers { val output = Tile().forward(input) output should be(expectOutput) } + + "Tile operation" should "handle empty multiples tensor" in { + val scalar = Tensor.scalar(1) + val multiply = Tensor[Int]() + Tile[Float]().forward(T(scalar, multiply)) should be(Tensor.scalar(1)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala index 8ddde5f96e1..234cda1cf2a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala @@ -29,6 +29,13 @@ class FillSpec extends FlatSpec with Matchers { layer.forward(T(shape, value)) should be(Tensor(T(T(0.1f, 0.1f, 0.1f), T(0.1f, 0.1f, 0.1f)))) } + "Fill forward scalar" should "be correct" in { + val layer = Fill[Double]() + val shape = Tensor[Int]() + val value = Tensor[Float](Array(0.1f), Array[Int]()) + layer.forward(T(shape, value)) should be(Tensor.scalar[Float](0.1f)) + } + "Fill backward" should "be correct" in { val layer = Fill() val shape = Tensor[Int](T(2, 3)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala index b7e40bf9800..56945c0bade 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala @@ -25,7 +25,7 @@ class SumSpec extends TorchSpec { "An Sum()" should "generate correct output and grad" in { torchCheck() - val layer = Sum[Double]() + val layer = Sum[Double, Double]() val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) @@ -54,7 +54,7 @@ class SumSpec extends TorchSpec { "An Sum(2)" should "generate correct output and grad" in { torchCheck() - val layer = Sum[Double](2) + val layer = Sum[Double, Double](2) val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) @@ -83,7 +83,7 @@ class SumSpec extends TorchSpec { "An Sum(2,1,true)" should "generate correct output and grad" in { torchCheck() - val layer = Sum[Double](2, 1, true) + val layer = Sum[Double, Double](2, 1, true) val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) @@ -112,7 +112,7 @@ class SumSpec extends TorchSpec { "An Sum(-1,1,true)" should "generate correct output and grad" in { torchCheck() - val layer = Sum[Double](-1, 1, true) + val layer = Sum[Double, Double](-1, 1, true) val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala index c9385087779..ebf129d6807 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala @@ -130,4 +130,10 @@ class UnsqueezeSpec extends TorchSpec { println("Test case : Unsqueeze, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } + + "A Unsqueeze(0)" should "generate correct output and grad" in { + val layer = new Unsqueeze[Double](0) + val input = Tensor[Double](2, 2).rand() + layer.forward(input).size() should be(Array(2, 2, 1)) + } } From c58ecedb5e034b0a88f126e783f308e4eea3a6ab Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 19 Oct 2017 10:08:24 +0800 Subject: [PATCH 0464/1065] fix decode raw and StridedSlice and Transpose serilization issue (#1673) --- .../analytics/bigdl/dllib/nn/Transpose.scala | 23 ++++-- .../bigdl/dllib/nn/ops/DecodeImage.scala | 65 ++++++++------- .../bigdl/dllib/utils/tf/Session.scala | 54 +++++++------ .../dllib/utils/tf/loaders/StridedSlice.scala | 26 +++--- .../dllib/utils/tf/loaders/Transpose.scala | 74 +++++++++--------- ...record => decode_image_test_case.tfrecord} | Bin .../bigdl/dllib/nn/ops/DecodeImageSpec.scala | 22 +++++- .../bigdl/dllib/torch/TransposeSpec.scala | 8 +- 8 files changed, 156 insertions(+), 116 deletions(-) rename scala/dllib/src/test/resources/tf/{mnist_test.tfrecord => decode_image_test_case.tfrecord} (100%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala index 3c68407638c..184273a3738 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl.{AttrValue, BigDLModule} @@ -31,29 +31,38 @@ import scala.reflect.runtime.universe */ @SerialVersionUID(8543726779794064339L) class Transpose[T: ClassTag]( - val permutations: Array[(Int, Int)])(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + val permutations: Array[(Int, Int)])(implicit ev: TensorNumeric[T]) + extends AbstractModule[Tensor[_], Tensor[_], T] { - var buffer: Tensor[T] = _ + var buffer: Tensor[_] = _ - override def updateOutput(input: Tensor[T]): Tensor[T] = { + override def updateOutput(input: Tensor[_]): Tensor[_] = { + if (output.getType() != input.getType()) { + output = input.emptyInstance() + } var i = 0 buffer = input while (i < permutations.length) { buffer = buffer.transpose(permutations(i)._1, permutations(i)._2) i += 1 } - output.resizeAs(buffer).copy(buffer) + output.resizeAs(buffer).asInstanceOf[Tensor[NumericWildcard]] + .copy(buffer.asInstanceOf[Tensor[NumericWildcard]]) output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + override def updateGradInput(input: Tensor[_], gradOutput: Tensor[_]): Tensor[_] = { + if (gradInput.getType() != input.getType()) { + gradInput = input.emptyInstance() + } var i = permutations.length - 1 buffer = gradOutput while (i >= 0) { buffer = buffer.transpose(permutations(i)._1, permutations(i)._2) i -= 1 } - gradInput.resizeAs(buffer).copy(buffer) + gradInput.resizeAs(buffer).asInstanceOf[Tensor[NumericWildcard]] + .copy(buffer.asInstanceOf[Tensor[NumericWildcard]]) gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala index 69b169b5267..924f70b0f3e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala @@ -174,11 +174,11 @@ class DecodeRaw[T: ClassTag](val outType: DataType, outType match { case DataType.DT_UINT8 => decodeUint8(input, buffer.array().length) case DataType.DT_INT8 => decodeInt8(input, buffer.array().length) - case DataType.DT_INT16 => decodeInt16(input, buffer.asShortBuffer().array().length) - case DataType.DT_INT32 => decodeInt32(input, buffer.asIntBuffer().array().length) - case DataType.DT_INT64 => decodeInt64(input, buffer.asLongBuffer().array().length) - case DataType.DT_FLOAT => decodeFloat(input, buffer.asFloatBuffer().array().length) - case DataType.DT_DOUBLE => decodeDouble(input, buffer.asDoubleBuffer().array().length) + case DataType.DT_INT16 => decodeInt16(input, buffer.asShortBuffer().capacity()) + case DataType.DT_INT32 => decodeInt32(input, buffer.asIntBuffer().capacity()) + case DataType.DT_INT64 => decodeInt64(input, buffer.asLongBuffer().capacity()) + case DataType.DT_FLOAT => decodeFloat(input, buffer.asFloatBuffer().capacity()) + case DataType.DT_DOUBLE => decodeDouble(input, buffer.asDoubleBuffer().capacity()) } output } @@ -199,11 +199,12 @@ class DecodeRaw[T: ClassTag](val outType: DataType, val bytes = inputData(inputOffset + i).toByteArray val buffer = ByteBuffer.wrap(bytes) buffer.order(byteOrder) - val typedInputData = buffer.asDoubleBuffer().array() - require(typedInputData.length == featureSize, - s"each element should have the same size, first elem size: $featureSize, " + - s"${i}th elem size: ${typedInputData.length}") - System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + val typedInputData = buffer.asDoubleBuffer() + var j = 0 + while (j < featureSize) { + outputData(outputOffset + i * featureSize + j) = typedInputData.get(j) + j = j + 1 + } i = i + 1 } } @@ -224,11 +225,12 @@ class DecodeRaw[T: ClassTag](val outType: DataType, val bytes = inputData(inputOffset + i).toByteArray val buffer = ByteBuffer.wrap(bytes) buffer.order(byteOrder) - val typedInputData = buffer.asFloatBuffer().array() - require(typedInputData.length == featureSize, - s"each element should have the same size, first elem size: $featureSize, " + - s"${i}th elem size: ${typedInputData.length}") - System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + val typedInputData = buffer.asFloatBuffer() + var j = 0 + while (j < featureSize) { + outputData(outputOffset + i * featureSize + j) = typedInputData.get(j) + j = j + 1 + } i = i + 1 } } @@ -249,11 +251,12 @@ class DecodeRaw[T: ClassTag](val outType: DataType, val bytes = inputData(inputOffset + i).toByteArray val buffer = ByteBuffer.wrap(bytes) buffer.order(byteOrder) - val typedInputData = buffer.asIntBuffer().array() - require(typedInputData.length == featureSize, - s"each element should have the same size, first elem size: $featureSize, " + - s"${i}th elem size: ${typedInputData.length}") - System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + val typedInputData = buffer.asIntBuffer() + var j = 0 + while (j < featureSize) { + outputData(outputOffset + i * featureSize + j) = typedInputData.get(j) + j = j + 1 + } i = i + 1 } } @@ -274,11 +277,12 @@ class DecodeRaw[T: ClassTag](val outType: DataType, val bytes = inputData(inputOffset + i).toByteArray val buffer = ByteBuffer.wrap(bytes) buffer.order(byteOrder) - val typedInputData = buffer.asLongBuffer().array() - require(typedInputData.length == featureSize, - s"each element should have the same size, first elem size: $featureSize, " + - s"${i}th elem size: ${typedInputData.length}") - System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + val typedInputData = buffer.asLongBuffer() + var j = 0 + while (j < featureSize) { + outputData(outputOffset + i * featureSize + j) = typedInputData.get(j) + j = j + 1 + } i = i + 1 } } @@ -299,11 +303,12 @@ class DecodeRaw[T: ClassTag](val outType: DataType, val bytes = inputData(inputOffset + i).toByteArray val buffer = ByteBuffer.wrap(bytes) buffer.order(byteOrder) - val typedInputData = buffer.asShortBuffer().array() - require(typedInputData.length == featureSize, - s"each element should have the same size, first elem size: $featureSize, " + - s"${i}th elem size: ${typedInputData.length}") - System.arraycopy(typedInputData, 0, outputData, outputOffset + i * featureSize, featureSize) + val typedInputData = buffer.asShortBuffer() + var j = 0 + while (j < featureSize) { + outputData(outputOffset + i * featureSize + j) = typedInputData.get(j) + j = j + 1 + } i = i + 1 } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index e2a256e7fa5..8af140539d8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -90,7 +90,7 @@ class BigDLSessionImpl[T: ClassTag]( val inputs = Seq(enqueueNode.element.getName) val result = constructLocalData(inputs, new DataCache()) if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { - result.flatMap(splitTensorByFirstDim) + result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) } else { result } @@ -232,7 +232,7 @@ class BigDLSessionImpl[T: ClassTag]( val inputs = Seq(enqueueNode.element.getName) val result = constructLocalData(inputs, new DataCache()) if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { - result.flatMap(splitTensorByFirstDim) + result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) } else { result } @@ -260,7 +260,7 @@ class BigDLSessionImpl[T: ClassTag]( val inputs = Seq(enqueueNode.element.getName) val result = constructDistributeData(inputs, cache) if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { - result.flatMap(splitTensorByFirstDim) + result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) } else { result } @@ -270,28 +270,6 @@ class BigDLSessionImpl[T: ClassTag]( rdd } - private def splitTensorByFirstDim(table: Table): Array[Table] = { - val nElem = table.length() - require(nElem >= 1, "EnqueueManyV2 encounter a empty table") - val first = table[Tensor[_]](1) - require(first.nDimension() >= 1) - val depth = first.size(1) - val result = new Array[Table](depth) - var i = 0 - while(i < depth) { - var j = 0 - val newTable = new Table() - while (j < nElem) { - val elem = table[Tensor[ByteString]](j + 1) - newTable.insert(elem(i + 1)) - j = j + 1 - } - result(i) = newTable - i = i + 1 - } - result - } - private def handleDistriDequeueManyNode(node: Node[NodeDef], cache: DataCache): RDD[Table] = { require(node.prevNodes.length == 2, "require QueueDequeueManyV2 only has two input") val queueNode = node.prevNodes.head @@ -305,7 +283,7 @@ class BigDLSessionImpl[T: ClassTag]( val inputs = Seq(enqueueNode.element.getName) val result = constructDistributeData(inputs, cache) if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { - result.flatMap(splitTensorByFirstDim) + result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) } else { result } @@ -560,3 +538,27 @@ class BigDLSessionImpl[T: ClassTag]( } + +object BigDLSessionImpl { + private def splitTensorByFirstDim(table: Table): Array[Table] = { + val nElem = table.length() + require(nElem >= 1, "EnqueueManyV2 encounter a empty table") + val first = table[Tensor[_]](1) + require(first.nDimension() >= 1) + val depth = first.size(1) + val result = new Array[Table](depth) + var i = 0 + while(i < depth) { + var j = 0 + val newTable = new Table() + while (j < nElem) { + val elem = table[Tensor[ByteString]](j + 1) + newTable.insert(elem(i + 1)) + j = j + 1 + } + result(i) = newTable + i = i + 1 + } + result + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala index 2b5329a1c9e..3b9932caef0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala @@ -32,7 +32,22 @@ class StridedSlice extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) (implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2, 3, 4), tensorArrays => { + + // this must be defined inside this function, otherwise the loader will be + // serialized + def oneDTensorToArray(tensor: Tensor[Int]): Array[Int] = { + require(tensor.nDimension() == 1, "1D tensor required") + val result = new Array[Int](tensor.nElement()) + var i = 0 + while(i < tensor.nElement()) { + result(i) = tensor.valueAt(i + 1) + i += 1 + } + result + } + val start = oneDTensorToArray(tensorArrays(0).asInstanceOf[Tensor[Int]]) val end = oneDTensorToArray(tensorArrays(1).asInstanceOf[Tensor[Int]]) val stride = oneDTensorToArray(tensorArrays(2).asInstanceOf[Tensor[Int]]) @@ -44,16 +59,5 @@ class StridedSlice extends TensorflowOpsLoader { StrideSlice[T](specs) }) } - - private def oneDTensorToArray(tensor: Tensor[Int]): Array[Int] = { - require(tensor.nDimension() == 1, "1D tensor required") - val result = new Array[Int](tensor.nElement()) - var i = 0 - while(i < tensor.nElement()) { - result(i) = tensor.valueAt(i + 1) - i += 1 - } - result - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala index aacaca4a415..30c45417bfe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala @@ -32,51 +32,53 @@ class Transpose extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) (implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(2), tensorArrays => { - val perm = tensorArrays(0).asInstanceOf[Tensor[Int]].storage().array() - val paris = permToPair(perm) - val layer = Sequential() - layer.add(Transpose[T](paris)) - layer.add(Contiguous()) - layer - }) - } - private def permToPair(perm: Array[Int]): Array[(Int, Int)] = { + Adapter[T](Array(2), tensorArrays => { + // this must be defined inside this function, otherwise the loader will be + // serialized + def permToPair(perm: Array[Int]): Array[(Int, Int)] = { + val numToRank = perm.zipWithIndex.toMap + val arr = perm.indices.toArray + val pairs = ArrayBuffer[(Int, Int)]() - val numToRank = perm.zipWithIndex.toMap - val arr = perm.indices.toArray - val pairs = ArrayBuffer[(Int, Int)]() + def sort(arr: Array[Int], low: Int, high: Int): Unit = { + var i = low + var j = high + val pivot = arr(low + (high - low)/2) - def sort(arr: Array[Int], low: Int, high: Int): Unit = { - var i = low - var j = high - val pivot = arr(low + (high - low)/2) + while (i <= j) { + while (arr(i) < pivot) i += 1 + while (arr(j) > pivot) j -= 1 - while (i <= j) { - while (arr(i) < pivot) i += 1 - while (arr(j) > pivot) j -= 1 + if (i <= j) { + exchangeNumbers(arr, i, j) + i += 1 + j -= 1 + } + } - if (i <= j) { - exchangeNumbers(arr, i, j) - i += 1 - j -= 1 + if (low < j) sort(arr, low, j) + if (i < high) sort(arr, i, high) } - } - if (low < j) sort(arr, low, j) - if (i < high) sort(arr, i, high) - } + def exchangeNumbers(arr: Array[Int], i: Int, j: Int): Unit = { + val temp = arr(i) + arr(i) = arr(j) + arr(j) = temp + pairs += ((i, j)) + } - def exchangeNumbers(arr: Array[Int], i: Int, j: Int): Unit = { - val temp = arr(i) - arr(i) = arr(j) - arr(j) = temp - pairs += ((i, j)) - } + sort(arr.map(numToRank), 0, arr.length-1) - sort(arr.map(numToRank), 0, arr.length-1) + pairs.filter(pair => pair._1 != pair._2).toArray + } - pairs.filter(pair => pair._1 != pair._2).toArray + val perm = tensorArrays(0).asInstanceOf[Tensor[Int]].storage().array() + val paris = permToPair(perm) + val layer = Sequential() + layer.add(Transpose[T](paris.map(x => (x._1 + 1, x._2 + 1)))) + layer.add(Contiguous()) + layer + }) } } diff --git a/scala/dllib/src/test/resources/tf/mnist_test.tfrecord b/scala/dllib/src/test/resources/tf/decode_image_test_case.tfrecord similarity index 100% rename from scala/dllib/src/test/resources/tf/mnist_test.tfrecord rename to scala/dllib/src/test/resources/tf/decode_image_test_case.tfrecord diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala index d38b33bb005..df407a5ba66 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.utils.tf.TFRecordIterator import org.scalatest.{FlatSpec, Matchers} import java.io.{File => JFile} -import java.nio.ByteOrder +import java.nio.{ByteBuffer, ByteOrder} import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} @@ -39,6 +39,24 @@ class DecodeImageSpec extends FlatSpec with Matchers { output.size() should be (Array(28*28)) } + "DecodeRaw " should "be able to decode float raw bytes" in { + + val data = ByteBuffer.allocate(16) + data.order(ByteOrder.LITTLE_ENDIAN) + data.putFloat(1.0f) + data.putFloat(2.0f) + data.putFloat(3.0f) + data.putFloat(4.0f) + + val input = Tensor.scalar(ByteString.copyFrom(data.array())) + + val decoder = new DecodeRaw[Float](DataType.DT_FLOAT, true) + + val output = decoder.forward(input).asInstanceOf[Tensor[Int]] + + output should be (Tensor[Float](Array(1.0f, 2.0f, 3.0f, 4.0f), Array(4))) + } + "DecodePng " should "be able to decode png" in { val input = getInputs("png") @@ -91,7 +109,7 @@ class DecodeImageSpec extends FlatSpec with Matchers { } val resource = getClass.getClassLoader.getResource("tf") - val path = resource.getPath + JFile.separator + "mnist_test.tfrecord" + val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" val file = new JFile(path) val bytesVector = new TFRecordIterator(file).toVector diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TransposeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TransposeSpec.scala index a0354b137f0..7070b97f9eb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TransposeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TransposeSpec.scala @@ -59,11 +59,11 @@ class TransposeSpec extends TorchSpec { val luaOutput1 = torchResult("output").asInstanceOf[Tensor[Double]] val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Double]] - luaOutput1.map(output, (v1, v2) => { + luaOutput1.map(output.asInstanceOf[Tensor[Double]], (v1, v2) => { assert(abs(v1 - v2) < 1e-6); v1 }) - luaOutput2.map(gradInput, (v1, v2) => { + luaOutput2.map(gradInput.asInstanceOf[Tensor[Double]], (v1, v2) => { assert(abs(v1 - v2) < 1e-6); v1 }) @@ -108,11 +108,11 @@ class TransposeSpec extends TorchSpec { val luaOutput1 = torchResult("output").asInstanceOf[Tensor[Double]] val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Double]] - luaOutput1.map(output, (v1, v2) => { + luaOutput1.map(output.asInstanceOf[Tensor[Double]], (v1, v2) => { assert(abs(v1 - v2) < 1e-6); v1 }) - luaOutput2.map(gradInput, (v1, v2) => { + luaOutput2.map(gradInput.asInstanceOf[Tensor[Double]], (v1, v2) => { assert(abs(v1 - v2) < 1e-6); v1 }) From 6bb13300c770efc32b3a7f49d9b1aba33a0a9b46 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Thu, 19 Oct 2017 12:58:55 +0800 Subject: [PATCH 0465/1065] Move classes.lst and img_class.lst to the model example folder. So user can find them easier. --- .../bigdl/dllib/models/inception/README.md | 2 +- .../bigdl/dllib/models/inception/classes.lst | 1000 + .../dllib/models/inception/img_class.lst | 50000 ++++++++++++++++ 3 files changed, 51001 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/classes.lst create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/img_class.lst diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md index 5274e602103..e6d8050d1ed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md @@ -11,7 +11,7 @@ You can download imagenet-2012 data from . After you download the files(**ILSVRC2012_img_train.tar** and **ILSVRC2012_img_val.tar**), run the follow commands to prepare the data. -classes.lst and img_class.lst used below can be found at bigdl_folder/scripts folder +classes.lst and img_class.lst used below can be found in the current folder. ```bash mkdir train mv ILSVRC2012_img_train.tar train/ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/classes.lst b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/classes.lst new file mode 100644 index 00000000000..88aa58f966b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/classes.lst @@ -0,0 +1,1000 @@ +n01440764 +n01443537 +n01484850 +n01491361 +n01494475 +n01496331 +n01498041 +n01514668 +n01514859 +n01518878 +n01530575 +n01531178 +n01532829 +n01534433 +n01537544 +n01558993 +n01560419 +n01580077 +n01582220 +n01592084 +n01601694 +n01608432 +n01614925 +n01616318 +n01622779 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+ILSVRC2012_val_00049998.JPEG n02106166 +ILSVRC2012_val_00049999.JPEG n10148035 +ILSVRC2012_val_00050000.JPEG n02437616 From 7a9334cdf0e355b6b5686f73a1c93d76dfc92b66 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Thu, 19 Oct 2017 14:24:36 +0800 Subject: [PATCH 0466/1065] add ptb model example (#1672) * add ptb model example * meet review --- .../example/languagemodel/PTBModel.scala | 66 ++++++++++ .../example/languagemodel/PTBWordLM.scala | 113 ++++++++++++++++++ .../dllib/example/languagemodel/README.md | 45 +++++++ .../dllib/example/languagemodel/Utils.scala | 113 ++++++++++++++++++ .../feature/dataset/text/Dictionary.scala | 16 ++- .../text/LabeledSentenceToSample.scala | 103 ++++++++++------ .../dataset/text/TextToLabeledSentence.scala | 39 ++++++ .../bigdl/dllib/models/rnn/Utils.scala | 45 +++++++ .../dllib/nn/TimeDistributedCriterion.scala | 43 +++---- 9 files changed, 524 insertions(+), 59 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBWordLM.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/Utils.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala new file mode 100644 index 00000000000..2f81ddf23d6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala @@ -0,0 +1,66 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.languagemodel + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Graph._ +import com.intel.analytics.bigdl.nn.{TimeDistributed, _} + +object PTBModel { + def apply( + inputSize: Int, + hiddenSize: Int, + outputSize: Int, + numLayers: Int, + keepProb: Float = 2.0f) + : Module[Float] = { + val input = Input[Float]() + val embeddingLookup = LookupTable[Float](inputSize, hiddenSize).inputs(input) + + val inputs = if (keepProb < 1) { + Dropout[Float](keepProb).inputs(embeddingLookup) + } else embeddingLookup + + val lstm = addLayer(hiddenSize, hiddenSize, 1, numLayers, inputs) + val linear = Linear[Float](hiddenSize, outputSize) + val output = TimeDistributed[Float](linear).inputs(lstm) + + Graph(input, output) + } + + private def addLayer(inputSize: Int, + hiddenSize: Int, + depth: Int, + numLayers: Int, + input: ModuleNode[Float]): ModuleNode[Float] = { + if (depth == numLayers) { + Recurrent[Float]() + .add(LSTM[Float](inputSize, hiddenSize, 0, null, null, null)) + .inputs(input) + } else { + addLayer( + inputSize, + hiddenSize, + depth + 1, + numLayers, + Recurrent[Float]() + .add(LSTM[Float](inputSize, hiddenSize, 0, null, null, null)) + .inputs(input) + ) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBWordLM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBWordLM.scala new file mode 100644 index 00000000000..523c955f01d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBWordLM.scala @@ -0,0 +1,113 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.languagemodel + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.text.{LabeledSentenceToSample, _} +import com.intel.analytics.bigdl.dataset.{DataSet, SampleToMiniBatch} +import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, Module, TimeDistributedCriterion} +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._ +import com.intel.analytics.bigdl.utils.Engine +import org.apache.log4j.{Level, Logger} +import org.apache.spark.SparkContext +import com.intel.analytics.bigdl.example.languagemodel.Utils._ +import com.intel.analytics.bigdl.models.rnn.SequencePreprocess + +object PTBWordLM { + Logger.getLogger("org").setLevel(Level.ERROR) + Logger.getLogger("akka").setLevel(Level.ERROR) + Logger.getLogger("breeze").setLevel(Level.ERROR) + Logger.getLogger("com.intel.analytics.bigdl.example").setLevel(Level.INFO) + val logger = Logger.getLogger(getClass) + def main(args: Array[String]): Unit = { + trainParser.parse(args, new TrainParams()).map(param => { + + val conf = Engine.createSparkConf() + .setAppName("Train ptbModel on text") + .set("spark.task.maxFailures", "1") + val sc = new SparkContext(conf) + Engine.init + + val (trainData, validData, testData, dictionary) = SequencePreprocess( + param.dataFolder, param.vocabSize) + + val trainSet = DataSet.rdd(sc.parallelize( + SequencePreprocess.reader(trainData, param.numSteps))) + .transform(TextToLabeledSentence[Float](param.numSteps)) + .transform(LabeledSentenceToSample[Float]( + oneHot = false, + fixDataLength = None, + fixLabelLength = None)) + .transform(SampleToMiniBatch[Float](param.batchSize)) + + val validationSet = DataSet.rdd(sc.parallelize( + SequencePreprocess.reader(validData, param.numSteps))) + .transform(TextToLabeledSentence[Float](param.numSteps)) + .transform(LabeledSentenceToSample[Float]( + oneHot = false, + fixDataLength = None, + fixLabelLength = None)) + .transform(SampleToMiniBatch[Float](param.batchSize)) + + val model = if (param.modelSnapshot.isDefined) { + Module.loadModule[Float](param.modelSnapshot.get) + } else { + val curModel = PTBModel( + inputSize = param.vocabSize, + hiddenSize = param.hiddenSize, + outputSize = param.vocabSize, + numLayers = param.numLayers, + keepProb = param.keepProb) + curModel.reset() + curModel + } + + val optimMethod = if (param.stateSnapshot.isDefined) { + OptimMethod.load[Float](param.stateSnapshot.get) + } else { + new Adagrad[Float](learningRate = param.learningRate, + learningRateDecay = param.learningRateDecay) + } + + val optimizer = Optimizer( + model = model, + dataset = trainSet, + criterion = TimeDistributedCriterion[Float]( + CrossEntropyCriterion[Float](), sizeAverage = false, dimension = 1) + ) + + if (param.checkpoint.isDefined) { + optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch) + } + + if(param.overWriteCheckpoint) { + optimizer.overWriteCheckpoint() + } + + optimizer + .setValidation(Trigger.everyEpoch, validationSet, Array(new Loss[Float]( + TimeDistributedCriterion[Float]( + CrossEntropyCriterion[Float](), + sizeAverage = false, dimension = 1)))) + .setOptimMethod(optimMethod) + .setEndWhen(Trigger.maxEpoch(param.nEpochs)) + .optimize() + sc.stop() + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md new file mode 100644 index 00000000000..9a5a56ee026 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md @@ -0,0 +1,45 @@ +# Language Model + +This example refers to [tensorflow ptb example](https://www.tensorflow.org/tutorials/recurrent#language_modeling), which shows how to train a recurrent neural network on a challenging task of language modeling. + +The core of our model consists of LSTM cells that process one word at a time and computes probabilities of the possible values for the next word in the sentence. + +Here we use [Penn Tree Bank (PTB)](https://catalog.ldc.upenn.edu/ldc99t42) as training dataset, which is a popular benchmark for measuring the quality of these models, whilst being small and relatively fast to train. + +## Get BigDL jar + +Please build BigDL referring to [Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page). + +## Prepare PTB Data +Download PTB dataset from [Tomas Mikolov's webpage](http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz) +Then, extract the PTB dataset underneath your home directory as follows: + +```tar xvfz simple-examples.tgz -C $HOME``` + +Dataset here we need is $HOME/simple-examples/data + +## Train the Model +Example command +``` +spark-submit \ +--master spark://... \ +--executor-cores cores_per_executor \ +--total-executor-cores total_cores_for_the_job \ +--class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM \ +dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +-f $HOME/simple-examples/data -b 420 --checkpoint $HOME/model --numLayers 2 --vocab 10001 -h 1500 --numSteps 35 -r 0.005 -e 20 --learningRateDecay 0.001 --keepProb 0.5 --overWriteCheckpoint +``` + +In the above commands: +```-f```: where you put your PTB data +```-b```: Total batch size. It is expected that batch size is a multiple of core_number +```--checkpoint```: Where you cache the model/train_state snapshot. +```--learningRate```: learning rate for adagrad +```--learningRateDecay```: learning rate decay for adagrad +```--hiddenSize```: hiddensize for lstm +```--vocabSize```: vocabulary size, default 10000 +```--nEpochs```: epochs to run +```--numLayers```: numbers of lstm cell, default 2 lstm cells +```--numSteps```: number of words per record in LM +```--overWriteCheckpoint```: do overwrite when saving checkpoint +```--keepProb```: the probability to do dropout \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/Utils.scala new file mode 100644 index 00000000000..127bd6d6ba0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/Utils.scala @@ -0,0 +1,113 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.languagemodel + +import scopt.OptionParser +import java.io._ + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.text._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.util.Random +import org.apache.log4j.Logger +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.io.Source +import scala.reflect.ClassTag + +object Utils { + + case class TrainParams( + dataFolder: String = "./", + modelSnapshot: Option[String] = None, + stateSnapshot: Option[String] = None, + checkpoint: Option[String] = None, + batchSize: Int = 20, + learningRate: Double = 0.01, + learningRateDecay: Double = 0.001, + hiddenSize: Int = 200, + vocabSize: Int = 10000, + nEpochs: Int = 4, + numLayers: Int = 2, + numSteps: Int = 20, + overWriteCheckpoint: Boolean = false, + keepProb: Float = 2.0f) + + val trainParser = new OptionParser[TrainParams]("BigDL ptbModel Train Example") { + opt[String]('f', "dataFolder") + .text("where you put the text data") + .action((x, c) => c.copy(dataFolder = x)) + .required() + + opt[String]("model") + .text("model snapshot location") + .action((x, c) => c.copy(modelSnapshot = Some(x))) + opt[String]("state") + .text("state snapshot location") + .action((x, c) => c.copy(stateSnapshot = Some(x))) + + opt[String]("checkpoint") + .text("where to cache the model and state") + .action((x, c) => c.copy(checkpoint = Some(x))) + + opt[Int]('b', "batchSize") + .text("batchSize of rnn") + .action((x, c) => c.copy(batchSize = x)) + .required() + + opt[Double]('r', "learningRate") + .text("learning rate") + .action((x, c) => c.copy(learningRate = x)) + + opt[Double]("learningRateDecay") + .text("learningRateDecay") + .action((x, c) => c.copy(learningRateDecay = x)) + + opt[Int]('h', "hidden") + .text("hidden size") + .action((x, c) => c.copy(hiddenSize = x)) + + opt[Int]("vocab") + .text("dictionary length | vocabulary size") + .action((x, c) => c.copy(vocabSize = x)) + + opt[Int]('e', "nEpochs") + .text("epoch numbers") + .action((x, c) => c.copy(nEpochs = x)) + + opt[Int]("numLayers") + .text("number of recurrent layers") + .action((x, c) => c.copy(numLayers = x)) + + opt[Int]("numSteps") + .text("number of words per record in LM") + .action((x, c) => c.copy(numSteps = x)) + + opt[Unit]("overWrite") + .text("overwrite checkpoint files") + .action( (_, c) => c.copy(overWriteCheckpoint = true) ) + + opt[Double]("keepProb") + .text("the probability p to do dropout") + .action((x, c) => c.copy(keepProb = x.toFloat)) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/Dictionary.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/Dictionary.scala index 1958faf769f..c2cfea9d4df 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/Dictionary.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/Dictionary.scala @@ -153,6 +153,17 @@ class Dictionary() update(freqDict, vocabSize) } + def this(words: Array[String], + vocabSize: Int) = { + this() + val freqDict = words + .foldLeft(Map.empty[String, Int]) { + (count, word) => count + (word -> (count.getOrElse(word, 0) + 1)) + }.toSeq.sortBy(_._2) + + update(freqDict, vocabSize) + } + def this(sentences: Stream[Array[String]], vocabSize: Int) = { this() @@ -211,9 +222,12 @@ class Dictionary() } object Dictionary { - def apply[S <: Iterator[Array[String]]](sentences: S, vocabSize: Int) + def apply(sentences: Iterator[Array[String]], vocabSize: Int) : Dictionary = new Dictionary(sentences, vocabSize) + def apply(words: Array[String], vocabSize: Int) + : Dictionary = new Dictionary(words, vocabSize) + def apply(dataset: Stream[Array[String]], vocabSize: Int) : Dictionary = new Dictionary(dataset, vocabSize) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/LabeledSentenceToSample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/LabeledSentenceToSample.scala index 0e2db559503..e6bc6cb3360 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/LabeledSentenceToSample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/LabeledSentenceToSample.scala @@ -36,16 +36,34 @@ object LabeledSentenceToSample { new LabeledSentenceToSample[T]( vocabLength, fixDataLength, - fixLabelLength) + fixLabelLength, + true) + def apply[T: ClassTag] + (oneHot: Boolean, + fixDataLength: Option[Int], + fixLabelLength: Option[Int]) + (implicit ev: TensorNumeric[T]) + : LabeledSentenceToSample[T] = + new LabeledSentenceToSample[T]( + vocabLength = 0, + fixDataLength, + fixLabelLength, + oneHot) } /** + * if oneHot = true: * Transform labeled sentences to one-hot format samples * e.g. sentence._data: [0, 2, 3] * sentence._label: [2, 3, 1] * vocabLength: 4 * => input: [[1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]] * target: [3, 4, 2] + * + * else: + * The model will use LookupTable for word embedding. + * => input: [1, 2, 3] + * => label: [2, 3, 4] * The input is an iterator of LabeledSentence class * The output is an iterator of Sample class * @@ -53,10 +71,11 @@ object LabeledSentenceToSample { * @param fixDataLength optional parameter for fixed length of input data * @param fixLabelLength optional parameter for fixed length of labels */ -class LabeledSentenceToSample[T: ClassTag](vocabLength: Int, - fixDataLength: Option[Int], - fixLabelLength: Option[Int]) - (implicit ev: TensorNumeric[T]) +class LabeledSentenceToSample[T: ClassTag]( + vocabLength: Int, + fixDataLength: Option[Int], + fixLabelLength: Option[Int], + oneHot: Boolean = true)(implicit ev: TensorNumeric[T]) extends Transformer[LabeledSentence[T], Sample[T]] { private val feature: Tensor[T] = Tensor() @@ -68,16 +87,17 @@ class LabeledSentenceToSample[T: ClassTag](vocabLength: Int, val dataLength = fixDataLength.getOrElse(sentence.dataLength()) val labelLength = fixLabelLength.getOrElse(sentence.labelLength()) - // initialize featureBuffer - feature.resize(dataLength, vocabLength) - feature.zero() - label.resize(labelLength) - label.zero() + if (oneHot) { + // initialize featureBuffer + feature.resize(dataLength, vocabLength) + feature.zero() + label.resize(labelLength) + label.zero() - val featureBuffer = feature.storage().array() - val labelBuffer = label.storage().array() + val featureBuffer = feature.storage().array() + val labelBuffer = label.storage().array() - /* One-Hot format for feature + /* One-Hot format for feature * Expected transformed format should be: * * Example1: Input = [0, 2, 3], label = [2, 3, 1], dictionary length = 4 @@ -90,31 +110,40 @@ class LabeledSentenceToSample[T: ClassTag](vocabLength: Int, */ - val startTokenIndex = sentence.getData(0) - val endTokenIndex = if (labelLength == 1) 0 - else ev.toType[Int](sentence.getLabel(sentence.labelLength - 1)) - - var i = 0 - while (i < sentence.dataLength) { - featureBuffer(i*vocabLength + ev.toType[Int](sentence.getData(i))) - = ev.fromType[Float](1.0f) - i += 1 - } - while (i < dataLength) { - featureBuffer(i*vocabLength + endTokenIndex) = ev.fromType[Float](1.0f) - i += 1 - } - - i = 0 - while (i < sentence.labelLength) { - labelBuffer(i) = ev.plus(sentence.label()(i), ev.fromType[Float](1.0f)) - i += 1 - } - while (i < labelLength) { - labelBuffer(i) = ev.plus(startTokenIndex, ev.fromType[Float](1.0f)) - i += 1 + val startTokenIndex = sentence.getData(0) + val endTokenIndex = if (labelLength == 1) 0 + else ev.toType[Int](sentence.getLabel(sentence.labelLength - 1)) + + var i = 0 + while (i < sentence.dataLength) { + featureBuffer(i * vocabLength + ev.toType[Int](sentence.getData(i))) + = ev.fromType[Float](1.0f) + i += 1 + } + while (i < dataLength) { + featureBuffer(i * vocabLength + endTokenIndex) = ev.fromType[Float](1.0f) + i += 1 + } + + i = 0 + while (i < sentence.labelLength) { + labelBuffer(i) = ev.plus(sentence.label()(i), ev.fromType[Float](1.0f)) + i += 1 + } + while (i < labelLength) { + labelBuffer(i) = ev.plus(startTokenIndex, ev.fromType[Float](1.0f)) + i += 1 + } + } else { + feature.resize(dataLength).zero + label.resize(labelLength).zero + + val featureBuffer = feature.storage().array() + val labelBuffer = label.storage().array() + + Array.copy(sentence.data, 0, featureBuffer, 0, dataLength) + Array.copy(sentence.label, 0, labelBuffer, 0, labelLength) } - Sample[T](feature, label) }) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/TextToLabeledSentence.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/TextToLabeledSentence.scala index 14d11b32b1f..b8124de111e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/TextToLabeledSentence.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/TextToLabeledSentence.scala @@ -27,6 +27,8 @@ object TextToLabeledSentence { (implicit ev: TensorNumeric[T]) : TextToLabeledSentence[T] = new TextToLabeledSentence[T](dictionary) + def apply[T: ClassTag](numSteps: Int)(implicit ev: TensorNumeric[T]) + : TextToSentenceWithSteps[T] = new TextToSentenceWithSteps[T](numSteps) } /** @@ -57,3 +59,40 @@ class TextToLabeledSentence[T: ClassTag](dictionary: Dictionary) }) } } + +/** + * Transform a sequence of integers to LabeledSentence. + * e.g. input = [0, 1, 2, 3, 4, 5, 6, ..] + * numSteps = 3 + * + * xbuffer = [0, 1, 2] + * ybuffer = [1, 2, 3] + * + * next: + * xbuffer = [3, 4, 5] + * ybuffer = [4, 5, 6] + * @param numSteps + * @param ev$1 + * @param ev + * @tparam T + */ +private[bigdl] class TextToSentenceWithSteps[T: ClassTag](numSteps: Int) + (implicit ev: TensorNumeric[T]) + extends Transformer[Array[T], LabeledSentence[T]] { + val xbuffer = new Array[T](numSteps) + val ybuffer = new Array[T](numSteps) + val buffer = new LabeledSentence[T]() + + override def apply(prev: Iterator[Array[T]]): Iterator[LabeledSentence[T]] = { + prev.map(sentence => { + require(sentence.length >= numSteps + 1, + "input sentence length should be numSteps + 1, " + + s"sentence.length = ${sentence.length}, numSteps = ${numSteps}") + Array.copy(sentence, 0, xbuffer, 0, numSteps) + Array.copy(sentence, 1, ybuffer, 0, numSteps) + + buffer.copy(xbuffer, ybuffer) + buffer + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Utils.scala index ace771fa046..bd43f7468c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/Utils.scala @@ -28,6 +28,7 @@ import org.apache.log4j.Logger import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD +import scala.collection.mutable.ArrayBuffer import scala.io.Source import scala.reflect.ClassTag @@ -196,6 +197,50 @@ object SequencePreprocess { tokens } + def apply( + fileDirect: String, + vocabSize: Int): (Array[Float], Array[Float], Array[Float], Dictionary) = { + + val trainPath = new File(fileDirect, "ptb.train.txt").toString + val validPath = new File(fileDirect, "ptb.valid.txt").toString + val testPath = new File(fileDirect, "ptb.test.txt").toString + + val dictionary = Dictionary(readWords(trainPath).toArray, vocabSize - 1) + val trainData = fileToWordIdx(trainPath, dictionary) + val validData = fileToWordIdx(validPath, dictionary) + val testData = fileToWordIdx(testPath, dictionary) + + (trainData.toArray, validData.toArray, testData.toArray, dictionary) + } + + def reader(rawData: Array[Float], numSteps: Int): Array[Array[Float]] = { + var offset = 0 + val length = rawData.length - 1 - numSteps + val buffer = new ArrayBuffer[Array[Float]] + while (offset <= length) { + val slice = new Array[Float](numSteps + 1) + Array.copy(rawData, offset, slice, 0, numSteps + 1) + buffer.append(slice) + offset += numSteps + } + buffer.toArray[Array[Float]] + } + + private[bigdl] def fileToWordIdx(fileName: String, dictionary: Dictionary) + : Iterator[Float] = { + val words = readWords(fileName) + words.map(x => dictionary.getIndex(x).toFloat + 1.0f) + } + + private[bigdl] def readWords(fileName: String): Iterator[String] = { + val buffer = new ArrayBuffer[String] + val readWords = Source.fromFile(fileName).getLines.foreach(x => { + val words = x.split(" ").foreach(t => buffer.append(t)) + buffer.append("") + }) + buffer.toIterator + } + private[bigdl] def load(fileName: String): Array[String] = { import scala.io.Source require(new File(fileName).exists(), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedCriterion.scala index 1fee232181d..3f9463e7d24 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedCriterion.scala @@ -30,12 +30,16 @@ import scala.reflect.ClassTag * Apply Any Provided Criterion to every temporal slice of an input. * * @param critrn embedded criterion - * @param sizeAverage whether to divide the sequence length + * @param sizeAverage whether to divide the length of input along dimension. + * @param dimension to compute criterion loss along the input and target dimension + * if sizeAverage=true, and dimension=1, it means to divide the batch size; + * if sizeAverage=true, and dimension=2, it means to divide the sequence length */ class TimeDistributedCriterion[T : ClassTag]( val critrn : TensorCriterion[T], - val sizeAverage: Boolean = false) + val sizeAverage: Boolean = false, + val dimension: Int = 2) (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { private var fInput: Tensor[T] = Tensor[T]() @@ -47,7 +51,6 @@ class TimeDistributedCriterion[T : ClassTag]( @transient protected var results: Array[Future[Unit]] = _ - /** * Clone N criterions; N depends on the time dimension of the input * @param times @@ -65,18 +68,17 @@ class TimeDistributedCriterion[T : ClassTag]( override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { /** * Take each time slice of input and target, and add up all outputs of slices - * For example + * Example with dimension=2: * input.size = [B, T, D] => fInput.size = [B, D] * target.size = [B, T] => fTarget.size = [B] - * If sizeAverage is true, the output is averaged through time dimension + * If sizeAverage is true, the output is averaged through time dim */ - val timeDim = 2 - require(input.size(timeDim) == target.size(timeDim), + require(input.size(dimension) == target.size(dimension), "target should have as many elements as input, " + - s"input ${input.size(timeDim)}, target ${target.size(timeDim)}") + s"input ${input.size(dimension)}, target ${target.size(dimension)}") output = ev.fromType[Int](0) - val nstep = input.size(timeDim) + val nstep = input.size(dimension) extend(nstep) if (results == null || results.length != nstep) { @@ -87,8 +89,8 @@ class TimeDistributedCriterion[T : ClassTag]( while (i < nstep) { val _i = i + 1 results(i) = Engine.model.invoke(() => { - fInput = input.select(timeDim, _i) - fTarget = target.select(timeDim, _i) + fInput = input.select(dimension, _i) + fTarget = target.select(dimension, _i) cells(_i - 1).updateOutput(fInput, fTarget) }) i += 1 @@ -108,23 +110,22 @@ class TimeDistributedCriterion[T : ClassTag]( override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { /** * Take each time slice of input and target, and calculate gradInput of each slice - * If sizeAverage is true, the gradInput is also averaged through time dimension + * If sizeAverage is true, the gradInput is also averaged through dimension */ - val timeDim = 2 - require(input.size(timeDim) == target.size(timeDim), + require(input.size(dimension) == target.size(dimension), s"target should have as many elements as input, " + - s"input ${input.size(timeDim)}, target ${target.size(timeDim)}") + s"input ${input.size(dimension)}, target ${target.size(dimension)}") gradInput.resizeAs(input).zero() - val nstep = input.size(timeDim) + val nstep = input.size(dimension) var i = 0 while (i < nstep) { val _i = i + 1 results(i) = Engine.model.invoke(() => { - fInput = input.select(timeDim, _i) - fTarget = target.select(timeDim, _i) - _gradInput = gradInput.select(timeDim, _i) + fInput = input.select(dimension, _i) + fTarget = target.select(dimension, _i) + _gradInput = gradInput.select(dimension, _i) _gradInput.copy(cells(_i - 1).updateGradInput(fInput, fTarget).toTensor[T]) if (sizeAverage) { _gradInput = _gradInput.div(ev.fromType[Int](nstep)) @@ -141,8 +142,8 @@ class TimeDistributedCriterion[T : ClassTag]( object TimeDistributedCriterion { def apply[@specialized(Float, Double) T: ClassTag]( - critrn: TensorCriterion[T] = null, sizeAverage: Boolean = false) + critrn: TensorCriterion[T] = null, sizeAverage: Boolean = false, dimension: Int = 2) (implicit ev: TensorNumeric[T]) : TimeDistributedCriterion[T] = { - new TimeDistributedCriterion[T](critrn, sizeAverage) + new TimeDistributedCriterion[T](critrn, sizeAverage, dimension) } } From 62f320c4b9061d7c0cc56d21d26aface6a7bb0dd Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 19 Oct 2017 14:35:17 +0800 Subject: [PATCH 0467/1065] Support load more tensorflow operations (#1656) * Support load more tf operation * refine session * fix style issue * meet code reivew --- .../analytics/bigdl/dllib/nn/Module.scala | 22 ++- .../dllib/utils/python/api/PythonBigDL.scala | 7 +- .../bigdl/dllib/utils/tf/Context.scala | 62 +++++++++ .../bigdl/dllib/utils/tf/Session.scala | 123 +++++++++-------- .../dllib/utils/tf/TensorflowLoader.scala | 127 +++++++++++++----- .../dllib/utils/tf/TensorflowToBigDL.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Add.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/AddN.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Assert.scala | 6 +- .../dllib/utils/tf/loaders/AvgPool.scala | 6 +- .../dllib/utils/tf/loaders/BiasAdd.scala | 5 +- .../dllib/utils/tf/loaders/BiasAddGrad.scala | 45 +++++++ .../tf/loaders/BroadcastGradientArgs.scala | 38 ++++++ .../bigdl/dllib/utils/tf/loaders/Cast.scala | 5 +- .../dllib/utils/tf/loaders/ConcatV2.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Const.scala | 7 +- .../bigdl/dllib/utils/tf/loaders/Conv2D.scala | 64 +++++++++ .../tf/loaders/Conv2DBackpropFilter.scala | 65 +++++++++ .../tf/loaders/Conv2DBackpropInput.scala | 6 +- .../dllib/utils/tf/loaders/DecodeGif.scala | 5 +- .../dllib/utils/tf/loaders/DecodeJpeg.scala | 5 +- .../dllib/utils/tf/loaders/DecodePng.scala | 5 +- .../dllib/utils/tf/loaders/DecodeRaw.scala | 5 +- .../utils/tf/loaders/DependencyNode.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Equal.scala | 6 +- .../dllib/utils/tf/loaders/ExpandDims.scala | 9 +- .../bigdl/dllib/utils/tf/loaders/Fill.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Floor.scala | 38 ++++++ .../dllib/utils/tf/loaders/Greater.scala | 6 +- .../dllib/utils/tf/loaders/Identity.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/L2Loss.scala | 38 ++++++ .../bigdl/dllib/utils/tf/loaders/Less.scala | 5 +- .../dllib/utils/tf/loaders/LogicalAnd.scala | 5 +- .../dllib/utils/tf/loaders/LogicalNot.scala | 5 +- .../dllib/utils/tf/loaders/LogicalOr.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/MatMul.scala | 5 +- .../dllib/utils/tf/loaders/MaxPool.scala | 6 +- .../dllib/utils/tf/loaders/MaxPoolGrad.scala | 71 ++++++++++ .../bigdl/dllib/utils/tf/loaders/Mean.scala | 8 +- .../bigdl/dllib/utils/tf/loaders/Merge.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Mul.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Neg.scala | 37 +++++ .../bigdl/dllib/utils/tf/loaders/NoOp.scala | 5 +- .../dllib/utils/tf/loaders/NotEqual.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/OneHot.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Pack.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Pad.scala | 6 +- .../dllib/utils/tf/loaders/ParseExample.scala | 7 +- .../dllib/utils/tf/loaders/Placeholder.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Pow.scala | 37 +++++ .../bigdl/dllib/utils/tf/loaders/Prod.scala | 5 +- .../utils/tf/loaders/QueueDequeueManyV2.scala | 6 +- .../utils/tf/loaders/QueueDequeueV2.scala | 6 +- .../utils/tf/loaders/QueueEnqueueManyV2.scala | 6 +- .../utils/tf/loaders/QueueEnqueueV2.scala | 6 +- .../utils/tf/loaders/RandomShuffle.scala | 6 +- .../utils/tf/loaders/RandomUniform.scala | 55 ++++++++ .../bigdl/dllib/utils/tf/loaders/Rank.scala | 6 +- .../dllib/utils/tf/loaders/ReaderReadV2.scala | 6 +- .../dllib/utils/tf/loaders/RealDiv.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Relu.scala | 6 +- .../dllib/utils/tf/loaders/ReluGrad.scala | 38 ++++++ .../dllib/utils/tf/loaders/Reshape.scala | 6 +- .../utils/tf/loaders/ResizeBilinear.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Rsqrt.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Select.scala | 38 ++++++ .../bigdl/dllib/utils/tf/loaders/Shape.scala | 6 +- .../dllib/utils/tf/loaders/Sigmoid.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Slice.scala | 5 +- .../dllib/utils/tf/loaders/Softmax.scala | 6 +- .../SoftmaxCrossEntropyWithLogits.scala | 36 +++++ .../bigdl/dllib/utils/tf/loaders/Split.scala | 5 +- .../dllib/utils/tf/loaders/Squeeze.scala | 8 +- .../dllib/utils/tf/loaders/StridedSlice.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Sub.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Substr.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Sum.scala | 62 ++++----- .../bigdl/dllib/utils/tf/loaders/Switch.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Tanh.scala | 6 +- .../tf/loaders/TensorflowOpsLoader.scala | 4 +- .../bigdl/dllib/utils/tf/loaders/Tile.scala | 33 +++++ .../dllib/utils/tf/loaders/Transpose.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Unpack.scala | 6 +- .../bigdl/dllib/utils/tf/loaders/Utils.scala | 6 +- .../dllib/utils/tf/loaders/VariableV2.scala | 40 ++++++ .../bigdl/dllib/utils/tf/SessionSpec.scala | 19 ++- .../dllib/utils/tf/TensorflowLoaderSpec.scala | 9 +- 87 files changed, 1179 insertions(+), 291 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Context.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Floor.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/L2Loss.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Neg.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pow.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomUniform.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Select.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftmaxCrossEntropyWithLogits.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tile.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala index 220e661a2c7..fb8015c6987 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala @@ -80,17 +80,31 @@ object Module { } /** * Load tensorflow model from its saved protobuf file. - * @param file where is the protobuf model file + * @param graphFile where is the protobuf model file * @param inputs input node names * @param outputs output node names, the output tensor order is same with the node order * @param byteOrder byte order in the tensorflow file. The default value is little endian + * @param binFile where is the model variable file * @return BigDL model */ - def loadTF[T: ClassTag](file: String, inputs: Seq[String], outputs: Seq[String], - byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN)( + def loadTF[T: ClassTag](graphFile: String, inputs: Seq[String], outputs: Seq[String], + byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, + binFile: Option[String] = None)( implicit ev: TensorNumeric[T]): Module[T] = { - TensorflowLoader.load(file, inputs, outputs, byteOrder) + TensorflowLoader.load(graphFile, inputs, outputs, byteOrder, binFile) + } + + /** + * Load tensorflow checkpoints + * @param graphFile + * @param binFile + * @tparam T + * @return + */ + def tensorflowCheckpoints[T: ClassTag](graphFile: String, binFile: String, + byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN)(implicit ev: TensorNumeric[T]): Session[T] = { + TensorflowLoader.checkpoints(graphFile, binFile, byteOrder) } def flatten[@specialized(Float, Double) T: ClassTag](parameters: Array[Tensor[T]])( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 20cb586ce07..64de7f87b45 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -37,7 +37,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} -import com.intel.analytics.bigdl.utils.tf.{BigDLSessionImpl, TensorflowDataFormat, TensorflowSaver} +import com.intel.analytics.bigdl.utils.tf.{BigDLSessionImpl, Context, TensorflowDataFormat, TensorflowSaver} import org.apache.log4j._ import org.apache.spark.SparkContext import org.tensorflow.framework.NodeDef @@ -1796,9 +1796,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab endWhen: Trigger): AbstractModule[Activity, Activity, T] = { val nodeList = parse(modelPath) - val context = - new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]() - val session = new BigDLSessionImpl[T](nodeList.asScala, samples.sparkContext, context) + val context = new Context[T]() + val session = new BigDLSessionImpl[T](nodeList.asScala, context, ByteOrder.LITTLE_ENDIAN) val dataset = batching(samples, batchSize) val model = session.train(Seq(output), dataset, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Context.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Context.scala new file mode 100644 index 00000000000..322c254480a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Context.scala @@ -0,0 +1,62 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf + +import com.intel.analytics.bigdl.tensor.Tensor + +import scala.collection.mutable + +class Context[T]( + tensorsMap: mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])] + ) { + + /** + * Return weight, gradient and shape info + * @param key + * @return + */ + def apply(key: String): (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]]) = tensorsMap(key) + + def update(key: String, value: (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])): this.type = { + tensorsMap(key) = value + this + } + + def containsTensor(key: String): Boolean = tensorsMap.contains(key) + + def tensors(): Iterable[(Tensor[T], Tensor[T], Option[scala.Seq[(Int, Int)]])] = { + tensorsMap.values + } + + def putTensor(key: String, value: (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])): this.type = { + update(key, value) + } + + def tensorNames(): Set[String] = { + tensorsMap.keySet.toSet + } + + def this() = this(new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]()) + + def assignGrads : Option[Set[(String, String)]] = assignGradsAndWeights + + def setAssignGrads(set: Set[(String, String)]): this.type = { + assignGradsAndWeights = Some(set) + this + } + + private var assignGradsAndWeights : Option[Set[(String, String)]] = None +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index 8af140539d8..cc1ae202f5b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -38,6 +38,12 @@ import scala.reflect.ClassTag abstract class Session[T: ClassTag] { + /** + * Train the model specified by the model output + * @param outputs model output + * @param dataSet the training data set + * @return trained model + */ def train(outputs: Seq[String], dataSet: DistributedDataSet[MiniBatch[T]], optMethod: OptimMethod[T], @@ -45,13 +51,34 @@ abstract class Session[T: ClassTag] { endWhen: Trigger): Graph[T] } -class BigDLSessionImpl[T: ClassTag]( - graph: Seq[NodeDef], - sc: SparkContext, - context: mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]) - (implicit ev: TensorNumeric[T]) extends Session[T] { +class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], + byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN) + (implicit ev: TensorNumeric[T]) extends Session[T] { + import scala.collection.JavaConverters._ + override def train(outputs: Seq[String], + dataSet: DistributedDataSet[MiniBatch[T]], + optMethod: OptimMethod[T], + criterion: Criterion[T], + endWhen: Trigger): Graph[T] = { + + val (model, input) = constructModel(outputs, byteOrder) + + require(input.element.getOp == "Placeholder", + "only support Placeholder as input when in-memory input data is provided") + + val opt = new DistriOptimizer( + model, + dataSet, + criterion + ) + val optMethod = new SGD[T]() + opt.setOptimMethod(optMethod).setEndWhen(endWhen) + .optimize() + model + } + private val inputOp = Set("ReaderReadV2", "QueueDequeueV2", "QueueDequeueManyV2", "Placeholder") private val dequeueOp = Set("QueueDequeueV2", "QueueDequeueManyV2", "ReaderReadV2") @@ -65,7 +92,8 @@ class BigDLSessionImpl[T: ClassTag]( private val name2Node = wholeTFGraph. DFS.filter(_.element != null).map(node => (node.element.getName, node)).toMap - private def handleReaderNode(node: Node[NodeDef], cache: DataCache): RDD[Table] = { + private def handleReaderNode(node: Node[NodeDef], cache: DataCache, + sc: SparkContext): RDD[Table] = { require(node.prevNodes.length == 2, "require ReaderReadV2 only has two inputs") val readerNode = node.prevNodes.head val queueNode = node.prevNodes(1) @@ -105,8 +133,8 @@ class BigDLSessionImpl[T: ClassTag]( } readerNode.element.getOp match { - case "TFRecordReaderV2" => readTFRecord(filesSeq) - case "FixedLengthRecordReaderV2" => readFixedLengthRecord(filesSeq, readerNode.element) + case "TFRecordReaderV2" => readTFRecord(filesSeq, sc) + case "FixedLengthRecordReaderV2" => readFixedLengthRecord(filesSeq, readerNode.element, sc) } } @@ -127,7 +155,7 @@ class BigDLSessionImpl[T: ClassTag]( result } - private def readTFRecord(filesTable: Seq[Table]): RDD[Table] = { + private def readTFRecord(filesTable: Seq[Table], sc: SparkContext): RDD[Table] = { val result = filesTable.map { t => require(t.length() == 1 && t(1).isInstanceOf[Tensor[ByteString]], "Reader can only read one file at a time") @@ -150,7 +178,8 @@ class BigDLSessionImpl[T: ClassTag]( resultRdd } - private def readFixedLengthRecord(filesTable: Seq[Table], readerNode: NodeDef): RDD[Table] = { + private def readFixedLengthRecord(filesTable: Seq[Table], readerNode: NodeDef, sc: SparkContext) + : RDD[Table] = { val footerBytes = readerNode.getAttrMap.get("footer_bytes").getI.toInt val headerBytes = readerNode.getAttrMap.get("header_bytes").getI.toInt @@ -248,7 +277,8 @@ class BigDLSessionImpl[T: ClassTag]( dataSeq } - private def handleDistriDequeue(node: Node[NodeDef], cache: DataCache): RDD[Table] = { + private def handleDistriDequeue(node: Node[NodeDef], cache: DataCache, + sc: SparkContext): RDD[Table] = { require(node.prevNodes.length == 1, "require QueueDequeueV2 only has one input") val queueNode = node.prevNodes.head val dequeueNodes = queueNode.nextNodes @@ -258,7 +288,7 @@ class BigDLSessionImpl[T: ClassTag]( val enqueueNodes = findEnqueueNodes(queueNode) val rdd = enqueueNodes.map { enqueueNode => val inputs = Seq(enqueueNode.element.getName) - val result = constructDistributeData(inputs, cache) + val result = constructDistributeData(inputs, cache, sc) if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) } else { @@ -270,7 +300,8 @@ class BigDLSessionImpl[T: ClassTag]( rdd } - private def handleDistriDequeueManyNode(node: Node[NodeDef], cache: DataCache): RDD[Table] = { + private def handleDistriDequeueManyNode(node: Node[NodeDef], cache: DataCache, + sc: SparkContext): RDD[Table] = { require(node.prevNodes.length == 2, "require QueueDequeueManyV2 only has two input") val queueNode = node.prevNodes.head val dequeueNodes = queueNode.nextNodes @@ -281,7 +312,7 @@ class BigDLSessionImpl[T: ClassTag]( // get previous rdd val rdd = enqueueNodes.map { enqueueNode => val inputs = Seq(enqueueNode.element.getName) - val result = constructDistributeData(inputs, cache) + val result = constructDistributeData(inputs, cache, sc) if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) } else { @@ -377,7 +408,7 @@ class BigDLSessionImpl[T: ClassTag]( } } - def constructLocalData(endPoints: Seq[String], cache: DataCache): Seq[Table] = { + private def constructLocalData(endPoints: Seq[String], cache: DataCache): Seq[Table] = { val isInputOp = (n: NodeDef) => inputOp(n.getOp) val (tfGraph, inputs, originInputs) = TensorflowLoader. buildTFGraph(graph.asJava, endPoints, isInputOp) @@ -387,7 +418,7 @@ class BigDLSessionImpl[T: ClassTag]( val adjustedInputs = adjustInputNames(originInputs) val transformer = TensorflowLoader.buildBigDLModel( tfGraph, - inputs, + inputs.toSeq.map(_._2).flatten, endPoints, ByteOrder.LITTLE_ENDIAN, "", @@ -427,7 +458,8 @@ class BigDLSessionImpl[T: ClassTag]( } } - private def constructDistributeData(endPoints: Seq[String], cache: DataCache): RDD[Table] = { + private def constructDistributeData(endPoints: Seq[String], cache: DataCache, + sc: SparkContext): RDD[Table] = { val isInputOp = (n: NodeDef) => inputOp(n.getOp) val (tfGraph, inputs, originInputs) = TensorflowLoader.buildTFGraph(graph.asJava, endPoints, isInputOp) @@ -440,7 +472,7 @@ class BigDLSessionImpl[T: ClassTag]( val transformer = TensorflowLoader.buildBigDLModel( tfGraph, - inputs, + inputs.toSeq.map(_._2).flatten, endPoints, ByteOrder.LITTLE_ENDIAN, "", @@ -450,9 +482,9 @@ class BigDLSessionImpl[T: ClassTag]( val inputRdds = inputNodes.map { node => // this is the input op node.element.getOp match { - case "ReaderReadV2" => handleReaderNode(node, cache) - case "QueueDequeueV2" => handleDistriDequeue(node, cache) - case "QueueDequeueManyV2" => handleDistriDequeueManyNode(node, cache) + case "ReaderReadV2" => handleReaderNode(node, cache, sc) + case "QueueDequeueV2" => handleDistriDequeue(node, cache, sc) + case "QueueDequeueManyV2" => handleDistriDequeueManyNode(node, cache, sc) } } val inputRdd = inputRdds.reduce { (rdd1, rdd2) => @@ -473,7 +505,8 @@ class BigDLSessionImpl[T: ClassTag]( } - private def constructModel(endPoints: Seq[String]): (Graph[T], Node[NodeDef]) = { + private def constructModel(endPoints: Seq[String], byteOrder: ByteOrder) + : (Graph[T], Node[NodeDef]) = { val isInputOp = (n: NodeDef) => inputOp(n.getOp) val (tfGraph, inputs, originInputs) = TensorflowLoader.buildTFGraph(graph.asJava, endPoints, isInputOp) @@ -488,55 +521,35 @@ class BigDLSessionImpl[T: ClassTag]( val model = TensorflowLoader.buildBigDLModel( tfGraph, - inputs, + inputs.toSeq.map(_._2).flatten, endPoints, - ByteOrder.LITTLE_ENDIAN, + byteOrder, "", Some(context) ).asInstanceOf[Graph[T]] (model, inputNodes.head) } - /** - * Train the model specified by the model output - * @param outputs model output - * @param dataSet the training data set - * @return trained model - */ - override def train(outputs: Seq[String], - dataSet: DistributedDataSet[MiniBatch[T]], - optMethod: OptimMethod[T], - criterion: Criterion[T], - endWhen: Trigger): Graph[T] = { - - val (model, input) = constructModel(outputs) - - require(input.element.getOp == "Placeholder", - "only support Placeholder as input when in-memory input data is provided") - - val opt = new DistriOptimizer( - model, - dataSet, - criterion - ) - - opt.setOptimMethod(optMethod).setEndWhen(endWhen) - .optimize() - model - } - /** * Get and calculate the data up to the specified endpoints, and * return as a RDD[Table] * @param endPoints output endpoints * @return */ - def getRDD(endPoints: Seq[String]): RDD[Table] = { + def getRDD(endPoints: Seq[String], sc: SparkContext): RDD[Table] = { val cache = new mutable.HashMap[String, Array[Seq[Table]]]() - constructDistributeData(endPoints, cache) + constructDistributeData(endPoints, cache, sc) } +} - +object TFUpdater { + def apply(node: NodeDef): Option[(String, String)] = { + node.getOp match { + case "ApplyRMSProp" => + Some((node.getInput(0), node.getInput(7))) + case _ => None + } + } } object BigDLSessionImpl { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index e818dc6f87d..29dce704fe4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -19,28 +19,28 @@ import java.io.{DataInputStream, InputStream, FileReader => JFileReader} import java.nio.ByteOrder import java.util import java.util.List +import java.util.{HashMap => JHashMap} import com.google.protobuf.{CodedInputStream, TextFormat} import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.{SwitchControlNode, SwitchOps} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.AssignGrad +import com.intel.analytics.bigdl.python.api.{JTensor, PythonBigDL, PythonBigDLUtils} +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{DirectedGraph, Edge, FileReader, Node} +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.nn.ops.{SwitchControlNode, SwitchOps} import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL._ import com.intel.analytics.bigdl.utils.tf.loaders.TensorflowOpsLoader -import com.intel.analytics.bigdl.utils.{DirectedGraph, FileReader, Node} import org.tensorflow.framework.{GraphDef, NodeDef} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +import scala.collection.JavaConverters._ object TensorflowLoader{ - - type Context[T] = mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])] - /** * Load tensorflow model from a prototxt file * @param graphPrototxt where is the tensorflow protobuf file @@ -50,17 +50,34 @@ object TensorflowLoader{ * @return */ def load[T: ClassTag](graphPrototxt: String, inputs: Seq[String], outputs: Seq[String], - byteOrder: ByteOrder)( + byteOrder: ByteOrder, binFile: Option[String] = None)( implicit ev: TensorNumeric[T]): Module[T] = { // Get node list val nodeList = parse(graphPrototxt) // Construct tf node graph - val (tfGraph, adjustedInputs, _) = + val (tfGraph, adjustedInputsMap, _) = buildTFGraph(nodeList, outputs, (node: NodeDef) => inputs.contains(node.getName)) + val adjustedInputs = ArrayBuffer[String]() + inputs.foreach(i => { + if (adjustedInputsMap.isDefinedAt(i)) { + adjustedInputsMap(i).foreach(n => adjustedInputs.append(n)) + } + }) + // Try to load variables + val context = binFile.map(loadBinFiles(_)) + // Build BigDL model from the tf node graph - buildBigDLModel(tfGraph, adjustedInputs, outputs, byteOrder, graphPrototxt) + buildBigDLModel(tfGraph, adjustedInputs, outputs, byteOrder, graphPrototxt, context) + } + + def checkpoints[T: ClassTag](graphFile: String, binFile: String, byteOrder: ByteOrder)( + implicit ev: TensorNumeric[T]): Session[T] = { + // Get node list + val nodeList = parse(graphFile) + + new BigDLSessionImpl[T](nodeList.asScala, loadBinFiles(binFile), byteOrder) } /** @@ -86,6 +103,20 @@ object TensorflowLoader{ } + private def loadBinFiles[T: ClassTag](file: String)(implicit ev: TensorNumeric[T]): Context[T] = { + val m = File.load(file).asInstanceOf[JHashMap[String, JTensor]].asScala + val map = new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]() + for (k <- m.keys) { + val tensor = ev.getType() match { + case FloatType => PythonBigDLUtils.toTensor(m(k), "float") + case DoubleType => PythonBigDLUtils.toTensor(m(k), "double") + } + + map(k) = (tensor, tensor.clone(), None) + } + new Context[T](map) + } + /** * Parse a tensorflow model protobuf text file, read a list of op nodes from it * @param graphProtoTxt where is the tf protobuf file @@ -112,8 +143,7 @@ object TensorflowLoader{ */ private[bigdl] def buildTFGraph(nodes : List[NodeDef], outputNodeNames: Seq[String], isInput: (NodeDef) => Boolean = (_: NodeDef) => false) - : (DirectedGraph[NodeDef], Seq[String], Seq[String]) = { - import scala.collection.JavaConverters._ + : (DirectedGraph[NodeDef], mutable.HashMap[String, ArrayBuffer[String]], Seq[String]) = { val name2Node = nodes.asScala.map(n => n.getName -> new Node(n)).toMap // Build graph @@ -126,12 +156,14 @@ object TensorflowLoader{ results } - def connect(nodes: Seq[Node[NodeDef]]): (Seq[String], Seq[String]) = { + def connect(nodes: Seq[Node[NodeDef]]): (mutable.HashMap[String, ArrayBuffer[String]], + Seq[String]) = { + var inputCounter = 0 - var dependencyCounter = 0 + var depCounter = 0 val queue = new mutable.Queue[Node[NodeDef]]() val visited = mutable.Set[Node[NodeDef]]() - val inputs = new mutable.ArrayBuffer[String]() + val inputs = new mutable.HashMap[String, ArrayBuffer[String]]() val originInputs = new mutable.ArrayBuffer[String]() // Do a BFS to connect the nodes @@ -164,9 +196,9 @@ object TensorflowLoader{ val dependencyNode = Node(NodeDef.newBuilder() .setOp("DependencyNode") .addInput(preNode.element.getName) - .setName(s"depends_on_${preNode.element.getName}_$dependencyCounter") + .setName(s"depends_on_${preNode.element.getName}$depCounter") .build()) - dependencyCounter = dependencyCounter + 1 + depCounter += 1 dependencyNode -> node dependencyNode } else { @@ -177,29 +209,35 @@ object TensorflowLoader{ queue.enqueue(preNode) } } else { + if (inputs.get(node.element.getName).isEmpty) { + inputs(node.element.getName) = new ArrayBuffer[String]() + } if (isInput(node.element) && node.element.getOp != "Placeholder") { // if the predefined input node is not a Placeholder, add one to match the Input node val inputNum = getInputNumber(node.element) - var i = 0 - while (i < inputNum) { - val name = s"input$inputCounter" - val placeholder = NodeDef.newBuilder() - .setName(name) - .setOp("Placeholder").build() - inputCounter = inputCounter + 1 - val n = Node(placeholder) - n -> node - inputs += name - i = i + 1 + if (inputNum == 0) { + inputs(node.element.getName).append(node.element.getName) + } else { + var i = 0 + while (i < inputNum) { + val name = s"input$inputCounter" + val placeholder = NodeDef.newBuilder() + .setName(name) + .setOp("Placeholder").build() + inputCounter = inputCounter + 1 + val n = Node(placeholder) + n -> node + inputs(node.element.getName).append(name) + i = i + 1 + } } originInputs += node.element.getName } else if (node.element.getOp == "Placeholder") { - inputs += node.element.getName + inputs(node.element.getName).append(node.element.getName) originInputs += node.element.getName } } } - } (inputs, originInputs) } @@ -241,8 +279,7 @@ object TensorflowLoader{ new mutable.HashMap[Node[AbstractModule[Activity, Activity, T]], Seq[Node[NodeDef]]]() val moduleToAllNodes = new mutable.HashMap[Node[AbstractModule[Activity, Activity, T]], Set[Node[NodeDef]]]() - val context = ctx.getOrElse( - new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]) + val context = ctx.getOrElse(new Context[T]) // BFS to keep the input order same tfGraph.BFS.foreach(n => { @@ -271,9 +308,11 @@ object TensorflowLoader{ val cls = Class.forName("com.intel.analytics.bigdl.utils.tf.loaders." + n.element.getOp) val builder = cls.getConstructors()(0).newInstance().asInstanceOf[TensorflowOpsLoader] - (builder.build[T](n.element, byteOrder), Seq(n).asJava, Seq(n)) + (builder.build[T](n.element, byteOrder, context), Seq(n).asJava, Seq(n)) } catch { case _ => + println("com.intel.analytics.bigdl.utils.tf.loaders." + + n.element.getOp) throw new UnsupportedOperationException(errorMsg) } }) @@ -310,6 +349,10 @@ object TensorflowLoader{ } }) + /** + * Go through all tensorflow nodes + * @param outputModuleNode + */ def connect(outputModuleNode: Seq[Node[AbstractModule[Activity, Activity, T]]]) = { val queue = new mutable.Queue[Node[AbstractModule[Activity, Activity, T]]]() val visited = mutable.Set[Node[AbstractModule[Activity, Activity, T]]]() @@ -322,7 +365,8 @@ object TensorflowLoader{ val inputNodes = moduleToInputNodes(currNode) val allNodes = moduleToAllNodes(currNode) val inputModuleNodes = inputNodes.flatMap(_.prevNodesAndEdges) - .filterNot(n => context.contains(n._1.element.getName)) + .filterNot(n => context.containsTensor(n._1.element.getName) && + n._1.element.getOp() != "VariableV2") .filterNot(n => allNodes(n._1)) .map(n => (convertedNode(n._1), n._2.newInstance())).filter(n => n._1 != currNode) inputModuleNodes.foreach(n => n._1.add(currNode, n._2)) @@ -343,12 +387,23 @@ object TensorflowLoader{ val weights = ArrayBuffer[Tensor[T]]() val gradients = ArrayBuffer[Tensor[T]]() - for ((weight, grad, _) <- context.values) { + for ((weight, grad, _) <- context.tensors) { weights += weight gradients += grad } - Graph(inputNodes.toArray, outputNodes.toArray, Some((weights.toArray, gradients.toArray)), + // Append assign nodes + val adjustOutputs = if (context.assignGrads.isDefined) { + outputNodes.map(n => { + val matchNode = context.assignGrads.get.filter(_._2 == n.element.getName()) + require(matchNode.size == 1, "Invalid gradients output") + new AssignGrad[T](context(matchNode.head._1)._2).inputs(n) + }) + } else { + outputNodes + } + + Graph(inputNodes.toArray, adjustOutputs.toArray, Some((weights.toArray, gradients.toArray)), generatedBackward) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala index 7243bceb984..8b613d08fd0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala @@ -30,7 +30,6 @@ import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.FullConnectionTF.getOrSetTensor import com.intel.analytics.bigdl.utils.{DirectedGraph, Node, T} -import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.Context import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL._ import scala.collection.mutable.ArrayBuffer @@ -64,7 +63,7 @@ trait TensorflowToBigDL { trans: Option[Seq[(Int, Int)]] = None)( implicit ev: TensorNumeric[T]): (Tensor[T], Tensor[T]) = { - if (context.contains(node.getName)) { + if (context.containsTensor(node.getName)) { val result = context(node.getName) (result._1, result._2) } else { @@ -78,7 +77,7 @@ trait TensorflowToBigDL { case _ => } val gradient = Tensor[T](weight.size()) - context.put(node.getName, (weight, gradient, trans)) + context.putTensor(node.getName, (weight, gradient, trans)) (weight, gradient) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala index 1b147395fd5..b9d6b67d882 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala @@ -20,13 +20,14 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.CAddTable import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Add extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { CAddTable[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AddN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AddN.scala index 72c619beeb2..149d7d13cdf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AddN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AddN.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.CAddTable +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class AddN extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { CAddTable[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala index de167a98a17..68af428add1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala @@ -19,7 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{Assert => AssertOperation} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class Assert extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new AssertOperation[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPool.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPool.scala index b13d1cc0f6f..089833cc698 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPool.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPool.scala @@ -20,7 +20,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.SpatialAveragePooling import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -29,8 +31,8 @@ class AvgPool extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attributes = nodeDef.getAttrMap val format = getString(attributes, "data_format") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAdd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAdd.scala index 678e71c87f5..f29ffc4be81 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAdd.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAdd.scala @@ -22,12 +22,13 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef import com.intel.analytics.bigdl.nn.tf.{BiasAdd => BiasAddOp} import Utils._ +import com.intel.analytics.bigdl.utils.tf.Context import scala.reflect.ClassTag class BiasAdd extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attr = nodeDef.getAttrMap require(getString(attr, "data_format") == "NHWC", "only support NHWC format") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala new file mode 100644 index 00000000000..15c0b22744d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.BiasAddGrad +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class BiasAddGrad extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + + val format = if (getString(nodeDef.getAttrMap, "data_format") == "NHWC") { + DataFormat.NHWC + } else { + DataFormat.NCHW + } + BiasAddGrad[T](format) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala new file mode 100644 index 00000000000..b3f68b0acc8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.ops.BroadcastGradientArgs +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class BroadcastGradientArgs extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + BroadcastGradientArgs[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala index b9938f19628..168f64697d8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala @@ -20,6 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.Cast import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} import scala.reflect.ClassTag @@ -28,8 +29,8 @@ class Cast extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attr = nodeDef.getAttrMap val dataType = getType(attr, "DstT") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala index c46f1898db8..ddc92cbd756 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.JoinTable import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -29,8 +30,8 @@ class ConcatV2 extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(-1), tensorArrays => { val axis = tensorArrays(0).value().asInstanceOf[Int] + 1 JoinTable[T](dimension = axis, nInputDims = -1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala index 798051bb803..3fdb592efa3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala @@ -20,15 +20,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.tf.TFUtils +import com.intel.analytics.bigdl.utils.tf.{Context, TFUtils} import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Const extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val value = TFUtils.parseTensor(nodeDef.getAttrMap.get("value").getTensor, byteOrder) Const(value).asInstanceOf[AbstractModule[Activity, Activity, T]] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala new file mode 100644 index 00000000000..d9e25965d79 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.Conv2D +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Conv2D extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val conv = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + Conv2D[T](strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + Conv2D[T](strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala new file mode 100644 index 00000000000..66e2c446eb0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala @@ -0,0 +1,65 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.Conv2DBackFilter +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Conv2DBackpropFilter extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val convBackFilter = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + Conv2DBackFilter[T](strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + Conv2DBackFilter[T](strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + convBackFilter.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala index 5143b88503b..70f6258d7d8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala @@ -25,10 +25,12 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag import Utils._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Context class Conv2DBackpropInput extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attributes = nodeDef.getAttrMap val (pW, pH) = diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala index 821e067538a..ff5924d80b0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala @@ -20,6 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{DecodeGif => DecodeGifOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +29,8 @@ class DecodeGif extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new DecodeGifOp[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala index b195643b0ea..cd21675e35c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala @@ -20,6 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{DecodeJpeg => DecodeJpegOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +29,8 @@ class DecodeJpeg extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attr = nodeDef.getAttrMap val channels = getInt(attr, "channels") val ratio = getInt(attr, "ratio") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala index ed336a0fec6..a94e91710be 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala @@ -20,6 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{DecodePng => DecodePngOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +29,8 @@ class DecodePng extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attr = nodeDef.getAttrMap val channels = getInt(attr, "channels") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala index 9e0e5a4e424..bb9e7f5f20d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala @@ -20,6 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{DecodeRaw => DecodeRawOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +29,8 @@ class DecodeRaw extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attrs = nodeDef.getAttrMap val endian = getBoolean(attrs, "little_endian") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala index ca04973f7a7..9dd04d0ae91 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.ControlDependency +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class DependencyNode extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { ControlDependency[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Equal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Equal.scala index fde173ced93..3f7ebb15e74 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Equal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Equal.scala @@ -19,7 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{Equal => EqualOperation} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class Equal extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new EqualOperation[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala index e22cb1e9377..ec84e3f7f90 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.ops.ExpandDims import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -30,11 +31,11 @@ class ExpandDims extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2), tensorArrays => { - val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 - ExpandDims[T](axis) + val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + ExpandDims[T](if (axis < 0) axis + 1 else axis + 1) }) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala index 801cc5a3b43..369671eb2db 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.tf.Fill +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Fill extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Fill[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Floor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Floor.scala new file mode 100644 index 00000000000..bf59b38f469 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Floor.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.ops.Floor +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Floor extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + Floor[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Greater.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Greater.scala index e51ef146deb..317ab688913 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Greater.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Greater.scala @@ -19,7 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{Greater => GreaterOperation} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class Greater extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new GreaterOperation[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Identity.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Identity.scala index a381a60bf8f..18474088060 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Identity.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Identity.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.{Identity => nnIdentity} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Identity extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { nnIdentity[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/L2Loss.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/L2Loss.scala new file mode 100644 index 00000000000..3a26be62e24 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/L2Loss.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.ops.L2Loss +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class L2Loss extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + L2Loss[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Less.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Less.scala index 7c28b4833e0..c899adc01ea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Less.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Less.scala @@ -21,12 +21,13 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef import com.intel.analytics.bigdl.nn.ops.{Less => LessOp} +import com.intel.analytics.bigdl.utils.tf.Context import scala.reflect.ClassTag class Less extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new LessOp[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalAnd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalAnd.scala index 9d9be1f427c..4299ef5f9e9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalAnd.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalAnd.scala @@ -21,12 +21,13 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef import com.intel.analytics.bigdl.nn.ops.{LogicalAnd => LogicalAndOp} +import com.intel.analytics.bigdl.utils.tf.Context import scala.reflect.ClassTag class LogicalAnd extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { LogicalAndOp() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalNot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalNot.scala index 5325c426dd0..f932b3cc674 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalNot.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalNot.scala @@ -21,12 +21,13 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef import com.intel.analytics.bigdl.nn.ops.{LogicalNot => LogicalNotOp} +import com.intel.analytics.bigdl.utils.tf.Context import scala.reflect.ClassTag class LogicalNot extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { LogicalNotOp() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalOr.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalOr.scala index 8f03ba93bb4..b981663ab50 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalOr.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogicalOr.scala @@ -21,12 +21,13 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef import com.intel.analytics.bigdl.nn.ops.{LogicalOr => LogicalOrOp} +import com.intel.analytics.bigdl.utils.tf.Context import scala.reflect.ClassTag class LogicalOr extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { LogicalOrOp() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MatMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MatMul.scala index dc6324ca16b..7716e939579 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MatMul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MatMul.scala @@ -22,12 +22,13 @@ import com.intel.analytics.bigdl.nn.MM import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef import Utils._ +import com.intel.analytics.bigdl.utils.tf.Context import scala.reflect.ClassTag class MatMul extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attr = nodeDef.getAttrMap MM[T](getBoolean(attr, "transpose_a"), getBoolean(attr, "transpose_b")) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPool.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPool.scala index 214fc095161..61a9c71c752 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPool.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPool.scala @@ -20,7 +20,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.SpatialMaxPooling import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ import scala.reflect.ClassTag class MaxPool extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attributes = nodeDef.getAttrMap val format = getString(attributes, "data_format") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala new file mode 100644 index 00000000000..213923938a5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.MaxPoolGrad +import com.intel.analytics.bigdl.nn.{Identity, SpatialMaxPooling} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class MaxPoolGrad extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + val kernelSize = getIntList(attributes, "ksize") + require(kernelSize.head == 1, s"not support kernel on batch") + + val format = getString(attributes, "data_format") + val poolgrad = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + val kW = kernelSize(1) + val kH = kernelSize(2) + MaxPoolGrad[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + val kW = kernelSize(2) + val kH = kernelSize(3) + MaxPoolGrad[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + poolgrad.asInstanceOf[Module[T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala index 667fe79a020..264f217cb2d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala @@ -18,9 +18,10 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.{Mean => MeanNN, Sequential} +import com.intel.analytics.bigdl.nn.{Sequential, Mean => MeanNN} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} import scala.collection.mutable.ArrayBuffer @@ -30,8 +31,9 @@ class Mean extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + Adapter[T](Array(2), tensorArrays => { val attr = nodeDef.getAttrMap val dataType = getType(attr, "T") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala index 0fab09fe8e7..4fca17b94c8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala @@ -20,13 +20,14 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.MergeOps import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Merge extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new MergeOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mul.scala index 5230284b010..0daf9a047c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mul.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.CMulTable +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Mul extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { CMulTable[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Neg.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Neg.scala new file mode 100644 index 00000000000..ea7e6bb07bc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Neg.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Identity, Negative} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Neg extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + Negative[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala index f3738cb159b..f49fe376224 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala @@ -20,13 +20,14 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.tf.ControlDependency import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class NoOp extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new ControlDependency[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NotEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NotEqual.scala index 17c7f7703c0..8c6c7f5ef70 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NotEqual.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NotEqual.scala @@ -20,6 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{NotEqual => NotEqualOperation} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +29,8 @@ class NotEqual extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new NotEqualOperation[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala index 378f8857c65..34287e89e56 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Power import com.intel.analytics.bigdl.nn.ops.{OneHot => OneHotOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.{TensorflowOpsLoader, Utils} import org.tensorflow.framework.NodeDef @@ -29,8 +30,8 @@ class OneHot extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val axis = getInt(nodeDef.getAttrMap, "axis") OneHotOp(axis) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala index 5a244d9ba7e..d0990e6a697 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala @@ -20,14 +20,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Pack import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Pack extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val dim = nodeDef.getAttrMap.get("axis").getI.toInt + 1 Pack[T](dim).asInstanceOf[AbstractModule[Activity, Activity, T]] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala index 454b92981b8..8d4b8d6fdb6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.{Padding, Sequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.tf.TFUtils +import com.intel.analytics.bigdl.utils.tf.{Context, TFUtils} import org.tensorflow.framework.NodeDef import scala.collection.mutable.ArrayBuffer @@ -31,8 +31,8 @@ class Pad extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2), tensorArrays => { val paddings = tensorArrays(0).asInstanceOf[Tensor[Int]] val pad = ArrayBuffer[Int]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala index cff19735c5e..e8d2ce438c0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala @@ -21,17 +21,18 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{ParseExample => ParseExampleOperation} import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} -import collection.JavaConverters._ +import collection.JavaConverters._ import scala.reflect.ClassTag class ParseExample extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val Ndense = nodeDef.getAttrMap.get("Ndense").getI.toInt val Tdense = nodeDef.getAttrMap.get("Tdense") .getList.getTypeList.asScala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala index 1510f66af75..f89bd4ace15 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala @@ -19,7 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class Placeholder extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Identity[T] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pow.scala new file mode 100644 index 00000000000..de604cbea46 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pow.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Pow +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Pow extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + Pow[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala index 6695a01be63..647768274a2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.ops.Prod import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -30,8 +31,8 @@ class Prod extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2), tensorArrays => { val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 val keepDims = getBoolean(nodeDef.getAttrMap, "keep_dims") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueManyV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueManyV2.scala index 9baa7c17739..fe97f92ec4c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueManyV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueManyV2.scala @@ -20,6 +20,8 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class QueueDequeueManyV2 extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new IdentityModule[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueV2.scala index d054f626a05..c112372397d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueDequeueV2.scala @@ -20,6 +20,8 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class QueueDequeueV2 extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new IdentityModule[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueManyV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueManyV2.scala index d81c39dfdd8..bfbffb1850c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueManyV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueManyV2.scala @@ -20,6 +20,8 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class QueueEnqueueManyV2 extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new IdentityModule[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueV2.scala index 628e4c7514a..59b58e84f71 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/QueueEnqueueV2.scala @@ -20,6 +20,8 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class QueueEnqueueV2 extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new IdentityModule[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomShuffle.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomShuffle.scala index 82a374b1f8b..2255852ff51 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomShuffle.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomShuffle.scala @@ -19,7 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class RandomShuffle extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new IdentityModule[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomUniform.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomUniform.scala new file mode 100644 index 00000000000..102fb30a7db --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomUniform.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.ops.RandomUniform +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class RandomUniform extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val seed = if (nodeDef.getAttrMap.containsKey("seed")) { + Some(nodeDef.getAttrMap.get("seed").getI().toInt) + } else { + None + } + + nodeDef.getAttrMap.get("dtype").getType match { + case DataType.DT_FLOAT => + val min = 0 + val max = 1 + RandomUniform[T, Float](min, max, seed) + case DataType.DT_DOUBLE => + val min = 0 + val max = 1 + RandomUniform[T, Double](min, max, seed) + case _ => + throw new IllegalArgumentException("Not support data type") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rank.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rank.scala index f882493bcb4..1a6e88d427b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rank.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rank.scala @@ -19,7 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{Rank => RankOperation} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class Rank extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new RankOperation[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReaderReadV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReaderReadV2.scala index e59683d2cec..6950104eab5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReaderReadV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReaderReadV2.scala @@ -20,6 +20,8 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.nn.{Identity => IdentityModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class ReaderReadV2 extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new IdentityModule[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala index cfda7028b14..bd72b7211ae 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala @@ -20,13 +20,14 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{RealDiv => RealDivOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class RealDiv extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { RealDivOp() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu.scala index be587d5a416..a8a3441c1a7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ReLU +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Relu extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { ReLU[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala new file mode 100644 index 00000000000..55561eb3d93 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.ReluGrad +import com.intel.analytics.bigdl.nn.{Identity, ReLU} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class ReluGrad extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + ReluGrad[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala index a19f46ed876..1d830a5005e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Reshape import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.tf.TFUtils +import com.intel.analytics.bigdl.utils.tf.{Context, TFUtils} import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -30,8 +30,8 @@ class Reshape extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2), tensorArrays => { val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala index ea2c1251752..91e94bf6cfc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.ResizeBilinearOps +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class ResizeBilinear extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val alignCorner = nodeDef.getAttrMap.get("align_corners").getB ResizeBilinearOps[T](alignCorner) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala index bd42cf15eb3..32b15e59aef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala @@ -19,7 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.{Identity, Power} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class Rsqrt extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Power[T](-0.5, 1, 0) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Select.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Select.scala new file mode 100644 index 00000000000..9d947e64f37 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Select.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.ops.Select +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Select extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + Select[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala index 602715b6182..d20604a2668 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.tf.Shape +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Shape extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Shape[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala index 076c45ef103..75447394b33 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Sigmoid +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Sigmoid extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Sigmoid[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala index 333946debb3..b577522bde9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.ops.Slice import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -30,8 +31,8 @@ class Slice extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2, 3), tensorArrays => { val size = tensorArrays(1).asInstanceOf[Tensor[Int]] Slice[T](toArray(tensorArrays(0).asInstanceOf[Tensor[Int]]), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softmax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softmax.scala index 3f08766a325..2c745fc83ac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softmax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softmax.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.SoftMax +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Softmax extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { SoftMax[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftmaxCrossEntropyWithLogits.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftmaxCrossEntropyWithLogits.scala new file mode 100644 index 00000000000..a93cbe9020c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftmaxCrossEntropyWithLogits.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.CrossEntropy +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class SoftmaxCrossEntropyWithLogits extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + CrossEntropy[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala index 349c41acb21..81c76e40395 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn.ConcatTable import com.intel.analytics.bigdl.nn.tf.SplitAndSelect import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -30,8 +31,8 @@ class Split extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(1), tensorArrays => { val numSplit = nodeDef.getAttrMap.get("num_split").getI.toInt val dim = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala index a49e74069ba..a40ec350d8c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala @@ -19,18 +19,20 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Squeeze +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef -import collection.JavaConverters._ +import collection.JavaConverters._ import scala.reflect.ClassTag class Squeeze extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val dims = nodeDef.getAttrOrThrow("squeeze_dims").getList().getIList() .asScala.map(_.toInt).toArray diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala index 3b9932caef0..f101498064e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn.tf.StrideSlice import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Node +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -30,11 +31,10 @@ class StridedSlice extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2, 3, 4), tensorArrays => { - // this must be defined inside this function, otherwise the loader will be // serialized def oneDTensorToArray(tensor: Tensor[Int]): Array[Int] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sub.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sub.scala index ca5a360dd16..853bd12001b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sub.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sub.scala @@ -19,7 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.CSubTable +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag @@ -28,8 +30,8 @@ class Sub extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { CSubTable[T] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Substr.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Substr.scala index 344079424a6..0c9654baa3b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Substr.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Substr.scala @@ -20,13 +20,14 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.{Substr => SubstrOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Substr extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { SubstrOp() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala index 14b48a72837..95b05dadca3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala @@ -18,51 +18,39 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.{Sequential, Sum => SumOp} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.Sum import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} -import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag class Sum extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(2), tensorArrays => { - val attr = nodeDef.getAttrMap - - val squeeze = !getBoolean(attr, "keep_dims") - val dims = tensorArrays(0).asInstanceOf[Tensor[Int]] - val dim = ArrayBuffer[Int]() - val sum = Sequential[T]() - for (i <- 1 to dims.size(1)) { - dim += dims.valueAt(i) + 1 - } - - val dataType = getType(attr, "T") - dataType match { - case DataType.DT_INT8 => - dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) - case DataType.DT_INT16 => - dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) - case DataType.DT_UINT8 => - dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) - case DataType.DT_UINT16 => - dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) - case DataType.DT_INT32 => - dim.foreach(i => sum.add(new SumOp[T, Int](i, squeeze = squeeze))) - case DataType.DT_INT64 => - dim.foreach(i => sum.add(new SumOp[T, Long](i, squeeze = squeeze))) - case DataType.DT_FLOAT => - dim.foreach(i => sum.add(new SumOp[T, Float](i, squeeze = squeeze))) - case DataType.DT_DOUBLE => - dim.foreach(i => sum.add(new SumOp[T, Double](i, squeeze = squeeze))) - } - sum - }) + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attr = nodeDef.getAttrMap + val keepDims = getBoolean(attr, "keep_dims") + val dataType = getType(attr, "T") + dataType match { + case DataType.DT_INT8 => + Sum[T, Int](keepDims, startFromZero = true) + case DataType.DT_INT16 => + Sum[T, Int](keepDims, startFromZero = true) + case DataType.DT_UINT8 => + Sum[T, Int](keepDims, startFromZero = true) + case DataType.DT_UINT16 => + Sum[T, Int](keepDims, startFromZero = true) + case DataType.DT_INT32 => + Sum[T, Int](keepDims, startFromZero = true) + case DataType.DT_INT64 => + Sum[T, Int](keepDims, startFromZero = true) + case DataType.DT_FLOAT => + Sum[T, Float](keepDims, startFromZero = true) + case DataType.DT_DOUBLE => + Sum[T, Double](keepDims, startFromZero = true) + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala index fc67996bd22..a6865b8ad52 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala @@ -20,13 +20,14 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ops.SwitchOps import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Switch extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new SwitchOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala index 2a0409b452a..bde5b9dc61f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala @@ -19,14 +19,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Tanh +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Tanh extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Tanh[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TensorflowOpsLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TensorflowOpsLoader.scala index 5f4f81d5220..af35bf770e1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TensorflowOpsLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TensorflowOpsLoader.scala @@ -18,13 +18,13 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Tanh import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag abstract class TensorflowOpsLoader() { - def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) + def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) (implicit ev: TensorNumeric[T]): Module[T] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tile.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tile.scala new file mode 100644 index 00000000000..de42991b934 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tile.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Tile +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Tile extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + Tile[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala index 30c45417bfe..5838dc48628 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.{Contiguous, Sequential, Transpose} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.collection.mutable.ArrayBuffer @@ -30,8 +31,8 @@ class Transpose extends TensorflowOpsLoader { import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { Adapter[T](Array(2), tensorArrays => { // this must be defined inside this function, otherwise the loader will be diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Unpack.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Unpack.scala index 9ffb2ff423d..ea40b1d0292 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Unpack.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Unpack.scala @@ -20,14 +20,16 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.SplitTable import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Unpack extends TensorflowOpsLoader { - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder) - (implicit ev: TensorNumeric[T]): Module[T] = { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val dim = nodeDef.getAttrMap.get("axis").getI.toInt + 1 SplitTable[T](dim) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala index 2927372b536..fe4ae32a42a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala @@ -21,7 +21,7 @@ import java.util import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.Context +import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL.toTensor import org.tensorflow.framework.{AttrValue, DataType, NodeDef} @@ -34,7 +34,7 @@ object Utils { trans: Option[Seq[(Int, Int)]] = None)( implicit ev: TensorNumeric[T]): (Tensor[T], Tensor[T]) = { - if (context.contains(node.getName)) { + if (context.containsTensor(node.getName)) { val result = context(node.getName) (result._1, result._2) } else { @@ -48,7 +48,7 @@ object Utils { case _ => } val gradient = Tensor[T](weight.size()) - context.put(node.getName, (weight, gradient, trans)) + context.putTensor(node.getName, (weight, gradient, trans)) (weight, gradient) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala new file mode 100644 index 00000000000..c264c3784a4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.tf.{Const, Variable, WithoutInput} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class VariableV2 extends TensorflowOpsLoader{ + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val w = context(nodeDef.getName)._1 + val g = context(nodeDef.getName)._2 + Variable[T](w, g) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala index aced5443c2d..42f65f92456 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala @@ -63,9 +63,8 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { val nodes = TensorflowLoader.parse(path) import scala.collection.JavaConverters._ - val context = - new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() - val session = new BigDLSessionImpl[Float](nodes.asScala, sc, context) + val context = new Context[Float]() + val session = new BigDLSessionImpl[Float](nodes.asScala, context) val data = new Array[Tensor[Float]](100) val label = new Array[Tensor[Float]](100) @@ -100,14 +99,13 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { val lenetModel = getLenetModel("lenet_batch_2.pbtxt") - val context = - new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() - val session = new BigDLSessionImpl[Float](lenetModel, sc, context) + val context = new Context[Float]() + val session = new BigDLSessionImpl[Float](lenetModel, context) val endpoints = Seq( "fifo_queue_Dequeue" ) - val rdd = session.getRDD(endpoints) + val rdd = session.getRDD(endpoints, sc) val result = rdd.collect() result.length should be (5) val imageSum = result.map(t => t[Tensor[Float]](1).sum()).sum @@ -121,14 +119,13 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { val lenetModel = getLenetModel("lenet_with_batch_3.pbtxt") - val context = - new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])]() - val session = new BigDLSessionImpl[Float](lenetModel, sc, context) + val context = new Context[Float]() + val session = new BigDLSessionImpl[Float](lenetModel, context) val endpoints = Seq( "fifo_queue_Dequeue" ) - val rdd = session.getRDD(endpoints) + val rdd = session.getRDD(endpoints, sc) val result = rdd.collect() result.length should be (4) result.head[Tensor[Float]](1).size(1) should be (3) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index f52c8922de5..56f5aa9b9a0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -516,9 +516,8 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ val (tfGraph, inputs, _) = TensorflowLoader.buildTFGraph(tfNodes, endPoints.map(_.split(":")(0)), (node: NodeDef) => node.getName == "input_node") - val context = - new mutable.HashMap[String, (Tensor[Float], Tensor[Float], Option[Seq[(Int, Int)]])] - val model = TensorflowLoader.buildBigDLModel(tfGraph, inputs, + val context = new Context[Float]() + val model = TensorflowLoader.buildBigDLModel(tfGraph, inputs.toSeq.map(_._2).flatten, endPoints.map(_.split(":")(0)), ByteOrder.LITTLE_ENDIAN, "", Some(context)) // Compare the tensor contents @@ -572,7 +571,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ } // find all gradients tensor of variables in tensorflow graph - val tfGradTensorsMap = context.keySet.map{ + val tfGradTensorsMap = context.tensorNames().map{ node => val t = tfNodes.asScala.filter(_.getName.contains(node + "_grad"))(0) t.getName -> @@ -582,7 +581,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ // do backward model.backward(input, gradInputs) - val pairs = context.keySet.map { x => + val pairs = context.tensorNames().map { x => val name = s"${x}_grad" var tensor = tfGradTensorsMap.get(name).orNull var (_, grad, trans) = context(x) From e382e26a292413064b3c2c96bbea8268eb7c1684 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 19 Oct 2017 17:02:21 +0800 Subject: [PATCH 0468/1065] populate and check spark.speculation to spark conf (#1683) * populate and check spark.speculation to spark conf * fix an error in pytest --- .../scala/com/intel/analytics/bigdl/utils/EngineSpec.scala | 6 ++++-- scala/dllib/src/main/resources/spark-bigdl.conf | 1 + 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala index 5f14ed33a98..91e59c18799 100644 --- a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala @@ -133,7 +133,8 @@ class EngineSpec extends FlatSpec with Matchers with BeforeAndAfter { val target = Map( "spark.shuffle.reduceLocality.enabled" -> "false", "spark.shuffle.blockTransferService" -> "nio", - "spark.scheduler.minRegisteredResourcesRatio" -> "1.0" + "spark.scheduler.minRegisteredResourcesRatio" -> "1.0", + "spark.speculation" -> "true" ) conf.length should be(target.keys.size) conf.foreach(s => { @@ -146,7 +147,8 @@ class EngineSpec extends FlatSpec with Matchers with BeforeAndAfter { val conf = Engine.readConf val target = Map( "spark.shuffle.reduceLocality.enabled" -> "false", - "spark.scheduler.minRegisteredResourcesRatio" -> "1.0" + "spark.scheduler.minRegisteredResourcesRatio" -> "1.0", + "spark.speculation" -> "true" ) conf.length should be(target.keys.size) conf.foreach(s => { diff --git a/scala/dllib/src/main/resources/spark-bigdl.conf b/scala/dllib/src/main/resources/spark-bigdl.conf index 9870f82a6bc..63b77c33d0d 100644 --- a/scala/dllib/src/main/resources/spark-bigdl.conf +++ b/scala/dllib/src/main/resources/spark-bigdl.conf @@ -30,3 +30,4 @@ spark.shuffle.reduceLocality.enabled false spark.shuffle.blockTransferService nio spark.scheduler.minRegisteredResourcesRatio 1.0 +spark.speculation true From 5881cf8e351cdd6fc8a02f7f5b0f50e7a72bd78c Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 19 Oct 2017 19:00:45 +0800 Subject: [PATCH 0469/1065] undo batch (#1651) --- .../bigdl/dllib/utils/tf/Session.scala | 134 ++++++++---------- .../bigdl/dllib/utils/tf/SessionSpec.scala | 29 ++-- 2 files changed, 80 insertions(+), 83 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index cc1ae202f5b..cf18825585d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -34,7 +34,9 @@ import com.intel.analytics.bigdl.models.utils.ModelBroadcast import TFTensorNumeric.NumericByteString import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +import scala.util.Random abstract class Session[T: ClassTag] { @@ -277,81 +279,20 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], dataSeq } - private def handleDistriDequeue(node: Node[NodeDef], cache: DataCache, - sc: SparkContext): RDD[Table] = { - require(node.prevNodes.length == 1, "require QueueDequeueV2 only has one input") - val queueNode = node.prevNodes.head - val dequeueNodes = queueNode.nextNodes - .filter(n => n.element != null && dequeueOp(n.element.getOp)) - .map(n => n.element.getName.split(":")(0)).toSet - require(dequeueNodes.size == 1, "only support one dequeue node after reader") - val enqueueNodes = findEnqueueNodes(queueNode) - val rdd = enqueueNodes.map { enqueueNode => - val inputs = Seq(enqueueNode.element.getName) - val result = constructDistributeData(inputs, cache, sc) - if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { - result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) - } else { - result - } - }.reduce { (rdd1, rdd2) => - rdd1.union(rdd2) - } - rdd - } - - private def handleDistriDequeueManyNode(node: Node[NodeDef], cache: DataCache, - sc: SparkContext): RDD[Table] = { - require(node.prevNodes.length == 2, "require QueueDequeueManyV2 only has two input") - val queueNode = node.prevNodes.head - val dequeueNodes = queueNode.nextNodes - .filter(n => n.element != null && dequeueOp(n.element.getOp)) - .map(n => n.element.getName.split(":")(0)).toSet - require(dequeueNodes.size == 1, "only support one dequeue node after reader") - val enqueueNodes = findEnqueueNodes(queueNode) - // get previous rdd - val rdd = enqueueNodes.map { enqueueNode => - val inputs = Seq(enqueueNode.element.getName) - val result = constructDistributeData(inputs, cache, sc) - if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { - result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) - } else { - result - } - }.reduce { (rdd1, rdd2) => - rdd1.union(rdd2) - } - - // get batch size - val batchSizeNode = node.prevNodes(1) - require(batchSizeNode.element.getOp == "Const", "batchsize must be a const") - - val batchSize = batchSizeNode.element.getAttrMap.get("value").getTensor.getIntVal(0) - - val batchRdd = rdd.mapPartitions { iter => + private def batchRdd(rdd: RDD[Table], batchSize: Int): RDD[Table] = { + rdd.mapPartitions { iter => new Iterator[Table] { - private var firstBatch: Array[Table] = null override def hasNext: Boolean = iter.hasNext override def next(): Table = { require(iter.hasNext, "Call next() on a empty iterator") - val tables = new Array[Table](batchSize) - var index = 0 - for (i <- 0 until batchSize) { - if (iter.hasNext) { - tables(i) = iter.next() - } else if (firstBatch == null) { - tables(i) = tables(index) - index = index + 1 - } else { - tables(i) = firstBatch(index) - index = index + 1 + + val tables = + for (i <- 0 until batchSize if iter.hasNext) yield { + iter.next() } - } - if (firstBatch == null) { - firstBatch = tables - } + val batch = tables.map(_.toSeq) val firstSeq = batch.head val sizes = firstSeq.map { tensor => @@ -387,7 +328,38 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], } } - batchRdd + } + + private def handleDistriDequeue(node: Node[NodeDef], cache: DataCache, + sc: SparkContext): RDD[Table] = { + val queueNode = node.prevNodes.head + val dequeueNodes = queueNode.nextNodes + .filter(n => n.element != null && dequeueOp(n.element.getOp)) + .map(n => n.element.getName.split(":")(0)).toSet + require(dequeueNodes.size == 1, "only support one dequeue node after reader") + val enqueueNodes = findEnqueueNodes(queueNode) + // get previous rdd + var rdd = enqueueNodes.map { enqueueNode => + val inputs = Seq(enqueueNode.element.getName) + val result = constructDistributeData(inputs, cache, sc) + if (enqueueNode.element.getOp == "QueueEnqueueManyV2") { + result.flatMap(BigDLSessionImpl.splitTensorByFirstDim) + } else { + result + } + }.reduce { (rdd1, rdd2) => + rdd1.union(rdd2) + } + + if (node.element.getOp == "QueueDequeueManyV2") { + // get batch size + val batchSizeNode = node.prevNodes(1) + require(batchSizeNode.element.getOp == "Const", "batchsize must be a const") + + val batchSize = batchSizeNode.element.getAttrMap.get("value").getTensor.getIntVal(0) + rdd = batchRdd(rdd, batchSize) + } + rdd } type DataCache = mutable.HashMap[String, Array[Seq[Table]]] @@ -484,7 +456,7 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], node.element.getOp match { case "ReaderReadV2" => handleReaderNode(node, cache, sc) case "QueueDequeueV2" => handleDistriDequeue(node, cache, sc) - case "QueueDequeueManyV2" => handleDistriDequeueManyNode(node, cache, sc) + case "QueueDequeueManyV2" => handleDistriDequeue(node, cache, sc) } } val inputRdd = inputRdds.reduce { (rdd1, rdd2) => @@ -534,11 +506,21 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], * Get and calculate the data up to the specified endpoints, and * return as a RDD[Table] * @param endPoints output endpoints + * @param hasToBatch indicate whether the subgraph to be executed already has + * to batch operation. If yes, the batch operation will be undone at + * the end of this execution, that is split each tensor by their first dimension. * @return */ - def getRDD(endPoints: Seq[String], sc: SparkContext): RDD[Table] = { + private[bigdl] def getRDD(endPoints: Seq[String], sc: SparkContext, + hasToBatch: Boolean = true): RDD[Table] = { val cache = new mutable.HashMap[String, Array[Seq[Table]]]() - constructDistributeData(endPoints, cache, sc) + val result = if (!hasToBatch) { + constructDistributeData(endPoints, cache, sc) + } else { + val batchRdd = constructDistributeData(endPoints, cache, sc) + batchRdd.flatMap(BigDLSessionImpl.splitTensorByFirstDim) + } + result } } @@ -574,4 +556,12 @@ object BigDLSessionImpl { } result } + + private def toSample[T: ClassTag](rdd: RDD[Table]) + (implicit ev: TensorNumeric[T]): RDD[Sample[T]] = { + rdd.map{ t => + val arr = t.toSeq[T].toArray + Sample[T](arr) + } + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala index 42f65f92456..df141577f98 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/SessionSpec.scala @@ -16,10 +16,10 @@ package com.intel.analytics.bigdl.utils.tf import com.intel.analytics.bigdl.dataset._ -import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, MSECriterion} +import com.intel.analytics.bigdl.nn.MSECriterion import com.intel.analytics.bigdl.optim.{SGD, Trigger} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{Engine, File, T, Table} +import com.intel.analytics.bigdl.utils.Engine import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -105,14 +105,16 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { val endpoints = Seq( "fifo_queue_Dequeue" ) - val rdd = session.getRDD(endpoints, sc) - val result = rdd.collect() - result.length should be (5) - val imageSum = result.map(t => t[Tensor[Float]](1).sum()).sum - val labelSum = result.map(t => t[Tensor[Float]](2).sum()).sum - (imageSum - (-6009.5)) < 1e-7 should be (true) - labelSum should be (10) + val rdd = session.getRDD(endpoints, sc) + val result1 = rdd.collect() + + result1.length should be (10) + val rowSum1 = result1.map(t => t[Tensor[Float]](1).sum()) + val allSum1 = rowSum1.sum + val labelSum1 = result1.map(t => t[Tensor[Float]](2).sum()).sum + (allSum1 - (-6009.5)) < 1e-7 should be (true) + labelSum1 should be (10) } "Session" should "be work with arbitrary batch size" in { @@ -127,8 +129,13 @@ class SessionSpec extends FlatSpec with Matchers with BeforeAndAfter { ) val rdd = session.getRDD(endpoints, sc) val result = rdd.collect() - result.length should be (4) - result.head[Tensor[Float]](1).size(1) should be (3) + result.length should be (10) + val labelSize = Array(10) + val featureSize = Array(28, 28, 1) + result.foreach { t => + t[Tensor[Float]](1).size should be (featureSize) + t[Tensor[Int]](2).size should be (labelSize) + } } private def getLenetModel(name: String) = { From e615b77d55858e5bf66b2a60d25c3773468dd807 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 20 Oct 2017 04:33:06 -0400 Subject: [PATCH 0470/1065] fix: make the length of weight and gradWeight in quantized conv same length (#1692) * fix: the different length of weight and gradWeight in quantized conv is very confused. So make them same length. * fix: return grad --- .../bigdl/dllib/models/utils/ModelBroadcast.scala | 1 - .../dllib/nn/quantized/SpatialConvolution.scala | 2 +- .../dllib/models/utils/ModelBroadcastSpec.scala | 12 ++++++++++++ 3 files changed, 13 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 1c2d430c57c..8652e207ac4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -105,7 +105,6 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria } // clear parameters clearTensor(parameters._1) - // because in quantized mode, the weight number may be different with gradWeight number clearTensor(parameters._2) weightsBias diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala index 36c9aa7e2cc..1908ab02ee1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -198,7 +198,7 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( } override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { - (weight :+ bias, Array(empty, empty)) + (weight :+ bias, Array.fill[Tensor[T]](nGroup + 1)(empty)) // nGroup's weight + bias } override def getParametersTable(): Table = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala index c75699d228c..ab89c578fa7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala @@ -16,6 +16,8 @@ package com.intel.analytics.bigdl.models.utils import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.nn.Sequential +import com.intel.analytics.bigdl.nn.SpatialConvolution import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -55,6 +57,16 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } + "quantized multi groups model" should "work properly" in { + val model = Sequential[Float]() + .add(SpatialConvolution[Float](2, 4, 4, 4, 1, 1, 0, 0, 2)) + .quantize() + + val modelBroadCast = ModelBroadcast[Float].broadcast(sc, model) + modelBroadCast.value().toString should be(model.toString) + modelBroadCast.value().parameters()._1 should be(model.parameters()._1) + } + after { if (sc != null) { sc.stop() From 717e28d2818efae62970bab0e315c6646ee0f7b0 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 20 Oct 2017 06:00:38 -0400 Subject: [PATCH 0471/1065] feat: make native to one artifactId bigdl-core (#1685) * feat: make native to one artifactId bigdl-core * fix: update core --- dl/pom.xml | 25 +++---------------------- 1 file changed, 3 insertions(+), 22 deletions(-) diff --git a/dl/pom.xml b/dl/pom.xml index 76edfcc9a24..5b87e8569fd 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -67,29 +67,10 @@ compile - com.intel.analytics.bigdl.native - ${mkl-java-os-version} + com.intel.analytics.bigdl + bigdl-core 0.3.0-SNAPSHOT - - - - com.intel.analytics.bigdl.native - bigdl-native - - - - - com.intel.analytics.bigdl.bigquant - bigquant-java - ${project.version} - - - com.intel.analytics.bigdl.bigquant - bigquant-native - - + pom org.apache.spark From d6d68eead89be3c1ed8a9de8e7fbdfae32683295 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Sun, 22 Oct 2017 20:59:35 +0800 Subject: [PATCH 0472/1065] Add new API to session to allow user train a tensorflow graph directly on BigDL (#1694) * Add new API to session to allow user train a tensorflow graph directly on BigDL * meet code review --- .../bigdl/dllib/feature/dataset/Sample.scala | 3 +- .../bigdl/dllib/utils/tf/Session.scala | 138 ++++++++++++++++-- .../dllib/utils/tf/TensorflowLoader.scala | 40 +++-- 3 files changed, 153 insertions(+), 28 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index 287ee20b775..6cdffb1d5b6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -289,8 +289,7 @@ object ArraySample { featureTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { val data = new Array[T](featureTensors.map(_.nElement()).sum) copy(data, featureTensors) - new ArraySample[T](featureTensors.flatMap(_.storage().array()), - getSize(featureTensors), null) + new ArraySample[T](data, getSize(featureTensors), null) } private def copy[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index cf18825585d..cc5d239438b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -20,7 +20,7 @@ import java.nio.{ByteOrder, DoubleBuffer, FloatBuffer} import com.intel.analytics.bigdl.Criterion import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Graph, Linear} -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractCriterion, AbstractModule, Activity} import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} @@ -32,6 +32,7 @@ import org.tensorflow.framework.{GraphDef, NodeDef} import com.google.protobuf.ByteString import com.intel.analytics.bigdl.models.utils.ModelBroadcast import TFTensorNumeric.NumericByteString +import com.intel.analytics.bigdl.utils.tf.BigDLSessionImpl.FakeCriterion import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -41,16 +42,63 @@ import scala.util.Random abstract class Session[T: ClassTag] { /** - * Train the model specified by the model output - * @param outputs model output - * @param dataSet the training data set - * @return trained model + * Train the tensorflow graph + * @param outputs + * @param dataSet + * @param optMethod + * @param criterion + * @param endWhen + * @return */ def train(outputs: Seq[String], dataSet: DistributedDataSet[MiniBatch[T]], optMethod: OptimMethod[T], criterion: Criterion[T], endWhen: Trigger): Graph[T] + + + /** + * Train the tensorflow graph. The model must be fed data with a queue + * @param endPoints + * @param optMethod + * @param endWhen + * @param isDataBatch if the model input is the batch + * @param batchSize batch size, which should be original batch size * total core number + * @param sc + * @param loss + * @return + */ + def train( + endPoints: Seq[String], + optMethod: OptimMethod[T], + endWhen: Trigger, + isDataBatch: Boolean, + batchSize: Int, + sc: SparkContext, + loss: Option[String] + ): this.type + + /** + * Predict data with tensorflow graph. The data must hold in a queue + * @param endPoints + * @param isDataBatch if the model input is the batch + * @param batchSize batch size, which should be original batch size * total core number + * @param sc + * @return + */ + def predict( + endPoints: Seq[String], + isDataBatch: Boolean, + batchSize: Int, + sc: SparkContext + ): RDD[Activity] + + /** + * Dump varaible contents to a file + * @param binFile + * @return + */ + def saveParameters(binFile: String): this.type } class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], @@ -65,7 +113,7 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], criterion: Criterion[T], endWhen: Trigger): Graph[T] = { - val (model, input) = constructModel(outputs, byteOrder) + val (model, input) = constructModel(outputs, byteOrder, true, None) require(input.element.getOp == "Placeholder", "only support Placeholder as input when in-memory input data is provided") @@ -75,12 +123,61 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], dataSet, criterion ) - val optMethod = new SGD[T]() opt.setOptimMethod(optMethod).setEndWhen(endWhen) .optimize() model } + override def train( + endPoints: Seq[String], + optMethod: OptimMethod[T], + endWhen: Trigger, + isDataBatch: Boolean, + batchSize: Int, + sc: SparkContext, + loss: Option[String] + ) + : this.type = { + val weightsAndGrads = endPoints.map(e => name2Node(e)).map(n => n.graph(true).DFS).flatten + .map(n => TFUpdater(n.element)).flatten.toSet + + require(weightsAndGrads.size != 0, "Cannot find updater nodes") + context.setAssignGrads(weightsAndGrads) + val modelOutputs = if (loss.isDefined) { + Seq(loss.get) ++ weightsAndGrads.map(_._2).toSeq + } else { + weightsAndGrads.map(_._2).toSeq + } + val (model, input) = constructModel(modelOutputs, byteOrder, false, Some(context)) + val data = BigDLSessionImpl.toSample[T](getRDD(Seq(input.element.getName), sc, isDataBatch)) + + val opt = Optimizer[T]( + model, + data, + new FakeCriterion[T](), + batchSize + ) + opt.setOptimMethod(optMethod).setEndWhen(endWhen) + .optimize() + this + } + + override def predict( + endPoints: Seq[String], + isDataBatch: Boolean, + batchSize: Int, + sc: SparkContext + ): RDD[Activity] = { + val (model, input) = constructModel(endPoints, byteOrder, true, Some(context)) + val data = BigDLSessionImpl.toSample[T](getRDD(Seq(input.element.getName), sc, isDataBatch)) + model.predict(data) + } + + override def saveParameters(binFile: String): this.type = { + TensorflowLoader.saveBinFile(binFile, context) + this + } + private val inputOp = Set("ReaderReadV2", "QueueDequeueV2", "QueueDequeueManyV2", "Placeholder") private val dequeueOp = Set("QueueDequeueV2", "QueueDequeueManyV2", "ReaderReadV2") @@ -394,7 +491,7 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], endPoints, ByteOrder.LITTLE_ENDIAN, "", - Some(context), + None, generatedBackward = false ).asInstanceOf[Graph[T]] @@ -448,7 +545,7 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], endPoints, ByteOrder.LITTLE_ENDIAN, "", - Some(context), + None, generatedBackward = false ).asInstanceOf[Graph[T]] @@ -477,7 +574,8 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], } - private def constructModel(endPoints: Seq[String], byteOrder: ByteOrder) + private def constructModel(endPoints: Seq[String], byteOrder: ByteOrder, + generateBackward: Boolean, context: Option[Context[T]]) : (Graph[T], Node[NodeDef]) = { val isInputOp = (n: NodeDef) => inputOp(n.getOp) val (tfGraph, inputs, originInputs) = @@ -497,7 +595,8 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], endPoints, byteOrder, "", - Some(context) + context, + generateBackward ).asInstanceOf[Graph[T]] (model, inputNodes.head) } @@ -535,6 +634,23 @@ object TFUpdater { } object BigDLSessionImpl { + + class FakeCriterion[T: ClassTag](enable: Boolean = false)(implicit ev: TensorNumeric[T]) + extends AbstractCriterion[Activity, Activity, T] { + + override def updateOutput(input: Activity, target: Activity): T = { + if (enable) { + ev.fromType(0.0) + } else { + input.toTable.apply[Tensor[T]](1).value() + } + } + + override def updateGradInput(input: Activity, target: Activity): Activity = { + null + } + } + private def splitTensorByFirstDim(table: Table): Array[Table] = { val nElem = table.length() require(nElem >= 1, "EnqueueManyV2 encounter a empty table") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 29dce704fe4..47b6bc7f0d2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -103,6 +103,22 @@ object TensorflowLoader{ } + private[bigdl] def saveBinFile[T: ClassTag](file: String, context: Context[T]) + (implicit ev: TensorNumeric[T]): Unit = { + val save = new JHashMap[String, JTensor]() + context.tensorNames().foreach(n => { + val tensor = context(n)._1 + val saveTensor = ev.getType() match { + case FloatType => new JTensor(tensor.asInstanceOf[Tensor[Float]].storage().array(), + tensor.size(), "float") + case DoubleType => new JTensor(tensor.asInstanceOf[Tensor[Double]].storage().array() + .map(_.toFloat), tensor.size(), "double") + } + save.put(n, saveTensor) + }) + File.save(save, file, true) + } + private def loadBinFiles[T: ClassTag](file: String)(implicit ev: TensorNumeric[T]): Context[T] = { val m = File.load(file).asInstanceOf[JHashMap[String, JTensor]].asScala val map = new mutable.HashMap[String, (Tensor[T], Tensor[T], Option[Seq[(Int, Int)]])]() @@ -291,15 +307,7 @@ object TensorflowLoader{ val errorMsg = s""" | Cannot convert the given tensorflow operation graph to BigDL model. The convert fails - | at node ${n.element.getName}. - | To investigate the model. Please use the dump_tf_graph.py to dump the graph, then use - | Tensorboard to visualize the model. - | - | python dump_tf_graph.py $graphPrototxt - | tensorboard --logdir ./log - | - | You can find the dump_tf_graph.py in the bin folder of the dist package, or scripts - | folder in the source code. + | at node ${n.element.getName}. Operation type is ${n.element.getOp} """.stripMargin val (module, nodes, inputNodes) = @@ -310,10 +318,8 @@ object TensorflowLoader{ val builder = cls.getConstructors()(0).newInstance().asInstanceOf[TensorflowOpsLoader] (builder.build[T](n.element, byteOrder, context), Seq(n).asJava, Seq(n)) } catch { - case _ => - println("com.intel.analytics.bigdl.utils.tf.loaders." + - n.element.getOp) - throw new UnsupportedOperationException(errorMsg) + case e: Throwable => + throw new UnsupportedOperationException(errorMsg, e) } }) @@ -396,8 +402,12 @@ object TensorflowLoader{ val adjustOutputs = if (context.assignGrads.isDefined) { outputNodes.map(n => { val matchNode = context.assignGrads.get.filter(_._2 == n.element.getName()) - require(matchNode.size == 1, "Invalid gradients output") - new AssignGrad[T](context(matchNode.head._1)._2).inputs(n) + require(matchNode.size <= 1, "Invalid gradients output") + if (matchNode.size == 1) { + new AssignGrad[T](context(matchNode.head._1)._2).inputs(n) + } else { + n + } }) } else { outputNodes From 9ffed543c01ff2c2cf38463d6b4f28e8d9a802b2 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Sun, 22 Oct 2017 21:00:16 +0800 Subject: [PATCH 0473/1065] fix some document issue (#1696) --- .../intel/analytics/bigdl/dllib/models/inception/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md index e6d8050d1ed..ec7f07793dc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md @@ -36,10 +36,11 @@ This command will transform the images into hadoop sequence files, which are more suitable for a distributed training. Bigdl has different versions, bigdl-VERSION-jar-with-dependencies-and-spark.jar used in the following command is a general name. -Please update it according to your bigdl version. +Please update it according to your bigdl version. As we only distribute jar without spark dependency, you need to build the +bigdl-VERSION-jar-with-dependencies-and-spark.jar from the source. ```bash -java -cp bigdl_folder/spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar com.intel.analytics.bigdl.models.utils.ImageNetSeqFileGenerator -f imagenet_folder -o output_folder -p cores_number +java -cp bigdl_source_folder/spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar com.intel.analytics.bigdl.models.utils.ImageNetSeqFileGenerator -f imagenet_folder -o output_folder -p cores_number ``` It will generate the hadoop sequence files in the output folder. From 8db149aacea7537be6948dc9b948a4df475e6c54 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 23 Oct 2017 09:51:19 +0800 Subject: [PATCH 0474/1065] Caffe converter fix (#1693) * converter fix * fix typo * fix unit test accordingly --- .../com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala | 2 +- .../analytics/bigdl/dllib/utils/caffe/LayerConverter.scala | 2 +- .../com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index 09aaa501eef..442585ccfbe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -140,7 +140,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { private def fromCaffeSoftmax(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val layerName = getLayerName(layer) - Seq(LogSoftMax().setName(layerName).inputs()) + Seq(SoftMax().setName(layerName).inputs()) } private def fromCaffeTanh(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index 3469e3c3954..dde5a31b832 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -80,7 +80,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert kw, kh, dw, dh, pw, ph, 0, 0, group, !withBias).setName(getLayerName(layer)).inputs()) } else { Seq(SpatialConvolution[T](nInputPlane.toInt, nOutPlane.toInt, - kw, kh, dw, dh, pw, ph, group, withBias).setName(getLayerName(layer)).inputs()) + kw, kh, dw, dh, pw, ph, group, withBias = withBias).setName(getLayerName(layer)).inputs()) } } else { val dilation = param.getDilation(0) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala index 6366f97c021..a671213add9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala @@ -204,7 +204,7 @@ class CaffeLoaderSpec extends FlatSpec with Matchers { .add(Convolution(4, 3, 2, 2).setName("conv2")) .add(View(27)).setName("view") .add(Linear(27, 2, withBias = false).setName("ip")) - .add(LogSoftMax().setName("softmax")) + .add(SoftMax().setName("softmax")) val staticInput = Tensor[Double](1, 3, 5, 5).apply1( e => Random.nextDouble()) From f87ac28bb5e12ef60654d9bc6d98a3c6f062bac5 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 23 Oct 2017 10:10:10 +0800 Subject: [PATCH 0475/1065] fix SparseTensor's resize (#1681) --- .../bigdl/dllib/tensor/SparseTensor.scala | 45 ++++++++++------ .../bigdl/dllib/tensor/SparseTensorBLAS.scala | 20 ------- .../bigdl/dllib/tensor/SparseTensorSpec.scala | 54 +++++++++++++++++++ 3 files changed, 83 insertions(+), 36 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 1eae98612aa..e36bddbc7a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -285,14 +285,17 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( override def set(): Tensor[T] = { if (this._indices != null) { - for (ind <- this._indices) - ind.resize(0) + _indices.foreach(ind => ind.resize(0)) + for (i <- 0 until _indicesOffset.length) { + _indicesOffset(i) = 0 + } } if (this._values != null) { this._values.resize(0) } this._nElement = 0 this._storageOffset = 0 + this.nDimension = 0 this._shape = Array() this } @@ -436,21 +439,31 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + private def resizeIndices(nElement: Int): Unit = { + var i = 0 + while (i < _indices.length) { + _indices(i).resize(nElement + _indicesOffset(i)) + i += 1 + } + } + override def resize(size: Array[Int], nElement: Int): Tensor[T] = { - if (this.nElement() < nElement) { - storage.resize(nElement) - if (size.length == _indices.length) { - _indices.foreach(_.resize(nElement)) - } else if (size.length < _indices.length) { - _indices = _indices.slice(0, size.length) - _indices.foreach(_.resize(nElement)) - } else { - val _addIndices = new Array[Storage[Int]](size.length - _indices.length) - for (i <- _addIndices.indices) _addIndices(i) = Storage[Int](nElement) - _indices ++= _addIndices - _indices.foreach(_.resize(nElement)) - } - _storageOffset = 0 + if (size.length < _indices.length) { + _indices = _indices.slice(0, size.length) + _indicesOffset = _indicesOffset.slice(0, size.length) + resizeIndices(nElement) + } else if (size.length > _indices.length) { + val _addIndices = new Array[Storage[Int]](size.length - _indices.length) + for (i <- _addIndices.indices) _addIndices(i) = Storage[Int](nElement) + _indicesOffset ++= new Array[Int](size.length - _indicesOffset.length) + _indices ++= _addIndices + resizeIndices(nElement) + } else if (_indices(0).length() - _indicesOffset(0) < nElement) { + resizeIndices(nElement) + } + + if (storage.length() - _storageOffset < nElement) { + storage.resize(nElement + _storageOffset) } _nElement = nElement _shape = size diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala index b4a3ad0292a..8af373d52bf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala @@ -166,11 +166,6 @@ object SparseTensorBLAS { val Cvals = C.storage().array() val cOffset = C.storageOffset() - 1 - require(ArowIndices.length == AcolIndices.length, s"A: row indices number " + - s"${ArowIndices.length()} is not equal to col indices number ${AcolIndices.length()}") - require(ArowIndices.length == Avals.length, s"A: indices length ${ArowIndices.length()}" + - s"is not equal to values length ${Avals.length}") - // Scale matrix first if `beta` is not equal to 1.0 if (beta != 1.0) { C.mul(beta) @@ -229,11 +224,6 @@ object SparseTensorBLAS { val Cvals = C.storage().array() val cOffset = C.storageOffset() - 1 - require(ArowIndices.length == AcolIndices.length, s"A: row indices number " + - s"${ArowIndices.length()} is not equal to col indices number ${AcolIndices.length()}") - require(ArowIndices.length == Avals.length, s"A: indices length ${ArowIndices.length()}" + - s"is not equal to values length ${Avals.length}") - // Scale matrix first if `beta` is not equal to 1.0 if (beta != 1.0) { C.mul(beta) @@ -292,11 +282,6 @@ object SparseTensorBLAS { val BcolIndices = B._indices(1) val BcolIndicesOffset = B._indicesOffset(1) - require(BrowIndices.length == BcolIndices.length, s"B: row indices number " + - s"${BrowIndices.length()} is not equal to col indices number ${BcolIndices.length()}") - require(BrowIndices.length == Bvals.length, s"B: indices length ${BrowIndices.length()}" + - s"is not equal to values length ${Bvals.length}") - // Scale matrix first if `beta` is not equal to 1.0 if (beta != 1.0) { C.mul(beta) @@ -354,11 +339,6 @@ object SparseTensorBLAS { val BcolIndices = B._indices(1) val BcolIndicesOffset = B._indicesOffset(1) - require(BrowIndices.length == BcolIndices.length, s"B: row indices number " + - s"${BrowIndices.length()} is not equal to col indices number ${BcolIndices.length()}") - require(BrowIndices.length == Bvals.length, s"B: indices length ${BrowIndices.length()}" + - s"is not equal to values length ${Bvals.length}") - // Scale matrix first if `beta` is not equal to 1.0 if (beta != 1.0) { C.mul(beta) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala index fbd36689d50..01724d5c944 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala @@ -45,4 +45,58 @@ class SparseTensorSpec extends FlatSpec with Matchers { sTensor3.storageOffset() should be (11) sTensor3.asInstanceOf[SparseTensor[Float]]._indicesOffset should be (Array(2, 0)) } + + "resize" should "return right result" in { + val sTensor = Tensor.sparse(Tensor(6, 5).range(1, 30, 1)) + sTensor.resize(Array(10, 10), 50) + sTensor.size() should be (Array(10, 10)) + sTensor.nElement() should be (50) + sTensor.storage().array.length should be (50) + } + + "resize on empty tensor" should "return right result" in { + val sTensor = Tensor.sparse(Tensor(6, 5).range(1, 30, 1)) + sTensor.set() + sTensor.resize(Array(10, 10), 50) + sTensor.size() should be (Array(10, 10)) + sTensor.nElement() should be (50) + sTensor.storage().array.length should be (50) + } + + "resize on narrowed tensor" should "return right result" in { + val sTensor = Tensor.sparse(Tensor(6, 5).range(1, 30, 1)).narrow(1, 2, 4) + sTensor.resize(Array(10, 10), 50) + sTensor.size() should be (Array(10, 10)) + sTensor.nElement() should be (50) + sTensor.storage().array.length should be (55) + sTensor.storageOffset() should be (6) + } + + "resize 2D tensor to 3D tensor" should "return right result" in { + val sTensor = Tensor.sparse(Tensor(6, 5).range(1, 30, 1)).narrow(1, 2, 4) + sTensor.resize(Array(10, 10, 10), 50) + sTensor.size() should be (Array(10, 10, 10)) + sTensor.nElement() should be (50) + sTensor.storage().array.length should be (55) + sTensor.storageOffset() should be (6) + } + + "resize 2D tensor to 1D tensor" should "return right result" in { + val sTensor = Tensor.sparse(Tensor(6, 5).range(1, 30, 1)).narrow(1, 2, 4) + sTensor.resize(Array(10), 5) + sTensor.size() should be (Array(10)) + sTensor.nElement() should be (5) + sTensor.storage().array.length should be (30) + sTensor.storageOffset() should be (6) + } + + "resize 2D tensor to 1D tensor" should "return right result2" in { + val sTensor = Tensor.sparse(Tensor(6, 5).range(1, 30, 1)) + sTensor.resize(Array(30), 30) + sTensor.size() should be (Array(30)) + sTensor.nElement() should be (30) + sTensor.storage().array.length should be (30) + sTensor.storageOffset() should be (1) + } + } From 7f3c1fb6e14a9619bf9d8538f335216fd3b22c9d Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 23 Oct 2017 10:45:34 +0800 Subject: [PATCH 0476/1065] add toGraph (#1189) * add toGraph * refactor toGraph * add getEndNodes for ParallelTable, MapTable * add unit test --- .../analytics/bigdl/dllib/nn/Concat.scala | 12 + .../bigdl/dllib/nn/ConcatTable.scala | 12 + .../analytics/bigdl/dllib/nn/Graph.scala | 3 + .../analytics/bigdl/dllib/nn/Input.scala | 7 +- .../analytics/bigdl/dllib/nn/MapTable.scala | 9 +- .../bigdl/dllib/nn/ParallelTable.scala | 14 ++ .../analytics/bigdl/dllib/nn/Sequential.scala | 13 +- .../dllib/nn/abstractnn/AbstractModule.scala | 25 +- .../bigdl/dllib/utils/GraphNodeSpec.scala | 226 ++++++++++++++++++ 9 files changed, 313 insertions(+), 8 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala index 891cee825f0..fb5e5ec8833 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala @@ -16,11 +16,13 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Engine +import scala.collection.mutable.ArrayBuffer import scala.concurrent.Future import scala.reflect.ClassTag @@ -325,6 +327,16 @@ class Concat[T: ClassTag](val dimension: Int)( forwardTime = 0 backwardTime = 0 } + + override def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { + val outputs = ArrayBuffer[ModuleNode[T]]() + var outputTuple: Array[ModuleNode[T]] = null + for (i <- 0 to modules.size - 1) { + outputTuple = modules(i).getEndNodes(startNodes) + outputs ++= outputTuple + } + Array(JoinTable(dimension, -1).inputs(outputs: _*)) + } } object Concat { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala index 1644627152e..d1f66bb8a3c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala @@ -16,11 +16,13 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -244,6 +246,16 @@ class ConcatTable[T : ClassTag] str = str + line + "}" str } + + override def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { + val outputs = ArrayBuffer[ModuleNode[T]]() + var outputTuple: Array[ModuleNode[T]] = null + for (i <- 0 to modules.size - 1) { + outputTuple = modules(i).getEndNodes(startNodes) + outputs ++= outputTuple + } + outputs.toArray + } } object ConcatTable { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 07c9b4c998c..03aaea59d6b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -279,6 +279,9 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], "A graph container should not be changed after it is constructed") } + // todo: expand the graph + override def toGraph(startNodes: ModuleNode[T]*): Graph[T] = this + // Add a dummy output node, to get an one end graph. So the nodes that are not dependent by // the outputs will be excluded private val dummyOutput = new ModuleNode[T](new Identity[T]()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala index af35eaad566..8a1669d7012 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Input.scala @@ -36,12 +36,13 @@ import scala.reflect.ClassTag * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] */ @SerialVersionUID(- 8525406230282608924L) -class Input[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T] { - override def updateOutput(input: Tensor[T]): Tensor[T] = { +class Input[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends AbstractModule[Activity, Activity, T] { + override def updateOutput(input: Activity): Activity = { output = input output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { gradInput = gradOutput gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala index 25c31a07dce..087b5659c02 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala @@ -15,12 +15,14 @@ */ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DeserializeContext, ModuleData, SerializeContext} import serialization.Bigdl.BigDLModule +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -41,11 +43,11 @@ class MapTable[T: ClassTag]( } private def extend(n: Int): Unit = { - var i = 1 + var i = 2 while (i <= n && modules.size <= i) { if (modules.length <= i) { modules.append(module - .cloneModule() + .cloneModule().setName(module.getName() + i) .asInstanceOf[AbstractModule[Activity, Activity, T]]) } i += 1 @@ -98,6 +100,9 @@ class MapTable[T: ClassTag]( } } + override def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { + throw new IllegalArgumentException("Can not transform Container MapTable to graph") + } override def zeroGradParameters(): Unit = { if (module != null) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala index 4a763de9c26..6525fc7540d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala @@ -15,9 +15,11 @@ */ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -55,6 +57,18 @@ class ParallelTable[T: ClassTag] } } + override def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { + val outputs = ArrayBuffer[ModuleNode[T]]() + var outputTuple: Array[ModuleNode[T]] = null + require(startNodes.length == modules.length, s"ParallelTable: " + + s"startNodes length ${startNodes.length} is more than modules length ${modules.length}") + for (i <- 0 to modules.size - 1) { + outputTuple = modules(i).getEndNodes(Array(startNodes(i))) + outputs ++= outputTuple + } + outputs.toArray + } + override def toString: String = { val tab = "\t" val line = "\n" diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala index 586d08bacc6..4141ed84f44 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala @@ -16,10 +16,12 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{Activity, AbstractModule} +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag +import scala.collection.mutable.ArrayBuffer /** * Sequential provides a means to plug layers together @@ -147,6 +149,15 @@ class Sequential[T: ClassTag] }$line}" } + override def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { + var startnodes = startNodes + var curNodes: Array[ModuleNode[T]] = null + for (i <- 0 to modules.size - 1) { + curNodes = modules(i).getEndNodes(startnodes) + startnodes = curNodes + } + curNodes + } } object Sequential { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index a39dc4c2913..6f4c18c1413 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -22,8 +22,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.tensor.{Tensor, TensorDataType} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ -import com.intel.analytics.bigdl.nn.{Graph, InitializationMethod, Module, Zeros} -import com.intel.analytics.bigdl.nn.{Module, Utils} +import com.intel.analytics.bigdl.nn.{Module, _} import com.intel.analytics.bigdl.utils.TorchObject.TYPE_MODULE import org.apache.commons.lang3.SerializationUtils import org.apache.spark.rdd.RDD @@ -709,5 +708,27 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, def quantize(): Module[T] = { Quantization.quantize(this) } + + + /** + * Generate end nodes of current module with start nodes + * @param startNodes: current start nodes + * @return current end nodes + */ + private[bigdl] def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { + val endNodes = Array(this.inputs(startNodes: _*)) + endNodes + } + + /** + * Generate graph module with start nodes + * @param startNodes + * @return + */ + def toGraph(startNodes: ModuleNode[T]*): Graph[T] = { + val starts = if (startNodes.isEmpty) Array(Input[T]()) else startNodes.toArray + val endNodes = this.getEndNodes(starts) + Graph(starts, endNodes) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala new file mode 100644 index 00000000000..da08a1e1799 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala @@ -0,0 +1,226 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + + +import com.intel.analytics.bigdl.example.loadmodel.AlexNet_OWT +import com.intel.analytics.bigdl.models.Inception +import com.intel.analytics.bigdl.models.resnet.ResNet +import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} +import spire.syntax.module + +import scala.util.Random + +class GraphNodeSpec extends FlatSpec with Matchers { + + "Inception bn to Graph" should "generate correct output" in { + val batchSize = 2 + Random.setSeed(3) + val input = Tensor[Float](batchSize, 3, 224, 224).apply1(e => Random.nextFloat()) + val labels = Tensor[Float](batchSize).apply1(e => Random.nextInt(1000)) + + val inputNew = input.clone() + + val seed = 100 + RNG.setSeed(seed) + val model = Inception.getModel[Float](1000, "inception-bn") + RNG.setSeed(seed) + val model2 = Inception.getModel[Float](1000, "inception-bn") + val graphModel = model2.toGraph() + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be(output2) + + val criterion = new ClassNLLCriterion[Float]() + val loss = criterion.forward(output1, labels) + val gradOutput = criterion.backward(output1, labels) + + + val gradInput1 = model.backward(input, gradOutput).toTensor + val gradInput2 = graphModel.backward(input, gradOutput).toTensor + + val arr1 = gradInput1.storage().array() + val arr2 = gradInput2.storage().array() + + for (i <- 0 to (arr1.length-1)) { + arr1(i) should be(arr2(i) +- 1e-5f) + } + + // gradInput1.equals(gradInput2) should be(true) + } + + "ResNet to Graph" should "generate correct output" in { + val inputSeed = 1 + val depth = 18 + val batchSize = 4 + val modelSeed = 101 + Random.setSeed(inputSeed) + val classNum: Int = 1000 + val input = Tensor[Float](batchSize, 3, 224, 224).apply1( e => Random.nextFloat()) + val labels = Tensor[Float](batchSize).apply1(e => Random.nextInt(classNum)) + val seed = modelSeed + RNG.setSeed(seed) + val model = ResNet(classNum, T("shortcutType" -> ShortcutType.B, + "depth" -> depth, "dataset" -> DatasetType.ImageNet)) + RNG.setSeed(seed) + val model2 = ResNet(classNum, T("shortcutType" -> ShortcutType.B, + "depth" -> depth, "dataset" -> DatasetType.ImageNet)) + + val (weights, grad) = model.getParameters() + val (w, g) = model2.getParameters() + w.copy(weights) + val graphModel = model2.toGraph() + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be (output2) + + val criterion = new ClassNLLCriterion[Float]() + val loss = criterion.forward(output1, labels) + val gradOutput = criterion.backward(output1, labels) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + + gradInput1 should be (gradInput2) + } + + "AlexNet to Graph" should "generate correct output" in { + Random.setSeed(1) + val input = Tensor[Float](8, 3, 224, 224).apply1(e => Random.nextFloat()) + val labels = Tensor[Float](8).apply1(e => Random.nextInt(100)) + + val seed = 100 + RNG.setSeed(seed) + val model = AlexNet_OWT(1000, false, true) + RNG.setSeed(seed) + val model2 = AlexNet_OWT(1000, false, true) + val graphModel = model2.toGraph() + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be (output2) + + val criterion = new ClassNLLCriterion[Float]() + val loss = criterion.forward(output1, labels) + val gradOutput = criterion.backward(output1, labels) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + + gradInput1 should be (gradInput2) + } + + "Recurrent+LSTM to graph" should "generate correct output" in { + Random.setSeed(1) + val input = Tensor[Float](8, 128, 128).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](8, 128, 128).apply1(e => Random.nextFloat()) + + val seed = 100 + val inputSize = 128 + val hiddenSize = 128 + val outputSize = 128 + + RNG.setSeed(seed) + val model = Sequential[Float]() + model.add(Recurrent[Float]() + .add(RnnCell[Float](inputSize, hiddenSize, Tanh[Float]()))) + .add(TimeDistributed[Float](Linear[Float](hiddenSize, outputSize))) + + val model2 = model.cloneModule() + val graphModel = model2.toGraph() + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be (output2) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + + gradInput1 should be (gradInput2) + } + + "ParallelTable to graph" should "generate correct output" in { + Random.setSeed(1) + val batchSize = 4 + val hiddenSize = 12 + val input = T(Tensor[Float](batchSize, hiddenSize).apply1(e => Random.nextFloat()), + Tensor[Float](batchSize, hiddenSize).apply1(e => Random.nextFloat())) + val gradOutput = T(Tensor[Float](batchSize, hiddenSize).apply1(e => Random.nextFloat()), + Tensor[Float](batchSize, hiddenSize).apply1(e => Random.nextFloat())) + + val seed = 100 + RNG.setSeed(seed) + val model = ParallelTable() + .add(Linear(hiddenSize, hiddenSize)) + .add(Linear(hiddenSize, hiddenSize)) + + val model2 = model.cloneModule() + val graphModel = model2.toGraph(Input(), Input()) + + val output1 = model.forward(input) + val output2 = graphModel.forward(input) + output1 should be (output2) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + + gradInput1 should be (gradInput2) + } + + "CAddTable to graph" should "generate correct output" in { + val module = CAddTable[Float]().toGraph(Input(), Input()) + val scalar = Tensor[Float](Array(2.0f), Array[Int]()) + val tensor = Tensor[Float](T(1, 2, 3)) + val output = module.forward(T(scalar, tensor)) + output should be(Tensor[Float](T(3, 4, 5))) + val grads = module.backward(T(scalar, tensor), Tensor[Float](T(1, 2, 3))).toTable + grads[Tensor[Float]](1).value() should be(6) + grads[Tensor[Float]](2) should be(Tensor[Float](T(1, 2, 3))) + } + + "Bottle to graph" should "generate correct output" in { + val seed = 100 + RNG.setSeed(seed) + val model = Bottle[Double](Linear[Double](10, 2), 2, 2) + model.add(Linear(10, 2)) + + val input = Tensor[Double](4, 5, 10).apply1(_ => Random.nextDouble()) + val gradOutput = Tensor[Double](4, 10).apply1(_ => Random.nextDouble()) + + val graphModel = model.cloneModule().toGraph(Input()) + + val output1 = model.forward(input) + val output2 = graphModel.forward(input) + output1 should be (output2) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + + gradInput1 should be (gradInput2) + } + +} From 9106bcfdb4867ca4bb4f2dcf7d38333778824339 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 23 Oct 2017 12:23:22 +0800 Subject: [PATCH 0477/1065] CaffeLoader supports multiple inputs and some additional outputs (#1699) * CaffeLoader supports multiple inputs and some additional outputs * Add input mapping --- .../bigdl/dllib/utils/caffe/CaffeLoader.scala | 68 +++++++++++++++++-- .../bigdl/dllib/utils/caffe/Converter.scala | 8 ++- .../dllib/utils/caffe/LayerConverter.scala | 10 +++ .../dllib/utils/caffe/V1LayerConverter.scala | 9 +++ 4 files changed, 87 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index b6bd3a6aae1..7adec400ca4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -262,19 +262,67 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, /** * Load caffe model from prototxt file and binary pre-trained model and converted * to BigDL graph module + * @param outputNames additional output layer names besides the default(layers without next nodes) * @return BigDL model and criterion */ - def createCaffeModel(): (Module[T], ParallelCriterion[T]) = { + def createCaffeModel(outputNames: Array[String] = Array[String]()) + : (Module[T], ParallelCriterion[T]) = { loadCaffe(prototxtPath, modelPath) registerCustomizedConverter() val layers = createLayers() val inputs = layers.filter(layer => layer.prevNodes.isEmpty).toArray - val outputs = layers.filter(layer => layer.nextNodes.isEmpty).toArray + val outputs = layers.filter(layer => layer.nextNodes.isEmpty || + outputNames.contains(layer.element.getName())).toArray val module = Graph(inputs, outputs) module.setName(netparam.getName) copyParameters(module) (module, criterions) } + + private val dataLayerList = Array("INPUT", "DATA", "DUMMYDATA", "ANNOTATEDDATA") + + private def tryConvertInput(layer: GeneratedMessage, layerType: String, + layers: ArrayBuffer[ModuleNode[T]], + top2LayerMap: mutable.HashMap[String, String], + layersMap: mutable.HashMap[String, ModuleNode[T]]): Boolean = { + val inputs = if (dataLayerList.contains(layerType)) convertCaffeLayer(layer) else null + addInputList(inputs, layers, top2LayerMap, layersMap) + } + + // try to get input list (without data layer) + private def tryConvertInput(netparam: Caffe.NetParameter, + layers: ArrayBuffer[ModuleNode[T]], + top2LayerMap: mutable.HashMap[String, String], + layersMap: mutable.HashMap[String, ModuleNode[T]]): Boolean = { + val inputNames = netparam.getInputList + val inputs = if (!inputNames.isEmpty) { + (0 until inputNames.size()).map(i => { + val input = Input() + input.element.setName(inputNames.get(i)) + input + }) + } else { + null + } + addInputList(inputs, layers, top2LayerMap, layersMap) + } + + private def addInputList(inputs: Seq[ModuleNode[T]], + layers: ArrayBuffer[ModuleNode[T]], + top2LayerMap: mutable.HashMap[String, String], + layersMap: mutable.HashMap[String, ModuleNode[T]]): Boolean = { + if (null != inputs) { + inputs.foreach(input => { + top2LayerMap(input.element.getName()) = input.element.getName() + layersMap(input.element.getName()) = input + layers.append(input) + }) + true + } else { + false + } + } + // create directed graph based on the module relationships private def createLayers() : ArrayBuffer[ModuleNode[T]] = { val layers = ArrayBuffer[ModuleNode[T]]() @@ -311,6 +359,8 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } }) } + + tryConvertInput(netparam, layers, top2LayerMap, layersMap) allLayers.foreach(layer => { var name : String = null val topList = new ArrayBuffer[String]() @@ -337,8 +387,12 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } else { // some criterion layers are not only for loss calculation, // we need to separate it with loss function and module - val isCriterionLayerOnly : Boolean = tryAddCriterion(layerType, name) - if (!isCriterionLayerOnly) { + val isCriterionLayerOnly: Boolean = tryAddCriterion(layerType, name) + val isInput = if (!isCriterionLayerOnly) { + tryConvertInput(layer, layerType, layers, top2LayerMap, layersMap) + } else false + + if (!isCriterionLayerOnly && !isInput) { val nodes = convertCaffeLayer(layer) if (nodes != null) { var curr = nodes.head @@ -472,13 +526,15 @@ object CaffeLoader { * @param defPath prototxt file which illustrate the caffe model structure * @param modelPath binary file containing the weight and bias * @param customizedConverters customized layer converter + * @param outputNames additional output layer names besides the default(layers without next nodes) * @tparam T data type * @return created module (graph) and criterion */ def loadCaffe[T: ClassTag](defPath: String, modelPath: String, - customizedConverters : mutable.HashMap[String, Customizable[T]] = null) + customizedConverters : mutable.HashMap[String, Customizable[T]] = null, + outputNames: Array[String] = Array[String]()) (implicit ev: TensorNumeric[T]): (Module[T], ParallelCriterion[T]) = { val caffeLoader = new CaffeLoader[T](defPath, modelPath, true, customizedConverters) - caffeLoader.createCaffeModel() + caffeLoader.createCaffeModel(outputNames) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index 442585ccfbe..24a8efe51cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -252,6 +252,8 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { protected def fromCaffeTile(layer : GeneratedMessage) : Seq[ModuleNode[T]] + protected def fromCaffeInput(layer: GeneratedMessage): Seq[ModuleNode[T]] + protected def getLayerType(layer : GeneratedMessage) : String protected def getLayerName(layer : GeneratedMessage) : String @@ -635,7 +637,9 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { caffe2BigDL("SLICE") = fromCaffeSlice caffe2BigDL("TILE") = fromCaffeTile caffe2BigDL("ELTWISE") = fromCaffeEltwise - caffe2BigDL("INPUT") = null - caffe2BigDL("DATA") = null + caffe2BigDL("INPUT") = fromCaffeInput + caffe2BigDL("DATA") = fromCaffeInput + caffe2BigDL("DUMMYDATA") = fromCaffeInput + caffe2BigDL("ANNOTATEDDATA") = fromCaffeInput } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index dde5a31b832..3ff94e6235b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -180,6 +180,16 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert Seq(Replicate[T](tiles, axis).setName(getLayerName(layer)).inputs()) } + override protected def fromCaffeInput(layer: GeneratedMessage): Seq[ModuleNode[T]] = { + val layerParam = layer.asInstanceOf[LayerParameter] + val tops = layerParam.getTopList + (0 until tops.size()).map(i => { + val input = Input() + input.element.setName(tops.get(i)) + input + }) + } + override protected def toCaffeConvolution(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { val layerParameter = LayerParameter.newBuilder() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala index c45c7ba4898..605ee3c67f8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala @@ -136,6 +136,15 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve throw new UnsupportedOperationException("Tile is not supported in V1 Layer") } + override protected def fromCaffeInput(layer: GeneratedMessage): Seq[ModuleNode[T]] = { + val tops = layer.asInstanceOf[V1LayerParameter].getTopList + (0 until tops.size()).map(i => { + val input = Input() + input.element.setName(tops.get(i)) + input + }) + } + override protected def toCaffeConvolution(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { val layerParameter = V1LayerParameter.newBuilder() From 54240aa1110ce9bc28d1461d46adc3beaaeb7e60 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 23 Oct 2017 13:08:43 +0800 Subject: [PATCH 0478/1065] Fix fromCaffeEltwise and fromCaffeBatchNorm (#1700) --- .../bigdl/dllib/utils/caffe/Converter.scala | 5 ++-- .../dllib/utils/caffe/LayerConverter.scala | 23 +++++++++++++++---- 2 files changed, 22 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index 24a8efe51cf..e4e15ae6abf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -221,12 +221,12 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { val param = getEltWiseParam(layer).get val layerName = getLayerName(layer) val opsType = param.getOperation - val coeff2 = if (param.getCoeffCount == 0) 1 else param.getCoeff(0) val ops = opsType match { case EltwiseOp.PROD => CMulTable[T]().setName(layerName).inputs() case EltwiseOp.MAX => CMaxTable[T]().setName(layerName).inputs() case EltwiseOp.SUM => - if (coeff2 < 0) { + val coeff2 = param.getCoeff(1) + if (coeff2 > 0) { CAddTable[T]().setName(layerName).inputs() } else { CSubTable[T]().setName(layerName).inputs() @@ -641,5 +641,6 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { caffe2BigDL("DATA") = fromCaffeInput caffe2BigDL("DUMMYDATA") = fromCaffeInput caffe2BigDL("ANNOTATEDDATA") = fromCaffeInput + caffe2BigDL("SILENCE") = null } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index 3ff94e6235b..a37a9a3c602 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -115,14 +115,29 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert } } - override protected def fromCaffeBatchNormalization(layer : GeneratedMessage) : + override protected def fromCaffeBatchNormalization(layer: GeneratedMessage): Seq[ModuleNode[T]] = { - val weightBlob = getBlob(layer, 0).get - val nOutPlane = if (weightBlob.hasShape) weightBlob.getShape.getDim(0) + val weightBlob = getBlob(layer, 0).get + val nOutPlane = if (weightBlob.hasShape) weightBlob.getShape.getDim(0).toInt else weightBlob.getNum val param = layer.asInstanceOf[LayerParameter].getBatchNormParam val eps = param.getEps - Seq(SpatialBatchNormalization[T](nOutPlane.toInt, eps).setName(getLayerName(layer)).inputs()) + val batchNorm = SpatialBatchNormalization[T](nOutPlane.toInt, eps, affine = false) + .setName(getLayerName(layer)) + val scaleData = getBlob(layer, 2).get.getData(0) + val scale = if (scaleData == 0) 0 else 1 / scaleData + val means = getBlob(layer, 0).get.getDataList + val variances = getBlob(layer, 1).get.getDataList + batchNorm.runningMean.resize(nOutPlane) + batchNorm.runningVar.resize(nOutPlane) + + batchNorm.saveMean.resize(nOutPlane) + batchNorm.saveStd.resize(nOutPlane) + (1 to nOutPlane).foreach(i => { + batchNorm.runningMean.setValue(i, ev.fromType[Float](means.get(i - 1) * scale)) + batchNorm.runningVar.setValue(i, ev.fromType[Float](variances.get(i - 1) * scale)) + }) + Seq(batchNorm.inputs()) } override protected def fromCaffeELU(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { From a44edf75bf3b1e03360eff7fac8906171cb4b845 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 23 Oct 2017 03:20:36 -0400 Subject: [PATCH 0479/1065] docs: quantization api usage (#1704) * docs: quantization api usage * fix: modification of docs --- .../intel/analytics/bigdl/dllib/utils/ConvertModel.scala | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ConvertModel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ConvertModel.scala index 877a9589872..42db93f4b42 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ConvertModel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ConvertModel.scala @@ -37,7 +37,8 @@ object ConvertModel { val fromSupports = Set("bigdl", "caffe", "torch", "tensorflow") val toSupports = Set("bigdl", "caffe", "torch") - val converterParser = new OptionParser[ConverterParam]("Convert caffe model to bigdl model") { + val converterParser = new OptionParser[ConverterParam]( + "Convert models between different dl frameworks") { opt[String]("from") .text(s"What's the type origin model ${fromSupports.mkString(",")}?") .action((x, c) => c.copy(from = x)) @@ -70,7 +71,8 @@ object ConvertModel { .text("Where's the caffe deploy prototxt?") .action((x, c) => c.copy(prototxt = x)) opt[Boolean]("quantize") - .text("Do you want to quantize the model?") + .text("Do you want to quantize the model? Only works when \"--to\" is bigdl;" + + "you can only perform inference using the new quantized model.") .action((x, c) => c.copy(quantize = x)) opt[String]("tf_inputs") .text("Inputs for Tensorflow") From 65c9e084f5f9942eb8e6f3bd81538054db659237 Mon Sep 17 00:00:00 2001 From: megaSpoon Date: Mon, 23 Oct 2017 03:05:47 -0700 Subject: [PATCH 0480/1065] feat: ML Pipeline: (#1654) * DLEstimator, DLClassifier, DLModel, DLClassiferModel API * add unit test test_dl_classifier --- .../org/apache/spark/ml/DLEstimator.scala | 6 +- .../dllib/utils/python/api/PythonBigDL.scala | 85 +++++++++++++++++++ 2 files changed, 89 insertions(+), 2 deletions(-) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index df341da9aa1..e06b49287d4 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -90,12 +90,14 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( def setMaxEpoch(value: Int): this.type = set(maxEpoch, value) /** - * learning rate for the optimizer. - * Default: 1.0 + * learning rate for the optimizer in the DLEstimator. + * Default: 0.001 */ val learningRate = new DoubleParam(this, "learningRate", "learningRate", ParamValidators.gt(0)) + setDefault(learningRate -> 1e-3) + def getLearningRate: Double = $(learningRate) def setLearningRate(value: Double): this.type = set(learningRate, value) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 64de7f87b45..ef2869048e2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -37,7 +37,12 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} + import com.intel.analytics.bigdl.utils.tf.{BigDLSessionImpl, Context, TensorflowDataFormat, TensorflowSaver} + +import org.apache.spark.ml.{DLClassifierModel, DLEstimator, DLClassifier, DLModel} +import org.apache.spark.sql.DataFrame + import org.apache.log4j._ import org.apache.spark.SparkContext import org.tensorflow.framework.NodeDef @@ -2026,6 +2031,86 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def quantize(module: AbstractModule[Activity, Activity, T]): Module[T] = { module.quantize() } + + def createDLEstimator(model: Module[T], criterion: Criterion[T], + featureSize: JArrayList[Int], + labelSize: JArrayList[Int]): DLEstimator[T] = { + new DLEstimator[T](model, criterion, featureSize.asScala.toArray, labelSize.asScala.toArray) + } + + def createDLClassifier(model: Module[T], criterion: Criterion[T], + featureSize: JArrayList[Int], + labelSize: JArrayList[Int]): DLClassifier[T] = { + new DLClassifier[T](model, criterion, featureSize.asScala.toArray) + } + + def fitEstimator(estimator: DLEstimator[T], dataSet: DataFrame): DLModel[T] = { + estimator.fit(dataSet) + } + + def fitClassifier(classifier: DLClassifier[T], dataSet: DataFrame): DLModel[T] = { + classifier.fit(dataSet) + } + + def setBatchSizeDLEstimator(estimator: DLEstimator[T], batchSize: Int): DLEstimator[T] = { + estimator.setBatchSize(batchSize) + } + + def setBatchSizeDLClassifier(classifier: DLClassifier[T], batchSize: Int): DLClassifier[T] = { + classifier.setBatchSize(batchSize) + } + + def setMaxEpochDLEstimator(estimator: DLEstimator[T], maxEpoch: Int): DLEstimator[T] = { + estimator.setMaxEpoch(maxEpoch) + } + + def setMaxEpochDLClassifier(classifier: DLClassifier[T], maxEpoch: Int): DLClassifier[T] = { + classifier.setMaxEpoch(maxEpoch) + } + + def setLearningRateDLEstimator(estimator: DLEstimator[T], lr: Double): DLEstimator[T] = { + estimator.setLearningRate(lr) + } + + def setLearningRateDLClassifier(classifier: DLClassifier[T], lr: Double): DLClassifier[T] = { + classifier.setLearningRate(lr) + } + + def createDLModel(model: Module[T], featureSize: JArrayList[Int]): DLModel[T] = { + new DLModel[T](model, featureSize.asScala.toArray) + } + + def createDLClassifierModel(model: Module[T], + featureSize: JArrayList[Int]): DLClassifierModel[T] = { + new DLClassifierModel[T](model, featureSize.asScala.toArray) + } + + def dlModelTransform(dlModel: DLModel[T], dataSet: DataFrame): DataFrame = { + dlModel.transform(dataSet) + } + + def dlClassifierModelTransform(dlClassifierModel: DLClassifierModel[T], + dataSet: DataFrame): DataFrame = { + dlClassifierModel.transform(dataSet) + } + + def setFeatureSizeDLModel(dlModel: DLModel[T], featureSize: JArrayList[Int]): DLModel[T] = { + dlModel.setFeatureSize(featureSize.asScala.toArray) + } + + def setFeatureSizeDLClassifierModel(dlClassifierModel: DLClassifierModel[T], + featureSize: JArrayList[Int]): DLClassifierModel[T] = { + dlClassifierModel.setFeatureSize(featureSize.asScala.toArray) + } + + def setBatchSizeDLModel(dlModel: DLModel[T], batchSize: Int): DLModel[T] = { + dlModel.setBatchSize(batchSize) + } + + def setBatchSizeDLClassifierModel(dlClassifierModel: DLClassifierModel[T], + batchSize: Int): DLClassifierModel[T] = { + dlClassifierModel.setBatchSize(batchSize) + } } object PythonBigDLUtils { From 7ffef5953f384577185ccd27ef438d54223a5a34 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 23 Oct 2017 18:31:42 +0800 Subject: [PATCH 0481/1065] fix jtensor (#1706) --- .../analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala | 2 ++ 1 file changed, 2 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala index 2e5c2740f11..ed2f5575ba3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala @@ -250,6 +250,8 @@ object BigDLSerDe extends BigDLSerDeBase with Serializable { saveBytes(out, pickler, floatArrayToBytes(jTensor.storage)) saveBytes(out, pickler, int32ArrayToBytes(jTensor.shape)) pickler.save(jTensor.bigdlType) + // TODO: Find a way to pass sparseTensor's indices back to python + // out.write(Opcodes.NONE) out.write(Opcodes.TUPLE3) } From d23772b0b0474b462907d6a73e61f822e466f2c5 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 25 Oct 2017 13:12:35 +0800 Subject: [PATCH 0482/1065] revert previous fix back for eltwise layer mapping (#1723) * revert previous fix back for eltwise layers * optimize and enhance the eltwise converter --- .../bigdl/dllib/utils/caffe/Converter.scala | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index e4e15ae6abf..cb3ae747de5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -225,11 +225,17 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { case EltwiseOp.PROD => CMulTable[T]().setName(layerName).inputs() case EltwiseOp.MAX => CMaxTable[T]().setName(layerName).inputs() case EltwiseOp.SUM => - val coeff2 = param.getCoeff(1) - if (coeff2 > 0) { + val coeff1 = if (param.getCoeffCount == 0) 1 else param.getCoeff(0) + val coeff2 = if (param.getCoeffCount == 0) 1 else param.getCoeff(1) + if (coeff1 == 1 && coeff2 == 1) { CAddTable[T]().setName(layerName).inputs() - } else { + } else if (coeff1 == 1 && coeff2 == -1) { CSubTable[T]().setName(layerName).inputs() + } else { + val mul1 = MulConstant[T](coeff1.toFloat).inputs() + val mul2 = MulConstant[T](coeff2.toFloat).inputs() + val caddTable = CAddTable[T]().setName(layerName).inputs(mul1, mul2) + Graph[T](Array(mul1, mul2), Array(caddTable)).inputs() } case _ => null } From 76da23d6ea651b93d7cfd33910a0ee3edc4d8e65 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 25 Oct 2017 13:57:33 +0800 Subject: [PATCH 0483/1065] update python version and bigdl-version-info on branch-0.3 (#1730) (#1735) --- scala/dllib/src/main/resources/bigdl-version-info.properties | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/resources/bigdl-version-info.properties b/scala/dllib/src/main/resources/bigdl-version-info.properties index 50c9bb007fa..09c134b077c 100644 --- a/scala/dllib/src/main/resources/bigdl-version-info.properties +++ b/scala/dllib/src/main/resources/bigdl-version-info.properties @@ -16,4 +16,4 @@ #BigDL version info config -version=0.3.0 \ No newline at end of file +version=0.3.0-SNAPSHOT \ No newline at end of file From 0c2778bc8e4a9c5a09df5b00460b44b13f701373 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 25 Oct 2017 17:09:46 +0800 Subject: [PATCH 0484/1065] change speculation from true to false (#1746) --- .../scala/com/intel/analytics/bigdl/utils/EngineSpec.scala | 4 ++-- scala/dllib/src/main/resources/spark-bigdl.conf | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala index 91e59c18799..9777f2ec5a1 100644 --- a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/EngineSpec.scala @@ -134,7 +134,7 @@ class EngineSpec extends FlatSpec with Matchers with BeforeAndAfter { "spark.shuffle.reduceLocality.enabled" -> "false", "spark.shuffle.blockTransferService" -> "nio", "spark.scheduler.minRegisteredResourcesRatio" -> "1.0", - "spark.speculation" -> "true" + "spark.speculation" -> "false" ) conf.length should be(target.keys.size) conf.foreach(s => { @@ -148,7 +148,7 @@ class EngineSpec extends FlatSpec with Matchers with BeforeAndAfter { val target = Map( "spark.shuffle.reduceLocality.enabled" -> "false", "spark.scheduler.minRegisteredResourcesRatio" -> "1.0", - "spark.speculation" -> "true" + "spark.speculation" -> "false" ) conf.length should be(target.keys.size) conf.foreach(s => { diff --git a/scala/dllib/src/main/resources/spark-bigdl.conf b/scala/dllib/src/main/resources/spark-bigdl.conf index 63b77c33d0d..63436abc6e7 100644 --- a/scala/dllib/src/main/resources/spark-bigdl.conf +++ b/scala/dllib/src/main/resources/spark-bigdl.conf @@ -30,4 +30,4 @@ spark.shuffle.reduceLocality.enabled false spark.shuffle.blockTransferService nio spark.scheduler.minRegisteredResourcesRatio 1.0 -spark.speculation true +spark.speculation false From d7ed4c6086a87c07d8e43caadadb8a47145f177c Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 25 Oct 2017 18:33:12 +0800 Subject: [PATCH 0485/1065] update readme about language model and ml pipeline (#1748) --- .../analytics/bigdl/dllib/example/MLPipeline/README.md | 10 +++++----- .../bigdl/dllib/example/languagemodel/README.md | 8 +++++--- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/README.md index 628f5f078cf..059ab505df1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/README.md @@ -42,7 +42,7 @@ Command to run the example in Spark local mode: spark-submit \ --master local[physcial_core_number] \ --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet \ -./dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ +./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size ``` @@ -52,9 +52,9 @@ spark-submit \ --master spark://... \ --executor-cores cores_per_executor \ --total-executor-cores total_cores_for_the_job \ ---driver-class-path dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ +--driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet \ -dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ +dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size ``` @@ -64,9 +64,9 @@ Command to run the example in Spark yarn mode: --deploy-mode client \ --executor-cores cores_per_executor \ --num-executors executors_number \ ---driver-class-path dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ +--driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet \ -dist/lib/bigdl-0.1.0-SNAPSHOT-jar-with-dependencies.jar \ +dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size ``` diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md index 9a5a56ee026..9eaa3950320 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md @@ -23,11 +23,13 @@ Example command ``` spark-submit \ --master spark://... \ +--driver-memory 40g \ +--executor-memory 100g \ --executor-cores cores_per_executor \ --total-executor-cores total_cores_for_the_job \ --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --f $HOME/simple-examples/data -b 420 --checkpoint $HOME/model --numLayers 2 --vocab 10001 -h 1500 --numSteps 35 -r 0.005 -e 20 --learningRateDecay 0.001 --keepProb 0.5 --overWriteCheckpoint +-f $HOME/simple-examples/data -b 40 --checkpoint $HOME/model --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 20 --learningRateDecay 0.001 --keepProb 0.5 --overWrite ``` In the above commands: @@ -36,10 +38,10 @@ In the above commands: ```--checkpoint```: Where you cache the model/train_state snapshot. ```--learningRate```: learning rate for adagrad ```--learningRateDecay```: learning rate decay for adagrad -```--hiddenSize```: hiddensize for lstm +```--hidden```: hiddensize for lstm ```--vocabSize```: vocabulary size, default 10000 ```--nEpochs```: epochs to run ```--numLayers```: numbers of lstm cell, default 2 lstm cells ```--numSteps```: number of words per record in LM -```--overWriteCheckpoint```: do overwrite when saving checkpoint +```--overWrite```: do overwrite when saving checkpoint ```--keepProb```: the probability to do dropout \ No newline at end of file From a2fefa77645047856b5f86b36970b312fd839ba5 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 25 Oct 2017 20:36:48 +0800 Subject: [PATCH 0486/1065] fix shape output tensor type from T to Int (#1737) --- .../com/intel/analytics/bigdl/dllib/nn/tf/Shape.scala | 8 ++++---- .../com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Shape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Shape.scala index 0a052487652..276acf8ef49 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Shape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Shape.scala @@ -26,14 +26,14 @@ import scala.reflect.ClassTag */ @SerialVersionUID(-907995771209831179L) private[bigdl] class Shape[T: ClassTag](implicit ev: TensorNumeric[T]) - extends AbstractModule[Tensor[T], Tensor[T], T] { + extends AbstractModule[Tensor[T], Tensor[Int], T] { - override def updateOutput(input: Tensor[T]): Tensor[T] = { - this.output = Tensor[T](input.size().map(ev.fromType(_)), Array(input.nDimension())) + override def updateOutput(input: Tensor[T]): Tensor[Int] = { + this.output = Tensor[Int](input.size(), Array(input.nDimension())) this.output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[Int]): Tensor[T] = { gradInput.resizeAs(input) gradInput.zero() gradInput diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala index 876beb10745..87a1c99e29d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala @@ -25,14 +25,14 @@ class ShapeSpec extends FlatSpec with Matchers { "Shape forward" should "be success" in { val layer = Shape() val input = Tensor(T(T(0.1f, 0.2f), T(0.1f, 0.2f), T(0.1f, 0.2f))) - layer.forward(input) should be(Tensor(T(3.0f, 2.0f))) + layer.forward(input) should be(Tensor[Int](T(3, 2))) } "Shape backward" should "be correct" in { val layer = Shape() val input = Tensor(T(T(0.1f, 0.2f), T(0.1f, 0.2f), T(0.1f, 0.2f))) - val gradOutput = Tensor(T(3.0f, 2.0f)) - layer.forward(input) should be(Tensor(T(3.0f, 2.0f))) + val gradOutput = Tensor[Int](T(3, 2)) + layer.forward(input) should be(Tensor[Int](T(3, 2))) layer.backward(input, gradOutput) should be(Tensor(T( T(0.0f, 0.0f), T(0.0f, 0.0f), From 7fab09c3dd4ecf00d7f59cf4ce20a56b24556451 Mon Sep 17 00:00:00 2001 From: dobachi Date: Thu, 26 Oct 2017 13:03:05 +0900 Subject: [PATCH 0487/1065] Use newInstance method to obtain FileSystem. (#1753) --- .../scala/com/intel/analytics/bigdl/dllib/utils/File.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala index 04ab90e413f..945422caec0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.utils import java.io._ +import java.net.URI import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FSDataInputStream, FSDataOutputStream, FileSystem, Path} @@ -199,7 +200,7 @@ object File { var fs: FileSystem = null var in: FSDataInputStream = null try { - fs = src.getFileSystem(new Configuration()) + fs = FileSystem.newInstance(new URI(fileName), new Configuration()) in = fs.open(src) val byteArrayOut = new ByteArrayOutputStream() IOUtils.copyBytes(in, byteArrayOut, 1024, true) From de7f5a3ed590f0223193bd0aa0f920a8032364a5 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Thu, 26 Oct 2017 13:03:42 +0800 Subject: [PATCH 0488/1065] Update README.md --- .../com/intel/analytics/bigdl/dllib/models/resnet/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md index 7e6080e91a2..9857c69fdef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md @@ -24,6 +24,7 @@ You can build one by refer to the * Spark local, example command ```shell spark-submit --master local[physical_core_number] \ +--driver-memory 3G \ --class com.intel.analytics.bigdl.models.resnet.Train \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f Cifar-10/ \ From 76ce8c99b493424ed7743bbb197ba817b8671765 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Thu, 26 Oct 2017 14:40:53 +0800 Subject: [PATCH 0489/1065] Add profile to minimize the size of jar (#1744) * perplatform * update * add quant dependencies --- dl/pom.xml | 40 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/dl/pom.xml b/dl/pom.xml index 5b87e8569fd..d97ac46cd7b 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -283,6 +283,46 @@ + + per_platform + + + com.intel.analytics.bigdl.native + ${mkl-java-os-version} + 0.3.0-SNAPSHOT + + + + com.intel.analytics.bigdl.native + bigdl-native + + + + + + com.intel.analytics.bigdl.bigquant + ${bigquant-java-os-version} + 0.3.0-SNAPSHOT + + + com.intel.analytics.bigdl.bigquant + bigquant-native + + + + + + com.intel.analytics.bigdl + bigdl-core + 0.3.0-SNAPSHOT + provided + pom + + + + parallel-tests From fd10f75be4b0918d46546c2a8114573ca387464e Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Thu, 26 Oct 2017 16:55:50 +0800 Subject: [PATCH 0490/1065] update rnn readme (#1767) --- .../com/intel/analytics/bigdl/dllib/models/rnn/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/README.md index 0fdc4b98677..efc48a49eae 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/README.md @@ -50,7 +50,7 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ ``` ## Test the Model -Please create a test.txt file under the folder in which you save your dictionary during training process. +Please create a test.txt file under the folder /path/saveDict in which you save your dictionary during training process. A sample test.txt can be as follows. Each line starts with several trigger words and ends with a period. The test script will load in the trained model and test.txt, then it will generate the following words per line. ``` Long live the. @@ -73,9 +73,10 @@ spark-submit \ --master spark://... \ --executor-cores cores_per_executor \ --total-executor-cores total_cores_for_the_job \ +--driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --class com.intel.analytics.bigdl.models.rnn.Test \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --f /dictDirect --model /modeldirectory/model.iterationNumber --words 20 +-f /path/saveDict --model /path/model/model.iterationNumber --words 20 ``` ## Preprocessing From 9fcc34692d28d331204f59de9c48340baa14c2a2 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 27 Oct 2017 11:24:40 +0800 Subject: [PATCH 0491/1065] Clone graph keep their original prevs nodes and next nodes sequence (#1763) * Clone graph keep their original prevs nodes and next nodes sequence * add comments * update * simplify table merge --- .../analytics/bigdl/dllib/nn/Graph.scala | 20 ++++++++++--------- .../bigdl/dllib/utils/DirectedGraph.scala | 16 +++++++++++---- .../bigdl/dllib/utils/DirectedGraphSpec.scala | 8 +++++++- 3 files changed, 30 insertions(+), 14 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 03aaea59d6b..9168f7d5884 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -508,17 +508,19 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], require(activity.isTensor, "Cannot add a table to a tensor") activity.toTensor[T].add(other.toTensor[T]) } else { + // if 'activity' and 'other' are both table, we need to merge 'other' to 'activity' + // if 'other' and 'activity' both contains the index, update 'activity' by sum + // if 'other' contains the index while 'activity' does not, + // just insert the corresponding tensor of 'other' to 'activity' val actTable = activity.toTable - val actLen = actTable.length() val otherTable = other.toTable - val otherLen = otherTable.length() - require(actLen == otherLen, "table length is not equal") - var i = 1 - while(i <= actLen) { - require(actTable[Activity](i) != null, "Invalid table element") - accActivity(actTable[Activity](i), otherTable[Activity](i)) - i += 1 - } + otherTable.keySet.foreach(index => { + if (actTable.contains(index)) { + accActivity(actTable[Activity](index), otherTable[Activity](index)) + } else { + actTable.insert(index.asInstanceOf[Int], otherTable(index)) + } + }) actTable } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala index 17b3894d35a..3c6551de270 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala @@ -146,14 +146,22 @@ class DirectedGraph[T](val source : Node[T], val reverse : Boolean = false) exte bfs.foreach(node => { oldToNew.put(node, new Node[T](node.element)) }) + // Keep the order in the nextNodes array and prevNodes array of the current node. + // As we go through all node in bfs from source, the prevNodes order can be preserved. + // For each node, we iterate and add their nextNodes, the nextNodes order can also be preserved. bfs.foreach(node => { if (reverseEdge) { - node.prevNodesAndEdges.foreach(prevNodeAndEdge => { - oldToNew.get(node).add(oldToNew.get(prevNodeAndEdge._1), prevNodeAndEdge._2) + node.nextNodesAndEdges.foreach(nextNodeAndEdge => { + // Some next nodes may be not included in the graph + if (oldToNew.containsKey(nextNodeAndEdge._1)) { + oldToNew.get(nextNodeAndEdge._1).add(oldToNew.get(node), nextNodeAndEdge._2) + } }) } else { - node.prevNodesAndEdges.foreach(prevNodeAndEdge => { - oldToNew.get(prevNodeAndEdge._1).add(oldToNew.get(node), prevNodeAndEdge._2) + node.nextNodesAndEdges.foreach(nextNodeAndEdge => { + if (oldToNew.containsKey(nextNodeAndEdge._1)) { + oldToNew.get(node).add(oldToNew.get(nextNodeAndEdge._1), nextNodeAndEdge._2) + } }) } }) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala index 1754cab9c87..7e909819865 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala @@ -315,7 +315,13 @@ class DirectedGraphSpec extends FlatSpec with Matchers { val graph = nodeA.graph() val cloneGraph = graph.cloneGraph() - graph.topologySort.map(_.element) should be(cloneGraph.topologySort.map(_.element)) + val sort1 = graph.topologySort + val sort2 = cloneGraph.topologySort + sort1.map(_.element) should be(sort2.map(_.element)) + sort1.zip(sort2).foreach(x => { + x._1.prevNodes.map(_.element) should be (x._2.prevNodes.map(_.element)) + x._1.nextNodes.map(_.element) should be (x._2.nextNodes.map(_.element)) + }) } "Clone graph" should "should reuse the edge" in { From 2bc7f07a0eb7e80baf71edbd90cc8bed6198cbcb Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 27 Oct 2017 14:19:38 +0800 Subject: [PATCH 0492/1065] MapTable: Add module back in clearstate (#1778) * MapTable: Add module back in clearstate * add unit test --- .../com/intel/analytics/bigdl/dllib/nn/MapTable.scala | 3 +++ .../com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala | 8 ++++++++ 2 files changed, 11 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala index 087b5659c02..92ea280e70d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala @@ -132,6 +132,9 @@ class MapTable[T: ClassTag]( override def clearState(): this.type = { modules.clear() + if ( module != null) { + this.add(module) + } this } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala index 5eaa89f0b42..3c128e62bb1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala @@ -75,4 +75,12 @@ class MapTableSpec extends FlatSpec with Matchers { mapGradInput should equal (expectedGradInput) } + + "A MapTable clearstate" should "add not change modules" in { + val linear1 = new Linear[Float](10, 3) + val map = new MapTable[Float](linear1) + + map.clearState() + map.modules.length should be (1) + } } From ce0cf2944ee455707a1ec055979c32c4a640191e Mon Sep 17 00:00:00 2001 From: jenniew Date: Thu, 26 Oct 2017 22:56:35 -0700 Subject: [PATCH 0493/1065] update locallenet docs --- .../analytics/bigdl/dllib/example/lenetLocal/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md index 324614a22ff..ac745bc98e6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md @@ -1,9 +1,9 @@ -# Running LeNet5 Model as a local Java/Scala program +# Running LeNet5 Model as a local Scala program This example shows how to run training, prediction and testing with LeNet5 model on local JVM with BigDL. Lenet5 is a classical CNN model used in digital number classification. For detail information, please refer to . -To run the BigDL model as a local Java/Scala program without Spark, user needs to set Java property `bigdl.localMode` to `true`. If user wants to specify how many cores to be used for training/testing/prediction, he needs to set Java property `bigdl.coreNumber` to the core number. User can either call `System.setProperty("bigdl.localMode", "true")` and `System.setProperty("bigdl.coreNumber", core_number)` in the Java/Scala code, or pass -Dbigdl.localMode=true and -Dbigdl.coreNumber=core_number when runing the program. In this example, we use the former way to set these Java properties. +To run the BigDL model as a local Scala program without Spark, user needs to set JVM property `bigdl.localMode` to `true`. If user wants to specify how many cores to be used for training/testing/prediction, he needs to set JVM property `bigdl.coreNumber` to the core number. User can either call `System.setProperty("bigdl.localMode", "true")` and `System.setProperty("bigdl.coreNumber", core_number)` in the Scala code, or pass -Dbigdl.localMode=true and -Dbigdl.coreNumber=core_number when runing the program. In this example, we use the former way to set these JVM properties. ## Prepare MNIST Data You can download the MNIST Data from [here](http://yann.lecun.com/exdb/mnist/). Unzip all the @@ -21,7 +21,7 @@ You can build one by refer to the ## Train the Model Example command ``` -java -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ +scala -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ com.intel.analytics.bigdl.example.lenetLocal.Train \ -f path_to_mnist_folder \ -c core_number \ @@ -45,7 +45,7 @@ use the model to do a validation. Example command ``` -java -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ +scala -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ com.intel.analytics.bigdl.example.lenetLocal.Test \ -f path_to_mnist_folder \ --model ./model/model.iteration \ @@ -63,7 +63,7 @@ The above commands will use the model in specified path(--checkpoint)to do a pre Example command ``` -java -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ +scala -cp spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar \ com.intel.analytics.bigdl.example.lenetLocal.Predict \ -f path_to_mnist_folder \ -c core_number \ From 3615bd6a7ec9b6e962a16d3ea27e4520286b6dc0 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 30 Oct 2017 13:47:24 +0800 Subject: [PATCH 0494/1065] fix SparseJoinTable (#1788) * fix SparseJoinTable * fix resize * add some comment --- .../bigdl/dllib/tensor/SparseTensor.scala | 45 +++++++++++++------ .../bigdl/dllib/nn/SparseJoinTableSpec.scala | 38 ++++++++++++++++ .../bigdl/dllib/tensor/SparseTensorSpec.scala | 9 ++++ 3 files changed, 78 insertions(+), 14 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index e36bddbc7a5..ac75e52fc58 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -43,10 +43,11 @@ import scala.reflect.ClassTag * * @param _indices non-zero elements' indices * @param _values values of the non-zero elements - * @param _storageOffset storageOffset + * @param _storageOffset storageOffset, both _values and _indices's storage offset. * @param _nElement number of non-zero elements * @param _shape dense shape * @param _indicesOffset indices' offset, Default is zeros, will vary in narrowed/selected tensor. + * The true indices should be (_indices - _indicesOffset). * @param nDimension dimensions. * @tparam T should be Double or Float */ @@ -152,7 +153,7 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( } override def transpose(dim1: Int, dim2: Int): Tensor[T] = { - throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + throw new UnsupportedOperationException(s"SparseTennewIndicesOffsetsor: Unimplemented method") } override def t(): Tensor[T] = { @@ -442,26 +443,36 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( private def resizeIndices(nElement: Int): Unit = { var i = 0 while (i < _indices.length) { - _indices(i).resize(nElement + _indicesOffset(i)) + _indices(i).resize(nElement + _storageOffset) i += 1 } } + /** + * Notice: if size.length < dimension, will delete last (dimension - size.length)th _indices. + * if size.length > dimension, will add indices to the front of _indices array. + */ override def resize(size: Array[Int], nElement: Int): Tensor[T] = { + // if reset number of _indices + // TODO: implement addSingletonDimension and squeeze to add/delete specified dimension. if (size.length < _indices.length) { + // need to delete last (_indices.length - size.length) dimension _indices = _indices.slice(0, size.length) _indicesOffset = _indicesOffset.slice(0, size.length) - resizeIndices(nElement) } else if (size.length > _indices.length) { + // add (size.length - _indices.length) dimension to the first dimension val _addIndices = new Array[Storage[Int]](size.length - _indices.length) - for (i <- _addIndices.indices) _addIndices(i) = Storage[Int](nElement) - _indicesOffset ++= new Array[Int](size.length - _indicesOffset.length) + for (i <- _addIndices.indices) _addIndices(i) = Storage[Int](nElement + _storageOffset) + _indicesOffset = new Array[Int](size.length - _indicesOffset.length) ++ _indicesOffset _indices ++= _addIndices - resizeIndices(nElement) - } else if (_indices(0).length() - _indicesOffset(0) < nElement) { + } + + // resize _indices's length + if (_indices(0).length() - _storageOffset < nElement) { resizeIndices(nElement) } + // resize _values's length if (storage.length() - _storageOffset < nElement) { storage.resize(nElement + _storageOffset) } @@ -910,7 +921,7 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( val storageOffset = _storageOffset val indicesOffset = _indicesOffset(0) for (i <- 0 until this.nElement) - sb.append((indices(0)(i + storageOffset) + indicesOffset) + sb.append((indices(0)(i + storageOffset) - indicesOffset) + " : " + values(i + storageOffset)).append('\n') s"${sb}[${this.getClass.getName} of size ${this.size(1)}]" @@ -1149,8 +1160,11 @@ object SparseTensor{ var offset = 0 while (index < tensors.size) { val currentTensor = tensors(index) - val findIndexStart = currentTensor._indices(0).array().indexOf(j, tensorsOffset(index)) - val findIndexEnd = currentTensor._indices(0).array().lastIndexOf(j) + val currentIndicesOffset = currentTensor._indicesOffset + val findIndexStart = currentTensor._indices(0).array().indexOf( + j + currentIndicesOffset(0), tensorsOffset(index)) + val findIndexEnd = currentTensor._indices(0).array().lastIndexOf( + j + currentIndicesOffset(0)) val curLength = if (findIndexStart != -1 && findIndexEnd != -1) { findIndexEnd - findIndexStart + 1 } else { @@ -1169,8 +1183,11 @@ object SparseTensor{ while (indicesIndex < numOfIndices) { val indicesIndexArray = currentTensor._indices(indicesIndex).array() val resultIndicesArray = res._indices(indicesIndex).array() - if (indicesIndex != dim - 1 || index == 0) { - // copy directly + if (indicesIndex == 0) { + // fill the first indices + res._indices(indicesIndex).fill(j, start + 1, curLength) + } else if (index == 0) { + // copy directly for the first tensor's indices System.arraycopy(currentTensor._indices(indicesIndex).array(), tensorsOffset(index), res._indices(indicesIndex).array(), start, curLength) } else { @@ -1178,7 +1195,7 @@ object SparseTensor{ var i = 0 while (i < curLength) { resultIndicesArray(start + i) = indicesIndexArray(tensorsOffset(index) + i) + - offset + offset - currentIndicesOffset(indicesIndex) i += 1 } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala index f224ea4f274..aec9374986d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala @@ -67,4 +67,42 @@ class SparseJoinTableSpec extends FlatSpec with Matchers { } + "Sparse JoinTable on narrowed table" should "return the same result" in { + Random.setSeed(2) + RandomGenerator.RNG.setSeed(1) + val input = Tensor(8, 10).apply1(_ => Random.nextInt(10) / 5 * Random.nextFloat()) + println(input) + val input2 = Tensor(4, 10).apply1(_ => Random.nextInt(10) / 5 * Random.nextFloat()) + println(input2) + val sparseModel = Sequential().add(ParallelTable().add(Identity()).add(Identity())) + .add(SparseJoinTable(2)) + val denseInput = Tensor(4, 20) + denseInput.narrow(2, 1, 10).copy(input.narrow(1, 4, 4)) + denseInput.narrow(2, 11, 10).copy(input2) + + val sparseInput = T(Tensor.sparse(input).narrow(1, 4, 4), Tensor.sparse(input2)) + val out1 = sparseModel.forward(sparseInput).toTensor[Float] + val exceptOut = Tensor.sparse(denseInput) + out1 shouldEqual exceptOut + Tensor.dense(out1) shouldEqual denseInput + } + + "Sparse JoinTable on narrowed table" should "return the same result 2" in { + Random.setSeed(2) + RandomGenerator.RNG.setSeed(1) + val input = Tensor(4, 10).apply1(_ => Random.nextInt(10) / 5 * Random.nextFloat()) + val input2 = Tensor(8, 10).apply1(_ => Random.nextInt(10) / 5 * Random.nextFloat()) + val sparseModel = Sequential().add(ParallelTable().add(Identity()).add(Identity())) + .add(SparseJoinTable(2)) + val denseInput = Tensor(4, 20) + denseInput.narrow(2, 1, 10).copy(input) + denseInput.narrow(2, 11, 10).copy(input2.narrow(1, 4, 4)) + + val sparseInput = T(Tensor.sparse(input), Tensor.sparse(input2).narrow(1, 4, 4)) + val out1 = sparseModel.forward(sparseInput).toTensor[Float] + val exceptOut = Tensor.sparse(denseInput) + out1 shouldEqual exceptOut + Tensor.dense(out1) shouldEqual denseInput + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala index 01724d5c944..6e964d1a377 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala @@ -99,4 +99,13 @@ class SparseTensorSpec extends FlatSpec with Matchers { sTensor.storageOffset() should be (1) } + "resize narrowed tensor" should "return right result" in { + val sTensor = Tensor.sparse(Tensor(30).range(1, 30, 1)).narrow(1, 6, 18) + sTensor.resize(Array(6, 3), 18) + sTensor.size() should be (Array(6, 3)) + sTensor.nElement() should be (18) + sTensor.storage().array.length should be (30) + sTensor.storageOffset() should be (6) + } + } From 159bb4a1ba75742b9af237608a60a14ee2daa004 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 31 Oct 2017 10:00:11 +0800 Subject: [PATCH 0495/1065] MSRA filler (#1802) --- .../bigdl/dllib/nn/InitializationMethod.scala | 30 ++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 4 +++ .../dllib/torch/SpatialConvolutionSpec.scala | 34 +++++++++++++++++++ 3 files changed, 68 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala index 8dcb9e0a79e..62db21e5f35 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.VariableFormat.Default import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.RandomGenerator /** * VariableFormat describe the meaning of each dimension of the variable @@ -280,6 +281,35 @@ case object Xavier extends InitializationMethod { } +/** + * A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically + * accounts for ReLU nonlinearities. + * + * Aside: for another perspective on the scaling factor, see the derivation of + * [Saxe, McClelland, and Ganguli 2013 (v3)]. + * + * It fills the incoming matrix by randomly sampling Gaussian data with std = + * sqrt(2 / n) where n is the fanIn, fanOut, or their average, depending on + * the varianceNormAverage parameter. + * + * @param varianceNormAverage VarianceNorm use average of (fanIn + fanOut) or just fanOut + */ +case class MsraFiller(varianceNormAverage: Boolean = true) extends InitializationMethod { + def init[T](variable: Tensor[T], dataFormat: VariableFormat) + (implicit ev: TensorNumeric[T]): Unit = { + val shape = variable.size() + val fanIn = dataFormat.getFanIn(shape) + val fanOut = dataFormat.getFanOut(shape) + val n = if (varianceNormAverage) { + (fanIn + fanOut) / 2 + } else { + fanOut + } + val std = math.sqrt(2.0 / n) + variable.apply1(_ => ev.fromType(RandomGenerator.RNG.normal(0, std))) + } +} + /** * Initialize the weight with coefficients for bilinear interpolation. * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ef2869048e2..80d28fb13e7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1970,6 +1970,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Xavier } + def createMsraFiller(varianceNormAverage: Boolean = true): MsraFiller = { + MsraFiller(varianceNormAverage) + } + def createBilinearFiller(): BilinearFiller.type = { BilinearFiller } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionSpec.scala index e3e3919b558..aced2ecf2d7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionSpec.scala @@ -175,4 +175,38 @@ class SpatialConvolutionSpec extends TorchSpec { require(output.equals(luaOutput) == true) } + + "A SpatialConvolution init with msrafiller" should "generate correct output" in { + torchCheck() + val seed = 100 + + val nInputPlane = 3 + val nOutputPlane = 64 + val kW = 11 + val kH = 11 + val dW = 4 + val dH = 4 + val padW = 2 + val padH = 2 + val layer = new SpatialConvolution[Double](nInputPlane, nOutputPlane, kW, kH, dW, dH, + padW, padH, withBias = false) + RNG.setSeed(seed) + layer.setInitMethod(MsraFiller(false), Zeros) + + val input = Tensor[Double](16, 3, 224, 224).apply1(e => Random.nextDouble()) + + val code = "layer = nn.SpatialConvolutionMM(3, 64, 11, 11, 4, 4, 2, 2)\n" + + "torch.manualSeed(" + seed + ")\n" + + "n = layer.kW*layer.kH*layer.nOutputPlane\n" + + "weight = layer.weight\n" + + "std = math.sqrt(2/n)" + + "weight:normal(0, std)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input), + Array("weight", "std")) + val luaWeight = torchResult("weight").asInstanceOf[Tensor[Double]] + val luaStd = torchResult("std") + + luaWeight should be (layer.weight.resize(64, 363)) + } } From dd603f43517c63f521cfcf5f0ea371e3da5a9f67 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 31 Oct 2017 10:01:25 +0800 Subject: [PATCH 0496/1065] support argmax operation (#1714) --- .../analytics/bigdl/dllib/nn/ops/ArgMax.scala | 50 +++++++++++++++++++ .../bigdl/dllib/utils/tf/loaders/ArgMax.scala | 32 ++++++++++++ .../bigdl/dllib/nn/ops/ArgMaxSpec.scala | 37 ++++++++++++++ 3 files changed, 119 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMax.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArgMax.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMaxSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMax.scala new file mode 100644 index 00000000000..62557f4d74b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMax.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.{IntType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + + +class ArgMax[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[Int], T] { + + override def updateOutput(input: Table): Tensor[Int] = { + if (output.getType() != IntType) { + output = Tensor[Int]() + } + val inputTensor = input[Tensor[_]](1) + val dimension = input[Tensor[Int]](2).value() + 1 + + val (_, result) = inputTensor + .asInstanceOf[Tensor[NumericWildcard]] + .max(dimension) + + output.resizeAs(result) + result.cast[Int](output) + output.squeeze(dimension) + + output + } + +} + +object ArgMax { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): ArgMax[T] = new ArgMax[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArgMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArgMax.scala new file mode 100644 index 00000000000..ca0ecdb3105 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArgMax.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{ArgMax => ArgMaxOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class ArgMax extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = { + ArgMaxOp[T]() + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMaxSpec.scala new file mode 100644 index 00000000000..8e885ec18c0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMaxSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + + +class ArgMaxSpec extends FlatSpec with Matchers { + + "ArgMax Float" should "work properly" in { + val dataTensor = Tensor[Float](T(T(1.0f, 2.0f), T(3.0f, 4.0f))) + val dimensionTensor = Tensor.scalar[Int](1) + val input = T(dataTensor, dimensionTensor) + val expectedOutput = Tensor[Int](T(2, 2)) + + val layer = ArgMax[Double]() + val result = layer.forward(input) + + result should be (expectedOutput) + } + +} From 83530464666d2d9f6668db82e09cfd896e3a6179 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 31 Oct 2017 11:16:36 +0800 Subject: [PATCH 0497/1065] bump bigdl version to 0.4.0-SNAPSHOT (#1807) --- dist/pom.xml | 2 +- dl/pom.xml | 10 +++++----- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- .../src/main/resources/bigdl-version-info.properties | 2 +- 7 files changed, 11 insertions(+), 11 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index 3e923601926..3db927605a8 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.3.0-SNAPSHOT + 0.4.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index d97ac46cd7b..faa6ab9980e 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.3.0-SNAPSHOT + 0.4.0-SNAPSHOT 4.0.0 @@ -69,7 +69,7 @@ com.intel.analytics.bigdl bigdl-core - 0.3.0-SNAPSHOT + 0.4.0-SNAPSHOT pom @@ -289,7 +289,7 @@ com.intel.analytics.bigdl.native ${mkl-java-os-version} - 0.3.0-SNAPSHOT + 0.4.0-SNAPSHOT x = (1/k)*log(exp(k*y)-1) // THEREFORE: // d/dx(f(x)) = (exp(k*y) - 1) / exp(k*y) - val func = new TensorFunc6[T] { - override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, - data3: Array[T], offset3: Int): Unit = { - val z = ev.exp(ev.times(data3(offset3), betaT)) - data1(offset1) = if (ev.isGreater(ev.times(data3(offset3), betaT), threshold)) { + val func = new TensorFunc6[D] { + override def apply(data1: Array[D], offset1: Int, data2: Array[D], offset2: Int, + data3: Array[D], offset3: Int): Unit = { + val z = ev2.exp(ev2.times(data3(offset3), betaT)) + data1(offset1) = if (ev2.isGreater(ev2.times(data3(offset3), betaT), threshold)) { data2(offset2) } else { - ev.times(data2(offset2), ev.divide(ev.minus(z, ev.fromType[Int](1)), z)) + ev2.times(data2(offset2), ev2.divide(ev2.minus(z, ev2.fromType[Int](1)), z)) } } } - DenseTensorApply.apply3[T](gradInput, gradOutput, output, func) + DenseTensorApply.apply3[D](gradInput, gradOutput, output, func) gradInput } } object SoftPlus { - def apply[@specialized(Float, Double) T: ClassTag]( - beta: Double = 1.0)(implicit ev: TensorNumeric[T]) : SoftPlus[T] = { - new SoftPlus[T](beta) + def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( + beta: Double = 1.0) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : SoftPlus[T, D] = { + new SoftPlus[T, D](beta) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala index 3221aa51bb5..73f37ee00b9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -28,37 +28,39 @@ import scala.reflect.ClassTag */ @SerialVersionUID(- 3936698382129844874L) -class SoftSign[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T] { +class SoftSign[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends AbstractModule[Tensor[D], Tensor[D], T] { - @transient private var temp: Tensor[T] = null - @transient private var tempGrad: Tensor[T] = null + @transient private var temp: Tensor[D] = null + @transient private var tempGrad: Tensor[D] = null - override def updateOutput(input: Tensor[T]): Tensor[T] = { + override def updateOutput(input: Tensor[D]): Tensor[D] = { if (null == temp) { temp = input.clone() } else { temp.resizeAs(input).copy(input) } - temp.abs().add(ev.fromType[Int](1)) + temp.abs().add(ev2.fromType[Int](1)) output.resizeAs(input).copy(input).cdiv(temp) output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { if (null == tempGrad) { tempGrad = input.clone() } else { tempGrad.resizeAs(output).copy(input) } - tempGrad.abs().add(ev.fromType[Int](1)).cmul(tempGrad) + tempGrad.abs().add(ev2.fromType[Int](1)).cmul(tempGrad) gradInput.resizeAs(input).copy(gradOutput).cdiv(tempGrad) gradInput } } object SoftSign { - def apply[@specialized(Float, Double) T: ClassTag]() - (implicit ev: TensorNumeric[T]) : SoftSign[T] = { - new SoftSign[T]() + def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : SoftSign[T, D] = { + new SoftSign[T, D]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala index 81b27b2aa82..a54eb1831aa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala @@ -628,14 +628,21 @@ class SpatialAveragePooling[T: ClassTag]( } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - require(input.dim() == 3 || input.dim() == 4, + val inputSize = input.size() + updateGradInputInternal(inputSize, gradOutput) + } + + private[bigdl] def updateGradInputInternal(inputSize: Array[Int], + gradOutput: Tensor[T]): Tensor[T] = { + require(inputSize.length == 3 || inputSize.length == 4, "SpatialAveragePooling: " + ErrorInfo.constrainInputAs3DOrBatch + - s"input dimension ${input.dim()}") - val (dimh, dimw, dimc) = format.getHWCDims(input.dim()) + s"input dimension ${inputSize.length}") + // dimh, dimw, dimc start with 1 + val (dimh, dimw, dimc) = format.getHWCDims(inputSize.length) - val nInputPlane = input.size(dimc) - val inputHeight = input.size(dimh) - val inputWidth = input.size(dimw) + val nInputPlane = inputSize(dimc - 1) + val inputHeight = inputSize(dimh - 1) + val inputWidth = inputSize(dimw - 1) val (padTop, padBottom, padLeft, padRight, outputHeight, outputWidth) = if (padW == -1 && padH == -1) { @@ -648,8 +655,8 @@ class SpatialAveragePooling[T: ClassTag]( Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW, padH, padW, ceilMode) } - gradInput.resizeAs(input).zero() - if (input.dim() == 3) { + gradInput.resize(inputSize).zero() + if (inputSize.length == 3) { format match { case DataFormat.NCHW => if (classTag[T] == classTag[Double]) { @@ -677,7 +684,7 @@ class SpatialAveragePooling[T: ClassTag]( } } } else { - val nBatch = input.size(1) + val nBatch = inputSize(0) if (results == null || results.length != nBatch) { results = new Array[Future[Unit]](nBatch) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ApproximateEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ApproximateEqual.scala new file mode 100644 index 00000000000..608bd8b4d05 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ApproximateEqual.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class ApproximateEqual[T: ClassTag](tolerance: Float) + (implicit ev: TensorNumeric[T]) extends Compare[T] { + override def compareFloat(a: Float, b: Float): Boolean = math.abs(a - b) < tolerance + + override def compareDouble(a: Double, b: Double): Boolean = math.abs(a - b) < tolerance + + override def compareChar(a: Char, b: Char): Boolean = math.abs(a - b) < tolerance + + override def compareLong(a: Long, b: Long): Boolean = math.abs(a - b) < tolerance + + override def compareShort(a: Short, b: Short): Boolean = math.abs(a - b) < tolerance + + override def compareInt(a: Int, b: Int): Boolean = math.abs(a - b) < tolerance + + override def compareBoolean(a: Boolean, b: Boolean): Boolean = { + throw new UnsupportedOperationException("Does not support ApproximateEqual on Boolean") + } + + override def compareByteString(a: ByteString, b: ByteString): Boolean = { + throw new UnsupportedOperationException("Does not support ApproximateEqual on ByteString") + } +} + +object ApproximateEqual { + def apply[T: ClassTag](tolerance: Float) + (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new ApproximateEqual(tolerance)) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AvgPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AvgPoolGrad.scala new file mode 100644 index 00000000000..6b4c9701955 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AvgPoolGrad.scala @@ -0,0 +1,72 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, SpatialMaxPooling} +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class AvgPoolGrad[T: ClassTag]( + kH: Int, + kW: Int, + strideW: Int, + strideH: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[T], T]{ + + private var module : SpatialAveragePooling[T] = _ + + override def updateOutput(input: Table): Tensor[T] = { + if (module == null) { + module = SpatialAveragePooling[T]( + kH, + kW, + strideH, + strideW, + padH, + padW, + countIncludePad = false, + format = format + ) + } + + val inputDataSize = input[Tensor[Int]](1).storage().array() + + val gradOutput = input[Tensor[T]](2) + output = module.updateGradInputInternal(inputDataSize, gradOutput) + output + } +} + +object AvgPoolGrad { + def apply[T: ClassTag]( + kH: Int, + kW: Int, + strideW: Int, + strideH: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): AvgPoolGrad[T] = + new AvgPoolGrad(kH, kW, strideW, strideH, padH, padW, format) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Compare.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Compare.scala new file mode 100644 index 00000000000..6bffb160953 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Compare.scala @@ -0,0 +1,95 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +abstract class Compare[T: ClassTag]() +(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { + + def compareFloat(a: Float, b: Float): Boolean + + def compareDouble(a: Double, b: Double): Boolean + + def compareChar(a: Char, b: Char): Boolean + + def compareLong(a: Long, b: Long): Boolean + + def compareShort(a: Short, b: Short): Boolean + + def compareInt(a: Int, b: Int): Boolean + + def compareBoolean(a: Boolean, b: Boolean): Boolean + + def compareByteString(a: ByteString, b: ByteString): Boolean + + output = Activity.allocate[Tensor[Boolean], Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { + output.resizeAs(input(1)) + input[Tensor[_]](1).getType() match { + case FloatType => + output.zipWith[Float, Float]( + input[Tensor[Float]](1), + input[Tensor[Float]](2), + (a, b) => compareFloat(a, b)) + case DoubleType => + output.zipWith[Double, Double]( + input[Tensor[Double]](1), + input[Tensor[Double]](2), + (a, b) => compareDouble(a, b)) + case CharType => + output.zipWith[Char, Char]( + input[Tensor[Char]](1), + input[Tensor[Char]](2), + (a, b) => compareChar(a, b)) + case LongType => + output.zipWith[Long, Long]( + input[Tensor[Long]](1), + input[Tensor[Long]](2), + (a, b) => compareLong(a, b)) + case ShortType => + output.zipWith[Short, Short]( + input[Tensor[Short]](1), + input[Tensor[Short]](2), + (a, b) => compareShort(a, b)) + case IntType => + output.zipWith[Int, Int]( + input[Tensor[Int]](1), + input[Tensor[Int]](2), + (a, b) => compareInt(a, b)) + case BooleanType => + output.zipWith[Boolean, Boolean]( + input[Tensor[Boolean]](1), + input[Tensor[Boolean]](2), + (a, b) => compareBoolean(a, b)) + case StringType => + output.zipWith[ByteString, ByteString]( + input[Tensor[ByteString]](1), + input[Tensor[ByteString]](2), + (a, b) => compareByteString(a, b)) + case _ => throw new RuntimeException("Unsupported tensor type") + } + + output + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala new file mode 100644 index 00000000000..d6bdde99153 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.{ELU => ELULayer} + +import scala.reflect.ClassTag + +class EluGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends UnaryGrad[T, D](true, true) { + + override val module: Module = ELULayer[T, D]() +} + +object EluGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): EluGrad[T, D] = new EluGrad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala index 430a714e7e5..9b096de5794 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Equal.scala @@ -24,58 +24,22 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class Equal[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { + (implicit ev: TensorNumeric[T]) extends Compare[T] { + override def compareFloat(a: Float, b: Float): Boolean = a == b - output = Activity.allocate[Tensor[Boolean], Boolean]() + override def compareDouble(a: Double, b: Double): Boolean = a == b - override def updateOutput(input: Table): Tensor[Boolean] = { - output.resizeAs(input(1)) - input[Tensor[_]](1).getType() match { - case FloatType => - output.zipWith[Float, Float]( - input[Tensor[Float]](1), - input[Tensor[Float]](2), - (a, b) => a == b) - case BooleanType => - output.zipWith[Boolean, Boolean]( - input[Tensor[Boolean]](1), - input[Tensor[Boolean]](2), - (a, b) => a == b) - case DoubleType => - output.zipWith[Double, Double]( - input[Tensor[Double]](1), - input[Tensor[Double]](2), - (a, b) => a == b) - case CharType => - output.zipWith[Char, Char]( - input[Tensor[Char]](1), - input[Tensor[Char]](2), - (a, b) => a == b) - case StringType => - output.zipWith[ByteString, ByteString]( - input[Tensor[ByteString]](1), - input[Tensor[ByteString]](2), - (a, b) => a.equals(b)) - case LongType => - output.zipWith[Long, Long]( - input[Tensor[Long]](1), - input[Tensor[Long]](2), - (a, b) => a == b) - case ShortType => - output.zipWith[Short, Short]( - input[Tensor[Short]](1), - input[Tensor[Short]](2), - (a, b) => a == b) - case IntType => - output.zipWith[Int, Int]( - input[Tensor[Int]](1), - input[Tensor[Int]](2), - (a, b) => a == b) - case _ => throw new RuntimeException("Unsupported tensor type") - } + override def compareChar(a: Char, b: Char): Boolean = a == b - output - } + override def compareLong(a: Long, b: Long): Boolean = a == b + + override def compareShort(a: Short, b: Short): Boolean = a == b + + override def compareInt(a: Int, b: Int): Boolean = a == b + + override def compareBoolean(a: Boolean, b: Boolean): Boolean = a == b + + override def compareByteString(a: ByteString, b: ByteString): Boolean = a.equals(b) } object Equal { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Greater.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Greater.scala index e5aa7f1f839..6128b451ce1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Greater.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Greater.scala @@ -15,60 +15,32 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class Greater[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { + (implicit ev: TensorNumeric[T]) extends Compare[T] { + override def compareFloat(a: Float, b: Float): Boolean = a > b - output = Activity.allocate[Tensor[Boolean], Boolean]() + override def compareDouble(a: Double, b: Double): Boolean = a > b - override def updateOutput(input: Table): Tensor[Boolean] = { - output.resizeAs(input(1)) - input[Tensor[_]](1).getType() match { - case FloatType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Float, Float]( - input[Tensor[Float]](1), - input[Tensor[Float]](2), - (a, b) => a > b) - case DoubleType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Double, Double]( - input[Tensor[Double]](1), - input[Tensor[Double]](2), - (a, b) => a > b) - case CharType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Char, Char]( - input[Tensor[Char]](1), - input[Tensor[Char]](2), - (a, b) => a > b) - case StringType => - output.asInstanceOf[Tensor[Boolean]].zipWith[String, String]( - input[Tensor[String]](1), - input[Tensor[String]](2), - (a, b) => a > b) - case LongType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Long, Long]( - input[Tensor[Long]](1), - input[Tensor[Long]](2), - (a, b) => a > b) - case ShortType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Short, Short]( - input[Tensor[Short]](1), - input[Tensor[Short]](2), - (a, b) => a > b) - case IntType => - output.asInstanceOf[Tensor[Boolean]].zipWith[Int, Int]( - input[Tensor[Int]](1), - input[Tensor[Int]](2), - (a, b) => a > b) - case _ => throw new RuntimeException("Unsupported tensor type") - } + override def compareChar(a: Char, b: Char): Boolean = a > b - output + override def compareLong(a: Long, b: Long): Boolean = a > b + + override def compareShort(a: Short, b: Short): Boolean = a > b + + override def compareInt(a: Int, b: Int): Boolean = a > b + + override def compareBoolean(a: Boolean, b: Boolean): Boolean = { + throw new UnsupportedOperationException("Does not support Greater on Boolean") + } + + override def compareByteString(a: ByteString, b: ByteString): Boolean = { + throw new UnsupportedOperationException("Does not support Greater on ByteString") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterEqual.scala new file mode 100644 index 00000000000..8606cee51ee --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterEqual.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class GreaterEqual[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Compare[T] { + override def compareFloat(a: Float, b: Float): Boolean = a >= b + + override def compareDouble(a: Double, b: Double): Boolean = a >= b + + override def compareChar(a: Char, b: Char): Boolean = a >= b + + override def compareLong(a: Long, b: Long): Boolean = a >= b + + override def compareShort(a: Short, b: Short): Boolean = a >= b + + override def compareInt(a: Int, b: Int): Boolean = a >= b + + override def compareBoolean(a: Boolean, b: Boolean): Boolean = { + throw new UnsupportedOperationException("Does not support GreaterEqual on Boolean") + } + + override def compareByteString(a: ByteString, b: ByteString): Boolean = { + throw new UnsupportedOperationException("Does not support GreaterEqual on ByteString") + } +} + +object GreaterEqual { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new GreaterEqual()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Less.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Less.scala index 56b88dd88f8..352e1441a40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Less.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Less.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -23,52 +24,25 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class Less[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Boolean], T] { + (implicit ev: TensorNumeric[T]) extends Compare[T] { + override def compareFloat(a: Float, b: Float): Boolean = a < b - output = Activity.allocate[Tensor[Boolean], Boolean]() + override def compareDouble(a: Double, b: Double): Boolean = a < b - override def updateOutput(input: Table): Tensor[Boolean] = { - output.resizeAs(input(1)) - input[Tensor[_]](1).getType() match { - case FloatType => - output.zipWith[Float, Float]( - input[Tensor[Float]](1), - input[Tensor[Float]](2), - (a, b) => a < b) - case DoubleType => - output.zipWith[Double, Double]( - input[Tensor[Double]](1), - input[Tensor[Double]](2), - (a, b) => a < b) - case CharType => - output.zipWith[Char, Char]( - input[Tensor[Char]](1), - input[Tensor[Char]](2), - (a, b) => a < b) - case StringType => - output.zipWith[String, String]( - input[Tensor[String]](1), - input[Tensor[String]](2), - (a, b) => a < b) - case LongType => - output.zipWith[Long, Long]( - input[Tensor[Long]](1), - input[Tensor[Long]](2), - (a, b) => a < b) - case ShortType => - output.zipWith[Short, Short]( - input[Tensor[Short]](1), - input[Tensor[Short]](2), - (a, b) => a < b) - case IntType => - output.zipWith[Int, Int]( - input[Tensor[Int]](1), - input[Tensor[Int]](2), - (a, b) => a < b) - case _ => throw new RuntimeException("Unsupported tensor type") - } + override def compareChar(a: Char, b: Char): Boolean = a < b - output + override def compareLong(a: Long, b: Long): Boolean = a < b + + override def compareShort(a: Short, b: Short): Boolean = a < b + + override def compareInt(a: Int, b: Int): Boolean = a < b + + override def compareBoolean(a: Boolean, b: Boolean): Boolean = { + throw new UnsupportedOperationException("Does not support Less on Boolean") + } + + override def compareByteString(a: ByteString, b: ByteString): Boolean = { + throw new UnsupportedOperationException("Does not support Less on ByteString") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessEqual.scala new file mode 100644 index 00000000000..6e9c41e41f6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessEqual.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class LessEqual[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Compare[T] { + override def compareFloat(a: Float, b: Float): Boolean = a <= b + + override def compareDouble(a: Double, b: Double): Boolean = a <= b + + override def compareChar(a: Char, b: Char): Boolean = a <= b + + override def compareLong(a: Long, b: Long): Boolean = a <= b + + override def compareShort(a: Short, b: Short): Boolean = a <= b + + override def compareInt(a: Int, b: Int): Boolean = a <= b + + override def compareBoolean(a: Boolean, b: Boolean): Boolean = { + throw new UnsupportedOperationException("Does not support LessEqual on Boolean") + } + + override def compareByteString(a: ByteString, b: ByteString): Boolean = { + throw new UnsupportedOperationException("Does not support LessEqual on ByteString") + } +} + +object LessEqual { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new LessEqual()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala new file mode 100644 index 00000000000..62d7f6bd2a4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.{ReLU6 => ReLU6Layer} + +import scala.reflect.ClassTag + +class Relu6Grad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends UnaryGrad[T, D](true) { + + val module: Module = ReLU6Layer[T, D]() +} + +object Relu6Grad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Relu6Grad[T, D] = + new Relu6Grad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala new file mode 100644 index 00000000000..bc5428da1c5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.Sigmoid +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class SigmoidGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + private val module = Sigmoid[D]() + override def updateOutput(input: Table): Tensor[D] = { + val (y, grads) = (input[Tensor[D]](1), input[Tensor[D]](2)) + + output = module.updateGradInputInternal(y, grads).toTensor[D] + output + } +} + +object SigmoidGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SigmoidGrad[T, D] = + new SigmoidGrad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala new file mode 100644 index 00000000000..5c065b48810 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.SoftPlus +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class SoftplusGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends UnaryGrad[T, D](true, true) { + + override val module: Module = SoftPlus[T, D]() +} + +object SoftplusGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SoftplusGrad[T, D] = + new SoftplusGrad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala new file mode 100644 index 00000000000..a9eb55a6e2c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.SoftSign +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class SoftsignGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends UnaryGrad[T, D](true) { + + override val module: Module = SoftSign[T, D]() +} + +object SoftsignGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SoftsignGrad[T, D] = + new SoftsignGrad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SquaredDifference.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SquaredDifference.scala new file mode 100644 index 00000000000..2275a9d7d41 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SquaredDifference.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath._ +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Returns (x - y)(x - y) element-wise. + */ +class SquaredDifference[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[_], T] { + + def updateOutput(inputs: Table): Tensor[_] = { + val x = inputs[Tensor[NumericWildcard]](1) + val y = inputs[Tensor[NumericWildcard]](2) + + require(x.getType() == y.getType(), "The numeric type of x and y must be the same, but got" + + s"x: ${x.getType()}, y: ${y.getType()}") + + if (output.getType() != x.getType()) { + output = x.emptyInstance() + } + + output.asInstanceOf[Tensor[NumericWildcard]] + .resizeAs(x).copy(x).sub(y).square() + + output + } + +} + +object SquaredDifference { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): SquaredDifference[T] + = new SquaredDifference() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala new file mode 100644 index 00000000000..5d0f73495fe --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +abstract class UnaryGrad[T: ClassTag, D: ClassTag]( + gradFirst: Boolean = false, + needForward: Boolean = false) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + type Module = AbstractModule[Tensor[D], Tensor[D], T] + + val module: Module + + override def updateOutput(input: Table): Tensor[D] = { + val (grads, inputs) = if (gradFirst) { + (input[Tensor[D]](1), input[Tensor[D]](2)) + } else { + (input[Tensor[D]](2), input[Tensor[D]](1)) + } + + if (needForward) { + module.forward(inputs) + } + + output = module.updateGradInput(inputs, grads).toTensor[D] + output + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala new file mode 100644 index 00000000000..e7cb4866f19 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.Log +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * The [[Log]] module applies a log transformation to the input data + */ +@SerialVersionUID(952324213749625368L) +class Log1p[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends AbstractModule[Tensor[D], Tensor[D], T] { + private val buffer: Tensor[D] = Tensor[D]() + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output.resizeAs(input) + .copy(input) + .log1p() + output + } + override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + buffer.resizeAs(input) + buffer.copy(input).add(ev2.fromType[Double](1.0)) + gradInput.resizeAs(input) + .fill(ev2.fromType[Double](1.0)) + .cdiv(buffer) + .cmul(gradOutput) + + gradInput + } +} + +object Log1p { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Log1p[T, D] = { + new Log1p[T, D]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index e9ba289ccf4..2a1fb8e2f74 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -1694,6 +1694,8 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( override def pow(n: T): Tensor[T] = DenseTensorMath.pow[T](this, this, n) + override def square(): Tensor[T] = pow(ev.fromType(2.0)) + override def log(x: Tensor[T]): Tensor[T] = DenseTensorMath.log[T](this, x) override def log(): Tensor[T] = DenseTensorMath.log[T](this, this) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index 894a2a20bc3..5431320600f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1130,6 +1130,9 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def pow(n: T): Tensor[T] = throw new UnsupportedOperationException(errorString) + override def square(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + /** * Get the top k smallest values and their indices. * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 437b9d555c0..496e37f61ed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -773,6 +773,10 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + override def square(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + override def topk(k: Int, dim: Int, increase: Boolean, result: Tensor[T], indices: Tensor[T], sortedResult: Boolean = true): (Tensor[T], Tensor[T]) = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala index ad92a5cebc9..3c4325642d9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala @@ -551,6 +551,13 @@ trait TensorMath[T] { def pow(n: T): Tensor[T] + /** + * Replaces all elements in-place with the elements of x squared + * + * @return current tensor reference + */ + def square(): Tensor[T] + /** * Populate the given tensor with the floor result of elements * @param y diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index 85a21318e7b..632e6a699f5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -156,7 +156,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { private def fromCaffeAbsVal(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val layerName = getLayerName(layer) - Seq(Abs[T]().setName(layerName).inputs()) + Seq(Abs[T, T]().setName(layerName).inputs()) } private def fromCaffeConcat(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -173,7 +173,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { private def fromCaffeLog(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val layerName = getLayerName(layer) - Seq(Log[T]().setName(layerName).inputs()) + Seq(Log[T, T]().setName(layerName).inputs()) } private def fromCaffePower(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -304,13 +304,13 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { case logSoftMax : LogSoftMax[_] => toCaffeLogSoftMax(moduleNode, bottoms, nextSize) case tanh : Tanh[_] => toCaffeTanh(moduleNode, bottoms, nextSize) case sigmoid : Sigmoid[_] => toCaffeSigmoid(moduleNode, bottoms, nextSize) - case abs : Abs[_] => toCaffeAbs(moduleNode, bottoms, nextSize) + case abs : Abs[_, _] => toCaffeAbs(moduleNode, bottoms, nextSize) case bartchNorm : SpatialBatchNormalization[_] => toCaffeBatchNormalization(moduleNode, bottoms, nextSize) case joinTable : JoinTable[_] => toCaffeConcat(moduleNode, bottoms, nextSize) - case elu : ELU[_] => toCaffeElu(moduleNode, bottoms, nextSize) + case elu : ELU[_, _] => toCaffeElu(moduleNode, bottoms, nextSize) case infershape : InferReshape[_] => toCaffeFlattern(moduleNode, bottoms, nextSize) - case log : Log[_] => toCaffeLog(moduleNode, bottoms, nextSize) + case log : Log[_, _] => toCaffeLog(moduleNode, bottoms, nextSize) case power : Power[_] => toCaffePower(moduleNode, bottoms, nextSize) case prelu : PReLU[_] => toCaffePReLu(moduleNode, bottoms, nextSize) case recurrent : Recurrent[_] => toCaffeRecurrent(moduleNode, bottoms, nextSize) @@ -543,7 +543,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { protected def toCaffeEluParam(module : AbstractModule[Activity, Activity, T]) : ELUParameter = { val eLUParameter = ELUParameter.newBuilder() - val layer = classOf[ELU[T]].cast(module) + val layer = classOf[ELU[T, T]].cast(module) eLUParameter.setAlpha(layer.alpha.toFloat) eLUParameter.build() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index e102f7198d6..82a20b16822 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -144,7 +144,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert val param = layer.asInstanceOf[LayerParameter].getEluParam var alpha = 1.0 if (param.hasAlpha) alpha = param.getAlpha - Seq(ELU[T](alpha).setName(getLayerName(layer)).inputs()) + Seq(ELU[T, T](alpha).setName(getLayerName(layer)).inputs()) } override protected def fromCaffeReshape(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 357d1530f67..7d0ab5d7484 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -488,8 +488,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createAbs() - : Abs[T] = { - Abs[T]() + : Abs[T, T] = { + Abs[T, T]() } def createAdd(inputSize: Int) @@ -592,8 +592,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createClamp(min: Int, max: Int) - : Clamp[T] = { - Clamp[T](min, + : Clamp[T, T] = { + Clamp[T, T](min, max) } @@ -632,8 +632,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createELU(alpha: Double = 1.0, inplace: Boolean = false) - : ELU[T] = { - ELU[T](alpha, + : ELU[T, T] = { + ELU[T, T](alpha, inplace) } @@ -669,8 +669,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createHardTanh(minValue: Double = -1, maxValue: Double = 1, inplace: Boolean = false) - : HardTanh[T] = { - HardTanh[T](minValue, + : HardTanh[T, T] = { + HardTanh[T, T](minValue, maxValue, inplace) } @@ -719,8 +719,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createLog() - : Log[T] = { - Log[T]() + : Log[T, T] = { + Log[T, T]() } def createLogSigmoid() @@ -874,8 +874,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createReLU6(inplace: Boolean = false) - : ReLU6[T] = { - ReLU6[T](inplace) + : ReLU6[T, T] = { + ReLU6[T, T](inplace) } def createReplicate(nFeatures: Int, @@ -927,8 +927,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSoftPlus(beta: Double = 1.0) - : SoftPlus[T] = { - SoftPlus[T](beta) + : SoftPlus[T, T] = { + SoftPlus[T, T](beta) } def createSoftShrink(lambda: Double = 0.5) @@ -937,8 +937,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSoftSign() - : SoftSign[T] = { - SoftSign[T]() + : SoftSign[T, T] = { + SoftSign[T, T]() } def createSpatialDilatedConvolution(nInputPlane: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Abs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Abs.scala new file mode 100644 index 00000000000..14017338eba --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Abs.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Abs, ELU} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Abs extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Abs[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Abs[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Abs when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqual.scala new file mode 100644 index 00000000000..5c651ad66aa --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqual.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.ops.ApproximateEqual +import com.intel.analytics.bigdl.utils.tf.Context + + +import scala.reflect.ClassTag + +class ApproximateEqual extends TensorflowOpsLoader { + import Utils._ + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val tolerance = getFloat(attributes, "tolerance") + ApproximateEqual[T](tolerance) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala new file mode 100644 index 00000000000..71bdfab6162 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala @@ -0,0 +1,69 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.AvgPoolGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class AvgPoolGrad extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + val kernelSize = getIntList(attributes, "ksize") + require(kernelSize.head == 1, s"not support kernel on batch") + + val format = getString(attributes, "data_format") + val poolgrad = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + val kW = kernelSize(1) + val kH = kernelSize(2) + AvgPoolGrad[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + val kW = kernelSize(2) + val kH = kernelSize(3) + AvgPoolGrad[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + poolgrad.asInstanceOf[Module[T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1.scala new file mode 100644 index 00000000000..97d0ef2dc2c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.tf.{BiasAdd => BiasAddOp} +import com.intel.analytics.bigdl.utils.tf.Context + +import scala.reflect.ClassTag + +class BiasAddV1 extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + BiasAddOp[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Div.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Div.scala new file mode 100644 index 00000000000..ea45344bf32 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Div.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{RealDiv => RealDivOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Div extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + RealDivOp() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Elu.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Elu.scala new file mode 100644 index 00000000000..9da0a0fceec --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Elu.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{ELU, ReLU6} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Elu extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + ELU[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + ELU[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load ELU when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala new file mode 100644 index 00000000000..aea85fa898b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.EluGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class EluGrad extends TensorflowOpsLoader { + + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + EluGrad[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + EluGrad[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load ReLU6 when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqual.scala new file mode 100644 index 00000000000..ffef48eb98b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqual.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.ops.GreaterEqual +import com.intel.analytics.bigdl.utils.tf.Context + +import scala.reflect.ClassTag + +class GreaterEqual extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + GreaterEqual[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqual.scala new file mode 100644 index 00000000000..261b5b97022 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqual.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.NodeDef +import com.intel.analytics.bigdl.nn.ops.LessEqual +import com.intel.analytics.bigdl.utils.tf.Context + +import scala.reflect.ClassTag + +class LessEqual extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + LessEqual[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log.scala new file mode 100644 index 00000000000..b16c2408236 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{ELU, Log} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType + +import scala.reflect.ClassTag + +class Log extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Log[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Log[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Log when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1p.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1p.scala new file mode 100644 index 00000000000..e7d109b1b40 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1p.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.tf.Log1p +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Context + +import scala.reflect.ClassTag + +class Log1p extends TensorflowOpsLoader { + import Utils._ + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Log1p[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Log1p[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Log1p when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmax.scala new file mode 100644 index 00000000000..2eb1514f7b1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmax.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.LogSoftMax +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class LogSoftmax extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + LogSoftMax[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala new file mode 100644 index 00000000000..b4ead966a65 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ReLU6 +import com.intel.analytics.bigdl.nn.tf.Log1p +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Relu6 extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + ReLU6[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + ReLU6[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load ReLU6 when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala new file mode 100644 index 00000000000..8cc7ddf823b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{EluGrad, Relu6Grad} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Relu6Grad extends TensorflowOpsLoader { + + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Relu6Grad[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Relu6Grad[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load ReLU6 when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala new file mode 100644 index 00000000000..d150092ef70 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.SigmoidGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class SigmoidGrad extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + SigmoidGrad[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + SigmoidGrad[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load SigmoidGrad when type is ${t}") + } + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softplus.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softplus.scala new file mode 100644 index 00000000000..a5eeea45477 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softplus.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.SoftPlus +import com.intel.analytics.bigdl.nn.ops.Relu6Grad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Softplus extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + SoftPlus[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + SoftPlus[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load SoftPlus when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala new file mode 100644 index 00000000000..fbdb46e4237 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.SoftPlus +import com.intel.analytics.bigdl.nn.ops.SoftplusGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class SoftplusGrad extends TensorflowOpsLoader { + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + SoftplusGrad[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + SoftplusGrad[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load SoftplusGrad when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softsign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softsign.scala new file mode 100644 index 00000000000..17ddaad87b0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softsign.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.SoftSign +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Softsign extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + SoftSign[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + SoftSign[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load SoftsignGrad when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala new file mode 100644 index 00000000000..7fc140e9036 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{SoftplusGrad, SoftsignGrad} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class SoftsignGrad extends TensorflowOpsLoader { + + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + SoftsignGrad[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + SoftsignGrad[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load SoftsignGrad when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifference.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifference.scala new file mode 100644 index 00000000000..7580429b7ce --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifference.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.SquaredDifference +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class SquaredDifference extends TensorflowOpsLoader { + + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + SquaredDifference[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala index fe4ae32a42a..709556cab2d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala @@ -61,6 +61,10 @@ object Utils { attrMap.get(key).getI.toInt } + private[loaders] def getFloat(attrMap: util.Map[String, AttrValue], key: String): Float = { + attrMap.get(key).getF + } + private[loaders] def getBoolean(attrMap: util.Map[String, AttrValue], key: String): Boolean = { attrMap.get(key).getB } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Log1pSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Log1pSpec.scala new file mode 100644 index 00000000000..2a89d879aa3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Log1pSpec.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.tf.Log1p +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import org.scalatest.{FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Parallel +class Log1pSpec extends FlatSpec with Matchers { + "A Log" should "generate correct output" in { + val input = Tensor(Storage[Double](Array(0.0, 1, 2, 3, 4, 5)), 1, Array(2, 3)) + + val output = Tensor(Storage(Array(0.0, 0.6931471805599453, 1.0986122886681098, + 1.3862943611198906, 1.6094379124341003, 1.791759469228055)), 1, Array(2, 3)) + + val log1p = new Log1p[Double, Double]() + + val logOutput = log1p.forward(input) + + logOutput should equal (output) + } + + "A Log" should "generate correct grad" in { + val input = Tensor(Storage[Double](Array(0.0, 1, 2, 3, 4, 5)), 1, Array(2, 3)) + + val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) + + val log1p = new Log1p[Double, Double]() + + val gradInput = log1p.backward(input, gradOutput) + + gradInput should equal (Tensor(Storage(Array(0.1, 0.1, 0.1, 0.1, 0.1, 0.1)), 1, Array(2, 3))) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala index bb6ee9bd4c4..3cf4f8c67bc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala @@ -26,7 +26,7 @@ class LogSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(0.0, 0.6931471805599453, 1.0986122886681098, 1.3862943611198906, 1.6094379124341003, 1.791759469228055)), 1, Array(2, 3)) - val log = new Log[Double]() + val log = new Log[Double, Double]() val logOutput = log.forward(input) @@ -38,7 +38,7 @@ class LogSpec extends FlatSpec with Matchers { val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) - val log = new Log[Double]() + val log = new Log[Double, Double]() val gradInput = log.backward(input, gradOutput) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala index ec381d780fa..163ede783d1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala @@ -26,7 +26,7 @@ class MMSpec extends FlatSpec with Matchers { val m2 = new MM[Double]() val m3 = new MM[Double](true, true) val m4 = new MM[Double]() - val log = new Log[Double]() + val log = new Log[Double, Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3, 3).randn() @@ -45,7 +45,7 @@ class MMSpec extends FlatSpec with Matchers { val m2 = new MM[Double]() val m3 = new MM[Double](true, true) val m4 = new MM[Double]() - val log = new Log[Double]() + val log = new Log[Double, Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3, 3).randn() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala index 792847e60fc..3e81f277e78 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala @@ -26,7 +26,7 @@ class MVSpec extends FlatSpec with Matchers { val m2 = new MV[Double]() val m3 = new MV[Double](true) val m4 = new MV[Double]() - val log = new Log[Double]() + val log = new Log[Double, Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3).randn() @@ -46,7 +46,7 @@ class MVSpec extends FlatSpec with Matchers { val m2 = new MV[Double]() val m3 = new MV[Double](true) val m4 = new MV[Double]() - val log = new Log[Double]() + val log = new Log[Double, Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3).randn() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala index aab3e63ca0b..06c0593ce79 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala @@ -28,7 +28,7 @@ class ModuleSpec extends FlatSpec with Matchers { "hashcode()" should "behave correctly" in { val r1 = new ReLU[Double]() val r2 = new ReLU[Double]() - val log = new Log[Double]() + val log = new Log[Double, Double]() val r3 = new ReLU[Float]() val r4 = new ReLU[Double]() val r5 = new ReLU[Double]() @@ -49,7 +49,7 @@ class ModuleSpec extends FlatSpec with Matchers { "equals()" should "behave correctly" in { val r1 = new ReLU[Double]() val r2 = new ReLU[Double]() - val log = new Log[Double]() + val log = new Log[Double, Double]() val mNull = null val r3 = new ReLU[Float]() val r4 = new ReLU[Double]() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala index c61fe5d9f2e..3a4613528cc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala @@ -27,7 +27,7 @@ class PairwiseDistanceSpec extends FlatSpec with Matchers { val m2 = new PairwiseDistance[Double]() val m3 = new PairwiseDistance[Double](3) val m4 = new PairwiseDistance[Double]() - val log = new Log[Double]() + val log = new Log[Double, Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3, 3).randn() @@ -47,7 +47,7 @@ class PairwiseDistanceSpec extends FlatSpec with Matchers { val m2 = new PairwiseDistance[Double]() val m3 = new PairwiseDistance[Double](3) val m4 = new PairwiseDistance[Double]() - val log = new Log[Double]() + val log = new Log[Double, Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3, 3).randn() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala index 1a6f38bec92..cd9c1fd4b75 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.utils.T @com.intel.analytics.bigdl.tags.Parallel class ParallelTableSpec extends FlatSpec with Matchers { "hashcode()" should "behave correctly" in { - val log = new Log[Double]() + val log = new Log[Double, Double]() val exp = new Exp[Double]() val m1 = new ParallelTable[Double]() m1.add(log) @@ -49,7 +49,7 @@ class ParallelTableSpec extends FlatSpec with Matchers { } "equals()" should "behave correctly" in { - val log = new Log[Double]() + val log = new Log[Double, Double]() val exp = new Exp[Double]() val m1 = new ParallelTable[Double]() m1.add(log) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala index 9eb782dd446..35a4b86ed3b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala @@ -72,19 +72,6 @@ class GreaterSpec extends FlatSpec with Matchers { output should be(expectOutput) } - "Greater String operation" should "works correctly" in { - val input = - T( - Tensor[String](T("abc", "bbb", "baa")), - Tensor[String](T("aaa", "ccc", "aaa")) - ) - - val expectOutput = Tensor[Boolean](T(true, false, true)) - - val output = Greater[Float]().forward(input) - output should be(expectOutput) - } - "Greater Short operation" should "works correctly" in { val input = T( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala index 24dd436aa15..f2a277f7bec 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala @@ -72,19 +72,6 @@ class LessSpec extends FlatSpec with Matchers { output should be(expectOutput) } - "Less String operation" should "works correctly" in { - val input = - T( - Tensor[String](T("abc", "bbb", "aaa")), - Tensor[String](T("aaa", "ccc", "aaa")) - ) - - val expectOutput = Tensor[Boolean](T(false, true, false)) - - val output = Less[Float]().forward(input) - output should be(expectOutput) - } - "Less Short operation" should "works correctly" in { val input = T( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala index f50b979e08d..d90f38bdd33 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.Tensor class AbsSpec extends TorchSpec { "A Abs Module " should "generate correct output and grad" in { torchCheck() - val module = new Abs[Double] + val module = new Abs[Double, Double] val input = Tensor[Double](2, 1, 2) input(Array(1, 1, 1)) = 21 input(Array(1, 1, 2)) = -29 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala index 1fc0a49e751..31e839e6d20 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala @@ -24,7 +24,7 @@ import scala.math._ class ClampSpec extends TorchSpec { "A Clamp Module " should "generate correct output and grad" in { torchCheck() - val module = new Clamp[Double](-10, 10) + val module = new Clamp[Double, Double](-10, 10) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala index 7190f973b76..acdd7f11e12 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala @@ -29,7 +29,7 @@ class ELUSpec extends TorchSpec { val seed = 100 RNG.setSeed(seed) - val module = new ELU[Double]() + val module = new ELU[Double, Double]() val input = Tensor[Double](2, 2, 2) input.apply1(x => random()) val gradOutput = Tensor[Double](2, 2, 2) @@ -62,7 +62,7 @@ class ELUSpec extends TorchSpec { val seed = 100 RNG.setSeed(seed) - val module = new ELU[Double](10, false) + val module = new ELU[Double, Double](10, false) val input = Tensor[Double](2, 2, 2) input.apply1(x => random()) val gradOutput = Tensor[Double](2, 2, 2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala index 98844446abe..91d297b6678 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala @@ -23,7 +23,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad not inplace with contiguous input" in { torchCheck() - val module = new HardTanh[Double]() + val module = new HardTanh[Double, Double]() val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 @@ -66,7 +66,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad inplace with contiguous input" in { torchCheck() - val module = new HardTanh[Double](inplace = true) + val module = new HardTanh[Double, Double](inplace = true) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 @@ -110,7 +110,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad not inplace with not contiguous input" in { torchCheck() - val module = new HardTanh[Double]() + val module = new HardTanh[Double, Double]() val input = Tensor[Double](2, 2) input(Array(1, 1)) = -0.97008799016476 input(Array(1, 2)) = -0.65073125436902 @@ -146,7 +146,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad inplace with not contiguous input" in { torchCheck() - val module = new HardTanh[Double](inplace = true) + val module = new HardTanh[Double, Double](inplace = true) val input = Tensor[Double](2, 2) input(Array(1, 1)) = -0.97008799016476 input(Array(1, 2)) = -0.65073125436902 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala index 771918dbed6..002e58c5577 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala @@ -24,7 +24,7 @@ class LogSpec extends TorchSpec { "A Log()" should "generate correct output and grad" in { torchCheck() def randomn(): Double = RandomGenerator.RNG.uniform(2, 10) - val layer = new Log[Double]() + val layer = new Log[Double, Double]() val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](2, 2, 2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala index 9a1fc1554c9..266a56b7770 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala @@ -25,7 +25,7 @@ import scala.math._ class ReLU6Spec extends TorchSpec { "A ReLU6 Module " should "generate correct output and grad not inplace" in { torchCheck() - val module = new ReLU6[Double]() + val module = new ReLU6[Double, Double]() val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 @@ -74,7 +74,7 @@ class ReLU6Spec extends TorchSpec { "A ReLU6 Module " should "generate correct output and grad inplace" in { torchCheck() - val module = new ReLU6[Double](true) + val module = new ReLU6[Double, Double](true) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala index 8c9f818390d..ee76910fdb7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala @@ -24,7 +24,7 @@ import scala.util.Random class SoftPlusSpec extends TorchSpec { "A SoftPlus 3D input" should "generate correct output and grad" in { torchCheck() - val layer = new SoftPlus[Double]() + val layer = new SoftPlus[Double, Double]() val input = Tensor[Double](2, 3, 4).apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](2, 3, 4).apply1(_ => Random.nextDouble()) @@ -51,7 +51,7 @@ class SoftPlusSpec extends TorchSpec { "A SoftPlus 4D input" should "generate correct output and grad" in { torchCheck() - val layer = new SoftPlus[Double](2.0) + val layer = new SoftPlus[Double, Double](2.0) val input = Tensor[Double](5, 4, 3, 2).apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](5, 4, 3, 2).apply1(_ => Random.nextDouble()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala index 8ccb818462a..9c99ce6d0aa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala @@ -25,7 +25,7 @@ import scala.util.Random class SoftSignSpec extends TorchSpec { "A SoftSign 3D input" should "generate correct output and grad" in { torchCheck() - val layer = new SoftSign[Double]() + val layer = new SoftSign[Double, Double]() val input = Tensor[Double](2, 3, 4).apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](2, 3, 4).apply1(_ => Random.nextDouble()) @@ -52,7 +52,7 @@ class SoftSignSpec extends TorchSpec { "A SoftSign 4D input" should "generate correct output and grad" in { torchCheck() - val layer = new SoftSign[Double]() + val layer = new SoftSign[Double, Double]() val input = Tensor[Double](5, 4, 3, 2).apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](5, 4, 3, 2).apply1(_ => Random.nextDouble()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AbsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AbsSpec.scala new file mode 100644 index 00000000000..d33489c719b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AbsSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class AbsSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Abs" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqualSpec.scala new file mode 100644 index 00000000000..04ebe66150a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqualSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor +import org.tensorflow.framework.AttrValue + + +class ApproximateEqualSpec extends BinaryOpBaseSpec { + override def getOpName: String = "ApproximateEqual" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) + + override def getAttrs: Seq[(String, AttrValue)] = + Seq(("tolerance", AttrValue.newBuilder().setF(1e-6f).build())) + + override def compareExactly: Boolean = true +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGradSpec.scala new file mode 100644 index 00000000000..c9164e23dbd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGradSpec.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class AvgPoolGradSpec extends TensorflowSpecHelper { + "Avg forward" should "be correct" in { + compare( + NodeDef.newBuilder() + .setName("avg_pool_grad_test") + .setOp("AvgPoolGrad") + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("ksize", kernelAttr(4, 4, TensorflowDataFormat.NHWC)) + .putAttr("strides", strideAttr(1, 1, TensorflowDataFormat.NHWC)) + .putAttr("padding", PaddingType.PADDING_SAME.value), + Seq(Tensor[Int](T(4, 32, 32, 3)), Tensor[Float](4, 32, 32, 3).rand()), + 0 + ) + + compare( + NodeDef.newBuilder() + .setName("avg_pool_grad_test") + .setOp("AvgPoolGrad") + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("ksize", kernelAttr(4, 4, TensorflowDataFormat.NHWC)) + .putAttr("strides", strideAttr(1, 1, TensorflowDataFormat.NHWC)) + .putAttr("padding", PaddingType.PADDING_VALID.value), + Seq(Tensor[Int](T(4, 32, 32, 3)), Tensor[Float](4, 29, 29, 3).rand()), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1Spec.scala new file mode 100644 index 00000000000..9c17217c425 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1Spec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class BiasAddV1Spec extends BinaryOpBaseSpec { + + override def getOpName: String = "BiasAddV1" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BinaryOpBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BinaryOpBaseSpec.scala new file mode 100644 index 00000000000..3133475761f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BinaryOpBaseSpec.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{AttrValue, DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +abstract class BinaryOpBaseSpec extends TensorflowSpecHelper { + + def getOpName: String + + def getInputs: Seq[Tensor[_]] + + def getAttrs: Seq[(String, AttrValue)] = Seq.empty + + def compareExactly: Boolean = false + + s"$getOpName forward" should "be correct" in { + + val builder = NodeDef.newBuilder() + .setName(s"${getOpName}Test") + .setOp(getOpName) + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + + for ((k, v) <- getAttrs) { + builder.putAttr(k, v) + } + + if (!compareExactly) { + compare( + builder, + getInputs, + 0 + ) + } else { + val (bigdl, tf) = getResult(builder, getInputs, 0) + bigdl should be (tf) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DivSpec.scala new file mode 100644 index 00000000000..5fd522145ec --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DivSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class DivSpec extends BinaryOpBaseSpec { + override def getOpName: String = "Div" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGradSpec.scala new file mode 100644 index 00000000000..6b55398ba77 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGradSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class EluGradSpec extends BinaryOpBaseSpec { + override def getOpName: String = "EluGrad" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluSpec.scala new file mode 100644 index 00000000000..80fbaa58d6c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class EluSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Elu" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqualSpec.scala new file mode 100644 index 00000000000..70da07b51ac --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqualSpec.scala @@ -0,0 +1,27 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class GreaterEqualSpec extends BinaryOpBaseSpec { + override def getOpName: String = "GreaterEqual" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) + + override def compareExactly: Boolean = true +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqualSpec.scala new file mode 100644 index 00000000000..053e4580b30 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqualSpec.scala @@ -0,0 +1,27 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class LessEqualSpec extends BinaryOpBaseSpec { + override def getOpName: String = "LessEqual" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) + + override def compareExactly: Boolean = true +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1pSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1pSpec.scala new file mode 100644 index 00000000000..95bfe05ba8c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1pSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class Log1pSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Log1p" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmaxSpec.scala new file mode 100644 index 00000000000..7a50befd9d4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmaxSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class LogSoftmaxSpec extends UnaryOpBaseSpec { + override def getOpName: String = "LogSoftmax" + + override def getInput: Tensor[_] = Tensor[Float](4, 32).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSpec.scala new file mode 100644 index 00000000000..23d629e3550 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class LogSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Log" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6GradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6GradSpec.scala new file mode 100644 index 00000000000..abf65de53f9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6GradSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class Relu6GradSpec extends BinaryOpBaseSpec { + override def getOpName: String = "Relu6Grad" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Spec.scala new file mode 100644 index 00000000000..5bda9524a61 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Spec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class Relu6Spec extends UnaryOpBaseSpec { + override def getOpName: String = "Relu6" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGradSpec.scala new file mode 100644 index 00000000000..dd281f44c38 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGradSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class SigmoidGradSpec extends BinaryOpBaseSpec { + override def getOpName: String = "SigmoidGrad" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGradSpec.scala new file mode 100644 index 00000000000..62cb7ff350a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGradSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class SoftplusGradSpec extends BinaryOpBaseSpec { + override def getOpName: String = "SoftplusGrad" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusSpec.scala new file mode 100644 index 00000000000..19052769d80 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class SoftplusSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Softplus" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGradSpec.scala new file mode 100644 index 00000000000..b8b58ce5147 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGradSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class SoftsignGradSpec extends BinaryOpBaseSpec { + override def getOpName: String = "SoftsignGrad" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignSpec.scala new file mode 100644 index 00000000000..7a57ac3c39c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor + +class SoftsignSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Softsign" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifferenceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifferenceSpec.scala new file mode 100644 index 00000000000..7fb40d27add --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifferenceSpec.scala @@ -0,0 +1,26 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor + +class SquaredDifferenceSpec extends BinaryOpBaseSpec { + + override def getOpName: String = "SquaredDifference" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](4, 32, 32, 3).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala new file mode 100644 index 00000000000..871616b47d3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +abstract class UnaryOpBaseSpec extends TensorflowSpecHelper { + + def getOpName: String + + def getInput: Tensor[_] + + s"$getOpName forward float" should "be correct" in { + compare( + NodeDef.newBuilder() + .setName(s"${getOpName}Test") + .setOp(s"$getOpName") + .putAttr("T", typeAttr(DataType.DT_FLOAT)), + Seq(getInput), + 0 + ) + } + +} From a1a5c708b61bf3401fde0e2fc9f6e4a055d97bec Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 3 Nov 2017 15:48:01 +0800 Subject: [PATCH 0507/1065] fix L1Regularization (#1826) --- .../bigdl/dllib/optim/Regularizer.scala | 9 ++++-- .../dllib/optim/DistriOptimizerSpec.scala | 28 +++++++++++++++++++ 2 files changed, 34 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Regularizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Regularizer.scala index 5fa0e3464ed..15f153984c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Regularizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Regularizer.scala @@ -134,11 +134,14 @@ class L1L2Regularizer[T: ClassTag]( gradParameter: Tensor[T], scale: Double ): Unit = { - if (alpha != 0 && scale != 0) gradParameter.add(ev.fromType(alpha*scale), - l1SignBuffer.resizeAs(parameter).copy(parameter).sign()) + if (alpha != 0 && scale != 0) { + if (null == l1SignBuffer) l1SignBuffer = Tensor() + gradParameter.add(ev.fromType(alpha*scale), + l1SignBuffer.resizeAs(parameter).copy(parameter).sign()) + } } - @transient private val l1SignBuffer = Tensor() + @transient private var l1SignBuffer: Tensor[T] = null /** * Accumulates the gradient of the l2 regularization of `parameter` diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index 4208498da5c..bdd17842719 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -568,4 +568,32 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] result2(Array(1)) should be(1.0 +- 5e-2) } + + "Train with L1Regularization" should "work properly in DistriOptimizer" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) + + RandomGenerator.RNG.setSeed(10) + val logdir = com.google.common.io.Files.createTempDir() + val mm = Sequential[Double]().add(Linear(4, 2, + wRegularizer = L1Regularizer(1), bRegularizer = L1Regularizer(1))) + .add(Sigmoid()) + .add(Linear(2, 1)) + .add(Sigmoid()) + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizer[Double]( + _model = mm, + dataset = dataSet, + criterion = new MSECriterion[Double]() + ) + + val optimMethod = new SGD[Double](learningRate = 20.0) + + optimizer.setOptimMethod(optimMethod) + .setEndWhen(Trigger.severalIteration(10)) + optimizer.setValidation(Trigger.everyEpoch, dataSet, + Array(new Top1Accuracy[Double]())) + val model = optimizer.optimize() + } } From fac53b2c0ed86eb91206f33258cb8832081ff46a Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 3 Nov 2017 16:47:38 +0800 Subject: [PATCH 0508/1065] Fix SmoothL1Criterion and SoftmaxWithCriterion (#1830) --- .../nn/SmoothL1CriterionWithWeights.scala | 40 ++++++++++--------- .../bigdl/dllib/nn/SoftmaxWithCriterion.scala | 6 ++- .../nn/SmoothL1CriterionWithWeightsSpec.scala | 22 ++++++++-- 3 files changed, 44 insertions(+), 24 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SmoothL1CriterionWithWeights.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SmoothL1CriterionWithWeights.scala index ae348ea73f2..3e413bbedc4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SmoothL1CriterionWithWeights.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SmoothL1CriterionWithWeights.scala @@ -42,7 +42,7 @@ class SmoothL1CriterionWithWeights[@specialized(Float, Double) T: ClassTag] @transient var buffer: Tensor[T] = _ // diff holds (input - gt) * w_in @transient var diff: Tensor[T] = _ - @transient val sigma2 = sigma * sigma + val sigma2 = sigma * sigma @transient var hasWeights = true override def updateOutput(input: Tensor[T], target: Table): T = { @@ -86,16 +86,17 @@ class SmoothL1CriterionWithWeights[@specialized(Float, Double) T: ClassTag] // |input - gt| * w_in buffer.resizeAs(diff).copy(diff).abs() val data = buffer.storage().array() + val dataOffset = buffer.storageOffset() - 1 var i = 0 - while (i < data.length) { + while (i < buffer.nElement()) { // f(x) = 0.5 * (sigma * x)^2 if |x| < 1 / sigma / sigma // |x| - 0.5 / sigma / sigma otherwise - if (ev.isGreater(ev.fromType(1.0 / sigma2), data(i))) { - data(i) = ev.times(ev.fromType[Double](sigma2), - ev.times(ev.fromType(0.5), ev.times(data(i), data(i)))) + if (ev.isGreater(ev.fromType(1.0 / sigma2), data(dataOffset + i))) { + data(dataOffset + i) = ev.times(ev.fromType[Double](sigma2), + ev.times(ev.fromType(0.5), ev.times(data(dataOffset + i), data(dataOffset + i)))) } else { - data(i) = ev.minus(data(i), ev.fromType[Double](0.5 / sigma2)) + data(dataOffset + i) = ev.minus(data(dataOffset + i), ev.fromType[Double](0.5 / sigma2)) } i += 1 } @@ -135,32 +136,33 @@ class SmoothL1CriterionWithWeights[@specialized(Float, Double) T: ClassTag] s"outsideW ${outsideW.nElement()}") } val data = diff.storage().array() + val dataOffset = diff.storageOffset() - 1 var i = 0 - while (i < data.length) { + while (i < diff.nElement()) { // f'(x) = sigma * sigma * x if |x| < 1 / sigma / sigma // = sign(x) - val x = data(i) + val x = data(dataOffset + i) if (ev.isGreater(ev.fromType[Double](1.0 / sigma2), ev.abs(x))) { - data(i) = ev.times(ev.fromType[Double](sigma2), x) + data(dataOffset + i) = ev.times(ev.fromType[Double](sigma2), x) } else { // sign(x) == (0 0) { - ev.fromType(1.0 / num) + + gradInput.resizeAs(diff).copy(diff) + if (num > 0) { + gradInput.div(ev.fromType(num)) } else { - ev.fromType(input.size(1)) + gradInput.div(ev.fromType(input.size(1))) } - - gradInput.resizeAs(diff).copy(diff).mul(alpha) if (hasWeights) { // scale by inside weight gradInput.cmul(insideW) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftmaxWithCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftmaxWithCriterion.scala index 09d9c476851..d436f06e6cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftmaxWithCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftmaxWithCriterion.scala @@ -73,8 +73,10 @@ class SoftmaxWithCriterion[@specialized(Float, Double) T: ClassTag](ignoreLabel: if (ignoreLabel.isEmpty || ignoreLabel.get != curTarget) { assert(curTarget >= 1 && curTarget <= nClasses, s"curTarget $curTarget is out of range 1 to ${ nClasses } ") - loss = ev.minus(loss, - ev.log(probData(i * dim + (curTarget - 1) * innerNum + j))) + // avoid log(0) + val prob = ev.max(probData(i * dim + (curTarget - 1) * innerNum + j), + ev.fromType(Double.MinPositiveValue)) + loss = ev.minus(loss, ev.log(prob)) count = count + 1 } j += 1 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SmoothL1CriterionWithWeightsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SmoothL1CriterionWithWeightsSpec.scala index 157b5736a94..0d523cf88df 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SmoothL1CriterionWithWeightsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SmoothL1CriterionWithWeightsSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import breeze.numerics.abs import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{T, Table} import org.scalatest.{FlatSpec, Matchers} class SmoothL1CriterionWithWeightsSpec extends FlatSpec with Matchers { @@ -91,8 +91,8 @@ class SmoothL1CriterionWithWeightsSpec extends FlatSpec with Matchers { "SmoothL1CriterionWithWeights with sigma 1 and without weights" should "have the same result as SmoothL1Criterion" in { val targetNoWeight = Tensor(Storage(targetArr.map(x => x.toFloat))) - val smcod = new SmoothL1CriterionWithWeights[Float](1f, input.nElement()) - val smc = new SmoothL1Criterion[Float](true) + val smcod = SmoothL1CriterionWithWeights[Float](1f, input.nElement()) + val smc = SmoothL1Criterion[Float](true) val out1 = smcod.forward(input, new Table().insert(targetNoWeight)) val out2 = smc.forward(input, targetNoWeight) assert(abs(out1 - out2) < 1e-6) @@ -104,4 +104,20 @@ class SmoothL1CriterionWithWeightsSpec extends FlatSpec with Matchers { v1 }) } + + "a SmoothL1CriterionWithWeights of object detection with num==0" should + "generate correct loss and grad" in { + val label = T() + label.insert(target(1)) + val smcod = SmoothL1CriterionWithWeights[Float](1f) + val smc = SmoothL1Criterion[Float](true) + smcod.forward(input, label) + smc.forward(input, target(1)) + + smcod.output should be(smc.output) + smcod.backward(input, label) + smc.backward(input, target(1)) + + smc.gradInput should be (smcod.gradInput) + } } From 31ccbf5518e83ad956c448a7b05b40713c4450b2 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 6 Nov 2017 00:24:49 -0500 Subject: [PATCH 0509/1065] fix: change mkl threads to user defined. (#1834) --- .../main/scala/com/intel/analytics/bigdl/utils/Engine.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index d4776bf37a3..ca478bb8524 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -140,7 +140,7 @@ object Engine { @volatile private var _default: ThreadPool = null // Thread pool for layer use - @volatile private var _model: ThreadPool = new ThreadPool(1).setMKLThread(MKL.getNumThreads) + @volatile private var _model: ThreadPool = new ThreadPool(1).setMKLThread(MKL.getMklNumThreads) /** * If user undefine the property bigdl.coreNumber, it will return physical core number @@ -253,7 +253,7 @@ object Engine { if(_model == null || _model.getPoolSize != modelPoolSize) { _model = new ThreadPool(modelPoolSize) - _model.setMKLThread(MKL.getNumThreads) + _model.setMKLThread(MKL.getMklNumThreads) } } From 6a9346b5d6133a076f185364bec4819f9d29f64b Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 6 Nov 2017 14:27:20 +0800 Subject: [PATCH 0510/1065] Add square related ops (#1823) * add square related ops * use another type parameter * fix error message --- .../analytics/bigdl/dllib/nn/Power.scala | 37 ++++++++-------- .../dllib/nn/SpatialWithinChannelLRN.scala | 4 +- .../intel/analytics/bigdl/dllib/nn/Sqrt.scala | 9 ++-- .../analytics/bigdl/dllib/nn/Square.scala | 9 ++-- .../bigdl/dllib/nn/ops/RsqrtGrad.scala | 40 ++++++++++++++++++ .../bigdl/dllib/nn/ops/SqrtGrad.scala | 40 ++++++++++++++++++ .../bigdl/dllib/utils/caffe/Converter.scala | 6 +-- .../dllib/utils/python/api/PythonBigDL.scala | 12 +++--- .../bigdl/dllib/utils/tf/loaders/Rsqrt.scala | 12 +++++- .../dllib/utils/tf/loaders/RsqrtGrad.scala | 42 +++++++++++++++++++ .../bigdl/dllib/utils/tf/loaders/Sqrt.scala | 42 +++++++++++++++++++ .../dllib/utils/tf/loaders/SqrtGrad.scala | 41 ++++++++++++++++++ .../bigdl/dllib/utils/tf/loaders/Square.scala | 42 +++++++++++++++++++ .../analytics/bigdl/dllib/nn/PowerSpec.scala | 14 +++---- .../bigdl/dllib/torch/PowerSpec.scala | 4 +- .../bigdl/dllib/torch/SqrtSpec.scala | 8 ++-- .../bigdl/dllib/torch/SquareSpec.scala | 8 ++-- .../utils/tf/loaders/RsqrtGradSpec.scala | 26 ++++++++++++ .../dllib/utils/tf/loaders/SqrtGradSpec.scala | 25 +++++++++++ .../dllib/utils/tf/loaders/SqrtSpec.scala | 24 +++++++++++ .../dllib/utils/tf/loaders/SquareSpec.scala | 24 +++++++++++ 21 files changed, 413 insertions(+), 56 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sqrt.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Square.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquareSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala index e72bee6bd40..aef00324a1d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -33,32 +33,33 @@ import scala.reflect.ClassTag */ @SerialVersionUID(- 6637789603381436472L) -class Power[T: ClassTag]( +class Power[T: ClassTag, D: ClassTag]( val power: Double, val scale : Double = 1, val shift : Double = 0) -(implicit ev: TensorNumeric[T]) extends TensorModule[T] { +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends AbstractModule[Tensor[D], Tensor[D], T] { val diffScale = power * scale - override def updateOutput(input: Tensor[T]): Tensor[T] = { + override def updateOutput(input: Tensor[D]): Tensor[D] = { output.resizeAs(input) output.copy(input) if(scale != 1) { - output.mul(ev.fromType[Double](scale)) + output.mul(ev2.fromType[Double](scale)) } if(shift != 0) { - output.add(ev.fromType[Double](shift)) + output.add(ev2.fromType[Double](shift)) } if(power != 1) { - output.pow(output, ev.fromType[Double](power)) + output.pow(output, ev2.fromType[Double](power)) } output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { gradInput.resizeAs(input) // Compute dy/dx = scale * power * (shift + scale * x)^(power - 1) // = diff_scale * y / (shift + scale * x) @@ -67,28 +68,28 @@ class Power[T: ClassTag]( // -> dy/dx = 2 * scale * (shift + scale * x) // = diff_scale * shift + diff_scale * scale * x gradInput.copy(input) - gradInput.mul(ev.fromType[Double](diffScale * scale)) + gradInput.mul(ev2.fromType[Double](diffScale * scale)) if(shift != 0) { - gradInput.add(ev.fromType(diffScale * shift)) + gradInput.add(ev2.fromType(diffScale * shift)) } } else if (shift == 0) { // Special case for y = (scale * x)^power // -> dy/dx = scale * power * (scale * x)^(power - 1) // = scale * power * (scale * x)^power * (scale * x)^(-1) // = power * y / x - gradInput.fill(ev.fromType[Int](0)) - gradInput.addcdiv(ev.fromType[Double](power), output, input) + gradInput.fill(ev2.fromType[Int](0)) + gradInput.addcdiv(ev2.fromType[Double](power), output, input) } else { gradInput.copy(input) if(scale != 1) { - gradInput.mul(ev.fromType[Double](scale)) + gradInput.mul(ev2.fromType[Double](scale)) } if(shift != 0) { - gradInput.add(ev.fromType[Double](shift)) + gradInput.add(ev2.fromType[Double](shift)) } gradInput.cdiv(output, gradInput) if (diffScale != 1) { - gradInput.mul(ev.fromType[Double](diffScale)) + gradInput.mul(ev2.fromType[Double](diffScale)) } } if(diffScale != 0) { @@ -105,10 +106,10 @@ class Power[T: ClassTag]( } object Power { - def apply[@specialized(Float, Double) T: ClassTag]( + def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( power: Double, scale : Double = 1, - shift : Double = 0)(implicit ev: TensorNumeric[T]) : Power[T] = { - new Power[T](power, scale, shift) + shift : Double = 0)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Power[T, D] = { + new Power[T, D](power, scale, shift) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRN.scala index 07b5312a39a..bc0195c340a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRN.scala @@ -41,10 +41,10 @@ class SpatialWithinChannelLRN[T: ClassTag] .add(new ConcatTable[T]() .add(Identity[T]()) .add(Sequential[T]() - .add(Power[T](2)) + .add(Power[T, T](2)) .add(SpatialAveragePooling[T](size, size, padW = (size - 1) / 2, padH = (size - 1) / 2).ceil()) - .add(Power[T](-beta, alpha, 1)))) + .add(Power[T, T](-beta, alpha, 1)))) .add(CMulTable[T]()) override def updateOutput(input: Tensor[T]): Tensor[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sqrt.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sqrt.scala index db67fda2b54..a27a4dcfce6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sqrt.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sqrt.scala @@ -24,12 +24,13 @@ import scala.reflect.ClassTag */ @SerialVersionUID(223597921741020277L) -class Sqrt[T: ClassTag](implicit ev: TensorNumeric[T]) extends Power[T](0.5, 1, 0) { +class Sqrt[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Power[T, D](0.5, 1, 0) { } object Sqrt { - def apply[@specialized(Float, Double) T: ClassTag]() - (implicit ev: TensorNumeric[T]) : Sqrt[T] = { - new Sqrt[T]() + def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Sqrt[T, D] = { + new Sqrt[T, D]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Square.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Square.scala index edf4c00e3f2..d949c3a3656 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Square.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Square.scala @@ -24,12 +24,13 @@ import scala.reflect.ClassTag */ @SerialVersionUID(5169592189338322411L) -class Square[T: ClassTag](implicit ev: TensorNumeric[T]) extends Power[T](2, 1, 0) { +class Square[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Power[T, D](2, 1, 0) { } object Square { - def apply[@specialized(Float, Double) T: ClassTag]() - (implicit ev: TensorNumeric[T]) : Square[T] = { - new Square[T]() + def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Square[T, D] = { + new Square[T, D]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala new file mode 100644 index 00000000000..92f56de89d1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class RsqrtGrad[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T] { + + override def updateOutput(inputs: Table): Tensor[D] = { + val grads = inputs[Tensor[D]](2) + val y = inputs[Tensor[D]](1) + + output.resizeAs(y).copy(y).pow(ev2.fromType(3.0)).mul(ev2.fromType(-0.5f)).cmul(grads) + + output + } +} + +object RsqrtGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): RsqrtGrad[T, D] = new RsqrtGrad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala new file mode 100644 index 00000000000..5bee1b6f9f0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class SqrtGrad[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T] { + + override def updateOutput(inputs: Table): Tensor[D] = { + val grads = inputs[Tensor[D]](2) + val y = inputs[Tensor[D]](1) + + output.resizeAs(grads).copy(grads).mul(ev2.fromType(0.5)).div(y) + + output + } +} + +object SqrtGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SqrtGrad[T, D] = new SqrtGrad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index 632e6a699f5..d0a3aaf83ae 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -184,7 +184,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { var shift = 0.0 if (param.hasScale) scale = param.getScale if (param.hasShift) shift = param.getShift - Seq(Power[T](power, scale, shift).setName(layerName).inputs()) + Seq(Power[T, T](power, scale, shift).setName(layerName).inputs()) } private def fromCaffePreLU(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -311,7 +311,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { case elu : ELU[_, _] => toCaffeElu(moduleNode, bottoms, nextSize) case infershape : InferReshape[_] => toCaffeFlattern(moduleNode, bottoms, nextSize) case log : Log[_, _] => toCaffeLog(moduleNode, bottoms, nextSize) - case power : Power[_] => toCaffePower(moduleNode, bottoms, nextSize) + case power : Power[_, _] => toCaffePower(moduleNode, bottoms, nextSize) case prelu : PReLU[_] => toCaffePReLu(moduleNode, bottoms, nextSize) case recurrent : Recurrent[_] => toCaffeRecurrent(moduleNode, bottoms, nextSize) case reshape : Reshape[_] => toCaffeReshape(moduleNode, bottoms, nextSize) @@ -551,7 +551,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { protected def toCaffePowerParam(module : AbstractModule[Activity, Activity, T]) : PowerParameter = { val powerParameter = PowerParameter.newBuilder - val layer = classOf[Power[T]].cast(module) + val layer = classOf[Power[T, T]].cast(module) powerParameter.setPower(layer.power.toFloat) powerParameter.setScale(layer.scale.toFloat) powerParameter.setShift(layer.shift.toFloat) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 7d0ab5d7484..ae8d7d0d23b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -858,8 +858,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createPower(power: Double, scale: Double = 1, shift: Double = 0) - : Power[T] = { - Power[T](power, + : Power[T, T] = { + Power[T, T](power, scale, shift) } @@ -1142,13 +1142,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSqrt() - : Sqrt[T] = { - Sqrt[T]() + : Sqrt[T, T] = { + Sqrt[T, T]() } def createSquare() - : Square[T] = { - Square[T]() + : Square[T, T] = { + Square[T, T]() } def createSqueeze(dim: Int = Int.MinValue, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala index 32b15e59aef..96579660eb0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala @@ -18,11 +18,12 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.RsqrtGrad import com.intel.analytics.bigdl.nn.{Identity, Power} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context -import org.tensorflow.framework.NodeDef +import org.tensorflow.framework.{DataType, NodeDef} import scala.reflect.ClassTag @@ -32,6 +33,13 @@ class Rsqrt extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Power[T](-0.5, 1, 0) + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Power[T, Float](-0.5, 1, 0) + } else if (t == DataType.DT_DOUBLE) { + Power[T, Double](-0.5, 1, 0) + } else { + throw new UnsupportedOperationException(s"Not support load Rsqrt when type is $t") + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala new file mode 100644 index 00000000000..a98b4bf04ea --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.RsqrtGrad +import com.intel.analytics.bigdl.nn.tf.Log1p +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class RsqrtGrad extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + RsqrtGrad[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + RsqrtGrad[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load RsqrtGrad when type is $t") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sqrt.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sqrt.scala new file mode 100644 index 00000000000..f1ce77d0d30 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sqrt.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Power +import com.intel.analytics.bigdl.nn.ops.RsqrtGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Sqrt extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Power[T, Float](0.5) + } else if (t == DataType.DT_DOUBLE) { + Power[T, Double](0.5) + } else { + throw new UnsupportedOperationException(s"Not support load Sqrt when type is $t") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala new file mode 100644 index 00000000000..feb913fe96f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{RsqrtGrad, SqrtGrad} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class SqrtGrad extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + SqrtGrad[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + SqrtGrad[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load SqrtGrad when type is $t") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Square.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Square.scala new file mode 100644 index 00000000000..ef2efc38337 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Square.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Power +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Square extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Power[T, Float](2.0) + } else if (t == DataType.DT_DOUBLE) { + Power[T, Double](2.0) + } else { + throw new UnsupportedOperationException(s"Not support load Square when type is $t") + } + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala index a770321de60..a609944780b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala @@ -26,7 +26,7 @@ class PowerSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(1.0, 4, 9, 16, 25, 36)), 1, Array(2, 3)) - val power = new Power[Double](2) + val power = new Power[Double, Double](2) val powerOutput = power.forward(input) @@ -38,7 +38,7 @@ class PowerSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(4.0, 16, 36, 64, 100, 144)), 1, Array(2, 3)) - val power = new Power[Double](2, 2) + val power = new Power[Double, Double](2, 2) val powerOutput = power.forward(input) @@ -50,7 +50,7 @@ class PowerSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(1.0, 4, 9, 16, 25, 36)), 1, Array(2, 3)) - val power = new Power[Double](2, 1, 1) + val power = new Power[Double, Double](2, 1, 1) val powerOutput = power.forward(input) @@ -62,7 +62,7 @@ class PowerSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(1.0, 9, 25, 49, 81, 121)), 1, Array(2, 3)) - val power = new Power[Double](2, 2, 1) + val power = new Power[Double, Double](2, 2, 1) val powerOutput = power.forward(input) @@ -74,7 +74,7 @@ class PowerSpec extends FlatSpec with Matchers { val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) - val power = new Power[Double](2, 2, 2) + val power = new Power[Double, Double](2, 2, 2) val output = power.forward(input) val gradInput = power.backward(input, gradOutput) @@ -89,7 +89,7 @@ class PowerSpec extends FlatSpec with Matchers { val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) - val power = new Power[Double](1, -1) + val power = new Power[Double, Double](1, -1) val output = power.forward(input) val gradInput = power.backward(input, gradOutput) @@ -104,7 +104,7 @@ class PowerSpec extends FlatSpec with Matchers { val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) - val power = new Power[Double](3, 2, 2) + val power = new Power[Double, Double](3, 2, 2) val output = power.forward(input) val gradInput = power.backward(input, gradOutput) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala index 345ca2e0182..eca00dbdd0b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.tensor.Tensor class PowerSpec extends TorchSpec { "A Power(2)" should "generate correct output and grad" in { torchCheck() - val layer = new Power[Double](2) + val layer = new Power[Double, Double](2) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = 1 input(Array(1, 1, 2)) = 2 @@ -66,7 +66,7 @@ class PowerSpec extends TorchSpec { "A Power(3)" should "generate correct output and grad" in { torchCheck() - val layer = new Power[Double](3) + val layer = new Power[Double, Double](3) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = 1 input(Array(1, 1, 2)) = 2 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala index c1ead4637d7..b2b09c05cdf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala @@ -25,7 +25,7 @@ import scala.util.Random class SqrtSpec extends TorchSpec { "A Sqrt 1D input" should "generate correct output and grad" in { torchCheck() - val layer = new Sqrt[Double]() + val layer = new Sqrt[Double, Double]() val input = Tensor[Double](10) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](10) @@ -54,7 +54,7 @@ class SqrtSpec extends TorchSpec { "A Sqrt 2D input" should "generate correct output and grad" in { torchCheck() - val layer = new Sqrt[Double]() + val layer = new Sqrt[Double, Double]() val input = Tensor[Double](3, 5) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](3, 5) @@ -83,7 +83,7 @@ class SqrtSpec extends TorchSpec { "A Sqrt 3D input" should "generate correct output and grad" in { torchCheck() - val layer = new Sqrt[Double]() + val layer = new Sqrt[Double, Double]() val input = Tensor[Double](4, 6, 6) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](4, 6, 6) @@ -112,7 +112,7 @@ class SqrtSpec extends TorchSpec { "A Sqrt 4D input" should "generate correct output and grad" in { torchCheck() - val layer = new Sqrt[Double]() + val layer = new Sqrt[Double, Double]() val input = Tensor[Double](3, 5, 6, 6) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](3, 5, 6, 6) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala index 569f44dab2a..c4842ea4a12 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala @@ -25,7 +25,7 @@ import scala.util.Random class SquareSpec extends TorchSpec { "A Square 1D input" should "generate correct output and grad" in { torchCheck() - val layer = new Square[Double]() + val layer = new Square[Double, Double]() val input = Tensor[Double](10) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](10) @@ -54,7 +54,7 @@ class SquareSpec extends TorchSpec { "A Square 2D input" should "generate correct output and grad" in { torchCheck() - val layer = new Square[Double]() + val layer = new Square[Double, Double]() val input = Tensor[Double](3, 5) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](3, 5) @@ -83,7 +83,7 @@ class SquareSpec extends TorchSpec { "A Square 3D input" should "generate correct output and grad" in { torchCheck() - val layer = new Square[Double]() + val layer = new Square[Double, Double]() val input = Tensor[Double](4, 6, 6) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](4, 6, 6) @@ -112,7 +112,7 @@ class SquareSpec extends TorchSpec { "A Square 4D input" should "generate correct output and grad" in { torchCheck() - val layer = new Square[Double]() + val layer = new Square[Double, Double]() val input = Tensor[Double](3, 5, 6, 6) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](3, 5, 6, 6) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGradSpec.scala new file mode 100644 index 00000000000..6bddce8b986 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGradSpec.scala @@ -0,0 +1,26 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class RsqrtGradSpec extends BinaryOpBaseSpec { + override def getOpName: String = "RsqrtGrad" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGradSpec.scala new file mode 100644 index 00000000000..b5ad63d6f7d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGradSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class SqrtGradSpec extends BinaryOpBaseSpec { + override def getOpName: String = "SqrtGrad" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtSpec.scala new file mode 100644 index 00000000000..dd5425140b1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor + +class SqrtSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Sqrt" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquareSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquareSpec.scala new file mode 100644 index 00000000000..5cedd217a81 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquareSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor + +class SquareSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Square" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} From 67c46fd5a74fabaadf05e6e80017213cefaad25c Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 6 Nov 2017 19:37:28 +0800 Subject: [PATCH 0511/1065] Add Maximum, Minimum, BatchMatMul (#1828) * add ops * fix style --- .../bigdl/dllib/nn/ops/BatchMatMul.scala | 101 ++++++++++++++ .../bigdl/dllib/nn/ops/Maximum.scala | 41 ++++++ .../bigdl/dllib/nn/ops/Minimum.scala | 42 ++++++ .../bigdl/dllib/tensor/DenseTensor.scala | 4 + .../bigdl/dllib/tensor/DenseTensorMath.scala | 15 ++ .../tensor/QuantizedTensorUnsupported.scala | 20 +++ .../bigdl/dllib/tensor/SparseTensor.scala | 8 ++ .../bigdl/dllib/tensor/TensorMath.scala | 18 +++ .../bigdl/dllib/tensor/TensorNumeric.scala | 16 +++ .../dllib/utils/tf/loaders/BatchMatMul.scala | 46 +++++++ .../dllib/utils/tf/loaders/Maximum.scala | 41 ++++++ .../dllib/utils/tf/loaders/Minimum.scala | 41 ++++++ .../utils/tf/loaders/BatchMatMulSpec.scala | 129 ++++++++++++++++++ .../dllib/utils/tf/loaders/MaximumSpec.scala | 25 ++++ .../dllib/utils/tf/loaders/MinimumSpec.scala | 25 ++++ 15 files changed, 572 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMul.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Maximum.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Minimum.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMulSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaximumSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MinimumSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala new file mode 100644 index 00000000000..5bfcb39ff26 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala @@ -0,0 +1,101 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + +/** + * Multiplies all slices of `Tensor` `x` and `y` (each slice can be + * viewed as an element of a batch), and arranges the individual results + * in a single output tensor of the same batch size. Each of the + * individual slices can optionally be adjointed (to adjoint a matrix + * means to transpose and conjugate it) before multiplication by setting + * the `adj_x` or `adj_y` flag to `True`, which are by default `False`. + * + */ + +class BatchMatMul[T: ClassTag, D: ClassTag]( + val adjX: Boolean = false, + val adjY: Boolean = false) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T] { + gradInput = T(Tensor[D], Tensor[D]()) + + override def updateOutput(input: Table): Tensor[D] = { + var x: Tensor[D] = input(1) + var y: Tensor[D] = input(2) + + require(x.dim() == y.dim(), "tensor x and tensor y must have the same number of dims") + require(x.dim() >= 2, "tensor dim num must be at least 2") + + if (x.dim() == 2) { + require(y.dim() == 2, "second input tensor must be 2D" + + s"second input dim ${y.dim()}") + + if (adjX) { + x = x.t() + } + if (adjY) { + y = y.t() + } + require(x.size(2) == y.size(1), "matrix sizes do not match" + + s"The sizes are ${x.size(2)} and ${y.size(1)}") + + output.resize(x.size(1), y.size(2)) + output.mm(x, y) + } else { + + require(x.size(1) == y.size(1), "inputs must contain the same number of minibatches" + + s"The minibatces of each are ${x.size(1)} and ${y.size(1)}") + + val dimNum = x.dim() + + val batchSize = x.size().slice(0, dimNum - 2).product + + var reshapedX = x.view(Array(batchSize, x.size(dimNum - 1), x.size(dimNum))) + var reshapedY = y.view(Array(batchSize, y.size(dimNum - 1), y.size(dimNum))) + + if (adjX) { + reshapedX = reshapedX.transpose(2, 3) + } + if (adjY) { + reshapedY = reshapedY.transpose(2, 3) + } + require(reshapedX.size(2) == reshapedY.size(3), "matrix sizes do not match" + + s"the matrix sizes are ${reshapedX.size(2)} and ${reshapedY.size(3)}") + + output.resize(batchSize, reshapedX.size(2), reshapedY.size(3)) + output.bmm(reshapedX, reshapedY) + val outputSize = x.size().slice(0, dimNum - 2) ++ Array(reshapedX.size(2), reshapedY.size(3)) + output.resize(outputSize) + } + + output + } +} + +object BatchMatMul { + def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( + adjX: Boolean = false, + adjY: Boolean = false) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): BatchMatMul[T, D] = { + new BatchMatMul[T, D](adjX, adjY) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala new file mode 100644 index 00000000000..467af5b080f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Maximum[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T] { + override def updateOutput(input: Table): Tensor[D] = { + val x = input[Tensor[D]](1) + val y = input[Tensor[D]](2) + + require(x.size().sameElements(y.size()), "require the shape of x, y to be the same") + + output.resizeAs(x).cmax(x, y) + } +} + +object Maximum { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Maximum[T, D] = + new Maximum[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala new file mode 100644 index 00000000000..78ecff68b68 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Minimum[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T] { + override def updateOutput(input: Table): Tensor[D] = { + val x = input[Tensor[D]](1) + val y = input[Tensor[D]](2) + + require(x.size().sameElements(y.size()), "require the shape of x, y to be the same") + + output.resizeAs(x).cmin(x, y) + } +} + +object Minimum { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Minimum[T, D] = + new Minimum[T, D]() +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 2a1fb8e2f74..061ed439b27 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -1160,6 +1160,10 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( */ override def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T] = DenseTensorMath.cmax(this, x, y) + override def cmin(y: Tensor[T]): Tensor[T] = DenseTensorMath.cmin(this, this, y) + + override def cmin(x: Tensor[T], y: Tensor[T]): Tensor[T] = DenseTensorMath.cmin(this, x, y) + override def mul(x: Tensor[T], value: T): Tensor[T] = DenseTensorMath.mul(this, x, value) override def mul(value: T): Tensor[T] = DenseTensorMath.mul(this, null, value) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala index d48dc63fef2..03c0355fa58 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala @@ -790,6 +790,21 @@ object DenseTensorMath { Apply.apply3[T](self, x, y, func) self } + def cmin[@specialized(Float, Double) T](self: DenseTensor[T], x: Tensor[T], y: Tensor[T]) + (implicit ev: TensorNumeric[T]): Tensor[T] = { + require(self.nElement() == y.nElement() && self.nElement() == x.nElement(), + "element number doesn't match") + // todo: the performance of contiguous tensor should be optimized + val func = new TensorFunc6[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, + data3: Array[T], offset3: Int): Unit = { + data1(offset1) = ev.min(data2(offset2), data3(offset3)) + } + } + Apply.apply3[T](self, x, y, func) + self + } + val doubleEpsilon = System.getProperty("DoubleTensorEpsilon", "0.0000001").toDouble val floatEpsilon = System.getProperty("FloatTensorEpsilon", "0.00001").toDouble diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index 5431320600f..a57d2cf7a9b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1318,6 +1318,16 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def cmax(y: Tensor[T]): Tensor[T] = throw new UnsupportedOperationException(errorString) + /** + * stores the element-wise maximum of x and y in x. + * x.cmin(y) = min(x, y) + * + * @param y tensor + * @return current tensor + */ + override def cmin(y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + /** * stores the element-wise maximum of x and y in z. * z.cmax(x, y) means z = max(x, y) @@ -1328,6 +1338,16 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T] = throw new UnsupportedOperationException(errorString) + /** + * stores the element-wise maximum of x and y in z. + * z.cmin(x, y) means z = min(x, y) + * + * @param x tensor + * @param y tensor + */ + override def cmin(x: Tensor[T], y: Tensor[T]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + /** * resize this tensor size to floor((xmax - xmin) / step) + 1 and set values from * xmin to xmax with step (default to 1). diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 496e37f61ed..55ea2845536 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -872,6 +872,14 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + override def cmin(y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def cmin(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + override def range(xmin: Double, xmax: Double, step: Int): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala index 3c4325642d9..632e96007dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala @@ -744,6 +744,15 @@ trait TensorMath[T] { */ def cmax(y: Tensor[T]): Tensor[T] + /** + * stores the element-wise maximum of x and y in x. + * x.cmin(y) = min(x, y) + * + * @param y tensor + * @return current tensor + */ + def cmin(y: Tensor[T]): Tensor[T] + /** * stores the element-wise maximum of x and y in z. * z.cmax(x, y) means z = max(x, y) @@ -753,6 +762,15 @@ trait TensorMath[T] { */ def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T] + /** + * stores the element-wise maximum of x and y in z. + * z.cmin(x, y) means z = min(x, y) + * + * @param x tensor + * @param y tensor + */ + def cmin(x: Tensor[T], y: Tensor[T]): Tensor[T] + /** * resize this tensor size to floor((xmax - xmin) / step) + 1 and set values from * xmin to xmax with step (default to 1). diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index fb5347a7390..96f1fa2349a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -69,6 +69,8 @@ object TensorNumericMath { def max(x: T, y: T): T + def min(x: T, y: T): T + def sqrt(x: T): T def tanh(x: T): T @@ -221,6 +223,10 @@ object TensorNumericMath { throw new UnsupportedOperationException(typeName + " in tensor does not support max operation") + def min(x: T, y: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support min operation") + def sqrt(x: T): T = throw new UnsupportedOperationException(typeName + " in tensor does not support sqrt operation") @@ -461,6 +467,8 @@ object TensorNumericMath { override def max(x: Float, y: Float): Float = java.lang.Math.max(x, y) + override def min(x: Float, y: Float): Float = java.lang.Math.min(x, y) + override def sqrt(x: Float): Float = Math.sqrt(x.toDouble).toFloat override def tanh(x: Float): Float = Math.tanh(x.toDouble).toFloat @@ -734,6 +742,8 @@ object TensorNumericMath { override def max(x: Double, y: Double): Double = java.lang.Math.max(x, y) + override def min(x: Double, y: Double): Double = java.lang.Math.min(x, y) + override def sqrt(x: Double): Double = Math.sqrt(x) override def tanh(x: Double): Double = Math.tanh(x) @@ -1060,6 +1070,8 @@ object TensorNumericMath { override def max(x: Int, y: Int): Int = java.lang.Math.max(x, y) + override def min(x: Int, y: Int): Int = java.lang.Math.min(x, y) + override def sqrt(x: Int): Int = Math.sqrt(x.toDouble).toInt override def tanh(x: Int): Int = Math.tanh(x.toDouble).toInt @@ -1145,6 +1157,8 @@ object TensorNumericMath { override def max(x: Long, y: Long): Long = java.lang.Math.max(x, y) + override def min(x: Long, y: Long): Long = java.lang.Math.min(x, y) + override def sqrt(x: Long): Long = Math.sqrt(x.toDouble).toLong override def tanh(x: Long): Long = Math.tanh(x.toDouble).toLong @@ -1216,6 +1230,8 @@ object TensorNumericMath { override def max(x: Short, y: Short): Short = java.lang.Math.max(x, y).toShort + override def min(x: Short, y: Short): Short = java.lang.Math.min(x, y).toShort + override def sqrt(x: Short): Short = Math.sqrt(x.toDouble).toShort override def tanh(x: Short): Short = Math.tanh(x.toDouble).toShort diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMul.scala new file mode 100644 index 00000000000..aa1d237c6c3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMul.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.BatchMatMul +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class BatchMatMul extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + val adjX = getBoolean(nodeDef.getAttrMap, "adj_x") + val adjY = getBoolean(nodeDef.getAttrMap, "adj_y") + if (t == DataType.DT_FLOAT) { + BatchMatMul[T, Float](adjX, adjY) + } else if (t == DataType.DT_DOUBLE) { + BatchMatMul[T, Double](adjX, adjY) + } else { + throw new UnsupportedOperationException(s"Not support load ReLU6 when type is $t") + } + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Maximum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Maximum.scala new file mode 100644 index 00000000000..46982aa4b9f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Maximum.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Maximum +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.{DataType, NodeDef} +import Utils._ +import com.intel.analytics.bigdl.utils.tf.Context + +import scala.reflect.ClassTag + +class Maximum extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Maximum[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Maximum[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Maximum when type is $t") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Minimum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Minimum.scala new file mode 100644 index 00000000000..127285f8822 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Minimum.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Minimum +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.tensorflow.framework.{DataType, NodeDef} +import Utils._ +import com.intel.analytics.bigdl.utils.tf.Context + +import scala.reflect.ClassTag + +class Minimum extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Minimum[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Minimum[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Maximum when type is $t") + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMulSpec.scala new file mode 100644 index 00000000000..2b766fd4aaa --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMulSpec.scala @@ -0,0 +1,129 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class BatchMatMulSpec extends TensorflowSpecHelper { + + "BatchMatMul with two dim forward" should "be correct" in { + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(false)) + .putAttr("adj_y", booleanAttr(false)), + Seq(Tensor[Float](4, 3).rand(), Tensor[Float](3, 4).rand()), + 0 + ) + + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(true)) + .putAttr("adj_y", booleanAttr(true)), + Seq(Tensor[Float](4, 3).rand(), Tensor[Float](3, 4).rand()), + 0 + ) + + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(false)) + .putAttr("adj_y", booleanAttr(true)), + Seq(Tensor[Float](4, 3).rand(), Tensor[Float](4, 3).rand()), + 0 + ) + } + + "BatchMatMul with three dim forward" should "be correct" in { + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(false)) + .putAttr("adj_y", booleanAttr(false)), + Seq(Tensor[Float](4, 3, 2).rand(), Tensor[Float](4, 2, 3).rand()), + 0 + ) + + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(true)) + .putAttr("adj_y", booleanAttr(true)), + Seq(Tensor[Float](4, 3, 2).rand(), Tensor[Float](4, 2, 3).rand()), + 0 + ) + + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(false)) + .putAttr("adj_y", booleanAttr(true)), + Seq(Tensor[Float](4, 3, 2).rand(), Tensor[Float](4, 3, 2).rand()), + 0 + ) + } + + "BatchMatMul with more dim forward" should "be correct" in { + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(false)) + .putAttr("adj_y", booleanAttr(false)), + Seq(Tensor[Float](2, 4, 3, 2).rand(), Tensor[Float](2, 4, 2, 3).rand()), + 0 + ) + + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(true)) + .putAttr("adj_y", booleanAttr(true)), + Seq(Tensor[Float](2, 4, 3, 2).rand(), Tensor[Float](2, 4, 2, 3).rand()), + 0 + ) + + compare( + NodeDef.newBuilder() + .setName("BatchMatMul_test") + .setOp("BatchMatMul") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("adj_x", booleanAttr(false)) + .putAttr("adj_y", booleanAttr(true)), + Seq(Tensor[Float](2, 4, 3, 2).rand(), Tensor[Float](2, 4, 3, 2).rand()), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaximumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaximumSpec.scala new file mode 100644 index 00000000000..4026e61cd5b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaximumSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class MaximumSpec extends BinaryOpBaseSpec { + override def getOpName: String = "Maximum" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MinimumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MinimumSpec.scala new file mode 100644 index 00000000000..37df8d9a95b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MinimumSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class MinimumSpec extends BinaryOpBaseSpec { + override def getOpName: String = "Maximum" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} From cdfb34641f891f0690ac305ac96ad749c228b2f3 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 7 Nov 2017 15:42:14 +0800 Subject: [PATCH 0512/1065] use shade to package jar-with-dependencies (#1839) * use shade to package jar-with-dependencies * ajust incident --- dl/pom.xml | 37 +++++++++++++++++++------------------ 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/dl/pom.xml b/dl/pom.xml index faa6ab9980e..b7ee8ffcbc5 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -167,11 +167,6 @@ maven-shade-plugin 3.0.0 - - - com.google.protobuf - - com.google.protobuf @@ -189,30 +184,36 @@ + shade package shade + + + + com.google.protobuf + + + - - - - - org.apache.maven.plugins - maven-assembly-plugin - without-spark - - - jar-with-dependencies - - package - single + shade + + true + jar-with-dependencies + + + + + org.apache.maven.plugins + maven-assembly-plugin + with-spark From d9d9fe00b105146fa204863628dacfa6e55c3724 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Tue, 7 Nov 2017 20:37:22 -0600 Subject: [PATCH 0513/1065] Expose local optimizer and predictor (#1689) * local refactor update update * nupdate * support list of input * add unit-test * update * style * compile * fix * fix --- .../bigdl/dllib/optim/LocalOptimizer.scala | 2 +- .../bigdl/dllib/optim/LocalPredictor.scala | 10 +- .../bigdl/dllib/utils/LocalModule.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 165 +++++++++++++----- .../bigdl/dllib/python/api/PythonSpec.scala | 48 ++++- 5 files changed, 169 insertions(+), 58 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index ba96007322c..c2b49a7d5dc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -38,7 +38,7 @@ object LocalOptimizer { * @param dataset data set * @param criterion criterion to be used */ -class LocalOptimizer[T: ClassTag] private[optim]( +class LocalOptimizer[T: ClassTag] ( model: Module[T], dataset: LocalDataSet[MiniBatch[T]], criterion: Criterion[T] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index e6c6374ac91..38523ded38c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -17,12 +17,12 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample, SampleToBatch} +import com.intel.analytics.bigdl.dataset._ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Engine, MklBlas} - +import com.intel.analytics.bigdl.dataset.SampleToMiniBatch import scala.reflect.ClassTag @@ -102,8 +102,8 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: def predict(dataSet: Array[Sample[T]]): Array[Activity] = { val iter = dataSet.iterator - val transformer = SampleToBatch[T]( - batchSize = batchPerCore * subModelNumber, None, None, None, + val transformer = SampleToMiniBatch[T]( + batchSize = batchPerCore * subModelNumber, None, None, partitionNum = Some(1)) val dataIter = transformer(iter) @@ -126,7 +126,7 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: val currentMiniBatch = batch.slice(offset, length) val input = currentMiniBatch.getInput() val output = workingModels(b).forward(input).toTensor[T] - output + output.clone() } ) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala index bab9ede9add..849768cd0f0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala @@ -53,7 +53,7 @@ object LocalModule { def apply[T: ClassTag](model: Module[T]) (implicit ev: TensorNumeric[T]): LocalModule[T] = { - val weightsBias = getAndClearWeightBias(model.parameters()) + val weightsBias = getAndClearWeightBias(model.cloneModule().parameters()) new LocalModule[T](model, weightsBias) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ae8d7d0d23b..8acff1455cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -184,21 +184,62 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def testSample(sample: Sample): Sample = { - val jsample = toSample(sample) + val jsample = toJSample(sample) toPySample(jsample) } - def toSample(record: Sample): JSample[T] = { + def toJSample(record: Sample): JSample[T] = { require(record.bigdlType == this.typeName, s"record.bigdlType: ${record.bigdlType} == this.typeName: ${this.typeName}") JSample[T](record.features.asScala.toArray.map(toTensor(_)), toTensor(record.label)) } - private def batching(rdd: RDD[Sample], batchSize: Int) - : DistributedDataSet[MiniBatch[T]] = { - val recordRDD = rdd.map(toSample(_)) - (DataSet.rdd(recordRDD) -> SampleToMiniBatch[T](batchSize)) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + def toJSample(psamples: RDD[Sample]): RDD[JSample[T]] = { + psamples.map(toJSample(_)) + } + + // The first dimension is batch for both X and y + private def toSampleArray(Xs: List[Tensor[T]], y: Tensor[T] = null): Array[JSample[T]] = { + require(!Xs.isEmpty, "Xs should not be empty") + val totalNum = Xs(0).size()(0) + var i = 1 + val samples = new Array[JSample[T]](totalNum) + + if (y != null) { + require(Xs(0).size()(0) == y.size()(0), + s"The batch dim should be equal, but we got: ${Xs(0).size()(0)} vs ${y.size()(0)}") + while (i <= totalNum) { + samples(i-1) = JSample(Xs.map{X => X.select(1, i)}.toArray, y.select(1, i)) + i += 1 + } + } else { + val dummyTensor = Tensor[T](1).fill(ev.fromType(1)) + while (i <= totalNum) { + samples(i-1) = JSample(Xs.map{X => X.select(1, i)}.toArray, dummyTensor) + i += 1 + } + } + + samples + } + + + def batching(dataset: DataSet[JSample[T]], batchSize: Int) + : DataSet[MiniBatch[T]] = { + dataset -> SampleToMiniBatch[T](batchSize) + } + + private def enrichOptimizer[T](optimizer: Optimizer[T, MiniBatch[T]], + endTrigger: Trigger, + optimMethod: OptimMethod[T]): Optimizer[T, MiniBatch[T]] = { + optimizer.setEndWhen(endTrigger) + + optimizer.setOptimMethod(optimMethod) + + // TODO: remove this + optimizer.disableCheckSingleton() + + optimizer } def createSequential(): Sequential[T] = { @@ -1510,11 +1551,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelEvaluate(model: AbstractModule[Activity, Activity, T], - valRDD: JavaRDD[Sample], - batchSize: Int, - valMethods: JList[ValidationMethod[T]]) + valRDD: JavaRDD[Sample], + batchSize: Int, + valMethods: JList[ValidationMethod[T]]) : JList[EvaluatedResult] = { - val resultArray = model.evaluate(valRDD.rdd.map(toSample(_)), + val resultArray = model.evaluate(valRDD.rdd.map(toJSample(_)), valMethods.asScala.toArray, Some(batchSize)) val testResultArray = resultArray.map { result => EvaluatedResult(result._1.result()._1, result._1.result()._2, @@ -1581,9 +1622,25 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab model.saveTF(scalaInputs, path, order, format) } + def predictLocal(model: AbstractModule[Activity, Activity, T], + features: JList[JTensor]): JList[JTensor] = { + val sampleArray = toSampleArray(features.asScala.toList.map{f => toTensor(f)}) + val localModel = LocalModule(model) + val result = localModel.predict(sampleArray) + result.map{a => toJTensor(a.asInstanceOf[Tensor[T]])}.toList.asJava + } + + def predictLocalClass(model: AbstractModule[Activity, Activity, T], + features: JList[JTensor]): JList[Int] = { + val sampleArray = toSampleArray(features.asScala.toList.map{f => toTensor(f)}) + val localModel = LocalModule(model) + val result = localModel.predictClass(sampleArray) + result.toList.asJava + } + def modelPredictRDD(model: AbstractModule[Activity, Activity, T], - dataRdd: JavaRDD[Sample]): JavaRDD[JTensor] = { - val tensorRDD = model.predict(dataRdd.rdd.map(toSample(_))) + dataRdd: JavaRDD[Sample]): JavaRDD[JTensor] = { + val tensorRDD = model.predict(dataRdd.rdd.map(toJSample(_))) val listRDD = tensorRDD.map { res => val tensor = res.asInstanceOf[Tensor[T]] val cloneTensor = tensor.clone() @@ -1599,8 +1656,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelPredictClass(model: AbstractModule[Activity, Activity, T], - dataRdd: JavaRDD[Sample]): JavaRDD[Int] = { - val tensorRDD = model.predictClass(dataRdd.rdd.map(toSample(_))) + dataRdd: JavaRDD[Sample]): JavaRDD[Int] = { + val sampleRdd = toJSample(dataRdd) + val tensorRDD = model.predictClass(sampleRdd) new JavaRDD[Int](tensorRDD) } @@ -1800,45 +1858,58 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def trainTF( - modelPath: String, - output: String, - samples: JavaRDD[Sample], - optMethod: OptimMethod[T], - criterion: Criterion[T], - batchSize: Int, - endWhen: Trigger): AbstractModule[Activity, Activity, T] = { + modelPath: String, + output: String, + samples: JavaRDD[Sample], + optMethod: OptimMethod[T], + criterion: Criterion[T], + batchSize: Int, + endWhen: Trigger): AbstractModule[Activity, Activity, T] = { val nodeList = parse(modelPath) val context = new Context[T]() val session = new BigDLSessionImpl[T](nodeList.asScala, context, ByteOrder.LITTLE_ENDIAN) - val dataset = batching(samples, batchSize) - + val dataset = batching(DataSet.rdd(toJSample(samples)), + batchSize).asInstanceOf[DistributedDataSet[MiniBatch[T]]] val model = session.train(Seq(output), dataset, optMethod, criterion, endWhen) model } - def createOptimizer(model: AbstractModule[Activity, Activity, T], - trainingRdd: JavaRDD[Sample], - criterion: Criterion[T], - optimMethod: OptimMethod[T], - endTrigger: Trigger, - batchSize: Int): Optimizer[T, MiniBatch[T]] = { + def createLocalOptimizer(features: JList[JTensor], + y: JTensor, + model: AbstractModule[Activity, Activity, T], + criterion: Criterion[T], + optimMethod: OptimMethod[T], + endTrigger: Trigger, + batchSize: Int, + localCores: Int): Optimizer[T, MiniBatch[T]] = { + val sampleArray = toSampleArray(features.asScala.toList.map{f => toTensor(f)}, toTensor(y)) + val optimizer = new LocalOptimizer[T]( + model, + batching(DataSet.array(sampleArray), batchSize) + .asInstanceOf[LocalDataSet[MiniBatch[T]]], + criterion + ).asInstanceOf[Optimizer[T, MiniBatch[T]]] + Engine.setNodeAndCore(1, localCores) + enrichOptimizer(optimizer, endTrigger, optimMethod) + } + + def createDistriOptimizer(model: AbstractModule[Activity, Activity, T], + trainingRdd: JavaRDD[Sample], + criterion: Criterion[T], + optimMethod: OptimMethod[T], + endTrigger: Trigger, + batchSize: Int): Optimizer[T, MiniBatch[T]] = { + val sampleRDD = toJSample(trainingRdd) + val optimizer = new DistriOptimizer( _model = model, - dataset = batching(trainingRdd, batchSize), + dataset = batching(DataSet.rdd(sampleRDD), batchSize) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]], criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] - // TODO: we should provide a more convenient way to create Table - - optimizer.setEndWhen(endTrigger) - - optimizer.setOptimMethod(optimMethod) - - // TODO: remove this - optimizer.disableCheckSingleton() - - optimizer + enrichOptimizer(optimizer, endTrigger, optimMethod) } def createL1L2Regularizer(l1: Double, l2: Double): L1L2Regularizer[T] = { @@ -1854,11 +1925,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def setValidation(optimizer: Optimizer[T, MiniBatch[T]], - batchSize: Int, - trigger: Trigger, - valRdd: JavaRDD[Sample], - vMethods: JList[ValidationMethod[T]]): Unit = { - optimizer.setValidation(trigger, batching(valRdd, batchSize.toInt), vMethods.asScala.toArray) + batchSize: Int, + trigger: Trigger, + valRdd: JavaRDD[Sample], + vMethods: JList[ValidationMethod[T]]): Unit = { + val sampleRDD = toJSample(valRdd) + optimizer.setValidation(trigger, batching(DataSet.rdd(sampleRDD), batchSize.toInt), + vMethods.asScala.toArray) } def setCheckPoint(optimizer: Optimizer[T, MiniBatch[T]], diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index b96dd052d60..86026d4980e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -185,7 +185,7 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { val pp = PythonBigDL.ofDouble() val optimMethod = new SGD[Double]() optimMethod.learningRateSchedule = SGD.Poly(0.5, math.ceil(1281167.toDouble / batchSize).toInt) - val optimizer = pp.createOptimizer( + val optimizer = pp.createDistriOptimizer( model, data.toJavaRDD(), ClassNLLCriterion[Double](), @@ -219,16 +219,16 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { val localData = data.collect() pp.toTensor(preResult.get(0)) should be - (trainedModel.forward(pp.toSample(localData(0)).feature)) + (trainedModel.forward(pp.toJSample(localData(0)).feature)) pp.toTensor(preResult.get(25)) should be - (trainedModel.forward(pp.toSample(localData(25)).feature)) + (trainedModel.forward(pp.toJSample(localData(25)).feature)) pp.toTensor(preResult.get(55)) should be - (trainedModel.forward(pp.toSample(localData(55)).feature)) + (trainedModel.forward(pp.toJSample(localData(55)).feature)) pp.toTensor(preResult.get(75)) should be - (trainedModel.forward(pp.toSample(localData(75)).feature)) + (trainedModel.forward(pp.toJSample(localData(75)).feature)) // TODO: verify the parameters result val parameters = pp.modelGetParameters(trainedModel) @@ -239,4 +239,42 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { valMethods = util.Arrays.asList(new Top1Accuracy())) println(testResult) } + + "local optimizer" should "be test" in { + + TestUtils.cancelOnWindows() + + Logger.getLogger("org").setLevel(Level.WARN) + Logger.getLogger("akka").setLevel(Level.WARN) + + import collection.JavaConverters._ + + val featuresShape = util.Arrays.asList(100) + val labelShape = util.Arrays.asList(1) + val pp = PythonBigDL.ofDouble() + + val X = pp.toJTensor(Tensor[Double](Array(100, 100)).randn()) + val y = pp.toJTensor(Tensor[Double](Array(100, 1)).zero().add(1)) + + val model = Sequential[Double]() + model.add(Linear[Double](100, 10)) + model.add(ReLU[Double]()) + model.add(LogSoftMax[Double]()) + val batchSize = 32 + val optimMethod = new SGD[Double]() + val optimizer = pp.createLocalOptimizer( + List(X).asJava, + y, + model, + ClassNLLCriterion[Double](), + optimMethod, + Trigger.maxEpoch(2), + 32, + 2) + val trainedModel = optimizer.optimize() + val predictedResult = pp.predictLocal( + trainedModel, List(pp.toJTensor(Tensor[Double](Array(34, 100)).randn())).asJava) + println(predictedResult) + } + } From 9dc785b8a0eb1588d7eb6974d96738e3bc56eca6 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 9 Nov 2017 08:40:58 +0800 Subject: [PATCH 0514/1065] Tf ParseExample add serialization support (#1852) * update bigdl download url * fix (#1719) * fix jtensor (#1722) * [Backport 0.3 PR1728]Bump script (#1729) * checkin bump-version script * add comments * add serialization support --- .../bigdl/dllib/nn/ops/ParseExample.scala | 89 ++++++++++++++++++- .../utils/serializer/ModuleSerializer.scala | 2 + .../serializer/ModuleSerializerSpec.scala | 51 ++++++++++- 3 files changed, 138 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala index 3dcfd26441a..d34f3fc89c2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala @@ -18,15 +18,19 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, Tens import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.{T, Table} import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleSerializable, SerializeContext} import org.tensorflow.example.{Example, Feature} import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString +import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.JavaConverters._ import scala.reflect.ClassTag +import scala.reflect.runtime.universe -class ParseExample[T: ClassTag](nDense: Int, - tDense: Seq[TensorDataType], - denseShape: Seq[Array[Int]]) +class ParseExample[T: ClassTag](val nDense: Int, + val tDense: Seq[TensorDataType], + val denseShape: Seq[Array[Int]]) (implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T] { @@ -84,3 +88,82 @@ class ParseExample[T: ClassTag](nDense: Int, throw new UnsupportedOperationException("no backward on ParseExample") } } + +object ParseExample extends ModuleSerializable { + def apply[T: ClassTag](nDense: Int, + tDense: Seq[TensorDataType], + denseShape: Seq[Array[Int]]) + (implicit ev: TensorNumeric[T]): ParseExample[T] = + new ParseExample[T](nDense, tDense, denseShape) + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + val attrMap = context.bigdlModule.getAttrMap + + val nDense = DataConverter.getAttributeValue(context, attrMap.get("nDense")). + asInstanceOf[Int] + + val tDense = DataConverter.getAttributeValue(context, attrMap.get("tDense")). + asInstanceOf[Array[String]].map(toTensorType(_)) + + val shapeSize = DataConverter.getAttributeValue(context, attrMap.get("shapeSize")). + asInstanceOf[Int] + + val denseShape = new Array[Array[Int]](shapeSize) + for (i <- 1 to shapeSize) { + denseShape(i - 1) = DataConverter.getAttributeValue(context, + attrMap.get(s"shapeSize_${i - 1}")). + asInstanceOf[Array[Int]] + } + ParseExample[T](nDense, tDense.toSeq, denseShape.toSeq) + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + bigDLModelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + + val parseExample = context.moduleData.module.asInstanceOf[ParseExample[T]] + + val nDenseBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, nDenseBuilder, parseExample.nDense, + universe.typeOf[Int]) + bigDLModelBuilder.putAttr("nDense", nDenseBuilder.build) + + val tensorTypeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, tensorTypeBuilder, + parseExample.tDense.toArray.map(fromTensorType(_)), + universe.typeOf[Array[String]]) + bigDLModelBuilder.putAttr("tDense", tensorTypeBuilder.build) + + val shapeSizeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, shapeSizeBuilder, + parseExample.denseShape.size, + universe.typeOf[Int]) + bigDLModelBuilder.putAttr("shapeSize", shapeSizeBuilder.build) + + parseExample.denseShape.zipWithIndex.foreach(shape => { + val shapeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, shapeBuilder, + parseExample.denseShape(shape._2), + universe.typeOf[Array[Int]]) + bigDLModelBuilder.putAttr(s"shapeSize_${shape._2}", shapeBuilder.build) + }) + + } + + private def fromTensorType(ttype : TensorDataType): String = { + ttype match { + case LongType => "Long" + case FloatType => "Float" + case StringType => "String" + } + } + + private def toTensorType(ttype : String): TensorDataType = { + ttype match { + case "Long" => LongType + case "Float" => FloatType + case "String" => StringType + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 131fa19d2da..787e0cdbee3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.nn._ import scala.collection.JavaConverters._ import scala.reflect.runtime.universe import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.ops.ParseExample import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.{Tensor, TensorNumericMath} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -197,6 +198,7 @@ object ModuleSerializer extends ModuleSerializable{ quantized.SpatialDilatedConvolution) registerModule("com.intel.analytics.bigdl.nn.quantized.Linear", quantized.Linear) + registerModule("com.intel.analytics.bigdl.nn.ops.ParseExample", ParseExample) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 21aeb024b0b..23018c898be 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -15,16 +15,19 @@ */ package com.intel.analytics.bigdl.utils.serializer +import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.ops.ParseExample import com.intel.analytics.bigdl.nn.{VolumetricFullConvolution, _} -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.caffe.CaffeLoader import com.intel.analytics.bigdl.utils.{T, Table} import org.scalatest.{FlatSpec, Matchers} +import org.tensorflow.example._ import serialization.Bigdl import serialization.Bigdl.AttrValue import serializer.TestCustomData @@ -1904,7 +1907,53 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res2 = loadedLinear.forward(input) res1 should be (res2) } + "ParseExample serializer" should "work properly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val floatBuilder = FloatList.newBuilder() + .addValue(0.0f).addValue(1.0f).addValue(2.0f) + val floatFeature = Feature.newBuilder().setFloatList(floatBuilder).build() + + val longBuilder = Int64List.newBuilder() + .addValue(0).addValue(1).addValue(2) + val longFeature = Feature.newBuilder().setInt64List(longBuilder).build() + + val bytesBuilder = BytesList.newBuilder().addValue(ByteString.copyFromUtf8("abcd")) + val bytesFeature = Feature.newBuilder().setBytesList(bytesBuilder).build() + + val features = Features.newBuilder() + .putFeature("floatFeature", floatFeature) + .putFeature("longFeature", longFeature) + .putFeature("bytesFeature", bytesFeature) + val example = Example.newBuilder().setFeatures(features).build() + val length = example.getSerializedSize + val data = new Array[Byte](length) + val outputStream = CodedOutputStream.newInstance(data) + example.writeTo(outputStream) + + val exampleParser = new ParseExample[Float](3, + Seq(FloatType, LongType, StringType), Seq(Array(3), Array(3), Array())) + + val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) + val names = Tensor[ByteString]() + val key1 = Tensor[ByteString](Array(ByteString.copyFromUtf8("floatFeature")), Array[Int]()) + val key2 = Tensor[ByteString](Array(ByteString.copyFromUtf8("longFeature")), Array[Int]()) + val key3 = Tensor[ByteString](Array(ByteString.copyFromUtf8("bytesFeature")), Array[Int]()) + + val default1 = Tensor[Float]() + val default2 = Tensor[Long]() + val default3 = Tensor[ByteString]() + + val input = T(serialized, names, key1, key2, key3, default1, default2, default3) + + val res1 = exampleParser.forward(input) + + ModulePersister.saveToFile("/tmp/exampleParser.bigdl", exampleParser, true) + val loadedExampleParser = ModuleLoader.loadFromFile[Float]("/tmp/exampleParser.bigdl") + val res2 = loadedExampleParser.forward(input) + res1 should be (res2) + + } "Customized Module " should "work properly" in { val testModule = new TestModule(CustomData(1.0)) DataConverter.registerConverter(universe.typeOf[CustomData].toString, TestCustomDataConverter) From 1c55b5d580c00b04c56d1ff1fda8d249dae41355 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 10 Nov 2017 13:28:07 +0800 Subject: [PATCH 0515/1065] time counting for paralleltable's forward/backward (#1861) --- .../bigdl/dllib/nn/ParallelTable.scala | 11 +++++++- .../bigdl/dllib/nn/ParallelTableSpec.scala | 25 +++++++++++++++++++ 2 files changed, 35 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala index 6525fc7540d..35cb2df67eb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala @@ -34,7 +34,7 @@ class ParallelTable[T: ClassTag] override def updateOutput(input: Table): Table = { var i = 0 while (i < input.length()) { - output.update(i + 1, modules(i).updateOutput(input(i + 1))) + output.update(i + 1, modules(i).forward(input(i + 1))) i += 1 } output @@ -57,6 +57,15 @@ class ParallelTable[T: ClassTag] } } + override def backward(input: Table, gradOutput: Table): Table = { + var i = 0 + while (i < input.length()) { + gradInput.update(i + 1, modules(i).backward(input(i + 1), gradOutput(i + 1))) + i += 1 + } + gradInput + } + override def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { val outputs = ArrayBuffer[ModuleNode[T]]() var outputTuple: Array[ModuleNode[T]] = null diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala index cd9c1fd4b75..a0d7170b926 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala @@ -103,4 +103,29 @@ class ParallelTableSpec extends FlatSpec with Matchers { mapGradInput should equal (expectedGradInput) } + + "A ParallelTable time counting" should "work fine" in { + val input = T( + Tensor[Float](10).randn(), + Tensor[Float](10).randn()) + + val gradOutput = T( + Tensor[Float](3).randn(), + Tensor[Float](3).randn()) + + val linear1 = new Linear[Float](10, 3) + val linear2 = new Linear[Float](10, 3) + + val module = new ParallelTable[Float]() + module.add(linear1) + module.add(linear2) + val mapOutput = module.forward(input) + + val mapGradInput = module.backward(input, gradOutput) + module.getTimes.foreach{m => + m._2 should be > 0L + m._3 should be > 0L + } + } + } From 86b3d63948e09b1349db96966f5c3f8ce0f4ef47 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 10 Nov 2017 14:18:47 +0800 Subject: [PATCH 0516/1065] LocalOptimizer-use modelbroadcast-like method to clone module (#1864) --- .../dllib/models/utils/ModelBroadcast.scala | 6 ++--- .../bigdl/dllib/optim/LocalOptimizer.scala | 24 +++++++++++++------ 2 files changed, 20 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 8652e207ac4..d86cc2c4363 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -71,7 +71,7 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria } - private def getAndClearWeightBias(parameters: (Array[Tensor[T]], Array[Tensor[T]])) + private[bigdl] def getAndClearWeightBias(parameters: (Array[Tensor[T]], Array[Tensor[T]])) : Array[Tensor[T]] = { if (parameters._1.length != 0) { var i = 0 @@ -124,7 +124,7 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria } } - private def putWeightBias( + private[bigdl] def putWeightBias( broadcastWeightBias: Array[Tensor[T]], localModel: Module[T]): Unit = { val localWeightBias = localModel.parameters()._1 @@ -137,7 +137,7 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria } } - private def initGradWeightBias( + private[bigdl] def initGradWeightBias( broadcastWeightBias: Array[Tensor[T]], localModel: Module[T]): Unit = { val (localWeightBias, localGradWeightBias) = localModel.parameters() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index c2b49a7d5dc..772a5d0a4bc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch} import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.Utils import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor @@ -56,6 +57,22 @@ class LocalOptimizer[T: ClassTag] ( case _ => throw new IllegalArgumentException } + private val workingModels = { + val modelBroadcast = ModelBroadcast() + model.getParameters() + val wb = modelBroadcast.getAndClearWeightBias(model.parameters()) + + val models = (1 to subModelNumber).map(i => { + logger.info(s"Clone $i model...") + val m = model.cloneModule() + modelBroadcast.putWeightBias(wb, m) + modelBroadcast.initGradWeightBias(wb, m) + m + }).toArray + modelBroadcast.putWeightBias(wb, model) + modelBroadcast.initGradWeightBias(wb, model) + models + } private val (weight, grad) = model.getParameters() private val gradLength = grad.nElement() private val syncGradTaskSize = gradLength / subModelNumber @@ -63,15 +80,8 @@ class LocalOptimizer[T: ClassTag] ( private val syncGradParallelNum = if (syncGradTaskSize == 0) syncGradExtraTask else subModelNumber - private val workingModels = (1 to subModelNumber).map(i => { - logger.info(s"Clone $i model...") - model.cloneModule() - }).toArray - private val workingModelWAndG = workingModels.map(_.getParameters()) - workingModelWAndG.foreach(_._1.storage().set(weight.storage())) - private val workingCriterion = (1 to subModelNumber).map(_ => criterion.cloneCriterion()).toArray From e176ddb525b77817e6e4fad03a5e5af8aaad9cc3 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 10 Nov 2017 14:59:32 +0800 Subject: [PATCH 0517/1065] fix adapter issue (#1859) * fix adapter issue * fix style issue --- .../utils/serializer/ModuleSerializable.scala | 2 +- .../dllib/utils/tf/loaders/Adapter.scala | 32 +++---- .../dllib/utils/tf/loaders/ConcatV2.scala | 13 ++- .../dllib/utils/tf/loaders/ExpandDims.scala | 13 ++- .../bigdl/dllib/utils/tf/loaders/Mean.scala | 76 +++++++++------- .../bigdl/dllib/utils/tf/loaders/Pad.scala | 35 ++++--- .../bigdl/dllib/utils/tf/loaders/Prod.scala | 14 ++- .../dllib/utils/tf/loaders/Reshape.scala | 43 +++++---- .../bigdl/dllib/utils/tf/loaders/Slice.scala | 18 ++-- .../bigdl/dllib/utils/tf/loaders/Split.scala | 24 +++-- .../dllib/utils/tf/loaders/StridedSlice.scala | 56 +++++++----- .../bigdl/dllib/utils/tf/loaders/TopKV2.scala | 39 +++++--- .../dllib/utils/tf/loaders/Transpose.scala | 91 ++++++++++--------- .../bigdl/dllib/utils/BigDLSpecHelper.scala | 2 +- .../bigdl/dllib/utils/tf/AdapterSpec.scala | 44 +++++---- 15 files changed, 295 insertions(+), 207 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 74a42ab3727..25510cdbcb9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -104,7 +104,7 @@ trait ModuleSerializable extends Loadable with Savable{ val cls = Class.forName(moduleType) val constructorMirror = getCostructorMirror(cls) val constructorFullParams = constructorMirror.symbol.paramss - val args = new Array[Object](constructorFullParams(0).size + constructorFullParams(1).size) + val args = new Array[Object](constructorFullParams.map(_.size).sum) var i = 0; lock.synchronized { constructorFullParams.foreach(map => { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Adapter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Adapter.scala index a15910201a8..a7ab5757ce8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Adapter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Adapter.scala @@ -29,12 +29,10 @@ import scala.reflect.ClassTag * * Please note you must guarantee the input parameter won't change each time. * @param configIndexes configuration tensor indexes, start from 1 and -1 specify the last one - * @param build build function * @tparam T Numeric type. Only support float/double now */ -class Adapter[T: ClassTag]( - val configIndexes: Array[Int], - val build: Array[Tensor[_]] => AbstractModule[Activity, Activity, T] +abstract class Adapter[T: ClassTag]( + val configIndexes: Array[Int] )(implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Activity, T]{ @@ -45,8 +43,21 @@ class Adapter[T: ClassTag]( private var realInput: Activity = _ private var initTensors: Array[Tensor[_]] = _ + def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] + override def updateOutput(input: Table): Activity = { + var rebuildModule = false if (module == null) { + rebuildModule = true + } else { + indexes.map(i => input[Tensor[_]](i)).zip(initTensors).foreach(tensors => { + if (tensors._1 != tensors._2) { + rebuildModule = true + } + }) + } + + if (rebuildModule) { val l = input.length() indexes = configIndexes.map(getPositiveIndex(_, l)) val tensors = indexes.map(i => input[Tensor[_]](i)) @@ -54,11 +65,6 @@ class Adapter[T: ClassTag]( module = build(tensors) dataIndexes = getDataIndexes(indexes, l) zeroGrads = tensors.map(t => t.emptyInstance().resizeAs(t)) - } else { - indexes.map(i => input[Tensor[_]](i)).zip(initTensors).foreach(tensors => { - require(tensors._1 == tensors._2, s"constant tensor is changed. " + - s"\noriginal\n${tensors._2}\nnow\n${tensors._1}") - }) } realInput = if (dataIndexes.length == 1) { @@ -105,11 +111,3 @@ class Adapter[T: ClassTag]( module.accGradParameters(realInput, gradOutput) } } - -object Adapter { - def apply[T: ClassTag]( - configIndexes: Array[Int], build: Array[Tensor[_]] => AbstractModule[Activity, Activity, T] - )(implicit ev: TensorNumeric[T]): Adapter[T] = { - new Adapter(configIndexes, build) - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala index ddc92cbd756..0b78af6181b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2.scala @@ -19,6 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.JoinTable +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context @@ -32,10 +33,14 @@ class ConcatV2 extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(-1), tensorArrays => { - val axis = tensorArrays(0).value().asInstanceOf[Int] + 1 - JoinTable[T](dimension = axis, nInputDims = -1) - }) + new ConcatV2LoadTF[T]() + } +} + +class ConcatV2LoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(-1)) { + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val axis = tensorArrays(0).value().asInstanceOf[Int] + 1 + JoinTable[T](dimension = axis, nInputDims = -1) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala index ec84e3f7f90..284930dd6d2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala @@ -19,6 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.ops.ExpandDims import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -33,9 +34,13 @@ class ExpandDims extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(2), tensorArrays => { - val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() - ExpandDims[T](if (axis < 0) axis + 1 else axis + 1) - }) + new ExpandDimsLoadTF[T]() + } +} + +class ExpandDimsLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) { + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + ExpandDims[T](if (axis < 0) axis + 1 else axis + 1) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala index 264f217cb2d..071563a1f91 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.{Sequential, Mean => MeanNN} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -33,37 +34,50 @@ class Mean extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - - Adapter[T](Array(2), tensorArrays => { - val attr = nodeDef.getAttrMap - val dataType = getType(attr, "T") - val squeeze = !getBoolean(attr, "keep_dims") - val dims = tensorArrays(0).asInstanceOf[Tensor[Int]] - val dim = ArrayBuffer[Int]() - val mean = Sequential[T]() - for (i <- 1 to dims.size(1)) { - dim += dims.valueAt(i) + 1 - } - dataType match { - case DataType.DT_INT8 => - dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) - case DataType.DT_INT16 => - dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) - case DataType.DT_UINT8 => - dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) - case DataType.DT_UINT16 => - dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) - case DataType.DT_INT32 => - dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) - case DataType.DT_INT64 => - dim.foreach(i => mean.add(new MeanNN[T, Long](i, squeeze = squeeze))) - case DataType.DT_FLOAT => - dim.foreach(i => mean.add(new MeanNN[T, Float](i, squeeze = squeeze))) - case DataType.DT_DOUBLE => - dim.foreach(i => mean.add(new MeanNN[T, Double](i, squeeze = squeeze))) - } - mean - }) + val attr = nodeDef.getAttrMap + val dataType = getType(attr, "T") + val squeeze = !getBoolean(attr, "keep_dims") + val dt = dataType match { + case DataType.DT_INT8 => + "Int" + case DataType.DT_INT16 => + "Int" + case DataType.DT_UINT8 => + "Int" + case DataType.DT_UINT16 => + "Int" + case DataType.DT_INT32 => + "Int" + case DataType.DT_INT64 => + "Long" + case DataType.DT_FLOAT => + "Float" + case DataType.DT_DOUBLE => + "Double" + } + new MeanLoadTF[T](dt, squeeze) } } +class MeanLoadTF[T: ClassTag](dataType: String, squeeze: Boolean)(implicit ev: TensorNumeric[T]) + extends Adapter[T](Array(2)) { + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val dims = tensorArrays(0).asInstanceOf[Tensor[Int]] + val dim = ArrayBuffer[Int]() + val mean = Sequential[T]() + for (i <- 1 to dims.size(1)) { + dim += dims.valueAt(i) + 1 + } + dataType match { + case "Int" => + dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) + case "Long" => + dim.foreach(i => mean.add(new MeanNN[T, Long](i, squeeze = squeeze))) + case "Float" => + dim.foreach(i => mean.add(new MeanNN[T, Float](i, squeeze = squeeze))) + case "Double" => + dim.foreach(i => mean.add(new MeanNN[T, Double](i, squeeze = squeeze))) + } + mean + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala index 8d4b8d6fdb6..3329cc458c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pad.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.{Padding, Sequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -33,24 +34,28 @@ class Pad extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(2), tensorArrays => { - val paddings = tensorArrays(0).asInstanceOf[Tensor[Int]] - val pad = ArrayBuffer[Int]() - val padding = Sequential[T]() - - for(dim <- 1 to paddings.size(1)) { - if (paddings.valueAt(dim, 1) != 0 || paddings.valueAt(dim, 2) != 0 ) { - if (paddings(Array(dim, 1)) != 0) { - padding.add(Padding[T](dim, -paddings.valueAt(dim, 1), 4)) - } - if (paddings(Array(dim, 2)) != 0) { - padding.add(Padding[T](dim, paddings.valueAt(dim, 2), 4)) - } + new PadLoadTF[T]() + } +} + +class PadLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) { + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val paddings = tensorArrays(0).asInstanceOf[Tensor[Int]] + val pad = ArrayBuffer[Int]() + val padding = Sequential[T]() + + for(dim <- 1 to paddings.size(1)) { + if (paddings.valueAt(dim, 1) != 0 || paddings.valueAt(dim, 2) != 0 ) { + if (paddings(Array(dim, 1)) != 0) { + padding.add(Padding[T](dim, -paddings.valueAt(dim, 1), 4)) + } + if (paddings(Array(dim, 2)) != 0) { + padding.add(Padding[T](dim, paddings.valueAt(dim, 2), 4)) } } + } - padding - }) + padding } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala index 647768274a2..0667e49f3cb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala @@ -19,6 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.ops.Prod import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -33,10 +34,13 @@ class Prod extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(2), tensorArrays => { - val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 - val keepDims = getBoolean(nodeDef.getAttrMap, "keep_dims") - Prod[T](axis) - }) + new ProdLoadTF[T]() + } +} + +class ProdLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) { + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 + Prod[T](axis) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala index 1d830a5005e..675180d9572 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala @@ -19,6 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Reshape +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.{Context, TFUtils} @@ -32,25 +33,29 @@ class Reshape extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(2), tensorArrays => { - val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] - - val batchMode = if (sizes.nDimension() >= 1) { - sizes.valueAt(1) == -1 - } else { - false - } - - val arraySize = new Array[Int](if (batchMode) sizes.nElement() - 1 else sizes.nElement()) - var i = if (batchMode) 2 else 1 - var k = 0 - while(i <= sizes.nElement()) { - arraySize(k) = sizes.valueAt(i) - k += 1 - i += 1 - } - Reshape[T](size = arraySize, Some(batchMode)) - }) + new ReshapeLoadTF[T]() + } +} + +class ReshapeLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) { + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] + + val batchMode = if (sizes.nDimension() >= 1) { + sizes.valueAt(1) == -1 + } else { + false + } + + val arraySize = new Array[Int](if (batchMode) sizes.nElement() - 1 else sizes.nElement()) + var i = if (batchMode) 2 else 1 + var k = 0 + while(i <= sizes.nElement()) { + arraySize(k) = sizes.valueAt(i) + k += 1 + i += 1 + } + Reshape[T](size = arraySize, Some(batchMode)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala index b577522bde9..e394fc79cd4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala @@ -19,6 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.ops.Slice import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -29,14 +30,19 @@ import scala.reflect.ClassTag class Slice extends TensorflowOpsLoader { - import Utils._ override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(2, 3), tensorArrays => { - val size = tensorArrays(1).asInstanceOf[Tensor[Int]] - Slice[T](toArray(tensorArrays(0).asInstanceOf[Tensor[Int]]), - toArray(tensorArrays(1).asInstanceOf[Tensor[Int]])) - }) + new SliceLoadTF[T]() + } +} + +class SliceLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2, 3)) { + import Utils._ + + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val size = tensorArrays(1).asInstanceOf[Tensor[Int]] + Slice[T](toArray(tensorArrays(0).asInstanceOf[Tensor[Int]]), + toArray(tensorArrays(1).asInstanceOf[Tensor[Int]])) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala index 81c76e40395..2e3ed4f1c5b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala @@ -19,6 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.ConcatTable +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.SplitAndSelect import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -33,15 +34,20 @@ class Split extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(1), tensorArrays => { - val numSplit = nodeDef.getAttrMap.get("num_split").getI.toInt - val dim = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 - val model = new ConcatTable[T]() - for (index <- Range(1, numSplit + 1)) { - model.add(SplitAndSelect[T](dim, index, numSplit)) - } - model - }) + val numSplit = nodeDef.getAttrMap.get("num_split").getI.toInt + new SplitLoadTF[T](numSplit) + } +} + +class SplitLoadTF[T: ClassTag](numSplit: Int)(implicit ev: TensorNumeric[T]) + extends Adapter[T](Array(1)) { + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val dim = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 + val model = new ConcatTable[T]() + for (index <- Range(1, numSplit + 1)) { + model.add(SplitAndSelect[T](dim, index, numSplit)) + } + model } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala index f101498064e..5da9afc9a23 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.StrideSlice import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -34,30 +35,37 @@ class StridedSlice extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Adapter[T](Array(2, 3, 4), tensorArrays => { - // this must be defined inside this function, otherwise the loader will be - // serialized - def oneDTensorToArray(tensor: Tensor[Int]): Array[Int] = { - require(tensor.nDimension() == 1, "1D tensor required") - val result = new Array[Int](tensor.nElement()) - var i = 0 - while(i < tensor.nElement()) { - result(i) = tensor.valueAt(i + 1) - i += 1 - } - result - } - - val start = oneDTensorToArray(tensorArrays(0).asInstanceOf[Tensor[Int]]) - val end = oneDTensorToArray(tensorArrays(1).asInstanceOf[Tensor[Int]]) - val stride = oneDTensorToArray(tensorArrays(2).asInstanceOf[Tensor[Int]]) - - val specs = (start zip end zip stride).zipWithIndex - .map(elem => (elem._2 + 1, elem._1._1._1 + 1, elem._1._1._2 + 1, elem._1._2)) - - - StrideSlice[T](specs) - }) + new StridedSliceLoadTF[T]() + } +} + +class StridedSliceLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends Adapter[T](Array(2, 3, 4)) { + import StridedSlice._ + + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val start = oneDTensorToArray(tensorArrays(0).asInstanceOf[Tensor[Int]]) + val end = oneDTensorToArray(tensorArrays(1).asInstanceOf[Tensor[Int]]) + val stride = oneDTensorToArray(tensorArrays(2).asInstanceOf[Tensor[Int]]) + + val specs = (start zip end zip stride).zipWithIndex + .map(elem => (elem._2 + 1, elem._1._1._1 + 1, elem._1._1._2 + 1, elem._1._2)) + + + StrideSlice[T](specs) + } +} + +object StridedSlice { + def oneDTensorToArray(tensor: Tensor[Int]): Array[Int] = { + require(tensor.nDimension() == 1, "1D tensor required") + val result = new Array[Int](tensor.nElement()) + var i = 0 + while(i < tensor.nElement()) { + result(i) = tensor.valueAt(i + 1) + i += 1 + } + result } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2.scala index 1943aa49b3b..d3ce67b8efd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.ops.TopK import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -39,19 +40,31 @@ class TopKV2 extends TensorflowOpsLoader { true } val t = getType(nodeDef.getAttrMap, "T") + val ts = if (t == DataType.DT_FLOAT) { + "Float" + } else if (t == DataType.DT_DOUBLE) { + "Double" + } else { + throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") + } + + new TopKV2LoadTF[T](s, ts) + } +} + +class TopKV2LoadTF[T: ClassTag](s: Boolean, t: String)(implicit ev: TensorNumeric[T]) + extends Adapter[T](Array(2)) { + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val kTensor = tensorArrays(0).asInstanceOf[Tensor[Int]] + require(kTensor.isScalar, "Invalid input k") + val k = kTensor.value() - Adapter[T](Array(2), tensorArrays => { - val kTensor = tensorArrays(0).asInstanceOf[Tensor[Int]] - require(kTensor.isScalar, "Invalid input k") - val k = kTensor.value() - - if (t == DataType.DT_FLOAT) { - TopK[T, Float](k, s, startIndex = 0) - } else if (t == DataType.DT_DOUBLE) { - TopK[T, Double](k, s, startIndex = 0) - } else { - throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") - } - }) + if (t == "Float") { + TopK[T, Float](k, s, startIndex = 0) + } else if (t == "Double") { + TopK[T, Double](k, s, startIndex = 0) + } else { + throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala index 5838dc48628..34200a2a30e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.{Contiguous, Sequential, Transpose} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -33,53 +34,59 @@ class Transpose extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + new TransposeLoadTF[T]() + } +} - Adapter[T](Array(2), tensorArrays => { - // this must be defined inside this function, otherwise the loader will be - // serialized - def permToPair(perm: Array[Int]): Array[(Int, Int)] = { - val numToRank = perm.zipWithIndex.toMap - val arr = perm.indices.toArray - val pairs = ArrayBuffer[(Int, Int)]() - - def sort(arr: Array[Int], low: Int, high: Int): Unit = { - var i = low - var j = high - val pivot = arr(low + (high - low)/2) - - while (i <= j) { - while (arr(i) < pivot) i += 1 - while (arr(j) > pivot) j -= 1 - - if (i <= j) { - exchangeNumbers(arr, i, j) - i += 1 - j -= 1 - } - } - - if (low < j) sort(arr, low, j) - if (i < high) sort(arr, i, high) - } +object TransposeLoadTF { - def exchangeNumbers(arr: Array[Int], i: Int, j: Int): Unit = { - val temp = arr(i) - arr(i) = arr(j) - arr(j) = temp - pairs += ((i, j)) - } + def permToPair(perm: Array[Int]): Array[(Int, Int)] = { + val numToRank = perm.zipWithIndex.toMap + val arr = perm.indices.toArray + val pairs = ArrayBuffer[(Int, Int)]() - sort(arr.map(numToRank), 0, arr.length-1) + def sort(arr: Array[Int], low: Int, high: Int): Unit = { + var i = low + var j = high + val pivot = arr(low + (high - low)/2) - pairs.filter(pair => pair._1 != pair._2).toArray + while (i <= j) { + while (arr(i) < pivot) i += 1 + while (arr(j) > pivot) j -= 1 + + if (i <= j) { + exchangeNumbers(arr, i, j) + i += 1 + j -= 1 + } } - val perm = tensorArrays(0).asInstanceOf[Tensor[Int]].storage().array() - val paris = permToPair(perm) - val layer = Sequential() - layer.add(Transpose[T](paris.map(x => (x._1 + 1, x._2 + 1)))) - layer.add(Contiguous()) - layer - }) + if (low < j) sort(arr, low, j) + if (i < high) sort(arr, i, high) + } + + def exchangeNumbers(arr: Array[Int], i: Int, j: Int): Unit = { + val temp = arr(i) + arr(i) = arr(j) + arr(j) = temp + pairs += ((i, j)) + } + + sort(arr.map(numToRank), 0, arr.length-1) + + pairs.filter(pair => pair._1 != pair._2).toArray + } +} + +class TransposeLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) { + import TransposeLoadTF._ + + override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { + val perm = tensorArrays(0).asInstanceOf[Tensor[Int]].storage().array() + val paris = permToPair(perm) + val layer = Sequential() + layer.add(Transpose[T](paris.map(x => (x._1 + 1, x._2 + 1)))) + layer.add(Contiguous()) + layer } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/BigDLSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/BigDLSpecHelper.scala index fd65089d3a8..18c85fae28e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/BigDLSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/BigDLSpecHelper.scala @@ -28,7 +28,7 @@ abstract class BigDLSpecHelper extends FlatSpec with Matchers with BeforeAndAfte private val tmpFiles : ArrayBuffer[JFile] = new ArrayBuffer[JFile]() protected def createTmpFile(): JFile = { - val file = java.io.File.createTempFile("UnitTest", "TensorflowLoaderSpecBase") + val file = java.io.File.createTempFile("UnitTest", "BigDLSpecBase") logger.info(s"created file $file") tmpFiles.append(file) file diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/AdapterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/AdapterSpec.scala index fd7bef74aa4..cfb8062a136 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/AdapterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/AdapterSpec.scala @@ -15,15 +15,39 @@ */ package com.intel.analytics.bigdl.utils.tf -import com.intel.analytics.bigdl.nn.Reshape +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.{Module, Reshape} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} import com.intel.analytics.bigdl.utils.tf.loaders.Adapter import org.scalatest.{FlatSpec, Matchers} -class AdapterSpec extends FlatSpec with Matchers { +class AdapterSpec extends BigDLSpecHelper { - private val module = Adapter[Float](Array(2), tensorArrays => { + private val module = new AdapterForTest() + + "Adapter" should "work correct" in { + module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) + module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) + } + + "Adapter" should "rebuild module when const tensor is changed" in { + module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) + module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(2, 6)))) should be(Tensor[Float](2, 6)) + } + + "Adapter" should "be able to serialized and deserialized" in { + val tmpFile = createTmpFile() + tmpFile.delete() + module.saveModule(tmpFile.getAbsolutePath) + val loadModule = Module.loadModule[Float](tmpFile.getAbsolutePath) + loadModule.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) + } +} + +class AdapterForTest extends Adapter[Float](Array(2)) { + override def build(tensorArrays: Array[Tensor[_]]) + : AbstractModule[Activity, Activity, Float] = { val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] val batchMode = sizes.valueAt(1) == -1 @@ -36,17 +60,5 @@ class AdapterSpec extends FlatSpec with Matchers { i += 1 } Reshape[Float](size = arraySize, Some(batchMode)) - }) - - "Adapter" should "work correct" in { - module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) - module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) - } - - "Adapter" should "throw exception when const tensor is changed" in { - module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(4, 3)))) should be(Tensor[Float](4, 3)) - intercept[Exception] { - module.forward(T(Tensor[Float](3, 4), Tensor[Int](T(2, 6)))) - } } } From a51b64b6ec1c740fe8e7c4cc43def2eff50bb664 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 10 Nov 2017 15:48:11 +0800 Subject: [PATCH 0518/1065] Support load more tensorflow operations (#1858) * add more ops * checkin reduce * add All * support all and any * add operation Round * support floordiv and truncatediv * support FloorMode/Mod/TruncateMod * add exp/expm1 * add InTopK * add rint * remove a JDK8 api * fix style issue * fix a bug in floordiv --- .../analytics/bigdl/dllib/nn/ops/All.scala | 101 ++++++++++++++++++ .../analytics/bigdl/dllib/nn/ops/Any.scala | 101 ++++++++++++++++++ .../analytics/bigdl/dllib/nn/ops/Exp.scala | 37 +++++++ .../analytics/bigdl/dllib/nn/ops/Expm1.scala | 37 +++++++ .../bigdl/dllib/nn/ops/FloorDiv.scala | 41 +++++++ .../bigdl/dllib/nn/ops/FloorMod.scala | 49 +++++++++ .../analytics/bigdl/dllib/nn/ops/InTopK.scala | 61 +++++++++++ .../analytics/bigdl/dllib/nn/ops/Mod.scala | 49 +++++++++ .../bigdl/dllib/nn/ops/RangeOps.scala | 50 +++++++++ .../analytics/bigdl/dllib/nn/ops/Rint.scala | 37 +++++++ .../analytics/bigdl/dllib/nn/ops/Round.scala | 41 +++++++ .../bigdl/dllib/nn/ops/TruncateDiv.scala | 42 ++++++++ .../bigdl/dllib/tensor/DenseTensor.scala | 13 +++ .../tensor/QuantizedTensorUnsupported.scala | 3 + .../bigdl/dllib/tensor/SparseTensor.scala | 4 + .../bigdl/dllib/tensor/TensorMath.scala | 9 ++ .../bigdl/dllib/tensor/TensorNumeric.scala | 80 ++++++++++++++ .../bigdl/dllib/utils/tf/TFUtils.scala | 11 ++ .../bigdl/dllib/utils/tf/Tensorflow.scala | 15 +++ .../bigdl/dllib/utils/tf/loaders/All.scala | 37 +++++++ .../bigdl/dllib/utils/tf/loaders/Any.scala | 37 +++++++ .../bigdl/dllib/utils/tf/loaders/Exp.scala | 43 ++++++++ .../bigdl/dllib/utils/tf/loaders/Expm1.scala | 43 ++++++++ .../dllib/utils/tf/loaders/FloorDiv.scala | 45 ++++++++ .../dllib/utils/tf/loaders/FloorMod.scala | 45 ++++++++ .../bigdl/dllib/utils/tf/loaders/InTopK.scala | 37 +++++++ .../bigdl/dllib/utils/tf/loaders/Mod.scala | 45 ++++++++ .../bigdl/dllib/utils/tf/loaders/Range.scala | 45 ++++++++ .../bigdl/dllib/utils/tf/loaders/Rint.scala | 36 +++++++ .../bigdl/dllib/utils/tf/loaders/Round.scala | 46 ++++++++ .../dllib/utils/tf/loaders/TruncateDiv.scala | 41 +++++++ .../dllib/utils/tf/loaders/TruncateMod.scala | 45 ++++++++ .../dllib/utils/tf/loaders/AllSpec.scala | 92 ++++++++++++++++ .../dllib/utils/tf/loaders/AnySpec.scala | 92 ++++++++++++++++ .../dllib/utils/tf/loaders/ExpSpec.scala | 47 ++++++++ .../dllib/utils/tf/loaders/Expm1Spec.scala | 45 ++++++++ .../dllib/utils/tf/loaders/FloorDivSpec.scala | 60 +++++++++++ .../dllib/utils/tf/loaders/FloorModSpec.scala | 48 +++++++++ .../dllib/utils/tf/loaders/InTopKSpec.scala | 40 +++++++ .../dllib/utils/tf/loaders/ModSpec.scala | 49 +++++++++ .../dllib/utils/tf/loaders/RangeSpec.scala | 44 ++++++++ .../dllib/utils/tf/loaders/RintSpec.scala | 35 ++++++ .../dllib/utils/tf/loaders/RoundSpec.scala | 47 ++++++++ .../utils/tf/loaders/TruncateDivSpec.scala | 37 +++++++ .../utils/tf/loaders/TruncateModSpec.scala | 48 +++++++++ 45 files changed, 2010 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/All.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Any.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Exp.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDiv.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorMod.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InTopK.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Mod.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOps.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rint.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Round.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDiv.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/All.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Any.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Exp.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDiv.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorMod.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopK.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mod.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Range.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rint.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Round.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDiv.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateMod.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AllSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AnySpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDivSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorModSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopKSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ModSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RangeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RintSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RoundSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDivSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateModSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/All.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/All.scala new file mode 100644 index 00000000000..459ebba3a21 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/All.scala @@ -0,0 +1,101 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class All[T: ClassTag](keepDim : Boolean = false, startFromZero : Boolean = false) + (implicit ev: TensorNumeric[T]) extends Operation[Table, + Tensor[Boolean], T] { + + output = Tensor[Boolean]() + + private var buffer = Tensor[Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { + val data = input[Tensor[Boolean]](1) + val indices = input[Tensor[Int]](2) + require(indices.nDimension() == 1 || indices.isScalar, "indices must be 1D tensor or scala") + output.resizeAs(data) + buffer.resizeAs(data).copy(data) + val reduceDims = new ArrayBuffer[Int]() + val size = output.size() + if (indices.isScalar) { + val dim = if (indices.value() < 0) { + data.nDimension() + indices.value() + 1 + } else if (startFromZero) { + indices.value() + 1 + } else { + indices.value() + } + + if (size(dim - 1) != 1) { + size(dim - 1) = 1 + reduceDims += dim + output.resize(size) + buffer.reduce(dim, output, (a, b) => a && b) + buffer.resizeAs(output).copy(output) + } + } else { + var i = 1 + while (i <= indices.size(1)) { + val dim = if (indices.valueAt(i) < 0) { + data.nDimension() + indices.valueAt(i) + 1 + } else if (startFromZero) { + indices.valueAt(i) + 1 + } else { + indices.valueAt(i) + } + if (size(dim - 1) != 1) { + size(dim - 1) = 1 + reduceDims += dim + output.resize(size) + buffer.reduce(dim, output, (a, b) => a && b) + buffer.resizeAs(output).copy(output) + } + i += 1 + } + } + + if (!keepDim) { + val sizeBuffer = new ArrayBuffer[Int]() + var i = 1 + while (i <= data.nDimension()) { + if (!reduceDims.contains(i)) sizeBuffer.append(data.size(i)) + i += 1 + } + output.resize(sizeBuffer.toArray) + } + output + } + + override def clearState(): this.type = { + super.clearState() + buffer.set() + this + } +} + +object All { + def apply[T: ClassTag](keepDim: Boolean = false, startFromZero : Boolean = false) + (implicit ev: TensorNumeric[T]): All[T] = new All[T](keepDim, startFromZero) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Any.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Any.scala new file mode 100644 index 00000000000..b20227cf4b4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Any.scala @@ -0,0 +1,101 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Any[T: ClassTag](keepDim : Boolean = false, startFromZero : Boolean = false) + (implicit ev: TensorNumeric[T]) extends Operation[Table, + Tensor[Boolean], T] { + + output = Tensor[Boolean]() + + private var buffer = Tensor[Boolean]() + + override def updateOutput(input: Table): Tensor[Boolean] = { + val data = input[Tensor[Boolean]](1) + val indices = input[Tensor[Int]](2) + require(indices.nDimension() == 1 || indices.isScalar, "indices must be 1D tensor or scala") + output.resizeAs(data) + buffer.resizeAs(data).copy(data) + val reduceDims = new ArrayBuffer[Int]() + val size = output.size() + if (indices.isScalar) { + val dim = if (indices.value() < 0) { + data.nDimension() + indices.value() + 1 + } else if (startFromZero) { + indices.value() + 1 + } else { + indices.value() + } + + if (size(dim - 1) != 1) { + size(dim - 1) = 1 + reduceDims += dim + output.resize(size) + buffer.reduce(dim, output, (a, b) => a || b) + buffer.resizeAs(output).copy(output) + } + } else { + var i = 1 + while (i <= indices.size(1)) { + val dim = if (indices.valueAt(i) < 0) { + data.nDimension() + indices.valueAt(i) + 1 + } else if (startFromZero) { + indices.valueAt(i) + 1 + } else { + indices.valueAt(i) + } + if (size(dim - 1) != 1) { + size(dim - 1) = 1 + reduceDims += dim + output.resize(size) + buffer.reduce(dim, output, (a, b) => a || b) + buffer.resizeAs(output).copy(output) + } + i += 1 + } + } + + if (!keepDim) { + val sizeBuffer = new ArrayBuffer[Int]() + var i = 1 + while (i <= data.nDimension()) { + if (!reduceDims.contains(i)) sizeBuffer.append(data.size(i)) + i += 1 + } + output.resize(sizeBuffer.toArray) + } + output + } + + override def clearState(): this.type = { + super.clearState() + buffer.set() + this + } +} + +object Any { + def apply[T: ClassTag](keepDim: Boolean = false, startFromZero : Boolean = false) + (implicit ev: TensorNumeric[T]): Any[T] = new Any[T](keepDim, startFromZero) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Exp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Exp.scala new file mode 100644 index 00000000000..0c45c410ec9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Exp.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class Exp[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[D], Tensor[D], T]{ + + output = Tensor[D]() + + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output.resizeAs(input) + output.map(input, (a, b) => ev2.exp(b)) + } +} + +object Exp { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + : Exp[T, D] = new Exp() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1.scala new file mode 100644 index 00000000000..667623bb106 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class Expm1[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[D], Tensor[D], T]{ + + output = Tensor[D]() + + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output.resizeAs(input) + output.map(input, (a, b) => ev2.minus(ev2.exp(b), ev2.one)) + } +} + +object Expm1 { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + : Expm1[T, D] = new Expm1() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDiv.scala new file mode 100644 index 00000000000..5d785f063bd --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDiv.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class FloorDiv[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + output = Tensor[D]() + + override def updateOutput(input: Table): Tensor[D] = { + val input1 = input[Tensor[D]](1) + val input2 = input[Tensor[D]](2) + output.resizeAs(input1).copy(input1) + output.map(input2, (a, b) => {ev2.floorDiv(a, b)}) + output + } +} + +object FloorDiv { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): + FloorDiv[T, D] = new FloorDiv() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorMod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorMod.scala new file mode 100644 index 00000000000..05e22c913e4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorMod.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class FloorMod[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + output = Tensor[D]() + private val buffer = Tensor[D]() + + override def updateOutput(input: Table): Tensor[D] = { + val input1 = input[Tensor[D]](1) + val input2 = input[Tensor[D]](2) + output.resizeAs(input1).copy(input1) + buffer.resizeAs(output).copy(output) + buffer.map(input2, (a, b) => ev2.floorDiv(a, b)).cmul(input2) + output.sub(buffer) + } + + override def clearState(): FloorMod.this.type = { + super.clearState() + buffer.set() + this + } +} + +object FloorMod { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + : FloorMod[T, D] = new FloorMod() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InTopK.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InTopK.scala new file mode 100644 index 00000000000..2feb5abc1e5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InTopK.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class InTopK[T: ClassTag](k: Int, startFromZero: Boolean = false)(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[Boolean], T]{ + + override def updateOutput(input: Table): Tensor[Boolean] = { + + output = Tensor[Boolean]() + + val predictions = input[Tensor[Float]](1) + val targets = input[Tensor[Int]](2) + + require(predictions.nDimension() == 2, "predictions should be 2D in InTopK") + require(targets.nDimension() == 1, "targets shoudl be 1D in InTopK") + + val batchSize = targets.size(1) + output.resizeAs(targets) + var i = 1 + while(i <= batchSize) { + var j = 1 + var largerNum = 0 + val d = if (startFromZero) targets.valueAt(i) + 1 else targets.valueAt(i) + val element = predictions.valueAt(i, d) + while(j <= predictions.size(2)) { + if (element < predictions.valueAt(i, j) && j != d) { + largerNum += 1 + } + j += 1 + } + output.setValue(i, largerNum < k) + i += 1 + } + output + } +} + +object InTopK { + def apply[T: ClassTag](k: Int, startFromZero: Boolean = false)(implicit ev: TensorNumeric[T]) + : InTopK[T] = new InTopK[T](k, startFromZero) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Mod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Mod.scala new file mode 100644 index 00000000000..3f5bc45f02d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Mod.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Mod[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + output = Tensor[D]() + private val buffer = Tensor[D]() + + override def updateOutput(input: Table): Tensor[D] = { + val input1 = input[Tensor[D]](1) + val input2 = input[Tensor[D]](2) + output.resizeAs(input1).copy(input1) + buffer.resizeAs(output).copy(output) + buffer.div(input2).apply1(ev2.truncate(_)).cmul(input2) + output.sub(buffer) + } + + override def clearState(): Mod.this.type = { + super.clearState() + buffer.set() + this + } +} + +object Mod { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + : Mod[T, D] = new Mod() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOps.scala new file mode 100644 index 00000000000..ba0ed27040f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOps.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class RangeOps[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T] { + + output = Tensor[D]() + + override def updateOutput(input: Table): Tensor[D] = { + require(input.length() == 3, s"require 3 tensors as input, but get ${input.length()}") + val start = input[Tensor[D]](1).value() + val limit = input[Tensor[D]](2).value() + val delta = input[Tensor[D]](3).value() + + output = Tensor[D](ev2.toType[Int](ev2.divide(ev2.minus(limit, start), delta))) + var i = 0 + while(i < output.size(1)) { + output.setValue(i + 1, ev2.plus(start, ev2.times(ev2.fromType(i), delta))) + i += 1 + } + output + } +} + +object RangeOps { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + : RangeOps[T, D] = new RangeOps[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rint.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rint.scala new file mode 100644 index 00000000000..51b147096fd --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rint.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class Rint[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends Operation[Tensor[Float], Tensor[Float], T]{ + + output = Tensor[Float]() + + override def updateOutput(input: Tensor[Float]): Tensor[Float] = { + output.resizeAs(input) + output.map(input, (a, b) => {Math.rint(b).toFloat}) + output + } +} + +object Rint { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Rint[T] = new Rint[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Round.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Round.scala new file mode 100644 index 00000000000..dcd6ee66e94 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Round.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, IntType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class Round[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[D], Tensor[D], T]{ + + output = Tensor[D]() + + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output.resizeAs(input) + + output.zipWith[D, D](input, output, (a, b) => { + ev2.round(a) + }) + output + } +} + +object Round { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): + Round[T, D] = new Round[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDiv.scala new file mode 100644 index 00000000000..0031db2e524 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDiv.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class TruncateDiv[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + output = Tensor[D]() + + override def updateOutput(input: Table): Tensor[D] = { + val input1 = input[Tensor[D]](1) + val input2 = input[Tensor[D]](2) + + output.resizeAs(input1).copy(input1) + output.div(input2).apply1(ev2.truncate(_)) + output + } +} + +object TruncateDiv { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): + TruncateDiv[T, D] = new TruncateDiv() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 061ed439b27..3aadc5ec4bf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2133,6 +2133,19 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( override def inv(): Tensor[T] = { this.apply1(a => ev.inv(a)) } + + override def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] = { + DenseTensorDimApply.dimApply2[T](result.asInstanceOf[DenseTensor[T]], this, dim - 1, + (r, rOffset, rStride, rSize, t, tOffset, tStride, tSize) => { + r(rOffset) = t(tOffset) + var i = 1 + while(i < tSize) { + r(rOffset) = reducer(r(rOffset), t(tOffset + i * tStride)) + i += 1 + } + }) + result + } } object DenseTensor { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index a57d2cf7a9b..f2bff95f683 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1423,4 +1423,7 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def inv(): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] = + throw new UnsupportedOperationException(errorString) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 55ea2845536..aef79240b30 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -1037,6 +1037,10 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( override def negative(x: Tensor[T]): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + + override def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } } object SparseTensor{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala index 632e96007dd..f0f4415e4d4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala @@ -787,4 +787,13 @@ trait TensorMath[T] { * @return this tensor */ def negative(x : Tensor[T]): Tensor[T] + + /** + * Reduce along the given dimension with the given reducer, and copy the result to the result + * tensor + * @param dim + * @param result + * @param reducer + */ + def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index 96f1fa2349a..6f65a2f645a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -184,6 +184,12 @@ object TensorNumericMath { def isNan(a: T): Boolean def isInf(a: T): Boolean + + def round(a: T): T + + def truncate(a: T): T + + def floorDiv(a: T, b: T): T } /** @@ -432,6 +438,18 @@ object TensorNumericMath { override def isNan(a: T): Boolean = throw new UnsupportedOperationException(typeName + " in tensor does not support isNan operation") + + override def round(a: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support round operation") + + override def truncate(a: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support truncate operation") + + override def floorDiv(a: T, b: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support floorDiv operation") } /** @@ -725,6 +743,22 @@ object TensorNumericMath { override def isNan(a: Float): Boolean = java.lang.Float.isNaN(a) override def isInf(a: Float): Boolean = java.lang.Float.isInfinite(a) + + override def round(a: Float): Float = Math.round(a).toFloat + + override def truncate(a: Float): Float = { + if (a >= 0) { + Math.floor(a).toFloat + } else if (a == Math.floor(a)) { + a + } else { + Math.floor(a).toFloat + 1 + } + } + + override def floorDiv(a: Float, b: Float): Float = { + Math.floor(a / b).toFloat + } } implicit object NumericDouble extends UndefinedTensorNumeric[Double]("Double") { @@ -1008,6 +1042,22 @@ object TensorNumericMath { override def isNan(a: Double): Boolean = java.lang.Double.isNaN(a) override def isInf(a: Double): Boolean = java.lang.Double.isInfinite(a) + + override def round(a: Double): Double = Math.round(a).toDouble + + override def truncate(a: Double): Double = { + if (a >= 0) { + Math.floor(a) + } else if (a == Math.floor(a)) { + a + } else { + Math.floor(a) + 1 + } + } + + override def floorDiv(a: Double, b: Double): Double = { + Math.floor(a / b) + } } implicit object NumericString extends UndefinedTensorNumeric[String]("String") { @@ -1138,6 +1188,36 @@ object TensorNumericMath { i += 1 } } + + override def round(a: Int): Int = a + + override def vDiv(n: Int, a: Array[Int], aOffset: Int, b: Array[Int], bOffset: Int, + y: Array[Int], yOffset: Int): Unit = { + var i = 0 + while(i < n) { + y(i + yOffset) = a(i + aOffset) / b(i + bOffset) + i += 1 + } + } + + override def vMul(n: Int, a: Array[Int], aOffset: Int, b: Array[Int], bOffset: Int, + y: Array[Int], yOffset: Int): Unit = { + var i = 0 + while(i < n) { + y(i + yOffset) = a(i + aOffset) * b(i + bOffset) + i += 1 + } + } + + override def truncate(a: Int): Int = a + + override def floorDiv(a: Int, b: Int): Int = { + var var2 = a / b + if ((a ^ b) < 0 && var2 * b != a) { + var2 -= 1 + } + var2 + } } implicit object NumericLong extends UndefinedTensorNumeric[Long]("Long") { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala index dd80a8aa5eb..fa2abf259fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TFUtils.scala @@ -135,6 +135,17 @@ object TFUtils { j += 1 } Tensor(tmp, shape) + case DataType.DT_BOOL => + val buffer = ByteBuffer.wrap(content) + buffer.order(endian) + val params = buffer + val tmp = new Array[Boolean](params.capacity()) + var j = 0 + while (j < params.capacity()) { + tmp(j) = if (params.get(j) == 0) false else true + j += 1 + } + Tensor(tmp, shape) case t => throw new IllegalArgumentException(s"DataType: $t not supported yet") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala index 69f476efabb..6d40cb9e7d2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala @@ -123,6 +123,8 @@ object Tensorflow { DataType.DT_FLOAT } else if (value.getType() == IntType) { DataType.DT_INT32 + } else if (value.getType() == BooleanType) { + DataType.DT_BOOL } else { throw new UnsupportedOperationException(s"data type ${value.getType()} is not supported") } @@ -507,6 +509,19 @@ object Tensorflow { i += 1 } (buffer, DataType.DT_INT32) + } else if (value.getType() == BooleanType) { + val array = value.asInstanceOf[Tensor[Boolean]].storage().array() + val offset = value.storageOffset() - 1 + val buffer = ByteBuffer.allocate(array.length) + buffer.order(byteOrder) + val t : Byte = 1 + val f : Byte = 0 + var i = 0 + while (i < value.nElement()) { + buffer.put(if (array(i + offset)) t else f) + i += 1 + } + (buffer, DataType.DT_BOOL) } else { throw new UnsupportedOperationException(s"") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/All.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/All.scala new file mode 100644 index 00000000000..e10e90fa543 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/All.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.All +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class All extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val keepDims = getBoolean(nodeDef.getAttrMap, "keep_dims") + All[T](keepDims, true) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Any.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Any.scala new file mode 100644 index 00000000000..855f6d8966b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Any.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Any +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Any extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val keepDims = getBoolean(nodeDef.getAttrMap, "keep_dims") + Any[T](keepDims, true) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Exp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Exp.scala new file mode 100644 index 00000000000..c8b52868b3d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Exp.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Exp, FloorDiv} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Exp extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Exp[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Exp[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Exp when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1.scala new file mode 100644 index 00000000000..b020183213a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Exp, Expm1} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Expm1 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Expm1[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Expm1[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Expm1 when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDiv.scala new file mode 100644 index 00000000000..f583f251cd0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDiv.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{FloorDiv, Round} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class FloorDiv extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + FloorDiv[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + FloorDiv[T, Double]() + } else if (t == DataType.DT_INT32) { + FloorDiv[T, Int]() + } else { + throw new UnsupportedOperationException(s"Not support load FloorDiv when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorMod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorMod.scala new file mode 100644 index 00000000000..901571d8175 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorMod.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{FloorMod, Mod} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class FloorMod extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + FloorMod[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + FloorMod[T, Double]() + } else if (t == DataType.DT_INT32) { + FloorMod[T, Int]() + } else { + throw new UnsupportedOperationException(s"Not support load Mod when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopK.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopK.scala new file mode 100644 index 00000000000..34a86f414a8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopK.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.InTopK +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class InTopK extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val k = getInt(nodeDef.getAttrMap, "k") + InTopK[T](k, true) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mod.scala new file mode 100644 index 00000000000..ff86dceee7b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mod.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{FloorDiv, Mod} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Mod extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Mod[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Mod[T, Double]() + } else if (t == DataType.DT_INT32) { + Mod[T, Int]() + } else { + throw new UnsupportedOperationException(s"Not support load Mod when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Range.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Range.scala new file mode 100644 index 00000000000..9ae249efd39 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Range.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.nn.ops.RangeOps + +import scala.reflect.ClassTag + +class Range extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "Tidx") + if (t == DataType.DT_FLOAT) { + RangeOps[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + RangeOps[T, Double]() + } else if (t == DataType.DT_INT32) { + RangeOps[T, Int]() + } else { + throw new UnsupportedOperationException(s"Not support load Log when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rint.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rint.scala new file mode 100644 index 00000000000..b8910bb8250 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rint.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Rint +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Rint extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + Rint[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Round.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Round.scala new file mode 100644 index 00000000000..533766f6a7c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Round.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Abs +import com.intel.analytics.bigdl.nn.ops.Round +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Round extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Round[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Round[T, Double]() + } else if (t == DataType.DT_INT32) { + Round[T, Int]() + } else { + throw new UnsupportedOperationException(s"Not support load Round when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDiv.scala new file mode 100644 index 00000000000..82be079ac08 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDiv.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{FloorDiv, TruncateDiv} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class TruncateDiv extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_INT32) { + TruncateDiv[T, Int]() + } else { + throw new UnsupportedOperationException(s"Not support load TruncateDiv when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateMod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateMod.scala new file mode 100644 index 00000000000..8ca415a420f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateMod.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Mod +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class TruncateMod extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Mod[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Mod[T, Double]() + } else if (t == DataType.DT_INT32) { + Mod[T, Int]() + } else { + throw new UnsupportedOperationException(s"Not support load Mod when type is ${t}") + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AllSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AllSpec.scala new file mode 100644 index 00000000000..921077c885a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AllSpec.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{booleanAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class AllSpec extends TensorflowSpecHelper { + "All ops" should "be correct when keep_dims is true" in { + val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val indice = Tensor[Int](T(1)) + val (t1, t2) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("all_test") + .putAttr("keep_dims", booleanAttr(true)) + .setOp("All"), + Seq(data, indice), + 0 + ) + t1.map(t2, (a, b) => { + require(a == b, "output not match") + b + }) + } + + "All ops" should "be correct when indice contains several value" in { + val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val indice = Tensor[Int](T(0, 1)) + val (t1, t2) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("all_test") + .putAttr("keep_dims", booleanAttr(true)) + .setOp("All"), + Seq(data, indice), + 0 + ) + t1.map(t2, (a, b) => { + require(a == b, "output not match") + b + }) + } + + "All ops" should "be correct when keep_dims is false" in { + val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val indice = Tensor[Int](T(1)) + val (t1, t2) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("all_test") + .putAttr("keep_dims", booleanAttr(false)) + .setOp("All"), + Seq(data, indice), + 0 + ) + t1.map(t2, (a, b) => { + require(a == b, "output not match") + b + }) + } + + "All ops" should "be correct when indice is scalar" in { + val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val indice = Tensor.scalar[Int](1) + val (t1, t2) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("all_test") + .putAttr("keep_dims", booleanAttr(false)) + .setOp("All"), + Seq(data, indice), + 0 + ) + t1.map(t2, (a, b) => { + require(a == b, "output not match") + b + }) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AnySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AnySpec.scala new file mode 100644 index 00000000000..defbba9e474 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AnySpec.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.booleanAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.NodeDef + +class AnySpec extends TensorflowSpecHelper { + "Any ops" should "be correct when keep_dims is true" in { + val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val indice = Tensor[Int](T(1)) + val (t1, t2) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("any_test") + .putAttr("keep_dims", booleanAttr(true)) + .setOp("Any"), + Seq(data, indice), + 0 + ) + t1.map(t2, (a, b) => { + require(a == b, "output not match") + b + }) + } + + "Any ops" should "be correct when indice contains several value" in { + val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val indice = Tensor[Int](T(0, 1)) + val (t1, t2) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("any_test") + .putAttr("keep_dims", booleanAttr(true)) + .setOp("Any"), + Seq(data, indice), + 0 + ) + t1.map(t2, (a, b) => { + require(a == b, "output not match") + b + }) + } + + "Any ops" should "be correct when keep_dims is false" in { + val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val indice = Tensor[Int](T(1)) + val (t1, t2) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("any_test") + .putAttr("keep_dims", booleanAttr(false)) + .setOp("Any"), + Seq(data, indice), + 0 + ) + t1.map(t2, (a, b) => { + require(a == b, "output not match") + b + }) + } + + "Any ops" should "be correct when indice is scalar" in { + val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val indice = Tensor.scalar[Int](1) + val (t1, t2) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("any_test") + .putAttr("keep_dims", booleanAttr(false)) + .setOp("Any"), + Seq(data, indice), + 0 + ) + t1.map(t2, (a, b) => { + require(a == b, "output not match") + b + }) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpSpec.scala new file mode 100644 index 00000000000..f92675d923c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpSpec.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class ExpSpec extends TensorflowSpecHelper { + + "Exp" should "be correct for float" in { + compare( + NodeDef.newBuilder() + .setName("exp_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("Exp"), + Seq(Tensor[Float](10).rand()), + 0 + ) + } + + "Exp" should "be correct for double" in { + compare( + NodeDef.newBuilder() + .setName("exp_test") + .putAttr("T", typeAttr(DataType.DT_DOUBLE)) + .setOp("Exp"), + Seq(Tensor[Double](10).rand()), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala new file mode 100644 index 00000000000..2cadd09b594 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class Expm1Spec extends TensorflowSpecHelper { + "Expm1" should "be correct for float" in { + compare( + NodeDef.newBuilder() + .setName("expm1_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("Expm1"), + Seq(Tensor[Float](10).rand()), + 0 + ) + } + + "Expm1" should "be correct for double" in { + compare( + NodeDef.newBuilder() + .setName("expm1_test") + .putAttr("T", typeAttr(DataType.DT_DOUBLE)) + .setOp("Expm1"), + Seq(Tensor[Double](10).rand()), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDivSpec.scala new file mode 100644 index 00000000000..3519ff34494 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDivSpec.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class FloorDivSpec extends TensorflowSpecHelper { + "FloorDiv" should "be correct for float" in { + compare( + NodeDef.newBuilder() + .setName("floorDiv_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("FloorDiv"), + Seq(Tensor[Float](T(1, 1.44, 4.8, -1, -1.44, -4.8, 5)), + Tensor[Float](T(1, 1.2, 3, 1, 1.2, 3, -2))), + 0 + ) + } + + "FloorDiv" should "be correct for Int" in { + compare( + NodeDef.newBuilder() + .setName("floorDiv_test") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .setOp("FloorDiv"), + Seq(Tensor[Int](T(1, 1.44, 4.8, -1, -1.44, -4.8, 5)), + Tensor[Int](T(1, 1.2, 3, 1, 1.2, 3, -2))), + 0 + ) + } + + "FloorDiv" should "be correct for double" in { + compare( + NodeDef.newBuilder() + .setName("floorDiv_test") + .putAttr("T", typeAttr(DataType.DT_DOUBLE)) + .setOp("FloorDiv"), + Seq(Tensor[Double](T(1, 1.44, 4.8, -1, -1.44, -4.8, 5)), + Tensor[Double](T(1, 1.2, 3, 1, 1.2, 3, -2))), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorModSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorModSpec.scala new file mode 100644 index 00000000000..d0eca923b89 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorModSpec.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class FloorModSpec extends TensorflowSpecHelper { + "FloorMod" should "be correct for Int" in { + compare( + NodeDef.newBuilder() + .setName("floor_mod_test") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .setOp("FloorMod"), + Seq(Tensor[Int](T(1, 5, 10, -1, 5, -5, -10, 10, -10)), + Tensor[Int](T(7, 5, 7, -7, -5, -5, 7, -7, -7))), + 0 + ) + } + + "FloorMod" should "be correct for float" in { + compare( + NodeDef.newBuilder() + .setName("floor_mod_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("FloorMod"), + Seq(Tensor[Float](T(1, 1.44, 4.8, -1, -1.44, -4.8)), + Tensor[Float](T(1, 1.2, 3, 1, 1.2, 3))), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopKSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopKSpec.scala new file mode 100644 index 00000000000..b66005dd6c7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopKSpec.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{intAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class InTopKSpec extends TensorflowSpecHelper { + "InTopK" should "be correct" in { + val (a, b) = getResult[Boolean]( + NodeDef.newBuilder() + .setName("inv_grad_test") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .putAttr("k", intAttr(2)) + .setOp("InTopK"), + Seq(Tensor[Float](T(T(1, 2, 3, 4), T(3, 2, 5, 6))), Tensor[Int](T(1, 2))), + 0 + ) + a.map(b, (e1, e2) => { + require(e1 == e2, "output not match") + e2 + }) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ModSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ModSpec.scala new file mode 100644 index 00000000000..f2773d2f0a5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ModSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class ModSpec extends TensorflowSpecHelper { + + "Mod" should "be correct for Int" in { + compare( + NodeDef.newBuilder() + .setName("mod_test") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .setOp("Mod"), + Seq(Tensor[Int](T(1, 5, 10, -1, 5, -5, -10, 10, -10)), + Tensor[Int](T(7, 5, 7, -7, -5, -5, 7, -7, -7))), + 0 + ) + } + + "Mod" should "be correct for float" in { + compare( + NodeDef.newBuilder() + .setName("mod_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("Mod"), + Seq(Tensor[Float](T(1, 1.44, 4.8, -1, -1.44, -4.8)), + Tensor[Float](T(1, 1.2, 3, 1, 1.2, 3))), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RangeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RangeSpec.scala new file mode 100644 index 00000000000..fcc0839ed1d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RangeSpec.scala @@ -0,0 +1,44 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class RangeSpec extends TensorflowSpecHelper { + "Range" should "be correct when input is int" in { + compare( + NodeDef.newBuilder() + .setName("range_test") + .setOp("Range") + .putAttr("Tidx", typeAttr(DataType.DT_INT32)), + Seq(Tensor.scalar[Int](3), Tensor.scalar[Int](18), Tensor.scalar[Int](3)), 0 + ) + } + + "Range" should "be correct when input is float" in { + compare( + NodeDef.newBuilder() + .setName("range_test") + .setOp("Range") + .putAttr("Tidx", typeAttr(DataType.DT_FLOAT)), + Seq(Tensor.scalar[Float](3), Tensor.scalar[Float](1), Tensor.scalar[Float](-0.5f)), 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RintSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RintSpec.scala new file mode 100644 index 00000000000..f6d2b081a22 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RintSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class RintSpec extends TensorflowSpecHelper { + "Rint" should "be correct" in { + compare( + NodeDef.newBuilder() + .setName("rint_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("Rint"), + Seq(Tensor[Float](T(-1.5, 0.5000001, -1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0))), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RoundSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RoundSpec.scala new file mode 100644 index 00000000000..368f0baa567 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RoundSpec.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class RoundSpec extends TensorflowSpecHelper { + "Round" should "be correct for float" in { + compare( + NodeDef.newBuilder() + .setName("round_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("Round"), + Seq(Tensor[Float](T(1, 2.2, 4.6, -1, -2.2, -4.6))), + 0 + ) + } + + + "Round" should "be correct for double" in { + compare( + NodeDef.newBuilder() + .setName("round_test") + .putAttr("T", typeAttr(DataType.DT_DOUBLE)) + .setOp("Round"), + Seq(Tensor[Double](T(1, 2.2, 4.6, -1, -2.2, -4.6))), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDivSpec.scala new file mode 100644 index 00000000000..e9ad8d2557e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDivSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class TruncateDivSpec extends TensorflowSpecHelper { + + "TruncateDiv" should "be correct for int" in { + compare( + NodeDef.newBuilder() + .setName("truncateDiv_test") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .setOp("TruncateDiv"), + Seq(Tensor[Int](T(1, 6, 8, -1, -6, -8)), + Tensor[Int](T(1, 5, 5, 1, 5, 5))), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateModSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateModSpec.scala new file mode 100644 index 00000000000..309f951559c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateModSpec.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class TruncateModSpec extends TensorflowSpecHelper { + "TruncateMod" should "be correct for Int" in { + compare( + NodeDef.newBuilder() + .setName("trunc_mod_test") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .setOp("TruncateMod"), + Seq(Tensor[Int](T(1, 5, 10, -1, 5, -5, -10, 10, -10)), + Tensor[Int](T(7, 5, 7, -7, -5, -5, 7, -7, -7))), + 0 + ) + } + + "TruncateMod" should "be correct for float" in { + compare( + NodeDef.newBuilder() + .setName("trunc_mod_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("TruncateMod"), + Seq(Tensor[Float](T(1, 1.44, 4.8, -1, -1.44, -4.8)), + Tensor[Float](T(1, 1.2, 3, 1, 1.2, 3))), + 0 + ) + } +} From a5f7ea4ba6858f441a248de9b2db9b90f641895f Mon Sep 17 00:00:00 2001 From: dding3 Date: Fri, 10 Nov 2017 15:16:15 -0500 Subject: [PATCH 0519/1065] support SAME padding in 3d conv and allows user config padding size in convlstm and convlstm3d (#1862) * support SAME padding in 3d conv * allows user config padding size in convlstm and convlstm3d --- .../bigdl/dllib/nn/ConvLSTMPeephole.scala | 36 ++++---- .../bigdl/dllib/nn/ConvLSTMPeephole3D.scala | 33 ++++---- .../bigdl/dllib/nn/NNPrimitive.scala | 56 ++++++++----- .../dllib/nn/SpatialAveragePooling.scala | 16 +++- .../bigdl/dllib/nn/SpatialConvolution.scala | 9 +- .../bigdl/dllib/nn/SpatialMaxPooling.scala | 9 +- .../analytics/bigdl/dllib/nn/Utils.scala | 84 +++++++++++++------ .../dllib/nn/VolumetricConvolution.scala | 72 +++++++++++++--- .../nn/quantized/SpatialConvolution.scala | 9 +- .../dllib/utils/python/api/PythonBigDL.scala | 6 +- .../dllib/nn/ConvLSTMPeephole3DSpec.scala | 21 +++-- .../torch/VolumetricConvolutionSpec.scala | 38 ++++++++- 12 files changed, 285 insertions(+), 104 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala index 25ea4272064..9577d8a026d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala @@ -33,7 +33,10 @@ import scala.reflect.ClassTag * @param outputSize number of output planes the convolution layer will produce * @param kernelI Convolutional filter size to convolve input * @param kernelC Convolutional filter size to convolve cell - * @param stride The step of the convolution + * @param stride The step of the convolution, default is 1 + * @param padding The step of the convolution, default is -1, + * behaves same with SAME padding in tensorflow. + * Default stride,padding ensure last 2 dim of output shape is the same with input * @param wRegularizer: instance of [[Regularizer]] (eg. L1 or L2 regularization), applied to the input weights matrices. * @param uRegularizer: instance [[Regularizer]] @@ -49,7 +52,8 @@ class ConvLSTMPeephole[T : ClassTag]( val outputSize: Int, val kernelI: Int, val kernelC: Int, - val stride: Int, + val stride: Int = 1, + val padding: Int = -1, var wRegularizer: Regularizer[T] = null, var uRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, @@ -70,7 +74,6 @@ class ConvLSTMPeephole[T : ClassTag]( // val joinDim = 2 override var preTopology: TensorModule[T] = null - // override var preTopology: AbstractModule[Activity, Activity, T] = null // override def preTopology: AbstractModule[Activity, Activity, T] = // Sequential() @@ -80,17 +83,17 @@ class ConvLSTMPeephole[T : ClassTag]( // def buildGate(offset: Int, length: Int): Sequential[T] = { // val i2g = Narrow(joinDim, offset, length) - def buildGate(): Sequential[T] = { + def buildGate(name: String = null): Sequential[T] = { val i2g = Sequential() .add(Contiguous()) .add(SpatialConvolution(inputSize, outputSize, kernelI, kernelI, - stride, stride, kernelI/2, kernelI/2, wRegularizer = wRegularizer, - bRegularizer = bRegularizer)) + stride, stride, padding, padding, wRegularizer = wRegularizer, + bRegularizer = bRegularizer).setName(name + "_i2g")) val h2g = Sequential() .add(Contiguous()) .add(SpatialConvolution(outputSize, outputSize, kernelC, kernelC, - stride, stride, kernelC/2, kernelC/2, withBias = false, - wRegularizer = uRegularizer)) + stride, stride, padding, padding, withBias = false, + wRegularizer = uRegularizer).setName(name + "_h2g")) val gate = Sequential() if (withPeephole) { @@ -113,19 +116,19 @@ class ConvLSTMPeephole[T : ClassTag]( def buildInputGate(): Sequential[T] = { // inputGate = buildGate(1 + outputSize, outputSize) - inputGate = buildGate() + inputGate = buildGate("InputGate") inputGate } def buildForgetGate(): Sequential[T] = { // forgetGate = buildGate(1, outputSize) - forgetGate = buildGate() + forgetGate = buildGate("ForgetGate") forgetGate } def buildOutputGate(): Sequential[T] = { // outputGate = buildGate(1 + 3 * outputSize, outputSize) - outputGate = buildGate() + outputGate = buildGate("OutputGate") outputGate } @@ -137,13 +140,13 @@ class ConvLSTMPeephole[T : ClassTag]( val i2h = Sequential() .add(Contiguous()) .add(SpatialConvolution(inputSize, outputSize, kernelI, kernelI, - stride, stride, kernelI/2, kernelI/2, wRegularizer = wRegularizer, - bRegularizer = bRegularizer)) + stride, stride, padding, padding, wRegularizer = wRegularizer, + bRegularizer = bRegularizer).setName("Hidden_i2h")) val h2h = Sequential() .add(Contiguous()) .add(SpatialConvolution(outputSize, outputSize, kernelC, kernelC, - stride, stride, kernelC/2, kernelC/2, withBias = false, - wRegularizer = uRegularizer)) + stride, stride, padding, padding, withBias = false, + wRegularizer = uRegularizer).setName("Hidden_h2h")) hidden .add(ParallelTable() @@ -248,13 +251,14 @@ object ConvLSTMPeephole { kernelI: Int, kernelC: Int, stride: Int = 1, + padding: Int = -1, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, cRegularizer: Regularizer[T] = null, withPeephole: Boolean = true )(implicit ev: TensorNumeric[T]): ConvLSTMPeephole[T] = { - new ConvLSTMPeephole[T](inputSize, outputSize, kernelI, kernelC, stride, + new ConvLSTMPeephole[T](inputSize, outputSize, kernelI, kernelC, stride, padding, wRegularizer, uRegularizer, bRegularizer, cRegularizer, withPeephole) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala index 46fba08e96e..c445fdfcd2a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3D.scala @@ -34,6 +34,9 @@ import scala.reflect.ClassTag * @param kernelI Convolutional filter size to convolve input * @param kernelC Convolutional filter size to convolve cell * @param stride The step of the convolution + * @param padding The step of the convolution, default is -1, + * behaves same with SAME padding in tensorflow. + * Default stride,padding ensure last 3 dim of output shape is the same with input * @param wRegularizer: instance of [[Regularizer]] (eg. L1 or L2 regularization), applied to the input weights matrices. * @param uRegularizer: instance [[Regularizer]] @@ -49,7 +52,8 @@ class ConvLSTMPeephole3D[T : ClassTag]( val outputSize: Int, val kernelI: Int, val kernelC: Int, - val stride: Int, + val stride: Int = 1, + val padding: Int = -1, var wRegularizer: Regularizer[T] = null, var uRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, @@ -69,17 +73,17 @@ class ConvLSTMPeephole3D[T : ClassTag]( override var preTopology: TensorModule[T] = null override var cell: AbstractModule[Activity, Activity, T] = buildModel() - def buildGate(): Sequential[T] = { + def buildGate(name: String = null): Sequential[T] = { val i2g = Sequential() .add(Contiguous()) .add(VolumetricConvolution(inputSize, outputSize, kernelI, kernelI, kernelI, - stride, stride, stride, kernelI/2, kernelI/2, kernelI/2, wRegularizer = wRegularizer, - bRegularizer = bRegularizer)) + stride, stride, stride, padding, padding, padding, wRegularizer = wRegularizer, + bRegularizer = bRegularizer).setName(name + "_i2g")) val h2g = Sequential() .add(Contiguous()) .add(VolumetricConvolution(outputSize, outputSize, kernelC, kernelC, kernelC, - stride, stride, stride, kernelC/2, kernelC/2, kernelC/2, wRegularizer = uRegularizer, - withBias = false)) + stride, stride, stride, padding, padding, padding, wRegularizer = uRegularizer, + withBias = false).setName(name + "_h2g")) val gate = Sequential() if (withPeephole) { @@ -101,17 +105,17 @@ class ConvLSTMPeephole3D[T : ClassTag]( } def buildInputGate(): Sequential[T] = { - inputGate = buildGate() + inputGate = buildGate("InputGate") inputGate } def buildForgetGate(): Sequential[T] = { - forgetGate = buildGate() + forgetGate = buildGate("ForgetGate") forgetGate } def buildOutputGate(): Sequential[T] = { - outputGate = buildGate() + outputGate = buildGate("OutputGate") outputGate } @@ -122,13 +126,13 @@ class ConvLSTMPeephole3D[T : ClassTag]( val i2h = Sequential() .add(Contiguous()) .add(VolumetricConvolution(inputSize, outputSize, kernelI, kernelI, kernelI, - stride, stride, stride, kernelI/2, kernelI/2, kernelI/2, wRegularizer = wRegularizer, - bRegularizer = bRegularizer)) + stride, stride, stride, padding, padding, padding, wRegularizer = wRegularizer, + bRegularizer = bRegularizer).setName("Hidden_i2h")) val h2h = Sequential() .add(Contiguous()) .add(VolumetricConvolution(outputSize, outputSize, kernelC, kernelC, kernelC, - stride, stride, stride, kernelC/2, kernelC/2, kernelC/2, withBias = false, - wRegularizer = uRegularizer)) + stride, stride, stride, padding, padding, padding, withBias = false, + wRegularizer = uRegularizer).setName("Hidden_h2h")) hidden .add(ParallelTable() @@ -233,13 +237,14 @@ object ConvLSTMPeephole3D { kernelI: Int, kernelC: Int, stride: Int = 1, + padding: Int = -1, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, cRegularizer: Regularizer[T] = null, withPeephole: Boolean = true )(implicit ev: TensorNumeric[T]): ConvLSTMPeephole3D[T] = { - new ConvLSTMPeephole3D[T](inputSize, outputSize, kernelI, kernelC, stride, + new ConvLSTMPeephole3D[T](inputSize, outputSize, kernelI, kernelC, stride, padding, wRegularizer, uRegularizer, bRegularizer, cRegularizer, withPeephole) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NNPrimitive.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NNPrimitive.scala index c471e976917..e1f5c72bd1d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NNPrimitive.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NNPrimitive.scala @@ -1249,7 +1249,10 @@ private[nn] object NNPrimitive { def unfoldedCopyVolDouble(fInput: Tensor[Double], input: Tensor[Double], kT: Int, kW: Int, kH: Int, - dT: Int, dW: Int, dH: Int, pT: Int, pW: Int, pH: Int, nInputPlane: Int, + dT: Int, dW: Int, dH: Int, + padFront: Int, padLeft: Int, padTop: Int, + padBack: Int, padRight: Int, padBottom: Int, + nInputPlane: Int, inputDepth: Int, inputWidth: Int, inputHeight: Int, outputDepth: Int, outputWidth: Int, outputHeight: Int): Unit = { val inputData = input.storage().array() @@ -1270,16 +1273,17 @@ private[nn] object NNPrimitive { kw * (outputDepth * outputHeight * outputWidth) + fInput.storageOffset() - 1 val srcOffset = nip * (inputDepth * inputHeight * inputWidth) + input.storageOffset() - 1 - if (pT > 0 || pH > 0 || pW > 0) { + if (padFront > 0 || padBack > 0 || padLeft > 0 || padRight > 0 || + padBottom > 0 || padTop > 0) { t = 0 while (t < outputDepth) { - it = t * dT - pT + kt + it = t * dT - padFront + kt var y = 0 while (y < outputHeight) { - iy = y * dH - pH + kh + iy = y * dH - padTop + kh x = 0 while (x < outputWidth) { - ix = x * dW - pW + kw + ix = x * dW - padLeft + kw if (it < 0 || it >= inputDepth || iy < 0 || iy >= inputHeight || ix < 0 || ix >= inputWidth) { fInputData(dstOffset + t * outputHeight * outputWidth + y * outputWidth + x) = 0 @@ -1318,7 +1322,10 @@ private[nn] object NNPrimitive { def unfoldedCopyVolFloat(fInput: Tensor[Float], input: Tensor[Float], kT: Int, kW: Int, kH: Int, - dT: Int, dW: Int, dH: Int, pT: Int, pW: Int, pH: Int, nInputPlane: Int, + dT: Int, dW: Int, dH: Int, + padFront: Int, padLeft: Int, padTop: Int, + padBack: Int, padRight: Int, padBottom: Int, + nInputPlane: Int, inputDepth: Int, inputWidth: Int, inputHeight: Int, outputDepth: Int, outputWidth: Int, outputHeight: Int): Unit = { val inputData = input.storage().array() @@ -1339,16 +1346,17 @@ private[nn] object NNPrimitive { kw * (outputDepth * outputHeight * outputWidth) + fInput.storageOffset() - 1 val srcOffset = nip * (inputDepth * inputHeight * inputWidth) + input.storageOffset() - 1 - if (pT > 0 || pH > 0 || pW > 0) { + if (padFront > 0 || padLeft > 0 || padTop > 0 || padBack > 0 + || padRight > 0 || padBottom > 0) { t = 0 while (t < outputDepth) { - it = t * dT - pT + kt + it = t * dT - padFront + kt var y = 0 while (y < outputHeight) { - iy = y * dH - pH + kh + iy = y * dH - padTop + kh x = 0 while (x < outputWidth) { - ix = x * dW - pW + kw + ix = x * dW - padLeft + kw if (it < 0 || it >= inputDepth || iy < 0 || iy >= inputHeight || ix < 0 || ix >= inputWidth) { fInputData(dstOffset + t * outputHeight * outputWidth + y * outputWidth + x) = 0f @@ -1386,7 +1394,10 @@ private[nn] object NNPrimitive { } def unfoldedAccVolDouble(fInput: Tensor[Double], input: Tensor[Double], kT: Int, kW: Int, kH: Int, - dT: Int, dW: Int, dH: Int, pT: Int, pW: Int, pH: Int, nInputPlane: Int, inputDepth: Int, + dT: Int, dW: Int, dH: Int, + padFront: Int, padLeft: Int, padTop: Int, + padBack: Int, padRight: Int, padBottom: Int, + nInputPlane: Int, inputDepth: Int, inputWidth: Int, inputHeight: Int, outputDepth: Int, outputWidth: Int, outputHeight: Int): Unit = { var nip, kt, kw, kh, t, y, x, it, ix, iy = 0 @@ -1407,16 +1418,17 @@ private[nn] object NNPrimitive { val dstOffset = nip * (inputDepth * inputHeight * inputWidth) + input.storageOffset() - 1 - if (pT > 0 || pH > 0 || pW > 0) { + if (padFront > 0 || padLeft > 0 || padTop > 0 || padBack > 0 + || padRight > 0 || padBottom > 0) { t = 0 while (t < outputDepth) { - it = t * dT - pT + kt + it = t * dT - padFront + kt y = 0 while (y < outputHeight) { - iy = y * dH - pH + kh + iy = y * dH - padTop + kh x = 0 while (x < outputWidth) { - ix = x * dW - pW + kw + ix = x * dW - padLeft + kw if (it < 0 || it >= inputDepth || iy < 0 || iy >= inputHeight || ix < 0 || ix >= inputWidth) { @@ -1463,7 +1475,10 @@ private[nn] object NNPrimitive { } def unfoldedAccVolFloat(fInput: Tensor[Float], input: Tensor[Float], kT: Int, kW: Int, kH: Int, - dT: Int, dW: Int, dH: Int, pT: Int, pW: Int, pH: Int, nInputPlane: Int, inputDepth: Int, + dT: Int, dW: Int, dH: Int, + padFront: Int, padLeft: Int, padTop: Int, + padBack: Int, padRight: Int, padBottom: Int, + nInputPlane: Int, inputDepth: Int, inputWidth: Int, inputHeight: Int, outputDepth: Int, outputWidth: Int, outputHeight: Int): Unit = { var nip, kt, kw, kh, t, y, x, it, ix, iy = 0 @@ -1484,16 +1499,17 @@ private[nn] object NNPrimitive { val dstOffset = nip * (inputDepth * inputHeight * inputWidth) + input.storageOffset() - 1 - if (pT > 0 || pH > 0 || pW > 0) { + if (padFront > 0 || padLeft > 0 || padTop > 0 || padBack > 0 + || padRight > 0 || padBottom > 0) { t = 0 while (t < outputDepth) { - it = t * dT - pT + kt + it = t * dT - padFront + kt y = 0 while (y < outputHeight) { - iy = y * dH - pH + kh + iy = y * dH - padTop + kh x = 0 while (x < outputWidth) { - ix = x * dW - pW + kw + ix = x * dW - padLeft + kw if (it < 0 || it >= inputDepth || iy < 0 || iy >= inputHeight || ix < 0 || ix >= inputWidth) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala index a54eb1831aa..5a9083af617 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala @@ -335,12 +335,18 @@ class SpatialAveragePooling[T: ClassTag]( kW = inputWidth } - val (padTop, padBottom, padLeft, padRight, outputHeight, outputWidth) = + val sizes = if (padW == -1 && padH == -1) { Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW) } else { Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW, padH, padW, ceilMode) } + val padTop = sizes(0) + val padBottom = sizes(1) + val padLeft = sizes(2) + val padRight = sizes(3) + val outputHeight = sizes(4) + val outputWidth = sizes(5) if (input.dim() == 3) { format match { @@ -644,7 +650,7 @@ class SpatialAveragePooling[T: ClassTag]( val inputHeight = inputSize(dimh - 1) val inputWidth = inputSize(dimw - 1) - val (padTop, padBottom, padLeft, padRight, outputHeight, outputWidth) = + val sizes = if (padW == -1 && padH == -1) { // no ceil/floor mode in SAME padding Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW) @@ -654,6 +660,12 @@ class SpatialAveragePooling[T: ClassTag]( require(kW / 2 >= padW && kH / 2 >= padH, "pad should be smaller than half of kernel size") Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW, padH, padW, ceilMode) } + val padTop = sizes(0) + val padBottom = sizes(1) + val padLeft = sizes(2) + val padRight = sizes(3) + val outputHeight = sizes(4) + val outputWidth = sizes(5) gradInput.resize(inputSize).zero() if (inputSize.length == 3) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala index fb44fb4deb9..adebd7d05d7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala @@ -240,7 +240,7 @@ class SpatialConvolution[T: ClassTag]( val inputWidth = input.size(dimWidth) val inputHeight = input.size(dimHeight) - val (padTop, padBottom, padLeft, padRight, outputHeight, outputWidth) = + val sizes = if (padW == -1 && padH == -1) { Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW) } else { @@ -248,6 +248,13 @@ class SpatialConvolution[T: ClassTag]( kernelH, kernelW, padH, padW, ceilMode = false) } + val padTop = sizes(0) + val padBottom = sizes(1) + val padLeft = sizes(2) + val padRight = sizes(3) + val outputHeight = sizes(4) + val outputWidth = sizes(5) + require(outputWidth >= 1 && outputHeight >= 1, s"output size is too small. outputWidth: $outputWidth, outputHeight: $outputHeight") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala index d27278dffd0..39348e6cfef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala @@ -97,7 +97,7 @@ class SpatialMaxPooling[T: ClassTag]( val inputHeight = input.size(dimh) val inputWidth = input.size(dimw) - val (padTop, _, padLeft, _, oHeight, oWidth) = + val sizes = if (padW == -1 && padH == -1) { // no ceil/floor mode in SAME padding Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW) @@ -112,6 +112,13 @@ class SpatialMaxPooling[T: ClassTag]( Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW, padH, padW, ceilMode) } + val padTop = sizes(0) + val padBottom = sizes(1) + val padLeft = sizes(2) + val padRight = sizes(3) + val oHeight = sizes(4) + val oWidth = sizes(5) + if (input.dim() == 3) { format match { case DataFormat.NCHW => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index 58190608ed5..0cb1b237d53 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -299,61 +299,97 @@ object Utils { /** * - * @return (padTop, padBottom, padLeft, padRight, outputHeight, outputWidth) + * @return Array(padTop, padBottom, padLeft, padRight, outputHeight, outputWidth) + * or Array(padFront, padBackward, padTop, padBottom, padLeft, padRight, + * outputDepth, outputHeight, outputWidth) */ private[nn] def getSAMEOutSizeAndPadding( - inputHeight: Int, - inputWidth: Int, - dH: Int, - dW: Int, - kH: Int, - kW: Int - ): (Int, Int, Int, Int, Int, Int) = { + inputHeight: Int, + inputWidth: Int, + dH: Int, + dW: Int, + kH: Int, + kW: Int, + inputDepth: Int = -1, + dT: Int = -1, + kT: Int = -1): Array[Int] = { val oW = Math.ceil(inputWidth.toFloat / dW.toFloat).toInt val oH = Math.ceil(inputHeight.toFloat / dH.toFloat).toInt val padAlongWidth = Math.max(0, (oW -1) * dW + kW - inputWidth) val padAlongHeight = Math.max(0, (oH - 1) * dH + kH - inputHeight) - (padAlongHeight/2, padAlongHeight - padAlongHeight/2, + if (inputDepth != -1) { + require(dT > 0 && kT > 0, "kernel size and strideSize cannot be smaller than 0") + val oT = Math.ceil(inputDepth.toFloat / dT.toFloat).toInt + val padAlongDepth = Math.max(0, (oT -1) * dT + kT - inputDepth) + return Array(padAlongDepth/2, padAlongDepth - padAlongDepth/2, padAlongHeight/2, + padAlongHeight - padAlongHeight/2, padAlongWidth/2, padAlongWidth - padAlongWidth/2, + oT, oH, oW) + } + Array(padAlongHeight/2, padAlongHeight - padAlongHeight/2, padAlongWidth/2, padAlongWidth - padAlongWidth/2, oH, oW) } /** * - * @return (padLeft, padRight, padTop, padBottom, outputHeight, outputWidth) + * @return Array(padLeft, padRight, padTop, padBottom, outputHeight, outputWidth) + * or Array(padFront, padBack, padLeft, padRight, padTop, padBottom, + * outputDepth, outputHeight, outputWidth) */ private[nn] def getOutSizeAndPadding( - inputHeight: Int, - inputWidth: Int, - dH: Int, - dW: Int, - kH: Int, - kW: Int, - padH: Int, - padW: Int, - ceilMode: Boolean, - dilationHeight: Int = 1, - dilationWidth: Int = 1 - ): (Int, Int, Int, Int, Int, Int) = { + inputHeight: Int, + inputWidth: Int, + dH: Int, + dW: Int, + kH: Int, + kW: Int, + padH: Int, + padW: Int, + ceilMode: Boolean, + dilationHeight: Int = 1, + dilationWidth: Int = 1, + inputdepth: Int = -1, + dt: Int = -1, + kt: Int = -1, + padt: Int = 0, + dilationDepth: Int = 1): Array[Int] = { var oheight = 0 var owidth = 0 + var odepth = 0 val dilationKernelHeight = dilationHeight * (kH - 1) + 1 val dilationKernelWidth = dilationWidth * (kW - 1) + 1 + val dilationKernelDepth = if (inputdepth > 0) dilationDepth * (kt - 1) + 1 else kt if (ceilMode) { oheight = math.ceil(1.0 * (inputHeight - dilationKernelHeight + 2*padH) / dH).toInt + 1 owidth = math.ceil(1.0 * (inputWidth - dilationKernelWidth + 2*padW) / dW).toInt + 1 + if (inputdepth > 0) { + require(dt > 0 && kt > 0 && padt >= 0, + "kernel size, stride size, padding size cannot be smaller than 0") + odepth = math.ceil(1.0 * (inputdepth - dilationKernelDepth + 2*padt) / dt).toInt + 1 + } } else { oheight = math.floor(1.0 * (inputHeight - dilationKernelHeight + 2*padH) / dH).toInt + 1 owidth = math.floor(1.0 * (inputWidth - dilationKernelWidth + 2*padW) / dW).toInt + 1 + if (inputdepth > 0) { + require(dt > 0 && kt > 0 && padt >= 0, + "kernel size, stride size, padding size cannot be smaller than 0") + odepth = math.floor(1.0 * (inputdepth - dilationKernelDepth + 2*padt) / dt).toInt + 1 + } } - if (padH != 0 || padW != 0) { + if (padH != 0 || padW != 0 || padt != 0) { if ((oheight - 1) * dH >= inputHeight + padH) oheight -= 1 if ((owidth - 1) * dW >= inputWidth + padW) owidth -= 1 + if (inputdepth > 0) { + if ((odepth - 1) * dt >= inputdepth + padt) odepth -= 1 + return Array(padt, padt, padH, padH, padW, padW, odepth, oheight, owidth) + } + } else if (inputdepth > 0) { + return Array(padt, padt, padH, padH, padW, padW, odepth, oheight, owidth) } - (padH, padH, padW, padW, oheight, owidth) + Array(padH, padH, padW, padW, oheight, owidth) } private[nn] def getOutputShape(outputHeight: Int, outputWidth: Int, nOutputPlane: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala index 8b72a1769ec..d28fcbdd3e9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala @@ -129,7 +129,8 @@ class VolumetricConvolution[T: ClassTag]( private def updateOutputFrame(input: Tensor[T], output: Tensor[T], weight: Tensor[T], bias: Tensor[T], fInput: Tensor[T], kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, - dH: Int, pT: Int, pW: Int, pH: Int, nInputPlane: Int, inputDepth: Int, + dH: Int, padFront: Int, padLeft: Int, padTop: Int, padBack: Int, padRight: Int, padBottom: Int, + nInputPlane: Int, inputDepth: Int, inputWidth: Int, inputHeight: Int, nOutputPlane: Int, outputDepth: Int, outputWidth: Int, outputHeight: Int): Unit = { val output2d = output.view(nOutputPlane, outputDepth * outputHeight * outputWidth) @@ -137,11 +138,15 @@ class VolumetricConvolution[T: ClassTag]( ev.getType() match { case DoubleType => NNPrimitive.unfoldedCopyVolDouble(fInput.asInstanceOf[Tensor[Double]], - input.asInstanceOf[Tensor[Double]], kT, kW, kH, dT, dW, dH, pT, pW, pH, nInputPlane, + input.asInstanceOf[Tensor[Double]], kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, + nInputPlane, inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) case FloatType => NNPrimitive.unfoldedCopyVolFloat(fInput.asInstanceOf[Tensor[Float]], - input.asInstanceOf[Tensor[Float]], kT, kW, kH, dT, dW, dH, pT, pW, pH, nInputPlane, + input.asInstanceOf[Tensor[Float]], kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, + nInputPlane, inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) } @@ -174,9 +179,23 @@ class VolumetricConvolution[T: ClassTag]( val inputHeight = input.size(dimHeight) val inputDepth = input.size(dimDepth) - val outputDepth = (inputDepth + 2 * padT - kT) / dT + 1 - val outputHeight = (inputHeight + 2 * padH - kH) / dH + 1 - val outputWidth = (inputWidth + 2 * padW - kW) / dW + 1 + val sizes = if (padW == -1 && padH == -1 && padT == -1) { + Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, + dW, kH, kW, inputDepth, dT, kT) + } else { + Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, + dW, kH, kW, padH, padW, ceilMode = false, inputdepth = inputDepth, + dt = dT, kt = kT, padt = padT) + } + val padFront = sizes(0) + val padBack = sizes(1) + val padLeft = sizes(4) + val padRight = sizes(5) + val padTop = sizes(2) + val padBottom = sizes(3) + val outputDepth = sizes(6) + val outputHeight = sizes(7) + val outputWidth = sizes(8) require(outputWidth >= 1 && outputDepth >= 1 && outputHeight >= 1, s"Given input size: (${ input.size().mkString("x") })." + @@ -199,7 +218,8 @@ class VolumetricConvolution[T: ClassTag]( fInput.resize(kT * kW * kH * nInputPlane, outputDepth * outputHeight * outputWidth) output.resize(nOutputPlane, outputDepth, outputHeight, outputWidth) updateOutputFrame(input, output, weightMM, bias, fInput, kT, kW, kH, dT, dW, dH, - padT, padW, padH, nInputPlane, inputDepth, inputWidth, inputHeight, + padFront, padLeft, padTop, padBack, padRight, padBottom, nInputPlane, + inputDepth, inputWidth, inputHeight, nOutputPlane, outputDepth, outputWidth, outputHeight) } else { fInput.resize(input.size(1), kT * kW * kH * nInputPlane, @@ -214,7 +234,7 @@ class VolumetricConvolution[T: ClassTag]( updateOutputFrame(inputT, outputT, weightMM, bias, fInputT, kT, kW, kH, dT, dW, dH, - padT, padW, padH, + padFront, padLeft, padTop, padBack, padRight, padBottom, nInputPlane, inputDepth, inputWidth, inputHeight, nOutputPlane, outputDepth, outputWidth, outputHeight) t += 1 @@ -225,7 +245,8 @@ class VolumetricConvolution[T: ClassTag]( private def updateGradInputFrame(gradInput: Tensor[T], gradOutput: Tensor[T], weight: Tensor[T], fGradInput: Tensor[T], kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, - pT: Int, pW: Int, pH: Int): Unit = { + padFront: Int, padLeft: Int, padTop: Int, padBack: Int, padRight: Int, padBottom: Int): + Unit = { val gradOutput2d = gradOutput.view(gradOutput.size(1), gradOutput.size(2) * gradOutput.size(3) * gradOutput.size(4)) fGradInput.addmm(ev.zero, fGradInput, @@ -235,12 +256,14 @@ class VolumetricConvolution[T: ClassTag]( ev.getType() match { case DoubleType => NNPrimitive.unfoldedAccVolDouble(fGradInput.asInstanceOf[Tensor[Double]], - gradInput.asInstanceOf[Tensor[Double]], kT, kW, kH, dT, dW, dH, pT, pW, pH, + gradInput.asInstanceOf[Tensor[Double]], kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, gradInput.size(1), gradInput.size(2), gradInput.size(4), gradInput.size(3), gradOutput.size(2), gradOutput.size(4), gradOutput.size(3)) case FloatType => NNPrimitive.unfoldedAccVolFloat(fGradInput.asInstanceOf[Tensor[Float]], - gradInput.asInstanceOf[Tensor[Float]], kT, kW, kH, dT, dW, dH, pT, pW, pH, + gradInput.asInstanceOf[Tensor[Float]], kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, gradInput.size(1), gradInput.size(2), gradInput.size(4), gradInput.size(3), gradOutput.size(2), gradOutput.size(4), gradOutput.size(3)) } @@ -257,6 +280,29 @@ class VolumetricConvolution[T: ClassTag]( override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { require(input.dim() == 4 || input.dim() == 5, s"4D or 5D (batch mode) tensor expected for input, but got: ${ input.dim() }d") + + val dimDepth = if (input.dim() == 4) 2 else 3 + val dimWidth = if (input.dim() == 4) 4 else 5 + val dimHeight = if (input.dim() == 4) 3 else 4 + + val inputWidth = input.size(dimWidth) + val inputHeight = input.size(dimHeight) + val inputDepth = input.size(dimDepth) + val sizes = if (padW == -1 && padH == -1 && padT == -1) { + Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, + dW, kH, kW, inputDepth, dT, kT) + } else { + Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, + dW, kH, kW, padH, padW, ceilMode = false, inputdepth = inputDepth, + dt = dT, kt = kT, padt = padT) + } + val padFront = sizes(0) + val padBack = sizes(1) + val padLeft = sizes(4) + val padRight = sizes(5) + val padTop = sizes(2) + val padBottom = sizes(3) + gradInput.resizeAs(input) fGradInput.resizeAs(fInput).zero() if (input.dim() == 4) { @@ -264,7 +310,7 @@ class VolumetricConvolution[T: ClassTag]( updateGradInputFrame(gradInput, gradOutput, weightMM.transpose(1, 2), fGradInput, kT, kW, kH, dT, dW, dH, - padT, padW, padH) + padFront, padLeft, padTop, padBack, padRight, padBottom) } else { // batch mode var t = 1 @@ -276,7 +322,7 @@ class VolumetricConvolution[T: ClassTag]( updateGradInputFrame(gradInputT, gradOutputT, weightMM.transpose(1, 2), fGradInputT, kT, kW, kH, dT, dW, dH, - padT, padW, padH) + padFront, padLeft, padTop, padBack, padRight, padBottom) t += 1 } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala index 1908ab02ee1..51677c7d57a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -104,7 +104,7 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( val inputWidth = input.size(dimWidth) val inputHeight = input.size(dimHeight) - val (padTop, padBottom, padLeft, padRight, outputHeight, outputWidth) = + val sizes = if (padW == -1 && padH == -1) { nn.Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW) @@ -114,6 +114,13 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( dilationHeight = dilationHeight) } + val padTop = sizes(0) + val padBottom = sizes(1) + val padLeft = sizes(2) + val padRight = sizes(3) + val outputHeight = sizes(4) + val outputWidth = sizes(5) + val batchSize = if (input.dim() == 3) { output.resize(nn.Utils.getOutputShape(outputHeight, outputWidth, nOutputPlane, format = format)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 8acff1455cc..186659c9c1a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -365,12 +365,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab kernelI: Int, kernelC: Int, stride: Int = 1, + padding: Int = -1, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, cRegularizer: Regularizer[T] = null, withPeephole: Boolean = true): ConvLSTMPeephole[T] = { - ConvLSTMPeephole[T](inputSize, outputSize, kernelI, kernelC, stride, + ConvLSTMPeephole[T](inputSize, outputSize, kernelI, kernelC, stride, padding, wRegularizer, uRegularizer, bRegularizer, cRegularizer, withPeephole) } @@ -380,12 +381,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab kernelI: Int, kernelC: Int, stride: Int = 1, + padding: Int = -1, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, cRegularizer: Regularizer[T] = null, withPeephole: Boolean = true): ConvLSTMPeephole3D[T] = { - ConvLSTMPeephole3D[T](inputSize, outputSize, kernelI, kernelC, stride, + ConvLSTMPeephole3D[T](inputSize, outputSize, kernelI, kernelC, stride, padding, wRegularizer, uRegularizer, bRegularizer, cRegularizer, withPeephole) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala index 050d6046d59..1970369908b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala @@ -30,13 +30,13 @@ import scala.math._ @com.intel.analytics.bigdl.tags.Parallel class ConvLSTMPeephole3DSpec extends FlatSpec with BeforeAndAfter with Matchers { - "A ConvLSTMPeepwhole3D" should " work in BatchMode" in { + "A ConvLSTMPeepwhole3D " should " work in BatchMode" in { val hiddenSize = 5 val inputSize = 3 val seqLength = 4 val batchSize = 2 - val kernalW = 3 - val kernalH = 3 + val kernalW = 2 + val kernalH = 2 val rec = Recurrent[Double]() val model = Sequential[Double]() .add(rec @@ -44,12 +44,17 @@ class ConvLSTMPeephole3DSpec extends FlatSpec with BeforeAndAfter with Matchers inputSize, hiddenSize, kernalW, kernalH, - 1, withPeephole = true))) + withPeephole = true))) - val input = Tensor[Double](batchSize, seqLength, inputSize, 3, 3, 3).rand + val input = Tensor[Double](batchSize, seqLength, inputSize, 5, 5, 5).rand for (i <- 1 to 3) { - val output = model.forward(input) + val output = model.forward(input).toTensor[Double] + for((value, j) <- output.size.view.zipWithIndex) { + if (j > 2) { + require(value == input.size(j + 1)) + } + } model.backward(input, output) } } @@ -66,7 +71,7 @@ class ConvLSTMPeephole3DSpec extends FlatSpec with BeforeAndAfter with Matchers inputSize, hiddenSize, kernalW, kernalH, - 1, withPeephole = true)) + withPeephole = true)) val input = Tensor[Double](batchSize, seqLength, inputSize, 3, 3, 3).rand @@ -107,7 +112,6 @@ class ConvLSTMPeephole3DSpec extends FlatSpec with BeforeAndAfter with Matchers inputSize, hiddenSize, kernalW, kernalH, - 1, withPeephole = true))) val (weights1, grad1) = model1.getParameters() @@ -118,7 +122,6 @@ class ConvLSTMPeephole3DSpec extends FlatSpec with BeforeAndAfter with Matchers inputSize, hiddenSize, kernalW, kernalH, - 1, wRegularizer = L2Regularizer(0.1), uRegularizer = L2Regularizer(0.1), bRegularizer = L2Regularizer(0.1), diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala index a1028244d29..01927e53ff9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.T @@ -439,5 +439,41 @@ class VolumetricConvolutionSpec extends TorchSpec { weights1 should be(weights2) loss1 should be(loss2) } + + "A VolumetricConvolution layer" should "work with SAME padding" in { + import tensor.TensorNumericMath.TensorNumeric.NumericFloat + val nInputPlane = 1 + val nOutputPlane = 1 + val kW = 1 + val kH = 1 + val kT = 1 + val dT = 2 + val dW = 2 + val dH = 2 + val padW = -1 + val padH = -1 + val padT = -1 + val layer = new VolumetricConvolution(nInputPlane, nOutputPlane, + kT, kW, kH, dT, dW, dH, padT, padW, padH) + + val inputData = Array( + 0.0f, 1.0f, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26 + ) + + val kernelData = Array( + 1.0f + ) + + val biasData = Array(0.0f) + + layer.weight.copy(Tensor(Storage(kernelData), 1, Array(nOutputPlane, + nInputPlane, kT, kH, kW))) + layer.bias.copy(Tensor(Storage(biasData), 1, Array(nOutputPlane))) + val input = Tensor(Storage(inputData), 1, Array(1, 3, 3, 3)) + val output = layer.updateOutput(input) + val gradInput = layer.backward(input, output) + output.storage().array() should be (Array(0.0f, 2, 6, 8, 18, 20, 24, 26)) + } } From 4f525fb4cfec5ec21fd00e9c7608a92837a41413 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 13 Nov 2017 17:00:30 +0800 Subject: [PATCH 0520/1065] fix squeeze bug and some other refinement (#1874) --- .../bigdl/dllib/utils/tf/Session.scala | 2 +- .../bigdl/dllib/utils/tf/Tensorflow.scala | 2 +- .../dllib/utils/tf/loaders/Squeeze.scala | 8 ++-- .../dllib/utils/tf/TensorflowSpecHelper.scala | 5 +- .../dllib/utils/tf/loaders/SqueezeSpec.scala | 46 +++++++++++++++++++ 5 files changed, 56 insertions(+), 7 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqueezeSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index 62ee7be0709..7e7e1ed91ba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -601,7 +601,7 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], endPoints, ByteOrder.LITTLE_ENDIAN, "", - None, + Some(context), generatedBackward = false ).asInstanceOf[Graph[T]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala index 6d40cb9e7d2..b0a21e40049 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala @@ -461,7 +461,7 @@ object Tensorflow { AttrValue.newBuilder().setI(value).build() } - private def listIntAttr(value: Seq[Int]): AttrValue = { + private[bigdl] def listIntAttr(value: Seq[Int]): AttrValue = { val list = ListValue.newBuilder() value.foreach(list.addI(_)) AttrValue.newBuilder().setList(list).build() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala index a40ec350d8c..e65df63978f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala @@ -34,10 +34,12 @@ class Squeeze extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - val dims = nodeDef.getAttrOrThrow("squeeze_dims").getList().getIList() - .asScala.map(_.toInt).toArray + var dims = nodeDef.getAttrOrThrow("squeeze_dims").getList().getIList() + .asScala.map(_.toInt + 1).toArray - Squeeze[T](dims, batchMode = true) + dims = if (dims.isEmpty) null else dims + + Squeeze[T](dims, batchMode = false) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala index 31a7ba3100a..1337b5718b8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala @@ -29,6 +29,7 @@ import com.intel.analytics.bigdl.utils.tf.Tensorflow.const import org.tensorflow.framework.{GraphDef, NodeDef} import scala.sys.process._ +import scala.util.control.NonFatal abstract class TensorflowSpecHelper extends BigDLSpecHelper { @@ -38,7 +39,7 @@ abstract class TensorflowSpecHelper extends BigDLSpecHelper { exitValue = ((Seq("python", "-c", "import sys; print ','.join(sys.path)"))!!) ((Seq("python", "-c", "import tensorflow"))!!) } catch { - case _: Throwable => cancel("python or tensorflow is not installed") + case NonFatal(e) => cancel("python or tensorflow is not installed", e) } if (!exitValue.contains("models")) { @@ -52,7 +53,7 @@ abstract class TensorflowSpecHelper extends BigDLSpecHelper { val proc = s"python $cmd".run return proc.exitValue() == 0 } catch { - case _: Throwable => false + case NonFatal(e) => false } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqueezeSpec.scala new file mode 100644 index 00000000000..9f2818b7407 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqueezeSpec.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class SqueezeSpec extends TensorflowSpecHelper { + + s"Squeeze forward float" should "be correct" in { + compare( + NodeDef.newBuilder() + .setName("SqueezeTest") + .setOp(s"Squeeze") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("squeeze_dims", listIntAttr(Seq[Int]())), + Seq(Tensor[Float](1, 2, 1, 3, 1, 1).rand()), + 0 + ) + + compare( + NodeDef.newBuilder() + .setName("SqueezeTest") + .setOp(s"Squeeze") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("squeeze_dims", listIntAttr(Seq[Int](0, 2))), + Seq(Tensor[Float](1, 2, 1, 3, 1, 1).rand()), + 0 + ) + } +} From 2138e7c255c0e03b28186ed3f27ee5e877a7f25f Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 14 Nov 2017 09:48:48 +0800 Subject: [PATCH 0521/1065] fix loading ds2 module issue (#1867) * fix loading ds2 module issue * refinemnt * refinement per review --- .../analytics/bigdl/dllib/nn/Sigmoid.scala | 2 +- .../utils/serializer/DataConverter.scala | 210 ++++++++++++------ .../dllib/utils/serializer/ModuleLoader.scala | 2 +- .../utils/serializer/DataConverterSpec.scala | 16 +- 4 files changed, 160 insertions(+), 70 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala index a5afde82146..87e72848ee3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala @@ -28,7 +28,7 @@ import scala.reflect.ClassTag * Sigmoid is defined as: f(x) = 1 / (1 + exp(-x)) */ @SerialVersionUID(6855417348268610044L) -class Sigmoid[@specialized(Float, Double) T: ClassTag]( +class Sigmoid[T: ClassTag]( implicit ev: TensorNumeric[T]) extends TensorModule[T] { private val buffer: Tensor[T] = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index af52953fc70..8ab9fd260cb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -246,6 +246,17 @@ object DataConverter extends DataConverter{ */ object TensorConverter extends DataConverter { + + private def isEmptyTensor(tensor : Tensor[_]): Boolean = { + val emptyTensor = tensor.getTensorType match { + case DenseType => + tensor.storage == null + case QuantizedType => + tensor.asInstanceOf[QuantizedTensor[_]].getStorage == null + } + emptyTensor + } + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) (implicit ev: TensorNumeric[T]): AnyRef = { @@ -323,76 +334,112 @@ object DataConverter extends DataConverter{ tensorType match { case TensorType.DENSE => val storage : Storage[Float] = if (created == null ) { - val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) - val newStorage = Storage[Float](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) + val newStorage = Storage[Float](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[Float]] Tensor[Float](storage, offSet, sizes, strides) case TensorType.QUANT => quant() } case DataType.DOUBLE => val storage : Storage[Double] = if (created == null ) { - val data = serializedStorage.getDoubleDataList.asScala.toArray.map(_.doubleValue()) - val newStorage = Storage[Double](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getDoubleDataList.asScala.toArray.map(_.doubleValue()) + val newStorage = Storage[Double](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[Double]] Tensor[Double](storage, offSet, sizes, strides) case DataType.BOOL => val storage : Storage[Boolean] = if (created == null ) { - val data = serializedStorage.getBoolDataList.asScala.toArray.map(_.booleanValue()) - val newStorage = Storage[Boolean](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getBoolDataList.asScala.toArray.map(_.booleanValue()) + val newStorage = Storage[Boolean](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[Boolean]] Tensor[Boolean](storage, offSet, sizes, strides) case DataType.CHAR => val storage: Storage[Char] = if (created == null ) { - val data = serializedStorage.getIntDataList.asScala.toArray.map(_.toChar.charValue()) - val newStorage = Storage[Char](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.toChar.charValue()) + val newStorage = Storage[Char](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[Char]] Tensor[Char](storage, offSet, sizes, strides) case DataType.STRING => val storage: Storage[String] = if (created == null ) { - val data = serializedStorage.getStringDataList.asScala.toArray - val newStorage = Storage[String](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getStringDataList.asScala.toArray + val newStorage = Storage[String](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[String]] Tensor[String](storage, offSet, sizes, strides) case DataType.INT32 => val storage: Storage[Int] = if (created == null ) { - val data = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) - val newStorage = Storage[Int](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) + val newStorage = Storage[Int](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[Int]] Tensor[Int](storage, offSet, sizes, strides) case DataType.SHORT => val storage: Storage[Short] = if (created == null ) { - val data = serializedStorage.getIntDataList.asScala.toArray.map(_.shortValue()) - val newStorage = Storage[Short](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.shortValue()) + val newStorage = Storage[Short](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[Short]] Tensor[Short](storage, offSet, sizes, strides) case DataType.INT64 => val storage: Storage[Long] = if (created == null ) { - val data = serializedStorage.getLongDataList.asScala.toArray.map(_.longValue()) - val newStorage = Storage[Long](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getLongDataList.asScala.toArray.map(_.longValue()) + val newStorage = Storage[Long](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[Long]] Tensor[Long](storage, offSet, sizes, strides) case DataType.BYTES => val storage: Storage[ByteString] = if (created == null ) { - val data = serializedStorage.getBytesDataList.asScala.toArray - val newStorage = Storage[ByteString](data) - storages(storageId) = newStorage - newStorage + if (storageId == -1) { + null + } else { + val data = serializedStorage.getBytesDataList.asScala.toArray + val newStorage = Storage[ByteString](data) + storages(storageId) = newStorage + newStorage + } } else created.asInstanceOf[Storage[ByteString]] Tensor[ByteString](storage, offSet, sizes, strides) case _ => throw new IllegalArgumentException(s"$dataType not supported in tensor now !") @@ -406,13 +453,18 @@ object DataConverter extends DataConverter{ val tensorNumeric = tensor.getTensorNumeric() val storageType = context.storageType + val isEmpty = isEmptyTensor(tensor) + val storageId = tensor.getTensorType match { case DenseType => - System.identityHashCode(tensor.storage().array()) + if (isEmpty) -1 else System.identityHashCode(tensor.storage().array()) case QuantizedType => - System.identityHashCode(tensor.asInstanceOf[QuantizedTensor[T]].getStorage) + if (isEmpty) { + -1 + } else { + System.identityHashCode(tensor.asInstanceOf[QuantizedTensor[T]].getStorage) + } } - val storages = context.storages if (storageType == ProtoStorageType) { if (storages.contains(storageId)) { @@ -425,15 +477,16 @@ object DataConverter extends DataConverter{ if (tensorNumeric == NumericFloat) { tensorBuilder.setDatatype(DataType.FLOAT) storageBuilder.setDatatype(DataType.FLOAT) - tensor.getTensorType match { - case DenseType => - tensor.storage().array().asInstanceOf[Array[Float]]. - foreach(data => storageBuilder.addFloatData(data)) - case QuantizedType => - val quantTensor = tensor.asInstanceOf[QuantizedTensor[Float]] - val bytes = quantTensor.getStorage - val bs = ByteString.copyFrom(bytes) - storageBuilder.addBytesData(bs) + if(!isEmpty) { + tensor.getTensorType match { + case DenseType => + tensor.storage().array().asInstanceOf[Array[Float]]. + foreach(data => storageBuilder.addFloatData(data)) + case QuantizedType => + val quantTensor = tensor.asInstanceOf[QuantizedTensor[Float]] + val bytes = quantTensor.getStorage + val bs = ByteString.copyFrom(bytes) + storageBuilder.addBytesData(bs) // max, min, and sum quantTensor.maxOfRow.foreach(data => storageBuilder.addFloatData(data)) @@ -451,52 +504,71 @@ object DataConverter extends DataConverter{ case LinearData => storageBuilder.addIntData(2) case LinearWeight => storageBuilder.addIntData(3) } + } } } else if (tensorNumeric == NumericDouble) { tensorBuilder.setDatatype(DataType.DOUBLE) storageBuilder.setDatatype(DataType.DOUBLE) - tensor.storage().array().asInstanceOf[Array[Double]]. - foreach(data => storageBuilder.addDoubleData(data)) + if(!tensor.isEmpty) { + tensor.storage().array().asInstanceOf[Array[Double]]. + foreach(data => storageBuilder.addDoubleData(data)) + } } else if (tensorNumeric == NumericChar) { tensorBuilder.setDatatype(DataType.CHAR) storageBuilder.setDatatype(DataType.CHAR) - tensor.storage().array().asInstanceOf[Array[Char]]. - foreach(data => storageBuilder.addIntData(data)) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Char]]. + foreach(data => storageBuilder.addIntData(data)) + } } else if (tensorNumeric == NumericBoolean) { tensorBuilder.setDatatype(DataType.BOOL) storageBuilder.setDatatype(DataType.BOOL) - tensor.storage().array().asInstanceOf[Array[Boolean]]. - foreach(data => storageBuilder.addBoolData(data)) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Boolean]]. + foreach(data => storageBuilder.addBoolData(data)) + } } else if (tensorNumeric == NumericString) { tensorBuilder.setDatatype(DataType.STRING) storageBuilder.setDatatype(DataType.STRING) - tensor.storage().array().asInstanceOf[Array[String]]. - foreach(data => storageBuilder.addStringData(data)) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[String]]. + foreach(data => storageBuilder.addStringData(data)) + } } else if (tensorNumeric == NumericInt) { tensorBuilder.setDatatype(DataType.INT32) storageBuilder.setDatatype(DataType.INT32) - tensor.storage().array().asInstanceOf[Array[Int]]. - foreach(data => storageBuilder.addIntData(data)) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Int]]. + foreach(data => storageBuilder.addIntData(data)) + } } else if (tensorNumeric == NumericShort) { tensorBuilder.setDatatype(DataType.SHORT) storageBuilder.setDatatype(DataType.SHORT) - tensor.storage().array().asInstanceOf[Array[Short]]. - foreach(data => storageBuilder.addIntData(data)) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Short]]. + foreach(data => storageBuilder.addIntData(data)) + } } else if (tensorNumeric == NumericLong) { tensorBuilder.setDatatype(DataType.INT64) storageBuilder.setDatatype(DataType.INT64) - tensor.storage().array().asInstanceOf[Array[Long]]. - foreach(data => storageBuilder.addLongData(data)) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Long]]. + foreach(data => storageBuilder.addLongData(data)) + } } else if (tensorNumeric == NumericByteString) { tensorBuilder.setDatatype(DataType.BYTES) storageBuilder.setDatatype(DataType.BYTES) - tensor.storage().array().asInstanceOf[Array[ByteString]]. - foreach(data => storageBuilder.addBytesData(data)) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[ByteString]]. + foreach(data => storageBuilder.addBytesData(data)) + } } storageBuilder.setId(storageId) val storage = storageBuilder.build tensorBuilder.setStorage(resetStorage(storage)) - storages(storageId) = storage + if (storageId != -1) { + storages(storageId) = storage + } } } else { throw new IllegalArgumentException(s"$storageType not supported") @@ -532,8 +604,12 @@ object DataConverter extends DataConverter{ tensorBuilder.setTensorType(TensorType.QUANT) } - tensor.size().foreach(size => tensorBuilder.addSize(size)) - tensor.stride().foreach(stride => tensorBuilder.addStride(stride)) + val tensorEmpty = isEmptyTensor(tensor) + + if (!tensorEmpty) { + tensor.size().foreach(size => tensorBuilder.addSize(size)) + tensor.stride().foreach(stride => tensorBuilder.addStride(stride)) + } setStorage(context, tensorBuilder, tensor) val tensorBuild = tensorBuilder.build attributeBuilder.setTensorValue(resetTensor(tensorBuild)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala index c9060772976..9c2bb7882bc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala @@ -185,7 +185,7 @@ object ModulePersister { storages.values.filter(_.isInstanceOf[BigDLTensor]).foreach(storage => { val bigdlTensor = storage.asInstanceOf[BigDLTensor] val storageId = bigdlTensor.getStorage.getId - if (!storageIds.contains(storageId)) { + if (!storageIds.contains(storageId) && storageId != -1) { val tensorBuilder = BigDLTensor.newBuilder(bigdlTensor) tensorBuilder.clearStorage() require(tensorStorages.contains(storageId), s"${storageId} does not exist") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index 6e163c054a4..a5d5b6d7ca6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -190,7 +190,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ retrievedValue should be (tensor) } - "Empty Tensor conversion" should "work properly" in { + "Null Tensor conversion" should "work properly" in { val tensor : Tensor[Float] = null val attriBulder = AttrValue.newBuilder map.clear() @@ -204,6 +204,20 @@ class DataConverterSpec extends FlatSpec with Matchers{ retrievedValue should be (tensor) } + "Empty Tensor conversion" should "work properly" in { + val tensor : Tensor[Float] = Tensor[Float]() + val attriBulder = AttrValue.newBuilder + map.clear() + DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), + attriBulder, tensor, ModuleSerializer.tensorType) + val attr = attriBulder.build + map.clear() + val retrievedValue = DataConverter. + getAttributeValue(DeserializeContext(null, map, ProtoStorageType), attr) + attr.getDataType should be (DataType.TENSOR) + retrievedValue should be (tensor) + } + "Two tensors to the same object conversion" should "work properly" in { val tensor1 = Tensor(5, 5).apply1(e => Random.nextFloat()) val tensor2 = tensor1 From 723ff97524d489bd8053d835e04beebccd7a089c Mon Sep 17 00:00:00 2001 From: Wang Yanzhang Date: Tue, 7 Nov 2017 03:56:00 -0500 Subject: [PATCH 0522/1065] refator: update initialize method for serialization --- .../bigdl/dllib/nn/quantized/Linear.scala | 13 +++++++----- .../nn/quantized/SpatialConvolution.scala | 21 ++++++++++++++----- .../quantized/SpatialDilatedConvolution.scala | 12 +++++++---- 3 files changed, 32 insertions(+), 14 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala index 794324ff9e1..f6df7b97b71 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala @@ -31,9 +31,10 @@ private[bigdl] class Linear[T: ClassTag]( val outputSize: Int, val withBias: Boolean = true )(implicit ev: TensorNumeric[T]) extends QuantizedModule[T](outputSize) { - + val params = LinearWeightParams(outputSize, inputSize) private val data: QuantizedTensor[T] = QuantizedDummyTensor[T]() - var weight: QuantizedTensor[T] = _ + val weight: QuantizedTensor[T] = QuantizedTensor[T](Tensor[T]( + Array(outputSize, inputSize)), params) val bias: Tensor[T] = Tensor[T](outputSize) private def initWeightAndBias(weightFP32: Tensor[T], biasFP32: Tensor[T]): this.type = { @@ -44,8 +45,8 @@ private[bigdl] class Linear[T: ClassTag]( } val weightFP32Tmp = weightFP32.view(Array(outputSize, inputSize)) - val params = LinearWeightParams(outputSize, inputSize) - weight = QuantizedTensor[T](weightFP32Tmp, params) + weight.release() + weight.set(QuantizedTensor[T](weightFP32Tmp, params)) this } @@ -175,7 +176,9 @@ object Linear extends QuantSerializer { module: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit = { val linear = module.module.asInstanceOf[Linear[T]] val attrMap = context.bigdlModule.getAttrMap - linear.weight = DataConverter.getAttributeValue(context, attrMap.get("weight")) + val weight = DataConverter.getAttributeValue(context, attrMap.get("weight")) .asInstanceOf[QuantizedTensor[T]] + linear.weight.release() + linear.weight.set(weight) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala index 51677c7d57a..544f13a4358 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -46,7 +46,16 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( require(nInputPlane % nGroup == 0, "Number of input channels should be multiples of group.") require(nOutputPlane % nGroup == 0, "Number of output channels should be multiples of group.") - var weight: Array[Tensor[T]] = null + val params = ConvWeightParams(nOutputPlane / nGroup, nInputPlane / nGroup, kernelH, kernelW, + quantFormat) + val weight: Array[Tensor[T]] = { + val array = new Array[Tensor[T]](nGroup) + for (i <- 0 until nGroup) { + array(i) = QuantizedTensor[T](Tensor[T](Array(nGroup, kernelH, kernelW, nInputPlane / nGroup, + nOutputPlane / nGroup)), params) + } + array + } private val data: QuantizedTensor[T] = QuantizedDummyTensor[T]() val bias: Tensor[T] = Tensor[T](nOutputPlane) @@ -77,13 +86,11 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( kernelH, kernelW)) } - weight = new Array[Tensor[T]](nGroup) - val params = ConvWeightParams(nOutputPlane / nGroup, nInputPlane / nGroup, kernelH, kernelW, - quantFormat) for (i <- 1 to nGroup) { val groupWeight = weightTmp.select(1, i) ev.getType() match { case FloatType => + weight(i - 1).asInstanceOf[QuantizedTensor[T]].release() weight(i - 1) = QuantizedTensor[T](groupWeight, params) case _ => throw new UnsupportedOperationException(s"Only support Float for quantized model") } @@ -308,8 +315,12 @@ object SpatialConvolution extends QuantSerializer { moduleData: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit = { val conv = moduleData.module.asInstanceOf[SpatialConvolution[T]] val attrMap = context.bigdlModule.getAttrMap - conv.weight = DataConverter.getAttributeValue(context, attrMap.get("weights")) + val weights = DataConverter.getAttributeValue(context, attrMap.get("weights")) .asInstanceOf[Array[Tensor[T]]] + for (i <- 0 until conv.weight.length) { + conv.weight(i).asInstanceOf[QuantizedTensor[T]].release() + conv.weight(i).set(weights(i)) + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala index 9108d3c972e..1c272136437 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn.quantized import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.{FloatType, QuantizedTensor, Tensor} import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleData, SerializeContext} import serialization.Bigdl.{AttrValue, BigDLModule} @@ -82,7 +82,7 @@ object SpatialDilatedConvolution extends QuantSerializer { override def serializeWeight[T: ClassTag](context: SerializeContext[T], modelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { val module = context.moduleData.module - val conv = module.asInstanceOf[SpatialConvolution[T]] + val conv = module.asInstanceOf[SpatialDilatedConvolution[T]] val weightBuilder = AttrValue.newBuilder ev.getType() match { case FloatType => @@ -95,10 +95,14 @@ object SpatialDilatedConvolution extends QuantSerializer { override def loadWeight[T: ClassTag](context: DeserializeContext, moduleData: ModuleData[T])(implicit ev: TensorNumeric[T]): Unit = { - val conv = moduleData.module.asInstanceOf[SpatialConvolution[T]] + val conv = moduleData.module.asInstanceOf[SpatialDilatedConvolution[T]] val attrMap = context.bigdlModule.getAttrMap - conv.weight = DataConverter.getAttributeValue(context, attrMap.get("weights")) + val weights = DataConverter.getAttributeValue(context, attrMap.get("weights")) .asInstanceOf[Array[Tensor[T]]] + for (i <- 0 until conv.weight.length) { + conv.weight(i).asInstanceOf[QuantizedTensor[T]].release() + conv.weight(i).set(weights(i)) + } } } From 101c56cd6d0f24da1a6f5143d855fa690db3d9df Mon Sep 17 00:00:00 2001 From: Wang Yanzhang Date: Tue, 7 Nov 2017 21:14:07 -0500 Subject: [PATCH 0523/1065] test: dilated serialization test --- .../serializer/ModuleSerializerSpec.scala | 32 +++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 23018c898be..1c753810a88 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.utils.serializer import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.nn.ops.ParseExample import com.intel.analytics.bigdl.nn.{VolumetricFullConvolution, _} @@ -1883,6 +1884,37 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } + "bigquant.SpatialDilatedConvolution serializer" should "work properly " in { + val nInputPlane = 1 + val nOutputPlane = 1 + val kW = 2 + val kH = 2 + val dW = 1 + val dH = 1 + val padW = 0 + val padH = 0 + + val kernelData = Array( + 2.0f, 3f, + 4f, 5f + ) + + val biasData = Array(0.0f) + + val input = Tensor(1, 1, 3, 3).apply1(_ => Random.nextFloat()) + val weight = Tensor(Storage(kernelData), 1, Array(nOutputPlane, nInputPlane, kH, kW)) + val bias = Tensor(Storage(biasData), 1, Array(nOutputPlane)) + val conv = quantized.SpatialDilatedConvolution[Float](nInputPlane, nOutputPlane, + kW, kH, dW, dH, padW, padH, initWeight = weight, initBias = bias) + + val res1 = conv.forward(input) + + ModulePersister.saveToFile("/tmp/bigquant.dilated.conv.bigdl", conv, true) + val loadedConv = ModuleLoader.loadFromFile("/tmp/bigquant.dilated.conv.bigdl") + val res2 = loadedConv.forward(input) + res1 should be (res2) + } + "bigquant.Linear serializer" should "work properly " in { val outputSize = 2 val inputSize = 2 From 250cb23840ddecf5da3c3e138d412f04034db511 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 14 Nov 2017 12:45:08 +0800 Subject: [PATCH 0524/1065] Support keras squared_hinge and sparse_categorial_crossentropy (#1865) * add squared hinge loss * classNLL support prob as input * add python api * meet code review * fix python tests * more doc * more doc --- .../bigdl/dllib/nn/ClassNLLCriterion.scala | 60 +++++++++++--- .../bigdl/dllib/nn/MarginCriterion.scala | 36 +++++++-- .../bigdl/dllib/tensor/TensorNumeric.scala | 17 ++++ .../dllib/utils/python/api/PythonBigDL.scala | 8 +- .../dllib/nn/ClassNLLCriterionSpec.scala | 48 ++++++++++++ .../bigdl/dllib/nn/MarginCriterionSpec.scala | 78 +++++++++++++++++++ 6 files changed, 224 insertions(+), 23 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MarginCriterionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala index 4284af9e3ac..2aa58f851e4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala @@ -30,17 +30,19 @@ import com.intel.analytics.bigdl.utils.Engine * classes. If provided, the optional argument weights should be a 1D Tensor assigning weight to * each of the classes. This is particularly useful when you have an unbalanced training set. * - * The input given through a forward() is expected to contain log-probabilities of each class: - * input has to be a 1D Tensor of size n. Obtaining log-probabilities in a neural network is easily - * achieved by adding a LogSoftMax layer in the last layer of your neural network. You may use - * CrossEntropyCriterion instead, if you prefer not to add an extra layer to your network. This - * criterion expects a class index (1 to the number of class) as target when calling - * forward(input, target) and backward(input, target). + * The input given through a forward() is expected to contain log-probabilities/probabilities of + * each class: input has to be a 1D Tensor of size n. Obtaining log-probabilities/probabilities + * in a neural network is easily achieved by adding a LogSoftMax/SoftMax layer in the last layer + * of your neural network. You may use CrossEntropyCriterion instead, if you prefer not to add + * an extra layer to your network. This criterion expects a class index (1 to the number of class) + * as target when calling forward(input, target) and backward(input, target). * + * In the log-probabilities case, * The loss can be described as: * loss(x, class) = -x[class] * or in the case of the weights argument it is specified as follows: * loss(x, class) = -weights[class] * x[class] + * * Due to the behaviour of the backend code, it is necessary to set sizeAverage to false when * calculating losses in non-batch mode. * @@ -51,14 +53,19 @@ import com.intel.analytics.bigdl.utils.Engine * By default, the losses are averaged over observations for each minibatch. However, if the field * sizeAverage is set to false, the losses are instead summed for each minibatch. * + * In particular, when weights=None, size_average=True and logProbAsInput=False, this is same as + * `sparse_categorical_crossentropy` loss in keras. + * * @param weights weights of each element of the input * @param sizeAverage size average of batch + * @param logProbAsInput indicating whether to accept log-probabilities or probabilities as input. + * True means accepting log-probabilities as input. * @param ev numeric operator * @tparam T numeric type */ @SerialVersionUID(- 8696382776046599502L) class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] -(weights: Tensor[T] = null, sizeAverage: Boolean = true) +(weights: Tensor[T] = null, sizeAverage: Boolean = true, logProbAsInput: Boolean = true) (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { private var total_weight = ev.fromType[Int](0) if (weights != null) require(weights.dim() == 1, @@ -70,6 +77,12 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] @transient private var resultsBackward: Array[Future[_]] = null + private val epsilon: T = ev.fromType(1e-8) + + private val oneMinusEpsilon: T = ev.minus(ev.one, epsilon) + + + override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { require(input.dim() == 1 || input.dim() == 2, "ClassNLLCriterion: " + @@ -85,7 +98,15 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] s"curTarget ${curTarget} is out of range, should be 1 to ${nClasses}") total_weight = if (weights != null) weights(Array(curTarget)) else ev.fromType[Int](1) output = if (curTarget == -1) ev.zero - else ev.times(ev.negative(input.valueAt(curTarget)), total_weight) + else { + if (!logProbAsInput) { + val clipped = ev.clip(input.valueAt(curTarget), epsilon, oneMinusEpsilon) + ev.times(ev.negative(ev.log(clipped)), total_weight) + } else { + ev.times(ev.negative(input.valueAt(curTarget)), total_weight) + } + } + } else if (input.dim() == 2) { val batchSize = input.size(1) val targetSize = target.size() @@ -111,7 +132,13 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] if (curTarget == -1) (ev.zero, ev.one) else { val curWeight = if (weights != null) weights.valueAt(curTarget) else ev.fromType[Int](1) - (ev.times(input.valueAt(_i, curTarget), curWeight), curWeight) + if (!logProbAsInput) { + val clipped = ev.clip(input.valueAt(_i, curTarget), epsilon, oneMinusEpsilon) + (ev.times(ev.log(clipped), curWeight), curWeight) + } else { + (ev.times(input.valueAt(_i, curTarget), curWeight), curWeight) + } + } }) i += 1 @@ -152,6 +179,11 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] else ev.fromType[Int](-1)) if (sizeAverage) gradInput.setValue(curTarget, ev.divide(gradInput.valueAt(curTarget), total_weight)) + if (!logProbAsInput) { + val clipped = ev.clip(input.valueAt(curTarget), epsilon, oneMinusEpsilon) + gradInput.setValue(curTarget, + ev.times(gradInput.valueAt(curTarget), ev.inv(clipped))) + } } else if (input.dim() == 2) { val batchSize = input.size(1) @@ -172,6 +204,11 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] else ev.fromType[Int](-1)) if (sizeAverage) gradInput.setValue(_i, curTarget, ev.divide(gradInput.valueAt(_i, curTarget), total_weight)) + if (!logProbAsInput) { + val clipped = ev.clip(input.valueAt(_i, curTarget), epsilon, oneMinusEpsilon) + gradInput.setValue(_i, curTarget, + ev.times(gradInput.valueAt(_i, curTarget), ev.inv(clipped))) + } } }) i += 1 @@ -191,7 +228,8 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] object ClassNLLCriterion { def apply[@specialized(Float, Double) T: ClassTag]( weights: Tensor[T] = null, - sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : ClassNLLCriterion[T] = { - new ClassNLLCriterion[T](weights, sizeAverage) + sizeAverage: Boolean = true, + logProbAsInput: Boolean = true)(implicit ev: TensorNumeric[T]) : ClassNLLCriterion[T] = { + new ClassNLLCriterion[T](weights, sizeAverage, logProbAsInput) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MarginCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MarginCriterion.scala index 724b4e7d161..8f561e22e5e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MarginCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MarginCriterion.scala @@ -22,16 +22,21 @@ import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc4, import scala.reflect.ClassTag /** - * Creates a criterion that optimizes a two-class classification hinge loss (margin-based loss) - * between input x (a Tensor of dimension 1) and output y. + * Creates a criterion that optimizes a two-class classification (squared) + * hinge loss (margin-based loss) between input x (a Tensor of dimension 1) and output y. + * + * When margin = 1, sizeAverage = True and squared = False, this is the same as hinge loss in keras; + * When margin = 1, sizeAverage = False and squared = True, this is the same as squared_hinge loss + * in keras. * * @param margin if unspecified, is by default 1. * @param sizeAverage whether to average the loss + * @param squared whether to calculate the squared hinge loss */ @SerialVersionUID( - 5028892499250398130L) class MarginCriterion[@specialized(Float, Double) T: ClassTag] - (val margin: Double = 1.0, val sizeAverage: Boolean = true) + (val margin: Double = 1.0, val sizeAverage: Boolean = true, squared: Boolean = false) (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { @@ -40,7 +45,13 @@ class MarginCriterion[@specialized(Float, Double) T: ClassTag] val func = new TensorFunc4[T] { override def apply(data1: Array[T], index1: Int, data2: Array[T], index2: Int): Unit = { val z = ev.minus(ev.fromType(margin), ev.times(data1(index1), data2(index2))) - if (ev.isGreater(z, ev.fromType(0))) sum = ev.plus(sum, z) + if (ev.isGreater(z, ev.fromType(0))) { + if (squared) { + sum = ev.plus(sum, ev.times(z, z)) + } else { + sum = ev.plus(sum, z) + } + } } } DenseTensorApply.apply2[T](input, target, func) @@ -49,7 +60,7 @@ class MarginCriterion[@specialized(Float, Double) T: ClassTag] } override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { - val norm = ev.fromType(if (sizeAverage) -1.0 / input.nElement() else 1.0) + val norm = ev.fromType(if (sizeAverage) -1.0 / input.nElement() else -1.0) gradInput.resizeAs(input) // todo: the performance of contiguous tensor should be optimized @@ -57,7 +68,15 @@ class MarginCriterion[@specialized(Float, Double) T: ClassTag] override def apply (data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, data3: Array[T], offset3: Int): Unit = { if (ev.isGreater(ev.fromType(margin), ev.times(data2(offset2), data3(offset3)))) { - data1(offset1) = ev.times(norm, data3(offset3)) + if (squared) { + // dl/dx = -2y(1-xy) + data1(offset1) = ev.times( + ev.times(ev.times(ev.fromType(2), norm), data3(offset3)), + ev.minus(ev.fromType(margin), + ev.times(data2(offset2), data3(offset3)))) + } else { + data1(offset1) = ev.times(norm, data3(offset3)) + } } } } @@ -90,7 +109,8 @@ class MarginCriterion[@specialized(Float, Double) T: ClassTag] object MarginCriterion { def apply[@specialized(Float, Double) T: ClassTag]( margin: Double = 1.0, - sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : MarginCriterion[T] = { - new MarginCriterion[T](margin, sizeAverage) + sizeAverage: Boolean = true, + squared: Boolean = false)(implicit ev: TensorNumeric[T]) : MarginCriterion[T] = { + new MarginCriterion[T](margin, sizeAverage, squared) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index 6f65a2f645a..842ad0cb87a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -190,6 +190,8 @@ object TensorNumericMath { def truncate(a: T): T def floorDiv(a: T, b: T): T + + def clip(a: T, lower: T, upper: T): T } /** @@ -450,6 +452,10 @@ object TensorNumericMath { override def floorDiv(a: T, b: T): T = throw new UnsupportedOperationException(typeName + " in tensor does not support floorDiv operation") + + def clip(a: T, lower: T, upper: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support clip operation") } /** @@ -759,6 +765,12 @@ object TensorNumericMath { override def floorDiv(a: Float, b: Float): Float = { Math.floor(a / b).toFloat } + + override def clip(a: Float, lower: Float, upper: Float): Float = { + require(lower <= upper, "lower bound must be less or equal than upper bound") + math.min(math.max(a, lower), upper) + } + } implicit object NumericDouble extends UndefinedTensorNumeric[Double]("Double") { @@ -1058,6 +1070,11 @@ object TensorNumericMath { override def floorDiv(a: Double, b: Double): Double = { Math.floor(a / b) } + + override def clip(a: Double, lower: Double, upper: Double): Double = { + require(lower <= upper, "lower bound must be less or equal than upper bound") + math.min(math.max(a, lower), upper) + } } implicit object NumericString extends UndefinedTensorNumeric[String]("String") { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 186659c9c1a..1962d864be6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1419,10 +1419,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createClassNLLCriterion(weights: JTensor = null, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true, logProbAsInput: Boolean = true) : ClassNLLCriterion[T] = { ClassNLLCriterion[T](if (weights == null) null else toTensor(weights), - sizeAverage) + sizeAverage, logProbAsInput) } def createMSECriterion: MSECriterion[T] = { @@ -1470,10 +1470,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createMarginCriterion(margin: Double = 1.0, - sizeAverage: Boolean = true) + sizeAverage: Boolean = true, squared: Boolean = false) : MarginCriterion[T] = { MarginCriterion[T](margin, - sizeAverage) + sizeAverage, squared) } def createMarginRankingCriterion(margin: Double = 1.0, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala index 44a3a45cf91..282965264b5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala @@ -234,4 +234,52 @@ class ClassNLLCriterionSpec extends FlatSpec with Matchers { v1 }) } + + "A ClassNLL Criterion with probabilities input" should "generate correct output and grad" in { + + val input = Tensor[Float](Array(4, 4)).rand() + val target = Tensor[Float](Array[Float](1, 2, 3, 4), Array(4)) + + val logSoftMax = LogSoftMax[Float]() + val softMax = SoftMax[Float]() + + val logProb = logSoftMax.forward(input) + val prob = softMax.forward(input) + + val referenceLayer = ClassNLLCriterion[Float]() + val testedLayer = ClassNLLCriterion[Float](logProbAsInput = false) + + val expectedLoss = referenceLayer.forward(logProb, target) + val loss = testedLayer.forward(prob, target) + + val expectedGradInput = logSoftMax.backward(input, referenceLayer.backward(logProb, target)) + val gradInput = softMax.backward(input, testedLayer.backward(prob, target)) + + math.abs(expectedLoss - loss) < 1e-5 should be (true) + expectedGradInput.almostEqual(gradInput, 1e-5) should be (true) + } + + "A ClassNLL Criterion with probabilities input 1d" should "generate correct output and grad" in { + + val input = Tensor[Float](Array(4)).rand() + val target = Tensor[Float](Array[Float](4), Array(1)) + + val logSoftMax = LogSoftMax[Float]() + val softMax = SoftMax[Float]() + + val logProb = logSoftMax.forward(input) + val prob = softMax.forward(input) + + val referenceLayer = ClassNLLCriterion[Float]() + val testedLayer = ClassNLLCriterion[Float](logProbAsInput = false) + + val expectedLoss = referenceLayer.forward(logProb, target) + val loss = testedLayer.forward(prob, target) + + val expectedGradInput = logSoftMax.backward(input, referenceLayer.backward(logProb, target)) + val gradInput = softMax.backward(input, testedLayer.backward(prob, target)) + + math.abs(expectedLoss - loss) < 1e-5 should be (true) + expectedGradInput.almostEqual(gradInput, 1e-5) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MarginCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MarginCriterionSpec.scala new file mode 100644 index 00000000000..4af02c2dbd0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MarginCriterionSpec.scala @@ -0,0 +1,78 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + + +class MarginCriterionSpec extends FlatSpec with Matchers { + + "MarginCriterion " should "calculate correct squared hinge loss" in { + val input = Tensor[Float](Array[Float](0.1f, 0.2f, 0.3f, 0.4f), Array(4)) + val target = Tensor[Float](Array[Float](0.4f, 0.3f, 0.2f, 0.1f), Array(4)) + + val criterion = MarginCriterion[Float](squared = true) + + val loss = criterion.forward(input, target) + + val gradInput = criterion.backward(input, target) + val expectedGradInput = + Tensor[Float](Array[Float](-0.192f, -0.141f, -0.094f, -0.048f), Array(4)) + + math.abs(loss - 0.9026) < 1e-5 should be (true) + + gradInput.almostEqual(expectedGradInput, 1e-5) should be (true) + + } + + "MarginCriterion " should "calculate correct squared hinge loss 2" in { + val input = Tensor[Float](Array[Float](1f, 0.2f, 3f, 0.4f), Array(4)) + val target = Tensor[Float](Array[Float](4f, 0.3f, 2f, 0.1f), Array(4)) + + val criterion = MarginCriterion[Float](squared = true) + + val loss = criterion.forward(input, target) + + val gradInput = criterion.backward(input, target) + val expectedGradInput = + Tensor[Float](Array[Float](-0.0f, -0.141f, -0.0f, -0.048f), Array(4)) + + math.abs(loss - 0.4513) < 1e-5 should be (true) + + gradInput.almostEqual(expectedGradInput, 1e-5) should be (true) + + } + + "MarginCriterion " should "calculate correct hinge loss" in { + val input = Tensor[Float](Array[Float](0.1f, 0.2f, 0.3f, 0.4f), Array(4)) + val target = Tensor[Float](Array[Float](0.4f, 0.3f, 0.2f, 0.1f), Array(4)) + + val criterion = MarginCriterion[Float](sizeAverage = false) + + val loss = criterion.forward(input, target) + + val gradInput = criterion.backward(input, target) + val expectedGradInput = + Tensor[Float](Array[Float](-0.4f, -0.3f, -0.2f, -0.1f), Array(4)) + + math.abs(loss - 3.8) < 1e-5 should be (true) + + gradInput.almostEqual(expectedGradInput, 1e-5) should be (true) + + } + +} From a980caf870ed38c1ad77cb4c29c1f1dca0188b07 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Tue, 14 Nov 2017 20:39:20 -0600 Subject: [PATCH 0525/1065] Test utility to verify layer implementation against Keras (#1878) * add keras test up update update * clean * clean * off style * add comment * change init to uniform --- .../bigdl/dllib/keras/KerasBaseSpec.scala | 59 ++++++++ .../bigdl/dllib/keras/KerasRunner.scala | 141 ++++++++++++++++++ .../bigdl/dllib/keras/TestKerasBaseSpec.scala | 54 +++++++ 3 files changed, 254 insertions(+) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala new file mode 100644 index 00000000000..adb1a4ecb26 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -0,0 +1,59 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class KerasBaseSpec extends FlatSpec with BeforeAndAfter with Matchers { + + private def defaultWeightConverter(in: Array[Tensor[Float]]) = in + + private def getFieldByReflect(obj: Object, name: String): Object = { + val fieldDefinition = obj.getClass().getDeclaredField(name) + fieldDefinition.setAccessible(true) + return fieldDefinition.get(obj) + } + // weightConverter: convert keras weight to BigDL format, + // do nothing for the default converter + def checkOutputAndGrad(bmodel: AbstractModule[Tensor[Float], Tensor[Float], Float], + kerasCode: String, + weightConverter: (Array[Tensor[Float]]) => Array[Tensor[Float]] + = defaultWeightConverter, + precision: Double = 1e-5): Unit = { + val (gradInput, gradWeight, weights, input, output) = KerasRunner.run(kerasCode) + // Ensure they share the same weights + bmodel.setWeightsBias(weightConverter(weights)) + + val boutput = bmodel.forward(input) + boutput.almostEqual(output, precision) should be(true) + + val bgradInput = bmodel.backward(input, boutput.clone().fill(1)) + bgradInput.almostEqual(gradInput, precision) should be(true) + + // assuming the first one is weight, the second one is bias + if (gradWeight != null) { + val bgradWeight = getFieldByReflect(bmodel, "gradWeight").asInstanceOf[Tensor[Float]] + bgradWeight.almostEqual(weightConverter(gradWeight)(0), precision) should be(true) + + if (gradWeight.length > 1) { + val bgradBias = getFieldByReflect(bmodel, "gradBias").asInstanceOf[Tensor[Float]] + bgradBias.almostEqual(weightConverter(gradWeight)(1), precision) should be(true) + } + } + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala new file mode 100644 index 00000000000..795a539ed5d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala @@ -0,0 +1,141 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras + +import java.io.{File, PrintWriter} + +import com.intel.analytics.bigdl.tensor.Tensor + +import scala.io.Source +import scala.sys.process._ + +object KerasRunner { + // scalastyle:off + val code_head = + """ + |from keras.layers.core import * + |from keras.layers.convolutional import * + |from keras.layers import * + |from keras.models import Model + |import keras.backend as K + |import numpy as np + |import tempfile + | + |np.random.seed(1337) # for reproducibility + | + |def create_tmp_path(name): + | tmp_file = tempfile.NamedTemporaryFile(prefix="UnitTest-keras-" + name + "-") + | tmp_file.close() + | return tmp_file.name + | + """.stripMargin + val code_bottom = + """ + |nb_sample= 1 + |input_shape = input_tensor.shape.as_list() + |input_shape[0] = nb_sample + |X = np.random.uniform(0, 1, input_shape) + | + |grad_input = K.get_session().run(K.gradients(model.output, model.input), feed_dict={input_tensor: X}) # grad_input + | + |grad_weight = K.get_session().run(K.gradients(model.output, model.trainable_weights), # grad_weight + | feed_dict={input_tensor: X}) + |weights = model.get_weights() + |output = model.predict(X) + |result_list = [] + |for item in [("weights", weights), ("input", X), ("grad_input", grad_input), ("grad_weight", grad_weight), ("output",output)]: + | if isinstance(item[1], list): + | if len(item[1]) > 1: + | for i in range(len(item[1])): + | result_list.append((item[0] + "_" + str(i), item[1][i])) + | elif len(item[1]) == 1: + | result_list.append((item[0], item[1][0])) + | else: + | continue + | else: + | result_list.append(item) + |for result in result_list: + | value_path = create_tmp_path(result[0] + "_value") + | shape_path = create_tmp_path(result[0] + "_shape") + | np.savetxt(shape_path, result[1].shape) + | np.savetxt(value_path, result[1].ravel()) + | print(shape_path) + | print(value_path) + | + | + """.stripMargin + // scalastyle:on + private def getWeightRelate(pvalues: Map[String, Array[Float]], + keyName: String): Array[Tensor[Float]] = { + if (!pvalues.keySet.filter(key => key.contains(keyName)).isEmpty) { + val weightNum = pvalues.keySet.filter(key => key.contains(keyName)).size / 2 + Range(0, weightNum).map {i => + Tensor[Float]( + data = pvalues(s"${keyName}_${i}_value"), + shape = pvalues(s"${keyName}_${i}_shape").map(_.toInt)) + }.toArray + } else { + null + } + } + + private def getNoneWeightRelate(pvalues: Map[String, Array[Float]], + keyName: String): Tensor[Float] = { + Tensor[Float]( + data = pvalues(s"${keyName}_value"), + shape = pvalues(s"${keyName}_shape").map(_.toInt)) + } + + // return: (grad_input, grad_weight, weights, input, output) + def run(code: String): (Tensor[Float], Array[Tensor[Float]], + Array[Tensor[Float]], Tensor[Float], Tensor[Float]) = { + val pcodeFile = java.io.File.createTempFile("UnitTest", "keras") + val writer = new PrintWriter(pcodeFile) + writer.write(code_head) + writer.write(code) + writer.write(code_bottom) + writer.close() + val pcodeFileAbsPath = pcodeFile.getAbsolutePath + println("python code file: " + pcodeFileAbsPath) + val resultPaths = s"python ${pcodeFileAbsPath}".!!.split("\n") + if (pcodeFile.exists()) { + pcodeFile.delete() + } + + val pvalues = resultPaths.map {file => + val value = Source.fromFile(file).getLines().map(_.toFloat).toArray + val key = file.split("-")(2) + key -> value + }.toMap + + resultPaths.foreach {path => + new File(path).delete() + } + + val grad_input = getNoneWeightRelate(pvalues, "grad_input") + + val grad_weight = getWeightRelate(pvalues, "grad_weight") + + val weights = getWeightRelate(pvalues, "weights") + + val input = getNoneWeightRelate(pvalues, "input") + + val output = getNoneWeightRelate(pvalues, "output") + + (grad_input, grad_weight, weights, input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala new file mode 100644 index 00000000000..59de85b5349 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala @@ -0,0 +1,54 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{Linear, ReLU} +import com.intel.analytics.bigdl.tensor.Tensor + +class TestKerasBaseSpec extends KerasBaseSpec{ + + "Layer with weights" should "be ok" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |output_tensor = Dense(2, init='uniform')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val linear = new Linear[Float](3, 2) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(0).t(), in(1)) + checkOutputAndGrad(linear, kerasCode, weightConverter) + + } + + "Layer without weights" should "be ok" in { + val code = + """ + |input_tensor = Input(shape=[2]) + |output_tensor = Activation(activation="relu")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val (gradInput, gradWeight, weights, input, output) = KerasRunner.run(code) + + val relu = new ReLU[Float]() + val boutput = relu.forward(input) + boutput should be (output) + + val bGradInput = relu.backward(input, boutput) + bGradInput.div(boutput) should be (gradInput) + } + + +} From 59338c1426bed8584960b9b5063945b7c79242a3 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Wed, 15 Nov 2017 09:15:24 -0600 Subject: [PATCH 0526/1065] Support keras model loading (#1827) * keras load model and api wrapper * fix batchnorm * fix averagepooling * remove function wrapper and clean code * add unit test * remove backend * fix and clean * update * separate unittest * x * remove -n * ignore application * separate test * pass parameter * try to fix test * disable shape checking * ignore --- .../dllib/nn/abstractnn/AbstractModule.scala | 4 ++++ .../dllib/utils/python/api/PythonBigDL.scala | 19 +++++++++++++++++++ 2 files changed, 23 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 6f4c18c1413..e8840721d87 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -555,6 +555,10 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, s" number of output ${newWeights.length}") val weights = parameters()._1 for(i <- newWeights.indices) { + // TODO: enable this checking as we don't respect shape right now. +// require(weights(i).size().deep == newWeights(i).size().deep, +// s"Mismatch shape, ${weights(i).size().mkString(",")}" + +// s" vs ${newWeights(i).size().mkString(",")} ") weights(i).copy(newWeights(i)) } this diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 1962d864be6..fa8d02549bd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -36,6 +36,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} +import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} import com.intel.analytics.bigdl.utils.tf.{BigDLSessionImpl, Context, TensorflowDataFormat, TensorflowSaver} @@ -2198,6 +2199,24 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab batchSize: Int): DLClassifierModel[T] = { dlClassifierModel.setBatchSize(batchSize) } + + def getContainerModules(module: Container[Activity, Activity, T]) + : JList[AbstractModule[Activity, Activity, T]] = { + module.modules.toList.asJava + } + + def isWithWeights(module: Module[T]): Boolean = { + val weights = module.getWeightsBias() + return weights != null && !weights.isEmpty + } + + def setRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit = { + module.runningMean.set(toTensor(runningMean)) + } + + def setRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit = { + module.runningVar.set(toTensor(runningStd)) + } } object PythonBigDLUtils { From bc8e846a8b5b38d6c4b14d79106e57bcf3eb5ed0 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Thu, 16 Nov 2017 01:41:36 -0600 Subject: [PATCH 0527/1065] fix keras tester (#1892) * fix bug * add clone back --- .../analytics/bigdl/dllib/keras/KerasBaseSpec.scala | 4 +++- .../bigdl/dllib/keras/TestKerasBaseSpec.scala | 12 ++---------- 2 files changed, 5 insertions(+), 11 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index adb1a4ecb26..9fb684d7942 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -36,7 +36,9 @@ class KerasBaseSpec extends FlatSpec with BeforeAndAfter with Matchers { precision: Double = 1e-5): Unit = { val (gradInput, gradWeight, weights, input, output) = KerasRunner.run(kerasCode) // Ensure they share the same weights - bmodel.setWeightsBias(weightConverter(weights)) + if (weights != null) { + bmodel.setWeightsBias(weightConverter(weights)) + } val boutput = bmodel.forward(input) boutput.almostEqual(output, precision) should be(true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala index 59de85b5349..57f17746b02 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala @@ -34,21 +34,13 @@ class TestKerasBaseSpec extends KerasBaseSpec{ } "Layer without weights" should "be ok" in { - val code = + val kerasCode = """ |input_tensor = Input(shape=[2]) |output_tensor = Activation(activation="relu")(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val (gradInput, gradWeight, weights, input, output) = KerasRunner.run(code) - val relu = new ReLU[Float]() - val boutput = relu.forward(input) - boutput should be (output) - - val bGradInput = relu.backward(input, boutput) - bGradInput.div(boutput) should be (gradInput) + checkOutputAndGrad(relu, kerasCode) } - - } From aa19752dcc0113c9be956764da47c5a886f37c93 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 17 Nov 2017 00:09:00 -0600 Subject: [PATCH 0528/1065] Keras support - add Hard-sigmoid layer (#1897) * add hard sigmoid * add python * add python api * fix typo * add doc --- .../bigdl/dllib/nn/HardSigmoid.scala | 74 +++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 4 + .../bigdl/dllib/keras/HardSigmoidSpec.scala | 38 ++++++++++ 3 files changed, 116 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HardSigmoidSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala new file mode 100644 index 00000000000..436d9f0858e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala @@ -0,0 +1,74 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Apply Segment-wise linear approximation of sigmoid. + * Faster than sigmoid + * ⎧ 0, if x < -2.5 + * f(x) = ⎨ 1, if x > 2.5 + * ⎩ 0.2 * x + 0.5, otherwise + */ +class HardSigmoid[T: ClassTag] +(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + + val minValue = ev.fromType[Double](-2.5) + val maxValue = ev.fromType[Double](2.5) + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + output.resizeAs(input) + output.map(input, (out, in) => { + if (ev.isGreater(in, maxValue)) { + ev.fromType[Int](1) + } else if (ev.isGreater(minValue, in)) { + ev.fromType[Int](0) + } else { + ev.fromType[Double](0.2 * ev.toType[Double](in) + 0.5) + } + }) + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(input.isSameSizeAs(gradOutput), + "Input should have the same size as gradOutput" + + s"input size(${input.dim()}) gradOutput size(${gradOutput.dim()})") + gradInput.resizeAs(input) + val func = new TensorFunc6[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], + offset2: Int, data3: Array[T], offset3: Int): Unit = { + if (ev.isGreater(data3(offset3), maxValue) + || ev.isGreater(minValue, data3(offset3))) { + data1(offset1) = ev.fromType[Double](0) + } else { + data1(offset1) = ev.times(data2(offset2), ev.fromType[Double](0.2)) + } + } + } + DenseTensorApply.apply3[T](gradInput, gradOutput, input, func) + gradInput + } +} + +object HardSigmoid { + def apply[T : ClassTag]()(implicit ev: TensorNumeric[T]): HardSigmoid[T] = new HardSigmoid[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index fa8d02549bd..4a7f87927a1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2062,6 +2062,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab BilinearFiller } + def createHardSigmoid : HardSigmoid[T] = { + HardSigmoid() + } + def setInitMethod(layer: Initializable, weightInitMethod: InitializationMethod, biasInitMethod: InitializationMethod): layer.type = { layer.setInitMethod(weightInitMethod, biasInitMethod) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HardSigmoidSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HardSigmoidSpec.scala new file mode 100644 index 00000000000..6c036f7939e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HardSigmoidSpec.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.HardSigmoid + + +class HardSigmoidSpec extends KerasBaseSpec { + + "Hard_sigmoid" should "be ok" in { + val sigmoidCode = + """ + |input_tensor = Input(shape=[5]) + |output_tensor = Activation(activation="hard_sigmoid")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val (gradInput, gradWeight, weights, input, output) = KerasRunner.run(sigmoidCode) + + val hardSigmoid = HardSigmoid[Float]() + + checkOutputAndGrad(hardSigmoid, sigmoidCode) + + } +} From f9a8a2a74f6db7062b4c8bab53037b389f8b1398 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Mon, 20 Nov 2017 04:23:48 -0600 Subject: [PATCH 0529/1065] Ignore keras test if environment is not ready and add test method for loss (#1899) * add check method for loss * clean * clean * comments * checking * fix * rebase * rebase --- .../bigdl/dllib/keras/HardSigmoidSpec.scala | 3 +- .../bigdl/dllib/keras/KerasBaseSpec.scala | 35 ++++++++-- .../bigdl/dllib/keras/KerasRunner.scala | 68 ++++++++++++------- .../bigdl/dllib/keras/TestKerasBaseSpec.scala | 20 +++++- 4 files changed, 93 insertions(+), 33 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HardSigmoidSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HardSigmoidSpec.scala index 6c036f7939e..da3df660d92 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HardSigmoidSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HardSigmoidSpec.scala @@ -25,11 +25,10 @@ class HardSigmoidSpec extends KerasBaseSpec { val sigmoidCode = """ |input_tensor = Input(shape=[5]) + |input = np.random.uniform(0, 1, [4, 5]) |output_tensor = Activation(activation="hard_sigmoid")(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val (gradInput, gradWeight, weights, input, output) = KerasRunner.run(sigmoidCode) - val hardSigmoid = HardSigmoid[Float]() checkOutputAndGrad(hardSigmoid, sigmoidCode) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index 9fb684d7942..1be4374813b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -14,11 +14,23 @@ * limitations under the License. */ package com.intel.analytics.bigdl.keras -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractCriterion, AbstractModule} import com.intel.analytics.bigdl.tensor.Tensor -import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.utils.BigDLSpecHelper -class KerasBaseSpec extends FlatSpec with BeforeAndAfter with Matchers { +import scala.sys.process._ + +abstract class KerasBaseSpec extends BigDLSpecHelper { + + protected def ifskipTest(): Unit = { + // Skip unitest if environment is not ready + try { + Seq("python", "-c", "import keras; import tensorflow").!! + } catch { + case e: Throwable => cancel("python or keras or tensorflow is not installed", e) + } + } private def defaultWeightConverter(in: Array[Tensor[Float]]) = in @@ -34,7 +46,7 @@ class KerasBaseSpec extends FlatSpec with BeforeAndAfter with Matchers { weightConverter: (Array[Tensor[Float]]) => Array[Tensor[Float]] = defaultWeightConverter, precision: Double = 1e-5): Unit = { - val (gradInput, gradWeight, weights, input, output) = KerasRunner.run(kerasCode) + val (gradInput, gradWeight, weights, input, target, output) = KerasRunner.run(kerasCode) // Ensure they share the same weights if (weights != null) { bmodel.setWeightsBias(weightConverter(weights)) @@ -57,5 +69,20 @@ class KerasBaseSpec extends FlatSpec with BeforeAndAfter with Matchers { } } } + + def checkOutputAndGradForLoss(bmodel: AbstractCriterion[Tensor[Float], Tensor[Float], Float], + kerasCode: String, + precision: Double = 1e-5): Unit = { + val (gradInput, gradWeight, weights, input, target, output) = + KerasRunner.run(kerasCode, is_loss = true) + + val boutput = bmodel.forward(input, target) + require(output.nElement() == 1, s"output should only contain 1 element, but we got: ${output}") + NumericFloat.nearlyEqual(boutput, output.storage.array()(0), precision) should be(true) + + val bgradInput = bmodel.backward(input, target.clone()) + bgradInput.almostEqual(gradInput, precision) should be(true) + + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala index 795a539ed5d..f976789b423 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala @@ -29,6 +29,7 @@ object KerasRunner { |from keras.layers.core import * |from keras.layers.convolutional import * |from keras.layers import * + |from keras.metrics import * |from keras.models import Model |import keras.backend as K |import numpy as np @@ -42,21 +43,29 @@ object KerasRunner { | return tmp_file.name | """.stripMargin - val code_bottom = + + val code_for_loss = + """ + |grad_input = K.get_session().run(K.gradients(loss, [input_tensor]), + | feed_dict={input_tensor: input, target_tensor: Y}) + |output = K.get_session().run(loss, feed_dict={input_tensor: input, target_tensor: Y}) + |weights = [] + |grad_weight = [] + """.stripMargin + val code_for_layer = """ - |nb_sample= 1 - |input_shape = input_tensor.shape.as_list() - |input_shape[0] = nb_sample - |X = np.random.uniform(0, 1, input_shape) - | - |grad_input = K.get_session().run(K.gradients(model.output, model.input), feed_dict={input_tensor: X}) # grad_input + |Y = [] + |grad_input = K.get_session().run(K.gradients(model.output, model.input), feed_dict={input_tensor: input}) # grad_input | |grad_weight = K.get_session().run(K.gradients(model.output, model.trainable_weights), # grad_weight - | feed_dict={input_tensor: X}) + | feed_dict={input_tensor: input}) + |output = model.predict(input) |weights = model.get_weights() - |output = model.predict(X) + + """.stripMargin + val code_for_save = """ |result_list = [] - |for item in [("weights", weights), ("input", X), ("grad_input", grad_input), ("grad_weight", grad_weight), ("output",output)]: + |for item in [("weights", weights), ("input", input), ("target", Y), ("grad_input", grad_input), ("grad_weight", grad_weight), ("output",output)]: | if isinstance(item[1], list): | if len(item[1]) > 1: | for i in range(len(item[1])): @@ -94,26 +103,28 @@ object KerasRunner { private def getNoneWeightRelate(pvalues: Map[String, Array[Float]], keyName: String): Tensor[Float] = { - Tensor[Float]( - data = pvalues(s"${keyName}_value"), - shape = pvalues(s"${keyName}_shape").map(_.toInt)) + if (!pvalues.keySet.filter(key => key.contains(keyName)).isEmpty) { + Tensor[Float]( + data = pvalues(s"${keyName}_value"), + shape = pvalues(s"${keyName}_shape").map(_.toInt)) + } else { + null + } } - // return: (grad_input, grad_weight, weights, input, output) - def run(code: String): (Tensor[Float], Array[Tensor[Float]], - Array[Tensor[Float]], Tensor[Float], Tensor[Float]) = { + // return: (grad_input, grad_weight, weights, input, target, output) + def run(code: String, is_loss: Boolean = false): (Tensor[Float], Array[Tensor[Float]], + Array[Tensor[Float]], Tensor[Float], Tensor[Float], Tensor[Float]) = { val pcodeFile = java.io.File.createTempFile("UnitTest", "keras") val writer = new PrintWriter(pcodeFile) writer.write(code_head) writer.write(code) - writer.write(code_bottom) + writer.write(if (is_loss) {code_for_loss} else {code_for_layer}) + writer.write(code_for_save) writer.close() val pcodeFileAbsPath = pcodeFile.getAbsolutePath println("python code file: " + pcodeFileAbsPath) val resultPaths = s"python ${pcodeFileAbsPath}".!!.split("\n") - if (pcodeFile.exists()) { - pcodeFile.delete() - } val pvalues = resultPaths.map {file => val value = Source.fromFile(file).getLines().map(_.toFloat).toArray @@ -121,10 +132,6 @@ object KerasRunner { key -> value }.toMap - resultPaths.foreach {path => - new File(path).delete() - } - val grad_input = getNoneWeightRelate(pvalues, "grad_input") val grad_weight = getWeightRelate(pvalues, "grad_weight") @@ -133,9 +140,18 @@ object KerasRunner { val input = getNoneWeightRelate(pvalues, "input") - val output = getNoneWeightRelate(pvalues, "output") + val target = getNoneWeightRelate(pvalues, "target") + + var output = getNoneWeightRelate(pvalues, "output") + + resultPaths.foreach {path => + new File(path).delete() + } + if (pcodeFile.exists()) { + pcodeFile.delete() + } - (grad_input, grad_weight, weights, input, output) + (grad_input, grad_weight, weights, input, target, output) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala index 57f17746b02..f798063b974 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala @@ -15,15 +15,17 @@ */ package com.intel.analytics.bigdl.keras -import com.intel.analytics.bigdl.nn.{Linear, ReLU} +import com.intel.analytics.bigdl.nn.{Linear, MSECriterion, ReLU} import com.intel.analytics.bigdl.tensor.Tensor class TestKerasBaseSpec extends KerasBaseSpec{ "Layer with weights" should "be ok" in { + ifskipTest() val kerasCode = """ |input_tensor = Input(shape=[3]) + |input = np.random.uniform(0, 1, [1, 3]) |output_tensor = Dense(2, init='uniform')(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin @@ -34,13 +36,29 @@ class TestKerasBaseSpec extends KerasBaseSpec{ } "Layer without weights" should "be ok" in { + ifskipTest() val kerasCode = """ |input_tensor = Input(shape=[2]) + |input = np.random.uniform(0, 1, [1, 2]) |output_tensor = Activation(activation="relu")(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin val relu = new ReLU[Float]() checkOutputAndGrad(relu, kerasCode) } + + "MSE loss" should "be ok" in { + ifskipTest() + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = mean_squared_error(input_tensor, target_tensor) + |input = np.random.uniform(0, 1, [2, 3]) + |Y = np.random.uniform(0, 1, [2, 3]) + """.stripMargin + val mse = new MSECriterion[Float]() + checkOutputAndGradForLoss(mse, kerasCode) + } } From eb90ed49d5383d2aa02d5b1eaa7b519c1e715856 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Mon, 20 Nov 2017 07:11:27 -0600 Subject: [PATCH 0530/1065] skip test should be transparent (#1904) --- .../com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala | 3 +++ .../intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala | 3 --- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index 1be4374813b..05d4f6fe679 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -46,6 +46,7 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { weightConverter: (Array[Tensor[Float]]) => Array[Tensor[Float]] = defaultWeightConverter, precision: Double = 1e-5): Unit = { + ifskipTest() val (gradInput, gradWeight, weights, input, target, output) = KerasRunner.run(kerasCode) // Ensure they share the same weights if (weights != null) { @@ -73,6 +74,8 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { def checkOutputAndGradForLoss(bmodel: AbstractCriterion[Tensor[Float], Tensor[Float], Float], kerasCode: String, precision: Double = 1e-5): Unit = { + ifskipTest() + val (gradInput, gradWeight, weights, input, target, output) = KerasRunner.run(kerasCode, is_loss = true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala index f798063b974..7cca6863bb3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/TestKerasBaseSpec.scala @@ -21,7 +21,6 @@ import com.intel.analytics.bigdl.tensor.Tensor class TestKerasBaseSpec extends KerasBaseSpec{ "Layer with weights" should "be ok" in { - ifskipTest() val kerasCode = """ |input_tensor = Input(shape=[3]) @@ -36,7 +35,6 @@ class TestKerasBaseSpec extends KerasBaseSpec{ } "Layer without weights" should "be ok" in { - ifskipTest() val kerasCode = """ |input_tensor = Input(shape=[2]) @@ -49,7 +47,6 @@ class TestKerasBaseSpec extends KerasBaseSpec{ } "MSE loss" should "be ok" in { - ifskipTest() val kerasCode = """ |input_tensor = Input(shape=[3]) From b067e66c16e8d580a9bfebb7686c3b8989353f9a Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Tue, 21 Nov 2017 11:09:43 +0800 Subject: [PATCH 0531/1065] Add more keras layers and test (#1891) * add, update and fix * add hard_sigmoid * fix python3.5 lambda in permute * fix syntax for python3.5 * fix pooling and conv layers * fix syntax * fix permute for py35 --- .../analytics/bigdl/dllib/nn/CAveTable.scala | 85 +++ .../analytics/bigdl/dllib/nn/CMinTable.scala | 2 +- .../dllib/nn/VolumetricAveragePooling.scala | 614 ++++++++++++++++++ .../bigdl/dllib/nn/VolumetricMaxPooling.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 20 + .../bigdl/dllib/nn/CAveTableSpec.scala | 35 + .../analytics/bigdl/dllib/nn/LinearSpec.scala | 4 +- .../torch/VolumetricAveragePoolingSpec.scala | 335 ++++++++++ 8 files changed, 1093 insertions(+), 4 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAveTable.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAveTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricAveragePoolingSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAveTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAveTable.scala new file mode 100644 index 00000000000..bf2c2d73268 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAveTable.scala @@ -0,0 +1,85 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect._ + +/** + * Merge the input tensors in the input table by element wise taking the average. The input table + * is actually an array of tensor with same size. + * @param inplace reuse the input memory + * @param ev numeric operator + * @tparam T Numeric type. Only support float/double now + */ +// TODO: add serialization +class CAveTable[T: ClassTag](val inplace: Boolean = false)( + implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { + + override def updateOutput(input: Table): Tensor[T] = { + if (inplace) { + output.set(input[Tensor[T]](1)) + } else { + output.resizeAs(input[Tensor[T]](1)).copy(input[Tensor[T]](1)) + } + var i = 2 + while (i <= input.length()) { + output.add(input[Tensor[T]](i)) + i += 1 + } + output.div(ev.fromType(input.length())) + } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]) : Table = { + var i = 1 + val size = input.length() + val gradResult = gradOutput.div(ev.fromType(size)) + while (i <= size) { + if (i > gradInput.length()) gradInput.insert(i, Tensor[T]().resizeAs(input(1))) + if (inplace) { + gradInput[Tensor[T]](i).set(gradResult) + } else { + gradInput[Tensor[T]](i).resizeAs(gradOutput).copy(gradResult) + } + i += 1 + } + i = input.length() + 1 + while (i <= gradInput.length) { + gradInput.remove(i) + } + gradInput + } + + override def clearState(): this.type = { + if (!inplace) { + super.clearState() + } + this + } +} + + +object CAveTable { + def apply[@specialized(Float, Double) T: ClassTag]( + inplace: Boolean = false)(implicit ev: TensorNumeric[T]) : CAveTable[T] = { + new CAveTable[T](inplace) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMinTable.scala index 2f5babac554..436b81bf58f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMinTable.scala @@ -79,7 +79,7 @@ class CMinTable[T: ClassTag](implicit ev: TensorNumeric[T]) gradInput } - override def canEqual(other: Any): Boolean = other.isInstanceOf[CMaxTable[T]] + override def canEqual(other: Any): Boolean = other.isInstanceOf[CMinTable[T]] override def equals(other: Any): Boolean = other match { case that: CMinTable[T] => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala new file mode 100644 index 00000000000..dd394283ac1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala @@ -0,0 +1,614 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer._ +import org.codehaus.jackson.map.DeserializationContext +import serialization.Bigdl.{AttrValue, BigDLModule} + +import scala.reflect._ +import scala.reflect.runtime.universe + +/** + * Applies 3D average-pooling operation in kTxkWxkH regions by step size dTxdWxdH. + * The number of output features is equal to the number of input planes / dT. + * The input can optionally be padded with zeros. Padding should be smaller than + * half of kernel size. That is, padT < kT/2, padW < kW/2 and padH < kH/2 + * @param kT The kernel size + * @param kW The kernel width + * @param kH The kernel height + * @param dT The step in the time dimension + * @param dW The step in the width dimension + * @param dH The step in the height dimension + * @param padT The padding in the time dimension + * @param padW The padding in the width dimension + * @param padH The padding in the height dimension + * @param countIncludePad Whether to include padding when dividing the + * number of elements in pooling region + * @param ceilMode Whether the output size is to be ceiled or floored + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +// TODO: add serialization +class VolumetricAveragePooling[T: ClassTag]( + val kT: Int, val kW: Int, val kH: Int, + val dT: Int, val dW: Int, val dH: Int, + val padT: Int = 0, val padW: Int = 0, val padH: Int = 0, + private var countIncludePad: Boolean = true, + private var ceilMode: Boolean = false) + (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + + def this(kT: Int, kW: Int, kH: Int)(implicit ev: TensorNumeric[T]) { + this(kT, kW, kH, kT, kW, kH) + } + + /** + * set ceil mode + * @return this + */ + def ceil(): VolumetricAveragePooling[T] = { + ceilMode = true + this + } + + /** + * set floor mode + * @return this + */ + def floor(): VolumetricAveragePooling[T] = { + ceilMode = false + this + } + + /** + * set countIncludePad to true + * @return this + */ + def setCountIncludePad(): VolumetricAveragePooling[T] = { + countIncludePad = true + this + } + + /** + * set countIncludePad to false + * @return this + */ + def setCountExcludePad(): VolumetricAveragePooling[T] = { + countIncludePad = false + this + } + + + require(kT > 0 && kW > 0 && kH > 0, + s"kernel size should be greater than zero, but got kT: $kT kH: $kH kW: $kW") + + require(dT > 0 && dW > 0 && dH > 0, + s"stride should be greater than zero, but got dT: $dT dH: $dH dW: $dW") + + require(kT / 2 >= padT && kW / 2 >= padW && kH / 2 >= padH, + "pad should be smaller than half of kernel size, but got " + + s"kT: $kT kH: $kH kW: $kW, padT: $padT, padW: $padW, padH: $padH") + + /** + * Computes the output using the current parameter set of the class and input. This function + * returns the result which is stored in the output field. + * @param input + * @return + */ + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 4 || input.dim() == 5, + s"4D or 5D (batch mode) tensor expected for input, but got: ${ input.dim() }") + require(input.isContiguous(), "input is not contiguous") + val dimt = input.dim() - 2 + val dimh = input.dim() - 1 + val dimw = input.dim() + require(input.size(dimw) >= kW && input.size(dimh) >= kH && input.size(dimt) >= kT, + s"input image (T: ${input.size(dimt)} H: ${input.size(dimh)} W: ${input.size(dimw)}) " + + s"smaller than kernel size (kT: $kT kH: $kH kW: $kW)") + + val nslices = input.size(input.dim() - 3) + val itime = input.size(dimt) + val iheight = input.size(dimh) + val iwidth = input.size(dimw) + + var otime: Int = 0 + var oheight: Int = 0 + var owidth: Int = 0 + + if (ceilMode) { + otime = math.ceil(1.0 * (itime - kT + 2 * padT) / dT).toInt + 1 + oheight = math.ceil(1.0 * (iheight - kH + 2 * padH) / dH).toInt + 1 + owidth = math.ceil(1.0 * (iwidth - kW + 2 * padW) / dW).toInt + 1 + } + else { + otime = math.floor(1.0 * (itime - kT + 2 * padT) / dT).toInt + 1 + oheight = math.floor(1.0 * (iheight - kH + 2 * padH) / dH).toInt + 1 + owidth = math.floor(1.0 * (iwidth - kW + 2 * padW) / dW).toInt + 1 + } + if (padT != 0 || padW != 0 || padH != 0) { + // ensure that the last pooling starts inside the image + if ((otime - 1) * dT >= itime + padT) otime -= 1 + if ((oheight - 1) * dH >= iheight + padH) oheight -= 1 + if ((owidth - 1) * dW >= iwidth + padW) owidth -= 1 + } + require(otime >= 1 && owidth >= 1 && oheight >= 1, + s"Given input size: (${ nslices }x${ itime }x${ iheight }x${ iwidth })." + + s" Calculated output size:" + + s" (${ nslices }x${ otime }x${ oheight }x${ owidth }). Output size is too small") + + if (input.dim() == 4) { + // non-batch mode + output.resize(nslices, otime, oheight, owidth) + if (classTag[T] == classTag[Double]) { + volumetricAveragePoolingForwardDouble( + input.asInstanceOf[Tensor[Double]].storage().array(), input.storageOffset() - 1, + output.asInstanceOf[Tensor[Double]].storage().array(), output.storageOffset() - 1, + countIncludePad, nslices, itime, iwidth, iheight, otime, owidth, oheight, + kT, kW, kH, dT, dW, dH, padT, padW, padH) + } else if (classTag[T] == classTag[Float]) { + volumetricAveragePoolingForwardFloat( + input.asInstanceOf[Tensor[Float]].storage().array(), input.storageOffset() - 1, + output.asInstanceOf[Tensor[Float]].storage().array(), output.storageOffset() - 1, + countIncludePad, nslices, itime, iwidth, iheight, otime, owidth, oheight, + kT, kW, kH, dT, dW, dH, padT, padW, padH) + } else { + throw new IllegalArgumentException("currently only support type float or double") + } + } else { + // batch mode + val nBatch = input.size(1) + + output.resize(nBatch, nslices, otime, oheight, owidth) + + var p = 0 + if (classTag[T] == classTag[Double]) { + while (p < nBatch) { + val curInput = input(p + 1) + val curOutput = output(p + 1) + volumetricAveragePoolingForwardDouble( + curInput.asInstanceOf[Tensor[Double]].storage().array(), + curInput.storageOffset() - 1, + curOutput.asInstanceOf[Tensor[Double]].storage().array(), + curOutput.storageOffset() - 1, countIncludePad, + nslices, itime, iwidth, iheight, otime, owidth, oheight, + kT, kW, kH, dT, dW, dH, padT, padW, padH) + p += 1 + } + } else if (classTag[T] == classTag[Float]) { + while (p < nBatch) { + val curInput = input(p + 1) + val curOutput = output(p + 1) + volumetricAveragePoolingForwardFloat( + curInput.asInstanceOf[Tensor[Float]].storage().array(), + curInput.storageOffset() - 1, + curOutput.asInstanceOf[Tensor[Float]].storage().array(), + curOutput.storageOffset() - 1, countIncludePad, + nslices, itime, iwidth, iheight, otime, owidth, oheight, + kT, kW, kH, dT, dW, dH, padT, padW, padH) + p += 1 + } + } else { + throw new IllegalArgumentException("currently only support type float or double") + } + + } + output + } + + /** + * Computing the gradient of the module with respect to its own input. This is returned in + * gradInput. Also, the gradInput state variable is updated accordingly. + * @param input + * @param gradOutput + * @return + */ + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val dimn = input.dim() - 3 + val dimt = input.dim() - 2 + val dimh = input.dim() - 1 + val dimw = input.dim() + + val nslices = input.size(dimn) + val itime = input.size(dimt) + val iheight = input.size(dimh) + val iwidth = input.size(dimw) + val otime = gradOutput.size(dimt) + val oheight = gradOutput.size(dimh) + val owidth = gradOutput.size(dimw) + + gradInput.resizeAs(input).zero() + require(gradOutput.isContiguous(), "gradOutput is not contiguous") + + if (input.dim() == 4) { + // non-batch mode + if (classTag[T] == classTag[Double]) { + volumetricAveragePoolingBackwardDouble( + gradInput.asInstanceOf[Tensor[Double]].storage().array(), gradInput.storageOffset() - 1, + gradOutput.asInstanceOf[Tensor[Double]].storage().array(), gradOutput.storageOffset() - 1, + countIncludePad, nslices, itime, iwidth, iheight, otime, owidth, oheight, + dT, dW, dH, padT, padW, padH) + } else if (classTag[T] == classTag[Float]) { + volumetricAveragePoolingBackwardFloat( + gradInput.asInstanceOf[Tensor[Float]].storage().array(), gradInput.storageOffset() - 1, + gradOutput.asInstanceOf[Tensor[Float]].storage().array(), gradOutput.storageOffset() - 1, + countIncludePad, nslices, itime, iwidth, iheight, otime, owidth, oheight, + dT, dW, dH, padT, padW, padH) + } else { + throw new IllegalArgumentException("currently only support type float or double") + } + } + else { + // batch mode + val nBatch = input.size(1) + var p = 0 + + if (classTag[T] == classTag[Double]) { + while (p < nBatch) { + val curGradInput = gradInput(p + 1) + val curGradOutput = gradOutput(p + 1) + volumetricAveragePoolingBackwardDouble( + curGradInput.asInstanceOf[Tensor[Double]].storage().array(), + curGradInput.storageOffset() - 1, + curGradOutput.asInstanceOf[Tensor[Double]].storage().array(), + curGradOutput.storageOffset() - 1, countIncludePad, + nslices, itime, iwidth, iheight, otime, owidth, oheight, + dT, dW, dH, padT, padW, padH) + p += 1 + } + } else if (classTag[T] == classTag[Float]) { + while (p < nBatch) { + val curGradInput = gradInput(p + 1) + val curGradOutput = gradOutput(p + 1) + volumetricAveragePoolingBackwardFloat( + curGradInput.asInstanceOf[Tensor[Float]].storage().array(), + curGradInput.storageOffset() - 1, + curGradOutput.asInstanceOf[Tensor[Float]].storage().array(), + curGradOutput.storageOffset() - 1, countIncludePad, + nslices, itime, iwidth, iheight, otime, owidth, oheight, + dT, dW, dH, padT, padW, padH) + p += 1 + } + } else { + throw new IllegalArgumentException("currently only support type float or double") + } + } + gradInput + } + + override def equals(obj: Any): Boolean = { + + if (!super.equals(obj)) { + return false + } + + if (!obj.isInstanceOf[VolumetricAveragePooling[T]]) { + return false + } + val other = obj.asInstanceOf[VolumetricAveragePooling[T]] + if (this.eq(other)) { + return true + } + + kT == other.kT && + kW == other.kW && + kH == other.kH && + dT == other.dT && + dW == other.dW && + dH == other.dH && + padT == other.padT && + padW == other.padW && + padH == other.padH && + ceilMode == other.ceilMode && + countIncludePad == other.countIncludePad + } + + override def hashCode(): Int = { + val seed = 37 + var hash = super.hashCode() + hash = hash * seed + kT.hashCode() + hash = hash * seed + kW.hashCode() + hash = hash * seed + kH.hashCode() + hash = hash * seed + dT.hashCode() + hash = hash * seed + dW.hashCode() + hash = hash * seed + dH.hashCode() + hash = hash * seed + padT.hashCode() + hash = hash * seed + padW.hashCode() + hash = hash * seed + padH.hashCode() + hash = hash * seed + ceilMode.hashCode() + hash = hash * seed + countIncludePad.hashCode() + + hash + } + + override def toString(): String = { + s"${getPrintName}($kT, $kW, $kH, $dT, $dW, $dH, $padT, $padW, $padH, " + + s"$countIncludePad, $ceilMode)" + } + + override def clearState(): this.type = { + super.clearState() + this + } + + private def volumetricAveragePoolingForwardDouble(input: Array[Double], inputOffset: Int, + output: Array[Double], outputOffset: Int, countIncludePad: Boolean, + nSlices: Int, iTime: Int, iWidth: Int, iHeight: Int, oTime: Int, oWidth: Int, oHeight: Int, + kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, padT: Int, padW: Int, padH: Int): Unit = { + var k = 0 + while (k < nSlices) { + val inputStart = inputOffset + k * iTime * iWidth * iHeight + val outputStart = outputOffset + k * oTime * oWidth * oHeight + var ti = 0 + while (ti < oTime) { + var i = 0 + while (i < oHeight) { + var j = 0 + while (j < oWidth) { + var tstart = ti * dT - padT + var hstart = i * dH - padH + var wstart = j * dW - padW + var tend = math.min(tstart + kT, iTime + padT) + var hend = math.min(hstart + kH, iHeight + padH) + var wend = math.min(wstart + kW, iWidth + padW) + var poolSize = (tend - tstart) * (hend - hstart) * (wend - wstart) + tstart = math.max(tstart, 0) + hstart = math.max(hstart, 0) + wstart = math.max(wstart, 0) + tend = math.min(tend, iTime) + hend = math.min(hend, iHeight) + wend = math.min(wend, iWidth) + + val divide_factor = if (countIncludePad) poolSize + else (tend - tstart) * (hend - hstart) * (wend - wstart) + + var sum = 0.0 + var z = tstart + while (z < tend) { + var y = hstart + while (y < hend) { + var x = wstart + while (x < wend) { + val value = input(z * iWidth * iHeight + y * iWidth + x + inputStart) + sum += value + x += 1 + } + y += 1 + } + z += 1 + } + output(ti * oWidth * oHeight + i * oWidth + j + outputStart) = sum / divide_factor + j += 1 + } + i += 1 + } + ti += 1 + } + k += 1 + } + } + + private def volumetricAveragePoolingForwardFloat(input: Array[Float], inputOffset: Int, + output: Array[Float], outputOffset: Int, countIncludePad: Boolean, + nSlices: Int, iTime: Int, iWidth: Int, iHeight: Int, oTime: Int, oWidth: Int, oHeight: Int, + kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, padT: Int, padW: Int, padH: Int): Unit = { + var k = 0 + while (k < nSlices) { + val inputStart = inputOffset + k * iTime * iWidth * iHeight + val outputStart = outputOffset + k * oTime * oWidth * oHeight + var ti = 0 + while (ti < oTime) { + var i = 0 + while (i < oHeight) { + var j = 0 + while (j < oWidth) { + var tstart = ti * dT - padT + var hstart = i * dH - padH + var wstart = j * dW - padW + var tend = math.min(tstart + kT, iTime + padT) + var hend = math.min(hstart + kH, iHeight + padH) + var wend = math.min(wstart + kW, iWidth + padW) + var poolSize = (tend - tstart) * (hend - hstart) * (wend - wstart) + tstart = math.max(tstart, 0) + hstart = math.max(hstart, 0) + wstart = math.max(wstart, 0) + tend = math.min(tend, iTime) + hend = math.min(hend, iHeight) + wend = math.min(wend, iWidth) + + val divide_factor = if (countIncludePad) poolSize + else (tend - tstart) * (hend - hstart) * (wend - wstart) + + var sum = 0.0f + var z = tstart + while (z < tend) { + var y = hstart + while (y < hend) { + var x = wstart + while (x < wend) { + val value = input(z * iWidth * iHeight + y * iWidth + x + inputStart) + sum += value + x += 1 + } + y += 1 + } + z += 1 + } + output(ti * oWidth * oHeight + i * oWidth + j + outputStart) = sum / divide_factor + j += 1 + } + i += 1 + } + ti += 1 + } + k += 1 + } + } + + + private def volumetricAveragePoolingBackwardDouble(gradInput: Array[Double], gradInputOffset: Int, + gradOutput: Array[Double], gradOutputOffset: Int, countIncludePad: Boolean, + nslices: Int, iTime: Int, iWidth: Int, iHeight: Int, + oTime: Int, oWidth: Int, oHeight: Int, + dT: Int, dW: Int, dH: Int, padT: Int, padW: Int, padH: Int): Unit = { + var k = 0 + while (k < nslices) { + val gradInputK = gradInputOffset + k * iTime * iWidth * iHeight + val gradOutputK = gradOutputOffset + k * oTime * oWidth * oHeight + var ti = 0 + while (ti < oTime) { + var i = 0 + while (i < oHeight) { + var j = 0 + while (j < oWidth) { + var tstart = ti * dT - padT + var hstart = i * dH - padH + var wstart = j * dW - padW + var tend = math.min(tstart + kT, iTime + padT) + var hend = math.min(hstart + kH, iHeight + padH) + var wend = math.min(wstart + kW, iWidth + padW) + val poolSize = (tend - tstart) * (hend - hstart) * (wend - wstart) + tstart = math.max(tstart, 0) + hstart = math.max(hstart, 0) + wstart = math.max(wstart, 0) + tend = math.min(tend, iTime) + hend = math.min(hend, iHeight) + wend = math.min(wend, iWidth) + + val divide_factor = if (countIncludePad) poolSize + else (tend - tstart) * (hend - hstart) * (wend - wstart) + + val s = gradOutput(ti * oWidth * oHeight + i * oWidth + j + gradOutputK) + var z = tstart + while (z < tend) { + var y = hstart + while (y < hend) { + var x = wstart + while (x < wend) { + gradInput(z * iWidth * iHeight + y * iWidth + + x + gradInputK) += s / divide_factor + x += 1 + } + y += 1 + } + z += 1 + } + j += 1 + } + i += 1 + } + ti += 1 + } + k += 1 + } + } + + private def volumetricAveragePoolingBackwardFloat(gradInput: Array[Float], gradInputOffset: Int, + gradOutput: Array[Float], gradOutputOffset: Int, countIncludePad: Boolean, + nslices: Int, iTime: Int, iWidth: Int, iHeight: Int, + oTime: Int, oWidth: Int, oHeight: Int, + dT: Int, dW: Int, dH: Int, padT: Int, padW: Int, padH: Int): Unit = { + var k = 0 + while (k < nslices) { + val gradInputK = gradInputOffset + k * iTime * iWidth * iHeight + val gradOutputK = gradOutputOffset + k * oTime * oWidth * oHeight + var ti = 0 + while (ti < oTime) { + var i = 0 + while (i < oHeight) { + var j = 0 + while (j < oWidth) { + var tstart = ti * dT - padT + var hstart = i * dH - padH + var wstart = j * dW - padW + var tend = math.min(tstart + kT, iTime + padT) + var hend = math.min(hstart + kH, iHeight + padH) + var wend = math.min(wstart + kW, iWidth + padW) + val poolSize = (tend - tstart) * (hend - hstart) * (wend - wstart) + tstart = math.max(tstart, 0) + hstart = math.max(hstart, 0) + wstart = math.max(wstart, 0) + tend = math.min(tend, iTime) + hend = math.min(hend, iHeight) + wend = math.min(wend, iWidth) + + val divide_factor = if (countIncludePad) poolSize + else (tend - tstart) * (hend - hstart) * (wend - wstart) + + val s = gradOutput(ti * oWidth * oHeight + i * oWidth + j + gradOutputK) + var z = tstart + while (z < tend) { + var y = hstart + while (y < hend) { + var x = wstart + while (x < wend) { + gradInput(z * iWidth * iHeight + y * iWidth + + x + gradInputK) += s / divide_factor + x += 1 + } + y += 1 + } + z += 1 + } + j += 1 + } + i += 1 + } + ti += 1 + } + k += 1 + } + } +} + +object VolumetricAveragePooling extends ModuleSerializable { + def apply[@specialized(Float, Double) T: ClassTag] + (kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, + padT: Int = 0, padW: Int = 0, padH: Int = 0, + countIncludePad: Boolean = true, ceilMode: Boolean = false)(implicit ev: TensorNumeric[T]) + : VolumetricAveragePooling[T] = + new VolumetricAveragePooling[T](kT, kW, kH, dT, dW, dH, padT, padW, padH, countIncludePad) + + def apply[@specialized(Float, Double) T: ClassTag] + (kT: Int, kW: Int, kH: Int)(implicit ev: TensorNumeric[T]) + : VolumetricAveragePooling[T] = new VolumetricAveragePooling[T](kT, kW, kH) + + override def doLoadModule[T: ClassTag](context : DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + + val averagePooling = super.doLoadModule(context).asInstanceOf[VolumetricAveragePooling[T]] + val attrMap = context.bigdlModule.getAttrMap + averagePooling.ceilMode = DataConverter.getAttributeValue(context, + attrMap.get("ceilMode")).asInstanceOf[Boolean] + averagePooling + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + volumetricAverageBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + val averagePooling = context.moduleData.module.asInstanceOf[VolumetricAveragePooling[T]] + + super.doSerializeModule(context, volumetricAverageBuilder) + + val ceilModeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, ceilModeBuilder, + averagePooling.ceilMode, universe.typeOf[Boolean]) + volumetricAverageBuilder.putAttr("ceilMode", ceilModeBuilder.build) + + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala index 65909102708..fd651f263f4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala @@ -552,7 +552,7 @@ object VolumetricMaxPooling extends ModuleSerializable { def apply[@specialized(Float, Double) T: ClassTag] (kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, padT: Int = 0, padW: Int = 0, padH: Int = 0)(implicit ev: TensorNumeric[T]) - : VolumetricMaxPooling[T] = new VolumetricMaxPooling[T](kT, kW, kH, dT, dW, dH, padT, padW) + : VolumetricMaxPooling[T] = new VolumetricMaxPooling[T](kT, kW, kH, dT, dW, dH, padT, padW, padH) def apply[@specialized(Float, Double) T: ClassTag] (kT: Int, kW: Int, kH: Int)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 4a7f87927a1..a3f8b2a02dc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -603,6 +603,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab CAddTable[T](inplace) } + def createCAveTable(inplace: Boolean = false) + : CAveTable[T] = { + CAveTable[T](inplace) + } + def createCDivTable() : CDivTable[T] = { CDivTable[T]() @@ -1368,6 +1373,21 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab VolumetricMaxPooling[T](kT, kW, kH, dT, dW, dH, padT, padW, padH) } + def createVolumetricAveragePooling(kT: Int, + kW: Int, + kH: Int, + dT: Int, + dW: Int, + dH: Int, + padT: Int = 0, + padW: Int = 0, + padH: Int = 0, + countIncludePad: Boolean = true, + ceilMode: Boolean = false): + VolumetricAveragePooling[T] = { + VolumetricAveragePooling[T](kT, kW, kH, dT, dW, dH, padT, padW, padH, countIncludePad, ceilMode) + } + def createSpatialDivisiveNormalization(nInputPlane: Int = 1, kernel: JTensor = null, threshold: Double = 1e-4, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAveTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAveTableSpec.scala new file mode 100644 index 00000000000..b323279a246 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAveTableSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Parallel +class CAveTableSpec extends FlatSpec with Matchers { + "CAveTable" should "be correct for multiple tensor inputs" in { + val module = CAveTable[Float]() + val tensor1 = Tensor[Float](T(1, 2, 3)) + val tensor2 = Tensor[Float](T(2, 3, 4)) + val tensor3 = Tensor[Float](T(3, 4, 5)) + module.forward(T(tensor1, tensor2, tensor3)) should be(Tensor[Float](T(2, 3, 4))) + val grads = module.backward(T(tensor1, tensor2), Tensor[Float](T(2, 4, 6))) + grads[Tensor[Float]](1) should be(Tensor[Float](T(1, 2, 3))) + grads[Tensor[Float]](2) should be(Tensor[Float](T(1, 2, 3))) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala index cb620459cd4..d50f12f2369 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala @@ -262,7 +262,7 @@ class LinearSpec extends FlatSpec with Matchers { assert(err < 1e-6) } - "Linear module in batch mode without bias" should "converate to correct weight and bias" in { + "Linear module in batch mode without bias" should "converge to correct weight and bias" in { val inputN = 5 val outputN = 2 val batchN = 3 @@ -388,7 +388,7 @@ class LinearSpec extends FlatSpec with Matchers { linear2.gradBias should be(linear.gradBias.mul(2)) } - "Xavier" should "init right in SpatialConvolution" in { + "Xavier" should "init right in Linear" in { RandomGenerator.RNG.setSeed(1) val linear = Linear[Float](3, 5) .setInitMethod(Xavier, Zeros) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricAveragePoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricAveragePoolingSpec.scala new file mode 100644 index 00000000000..23e51e6d7d6 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricAveragePoolingSpec.scala @@ -0,0 +1,335 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.{GradientChecker, VolumetricAveragePooling} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ + +import scala.util.Random + +@com.intel.analytics.bigdl.tags.Serial +class VolumetricAveragePoolingSpec extends TorchSpec { + + "VolumetricAveragePooling Forward dim 4 Double" should "work properly" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + val from = RNG.uniform(2, 4).toInt + val to = RNG.uniform(1, 4).toInt + val kt = RNG.uniform(2, 4).toInt + val ki = RNG.uniform(2, 4).toInt + val kj = RNG.uniform(2, 4).toInt + val st = RNG.uniform(1, 3).toInt + val si = RNG.uniform(1, 3).toInt + val sj = RNG.uniform(1, 3).toInt + val padT = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padW = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padH = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val outt = RNG.uniform(5, 7).toInt + val outi = RNG.uniform(5, 7).toInt + val outj = RNG.uniform(5, 7).toInt + val batch = RNG.uniform(2, 7).toInt + val int = (outt - 1) * st + kt - padT * 2 + val ini = (outi - 1) * si + ki - padW * 2 + val inj = (outj - 1) * sj + kj - padH * 2 + val layer = VolumetricAveragePooling[Double](kt, ki, kj, st, si, sj, + padT, padW, padH) + + val input = Tensor[Double](from, int, ini, inj).apply1(e => Random.nextDouble()) + val output = layer.updateOutput(input) + + val code = "torch.manualSeed(" + seed + ")\n" + + s"layer = nn.VolumetricAveragePooling($kt, $ki, $kj, $st, $si, $sj, $padT," + + s" $padW, $padH)\n" + + "output = layer:forward(input)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input), + Array("output")) + + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + + output.size() should be(luaOutput.size()) + output should be(luaOutput) + } + + "VolumetricAveragePooling Forward dim 5 Double" should "work properly" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + val from = RNG.uniform(2, 4).toInt + val to = RNG.uniform(1, 4).toInt + val kt = RNG.uniform(2, 4).toInt + val ki = RNG.uniform(2, 4).toInt + val kj = RNG.uniform(2, 4).toInt + val st = RNG.uniform(1, 3).toInt + val si = RNG.uniform(1, 3).toInt + val sj = RNG.uniform(1, 3).toInt + val padT = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padW = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padH = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val outt = RNG.uniform(5, 7).toInt + val outi = RNG.uniform(5, 7).toInt + val outj = RNG.uniform(5, 7).toInt + val batch = RNG.uniform(2, 7).toInt + val int = (outt - 1) * st + kt - padT * 2 + val ini = (outi - 1) * si + ki - padW * 2 + val inj = (outj - 1) * sj + kj - padH * 2 + val layer = VolumetricAveragePooling[Double](kt, ki, kj, st, si, sj, + padT, padW, padH) + + val input = Tensor[Double](batch, from, int, ini, inj).apply1(e => Random.nextDouble()) + + val output = layer.updateOutput(input) + + val code = "torch.manualSeed(" + seed + ")\n" + + s"layer = nn.VolumetricAveragePooling($kt, $ki, $kj, $st, $si, $sj, $padT," + + s" $padW, $padH)\n" + + "output = layer:forward(input)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input), + Array("weight", "bias", "output")) + + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + + output should be(luaOutput) + } + + "forward backward Double batch" should "work properly" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + + val from = RNG.uniform(3, 4).toInt + val kt = RNG.uniform(2, 7).toInt + val ki = RNG.uniform(2, 7).toInt + val kj = RNG.uniform(2, 7).toInt + val st = RNG.uniform(1, 3).toInt + val si = RNG.uniform(1, 3).toInt + val sj = RNG.uniform(1, 3).toInt + val padT = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padW = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padH = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val outt = RNG.uniform(5, 7).toInt + val outi = RNG.uniform(5, 7).toInt + val outj = RNG.uniform(5, 7).toInt + val batch = RNG.uniform(2, 7).toInt + + val int = (outt - 1) * st + kt - padT * 2 + val ini = (outi - 1) * si + ki - padW * 2 + val inj = (outj - 1) * sj + kj - padH * 2 + val module = VolumetricAveragePooling[Double](kt, ki, kj, st, si, sj, + padT, padW, padH) + + Random.setSeed(seed) + val input = Tensor[Double](batch, from, int, ini, inj).apply1(e => Random.nextDouble()) + + val output = module.updateOutput(input) + + val code = "torch.manualSeed(" + seed + ")\n" + + s"module = nn.VolumetricAveragePooling($kt, $ki, $kj, $st, $si, $sj, $padT," + + s" $padW, $padH)\n" + + "output = module:forward(input)\n" + + "gradInput = module:backward(input, gradOutput)" + + + val gradOutput = Tensor[Double]().resizeAs(output).rand() + val gradInput = module.backward(input, gradOutput) + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, + "gradOutput" -> gradOutput), Array("output", "gradInput")) + + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + output should be(luaOutput) + val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] + gradInput should be(luaGradInput) + } + + "gradient check Double batch" should "work properly" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + + val from = RNG.uniform(2, 4).toInt + val kt = RNG.uniform(2, 7).toInt + val ki = RNG.uniform(2, 7).toInt + val kj = RNG.uniform(2, 7).toInt + val st = RNG.uniform(1, 3).toInt + val si = RNG.uniform(1, 3).toInt + val sj = RNG.uniform(1, 3).toInt + val padT = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padW = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padH = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val outt = RNG.uniform(5, 7).toInt + val outi = RNG.uniform(5, 7).toInt + val outj = RNG.uniform(5, 7).toInt + val batch = RNG.uniform(2, 7).toInt + + val int = (outt - 1) * st + kt - padT * 2 + val ini = (outi - 1) * si + ki - padW * 2 + val inj = (outj - 1) * sj + kj - padH * 2 + val module = VolumetricAveragePooling[Double](kt, ki, kj, st, si, sj, + padT, padW, padH) + + Random.setSeed(seed) + val input = Tensor[Double](batch, from, int, ini, inj).apply1(e => Random.nextDouble()) + + val checker = new GradientChecker(1e-5) + checker.checkLayer[Double](module, input, 1e-3) should be (true) + } + + + "VolumetricAveragePooling Forward dim 4 Float" should "work properly" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + val from = RNG.uniform(2, 4).toInt + val to = RNG.uniform(1, 4).toInt + val kt = RNG.uniform(2, 4).toInt + val ki = RNG.uniform(2, 4).toInt + val kj = RNG.uniform(2, 4).toInt + val st = RNG.uniform(1, 3).toInt + val si = RNG.uniform(1, 3).toInt + val sj = RNG.uniform(1, 3).toInt + val padT = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padW = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padH = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val outt = RNG.uniform(5, 7).toInt + val outi = RNG.uniform(5, 7).toInt + val outj = RNG.uniform(5, 7).toInt + val batch = RNG.uniform(2, 7).toInt + val int = (outt - 1) * st + kt - padT * 2 + val ini = (outi - 1) * si + ki - padW * 2 + val inj = (outj - 1) * sj + kj - padH * 2 + val layer = VolumetricAveragePooling[Float](kt, ki, kj, st, si, sj, + padT, padW, padH) + + val input = Tensor[Float](from, int, ini, inj).apply1(e => Random.nextFloat()) + val output = layer.updateOutput(input) + + val code = "torch.manualSeed(" + seed + ")\n" + + "torch.setdefaulttensortype('torch.FloatTensor')" + + s"layer = nn.VolumetricAveragePooling($kt, $ki, $kj, $st, $si, $sj, $padT," + + s" $padW, $padH)\n" + + "output = layer:forward(input)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input), + Array("output")) + + val luaOutput = torchResult("output").asInstanceOf[Tensor[Float]] + + output.size() should be(luaOutput.size()) + output should be(luaOutput) + } + + + "VolumetricAveragePooling Forward dim 5 Float" should "work properly" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + val from = RNG.uniform(2, 4).toInt + val to = RNG.uniform(1, 4).toInt + val kt = RNG.uniform(2, 4).toInt + val ki = RNG.uniform(2, 4).toInt + val kj = RNG.uniform(2, 4).toInt + val st = RNG.uniform(1, 3).toInt + val si = RNG.uniform(1, 3).toInt + val sj = RNG.uniform(1, 3).toInt + val padT = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padW = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padH = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val outt = RNG.uniform(5, 7).toInt + val outi = RNG.uniform(5, 7).toInt + val outj = RNG.uniform(5, 7).toInt + val batch = RNG.uniform(2, 7).toInt + val int = (outt - 1) * st + kt - padT * 2 + val ini = (outi - 1) * si + ki - padW * 2 + val inj = (outj - 1) * sj + kj - padH * 2 + val layer = VolumetricAveragePooling[Float](kt, ki, kj, st, si, sj, + padT, padW, padH) + + val input = Tensor[Float](batch, from, int, ini, inj).apply1(e => Random.nextFloat()) + + val output = layer.updateOutput(input) + + val code = "torch.manualSeed(" + seed + ")\n" + + "torch.setdefaulttensortype('torch.FloatTensor')" + + s"layer = nn.VolumetricAveragePooling($kt, $ki, $kj, $st, $si, $sj, $padT," + + s" $padW, $padH)\n" + + "output = layer:forward(input)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input), + Array("weight", "bias", "output")) + + val luaOutput = torchResult("output").asInstanceOf[Tensor[Float]] + + output should be(luaOutput) + } + + + "forward backward Float batch" should "work properly" in { + torchCheck() + val seed = 100 + RNG.setSeed(seed) + + val from = RNG.uniform(3, 5).toInt + val kt = RNG.uniform(2, 7).toInt + val ki = RNG.uniform(2, 7).toInt + val kj = RNG.uniform(2, 7).toInt + val st = RNG.uniform(1, 3).toInt + val si = RNG.uniform(1, 3).toInt + val sj = RNG.uniform(1, 3).toInt + val padT = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padW = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val padH = Math.min(RNG.uniform(0, 2).toInt, math.floor(kt / 2).toInt) + val outt = RNG.uniform(5, 7).toInt + val outi = RNG.uniform(5, 7).toInt + val outj = RNG.uniform(5, 7).toInt + val batch = RNG.uniform(2, 7).toInt + + val int = (outt - 1) * st + kt - padT * 2 + val ini = (outi - 1) * si + ki - padW * 2 + val inj = (outj - 1) * sj + kj - padH * 2 + val module = VolumetricAveragePooling[Float](kt, ki, kj, st, si, sj, + padT, padW, padH) + + Random.setSeed(seed) + val input = Tensor[Float](batch, from, int, ini, inj).apply1(e => Random.nextFloat()) + + val output = module.updateOutput(input) + + val code = "torch.manualSeed(" + seed + ")\n" + + "torch.setdefaulttensortype('torch.FloatTensor')" + + s"module = nn.VolumetricAveragePooling($kt, $ki, $kj, $st, $si, $sj, $padT," + + s" $padW, $padH)\n" + + "output = module:forward(input)\n" + + "gradInput = module:backward(input, gradOutput)" + + + val gradOutput = Tensor[Float]() + gradOutput.resizeAs(output).rand() + val gradInput = module.backward(input, gradOutput) + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, + "gradOutput" -> gradOutput), Array("output", "gradInput")) + + val luaOutput = torchResult("output").asInstanceOf[Tensor[Float]] + output should be(luaOutput) + val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] + gradInput should be(luaGradInput) + } + +} From 7a5bb26b26d1cf47cfce4d3ce1f795fb49c90759 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Thu, 23 Nov 2017 00:17:28 -0600 Subject: [PATCH 0532/1065] Use `parameters()` to get gradWeights instead of reflection (#1921) --- .../bigdl/dllib/keras/KerasBaseSpec.scala | 17 +++++------------ 1 file changed, 5 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index 05d4f6fe679..37cf55d8422 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -34,11 +34,6 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { private def defaultWeightConverter(in: Array[Tensor[Float]]) = in - private def getFieldByReflect(obj: Object, name: String): Object = { - val fieldDefinition = obj.getClass().getDeclaredField(name) - fieldDefinition.setAccessible(true) - return fieldDefinition.get(obj) - } // weightConverter: convert keras weight to BigDL format, // do nothing for the default converter def checkOutputAndGrad(bmodel: AbstractModule[Tensor[Float], Tensor[Float], Float], @@ -59,14 +54,12 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { val bgradInput = bmodel.backward(input, boutput.clone().fill(1)) bgradInput.almostEqual(gradInput, precision) should be(true) - // assuming the first one is weight, the second one is bias - if (gradWeight != null) { - val bgradWeight = getFieldByReflect(bmodel, "gradWeight").asInstanceOf[Tensor[Float]] - bgradWeight.almostEqual(weightConverter(gradWeight)(0), precision) should be(true) - if (gradWeight.length > 1) { - val bgradBias = getFieldByReflect(bmodel, "gradBias").asInstanceOf[Tensor[Float]] - bgradBias.almostEqual(weightConverter(gradWeight)(1), precision) should be(true) + val parameters = bmodel.parameters() + if (parameters != null) { + val bgradWeights = parameters._2 + (bgradWeights, weightConverter(gradWeight)).zipped.foreach { (bgrad, kgrad) => + bgrad.almostEqual(kgrad, precision) should be(true) } } } From a969b9cc9f88adba3313857166bac24aaeff3d97 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 23 Nov 2017 15:12:44 +0800 Subject: [PATCH 0533/1065] Add tensorflow transfer learning example (#1895) * add transfer learning example * add readme * rm tensorflow files * make getRDD public * meet code review * update doc * fix tests * add a link in document * refine doc --- .../tensorflow/{ => loadandsave}/.gitignore | 0 .../tensorflow/{ => loadandsave}/Load.scala | 2 +- .../tensorflow/{ => loadandsave}/README.md | 0 .../tensorflow/{ => loadandsave}/Save.scala | 4 +- .../tensorflow/{ => loadandsave}/model.py | 0 .../tensorflow/transferlearning/README.md | 170 ++++++++++++++++++ .../transferlearning/TransferLearning.scala | 123 +++++++++++++ .../transferlearning/dump_model_example.py | 74 ++++++++ .../transferlearning/inception_model.py | 113 ++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 11 +- .../bigdl/dllib/utils/tf/Session.scala | 2 +- 11 files changed, 488 insertions(+), 11 deletions(-) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/{ => loadandsave}/.gitignore (100%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/{ => loadandsave}/Load.scala (94%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/{ => loadandsave}/README.md (100%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/{ => loadandsave}/Save.scala (96%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/{ => loadandsave}/model.py (100%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/TransferLearning.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/dump_model_example.py create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/inception_model.py diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/.gitignore b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/.gitignore similarity index 100% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/.gitignore rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/.gitignore diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Load.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/Load.scala similarity index 94% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Load.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/Load.scala index db2af15ae69..07971006d54 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Load.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/Load.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.example.tensorflow +package com.intel.analytics.bigdl.example.tensorflow.loadandsave import com.intel.analytics.bigdl.nn.Module import com.intel.analytics.bigdl.numeric.NumericFloat diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md similarity index 100% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/README.md rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Save.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/Save.scala similarity index 96% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Save.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/Save.scala index 2fd464fa2ea..38c7f283217 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/Save.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/Save.scala @@ -13,11 +13,11 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.example.tensorflow +package com.intel.analytics.bigdl.example.tensorflow.loadandsave import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.utils.tf.TensorflowSaver import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.tf.TensorflowSaver /** * This example shows how to define a BigDL model and save it as tensorflow format diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/model.py b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/model.py similarity index 100% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/model.py rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/model.py diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md new file mode 100644 index 00000000000..abf0e573abb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md @@ -0,0 +1,170 @@ +# Summary + +Training a big model on a new domain from scratch can be very hard job. Since a lot of data in +the new domain is required to prevent over-fitting and the training process can be extremely +time-consuming due to the size of the model and training data. A very common approach to this +problem is transfer learning, which uses a pre-trained model of a similar task and fine-tunes it +for the task at hand. + +In this example, we will show you how to use a pre-trained tensorflow inception-v1 model trained on +imagenet dataset to solve the flowers classification problem by fine-tune it in BigDL. As the flowers +dataset contains only thousands of images, we will treat the inception-v1 model as a feature extractor +and only train a linear model on these features. + +# Preparation + +## Make sure Spark, BigDL (both scala and python api) and Tensorflow are successfully install + +Please refer to [BigDL](https://bigdl-project.github.io/master/), [Tensorflow](https://www.tensorflow.org/versions/r1.2/install/) for more information. + +We currently support Tensorflow r1.2. + +## Install the TF-slim image models library + +Please checkout this [page](https://github.com/tensorflow/models/tree/master/research/slim#installing-the-tf-slim-image-models-library) +to install the TF-slim image models library. And add the library to `PYTHONPATH`. + +For example, + +```shell +SLIM_PATH=/your/path/to/slim/model/ +export PYTHONPATH=$SLIM_PATH:$PYTHONPATH +``` + +## Get the flowers datasets + +```shell +mkdir /tmp/flowers +cd /tmp/flowers +wget http://download.tensorflow.org/data/flowers.tar.gz +tar -xzvf flowers.tar.gz +``` + +## Get the inception-v1 checkpoint file + +```shell +mkdir /tmp/checkpoints +cd /tmp/checkpoints +wget http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz +tar -xzvf inception_v1_2016_08_28.tar.gz +``` + +## Dump the pre-trained tensorflow model to a BigDL readable format + +By reading directly from tensorflow's computational graph, BigDL can recover +the entire training pipeline, including the in-graph preprocessing step and the +model, and return the extracted features as an RDD. + +To apply this pipeline to a new dataset, you should made a few modifications to your original training +code as illustrated in the dump_model_example.py script: + + 1. Change the original dataset to the new one of the problem at hand. + 2. We may also set the model in evaluation model, since we only treat + it as a feature extractor and we do not want the additional stochasticity + introduced by Dropout layer or data augmentation process. + 3. Comment out the actually training code and use BigDL `dump_model` function + to write the model as BigDL readable format. + +You can easily apply these steps to your own model. + +For this example, you can simply run the following commands: + +```shell +mkdir /tmp/tf_model_train/ +python dump_model_example.py --data_split=train --dump_model_path=/tmp/tf_model_train/ +mkdir /tmp/tf_model_validation/ +python dump_model_example.py --data_split=validation --dump_model_path=/tmp/tf_model_validation/ +``` + +One thing to note is that data path is hard coded in tensorflow's computational graph, so we need +to dump two pipeline, one for training and one for validation. + +# Training for flowers classification + +## Loading the Pipeline + +Provided the pipeline definition in `model.pb`, pre-trained weights in `model.bin` and +the specified end-points, we can load the entire pipeline and return the results as an +RDD. In this case, the resulting rdd contains the features extracted from inception-v1 model +as well as their corresponding label. + +```scala + + val training_graph_file = modelDir + "/model.pb" + val training_bin_file = modelDir + "/model.bin" + + val featureOutputNode = "InceptionV1/Logits/AvgPool_0a_7x7/AvgPool" + val labelNode = "OneHotEncoding/one_hot" + + val session = TensorflowLoader.checkpoints[Float](training_graph_file, + training_bin_file, ByteOrder.LITTLE_ENDIAN) + .asInstanceOf[BigDLSessionImpl[Float]] + + val rdd = session.getRDD(Seq(featureOutputNode, labelNode), sc) + +``` + +## Train a Classifier + +We will only train a linear using the features extracted by inception-v1 model. + +```scala + + val model = Sequential[Float]() + model.add(Squeeze[Float](null, batchMode = true)) + model.add(Linear[Float](1024, 5)) + + val criterion = CrossEntropyCriterion[Float]() + + val optimizer = Optimizer[Float](model, trainingData, criterion, param.batchSize) + + val endWhen = Trigger.maxEpoch(param.nEpochs) + val optim = new RMSprop[Float](learningRate = 0.001, decayRate = 0.9) + + optimizer.setEndWhen(endWhen) + optimizer.setOptimMethod(optim) + + optimizer.optimize() +``` + +The `TransferLearning.scala` contains the entire code for this task. + +To run this example, you can modify and execute the following command: + +```shell +SPARK_HOME=... +BIGDL_HOME=... +BIGDL_VERSION=... +$SPARK_HOME/bin/spark-submit \ +--master spark://... \ +--driver-memory driver-memory \ +--executor-memory executor-memory \ +--executor-cores cores_per_executor \ +--total-executor-cores total_cores_for_the_job \ +--driver-class-path $BIGDL_HOME/lib/bigdl-$BIGDL_VERSION-jar-with-dependencies.jar \ +--class com.intel.analytics.bigdl.example.tensorflow.transferLearning.TransferLearning \ +$BIGDL_HOME/lib/bigdl-$BIGDL_VERSION-jar-with-dependencies.jar \ +-t /tmp/tf_model_train/ -v /tmp/tf_model_validation/ \ +-b batch_size -e nEpochs +``` + +After training, you should see something like this in the console: + +``` +2017-11-16 12:13:02 INFO DistriOptimizer$:330 - [Epoch 10 3328/3320][Iteration 2080][Wall Clock 201.064860541s] Trained 16 records in 0.01422112 seconds. Throughput is 1125.0872 records/second. Loss is 0.15326343. +2017-11-16 12:13:02 INFO DistriOptimizer$:374 - [Epoch 10 3328/3320][Iteration 2080][Wall Clock 201.064860541s] Epoch finished. Wall clock time is 201105.290228 ms +2017-11-16 12:13:02 INFO DistriOptimizer$:626 - [Wall Clock 201.105290228s] Validate model... +2017-11-16 12:13:02 INFO DistriOptimizer$:668 - Top1Accuracy is Accuracy(correct: 306, count: 350, accuracy: 0.8742857142857143) +``` +As we can see, training a linear classifier for 10 epochs, we can achieve a +87.4% accuracy on the validation set. + + + + + + + + + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/TransferLearning.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/TransferLearning.scala new file mode 100644 index 00000000000..015fb9128f5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/TransferLearning.scala @@ -0,0 +1,123 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.tensorflow.transferlearning + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} +import com.intel.analytics.bigdl.utils.tf.{BigDLSessionImpl, TensorflowLoader} +import org.apache.spark.SparkContext +import scopt.OptionParser + + +object Utils { + + case class TrainParams( + trainingModelDir: String = "/tmp/tfmodel", + validationModelDir: Option[String] = None, + batchSize: Int = 16, + nEpochs: Int = 10) + + val trainParser = new OptionParser[TrainParams]("BigDL ptbModel Train Example") { + opt[String]('t', "trainingModelDir") + .text("tensorflow training model location") + .action((x, c) => c.copy(trainingModelDir = x)) + .required() + + opt[String]('v', "validationModelDir") + .text("tensorflow validation model location") + .action((x, c) => c.copy(validationModelDir = Some(x))) + + opt[Int]('b', "batchSize") + .text("batchSize") + .action((x, c) => c.copy(batchSize = x)) + + opt[Int]('e', "nEpochs") + .text("epoch numbers") + .action((x, c) => c.copy(nEpochs = x)) + } +} + +object TransferLearning { + + LoggerFilter.redirectSparkInfoLogs() + + def main(args: Array[String]): Unit = { + Utils.trainParser.parse(args, Utils.TrainParams()).map(param => { + + val conf = Engine.createSparkConf() + .setAppName("Transfer Learning") + .set("spark.task.maxFailures", "1") + + val sc = new SparkContext(conf) + Engine.init + + val trainingData = getData(param.trainingModelDir, sc) + + val model = Sequential[Float]() + model.add(Squeeze[Float](null, batchMode = true)) + model.add(Linear[Float](1024, 5)) + + val criterion = CrossEntropyCriterion[Float]() + + val optimizer = Optimizer[Float](model, trainingData, criterion, param.batchSize) + + val endWhen = Trigger.maxEpoch(param.nEpochs) + val optim = new RMSprop[Float](learningRate = 0.001, decayRate = 0.9) + + optimizer.setEndWhen(endWhen) + optimizer.setOptimMethod(optim) + + if (param.validationModelDir.isDefined) { + val validationData = getData(param.validationModelDir.get, sc) + optimizer.setValidation(Trigger.everyEpoch, validationData, + Array(new Top1Accuracy[Float]()), param.batchSize) + } + + optimizer.optimize() + + sc.stop() + }) + } + + private def getData(modelDir: String, sc: SparkContext) = { + val training_graph_file = modelDir + "/model.pb" + val training_bin_file = modelDir + "/model.bin" + + val featureOutputNode = "InceptionV1/Logits/AvgPool_0a_7x7/AvgPool" + val labelNode = "OneHotEncoding/one_hot" + + val session = TensorflowLoader.checkpoints[Float](training_graph_file, + training_bin_file, ByteOrder.LITTLE_ENDIAN) + .asInstanceOf[BigDLSessionImpl[Float]] + + val rdd = session.getRDD(Seq(featureOutputNode, labelNode), sc) + + val sampleRdd = rdd.map { t => + val feature = t[Tensor[Float]](1) + val onehotLabel = t[Tensor[Float]](2) + val label = onehotLabel.max(1)._2 + Sample(feature, label) + } + sampleRdd + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/dump_model_example.py b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/dump_model_example.py new file mode 100644 index 00000000000..39a87bbdd68 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/dump_model_example.py @@ -0,0 +1,74 @@ +# +# Copyright 2016 The BigDL Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import tensorflow as tf +from datasets import flowers +from inception_model import InceptionModel + + +slim = tf.contrib.slim + +tf.app.flags.DEFINE_string( + 'train_dir', '/tmp/inception_finetuned/', + 'Directory where checkpoints and event logs are written to.') + +tf.app.flags.DEFINE_string( + 'data_dir', '/tmp/flowers/', + 'Directory contains the flowers data.') + +tf.app.flags.DEFINE_string( + 'checkpoint_file_path', '/tmp/checkpoints/inception_v1.ckpt', + 'The path to a checkpoint from which to fine-tune.') + +tf.app.flags.DEFINE_string( + 'dump_model_path', '/tmp/tfmodel/', + 'Directory where graph file and variable bin file are written to.' +) + +tf.app.flags.DEFINE_string( + 'data_split', 'train', + 'Directory where graph file and variable bin file are written to.' +) + +FLAGS = tf.app.flags.FLAGS + + +def main(_): + + # 1. change the dataset + # dataset = imagenet.get_split('train'), FLAGS.data_dir) + dataset = flowers.get_split(FLAGS.data_split, FLAGS.data_dir) + + model = InceptionModel(checkpoints_file=FLAGS.checkpoint_file_path) + + # 2. set the model to training mode + # op, graph model.build(dataset, image_height=224, image_width=224, num_classes=1000, is_training=True) + op, graph = model.build(dataset, image_height=224, image_width=224, num_classes=1000, is_training=False) + + # 3. comment out the actual training code + # slim.learning.train( + # op, + # logdir=train_dir, + # init_fn=model.init_fn, + # number_of_steps=100) + + # 4. dump model to the specified path + from bigdl.util.tf_utils import dump_model + dump_model(path=FLAGS.dump_model_path, ckpt_file=FLAGS.checkpoint_file_path, graph=graph) + +if __name__ == '__main__': + tf.app.run() + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/inception_model.py b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/inception_model.py new file mode 100644 index 00000000000..8fb2f91497e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/inception_model.py @@ -0,0 +1,113 @@ +import tensorflow as tf +import os +from nets.inception import inception_v1 +from nets.inception import inception_v1_arg_scope + +slim = tf.contrib.slim + + +class InceptionModel(object): + + def __init__(self, trainable_scopes=None, checkpoint_exclude_scopes=None, checkpoints_file=None): + if trainable_scopes is None: + self.trainable_scopes = [] + else: + self.trainable_scopes=trainable_scopes + + if checkpoint_exclude_scopes is None: + self.checkpoint_exclude_scopes = [] + else: + self.checkpoint_exclude_scopes = checkpoint_exclude_scopes + self.checkpoints_file = checkpoints_file + + self.init_fn = None + + def _get_init_fn(self): + + exclusions = [scope.strip() for scope in self.checkpoint_exclude_scopes] + + variables_to_restore = [] + for var in slim.get_model_variables(): + excluded = False + for exclusion in exclusions: + if var.op.name.startswith(exclusion): + excluded = True + break + if not excluded: + variables_to_restore.append(var) + + return slim.assign_from_checkpoint_fn(self.checkpoints_file, variables_to_restore) + + def _load_batch(self, dataset, batch_size=32, height=224, width=224): + + data_provider = slim.dataset_data_provider.DatasetDataProvider( + dataset, common_queue_capacity=32, + common_queue_min=8) + image_raw, label = data_provider.get(['image', 'label']) + + # Preprocess image for usage by Inception. + image = self._preprocess(image_raw, height, width) + + # Batch it up. + images, labels = tf.train.batch( + [image, label], + batch_size=batch_size, + num_threads=1, + capacity=2 * batch_size) + + return images, labels + + def _preprocess(self, image, height, width, scope=None): + with tf.name_scope(scope, 'eval_image', [image, height, width]): + if image.dtype != tf.float32: + image = tf.image.convert_image_dtype(image, dtype=tf.float32) + # Crop the central region of the image with an area containing 87.5% of + # the original image. + # if central_fraction: + # image = tf.image.central_crop(image, central_fraction=central_fraction) + + if height and width: + # Resize the image to the specified height and width. + image = tf.expand_dims(image, 0) + image = tf.image.resize_bilinear(image, [height, width], + align_corners=False) + image = tf.squeeze(image, [0]) + image = tf.subtract(image, 0.5) + image = tf.multiply(image, 2.0) + return image + + def build(self, dataset, image_height, image_width, num_classes, is_training): + + images, labels = self._load_batch(dataset, height=image_height, width=image_width) + + # Create the model, use the default arg scope to configure the batch norm parameters. + with slim.arg_scope(inception_v1_arg_scope()): + logits, end_points = inception_v1(images, num_classes=num_classes, is_training=is_training) + + # Specify the loss function: + one_hot_labels = slim.one_hot_encoding(labels, num_classes) + loss = slim.losses.softmax_cross_entropy(logits, one_hot_labels) + + # Create some summaries to visualize the training process: + tf.summary.scalar('losses/Total Loss', loss) + + if is_training: + # Specify the optimizer and create the train op: + optimizer = tf.train.RMSPropOptimizer(learning_rate=0.01) + variables_to_train = [] + for scope in self.trainable_scopes: + variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope) + variables_to_train.extend(variables) + + variables = slim.get_model_variables("InceptionV1/Logits") + exec_op = slim.learning.create_train_op(loss, optimizer, variables_to_train=variables) + + else: + exec_op = end_points['Predictions'] + + if self.checkpoints_file is not None: + self.init_fn = self._get_init_fn() + + return exec_op, tf.get_default_graph() + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a3f8b2a02dc..bd36c52068e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -38,12 +38,9 @@ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} - -import com.intel.analytics.bigdl.utils.tf.{BigDLSessionImpl, Context, TensorflowDataFormat, TensorflowSaver} - -import org.apache.spark.ml.{DLClassifierModel, DLEstimator, DLClassifier, DLModel} +import com.intel.analytics.bigdl.utils.tf._ +import org.apache.spark.ml.{DLClassifier, DLClassifierModel, DLEstimator, DLModel} import org.apache.spark.sql.DataFrame - import org.apache.log4j._ import org.apache.spark.SparkContext import org.tensorflow.framework.NodeDef @@ -1611,13 +1608,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def loadTF(path: String, inputs: JList[String], outputs: JList[String], - byteOrder: String): AbstractModule[Activity, Activity, T] = { + byteOrder: String, binFile: String = null): AbstractModule[Activity, Activity, T] = { val order = byteOrder match { case "little_endian" => ByteOrder.LITTLE_ENDIAN case "big_endian" => ByteOrder.BIG_ENDIAN case _ => throw new IllegalArgumentException(s"No support byte order $byteOrder") } - Module.loadTF[T](path, inputs.asScala, outputs.asScala, order) + Module.loadTF[T](path, inputs.asScala, outputs.asScala, order, Option(binFile)) } def saveTF(model: AbstractModule[Activity, Activity, T], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index 7e7e1ed91ba..d0fca80bd10 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -666,7 +666,7 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], * the end of this execution, that is split each tensor by their first dimension. * @return */ - private[bigdl] def getRDD(endPoints: Seq[String], sc: SparkContext, + def getRDD(endPoints: Seq[String], sc: SparkContext, hasToBatch: Boolean = true): RDD[Table] = { val cache = new mutable.HashMap[String, Array[Seq[Table]]]() val result = if (!hasToBatch) { From 5e73f95faa4ce2c410b8b245596058001526f8fd Mon Sep 17 00:00:00 2001 From: Dongjie Shi Date: Thu, 23 Nov 2017 18:57:16 +0800 Subject: [PATCH 0534/1065] override joda-time in hadoop-aws to reduce compile time (#1880) override joda-time in hadoop-aws to reduce compile time --- dl/pom.xml | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/dl/pom.xml b/dl/pom.xml index b7ee8ffcbc5..6fb2ff20b7a 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -41,6 +41,12 @@ hadoop-client ${spark-scope} + + joda-time + joda-time + 2.9.9 + ${spark-scope} + org.apache.hadoop hadoop-aws @@ -58,6 +64,10 @@ com.fasterxml.jackson.core jackson-databind + + joda-time + joda-time + From 381996c5c29ac7356a8be927de83e7a8ef700280 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Thu, 23 Nov 2017 23:13:56 -0600 Subject: [PATCH 0535/1065] use random value as gradoutput instead of 1 (#1929) --- .../intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala | 2 +- .../intel/analytics/bigdl/dllib/keras/KerasRunner.scala | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index 37cf55d8422..d99d2500ba1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -51,7 +51,7 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { val boutput = bmodel.forward(input) boutput.almostEqual(output, precision) should be(true) - val bgradInput = bmodel.backward(input, boutput.clone().fill(1)) + val bgradInput = bmodel.backward(input, boutput.clone()) bgradInput.almostEqual(gradInput, precision) should be(true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala index f976789b423..88576b5d616 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala @@ -55,11 +55,12 @@ object KerasRunner { val code_for_layer = """ |Y = [] - |grad_input = K.get_session().run(K.gradients(model.output, model.input), feed_dict={input_tensor: input}) # grad_input + |output = model.predict(input) + | + |grad_input = K.get_session().run(K.gradients(model.output * output, model.input), feed_dict={input_tensor: input}) # grad_input | - |grad_weight = K.get_session().run(K.gradients(model.output, model.trainable_weights), # grad_weight + |grad_weight = K.get_session().run(K.gradients(model.output * output, model.trainable_weights), # grad_weight | feed_dict={input_tensor: input}) - |output = model.predict(input) |weights = model.get_weights() """.stripMargin @@ -86,6 +87,7 @@ object KerasRunner { | | """.stripMargin + // scalastyle:on private def getWeightRelate(pvalues: Map[String, Array[Float]], keyName: String): Array[Tensor[Float]] = { From f924ab505fcb8a04e9eec215153ea069eb4e2a67 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 24 Nov 2017 13:18:33 +0800 Subject: [PATCH 0536/1065] Fix tensor resize to 0 tensor issue (#1924) * Fix tensor resize to 0 tensor * Fix tensor set * add test * fix ut --- .../bigdl/dllib/tensor/DenseTensor.scala | 26 +++++++-------- .../dllib/utils/python/api/PythonBigDL.scala | 18 +++++++++-- .../dllib/utils/tf/loaders/Reshape.scala | 2 +- .../bigdl/dllib/tensor/DenseTensorSpec.scala | 32 +++++++++++++++++++ .../bigdl/dllib/tensor/TensorSpec.scala | 12 +++++++ 5 files changed, 71 insertions(+), 19 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 3aadc5ec4bf..cbb4008f881 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -373,10 +373,11 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } override def set(): Tensor[T] = { - this.resize(0) - if(this._storage != null) { + if (this._storage != null) { this._storage.resize(0) } + this.nDimension = 0 + this._size = null this } @@ -2247,19 +2248,14 @@ object DenseTensor { var hasCorrectSize = true var nDim_ = 0 var d = 0 - var break = false - while (d < nDim && !break) { - if (_size(d) > 0) { - nDim_ = nDim_ + 1 - if (self.nDimension > d && _size(d) != self._size(d)) { - hasCorrectSize = false - } - if (self.nDimension > d && _stride != null && _stride(d) >= 0 && - _stride(d) != self._stride(d)) { - hasCorrectSize = false - } - } else { - break = true + while (d < nDim) { + nDim_ = nDim_ + 1 + if (self.nDimension > d && _size(d) != self._size(d)) { + hasCorrectSize = false + } + if (self.nDimension > d && _stride != null && _stride(d) >= 0 && + _stride(d) != self._stride(d)) { + hasCorrectSize = false } d += 1 } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index bd36c52068e..70277abbda7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -129,13 +129,21 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab this.typeName match { case "float" => if (null == jTensor.indices) { - Tensor(jTensor.storage.map(x => ev.fromType(x)), jTensor.shape) + if (jTensor.shape == null || jTensor.shape.length == 0) { + Tensor() + } else { + Tensor(jTensor.storage.map(x => ev.fromType(x)), jTensor.shape) + } } else { Tensor.sparse(jTensor.indices, jTensor.storage.map(x => ev.fromType(x)), jTensor.shape) } case "double" => if (null == jTensor.indices) { - Tensor(jTensor.storage.map(x => ev.fromType(x.toDouble)), jTensor.shape) + if (jTensor.shape == null || jTensor.shape.length == 0) { + Tensor() + } else { + Tensor(jTensor.storage.map(x => ev.fromType(x.toDouble)), jTensor.shape) + } } else { Tensor.sparse(jTensor.indices, jTensor.storage.map(x => ev.fromType(x.toDouble)), jTensor.shape) @@ -162,7 +170,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } case DenseType => if (tensor.nElement() == 0) { - JTensor(Array(), Array(0), bigdlType = typeName) + if (tensor.dim() == 0) { + JTensor(null, null, bigdlType = typeName) + } else { + JTensor(Array(), tensor.size(), bigdlType = typeName) + } } else { val cloneTensor = tensor.clone() val result = JTensor(cloneTensor.storage().array().map(i => ev.toType[Float](i)), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala index 675180d9572..2731fa0051d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala @@ -41,7 +41,7 @@ class ReshapeLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapte override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] - val batchMode = if (sizes.nDimension() >= 1) { + val batchMode = if (sizes.nDimension() >= 1 && sizes.nElement() > 0) { sizes.valueAt(1) == -1 } else { false diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index cf5925e82ce..da021211ed5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -317,6 +317,38 @@ class DenseTensorSpec extends FlatSpec with Matchers { t.storage().size should be(t1.storage().size) } + "resize 0 tensor" should "work properly" in { + val t = Tensor[Double](10) + t.resize(0) + t.nElement() should be (0) + t.dim() should be (1) + } + + "resize 2d tensor to 0" should "work properly" in { + val t = Tensor[Double](2, 10) + t.resize(0, 10) + t.nElement() should be (0) + t.dim() should be (2) + + val t1 = Tensor[Double](2, 10) + t1.resize(10, 0) + t1.nElement() should be (0) + t1.dim() should be (2) + } + + "resize 0 element 2d tensor to > 0 element" should "work properly" in { + val t = Tensor[Double](0, 10) + t.resize(1, 10) + t.nElement() should be (10) + t.dim() should be (2) + + val t1 = Tensor[Double](10, 0) + t1.resize(10, 1) + t1.nElement() should be (10) + t1.dim() should be (2) + } + + "nElement" should "return correct value" in { val t: Tensor[Double] = new DenseTensor[Double](3, 4) t.nElement() should be(12) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala index caf679e0be6..9d671882a3a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/TensorSpec.scala @@ -53,4 +53,16 @@ class TensorSpec extends FlatSpec with Matchers { tensor.isEmpty should be (true) } + "zero Tensor set " should "work" in { + val tensor = Tensor[Int](0) + tensor.set() + tensor.isEmpty should be (true) + } + + "Empty Tensor set " should "work" in { + val tensor = Tensor[Int]() + tensor.set() + tensor.isEmpty should be (true) + } + } From 9863e3e6587a05687c6cc4851881e2c04e0cbd8f Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Fri, 24 Nov 2017 02:13:19 -0600 Subject: [PATCH 0537/1065] Support nested Model and enable test_application (#1927) * good * update * up * goodgood * clean code * fix and enable application test --- .../dllib/utils/python/api/PythonBigDL.scala | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 70277abbda7..22539ab0df3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2238,6 +2238,24 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab module.modules.toList.asJava } + def getFlattenModules(module: Container[Activity, Activity, T]) + : JList[AbstractModule[Activity, Activity, T]] = { + val result = ArrayBuffer[AbstractModule[Activity, Activity, T]]() + doGetFlattenModules(module, result) + result.toList.asJava + } + + private def doGetFlattenModules(module: Container[Activity, Activity, T], + result: ArrayBuffer[AbstractModule[Activity, Activity, T]]): Unit = { + module.modules.foreach {m => + if (m.isInstanceOf[Container[Activity, Activity, T]]) { + doGetFlattenModules(m.asInstanceOf[Container[Activity, Activity, T]], result) + } else { + result.append(m) + } + } + } + def isWithWeights(module: Module[T]): Boolean = { val weights = module.getWeightsBias() return weights != null && !weights.isEmpty From c8028cf57e0efc6f681ab9b0e0a060f40d3f8b12 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Mon, 27 Nov 2017 09:06:20 +0800 Subject: [PATCH 0538/1065] Add serialization test for CAveTable and VolumetricAveragePooling (#1926) * add serialization uid * add ut for serialization --- .../analytics/bigdl/dllib/nn/CAveTable.scala | 2 +- .../dllib/nn/VolumetricAveragePooling.scala | 32 ++----------------- .../serializer/ModuleSerializerSpec.scala | 31 ++++++++++++++++++ 3 files changed, 35 insertions(+), 30 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAveTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAveTable.scala index bf2c2d73268..536cb4dd110 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAveTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAveTable.scala @@ -30,7 +30,7 @@ import scala.reflect._ * @param ev numeric operator * @tparam T Numeric type. Only support float/double now */ -// TODO: add serialization +@SerialVersionUID(- 4230815940936944708L) class CAveTable[T: ClassTag](val inplace: Boolean = false)( implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala index dd394283ac1..e3873c13659 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala @@ -45,7 +45,7 @@ import scala.reflect.runtime.universe * @param ceilMode Whether the output size is to be ceiled or floored * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] */ -// TODO: add serialization +@SerialVersionUID(- 7829953407414301872L) class VolumetricAveragePooling[T: ClassTag]( val kT: Int, val kW: Int, val kH: Int, val dT: Int, val dW: Int, val dH: Int, @@ -582,33 +582,7 @@ object VolumetricAveragePooling extends ModuleSerializable { padT: Int = 0, padW: Int = 0, padH: Int = 0, countIncludePad: Boolean = true, ceilMode: Boolean = false)(implicit ev: TensorNumeric[T]) : VolumetricAveragePooling[T] = - new VolumetricAveragePooling[T](kT, kW, kH, dT, dW, dH, padT, padW, padH, countIncludePad) + new VolumetricAveragePooling[T](kT, kW, kH, dT, dW, dH, + padT, padW, padH, countIncludePad, ceilMode) - def apply[@specialized(Float, Double) T: ClassTag] - (kT: Int, kW: Int, kH: Int)(implicit ev: TensorNumeric[T]) - : VolumetricAveragePooling[T] = new VolumetricAveragePooling[T](kT, kW, kH) - - override def doLoadModule[T: ClassTag](context : DeserializeContext) - (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - - val averagePooling = super.doLoadModule(context).asInstanceOf[VolumetricAveragePooling[T]] - val attrMap = context.bigdlModule.getAttrMap - averagePooling.ceilMode = DataConverter.getAttributeValue(context, - attrMap.get("ceilMode")).asInstanceOf[Boolean] - averagePooling - } - - override def doSerializeModule[T: ClassTag](context: SerializeContext[T], - volumetricAverageBuilder : BigDLModule.Builder) - (implicit ev: TensorNumeric[T]) : Unit = { - val averagePooling = context.moduleData.module.asInstanceOf[VolumetricAveragePooling[T]] - - super.doSerializeModule(context, volumetricAverageBuilder) - - val ceilModeBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(context, ceilModeBuilder, - averagePooling.ceilMode, universe.typeOf[Boolean]) - volumetricAverageBuilder.putAttr("ceilMode", ceilModeBuilder.build) - - } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 1c753810a88..c97bded0c6d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -218,6 +218,37 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } + "CAveTable serializer" should "work properly" in { + val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor(5, 5).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + + val caveTable = CAveTable(false) + + val res1 = caveTable.forward(input) + ModulePersister.saveToFile("/tmp/caveTable.bigdl", caveTable, true) + val loadedCaddTable = ModuleLoader.loadFromFile("/tmp/caveTable.bigdl") + val res2 = loadedCaddTable.forward(input) + res1 should be (res2) + } + + "VolumetricAveragePooling serializer" should "work properly" in { + val volumetricAveragePooling = VolumetricAveragePooling(2, 2, 2, 1, 1, 1, 0, 0, 0) + val input1 = Tensor(1, 2, 3, 3).apply1(_ => Random.nextFloat()) + val input2 = Tensor(1, 2, 3, 3) + input2.copy(input1) + val res1 = volumetricAveragePooling.forward(input1) + + ModulePersister.saveToFile("/tmp/volumetricAveragePooling.bigdl", + volumetricAveragePooling, true) + val loadedVolumetricAveragePooling = + ModuleLoader.loadFromFile("/tmp/volumetricAveragePooling.bigdl") + val res2 = loadedVolumetricAveragePooling.forward(input1) + res1 should be (res2) + } + "CDivTable serializer" should "work properly" in { val cdivTable = new CDivTable() From 5238ee8ccd37e47fe68f222ff25ccbb1cd8fd247 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 27 Nov 2017 02:04:59 -0600 Subject: [PATCH 0539/1065] fix graph missing generatebackward (#1877) --- .../com/intel/analytics/bigdl/dllib/nn/Graph.scala | 13 +++++++++++-- .../intel/analytics/bigdl/dllib/nn/Reshape.scala | 2 +- .../dllib/utils/serializer/DataConverter.scala | 2 +- .../utils/serializer/ModuleSerializerSpec.scala | 4 ++-- 4 files changed, 15 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 9168f7d5884..ef938dafb39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -72,7 +72,7 @@ import org.tensorflow.framework.GraphDef class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], private val outputs : Seq[ModuleNode[T]], private val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, - generateBackward: Boolean = true + private val generateBackward: Boolean = true )(implicit ev: TensorNumeric[T]) extends Container[Activity, Activity, T]{ @@ -667,7 +667,11 @@ object Graph extends ContainerSerializable { .asInstanceOf[Array[Tensor[T]]] sharedVariables = Some(weightArray, biasArray) } - Graph[T](inputs.toArray, outputs.toArray, sharedVariables) + + val generateBackward = DataConverter.getAttributeValue(context, attributes + .get("generateBackward")).asInstanceOf[Boolean] + + Graph[T](inputs.toArray, outputs.toArray, sharedVariables, generateBackward) } override def doSerializeModule[T: ClassTag](context: SerializeContext[T], @@ -733,5 +737,10 @@ object Graph extends ContainerSerializable { outputsNames, universe.typeOf[Array[String]]) graphBuilder.putAttr("outputNames", outputNamesBuilder.build) + val generateBackwardBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, generateBackwardBuilder, + graph.generateBackward, universe.typeOf[Boolean]) + graphBuilder.putAttr("generateBackward", generateBackwardBuilder.build) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala index dc2eb088219..f1cd205ab0f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala @@ -41,7 +41,7 @@ import scala.reflect.runtime.universe * */ @SerialVersionUID(- 830146931795053244L) -class Reshape[@specialized(Float, Double) T: ClassTag]( +class Reshape[T: ClassTag]( val size: Array[Int], var batchMode: Option[Boolean] = None)( implicit ev: TensorNumeric[T]) extends TensorModule[T] { val batchSize = new Array[Int](size.length + 1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index 8ab9fd260cb..72268921b99 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -250,7 +250,7 @@ object DataConverter extends DataConverter{ private def isEmptyTensor(tensor : Tensor[_]): Boolean = { val emptyTensor = tensor.getTensorType match { case DenseType => - tensor.storage == null + tensor.isEmpty case QuantizedType => tensor.asInstanceOf[QuantizedTensor[_]].getStorage == null } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index c97bded0c6d..e66a6b9fb18 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -631,13 +631,13 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } - "Graph with variables serializer " should "work properly" in { + "Graph with variables serializer" should "work properly" in { val linear = Linear(2, 2) val linearNode = linear.inputs() val linearWeight = linear.weight val linearBias = linear.bias val variables = Some(Array(linearWeight), Array(linearBias)) - val graph = Graph(Array(linearNode), Array(linearNode), variables) + val graph = Graph(Array(linearNode), Array(linearNode), variables, false) val tensor1 = Tensor(2).apply1(_ => Random.nextFloat()) val tensor2 = Tensor() val res1 = graph.forward(tensor1) From 6c7f86fca097d114ffd0e7f411615edc39becd58 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Mon, 27 Nov 2017 16:54:13 +0800 Subject: [PATCH 0540/1065] add activation for recurrent layers (#1909) --- .../bigdl/dllib/nn/ConvLSTMPeephole.scala | 27 +++++++++++--- .../intel/analytics/bigdl/dllib/nn/GRU.scala | 24 +++++++++--- .../analytics/bigdl/dllib/nn/Graph.scala | 2 +- .../intel/analytics/bigdl/dllib/nn/LSTM.scala | 37 ++++++++++++++----- .../intel/analytics/bigdl/dllib/nn/RNN.scala | 2 + .../dllib/utils/python/api/PythonBigDL.scala | 15 ++++++-- 6 files changed, 84 insertions(+), 23 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala index 9577d8a026d..0d52a1813bf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole.scala @@ -35,8 +35,11 @@ import scala.reflect.ClassTag * @param kernelC Convolutional filter size to convolve cell * @param stride The step of the convolution, default is 1 * @param padding The step of the convolution, default is -1, - * behaves same with SAME padding in tensorflow. + * behaves same with SAME padding in tensorflow. * Default stride,padding ensure last 2 dim of output shape is the same with input + * @param activation: activation function, by default to be Tanh if not specified. + * @param innerActivation: activation function for inner cells, + * by default to be Sigmoid if not specified. * @param wRegularizer: instance of [[Regularizer]] (eg. L1 or L2 regularization), applied to the input weights matrices. * @param uRegularizer: instance [[Regularizer]] @@ -54,6 +57,8 @@ class ConvLSTMPeephole[T : ClassTag]( val kernelC: Int, val stride: Int = 1, val padding: Int = -1, + var activation: TensorModule[T] = null, + var innerActivation: TensorModule[T] = null, var wRegularizer: Regularizer[T] = null, var uRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, @@ -69,6 +74,8 @@ class ConvLSTMPeephole[T : ClassTag]( var outputGate: Sequential[T] = _ var hiddenLayer: Sequential[T] = _ var cellLayer: Sequential[T] = _ + if (activation == null) activation = Tanh[T]() + if (innerActivation == null) innerActivation = Sigmoid[T]() override var cell: AbstractModule[Activity, Activity, T] = buildModel() // val joinDim = 2 @@ -110,8 +117,12 @@ class ConvLSTMPeephole[T : ClassTag]( .add(h2g)) } + // make a new instance of inner activation for each build gate + val inner = innerActivation.cloneModule() + inner.setName(Integer.toHexString(java.util.UUID.randomUUID().hashCode())) + gate.add(CAddTable()) - .add(Sigmoid()) + .add(inner) } def buildInputGate(): Sequential[T] = { @@ -153,7 +164,7 @@ class ConvLSTMPeephole[T : ClassTag]( .add(i2h) .add(h2h)) .add(CAddTable()) - .add(Tanh()) + .add(activation) this.hiddenLayer = hidden hidden @@ -190,6 +201,9 @@ class ConvLSTMPeephole[T : ClassTag]( buildCell() buildOutputGate() + val activation2 = activation.cloneModule() + activation2.setName(Integer.toHexString(java.util.UUID.randomUUID().hashCode())) + val convlstm = Sequential() .add(FlattenTable()) .add(ConcatTable() @@ -203,7 +217,7 @@ class ConvLSTMPeephole[T : ClassTag]( .add(outputGate) .add(Sequential() .add(SelectTable(3)) - .add(Tanh()))) + .add(activation2))) .add(CMulTable()) .add(Contiguous())) .add(SelectTable(3))) @@ -252,13 +266,16 @@ object ConvLSTMPeephole { kernelC: Int, stride: Int = 1, padding: Int = -1, + activation: TensorModule[T] = null, + innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, cRegularizer: Regularizer[T] = null, withPeephole: Boolean = true )(implicit ev: TensorNumeric[T]): ConvLSTMPeephole[T] = { - new ConvLSTMPeephole[T](inputSize, outputSize, kernelI, kernelC, stride, padding, + new ConvLSTMPeephole[T](inputSize, outputSize, kernelI, kernelC, + stride, padding, activation, innerActivation, wRegularizer, uRegularizer, bRegularizer, cRegularizer, withPeephole) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala index 864d72ee852..f79eec85a82 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GRU.scala @@ -44,6 +44,9 @@ import scala.reflect.ClassTag * (http://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf) * [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks] * (https://arxiv.org/pdf/1512.05287.pdf) + * @param activation: activation function, by default to be Tanh if not specified. + * @param innerActivation: activation function for inner cells, + * by default to be Sigmoid if not specified. * @param wRegularizer: instance of [[Regularizer]] * (eg. L1 or L2 regularization), applied to the input weights matrices. * @param uRegularizer: instance [[Regularizer]] @@ -56,6 +59,8 @@ class GRU[T : ClassTag] ( val inputSize: Int, val outputSize: Int, val p: Double = 0, + var activation: TensorModule[T] = null, + var innerActivation: TensorModule[T] = null, var wRegularizer: Regularizer[T] = null, var uRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null)(implicit ev: TensorNumeric[T]) @@ -63,6 +68,9 @@ class GRU[T : ClassTag] ( hiddensShape = Array(outputSize), regularizers = Array(wRegularizer, uRegularizer, bRegularizer) ) { + + if (activation == null) activation = Tanh[T]() + if (innerActivation == null) innerActivation = Sigmoid[T]() var i2g: ModuleNode[T] = _ var h2g: ModuleNode[T] = _ val featDim = 2 @@ -106,10 +114,13 @@ class GRU[T : ClassTag] ( val narrow1 = Narrow[T](featDim, 1, outputSize).inputs(cadd) val narrow2 = Narrow[T](featDim, 1 + outputSize, outputSize).inputs(cadd) - val sigmoid1 = Sigmoid().inputs(narrow1) - val sigmoid2 = Sigmoid().inputs(narrow2) + val innerActivation2 = innerActivation.cloneModule() + innerActivation2.setName(Integer.toHexString(java.util.UUID.randomUUID().hashCode())) + + val inner1 = innerActivation.inputs(narrow1) + val inner2 = innerActivation2.inputs(narrow2) - (sigmoid1, sigmoid2) + (inner1, inner2) } def buildModel(): Graph[T] = { @@ -133,7 +144,7 @@ class GRU[T : ClassTag] ( wRegularizer = uRegularizer).inputs(drop2) val cadd2 = CAddTable(true).inputs(f2g, linear2) - val h_hat = Tanh().inputs(cadd2) + val h_hat = activation.inputs(cadd2) // h_t (1 - z) * h + z * h_hat val mulConst = MulConstant(-1).inputs(z) @@ -173,9 +184,12 @@ object GRU { inputSize: Int = 4, outputSize: Int = 3, p: Double = 0, + activation: TensorModule[T] = null, + innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null)(implicit ev: TensorNumeric[T]): GRU[T] = { - new GRU[T](inputSize, outputSize, p, wRegularizer, uRegularizer, bRegularizer) + new GRU[T](inputSize, outputSize, p, activation, innerActivation, + wRegularizer, uRegularizer, bRegularizer) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index ef938dafb39..604c585d4d0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -379,7 +379,7 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], private def checkRoots: Unit = { require(forwardNodes.map(_.element.getName()).distinct.length == forwardNodes.length, - s"the name of node in the graph should be unique, but find dumplicated name " + + s"the name of node in the graph should be unique, but find duplicated name " + s"${duplicatedNames(forwardNodes.map(_.element.getName())).mkString(", ")}") val roots = forwardNodes.filter(_.prevNodes.size == 0) .filter(node => !node.element.isInstanceOf[WithoutInput] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala index a41e6efc248..9e11aa0551b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTM.scala @@ -34,12 +34,15 @@ import scala.reflect.ClassTag * * @param inputSize the size of each input vector * @param hiddenSize Hidden unit size in the LSTM - * @param p is used for [[Dropout]] probability. For more details about + * @param p is used for [[Dropout]] probability. For more details about * RNN dropouts, please refer to * [RnnDrop: A Novel Dropout for RNNs in ASR] * (http://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf) * [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks] * (https://arxiv.org/pdf/1512.05287.pdf) + * @param activation: activation function, by default to be Tanh if not specified. + * @param innerActivation: activation function for inner cells, + * by default to be Sigmoid if not specified. * @param wRegularizer: instance of [[Regularizer]] * (eg. L1 or L2 regularization), applied to the input weights matrices. * @param uRegularizer: instance [[Regularizer]] @@ -52,6 +55,8 @@ class LSTM[T : ClassTag] ( val inputSize: Int, val hiddenSize: Int, val p: Double = 0, + var activation: TensorModule[T] = null, + var innerActivation: TensorModule[T] = null, var wRegularizer: Regularizer[T] = null, var uRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null @@ -61,6 +66,9 @@ class LSTM[T : ClassTag] ( hiddensShape = Array(hiddenSize, hiddenSize), regularizers = Array(wRegularizer, uRegularizer, bRegularizer) ) { + + if (activation == null) activation = Tanh[T]() + if (innerActivation == null) innerActivation = Sigmoid[T]() var gates: Sequential[T] = _ var cellLayer: Sequential[T] = _ @@ -126,10 +134,16 @@ class LSTM[T : ClassTag] ( val split3 = Select(2, 3).inputs(reshape) val split4 = Select(2, 4).inputs(reshape) - (Sigmoid().inputs(split1), - Tanh().inputs(split2), - Sigmoid().inputs(split3), - Sigmoid().inputs(split4)) + // make different instances of inner activation + val innerActivation2 = innerActivation.cloneModule() + innerActivation2.setName(Integer.toHexString(java.util.UUID.randomUUID().hashCode())) + val innerActivation3 = innerActivation.cloneModule() + innerActivation3.setName(Integer.toHexString(java.util.UUID.randomUUID().hashCode())) + + (innerActivation.inputs(split1), + activation.inputs(split2), + innerActivation2.inputs(split3), + innerActivation3.inputs(split4)) } def buildModel(): Sequential[T] = { @@ -148,7 +162,7 @@ class LSTM[T : ClassTag] ( val (in, hid, forg, out) = buildGates()(input1, input2) /** - * g: Tanh + * g: activation * cMult1 = in * hid * cMult2 = forg * input3 * cMult3 = out * g(cMult1 + cMult2) @@ -156,8 +170,10 @@ class LSTM[T : ClassTag] ( val cMult1 = CMulTable().inputs(in, hid) val cMult2 = CMulTable().inputs(forg, input3) val cadd = CAddTable(true).inputs(cMult1, cMult2) - val tanh = Tanh().inputs(cadd) - val cMult3 = CMulTable().inputs(tanh, out) + val activation2 = activation.cloneModule() + activation2.setName(Integer.toHexString(java.util.UUID.randomUUID().hashCode())) + val activate = activation2.inputs(cadd) + val cMult3 = CMulTable().inputs(activate, out) val out1 = Identity().inputs(cMult3) val out2 = Identity().inputs(cMult3) @@ -202,11 +218,14 @@ object LSTM { inputSize: Int, hiddenSize: Int, p: Double = 0, + activation: TensorModule[T] = null, + innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null ) (implicit ev: TensorNumeric[T]): LSTM[T] = { - new LSTM[T](inputSize, hiddenSize, p, wRegularizer, uRegularizer, bRegularizer) + new LSTM[T](inputSize, hiddenSize, p, activation, innerActivation, + wRegularizer, uRegularizer, bRegularizer) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala index 350326d02e8..d8670f72c82 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RNN.scala @@ -37,6 +37,8 @@ import scala.reflect.ClassTag * @param inputSize input size * @param hiddenSize hidden layer size * @param activation activation function f for non-linearity + * @param isInputWithBias boolean + * @param isHiddenWithBias boolean * @param wRegularizer: instance of [[Regularizer]] * (eg. L1 or L2 regularization), applied to the input weights matrices. * @param uRegularizer: instance [[Regularizer]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 22539ab0df3..34bdbb81b2a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -335,20 +335,26 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab inputSize: Int, outputSize: Int, p: Double = 0, + activation: TensorModule[T] = null, + innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): GRU[T] = { - GRU[T](inputSize, outputSize, p, wRegularizer, uRegularizer, bRegularizer) + GRU[T](inputSize, outputSize, p, activation, innerActivation, + wRegularizer, uRegularizer, bRegularizer) } def createLSTM( inputSize: Int, hiddenSize: Int, p: Double = 0, + activation: TensorModule[T] = null, + innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): LSTM[T] = { - LSTM[T](inputSize, hiddenSize, p, wRegularizer, uRegularizer, bRegularizer) + LSTM[T](inputSize, hiddenSize, p, activation, innerActivation, + wRegularizer, uRegularizer, bRegularizer) } def createLSTMPeephole( @@ -376,12 +382,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab kernelC: Int, stride: Int = 1, padding: Int = -1, + activation: TensorModule[T] = null, + innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, cRegularizer: Regularizer[T] = null, withPeephole: Boolean = true): ConvLSTMPeephole[T] = { - ConvLSTMPeephole[T](inputSize, outputSize, kernelI, kernelC, stride, padding, + ConvLSTMPeephole[T](inputSize, outputSize, kernelI, kernelC, + stride, padding, activation, innerActivation, wRegularizer, uRegularizer, bRegularizer, cRegularizer, withPeephole) } From 7c2f2ba72b45264ed02bfb7d6e0485f72a2d8014 Mon Sep 17 00:00:00 2001 From: Shane Huang Date: Mon, 27 Nov 2017 17:47:46 +0800 Subject: [PATCH 0541/1065] add GaussianDropout, GaussianNoise layers (#1894) * add GaussianDropout, GaussianNoise layers, unit tests, python wrapper, module serializer unit test and docs --- .../bigdl/dllib/nn/GaussianDropout.scala | 90 +++++++++++++++++++ .../bigdl/dllib/nn/GaussianNoise.scala | 81 +++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 10 +++ .../bigdl/dllib/nn/GaussianDropoutSpec.scala | 80 +++++++++++++++++ .../bigdl/dllib/nn/GaussianNoiseSpec.scala | 71 +++++++++++++++ .../serializer/ModuleSerializerSpec.scala | 32 +++++++ 6 files changed, 364 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropoutSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoiseSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala new file mode 100644 index 00000000000..266297f6a42 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala @@ -0,0 +1,90 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Apply multiplicative 1-centered Gaussian noise. + * The multiplicative noise will have standard deviation `sqrt(rate / (1 - rate)). + * + * As it is a regularization layer, it is only active at training time. + * + * Output shape is the same as input. + * + * + * @param rate double, drop probability (as with `Dropout`). + * + */ + +@SerialVersionUID(- 1575781981601306833L) +class GaussianDropout[T: ClassTag]( + val rate: Double + )(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ + + require(rate < 1 && rate >= 0, s"rate should be in range [0,1)") + val stddev: Double = Math.sqrt(rate / (1.0-rate)) + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + + this.output.resizeAs(input).copy(input) + + if(train) { + // generate a new random noise tensor in each forward and backward + // following the behavior of tensorflow + val noise = Tensor[T]() + noise.resizeAs(input) + noise.randn(1.0, stddev) + this.output.cmul(noise) + } else { + this.output + } + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + + this.gradInput.resizeAs(gradOutput).copy(gradOutput) + + if (train) { + val noise = Tensor[T]() + noise.resizeAs(gradOutput) + noise.randn(1.0, stddev) + this.gradInput.cmul(noise) + } else { + throw new IllegalArgumentException("backprop only defined while training") + } + this.gradInput + } + + override def toString(): String = { + s"${getPrintName}($rate)" + } + + + +} + +object GaussianDropout { + def apply[@specialized(Float, Double) T: ClassTag]( + rate: Double + )(implicit ev: TensorNumeric[T]) : GaussianDropout[T] = { + new GaussianDropout[T](rate) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala new file mode 100644 index 00000000000..78525ae5974 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala @@ -0,0 +1,81 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Apply additive zero-centered Gaussian noise. + * This is useful to mitigate overfitting (you could see it as a form of random data + * augmentation). + * Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs. + * As it is a regularization layer, it is only active at training time. + * + * Output shape is the same as input. + * + * + * @param stddev double, standard deviation of the noise distribution. + * + */ + +@SerialVersionUID(- 2590701089601246637L) +class GaussianNoise[T: ClassTag]( + val stddev: Double + )(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ + + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + + this.output.resizeAs(input).copy(input) + + if(train) { + val noise = Tensor[T]() + noise.resizeAs(input) + noise.randn(0.0, stddev) + this.output.add(noise) + } else { + this.output + } + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + + if (train) { + this.gradInput.resizeAs(gradOutput).copy(gradOutput) + } else { + throw new IllegalArgumentException("backprop only defined while training") + } + this.gradInput + } + + override def toString(): String = { + s"${getPrintName}($stddev)" + } + + +} + +object GaussianNoise { + def apply[@specialized(Float, Double) T: ClassTag]( + stddev: Double + )(implicit ev: TensorNumeric[T]) : GaussianNoise[T] = { + new GaussianNoise[T](stddev) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 34bdbb81b2a..b54d0ca50bc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -545,6 +545,16 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Dropout[T](initP, inplace, scale) } + def createGaussianDropout(rate: Double) + : GaussianDropout[T] = { + GaussianDropout[T](rate) + } + + def createGaussianNoise(stddev: Double) + : GaussianNoise[T] = { + GaussianNoise[T](stddev) + } + def createView(sizes: JList[Int], num_input_dims: Int = 0): View[T] = { View[T](sizes.asScala.toArray).setNumInputDims(num_input_dims) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropoutSpec.scala new file mode 100644 index 00000000000..ac3ebc9d202 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropoutSpec.scala @@ -0,0 +1,80 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + + /** + * Unit test for GaussianDropout. + */ +@com.intel.analytics.bigdl.tags.Parallel +class GaussianDropoutSpec extends FlatSpec with Matchers { + "GaussianDropout" should "run through without problem in training mode" in { + val batchN = 3 + val inputN = 5 + val outputN = inputN + + val input = Tensor[Double](batchN, inputN).rand() + val gradOutput = Tensor[Double](batchN, outputN).rand() + + + val module = new GaussianDropout[Double](0.5) + // training mode + module.training() + val output = module.forward(input) + // check size, output should be same size as input + assertIntArrayEqual(output.size(), input.size()) + + val gradInput = module.backward(input, gradOutput) + assertIntArrayEqual(gradInput.size(), gradOutput.size()) + } + + "GaussianDropout" should "run correctly in evaluation mode" in { + val batchN = 3 + val inputN = 5 + val outputN = inputN + + val input = Tensor[Double](batchN, inputN).rand() + val gradOutput = Tensor[Double](batchN, outputN).rand() + + val module = new GaussianDropout[Double](0.5) + module.evaluate() + val outputEval = module.forward(input) + // output should be the same as input + assert(input equals outputEval) + // backward reports error in evaluation mode + intercept[IllegalArgumentException] { + module.backward(input, gradOutput) + } + + } + + "GaussianDropout" should "throw exception for illegal rate argument" in { + intercept[IllegalArgumentException] { + val module = new GaussianDropout[Double](-0.1) + } + intercept[IllegalArgumentException] { + val module = new GaussianDropout[Double](2) + } + + } + + def assertIntArrayEqual(a1: Array[Int], a2: Array[Int]): Unit = { + (a1 zip a2).foreach(x => assert(x._1 == x._2)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoiseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoiseSpec.scala new file mode 100644 index 00000000000..9ae5f004947 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoiseSpec.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + + /** + * Unit test for GaussianNoise + */ +@com.intel.analytics.bigdl.tags.Parallel +class GaussianNoiseSpec extends FlatSpec with Matchers { + "GaussianNoise" should "run through in training mode" in { + val batchN = 3 + val inputN = 5 + val outputN = inputN + + val input = Tensor[Double](batchN, inputN).rand() + val gradOutput = Tensor[Double](batchN, outputN).rand() + + val module = new GaussianNoise[Double](0.2) + + // training mode + module.training() + val output = module.forward(input) + assertIntArrayEqual(output.size(), input.size()) + + val gradInput = module.backward(input, gradOutput) + assertIntArrayEqual(gradInput.size(), gradOutput.size()) + + } + + "GaussianNoise" should "run correctly in evaluation mode" in { + val batchN = 3 + val inputN = 5 + val outputN = inputN + + val input = Tensor[Double](batchN, inputN).rand() + val gradOutput = Tensor[Double](batchN, outputN).rand() + + val module = new GaussianNoise[Double](0.2) + + // evaluation mode + module.evaluate() + val outputEval = module.forward(input) + // output should be the same as input + assert(input equals outputEval) + + intercept[IllegalArgumentException] { + module.backward(input, gradOutput) + } + } + + def assertIntArrayEqual(a1: Array[Int], a2: Array[Int]): Unit = { + (a1 zip a2).foreach(x => assert(x._1 == x._2)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index e66a6b9fb18..8fe3b2ce665 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -604,6 +604,38 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } + "GaussianDropout serializer" should "work properly" in { + RNG.setSeed(100) + val gd = GaussianDropout(0.5) + + val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) + val tensor2 = Tensor() + tensor2.resizeAs(tensor1).copy(tensor1) + val res1 = gd.forward(tensor1) + + ModulePersister.saveToFile("/tmp/gaussianDropout.bigdl", gd, true) + RNG.setSeed(100) + val loadedGd = ModuleLoader.loadFromFile("/tmp/gaussianDropout.bigdl") + val res2 = loadedGd.forward(tensor2) + res1 should be (res2) + } + + "GaussianNoise serializer" should "work properly" in { + RNG.setSeed(100) + val gn = GaussianNoise(0.5) + + val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) + val tensor2 = Tensor() + tensor2.resizeAs(tensor1).copy(tensor1) + val res1 = gn.forward(tensor1) + + ModulePersister.saveToFile("/tmp/gaussianNoise.bigdl", gn, true) + RNG.setSeed(100) + val loadedGn = ModuleLoader.loadFromFile("/tmp/gaussianNoise.bigdl") + val res2 = loadedGn.forward(tensor2) + res1 should be (res2) + } + "GradientReversal serializer" should "work properly" in { val gradientReversal = GradientReversal() From b5c8450a2fcb9807d8f4b17a875292772e00d95a Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 27 Nov 2017 19:09:19 -0600 Subject: [PATCH 0542/1065] Keras support - add cosine proximity criterion (#1902) * keras support to add CosineProximityCriterion * fix criterion * add unit test * fix typo * add comment --- .../dllib/nn/CosineProximityCriterion.scala | 139 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 4 + .../dllib/keras/CosineCriterionSpec.scala | 35 +++++ .../serializer/ModuleSerializerSpec.scala | 14 ++ 4 files changed, 192 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineProximityCriterion.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/CosineCriterionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineProximityCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineProximityCriterion.scala new file mode 100644 index 00000000000..75731ad1304 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineProximityCriterion.scala @@ -0,0 +1,139 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * The negative of the mean cosine proximity between predictions and targets. + * The cosine proximity is defined as below: + * x'(i) = x(i) / sqrt(max(sum(x(i)^2), 1e-12)) + * y'(i) = y(i) / sqrt(max(sum(x(i)^2), 1e-12)) + * cosine_proximity(x, y) = mean(-1 * x'(i) * y'(i)) + * + * Both batch and un-batched inputs are supported + */ +class CosineProximityCriterion[@specialized(Float, Double) T: ClassTag] +(implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { + + override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { + val x = l2Norm(input) + val y = l2Norm(target) + val mul = x.cmul(y) + var loss = ev.fromType[Double](0.0) + val func = new TensorFunc2[T] { + override def apply(v1: Array[T], v2: Int): Unit = { + loss = ev.plus(ev.negative(v1(v2)), loss) + } + } + DenseTensorApply.apply1(mul, func) + loss = ev.divide(loss, ev.fromType(mul.nElement())) + loss + } + + private def l2Norm(input : Tensor[T]) : Tensor[T] = { + + val tiled = reduceSum(input) + tiled.apply1((t) => ev.divide(ev.fromType[Double](1.0), + ev.sqrt(ev.max(t, ev.fromType[Double](1e-12))))) + val result = Tensor[T]() + result.resizeAs(input) + result.cmul(input, tiled) + result + } + + private def reduceSum(input : Tensor[T]): Tensor[T] = { + val square = Tensor[T]() + val dim = input.dim() + square.resizeAs(input).copy(input) + square.apply1((t) => ev.pow(t, ev.fromType[Double](2.0))) + // apply sum to last dim + val squareSum = square.sum(dim) + tileLastDim(squareSum, input.size()) + } + + private def tileLastDim(input : Tensor[T], sizeRef : Array[Int]): Tensor[T] = { + val sizes = new Array[Int](sizeRef.length) + var index = 0 + sizeRef.foreach(size => { + index += 1 + sizes(index - 1) = if (index == sizeRef.length) sizeRef(sizeRef.length - 1) else 1 + }) + input.repeatTensor(sizes) + } + + override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { + + gradInput.resizeAs(input) + + // Calculate target norm (yi/Sqrt(y1^2 + y2^2 .... + yn^2)) + val targetNorm = l2Norm(target) + + // expand as the input sizes and tile each element `dim` size + val tiled = reduceSum(input) + + // Calculate xi * norm(yi) + + val inputTargetNom = Tensor[T]().resizeAs(input) + inputTargetNom.cmul(input, targetNorm) + + val inputTargetNomSum = inputTargetNom.sum(inputTargetNom.dim()) + + val inputTargetNomSumTiled = tileLastDim(inputTargetNomSum, input.size()) + + // First calculate xi * (x1 * norm(y1)... xn * norm(yn)) + val func1 = new TensorFunc6[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], + offset2: Int, data3: Array[T], offset3: Int): Unit = { + data1(offset1) = ev.times(data2(offset2), data3(offset3)) + + } + } + + DenseTensorApply.apply3[T](gradInput, input, inputTargetNomSumTiled, func1) + + // Then appy -1/sqrt((x1^2 + ... xn^2)^(3/2)) * ((x1^2 + ... xn^2) - grad(xi)) + + val total = input.nElement() + + val func2 = new TensorFunc6[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], + offset2: Int, data3: Array[T], offset3: Int): Unit = { + if (ev.isGreater(ev.fromType[Double](1e-12f), data2(offset2))) { + data1(offset1) = ev.negative(ev.fromType[Double](1.0/Math.sqrt(1e-12))) + } else { + val f1 = ev.divide(ev.fromType[Double](1.0), + ev.times(data3(offset3), ev.sqrt(data3(offset3)))) + val s1 = ev.times(data3(offset3), data2(offset2)) + data1(offset1) = ev.divide(ev.negative(ev.times(f1, ev.minus(s1, data1(offset1)))) + , ev.fromType(total)) + } + } + } + DenseTensorApply.apply3[T](gradInput, targetNorm, tiled, func2) + gradInput + } +} + +object CosineProximityCriterion { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): + CosineProximityCriterion[T] = new CosineProximityCriterion[T] +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index b54d0ca50bc..8c59373a4fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2287,6 +2287,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def setRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit = { module.runningVar.set(toTensor(runningStd)) } + + def createCosineProximityCriterion(): CosineProximityCriterion[T] = { + CosineProximityCriterion[T]() + } } object PythonBigDLUtils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/CosineCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/CosineCriterionSpec.scala new file mode 100644 index 00000000000..ffcd5f611a4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/CosineCriterionSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{CosineProximityCriterion, MSECriterion} + +class CosineCriterionSpec extends KerasBaseSpec{ + "Cosine proximity loss" should "be ok" in { + ifskipTest() + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = cosine_proximity(input_tensor, target_tensor) + |input = np.random.uniform(0, 1, [2, 3]) + |Y = np.random.uniform(0, 1, [2, 3]) + """.stripMargin + val cosineProximity = new CosineProximityCriterion[Float]() + checkOutputAndGradForLoss(cosineProximity, kerasCode) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 8fe3b2ce665..b7c8b8022a7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -2121,6 +2121,20 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { loadedModel.isTraining() should be (false) } + "HardSigmoid serialization" should "work properly" in { + val hardSigmoid = HardSigmoid() + ModulePersister.saveToFile("/tmp/hardSigmoid.bigdl", hardSigmoid, true) + val loadedModel = ModuleLoader.loadFromFile("/tmp/hardSigmoid.bigdl") + + val input = Tensor(2, 2).rand() + + val res1 = hardSigmoid.forward(input) + + val res2 = loadedModel.forward(input) + + res1 should be (res2) + } + } class TestModule[T: ClassTag](val custom: CustomData) From 801652d4371b0381ccd17fc5fca5474cae28048b Mon Sep 17 00:00:00 2001 From: Xianyan Date: Tue, 28 Nov 2017 13:50:40 +0800 Subject: [PATCH 0543/1065] fix tensor set (#1939) --- .../intel/analytics/bigdl/dllib/tensor/DenseTensor.scala | 2 +- .../analytics/bigdl/dllib/tensor/DenseTensorSpec.scala | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index cbb4008f881..1f166bcab66 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -377,7 +377,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this._storage.resize(0) } this.nDimension = 0 - this._size = null + this._size = Array[Int]() this } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index da021211ed5..99b3bb77c38 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -1066,4 +1066,12 @@ class DenseTensorSpec extends FlatSpec with Matchers { o.mm(x, y) o should be(Tensor[Float](T(T(22, 28), T(49, 64)))) } + + "set" should "work properly" in { + val t = Tensor[Float](1, 3) + t.set() + t.size() should be (Array[Int]()) + t.nElement() should be (0) + t.dim() should be (0) + } } From e67a6950d256f0648ca77d98378c6e7b37fd36df Mon Sep 17 00:00:00 2001 From: Xianyan Date: Tue, 28 Nov 2017 19:05:11 +0800 Subject: [PATCH 0544/1065] Add keras highway (#1913) * Add highway * Fix unit test * Add python support * fix ut * add module seri * fix ut * Add API doc * Add Regularizer --- .../analytics/bigdl/dllib/nn/Highway.scala | 64 +++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 6 ++ .../bigdl/dllib/nn/HighwaySpec.scala | 102 ++++++++++++++++++ 3 files changed, 172 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Highway.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Highway.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Highway.scala new file mode 100644 index 00000000000..11fcb1d65c3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Highway.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + + +object Highway { + private def getAct[@specialized(Float, Double) T: ClassTag](name: String) + (implicit ev: TensorNumeric[T]): TensorModule[T] = name match { + case "tanh" => Tanh[T]() + case _ => null + } + + /** + * Densely connected highway network. + * Highway layers are a natural extension of LSTMs to feedforward networks. + * + * @param size input size + * @param withBias whether to include a bias + * @param activation name of activation function to use + * @param wRegularizer: instance of [[Regularizer]] + * (eg. L1 or L2 regularization), applied to the input weights matrices. + * @param bRegularizer: instance of [[Regularizer]] + * applied to the bias. + */ + def apply[@specialized(Float, Double) T: ClassTag](size: Int, withBias: Boolean = true, + activation: String = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null) + (implicit ev: TensorNumeric[T]): Graph[T] = { + val input = Input() + val l1 = Linear(size, size, withBias = withBias, wRegularizer = wRegularizer, + bRegularizer = bRegularizer).inputs(input) + val transformWeight = Sigmoid().inputs(l1) + val negatedGate = AddConstant(1).inputs(Negative().inputs(transformWeight)) + val l2 = Linear(size, size, withBias = withBias, wRegularizer = wRegularizer, + bRegularizer = bRegularizer).inputs(input) + val transformed = if (null != activation) getAct(activation).inputs(l2) else l2 + val transformedGated = CMulTable().inputs(transformWeight, transformed) + val identityGate = CMulTable().inputs(negatedGate, input) + val value = CAddTable().inputs(transformedGated, identityGate) + Graph(Array(input), Array(value)) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 8c59373a4fb..c0251a8f406 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2160,6 +2160,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab alignCorner) } + def createHighway(size: Int, withBias: Boolean, activation: String, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null): Graph[T] = { + Highway(size, withBias, activation, wRegularizer, bRegularizer) + } + def redirectSparkLogs(logPath: String): Unit = { LoggerFilter.redirectSparkInfoLogs(logPath) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala new file mode 100644 index 00000000000..20594a92bf7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala @@ -0,0 +1,102 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.keras.{KerasBaseSpec, KerasRunner} +import com.intel.analytics.bigdl.tensor.Tensor + +class HighwaySpec extends KerasBaseSpec { + "highway forward backward" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[2]) + |input = np.random.uniform(0, 1, [3, 2]) + |output_tensor = Highway(activation='tanh')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val highway = Highway[Float](2, activation = "tanh") + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = + Array(in(1).t(), in(3), in(0).t(), in(2)) + checkHighwayOutputAndGrad(highway, kerasCode, weightConverter) + } + + "highway forward backward noBias" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[2]) + |input = np.random.uniform(0, 1, [3, 2]) + |output_tensor = Highway(activation='tanh', bias=None)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val highway = Highway[Float](2, activation = "tanh", withBias = false) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = + Array(in(1).t(), in(0).t()) + + checkHighwayOutputAndGrad(highway, kerasCode, weightConverter) + } + + "highway forward backward no activation" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[2]) + |input = np.random.uniform(0, 1, [3, 2]) + |output_tensor = Highway(bias=None)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val highway = Highway[Float](2, withBias = false) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = + Array(in(1).t(), in(0).t()) + + checkHighwayOutputAndGrad(highway, kerasCode, weightConverter) + } + + def checkHighwayOutputAndGrad(bmodel: Graph[Float], + kerasCode: String, + weightConverter: (Array[Tensor[Float]]) => Array[Tensor[Float]], + precision: Double = 1e-5): Unit = { + ifskipTest() + val (gradInput, gradWeight, weights, input, target, output) = KerasRunner.run(kerasCode) + // Ensure they share the same weights + if (weights != null) { + bmodel.setWeightsBias(weightConverter(weights)) + } + val boutput = bmodel.forward(input).toTensor[Float] + boutput.almostEqual(output, precision) should be(true) + + val bgradInput = bmodel.backward(input, boutput.clone()).toTensor[Float] + bgradInput.almostEqual(gradInput, precision) should be(true) + } + + "Highway serializer" should "work properly" in { + val module = Highway[Float](2, activation = "tanh") + + val input = Tensor[Float](3, 2).randn() + val res1 = module.forward(input.clone()).toTensor[Float].clone() + val clone = module.cloneModule() + val tmpFile = java.io.File.createTempFile("module", ".bigdl") + module.saveModule(tmpFile.getAbsolutePath, true) + val loaded = Module.loadModule[Float](tmpFile.getAbsolutePath) + + val res2 = loaded.forward(input.clone()) + val namedModule = Utils.getNamedModules[Float](clone) + val namedModule2 = Utils.getNamedModules[Float](loaded) + res1 should be(res2) + if (tmpFile.exists()) { + tmpFile.delete() + } + } +} From e8b898813309a4cdec1cf7a5b5ffafb0829a133f Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Wed, 29 Nov 2017 15:12:28 +0800 Subject: [PATCH 0545/1065] Always averaged the returning value (#1951) * always averaged the returning value * try to fix random err on jenkins --- .../analytics/bigdl/dllib/keras/KerasBaseSpec.scala | 9 ++++----- .../intel/analytics/bigdl/dllib/keras/KerasRunner.scala | 2 +- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index d99d2500ba1..952cecf9411 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -73,12 +73,11 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { KerasRunner.run(kerasCode, is_loss = true) val boutput = bmodel.forward(input, target) - require(output.nElement() == 1, s"output should only contain 1 element, but we got: ${output}") - NumericFloat.nearlyEqual(boutput, output.storage.array()(0), precision) should be(true) - + val koutput = output.mean() // the return value from keras is not always averaged. + NumericFloat.nearlyEqual(boutput, koutput, precision) should be(true) + val kgradInput = gradInput.div(output.nElement()) // div is an in-place operation. val bgradInput = bmodel.backward(input, target.clone()) - bgradInput.almostEqual(gradInput, precision) should be(true) - + bgradInput.almostEqual(kgradInput, precision) should be(true) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala index 88576b5d616..7e930b41f8e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala @@ -29,7 +29,7 @@ object KerasRunner { |from keras.layers.core import * |from keras.layers.convolutional import * |from keras.layers import * - |from keras.metrics import * + |from keras.objectives import * |from keras.models import Model |import keras.backend as K |import numpy as np From 4451406c2eaea0eabb8c8368abd5eb879ea0e7ec Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 29 Nov 2017 20:19:04 -0600 Subject: [PATCH 0546/1065] Fix Java based model saving issue with size > 2GB (#1922) * refinement for saving obj * add integration test * update test name * add output for testing * testing debug * testing * test small one for debugging * test bigger one * remove forkmodel * add unit test * add unit test * delete tmp file --- .../analytics/bigdl/dllib/utils/File.scala | 37 ++++++++++++++----- .../bigdl/dllib/utils/FileSpec.scala | 18 +++++++++ 2 files changed, 45 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala index 945422caec0..5a87fe841fa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/File.scala @@ -66,11 +66,19 @@ object File { * @param isOverwrite if overwrite. */ def save(obj: Serializable, fileName: String, isOverwrite: Boolean = false): Unit = { - - val byteArrayOut = new ByteArrayOutputStream() - val objFile = new ObjectOutputStream(byteArrayOut) - objFile.writeObject(obj) - saveBytes(byteArrayOut.toByteArray, fileName, isOverwrite) + var fw: FileWriter = null + var out: OutputStream = null + var objFile: ObjectOutputStream = null + try { + fw = FileWriter(fileName) + out = fw.create(isOverwrite) + objFile = new ObjectOutputStream(new BufferedOutputStream(out)) + objFile.writeObject(obj) + } finally { + if (null != objFile) objFile.close() + if (null != out) out.close() + if (null != fw) fw.close() + } } def saveBytes(bytes: Array[Byte], fileName: String, isOverwrite: Boolean = false) : Unit = { @@ -166,13 +174,22 @@ object File { * @param fileName file name. */ def load[T](fileName: String): T = { - - val objFile = new ObjectInputStream(new ByteArrayInputStream(readBytes(fileName))) - val result = objFile.readObject() - result.asInstanceOf[T] + var fr: FileReader = null + var in: InputStream = null + var objFile: ObjectInputStream = null + try { + fr = FileReader(fileName) + in = fr.open() + val bis = new BufferedInputStream(in) + val objFile = new ObjectInputStream(bis) + objFile.readObject().asInstanceOf[T] + } finally { + if (null != in) in.close() + if (null != fr) fr.close() + if (null != objFile) objFile.close() + } } - def readBytes[T](fileName : String) : Array[Byte] = { var fr: FileReader = null var in: InputStream = null diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/FileSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/FileSpec.scala index fdbc0cb95c9..ddda8335497 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/FileSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/FileSpec.scala @@ -68,4 +68,22 @@ class FileSpec extends FlatSpec with Matchers { testModule should be(module) } + + "save/load big size model" should "work properly" in { + val tmpFile = java.io.File.createTempFile("module", "obj") + val absolutePath = tmpFile.getAbsolutePath + + + val module = Linear[Float](40000, 8000) + + File.save(module, absolutePath, true) + val testModule: Module[Double] = File.load(absolutePath) + + testModule should be(module) + + if (tmpFile.exists()) { + tmpFile.delete() + } + } + } From de2aac3b344700fbadc6a329f20e977f781d0e77 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 30 Nov 2017 10:30:08 +0800 Subject: [PATCH 0547/1065] feat: refactor of bigdl core and enable opencv (#1955) * feat: refactor of bigdl core and enable opencv * fix: skip opencv on rh5 --- dl/pom.xml | 49 ++++++++++++------------------------------------- 1 file changed, 12 insertions(+), 37 deletions(-) diff --git a/dl/pom.xml b/dl/pom.xml index 6fb2ff20b7a..f75566a260a 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -77,10 +77,10 @@ compile - com.intel.analytics.bigdl - bigdl-core + com.intel.analytics.bigdl.core.dist + all 0.4.0-SNAPSHOT - pom + ${bigdl-core-all-scope} org.apache.spark @@ -296,41 +296,16 @@ per_platform + + provided + - - com.intel.analytics.bigdl.native - ${mkl-java-os-version} - 0.4.0-SNAPSHOT - - - - com.intel.analytics.bigdl.native - bigdl-native - - - - - - com.intel.analytics.bigdl.bigquant - ${bigquant-java-os-version} - 0.4.0-SNAPSHOT - - - com.intel.analytics.bigdl.bigquant - bigquant-native - - - - - - com.intel.analytics.bigdl - bigdl-core - 0.4.0-SNAPSHOT - provided - pom - + + com.intel.analytics.bigdl.core.dist + ${os-flag} + 0.4.0-SNAPSHOT + pom + From b9db95631e773518b8ac27b3a020c2c7ee43ef3c Mon Sep 17 00:00:00 2001 From: Xianyan Date: Thu, 30 Nov 2017 13:26:32 +0800 Subject: [PATCH 0548/1065] Add Upsample3d (#1911) * add * Add upsample3d * add SerialVersionUID * add python * Fix ut * add saveModule test * add updample3d serializer and markdown doc * meet code review * meet code review * fix ut * fix ut * resolve conflicts --- .../bigdl/dllib/nn/UpSampling3D.scala | 228 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 4 + .../bigdl/dllib/keras/UpSampling3DSpec.scala | 73 ++++++ 3 files changed, 305 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3D.scala new file mode 100644 index 00000000000..3505114dc48 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3D.scala @@ -0,0 +1,228 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Upsampling layer for 3D inputs. + * Repeats the 1st, 2nd and 3rd dimensions + * of the data by size[0], size[1] and size[2] respectively. + * The input data is assumed to be of the form `minibatch x channels x depth x height x width`. + * + * @param size Repeats the depth, height, width dimensions of the data by + * size[0], size[1] and size[2] respectively. + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +@SerialVersionUID(3462228835945094156L) +class UpSampling3D[T: ClassTag](val size: Array[Int]) + (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + + require(size != null && size.length == 3, "the size should be 3 dims") + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 5, "only supports 5d tensors") + require(input.isContiguous(), "input need to be contiguous") + + val inputDepth = input.size(3) + val inputHeight = input.size(4) + val inputWidth = input.size(5) + + val dT = size(0) + val dH = size(1) + val dW = size(2) + + val outputDepth = inputDepth * dT + val outputHeight = inputHeight * dH + val outputWidth = inputWidth * dW + + output.resize(input.size(1), input.size(2), outputDepth, outputHeight, outputWidth) + + // dims + val idim = input.dim() + val xDim = idim - 1 + val yDim = idim - 2 + val zDim = idim - 3 + + val osz0 = output.size(1) + val osz1 = output.size(2) + val osz2 = output.size(3) + val osz3 = output.size(4) + val osz4 = output.size(5) + + // get strides + val is = input.stride() + val os = output.stride() + + // get raw pointers + val pin = input.storage().array() + val inOffset = input.storageOffset() - 1 + val pout = output.storage().array() + val outOffset = output.storageOffset() - 1 + + // perform the upsampling + var i0, i1, i2, i3, i4, isrc, idst = 0 + val iout = new Array[Int](5) // Output indices + val iin = new Array[Int](5) // Input indices + + i0 = 0 + while (i0 < osz0) { + iout(0) = i0 + iin(0) = i0 + i1 = 0 + while (i1 < osz1) { + iout(1) = i1 + iin(1) = i1 + i2 = 0 + while (i2 < osz2) { + iout(2) = i2 + iin(2) = i2 + i3 = 0 + while (i3 < osz3) { + iout(3) = i3 + iin(3) = i3 + i4 = 0 + while (i4 < osz4) { + iout(4) = i4 + iin(4) = i4 + // set the indices for the upsampled dimensions + iin(xDim) = iout(xDim) / dW + iin(yDim) = iout(yDim) / dH + iin(zDim) = iout(zDim) / dT + + idst = i0 * os(0) + i1 * os(1) + i2 * os(2) + i3 * os(3) + isrc = iin(0) * is(0) + iin(1) * is(1) + iin(2) * is(2) + iin(3) * is(3) + if (idim > 4) { + idst += i4 * os(4) + isrc += iin(4) * is(4) + } + pout(outOffset + idst) = pin(inOffset + isrc) + i4 += 1 + } + i3 += 1 + } + i2 += 1 + } + i1 += 1 + } + i0 += 1 + } + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + gradInput.resizeAs(input).zero() + val dT = size(0) + val dH = size(1) + val dW = size(2) + + // dims + val idim = gradInput.dim() + val xDim = idim - 1 + val yDim = idim - 2 + val zDim = idim - 3 + + val isz0 = gradInput.size(1) + val isz1 = gradInput.size(2) + val isz2 = gradInput.size(3) + val isz3 = gradInput.size(4) + val isz4 = gradInput.size(5) + + val is = gradInput.stride() + val os = gradOutput.stride() + + val pin = gradInput.storage().array() + val pout = gradOutput.storage().array() + + + val inOffset = gradInput.storageOffset() - 1 + val outOffset = gradOutput.storageOffset() - 1 + + // perform the upsampling + var i0, i1, i2, i3, i4, isrc, idst, x, y, z = 0 + val iin = new Array[Int](5) // Input indices + val iout = new Array[Int](5) // Output indices + + i0 = 0 + while (i0 < isz0) { + iout(0) = i0 + iin(0) = i0 + i1 = 0 + while (i1 < isz1) { + iout(1) = i1 + iin(1) = i1 + i2 = 0 + while (i2 < isz2) { + iout(2) = i2 + iin(2) = i2 + i3 = 0 + while (i3 < isz3) { + iout(3) = i3 + iin(3) = i3 + i4 = 0 + while (i4 < isz4) { + iout(4) = i4 + iin(4) = i4 + + idst = i0 * is(0) + i1 * is(1) + i2 * is(2) + i3 * is(3) + + if (idim > 4) { + idst += i4 * is(4) + } + // Now accumulate the gradients from gradOutput + z = 0 + while (z < dT) { + y = 0 + while (y < dH) { + x = 0 + while (x < dW) { + iout(xDim) = dW * iin(xDim) + x + iout(yDim) = dH * iin(yDim) + y + iout(zDim) = dT * iin(zDim) + z + isrc = iout(0) * os(0) + iout(1) * os(1) + iout(2) * os(2) + iout(3) * os(3) + if (idim > 4) { + isrc += iout(4) * os(4) + } + pin(inOffset + idst) = ev.plus(pin(inOffset + idst), pout(outOffset + isrc)) + x += 1 + } + y += 1 + } + z += 1 + } + i4 += 1 + } + i3 += 1 + } + i2 += 1 + } + i1 += 1 + } + i0 += 1 + } + gradInput + } +} + +object UpSampling3D { + def apply[@specialized(Float, Double) T: ClassTag](size: Array[Int]) + (implicit ev: TensorNumeric[T]): UpSampling3D[T] = new UpSampling3D(size) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c0251a8f406..5f1380be620 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2166,6 +2166,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Highway(size, withBias, activation, wRegularizer, bRegularizer) } + def createUpSampling3D(size: JList[Int]): UpSampling3D[T] = { + UpSampling3D(size.asScala.toArray) + } + def redirectSparkLogs(logPath: String): Unit = { LoggerFilter.redirectSparkInfoLogs(logPath) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala new file mode 100644 index 00000000000..fccb77dd259 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala @@ -0,0 +1,73 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.tensor.Tensor + +class UpSampling3DSpec extends KerasBaseSpec { + "updample3d forward with size 1" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[1, 2, 3, 4]) + |input = np.random.uniform(0, 1, [2, 1, 2, 3, 4]) + |output_tensor = UpSampling3D((1, 1, 1))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = UpSampling3D[Float](Array(1, 1, 1)) + checkOutputAndGrad(model, kerasCode) + } + + "updample3d forward with size 2" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[1, 1, 2, 4]) + |input = np.random.uniform(0, 1, [2, 1, 1, 2, 4]) + |output_tensor = UpSampling3D((2, 2, 2), dim_ordering = 'th')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = UpSampling3D[Float](Array(2, 2, 2)) + checkOutputAndGrad(model, kerasCode) + } + + "updample3d forward with size 2, 3, 4" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[2, 3, 2, 4]) + |input = np.random.uniform(0, 1, [2, 2, 3, 2, 4]) + |output_tensor = UpSampling3D((2, 3, 4), dim_ordering = 'th')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = UpSampling3D[Float](Array(2, 3, 4)) + checkOutputAndGrad(model, kerasCode) + } + + "updample3d serializer" should "work properly" in { + val module = UpSampling3D[Float](Array(2, 2, 2)) + + val input = Tensor[Float](1, 2, 2, 2, 2).randn() + val res1 = module.forward(input).clone() + val tmpFile = java.io.File.createTempFile("module", ".bigdl") + module.saveModule(tmpFile.getAbsolutePath, true) + val loaded = Module.loadModule[Float](tmpFile.getAbsolutePath) + val res2 = loaded.forward(input) + res1 should be(res2) + if (tmpFile.exists()) { + tmpFile.delete() + } + } +} From 698a9f32c43a2206ef87a400e250cad9db5adc72 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Thu, 30 Nov 2017 13:31:52 +0800 Subject: [PATCH 0549/1065] Add OpenCV Vision Transformer Structure (#1954) * Add OpenCV Vision Transformer * Add more unit test * Add scala docs and python docs * Fix python spark context * update image --- .../transform/vision/image/Convertor.scala | 55 ++++ .../vision/image/FeatureTransformer.scala | 113 +++++++ .../transform/vision/image/ImageFeature.scala | 308 ++++++++++++++++++ .../transform/vision/image/ImageFrame.scala | 192 +++++++++++ .../vision/image/augmentation/HFlip.scala | 40 +++ .../vision/image/opencv/OpenCVMat.scala | 183 +++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 155 ++++++++- .../src/test/resources/pascal/000025.jpg | Bin 0 -> 95959 bytes .../vision/image/ImageFrameSpec.scala | 91 ++++++ .../vision/image/augmentation/HFlipSpec.scala | 31 ++ .../vision/image/opencv/OpenCVMatSpec.scala | 81 +++++ 11 files changed, 1244 insertions(+), 5 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/FeatureTransformer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/HFlip.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala create mode 100644 scala/dllib/src/test/resources/pascal/000025.jpg create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HFlipSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala new file mode 100644 index 00000000000..2aecd5e784c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import org.apache.log4j.Logger + +/** + * Transform byte array(original image file in byte) to OpenCVMat + */ +class BytesToMat() + extends FeatureTransformer { + + override def transform(feature: ImageFeature): ImageFeature = { + BytesToMat.transform(feature) + } +} + +object BytesToMat { + val logger = Logger.getLogger(getClass) + def apply(): BytesToMat = new BytesToMat() + + def transform(feature: ImageFeature): ImageFeature = { + if (!feature.isValid) return feature + val bytes = feature[Array[Byte]](ImageFeature.bytes) + var mat: OpenCVMat = null + try { + require(null != bytes && bytes.length > 0, "image file bytes should not be empty") + mat = OpenCVMat.fromImageBytes(bytes) + feature(ImageFeature.mat) = mat + feature(ImageFeature.originalSize) = mat.shape() + } catch { + case e: Exception => + val uri = feature.uri() + logger.warn(s"convert byte to mat fail for $uri") + feature(ImageFeature.originalSize) = (-1, -1, -1) + feature.isValid = false + } + feature + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/FeatureTransformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/FeatureTransformer.scala new file mode 100644 index 00000000000..bd9e47a8411 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/FeatureTransformer.scala @@ -0,0 +1,113 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image + +import com.intel.analytics.bigdl.dataset.{ChainedTransformer, Transformer} +import com.intel.analytics.bigdl.opencv.OpenCV +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import org.apache.log4j.Logger + +/** + * FeatureTransformer is a transformer that transform ImageFeature + */ +abstract class FeatureTransformer() extends Transformer[ImageFeature, ImageFeature] { + + import FeatureTransformer.logger + + private var outKey: Option[String] = None + + /** + * set the output key to store current transformed result + * if the key is not set, or same as default, then the transformed result + * will be overwritted by the following transformer + * @param key output key + */ + def setOutKey(key: String): this.type = { + outKey = Some(key) + this + } + + /** + * transform mat + */ + protected def transformMat(feature: ImageFeature): Unit = {} + + /** + * transform feature + * @param feature ImageFeature + * @return ImageFeature + */ + def transform(feature: ImageFeature): ImageFeature = { + require(OpenCV.isOpenCVLoaded, "opencv isn't loaded") + if (!feature.isValid) return feature + try { + transformMat(feature) + if (outKey.isDefined) { + require(outKey.get != ImageFeature.mat, s"the output key should not equal to" + + s" ${ImageFeature.mat}, please give another name") + if (feature.contains(outKey.get)) { + val mat = feature[OpenCVMat](outKey.get) + feature.opencvMat().copyTo(mat) + } else { + feature(outKey.get) = feature.opencvMat().clone() + } + } + } catch { + case e: Exception => + val path = if (feature.contains(ImageFeature.uri)) feature(ImageFeature.uri) else "" + logger.warn(s"failed ${path} in transformer ${getClass}") + e.printStackTrace() + feature.isValid = false + } + feature + } + + override def apply(prev: Iterator[ImageFeature]): Iterator[ImageFeature] = { + prev.map(transform) + } + + def apply(imageFrame: ImageFrame): ImageFrame = { + imageFrame.transform(this) + } + + // scalastyle:off methodName + // scalastyle:off noSpaceBeforeLeftBracket + def -> (other: FeatureTransformer): FeatureTransformer = { + new ChainedFeatureTransformer(this, other) + } + + // scalastyle:off methodName + // scalastyle:off noSpaceBeforeLeftBracket + override def -> [C](other: Transformer[ImageFeature, C]): Transformer[ImageFeature, C] = { + new ChainedTransformer(this, other) + } +} + +object FeatureTransformer { + val logger = Logger.getLogger(getClass) +} + +/** + * A transformer chain two FeatureTransformer together. + */ +class ChainedFeatureTransformer(first: FeatureTransformer, last: FeatureTransformer) extends + FeatureTransformer { + + override def transform(prev: ImageFeature): ImageFeature = { + last.transform(first.transform(prev)) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala new file mode 100644 index 00000000000..952bf9c9bc9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala @@ -0,0 +1,308 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image + +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.utils.T +import org.apache.log4j.Logger + +import scala.collection.{Set, mutable} +import scala.reflect.ClassTag + +/** + * Each ImageFeature keeps information about single image, + * it can include various status of an image, + * e.g. original bytes read from image file, an opencv mat, + * pixels in float array, image label, meta data and so on. + * it uses HashMap to store all these data, + * the key is string that identify the corresponding value + */ +class ImageFeature extends Serializable { + + import ImageFeature.logger + + /** + * Create ImageFeature from original image in byte array, label and uri + * + * @param bytes image file in bytes + * @param label label + * @param uri image uri + */ + def this(bytes: Array[Byte], label: Any = null, uri: String = null) { + this + state(ImageFeature.bytes) = bytes + if (null != uri) { + state(ImageFeature.uri) = uri + } + if (null != label) { + state(ImageFeature.label) = label + } + } + + private val state = new mutable.HashMap[String, Any]() + + /** + * whether this image feature is valid + */ + var isValid = true + + def apply[T](key: String): T = { + if (contains(key)) state(key).asInstanceOf[T] else null.asInstanceOf[T] + } + + def update(key: String, value: Any): Unit = state(key) = value + + def contains(key: String): Boolean = state.contains(key) + + /** + * get opencv mat from ImageFeature, note that it may be empty if it is released + */ + def opencvMat(): OpenCVMat = apply[OpenCVMat](ImageFeature.mat) + + def keys(): Set[String] = state.keySet + + /** + * whether this ImageFeature contains label + * @return + */ + def hasLabel(): Boolean = state.contains(ImageFeature.label) + + /** + * image file in bytes + */ + def bytes(): Array[Byte] = apply[Array[Byte]](ImageFeature.bytes) + + /** + * get uri from ImageFeature + * @return + */ + def uri(): String = apply[String](ImageFeature.uri) + + /** + * image pixels in float array + * + * @param key key that maps float array + * @return float array + */ + def floats(key: String = ImageFeature.floats): Array[Float] = { + apply[Array[Float]](key) + } + + /** + * get prediction result from ImageFeature + * @param key key that maps prediction result + */ + def predict(key: String = ImageFeature.predict): Any = { + apply(key) + } + + /** + * get current image size in (height, width, channel) + * + * @return (height, width, channel) + */ + def getSize: (Int, Int, Int) = { + val mat = opencvMat() + if (!mat.isReleased) { + mat.shape() + } else if (contains(ImageFeature.size)) { + apply[(Int, Int, Int)](ImageFeature.size) + } else { + getOriginalSize + } + } + + /** + * get current height + */ + def getHeight(): Int = getSize._1 + + /** + * get current width + */ + def getWidth(): Int = getSize._2 + + /** + * get current channel + */ + def getChannel(): Int = getSize._3 + + /** + * get original image size in (height, width, channel) + * + * @return (height, width, channel) + */ + def getOriginalSize: (Int, Int, Int) = { + if (contains(ImageFeature.originalSize)) { + apply[(Int, Int, Int)](ImageFeature.originalSize) + } else { + logger.warn("there is no original size stored") + (-1, -1, -1) + } + } + + /** + * get original width + */ + def getOriginalWidth: Int = getOriginalSize._2 + + /** + * get original height + */ + def getOriginalHeight: Int = getOriginalSize._1 + + /** + * get label from ImageFeature + */ + def getLabel[T: ClassTag]: T = apply[T](ImageFeature.label) + + /** + * imInfo is a tensor that contains height, width, scaleInHeight, scaleInWidth + * e.g. it is used in SSD and Faster-RCNN to post process the roi detection + */ + def getImInfo(): Tensor[Float] = { + val (height, width, _) = getSize + val (oh, ow, _) = getOriginalSize + Tensor[Float](T(height, width, height.toFloat / oh, width.toFloat / ow)) + } + + /** + * clear ImageFeature + */ + def clear(): Unit = { + state.clear() + isValid = true + } + + + /** + * copy the float array to a storage + * @param storage destination array + * @param offset offset to copy + * @param floatKey key that maps float array + * @param toRGB BGR to RGB + */ + def copyTo(storage: Array[Float], offset: Int, floatKey: String = ImageFeature.floats, + toRGB: Boolean = true): Unit = { + require(contains(floatKey), s"there should be ${floatKey} in ImageFeature") + val data = floats(floatKey) + require(data.length >= getWidth() * getHeight() * 3, + s"float array length should be larger than 3 * ${getWidth()} * ${getHeight()}") + val frameLength = getWidth() * getHeight() + require(frameLength * 3 + offset <= storage.length) + var j = 0 + if (toRGB) { + while (j < frameLength) { + storage(offset + j) = data(j * 3 + 2) + storage(offset + j + frameLength) = data(j * 3 + 1) + storage(offset + j + frameLength * 2) = data(j * 3) + j += 1 + } + } else { + while (j < frameLength) { + storage(offset + j) = data(j * 3) + storage(offset + j + frameLength) = data(j * 3 + 1) + storage(offset + j + frameLength * 2) = data(j * 3 + 2) + j += 1 + } + } + } + + /** + * Convert ImageFeature to image tensor + * @param floatKey key that maps the float array + * @param toChw transpose the image from hwc to chw + * @return tensor that represents an image + */ + def toTensor(floatKey: String, toChw: Boolean = true): Tensor[Float] = { + val (data, size) = if (contains(floatKey)) { + (floats(floatKey), + Array(getHeight(), getWidth(), 3)) + } else { + logger.warn(s"please add MatToFloats(out_key = $floatKey) in the end of pipeline if you" + + s" are transforming an rdd") + val mat = opencvMat() + val floats = new Array[Float](mat.height() * mat.width() * 3) + OpenCVMat.toFloatPixels(mat, floats) + (floats, Array(mat.height(), mat.width(), 3)) + } + var image = Tensor(Storage(data)).resize(size) + if (toChw) { + // transpose the shape of image from (h, w, c) to (c, h, w) + image = image.transpose(1, 3).transpose(2, 3).contiguous() + } + image + } +} + +object ImageFeature { + /** + * key: uri that identifies image + */ + val uri = "uri" + /** + * key: image in OpenCVMat + */ + val mat = "mat" + /** + * key: image file in bytes + */ + val bytes = "bytes" + /** + * key: image pixels in float array + */ + val floats = "floats" + /** + * key: current image size + */ + val size = "size" + /** + * key: original image size + */ + val originalSize = "originalSize" + /** + * key: label + */ + val label = "label" + /** + * key: image prediction result + */ + val predict = "predict" + /** + * key: store cropped box in Crop + */ + val cropBbox = "cropBbox" + /** + * key: store expand box in Expand + */ + val expandBbox = "expandBbox" + + /** + * Create ImageFeature + * + * @param bytes image file in bytes + * @param label label + * @param uri image uri + */ + def apply(bytes: Array[Byte], label: Any = null, uri: String = null) + : ImageFeature = new ImageFeature(bytes, label, uri) + + def apply(): ImageFeature = new ImageFeature() + + val logger = Logger.getLogger(getClass) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala new file mode 100644 index 00000000000..eaae6e3aac1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -0,0 +1,192 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image + +import java.io.{File, FilenameFilter} + +import org.apache.commons.io.FileUtils +import org.apache.commons.io.filefilter.WildcardFileFilter +import org.apache.log4j.Logger +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{SQLContext} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +/** + * ImageFrame wraps a set of ImageFeature + */ +trait ImageFrame { + + /** + * transform ImageFrame + * @param transformer FeatureTransformer + * @return transformed ImageFrame + */ + def transform(transformer: FeatureTransformer): ImageFrame + + // scalastyle:off methodName + // scalastyle:off noSpaceBeforeLeftBracket + def -> [C: ClassTag](transformer: FeatureTransformer): ImageFrame = { + this.transform(transformer) + } + + /** + * whether this is a LocalImageFrame + */ + def isLocal(): Boolean + + /** + * whether this is a DistributedImageFrame + */ + def isDistributed(): Boolean +} + +object ImageFrame { + val logger = Logger.getLogger(getClass) + + /** + * create LocalImageFrame + * @param data array of ImageFeature + */ + def array(data: Array[ImageFeature]): LocalImageFrame = { + new LocalImageFrame(data) + } + + /** + * create DistributedImageFrame + * @param data rdd of ImageFeature + */ + def rdd(data: RDD[ImageFeature]): DistributedImageFrame = { + new DistributedImageFrame(data) + } + + /** + * Read images as Image Frame + * if sc is defined, Read image as DistributedImageFrame from local file system or HDFS + * if sc is null, Read image as LocalImageFrame from local file system + * + * @param path path to read images + * if sc is defined, path can be local or HDFS. Wildcard character are supported. + * if sc is null, path is local directory/image file/image file with wildcard character + * @param sc SparkContext + * @return ImageFrame + */ + def read(path: String, sc: SparkContext = null): ImageFrame = { + if (null != sc) { + val images = sc.binaryFiles(path).map { case (p, stream) => + ImageFeature(stream.toArray(), uri = p) + }.map(BytesToMat.transform) + ImageFrame.rdd(images) + } else { + val files = listLocalFiles(path) + val images = files.map { p => + ImageFeature(FileUtils.readFileToByteArray(p), uri = p.getAbsolutePath) + }.map(BytesToMat.transform) + ImageFrame.array(images) + } + } + + private def listLocalFiles(path: String): Array[File] = { + val files = new ArrayBuffer[File]() + listFiles(path, files) + files.toArray + } + + private def listFiles(path: String, files: ArrayBuffer[File]): Unit = { + val file = new File(path) + if (file.isDirectory) { + file.listFiles().foreach(x => listFiles(x.getAbsolutePath, files)) + } else if (file.isFile) { + files.append(file) + } else { + val filter = new WildcardFileFilter(file.getName) + file.getParentFile.listFiles(new FilenameFilter { + override def accept(dir: File, name: String): Boolean = filter.accept(dir, name) + }).foreach(x => listFiles(x.getAbsolutePath, files)) + } + } + + + /** + * Read parquet file as DistributedImageFrame + * + * @param path Parquet file path + * @return DistributedImageFrame + */ + def readParquet(path: String, sqlContext: SQLContext): DistributedImageFrame = { + val df = sqlContext.read.parquet(path) + val images = df.rdd.map(row => { + val uri = row.getAs[String](ImageFeature.uri) + val image = row.getAs[Array[Byte]](ImageFeature.bytes) + ImageFeature(image, uri = uri) + }).map(BytesToMat.transform) + ImageFrame.rdd(images) + } + + /** + * Write images as parquet file + * + * @param path path to read images. Local or HDFS. Wildcard character are supported. + * @param output Parquet file path + */ + def writeParquet(path: String, output: String, sqlContext: SQLContext): Unit = { + import sqlContext.implicits._ + val df = sqlContext.sparkContext.binaryFiles(path) + .map { case (p, stream) => + (p, stream.toArray()) + }.toDF(ImageFeature.uri, ImageFeature.bytes) + df.write.parquet(output) + } +} + +/** + * Local ImageFrame, keeps an array of ImageFeature + * @param array array of ImageFeature + */ +class LocalImageFrame(var array: Array[ImageFeature]) extends ImageFrame { + + def toDistributed(sc: SparkContext): DistributedImageFrame = { + new DistributedImageFrame(sc.parallelize(array)) + } + + override def transform(transformer: FeatureTransformer): ImageFrame = { + array = array.map(transformer.transform) + this + } + + override def isLocal(): Boolean = true + + override def isDistributed(): Boolean = false +} + +/** + * Distributerd ImageFrame, it keeps an rdd of ImageFeature + * @param rdd rdd of ImageFeature + */ +class DistributedImageFrame(var rdd: RDD[ImageFeature]) extends ImageFrame { + + override def transform(transformer: FeatureTransformer): ImageFrame = { + rdd = transformer(rdd) + this + } + + override def isLocal(): Boolean = false + + override def isDistributed(): Boolean = true +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/HFlip.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/HFlip.scala new file mode 100644 index 00000000000..edd086e1d9f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/HFlip.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import org.opencv.core.Core + +/** + * Flip the image horizontally + */ +class HFlip() extends FeatureTransformer { + + override def transformMat(feature: ImageFeature): Unit = { + HFlip.transform(feature.opencvMat(), feature.opencvMat()) + } +} + +object HFlip { + def apply(): HFlip = new HFlip() + + def transform(input: OpenCVMat, output: OpenCVMat): OpenCVMat = { + Core.flip(input, output, 1) + output + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala new file mode 100644 index 00000000000..b29ac7fe450 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala @@ -0,0 +1,183 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.opencv + +import java.io.{File, IOException, ObjectInputStream, ObjectOutputStream} + +import com.intel.analytics.bigdl.opencv.OpenCV +import org.apache.commons.io.FileUtils +import org.opencv.core.{CvType, Mat, MatOfByte} +import org.opencv.imgcodecs.Imgcodecs + +/** + * OpenCVMat is a Serializable wrapper of original Mat + */ +class OpenCVMat() extends Mat with Serializable { + + def this(mat: Mat) { + this() + mat.copyTo(this) + } + + @throws(classOf[IOException]) + private def writeObject(out: ObjectOutputStream): Unit = { + out.writeInt(rows()) + out.writeInt(cols()) + out.writeInt(`type`()) + val size = (elemSize() * rows() * cols()).toInt + out.writeInt(size) + val bytes = new Array[Byte](size) + get(rows(), cols(), bytes) + out.write(bytes) + } + + @throws(classOf[IOException]) + private def readObject(input: ObjectInputStream): Unit = { + val rows = input.readInt() + val cols = input.readInt() + val t = input.readInt() + val size = input.readInt() + val data = new Array[Byte](size) + input.read(data) + create(rows, cols, t) + put(rows, cols, data) + } + + var isReleased: Boolean = false + override def release(): Unit = { + super.release() + isReleased = true + } + + /** + * get shape of mat + * @return (height, width, channel) + */ + def shape(): (Int, Int, Int) = { + (height(), width(), channels()) + } +} + +object OpenCVMat { + OpenCV.isOpenCVLoaded + + /** + * read local image path as opencv mat + * + * @param path local image path + * @return mat + */ + def read(path: String): OpenCVMat = { + val bytes = FileUtils.readFileToByteArray(new File(path)) + fromImageBytes(bytes) + } + + /** + * convert image file in bytes to opencv mat + * + * @param fileContent bytes from an image file + * @return opencv mat + */ + def fromImageBytes(fileContent: Array[Byte]): OpenCVMat = { + var mat: Mat = null + var matOfByte: MatOfByte = null + var result: OpenCVMat = null + try { + matOfByte = new MatOfByte(fileContent: _*) + mat = Imgcodecs.imdecode(matOfByte, Imgcodecs.CV_LOAD_IMAGE_COLOR) + result = new OpenCVMat(mat) + } catch { + case e: Exception => + if (null != result) result.release() + throw e + } finally { + if (null != mat) mat.release() + if (null != matOfByte) matOfByte.release() + } + result + } + + /** + * convert opencv mat to image bytes + * + * @param mat opencv mat + * @param encoding encoding type + * @return bytes that represent an image + */ + def imencode(mat: OpenCVMat, encoding: String = "png"): Array[Byte] = { + val buf = new MatOfByte() + try { + Imgcodecs.imencode("." + encoding, mat, buf) + buf.toArray + } finally { + buf.release() + } + } + + /** + * Convert float array(pixels) to OpenCV mat + * + * @param floats float array that represents pixels + * @param height image height + * @param width image width + * @return image in mat + */ + def fromFloats(floats: Array[Float], height: Int, width: Int): OpenCVMat = { + val mat = new Mat(height, width, CvType.CV_32FC3) + mat.put(0, 0, floats) + new OpenCVMat(mat) + } + + /** + * convert mat to byte array that represents pixels + * + * @param input opencv mat + * @param buffer + * @return + */ + def toBytePixels(input: Mat, buffer: Array[Byte] = null): (Array[Byte], Int, Int) = { + var bytes = buffer + val length = input.channels() * input.height() * input.width() + if (null == buffer || buffer.length < length) { + bytes = new Array[Byte](length) + } + input.get(0, 0, bytes) + (bytes, input.height(), input.width()) + } + + + /** + * convert mat to float array that represents pixels + * + * @param input mat + * @param buffer float array + * @return + */ + def toFloatPixels(input: Mat, + buffer: Array[Float] = null): (Array[Float], Int, Int) = { + var floats = buffer + val length = input.channels() * input.height() * input.width() + if (null == buffer || buffer.length < length) { + floats = new Array[Float](length) + } + if (input.`type`() != CvType.CV_32FC3) { + input.convertTo(input, CvType.CV_32FC3) + } + input.get(0, 0, floats) + (floats, input.height(), input.width()) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 5f1380be620..038a4d77df0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -29,24 +29,25 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Table, _} import com.intel.analytics.bigdl.visualization.{Summary, TrainSummary, ValidationSummary} import com.intel.analytics.bigdl.nn.Zeros -import org.apache.spark.api.java.JavaRDD +import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD import java.lang.{Integer, Boolean => JBoolean} import java.nio.ByteOrder +import java.util import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} import com.intel.analytics.bigdl.utils.tf._ import org.apache.spark.ml.{DLClassifier, DLClassifierModel, DLEstimator, DLModel} -import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.{DataFrame, SQLContext} import org.apache.log4j._ -import org.apache.spark.SparkContext -import org.tensorflow.framework.NodeDef import scala.collection.JavaConverters._ -import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.language.existentials import scala.reflect.ClassTag @@ -2301,6 +2302,150 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createCosineProximityCriterion(): CosineProximityCriterion[T] = { CosineProximityCriterion[T]() } + + def createHFlip(): HFlip = { + HFlip() + } + + def transformImageFeature(transformer: FeatureTransformer, feature: ImageFeature) + : ImageFeature = { + transformer.transform(feature) + } + + + def transformImageFrame(transformer: FeatureTransformer, + imageFrame: ImageFrame): ImageFrame = { + imageFrame.transform(transformer) + } + + def createDistributedImageFrame(imageRdd: JavaRDD[JTensor], labelRdd: JavaRDD[JTensor]) + : DistributedImageFrame = { + require(null != imageRdd, "imageRdd cannot be null") + val featureRdd = if (null != labelRdd) { + imageRdd.rdd.zip(labelRdd.rdd).map(data => { + createImageFeature(data._1, data._2) + }) + } else { + imageRdd.rdd.map(image => { + createImageFeature(image, null) + }) + } + new DistributedImageFrame(featureRdd) + } + + def createLocalImageFrame(images: JList[JTensor], labels: JList[JTensor]) + : LocalImageFrame = { + require(null != images, "images cannot be null") + val features = if (null != labels) { + (0 until images.size()).map(i => { + createImageFeature(images.get(i), labels.get(i)) + }) + } else { + (0 until images.size()).map(i => { + createImageFeature(images.get(i), null) + }) + } + new LocalImageFrame(features.toArray) + } + + def createPipeline(list: JList[FeatureTransformer]): FeatureTransformer = { + var cur = list.get(0) + (1 until list.size()).foreach(t => cur = cur -> list.get(t)) + cur + } + + + def createImageFeature(data: JTensor = null, label: JTensor = null, uri: String = null) + : ImageFeature = { + val feature = new ImageFeature() + if (null != data) { + val mat = OpenCVMat.fromFloats(data.storage, data.shape(0), data.shape(1)) + feature(ImageFeature.mat) = mat + feature(ImageFeature.size) = mat.shape() + } + if (null != label) { + // todo: may need a method to change label format if needed + feature(ImageFeature.label) = toTensor(label) + } + if (null != uri) { + feature(ImageFeature.uri) = uri + } + feature + } + + def imageFeatureToSample(imageFeature: ImageFeature, + floatKey: String = ImageFeature.floats, toChw: Boolean = true, + withImInfo: Boolean = false): Sample = { + val imageTensor = imageFeatureToImageTensor(imageFeature, floatKey, toChw) + val features = new util.ArrayList[JTensor]() + features.add(imageTensor) + if (withImInfo) { + val imInfo = imageFeature.getImInfo() + features.add(toJTensor(imInfo.asInstanceOf[Tensor[T]])) + } + val label = imageFeatureToLabelTensor(imageFeature) + Sample(features, label, "float") + } + + def imageFeatureGetKeys(imageFeature: ImageFeature): JList[String] = { + imageFeature.keys().toList.asJava + } + + def distributedImageFrameToSampleRdd(imageFrame: DistributedImageFrame, + floatKey: String = ImageFeature.floats, toChw: Boolean = true, withImInfo: Boolean = false) + : JavaRDD[Sample] = { + imageFrame.rdd.map(imageFeatureToSample(_, floatKey, toChw, withImInfo)).toJavaRDD() + } + + def distributedImageFrameToImageTensorRdd(imageFrame: DistributedImageFrame, + floatKey: String = ImageFeature.floats, toChw: Boolean = true): JavaRDD[JTensor] = { + imageFrame.rdd.map(imageFeatureToImageTensor(_, floatKey, toChw)).toJavaRDD() + } + + def distributedImageFrameToLabelTensorRdd(imageFrame: DistributedImageFrame): JavaRDD[JTensor] = { + imageFrame.rdd.map(imageFeatureToLabelTensor).toJavaRDD() + } + + def localImageFrameToSample(imageFrame: LocalImageFrame, + floatKey: String = ImageFeature.floats, toChw: Boolean = true, withImInfo: Boolean = false) + : JList[Sample] = { + imageFrame.array.map(imageFeatureToSample(_, floatKey, toChw, withImInfo)).toList.asJava + } + + def localImageFrameToImageTensor(imageFrame: LocalImageFrame, + floatKey: String = ImageFeature.floats, toChw: Boolean = true): JList[JTensor] = { + imageFrame.array.map(imageFeatureToImageTensor(_, floatKey, toChw)).toList.asJava + } + + def localImageFrameToLabelTensor(imageFrame: LocalImageFrame): JList[JTensor] = { + imageFrame.array.map(imageFeatureToLabelTensor).toList.asJava + } + + def imageFeatureToImageTensor(imageFeature: ImageFeature, + floatKey: String = ImageFeature.floats, toChw: Boolean = true): JTensor = { + toJTensor(imageFeature.toTensor(floatKey, toChw).asInstanceOf[Tensor[T]]) + } + + def imageFeatureToLabelTensor(imageFeature: ImageFeature): JTensor = { + val label = if (imageFeature.hasLabel()) { + imageFeature.getLabel[Tensor[T]] + } else { + Tensor[T](1).fill(ev.fromType[Float](-1f)) + } + toJTensor(label) + } + + def read(path: String, sc: JavaSparkContext): ImageFrame = { + if (sc == null) ImageFrame.read(path, null) else ImageFrame.read(path, sc.sc) + } + + def readParquet(path: String, sqlContext: SQLContext): DistributedImageFrame = { + ImageFrame.readParquet(path, sqlContext) + } + + def isLocal(imageFrame: ImageFrame): Boolean = imageFrame.isLocal() + + def isDistributed(imageFrame: ImageFrame): Boolean = imageFrame.isDistributed() } object PythonBigDLUtils { diff --git a/scala/dllib/src/test/resources/pascal/000025.jpg b/scala/dllib/src/test/resources/pascal/000025.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b1b0359c9aea428d497108f161e31c9c16a913e2 GIT binary patch literal 95959 zcmbTdWmH>F)c+gYiWe!~VuciUFQr8b6e+a0yK8WFhXO5HN-0p>-5rV)OL3AED4rl8 zKnQ<+&$HIOZ|;kG?_`}>$vSi9?C&`vdp>(||L1-iK%uUzrVPNs0syccK7ji*fD!;3 z>%a70j{RT4#rZGC!^OqH#mB?P|DS_^h!CHEhyWj-kc5zk_`meiPGgR%dcu&{A( z@$d;A%piFv&_eNGJ`T=<(RdFQKa>u6I1j+3#G`sDq=-+gV@dGL^RaM5MhPMNtA+s@ z-QREy5i74qB4S!P`X>yWT-?ujctyp;B_yS!m0rJ5R#8<`*VBJ*VEEyqk+qGjoxOvj 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zhI~o=jJ#vvQ*zc9_Sf;+#L`bDM0-F0oCab~72#Lj99E3sfdqo2_7qWFiCpBChGOV( zqizIs>HdDT@87b=!Ch;xog8@s67gzehEox>+^qKeiMlaBkamd%uPTc^63^G>i+rb?F)$9n`gDCK}F zDz7RC40eIH7(zfht11y`sA@CY#!QjRI;2s%5#@zqRgV}YRd*Z?OJH}Rio#NG)e!e4 xZEC|%V{v=pdpPCK&mN_3a9s~v0}x println(x.uri, x.bytes.length)) + val imageFeature = local.toDistributed(sc).rdd.first() + assert(imageFeature.uri.endsWith("000025.jpg")) + assert(imageFeature.bytes.length == 95959) + imageFeature.opencvMat().shape() should be((375, 500, 3)) + } + + "read DistributedImageFrame" should "work properly" in { + val distributed = ImageFrame.read(resource.getFile, sc) + .asInstanceOf[DistributedImageFrame] + val imageFeature = distributed.rdd.first() + assert(imageFeature.uri.endsWith("000025.jpg")) + assert(imageFeature.bytes.length == 95959) + imageFeature.opencvMat().shape() should be((375, 500, 3)) + } + + "SequenceFile write and read" should "work properly" in { + val tmpFile = Files.createTempDir() + val dir = tmpFile.toString + "/parque" + ImageFrame.writeParquet(resource.getFile, dir, sqlContext) + + val distributed = ImageFrame.readParquet(dir, sqlContext) + val imageFeature = distributed.rdd.first() + assert(imageFeature.uri.endsWith("000025.jpg")) + assert(imageFeature.bytes.length == 95959) + FileUtils.deleteDirectory(tmpFile) + } + + "read local" should "work" in { + val images = ImageFrame.read(resource.getFile).asInstanceOf[LocalImageFrame] + images.array.length should be (1) + + val images2 = ImageFrame.read(resource.getFile + "*.jpg").asInstanceOf[LocalImageFrame] + images2.array.length should be (1) + + val images3 = ImageFrame.read(resource.getFile + "000025.jpg").asInstanceOf[LocalImageFrame] + images3.array.length should be (1) + + val images4 = ImageFrame.read(resource.getFile + "0000251.jpg").asInstanceOf[LocalImageFrame] + images4.array.length should be (0) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HFlipSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HFlipSpec.scala new file mode 100644 index 00000000000..da7a6604714 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HFlipSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame} +import org.scalatest.{FlatSpec, Matchers} + +class HFlipSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "HFlip" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val hFlip = HFlip() + val transformed = hFlip(data).asInstanceOf[LocalImageFrame] + transformed.array(0).getHeight() should be (transformed.array(0).getOriginalHeight) + transformed.array(0).getWidth() should be (transformed.array(0).getOriginalWidth) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala new file mode 100644 index 00000000000..e23f1449f74 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala @@ -0,0 +1,81 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.opencv + +import java.io.File + +import com.intel.analytics.bigdl.opencv.OpenCV +import org.apache.commons.io.FileUtils +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class OpenCVMatSpec extends FlatSpec with Matchers { + val resource = getClass().getClassLoader().getResource("pascal/000025.jpg") + + "toFloatsPixels" should "work properly" in { + val img = OpenCVMat.read(resource.getFile) + val floats = new Array[Float](img.height() * img.width() * img.channels()) + val out = OpenCVMat.toFloatPixels(img, floats) + out._2 should be(375) + out._3 should be(500) + + // without buffer + val out2 = OpenCVMat.toFloatPixels(img) + out2._2 should be(375) + out2._3 should be(500) + + out._1 should equal(out2._1) + } + + "toBytesInPixels" should "work properly" in { + val img = OpenCVMat.read(resource.getFile) + val bytes = new Array[Byte](img.height() * img.width() * img.channels()) + val out = OpenCVMat.toBytePixels(img, bytes) + out._2 should be(375) + out._3 should be(500) + + // without buffer + val out2 = OpenCVMat.toBytePixels(img) + out2._2 should be(375) + out2._3 should be(500) + + out._1 should equal(out2._1) + } + + "fromImageBytes" should "work properly" in { + OpenCV.isOpenCVLoaded + val img = Imgcodecs.imread(resource.getFile) + val bytes = FileUtils.readFileToByteArray(new File(resource.getFile)) + val mat = OpenCVMat.fromImageBytes(bytes) + img.height() should be (mat.height()) + img.width() should be (mat.width()) + img.channels() should be (mat.channels()) + val bytes1 = OpenCVMat.toBytePixels(img) + val bytes2 = OpenCVMat.toBytePixels(mat) + bytes1._1 should equal(bytes2._1) + } + + "imencode" should "work not affect pixels" in { + val img = OpenCVMat.read(resource.getFile) + val bytes = OpenCVMat.imencode(img) + val mat = OpenCVMat.fromImageBytes(bytes) + val bytes1 = OpenCVMat.toBytePixels(img) + val bytes2 = OpenCVMat.toBytePixels(mat) + bytes1._1 should equal(bytes2._1) + } + +} From 00ac64282aea5296f36331fa1e7cbfdaf2cea13d Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 1 Dec 2017 13:57:12 +0800 Subject: [PATCH 0550/1065] add summary option for vgg example (#1967) --- .../bigdl/dllib/models/vgg/README.md | 6 +++++- .../bigdl/dllib/models/vgg/Train.scala | 19 ++++++++++++++++++- .../bigdl/dllib/models/vgg/Utils.scala | 4 ++++ 3 files changed, 27 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/README.md index 35c722de6e5..fca2e395922 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/README.md @@ -17,7 +17,8 @@ spark-submit --master local[physical_core_number] \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f Cifar-folder \ -b batch_size \ ---checkpoint ~/model +--summary ./log \ +--checkpoint ./model ``` Standalone cluster mode, example command @@ -30,6 +31,7 @@ spark-submit \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f Cifar-folder \ -b batchsize \ +--summary ./log \ --checkpoint ./model ``` Yarn cluster mode, example command @@ -44,6 +46,7 @@ spark-submit \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f Cifar-folder \ -b batch_size \ +--summary ./log \ --checkpoint ./model ``` In the above commands @@ -54,6 +57,7 @@ model.#iteration_number, and train state will be named as state.#iteration_numbe there are some files already exist in the folder, the old file will not be overwrite for the safety of your model files. * -b: The mini-batch size. It is expected that the mini-batch size is a multiple of node_number * core_number. +* --summary: Where you store the training metainfo, which can be visualized in tensorboard ## Test Model Example command for running in Spark local mode ``` diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Train.scala index 0b57a38cd1a..911b031af7d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Train.scala @@ -15,6 +15,9 @@ */ package com.intel.analytics.bigdl.models.vgg +import java.text.SimpleDateFormat +import java.util.Date + import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.DataSet import com.intel.analytics.bigdl.dataset.image._ @@ -22,6 +25,7 @@ import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._ import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T, Table} +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext @@ -70,9 +74,22 @@ object Train { if (param.checkpoint.isDefined) { optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch) } - if(param.overWriteCheckpoint) { + + if (param.overWriteCheckpoint) { optimizer.overWriteCheckpoint() } + + if (param.summaryPath.isDefined) { + val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss") + val timeStamp = sdf.format(new Date()) + val trainSummry = new TrainSummary(param.summaryPath.get, + s"vgg-on-cifar10-train-$timeStamp") + optimizer.setTrainSummary(trainSummry) + val validationSummary = new ValidationSummary(param.summaryPath.get, + s"vgg-on-cifar10-val-$timeStamp") + optimizer.setValidationSummary(validationSummary) + } + optimizer .setValidation(Trigger.everyEpoch, validateSet, Array(new Top1Accuracy[Float])) .setOptimMethod(optimMethod) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Utils.scala index 9da577bde8a..1ad952cfba5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Utils.scala @@ -37,6 +37,7 @@ object Utils { checkpoint: Option[String] = None, modelSnapshot: Option[String] = None, stateSnapshot: Option[String] = None, + summaryPath: Option[String] = None, batchSize: Int = 112, maxEpoch: Int = 90, overWriteCheckpoint: Boolean = false, @@ -58,6 +59,9 @@ object Utils { opt[String]("checkpoint") .text("where to cache the model and state") .action((x, c) => c.copy(checkpoint = Some(x))) + opt[String]("summary") + .text("where to store the training summary") + .action((x, c) => c.copy(summaryPath = Some(x))) opt[Int]('e', "maxEpoch") .text("epoch numbers") .action((x, c) => c.copy(maxEpoch = x)) From 7479f8fec98f2d40a6c4da660600f0a730d71498 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 1 Dec 2017 16:51:13 +0800 Subject: [PATCH 0551/1065] Add more image transformers (#1965) * Add OpenCV Vision Transformer * Add more transformers * update image * Add more transformers * Add more test * Refine documents * update * Add visualize test * Make BoundingBox Serializable * Fix ut * Add more python test * remove head from imageframe --- .../transform/vision/image/Convertor.scala | 64 +++++ .../transform/vision/image/ImageFeature.scala | 9 +- .../transform/vision/image/ImageFrame.scala | 8 +- .../image/augmentation/Brightness.scala | 57 +++++ .../image/augmentation/ChannelNormalize.scala | 78 ++++++ .../image/augmentation/ChannelOrder.scala | 48 ++++ .../image/augmentation/ColorJitter.scala | 90 +++++++ .../vision/image/augmentation/Contrast.scala | 49 ++++ .../vision/image/augmentation/Crop.scala | 182 ++++++++++++++ .../vision/image/augmentation/Expand.scala | 94 ++++++++ .../vision/image/augmentation/Filler.scala | 62 +++++ .../vision/image/augmentation/Hue.scala | 64 +++++ .../image/augmentation/PixelNormalizer.scala | 62 +++++ .../augmentation/RandomTransformer.scala | 45 ++++ .../vision/image/augmentation/Resize.scala | 134 +++++++++++ .../image/augmentation/Saturation.scala | 63 +++++ .../vision/image/label/roi/BatchSampler.scala | 134 +++++++++++ .../image/label/roi/RandomSampler.scala | 69 ++++++ .../vision/image/label/roi/RoiLabel.scala | 62 +++++ .../image/label/roi/RoiTransformer.scala | 116 +++++++++ .../vision/image/opencv/OpenCVMat.scala | 30 ++- .../vision/image/util/BboxUtil.scala | 42 ++++ .../vision/image/util/BoundingBox.scala | 167 +++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 112 +++++++++ .../vision/image/FeatureTransformerSpec.scala | 123 ++++++++++ .../vision/image/ImageFrameSpec.scala | 16 +- .../image/augmentation/BrightnessSpec.scala | 38 +++ .../augmentation/ChannelNormalizeSpec.scala | 53 +++++ .../image/augmentation/ChannelOrderSpec.scala | 36 +++ .../image/augmentation/ColorJitterSpec.scala | 49 ++++ .../image/augmentation/ContrastSpec.scala | 37 +++ .../vision/image/augmentation/CropSpec.scala | 106 +++++++++ .../image/augmentation/ExpandSpec.scala | 38 +++ .../image/augmentation/FillerSepc.scala | 52 ++++ .../vision/image/augmentation/HFlipSpec.scala | 7 +- .../vision/image/augmentation/HueSpec.scala | 37 +++ .../augmentation/PixelNormalizerSpec.scala | 47 ++++ .../augmentation/RandomTransformerSpec.scala | 42 ++++ .../image/augmentation/ResizeSpec.scala | 68 ++++++ .../image/augmentation/SaturationSpec.scala | 37 +++ .../image/label/roi/BatchSamplerSpec.scala | 100 ++++++++ .../image/label/roi/RoiTransformerSpec.scala | 225 ++++++++++++++++++ .../vision/image/opencv/OpenCVMatSpec.scala | 50 +++- .../vision/image/util/BoundingBoxSpec.scala | 41 ++++ 44 files changed, 3022 insertions(+), 21 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Brightness.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelNormalize.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelOrder.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ColorJitter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Contrast.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Crop.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Filler.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Hue.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/PixelNormalizer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomTransformer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Saturation.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/BatchSampler.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RandomSampler.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiTransformer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BoundingBox.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/FeatureTransformerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/BrightnessSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelNormalizeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelOrderSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ColorJitterSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ContrastSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/CropSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ExpandSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/FillerSepc.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HueSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/PixelNormalizerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomTransformerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/SaturationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/label/roi/BatchSamplerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/label/roi/RoiTransformerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/util/BoundingBoxSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala index 2aecd5e784c..5eef5128602 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala @@ -53,3 +53,67 @@ object BytesToMat { feature } } + + +/** + * Transform OpenCVMat to float array, note that in this transformer, the mat is released + * @param validHeight valid height in case the mat is invalid + * @param validWidth valid width in case the mat is invalid + * @param validChannels valid channel in case the mat is invalid + * @param meanRGB meansRGB to subtract, it can be replaced by ChannelNormalize + * @param outKey key to store float array + */ +class MatToFloats(validHeight: Int, validWidth: Int, validChannels: Int, + meanRGB: Option[(Float, Float, Float)] = None, outKey: String = ImageFeature.floats) + extends FeatureTransformer { + @transient private var data: Array[Float] = _ + + private def normalize(img: Array[Float], + meanR: Float, meanG: Float, meanB: Float): Array[Float] = { + val content = img + require(content.length % 3 == 0) + var i = 0 + while (i < content.length) { + content(i + 2) = content(i + 2) - meanR + content(i + 1) = content(i + 1) - meanG + content(i + 0) = content(i + 0) - meanB + i += 3 + } + img + } + + override def transform(feature: ImageFeature): ImageFeature = { + var input: OpenCVMat = null + val (height, width, channel) = if (feature.isValid) { + input = feature.opencvMat() + (input.height(), input.width(), input.channels()) + } else { + (validHeight, validWidth, validChannels) + } + if (null == data || data.length < height * width * channel) { + data = new Array[Float](height * width * channel) + } + if (feature.isValid) { + try { + OpenCVMat.toFloatPixels(input, data) + if (meanRGB.isDefined) { + normalize(data, meanRGB.get._1, meanRGB.get._2, meanRGB.get._3) + } + } finally { + if (null != input) input.release() + } + } + feature(outKey) = data + feature(ImageFeature.size) = (height, width, channel) + feature + } +} + +object MatToFloats { + val logger = Logger.getLogger(getClass) + + def apply(validHeight: Int = 300, validWidth: Int = 300, validChannels: Int = 3, + meanRGB: Option[(Float, Float, Float)] = None, + outKey: String = ImageFeature.floats): MatToFloats = + new MatToFloats(validHeight, validWidth, validChannels, meanRGB, outKey) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala index 952bf9c9bc9..b38974f5952 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala @@ -284,13 +284,10 @@ object ImageFeature { */ val predict = "predict" /** - * key: store cropped box in Crop + * key: store boundingBox of current image + * it may be used in crop/expand that may change the size of image */ - val cropBbox = "cropBbox" - /** - * key: store expand box in Expand - */ - val expandBbox = "expandBbox" + val boundingBox = "boundingBox" /** * Create ImageFeature diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala index eaae6e3aac1..dab58bae5a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -91,14 +91,14 @@ object ImageFrame { if (null != sc) { val images = sc.binaryFiles(path).map { case (p, stream) => ImageFeature(stream.toArray(), uri = p) - }.map(BytesToMat.transform) - ImageFrame.rdd(images) + } + ImageFrame.rdd(images) -> BytesToMat() } else { val files = listLocalFiles(path) val images = files.map { p => ImageFeature(FileUtils.readFileToByteArray(p), uri = p.getAbsolutePath) - }.map(BytesToMat.transform) - ImageFrame.array(images) + } + ImageFrame.array(images) -> BytesToMat() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Brightness.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Brightness.scala new file mode 100644 index 00000000000..26adf4e64ff --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Brightness.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.utils.RandomGenerator._ + +/** + * adjust the image brightness + * + * @param deltaLow brightness parameter: low bound + * @param deltaHigh brightness parameter: high bound + */ +class Brightness(deltaLow: Double, deltaHigh: Double) + extends FeatureTransformer { + require(deltaLow <= deltaHigh) + override def transformMat(feature: ImageFeature): Unit = { + Brightness.transform(feature.opencvMat(), feature.opencvMat(), RNG.uniform(deltaLow, deltaHigh)) + } +} + +object Brightness { + def apply(deltaLow: Double, deltaHigh: Double): Brightness + = new Brightness(deltaLow, deltaHigh) + + /** + * if delta > 0, increase the brightness + * if delta < 0, decrease the brightness + * @param input input mat + * @param output output mat + * @param delta brightness parameter + * @return output mat + */ + def transform(input: OpenCVMat, output: OpenCVMat, delta: Double): OpenCVMat = { + if (delta != 0) { + input.convertTo(output, -1, 1, delta) + } else { + if (input != output) input.copyTo(output) + } + output + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelNormalize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelNormalize.scala new file mode 100644 index 00000000000..38f1326638a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelNormalize.scala @@ -0,0 +1,78 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import java.util + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import org.opencv.core.{Core, CvType, Mat, Scalar} + +/** + * image channel normalize + * @param meanR mean value in R channel + * @param meanG mean value in G channel + * @param meanB mean value in B channel + * @param stdR std value in R channel + * @param stdG std value in G channel + * @param stdB std value in B channel + */ +class ChannelNormalize(meanR: Float, meanG: Float, meanB: Float, + stdR: Float = 1, stdG: Float = 1, stdB: Float = 1) + extends FeatureTransformer { + override def transformMat(feature: ImageFeature): Unit = { + ChannelNormalize.transform(feature.opencvMat(), feature.opencvMat(), + meanR, meanG, meanB, stdR, stdG, stdB) + } +} + +object ChannelNormalize { + def apply(meanR: Float, meanG: Float, meanB: Float, + stdR: Float = 1, stdG: Float = 1, stdB: Float = 1): ChannelNormalize = { + new ChannelNormalize(meanR, meanG, meanB, stdR, stdG, stdB) + } + + def transform(input: OpenCVMat, output: OpenCVMat, + meanR: Float, meanG: Float, meanB: Float, + stdR: Float = 1, stdG: Float = 1, stdB: Float = 1): Unit = { + if (input.`type`() != CvType.CV_32FC3) { + input.convertTo(input, CvType.CV_32FC3) + } + val inputChannels = new util.ArrayList[Mat]() + Core.split(input, inputChannels) + require(inputChannels.size() == 3) + val outputChannels = if (output != input) { + output.create(input.rows(), input.cols(), input.`type`()) + val channels = new util.ArrayList[Mat]() + Core.split(output, channels) + channels + } else inputChannels + + Core.subtract(inputChannels.get(0), new Scalar(meanB), outputChannels.get(0)) + Core.subtract(inputChannels.get(1), new Scalar(meanG), outputChannels.get(1)) + Core.subtract(inputChannels.get(2), new Scalar(meanR), outputChannels.get(2)) + if (stdB != 1) Core.divide(outputChannels.get(0), new Scalar(stdB), outputChannels.get(0)) + if (stdG != 1) Core.divide(outputChannels.get(1), new Scalar(stdG), outputChannels.get(1)) + if (stdR != 1) Core.divide(outputChannels.get(2), new Scalar(stdR), outputChannels.get(2)) + Core.merge(outputChannels, output) + + (0 until inputChannels.size()).foreach(inputChannels.get(_).release()) + if (input != output) { + (0 until outputChannels.size()).foreach(outputChannels.get(_).release()) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelOrder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelOrder.scala new file mode 100644 index 00000000000..9d1b90e3589 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelOrder.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import java.util +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import org.opencv.core.{Core, Mat} + +/** + * random change the channel of an image + */ +class ChannelOrder() extends FeatureTransformer { + override def transformMat(feature: ImageFeature): Unit = { + ChannelOrder.transform(feature.opencvMat(), feature.opencvMat()) + } + +} + +object ChannelOrder { + def apply(): ChannelOrder = new ChannelOrder() + + def transform(input: OpenCVMat, output: OpenCVMat): OpenCVMat = { + // Split the image to 3 channels. + val channels = new util.ArrayList[Mat]() + Core.split(output, channels) + // Shuffle the channels. + util.Collections.shuffle(channels) + Core.merge(channels, output) + // release memory + (0 until channels.size()).foreach(channels.get(_).release()) + output + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ColorJitter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ColorJitter.scala new file mode 100644 index 00000000000..5019287b249 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ColorJitter.scala @@ -0,0 +1,90 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import scala.util.Random + +/** + * Random adjust brightness, contrast, hue, saturation + * + * @param brightnessProb probability to adjust brightness + * @param brightnessDelta brightness parameter + * @param contrastProb probability to adjust contrast + * @param contrastLower contrast lower parameter + * @param contrastUpper contrast upper parameter + * @param hueProb probability to adjust hue + * @param hueDelta hue parameter + * @param saturationProb probability to adjust saturation + * @param saturationLower saturation lower parameter + * @param saturationUpper saturation upper parameter + * @param randomChannelOrderProb random order for different operation + * @param shuffle shuffle the transformers + */ +class ColorJitter( + brightnessProb: Double, brightnessDelta: Double, + contrastProb: Double, contrastLower: Double, contrastUpper: Double, + hueProb: Double, hueDelta: Double, + saturationProb: Double, saturationLower: Double, saturationUpper: Double, + randomChannelOrderProb: Double, shuffle: Boolean = false) extends FeatureTransformer { + + private val brightness = RandomTransformer( + Brightness(-brightnessDelta, brightnessDelta), brightnessProb) + private val contrast = RandomTransformer( + Contrast(contrastLower, contrastUpper), contrastProb) + private val saturation = RandomTransformer( + Saturation(saturationLower, saturationUpper), saturationProb) + private val hue = RandomTransformer(Hue(-hueDelta, hueDelta), hueProb) + private val channelOrder = RandomTransformer(ChannelOrder(), randomChannelOrderProb) + + private val order1 = brightness -> contrast -> saturation -> hue -> channelOrder + private val order2 = brightness -> saturation -> hue -> contrast -> channelOrder + + private val transformers = Array(brightness, contrast, saturation, hue, channelOrder) + + override def transformMat(feature: ImageFeature): Unit = { + if (!shuffle) { + val prob = RNG.uniform(0, 1) + if (prob > 0.5) { + order1.transform(feature) + } else { + order2.transform(feature) + } + } else { + val order = Random.shuffle(List.range(0, transformers.length)) + var i = 0 + while (i < order.length) { + transformers(order(i)).transform(feature) + i += 1 + } + } + } +} + +object ColorJitter { + def apply( + brightnessProb: Double = 0.5, brightnessDelta: Double = 32, + contrastProb: Double = 0.5, contrastLower: Double = 0.5, contrastUpper: Double = 1.5, + hueProb: Double = 0.5, hueDelta: Double = 18, + saturationProb: Double = 0.5, saturationLower: Double = 0.5, saturationUpper: Double = 1.5, + randomOrderProb: Double = 0, shuffle: Boolean = false + ): ColorJitter = + new ColorJitter(brightnessProb, brightnessDelta, contrastProb, + contrastLower, contrastUpper, hueProb, hueDelta, saturationProb, + saturationLower, saturationUpper, randomOrderProb, shuffle) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Contrast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Contrast.scala new file mode 100644 index 00000000000..21f3ecd1b97 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Contrast.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.utils.RandomGenerator._ + +/** + * Adjust the image contrast + * @param deltaLow contrast parameter low bound + * @param deltaHigh contrast parameter high bound + */ +class Contrast(deltaLow: Double, deltaHigh: Double) + extends FeatureTransformer { + + require(deltaHigh >= deltaLow, "contrast upper must be >= lower.") + require(deltaLow >= 0, "contrast lower must be non-negative.") + override def transformMat(feature: ImageFeature): Unit = { + Contrast.transform(feature.opencvMat(), feature.opencvMat(), RNG.uniform(deltaLow, deltaHigh)) + } +} + +object Contrast { + def apply(deltaLow: Double, deltaHigh: Double): Contrast = new Contrast(deltaLow, deltaHigh) + + def transform(input: OpenCVMat, output: OpenCVMat, delta: Double): OpenCVMat = { + if (Math.abs(delta - 1) > 1e-3) { + input.convertTo(output, -1, delta, 0) + } else { + if (input != output) input.copyTo(output) + } + output + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Crop.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Crop.scala new file mode 100644 index 00000000000..5fbe3723cee --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Crop.scala @@ -0,0 +1,182 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.util.{BboxUtil, BoundingBox} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.opencv.core.Rect + +/** + * Abstract crop transformer, other crop transformer need to override generateRoi + * + * @param normalized whether the roi is normalized + * @param isClip whether to clip the roi to image boundaries + */ +abstract class Crop(normalized: Boolean = true, isClip: Boolean = true) extends FeatureTransformer { + + /** + * how to generate crop roi + * @param feature image feature + * @return crop roi + */ + def generateRoi(feature: ImageFeature): BoundingBox + + override def transformMat(feature: ImageFeature): Unit = { + val cropBox = generateRoi(feature) + Crop.transform(feature.opencvMat(), feature.opencvMat(), + cropBox.x1, cropBox.y1, cropBox.x2, cropBox.y2, normalized, isClip) + if (feature.hasLabel()) { + feature(ImageFeature.boundingBox) = cropBox + } + } +} + +object Crop { + + def transform(input: OpenCVMat, output: OpenCVMat, + wStart: Float, hStart: Float, wEnd: Float, hEnd: Float, normalized: Boolean = true, + isClip: Boolean = true): Unit = { + val width = input.width + val height = input.height + var (x1, y1, x2, y2) = if (normalized) { + // scale back to original size + (wStart * width, hStart * height, wEnd * width, hEnd * height) + } else { + (wStart, hStart, wEnd, hEnd) + } + if (isClip) { + // clip to image boundary + x1 = Math.max(Math.min(x1, width), 0f) + y1 = Math.max(Math.min(y1, height), 0f) + x2 = Math.max(Math.min(x2, width), 0f) + y2 = Math.max(Math.min(y2, height), 0f) + } + val rect = new Rect(x1.toInt, y1.toInt, (x2 - x1).toInt, (y2 - y1).toInt) + input.submat(rect).copyTo(output) + } +} + +/** + * Crop a `cropWidth` x `cropHeight` patch from center of image. + * The patch size should be less than the image size. + * + * @param cropWidth width after crop + * @param cropHeight height after crop + * @param isClip whether to clip the roi to image boundaries + */ +class CenterCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean) extends Crop(false, isClip) { + override def generateRoi(feature: ImageFeature): BoundingBox = { + val mat = feature.opencvMat() + val height = mat.height().toFloat + val width = mat.width().toFloat + val startH = (height - cropHeight) / 2 + val startW = (width - cropWidth) / 2 + BoundingBox(startW, startH, startW + cropWidth, startH + cropHeight, false) + } +} + +object CenterCrop { + def apply(cropWidth: Int, cropHeight: Int, isClip: Boolean = true) + : CenterCrop = new CenterCrop(cropWidth, cropHeight, isClip) +} + +/** + * Random crop a `cropWidth` x `cropHeight` patch from an image. + * The patch size should be less than the image size. + * + * @param cropWidth width after crop + * @param cropHeight height after crop + * @param isClip whether to clip the roi to image boundaries + */ +class RandomCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean) extends Crop(false, isClip) { + + override def generateRoi(feature: ImageFeature): BoundingBox = { + val mat = feature.opencvMat() + val height = mat.height().toFloat + val width = mat.width().toFloat + val startH = math.ceil(RNG.uniform(1e-2, height - cropHeight)).toFloat + val startW = math.ceil(RNG.uniform(1e-2, width - cropWidth)).toFloat + BoundingBox(startW, startH, startW + cropWidth, startH + cropHeight, false) + } +} + +object RandomCrop { + def apply(cropWidth: Int, cropHeight: Int, isClip: Boolean = true): RandomCrop = + new RandomCrop(cropWidth, cropHeight, isClip) +} + +/** + * Crop a fixed area of image + * + * @param x1 start in width + * @param y1 start in height + * @param x2 end in width + * @param y2 end in height + * @param normalized whether args are normalized, i.e. in range [0, 1] + * @param isClip whether to clip the roi to image boundaries + */ +class FixedCrop(x1: Float, y1: Float, x2: Float, y2: Float, normalized: Boolean, + isClip: Boolean) + extends Crop(normalized, isClip) { + + @transient private var cropBox: BoundingBox = _ + + override def generateRoi(feature: ImageFeature): BoundingBox = { + if (null == cropBox) cropBox = BoundingBox(x1, y1, x2, y2, normalized) + cropBox + } +} + +object FixedCrop { + def apply(x1: Float, y1: Float, x2: Float, y2: Float, normalized: Boolean, + isClip: Boolean = true) + : FixedCrop = new FixedCrop(x1, y1, x2, y2, normalized, isClip) +} + +/** + * Crop from object detections, each image should has a tensor detection, + * which is stored in ImageFeature + * + * @param roiKey roiKey that map a tensor detection + * @param normalized whether is detection is normalized, i.e. in range [0, 1] + */ +class DetectionCrop(roiKey: String, normalized: Boolean = true) extends Crop(normalized, true) { + + override def generateRoi(feature: ImageFeature): BoundingBox = { + require(feature(roiKey).isInstanceOf[Tensor[Float]], "currently only support tensor detection") + var roi = feature(roiKey).asInstanceOf[Tensor[Float]] + if (roi.dim() == 1) { + roi = BboxUtil.decodeRois(roi) + } + if (roi.nElement() >= 6 && roi.dim() == 2) { + BoundingBox(roi.valueAt(1, 3), roi.valueAt(1, 4), + roi.valueAt(1, 5), roi.valueAt(1, 6), normalized) + } else { + BoundingBox(0, 0, 1, 1, normalized) + } + } +} + +object DetectionCrop { + def apply(roiKey: String, normalized: Boolean = true): DetectionCrop = + new DetectionCrop(roiKey, normalized) +} + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala new file mode 100644 index 00000000000..5bce28a00df --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala @@ -0,0 +1,94 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import java.util + +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.util.BoundingBox +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.opencv.core.{Core, Mat, Rect, Scalar} + +/** + * expand image, fill the blank part with the meanR, meanG, meanB + * + * @param meansR means in R channel + * @param meansG means in G channel + * @param meansB means in B channel + * @param minExpandRatio min expand ratio + * @param maxExpandRatio max expand ratio + */ +class Expand(meansR: Int = 123, meansG: Int = 117, meansB: Int = 104, + minExpandRatio: Double = 1, maxExpandRatio: Double = 4.0) + extends FeatureTransformer { + + var expandMat: OpenCVMat = _ + + def transform(input: OpenCVMat, + output: OpenCVMat): BoundingBox = { + val imgHeight = input.rows() + val imgWidth = input.cols() + val expandRatio = RNG.uniform(minExpandRatio, maxExpandRatio) + val height = (imgHeight * expandRatio).toInt + val width = (imgWidth * expandRatio).toInt + val hOff = RNG.uniform(0, height - imgHeight).floor.toFloat + val wOff = RNG.uniform(0, width - imgWidth).floor.toFloat + val expandBbox = new BoundingBox() + expandBbox.x1 = -wOff / imgWidth + expandBbox.y1 = -hOff / imgHeight + expandBbox.x2 = (width - wOff) / imgWidth + expandBbox.y2 = (height - hOff) / imgHeight + val bboxRoi = new Rect(wOff.toInt, hOff.toInt, imgWidth.toInt, imgHeight.toInt) + + output.create(height, width, input.`type`()) + + // Split the image to 3 channels. + val channels = new util.ArrayList[Mat]() + Core.split(output, channels) + require(channels.size() == 3) + channels.get(0).setTo(new Scalar(meansB)) + channels.get(1).setTo(new Scalar(meansG)) + channels.get(2).setTo(new Scalar(meansR)) + Core.merge(channels, output) + input.copyTo(output.submat(bboxRoi)) + // release memory + (0 to 2).foreach(channels.get(_).release()) + expandBbox + } + + override def transformMat(prev: ImageFeature): Unit = { + val mat = prev.opencvMat() + if (Math.abs(maxExpandRatio - 1) >= 1e-2) { + if (null == expandMat) expandMat = new OpenCVMat() + val expandBbox = transform(mat, expandMat) + expandMat.copyTo(mat) + if (prev.hasLabel()) { + prev(ImageFeature.boundingBox) = expandBbox + } + if (null != expandMat) expandMat.release() + } + } +} + +object Expand { + def apply(meansR: Int = 123, meansG: Int = 117, meansB: Int = 104, + minExpandRatio: Double = 1.0, maxExpandRatio: Double = 4.0): Expand = + new Expand(meansR, meansG, meansB, minExpandRatio, maxExpandRatio) +} + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Filler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Filler.scala new file mode 100644 index 00000000000..bb0b727059e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Filler.scala @@ -0,0 +1,62 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import org.opencv.core +import org.opencv.core.Mat + +/** + * Fill part of image with certain pixel value + * + * @param startX start x ratio + * @param startY start y ratio + * @param endX end x ratio + * @param endY end y ratio + * @param value filling value + */ +class Filler(startX: Float, startY: Float, endX: Float, endY: Float, value: Int = 255) + extends FeatureTransformer { + + require(startX >= 0 && startX <= 1, s"$startX should be in the range [0, 1]") + require(startY >= 0 && startY <= 1, s"$startY should be in the range [0, 1]") + require(endX >= 0 && endX <= 1, s"$endX should be in the range [0, 1]") + require(endY >= 0 && endY <= 1, s"$endY should be in the range [0, 1]") + require(endX > startX, s"$endX should be greater than $startX") + require(endY > startY, s"$endY should be greater than $startY") + + override def transformMat(feature: ImageFeature): Unit = { + var fillMat: Mat = null + try { + val mat = feature.opencvMat() + val x1 = (startX * mat.cols()).ceil.toInt + val x2 = (endX * mat.cols()).ceil.toInt + val y1 = (startY * mat.rows()).ceil.toInt + val y2 = (endY * mat.rows()).ceil.toInt + fillMat = new core.Mat(y2 - y1, x2 - x1, mat.`type`(), new core.Scalar(value, value, value)) + fillMat.copyTo(mat.submat(y1, y2, x1, x2)) + } finally { + if (null != fillMat) fillMat.release() + } + } +} + +object Filler { + def apply(startX: Float, startY: Float, endX: Float, endY: Float, value: Int = 255): Filler + = new Filler(startX, startY, endX, endY, value) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Hue.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Hue.scala new file mode 100644 index 00000000000..4aa9092a821 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Hue.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import java.util + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.opencv.core.{Core, Mat} +import org.opencv.imgproc.Imgproc + +/** + * Adjust image hue + * @param deltaLow hue parameter: low bound + * @param deltaHigh hue parameter: high bound + */ +class Hue(deltaLow: Double, deltaHigh: Double) + extends FeatureTransformer { + override def transformMat(feature: ImageFeature): Unit = { + Hue.transform(feature.opencvMat(), feature.opencvMat(), RNG.uniform(deltaLow, deltaHigh)) + } +} + +object Hue { + def apply(deltaLow: Double, deltaHigh: Double): Hue = new Hue(deltaLow, deltaHigh) + + def transform(input: OpenCVMat, output: OpenCVMat, delta: Double): OpenCVMat = { + if (delta != 0) { + // Convert to HSV colorspae + Imgproc.cvtColor(input, output, Imgproc.COLOR_BGR2HSV) + + // Split the image to 3 channels. + val channels = new util.ArrayList[Mat]() + Core.split(output, channels) + + // Adjust the hue. + channels.get(0).convertTo(channels.get(0), -1, 1, delta) + Core.merge(channels, output) + + (0 until channels.size()).foreach(channels.get(_).release()) + + // Back to BGR colorspace. + Imgproc.cvtColor(output, output, Imgproc.COLOR_HSV2BGR) + } else { + if (input != output) input.copyTo(output) + } + output + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/PixelNormalizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/PixelNormalizer.scala new file mode 100644 index 00000000000..452602930bd --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/PixelNormalizer.scala @@ -0,0 +1,62 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import org.opencv.core.CvType + +/** + * Pixel level normalizer, data(i) = data(i) - mean(i) + * + * @param means pixel level mean, following H * W * C order + */ +class PixelNormalizer(means: Array[Float]) extends FeatureTransformer { + + private var data: Array[Float] = _ + + override def transformMat(feature: ImageFeature): Unit = { + val openCVMat = feature.opencvMat() + if (openCVMat.`type`() != CvType.CV_32FC3) { + openCVMat.convertTo(openCVMat, CvType.CV_32FC3) + } + + if (data == null) { + data = new Array[Float](means.length) + } + require(data.length == openCVMat.height() * openCVMat.width() * openCVMat.channels(), + "the means provided must have the same length as image") + openCVMat.get(0, 0, data) + + require(means.length == data.length, s"Image size expected :" + + s"${means.length}, actual : ${data.length}") + + var i = 0 + while (i < data.length) { + data(i + 2) = data(i + 2) - means(i + 2) + data(i + 1) = data(i + 1) - means(i + 1) + data(i + 0) = data(i + 0) - means(i + 0) + i += 3 + } + + openCVMat.put(0, 0, data) + } + +} + +object PixelNormalizer { + def apply(means: Array[Float]): PixelNormalizer = new PixelNormalizer(means) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomTransformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomTransformer.scala new file mode 100644 index 00000000000..5b90e04a96b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomTransformer.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.utils.RandomGenerator._ + +/** + * It is a wrapper for transformers to control the transform probability + * @param transformer transformer to apply randomness + * @param maxProb max prob + */ +class RandomTransformer(transformer: FeatureTransformer, maxProb: Double) + extends FeatureTransformer { + override def transform(prev: ImageFeature): ImageFeature = { + if (RNG.uniform(0, 1) < maxProb) { + transformer.transform(prev) + } + prev + } + + override def toString: String = { + s"Random[${transformer.getClass.getCanonicalName}, $maxProb]" + } +} + +object RandomTransformer { + def apply(transformer: FeatureTransformer, maxProb: Double): RandomTransformer = + new RandomTransformer(transformer, maxProb) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala new file mode 100644 index 00000000000..472d4fd4136 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala @@ -0,0 +1,134 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import org.apache.log4j.Logger +import org.opencv.core.Size +import org.opencv.imgproc.Imgproc + +import scala.util.Random + +/** + * Resize image + * @param resizeH height after resize + * @param resizeW width after resize + * @param resizeMode if resizeMode = -1, random select a mode from + * (Imgproc.INTER_LINEAR, Imgproc.INTER_CUBIC, Imgproc.INTER_AREA, + * Imgproc.INTER_NEAREST, Imgproc.INTER_LANCZOS4) + */ +class Resize(resizeH: Int, resizeW: Int, + resizeMode: Int = Imgproc.INTER_LINEAR) + extends FeatureTransformer { + + private val interpMethods = Array(Imgproc.INTER_LINEAR, Imgproc.INTER_CUBIC, Imgproc.INTER_AREA, + Imgproc.INTER_NEAREST, Imgproc.INTER_LANCZOS4) + + override def transformMat(feature: ImageFeature): Unit = { + val interpMethod = if (resizeMode == -1) { + interpMethods(new Random().nextInt(interpMethods.length)) + } else { + resizeMode + } + Resize.transform(feature.opencvMat(), feature.opencvMat(), resizeW, resizeH, interpMethod) + } +} + +object Resize { + val logger = Logger.getLogger(getClass) + + def apply(resizeH: Int, resizeW: Int, + resizeMode: Int = Imgproc.INTER_LINEAR): Resize = + new Resize(resizeH, resizeW, resizeMode) + + def transform(input: OpenCVMat, output: OpenCVMat, resizeW: Int, resizeH: Int, + mode: Int = Imgproc.INTER_LINEAR) + : OpenCVMat = { + Imgproc.resize(input, output, new Size(resizeW, resizeH), 0, 0, mode) + output + } +} + +/** + * Resize the image, keep the aspect ratio. scale according to the short edge + * @param scale scale size, apply to short edge + * @param scaleMultipleOf make the scaled size multiple of some value + * @param maxSize max size after scale + */ +class AspectScale(scale: Int, scaleMultipleOf: Int = 1, + maxSize: Int = 1000) extends FeatureTransformer { + + override def transformMat(feature: ImageFeature): Unit = { + val (height, width) = AspectScale.getHeightWidthAfterRatioScale(feature.opencvMat(), + scale, maxSize, scaleMultipleOf) + Resize.transform(feature.opencvMat(), feature.opencvMat(), width, height) + } +} + +object AspectScale { + + def apply(scale: Int, scaleMultipleOf: Int = 1, + maxSize: Int = 1000): AspectScale = new AspectScale(scale, scaleMultipleOf, maxSize) + /** + * get the width and height of scaled image + * @param img original image + */ + def getHeightWidthAfterRatioScale(img: OpenCVMat, scaleTo: Float, + maxSize: Int, scaleMultipleOf: Int): (Int, Int) = { + val imSizeMin = Math.min(img.width(), img.height()) + val imSizeMax = Math.max(img.width(), img.height()) + var imScale = scaleTo.toFloat / imSizeMin.toFloat + // Prevent the biggest axis from being more than MAX_SIZE + if (Math.round(imScale * imSizeMax) > maxSize) { + imScale = maxSize / imSizeMax.toFloat + } + + val imScaleH = (Math.floor(img.height() * imScale / scaleMultipleOf) * + scaleMultipleOf / img.height()).toFloat + val imScaleW = (Math.floor(img.width() * imScale / scaleMultipleOf) * + scaleMultipleOf / img.width()).toFloat + val width = imScaleW * img.width() + val height = imScaleH * img.height() + (height.toInt, width.toInt) + } +} + + +/** + * resize the image by randomly choosing a scale + * @param scales array of scale options that for random choice + * @param scaleMultipleOf Resize test images so that its width and height are multiples of + * @param maxSize Max pixel size of the longest side of a scaled input image + */ +class RandomAspectScale(scales: Array[Int], scaleMultipleOf: Int = 1, + maxSize: Int = 1000) extends FeatureTransformer { + + override def transformMat(feature: ImageFeature): Unit = { + val scaleTo = scales(Random.nextInt(scales.length)) + val (height, width) = AspectScale.getHeightWidthAfterRatioScale(feature.opencvMat(), + scaleTo, maxSize, scaleMultipleOf) + Resize.transform(feature.opencvMat(), feature.opencvMat(), width, height) + } +} + +object RandomAspectScale { + def apply(scales: Array[Int], scaleMultipleOf: Int = 1, + maxSize: Int = 1000): RandomAspectScale = + new RandomAspectScale(scales, scaleMultipleOf, maxSize) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Saturation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Saturation.scala new file mode 100644 index 00000000000..bddcc053090 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Saturation.scala @@ -0,0 +1,63 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import java.util + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.opencv.core.{Core, Mat} +import org.opencv.imgproc.Imgproc + +/** + * Adjust image saturation + */ +class Saturation(deltaLow: Double, deltaHigh: Double) + extends FeatureTransformer { + + require(deltaHigh >= deltaLow, "saturation upper must be >= lower.") + require(deltaLow >= 0, "saturation lower must be non-negative.") + override def transformMat(feature: ImageFeature): Unit = { + Saturation.transform(feature.opencvMat(), feature.opencvMat(), RNG.uniform(deltaLow, deltaHigh)) + } +} + +object Saturation { + def apply(deltaLow: Double, deltaHigh: Double): Saturation = new Saturation(deltaLow, deltaHigh) + + def transform(input: OpenCVMat, output: OpenCVMat, delta: Double): OpenCVMat = { + if (Math.abs(delta - 1) != 1e-3) { + // Convert to HSV colorspace + Imgproc.cvtColor(input, output, Imgproc.COLOR_BGR2HSV) + + // Split the image to 3 channels. + val channels = new util.ArrayList[Mat]() + Core.split(output, channels) + + // Adjust the saturation. + channels.get(1).convertTo(channels.get(1), -1, delta, 0) + Core.merge(channels, output) + (0 until channels.size()).foreach(channels.get(_).release()) + // Back to BGR colorspace. + Imgproc.cvtColor(output, output, Imgproc.COLOR_HSV2BGR) + } else { + if (input != output) input.copyTo(output) + } + output + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/BatchSampler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/BatchSampler.scala new file mode 100644 index 00000000000..d34a50eba4c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/BatchSampler.scala @@ -0,0 +1,134 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.label.roi + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.util.{BboxUtil, BoundingBox} +import com.intel.analytics.bigdl.utils.RandomGenerator._ + +import scala.collection.mutable.ArrayBuffer + + +/** + * sample box from given parameters, and regard it as positive if it satisfies overlap + * constraints + * + * @param maxSample maximum random crop samples to be generated + * @param maxTrials maximum trials, if exceed this number, give up anyway + * @param minScale min scale + * @param maxScale max scale + * @param minAspectRatio min aspect ratio + * @param maxAspectRatio max aspect ratio + * @param minOverlap min overlap between sampled box and gt box + * @param maxOverlap max overlap between sampled box and gt box + */ +class BatchSampler(maxSample: Int = 1, maxTrials: Int = 50, + minScale: Double = 1, maxScale: Double = 1, + minAspectRatio: Double = 1, maxAspectRatio: Double = 1, + minOverlap: Option[Double] = None, + maxOverlap: Option[Double] = None) extends Serializable { + + require(minScale <= maxScale, "minScale must <= maxScale") + require(minScale > 0 && minScale <= 1, "minScale must in (0, 1]") + require(maxScale > 0 && maxScale <= 1, "maxScale must in (0, 1]") + require(minAspectRatio > 0 && minAspectRatio <= 1, "minAspectRatio must in (0, 1]") + require(maxAspectRatio >= 1, "minAspectRatio must >= 1") + if (minOverlap.isDefined) { + require(minOverlap.get >= 0 && minOverlap.get <= 1, "minOverlap must in [0, 1]") + } + + def satisfySampleConstraint(sampledBox: BoundingBox, target: RoiLabel): Boolean = { + // By default, the sampled_bbox is "positive" if no constraints are defined. + if (minOverlap.isEmpty && maxOverlap.isEmpty) return true + var i = 1 + while (i <= target.size()) { + val overlap = jaccardOverlap(sampledBox, target.bboxes, i) + if (minOverlap.isEmpty || overlap >= minOverlap.get) { + if (maxOverlap.isEmpty || overlap <= maxOverlap.get) { + return true + } + } + i += 1 + } + false + } + + def sample(sourceBox: BoundingBox, target: RoiLabel, sampledBoxes: ArrayBuffer[BoundingBox]) + : Unit = { + var found = 0 + var trial = 0 + while (trial < maxTrials) { + if (found >= maxSample) { + return + } + // Generate sampled_bbox in the normalized space [0, 1]. + val sampledBox = sampleBox() + // Transform the sampled_bbox w.r.t. source_bbox. + sourceBox.locateBBox(sampledBox, sampledBox) + // Determine if the sampled bbox is positive or negative by the constraint. + if (satisfySampleConstraint(sampledBox, target)) { + found += 1 + sampledBoxes.append(sampledBox) + } + trial += 1 + } + } + + private def sampleBox(): BoundingBox = { + val scale = RNG.uniform(minScale, maxScale) + var ratio = RNG.uniform(minAspectRatio, maxAspectRatio) + ratio = Math.max(ratio, scale * scale) + ratio = Math.min(ratio, 1 / scale / scale) + val width = scale * Math.sqrt(ratio) + val height = scale / Math.sqrt(ratio) + val x1 = RNG.uniform(0, 1 - width).toFloat + val y1 = RNG.uniform(0, 1 - height).toFloat + val x2 = x1 + width.toFloat + val y2 = y1 + height.toFloat + BoundingBox(x1, y1, x2, y2) + } + + private def jaccardOverlap(bbox: BoundingBox, gtBoxes: Tensor[Float], i: Int): Float = { + val gtBox = BoundingBox(gtBoxes.valueAt(i, 1), + gtBoxes.valueAt(i, 2), + gtBoxes.valueAt(i, 3), + gtBoxes.valueAt(i, 4)) + + bbox.jaccardOverlap(gtBox) + } + +} + +object BatchSampler { + + /** + * generate batch samples + * @param label normalized + * @param batchSamplers + * @param sampledBoxes + */ + def generateBatchSamples(label: RoiLabel, batchSamplers: Array[BatchSampler], + sampledBoxes: ArrayBuffer[BoundingBox]): Unit = { + sampledBoxes.clear() + var i = 0 + val unitBox = BoundingBox(0, 0, 1, 1) + while (i < batchSamplers.length) { + batchSamplers(i).sample(unitBox, label, sampledBoxes) + i += 1 + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RandomSampler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RandomSampler.scala new file mode 100644 index 00000000000..b3173a763f0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RandomSampler.scala @@ -0,0 +1,69 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.label.roi + +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.Crop +import com.intel.analytics.bigdl.transform.vision.image.util.{BoundingBox} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.opencv.core.Mat + +import scala.collection.mutable.ArrayBuffer + +/** + * Random sample a bounding box given some constraints and crop the image + * This is used in SSD training augmentation + */ +class RandomSampler extends Crop { + // random cropping samplers + val batchSamplers = Array( + new BatchSampler(maxTrials = 1), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.1)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.3)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.5)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.7)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.9)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + maxOverlap = Some(1.0))) + + def generateRoi(feature: ImageFeature): BoundingBox = { + val roiLabel = feature(ImageFeature.label).asInstanceOf[RoiLabel] + val boxesBuffer = new ArrayBuffer[BoundingBox]() + BatchSampler.generateBatchSamples(roiLabel, + batchSamplers, boxesBuffer) + + // randomly pick up one as input data + if (boxesBuffer.nonEmpty) { + // Randomly pick a sampled bbox and crop the expand_datum. + val index = (RNG.uniform(0, 1) * boxesBuffer.length).toInt + boxesBuffer(index) + } else { + BoundingBox(0, 0, 1, 1) + } + } +} + +object RandomSampler { + def apply(): FeatureTransformer = { + new RandomSampler() -> RoiProject() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala new file mode 100644 index 00000000000..f6da79287d6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -0,0 +1,62 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.label.roi + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{T, Table} + +/** + * image target with classes and bounding boxes + * + * @param classes N (class labels) or 2 * N, the first row is class labels, + * the second line is difficults + * @param bboxes N * 4 + */ +case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float]) { + def copy(target: RoiLabel): Unit = { + classes.resizeAs(target.classes).copy(target.classes) + bboxes.resizeAs(target.bboxes).copy(target.bboxes) + } + + if (classes.dim() == 1) { + require(classes.size(1) == bboxes.size(1), "the number of classes should be" + + " equal to the number of bounding box numbers") + } else if (classes.nElement() > 0 && classes.dim() == 2) { + require(classes.size(2) == bboxes.size(1), s"the number of classes ${ classes.size(2) }" + + s"should be equal to the number of bounding box numbers ${ bboxes.size(1) }") + } + + + def toTable: Table = { + val table = T() + table.insert(classes) + table.insert(bboxes) + } + + def size(): Int = { + if (bboxes.nElement() < 4) 0 else bboxes.size(1) + } +} + +object RoiLabel { + def fromTensor(tensor: Tensor[Float]): RoiLabel = { + val label = tensor.narrow(2, 1, 2).transpose(1, 2).contiguous() + val rois = tensor.narrow(2, 3, 4) + RoiLabel(label, rois) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiTransformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiTransformer.scala new file mode 100644 index 00000000000..5b570ecfb11 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiTransformer.scala @@ -0,0 +1,116 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.label.roi + +import com.intel.analytics.bigdl.transform.vision.image.util.{BboxUtil, BoundingBox} +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} + +import scala.collection.mutable.ArrayBuffer + +/** + * Normalize Roi to [0, 1] + */ +case class RoiNormalize() extends FeatureTransformer { + override def transformMat(feature: ImageFeature): Unit = { + val height = feature.getHeight() + val width = feature.getWidth() + val label = feature(ImageFeature.label).asInstanceOf[RoiLabel] + BboxUtil.scaleBBox(label.bboxes, 1.0f / height, 1.0f / width) + } +} + +/** + * horizontally flip the roi + * @param normalized whether the roi is normalized, i.e. in range [0, 1] + */ +case class RoiHFlip(normalized: Boolean = true) extends FeatureTransformer { + override def transformMat(feature: ImageFeature): Unit = { + val roiLabel = feature.getLabel[RoiLabel] + var i = 1 + val width = if (normalized) 1 else feature.getWidth() + while (i <= roiLabel.size()) { + val x1 = width - roiLabel.bboxes.valueAt(i, 1) + roiLabel.bboxes.setValue(i, 1, width - roiLabel.bboxes.valueAt(i, 3)) + roiLabel.bboxes.setValue(i, 3, x1) + i += 1 + } + } +} + +/** + * resize the roi according to scale + * @param normalized whether the roi is normalized, i.e. in range [0, 1] + */ +case class RoiResize(normalized: Boolean = false) extends FeatureTransformer { + override def transformMat(feature: ImageFeature): Unit = { + if (!normalized) { + val scaledW = feature.getWidth().toFloat / feature.getOriginalWidth + val scaledH = feature.getHeight().toFloat / feature.getOriginalHeight + val target = feature.getLabel[RoiLabel] + BboxUtil.scaleBBox(target.bboxes, scaledH, scaledW) + } + } +} + +/** + * Project gt boxes onto the coordinate system defined by image boundary + * @param needMeetCenterConstraint whether need to meet center constraint, i.e., the center of + * gt box need be within image boundary + */ +case class RoiProject(needMeetCenterConstraint: Boolean = true) extends FeatureTransformer { + val transformedAnnot = new ArrayBuffer[BoundingBox]() + override def transformMat(feature: ImageFeature): Unit = { + val imageBoundary = feature[BoundingBox](ImageFeature.boundingBox) + if (!imageBoundary.normalized) { + imageBoundary.scaleBox(1.0f / feature.getHeight(), 1f / feature.getWidth(), imageBoundary) + } + val target = feature[RoiLabel](ImageFeature.label) + transformedAnnot.clear() + // Transform the annotation according to bounding box. + var i = 1 + while (i <= target.size()) { + val gtBoxes = BoundingBox(target.bboxes.valueAt(i, 1), + target.bboxes.valueAt(i, 2), + target.bboxes.valueAt(i, 3), + target.bboxes.valueAt(i, 4)) + if (!needMeetCenterConstraint || + imageBoundary.meetEmitCenterConstraint(gtBoxes)) { + val transformedBox = new BoundingBox() + if (imageBoundary.projectBbox(gtBoxes, transformedBox)) { + transformedBox.setLabel(target.classes.valueAt(1, i)) + transformedBox.setDifficult(target.classes.valueAt(2, i)) + transformedAnnot.append(transformedBox) + } + } + i += 1 + } + // write the transformed annotation back to target + target.bboxes.resize(transformedAnnot.length, 4) + target.classes.resize(2, transformedAnnot.length) + + i = 1 + while (i <= transformedAnnot.length) { + target.bboxes.setValue(i, 1, transformedAnnot(i - 1).x1) + target.bboxes.setValue(i, 2, transformedAnnot(i - 1).y1) + target.bboxes.setValue(i, 3, transformedAnnot(i - 1).x2) + target.bboxes.setValue(i, 4, transformedAnnot(i - 1).y2) + target.classes.setValue(1, i, transformedAnnot(i - 1).label) + target.classes.setValue(2, i, transformedAnnot(i - 1).difficult) + i += 1 + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala index b29ac7fe450..87bd0666df4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala @@ -19,9 +19,11 @@ package com.intel.analytics.bigdl.transform.vision.image.opencv import java.io.{File, IOException, ObjectInputStream, ObjectOutputStream} import com.intel.analytics.bigdl.opencv.OpenCV +import com.intel.analytics.bigdl.transform.vision.image.util.BoundingBox import org.apache.commons.io.FileUtils -import org.opencv.core.{CvType, Mat, MatOfByte} +import org.opencv.core._ import org.opencv.imgcodecs.Imgcodecs +import org.opencv.imgproc.Imgproc /** * OpenCVMat is a Serializable wrapper of original Mat @@ -58,6 +60,7 @@ class OpenCVMat() extends Mat with Serializable { } var isReleased: Boolean = false + override def release(): Unit = { super.release() isReleased = true @@ -65,11 +68,36 @@ class OpenCVMat() extends Mat with Serializable { /** * get shape of mat + * * @return (height, width, channel) */ def shape(): (Int, Int, Int) = { (height(), width(), channels()) } + + /** + * draw bounding box on current mat + * @param bbox bounding box + * @param text text + * @param font text fond + * @param boxColor bounding box color + * @param textColor text color + * @return + */ + def drawBoundingBox(bbox: BoundingBox, text: String, + font: Int = Core.FONT_HERSHEY_COMPLEX_SMALL, + boxColor: (Double, Double, Double) = (0, 255, 0), + textColor: (Double, Double, Double) = (255, 255, 255)): this.type = { + Imgproc.rectangle(this, + new Point(bbox.x1, bbox.y1), + new Point(bbox.x2, bbox.y2), + new Scalar(boxColor._1, boxColor._2, boxColor._3), 3) + Imgproc.putText(this, text, + new Point(bbox.x1, bbox.y1 - 2), + font, 1, + new Scalar(textColor._1, textColor._2, textColor._3), 1) + this + } } object OpenCVMat { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala new file mode 100644 index 00000000000..855a3ac6f0b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.util + +import com.intel.analytics.bigdl.tensor.Tensor + +object BboxUtil { + def decodeRois(output: Tensor[Float]): Tensor[Float] = { + // ignore if decoded + if (output.nElement() < 6 || output.dim() == 2) return output + val num = output.valueAt(1).toInt + require(num >= 0) + if (num == 0) { + Tensor[Float]() + } else { + output.narrow(1, 2, num * 6).view(num, 6) + } + } + + // inplace scale + def scaleBBox(bboxes: Tensor[Float], height: Float, width: Float): Unit = { + if (bboxes.nElement() == 0) return + bboxes.select(2, 1).mul(width) + bboxes.select(2, 2).mul(height) + bboxes.select(2, 3).mul(width) + bboxes.select(2, 4).mul(height) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BoundingBox.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BoundingBox.scala new file mode 100644 index 00000000000..20574672d21 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BoundingBox.scala @@ -0,0 +1,167 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.util + +class BoundingBox(var x1: Float, var y1: Float, var x2: Float, var y2: Float, + var normalized: Boolean = true) extends Serializable { + + var label: Float = -1 + + def setLabel(l: Float): this.type = { + label = l + this + } + + var difficult: Float = -1 + + def setDifficult(d: Float): this.type = { + difficult = d + this + } + + def this(other: BoundingBox) { + this(other.x1, other.y1, other.x2, other.y2) + } + + def centerX(): Float = { + (x1 + x2) / 2 + } + + def centerY(): Float = { + (y1 + y2) / 2 + } + + def this() = { + this(0f, 0f, 1f, 1f) + } + + def width(): Float = x2 - x1 + + def height(): Float = y2 - y1 + + def area(): Float = width() * height() + + def clipBox(clipedBox: BoundingBox): Unit = { + if (normalized) { + clipedBox.x1 = Math.max(Math.min(x1, 1f), 0f) + clipedBox.y1 = Math.max(Math.min(y1, 1f), 0f) + clipedBox.x2 = Math.max(Math.min(x2, 1f), 0f) + clipedBox.y2 = Math.max(Math.min(y2, 1f), 0f) + } + } + + def scaleBox(height: Float, width: Float, scaledBox: BoundingBox): Unit = { + scaledBox.x1 = x1 * width + scaledBox.y1 = y1 * height + scaledBox.x2 = x2 * width + scaledBox.y2 = y2 * height + } + + /** + * Whether the center of given bbox lies in current bbox + */ + def meetEmitCenterConstraint(bbox: BoundingBox): Boolean = { + val xCenter = bbox.centerX() + val yCenter = bbox.centerY() + if (xCenter >= x1 && xCenter <= x2 && + yCenter >= y1 && yCenter <= y2) { + true + } else { + false + } + } + + /** + * whether overlaps with given bbox + */ + def isOverlap(bbox: BoundingBox): Boolean = { + if (bbox.x1 >= x2 || bbox.x2 <= x1 || bbox.y1 >= y2 || bbox.y2 <= y1) { + false + } else { + true + } + } + + def jaccardOverlap(bbox: BoundingBox): Float = { + val w = math.min(x2, bbox.x2) - math.max(x1, bbox.x1) + if (w < 0) return 0 + val h = math.min(y2, bbox.y2) - math.max(y1, bbox.y1) + if (h < 0) return 0 + val overlap = w * h + overlap / ((area() + bbox.area()) - overlap) + } + + /** + * Project bbox onto the coordinate system defined by current bbox. + * @param bbox + * @param projBox + * @return + */ + def projectBbox(bbox: BoundingBox, projBox: BoundingBox): Boolean = { + if (!isOverlap(bbox)) { + return false + } + val srcWidth = width() + val srcHeight = height() + projBox.x1 = (bbox.x1 - x1) / srcWidth + projBox.y1 = (bbox.y1 - y1) / srcHeight + projBox.x2 = (bbox.x2 - x1) / srcWidth + projBox.y2 = (bbox.y2 - y1) / srcHeight + projBox.clipBox(projBox) + if (projBox.area() > 0) true else false + } + + def locateBBox(box: BoundingBox, locBox: BoundingBox) + : Unit = { + val srcW = width() + val srcH = height() + locBox.x1 = x1 + box.x1 * srcW + locBox.y1 = y1 + box.y1 * srcH + locBox.x2 = x1 + box.x2 * srcW + locBox.y2 = y1 + box.y2 * srcH + } + + override def equals(obj: Any): Boolean = { + obj match { + case box: BoundingBox => + if (box.x1 == x1 && box.x2 == x2 && box.y1 == y1 && box.y2 == y2) true + else false + case _ => false + } + } + + override def hashCode() : Int = { + val seed = 37 + var hash = super.hashCode() + hash = hash * seed + x1.hashCode() + hash = hash * seed + y1.hashCode() + hash = hash * seed + x2.hashCode() + hash = hash * seed + y2.hashCode() + + hash + } + + override def toString: String = { + s"BoundingBox ($x1, $y1, $x2, $y2)" + } +} + +object BoundingBox { + def apply(x1: Float, y1: Float, x2: Float, y2: Float, + normalized: Boolean = true): BoundingBox = + new BoundingBox(x1, y1, x2, y2, normalized) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 038a4d77df0..0f41e49666e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -39,6 +39,7 @@ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.transform.vision.image.label.roi._ import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} @@ -46,6 +47,7 @@ import com.intel.analytics.bigdl.utils.tf._ import org.apache.spark.ml.{DLClassifier, DLClassifierModel, DLEstimator, DLModel} import org.apache.spark.sql.{DataFrame, SQLContext} import org.apache.log4j._ +import org.opencv.imgproc.Imgproc import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer @@ -2307,6 +2309,112 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab HFlip() } + def createResize(resizeH: Int, resizeW: Int, resizeMode: Int = Imgproc.INTER_LINEAR): Resize = { + Resize(resizeH, resizeW, resizeMode) + } + + def createColorJitter(brightnessProb: Double = 0.5, brightnessDelta: Double = 32, + contrastProb: Double = 0.5, contrastLower: Double = 0.5, contrastUpper: Double = 1.5, + hueProb: Double = 0.5, hueDelta: Double = 18, + saturationProb: Double = 0.5, saturationLower: Double = 0.5, saturationUpper: Double = 1.5, + randomOrderProb: Double = 0, shuffle: Boolean = false): ColorJitter = { + ColorJitter(brightnessProb, brightnessDelta, contrastProb, + contrastLower, contrastUpper, hueProb, hueDelta, saturationProb, + saturationLower, saturationUpper, randomOrderProb, shuffle) + } + + def createBrightness(deltaLow: Double, deltaHigh: Double): Brightness = { + Brightness(deltaLow, deltaHigh) + } + + def createChannelOrder(): ChannelOrder = { + ChannelOrder() + } + + def createContrast(deltaLow: Double, deltaHigh: Double): Contrast = { + Contrast(deltaLow, deltaHigh) + } + + def createRandomCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean): RandomCrop = { + RandomCrop(cropWidth, cropHeight, isClip) + } + + def createCenterCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean): CenterCrop = { + CenterCrop(cropWidth, cropHeight, isClip) + } + + def createFixedCrop(wStart: Double, + hStart: Double, wEnd: Double, hEnd: Double, normalized: Boolean, + isClip: Boolean): FixedCrop = { + FixedCrop(wStart.toFloat, hStart.toFloat, wEnd.toFloat, hEnd.toFloat, normalized, isClip) + } + + def createDetectionCrop(roiKey: String, normalized: Boolean): DetectionCrop = { + DetectionCrop(roiKey, normalized) + } + + def createExpand(meansR: Int = 123, meansG: Int = 117, meansB: Int = 104, + minExpandRatio: Double = 1.0, + maxExpandRatio: Double = 4.0): Expand = { + Expand(meansR, meansG, meansB, minExpandRatio, maxExpandRatio) + } + + def createRandomAspectScale(scales: JList[Int], scaleMultipleOf: Int = 1, + maxSize: Int = 1000): RandomAspectScale = { + RandomAspectScale(scales.asScala.toArray, scaleMultipleOf, maxSize) + } + + def createHue(deltaLow: Double, deltaHigh: Double): Hue = { + Hue(deltaLow, deltaHigh) + } + + def createRandomTransformer(transformer: FeatureTransformer, prob: Double): RandomTransformer = { + RandomTransformer(transformer, prob) + } + + def createSaturation(deltaLow: Double, deltaHigh: Double): Saturation = { + Saturation(deltaLow, deltaHigh) + } + + def createRandomSampler(): FeatureTransformer = { + RandomSampler() + } + + def createChannelNormalize(meanR: Double, meanG: Double, meanB: Double, + stdR: Double = 1, stdG: Double = 1, stdB: Double = 1): FeatureTransformer = { + ChannelNormalize(meanR.toFloat, meanG.toFloat, meanB.toFloat, + stdR.toFloat, stdG.toFloat, stdB.toFloat) + } + + def createAspectScale(scale: Int, scaleMultipleOf: Int, maxSize: Int): FeatureTransformer = { + AspectScale(scale, scaleMultipleOf, maxSize) + } + + def createFiller(startX: Double, startY: Double, endX: Double, endY: Double, + value: Int = 255): Filler = { + Filler(startX.toFloat, startY.toFloat, endX.toFloat, endY.toFloat, value) + } + + def createPixelNormalize(means: JList[Double]): PixelNormalizer = { + PixelNormalizer(means.asScala.toArray.map(_.toFloat)) + } + + def createRoiProject(needMeetCenterConstraint: Boolean): RoiProject = { + RoiProject(needMeetCenterConstraint) + } + + def createRoiResize(normalized: Boolean): RoiResize = { + RoiResize(normalized) + } + + def createRoiHFlip(normalized: Boolean = true): RoiHFlip = { + RoiHFlip(normalized) + } + + def createRoiNormalize(): RoiNormalize = { + RoiNormalize() + } + def transformImageFeature(transformer: FeatureTransformer, feature: ImageFeature) : ImageFeature = { transformer.transform(feature) @@ -2443,6 +2551,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ImageFrame.readParquet(path, sqlContext) } + def createBytesToMat(): BytesToMat = { + BytesToMat() + } + def isLocal(imageFrame: ImageFrame): Boolean = imageFrame.isLocal() def isDistributed(imageFrame: ImageFrame): Boolean = imageFrame.isDistributed() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/FeatureTransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/FeatureTransformerSpec.scala new file mode 100644 index 00000000000..0f195203597 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/FeatureTransformerSpec.scala @@ -0,0 +1,123 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image + +import java.nio.file.{Files, Paths} + +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.transform.vision.image.label.roi._ +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.util.{BboxUtil, BoundingBox} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class FeatureTransformerSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + + "Image Transformer with empty byte input" should "work properly" in { + val img = Array[Byte]() + val imageFeature = ImageFeature(img) + val imageFrame = new LocalImageFrame(Array(imageFeature)) + val imgAug = Resize(1, 1, -1) -> + FixedCrop(-1, -1, -1, -1, normalized = false) -> + MatToFloats(validHeight = 1, validWidth = 1) + val out = imgAug(imageFrame) + imageFeature.floats().length should be(3) + imageFeature.isValid should be(false) + } + + "Image Transformer with exception" should "work properly" in { + val images = ImageFrame.read(resource.getFile) + val imgAug = FixedCrop(-1, -1, -1, -1, normalized = false) -> + Resize(300, 300, -1) -> + MatToFloats(validHeight = 300, validWidth = 300) + val out = imgAug(images) + val imageFeature = out.asInstanceOf[LocalImageFrame].array(0) + imageFeature.floats().length should be(3 * 300 * 300) + imageFeature.isValid should be(false) + } + + "ImageAugmentation with label and random" should "work properly" in { + val img = Files.readAllBytes(Paths.get(resource.getFile + "/000025.jpg")) + val classes = Array(11.0, 11.0, 11.0, 16.0, 16.0, 16.0, 11.0, 16.0, + 16.0, 16.0, 16.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 1.0).map(_.toFloat) + val boxes = Array(2.0, 84.0, 59.0, 248.0, + 68.0, 115.0, 233.0, 279.0, + 64.0, 173.0, 377.0, 373.0, + 320.0, 2.0, 496.0, 375.0, + 221.0, 4.0, 341.0, 374.0, + 135.0, 14.0, 220.0, 148.0, + 69.0, 43.0, 156.0, 177.0, + 58.0, 54.0, 104.0, 139.0, + 279.0, 1.0, 331.0, 86.0, + 320.0, 22.0, 344.0, 96.0, + 337.0, 1.0, 390.0, 107.0).map(_.toFloat) + val label = RoiLabel(Tensor(Storage(classes)).resize(2, 11), + Tensor(Storage(boxes)).resize(11, 4)) + + val feature = ImageFeature(img, label, resource.getFile) + val imgAug = BytesToMat() -> RoiNormalize() -> + ColorJitter() -> + RandomTransformer(Expand() -> RoiProject(), 0.5) -> + RandomSampler() -> + Resize(300, 300, -1) -> + RandomTransformer(HFlip() -> RoiHFlip(), 0.5) + + val imageFrame = new LocalImageFrame(Array(feature)) + val out = imgAug(imageFrame) + + feature.isValid should be(true) + feature.getOriginalHeight should be (375) + feature.getOriginalWidth should be (500) + feature.getHeight should be (300) + feature.getWidth should be (300) + + val bboxes = feature.getLabel[RoiLabel].bboxes + BboxUtil.scaleBBox(bboxes, 300, 300) + visualize(feature.opencvMat(), feature.getLabel[RoiLabel].bboxes) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, feature.opencvMat()) + println(tmpFile) + } + + private def visualize(mat: OpenCVMat, boxes: Tensor[Float]): Unit = { + (1 to boxes.size(1)).foreach(i => { + val bbox = BoundingBox(boxes.valueAt(i, 1), boxes.valueAt(i, 2), + boxes.valueAt(i, 3), boxes.valueAt(i, 4)) + mat.drawBoundingBox(bbox, "") + }) + } + + "ImageAugmentation" should "work properly" in { + val imageFrame = ImageFrame.read(resource.getFile) + val imgAug = ColorJitter() -> + Expand() -> + Resize(300, 300, -1) -> + HFlip() -> + ChannelNormalize(123, 117, 104) -> + MatToFloats(validHeight = 300, validWidth = 300) + val out = imgAug(imageFrame) + val feature = out.asInstanceOf[LocalImageFrame].array(0) + feature.isValid should be(true) + feature.getOriginalHeight should be (375) + feature.getOriginalWidth should be (500) + feature.getHeight should be (300) + feature.getWidth should be (300) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala index ac9a5d722da..933356e62bd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala @@ -17,10 +17,11 @@ package com.intel.analytics.bigdl.transform.vision.image import com.google.common.io.Files +import com.intel.analytics.bigdl.transform.vision.image.augmentation.HFlip import com.intel.analytics.bigdl.utils.Engine import org.apache.commons.io.FileUtils import org.apache.spark.SparkContext -import org.apache.spark.sql.{SQLContext} +import org.apache.spark.sql.SQLContext import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class ImageFrameSpec extends FlatSpec with Matchers with BeforeAndAfter { @@ -40,9 +41,10 @@ class ImageFrameSpec extends FlatSpec with Matchers with BeforeAndAfter { "read LocalImageFrame" should "work properly" in { val local = ImageFrame.read(resource.getFile).asInstanceOf[LocalImageFrame] local.array.length should be(1) - assert(local.array(0).uri.endsWith("000025.jpg")) - assert(local.array(0).bytes.length == 95959) - local.array(0).opencvMat().shape() should be((375, 500, 3)) + val imf = local.array(0) + assert(imf.uri.endsWith("000025.jpg")) + assert(imf.bytes.length == 95959) + imf.opencvMat().shape() should be((375, 500, 3)) } "LocalImageFrame toDistributed" should "work properly" in { @@ -88,4 +90,10 @@ class ImageFrameSpec extends FlatSpec with Matchers with BeforeAndAfter { val images4 = ImageFrame.read(resource.getFile + "0000251.jpg").asInstanceOf[LocalImageFrame] images4.array.length should be (0) } + + "transform" should "work" in { + val transformer = BytesToMat() -> HFlip() + val images = ImageFrame.read(resource.getFile) + images.transform(transformer) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/BrightnessSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/BrightnessSpec.scala new file mode 100644 index 00000000000..ca8336db569 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/BrightnessSpec.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class BrightnessSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + + "Brightness" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Brightness(0, 32) + val transformed = transformer(data).asInstanceOf[LocalImageFrame] + val imf = transformed.array.head + imf.getHeight() should be (imf.getOriginalHeight) + imf.getWidth() should be (imf.getOriginalWidth) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelNormalizeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelNormalizeSpec.scala new file mode 100644 index 00000000000..333e17b6848 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelNormalizeSpec.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame, MatToFloats} +import org.scalatest.{FlatSpec, Matchers} + +class ChannelNormalizeSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + + "ChannelNormalize" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = ChannelNormalize(100, 200, 300) -> MatToFloats() + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + + val toFloat = MatToFloats(meanRGB = Some(100f, 200f, 300f)) + val data2 = ImageFrame.read(resource.getFile) + val transformed2 = toFloat(data2) + val imf2 = transformed2.asInstanceOf[LocalImageFrame].array(0) + imf2.floats().length should be (375 * 500 * 3) + imf2.floats() should equal(imf.floats()) + } + + "ChannelNormalize with std not 1" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = ChannelNormalize(100, 200, 300, 2, 2, 2) -> MatToFloats() + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + + val data2 = ImageFrame.read(resource.getFile) + val toFloat = MatToFloats(meanRGB = Some(100f, 200f, 300f)) + val transformed2 = toFloat(data2) + val imf2 = transformed2.asInstanceOf[LocalImageFrame].array(0) + + imf2.floats().length should be (375 * 500 * 3) + imf2.floats().map(_ / 2) should equal(imf.floats()) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelOrderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelOrderSpec.scala new file mode 100644 index 00000000000..e70f4e41aa8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelOrderSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class ChannelOrderSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "ChannelOrder" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = ChannelOrder() + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (imf.getOriginalHeight) + imf.getWidth() should be (imf.getOriginalWidth) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ColorJitterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ColorJitterSpec.scala new file mode 100644 index 00000000000..32ca1b4c1d5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ColorJitterSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class ColorJitterSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "ColorJitter" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = ColorJitter() + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (imf.getOriginalHeight) + imf.getWidth() should be (imf.getOriginalWidth) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } + + "ColorJitter shuffle" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = ColorJitter(shuffle = true) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (imf.getOriginalHeight) + imf.getWidth() should be (imf.getOriginalWidth) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ContrastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ContrastSpec.scala new file mode 100644 index 00000000000..d8533b43f82 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ContrastSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class ContrastSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "Contrast" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Contrast(0.5, 1.5) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (imf.getOriginalHeight) + imf.getWidth() should be (imf.getOriginalWidth) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/CropSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/CropSpec.scala new file mode 100644 index 00000000000..10dcc76cb76 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/CropSpec.scala @@ -0,0 +1,106 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import com.intel.analytics.bigdl.utils.T +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class CropSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "centercrop" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = CenterCrop(50, 50) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (50) + imf.getWidth() should be (50) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } + + "randomcrop" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = RandomCrop(200, 200) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (200) + imf.getWidth() should be (200) + + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } + + "fixedCrop" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = FixedCrop(0, 0, 50, 50, false) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (50) + imf.getWidth() should be (50) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } + + "fixedCrop normalized" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = FixedCrop(0, 0, 50 / 500f, 50 / 375f, true) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (50) + imf.getWidth() should be (50) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } + + "fixedCrop clip" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = FixedCrop(0, 0, 600f, 700f, true) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (375) + imf.getWidth() should be (500) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } + + "Detection Crop" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val imf = data.asInstanceOf[LocalImageFrame].array(0) + imf("roi") = Tensor[Float](T(1, 1, 0.2, 0, 0, 0.5, 0.5)) + val transformer = DetectionCrop("roi") + transformer(data) + imf.getHeight() should be (187) + imf.getWidth() should be (250) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ExpandSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ExpandSpec.scala new file mode 100644 index 00000000000..2ea9954b9f1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ExpandSpec.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class ExpandSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "expand" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Expand(minExpandRatio = 2, maxExpandRatio = 2) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (375 * 2) + imf.getWidth() should be (500 * 2) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/FillerSepc.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/FillerSepc.scala new file mode 100644 index 00000000000..aeda2b969cb --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/FillerSepc.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame, MatToFloats} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class FillerSepc extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + + "Filler all" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Filler(0, 0, 1, 1, 255) -> MatToFloats() + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.floats().forall(x => x == 255) should be (true) + } + + "Filler part" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Filler(0, 0, 1, 0.5f, 255) -> MatToFloats() + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.floats().slice(0, 3 * 375 * 250).forall(_ == 255) should be (true) + } + + "Filler part2" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Filler(0, 0, 1, 0.5f, 255) + val transformed = transformer(data) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HFlipSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HFlipSpec.scala index da7a6604714..c0600cffd44 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HFlipSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HFlipSpec.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.transform.vision.image.augmentation -import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame} +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs import org.scalatest.{FlatSpec, Matchers} class HFlipSpec extends FlatSpec with Matchers { @@ -27,5 +28,9 @@ class HFlipSpec extends FlatSpec with Matchers { val transformed = hFlip(data).asInstanceOf[LocalImageFrame] transformed.array(0).getHeight() should be (transformed.array(0).getOriginalHeight) transformed.array(0).getWidth() should be (transformed.array(0).getOriginalWidth) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, transformed.array(0).opencvMat()) + println(tmpFile) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HueSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HueSpec.scala new file mode 100644 index 00000000000..01c6752be92 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/HueSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class HueSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "Hue" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Hue(-18, 18) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be (imageFeature.getOriginalHeight) + imageFeature.getWidth() should be (imageFeature.getOriginalWidth) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/PixelNormalizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/PixelNormalizerSpec.scala new file mode 100644 index 00000000000..d356fd84f9b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/PixelNormalizerSpec.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame, MatToFloats} +import org.scalatest.{FlatSpec, Matchers} + +class PixelNormalizerSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + + "PixelNormalizer" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val means = new Array[Float](375 * 500 * 3) + var i = 0 + while (i < 375 * 500 * 3) { + means(i) = 300f + means(i + 1) = 200f + means(i + 2) = 100f + i += 3 + } + val transformer = PixelNormalizer(means) -> MatToFloats() + val transformed = transformer(data) + + val data2 = ImageFrame.read(resource.getFile) + val toFloat = MatToFloats(meanRGB = Some(100f, 200f, 300f)) + val transformed2 = toFloat(data2) + + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + val imageFeature2 = transformed2.asInstanceOf[LocalImageFrame].array(0) + imageFeature2.floats().length should be (375 * 500 * 3) + imageFeature2.floats() should equal(imageFeature.floats()) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomTransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomTransformerSpec.scala new file mode 100644 index 00000000000..93efeff2b9d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomTransformerSpec.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import org.scalatest.{FlatSpec, Matchers} + +class RandomTransformerSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + + "RandomTransformer with 0" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = RandomTransformer(FixedCrop(0, 0, 50, 50, false), 0) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be(375) + imageFeature.getWidth() should be(500) + } + + "RandomTransformer with 1" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = RandomTransformer(FixedCrop(0, 0, 50, 50, false), 1) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be(50) + imageFeature.getWidth() should be(50) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala new file mode 100644 index 00000000000..f6a57cebc39 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class ResizeSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "resize" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Resize(300, 300) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be(300) + imageFeature.getWidth() should be(300) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + println(tmpFile) + } + + "AspectScale" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = AspectScale(750, maxSize = 3000) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be(750) + imageFeature.getWidth() should be(1000) + + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + println(tmpFile) + } + + "RandomAspectScale" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = RandomAspectScale(Array(750), maxSize = 3000) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be(750) + imageFeature.getWidth() should be(1000) + } + + "getWidthHeightAfterRatioScale" should "work" in { + val img = OpenCVMat.read(resource.getFile + "/000025.jpg") + val (height, width) = AspectScale.getHeightWidthAfterRatioScale(img, 600, 1000, 1) + height should be (600) + width should be (800) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/SaturationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/SaturationSpec.scala new file mode 100644 index 00000000000..78372c5e674 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/SaturationSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFeature, ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class SaturationSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "Saturation" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Saturation(10, 20) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be (imageFeature.getOriginalHeight) + imageFeature.getWidth() should be (imageFeature.getOriginalWidth) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/label/roi/BatchSamplerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/label/roi/BatchSamplerSpec.scala new file mode 100644 index 00000000000..33f5ed720d0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/label/roi/BatchSamplerSpec.scala @@ -0,0 +1,100 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.label.roi + +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.transform.vision.image.util.BoundingBox +import org.scalatest.{FlatSpec, Matchers} + +import scala.collection.mutable.ArrayBuffer + +class BatchSamplerSpec extends FlatSpec with Matchers { + "batch sampler with no change" should "work properly" in { + val sampler = new BatchSampler(maxTrials = 1) + val unitBox = BoundingBox(0, 0, 1, 1) + val boxes = Tensor(Storage(Array(0.582296, 0.334719, 0.673582, 0.52183, + 0.596127, 0.282744, 0.670816, 0.449064, + 0.936376, 0.627859, 0.961272, 0.733888, + 0.896266, 0.640333, 0.923928, 0.740125).map(x => x.toFloat))).resize(4, 4) + val classes = Tensor[Float](4).randn() + val target = RoiLabel(classes, boxes) + val sampledBoxes = new ArrayBuffer[BoundingBox]() + sampler.sample(unitBox, target, sampledBoxes) + + sampledBoxes.length should be(1) + sampledBoxes(0) should be(unitBox) + } + + "satisfySampleConstraint with minOverlap 0.1" should "work properly" in { + val boxes = Tensor(Storage(Array(0.418, 0.396396, 0.55, 0.666667, + 0.438, 0.321321, 0.546, 0.561562, + 0.93, 0.81982, 0.966, 0.972973, + 0.872, 0.837838, 0.912, 0.981982).map(x => x.toFloat))).resize(4, 4) + val classes = Tensor[Float](4).randn() + val target = RoiLabel(classes, boxes) + + val sampledBox = BoundingBox(0.114741f, 0.248062f, 0.633665f, 0.763736f) + val sampler = new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.1)) + + sampler.satisfySampleConstraint(sampledBox, target) should be(true) + } + + "satisfySampleConstraint with minOverlap 0.3" should "work properly" in { + val boxes = Tensor(Storage(Array(0.418, 0.396396, 0.55, 0.666667, + 0.438, 0.321321, 0.546, 0.561562, + 0.93, 0.81982, 0.966, 0.972973, + 0.872, 0.837838, 0.912, 0.981982).map(x => x.toFloat))).resize(4, 4) + val classes = Tensor[Float](4).randn() + val target = RoiLabel(classes, boxes) + + val sampledBox = BoundingBox(0.266885f, 0.416113f, 0.678256f, 0.67208f) + val sampler = new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.3)) + + sampler.satisfySampleConstraint(sampledBox, target) should be(true) + } + + "batch samplers" should "work properly" in { + val boxes = Tensor(Storage(Array(0.418, 0.396396, 0.55, 0.666667, + 0.438, 0.321321, 0.546, 0.561562, + 0.93, 0.81982, 0.966, 0.972973, + 0.872, 0.837838, 0.912, 0.981982).map(x => x.toFloat))).resize(4, 4) + val classes = Tensor[Float](4).randn() + val target = RoiLabel(classes, boxes) + val sampledBoxes = new ArrayBuffer[BoundingBox]() + val batchSamplers = Array( + new BatchSampler(maxTrials = 1), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.1)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.3)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.5)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.7)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + minOverlap = Some(0.9)), + new BatchSampler(minScale = 0.3, minAspectRatio = 0.5, maxAspectRatio = 2, + maxOverlap = Some(1.0))) + BatchSampler.generateBatchSamples(target, batchSamplers, sampledBoxes) + + sampledBoxes.foreach(box => { + println(box) + }) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/label/roi/RoiTransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/label/roi/RoiTransformerSpec.scala new file mode 100644 index 00000000000..c031d4d093d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/label/roi/RoiTransformerSpec.scala @@ -0,0 +1,225 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.label.roi + +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, HFlip, Resize} +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.transform.vision.image.util.{BboxUtil, BoundingBox} +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFeature, ImageFrame, LocalImageFrame} +import com.intel.analytics.bigdl.utils.T +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class RoiTransformerSpec extends FlatSpec with Matchers { + private def classes = Array(11.0, 11.0, 11.0, 16.0, 16.0, 16.0, 11.0, 16.0, + 16.0, 16.0, 16.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 1.0).map(_.toFloat) + + private def visualize(mat: OpenCVMat, boxes: Tensor[Float]): Unit = { + (1 to boxes.size(1)).foreach(i => { + val bbox = BoundingBox(boxes.valueAt(i, 1), boxes.valueAt(i, 2), + boxes.valueAt(i, 3), boxes.valueAt(i, 4)) + mat.drawBoundingBox(bbox, "") + }) + } + + private def boxes = Array(2.0, 84.0, 59.0, 248.0, + 68.0, 115.0, 233.0, 279.0, + 64.0, 173.0, 377.0, 373.0, + 320.0, 2.0, 496.0, 375.0, + 221.0, 4.0, 341.0, 374.0, + 135.0, 14.0, 220.0, 148.0, + 69.0, 43.0, 156.0, 177.0, + 58.0, 54.0, 104.0, 139.0, + 279.0, 1.0, 331.0, 86.0, + 320.0, 22.0, 344.0, 96.0, + 337.0, 1.0, 390.0, 107.0).map(_.toFloat) + + private def getLables = { + RoiLabel(Tensor(Storage(classes)).resize(2, 11), + Tensor(Storage(boxes)).resize(11, 4)) + } + + val resource = getClass.getClassLoader.getResource("pascal/") + + "RoiNormalize" should "work properly" in { + val label = getLables + val images = ImageFrame.read(resource.getFile) + val imageFeature = images.asInstanceOf[LocalImageFrame].array(0) + imageFeature(ImageFeature.label) = label + + val transformer = RoiNormalize() + transformer(images) + + imageFeature.getLabel[RoiLabel].classes should be(label.classes) + + val expected = Tensor[Float](T(0.004, 0.22399999, 0.11800001, 0.6613333, + 0.136, 0.30666667, 0.46600002, 0.744, + 0.128, 0.46133333, 0.754, 0.99466664, + 0.64000005, 0.0053333333, 0.99200004, 1.0, + 0.44200003, 0.010666667, 0.68200004, 0.99733335, + 0.27, 0.037333332, 0.44000003, 0.39466667, + 0.13800001, 0.11466666, 0.312, 0.472, + 0.116000004, 0.144, 0.208, 0.37066665, + 0.558, 0.0026666666, 0.66200006, 0.22933333, + 0.64000005, 0.058666665, 0.688, 0.25599998, + 0.674, 0.0026666666, 0.78000003, 0.28533334)).resize(11, 4) + + imageFeature.getLabel[RoiLabel].bboxes should be(expected) + } + + "RoiHFlip" should "work properly" in { + val label = getLables + val images = ImageFrame.read(resource.getFile) + val imageFeature = images.asInstanceOf[LocalImageFrame].array(0) + imageFeature(ImageFeature.label) = label + + val transformer = HFlip() -> RoiHFlip(false) + transformer(images) + + imageFeature.getLabel[RoiLabel].classes should be(label.classes) + + val boxes = imageFeature.getLabel[RoiLabel].bboxes + val expected = Tensor[Float](T(441.0, 84.0, 498.0, 248.0, + 267.0, 115.0, 432.0, 279.0, + 123.0, 173.0, 436.0, 373.0, + 4.0, 2.0, 180.0, 375.0, + 159.0, 4.0, 279.0, 374.0, + 280.0, 14.0, 365.0, 148.0, + 344.0, 43.0, 431.0, 177.0, + 396.0, 54.0, 442.0, 139.0, + 169.0, 1.0, 221.0, 86.0, + 156.0, 22.0, 180.0, 96.0, + 110.0, 1.0, 163.0, 107.0)).resize(11, 4) + boxes should be(expected) + + visualize(imageFeature.opencvMat(), expected) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + println(tmpFile) + } + + "RoiHFlip normalized" should "work properly" in { + val label = getLables + val images = ImageFrame.read(resource.getFile) + val imageFeature = images.asInstanceOf[LocalImageFrame].array(0) + imageFeature(ImageFeature.label) = label + + val transformer = RoiNormalize() -> RoiHFlip() + transformer(images) + + imageFeature.getLabel[RoiLabel].classes should be(label.classes) + + val boxes = imageFeature.getLabel[RoiLabel].bboxes + val expected = Tensor[Float](T(0.88199997, 0.22399999, 0.996, 0.6613333, + 0.534, 0.30666667, 0.86399996, 0.744, + 0.24599999, 0.46133333, 0.872, 0.99466664, + 0.007999957, 0.0053333333, 0.35999995, 1.0, + 0.31799996, 0.010666667, 0.55799997, 0.99733335, + 0.55999994, 0.037333332, 0.73, 0.39466667, + 0.68799996, 0.11466666, 0.862, 0.472, + 0.792, 0.144, 0.884, 0.37066665, + 0.33799994, 0.0026666666, 0.44199997, 0.22933333, + 0.31199998, 0.058666665, 0.35999995, 0.25599998, + 0.21999997, 0.0026666666, 0.32599998, 0.28533334)).resize(11, 4) + boxes should be(expected) + } + + "RoiResize normalized" should "work properly" in { + val label = getLables + val images = ImageFrame.read(resource.getFile) -> Resize(300, 300) + val imageFeature = images.asInstanceOf[LocalImageFrame].array(0) + imageFeature(ImageFeature.label) = label + + val transformer = RoiNormalize() -> RoiResize() + transformer(images) + + imageFeature.getLabel[RoiLabel].classes should be(label.classes) + + val boxes = imageFeature.getLabel[RoiLabel].bboxes + val expected = Tensor[Float](T(0.004, 0.22399999, 0.11800001, 0.6613333, + 0.136, 0.30666667, 0.46600002, 0.744, + 0.128, 0.46133333, 0.754, 0.99466664, + 0.64000005, 0.0053333333, 0.99200004, 1.0, + 0.44200003, 0.010666667, 0.68200004, 0.99733335, + 0.27, 0.037333332, 0.44000003, 0.39466667, + 0.13800001, 0.11466666, 0.312, 0.472, + 0.116000004, 0.144, 0.208, 0.37066665, + 0.558, 0.0026666666, 0.66200006, 0.22933333, + 0.64000005, 0.058666665, 0.688, 0.25599998, + 0.674, 0.0026666666, 0.78000003, 0.28533334)).resize(11, 4) + boxes should be(expected) + } + + "RoiResize normalized == false" should "work properly" in { + val label = getLables + val images = ImageFrame.read(resource.getFile) -> Resize(300, 300) + val imageFeature = images.asInstanceOf[LocalImageFrame].array(0) + imageFeature(ImageFeature.label) = label + + val transformer = RoiResize() + transformer(images) + + imageFeature.getLabel[RoiLabel].classes should be(label.classes) + + val boxes = imageFeature.getLabel[RoiLabel].bboxes + val expected = Tensor[Float](T(1.2, 67.200005, 35.4, 198.40001, + 40.800003, 92.0, 139.8, 223.2, + 38.4, 138.40001, 226.20001, 298.4, + 192.0, 1.6, 297.6, 300.0, + 132.6, 3.2, 204.6, 299.2, + 81.0, 11.2, 132.0, 118.4, + 41.4, 34.4, 93.600006, 141.6, + 34.800003, 43.2, 62.4, 111.200005, + 167.40001, 0.8, 198.6, 68.8, + 192.0, 17.6, 206.40001, 76.8, + 202.20001, 0.8, 234.00002, 85.6)).resize(11, 4) + boxes should be(expected) + + visualize(imageFeature.opencvMat(), expected) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + println(tmpFile) + } + + "RoiProject" should "work properly" in { + val label = getLables + val images = ImageFrame.read(resource.getFile) + val imageFeature = images.asInstanceOf[LocalImageFrame].array(0) + imageFeature(ImageFeature.label) = label + + val transformer = CenterCrop(300, 300) -> RoiNormalize() -> RoiProject() + transformer(images) + + val boxes = imageFeature.getLabel[RoiLabel].bboxes + val expected = Tensor[Float](T(0.0, 0.25833336, 0.44333336, 0.805, + 0.0, 0.45166665, 0.9233333, 1.0, + 0.40333334, 0.0, 0.8033333, 1.0, + 0.116666675, 0.0, 0.4, 0.36833334, + 0.0, 0.01833333, 0.1866667, 0.46500003, + 0.5966667, 0.0, 0.77, 0.16166666, + 0.73333335, 0.0, 0.8133333, 0.195, + 0.78999996, 0.0, 0.9666667, 0.23166668)).resize(8, 4) + boxes should be(expected) + + BboxUtil.scaleBBox(expected, 300, 300) + visualize(imageFeature.opencvMat(), expected) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala index e23f1449f74..0bce05388c7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala @@ -19,11 +19,14 @@ package com.intel.analytics.bigdl.transform.vision.image.opencv import java.io.File import com.intel.analytics.bigdl.opencv.OpenCV +import com.intel.analytics.bigdl.transform.vision.image.util.BoundingBox +import com.intel.analytics.bigdl.utils.Engine import org.apache.commons.io.FileUtils +import org.apache.spark.SparkContext import org.opencv.imgcodecs.Imgcodecs -import org.scalatest.{FlatSpec, Matchers} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} -class OpenCVMatSpec extends FlatSpec with Matchers { +class OpenCVMatSpec extends FlatSpec with Matchers with BeforeAndAfter { val resource = getClass().getClassLoader().getResource("pascal/000025.jpg") "toFloatsPixels" should "work properly" in { @@ -61,9 +64,9 @@ class OpenCVMatSpec extends FlatSpec with Matchers { val img = Imgcodecs.imread(resource.getFile) val bytes = FileUtils.readFileToByteArray(new File(resource.getFile)) val mat = OpenCVMat.fromImageBytes(bytes) - img.height() should be (mat.height()) - img.width() should be (mat.width()) - img.channels() should be (mat.channels()) + img.height() should be(mat.height()) + img.width() should be(mat.width()) + img.channels() should be(mat.channels()) val bytes1 = OpenCVMat.toBytePixels(img) val bytes2 = OpenCVMat.toBytePixels(mat) bytes1._1 should equal(bytes2._1) @@ -78,4 +81,41 @@ class OpenCVMatSpec extends FlatSpec with Matchers { bytes1._1 should equal(bytes2._1) } + + var sc: SparkContext = null + before { + val conf = Engine.createSparkConf().setAppName("ImageSpec").setMaster("local[2]") + sc = new SparkContext(conf) + Engine.init + } + + after { + if (null != sc) sc.stop() + } + + "serialize" should "work properly" in { + val img = OpenCVMat.read(resource.getFile) + val shape = img.shape() + val rdd = sc.parallelize(Array(img)) + val collect = rdd.collect() + collect(0).shape() should be(shape) + } + + "release" should "work properly" in { + val img = OpenCVMat.read(resource.getFile) + img.release() + img.isReleased should be (true) + img.shape() should be (0, 0, 3) + } + + "drawBoundingBox" should "work properly" in { + val img = OpenCVMat.read(resource.getFile) + val boundingBox = BoundingBox(2.0f, 84.0f, 59.0f, 248.0f, false) + val boundingBox2 = BoundingBox(68.0f, 115.0f, 233.0f, 279.0f, false) + img.drawBoundingBox(boundingBox, "boundingBox") + img.drawBoundingBox(boundingBox2, "boundingBox2") + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, img) + println(tmpFile) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/util/BoundingBoxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/util/BoundingBoxSpec.scala new file mode 100644 index 00000000000..51c2cd67062 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/util/BoundingBoxSpec.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.util + +import org.scalatest.{FlatSpec, Matchers} + +class BoundingBoxSpec extends FlatSpec with Matchers { + "scaleBBox" should "work properly" in { + val bbox = BoundingBox(1f, 4f, 5f, 6f, false) + val scaled = new BoundingBox() + bbox.scaleBox(1.0f / 4, 1.0f / 2, scaled) + + scaled should be (BoundingBox(0.5f, 1, 2.5f, 1.5f)) + } + + "meetEmitCenterConstraint" should "work properly" in { + val bbox = BoundingBox(0, 0, 5, 3, false) + val bbox2 = BoundingBox(1, 0, 7, 4, false) + bbox.meetEmitCenterConstraint(bbox2) should be (true) + } + + "meetEmitCenterConstraint false" should "work properly" in { + val bbox = BoundingBox(0, 0, 5, 3, false) + val bbox2 = BoundingBox(4, 0, 7, 4, false) + bbox.meetEmitCenterConstraint(bbox2) should be (false) + } +} From 04c55e74f03e696d3751916758b3e7e8d654e9ae Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Mon, 4 Dec 2017 13:12:38 +0800 Subject: [PATCH 0552/1065] Add include_container option for flattened_layers (#1941) * update * style --- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0f41e49666e..26e11285b03 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2270,18 +2270,25 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab module.modules.toList.asJava } - def getFlattenModules(module: Container[Activity, Activity, T]) + def getFlattenModules(module: Container[Activity, Activity, T], + includeContainer: Boolean) : JList[AbstractModule[Activity, Activity, T]] = { val result = ArrayBuffer[AbstractModule[Activity, Activity, T]]() - doGetFlattenModules(module, result) + doGetFlattenModules(module, includeContainer, result) result.toList.asJava } private def doGetFlattenModules(module: Container[Activity, Activity, T], + includeContainer: Boolean, result: ArrayBuffer[AbstractModule[Activity, Activity, T]]): Unit = { + if (includeContainer) { + result.append(module) + } module.modules.foreach {m => if (m.isInstanceOf[Container[Activity, Activity, T]]) { - doGetFlattenModules(m.asInstanceOf[Container[Activity, Activity, T]], result) + doGetFlattenModules(m.asInstanceOf[Container[Activity, Activity, T]], + includeContainer, + result) } else { result.append(m) } From bd34178f4a6bfd6470d5a6d35ba35b04c50be4db Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Mon, 4 Dec 2017 15:53:38 +0800 Subject: [PATCH 0553/1065] fix issue 1890 (#1974) * change default platform to all * fix error --- dist/pom.xml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index 3db927605a8..8ce97ad82bd 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -9,7 +9,7 @@ 4.0.0 - dist-spark-${spark.version}-scala-${scala.version}-${os-flag} + dist-spark-${spark.version}-scala-${scala.version}-${dist-os-name} jar @@ -40,7 +40,7 @@ single - bigdl-${project.version}-spark-${spark.version}-scala-${scala.version}-${os-flag} + bigdl-${project.version}-spark-${spark.version}-scala-${scala.version}-${dist-os-name} assembly/dist.xml From 5420625b016f8094b0f85a8599d0a273c9e49242 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Mon, 4 Dec 2017 16:31:47 +0800 Subject: [PATCH 0554/1065] Add an API to get node from graph model with given name (#1871) * get node from graph * add unit test * add Graph.node api in python --- .../analytics/bigdl/dllib/nn/Graph.scala | 15 +++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 4 ++++ .../analytics/bigdl/dllib/nn/GraphSpec.scala | 22 +++++++++++++++++++ 3 files changed, 41 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 604c585d4d0..fb02b3131a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -282,6 +282,21 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], // todo: expand the graph override def toGraph(startNodes: ModuleNode[T]*): Graph[T] = this + /** + * Return the corresponding node has the given name. If the given name doesn't match any node, + * NoSuchElementException will be thrown + * @param name + * @return + */ + def node(name: String): ModuleNode[T] = { + val matchNodes = backGraph.BFS.filter(_.element.getName() == name).toArray + if (matchNodes.length == 0) { + throw new NoSuchElementException(s"Can not find node with name $name") + } else { + return matchNodes.head + } + } + // Add a dummy output node, to get an one end graph. So the nodes that are not dependent by // the outputs will be excluded private val dummyOutput = new ModuleNode[T](new Identity[T]()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 26e11285b03..1235b750183 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2265,6 +2265,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab dlClassifierModel.setBatchSize(batchSize) } + def findGraphNode(model: Graph[T], name: String): ModuleNode[T] = { + model.node(name) + } + def getContainerModules(module: Container[Activity, Activity, T]) : JList[AbstractModule[Activity, Activity, T]] = { module.modules.toList.asJava diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index a54cc863f66..9eb17e81232 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -1238,6 +1238,28 @@ class GraphSpec extends FlatSpec with Matchers { isExecuted2 should be(false) isExecuted3 should be(false) } + + "Graph get name" should "be correct" in { + val data = Identity().setName("input").inputs() + val const = Const(Tensor(T(1, 2))).setName("const").inputs() + var isExecuted1 = false + val l1 = Echo().setName("l1").setBeval((a, b, c) => isExecuted1 = true).inputs(const) + val cadd = CAddTable().setName("cadd").inputs(data, l1) + val l2 = Identity().setName("l2").inputs(cadd) + var isExecuted2 = false + var isExecuted3 = false + val echo = Echo().setName("echo") + .setFeval((a, b) => isExecuted2 = true) + .setBeval((a, b, c) => isExecuted3 = true).inputs(cadd) + val l3 = Identity().setName("l3").inputs(echo) + + val model = Graph(data, l2) + model.node("l1") should be(l1) + + intercept[NoSuchElementException] { + model.node("ll1") + } + } } object ModelUntils { From 580e38435a41f066a5e952402cedccce312f1ea3 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 4 Dec 2017 17:24:31 +0800 Subject: [PATCH 0555/1065] Add loadTF API doc (#1959) * add loadTF API doc * refine * mention dump_model * pack script * refine --- dist/assembly/dist.xml | 1 + 1 file changed, 1 insertion(+) diff --git a/dist/assembly/dist.xml b/dist/assembly/dist.xml index 389a9246643..9a05ebca764 100644 --- a/dist/assembly/dist.xml +++ b/dist/assembly/dist.xml @@ -24,6 +24,7 @@ run.example.sh dump_tf_graph.py launch-dataproc.sh + export_tf_checkpoint.py From f341d338a00b8132c3fca2fd401bb59f888f2d1b Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Tue, 5 Dec 2017 09:15:49 +0800 Subject: [PATCH 0556/1065] An Activity wrapper for python to simplify the returning value (#1962) * update add test remove jtable clean clean clean * sort list * clean * update and use toSeq * add toSeqActivity clean * use D --- .../analytics/bigdl/dllib/utils/Table.scala | 6 +- .../dllib/utils/python/api/BigDLSerde.scala | 37 ++++++++- .../dllib/utils/python/api/PythonBigDL.scala | 4 + .../python/api/PythonBigDLValidator.scala | 81 +++++++++++++++++++ .../bigdl/dllib/utils/tf/Session.scala | 4 +- .../bigdl/dllib/optim/TableSpec.scala | 2 +- .../bigdl/dllib/python/api/PythonSpec.scala | 11 ++- 7 files changed, 137 insertions(+), 8 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLValidator.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala index e567af0b5ff..455e0616a70 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala @@ -293,12 +293,12 @@ class Table private[bigdl]( * Return the elements of this table as a Seq. * This method assumes the key of this table are all * the integers between 1 to this.length(), - * the values are all Tensor[T] + * the values are all D */ - def toSeq[T]: Seq[Tensor[T]] = { + def toSeq[D]: Seq[D] = { for (i <- 0 until this.length()) yield { try { - this(i + 1).asInstanceOf[Tensor[T]] + this(i + 1).asInstanceOf[D] } catch { case e: NoSuchElementException => throw new UnsupportedOperationException("toSeq requires the key of this table are" + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala index ed2f5575ba3..fc2273dedd4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala @@ -23,7 +23,9 @@ import java.nio.charset.StandardCharsets import java.nio.{ByteBuffer, ByteOrder} import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} -import com.intel.analytics.bigdl.python.api.{EvaluatedResult, JTensor, Sample} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.python.api._ +import com.intel.analytics.bigdl.utils.Table import net.razorvine.pickle._ import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.python.SerDeUtil @@ -223,6 +225,38 @@ object BigDLSerDe extends BigDLSerDeBase with Serializable { } } + private[python] class JActivityPickler extends BigDLBasePickler[JActivity] { + private def doConvertTable(table: Table): Any = { + val valuesOrderByKey = table.toSeq[Activity] + if (valuesOrderByKey.isEmpty) { + throw new RuntimeException("Found empty table") + } + return valuesOrderByKey.map { item => doConvertActivity(item) }.asJava + } + private def doConvertActivity(activity: Activity): Any = { + if (activity.isTable) { + return doConvertTable(activity.toTable) + } else if (activity.isTensor) { + return PythonBigDL.ofFloat().toJTensor(activity.toTensor) + } else { + throw new RuntimeException(s"""not supported type: + ${activity.getClass.getSimpleName}""") + } + } + + def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = { + val record = obj.asInstanceOf[JActivity] + saveObjects(out, + pickler, + doConvertActivity(record.value)) + } + + def construct(args: Array[Object]): Object = { + throw new RuntimeException("haven't be implemented") + } + } + + private[python] class TestResultPickler extends BigDLBasePickler[EvaluatedResult] { def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = { @@ -286,6 +320,7 @@ object BigDLSerDe extends BigDLSerDeBase with Serializable { new SamplePickler().register() new TestResultPickler().register() new JTensorPickler().register() + new JActivityPickler().register() initialized = true } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 1235b750183..d6000a2074b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -51,6 +51,8 @@ import org.opencv.imgproc.Imgproc import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable +import scala.collection.mutable.{ArrayBuffer, Map} import scala.language.existentials import scala.reflect.ClassTag @@ -68,6 +70,8 @@ case class Sample(features: JList[JTensor], case class JTensor(storage: Array[Float], shape: Array[Int], bigdlType: String, indices: Array[Array[Int]] = null) +case class JActivity(value: Activity) + /** * [[ValidationResult]] for python * @param result result diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLValidator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLValidator.scala new file mode 100644 index 00000000000..6d1b6e46afa --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLValidator.scala @@ -0,0 +1,81 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.python.api + +import java.lang.{Boolean => JBoolean} +import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.collection.JavaConverters._ +import scala.collection.mutable.Map +import scala.language.existentials +import scala.reflect.ClassTag + +object PythonBigDLValidator { + + def ofFloat(): PythonBigDLValidator[Float] = new PythonBigDLValidator[Float]() + + def ofDouble(): PythonBigDLValidator[Double] = new PythonBigDLValidator[Double]() +} + +class PythonBigDLValidator[T: ClassTag](implicit ev: TensorNumeric[T]) extends PythonBigDL[T]{ + + def testDict(): JMap[String, String] = { + return Map("jack" -> "40", "lucy" -> "50").asJava + } + + def testDictJTensor(): JMap[String, JTensor] = { + return Map("jack" -> JTensor(Array(1.0f, 2.0f, 3.0f, 4.0f), Array(4, 1), "float")).asJava + } + + def testDictJMapJTensor(): JMap[String, JMap[String, JTensor]] = { + val table = new Table() + val tensor = JTensor(Array(1.0f, 2.0f, 3.0f, 4.0f), Array(4, 1), "float") + val result = Map("jack" -> tensor).asJava + table.insert(tensor) + return Map("nested" -> result).asJava + } + + def testActivityWithTensor(): JActivity = { + val tensor = Tensor(Array(1.0f, 2.0f, 3.0f, 4.0f), Array(4, 1)) + return JActivity(tensor) + } + + def testActivityWithTableOfTensor(): JActivity = { + val tensor1 = Tensor(Array(1.0f, 1.0f), Array(2)) + val tensor2 = Tensor(Array(2.0f, 2.0f), Array(2)) + val tensor3 = Tensor(Array(3.0f, 3.0f), Array(2)) + val table = new Table() + table.insert(tensor1) + table.insert(tensor2) + table.insert(tensor3) + return JActivity(table) + } + + def testActivityWithTableOfTable(): JActivity = { + val tensor = Tensor(Array(1.0f, 2.0f, 3.0f, 4.0f), Array(4, 1)) + val table = new Table() + table.insert(tensor) + val nestedTable = new Table() + nestedTable.insert(table) + nestedTable.insert(table) + return JActivity(nestedTable) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala index d0fca80bd10..75d686392fd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Session.scala @@ -446,7 +446,7 @@ class BigDLSessionImpl[T: ClassTag](graph: Seq[NodeDef], context: Context[T], iter.next() } - val batch = tables.map(_.toSeq) + val batch = tables.map(_.toSeq[Tensor[T]]) val firstSeq = batch.head val sizes = firstSeq.map { tensor => val nDim = tensor.nDimension() @@ -732,7 +732,7 @@ object BigDLSessionImpl { private def toSample[T: ClassTag](rdd: RDD[Table]) (implicit ev: TensorNumeric[T]): RDD[Sample[T]] = { rdd.map{ t => - val arr = t.toSeq[T].toArray + val arr = t.toSeq[Tensor[T]].toArray Sample[T](arr) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/TableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/TableSpec.scala index a24a309bdff..965295efd0b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/TableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/TableSpec.scala @@ -286,6 +286,6 @@ class TableSpec extends FlatSpec with Matchers { val t = T(Tensor[Double](T(1.0)), Tensor[Double](T(2.0))) - t.toSeq[Double] should be (Seq(Tensor[Double](T(1.0)), Tensor[Double](T(2.0)))) + t.toSeq[Tensor[Double]] should be (Seq(Tensor[Double](T(1.0)), Tensor[Double](T(2.0)))) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index 86026d4980e..8b9c8834c77 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -22,7 +22,7 @@ import java.util.{ArrayList => JArrayList, List => JList, Map => JMap} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{Loss, SGD, Top1Accuracy, Trigger} -import com.intel.analytics.bigdl.utils.{Engine, T, TestUtils} +import com.intel.analytics.bigdl.utils.{Engine, T, Table, TestUtils} import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext @@ -54,6 +54,15 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { } } + "pickle activity" should "be test" in { + val tensor = Tensor(Array(1.0f, 2.0f, 3.0f, 4.0f), Array(4, 1)) + val table = new Table() + table.insert(tensor) + table.insert(tensor) + val r = JActivity(table) + org.apache.spark.bigdl.api.python.BigDLSerDe.dumps(r) + } + "model forward and backward with sigle tensor input" should "be test" in { val linear = Linear[Float](4, 5) val input: Tensor[Float] = Tensor[Float](4).apply1(_ => Random.nextFloat()) From bff06767b4370856a5fdf3060d1e6db3d80a3f4e Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 5 Dec 2017 15:07:59 +0800 Subject: [PATCH 0557/1065] Support NHWC for LRN and batch normalization, and optimize batch normalization speed (#1936) * add LRN and LRNGrad * add LRNGrad * support bn with NHWC * support bn with NHWC * add batch norm grad * support tf bnv2/bngradv2 * fix uts * fix uts * fix ut * fix ut * fix ut * fix ut * fix style issue * fix ut * fix ut * fix ut * update python api and docs * meet code review * meet code review * fix ut * fix ut * fix ut --- .../bigdl/dllib/nn/BatchNormalization.scala | 682 ++----- .../dllib/nn/SpatialBatchNormalization.scala | 1744 ++++++++++++++++- .../bigdl/dllib/nn/SpatialCrossMapLRN.scala | 158 +- .../bigdl/dllib/nn/ops/FusedBatchNorm.scala | 125 ++ .../dllib/nn/ops/FusedBatchNormGrad.scala | 86 + .../bigdl/dllib/nn/ops/LRNGrad.scala | 76 + .../bigdl/dllib/tensor/DenseTensor.scala | 4 +- .../dllib/utils/python/api/PythonBigDL.scala | 11 +- .../bigdl/dllib/utils/tf/Tensorflow.scala | 4 + .../utils/tf/loaders/FusedBatchNorm.scala | 46 + .../utils/tf/loaders/FusedBatchNormGrad.scala | 42 + .../tf/loaders/FusedBatchNormGradV2.scala | 42 + .../utils/tf/loaders/FusedBatchNormV2.scala | 48 + .../bigdl/dllib/utils/tf/loaders/LRN.scala | 47 + .../dllib/utils/tf/loaders/LRNGrad.scala | 41 + .../nn/SpatialBatchNormalizationSpec.scala | 104 + .../bigdl/dllib/nn/TimeDistributedSpec.scala | 23 +- .../dllib/torch/BatchNormalizationSpec.scala | 6 +- .../bigdl/dllib/torch/ConcatSpec.scala | 30 +- .../torch/SpatialBatchNormalizationSpec.scala | 37 +- .../tf/loaders/FusedBatchNormGradSpec.scala | 145 ++ .../tf/loaders/FusedBatchNormGradV2Spec.scala | 157 ++ .../utils/tf/loaders/FusedBatchNormSpec.scala | 145 ++ .../tf/loaders/FusedBatchNormV2Spec.scala | 156 ++ .../dllib/utils/tf/loaders/LRNGradSpec.scala | 64 + .../dllib/utils/tf/loaders/LRNSpec.scala | 55 + 26 files changed, 3426 insertions(+), 652 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNorm.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRN.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index b74466abac5..e8f0b813188 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -17,15 +17,13 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Initializable, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} -import com.intel.analytics.bigdl.utils.{Engine, T, Table} -import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} -import scala.concurrent.Future import scala.reflect.ClassTag /** @@ -60,13 +58,14 @@ class BatchNormalization[T: ClassTag]( private val initGradBias: Tensor[T] = null )(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { - require(nOutput > 0) + require(nOutput > 0, "output feature map number must be greater than zero") val nDim = 2 + val channelDim = 2 var runningMean = if (affine) Tensor[T](nOutput) else Tensor[T]() - var runningVar = if (affine) Tensor[T](nOutput).fill(ev.fromType[Int](1)) else Tensor[T]() + var runningVar = if (affine) Tensor[T](nOutput).fill(ev.one) else Tensor[T]() var saveMean = if (affine) Tensor[T](nOutput) else Tensor[T]() - var saveStd = if (affine) Tensor[T](nOutput).fill(ev.fromType[Int](1)) else Tensor[T]() + var saveStd = if (affine) Tensor[T](nOutput).fill(ev.zero) else Tensor[T]() val weight: Tensor[T] = if (initWeight != null) initWeight else if (affine) Tensor[T](nOutput) else null @@ -78,13 +77,11 @@ class BatchNormalization[T: ClassTag]( val gradBias: Tensor[T] = if (initGradBias != null) initGradBias else if (affine) Tensor[T](nOutput) else null - @transient - private var results : Array[Future[_]] = null @transient // BatchNormalization has internal parameters (saveMean, saveStd) // that changes at every forward, so a standard gradcheck won't work with this module. // if you want to do a gradcheck, you will need to fix those variables, otherwise not fix. - private var needFix: Boolean = false + protected var needFix: Boolean = false { val wInit = RandomUniform(0, 1) @@ -112,13 +109,13 @@ class BatchNormalization[T: ClassTag]( } @inline - private def checkInputDim(input: Tensor[T]): Unit = { + protected def checkInputDim(input: Tensor[T]): Unit = { require(input.dim() == nDim || (input.dim() == nDim - 1 && train == false), s"only mini-batch supported (${nDim}D tensor), got ${input.dim()}D tensor instead") } @inline - private def makeBatch(input: Tensor[T]): Tensor[T] = { + protected def makeBatch(input: Tensor[T]): Tensor[T] = { if (input.dim() == nDim - 1 && train == false) { input.addSingletonDimension() } else { @@ -127,544 +124,21 @@ class BatchNormalization[T: ClassTag]( } @inline - private def initializeBuffer(nOutput: Int): Unit = { - runningMean.resize(nOutput).zero - runningVar.resize(nOutput).fill(ev.fromType[Int](1)) - } - - override def updateOutput(input: Tensor[T]): Tensor[T] = { - checkInputDim(input) - - output.resizeAs(input) - - val _input = makeBatch(input) - val nInput = _input.size(2) - - if (runningMean.nElement == 0 || runningMean.nElement < nInput) { - initializeBuffer(nInput) - } - - saveMean.resizeAs(runningMean).zero - saveStd.resizeAs(runningVar).fill(ev.fromType[Int](1)) - - if (results == null || results.length > nInput) { - results = new Array[Future[_]](nInput) - } - val n = _input.nElement() / nInput - ev.getType() match { - case DoubleType => - val inputDouble = _input.asInstanceOf[Tensor[Double]] - val inputData = inputDouble.storage().array() - val inputOffset = inputDouble.storageOffset() - 1 - val inputStride = _input.stride(1) - val inputStride2 = _input.stride(2) - val outputDouble = output.asInstanceOf[Tensor[Double]] - val outputData = outputDouble.storage().array() - val outputOffset = outputDouble.storageOffset() - 1 - val outputStride = output.stride(1) - updateOutputDouble(inputData, inputOffset, inputStride, outputData, outputOffset, - outputStride, nInput, n, inputStride2) - - case FloatType => - val inputFloat = _input.asInstanceOf[Tensor[Float]] - val inputData = inputFloat.storage().array() - val inputOffset = inputFloat.storageOffset() - 1 - val inputStride = _input.stride(1) - val inputStride2 = _input.stride(2) - val outputFloat = output.asInstanceOf[Tensor[Float]] - val outputData = outputFloat.storage().array() - val outputOffset = outputFloat.storageOffset() - 1 - val outputStride = output.stride(1) - updateOutputFloat(inputData, inputOffset, inputStride, outputData, outputOffset, - outputStride, nInput, n, inputStride2) - } - - output - } - - private def updateOutputDouble(input: Array[Double], inputOffset: Int, inputStride: Int, - output: Array[Double], outputOffset: Int, outputStride: Int, - nInput: Int, n: Int, stride2: Int - ): Unit = { - var f = 0 - while (f < nInput) { - val _f = f + 1 - results(f) = Engine.model.invoke(() => { - var mean = 0.0 - var invstd = 0.0 - if (train) { - var sum = 0.0 - var i = 0 - while (i < n) { - sum += input(i % stride2 + (_f - 1) * stride2 + inputOffset + - (i / stride2) * inputStride) - i += 1 - } - mean = sum / n - saveMean.setValue(_f, ev.fromType[Double](mean)) - sum = 0.0 - i = 0 - while (i < n) { - sum += (input(i % stride2 + (_f - 1) * stride2 + inputOffset + - (i / stride2) * inputStride) - mean) * (input(i % stride2 + (_f - 1) * stride2 + - inputOffset + (i / stride2) * inputStride) - mean) - i += 1 - } - - invstd = if (sum == 0 && eps == 0.0) { - 0.0 - } else { - 1 / Math.sqrt(sum / n + eps) - } - saveStd.setValue(_f, ev.fromType[Double](invstd)) - - runningMean.setValue(_f, ev.fromType[Double](momentum * mean + (1 - momentum) * - ev.toType[Double](runningMean.valueAt(_f)))) - - val unbiasedVar = sum / (n - 1) - runningVar.setValue(_f, ev.fromType[Double](momentum * unbiasedVar + (1 - momentum) * - ev.toType[Double](runningVar.storage().array()(_f - 1)))) - } else { - mean = ev.toType[Double](runningMean.valueAt(_f)) - invstd = 1 / Math.sqrt(ev.toType[Double](runningVar.valueAt(_f)) + eps) - } - - if (needFix) { - mean = 0 - invstd = 0.0001 - saveMean.zero().fill(ev.fromType(mean)) - saveStd.zero().fill(ev.fromType(invstd)) - } - val w = if (null != weight) ev.toType[Double](weight.valueAt(_f)) else 1.0 - val b = if (null != bias) ev.toType[Double](bias.valueAt(_f)) else 0.0 - - var i = 0 - while (i < n) { - output(i % stride2 + (_f - 1) * stride2 + - inputOffset + (i / stride2) * inputStride) = (input(i % stride2 + (_f - 1) * stride2 + - inputOffset + (i / stride2) * inputStride) - mean) * invstd * w + b - i += 1 - } - }) - f += 1 - } - Engine.model.sync(results) - } - - private def updateOutputFloat(input: Array[Float], inputOffset: Int, inputStride: Int, - output: Array[Float], outputOffset: Int, outputStride: Int, - nInput: Int, n: Int, stride2: Int - ): Unit = { - var f = 0 - while (f < nInput) { - val _f = f + 1 - results(f) = Engine.model.invoke(() => { - var mean = 0.0f - var invstd = 0.0f - if (train) { - var sum = 0.0f - var i = 0 - while (i < n) { - sum += input(i % stride2 + (_f - 1) * stride2 + inputOffset + - (i / stride2) * inputStride) - i += 1 - } - mean = sum / n - saveMean.setValue(_f, ev.fromType(mean)) - - sum = 0.0f - i = 0 - while (i < n) { - sum += (input(i % stride2 + (_f - 1) * stride2 + inputOffset + - (i / stride2) * inputStride) - mean) * (input(i % stride2 + (_f - 1) * stride2 + - inputOffset + (i / stride2) * inputStride) - mean) - i += 1 - } - - invstd = if (sum == 0 && eps == 0.0) { - 0.0f - } else { - 1.0f / Math.sqrt(sum / n + eps).toFloat - } - saveStd.setValue(_f, ev.fromType(invstd)) - - runningMean.setValue(_f, ev.fromType(momentum * mean + (1 - momentum) * - ev.toType[Double](runningMean.valueAt(_f)))) - - val unbiasedVar = sum / (n - 1) - runningVar.setValue(_f, ev.fromType[Double](momentum * unbiasedVar + (1 - momentum) * - ev.toType[Double](runningVar.storage().array()(_f - 1)))) - } else { - mean = ev.toType[Float](runningMean.valueAt(_f)) - invstd = 1 / Math.sqrt(ev.toType[Double](runningVar.valueAt(_f)) + eps).toFloat - } - - if (needFix) { - mean = 0 - invstd = 0.0001f - saveMean.zero().fill(ev.fromType(mean)) - saveStd.zero().fill(ev.fromType(invstd)) - } - - val w = if (null != weight) ev.toType[Float](weight.valueAt(_f)) else 1.0f - val b = if (null != bias) ev.toType[Float](bias.valueAt(_f)) else 0.0f - - var i = 0 - while (i < n) { - output(i % stride2 + (_f - 1) * stride2 + - inputOffset + (i / stride2) * inputStride) = (input(i % stride2 + (_f - 1) * stride2 + - inputOffset + (i / stride2) * inputStride) - mean) * invstd * w + b - i += 1 - } - }) - f += 1 - } - Engine.model.sync(results) - } - - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - backward(input, gradOutput, gradInput, null, null) - } - - override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { - backward(input, gradOutput, null, gradWeight, gradBias) - } - - override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - checkInputDim(input) - checkInputDim(gradOutput) - val before = System.nanoTime() - val result = backward(input, gradOutput, gradInput, gradWeight, gradBias) - backwardTime += System.nanoTime() - before - result - } - - def backward(input: Tensor[T], gradOutput: Tensor[T], - theGradInput: Tensor[T] = null, theGradWeight: Tensor[T] = null, - theGradBias: Tensor[T] = null): Tensor[T] = { - require(train, "should be in training mode when this.train is true") - require(null != saveMean && null != saveStd, "must call updateOutput() first") - - if (null != theGradInput) { - theGradInput.resizeAs(gradOutput) - } - - val nInput = input.size(2) - if (results == null || results.length > nInput) { - results = new Array[Future[_]](nInput) - } - val n = input.nElement() / nInput - - ev.getType() match { - case DoubleType => - val inputDouble = input.asInstanceOf[Tensor[Double]] - val inputData = inputDouble.storage().array() - val inputOffset = inputDouble.storageOffset() - 1 - val inputStride = input.stride(1) - val inputStride2 = input.stride(2) - val gradOutputDouble = gradOutput.asInstanceOf[Tensor[Double]] - val gradOutputData = gradOutputDouble.storage().array() - val gradOutputOffset = gradOutputDouble.storageOffset() - 1 - val gradOutputStride = gradOutputDouble.stride(1) - val gradOutputStride2 = gradOutputDouble.stride(2) - if (affine) { - if (theGradInput != null) { - val gradInputDouble = theGradInput.asInstanceOf[Tensor[Double]] - val gradInputData = gradInputDouble.storage().array() - val gradInputOffset = gradInputDouble.storageOffset() - 1 - val gradInputStride = gradInputDouble.stride(1) - val gradInputStride2 = gradInputDouble.stride(2) - if (theGradWeight != null && theGradBias != null) { - val gradWeightDouble = theGradWeight.asInstanceOf[Tensor[Double]] - val gradWeightData = gradWeightDouble.storage().array() - val gradWeightOffset = gradWeightDouble.storageOffset() - 1 - val gradBiasDouble = theGradBias.asInstanceOf[Tensor[Double]] - val gradBiasData = gradBiasDouble.storage().array() - val gradBiasOffset = gradBiasDouble.storageOffset() - 1 - backwardDouble(inputData, inputOffset, inputStride, inputStride2, gradOutputData, - gradOutputOffset, gradOutputStride, gradOutputStride2, - gradInputData, gradInputOffset, gradInputStride, gradInputStride2, nInput, n, - scaleW, scaleB, - gradWeightData, gradWeightOffset, gradBiasData, - gradBiasOffset) - } else { - backwardDouble(inputData, inputOffset, inputStride, inputStride2, gradOutputData, - gradOutputOffset, gradOutputStride, gradOutputStride2, - gradInputData, gradInputOffset, gradInputStride, gradInputStride2, nInput, n, - scaleW, scaleB, null, 0, null, 0) - } - } else { - val gradWeightDouble = theGradWeight.asInstanceOf[Tensor[Double]] - val gradWeightData = gradWeightDouble.storage().array() - val gradWeightOffset = gradWeightDouble.storageOffset() - 1 - val gradBiasDouble = theGradBias.asInstanceOf[Tensor[Double]] - val gradBiasData = gradBiasDouble.storage().array() - val gradBiasOffset = gradBiasDouble.storageOffset() - 1 - backwardDouble(inputData, inputOffset, inputStride, inputStride2, gradOutputData, - gradOutputOffset, gradOutputStride, gradOutputStride2, - null, 0, 0, 0, nInput, n, scaleW, scaleB, - gradWeightData, gradWeightOffset, - gradBiasData, gradBiasOffset) - } - } else if (null != theGradInput) { - val gradInputDouble = theGradInput.asInstanceOf[Tensor[Double]] - val gradInputData = gradInputDouble.storage().array() - val gradInputOffset = gradInputDouble.storageOffset() - 1 - val gradInputStride = gradInputDouble.stride(1) - val gradInputStride2 = gradInputDouble.stride(2) - backwardDouble(inputData, inputOffset, inputStride, inputStride2, gradOutputData, - gradOutputOffset, gradOutputStride, gradOutputStride2, - gradInputData, gradInputOffset, gradInputStride, gradInputStride2, nInput, n, - scaleW, scaleB, null, 0, null, 0) - } - - case FloatType => - val inputFloat = input.asInstanceOf[Tensor[Float]] - val inputData = inputFloat.storage().array() - val inputOffset = inputFloat.storageOffset() - 1 - val inputStride = input.stride(1) - val inputStride2 = input.stride(2) - val gradOutputFloat = gradOutput.asInstanceOf[Tensor[Float]] - val gradOutputData = gradOutputFloat.storage().array() - val gradOutputOffset = gradOutputFloat.storageOffset() - 1 - val gradOutputStride = gradOutputFloat.stride(1) - val gradOutputStride2 = gradOutputFloat.stride(2) - if (affine) { - if (theGradInput != null) { - val gradInputFloat = theGradInput.asInstanceOf[Tensor[Float]] - val gradInputData = gradInputFloat.storage().array() - val gradInputOffset = gradInputFloat.storageOffset() - 1 - val gradInputStride = gradInputFloat.stride(1) - val gradInputStride2 = gradInputFloat.stride(2) - if (theGradWeight != null && theGradBias != null) { - val gradWeightFloat = theGradWeight.asInstanceOf[Tensor[Float]] - val gradWeightData = gradWeightFloat.storage().array() - val gradWeightOffset = gradWeightFloat.storageOffset() - 1 - val gradBiasFloat = theGradBias.asInstanceOf[Tensor[Float]] - val gradBiasData = gradBiasFloat.storage().array() - val gradBiasOffset = gradBiasFloat.storageOffset() - 1 - backwardFloat(inputData, inputOffset, inputStride, inputStride2, gradOutputData, - gradOutputOffset, gradOutputStride, gradOutputStride2, - gradInputData, gradInputOffset, gradInputStride, gradInputStride2, nInput, n, - ev.toType[Float](ev.fromType[Double](scaleW)), - ev.toType[Float](ev.fromType[Double](scaleB)), - gradWeightData, gradWeightOffset, gradBiasData, - gradBiasOffset) - } else { - backwardFloat(inputData, inputOffset, inputStride, inputStride2, gradOutputData, - gradOutputOffset, gradOutputStride, gradOutputStride2, - gradInputData, gradInputOffset, gradInputStride, gradInputStride2, nInput, n, - ev.toType[Float](ev.fromType[Double](scaleW)), - ev.toType[Float](ev.fromType[Double](scaleB)), - null, 0, null, 0) - } - } else { - val gradWeightFloat = theGradWeight.asInstanceOf[Tensor[Float]] - val gradWeightData = gradWeightFloat.storage().array() - val gradWeightOffset = gradWeightFloat.storageOffset() - 1 - val gradBiasFloat = theGradBias.asInstanceOf[Tensor[Float]] - val gradBiasData = gradBiasFloat.storage().array() - val gradBiasOffset = gradBiasFloat.storageOffset() - 1 - backwardFloat(inputData, inputOffset, inputStride, inputStride2, gradOutputData, - gradOutputOffset, gradOutputStride, gradOutputStride2, - null, 0, 0, 0, nInput, n, - ev.toType[Float](ev.fromType[Double](scaleW)), - ev.toType[Float](ev.fromType[Double](scaleB)), - gradWeightData, gradWeightOffset, - gradBiasData, gradBiasOffset) - } - } else if (null != theGradInput) { - val gradInputFloat = theGradInput.asInstanceOf[Tensor[Float]] - val gradInputData = gradInputFloat.storage().array() - val gradInputOffset = gradInputFloat.storageOffset() - 1 - val gradInputStride = gradInputFloat.stride(1) - val gradInputStride2 = gradInputFloat.stride(2) - backwardFloat(inputData, inputOffset, inputStride, inputStride2, gradOutputData, - gradOutputOffset, gradOutputStride, gradOutputStride2, - gradInputData, gradInputOffset, gradInputStride, gradInputStride2, nInput, n, - ev.toType[Float](ev.fromType[Double](scaleW)), - ev.toType[Float](ev.fromType[Double](scaleB)), - null, 0, null, 0) - } - } - - gradInput + protected def initializeBuffer(channels: Int): Unit = { + runningMean.resize(channels).zero + runningVar.resize(channels).fill(ev.one) } - private def backwardDouble(input: Array[Double], inputOffset: Int, inputStride: Int, - inputStride2: Int, gradOutput: Array[Double], gradOutputOffset: Int, gradOutputStride: Int, - gradOutputStride2: Int, gradInput: Array[Double], gradInputOffset: Int, gradInputStride: Int, - gradInputStride2: Int, nInput: Int, n: Int, scaleW: Double, scaleB: Double, - gradWeight: Array[Double], gradWeightOffset: Int, gradBias: Array[Double], gradBiasOffset: Int - ): Unit = { - var f = 0 - while (f < nInput) { - val _f = f + 1 - results(f) = Engine.model.invoke(() => { - val w = if (null != weight) ev.toType[Double](weight.valueAt(_f)) else 1.0 - val (mean, invstd) = if (train) { - (ev.toType[Double](saveMean.valueAt(_f)), ev.toType[Double](saveStd.valueAt(_f))) - } else { - (ev.toType[Double](runningMean.valueAt(_f)), - 1 / Math.sqrt(ev.toType[Double](runningVar.valueAt(_f)) + eps)) - } - - var sum = 0.0 - var i = 0 - while (i < n) { - val index = i % gradOutputStride2 + (_f - 1) * gradOutputStride2 + gradOutputOffset + - (i / gradOutputStride2) * gradOutputStride - sum += gradOutput(index) - i += 1 - } - - var dotp = 0.0 - i = 0 - while (i < n) { - val inputIndex = i % inputStride2 + (_f - 1) * inputStride2 + inputOffset + - (i / inputStride2) * inputStride - val gradOutputIndex = i % gradOutputStride2 + (_f - 1) * gradOutputStride2 + - gradOutputOffset + (i / gradOutputStride2) * gradOutputStride - dotp += (input(inputIndex) - mean) * gradOutput(gradOutputIndex) - i += 1 - } - - if (null != gradInput) { - if (train) { - val k = dotp * invstd * invstd / n - i = 0 - while (i < n) { - val inputIndex = i % inputStride2 + (_f - 1) * inputStride2 + inputOffset + - (i / inputStride2) * inputStride - val gradInputIndex = i % gradInputStride2 + (_f - 1) * gradInputStride2 + - gradInputOffset + (i / gradInputStride2) * gradInputStride - gradInput(gradInputIndex) = (input(inputIndex) - mean) * k - i += 1 - } - - val gradMean = sum / n - i = 0 - while (i < n) { - val gradInputIndex = i % gradInputStride2 + (_f - 1) * gradInputStride2 + - gradInputOffset + (i / gradInputStride2) * gradInputStride - val gradOutputIndex = i % gradOutputStride2 + (_f - 1) * gradOutputStride2 + - gradOutputOffset + (i / gradOutputStride2) * gradOutputStride - gradInput(gradInputIndex) = (gradOutput(gradOutputIndex) - gradMean - - gradInput(gradInputIndex)) * invstd * w - i += 1 - } - } else { - var i = 0 - while (i < n) { - val gradInputIndex = i % gradInputStride2 + (_f - 1) * gradInputStride2 + - gradInputOffset + (i / gradInputStride2) * gradInputStride - val gradOutputIndex = i % gradOutputStride2 + (_f - 1) * gradOutputStride2 + - gradOutputOffset + (i / gradOutputStride2) * gradOutputStride - gradInput(gradInputIndex) = gradOutput(gradOutputIndex) * invstd * w - i += 1 - } - } - } - - if (null != gradWeight && scaleW != 0) { - gradWeight(_f - 1 + gradWeightOffset) += scaleW * dotp * invstd - } - - if (null != gradBias && scaleB != 0) { - gradBias(_f - 1 + gradBiasOffset) += scaleB * sum - } - }) - f += 1 - } - Engine.model.sync(results) - } + protected val gMean = Tensor[T]() + protected val gxMean = Tensor[T]() + protected val _input = Tensor[T]() + protected val _gradOutput = Tensor[T]() - private def backwardFloat(input: Array[Float], inputOffset: Int, inputStride: Int, - inputStride2: Int, gradOutput: Array[Float], gradOutputOffset: Int, gradOutputStride: Int, - gradOutputStride2: Int, gradInput: Array[Float], gradInputOffset: Int, gradInputStride: Int, - gradInputStride2: Int, nInput: Int, n: Int, scaleW: Float, scaleB: Float, - gradWeight: Array[Float], gradWeightOffset: Int, gradBias: Array[Float], gradBiasOffset: Int - ): Unit = { - var f = 0 - while (f < nInput) { - val _f = f + 1 - results(f) = Engine.model.invoke(() => { - val w = if (null != weight) ev.toType[Float](weight.valueAt(_f)) else 1.0f - val (mean, invstd) = if (train) { - (ev.toType[Float](saveMean.valueAt(_f)), ev.toType[Float](saveStd.valueAt(_f))) - } else { - (ev.toType[Float](runningMean.valueAt(_f)), - 1 / Math.sqrt(ev.toType[Float](runningVar.valueAt(_f)) + eps).toFloat) - } - - var sum = 0.0f - var i = 0 - while (i < n) { - val index = i % gradOutputStride2 + (_f - 1) * gradOutputStride2 + gradOutputOffset + - (i / gradOutputStride2) * gradOutputStride - sum += gradOutput(index) - i += 1 - } - - var dotp = 0.0f - i = 0 - while (i < n) { - val inputIndex = i % inputStride2 + (_f - 1) * inputStride2 + inputOffset + - (i / inputStride2) * inputStride - val gradOutputIndex = i % gradOutputStride2 + (_f - 1) * gradOutputStride2 + - gradOutputOffset + (i / gradOutputStride2) * gradOutputStride - dotp += (input(inputIndex) - mean) * gradOutput(gradOutputIndex) - i += 1 - } - - if (null != gradInput) { - if (train) { - val k = dotp * invstd * invstd / n - i = 0 - while (i < n) { - val inputIndex = i % inputStride2 + (_f - 1) * inputStride2 + inputOffset + - (i / inputStride2) * inputStride - val gradInputIndex = i % gradInputStride2 + (_f - 1) * gradInputStride2 + - gradInputOffset + (i / gradInputStride2) * gradInputStride - gradInput(gradInputIndex) = (input(inputIndex) - mean) * k - i += 1 - } - - val gradMean = sum / n - i = 0 - while (i < n) { - val gradInputIndex = i % gradInputStride2 + (_f - 1) * gradInputStride2 + - gradInputOffset + (i / gradInputStride2) * gradInputStride - val gradOutputIndex = i % gradOutputStride2 + (_f - 1) * gradOutputStride2 + - gradOutputOffset + (i / gradOutputStride2) * gradOutputStride - gradInput(gradInputIndex) = (gradOutput(gradOutputIndex) - gradMean - - gradInput(gradInputIndex)) * invstd * w - i += 1 - } - } else { - var i = 0 - while (i < n) { - val gradInputIndex = i % gradInputStride2 + (_f - 1) * gradInputStride2 + - gradInputOffset + (i / gradInputStride2) * gradInputStride - val gradOutputIndex = i % gradOutputStride2 + (_f - 1) * gradOutputStride2 + - gradOutputOffset + (i / gradOutputStride2) * gradOutputStride - gradInput(gradInputIndex) = gradOutput(gradOutputIndex) * invstd * w - i += 1 - } - } - } - - if (null != gradWeight && scaleW != 0) { - gradWeight(_f - 1 + gradWeightOffset) += scaleW * dotp * invstd - } - - if (null != gradBias && scaleB != 0) { - gradBias(_f - 1 + gradBiasOffset) += scaleB * sum - } - }) - f += 1 - } - Engine.model.sync(results) + override def clearState(): this.type = { + super.clearState() + gMean.set() + gxMean.set() + this } override def copyStatus(src: Module[T]): this.type = { @@ -727,6 +201,113 @@ class BatchNormalization[T: ClassTag]( nOutput, eps, momentum, affine) state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + checkInputDim(input) + output.resizeAs(input) + + _input.set(input) + makeBatch(_input) + _input.addSingletonDimension(_input, 3) + _input.addSingletonDimension(_input, 4) + val nInput = _input.size(channelDim) + + if (runningMean.nElement == 0 || runningMean.nElement < nInput) { + initializeBuffer(nInput) + } + + saveMean.resizeAs(runningMean).zero + saveStd.resizeAs(runningVar).fill(ev.zero) + + if (train) { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateOutputNCHWTrainFloat( + _input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], + runningMean.asInstanceOf[Tensor[Float]], runningVar.asInstanceOf[Tensor[Float]], + weight.asInstanceOf[Tensor[Float]], bias.asInstanceOf[Tensor[Float]], + eps.toFloat, momentum.toFloat) + } else { + SpatialBatchNormalization.updateOutputNCHWTrainDouble( + _input.asInstanceOf[Tensor[Double]], output.asInstanceOf[Tensor[Double]], + saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], + runningMean.asInstanceOf[Tensor[Double]], runningVar.asInstanceOf[Tensor[Double]], + weight.asInstanceOf[Tensor[Double]], bias.asInstanceOf[Tensor[Double]], + eps, momentum) + } + } else { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateOutputNCHWInferFloat( + _input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + runningMean.asInstanceOf[Tensor[Float]], runningVar.asInstanceOf[Tensor[Float]], + weight.asInstanceOf[Tensor[Float]], bias.asInstanceOf[Tensor[Float]], eps.toFloat) + } else { + SpatialBatchNormalization.updateOutputNCHWInferDouble( + _input.asInstanceOf[Tensor[Double]], output.asInstanceOf[Tensor[Double]], + runningMean.asInstanceOf[Tensor[Double]], runningVar.asInstanceOf[Tensor[Double]], + weight.asInstanceOf[Tensor[Double]], bias.asInstanceOf[Tensor[Double]], eps) + } + } + + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + _gradOutput.set(gradOutput) + makeBatch(_gradOutput) + _gradOutput.addSingletonDimension(_gradOutput, 3) + _gradOutput.addSingletonDimension(_gradOutput, 4) + gxMean.zero() + gMean.zero() + if (train) { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateGradInputNCHWTrainFloat( + _input.asInstanceOf[Tensor[Float]], _gradOutput.asInstanceOf[Tensor[Float]], + gradInput.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], + saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], + gMean.asInstanceOf[Tensor[Float]], gxMean.asInstanceOf[Tensor[Float]]) + } else { + SpatialBatchNormalization.updateGradInputNCHWTrainDouble( + _input.asInstanceOf[Tensor[Double]], _gradOutput.asInstanceOf[Tensor[Double]], + gradInput.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], + saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], + gMean.asInstanceOf[Tensor[Double]], gxMean.asInstanceOf[Tensor[Double]]) + } + } else { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateGradInputNCHWInferFloat( + _gradOutput.asInstanceOf[Tensor[Float]], + gradInput.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], + bias.asInstanceOf[Tensor[Float]]) + } else { + SpatialBatchNormalization.updateGradInputNCHWInferDouble( + _gradOutput.asInstanceOf[Tensor[Double]], + gradInput.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], + bias.asInstanceOf[Tensor[Double]]) + } + } + gradInput.squeeze(4) + gradInput.squeeze(3) + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + if (weight == null || scaleW == 0) { + return + } + + if (ev.getType() == FloatType) { + SpatialBatchNormalization.accGradientNCHWFloat(_gradOutput.asInstanceOf[Tensor[Float]], + gradWeight.asInstanceOf[Tensor[Float]], gradBias.asInstanceOf[Tensor[Float]], + _input.asInstanceOf[Tensor[Float]], saveMean.asInstanceOf[Tensor[Float]], + saveStd.asInstanceOf[Tensor[Float]], scaleW.toFloat, scaleB.toFloat) + } else { + SpatialBatchNormalization.accGradientNCHWDouble(_gradOutput.asInstanceOf[Tensor[Double]], + gradWeight.asInstanceOf[Tensor[Double]], gradBias.asInstanceOf[Tensor[Double]], + _input.asInstanceOf[Tensor[Double]], saveMean.asInstanceOf[Tensor[Double]], + saveStd.asInstanceOf[Tensor[Double]], scaleW, scaleB) + } + } } object BatchNormalization extends ModuleSerializable { @@ -738,7 +319,9 @@ object BatchNormalization extends ModuleSerializable { initWeight: Tensor[T] = null, initBias: Tensor[T] = null, initGradWeight: Tensor[T] = null, - initGradBias: Tensor[T] = null)(implicit ev: TensorNumeric[T]): BatchNormalization[T] = { + initGradBias: Tensor[T] = null) + (implicit ev: TensorNumeric[T]): BatchNormalization[T] = { + new BatchNormalization[T]( nOutput, eps, momentum, affine, initWeight, initBias, initGradWeight, initGradBias) } @@ -797,6 +380,5 @@ object BatchNormalization extends ModuleSerializable { DataConverter.setAttributeValue(context, saveStdBuilder, batchNorm.saveStd, ModuleSerializer.tensorType) batchNormBuilder.putAttr("saveStd", saveStdBuilder.build) - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala index 87d0425be48..376e0b5dbd8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag @@ -42,12 +43,189 @@ class SpatialBatchNormalization[T: ClassTag]( initWeight: Tensor[T] = null, initBias: Tensor[T] = null, initGradWeight: Tensor[T] = null, - initGradBias: Tensor[T] = null)( + initGradBias: Tensor[T] = null, dataFormat: DataFormat = DataFormat.NCHW)( implicit ev: TensorNumeric[T]) extends BatchNormalization[T](nOutput, eps, momentum, affine, initWeight, initBias, initGradWeight, initGradBias) { override val nDim = 4 + override def updateOutput(input: Tensor[T]): Tensor[T] = { + checkInputDim(input) + output.resizeAs(input) + + _input.set(input) + makeBatch(_input) + val nInput = _input.size(channelDim) + + if (runningMean.nElement == 0 || runningMean.nElement < nInput) { + initializeBuffer(nInput) + } + + saveMean.resizeAs(runningMean).zero + saveStd.resizeAs(runningVar).fill(ev.zero) + + if (dataFormat == DataFormat.NCHW) { + if (train) { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateOutputNCHWTrainFloat( + _input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], + runningMean.asInstanceOf[Tensor[Float]], runningVar.asInstanceOf[Tensor[Float]], + weight.asInstanceOf[Tensor[Float]], bias.asInstanceOf[Tensor[Float]], + eps.toFloat, momentum.toFloat, needFix = needFix) + } else { + SpatialBatchNormalization.updateOutputNCHWTrainDouble( + _input.asInstanceOf[Tensor[Double]], output.asInstanceOf[Tensor[Double]], + saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], + runningMean.asInstanceOf[Tensor[Double]], runningVar.asInstanceOf[Tensor[Double]], + weight.asInstanceOf[Tensor[Double]], bias.asInstanceOf[Tensor[Double]], + eps, momentum, needFix = needFix) + } + } else { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateOutputNCHWInferFloat( + _input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + runningMean.asInstanceOf[Tensor[Float]], runningVar.asInstanceOf[Tensor[Float]], + weight.asInstanceOf[Tensor[Float]], bias.asInstanceOf[Tensor[Float]], eps.toFloat) + } else { + SpatialBatchNormalization.updateOutputNCHWInferDouble( + _input.asInstanceOf[Tensor[Double]], output.asInstanceOf[Tensor[Double]], + runningMean.asInstanceOf[Tensor[Double]], runningVar.asInstanceOf[Tensor[Double]], + weight.asInstanceOf[Tensor[Double]], bias.asInstanceOf[Tensor[Double]], eps) + } + } + } else { + if (train) { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateOutputNHWCTrainFloat( + _input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], + runningMean.asInstanceOf[Tensor[Float]], runningVar.asInstanceOf[Tensor[Float]], + weight.asInstanceOf[Tensor[Float]], bias.asInstanceOf[Tensor[Float]], + eps.toFloat, momentum.toFloat) + } else { + SpatialBatchNormalization.updateOutputNHWCTrainDouble( + _input.asInstanceOf[Tensor[Double]], output.asInstanceOf[Tensor[Double]], + saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], + runningMean.asInstanceOf[Tensor[Double]], runningVar.asInstanceOf[Tensor[Double]], + weight.asInstanceOf[Tensor[Double]], bias.asInstanceOf[Tensor[Double]], + eps.toFloat, momentum.toFloat) + } + } else { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateOutputNHWCInferFloat( + _input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + runningMean.asInstanceOf[Tensor[Float]], runningVar.asInstanceOf[Tensor[Float]], + weight.asInstanceOf[Tensor[Float]], bias.asInstanceOf[Tensor[Float]], eps.toFloat) + } else { + SpatialBatchNormalization.updateOutputNHWCInferDouble( + _input.asInstanceOf[Tensor[Double]], output.asInstanceOf[Tensor[Double]], + runningMean.asInstanceOf[Tensor[Double]], runningVar.asInstanceOf[Tensor[Double]], + weight.asInstanceOf[Tensor[Double]], bias.asInstanceOf[Tensor[Double]], eps) + } + } + } + + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + _gradOutput.set(gradOutput) + makeBatch(_gradOutput) + gxMean.zero() + gMean.zero() + if (dataFormat == DataFormat.NCHW) { + if (train) { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateGradInputNCHWTrainFloat( + _input.asInstanceOf[Tensor[Float]], _gradOutput.asInstanceOf[Tensor[Float]], + gradInput.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], + saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], + gMean.asInstanceOf[Tensor[Float]], gxMean.asInstanceOf[Tensor[Float]]) + } else { + SpatialBatchNormalization.updateGradInputNCHWTrainDouble( + _input.asInstanceOf[Tensor[Double]], _gradOutput.asInstanceOf[Tensor[Double]], + gradInput.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], + saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], + gMean.asInstanceOf[Tensor[Double]], gxMean.asInstanceOf[Tensor[Double]]) + } + } else { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateGradInputNCHWInferFloat( + _gradOutput.asInstanceOf[Tensor[Float]], + gradInput.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], + bias.asInstanceOf[Tensor[Float]]) + } else { + SpatialBatchNormalization.updateGradInputNCHWInferDouble( + _gradOutput.asInstanceOf[Tensor[Double]], + gradInput.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], + bias.asInstanceOf[Tensor[Double]]) + } + } + } else { + if (train) { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateGradInputNHWCTrainFloat( + _input.asInstanceOf[Tensor[Float]], _gradOutput.asInstanceOf[Tensor[Float]], + gradInput.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], + saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], + gMean.asInstanceOf[Tensor[Float]], gxMean.asInstanceOf[Tensor[Float]]) + } else { + SpatialBatchNormalization.updateGradInputNHWCTrainDouble( + _input.asInstanceOf[Tensor[Double]], _gradOutput.asInstanceOf[Tensor[Double]], + gradInput.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], + saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], + gMean.asInstanceOf[Tensor[Double]], gxMean.asInstanceOf[Tensor[Double]]) + } + } else { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.updateGradInputNHWCInferFloat( + _gradOutput.asInstanceOf[Tensor[Float]], + gradInput.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], + bias.asInstanceOf[Tensor[Float]]) + } else { + SpatialBatchNormalization.updateGradInputNHWCInferDouble( + _gradOutput.asInstanceOf[Tensor[Double]], + gradInput.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], + bias.asInstanceOf[Tensor[Double]]) + } + } + } + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + if (weight == null || scaleW == 0) { + return + } + + if (dataFormat == DataFormat.NCHW) { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.accGradientNCHWFloat(_gradOutput.asInstanceOf[Tensor[Float]], + gradWeight.asInstanceOf[Tensor[Float]], gradBias.asInstanceOf[Tensor[Float]], + _input.asInstanceOf[Tensor[Float]], saveMean.asInstanceOf[Tensor[Float]], + saveStd.asInstanceOf[Tensor[Float]], scaleW.toFloat, scaleB.toFloat) + } else { + SpatialBatchNormalization.accGradientNCHWDouble(_gradOutput.asInstanceOf[Tensor[Double]], + gradWeight.asInstanceOf[Tensor[Double]], gradBias.asInstanceOf[Tensor[Double]], + _input.asInstanceOf[Tensor[Double]], saveMean.asInstanceOf[Tensor[Double]], + saveStd.asInstanceOf[Tensor[Double]], scaleW, scaleB) + } + } else { + if (ev.getType() == FloatType) { + SpatialBatchNormalization.accGradientNHWCFloat(_gradOutput.asInstanceOf[Tensor[Float]], + gradWeight.asInstanceOf[Tensor[Float]], gradBias.asInstanceOf[Tensor[Float]], + _input.asInstanceOf[Tensor[Float]], saveMean.asInstanceOf[Tensor[Float]], + saveStd.asInstanceOf[Tensor[Float]], scaleW.toFloat, scaleB.toFloat) + } else { + SpatialBatchNormalization.accGradientNHWCDouble(_gradOutput.asInstanceOf[Tensor[Double]], + gradWeight.asInstanceOf[Tensor[Double]], gradBias.asInstanceOf[Tensor[Double]], + _input.asInstanceOf[Tensor[Double]], saveMean.asInstanceOf[Tensor[Double]], + saveStd.asInstanceOf[Tensor[Double]], scaleW, scaleB) + } + } + } + override def toString(): String = { s"${getPrintName}[${ev.getType()}]($nOutput, $eps, $momentum, $affine)" } @@ -55,16 +233,1560 @@ class SpatialBatchNormalization[T: ClassTag]( object SpatialBatchNormalization { def apply[@specialized(Float, Double) T: ClassTag]( - nOutput: Int, - eps: Double = 1e-5, - momentum: Double = 0.1, - affine: Boolean = true, - initWeight: Tensor[T] = null, - initBias: Tensor[T] = null, - initGradWeight: Tensor[T] = null, - initGradBias: Tensor[T] = null)(implicit ev: TensorNumeric[T]) + nOutput: Int, + eps: Double = 1e-5, + momentum: Double = 0.1, + affine: Boolean = true, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null, + dataFormat: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]) : SpatialBatchNormalization[T] = { new SpatialBatchNormalization[T](nOutput, eps, momentum, affine, - initWeight, initBias, initGradWeight, initGradBias) + initWeight, initBias, initGradWeight, initGradBias, dataFormat) + } + + private[bigdl] def updateOutputNHWCInferFloat(input: Tensor[Float], output: Tensor[Float], + mean: Tensor[Float], variance: Tensor[Float], scale: Tensor[Float], offset: Tensor[Float], + eps: Float): Unit = { + + require(input.isContiguous(), "BatchNorm NHWC require a contiguous input") + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + val nChannels = input.size(4) + val n = input.nElement() + val meanData = mean.storage().array() + val meanOffset = mean.storageOffset() - 1 + val varData = variance.storage().array() + val varOffset = variance.storageOffset() - 1 + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + val offsetData = offset.storage().array() + val offsetOffset = offset.storageOffset() - 1 + var i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps).toFloat + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(c + meanOffset)) * invStd * scaleData(scaleOffset + c) + + offsetData(offsetOffset + c) + c += 1 + } + i += nChannels + } + } else { + var i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps).toFloat + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(c + meanOffset)) * invStd + c += 1 + } + i += nChannels + } + } + } + + private[bigdl] def updateOutputNHWCInferDouble(input: Tensor[Double], output: Tensor[Double], + mean: Tensor[Double], variance: Tensor[Double], scale: Tensor[Double], offset: Tensor[Double], + eps: Double): Unit = { + + require(input.isContiguous(), "BatchNorm NHWC require a contiguous input") + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + val nChannels = input.size(4) + val n = input.nElement() + val meanData = mean.storage().array() + val meanOffset = mean.storageOffset() - 1 + val varData = variance.storage().array() + val varOffset = variance.storageOffset() - 1 + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + val offsetData = offset.storage().array() + val offsetOffset = offset.storageOffset() - 1 + var i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps) + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(meanOffset + c)) * invStd * scaleData(scaleOffset + c) + + offsetData(offsetOffset + c) + c += 1 + } + i += nChannels + } + } else { + var i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps) + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(meanOffset + c)) * invStd + c += 1 + } + i += nChannels + } + } + } + + private[bigdl] def updateOutputNHWCTrainFloat(input: Tensor[Float], output: Tensor[Float], + saveMean: Tensor[Float], saveStd: Tensor[Float], runningMean: Tensor[Float], + runningVar: Tensor[Float], scale: Tensor[Float], offset: Tensor[Float], + eps: Float, momentum: Float, + batchVar: Tensor[Float] = null, saveVar: Tensor[Float] = null): Unit = { + require(input.isContiguous(), "BatchNorm NHWC require a contiguous input") + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + val nChannels = input.size(4) + if(saveMean.size(1) != nChannels) { + saveMean.resize(nChannels) + saveStd.resize(nChannels) + runningMean.resize(nChannels) + runningVar.resize(nChannels) + } + val meanData = saveMean.storage().array() + val meanOffset = saveMean.storageOffset() - 1 + var i = 0 + val n = input.nElement() + val frameSize = n / nChannels + while(i < n) { + var c = 0 + while(c < nChannels) { + meanData(meanOffset + c) += inputData(inputOffset + i + c) + c += 1 + } + i += nChannels + } + + var c = 0 + val runningMeanData = runningMean.storage().array() + val runningMeanDataOffset = runningMean.storageOffset() - 1 + while(c < nChannels) { + meanData(meanOffset + c) /= frameSize + runningMeanData(runningMeanDataOffset + c) = meanData(meanOffset + c) * momentum + + (1 - momentum) * runningMeanData(c + runningMeanDataOffset) + c += 1 + } + + val stdData = saveStd.storage().array() + val stdOffset = saveStd.storageOffset() - 1 + i = 0 + while(i < n) { + var c = 0 + while(c < nChannels) { + val diff = (inputData(inputOffset + i + c) - meanData(meanOffset + c)) + stdData(stdOffset + c) += diff * diff + c += 1 + } + i += nChannels + } + + c = 0 + val runningVarData = runningVar.storage().array() + val runningVarOffset = runningVar.storageOffset() - 1 + while(c < nChannels) { + if (stdData(c + stdOffset) == 0 && eps == 0) { + stdData(c + stdOffset) = 0 + if (saveVar != null) { + saveVar.setValue(c + 1, 0f) + } + if (batchVar != null) { + batchVar.setValue(c + 1, 0f) + } + } else { + val s = stdData(c + stdOffset) + val unbiasedVar = s / (frameSize - 1) + if (saveVar != null) { + saveVar.setValue(c + 1, s / frameSize) + } + if (batchVar != null) { + batchVar.setValue(c + 1, unbiasedVar) + } + stdData(c + stdOffset) = 1.0f / Math.sqrt(s / frameSize + eps).toFloat + runningVarData(c + runningVarOffset) = momentum * unbiasedVar + + (1 - momentum) * runningVarData(c + runningVarOffset) + } + c += 1 + } + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + val offsetData = offset.storage().array() + val offsetOffset = offset.storageOffset() - 1 + i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(meanOffset + c)) * stdData(c + stdOffset) * + scaleData(scaleOffset + c) + offsetData(offsetOffset + c) + c += 1 + } + i += nChannels + } + } else { + i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(meanOffset + c)) * stdData(c + stdOffset) + c += 1 + } + i += nChannels + } + } + } + + private[bigdl] def updateOutputNHWCTrainDouble(input: Tensor[Double], output: Tensor[Double], + saveMean: Tensor[Double], saveStd: Tensor[Double], runningMean: Tensor[Double], + runningVar: Tensor[Double], scale: Tensor[Double], offset: Tensor[Double], + eps: Double, momentum: Double, + batchVar: Tensor[Double] = null, saveVar: Tensor[Double] = null): Unit = { + require(input.isContiguous(), "BatchNorm NHWC require a contiguous input") + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + val nChannels = input.size(4) + if(saveMean.size(1) != nChannels) { + saveMean.resize(nChannels) + saveStd.resize(nChannels) + runningMean.resize(nChannels) + runningVar.resize(nChannels) + } + val meanData = saveMean.storage().array() + val meanOffset = saveMean.storageOffset() - 1 + var i = 0 + val n = input.nElement() + val frameSize = n / nChannels + while(i < n) { + var c = 0 + while(c < nChannels) { + meanData(c + meanOffset) += inputData(inputOffset + i + c) + c += 1 + } + i += nChannels + } + + var c = 0 + val runningMeanData = runningMean.storage().array() + val runningMeanOffset = runningMean.storageOffset() - 1 + while(c < nChannels) { + meanData(c + meanOffset) /= frameSize + runningMeanData(c + runningMeanOffset) = meanData(c + meanOffset) * momentum + + (1 - momentum) * runningMeanData(c + runningMeanOffset) + c += 1 + } + + val stdData = saveStd.storage().array() + val stdOffset = saveStd.storageOffset() - 1 + i = 0 + while(i < n) { + var c = 0 + while(c < nChannels) { + val diff = (inputData(inputOffset + i + c) - meanData(c + meanOffset)) + stdData(c + stdOffset) += diff * diff + c += 1 + } + i += nChannels + } + + c = 0 + val runningVarData = runningVar.storage().array() + val runningVarOffset = runningVar.storageOffset() - 1 + while(c < nChannels) { + if (stdData(c + stdOffset) == 0 && eps == 0) { + stdData(c + stdOffset) = 0 + if (saveVar != null) { + saveVar.setValue(c + 1, 0f) + } + if (batchVar != null) { + batchVar.setValue(c + 1, 0f) + } + } else { + val s = stdData(c + stdOffset) + val unbiasedVar = s / (frameSize - 1) + if (saveVar != null) { + saveVar.setValue(c + 1, s / frameSize) + } + if (batchVar != null) { + batchVar.setValue(c + 1, unbiasedVar) + } + stdData(c + stdOffset) = 1.0f / Math.sqrt(s / frameSize + eps).toFloat + runningVarData(c + runningVarOffset) = momentum * unbiasedVar + + (1 - momentum) * runningVarData(c + runningVarOffset) + } + c += 1 + } + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + val offsetData = offset.storage().array() + val offsetOffset = offset.storageOffset() - 1 + i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(c + meanOffset)) * stdData(c + stdOffset) * + scaleData(c + scaleOffset) + offsetData(c + offsetOffset) + c += 1 + } + i += nChannels + } + } else { + i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(c + meanOffset)) * stdData(c + stdOffset) + c += 1 + } + i += nChannels + } + } + } + + private[bigdl] def updateOutputNCHWInferFloat(input: Tensor[Float], output: Tensor[Float], + mean: Tensor[Float], variance: Tensor[Float], scale: Tensor[Float], + offset: Tensor[Float], eps: Float): Unit = { + + require(input.isContiguous(), "BatchNorm NCHW require a contiguous input") + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + val meanData = mean.storage().array() + val meanOffset = mean.storageOffset() - 1 + val varData = variance.storage().array() + val varOffset = variance.storageOffset() - 1 + val nChannels = input.size(2) + val nBatch = input.size(1) + val nFrame = input.size(3) * input.size(4) + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + val offsetData = offset.storage().array() + val offsetOffset = offset.storageOffset() - 1 + var i = 0 + var b = 0 + while (b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + while (k < nFrame) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps).toFloat + outputData(i + outputOffset) = (inputData(i + inputOffset) - meanData(c + meanOffset)) * + invStd * scaleData(c + scaleOffset) + offsetData(c + offsetOffset) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } else { + var i = 0 + var b = 0 + while (b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + while (k < nFrame) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps).toFloat + outputData(i + outputOffset) = (inputData(i + inputOffset) - meanData(c + meanOffset)) * + invStd + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + } + + private[bigdl] def updateOutputNCHWInferDouble(input: Tensor[Double], output: Tensor[Double], + mean: Tensor[Double], variance: Tensor[Double], scale: Tensor[Double], offset: Tensor[Double], + eps: Double) + : Unit = { + + require(input.isContiguous(), "BatchNorm NCHW require a contiguous input") + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + val meanData = mean.storage().array() + val meanOffset = mean.storageOffset() - 1 + val varData = variance.storage().array() + val varOffset = variance.storageOffset() - 1 + val nChannels = input.size(2) + val nBatch = input.size(1) + val nFrame = input.size(3) * input.size(4) + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + val offsetData = offset.storage().array() + val offsetOffset = offset.storageOffset() - 1 + var i = 0 + var b = 0 + while (b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + while (k < nFrame) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps) + outputData(i + outputOffset) = (inputData(i + inputOffset) - meanData(c + meanOffset)) * + invStd * scaleData(c + scaleOffset) + offsetData(c + offsetOffset) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } else { + var i = 0 + var b = 0 + while (b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + while (k < nFrame) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps) + outputData(i + outputOffset) = (inputData(i + inputOffset) - meanData(c + meanOffset)) * + invStd + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + } + + private[bigdl] def updateGradInputNHWCTrainFloat( + input: Tensor[Float], + gradOutput: Tensor[Float], + gradInput: Tensor[Float], + scale: Tensor[Float], + saveMean: Tensor[Float], + saveStd: Tensor[Float], + gMean: Tensor[Float], + gxMean: Tensor[Float] + ): Unit = { + require(input.nDimension() == 4, "BN require a 4D input") + require(input.isContiguous(), "input is not contiguous") + require(gradOutput.nDimension() == 4, "BN require a 4D gradient") + require(gradOutput.isContiguous(), "gradient is not contiguous") + val nChannel = gradOutput.size(4) + require(scale.size(1) == nChannel, "scale length is not consist with channel number") + require(saveMean.size(1) == nChannel, "saveMean length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + + gradInput.resizeAs(gradOutput) + if (gMean.isEmpty) { + gMean.resize(scale.size(1)) + gxMean.resize(scale.size(1)) + } + + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val saveMeanData = saveMean.storage().array() + val saveMeanOffset = saveMean.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + val gMeanData = gMean.storage().array() + val gxMeanData = gxMean.storage().array() + + val n = gradOutput.nElement() + var i = 0 + while(i < n) { + var c = 0 + while(c < nChannel) { + gMeanData(c) += gradOutputData(i + gradOutputOffset) + gxMeanData(c) += gradOutputData(i + gradOutputOffset) * + (inputData(i + inputOffset) - saveMeanData(c + saveMeanOffset)) + c += 1 + i += 1 + } + } + + var c = 0 + val size = n / nChannel + while(c < nChannel) { + gMeanData(c) /= size + gxMeanData(c) /= size + c += 1 + } + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + i = 0 + while (i < n) { + var c = 0 + while (c < nChannel) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = scaleData(scaleOffset + c) * + invStd * (gradOutputData(gradOutputOffset + i) - gMeanData(c) - + gxMeanData(c) * invStd * invStd * (inputData(inputOffset + i) - + saveMeanData(saveMeanOffset + c))) + c += 1 + i += 1 + } + } + } else { + i = 0 + while (i < n) { + var c = 0 + while (c < nChannel) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = + invStd * (gradOutputData(gradOutputOffset + i) - gMeanData(c) - + gxMeanData(c) * invStd * invStd * (inputData(inputOffset + i) - + saveMeanData(saveMeanOffset + c))) + c += 1 + i += 1 + } + } + } + } + + private[bigdl] def updateGradInputNHWCTrainDouble( + input: Tensor[Double], + gradOutput: Tensor[Double], + gradInput: Tensor[Double], + scale: Tensor[Double], + saveMean: Tensor[Double], + saveStd: Tensor[Double], + gMean: Tensor[Double], + gxMean: Tensor[Double] + ): Unit = { + require(input.nDimension() == 4, "BN require a 4D input") + require(input.isContiguous(), "input is not contiguous") + require(gradOutput.nDimension() == 4, "BN require a 4D gradient") + require(gradOutput.isContiguous(), "gradient is not contiguous") + val nChannel = gradOutput.size(4) + require(scale.size(1) == nChannel, "scale length is not consist with channel number") + require(saveMean.size(1) == nChannel, "saveMean length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + + gradInput.resizeAs(gradOutput) + if (gMean.isEmpty) { + gMean.resize(scale.size(1)) + gxMean.resize(scale.size(1)) + } + + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val saveMeanData = saveMean.storage().array() + val saveMeanOffset = saveMean.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + val gMeanData = gMean.storage().array() + val gxMeanData = gxMean.storage().array() + + val n = gradOutput.nElement() + var i = 0 + while(i < n) { + var c = 0 + while(c < nChannel) { + gMeanData(c) += gradOutputData(i + gradOutputOffset) + gxMeanData(c) += gradOutputData(i + gradOutputOffset) * + (inputData(i + inputOffset) - saveMeanData(c + saveMeanOffset)) + c += 1 + i += 1 + } + } + + var c = 0 + val size = n / nChannel + while(c < nChannel) { + gMeanData(c) /= size + gxMeanData(c) /= size + c += 1 + } + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + i = 0 + while (i < n) { + var c = 0 + while (c < nChannel) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = scaleData(scaleOffset + c) * + invStd * (gradOutputData(gradOutputOffset + i) - gMeanData(c) - + gxMeanData(c) * invStd * invStd * (inputData(inputOffset + i) - + saveMeanData(saveMeanOffset + c))) + c += 1 + i += 1 + } + } + } else { + i = 0 + while (i < n) { + var c = 0 + while (c < nChannel) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = + invStd * (gradOutputData(gradOutputOffset + i) - gMeanData(c) - + gxMeanData(c) * invStd * invStd * (inputData(inputOffset + i) - + saveMeanData(saveMeanOffset + c))) + c += 1 + i += 1 + } + } + } + } + + private[bigdl] def updateGradInputNHWCInferFloat( + gradOutput: Tensor[Float], + gradInput: Tensor[Float], + scale: Tensor[Float], + saveStd: Tensor[Float] + ): Unit = { + require(gradOutput.nDimension() == 4, "BN require a 4D gradient") + require(gradOutput.isContiguous(), "gradient is not contiguous") + val nChannel = gradOutput.size(4) + require(scale.size(1) == nChannel, "scale length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + + gradInput.resizeAs(gradOutput) + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + + val n = gradOutput.nElement() + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + var i = 0 + while (i < n) { + var c = 0 + while (c < nChannel) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = scaleData(scaleOffset + c) * + invStd * gradOutputData(gradOutputOffset + i) + c += 1 + i += 1 + } + } + } else { + var i = 0 + while (i < n) { + var c = 0 + while (c < nChannel) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = + invStd * gradOutputData(gradOutputOffset + i) + c += 1 + i += 1 + } + } + } + } + + private[bigdl] def updateGradInputNHWCInferDouble( + gradOutput: Tensor[Double], + gradInput: Tensor[Double], + scale: Tensor[Double], + saveStd: Tensor[Double] + ): Unit = { + require(gradOutput.nDimension() == 4, "BN require a 4D gradient") + require(gradOutput.isContiguous(), "gradient is not contiguous") + val nChannel = gradOutput.size(4) + require(scale.size(1) == nChannel, "scale length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + + gradInput.resizeAs(gradOutput) + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + + val n = gradOutput.nElement() + var i = 0 + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + while (i < n) { + var c = 0 + while (c < nChannel) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = scaleData(scaleOffset + c) * + invStd * gradOutputData(gradOutputOffset + i) + c += 1 + i += 1 + } + } + } else { + while (i < n) { + var c = 0 + while (c < nChannel) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = + invStd * gradOutputData(gradOutputOffset + i) + c += 1 + i += 1 + } + } + } + } + + private[bigdl] def updateGradInputNCHWTrainFloat( + input: Tensor[Float], + gradOutput: Tensor[Float], + gradInput: Tensor[Float], + scale: Tensor[Float], + saveMean: Tensor[Float], + saveStd: Tensor[Float], + gMean: Tensor[Float], + gxMean: Tensor[Float] + ): Unit = { + require(input.nDimension() == 4, "BN require a 4D input") + require(input.isContiguous(), "input is not contiguous") + require(gradOutput.nDimension() == 4, "BN require a 4D gradient") + require(gradOutput.isContiguous(), "gradient is not contiguous") + val nChannel = gradOutput.size(2) + require(scale.size(1) == nChannel, "scale length is not consist with channel number") + require(saveMean.size(1) == nChannel, "saveMean length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + + gradInput.resizeAs(gradOutput) + if (gMean.isEmpty) { + gMean.resize(scale.size(1)) + gxMean.resize(scale.size(1)) + } + + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val saveMeanData = saveMean.storage().array() + val saveMeanOffset = saveMean.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + val gMeanData = gMean.storage().array() + val gxMeanData = gxMean.storage().array() + + val nBatch = gradOutput.size(1) + val frameSize = gradOutput.size(3) * gradOutput.size(4) + val n = gradOutput.nElement() + var b = 0 + var i = 0 + while(b < nBatch) { + var c = 0 + while(c < nChannel) { + var k = 0 + while(k < frameSize) { + gMeanData(c) += gradOutputData(i + gradOutputOffset) + gxMeanData(c) += gradOutputData(i + gradOutputOffset) * + (inputData(i + inputOffset) - saveMeanData(c + saveMeanOffset)) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + + var c = 0 + val size = n / nChannel + while(c < nChannel) { + gMeanData(c) /= size + gxMeanData(c) /= size + c += 1 + } + + i = 0 + b = 0 + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + while (b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while (k < frameSize) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = scaleData(scaleOffset + c) * + invStd * (gradOutputData(gradOutputOffset + i) - gMeanData(c) - + gxMeanData(c) * invStd * invStd * (inputData(inputOffset + i) - + saveMeanData(saveMeanOffset + c))) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } else { + while (b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while (k < frameSize) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = + invStd * (gradOutputData(gradOutputOffset + i) - gMeanData(c) - + gxMeanData(c) * invStd * invStd * (inputData(inputOffset + i) - + saveMeanData(saveMeanOffset + c))) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + } + + private[bigdl] def updateOutputNCHWTrainFloat(input: Tensor[Float], output: Tensor[Float], + saveMean: Tensor[Float], saveStd: Tensor[Float], runningMean: Tensor[Float], + runningVar: Tensor[Float], scale: Tensor[Float], offset: Tensor[Float], + eps: Float, momentum: Float, + batchVar: Tensor[Float] = null, saveVar: Tensor[Float] = null, needFix: Boolean = false) + : Unit = { + require(input.isContiguous(), "BatchNorm NCHW require a contiguous input") + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + val nChannels = input.size(2) + val nBatch = input.size(1) + val nFrame = input.size(3) * input.size(4) + if(saveMean.size(1) != nChannels) { + saveMean.resize(nChannels) + saveStd.resize(nChannels) + runningMean.resize(nChannels) + runningVar.resize(nChannels) + } + val meanData = saveMean.storage().array() + val meanOffset = saveMean.storageOffset() - 1 + var i = 0 + var b = 0 + while(b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + var meanSum = 0f + while(k < nFrame) { + meanSum += inputData(i + inputOffset) + k += 1 + i += 1 + } + meanData(c + meanOffset) += meanSum + c += 1 + } + b += 1 + } + + val n = input.nElement() + val frameSize = n / nChannels + var c = 0 + val runningMeanData = runningMean.storage().array() + val runningMeanOffset = runningMean.storageOffset() - 1 + while(c < nChannels) { + meanData(c + meanOffset) /= frameSize + runningMeanData(c + runningMeanOffset) = meanData(c + meanOffset) * momentum + + (1 - momentum) * runningMeanData(c + runningMeanOffset) + c += 1 + } + + val stdData = saveStd.storage().array() + val stdOffset = saveStd.storageOffset() - 1 + i = 0 + b = 0 + while(b < nBatch) { + var c = 0 + while(c < nChannels) { + var k = 0 + var stdSum = 0f + while(k < nFrame) { + val diff = (inputData(i + inputOffset) - meanData(c + meanOffset)) + stdSum += diff * diff + k += 1 + i += 1 + } + stdData(c + stdOffset) += stdSum + c += 1 + } + b += 1 + } + + c = 0 + val runningVarData = runningVar.storage().array() + val runningVarOffset = runningVar.storageOffset() - 1 + while(c < nChannels) { + if (stdData(c + stdOffset) == 0 && eps == 0) { + stdData(c + stdOffset) = 0 + if (saveVar != null) { + saveVar.setValue(c + 1, 0f) + } + if (batchVar != null) { + batchVar.setValue(c + 1, 0f) + } + } else { + val s = stdData(c + stdOffset) + val unbiasedVar = s / (frameSize - 1) + if (saveVar != null) { + saveVar.setValue(c + 1, s / frameSize) + } + if (batchVar != null) { + batchVar.setValue(c + 1, unbiasedVar) + } + stdData(c + stdOffset) = 1.0f / Math.sqrt(s / frameSize + eps).toFloat + runningVarData(c + runningVarOffset) = momentum * unbiasedVar + + (1 - momentum) * runningVarData(c + runningVarOffset) + } + c += 1 + } + + if (needFix) { + c = 0 + while(c < nChannels) { + meanData(c + meanOffset) = 0 + stdData(c + stdOffset) = 0.0001f + c += 1 + } + } + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + val offsetData = offset.storage().array() + val offsetOffset = offset.storageOffset() - 1 + i = 0 + b = 0 + while (b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + while (k < nFrame) { + outputData(i + outputOffset) = (inputData(i + inputOffset) - + meanData(c + meanOffset)) * stdData(c + stdOffset) * + scaleData(c + scaleOffset) + offsetData(c + offsetOffset) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } else { + i = 0 + b = 0 + while (b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + while (k < nFrame) { + outputData(i + outputOffset) = (inputData(i + inputOffset) - + meanData(c + meanOffset)) * stdData(c + stdOffset) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + } + + private[bigdl] def updateOutputNCHWTrainDouble(input: Tensor[Double], output: Tensor[Double], + saveMean: Tensor[Double], saveStd: Tensor[Double], runningMean: Tensor[Double], + runningVar: Tensor[Double], scale: Tensor[Double], offset: Tensor[Double], + eps: Double, momentum: Double, + batchVar: Tensor[Double] = null, saveVar: Tensor[Double] = null, needFix: Boolean = false) + : Unit = { + require(input.isContiguous(), "BatchNorm NCHW require a contiguous input") + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + val nChannels = input.size(2) + val nBatch = input.size(1) + val nFrame = input.size(3) * input.size(4) + if(saveMean.size(1) != nChannels) { + saveMean.resize(nChannels) + saveStd.resize(nChannels) + runningMean.resize(nChannels) + runningVar.resize(nChannels) + } + val meanData = saveMean.storage().array() + val meanOffset = saveMean.storageOffset() - 1 + var i = 0 + var b = 0 + while(b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + var meanSum = 0d + while(k < nFrame) { + meanSum += inputData(i + inputOffset) + k += 1 + i += 1 + } + meanData(c + meanOffset) += meanSum + c += 1 + } + b += 1 + } + + val n = input.nElement() + val frameSize = n / nChannels + var c = 0 + val runningMeanData = runningMean.storage().array() + val runningMeanOffset = runningMean.storageOffset() - 1 + while(c < nChannels) { + meanData(c + meanOffset) /= frameSize + runningMeanData(c + runningMeanOffset) = meanData(c + meanOffset) * momentum + + (1 - momentum) * runningMeanData(c + runningMeanOffset) + c += 1 + } + + val stdData = saveStd.storage().array() + val stdOffset = saveStd.storageOffset() - 1 + i = 0 + b = 0 + while(b < nBatch) { + var c = 0 + while(c < nChannels) { + var k = 0 + while(k < nFrame) { + val diff = (inputData(i + inputOffset) - meanData(c + meanOffset)) + stdData(c + stdOffset) += diff * diff + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + + c = 0 + val runningVarData = runningVar.storage().array() + val runningVarOffset = runningVar.storageOffset() - 1 + while(c < nChannels) { + if (stdData(c + stdOffset) == 0 && eps == 0) { + stdData(c + stdOffset) = 0 + if (saveVar != null) { + saveVar.setValue(c + 1, 0f) + } + if (batchVar != null) { + batchVar.setValue(c + 1, 0f) + } + } else { + val s = stdData(c + stdOffset) + val unbiasedVar = s / (frameSize - 1) + if (saveVar != null) { + saveVar.setValue(c + 1, s / frameSize) + } + if (batchVar != null) { + batchVar.setValue(c + 1, unbiasedVar) + } + stdData(c + stdOffset) = 1.0 / Math.sqrt(s / frameSize + eps) + runningVarData(c + stdOffset) = momentum * unbiasedVar + (1 - momentum) * + runningVarData(c + runningVarOffset) + } + c += 1 + } + + if (needFix) { + c = 0 + while(c < nChannels) { + meanData(c + meanOffset) = 0 + stdData(c + stdOffset) = 0.0001 + c += 1 + } + } + + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + val offsetData = offset.storage().array() + val offsetOffset = offset.storageOffset() - 1 + i = 0 + b = 0 + while (b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + while (k < nFrame) { + outputData(i + outputOffset) = (inputData(i + inputOffset) - + meanData(c + meanOffset)) * stdData(c + stdOffset) * + scaleData(c + scaleOffset) + offsetData(c + offsetOffset) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } else { + i = 0 + b = 0 + while (b < nBatch) { + var c = 0 + while (c < nChannels) { + var k = 0 + while (k < nFrame) { + outputData(i + outputOffset) = (inputData(i + inputOffset) - + meanData(c + meanOffset)) * stdData(c + stdOffset) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + } + + private[bigdl] def updateGradInputNCHWTrainDouble( + input: Tensor[Double], + gradOutput: Tensor[Double], + gradInput: Tensor[Double], + scale: Tensor[Double], + saveMean: Tensor[Double], + saveStd: Tensor[Double], + gMean: Tensor[Double], + gxMean: Tensor[Double] + ): Unit = { + require(input.nDimension() == 4, "BN require a 4D input") + require(input.isContiguous(), "input is not contiguous") + require(gradOutput.nDimension() == 4, "BN require a 4D gradient") + require(gradOutput.isContiguous(), "gradient is not contiguous") + val nChannel = gradOutput.size(2) + require(saveMean.size(1) == nChannel, "saveMean length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + + gradInput.resizeAs(gradOutput) + if (gMean.isEmpty) { + gMean.resize(saveMean.size(1)) + gxMean.resize(saveMean.size(1)) + } + + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val saveMeanData = saveMean.storage().array() + val saveMeanOffset = saveMean.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + val gMeanData = gMean.storage().array() + val gxMeanData = gxMean.storage().array() + + val nBatch = gradOutput.size(1) + val frameSize = gradOutput.size(3) * gradOutput.size(4) + val n = gradOutput.nElement() + var b = 0 + var i = 0 + while(b < nBatch) { + var c = 0 + while(c < nChannel) { + var k = 0 + while(k < frameSize) { + gMeanData(c) += gradOutputData(i + gradOutputOffset) + gxMeanData(c) += gradOutputData(i + gradOutputOffset) * + (inputData(i + inputOffset) - saveMeanData(c + saveMeanOffset)) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + + var c = 0 + val size = n / nChannel + while(c < nChannel) { + gMeanData(c) /= size + val invStd = saveStdData(saveStdOffset + c) + gxMeanData(c) = gxMeanData(c) * invStd * invStd / size + c += 1 + } + + i = 0 + b = 0 + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + require(scale.size(1) == nChannel, "scale length is not consist with channel number") + while (b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while (k < frameSize) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = (gradOutputData(gradOutputOffset + i) - + gMeanData(c) - (inputData(inputOffset + i) - saveMeanData(saveMeanOffset + c)) * + gxMeanData(c)) * invStd * scaleData(scaleOffset + c) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } else { + while (b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while (k < frameSize) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = (gradOutputData(gradOutputOffset + i) - + gMeanData(c) - (inputData(inputOffset + i) - saveMeanData(saveMeanOffset + c)) * + gxMeanData(c)) * invStd + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + } + + private[bigdl] def updateGradInputNCHWInferFloat( + gradOutput: Tensor[Float], + gradInput: Tensor[Float], + scale: Tensor[Float], + saveStd: Tensor[Float] + ): Unit = { + require(gradOutput.nDimension() == 4, "BN require a 4D gradient") + require(gradOutput.isContiguous(), "gradient is not contiguous") + val nChannel = gradOutput.size(2) + require(scale.size(1) == nChannel, "scale length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + + gradInput.resizeAs(gradOutput) + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + + val nBatch = gradOutput.size(1) + val frameSize = gradOutput.size(3) * gradOutput.size(4) + var b = 0 + var i = 0 + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + while (b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while (k < frameSize) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = scaleData(scaleOffset + c) * + invStd * gradOutputData(gradOutputOffset + i) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } else { + while (b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while (k < frameSize) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = + invStd * gradOutputData(gradOutputOffset + i) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + } + + private[bigdl] def updateGradInputNCHWInferDouble( + gradOutput: Tensor[Double], + gradInput: Tensor[Double], + scale: Tensor[Double], + saveStd: Tensor[Double] + ): Unit = { + require(gradOutput.nDimension() == 4, "BN require a 4D gradient") + require(gradOutput.isContiguous(), "gradient is not contiguous") + val nChannel = gradOutput.size(2) + require(scale.size(1) == nChannel, "scale length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + + gradInput.resizeAs(gradOutput) + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + + val nBatch = gradOutput.size(1) + val frameSize = gradOutput.size(3) * gradOutput.size(4) + var b = 0 + var i = 0 + if (scale != null) { + val scaleData = scale.storage().array() + val scaleOffset = scale.storageOffset() - 1 + while (b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while (k < frameSize) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = scaleData(scaleOffset + c) * + invStd * gradOutputData(gradOutputOffset + i) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } else { + while (b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while (k < frameSize) { + val invStd = saveStdData(saveStdOffset + c) + gradInputData(gradInputOffset + i) = + invStd * gradOutputData(gradOutputOffset + i) + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + } + + private[bigdl] def accGradientNHWCFloat(gradOutput: Tensor[Float], + gradWeight: Tensor[Float], gradBias: Tensor[Float], + input: Tensor[Float], saveMean: Tensor[Float], + saveStd: Tensor[Float], scaleW: Float, scaleB: Float): Unit = { + require(gradOutput.isContiguous(), "gradOutput must be contiguous") + require(gradWeight.isContiguous(), "gradWeight must be contiguous") + require(gradBias.isContiguous(), "gradBias must be contiguous") + require(input.isContiguous(), "gradWeight must be contiguous") + require(saveMean.nDimension() == 1, "saveMean must be 1D") + require(saveStd.nDimension() == 1, "saveStd must be 1D") + val nChannel = saveMean.size(1) + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradWeightData = gradWeight.storage().array() + val gradWeightOffset = gradWeight.storageOffset() - 1 + val gradBiasData = gradBias.storage().array() + val gradBiasOffset = gradBias.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val saveMeanData = saveMean.storage().array() + val saveMeanOffset = saveMean.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + + var i = 0 + val n = input.nElement() + while(i < n) { + var c = 0 + while(c < nChannel) { + val g = gradOutputData(gradOutputOffset + i) + gradWeightData(c + gradWeightOffset) += g * + (inputData(inputOffset + i) - saveMeanData(saveMeanOffset + c)) * + saveStdData(saveStdOffset + c) * scaleW + gradBiasData(c + gradBiasOffset) += g * scaleB + i += 1 + c += 1 + } + } + } + + private[bigdl] def accGradientNHWCDouble(gradOutput: Tensor[Double], + gradWeight: Tensor[Double], gradBias: Tensor[Double], + input: Tensor[Double], saveMean: Tensor[Double], + saveStd: Tensor[Double], scaleW: Double, scaleB: Double): Unit = { + require(gradOutput.isContiguous(), "gradOutput must be contiguous") + require(gradWeight.isContiguous(), "gradWeight must be contiguous") + require(gradBias.isContiguous(), "gradBias must be contiguous") + require(input.isContiguous(), "gradWeight must be contiguous") + require(saveMean.nDimension() == 1, "saveMean must be 1D") + require(saveStd.nDimension() == 1, "saveStd must be 1D") + val nChannel = saveMean.size(1) + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradWeightData = gradWeight.storage().array() + val gradWeightOffset = gradWeight.storageOffset() - 1 + val gradBiasData = gradBias.storage().array() + val gradBiasOffset = gradBias.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val saveMeanData = saveMean.storage().array() + val saveMeanOffset = saveMean.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + + var i = 0 + val n = input.nElement() + while(i < n) { + var c = 0 + while(c < nChannel) { + val g = gradOutputData(gradOutputOffset + i) + gradWeightData(c + gradWeightOffset) += g * + (inputData(inputOffset + i) - saveMeanData(saveMeanOffset + c)) * + saveStdData(saveStdOffset + c) * scaleW + gradBiasData(c + gradBiasOffset) += g * scaleB + i += 1 + c += 1 + } + } + } + + private[bigdl] def accGradientNCHWFloat(gradOutput: Tensor[Float], + gradWeight: Tensor[Float], gradBias: Tensor[Float], + input: Tensor[Float], saveMean: Tensor[Float], + saveStd: Tensor[Float], scaleW: Float, scaleB: Float): Unit = { + require(gradOutput.isContiguous(), "gradOutput must be contiguous") + require(gradWeight.isContiguous(), "gradWeight must be contiguous") + require(gradBias.isContiguous(), "gradBias must be contiguous") + require(input.isContiguous(), "gradWeight must be contiguous") + require(saveMean.nDimension() == 1, "saveMean must be 1D") + require(saveStd.nDimension() == 1, "saveStd must be 1D") + val nChannel = saveMean.size(1) + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradWeightData = gradWeight.storage().array() + val gradWeightOffset = gradWeight.storageOffset() - 1 + val gradBiasData = gradBias.storage().array() + val gradBiasOffset = gradBias.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val saveMeanData = saveMean.storage().array() + val saveMeanOffset = saveMean.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + + val nBatch = gradOutput.size(1) + val frameSize = gradOutput.size(3) * gradOutput.size(4) + var i = 0 + var b = 0 + while(b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while(k < frameSize) { + val g = gradOutputData(gradOutputOffset + i) + gradWeightData(c + gradWeightOffset) += g * + (inputData(inputOffset + i) - saveMeanData(saveMeanOffset + c)) * + saveStdData(saveStdOffset + c) * scaleW + gradBiasData(c + gradBiasOffset) += g * scaleB + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } + } + + private[bigdl] def accGradientNCHWDouble(gradOutput: Tensor[Double], + gradWeight: Tensor[Double], gradBias: Tensor[Double], + input: Tensor[Double], saveMean: Tensor[Double], + saveStd: Tensor[Double], scaleW: Double, scaleB: Double): Unit = { + require(gradOutput.isContiguous(), "gradOutput must be contiguous") + require(gradWeight.isContiguous(), "gradWeight must be contiguous") + require(gradBias.isContiguous(), "gradBias must be contiguous") + require(input.isContiguous(), "gradWeight must be contiguous") + require(saveMean.nDimension() == 1, "saveMean must be 1D") + require(saveStd.nDimension() == 1, "saveStd must be 1D") + val nChannel = saveMean.size(1) + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradWeightData = gradWeight.storage().array() + val gradWeightOffset = gradWeight.storageOffset() - 1 + val gradBiasData = gradBias.storage().array() + val gradBiasOffset = gradBias.storageOffset() - 1 + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + val saveMeanData = saveMean.storage().array() + val saveMeanOffset = saveMean.storageOffset() - 1 + val saveStdData = saveStd.storage().array() + val saveStdOffset = saveStd.storageOffset() - 1 + + val nBatch = gradOutput.size(1) + val frameSize = gradOutput.size(3) * gradOutput.size(4) + var i = 0 + var b = 0 + while(b < nBatch) { + var c = 0 + while (c < nChannel) { + var k = 0 + while(k < frameSize) { + val g = gradOutputData(gradOutputOffset + i) + gradWeightData(c + gradWeightOffset) += scaleW * (inputData(inputOffset + i) - + saveMeanData(saveMeanOffset + c)) * g * saveStdData(saveStdOffset + c) + gradBiasData(c + gradBiasOffset) += g * scaleB + k += 1 + i += 1 + } + c += 1 + } + b += 1 + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRN.scala index de488152646..6396cb5a02e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRN.scala @@ -16,8 +16,8 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Engine @@ -41,7 +41,8 @@ import scala.reflect._ */ @SerialVersionUID(3641570491004969703L) class SpatialCrossMapLRN[T: ClassTag] -(val size: Int = 5, val alpha: Double = 1.0, val beta: Double = 0.75, val k: Double = 1.0)( +(val size: Int = 5, val alpha: Double = 1.0, val beta: Double = 0.75, val k: Double = 1.0, + val format: DataFormat = DataFormat.NCHW)( implicit ev: TensorNumeric[T]) extends TensorModule[T] { @transient @@ -113,8 +114,19 @@ class SpatialCrossMapLRN[T: ClassTag] while (b <= batchNum) { val _b = b results(b - 1) = Engine.model.invoke(() => { - SpatialCrossMapLRN.forwardFrame(input.select(1, _b), output.select(1, _b), - scale.select(1, _b), alpha, size, beta, k) + if (format == DataFormat.NCHW) { + SpatialCrossMapLRN.forwardFrameNCHW(input.select(1, _b), output.select(1, _b), + scale.select(1, _b), alpha, size, beta, k) + } else { + if (ev.getType() == FloatType) { + SpatialCrossMapLRN.forwardFrameNHWCFloat( + input.select(1, _b).asInstanceOf[Tensor[Float]], + output.select(1, _b).asInstanceOf[Tensor[Float]], + alpha, size, beta, k) + } else { + throw new NotImplementedError(s"Not support numeric type ${ev.getType()} in NHWC") + } + } }) b += 1 } @@ -151,9 +163,22 @@ class SpatialCrossMapLRN[T: ClassTag] while (b <= batchNum) { val _b = b results(b - 1) = Engine.model.invoke(() => { - SpatialCrossMapLRN.backwardFrame(input.select(1, _b), output.select(1, _b), - scale.select(1, _b), gradOutput.select(1, _b), gradInput.select(1, _b), - paddedRatio.select(1, _b), accumRatio.select(1, _b), alpha, size, beta) + if (format == DataFormat.NCHW) { + SpatialCrossMapLRN.backwardFrameNCHW(input.select(1, _b), output.select(1, _b), + scale.select(1, _b), gradOutput.select(1, _b), gradInput.select(1, _b), + paddedRatio.select(1, _b), accumRatio.select(1, _b), alpha, size, beta) + } else { + if (ev.getType() == FloatType) { + SpatialCrossMapLRN.backwardFrameNHWCFloat( + gradOutput.select(1, _b).asInstanceOf[Tensor[Float]], + input.select(1, _b).asInstanceOf[Tensor[Float]], + gradInput.select(1, _b).asInstanceOf[Tensor[Float]], + output.select(1, _b).asInstanceOf[Tensor[Float]], + alpha, size, beta, k) + } else { + throw new NotImplementedError(s"Not support numeric type ${ev.getType()} in NHWC") + } + } }) b += 1 } @@ -169,11 +194,13 @@ object SpatialCrossMapLRN { size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75, - k: Double = 1.0)(implicit ev: TensorNumeric[T]) : SpatialCrossMapLRN[T] = { - new SpatialCrossMapLRN[T](size, alpha, beta, k) + k: Double = 1.0, + format: DataFormat = DataFormat.NCHW) + (implicit ev: TensorNumeric[T]) : SpatialCrossMapLRN[T] = { + new SpatialCrossMapLRN[T](size, alpha, beta, k, format) } - private def forwardFrame[T](input: Tensor[T], output: Tensor[T], + private[bigdl] def forwardFrameNCHW[T](input: Tensor[T], output: Tensor[T], scale: Tensor[T], alpha: Double, size: Int, beta: Double, k: Double) (implicit ev: TensorNumeric[T]): Unit = { val channels = input.size(1) @@ -209,9 +236,56 @@ object SpatialCrossMapLRN { scale.mul(ev.fromType(alpha / size)).add(ev.fromType(k)) output.pow(scale, ev.fromType(-beta)) output.cmul(input) + output } - private def backwardFrame[T]( + def forwardFrameNHWCFloat( + input: Tensor[Float], + output: Tensor[Float], + alpha: Double, + size: Int, + beta: Double, + k: Double + ): Unit = { + require(input.isContiguous(), "input of LRN for NHWC should be contiguous") + require(output.isContiguous(), "output of LRN for NHWC should be contiguous") + val channel = input.size(3) + val inputOffset = input.storageOffset() - 1 + val inputArray = input.storage().array() + val outputOffset = output.storageOffset() - 1 + val outputArray = output.storage().array() + val nElement = output.nElement() + var l2sum = 0f + var i = 0 + while(i < nElement) { + val p = i % channel + if (p == 0) { + var c = 0 + l2sum = 0 + val depth = Math.min((size - 1) / 2 + 1, channel) + while (c < depth) { + val x = inputArray(inputOffset + i + c) + l2sum += x * x + c += 1 + } + } else { + if (p + (size - 1) / 2 < channel) { + val x = inputArray(inputOffset + i + (size - 1) / 2) + l2sum += x * x + } + if (p - (size - 1) / 2 > 0) { + val x = inputArray(inputOffset + i - (size - 1) / 2 - 1) + l2sum -= x * x + } + } + outputArray(outputOffset + i) = inputArray(inputOffset + i) * + Math.pow(k + alpha / size * l2sum, -beta).toFloat + i += 1 + } + output + } + + private def backwardFrameNCHW[T]( input: Tensor[T], output: Tensor[T], scale: Tensor[T], gradOutput: Tensor[T], gradInput: Tensor[T], paddedRatio: Tensor[T], accumRatio: Tensor[T], alpha: Double, size: Int, beta: Double) @@ -234,4 +308,64 @@ object SpatialCrossMapLRN { c += 1 } } + + private[bigdl] def backwardFrameNHWCFloat( + gradOutput: Tensor[Float], + input: Tensor[Float], + gradInput: Tensor[Float], + output: Tensor[Float], + alpha: Double, + size: Int, + beta: Double, + k: Double + ): Unit = { + gradInput.copy(input) + val channel = input.size(3) + val inputOffset = input.storageOffset() - 1 + val inputArray = input.storage().array() + val outputOffset = output.storageOffset() - 1 + val outputArray = output.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + val gradOutputArray = gradOutput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val gradInputArray = gradInput.storage().array() + val nElement = gradInput.nElement() + var glsum = 0f + var i = 0 + while(i < nElement) { + val p = i % channel + if (p == 0) { + var c = 0 + glsum = 0 + val depth = Math.min((size - 1) / 2 + 1, channel) + while (c < depth) { + val x = inputArray(inputOffset + i + c) + val g = gradOutputArray(gradOutputOffset + i + c) + val o = outputArray(outputOffset + i + c) + glsum += g * Math.pow(o / x, (beta + 1) / beta).toFloat * x + c += 1 + } + } else { + if (p + (size - 1) / 2 < channel) { + val x = inputArray(inputOffset + i + (size - 1) / 2) + val g = gradOutputArray(gradOutputOffset + i + (size - 1) / 2) + val o = outputArray(outputOffset + i + (size - 1) / 2) + glsum += g * Math.pow(o / x, (beta + 1) / beta).toFloat * x + } + if (p - (size - 1) / 2 - 1>= 0) { + val x = inputArray(inputOffset + i - (size - 1) / 2 - 1) + val g = gradOutputArray(gradOutputOffset + i - (size - 1) / 2 - 1) + val o = outputArray(outputOffset + i - (size - 1) / 2 - 1) + glsum -= g * Math.pow(o / x, (beta + 1) / beta).toFloat * x + } + } + val x = inputArray(inputOffset + i) + val g = gradOutputArray(gradOutputOffset + i) + val o = outputArray(outputOffset + i) + gradInputArray(gradInputOffset + i) = + (o / x * g - 2 * beta * alpha / size * x * glsum).toFloat + + i += 1 + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNorm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNorm.scala new file mode 100644 index 00000000000..c18d8a68afe --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNorm.scala @@ -0,0 +1,125 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.SpatialBatchNormalization +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * This is similar to SpatialBatchNormalization. + * + * When isTraining is true, it takes three tensors as inputs, which is image, + * scale, offset. + * + * The operation implemented is: + * + * ( image - batch-mean(x) ) + * y = ---------------------------------- * weight + offset + * batch-standard-deviation(x) + * + * The operation will output y, mean and variance tensors. + * + * If the isTraining is false, it takes five tensors as inputs, which is image, scale, offset, mean, + * and variance. + * + * @param epsilon + * @param isTraining + * @param momentum + * @param dataFormat + * @param ev$1 + * @param ev + * @tparam T Numeric type. Only support float/double now + */ +class FusedBatchNorm[T: ClassTag]( + epsilon: Float = 0.0001f, + isTraining: Boolean = true, + momentum: Float = 0.1f, + dataFormat: DataFormat = DataFormat.NHWC +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T]{ + + @transient + private var runningMean: Tensor[Float] = null + + @transient + private var runningVar: Tensor[Float] = null + + @transient + private var saveStd: Tensor[Float] = null + + override def updateOutput(input: Table): Table = { + val x = input[Tensor[Float]](1) + val scale = input[Tensor[Float]](2) + val offset = input[Tensor[Float]](3) + val mean = input[Tensor[Float]](4) + val variance = input[Tensor[Float]](5) + + if (output.length() == 0) { + output(1) = Tensor[Float]().resizeAs(x) // y + output(2) = Tensor[Float](x.size(4)) // batch mean + output(3) = Tensor[Float](x.size(4)) // batch var + output(4) = Tensor[Float](x.size(4)) // save mean + output(5) = Tensor[Float](x.size(4)) // save var + runningMean = Tensor[Float](x.size(4)) // running mean + runningVar = Tensor[Float](x.size(4)) // running var + saveStd = Tensor[Float](x.size(4)) // save std + } + + val y = output[Tensor[Float]](1) + val batchMean = output[Tensor[Float]](2) + val batchVar = output[Tensor[Float]](3) + val saveMean = output[Tensor[Float]](4) + val saveVar = output[Tensor[Float]](5) + + if (isTraining) { + if (dataFormat == DataFormat.NHWC) { + SpatialBatchNormalization.updateOutputNHWCTrainFloat( + x, y, batchMean, saveStd, runningMean, runningVar, scale, offset, epsilon, momentum, + batchVar, saveVar + ) + } else { + SpatialBatchNormalization.updateOutputNCHWTrainFloat( + x, y, batchMean, saveStd, runningMean, runningVar, scale, offset, epsilon, momentum, + batchVar, saveVar + ) + } + saveMean.copy(batchMean) + } else { + if (dataFormat == DataFormat.NHWC) { + SpatialBatchNormalization.updateOutputNHWCInferFloat( + x, y, mean, variance, scale, offset, epsilon + ) + } else { + SpatialBatchNormalization.updateOutputNCHWInferFloat( + x, y, mean, variance, scale, offset, epsilon + ) + } + } + + output + } +} + +object FusedBatchNorm { + def apply[T: ClassTag](epsilon: Float = 0.0001f, isTrainning: Boolean = true, + momentum: Float = 0.1f, dataFormat: DataFormat = DataFormat.NHWC) + (implicit ev: TensorNumeric[T]): FusedBatchNorm[T] + = new FusedBatchNorm(epsilon, isTrainning, momentum, dataFormat) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala new file mode 100644 index 00000000000..d35bc6078b3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala @@ -0,0 +1,86 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.{BatchNormalization, SpatialBatchNormalization} +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * This is the gradient operation coressponding to the FusedBatchNorm. It will calculate the + * activity, weight and bias gradients of the spatial batch normalization. + * + * The formula is + * x_backprop = scale * rsqrt(variance + epsilon) * [y_backprop - mean(y_backprop) - + * (x - mean(x)) * mean(y_backprop * (x - mean(x))) / (variance + epsilon)] + * weight_backprop = sum(y_backprop * (x - mean(x)) * rsqrt(variance + epsilon)) + * bias_backprop = sum(y_backprop) + * + * @param epsilon + * @param dataFormat + * @param isTraining + * @param ev$1 + * @param ev + * @tparam T Numeric type. Only support float/double now + */ +class FusedBatchNormGrad[T: ClassTag]( + epsilon: Float, dataFormat: DataFormat, isTraining: Boolean)(implicit ev: TensorNumeric[T]) + extends Operation[Table, Table, T]{ + + private val gMean = Tensor[Float]() + private val gxMean = Tensor[Float]() + private val saveStd = Tensor[Float]() + + override def updateOutput(input: Table): Table = { + val gradOutput = input[Tensor[Float]](1) + val x = input[Tensor[Float]](2) + val scale = input[Tensor[Float]](3) + val saveMean = input[Tensor[Float]](4) + val saveVar = input[Tensor[Float]](5) + + if (output.length() == 0) { + output(1) = Tensor[Float]().resizeAs(x) // gradInput + output(2) = Tensor[Float](x.size(4)) // weight gradient + output(3) = Tensor[Float](x.size(4)) // bias gradient + saveStd.resize(x.size(4)) // bias gradient + } + saveStd.copy(saveVar) + saveStd.add(epsilon).pow(-0.5f) + val gradInput = output[Tensor[Float]](1) + val gradWeight = output[Tensor[Float]](2) + val gradBias = output[Tensor[Float]](3) + + SpatialBatchNormalization.updateGradInputNHWCTrainFloat( + x, gradOutput, gradInput, scale, saveMean, saveStd, gMean, gxMean) + + gradWeight.zero() + gradBias.zero() + SpatialBatchNormalization.accGradientNHWCFloat( + gradOutput, gradWeight, gradBias, x, saveMean, saveStd, 1.0f, 1.0f) + + output + } +} + +object FusedBatchNormGrad { + def apply[T: ClassTag](epsilon: Float = 0.0001f, dataFormat: DataFormat = DataFormat.NHWC, + isTraining: Boolean = true)(implicit ev: TensorNumeric[T]): FusedBatchNormGrad[T] = + new FusedBatchNormGrad(epsilon, dataFormat, isTraining) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala new file mode 100644 index 00000000000..33c7aaf15e7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala @@ -0,0 +1,76 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.SpatialCrossMapLRN +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * LRNGrad calculate the backprop gradients of the Local response normalization layer. + * + * @param depthRadius + * @param bias + * @param alpha + * @param beta + * @param ev$1 + * @param ev + * @param ev2 + * @tparam T Numeric type. Only support float/double now + */ +class LRNGrad[T: ClassTag]( + depthRadius: Int = 5, + bias: Float = 1.0f, + alpha: Float = 1.0f, + beta: Float = 0.5f +)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[Float]) + extends Operation[Table, Tensor[Float], T] { + + output = Tensor[Float]() + + override def updateOutput(input: Table): Tensor[Float] = { + val gradOutput = input[Tensor[Float]](1) + val inputTensor = input[Tensor[Float]](2) + val outputTensor = input[Tensor[Float]](3) + + output.resizeAs(inputTensor) + var b = 1 + while(b <= inputTensor.size(1)) { + SpatialCrossMapLRN.backwardFrameNHWCFloat( + gradOutput.select(1, b), + inputTensor.select(1, b), + output.select(1, b), + outputTensor.select(1, b), + alpha * (2 * depthRadius + 1), 2 * depthRadius + 1, beta, bias + ) + b += 1 + } + output + } +} + +object LRNGrad { + def apply[T: ClassTag]( + depthRadius: Int = 5, + bias: Float = 1.0f, + alpha: Float = 1.0f, + beta: Float = 0.5f + )(implicit ev: TensorNumeric[T]): LRNGrad[T] + = new LRNGrad(depthRadius, bias, alpha, beta) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 1f166bcab66..36b871f99f5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -255,6 +255,8 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( private[tensor] def this()(implicit ev: TensorNumeric[T]) = this(null, 0, null, null, 0) override def fill(v: T): Tensor[T] = { + if (this.storage() == null) return this + if (this.isContiguous()) { this.storage().fill(v, this.storageOffset(), this.nElement()) } else { @@ -273,7 +275,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } override def zero(): Tensor[T] = { - this.fill(ev.fromType[Int](0)) + this.fill(ev.zero) } override def randn(): Tensor[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index d6000a2074b..e4b9a574d37 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -531,18 +531,21 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, - initGradBias: JTensor = null) + initGradBias: JTensor = null, dataFormat: String = "NCHW") : SpatialBatchNormalization[T] = { SpatialBatchNormalization[T](nOutput, eps, momentum, affine, - toTensor(initWeight), toTensor(initBias), toTensor(initGradWeight), toTensor(initBias)) + toTensor(initWeight), toTensor(initBias), toTensor(initGradWeight), toTensor(initBias), + DataFormat(dataFormat) + ) } def createSpatialCrossMapLRN(size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75, - k: Double = 1.0) + k: Double = 1.0, + dataFormat: String = "NCHW") : SpatialCrossMapLRN[T] = { - SpatialCrossMapLRN[T](size, alpha, beta, k) + SpatialCrossMapLRN[T](size, alpha, beta, k, DataFormat(dataFormat)) } def createDropout(initP: Double = 0.5, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala index b0a21e40049..d2b318c79fa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala @@ -461,6 +461,10 @@ object Tensorflow { AttrValue.newBuilder().setI(value).build() } + private[bigdl] def floatAttr(value: Float): AttrValue = { + AttrValue.newBuilder().setF(value).build() + } + private[bigdl] def listIntAttr(value: Seq[Int]): AttrValue = { val list = ListValue.newBuilder() value.foreach(list.addI(_)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala new file mode 100644 index 00000000000..a75d2de1ca8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.FusedBatchNorm +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class FusedBatchNorm extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + require(t == DataType.DT_FLOAT, "Only support float batch normal") + val eps = getFloat(nodeDef.getAttrMap, "epsilon") + val dataFormat = getString(nodeDef.getAttrMap, "data_format") + val isTrain = getBoolean(nodeDef.getAttrMap, "is_training") + if (dataFormat == "NHWC") { + FusedBatchNorm[T](eps, isTrain, dataFormat = DataFormat.NHWC) + } else { + FusedBatchNorm[T](eps, isTrain, dataFormat = DataFormat.NCHW) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala new file mode 100644 index 00000000000..a0156923b88 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.FusedBatchNormGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class FusedBatchNormGrad extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val eps = getFloat(nodeDef.getAttrMap, "epsilon") + val dataFormat = getString(nodeDef.getAttrMap, "data_format") + val isTrain = getBoolean(nodeDef.getAttrMap, "is_training") + FusedBatchNormGrad[T](eps, + if (dataFormat == "NHWC") DataFormat.NHWC else DataFormat.NCHW, + isTrain) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala new file mode 100644 index 00000000000..6e29af5f646 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.FusedBatchNormGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class FusedBatchNormGradV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val eps = getFloat(nodeDef.getAttrMap, "epsilon") + val dataFormat = getString(nodeDef.getAttrMap, "data_format") + val isTrain = getBoolean(nodeDef.getAttrMap, "is_training") + FusedBatchNormGrad[T](eps, + if (dataFormat == "NHWC") DataFormat.NHWC else DataFormat.NCHW, + isTrain) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala new file mode 100644 index 00000000000..9498b7104ee --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.FusedBatchNorm +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class FusedBatchNormV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + val u = getType(nodeDef.getAttrMap, "U") + require(t == DataType.DT_FLOAT, "T: Only support float batch normal") + require(u == DataType.DT_FLOAT, "U: Only support float batch normal") + val eps = getFloat(nodeDef.getAttrMap, "epsilon") + val dataFormat = getString(nodeDef.getAttrMap, "data_format") + val isTrain = getBoolean(nodeDef.getAttrMap, "is_training") + if (dataFormat == "NHWC") { + FusedBatchNorm[T](eps, isTrain, dataFormat = DataFormat.NHWC) + } else { + FusedBatchNorm[T](eps, isTrain, dataFormat = DataFormat.NCHW) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRN.scala new file mode 100644 index 00000000000..d7f2a88a8bf --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRN.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.SpatialCrossMapLRN +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class LRN extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val size = getInt(nodeDef.getAttrMap, "depth_radius") + val k = getFloat(nodeDef.getAttrMap, "bias") + val alpha = getFloat(nodeDef.getAttrMap, "alpha") + val beta = getFloat(nodeDef.getAttrMap, "beta") + SpatialCrossMapLRN[T]( + size = size * 2 + 1, + k = k, + alpha = alpha * (size * 2 + 1), + beta = beta, + format = DataFormat.NHWC + ) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala new file mode 100644 index 00000000000..355e2d78a8a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.LRNGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class LRNGrad extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val size = getInt(nodeDef.getAttrMap, "depth_radius") + val k = getFloat(nodeDef.getAttrMap, "bias") + val alpha = getFloat(nodeDef.getAttrMap, "alpha") + val beta = getFloat(nodeDef.getAttrMap, "beta") + + LRNGrad[T](size, k, alpha, beta) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala new file mode 100644 index 00000000000..10d7acf2b44 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala @@ -0,0 +1,104 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { + "SpatialBatchNormalization module in batch mode" should "be good in gradient check " + + "for input" in { + val seed = 100 + RNG.setSeed(seed) + val sbn = new SpatialBatchNormalization[Double](3, 1e-3) + val input = Tensor[Double](16, 3, 4, 4).apply1(e => Random.nextDouble()) + + val checker = new GradientChecker(1e-4) + checker.checkLayer[Double](sbn, input, 1e-3) should be(true) + } + + "SpatialBatchNormalization module in batch mode" should "be good in gradient check " + + "for weight" in { + val seed = 100 + RNG.setSeed(seed) + val sbn = new SpatialBatchNormalization[Double](3, 1e-3) + val input = Tensor[Double](16, 3, 4, 4).apply1(e => Random.nextDouble()) + + val checker = new GradientChecker(1e-4) + checker.checkWeight[Double](sbn, input, 1e-3) should be(true) + } + + "A SpatialBatchNormalization" should "generate same output for NHWC and NCHW" in { + val inputNCHW = Tensor[Float](4, 256, 8, 8).rand() + val inputNHWC = inputNCHW.transpose(2, 4).transpose(2, 3).contiguous() + val weight = Tensor[Float](256).rand() + val bias = Tensor[Float](256).rand() + val bnNCHW = SpatialBatchNormalization[Float](nOutput = 256, initWeight = weight, + initBias = bias) + val bnNHWC = SpatialBatchNormalization[Float](nOutput = 256, dataFormat = DataFormat.NHWC, + initWeight = weight, initBias = bias) + val outputNCHW = bnNCHW.forward(inputNCHW) + val outputNHWC = bnNHWC.forward(inputNHWC) + outputNCHW.almostEqual(outputNHWC.transpose(2, 4).transpose(3, 4), 1e-5) should be(true) + } + + "A SpatialBatchNormalization update gradinput" should + "generate same output for NHWC and NCHW" in { + val inputNCHW = Tensor[Float](4, 256, 8, 8).rand() + val inputNHWC = inputNCHW.transpose(2, 4).transpose(2, 3).contiguous() + val gradientNCHW = Tensor[Float](4, 256, 8, 8).rand() + val gradientNHWC = gradientNCHW.transpose(2, 4).transpose(2, 3).contiguous() + val weight = Tensor[Float](256).rand() + val bias = Tensor[Float](256).rand() + val bnNCHW = SpatialBatchNormalization[Float](nOutput = 256, initWeight = weight, + initBias = bias) + val bnNHWC = SpatialBatchNormalization[Float](nOutput = 256, dataFormat = DataFormat.NHWC, + initWeight = weight, initBias = bias) + val outputNCHW = bnNCHW.forward(inputNCHW) + val outputNHWC = bnNHWC.forward(inputNHWC) + + val backpropNCHW = bnNCHW.updateGradInput(inputNCHW, gradientNCHW) + val backpropNHWC = bnNHWC.updateGradInput(inputNHWC, gradientNHWC) + + backpropNCHW.almostEqual(backpropNHWC.transpose(2, 4).transpose(3, 4), 1e-5) should be(true) + } + + "A SpatialBatchNormalization acc gradient" should "generate same output for NHWC and NCHW" in { + val inputNCHW = Tensor[Float](4, 256, 8, 8).rand() + val inputNHWC = inputNCHW.transpose(2, 4).transpose(2, 3).contiguous() + val gradientNCHW = Tensor[Float](4, 256, 8, 8).rand() + val gradientNHWC = gradientNCHW.transpose(2, 4).transpose(2, 3).contiguous() + val weight = Tensor[Float](256).rand() + val bias = Tensor[Float](256).rand() + val bnNCHW = SpatialBatchNormalization[Float](nOutput = 256, initWeight = weight, + initBias = bias) + val bnNHWC = SpatialBatchNormalization[Float](nOutput = 256, dataFormat = DataFormat.NHWC, + initWeight = weight, initBias = bias) + val outputNCHW = bnNCHW.forward(inputNCHW) + val outputNHWC = bnNHWC.forward(inputNHWC) + + bnNCHW.backward(inputNCHW, gradientNCHW) + bnNHWC.backward(inputNHWC, gradientNHWC) + + bnNCHW.gradWeight.almostEqual(bnNHWC.gradWeight, 1e-5) + bnNCHW.gradBias.almostEqual(bnNHWC.gradBias, 1e-5) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala index 258c14e65df..f07fdad0ddb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala @@ -22,19 +22,18 @@ import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.scalatest.{FlatSpec, Matchers} class TimeDistributedSpec extends FlatSpec with Matchers { - "A TimeDistributed Module" should " copyStatus correctly" in { + "A TimeDistributed Module" should "copyStatus correctly" in { RNG.setSeed(100) val batchSize = 5 val times = 5 - val inputDim = 3 - val outputDim = 4 + val channels = 3 val timeDim = 1 - val input1 = Tensor[Float](Array(batchSize, times, inputDim)).randn() - val gradOutput1 = Tensor[Float](Array(batchSize, times, outputDim)).randn() - val input2 = Tensor[Float](Array(batchSize, times, inputDim)).randn() - val gradOutput2 = Tensor[Float](Array(batchSize, times, outputDim)).randn() - val bnorm1 = BatchNormalization[Float](inputDim, outputDim) - val bnorm2 = BatchNormalization[Float](inputDim, outputDim) + val input1 = Tensor[Float](Array(batchSize, times, channels)).randn() + val gradOutput1 = Tensor[Float](Array(batchSize, times, channels)).randn() + val input2 = Tensor[Float](Array(batchSize, times, channels)).randn() + val gradOutput2 = Tensor[Float](Array(batchSize, times, channels)).randn() + val bnorm1 = BatchNormalization[Float](channels) + val bnorm2 = BatchNormalization[Float](channels) val model1 = TimeDistributed[Float](bnorm1) val model2 = TimeDistributed[Float](bnorm2) @@ -50,7 +49,7 @@ class TimeDistributedSpec extends FlatSpec with Matchers { bnorm1.runningVar should be (bnorm2.runningVar) } - "A TimeDistributed Module" should " reset correctly" in { + "A TimeDistributed Module" should "reset correctly" in { RNG.setSeed(100) val batchSize = 5 val times = 5 @@ -71,7 +70,7 @@ class TimeDistributedSpec extends FlatSpec with Matchers { gradInput should not be (null) } - "A TimeDistributed Module" should " hash code correctly" in { + "A TimeDistributed Module" should "hash code correctly" in { RNG.setSeed(100) val batchSize = 5 val times = 5 @@ -95,7 +94,7 @@ class TimeDistributedSpec extends FlatSpec with Matchers { val hashCode2 = model2.hashCode() hashCode1 should be(hashCode2) } - "A TimeDistributed Module" should " getParaemtersTable correctly" in { + "A TimeDistributed Module" should "getParaemtersTable correctly" in { RNG.setSeed(100) val batchSize = 5 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BatchNormalizationSpec.scala index de6424c6872..273db78bb47 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BatchNormalizationSpec.scala @@ -108,11 +108,7 @@ class BatchNormalizationSpec extends TorchSpec { v1 }) - gradparametersTorch.map(gradparameters, (v1, v2) => { - assert(abs(v1 - v2) == 0) - v1 - }) - + gradparametersTorch.almostEqual(gradparameters, 1e-10) } "A SpatialBatchNormalization evaluating" should "generate correct output" in { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala index 3140db39d6b..2d7c11bdf0c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala @@ -75,14 +75,8 @@ class ConcatSpec extends TorchSpec { val end = System.nanoTime() val scalaTime = end - start - luaOutput.map(output, (v1, v2) => { - assert(abs(v1 - v2) == 0) - v1 - }) - luaGradInput.map(gradInput, (v1, v2) => { - assert(abs(v1 - v2) == 0) - v1 - }) + luaOutput.almostEqual(output, 1e-15) + luaGradInput.almostEqual(gradInput, 1e-15) println("Test case : Concat, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } @@ -142,22 +136,10 @@ class ConcatSpec extends TorchSpec { val end = System.nanoTime() val scalaTime = end - start - luaOutput.map(output, (v1, v2) => { - assert(abs(v1 - v2) == 0) - v1 - }) - luaGradInput.map(gradInput, (v1, v2) => { - assert(abs(v1 - v2) == 0) - v1 - }) - gradParametersLua.map(gradParameters, (v1, v2) => { - assert(abs(v1 - v2) == 0) - v1 - }) - parametersLua.map(parameters, (v1, v2) => { - assert(abs(v1 - v2) == 0) - v1 - }) + luaOutput.almostEqual(output, 1e-15) + luaGradInput.almostEqual(gradInput, 1e-15) + gradParametersLua.almostEqual(gradParameters, 1e-11) + parametersLua.almostEqual(parameters, 1e-11) println("Test case : Concat, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialBatchNormalizationSpec.scala index ce5c8a0558b..96caae78753 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialBatchNormalizationSpec.scala @@ -17,12 +17,13 @@ package com.intel.analytics.bigdl.torch import breeze.numerics.abs -import com.intel.analytics.bigdl.nn.{GradientChecker, SpatialBatchNormalization} +import com.intel.analytics.bigdl.nn.{BatchNormalization, GradientChecker, SpatialBatchNormalization} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ import scala.util.Random import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat @com.intel.analytics.bigdl.tags.Serial class SpatialBatchNormalizationSpec extends TorchSpec { @@ -102,15 +103,11 @@ class SpatialBatchNormalizationSpec extends TorchSpec { }) gradInputTorch.map(gradInput, (v1, v2) => { - if (abs(v1 - v2) != 0) println(s"$v1 $v2") - v1 - }) - - gradparametersTorch.map(gradparameters, (v1, v2) => { - if (abs(v1 - v2) != 0) println(s"$v1 $v2") + assert(abs(v1 - v2) == 0) v1 }) + gradparametersTorch.almostEqual(gradparameters, 1e-10) } "A SpatialBatchNormalization evaluating" should "generate correct output" in { @@ -193,31 +190,5 @@ class SpatialBatchNormalizationSpec extends TorchSpec { assert(abs(v1 - v2) == 0) v1 }) - - } - - "SpatialBatchNormalization module in batch mode" should "be good in gradient check " + - "for input" in { - torchCheck() - val seed = 100 - RNG.setSeed(seed) - val sbn = new SpatialBatchNormalization[Double](3, 1e-3) - val input = Tensor[Double](16, 3, 4, 4).apply1(e => Random.nextDouble()) - - val checker = new GradientChecker(1e-4) - checker.checkLayer[Double](sbn, input, 1e-3) should be(true) - } - - "SpatialBatchNormalization module in batch mode" should "be good in gradient check " + - "for weight" in { - torchCheck() - val seed = 100 - RNG.setSeed(seed) - val sbn = new SpatialBatchNormalization[Double](3, 1e-3) - val input = Tensor[Double](16, 3, 4, 4).apply1(e => Random.nextDouble()) - - val checker = new GradientChecker(1e-4) - checker.checkWeight[Double](sbn, input, 1e-3) should be(true) } - } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala new file mode 100644 index 00000000000..c76cbeb4183 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala @@ -0,0 +1,145 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{booleanAttr, floatAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} + +class FusedBatchNormGradSpec extends TensorflowSpecHelper { + + "FusedBatchNormGrad gradInput" should "be correct when is training is true" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormGrad"), + Seq(g, x, scale, mean, variance), + 0, 1e-3 + ) + } + + "FusedBatchNormGrad weight" should "be correct when is training is true" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormGrad"), + Seq(g, x, scale, mean, variance), + 1, 1e-3 + ) + } + + "FusedBatchNormGrad bias" should "be correct when is training is true" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormGrad"), + Seq(g, x, scale, mean, variance), + 2 + ) + } + + "FusedBatchNormGrad gradInput" should "be correct when is training is false" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(false)) + .setOp("FusedBatchNormGrad"), + Seq(g, x, scale, mean, variance), + 0, 1e-3 + ) + } + + "FusedBatchNormGrad weight" should "be correct when is training is false" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(false)) + .setOp("FusedBatchNormGrad"), + Seq(g, x, scale, mean, variance), + 1, 1e-3 + ) + } + + "FusedBatchNormGrad bias" should "be correct when is training is false" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(false)) + .setOp("FusedBatchNormGrad"), + Seq(g, x, scale, mean, variance), + 2 + ) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2Spec.scala new file mode 100644 index 00000000000..733bd3f3ea2 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2Spec.scala @@ -0,0 +1,157 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{booleanAttr, floatAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} + +class FusedBatchNormGradV2Spec extends TensorflowSpecHelper { + + "FusedBatchNormGradV2 gradInput" should "be correct when is training is true" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormGradV2"), + Seq(g, x, scale, mean, variance), + 0, 1e-3 + ) + } + + "FusedBatchNormGradV2 weight" should "be correct when is training is true" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormGradV2"), + Seq(g, x, scale, mean, variance), + 1, 1e-3 + ) + } + + "FusedBatchNormGradV2 bias" should "be correct when is training is true" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormGradV2"), + Seq(g, x, scale, mean, variance), + 2 + ) + } + + "FusedBatchNormGradV2 gradInput" should "be correct when is training is false" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(false)) + .setOp("FusedBatchNormGradV2"), + Seq(g, x, scale, mean, variance), + 0, 1e-3 + ) + } + + "FusedBatchNormGradV2 weight" should "be correct when is training is false" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(false)) + .setOp("FusedBatchNormGradV2"), + Seq(g, x, scale, mean, variance), + 1, 1e-3 + ) + } + + "FusedBatchNormGradV2 bias" should "be correct when is training is false" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val g = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand() + + compare( + NodeDef.newBuilder() + .setName("fusedbn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(false)) + .setOp("FusedBatchNormGradV2"), + Seq(g, x, scale, mean, variance), + 2 + ) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormSpec.scala new file mode 100644 index 00000000000..6157bebfc10 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormSpec.scala @@ -0,0 +1,145 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{booleanAttr, floatAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} + +class FusedBatchNormSpec extends TensorflowSpecHelper { + "FusedBatchNorm y" should "be correct when is training is true" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNorm"), + Seq(x, scale, offset, mean, variance), + 0 + ) + } + + "FusedBatchNorm batch_mean" should "be correct when is training is true" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNorm"), + Seq(x, scale, offset, mean, variance), + 1 + ) + } + + "FusedBatchNorm reserve_space_1" should "be correct when is training is true" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNorm"), + Seq(x, scale, offset, mean, variance), + 3 + ) + } + + "FusedBatchNorm batch_variance" should "be correct when is training is true" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNorm"), + Seq(x, scale, offset, mean, variance), + 2 + ) + } + + "FusedBatchNorm reserve_space_2" should "be correct when is training is true" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNorm"), + Seq(x, scale, offset, mean, variance), + 4 + ) + } + + "FusedBatchNorm" should "be correct when is training is false" in { + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand().add(1f) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(false)) + .setOp("FusedBatchNorm"), + Seq(x, scale, offset, mean, variance), + 0 + ) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2Spec.scala new file mode 100644 index 00000000000..1a96e2ffdc0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2Spec.scala @@ -0,0 +1,156 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{booleanAttr, floatAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} + +class FusedBatchNormV2Spec extends TensorflowSpecHelper { + "FusedBatchNormV2 y" should "be correct when is training is true" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormV2"), + Seq(x, scale, offset, mean, variance), + 0 + ) + } + + "FusedBatchNormV2 batch_mean" should "be correct when is training is true" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormV2"), + Seq(x, scale, offset, mean, variance), + 1 + ) + } + + "FusedBatchNormV2 reserve_space_1" should "be correct when is training is true" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormV2"), + Seq(x, scale, offset, mean, variance), + 3 + ) + } + + "FusedBatchNormV2 batch_variance" should "be correct when is training is true" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormV2"), + Seq(x, scale, offset, mean, variance), + 2 + ) + } + + "FusedBatchNormV2 reserve_space_2" should "be correct when is training is true" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](0) + val variance = Tensor[Float](0) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(true)) + .setOp("FusedBatchNormV2"), + Seq(x, scale, offset, mean, variance), + 4 + ) + } + + "FusedBatchNormV2" should "be correct when is training is false" in { + cancel("Cancel this test as the jenkins server is still using old tensorflow") + val x = Tensor[Float](4, 8, 8, 256).rand() + val scale = Tensor[Float](256).rand() + val offset = Tensor[Float](256).rand() + val mean = Tensor[Float](256).rand() + val variance = Tensor[Float](256).rand().add(1f) + + compare( + NodeDef.newBuilder() + .setName("fusedbn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("U", typeAttr(DataType.DT_FLOAT)) + .putAttr("epsilon", floatAttr(0.0001f)) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("is_training", booleanAttr(false)) + .setOp("FusedBatchNormV2"), + Seq(x, scale, offset, mean, variance), + 0, 1e-4 + ) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala new file mode 100644 index 00000000000..b8a9c0a9031 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.nn.SpatialCrossMapLRN +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{floatAttr, intAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class LRNGradSpec extends TensorflowSpecHelper { + "LRNGrad" should "be correct for float tensor" in { + val op = SpatialCrossMapLRN[Float](7, 7, 0.5, 1, DataFormat.NHWC) + val input = Tensor[Float](4, 8, 8, 3).rand() + val t = op.forward(input) + val g = Tensor[Float](4, 8, 8, 3).rand() + compare( + NodeDef.newBuilder() + .setName("lrn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("depth_radius", intAttr(3)) + .putAttr("beta", floatAttr(0.5f)) + .putAttr("alpha", floatAttr(1)) + .putAttr("bias", floatAttr(1)) + .setOp("LRNGrad"), + Seq(g, input, t), + 0, 1e-2 + ) + } + + "LRNGrad" should "be correct for float tensor2" in { + val op = SpatialCrossMapLRN[Float](3, 3, 1, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 8, 8, 3).rand() + val t = op.forward(input) + val g = Tensor[Float](4, 8, 8, 3).rand() + compare( + NodeDef.newBuilder() + .setName("lrn_grad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("depth_radius", intAttr(1)) + .putAttr("beta", floatAttr(1f)) + .putAttr("alpha", floatAttr(1)) + .putAttr("bias", floatAttr(0)) + .setOp("LRNGrad"), + Seq(g, input, t), + 0, 1e-2 + ) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNSpec.scala new file mode 100644 index 00000000000..a88e5f3822f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNSpec.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{floatAttr, intAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class LRNSpec extends TensorflowSpecHelper { + "LRN" should "be correct for float tensor" in { + compare( + NodeDef.newBuilder() + .setName("lrn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("depth_radius", intAttr(3)) + .putAttr("beta", floatAttr(0.5f)) + .putAttr("alpha", floatAttr(1)) + .putAttr("bias", floatAttr(1)) + .setOp("LRN"), + Seq(Tensor[Float](4, 8, 8, 3).rand()), + 0 + ) + } + + "LRN" should "be correct for float tensor2" in { + val t = Tensor[Float](4, 8, 8, 3).fill(1f) + compare( + NodeDef.newBuilder() + .setName("lrn_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("depth_radius", intAttr(1)) + .putAttr("beta", floatAttr(1f)) + .putAttr("alpha", floatAttr(1)) + .putAttr("bias", floatAttr(0)) + .setOp("LRN"), + Seq(t), + 0 + ) + } +} + From c3bead2ce70b6ed8963996d9cacabaf2de8fc662 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 5 Dec 2017 02:05:32 -0600 Subject: [PATCH 0558/1065] Pb support bigmodel (#1976) * support new format * add unit test * add unit test * refinement * fix typo * fix unit test --- .../analytics/bigdl/dllib/nn/Module.scala | 6 +- .../dllib/nn/abstractnn/AbstractModule.scala | 6 +- .../dllib/utils/python/api/PythonBigDL.scala | 9 +- .../utils/serializer/DataConverter.scala | 134 +------- .../utils/serializer/DataReaderWriter.scala | 238 ++++++++++++++ .../dllib/utils/serializer/ModuleLoader.scala | 147 ++++++++- .../utils/serializer/ModuleSerializable.scala | 36 +-- .../serializer/TensorStorageManager.scala | 253 +++++++++++++++ .../bigdl/dllib/utils/serializer/Types.scala | 51 +++ .../bigdl/dllib/keras/UpSampling3DSpec.scala | 2 +- .../bigdl/dllib/nn/HighwaySpec.scala | 2 +- .../utils/serializer/DataConverterSpec.scala | 2 +- .../serializer/DataReaderWriterSpec.scala | 137 ++++++++ .../serializer/ModuleSerializerSpec.scala | 296 ++++++++++-------- 14 files changed, 1010 insertions(+), 309 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorStorageManager.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriterSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala index fb8015c6987..93ae211446b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala @@ -50,12 +50,14 @@ object Module { * @param path path to save module, local file system, HDFS and Amazon S3 is supported. * HDFS path should be like "hdfs://[host]:[port]/xxx" * Amazon S3 path should be like "s3a://bucket/xxx" + * @param weightPath : where weight is stored * @tparam T numeric type * @return model loaded from path */ - def loadModule[T: ClassTag](path : String)(implicit ev: TensorNumeric[T]) + def loadModule[T: ClassTag](path : String, + weightPath : String = null)(implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - ModuleLoader.loadFromFile(path) + ModuleLoader.loadFromFile(path, weightPath) } def loadTorch[T: ClassTag](path : String) : AbstractModule[Activity, Activity, T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index e8840721d87..62b82d7bf82 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -464,12 +464,14 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param path path to save module, local file system, HDFS and Amazon S3 is supported. * HDFS path should be like "hdfs://[host]:[port]/xxx" * Amazon S3 path should be like "s3a://bucket/xxx" + * @param weightPath where to store weight * @param overWrite if overwrite * @return self */ - def saveModule(path : String, overWrite: Boolean = false) : this.type = { + def saveModule(path : String, weightPath : String = null, + overWrite: Boolean = false) : this.type = { this.clearState() - ModulePersister.saveToFile(path, this, overWrite) + ModulePersister.saveToFile(path, weightPath, this, overWrite) this } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index e4b9a574d37..db6a15bf3bd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1629,8 +1629,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Module.load[T](path) } - def loadBigDLModule(path: String): AbstractModule[Activity, Activity, T] = { - Module.loadModule[T](path) + def loadBigDLModule(modulePath: String, + weightPath : String): AbstractModule[Activity, Activity, T] = { + Module.loadModule[T](modulePath, weightPath) } def loadTorch(path: String): AbstractModule[Activity, Activity, T] = { @@ -1749,8 +1750,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def saveBigDLModule(module: AbstractModule[Activity, Activity, T], - path: String, overWrite: Boolean): Unit = { - module.saveModule(path, overWrite) + modulePath: String, weightPath: String, overWrite: Boolean): Unit = { + module.saveModule(modulePath, weightPath, overWrite) } def saveCaffe(module: AbstractModule[Activity, Activity, T], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index 72268921b99..58c2a599787 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -250,7 +250,7 @@ object DataConverter extends DataConverter{ private def isEmptyTensor(tensor : Tensor[_]): Boolean = { val emptyTensor = tensor.getTensorType match { case DenseType => - tensor.isEmpty + tensor.storage == null case QuantizedType => tensor.asInstanceOf[QuantizedTensor[_]].getStorage == null } @@ -284,7 +284,12 @@ object DataConverter extends DataConverter{ } def quant(): Tensor[T] = { - val bytes = serializedStorage.getBytesDataList.asScala.toArray.head.toByteArray + var bytes: Array[Byte] = null + if (context.storageType == ProtoStorageType) { + bytes = serializedStorage.getBytesDataList.asScala.toArray.head.toByteArray + } else { + created + } val serializedParams = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) val paramsNum = serializedParams.head val paramsArray = serializedParams.slice(1, paramsNum + 1) @@ -450,126 +455,11 @@ object DataConverter extends DataConverter{ private def setStorage[T: ClassTag](context: SerializeContext[T], tensorBuilder: BigDLTensor.Builder, tensor: Tensor[_]): Unit = { - val tensorNumeric = tensor.getTensorNumeric() val storageType = context.storageType - - val isEmpty = isEmptyTensor(tensor) - - val storageId = tensor.getTensorType match { - case DenseType => - if (isEmpty) -1 else System.identityHashCode(tensor.storage().array()) - case QuantizedType => - if (isEmpty) { - -1 - } else { - System.identityHashCode(tensor.asInstanceOf[QuantizedTensor[T]].getStorage) - } - } - val storages = context.storages if (storageType == ProtoStorageType) { - if (storages.contains(storageId)) { - val storage = storages(storageId).asInstanceOf[TensorStorage] - tensorBuilder.setStorage(resetStorage(storage)) - // we should set back the datatype from existed storage - tensorBuilder.setDatatype(storage.getDatatype) - } else { - val storageBuilder = TensorStorage.newBuilder - if (tensorNumeric == NumericFloat) { - tensorBuilder.setDatatype(DataType.FLOAT) - storageBuilder.setDatatype(DataType.FLOAT) - if(!isEmpty) { - tensor.getTensorType match { - case DenseType => - tensor.storage().array().asInstanceOf[Array[Float]]. - foreach(data => storageBuilder.addFloatData(data)) - case QuantizedType => - val quantTensor = tensor.asInstanceOf[QuantizedTensor[Float]] - val bytes = quantTensor.getStorage - val bs = ByteString.copyFrom(bytes) - storageBuilder.addBytesData(bs) - - // max, min, and sum - quantTensor.maxOfRow.foreach(data => storageBuilder.addFloatData(data)) - quantTensor.minOfRow.foreach(data => storageBuilder.addFloatData(data)) - quantTensor.sumOfRow.foreach(data => storageBuilder.addFloatData(data)) - - // params and desc type - val params = quantTensor.params.array - storageBuilder.addIntData(params.length) - params.foreach(param => storageBuilder.addIntData(param.asInstanceOf[Int])) - - quantTensor.params.getType match { - case ConvData => storageBuilder.addIntData(0) - case ConvWeight => storageBuilder.addIntData(1) - case LinearData => storageBuilder.addIntData(2) - case LinearWeight => storageBuilder.addIntData(3) - } - } - } - } else if (tensorNumeric == NumericDouble) { - tensorBuilder.setDatatype(DataType.DOUBLE) - storageBuilder.setDatatype(DataType.DOUBLE) - if(!tensor.isEmpty) { - tensor.storage().array().asInstanceOf[Array[Double]]. - foreach(data => storageBuilder.addDoubleData(data)) - } - } else if (tensorNumeric == NumericChar) { - tensorBuilder.setDatatype(DataType.CHAR) - storageBuilder.setDatatype(DataType.CHAR) - if(!isEmpty) { - tensor.storage().array().asInstanceOf[Array[Char]]. - foreach(data => storageBuilder.addIntData(data)) - } - } else if (tensorNumeric == NumericBoolean) { - tensorBuilder.setDatatype(DataType.BOOL) - storageBuilder.setDatatype(DataType.BOOL) - if(!isEmpty) { - tensor.storage().array().asInstanceOf[Array[Boolean]]. - foreach(data => storageBuilder.addBoolData(data)) - } - } else if (tensorNumeric == NumericString) { - tensorBuilder.setDatatype(DataType.STRING) - storageBuilder.setDatatype(DataType.STRING) - if(!isEmpty) { - tensor.storage().array().asInstanceOf[Array[String]]. - foreach(data => storageBuilder.addStringData(data)) - } - } else if (tensorNumeric == NumericInt) { - tensorBuilder.setDatatype(DataType.INT32) - storageBuilder.setDatatype(DataType.INT32) - if(!isEmpty) { - tensor.storage().array().asInstanceOf[Array[Int]]. - foreach(data => storageBuilder.addIntData(data)) - } - } else if (tensorNumeric == NumericShort) { - tensorBuilder.setDatatype(DataType.SHORT) - storageBuilder.setDatatype(DataType.SHORT) - if(!isEmpty) { - tensor.storage().array().asInstanceOf[Array[Short]]. - foreach(data => storageBuilder.addIntData(data)) - } - } else if (tensorNumeric == NumericLong) { - tensorBuilder.setDatatype(DataType.INT64) - storageBuilder.setDatatype(DataType.INT64) - if(!isEmpty) { - tensor.storage().array().asInstanceOf[Array[Long]]. - foreach(data => storageBuilder.addLongData(data)) - } - } else if (tensorNumeric == NumericByteString) { - tensorBuilder.setDatatype(DataType.BYTES) - storageBuilder.setDatatype(DataType.BYTES) - if(!isEmpty) { - tensor.storage().array().asInstanceOf[Array[ByteString]]. - foreach(data => storageBuilder.addBytesData(data)) - } - } - storageBuilder.setId(storageId) - val storage = storageBuilder.build - tensorBuilder.setStorage(resetStorage(storage)) - if (storageId != -1) { - storages(storageId) = storage - } - } + ProtoTensorStorageManager.setStorage(context, tensorBuilder, tensor) + } else if (storageType == BigDLStorage) { + BigDLTensorStorageManager.setStorage(context, tensorBuilder, tensor) } else { throw new IllegalArgumentException(s"$storageType not supported") } @@ -631,7 +521,9 @@ object DataConverter extends DataConverter{ tensorBuilder.clearStorage tensorBuilder.setDatatype(originTensor.getDatatype) tensorBuilder.setId(originTensor.getId) - tensorBuilder.setStorage(resetStorage(originTensor.getStorage)) + if (originTensor.hasStorage) { + tensorBuilder.setStorage(resetStorage(originTensor.getStorage)) + } tensorBuilder.build } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriter.scala new file mode 100644 index 00000000000..b206f9877a1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriter.scala @@ -0,0 +1,238 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.serializer + +import java.io.{DataInputStream, DataOutputStream} + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Storage +import com.intel.analytics.bigdl.utils.serializer.BigDLDataType.BigDLDataType + +/** + * DataReaderWriter defines how to read/write weight data from bin file + */ +trait DataReaderWriter { + def write(outputStream: DataOutputStream, data: Array[_]): Unit + def read(inputStream: DataInputStream, size: Int): Any + def dataType(): BigDLDataType +} + +object FloatReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(d => outputStream.writeFloat(d.asInstanceOf[Float])) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[Float](size) + for (i <- 0 until size) { + data(i) = inputStream.readFloat + } + Storage[Float](data) + } + + def dataType(): BigDLDataType = BigDLDataType.FLOAT +} + +object DoubleReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(d => outputStream.writeDouble(d.asInstanceOf[Double])) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[Double](size) + for (i <- 0 until size) { + data(i) = inputStream.readDouble + } + Storage[Double](data) + } + + def dataType(): BigDLDataType = BigDLDataType.DOUBLE +} + +object CharReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(d => outputStream.writeChar(d.asInstanceOf[Char])) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[Char](size) + for (i <- 0 until size) { + data(i) = inputStream.readChar + } + Storage[Char](data) + } + + def dataType(): BigDLDataType = BigDLDataType.CHAR +} + +object BoolReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(d => outputStream.writeBoolean(d.asInstanceOf[Boolean])) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[Boolean](size) + for (i <- 0 until size) { + data(i) = inputStream.readBoolean + } + Storage[Boolean](data) + } + + def dataType(): BigDLDataType = BigDLDataType.BOOL +} + +object StringReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(str => { + val value = str.asInstanceOf[String].getBytes("utf-8") + outputStream.writeInt(value.size) + outputStream.write(value) + }) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[String](size) + for (i <- 0 until size) { + val ssize = inputStream.readInt + val buffer = new Array[Byte](ssize) + inputStream.read(buffer) + data(i) = new String(buffer, "utf-8") + } + Storage[String](data) + } + + def dataType(): BigDLDataType = BigDLDataType.STRING +} + +object IntReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(d => outputStream.writeInt(d.asInstanceOf[Int])) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[Int](size) + for (i <- 0 until size) { + data(i) = inputStream.readInt + } + Storage[Int](data) + } + + def dataType(): BigDLDataType = BigDLDataType.INT +} + +object ShortReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(d => outputStream.writeShort(d.asInstanceOf[Short])) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[Short](size) + for (i <- 0 until size) { + data(i) = inputStream.readShort + } + Storage[Short](data) + } + + def dataType(): BigDLDataType = BigDLDataType.SHORT +} + +object LongReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(d => outputStream.writeLong(d.asInstanceOf[Long])) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[Long](size) + for (i <- 0 until size) { + data(i) = inputStream.readLong + } + Storage[Long](data) + } + + def dataType(): BigDLDataType = BigDLDataType.LONG +} + +object ByteStringReaderWriter extends DataReaderWriter { + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + data.foreach(str => { + val value = str.asInstanceOf[ByteString].toByteArray + outputStream.writeInt(value.size) + outputStream.write(value) + }) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[ByteString](size) + for (i <- 0 until size) { + val ssize = inputStream.readInt + val buffer = new Array[Byte](ssize) + inputStream.read(buffer) + data(i) = ByteString.copyFrom(buffer) + } + Storage[ByteString](data) + } + + def dataType(): BigDLDataType = BigDLDataType.BYTESTRING +} + +object ByteReaderWriter extends DataReaderWriter { + + override def write(outputStream: DataOutputStream, data: Array[_]): Unit = { + outputStream.write(data.asInstanceOf[Array[Byte]]) + } + + override def read(inputStream: DataInputStream, size: Int): Any = { + val data = new Array[Byte](size) + inputStream.read(data) + Storage[Byte](data) + } + + override def dataType(): BigDLDataType = BigDLDataType.BYTE +} + +object DataReaderWriter { + def apply(datas : Array[_]): DataReaderWriter = { + datas match { + case flats: Array[Float] => FloatReaderWriter + case dbls: Array[Double] => DoubleReaderWriter + case chs: Array[Char] => CharReaderWriter + case bools: Array[Boolean] => BoolReaderWriter + case strs : Array[String] => StringReaderWriter + case ints : Array[Int] => IntReaderWriter + case shorts : Array[Short] => ShortReaderWriter + case longs : Array[Long] => LongReaderWriter + case bytestrs : Array[ByteString] => ByteStringReaderWriter + case bytes : Array[Byte] => ByteReaderWriter + case _ => throw new RuntimeException("Unsupported Type") + } + } + + def apply(dataType : BigDLDataType): DataReaderWriter = { + dataType match { + case BigDLDataType.FLOAT => FloatReaderWriter + case BigDLDataType.DOUBLE => DoubleReaderWriter + case BigDLDataType.CHAR => CharReaderWriter + case BigDLDataType.BOOL => BoolReaderWriter + case BigDLDataType.STRING => StringReaderWriter + case BigDLDataType.INT => IntReaderWriter + case BigDLDataType.SHORT => ShortReaderWriter + case BigDLDataType.LONG => LongReaderWriter + case BigDLDataType.BYTESTRING => ByteStringReaderWriter + case BigDLDataType.BYTE => ByteReaderWriter + case _ => throw new RuntimeException("Unsupported Type") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala index 9c2bb7882bc..4f256c1dbfa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala @@ -16,6 +16,8 @@ package com.intel.analytics.bigdl.utils.serializer import java.io._ +import java.nio.ByteBuffer +import java.security.{DigestInputStream, DigestOutputStream, MessageDigest} import scala.collection.JavaConverters._ import com.google.protobuf.CodedInputStream @@ -24,7 +26,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.{DenseType, QuantizedTensor, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.DataConverter.TensorConverter -import com.intel.analytics.bigdl.utils.{File, Table} +import com.intel.analytics.bigdl.utils.{File, FileReader, FileWriter, Table} import serialization.Bigdl._ import scala.collection.mutable @@ -36,11 +38,12 @@ object ModuleLoader { /** * load module from `modelPath` * @param modelPath path where protobuf formatted module is stored + * @param weightPath optional : weight path * @param ev numeric ops * @tparam T data type * @return loaded BigDL module */ - def loadFromFile[T: ClassTag](modelPath : String) + def loadFromFile[T: ClassTag](modelPath : String, weightPath : String = null) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { val modelBuilder = BigDLModule.newBuilder val inputBytes = File.readBytes(modelPath) @@ -49,16 +52,73 @@ object ModuleLoader { modelBuilder.mergeFrom(cis) val bigDLModel = modelBuilder.build() val storages = new mutable.HashMap[Int, Any]() - val deserializationContext = DeserializeContext(bigDLModel, storages, ProtoStorageType) - initTensorStorage(deserializationContext) - ModuleSerializer.load(DeserializeContext(bigDLModel, storages, ProtoStorageType)).module + var deserializationContext : DeserializeContext = null + if (weightPath == null) { + deserializationContext = DeserializeContext(bigDLModel, storages, ProtoStorageType) + initTensorStorage(deserializationContext) + } else { + deserializationContext = DeserializeContext(bigDLModel, storages, BigDLStorage) + initTensorStorage(deserializationContext, weightPath) + } + ModuleSerializer.load(deserializationContext).module + } + + private def initTensorStorage[T: ClassTag](context: DeserializeContext, weightPath : String) + (implicit ev: TensorNumeric[T]): Unit = { + val magicNo = SerConst.MAGIC_NO + var fr: FileReader = null + var in: InputStream = null + var objFile: ObjectInputStream = null + val storages = context.storages + try { + fr = FileReader(weightPath) + in = fr.open() + val digest = MessageDigest.getInstance(SerConst.DIGEST_TYPE) + val digestInputStream = new DigestInputStream(in, digest) + val dataInputStream = new DataInputStream(digestInputStream) + digestInputStream.on(true) + val magicNumber = dataInputStream.readInt + require(magicNumber == magicNo, + s"Magic number mismatch, expected $magicNo, actual $magicNumber") + + val totalCount = dataInputStream.readInt + // Read each storage data and convert to storage + for (i <- 0 until totalCount) { + val storageId = dataInputStream.readInt + val dataType = BigDLDataType(dataInputStream.readInt) + val reader = DataReaderWriter(dataType) + val size = dataInputStream.readInt + val data = reader.read(dataInputStream, size) + storages(storageId) = data + } + digestInputStream.on(false) + + val digestLen = dataInputStream.readInt + + val storedDigest = new Array[Byte](digestLen) + + dataInputStream.read(storedDigest) + + val calculatedDigest = digestInputStream.getMessageDigest.digest + + require(calculatedDigest.length == digestLen, "checksum error, size mismatch") + + for (i <- 0 until digestLen) { + require(calculatedDigest(i) == storedDigest(i), "check sum error, please check weight file") + } + + } finally { + if (null != in) in.close() + if (null != fr) fr.close() + if (null != objFile) objFile.close() + } } private def initTensorStorage[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]): Unit = { val attrMap = context.bigdlModule.getAttrMap - val storagesMap = attrMap.get("global_storage").getNameAttrListValue.getAttrMap + val storagesMap = attrMap.get(SerConst.GLOBAL_STORAGE).getNameAttrListValue.getAttrMap storagesMap.asScala.foreach(map => { val storages = context.storages @@ -165,23 +225,84 @@ object ModulePersister { * @param ev numeric ops * @tparam T data type */ - def saveToFile[T: ClassTag](modelPath: String, module: AbstractModule[Activity, Activity, T], + def saveToFile[T: ClassTag](modelPath: String, + weightPath: String = null, + module: AbstractModule[Activity, Activity, T], overwrite: Boolean = false) (implicit ev: TensorNumeric[T]): Unit = { + + if (weightPath == null) { + val serializeResult = serializeModule(module, ProtoStorageType) + setTensorStorage(serializeResult.bigDLModule, serializeResult.storages) + File.saveBytes(serializeResult.bigDLModule.build.toByteArray, modelPath, overwrite) + } else { + val serializeResult = serializeModule(module, BigDLStorage) + val tensorStorages = serializeResult.storages.filter(_._2.isInstanceOf[Array[_]]) + File.saveBytes(serializeResult.bigDLModule.build.toByteArray, modelPath, overwrite) + saveWeightsToFile(weightPath, tensorStorages, overwrite) + } + } + + private def serializeModule[T : ClassTag](module: AbstractModule[Activity, Activity, T], + storageType: StorageType)(implicit ev: TensorNumeric[T]): SerializeResult = { val bigDLModule = ModuleData(module , new ArrayBuffer[String](), new ArrayBuffer[String]()) val storages = new mutable.HashMap[Int, Any]() - val context = SerializeContext(bigDLModule, storages, ProtoStorageType) - val serializeResult = ModuleSerializer.serialize(context) - setTensorStorage(serializeResult.bigDLModule, serializeResult.storages) - File.saveBytes(serializeResult.bigDLModule.build.toByteArray, modelPath, overwrite) + val context = SerializeContext(bigDLModule, storages, storageType) + ModuleSerializer.serialize(context) } + private def saveWeightsToFile(weightPath: String, storages: mutable.HashMap[Int, Any], + overwrite: Boolean = false): Unit = { + val magicNo = SerConst.MAGIC_NO + val total = storages.size + var fw: FileWriter = null + var out: OutputStream = null + var objFile: ObjectOutputStream = null + var digestOutputStream : DigestOutputStream = null + var dataOutputStream: DataOutputStream = null + try { + fw = FileWriter(weightPath) + out = fw.create(overwrite) + val digest = MessageDigest.getInstance(SerConst.DIGEST_TYPE) + digestOutputStream = new DigestOutputStream(out, digest); + dataOutputStream = new DataOutputStream(digestOutputStream) + digestOutputStream.on(true) + dataOutputStream.writeInt(magicNo) + dataOutputStream.writeInt(total) + storages.foreach(storage => { + val storageId = storage._1 + val dataArray = storage._2.asInstanceOf[Array[_]] + val writer = DataReaderWriter(dataArray) + dataOutputStream.writeInt(storageId) + dataOutputStream.writeInt(writer.dataType().id) + dataOutputStream.writeInt(dataArray.size) + writer.write(dataOutputStream, dataArray) + }) + digestOutputStream.on(false) + val digestContent = digestOutputStream.getMessageDigest.digest + dataOutputStream.writeInt(digestContent.length) + dataOutputStream.write(digestContent) + } finally { + if (null != objFile) objFile.close() + if (null != out) out.close() + if (null != fw) fw.close() + if (null != digestOutputStream) { + digestOutputStream.flush() + digestOutputStream.close() + } + if (null != dataOutputStream) { + dataOutputStream.close() + } + } + } + + private def setTensorStorage(bigDLModule: BigDLModule.Builder, storages: mutable.HashMap[Int, Any]) : Unit = { val storageIds = new mutable.HashSet[Int] val tensorStorages = storages.filter(_._2.isInstanceOf[TensorStorage]) - var nameAttributes = NameAttrList.newBuilder().setName("global_storage") + var nameAttributes = NameAttrList.newBuilder().setName(SerConst.GLOBAL_STORAGE) storages.values.filter(_.isInstanceOf[BigDLTensor]).foreach(storage => { val bigdlTensor = storage.asInstanceOf[BigDLTensor] val storageId = bigdlTensor.getStorage.getId @@ -199,7 +320,7 @@ object ModulePersister { }) val attrValueBuilder = AttrValue.newBuilder attrValueBuilder.setNameAttrListValue(nameAttributes) - bigDLModule.putAttr("global_storage", attrValueBuilder.build) + bigDLModule.putAttr(SerConst.GLOBAL_STORAGE, attrValueBuilder.build) } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 25510cdbcb9..655a06f1d46 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -270,19 +270,15 @@ trait ModuleSerializable extends Loadable with Savable{ } else null val storageType = context.storageType - if (storageType == ProtoStorageType) { - if (weight != null) { - val weightAttr = AttrValue.newBuilder - TensorConverter.setAttributeValue(context, weightAttr, weight) - modelBuilder.setWeight(weightAttr.getTensorValue) - } - if (bias != null) { - val biasAttr = AttrValue.newBuilder - TensorConverter.setAttributeValue(context, biasAttr, bias) - modelBuilder.setBias(biasAttr.getTensorValue) - } - } else { - throw new IllegalArgumentException(s"$storageType not supported!") + if (weight != null) { + val weightAttr = AttrValue.newBuilder + TensorConverter.setAttributeValue(context, weightAttr, weight) + modelBuilder.setWeight(weightAttr.getTensorValue) + } + if (bias != null) { + val biasAttr = AttrValue.newBuilder + TensorConverter.setAttributeValue(context, biasAttr, bias) + modelBuilder.setBias(biasAttr.getTensorValue) } } } @@ -322,20 +318,6 @@ trait ContainerSerializable extends ModuleSerializable { object ContainerSerializer extends ContainerSerializable -trait StorageType -object ProtoStorageType extends StorageType - -case class SerializeContext[T: ClassTag](moduleData: ModuleData[T], - storages: mutable.HashMap[Int, Any], - storageType: StorageType) -case class DeserializeContext(bigdlModule : BigDLModule, - storages: mutable.HashMap[Int, Any], - storageType: StorageType) - -case class SerializeResult(bigDLModule: BigDLModule.Builder, storages: mutable.HashMap[Int, Any]) - -case class ModuleData[T: ClassTag](module : AbstractModule[Activity, Activity, T], - pre : Seq[String], next : Seq[String]) trait Loadable { def loadModule[T: ClassTag](context: DeserializeContext) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorStorageManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorStorageManager.scala new file mode 100644 index 00000000000..1fbf0fdcad5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorStorageManager.scala @@ -0,0 +1,253 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.quantized.{ConvData, ConvWeight, LinearData, LinearWeight} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericShort, NumericString} +import com.intel.analytics.bigdl.tensor.{DenseType, QuantizedTensor, QuantizedType, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ProtoTensorStorageManager.isEmptyTensor +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString +import serialization.Bigdl.{BigDLTensor, DataType, TensorStorage} + +import scala.reflect.ClassTag + + +trait TensorStorageManager { + def setStorage[T: ClassTag](context: SerializeContext[T], + tensorBuilder: BigDLTensor.Builder, tensor: Tensor[_]): Unit + + protected def isEmptyTensor(tensor : Tensor[_]): Boolean = { + val emptyTensor = tensor.getTensorType match { + case DenseType => + tensor.storage == null + case QuantizedType => + tensor.asInstanceOf[QuantizedTensor[_]].getStorage == null + } + emptyTensor + } + + protected def getStorageId[T: ClassTag](tensor: Tensor[_]): Int = { + val isEmpty = isEmptyTensor(tensor) + tensor.getTensorType match { + case DenseType => + if (isEmpty) -1 else System.identityHashCode(tensor.storage().array()) + case QuantizedType => + if (isEmpty) { + -1 + } else { + System.identityHashCode(tensor.asInstanceOf[QuantizedTensor[T]].getStorage) + } + } + } + + protected def resetStorage(originStorage : TensorStorage) : TensorStorage = { + val storageBuilder = TensorStorage.newBuilder + storageBuilder.setDatatype(originStorage.getDatatype) + storageBuilder.setId(originStorage.getId) + storageBuilder.build + } +} + +object BigDLTensorStorageManager extends TensorStorageManager { + override def setStorage[T: ClassTag](context: SerializeContext[T], + tensorBuilder: BigDLTensor.Builder, tensor: Tensor[_]): Unit = { + val tensorNumeric = tensor.getTensorNumeric() + val storageId = getStorageId(tensor) + val storages = context.storages + val storageBuilder = TensorStorage.newBuilder + storageBuilder.setId(storageId) + if (tensorNumeric == NumericFloat) { + tensorBuilder.setDatatype(DataType.FLOAT) + storageBuilder.setDatatype(DataType.FLOAT) + if(tensor.getTensorType == QuantizedType) { + val quantTensor = tensor.asInstanceOf[QuantizedTensor[Float]] + val bytes = quantTensor.getStorage + val bs = ByteString.copyFrom(bytes) + storageBuilder.addBytesData(bs) + + // max, min, and sum + quantTensor.maxOfRow.foreach(data => storageBuilder.addFloatData(data)) + quantTensor.minOfRow.foreach(data => storageBuilder.addFloatData(data)) + quantTensor.sumOfRow.foreach(data => storageBuilder.addFloatData(data)) + + // params and desc type + val params = quantTensor.params.array + storageBuilder.addIntData(params.length) + params.foreach(param => storageBuilder.addIntData(param.asInstanceOf[Int])) + + quantTensor.params.getType match { + case ConvData => storageBuilder.addIntData(0) + case ConvWeight => storageBuilder.addIntData(1) + case LinearData => storageBuilder.addIntData(2) + case LinearWeight => storageBuilder.addIntData(3) + } + } + } else if (tensorNumeric == NumericDouble) { + tensorBuilder.setDatatype(DataType.DOUBLE) + storageBuilder.setDatatype(DataType.DOUBLE) + } else if (tensorNumeric == NumericChar) { + tensorBuilder.setDatatype(DataType.CHAR) + storageBuilder.setDatatype(DataType.CHAR) + } else if (tensorNumeric == NumericBoolean) { + tensorBuilder.setDatatype(DataType.BOOL) + storageBuilder.setDatatype(DataType.BOOL) + } else if (tensorNumeric == NumericString) { + tensorBuilder.setDatatype(DataType.STRING) + storageBuilder.setDatatype(DataType.STRING) + } else if (tensorNumeric == NumericInt) { + tensorBuilder.setDatatype(DataType.INT32) + storageBuilder.setDatatype(DataType.INT32) + } else if (tensorNumeric == NumericShort) { + tensorBuilder.setDatatype(DataType.SHORT) + storageBuilder.setDatatype(DataType.SHORT) + } else if (tensorNumeric == NumericLong) { + tensorBuilder.setDatatype(DataType.INT64) + storageBuilder.setDatatype(DataType.INT64) + } else if (tensorNumeric == NumericByteString) { + tensorBuilder.setDatatype(DataType.BYTES) + storageBuilder.setDatatype(DataType.BYTES) + } + + val storage = tensor.getTensorType match { + case DenseType => + if (tensor.storage() == null) null else tensor.storage().array() + case QuantizedType => + tensor.asInstanceOf[QuantizedTensor[Float]].getStorage + } + + if (storage != null) { + storages(storageId) = storage + } + + tensorBuilder.setStorage(storageBuilder.build()) + } +} + +object ProtoTensorStorageManager extends TensorStorageManager { + + override def setStorage[T: ClassTag] + (context: SerializeContext[T], tensorBuilder: BigDLTensor.Builder, tensor: Tensor[_]): Unit = { + val tensorNumeric = tensor.getTensorNumeric() + val isEmpty = isEmptyTensor(tensor) + val storageId = getStorageId(tensor) + val storages = context.storages + if (storages.contains(storageId)) { + val storage = storages(storageId).asInstanceOf[TensorStorage] + tensorBuilder.setStorage(resetStorage(storage)) + // we should set back the datatype from existed storage + tensorBuilder.setDatatype(storage.getDatatype) + } else { + val storageBuilder = TensorStorage.newBuilder + if (tensorNumeric == NumericFloat) { + tensorBuilder.setDatatype(DataType.FLOAT) + storageBuilder.setDatatype(DataType.FLOAT) + if(!isEmpty) { + tensor.getTensorType match { + case DenseType => + tensor.storage().array().asInstanceOf[Array[Float]]. + foreach(data => storageBuilder.addFloatData(data)) + case QuantizedType => + val quantTensor = tensor.asInstanceOf[QuantizedTensor[Float]] + val bytes = quantTensor.getStorage + val bs = ByteString.copyFrom(bytes) + storageBuilder.addBytesData(bs) + + // max, min, and sum + quantTensor.maxOfRow.foreach(data => storageBuilder.addFloatData(data)) + quantTensor.minOfRow.foreach(data => storageBuilder.addFloatData(data)) + quantTensor.sumOfRow.foreach(data => storageBuilder.addFloatData(data)) + + // params and desc type + val params = quantTensor.params.array + storageBuilder.addIntData(params.length) + params.foreach(param => storageBuilder.addIntData(param.asInstanceOf[Int])) + + quantTensor.params.getType match { + case ConvData => storageBuilder.addIntData(0) + case ConvWeight => storageBuilder.addIntData(1) + case LinearData => storageBuilder.addIntData(2) + case LinearWeight => storageBuilder.addIntData(3) + } + } + } + } else if (tensorNumeric == NumericDouble) { + tensorBuilder.setDatatype(DataType.DOUBLE) + storageBuilder.setDatatype(DataType.DOUBLE) + if(!tensor.isEmpty) { + tensor.storage().array().asInstanceOf[Array[Double]]. + foreach(data => storageBuilder.addDoubleData(data)) + } + } else if (tensorNumeric == NumericChar) { + tensorBuilder.setDatatype(DataType.CHAR) + storageBuilder.setDatatype(DataType.CHAR) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Char]]. + foreach(data => storageBuilder.addIntData(data)) + } + } else if (tensorNumeric == NumericBoolean) { + tensorBuilder.setDatatype(DataType.BOOL) + storageBuilder.setDatatype(DataType.BOOL) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Boolean]]. + foreach(data => storageBuilder.addBoolData(data)) + } + } else if (tensorNumeric == NumericString) { + tensorBuilder.setDatatype(DataType.STRING) + storageBuilder.setDatatype(DataType.STRING) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[String]]. + foreach(data => storageBuilder.addStringData(data)) + } + } else if (tensorNumeric == NumericInt) { + tensorBuilder.setDatatype(DataType.INT32) + storageBuilder.setDatatype(DataType.INT32) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Int]]. + foreach(data => storageBuilder.addIntData(data)) + } + } else if (tensorNumeric == NumericShort) { + tensorBuilder.setDatatype(DataType.SHORT) + storageBuilder.setDatatype(DataType.SHORT) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Short]]. + foreach(data => storageBuilder.addIntData(data)) + } + } else if (tensorNumeric == NumericLong) { + tensorBuilder.setDatatype(DataType.INT64) + storageBuilder.setDatatype(DataType.INT64) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[Long]]. + foreach(data => storageBuilder.addLongData(data)) + } + } else if (tensorNumeric == NumericByteString) { + tensorBuilder.setDatatype(DataType.BYTES) + storageBuilder.setDatatype(DataType.BYTES) + if(!isEmpty) { + tensor.storage().array().asInstanceOf[Array[ByteString]]. + foreach(data => storageBuilder.addBytesData(data)) + } + } + storageBuilder.setId(storageId) + val storage = storageBuilder.build + tensorBuilder.setStorage(resetStorage(storage)) + if (storageId != -1) { + storages(storageId) = storage + } + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala new file mode 100644 index 00000000000..35c1add75f9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import serialization.Bigdl.BigDLModule + +import scala.collection.mutable +import scala.reflect.ClassTag + + +trait StorageType +object ProtoStorageType extends StorageType +object BigDLStorage extends StorageType + +case class SerializeContext[T: ClassTag](moduleData: ModuleData[T], + storages: mutable.HashMap[Int, Any], + storageType: StorageType) +case class DeserializeContext(bigdlModule : BigDLModule, + storages: mutable.HashMap[Int, Any], + storageType: StorageType) + +case class SerializeResult(bigDLModule: BigDLModule.Builder, storages: mutable.HashMap[Int, Any]) + +case class ModuleData[T: ClassTag](module : AbstractModule[Activity, Activity, T], + pre : Seq[String], next : Seq[String]) + +object BigDLDataType extends Enumeration{ + type BigDLDataType = Value + val FLOAT, DOUBLE, CHAR, BOOL, STRING, INT, SHORT, LONG, BYTESTRING, BYTE = Value +} + +object SerConst { + val MAGIC_NO = 3721 + val DIGEST_TYPE = "MD5" + val GLOBAL_STORAGE = "global_storage" +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala index fccb77dd259..aaab4065f1f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala @@ -62,7 +62,7 @@ class UpSampling3DSpec extends KerasBaseSpec { val input = Tensor[Float](1, 2, 2, 2, 2).randn() val res1 = module.forward(input).clone() val tmpFile = java.io.File.createTempFile("module", ".bigdl") - module.saveModule(tmpFile.getAbsolutePath, true) + module.saveModule(tmpFile.getAbsolutePath, null, true) val loaded = Module.loadModule[Float](tmpFile.getAbsolutePath) val res2 = loaded.forward(input) res1 should be(res2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala index 20594a92bf7..512f86a8191 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala @@ -88,7 +88,7 @@ class HighwaySpec extends KerasBaseSpec { val res1 = module.forward(input.clone()).toTensor[Float].clone() val clone = module.cloneModule() val tmpFile = java.io.File.createTempFile("module", ".bigdl") - module.saveModule(tmpFile.getAbsolutePath, true) + module.saveModule(tmpFile.getAbsolutePath, null, true) val loaded = Module.loadModule[Float](tmpFile.getAbsolutePath) val res2 = loaded.forward(input.clone()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index a5d5b6d7ca6..0d1350cf7b0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -411,7 +411,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ val linear = Linear(5, 5).setName("linear") val moduleData = ModuleData(linear, Seq(), Seq()) map.clear() - ModulePersister.saveToFile("/tmp/linear.bigdl", linear, true) + ModulePersister.saveToFile("/tmp/linear.bigdl", null, linear, true) map.clear() val retrievedValue = ModuleLoader.loadFromFile("/tmp/linear.bigdl") retrievedValue should be (linear) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriterSpec.scala new file mode 100644 index 00000000000..59eed48c70b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriterSpec.scala @@ -0,0 +1,137 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.serializer + +import java.io._ + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Storage +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class DataReaderWriterSpec extends FlatSpec with Matchers with BeforeAndAfter { + + var inputStream : DataInputStream = null + var outputStream : DataOutputStream = null + var tmpFile : File = null + + before { + tmpFile = File.createTempFile("testWeight", "bin") + inputStream = new DataInputStream(new FileInputStream(tmpFile)) + outputStream = new DataOutputStream(new FileOutputStream(tmpFile)) + } + + "Float read/write" should "work properly" in { + val flts = Array[Float](1.0f, 2.0f) + FloatReaderWriter.write(outputStream, flts) + outputStream.flush + val readFloats = FloatReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[Float]].array + flts should be (readFloats) + } + + "Double read/write" should "work properly" in { + val dbs = Array[Double](1.0, 2.0) + DoubleReaderWriter.write(outputStream, dbs) + outputStream.flush + val readDoubles = DoubleReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[Double]].array + dbs should be (readDoubles) + } + + "Char read/write" should "work properly" in { + val chs = Array[Char]('a', 'b') + CharReaderWriter.write(outputStream, chs) + outputStream.flush + val readChars = CharReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[Char]].array + chs should be (readChars) + } + + "Bool read/write" should "work properly" in { + val bools = Array[Boolean](true, false) + BoolReaderWriter.write(outputStream, bools) + outputStream.flush + val readBools = BoolReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[Boolean]].array + bools should be (readBools) + } + + "String read/write" should "work properly" in { + val strs = Array[String]("abc", "123") + StringReaderWriter.write(outputStream, strs) + outputStream.flush + val readStrs = StringReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[String]].array + strs should be (readStrs) + } + + "Int read/write" should "work properly" in { + val ints = Array[Int](1, 2) + IntReaderWriter.write(outputStream, ints) + outputStream.flush + val readInts = IntReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[Int]].array + ints should be (readInts) + } + + "Short read/write" should "work properly" in { + val shorts = Array[Short](1, 2) + ShortReaderWriter.write(outputStream, shorts) + outputStream.flush + val readShorts = ShortReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[Short]].array + shorts should be (readShorts) + } + + "Long read/write" should "work properly" in { + val longs = Array[Long](1, 2) + LongReaderWriter.write(outputStream, longs) + outputStream.flush + val readLongs = LongReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[Long]].array + longs should be (readLongs) + } + + "ByteString read/write" should "work properly" in { + val bytStrs = Array[ByteString](ByteString.copyFromUtf8("abc")) + ByteStringReaderWriter.write(outputStream, bytStrs) + outputStream.flush + val readBytStrs = ByteStringReaderWriter.read(inputStream, 1). + asInstanceOf[Storage[ByteString]].array + bytStrs should be (readBytStrs) + } + + "Byte read/write" should "work properly" in { + val byts = Array[Byte](1, 2) + ByteReaderWriter.write(outputStream, byts) + outputStream.flush + val readBytes = ByteReaderWriter.read(inputStream, 2). + asInstanceOf[Storage[Byte]].array + byts should be (readBytes) + } + + after { + if (tmpFile.exists) { + tmpFile.delete + } + if (inputStream != null) { + inputStream.close + } + if (outputStream != null) { + outputStream.close + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index b7c8b8022a7..4aeb19b6f80 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -15,6 +15,8 @@ */ package com.intel.analytics.bigdl.utils.serializer +import java.io.File + import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.models.lenet.LeNet5 @@ -45,7 +47,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = abs.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/abs.bigdl", abs, true) + ModulePersister.saveToFile("/tmp/abs.bigdl", null, abs, true) val loadedModule = ModuleLoader.loadFromFile("/tmp/abs.bigdl") val res2 = loadedModule.forward(tensor2) res1 should be (res2) @@ -57,7 +59,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = add.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/add.bigdl", add, true) + ModulePersister.saveToFile("/tmp/add.bigdl", null, add, true) val loadedAdd = ModuleLoader.loadFromFile("/tmp/add.bigdl") val res2 = loadedAdd.forward(tensor2) res1 should be (res2) @@ -69,7 +71,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = addconst.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/addconst.bigdl", addconst, true) + ModulePersister.saveToFile("/tmp/addconst.bigdl", null, addconst, true) val loadedAddConst = ModuleLoader.loadFromFile("/tmp/addconst.bigdl") val res2 = loadedAddConst.forward(tensor2) res1 should be (res2) @@ -81,7 +83,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() tensor2.resizeAs(tensor1).copy(tensor1) val res1 = batchNorm.forward(tensor1) - ModulePersister.saveToFile("/tmp/batchNorm.bigdl", batchNorm, true) + ModulePersister.saveToFile("/tmp/batchNorm.bigdl", null, batchNorm, true) val loadedBatchNorm = ModuleLoader.loadFromFile("/tmp/batchNorm.bigdl") val res2 = loadedBatchNorm.forward(tensor2) res1 should be (res2) @@ -96,7 +98,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val biLinear = Bilinear(5, 3, 2) val res1 = biLinear.forward(input) - ModulePersister.saveToFile("/tmp/biLinear.bigdl", biLinear, true) + ModulePersister.saveToFile("/tmp/biLinear.bigdl", null, biLinear, true) val loadedBiLinear = ModuleLoader.loadFromFile("/tmp/biLinear.bigdl") val res2 = loadedBiLinear.forward(input) res1 should be (res2) @@ -126,7 +128,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = binaryTreeLSTM.forward(input) val res11 = binaryTreeLSTM.forward(input) res1 should be (res11) - ModulePersister.saveToFile("/tmp/binaryTreeLSTM.bigdl", binaryTreeLSTM, true) + ModulePersister.saveToFile("/tmp/binaryTreeLSTM.bigdl", null, binaryTreeLSTM, true) RNG.setSeed(1000) val loadedBinaryTreeLSTM = ModuleLoader.loadFromFile("/tmp/binaryTreeLSTM.bigdl") val res2 = loadedBinaryTreeLSTM.forward(input) @@ -141,7 +143,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { RNG.setSeed(100) val biRecurrent = BiRecurrent().add(RnnCell(6, 4, Sigmoid())) val res1 = biRecurrent.forward(input1) - ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", biRecurrent, true) + ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", null, biRecurrent, true) RNG.setSeed(100) val loadedRecurent = ModuleLoader.loadFromFile("/tmp/biRecurrent.bigdl") val res2 = loadedRecurent.forward(input2) @@ -155,7 +157,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { RNG.setSeed(100) val biRecurrent = BiRecurrent(batchNormParams = BatchNormParams()).add(RnnCell(6, 4, Sigmoid())) val res1 = biRecurrent.forward(input1) - ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", biRecurrent, true) + ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", null, biRecurrent, true) RNG.setSeed(100) val loadedRecurent = ModuleLoader.loadFromFile("/tmp/biRecurrent.bigdl") val res2 = loadedRecurent.forward(input2) @@ -169,7 +171,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { RNG.setSeed(100) val biRecurrent = BiRecurrent(isSplitInput = false).add(RnnCell(6, 4, Sigmoid())) val res1 = biRecurrent.forward(input1) - ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", biRecurrent, true) + ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", null, biRecurrent, true) RNG.setSeed(100) val loadedRecurent = ModuleLoader.loadFromFile("/tmp/biRecurrent.bigdl") val res2 = loadedRecurent.forward(input2) @@ -184,7 +186,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val bottle = new Bottle(Linear(10, 2).asInstanceOf[Module[Float]], 2, 2) val res1 = bottle.forward(input1) - ModulePersister.saveToFile("/tmp/bottle.bigdl", bottle, true) + ModulePersister.saveToFile("/tmp/bottle.bigdl", null, bottle, true) val loadedBottle = ModuleLoader.loadFromFile("/tmp/bottle.bigdl") val res2 = loadedBottle.forward(input2) res1 should be (res2) @@ -196,7 +198,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.resizeAs(input1).copy(input1) val cadd = CAdd(Array(5, 1)) val res1 = cadd.forward(input1) - ModulePersister.saveToFile("/tmp/cadd.bigdl", cadd, true) + ModulePersister.saveToFile("/tmp/cadd.bigdl", null, cadd, true) val loadedCadd = ModuleLoader.loadFromFile("/tmp/cadd.bigdl") val res2 = loadedCadd.forward(input2) res1 should be (res2) @@ -212,7 +214,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val caddTable = CAddTable(false) val res1 = caddTable.forward(input) - ModulePersister.saveToFile("/tmp/caddTable.bigdl", caddTable, true) + ModulePersister.saveToFile("/tmp/caddTable.bigdl", null, caddTable, true) val loadedCaddTable = ModuleLoader.loadFromFile("/tmp/caddTable.bigdl") val res2 = loadedCaddTable.forward(input) res1 should be (res2) @@ -228,7 +230,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val caveTable = CAveTable(false) val res1 = caveTable.forward(input) - ModulePersister.saveToFile("/tmp/caveTable.bigdl", caveTable, true) + ModulePersister.saveToFile("/tmp/caveTable.bigdl", null, caveTable, true) val loadedCaddTable = ModuleLoader.loadFromFile("/tmp/caveTable.bigdl") val res2 = loadedCaddTable.forward(input) res1 should be (res2) @@ -241,7 +243,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = volumetricAveragePooling.forward(input1) - ModulePersister.saveToFile("/tmp/volumetricAveragePooling.bigdl", + ModulePersister.saveToFile("/tmp/volumetricAveragePooling.bigdl", null, volumetricAveragePooling, true) val loadedVolumetricAveragePooling = ModuleLoader.loadFromFile("/tmp/volumetricAveragePooling.bigdl") @@ -260,7 +262,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = cdivTable.forward(input) - ModulePersister.saveToFile("/tmp/cdivTable.bigdl", cdivTable, true) + ModulePersister.saveToFile("/tmp/cdivTable.bigdl", null, cdivTable, true) val loadedCdivTable = ModuleLoader.loadFromFile("/tmp/cdivTable.bigdl") val res2 = cdivTable.forward(input) res1 should be (res2) @@ -276,7 +278,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val clamp = Clamp(1, 10) val res1 = clamp.forward(input1) - ModulePersister.saveToFile("/tmp/clamp.bigdl", clamp, true) + ModulePersister.saveToFile("/tmp/clamp.bigdl", null, clamp, true) val loadedClamp = ModuleLoader.loadFromFile("/tmp/clamp.bigdl") val res2 = loadedClamp.forward(input2) res1 should be (res2) @@ -292,7 +294,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = cmaxTable.forward(input) - ModulePersister.saveToFile("/tmp/cmaxTable.bigdl", cmaxTable, true) + ModulePersister.saveToFile("/tmp/cmaxTable.bigdl", null, cmaxTable, true) val loadedCmaxTable = ModuleLoader.loadFromFile("/tmp/cmaxTable.bigdl") val res2 = loadedCmaxTable.forward(input) res1 should be (res2) @@ -308,7 +310,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = cminTable.forward(input) - ModulePersister.saveToFile("/tmp/cminTable.bigdl", cminTable, true) + ModulePersister.saveToFile("/tmp/cminTable.bigdl", null, cminTable, true) val loadedCminTable = ModuleLoader.loadFromFile("/tmp/cminTable.bigdl") val res2 = loadedCminTable.forward(input) res1 should be (res2) @@ -322,7 +324,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val cmul = CMul(Array(5, 1)) val res1 = cmul.forward(input1) - ModulePersister.saveToFile("/tmp/cmul.bigdl", cmul, true) + ModulePersister.saveToFile("/tmp/cmul.bigdl", null, cmul, true) val loadedCmul = ModuleLoader.loadFromFile("/tmp/cmul.bigdl") val res2 = loadedCmul.forward(input2) res1 should be (res2) @@ -338,7 +340,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val cmulTable = CMulTable() val res1 = cmulTable.forward(input) - ModulePersister.saveToFile("/tmp/cmulTable.bigdl", cmulTable, true) + ModulePersister.saveToFile("/tmp/cmulTable.bigdl", null, cmulTable, true) val loadedCmulTable = ModuleLoader.loadFromFile("/tmp/cmulTable.bigdl") val res2 = loadedCmulTable.forward(input) res1 should be (res2) @@ -355,7 +357,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { concat.add(Abs()) val res1 = concat.forward(input1) - ModulePersister.saveToFile("/tmp/concat.bigdl", concat, true) + ModulePersister.saveToFile("/tmp/concat.bigdl", null, concat, true) val loadedConcat = ModuleLoader.loadFromFile("/tmp/concat.bigdl") val res2 = loadedConcat.forward(input2) res1 should be (res2) @@ -371,7 +373,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = concatTable.forward(tensor1) - ModulePersister.saveToFile("/tmp/concatTable.bigdl", concatTable, true) + ModulePersister.saveToFile("/tmp/concatTable.bigdl", null, concatTable, true) val loadedConcatTable = ModuleLoader.loadFromFile("/tmp/concatTable.bigdl") val res2 = loadedConcatTable.forward(tensor2) res1 should be (res2) @@ -386,7 +388,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = contiguous.forward(tensor1) - ModulePersister.saveToFile("/tmp/contiguous.bigdl", contiguous, true) + ModulePersister.saveToFile("/tmp/contiguous.bigdl", null, contiguous, true) val loadedContiguous = ModuleLoader.loadFromFile("/tmp/contiguous.bigdl") val res2 = loadedContiguous.forward(tensor2) res1 should be (res2) @@ -415,7 +417,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = convLSTMPeephole2d.forward(input1) - ModulePersister.saveToFile("/tmp/convLSTMPeephole2d.bigdl", convLSTMPeephole2d, true) + ModulePersister.saveToFile("/tmp/convLSTMPeephole2d.bigdl", null, convLSTMPeephole2d, true) val loadedConvLSTMPeephole2d = ModuleLoader.loadFromFile("/tmp/convLSTMPeephole2d.bigdl") val res2 = loadedConvLSTMPeephole2d.forward(input2) res1 should be (res2) @@ -444,7 +446,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = convLSTMPeephole3d.forward(input1) - ModulePersister.saveToFile("/tmp/convLSTMPeephole3d.bigdl", convLSTMPeephole3d, true) + ModulePersister.saveToFile("/tmp/convLSTMPeephole3d.bigdl", null, convLSTMPeephole3d, true) val loadedConvLSTMPeephole3d = ModuleLoader.loadFromFile("/tmp/convLSTMPeephole3d.bigdl") val res2 = loadedConvLSTMPeephole3d.forward(input2) res1 should be (res2) @@ -459,7 +461,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = cosine.forward(tensor1) - ModulePersister.saveToFile("/tmp/cosine.bigdl", cosine, true) + ModulePersister.saveToFile("/tmp/cosine.bigdl", null, cosine, true) val loadedCosine = ModuleLoader.loadFromFile("/tmp/cosine.bigdl") val res2 = loadedCosine.forward(tensor2) res1 should be (res2) @@ -476,7 +478,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = cosineDistance.forward(input) - ModulePersister.saveToFile("/tmp/cosineDistance.bigdl", cosineDistance, true) + ModulePersister.saveToFile("/tmp/cosineDistance.bigdl", null, cosineDistance, true) val loadedCosineDistance = ModuleLoader.loadFromFile("/tmp/cosineDistance.bigdl") val res2 = loadedCosineDistance.forward(input) res1 should be (res2) @@ -493,7 +495,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = csubTable.forward(input) - ModulePersister.saveToFile("/tmp/csubTable.bigdl", csubTable, true) + ModulePersister.saveToFile("/tmp/csubTable.bigdl", null, csubTable, true) val loadedCSubTable = ModuleLoader.loadFromFile("/tmp/csubTable.bigdl") val res2 = loadedCSubTable.forward(input) res1 should be (res2) @@ -511,7 +513,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = dotProduct.forward(input) - ModulePersister.saveToFile("/tmp/dotProduct.bigdl", dotProduct, true) + ModulePersister.saveToFile("/tmp/dotProduct.bigdl", null, dotProduct, true) val loadedDotProduct = ModuleLoader.loadFromFile("/tmp/dotProduct.bigdl") val res2 = loadedDotProduct.forward(input) res1 should be (res2) @@ -526,7 +528,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = dropout.forward(tensor1) - ModulePersister.saveToFile("/tmp/dropout.bigdl", dropout, true) + ModulePersister.saveToFile("/tmp/dropout.bigdl", null, dropout, true) RNG.setSeed(100) val loadedDropout = ModuleLoader.loadFromFile("/tmp/dropout.bigdl") val res2 = loadedDropout.forward(tensor2) @@ -539,7 +541,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() tensor2.resizeAs(tensor1).copy(tensor1) val res1 = echo.forward(tensor1) - ModulePersister.saveToFile("/tmp/echo.bigdl", echo, true) + ModulePersister.saveToFile("/tmp/echo.bigdl", null, echo, true) val loadedEcho = ModuleLoader.loadFromFile("/tmp/echo.bigdl") val res2 = loadedEcho.forward(tensor2) res1 should be (res2) @@ -553,7 +555,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = elu.forward(tensor1) - ModulePersister.saveToFile("/tmp/elu.bigdl", elu, true) + ModulePersister.saveToFile("/tmp/elu.bigdl", null, elu, true) val loadedElu = ModuleLoader.loadFromFile("/tmp/elu.bigdl") val res2 = loadedElu.forward(tensor2) res1 should be (res2) @@ -567,7 +569,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = euclidean.forward(tensor1) - ModulePersister.saveToFile("/tmp/euclidean.bigdl", euclidean, true) + ModulePersister.saveToFile("/tmp/euclidean.bigdl", null, euclidean, true) val loadedEuclidean = ModuleLoader.loadFromFile("/tmp/euclidean.bigdl") val res2 = loadedEuclidean.forward(tensor2) res1 should be (res2) @@ -581,7 +583,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = exp.forward(tensor1) - ModulePersister.saveToFile("/tmp/exp.bigdl", exp, true) + ModulePersister.saveToFile("/tmp/exp.bigdl", null, exp, true) val loadedExp = ModuleLoader.loadFromFile("/tmp/exp.bigdl") val res2 = loadedExp.forward(tensor2) res1 should be (res2) @@ -598,7 +600,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = flattenTable.forward(input) - ModulePersister.saveToFile("/tmp/flattenTable.bigdl", flattenTable, true) + ModulePersister.saveToFile("/tmp/flattenTable.bigdl", null, flattenTable, true) val loadedFlattenTable = ModuleLoader.loadFromFile("/tmp/flattenTable.bigdl") val res2 = loadedFlattenTable.forward(input) res1 should be (res2) @@ -613,7 +615,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = gd.forward(tensor1) - ModulePersister.saveToFile("/tmp/gaussianDropout.bigdl", gd, true) + ModulePersister.saveToFile("/tmp/gaussianDropout.bigdl", null, gd, true) RNG.setSeed(100) val loadedGd = ModuleLoader.loadFromFile("/tmp/gaussianDropout.bigdl") val res2 = loadedGd.forward(tensor2) @@ -629,7 +631,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = gn.forward(tensor1) - ModulePersister.saveToFile("/tmp/gaussianNoise.bigdl", gn, true) + ModulePersister.saveToFile("/tmp/gaussianNoise.bigdl", null, gn, true) RNG.setSeed(100) val loadedGn = ModuleLoader.loadFromFile("/tmp/gaussianNoise.bigdl") val res2 = loadedGn.forward(tensor2) @@ -644,7 +646,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = gradientReversal.forward(tensor1) - ModulePersister.saveToFile("/tmp/gradientReversal.bigdl", gradientReversal, true) + ModulePersister.saveToFile("/tmp/gradientReversal.bigdl", null, gradientReversal, true) val loadedGradientReversal = ModuleLoader.loadFromFile("/tmp/gradientReversal.bigdl") val res2 = loadedGradientReversal.forward(tensor2) res1 should be (res2) @@ -657,7 +659,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = graph.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/graph.bigdl", graph, true) + ModulePersister.saveToFile("/tmp/graph.bigdl", null, graph, true) val loadedGraph = ModuleLoader.loadFromFile("/tmp/graph.bigdl") val res2 = loadedGraph.forward(tensor2) res1 should be (res2) @@ -674,7 +676,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = graph.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/graph.bigdl", graph, true) + ModulePersister.saveToFile("/tmp/graph.bigdl", null, graph, true) val loadedGraph = ModuleLoader.loadFromFile("/tmp/graph.bigdl") val res2 = loadedGraph.forward(tensor2) res1 should be (res2) @@ -689,7 +691,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { RNG.setSeed(100) val res1 = gru.forward(input1) - ModulePersister.saveToFile("/tmp/gru.bigdl", gru, true) + ModulePersister.saveToFile("/tmp/gru.bigdl", null, gru, true) RNG.setSeed(100) val loadedGRU = ModuleLoader.loadFromFile("/tmp/gru.bigdl") RNG.setSeed(100) @@ -705,7 +707,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = hardShrink.forward(tensor1) - ModulePersister.saveToFile("/tmp/hardShrink.bigdl", hardShrink, true) + ModulePersister.saveToFile("/tmp/hardShrink.bigdl", null, hardShrink, true) val loadedHardShrink = ModuleLoader.loadFromFile("/tmp/hardShrink.bigdl") val res2 = loadedHardShrink.forward(tensor2) res1 should be (res2) @@ -719,7 +721,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = hardTanh.forward(tensor1) - ModulePersister.saveToFile("/tmp/hardTanh.bigdl", hardTanh, true) + ModulePersister.saveToFile("/tmp/hardTanh.bigdl", null, hardTanh, true) val loadedHardTanh = ModuleLoader.loadFromFile("/tmp/hardTanh.bigdl") val res2 = loadedHardTanh.forward(tensor2) res1 should be (res2) @@ -733,7 +735,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = identity.forward(tensor1) - ModulePersister.saveToFile("/tmp/identity.bigdl", identity, true) + ModulePersister.saveToFile("/tmp/identity.bigdl", null, identity, true) val loadedIdentity = ModuleLoader.loadFromFile("/tmp/identity.bigdl") val res2 = loadedIdentity.forward(tensor2) res1 should be (res2) @@ -756,7 +758,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = index.forward(input) - ModulePersister.saveToFile("/tmp/index.bigdl", index, true) + ModulePersister.saveToFile("/tmp/index.bigdl", null, index, true) val loadedIndex = ModuleLoader.loadFromFile("/tmp/index.bigdl") val res2 = loadedIndex.forward(input) res1 should be (res2) @@ -769,7 +771,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { tensor2.resizeAs(tensor1).copy(tensor1) val res1 = inferReshape.forward(tensor1) - ModulePersister.saveToFile("/tmp/inferReshape.bigdl", inferReshape, true) + ModulePersister.saveToFile("/tmp/inferReshape.bigdl", null, inferReshape, true) val loadedInferReshape = ModuleLoader.loadFromFile("/tmp/inferReshape.bigdl") val res2 = loadedInferReshape.forward(tensor2) res1 should be (res2) @@ -783,7 +785,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = input.forward(tensor1) - ModulePersister.saveToFile("/tmp/input.bigdl", input, true) + ModulePersister.saveToFile("/tmp/input.bigdl", null, input, true) val loadedInferInput = ModuleLoader.loadFromFile("/tmp/input.bigdl") val res2 = loadedInferInput.forward(tensor2) res1 should be (res2) @@ -800,7 +802,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = joinTable.forward(input) - ModulePersister.saveToFile("/tmp/joinTable.bigdl", joinTable, true) + ModulePersister.saveToFile("/tmp/joinTable.bigdl", null, joinTable, true) val loadedJoinTable = ModuleLoader.loadFromFile("/tmp/joinTable.bigdl") val res2 = loadedJoinTable.forward(input) res1 should be (res2) @@ -816,7 +818,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = l1Penalty.forward(tensor1) - ModulePersister.saveToFile("/tmp/l1Penalty.bigdl", l1Penalty, true) + ModulePersister.saveToFile("/tmp/l1Penalty.bigdl", null, l1Penalty, true) val loadedL1Penalty = ModuleLoader.loadFromFile("/tmp/l1Penalty.bigdl") @@ -833,7 +835,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = leakyReLU.forward(tensor1) - ModulePersister.saveToFile("/tmp/leakyReLU.bigdl", leakyReLU, true) + ModulePersister.saveToFile("/tmp/leakyReLU.bigdl", null, leakyReLU, true) val loadedLeakyReLU = ModuleLoader.loadFromFile("/tmp/leakyReLU.bigdl") @@ -847,7 +849,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = linear.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/linear.bigdl", linear, true) + ModulePersister.saveToFile("/tmp/linear.bigdl", null, linear, true) val loadedLinear = ModuleLoader.loadFromFile("/tmp/linear.bigdl") val res2 = loadedLinear.forward(tensor2) res1 should be (res2) @@ -859,7 +861,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = log.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/log.bigdl", log, true) + ModulePersister.saveToFile("/tmp/log.bigdl", null, log, true) val loadedLog = ModuleLoader.loadFromFile("/tmp/log.bigdl") val res2 = loadedLog.forward(tensor2) res1 should be (res2) @@ -871,7 +873,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = logSigmoid.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/logSigmoid.bigdl", logSigmoid, true) + ModulePersister.saveToFile("/tmp/logSigmoid.bigdl", null, logSigmoid, true) val loadedLogSigmoid = ModuleLoader.loadFromFile("/tmp/logSigmoid.bigdl") val res2 = loadedLogSigmoid.forward(tensor2) res1 should be (res2) @@ -883,7 +885,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() val res1 = logSigmoid.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/logSigmoid.bigdl", logSigmoid, true) + ModulePersister.saveToFile("/tmp/logSigmoid.bigdl", null, logSigmoid, true) val loadedLogSigmoid = ModuleLoader.loadFromFile("/tmp/logSigmoid.bigdl") val res2 = loadedLogSigmoid.forward(tensor2) res1 should be (res2) @@ -903,7 +905,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = lookupTable.forward(tensor1) - ModulePersister.saveToFile("/tmp/lookupTable.bigdl", lookupTable, true) + ModulePersister.saveToFile("/tmp/lookupTable.bigdl", null, lookupTable, true) val loadedLookupTable = ModuleLoader.loadFromFile("/tmp/lookupTable.bigdl") val res2 = loadedLookupTable.forward(tensor2) res1 should be (res2) @@ -919,7 +921,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = lstm.forward(input1) - ModulePersister.saveToFile("/tmp/lstm.bigdl", lstm, true) + ModulePersister.saveToFile("/tmp/lstm.bigdl", null, lstm, true) val loadedLSTM = ModuleLoader.loadFromFile("/tmp/lstm.bigdl") val res2 = loadedLSTM.forward(input1) res1 should be (res2) @@ -935,7 +937,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = lstmPeephole.forward(input1) - ModulePersister.saveToFile("/tmp/lstmPeephole.bigdl", lstmPeephole, true) + ModulePersister.saveToFile("/tmp/lstmPeephole.bigdl", null, lstmPeephole, true) val loadedLSTMPeephole = ModuleLoader.loadFromFile("/tmp/lstmPeephole.bigdl") val res2 = loadedLSTMPeephole.forward(input2) res1 should be (res2) @@ -954,7 +956,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = mapTable.forward(input) - ModulePersister.saveToFile("/tmp/mapTable.bigdl", mapTable, true) + ModulePersister.saveToFile("/tmp/mapTable.bigdl", null, mapTable, true) val loadedMapTable = ModuleLoader.loadFromFile("/tmp/mapTable.bigdl") val res2 = loadedMapTable.forward(input) res1 should be (res2) @@ -978,7 +980,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val gradInput = maskedSelect.backward(input, gradOutput) - ModulePersister.saveToFile("/tmp/maskedSelect.bigdl", maskedSelect, true) + ModulePersister.saveToFile("/tmp/maskedSelect.bigdl", null, maskedSelect, true) val loadedMaskedSelect = ModuleLoader.loadFromFile("/tmp/maskedSelect.bigdl") val res2 = loadedMaskedSelect.forward(input) res1 should be (res2) @@ -993,7 +995,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = max.forward(input1) - ModulePersister.saveToFile("/tmp/max.bigdl", max, true) + ModulePersister.saveToFile("/tmp/max.bigdl", null, max, true) val loadedMax = ModuleLoader.loadFromFile("/tmp/max.bigdl") val res2 = loadedMax.forward(input2) res1 should be (res2) @@ -1006,7 +1008,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.resizeAs(input1).copy(input1) val res1 = mean.forward(input1) - ModulePersister.saveToFile("/tmp/mean.bigdl", mean, true) + ModulePersister.saveToFile("/tmp/mean.bigdl", null, mean, true) val loadedMean = ModuleLoader.loadFromFile("/tmp/mean.bigdl") val res2 = loadedMean.forward(input2) res1 should be (res2) @@ -1019,7 +1021,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.resizeAs(input1).copy(input1) val res1 = min.forward(input1) - ModulePersister.saveToFile("/tmp/min.bigdl", min, true) + ModulePersister.saveToFile("/tmp/min.bigdl", null, min, true) val loadedMin = ModuleLoader.loadFromFile("/tmp/min.bigdl") val res2 = loadedMin.forward(input2) res1 should be (res2) @@ -1036,7 +1038,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = mixTureTable.forward(input) - ModulePersister.saveToFile("/tmp/mixTureTable.bigdl", mixTureTable, true) + ModulePersister.saveToFile("/tmp/mixTureTable.bigdl", null, mixTureTable, true) val loadedMixtureTable = ModuleLoader.loadFromFile("/tmp/mixTureTable.bigdl") val res2 = loadedMixtureTable.forward(input) res1 should be (res2) @@ -1054,7 +1056,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = mm.forward(input) - ModulePersister.saveToFile("/tmp/mm.bigdl", mm, true) + ModulePersister.saveToFile("/tmp/mm.bigdl", null, mm, true) val loadedMM = ModuleLoader.loadFromFile("/tmp/mm.bigdl") val res2 = loadedMM.forward(input) res1 should be (res2) @@ -1069,7 +1071,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = mul.forward(input1) - ModulePersister.saveToFile("/tmp/mul.bigdl", mul, true) + ModulePersister.saveToFile("/tmp/mul.bigdl", null, mul, true) val loadedMul = ModuleLoader.loadFromFile("/tmp/mul.bigdl") val res2 = loadedMul.forward(input2) res1 should be (res2) @@ -1083,7 +1085,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = mulConst.forward(input1) - ModulePersister.saveToFile("/tmp/mulConst.bigdl", mulConst, true) + ModulePersister.saveToFile("/tmp/mulConst.bigdl", null, mulConst, true) val loadedMulConstant = ModuleLoader.loadFromFile("/tmp/mulConst.bigdl") val res2 = loadedMulConstant.forward(input2) res1 should be (res2) @@ -1100,7 +1102,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = mv.forward(input) - ModulePersister.saveToFile("/tmp/mv.bigdl", mv, true) + ModulePersister.saveToFile("/tmp/mv.bigdl", null, mv, true) val loadedMV = ModuleLoader.loadFromFile("/tmp/mv.bigdl") val res2 = loadedMV.forward(input) res1 should be (res2) @@ -1114,7 +1116,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = narrow.forward(input1) - ModulePersister.saveToFile("/tmp/narrow.bigdl", narrow, true) + ModulePersister.saveToFile("/tmp/narrow.bigdl", null, narrow, true) val loadedNarrow = ModuleLoader.loadFromFile("/tmp/narrow.bigdl") val res2 = loadedNarrow.forward(input2) res1 should be (res2) @@ -1128,7 +1130,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input(3.0) = Tensor(2, 2).apply1(e => Random.nextFloat()) val res1 = narrowTable.forward(input) - ModulePersister.saveToFile("/tmp/narrowTable.bigdl", narrowTable, true) + ModulePersister.saveToFile("/tmp/narrowTable.bigdl", null, narrowTable, true) val loadedNarrowTable = ModuleLoader.loadFromFile("/tmp/narrowTable.bigdl") val res2 = loadedNarrowTable.forward(input) res1 should be (res2) @@ -1142,7 +1144,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = normalizer.forward(input1) - ModulePersister.saveToFile("/tmp/normalizer.bigdl", normalizer, true) + ModulePersister.saveToFile("/tmp/normalizer.bigdl", null, normalizer, true) val loadedNormalize = ModuleLoader.loadFromFile("/tmp/normalizer.bigdl") val res2 = loadedNormalize.forward(input2) res1 should be (res2) @@ -1156,7 +1158,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input(1.0f) = input1 input(2.0f) = input2 val res1 = pack.forward(input) - ModulePersister.saveToFile("/tmp/pack.bigdl", pack, true) + ModulePersister.saveToFile("/tmp/pack.bigdl", null, pack, true) val loadedPack = ModuleLoader.loadFromFile("/tmp/pack.bigdl") val res2 = loadedPack.forward(input) res1 should be (res2) @@ -1167,7 +1169,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val padding = Padding(1, -1, 4, -0.8999761, 14) val input = Tensor(3, 13, 11).apply1(e => Random.nextFloat()) val res1 = padding.forward(input) - ModulePersister.saveToFile("/tmp/padding.bigdl", padding, true) + ModulePersister.saveToFile("/tmp/padding.bigdl", null, padding, true) val loadedPadding = ModuleLoader.loadFromFile("/tmp/padding.bigdl") val res2 = loadedPadding.forward(input) res1 should be (res2) @@ -1179,7 +1181,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor(3, 3).apply1(e => Random.nextFloat()) val input = T(1.0f -> input1, 2.0f -> input2) val res1 = pairwiseDistance.forward(input) - ModulePersister.saveToFile("/tmp/pairwiseDistance.bigdl", pairwiseDistance, true) + ModulePersister.saveToFile("/tmp/pairwiseDistance.bigdl", null, pairwiseDistance, true) val loadedPairwiseDistance = ModuleLoader.loadFromFile("/tmp/pairwiseDistance.bigdl") val res2 = loadedPairwiseDistance.forward(input) res1 should be (res2) @@ -1198,7 +1200,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input1 = T(1.0f -> input11, 2.0f -> input12) val input2 = T(1.0f -> input21, 2.0f -> input22) val res1 = parallelTable.forward(input1) - ModulePersister.saveToFile("/tmp/parallelTable.bigdl", parallelTable, true) + ModulePersister.saveToFile("/tmp/parallelTable.bigdl", null, parallelTable, true) val loadedParallelTable = ModuleLoader.loadFromFile("/tmp/parallelTable.bigdl") val res2 = loadedParallelTable.forward(input1) res1 should be (res2) @@ -1212,7 +1214,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = power.forward(input1) - ModulePersister.saveToFile("/tmp/power.bigdl", power, true) + ModulePersister.saveToFile("/tmp/power.bigdl", null, power, true) val loadedPower = ModuleLoader.loadFromFile("/tmp/power.bigdl") val res2 = loadedPower.forward(input1) res1 should be (res2) @@ -1225,7 +1227,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = preLu.forward(input1) - ModulePersister.saveToFile("/tmp/preLu.bigdl", preLu, true) + ModulePersister.saveToFile("/tmp/preLu.bigdl", null, preLu, true) val loadedPReLU = ModuleLoader.loadFromFile("/tmp/preLu.bigdl") val res2 = loadedPReLU.forward(input1) res1 should be (res2) @@ -1240,7 +1242,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = recurrent.forward(input1) - ModulePersister.saveToFile("/tmp/recurrent.bigdl", recurrent, true) + ModulePersister.saveToFile("/tmp/recurrent.bigdl", null, recurrent, true) val loadedRecurrent = ModuleLoader.loadFromFile("/tmp/recurrent.bigdl") val res2 = loadedRecurrent.forward(input1) res1 should be (res2) @@ -1256,7 +1258,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = recurrent.forward(input1) - ModulePersister.saveToFile("/tmp/recurrent.bigdl", recurrent, true) + ModulePersister.saveToFile("/tmp/recurrent.bigdl", null, recurrent, true) val loadedRecurrent = ModuleLoader.loadFromFile("/tmp/recurrent.bigdl") val res2 = loadedRecurrent.forward(input1) res1 should be (res2) @@ -1270,7 +1272,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = relu.forward(input1) - ModulePersister.saveToFile("/tmp/relu.bigdl", relu, true) + ModulePersister.saveToFile("/tmp/relu.bigdl", null, relu, true) val loadedReLU = ModuleLoader.loadFromFile("/tmp/relu.bigdl") val res2 = loadedReLU.forward(input1) res1 should be (res2) @@ -1283,7 +1285,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.resizeAs(input1).copy(input1) val res1 = relu6.forward(input1) - ModulePersister.saveToFile("/tmp/relu6.bigdl", relu6, true) + ModulePersister.saveToFile("/tmp/relu6.bigdl", null, relu6, true) val loadedReLU6 = ModuleLoader.loadFromFile("/tmp/relu6.bigdl") val res2 = loadedReLU6.forward(input2) res1 should be (res2) @@ -1295,7 +1297,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor(10) input2.copy(input1) val res1 = replicate.forward(input1) - ModulePersister.saveToFile("/tmp/replicate.bigdl", replicate, true) + ModulePersister.saveToFile("/tmp/replicate.bigdl", null, replicate, true) val loadedReplicate = ModuleLoader.loadFromFile("/tmp/replicate.bigdl") val res2 = loadedReplicate.forward(input2) res1 should be (res2) @@ -1307,7 +1309,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor(2, 2, 5) input2.copy(input1) val res1 = reshape.forward(input1) - ModulePersister.saveToFile("/tmp/reshape.bigdl", reshape, true) + ModulePersister.saveToFile("/tmp/reshape.bigdl", null, reshape, true) val loadedReshape = ModuleLoader.loadFromFile("/tmp/reshape.bigdl") val res2 = loadedReshape.forward(input2) res1 should be (res2) @@ -1319,7 +1321,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor(10) input2.copy(input1) val res1 = reverse.forward(input1) - ModulePersister.saveToFile("/tmp/reverse.bigdl", reverse, true) + ModulePersister.saveToFile("/tmp/reverse.bigdl", null, reverse, true) val loadedReverse = ModuleLoader.loadFromFile("/tmp/reverse.bigdl") val res2 = loadedReverse.forward(input2) res1 should be (res2) @@ -1338,7 +1340,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input(2.0f) = input2 val res1 = rnnCell.forward(input) - ModulePersister.saveToFile("/tmp/rnnCell.bigdl", rnnCell, true) + ModulePersister.saveToFile("/tmp/rnnCell.bigdl", null, rnnCell, true) val loadedRnnCell = ModuleLoader.loadFromFile("/tmp/rnnCell.bigdl") val res2 = loadedRnnCell.forward(input) res1 should be (res2) @@ -1361,7 +1363,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val roiPooling = new RoiPooling[Float](pooledW = 3, pooledH = 2, 1.0f) val res1 = roiPooling.forward(input1) val res3 = roiPooling.forward(input1) - ModulePersister.saveToFile("/tmp/roiPooling.bigdl", roiPooling, true) + ModulePersister.saveToFile("/tmp/roiPooling.bigdl", null, roiPooling, true) val loadedRoiPooling = ModuleLoader.loadFromFile("/tmp/roiPooling.bigdl") val res2 = loadedRoiPooling.forward(input2) res1 should be (res2) @@ -1373,7 +1375,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor(2, 2, 2) input2.copy(input1) val res1 = rrelu.forward(input1) - ModulePersister.saveToFile("/tmp/rrelu.bigdl", rrelu, true) + ModulePersister.saveToFile("/tmp/rrelu.bigdl", null, rrelu, true) val loadedRReLU = ModuleLoader.loadFromFile("/tmp/rrelu.bigdl") val res2 = loadedRReLU.forward(input2) res1 should be (res2) @@ -1385,7 +1387,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor(1, 4, 5, 6) input2.copy(input1) val res1 = scale.forward(input1) - ModulePersister.saveToFile("/tmp/scale.bigdl", scale, true) + ModulePersister.saveToFile("/tmp/scale.bigdl", null, scale, true) val loadedScale = ModuleLoader.loadFromFile("/tmp/scale.bigdl") val res2 = loadedScale.forward(input2) res1 should be (res2) @@ -1398,7 +1400,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor(5, 5, 5) input2.copy(input1) val res1 = select.forward(input1) - ModulePersister.saveToFile("/tmp/select.bigdl", select, true) + ModulePersister.saveToFile("/tmp/select.bigdl", null, select, true) val loadedSelect = ModuleLoader.loadFromFile("/tmp/select.bigdl") val res2 = loadedSelect.forward(input2) res1 should be (res2) @@ -1411,7 +1413,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input3 = Tensor(10).apply1(_ => Random.nextFloat()) val input = T(1.0 -> input1, 2.0 -> input2, 3.0 -> input3) val res1 = selectTable.forward(input) - ModulePersister.saveToFile("/tmp/selectTable.bigdl", selectTable, true) + ModulePersister.saveToFile("/tmp/selectTable.bigdl", null, selectTable, true) val loadedSelectTable = ModuleLoader.loadFromFile("/tmp/selectTable.bigdl") val res2 = loadedSelectTable.forward(input) res1 should be (res2) @@ -1425,7 +1427,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = sequential.forward(input1) - ModulePersister.saveToFile("/tmp/sequential.bigdl", sequential, true) + ModulePersister.saveToFile("/tmp/sequential.bigdl", null, sequential, true) val loadedSequential = ModuleLoader.loadFromFile("/tmp/sequential.bigdl") val res2 = loadedSequential.forward(input2) res1 should be (res2) @@ -1437,7 +1439,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = sigmoid.forward(input1) - ModulePersister.saveToFile("/tmp/sigmoid.bigdl", sigmoid, true) + ModulePersister.saveToFile("/tmp/sigmoid.bigdl", null, sigmoid, true) val loadedSigmoid = ModuleLoader.loadFromFile("/tmp/sigmoid.bigdl") val res2 = loadedSigmoid.forward(input2) res1 should be (res2) @@ -1449,7 +1451,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = softMax.forward(input1) - ModulePersister.saveToFile("/tmp/softMax.bigdl", softMax, true) + ModulePersister.saveToFile("/tmp/softMax.bigdl", null, softMax, true) val loadedSoftMax = ModuleLoader.loadFromFile("/tmp/softMax.bigdl") val res2 = loadedSoftMax.forward(input2) res1 should be (res2) @@ -1461,7 +1463,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = softMin.forward(input1) - ModulePersister.saveToFile("/tmp/softMin.bigdl", softMin, true) + ModulePersister.saveToFile("/tmp/softMin.bigdl", null, softMin, true) val loadedSoftMin = ModuleLoader.loadFromFile("/tmp/softMin.bigdl") val res2 = loadedSoftMin.forward(input2) res1 should be (res2) @@ -1473,7 +1475,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = softPlus.forward(input1) - ModulePersister.saveToFile("/tmp/softPlus.bigdl", softPlus, true) + ModulePersister.saveToFile("/tmp/softPlus.bigdl", null, softPlus, true) val loadedSoftPlus = ModuleLoader.loadFromFile("/tmp/softPlus.bigdl") val res2 = loadedSoftPlus.forward(input2) res1 should be (res2) @@ -1485,7 +1487,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = softShrink.forward(input1) - ModulePersister.saveToFile("/tmp/softShrink.bigdl", softShrink, true) + ModulePersister.saveToFile("/tmp/softShrink.bigdl", null, softShrink, true) val loadedSoftShrink = ModuleLoader.loadFromFile("/tmp/softShrink.bigdl") val res2 = loadedSoftShrink.forward(input2) res1 should be (res2) @@ -1497,7 +1499,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = softSign.forward(input1) - ModulePersister.saveToFile("/tmp/softSign.bigdl", softSign, true) + ModulePersister.saveToFile("/tmp/softSign.bigdl", null, softSign, true) val loadedSoftSign = ModuleLoader.loadFromFile("/tmp/softSign.bigdl") val res2 = loadedSoftSign.forward(input2) res1 should be (res2) @@ -1509,7 +1511,8 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialAveragePooling.forward(input1) - ModulePersister.saveToFile("/tmp/spatialAveragePooling.bigdl", spatialAveragePooling, true) + ModulePersister.saveToFile("/tmp/spatialAveragePooling.bigdl", + null, spatialAveragePooling, true) val loadedSpatialAveragePooling = ModuleLoader.loadFromFile("/tmp/spatialAveragePooling.bigdl") val res2 = loadedSpatialAveragePooling.forward(input2) res1 should be (res2) @@ -1521,7 +1524,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialBatchNorm.forward(input1) - ModulePersister.saveToFile("/tmp/spatialBatchNorm.bigdl", spatialBatchNorm, true) + ModulePersister.saveToFile("/tmp/spatialBatchNorm.bigdl", null, spatialBatchNorm, true) val loadedSpatialBatchNorm = ModuleLoader.loadFromFile("/tmp/spatialBatchNorm.bigdl") val res2 = loadedSpatialBatchNorm.forward(input2) res1 should be (res2) @@ -1534,7 +1537,8 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialContrastiveNorm.forward(input1) - ModulePersister.saveToFile("/tmp/spatialContrastiveNorm.bigdl", spatialContrastiveNorm, true) + ModulePersister.saveToFile("/tmp/spatialContrastiveNorm.bigdl", + null, spatialContrastiveNorm, true) RNG.setSeed(100) val loadedSpatialContrastiveNorm = ModuleLoader. loadFromFile("/tmp/spatialContrastiveNorm.bigdl") @@ -1548,7 +1552,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/spatialConvolution.bigdl", spatialConvolution, true) + ModulePersister.saveToFile("/tmp/spatialConvolution.bigdl", null, spatialConvolution, true) val loadedSpatialConvolution = ModuleLoader.loadFromFile("/tmp/spatialConvolution.bigdl") val res2 = loadedSpatialConvolution.forward(input2) res1 should be (res2) @@ -1561,7 +1565,8 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialConvolutionMap.forward(input1) - ModulePersister.saveToFile("/tmp/spatialConvolutionMap.bigdl", spatialConvolutionMap, true) + ModulePersister.saveToFile("/tmp/spatialConvolutionMap.bigdl", + null, spatialConvolutionMap, true) val loadedSpatialConvolutionMap = ModuleLoader. loadFromFile("/tmp/spatialConvolutionMap.bigdl") val res2 = loadedSpatialConvolutionMap.forward(input2) @@ -1574,7 +1579,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialCrossMapLRN.forward(input1) - ModulePersister.saveToFile("/tmp/spatialCrossMapLRN.bigdl", spatialCrossMapLRN, true) + ModulePersister.saveToFile("/tmp/spatialCrossMapLRN.bigdl", null, spatialCrossMapLRN, true) val loadedSpatialCrossMapLRN = ModuleLoader.loadFromFile("/tmp/spatialCrossMapLRN.bigdl") val res2 = loadedSpatialCrossMapLRN.forward(input2) res1 should be (res2) @@ -1588,7 +1593,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialDilatedConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/spatialDilatedConvolution.bigdl", + ModulePersister.saveToFile("/tmp/spatialDilatedConvolution.bigdl", null, spatialDilatedConvolution, true) val loadedSpatialDilatedConvolution = ModuleLoader. loadFromFile("/tmp/spatialDilatedConvolution.bigdl") @@ -1603,7 +1608,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = spatialDivisiveNormalization.forward(input1) - ModulePersister.saveToFile("/tmp/spatialDivisiveNormalization.bigdl", + ModulePersister.saveToFile("/tmp/spatialDivisiveNormalization.bigdl", null, spatialDivisiveNormalization, true) val loadedSpatialDivisiveNormalization = ModuleLoader. loadFromFile("/tmp/spatialDivisiveNormalization.bigdl") @@ -1621,7 +1626,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = spatialFullConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/spatialFullConvolution.bigdl", + ModulePersister.saveToFile("/tmp/spatialFullConvolution.bigdl", null, spatialFullConvolution, true) val loadedSpatialFullConvolution = ModuleLoader. loadFromFile("/tmp/spatialFullConvolution.bigdl") @@ -1635,7 +1640,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialMaxPooling.forward(input1) - ModulePersister.saveToFile("/tmp/spatialMaxPooling.bigdl", + ModulePersister.saveToFile("/tmp/spatialMaxPooling.bigdl", null, spatialMaxPooling, true) val loadedSpatialMaxPooling = ModuleLoader. loadFromFile("/tmp/spatialMaxPooling.bigdl") @@ -1649,7 +1654,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialShareConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/spatialShareConvolution.bigdl", + ModulePersister.saveToFile("/tmp/spatialShareConvolution.bigdl", null, spatialShareConvolution, true) val loadedSpatialShareConvolution = ModuleLoader. loadFromFile("/tmp/spatialShareConvolution.bigdl") @@ -1664,7 +1669,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialSubtractiveNormalization.forward(input1) - ModulePersister.saveToFile("/tmp/spatialSubtractiveNormalization.bigdl", + ModulePersister.saveToFile("/tmp/spatialSubtractiveNormalization.bigdl", null, spatialSubtractiveNormalization, true) val loadedSpatialSubtractiveNormalization = ModuleLoader. loadFromFile("/tmp/spatialSubtractiveNormalization.bigdl") @@ -1678,7 +1683,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = spatialWithinChannelLRN.forward(input1) - ModulePersister.saveToFile("/tmp/spatialWithinChannelLRN.bigdl", + ModulePersister.saveToFile("/tmp/spatialWithinChannelLRN.bigdl", null, spatialWithinChannelLRN, true) val loadedSpatialWithinChannelLRN = ModuleLoader. loadFromFile("/tmp/spatialWithinChannelLRN.bigdl") @@ -1692,7 +1697,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor(3, 3, 3) input2.copy(input1) val res1 = spatialZeroPadding.forward(input1) - ModulePersister.saveToFile("/tmp/spatialZeroPadding.bigdl", + ModulePersister.saveToFile("/tmp/spatialZeroPadding.bigdl", null, spatialZeroPadding, true) val loadedSpatialSpatialZeroPadding = ModuleLoader. loadFromFile("/tmp/spatialZeroPadding.bigdl") @@ -1707,7 +1712,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = splitTable.forward(input1) - ModulePersister.saveToFile("/tmp/splitTable.bigdl", splitTable, true) + ModulePersister.saveToFile("/tmp/splitTable.bigdl", null, splitTable, true) val loadedSplitTable = ModuleLoader.loadFromFile("/tmp/splitTable.bigdl") val res2 = loadedSplitTable.forward(input2) res1 should be (res2) @@ -1719,7 +1724,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = sqrt.forward(input1) - ModulePersister.saveToFile("/tmp/sqrt.bigdl", sqrt, true) + ModulePersister.saveToFile("/tmp/sqrt.bigdl", null, sqrt, true) val loadedSqrt = ModuleLoader.loadFromFile("/tmp/sqrt.bigdl") val res2 = loadedSqrt.forward(input2) res1 should be (res2) @@ -1731,7 +1736,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = square.forward(input1) - ModulePersister.saveToFile("/tmp/square.bigdl", square, true) + ModulePersister.saveToFile("/tmp/square.bigdl", null, square, true) val loadedSquare = ModuleLoader.loadFromFile("/tmp/square.bigdl") val res2 = loadedSquare.forward(input2) res1 should be (res2) @@ -1743,7 +1748,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input2 = Tensor() input2.resizeAs(input1).copy(input1) val res1 = squeeze.forward(input1) - ModulePersister.saveToFile("/tmp/squeeze.bigdl", squeeze, true) + ModulePersister.saveToFile("/tmp/squeeze.bigdl", null, squeeze, true) val loadedSqueeze = ModuleLoader.loadFromFile("/tmp/squeeze.bigdl") val res2 = loadedSqueeze.forward(input2) res1 should be (res2) @@ -1756,7 +1761,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.resizeAs(input1).copy(input1) val res1 = sum.forward(input1) - ModulePersister.saveToFile("/tmp/sum.bigdl", sum, true) + ModulePersister.saveToFile("/tmp/sum.bigdl", null, sum, true) val loadedSum = ModuleLoader.loadFromFile("/tmp/sum.bigdl") val res2 = loadedSum.forward(input2) res1 should be (res2) @@ -1769,7 +1774,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.resizeAs(input1).copy(input1) val res1 = tanh.forward(input1) - ModulePersister.saveToFile("/tmp/tanh.bigdl", tanh, true) + ModulePersister.saveToFile("/tmp/tanh.bigdl", null, tanh, true) val loadedTanh = ModuleLoader.loadFromFile("/tmp/tanh.bigdl") val res2 = loadedTanh.forward(input2) res1 should be (res2) @@ -1782,7 +1787,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.resizeAs(input1).copy(input1) val res1 = tanhShrink.forward(input1) - ModulePersister.saveToFile("/tmp/tanhShrink.bigdl", tanhShrink, true) + ModulePersister.saveToFile("/tmp/tanhShrink.bigdl", null, tanhShrink, true) val loadedTanhShrink = ModuleLoader.loadFromFile("/tmp/tanhShrink.bigdl") val res2 = loadedTanhShrink.forward(input2) res1 should be (res2) @@ -1796,7 +1801,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = temporalConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/temporalConvolution.bigdl", temporalConvolution, true) + ModulePersister.saveToFile("/tmp/temporalConvolution.bigdl", null, temporalConvolution, true) val loadedTemporalConvolution = ModuleLoader.loadFromFile("/tmp/temporalConvolution.bigdl") val res2 = loadedTemporalConvolution.forward(input2) res1 should be (res2) @@ -1809,7 +1814,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = threshold.forward(input1) - ModulePersister.saveToFile("/tmp/threshold.bigdl", threshold, true) + ModulePersister.saveToFile("/tmp/threshold.bigdl", null, threshold, true) val loadedThreshold = ModuleLoader.loadFromFile("/tmp/threshold.bigdl") val res2 = loadedThreshold.forward(input1) res1 should be (res2) @@ -1822,7 +1827,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = timeDistributed.forward(input1) - ModulePersister.saveToFile("/tmp/timeDistributed.bigdl", timeDistributed, true) + ModulePersister.saveToFile("/tmp/timeDistributed.bigdl", null, timeDistributed, true) val loadedTimeDistributed = ModuleLoader.loadFromFile("/tmp/timeDistributed.bigdl") val res2 = loadedTimeDistributed.forward(input1) res1 should be (res2) @@ -1836,7 +1841,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = transpose.forward(input1) - ModulePersister.saveToFile("/tmp/transpose.bigdl", transpose, true) + ModulePersister.saveToFile("/tmp/transpose.bigdl", null, transpose, true) val loadedTranspose = ModuleLoader.loadFromFile("/tmp/transpose.bigdl") val res2 = loadedTranspose.forward(input1) res1 should be (res2) @@ -1850,7 +1855,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = unsqueeze.forward(input1) - ModulePersister.saveToFile("/tmp/unsqueeze.bigdl", unsqueeze, true) + ModulePersister.saveToFile("/tmp/unsqueeze.bigdl", null, unsqueeze, true) val loadedUnsqueeze = ModuleLoader.loadFromFile("/tmp/unsqueeze.bigdl") val res2 = loadedUnsqueeze.forward(input1) res1 should be (res2) @@ -1863,7 +1868,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = view.forward(input1) - ModulePersister.saveToFile("/tmp/view.bigdl", view, true) + ModulePersister.saveToFile("/tmp/view.bigdl", null, view, true) val loadedView = ModuleLoader.loadFromFile("/tmp/view.bigdl") val res2 = loadedView.forward(input1) res1 should be (res2) @@ -1877,7 +1882,8 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = volumetricConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/volumetricConvolution.bigdl", volumetricConvolution, true) + ModulePersister.saveToFile("/tmp/volumetricConvolution.bigdl", + null, volumetricConvolution, true) val loadedVolumetricConvolution = ModuleLoader.loadFromFile("/tmp/volumetricConvolution.bigdl") val res2 = loadedVolumetricConvolution.forward(input1) res1 should be (res2) @@ -1893,7 +1899,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = volumetricFullConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/volumetricFullConvolution.bigdl", + ModulePersister.saveToFile("/tmp/volumetricFullConvolution.bigdl", null, volumetricFullConvolution, true) val loadedVolumetricFullConvolution = ModuleLoader. loadFromFile("/tmp/volumetricFullConvolution.bigdl") @@ -1909,7 +1915,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { input2.copy(input1) val res1 = volumetricMaxPooling.forward(input1) - ModulePersister.saveToFile("/tmp/volumetricMaxPooling.bigdl", volumetricMaxPooling, true) + ModulePersister.saveToFile("/tmp/volumetricMaxPooling.bigdl", null, volumetricMaxPooling, true) val loadedVolumetricMaxPooling = ModuleLoader.loadFromFile("/tmp/volumetricMaxPooling.bigdl") val res2 = loadedVolumetricMaxPooling.forward(input1) res1 should be (res2) @@ -1941,7 +1947,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = conv.forward(input) - ModulePersister.saveToFile("/tmp/bigquant.conv.bigdl", conv, true) + ModulePersister.saveToFile("/tmp/bigquant.conv.bigdl", null, conv, true) val loadedConv = ModuleLoader.loadFromFile("/tmp/bigquant.conv.bigdl") val res2 = loadedConv.forward(input) res1 should be (res2) @@ -1972,7 +1978,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = conv.forward(input) - ModulePersister.saveToFile("/tmp/bigquant.dilated.conv.bigdl", conv, true) + ModulePersister.saveToFile("/tmp/bigquant.dilated.conv.bigdl", null, conv, true) val loadedConv = ModuleLoader.loadFromFile("/tmp/bigquant.dilated.conv.bigdl") val res2 = loadedConv.forward(input) res1 should be (res2) @@ -1997,7 +2003,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = linear.forward(input) - ModulePersister.saveToFile("/tmp/bigquant.linear.bigdl", linear, true) + ModulePersister.saveToFile("/tmp/bigquant.linear.bigdl", null, linear, true) val loadedLinear = ModuleLoader.loadFromFile("/tmp/bigquant.linear.bigdl") val res2 = loadedLinear.forward(input) res1 should be (res2) @@ -2043,7 +2049,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = exampleParser.forward(input) - ModulePersister.saveToFile("/tmp/exampleParser.bigdl", exampleParser, true) + ModulePersister.saveToFile("/tmp/exampleParser.bigdl", null, exampleParser, true) val loadedExampleParser = ModuleLoader.loadFromFile[Float]("/tmp/exampleParser.bigdl") val res2 = loadedExampleParser.forward(input) res1 should be (res2) @@ -2056,7 +2062,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val tensor2 = Tensor() tensor2.resizeAs(tensor1).copy(tensor1) val res1 = testModule.forward(tensor1) - ModulePersister.saveToFile("/tmp/testModule.bigdl", testModule, true) + ModulePersister.saveToFile("/tmp/testModule.bigdl", null, testModule, true) val loadedModule = ModuleLoader.loadFromFile("/tmp/testModule.bigdl") val res2 = loadedModule.forward(tensor2) res1 should be (res2) @@ -2075,7 +2081,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val res1 = model.forward(input) - ModulePersister.saveToFile("/tmp/2linears.with.a.storage.bigdl", model, true) + ModulePersister.saveToFile("/tmp/2linears.with.a.storage.bigdl", null, model, true) val loadedModel = ModuleLoader.loadFromFile("/tmp/2linears.with.a.storage.bigdl") val res2 = loadedModel.forward(input) @@ -2085,7 +2091,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { "Load by definition " should " work properly" in { val linear1 = Linear(2, 2).setName("linear") val sequential = Sequential().setName("sequential").add(linear1) - ModulePersister.saveToFile("/tmp/loadDef.bigdl", sequential, true) + ModulePersister.saveToFile("/tmp/loadDef.bigdl", null, sequential, true) val linear2 = Linear(2, 2).setName("linear") val definition = Sequential().setName("sequential").add(linear2) ModuleLoader.loadFromDefinition(definition, "/tmp/loadDef.bigdl") @@ -2099,7 +2105,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { "Module toString" should "have same result" in { val linear = Linear(2, 2) - ModulePersister.saveToFile("/tmp/mstr.bigdl", linear, true) + ModulePersister.saveToFile("/tmp/mstr.bigdl", null, linear, true) val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") linear.toString() should be (loadedModel.toString()) @@ -2107,7 +2113,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { "Module in tain " should " keep the state" in { val linear = Linear(2, 2).training() - ModulePersister.saveToFile("/tmp/mstr.bigdl", linear, true) + ModulePersister.saveToFile("/tmp/mstr.bigdl", null, linear, true) val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") loadedModel.isTraining() should be (true) @@ -2115,7 +2121,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { "Module in evaluate " should " keep the state" in { val linear = Linear(2, 2).evaluate() - ModulePersister.saveToFile("/tmp/mstr.bigdl", linear, true) + ModulePersister.saveToFile("/tmp/mstr.bigdl", null, linear, true) val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") loadedModel.isTraining() should be (false) @@ -2123,7 +2129,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { "HardSigmoid serialization" should "work properly" in { val hardSigmoid = HardSigmoid() - ModulePersister.saveToFile("/tmp/hardSigmoid.bigdl", hardSigmoid, true) + ModulePersister.saveToFile("/tmp/hardSigmoid.bigdl", null, hardSigmoid, true) val loadedModel = ModuleLoader.loadFromFile("/tmp/hardSigmoid.bigdl") val input = Tensor(2, 2).rand() @@ -2135,6 +2141,22 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } + "Save model and weight separately" should "work properly" in { + val linear = Linear(3, 2) + val input = Tensor(2, 3).rand() + linear.saveModule("/tmp/linear.def", "/tmp/linear.bin", true) + val loaded = Module.loadModule("/tmp/linear.def", "/tmp/linear.bin") + val res1 = linear.forward(input) + + val res2 = loaded.forward(input) + + res1 should be (res2) + } + + "FindPath" should "work properly" in { + val file = new File("linear.bin") + println(file.getAbsolutePath) + } } class TestModule[T: ClassTag](val custom: CustomData) From 33f5ee5f73accd8cd946526dcb1153afcabd58fa Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 5 Dec 2017 17:42:49 +0800 Subject: [PATCH 0559/1065] feat: SReLU and ActivityRegularization support (#1917) * feat: SReLU and ActivityRegularization support * fix: update doc * fix: refactor the weights and gradWeights of SReLU * fix: serialization for srelu * fix: backward computing errors * fix: ActivityRegularization to layer * fix: change test location * fix: add python api * fix: shared axes have not consider enough --- .../dllib/nn/ActivityRegularization.scala | 52 +++ .../bigdl/dllib/nn/InitializationMethod.scala | 12 +- .../analytics/bigdl/dllib/nn/SReLU.scala | 386 ++++++++++++++++++ .../bigdl/dllib/optim/Regularizer.scala | 1 + .../dllib/utils/python/api/PythonBigDL.scala | 13 + .../utils/serializer/ModuleSerializer.scala | 1 + .../bigdl/dllib/keras/KerasBaseSpec.scala | 2 +- .../bigdl/dllib/keras/KerasRunner.scala | 25 +- .../dllib/nn/ActivityRegularizationSpec.scala | 52 +++ .../analytics/bigdl/dllib/nn/SReLUSpec.scala | 74 ++++ .../serializer/ModuleSerializerSpec.scala | 11 + 11 files changed, 624 insertions(+), 5 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularization.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularizationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularization.scala new file mode 100644 index 00000000000..3ff3a334ccf --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularization.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class ActivityRegularization[T: ClassTag](val l1: Double, val l2: Double) + (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + var loss: T = ev.fromType(0) + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + loss = ev.plus(ev.times(input.norm(1), ev.fromType(l1)), // l1 + ev.times(ev.pow(input.norm(2), ev.fromType(2)), ev.fromType(l2))) // l2 + + output = input + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + gradInput.resizeAs(input) + .copy(input).sign().mul(ev.fromType(l1)) // l1 + .add(input.mul(ev.fromType(2 * l2))) // l2 + .add(gradOutput) // add all the gradients of branches + + gradInput + } +} + +object ActivityRegularization { + def apply[T: ClassTag](l1: Double, l2: Double)( + implicit ev: TensorNumeric[T]): ActivityRegularization[T] = { + new ActivityRegularization[T](l1, l2) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala index 62db21e5f35..1272923b277 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala @@ -41,7 +41,16 @@ object VariableFormat { * The default VariableFormat used when we do not care about * the specified format of this variable. */ - case object Default extends VariableFormat + case object Default extends VariableFormat { + override def getFanIn(shape: Array[Int]): Int = { + shape.product + } + + override def getFanOut(shape: Array[Int]): Int = { + shape.product + } + + } case object ONE_D extends VariableFormat { override def getFanIn(shape: Array[Int]): Int = { @@ -143,7 +152,6 @@ object VariableFormat { shape(3) * receptiveFieldSize } } - } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala new file mode 100644 index 00000000000..af978b3aa66 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala @@ -0,0 +1,386 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.{T, Table} +import serialization.Bigdl.{AttrValue, BigDLModule} + +import scala.reflect.ClassTag + +/** + * S-shaped Rectified Linear Unit. + * It follows: + * `f(x) = t^r + a^r(x - t^r) for x >= t^r`, + * `f(x) = x for t^r > x > t^l`, + * `f(x) = t^l + a^l(x - t^l) for x <= t^l`. + * + * [Deep Learning with S-shaped Rectified Linear Activation Units](http://arxiv.org/abs/1512.07030) + * + * @param sharedAxes the axes along which to share learnable parameters + * for the activation function. + * For example, if the incoming feature maps are from a 2D convolution + * with output shape `(batch, height, width, channels)`, + * and you wish to share parameters across space + * so that each filter only has one set of parameters, + * set `shared_axes=[1, 2]`. + */ + +@SerialVersionUID(7173457290010080259L) +class SReLU[T: ClassTag](sharedAxes: Array[Int] = null)( + implicit ev: TensorNumeric[T]) extends TensorModule[T] { + import SReLU._ + val weightsLen = 4 + val weights: Array[Tensor[T]] = Array.fill[Tensor[T]](4)(Tensor[T]()) + val gradWeights: Array[Tensor[T]] = Array.fill[Tensor[T]](4)(Tensor[T]()) + + val weightsInit: Array[InitializationMethod] = Array(Zeros, Xavier, Xavier, Ones) + + // this attribute for computing the offset in weight because of sharedAxes + private var indexes: Array[Int] = null + + private def init(input: Tensor[T]): Unit = { + val shape = input.size().slice(1, input.size().length) + if (sharedAxes != null) { + var i = 0 + while (i < sharedAxes.length) { + shape(sharedAxes(i) - 1) = 1 + i += 1 + } + } + + val variableFormat = shape.length match { + case 2 => VariableFormat.IN_OUT + case 4 => VariableFormat.OUT_IN_KW_KH + case 5 => VariableFormat.OUT_IN_KT_KH_KW + case _ => VariableFormat.Default + } + + var i = 0 + while (i < weightsLen) { + weights(i).resize(shape) + weightsInit(i).init(weights(i), variableFormat) + + gradWeights(i).resize(shape) + gradWeights(i).resizeAs(weights(i)).zero() + + i += 1 + } + + // ensure the the right part is always to the right of the left + weights(tRight).abs().add(weights(tLeft)) + } + + private def getIndex(indexes: Array[Int], stride: Array[Int], ndim: Int, offset: Int): Unit = { + var i = 0 + var tmp = offset + while (i < ndim) { + indexes(i) = tmp / stride(i) + 1 // 1 based + tmp = tmp % stride(i) + i += 1 + } + + // set back the shared axes + if (sharedAxes != null) { + i = 0 + while (i < sharedAxes.length) { + indexes(sharedAxes(i) - 1) = 1 + i += 1 + } + } + } + + private def setValue(w: Array[Tensor[T]], i: Int, t: Int, v: T): Unit = { + w(t).storage().array()(w(t).storageOffset() - 1 + i) = ev.plus( + w(t).storage().array()(w(t).storageOffset() - 1 + i), + v) + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.isContiguous(), s"the input of SReLU must be contiguous") + output.resizeAs(input) + + // the weight's size depends on the input + if (weights.exists(_.isEmpty)) { + init(input) + } + + // temp buf for indexes + if (indexes == null) { + indexes = new Array[Int](weights(tRight).nDimension()) + } + + var batch = 0 + while (batch < input.size(1)) { + val sliceInput = input.select(1, batch + 1) + val sliceOutput = output.select(1, batch + 1) + + val xArr = sliceInput.storage().array() + val yArr = sliceOutput.storage().array() + val yOffset = sliceOutput.storageOffset() - 1 + val xOffset = sliceInput.storageOffset() - 1 + + var i = 0 + while (i < sliceInput.nElement()) { + getIndex(indexes, sliceInput.stride(), sliceInput.nDimension(), i) + + val tr = weights(tRight).apply(indexes) + val ar = weights(aRight).apply(indexes) + val tl = weights(tLeft).apply(indexes) + val al = weights(aLeft).apply(indexes) + + val x = xArr(xOffset + i) + + yArr(yOffset + i) = if (ev.isGreaterEq(x, tr)) { + // right: x_i >= t_i^r + ev.plus(tr, ev.times(ar, ev.minus(x, tr))) + } else if (ev.isGreaterEq(tl, x)) { + // left: x_i <= t_i^l + ev.plus(tl, ev.times(al, ev.minus(x, tl))) + } else { + // else x_i = x_i + x + } + + i += 1 + } + + batch += 1 + } + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(input.isContiguous(), s"the input of SReLU must be contiguous") + require(gradOutput.isContiguous(), s"the gradOutput of SReLU must be contiguous") + gradInput.resizeAs(input) + + var batch = 0 + while (batch < gradInput.size(1)) { + val sliceInput = input.select(1, batch + 1) + val sliceGradInput = gradInput.select(1, batch + 1) + val sliceGradOutput = gradOutput.select(1, batch + 1) + + val xArr = sliceInput.storage().array() + var xOffset = sliceInput.storageOffset() - 1 + + val yArr = sliceGradInput.storage().array() + var yOffset = sliceGradInput.storageOffset() - 1 + + val zArr = sliceGradOutput.storage().array() + var zOffset = sliceGradOutput.storageOffset() - 1 + + var i = 0 + + while (i < sliceGradInput.nElement()) { + getIndex(indexes, sliceInput.stride(), sliceInput.nDimension(), i) + + val tr = weights(tRight).apply(indexes) + val ar = weights(aRight).apply(indexes) + val tl = weights(tLeft).apply(indexes) + val al = weights(aLeft).apply(indexes) + val x = xArr(xOffset + i) + + val t = if (ev.isGreaterEq(x, tr)) { + ev.times(ar, zArr(zOffset + i)) + } else if (ev.isGreaterEq(tl, x)) { + ev.times(al, zArr(zOffset + i)) + } else { + zArr(zOffset + i) + } + yArr(yOffset + i) = ev.plus(yArr(yOffset + i), t) + i += 1 + } + + batch += 1 + } + + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + var batch = 0 + while (batch < gradInput.size(1)) { + val sliceInput = input.select(1, batch + 1) + val sliceGradOutput = gradOutput.select(1, batch + 1) + + val xArr = sliceInput.storage().array() + val xOffset = sliceInput.storageOffset() - 1 + + val zArr = sliceGradOutput.storage().array() + val zOffset = sliceGradOutput.storageOffset() - 1 + + var i = 0 + while (i < sliceInput.nElement()) { + getIndex(indexes, sliceInput.stride(), sliceInput.nDimension(), i) + + // weight offset + var wOffset = 0 + var j = 0 + while (j < indexes.length) { + // because indexes is 1 based, so we should minus 1 here + wOffset += (indexes(j) - 1) * gradWeights(tLeft).stride(j + 1) + j += 1 + } + + val tr = weights(tRight).apply(indexes) + val ar = weights(aRight).apply(indexes) + val tl = weights(tLeft).apply(indexes) + val al = weights(aLeft).apply(indexes) + val x = xArr(xOffset + i) + + if (ev.isGreaterEq(x, tr)) { + setValue(gradWeights, wOffset, tRight, ev.times(ev.minus(ev.fromType(1), ar), + zArr(zOffset + i))) + setValue(gradWeights, wOffset, aRight, ev.times(ev.minus(x, tr), + zArr(zOffset + i))) + } else { + setValue(gradWeights, wOffset, tRight, ev.fromType(0)) + setValue(gradWeights, wOffset, aRight, ev.fromType(0)) + } + + if (ev.isGreaterEq(tl, x)) { + setValue(gradWeights, wOffset, tLeft, ev.times(ev.minus(ev.fromType(1), al), + zArr(zOffset + i))) + setValue(gradWeights, wOffset, aLeft, ev.times(ev.minus(xArr(xOffset + i), tl), + zArr(zOffset + i))) + } else { + setValue(gradWeights, wOffset, tLeft, ev.fromType(0)) + setValue(gradWeights, wOffset, aLeft, ev.fromType(0)) + } + + i += 1 + } + + batch += 1 + } + } + + override def setWeightsBias(newWeights: Array[Tensor[T]]): this.type = { + // SReLU will split the weights from a tensor + if (!newWeights.isEmpty) { + var i = 0 + while (i < weightsLen) { + val weight = newWeights(i) + weights(i).resizeAs(weight).set(weight) + gradWeights(i) = Tensor[T]().resizeAs(weight) + + i += 1 + } + + // ensure the the right part is always to the right of the left + weights(tRight).abs().add(weights(tLeft)) + } + + this + } + + override def getParametersTable(): Table = { + T(getName() -> T( + "tLeft" -> weights(tLeft), + "aLeft" -> weights(aLeft), + "tRight" -> weights(tRight), + "aRight" -> weights(aRight))) + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (weights, gradWeights) + } + + def setInitMethod( + tLeftInit: InitializationMethod = null, + aLeftInit: InitializationMethod = null, + tRightInit: InitializationMethod = null, + aRightInit: InitializationMethod = null): this.type = { + val inits = Array(tLeftInit, aLeftInit, tRightInit, aRightInit) + + for (i <- Array(tLeft, aLeft, tRight, aRight)) { + if (inits(i) != null) { + weightsInit(i) = inits(i) + } + } + + this + } +} + + +object SReLU extends ModuleSerializable { + def apply[T: ClassTag](share_axes: Array[Int] = null)(implicit ev: TensorNumeric[T]) + : SReLU[T] = { + new SReLU[T](share_axes) + } + + val (tLeft, aLeft, tRight, aRight) = (0, 1, 2, 3) + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val attrMap = context.bigdlModule.getAttrMap + val srelu = super.doLoadModule(context).asInstanceOf[SReLU[T]] + + srelu.weights(tLeft) = DataConverter. + getAttributeValue(context, attrMap.get("tLeft")). + asInstanceOf[Tensor[T]] + + srelu.weights(aLeft) = DataConverter. + getAttributeValue(context, attrMap.get("aLeft")). + asInstanceOf[Tensor[T]] + + srelu.weights(tRight) = DataConverter. + getAttributeValue(context, attrMap.get("tRight")). + asInstanceOf[Tensor[T]] + + srelu.weights(aRight) = DataConverter. + getAttributeValue(context, attrMap.get("aRight")). + asInstanceOf[Tensor[T]] + + + srelu + } + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + sreluBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + + super.doSerializeModule(context, sreluBuilder) + + val srelu = context.moduleData.module.asInstanceOf[SReLU[T]] + + val runningMeanBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, runningMeanBuilder, + srelu.weights(tLeft), ModuleSerializer.tensorType) + sreluBuilder.putAttr("tLeft", runningMeanBuilder.build) + + val runningVarBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, runningVarBuilder, + srelu.weights(aLeft), ModuleSerializer.tensorType) + sreluBuilder.putAttr("aLeft", runningVarBuilder.build) + + val saveMeanBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, saveMeanBuilder, + srelu.weights(tRight), ModuleSerializer.tensorType) + sreluBuilder.putAttr("tRight", saveMeanBuilder.build) + + val saveStdBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, saveStdBuilder, + srelu.weights(aRight), ModuleSerializer.tensorType) + sreluBuilder.putAttr("aRight", saveStdBuilder.build) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Regularizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Regularizer.scala index 15f153984c6..7c31c63eaed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Regularizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Regularizer.scala @@ -190,3 +190,4 @@ case class L2Regularizer[T: ClassTag]( override val l2: Double ) (implicit ev: TensorNumeric[T]) extends L1L2Regularizer[T](0, l2) + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index db6a15bf3bd..3772c8d057b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -919,6 +919,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab PReLU[T](nOutputPlane) } + def createSReLU(shareAxes: JArrayList[Int] = null): SReLU[T] = { + val argv: Array[Int] = if (shareAxes == null) { + null + } else { + shareAxes.asScala.toArray + } + SReLU[T](argv) + } + + def createActivityRegularization(l1: Double, l2: Double): ActivityRegularization[T] = { + ActivityRegularization[T](l1, l2) + } + def createPadding(dim: Int, pad: Int, nInputDim: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 787e0cdbee3..5d1859b2db0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -199,6 +199,7 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.quantized.Linear", quantized.Linear) registerModule("com.intel.analytics.bigdl.nn.ops.ParseExample", ParseExample) + registerModule("com.intel.analytics.bigdl.nn.SReLU", SReLU) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index 952cecf9411..2b3553e829f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -70,7 +70,7 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { ifskipTest() val (gradInput, gradWeight, weights, input, target, output) = - KerasRunner.run(kerasCode, is_loss = true) + KerasRunner.run(kerasCode, Loss) val boutput = bmodel.forward(input, target) val koutput = output.mean() // the return value from keras is not always averaged. diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala index 7e930b41f8e..8ad9892f743 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala @@ -22,6 +22,11 @@ import com.intel.analytics.bigdl.tensor.Tensor import scala.io.Source import scala.sys.process._ +sealed trait MainCodeType +object Loss extends MainCodeType +object Layer extends MainCodeType +object Regularizer extends MainCodeType + object KerasRunner { // scalastyle:off val code_head = @@ -88,6 +93,17 @@ object KerasRunner { | """.stripMargin + val code_for_regularizer = + """ + |Y = K.get_session().run(model.losses, feed_dict={input_tensor: input}) + |output = model.predict(input) + |grad_input = K.get_session().run(K.gradients(model.losses, [input_tensor]), + | feed_dict={input_tensor: input}) + |grad_input += output # they're two branches, we should gather them. + |weights = [] + |grad_weight = [] + """.stripMargin + // scalastyle:on private def getWeightRelate(pvalues: Map[String, Array[Float]], keyName: String): Array[Tensor[Float]] = { @@ -115,13 +131,18 @@ object KerasRunner { } // return: (grad_input, grad_weight, weights, input, target, output) - def run(code: String, is_loss: Boolean = false): (Tensor[Float], Array[Tensor[Float]], + def run(code: String, codeType: MainCodeType = Layer): (Tensor[Float], Array[Tensor[Float]], Array[Tensor[Float]], Tensor[Float], Tensor[Float], Tensor[Float]) = { val pcodeFile = java.io.File.createTempFile("UnitTest", "keras") val writer = new PrintWriter(pcodeFile) writer.write(code_head) writer.write(code) - writer.write(if (is_loss) {code_for_loss} else {code_for_layer}) + writer.write( + codeType match { + case Layer => code_for_layer + case Loss => code_for_loss + case Regularizer => code_for_regularizer + }) writer.write(code_for_save) writer.close() val pcodeFileAbsPath = pcodeFile.getAbsolutePath diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularizationSpec.scala new file mode 100644 index 00000000000..042ee53e42b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularizationSpec.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.keras.{KerasBaseSpec, KerasRunner, Regularizer} + +class ActivityRegularizationSpec extends KerasBaseSpec { + "ActivityRegularization" should "same as keras" in { + ifskipTest() + + val keras = + """ + |act_reg = core.ActivityRegularization(l1=0.01, l2=0.01) + | + |input_tensor = Input(shape=(2,)) + |output_tensor = act_reg(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + | + |input = np.random.random((2, 2)) + |loss = model.losses + | + |Y = [] + """.stripMargin + + val ar = ActivityRegularization[Float](0.01, 0.01) + + val (gradInput, gradWeight, weights, input, target, output) = KerasRunner.run(keras, + Regularizer) + + val boutput = ar.forward(input) + boutput.almostEqual(output, 1e-5) should be(true) + + ar.loss.toDouble should be (target.value().toDouble +- 1e-5) + + val bgradInput = ar.backward(input, boutput.clone()) + bgradInput.almostEqual(gradInput, 1e-5) should be(true) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala new file mode 100644 index 00000000000..50e221c6041 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala @@ -0,0 +1,74 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.{ModuleLoader, ModulePersister} + +class SReLUSpec extends KerasBaseSpec { + "SReLU without share axes" should "same as keras" in { + val keras = + """ + |input_tensor = Input(shape=[3, 4]) + |input = np.random.uniform(-1, 1, [2, 3, 4]) + |output_tensor = SReLU()(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + + val srelu = SReLU[Float]() + checkOutputAndGrad(srelu, keras) + } + + "SReLU with share axes" should "same as keras" in { + + val keras = + """ + |input_tensor = Input(shape=[2, 3, 4]) + |input = np.random.uniform(-1, 1, [5, 2, 3, 4]) + |share_axes = [1, 2] + |output_tensor = SReLU(shared_axes = share_axes)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + + val srelu = SReLU[Float](Array(1, 2)) + checkOutputAndGrad(srelu, keras) + } + + "SReLU with share axes not contiguous" should "same as keras" in { + + val keras = + """ + |input_tensor = Input(shape=[2, 3, 4, 5]) + |input = np.random.uniform(-1, 1, [6, 2, 3, 4, 5]) + |share_axes = [2, 4] + |output_tensor = SReLU(shared_axes = share_axes)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + + val srelu = SReLU[Float](Array(2, 4)) + checkOutputAndGrad(srelu, keras) + } + + // do not delete this, it's for testing the initialization of SReLU + "SReLU init" should "same as keras" in { + val srelu = SReLU[Float]() + val input = Tensor[Float](5, 2, 3, 4).randn() + srelu.forward(input) + println(srelu.output) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 4aeb19b6f80..e7f7360eddf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -2141,6 +2141,17 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { res1 should be (res2) } + "SReLU serialize" should "work correctly" in { + val srelu = SReLU[Float]() + val input = Tensor[Float](5, 2, 3, 4).randn() + val res1 = srelu.forward(input) + + ModulePersister.saveToFile[Float]("/tmp/srelu.bigdl", null, srelu, true) + val loadSrelu = ModuleLoader.loadFromFile[Float]("/tmp/srelu.bigdl") + val res2 = loadSrelu.forward(input) + res1 should be (res2) + } + "Save model and weight separately" should "work properly" in { val linear = Linear(3, 2) val input = Tensor(2, 3).rand() From 7db9c06c119ef611c19d5652c64bf6508f0329d3 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 5 Dec 2017 19:57:44 -0600 Subject: [PATCH 0560/1065] add support for multiple tensornumerics (#1984) --- .../intel/analytics/bigdl/dllib/nn/Abs.scala | 5 ++ .../bigdl/dllib/nn/BinaryThreshold.scala | 2 +- .../intel/analytics/bigdl/dllib/nn/ELU.scala | 5 ++ .../bigdl/dllib/nn/GaussianSampler.scala | 2 +- .../analytics/bigdl/dllib/nn/HardTanh.scala | 5 ++ .../intel/analytics/bigdl/dllib/nn/Log.scala | 5 ++ .../analytics/bigdl/dllib/nn/Power.scala | 5 ++ .../analytics/bigdl/dllib/nn/SoftPlus.scala | 5 ++ .../analytics/bigdl/dllib/nn/SoftSign.scala | 5 ++ .../intel/analytics/bigdl/dllib/nn/Sum.scala | 5 ++ .../intel/analytics/bigdl/dllib/nn/Tanh.scala | 2 +- .../dllib/nn/abstractnn/AbstractModule.scala | 4 ++ .../bigdl/dllib/nn/ops/BatchMatMul.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Cast.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Ceil.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Exp.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Expm1.scala | 5 ++ .../bigdl/dllib/nn/ops/FloorDiv.scala | 5 ++ .../bigdl/dllib/nn/ops/FloorMod.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Inv.scala | 5 ++ .../bigdl/dllib/nn/ops/InvGrad.scala | 5 ++ .../bigdl/dllib/nn/ops/IsFinite.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/IsInf.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/IsNan.scala | 5 ++ .../bigdl/dllib/nn/ops/Maximum.scala | 5 ++ .../bigdl/dllib/nn/ops/Minimum.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Mod.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/OneHot.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Pad.scala | 5 ++ .../bigdl/dllib/nn/ops/RandomUniform.scala | 5 ++ .../bigdl/dllib/nn/ops/RangeOps.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Round.scala | 5 ++ .../bigdl/dllib/nn/ops/RsqrtGrad.scala | 5 ++ .../bigdl/dllib/nn/ops/SigmoidGrad.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Sign.scala | 5 ++ .../bigdl/dllib/nn/ops/SoftplusGrad.scala | 5 ++ .../bigdl/dllib/nn/ops/SqrtGrad.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/Sum.scala | 5 ++ .../analytics/bigdl/dllib/nn/ops/TopK.scala | 5 ++ .../bigdl/dllib/nn/ops/TruncateDiv.scala | 5 ++ .../bigdl/dllib/nn/ops/TruncatedNormal.scala | 5 ++ .../bigdl/dllib/nn/ops/UnaryGrad.scala | 5 ++ .../analytics/bigdl/dllib/nn/tf/Const.scala | 5 ++ .../analytics/bigdl/dllib/nn/tf/Log1p.scala | 5 ++ .../utils/serializer/ModuleSerializable.scala | 41 +++++++++++++-- .../bigdl/dllib/utils/serializer/Types.scala | 50 +++++++++++++++++++ .../serializer/ModuleSerializerSpec.scala | 6 +-- 47 files changed, 296 insertions(+), 11 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala index a90e822e219..71be5b4a0e3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala @@ -69,6 +69,11 @@ class Abs[T: ClassTag, D: ClassTag] val state = Seq(super.hashCode()) state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Abs { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThreshold.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThreshold.scala index 978612de5a9..2b5b4cc7ba7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThreshold.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThreshold.scala @@ -34,7 +34,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(4932292249027276581L) -class BinaryThreshold[@specialized(Float, Double) T: ClassTag]( +class BinaryThreshold[T: ClassTag]( th: Double = 1e-6, ip: Boolean = false)( implicit ev: TensorNumeric[T]) extends TensorModule[T] { var threshold = th diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala index 104412a8eac..e9562c9b986 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala @@ -95,6 +95,11 @@ class ELU[T: ClassTag, D: ClassTag]( } this } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object ELU { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSampler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSampler.scala index 6d543b95d26..b32d036d04c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSampler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSampler.scala @@ -26,7 +26,7 @@ import scala.reflect.ClassTag /** * Takes {mean, log_variance} as input and samples from the Gaussian distribution */ -class GaussianSampler[@specialized(Float, Double) T: ClassTag]( +class GaussianSampler[T: ClassTag]( implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { val eps = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala index 1c07008f532..954fda9d91e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala @@ -194,6 +194,11 @@ class HardTanh[T: ClassTag, D: ClassTag]( } this } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object HardTanh { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala index 0e28c74b8a4..476680be4ec 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala @@ -42,6 +42,11 @@ class Log[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorN gradInput } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Log { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala index aef00324a1d..f9f47dbf9d7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala @@ -103,6 +103,11 @@ class Power[T: ClassTag, D: ClassTag]( s"${getPrintName}($power, $scale, $shift)" } + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } + } object Power { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala index 126e5da30cb..5bce9aef263 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala @@ -81,6 +81,11 @@ class SoftPlus[T: ClassTag, D: ClassTag]( gradInput } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object SoftPlus { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala index 73f37ee00b9..0f36474a316 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala @@ -56,6 +56,11 @@ class SoftSign[T: ClassTag, D: ClassTag]() gradInput.resizeAs(input).copy(gradOutput).cdiv(tempGrad) gradInput } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object SoftSign { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala index bda7160f0a4..c9198b4b1f9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala @@ -111,6 +111,11 @@ class Sum[T: ClassTag, D: ClassTag]( gradInput } + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, evd)) + } + override def toString: String = s"nn.Sum" } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala index 0cdc6e02052..779364fd369 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala @@ -29,7 +29,7 @@ import scala.reflect.ClassTag * Tanh is defined as f(x) = (exp(x)-exp(-x))/(exp(x)+exp(-x)). */ @SerialVersionUID(9062199894710333035L) -class Tanh[@specialized(Float, Double) T: ClassTag]( +class Tanh[T: ClassTag]( implicit ev: TensorNumeric[T]) extends TensorModule[T] { private val buffer: Tensor[T] = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 62b82d7bf82..476bbb9a5d0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -736,5 +736,9 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, val endNodes = this.getEndNodes(starts) Graph(starts, endNodes) } + + def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T]), Array(ev)) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala index 5bfcb39ff26..9dcb2f8d535 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala @@ -89,6 +89,11 @@ class BatchMatMul[T: ClassTag, D: ClassTag]( output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object BatchMatMul { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Cast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Cast.scala index f080d4f479a..22c250bb059 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Cast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Cast.scala @@ -39,6 +39,11 @@ class Cast[T: ClassTag, D: ClassTag]() output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Cast { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Ceil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Ceil.scala index 864f1ed909c..8473c7de1f5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Ceil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Ceil.scala @@ -30,6 +30,11 @@ class Ceil[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tenso output.copy(input).ceil() output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Ceil { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Exp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Exp.scala index 0c45c410ec9..0d12ac0b5bb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Exp.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Exp.scala @@ -29,6 +29,11 @@ class Exp[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tensor output.resizeAs(input) output.map(input, (a, b) => ev2.exp(b)) } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Exp { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1.scala index 667623bb106..568130ce3da 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1.scala @@ -29,6 +29,11 @@ class Expm1[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tens output.resizeAs(input) output.map(input, (a, b) => ev2.minus(ev2.exp(b), ev2.one)) } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Expm1 { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDiv.scala index 5d785f063bd..50c19478c59 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDiv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDiv.scala @@ -33,6 +33,11 @@ class FloorDiv[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: T output.map(input2, (a, b) => {ev2.floorDiv(a, b)}) output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object FloorDiv { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorMod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorMod.scala index 05e22c913e4..e7aac1903aa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorMod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorMod.scala @@ -41,6 +41,11 @@ class FloorMod[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: T buffer.set() this } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object FloorMod { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Inv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Inv.scala index c1ebf653c45..d814633224d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Inv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Inv.scala @@ -28,6 +28,11 @@ class Inv[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tensor output.resizeAs(input).copy(input).inv() output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Inv { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGrad.scala index 3e74e44559f..8429b04c34b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGrad.scala @@ -37,6 +37,11 @@ class InvGrad[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Te output.copy(x).pow(ev2.fromType(2)).cmul(d).mul(ev2.fromType(-1)) output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object InvGrad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsFinite.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsFinite.scala index 167ec7c63c5..a73596d1f35 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsFinite.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsFinite.scala @@ -32,6 +32,11 @@ class IsFinite[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: T }) output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object IsFinite { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsInf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsInf.scala index 7d58457d792..2d647587d83 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsInf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsInf.scala @@ -31,6 +31,11 @@ class IsInf[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tens ev2.isInf(b) }) } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object IsInf { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsNan.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsNan.scala index 718b7e9eaca..ecc3249ec31 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsNan.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsNan.scala @@ -31,6 +31,11 @@ class IsNan[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tens ev2.isNan(b) }) } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object IsNan { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala index 467af5b080f..33bededf853 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala @@ -32,6 +32,11 @@ class Maximum[T: ClassTag, D: ClassTag] output.resizeAs(x).cmax(x, y) } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Maximum { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala index 78ecff68b68..7afa4bfe88b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala @@ -32,6 +32,11 @@ class Minimum[T: ClassTag, D: ClassTag] output.resizeAs(x).cmin(x, y) } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Minimum { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Mod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Mod.scala index 3f5bc45f02d..90df793f3a0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Mod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Mod.scala @@ -41,6 +41,11 @@ class Mod[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tensor buffer.set() this } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Mod { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala index feb752d94dc..0615f1d25d8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHot.scala @@ -127,6 +127,11 @@ class OneHot[T: ClassTag, D: ClassTag]( } output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev1)) + } } object OneHot { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala index 334dd00ee24..f30d43d5dc2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala @@ -158,6 +158,11 @@ class Pad[T: ClassTag, D: ClassTag]( output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev)) + } } object Pad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala index f14dc25747a..c382addabe3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala @@ -45,6 +45,11 @@ class RandomUniform[T: ClassTag, D: ClassTag]( output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object RandomUniform { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOps.scala index ba0ed27040f..fb047d3efac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOps.scala @@ -42,6 +42,11 @@ class RangeOps[T: ClassTag, D: ClassTag]() } output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object RangeOps { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Round.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Round.scala index dcd6ee66e94..6989aae0f83 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Round.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Round.scala @@ -33,6 +33,11 @@ class Round[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tens }) output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Round { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala index 92f56de89d1..1ef5768ca2a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala @@ -32,6 +32,11 @@ class RsqrtGrad[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: Te output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object RsqrtGrad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala index bc5428da1c5..ea22a321a8d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala @@ -33,6 +33,11 @@ class SigmoidGrad[T: ClassTag, D: ClassTag] output = module.updateGradInputInternal(y, grads).toTensor[D] output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object SigmoidGrad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sign.scala index a80184e1c78..e88c8b93d9b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sign.scala @@ -29,6 +29,11 @@ class Sign[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Tenso output.resizeAs(input).copy(input).sign() output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Sign { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala index 5c065b48810..2c3b76da64c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala @@ -25,6 +25,11 @@ class SoftplusGrad[T: ClassTag, D: ClassTag] extends UnaryGrad[T, D](true, true) { override val module: Module = SoftPlus[T, D]() + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object SoftplusGrad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala index 5bee1b6f9f0..9d6abb77279 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala @@ -32,6 +32,11 @@ class SqrtGrad[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: Ten output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object SqrtGrad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala index 091ad76bd95..b2fb172a339 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala @@ -62,6 +62,11 @@ class Sum[T: ClassTag, D: ClassTag](keepDims: Boolean, startFromZero: Boolean = output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Sum { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala index 84466fcf511..71ecbf4e365 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala @@ -42,6 +42,11 @@ class TopK[T: ClassTag, D: ClassTag]( }) output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object TopK { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDiv.scala index 0031db2e524..26afecb651b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDiv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDiv.scala @@ -34,6 +34,11 @@ class TruncateDiv[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2 output.div(input2).apply1(ev2.truncate(_)) output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object TruncateDiv { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala index 3e2a011a8d1..ff72d080bf4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala @@ -38,6 +38,11 @@ class TruncatedNormal[T: ClassTag, DataType: ClassTag]( output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[DataType]), + Array[TensorNumeric[_]](ev)) + } } object TruncatedNormal { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala index 5d0f73495fe..44d513db078 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala @@ -46,4 +46,9 @@ abstract class UnaryGrad[T: ClassTag, D: ClassTag]( output = module.updateGradInput(inputs, grads).toTensor[D] output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala index 74c0722bcc9..c9919f827ca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala @@ -65,6 +65,11 @@ private[bigdl] class Const[T: ClassTag, B: ClassTag](val value: Tensor[B]) } gradInput } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[B]), + Array[TensorNumeric[_]](ev)) + } } private[bigdl] object Const { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala index e7cb4866f19..8ae026cb32f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala @@ -46,6 +46,11 @@ class Log1p[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: Tenso gradInput } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } } object Log1p { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 655a06f1d46..b57932ff5c9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -97,6 +97,9 @@ trait ModuleSerializable extends Loadable with Savable{ protected def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val (tags, numerics) = getTypes(context) + val tagIter = tags.iterator + val numericIter = numerics.iterator val evidence = scala.reflect.classTag[T] val model = context.bigdlModule val modelAttributes = model.getAttrMap @@ -105,7 +108,7 @@ trait ModuleSerializable extends Loadable with Savable{ val constructorMirror = getCostructorMirror(cls) val constructorFullParams = constructorMirror.symbol.paramss val args = new Array[Object](constructorFullParams.map(_.size).sum) - var i = 0; + var i = 0 lock.synchronized { constructorFullParams.foreach(map => { map.foreach(param => { @@ -113,10 +116,10 @@ trait ModuleSerializable extends Loadable with Savable{ val ptype = param.typeSignature if (ptype <:< universe.typeOf[ClassTag[_]]|| ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { - args(i) = evidence + args(i) = tagIter.next } else if (ptype <:< universe.typeOf[TensorNumeric[_]] || ptype.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { - args(i) = ev + args(i) = numericIter.next } else { require(modelAttributes.containsKey(name), s"$name value cannot be found") val attribute = modelAttributes.get(name) @@ -131,6 +134,16 @@ trait ModuleSerializable extends Loadable with Savable{ asInstanceOf[AbstractModule[Activity, Activity, T]] } + private def getTypes(context: DeserializeContext): + (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + val attrMap = context.bigdlModule.getAttrMap + val tags = attrMap.get(SerConst.MODULE_TAGES).getArrayValue.getStrList.asScala + .map(ClassTagMapper.apply(_)).toArray + val numeris = attrMap.get(SerConst.MODULE_NUMERICS).getArrayValue.getStrList. + asScala.map(TensorNumericMapper.apply(_)).toArray + (tags, numeris) + } + /** * Default serialization skeleton using reflection * @param context Serialization context @@ -150,13 +163,31 @@ trait ModuleSerializable extends Loadable with Savable{ // step 2: set module type bigDLModelBuilder.setModuleType(cls.getName) - // step 3 : apply module specific logic to create module + // step 3 : set data types (ClassTag and TensorNumric) + setDataTypes(context, bigDLModelBuilder) + + // step 4 : apply module specific logic to create module doSerializeModule(context, bigDLModelBuilder) - // step 4 : copy params (weight & bias) a and linkage + // step 5 : copy params (weight & bias) a and linkage createSerializeBigDLModule(bigDLModelBuilder, context) } + protected def setDataTypes[T: ClassTag](context: SerializeContext[T], + bigDLModelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val (tags, numerics) = context.moduleData.module.getClassTagNumerics + val tagsSer = tags.map(ClassTagMapper.apply(_)) + val tagAttrValue = AttrValue.newBuilder + DataConverter.setAttributeValue[T](context, tagAttrValue, + tagsSer, universe.typeOf[Array[String]]) + bigDLModelBuilder.putAttr(SerConst.MODULE_TAGES, tagAttrValue.build) + val numericAttrValue = AttrValue.newBuilder + val numericSer = numerics.map(TensorNumericMapper.apply(_)) + DataConverter.setAttributeValue[T](context, + numericAttrValue, numericSer, universe.typeOf[Array[String]]) + bigDLModelBuilder.putAttr(SerConst.MODULE_NUMERICS, numericAttrValue.build) + } + protected def doSerializeModule[T: ClassTag](context: SerializeContext[T], bigDLModelBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala index 35c1add75f9..2d1efe30792 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala @@ -16,7 +16,11 @@ package com.intel.analytics.bigdl.utils.serializer +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericString} +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString import serialization.Bigdl.BigDLModule import scala.collection.mutable @@ -48,4 +52,50 @@ object SerConst { val MAGIC_NO = 3721 val DIGEST_TYPE = "MD5" val GLOBAL_STORAGE = "global_storage" + val MODULE_TAGES = "module_tags" + val MODULE_NUMERICS = "module_numerics" +} + +object ClassTagMapper { + def apply(tpe : String): ClassTag[_] = { + tpe match { + case "Float" => scala.reflect.classTag[Float] + case "Double" => scala.reflect.classTag[Double] + case "Char" => scala.reflect.classTag[Char] + case "Boolean" => scala.reflect.classTag[Boolean] + case "String" => scala.reflect.classTag[String] + case "Int" => scala.reflect.classTag[Int] + case "Long" => scala.reflect.classTag[Long] + case "ByteString" => scala.reflect.classTag[ByteString] + } + } + + def apply(classTag: ClassTag[_]): String = classTag.toString +} +object TensorNumericMapper { + def apply(tpe : String): TensorNumeric[_] = { + tpe match { + case "Float" => NumericFloat + case "Double" => NumericDouble + case "Char" => NumericChar + case "Boolean" => NumericBoolean + case "String" => NumericString + case "Int" => NumericInt + case "Long" => NumericLong + case "ByteString" => NumericByteString + } + } + + def apply(tensorNumeric: TensorNumeric[_]): String = { + tensorNumeric match { + case NumericFloat => "Float" + case NumericDouble => "Double" + case NumericChar => "Char" + case NumericBoolean => "Boolean" + case NumericString => "String" + case NumericInt => "Int" + case NumericLong => "Long" + case NumericByteString => "ByteString" + } + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index e7f7360eddf..e39de81ee1e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -42,9 +42,9 @@ import scala.util.Random class ModuleSerializerSpec extends FlatSpec with Matchers { "Abs serializer" should "work properly" in { - val abs = Abs().setName("abs") - val tensor1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() + val abs = Abs[Float, Float]().setName("abs") + val tensor1 = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + val tensor2 = Tensor[Float]() val res1 = abs.forward(tensor1) tensor2.resizeAs(tensor1).copy(tensor1) ModulePersister.saveToFile("/tmp/abs.bigdl", null, abs, true) From 0f7cdf962c0da552b51c58a9af0dbcddc7d7bf9a Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Wed, 6 Dec 2017 10:45:08 +0800 Subject: [PATCH 0561/1065] Support table for sample-label (#1977) * support table for label * fix ut * add unit test * add doc * update --- .../bigdl/dllib/utils/python/api/BigDLSerde.scala | 2 +- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 12 ++++++++---- .../bigdl/dllib/python/api/PythonSpec.scala | 4 +++- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala index fc2273dedd4..6c50bc9fa7b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala @@ -220,7 +220,7 @@ object BigDLSerDe extends BigDLSerDeBase with Serializable { throw new PickleException("should be 3, not : " + args.length) } Sample(args(0).asInstanceOf[JList[JTensor]], - args(1).asInstanceOf[JTensor], + args(1).asInstanceOf[JList[JTensor]], args(2).asInstanceOf[String]) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 3772c8d057b..23e4a69f92d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -64,7 +64,7 @@ import scala.reflect.ClassTag * @param bigdlType bigdl numeric type */ case class Sample(features: JList[JTensor], - label: JTensor, + label: JList[JTensor], bigdlType: String) case class JTensor(storage: Array[Float], shape: Array[Int], @@ -127,7 +127,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab val cls = implicitly[ClassTag[T]].runtimeClass val features = new JArrayList[JTensor]() features.add(toJTensor(sample.feature())) - Sample(features, toJTensor(sample.label()), cls.getSimpleName) + val label = new JArrayList[JTensor]() + label.add(toJTensor(sample.label())) + Sample(features, label, cls.getSimpleName) } def toTensor(jTensor: JTensor): Tensor[T] = { @@ -208,7 +210,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def toJSample(record: Sample): JSample[T] = { require(record.bigdlType == this.typeName, s"record.bigdlType: ${record.bigdlType} == this.typeName: ${this.typeName}") - JSample[T](record.features.asScala.toArray.map(toTensor(_)), toTensor(record.label)) + JSample[T](record.features.asScala.toArray.map(toTensor(_)), + record.label.asScala.toArray.map(toTensor(_))) } def toJSample(psamples: RDD[Sample]): RDD[JSample[T]] = { @@ -2523,7 +2526,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab val imInfo = imageFeature.getImInfo() features.add(toJTensor(imInfo.asInstanceOf[Tensor[T]])) } - val label = imageFeatureToLabelTensor(imageFeature) + val label = new util.ArrayList[JTensor]() + label.add(imageFeatureToLabelTensor(imageFeature)) Sample(features, label, "float") } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index 8b9c8834c77..5f817fbcc1f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -169,11 +169,13 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { val labelShape = util.Arrays.asList(1) val data = sc.parallelize(0 to 100).map {i => - val label = JTensor(Array(i % 2 + 1.0f), Array(1), "double") + val l = JTensor(Array(i % 2 + 1.0f), Array(1), "double") val feature = JTensor(Range(0, 100).map(_ => Random.nextFloat()).toArray, Array(100), "double") val features = new JArrayList[JTensor]() features.add(feature) + val label = new JArrayList[JTensor]() + label.add(l) Sample(features, label, "double") } From 4ee079e5cf4c92fa1f421620e41c0814c0e112f3 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 6 Dec 2017 15:20:30 +0800 Subject: [PATCH 0562/1065] Refine python run doc (#1832) * checkin some script to make run bigdl easier and simplify the documeent * refine doc * meet code review --- dist/assembly/dist.xml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/dist/assembly/dist.xml b/dist/assembly/dist.xml index 9a05ebca764..39088ba01bf 100644 --- a/dist/assembly/dist.xml +++ b/dist/assembly/dist.xml @@ -25,6 +25,10 @@ dump_tf_graph.py launch-dataproc.sh export_tf_checkpoint.py + pyspark-with-bigdl.sh + spark-submit-with-bigdl.sh + juptyer-with-bigdl.sh + spark-shell-with-bigdl.sh From 023b46bdf6991daa2d5f5c75ae8e617ff2a87d5a Mon Sep 17 00:00:00 2001 From: dding3 Date: Wed, 6 Dec 2017 14:17:03 -0500 Subject: [PATCH 0563/1065] Support Keras masking and maxoutdense (#1918) * support keras Masking layer * support keras maxoutdense --- .../analytics/bigdl/dllib/nn/Masking.scala | 91 +++++++++++++++ .../analytics/bigdl/dllib/nn/Maxout.scala | 87 ++++++++++++++ .../analytics/bigdl/dllib/tensor/Tensor.scala | 16 +++ .../dllib/utils/python/api/PythonBigDL.scala | 13 +++ .../bigdl/dllib/keras/KerasBaseSpec.scala | 1 - .../bigdl/dllib/nn/MaskingSpec.scala | 64 +++++++++++ .../analytics/bigdl/dllib/nn/MaxoutSpec.scala | 106 ++++++++++++++++++ 7 files changed, 377 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala new file mode 100644 index 00000000000..547a5022a50 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala @@ -0,0 +1,91 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * [[Masking]] Use a mask value to skip timesteps for a sequence + * + * @param maskValue mask value + */ +class Masking[T: ClassTag](maskValue: Double = 0.0) +(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + val batchDim = 1 + val timeDim = 2 + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + output.resizeAs(input) + var timeIndex = 1 + var batchIndex = 1 + val fillValue = ev.fromType(0.0) + while(batchIndex <= input.size(batchDim)) { + val batchInput = input.select(batchDim, batchIndex) + val batchOutput = output.select(batchDim, batchIndex) + while(timeIndex <= input.size(timeDim)) { + val slicedTensor = batchInput.select(timeDim - 1, timeIndex) + if (!slicedTensor.notEqualValue(maskValue)) { + batchOutput.select(timeDim - 1, timeIndex).fill(fillValue) + } else { + batchOutput.select(timeDim - 1, timeIndex).copy(slicedTensor) + } + timeIndex += 1 + } + batchIndex += 1 + timeIndex = 1 + } + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(input.isSameSizeAs(gradOutput), + "Input should have the same size as gradOutput" + + s"input size(${input.size().foreach(x => x)})" + + s"gradOutput size(${gradOutput.size().foreach(x => x)})") + gradInput.resizeAs(input) + var timeIndex = 1 + var batchIndex = 1 + val fillValue = ev.fromType(0.0) + while(batchIndex <= input.size(batchDim)) { + val batchInput = input.select(batchDim, batchIndex) + val batchgradOutput = gradOutput.select(batchDim, batchIndex) + val batchgradInput = gradInput.select(batchDim, batchIndex) + while(timeIndex <= input.size(timeDim)) { + val slicedTensor = batchInput.select(timeDim - 1, timeIndex) + if (!slicedTensor.notEqualValue(maskValue)) { + batchgradInput.select(timeDim - 1, timeIndex).fill(fillValue) + } else { + batchgradInput.select(timeDim - 1, timeIndex).copy( + batchgradOutput.select(timeDim - 1, timeIndex)) + } + timeIndex += 1 + } + batchIndex += 1 + timeIndex = 1 + } + gradInput + } +} + +object Masking { + def apply[T : ClassTag](maskValue: Double)(implicit ev: TensorNumeric[T]): Masking[T] + = new Masking[T](maskValue) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala new file mode 100644 index 00000000000..28cc79b9440 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala @@ -0,0 +1,87 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * [[Maxout]] A linear maxout layer + * Maxout layer select the element-wise maximum value of + * maxoutNumber Linear(inputSize, outputSize) layers + * + * @param inputSize: the size the each input sample + * @param outputSize: the size of the module output of each sample + * @param maxoutNumber: number of Linear layers to use + * @param withBias: whether use bias in Linear + * @param wRegularizer: instance of [[Regularizer]] + * (eg. L1 or L2 regularization), applied to the input weights matrices. + * @param bRegularizer: instance of [[Regularizer]] + * applied to the bias. + * @param initWeight: initial weight + * @param initBias: initial bias + */ +class Maxout[T: ClassTag](inputSize: Int, outputSize: Int, maxoutNumber: Int, + withBias: Boolean = true, wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, initWeight: Tensor[T] = null, initBias: Tensor[T] = null) + (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + val layer = Sequential().add(Linear(inputSize, outputSize * maxoutNumber, withBias = withBias, + wRegularizer = wRegularizer, bRegularizer = bRegularizer, initWeight = initWeight, + initBias = initBias)) + .add(View(maxoutNumber, outputSize).setNumInputDims(1)) + .add(Max(1, 2)) + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + output = layer.updateOutput(input) + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + gradInput = layer.updateGradInput(input, gradOutput) + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + layer.accGradParameters(input, gradOutput) + } + + override def zeroGradParameters(): Unit = { + layer.zeroGradParameters() + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + layer.parameters() + } + + override def getParametersTable(): Table = { + layer.getParametersTable() + } +} + +object Maxout { + def apply[T : ClassTag](inputSize: Int, outputSize: Int, maxoutNumber: Int, + withBias: Boolean = true, wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, initWeight: Tensor[T] = null, initBias: Tensor[T] = null) + (implicit ev: TensorNumeric[T]): Maxout[T] + = new Maxout[T](inputSize, outputSize, maxoutNumber, withBias, wRegularizer, + bRegularizer, initWeight, initBias) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 6a52301ac22..3a7b42041ba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -763,6 +763,22 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { }) return result } + + /** + * Element wise inequality between tensor and given value + * @param value + * @return + */ + def notEqualValue(value : Double): Boolean = { + var j = 0 + while (j < this.nElement()) { + if (this.storage.apply(j + this.storageOffset() - 1) != value) { + return true + } + j += 1 + } + return false + } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 23e4a69f92d..6f0c664d220 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2336,6 +2336,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab module.runningVar.set(toTensor(runningStd)) } + def createMasking(maskValue: Double) + : Masking[T] = { + Masking[T](maskValue) + } + + def createMaxout(inputSize: Int, outputSize: Int, maxoutNumber: Int, withBias: Boolean = true, + wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, initBias: Tensor[T] = null) + : Maxout[T] = { + Maxout[T](inputSize, outputSize, maxoutNumber, withBias, wRegularizer, bRegularizer, + initWeight, initBias) + } + def createCosineProximityCriterion(): CosineProximityCriterion[T] = { CosineProximityCriterion[T]() } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index 2b3553e829f..31ad485ab06 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -54,7 +54,6 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { val bgradInput = bmodel.backward(input, boutput.clone()) bgradInput.almostEqual(gradInput, precision) should be(true) - val parameters = bmodel.parameters() if (parameters != null) { val bgradWeights = parameters._2 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskingSpec.scala new file mode 100644 index 00000000000..a99e53ec8e9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskingSpec.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{Masking} + +class MaskingSpec extends KerasBaseSpec { + + "Masking" should "generate corrent result when batchsize == 1" in { + val inputSize = 2 + val times = 7 + val batchSize = 1 + val mask_value = -1 + + val sigmoidCode = + s""" + |input_tensor = Input(shape=[${times}, ${inputSize}]) + |input = np.array([1, 1, ${mask_value}, 2, 3, 3, 4, 4, ${mask_value}, ${mask_value}, 6, 6, + |7, 7]).reshape(${batchSize}, ${times}, ${inputSize}) + |output_tensor = Masking(${mask_value})(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + + val masking = Masking[Float](mask_value) + + checkOutputAndGrad(masking, sigmoidCode) + } + + "Masking" should "generate corrent result when batchsize != 1" in { + val inputSize = 2 + val times = 7 + val batchSize = 3 + val mask_value = -1 + + val sigmoidCode = + s""" + |input_tensor = Input(shape=[${times}, ${inputSize}]) + |input = np.array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, ${mask_value}, 1, 2, + | 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 1, 1, 2, 2, 3, 3, + | ${mask_value}, ${mask_value}, 5, + | 5, 6, 6, 7, 7]).reshape(${batchSize}, ${times}, ${inputSize}) + |output_tensor = Masking(${mask_value})(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + + val masking = Masking[Float](mask_value) + + checkOutputAndGrad(masking, sigmoidCode) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala new file mode 100644 index 00000000000..735dbad096e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala @@ -0,0 +1,106 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.Maxout +import com.intel.analytics.bigdl.tensor.Tensor + +class MaxoutSpec extends KerasBaseSpec { + "Maxout" should "generate corrent result when batchsize == 1" in { + val inputSize = 2 + val outputSize = 4 + val maxoutNumber = 3 + val batchSize = 1 + + val sigmoidCode = + s""" + |input_tensor = Input(shape=[${inputSize}]) + |input = np.random.uniform(0, 1, [${batchSize}, ${inputSize}]) + |output_tensor = MaxoutDense(output_dim=${outputSize}, input_dim=${inputSize}, + |nb_feature=${maxoutNumber})(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + + val maxout = Maxout[Float](inputSize, outputSize, maxoutNumber) + + val wc = (data: Array[Tensor[Float]]) => { + val out = new Array[Tensor[Float]](data.length) + out(0) = Tensor(inputSize, maxoutNumber * outputSize) + val weight = out.head.storage().array() + var index = 0 + for (i <- 1 to maxoutNumber) { + val sliceW = data(0).select(1, i).t.clone().storage().array() + System.arraycopy(sliceW, 0, weight, index, sliceW.size) + index += sliceW.size + } + + if (data.length > 1) { + out(1) = data(1) + } + out + } + checkOutputAndGrad(maxout, sigmoidCode, weightConverter = wc) + } + + "Maxout" should "generate corrent result when batchsize != 1" in { + val inputSize = 5 + val outputSize = 4 + val maxoutNumber = 3 + val batchSize = 4 + + val sigmoidCode = + s""" + |#w1 = np.array([[[1.0, 2.0, 3.0, 4.0], + |# [5, 6, 7, 8.0]], + |# [[-1, -2, -3, -4], + |# [-5, -6, -7, -8]], + |# [[9, 10, 11, 12], + |# [-9, -10, -11, -12]]]) + |#b = np.array([[ 0.0, 0.0, 0.0, 0.0], + |# [ 0.0, 0.0, 0.0, 0.0], + |# [ 0.0, 0.0, 0.0, 0.0]]) + |# w = [w1, b] + | + |input_tensor = Input(shape=[${inputSize}]) + |input = np.random.uniform(0, 1, [${batchSize}, ${inputSize}]) + |#output_tensor=MaxoutDense(output_dim=4,input_dim=2,nb_feature=3,weights=w)(input_tensor) + |output_tensor = MaxoutDense(output_dim=${outputSize}, input_dim=${inputSize}, + |nb_feature=${maxoutNumber})(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + + val maxout = Maxout[Float](inputSize, outputSize, maxoutNumber) + + val wc = (data: Array[Tensor[Float]]) => { + val out = new Array[Tensor[Float]](data.length) + out(0) = Tensor(inputSize, maxoutNumber * outputSize) + val weight = out.head.storage().array() + var index = 0 + for (i <- 1 to maxoutNumber) { + val sliceW = data(0).select(1, i).t.clone().storage().array() + System.arraycopy(sliceW, 0, weight, index, sliceW.size) + index += sliceW.size + } + + if (data.length > 1) { + out(1) = data(1) + } + out + } + checkOutputAndGrad(maxout, sigmoidCode, weightConverter = wc) + } +} From a6be254c3a1deb537fea56529af5ddd1ab658bb4 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 7 Dec 2017 13:22:57 +0800 Subject: [PATCH 0564/1065] fix output allocation bug (#1982) * fix output allocation bug * fix test * fix rebase * fix test * fix ut --- .../intel/analytics/bigdl/dllib/nn/Abs.scala | 4 +++ .../intel/analytics/bigdl/dllib/nn/ELU.scala | 4 +++ .../analytics/bigdl/dllib/nn/HardTanh.scala | 3 ++ .../intel/analytics/bigdl/dllib/nn/Log.scala | 3 ++ .../analytics/bigdl/dllib/nn/Power.scala | 3 ++ .../analytics/bigdl/dllib/nn/SoftPlus.scala | 3 ++ .../analytics/bigdl/dllib/nn/SoftSign.scala | 3 ++ .../analytics/bigdl/dllib/nn/ops/ArgMax.scala | 6 ++-- .../analytics/bigdl/dllib/nn/ops/Assign.scala | 4 +++ .../bigdl/dllib/nn/ops/BatchMatMul.scala | 1 + .../analytics/bigdl/dllib/nn/ops/Ceil.scala | 6 ++-- .../bigdl/dllib/nn/ops/Maximum.scala | 2 ++ .../bigdl/dllib/nn/ops/Minimum.scala | 3 ++ .../analytics/bigdl/dllib/nn/ops/Rank.scala | 6 ++-- .../bigdl/dllib/nn/ops/RsqrtGrad.scala | 2 ++ .../bigdl/dllib/nn/ops/SqrtGrad.scala | 2 ++ .../analytics/bigdl/dllib/nn/ops/Sum.scala | 5 ++- .../bigdl/dllib/nn/ops/TruncatedNormal.scala | 5 ++- .../analytics/bigdl/dllib/nn/tf/Log1p.scala | 4 +++ .../analytics/bigdl/dllib/nn/PowerSpec.scala | 12 +++++++ .../bigdl/dllib/nn/ops/AssignSpec.scala | 10 +++--- .../bigdl/dllib/nn/ops/RankSpec.scala | 16 ++++----- .../bigdl/dllib/torch/HardTanhSpec.scala | 2 +- .../dllib/utils/tf/TensorflowSpecHelper.scala | 34 +++++++++++-------- .../dllib/utils/tf/loaders/AllSpec.scala | 8 ++--- .../dllib/utils/tf/loaders/AnySpec.scala | 8 ++--- .../utils/tf/loaders/AvgPoolGradSpec.scala | 4 +-- .../dllib/utils/tf/loaders/AvgPoolSpec.scala | 1 + .../utils/tf/loaders/BatchMatMulSpec.scala | 1 + .../utils/tf/loaders/BiasAddV1Spec.scala | 16 ++++++--- .../utils/tf/loaders/BinaryOpBaseSpec.scala | 29 ++++++++++++++-- .../dllib/utils/tf/loaders/CeilSpec.scala | 15 ++++++-- .../dllib/utils/tf/loaders/ExpSpec.scala | 30 +++++++++++++--- .../dllib/utils/tf/loaders/Expm1Spec.scala | 4 +-- .../dllib/utils/tf/loaders/FloorDivSpec.scala | 6 ++-- .../dllib/utils/tf/loaders/FloorModSpec.scala | 4 +-- .../tf/loaders/FusedBatchNormGradSpec.scala | 12 +++---- .../tf/loaders/FusedBatchNormGradV2Spec.scala | 12 +++---- .../utils/tf/loaders/FusedBatchNormSpec.scala | 12 +++---- .../tf/loaders/FusedBatchNormV2Spec.scala | 12 +++---- .../dllib/utils/tf/loaders/InTopKSpec.scala | 2 +- .../dllib/utils/tf/loaders/InvGradSpec.scala | 2 +- .../dllib/utils/tf/loaders/InvSpec.scala | 2 +- .../dllib/utils/tf/loaders/IsFiniteSpec.scala | 2 +- .../dllib/utils/tf/loaders/IsInfSpec.scala | 2 +- .../dllib/utils/tf/loaders/IsNanSpec.scala | 2 +- .../dllib/utils/tf/loaders/LRNGradSpec.scala | 4 +-- .../dllib/utils/tf/loaders/LRNSpec.scala | 4 +-- .../utils/tf/loaders/LogSoftmaxSpec.scala | 18 ++++++++-- .../dllib/utils/tf/loaders/ModSpec.scala | 4 +-- .../dllib/utils/tf/loaders/RangeSpec.scala | 4 +-- .../utils/tf/loaders/ReciprocalGradSpec.scala | 2 +- .../utils/tf/loaders/ReciprocalSpec.scala | 2 +- .../dllib/utils/tf/loaders/RintSpec.scala | 2 +- .../dllib/utils/tf/loaders/RoundSpec.scala | 4 +-- .../dllib/utils/tf/loaders/SignSpec.scala | 2 +- .../dllib/utils/tf/loaders/SqueezeSpec.scala | 4 +-- .../dllib/utils/tf/loaders/TopKSpec.scala | 12 +++---- .../dllib/utils/tf/loaders/TopKV2Spec.scala | 12 +++---- .../utils/tf/loaders/TruncateDivSpec.scala | 2 +- .../utils/tf/loaders/TruncateModSpec.scala | 4 +-- .../utils/tf/loaders/UnaryOpBaseSpec.scala | 15 ++++++-- 62 files changed, 285 insertions(+), 139 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala index 71be5b4a0e3..3fcad3f632c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala @@ -29,6 +29,10 @@ class Abs[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends AbstractModule[Tensor[D], Tensor[D], T] { + output = Tensor[D]() + + gradInput = Tensor[D]() + override def updateOutput(input: Tensor[D]): Tensor[D] = { output.resizeAs(input) output.abs(input) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala index e9562c9b986..59860348669 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala @@ -33,6 +33,10 @@ class ELU[T: ClassTag, D: ClassTag]( val inplace: Boolean = false)( implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends AbstractModule[Tensor[D], Tensor[D], T] { + + output = Tensor[D]() + gradInput = Tensor[D]() + val _alpha = ev2.fromType[Double](alpha) // Todo: Improve the performance of contiguous tensor diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala index 954fda9d91e..9d1c40869c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala @@ -44,6 +44,9 @@ class HardTanh[T: ClassTag, D: ClassTag]( s"maxValue ${maxValue}, " + s"minValue ${minValue}") + output = Tensor[D]() + gradInput = Tensor[D]() + val min = ev2.fromType[Double](minValue) val max = ev2.fromType[Double](maxValue) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala index 476680be4ec..dc36cc8f73d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala @@ -28,6 +28,9 @@ import scala.reflect.ClassTag @SerialVersionUID(- 5175095570714684226L) class Log[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends AbstractModule[Tensor[D], Tensor[D], T] { + output = Tensor[D]() + gradInput = Tensor[D]() + override def updateOutput(input: Tensor[D]): Tensor[D] = { output.resizeAs(input) .copy(input) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala index f9f47dbf9d7..3b469a8fd5a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala @@ -40,6 +40,9 @@ class Power[T: ClassTag, D: ClassTag]( (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends AbstractModule[Tensor[D], Tensor[D], T] { + output = Tensor[D]() + gradInput = Tensor[D]() + val diffScale = power * scale override def updateOutput(input: Tensor[D]): Tensor[D] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala index 5bce9aef263..18a031fee73 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala @@ -40,6 +40,9 @@ class SoftPlus[T: ClassTag, D: ClassTag]( private val threshold = ev2.fromType[Double](20.0) private val betaT = ev2.fromType[Double](beta) + output = Tensor[D]() + gradInput = Tensor[D]() + override def updateOutput(input: Tensor[D]): Tensor[D] = { output.resizeAs(input) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala index 0f36474a316..fe443796086 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala @@ -35,6 +35,9 @@ class SoftSign[T: ClassTag, D: ClassTag]() @transient private var temp: Tensor[D] = null @transient private var tempGrad: Tensor[D] = null + output = Tensor[D]() + gradInput = Tensor[D]() + override def updateOutput(input: Tensor[D]): Tensor[D] = { if (null == temp) { temp = input.clone() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMax.scala index 62557f4d74b..0d3b51cca33 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMax.scala @@ -25,10 +25,10 @@ import scala.reflect.ClassTag class ArgMax[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[Int], T] { + output = Tensor[Int]() + override def updateOutput(input: Table): Tensor[Int] = { - if (output.getType() != IntType) { - output = Tensor[Int]() - } + val inputTensor = input[Tensor[_]](1) val dimension = input[Tensor[Int]](2).value() + 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala index 221d6425df5..1fb5c34b2de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala @@ -54,6 +54,10 @@ class Assign[T: ClassTag]( require(input1.getType() == input2.getType(), "ref and value must have the same tensor numeric type") + if (output.getType() != input2.getType()) { + output = input2.emptyInstance() + } + if (validateShape) { var i = 1 while (i <= input1.dim()) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala index 9dcb2f8d535..2cfc9c16c0b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala @@ -37,6 +37,7 @@ class BatchMatMul[T: ClassTag, D: ClassTag]( (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { gradInput = T(Tensor[D], Tensor[D]()) + output = Tensor[D]() override def updateOutput(input: Table): Tensor[D] = { var x: Tensor[D] = input(1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Ceil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Ceil.scala index 8473c7de1f5..50d0392db93 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Ceil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Ceil.scala @@ -22,10 +22,10 @@ import scala.reflect.ClassTag class Ceil[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Tensor[D], Tensor[D], T] { + + output = Tensor[D]() + override def updateOutput(input: Tensor[D]): Tensor[D] = { - if (input.getType() != output.getType()) { - output = input.emptyInstance() - } output.resizeAs(input) output.copy(input).ceil() output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala index 33bededf853..0abea2965c4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Maximum.scala @@ -24,6 +24,8 @@ import scala.reflect.ClassTag class Maximum[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { + + output = Tensor[D]() override def updateOutput(input: Table): Tensor[D] = { val x = input[Tensor[D]](1) val y = input[Tensor[D]](2) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala index 7afa4bfe88b..3eb2f7723ed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Minimum.scala @@ -24,6 +24,9 @@ import scala.reflect.ClassTag class Minimum[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { + + output = Tensor[D]() + override def updateOutput(input: Table): Tensor[D] = { val x = input[Tensor[D]](1) val y = input[Tensor[D]](2) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala index 2ca66798044..d3ea4b5277f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Rank.scala @@ -24,10 +24,10 @@ import scala.reflect.ClassTag class Rank[T: ClassTag]() (implicit ev: TensorNumeric[T]) extends Operation[Tensor[_], Tensor[Int], T] { + output = Tensor[Int]() + override def updateOutput(input: Tensor[_]): Tensor[Int] = { - if (output.getType() != IntType) { - output = Tensor[Int]() - } + output.resize(Array[Int]()) output.setValue(input.nDimension()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala index 1ef5768ca2a..7227baeecd1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala @@ -24,6 +24,8 @@ import scala.reflect.ClassTag class RsqrtGrad[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { + output = Tensor[D]() + override def updateOutput(inputs: Table): Tensor[D] = { val grads = inputs[Tensor[D]](2) val y = inputs[Tensor[D]](1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala index 9d6abb77279..dd905d8bd11 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala @@ -24,6 +24,8 @@ import scala.reflect.ClassTag class SqrtGrad[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { + output = Tensor[D]() + override def updateOutput(inputs: Table): Tensor[D] = { val grads = inputs[Tensor[D]](2) val y = inputs[Tensor[D]](1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala index b2fb172a339..ab85b6b4bf9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala @@ -29,13 +29,12 @@ class Sum[T: ClassTag, D: ClassTag](keepDims: Boolean, startFromZero: Boolean = private val sum: SumLayer[T, D] = SumLayer[T, D](squeeze = !keepDims) + output = Tensor[D]() + override def updateOutput(input: Table): Tensor[D] = { val data = input[Tensor[D]](1) val dims = input[Tensor[Int]](2) - if (output.getType() != data.getType()) { - output = data.emptyInstance() - } output.resizeAs(data).copy(data) val sumDims = if (dims.isEmpty) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala index ff72d080bf4..137b5e8111d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala @@ -26,7 +26,10 @@ class TruncatedNormal[T: ClassTag, DataType: ClassTag]( stddev: DataType = 1.0, seed: Int = 0 ) - (implicit ev: TensorNumeric[T]) extends Operation[Tensor[Int], Tensor[DataType], T] { + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[DataType]) + extends Operation[Tensor[Int], Tensor[DataType], T] { + + output = Tensor[DataType]() def updateOutput(input: Tensor[Int]): Tensor[DataType] = { require(input.nDimension() == 1, "the shape should be a one-dimensional tensor.") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala index 8ae026cb32f..0f94c4e886b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala @@ -29,6 +29,10 @@ import scala.reflect.ClassTag @SerialVersionUID(952324213749625368L) class Log1p[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends AbstractModule[Tensor[D], Tensor[D], T] { + + output = Tensor[D]() + gradInput = Tensor[D]() + private val buffer: Tensor[D] = Tensor[D]() override def updateOutput(input: Tensor[D]): Tensor[D] = { output.resizeAs(input) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala index a609944780b..37f9a6a7ba8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala @@ -33,6 +33,18 @@ class PowerSpec extends FlatSpec with Matchers { powerOutput should be (output) } + "A float Power" should "generate correct output" in { + val input = Tensor(Storage[Double](Array(1.0, 2, 3, 4, 5, 6)), 1, Array(2, 3)) + + val output = Tensor(Storage(Array(1.0, 4, 9, 16, 25, 36)), 1, Array(2, 3)) + + val power = new Power[Float, Double](2) + + val powerOutput = power.forward(input) + + powerOutput should be (output) + } + "A Power with scale" should "generate correct output" in { val input = Tensor(Storage[Double](Array(1.0, 2, 3, 4, 5, 6)), 1, Array(2, 3)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala index 05b41a1f546..6c0d8d1132c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala @@ -35,16 +35,16 @@ class AssignSpec extends FlatSpec with Matchers { } "Assign operation Double" should "works correctly" in { - import com.intel.analytics.bigdl.numeric.NumericDouble + import com.intel.analytics.bigdl.numeric.NumericFloat val input = T( - Tensor(T(1.0, 2.0, 3.0)), - Tensor(T(2.0, 2.0, 4.0)) + Tensor(T(1.0f, 2.0f, 3.0f)), + Tensor(T(2.0f, 2.0f, 4.0f)) ) - val expectOutput = Tensor(T(2.0, 2.0, 4.0)) + val expectOutput = Tensor(T(2.0f, 2.0f, 4.0f)) - val output = Assign().forward(input) + val output = Assign[Double]().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala index 82475f783ac..66906e276e9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala @@ -26,7 +26,7 @@ class RankSpec extends FlatSpec with Matchers { val expectOutput = Tensor.scalar(1) - val output = Rank[Int]().forward(input) + val output = Rank[Float]().forward(input) output should be(expectOutput) } @@ -36,7 +36,7 @@ class RankSpec extends FlatSpec with Matchers { val expectOutput = Tensor.scalar(1) - val output = Rank[Int]().forward(input) + val output = Rank[Float]().forward(input) output should be(expectOutput) } @@ -46,7 +46,7 @@ class RankSpec extends FlatSpec with Matchers { val expectOutput = Tensor.scalar(1) - val output = Rank[Int]().forward(input) + val output = Rank[Float]().forward(input) output should be(expectOutput) } @@ -56,7 +56,7 @@ class RankSpec extends FlatSpec with Matchers { val expectOutput = Tensor.scalar(1) - val output = Rank[Int]().forward(input) + val output = Rank[Float]().forward(input) output should be(expectOutput) } @@ -66,7 +66,7 @@ class RankSpec extends FlatSpec with Matchers { val expectOutput = Tensor.scalar(1) - val output = Rank[Int]().forward(input) + val output = Rank[Float]().forward(input) output should be(expectOutput) } @@ -76,7 +76,7 @@ class RankSpec extends FlatSpec with Matchers { val expectOutput = Tensor.scalar(1) - val output = Rank[Int]().forward(input) + val output = Rank[Float]().forward(input) output should be(expectOutput) } @@ -86,7 +86,7 @@ class RankSpec extends FlatSpec with Matchers { val expectOutput = Tensor.scalar(1) - val output = Rank[Int]().forward(input) + val output = Rank[Float]().forward(input) output should be(expectOutput) } @@ -96,7 +96,7 @@ class RankSpec extends FlatSpec with Matchers { val expectOutput = Tensor.scalar(1) - val output = Rank[Int]().forward(input) + val output = Rank[Float]().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala index 91d297b6678..4cd5bf0956e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala @@ -146,7 +146,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad inplace with not contiguous input" in { torchCheck() - val module = new HardTanh[Double, Double](inplace = true) + val module = new HardTanh[Float, Double](inplace = true) val input = Tensor[Double](2, 2) input(Array(1, 1)) = -0.97008799016476 input(Array(1, 2)) = -0.65073125436902 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala index 1337b5718b8..4fc8922a92f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala @@ -23,11 +23,12 @@ import com.google.protobuf.CodedOutputStream import com.intel.analytics.bigdl.nn.Module import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.NumericWildCard +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, FileWriter, T} import com.intel.analytics.bigdl.utils.tf.Tensorflow.const import org.tensorflow.framework.{GraphDef, NodeDef} +import scala.reflect.ClassTag import scala.sys.process._ import scala.util.control.NonFatal @@ -72,34 +73,35 @@ abstract class TensorflowSpecHelper extends BigDLSpecHelper { * @param outputIndex start from 0 * @param delta error tolerant */ - protected def compare(nodeDefBuilder: NodeDef.Builder, inputs: Seq[Tensor[_]], outputIndex: Int, - delta: Double = 1e-5) + protected def compare[T: ClassTag](nodeDefBuilder: NodeDef.Builder, + inputs: Seq[Tensor[_]], outputIndex: Int, + delta: Double = 1e-5)(implicit ev: TensorNumeric[T]) : Unit = { val graphFile = saveGraph(nodeDefBuilder, inputs) - val bigdlOutput = runGraphBigDL(graphFile, nodeDefBuilder.getName) + val bigdlOutput = runGraphBigDL[T](graphFile, nodeDefBuilder.getName) val bigdlOutputTensor = if (bigdlOutput.isTensor) { require(outputIndex == 0, s"invalid output index $outputIndex") bigdlOutput.asInstanceOf[Tensor[_]] } else { bigdlOutput.toTable.apply[Tensor[_]](outputIndex + 1) } - val tfOutput = runGraphTF(graphFile, nodeDefBuilder.getName + s":$outputIndex") + val tfOutput = runGraphTF[T](graphFile, nodeDefBuilder.getName + s":$outputIndex") bigdlOutputTensor.asInstanceOf[Tensor[NumericWildCard]] .almostEqual(tfOutput.asInstanceOf[Tensor[NumericWildCard]], delta) should be(true) } - protected def getResult[T](nodeDefBuilder: NodeDef.Builder, inputs: Seq[Tensor[_]], - outputIndex: Int): (Tensor[T], Tensor[T]) = { + protected def getResult[T: ClassTag, D](nodeDefBuilder: NodeDef.Builder, inputs: Seq[Tensor[_]], + outputIndex: Int)(implicit ev: TensorNumeric[T]): (Tensor[D], Tensor[D]) = { val graphFile = saveGraph(nodeDefBuilder, inputs) - val bigdlOutput = runGraphBigDL(graphFile, nodeDefBuilder.getName) + val bigdlOutput = runGraphBigDL[T](graphFile, nodeDefBuilder.getName) val bigdlOutputTensor = if (bigdlOutput.isTensor) { require(outputIndex == 0, s"invalid output index $outputIndex") - bigdlOutput.asInstanceOf[Tensor[T]] + bigdlOutput.asInstanceOf[Tensor[D]] } else { - bigdlOutput.toTable.apply[Tensor[T]](outputIndex + 1) + bigdlOutput.toTable.apply[Tensor[D]](outputIndex + 1) } val tfOutput = runGraphTF(graphFile, nodeDefBuilder.getName + s":$outputIndex") - (bigdlOutputTensor, tfOutput.asInstanceOf[Tensor[T]]) + (bigdlOutputTensor, tfOutput.asInstanceOf[Tensor[D]]) } private def saveGraph(nodeDefBuilder: NodeDef.Builder, inputs: Seq[Tensor[_]]): String = { @@ -132,12 +134,14 @@ abstract class TensorflowSpecHelper extends BigDLSpecHelper { } } - private def runGraphBigDL(graph: String, output: String): Activity = { - val m = Module.loadTF[Float](graph, Seq(), Seq(output)) + private def runGraphBigDL[T: ClassTag](graph: String, output: String) + (implicit ev: TensorNumeric[T]): Activity = { + val m = Module.loadTF[T](graph, Seq(), Seq(output)) m.forward(null) } - private def runGraphTF(graph: String, output: String): Tensor[_] = { + private def runGraphTF[T: ClassTag](graph: String, output: String) + (implicit ev: TensorNumeric[T]): Tensor[_] = { tfCheck() val outputFile = createTmpFile() val outputFolder = getFileFolder(outputFile.getAbsolutePath()) @@ -146,7 +150,7 @@ abstract class TensorflowSpecHelper extends BigDLSpecHelper { val path = processPath(resource.getPath()) + JFile.separator + s"run-graph.py $graph $output $outputFolder $outputFileName result" runPython(path) - val m = Module.loadTF[Float](outputFile.getAbsolutePath, Seq(), Seq("result")) + val m = Module.loadTF[T](outputFile.getAbsolutePath, Seq(), Seq("result")) m.forward(null).asInstanceOf[Tensor[_]] } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AllSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AllSpec.scala index 921077c885a..a238f6775e1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AllSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AllSpec.scala @@ -25,7 +25,7 @@ class AllSpec extends TensorflowSpecHelper { "All ops" should "be correct when keep_dims is true" in { val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) val indice = Tensor[Int](T(1)) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("all_test") .putAttr("keep_dims", booleanAttr(true)) @@ -42,7 +42,7 @@ class AllSpec extends TensorflowSpecHelper { "All ops" should "be correct when indice contains several value" in { val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) val indice = Tensor[Int](T(0, 1)) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("all_test") .putAttr("keep_dims", booleanAttr(true)) @@ -59,7 +59,7 @@ class AllSpec extends TensorflowSpecHelper { "All ops" should "be correct when keep_dims is false" in { val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) val indice = Tensor[Int](T(1)) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("all_test") .putAttr("keep_dims", booleanAttr(false)) @@ -76,7 +76,7 @@ class AllSpec extends TensorflowSpecHelper { "All ops" should "be correct when indice is scalar" in { val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) val indice = Tensor.scalar[Int](1) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("all_test") .putAttr("keep_dims", booleanAttr(false)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AnySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AnySpec.scala index defbba9e474..ffff5c32c69 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AnySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AnySpec.scala @@ -25,7 +25,7 @@ class AnySpec extends TensorflowSpecHelper { "Any ops" should "be correct when keep_dims is true" in { val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) val indice = Tensor[Int](T(1)) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("any_test") .putAttr("keep_dims", booleanAttr(true)) @@ -42,7 +42,7 @@ class AnySpec extends TensorflowSpecHelper { "Any ops" should "be correct when indice contains several value" in { val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) val indice = Tensor[Int](T(0, 1)) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("any_test") .putAttr("keep_dims", booleanAttr(true)) @@ -59,7 +59,7 @@ class AnySpec extends TensorflowSpecHelper { "Any ops" should "be correct when keep_dims is false" in { val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) val indice = Tensor[Int](T(1)) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("any_test") .putAttr("keep_dims", booleanAttr(false)) @@ -76,7 +76,7 @@ class AnySpec extends TensorflowSpecHelper { "Any ops" should "be correct when indice is scalar" in { val data = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) val indice = Tensor.scalar[Int](1) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("any_test") .putAttr("keep_dims", booleanAttr(false)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGradSpec.scala index c9164e23dbd..447867d93c8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGradSpec.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.utils.tf.Tensorflow._ class AvgPoolGradSpec extends TensorflowSpecHelper { "Avg forward" should "be correct" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("avg_pool_grad_test") .setOp("AvgPoolGrad") @@ -36,7 +36,7 @@ class AvgPoolGradSpec extends TensorflowSpecHelper { 0 ) - compare( + compare[Float]( NodeDef.newBuilder() .setName("avg_pool_grad_test") .setOp("AvgPoolGrad") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolSpec.scala index 8611ac0b653..9e75473759d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolSpec.scala @@ -21,6 +21,7 @@ import org.tensorflow.framework.{DataType, NodeDef} import com.intel.analytics.bigdl.utils.tf.Tensorflow._ class AvgPoolSpec extends TensorflowSpecHelper { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat "Avg forward" should "be correct" in { compare( NodeDef.newBuilder() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMulSpec.scala index 2b766fd4aaa..59cfd0545f4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMulSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMulSpec.scala @@ -21,6 +21,7 @@ import org.tensorflow.framework.{DataType, NodeDef} import com.intel.analytics.bigdl.utils.tf.Tensorflow._ class BatchMatMulSpec extends TensorflowSpecHelper { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat "BatchMatMul with two dim forward" should "be correct" in { compare( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1Spec.scala index 9c17217c425..176ed780e38 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddV1Spec.scala @@ -20,10 +20,16 @@ import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, Te import org.tensorflow.framework.{DataType, NodeDef} import com.intel.analytics.bigdl.utils.tf.Tensorflow._ -class BiasAddV1Spec extends BinaryOpBaseSpec { +class BiasAddV1Spec extends TensorflowSpecHelper { - override def getOpName: String = "BiasAddV1" - - override def getInputs: Seq[Tensor[_]] = - Seq(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3).rand()) + "BatchMatMul with two dim forward" should "be correct" in { + compare[Float]( + NodeDef.newBuilder() + .setName("BiasAddV1_test") + .setOp("BiasAddV1") + .putAttr("T", typeAttr(DataType.DT_FLOAT)), + Seq(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3).rand()), + 0 + ) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BinaryOpBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BinaryOpBaseSpec.scala index 3133475761f..cda0b978432 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BinaryOpBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BinaryOpBaseSpec.scala @@ -30,7 +30,7 @@ abstract class BinaryOpBaseSpec extends TensorflowSpecHelper { def compareExactly: Boolean = false - s"$getOpName forward" should "be correct" in { + s"$getOpName forward with float model" should "be correct" in { val builder = NodeDef.newBuilder() .setName(s"${getOpName}Test") @@ -42,13 +42,36 @@ abstract class BinaryOpBaseSpec extends TensorflowSpecHelper { } if (!compareExactly) { - compare( + compare[Float]( builder, getInputs, 0 ) } else { - val (bigdl, tf) = getResult(builder, getInputs, 0) + val (bigdl, tf) = getResult[Float, Float](builder, getInputs, 0) + bigdl should be (tf) + } + } + + s"$getOpName forward with double model" should "be correct" in { + + val builder = NodeDef.newBuilder() + .setName(s"${getOpName}Test") + .setOp(getOpName) + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + + for ((k, v) <- getAttrs) { + builder.putAttr(k, v) + } + + if (!compareExactly) { + compare[Double]( + builder, + getInputs, + 0 + ) + } else { + val (bigdl, tf) = getResult[Double, Float](builder, getInputs, 0) bigdl should be (tf) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/CeilSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/CeilSpec.scala index 7874cee55a0..c9388d60928 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/CeilSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/CeilSpec.scala @@ -21,8 +21,19 @@ import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper import org.tensorflow.framework.{DataType, NodeDef} class CeilSpec extends TensorflowSpecHelper { - "Ceil" should "be correct for float tensor" in { - compare( + "Ceil with model float" should "be correct for float tensor" in { + compare[Float]( + NodeDef.newBuilder() + .setName("ceil_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("Ceil"), + Seq(Tensor[Float](4, 32, 32, 3).rand()), + 0 + ) + } + + "Ceil with model double" should "be correct for float tensor" in { + compare[Double]( NodeDef.newBuilder() .setName("ceil_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpSpec.scala index f92675d923c..5d8f0f4d77e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpSpec.scala @@ -23,8 +23,8 @@ import org.tensorflow.framework.{DataType, NodeDef} class ExpSpec extends TensorflowSpecHelper { - "Exp" should "be correct for float" in { - compare( + "Exp with model float" should "be correct for float" in { + compare[Float]( NodeDef.newBuilder() .setName("exp_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -34,8 +34,30 @@ class ExpSpec extends TensorflowSpecHelper { ) } - "Exp" should "be correct for double" in { - compare( + "Exp with model float" should "be correct for double" in { + compare[Float]( + NodeDef.newBuilder() + .setName("exp_test") + .putAttr("T", typeAttr(DataType.DT_DOUBLE)) + .setOp("Exp"), + Seq(Tensor[Double](10).rand()), + 0 + ) + } + + "Exp with model double" should "be correct for float" in { + compare[Double]( + NodeDef.newBuilder() + .setName("exp_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .setOp("Exp"), + Seq(Tensor[Float](10).rand()), + 0 + ) + } + + "Exp with model double" should "be correct for double" in { + compare[Double]( NodeDef.newBuilder() .setName("exp_test") .putAttr("T", typeAttr(DataType.DT_DOUBLE)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala index 2cadd09b594..9d22bad71cf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala @@ -22,7 +22,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class Expm1Spec extends TensorflowSpecHelper { "Expm1" should "be correct for float" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("expm1_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -33,7 +33,7 @@ class Expm1Spec extends TensorflowSpecHelper { } "Expm1" should "be correct for double" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("expm1_test") .putAttr("T", typeAttr(DataType.DT_DOUBLE)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDivSpec.scala index 3519ff34494..0eb10eeb055 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDivSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDivSpec.scala @@ -23,7 +23,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class FloorDivSpec extends TensorflowSpecHelper { "FloorDiv" should "be correct for float" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("floorDiv_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -35,7 +35,7 @@ class FloorDivSpec extends TensorflowSpecHelper { } "FloorDiv" should "be correct for Int" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("floorDiv_test") .putAttr("T", typeAttr(DataType.DT_INT32)) @@ -47,7 +47,7 @@ class FloorDivSpec extends TensorflowSpecHelper { } "FloorDiv" should "be correct for double" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("floorDiv_test") .putAttr("T", typeAttr(DataType.DT_DOUBLE)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorModSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorModSpec.scala index d0eca923b89..a9dc10321de 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorModSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorModSpec.scala @@ -23,7 +23,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class FloorModSpec extends TensorflowSpecHelper { "FloorMod" should "be correct for Int" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("floor_mod_test") .putAttr("T", typeAttr(DataType.DT_INT32)) @@ -35,7 +35,7 @@ class FloorModSpec extends TensorflowSpecHelper { } "FloorMod" should "be correct for float" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("floor_mod_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala index c76cbeb4183..298c292bd5b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala @@ -29,7 +29,7 @@ class FusedBatchNormGradSpec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -49,7 +49,7 @@ class FusedBatchNormGradSpec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -69,7 +69,7 @@ class FusedBatchNormGradSpec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -89,7 +89,7 @@ class FusedBatchNormGradSpec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -109,7 +109,7 @@ class FusedBatchNormGradSpec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -129,7 +129,7 @@ class FusedBatchNormGradSpec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2Spec.scala index 733bd3f3ea2..3ae15fb46a2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2Spec.scala @@ -30,7 +30,7 @@ class FusedBatchNormGradV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -52,7 +52,7 @@ class FusedBatchNormGradV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -74,7 +74,7 @@ class FusedBatchNormGradV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -96,7 +96,7 @@ class FusedBatchNormGradV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -118,7 +118,7 @@ class FusedBatchNormGradV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -140,7 +140,7 @@ class FusedBatchNormGradV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormSpec.scala index 6157bebfc10..416671e086f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormSpec.scala @@ -29,7 +29,7 @@ class FusedBatchNormSpec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -49,7 +49,7 @@ class FusedBatchNormSpec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -69,7 +69,7 @@ class FusedBatchNormSpec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -89,7 +89,7 @@ class FusedBatchNormSpec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -109,7 +109,7 @@ class FusedBatchNormSpec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -129,7 +129,7 @@ class FusedBatchNormSpec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand().add(1f) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2Spec.scala index 1a96e2ffdc0..e21d4f6747c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2Spec.scala @@ -29,7 +29,7 @@ class FusedBatchNormV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -51,7 +51,7 @@ class FusedBatchNormV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -73,7 +73,7 @@ class FusedBatchNormV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -95,7 +95,7 @@ class FusedBatchNormV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -117,7 +117,7 @@ class FusedBatchNormV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](0) val variance = Tensor[Float](0) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -139,7 +139,7 @@ class FusedBatchNormV2Spec extends TensorflowSpecHelper { val mean = Tensor[Float](256).rand() val variance = Tensor[Float](256).rand().add(1f) - compare( + compare[Float]( NodeDef.newBuilder() .setName("fusedbn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopKSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopKSpec.scala index b66005dd6c7..337a895f3c2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopKSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopKSpec.scala @@ -23,7 +23,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class InTopKSpec extends TensorflowSpecHelper { "InTopK" should "be correct" in { - val (a, b) = getResult[Boolean]( + val (a, b) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("inv_grad_test") .putAttr("T", typeAttr(DataType.DT_INT32)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvGradSpec.scala index 7ad5636637e..f0ffad55c2d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvGradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvGradSpec.scala @@ -23,7 +23,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class InvGradSpec extends TensorflowSpecHelper { "Invgrad" should "be correct for float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("inv_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvSpec.scala index 0261affe517..2d023d88281 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvSpec.scala @@ -22,7 +22,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class InvSpec extends TensorflowSpecHelper { "Inv" should "be correct for float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("inv_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsFiniteSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsFiniteSpec.scala index a3b7446386c..28852185ad2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsFiniteSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsFiniteSpec.scala @@ -26,7 +26,7 @@ class IsFiniteSpec extends TensorflowSpecHelper { val t = Tensor[Float](4, 4).rand() t.setValue(2, 3, Float.NegativeInfinity) t.setValue(4, 4, Float.PositiveInfinity) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("isfinite_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsInfSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsInfSpec.scala index 7d9f9b623c5..dabbbabea1e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsInfSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsInfSpec.scala @@ -25,7 +25,7 @@ class IsInfSpec extends TensorflowSpecHelper { val t = Tensor[Float](4, 4).rand() t.setValue(2, 3, Float.NegativeInfinity) t.setValue(4, 4, Float.PositiveInfinity) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("isinf_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsNanSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsNanSpec.scala index 29dbb18a2f5..adfcb322107 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsNanSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsNanSpec.scala @@ -25,7 +25,7 @@ class IsNanSpec extends TensorflowSpecHelper { val t = Tensor[Float](4, 4).rand() t.setValue(2, 3, Float.NaN) t.setValue(4, 4, Float.NaN) - val (t1, t2) = getResult[Boolean]( + val (t1, t2) = getResult[Float, Boolean]( NodeDef.newBuilder() .setName("isnan_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala index b8a9c0a9031..6bcfb9ae239 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala @@ -28,7 +28,7 @@ class LRNGradSpec extends TensorflowSpecHelper { val input = Tensor[Float](4, 8, 8, 3).rand() val t = op.forward(input) val g = Tensor[Float](4, 8, 8, 3).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("lrn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -47,7 +47,7 @@ class LRNGradSpec extends TensorflowSpecHelper { val input = Tensor[Float](4, 8, 8, 3).rand() val t = op.forward(input) val g = Tensor[Float](4, 8, 8, 3).rand() - compare( + compare[Float]( NodeDef.newBuilder() .setName("lrn_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNSpec.scala index a88e5f3822f..c4fa8d43cd6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNSpec.scala @@ -22,7 +22,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class LRNSpec extends TensorflowSpecHelper { "LRN" should "be correct for float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("lrn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -38,7 +38,7 @@ class LRNSpec extends TensorflowSpecHelper { "LRN" should "be correct for float tensor2" in { val t = Tensor[Float](4, 8, 8, 3).fill(1f) - compare( + compare[Float]( NodeDef.newBuilder() .setName("lrn_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmaxSpec.scala index 7a50befd9d4..ac6e46fc77a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LogSoftmaxSpec.scala @@ -15,10 +15,22 @@ */ package com.intel.analytics.bigdl.utils.tf.loaders import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} -class LogSoftmaxSpec extends UnaryOpBaseSpec { - override def getOpName: String = "LogSoftmax" +class LogSoftmaxSpec extends TensorflowSpecHelper { - override def getInput: Tensor[_] = Tensor[Float](4, 32).rand() + + s"LogSoftmax forward with double model " should "be correct" in { + compare[Float]( + NodeDef.newBuilder() + .setName(s"LogSoftmaxTest") + .setOp(s"LogSoftmax") + .putAttr("T", typeAttr(DataType.DT_FLOAT)), + Seq(Tensor[Float](4, 10).rand()), + 0 + ) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ModSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ModSpec.scala index f2773d2f0a5..a59a7ada098 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ModSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ModSpec.scala @@ -24,7 +24,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class ModSpec extends TensorflowSpecHelper { "Mod" should "be correct for Int" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("mod_test") .putAttr("T", typeAttr(DataType.DT_INT32)) @@ -36,7 +36,7 @@ class ModSpec extends TensorflowSpecHelper { } "Mod" should "be correct for float" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("mod_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RangeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RangeSpec.scala index fcc0839ed1d..8c3ba48d1df 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RangeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RangeSpec.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.utils.tf.Tensorflow._ class RangeSpec extends TensorflowSpecHelper { "Range" should "be correct when input is int" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("range_test") .setOp("Range") @@ -33,7 +33,7 @@ class RangeSpec extends TensorflowSpecHelper { } "Range" should "be correct when input is float" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("range_test") .setOp("Range") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalGradSpec.scala index 525c23ef3bb..bd0a10dc00b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalGradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalGradSpec.scala @@ -22,7 +22,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class ReciprocalGradSpec extends TensorflowSpecHelper { "Reciprocal" should "be correct for float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("reciprocal_grad_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalSpec.scala index ca1f0e73dad..b199e972e20 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalSpec.scala @@ -22,7 +22,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class ReciprocalSpec extends TensorflowSpecHelper { "Reciprocal" should "be correct for float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("reciprocal_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RintSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RintSpec.scala index f6d2b081a22..c098b9f8e0c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RintSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RintSpec.scala @@ -23,7 +23,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class RintSpec extends TensorflowSpecHelper { "Rint" should "be correct" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("rint_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RoundSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RoundSpec.scala index 368f0baa567..6bf4b76b3d1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RoundSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RoundSpec.scala @@ -23,7 +23,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class RoundSpec extends TensorflowSpecHelper { "Round" should "be correct for float" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("round_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -35,7 +35,7 @@ class RoundSpec extends TensorflowSpecHelper { "Round" should "be correct for double" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("round_test") .putAttr("T", typeAttr(DataType.DT_DOUBLE)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SignSpec.scala index 93c40207ac8..0537b0f7dba 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SignSpec.scala @@ -29,7 +29,7 @@ class SignSpec extends TensorflowSpecHelper { t.setValue(2, 2, Float.PositiveInfinity) t.setValue(2, 3, Float.NegativeInfinity) t.setValue(4, 5, Float.NaN) - compare( + compare[Float]( NodeDef.newBuilder() .setName("sign_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqueezeSpec.scala index 9f2818b7407..749330c9d65 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqueezeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqueezeSpec.scala @@ -23,7 +23,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class SqueezeSpec extends TensorflowSpecHelper { s"Squeeze forward float" should "be correct" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("SqueezeTest") .setOp(s"Squeeze") @@ -33,7 +33,7 @@ class SqueezeSpec extends TensorflowSpecHelper { 0 ) - compare( + compare[Float]( NodeDef.newBuilder() .setName("SqueezeTest") .setOp(s"Squeeze") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKSpec.scala index 420b8fdc041..efbe63d0a9a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKSpec.scala @@ -22,7 +22,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class TopKSpec extends TensorflowSpecHelper { "TopK" should "be correct for float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -32,7 +32,7 @@ class TopKSpec extends TensorflowSpecHelper { 0 ) - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -44,7 +44,7 @@ class TopKSpec extends TensorflowSpecHelper { } "TopK" should "be correct for 1D float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -54,7 +54,7 @@ class TopKSpec extends TensorflowSpecHelper { 0 ) - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -66,7 +66,7 @@ class TopKSpec extends TensorflowSpecHelper { } "TopK" should "be correct for float tensor when sorted is false" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -77,7 +77,7 @@ class TopKSpec extends TensorflowSpecHelper { 0 ) - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala index 7f676e395e8..921823206b7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala @@ -22,7 +22,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class TopKV2Spec extends TensorflowSpecHelper { "TopKV2" should "be correct for float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -31,7 +31,7 @@ class TopKV2Spec extends TensorflowSpecHelper { 0 ) - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -42,7 +42,7 @@ class TopKV2Spec extends TensorflowSpecHelper { } "TopKV2" should "be correct for 1D float tensor" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -51,7 +51,7 @@ class TopKV2Spec extends TensorflowSpecHelper { 0 ) - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -62,7 +62,7 @@ class TopKV2Spec extends TensorflowSpecHelper { } "TopKV2" should "be correct for float tensor when sorted is false" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) @@ -72,7 +72,7 @@ class TopKV2Spec extends TensorflowSpecHelper { 0 ) - compare( + compare[Float]( NodeDef.newBuilder() .setName("topk_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDivSpec.scala index e9ad8d2557e..00968c6e484 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDivSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDivSpec.scala @@ -24,7 +24,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class TruncateDivSpec extends TensorflowSpecHelper { "TruncateDiv" should "be correct for int" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("truncateDiv_test") .putAttr("T", typeAttr(DataType.DT_INT32)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateModSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateModSpec.scala index 309f951559c..3040a6ec440 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateModSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateModSpec.scala @@ -23,7 +23,7 @@ import org.tensorflow.framework.{DataType, NodeDef} class TruncateModSpec extends TensorflowSpecHelper { "TruncateMod" should "be correct for Int" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("trunc_mod_test") .putAttr("T", typeAttr(DataType.DT_INT32)) @@ -35,7 +35,7 @@ class TruncateModSpec extends TensorflowSpecHelper { } "TruncateMod" should "be correct for float" in { - compare( + compare[Float]( NodeDef.newBuilder() .setName("trunc_mod_test") .putAttr("T", typeAttr(DataType.DT_FLOAT)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala index 871616b47d3..999b4099052 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala @@ -26,8 +26,19 @@ abstract class UnaryOpBaseSpec extends TensorflowSpecHelper { def getInput: Tensor[_] - s"$getOpName forward float" should "be correct" in { - compare( + s"$getOpName forward with float model" should "be correct" in { + compare[Float]( + NodeDef.newBuilder() + .setName(s"${getOpName}Test") + .setOp(s"$getOpName") + .putAttr("T", typeAttr(DataType.DT_FLOAT)), + Seq(getInput), + 0 + ) + } + + s"$getOpName forward with double model " should "be correct" in { + compare[Double]( NodeDef.newBuilder() .setName(s"${getOpName}Test") .setOp(s"$getOpName") From f5e8e3fe40c8ca32c89a7f4f40b858131e866f8d Mon Sep 17 00:00:00 2001 From: dding3 Date: Thu, 7 Dec 2017 15:15:46 -0500 Subject: [PATCH 0565/1065] Add multiple rnn cell and support get/setHiddenState in Recurrent(#1591) * add multicell * add get/setHiddenStates --- .../bigdl/dllib/nn/LSTMPeephole.scala | 1 - .../bigdl/dllib/nn/MultiRNNCell.scala | 179 +++++ .../analytics/bigdl/dllib/nn/Recurrent.scala | 3 + .../bigdl/dllib/nn/RecurrentDecoder.scala | 12 +- .../dllib/utils/python/api/PythonBigDL.scala | 13 +- .../bigdl/dllib/nn/MultiRNNCellSpec.scala | 637 ++++++++++++++++++ .../bigdl/dllib/nn/RecurrentDecoderSpec.scala | 2 +- .../bigdl/dllib/torch/LSTMPeepholeSpec.scala | 2 - 8 files changed, 831 insertions(+), 18 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCellSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala index 42eef3897c5..c7d722b2916 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeephole.scala @@ -144,7 +144,6 @@ class LSTMPeephole[T : ClassTag] ( /** * f(input1 + W * input2) */ - var i2h: ModuleNode[T] = null var h2h: ModuleNode[T] = null if (p != 0) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala new file mode 100644 index 00000000000..8ef8dd7055f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala @@ -0,0 +1,179 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +/** + * Enable user stack multiple simple cells. + */ +class MultiRNNCell[T : ClassTag](val cells: Array[Cell[T]])(implicit ev: TensorNumeric[T]) + extends Cell[T]( + hiddensShape = cells.last.hiddensShape, + regularizers = cells.flatMap(_.regularizers)) { + // inputDim and hidDim must be the same with Recurrent + private val inputDim = Recurrent.inputDim + private val hidDim = Recurrent.hidDim + + override var preTopology: TensorModule[T] = null + + override var cell: AbstractModule[Activity, Activity, T] = buildModel() + + override def hidResize(hidden: Activity, batchSize: Int, stepShape: Array[Int]): Activity = { + if (hidden == null) { + hidResize(T(), batchSize, stepShape) + } else { + var i = 0 + while (i < cells.size) { + hidden.toTable.insert(cells(i).hidResize(null, batchSize, stepShape)) + i += 1 + } + hidden + } + } + + def buildModel(): Sequential[T] = { + val seq = Sequential() + cells.foreach{ cell => + if (cell.preTopology != null) { + cell.includePreTopology = true + } + seq.add(cell) + } + seq + } + + override def updateOutput(input: Table): Table = { + val result = T() + result(inputDim) = input(inputDim) + // states and outputStates is 1 based + val states = input(hidDim).asInstanceOf[Table] + val outputStates = T() + + var i = 0 + while (i < cells.length) { + result(hidDim) = states(i + 1) + cells(i).forward(result).toTable + result(inputDim) = cells(i).output.toTable(inputDim) + outputStates.insert(cells(i).output.toTable(hidDim)) + i += 1 + } + + result(hidDim) = outputStates + this.output = result + output + } + + override def updateGradInput(input: Table, gradOutput: Table): Table = { + var i = cells.length - 1 + var error = T() + error(inputDim) = gradOutput(inputDim) + val states = input(hidDim).asInstanceOf[Table] + val gradStates = gradOutput(hidDim).asInstanceOf[Table] + val outputGradStates = T() + + val nextInput = T() + while (i >= 0) { + val input0: Tensor[T] = if (i > 0) { + cells(i - 1).output.toTable(inputDim) + } else input(inputDim) + nextInput(inputDim) = input0 + + nextInput(hidDim) = states(i + 1) + error(hidDim) = gradStates(i + 1) + error = cells(i).updateGradInput(nextInput, error) + outputGradStates(i + 1) = error(hidDim) + i -= 1 + } + + this.gradInput = error + gradInput(hidDim) = outputGradStates + gradInput + } + + override def accGradParameters(input: Table, gradOutput: Table): Unit = { + var i = cells.length - 1 + val error = T() + error(inputDim) = gradOutput(inputDim) + val states = input(hidDim).asInstanceOf[Table] + val gradStates = gradOutput(hidDim).asInstanceOf[Table] + + val nextInput = T() + while (i >= 0) { + val input0: Tensor[T] = if (i > 0) { + cells(i - 1).output.toTable(inputDim) + } else input(inputDim) + nextInput(inputDim) = input0 + + nextInput(hidDim) = states(i + 1) + error(hidDim) = gradStates(i + 1) + cells(i).accGradParameters(nextInput, error) + error(inputDim) = cells(i).gradInput.toTable(inputDim) + i -= 1 + } + } + + override def backward(input: Table, gradOutput: Table): Table = { + var i = cells.length - 1 + var error = T() + error(inputDim) = gradOutput(inputDim) + val states = input(hidDim).asInstanceOf[Table] + val gradStates = gradOutput(hidDim).asInstanceOf[Table] + val outputGradStates = T() + + val nextInput = T() + while (i >= 0) { + val input0: Tensor[T] = if (i > 0) { + cells(i - 1).output.toTable(inputDim) + } else input(inputDim) + nextInput(inputDim) = input0 + + nextInput(hidDim) = states(i + 1) + error(hidDim) = gradStates(i + 1) + error = cells(i).backward(nextInput, error) + outputGradStates(i + 1) = error(hidDim) + i -= 1 + } + + this.gradInput = error + gradInput(hidDim) = outputGradStates + gradInput + } + + override def zeroGradParameters(): Unit = { + cells.foreach(_.zeroGradParameters()) + } + + override def reset(): Unit = { + cells.foreach(_.reset()) + } +} + +object MultiRNNCell { + def apply[@specialized(Float, Double) T: ClassTag](cells: Array[Cell[T]] + )(implicit ev: TensorNumeric[T]): MultiRNNCell[T] = { + new MultiRNNCell[T](cells) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index 2472ab86656..e6d5990d5f1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -74,6 +74,9 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Recurrent.this.type = { require(module.isInstanceOf[Cell[T]], "Recurrent: added module should be Cell type!") + require(!module.isInstanceOf[MultiRNNCell[T]], + "Recurrent: added module cannot be MultiRNNCell," + + "use Sequential().add(Recurrent(cell)).add(Recurrent(cell))... instead!") topology = module.asInstanceOf[Cell[T]] preTopology = if (topology.preTopology != null) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala index 52933f5a6dd..8b77b60792d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala @@ -46,8 +46,8 @@ class RecurrentDecoder[T : ClassTag](val seqLength: Int) /** * - * modules: -- preTopology - * |- topology (cell) + * modules: -- preTopology + * |- topology (cell) * * The topology (or cell) will be cloned for N times w.r.t the time dimension. * The preTopology will be execute only once before the recurrence. @@ -56,9 +56,8 @@ class RecurrentDecoder[T : ClassTag](val seqLength: Int) * @return this container */ override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): - RecurrentDecoder.this.type = { - require(module.isInstanceOf[Cell[T]], - "Recurrent: contained module should be Cell type") + RecurrentDecoder.this.type = { + require(module.isInstanceOf[Cell[T]], "Recurrent: contained module should be Cell type") topology = module.asInstanceOf[Cell[T]] preTopology = topology.preTopology @@ -89,6 +88,7 @@ class RecurrentDecoder[T : ClassTag](val seqLength: Int) output.resize(Array(batchSize, times) ++ featureSizes) // Clone N modules along the sequence dimension. initHidden(featureSizes) + cloneCells() /** * currentInput forms a T() type. It contains two elements, hidden and input. @@ -97,8 +97,6 @@ class RecurrentDecoder[T : ClassTag](val seqLength: Int) * identical elements T(output, output). One of the elements from the cell output is * the updated hidden. Thus the currentInput will update its hidden element with this output. */ - // Clone N modules along the sequence dimension. - cloneCells() var i = 1 while (i <= times) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 6f0c664d220..0ff8c19d08d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2146,13 +2146,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab layer.setInitMethod(weightInitMethod, biasInitMethod) } - def getHiddenStates(rec: Recurrent[T]): JList[JTensor] = { - val states = rec.getHiddenState() - activityToJTensors(states) - } - - def setHiddenStates(rec: Recurrent[T], hiddenStates: JList[JTensor], isTable: Boolean): Unit = { - rec.setHiddenState(jTensorsToActivity(hiddenStates, isTable)) + def getHiddenState(rec: Recurrent[T]): JActivity = { + JActivity(rec.getHiddenState()) } def freeze(model: AbstractModule[Activity, Activity, T], freezeLayers: JList[String]) @@ -2187,6 +2182,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab alignCorner) } + def createMultiRNNCell(cells: JList[Cell[T]]): MultiRNNCell[T] = { + MultiRNNCell(cells.asScala.toArray) + } + def createHighway(size: Int, withBias: Boolean, activation: String, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): Graph[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCellSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCellSpec.scala new file mode 100644 index 00000000000..cc15aa558bd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCellSpec.scala @@ -0,0 +1,637 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.TorchObject.TYPE_DOUBLE_TENSOR +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.math._ + +@com.intel.analytics.bigdl.tags.Parallel +class MultiRNNCellSpec extends FlatSpec with BeforeAndAfter with Matchers { + + "A MultiRNNCell " should "work in BatchMode" in { + val hiddenSize = 5 + val inputSize = 5 + val seqLength = 4 + val batchSize = 2 + val kernalW = 3 + val kernalH = 3 + val rec = RecurrentDecoder[Double](seqLength) + val cells = Array(ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1), ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1), ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1)).asInstanceOf[Array[Cell[Double]]] + + val model = Sequential[Double]() + .add(rec + .add(MultiRNNCell[Double](cells))) + + val input = Tensor[Double](batchSize, inputSize, 10, 10).rand + val output = model.forward(input).toTensor[Double] + for (i <- 1 to 3) { + val output = model.forward(input) + model.backward(input, output) + } + } + +// "A MultiRNNCell " should "generate correct output with convlstm" in { +// val hiddenSize = 7 +// val inputSize = 7 +// val seqLength = 3 +// val batchSize = 2 +// val kernalW = 3 +// val kernalH = 3 +// val rec = Recurrent[Double]() +// val cells = Array(ConvLSTMPeephole[Double]( +// inputSize, +// hiddenSize, +// kernalW, kernalH, +// 1), ConvLSTMPeephole[Double]( +// inputSize, +// hiddenSize, +// kernalW, kernalH, +// 1)).asInstanceOf[Array[Cell[Double]]] +// +// val model = Sequential[Double]() +// .add(rec +// .add(MultiRNNCell[Double](cells))) +// val weights = model.getParameters()._1.clone() +// +// val input = Tensor[Double](batchSize, seqLength, inputSize, 3, 3).rand +// val gradOutput = Tensor[Double](batchSize, seqLength, inputSize, 3, 3).rand +// val output = model.forward(input).toTensor[Double] +// val gradInput = model.backward(input, gradOutput).toTensor[Double] +// +// val model2 = Sequential[Double]() +// .add(Recurrent[Double]().add(ConvLSTMPeephole[Double]( +// inputSize, +// hiddenSize, +// kernalW, kernalH, +// 1))) +// .add(Recurrent[Double]().add(ConvLSTMPeephole[Double]( +// inputSize, +// hiddenSize, +// kernalW, kernalH, +// 1))) +// model2.getParameters()._1.copy(weights) +// +// val output2 = model2.forward(input).toTensor[Double] +// val gradInput2 = model2.backward(input, gradOutput).toTensor[Double] +// +// output.map(output2, (v1, v2) => { +// assert(abs(v1 - v2) < 1e-6) +// v1 +// }) +// +// gradInput.map(gradInput2, (v1, v2) => { +// assert(abs(v1 - v2) < 1e-6) +// v1 +// }) +// } + +// "A MultiCell " should "generate correct output with lstm" in { +// val hiddenSize = 10 +// val inputSize = 10 +// val seqLength = 5 +// val batchSize = 2 +// val rec = Recurrent[Double]() +// val cells = Array(LSTM[Double]( +// inputSize, +// hiddenSize), +// LSTM[Double]( +// inputSize, +// hiddenSize)).asInstanceOf[Array[Cell[Double]]] +// +// val model = Sequential[Double]() +// .add(rec +// .add(MultiRNNCell[Double](cells))) +// val weights = model.getParameters()._1.clone() +// +// val input = Tensor[Double](batchSize, seqLength, inputSize).rand +// val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand +// val output = model.forward(input).toTensor[Double] +// val gradInput = model.backward(input, gradOutput).toTensor[Double] +// val gradient = model.getParameters()._2 +// +// val model2 = Sequential[Double]() +// .add(Recurrent[Double]().add(LSTM[Double]( +// inputSize, +// hiddenSize))) +// .add(Recurrent[Double]().add(LSTM[Double]( +// inputSize, +// hiddenSize))) +// model2.getParameters()._1.copy(weights) +// +// val output2 = model2.forward(input).toTensor[Double] +// val gradInput2 = model2.backward(input, gradOutput).toTensor[Double] +// val gradient2 = model2.getParameters()._2 +// +// output.map(output2, (v1, v2) => { +// assert(abs(v1 - v2) < 1e-6) +// v1 +// }) +// +// gradInput.map(gradInput2, (v1, v2) => { +// assert(abs(v1 - v2) < 1e-6) +// v1 +// }) +// +// require(gradient.almostEqual(gradient2, 1e-8) == true) +// } + + "A MultiRNNCell " should "generate correct output with convlstm RecurrentDecoder" in { + val hiddenSize = 7 + val inputSize = 7 + val seqLength = 3 + val batchSize = 2 + val kernalW = 3 + val kernalH = 3 + val rec = RecurrentDecoder[Double](seqLength) + val cells = Array(ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1), ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1)).asInstanceOf[Array[Cell[Double]]] + + val model = Sequential[Double]() + .add(rec + .add(MultiRNNCell[Double](cells))) + val weights = model.getParameters()._1.clone() + + val input = Tensor[Double](batchSize, inputSize, 3, 3).rand + val gradOutput = Tensor[Double](batchSize, seqLength, inputSize, 3, 3).rand + val output = model.forward(input).toTensor[Double] + val gradInput = model.backward(input, gradOutput).toTensor[Double] + + val input2 = Tensor[Double](Array(batchSize, seqLength, inputSize, 3, 3)) + input2.narrow(2, 1, 1).copy(input) + input2.narrow(2, 2, seqLength-1).copy(output.narrow(2, 1, seqLength-1)) + val model2 = Sequential[Double]() + .add(Recurrent[Double]().add(ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1))) + .add(Recurrent[Double]().add(ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1))) + model2.getParameters()._1.copy(weights) + + val output2 = model2.forward(input2).toTensor[Double] + + output.map(output2, (v1, v2) => { + assert(abs(v1 - v2) < 1e-6) + v1 + }) + } + + "A MultiRNCell backward" should "work with ConvLSTMPeepwhole RecurrentDecoder" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 3 + val inputSize = 3 + val seqLength = 2 + val seed = 100 + val kernalW = 3 + val kernalH = 3 + val batchSize = 2 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize, 3, 3).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3).rand + val rec = RecurrentDecoder(seqLength) + val cells = Array(ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1), ConvLSTMPeephole[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1)).asInstanceOf[Array[Cell[Double]]] + val model = rec + .add(MultiRNNCell(cells)) + + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + val output = model.forward(input).toTensor + val gradInput = model.backward(input, gradOutput).toTensor + val gradient = model.getParameters()._2.clone() + + val input2 = input.clone() + input2.resize(batchSize, 1, inputSize, 3, 3) + val model2 = ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1) + model2.getParameters()._1.copy(weights.narrow(1, 1, weights.nElement()/2)) + model2.zeroGradParameters() + val model4 = ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1) + model4.getParameters()._1 + .copy(weights.narrow(1, weights.nElement()/2 + 1, weights.nElement()/2)) + model4.zeroGradParameters() + + val model3 = ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1) + var i = 0 + while (i < model3.parameters()._1.length) { + model3.parameters()._1(i).set(model2.parameters()._1(i)) + i += 1 + } + i = 0 + while (i < model3.parameters()._2.length) { + model3.parameters()._2(i).set(model2.parameters()._2(i)) + i += 1 + } + + val model5 = ConvLSTMPeephole(inputSize, hiddenSize, 3, 3, 1) + i = 0 + while (i < model5.parameters()._1.length) { + model5.parameters()._1(i).set(model4.parameters()._1(i)) + i += 1 + } + i = 0 + while (i < model5.parameters()._2.length) { + model5.parameters()._2(i).set(model4.parameters()._2(i)) + i += 1 + } + + val state = T(Tensor[Double](batchSize, hiddenSize, 3, 3), + Tensor[Double](batchSize, hiddenSize, 3, 3)) + val state2 = T(Tensor[Double](batchSize, hiddenSize, 3, 3), + Tensor[Double](batchSize, hiddenSize, 3, 3)) + val output2 = model2.forward(T(input, state)) + val output4 = model4.forward(T(output2(1), state2)) + + val input3 = T() + input3(1) = output4(1) + input3(2) = output2(2) + val output3 = model3.forward(input3) + val input5 = T() + input5(1) = output3(1) + input5(2) = output4(2) + val output5 = model5.forward(input5) + + val gradState = T(Tensor[Double](batchSize, hiddenSize, 3, 3), + Tensor[Double](batchSize, hiddenSize, 3, 3)) + val gradState2 = T(Tensor[Double](batchSize, hiddenSize, 3, 3), + Tensor[Double](batchSize, hiddenSize, 3, 3)) + val gradOutput5 = gradOutput.select(2, 2) + val gradInput5 = model5.backward(input5, T(gradOutput5, gradState)) + + val gradInput3 = model3.backward(input3, T(gradInput5(1), gradState2)) + val tmp_gradInput = gradInput3.clone + tmp_gradInput(1) = gradOutput.select(2, 1).add(gradInput3.toTable[Tensor[Double]](1)) + tmp_gradInput(2) = gradInput5(2) + val gradInput4 = model4.backward(T(output2(1), state2), tmp_gradInput) + val gradOutput2 = T() + gradOutput2(1) = gradInput4(1) + gradOutput2(2) = gradInput3(2) + val gradInput2 = model2.backward(T(input, state), gradOutput2) + + val finalOutput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3) + finalOutput.narrow(2, 1, 1).copy(output4.toTable[Tensor[Double]](1)) + finalOutput.narrow(2, 2, 1).copy(output5.toTable[Tensor[Double]](1)) + require(output.almostEqual(finalOutput, 1e-8) == true) + + require(gradient.narrow(1, 1, gradient.nElement()/2) + .almostEqual(model2.getParameters()._2, 1e-8) == true) + require(gradient.narrow(1, gradient.nElement()/2 + 1, gradient.nElement()/2) + .almostEqual(model4.getParameters()._2, 1e-8) == true) + + val newGradInput = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3) + newGradInput.narrow(2, 1, 1).copy(gradInput2.toTable[Tensor[Double]](1)) + newGradInput.narrow(2, 2, 1).copy(gradInput3.toTable[Tensor[Double]](1)) + require(gradInput.almostEqual(newGradInput, 1e-8) == true) + } + + "A MultiRNNCell " should "generate correct output with lstm RecurrentDecoder" in { + val hiddenSize = 7 + val inputSize = 7 + val seqLength = 3 + val batchSize = 2 + val rec = RecurrentDecoder[Double](seqLength) + val cells = Array(LSTM[Double]( + inputSize, + hiddenSize), LSTM[Double]( + inputSize, + hiddenSize)).asInstanceOf[Array[Cell[Double]]] + + val model = Sequential[Double]() + .add(rec + .add(MultiRNNCell[Double](cells))) + val weights = model.getParameters()._1.clone() + + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, inputSize).rand + val output = model.forward(input).toTensor[Double] + val gradInput = model.backward(input, gradOutput).toTensor[Double] + + val input2 = Tensor[Double](Array(batchSize, seqLength, inputSize)) + input2.narrow(2, 1, 1).copy(input) + input2.narrow(2, 2, seqLength-1).copy(output.narrow(2, 1, seqLength-1)) + val model2 = Sequential[Double]() + .add(Recurrent[Double]().add(LSTM[Double]( + inputSize, + hiddenSize))) + .add(Recurrent[Double]().add(LSTM[Double]( + inputSize, + hiddenSize))) + model2.getParameters()._1.copy(weights) + + val output2 = model2.forward(input2).toTensor[Double] + + output.map(output2, (v1, v2) => { + assert(abs(v1 - v2) < 1e-6) + v1 + }) + } + + "A MultiRNCell backward" should "work with lstm RecurrentDecoder" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 3 + val inputSize = 3 + val seqLength = 2 + val seed = 100 + val batchSize = 2 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand + val rec = RecurrentDecoder(seqLength) + val cells = Array(LSTM[Double]( + inputSize, + hiddenSize), LSTM[Double]( + inputSize, + hiddenSize)).asInstanceOf[Array[Cell[Double]]] + val model = rec + .add(MultiRNNCell(cells)) + + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + val output = model.forward(input).toTensor + val gradInput = model.backward(input, gradOutput).toTensor + val gradient = model.getParameters()._2.clone() + + val input2 = input.clone() + input2.resize(batchSize, 1, inputSize) + val model2 = LSTM(inputSize, hiddenSize) + model2.includePreTopology = true + model2.getParameters()._1.copy(weights.narrow(1, 1, weights.nElement()/2)) + model2.zeroGradParameters() + val model4 = LSTM(inputSize, hiddenSize) + model4.includePreTopology = true + model4.getParameters()._1 + .copy(weights.narrow(1, weights.nElement()/2 + 1, weights.nElement()/2)) + model4.zeroGradParameters() + + val model3 = LSTM(inputSize, hiddenSize) + model3.includePreTopology = true + var i = 0 + while (i < model3.parameters()._1.length) { + model3.parameters()._1(i).set(model2.parameters()._1(i)) + i += 1 + } + i = 0 + while (i < model3.parameters()._2.length) { + model3.parameters()._2(i).set(model2.parameters()._2(i)) + i += 1 + } + + val model5 = LSTM(inputSize, hiddenSize) + model5.includePreTopology = true + i = 0 + while (i < model5.parameters()._1.length) { + model5.parameters()._1(i).set(model4.parameters()._1(i)) + i += 1 + } + i = 0 + while (i < model5.parameters()._2.length) { + model5.parameters()._2(i).set(model4.parameters()._2(i)) + i += 1 + } + + val state = T(Tensor[Double](batchSize, hiddenSize), + Tensor[Double](batchSize, hiddenSize)) + val state2 = T(Tensor[Double](batchSize, hiddenSize), + Tensor[Double](batchSize, hiddenSize)) + val output2 = model2.forward(T(input, state)) + val output4 = model4.forward(T(output2(1), state2)) + + val input3 = T() + input3(1) = output4(1) + input3(2) = output2(2) + val output3 = model3.forward(input3) + val input5 = T() + input5(1) = output3(1) + input5(2) = output4(2) + val output5 = model5.forward(input5) + + val gradState = T(Tensor[Double](batchSize, hiddenSize), + Tensor[Double](batchSize, hiddenSize)) + val gradState2 = T(Tensor[Double](batchSize, hiddenSize), + Tensor[Double](batchSize, hiddenSize)) + val gradOutput5 = gradOutput.select(2, 2) + val gradInput5 = model5.backward(input5, T(gradOutput5, gradState)) + + val gradInput3 = model3.backward(input3, T(gradInput5(1), gradState2)) + val tmp_gradInput = gradInput3.clone + tmp_gradInput(1) = gradOutput.select(2, 1).add(gradInput3.toTable[Tensor[Double]](1)) + tmp_gradInput(2) = gradInput5(2) + val gradInput4 = model4.backward(T(output2(1), state2), tmp_gradInput) + val gradOutput2 = T() + gradOutput2(1) = gradInput4(1) + gradOutput2(2) = gradInput3(2) + val gradInput2 = model2.backward(T(input, state), gradOutput2) + + val finalOutput = Tensor[Double](batchSize, seqLength, hiddenSize) + finalOutput.narrow(2, 1, 1).copy(output4.toTable[Tensor[Double]](1)) + finalOutput.narrow(2, 2, 1).copy(output5.toTable[Tensor[Double]](1)) + require(output.almostEqual(finalOutput, 1e-8) == true) + + require(gradient.narrow(1, 1, gradient.nElement()/2) + .almostEqual(model2.getParameters()._2, 1e-8) == true) + require(gradient.narrow(1, gradient.nElement()/2 + 1, gradient.nElement()/2) + .almostEqual(model4.getParameters()._2, 1e-8) == true) + + val newGradInput = Tensor[Double](batchSize, seqLength, hiddenSize) + newGradInput.narrow(2, 1, 1).copy(gradInput2.toTable[Tensor[Double]](1)) + newGradInput.narrow(2, 2, 1).copy(gradInput3.toTable[Tensor[Double]](1)) + require(gradInput.almostEqual(newGradInput, 1e-8) == true) + } + + "A MultiRNCell updateGradInput/acc" should "work with lstm RecurrentDecoder" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 3 + val inputSize = 3 + val seqLength = 2 + val seed = 100 + val batchSize = 2 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand + val rec = RecurrentDecoder(seqLength) + val cells = Array(LSTM[Double]( + inputSize, + hiddenSize), LSTM[Double]( + inputSize, + hiddenSize)).asInstanceOf[Array[Cell[Double]]] + val model = rec + .add(MultiRNNCell(cells)) + + val rec2 = RecurrentDecoder(seqLength) + val cells2 = Array(LSTM[Double]( + inputSize, + hiddenSize), LSTM[Double]( + inputSize, + hiddenSize)).asInstanceOf[Array[Cell[Double]]] + val model2 = rec2 + .add(MultiRNNCell(cells2)) + + val weights = model.getParameters()._1.clone() + model.zeroGradParameters() + model2.getParameters()._1.copy(weights) + model2.zeroGradParameters() + + val output = model.forward(input).toTensor + val gradInput = model.backward(input, gradOutput).toTensor + val gradient = model.getParameters()._2 + + val output2 = model2.forward(input).toTensor + val gradInput2 = model2.updateGradInput(input, gradOutput).toTensor + model2.accGradParameters(input, gradOutput) + val gradient2 = model2.getParameters()._2 + + require(output.almostEqual(output2, 1e-8) == true) + require(gradient.almostEqual(gradient2, 1e-8) == true) + require(gradInput.almostEqual(gradInput2, 1e-8) == true) + } + + "A MultiRNNCell " should "work with set/getHiddenState" in { + val hiddenSize = 7 + val inputSize = 7 + val seqLength = 3 + val batchSize = 2 + val rec = RecurrentDecoder[Double](seqLength) + val cells = Array(LSTM[Double]( + inputSize, + hiddenSize), LSTM[Double]( + inputSize, + hiddenSize)).asInstanceOf[Array[Cell[Double]]] + + val model = Sequential[Double]() + .add(rec + .add(MultiRNNCell[Double](cells))) + val weights = model.getParameters()._1.clone() + + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, inputSize).rand + val map0 = mutable.HashMap[Any, Any]() + val hidden0 = Tensor[Double](batchSize, hiddenSize).rand + val state0 = T(hidden0, + Tensor[Double](batchSize, hiddenSize).rand) + map0.put(1, state0) + val map1 = mutable.HashMap[Any, Any]() + val state1 = T(Tensor[Double](batchSize, hiddenSize).rand, + Tensor[Double](batchSize, hiddenSize).rand) + map1.put(1, state1) + val map = mutable.HashMap[Any, Any]() + map.put(1, state0) + map.put(2, state1) + val initStates = new Table(map) + val initStates_0 = new Table(map0) + val initStates_1 = new Table(map1) + rec.setHiddenState(initStates) + val output = model.forward(input).toTensor[Double] + val gradInput = model.backward(input, gradOutput).toTensor[Double] + + val input2 = Tensor[Double](Array(batchSize, seqLength, inputSize)) + input2.narrow(2, 1, 1).copy(input) + input2.narrow(2, 2, seqLength-1).copy(output.narrow(2, 1, seqLength-1)) + val rec0 = Recurrent[Double]().add(LSTM[Double]( + inputSize, + hiddenSize)) + rec0.setHiddenState(initStates_0) + val rec1 = Recurrent[Double]().add(LSTM[Double]( + inputSize, + hiddenSize)) + rec1.setHiddenState(initStates_1) + val model2 = Sequential[Double]() + .add(rec0) + .add(rec1) + model2.getParameters()._1.copy(weights) + + val output2 = model2.forward(input2).toTensor[Double] + + output.map(output2, (v1, v2) => { + assert(abs(v1 - v2) < 1e-6) + v1 + }) + + val state_decoder0 = rec.getHiddenState().toTable[Table](1).getState() + val state_decoder1 = rec.getHiddenState().toTable[Table](2).getState() + val stateGet0 = rec0.getHiddenState().toTable.getState() + val stateGet1 = rec1.getHiddenState().toTable.getState() + for (k <- state_decoder0.keys) { + val t1 = state_decoder0(k).asInstanceOf[Tensor[Double]] + val t2 = stateGet0(k).asInstanceOf[Tensor[Double]] + t1.map(t2, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + } + + for (k <- state_decoder1.keys) { + val t1 = state_decoder1(k).asInstanceOf[Tensor[Double]] + val t2 = stateGet1(k).asInstanceOf[Tensor[Double]] + t1.map(t2, (v1, v2) => { + assert(abs(v1 - v2) <= 1e-8) + v1 + }) + } + + // init states shoule remain unchanged + initStates.get(1).get.asInstanceOf[Table].get(1).get + .asInstanceOf[Tensor[Double]].map(hidden0, (v1, v2) => { + assert(v1 == v2) + v1 + }) + + rec.setHiddenState(rec.getHiddenState()) + model.forward(input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala index e04d4ad6fdf..28a8db02948 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala @@ -314,7 +314,7 @@ class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { require(gradient.almostEqual(gradient2, 1e-8) == true) } - "A ConvLSTMPeepwhole " should "work with RecurrentDecoder get/setStates" in { + "A ConvLSTMPeepwhole " should "work with RecurrentDecoder get/setHiddenStates" in { import com.intel.analytics.bigdl.numeric.NumericDouble val hiddenSize = 3 val inputSize = 3 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala index fd0fe801b02..5e32e9742c7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala @@ -707,9 +707,7 @@ class LSTMPeepholeSpec extends TorchSpec { Tensor[Double](batchSize, hiddenSize).rand) val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand val rec = Recurrent() - rec.setHiddenState(state) - val model = Sequential() .add(rec .add(LSTMPeephole(inputSize, hiddenSize))) From 02e06ed4cdfe96cba2e2175ff412b3a25648092d Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Fri, 8 Dec 2017 14:12:41 +0800 Subject: [PATCH 0566/1065] Fix sample table-label for tutorial nightly build (#1991) * fix * add feature=features[0] * update docs --- .../dllib/utils/python/api/BigDLSerde.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 18 +++++++++--------- .../bigdl/dllib/python/api/PythonSpec.scala | 8 ++++---- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala index 6c50bc9fa7b..4ee380e35b9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/BigDLSerde.scala @@ -211,7 +211,7 @@ object BigDLSerDe extends BigDLSerDeBase with Serializable { saveObjects(out, pickler, record.features, - record.label, + record.labels, record.bigdlType) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0ff8c19d08d..aade39c5d34 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -60,11 +60,11 @@ import scala.reflect.ClassTag /** * [[com.intel.analytics.bigdl.dataset.Sample]] for python. * @param features features - * @param label labels + * @param labels labels * @param bigdlType bigdl numeric type */ case class Sample(features: JList[JTensor], - label: JList[JTensor], + labels: JList[JTensor], bigdlType: String) case class JTensor(storage: Array[Float], shape: Array[Int], @@ -127,9 +127,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab val cls = implicitly[ClassTag[T]].runtimeClass val features = new JArrayList[JTensor]() features.add(toJTensor(sample.feature())) - val label = new JArrayList[JTensor]() - label.add(toJTensor(sample.label())) - Sample(features, label, cls.getSimpleName) + val labels = new JArrayList[JTensor]() + labels.add(toJTensor(sample.label())) + Sample(features, labels, cls.getSimpleName) } def toTensor(jTensor: JTensor): Tensor[T] = { @@ -211,7 +211,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab require(record.bigdlType == this.typeName, s"record.bigdlType: ${record.bigdlType} == this.typeName: ${this.typeName}") JSample[T](record.features.asScala.toArray.map(toTensor(_)), - record.label.asScala.toArray.map(toTensor(_))) + record.labels.asScala.toArray.map(toTensor(_))) } def toJSample(psamples: RDD[Sample]): RDD[JSample[T]] = { @@ -2538,9 +2538,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab val imInfo = imageFeature.getImInfo() features.add(toJTensor(imInfo.asInstanceOf[Tensor[T]])) } - val label = new util.ArrayList[JTensor]() - label.add(imageFeatureToLabelTensor(imageFeature)) - Sample(features, label, "float") + val labels = new util.ArrayList[JTensor]() + labels.add(imageFeatureToLabelTensor(imageFeature)) + Sample(features, labels, "float") } def imageFeatureGetKeys(imageFeature: ImageFeature): JList[String] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index 5f817fbcc1f..b8daf5db0bb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -169,14 +169,14 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { val labelShape = util.Arrays.asList(1) val data = sc.parallelize(0 to 100).map {i => - val l = JTensor(Array(i % 2 + 1.0f), Array(1), "double") + val label = JTensor(Array(i % 2 + 1.0f), Array(1), "double") val feature = JTensor(Range(0, 100).map(_ => Random.nextFloat()).toArray, Array(100), "double") val features = new JArrayList[JTensor]() features.add(feature) - val label = new JArrayList[JTensor]() - label.add(l) - Sample(features, label, "double") + val labels = new JArrayList[JTensor]() + labels.add(label) + Sample(features, labels, "double") } BigDLSerDe.javaToPython(data.toJavaRDD().asInstanceOf[JavaRDD[Any]]) From f72d0fdb61b3d1a0888ad4161a75359a0878d2ee Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Fri, 8 Dec 2017 18:13:02 +0800 Subject: [PATCH 0567/1065] Map newly added keras layers on python side (#1990) * update * add maxoutdense * add masking * add srelu * highway activation as string; update docs * code clean --- .../com/intel/analytics/bigdl/dllib/nn/Highway.scala | 12 +++--------- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 3 ++- .../intel/analytics/bigdl/dllib/nn/HighwaySpec.scala | 6 +++--- 3 files changed, 8 insertions(+), 13 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Highway.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Highway.scala index 11fcb1d65c3..63a21135b56 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Highway.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Highway.scala @@ -25,26 +25,20 @@ import scala.reflect.ClassTag object Highway { - private def getAct[@specialized(Float, Double) T: ClassTag](name: String) - (implicit ev: TensorNumeric[T]): TensorModule[T] = name match { - case "tanh" => Tanh[T]() - case _ => null - } - /** * Densely connected highway network. * Highway layers are a natural extension of LSTMs to feedforward networks. * * @param size input size * @param withBias whether to include a bias - * @param activation name of activation function to use + * @param activation activation function * @param wRegularizer: instance of [[Regularizer]] * (eg. L1 or L2 regularization), applied to the input weights matrices. * @param bRegularizer: instance of [[Regularizer]] * applied to the bias. */ def apply[@specialized(Float, Double) T: ClassTag](size: Int, withBias: Boolean = true, - activation: String = null, + activation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null) (implicit ev: TensorNumeric[T]): Graph[T] = { @@ -55,7 +49,7 @@ object Highway { val negatedGate = AddConstant(1).inputs(Negative().inputs(transformWeight)) val l2 = Linear(size, size, withBias = withBias, wRegularizer = wRegularizer, bRegularizer = bRegularizer).inputs(input) - val transformed = if (null != activation) getAct(activation).inputs(l2) else l2 + val transformed = if (null != activation) activation.inputs(l2) else l2 val transformedGated = CMulTable().inputs(transformWeight, transformed) val identityGate = CMulTable().inputs(negatedGate, input) val value = CAddTable().inputs(transformedGated, identityGate) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index aade39c5d34..48ade123459 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2186,7 +2186,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab MultiRNNCell(cells.asScala.toArray) } - def createHighway(size: Int, withBias: Boolean, activation: String, + def createHighway(size: Int, withBias: Boolean, + activation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): Graph[T] = { Highway(size, withBias, activation, wRegularizer, bRegularizer) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala index 512f86a8191..82f5f227037 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala @@ -28,7 +28,7 @@ class HighwaySpec extends KerasBaseSpec { |output_tensor = Highway(activation='tanh')(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val highway = Highway[Float](2, activation = "tanh") + val highway = Highway[Float](2, activation = Tanh[Float]) def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(1).t(), in(3), in(0).t(), in(2)) checkHighwayOutputAndGrad(highway, kerasCode, weightConverter) @@ -42,7 +42,7 @@ class HighwaySpec extends KerasBaseSpec { |output_tensor = Highway(activation='tanh', bias=None)(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val highway = Highway[Float](2, activation = "tanh", withBias = false) + val highway = Highway[Float](2, activation = Tanh[Float], withBias = false) def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(1).t(), in(0).t()) @@ -82,7 +82,7 @@ class HighwaySpec extends KerasBaseSpec { } "Highway serializer" should "work properly" in { - val module = Highway[Float](2, activation = "tanh") + val module = Highway[Float](2, activation = Tanh[Float]) val input = Tensor[Float](3, 2).randn() val res1 = module.forward(input.clone()).toTensor[Float].clone() From 8d380a297e2220c165cd3d01e0a35c4805f5d7ad Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Mon, 11 Dec 2017 13:45:39 +0800 Subject: [PATCH 0568/1065] fix amc (#2004) --- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 48ade123459..b1d372c902f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2309,9 +2309,6 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab private def doGetFlattenModules(module: Container[Activity, Activity, T], includeContainer: Boolean, result: ArrayBuffer[AbstractModule[Activity, Activity, T]]): Unit = { - if (includeContainer) { - result.append(module) - } module.modules.foreach {m => if (m.isInstanceOf[Container[Activity, Activity, T]]) { doGetFlattenModules(m.asInstanceOf[Container[Activity, Activity, T]], @@ -2321,6 +2318,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab result.append(m) } } + if (includeContainer) { + result.append(module) + } } def isWithWeights(module: Module[T]): Boolean = { From 34dbc50feef0d2026301a958e30798defeb5a3ca Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 11 Dec 2017 15:18:41 +0800 Subject: [PATCH 0569/1065] add Ser support to all tf related layers (#2002) * add Ser support to all tf related layers * fix style --- .../bigdl/dllib/nn/BinaryThreshold.scala | 2 +- .../bigdl/dllib/nn/RecurrentDecoder.scala | 31 ++++++---- .../analytics/bigdl/dllib/nn/ops/Assign.scala | 4 +- .../bigdl/dllib/nn/ops/DecodeImage.scala | 32 +++++++++- .../bigdl/dllib/nn/ops/MaxPool.scala | 8 +-- .../dllib/nn/ops/ModuleToOperation.scala | 2 +- .../analytics/bigdl/dllib/nn/ops/Pad.scala | 8 +-- .../bigdl/dllib/nn/ops/RandomUniform.scala | 58 +++++++++++++++---- .../analytics/bigdl/dllib/nn/ops/Sum.scala | 2 +- .../analytics/bigdl/dllib/nn/ops/TopK.scala | 6 +- .../bigdl/dllib/nn/ops/TruncatedNormal.scala | 13 +++-- .../bigdl/dllib/nn/tf/SplitAndSelect.scala | 3 +- .../bigdl/dllib/nn/tf/StrideSlice.scala | 53 +++++++++++++++-- .../utils/serializer/ModuleSerializer.scala | 7 ++- .../bigdl/dllib/utils/tf/loaders/Mean.scala | 3 +- .../bigdl/dllib/utils/tf/loaders/Split.scala | 2 +- 16 files changed, 182 insertions(+), 52 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThreshold.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThreshold.scala index 2b5b4cc7ba7..dfda7bdff7e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThreshold.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThreshold.scala @@ -35,7 +35,7 @@ import scala.reflect.ClassTag @SerialVersionUID(4932292249027276581L) class BinaryThreshold[T: ClassTag]( - th: Double = 1e-6, ip: Boolean = false)( + val th: Double = 1e-6, val ip: Boolean = false)( implicit ev: TensorNumeric[T]) extends TensorModule[T] { var threshold = th var inPlace = ip diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala index 8b77b60792d..b39f012f232 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala @@ -230,11 +230,14 @@ object RecurrentDecoder extends ContainerSerializable { new RecurrentDecoder[T](outputLength) } - override def loadModule[T: ClassTag](context: DeserializeContext) - (implicit ev: TensorNumeric[T]) : ModuleData[T] = { - val moduleData = super.loadModule(context) - val recurrentDecoder = moduleData.module.asInstanceOf[RecurrentDecoder[T]] + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val attrMap = context.bigdlModule.getAttrMap + // val module = super.doLoadModule(context) + + val seqLen = attrMap.get("seqLength") + val recurrentDecoder = RecurrentDecoder[T](seqLen.getInt32Value) val topologyAttr = attrMap.get("topology") recurrentDecoder.topology = DataConverter. @@ -250,27 +253,33 @@ object RecurrentDecoder extends ContainerSerializable { } recurrentDecoder.modules.append(recurrentDecoder.topology) - moduleData + recurrentDecoder } - override def serializeModule[T: ClassTag](context: SerializeContext[T]) - (implicit ev: TensorNumeric[T]) : SerializeResult = { - val containerBuilder = (super.serializeModule(context).bigDLModule) + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + recurrentBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { val recurrentDecoder = context.moduleData.module.asInstanceOf[RecurrentDecoder[T]] + val outputLengthBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, + outputLengthBuilder, recurrentDecoder.seqLength, + universe.typeOf[Int]) + recurrentBuilder.putAttr("seqLength", outputLengthBuilder.build) + val topologyBuilder = AttrValue.newBuilder DataConverter.setAttributeValue(context, topologyBuilder, recurrentDecoder.topology, ModuleSerializer.abstractModuleType) - containerBuilder.putAttr("topology", topologyBuilder.build) + recurrentBuilder.putAttr("topology", topologyBuilder.build) val preTopologyBuilder = AttrValue.newBuilder DataConverter.setAttributeValue(context, preTopologyBuilder, recurrentDecoder.preTopology, ModuleSerializer.tensorModuleType) - containerBuilder.putAttr("preTopology", topologyBuilder.build) + recurrentBuilder.putAttr("preTopology", topologyBuilder.build) - SerializeResult(containerBuilder, context.storages) } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala index 1fb5c34b2de..a5fe4e93fc6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala @@ -42,8 +42,8 @@ import scala.reflect.ClassTag * @tparam T Numeric type. Only support float/double now */ class Assign[T: ClassTag]( - validateShape: Boolean = true, - useLocking: Boolean = true + val validateShape: Boolean = true, + val useLocking: Boolean = true ) (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[_], T] { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala index 924f70b0f3e..99cbfde29e3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala @@ -24,9 +24,12 @@ import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleSerializable, SerializeContext} import org.tensorflow.framework.DataType +import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag +import scala.reflect.runtime.universe class DecodeImage[T: ClassTag](val channels: Int)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[ByteString], Tensor[Int], T] { @@ -91,7 +94,7 @@ class DecodeImage[T: ClassTag](val channels: Int)(implicit ev: TensorNumeric[T]) } } -class DecodeJpeg[T: ClassTag](channels: Int, ratio: Int = 1)(implicit ev: TensorNumeric[T]) +class DecodeJpeg[T: ClassTag](channels: Int, val ratio: Int = 1)(implicit ev: TensorNumeric[T]) extends DecodeImage[T](channels) { require(ratio == 1, "currently not supported sub-sampling") } @@ -374,5 +377,32 @@ class DecodeRaw[T: ClassTag](val outType: DataType, } } +object DecodeRawSerializer extends ModuleSerializable { + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + val attrMap = context.bigdlModule.getAttrMap + // val module = super.doLoadModule(context) + val outType = attrMap.get("outType").getInt32Value + val littleBoolean = attrMap.get("littleEndian").getBoolValue + new DecodeRaw[T](DataType.forNumber(outType), littleBoolean) + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + bigDLModelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val decodeImage = context.moduleData.module.asInstanceOf[DecodeRaw[_]] + val outTypeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, + outTypeBuilder, decodeImage.outType.getNumber, + universe.typeOf[Int]) + bigDLModelBuilder.putAttr("outType", outTypeBuilder.build) + val littleEndianBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, + outTypeBuilder, decodeImage.littleEndian, + universe.typeOf[Boolean]) + bigDLModelBuilder.putAttr("littleEndian", littleEndianBuilder.build) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala index a2aa411d647..1c6058d3b92 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala @@ -23,10 +23,10 @@ import com.intel.analytics.bigdl.tensor._ import scala.reflect.ClassTag class MaxPool[T: ClassTag]( - ksize: Array[Int], - strides: Array[Int], - padding: String, - format: DataFormat = DataFormat.NHWC + val ksize: Array[Int], + val strides: Array[Int], + val padding: String, + val format: DataFormat = DataFormat.NHWC )(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { val pool: SpatialMaxPooling[T] = format match { case DataFormat.NHWC => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala index 87c6ae8390d..891fef60baf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala @@ -28,7 +28,7 @@ import scala.reflect.ClassTag * @tparam T Numeric type. Only support float/double now */ class ModuleToOperation[T: ClassTag] -(module: AbstractModule[Activity, Activity, T]) +(val module: AbstractModule[Activity, Activity, T]) (implicit ev: TensorNumeric[T]) extends Operation[Activity, Activity, T]{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala index f30d43d5dc2..539f0064acb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Pad.scala @@ -23,8 +23,8 @@ import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag class Pad[T: ClassTag, D: ClassTag]( - mode: String, - constantValue: D) + val mode: String, + val constantValue: Double) (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[D], T] { output = Activity.allocate[Tensor[D], D]() @@ -168,8 +168,8 @@ class Pad[T: ClassTag, D: ClassTag]( object Pad { def apply[T: ClassTag, D: ClassTag]( mode: String, - constantValue: D) + constantValue: Double) (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] = ModuleToOperation[T]( - new Pad(mode = mode, constantValue = constantValue)) + new Pad[T, D](mode = mode, constantValue = constantValue)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala index c382addabe3..875f3c44d16 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala @@ -15,17 +15,20 @@ */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.RandomGenerator +import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleSerializable, SerializeContext} +import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag +import scala.reflect.runtime.universe private[bigdl] trait RandomNode class RandomUniform[T: ClassTag, D: ClassTag]( - minVal: D, maxVal: D, seed: Option[Int] = None + val minVal: Double, val maxVal: Double, val seed: Option[Int] = None )(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Tensor[Int], Tensor[D], T] with RandomNode { @@ -40,8 +43,8 @@ class RandomUniform[T: ClassTag, D: ClassTag]( val shape = input.storage().toArray output.resize(shape).rand( - ev2.toType[Double](minVal), - ev2.toType[Double](maxVal)) + minVal, + maxVal) output } @@ -52,12 +55,47 @@ class RandomUniform[T: ClassTag, D: ClassTag]( } } -object RandomUniform { +object RandomUniform extends ModuleSerializable { def apply[T: ClassTag, D: ClassTag]( - minVal: D, - maxVal: D, - seed: Option[Int] = None) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): + minVal: Double, + maxVal: Double, + seed: Option[Int] = None) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Operation[Activity, Activity, T] - = ModuleToOperation[T](new RandomUniform(minVal, maxVal, seed)) + = ModuleToOperation[T](new RandomUniform[T, D](minVal, maxVal, seed)) + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + bigDLModelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val randomUniform = context.moduleData.module.asInstanceOf[RandomUniform[T, _]] + + val minValBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, minValBuilder, randomUniform.minVal, + universe.typeOf[Double]) + bigDLModelBuilder.putAttr("minVal", minValBuilder.build) + + val maxValBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, maxValBuilder, randomUniform.maxVal, + universe.typeOf[Double]) + bigDLModelBuilder.putAttr("maxVal", maxValBuilder.build) + + if (randomUniform.seed.isDefined) { + val seedBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, seedBuilder, randomUniform.seed.get, + universe.typeOf[Int]) + bigDLModelBuilder.putAttr("seed", seedBuilder.build) + } + } + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + val attrMap = context.bigdlModule.getAttrMap + val minVal = attrMap.get("minVal").getDoubleValue + val maxVal = attrMap.get("maxVal").getDoubleValue + var seed : Option[Int] = None + if (attrMap.containsKey("seed")) { + seed = Option[Int](attrMap.get("seed").getInt32Value) + } + RandomUniform(minVal, maxVal, seed) + } } + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala index ab85b6b4bf9..b96cf99dd70 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.nn.{Sum => SumLayer} import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag -class Sum[T: ClassTag, D: ClassTag](keepDims: Boolean, startFromZero: Boolean = false) +class Sum[T: ClassTag, D: ClassTag](val keepDims: Boolean, val startFromZero: Boolean = false) (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala index 71ecbf4e365..159fec33248 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala @@ -22,9 +22,9 @@ import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.ClassTag class TopK[T: ClassTag, D: ClassTag]( - k: Int, - sorted: Boolean = true, - startIndex: Int = 1 + val k: Int, + val sorted: Boolean = true, + val startIndex: Int = 1 )(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Tensor[D], Table, T] { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala index 137b5e8111d..7c1d934ccce 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormal.scala @@ -22,9 +22,9 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag class TruncatedNormal[T: ClassTag, DataType: ClassTag]( - mean: DataType = 0.0, - stddev: DataType = 1.0, - seed: Int = 0 + val mean: Double = 0.0, + val stddev: Double = 1.0, + val seed: Int = 0 ) (implicit ev: TensorNumeric[T], ev2: TensorNumeric[DataType]) extends Operation[Tensor[Int], Tensor[DataType], T] { @@ -44,7 +44,7 @@ class TruncatedNormal[T: ClassTag, DataType: ClassTag]( override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[DataType]), - Array[TensorNumeric[_]](ev)) + Array[TensorNumeric[_]](ev, ev2)) } } @@ -53,7 +53,8 @@ object TruncatedNormal { mean: Double = 0.0, stddev: Double = 1.0, seed: Int = 0) - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[DataType] + ): Operation[Activity, Activity, T] = ModuleToOperation[T]( - new TruncatedNormal(mean, stddev, seed)) + new TruncatedNormal[T, DataType](mean, stddev, seed)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/SplitAndSelect.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/SplitAndSelect.scala index 2706cdb8a7c..e4570db4363 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/SplitAndSelect.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/SplitAndSelect.scala @@ -26,7 +26,8 @@ import scala.reflect.ClassTag * then select the [[index]]th one */ @SerialVersionUID(-9096120159559947483L) -private[bigdl] class SplitAndSelect[T: ClassTag](dimension: Int, index: Int, numSplit: Int) +private[bigdl] class SplitAndSelect[T: ClassTag] + (val dimension: Int, val index: Int, val numSplit: Int) (implicit ev: TensorNumeric[T]) extends TensorModule[T] { override def updateOutput(input: Tensor[T]): Tensor[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala index 7338777a889..0c0048b61a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala @@ -15,18 +15,21 @@ */ package com.intel.analytics.bigdl.nn.tf -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleSerializable, SerializeContext} +import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag +import scala.reflect.runtime.universe /** * Extracts a strided slice from a tensor. * @param sliceSpecs Array(dim, begin_index, end_index, stride) */ @SerialVersionUID(4436600172725317184L) -private[bigdl] class StrideSlice[T: ClassTag](sliceSpecs: Array[(Int, Int, Int, Int)]) +private[bigdl] class StrideSlice[T: ClassTag](val sliceSpecs: Array[(Int, Int, Int, Int)]) (implicit ev: TensorNumeric[T]) extends TensorModule[T] { require(sliceSpecs.map(_._4 == 1).reduce(_ && _), "only support stride 1 for now") @@ -57,9 +60,51 @@ private[bigdl] class StrideSlice[T: ClassTag](sliceSpecs: Array[(Int, Int, Int, } -private[bigdl] object StrideSlice { +private[bigdl] object StrideSlice extends ModuleSerializable { def apply[T: ClassTag](sliceSpecs: Array[(Int, Int, Int, Int)]) - (implicit ev: TensorNumeric[T]) : StrideSlice[T] = { + (implicit ev: TensorNumeric[T]) : StrideSlice[T] = { new StrideSlice[T](sliceSpecs) } + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + + val attrMap = context.bigdlModule.getAttrMap + // val module = super.doLoadModule(context) + + val sliceLen = attrMap.get("sliceLen").getInt32Value + + val specs = new Array[(Int, Int, Int, Int)](sliceLen) + for (i <- 0 until sliceLen) { + val spec = attrMap.get(s"spec_$i") + val lst = DataConverter. + getAttributeValue(context, spec).asInstanceOf[Array[Int]] + specs(i) = (lst(0), lst(1), lst(2), lst(3)) + } + StrideSlice[T](specs) + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + recurrentBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + + val strideSlice = context.moduleData.module.asInstanceOf[StrideSlice[T]] + + val sliceSpecs = strideSlice.sliceSpecs + + val lengthBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, + lengthBuilder, sliceSpecs.length, + universe.typeOf[Int]) + recurrentBuilder.putAttr("sliceLen", lengthBuilder.build) + + sliceSpecs.zipWithIndex.foreach(pair => { + val specBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, + specBuilder, Array[Int](pair._1._1, pair._1._2, pair._1._3, pair._1._4), + universe.typeOf[Array[Int]]) + recurrentBuilder.putAttr(s"spec_${pair._2}", specBuilder.build) + }) + } } + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 5d1859b2db0..06faa4db6af 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -23,7 +23,8 @@ import com.intel.analytics.bigdl.nn._ import scala.collection.JavaConverters._ import scala.reflect.runtime.universe import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} -import com.intel.analytics.bigdl.nn.ops.ParseExample +import com.intel.analytics.bigdl.nn.ops.{DecodeRawSerializer, ParseExample, RandomUniform => RandomUniformOps} +import com.intel.analytics.bigdl.nn.tf.StrideSlice import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.{Tensor, TensorNumericMath} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -177,6 +178,7 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.MapTable", MapTable) registerModule("com.intel.analytics.bigdl.nn.MaskedSelect", MaskedSelect) registerModule("com.intel.analytics.bigdl.nn.Recurrent", Recurrent) + registerModule("com.intel.analytics.bigdl.nn.RecurrentDecoder", RecurrentDecoder) registerModule("com.intel.analytics.bigdl.nn.Reshape", Reshape) registerModule("com.intel.analytics.bigdl.nn.Scale", Scale) registerModule("com.intel.analytics.bigdl.nn.SpatialContrastiveNormalization", @@ -200,6 +202,9 @@ object ModuleSerializer extends ModuleSerializable{ quantized.Linear) registerModule("com.intel.analytics.bigdl.nn.ops.ParseExample", ParseExample) registerModule("com.intel.analytics.bigdl.nn.SReLU", SReLU) + registerModule("com.intel.analytics.bigdl.nn.ops.DecodeRaw", DecodeRawSerializer) + registerModule("com.intel.analytics.bigdl.nn.ops.RandomUniform", RandomUniformOps) + registerModule("com.intel.analytics.bigdl.nn.tf.StrideSlice", StrideSlice) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala index 071563a1f91..6b19ecf81df 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala @@ -59,7 +59,8 @@ class Mean extends TensorflowOpsLoader { } } -class MeanLoadTF[T: ClassTag](dataType: String, squeeze: Boolean)(implicit ev: TensorNumeric[T]) +class MeanLoadTF[T: ClassTag](val dataType: String, + val squeeze: Boolean)(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) { override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { val dims = tensorArrays(0).asInstanceOf[Tensor[Int]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala index 2e3ed4f1c5b..a4765a72aa2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Split.scala @@ -39,7 +39,7 @@ class Split extends TensorflowOpsLoader { } } -class SplitLoadTF[T: ClassTag](numSplit: Int)(implicit ev: TensorNumeric[T]) +class SplitLoadTF[T: ClassTag](val numSplit: Int)(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(1)) { override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { val dim = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 From d4dbc09a74c381754aca6ef22e70ad462e06e140 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 11 Dec 2017 16:49:59 +0800 Subject: [PATCH 0570/1065] add MeanAbsolutePercentageCriterion & MeanSquaredLogarithmicCriterion (#1916) * add MeanAbsolutePercentageCriterion & MeanSquaredLogarithmicCriterion * add docs * meet pr comments * meet pr comments * exchange keras input target * update readme --- .../nn/MeanAbsolutePercentageCriterion.scala | 111 ++++++++++++++++++ .../nn/MeanSquaredLogarithmicCriterion.scala | 97 +++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 8 ++ .../MeanAbsolutePercentageCriterionSpec.scala | 51 ++++++++ .../MeanSquaredLogarithmicCriterionSpec.scala | 46 ++++++++ 5 files changed, 313 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MeanAbsolutePercentageCriterion.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MeanSquaredLogarithmicCriterion.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MeanAbsolutePercentageCriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MeanSquaredLogarithmicCriterionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MeanAbsolutePercentageCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MeanAbsolutePercentageCriterion.scala new file mode 100644 index 00000000000..0c677fde0a8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MeanAbsolutePercentageCriterion.scala @@ -0,0 +1,111 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion +import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * This method is same as `mean_absolute_percentage_error` loss in keras. + * It caculates diff = K.abs((y - x) / K.clip(K.abs(y), K.epsilon(), Double.MaxValue)) + * and return 100 * K.mean(diff) as outpout + * Here, the x and y can have or not have a batch. + * @param ev$1 + * @param ev + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class MeanAbsolutePercentageCriterion[T: ClassTag] + (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { + + @transient + private var buffer1 : Tensor[T] = null + @transient + private var buffer2 : Tensor[T] = null + private val epsilon: T = ev.fromType(1e-07) + private val maxValue: T = ev.fromType(Double.MaxValue) + private val negativeOne: T = ev.fromType(-1) + + + override def updateOutput(input: Tensor[T], target : Tensor[T]): T = { + if (buffer1 == null) buffer1 = Tensor[T]() + if (buffer2 == null) buffer2 = Tensor[T]() + buffer1.resizeAs(input).copy(input) + buffer2.resizeAs(target).copy(target) + buffer1.sub(target).abs() + // buffer2 = K.clip(K.abs(y), K.epsilon(), Double.MaxValue) + buffer2.apply1(e => ev.clip(ev.abs(e), epsilon, maxValue)) + buffer1.div(buffer2) + + output = ev.times(buffer1.mean(), ev.fromType(100.0)) + output + } + + override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { + val norm : T = ev.fromType(100.0 / input.nElement()) + + buffer1.resizeAs(input).copy(input) + gradInput.resizeAs(target).copy(target) + + val func = new TensorFunc6[T] { + override def apply(inputBuf: Array[T], inputOffset: Int, targetClipBuf: Array[T], + targetClipOffset: Int, gradInputBuf: Array[T], gradInputOffset: Int): Unit = { + val a = inputBuf(inputOffset) + val b = targetClipBuf(targetClipOffset) + val c = gradInputBuf(gradInputOffset) + + if (a == c) { + // x=y, gradInput value = 0 + gradInputBuf(gradInputOffset) = ev.zero + } else if (ev.isGreater(a, c)) { + // x > y, gradInput value = 1/K.clip(K.abs(y), K.epsilon(), Double.MaxValue) + gradInputBuf(gradInputOffset) = ev.divide(ev.one, b) + } else { + // x < y, , gradInput value = -1/K.clip(K.abs(y), K.epsilon(), Double.MaxValue) + gradInputBuf(gradInputOffset) = ev.divide(negativeOne, b) + } + } + } + DenseTensorApply.apply3(buffer1, buffer2, gradInput, func) + + gradInput.mul(norm) + gradInput + } + + + override def canEqual(other: Any): Boolean = + other.isInstanceOf[MeanAbsolutePercentageCriterion[T]] + + override def equals(other: Any): Boolean = other match { + case that: MeanAbsolutePercentageCriterion[T] => + super.equals(that) && + (that canEqual this) + case _ => false + } + + override def hashCode(): Int = { + val state = Seq(super.hashCode()) + state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) + } +} + +object MeanAbsolutePercentageCriterion { + def apply[T : ClassTag]()(implicit ev: TensorNumeric[T]): MeanAbsolutePercentageCriterion[T] + = new MeanAbsolutePercentageCriterion[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MeanSquaredLogarithmicCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MeanSquaredLogarithmicCriterion.scala new file mode 100644 index 00000000000..288e1c9a915 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MeanSquaredLogarithmicCriterion.scala @@ -0,0 +1,97 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.T + +import scala.reflect.ClassTag + +/** + * This method is same as `mean_squared_logarithmic_error` loss in keras. + * It calculates: + * first_log = K.log(K.clip(y, K.epsilon(), Double.MaxValue) + 1.) + * second_log = K.log(K.clip(x, K.epsilon(), Double.MaxValue) + 1.) + * and output K.mean(K.square(first_log - second_log)) + * Here, the x and y can have or not have a batch. + * @param ev$1 + * @param ev + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class MeanSquaredLogarithmicCriterion[T: ClassTag] + (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { + + private val epsilon: T = ev.fromType(1e-07) + private val maxValue: T = ev.fromType(Double.MaxValue) + + @transient + private var buffer1: Tensor[T] = null // first_log + @transient + private var buffer2: Tensor[T] = null // second_log + + override def updateOutput(input: Tensor[T], target : Tensor[T]): T = { + if (buffer1 == null) buffer1 = Tensor[T]() + if (buffer2 == null) buffer2 = Tensor[T]() + buffer1.resizeAs(target).copy(target) + buffer2.resizeAs(input).copy(input) + + buffer1.apply1(e => ev.clip(e, epsilon, ev.fromType(Double.MaxValue))) + buffer1.add(ev.one).log() + + buffer2.apply1(e => ev.clip(e, epsilon, ev.fromType(Double.MaxValue))) + buffer2.add(ev.one) + // keep result K.clip(x K.epsilon(), Double.MaxValue) + 1. + gradInput.resizeAs(buffer2).copy(buffer2) + buffer2.log() + + buffer1.sub(buffer2) + buffer2.copy(buffer1) // keep result of (first_log - second_log) + output = buffer1.square().mean() + output + } + + override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { + val norm : Double = -2.0 / input.nElement() + buffer2.mul(ev.fromType(norm)) + buffer2.div(gradInput) + + gradInput.resizeAs(input).copy(input) + val gradArray = gradInput.storage().array() + val gradOffset = gradInput.storageOffset() - 1 + val bufferArray = buffer2.storage().array() + val bufferOffset = buffer2.storageOffset() - 1 + + var i = 0 + while(i < gradInput.nElement()) { + val z = gradArray(i + gradOffset) + gradArray(i + gradOffset) = if (ev.isGreaterEq(z, epsilon) && ev.isGreaterEq(maxValue, z)) { + bufferArray(i + bufferOffset) + } else { + ev.zero + } + i += 1 + } + gradInput + } +} + +object MeanSquaredLogarithmicCriterion { + def apply[T : ClassTag]()(implicit ev: TensorNumeric[T]): MeanSquaredLogarithmicCriterion[T] + = new MeanSquaredLogarithmicCriterion[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index b1d372c902f..c707c630cdf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2141,6 +2141,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab HardSigmoid() } + def createMeanAbsolutePercentageCriterion: MeanAbsolutePercentageCriterion[T] = { + MeanAbsolutePercentageCriterion() + } + + def createMeanSquaredLogarithmicCriterion: MeanSquaredLogarithmicCriterion[T] = { + MeanSquaredLogarithmicCriterion() + } + def setInitMethod(layer: Initializable, weightInitMethod: InitializationMethod, biasInitMethod: InitializationMethod): layer.type = { layer.setInitMethod(weightInitMethod, biasInitMethod) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MeanAbsolutePercentageCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MeanAbsolutePercentageCriterionSpec.scala new file mode 100644 index 00000000000..46f46c86faf --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MeanAbsolutePercentageCriterionSpec.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{Abs, MeanAbsolutePercentageCriterion} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.{ModuleLoader, ModulePersister} + +import scala.util.Random + +class MeanAbsolutePercentageCriterionSpec extends KerasBaseSpec { + "MeanAbsolutePercentageCriterion" should "be ok" in { + val kerasCode = + """ + |input_tensor = Input(shape=[10]) + |target_tensor = Input(shape=[10]) + |loss = mean_absolute_percentage_error(target_tensor, input_tensor) + |input = np.random.uniform(-1000, 1000, [2, 10]) + |Y = np.random.uniform(-1000, 1000, [2, 10]) + """.stripMargin + val criterion = MeanAbsolutePercentageCriterion[Float]() + checkOutputAndGradForLoss(criterion, kerasCode) + } + + "MeanAbsolutePercentageCriterion" should "be ok with epsilon" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = mean_absolute_percentage_error(target_tensor, input_tensor) + |input = np.array([[1e-07, 1e-06, 1e-08]]) + |Y = np.array([[1, 2, 3]]) + """.stripMargin + val criterion = MeanAbsolutePercentageCriterion[Float]() + checkOutputAndGradForLoss(criterion, kerasCode) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MeanSquaredLogarithmicCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MeanSquaredLogarithmicCriterionSpec.scala new file mode 100644 index 00000000000..541b504dcdd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MeanSquaredLogarithmicCriterionSpec.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{MeanAbsolutePercentageCriterion, MeanSquaredLogarithmicCriterion} + +class MeanSquaredLogarithmicCriterionSpec extends KerasBaseSpec { + "MeanSquaredLogarithmicCriterion" should "be ok" in { + val kerasCode = + """ + |input_tensor = Input(shape=[10]) + |target_tensor = Input(shape=[10]) + |loss = mean_squared_logarithmic_error(target_tensor, input_tensor) + |input = np.random.uniform(-1, 1, [5, 10]) + |Y = np.random.uniform(-1, 1, [5, 10]) + """.stripMargin + val criterion = MeanSquaredLogarithmicCriterion[Float]() + checkOutputAndGradForLoss(criterion, kerasCode) + } + + "MeanSquaredLogarithmicCriterion" should "be ok with epsilon" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = mean_squared_logarithmic_error(target_tensor, input_tensor) + |input = np.array([[1e-07, 1e-06, 1e-08]]) + |Y = np.array([[1, 2, 3]]) + """.stripMargin + val criterion = MeanSquaredLogarithmicCriterion[Float]() + checkOutputAndGradForLoss(criterion, kerasCode) + } +} From a5f17535598dedad6c576a3235729dca187cbad0 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Mon, 11 Dec 2017 18:21:56 -0800 Subject: [PATCH 0571/1065] [keras][loss] kullback_leibler_divergence and poisson (#1938) * add kdl and poisson * add extra unittest * poisson update * kullback * update kld * kld update * style fix * add transient * add extra ut * init with null * resolve conflict --- .../KullbackLeiblerDivergenceCriterion.scala | 104 ++++++++++++++++++ .../bigdl/dllib/nn/PoissonCriterion.scala | 65 +++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 8 ++ ...llbackLeiblerDivergenceCriterionSpec.scala | 61 ++++++++++ .../dllib/keras/PoissonCriterionSpec.scala | 35 ++++++ 5 files changed, 273 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KullbackLeiblerDivergenceCriterion.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PoissonCriterion.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KullbackLeiblerDivergenceCriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/PoissonCriterionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KullbackLeiblerDivergenceCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KullbackLeiblerDivergenceCriterion.scala new file mode 100644 index 00000000000..8aadaefe5e4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KullbackLeiblerDivergenceCriterion.scala @@ -0,0 +1,104 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion +import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * This method is same as `kullback_leibler_divergence` loss in keras. + * Loss calculated as: + * y_true = K.clip(y_true, K.epsilon(), 1) + * y_pred = K.clip(y_pred, K.epsilon(), 1) + * and output K.sum(y_true * K.log(y_true / y_pred), axis=-1) + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class KullbackLeiblerDivergenceCriterion[T: ClassTag] + (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { + + private val epsilon: T = ev.fromType(1e-7) + private val upperlimit = ev.fromType(1.0) + + @transient var bufferInput: Tensor[T] = null + @transient var bufferTarget: Tensor[T] = null + + /** + * It calculates: + * y_true = K.clip(y_true, K.epsilon(), 1) + * y_pred = K.clip(y_pred, K.epsilon(), 1) + * and output K.sum(y_true * K.log(y_true / y_pred), axis=-1) + */ + override def updateOutput(input: Tensor[T], target : Tensor[T]): T = { + if (bufferInput == null) bufferInput = Tensor[T]() + if (bufferTarget == null) bufferTarget = Tensor[T]() + require(input.isSameSizeAs(target), + s"Input should have the same size as target. input size: (${input.size().mkString(", ")});" + + s" target size: (${target.size().mkString(", ")}).") + + bufferInput.resizeAs(input).copy(input) + bufferTarget.resizeAs(target).copy(target) + bufferInput.apply1(e => ev.clip(e, epsilon, upperlimit)) + bufferTarget.apply1(e => ev.clip(e, epsilon, upperlimit)) + gradInput = bufferTarget.clone().div(bufferInput) + + // use bufferInput hold the intermediate value + bufferInput.copy(gradInput) + val mul = bufferInput.log().cmul(bufferTarget).sum() + val batchSize = if (input.nDimension() == 1) 1 else input.size(1) + ev.divide(mul, ev.fromType(batchSize)) + } + + /** + * back propagation with: - target / input + */ + override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { + require(input.isSameSizeAs(target), + s"Input should have the same size as target. input size: (${input.size().mkString(", ")});" + + s" target size: (${target.size().mkString(", ")}).") + + val batchSize = if (input.nDimension() == 1) 1 else input.size(1) + gradInput.div(ev.fromType(-batchSize)) + + // keep consistent with Keras for values out of clip boundary + val func1 = new TensorFunc6[T] { + private val nonGradient = ev.fromType(0) + override def apply( + gradInputBuf: Array[T], gradInputOffset: Int, + inputBuf: Array[T], InputOffset: Int, + targetBuf: Array[T], targetOffset: Int + ): Unit = { + if (ev.isGreater(inputBuf(InputOffset), upperlimit) + && ev.isGreater(targetBuf(targetOffset), upperlimit)) { + gradInputBuf(gradInputOffset) = nonGradient + } else if (ev.isGreater(epsilon, inputBuf(InputOffset))) { + gradInputBuf(gradInputOffset) = nonGradient + } + } + } + + DenseTensorApply.apply3[T](gradInput, input, target, func1) + gradInput + } +} + +object KullbackLeiblerDivergenceCriterion { + def apply[T : ClassTag]()(implicit ev: TensorNumeric[T]): KullbackLeiblerDivergenceCriterion[T] = + new KullbackLeiblerDivergenceCriterion[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PoissonCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PoissonCriterion.scala new file mode 100644 index 00000000000..7c4c5f75b2c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PoissonCriterion.scala @@ -0,0 +1,65 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * This class is same as `Poisson` loss in keras. + * Loss calculated as: + * K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1) + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class PoissonCriterion[T: ClassTag] +(implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { + + private val epsilon: T = ev.fromType(1e-07) + + /* + * K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1) + */ + override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { + require(input.isSameSizeAs(target), + s"Input should have the same size as target. input size: (${input.size().mkString(", ")});" + + s" target size: (${target.size().mkString(", ")}).") + // use gradInput as buffer + gradInput.resizeAs(input).copy(input) + gradInput.add(epsilon).log().cmul(target).negative(gradInput).add(input).mean() + } + + /* + * back propagation with: 1 - y_true/y_pred + */ + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(input.isSameSizeAs(gradOutput), + s"Input should have the same size as target. input size: (${input.size().mkString(", ")});" + + s" target size: (${gradOutput.size().mkString(", ")}).") + + gradInput.resizeAs(gradOutput).copy(gradOutput) + gradInput.div(input).negative(gradInput).add(ev.fromType[Double](1.0)) + .div(ev.fromType[Int](input.nElement())) + } +} + +object PoissonCriterion { + def apply[T : ClassTag]()(implicit ev: TensorNumeric[T]): PoissonCriterion[T] = + new PoissonCriterion[T]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c707c630cdf..c1d08e575ae 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2149,6 +2149,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab MeanSquaredLogarithmicCriterion() } + def createKullbackLeiblerDivergenceCriterion: KullbackLeiblerDivergenceCriterion[T] = { + KullbackLeiblerDivergenceCriterion() + } + + def createPoissonCriterion: PoissonCriterion[T] = { + PoissonCriterion() + } + def setInitMethod(layer: Initializable, weightInitMethod: InitializationMethod, biasInitMethod: InitializationMethod): layer.type = { layer.setInitMethod(weightInitMethod, biasInitMethod) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KullbackLeiblerDivergenceCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KullbackLeiblerDivergenceCriterionSpec.scala new file mode 100644 index 00000000000..0e974793073 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KullbackLeiblerDivergenceCriterionSpec.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.KullbackLeiblerDivergenceCriterion + +class KullbackLeiblerDivergenceCriterionSpec extends KerasBaseSpec { + + "KullbackLeiblerDivergenceCriterion" should "match Keras for batch input" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = kullback_leibler_divergence(target_tensor, input_tensor) + |input = np.random.uniform(0, 1, [2, 3]) + |Y = np.random.uniform(0, 1, [2, 3]) + """.stripMargin + val kld = new KullbackLeiblerDivergenceCriterion[Float]() + checkOutputAndGradForLoss(kld, kerasCode) + } + + "KullbackLeiblerDivergenceCriterion" should "match Keras for values out of clip boundary" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = kullback_leibler_divergence(target_tensor, input_tensor) + |input = np.random.uniform(-1, 2, [2, 3]) + |Y = np.random.uniform(-1, 2, [2, 3]) + """.stripMargin + val kld = new KullbackLeiblerDivergenceCriterion[Float]() + checkOutputAndGradForLoss(kld, kerasCode) + } + + "KullbackLeiblerDivergenceCriterion" should "be ok with input close to epsilon" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = kullback_leibler_divergence(target_tensor, input_tensor) + |input = np.array([[1e-8, 1e-7, 1e-6]]) + |Y = np.array([[1.0, 1.0, 1.0]]) + """.stripMargin + val criterion = KullbackLeiblerDivergenceCriterion[Float]() + checkOutputAndGradForLoss(criterion, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/PoissonCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/PoissonCriterionSpec.scala new file mode 100644 index 00000000000..792ca358c96 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/PoissonCriterionSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.PoissonCriterion + +class PoissonCriterionSpec extends KerasBaseSpec { + + "PoissonCriterion" should "be ok" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = poisson(target_tensor, input_tensor) + |input = np.random.uniform(0, 1, [2, 3]) + |Y = np.random.uniform(0, 1, [2, 3]) + """.stripMargin + val kld = new PoissonCriterion[Float]() + checkOutputAndGradForLoss(kld, kerasCode) + } + +} From ab054568248659abc7b0b306ef5c7cc993b00eb5 Mon Sep 17 00:00:00 2001 From: Hawkwood <2041829103@qq.com> Date: Tue, 12 Dec 2017 10:48:43 +0800 Subject: [PATCH 0572/1065] Integration Test related (#1958) * recover * update optimization fixed #1521 * solve conflict * Update version.py * Update release.sh * Update version.py * Update version.py * Update version.py * Update version.py * Update version.py * Update version.py * Update version.py * Update version.py * secure * enable set Epoch on MLpipeline * Quantization4IT --- .../MLPipeline/DLClassifierLeNet.scala | 1 + .../dllib/integration/Quantization.scala | 180 ++++++++++++++++++ 2 files changed, 181 insertions(+) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/Quantization.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala index 59eeb946d52..eddb3d3b655 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala @@ -71,6 +71,7 @@ object DLClassifierLeNet { .setFeaturesCol(inputs(0)) .setLabelCol(inputs(1)) .setBatchSize(param.batchSize) + .setMaxEpoch(param.maxEpoch) val transformer = estimator.fit(trainingDF) val validationSet = DataSet.array(load(validationData, validationLabel), sc) -> diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/Quantization.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/Quantization.scala new file mode 100644 index 00000000000..8a36d975d75 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/Quantization.scala @@ -0,0 +1,180 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.integration + +import java.nio.file.Paths + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.dataset.image._ +import com.intel.analytics.bigdl.dataset.{ByteRecord, Sample, Transformer} +import com.intel.analytics.bigdl.models.lenet.{Utils => LeNetUtils} +import com.intel.analytics.bigdl.models.resnet.{Utils => ResNetUtils} +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.optim.{Top1Accuracy, Top5Accuracy, ValidationMethod, ValidationResult} +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} +import org.apache.log4j.{Level, Logger} +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Integration +class QuantizationSpec extends FlatSpec with Matchers with BeforeAndAfter{ + def test(model: Module[Float], evaluationSet: RDD[Sample[Float]], batchSize: Int) + : Array[(ValidationResult, ValidationMethod[Float])] = { + println(model) + val result = model.evaluate(evaluationSet, Array(new Top1Accuracy[Float], + new Top5Accuracy[Float]), Some(batchSize)) + result.foreach(r => println(s"${r._2} is ${r._1}")) + result + } + + type Result[Float] = Array[(ValidationResult, ValidationMethod[Float])] + def checkResult(fp32: Result[Float], int8: Result[Float]): Unit = { + fp32.zip(int8).foreach{ r => + val a1 = r._1._1.result()._1 + val a2 = r._2._1.result()._1 + require(Math.abs(a1 - a2) < 0.01, s"accuracy of quantized model seems wrong") + } + } + + def getRddData(model: String, sc: SparkContext, partitionNum: Int, + folder: String): RDD[ByteRecord] = { + model match { + case "lenet" => + val validationData = folder + "/t10k-images-idx3-ubyte" + val validationLabel = folder + "/t10k-labels-idx1-ubyte" + sc.parallelize(LeNetUtils.load(validationData, validationLabel), partitionNum) + + case "resnet" => + sc.parallelize(ResNetUtils.loadTest(folder), partitionNum) + + case _ => throw new UnsupportedOperationException(s"unknown model: $model") + } + } + + def getTransformer(model: String): Transformer[ByteRecord, Sample[Float]] = { + model match { + case "lenet" => + BytesToGreyImg(28, 28) -> GreyImgNormalizer(LeNetUtils.testMean, + LeNetUtils.testStd) -> GreyImgToSample() + + case "resnet" => + import com.intel.analytics.bigdl.models.resnet.Cifar10DataSet + + BytesToBGRImg() -> BGRImgNormalizer(Cifar10DataSet.trainMean, + Cifar10DataSet.trainStd) -> BGRImgToSample() + + case _ => throw new UnsupportedOperationException(s"unknown model: $model") + } + } + + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + + "Quantize LeNet5" should "generate the same top1 accuracy" in { + val lenetFP32Model = System.getenv("lenet.fp32.model") + val mnist = System.getenv("mnist") + + val conf = Engine.createSparkConf() + .setAppName(s"Test LeNet5 with quantization") + .set("spark.akka.frameSize", 64.toString) + .setMaster("local[4]") + val sc = new SparkContext(conf) + Engine.init + + val partitionNum = Engine.nodeNumber() * Engine.coreNumber() + val rddData = getRddData("lenet", sc, partitionNum, mnist) + val transformer = getTransformer("lenet") + val evaluationSet = transformer(rddData) + + val batchSize = Engine.coreNumber() * Engine.nodeNumber() * 4 + + val model = Module.loadModule(lenetFP32Model) + val fp32Result = test(model, evaluationSet, batchSize) + + val int8Model = model.quantize() + val int8Result = test(int8Model, evaluationSet, batchSize) + + checkResult(fp32Result, int8Result) + + val tempDir = Paths.get(System.getProperty("java.io.tmpdir")) + val modelPath = Paths.get(tempDir.toString, "lenet.quantized.bigdlmodel") + int8Model.saveModule(modelPath.toString, overWrite = true) + sc.stop() + } + + "Quantize ResNet on Cifar" should "generate the same top1 accuracy" in { + val resnetFP32Model = System.getenv("resnet.fp32.model") + val cifar10 = System.getenv("cifar10") + + val conf = Engine.createSparkConf() + .setAppName(s"Test ResNet on Cifar10 with quantization") + .set("spark.akka.frameSize", 64.toString) + .setMaster("local[4]") + val sc = new SparkContext(conf) + Engine.init + + val partitionNum = Engine.nodeNumber() * Engine.coreNumber() + val batchSize = Engine.coreNumber() * Engine.nodeNumber() * 4 + + val rddData = getRddData("resnet", sc, partitionNum, cifar10) + val transformer = getTransformer("resnet") + val evaluationSet = transformer(rddData) + + val model = Module.loadModule(resnetFP32Model) + val fp32Result = test(model, evaluationSet, batchSize) + + val int8Model = model.quantize() + val int8Result = test(int8Model, evaluationSet, batchSize) + + checkResult(fp32Result, int8Result) + sc.stop() + } + + "Load quantized model of LeNet5 on mnist" should "generate the same top1 accuracy" in { + val lenetFP32Model = System.getenv("lenet.fp32.model") + val mnist = System.getenv("mnist") + + val tempDir = Paths.get(System.getProperty("java.io.tmpdir")) + val modelPath = Paths.get(tempDir.toString, "lenet.quantized.bigdlmodel") + val lenetInt8Model = modelPath.toString + + + val conf = Engine.createSparkConf() + .setAppName(s"Test LeNet5 with quantization") + .set("spark.akka.frameSize", 64.toString) + .setMaster("local[4]") + val sc = new SparkContext(conf) + Engine.init + + val partitionNum = Engine.nodeNumber() * Engine.coreNumber() + val rddData = getRddData("lenet", sc, partitionNum, mnist) + val transformer = getTransformer("lenet") + val evaluationSet = transformer(rddData) + + val batchSize = Engine.coreNumber() * Engine.nodeNumber() * 4 + + val model = Module.loadModule(lenetFP32Model) + val fp32Result = test(model, evaluationSet, batchSize) + + val int8Model = Module.loadModule(lenetInt8Model) + val int8Result = test(int8Model, evaluationSet, batchSize) + + checkResult(fp32Result, int8Result) + sc.stop() + } +} From d6564348ed13c78e38ab99dec578f9b0a9bb8531 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 12 Dec 2017 15:06:53 +0800 Subject: [PATCH 0573/1065] Support SperableConv2D and one hot crossentropy (#1944) * Support SperableConv2D and one hot crossentropy * fix compile error * add python wrapper * add new ut * add ut * fix unit test * fix ut * refine depthwiseConv2D * fix seperable conv2d unit tests * support DepthWiseConv2dBackpropInput and DepthWiseConv2DBackpropFilter * refine the code * add more test * fix failed test and style issue * fix inconsistant API with keras1 * add serialization unit test * fix failed unit test --- .../dllib/nn/CategoricalCrossEntropy.scala | 60 ++++ .../bigdl/dllib/nn/ClassNLLCriterion.scala | 2 +- .../nn/SpatialSeperableConvolution.scala | 264 ++++++++++++++++++ .../analytics/bigdl/dllib/nn/SplitTable.scala | 17 +- .../dllib/nn/abstractnn/AbstractModule.scala | 13 + .../analytics/bigdl/dllib/nn/ops/Conv2D.scala | 31 +- .../bigdl/dllib/nn/ops/DepthwiseConv2D.scala | 182 ++++++++++++ .../bigdl/dllib/tensor/DenseTensor.scala | 13 + .../tensor/QuantizedTensorUnsupported.scala | 3 + .../bigdl/dllib/tensor/SparseTensor.scala | 4 + .../analytics/bigdl/dllib/tensor/Tensor.scala | 6 + .../dllib/utils/python/api/PythonBigDL.scala | 38 +++ .../tf/loaders/DepthwiseConv2dNative.scala | 63 +++++ .../DepthwiseConv2dNativeBackpropFilter.scala | 64 +++++ .../DepthwiseConv2dNativeBackpropInput.scala | 64 +++++ .../keras/CategoricalCrossEntropySpec.scala | 36 +++ .../SpatialSeperableConvolutionSpec.scala | 47 ++++ .../nn/SpatialSeperableConvolutionSpec.scala | 97 +++++++ .../dllib/nn/ops/DepthwiseConv2DSpec.scala | 43 +++ .../loaders/DepthwiseConv2DNativeSpec.scala | 57 ++++ ...thwiseConv2dNativeBackpropFilterSpec.scala | 61 ++++ ...pthwiseConv2dNativeBackpropInputSpec.scala | 59 ++++ .../dllib/utils/tf/loaders/LRNGradSpec.scala | 2 + 23 files changed, 1209 insertions(+), 17 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CategoricalCrossEntropy.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropFilter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropInput.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/CategoricalCrossEntropySpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeperableConvolutionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2DNativeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropFilterSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropInputSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CategoricalCrossEntropy.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CategoricalCrossEntropy.scala new file mode 100644 index 00000000000..ebd5e2533bc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CategoricalCrossEntropy.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractCriterion +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * This is same with cross entropy criterion, except the target tensor is a one-hot tensor + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class CategoricalCrossEntropy[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends AbstractCriterion[Tensor[T], Tensor[T], T]{ + + private val crxEntropy = CrossEntropyCriterion[T]() + + import CategoricalCrossEntropy._ + + private val buffer = Tensor[T]() + + override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { + buffer.resizeAs(input) + crxEntropy.forward(buffer.log(input), convertTensor(target)) + } + + override def backward(input: Tensor[T], target: Tensor[T]): Tensor[T] = { + gradInput = crxEntropy.backward(buffer, convertTensor(target)) + gradInput.div(input) + } + + override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { + gradInput = crxEntropy.updateGradInput(buffer, convertTensor(target)) + gradInput.div(input) + } +} + +object CategoricalCrossEntropy { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): CategoricalCrossEntropy[T] = + new CategoricalCrossEntropy() + + private def convertTensor[T](tensor: Tensor[T]): Tensor[T] = { + tensor.max(2)._2 + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala index 2aa58f851e4..482d82ee056 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala @@ -113,7 +113,7 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] target.squeeze() require(target.dim() == 1, "ClassNLLCriterion: illegal target! Target should be 1D tensor after squeeze," + - s"but target's size is: ${ target.size() }, please check your data.") + s"but target's dimension is: ${ target.dim() }, please check your data.") total_weight = ev.fromType[Int](0) output = ev.fromType[Int](0) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala new file mode 100644 index 00000000000..fef3f3df386 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala @@ -0,0 +1,264 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Separable convolutions consist in first performing a depthwise spatial convolution (which acts + * on each input channel separately) followed by a pointwise convolution which mixes together the + * resulting output channels. The depthMultiplier argument controls how many output channels are + * generated per input channel in the depthwise step. + * + * @param nInputChannel + * @param nOutputChannel + * @param depthMultiplier + * @param kW + * @param kH + * @param sW + * @param sH + * @param pW + * @param pH + * @param hasBias + * @param dataFormat + * @param wRegularizer + * @param bRegularizer + * @param pRegularizer + * @tparam T Numeric type. Only support float/double now + */ +class SpatialSeperableConvolution[T: ClassTag]( + val nInputChannel: Int, + val nOutputChannel: Int, + val depthMultiplier: Int, + val kW: Int, val kH: Int, + val sW: Int, val sH: Int, + val pW: Int, val pH: Int, + val hasBias: Boolean, + val dataFormat: DataFormat, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + var pRegularizer: Regularizer[T] = null, + val initDepthWeight: Tensor[T] = null, + val initPointWeight: Tensor[T] = null, + val initBias: Tensor[T] = null +)(implicit ev: TensorNumeric[T]) + extends AbstractModule[Tensor[T], Tensor[T], T]{ + + private val internalChannel = nInputChannel * depthMultiplier + + private val channelDim = if (dataFormat == DataFormat.NCHW) 2 else 4 + + private val depthWeight = if (initDepthWeight != null) { + initDepthWeight + } else if (dataFormat == DataFormat.NCHW) { + Tensor[T](depthMultiplier, nInputChannel, kW, kH) + } else { + Tensor[T](kW, kH, nInputChannel, depthMultiplier) + } + + private val depthGradWeight = Tensor[T].resizeAs(depthWeight) + + private val pointWeight = if (initPointWeight != null) { + initPointWeight + } else if (dataFormat == DataFormat.NCHW) { + Tensor[T](nOutputChannel, internalChannel, 1, 1) + } else { + Tensor[T](1, 1, internalChannel, nOutputChannel) + } + + private val pointGradWeight = Tensor[T].resizeAs(pointWeight) + + private val bias = if (initBias != null) initBias else Tensor[T](nOutputChannel) + + private val gradBias = Tensor[T].resizeAs(bias) + + private val depthConv = SpatialConvolution[T]( + nInputPlane = nInputChannel, + nOutputPlane = internalChannel, + kernelW = kW, + kernelH = kH, + strideW = sW, + strideH = sH, + padW = pW, + padH = pH, + wRegularizer = wRegularizer, + bRegularizer = null, + withBias = false, + format = dataFormat + ) + + private val pointWiseConv2D = SpatialConvolution[T]( + nInputPlane = internalChannel, + nOutputPlane = nOutputChannel, + kernelW = 1, + kernelH = 1, + strideW = 1, + strideH = 1, + padW = 0, + padH = 0, + wRegularizer = pRegularizer, + bRegularizer = bRegularizer, + withBias = hasBias, + format = dataFormat, + initWeight = pointWeight, + initGradWeight = pointGradWeight, + initBias = bias, + initGradBias = gradBias + ) + + reset() + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (Array(depthWeight, pointWeight, bias), Array(depthGradWeight, pointGradWeight, gradBias)) + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") + require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") + require(nInputChannel == input.size(channelDim), + s"input tensor channel dimension size(${input.size(channelDim)}) doesn't " + + s"match layer nInputChannel $nInputChannel") + + SpatialSeperableConvolution.copyWeight(depthConv.weight, input.size(channelDim), + depthMultiplier, depthWeight, dataFormat) + + depthConv.forward(input) + output = pointWiseConv2D.forward(depthConv.output) + output + } + + override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") + require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") + require(nInputChannel == input.size(channelDim), + "input tensor channel dimension size doesn't match layer nInputChannel") + + require(gradOutput.nDimension() == 4, "SpatialSeperableConvolution only accept 4D gradOutput") + require(gradOutput.isContiguous(), "SpatialSeperableConvolution require contiguous gradOutput") + require(nOutputChannel == gradOutput.size(channelDim), + "gradOutput tensor channel dimension size doesn't match layer nOutputChannel") + + pointWiseConv2D.backward(depthConv.output, gradOutput) + gradInput = depthConv.backward(input, pointWiseConv2D.gradInput) + SpatialSeperableConvolution.copyDepthGradWeight(nInputChannel, depthMultiplier, + depthConv.gradWeight, depthGradWeight, dataFormat) + gradInput + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") + require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") + require(nInputChannel == input.size(channelDim), + "input tensor channel dimension size doesn't match layer nInputChannel") + + require(gradOutput.nDimension() == 4, "SpatialSeperableConvolution only accept 4D gradOutput") + require(gradOutput.isContiguous(), "SpatialSeperableConvolution require contiguous gradOutput") + require(nOutputChannel == gradOutput.size(channelDim), + "gradOutput tensor channel dimension size doesn't match layer nOutputChannel") + + pointWiseConv2D.updateGradInput(depthConv.output, gradOutput) + gradInput = depthConv.updateGradInput(input, pointWiseConv2D.gradInput) + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") + require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") + require(nInputChannel == input.size(channelDim), + "input tensor channel dimension size doesn't match layer nInputChannel") + + require(gradOutput.nDimension() == 4, "SpatialSeperableConvolution only accept 4D gradOutput") + require(gradOutput.isContiguous(), "SpatialSeperableConvolution require contiguous gradOutput") + require(nOutputChannel == gradOutput.size(channelDim), + "gradOutput tensor channel dimension size doesn't match layer nOutputChannel") + + pointWiseConv2D.accGradParameters(depthConv.output, gradOutput) + depthConv.accGradParameters(input, pointWiseConv2D.gradInput) + SpatialSeperableConvolution.copyDepthGradWeight(nInputChannel, depthMultiplier, + depthConv.gradWeight, depthGradWeight, dataFormat) + } + + override def reset(): Unit = { + if (initDepthWeight == null) depthWeight.rand() + if (initPointWeight == null) pointWeight.rand() + if (initBias == null) bias.zero() + zeroGradParameters() + } + + override def zeroGradParameters(): Unit = { + depthWeight.zero() + pointWeight.zero() + bias.zero() + } +} + +object SpatialSeperableConvolution { + def apply[T: ClassTag](nInputChannel: Int, nOutputChannel: Int, depthMultiplier: Int, + kW: Int, kH: Int, sW: Int = 1, sH: Int = 1, pW: Int = 0, pH: Int = 0, + hasBias: Boolean = true, dataFormat: DataFormat = DataFormat.NCHW, + wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, + pRegularizer: Regularizer[T] = null, initDepthWeight: Tensor[T] = null, + initPointWeight: Tensor[T] = null, initBias: Tensor[T] = null + )(implicit ev: TensorNumeric[T]) + : SpatialSeperableConvolution[T] = new SpatialSeperableConvolution[T](nInputChannel, + nOutputChannel, depthMultiplier, kW, kH, sW, sH, pW, pH, hasBias, dataFormat, wRegularizer, + bRegularizer, pRegularizer, initDepthWeight, initPointWeight, initBias) + + private[bigdl] def copyWeight[T](weight: Tensor[T], nInputChannel: Int, + depthMultiplier: Int, sourceWeight: Tensor[T], dataFormat: DataFormat): Unit = { + val kInputDim = if (dataFormat == DataFormat.NHWC) 3 else 2 + val kOutputDim = if (dataFormat == DataFormat.NHWC) 4 else 1 + val delta = if (dataFormat == DataFormat.NHWC) 0 else 1 + weight.zero() + var in = 0 + while(in < nInputChannel) { + var out = 0 + while(out < depthMultiplier) { + // weight is a 5D tensor with a group dimension + weight.select(kInputDim + 1, in + 1) + .select(kOutputDim + delta, in * depthMultiplier + out + 1) + .copy(sourceWeight.select(kInputDim, in + 1).select(kOutputDim - 1 + delta, out + 1)) + out += 1 + } + in += 1 + } + } + + private[bigdl] def copyDepthGradWeight[T]( + nInputChannel: Int, depthMultiplier: Int, + sourceGrad: Tensor[T], targetGrad: Tensor[T], dataFormat: DataFormat + ): Unit = { + val kInputDim = if (dataFormat == DataFormat.NHWC) 3 else 2 + val kOutputDim = if (dataFormat == DataFormat.NHWC) 4 else 1 + val delta = if (dataFormat == DataFormat.NHWC) 0 else 1 + var in = 0 + while(in < nInputChannel) { + var out = 0 + while(out < depthMultiplier) { + targetGrad.select(kInputDim, in + 1).select(kOutputDim - 1 + delta, out + 1) + .copy(sourceGrad.select(kInputDim + 1, in + 1).select(kOutputDim + delta, + in * depthMultiplier + out + 1)) + out += 1 + } + in += 1 + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SplitTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SplitTable.scala index 95de815b5d4..7274f9a77a0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SplitTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SplitTable.scala @@ -41,8 +41,10 @@ import scala.reflect.ClassTag @SerialVersionUID(- 4318640284973082779L) class SplitTable[T: ClassTag]( var dimension: Int, - var nInputDims: Int = -1) - (implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[T], Table, T]{ + var nInputDims: Int = -1, + var keepDim: Boolean = false, + var contiguousOutput: Boolean = false +)(implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[T], Table, T]{ private def getPositiveDimension(input: Tensor[T]): Int = { if (dimension < 0) { @@ -61,7 +63,9 @@ class SplitTable[T: ClassTag]( val currentOutput = T() var i = 1 while (i <= slices) { - currentOutput.insert(input.select(dim, i)) + val t = input.select(dim, i) + if (keepDim) t.addSingletonDimension(t, dim) + currentOutput.insert(if (contiguousOutput) t.contiguous() else t) i += 1 } output = currentOutput @@ -109,7 +113,10 @@ class SplitTable[T: ClassTag]( object SplitTable { def apply[@specialized(Float, Double) T: ClassTag]( dimension: Int, - nInputDims: Int = -1)(implicit ev: TensorNumeric[T]) : SplitTable[T] = { - new SplitTable[T](dimension, nInputDims) + nInputDims: Int = -1, + keepDim: Boolean = false, + contiguousOutput: Boolean = false + )(implicit ev: TensorNumeric[T]) : SplitTable[T] = { + new SplitTable[T](dimension, nInputDims, keepDim, contiguousOutput) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 476bbb9a5d0..05d76881d3f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -661,6 +661,19 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, curNode } + /** + * Build graph: some other modules point to current module + * @param nodes upstream module nodes in an array + * @return node containing current module + */ + def inputs(nodes : Array[ModuleNode[T]]): ModuleNode[T] = { + val curNode = new ModuleNode[T](this) + nodes.foreach(node => { + node.add(curNode, Edge()) + }) + curNode + } + /** * Build graph: some other modules point to current module * @param first distinguish from another inputs when input parameter list is empty diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala index 10b0fd48e5b..ec579913c13 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala @@ -37,12 +37,17 @@ class Conv2D[T: ClassTag]( val input: Tensor[T] = inputs[Tensor[T]](1) val filter: Tensor[T] = inputs[Tensor[T]](2) + + val channelDim = if (format == DataFormat.NHWC) 4 else 2 + val kHDim = if (format == DataFormat.NHWC) 1 else 3 + val kWDim = if (format == DataFormat.NHWC) 2 else 4 + if (conv == null) { conv = SpatialConvolution( - nInputPlane = input.size(4), - nOutputPlane = filter.size(4), - kernelH = filter.size(1), - kernelW = filter.size(2), + nInputPlane = input.size(channelDim), + nOutputPlane = filter.size(channelDim), + kernelH = filter.size(kHDim), + kernelW = filter.size(kWDim), strideH = strideH, strideW = strideW, padH = padH, @@ -53,7 +58,7 @@ class Conv2D[T: ClassTag]( } conv.setWeightsBias(Array(filter)) - output = conv.updateOutput(input) + output = conv.forward(input) output } } @@ -99,12 +104,16 @@ class Conv2DTranspose[T: ClassTag]( (data.size(4), inputSizes.valueAt(4)) } + val kHDim = if (format == DataFormat.NHWC) 1 else 3 + val kWDim = if (format == DataFormat.NHWC) 2 else 4 + + if (module == null) { module = new SpatialConvolution[T]( nInputPlane = nInputPlane, nOutputPlane = nOutputPlane, - kernelW = kernel.size(2), - kernelH = kernel.size(1), + kernelW = kernel.size(kWDim), + kernelH = kernel.size(kHDim), strideH = strideH, strideW = strideW, padH = padH, @@ -116,7 +125,6 @@ class Conv2DTranspose[T: ClassTag]( dummyInput = Tensor[T](inputSizes.valueAt(1), inputSizes.valueAt(2), inputSizes.valueAt(3), inputSizes.valueAt(4)) - module.forward(dummyInput) } else { val (nOutputPlanbe, nInputPlane) = if (format == DataFormat.NCHW) { (data.size(2), inputSizes.valueAt(2)) @@ -126,8 +134,8 @@ class Conv2DTranspose[T: ClassTag]( require(module.nInputPlane == nInputPlane, "nInputPlane is not valid") require(module.nOutputPlane == nOutputPlane, "nOutputPlane is not valid") - require(module.kernelH == kernel.size(1), "kernelH is not valid") - require(module.kernelW == kernel.size(2), "kernelW is not valid") + require(module.kernelH == kernel.size(kWDim), "kernelH is not valid") + require(module.kernelW == kernel.size(kWDim), "kernelW is not valid") require(kernel.size(3) == nInputPlane, "kernel nInputPlane is not valid") require(kernel.size(4) == nOutputPlane, "kernel nOutputPlane is not valid") require(dummyInput.size(1) == inputSizes.valueAt(1), "size 1 is not correct") @@ -136,6 +144,7 @@ class Conv2DTranspose[T: ClassTag]( require(dummyInput.size(4) == inputSizes.valueAt(4), "size 1 is not correct") } + module.forward(dummyInput) module.weight.set(kernel) module.updateGradInput(dummyInput, data) output = module.gradInput @@ -198,7 +207,6 @@ class Conv2DBackFilter[T: ClassTag]( format = format, withBias = false ) - module.forward(inputActivity) } else { val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { (grads.size(2), inputActivity.size(2)) @@ -214,6 +222,7 @@ class Conv2DBackFilter[T: ClassTag]( require(kernelSize.valueAt(4) == nOutputPlane, "kernel nOutputPlane is not valid") } + module.forward(inputActivity) gradWeight.zero() module.accGradParameters(inputActivity, grads) output = module.gradWeight diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala new file mode 100644 index 00000000000..552746c0bf0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala @@ -0,0 +1,182 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.{SpatialConvolution, SpatialSeperableConvolution} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.tf.loaders.Adapter + +import scala.reflect.ClassTag + +class DepthwiseConv2D[T: ClassTag]( + strideW: Int, strideH: Int, + padW: Int, padH: Int, + dataFormat: DataFormat +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private var conv: SpatialConvolution[T] = _ + private var channelMultiplier = 0 + + override def updateOutput(inputs: Table): Tensor[T] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + val filter: Tensor[T] = inputs[Tensor[T]](2) + val channelDim = if (dataFormat == DataFormat.NHWC) 4 else 2 + val kHDim = if (dataFormat == DataFormat.NHWC) 1 else 3 + val kWDim = if (dataFormat == DataFormat.NHWC) 2 else 4 + + if (conv == null) { + channelMultiplier = filter.size(channelDim) + conv = SpatialConvolution( + nInputPlane = input.size(channelDim), + nOutputPlane = channelMultiplier * input.size(channelDim), + kernelH = filter.size(kHDim), + kernelW = filter.size(kWDim), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + withBias = false, + format = dataFormat + ) + conv.weight.zero() + } + + SpatialSeperableConvolution.copyWeight(conv.weight, input.size(channelDim), channelMultiplier, + filter, dataFormat) + output = conv.forward(input) + output + } +} + +object DepthwiseConv2D { + def apply[T: ClassTag]( + strideW: Int, strideH: Int, + padW: Int, padH: Int, + dataFormat: DataFormat = DataFormat.NHWC + )(implicit ev: TensorNumeric[T]): DepthwiseConv2D[T] = + new DepthwiseConv2D(strideW, strideH, padW, padH, dataFormat) +} + +class DepthwiseConv2DBackpropInput[T: ClassTag]( + strideW: Int, strideH: Int, + padW: Int, padH: Int, + dataFormat: DataFormat +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private var conv: SpatialConvolution[T] = _ + private var channelMultiplier = 0 + private val dummyInput = Tensor[T]() + + override def updateOutput(inputs: Table): Tensor[T] = { + val inputSize: Tensor[Int] = inputs[Tensor[Int]](1) + val filter: Tensor[T] = inputs[Tensor[T]](2) + val gradOutput: Tensor[T] = inputs[Tensor[T]](3) + val channelDim = if (dataFormat == DataFormat.NHWC) 4 else 2 + val kHDim = if (dataFormat == DataFormat.NHWC) 1 else 3 + val kWDim = if (dataFormat == DataFormat.NHWC) 2 else 4 + dummyInput.resize(inputSize.toArray()) + + if (conv == null) { + channelMultiplier = filter.size(4) + conv = SpatialConvolution( + nInputPlane = inputSize.valueAt(channelDim), + nOutputPlane = channelMultiplier * inputSize.valueAt(channelDim), + kernelH = filter.size(kHDim), + kernelW = filter.size(kWDim), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + withBias = false, + format = dataFormat + ) + conv.weight.zero() + conv.forward(dummyInput) + } + + SpatialSeperableConvolution.copyWeight(conv.weight, inputSize.valueAt(channelDim), + channelMultiplier, filter, dataFormat) + output = conv.updateGradInput(dummyInput, gradOutput) + output + } +} + +object DepthwiseConv2DBackpropInput { + def apply[T: ClassTag]( + strideW: Int, strideH: Int, + padW: Int, padH: Int, + dataFormat: DataFormat + )(implicit ev: TensorNumeric[T]): DepthwiseConv2DBackpropInput[T] = + new DepthwiseConv2DBackpropInput(strideW, strideH, padW, padH, dataFormat) +} + +class DepthwiseConv2DBackpropFilter[T: ClassTag]( + strideW: Int, strideH: Int, + padW: Int, padH: Int, + dataFormat: DataFormat +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private var conv: SpatialConvolution[T] = _ + private var channelMultiplier = 0 + + override def updateOutput(inputs: Table): Tensor[T] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + val filterSize: Tensor[Int] = inputs[Tensor[Int]](2) + val gradOutput: Tensor[T] = inputs[Tensor[T]](3) + val channelDim = if (dataFormat == DataFormat.NHWC) 4 else 2 + val kHDim = if (dataFormat == DataFormat.NHWC) 1 else 3 + val kWDim = if (dataFormat == DataFormat.NHWC) 2 else 4 + + + if (conv == null) { + channelMultiplier = filterSize.valueAt(4) + conv = SpatialConvolution( + nInputPlane = input.size(channelDim), + nOutputPlane = channelMultiplier * input.size(channelDim), + kernelH = filterSize.valueAt(kHDim), + kernelW = filterSize.valueAt(kWDim), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + withBias = false, + format = dataFormat + ) + } + + conv.forward(input) + conv.zeroGradParameters() + conv.accGradParameters(input, gradOutput) + output.resize(filterSize.toArray()) + + SpatialSeperableConvolution.copyDepthGradWeight(input.size(channelDim), channelMultiplier, + conv.gradWeight, output, dataFormat) + + output + } +} + +object DepthwiseConv2DBackpropFilter { + def apply[T: ClassTag]( + strideW: Int, strideH: Int, + padW: Int, padH: Int, + dataFormat: DataFormat + )(implicit ev: TensorNumeric[T]): DepthwiseConv2DBackpropFilter[T] = + new DepthwiseConv2DBackpropFilter(strideW, strideH, padW, padH, dataFormat) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 36b871f99f5..d78eb970471 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2149,6 +2149,19 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( }) result } + + override def toArray(): Array[T] = { + require(this.dim() == 1, "toArray only support 1D tensor") + val n = this.nElement() + val array = new Array[T](n) + var i = 0 + while(i < n) { + array(i) = this.valueAt(i + 1) + i += 1 + } + + array + } } object DenseTensor { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index f2bff95f683..6d40ae9c15c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1426,4 +1426,7 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] = throw new UnsupportedOperationException(errorString) + + override def toArray(): Array[T] = + throw new UnsupportedOperationException(errorString) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index aef79240b30..7bd1d16bd00 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -1041,6 +1041,10 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( override def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + + override def toArray(): Array[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } } object SparseTensor{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 3a7b42041ba..87a7ec5ade9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -764,6 +764,12 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { return result } + /** + * Convert 1D tensor to an array. If the tensor is not 1D, an exception will be thrown out. + * @return + */ + def toArray(): Array[T] + /** * Element wise inequality between tensor and given value * @param value diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c1d08e575ae..a82a2c77777 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -498,6 +498,40 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ) } + def createSpatialSeperableConvolution( + nInputChannel: Int, + nOutputChannel: Int, + depthMultiplier: Int, + kW: Int, + kH: Int, + sW: Int = 1, + sH: Int = 1, + pW: Int = 0, + pH: Int = 0, + withBias: Boolean = true, + dataFormat: String = "NCHW", + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + pRegularizer: Regularizer[T] = null + ) + : SpatialSeperableConvolution[T] = { + SpatialSeperableConvolution[T](nInputChannel, + nOutputChannel, + depthMultiplier, + kW, + kH, + sW, + sH, + pW, + pH, + withBias, + DataFormat(dataFormat), + wRegularizer, + bRegularizer, + pRegularizer + ) + } + def createReshape(size: JList[Int], batchMode: JBoolean = null): Reshape[T] = { val mappedBatchMode = batchMode match { case JBoolean.TRUE => Some(true) @@ -1465,6 +1499,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab SoftMarginCriterion[T](sizeAverage) } + def createCategoricalCrossEntropy(): CategoricalCrossEntropy[T] = { + CategoricalCrossEntropy[T]() + } + // Optimizer def createPoly(power: Double, maxIteration: Int): SGD.Poly = { SGD.Poly(power, maxIteration) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala new file mode 100644 index 00000000000..b1e901eaff9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala @@ -0,0 +1,63 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.{Conv2D, DepthwiseConv2D} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DepthwiseConv2dNative extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val conv = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + DepthwiseConv2D[T](strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + DepthwiseConv2D[T](strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropFilter.scala new file mode 100644 index 00000000000..e3b5b62edef --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropFilter.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.{DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DepthwiseConv2dNativeBackpropFilter extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val attributes = nodeDef.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val conv = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + DepthwiseConv2DBackpropFilter[T](strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + DepthwiseConv2DBackpropFilter[T](strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropInput.scala new file mode 100644 index 00000000000..945ce2050fb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropInput.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.{DepthwiseConv2D, DepthwiseConv2DBackpropInput} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DepthwiseConv2dNativeBackpropInput extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + + val attributes = nodeDef.getAttrMap + val (pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1) + } else { + (0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val conv = format match { + case "NHWC" => + require(strideList(3) == 1, s"not support strides on depth") + val strideW = strideList(1) + val strideH = strideList(2) + DepthwiseConv2DBackpropInput[T](strideW, strideH, pW, pH, DataFormat.NHWC) + + case "NCHW" => + require(strideList(1) == 1, s"not support strides on depth") + val strideW = strideList(2) + val strideH = strideList(3) + DepthwiseConv2DBackpropInput[T](strideW, strideH, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/CategoricalCrossEntropySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/CategoricalCrossEntropySpec.scala new file mode 100644 index 00000000000..c19569897b1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/CategoricalCrossEntropySpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{CategoricalCrossEntropy, CosineProximityCriterion} + +class CategoricalCrossEntropySpec extends KerasBaseSpec{ + "CategoricalCrossEntropy loss" should "be ok" in { + ifskipTest() + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |target_tensor = Input(shape=[3]) + |loss = categorical_crossentropy(target_tensor, input_tensor) + |input = np.random.uniform(0, 1, [2, 3]) + |Y = np.zeros((2, 3)) + |index = np.array([1, 2]) + |Y[np.arange(2), index] = 1 + """.stripMargin + val criterion = CategoricalCrossEntropy[Float]() + checkOutputAndGradForLoss(criterion, kerasCode) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeperableConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeperableConvolutionSpec.scala new file mode 100644 index 00000000000..5c7c809f8f7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeperableConvolutionSpec.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.{CategoricalCrossEntropy, SpatialSeperableConvolution} + +class SpatialSeperableConvolutionSpec extends KerasBaseSpec { + "SpatialSeperableConvolution" should "be ok" in { + ifskipTest() + val kerasCode = + """ + |input_tensor = Input(shape=[5, 5, 2]) + |output_tensor = SeparableConv2D(2, 2, 2)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + |input = np.random.uniform(0, 1, [2, 5, 5, 2]) + """.stripMargin + val layer = SpatialSeperableConvolution[Float](2, 2, 1, 2, 2, dataFormat = DataFormat.NHWC) + checkOutputAndGrad(layer, kerasCode) + } + + "SpatialSeperableConvolution" should "be ok when depth multipler is not 1" in { + ifskipTest() + val kerasCode = + """ + |input_tensor = Input(shape=[5, 5, 2]) + |output_tensor = SeparableConv2D(4, 2, 2, depth_multiplier=2)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + |input = np.random.uniform(0, 1, [2, 5, 5, 2]) + """.stripMargin + val layer = SpatialSeperableConvolution[Float](2, 4, 2, 2, 2, dataFormat = DataFormat.NHWC) + checkOutputAndGrad(layer, kerasCode) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala new file mode 100644 index 00000000000..a899fd0aba9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala @@ -0,0 +1,97 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.BigDLSpecHelper + +class SpatialSeperableConvolutionSpec extends BigDLSpecHelper { + "SpatialSeperableConvolution NHWC and NCHW" should "have same output" in { + val depthWeightNHWC = Tensor[Float](2, 2, 3, 1).rand() + val depthWeightNCHW = depthWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) + .contiguous() + val pointWeightNHWC = Tensor[Float](1, 1, 3, 6).rand() + val pointWeightNCHW = pointWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) + .contiguous() + val convNHWC = SpatialSeperableConvolution[Float](3, 6, 1, 2, 2, dataFormat = DataFormat.NHWC, + initDepthWeight = depthWeightNHWC, initPointWeight = pointWeightNHWC) + val convNCHW = SpatialSeperableConvolution[Float](3, 6, 1, 2, 2, dataFormat = DataFormat.NCHW, + initDepthWeight = depthWeightNCHW, initPointWeight = pointWeightNCHW) + val inputNHWC = Tensor[Float](2, 24, 24, 3).rand() + val inputNCHW = inputNHWC.transpose(2, 4).transpose(3, 4).contiguous() + val outputNHWC = convNHWC.forward(inputNHWC) + val outputNCHW = convNCHW.forward(inputNCHW) + val convert = outputNHWC.transpose(2, 4).transpose(3, 4).contiguous() + convert.almostEqual(outputNCHW, 1e-5) should be(true) + val gradOutputNHWC = Tensor[Float](2, 23, 23, 6).rand() + val gradOutputNCHW = gradOutputNHWC.transpose(2, 4).transpose(3, 4).contiguous() + val gradInputNHWC = convNHWC.backward(inputNHWC, gradOutputNHWC) + val gradInputNCHW = convNCHW.backward(inputNCHW, gradOutputNCHW) + val convertGradInput = gradInputNHWC.transpose(2, 4).transpose(3, 4).contiguous() + convertGradInput.almostEqual(gradInputNCHW, 1e-5) should be(true) + + convNHWC.parameters()._2.zip(convNCHW.parameters()._2).map { case(p1, p2) => + if (p1.nDimension() == 4) { + val convert = p2.transpose(1, 4).transpose(1, 3).transpose(2, 3) + p1.almostEqual(convert, 1e-3) should be(true) + } else { + p1.almostEqual(p2, 1e-3) should be(true) + } + } + } + + "SpatialSeperableConvolution NHWC and NCHW" should "have same output when depth mul is 2" in { + val depthWeightNHWC = Tensor[Float](2, 2, 3, 2).rand() + val depthWeightNCHW = depthWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) + .contiguous() + val pointWeightNHWC = Tensor[Float](1, 1, 6, 6).rand() + val pointWeightNCHW = pointWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) + .contiguous() + val convNHWC = SpatialSeperableConvolution[Float](3, 6, 2, 2, 2, dataFormat = DataFormat.NHWC, + initDepthWeight = depthWeightNHWC, initPointWeight = pointWeightNHWC) + val convNCHW = SpatialSeperableConvolution[Float](3, 6, 2, 2, 2, dataFormat = DataFormat.NCHW, + initDepthWeight = depthWeightNCHW, initPointWeight = pointWeightNCHW) + val inputNHWC = Tensor[Float](2, 24, 24, 3).rand() + val inputNCHW = inputNHWC.transpose(2, 4).transpose(3, 4).contiguous() + val outputNHWC = convNHWC.forward(inputNHWC) + val outputNCHW = convNCHW.forward(inputNCHW) + val convert = outputNHWC.transpose(2, 4).transpose(3, 4).contiguous() + convert.almostEqual(outputNCHW, 1e-5) should be(true) + val gradOutputNHWC = Tensor[Float](2, 23, 23, 6).rand() + val gradOutputNCHW = gradOutputNHWC.transpose(2, 4).transpose(3, 4).contiguous() + val gradInputNHWC = convNHWC.backward(inputNHWC, gradOutputNHWC) + val gradInputNCHW = convNCHW.backward(inputNCHW, gradOutputNCHW) + val convertGradInput = gradInputNHWC.transpose(2, 4).transpose(3, 4).contiguous() + convertGradInput.almostEqual(gradInputNCHW, 1e-5) should be(true) + + convNHWC.parameters()._2.zip(convNCHW.parameters()._2).map { case(p1, p2) => + if (p1.nDimension() == 4) { + val convert = p2.transpose(1, 4).transpose(1, 3).transpose(2, 3) + p1.almostEqual(convert, 1e-3) should be(true) + } else { + p1.almostEqual(p2, 1e-3) should be(true) + } + } + } + + "SpatialSeperableConvolution" should "be able to serialized" in { + val conv = SpatialSeperableConvolution[Float](3, 6, 2, 2, 2) + val file = createTmpFile() + conv.saveModule(file.getAbsolutePath, overWrite = true) + val conv2 = Module.loadModule[Float](file.getAbsolutePath) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DSpec.scala new file mode 100644 index 00000000000..73470ebbcc2 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.utils.BigDLSpecHelper + +class DepthwiseConv2DSpec extends BigDLSpecHelper { + "DepthwiseConv2D" should "be able to save and load" in { + val module = DepthwiseConv2D[Float](1, 1, 0, 0) + val file = createTmpFile() + module.saveModule(file.getAbsolutePath, overWrite = true) + Module.loadModule[Float](file.getAbsolutePath) + } + + "DepthwiseConv2DBackpropInput" should "be able to save and load" in { + val module = DepthwiseConv2DBackpropInput[Float](1, 1, 0, 0, DataFormat.NHWC) + val file = createTmpFile() + module.saveModule(file.getAbsolutePath, overWrite = true) + Module.loadModule[Float](file.getAbsolutePath) + } + + "DepthwiseConv2DBackpropFilter" should "be able to save and load" in { + val module = DepthwiseConv2DBackpropFilter[Float](1, 1, 0, 0, DataFormat.NHWC) + val file = createTmpFile() + module.saveModule(file.getAbsolutePath, overWrite = true) + Module.loadModule[Float](file.getAbsolutePath) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2DNativeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2DNativeSpec.scala new file mode 100644 index 00000000000..156ad15681f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2DNativeSpec.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{listIntAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} + +class DepthwiseConv2DNativeSpec extends TensorflowSpecHelper { + "DepthwiseConv2DNative forward" should "be correct when depth multiplyer is 1" in { + RNG.setSeed(100) + val filter = Tensor[Float](2, 2, 3, 1).rand() + compare[Float]( + NodeDef.newBuilder() + .setName("depthwise_conv2d_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("strides", listIntAttr(Seq(1, 1, 1, 1))) + .setOp("DepthwiseConv2dNative"), + Seq(Tensor[Float](4, 24, 24, 3).rand(), filter), + 0 + ) + } + + "DepthwiseConv2DNative forward" should "be correct when depth multiplyer is 2" in { + RNG.setSeed(100) + val filter = Tensor[Float](2, 2, 3, 2).rand() + compare[Float]( + NodeDef.newBuilder() + .setName("depthwise_conv2d_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("strides", listIntAttr(Seq(1, 1, 1, 1))) + .setOp("DepthwiseConv2dNative"), + Seq(Tensor[Float](4, 24, 24, 3).rand(), filter), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropFilterSpec.scala new file mode 100644 index 00000000000..b7fb01e4aab --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropFilterSpec.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{listIntAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} + +class DepthwiseConv2dNativeBackpropFilterSpec extends TensorflowSpecHelper { + "DepthwiseConv2dNativeBackpropFilter forward" should "be correct when depth multiplyer is 1" in { + RNG.setSeed(100) + val input = Tensor[Float](4, 24, 24, 3).rand() + val gradOutput = Tensor[Float](4, 23, 23, 3).rand() + val filterSize = Tensor[Int](T(2, 2, 3, 1)) + compare[Float]( + NodeDef.newBuilder() + .setName("depthwise_conv2d_backfilter_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("strides", listIntAttr(Seq(1, 1, 1, 1))) + .setOp("DepthwiseConv2dNativeBackpropFilter"), + Seq(input, filterSize, gradOutput), + 0, 1e-3 + ) + } + + "DepthwiseConv2dNativeBackpropFilter forward" should "be correct when depth multiplyer is 2" in { + RNG.setSeed(100) + val input = Tensor[Float](4, 24, 24, 3).rand() + val gradOutput = Tensor[Float](4, 23, 23, 6).rand() + val filterSize = Tensor[Int](T(2, 2, 3, 2)) + compare[Float]( + NodeDef.newBuilder() + .setName("depthwise_conv2d_backfilter_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("strides", listIntAttr(Seq(1, 1, 1, 1))) + .setOp("DepthwiseConv2dNativeBackpropFilter"), + Seq(input, filterSize, gradOutput), + 0, 1e-3 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropInputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropInputSpec.scala new file mode 100644 index 00000000000..e4109c581ff --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNativeBackpropInputSpec.scala @@ -0,0 +1,59 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{listIntAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} + +class DepthwiseConv2dNativeBackpropInputSpec extends TensorflowSpecHelper { + "DepthwiseConv2dNativeBackpropInput forward" should "be correct when depth multiplyer is 1" in { + RNG.setSeed(100) + val filter = Tensor[Float](2, 2, 3, 1).rand() + val size = Tensor[Int](T(4, 24, 24, 3)) + compare[Float]( + NodeDef.newBuilder() + .setName("depthwise_conv2d_backinput_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("strides", listIntAttr(Seq(1, 1, 1, 1))) + .setOp("DepthwiseConv2dNativeBackpropInput"), + Seq(size, filter, Tensor[Float](4, 23, 23, 3).rand()), + 0 + ) + } + + "DepthwiseConv2dNativeBackpropInput forward" should "be correct when depth multiplyer is 2" in { + RNG.setSeed(100) + val filter = Tensor[Float](2, 2, 3, 2).rand() + val size = Tensor[Int](T(4, 24, 24, 3)) + compare[Float]( + NodeDef.newBuilder() + .setName("depthwise_conv2d_backinput_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", TensorflowDataFormat.NHWC.value) + .putAttr("strides", listIntAttr(Seq(1, 1, 1, 1))) + .setOp("DepthwiseConv2dNativeBackpropInput"), + Seq(size, filter, Tensor[Float](4, 23, 23, 6).rand()), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala index 6bcfb9ae239..2e92d02d385 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGradSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import com.intel.analytics.bigdl.nn.SpatialCrossMapLRN import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator import com.intel.analytics.bigdl.utils.tf.Tensorflow.{floatAttr, intAttr, typeAttr} import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper import org.tensorflow.framework.{DataType, NodeDef} @@ -43,6 +44,7 @@ class LRNGradSpec extends TensorflowSpecHelper { } "LRNGrad" should "be correct for float tensor2" in { + RandomGenerator.RNG.setSeed(1000) val op = SpatialCrossMapLRN[Float](3, 3, 1, 0, DataFormat.NHWC) val input = Tensor[Float](4, 8, 8, 3).rand() val t = op.forward(input) From 062f8743e6660edffc24d479ab1fe225cfdb28aa Mon Sep 17 00:00:00 2001 From: Xianyan Date: Tue, 12 Dec 2017 16:54:13 +0800 Subject: [PATCH 0574/1065] Add object detection related layers for model zoo and keras support (#2015) * Add object detection model zoo support * Add unit test * Add more doc and python wrapper * revert some and add createProposal * Fix ut --- .../vision/image/util/BboxUtil.scala | 438 ++++++++++++++++++ .../analytics/bigdl/dllib/nn/Anchor.scala | 228 +++++++++ .../bigdl/dllib/nn/DetectionOutputFrcnn.scala | 260 +++++++++++ .../bigdl/dllib/nn/DetectionOutputSSD.scala | 301 ++++++++++++ .../intel/analytics/bigdl/dllib/nn/Nms.scala | 113 ++++- .../bigdl/dllib/nn/NormalizeScale.scala | 80 ++++ .../analytics/bigdl/dllib/nn/PriorBox.scala | 324 +++++++++++++ .../analytics/bigdl/dllib/nn/Proposal.scala | 204 ++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 40 ++ .../bigdl/dllib/nn/NormalizeScaleSpec.scala | 363 +++++++++++++++ .../bigdl/dllib/nn/PriorBoxSpec.scala | 51 ++ .../vision/image/util/BoundingBoxSpec.scala | 225 ++++++++- 12 files changed, 2604 insertions(+), 23 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnn.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSD.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PriorBox.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Proposal.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PriorBoxSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala index 855a3ac6f0b..5cddd22f4b3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala @@ -17,8 +17,12 @@ package com.intel.analytics.bigdl.transform.vision.image.util import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import org.apache.log4j.Logger object BboxUtil { + val logger = Logger.getLogger(getClass) + def decodeRois(output: Tensor[Float]): Tensor[Float] = { // ignore if decoded if (output.nElement() < 6 || output.dim() == 2) return output @@ -39,4 +43,438 @@ object BboxUtil { bboxes.select(2, 3).mul(width) bboxes.select(2, 4).mul(height) } + + /** + * Note that the output are stored in input deltas + * @param boxes (N, 4) + * @param deltas (N, 4a) + * @return + */ + def bboxTransformInv(boxes: Tensor[Float], deltas: Tensor[Float]): Tensor[Float] = { + if (boxes.size(1) == 0) { + return boxes + } + val output = Tensor[Float]().resizeAs(deltas).copy(deltas) + require(boxes.size(2) == 4, + s"boxes size ${boxes.size().mkString(",")} do not satisfy N*4 size") + require(output.size(2) % 4 == 0, + s"and deltas size ${output.size().mkString(",")} do not satisfy N*4a size") + val boxesArr = boxes.storage().array() + var offset = boxes.storageOffset() - 1 + val rowLength = boxes.stride(1) + val deltasArr = output.storage().array() + var i = 0 + val repeat = output.size(2) / boxes.size(2) + var deltasoffset = output.storageOffset() - 1 + while (i < boxes.size(1)) { + val x1 = boxesArr(offset) + val y1 = boxesArr(offset + 1) + val width = boxesArr(offset + 2) - x1 + 1 + val height = boxesArr(offset + 3) - y1 + 1 + var j = 0 + while (j < repeat) { + j += 1 + // dx1*width + centerX + val predCtrX = deltasArr(deltasoffset) * width + x1 + width / 2 + // dy1*height + centerY + val predCtrY = deltasArr(deltasoffset + 1) * height + y1 + height / 2 + // exp(dx2)*width/2 + val predW = Math.exp(deltasArr(deltasoffset + 2)).toFloat * width / 2 + // exp(dy2)*height/2 + val predH = Math.exp(deltasArr(deltasoffset + 3)).toFloat * height / 2 + deltasArr(deltasoffset) = predCtrX - predW + deltasArr(deltasoffset + 1) = predCtrY - predH + deltasArr(deltasoffset + 2) = predCtrX + predW + deltasArr(deltasoffset + 3) = predCtrY + predH + deltasoffset += rowLength + } + offset += rowLength + i += 1 + } + output + } + + /** + * Clip boxes to image boundaries. + * set the score of all boxes with any side smaller than minSize to 0 + * @param boxes N * 4a + * @param height height of image + * @param width width of image + * @param minH min height limit + * @param minW min width limit + * @param scores scores for boxes + * @return the number of boxes kept (score > 0) + */ + def clipBoxes(boxes: Tensor[Float], height: Float, width: Float, minH: Float = 0, + minW: Float = 0, scores: Tensor[Float] = null): Int = { + require(boxes.size(2) % 4 == 0, "boxes should have the shape N*4a") + val boxesArr = boxes.storage().array() + var offset = boxes.storageOffset() - 1 + val scoresArr = if (scores != null) scores.storage().array() else null + var scoreOffset = if (scores != null) scores.storageOffset() - 1 else -1 + var i = 0 + var count = 0 + val h = height - 1 + val w = width - 1 + val repeat = boxes.size(2) / 4 + while (i < boxes.size(1)) { + var r = 0 + while (r < repeat) { + boxesArr(offset) = Math.max(Math.min(boxesArr(offset), w), 0) + boxesArr(offset + 1) = Math.max(Math.min(boxesArr(offset + 1), h), 0) + boxesArr(offset + 2) = Math.max(Math.min(boxesArr(offset + 2), w), 0) + boxesArr(offset + 3) = Math.max(Math.min(boxesArr(offset + 3), h), 0) + + if (scores != null) { + val width = boxesArr(offset + 2) - boxesArr(offset) + 1 + if (width < minW) { + scoresArr(scoreOffset) = 0 + } else { + val height = boxesArr(offset + 3) - boxesArr(offset + 1) + 1 + if (height < minH) scoresArr(scoreOffset) = 0 + else count += 1 + } + scoreOffset += 1 + } + r += 1 + offset += 4 + } + i += 1 + } + count + } + + def getLocPredictions(loc: Tensor[Float], numPredsPerClass: Int, numClasses: Int, + shareLocation: Boolean, locPredsBuf: Array[Array[Tensor[Float]]] = null) + : Array[Array[Tensor[Float]]] = { + // the outer array is the batch, each img contains an array of results, grouped by class + val locPreds = if (locPredsBuf == null) { + val out = new Array[Array[Tensor[Float]]](loc.size(1)) + var i = 0 + while (i < loc.size(1)) { + out(i) = new Array[Tensor[Float]](numClasses) + var c = 0 + while (c < numClasses) { + out(i)(c) = Tensor[Float](numPredsPerClass, 4) + c += 1 + } + i += 1 + } + out + } else { + locPredsBuf + } + var i = 0 + val locData = loc.storage().array() + var locDataOffset = loc.storageOffset() - 1 + while (i < loc.size(1)) { + val labelBbox = locPreds(i) + var p = 0 + while (p < numPredsPerClass) { + val startInd = p * numClasses * 4 + locDataOffset + var c = 0 + while (c < numClasses) { + val label = if (shareLocation) labelBbox.length - 1 else c + val boxData = labelBbox(label).storage().array() + val boxOffset = p * 4 + labelBbox(label).storageOffset() - 1 + val offset = startInd + c * 4 + boxData(boxOffset) = locData(offset) + boxData(boxOffset + 1) = locData(offset + 1) + boxData(boxOffset + 2) = locData(offset + 2) + boxData(boxOffset + 3) = locData(offset + 3) + c += 1 + } + p += 1 + } + locDataOffset += numPredsPerClass * numClasses * 4 + i += 1 + } + locPreds + } + + def getConfidenceScores(conf: Tensor[Float], numPredsPerClass: Int, numClasses: Int, + confBuf: Array[Array[Tensor[Float]]] = null) + : Array[Array[Tensor[Float]]] = { + val confPreds = if (confBuf == null) { + val out = new Array[Array[Tensor[Float]]](conf.size(1)) + var i = 0 + while (i < conf.size(1)) { + out(i) = new Array[Tensor[Float]](numClasses) + var c = 0 + while (c < numClasses) { + out(i)(c) = Tensor[Float](numPredsPerClass) + c += 1 + } + i += 1 + } + out + } + else confBuf + val confData = conf.storage().array() + var confDataOffset = conf.storageOffset() - 1 + var i = 0 + while (i < conf.size(1)) { + val labelScores = confPreds(i) + var p = 0 + while (p < numPredsPerClass) { + val startInd = p * numClasses + confDataOffset + var c = 0 + while (c < numClasses) { + labelScores(c).setValue(p + 1, confData(startInd + c)) + c += 1 + } + p += 1 + } + confDataOffset += numPredsPerClass * numClasses + i += 1 + } + confPreds + } + + def getPriorBboxes(prior: Tensor[Float], nPriors: Int): (Tensor[Float], Tensor[Float]) = { + val array = prior.storage() + val aOffset = prior.storageOffset() + val priorBoxes = Tensor(array, aOffset, Array(nPriors, 4)) + val priorVariances = Tensor(array, aOffset + nPriors * 4, Array(nPriors, 4)) + (priorBoxes, priorVariances) + } + + def decodeBboxesAll(allLocPreds: Array[Array[Tensor[Float]]], priorBoxes: Tensor[Float], + priorVariances: Tensor[Float], nClasses: Int, bgLabel: Int, clipBoxes: Boolean, + varianceEncodedInTarget: Boolean, shareLocation: Boolean, + output: Array[Array[Tensor[Float]]] = null) + : Array[Array[Tensor[Float]]] = { + val batch = allLocPreds.length + val allDecodeBboxes = if (output == null) { + val all = new Array[Array[Tensor[Float]]](batch) + var i = 0 + while (i < batch) { + all(i) = new Array[Tensor[Float]](nClasses) + i += 1 + } + all + } else { + require(output.length == batch) + output + } + var i = 0 + while (i < batch) { + val decodedBoxes = allDecodeBboxes(i) + var c = 0 + while (c < nClasses) { + // Ignore background class. + if (shareLocation || c != bgLabel) { + // Something bad happened if there are no predictions for current label. + if (allLocPreds(i)(c).nElement() == 0) { + logger.warn(s"Could not find location predictions for label $c") + } + val labelLocPreds = allLocPreds(i)(c) + decodedBoxes(c) = decodeBoxes(priorBoxes, priorVariances, clipBoxes, + labelLocPreds, varianceEncodedInTarget, labelLocPreds) + } + c += 1 + } + allDecodeBboxes(i) = decodedBoxes + i += 1 + } + allDecodeBboxes + } + + def decodeBoxes(priorBoxes: Tensor[Float], priorVariances: Tensor[Float], + isClipBoxes: Boolean, bboxes: Tensor[Float], + varianceEncodedInTarget: Boolean, output: Tensor[Float] = null): Tensor[Float] = { + require(priorBoxes.size(1) == priorVariances.size(1)) + require(priorBoxes.size(1) == bboxes.size(1)) + val numBboxes = priorBoxes.size(1) + if (numBboxes > 0) { + require(priorBoxes.size(2) == 4) + } + val decodedBboxes = if (output == null) Tensor[Float](numBboxes, 4) + else output.resizeAs(priorBoxes) + var i = 1 + while (i <= numBboxes) { + decodeSingleBbox(i, priorBoxes, + priorVariances, isClipBoxes, bboxes, varianceEncodedInTarget, decodedBboxes) + i += 1 + } + decodedBboxes + } + + private def decodeSingleBbox(i: Int, priorBox: Tensor[Float], priorVariance: Tensor[Float], + isClipBoxes: Boolean, bbox: Tensor[Float], varianceEncodedInTarget: Boolean, + decodedBoxes: Tensor[Float]): Unit = { + val x1 = priorBox.valueAt(i, 1) + val y1 = priorBox.valueAt(i, 2) + val x2 = priorBox.valueAt(i, 3) + val y2 = priorBox.valueAt(i, 4) + val priorWidth = x2 - x1 + require(priorWidth > 0) + val priorHeight = y2 - y1 + require(priorHeight > 0) + val pCenterX = (x1 + x2) / 2 + val pCenterY = (y1 + y2) / 2 + var decodeCenterX = 0f + var decodeCenterY = 0f + var decodeWidth = 0f + var decodedHeight = 0f + if (varianceEncodedInTarget) { + // variance is encoded in target, we simply need to retore the offset + // predictions. + decodeCenterX = bbox.valueAt(i, 1) * priorWidth + pCenterX + decodeCenterY = bbox.valueAt(i, 2) * priorHeight + pCenterY + decodeWidth = Math.exp(bbox.valueAt(i, 3)).toFloat * priorWidth + decodedHeight = Math.exp(bbox.valueAt(i, 4)).toFloat * priorHeight + } else { + // variance is encoded in bbox, we need to scale the offset accordingly. + decodeCenterX = priorVariance.valueAt(i, 1) * bbox.valueAt(i, 1) * priorWidth + pCenterX + decodeCenterY = priorVariance.valueAt(i, 2) * bbox.valueAt(i, 2) * priorHeight + pCenterY + decodeWidth = Math.exp(priorVariance.valueAt(i, 3) * bbox.valueAt(i, 3)).toFloat * priorWidth + decodedHeight = Math.exp(priorVariance.valueAt(i, 4) * bbox.valueAt(i, 4)) + .toFloat * priorHeight + } + decodedBoxes.setValue(i, 1, decodeCenterX - decodeWidth / 2) + decodedBoxes.setValue(i, 2, decodeCenterY - decodedHeight / 2) + decodedBoxes.setValue(i, 3, decodeCenterX + decodeWidth / 2) + decodedBoxes.setValue(i, 4, decodeCenterY + decodedHeight / 2) + if (isClipBoxes) { + clipBoxes(decodedBoxes) + } + } + + def clipBoxes(bboxes: Tensor[Float]): Tensor[Float] = { + bboxes.cmax(0).apply1(x => Math.min(1, x)) + } + + /** + * + * @param scoresNms N + * @param bboxNms N * 4 + * @param scoresAll M + * @param bboxAll M * 4 + * @return + */ + def bboxVote(scoresNms: Tensor[Float], bboxNms: Tensor[Float], + scoresAll: Tensor[Float], bboxAll: Tensor[Float], + areasBuf: Tensor[Float] = null): RoiLabel = { + var accBox: Tensor[Float] = null + var accScore = 0f + var box: Tensor[Float] = null + val areasAll = if (areasBuf == null) { + Tensor[Float] + } else areasBuf + getAreas(bboxAll, areasAll) + var i = 1 + while (i <= scoresNms.size(1)) { + box = bboxNms(i) + if (accBox == null) { + accBox = Tensor[Float](4) + } else { + accBox.fill(0f) + } + accScore = 0f + var m = 1 + while (m <= scoresAll.size(1)) { + val boxA = bboxAll(m) + val iw = Math.min(box.valueAt(3), boxA.valueAt(3)) - + Math.max(box.valueAt(1), boxA.valueAt(1)) + 1 + val ih = Math.min(box.valueAt(4), boxA.valueAt(4)) - + Math.max(box.valueAt(2), boxA.valueAt(2)) + 1 + + if (iw > 0 && ih > 0) { + val ua = getArea(box) + areasAll.valueAt(m) - iw * ih + val ov = iw * ih / ua + if (ov >= 0.5) { + accBox.add(scoresAll.valueAt(m), boxA) + accScore += scoresAll.valueAt(m) + } + } + m += 1 + } + var x = 1 + while (x <= 4) { + bboxNms.setValue(i, x, accBox.valueAt(x) / accScore) + x += 1 + } + i += 1 + } + RoiLabel(scoresNms, bboxNms) + } + + private def getArea(box: Tensor[Float]): Float = { + require(box.dim() == 1 && box.nElement() >= 4) + (box.valueAt(3) - box.valueAt(1) + 1) * (box.valueAt(4) - box.valueAt(2) + 1) + } + + /** + * get the areas of boxes + * @param boxes N * 4 tensor + * @param areas buffer to store the results + * @return areas array + */ + def getAreas(boxes: Tensor[Float], areas: Tensor[Float], startInd: Int = 1, + normalized: Boolean = false): Tensor[Float] = { + if (boxes.nElement() == 0) return areas + require(boxes.size(2) >= 4) + areas.resize(boxes.size(1)) + val boxesArr = boxes.storage().array() + val offset = boxes.storageOffset() - 1 + val rowLength = boxes.stride(1) + var i = 0 + var boffset = offset + startInd - 1 + while (i < boxes.size(1)) { + val x1 = boxesArr(boffset) + val y1 = boxesArr(boffset + 1) + val x2 = boxesArr(boffset + 2) + val y2 = boxesArr(boffset + 3) + if (normalized) areas.setValue(i + 1, (x2 - x1) * (y2 - y1)) + else areas.setValue(i + 1, (x2 - x1 + 1) * (y2 - y1 + 1)) + boffset += rowLength + i += 1 + } + areas + } + + def getGroundTruths(result: Tensor[Float]): Map[Int, Tensor[Float]] = { + val indices = getGroundTruthIndices(result).toArray.sortBy(_._1) + var gtMap = Map[Int, Tensor[Float]]() + var ind = 0 + val iter = indices.iterator + while (iter.hasNext) { + val x = iter.next() + val gt = result.narrow(1, x._2._1, x._2._2) + // -1 represent those images without label + if (gt.size(1) > 1 || gt.valueAt(1, 2) != -1) { + gtMap += (ind -> gt) + } + ind += 1 + } + gtMap + // indices.map(x => x._1 -> result.narrow(1, x._2._1, x._2._2)) + } + + def getGroundTruthIndices(result: Tensor[Float]): Map[Int, (Int, Int)] = { + var indices = Map[Int, (Int, Int)]() + if (result.nElement() == 0) return indices + var prev = -1f + var i = 1 + var start = 1 + if (result.size(1) == 1) { + indices += (result.valueAt(i, 1).toInt -> (1, 1)) + return indices + } + while (i <= result.size(1)) { + if (prev != result.valueAt(i, 1)) { + if (prev >= 0) { + indices += (prev.toInt -> (start, i - start)) + } + start = i + } + prev = result.valueAt(i, 1) + if (i == result.size(1)) { + indices += (prev.toInt -> (start, i - start + 1)) + } + i += 1 + } + indices + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala new file mode 100644 index 00000000000..8010cc4c761 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala @@ -0,0 +1,228 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} + +/** + * Generates a regular grid of multi-scale, multi-aspect anchor boxes. + */ +class Anchor(ratios: Array[Float], scales: Array[Float]) extends Serializable { + + private val basicAnchors: Tensor[Float] = generateBasicAnchors(ratios, scales) + + val anchorNum = ratios.length * scales.length + /** + * first generate shiftX and shiftY over the whole feature map + * then apply shifts for each basic anchors + * @param width feature map width + * @param height feature map height + * @param featStride stride to move + * @return all anchors over the feature map + */ + def generateAnchors(width: Int, height: Int, featStride: Float = 16): Tensor[Float] = { + val (shiftX, shiftY) = generateShifts(width, height, featStride) + getAllAnchors(shiftX, shiftY, basicAnchors) + } + + @transient private var shiftX: Tensor[Float] = _ + @transient private var shiftY: Tensor[Float] = _ + + /** + * generate shifts wrt width, height and featStride + * in order to generate anchors over the whole feature map + * @param width feature map width + * @param height feature map height + * @param featStride stride to move + * @return shiftX and shiftY + */ + private[nn] def generateShifts(width: Int, height: Int, featStride: Float): + (Tensor[Float], Tensor[Float]) = { + if (shiftX == null) { + shiftX = Tensor[Float] + shiftY = Tensor[Float] + } + var i = -1 + shiftX.resize(width).apply1 { x => i += 1; i * featStride } // 0, f, 2f, ..., wf + i = -1 + shiftY.resize(height).apply1 { x => i += 1; i * featStride } // 0, f, 2f, ..., hf + (shiftX, shiftY) + } + + @transient private var allAnchors: Tensor[Float] = _ + + /** + * each anchor add with shiftX and shiftY + * @param shiftX a list of shift in X direction + * @param shiftY a list of shift in Y direction + * @param anchors basic anchors that will apply shifts + * @return anchors with all shifts + */ + private def getAllAnchors(shiftX: Tensor[Float], shiftY: Tensor[Float], + anchors: Tensor[Float]): Tensor[Float] = { + if (allAnchors == null) { + allAnchors = Tensor[Float] + } + val S = shiftX.nElement() * shiftY.nElement() + val A = anchors.size(1) + allAnchors.resize(S * A, 4) + val xsArr = shiftX.storage().array() + val ysArr = shiftY.storage().array() + val allAnchorArr = allAnchors.storage().array() + var aOffset = allAnchors.storageOffset() - 1 + val anchorArr = anchors.storage().array() + var ysOffset = shiftY.storageOffset() - 1 + var ys = 0 + while (ys < shiftY.nElement()) { + var xs = 0 + var xsOffset = shiftX.storageOffset() - 1 + while (xs < shiftX.nElement()) { + var a = 0 + var anchorOffset = anchors.storageOffset() - 1 + while (a < A) { + allAnchorArr(aOffset) = anchorArr(anchorOffset) + xsArr(xsOffset) + allAnchorArr(aOffset + 1) = anchorArr(anchorOffset + 1) + ysArr(ysOffset) + allAnchorArr(aOffset + 2) = anchorArr(anchorOffset + 2) + xsArr(xsOffset) + allAnchorArr(aOffset + 3) = anchorArr(anchorOffset + 3) + ysArr(ysOffset) + aOffset += 4 + anchorOffset += 4 + a += 1 + } + xs += 1 + xsOffset += 1 + } + ys += 1 + ysOffset += 1 + } + allAnchors + } + + /** + * Generate anchor (reference) windows by enumerating aspect ratios(M) X scales(N) + * wrt a reference (0, 0, 15, 15) window. + * 1. generate anchors for different ratios (N, 4) + * 2. for each anchors generated in 1, scale them to get scaled anchors (M*N, 4) + */ + private[nn] def generateBasicAnchors(_ratios: Array[Float], _scales: Array[Float], + baseSize: Float = 16): Tensor[Float] = { + val ratios = Tensor(Storage(_ratios)) + val scales = Tensor(Storage(_scales)) + val baseAnchor = Tensor(Storage(Array(0, 0, baseSize - 1, baseSize - 1))) + val ratioAnchors = ratioEnum(baseAnchor, ratios) + val anchors = Tensor(scales.size(1) * ratioAnchors.size(1), 4) + var idx = 1 + var i = 1 + while (i <= ratioAnchors.size(1)) { + val scaleAnchors = scaleEnum(ratioAnchors(i), scales) + var j = 1 + while (j <= scaleAnchors.size(1)) { + anchors.update(idx, scaleAnchors(j)) + idx = idx + 1 + j += 1 + } + i += 1 + } + anchors + } + + /** + * Given a vector of widths (ws) and heights (hs) around a center + * (x_ctr, y_ctr), output a set of anchors (windows). + * note that the value of ws and hs is changed after mkAnchors (half) + * x1 = xCtr - (ws-1)/2 = xCtr - ws/2 + 0.5 + * y1 = yCtr - (hs-1)/2 = yCtr - hs/2 + 0.5 + * x2 = xCtr + (ws-1)/2 = xCtr + ws/2 - 0.5 + * y2 = yCtr + (hs-1)/2 = yCtr + hs/2 - 0.5 + * @param ws widths + * @param hs heights + * @param xCtr center x + * @param yCtr center y + * @return anchors around this center, with shape (4, N) + */ + private def mkAnchors(ws: Tensor[Float], hs: Tensor[Float], + xCtr: Float, yCtr: Float): Tensor[Float] = { + require(ws.size(1) == hs.size(1)) + val anchors = Tensor(ws.size(1), 4) + var i = 1 + while (i <= ws.size(1)) { + val w = ws.valueAt(i) / 2 - 0.5f + val h = hs.valueAt(i) / 2 - 0.5f + anchors.setValue(i, 1, xCtr - w) + anchors.setValue(i, 2, yCtr - h) + anchors.setValue(i, 3, xCtr + w) + anchors.setValue(i, 4, yCtr + h) + i += 1 + } + anchors + } + + /** + * Return width, height, x center, and y center for an anchor (window). + */ + private def getBasicAchorInfo(anchor: Tensor[Float]): (Float, Float, Float, Float) = { + val w = anchor.valueAt(3) - anchor.valueAt(1) + 1 + val h = anchor.valueAt(4) - anchor.valueAt(2) + 1 + val xCtr = anchor.valueAt(1) + 0.5f * (w - 1) + val yCtr = anchor.valueAt(2) + 0.5f * (h - 1) + (w, h, xCtr, yCtr) + } + + @transient var ws: Tensor[Float] = _ + @transient var hs: Tensor[Float] = _ + + /** + * Enumerate a set of anchors for each aspect ratio with respect to an anchor. + * ratio = height / width + */ + private def ratioEnum(anchor: Tensor[Float], ratios: Tensor[Float]): Tensor[Float] = { + val (width, height, xCtr, yCtr) = getBasicAchorInfo(anchor) + val area = width * height + if (ws == null) { + ws = Tensor() + hs = Tensor() + } + // get a set of widths + ws.resizeAs(ratios).map(ratios, (w, ratio) => Math.sqrt(area / ratio).round) + // get corresponding heights + hs.resizeAs(ws).cmul(ws, ratios).apply1(Math.round) + mkAnchors(ws, hs, xCtr, yCtr) + } + + /** + * Enumerate a set of anchors for each scale wrt an anchor. + */ + private def scaleEnum(anchor: Tensor[Float], scales: Tensor[Float]): Tensor[Float] = { + if (ws == null) { + ws = Tensor() + hs = Tensor() + } + val (width, height, xCtr, yCtr) = getBasicAchorInfo(anchor) + ws.resizeAs(scales).mul(scales, width) + hs.resizeAs(scales).mul(scales, height) + mkAnchors(ws, hs, xCtr, yCtr) + } +} + +object Anchor { + def apply(ratios: Array[Float], scales: Array[Float]): Anchor = new Anchor(ratios, scales) +} + + + + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnn.scala new file mode 100644 index 00000000000..de3972d2e3f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnn.scala @@ -0,0 +1,260 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil +import com.intel.analytics.bigdl.utils.Table +import org.apache.log4j.Logger + +import scala.collection.mutable.ArrayBuffer + + +object DetectionOutputFrcnn { + val logger = Logger.getLogger(this.getClass) + + def apply(nmsThresh: Float = 0.3f, nClasses: Int, + bboxVote: Boolean, maxPerImage: Int = 100, thresh: Double = 0.05)( + implicit ev: TensorNumeric[Float]): DetectionOutputFrcnn = + new DetectionOutputFrcnn(nmsThresh, nClasses, bboxVote, maxPerImage, thresh) +} + +/** + * Post process Faster-RCNN models + * @param nmsThresh nms threshold + * @param nClasses number of classes + * @param bboxVote whether to vote for detections + * @param maxPerImage limit max number of detections per image + * @param thresh score threshold + */ +@SerialVersionUID(5253792953255433914L) +class DetectionOutputFrcnn(var nmsThresh: Float = 0.3f, val nClasses: Int, + var bboxVote: Boolean, var maxPerImage: Int = 100, var thresh: Double = 0.05)( + implicit ev: TensorNumeric[Float]) extends AbstractModule[Table, Activity, Float] { + + @transient var nmsTool: Nms = _ + + // scores (N, clsNum) + // boxes (N, 4 * clsNum) + private def postProcess(scores: Tensor[Float], boxes: Tensor[Float]) + : Array[RoiLabel] = { + require(scores.size(1) == boxes.size(1)) + val results = new Array[RoiLabel](nClasses) + // skip j = 0, because it's the background class + var clsInd = 1 + while (clsInd < nClasses) { + results(clsInd) = postProcessOneClass(scores, boxes, clsInd) + clsInd += 1 + } + + // Limit to max_per_image detections *over all classes* + if (maxPerImage > 0) { + limitMaxPerImage(results) + } + results + } + + private def resultToTensor(results: Array[RoiLabel]): Tensor[Float] = { + var maxDetection = 0 + results.foreach(res => { + if (null != res) { + maxDetection += res.size() + } + }) + val out = Tensor[Float](1, 1 + maxDetection * 6) + val outi = out(1) + + outi.setValue(1, maxDetection) + var offset = 2 + (0 until nClasses).foreach(c => { + val label = results(c) + if (null != label) { + (1 to label.size()).foreach(j => { + outi.setValue(offset, c) + outi.setValue(offset + 1, label.classes.valueAt(j)) + outi.setValue(offset + 2, label.bboxes.valueAt(j, 1)) + outi.setValue(offset + 3, label.bboxes.valueAt(j, 2)) + outi.setValue(offset + 4, label.bboxes.valueAt(j, 3)) + outi.setValue(offset + 5, label.bboxes.valueAt(j, 4)) + offset += 6 + }) + } + }) + out + } + + @transient private var areas: Tensor[Float] = _ + + private def postProcessOneClass(scores: Tensor[Float], boxes: Tensor[Float], + clsInd: Int): RoiLabel = { + val inds = (1 to scores.size(1)).filter(ind => + scores.valueAt(ind, clsInd + 1) > thresh).toArray + if (inds.length == 0) return null + val clsScores = selectTensor(scores.select(2, clsInd + 1), inds, 1) + val clsBoxes = selectTensor(boxes.narrow(2, clsInd * 4 + 1, 4), inds, 1) + + val keepN = nmsTool.nms(clsScores, clsBoxes, nmsThresh, inds) + + val bboxNms = selectTensor(clsBoxes, inds, 1, keepN) + val scoresNms = selectTensor(clsScores, inds, 1, keepN) + if (bboxVote) { + if (areas == null) areas = Tensor[Float] + BboxUtil.bboxVote(scoresNms, bboxNms, clsScores, clsBoxes, areas) + } else { + RoiLabel(scoresNms, bboxNms) + } + } + + private def selectTensor(matrix: Tensor[Float], indices: Array[Int], + dim: Int, indiceLen: Int = -1, out: Tensor[Float] = null): Tensor[Float] = { + assert(dim == 1 || dim == 2) + var i = 1 + val n = if (indiceLen == -1) indices.length else indiceLen + if (matrix.nDimension() == 1) { + val res = if (out == null) { + Tensor[Float](n) + } else { + out.resize(n) + } + while (i <= n) { + res.update(i, matrix.valueAt(indices(i - 1))) + i += 1 + } + return res + } + // select rows + if (dim == 1) { + val res = if (out == null) { + Tensor[Float](n, matrix.size(2)) + } else { + out.resize(n, matrix.size(2)) + } + while (i <= n) { + res.update(i, matrix(indices(i - 1))) + i += 1 + } + res + } else { + val res = if (out == null) { + Tensor[Float](matrix.size(1), n) + } else { + out.resize(matrix.size(1), n) + } + while (i <= n) { + var rid = 1 + val value = matrix.select(2, indices(i - 1)) + while (rid <= res.size(1)) { + res.setValue(rid, i, value.valueAt(rid)) + rid += 1 + } + i += 1 + } + res + } + } + + def limitMaxPerImage(results: Array[RoiLabel]): Unit = { + val nImageScores = (1 until nClasses).map(j => if (results(j) == null) 0 + else results(j).classes.size(1)).sum + if (nImageScores > maxPerImage) { + val imageScores = ArrayBuffer[Float]() + var j = 1 + while (j < nClasses) { + if (results(j) != null) { + val res = results(j).classes + if (res.nElement() > 0) { + res.apply1(x => { + imageScores.append(x) + x + }) + } + } + j += 1 + } + val imageThresh = imageScores.sortWith(_ < _)(imageScores.length - maxPerImage) + j = 1 + while (j < nClasses) { + if (results(j) != null) { + val box = results(j).bboxes + val keep = (1 to box.size(1)).filter(x => + box.valueAt(x, box.size(2)) >= imageThresh).toArray + val selectedScores = selectTensor(results(j).classes, keep, 1) + val selectedBoxes = selectTensor(results(j).bboxes, keep, 1) + if (selectedScores.nElement() == 0) { + results(j).classes.set() + results(j).bboxes.set() + } else { + results(j).classes.resizeAs(selectedScores).copy(selectedScores) + results(j).bboxes.resizeAs(selectedBoxes).copy(selectedBoxes) + } + } + j += 1 + } + } + } + + @transient var boxesBuf: Tensor[Float] = _ + + def process(scores: Tensor[Float], + boxDeltas: Tensor[Float], + rois: Tensor[Float], + imInfo: Tensor[Float]): Array[RoiLabel] = { + if (nmsTool == null) nmsTool = new Nms + // post process + // unscale back to raw image space + if (boxesBuf == null) boxesBuf = Tensor[Float] + boxesBuf.resize(rois.size(1), 4).copy(rois.narrow(2, 2, 4)) + BboxUtil.scaleBBox(boxesBuf, 1 / imInfo.valueAt(1, 3), 1 / imInfo.valueAt(1, 4)) + // Apply bounding-box regression deltas + val predBoxes = BboxUtil.bboxTransformInv(boxesBuf, boxDeltas) + BboxUtil.clipBoxes(predBoxes, imInfo.valueAt(1, 1) / imInfo.valueAt(1, 3), + imInfo.valueAt(1, 2) / imInfo.valueAt(1, 4)) + val res = postProcess(scores, predBoxes) + res + } + + override def updateOutput(input: Table): Activity = { + if (isTraining()) { + output = input + return output + } + val imInfo = input[Tensor[Float]](1) + val roisData = input[Activity](2) + val rois = if (roisData.isTable) roisData.toTable[Tensor[Float]](1) + else roisData.toTensor[Float] + val boxDeltas = input[Tensor[Float]](3) + val scores = input[Tensor[Float]](4) + require(imInfo.dim() == 2 && imInfo.size(1) == 1 && imInfo.size(2) == 4, + s"imInfo should be a 1x4 tensor, while actual is ${imInfo.size().mkString("x")}") + require(rois.size(2) == 5, + s"rois is a Nx5 tensor, while actual is ${rois.size().mkString("x")}") + require(boxDeltas.size(2) == nClasses * 4, + s"boxDeltas is a Nx(nClasses * 4) tensor, while actual is ${boxDeltas.size().mkString("x")}") + require(scores.size(2) == nClasses, + s"scores is a NxnClasses tensor, while actual is ${scores.size().mkString("x")}") + output = resultToTensor(process(scores, boxDeltas, rois, imInfo)) + output + } + + override def updateGradInput(input: Table, gradOutput: Activity): Table = { + gradInput = gradOutput.toTable + gradInput + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSD.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSD.scala new file mode 100644 index 00000000000..3c3e6b36a54 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSD.scala @@ -0,0 +1,301 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.BinaryTreeLSTM.{apply => _} +import com.intel.analytics.bigdl.nn.Reshape.{apply => _, createBigDLModule => _, createSerializeBigDLModule => _, getClass => _} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.{Module => _, _} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil +import com.intel.analytics.bigdl.utils.Table +import org.apache.log4j.Logger +import DetectionOutputSSD.logger + +import scala.reflect.ClassTag + +/** + * Layer to Post-process SSD output + * @param nClasses number of classes + * @param shareLocation whether to share location, default is true + * @param bgLabel background label + * @param nmsThresh nms threshold + * @param nmsTopk nms topk + * @param keepTopK result topk + * @param confThresh confidence threshold + * @param varianceEncodedInTarget if variance is encoded in target, + * we simply need to retore the offset predictions, + * else if variance is encoded in bbox, + * we need to scale the offset accordingly. + * @param confPostProcess whether add some additional post process to confidence prediction + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +@SerialVersionUID(5253792953255433914L) +class DetectionOutputSSD[T: ClassTag](val nClasses: Int = 21, + val shareLocation: Boolean = true, + val bgLabel: Int = 0, + val nmsThresh: Float = 0.45f, + val nmsTopk: Int = 400, + var keepTopK: Int = 200, + val confThresh: Float = 0.01f, + val varianceEncodedInTarget: Boolean = false, + val confPostProcess: Boolean = true) + (implicit ev: TensorNumeric[T]) + extends AbstractModule[Table, Activity, T] { + @transient private var nms: Nms = _ + + def setTopK(topK: Int): this.type = { + keepTopK = topK + this + } + + private def filterBboxes(decodedBboxes: Array[Tensor[Float]], + confScores: Array[Tensor[Float]], indices: Array[Array[Int]], + indicesNum: Array[Int]): Int = { + var numDet = 0 + var c = 0 + while (c < nClasses) { + if (c != bgLabel) { + val scores = confScores(c) + if (scores.nElement() == 0) { + logger.warn(s"Could not find confidence predictions for label $c") + } + val label = if (shareLocation) decodedBboxes.length - 1 else c + val bboxes = decodedBboxes(label) + if (bboxes == null || bboxes.nElement() == 0) { + logger.warn(s"Could not find locƒation predictions for label $label") + return 0 + } + indicesNum(c) = nms.nmsFast(scores, bboxes, nmsThresh, + confThresh, indices(c), nmsTopk, normalized = true) + + numDet += indicesNum(c) + } + c += 1 + } + if (keepTopK > -1 && numDet > keepTopK) { + val scoreClassIndex = new Array[(Float, Int, Int)](numDet) + var c = 0 + var count = 0 + while (c < indices.length) { + var j = 0 + while (j < indicesNum(c)) { + val idx = indices(c)(j) + scoreClassIndex(count) = (confScores(c).valueAt(idx), c, idx) + count += 1 + j += 1 + } + indicesNum(c) = 0 + c += 1 + } + // keep top k results per image + val sortedPairs = scoreClassIndex.sortBy(x => -x._1) + var i = 0 + while (i < keepTopK) { + val label = sortedPairs(i)._2 + val idx = sortedPairs(i)._3 + indices(label)(indicesNum(label)) = idx + indicesNum(label) += 1 + i += 1 + } + keepTopK + } else { + numDet + } + } + + @transient private var allLocPreds: Array[Array[Tensor[Float]]] = _ + @transient private var allConfScores: Array[Array[Tensor[Float]]] = _ + @transient private var allIndices: Array[Array[Array[Int]]] = _ + @transient private var allIndicesNum: Array[Array[Int]] = _ + + private def init(batch: Int, numLocClasses: Int, nPriors: Int): Unit = { + var i = 0 + if (allLocPreds == null || allLocPreds.length < batch) { + // the outer array is the batch, each img contains an array of results, grouped by class + allLocPreds = new Array[Array[Tensor[Float]]](batch) + allConfScores = new Array[Array[Tensor[Float]]](batch) + allIndices = new Array[Array[Array[Int]]](batch) + allIndicesNum = new Array[Array[Int]](batch) + i = 0 + while (i < batch) { + allLocPreds(i) = new Array[Tensor[Float]](numLocClasses) + allConfScores(i) = new Array[Tensor[Float]](nClasses) + allIndices(i) = new Array[Array[Int]](nClasses) + allIndicesNum(i) = new Array[Int](nClasses) + var c = 0 + while (c < numLocClasses) { + allLocPreds(i)(c) = Tensor[Float](nPriors, 4) + c += 1 + } + c = 0 + while (c < nClasses) { + allConfScores(i)(c) = Tensor[Float](nPriors) + if (c != bgLabel) allIndices(i)(c) = new Array[Int](nPriors) + c += 1 + } + i += 1 + } + + } else { + i = 0 + while (i < batch) { + var c = 0 + while (c < numLocClasses) { + allLocPreds(i)(c).resize(nPriors, 4) + c += 1 + } + c = 0 + while (c < nClasses) { + allConfScores(i)(c).resize(nPriors) + if (c != bgLabel && allIndices(i)(c).length < nPriors) { + allIndices(i)(c) = new Array[Int](nPriors) + } + c += 1 + } + i += 1 + } + } + } + + + private val confPost = if (confPostProcess) { + Sequential[T]() + .add(InferReshape[T](Array(0, -1, nClasses)).setName("mbox_conf_reshape")) + .add(TimeDistributed[T](SoftMax[T]()).setName("mbox_conf_softmax")) + .add(InferReshape[T](Array(0, -1)).setName("mbox_conf_flatten")) + } else { + null + } + + override def updateOutput(input: Table): Activity = { + if (isTraining()) { + output = input + return output + } + if (nms == null) nms = new Nms() + val loc = input[Tensor[Float]](1) + val conf = if (confPostProcess) { + confPost.forward(input[Tensor[Float]](2)).toTensor[Float] + } else { + input[Tensor[Float]](2) + } + val prior = input[Tensor[Float]](3) + val batch = loc.size(1) + val numLocClasses = if (shareLocation) 1 else nClasses + val nPriors = prior.size(3) / 4 + + var i = 0 + + init(batch, numLocClasses, nPriors) + + BboxUtil.getLocPredictions(loc, nPriors, numLocClasses, shareLocation, + allLocPreds) + + BboxUtil.getConfidenceScores(conf, nPriors, nClasses, allConfScores) + val (priorBoxes, priorVariances) = BboxUtil.getPriorBboxes(prior, nPriors) + + val allDecodedBboxes = BboxUtil.decodeBboxesAll(allLocPreds, priorBoxes, priorVariances, + numLocClasses, bgLabel, false, varianceEncodedInTarget, shareLocation, + allLocPreds) + val numKepts = new Array[Int](batch) + var maxDetection = 0 + + i = 0 + while (i < batch) { + val num = filterBboxes(allDecodedBboxes(i), allConfScores(i), + allIndices(i), allIndicesNum(i)) + numKepts(i) = num + maxDetection = Math.max(maxDetection, num) + i += 1 + } + // the first element is the number of detection numbers + val out = Tensor[Float](batch, 1 + maxDetection * 6) + if (numKepts.sum > 0) { + i = 0 + while (i < batch) { + val outi = out(i + 1) + var c = 0 + outi.setValue(1, numKepts(i)) + var offset = 2 + while (c < allIndices(i).length) { + val indices = allIndices(i)(c) + if (indices != null) { + val indicesNum = allIndicesNum(i)(c) + val locLabel = if (shareLocation) allDecodedBboxes(i).length - 1 else c + val bboxes = allDecodedBboxes(i)(locLabel) + var j = 0 + while (j < indicesNum) { + val idx = indices(j) + outi.setValue(offset, c) + outi.setValue(offset + 1, allConfScores(i)(c).valueAt(idx)) + outi.setValue(offset + 2, bboxes.valueAt(idx, 1)) + outi.setValue(offset + 3, bboxes.valueAt(idx, 2)) + outi.setValue(offset + 4, bboxes.valueAt(idx, 3)) + outi.setValue(offset + 5, bboxes.valueAt(idx, 4)) + offset += 6 + j += 1 + } + } + c += 1 + } + i += 1 + } + } + output = out + output + } + + override def updateGradInput(input: Table, gradOutput: Activity): Table = { + gradInput = gradOutput.toTable + gradInput + } + + override def clearState(): DetectionOutputSSD.this.type = { + nms = null + allLocPreds = null + allConfScores = null + allIndices = null + allIndicesNum = null + if (null != confPost) confPost.clearState() + this + } +} + +object DetectionOutputSSD { + val logger = Logger.getLogger(getClass) + + def apply[@specialized(Float) T: ClassTag] + (param: DetectionOutputParam, postProcess: Boolean = true) + (implicit ev: TensorNumeric[T]): DetectionOutputSSD[T] = + new DetectionOutputSSD[T](param.nClasses, + param.shareLocation, + param.bgLabel, + param.nmsThresh, + param.nmsTopk, + param.keepTopK, + param.confThresh, + param.varianceEncodedInTarget, + postProcess) +} + + +case class DetectionOutputParam(nClasses: Int = 21, shareLocation: Boolean = true, bgLabel: Int = 0, + nmsThresh: Float = 0.45f, nmsTopk: Int = 400, var keepTopK: Int = 200, + confThresh: Float = 0.01f, + varianceEncodedInTarget: Boolean = false) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Nms.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Nms.scala index fd225abef50..f1a27af7fbd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Nms.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Nms.scala @@ -57,18 +57,16 @@ class Nms extends Serializable { * 3. update the indices by keeping those bboxes with overlap less than thresh * 4. repeat 2 and 3 until the indices are empty * @param scores score tensor - * @param boxes box tensor, with the size N*4 + * @param boxes box tensor, with the size N*4 * @param thresh overlap thresh * @param indices buffer to store indices after nms + * @param sorted whether the scores are sorted * @return the length of indices after nms */ def nms(scores: Tensor[Float], boxes: Tensor[Float], thresh: Float, - indices: Array[Int]): Int = { + indices: Array[Int], sorted: Boolean = false): Int = { if (scores.nElement() == 0) return 0 - require(indices.length >= scores.nElement() && boxes.size(2) == 4, - "indices length should exceed the number scores'elements and boxes row count should be 4" + - s"indices length ${indices.length}, " + - s"number of elements ${scores.nElement()}, boxes row count ${boxes.size(2)}") + require(indices.length >= scores.nElement() && boxes.size(2) == 4) init(scores.nElement()) val boxArray = boxes.storage().array() @@ -76,7 +74,17 @@ class Nms extends Serializable { val rowLength = boxes.stride(1) getAreas(boxArray, offset, rowLength, boxes.size(1), areas) // indices start from 0 - val orderLength = getSortedScoreInds(scores, sortIndBuffer) + // indices start from 0 + val orderLength = if (!sorted) { + getSortedScoreInds(scores, sortIndBuffer) + } else { + var i = 0 + while ( i < scores.nElement()) { + sortIndBuffer(i) = i + i += 1 + } + scores.nElement() + } var indexLenth = 0 var i = 0 var curInd = 0 @@ -102,49 +110,118 @@ class Nms extends Serializable { indexLenth } - private def getSortedScoreInds(scores: Tensor[Float], resultBuffer: Array[Int]): Int = { + def isKeepCurIndex(boxArray: Array[Float], offset: Int, rowLength: Int, areas: Array[Float], + curInd: Int, adaptiveThresh: Float, indices: Array[Int], indexLength: Int, + normalized: Boolean): Boolean = { + var keep = true + var k = 0 + while (k < indexLength) { + if (keep) { + val keptInd = indices(k) - 1 + keep = !isOverlapRatioGtThresh(boxArray, offset, rowLength, areas, curInd, + keptInd, adaptiveThresh, normalized) + k += 1 + } else { + return false + } + } + keep + } + + def nmsFast(scores: Tensor[Float], boxes: Tensor[Float], nmsThresh: Float, scoreThresh: Float, + indices: Array[Int], topk: Int = -1, eta: Float = 1, normalized: Boolean = true): Int = { + init(scores.nElement()) + val boxArray = boxes.storage().array() + val offset = boxes.storageOffset() - 1 + val rowLength = boxes.stride(1) + getAreas(boxArray, offset, rowLength, boxes.size(1), areas, normalized) + var adaptiveThresh = nmsThresh + val orderLength = getSortedScoreInds(scores, sortIndBuffer, scoreThresh, topk) + var i = 0 + var curInd = 0 + var indexLength = 0 + while (i < orderLength) { + curInd = sortIndBuffer(i) + + val keep = isKeepCurIndex(boxArray, offset, rowLength, areas, curInd, + adaptiveThresh, indices, indexLength, normalized) + if (keep) { + indices(indexLength) = curInd + 1 + indexLength += 1 + } + if (keep && eta < 1 && adaptiveThresh > 0.5) { + adaptiveThresh *= eta + } + i += 1 + } + indexLength + } + + private def getSortedScoreInds(scores: Tensor[Float], resultBuffer: Array[Int], + scoreThresh: Float = 0, topK: Int = -1): Int = { + var num = 0 + if (scoreThresh > 0) { + scores.apply1(x => if (x < scoreThresh) { + 0f + } else { + num += 1 + x + }) + } else { + num = scores.nElement() + } + if (topK > 0) num = Math.min(topK, num) + if (num == 0) return num // note that when the score is the same, // the order of the indices are different in python and here - scores.topk(scores.nElement(), dim = 1, increase = false, result = sortedScores, + scores.topk(num, dim = 1, increase = false, result = sortedScores, indices = sortedInds ) + var i = 0 - while (i < scores.nElement()) { - sortIndBuffer(i) = sortedInds.valueAt(i + 1).toInt - 1 + while (i < num) { + resultBuffer(i) = sortedInds.valueAt(i + 1).toInt - 1 i += 1 } - scores.nElement() + num } private def getAreas(boxesArr: Array[Float], offset: Int, rowLength: Int, total: Int, - areas: Array[Float]): Array[Float] = { + areas: Array[Float], normalized: Boolean = false): Array[Float] = { var i = 0 while (i < total) { val x1 = boxesArr(offset + rowLength * i) val y1 = boxesArr(offset + 1 + rowLength * i) val x2 = boxesArr(offset + 2 + rowLength * i) val y2 = boxesArr(offset + 3 + rowLength * i) - areas(i) = (x2 - x1 + 1) * (y2 - y1 + 1) + areas(i) = if (!normalized) { + (x2 - x1 + 1) * (y2 - y1 + 1) + } else { + // If bbox is within range [0, 1]. + (x2 - x1) * (y2 - y1) + } i += 1 } areas } private def isOverlapRatioGtThresh(boxArr: Array[Float], offset: Int, rowLength: Int, - areas: Array[Float], ind: Int, ind2: Int, thresh: Float): Boolean = { + areas: Array[Float], ind: Int, ind2: Int, thresh: Float, + normalized: Boolean = false): Boolean = { val b1x1 = boxArr(offset + 2 + rowLength * ind2) val b1x2 = boxArr(offset + rowLength * ind2) val b2x1 = boxArr(offset + 2 + rowLength * ind) val b2x2 = boxArr(offset + rowLength * ind) - val w = math.min(b1x1, b2x1) - - math.max(b1x2, b2x2) + 1 + val w = if (normalized) math.min(b1x1, b2x1) - math.max(b1x2, b2x2) + else math.min(b1x1, b2x1) - math.max(b1x2, b2x2) + 1 if (w < 0) return false val b1y1 = boxArr(offset + 3 + rowLength * ind2) val b1y2 = boxArr(offset + 1 + rowLength * ind2) val b2y1 = boxArr(offset + 3 + rowLength * ind) val b2y2 = boxArr(offset + 1 + rowLength * ind) - val h = math.min(b1y1, b2y1) - math.max(b1y2, b2y2) + 1 + val h = if (normalized) math.min(b1y1, b2y1) - math.max(b1y2, b2y2) + else math.min(b1y1, b2y1) - math.max(b1y2, b2y2) + 1 if (h < 0) return false val overlap = w * h diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala new file mode 100644 index 00000000000..34892300df5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala @@ -0,0 +1,80 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + +/** + * NormalizeScale is conposed of normalize and scale, this is equal to caffe Normalize layer + * @param p L_p norm + * @param eps smoothing parameter + * @param scale scale parameter + * @param size size of scale input + * @param wRegularizer weight regularizer + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +@SerialVersionUID(8394549762420197622L) +class NormalizeScale[T: ClassTag](val p: Double, val eps: Double = 1e-10, + val scale: Double, val size: Array[Int], + var wRegularizer: Regularizer[T] = null)(implicit ev: TensorNumeric[T]) + extends TensorModule[T] { + val normalize = Normalize[T](p, eps) + val cmul = CMul[T](size, wRegularizer = wRegularizer) + cmul.weight.fill(ev.fromType(scale)) + + override def setScaleW(w: Double): this.type = { + cmul.setScaleW(w) + this + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + normalize.forward(input) + output = cmul.forward(normalize.output) + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + gradInput = cmul.updateGradInput(output, normalize.updateGradInput(input, gradOutput)) + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + cmul.accGradParameters(input, gradOutput) + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (Array(cmul.weight), Array(cmul.gradWeight)) + } + + override def getParametersTable(): Table = { + T(getName() -> T("weight" -> cmul.weight, "gradWeight" -> cmul.gradWeight)) + } +} + +object NormalizeScale { + def apply[@specialized(Float, Double) T: ClassTag] + (p: Double, eps: Double = 1e-10, scale: Double, size: Array[Int], + wRegularizer: Regularizer[T] = null) + (implicit ev: TensorNumeric[T]): NormalizeScale[T] = + new NormalizeScale[T](p, eps, scale, size, wRegularizer) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PriorBox.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PriorBox.scala new file mode 100644 index 00000000000..92485068f0f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PriorBox.scala @@ -0,0 +1,324 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericDouble, NumericFloat} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect._ + +/** + * Generate the prior boxes of designated sizes and aspect ratios across + * all dimensions (H * W) + * Intended for use with MultiBox detection method to generate prior + * + * @param minSizes minimum box size in pixels. can be multiple. required! + * @param maxSizes maximum box size in pixels. can be ignored or same as the + * # of min_size. + * @param _aspectRatios optional aspect ratios of the boxes. can be multiple + * @param isFlip optional bool, default true. if set, flip the aspect ratio. + * @param isClip whether to clip the prior's coordidate such that it is within [0, 1] + * @tparam T Numeric type. Only support float/double now + */ +@SerialVersionUID(7934178172129260471L) +class PriorBox[T: ClassTag](minSizes: Array[Float], maxSizes: Array[Float] = null, + _aspectRatios: Array[Float] = null, isFlip: Boolean = true, isClip: Boolean = false, + var variances: Array[Float] = null, offset: Float = 0.5f, + var imgH: Int = 0, var imgW: Int = 0, imgSize: Int = 0, + var stepH: Float = 0, var stepW: Float = 0, step: Float = 0) + (implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Tensor[T], T] { + + private var aspectRatios: ArrayBuffer[Float] = _ + private var numPriors = 0 + + init() + + private def init(): Unit = { + require(minSizes != null && minSizes.length > 0, "must provide minSize") + if (aspectRatios == null) aspectRatios = new ArrayBuffer[Float]() + else aspectRatios.clear() + aspectRatios.append(1) + var i = 0 + if (_aspectRatios != null) { + while (i < _aspectRatios.length) { + val ar = _aspectRatios(i) + if (!checkExist(aspectRatios, ar)) { + aspectRatios.append(ar) + } + if (isFlip) { + aspectRatios.append(1 / ar) + } + i += 1 + } + } + + numPriors = aspectRatios.length * minSizes.length + if (maxSizes != null && maxSizes.length > 0) { + require(minSizes.length == maxSizes.length) + i = 0 + while (i < maxSizes.length) { + require(maxSizes(i) > minSizes(i)) + numPriors += 1 + i += 1 + } + } + + if (variances == null) { + variances = Array[Float](0.1f) + } else if (variances.length > 1) { + require(variances.length == 4, "Must and only provide 4 variance.") + } + + if (imgH != 0 && imgW != 0) { + require(imgW > 0 && imgH > 0) + } else if (imgSize != 0) { + require(imgSize > 0) + imgH = imgSize + imgW = imgSize + } + + if (stepH != 0 && stepW != 0) { + require(stepW > 0 && stepH > 0) + } else if (step != 0) { + require(step > 0) + stepH = step + stepW = step + } + } + + private def checkExist(aspectRatios: ArrayBuffer[Float], ar: Float): Boolean = { + var j = 0 + while (j < aspectRatios.length) { + if (Math.abs(ar - aspectRatios(j)) < 1e-6) { + return true + } + j += 1 + } + false + } + + /** + * Computes the output using the current parameter set of the class and input. This function + * returns the result which is stored in the output field. + * + * @param input + * @return + */ + override def updateOutput(input: Activity): Tensor[T] = { + require(imgW > 0 && imgH > 0, "imgW and imgH must > 0") + val feature = if (input.isTensor) input.toTensor[Float] else input.toTable[Tensor[Float]](1) + val layerW = feature.size(4) + val layerH = feature.size(3) + if (stepW == 0 || stepH == 0) { + stepW = imgW / layerW.toFloat + stepH = imgH / layerH.toFloat + } + val dim = layerH * layerW * numPriors * 4 + if (output.nElement() == 2 * dim && output.dim() == 3 && + output.size(1) == 1 && output.size(2) == 2 && output.size(3) == dim) { + return output + } + // Since all images in a batch has same height and width, we only need to + // generate one set of priors which can be shared across all images. + // 2 channels. First channel stores the mean of each prior coordinate. + // Second channel stores the variance of each prior coordinate. + output.resize(1, 2, dim) + val offset = output.storageOffset() - 1 + if (classTag[T] == classTag[Float]) { + val outputData = output.storage().array().asInstanceOf[Array[Float]] + computPriorBoxFloat(layerW, layerH, imgW, imgH, dim, outputData, offset) + } else if (classTag[T] == classTag[Double]) { + val outputData = output.storage().array().asInstanceOf[Array[Double]] + computPriorBoxDouble(layerW, layerH, imgW, imgH, dim, outputData, offset) + } + output + } + + def computPriorBoxFloat(layerW: Int, layerH: Int, imgWidth: Float, imgHeight: Float, + dim: Int, outputData: Array[Float], outputOffset: Int): Unit = { + var idx = outputOffset + var h = 0 + while (h < layerH) { + var w = 0 + while (w < layerW) { + val centerX = (w + offset) * stepW + val centerY = (h + offset) * stepH + var halfBoxW = 0f + var halfBoxH = 0f + var s = 0 + while (s < minSizes.length) { + val minSize = minSizes(s).toInt + halfBoxW = minSize / 2.0f + halfBoxH = minSize / 2.0f + outputData(idx) = (centerX - halfBoxW) / imgWidth // xmin + outputData(idx + 1) = (centerY - halfBoxH) / imgHeight // ymin + outputData(idx + 2) = (centerX + halfBoxW) / imgWidth // xmax + outputData(idx + 3) = (centerY + halfBoxH) / imgHeight // ymax + idx += 4 + if (maxSizes != null && maxSizes.length > 0) { + val maxSize = maxSizes(s).toInt + // second prior: aspect_ratio = 1, size = sqrt(min_size * max_size) + halfBoxW = Math.sqrt(minSize * maxSize).toFloat / 2 + halfBoxH = halfBoxW + outputData(idx) = (centerX - halfBoxW) / imgWidth // xmin + outputData(idx + 1) = (centerY - halfBoxH) / imgHeight // ymin + outputData(idx + 2) = (centerX + halfBoxW) / imgWidth // xmax + outputData(idx + 3) = (centerY + halfBoxH) / imgHeight // ymax + idx += 4 + } + + var r = 0 + // rest of priors + while (r < aspectRatios.length) { + val ar = aspectRatios(r) + if (Math.abs(ar - 1) >= 1e-6) { + val v = Math.sqrt(ar).toFloat + halfBoxW = minSize * v / 2 + halfBoxH = minSize / v / 2 + outputData(idx) = (centerX - halfBoxW) / imgWidth // xmin + outputData(idx + 1) = (centerY - halfBoxH) / imgHeight // ymin + outputData(idx + 2) = (centerX + halfBoxW) / imgWidth // xmax + outputData(idx + 3) = (centerY + halfBoxH) / imgHeight // ymax + idx += 4 + } + r += 1 + } + s += 1 + } + w += 1 + } + h += 1 + } + // clip the prior's coordidate such that it is within [0, 1] + if (isClip) { + var d = outputOffset + while (d < dim) { + outputData(d) = Math.min(Math.max(outputData(d), 0), 1) + d += 1 + } + } + // set the variance. + // var outputDataOffset = output.storageOffset() - 1 + offset(0, 1, sizes = output.size()) + if (variances.length == 1) { + NumericFloat.fill(outputData, idx, output.nElement(), variances(0)) + } else { + var d = 0 + while (d < dim) { + Array.copy(variances, 0, outputData, idx, 4) + idx += 4 + d += 4 + } + } + } + + def computPriorBoxDouble(layerW: Int, layerH: Int, imgWidth: Float, imgHeight: Float, + dim: Int, outputData: Array[Double], outputOffset: Int): Unit = { + var idx = outputOffset + var h = 0 + while (h < layerH) { + var w = 0 + while (w < layerW) { + val centerX = (w + offset) * stepW + val centerY = (h + offset) * stepH + var halfBoxW = 0f + var halfBoxH = 0f + var s = 0 + while (s < minSizes.length) { + val minSize = minSizes(s) + halfBoxW = minSize / 2 + halfBoxH = minSize / 2 + outputData(idx) = (centerX - halfBoxW) / imgWidth // xmin + outputData(idx + 1) = (centerY - halfBoxH) / imgHeight // ymin + outputData(idx + 2) = (centerX + halfBoxW) / imgWidth // xmax + outputData(idx + 3) = (centerY + halfBoxH) / imgHeight // ymax + idx += 4 + if (maxSizes != null && maxSizes.length > 0) { + val maxSize = maxSizes(s) + // second prior: aspect_ratio = 1, size = sqrt(min_size * max_size) + halfBoxW = Math.sqrt(minSize * maxSize).toFloat / 2 + halfBoxH = halfBoxW + outputData(idx) = (centerX - halfBoxW) / imgWidth // xmin + outputData(idx + 1) = (centerY - halfBoxH) / imgHeight // ymin + outputData(idx + 2) = (centerX + halfBoxW) / imgWidth // xmax + outputData(idx + 3) = (centerY + halfBoxH) / imgHeight // ymax + idx += 4 + } + + var r = 0 + // rest of priors + while (r < aspectRatios.length) { + val ar = aspectRatios(r) + if (Math.abs(ar - 1) >= 1e-6) { + val v = Math.sqrt(ar).toFloat + halfBoxW = minSize * v / 2 + halfBoxH = minSize / v / 2 + outputData(idx) = (centerX - halfBoxW) / imgWidth // xmin + outputData(idx + 1) = (centerY - halfBoxH) / imgHeight // ymin + outputData(idx + 2) = (centerX + halfBoxW) / imgWidth // xmax + outputData(idx + 3) = (centerY + halfBoxH) / imgHeight // ymax + idx += 4 + } + r += 1 + } + s += 1 + } + w += 1 + } + h += 1 + } + // clip the prior's coordidate such that it is within [0, 1] + if (isClip) { + var d = outputOffset + while (d < dim) { + outputData(d) = Math.min(Math.max(outputData(d), 0), 1) + d += 1 + } + } + // set the variance. + // var outputDataOffset = output.storageOffset() - 1 + offset(0, 1, sizes = output.size()) + if (variances.length == 1) { + NumericDouble.fill(outputData, idx, output.nElement(), variances(0)) + } else { + var d = 0 + while (d < dim) { + Array.copy(variances, 0, outputData, idx, 4) + idx += 4 + d += 4 + } + } + } + + override def updateGradInput(input: Activity, gradOutput: Tensor[T]): Activity = { + gradInput = null + gradInput + } +} + +object PriorBox { + def apply[@specialized(Float, Double) T: ClassTag](minSizes: Array[Float], + maxSizes: Array[Float] = null, + _aspectRatios: Array[Float] = null, isFlip: Boolean = true, isClip: Boolean = false, + variances: Array[Float] = null, offset: Float = 0.5f, + imgH: Int = 0, imgW: Int = 0, imgSize: Int = 0, + stepH: Float = 0, stepW: Float = 0, step: Float = 0) + (implicit ev: TensorNumeric[T]): PriorBox[T] = + new PriorBox[T](minSizes, maxSizes, _aspectRatios, isFlip, isClip, variances, offset, imgH, + imgW, imgSize, stepH, stepW, step) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Proposal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Proposal.scala new file mode 100644 index 00000000000..ae1df591f15 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Proposal.scala @@ -0,0 +1,204 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil +import com.intel.analytics.bigdl.utils.Table + +/** + * Outputs object detection proposals by applying estimated bounding-box + * transformations to a set of regular boxes (called "anchors"). + * rois: holds R regions of interest, each is a 5-tuple + * (n, x1, y1, x2, y2) specifying an image batch index n and a rectangle (x1, y1, x2, y2) + * scores: holds scores for R regions of interest + * + */ +@SerialVersionUID(5313615238114647805L) +class Proposal(preNmsTopNTest: Int, postNmsTopNTest: Int, val ratios: Array[Float], + val scales: Array[Float], rpnPreNmsTopNTrain: Int, rpnPostNmsTopNTrain: Int)( + implicit ev: TensorNumeric[Float]) extends AbstractModule[Table, Tensor[Float], Float] { + + private val anchorUtil: Anchor = Anchor(ratios, scales) + @transient private var nms: Nms = _ + @transient private var bboxDeltas: Tensor[Float] = _ + @transient private var scores: Tensor[Float] = _ + @transient private var keep: Array[Int] = _ + @transient private var sortedScores: Tensor[Float] = _ + @transient private var sortedInds: Tensor[Float] = _ + @transient private var filteredProposals: Tensor[Float] = _ + // Proposal height and width both need to be greater than minSize (at orig image scale) + private val minSize = 16 + + private def init(): Unit = { + if (nms == null) { + nms = new Nms() + bboxDeltas = Tensor[Float] + scores = Tensor[Float] + sortedScores = Tensor[Float] + sortedInds = Tensor[Float] + filteredProposals = Tensor[Float] + } + } + + /** + * Algorithm: + * for each (H, W) location i + * generate A anchor boxes centered on cell i + * apply predicted bbox deltas at cell i to each of the A anchors + * clip predicted boxes to image + * remove predicted boxes with either height or width < threshold + * sort all (proposal, score) pairs by score from highest to lowest + * take top pre_nms_topN proposals before NMS + * apply NMS with threshold to remaining proposals + * take after_nms_topN proposals after NMS + * return the top proposals (-> RoIs top, scores top) + * @param input input(1): cls scores + * input(2): bbox pred + * input(3): im_info + * @return output + * output(1): rpn_rois + * output(2): rpn_scores + */ + override def updateOutput(input: Table): Tensor[Float] = { + val inputScore = input[Tensor[Float]](1) + val imInfo = input[Tensor[Float]](3) + require(inputScore.size(1) == 1 && imInfo.size(1) == 1, "currently only support single batch") + init() + // transpose from (1, 4A, H, W) to (H * W * A, 4) + transposeAndReshape(input[Tensor[Float]](2), 4, bboxDeltas) + + // select scores for object (while the remaining is the score for background) + // transpose from (1, 2A, H, W) to (H * W * A) + val scoresOri = inputScore.narrow(2, anchorUtil.anchorNum + 1, anchorUtil.anchorNum) + transposeAndReshape(scoresOri, 1, scores) + + + // Generate proposals from bbox deltas and shifted anchors + // Enumerate all shifts + val anchors = anchorUtil.generateAnchors(inputScore.size(4), inputScore.size(3)) + // Convert anchors into proposals via bbox transformations + val proposals = BboxUtil.bboxTransformInv(anchors, bboxDeltas) + // clip predicted boxes to image + // original faster rcnn way + // minimum box width & height + val minBoxH = minSize * imInfo.valueAt(1, 3) + val minBoxW = minSize * imInfo.valueAt(1, 4) + var keepN = BboxUtil.clipBoxes(proposals, imInfo.valueAt(1, 1), imInfo.valueAt(1, 2), minBoxH + , minBoxW, scores) + + val preNmsTopN = if (isTraining()) rpnPreNmsTopNTrain else preNmsTopNTest + val postNmsTopN = if (isTraining()) rpnPostNmsTopNTrain else postNmsTopNTest + val topNum = Math.min(preNmsTopN, keepN) + scores.topk(topNum, dim = 1, increase = false, + result = sortedScores, indices = sortedInds) + if (keep == null || keep.length < sortedInds.nElement()) { + keep = new Array[Int](sortedInds.nElement()) + } + var k = 1 + while (k <= sortedInds.nElement()) { + keep(k - 1) = sortedInds.valueAt(k).toInt - 1 + k += 1 + } + filteredProposals.resize(topNum, proposals.size(2)) + k = 1 + while (k <= topNum) { + filteredProposals.update(k, proposals(keep(k - 1) + 1)) + k += 1 + } + + // apply nms (e.g. threshold = 0.7) + // take after_nms_topN (e.g. 300) + // return the top proposals (-> RoIs topN + keepN = nms.nms(sortedScores, filteredProposals, 0.7f, keep, sorted = true) + if (postNmsTopN > 0) { + keepN = Math.min(keepN, postNmsTopN) + } + + var i = 1 + var j = 2 + + output.resize(keepN, filteredProposals.size(2) + 1) + while (i <= keepN) { + output.setValue(i, 1, 0) + j = 2 + while (j <= output.size(2)) { + output.setValue(i, j, filteredProposals.valueAt(keep(i - 1), j - 1)) + j += 1 + } + i += 1 + } + output + } + + // Transpose and reshape predicted bbox transformations to get them + // into the same order as the anchors: + // bbox deltas will be (1, 4 * A, H, W) format + // transpose to (1, H, W, 4 * A) + // reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a) + // in slowest to fastest order + private def transposeAndReshape(mat: Tensor[Float], cols: Int, + out: Tensor[Float]): Tensor[Float] = { + if (cols == 1) { + out.resize(mat.nElement()) + } else { + out.resize(mat.nElement() / cols, cols) + } + val matArr = mat.storage().array() + val matOffset = mat.storageOffset() - 1 + val st2 = mat.stride(2) + val st3 = mat.stride(3) + val outArr = out.storage().array() + var outOffset = out.storageOffset() - 1 + var ind = 0 + var r = 0 + while (r < mat.size(3)) { + var c = 0 + val offset3 = r * st3 + while (c < mat.size(4)) { + var i = 0 + while (i < mat.size(2)) { + var j = 0 + while (j < cols) { + outArr(outOffset) = matArr(matOffset + (i + j) * st2 + offset3 + c) + outOffset += 1 + j += 1 + } + i += cols + ind += 1 + } + c += 1 + } + r += 1 + } + out + } + + override def updateGradInput(input: Table, gradOutput: Tensor[Float]): Table = { + gradInput = null + gradInput + } +} + +object Proposal { + def apply(preNmsTopN: Int, postNmsTopN: Int, ratios: Array[Float], scales: Array[Float], + rpnPreNmsTopNTrain: Int = 12000, rpnPostNmsTopNTrain: Int = 2000) + (implicit ev: TensorNumeric[Float]): Proposal + = new Proposal(preNmsTopN, postNmsTopN, ratios, scales, rpnPreNmsTopNTrain, rpnPostNmsTopNTrain) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a82a2c77777..a4c3a85ff67 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2407,6 +2407,46 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab CosineProximityCriterion[T]() } + def createPriorBox(minSizes: JList[Double], maxSizes: JList[Double] = null, + aspectRatios: JList[Double] = null, isFlip: Boolean = true, isClip: Boolean = false, + variances: JList[Double] = null, offset: Float = 0.5f, + imgH: Int = 0, imgW: Int = 0, imgSize: Int = 0, + stepH: Float = 0, stepW: Float = 0, step: Float = 0): PriorBox[T] = { + val maxS = if (maxSizes == null) null else maxSizes.asScala.toArray.map(_.toFloat) + val aspectR = if (aspectRatios == null) null else aspectRatios.asScala.toArray.map(_.toFloat) + val vars = if (variances == null) null else variances.asScala.toArray.map(_.toFloat) + new PriorBox[T](minSizes.asScala.toArray.map(_.toFloat), + maxS, aspectR, isFlip, isClip, vars, offset, imgH, imgW, imgSize, stepH, stepW, step) + } + + def createNormalizeScale(p: Double, eps: Double = 1e-10, scale: Double, size: JList[Int], + wRegularizer: Regularizer[T] = null): NormalizeScale[T] = + new NormalizeScale[T](p, eps, scale, size.asScala.toArray, wRegularizer) + + def createDetectionOutputSSD(nClasses: Int, + shareLocation: Boolean, + bgLabel: Int, + nmsThresh: Double, + nmsTopk: Int, + keepTopK: Int, + confThresh: Double, + varianceEncodedInTarget: Boolean, + confPostProcess: Boolean): DetectionOutputSSD[T] = + new DetectionOutputSSD[T](nClasses, shareLocation, bgLabel, nmsThresh.toFloat, + nmsTopk, keepTopK, confThresh.toFloat, varianceEncodedInTarget, confPostProcess) + + def createDetectionOutputFrcnn(nmsThresh: Float = 0.3f, nClasses: Int, + bboxVote: Boolean, maxPerImage: Int = 100, thresh: Double = 0.05): DetectionOutputFrcnn = { + new DetectionOutputFrcnn(nmsThresh, nClasses, bboxVote, maxPerImage, thresh) + } + + def createProposal(preNmsTopN: Int, postNmsTopN: Int, + ratios: JList[Double], scales: JList[Double], + rpnPreNmsTopNTrain: Int = 12000, rpnPostNmsTopNTrain: Int = 2000): Proposal = { + new Proposal(preNmsTopN, postNmsTopN, ratios.asScala.toArray.map(_.toFloat), + scales.asScala.toArray.map(_.toFloat), rpnPreNmsTopNTrain, rpnPostNmsTopNTrain) + } + def createHFlip(): HFlip = { HFlip() } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala new file mode 100644 index 00000000000..31287c35199 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala @@ -0,0 +1,363 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.optim.L2Regularizer +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import org.scalatest.{FlatSpec, Matchers} + +class NormalizeScaleSpec extends FlatSpec with Matchers { + "Normalize with 4d" should "work properly" in { + val input = Tensor(Storage(Array( + 0.5507978797, 0.7081478238, 0.2909047306, 0.5108276010, + 0.8929469585, 0.8962931037, 0.1255853176, 0.2072428763, + 0.0514672026, 0.4408098459, 0.0298762117, 0.4568332136, + 0.6491440535, 0.2784872949, 0.6762549281, 0.5908628106, + 0.0239818823, 0.5588541031, 0.2592524588, 0.4151012003, + 0.2835250795, 0.6931379437, 0.4404537082, 0.1568677425, + 0.5446490049, 0.7803147435, 0.3063635230, 0.2219578773, + 0.3879712522, 0.9363836646, 0.9759954214, 0.6723836660, + 0.9028341174, 0.8457508683, 0.3779940307, 0.0922170058) + .map(x => x.toFloat))).resize(2, 3, 2, 3) + + val expectedOutput = Tensor(Storage(Array( + 12.8011369705, 17.9583473206, 7.8839497566, 11.3913326263, + 19.9816169739, 15.5767812729, 2.9187383652, 5.2555966377, + 1.3948376179, 9.8299531937, 0.6685447693, 7.9393572807, + 15.0868082047, 7.0623264313, 18.3275127411, 13.1760978699, + 0.5366464257, 9.7123899460, 4.5191359520, 7.4756622314, + 5.7009272575, 12.4241008759, 12.6179037094, 3.2889757156, + 9.4940004349, 14.0528850555, 6.1601471901, 3.9784679413, + 11.1144113541, 19.6327362061, 17.0129756927, 12.1091270447, + 18.1535682678, 15.1595993042, 10.8285894394, 1.9334726334) + .map(x => x.toFloat))).resize(2, 3, 2, 3) + + + val normalizer = Normalize[Float](2) + val output = normalizer.forward(input) + val mul = CMul[Float](size = Array(1, 3, 1, 1)) + mul.weight.setValue(1, 1, 1, 1, 20) + mul.weight.setValue(1, 2, 1, 1, 20) + mul.weight.setValue(1, 3, 1, 1, 20) + mul.forward(output) should be(expectedOutput) + } + + "normalize with more data" should "work properly" in { + val input = Tensor(Storage(Array( + 0.5507978797, 0.7081478238, 0.2909047306, 0.5108276010, + 0.8929469585, 0.8962931037, 0.1255853176, 0.2072428763, + 0.0514672026, 0.4408098459, 0.0298762117, 0.4568332136, + 0.6491440535, 0.2784872949, 0.6762549281, 0.5908628106, + 0.0239818823, 0.5588541031, 0.2592524588, 0.4151012003, + 0.2835250795, 0.6931379437, 0.4404537082, 0.1568677425, + 0.5446490049, 0.7803147435, 0.3063635230, 0.2219578773, + 0.3879712522, 0.9363836646, 0.9759954214, 0.6723836660, + 0.9028341174, 0.8457508683, 0.3779940307, 0.0922170058, + 0.6534109116, 0.5578407645, 0.3615647554, 0.2250545025, + 0.4065199196, 0.4689402580, 0.2692355812, 0.2917927802, + 0.4576863945, 0.8605338931, 0.5862529278, 0.2834878564, + 0.2779774964, 0.4546220899, 0.2054103464, 0.2013787180, + 0.5140350461, 0.0872293711, 0.4835855365, 0.3621762097, + 0.7076866031, 0.7467462420, 0.6910929084, 0.6891804338, + 0.3736001253, 0.6681348085, 0.3398486674, 0.5727938414, + 0.3258071542, 0.4451450408, 0.0615289323, 0.2426754236, + 0.9716026187, 0.2305842042, 0.6914775372, 0.6504768729, + 0.7239391208, 0.4750885963, 0.5966637731, 0.0669694245, + 0.0725621358, 0.1989760250, 0.1518609971, 0.1001043469, + 0.1292938590, 0.5532777309, 0.1878148317, 0.9521012306, + 0.6816117764, 0.5410196781, 0.7071806192, 0.2638866603, + 0.9267256856, 0.8391930461) + .map(x => x.toFloat))).reshape(Array(2, 5, 3, 3)) + + val expectedOutput = Tensor(Storage(Array( + 10.7962512970, 13.2859182358, 4.9504461288, 6.9418644905, + 13.7528429031, 11.7681846619, 2.0863511562, 4.4173331261, + 1.3001513481, 8.6403636932, 0.5605226755, 7.7741193771, + 8.8215084076, 4.2891592979, 8.8791189194, 9.8160142899, + 0.5111681819, 14.1176309586, 5.0816369057, 7.7879228592, + 4.8248634338, 9.4193611145, 6.7837071419, 2.0596482754, + 9.0482635498, 16.6322250366, 7.7392773628, 4.3506212234, + 7.2789239883, 15.9348278046, 13.2632369995, 10.3558073044, + 11.8540668488, 14.0504741669, 8.0568532944, 2.3295626640, + 12.8075809479, 10.4659318924, 6.1528968811, 3.0583662987, + 6.2610712051, 6.1571102142, 4.4728155136, 6.2194943428, + 11.5619573593, 11.7851772308, 12.7779006958, 3.9678306580, + 5.2822084427, 8.9109497070, 2.9043090343, 7.0540165901, + 7.1670479774, 1.5497876406, 6.6227970123, 7.8939504623, + 9.9051179886, 14.1898860931, 13.5459632874, 9.7443618774, + 13.0866928101, 9.3156175613, 6.0380268097, 7.8445219994, + 7.1012549400, 6.2304615974, 1.1691904068, 4.7566289902, + 13.7375459671, 8.0770444870, 9.6410789490, 11.5568981171, + 9.9144859314, 10.3549757004, 8.3511896133, 1.2725722790, + 1.4222748280, 2.8133335114, 5.3194799423, 1.3957271576, + 2.2971394062, 7.5772452354, 4.0935902596, 13.3260612488, + 12.9521827698, 10.6044101715, 9.9988679886, 9.2435827255, + 12.9210777283, 14.9097824097) + .map(x => x.toFloat))).resize(2, 5, 3, 3) + + + val normalizer = new NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1)) + val output = normalizer.forward(input) + + output should be(expectedOutput) + } + + "A Normalize Module" should "generate correct gradInput with data" in { + var input = Tensor(Storage(Array( + 0.5507978797, 0.7081478238, 0.2909047306, 0.5108276010, + 0.8929469585, 0.8962931037, 0.1255853176, 0.2072428763, + 0.0514672026, 0.4408098459, 0.0298762117, 0.4568332136, + 0.6491440535, 0.2784872949, 0.6762549281, 0.5908628106, + 0.0239818823, 0.5588541031, 0.2592524588, 0.4151012003, + 0.2835250795, 0.6931379437, 0.4404537082, 0.1568677425, + 0.5446490049, 0.7803147435, 0.3063635230, 0.2219578773, + 0.3879712522, 0.9363836646, 0.9759954214, 0.6723836660, + 0.9028341174, 0.8457508683, 0.3779940307, 0.0922170058, + 0.6534109116, 0.5578407645, 0.3615647554, 0.2250545025, + 0.4065199196, 0.4689402580, 0.2692355812, 0.2917927802, + 0.4576863945, 0.8605338931, 0.5862529278, 0.2834878564, + 0.2779774964, 0.4546220899, 0.2054103464, 0.2013787180, + 0.5140350461, 0.0872293711, 0.4835855365, 0.3621762097, + 0.7076866031, 0.7467462420, 0.6910929084, 0.6891804338, + 0.3736001253, 0.6681348085, 0.3398486674, 0.5727938414, + 0.3258071542, 0.4451450408, 0.0615289323, 0.2426754236, + 0.9716026187, 0.2305842042, 0.6914775372, 0.6504768729, + 0.7239391208, 0.4750885963, 0.5966637731, 0.0669694245, + 0.0725621358, 0.1989760250, 0.1518609971, 0.1001043469, + 0.1292938590, 0.5532777309, 0.1878148317, 0.9521012306, + 0.6816117764, 0.5410196781, 0.7071806192, 0.2638866603, + 0.9267256856, 0.8391930461) + .map(x => x.toFloat))).reshape(Array(2, 5, 3, 3)) + + var gradOut = Tensor(Storage(Array( + 0.7263194919, 0.4802399576, 0.8421031833, 0.7447523475, + 0.6603258848, 0.9139752388, 0.6336655617, 0.3659405708, + 0.5528445840, 0.1963805705, 0.1920723021, 0.7256696224, + 0.7849367261, 0.9720983505, 0.8509714007, 0.5435943007, + 0.0897908732, 0.4888732433, 0.9279363751, 0.7876182199, + 0.4850942194, 0.4552793503, 0.2179857641, 0.1772133857, + 0.0736236721, 0.8923931718, 0.6401765943, 0.1433323175, + 0.4141269326, 0.0491089262, 0.2093733549, 0.7307081223, + 0.6511227489, 0.4789783061, 0.2747805119, 0.6522231102, + 0.9564495087, 0.4355205595, 0.0701325014, 0.0577314869, + 0.0828710198, 0.9597072005, 0.5407608151, 0.8374624252, + 0.1700335443, 0.2603450716, 0.6919775009, 0.8955703378, + 0.3406884968, 0.0646732002, 0.8641196489, 0.2908724546, + 0.7410824299, 0.1580336541, 0.6949634552, 0.8414196372, + 0.7271520495, 0.3591075242, 0.7266897559, 0.1394671202, + 0.3138191104, 0.4195827544, 0.8772120476, 0.1537402123, + 0.8801248074, 0.7989643216, 0.9716243148, 0.3677029908, + 0.2049397677, 0.2405703217, 0.8278627992, 0.9652281404, + 0.6988099813, 0.4824970365, 0.2870497704, 0.8336879015, + 0.8721795082, 0.0921315923, 0.2159494758, 0.8317610621, + 0.8483039141, 0.3146530092, 0.2792946100, 0.4308150113, + 0.5394464731, 0.0955668166, 0.8369121552, 0.5347348452, + 0.7749677896, 0.2308362722) + .map(x => x.toFloat))).reshape(Array(2, 5, 3, 3)) + + val expectedOutput = Tensor(Storage(Array( + 10.7962512970, 13.2859182358, 4.9504461288, 6.9418644905, + 13.7528429031, 11.7681846619, 2.0863511562, 4.4173331261, + 1.3001513481, 8.6403636932, 0.5605226755, 7.7741193771, + 8.8215084076, 4.2891592979, 8.8791189194, 9.8160142899, + 0.5111681819, 14.1176309586, 5.0816369057, 7.7879228592, + 4.8248634338, 9.4193611145, 6.7837071419, 2.0596482754, + 9.0482635498, 16.6322250366, 7.7392773628, 4.3506212234, + 7.2789239883, 15.9348278046, 13.2632369995, 10.3558073044, + 11.8540668488, 14.0504741669, 8.0568532944, 2.3295626640, + 12.8075809479, 10.4659318924, 6.1528968811, 3.0583662987, + 6.2610712051, 6.1571102142, 4.4728155136, 6.2194943428, + 11.5619573593, 11.7851772308, 12.7779006958, 3.9678306580, + 5.2822084427, 8.9109497070, 2.9043090343, 7.0540165901, + 7.1670479774, 1.5497876406, 6.6227970123, 7.8939504623, + 9.9051179886, 14.1898860931, 13.5459632874, 9.7443618774, + 13.0866928101, 9.3156175613, 6.0380268097, 7.8445219994, + 7.1012549400, 6.2304615974, 1.1691904068, 4.7566289902, + 13.7375459671, 8.0770444870, 9.6410789490, 11.5568981171, + 9.9144859314, 10.3549757004, 8.3511896133, 1.2725722790, + 1.4222748280, 2.8133335114, 5.3194799423, 1.3957271576, + 2.2971394062, 7.5772452354, 4.0935902596, 13.3260612488, + 12.9521827698, 10.6044101715, 9.9988679886, 9.2435827255, + 12.9210777283, 14.9097824097) + .map(x => x.toFloat))).resize(2, 5, 3, 3) + + var expectedGradInput = Tensor(Storage(Array( + -0.1148514375, -3.5743882656, 11.5156641006, 5.5605020523, + -1.9116518497, -0.4783028662, 9.0997104645, 2.1677801609, + 12.6471023560, -7.6364088058, 3.0726387501, 7.9287652969, + 4.8718037605, 11.2039165497, 1.7579507828, 2.3150684834, + 1.2621252537, -1.9694392681, 11.4335355759, 7.4001874924, + 5.5116991997, -0.0008006793, -2.6020886898, 0.1427903473, + -4.9673018456, -2.1851525307, 8.3221836090, -2.9738335609, + 0.8750523329, -8.2246103287, -5.8676581383, 2.1566159725, + -4.0205812454, -1.6554248333, -4.4157114029, 14.1134653091, + 1.7222809792, -1.7423020601, -2.3049762249, -1.2245724201, + -4.2239460945, 6.0719752312, 5.9235711098, 9.9203786850, + -7.4317178726, -3.7739505768, -4.3251948357, 9.2204341888, + 2.4390163422, -5.0301928520, 10.6459598541, 1.4211689234, + 3.2438371181, 1.2873532772, 5.3931946754, 6.3499174118, + 1.9036595821, -4.0151600838, 4.6700425148, -3.3019390106, + -5.2732696533, -3.3638772964, 9.6617259979, -2.7798123360, + 8.3975009918, 5.9782786369, 17.5699920654, 3.8455066681, + -4.5374197960, -1.6124024391, 2.0067462921, 5.8112678528, + 3.3959164619, -5.2109961510, -2.9581990242, 14.8699150085, + 16.0902061462, -0.2199868113, 0.9526393414, 10.2165107727, + 12.8180732727, -0.4096293151, -0.1299941391, -5.1015834808, + 0.3571199179, -5.6215124130, 6.4215326309, 7.2418398857, + -1.9749751091, -10.5258388519) + .map(x => x.toFloat))).reshape(Array(2, 5, 3, 3)) + + val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1)) + + val out = module.forward(input) + out should be(expectedOutput) + val gradInput = module.backward(input, gradOut) + gradInput.map(expectedGradInput, (a, b) => { + assert(Math.abs(a - b) < 1e-5) + a + }) + + input = Tensor(Storage(Array( + 0.9652933478, 0.7510272861, 0.3430938721, 0.9485276341, + 0.7005117536, 0.8405610919, 0.0454973057, 0.0556415394, + 0.7427372932, 0.3046864271, 0.5167843699, 0.1562624276, + 0.9779524207, 0.5027510524, 0.8290010691, 0.0740377977, + 0.4789154530, 0.0622794814, 0.8842414021, 0.4458101690, + 0.0685499161, 0.0764962807, 0.5387926698, 0.0755664036, + 0.1837723106, 0.4363570809, 0.4977828264, 0.5833119154, + 0.6205126643, 0.3728114963, 0.6187365651, 0.1572446525, + 0.2755084634, 0.7987182736, 0.1530892998, 0.2233229727, + 0.2429781854, 0.4795072973, 0.0007455220, 0.0303113610, + 0.4615481496, 0.1625206918, 0.6795018315, 0.7952045798, + 0.5781633854, 0.6947649717, 0.3909580112, 0.0462962016, + 0.4394215345, 0.3719803095, 0.5970032215, 0.1342181712, + 0.2277128994, 0.8147824407, 0.2643255293, 0.4103201926, + 0.9359721541, 0.2755524516, 0.1452899575, 0.7020201087, + 0.5670665503, 0.6115938425, 0.0420308523, 0.4172669053, + 0.0042664343, 0.2465354651, 0.7060561776, 0.0615407154, + 0.2946934998, 0.9881127477, 0.9712222219, 0.4818463922, + 0.7356933951, 0.6326557994, 0.4656930566, 0.8570664525, + 0.1725182533, 0.8284319043, 0.0420695245, 0.8669536710, + 0.6759966016, 0.4459141195, 0.6715702415, 0.4603685141, + 0.7800032496, 0.1134440824, 0.5081574321, 0.8714895248, + 0.9275292754, 0.2203363329) + .map(x => x.toFloat))).reshape(Array(2, 5, 3, 3)) + + gradOut = Tensor(Storage(Array( + 0.2519302964, 0.9040125012, 0.3455173373, 0.0568725727, + 0.7148866653, 0.4144188464, 0.7353242040, 0.4770916402, + 0.5005655289, 0.4175619185, 0.2904849052, 0.9007108808, + 0.6687875986, 0.7888323665, 0.8957395554, 0.2501889467, + 0.9198206067, 0.8626050949, 0.5060852766, 0.5679021478, + 0.1149322391, 0.7621158361, 0.1732250005, 0.5338360667, + 0.1264373064, 0.0729170963, 0.6090274453, 0.4912031293, + 0.6748099327, 0.7908541560, 0.9826874733, 0.0840806812, + 0.6312121153, 0.5769748688, 0.6506421566, 0.5431794524, + 0.5573673844, 0.8685546517, 0.4520116150, 0.5349755883, + 0.6909636855, 0.0894949883, 0.4319253266, 0.1021342203, + 0.1378098875, 0.0021255584, 0.2097064406, 0.2166372687, + 0.5162444711, 0.5702082515, 0.8239611983, 0.4867111742, + 0.8914545774, 0.3950607777, 0.7446114421, 0.5527572632, + 0.6238250732, 0.2703920007, 0.9375950098, 0.7696546912, + 0.1455950290, 0.5374343991, 0.9957863092, 0.0490563326, + 0.3028198481, 0.7408133745, 0.0393614471, 0.7466710806, + 0.5734212995, 0.7317056656, 0.5508325696, 0.9394034743, + 0.3464016914, 0.5744694471, 0.0507799797, 0.5534328222, + 0.4193929434, 0.9831618071, 0.9456073046, 0.1382794082, + 0.0416156426, 0.1206750646, 0.1927469671, 0.7261679769, + 0.5439779758, 0.5349253416, 0.4645369649, 0.0066942186, + 0.3924719095, 0.9989384413) + .map(x => x.toFloat))).reshape(Array(2, 5, 3, 3)) + + val expectedOut = Tensor(Storage(Array( + 12.9988918304, 11.7118844986, 12.8350353241, 12.6591348648, + 12.4246129990, 13.7180128098, 0.8518700600, 1.0714917183, + 13.6318149567, 4.1029872894, 8.0589866638, 5.8457288742, + 13.0518417358, 8.9170351028, 13.5293521881, 1.3862485886, + 9.2224969864, 1.1430453062, 11.9074258804, 6.9521808624, + 2.5644311905, 1.0209262371, 9.5562858582, 1.2332487106, + 3.4408659935, 8.4029483795, 9.1360473633, 7.8550300598, + 9.6765766144, 13.9467630386, 8.2577142715, 2.7889668941, + 4.4963164330, 14.9548234940, 2.9480478764, 4.0987539291, + 3.2720077038, 7.4776701927, 0.0278897490, 0.4045381546, + 8.1862401962, 2.6523485184, 12.7226715088, 15.3132915497, + 10.6113109589, 11.4732933044, 7.2211399078, 0.7917130589, + 6.0494828224, 16.3954753876, 8.7067680359, 1.8625209332, + 2.6377274990, 13.7559747696, 4.3650503159, 7.5787663460, + 16.0060958862, 3.7935094833, 6.4038276672, 10.2383470535, + 7.8690776825, 7.0844383240, 0.7096070051, 6.8907122612, + 0.0788026229, 4.2160124779, 9.7202215195, 2.7124800682, + 4.2978463173, 13.7118587494, 11.2502174377, 8.1350145340, + 12.1491813660, 11.6853866577, 7.9638342857, 11.7991685867, + 7.6039476395, 12.0819520950, 0.5837910175, 10.0424156189, + 11.4128532410, 7.3637895584, 12.4041509628, 7.8727793694, + 10.7382450104, 5.0001831055, 7.4110307693, 12.0935001373, + 10.7440967560, 3.7199389935) + .map(x => x.toFloat))).reshape(Array(2, 5, 3, 3)) + + expectedGradInput = Tensor(Storage(Array( + -3.9140403271, 0.4445342720, -12.3271293640, -7.0794482231, + -0.2464490235, -5.2912111282, 13.1485214233, 8.5121822357, + -1.4850496054, 3.3167335987, -4.8647637367, 22.1938934326, + 0.8440631032, 4.7142181396, 2.7297582626, 3.6765637398, + 11.9015321732, 14.9369316101, 0.1219845936, 0.7516693473, + -0.7459176183, 9.5391139984, -6.8695392609, 7.6285362244, + -0.1342885494, -3.8908934593, 4.0252819061, 2.1994044781, + -0.7571096420, 2.1454718113, 8.0018997192, -1.4102233648, + 6.3503351212, -0.0697237849, 10.6717529297, 6.7603731155, + 5.6664791107, 4.8276038170, 16.8547420502, 6.8893437386, + 3.7386598587, -0.8701580763, -1.1627024412, -7.6827645302, + -5.7781653404, -4.0904965401, -1.0719879866, 3.0480358601, + 3.5878252983, -16.8572063446, 1.5917342901, 5.9322166443, + 8.9542407990, -3.7660286427, 10.7268571854, 5.0193696022, + -2.6081981659, 1.5156005621, 24.9250202179, -1.0341093540, + -1.4516141415, 2.5405220985, 16.2735252380, -1.6676687002, + 5.5392270088, 9.1717061996, -5.1128301620, 25.9635906219, + 3.2168364525, 4.1037721634, 0.5289410949, 9.6884002686, + 1.3518137932, 2.6080131531, -5.7372117043, 0.7549284697, + -0.9889311790, -0.1277296096, 12.8644371033, -3.6216723919, + -7.9556565285, -0.6550734639, -4.9347825050, 5.8881497383, + 1.2419538498, 10.7716960907, -2.0987062454, -5.2430224419, + -1.0421879292, 14.0429840088) + .map(x => x.toFloat))).reshape(Array(2, 5, 3, 3)) + + val out2 = module.forward(input) + val gradInput2 = module.backward(input, gradOut) + + out2 should be(expectedOut) + gradInput2.map(expectedGradInput, (a, b) => { + assert(Math.abs(a - b) < 1e-5) + a + }) + } + + "NormalizeScale serializer" should "work properly" in { + val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1), + wRegularizer = L2Regularizer[Float](0.2)) + + val input = Tensor[Float](1, 5, 3, 4).randn() + val res1 = module.forward(input).clone() + val tmpFile = java.io.File.createTempFile("module", ".bigdl") + module.saveModule(tmpFile.getAbsolutePath, overWrite = true) + val loaded = Module.loadModule[Float](tmpFile.getAbsolutePath) + val res2 = loaded.forward(input) + res1 should be(res2) + if (tmpFile.exists()) { + tmpFile.delete() + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PriorBoxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PriorBoxSpec.scala new file mode 100644 index 00000000000..c0116f2951f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PriorBoxSpec.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import org.scalatest.FlatSpec + +class PriorBoxSpec extends FlatSpec { + "Priorbox" should "work" in { + val isClip = false + val isFlip = true + val variances = Array(0.1f, 0.1f, 0.2f, 0.2f) + val minSizes = Array(460.8f) + val maxSizes = Array(537.6f) + val aspectRatios = Array(2f) +// val param = ComponetParam(256, 4, minSizes = Array(460.8f), +// maxSizes = Array(537.6f), aspectRatios = Array(2), isFlip, isClip, variances, 512) + val layer = PriorBox[Float](minSizes = minSizes, maxSizes = maxSizes, + _aspectRatios = aspectRatios, isFlip = isFlip, isClip = isClip, + variances = variances, step = 0, offset = 0.5f, imgH = 512, imgW = 512) + val input = Tensor[Float](8, 256, 1, 1) + + val out = layer.forward(input) + + val expectedStr = "0.0507812\n0.0507812\n0.949219\n0.949219\n0.0146376\n" + + "0.0146376\n0.985362\n0.985362\n-0.135291\n0.182354\n1.13529\n0.817646\n" + + "0.182354\n-0.135291\n0.817646\n1.13529\n0.1\n0.1\n0.2\n0.2\n0.1\n0.1\n0.2\n" + + "0.2\n0.1\n0.1\n0.2\n0.2\n0.1\n0.1\n0.2\n0.2" + + val expected = Tensor(Storage(expectedStr.split("\n").map(_.toFloat))).resize(1, 2, 16) + + out.map(expected, (a, b) => { + assert((a - b).abs < 1e-5); + a + }) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/util/BoundingBoxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/util/BoundingBoxSpec.scala index 51c2cd67062..1ebcde6da25 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/util/BoundingBoxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/util/BoundingBoxSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.transform.vision.image.util +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import org.scalatest.{FlatSpec, Matchers} class BoundingBoxSpec extends FlatSpec with Matchers { @@ -24,18 +25,232 @@ class BoundingBoxSpec extends FlatSpec with Matchers { val scaled = new BoundingBox() bbox.scaleBox(1.0f / 4, 1.0f / 2, scaled) - scaled should be (BoundingBox(0.5f, 1, 2.5f, 1.5f)) + scaled should be(BoundingBox(0.5f, 1, 2.5f, 1.5f)) } - "meetEmitCenterConstraint" should "work properly" in { + "meetEmitCenterConstraint" should "work properly" in { val bbox = BoundingBox(0, 0, 5, 3, false) val bbox2 = BoundingBox(1, 0, 7, 4, false) - bbox.meetEmitCenterConstraint(bbox2) should be (true) + bbox.meetEmitCenterConstraint(bbox2) should be(true) } - "meetEmitCenterConstraint false" should "work properly" in { + "meetEmitCenterConstraint false" should "work properly" in { val bbox = BoundingBox(0, 0, 5, 3, false) val bbox2 = BoundingBox(4, 0, 7, 4, false) - bbox.meetEmitCenterConstraint(bbox2) should be (false) + bbox.meetEmitCenterConstraint(bbox2) should be(false) + } + + "jaccardOverlap partial overlap" should "work properly" in { + val bbox1 = BoundingBox(0.2f, 0.3f, 0.3f, 0.5f) + val bbox2 = BoundingBox(0.1f, 0.1f, 0.3f, 0.4f) + val overlap = bbox1.jaccardOverlap(bbox2) + assert(Math.abs(overlap - 1.0 / 7) < 1e-6) + } + + "jaccardOverlap fully contain" should "work properly" in { + val bbox1 = BoundingBox(0.2f, 0.3f, 0.3f, 0.5f) + val bbox2 = BoundingBox(0.1f, 0.1f, 0.4f, 0.6f) + val overlap = bbox1.jaccardOverlap(bbox2) + assert(Math.abs(overlap - 2.0 / 15) < 1e-6) + } + + "jaccardOverlap outside" should "work properly" in { + val bbox1 = BoundingBox(0.2f, 0.3f, 0.3f, 0.5f) + val bbox2 = BoundingBox(0f, 0f, 0.1f, 0.1f) + val overlap = bbox1.jaccardOverlap(bbox2) + assert(Math.abs(overlap - 0) < 1e-6) + } + + "projectBbox" should "work properly" in { + val box1 = BoundingBox(0.222159f, 0.427017f, 0.606492f, 0.679355f) + val box2 = BoundingBox(0.418f, 0.396396f, 0.55f, 0.666667f) + val projBox = new BoundingBox() + val state = box1.projectBbox(box2, projBox) + state should be(true) + assert(Math.abs(projBox.x1 - 0.509561f) < 1e-5) + assert(Math.abs(projBox.y1 - 0f) < 1e-5) + assert(Math.abs(projBox.x2 - 0.853014f) < 1e-5) + assert(Math.abs(projBox.y2 - 0.949717f) < 1e-5) + } + + "meetEmitCenterConstraint true" should "work properly" in { + val box1 = BoundingBox(0.222159f, 0.427017f, 0.606492f, 0.679355f) + val box2 = BoundingBox(0.418f, 0.396396f, 0.55f, 0.666667f) + + val state = box1.meetEmitCenterConstraint(box2) + + state should be(true) + } + + "meetEmitCenterConstraint normalized false" should "work properly" in { + val box1 = BoundingBox(0.0268208f, 0.388175f, 0.394421f, 0.916685f) + val box2 = BoundingBox(0.418f, 0.396396f, 0.55f, 0.666667f) + + val state = box1.meetEmitCenterConstraint(box2) + + state should be(false) + } + + "getLocPredictions shared" should "work properly" in { + val num = 2 + val numPredsPerClass = 2 + val numLocClasses = 1 + val shareLoc = true + val dim = numPredsPerClass * numLocClasses * 4 + val loc = Tensor[Float](num, dim, 1, 1) + + val locData = loc.storage().array() + (0 until num).foreach(i => { + (0 until numPredsPerClass).foreach(j => { + val idx = i * dim + j * 4 + locData(idx) = i * numPredsPerClass * 0.1f + j * 0.1f + locData(idx + 1) = i * numPredsPerClass * 0.1f + j * 0.1f + locData(idx + 2) = i * numPredsPerClass * 0.1f + j * 0.1f + 0.2f + locData(idx + 3) = i * numPredsPerClass * 0.1f + j * 0.1f + 0.2f + }) + }) + + val out = BboxUtil.getLocPredictions(loc, numPredsPerClass, numLocClasses, shareLoc) + + assert(out.length == num) + + (0 until num).foreach(i => { + assert(out(i).length == 1) + val bboxes = out(i)(0) + assert(bboxes.size(1) == numPredsPerClass) + val startValue = i * numPredsPerClass * 0.1f + var j = 0 + while (j < numPredsPerClass) { + expectNear(bboxes(j + 1).valueAt(1), startValue + j * 0.1, 1e-6) + expectNear(bboxes(j + 1).valueAt(2), startValue + j * 0.1, 1e-6) + expectNear(bboxes(j + 1).valueAt(3), startValue + j * 0.1 + 0.2, 1e-6) + expectNear(bboxes(j + 1).valueAt(4), startValue + j * 0.1 + 0.2, 1e-6) + j += 1 + } + }) + } + + def expectNear(v1: Float, v2: Double, eps: Double): Unit = { + assert(Math.abs(v1 - v2) < eps) + } + + "decodeBoxes" should "work properly" in { + val priorBoxes = Tensor[Float](4, 4) + val priorVariances = Tensor[Float](4, 4) + val bboxes = Tensor[Float](4, 4) + var i = 1 + while (i < 5) { + priorBoxes.setValue(i, 1, 0.1f * i) + priorBoxes.setValue(i, 2, 0.1f * i) + priorBoxes.setValue(i, 3, 0.1f * i + 0.2f) + priorBoxes.setValue(i, 4, 0.1f * i + 0.2f) + + priorVariances.setValue(i, 1, 0.1f) + priorVariances.setValue(i, 2, 0.1f) + priorVariances.setValue(i, 3, 0.2f) + priorVariances.setValue(i, 4, 0.2f) + + bboxes.setValue(i, 1, 0f) + bboxes.setValue(i, 2, 0.75f) + bboxes.setValue(i, 3, Math.log(2).toFloat) + bboxes.setValue(i, 4, Math.log(3f / 2).toFloat) + i += 1 + } + + val decodedBboxes = BboxUtil.decodeBoxes(priorBoxes, priorVariances, false, bboxes, true) + + assert(decodedBboxes.size(1) == 4) + + i = 1 + while (i < 5) { + expectNear(decodedBboxes.valueAt(i, 1), 0 + (i - 1) * 0.1, 1e-5) + expectNear(decodedBboxes.valueAt(i, 2), 0.2 + (i - 1) * 0.1, 1e-5) + expectNear(decodedBboxes.valueAt(i, 3), 0.4 + (i - 1) * 0.1, 1e-5) + expectNear(decodedBboxes.valueAt(i, 4), 0.5 + (i - 1) * 0.1, 1e-5) + i += 1 + } + + } + + + "getPriorVariance" should "work properly" in { + val num_channels = 2 + val num_priors = 2 + val dim = num_priors * 4 + val prior = Tensor[Float](1, num_channels, dim, 1) + val prior_data = prior.storage().array() + for (i <- 0 until num_priors) { + prior_data(i * 4) = i * 0.1f + prior_data(i * 4 + 1) = i * 0.1f + prior_data(i * 4 + 2) = i * 0.1f + 0.2f + prior_data(i * 4 + 3) = i * 0.1f + 0.1f + for (j <- 0 until 4) { + prior_data(dim + i * 4 + j) = 0.1f + } + } + + val (boxes, variances) = BboxUtil.getPriorBboxes(prior, num_priors) + assert(boxes.size(1) == num_priors) + assert(variances.size(1) == num_priors) + for (i <- 0 until num_priors) { + expectNear(boxes.valueAt(i + 1, 1), i * 0.1, 1e-5) + expectNear(boxes.valueAt(i + 1, 2), i * 0.1, 1e-5) + expectNear(boxes.valueAt(i + 1, 3), i * 0.1 + 0.2, 1e-5) + expectNear(boxes.valueAt(i + 1, 4), i * 0.1 + 0.1, 1e-5) + expectNear(variances.valueAt(i + 1, 1), 0.1, 1e-5) + expectNear(variances.valueAt(i + 1, 2), 0.1, 1e-5) + expectNear(variances.valueAt(i + 1, 3), 0.1, 1e-5) + expectNear(variances.valueAt(i + 1, 4), 0.1, 1e-5) + } + } + + "getGroundTruths" should "work properly" in { + val input = Tensor(Storage(Array( + 0.0f, 1.0f, 0.14285715f, 0.1904762f, 0.23809524f, 0.2857143f, 0.33333334f, + 0.0f, 1.0f, 0.47619048f, 0.52380955f, 0.5714286f, 0.61904764f, 0.6666667f, + 1.0f, 3.0f, 0.8095238f, 0.85714287f, 0.9047619f, 0.95238096f, 1.0f + ))).resize(3, 7) + + val gt0 = Tensor(Storage(Array( + 0.0f, 1.0f, 0.14285715f, 0.1904762f, 0.23809524f, 0.2857143f, 0.33333334f, + 0.0f, 1.0f, 0.47619048f, 0.52380955f, 0.5714286f, 0.61904764f, 0.6666667f + ))).resize(2, 7) + + val gt1 = Tensor(Storage(Array( + 1.0f, 3.0f, 0.8095238f, 0.85714287f, 0.9047619f, 0.95238096f, 1.0f + ))).resize(1, 7) + + val gts = BboxUtil.getGroundTruths(input) + + gts(0) should be(gt0) + gts(1) should be(gt1) + + val gts2 = BboxUtil.getGroundTruths(gt1) + + gts2(0) should be(gt1) + + val label = Tensor(Storage(Array( + 3.0, 8.0, 0.0, 0.241746, 0.322738, 0.447184, 0.478388, + 3.0, 8.0, 0.0, 0.318659, 0.336546, 0.661729, 0.675461, + 3.0, 8.0, 0.0, 0.56154, 0.300144, 0.699173, 0.708098, + 3.0, 8.0, 0.0, 0.220494, 0.327759, 0.327767, 0.396797, + 3.0, 8.0, 0.0, 0.194182, 0.317717, 0.279191, 0.389266, + 4.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, + 5.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, + 6.0, 10.0, 0.0, 0.67894, 0.471823, 0.929308, 0.632044, + 6.0, 10.0, 0.0, 0.381443, 0.572376, 0.892489, 0.691713, + 7.0, 9.0, 0.0, 0.0, 0.0620616, 0.667269, 1.0 + ).map(_.toFloat))).resize(10, 7) + + + val labelgt = BboxUtil.getGroundTruths(label) + + labelgt.size should be(3) + labelgt(0).size(1) should be(5) + labelgt(0).valueAt(1, 1) should be(3) + labelgt(3).size(1) should be(2) + labelgt(3).valueAt(1, 1) should be(6) + labelgt(4).size(1) should be(1) + labelgt(4).valueAt(1, 1) should be(7) } } From d634c5de08968e32285d229bb08daddf3ef7fdec Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 12 Dec 2017 17:21:25 +0800 Subject: [PATCH 0575/1065] support serialization unittest auto-check (#2011) * support serialization auto check * fix typo * merge master --- dl/pom.xml | 5 + .../intel/analytics/bigdl/dllib/nn/Cell.scala | 35 +- .../analytics/bigdl/dllib/nn/Maxout.scala | 7 +- .../bigdl/dllib/nn/MultiRNNCell.scala | 34 +- .../dllib/nn/ops/FusedBatchNormGrad.scala | 6 +- .../bigdl/dllib/nn/ops/LRNGrad.scala | 5 + .../utils/serializer/DataConverter.scala | 2 +- .../utils/serializer/ModuleSerializer.scala | 1 + .../serializer/ModuleSerializerSpec.scala | 3321 ++++++++--------- 9 files changed, 1728 insertions(+), 1688 deletions(-) diff --git a/dl/pom.xml b/dl/pom.xml index f75566a260a..4af7aa3cc42 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -122,6 +122,11 @@ scalatest_${scala.major.version} test + + org.reflections + reflections 0.9.9-RC1 + test + com.google.guava guava diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala index b3b28a920b9..160eabe8654 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala @@ -309,31 +309,32 @@ abstract class Cell[T : ClassTag]( object CellSerializer extends ModuleSerializable { - override def doLoadModule[T: ClassTag](context : DeserializeContext) + private[nn] def populateCellAttributes[T: ClassTag](context : DeserializeContext, + cell : Cell[T]) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val module = super.doLoadModule(context) - val cellModule = module.asInstanceOf[Cell[T]] - val attrMap = context.bigdlModule.getAttrMap - cellModule.cell = DataConverter.getAttributeValue(context, attrMap.get("cell")). + cell.cell = DataConverter.getAttributeValue(context, attrMap.get("cell")). asInstanceOf[AbstractModule[Activity, Activity, T]] val preTopologyAttr = attrMap.get("preTopology") - cellModule.preTopology = DataConverter.getAttributeValue(context, preTopologyAttr). + cell.preTopology = DataConverter.getAttributeValue(context, preTopologyAttr). asInstanceOf[TensorModule[T]] val includePreTopologyAttr = attrMap.get("includePreTopology") - cellModule.includePreTopology = DataConverter.getAttributeValue(context, + cell.includePreTopology = DataConverter.getAttributeValue(context, includePreTopologyAttr).asInstanceOf[Boolean] - - cellModule + cell } - override def doSerializeModule[T: ClassTag](context: SerializeContext[T], - cellModuleBuilder : BigDLModule.Builder) - (implicit ev: TensorNumeric[T]) : Unit = { + override def doLoadModule[T: ClassTag](context : DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val cell = super.doLoadModule(context).asInstanceOf[Cell[T]] + populateCellAttributes(context, cell) + } - super.doSerializeModule(context, cellModuleBuilder) + private[nn] def saveCellAttributes[T: ClassTag](context: SerializeContext[T], + cellModuleBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { val cellModule = context.moduleData.module.asInstanceOf[Cell[T]] val cellSerializerFlagBuilder = AttrValue.newBuilder @@ -356,4 +357,12 @@ object CellSerializer extends ModuleSerializable { cellModule.includePreTopology, scala.reflect.runtime.universe.typeOf[Boolean]) cellModuleBuilder.putAttr("includePreTopology", includePreTopologyBuilder.build) } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + cellModuleBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + + super.doSerializeModule(context, cellModuleBuilder) + saveCellAttributes(context, cellModuleBuilder) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala index 28cc79b9440..43af792d1dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala @@ -40,9 +40,10 @@ import scala.reflect.ClassTag * @param initWeight: initial weight * @param initBias: initial bias */ -class Maxout[T: ClassTag](inputSize: Int, outputSize: Int, maxoutNumber: Int, - withBias: Boolean = true, wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null, initWeight: Tensor[T] = null, initBias: Tensor[T] = null) +class Maxout[T: ClassTag](val inputSize: Int, val outputSize: Int, val maxoutNumber: Int, + val withBias: Boolean = true, val wRegularizer: Regularizer[T] = null, + val bRegularizer: Regularizer[T] = null, val initWeight: Tensor[T] = null, + val initBias: Tensor[T] = null) (implicit ev: TensorNumeric[T]) extends TensorModule[T] { val layer = Sequential().add(Linear(inputSize, outputSize * maxoutNumber, withBias = withBias, wRegularizer = wRegularizer, bRegularizer = bRegularizer, initWeight = initWeight, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala index 8ef8dd7055f..03f95a14fcf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala @@ -19,8 +19,10 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, ModuleData, ModuleSerializable, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer.DataConverter.ArrayConverter +import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.{T, Table} +import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -171,9 +173,37 @@ class MultiRNNCell[T : ClassTag](val cells: Array[Cell[T]])(implicit ev: TensorN } } -object MultiRNNCell { +object MultiRNNCell extends ModuleSerializable { def apply[@specialized(Float, Double) T: ClassTag](cells: Array[Cell[T]] )(implicit ev: TensorNumeric[T]): MultiRNNCell[T] = { new MultiRNNCell[T](cells) } + + override def doLoadModule[T: ClassTag](context : DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + + val attrMap = context.bigdlModule.getAttrMap + + val cells = DataConverter.getAttributeValue(context, attrMap.get("cells")). + asInstanceOf[Array[AbstractModule[_, _, T]]].map(_.asInstanceOf[Cell[T]]) + + val multiRNNCell = MultiRNNCell[T](cells) + + CellSerializer.populateCellAttributes(context, multiRNNCell) + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + cellModuleBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + + CellSerializer.saveCellAttributes(context, cellModuleBuilder) + + val cellsBuilder = AttrValue.newBuilder + ArrayConverter.setAttributeValue(context, cellsBuilder, + context.moduleData.module.asInstanceOf[MultiRNNCell[T]].cells, + scala.reflect.runtime.universe.typeOf[Array[_ <: + AbstractModule[_ <: Activity, _ <: Activity, _ <: Any]]]) + cellModuleBuilder.putAttr("cells", cellsBuilder.build) + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala index d35bc6078b3..00a42855276 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala @@ -35,15 +35,17 @@ import scala.reflect.ClassTag * * @param epsilon * @param dataFormat - * @param isTraining + * @param isTrain * @param ev$1 * @param ev * @tparam T Numeric type. Only support float/double now */ class FusedBatchNormGrad[T: ClassTag]( - epsilon: Float, dataFormat: DataFormat, isTraining: Boolean)(implicit ev: TensorNumeric[T]) + val epsilon: Float, val dataFormat: DataFormat, + val isTrain: Boolean = false)(implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T]{ + private val gMean = Tensor[Float]() private val gxMean = Tensor[Float]() private val saveStd = Tensor[Float]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala index 33c7aaf15e7..b7b53099ed0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala @@ -63,6 +63,11 @@ class LRNGrad[T: ClassTag]( } output } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T]), + Array[TensorNumeric[_]](ev, ev2)) + } } object LRNGrad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index 58c2a599787..f9e74813381 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -984,7 +984,7 @@ object DataConverter extends DataConverter{ arrayBuilder.setSize(methods.size) } } else if (valueType <:< universe. - typeOf[Array[_ <: AbstractModule[Activity, Activity, _ <: Any]]]) { + typeOf[Array[_ <: AbstractModule[_ <: Activity, _ <: Activity, _ <: Any]]]) { arrayBuilder.setDatatype(DataType.MODULE) if (value != null) { val modules = value.asInstanceOf[Array[_ <: AbstractModule[Activity, Activity, T]]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 06faa4db6af..e4319e495fd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -205,6 +205,7 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.ops.DecodeRaw", DecodeRawSerializer) registerModule("com.intel.analytics.bigdl.nn.ops.RandomUniform", RandomUniformOps) registerModule("com.intel.analytics.bigdl.nn.tf.StrideSlice", StrideSlice) + registerModule("com.intel.analytics.bigdl.nn.MultiRNNCell", MultiRNNCell) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index e39de81ee1e..7f2c0b16e17 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -16,107 +16,154 @@ package com.intel.analytics.bigdl.utils.serializer import java.io.File +import java.io.{File => JFile} +import java.lang.reflect.Modifier import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.models.lenet.LeNet5 -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule -import com.intel.analytics.bigdl.nn.ops.ParseExample -import com.intel.analytics.bigdl.nn.{VolumetricFullConvolution, _} +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC + +import scala.collection.JavaConverters._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, EluGrad, Equal, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.tf._ +import com.intel.analytics.bigdl.nn.{DenseToSparse, _} import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator.RNG -import com.intel.analytics.bigdl.utils.caffe.CaffeLoader +import com.intel.analytics.bigdl.utils.tf.TFRecordIterator +import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _, _} import com.intel.analytics.bigdl.utils.{T, Table} -import org.scalatest.{FlatSpec, Matchers} +import org.reflections.Reflections +import org.reflections.scanners.SubTypesScanner +import org.reflections.util.{ClasspathHelper, ConfigurationBuilder, FilterBuilder} +import org.scalatest.{BeforeAndAfterAll, FlatSpec, Matchers} import org.tensorflow.example._ -import serialization.Bigdl -import serialization.Bigdl.AttrValue -import serializer.TestCustomData +import org.tensorflow.framework.DataType -import scala.reflect.ClassTag -import scala.reflect.runtime.universe +import scala.collection.mutable import scala.util.Random -class ModuleSerializerSpec extends FlatSpec with Matchers { + +class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll { + + val pkg = "com.intel.analytics.bigdl.nn" + val postFix = "bigdl" + val excluded = new mutable.HashSet[String]() + val expected = new mutable.HashSet[String]() + val tested = new mutable.HashSet[String]() + + private def addExcluded(): Unit = { + excluded.add("com.intel.analytics.bigdl.nn.CellUnit") + excluded.add("com.intel.analytics.bigdl.nn.tf.ControlDependency") + excluded.add("com.intel.analytics.bigdl.utils.tf.AdapterForTest") + excluded.add("com.intel.analytics.bigdl.utils.serializer.TestModule") + excluded.add("com.intel.analytics.bigdl.utils.ExceptionTest") + } + + override protected def beforeAll() = { + addExcluded + val reflections = new Reflections(new ConfigurationBuilder() + .filterInputsBy(new FilterBuilder(). + excludePackage("com.intel.analytics.bigdl.utils.tf.loaders")) + .setUrls(ClasspathHelper.forPackage(pkg)) + .setScanners(new SubTypesScanner())) + + val subTypes = reflections.getSubTypesOf(classOf[AbstractModule[_, _, _]]) + .asScala.filter(sub => !Modifier.isAbstract(sub.getModifiers)). + filter(sub => !excluded.contains(sub.getName)) + subTypes.foreach(sub => expected.add(sub.getName)) + } + + private def runSerializationTest(module : AbstractModule[_, _, Float], + input : Activity, cls: Class[_] = null) : Unit = { + val name = module.getName + val serFile = File.createTempFile(name, postFix) + val originForward = module.evaluate().forward(input) + + ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) + RNG.setSeed(1000) + val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) + + val afterLoadForward = loadedModule.forward(input) + + if (serFile.exists) { + serFile.delete + } + + afterLoadForward should be (originForward) + if (cls != null) { + tested.add(cls.getName) + } else { + tested.add(module.getClass.getName) + } + } "Abs serializer" should "work properly" in { val abs = Abs[Float, Float]().setName("abs") - val tensor1 = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor[Float]() - val res1 = abs.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/abs.bigdl", null, abs, true) - val loadedModule = ModuleLoader.loadFromFile("/tmp/abs.bigdl") - val res2 = loadedModule.forward(tensor2) - res1 should be (res2) + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(abs, input) + } + + "ActivityRegularization serializer" should "work properly" in { + val activityRegularization = ActivityRegularization[Float](l1 = 0.01, l2 = 0.01). + setName("activityRegularization") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(activityRegularization, input) } "Add serializer" should "work properly" in { - val add = Add(5) - val tensor1 = Tensor(5).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - val res1 = add.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/add.bigdl", null, add, true) - val loadedAdd = ModuleLoader.loadFromFile("/tmp/add.bigdl") - val res2 = loadedAdd.forward(tensor2) - res1 should be (res2) + val add = Add[Float](5).setName("add") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(add, input) } "AddConst serializer" should "work properly" in { - val addconst = AddConstant(5) - val tensor1 = Tensor(5).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - val res1 = addconst.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/addconst.bigdl", null, addconst, true) - val loadedAddConst = ModuleLoader.loadFromFile("/tmp/addconst.bigdl") - val res2 = loadedAddConst.forward(tensor2) - res1 should be (res2) + val addconst = AddConstant[Float](5).setName("addconst") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(addconst, input) } "BatchNormalization serializer" should "work properly" in { - val batchNorm = BatchNormalization(5) - val tensor1 = Tensor(2, 5).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = batchNorm.forward(tensor1) - ModulePersister.saveToFile("/tmp/batchNorm.bigdl", null, batchNorm, true) - val loadedBatchNorm = ModuleLoader.loadFromFile("/tmp/batchNorm.bigdl") - val res2 = loadedBatchNorm.forward(tensor2) - res1 should be (res2) + val batchNorm = BatchNormalization[Float](5).setName("batchNorm") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(batchNorm, input) + } + + "BifurcateSplitTable serializer" should "work properly" in { + val batchNorm = BifurcateSplitTable[Float](1).setName("batchNorm") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(batchNorm, input) } "BiLinear serializer" should "work properly" in { - val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(5, 3).apply1(e => Random.nextFloat()) + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 3).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 + val biLinear = Bilinear[Float](5, 3, 2) + runSerializationTest(biLinear, input) + } - val biLinear = Bilinear(5, 3, 2) - val res1 = biLinear.forward(input) - ModulePersister.saveToFile("/tmp/biLinear.bigdl", null, biLinear, true) - val loadedBiLinear = ModuleLoader.loadFromFile("/tmp/biLinear.bigdl") - val res2 = loadedBiLinear.forward(input) - res1 should be (res2) + "BinaryThreshold serializer" should "work properly" in { + val binaryThreshold = BinaryThreshold[Float]().setName("binaryThreshold") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(binaryThreshold, input) } "BinaryTreeLSTM serializer" should "work properly" in { RNG.setSeed(1000) - val binaryTreeLSTM = BinaryTreeLSTM(2, 2) + val binaryTreeLSTM = BinaryTreeLSTM[Float](2, 2).setName("binaryTreeLSTM") val inputs = - Tensor( + Tensor[Float]( T(T(T(1f, 2f), T(2f, 3f), T(4f, 5f)))) val tree = - Tensor( + Tensor[Float]( T(T(T(2f, 5f, -1f), T(0f, 0f, 1f), T(0f, 0f, 2f), @@ -125,1803 +172,1064 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val input = T(inputs, tree) - val res1 = binaryTreeLSTM.forward(input) - val res11 = binaryTreeLSTM.forward(input) - res1 should be (res11) - ModulePersister.saveToFile("/tmp/binaryTreeLSTM.bigdl", null, binaryTreeLSTM, true) - RNG.setSeed(1000) - val loadedBinaryTreeLSTM = ModuleLoader.loadFromFile("/tmp/binaryTreeLSTM.bigdl") - val res2 = loadedBinaryTreeLSTM.forward(input) - res1 should be (res2) - + runSerializationTest(binaryTreeLSTM, input) } "BiRecurrent serializer" should "work properly" in { - val input1 = Tensor(1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - RNG.setSeed(100) - val biRecurrent = BiRecurrent().add(RnnCell(6, 4, Sigmoid())) - val res1 = biRecurrent.forward(input1) - ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", null, biRecurrent, true) + val input = Tensor[Float](1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) RNG.setSeed(100) - val loadedRecurent = ModuleLoader.loadFromFile("/tmp/biRecurrent.bigdl") - val res2 = loadedRecurent.forward(input2) - res1 should be (res2) + val biRecurrent = BiRecurrent[Float]().add(RnnCell[Float](6, 4, + Sigmoid[Float]())).setName("biRecurrent") + runSerializationTest(biRecurrent, input) } - "BiRecurrent serializer" should "work properly with BatchNormParams" in { - val input1 = Tensor(1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) + "BiRecurrent serializer with BatchNormParams" should "work properly" in { + val input = Tensor[Float](1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) RNG.setSeed(100) - val biRecurrent = BiRecurrent(batchNormParams = BatchNormParams()).add(RnnCell(6, 4, Sigmoid())) - val res1 = biRecurrent.forward(input1) - ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", null, biRecurrent, true) - RNG.setSeed(100) - val loadedRecurent = ModuleLoader.loadFromFile("/tmp/biRecurrent.bigdl") - val res2 = loadedRecurent.forward(input2) - res1 should be (res2) + val biRecurrent = BiRecurrent[Float](batchNormParams = + BatchNormParams()).add(RnnCell[Float](6, 4, Sigmoid[Float]())).setName("biRecurrentWithNorm") + runSerializationTest(biRecurrent, input) } + "BiRecurrent serializer" should "work properly with isSplitInput" in { - val input1 = Tensor(1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - RNG.setSeed(100) - val biRecurrent = BiRecurrent(isSplitInput = false).add(RnnCell(6, 4, Sigmoid())) - val res1 = biRecurrent.forward(input1) - ModulePersister.saveToFile("/tmp/biRecurrent.bigdl", null, biRecurrent, true) - RNG.setSeed(100) - val loadedRecurent = ModuleLoader.loadFromFile("/tmp/biRecurrent.bigdl") - val res2 = loadedRecurent.forward(input2) - res1 should be (res2) + val input = Tensor[Float](1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) + val biRecurrent = BiRecurrent[Float](isSplitInput = false) + .add(RnnCell[Float](6, 4, Sigmoid[Float]())).setName("biRecurrentWithSplit") + runSerializationTest(biRecurrent, input) } "Bottle serializer" should "work properly" in { - val input1 = Tensor(10).apply1(e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - - val bottle = new Bottle(Linear(10, 2).asInstanceOf[Module[Float]], 2, 2) + val input = Tensor[Float](10).apply1(e => Random.nextFloat()) - val res1 = bottle.forward(input1) - ModulePersister.saveToFile("/tmp/bottle.bigdl", null, bottle, true) - val loadedBottle = ModuleLoader.loadFromFile("/tmp/bottle.bigdl") - val res2 = loadedBottle.forward(input2) - res1 should be (res2) + val bottle = new Bottle[Float](Linear[Float](10, 2). + asInstanceOf[Module[Float]], 2, 2).setName("bottle") + runSerializationTest(bottle, input) } "Caddserializer" should "work properly" in { - val input1 = Tensor(5, 1).apply1(e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val cadd = CAdd(Array(5, 1)) - val res1 = cadd.forward(input1) - ModulePersister.saveToFile("/tmp/cadd.bigdl", null, cadd, true) - val loadedCadd = ModuleLoader.loadFromFile("/tmp/cadd.bigdl") - val res2 = loadedCadd.forward(input2) - res1 should be (res2) + val input = Tensor[Float](5, 1).apply1(e => Random.nextFloat()) + val cadd = CAdd[Float](Array(5, 1)).setName("cadd") + runSerializationTest(cadd, input) } "CaddTable serializer" should "work properly" in { - val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val caddTable = CAddTable(false) - - val res1 = caddTable.forward(input) - ModulePersister.saveToFile("/tmp/caddTable.bigdl", null, caddTable, true) - val loadedCaddTable = ModuleLoader.loadFromFile("/tmp/caddTable.bigdl") - val res2 = loadedCaddTable.forward(input) - res1 should be (res2) + val caddTable = CAddTable[Float](false).setName("caddTable") + runSerializationTest(caddTable, input) } "CAveTable serializer" should "work properly" in { - val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val caveTable = CAveTable(false) - - val res1 = caveTable.forward(input) - ModulePersister.saveToFile("/tmp/caveTable.bigdl", null, caveTable, true) - val loadedCaddTable = ModuleLoader.loadFromFile("/tmp/caveTable.bigdl") - val res2 = loadedCaddTable.forward(input) - res1 should be (res2) + val caveTable = CAveTable[Float](false).setName("caveTable") + runSerializationTest(caveTable, input) } - "VolumetricAveragePooling serializer" should "work properly" in { - val volumetricAveragePooling = VolumetricAveragePooling(2, 2, 2, 1, 1, 1, 0, 0, 0) - val input1 = Tensor(1, 2, 3, 3).apply1(_ => Random.nextFloat()) - val input2 = Tensor(1, 2, 3, 3) - input2.copy(input1) - val res1 = volumetricAveragePooling.forward(input1) - - ModulePersister.saveToFile("/tmp/volumetricAveragePooling.bigdl", null, - volumetricAveragePooling, true) - val loadedVolumetricAveragePooling = - ModuleLoader.loadFromFile("/tmp/volumetricAveragePooling.bigdl") - val res2 = loadedVolumetricAveragePooling.forward(input1) - res1 should be (res2) - } - - "CDivTable serializer" should "work properly" in { - val cdivTable = new CDivTable() - val input1 = Tensor(10).apply1(e => Random.nextFloat()) - val input2 = Tensor(10).apply1(e => Random.nextFloat()) + val cdivTable = new CDivTable[Float]().setName("cdivTable") + val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val res1 = cdivTable.forward(input) - - ModulePersister.saveToFile("/tmp/cdivTable.bigdl", null, cdivTable, true) - val loadedCdivTable = ModuleLoader.loadFromFile("/tmp/cdivTable.bigdl") - val res2 = cdivTable.forward(input) - res1 should be (res2) + runSerializationTest(cdivTable, input) } "Clamp serializer" should "work properly" in { - val input1 = Tensor(10).apply1(e => Random.nextFloat()) - - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - - val clamp = Clamp(1, 10) - val res1 = clamp.forward(input1) - - ModulePersister.saveToFile("/tmp/clamp.bigdl", null, clamp, true) - val loadedClamp = ModuleLoader.loadFromFile("/tmp/clamp.bigdl") - val res2 = loadedClamp.forward(input2) - res1 should be (res2) + val input = Tensor[Float](10).apply1(e => Random.nextFloat()) + val clamp = Clamp[Float, Float](1, 10).setName("clamp") + runSerializationTest(clamp, input) } "CMaxTable serializer" should "work properly" in { - val cmaxTable = new CMaxTable() - val input1 = Tensor(10).apply1(e => Random.nextFloat()) - val input2 = Tensor(10).apply1(e => Random.nextFloat()) + val cmaxTable = new CMaxTable[Float]().setName("cmaxTable") + val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val res1 = cmaxTable.forward(input) - - ModulePersister.saveToFile("/tmp/cmaxTable.bigdl", null, cmaxTable, true) - val loadedCmaxTable = ModuleLoader.loadFromFile("/tmp/cmaxTable.bigdl") - val res2 = loadedCmaxTable.forward(input) - res1 should be (res2) + runSerializationTest(cmaxTable, input) } "CMinTable serializer" should "work properly" in { - val cminTable = new CMinTable() - val input1 = Tensor(10).apply1(e => Random.nextFloat()) - val input2 = Tensor(10).apply1(e => Random.nextFloat()) + val cminTable = new CMinTable[Float]().setName("cminTable") + val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val res1 = cminTable.forward(input) - - ModulePersister.saveToFile("/tmp/cminTable.bigdl", null, cminTable, true) - val loadedCminTable = ModuleLoader.loadFromFile("/tmp/cminTable.bigdl") - val res2 = loadedCminTable.forward(input) - res1 should be (res2) + runSerializationTest(cminTable, input) } - "CMulserializer" should "work properly" in { - val input1 = Tensor(5, 1).apply1(e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - - val cmul = CMul(Array(5, 1)) - - val res1 = cmul.forward(input1) - ModulePersister.saveToFile("/tmp/cmul.bigdl", null, cmul, true) - val loadedCmul = ModuleLoader.loadFromFile("/tmp/cmul.bigdl") - val res2 = loadedCmul.forward(input2) - res1 should be (res2) + "CMul serializer" should "work properly" in { + val input = Tensor[Float](5, 1).apply1(e => Random.nextFloat()) + val cmul = CMul[Float](Array(5, 1)).setName("cmul") + runSerializationTest(cmul, input) } "CMulTable serializer" should "work properly" in { - val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - val cmulTable = CMulTable() - - val res1 = cmulTable.forward(input) - ModulePersister.saveToFile("/tmp/cmulTable.bigdl", null, cmulTable, true) - val loadedCmulTable = ModuleLoader.loadFromFile("/tmp/cmulTable.bigdl") - val res2 = loadedCmulTable.forward(input) - res1 should be (res2) + val cmulTable = CMulTable[Float]().setName("cmulTable") + runSerializationTest(cmulTable, input) } "Concatserializer" should "work properly" in { - val input1 = Tensor(2, 2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - - val concat = Concat(2) - - concat.add(Abs()) - concat.add(Abs()) - - val res1 = concat.forward(input1) - ModulePersister.saveToFile("/tmp/concat.bigdl", null, concat, true) - val loadedConcat = ModuleLoader.loadFromFile("/tmp/concat.bigdl") - val res2 = loadedConcat.forward(input2) - res1 should be (res2) + val input = Tensor[Float](2, 2, 2).apply1(e => Random.nextFloat()) + val concat = Concat[Float](2).setName("concat") + concat.add(Abs[Float, Float]()) + concat.add(Abs[Float, Float]()) + runSerializationTest(concat, input) } "ConcatTable serializer" should "work properly" in { - val concatTable = ConcatTable() - concatTable.add(Linear(10, 2)) - concatTable.add(Linear(10, 2)) - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = concatTable.forward(tensor1) - - ModulePersister.saveToFile("/tmp/concatTable.bigdl", null, concatTable, true) - val loadedConcatTable = ModuleLoader.loadFromFile("/tmp/concatTable.bigdl") - val res2 = loadedConcatTable.forward(tensor2) - res1 should be (res2) + val concatTable = new ConcatTable[Float]().setName("concatTable") + concatTable.add(Linear[Float](10, 2)) + concatTable.add(Linear[Float](10, 2)) + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(concatTable, input) } "Contiguous serializer" should "work properly" in { - val contiguous = Contiguous() - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - - val res1 = contiguous.forward(tensor1) - - ModulePersister.saveToFile("/tmp/contiguous.bigdl", null, contiguous, true) - val loadedContiguous = ModuleLoader.loadFromFile("/tmp/contiguous.bigdl") - val res2 = loadedContiguous.forward(tensor2) - res1 should be (res2) + val contiguous = Contiguous[Float]().setName("contiguous") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(contiguous, input) } - "ConvLSTMPeephole2D serializer" should " work properly" in { + "ConvLSTMPeephole2D serializer" should "work properly" in { val hiddenSize = 5 val inputSize = 3 val seqLength = 4 val batchSize = 2 val kernalW = 3 val kernalH = 3 - val convLSTMPeephole2d = Recurrent() - val model = Sequential() + val c2d = ConvLSTMPeephole[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1, + withPeephole = false) + val convLSTMPeephole2d = Recurrent[Float]().setName("convLSTMPeephole2d") + val model = Sequential[Float]() .add(convLSTMPeephole2d - .add(ConvLSTMPeephole( - inputSize, - hiddenSize, - kernalW, kernalH, - 1, - withPeephole = false))) - .add(View(hiddenSize * kernalH * kernalW)) - - val input1 = Tensor(batchSize, seqLength, inputSize, kernalW, kernalH).rand - - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = convLSTMPeephole2d.forward(input1) - ModulePersister.saveToFile("/tmp/convLSTMPeephole2d.bigdl", null, convLSTMPeephole2d, true) - val loadedConvLSTMPeephole2d = ModuleLoader.loadFromFile("/tmp/convLSTMPeephole2d.bigdl") - val res2 = loadedConvLSTMPeephole2d.forward(input2) - res1 should be (res2) - } - - "ConvLSTMPeephole3D serializer" should " work properly" in { + .add(c2d)) + .add(View[Float](hiddenSize * kernalH * kernalW)) + + val input = Tensor[Float](batchSize, seqLength, inputSize, kernalW, kernalH).rand + runSerializationTest(convLSTMPeephole2d, input, c2d.getClass) + } + + "ConvLSTMPeephole3D serializer" should "work properly" in { val hiddenSize = 5 val inputSize = 3 val seqLength = 4 val batchSize = 2 val kernalW = 3 val kernalH = 3 - val convLSTMPeephole3d = Recurrent() - val model = Sequential() + val c3d = ConvLSTMPeephole3D[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1, + withPeephole = false) + val convLSTMPeephole3d = Recurrent[Float]().setName("convLSTMPeephole3d") + val model = Sequential[Float]() .add(convLSTMPeephole3d - .add(ConvLSTMPeephole3D( - inputSize, - hiddenSize, - kernalW, kernalH, - 1, - withPeephole = false))) - .add(View(hiddenSize * kernalH * kernalW)) - - val input1 = Tensor(batchSize, seqLength, inputSize, kernalW, kernalH, 3).rand + .add(c3d)) + .add(View[Float](hiddenSize * kernalH * kernalW)) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = convLSTMPeephole3d.forward(input1) - ModulePersister.saveToFile("/tmp/convLSTMPeephole3d.bigdl", null, convLSTMPeephole3d, true) - val loadedConvLSTMPeephole3d = ModuleLoader.loadFromFile("/tmp/convLSTMPeephole3d.bigdl") - val res2 = loadedConvLSTMPeephole3d.forward(input2) - res1 should be (res2) + val input = Tensor[Float](batchSize, seqLength, inputSize, kernalW, kernalH, 3).rand + runSerializationTest(convLSTMPeephole3d, input, c3d.getClass) } "Cosine serializer" should "work properly" in { - val cosine = Cosine(5, 5) - - val tensor1 = Tensor(5).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - - val res1 = cosine.forward(tensor1) - - ModulePersister.saveToFile("/tmp/cosine.bigdl", null, cosine, true) - val loadedCosine = ModuleLoader.loadFromFile("/tmp/cosine.bigdl") - val res2 = loadedCosine.forward(tensor2) - res1 should be (res2) + val cosine = Cosine[Float](5, 5).setName("cosine") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(cosine, input) } "CosineDistance serializer" should "work properly" in { - val cosineDistance = CosineDistance() - - val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val cosineDistance = CosineDistance[Float]().setName("cosineDistance") + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val res1 = cosineDistance.forward(input) - - ModulePersister.saveToFile("/tmp/cosineDistance.bigdl", null, cosineDistance, true) - val loadedCosineDistance = ModuleLoader.loadFromFile("/tmp/cosineDistance.bigdl") - val res2 = loadedCosineDistance.forward(input) - res1 should be (res2) + runSerializationTest(cosineDistance, input) } "CSubTable serializer" should "work properly" in { - val csubTable = CSubTable() + val csubTable = CSubTable[Float]().setName("csubTable") - val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 + runSerializationTest(csubTable, input) + } - val res1 = csubTable.forward(input) - - ModulePersister.saveToFile("/tmp/csubTable.bigdl", null, csubTable, true) - val loadedCSubTable = ModuleLoader.loadFromFile("/tmp/csubTable.bigdl") - val res2 = loadedCSubTable.forward(input) - res1 should be (res2) + "DenseToSparse serializer" should "work properly" in { + val denseToSparse = DenseToSparse[Float]().setName("denseToSparse") + val input = Tensor.range[Float](1, 12, 1) + runSerializationTest(denseToSparse, input) } "Dotproduct serializer" should "work properly" in { - - val dotProduct = DotProduct() - - val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val dotProduct = DotProduct[Float]().setName("dotProduct") + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val res1 = dotProduct.forward(input) - - ModulePersister.saveToFile("/tmp/dotProduct.bigdl", null, dotProduct, true) - val loadedDotProduct = ModuleLoader.loadFromFile("/tmp/dotProduct.bigdl") - val res2 = loadedDotProduct.forward(input) - res1 should be (res2) + runSerializationTest(dotProduct, input) } "Dropout serializer" should "work properly" in { RNG.setSeed(100) - val dropout = Dropout() - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = dropout.forward(tensor1) - - ModulePersister.saveToFile("/tmp/dropout.bigdl", null, dropout, true) - RNG.setSeed(100) - val loadedDropout = ModuleLoader.loadFromFile("/tmp/dropout.bigdl") - val res2 = loadedDropout.forward(tensor2) - res1 should be (res2) + val dropout = Dropout[Float]().setName("dropout") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(dropout, input) } "Echo serializer" should "work properly" in { - val echo = Echo() - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = echo.forward(tensor1) - ModulePersister.saveToFile("/tmp/echo.bigdl", null, echo, true) - val loadedEcho = ModuleLoader.loadFromFile("/tmp/echo.bigdl") - val res2 = loadedEcho.forward(tensor2) - res1 should be (res2) + val echo = Echo[Float]().setName("echo") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(echo, input) } "ELU serializer" should "work properly" in { - val elu = ELU() - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = elu.forward(tensor1) - - ModulePersister.saveToFile("/tmp/elu.bigdl", null, elu, true) - val loadedElu = ModuleLoader.loadFromFile("/tmp/elu.bigdl") - val res2 = loadedElu.forward(tensor2) - res1 should be (res2) + val elu = ELU[Float, Float]().setName("elu") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(elu, input) } "Euclidena serializer" should "work properly" in { - val euclidean = Euclidean(7, 7) - - val tensor1 = Tensor(8, 7).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = euclidean.forward(tensor1) - - ModulePersister.saveToFile("/tmp/euclidean.bigdl", null, euclidean, true) - val loadedEuclidean = ModuleLoader.loadFromFile("/tmp/euclidean.bigdl") - val res2 = loadedEuclidean.forward(tensor2) - res1 should be (res2) + val euclidean = Euclidean[Float](7, 7).setName("euclidean") + val input = Tensor[Float](8, 7).apply1(_ => Random.nextFloat()) + runSerializationTest(euclidean, input) } "Exp serializer" should "work properly" in { - val exp = Exp() - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = exp.forward(tensor1) - - ModulePersister.saveToFile("/tmp/exp.bigdl", null, exp, true) - val loadedExp = ModuleLoader.loadFromFile("/tmp/exp.bigdl") - val res2 = loadedExp.forward(tensor2) - res1 should be (res2) + val exp = Exp[Float]().setName("exp") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(exp, input) } "FlattenTable serializer" should "work properly" in { - val flattenTable = FlattenTable() - - val input1 = Tensor(5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(5, 5).apply1(e => Random.nextFloat()) + val flattenTable = FlattenTable[Float]().setName("flattenTable") + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) var input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val res1 = flattenTable.forward(input) - - ModulePersister.saveToFile("/tmp/flattenTable.bigdl", null, flattenTable, true) - val loadedFlattenTable = ModuleLoader.loadFromFile("/tmp/flattenTable.bigdl") - val res2 = loadedFlattenTable.forward(input) - res1 should be (res2) + runSerializationTest(flattenTable, input) } "GaussianDropout serializer" should "work properly" in { - RNG.setSeed(100) - val gd = GaussianDropout(0.5) - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = gd.forward(tensor1) - - ModulePersister.saveToFile("/tmp/gaussianDropout.bigdl", null, gd, true) - RNG.setSeed(100) - val loadedGd = ModuleLoader.loadFromFile("/tmp/gaussianDropout.bigdl") - val res2 = loadedGd.forward(tensor2) - res1 should be (res2) + RNG.setSeed(1000) + val gaussianDropout = GaussianDropout[Float](0.5).setName("gaussianDropout") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(gaussianDropout, input) } "GaussianNoise serializer" should "work properly" in { - RNG.setSeed(100) - val gn = GaussianNoise(0.5) - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = gn.forward(tensor1) + RNG.setSeed(1000) + val gaussianNoise = GaussianNoise[Float](0.5).setName("gaussianNoise") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(gaussianNoise, input) + } - ModulePersister.saveToFile("/tmp/gaussianNoise.bigdl", null, gn, true) - RNG.setSeed(100) - val loadedGn = ModuleLoader.loadFromFile("/tmp/gaussianNoise.bigdl") - val res2 = loadedGn.forward(tensor2) - res1 should be (res2) + "GaussianSampler serializer" should "work properly" in { + val input1 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input2 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input = T(input1, input2) + RNG.setSeed(1000) + val gaussianSampler = GaussianSampler[Float]().setName("gaussianSampler") + runSerializationTest(gaussianSampler, input) } "GradientReversal serializer" should "work properly" in { - val gradientReversal = GradientReversal() - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = gradientReversal.forward(tensor1) - - ModulePersister.saveToFile("/tmp/gradientReversal.bigdl", null, gradientReversal, true) - val loadedGradientReversal = ModuleLoader.loadFromFile("/tmp/gradientReversal.bigdl") - val res2 = loadedGradientReversal.forward(tensor2) - res1 should be (res2) + val gradientReversal = GradientReversal[Float]().setName("gradientReversal") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(gradientReversal, input) } - "Graph serializer " should "work properly" in { - val linear = Linear(10, 2).inputs() - val graph = Graph(linear, linear) - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - val res1 = graph.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/graph.bigdl", null, graph, true) - val loadedGraph = ModuleLoader.loadFromFile("/tmp/graph.bigdl") - val res2 = loadedGraph.forward(tensor2) - res1 should be (res2) + "Graph serializer" should "work properly" in { + val linear = Linear[Float](10, 2).inputs() + val graph = Graph[Float](linear, linear).setName("graph") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(graph, input) } "Graph with variables serializer" should "work properly" in { - val linear = Linear(2, 2) + val linear = Linear[Float](2, 2) val linearNode = linear.inputs() val linearWeight = linear.weight val linearBias = linear.bias val variables = Some(Array(linearWeight), Array(linearBias)) - val graph = Graph(Array(linearNode), Array(linearNode), variables, false) - val tensor1 = Tensor(2).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - val res1 = graph.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/graph.bigdl", null, graph, true) - val loadedGraph = ModuleLoader.loadFromFile("/tmp/graph.bigdl") - val res2 = loadedGraph.forward(tensor2) - res1 should be (res2) - } - - "GRU serializer " should "work properly" in { - RNG.setSeed(100) - val gru = Recurrent().add(GRU(100, 100)) - val input1 = Tensor(2, 20, 100).apply1(e => Random.nextFloat()) - val input2 = Tensor(2, 20, 100) - input2.copy(input1) + val graphWithVariable = Graph[Float](Array(linearNode), Array(linearNode), + variables, false).setName("graphWithVariable") + val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) + runSerializationTest(graphWithVariable, input) + } + "GRU serializer" should "work properly" in { RNG.setSeed(100) - val res1 = gru.forward(input1) - ModulePersister.saveToFile("/tmp/gru.bigdl", null, gru, true) - RNG.setSeed(100) - val loadedGRU = ModuleLoader.loadFromFile("/tmp/gru.bigdl") - RNG.setSeed(100) - val res2 = loadedGRU.forward(input2) - res1 should be (res2) + val gru = GRU[Float](100, 100) + val gruModel = Recurrent[Float]().add(gru).setName("gru") + val input = Tensor[Float](2, 20, 100).apply1(e => Random.nextFloat()) + runSerializationTest(gruModel, input, gru.getClass) } "HardShrink serializer" should "work properly" in { - val hardShrink = HardShrink() - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = hardShrink.forward(tensor1) - - ModulePersister.saveToFile("/tmp/hardShrink.bigdl", null, hardShrink, true) - val loadedHardShrink = ModuleLoader.loadFromFile("/tmp/hardShrink.bigdl") - val res2 = loadedHardShrink.forward(tensor2) - res1 should be (res2) + val hardShrink = HardShrink[Float]().setName("hardShrink") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(hardShrink, input) } "HardTanh serializer" should "work properly" in { - val hardTanh = HardTanh() - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = hardTanh.forward(tensor1) + val hardTanh = HardTanh[Float, Float]().setName("hardTanh") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(hardTanh, input) + } - ModulePersister.saveToFile("/tmp/hardTanh.bigdl", null, hardTanh, true) - val loadedHardTanh = ModuleLoader.loadFromFile("/tmp/hardTanh.bigdl") - val res2 = loadedHardTanh.forward(tensor2) - res1 should be (res2) + "HardSigmoid serialization" should "work properly" in { + val hardSigmoid = HardSigmoid[Float]().setName("hardSigmoid") + val input = Tensor[Float](2, 2).rand() + runSerializationTest(hardSigmoid, input) } "Identity serializer" should "work properly" in { - val identity = Identity() - - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = identity.forward(tensor1) - - ModulePersister.saveToFile("/tmp/identity.bigdl", null, identity, true) - val loadedIdentity = ModuleLoader.loadFromFile("/tmp/identity.bigdl") - val res2 = loadedIdentity.forward(tensor2) - res1 should be (res2) + val identity = Identity[Float]().setName("identity") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(identity, input) } "Index serializer" should "work properly" in { - val index = Index(1) - - val input1 = Tensor(3).apply1(e => Random.nextFloat()) - val input2 = Tensor(4) + val index = Index[Float](1).setName("index") + val input1 = Tensor[Float](3).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](4) input2(Array(1)) = 1 input2(Array(2)) = 2 input2(Array(3)) = 2 input2(Array(4)) = 3 - val gradOutput = Tensor(4).apply1(e => Random.nextFloat()) - val input = new Table() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val res1 = index.forward(input) - - ModulePersister.saveToFile("/tmp/index.bigdl", null, index, true) - val loadedIndex = ModuleLoader.loadFromFile("/tmp/index.bigdl") - val res2 = loadedIndex.forward(input) - res1 should be (res2) + runSerializationTest(index, input) } - "InferReshape serializer" should " work properly" in { - val inferReshape = InferReshape(Array(-1, 2, 0, 5)) - val tensor1 = Tensor(2, 5, 2, 2).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = inferReshape.forward(tensor1) - - ModulePersister.saveToFile("/tmp/inferReshape.bigdl", null, inferReshape, true) - val loadedInferReshape = ModuleLoader.loadFromFile("/tmp/inferReshape.bigdl") - val res2 = loadedInferReshape.forward(tensor2) - res1 should be (res2) + "InferReshape serializer" should "work properly" in { + val inferReshape = InferReshape[Float](Array(-1, 2, 0, 5)).setName("inferReshape") + val input = Tensor[Float](2, 5, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(inferReshape, input) } - "Input serializer " should " work properly " in { - val input = Input().element - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor(10) - tensor2.copy(tensor1) - - val res1 = input.forward(tensor1) - - ModulePersister.saveToFile("/tmp/input.bigdl", null, input, true) - val loadedInferInput = ModuleLoader.loadFromFile("/tmp/input.bigdl") - val res2 = loadedInferInput.forward(tensor2) - res1 should be (res2) + "Input serializer" should "work properly " in { + val inputl = Input[Float]().element.setName("input") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(inputl, input) } - "JoinTable serializer " should "work properly" in { - val joinTable = JoinTable(2, 2) - val input1 = Tensor(2, 2).apply1(_ => Random.nextFloat()) - val input2 = Tensor(2, 2).apply1(_ => Random.nextFloat()) - + "JoinTable serializer" should "work properly" in { + val joinTable = JoinTable[Float](2, 2).setName("joinTable") + val input1 = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) val input = T() input(1.toFloat) = input1 input(2.toFloat) = input2 - - val res1 = joinTable.forward(input) - - ModulePersister.saveToFile("/tmp/joinTable.bigdl", null, joinTable, true) - val loadedJoinTable = ModuleLoader.loadFromFile("/tmp/joinTable.bigdl") - val res2 = loadedJoinTable.forward(input) - res1 should be (res2) + runSerializationTest(joinTable, input) } - "L1Penalty serializer " should " work properly" in { - val l1Penalty = L1Penalty(1, true, true) - - val tensor1 = Tensor(3, 3).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - - val res1 = l1Penalty.forward(tensor1) - - ModulePersister.saveToFile("/tmp/l1Penalty.bigdl", null, l1Penalty, true) - - val loadedL1Penalty = ModuleLoader.loadFromFile("/tmp/l1Penalty.bigdl") - - val res2 = loadedL1Penalty.forward(tensor2) - res1 should be (res2) + "L1Penalty serializer" should "work properly" in { + val l1Penalty = L1Penalty[Float](1, true, true).setName("l1Penalty") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(l1Penalty, input) } - "LeakReLu serializer " should " work properly" in { - val leakyReLU = LeakyReLU(0.01, true) - - val tensor1 = Tensor(3, 3).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - - val res1 = leakyReLU.forward(tensor1) - - ModulePersister.saveToFile("/tmp/leakyReLU.bigdl", null, leakyReLU, true) - - val loadedLeakyReLU = ModuleLoader.loadFromFile("/tmp/leakyReLU.bigdl") - - val res2 = loadedLeakyReLU.forward(tensor2) - res1 should be (res2) + "LeakReLu serializer" should "work properly" in { + val leakyReLU = LeakyReLU[Float](0.01, true).setName("leakyReLU") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(leakyReLU, input) } - "Linear serializer " should "work properly" in { - val linear = Linear(10, 2) - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - val res1 = linear.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/linear.bigdl", null, linear, true) - val loadedLinear = ModuleLoader.loadFromFile("/tmp/linear.bigdl") - val res2 = loadedLinear.forward(tensor2) - res1 should be (res2) + "Linear serializer" should "work properly" in { + val linear = Linear[Float](10, 2).setName("linear") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(linear, input) } - "Log Serializer " should " work properly" in { - val log = Log() - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - val res1 = log.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/log.bigdl", null, log, true) - val loadedLog = ModuleLoader.loadFromFile("/tmp/log.bigdl") - val res2 = loadedLog.forward(tensor2) - res1 should be (res2) + "Log Serializer" should "work properly" in { + val log = Log[Float, Float]().setName("log") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(log, input) } - "LogSigmoid serializer" should " work properly" in { - val logSigmoid = LogSigmoid() - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - val res1 = logSigmoid.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/logSigmoid.bigdl", null, logSigmoid, true) - val loadedLogSigmoid = ModuleLoader.loadFromFile("/tmp/logSigmoid.bigdl") - val res2 = loadedLogSigmoid.forward(tensor2) - res1 should be (res2) + "LogSigmoid serializer" should "work properly" in { + val logSigmoid = LogSigmoid[Float]().setName("logSigmoid") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(logSigmoid, input) } - "LogSogMax serializer" should " work properly" in { - val logSigmoid = LogSoftMax() - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - val res1 = logSigmoid.forward(tensor1) - tensor2.resizeAs(tensor1).copy(tensor1) - ModulePersister.saveToFile("/tmp/logSigmoid.bigdl", null, logSigmoid, true) - val loadedLogSigmoid = ModuleLoader.loadFromFile("/tmp/logSigmoid.bigdl") - val res2 = loadedLogSigmoid.forward(tensor2) - res1 should be (res2) + "LogSogMax serializer" should "work properly" in { + val logSoftMax = LogSoftMax[Float]().setName("logSoftMax") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(logSoftMax, input) } - "LookupTable serializer " should " work properly" in { - val lookupTable = LookupTable(9, 4, 2, 0.1, 2.0, true) - val tensor1 = Tensor(5) - tensor1(Array(1)) = 5 - tensor1(Array(2)) = 2 - tensor1(Array(3)) = 6 - tensor1(Array(4)) = 9 - tensor1(Array(5)) = 4 - - val tensor2 = Tensor(5) - tensor2.copy(tensor1) - - val res1 = lookupTable.forward(tensor1) - - ModulePersister.saveToFile("/tmp/lookupTable.bigdl", null, lookupTable, true) - val loadedLookupTable = ModuleLoader.loadFromFile("/tmp/lookupTable.bigdl") - val res2 = loadedLookupTable.forward(tensor2) - res1 should be (res2) + "LookupTable serializer" should "work properly" in { + val lookupTable = LookupTable[Float](9, 4, 2, 0.1, 2.0, true).setName("lookupTable") + val input = Tensor[Float](5) + input(Array(1)) = 5 + input(Array(2)) = 2 + input(Array(3)) = 6 + input(Array(4)) = 9 + input(Array(5)) = 4 + runSerializationTest(lookupTable, input) } - "LSTM serializer " should " work properly" in { - - val lstm = Recurrent().add(LSTM(6, 4)) - - val input1 = Tensor(Array(1, 5, 6)).apply1(_ => Random.nextFloat()) - val input2 = Tensor(1, 5, 6) - input2.copy(input1) - - val res1 = lstm.forward(input1) - - ModulePersister.saveToFile("/tmp/lstm.bigdl", null, lstm, true) - val loadedLSTM = ModuleLoader.loadFromFile("/tmp/lstm.bigdl") - val res2 = loadedLSTM.forward(input1) - res1 should be (res2) - + "LSTM serializer" should "work properly" in { + val lstm = LSTM[Float](6, 4) + val lstmModel = Recurrent[Float]().add(lstm).setName("lstm") + val input = Tensor[Float](Array(1, 5, 6)).apply1(_ => Random.nextFloat()) + runSerializationTest(lstmModel, input, lstm.getClass) } - "LSTMPeephole serializer " should " work properly" in { - val lstmPeephole = Recurrent().add(LSTMPeephole(6, 4)) - - val input1 = Tensor(Array(1, 5, 6)).apply1(_ => Random.nextFloat()) - val input2 = Tensor(1, 5, 6) - input2.copy(input1) - - val res1 = lstmPeephole.forward(input1) - - ModulePersister.saveToFile("/tmp/lstmPeephole.bigdl", null, lstmPeephole, true) - val loadedLSTMPeephole = ModuleLoader.loadFromFile("/tmp/lstmPeephole.bigdl") - val res2 = loadedLSTMPeephole.forward(input2) - res1 should be (res2) - + "LSTMPeephole serializer" should "work properly" in { + val lstmPeephole = LSTMPeephole[Float](6, 4) + val lstmPeepholeModel = Recurrent[Float]().add(lstmPeephole).setName("lstmPeephole") + val input = Tensor[Float](Array(1, 5, 6)).apply1(_ => Random.nextFloat()) + runSerializationTest(lstmPeepholeModel, input, lstmPeephole.getClass) } - "MapTable serializer " should " work properly" in { - val linear = Linear(2, 2) - val mapTable = new MapTable() + "MapTable serializer" should "work properly" in { + val linear = Linear[Float](2, 2) + val mapTable = new MapTable[Float]().setName("mapTable") mapTable.add(linear) - val input1 = Tensor(2).apply1(_ => Random.nextFloat()) - val input2 = Tensor(2).apply1(_ => Random.nextFloat()) + val input1 = Tensor[Float](2).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](2).apply1(_ => Random.nextFloat()) val input = T() input(1.0.toFloat) = input1 input(2.0.toFloat) = input2 - - val res1 = mapTable.forward(input) - - ModulePersister.saveToFile("/tmp/mapTable.bigdl", null, mapTable, true) - val loadedMapTable = ModuleLoader.loadFromFile("/tmp/mapTable.bigdl") - val res2 = loadedMapTable.forward(input) - res1 should be (res2) + runSerializationTest(mapTable, input) } - "MaskedSelect serializer" should " work properly" in { - val maskedSelect = MaskedSelect() - val input1 = Tensor(2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor(2, 2) + "MaskedSelect serializer" should "work properly" in { + val maskedSelect = MaskedSelect[Float]().setName("maskedSelect") + val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 2) input2(Array(1, 1)) = 1 input2(Array(1, 2)) = 0 input2(Array(2, 1)) = 0 input2(Array(2, 2)) = 1 - val gradOutput = Tensor(5).apply1(e => Random.nextFloat()) - val input = new Table() input(1.0f) = input1 input(2.0f) = input2 + runSerializationTest(maskedSelect, input) + } - val res1 = maskedSelect.forward(input) - - val gradInput = maskedSelect.backward(input, gradOutput) - - ModulePersister.saveToFile("/tmp/maskedSelect.bigdl", null, maskedSelect, true) - val loadedMaskedSelect = ModuleLoader.loadFromFile("/tmp/maskedSelect.bigdl") - val res2 = loadedMaskedSelect.forward(input) - res1 should be (res2) - + "Masking serializer" should "work properly" in { + val masking = Masking[Float](0.1).setName("masking") + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(masking, input) } - "Max serializer " should " work properly" in { - val max = new Max(2) - val input1 = Tensor(2, 3, 4).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) + "Max serializer" should "work properly" in { + val max = new Max[Float](2).setName("max") + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(max, input) + } - val res1 = max.forward(input1) + "Maxout serializer" should "work properly" in { + val maxout = Maxout[Float](2, 4, 5).setName("maxout") + val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) + runSerializationTest(maxout, input) + } - ModulePersister.saveToFile("/tmp/max.bigdl", null, max, true) - val loadedMax = ModuleLoader.loadFromFile("/tmp/max.bigdl") - val res2 = loadedMax.forward(input2) - res1 should be (res2) + "Mean serializer" should "work properly " in { + val mean = Mean[Float](2).setName("mean") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(mean, input) } - "Mean serializer " should " work properly " in { - val mean = Mean(2) - val input1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = mean.forward(input1) + "Min serializer" should "work properly " in { + val min = Min[Float](2).setName("min") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(min, input) + } - ModulePersister.saveToFile("/tmp/mean.bigdl", null, mean, true) - val loadedMean = ModuleLoader.loadFromFile("/tmp/mean.bigdl") - val res2 = loadedMean.forward(input2) - res1 should be (res2) + "MixtureTable Serializer" should "work properly " in { + val mixTureTable = MixtureTable[Float]().setName("mixTureTable") + val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input = new Table() + input(1.0f) = input1 + input(2.0f) = input2 + runSerializationTest(mixTureTable, input) } - "Min serializer " should " work properly " in { - val min = Min(2) - val input1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = min.forward(input1) + "MM Serializer" should "work properly" in { + val mm = MM[Float]().setName("mm_layer") + val input1 = Tensor[Float](2, 3).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](3, 4).apply1(e => Random.nextFloat()) + val input = new Table() + input(1.0f) = input1 + input(2.0f) = input2 + runSerializationTest(mm, input) + } - ModulePersister.saveToFile("/tmp/min.bigdl", null, min, true) - val loadedMin = ModuleLoader.loadFromFile("/tmp/min.bigdl") - val res2 = loadedMin.forward(input2) - res1 should be (res2) + "Mul Serializer" should "work properly" in { + val mul = Mul[Float]().setName("mul") + val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(mul, input) } - "MixtureTable Serializer " should " work properly " in { - val mixTureTable = MixtureTable() - val input1 = Tensor(2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor(2, 2).apply1(e => Random.nextFloat()) + "MulConst Serializer" should "work properly" in { + val mulConst = MulConstant[Float](1.0).setName("mulConst") + val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(mulConst, input) + } + "MultiRNNCell serializer" should "work properly" in { + val hiddenSize = 5 + val inputSize = 5 + val seqLength = 4 + val batchSize = 2 + val kernalW = 3 + val kernalH = 3 + val rec = RecurrentDecoder[Float](seqLength) + val cells = Array(ConvLSTMPeephole[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1), ConvLSTMPeephole[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1), ConvLSTMPeephole[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1)).asInstanceOf[Array[Cell[Float]]] + + val multiRNNCell = MultiRNNCell[Float](cells) + + val model = Sequential[Float]() + .add(rec + .add(multiRNNCell)).setName("multiRNNCell") + + val input = Tensor[Float](batchSize, inputSize, 10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input, multiRNNCell.getClass) + } + + "MV Serializer" should "work properly" in { + val mv = MV[Float]().setName("mv_layer") + val input1 = Tensor[Float](2, 3).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](3).apply1(e => Random.nextFloat()) val input = new Table() input(1.0f) = input1 input(2.0f) = input2 + runSerializationTest(mv, input) + } - val res1 = mixTureTable.forward(input) + "Narrow serializer" should "work properly" in { + val narrow = Narrow[Float](1, 3, -3).setName("narrow") + val input = Tensor[Float](9, 4, 14).apply1(e => Random.nextFloat()) + runSerializationTest(narrow, input) + } - ModulePersister.saveToFile("/tmp/mixTureTable.bigdl", null, mixTureTable, true) - val loadedMixtureTable = ModuleLoader.loadFromFile("/tmp/mixTureTable.bigdl") - val res2 = loadedMixtureTable.forward(input) - res1 should be (res2) + "NarrowTable serializer" should "work properly" in { + val narrowTable = NarrowTable[Float](1, 1) + val input = T() + input(1.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + input(2.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + input(3.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + runSerializationTest(narrowTable, input) } - "MM Serializer" should "work properly" in { - val mm = MM() + "Negative serializer" should "work properly" in { + val negative = Negative[Float]().setName("negative") + val input = Tensor[Float](10).apply1(e => Random.nextFloat()) + runSerializationTest(negative, input) + } - val input1 = Tensor(2, 3).apply1(e => Random.nextFloat()) - val input2 = Tensor(3, 4).apply1(e => Random.nextFloat()) + "Normlize serializer" should "work properly" in { + val normalizer = Normalize[Float](2).setName("normalizer") + val input = Tensor[Float](2, 3, 4, 4).apply1(e => Random.nextFloat()) + runSerializationTest(normalizer, input) + } - val input = new Table() + "Pack serializer" should "work properly" in { + val pack = new Pack[Float](1).setName("pack") + val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input = T() input(1.0f) = input1 input(2.0f) = input2 + runSerializationTest(pack, input) + } - val res1 = mm.forward(input) + "Padding serializer" should "work properly" in { + val padding = Padding[Float](1, -1, 4, -0.8999761, 14).setName("padding") + val input = Tensor[Float](3, 13, 11).apply1(e => Random.nextFloat()) + runSerializationTest(padding, input) + } - ModulePersister.saveToFile("/tmp/mm.bigdl", null, mm, true) - val loadedMM = ModuleLoader.loadFromFile("/tmp/mm.bigdl") - val res2 = loadedMM.forward(input) - res1 should be (res2) + "PairwiseDistance serializer" should "work properly" in { + val pairwiseDistance = new PairwiseDistance[Float](3).setName("pairwiseDistance") + val input1 = Tensor[Float](3, 3).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](3, 3).apply1(e => Random.nextFloat()) + val input = T(1.0f -> input1, 2.0f -> input2) + runSerializationTest(pairwiseDistance, input) + } + "ParallelTable serializer" should "work properly" in { + val parallelTable = ParallelTable[Float]().setName("parallelTable") + parallelTable.add(Linear[Float](2, 2)) + parallelTable.add(Linear[Float](2, 2)) + val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input = T(1.0f -> input1, 2.0f -> input2) + runSerializationTest(parallelTable, input) } - "Mul Serializer " should "work properly" in { - val mul = Mul() - val input1 = Tensor(10, 10).apply1(_ => Random.nextFloat()) - val input2 = Tensor(10, 10) - input2.copy(input1) + "Power serializer" should "work properly" in { + val power = Power[Float, Float](2.0).setName("power") + val input = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + runSerializationTest(power, input) + } - val res1 = mul.forward(input1) + "PReLU serializer" should "work properly" in { + val preLu = PReLU[Float](2).setName("preLu") + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(preLu, input) + } - ModulePersister.saveToFile("/tmp/mul.bigdl", null, mul, true) - val loadedMul = ModuleLoader.loadFromFile("/tmp/mul.bigdl") - val res2 = loadedMul.forward(input2) - res1 should be (res2) + "Recurrent serializer" should "work properly" in { + val recurrent = Recurrent[Float]().setName("recurrent") + .add(RnnCell[Float](5, 4, Tanh[Float]())) + val input = Tensor[Float](Array(10, 5, 5)).apply1(_ => Random.nextFloat()) + runSerializationTest(recurrent, input) } - "MulConst Serializer " should "work properly" in { - val mulConst = MulConstant(1.0) - val input1 = Tensor(10, 10).apply1(_ => Random.nextFloat()) - val input2 = Tensor(10, 10) - input2.copy(input1) + "Recurrent serializer" should "work properly with BatchNormParams" in { + val recurrent = Recurrent[Float](BatchNormParams()).setName("recurrentWithNorm") + .add(RnnCell[Float](5, 4, Tanh[Float]())) + val input = Tensor[Float](Array(10, 5, 5)).apply1(_ => Random.nextFloat()) + runSerializationTest(recurrent, input) + } - val res1 = mulConst.forward(input1) + "RecurrentDecoder serializer" should "work properly" in { + val recDecoder = RecurrentDecoder[Float](5). + add(ConvLSTMPeephole[Float](7, 7, 3, 3, 1)) + val input = Tensor[Float](4, 7, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(recDecoder, input) + } - ModulePersister.saveToFile("/tmp/mulConst.bigdl", null, mulConst, true) - val loadedMulConstant = ModuleLoader.loadFromFile("/tmp/mulConst.bigdl") - val res2 = loadedMulConstant.forward(input2) - res1 should be (res2) + "ReLU serializer" should "work properly" in { + val relu = ReLU[Float]().setName("relu") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(relu, input) } - "MV Serializer " should " work properly" in { - val mv = MV() - val input1 = Tensor(2, 3).apply1(e => Random.nextFloat()) - val input2 = Tensor(3).apply1(e => Random.nextFloat()) + "ReLU6 serializer" should "work properly" in { + val relu6 = ReLU6[Float, Float](false).setName("relu6") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(relu6, input) + } - val input = new Table() + "Replicate serializer" should "work properly" in { + val replicate = new Replicate[Float](3).setName("replicate") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(replicate, input) + } + + "Reshape serializer" should "work properly" in { + val reshape = Reshape[Float](Array(1, 4, 5)).setName("reshape") + val input = Tensor[Float](2, 2, 5).apply1( _ => Random.nextFloat()) + runSerializationTest(reshape, input) + } + + "ResizeBilinear serializer" should "work properly" in { + val input = Tensor[Float](1, 3, 2, 3).apply1( _ => Random.nextFloat()) + val resizeBilinear = ResizeBilinear[Float](3, 2).setName("resizeBilinear") + runSerializationTest(resizeBilinear, input) + } + + "Reverse serializer" should "work properly" in { + val reverse = Reverse[Float]().setName("reverse") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(reverse, input) + } + + "RnnCell serializer" should "work properly" in { + val rnnCell = RnnCell[Float](6, 4, Sigmoid[Float]()).setName("rnnCell") + val input1 = Tensor[Float](Array(1, 4)).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](Array(1, 4)).apply1(_ => Random.nextFloat()) + val input = T() input(1.0f) = input1 input(2.0f) = input2 + runSerializationTest(rnnCell, input) + } - val res1 = mv.forward(input) + "RoiPooling serializer" should " work properly" in { + val input = T() + val input1 = Tensor[Float](1, 1, 2, 2).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](1, 5).apply1(_ => Random.nextFloat()) + input(1.0f) = input1 + input(2.0f) = input2 + val roiPooling = new RoiPooling[Float](pooledW = 3, + pooledH = 2, 1.0f).setName("roiPooling") + runSerializationTest(roiPooling, input) + } - ModulePersister.saveToFile("/tmp/mv.bigdl", null, mv, true) - val loadedMV = ModuleLoader.loadFromFile("/tmp/mv.bigdl") - val res2 = loadedMV.forward(input) - res1 should be (res2) + "RReLU serializer" should "work properly" in { + val rrelu = new RReLU[Float](inplace = false).setName("rrelu") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(rrelu, input) } - "Narrow serializer " should " work properly" in { - val narrow = Narrow(1, 3, -3) - val input1 = Tensor(9, 4, 14).apply1(e => Random.nextFloat()) - val input2 = Tensor(9, 4, 14) - input2.copy(input1) + "Scale serializer" should "work properly" in { + val scale = Scale[Float](Array(1, 4, 1, 1)).setName("scale") + val input = Tensor[Float](1, 4, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(scale, input) + } - val res1 = narrow.forward(input1) + "Select serializer" should "work properly" in { + val select = Select[Float](2, 2).setName("select") + val input = Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(select, input) + } - ModulePersister.saveToFile("/tmp/narrow.bigdl", null, narrow, true) - val loadedNarrow = ModuleLoader.loadFromFile("/tmp/narrow.bigdl") - val res2 = loadedNarrow.forward(input2) - res1 should be (res2) + "SelectTable serializer" should "work properly" in { + val selectTable = SelectTable[Float](2).setName("selectTable") + val input1 = Tensor[Float](10).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](10).apply1(_ => Random.nextFloat()) + val input3 = Tensor[Float](10).apply1(_ => Random.nextFloat()) + val input = T(1.0 -> input1, 2.0 -> input2, 3.0 -> input3) + runSerializationTest(selectTable, input) } - "NarrowTable serializer " should " work properly" in { - val narrowTable = NarrowTable(1, 1) - val input = T() - input(1.0) = Tensor(2, 2).apply1(e => Random.nextFloat()) - input(2.0) = Tensor(2, 2).apply1(e => Random.nextFloat()) - input(3.0) = Tensor(2, 2).apply1(e => Random.nextFloat()) - val res1 = narrowTable.forward(input) + "Sequential Container" should "work properly" in { + val sequential = Sequential[Float]().setName("sequential") + val linear = Linear[Float](10, 2) + sequential.add(linear) + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(sequential, input) + } - ModulePersister.saveToFile("/tmp/narrowTable.bigdl", null, narrowTable, true) - val loadedNarrowTable = ModuleLoader.loadFromFile("/tmp/narrowTable.bigdl") - val res2 = loadedNarrowTable.forward(input) - res1 should be (res2) + "Sigmoid serializer" should "work properly" in { + val sigmoid = Sigmoid[Float]().setName("sigmoid") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(sigmoid, input) } - "Normlize serializer " should " work properly" in { - val normalizer = Normalize(2) - val input1 = Tensor(2, 3, 4, 4).apply1(e => Random.nextFloat()) - val input2 = Tensor(2, 3, 4, 4) - input2.copy(input1) + "SoftMax serializer" should "work properly" in { + val softMax = SoftMax[Float]().setName("softMax") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(softMax, input) + } - val res1 = normalizer.forward(input1) + "SoftMin serializer" should "work properly" in { + val softMin = SoftMin[Float]().setName("softMin") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(softMin, input) + } - ModulePersister.saveToFile("/tmp/normalizer.bigdl", null, normalizer, true) - val loadedNormalize = ModuleLoader.loadFromFile("/tmp/normalizer.bigdl") - val res2 = loadedNormalize.forward(input2) - res1 should be (res2) + "SoftPlus serializer" should "work properly" in { + val softPlus = SoftPlus[Float, Float]().setName("softPlus") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(softPlus, input) } - "Pack serializer " should " work properly" in { - val pack = new Pack(1) - val input1 = Tensor(2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor(2, 2).apply1(e => Random.nextFloat()) - val input = T() - input(1.0f) = input1 - input(2.0f) = input2 - val res1 = pack.forward(input) - ModulePersister.saveToFile("/tmp/pack.bigdl", null, pack, true) - val loadedPack = ModuleLoader.loadFromFile("/tmp/pack.bigdl") - val res2 = loadedPack.forward(input) - res1 should be (res2) + "SoftShrink serializer" should "work properly" in { + val softShrink = SoftShrink[Float]().setName("softShrink") + val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(softShrink, input) + } + "SoftSign serializer" should "work properly" in { + val softSign = SoftSign[Float, Float]().setName("softSign") + val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(softSign, input) } - "Padding serializer " should " work properly" in { - val padding = Padding(1, -1, 4, -0.8999761, 14) - val input = Tensor(3, 13, 11).apply1(e => Random.nextFloat()) - val res1 = padding.forward(input) - ModulePersister.saveToFile("/tmp/padding.bigdl", null, padding, true) - val loadedPadding = ModuleLoader.loadFromFile("/tmp/padding.bigdl") - val res2 = loadedPadding.forward(input) - res1 should be (res2) + "SparseJoinTable serializer" should "work properly" in { + val sparseJoinTable = SparseJoinTable[Float](2).setName("sparseJoinTable") + val sparseModel = Sequential[Float](). + add(ParallelTable[Float]().add(Identity[Float]()).add(Identity[Float]())) + .add(sparseJoinTable) + val input1 = Tensor[Float](4, 3).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + val input2 = Tensor[Float](4, 2).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + val sparseInput = T(Tensor.sparse(input1), Tensor.sparse(input2)) + runSerializationTest(sparseJoinTable, sparseInput) } - "PairwiseDistance serializer " should " work properly" in { - val pairwiseDistance = new PairwiseDistance(3) - val input1 = Tensor(3, 3).apply1(e => Random.nextFloat()) - val input2 = Tensor(3, 3).apply1(e => Random.nextFloat()) - val input = T(1.0f -> input1, 2.0f -> input2) - val res1 = pairwiseDistance.forward(input) - ModulePersister.saveToFile("/tmp/pairwiseDistance.bigdl", null, pairwiseDistance, true) - val loadedPairwiseDistance = ModuleLoader.loadFromFile("/tmp/pairwiseDistance.bigdl") - val res2 = loadedPairwiseDistance.forward(input) - res1 should be (res2) - } - - "ParallelTable serializer " should " work properly" in { - val parallelTable = ParallelTable() - parallelTable.add(Linear(2, 2)) - parallelTable.add(Linear(2, 2)) - val input11 = Tensor(2, 2).apply1(e => Random.nextFloat()) - val input21 = Tensor(2, 2) - input21.copy(input11) - val input12 = Tensor(2, 2).apply1(e => Random.nextFloat()) - val input22 = Tensor(2, 2) - input22.copy(input12) - val input1 = T(1.0f -> input11, 2.0f -> input12) - val input2 = T(1.0f -> input21, 2.0f -> input22) - val res1 = parallelTable.forward(input1) - ModulePersister.saveToFile("/tmp/parallelTable.bigdl", null, parallelTable, true) - val loadedParallelTable = ModuleLoader.loadFromFile("/tmp/parallelTable.bigdl") - val res2 = loadedParallelTable.forward(input1) - res1 should be (res2) - } - - "Power serializer " should " work properly" in { - val power = Power(2.0) - val input1 = Tensor(2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor(2, 2) - input2.copy(input1) - - val res1 = power.forward(input1) - - ModulePersister.saveToFile("/tmp/power.bigdl", null, power, true) - val loadedPower = ModuleLoader.loadFromFile("/tmp/power.bigdl") - val res2 = loadedPower.forward(input1) - res1 should be (res2) - } - - "PReLU serializer " should " work properly" in { - val preLu = PReLU(2) - val input1 = Tensor(2, 3, 4).apply1(_ => Random.nextFloat()) - val input2 = Tensor(2, 3, 4) - input2.copy(input1) - val res1 = preLu.forward(input1) - - ModulePersister.saveToFile("/tmp/preLu.bigdl", null, preLu, true) - val loadedPReLU = ModuleLoader.loadFromFile("/tmp/preLu.bigdl") - val res2 = loadedPReLU.forward(input1) - res1 should be (res2) - } - - "Recurrent serializer " should "work properly" in { - val recurrent = Recurrent() - .add(RnnCell(5, 4, Tanh())) - val input1 = Tensor(Array(10, 5, 5)) - - val input2 = Tensor(10, 5, 5) - input2.copy(input1) - val res1 = recurrent.forward(input1) - - ModulePersister.saveToFile("/tmp/recurrent.bigdl", null, recurrent, true) - val loadedRecurrent = ModuleLoader.loadFromFile("/tmp/recurrent.bigdl") - val res2 = loadedRecurrent.forward(input1) - res1 should be (res2) - - } - - "Recurrent serializer " should "work properly with BatchNormParams" in { - val recurrent = Recurrent(BatchNormParams()) - .add(RnnCell(5, 4, Tanh())) - val input1 = Tensor(Array(10, 5, 5)) - - val input2 = Tensor(10, 5, 5) - input2.copy(input1) - val res1 = recurrent.forward(input1) - - ModulePersister.saveToFile("/tmp/recurrent.bigdl", null, recurrent, true) - val loadedRecurrent = ModuleLoader.loadFromFile("/tmp/recurrent.bigdl") - val res2 = loadedRecurrent.forward(input1) - res1 should be (res2) - - } - - "ReLU serializer " should " work properly" in { - val relu = ReLU() - val input1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor(5, 5) - input2.copy(input1) - val res1 = relu.forward(input1) - - ModulePersister.saveToFile("/tmp/relu.bigdl", null, relu, true) - val loadedReLU = ModuleLoader.loadFromFile("/tmp/relu.bigdl") - val res2 = loadedReLU.forward(input1) - res1 should be (res2) - } - - "ReLU6 serializer" should " work properly " in { - val relu6 = ReLU6(false) - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = relu6.forward(input1) + "SparseLinear serializer" should "work properly" in { + val sparseLinear = SparseLinear[Float](4, 2).setName("sparseLinear") + val input = Tensor[Float](2, 4).apply1(_ => Random.nextFloat()) + val sparseInput = Tensor.sparse(input) + runSerializationTest(sparseLinear, sparseInput) + } - ModulePersister.saveToFile("/tmp/relu6.bigdl", null, relu6, true) - val loadedReLU6 = ModuleLoader.loadFromFile("/tmp/relu6.bigdl") - val res2 = loadedReLU6.forward(input2) - res1 should be (res2) - } - - "Replicate serializer " should " work properly" in { - val replicate = new Replicate(3) - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor(10) - input2.copy(input1) - val res1 = replicate.forward(input1) - ModulePersister.saveToFile("/tmp/replicate.bigdl", null, replicate, true) - val loadedReplicate = ModuleLoader.loadFromFile("/tmp/replicate.bigdl") - val res2 = loadedReplicate.forward(input2) - res1 should be (res2) + "SpatialAveragePooling serializer" should "work properly" in { + val spatialAveragePooling = new SpatialAveragePooling[Float](3, 2, 2, 1). + setName("spatialAveragePooling") + val input = Tensor[Float](1, 4, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialAveragePooling, input) } - "Reshape serializer " should " work properly " in { - val reshape = Reshape(Array(1, 4, 5)) - val input1 = Tensor(2, 2, 5).apply1( _ => Random.nextFloat()) - val input2 = Tensor(2, 2, 5) - input2.copy(input1) - val res1 = reshape.forward(input1) - ModulePersister.saveToFile("/tmp/reshape.bigdl", null, reshape, true) - val loadedReshape = ModuleLoader.loadFromFile("/tmp/reshape.bigdl") - val res2 = loadedReshape.forward(input2) - res1 should be (res2) + "SpatialBatchNormalization serializer" should "work properly" in { + val spatialBatchNorm = SpatialBatchNormalization[Float](5). + setName("spatialBatchNorm") + val input = Tensor[Float](2, 5, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialBatchNorm, input) } - "Reverse serializer " should " work properly " in { - val reverse = Reverse() - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor(10) - input2.copy(input1) - val res1 = reverse.forward(input1) - ModulePersister.saveToFile("/tmp/reverse.bigdl", null, reverse, true) - val loadedReverse = ModuleLoader.loadFromFile("/tmp/reverse.bigdl") - val res2 = loadedReverse.forward(input2) - res1 should be (res2) + "SpatialContrastiveNormalization serializer" should "work properly" in { + RNG.setSeed(100) + val spatialContrastiveNorm = new SpatialContrastiveNormalization[Float](). + setName("spatialContrastiveNorm") + val input = Tensor[Float](1, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialContrastiveNorm, input) } - "RnnCell serializer " should " work properly " in { + "SpatialConvolution serializer" should "work properly" in { + val spatialConvolution = SpatialConvolution[Float](3, 4, 2, 2). + setName("spatialConvolution") + val input = Tensor[Float](1, 3, 5, 5).apply1( e => Random.nextFloat()) + runSerializationTest(spatialConvolution, input) + } - val rnnCell = RnnCell(6, 4, Sigmoid()) + "SpatialConvolutionMap serializer" should "work properly" in { + val spatialConvolutionMap = SpatialConvolutionMap[Float]( + SpatialConvolutionMap.random(1, 1, 1), 2, 2).setName("spatialConvolutionMap") + val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) + runSerializationTest(spatialConvolutionMap, input) + } - val input1 = Tensor(Array(1, 4)).apply1(_ => Random.nextFloat()) + "SpatialCrossMapLRN serializer" should "work properly" in { + val spatialCrossMapLRN = SpatialCrossMapLRN[Float](5, 0.01, 0.75, 1.0). + setName("spatialCrossMapLRN") + val input = Tensor[Float](2, 2, 2, 2).apply1( e => Random.nextFloat()) + runSerializationTest(spatialCrossMapLRN, input) + } - val input2 = Tensor(Array(1, 4)).apply1(_ => Random.nextFloat()) + "SpatialDilatedConvolution serializer" should "work properly" in { - val input = T() - input(1.0f) = input1 - input(2.0f) = input2 - val res1 = rnnCell.forward(input) - - ModulePersister.saveToFile("/tmp/rnnCell.bigdl", null, rnnCell, true) - val loadedRnnCell = ModuleLoader.loadFromFile("/tmp/rnnCell.bigdl") - val res2 = loadedRnnCell.forward(input) - res1 should be (res2) - } - - "RoiPooling serializer " should " work properly " in { - val input1 = T() - val input2 = T() - val input11 = Tensor(1, 1, 2, 2).apply1(_ => Random.nextFloat()) - val input21 = Tensor(1, 1, 2, 2) - input21.copy(input11) - val input12 = Tensor(1, 5).apply1(_ => Random.nextFloat()) - val input22 = Tensor(1, 5) - input22.copy(input12) - input1(1.0f) = input11 - input1(2.0f) = input12 - input2(1.0f) = input21 - input2(2.0f) = input22 - - val roiPooling = new RoiPooling[Float](pooledW = 3, pooledH = 2, 1.0f) - val res1 = roiPooling.forward(input1) - val res3 = roiPooling.forward(input1) - ModulePersister.saveToFile("/tmp/roiPooling.bigdl", null, roiPooling, true) - val loadedRoiPooling = ModuleLoader.loadFromFile("/tmp/roiPooling.bigdl") - val res2 = loadedRoiPooling.forward(input2) - res1 should be (res2) - } - - "RReLU serializer " should " work properly " in { - val rrelu = new RReLU(inplace = false) - val input1 = Tensor(2, 2, 2).apply1(_ => Random.nextFloat()) - val input2 = Tensor(2, 2, 2) - input2.copy(input1) - val res1 = rrelu.forward(input1) - ModulePersister.saveToFile("/tmp/rrelu.bigdl", null, rrelu, true) - val loadedRReLU = ModuleLoader.loadFromFile("/tmp/rrelu.bigdl") - val res2 = loadedRReLU.forward(input2) - res1 should be (res2) - } - - "Scale serializer " should " work properly " in { - val scale = Scale(Array(1, 4, 1, 1)) - val input1 = Tensor(1, 4, 5, 6).apply1(_ => Random.nextFloat()) - val input2 = Tensor(1, 4, 5, 6) - input2.copy(input1) - val res1 = scale.forward(input1) - ModulePersister.saveToFile("/tmp/scale.bigdl", null, scale, true) - val loadedScale = ModuleLoader.loadFromFile("/tmp/scale.bigdl") - val res2 = loadedScale.forward(input2) - res1 should be (res2) - - } - - "Select serializer " should " work properly " in { - val select = Select(2, 2) - val input1 = Tensor(5, 5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor(5, 5, 5) - input2.copy(input1) - val res1 = select.forward(input1) - ModulePersister.saveToFile("/tmp/select.bigdl", null, select, true) - val loadedSelect = ModuleLoader.loadFromFile("/tmp/select.bigdl") - val res2 = loadedSelect.forward(input2) - res1 should be (res2) - } - - "SelectTable serializer " should " work properly " in { - val selectTable = SelectTable(2) - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor(10).apply1(_ => Random.nextFloat()) - val input3 = Tensor(10).apply1(_ => Random.nextFloat()) - val input = T(1.0 -> input1, 2.0 -> input2, 3.0 -> input3) - val res1 = selectTable.forward(input) - ModulePersister.saveToFile("/tmp/selectTable.bigdl", null, selectTable, true) - val loadedSelectTable = ModuleLoader.loadFromFile("/tmp/selectTable.bigdl") - val res2 = loadedSelectTable.forward(input) - res1 should be (res2) + val spatialDilatedConvolution = SpatialDilatedConvolution[Float](1, 1, + 2, 2, 1, 1, 0, 0).setName("spatialDilatedConvolution") + val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) + runSerializationTest(spatialDilatedConvolution, input) } - "Sequential Container" should "work properly" in { - val sequential = Sequential() - val linear = Linear(10, 2) - sequential.add(linear) - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = sequential.forward(input1) - ModulePersister.saveToFile("/tmp/sequential.bigdl", null, sequential, true) - val loadedSequential = ModuleLoader.loadFromFile("/tmp/sequential.bigdl") - val res2 = loadedSequential.forward(input2) - res1 should be (res2) - } - - "Sigmoid serializer " should " work properly" in { - val sigmoid = Sigmoid() - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = sigmoid.forward(input1) - ModulePersister.saveToFile("/tmp/sigmoid.bigdl", null, sigmoid, true) - val loadedSigmoid = ModuleLoader.loadFromFile("/tmp/sigmoid.bigdl") - val res2 = loadedSigmoid.forward(input2) - res1 should be (res2) - } - - "SoftMax serializer " should " work properly" in { - val softMax = SoftMax() - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = softMax.forward(input1) - ModulePersister.saveToFile("/tmp/softMax.bigdl", null, softMax, true) - val loadedSoftMax = ModuleLoader.loadFromFile("/tmp/softMax.bigdl") - val res2 = loadedSoftMax.forward(input2) - res1 should be (res2) - } - - "SoftMin serializer " should " work properly " in { - val softMin = SoftMin() - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = softMin.forward(input1) - ModulePersister.saveToFile("/tmp/softMin.bigdl", null, softMin, true) - val loadedSoftMin = ModuleLoader.loadFromFile("/tmp/softMin.bigdl") - val res2 = loadedSoftMin.forward(input2) - res1 should be (res2) - } - - "SoftPlus serializer " should " work properly" in { - val softPlus = SoftPlus() - val input1 = Tensor(10).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = softPlus.forward(input1) - ModulePersister.saveToFile("/tmp/softPlus.bigdl", null, softPlus, true) - val loadedSoftPlus = ModuleLoader.loadFromFile("/tmp/softPlus.bigdl") - val res2 = loadedSoftPlus.forward(input2) - res1 should be (res2) - } - - "SoftShrink serializer " should " work properly" in { - val softShrink = SoftShrink() - val input1 = Tensor(10, 10).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = softShrink.forward(input1) - ModulePersister.saveToFile("/tmp/softShrink.bigdl", null, softShrink, true) - val loadedSoftShrink = ModuleLoader.loadFromFile("/tmp/softShrink.bigdl") - val res2 = loadedSoftShrink.forward(input2) - res1 should be (res2) - } - - "SoftSign serializer " should "work properly" in { - val softSign = SoftSign() - val input1 = Tensor(10, 10).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = softSign.forward(input1) - ModulePersister.saveToFile("/tmp/softSign.bigdl", null, softSign, true) - val loadedSoftSign = ModuleLoader.loadFromFile("/tmp/softSign.bigdl") - val res2 = loadedSoftSign.forward(input2) - res1 should be (res2) - } - - "SpatialAveragePooling serializer " should " work properly " in { - val spatialAveragePooling = new SpatialAveragePooling(3, 2, 2, 1) - val input1 = Tensor(1, 4, 3).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialAveragePooling.forward(input1) - ModulePersister.saveToFile("/tmp/spatialAveragePooling.bigdl", - null, spatialAveragePooling, true) - val loadedSpatialAveragePooling = ModuleLoader.loadFromFile("/tmp/spatialAveragePooling.bigdl") - val res2 = loadedSpatialAveragePooling.forward(input2) - res1 should be (res2) - } - - "SpatialBatchNormalization serializer " should " work properly " in { - val spatialBatchNorm = SpatialBatchNormalization(5) - val input1 = Tensor(2, 5, 4, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialBatchNorm.forward(input1) - ModulePersister.saveToFile("/tmp/spatialBatchNorm.bigdl", null, spatialBatchNorm, true) - val loadedSpatialBatchNorm = ModuleLoader.loadFromFile("/tmp/spatialBatchNorm.bigdl") - val res2 = loadedSpatialBatchNorm.forward(input2) - res1 should be (res2) - } - - "SpatialContrastiveNormalization serializer " should " work properly" in { - RNG.setSeed(100) - val spatialContrastiveNorm = new SpatialContrastiveNormalization() - val input1 = Tensor(1, 5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialContrastiveNorm.forward(input1) - ModulePersister.saveToFile("/tmp/spatialContrastiveNorm.bigdl", - null, spatialContrastiveNorm, true) - RNG.setSeed(100) - val loadedSpatialContrastiveNorm = ModuleLoader. - loadFromFile("/tmp/spatialContrastiveNorm.bigdl") - val res2 = loadedSpatialContrastiveNorm.forward(input2) - res1 should be (res2) - } - - "SpatialConvolution serializer " should " work properly" in { - val spatialConvolution = SpatialConvolution(3, 4, 2, 2) - val input1 = Tensor(1, 3, 5, 5).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/spatialConvolution.bigdl", null, spatialConvolution, true) - val loadedSpatialConvolution = ModuleLoader.loadFromFile("/tmp/spatialConvolution.bigdl") - val res2 = loadedSpatialConvolution.forward(input2) - res1 should be (res2) - } - - "SpatialConvolutionMap serializer" should " work properly" in { - val spatialConvolutionMap = SpatialConvolutionMap( - SpatialConvolutionMap.random(1, 1, 1), 2, 2) - val input1 = Tensor(1, 3, 3).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialConvolutionMap.forward(input1) - ModulePersister.saveToFile("/tmp/spatialConvolutionMap.bigdl", - null, spatialConvolutionMap, true) - val loadedSpatialConvolutionMap = ModuleLoader. - loadFromFile("/tmp/spatialConvolutionMap.bigdl") - val res2 = loadedSpatialConvolutionMap.forward(input2) - res1 should be (res2) - } - - "SpatialCrossMapLRN serializer " should " work properly " in { - val spatialCrossMapLRN = SpatialCrossMapLRN(5, 0.01, 0.75, 1.0) - val input1 = Tensor(2, 2, 2, 2).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialCrossMapLRN.forward(input1) - ModulePersister.saveToFile("/tmp/spatialCrossMapLRN.bigdl", null, spatialCrossMapLRN, true) - val loadedSpatialCrossMapLRN = ModuleLoader.loadFromFile("/tmp/spatialCrossMapLRN.bigdl") - val res2 = loadedSpatialCrossMapLRN.forward(input2) - res1 should be (res2) - } - - "SpatialDilatedConvolution serializer " should "work properly" in { - - val spatialDilatedConvolution = SpatialDilatedConvolution(1, 1, - 2, 2, 1, 1, 0, 0) - val input1 = Tensor(1, 3, 3).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialDilatedConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/spatialDilatedConvolution.bigdl", null, - spatialDilatedConvolution, true) - val loadedSpatialDilatedConvolution = ModuleLoader. - loadFromFile("/tmp/spatialDilatedConvolution.bigdl") - val res2 = loadedSpatialDilatedConvolution.forward(input2) - res1 should be (res2) - } - - "SpatialDivisiveNormalization serializer " should " work properly" in { - val spatialDivisiveNormalization = SpatialDivisiveNormalization() - val input1 = Tensor(1, 5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor(1, 5, 5) - input2.copy(input1) - - val res1 = spatialDivisiveNormalization.forward(input1) - ModulePersister.saveToFile("/tmp/spatialDivisiveNormalization.bigdl", null, - spatialDivisiveNormalization, true) - val loadedSpatialDivisiveNormalization = ModuleLoader. - loadFromFile("/tmp/spatialDivisiveNormalization.bigdl") - val res2 = loadedSpatialDivisiveNormalization.forward(input2) - res1 should be (res2) - - } - - "SpatialFullConvolution serializer " should " work properly" in { - - val spatialFullConvolution = SpatialFullConvolution(1, 1, - 2, 2, 1, 1, 0, 0) - val input1 = Tensor(1, 3, 3).apply1(e => Random.nextFloat()) - val input2 = Tensor(1, 3, 3) - input2.copy(input1) - - val res1 = spatialFullConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/spatialFullConvolution.bigdl", null, - spatialFullConvolution, true) - val loadedSpatialFullConvolution = ModuleLoader. - loadFromFile("/tmp/spatialFullConvolution.bigdl") - val res2 = loadedSpatialFullConvolution.forward(input2) - res1 should be (res2) - } - - "SpatialMaxPooling serializer " should " work properly " in { - val spatialMaxPooling = SpatialMaxPooling(2, 2, 2, 2) - val input1 = Tensor(1, 3, 3).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialMaxPooling.forward(input1) - ModulePersister.saveToFile("/tmp/spatialMaxPooling.bigdl", null, - spatialMaxPooling, true) - val loadedSpatialMaxPooling = ModuleLoader. - loadFromFile("/tmp/spatialMaxPooling.bigdl") - val res2 = loadedSpatialMaxPooling.forward(input2) - res1 should be (res2) - } - - "SpatialShareConvolution serializer " should "work properly" in { - val spatialShareConvolution = SpatialShareConvolution(1, 1, 2, 2, 1, 1) - val input1 = Tensor(3, 1, 3, 4).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialShareConvolution.forward(input1) - ModulePersister.saveToFile("/tmp/spatialShareConvolution.bigdl", null, - spatialShareConvolution, true) - val loadedSpatialShareConvolution = ModuleLoader. - loadFromFile("/tmp/spatialShareConvolution.bigdl") - val res2 = loadedSpatialShareConvolution.forward(input2) - res1 should be (res2) - } - - "SpatialSubtractiveNormalization serializer " should " work properly" in { - val kernel = Tensor(3, 3).apply1( e => Random.nextFloat()) - val spatialSubtractiveNormalization = SpatialSubtractiveNormalization(1, kernel) - val input1 = Tensor(1, 1, 1, 5).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialSubtractiveNormalization.forward(input1) - ModulePersister.saveToFile("/tmp/spatialSubtractiveNormalization.bigdl", null, - spatialSubtractiveNormalization, true) - val loadedSpatialSubtractiveNormalization = ModuleLoader. - loadFromFile("/tmp/spatialSubtractiveNormalization.bigdl") - val res2 = loadedSpatialSubtractiveNormalization.forward(input2) - res1 should be (res2) - } - - "SpatialWithinChannelLRN serializer " should " work properly" in { - val spatialWithinChannelLRN = new SpatialWithinChannelLRN[Float](5, 5e-4, 0.75) - val input1 = Tensor(1, 4, 7, 6).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = spatialWithinChannelLRN.forward(input1) - ModulePersister.saveToFile("/tmp/spatialWithinChannelLRN.bigdl", null, - spatialWithinChannelLRN, true) - val loadedSpatialWithinChannelLRN = ModuleLoader. - loadFromFile("/tmp/spatialWithinChannelLRN.bigdl") - val res2 = loadedSpatialWithinChannelLRN.forward(input2) - res1 should be (res2) - } - - "SpatialZeroPadding serializer " should " work properly" in { - val spatialZeroPadding = SpatialZeroPadding(1, 0, -1, 0) - val input1 = Tensor(3, 3, 3).apply1(_ => Random.nextFloat()) - val input2 = Tensor(3, 3, 3) - input2.copy(input1) - val res1 = spatialZeroPadding.forward(input1) - ModulePersister.saveToFile("/tmp/spatialZeroPadding.bigdl", null, - spatialZeroPadding, true) - val loadedSpatialSpatialZeroPadding = ModuleLoader. - loadFromFile("/tmp/spatialZeroPadding.bigdl") - val res2 = loadedSpatialSpatialZeroPadding.forward(input2) - res1 should be (res2) - - } - - "SplitTable serializer " should " work properly" in { - val splitTable = SplitTable(2) - val input1 = Tensor(2, 10).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = splitTable.forward(input1) - ModulePersister.saveToFile("/tmp/splitTable.bigdl", null, splitTable, true) - val loadedSplitTable = ModuleLoader.loadFromFile("/tmp/splitTable.bigdl") - val res2 = loadedSplitTable.forward(input2) - res1 should be (res2) - } - - "Sqrt serializer " should " work properly" in { - val sqrt = Sqrt() - val input1 = Tensor(10).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = sqrt.forward(input1) - ModulePersister.saveToFile("/tmp/sqrt.bigdl", null, sqrt, true) - val loadedSqrt = ModuleLoader.loadFromFile("/tmp/sqrt.bigdl") - val res2 = loadedSqrt.forward(input2) - res1 should be (res2) - } - - "Square serializer " should " work properly " in { - val square = Square() - val input1 = Tensor(10).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = square.forward(input1) - ModulePersister.saveToFile("/tmp/square.bigdl", null, square, true) - val loadedSquare = ModuleLoader.loadFromFile("/tmp/square.bigdl") - val res2 = loadedSquare.forward(input2) - res1 should be (res2) - } - - "Squeeze serializer " should " work properly" in { - val squeeze = Squeeze(2) - val input1 = Tensor(2, 1, 2).apply1( e => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = squeeze.forward(input1) - ModulePersister.saveToFile("/tmp/squeeze.bigdl", null, squeeze, true) - val loadedSqueeze = ModuleLoader.loadFromFile("/tmp/squeeze.bigdl") - val res2 = loadedSqueeze.forward(input2) - res1 should be (res2) + "SpatialDivisiveNormalization serializer" should "work properly" in { + val spatialDivisiveNormalization = SpatialDivisiveNormalization[Float](). + setName("spatialDivisiveNormalization") + val input = Tensor[Float](1, 5, 5).apply1(e => Random.nextFloat()) + runSerializationTest(spatialDivisiveNormalization, input) } - "Sum serializer" should "work properly" in { - val sum = Sum(2) - val input1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = sum.forward(input1) + "SpatialFullConvolution serializer" should "work properly" in { - ModulePersister.saveToFile("/tmp/sum.bigdl", null, sum, true) - val loadedSum = ModuleLoader.loadFromFile("/tmp/sum.bigdl") - val res2 = loadedSum.forward(input2) - res1 should be (res2) + val spatialFullConvolution = SpatialFullConvolution[Float](1, 1, + 2, 2, 1, 1, 0, 0).setName("spatialFullConvolution") + val input = Tensor[Float](1, 3, 3).apply1(e => Random.nextFloat()) + runSerializationTest(spatialFullConvolution, input) } - "Tanh serializer" should " work properly" in { - val tanh = Tanh() - val input1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = tanh.forward(input1) - - ModulePersister.saveToFile("/tmp/tanh.bigdl", null, tanh, true) - val loadedTanh = ModuleLoader.loadFromFile("/tmp/tanh.bigdl") - val res2 = loadedTanh.forward(input2) - res1 should be (res2) - } - - "TanhShrink serializer " should " work properly" in { - val tanhShrink = TanhShrink() - val input1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor() - input2.resizeAs(input1).copy(input1) - val res1 = tanhShrink.forward(input1) - - ModulePersister.saveToFile("/tmp/tanhShrink.bigdl", null, tanhShrink, true) - val loadedTanhShrink = ModuleLoader.loadFromFile("/tmp/tanhShrink.bigdl") - val res2 = loadedTanhShrink.forward(input2) - res1 should be (res2) + "SpatialMaxPooling serializer" should "work properly" in { + val spatialMaxPooling = SpatialMaxPooling[Float](2, 2, 2, 2). + setName("spatialMaxPooling") + val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) + runSerializationTest(spatialMaxPooling, input) } - "TemporalConvolution serializer " should " work properly" in { - val temporalConvolution = TemporalConvolution(10, 8, 5, 2) - val input1 = Tensor(100, 10).apply1(e => Random.nextFloat()) - val input2 = Tensor(100, 10) - input2.copy(input1) + "SpatialShareConvolution serializer" should "work properly" in { + val spatialShareConvolution = SpatialShareConvolution[Float](1, 1, 2, 2, 1, 1). + setName("spatialShareConvolution") + val input = Tensor[Float](3, 1, 3, 4).apply1( e => Random.nextFloat()) + runSerializationTest(spatialShareConvolution, input) + } - val res1 = temporalConvolution.forward(input1) + "SpatialSubtractiveNormalization serializer" should "work properly" in { + val kernel = Tensor[Float](3, 3).apply1( e => Random.nextFloat()) + val spatialSubtractiveNormalization = SpatialSubtractiveNormalization[Float](1, kernel). + setName("spatialSubtractiveNormalization") + val input = Tensor[Float](1, 1, 1, 5).apply1( e => Random.nextFloat()) + runSerializationTest(spatialSubtractiveNormalization, input) + } + + "SpatialWithinChannelLRN serializer" should "work properly" in { + val spatialWithinChannelLRN = new SpatialWithinChannelLRN[Float](5, 5e-4, 0.75). + setName("spatialWithinChannelLRN") + val input = Tensor[Float](1, 4, 7, 6).apply1( e => Random.nextFloat()) + runSerializationTest(spatialWithinChannelLRN, input) + } - ModulePersister.saveToFile("/tmp/temporalConvolution.bigdl", null, temporalConvolution, true) - val loadedTemporalConvolution = ModuleLoader.loadFromFile("/tmp/temporalConvolution.bigdl") - val res2 = loadedTemporalConvolution.forward(input2) - res1 should be (res2) + "SpatialZeroPadding serializer" should "work properly" in { + val spatialZeroPadding = SpatialZeroPadding[Float](1, 0, -1, 0). + setName("spatialZeroPadding") + val input = Tensor[Float](3, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialZeroPadding, input) } - "Threshold serializer " should " work properly" in { - val threshold = Threshold(0.5) - val input1 = Tensor(5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor(5, 5) - input2.copy(input1) - val res1 = threshold.forward(input1) + "SpatialSeperableConvolution serializer" should "work properly" in { + val seprableConv = SpatialSeperableConvolution[Float](2, 2, 1, 2, 2, + dataFormat = DataFormat.NHWC).setName("seprableConv") + val input = Tensor[Float](1, 5, 5, 2).apply1( e => Random.nextFloat()) + runSerializationTest(seprableConv, input) + } - ModulePersister.saveToFile("/tmp/threshold.bigdl", null, threshold, true) - val loadedThreshold = ModuleLoader.loadFromFile("/tmp/threshold.bigdl") - val res2 = loadedThreshold.forward(input1) - res1 should be (res2) + "SplitTable serializer" should "work properly" in { + val splitTable = SplitTable[Float](2).setName("splitTable") + val input = Tensor[Float](2, 10).apply1( e => Random.nextFloat()) + runSerializationTest(splitTable, input) } - "TimeDistributed serializer " should " work properly" in { - val timeDistributed = TimeDistributed(Linear(5, 5)) - val input1 = Tensor(2, 5, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor(2, 5, 5) - input2.copy(input1) - val res1 = timeDistributed.forward(input1) + "Sqrt serializer" should "work properly" in { + val sqrt = Sqrt[Float, Float]().setName("sqrt") + val input = Tensor[Float](10).apply1( e => Random.nextFloat()) + runSerializationTest(sqrt, input) + } - ModulePersister.saveToFile("/tmp/timeDistributed.bigdl", null, timeDistributed, true) - val loadedTimeDistributed = ModuleLoader.loadFromFile("/tmp/timeDistributed.bigdl") - val res2 = loadedTimeDistributed.forward(input1) - res1 should be (res2) + "Square serializer" should "work properly" in { + val square = Square[Float, Float]().setName("square") + val input = Tensor[Float](10).apply1( e => Random.nextFloat()) + runSerializationTest(square, input) } - "Transpose serializer " should " work properly" in { - val transpose = Transpose(Array((1, 2))) - val input1 = Tensor().resize(Array(2, 3)).apply1(_ => Random.nextFloat()) - val input2 = Tensor(2, 3) - input2.copy(input1) + "Squeeze serializer" should "work properly" in { + val squeeze = Squeeze[Float](2).setName("squeeze") + val input = Tensor[Float](2, 1, 2).apply1( e => Random.nextFloat()) + runSerializationTest(squeeze, input) + } - val res1 = transpose.forward(input1) + "SReLU serilalizer" should "work properly" in { + val srelu = SReLU[Float]().setName("srelu") + val input = Tensor[Float](3, 4).apply1( e => Random.nextFloat()) + runSerializationTest(srelu, input) + } - ModulePersister.saveToFile("/tmp/transpose.bigdl", null, transpose, true) - val loadedTranspose = ModuleLoader.loadFromFile("/tmp/transpose.bigdl") - val res2 = loadedTranspose.forward(input1) - res1 should be (res2) + "Sum serializer" should "work properly" in { + val sum = Sum[Float, Float](2).setName("sum") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(sum, input) + } + "Tanh serializer" should "work properly" in { + val tanh = Tanh[Float]().setName("tanh") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(tanh, input) } - "Unsqueeze serializer" should " work properly" in { - val unsqueeze = Unsqueeze(2) - val input1 = Tensor(2, 2, 2).apply1(_ => Random.nextFloat()) - val input2 = Tensor(2, 2, 2) - input2.copy(input1) - val res1 = unsqueeze.forward(input1) + "TanhShrink serializer" should "work properly" in { + val tanhShrink = TanhShrink[Float]().setName("tanhShrink") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(tanhShrink, input) + } - ModulePersister.saveToFile("/tmp/unsqueeze.bigdl", null, unsqueeze, true) - val loadedUnsqueeze = ModuleLoader.loadFromFile("/tmp/unsqueeze.bigdl") - val res2 = loadedUnsqueeze.forward(input1) - res1 should be (res2) + "TemporalConvolution serializer" should "work properly" in { + val temporalConvolution = TemporalConvolution[Float](10, 8, 5, 2). + setName("temporalConvolution") + val input = Tensor[Float](100, 10).apply1(e => Random.nextFloat()) + runSerializationTest(temporalConvolution, input) } - "View serializer" should " work properly " in { - val view = View(Array(2, 5)) - val input1 = Tensor(1, 10).apply1(_ => Random.nextFloat()) - val input2 = Tensor(1, 10) - input2.copy(input1) - val res1 = view.forward(input1) + "TemporalMaxPooling serializer" should "work properly" in { + val temporalMaxPooling = new TemporalMaxPooling[Float](4).setName("temporalMaxPooling") + val input = Tensor[Float](5, 4, 5).apply1(e => Random.nextFloat()) + runSerializationTest(temporalMaxPooling, input) + } - ModulePersister.saveToFile("/tmp/view.bigdl", null, view, true) - val loadedView = ModuleLoader.loadFromFile("/tmp/view.bigdl") - val res2 = loadedView.forward(input1) - res1 should be (res2) + "Threshold serializer" should "work properly" in { + val threshold = Threshold[Float](0.5).setName("threshold") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(threshold, input) } - "VolumetricConvolution serializer " should " work properly " in { - val volumetricConvolution = VolumetricConvolution(2, 3, 2, 2, 2, dT = 1, dW = 1, dH = 1, - padT = 0, padW = 0, padH = 0, withBias = true) - val input1 = Tensor(2, 2, 2, 2).apply1(_ => Random.nextFloat()) - val input2 = Tensor(2, 2, 2, 2) - input2.copy(input1) - val res1 = volumetricConvolution.forward(input1) + "Tile serializer" should "work properly" in { + val tile = Tile[Float](1).setName("tile") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(tile, input) + } - ModulePersister.saveToFile("/tmp/volumetricConvolution.bigdl", - null, volumetricConvolution, true) - val loadedVolumetricConvolution = ModuleLoader.loadFromFile("/tmp/volumetricConvolution.bigdl") - val res2 = loadedVolumetricConvolution.forward(input1) - res1 should be (res2) + "TimeDistributed serializer" should "work properly" in { + val timeDistributed = TimeDistributed[Float](Linear[Float](5, 5)). + setName("timeDistributed") + val input = Tensor[Float](2, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(timeDistributed, input) } - "VolumetricFullConvolution serializer " should " work properly " in { + "Transpose serializer" should "work properly" in { + val transpose = Transpose[Float](Array((1, 2))).setName("transpose") + val input = Tensor[Float]().resize(Array(2, 3)).apply1(_ => Random.nextFloat()) + runSerializationTest(transpose, input) + } - val volumetricFullConvolution = new VolumetricFullConvolution(3, 6, - 4, 3, 3, 2, 1, 1, 2, 2, 2) + "Unsqueeze serializer" should "work properly" in { + val unsqueeze = Unsqueeze[Float](2).setName("unsqueeze") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(unsqueeze, input) + } - val input1 = Tensor(3, 3, 3, 6, 6).apply1(e => Random.nextFloat()) - val input2 = Tensor(3, 3, 3, 6, 6).copy(input1) + "UpSampling3D serializer" should "work properly" in { + val upSampling3D = UpSampling3D[Float](Array(2, 2, 2)).setName("upSampling3D") + val input = Tensor[Float](1, 2, 2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(upSampling3D, input) + } - val res1 = volumetricFullConvolution.forward(input1) + "View serializer" should "work properly" in { + val view = View[Float](Array(2, 5)).setName("view") + val input = Tensor[Float](1, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(view, input) + } - ModulePersister.saveToFile("/tmp/volumetricFullConvolution.bigdl", null, - volumetricFullConvolution, true) - val loadedVolumetricFullConvolution = ModuleLoader. - loadFromFile("/tmp/volumetricFullConvolution.bigdl") - val res2 = loadedVolumetricFullConvolution.forward(input1) - res1 should be (res2) + "VolumetricAveragePooling serializer" should "work properly" in { + val volumetricAveragePooling = VolumetricAveragePooling[Float](2, 2, 2, 1, 1, 1, 0, 0, 0). + setName("volumetricAveragePooling") + val input = Tensor[Float](1, 2, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(volumetricAveragePooling, input) + } + "VolumetricConvolution serializer" should "work properly" in { + val volumetricConvolution = VolumetricConvolution[Float](2, 3, 2, 2, 2, dT = 1, dW = 1, dH = 1, + padT = 0, padW = 0, padH = 0, withBias = true).setName("volumetricConvolution") + val input = Tensor[Float](2, 2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(volumetricConvolution, input) } - "VolumetricMaxPooling serializer " should " work properly " in { - val volumetricMaxPooling = VolumetricMaxPooling(2, 2, 2, 1, 1, 1, 0, 0, 0) - val input1 = Tensor(1, 2, 3, 3).apply1(_ => Random.nextFloat()) - val input2 = Tensor(1, 2, 3, 3) - input2.copy(input1) - val res1 = volumetricMaxPooling.forward(input1) + "VolumetricFullConvolution serializer" should "work properly" in { + + val volumetricFullConvolution = new VolumetricFullConvolution[Float](3, 6, + 4, 3, 3, 2, 1, 1, 2, 2, 2).setName("volumetricFullConvolution") + val input = Tensor[Float](3, 3, 3, 6, 6).apply1(e => Random.nextFloat()) + runSerializationTest(volumetricFullConvolution, input) + } - ModulePersister.saveToFile("/tmp/volumetricMaxPooling.bigdl", null, volumetricMaxPooling, true) - val loadedVolumetricMaxPooling = ModuleLoader.loadFromFile("/tmp/volumetricMaxPooling.bigdl") - val res2 = loadedVolumetricMaxPooling.forward(input1) - res1 should be (res2) + "VolumetricMaxPooling serializer" should "work properly" in { + val volumetricMaxPooling = VolumetricMaxPooling[Float](2, 2, 2, 1, 1, 1, 0, 0, 0). + setName("volumetricMaxPooling") + val input = Tensor[Float](1, 2, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(volumetricMaxPooling, input) } - "bigquant.SpatialConvolution serializer" should "work properly " in { + "bigquant.SpatialConvolution serializer" should "work properly" in { val nInputPlane = 1 val nOutputPlane = 1 val kW = 2 @@ -1938,22 +1246,16 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val biasData = Array(0.0f) - val input = Tensor(1, 1, 3, 3).apply1(_ => Random.nextFloat()) - val weight = Tensor(Storage(kernelData), 1, Array(nOutputPlane, nInputPlane, kH, kW)) - val bias = Tensor(Storage(biasData), 1, Array(nOutputPlane)) + val input = Tensor[Float](1, 1, 3, 3).apply1(_ => Random.nextFloat()) + val weight = Tensor[Float](Storage(kernelData), 1, Array(nOutputPlane, nInputPlane, kH, kW)) + val bias = Tensor[Float](Storage(biasData), 1, Array(nOutputPlane)) val conv = quantized.SpatialConvolution[Float](nInputPlane, nOutputPlane, - kW, kH, dW, dH, padW, padH, initWeight = weight, initBias = bias) - - - val res1 = conv.forward(input) + kW, kH, dW, dH, padW, padH, initWeight = weight, initBias = bias).setName("quantConv") - ModulePersister.saveToFile("/tmp/bigquant.conv.bigdl", null, conv, true) - val loadedConv = ModuleLoader.loadFromFile("/tmp/bigquant.conv.bigdl") - val res2 = loadedConv.forward(input) - res1 should be (res2) + runSerializationTest(conv, input) } - "bigquant.SpatialDilatedConvolution serializer" should "work properly " in { + "bigquant.SpatialDilatedConvolution serializer" should "work properly" in { val nInputPlane = 1 val nOutputPlane = 1 val kW = 2 @@ -1970,18 +1272,14 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val biasData = Array(0.0f) - val input = Tensor(1, 1, 3, 3).apply1(_ => Random.nextFloat()) - val weight = Tensor(Storage(kernelData), 1, Array(nOutputPlane, nInputPlane, kH, kW)) - val bias = Tensor(Storage(biasData), 1, Array(nOutputPlane)) + val input = Tensor[Float](1, 1, 3, 3).apply1(_ => Random.nextFloat()) + val weight = Tensor[Float](Storage(kernelData), 1, Array(nOutputPlane, nInputPlane, kH, kW)) + val bias = Tensor[Float](Storage(biasData), 1, Array(nOutputPlane)) val conv = quantized.SpatialDilatedConvolution[Float](nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, initWeight = weight, initBias = bias) + .setName("quantDilatedConv") - val res1 = conv.forward(input) - - ModulePersister.saveToFile("/tmp/bigquant.dilated.conv.bigdl", null, conv, true) - val loadedConv = ModuleLoader.loadFromFile("/tmp/bigquant.dilated.conv.bigdl") - val res2 = loadedConv.forward(input) - res1 should be (res2) + runSerializationTest(conv, input) } "bigquant.Linear serializer" should "work properly " in { @@ -1995,19 +1293,532 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val biasData = Array(0.0f, 0.1f) - val input = Tensor(2, 2).apply1(_ => Random.nextFloat()) - val weight = Tensor(Storage(kernelData), 1, Array(outputSize, inputSize)) - val bias = Tensor(Storage(biasData), 1, Array(outputSize)) + val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + val weight = Tensor[Float](Storage(kernelData), 1, Array(outputSize, inputSize)) + val bias = Tensor[Float](Storage(biasData), 1, Array(outputSize)) val linear = quantized.Linear[Float](outputSize, inputSize, initWeight = weight, - initBias = bias) + initBias = bias).setName("quantLinear") + runSerializationTest(linear, input) + } + + // Below are TF Ops + "All serializer" should "work properly" in { + val all = All[Float]().setName("all") + val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val input2 = Tensor[Int](T(2, 1, 2)) + val input = T() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(all, input) + } + + "Any serializer" should "work properly" in { + val any = Any[Float]().setName("any") + val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val input2 = Tensor[Int](T(2, 1, 2)) + val input = T() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(any, input) + } + + "ApproximateEqual serializer" should "work properly" in { + val approximateEqual = ApproximateEqual[Float](0.01f).setName("approximateEqual") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(approximateEqual, input, approximateEqual. + asInstanceOf[ModuleToOperation[Float]].module.getClass + ) + } + + "ArgMax serializer" should "work properly" in { + val argMax = ArgMax[Float].setName("argMax") + val dataTensor = Tensor[Float](T(T(1.0f, 2.0f), T(3.0f, 4.0f))) + val dimensionTensor = Tensor.scalar[Int](1) + val input = T(dataTensor, dimensionTensor) + runSerializationTest(argMax, input) + } + + "Assert serializer" should "work properly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val assert = new Assert[Float]().setName("assert") + val predictTensor = Tensor[Boolean](Array(1)) + predictTensor.setValue(1, true) + val msg = Tensor[ByteString](Array(1)) + msg.setValue(1, ByteString.copyFromUtf8("must be true")) + val input = T(predictTensor, msg) + runSerializationTest(assert, input) + } + + "Assign serializer" should "work properly" in { + val assign = new Assign[Float]().setName("assign") + val input = + T( + Tensor[Float](T(1f, 2f, 3f)), + Tensor[Float](T(2f, 2f, 4f)) + ) + runSerializationTest(assign, input) + } + + "AssignGrad serializer" should "work properly" in { + val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val assignGrad = new AssignGrad[Float](grad).setName("assignGrad") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(assignGrad, input) + } + + "AvgPoolGrad serializer" should "work properly" in { + val avgPoolGrad = AvgPoolGrad[Float](4, 4, 1, 1, -1, -1, DataFormat.NHWC). + setName("avgPoolGrad") + val input1 = Tensor[Int](T(4, 32, 32, 3)) + val input2 = Tensor[Float](4, 32, 32, 3).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(avgPoolGrad, input) + } + + "BatchMatMul serializer" should "work properly" in { + val batchMatMul = BatchMatMul[Float, Float]().setName("batchMatMul") + val input = + T( + Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(batchMatMul, input) + } + + "BiasAddGrad serializer" should "work properly" in { + val biasAddGrad = BiasAddGrad[Float](DataFormat.NCHW). + setName("biasAddGrad") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(biasAddGrad, input) + } + + "BroadcastGradientArgs serializer" should "work properly" in { + val broadcastGradientArgs = BroadcastGradientArgs[Float](). + setName("broadcastGradientArgs") + val input = + T( + Tensor[Int](T(1, 2, 3)), + Tensor[Int](T(2, 2, 1)) + ) + runSerializationTest(broadcastGradientArgs, input, broadcastGradientArgs. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Cast serializer" should "work properly" in { + val cast = Cast[Float, Float]().setName("cast") + val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(cast, input, cast. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Ceil serializer" should "work properly" in { + val ceil = Ceil[Float, Float]().setName("ceil") + val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(ceil, input) + } + + "MergeOps serializer" should "work properly" in { + val mergeOps = new MergeOps[Float](1).setName("mergeOps") + val input = + T( + Tensor[Float](T(1.0f, 2.0f, 3.0f)), + Tensor[Float](T(2.0f, 2.0f, 1.0f)) + ) + runSerializationTest(mergeOps, input) + } + + "SwitchOps serializer" should "work properly" in { + val switchOps = new SwitchOps[Float]().setName("switchOps") + val input = + T( + Tensor[Float](T(1.0f, 2.0f, 3.0f)), + Tensor[Boolean](T(true)) + ) + runSerializationTest(switchOps, input) + } + + "Conv2D serializer" should "work properly" in { + val conv2d = Conv2D[Float](2, 1, -1, -1).setName("conv2d") + val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + val filter = Tensor[Float](4, 3, 3, 2).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, filter) + runSerializationTest(conv2d, input) + } + + "Conv2DBackFilter serializer" should "work properly" in { + val conv2dBackFilter = Conv2DBackFilter[Float](2, 2, -1, -1, DataFormat.NHWC). + setName("conv2dBackFilter") + val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + val kernelSize = Tensor[Int](T(2, 2, 3, 3)) + val grad = Tensor[Float](1, 2, 2, 3).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, kernelSize, grad) + runSerializationTest(conv2dBackFilter, input) + } + + "Conv2DTranspose Serializer" should "work properly" in { + val conv2dTranspose = Conv2DTranspose[Float](2, 2, -1, -1, DataFormat.NHWC). + setName("conv2dTranspose") + val inputTensor = Tensor[Int](T(1, 4, 3, 3)) + val kernelSize = Tensor[Float](2, 2, 3, 3).apply1(_ => Random.nextFloat()) + val data = Tensor[Float](1, 2, 2, 3)apply1(_ => Random.nextFloat()) + val input = T(inputTensor, kernelSize, data) + runSerializationTest(conv2dTranspose, input) + } + + "CrossEntropy serializer" should "work properly" in { + val crossEntropy = CrossEntropy[Float]().setName("crossEntropy") + val output = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val label = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val input = T(output, label) + runSerializationTest(crossEntropy, input) + } + + private def getInputs(name: String): Tensor[ByteString] = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" + val file = new JFile(path) + + val bytesVector = TFRecordIterator(file).toVector + val pngBytes = bytesVector(index) + + val example = Example.parseFrom(pngBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } + + "DecodeImage Serializer" should "work properly" in { + val decodeImage = new DecodeImage[Float](1).setName("decodeImage") + val input = getInputs("png") + runSerializationTest(decodeImage, input) + } + + "DecodeGif Serializer" should "work properly" in { + val decodeGif = new DecodeGifOps[Float]().setName("decodeGif") + val input = getInputs("gif") + runSerializationTest(decodeGif, input) + } + + "DecodeJpeg Serializer" should "work properly" in { + val decodeJpeg = new DecodeJpegOps[Float](1).setName("decodeJpeg") + val input = getInputs("jpeg") + runSerializationTest(decodeJpeg, input) + } + + "DecodePng Serializer" should "work properly" in { + val decodePng = new DecodePngOps[Float](1).setName("decodePng") + val input = getInputs("png") + runSerializationTest(decodePng, input) + } + + + "DecodeRaw Serializer" should "work properly" in { + val decodeRaw = new DecodeRawOps[Float](DataType.DT_UINT8, true).setName("decodeRaw") + val input = getInputs("raw") + runSerializationTest(decodeRaw, input) + } + + "DepthwiseConv2DBackpropInput serializer" should "work properly" in { + val depWiseBackprop = + DepthwiseConv2DBackpropInput[Float](1, 1, 0, 0, DataFormat.NHWC). + setName("depWiseBackprop") + val input = T(Tensor[Int](T(4, 24, 24, 3)), + Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(depWiseBackprop, input) + } + + "DepthwiseConv2D serializer" should "work properly" in { + val depWIseConv2d = DepthwiseConv2D[Float](1, 1, 0, 0).setName("depWIseConv2d") + val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat())) + runSerializationTest(depWIseConv2d, input) + } + + "DepthwiseConv2DBackpropFilter serializer" should "work properly" in { + val depWiseConv2dBackProp = DepthwiseConv2DBackpropFilter[Float](1, + 1, 0, 0, DataFormat.NHWC).setName("depWiseConv2dBackProp") + val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(2, 2, 3, 1)), + Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(depWiseConv2dBackProp, input) + } + + "EluGrad serializer" should "work properly" in { + val eluGrad = EluGrad[Float, Float]().setName("eluGrad") + val inputTensor = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, grad) + runSerializationTest(eluGrad, input) + } + + "Equal serializer" should "work properly" in { + val equal = Equal[Float]().setName("equal") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(equal, input, + equal.asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "ExpOps serializer" should "work properly" in { + val expOps = ExpOps[Float, Float]().setName("expOps") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(expOps, input) + } + + "Expm1 serializer" should "work properly" in { + val expm1 = Expm1[Float, Float]().setName("expm1") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(expm1, input) + } + + "Floor serializer" should "work properly" in { + val floor = Floor[Float]().setName("floor") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(floor, input, floor. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "FloorDiv serializer" should "work properly" in { + val floorDiv = FloorDiv[Float, Float]().setName("floorDiv") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(floorDiv, input) + } + + "FloorMod serializer" should "work properly" in { + val floorMod = FloorMod[Float, Float]().setName("floorMod") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(floorMod, input) + } + + "FusedBatchNorm serializer" should "work properly" in { + val fusedBatchNorm = FusedBatchNorm[Float]().setName("fusedBatchNorm") + val input = T(Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](0), + Tensor[Float](0)) + runSerializationTest(fusedBatchNorm, input) + } + + "FusedBatchNormGrad serializer" should "work properly" in { + val fbatchNormGrad = FusedBatchNormGrad[Float]().setName("fbatchNormGrad") + val input = T(Tensor[Float](4, 8, 8, 256).rand(), + Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat())) + runSerializationTest(fbatchNormGrad, input) + } - val res1 = linear.forward(input) + "Greater serializer" should "work properly" in { + val greater = Greater[Float]().setName("greater") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(greater, input, greater. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "GreaterEqual serializer" should "work properly" in { + val greaterEqual = GreaterEqual[Float]().setName("greaterEqual") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(greaterEqual, input, greaterEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "InTopK serializer" should "work properly" in { + val inTopK = InTopK[Float](2).setName("inTopK") + val input1 = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Int](2).fill(1) + val input = T(input1, input2) + runSerializationTest(inTopK, input) + } + + "Inv serializer" should "work properly" in { + val inv = Inv[Float, Float]().setName("inv") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(inv, input) + } + + "InvGrad serializer" should "work properly" in { + val invGrad = InvGrad[Float, Float]().setName("invGrad") + val input = T(Tensor[Float](2, 5).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 5).apply1(_ => Random.nextFloat())) + runSerializationTest(invGrad, input) + } + + "IsFinite serializer" should "work properly" in { + val isFinite = IsFinite[Float, Float]().setName("isFinite") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isFinite, input) + } + + "IsInf serializer" should "work properly" in { + val isInf = IsInf[Float, Float]().setName("isInf") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isInf, input) + } + + "IsNan serializer" should "work properly" in { + val isNan = IsNan[Float, Float]().setName("isInf") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isNan, input) + } + + "L2Loss serializer" should "work properly" in { + val l2loss = L2Loss[Float]().setName("l2loss") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(l2loss, input, + l2loss.asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Less serializer" should "work properly" in { + val less = Less[Float]().setName("less") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(less, input, less + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LessEqual serializer" should "work properly" in { + val lessEqual = LessEqual[Float]().setName("lessEqual") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(lessEqual, input, lessEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LogicalAnd serializer" should "work properly" in { + val logicalAnd = LogicalAnd[Float].setName("logicalAnd") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(logicalAnd, input, logicalAnd. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LogicalNot serializer" should "work properly" in { + val logicalNot = LogicalNot[Float].setName("logicalNot") + val input = Tensor[Boolean](T(true, false)) + runSerializationTest(logicalNot, input, logicalNot + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LogicalOr serializer" should "work properly" in { + val logicalOr = LogicalOr[Float].setName("logicalOr") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(logicalOr, input, logicalOr + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LRNGrad serializer" should "work properly" in { + val lrnGrad = LRNGrad[Float]().setName("lrnGrad") + val input = T(Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(lrnGrad, input) + } + + "Maximum serializer" should "work properly" in { + val maxiMum = Maximum[Float, Float]().setName("maxiMum") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(maxiMum, input) + } + + "MaxPool serializer" should "work properly" in { + val maxPool = MaxPool[Float]( + Array(1, 2, 3, 1), + Array(1, 2, 1, 1), + "VALID").setName("maxPool") + val input = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(maxPool, input, maxPool. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + + } + + "MaxPoolGrad serializer" should "work properly" in { + val maxPoolGrad = MaxPoolGrad[Float](2, 1, 1, 1, 0, 0, DataFormat.NCHW). + setName("maxPoolGrad") + val input = T(Tensor[Float](1, 3, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](), + Tensor[Float](1, 1, 1).apply1(_ => Random.nextFloat())) + runSerializationTest(maxPoolGrad, input) + } + + "Mimimum serializer" should "work properly" in { + val minimum = Minimum[Float, Float]().setName("minimum") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(minimum, input) + } - ModulePersister.saveToFile("/tmp/bigquant.linear.bigdl", null, linear, true) - val loadedLinear = ModuleLoader.loadFromFile("/tmp/bigquant.linear.bigdl") - val res2 = loadedLinear.forward(input) - res1 should be (res2) + "Mod serializer" should "work properly" in { + val mod = Mod[Float, Float]().setName("mod") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(mod, input) } + + "ModuleToOperation serializer" should "work properly" in { + val moduleToOperation = ModuleToOperation[Float](new LogicalOr()). + setName("moduleToOperation") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(moduleToOperation, input) + } + + "NoOp serializer" should "work properly" in { + val noOp = NoOp[Float]().setName("noOp") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(noOp, input) + } + + "NotEqual serializer" should "work properly" in { + val notEqual = NotEqual[Float].setName("notEqual") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(notEqual, input, notEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "OneHot serializer" should "work properly" in { + val oneHot = OneHot[Float, Float](axis = -1).setName("oneHot") + val input = + T(Tensor[Long](T(0, 2, -1, 1)), + Tensor[Int](Array(3), shape = Array[Int]()), + Tensor[Float](Array(0.5f), shape = Array[Int]()), + Tensor[Float](Array(0.0f), shape = Array[Int]())) + runSerializationTest(oneHot, input, oneHot + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Pad serializer" should "work properly" in { + val pad = Pad[Float, Float](mode = "CONSTANT", 0.0f).setName("pad") + val inputTensor = Tensor[Float](2, 2, 3).apply1(_ => Random.nextFloat()) + val padding = Tensor[Int](T(T(1, 2), T(1, 2), T(1, 2))) + val input = T(inputTensor, padding) + runSerializationTest(pad, input, pad. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + "ParseExample serializer" should "work properly" in { import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString @@ -2032,8 +1843,8 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val outputStream = CodedOutputStream.newInstance(data) example.writeTo(outputStream) - val exampleParser = new ParseExample[Float](3, - Seq(FloatType, LongType, StringType), Seq(Array(3), Array(3), Array())) + val exampleParser = new ParseExample[Float](3, Seq(FloatType, LongType, StringType), + Seq(Array(3), Array(3), Array())).setName("parseExample") val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) val names = Tensor[ByteString]() @@ -2044,163 +1855,339 @@ class ModuleSerializerSpec extends FlatSpec with Matchers { val default1 = Tensor[Float]() val default2 = Tensor[Long]() val default3 = Tensor[ByteString]() - val input = T(serialized, names, key1, key2, key3, default1, default2, default3) + runSerializationTest(exampleParser, input) + } - val res1 = exampleParser.forward(input) + "PowOps serializer" should "work properly" in { + val pow = PowOps[Float]().setName("powOps") + val v = Tensor[Float](T(2)) + val t = Tensor[Float](T(1, 2, 3)) + val input = (T(t, v)) + runSerializationTest(pow, input) + } - ModulePersister.saveToFile("/tmp/exampleParser.bigdl", null, exampleParser, true) - val loadedExampleParser = ModuleLoader.loadFromFile[Float]("/tmp/exampleParser.bigdl") - val res2 = loadedExampleParser.forward(input) - res1 should be (res2) + "Prod serializer" should "work properly" in { + val prod = Prod[Float](-1, false).setName("prod") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(prod, input, prod. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + "RandomUniform serializer" should "work properly" in { + val randomUniform = RandomUniform[Float, Float](10, 20). + setName("randomUniform") + val input = Tensor[Int](T(1, 2, 3)) + runSerializationTest(randomUniform, input, randomUniform. + asInstanceOf[ModuleToOperation[Float]].module.getClass) } - "Customized Module " should "work properly" in { - val testModule = new TestModule(CustomData(1.0)) - DataConverter.registerConverter(universe.typeOf[CustomData].toString, TestCustomDataConverter) - val tensor1 = Tensor(10).apply1(_ => Random.nextFloat()) - val tensor2 = Tensor() - tensor2.resizeAs(tensor1).copy(tensor1) - val res1 = testModule.forward(tensor1) - ModulePersister.saveToFile("/tmp/testModule.bigdl", null, testModule, true) - val loadedModule = ModuleLoader.loadFromFile("/tmp/testModule.bigdl") - val res2 = loadedModule.forward(tensor2) - res1 should be (res2) + + "RangeOps serializer" should "work properly" in { + val rangeOps = RangeOps[Float, Float]().setName("rangeOps") + val input = T(Tensor[Float](T(1)), Tensor[Float](T(10)), Tensor[Float](T(1))) + runSerializationTest(rangeOps, input) } - "2 Linears's weights use same storage" should "work properly" in { - val weight = Array(0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f) - val weight1 = Tensor(Storage(weight), 1, Array(2, 2)) - val weight2 = Tensor(Storage(weight), 5, Array(2, 2)) + "Rank serializer" should "work properly" in { + val rank = Rank[Float].setName("rank") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(rank, input, rank. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } - val linear1 = Linear(2, 2, initWeight = weight1) - val linear2 = Linear(2, 2, initWeight = weight2) - val model = Sequential().add(linear1).add(linear2) + "Relu6Grad serializer" should "work properly" in { + val relu6Grad = Relu6Grad[Float, Float]().setName("relu6Grad") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(relu6Grad, input) + } - val input = Tensor(4, 2).rand + "ReluGrad serializer" should "work properly" in { + val reluGrad = ReluGrad[Float] + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(reluGrad, input) + } - val res1 = model.forward(input) + "ResizeBilinearOps serializer" should "work properly" in { + val resizeBilinearOps = ResizeBilinearOps[Float](false). + setName("resizeBiLinearOps") + val input = T(Tensor[Float](1, 3, 2, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(3, 2))) + runSerializationTest(resizeBilinearOps, input) + } - ModulePersister.saveToFile("/tmp/2linears.with.a.storage.bigdl", null, model, true) - val loadedModel = ModuleLoader.loadFromFile("/tmp/2linears.with.a.storage.bigdl") - val res2 = loadedModel.forward(input) + "Rint serializer" should "work properly" in { + val rint = Rint[Float]().setName("rint") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(rint, input) + } - res1 should be (res2) + "Round serializer" should "work properly" in { + val round = Round[Float, Float]().setName("round") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(round, input) } - "Load by definition " should " work properly" in { - val linear1 = Linear(2, 2).setName("linear") - val sequential = Sequential().setName("sequential").add(linear1) - ModulePersister.saveToFile("/tmp/loadDef.bigdl", null, sequential, true) - val linear2 = Linear(2, 2).setName("linear") - val definition = Sequential().setName("sequential").add(linear2) - ModuleLoader.loadFromDefinition(definition, "/tmp/loadDef.bigdl") + "RsqrtGrad serializer" should "work properly" in { + val rsqrtGrad = RsqrtGrad[Float, Float].setName("rsqrtGrad") + val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](3, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(rsqrtGrad, input) + } - val weight1 = linear1.weight + "SelectOps serializer" should "work properly" in { + val select = SelectOps[Float]().setName("select") + val cond = Tensor.scalar[Boolean](true) + val t = Tensor[Int](T(1)) + val e = Tensor[Int](T(2)) + val input = T(cond, t, e) + runSerializationTest(select, input) + } - val weight2 = linear2.weight + "SigmoidGrad serializer" should "work properly" in { + val sigMoidGrad = SigmoidGrad[Float, Float]().setName("sigMoidGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sigMoidGrad, input) + } - weight1 should be (weight2) + "Sign serializer" should "work properly" in { + val sign = Sign[Float, Float]().setName("sign") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(sign, input) } - "Module toString" should "have same result" in { - val linear = Linear(2, 2) - ModulePersister.saveToFile("/tmp/mstr.bigdl", null, linear, true) - val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") + "Slice serializer" should "work properly" in { + val slice = Slice[Float](begin = Array(0, 1, 1), + size = Array(2, -1, 1)).setName("slice") + val input = Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(slice, input, slice. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } - linear.toString() should be (loadedModel.toString()) + "SoftplusGrad serializer" should "work properly" in { + val sofplusGrad = SoftplusGrad[Float, Float].setName("sofplusGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sofplusGrad, input) } - "Module in tain " should " keep the state" in { - val linear = Linear(2, 2).training() - ModulePersister.saveToFile("/tmp/mstr.bigdl", null, linear, true) - val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") + "SoftSignGrad serializer" should "work properly" in { + val softSign = SoftsignGrad[Float, Float].setName("softSign") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(softSign, input) + } - loadedModel.isTraining() should be (true) + "SqrtGrad serializer" should "work properly" in { + val sqrtGrad = SqrtGrad[Float, Float].setName("sqrtGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sqrtGrad, input) } - "Module in evaluate " should " keep the state" in { - val linear = Linear(2, 2).evaluate() - ModulePersister.saveToFile("/tmp/mstr.bigdl", null, linear, true) - val loadedModel = ModuleLoader.loadFromFile("/tmp/mstr.bigdl") + "SquaredDifference serializer" should "work properly" in { + val squareDiff = SquaredDifference[Float]().setName("squareDiff") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(squareDiff, input) + } - loadedModel.isTraining() should be (false) + "Substr serializer" should "work properly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val subStr = Substr[Float]().setName("subStr") + val input = T(Tensor.scalar[ByteString](ByteString.copyFromUtf8("HelloBigDL")), + Tensor.scalar[Int](0), Tensor.scalar[Int](5)) + runSerializationTest(subStr, input) } - "HardSigmoid serialization" should "work properly" in { - val hardSigmoid = HardSigmoid() - ModulePersister.saveToFile("/tmp/hardSigmoid.bigdl", null, hardSigmoid, true) - val loadedModel = ModuleLoader.loadFromFile("/tmp/hardSigmoid.bigdl") + "SumOps serializer" should "work properly" in { + val sumOps = SumOps[Float, Float]().setName("sumOps") + val input = T(Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float]()) + runSerializationTest(sumOps, input) + } - val input = Tensor(2, 2).rand() + "TileOps serializer" should "work properly" in { + val tileOps = TileOps[Float]().setName("tileOps") + val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(2, 1, 2))) + runSerializationTest(tileOps, input, tileOps. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } - val res1 = hardSigmoid.forward(input) + "TopK serializer" should "work properly" in { + val topk = TopK[Float, Float](2).setName("topK") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(topk, input) + } - val res2 = loadedModel.forward(input) + "TruncateDiv serializer" should "work properly" in { + val truncateDiv = TruncateDiv[Float, Float]().setName("truncateDiv") + val input = T(Tensor[Float](5).fill(1.0f), Tensor[Float](5).fill(2.0f)) + runSerializationTest(truncateDiv, input) + } - res1 should be (res2) + "TruncatedNormal serializer" should "work properly" in { + val truncateNormal = TruncatedNormal[Float, Float](10, 20).setName("truncateNormal") + val input = Tensor[Int](T(1, 2, 3)) + runSerializationTest(truncateNormal, input, truncateNormal. + asInstanceOf[ModuleToOperation[Float]].module.getClass) } - "SReLU serialize" should "work correctly" in { - val srelu = SReLU[Float]() - val input = Tensor[Float](5, 2, 3, 4).randn() - val res1 = srelu.forward(input) + // nn.tf package - ModulePersister.saveToFile[Float]("/tmp/srelu.bigdl", null, srelu, true) - val loadSrelu = ModuleLoader.loadFromFile[Float]("/tmp/srelu.bigdl") - val res2 = loadSrelu.forward(input) - res1 should be (res2) + "BiasAdd serializer" should "work properly" in { + val biasAdd = BiasAdd[Float]().setName("biasAdd") + val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](3).apply1(_ => Random.nextFloat())) + runSerializationTest(biasAdd, input) + } + "Const serializer" should "work properly" in { + val value = Tensor[Float](3).apply1(_ => Random.nextFloat()) + val const = Const[Float, Float](value).setName("const") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(const, input) } - "Save model and weight separately" should "work properly" in { - val linear = Linear(3, 2) - val input = Tensor(2, 3).rand() - linear.saveModule("/tmp/linear.def", "/tmp/linear.bin", true) - val loaded = Module.loadModule("/tmp/linear.def", "/tmp/linear.bin") - val res1 = linear.forward(input) + "Fill serializer" should "work properly" in { + val fill = Fill[Float]().setName("fill") + val shape = Tensor[Int](T(2, 3)) + val value = Tensor[Float](Array(0.1f), Array[Int]()) + val input = T(shape, value) + runSerializationTest(fill, input) + } - val res2 = loaded.forward(input) + "Log1p serializer" should "work properly" in { + val log1p = Log1p[Float, Float]().setName("log1p") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(log1p, input) + } - res1 should be (res2) + "Shape serializer" should "work properly" in { + val shape = Shape[Float]().setName("shape") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(shape, input) } - "FindPath" should "work properly" in { - val file = new File("linear.bin") - println(file.getAbsolutePath) + "SplitAndSelect serializer" should "work properly" in { + val splitAndSelect = SplitAndSelect[Float](2, 1, 2).setName("splitSelect") + val input = Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(splitAndSelect, input) } -} -class TestModule[T: ClassTag](val custom: CustomData) - (implicit ev: TensorNumeric[T]) extends TensorModule[T] { - val addConst = AddConstant(custom.constant_scalar) - override def updateOutput(input: Tensor[T]): Tensor[T] = { - output = addConst.forward(input).asInstanceOf[Tensor[T]] - output + "StrideSlice serialier" should "work properly" in { + val strideSlice = new StrideSlice[Float](Array((1, 1, 2, 1))).setName("strideSlice") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(strideSlice, input) } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - gradInput = addConst.updateGradInput(input, gradOutput).asInstanceOf[Tensor[T]] - gradInput + "Variable serializer" should "work properly" in { + val out = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + val grad = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + val variable = Variable[Float](out, grad).setName("variable") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(variable, input) } -} -case class CustomData(val constant_scalar: Double) -case object TestSerializer extends ModuleSerializable -object TestCustomDataConverter extends DataConverter { + // tf.loaders - override def getAttributeValue[T: ClassTag](context: DeserializeContext, - attribute: Bigdl.AttrValue) - (implicit ev: TensorNumeric[T]): AnyRef = { - val customData = attribute.getCustomValue - val customMsg = customData.unpack(classOf[TestCustomData.CustomData]) - CustomData(customMsg.getScalar) + "MeanLoadTF serializer" should "work properly" in { + val meanLoadTF = new MeanLoadTF[Float]("Float", false).setName("meanLoadTF") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1, 1))) + runSerializationTest(meanLoadTF, input) } - override def setAttributeValue[T: ClassTag](context: SerializeContext[T], - attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type) - (implicit ev: TensorNumeric[T]): Unit = { - val testCustomData = TestCustomData.CustomData.newBuilder - testCustomData.setScalar(value.asInstanceOf[CustomData].constant_scalar) - attributeBuilder.setCustomValue(com.google.protobuf.Any.pack(testCustomData.build())) + "ConcatV2LoadTF serializer" should "work properly" in { + val concatv2 = new ConcatV2LoadTF[Float]().setName("concatv2LoadTF") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1))) + runSerializationTest(concatv2, input) } + + "ExpandDimsLoadTF serializer" should "work properly" in { + val expandDim = new ExpandDimsLoadTF[Float]().setName("expandDim") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](1)) + runSerializationTest(expandDim, input) + } + + "PadLoadTF serializer" should "work properly" in { + val padLoadTF = new PadLoadTF[Float]().setName("PadLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor[Int](T(T(1, 1), T(1, 1)))) + runSerializationTest(padLoadTF, input) + } + + "ProdLoadTF serializer" should "work properly" in { + val prodLoadTF = new ProdLoadTF[Float]().setName("prodLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](1)) + runSerializationTest(prodLoadTF, input) + } + + "ReshapeLoadTF serializer" should "work properly" in { + val reshapeLoadTF = new ReshapeLoadTF[Float]().setName("reshapeLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1, 5, 25))) + runSerializationTest(reshapeLoadTF, input) + } + + "SliceLoadTF serializer" should "work properly" in { + val sliceLoadTF = new SliceLoadTF[Float]().setName("sliceLoadTF") + val input = T(Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 1, 1)), + Tensor[Int](T(2, -1, 1))) + runSerializationTest(sliceLoadTF, input) + } + + "StridedSliceLoadTF serializer" should "work properly" in { + val strideSliceLoadTF = new StridedSliceLoadTF[Float](). + setName("strideSliceLoadTF") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0)), + Tensor[Int](T(1)), + Tensor[Int](T(1)) + ) + runSerializationTest(strideSliceLoadTF, input) + } + + "SplitLoadTF serializer" should "work properly" in { + val splitLoadTF = new SplitLoadTF[Float](1).setName("splitLoadTD") + val input = T(Tensor[Int](T(1)), + Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(splitLoadTF, input) + } + + "TransposeLoadTF serializer" should "work properly" in { + val transposeLoadTF = new TransposeLoadTF[Float]().setName("transposeLoadTF") + val input = T(Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 1)) + ) + runSerializationTest(transposeLoadTF, input) + } + + "TopKV2LoadTF serializer" should "work properly" in { + val topkv2LoadTF = new TopKV2LoadTF[Float](false, "Float"). + setName("topkv2LoadTF") + val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](2) + ) + runSerializationTest(topkv2LoadTF, input) + } + + override protected def afterAll() = { + var total = 0 + expected.foreach(exp => { + require(tested.contains(exp), s" $exp not included in the test!") + total += 1 + }) + println(s"total $total, remaining ${expected.size - total}") + } + } + From 43b8981bf52a242752380202d7eea18d7869ff03 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Tue, 12 Dec 2017 18:04:07 +0800 Subject: [PATCH 0576/1065] Add image frame predict (#1972) * Add VisionPredictor * share convolution * inception example can work * move predictImage To Predictor add matToTensor, remove postPorcessor add python Add more doc * meet code review and fix unit test * fix test * remove unnecessary ClassTag in ImageFrame --- .../dllib/feature/dataset/Transformer.scala | 5 +- .../transform/vision/image/Convertor.scala | 119 ++++++++++++++---- .../transform/vision/image/ImageFeature.scala | 66 +++++++--- .../transform/vision/image/ImageFrame.scala | 2 +- .../dllib/nn/abstractnn/AbstractModule.scala | 20 +++ .../bigdl/dllib/optim/Predictor.scala | 57 ++++++++- .../dllib/utils/python/api/PythonBigDL.scala | 57 +++++---- .../bigdl/dllib/optim/PredictorSpec.scala | 46 +++++++ .../vision/image/ConvertorSpec.scala | 35 ++++++ .../augmentation/ChannelNormalizeSpec.scala | 54 +++++++- .../augmentation/PixelNormalizerSpec.scala | 2 +- 11 files changed, 391 insertions(+), 72 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ConvertorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala index 28dda397652..a0b3038aa98 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Transformer.scala @@ -316,13 +316,12 @@ class SampleToMiniBatch[T: ClassTag] private[bigdl]( private val batchPerPartition = Utils.getBatchSize(totalBatch, partitionNum) var miniBatchBuffer = miniBatch.orNull + private val batchSize = batchPerPartition + private val sampleData = new Array[Sample[T]](batchSize) override def apply(prev: Iterator[Sample[T]]): Iterator[MiniBatch[T]] = { - val batchSizePerPartition = batchPerPartition new Iterator[MiniBatch[T]] { - private val batchSize = batchSizePerPartition - private val sampleData = new Array[Sample[T]](batchSize) override def hasNext: Boolean = prev.hasNext override def next(): MiniBatch[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala index 5eef5128602..2054f45d841 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala @@ -16,9 +16,14 @@ package com.intel.analytics.bigdl.transform.vision.image +import com.intel.analytics.bigdl.dataset.ArraySample +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat import org.apache.log4j.Logger +import scala.reflect._ + /** * Transform byte array(original image file in byte) to OpenCVMat */ @@ -45,6 +50,7 @@ object BytesToMat { feature(ImageFeature.originalSize) = mat.shape() } catch { case e: Exception => + e.printStackTrace() val uri = feature.uri() logger.warn(s"convert byte to mat fail for $uri") feature(ImageFeature.originalSize) = (-1, -1, -1) @@ -60,28 +66,13 @@ object BytesToMat { * @param validHeight valid height in case the mat is invalid * @param validWidth valid width in case the mat is invalid * @param validChannels valid channel in case the mat is invalid - * @param meanRGB meansRGB to subtract, it can be replaced by ChannelNormalize * @param outKey key to store float array */ class MatToFloats(validHeight: Int, validWidth: Int, validChannels: Int, - meanRGB: Option[(Float, Float, Float)] = None, outKey: String = ImageFeature.floats) + outKey: String = ImageFeature.floats) extends FeatureTransformer { @transient private var data: Array[Float] = _ - private def normalize(img: Array[Float], - meanR: Float, meanG: Float, meanB: Float): Array[Float] = { - val content = img - require(content.length % 3 == 0) - var i = 0 - while (i < content.length) { - content(i + 2) = content(i + 2) - meanR - content(i + 1) = content(i + 1) - meanG - content(i + 0) = content(i + 0) - meanB - i += 3 - } - img - } - override def transform(feature: ImageFeature): ImageFeature = { var input: OpenCVMat = null val (height, width, channel) = if (feature.isValid) { @@ -96,11 +87,9 @@ class MatToFloats(validHeight: Int, validWidth: Int, validChannels: Int, if (feature.isValid) { try { OpenCVMat.toFloatPixels(input, data) - if (meanRGB.isDefined) { - normalize(data, meanRGB.get._1, meanRGB.get._2, meanRGB.get._3) - } } finally { if (null != input) input.release() + feature(ImageFeature.mat) = null } } feature(outKey) = data @@ -113,7 +102,95 @@ object MatToFloats { val logger = Logger.getLogger(getClass) def apply(validHeight: Int = 300, validWidth: Int = 300, validChannels: Int = 3, - meanRGB: Option[(Float, Float, Float)] = None, outKey: String = ImageFeature.floats): MatToFloats = - new MatToFloats(validHeight, validWidth, validChannels, meanRGB, outKey) + new MatToFloats(validHeight, validWidth, validChannels, outKey) +} + +/** + * transform opencv mat to tensor + * @param toRGB BGR to RGB (default is BGR) + * @param tensorKey key to store transformed tensor + */ +class MatToTensor[T: ClassTag](toRGB: Boolean = false, + tensorKey: String = ImageFeature.imageTensor)(implicit ev: TensorNumeric[T]) + extends FeatureTransformer { + private val imageTensor: Tensor[T] = Tensor[T]() + private val matToFloats = MatToFloats() + + override def transform(feature: ImageFeature): ImageFeature = { + if (!feature.isValid) return feature + try { + matToFloats.transform(feature) + imageTensor.resize(3, feature.getHeight(), feature.getWidth()) + feature.copyTo[T](imageTensor.storage().array(), 0, ImageFeature.floats, toRGB) + feature(tensorKey) = imageTensor + } catch { + case e: Exception => + val uri = feature.uri() + MatToTensor.logger.warn(s"float to tensor fail for ${uri}") + e.printStackTrace() + feature.isValid = false + } + feature + } +} + +object MatToTensor { + val logger = Logger.getLogger(getClass) + + def apply[T: ClassTag](toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor) + (implicit ev: TensorNumeric[T]) + : MatToTensor[T] = new MatToTensor[T](toRGB, tensorKey) +} + +/** + * transform imageframe to samples + * @param inputKeys keys that maps inputs (each input should be a tensor) + * @param targetKeys keys that maps targets (each target should be a tensor) + * @param sampleKey key to store sample + */ +class ImageFrameToSample[T: ClassTag](inputKeys: Array[String] = Array(ImageFeature.imageTensor), + targetKeys: Array[String] = null, + sampleKey: String = ImageFeature.sample) + (implicit ev: TensorNumeric[T]) extends FeatureTransformer { + + + override def transform(feature: ImageFeature): ImageFeature = { + if (!feature.isValid) return feature + try { + val inputs = inputKeys.map(key => { + val input = feature[Tensor[T]](key) + require(input.isInstanceOf[Tensor[T]], s"the input $key should be tensor") + input.asInstanceOf[Tensor[T]] + }) + val sample = if (targetKeys == null) { + ArraySample[T](inputs) + } else { + val targets = targetKeys.map(key => { + val target = feature[Tensor[T]](key) + require(target.isInstanceOf[Tensor[T]], s"the target $key should be tensor") + target.asInstanceOf[Tensor[T]] + }) + ArraySample[T](inputs, targets) + } + feature(sampleKey) = sample + } catch { + case e: Exception => + e.printStackTrace() + val uri = feature.uri() + ImageFrameToSample.logger.warn(s"convert imageframe to sample fail for $uri") + feature(ImageFeature.originalSize) = (-1, -1, -1) + feature.isValid = false + } + feature + } +} + +object ImageFrameToSample { + val logger = Logger.getLogger(getClass) + + def apply[T: ClassTag](inputKeys: Array[String] = Array(ImageFeature.imageTensor), + targetKeys: Array[String] = null, + sampleKey: String = ImageFeature.sample)(implicit ev: TensorNumeric[T]) + : ImageFrameToSample[T] = new ImageFrameToSample[T](inputKeys, targetKeys, sampleKey) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala index b38974f5952..f521091ad86 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala @@ -16,13 +16,14 @@ package com.intel.analytics.bigdl.transform.vision.image +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat import com.intel.analytics.bigdl.utils.T import org.apache.log4j.Logger import scala.collection.{Set, mutable} -import scala.reflect.ClassTag +import scala.reflect._ /** * Each ImageFeature keeps information about single image, @@ -118,7 +119,7 @@ class ImageFeature extends Serializable { */ def getSize: (Int, Int, Int) = { val mat = opencvMat() - if (!mat.isReleased) { + if (mat != null && !mat.isReleased) { mat.shape() } else if (contains(ImageFeature.size)) { apply[(Int, Int, Int)](ImageFeature.size) @@ -197,28 +198,49 @@ class ImageFeature extends Serializable { * @param floatKey key that maps float array * @param toRGB BGR to RGB */ - def copyTo(storage: Array[Float], offset: Int, floatKey: String = ImageFeature.floats, - toRGB: Boolean = true): Unit = { + def copyTo[T: ClassTag](storage: Array[T], offset: Int, floatKey: String = ImageFeature.floats, + toRGB: Boolean = true)(implicit ev: TensorNumeric[T]): Unit = { require(contains(floatKey), s"there should be ${floatKey} in ImageFeature") val data = floats(floatKey) require(data.length >= getWidth() * getHeight() * 3, s"float array length should be larger than 3 * ${getWidth()} * ${getHeight()}") val frameLength = getWidth() * getHeight() require(frameLength * 3 + offset <= storage.length) - var j = 0 - if (toRGB) { - while (j < frameLength) { - storage(offset + j) = data(j * 3 + 2) - storage(offset + j + frameLength) = data(j * 3 + 1) - storage(offset + j + frameLength * 2) = data(j * 3) - j += 1 + if (classTag[T] == classTag[Float]) { + val storageFloat = storage.asInstanceOf[Array[Float]] + var j = 0 + if (toRGB) { + while (j < frameLength) { + storageFloat(offset + j) = data(j * 3 + 2) + storageFloat(offset + j + frameLength) = data(j * 3 + 1) + storageFloat(offset + j + frameLength * 2) = data(j * 3) + j += 1 + } + } else { + while (j < frameLength) { + storageFloat(offset + j) = data(j * 3) + storageFloat(offset + j + frameLength) = data(j * 3 + 1) + storageFloat(offset + j + frameLength * 2) = data(j * 3 + 2) + j += 1 + } } - } else { - while (j < frameLength) { - storage(offset + j) = data(j * 3) - storage(offset + j + frameLength) = data(j * 3 + 1) - storage(offset + j + frameLength * 2) = data(j * 3 + 2) - j += 1 + } else if (classTag[T] == classTag[Double]) { + val storageDouble = storage.asInstanceOf[Array[Double]] + var j = 0 + if (toRGB) { + while (j < frameLength) { + storageDouble(offset + j) = data(j * 3 + 2) + storageDouble(offset + j + frameLength) = data(j * 3 + 1) + storageDouble(offset + j + frameLength * 2) = data(j * 3) + j += 1 + } + } else { + while (j < frameLength) { + storageDouble(offset + j) = data(j * 3) + storageDouble(offset + j + frameLength) = data(j * 3 + 1) + storageDouble(offset + j + frameLength * 2) = data(j * 3 + 2) + j += 1 + } } } } @@ -289,6 +311,16 @@ object ImageFeature { */ val boundingBox = "boundingBox" + /** + * key: sample + */ + val sample = "sample" + + /** + * key: Image Tensor + */ + val imageTensor = "imageTensor" + /** * Create ImageFeature * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala index dab58bae5a5..6cfa43fbf77 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -42,7 +42,7 @@ trait ImageFrame { // scalastyle:off methodName // scalastyle:off noSpaceBeforeLeftBracket - def -> [C: ClassTag](transformer: FeatureTransformer): ImageFrame = { + def -> (transformer: FeatureTransformer): ImageFrame = { this.transform(transformer) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 05d76881d3f..f2c23d5bf97 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -30,6 +30,7 @@ import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.quantized.Quantization +import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame} import com.intel.analytics.bigdl.utils.caffe.CaffePersister import com.intel.analytics.bigdl.utils.serializer.ModulePersister import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} @@ -543,6 +544,25 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, Predictor(this).predictClass(dataset, batchSize) } + /** + * model predict images, return imageFrame with predicted tensor + * @param imageFrame imageFrame that contains images + * @param outputLayer if outputLayer is not null, the output of layer that matches + * outputLayer will be used as predicted output + * @param shareBuffer whether to share same memory for each batch predict results + * @param batchPerPartition batch size per partition, default is 4 + * @param predictKey key to store predicted result + * @return + */ + def predictImage(imageFrame: ImageFrame, + outputLayer: String = null, + shareBuffer: Boolean = false, + batchPerPartition: Int = 4, + predictKey: String = ImageFeature.predict): DistributedImageFrame = { + Predictor(this).predictImage(imageFrame, outputLayer, + shareBuffer, batchPerPartition, predictKey) + } + /** * Set weight and bias for the module * @param newWeights array of weights and bias diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index a057583536a..c50c2d7094b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -19,10 +19,11 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, SampleToMiniBatch, Utils, DataSet => _} import com.intel.analytics.bigdl.models.utils.ModelBroadcast +import com.intel.analytics.bigdl.nn.SpatialShareConvolution import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, FeatureTransformer, ImageFeature, ImageFrame} import org.apache.spark.rdd.RDD -import org.dmg.pmml.False import scala.reflect.ClassTag @@ -78,4 +79,58 @@ class Predictor[T: ClassTag] private[optim]( }) } } + + + /** + * model predict images, return imageFrame with predicted tensor + * @param imageFrame imageFrame that contains images + * @param outputLayer if outputLayer is not null, the output of layer that matches + * outputLayer will be used as predicted output + * @param shareBuffer whether to share same memory for each batch predict results + * @param batchPerPartition batch size per partition, default is 4 + * @param predictKey key to store predicted result + */ + def predictImage(imageFrame: ImageFrame, + outputLayer: String = null, + shareBuffer: Boolean = false, + batchPerPartition: Int = 4, + predictKey: String = ImageFeature.predict): DistributedImageFrame = { + require(imageFrame.isDistributed(), "please provide a distributed imageframe") + // share convolution fInput + SpatialShareConvolution.shareConvolution(model) + val rdd = imageFrame.asInstanceOf[DistributedImageFrame].rdd + val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, model.evaluate()) + val partitionNum = rdd.partitions.length + val toBatchBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( + batchSize = partitionNum * batchPerPartition, + partitionNum = Some(partitionNum)), shareBuffer) + val result = rdd.mapPartitions(partition => { + val localModel = modelBroad.value() + val localToBatch = toBatchBroad.value._1.cloneTransformer() + + partition.grouped(batchPerPartition).flatMap(imageFeatures => { + val validImageFeatures = imageFeatures.filter(_.isValid) + val samples = validImageFeatures.map(x => x[Sample[T]](ImageFeature.sample)) + val batch = localToBatch(samples.toIterator).next() + if (batch != null) { + localModel.forward(batch.getInput()) + val result = if (outputLayer == null) { + localModel.output.toTensor[T] + } else { + localModel(outputLayer).get.output.toTensor[T] + } + val batchOut = if (result.dim() == 1) { + Array(result) + } else { + result.split(1) + } + validImageFeatures.zip(batchOut).foreach(tuple => { + tuple._1(predictKey) = tuple._2 + }) + } + imageFeatures + }) + }) + ImageFrame.rdd(result) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a4c3a85ff67..c0536ccfdb3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1766,6 +1766,16 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new JavaRDD[JTensor](listRDD) } + def modelPredictImage(model: AbstractModule[Activity, Activity, T], + imageFrame: ImageFrame, + featLayerName: String, + shareBuffer: Boolean, + batchPerPartition: Int, + predictKey: String) + : DistributedImageFrame = { + model.predictImage(imageFrame, featLayerName, shareBuffer, batchPerPartition, predictKey) + } + def evaluate(module: AbstractModule[Activity, Activity, T]): AbstractModule[Activity, Activity, T] = { module.evaluate() @@ -2623,31 +2633,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab feature } - def imageFeatureToSample(imageFeature: ImageFeature, - floatKey: String = ImageFeature.floats, toChw: Boolean = true, - withImInfo: Boolean = false): Sample = { - val imageTensor = imageFeatureToImageTensor(imageFeature, floatKey, toChw) - val features = new util.ArrayList[JTensor]() - features.add(imageTensor) - if (withImInfo) { - val imInfo = imageFeature.getImInfo() - features.add(toJTensor(imInfo.asInstanceOf[Tensor[T]])) - } - val labels = new util.ArrayList[JTensor]() - labels.add(imageFeatureToLabelTensor(imageFeature)) - Sample(features, labels, "float") - } - def imageFeatureGetKeys(imageFeature: ImageFeature): JList[String] = { imageFeature.keys().toList.asJava } - def distributedImageFrameToSampleRdd(imageFrame: DistributedImageFrame, - floatKey: String = ImageFeature.floats, toChw: Boolean = true, withImInfo: Boolean = false) - : JavaRDD[Sample] = { - imageFrame.rdd.map(imageFeatureToSample(_, floatKey, toChw, withImInfo)).toJavaRDD() - } - def distributedImageFrameToImageTensorRdd(imageFrame: DistributedImageFrame, floatKey: String = ImageFeature.floats, toChw: Boolean = true): JavaRDD[JTensor] = { imageFrame.rdd.map(imageFeatureToImageTensor(_, floatKey, toChw)).toJavaRDD() @@ -2657,10 +2646,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab imageFrame.rdd.map(imageFeatureToLabelTensor).toJavaRDD() } - def localImageFrameToSample(imageFrame: LocalImageFrame, - floatKey: String = ImageFeature.floats, toChw: Boolean = true, withImInfo: Boolean = false) - : JList[Sample] = { - imageFrame.array.map(imageFeatureToSample(_, floatKey, toChw, withImInfo)).toList.asJava + + def distributedImageFrameToPredict(imageFrame: DistributedImageFrame, key: String) + : JavaRDD[JList[Any]] = { + imageFrame.rdd.map(x => List[Any](x.uri(), toJTensor(x[Tensor[T]](key))).asJava) + } + + def localImageFrameToPredict(imageFrame: LocalImageFrame, key: String) + : JList[(String, JTensor)] = { + imageFrame.array.map(x => (x.uri(), toJTensor(x[Tensor[T]](key)))).toList.asJava } def localImageFrameToImageTensor(imageFrame: LocalImageFrame, @@ -2698,9 +2692,22 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab BytesToMat() } + def createMatToFloats(validHeight: Int = 300, validWidth: Int = 300, validChannels: Int = 3, + outKey: String = ImageFeature.floats): MatToFloats = + new MatToFloats(validHeight, validWidth, validChannels, outKey) + + def createMatToTensor(toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor) + : MatToTensor[T] = new MatToTensor[T](toRGB, tensorKey) + def isLocal(imageFrame: ImageFrame): Boolean = imageFrame.isLocal() def isDistributed(imageFrame: ImageFrame): Boolean = imageFrame.isDistributed() + + def createImageFrameToSample(inputKeys: JList[String], + targetKeys: JList[String], sampleKey: String): ImageFrameToSample[T] = { + val targets = if (targetKeys == null) null else targetKeys.asScala.toArray + ImageFrameToSample[T](inputKeys.asScala.toArray, targets, sampleKey) + } } object PythonBigDLUtils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index af742c2cfd3..b6fea9df50c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -17,8 +17,12 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.nn.{Sequential, SpatialConvolution, Tanh} import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.spark.{SparkConf, SparkContext} @@ -109,4 +113,46 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ (model.forward(data(91).feature ).toTensor[Float].max(1)._2.valueAt(1).toInt) } + + "model.predictImage" should "be correct" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile, sc) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 20) + val detection = model.predictImage(imageFrame) + val feature = detection.rdd.first() + println(feature(ImageFeature.predict)) + + val imageFeatures = detection.rdd.collect() + val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) + val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) + prob(0) should be (model.forward(data(0).feature.reshape(Array(1, 3, 224, 224))) + .toTensor[Float].split(1)(0)) + } + + "model.predictImage with simple model" should "be correct" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile, sc) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Sequential() + model.add(SpatialConvolution(3, 6, 5, 5)) + model.add(Tanh()) + val detection = model.predictImage(imageFrame) + val feature = detection.rdd.first() + println(feature(ImageFeature.predict)) + + val imageFeatures = detection.rdd.collect() + val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) + val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) + prob(0) should be (model.forward(data(0).feature.reshape(Array(1, 3, 224, 224))) + .toTensor[Float].split(1)(0)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ConvertorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ConvertorSpec.scala new file mode 100644 index 00000000000..843495dfa6c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ConvertorSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + +class ConvertorSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + + "MatToTensor" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val imF = data.asInstanceOf[LocalImageFrame].array.head + val tensor2 = imF.toTensor(ImageFeature.floats) + val transformer = MatToTensor[Float]() + transformer(data) + val tensor = imF[Tensor[Float]](ImageFeature.imageTensor) + tensor should be (tensor2) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelNormalizeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelNormalizeSpec.scala index 333e17b6848..654a721cd77 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelNormalizeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ChannelNormalizeSpec.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.transform.vision.image.augmentation -import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame, MatToFloats} +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat import org.scalatest.{FlatSpec, Matchers} class ChannelNormalizeSpec extends FlatSpec with Matchers { @@ -28,7 +29,7 @@ class ChannelNormalizeSpec extends FlatSpec with Matchers { val transformed = transformer(data) val imf = transformed.asInstanceOf[LocalImageFrame].array(0) - val toFloat = MatToFloats(meanRGB = Some(100f, 200f, 300f)) + val toFloat = new MatToFloatsWithNorm(meanRGB = Some(100f, 200f, 300f)) val data2 = ImageFrame.read(resource.getFile) val transformed2 = toFloat(data2) val imf2 = transformed2.asInstanceOf[LocalImageFrame].array(0) @@ -43,7 +44,7 @@ class ChannelNormalizeSpec extends FlatSpec with Matchers { val imf = transformed.asInstanceOf[LocalImageFrame].array(0) val data2 = ImageFrame.read(resource.getFile) - val toFloat = MatToFloats(meanRGB = Some(100f, 200f, 300f)) + val toFloat = new MatToFloatsWithNorm(meanRGB = Some(100f, 200f, 300f)) val transformed2 = toFloat(data2) val imf2 = transformed2.asInstanceOf[LocalImageFrame].array(0) @@ -51,3 +52,50 @@ class ChannelNormalizeSpec extends FlatSpec with Matchers { imf2.floats().map(_ / 2) should equal(imf.floats()) } } + +class MatToFloatsWithNorm(validHeight: Int = 300, validWidth: Int = 300, validChannels: Int = 3, + meanRGB: Option[(Float, Float, Float)] = None, outKey: String = ImageFeature.floats) + extends FeatureTransformer { + @transient private var data: Array[Float] = _ + + private def normalize(img: Array[Float], + meanR: Float, meanG: Float, meanB: Float): Array[Float] = { + val content = img + require(content.length % 3 == 0) + var i = 0 + while (i < content.length) { + content(i + 2) = content(i + 2) - meanR + content(i + 1) = content(i + 1) - meanG + content(i + 0) = content(i + 0) - meanB + i += 3 + } + img + } + + override def transform(feature: ImageFeature): ImageFeature = { + var input: OpenCVMat = null + val (height, width, channel) = if (feature.isValid) { + input = feature.opencvMat() + (input.height(), input.width(), input.channels()) + } else { + (validHeight, validWidth, validChannels) + } + if (null == data || data.length < height * width * channel) { + data = new Array[Float](height * width * channel) + } + if (feature.isValid) { + try { + OpenCVMat.toFloatPixels(input, data) + if (meanRGB.isDefined) { + normalize(data, meanRGB.get._1, meanRGB.get._2, meanRGB.get._3) + } + } finally { + if (null != input) input.release() + feature(ImageFeature.mat) = null + } + } + feature(outKey) = data + feature(ImageFeature.size) = (height, width, channel) + feature + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/PixelNormalizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/PixelNormalizerSpec.scala index d356fd84f9b..54328de9366 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/PixelNormalizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/PixelNormalizerSpec.scala @@ -36,7 +36,7 @@ class PixelNormalizerSpec extends FlatSpec with Matchers { val transformed = transformer(data) val data2 = ImageFrame.read(resource.getFile) - val toFloat = MatToFloats(meanRGB = Some(100f, 200f, 300f)) + val toFloat = new MatToFloatsWithNorm(meanRGB = Some(100f, 200f, 300f)) val transformed2 = toFloat(data2) val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) From de28da09431f35ad87c4e62890d8f8b2b7ff2cf5 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Wed, 13 Dec 2017 10:29:18 +0800 Subject: [PATCH 0577/1065] Add serialize unit test for new add layer (#2021) * Add serialize unit test for new add layer * add PriorBox serializer --- .../bigdl/dllib/nn/DetectionOutputFrcnn.scala | 8 +-- .../bigdl/dllib/nn/NormalizeScaleSpec.scala | 16 ----- .../serializer/ModuleSerializerSpec.scala | 64 +++++++++++++++++++ 3 files changed, 68 insertions(+), 20 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnn.scala index de3972d2e3f..ce12a2831db 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnn.scala @@ -30,8 +30,8 @@ import scala.collection.mutable.ArrayBuffer object DetectionOutputFrcnn { val logger = Logger.getLogger(this.getClass) - def apply(nmsThresh: Float = 0.3f, nClasses: Int, - bboxVote: Boolean, maxPerImage: Int = 100, thresh: Double = 0.05)( + def apply(nmsThresh: Float = 0.3f, nClasses: Int = 21, + bboxVote: Boolean = false, maxPerImage: Int = 100, thresh: Double = 0.05)( implicit ev: TensorNumeric[Float]): DetectionOutputFrcnn = new DetectionOutputFrcnn(nmsThresh, nClasses, bboxVote, maxPerImage, thresh) } @@ -45,8 +45,8 @@ object DetectionOutputFrcnn { * @param thresh score threshold */ @SerialVersionUID(5253792953255433914L) -class DetectionOutputFrcnn(var nmsThresh: Float = 0.3f, val nClasses: Int, - var bboxVote: Boolean, var maxPerImage: Int = 100, var thresh: Double = 0.05)( +class DetectionOutputFrcnn(var nmsThresh: Float = 0.3f, val nClasses: Int = 21, + var bboxVote: Boolean = false, var maxPerImage: Int = 100, var thresh: Double = 0.05)( implicit ev: TensorNumeric[Float]) extends AbstractModule[Table, Activity, Float] { @transient var nmsTool: Nms = _ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala index 31287c35199..262ff010d71 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala @@ -344,20 +344,4 @@ class NormalizeScaleSpec extends FlatSpec with Matchers { a }) } - - "NormalizeScale serializer" should "work properly" in { - val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1), - wRegularizer = L2Regularizer[Float](0.2)) - - val input = Tensor[Float](1, 5, 3, 4).randn() - val res1 = module.forward(input).clone() - val tmpFile = java.io.File.createTempFile("module", ".bigdl") - module.saveModule(tmpFile.getAbsolutePath, overWrite = true) - val loaded = Module.loadModule[Float](tmpFile.getAbsolutePath) - val res2 = loaded.forward(input) - res1 should be(res2) - if (tmpFile.exists()) { - tmpFile.delete() - } - } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 7f2c0b16e17..089497fbd51 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -28,6 +28,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFo import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, EluGrad, Equal, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{DenseToSparse, _} +import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.tf.TFRecordIterator @@ -2180,6 +2181,69 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(topkv2LoadTF, input) } + "Proposal serializer" should "work properly" in { + val proposal = Proposal(200, 100, Array[Float](0.1f, 0.2f, 0.3f), Array[Float](4, 5, 6)) + val score = Tensor[Float](1, 18, 20, 30).randn() + val boxes = Tensor[Float](1, 36, 20, 30).randn() + val imInfo = Tensor[Float](T(300, 300, 1, 1)).resize(1, 4) + val input = T(score, boxes, imInfo) + runSerializationTest(proposal, input) + } + + "NormalizeScale serializer" should "work properly" in { + val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1), + wRegularizer = L2Regularizer[Float](0.2)).setName("NormalizeScale") + + val input = Tensor[Float](1, 5, 3, 4).randn() + runSerializationTest(module, input) + } + + "DetectionOutputSSD serializer" should "work properly" in { + val module = DetectionOutputSSD[Float](DetectionOutputParam()).setName("DetectionOutputSSD") + val name = module.getName + val serFile = File.createTempFile(name, postFix) + + ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) + RNG.setSeed(1000) + val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) + + + if (serFile.exists) { + serFile.delete + } + tested.add(module.getClass.getName) + } + + "DetectionOutputFrcnn serializer" should "work properly" in { + val module = DetectionOutputFrcnn().setName("DetectionOutputFrcnn") + val name = module.getName + val serFile = File.createTempFile(name, postFix) + + ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) + RNG.setSeed(1000) + val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) + + + if (serFile.exists) { + serFile.delete + } + tested.add(module.getClass.getName) + } + + "PriorBox serializer" should "work properly" in { + val isClip = false + val isFlip = true + val variances = Array(0.1f, 0.1f, 0.2f, 0.2f) + val minSizes = Array(460.8f) + val maxSizes = Array(537.6f) + val aspectRatios = Array(2f) + val module = PriorBox[Float](minSizes = minSizes, maxSizes = maxSizes, + _aspectRatios = aspectRatios, isFlip = isFlip, isClip = isClip, + variances = variances, step = 0, offset = 0.5f, imgH = 512, imgW = 512) + val input = Tensor[Float](8, 256, 1, 1) + runSerializationTest(module, input) + } + override protected def afterAll() = { var total = 0 expected.foreach(exp => { From 5be6ec12fcc7207ab7071eb26e2776402ca3a893 Mon Sep 17 00:00:00 2001 From: Shane Huang Date: Wed, 13 Dec 2017 14:31:08 +0800 Subject: [PATCH 0578/1065] add setTrainData and setCriterion for optimizer reuse (#1986) * add setTrainData and setCriterion for optimizer reuse, both scala and python api * refactor the apply of Optimizer and setTrainData, changed docs --- .../bigdl/dllib/optim/DistriOptimizer.scala | 52 +++++-- .../bigdl/dllib/optim/Optimizer.scala | 140 +++++++++++------- .../dllib/utils/python/api/PythonBigDL.scala | 16 +- .../dllib/optim/DistriOptimizerSpec.scala | 55 ++++++- .../bigdl/dllib/optim/OptimizerSpec.scala | 56 ++++++- 5 files changed, 244 insertions(+), 75 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 79f6f38ec01..2026bf680f9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -17,7 +17,8 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.{Module, _} -import com.intel.analytics.bigdl.dataset.{DistributedDataSet, MiniBatch} +import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, + MiniBatch, SampleToMiniBatch, Sample, PaddingParam} import com.intel.analytics.bigdl.nn.{Module, Utils} import com.intel.analytics.bigdl.parameters.AllReduceParameter import com.intel.analytics.bigdl.tensor.Tensor @@ -722,16 +723,16 @@ object DistriOptimizer { * The optimizer run on a distributed cluster. * * @param _model train model - * @param dataset train dataset - * @param criterion loss function + * @param _dataset train dataset + * @param _criterion loss function */ class DistriOptimizer[T: ClassTag] ( _model: Module[T], - dataset: DistributedDataSet[MiniBatch[T]], - criterion: Criterion[T] + _dataset: DistributedDataSet[MiniBatch[T]], + _criterion: Criterion[T] )(implicit ev: TensorNumeric[T]) extends Optimizer[T, MiniBatch[T]]( - _model, dataset, criterion) { + _model, _dataset, _criterion) { val metrics = new Metrics private var models: RDD[DistriOptimizer.Cache[T]] = null @@ -750,15 +751,41 @@ class DistriOptimizer[T: ClassTag] ( }).count() } + + override def setTrainData(sampleRDD: RDD[Sample[T]], + batchSize: Int, + miniBatch: MiniBatch[T]): this.type = { + this.dataset = (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(miniBatch, batchSize, None)) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + this + } + + override def setTrainData(sampleRDD: RDD[Sample[T]], + batchSize: Int, + featurePaddingParam: PaddingParam[T] = null, + labelPaddingParam: PaddingParam[T] = null) : this.type = { + val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None + val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None + dataset = (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(batchSize, _featurePaddingParam, _labelPaddingParam)) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + this + } + + override def prepareInput(): Unit = { import DistriOptimizer._ - if (!dataset.isCached) { + if (!dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].isCached) { logger.info("caching training rdd ...") - dataset.cache() + dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].cache() } } override def optimize(): Module[T] = { + + val distDataset = dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]] + optimMethod.clearHistory() optimMethod.loadFromTable(state) state("dropPercentage") = dropPercentage @@ -770,13 +797,13 @@ class DistriOptimizer[T: ClassTag] ( val nodeNumber = Engine.nodeNumber() val coresPerNode = Engine.coreNumber() - val partitionNum = dataset.originRDD().partitions.length + val partitionNum = distDataset.originRDD().partitions.length val size = model.getParameters()._1.nElement() val parameters = AllReduceParameter.newParameter(partitionNum, size) prepareInput() - models = DistriOptimizer.initThreadModels(model, dataset, criterion, state, + models = DistriOptimizer.initThreadModels(model, distDataset, criterion, state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, optimMethod) if (checkpointPath.isDefined) { @@ -790,10 +817,11 @@ class DistriOptimizer[T: ClassTag] ( val maxRetry = System.getProperty("bigdl.failure.retryTimes", "5").toInt val retryTimeInterval = System.getProperty("bigdl.failure.retryTimeInterval", "120").toInt var lastFailureTimestamp = System.nanoTime() + while (retryNum < maxRetry) { try { DistriOptimizer.optimize( - dataset, + distDataset, coresPerNode, state, endWhen, @@ -846,7 +874,7 @@ class DistriOptimizer[T: ClassTag] ( DistriOptimizer.logger.info("Recover from origin model") } optimMethod.clearHistory() - models = DistriOptimizer.initThreadModels(newModel, dataset, criterion, state, + models = DistriOptimizer.initThreadModels(newModel, distDataset, criterion, state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, optimMethod) } else { throw t diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index a84fa7a9dc7..15ac0f49cbb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -41,8 +41,8 @@ import scala.reflect.ClassTag // TODO: remove D to be MiniBatch[T] abstract class Optimizer[T: ClassTag, D]( protected var model: Module[T], - protected val dataset: DataSet[D], - protected val criterion: Criterion[T])(implicit ev : TensorNumeric[T]) + protected var dataset: DataSet[D], + protected var criterion: Criterion[T])(implicit ev : TensorNumeric[T]) { protected var state: Table = T() protected var optimMethod: OptimMethod[T] = new SGD[T]() @@ -232,6 +232,59 @@ abstract class Optimizer[T: ClassTag, D]( this } + + /** + * Set new train dataset. + * User can supply a customized implementation of trait MiniBatch to define + * how data is organized and retrieved in a mini batch. + * + * @param sampleRDD training Samples + * @param batchSize mini batch size + * @param miniBatchImpl An User-Defined MiniBatch implementation. + * @return the Optimizer + */ + def setTrainData(sampleRDD: RDD[Sample[T]], + batchSize: Int, + miniBatchImpl: MiniBatch[T]): this.type = { + throw new UnsupportedOperationException( + s"setTrainData(sampleRDD, batchSize,miniBatch) " + + s"is only supported in distributed optimizer") + this + } + + /** + * Set new train dataset. + * + * @param sampleRDD training Samples + * @param batchSize mini batch size + * @param featurePaddingParam feature padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * @param labelPaddingParam label padding strategy, see + * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. + * @return the optimizer + */ + def setTrainData(sampleRDD: RDD[Sample[T]], + batchSize: Int, + featurePaddingParam: PaddingParam[T] = null, + labelPaddingParam: PaddingParam[T] = null): this.type = { + throw new UnsupportedOperationException( + s"setTrainData(sampleRDD,batchSize,featurePaddingParam=null,labelPaddingParam=null) " + + s"is only supported in distributed optimizer") + this + } + + + /** + * Set a new criterion to the optimizer + * + * @param newCriterion new criterion + */ + def setCriterion(newCriterion: Criterion[T]): this.type = { + this.criterion = newCriterion + this + } + + /** * Set a state(learning rate, epochs...) to the optimizer * @@ -342,25 +395,11 @@ object Optimizer { } } - def apply[T: ClassTag]( - model: Module[T], - sampleRDD: RDD[Sample[T]], - criterion: Criterion[T], - batchSize: Int - )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { - new DistriOptimizer[T]( - _model = model, - dataset = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(batchSize)) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]], - criterion = criterion - ).asInstanceOf[Optimizer[T, MiniBatch[T]]] - } /** - * Apply an Optimizer who could apply padding to the Samples - * with a padding strategy. + * Apply an Optimizer. * - * @param model model will be optimizied + * @param model model will be optimized * @param sampleRDD training Samples * @param criterion loss function * @param batchSize mini batch size @@ -375,60 +414,59 @@ object Optimizer { sampleRDD: RDD[Sample[T]], criterion: Criterion[T], batchSize: Int, - featurePaddingParam: PaddingParam[T], - labelPaddingParam: PaddingParam[T] + featurePaddingParam: PaddingParam[T] = null, + labelPaddingParam: PaddingParam[T] = null )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { - new DistriOptimizer[T]( - _model = model, - dataset = (DataSet.rdd(sampleRDD) -> - SampleToMiniBatch(batchSize, Some(featurePaddingParam), Some(labelPaddingParam))) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]], - criterion = criterion - ).asInstanceOf[Optimizer[T, MiniBatch[T]]] - } - def apply[T: ClassTag]( - model: Module[T], - sampleRDD: RDD[Sample[T]], - criterion: Criterion[T], - batchSize: Int, - featurePaddingParam: PaddingParam[T] - )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { + val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None + val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None + new DistriOptimizer[T]( - _model = model, - dataset = (DataSet.rdd(sampleRDD) -> - SampleToMiniBatch(batchSize, Some(featurePaddingParam))) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]], - criterion = criterion - ).asInstanceOf[Optimizer[T, MiniBatch[T]]] + _model = model, + _dataset = (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(batchSize, _featurePaddingParam, _labelPaddingParam)) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]], + _criterion = criterion + ).asInstanceOf[Optimizer[T, MiniBatch[T]]] } + /** - * Apply an optimizer with User-Defined MiniBatch. + * Apply an optimizer. + * User can supply a customized implementation of trait MiniBatch to define + * how data is organize and retrieved in a mini batch. * - * @param model model will be optimizied + * @param model model will be optimized * @param sampleRDD training Samples * @param criterion loss function * @param batchSize mini batch size - * @param miniBatch An User-Defined MiniBatch to construct a mini batch. - * @return an Optimizer + * @param miniBatchImpl An User-Defined MiniBatch implementation + * @return an new Optimizer */ def apply[T: ClassTag]( model: Module[T], sampleRDD: RDD[Sample[T]], criterion: Criterion[T], batchSize: Int, - miniBatch: MiniBatch[T] + miniBatchImpl: MiniBatch[T] )(implicit ev: TensorNumeric[T]): Optimizer[T, MiniBatch[T]] = { new DistriOptimizer[T]( _model = model, - dataset = (DataSet.rdd(sampleRDD) -> - SampleToMiniBatch(miniBatch, batchSize, None)) + _dataset = (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(miniBatchImpl, batchSize, None)) .asInstanceOf[DistributedDataSet[MiniBatch[T]]], - criterion = criterion + _criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] } + /** + * Apply an optimizer. + * + * @param model model will be optimizied + * @param dataset the input dataset - determines the type of optimizer + * @param criterion loss function + * @return an new Optimizer + */ def apply[T: ClassTag, D]( model: Module[T], dataset: DataSet[D], @@ -438,8 +476,8 @@ object Optimizer { case d: DistributedDataSet[_] => new DistriOptimizer[T]( _model = model, - dataset = d.asInstanceOf[DistributedDataSet[MiniBatch[T]]], - criterion = criterion + _dataset = d.asInstanceOf[DistributedDataSet[MiniBatch[T]]], + _criterion = criterion ).asInstanceOf[Optimizer[T, D]] case d: LocalDataSet[_] => new LocalOptimizer[T]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c0536ccfdb3..5f32cb4733c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2031,9 +2031,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab val optimizer = new DistriOptimizer( _model = model, - dataset = batching(DataSet.rdd(sampleRDD), batchSize) + _dataset = batching(DataSet.rdd(sampleRDD), batchSize) .asInstanceOf[DistributedDataSet[MiniBatch[T]]], - criterion = criterion + _criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] enrichOptimizer(optimizer, endTrigger, optimMethod) } @@ -2060,6 +2060,18 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab vMethods.asScala.toArray) } + def setTrainData(optimizer: Optimizer[T, MiniBatch[T]], + trainingRdd: JavaRDD[Sample], + batchSize: Int): Unit = { + val sampleRDD = toJSample(trainingRdd) + optimizer.setTrainData(sampleRDD, batchSize) + } + + def setCriterion(optimizer: Optimizer[T, MiniBatch[T]], + criterion: Criterion[T]): Unit = { + optimizer.setCriterion(criterion) + } + def setCheckPoint(optimizer: Optimizer[T, MiniBatch[T]], trigger: Trigger, checkPointPath: String, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index bdd17842719..c9ecb34aa1e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -20,9 +20,10 @@ import java.nio.file.{Files, Paths} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.image.{BGRImgToBatch, LabeledBGRImage} -import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch} +import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.{Storage, Tensor, DenseTensor} import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.visualization.TrainSummary import org.apache.log4j.{Level, Logger} @@ -520,8 +521,8 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { mm.getParameters()._1.fill(0.125) val optimizer = new DistriOptimizer[Double]( _model = mm, - dataset = dataSet, - criterion = new MSECriterion[Double]() + _dataset = dataSet, + _criterion = new MSECriterion[Double]() ) val optimMethod = new SGD[Double](learningRate = 20.0, learningRateSchedule = @@ -549,8 +550,8 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { mm.getParameters()._1.fill(0.125) val optimizer = new DistriOptimizer[Double]( _model = mm, - dataset = dataSet, - criterion = new MSECriterion[Double]() + _dataset = dataSet, + _criterion = new MSECriterion[Double]() ) val optimMethod = new SGD[Double](learningRate = 20.0, learningRateSchedule = @@ -584,8 +585,8 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { mm.getParameters()._1.fill(0.125) val optimizer = new DistriOptimizer[Double]( _model = mm, - dataset = dataSet, - criterion = new MSECriterion[Double]() + _dataset = dataSet, + _criterion = new MSECriterion[Double]() ) val optimMethod = new SGD[Double](learningRate = 20.0) @@ -596,4 +597,42 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { Array(new Top1Accuracy[Double]())) val model = optimizer.optimize() } + + "setTrainData" should "work properly" in { + + RandomGenerator.RNG.setSeed(10) + val rdd = sc.parallelize(1 to (2 * nodeNumber), nodeNumber) + .map(_ => Sample[Double](Tensor[Double](2, 3).fill(2.0), Tensor[Double](1).fill(1.0))) + + val inputOri = rdd.map{s => s.feature} + val targetOri = rdd.map{s => s.label} + val inputOriArr = inputOri.collect() + val targetOriArr = targetOri.collect() + + + val myOpt = new DistriOptimizer[Double](null, dataSet, null) { + override def optimize(): Module[Double] = { + val dds = this.dataset.asInstanceOf[DistributedDataSet[MiniBatch[Double]]] + val rdd = dds.data(train = false) + // flatmap to break minibatches into single tensors + val input = rdd.flatMap[Tensor[Double]]{ + data => data.getInput().asInstanceOf[Tensor[Double]].split(dim = 1)} + val target = rdd.flatMap[Tensor[Double]]{ + data => data.getTarget().asInstanceOf[Tensor[Double]].split(dim = 1)} + val inputArr = input.collect() + val targetArr = target.collect() + + inputArr.sameElements(inputOriArr) should be (true) + targetArr.sameElements(targetOriArr) should be (true) + + // println(s"get=(input=${inputArr.mkString("\n")}\ntarget=${targetArr.mkString("\n")})") + // println(s"original=(input=${inputOriArray.mkString("\n")}" + // + s"\ntarget=${targetOriArray.mkString("\n")})") + model + } + } + + myOpt.setTrainData(rdd, 2*nodeNumber) + myOpt.optimize() + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimizerSpec.scala index 3f20c0fa43f..550643b3e05 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimizerSpec.scala @@ -18,20 +18,32 @@ package com.intel.analytics.bigdl.optim import java.nio.file.{Files, Paths} -import com.intel.analytics.bigdl.dataset.{DistributedDataSet, LocalDataSet} +import com.intel.analytics.bigdl.dataset.{DistributedDataSet, LocalDataSet, Sample} import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Linear, Sequential} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.example.loadmodel.AlexNet +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{Engine, File, T, Table} +import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Parallel class OptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val model = new Sequential[Float]() + private var sc: SparkContext = _ + private val nodeNumber = 1 + private val coreNumber = 4 before { - Engine.setNodeAndCore(1, 4) + Engine.setNodeAndCore(nodeNumber, coreNumber) + sc = new SparkContext(s"local[$coreNumber]", "OptimizerSpec") + } + + after { + if (sc != null) { + sc.stop() + } } "Optimizer" should "end with maxEpoch" in { @@ -243,4 +255,44 @@ class OptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { res.isInstanceOf[DistriOptimizer[Float]] should be(false) res.isInstanceOf[LocalOptimizer[Float]] should be(true) } + + + "setTrainData" should "work in distributed optimizer" in { + val ds = new DistributedDataSet[Float] { + override def originRDD(): RDD[_] = null + override def data(train: Boolean): RDD[Float] = null + override def size(): Long = 0 + override def shuffle(): Unit = {} + } + + val model = Linear[Float](4, 3) + val criterion = ClassNLLCriterion[Float]() + val opt = Optimizer(model, ds, criterion) + + val rdd = sc.parallelize(1 to (256 * nodeNumber), nodeNumber) + .map(_ => Sample[Float](Tensor[Float](2, 3).fill(1.0f))) + + opt.setTrainData(rdd, 16) + } + + "setTrainData" should "throw exception in local optimizer" in { + val ds = new LocalDataSet[Float] { + override def data(train: Boolean): Iterator[Float] = null + override def size(): Long = 0 + override def shuffle(): Unit = {} + } + val model = Linear[Float](4, 3) + val criterion = ClassNLLCriterion[Float]() + val opt = Optimizer(model, ds, criterion) + + val rdd = sc.parallelize(1 to (256 * nodeNumber), nodeNumber) + .map(_ => Sample[Float](Tensor[Float](2, 3).fill(1.0f))) + + intercept[UnsupportedOperationException] { + opt.setTrainData(rdd, 16) + } + + } + + } From 1e21a642baacff1b42089ef7014f02189ca6c837 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Wed, 13 Dec 2017 15:10:53 +0800 Subject: [PATCH 0579/1065] Add partition number option for ImageFrame read (#2022) * add partition number * update * Fix ut * rename partitionNum to minPartitions --- dl/pom.xml | 2 +- .../transform/vision/image/ImageFrame.scala | 11 +++++++---- .../dllib/utils/python/api/PythonBigDL.scala | 8 ++++++-- .../transform/vision/image/ImageFrameSpec.scala | 17 ++++++++++++++++- 4 files changed, 30 insertions(+), 8 deletions(-) diff --git a/dl/pom.xml b/dl/pom.xml index 4af7aa3cc42..cd1179ea9c3 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -9,7 +9,7 @@ 4.0.0 - bigdl + bigdl-SPARK_2.1 jar diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala index 6cfa43fbf77..24c1d1b443f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -85,11 +85,12 @@ object ImageFrame { * if sc is defined, path can be local or HDFS. Wildcard character are supported. * if sc is null, path is local directory/image file/image file with wildcard character * @param sc SparkContext + * @param minPartitions A suggestion value of the minimal splitting number for input data. * @return ImageFrame */ - def read(path: String, sc: SparkContext = null): ImageFrame = { + def read(path: String, sc: SparkContext = null, minPartitions: Int = 1): ImageFrame = { if (null != sc) { - val images = sc.binaryFiles(path).map { case (p, stream) => + val images = sc.binaryFiles(path, minPartitions).map { case (p, stream) => ImageFeature(stream.toArray(), uri = p) } ImageFrame.rdd(images) -> BytesToMat() @@ -144,10 +145,12 @@ object ImageFrame { * * @param path path to read images. Local or HDFS. Wildcard character are supported. * @param output Parquet file path + * @param partitionNum partition number */ - def writeParquet(path: String, output: String, sqlContext: SQLContext): Unit = { + def writeParquet(path: String, output: String, sqlContext: SQLContext, + partitionNum: Int = 1): Unit = { import sqlContext.implicits._ - val df = sqlContext.sparkContext.binaryFiles(path) + val df = sqlContext.sparkContext.binaryFiles(path, partitionNum) .map { case (p, stream) => (p, stream.toArray()) }.toDF(ImageFeature.uri, ImageFeature.bytes) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 5f32cb4733c..c5aa4d81847 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2692,8 +2692,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab toJTensor(label) } - def read(path: String, sc: JavaSparkContext): ImageFrame = { - if (sc == null) ImageFrame.read(path, null) else ImageFrame.read(path, sc.sc) + def read(path: String, sc: JavaSparkContext, minPartitions: Int): ImageFrame = { + if (sc == null) { + ImageFrame.read(path, null, minPartitions) + } else { + ImageFrame.read(path, sc.sc, minPartitions) + } } def readParquet(path: String, sqlContext: SQLContext): DistributedImageFrame = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala index 933356e62bd..b8fcbd2503b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala @@ -16,6 +16,8 @@ package com.intel.analytics.bigdl.transform.vision.image +import java.io.File + import com.google.common.io.Files import com.intel.analytics.bigdl.transform.vision.image.augmentation.HFlip import com.intel.analytics.bigdl.utils.Engine @@ -65,10 +67,23 @@ class ImageFrameSpec extends FlatSpec with Matchers with BeforeAndAfter { imageFeature.opencvMat().shape() should be((375, 500, 3)) } + "read DistributedImageFrame with partition number" should "work properly" in { + val tmpFile = Files.createTempDir() + val dir = new File(tmpFile.toString + "/images") + dir.mkdir() + (1 to 10).foreach(i => { + Files.copy(new File(resource.getFile + "000025.jpg"), new File(dir + s"/$i.jpg")) + }) + + val distributed = ImageFrame.read(dir.toString, sc, 5) + .asInstanceOf[DistributedImageFrame] + if (tmpFile.exists()) FileUtils.deleteDirectory(tmpFile) + } + "SequenceFile write and read" should "work properly" in { val tmpFile = Files.createTempDir() val dir = tmpFile.toString + "/parque" - ImageFrame.writeParquet(resource.getFile, dir, sqlContext) + ImageFrame.writeParquet(resource.getFile, dir, sqlContext, 1) val distributed = ImageFrame.readParquet(dir, sqlContext) val imageFeature = distributed.rdd.first() From ff62c7c9df204a929612b5feb1c1aec2a24bbf54 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 13 Dec 2017 16:13:58 +0800 Subject: [PATCH 0580/1065] UpSampling1D UpSampling2D (#1928) --- .../bigdl/dllib/nn/UpSampling1D.scala | 107 +++++++ .../bigdl/dllib/nn/UpSampling2D.scala | 270 ++++++++++++++++++ .../dllib/nn/abstractnn/AbstractModule.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 8 + .../bigdl/dllib/keras/UpSampling1DSpec.scala | 46 +++ .../bigdl/dllib/keras/UpSampling2DSpec.scala | 47 +++ .../serializer/ModuleSerializerSpec.scala | 12 + 7 files changed, 491 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala new file mode 100644 index 00000000000..b72a059b26f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala @@ -0,0 +1,107 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import java.util + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Upsampling layer for 1D inputs. + * Repeats each temporal step length times along the time axis. + * + * If input's size is (batch, steps, features), + * then the output's size is (batch, steps * length, features) + * + * @param length integer, upsampling factor. + * @tparam T The numeric type in this module, usually which are [[Float]] or [[Double]] + */ +class UpSampling1D[T: ClassTag] (val length: Int) + (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + require(length > 0, "UpSampling1D's length should be bigger than 0," + + s"but got $length") + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 3, "UpSampling1D only supports 3D input") + require(input.isContiguous(), "input should be contiguous") + + val inputLength = input.size(3) + val outputLength = inputLength * length + + output.resize(input.size(1), input.size(2) * length, input.size(3)) + + val inputData = input.storage().array() + val inputOffset = input.storageOffset() - 1 + + val outputData = output.storage().array() + val outputOffset = output.storageOffset() - 1 + + var i = 0 + while (i < input.size(1) * input.size(2)) { + var j = 0 + while (j < length) { + ev.arraycopy(inputData, inputOffset + i * inputLength, + outputData, outputOffset + i * outputLength + inputLength * j, inputLength) + j += 1 + } + i += 1 + } + + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(gradOutput.dim() == 3, "UpSampling1D only supports 3D input") + require(gradOutput.isContiguous(), "gradOutput should be contiguous") + gradInput.resizeAs(input).zero() + + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + + val gradInputLength = gradInput.size(3) + val gradOutputLength = gradInputLength * length + + + var i = 0 + while (i < input.size(1) * input.size(2)) { + var j = 0 + while (j < length) { + ev.axpy(gradInputLength, ev.one, gradOutputData, + gradOutputOffset + i * gradOutputLength + gradInputLength * j, 1, + gradInputData, gradInputOffset + i * gradInputLength, 1) + j += 1 + } + i += 1 + } + + gradInput + } +} + +object UpSampling1D { + def apply[T: ClassTag](length: Int) + (implicit ev: TensorNumeric[T]): UpSampling1D[T] = { + new UpSampling1D(length) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2D.scala new file mode 100644 index 00000000000..2c3709c0039 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2D.scala @@ -0,0 +1,270 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Upsampling layer for 2D inputs. + * Repeats the heights and widths of the data by size(0) and size(1) respectively. + * + * If input's dataformat is NCHW, then the size of output is (N, C, H * size(0), W * size(1)) + * + * @param size tuple of 2 integers. The upsampling factors for heights and widths. + * @param format DataFormat, NCHW or NHWC + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class UpSampling2D[T: ClassTag] (val size: Array[Int], val format: DataFormat = DataFormat.NCHW) + (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + require(size.length == 2, s"UpSampling2D's size should be an array containing" + + s" 2 elements, but got ${size.mkString("x")}") + require(size(0) > 0 && size(1) > 0, "UpSampling2D's size should be bigger than 0," + + s"but got ${size.mkString("x")}") + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 4, "UpSampling2D only supports 4D input") + require(input.isContiguous(), "input should be contiguous") + + format match { + case DataFormat.NCHW => + UpSampling2D.updateOutputNchw(input, output, size) + case DataFormat.NHWC => + UpSampling2D.updateOutputNhwc(input, output, size) + case _ => + throw new IllegalArgumentException("UpSampling2D: unsupported data format.") + } + + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(gradOutput.dim() == 4, "UpSampling2D only supports 4D gradOutput") + require(gradOutput.isContiguous(), "gradOutput should be contiguous") + gradInput.resizeAs(input).zero() + + format match { + case DataFormat.NCHW => + UpSampling2D.updateGradInputNchw(gradInput, gradOutput, size) + case DataFormat.NHWC => + UpSampling2D.updateGradInputNhwc(gradInput, gradOutput, size) + case _ => + throw new IllegalArgumentException("UpSampling2D: unsupported data format.") + } + + gradInput + } +} + +object UpSampling2D { + def apply[T: ClassTag](size: Array[Int], format: DataFormat = DataFormat.NCHW) + (implicit ev: TensorNumeric[T]): UpSampling2D[T] = { + new UpSampling2D(size, format) + } + + protected def updateOutputNchw[T: ClassTag]( + input: Tensor[T], + output: Tensor[T], + size: Array[Int])(implicit ev: TensorNumeric[T]) : Tensor[T] = { + val inputHeight = input.size(3) + val inputWeight = input.size(4) + val outputHeight = inputHeight * size(0) + val outputWeight = inputWeight * size(1) + output.resize(input.size(1), input.size(2), outputHeight, outputWeight) + + + val inputData = input.storage().array() + var inputOffset = input.storageOffset() - 1 + + val outputData = output.storage().array() + var outputOffset = output.storageOffset() - 1 + + var i = 0 + while (i < input.size(1) * input.size(2)) { + var rowIndex = 0 + while (rowIndex < input.size(3)) { + var columnIndex = 0 + // copy column + while (columnIndex < input.size(4)) { + var colReplicate = 0 + while (colReplicate < size(1)) { + outputData(outputOffset) = inputData(inputOffset) + outputOffset += 1 + colReplicate += 1 + } + inputOffset += 1 + columnIndex += 1 + } + + // copy row + var rowReplicate = 1 + while (rowReplicate < size(0)) { + ev.arraycopy(outputData, outputOffset - outputWeight, + outputData, outputOffset + (rowReplicate - 1) * outputWeight, outputWeight) + rowReplicate += 1 + } + outputOffset += outputWeight * (size(0) - 1) + + rowIndex += 1 + } + + i += 1 + } + + output + + } + + protected def updateOutputNhwc[T: ClassTag]( + input: Tensor[T], + output: Tensor[T], + size: Array[Int])(implicit ev: TensorNumeric[T]) : Tensor[T] = { + val inputHeight = input.size(2) + val inputWeight = input.size(3) + val outputHeight = inputHeight * size(0) + val outputWeight = inputWeight * size(1) + output.resize(input.size(1), outputHeight, outputWeight, input.size(4)) + + val channel = input.size(4) + val owc = outputWeight * channel + + val inputData = input.storage().array() + var inputOffset = input.storageOffset() - 1 + + val outputData = output.storage().array() + var outputOffset = output.storageOffset() - 1 + + var i = 0 + while (i < input.size(1)) { + var rowIndex = 0 + while (rowIndex < input.size(2)) { + var columnIndex = 0 + // copy column + while (columnIndex < input.size(3)) { + var colReplicate = 0 + while (colReplicate < size(1)) { + ev.arraycopy(inputData, inputOffset, + outputData, outputOffset + colReplicate * channel, channel) + colReplicate += 1 + } + outputOffset += channel * size(1) + inputOffset += channel + columnIndex += 1 + } + + // copy row + var rowReplicate = 1 + while (rowReplicate < size(0)) { + ev.arraycopy(outputData, outputOffset - owc, outputData, + outputOffset + (rowReplicate - 1) * owc, owc) + rowReplicate += 1 + } + outputOffset += owc * (size(0) - 1) + + rowIndex += 1 + } + + i += 1 + } + output + } + + protected def updateGradInputNhwc[T: ClassTag]( + gradInput: Tensor[T], + gradOutput: Tensor[T], + size: Array[Int])(implicit ev: TensorNumeric[T]) : Tensor[T] = { + val gradInputData = gradInput.storage().array() + var gradInputOffset = gradInput.storageOffset() - 1 + + val gradOutputData = gradOutput.storage().array() + var gradOutputOffset = gradOutput.storageOffset() - 1 + + val gradInputWidth = gradInput.size(4) + val gradOutputWidth = gradOutput.size(4) + + val channel = gradInput.size(4) + val ocw = gradOutput.size(3) * gradOutput.size(4) + + var i = 0 + while (i < gradInput.size(1)) { + var rowIndex = 0 + while (rowIndex < gradInput.size(2)) { + var colIndex = 0 + while (colIndex < gradInput.size(3)) { + var rowReplicate = 0 + while (rowReplicate < size(0)) { + var colReplicate = 0 + while (colReplicate < size(1)) { + ev.axpy(channel, ev.one, gradOutputData, + gradOutputOffset + channel * colReplicate + rowReplicate * ocw, 1, + gradInputData, gradInputOffset, 1) + colReplicate += 1 + } + rowReplicate += 1 + } + gradInputOffset += channel + gradOutputOffset += size(1) * channel + colIndex += 1 + } + gradOutputOffset += (size(0) - 1) * ocw + rowIndex += 1 + } + + i += 1 + } + + gradInput + } + + protected def updateGradInputNchw[T: ClassTag]( + gradInput: Tensor[T], + gradOutput: Tensor[T], + size: Array[Int])(implicit ev: TensorNumeric[T]) : Tensor[T] = { + + val gradInputData = gradInput.storage().array() + var gradInputOffset = gradInput.storageOffset() - 1 + + val gradOutputData = gradOutput.storage().array() + var gradOutputOffset = gradOutput.storageOffset() - 1 + + val gradInputWidth = gradInput.size(4) + val gradOutputWidth = gradOutput.size(4) + + var i = 0 + while (i < gradInput.size(1) * gradInput.size(2) * gradInput.size(3)) { + var row = 0 + while (row < size(0)) { + var col = 0 + while (col < size(1)) { + ev.axpy(gradInputWidth, ev.one, gradOutputData, + gradOutputOffset + col, size(1), + gradInputData, gradInputOffset, 1) + col += 1 + } + gradOutputOffset += gradOutputWidth + row += 1 + } + gradInputOffset += gradInputWidth + i += 1 + } + + gradInput + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index f2c23d5bf97..397fb7d40e9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -41,7 +41,7 @@ import scala.reflect.ClassTag * [[TensorModule]] is an abstract sub-class of [[AbstractModule]], whose * input and output type both are [[Tensor]]. * - * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + * @tparam T The numeric type in this module, usually which are [[Float]] or [[Double]] */ abstract class TensorModule[T: ClassTag] (implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[T], Tensor[T], T] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c5aa4d81847..8e190d42688 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -826,6 +826,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab L1Cost[T]() } + def createUpSampling1D(length: Int): UpSampling1D[T] = { + UpSampling1D(length) + } + + def createUpSampling2D(size: JList[Int], dataFormat: String): UpSampling2D[T] = { + UpSampling2D(size.asScala.toArray, DataFormat(dataFormat)) + } + def createL1Penalty(l1weight: Int, sizeAverage: Boolean = false, provideOutput: Boolean = true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1DSpec.scala new file mode 100644 index 00000000000..cc5f56828dc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1DSpec.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn._ + +class UpSampling1DSpec extends KerasBaseSpec { + "updample1d forward with size 1" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4]) + |input = np.random.uniform(-1, 1, [2, 3, 4]) + |output_tensor = UpSampling1D(1)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = UpSampling1D[Float](1) + checkOutputAndGrad(model, kerasCode) + } + + "updample1d forward with size 2" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4]) + |input = np.random.uniform(-1, 1, [2, 3, 4]) + |output_tensor = UpSampling1D(2)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = UpSampling1D[Float](2) + checkOutputAndGrad(model, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2DSpec.scala new file mode 100644 index 00000000000..31e850248a5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2DSpec.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat + +class UpSampling2DSpec extends KerasBaseSpec { + "updample2D nchw" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[5, 3, 4]) + |input = np.random.uniform(-1, 1, [2, 5, 3, 4]) + |output_tensor = UpSampling2D(size=[2, 3], dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = UpSampling2D[Float](Array(2, 3)) + checkOutputAndGrad(model, kerasCode) + } + + "updample2D nhwc" should "work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5]) + |input = np.random.uniform(-1, 1, [2, 3, 4, 5]) + |output_tensor = UpSampling2D([2, 3], dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = UpSampling2D[Float](Array(2, 3), DataFormat.NHWC) + checkOutputAndGrad(model, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 089497fbd51..4248b5ca272 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -112,6 +112,18 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(activityRegularization, input) } + "UpSampling1D serializer" should "work properly" in { + val upsampling = UpSampling1D[Float](2).setName("upsampling") + val input = Tensor[Float](2, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(upsampling, input) + } + + "UpSampling2D serializer" should "work properly" in { + val upsampling = UpSampling2D[Float](Array(2, 3)).setName("upsampling") + val input = Tensor[Float](2, 3, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(upsampling, input) + } + "Add serializer" should "work properly" in { val add = Add[Float](5).setName("add") val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) From ef0eb269761cf7514d1322d57b2e39dc1a8e07b7 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 13 Dec 2017 17:51:12 +0800 Subject: [PATCH 0581/1065] Fix some issues found when save bigdl model to tensorflow format file (#1997) * fix BN save tf issue when scale and offset is null; fix conv2d save tf issue when bias is null; support save LRN layer to tf format * fix get padding info not correct when ceilMode=true and pad=0 * support save Scale in tf format * refind some code * fix unit test --- .../intel/analytics/bigdl/utils/Engine.scala | 15 +- .../analytics/bigdl/dllib/nn/Scale.scala | 4 +- .../dllib/nn/SpatialAveragePooling.scala | 6 +- .../bigdl/dllib/nn/SpatialMaxPooling.scala | 4 + .../intel/analytics/bigdl/dllib/nn/View.scala | 2 + .../dllib/nn/abstractnn/AbstractModule.scala | 7 + .../dllib/utils/tf/BigDLToTensorflow.scala | 200 +++++++++++++----- .../bigdl/dllib/utils/tf/Tensorflow.scala | 84 ++++++-- .../dllib/utils/tf/TensorflowSaver.scala | 55 ++++- .../dllib/utils/tf/TensorflowSaverSpec.scala | 53 ++++- 10 files changed, 346 insertions(+), 84 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index ca478bb8524..14d3b90aef5 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -21,8 +21,7 @@ import java.util.Locale import java.util.concurrent.atomic.AtomicBoolean import org.apache.log4j.Logger -import org.apache.spark.{SparkConf, SparkContext} - +import org.apache.spark.{SparkConf, SparkContext, SparkException} import com.intel.analytics.bigdl.mkl.MKL /** @@ -342,7 +341,17 @@ object Engine { * @return (nExecutor, executorCore) */ private[utils] def sparkExecutorAndCore(): Option[(Int, Int)] = { - parseExecutorAndCore(SparkContext.getOrCreate().getConf) + try { + parseExecutorAndCore(SparkContext.getOrCreate().getConf) + } catch { + case s: SparkException => + if (s.getMessage.contains("A master URL must be set in your configuration")) { + throw new IllegalArgumentException("A master URL must be set in your configuration." + + " Or if you want to run BigDL in a local JVM environment, you should set Java " + + "property bigdl.localMode=true") + } + throw s + } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala index 7eae5192f72..59b6521d2a3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala @@ -36,8 +36,8 @@ import scala.reflect.ClassTag class Scale[T: ClassTag](val size: Array[Int]) (implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[T], Tensor[T], T] { - private var cmul = new CMul[T](size) - private var cadd = new CAdd[T](size) + private[bigdl] var cmul = new CMul[T](size) + private[bigdl] var cadd = new CAdd[T](size) /** * Computes the output using the current parameter set of the class and input. This function diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala index 5a9083af617..25576884e6e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePooling.scala @@ -67,7 +67,7 @@ class SpatialAveragePooling[T: ClassTag]( val padW: Int = 0, val padH: Int = 0, val globalPooling: Boolean = false, - private var ceilMode: Boolean = false, + var ceilMode: Boolean = false, private var countIncludePad: Boolean = true, private var divide: Boolean = true, val format: DataFormat = DataFormat.NCHW @@ -348,6 +348,10 @@ class SpatialAveragePooling[T: ClassTag]( val outputHeight = sizes(4) val outputWidth = sizes(5) + if (ceilMode && padW == 0 && (inputWidth - kW) % dW == 0) { + ceilMode = false // The ceil mode is not needed. + } + if (input.dim() == 3) { format match { case DataFormat.NCHW => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala index 39348e6cfef..b4baa375ec5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala @@ -119,6 +119,10 @@ class SpatialMaxPooling[T: ClassTag]( val oHeight = sizes(4) val oWidth = sizes(5) + if (ceilMode && padW == 0 && (inputWidth - kW) % dW == 0) { + ceilMode = false // The ceil mode is not needed. + } + if (input.dim() == 3) { format match { case DataFormat.NCHW => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/View.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/View.scala index 004f858b8b1..7b4f01fb8e6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/View.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/View.scala @@ -63,6 +63,8 @@ class View[T: ClassTag](val sizes: Array[Int])( this } + def getNumInputDims(): Int = numInputDims + private def batchSize( input: Tensor[T], size: Array[Int], numberInputDims: Int, numElements: Int): Int = { val ind = input.nDimension() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 397fb7d40e9..18beaa804cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -510,7 +510,14 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC): this.type = { require(this.isInstanceOf[Graph[T]], "only Graph container can be saved as Tensorflow model") this.clearState() + val inTrainMode = train + if (inTrainMode) { + this.evaluate() + } TensorflowSaver.saveGraph(this.asInstanceOf[Graph[T]], inputs, path, byteOrder, dataFormat) + if (inTrainMode) { + this.training() + } this } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala index b8e09bc0657..029691f9f69 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala @@ -97,8 +97,8 @@ object LinearToTF extends BigDLToTensorflow { val mm = matmul(inputs(0), weightReader, linear.getName() + "/matmul") val bias = const(linear.bias, linear.getName() + "/bias", byteOrder) val biasReader = identity(bias, linear.getName() + "/biasReader") - val add = biasAdd(mm, biasReader, getDataFormat(), linear.getName() + "/biasAdd") - Seq(add, biasReader, bias, mm, weightReader, weight) + val addNode = add(mm, biasReader, linear.getName() + "/add") + Seq(addNode, biasReader, bias, mm, weightReader, weight) } } @@ -107,29 +107,72 @@ object SpatialConvolutionToTF extends BigDLToTensorflow { byteOrder: ByteOrder): Seq[NodeDef] = { require(inputs.length == 1, "SpatialConvolution only accept one input") val spatialConv = module.asInstanceOf[SpatialConvolution[_]] - // squeeze will modify the weight tensor - // GOIHW -> HWIO - require(spatialConv.weight.size(1) == 1, "convolution group is not supported") - val (dataFormat, filterTensor) = if (spatialConv.format == DataFormat.NCHW) { - (TensorflowDataFormat.NCHW, - spatialConv.weight.select(1, 1) + if (spatialConv.nGroup == 1) { + val (dataFormat, filterTensor) = if (spatialConv.format == DataFormat.NCHW) { + (TensorflowDataFormat.NCHW, + spatialConv.weight.select(1, 1) + .transpose(2, 3).transpose(3, 4) + .transpose(1, 2).transpose(2, 3) + .transpose(3, 4).contiguous()) + } else { + (TensorflowDataFormat.NHWC, spatialConv.weight.select(1, 1)) + } + + val filter = const(filterTensor, spatialConv.getName() + "/filter", byteOrder) + val filterReader = identity(filter, spatialConv.getName() + "/filterReader") + val conv = conv2D(inputs(0), filterReader, spatialConv.strideW, spatialConv.strideH, + spatialConv.kernelW, spatialConv.kernelH, spatialConv.padW, spatialConv.padH, + dataFormat, spatialConv.getName() + "/conv2D") + if (spatialConv.bias != null) { + val bias = const(spatialConv.bias, spatialConv.getName() + "/bias", byteOrder) + val biasReader = identity(bias, spatialConv.getName() + "/biasReader") + val add = biasAdd(conv, biasReader, dataFormat, + spatialConv.getName() + "/biasAdd") + Seq(add, biasReader, bias, conv, filterReader, filter) + } else { + Seq(conv, filterReader, filter) + } + } else { + require(spatialConv.format == DataFormat.NCHW, "Only NCHW support conv group") + val nodes = new ArrayBuffer[NodeDef]() + val splitDim = const(Tensor.scalar[Int](1), spatialConv.getName() + "/split_dim", + ByteOrder.LITTLE_ENDIAN) + val splits = split(splitDim, inputs(0), spatialConv.nGroup, spatialConv.getName() + "/split") + nodes.append(splitDim) + nodes.appendAll(splits) + val axis = const(Tensor.scalar[Int](1), spatialConv.getName() + "/concat/axis", + ByteOrder.LITTLE_ENDIAN) + nodes.append(axis) + val outputs = (0 until spatialConv.nGroup).map(g => { + val filterTensor = spatialConv.weight.select(1, g + 1) .transpose(2, 3).transpose(3, 4) .transpose(1, 2).transpose(2, 3) - .transpose(3, 4).contiguous()) - } else { - (TensorflowDataFormat.NHWC, spatialConv.weight.select(1, 1)) + .transpose(3, 4).contiguous() + + val filter = const(filterTensor, spatialConv.getName() + s"/group$g/filter", byteOrder) + val filterReader = identity(filter, spatialConv.getName() + s"/group$g/filterReader") + val conv = conv2D(splits(g), filterReader, spatialConv.strideW, spatialConv.strideH, + spatialConv.kernelW, spatialConv.kernelH, spatialConv.padW, spatialConv.padH, + TensorflowDataFormat.NCHW, spatialConv.getName() + s"/group$g/conv2D") + if (spatialConv.bias != null) { + val bias = const(spatialConv.bias.narrow(1, + g * spatialConv.nOutputPlane / spatialConv.nGroup + 1, + spatialConv.nOutputPlane / spatialConv.nGroup), + spatialConv.getName() + s"/group$g/bias", byteOrder) + val biasReader = identity(bias, spatialConv.getName() + s"/group$g/biasReader") + val add = biasAdd(conv, biasReader, TensorflowDataFormat.NCHW, + spatialConv.getName() + s"/group$g/biasAdd") + nodes.append(add, biasReader, bias, conv, filterReader, filter) + add + } else { + nodes.append(conv, filterReader, filter) + conv + } + }) ++ Seq(axis) + + val concatNode = concat(outputs, spatialConv.getName() + "/concat/output") + Seq(concatNode) ++ nodes } - - val filter = const(filterTensor, spatialConv.getName() + "/filter", byteOrder) - val filterReader = identity(filter, spatialConv.getName() + "/filterReader") - val conv = conv2D(inputs(0), filterReader, spatialConv.strideW, spatialConv.strideH, - spatialConv.kernelW, spatialConv.kernelH, spatialConv.padW, spatialConv.padH, - dataFormat, spatialConv.getName() + "/conv2D") - val bias = const(spatialConv.bias, spatialConv.getName() + "/bias", byteOrder) - val biasReader = identity(bias, spatialConv.getName() + "/biasReader") - val add = biasAdd(conv, biasReader, dataFormat, - spatialConv.getName() + "/biasAdd") - Seq(add, biasReader, bias, conv, filterReader, filter) } } @@ -208,12 +251,13 @@ object ViewToTF extends BigDLToTensorflow { byteOrder: ByteOrder): Seq[NodeDef] = { require(inputs.length == 1, "Reshape only accept one input") val viewLayer = module.asInstanceOf[View[_]] - val size = Tensor[Int](viewLayer.sizes.length) - var i = 0 - while(i < viewLayer.sizes.length) { - size.setValue(i + 1, viewLayer.sizes(i)) + val size = Tensor[Int](viewLayer.sizes.length + 1).setValue(1, -1) + var i = 1 + while(i < viewLayer.sizes.length + 1) { + size.setValue(i + 1, viewLayer.sizes(i - 1)) i += 1 } + val shape = const(size, viewLayer.getName() + "/shape", byteOrder) val reshapeNode = reshape(inputs(0), shape, viewLayer.getName()) Seq(reshapeNode, shape) @@ -231,7 +275,7 @@ object MaxpoolToTF extends BigDLToTensorflow { TensorflowDataFormat.NCHW } Seq(maxPool(inputs(0), layer.kW, layer.kH, layer.padW, layer.padH, - layer.dW, layer.dH, dataFormat, layer.getName())) + layer.dW, layer.dH, dataFormat, layer.getName(), layer.ceilMode)) } } @@ -266,7 +310,7 @@ object AvgpoolToTF extends BigDLToTensorflow { TensorflowDataFormat.NCHW } Seq(avgPool(inputs(0), layer.kW, layer.kH, layer.padW, layer.padH, - layer.dW, layer.dH, dataFormat, layer.getName())) + layer.dW, layer.dH, dataFormat, layer.getName(), layer.ceilMode)) } } @@ -289,6 +333,18 @@ object DropoutToTF extends BigDLToTensorflow { } } +object ScaleToTF extends BigDLToTensorflow { + override def toTFDef(module: AbstractModule[_, _, _], inputs: Seq[NodeDef], + byteOrder: ByteOrder): Seq[NodeDef] = { + val layer = module.asInstanceOf[Scale[_]] + val weight = const(layer.cmul.weight, layer.getName() + "/mul/weight", ByteOrder.LITTLE_ENDIAN) + val mulNode = multiply(weight, inputs(0), layer.getName() + "/mul/mul") + val bias = const(layer.cadd.bias, layer.getName() + "/add/bias", ByteOrder.LITTLE_ENDIAN) + val output = add(mulNode, bias, layer.getName() + "/add/add") + Seq(output, bias, mulNode, weight) + } +} + object CAddTableToTF extends BigDLToTensorflow { override def toTFDef(module: AbstractModule[_, _, _], inputs: Seq[NodeDef], byteOrder: ByteOrder): Seq[NodeDef] = { @@ -313,7 +369,7 @@ object JoinTableToTF extends BigDLToTensorflow { val updateInputs = new ArrayBuffer[NodeDef]() updateInputs ++= inputs.reverse updateInputs.append(axis) - Seq(concat(updateInputs, layer.dimension - 1, layer.getName()), axis) + Seq(concat(updateInputs, layer.getName()), axis) } } @@ -359,28 +415,68 @@ object BatchNorm2DToTF extends BigDLToTensorflow { for (i <- 0 until layer.nDim) { size.setValue(i + 1, 1) } - size(2) = layer.weight.size(1) - val shapeVar = const(size, layer.getName() + "/reshape_1/shape", byteOrder) - val shapeMean = const(size, layer.getName() + "/reshape_2/shape", byteOrder) - val shapeScale = const(size, layer.getName() + "/reshape_3/shape", byteOrder) - val shapeOffset = const(size, layer.getName() + "/reshape_4/shape", byteOrder) - val varNode = const(layer.runningVar, layer.getName() + "/std", byteOrder) - val mean = const(layer.runningMean, layer.getName() + "/mean", byteOrder) - val scale = const(layer.weight, layer.getName() + "/scale", byteOrder) - val offset = const(layer.bias, layer.getName() + "/offset", byteOrder) - val reshapeVar = reshape(varNode, shapeVar, s"${layer.getName()}/reshape_1") - val reshapeMean = reshape(mean, shapeMean, s"${layer.getName()}/reshape_2") - val reshapeScale = reshape(scale, shapeScale, s"${layer.getName()}/reshape_3") - val reshapeOffset = reshape(offset, shapeOffset, s"${layer.getName()}/reshape_4") - // construct graph - val sqrtVar = rsqrt(reshapeVar, layer.getName() + "/stdvar") - val mul0 = multiply(reshapeScale, sqrtVar, layer.getName() + "/mul0") - val mul1 = multiply(inputs(0), mul0, layer.getName() + "/mul1") - val mul2 = multiply(reshapeMean, mul0, layer.getName() + "/mul2") - val sub = subtract(reshapeOffset, mul2, layer.getName() + "/sub") - val output = add(mul1, sub, layer.getName() + "/output") - Seq(output, sub, mul2, mul1, mul0, reshapeOffset, reshapeMean, reshapeScale, - shapeOffset, shapeMean, shapeScale, offset, scale, mean, - sqrtVar, reshapeVar, shapeVar, varNode) + + size(2) = layer.runningVar.size(1) + if (layer.weight != null) { + val shapeVar = const(size, layer.getName() + "/reshape_1/shape", byteOrder) + val shapeMean = const(size, layer.getName() + "/reshape_2/shape", byteOrder) + val shapeScale = const(size, layer.getName() + "/reshape_3/shape", byteOrder) + val shapeOffset = const(size, layer.getName() + "/reshape_4/shape", byteOrder) + + val varNode = const(layer.runningVar, layer.getName() + "/var", byteOrder) + val mean = const(layer.runningMean, layer.getName() + "/mean", byteOrder) + val scale = const(layer.weight, layer.getName() + "/scale", byteOrder) + val offset = const(layer.bias, layer.getName() + "/offset", byteOrder) + val reshapeVar = reshape(varNode, shapeVar, s"${layer.getName()}/reshape_1") + val reshapeMean = reshape(mean, shapeMean, s"${layer.getName()}/reshape_2") + val reshapeScale = reshape(scale, shapeScale, s"${layer.getName()}/reshape_3") + val reshapeOffset = reshape(offset, shapeOffset, s"${layer.getName()}/reshape_4") + // construct graph + val sqrtVar = rsqrt(reshapeVar, layer.getName() + "/sqrtvar") + val mul0 = multiply(reshapeScale, sqrtVar, layer.getName() + "/mul0") + val mul1 = multiply(inputs(0), mul0, layer.getName() + "/mul1") + val mul2 = multiply(reshapeMean, mul0, layer.getName() + "/mul2") + val sub = subtract(reshapeOffset, mul2, layer.getName() + "/sub") + val output = add(mul1, sub, layer.getName() + "/output") + Seq(output, sub, mul2, mul1, mul0, reshapeOffset, reshapeMean, reshapeScale, + shapeOffset, shapeMean, shapeScale, offset, scale, mean, + sqrtVar, reshapeVar, shapeVar, varNode) + } else { + val shapeVar = const(size, layer.getName() + "/reshape_1/shape", byteOrder) + val shapeMean = const(size, layer.getName() + "/reshape_2/shape", byteOrder) + + val varNode = const(layer.runningVar, layer.getName() + "/var", byteOrder) + val mean = const(layer.runningMean, layer.getName() + "/mean", byteOrder) + val reshapeVar = reshape(varNode, shapeVar, s"${layer.getName()}/reshape_1") + val reshapeMean = reshape(mean, shapeMean, s"${layer.getName()}/reshape_2") + // construct graph + val sqrtVar = rsqrt(reshapeVar, layer.getName() + "/sqrtvar") + val mul1 = multiply(inputs(0), sqrtVar, layer.getName() + "/mul1") + val mul2 = multiply(reshapeMean, sqrtVar, layer.getName() + "/mul2") + val output = subtract(mul1, mul2, layer.getName() + "/output") + Seq(output, mul2, mul1, reshapeMean, shapeMean, mean, sqrtVar, reshapeVar, shapeVar, varNode) + } + } +} + +object LRNToTF extends BigDLToTensorflow { + override def toTFDef(module: AbstractModule[_, _, _], inputs: Seq[NodeDef], + byteOrder: ByteOrder): Seq[NodeDef] = { + val layer = module.asInstanceOf[SpatialCrossMapLRN[_]] + if (layer.format == DataFormat.NHWC) { + Seq(lrn(inputs(0), (layer.size - 1) / 2, layer.k.toFloat, (layer.alpha / layer.size).toFloat, + layer.beta.toFloat, module.getName())) + } else { + val perm1 = const(Tensor[Int](T(0, 2, 3, 1)), module.getName() + "/perm1", + ByteOrder.LITTLE_ENDIAN) + val transpose1 = transpose(inputs(0), perm1, module.getName() + "/transpose1") + val lrnNode = lrn(transpose1, (layer.size - 1) / 2, layer.k.toFloat, + (layer.alpha / layer.size).toFloat, + layer.beta.toFloat, module.getName() + "/lrn") + val perm2 = const(Tensor[Int](T(0, 3, 1, 2)), module.getName() + "/perm2", + ByteOrder.LITTLE_ENDIAN) + val output = transpose(lrnNode, perm2, module.getName() + "/transpose2") + Seq(output, perm1, transpose1, lrnNode, perm2) + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala index d2b318c79fa..90e9a69e5cd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala @@ -19,8 +19,10 @@ import java.nio.{ByteBuffer, ByteOrder} import java.nio.charset.Charset import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.{Graph, Module} import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Engine import org.tensorflow.framework.AttrValue.ListValue import org.tensorflow.framework._ import org.tensorflow.framework.TensorShapeProto.Dim @@ -174,6 +176,17 @@ object Tensorflow { .build() } + def transpose(x: NodeDef, perm: NodeDef, name: String): NodeDef = { + NodeDef.newBuilder() + .setName(name) + .setOp("Transpose") + .addInput(x.getName) + .addInput(perm.getName) + .putAttr("T", getDataType(x)) + .putAttr("Tperm", getDataType(perm)) + .build() + } + /** * Generate a biasAdd tensorflow protobuf node * @param value @@ -270,7 +283,15 @@ object Tensorflow { } def maxPool(value: NodeDef, kW: Int, kH: Int, pW: Int, pH: Int, sW: Int, sH: Int, - dataFormat: TensorflowDataFormat, name: String): NodeDef = { + dataFormat: TensorflowDataFormat, name: String, + ceilMode: Boolean = false): NodeDef = { + + val paddingType = if (ceilMode) { + PaddingType.PADDING_SAME.value + } else { + getPaddingType(pW, pH, kW, kH, sW, sH).value + } + NodeDef.newBuilder() .setName(name) .setOp("MaxPool") @@ -278,13 +299,19 @@ object Tensorflow { .putAttr("T", getDataType(value)) .putAttr("data_format", dataFormat.value) .putAttr("ksize", kernelAttr(kW, kH, dataFormat)) - .putAttr("padding", getPaddingType(pW, pH, kW, kH, sW, sH).value) + .putAttr("padding", paddingType) .putAttr("strides", strideAttr(sW, sH, dataFormat)) .build() } def avgPool(value: NodeDef, kW: Int, kH: Int, pW: Int, pH: Int, sW: Int, sH: Int, - dataFormat: TensorflowDataFormat, name: String): NodeDef = { + dataFormat: TensorflowDataFormat, name: String, ceilMode: Boolean): NodeDef = { + val paddingType = if (ceilMode) { + PaddingType.PADDING_SAME.value + } else { + getPaddingType(pW, pH, kW, kH, sW, sH).value + } + NodeDef.newBuilder() .setName(name) .setOp("AvgPool") @@ -292,7 +319,7 @@ object Tensorflow { .addInput(value.getName) .putAttr("data_format", dataFormat.value) .putAttr("ksize", kernelAttr(kW, kH, dataFormat)) - .putAttr("padding", getPaddingType(pW, pH, kW, kH, sW, sH).value) + .putAttr("padding", paddingType) .putAttr("strides", strideAttr(sW, sH, dataFormat)) .build() } @@ -388,7 +415,7 @@ object Tensorflow { node.build() } - def concat(inputs: Seq[NodeDef], axis: Int, name: String): NodeDef = { + def concat(inputs: Seq[NodeDef], name: String): NodeDef = { require(inputs.length >= 1, "at least one inputs for addN") val node = NodeDef.newBuilder() @@ -426,6 +453,25 @@ object Tensorflow { .build() } + def split(splitDim: NodeDef, value: NodeDef, numSplit: Int, name: String): Seq[NodeDef] = { + val splitNode = NodeDef.newBuilder() + .setName(name + "/split") + .setOp("Split") + .putAttr("T", getDataType(value)) + .putAttr("num_split", intAttr(numSplit)) + .addInput(splitDim.getName) + .addInput(value.getName) + .build() + (0 until numSplit).map(i => { + NodeDef.newBuilder() + .setName(name + s"/reader$i") + .setOp("Identity") + .addInput(name + s"/split:$i") + .putAttr("T", getDataType(value)) + .build() + }) ++ Seq(splitNode) + } + def softmax(logits: NodeDef, name: String): NodeDef = { NodeDef.newBuilder() .setName(name) @@ -444,6 +490,20 @@ object Tensorflow { .build() } + def lrn(input: NodeDef, depthRadius: Int, bias: Float, alpha: Float, beta: Float, + name: String): NodeDef = { + NodeDef.newBuilder() + .setName(name) + .setOp("LRN") + .putAttr("depth_radius", intAttr(depthRadius)) + .putAttr("bias", floatAttr(bias)) + .putAttr("beta", floatAttr(beta)) + .putAttr("alpha", floatAttr(alpha)) + .putAttr("T", getDataType(input)) + .addInput(input.getName) + .build() + } + def rsqrt(x: NodeDef, name: String): NodeDef = { NodeDef.newBuilder() .setName(name) @@ -483,7 +543,7 @@ object Tensorflow { val (content, dtype) = if (value.getType() == DoubleType) { val array = value.asInstanceOf[Tensor[Double]].storage().array() val offset = value.storageOffset() - 1 - val buffer = ByteBuffer.allocate(array.length * 8) + val buffer = ByteBuffer.allocate(value.nElement() * 8) buffer.order(byteOrder) var i = 0 while (i < value.nElement()) { @@ -494,7 +554,7 @@ object Tensorflow { } else if (value.getType() == FloatType) { val array = value.asInstanceOf[Tensor[Float]].storage().array() val offset = value.storageOffset() - 1 - val buffer = ByteBuffer.allocate(array.length * 4) + val buffer = ByteBuffer.allocate(value.nElement() * 4) buffer.order(byteOrder) var i = 0 while (i < value.nElement()) { @@ -505,7 +565,7 @@ object Tensorflow { } else if (value.getType() == IntType) { val array = value.asInstanceOf[Tensor[Int]].storage().array() val offset = value.storageOffset() - 1 - val buffer = ByteBuffer.allocate(array.length * 4) + val buffer = ByteBuffer.allocate(value.nElement() * 4) buffer.order(byteOrder) var i = 0 while (i < value.nElement()) { @@ -516,7 +576,7 @@ object Tensorflow { } else if (value.getType() == BooleanType) { val array = value.asInstanceOf[Tensor[Boolean]].storage().array() val offset = value.storageOffset() - 1 - val buffer = ByteBuffer.allocate(array.length) + val buffer = ByteBuffer.allocate(value.nElement()) buffer.order(byteOrder) val t : Byte = 1 val f : Byte = 0 @@ -590,12 +650,8 @@ object Tensorflow { : PaddingType = { if (padW == 0 && padH == 0) { return PaddingType.PADDING_VALID - } else if (2 * padW == (kW - sW) && 2 * padH == (kH - sH)) { - return PaddingType.PADDING_SAME } else { - throw new IllegalArgumentException( - s"Can not get padding type from given parameter " + - s"(padW: $padW, padH: $padH, kW: $kW, kH: $kH, sW: $sW, sH: $sH )") + return PaddingType.PADDING_SAME } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala index 0c570cb588d..9aecd49eeb1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaver.scala @@ -22,7 +22,8 @@ import com.google.protobuf.CodedOutputStream import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{File, FileWriter} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{File, FileWriter, T} import org.apache.log4j.Logger import org.tensorflow.framework._ @@ -30,6 +31,8 @@ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import com.intel.analytics.bigdl.utils.tf.Tensorflow._ +import scala.reflect.ClassTag + object TensorflowSaver { /** * Save a graph model to protobuf files so that it can be used in tensorflow inference. @@ -51,24 +54,25 @@ object TensorflowSaver { byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, extraNodes: Set[NodeDef] = Set()): Unit = { val inputNodeCache = - new mutable.HashMap[AbstractModule[Activity, Activity, T], ArrayBuffer[NodeDef]]() + new mutable.HashMap[String, ArrayBuffer[NodeDef]]() model.inputs.zip(inputs).foreach(n => { - inputNodeCache(n._1.element) = ArrayBuffer(n._2) + inputNodeCache(n._1.element.getName()) = ArrayBuffer(n._2) }) val graphBuilder = GraphDef.newBuilder() inputs.foreach(graphBuilder.addNode(_)) model.getSortedForwardExecutions.foreach(n => { - val nodeDefs = maps(n.element.getClass.getName).toTFDef(n.element, inputNodeCache(n.element), + val nodeDefs = maps(n.element.getClass.getName).toTFDef(n.element, + inputNodeCache(n.element.getName()), byteOrder) nodeDefs.foreach(nDef => { graphBuilder.addNode(nDef) }) n.nextNodes.foreach(n => { - val list = inputNodeCache.getOrElse(n.element, ArrayBuffer()) + val list = inputNodeCache.getOrElse(n.element.getName(), ArrayBuffer()) list.append(nodeDefs(0)) - inputNodeCache(n.element) = list + inputNodeCache(n.element.getName()) = list }) }) @@ -109,12 +113,43 @@ object TensorflowSaver { * @param dataFormat model data format * @tparam T */ - def saveGraph[T]( + def saveGraph[T: ClassTag]( model : Graph[T], inputs : Seq[(String, Seq[Int])], path: String, byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, - dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC): Unit = { + dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC)( + implicit ev: TensorNumeric[T]): Unit = { + // Check if there's pooling layer in which ceilMode is enable and pad is zero, we need double + // check if the ceilMode is real needed + val ceiledPoolingModules = model.modules.filter(m => + if (m.isInstanceOf[SpatialMaxPooling[_]]) { + val a = m.asInstanceOf[SpatialMaxPooling[_]] + a.ceilMode == true && a.padH == 0 && a.padW == 0 + } else if (m.isInstanceOf[SpatialAveragePooling[_]]) { + val a = m.asInstanceOf[SpatialAveragePooling[_]] + a.ceilMode == true && a.padH == 0 && a.padW == 0 + } else { + false + }) + + if (ceiledPoolingModules.size != 0) { + val inputTensors = inputs.map(shape => Tensor[T]().resize(shape._2.toArray)) + val inputActivity = if (inputTensors.size == 1) { + inputTensors.head + } else { + val t = T() + var i = 1 + inputTensors.foreach(tensor => { + t(i) = tensor + i += 1 + }) + t + } + model.forward(inputActivity) + } + + val inputNodeDefs = inputs.map(input => placeholder(model.getNumericType(), input._2, input._1) ) @@ -154,7 +189,9 @@ object TensorflowSaver { getNameFromObj(LogSoftMax.getClass.getName) -> LogSoftMaxToTF, getNameFromObj(SpatialBatchNormalization.getClass.getName) -> BatchNorm2DToTF, getNameFromObj(Input.getClass.getName) -> InputToTF, - getNameFromObj(Sigmoid.getClass.getName) -> SigmoidToTF + getNameFromObj(Sigmoid.getClass.getName) -> SigmoidToTF, + getNameFromObj(Scale.getClass.getName) -> ScaleToTF, + getNameFromObj(SpatialCrossMapLRN.getClass.getName) -> LRNToTF ) private def getNameFromObj(name: String) : String = name.substring(0, name.length - 1) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala index 36aad1e767b..76524b832db 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSaverSpec.scala @@ -57,7 +57,7 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { T(1.0f, 2.0f, 5.0f), T(-3.0f, -4.0f, -7.0f) )) - test(layer, input, "/biasAdd") should be(true) + test(layer, input, "/add") should be(true) } "AvgPooling NHWC" should "be correctly saved" in { @@ -153,6 +153,32 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { test(layer, input, "/biasAdd") should be(true) } + "SpatialConvolution NHWC without bias" should "be correctly saved" in { + val layer = SpatialConvolution(3, 5, 2, 2, format = DataFormat.NHWC, withBias = false) + val input = Tensor[Float](4, 5, 5, 3).rand() + test(layer, input, "/conv2D") should be(true) + } + + "SpatialConvolution NCHW with conv group" should "be correctly saved" in { + cancel("tf cpu only support NHWC, this can be test on tf with MKL") + val layer = SpatialConvolution(6, 10, 2, 2, nGroup = 2) + val input = Tensor[Float](4, 6, 24, 24).rand() + test(layer, input, "/concat/output") should be(true) + } + + "SpatialConvolution NCHW with conv group without bias" should "be correctly saved" in { + cancel("tf cpu only support NHWC, this can be test on tf with MKL") + val layer = SpatialConvolution(6, 10, 2, 2, nGroup = 2, withBias = false) + val input = Tensor[Float](4, 6, 24, 24).rand() + test(layer, input, "/concat/output") should be(true) + } + + "Scale" should "be correctly saved" in { + val layer = Scale[Float](Array(10)) + val input = Tensor[Float](4, 10) + test(layer, input, "/add/add") should be(true) + } + "TemporalConvolution" should "be correctly saved" in { val layer = TemporalConvolution(3, 5, 2, 2) val input = Tensor[Float](4, 16, 3).rand() @@ -182,6 +208,15 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { test(layer, input, "/output") should be(true) } + "Batch Norm2D NCHW without affine" should "be correctly saved" in { + val layer = SpatialBatchNormalization(2, affine = false) + layer.evaluate() + layer.runningVar.resize(2).rand(0.9, 1.1) + layer.runningMean.resize(2).rand() + val input = Tensor[Float](3, 2, 4, 5).rand() + test(layer, input, "/output") should be(true) + } + "Dropout" should "be correctly saved" in { val layer = Dropout() layer.evaluate() @@ -189,12 +224,24 @@ class TensorflowSaverSpec extends TensorflowSpecHelper { test(layer, input) should be(true) } - "View" should "be correctly saved" in { + "View" should "be correctly saved when batch is enabled" in { val layer = View(2, 4) - val input = Tensor[Float](2, 2, 2).rand() + val input = Tensor[Float](4, 2, 2, 2).rand() test(layer, input) should be(true) } + "LRN" should "be correct in NHWC" in { + val layer = SpatialCrossMapLRN(format = DataFormat.NHWC) + val input = Tensor[Float](4, 24, 24, 3).rand() + test(layer, input) should be(true) + } + + "LRN" should "be correct in NCHW" in { + val layer = SpatialCrossMapLRN(format = DataFormat.NCHW) + val input = Tensor[Float](4, 3, 24, 24).rand() + test(layer, input, "/transpose2") should be(true) + } + "Reshape" should "be correctly saved" in { val layer = Reshape(Array(2, 4)) val input = Tensor[Float](2, 2, 2).rand() From 862f7fda8ad5431076ab702d64b9bd1545e5cbf3 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 13 Dec 2017 18:01:06 +0800 Subject: [PATCH 0582/1065] add more ops (#1847) --- .../intel/analytics/bigdl/dllib/nn/Tanh.scala | 5 ++ .../bigdl/dllib/nn/ops/Digamma.scala | 40 ++++++++++++++++ .../analytics/bigdl/dllib/nn/ops/Erf.scala | 40 ++++++++++++++++ .../analytics/bigdl/dllib/nn/ops/Erfc.scala | 40 ++++++++++++++++ .../analytics/bigdl/dllib/nn/ops/Lgamma.scala | 40 ++++++++++++++++ .../bigdl/dllib/nn/ops/TanhGrad.scala | 46 +++++++++++++++++++ .../bigdl/dllib/tensor/DenseTensor.scala | 17 +++++++ .../tensor/QuantizedTensorUnsupported.scala | 13 ++++++ .../bigdl/dllib/tensor/SparseTensor.scala | 16 +++++++ .../bigdl/dllib/tensor/TensorMath.scala | 26 +++++++++++ .../bigdl/dllib/tensor/TensorNumeric.scala | 43 +++++++++++++++++ .../dllib/utils/tf/loaders/Digamma.scala | 43 +++++++++++++++++ .../bigdl/dllib/utils/tf/loaders/Erf.scala | 43 +++++++++++++++++ .../bigdl/dllib/utils/tf/loaders/Erfc.scala | 43 +++++++++++++++++ .../bigdl/dllib/utils/tf/loaders/Lgamma.scala | 43 +++++++++++++++++ .../dllib/utils/tf/loaders/TanhGrad.scala | 42 +++++++++++++++++ .../serializer/ModuleSerializerSpec.scala | 37 ++++++++++++++- .../dllib/utils/tf/loaders/DigammaSpec.scala | 28 +++++++++++ .../dllib/utils/tf/loaders/ErfSpec.scala | 24 ++++++++++ .../dllib/utils/tf/loaders/ErfcSpec.scala | 25 ++++++++++ .../dllib/utils/tf/loaders/LgammaSpec.scala | 24 ++++++++++ .../dllib/utils/tf/loaders/TanhGradSpec.scala | 25 ++++++++++ .../utils/tf/loaders/UnaryOpBaseSpec.scala | 8 +++- 23 files changed, 708 insertions(+), 3 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Digamma.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Erf.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Erfc.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Lgamma.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TanhGrad.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Digamma.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erf.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erfc.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Lgamma.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DigammaSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ErfSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ErfcSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LgammaSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGradSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala index 779364fd369..16e54bdf29f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala @@ -41,6 +41,11 @@ class Tanh[T: ClassTag]( } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + updateGradInputInternal(output, gradOutput) + } + + private[bigdl] def updateGradInputInternal(output: Tensor[T], + gradOutput: Tensor[T]): Tensor[T] = { gradInput.resizeAs(gradOutput) buffer.resizeAs(output) buffer.pow(output, ev.fromType(2)).cmul(gradOutput) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Digamma.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Digamma.scala new file mode 100644 index 00000000000..545b8ff597b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Digamma.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class Digamma[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[D], Tensor[D], T] { + output = Tensor[D]() + + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output.resizeAs(input).copy(input).digamma() + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +object Digamma { + def apply[T: ClassTag, D: ClassTag]()( + implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Digamma[T, D] = new Digamma() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Erf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Erf.scala new file mode 100644 index 00000000000..17be146be8d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Erf.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class Erf[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[D], Tensor[D], T] { + output = Tensor[D]() + + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output.resizeAs(input).copy(input).erf() + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +object Erf { + def apply[T: ClassTag, D: ClassTag]()( + implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Erf[T, D] = new Erf() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Erfc.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Erfc.scala new file mode 100644 index 00000000000..54e6b06b3e4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Erfc.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class Erfc[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[D], Tensor[D], T] { + output = Tensor[D]() + + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output.resizeAs(input).copy(input).erfc() + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +object Erfc { + def apply[T: ClassTag, D: ClassTag]()( + implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Erfc[T, D] = new Erfc() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Lgamma.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Lgamma.scala new file mode 100644 index 00000000000..260c35b640e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Lgamma.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +class Lgamma[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Tensor[D], Tensor[D], T] { + output = Tensor[D]() + + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output.resizeAs(input).copy(input).logGamma() + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +object Lgamma { + def apply[T: ClassTag, D: ClassTag]()( + implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Lgamma[T, D] = new Lgamma() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TanhGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TanhGrad.scala new file mode 100644 index 00000000000..cce4b8cc1b1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TanhGrad.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.nn.{Sigmoid, Tanh} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class TanhGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + private val module = Tanh[D]() + override def updateOutput(input: Table): Tensor[D] = { + val (y, grads) = (input[Tensor[D]](1), input[Tensor[D]](2)) + + output = module.updateGradInputInternal(y, grads).toTensor[D] + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +object TanhGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): TanhGrad[T, D] = + new TanhGrad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index d78eb970471..21b07b3dc5a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2162,6 +2162,23 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( array } + + override def erf(): Tensor[T] = { + this.apply1(a => ev.erf(a)) + } + + override def erfc(): Tensor[T] = { + this.apply1(a => ev.erfc(a)) + } + + override def logGamma(): Tensor[T] = { + this.apply1(a => ev.logGamma(a)) + } + + override def digamma(): Tensor[T] = { + this.apply1(a => ev.digamma(a)) + } + } object DenseTensor { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index 6d40ae9c15c..9221239404b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1429,4 +1429,17 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def toArray(): Array[T] = throw new UnsupportedOperationException(errorString) + + override def erf(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def erfc(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def logGamma(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def digamma(): Tensor[T] = + + throw new UnsupportedOperationException(errorString) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 7bd1d16bd00..059b4a8624f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -1045,6 +1045,22 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( override def toArray(): Array[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + + override def erf(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def erfc(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def logGamma(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + + override def digamma(): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } } object SparseTensor{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala index f0f4415e4d4..030ac6c026e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala @@ -583,6 +583,32 @@ trait TensorMath[T] { */ def inv(): Tensor[T] + /** + * Computes the reciprocal of this tensor element-wise and update the content inplace + * @return + */ + def erf(): Tensor[T] + + /** + * Computes the reciprocal of this tensor element-wise and update the content inplace + * @return + */ + def erfc(): Tensor[T] + + /** + * Computes the log of the absolute value of `Gamma(x)` element-wise, + * and update the content inplace + * @return + */ + def logGamma(): Tensor[T] + + /** + * Computes Psi, the derivative of Lgamma (the log of the absolute value of + * `Gamma(x)`), element-wise and update the content inplace + * @return + */ + def digamma(): Tensor[T] + /** * Get the top k smallest values and their indices. * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index 842ad0cb87a..7c6be529b62 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -138,6 +138,14 @@ object TensorNumericMath { def inv(v: T): T + def erf(v: T): T + + def erfc(v: T): T + + def logGamma(v: T): T + + def digamma(v: T): T + def add(n: Int, a: Array[T], offset: Int, v: T, stride: Int): Unit def sub(n: Int, a: Array[T], offset: Int, v: T, stride: Int): Unit @@ -456,6 +464,22 @@ object TensorNumericMath { def clip(a: T, lower: T, upper: T): T = throw new UnsupportedOperationException(typeName + " in tensor does not support clip operation") + + def erf(x: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support erf operation") + + def erfc(x: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support erf operation") + + def logGamma(v: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support erf operation") + + def digamma(v: T): T = + throw new UnsupportedOperationException(typeName + + " in tensor does not support erf operation") } /** @@ -771,6 +795,15 @@ object TensorNumericMath { math.min(math.max(a, lower), upper) } + override def erf(a: Float): Float = org.apache.commons.math3.special.Erf.erf(a).toFloat + + override def erfc(a: Float): Float = org.apache.commons.math3.special.Erf.erfc(a).toFloat + + override def logGamma(a: Float): Float = + org.apache.commons.math3.special.Gamma.logGamma(a).toFloat + + override def digamma(a: Float): Float = + org.apache.commons.math3.special.Gamma.digamma(a).toFloat } implicit object NumericDouble extends UndefinedTensorNumeric[Double]("Double") { @@ -1075,6 +1108,16 @@ object TensorNumericMath { require(lower <= upper, "lower bound must be less or equal than upper bound") math.min(math.max(a, lower), upper) } + + override def erf(a: Double): Double = org.apache.commons.math3.special.Erf.erf(a) + + override def erfc(a: Double): Double = org.apache.commons.math3.special.Erf.erfc(a) + + override def logGamma(a: Double): Double = + org.apache.commons.math3.special.Gamma.logGamma(a) + + override def digamma(a: Double): Double = + org.apache.commons.math3.special.Gamma.digamma(a) } implicit object NumericString extends UndefinedTensorNumeric[String]("String") { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Digamma.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Digamma.scala new file mode 100644 index 00000000000..43f16d745bd --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Digamma.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Digamma +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Digamma extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Digamma[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Digamma[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Digamma when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erf.scala new file mode 100644 index 00000000000..13c6ede4ce1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erf.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Erf +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Erf extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Erf[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Erf[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Erf when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erfc.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erfc.scala new file mode 100644 index 00000000000..711607ad62f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erfc.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Erfc +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Erfc extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Erfc[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Erfc[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Erfc when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Lgamma.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Lgamma.scala new file mode 100644 index 00000000000..1611f081cef --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Lgamma.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Lgamma +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Lgamma extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + Lgamma[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + Lgamma[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load Lgamma when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala new file mode 100644 index 00000000000..3f1f29fb567 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala @@ -0,0 +1,42 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.TanhGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class TanhGrad extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + if (t == DataType.DT_FLOAT) { + TanhGrad[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + TanhGrad[T, Double]() + } else { + throw new UnsupportedOperationException(s"Not support load TanhGrad when type is ${t}") + } + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 4248b5ca272..073230811c9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, EluGrad, Equal, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{DenseToSparse, _} import com.intel.analytics.bigdl.optim.L2Regularizer @@ -2210,6 +2210,41 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(module, input) } + "Digamma serializer" should "work properly" in { + val module = Digamma[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } + + "Lgamma serializer" should "work properly" in { + val module = Lgamma[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } + + "Erf serializer" should "work properly" in { + val module = Erf[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } + + "Erfc serializer" should "work properly" in { + val module = Erfc[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } + + "TanhGrad serializer" should "work properly" in { + val module = TanhGrad[Float, Float]() + + val input = T(Tensor[Float](1, 5, 3, 4).rand(), Tensor[Float](1, 5, 3, 4).rand()) + runSerializationTest(module, input) + } + "DetectionOutputSSD serializer" should "work properly" in { val module = DetectionOutputSSD[Float](DetectionOutputParam()).setName("DetectionOutputSSD") val name = module.getName diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DigammaSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DigammaSpec.scala new file mode 100644 index 00000000000..0c31deac5d5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DigammaSpec.scala @@ -0,0 +1,28 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class DigammaSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Digamma" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() + + override def getDelta: Double = 1e-3 + + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ErfSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ErfSpec.scala new file mode 100644 index 00000000000..511a12af58a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ErfSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class ErfSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Erf" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ErfcSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ErfcSpec.scala new file mode 100644 index 00000000000..06efb632b1e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ErfcSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor + + +class ErfcSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Erfc" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LgammaSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LgammaSpec.scala new file mode 100644 index 00000000000..0a94a0f4841 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LgammaSpec.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class LgammaSpec extends UnaryOpBaseSpec { + override def getOpName: String = "Lgamma" + + override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGradSpec.scala new file mode 100644 index 00000000000..059e4d3cbc4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGradSpec.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import com.intel.analytics.bigdl.tensor.Tensor + + +class TanhGradSpec extends BinaryOpBaseSpec { + override def getOpName: String = "TanhGrad" + + override def getInputs: Seq[Tensor[_]] = + Seq(Tensor[Float](4).rand(), Tensor[Float](4).rand()) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala index 999b4099052..08fe0e5332e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/UnaryOpBaseSpec.scala @@ -26,6 +26,8 @@ abstract class UnaryOpBaseSpec extends TensorflowSpecHelper { def getInput: Tensor[_] + def getDelta: Double = 1e-5 + s"$getOpName forward with float model" should "be correct" in { compare[Float]( NodeDef.newBuilder() @@ -33,7 +35,8 @@ abstract class UnaryOpBaseSpec extends TensorflowSpecHelper { .setOp(s"$getOpName") .putAttr("T", typeAttr(DataType.DT_FLOAT)), Seq(getInput), - 0 + 0, + getDelta ) } @@ -44,7 +47,8 @@ abstract class UnaryOpBaseSpec extends TensorflowSpecHelper { .setOp(s"$getOpName") .putAttr("T", typeAttr(DataType.DT_FLOAT)), Seq(getInput), - 0 + 0, + getDelta ) } From b9ca2292da22999fdf7a0d43c361ac87dcb62d6b Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 13 Dec 2017 18:55:48 +0800 Subject: [PATCH 0583/1065] Added Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput (#1853) * add ops * fix style * add more docs * address comments * fix test * add serilization tests --- .../analytics/bigdl/dllib/nn/Utils.scala | 15 + .../bigdl/dllib/nn/ops/Dilation2D.scala | 278 ++++++++++++++++++ .../nn/ops/Dilation2DBackpropFilter.scala | 262 +++++++++++++++++ .../nn/ops/Dilation2DBackpropInput.scala | 265 +++++++++++++++++ .../dllib/utils/tf/loaders/Dilation2D.scala | 49 +++ .../tf/loaders/Dilation2DBackpropFilter.scala | 49 +++ .../tf/loaders/Dilation2DBackpropInput.scala | 49 +++ .../serializer/ModuleSerializerSpec.scala | 34 ++- .../Dilation2DBackpropFilterSpec.scala | 83 ++++++ .../loaders/Dilation2DBackpropInputSpec.scala | 83 ++++++ .../utils/tf/loaders/Dilation2DSpec.scala | 75 +++++ 11 files changed, 1241 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInput.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInput.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilterSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInputSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index 0cb1b237d53..08eb046bcac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -411,6 +411,21 @@ object Utils { } } + private[nn] def getOutputSize(inputSize: Int, filterSize: Int, + stride: Int, padding: String) = { + padding.toLowerCase() match { + case "valid" => + val outputSize = (inputSize - filterSize + stride) / stride + (outputSize, 0, 0) + case "same" => + val outputSize = (inputSize + stride - 1) / stride + val paddingNeeded = math.max(0, (outputSize - 1) * stride + filterSize - inputSize) + val padBefore = paddingNeeded / 2 + val padAfter = paddingNeeded - padBefore + (outputSize, padBefore, padAfter) + } + } + def shuffle[T: ClassTag](src: Tensor[T], permutation: Array[Int], buffer: Tensor[T] = null)( implicit ev: TensorNumeric[T]): Tensor[T] = { require(permutation.length == src.nDimension, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2D.scala new file mode 100644 index 00000000000..55cb5194206 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2D.scala @@ -0,0 +1,278 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors. + * + * This layer takes a Table of two tensors as inputs, namely `input` and `filter`. + * The `input` tensor has shape `[batch, in_height, in_width, depth]` and the `filter` + * tensor has shape `[filter_height, filter_width, depth]`, i.e., each input channel is + * processed independently of the others with its own structing fucntion. The `output` tensor + * has shape `[batch, out_height, out_width, depth]`. The spatial dimensions of the output + * tensor depend on the `padding` algorithm. We currently only support the "NHWC" DataFormat. + * + * In detail, the grayscale morphological 2-D dilation is the max-sum correlation + * + * output[b, y, x, c] = + * max_{dy, dx} input[b, + * strides[1] * y + rates[1] * dy, + * strides[2] * x + rates[2] * dx, + * c] + + * filter[dy, dx, c] + * + * Max-pooling is a special case when the filter has size equal to the pooling kernel size and + * contains all zeros. + * + * Note on duality: The dilation of `input` by the `filter` is equal to the negation of the + * erosion of `-input` by the reflected `filter`. + * + */ +class Dilation2D[T: ClassTag, D: ClassTag](val strides: Array[Int], + val rates: Array[Int], + val padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T] { + + output = Tensor[D]() + + require(strides.length == 4, s"strides must have a length of 4, but got ${strides.length}") + require(rates.length == 4, s"rates must have a lenght of 4, but got ${rates.length}") + require(padding.toLowerCase() == "same" || padding.toLowerCase() == "valid", + s"padding must be one of same or valid, but got $padding") + + private def getOutputSize(inputSize: Int, filterSize: Int, stride: Int, padding: String) = { + padding.toLowerCase() match { + case "valid" => + val outputSize = (inputSize - filterSize + stride) / stride + (outputSize, 0, 0) + case "same" => + val outputSize = (inputSize + stride - 1) / stride + val paddingNeeded = math.max(0, (outputSize - 1) * stride + filterSize - inputSize) + val padBefore = paddingNeeded / 2 + val padAfter = paddingNeeded - padBefore + (outputSize, padBefore, padAfter) + } + } + + private def dilationFloat(input: Tensor[Float], filter: Tensor[Float], output: Tensor[Float], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) + val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) + + + val (outputRows, padTop, _) = + getOutputSize(inputRows, filterRowsEff, strideRows, padding) + val (outputCols, padLeft, _) = + getOutputSize(inputCols, filterColsEff, strideCols, padding) + + output.resize(Array(batch, outputRows, outputCols, depth)) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outputData = output.storage().array() + val outputDataOffset = output.storageOffset() - 1 + + var b = 0 + while(b < batch) { + var hOut = 0 + while (hOut < outputRows) { + val hBeg = hOut * strideRows - padTop + + var wOut = 0 + while (wOut < outputCols) { + val wBeg = wOut * strideCols - padLeft + + var d = 0 + while (d < depth) { + var curVal: Float = Float.MinValue + + var h = 0 + while(h < filterRows) { + val hIn = hBeg + h * rateRows + if (hIn >= 0 && hIn < inputRows) { + var w = 0 + while (w < filterCols) { + val wIn = wBeg + w * rateCols + if (wIn >= 0 && wIn < inputCols) { + val inputIndex = ((b * inputRows + hIn) * inputCols + wIn) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value = inputValue + filterValue + if (value > curVal) { + curVal = value + } + } + w += 1 + } + } + h += 1 + } + val outputIndex = ((b * outputRows + hOut) * outputCols + wOut) * depth + d + outputData(outputDataOffset + outputIndex) = curVal + d += 1 + } + + wOut += 1 + } + + hOut += 1 + } + b += 1 + } + + } + + private def dilationDouble(input: Tensor[Double], filter: Tensor[Double], output: Tensor[Double], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) + val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) + + + val (outputRows, padTop, _) = + getOutputSize(inputRows, filterRowsEff, strideRows, padding) + val (outputCols, padLeft, _) = + getOutputSize(inputCols, filterColsEff, strideCols, padding) + + output.resize(Array(batch, outputRows, outputCols, depth)) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outputData = output.storage().array() + val outputDataOffset = output.storageOffset() - 1 + + var b = 0 + while(b < batch) { + var hOut = 0 + while (hOut < outputRows) { + val hBeg = hOut * strideRows - padTop + var wOut = 0 + while (wOut < outputCols) { + val wBeg = wOut * strideCols - padLeft + var d = 0 + while (d < depth) { + var curVal: Double = Double.MinValue + var h = 0 + while(h < filterRows) { + val hIn = hBeg + h * rateRows + if (hIn >= 0 && hIn < inputRows) { + var w = 0 + while (w < filterCols) { + val wIn = wBeg + w * rateCols + if (wIn >= 0 && wIn < inputCols) { + val inputIndex = ((b * inputRows + hIn) * inputCols + wIn) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value = inputValue + filterValue + if (value > curVal) { + curVal = value + } + } + w += 1 + } + } + h += 1 + } + val outputIndex = ((b * outputRows + hOut) * outputCols + wOut) * depth + d + outputData(outputDataOffset + outputIndex) = curVal + d += 1 + } + wOut += 1 + } + hOut += 1 + } + b += 1 + } + + } + + override def updateOutput(inputs: Table): Tensor[D] = { + + val input = inputs[Tensor[D]](1) + val filter = inputs[Tensor[D]](2) + + require(input.dim() == 4, "input must have 4 dims") + require(filter.dim() == 3, "filter must have 3 dims") + + + val strideRows = strides(1) + val strideCols = strides(2) + + val rateRows = rates(1) + val rateCols = rates(2) + + if (ev2.getType() == FloatType) { + val inputTensor = input.asInstanceOf[Tensor[Float]] + val filterTensor = filter.asInstanceOf[Tensor[Float]] + val outputTensor = output.asInstanceOf[Tensor[Float]] + dilationFloat(inputTensor, filterTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else if (ev2.getType() == DoubleType) { + val inputTensor = input.asInstanceOf[Tensor[Double]] + val filterTensor = filter.asInstanceOf[Tensor[Double]] + val outputTensor = output.asInstanceOf[Tensor[Double]] + dilationDouble(inputTensor, filterTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else { + throw new IllegalArgumentException(s"does not support datatype ${ev2.getType()}") + } + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +object Dilation2D { + def apply[T: ClassTag, D: ClassTag](strides: Array[Int], rates: Array[Int], padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2D[T, D] = + new Dilation2D(strides, rates, padding) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilter.scala new file mode 100644 index 00000000000..b50582b6a7c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilter.scala @@ -0,0 +1,262 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.Utils +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Dilation2DBackpropFilter[T: ClassTag, D: ClassTag]( + strides: Array[Int], + rates: Array[Int], + padding: String)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + output = Tensor[D]() + + private def dilation2DBackpropFilterFloat( + input: Tensor[Float], + filter: Tensor[Float], + outBackprop: Tensor[Float], + filterBackprop: Tensor[Float], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) + val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) + + val (outputRows, padTop, _) = + Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) + val (outputCols, padLeft, _) = + Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) + + filterBackprop.resizeAs(filter) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outBackpropData = outBackprop.storage().array() + val outBackpropDataOffset = outBackprop.storageOffset() - 1 + + val filterBackpropData = filterBackprop.storage().array() + val filterBackpropDataOffset = filterBackprop.storageOffset() - 1 + + var b = 0 + while (b < batch) { + var h_out = 0 + while (h_out < outputRows) { + val h_beg = h_out * strideRows - padTop + var w_out = 0 + while (w_out < outputCols) { + val w_beg = w_out * strideCols - padLeft + var d = 0 + while (d < depth) { + var cur_val = Float.MinValue + var h_max = 0 + var w_max = 0 + var h = 0 + while (h < filterRows) { + val h_in = h_beg + h * rateRows + if (h_in >= 0 && h_in < inputRows) { + var w = 0 + while (w < filterCols) { + val w_in = w_beg + w * rateCols + if (w_in >= 0 && w_in < inputCols) { + val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value: Float = inputValue + filterValue + if (value > cur_val) { + cur_val = value + h_max = h + w_max = w + } + } + w += 1 + } + } + h += 1 + } + val filterBackPropIndex = + (h_max * filterCols + w_max) * depth + d + val outputBackPropIndex = + ((b * outputRows + h_out) * outputCols + w_out) * depth + d + filterBackpropData(filterBackpropDataOffset + filterBackPropIndex) += + outBackpropData(outBackpropDataOffset + outputBackPropIndex) + d += 1 + } + w_out += 1 + } + h_out += 1 + } + b += 1 + } + + } + + + + private def dilation2DBackpropFilterDouble(input: Tensor[Double], + filter: Tensor[Double], + outBackprop: Tensor[Double], + filterBackprop: Tensor[Double], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) + val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) + + val (outputRows, padTop, _) = + Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) + val (outputCols, padLeft, _) = + Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) + + filterBackprop.resizeAs(filter) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outBackpropData = outBackprop.storage().array() + val outBackpropDataOffset = outBackprop.storageOffset() - 1 + + val filterBackpropData = filterBackprop.storage().array() + val filterBackpropDataOffset = filterBackprop.storageOffset() - 1 + + var b = 0 + while (b < batch) { + var h_out = 0 + while (h_out < outputRows) { + val h_beg = h_out * strideRows - padTop + var w_out = 0 + while (w_out < outputCols) { + val w_beg = w_out * strideCols - padLeft + var d = 0 + while (d < depth) { + var cur_val = Double.MinValue + var h_max = 0 + var w_max = 0 + var h = 0 + while (h < filterRows) { + val h_in = h_beg + h * rateRows + if (h_in >= 0 && h_in < inputRows) { + var w = 0 + while (w < filterCols) { + val w_in = w_beg + w * rateCols + if (w_in >= 0 && w_in < inputCols) { + val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value: Double = inputValue + filterValue + if (value > cur_val) { + cur_val = value + h_max = h + w_max = w + } + } + w += 1 + } + } + h += 1 + } + val filterBackPropIndex = + (h_max * filterCols + w_max) * depth + d + val outputBackPropIndex = + ((b * outputRows + h_out) * outputCols + w_out) * depth + d + filterBackpropData(filterBackpropDataOffset + filterBackPropIndex) += + outBackpropData(outBackpropDataOffset + outputBackPropIndex) + d += 1 + } + w_out += 1 + } + h_out += 1 + } + b += 1 + } + } + + override def updateOutput(inputs: Table): Tensor[D] = { + val input = inputs[Tensor[D]](1) + val filter = inputs[Tensor[D]](2) + val outBackprop = inputs[Tensor[D]](3) + + require(input.dim() == 4, "input must have 4 dims") + require(filter.dim() == 3, "filter must have 3 dims") + + + val strideRows = strides(1) + val strideCols = strides(2) + + val rateRows = rates(1) + val rateCols = rates(2) + + if (ev2.getType() == FloatType) { + val inputTensor = input.asInstanceOf[Tensor[Float]] + val filterTensor = filter.asInstanceOf[Tensor[Float]] + val outBackpropTensor = outBackprop.asInstanceOf[Tensor[Float]] + val outputTensor = output.asInstanceOf[Tensor[Float]] + dilation2DBackpropFilterFloat(inputTensor, filterTensor, outBackpropTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else if (ev2.getType() == DoubleType) { + val inputTensor = input.asInstanceOf[Tensor[Double]] + val filterTensor = filter.asInstanceOf[Tensor[Double]] + val outBackpropTensor = output.asInstanceOf[Tensor[Double]] + val outputTensor = output.asInstanceOf[Tensor[Double]] + dilation2DBackpropFilterDouble(inputTensor, filterTensor, outBackpropTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else { + throw new IllegalArgumentException(s"does not support datatype ${ev2.getType()}") + } + + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +object Dilation2DBackpropFilter { + def apply[T: ClassTag, D: ClassTag](strides: Array[Int], rates: Array[Int], padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2DBackpropFilter[T, D] = + new Dilation2DBackpropFilter(strides, rates, padding) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInput.scala new file mode 100644 index 00000000000..ecaae69b384 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInput.scala @@ -0,0 +1,265 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.Utils +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Dilation2DBackpropInput[T: ClassTag, D: ClassTag](strides: Array[Int], + rates: Array[Int], + padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + output = Tensor[D] + + private def dilationBackpropInputFloat(input: Tensor[Float], + filter: Tensor[Float], + outBackprop: Tensor[Float], + inputBackprop: Tensor[Float], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) + val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) + + val (outputRows, padTop, _) = + Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) + val (outputCols, padLeft, _) = + Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) + + inputBackprop.resizeAs(input) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outBackpropData = outBackprop.storage().array() + val outBackpropDataOffset = outBackprop.storageOffset() - 1 + + val inputBackpropData = inputBackprop.storage().array() + val inputBackpropDataOffset = inputBackprop.storageOffset() - 1 + + var b = 0 + while (b < batch) { + var h_out = 0 + while (h_out < outputRows) { + val h_beg = h_out * strideRows - padTop + var w_out = 0 + while (w_out < outputCols) { + val w_beg = w_out * strideCols - padLeft + var d = 0 + while (d < depth) { + var cur_val = Float.MinValue + var h_in_max = if (h_beg < 0) 0 else h_beg + var w_in_max = if (w_beg < 0) 0 else w_beg + var h = 0 + while (h < filterRows) { + val h_in = h_beg + h * rateRows + if (h_in >= 0 && h_in < inputRows) { + var w = 0 + while (w < filterCols) { + val w_in = w_beg + w * rateCols + if (w_in >= 0 && w_in < inputCols) { + val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value: Float = inputValue + filterValue + if (value > cur_val) { + cur_val = value + h_in_max = h_in + w_in_max = w_in + } + } + w += 1 + } + } + h += 1 + } + val inputBackPropIndex = + ((b * inputRows + h_in_max) * inputCols + w_in_max) * depth + d + val outputBackPropIndex = + ((b * outputRows + h_out) * outputCols + w_out) * depth + d + inputBackpropData(inputBackpropDataOffset + inputBackPropIndex) += + outBackpropData(outBackpropDataOffset + outputBackPropIndex) + d += 1 + } + w_out += 1 + } + h_out += 1 + } + b += 1 + } + + } + + + + private def dilationBackpropInputDouble(input: Tensor[Double], + filter: Tensor[Double], + outBackprop: Tensor[Double], + inputBackprop: Tensor[Double], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val outputRows = outBackprop.size(2) + val outputCols = outBackprop.size(3) + + val (padTop, padLeft) = padding.toLowerCase() match { + case "same" => + val top = (outputRows - inputRows) / 2 + val left = (outputCols - inputCols) / 2 + (top, left) + case "valid" => + (0, 0) + } + + inputBackprop.resizeAs(input) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outBackpropData = outBackprop.storage().array() + val outBackpropDataOffset = outBackprop.storageOffset() - 1 + + val inputBackpropData = inputBackprop.storage().array() + val inputBackpropDataOffset = inputBackprop.storageOffset() - 1 + + var b = 0 + while (b < batch) { + var h_out = 0 + while (h_out < outputRows) { + val h_beg = h_out * strideRows - padTop + var w_out = 0 + while (w_out < outputCols) { + val w_beg = w_out * strideCols - padLeft + var d = 0 + while (d < depth) { + var cur_val = Double.MinValue + var h_in_max = if (h_beg < 0) 0 else h_beg + var w_in_max = if (w_beg < 0) 0 else w_beg + var h = 0 + while (h < filterRows) { + val h_in = h_beg + h * rateRows + if (h_in >= 0 && h_in < inputRows) { + var w = 0 + while (w < filterCols) { + val w_in = w_beg + w * rateCols + if (w_in >= 0 && w_in < inputCols) { + val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value: Double = inputValue + filterValue + if (value > cur_val) { + cur_val = value + h_in_max = h_in + w_in_max = w_in + } + } + w += 1 + } + } + h += 1 + } + val inputBackPropIndex = + ((b * inputRows + h_in_max) * inputCols + w_in_max) * depth + d + val outputBackPropIndex = + ((b * outputRows + h_out) * outputCols + w_out) * depth + d + inputBackpropData(inputBackpropDataOffset + inputBackPropIndex) += + outBackpropData(outBackpropDataOffset + outputBackPropIndex) + d += 1 + } + w_out += 1 + } + h_out += 1 + } + b += 1 + } + } + + override def updateOutput(inputs: Table): Tensor[D] = { + val input = inputs[Tensor[D]](1) + val filter = inputs[Tensor[D]](2) + val outBackprop = inputs[Tensor[D]](3) + + require(input.dim() == 4, "input must have 4 dims") + require(filter.dim() == 3, "filter must have 3 dims") + + + val strideRows = strides(1) + val strideCols = strides(2) + + val rateRows = rates(1) + val rateCols = rates(2) + + if (ev2.getType() == FloatType) { + val inputTensor = input.asInstanceOf[Tensor[Float]] + val filterTensor = filter.asInstanceOf[Tensor[Float]] + val outBackpropTensor = outBackprop.asInstanceOf[Tensor[Float]] + val outputTensor = output.asInstanceOf[Tensor[Float]] + dilationBackpropInputFloat(inputTensor, filterTensor, outBackpropTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else if (ev2.getType() == DoubleType) { + val inputTensor = input.asInstanceOf[Tensor[Double]] + val filterTensor = filter.asInstanceOf[Tensor[Double]] + val outBackpropTensor = output.asInstanceOf[Tensor[Double]] + val outputTensor = output.asInstanceOf[Tensor[Double]] + dilationBackpropInputDouble(inputTensor, filterTensor, outBackpropTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else { + throw new IllegalArgumentException(s"does not support datatype ${ev2.getType()}") + } + + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +object Dilation2DBackpropInput { + def apply[T: ClassTag, D: ClassTag](strides: Array[Int], rates: Array[Int], padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2DBackpropInput[T, D] = + new Dilation2DBackpropInput(strides, rates, padding) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2D.scala new file mode 100644 index 00000000000..b80a8fc08ab --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2D.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ELU +import com.intel.analytics.bigdl.nn.ops.Dilation2D +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Dilation2D extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val padding = getString(attributes, "padding") + val strides = getIntList(attributes, "strides").toArray + val rates = getIntList(attributes, "rates").toArray + val t = getType(nodeDef.getAttrMap, "T") + + if (t == DataType.DT_FLOAT) { + Dilation2D[T, Float](strides, rates, padding) + } else if (t == DataType.DT_DOUBLE) { + Dilation2D[T, Double](strides, rates, padding) + } else { + throw new UnsupportedOperationException(s"Not support load Dilation2D when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilter.scala new file mode 100644 index 00000000000..95086817bea --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilter.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Dilation2DBackpropFilter +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Dilation2DBackpropFilter extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val padding = getString(attributes, "padding") + val strides = getIntList(attributes, "strides").toArray + val rates = getIntList(attributes, "rates").toArray + val t = getType(nodeDef.getAttrMap, "T") + + if (t == DataType.DT_FLOAT) { + Dilation2DBackpropFilter[T, Float](strides, rates, padding) + } else if (t == DataType.DT_DOUBLE) { + Dilation2DBackpropFilter[T, Double](strides, rates, padding) + } else { + throw new UnsupportedOperationException( + s"Not support load Dilation2DBackpropFilter when type is ${t}") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInput.scala new file mode 100644 index 00000000000..198c336fe3e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInput.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.Dilation2DBackpropInput +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Dilation2DBackpropInput extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val padding = getString(attributes, "padding") + val strides = getIntList(attributes, "strides").toArray + val rates = getIntList(attributes, "rates").toArray + val t = getType(nodeDef.getAttrMap, "T") + + if (t == DataType.DT_FLOAT) { + Dilation2DBackpropInput[T, Float](strides, rates, padding) + } else if (t == DataType.DT_DOUBLE) { + Dilation2DBackpropInput[T, Double](strides, rates, padding) + } else { + throw new UnsupportedOperationException( + s"Not support load Dilation2DBackpropInput when type is ${t}") + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 073230811c9..4b08fbd47a0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{DenseToSparse, _} import com.intel.analytics.bigdl.optim.L2Regularizer @@ -2242,6 +2242,38 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll val module = TanhGrad[Float, Float]() val input = T(Tensor[Float](1, 5, 3, 4).rand(), Tensor[Float](1, 5, 3, 4).rand()) + + runSerializationTest(module, input) + } + + "Dilation2D serializer" should "work properly" in { + val module = Dilation2D[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()) + + runSerializationTest(module, input) + } + + "Dilation2DBackpropFilter serializer" should "work properly" in { + val module = Dilation2DBackpropFilter[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()) + + runSerializationTest(module, input) + } + + "Dilation2DBackpropInput serializer" should "work properly" in { + val module = Dilation2DBackpropInput[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()) + runSerializationTest(module, input) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilterSpec.scala new file mode 100644 index 00000000000..1632e469a1c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilterSpec.scala @@ -0,0 +1,83 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class Dilation2DBackpropFilterSpec extends TensorflowSpecHelper { + "Dilation2DBackpropFilter forward" should "be correct" in { + compare[Float]( + NodeDef.newBuilder() + .setName("Dilation2DBackpropFilter_test") + .setOp("Dilation2DBackpropFilter") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()), + 0 + ) + + compare[Float]( + NodeDef.newBuilder() + .setName("Dilation2DBackpropFilter_test") + .setOp("Dilation2DBackpropFilter") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 10, 12, 3).rand()), + 0 + ) + } + + "Dilation2DBackpropFilter forward with double model" should "be correct" in { + compare[Double]( + NodeDef.newBuilder() + .setName("Dilation2DBackpropFilter_test") + .setOp("Dilation2DBackpropFilter") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()), + 0 + ) + + compare[Double]( + NodeDef.newBuilder() + .setName("Dilation2DBackpropFilter_test") + .setOp("Dilation2DBackpropFilter") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 10, 12, 3).rand()), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInputSpec.scala new file mode 100644 index 00000000000..156c9249dd4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInputSpec.scala @@ -0,0 +1,83 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class Dilation2DBackpropInputSpec extends TensorflowSpecHelper { + "Dilation2DBackpropInput forward" should "be correct" in { + compare[Float]( + NodeDef.newBuilder() + .setName("Dilation2DBackpropInput_test") + .setOp("Dilation2DBackpropInput") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()), + 0 + ) + + compare[Float]( + NodeDef.newBuilder() + .setName("Dilation2DBackpropInput_test") + .setOp("Dilation2DBackpropInput") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 10, 12, 3).rand()), + 0 + ) + } + + "Dilation2DBackpropInput forward with double model" should "be correct" in { + compare[Double]( + NodeDef.newBuilder() + .setName("Dilation2DBackpropInput_test") + .setOp("Dilation2DBackpropInput") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()), + 0 + ) + + compare[Double]( + NodeDef.newBuilder() + .setName("Dilation2DBackpropInput_test") + .setOp("Dilation2DBackpropInput") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 10, 12, 3).rand()), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DSpec.scala new file mode 100644 index 00000000000..430bdc75526 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DSpec.scala @@ -0,0 +1,75 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class Dilation2DSpec extends TensorflowSpecHelper { + "Dilation2D forward" should "be correct" in { + compare[Float]( + NodeDef.newBuilder() + .setName("Dilation2D_test") + .setOp("Dilation2D") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()), + 0 + ) + + compare[Float]( + NodeDef.newBuilder() + .setName("Dilation2D_test") + .setOp("Dilation2D") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()), + 0 + ) + } + + "Dilation2D forward with double model" should "be correct" in { + compare[Double]( + NodeDef.newBuilder() + .setName("Dilation2D_test") + .setOp("Dilation2D") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()), + 0 + ) + + compare[Double]( + NodeDef.newBuilder() + .setName("Dilation2D_test") + .setOp("Dilation2D") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("rates", listIntAttr(Seq(1, 2, 3, 1))) + .putAttr("strides", listIntAttr(Seq(1, 3, 2, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value), + Seq(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()), + 0 + ) + } +} From 60f86908882992483e789cf22ac190362baf5665 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 14 Dec 2017 09:46:22 +0800 Subject: [PATCH 0584/1065] Add conv3d related operations (#1915) * add conv3d * backpropinput * fix conv3d * add backpropinputv2 * add backpropfilter and refactor * fix style * address comments * add serialization tests * fix tests --- .../dllib/nn/VolumetricConvolution.scala | 400 +++++++++++++----- .../analytics/bigdl/dllib/nn/ops/Conv3D.scala | 93 ++++ .../dllib/nn/ops/Conv3DBackpropFilter.scala | 102 +++++ .../dllib/nn/ops/Conv3DBackpropFilterV2.scala | 60 +++ .../dllib/nn/ops/Conv3DBackpropInput.scala | 114 +++++ .../dllib/nn/ops/Conv3DBackpropInputV2.scala | 72 ++++ .../bigdl/dllib/utils/tf/loaders/Conv3D.scala | 67 +++ .../tf/loaders/Conv3DBackpropFilter.scala | 51 +++ .../tf/loaders/Conv3DBackpropFilterV2.scala | 64 +++ .../tf/loaders/Conv3DBackpropInput.scala | 51 +++ .../tf/loaders/Conv3DBackpropInputV2.scala | 64 +++ .../serializer/ModuleSerializerSpec.scala | 45 +- .../tf/loaders/Conv3DBackpropFilterSpec.scala | 71 ++++ .../loaders/Conv3DBackpropFilterV2Spec.scala | 81 ++++ .../tf/loaders/Conv3DBackpropInputSpec.scala | 68 +++ .../loaders/Conv3DBackpropInputV2Spec.scala | 79 ++++ .../dllib/utils/tf/loaders/Conv3DSpec.scala | 75 ++++ 17 files changed, 1441 insertions(+), 116 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInput.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala index d28fcbdd3e9..efeff895704 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala @@ -127,35 +127,6 @@ class VolumetricConvolution[T: ClassTag]( } } - private def updateOutputFrame(input: Tensor[T], output: Tensor[T], weight: Tensor[T], - bias: Tensor[T], fInput: Tensor[T], kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, - dH: Int, padFront: Int, padLeft: Int, padTop: Int, padBack: Int, padRight: Int, padBottom: Int, - nInputPlane: Int, inputDepth: Int, - inputWidth: Int, inputHeight: Int, nOutputPlane: Int, outputDepth: Int, outputWidth: Int, - outputHeight: Int): Unit = { - val output2d = output.view(nOutputPlane, outputDepth * outputHeight * outputWidth) - - ev.getType() match { - case DoubleType => - NNPrimitive.unfoldedCopyVolDouble(fInput.asInstanceOf[Tensor[Double]], - input.asInstanceOf[Tensor[Double]], kT, kW, kH, dT, dW, dH, - padFront, padLeft, padTop, padBack, padRight, padBottom, - nInputPlane, - inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) - case FloatType => - NNPrimitive.unfoldedCopyVolFloat(fInput.asInstanceOf[Tensor[Float]], - input.asInstanceOf[Tensor[Float]], kT, kW, kH, dT, dW, dH, - padFront, padLeft, padTop, padBack, padRight, padBottom, - nInputPlane, - inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) - } - - output2d.addmm(ev.zero, output2d, ev.one, weight, fInput) - if (withBias) { - output2d.addr(ev.one, bias, onesBias) - } - } - /** * Computes the output using the current parameter set of the class and input. This function * returns the result which is stored in the output field. @@ -171,6 +142,86 @@ class VolumetricConvolution[T: ClassTag]( weightMM = weight.view(nOutputPlane, nInputPlane * kT * kH * kW) } + require(weight.dim() == 2 || weight.dim() == 5, + s"weight tensor should be 2D or 5D - got ${ weight.dim() }") + + + if (input.dim() == 4) { + require(input.size(1) == nInputPlane, s"input.size(1) should be equal to nInputPlane. " + + s"But In ${this.getName()} : input.size(1) is: ${ input.size(1) } ," + + s" nInputPlane is: ${ nInputPlane }") + } + + VolumetricConvolution.conv3d(input, output, weightMM, bias, onesBias, fInput, + nInputPlane, nOutputPlane, withBias, kT, kW, kH, dT, dW, dH, padT, padW, padH) + output + } + + /** + * Computing the gradient of the module with respect to its own input. This is returned in + * gradInput. Also, the gradInput state variable is updated accordingly. + * @param input + * @param gradOutput + * @return + */ + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + require(input.dim() == 4 || input.dim() == 5, + s"4D or 5D (batch mode) tensor expected for input, but got: ${ input.dim() }d") + + VolumetricConvolution.conv3DBackpropInput(input, gradInput, gradOutput, weightMM, + fGradInput, kT, kW, kH, dT, dW, dH, padT, padW, padH) + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + require(gradOutput.isContiguous(), "gradOutput should be contiguous") + if (gradWeightMM == null || gradWeightMM.storage().isEmpty) { + gradWeightMM = gradWeight.view(nOutputPlane, nInputPlane * kT * kH * kW) + } + + VolumetricConvolution.conv3DBackpropFilter(input, gradOutput, gradWeightMM, gradBias, + fInput, scaleB, scaleW, withBias) + + if (null != wRegularizer) { + wRegularizer.accRegularization(weight, gradWeight, scaleW) + } + if (withBias && null != bRegularizer) { + bRegularizer.accRegularization(bias, gradBias, scaleB) + } + } + + override def toString: String = { + s"nn.VolumetricConvolution($nInputPlane -> $nOutputPlane, $kT x $kW x" + + s" $kH, $dT, $dW, $dH, $padT, $padW, $padH)" + } +} + +object VolumetricConvolution { + def apply[@specialized(Float, Double) T: ClassTag]( + nInputPlane: Int, nOutputPlane: Int, + kT: Int, kW: Int, kH: Int, + dT: Int = 1, dW: Int = 1, dH: Int = 1, + padT: Int = 0, padW: Int = 0, padH: Int = 0, withBias: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null + )(implicit ev: TensorNumeric[T]): VolumetricConvolution[T] = { + new VolumetricConvolution[T](nInputPlane, nOutputPlane, kT, kW, kH, + dT, dW, dH, padT, padW, padH, withBias, wRegularizer, bRegularizer) + } + + private[bigdl] def conv3d[T](input: Tensor[T], + output: Tensor[T], + weightMM: Tensor[T], + bias: Tensor[T], + onesBias: Tensor[T], + fInput: Tensor[T], + nInputPlane: Int, + nOutputPlane: Int, + withBias: Boolean, + kT: Int, kW: Int, kH: Int, + dT: Int, dW: Int, dH: Int, + padT: Int, padW: Int, padH: Int + )(implicit ev: TensorNumeric[T]): Unit = { val dimDepth = if (input.dim() == 4) 2 else 3 val dimWidth = if (input.dim() == 4) 4 else 5 val dimHeight = if (input.dim() == 4) 3 else 4 @@ -203,24 +254,18 @@ class VolumetricConvolution[T: ClassTag]( s" (${ nOutputPlane }x${ outputDepth }x${ outputHeight }x${ outputWidth })." + s" Output size is too small") - require(weight.dim() == 2 || weight.dim() == 5, - s"weight tensor should be 2D or 5D - got ${ weight.dim() }") - if (withBias && (onesBias.dim() != 1 || onesBias.size(1) != outputHeight * outputWidth * outputDepth)) { onesBias.resize(Array(outputHeight * outputWidth * outputDepth)).fill(ev.one) } if (input.dim() == 4) { - require(input.size(1) == nInputPlane, s"input.size(1) should be equal to nInputPlane. " + - s"But In ${this.getName()} : input.size(1) is: ${ input.size(1) } ," + - s" nInputPlane is: ${ nInputPlane }") fInput.resize(kT * kW * kH * nInputPlane, outputDepth * outputHeight * outputWidth) output.resize(nOutputPlane, outputDepth, outputHeight, outputWidth) updateOutputFrame(input, output, weightMM, bias, fInput, kT, kW, kH, dT, dW, dH, padFront, padLeft, padTop, padBack, padRight, padBottom, nInputPlane, inputDepth, inputWidth, inputHeight, - nOutputPlane, outputDepth, outputWidth, outputHeight) + nOutputPlane, outputDepth, outputWidth, outputHeight, withBias, onesBias) } else { fInput.resize(input.size(1), kT * kW * kH * nInputPlane, outputDepth * outputHeight * outputWidth) @@ -236,17 +281,129 @@ class VolumetricConvolution[T: ClassTag]( dT, dW, dH, padFront, padLeft, padTop, padBack, padRight, padBottom, nInputPlane, inputDepth, inputWidth, inputHeight, - nOutputPlane, outputDepth, outputWidth, outputHeight) + nOutputPlane, outputDepth, outputWidth, outputHeight, withBias, onesBias) t += 1 } } - output } - private def updateGradInputFrame(gradInput: Tensor[T], gradOutput: Tensor[T], weight: Tensor[T], + private def updateOutputFrame[T]( + input: Tensor[T], output: Tensor[T], weight: Tensor[T], + bias: Tensor[T], fInput: Tensor[T], kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, + padFront: Int, padLeft: Int, padTop: Int, padBack: Int, padRight: Int, padBottom: Int, + nInputPlane: Int, inputDepth: Int, inputWidth: Int, inputHeight: Int, + nOutputPlane: Int, outputDepth: Int, outputWidth: Int, outputHeight: Int, + withBias: Boolean, onesBias: Tensor[T]) + (implicit ev: TensorNumeric[T]): Unit = { + val output2d = output.view(nOutputPlane, outputDepth * outputHeight * outputWidth) + + ev.getType() match { + case DoubleType => + NNPrimitive.unfoldedCopyVolDouble(fInput.asInstanceOf[Tensor[Double]], + input.asInstanceOf[Tensor[Double]], kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, + nInputPlane, + inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) + case FloatType => + NNPrimitive.unfoldedCopyVolFloat(fInput.asInstanceOf[Tensor[Float]], + input.asInstanceOf[Tensor[Float]], kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, + nInputPlane, + inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) + } + + output2d.addmm(ev.zero, output2d, ev.one, weight, fInput) + if (withBias) { + output2d.addr(ev.one, bias, onesBias) + } + } + + private[bigdl] def conv3DBackpropInput[T](inputSize: Array[Int], + gradInput: Tensor[T], + gradOutput: Tensor[T], + weightMM: Tensor[T], + fGradInput: Tensor[T], + kT: Int, kW: Int, kH: Int, + dT: Int, dW: Int, dH: Int, + padT: Int, padW: Int, padH: Int + )(implicit ev: TensorNumeric[T]): Unit = { + val dimChannel = if (inputSize.length == 4) 1 else 2 + val dimDepth = if (inputSize.length == 4) 2 else 3 + val dimWidth = if (inputSize.length == 4) 4 else 5 + val dimHeight = if (inputSize.length == 4) 3 else 4 + + val nInputPlane = inputSize(dimChannel - 1) + val inputWidth = inputSize(dimWidth - 1) + val inputHeight = inputSize(dimHeight - 1) + val inputDepth = inputSize(dimDepth - 1) + + + val outputDepth = gradOutput.size(dimDepth) + val outputHeight = gradOutput.size(dimHeight) + val outputWidth = gradOutput.size(dimWidth) + + val sizes = if (padW == -1 && padH == -1 && padT == -1) { + Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, + dW, kH, kW, inputDepth, dT, kT) + } else { + Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, + dW, kH, kW, padH, padW, ceilMode = false, inputdepth = inputDepth, + dt = dT, kt = kT, padt = padT) + } + val padFront = sizes(0) + val padBack = sizes(1) + val padLeft = sizes(4) + val padRight = sizes(5) + val padTop = sizes(2) + val padBottom = sizes(3) + + gradInput.resize(inputSize) + + if (inputSize.length == 4) { + fGradInput.resize(kT * kW * kH * nInputPlane, outputDepth * outputHeight * outputWidth) + require(gradOutput.isContiguous(), "gradOutput should be contiguous") + updateGradInputFrame(gradInput, gradOutput, weightMM.transpose(1, 2), fGradInput, + kT, kW, kH, + dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom) + } else { + fGradInput.resize(inputSize(0), kT * kW * kH * nInputPlane, + outputDepth * outputHeight * outputWidth) + // batch mode + var t = 1 + while (t <= inputSize(0)) { + val gradInputT = gradInput.select(1, t) + val gradOutputT = gradOutput.select(1, t) + val fGradInputT = fGradInput.select(1, t) + require(gradOutputT.isContiguous(), "each batch of gradOutput should be contiguous") + updateGradInputFrame(gradInputT, gradOutputT, weightMM.transpose(1, 2), fGradInputT, + kT, kW, kH, + dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom) + t += 1 + } + } + } + + private[bigdl] def conv3DBackpropInput[T](input: Tensor[T], + gradInput: Tensor[T], + gradOutput: Tensor[T], + weightMM: Tensor[T], + fGradInput: Tensor[T], + kT: Int, kW: Int, kH: Int, + dT: Int, dW: Int, dH: Int, + padT: Int, padW: Int, padH: Int + )(implicit ev: TensorNumeric[T]): Unit = { + conv3DBackpropInput(input.size(), gradInput, gradOutput, weightMM, fGradInput, + kT, kW, kH, dT, dW, dH, padT, padW, padH) + } + + private def updateGradInputFrame[T]( + gradInput: Tensor[T], gradOutput: Tensor[T], weight: Tensor[T], fGradInput: Tensor[T], kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, - padFront: Int, padLeft: Int, padTop: Int, padBack: Int, padRight: Int, padBottom: Int): - Unit = { + padFront: Int, padLeft: Int, padTop: Int, padBack: Int, padRight: Int, padBottom: Int) + (implicit ev: TensorNumeric[T]): + Unit = { val gradOutput2d = gradOutput.view(gradOutput.size(1), gradOutput.size(2) * gradOutput.size(3) * gradOutput.size(4)) fGradInput.addmm(ev.zero, fGradInput, @@ -270,17 +427,15 @@ class VolumetricConvolution[T: ClassTag]( } - /** - * Computing the gradient of the module with respect to its own input. This is returned in - * gradInput. Also, the gradInput state variable is updated accordingly. - * @param input - * @param gradOutput - * @return - */ - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - require(input.dim() == 4 || input.dim() == 5, - s"4D or 5D (batch mode) tensor expected for input, but got: ${ input.dim() }d") - + private[bigdl] def populateFInput[T]( + input: Tensor[T], + fInput: Tensor[T], + nInputPlane: Int, + nOutputPlane: Int, + kT: Int, kW: Int, kH: Int, + dT: Int, dW: Int, dH: Int, + padT: Int, padW: Int, padH: Int + )(implicit ev: TensorNumeric[T]): Unit = { val dimDepth = if (input.dim() == 4) 2 else 3 val dimWidth = if (input.dim() == 4) 4 else 5 val dimHeight = if (input.dim() == 4) 3 else 4 @@ -288,13 +443,14 @@ class VolumetricConvolution[T: ClassTag]( val inputWidth = input.size(dimWidth) val inputHeight = input.size(dimHeight) val inputDepth = input.size(dimDepth) + val sizes = if (padW == -1 && padH == -1 && padT == -1) { Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW, inputDepth, dT, kT) } else { Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW, padH, padW, ceilMode = false, inputdepth = inputDepth, - dt = dT, kt = kT, padt = padT) + dt = dT, kt = kT, padt = padT) } val padFront = sizes(0) val padBack = sizes(1) @@ -302,35 +458,94 @@ class VolumetricConvolution[T: ClassTag]( val padRight = sizes(5) val padTop = sizes(2) val padBottom = sizes(3) + val outputDepth = sizes(6) + val outputHeight = sizes(7) + val outputWidth = sizes(8) + + require(outputWidth >= 1 && outputDepth >= 1 && outputHeight >= 1, + s"Given input size: (${ input.size().mkString("x") })." + + s" Calculated output size:" + + s" (${ nOutputPlane }x${ outputDepth }x${ outputHeight }x${ outputWidth })." + + s" Output size is too small") + - gradInput.resizeAs(input) - fGradInput.resizeAs(fInput).zero() if (input.dim() == 4) { - require(gradOutput.isContiguous(), "gradOutput should be contiguous") - updateGradInputFrame(gradInput, gradOutput, weightMM.transpose(1, 2), fGradInput, - kT, kW, kH, - dT, dW, dH, - padFront, padLeft, padTop, padBack, padRight, padBottom) + fInput.resize(kT * kW * kH * nInputPlane, outputDepth * outputHeight * outputWidth) + im2colWrapper(input, fInput, kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, nInputPlane, + inputDepth, inputWidth, inputHeight, + nOutputPlane, outputDepth, outputWidth, outputHeight) } else { - // batch mode + fInput.resize(input.size(1), kT * kW * kH * nInputPlane, + outputDepth * outputHeight * outputWidth) + var t = 1 while (t <= input.size(1)) { - val gradInputT = gradInput.select(1, t) - val gradOutputT = gradOutput.select(1, t) - val fGradInputT = fGradInput.select(1, t) - require(gradOutputT.isContiguous(), "each batch of gradOutput should be contiguous") - updateGradInputFrame(gradInputT, gradOutputT, weightMM.transpose(1, 2), fGradInputT, + val inputT = input.select(1, t) + val fInputT = fInput.select(1, t) + im2colWrapper(inputT, fInputT, kT, kW, kH, dT, dW, dH, - padFront, padLeft, padTop, padBack, padRight, padBottom) + padFront, padLeft, padTop, padBack, padRight, padBottom, + nInputPlane, inputDepth, inputWidth, inputHeight, + nOutputPlane, outputDepth, outputWidth, outputHeight) t += 1 } } - gradInput } - def accGradParametersFrame(gradOutput: Tensor[T], gradWeight: Tensor[T], gradBias: Tensor[T], - fInput: Tensor[T], scaleW: T, scaleB: T): Unit = { + private def im2colWrapper[T]( + input: Tensor[T], + fInput: Tensor[T], kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, + padFront: Int, padLeft: Int, padTop: Int, padBack: Int, padRight: Int, padBottom: Int, + nInputPlane: Int, inputDepth: Int, inputWidth: Int, inputHeight: Int, + nOutputPlane: Int, outputDepth: Int, outputWidth: Int, outputHeight: Int) + (implicit ev: TensorNumeric[T]): Unit = { + ev.getType() match { + case DoubleType => + NNPrimitive.unfoldedCopyVolDouble(fInput.asInstanceOf[Tensor[Double]], + input.asInstanceOf[Tensor[Double]], kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, + nInputPlane, + inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) + case FloatType => + NNPrimitive.unfoldedCopyVolFloat(fInput.asInstanceOf[Tensor[Float]], + input.asInstanceOf[Tensor[Float]], kT, kW, kH, dT, dW, dH, + padFront, padLeft, padTop, padBack, padRight, padBottom, + nInputPlane, + inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) + } + } + + private[bigdl] def conv3DBackpropFilter[T](input: Tensor[T], + gradOutput: Tensor[T], + gradWeightMM: Tensor[T], + gradBias: Tensor[T], + fInput: Tensor[T], + scaleW: Double, scaleB: Double, + withBias: Boolean) + (implicit ev: TensorNumeric[T]): Unit = { + + if (input.dim() == 4) { + accGradParametersFrame(gradOutput, gradWeightMM, gradBias, fInput, + ev.fromType[Double](scaleW), ev.fromType[Double](scaleB), withBias) + } else { + // batch mode + var t = 1 + while (t <= input.size(1)) { + val gradOutputT = gradOutput.select(1, t) + val fInputT = fInput.select(1, t) + accGradParametersFrame(gradOutputT, gradWeightMM, gradBias, fInputT, + ev.fromType[Double](scaleW), ev.fromType[Double](scaleB), withBias) + t += 1 + } + } + } + + private def accGradParametersFrame[T]( + gradOutput: Tensor[T], gradWeight: Tensor[T], gradBias: Tensor[T], + fInput: Tensor[T], scaleW: T, scaleB: T, withBias: Boolean) + (implicit ev: TensorNumeric[T]): Unit = { val gradOutput2d = gradOutput.view(gradOutput.size(1), gradOutput.size(2) * gradOutput.size(3) * gradOutput.size(4)) val fInputT = fInput.transpose(1, 2) @@ -355,49 +570,4 @@ class VolumetricConvolution[T: ClassTag]( } } - override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { - require(gradOutput.isContiguous(), "gradOutput should be contiguous") - if (gradWeightMM == null || gradWeightMM.storage().isEmpty) { - gradWeightMM = gradWeight.view(nOutputPlane, nInputPlane * kT * kH * kW) - } - if (input.dim() == 4) { - accGradParametersFrame(gradOutput, gradWeightMM, gradBias, fInput, - ev.fromType[Double](scaleW), ev.fromType[Double](scaleB)) - } else { - // batch mode - var t = 1 - while (t <= input.size(1)) { - val gradOutputT = gradOutput.select(1, t) - val fInputT = fInput.select(1, t) - accGradParametersFrame(gradOutputT, gradWeightMM, gradBias, fInputT, - ev.fromType[Double](scaleW), ev.fromType[Double](scaleB)) - t += 1 - } - } - if (null != wRegularizer) { - wRegularizer.accRegularization(weight, gradWeight, scaleW) - } - if (withBias && null != bRegularizer) { - bRegularizer.accRegularization(bias, gradBias, scaleB) - } - } - - override def toString: String = { - s"nn.VolumetricConvolution($nInputPlane -> $nOutputPlane, $kT x $kW x" + - s" $kH, $dT, $dW, $dH, $padT, $padW, $padH)" - } -} - -object VolumetricConvolution { - def apply[@specialized(Float, Double) T: ClassTag]( - nInputPlane: Int, nOutputPlane: Int, - kT: Int, kW: Int, kH: Int, - dT: Int = 1, dW: Int = 1, dH: Int = 1, - padT: Int = 0, padW: Int = 0, padH: Int = 0, withBias: Boolean = true, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null - )(implicit ev: TensorNumeric[T]): VolumetricConvolution[T] = { - new VolumetricConvolution[T](nInputPlane, nOutputPlane, kT, kW, kH, - dT, dW, dH, padT, padW, padH, withBias, wRegularizer, bRegularizer) - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3D.scala new file mode 100644 index 00000000000..6dfdad1194b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3D.scala @@ -0,0 +1,93 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.VolumetricConvolution +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Conv3D[T: ClassTag]( + dT: Int, dH: Int, dW: Int, + padT: Int, padH: Int, padW: Int, + format: DataFormat) + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private val fInput = Tensor[T]() + + + override def updateOutput(inputs: Table): Tensor[T] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + val filter: Tensor[T] = inputs[Tensor[T]](2) + + val kT = filter.size(1) + val kH = filter.size(2) + val kW = filter.size(3) + val nInputPlane = filter.size(4) + val nOutputPlane = filter.size(5) + + val transInput = if (format == DataFormat.NHWC) { + var buffer = input + buffer = buffer.transpose(2, 5) + buffer = buffer.transpose(3, 5) + buffer = buffer.transpose(4, 5) + buffer = buffer.contiguous() + + buffer + } else { + input + } + + var transWeight = filter.transpose(1, 5) + transWeight = transWeight.transpose(2, 4) + transWeight = transWeight.transpose(3, 5) + transWeight = transWeight.contiguous() + val weightMM = transWeight.view(nOutputPlane, nInputPlane * kT * kH * kW) + + VolumetricConvolution.conv3d(transInput, output, weightMM, bias = null, onesBias = null, fInput, + nInputPlane, nOutputPlane, withBias = false, kT, kW, kH, dT, dW, dH, padT, padW, padH) + + if (format == DataFormat.NHWC) { + output = output.transpose(2, 5) + output = output.transpose(2, 4) + output = output.transpose(2, 3) + output = output.contiguous() + } + output + } + + override def clearState(): Conv3D.this.type = { + super.clearState() + fInput.set() + this + } +} + +object Conv3D { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3D[T] + = new Conv3D[T](dT, dH, dW, padT, padH, padW, format) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilter.scala new file mode 100644 index 00000000000..b72683f016a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilter.scala @@ -0,0 +1,102 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.VariableFormat.OUT_IN_KT_KH_KW +import com.intel.analytics.bigdl.nn.VolumetricConvolution +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Conv3DBackpropFilter[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private val fInput = Tensor[T]() + + + protected def getParams(inputs: Table): (Int, Int, Int, Int, Int) = { + val filter: Tensor[T] = inputs[Tensor[T]](2) + + val kT = filter.size(1) + val kH = filter.size(2) + val kW = filter.size(3) + val nInputPlane = filter.size(4) + val nOutputPlane = filter.size(5) + + (kT, kH, kW, nInputPlane, nOutputPlane) + } + override def updateOutput(inputs: Table): Tensor[T] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + val outputBackprop: Tensor[T] = inputs[Tensor[T]](3) + + val (transInput, transOutBackprop) = if (format == DataFormat.NHWC) { + // backpropInput only use input size, so we do not need it to be contiguous + val in = input.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() + val out = outputBackprop.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() + (in, out) + } else { + (input, outputBackprop) + } + + val (kT, kH, kW, nInputPlane, nOutputPlane) = getParams(inputs) + + val gradWeightMM = Tensor[T](nOutputPlane, nInputPlane * kT * kH * kW) + + VolumetricConvolution.populateFInput(transInput, fInput, nInputPlane, nOutputPlane, + kT, kW, kH, dT, dW, dH, padT, padW, padH) + + VolumetricConvolution.conv3DBackpropFilter(transInput, transOutBackprop, gradWeightMM, + null, fInput, 1.0, 1.0, false) + + output = if (format == DataFormat.NHWC) { + val gradWeight = gradWeightMM.view(nOutputPlane, nInputPlane, kT, kH, kW) + gradWeight.transpose(1, 5).transpose(2, 4).transpose(1, 3).contiguous() + } else { + gradWeightMM.view(nOutputPlane, nInputPlane, kT, kH, kW) + } + + output + } + + override def clearState(): Conv3DBackpropFilter.this.type = { + super.clearState() + fInput.set() + this + } +} + +object Conv3DBackpropFilter { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3DBackpropFilter[T] + = new Conv3DBackpropFilter[T](dT, dH, dW, padT, padH, padW, format) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2.scala new file mode 100644 index 00000000000..68b2a6decf2 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Conv3DBackpropFilterV2[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat) + (implicit ev: TensorNumeric[T]) + extends Conv3DBackpropFilter[T](dT, dH, dW, padT, padH, padW, format) { + + override protected def getParams(inputs: Table): (Int, Int, Int, Int, Int) = { + val filterSize: Tensor[Int] = inputs[Tensor[Int]](2) + + val kT = filterSize.valueAt(1) + val kH = filterSize.valueAt(2) + val kW = filterSize.valueAt(3) + val nInputPlane = filterSize.valueAt(4) + val nOutputPlane = filterSize.valueAt(5) + + (kT, kH, kW, nInputPlane, nOutputPlane) + } +} + +object Conv3DBackpropFilterV2 { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3DBackpropFilterV2[T] + = new Conv3DBackpropFilterV2[T](dT, dH, dW, padT, padH, padW, format) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInput.scala new file mode 100644 index 00000000000..bcf125397a9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInput.scala @@ -0,0 +1,114 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.VolumetricConvolution +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Conv3DBackpropInput[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private val fGradInput = Tensor[T]() + + protected def getInputSize(inputs: Table): Array[Int] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + + if (format == DataFormat.NHWC) { + val N = input.size(1) + val D = input.size(2) + val H = input.size(3) + val W = input.size(4) + val C = input.size(5) + Array(N, C, D, H, W) + } else { + val N = input.size(1) + val C = input.size(2) + val D = input.size(3) + val H = input.size(4) + val W = input.size(5) + Array(N, C, D, H, W) + } + } + + override def updateOutput(inputs: Table): Tensor[T] = { + + val filter: Tensor[T] = inputs[Tensor[T]](2) + val outputBackprop: Tensor[T] = inputs[Tensor[T]](3) + + val transOutBackprop = if (format == DataFormat.NHWC) { + // backpropInput only use input size, so we do not need it to be contiguous + outputBackprop.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() + } else { + outputBackprop + } + + val transInputSize = getInputSize(inputs) + + val kT = filter.size(1) + val kH = filter.size(2) + val kW = filter.size(3) + val nInputPlane = filter.size(4) + val nOutputPlane = filter.size(5) + + var transWeight = filter.transpose(1, 5) + transWeight = transWeight.transpose(2, 4) + transWeight = transWeight.transpose(3, 5) + transWeight = transWeight.contiguous() + val weightMM = transWeight.view(nOutputPlane, nInputPlane * kT * kH * kW) + + VolumetricConvolution.conv3DBackpropInput(transInputSize, output, transOutBackprop, + weightMM, fGradInput, kT, kW, kH, dT, dW, dH, padT, padW, padH) + + if (format == DataFormat.NHWC) { + output = output.transpose(2, 5) + output = output.transpose(2, 3) + output = output.transpose(3, 4) + output = output.contiguous() + } + output + } + + override def clearState(): Conv3DBackpropInput.this.type = { + super.clearState() + fGradInput.set() + this + } +} + +object Conv3DBackpropInput { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3DBackpropInput[T] + = new Conv3DBackpropInput[T](dT, dH, dW, padT, padH, padW, format) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2.scala new file mode 100644 index 00000000000..17b919e3026 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2.scala @@ -0,0 +1,72 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.VolumetricConvolution +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +class Conv3DBackpropInputV2[T: ClassTag](dT: Int, dH: Int, dW: Int, + padT: Int, padH: Int, padW: Int, + format: DataFormat) + (implicit ev: TensorNumeric[T]) + extends Conv3DBackpropInput[T](dT, dH, dW, padT, padH, padW, format) { + + private val fGradInput = Tensor[T]() + + override protected def getInputSize(inputs: Table): Array[Int] = { + val inputSize: Tensor[Int] = inputs[Tensor[Int]](1) + + if (format == DataFormat.NHWC) { + val N = inputSize.valueAt(1) + val D = inputSize.valueAt(2) + val H = inputSize.valueAt(3) + val W = inputSize.valueAt(4) + val C = inputSize.valueAt(5) + Array(N, C, D, H, W) + } else { + val N = inputSize.valueAt(1) + val C = inputSize.valueAt(2) + val D = inputSize.valueAt(3) + val H = inputSize.valueAt(4) + val W = inputSize.valueAt(5) + Array(N, C, D, H, W) + } + } + + override def clearState(): Conv3DBackpropInputV2.this.type = { + super.clearState() + fGradInput.set() + this + } +} + +object Conv3DBackpropInputV2 { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3DBackpropInputV2[T] + = new Conv3DBackpropInputV2[T](dT, dH, dW, padT, padH, padW, format) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala new file mode 100644 index 00000000000..edcb6ae3261 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.{Conv2D, Conv3D} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Node +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Conv3D extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pT, pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1, -1) + } else { + (0, 0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val conv = format match { + case "NDHWC" => + require(strideList(4) == 1, s"not support strides on depth") + val dT = strideList(1) + val dW = strideList(2) + val dH = strideList(3) + + Conv3D[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + case "NCDHW" => + require(strideList(1) == 1, s"not support strides on depth") + val dT = strideList(2) + val dW = strideList(3) + val dH = strideList(4) + Conv3D[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala new file mode 100644 index 00000000000..cffefcb1c56 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.Conv3DBackpropFilter +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Conv3DBackpropFilter extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pT, pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1, -1) + } else { + (0, 0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + require(strideList(4) == 1, s"not support strides on depth") + val dT = strideList(1) + val dW = strideList(2) + val dH = strideList(3) + Conv3DBackpropFilter[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala new file mode 100644 index 00000000000..48ace2b70ab --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.Conv3DBackpropFilterV2 +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Conv3DBackpropFilterV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pT, pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1, -1) + } else { + (0, 0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val conv = format match { + case "NDHWC" => + require(strideList(4) == 1, s"not support strides on depth") + val dT = strideList(1) + val dW = strideList(2) + val dH = strideList(3) + Conv3DBackpropFilterV2[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + case "NCDHW" => + require(strideList(1) == 1, s"not support strides on depth") + val dT = strideList(2) + val dW = strideList(3) + val dH = strideList(4) + Conv3DBackpropFilterV2[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala new file mode 100644 index 00000000000..7b39b52da34 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.Conv3DBackpropInput +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Conv3DBackpropInput extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pT, pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1, -1) + } else { + (0, 0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + require(strideList(4) == 1, s"not support strides on depth") + val dT = strideList(1) + val dW = strideList(2) + val dH = strideList(3) + Conv3DBackpropInput[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala new file mode 100644 index 00000000000..aa0f3ddeb8b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.Conv3DBackpropInputV2 +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class Conv3DBackpropInputV2 extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attributes = nodeDef.getAttrMap + val (pT, pW, pH) = + if (getString(attributes, "padding") == "SAME") { + (-1, -1, -1) + } else { + (0, 0, 0) + } + val strideList = getIntList(attributes, "strides") + require(strideList.head == 1, s"not support strides on batch") + + val format = getString(attributes, "data_format") + val conv = format match { + case "NDHWC" => + require(strideList(4) == 1, s"not support strides on depth") + val dT = strideList(1) + val dW = strideList(2) + val dH = strideList(3) + Conv3DBackpropInputV2[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + case "NCDHW" => + require(strideList(1) == 1, s"not support strides on depth") + val dT = strideList(2) + val dW = strideList(3) + val dH = strideList(4) + Conv3DBackpropInputV2[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) + case _ => + throw new IllegalArgumentException(s"not supported data format: $format") + } + conv.asInstanceOf[AbstractModule[Activity, Activity, T]] + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 4b08fbd47a0..9f0a2dca284 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{DenseToSparse, _} import com.intel.analytics.bigdl.optim.L2Regularizer @@ -2277,6 +2277,49 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(module, input) } + "Conv3D serializer" should "work properly" in { + val module = Conv3D[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + runSerializationTest(module, T(input, filter)) + } + + "Conv3DBackpropFilter serializer" should "work properly" in { + val module = Conv3DBackpropFilter[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4) + + runSerializationTest(module, T(input, filter, outputBackprop)) + } + + "Conv3DBackpropInput serializer" should "work properly" in { + val module = Conv3DBackpropInput[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(input, filter, outputBackprop)) + } + + "Conv3DBackpropFilterV2 serializer" should "work properly" in { + val module = Conv3DBackpropFilterV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Int](Array(2, 3, 4, 3, 4), Array(5)) + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(input, filter, outputBackprop)) + } + + "Conv3DBackpropInputV2 serializer" should "work properly" in { + val module = Conv3DBackpropInputV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val inputSize = Tensor[Int](Array(4, 20, 30, 40, 3), Array(5)) + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(inputSize, filter, outputBackprop)) + } + "DetectionOutputSSD serializer" should "work properly" in { val module = DetectionOutputSSD[Float](DetectionOutputParam()).setName("DetectionOutputSSD") val name = module.getName diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterSpec.scala new file mode 100644 index 00000000000..d8417d4a342 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterSpec.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class Conv3DBackpropFilterSpec extends TensorflowSpecHelper { + + "Conv3DBackpropFilter forward with VALID padding" should "be correct" in { + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DBackpropFilterTest") + .setOp("Conv3DBackpropFilter") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value) + + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + // the output in this case is typical the scale of thousands, + // so it is ok to have 1e-2 absolute error tolerance + compare[Float]( + builder, + Seq(input, filter, outputBackprop), + 0, + 1e-2 + ) + } + + "Conv3DBackpropFilter forward with SAME padding" should "be correct" in { + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DBackpropFilterTest") + .setOp("Conv3DBackpropFilter") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value) + + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 20, 15, 14, 4).rand() + + // the output in this case is typical the scale of thousands, + // so it is ok to have 1e-2 absolute error tolerance + compare[Float]( + builder, + Seq(input, filter, outputBackprop), + 0, + 1e-2 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2Spec.scala new file mode 100644 index 00000000000..659d99b7352 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2Spec.scala @@ -0,0 +1,81 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.charset.Charset + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowSpecHelper} +import org.tensorflow.framework.{AttrValue, DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class Conv3DBackpropFilterV2Spec extends TensorflowSpecHelper { + + "Conv3DBackpropFilter forward with VALID padding" should "be correct" in { + + val dataFormat = AttrValue.newBuilder().setS(ByteString + .copyFrom("NDHWC", Charset.defaultCharset())).build() + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DBackpropFilterV2Test") + .setOp("Conv3DBackpropFilterV2") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", dataFormat) + + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Int](Array(2, 3, 4, 3, 4), Array(5)) + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + // the output in this case is typical the scale of thousands, + // so it is ok to have 1e-2 absolute error tolerance + compare[Float]( + builder, + Seq(input, filter, outputBackprop), + 0, + 1e-2 + ) + } + + "Conv3DBackpropFilter forward with SAME padding" should "be correct" in { + + val dataFormat = AttrValue.newBuilder().setS(ByteString + .copyFrom("NDHWC", Charset.defaultCharset())).build() + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DBackpropFilterV2Test") + .setOp("Conv3DBackpropFilterV2") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value) + .putAttr("data_format", dataFormat) + + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Int](Array(2, 3, 4, 3, 4), Array(5)) + val outputBackprop = Tensor[Float](4, 20, 15, 14, 4).rand() + + // the output in this case is typical the scale of thousands, + // so it is ok to have 1e-2 absolute error tolerance + compare[Float]( + builder, + Seq(input, filter, outputBackprop), + 0, + 1e-2 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputSpec.scala new file mode 100644 index 00000000000..0d9bbbc3b64 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputSpec.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class Conv3DBackpropInputSpec extends TensorflowSpecHelper { + + "Conv3DBackpropInput forward with VALID padding" should "be correct" in { + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DBackpropInputTest") + .setOp("Conv3DBackpropInput") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value) + + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + compare[Float]( + builder, + Seq(input, filter, outputBackprop), + 0, + 1e-4 + ) + } + + "Conv3DBackpropInput forward with SAME padding" should "be correct" in { + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DBackpropInputTest") + .setOp("Conv3DBackpropInput") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value) + + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 20, 15, 14, 4).rand() + + compare[Float]( + builder, + Seq(input, filter, outputBackprop), + 0, + 1e-4 + ) + } + + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2Spec.scala new file mode 100644 index 00000000000..7ae55a4f8d0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2Spec.scala @@ -0,0 +1,79 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.charset.Charset + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowSpecHelper} +import org.tensorflow.framework.{AttrValue, DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class Conv3DBackpropInputV2Spec extends TensorflowSpecHelper { + + "Conv3DBackpropInputV2 forward with VALID padding" should "be correct" in { + + val dataFormat = AttrValue.newBuilder().setS(ByteString + .copyFrom("NDHWC", Charset.defaultCharset())).build() + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DBackpropInputV2Test") + .setOp("Conv3DBackpropInputV2") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", dataFormat) + + val inputSize = Tensor[Int](Array(4, 20, 30, 40, 3), Array(5)) + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + compare[Float]( + builder, + Seq(inputSize, filter, outputBackprop), + 0, + 1e-4 + ) + } + + "Conv3DBackpropInputV2 forward with SAME padding" should "be correct" in { + + val dataFormat = AttrValue.newBuilder().setS(ByteString + .copyFrom("NDHWC", Charset.defaultCharset())).build() + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DBackpropInputV2Test") + .setOp("Conv3DBackpropInputV2") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value) + .putAttr("data_format", dataFormat) + + val inputSize = Tensor[Int](Array(4, 20, 30, 40, 3), Array(5)) + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 20, 15, 14, 4).rand() + + compare[Float]( + builder, + Seq(inputSize, filter, outputBackprop), + 0, + 1e-4 + ) + } + + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DSpec.scala new file mode 100644 index 00000000000..e739ad5f848 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DSpec.scala @@ -0,0 +1,75 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.charset.Charset + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowSpecHelper} +import org.tensorflow.framework.{AttrValue, DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class Conv3DSpec extends TensorflowSpecHelper { + + "Conv3D forward with VALID padding" should "be correct" in { + + val dataFormat = AttrValue.newBuilder().setS(ByteString + .copyFrom("NDHWC", Charset.defaultCharset())).build() + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DTest") + .setOp("Conv3D") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("data_format", dataFormat) + + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + + compare[Float]( + builder, + Seq(input, filter), + 0, + 1e-4 + ) + } + + "Conv3D forward with SAME padding" should "be correct" in { + + val dataFormat = AttrValue.newBuilder().setS(ByteString + .copyFrom("NDHWC", Charset.defaultCharset())).build() + + val builder = NodeDef.newBuilder() + .setName(s"Conv3DTest") + .setOp("Conv3D") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("strides", listIntAttr(Seq(1, 1, 2, 3, 1))) + .putAttr("padding", PaddingType.PADDING_SAME.value) + .putAttr("data_format", dataFormat) + + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + + compare[Float]( + builder, + Seq(input, filter), + 0, + 1e-4 + ) + } +} From 4c5c3b36b414ca7743147efb258bc6101d47ece8 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Thu, 14 Dec 2017 11:01:35 +0800 Subject: [PATCH 0585/1065] add tensorflow operation SegmentSum (#1956) --- .../bigdl/dllib/nn/ops/SegmentSum.scala | 55 +++++++++++++++++++ .../dllib/utils/tf/loaders/SegmentSum.scala | 33 +++++++++++ .../serializer/ModuleSerializerSpec.scala | 9 ++- .../utils/tf/loaders/SegmentSumSpec.scala | 38 +++++++++++++ 4 files changed, 134 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SegmentSum.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSum.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSumSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SegmentSum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SegmentSum.scala new file mode 100644 index 00000000000..d38cfa897b0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SegmentSum.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Computes the sum along segments of a tensor. + */ +class SegmentSum[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[T], T]{ + + def updateOutput(inputs: Table): Tensor[T] = { + val x = inputs[Tensor[T]](1) + val y = inputs[Tensor[Int]](2) // zero-indices + require(y.nDimension() == 1, "segment ids should be 1D tensor") + require(y.size(1) == x.size(1), "segment ids should be the same size as" + + s" first dimension of input, excepted ${x.size(1)}, but got ${y.size(1)}") + val newSize = x.size() + newSize(0) = y.valueAt(y.nElement()) + 1 + output.resize(newSize).zero() + + var i = 0 + while(i < y.nElement()) { + output.select(1, y.valueAt(i + 1) + 1).add(x.select(1, i + 1)) + i += 1 + } + + output + } + +} + +object SegmentSum { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): SegmentSum[T] = { + new SegmentSum() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSum.scala new file mode 100644 index 00000000000..349c92d48a3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSum.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.SegmentSum +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class SegmentSum extends TensorflowOpsLoader { + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + SegmentSum[T]() + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 9f0a2dca284..57972e4e87d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{DenseToSparse, _} import com.intel.analytics.bigdl.optim.L2Regularizer @@ -1949,6 +1949,13 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(rsqrtGrad, input) } + "SegmentSum serializer" should "work properly" in { + val sgSum = SegmentSum[Float].setName("segmentSum") + val input = T(Tensor[Float](10, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 0, 0, 1, 2, 3, 3, 4, 4, 4))) + runSerializationTest(sgSum, input) + } + "SelectOps serializer" should "work properly" in { val select = SelectOps[Float]().setName("select") val cond = Tensor.scalar[Boolean](true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSumSpec.scala new file mode 100644 index 00000000000..6f515f39c17 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSumSpec.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.{PaddingType, TensorflowDataFormat, TensorflowSpecHelper} +import org.tensorflow.framework.{DataType, NodeDef} +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ + +class SegmentSumSpec extends TensorflowSpecHelper { + + "SegmentSum forward" should "work correctly" in { + + compare[Float]( + NodeDef.newBuilder() + .setName("segment_sum") + .setOp("SegmentSum") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Tindices", typeAttr(DataType.DT_INT32)), + Seq(Tensor[Float](10, 20).rand(), Tensor[Int](T(0, 0, 1, 1, 1, 2, 2, 2, 3, 4))), + 0 + ) + } +} From d5d2c8feb39277ee68cfab25bd58737c213933de Mon Sep 17 00:00:00 2001 From: Xianyan Date: Thu, 14 Dec 2017 12:47:11 +0800 Subject: [PATCH 0586/1065] revert dl pom (#2030) --- dl/pom.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dl/pom.xml b/dl/pom.xml index cd1179ea9c3..4af7aa3cc42 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -9,7 +9,7 @@ 4.0.0 - bigdl-SPARK_2.1 + bigdl jar From e7041f124171cb5491464fa89004f8008dabcdc3 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 14 Dec 2017 12:47:29 +0800 Subject: [PATCH 0587/1065] set random seed on loaders spec (#2029) * set random seed on loaders spec * meet code review --- .../bigdl/dllib/utils/tf/TensorflowSpecHelper.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/DigammaSpec.scala | 9 +++++++-- .../bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala | 7 +++++++ 3 files changed, 15 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala index 4fc8922a92f..f7767f26a79 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.nn.Module import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} -import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, FileWriter, T} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, FileWriter, RandomGenerator, T} import com.intel.analytics.bigdl.utils.tf.Tensorflow.const import org.tensorflow.framework.{GraphDef, NodeDef} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DigammaSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DigammaSpec.scala index 0c31deac5d5..cd4d9286b2f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DigammaSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DigammaSpec.scala @@ -15,14 +15,19 @@ */ package com.intel.analytics.bigdl.utils.tf.loaders import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator class DigammaSpec extends UnaryOpBaseSpec { + + override def doBefore(): Unit = { + super.doBefore() + RandomGenerator.RNG.setSeed(1L) + } + override def getOpName: String = "Digamma" override def getInput: Tensor[_] = Tensor[Float](4, 32, 32, 3).rand() override def getDelta: Double = 1e-3 - - } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala index 921823206b7..5ec9746d95f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2Spec.scala @@ -16,11 +16,18 @@ package com.intel.analytics.bigdl.utils.tf.loaders import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator import com.intel.analytics.bigdl.utils.tf.Tensorflow.{booleanAttr, intAttr, typeAttr} import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper import org.tensorflow.framework.{DataType, NodeDef} class TopKV2Spec extends TensorflowSpecHelper { + + override def doBefore(): Unit = { + super.doBefore() + RandomGenerator.RNG.setSeed(1L) + } + "TopKV2" should "be correct for float tensor" in { compare[Float]( NodeDef.newBuilder() From 5c32ccd07ab4a44413ae1ef00e1f39ab917f76d0 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 14 Dec 2017 14:09:49 +0800 Subject: [PATCH 0588/1065] Fix 1923: Use static graph execution when there's no control operation in graph container (#2008) * checkin dynamic graph and static graph * fix compile error * refine graph * meet code review * fix serialize/load test * fix failed unit test * add unit test for dynamic graph * merge duplicate code in graph * fix generateback issue * fix python test * fix style issue * meet code review * use array to cache output instead of hashmap * meet code review * fix a confused part mentioned in code review --- .../dllib/models/inception/Inception_v1.scala | 6 +- .../dllib/models/vgg/VggForCifar10.scala | 9 +- .../bigdl/dllib/nn/DynamicGraph.scala | 132 ++ .../analytics/bigdl/dllib/nn/Graph.scala | 585 ++++---- .../bigdl/dllib/nn/StaticGraph.scala | 137 ++ .../bigdl/dllib/nn/quantized/Quantizer.scala | 2 +- .../analytics/bigdl/dllib/utils/Util.scala | 26 + .../utils/serializer/ModuleSerializer.scala | 3 +- .../dllib/utils/tf/TensorflowLoader.scala | 3 +- .../dllib/models/DynamicTestModels.scala | 273 ++++ .../bigdl/dllib/nn/DynamicGraphSpec.scala | 1278 +++++++++++++++++ .../analytics/bigdl/dllib/nn/GraphSpec.scala | 37 +- .../bigdl/dllib/utils/UtilSpec.scala | 26 + .../serializer/ModuleSerializerSpec.scala | 12 + 14 files changed, 2165 insertions(+), 364 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/DynamicTestModels.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/UtilSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala index e7ac549aaf6..651efbdbdda 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala @@ -131,7 +131,8 @@ object Inception_v1_NoAuxClassifier { model } - def graph(classNum: Int, hasDropout: Boolean = true): Module[Float] = { + def graph(classNum: Int, hasDropout: Boolean = true) + : Module[Float] = { val input = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) .setInitMethod(weightInitMethod = Xavier, Zeros).setName("conv1/7x7_s2").inputs() val conv1_relu = ReLU(true).setName("conv1/relu_7x7").inputs(input) @@ -173,8 +174,7 @@ object Inception_v1_NoAuxClassifier { .setName("loss3/classifier").inputs(view) val loss = LogSoftMax().setName("loss3/loss3").inputs(classifier) - val model = Graph(input, loss) - model + Graph(input, loss) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/VggForCifar10.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/VggForCifar10.scala index a36cc1dafba..d2c3542aa0e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/VggForCifar10.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/VggForCifar10.scala @@ -76,7 +76,8 @@ object VggForCifar10 { vggBnDo } - def graph(classNum: Int, hasDropout: Boolean = true): Module[Float] = { + def graph(classNum: Int, hasDropout: Boolean = true) + : Module[Float] = { val input = Input() def convBNReLU(nInputPlane: Int, nOutPutPlane: Int)(input: ModuleNode[Float]) : ModuleNode[Float] = { @@ -179,7 +180,8 @@ object Vgg_16 { model } - def graph(classNum: Int, hasDropout: Boolean = true): Module[Float] = { + def graph(classNum: Int, hasDropout: Boolean = true) + : Module[Float] = { val conv1 = SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1).inputs() val relu1 = ReLU(true).inputs(conv1) val conv2 = SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1).inputs(relu1) @@ -288,7 +290,8 @@ object Vgg_19 { model } - def graph(classNum: Int, hasDropout: Boolean = true): Module[Float] = { + def graph(classNum: Int, hasDropout: Boolean = true) + : Module[Float] = { val conv1 = SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1).inputs() val relu1 = ReLU(true).inputs(conv1) val conv2 = SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1).inputs(relu1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala new file mode 100644 index 00000000000..ebf077b49e9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala @@ -0,0 +1,132 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.ops.ControlOps +import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.T + +import scala.collection.mutable +import scala.reflect.ClassTag + +class DynamicGraph[T: ClassTag]( + private val _inputs : Seq[ModuleNode[T]], + private val _outputs : Seq[ModuleNode[T]], + private val _variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, + val generateBackward: Boolean = true +)(implicit ev: TensorNumeric[T]) extends Graph[T](_inputs, _outputs, _variables) { + private val forwardScheduler = new Scheduler( + forwardNodes.filter(_.prevNodes.length == 0), + Seq(dummyOutput), + forwardNodes.map(_.element.getName()).toSet + ) + private var backwardScheduler : Scheduler[T] = _ + private val inputCache = new mutable.HashMap[String, Activity]() + private val gradOutputCache = new mutable.HashMap[String, Activity]() + + buildBackwardGraph() + + override def updateOutput(input: Activity): Activity = { + forwardScheduler.reset() + while (!forwardScheduler.isFinished()) { + val node = forwardScheduler.fetch() + val nodeInput = findInput(node, input) + inputCache(node.element.getName()) = nodeInput + node.element.forward(nodeInput) + forwardScheduler.schedule(node) + } + + output = dummyOutput.element.output + output + } + + override def backward(input: Activity, gradOutput: Activity): Activity = { + val before = System.nanoTime() + val result = backwardExecution(input, gradOutput, true) + backwardTime = System.nanoTime() - before + result + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + backwardExecution(input, gradOutput, false) + } + + /** + * Generate backward graph and apply the stopGrad + */ + override private[bigdl] def buildBackwardGraph(): this.type = { + if (!generateBackward) return this + + forwardNodes.foreach(n => require(!n.element.isInstanceOf[ControlOps[_]], + "Not suppot generate back graph with control ops node")) + + super.buildBackwardGraph() + val forwardNodeNames = forwardNodes.map(_.element.getName()).toSet + val executableNodes = backwardGraph.DFS.map(_.element.getName()) + .filter(forwardNodeNames.contains(_)).toSet + + val inputNames = inputs.map(_.element.getName()).toSet + val backwardTargets = backwardNodes + .filter(n => (n.element.parameters() != null && n.element.parameters()._1.length != 0) + || inputNames.contains(n.element.getName())) + + backwardScheduler = new Scheduler[T]( + Seq(dummyOutputGrad), + backwardTargets, + executableNodes + ) + clearState() + this + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + var i = 0 + while (i < backwardNodes.length) { + val curNode = backwardNodes(i) + curNode.element.accGradParameters(inputCache(curNode.element.getName()), + gradOutputCache(curNode.element.getName())) + i += 1 + } + } + + private def backwardExecution(input: Activity, gradOutput: Activity, isBackward: Boolean) + : Activity = { + if (!generateBackward) return null + backwardScheduler.reset() + while (!backwardScheduler.isFinished()) { + val curNode = backwardScheduler.fetch() + val curGradOutput = findGradOutput(curNode, gradOutput) + gradOutputCache(curNode.element.getName()) = curGradOutput + if (!isStopGradient(curNode.element)) { + if (isBackward) { + curNode.element.backward(inputCache(curNode.element.getName()), curGradOutput) + } else { + curNode.element.updateGradInput(inputCache(curNode.element.getName()), curGradOutput) + } + } else if (isBackward) { + curNode.element.accGradParameters(inputCache(curNode.element.getName()), curGradOutput) + } + backwardScheduler.schedule(curNode) + } + + gradInput = fetchModelGradInput() + gradInput + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index fb02b3131a4..123a2b3e62a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -17,8 +17,6 @@ package com.intel.analytics.bigdl.nn import java.util -import com.intel.analytics.bigdl.Module - import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} @@ -28,7 +26,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils._ -import com.intel.analytics.bigdl.utils.tf.{BigDLToTensorflow, Tensorflow, TensorflowSaver} +import com.intel.analytics.bigdl.utils.tf.Tensorflow import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable @@ -39,24 +37,24 @@ import com.intel.analytics.bigdl.visualization.tensorboard.{FileWriter => TFFile import org.tensorflow.framework.GraphDef /** - * A graph container. Each node can have multiple inputs. The output of the node should be a tensor. - * The output tensor can be connected to multiple nodes. So the module in each node can have a - * tensor or table input, and should have a tensor output. - * - * The graph container can have multiple inputs and multiple outputs. If there's one input, the - * input data fed to the graph module should be a tensor. If there're multiple inputs, the input - * data fed to the graph module should be a table, which is actually an sequence of tensor. The - * order of the input tensors should be same with the order of the input nodes. This is also - * applied to the gradient from the module in the back propagation. + * A graph container. The modules in the container are connected as a directed Graph. Each module + * can output one tensor or multiple tensors(as table). The edges between modules in the graph + * define how these tensors are passed. For example, if a module outputs two tensors, you can + * pass these two tensors together to its following module, or pass only one of them + * to its following module. If a tensor in the module output is connected to multiple modules, in + * the back propagation, the gradients from multiple connection will be accumulated. If multiple + * edges point to one module, the tensors from these edges will be stack as a table, then pass to + * that module. In the back propagation, the gradients will be splited based on how the input + * tensors stack. * - * All of the input modules must accept a tensor input. If your input module accept multiple - * tensors as input, you should add some Input module before it as input nodes and connect the - * output of the Input modules to that module. + * The graph container has multiple inputs and multiple outputs. The order of the input tensors + * should be same with the order of the input nodes when you construct the graph container. In the + * back propagation, the order of the gradients tensors should be the same with the order of the + * output nodes. * * If there's one output, the module output is a tensor. If there're multiple outputs, the module * output is a table, which is actually an sequence of tensor. The order of the output tensors is - * same with the order of the output modules. This is also applied to the gradient passed to the - * module in the back propagation. + * same with the order of the output modules. * * All inputs should be able to connect to outputs through some paths in the graph. It is * allowed that some successors of the inputs node are not connect to outputs. If so, these nodes @@ -69,107 +67,21 @@ import org.tensorflow.framework.GraphDef * @tparam T Numeric type. Only support float/double now */ @SerialVersionUID(- 2896121321564992779L) -class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], +abstract class Graph[T: ClassTag]( + val inputs : Seq[ModuleNode[T]], private val outputs : Seq[ModuleNode[T]], - private val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, - private val generateBackward: Boolean = true -)(implicit ev: TensorNumeric[T]) - extends Container[Activity, Activity, T]{ - - type absModule = AbstractModule[_ <: Activity, _ <: Activity, T] - - override def updateOutput(input: Activity): Activity = { - forwardScheduler.reset() - while (!forwardScheduler.isFinished()) { - val node = forwardScheduler.fetch() - val nodeInput = if (node.prevNodes.isEmpty && !node.element.isInstanceOf[WithoutInput]) { - inputData(node, input) - } else { - val prevActivities = node.prevNodesAndEdges - .filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) - .map(n => { - n._2.fromIndex match { - case Some(i) => - if (n._1.element.output == null || (i == 1 && n._1.element.output.isTensor)) { - n._1.element.output - } else { - n._1.element.output.toTable.apply[Activity](i) - } - case None => n._1.element.output - } - }) - if (prevActivities.length == 1) { - prevActivities.head - } else { - seqToTable(prevActivities) - } - } - node.element.forward(nodeInput) - inputCache(node.element.getName()) = nodeInput - forwardScheduler.schedule(node) - } - - output = dummyOutput.element.output - output - } - - override def backward(input: Activity, gradOutput: Activity): Activity = { - if (!generateBackward) return null - - val before = System.nanoTime() - backwardScheduler.reset() - while (!backwardScheduler.isFinished()) { - val curNode = backwardScheduler.fetch() - var curGradOutput : Activity = if (curNode.eq(dummyOutputGrad)) gradOutput else null - - curNode.prevNodesAndEdges.filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) - .foreach(n => { - val otherActivity = if (n._1.element.gradInput.isTensor || n._1.nextEdges.length == 1) { - n._1.element.gradInput - } else { - val index = n._1.nextEdges.indexOf(n._2) + 1 - n._1.element.gradInput.toTable.apply[Activity](index) - } - - n._2.fromIndex match { - case Some(i) => - if (i == 1 && curNode.element.output.isTensor) { - curGradOutput = accActivity(curGradOutput, otherActivity) - } else { - if (curNode.element.output.isTable && curGradOutput == null) { - curGradOutput = T() - } - val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) - curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) - } - case None => - curGradOutput = accActivity(curGradOutput, otherActivity) - } - }) - - if (curNode.element.output.isTable) { - addZeroTensorToMissingGradOutput(curNode.element.output.toTable, curGradOutput.toTable) - } - - gradOutputCache(curNode.element.getName()) = curGradOutput - if (!isStopGradient(curNode.element)) { - curNode.element.backward(inputCache(curNode.element.getName()), curGradOutput) - } else { - curNode.element.accGradParameters(inputCache(curNode.element.getName()), curGradOutput) - } - backwardScheduler.schedule(curNode) - } + private val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None +)(implicit ev: TensorNumeric[T]) extends Container[Activity, Activity, T]{ - gradInput = if (inputs.length == 1) { - inputs.head.element.gradInput - } else { - seqToTable(inputs.map(n => n.element.gradInput)) - } - backwardTime = System.nanoTime() - before - gradInput - } - - private def addZeroTensorToMissingGradOutput(output: Table, gradOutput: Table): Unit = { + /** + * For a multi-tensor output module, some output tensors may not contributed to the final forward + * result. So in the back propagation, the gradient on these positions are missing. And we use + * zero tensor to populate. + * + * @param output + * @param gradOutput + */ + protected def addZeroTensorToMissingGradOutput(output: Table, gradOutput: Table): Unit = { var i = 0 while (i < output.length()) { if (!gradOutput.contains(i + 1)) { @@ -181,7 +93,8 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } } - private def calcSumTimesOfAllNodes(timesOfAllNodes: Array[(absModule, Long, Long)]) + private def calcSumTimesOfAllNodes( + timesOfAllNodes: Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)]) : (Long, Long) = { var sumForward = 0L var sumBackward = 0L @@ -200,71 +113,9 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } override def resetTimes(): Unit = { + super.resetTimes() this.forwardTime = 0L this.backwardTime = 0L - modules.foreach(_.resetTimes()) - } - - override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { - if (!generateBackward) return null - - backwardScheduler.reset() - while (!backwardScheduler.isFinished()) { - val curNode = backwardScheduler.fetch() - var curGradOutput : Activity = if (curNode.eq(dummyOutputGrad)) gradOutput else null - - curNode.prevNodesAndEdges.filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) - .foreach(n => { - val otherActivity = if (n._1.element.gradInput.isTensor || n._1.nextEdges.length == 1) { - n._1.element.gradInput - } else { - val index = n._1.nextEdges.indexOf(n._2) + 1 - n._1.element.gradInput.toTable.apply[Activity](index) - } - - n._2.fromIndex match { - case Some(i) => - if (i == 1 && curNode.element.output.isTensor) { - curGradOutput = accActivity(curGradOutput, otherActivity) - } else { - if (curNode.element.output.isTable && curGradOutput == null) { - curGradOutput = T() - } - val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) - curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) - } - case None => - curGradOutput = accActivity(curGradOutput, otherActivity) - } - }) - - if (curNode.element.output.isTable) { - addZeroTensorToMissingGradOutput(curNode.element.output.toTable, curGradOutput.toTable) - } - - gradOutputCache(curNode.element.getName()) = curGradOutput - if (!isStopGradient(curNode.element)) { - curNode.element.updateGradInput(inputCache(curNode.element.getName()), curGradOutput) - } - backwardScheduler.schedule(curNode) - } - - gradInput = if (inputs.length == 1) { - inputs.head.element.gradInput - } else { - seqToTable(inputs.map(n => n.element.gradInput)) - } - gradInput - } - - override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { - var i = 0 - while (i < backwardNodes.length) { - val curNode = backwardNodes(i) - curNode.element.accGradParameters(inputCache(curNode.element.getName()), - gradOutputCache(curNode.element.getName())) - i += 1 - } } override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { @@ -289,7 +140,7 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], * @return */ def node(name: String): ModuleNode[T] = { - val matchNodes = backGraph.BFS.filter(_.element.getName() == name).toArray + val matchNodes = forwardNodes.filter(_.element.getName() == name).toArray if (matchNodes.length == 0) { throw new NoSuchElementException(s"Can not find node with name $name") } else { @@ -297,154 +148,136 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } } - // Add a dummy output node, to get an one end graph. So the nodes that are not dependent by - // the outputs will be excluded - private val dummyOutput = new ModuleNode[T](new Identity[T]()) - // Add a dummy output node for backward graph, - // dummyOutputGrad has the same function as dummyOutput - // used to construct a backward graph - private var dummyOutputGrad: ModuleNode[T] = _ + // Add a dummy output node, to get an one end forward graph. So the nodes that are not dependent + // by the outputs will be excluded + protected val dummyOutput = new ModuleNode[T](new Identity[T]()) outputs.foreach(_ -> dummyOutput) + protected val forwardGraph = dummyOutput.graph(reverse = true) + protected val forwardNodes = forwardGraph.DFS.toArray - /** - * Computing backgraph - */ - private val backGraph = dummyOutput.graph(reverse = true) - private var gradGraph: DirectedGraph[AbstractModule[Activity, Activity, T]] = _ - - /** - * Execution plan - */ - private val forwardNodes = backGraph.DFS.toArray - private val forwardScheduler = new Scheduler( - forwardNodes.filter(_.prevNodes.length == 0), - Seq(dummyOutput), - forwardNodes.map(_.element.getName()).toSet + modules.appendAll( + forwardGraph.topologySort + // todo: convert control dep node to edge + .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse + .filter(n => !n.eq(dummyOutput)).map(_.element) ) - private var backwardScheduler : Scheduler[T] = _ - private var backwardNodes: Array[Node[AbstractModule[Activity, Activity, T]]] = _ + // Check all inputs of the graph should be passed in + checkRoots + + // Check if the graph is correct + private def checkRoots: Unit = { + def duplicatedNames(names: Seq[String]): mutable.Set[String] = { + names.sortWith(_ < _) + val buffer = new mutable.HashSet[String]() + var i = 1 + while(i < names.length) { + if (names(i) == names(i - 1)) buffer.add(names(i)) + i += 1 + } + buffer + } + + require(forwardNodes.map(_.element.getName()).distinct.length == forwardNodes.length, + s"the name of node in the graph should be unique, but find duplicated name " + + s"${duplicatedNames(forwardNodes.map(_.element.getName())).mkString(", ")}") + val roots = forwardNodes.filter(_.prevNodes.size == 0) + .filterNot(_.element.isInstanceOf[WithoutInput]) + .filterNot(_.element.isInstanceOf[ControlDependency[_]]) - modules.appendAll(backGraph.topologySort - .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse - .filter(n => !n.eq(dummyOutput)).map(_.element)) + val realInputs = inputs.filterNot(_.element.isInstanceOf[WithoutInput]) + require(roots.size == realInputs.length, s"There're ${realInputs.length} inputs, " + + s"but graph has ${roots.size} roots") + + realInputs.foreach(n => + require(roots.contains(n), "inputs and graph roots are not match") + ) + } + + protected var dummyOutputGrad: ModuleNode[T] = _ + protected var backwardGraph: DirectedGraph[AbstractModule[Activity, Activity, T]] = _ + protected var backwardNodes: Array[Node[AbstractModule[Activity, Activity, T]]] = _ /** * Generate backward graph and apply the stopGrad */ - private[bigdl] def build(): this.type = { - val gradGraph = backGraph.cloneGraph(true) + private[bigdl] def buildBackwardGraph(): this.type = { + // Clone the forward graph and reverse the edge + val gradGraph = forwardGraph.cloneGraph(reverseEdge = true) dummyOutputGrad = gradGraph.source - val originalNodes = gradGraph.DFS - originalNodes.filter(x => isStopGradient(x.element)).foreach(removeStopNodes(_)) - backwardNodes = gradGraph.DFS.filter(n => !n.eq(dummyOutputGrad)) + gradGraph.DFS.filter(x => isStopGradient(x.element)).foreach(removeStopNodes(_)) + backwardNodes = gradGraph.DFS + .filterNot(_.eq(dummyOutputGrad)) .filterNot(_.element.isInstanceOf[ControlDependency[_]]).toArray val inputNames = inputs.map(_.element.getName()).toSet - val dummyBackwardEnd = Input() + val dummyBackwardEnd = Identity().inputs() val backwardTargets = backwardNodes .filter(n => (n.element.parameters() != null && n.element.parameters()._1.length != 0) || inputNames.contains(n.element.getName())) backwardTargets.foreach(_ -> dummyBackwardEnd) - val graph = dummyBackwardEnd.graph(true) - val forwardNodeNames = forwardNodes.map(_.element.getName()).toSet - val executableNodes = graph.DFS.map(_.element.getName()) - .filter(forwardNodeNames.contains(_)).toSet - dummyBackwardEnd.removePrevEdges() - - backwardScheduler = new Scheduler[T]( - Seq(dummyOutputGrad), - backwardTargets, - executableNodes - ) + backwardGraph = dummyBackwardEnd.graph(true) clearState() this } - private[bigdl] def removeStopNodes(n: Node[_]): Unit = { - val nodes = n.nextNodes - n.removeNextEdges() - nodes.filter(_.prevNodes.length == 0).foreach(removeStopNodes(_)) - } - - - private val inputCache = new mutable.HashMap[String, Activity]() + private var stopGradientLayers: util.HashSet[String] = _ - // Check all inputs of the graph should be passed in - checkRoots - if (generateBackward) { - forwardNodes.foreach(n => require(!n.element.isInstanceOf[ControlOps[_]], - "Not suppot generate back graph with control ops node")) - build() + /** + * whether stop propagating gradInput back + * @return + */ + protected def isStopGradient(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Boolean = { + null != stopGradientLayers && stopGradientLayers.contains(module.getName()) } - private val gradOutputCache = new mutable.HashMap[String, Activity]() + /** + * stop the input gradient of layers that match the given ```names``` + * their input gradient are not computed. + * And they will not contributed to the input gradient computation of + * layers that depend on them. + * @param names an array of layer names + * @return current graph model + */ + def stopGradient(names: Array[String]): this.type = { + if (stopGradientLayers == null) stopGradientLayers = new util.HashSet[String]() - private def duplicatedNames(names: Seq[String]): mutable.Set[String] = { - names.sortWith(_ < _) - val buffer = new mutable.HashSet[String]() - var i = 1 - while(i < names.length) { - if (names(i) == names(i - 1)) buffer.add(names(i)) - i += 1 - } - buffer + names.foreach(name => { + val layer = this (name) + require(layer.isDefined, s"cannot find layer match ${name}") + stopGradientLayers.add(layer.get.getName()) + }) + buildBackwardGraph() + this } - private def checkRoots: Unit = { - require(forwardNodes.map(_.element.getName()).distinct.length == forwardNodes.length, - s"the name of node in the graph should be unique, but find duplicated name " + - s"${duplicatedNames(forwardNodes.map(_.element.getName())).mkString(", ")}") - val roots = forwardNodes.filter(_.prevNodes.size == 0) - .filter(node => !node.element.isInstanceOf[WithoutInput] - && !node.element.isInstanceOf[ControlDependency[_]]) - require(roots.size == inputs.filter(node => !node.element.isInstanceOf[WithoutInput]).length, - s"There're ${inputs.length} inputs, but graph has ${roots.size} roots") - inputs.filter(node => !node.element.isInstanceOf[WithoutInput]).foreach(n => - require(roots.contains(n), "inputs and graph roots are not match") - ) + /** + * set an array of layers that match the given ```names``` to be "freezed", + * i.e. their parameters(weight/bias, if exists) are not changed in training process + * @param names an array of layer names + * @return current graph model + */ + def freeze(names: Array[String]): this.type = { + names.foreach(name => { + val layer = this (name) + require(layer.isDefined, s"cannot find layer match ${name}") + layer.get.setScaleW(0) + layer.get.setScaleB(0) + }) + this } - private[nn] def shift[B](data : Array[B], from : Int, to : Int): Array[B] = { - require(from < data.length && from >= 0, s"invalid from $from array length is ${data.length}") - require(to < data.length && to >= 0, s"invalid to $to array length is ${data.length}") - if (from == to) { - data - } else if (from < to) { - var i = from - while(i < to) { - val tmp = data(i) - data(i) = data(i + 1) - data(i + 1) = tmp - i += 1 - } - data - } else { - var i = from - while(i > to) { - val tmp = data(i) - data(i) = data(i - 1) - data(i - 1) = tmp - i -= 1 - } - data - } + private[bigdl] def removeStopNodes(n: Node[_]): Unit = { + val nodes = n.nextNodes + n.removeNextEdges() + nodes.filter(_.prevNodes.length == 0).foreach(removeStopNodes(_)) } - private def seqToTable(inputs: Seq[Activity]) : Table = { - val t = T() - var j = 1 - inputs.foreach(tensor => { - t(j) = tensor - j += 1 - }) - t - } - private def inputData( - node: Node[AbstractModule[Activity, Activity, T]], - input: Activity + protected def getInput( + node: Node[AbstractModule[Activity, Activity, T]], + input: Activity ): Activity = { if (inputs.length == 1) { require(inputs(0).eq(node), "input node is not in the input list") @@ -456,41 +289,81 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], } } - private var stopGradientLayers: util.HashSet[String] = _ + protected def findInput(node: ModuleNode[T], input: Activity): Activity = { + if (node.element.isInstanceOf[WithoutInput]) return null - /** - * whether stop propagating gradInput back - * @return - */ - private def isStopGradient(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Boolean = { - null != stopGradientLayers && stopGradientLayers.contains(module.getName()) + val nodeInput = if (node.prevNodes.isEmpty) { + getInput(node, input) + } else { + val prevActivities = node.prevNodesAndEdges + .filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) + .map(n => { + n._2.fromIndex match { + case Some(i) => + if (n._1.element.output == null || (i == 1 && n._1.element.output.isTensor)) { + n._1.element.output + } else { + n._1.element.output.toTable.apply[Activity](i) + } + case None => n._1.element.output + } + }) + if (prevActivities.length == 1) { + prevActivities.head + } else { + T.seq(prevActivities) + } + } + nodeInput } - /** - * stop the input gradient of layers that match the given ```names``` - * their input gradient are not computed. - * And they will not contributed to the input gradient computation of - * layers that depend on them. - * @param names an array of layer names - * @return current graph model - */ - def stopGradient(names: Array[String]): this.type = { - names.foreach(name => { - val layer = this (name) - require(layer.isDefined, s"cannot find layer match ${name}") - if (stopGradientLayers == null) stopGradientLayers = - new util.HashSet[String]() - stopGradientLayers.add(layer.get.getName()) - }) - build() - this + protected def findGradOutput(curNode: ModuleNode[T], gradOutput: Activity): Activity = { + var curGradOutput : Activity = if (curNode.eq(dummyOutputGrad)) gradOutput else null + + curNode.prevNodesAndEdges.filterNot(n => n._1.element.isInstanceOf[ControlDependency[T]]) + .foreach(n => { + val otherActivity = if (n._1.element.gradInput.isTensor || n._1.nextEdges.length == 1) { + n._1.element.gradInput + } else { + val index = n._1.nextEdges.indexOf(n._2) + 1 + n._1.element.gradInput.toTable.apply[Activity](index) + } + + n._2.fromIndex match { + case Some(i) => + if (i == 1 && curNode.element.output.isTensor) { + curGradOutput = accActivity(curGradOutput, otherActivity) + } else { + if (curNode.element.output.isTable && curGradOutput == null) { + curGradOutput = T() + } + val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) + curGradOutput.toTable(i) = accActivity(curActivity, otherActivity) + } + case None => + curGradOutput = accActivity(curGradOutput, otherActivity) + } + }) + + if (curNode.element.output.isTable) { + addZeroTensorToMissingGradOutput(curNode.element.output.toTable, curGradOutput.toTable) + } + + curGradOutput } + protected def fetchModelGradInput(): Activity = { + if (inputs.length == 1) { + inputs.head.element.gradInput + } else { + T.seq(inputs.map(n => n.element.gradInput)) + } + } override def reset(): Unit = { if (null != stopGradientLayers) stopGradientLayers.clear() unFreeze() - build() + buildBackwardGraph() } /** @@ -498,7 +371,7 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], * @return */ def getForwardExecutions: Array[Node[AbstractModule[Activity, Activity, T]]] = { - forwardNodes.filter(n => !n.eq(dummyOutput)) + forwardNodes.filterNot(_.eq(dummyOutput)) } /** @@ -509,13 +382,13 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], * @return */ def getSortedForwardExecutions: Array[Node[AbstractModule[Activity, Activity, T]]] = { - backGraph.topologySort + forwardGraph.topologySort .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse .filter(n => !n.eq(dummyOutput)) } @inline - private def accActivity(activity: Activity, other: Activity): Activity = { + protected def accActivity(activity: Activity, other: Activity): Activity = { if (activity == null) { other } else { @@ -568,7 +441,7 @@ class Graph[T: ClassTag](val inputs : Seq[ModuleNode[T]], def resetModules(): Unit = { modules.clear() - modules.appendAll(backGraph.topologySort + modules.appendAll(forwardGraph.topologySort .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse .filter(n => !n.eq(dummyOutput)).map(_.element)) } @@ -587,10 +460,20 @@ object Graph extends ContainerSerializable { * @param output output node * @return a graph container */ - def apply[T: ClassTag](input : Array[ModuleNode[T]], output : Array[ModuleNode[T]], - variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, + def apply[T: ClassTag]( + input : Array[ModuleNode[T]], + output : Array[ModuleNode[T]], + variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None + )(implicit ev: TensorNumeric[T]) : Graph[T] = { + new StaticGraph[T](input, output, variables) + } + + def dynamic[T: ClassTag]( + input : Array[ModuleNode[T]], + output : Array[ModuleNode[T]], + variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, generateBackward: Boolean = true)(implicit ev: TensorNumeric[T]) : Graph[T] = { - new Graph[T](input, output, variables, generateBackward) + new DynamicGraph[T](input, output, variables, generateBackward) } /** @@ -601,7 +484,12 @@ object Graph extends ContainerSerializable { */ def apply[T: ClassTag](input : ModuleNode[T], output : Array[ModuleNode[T]]) (implicit ev: TensorNumeric[T]) : Graph[T] = { - new Graph[T](Array(input), output) + new StaticGraph[T](Seq(input), output) + } + + def dynamic[T: ClassTag](input : ModuleNode[T], output : Array[ModuleNode[T]]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { + new DynamicGraph[T](Array(input), output, None, true) } /** @@ -612,7 +500,12 @@ object Graph extends ContainerSerializable { */ def apply[T: ClassTag](input : Array[ModuleNode[T]], output : ModuleNode[T]) (implicit ev: TensorNumeric[T]) : Graph[T] = { - new Graph[T](input, Array(output)) + new StaticGraph[T](input, Seq(output)) + } + + def dynamic[T: ClassTag](input : Array[ModuleNode[T]], output : ModuleNode[T]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { + new DynamicGraph[T](input, Array(output), None, true) } /** @@ -623,7 +516,12 @@ object Graph extends ContainerSerializable { */ def apply[T: ClassTag](input : ModuleNode[T], output : ModuleNode[T]) (implicit ev: TensorNumeric[T]) : Graph[T] = { - new Graph[T](Array(input), Array(output)) + new StaticGraph[T](Seq(input), Seq(output)) + } + + def dynamic[T: ClassTag](input : ModuleNode[T], output : ModuleNode[T]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { + new DynamicGraph[T](Array(input), Array(output), None, true) } override def doLoadModule[T: ClassTag](context: DeserializeContext) @@ -683,10 +581,14 @@ object Graph extends ContainerSerializable { sharedVariables = Some(weightArray, biasArray) } - val generateBackward = DataConverter.getAttributeValue(context, attributes - .get("generateBackward")).asInstanceOf[Boolean] - - Graph[T](inputs.toArray, outputs.toArray, sharedVariables, generateBackward) + val generateBackwardValue = attributes.get("generateBackward") + if (generateBackwardValue != null) { + val generateBackward = DataConverter.getAttributeValue(context, generateBackwardValue) + .asInstanceOf[Boolean] + Graph.dynamic[T](inputs.toArray, outputs.toArray, sharedVariables, generateBackward) + } else { + Graph[T](inputs.toArray, outputs.toArray, sharedVariables) + } } override def doSerializeModule[T: ClassTag](context: SerializeContext[T], @@ -752,10 +654,11 @@ object Graph extends ContainerSerializable { outputsNames, universe.typeOf[Array[String]]) graphBuilder.putAttr("outputNames", outputNamesBuilder.build) - val generateBackwardBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(context, generateBackwardBuilder, - graph.generateBackward, universe.typeOf[Boolean]) - graphBuilder.putAttr("generateBackward", generateBackwardBuilder.build) - + if (graph.isInstanceOf[DynamicGraph[_]]) { + val generateBackwardBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, generateBackwardBuilder, + graph.asInstanceOf[DynamicGraph[_]].generateBackward, universe.typeOf[Boolean]) + graphBuilder.putAttr("generateBackward", generateBackwardBuilder.build) + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala new file mode 100644 index 00000000000..993b66ed1ab --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -0,0 +1,137 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import java.util + +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Node, T} + +import scala.collection.mutable +import scala.reflect.ClassTag + +/** + * A graph container. The modules in the container are connected as a DAG graph. + * + * @param _inputs inputs modules, user can feed data into these modules in the forward method + * @param _outputs output modules + * @param _variables + * @tparam T Numeric type. Only support float/double now + */ +class StaticGraph[T: ClassTag]( + private val _inputs : Seq[ModuleNode[T]], + private val _outputs : Seq[ModuleNode[T]], + private val _variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None +)(implicit ev: TensorNumeric[T]) extends Graph[T](_inputs, _outputs, _variables) { + private val forwardExecution = forwardGraph.topologySort.reverse + private var backwardExecution: Array[Node[AbstractModule[Activity, Activity, T]]] = _ + private val inputCache = new Array[Activity](forwardExecution.length) + private var backId2ForwardId: Array[Int] = _ + private var gradOutputCache: Array[Activity] = _ + + buildBackwardGraph() + + override def updateOutput(input: Activity): Activity = { + var i = 0 + while(i < forwardExecution.length) { + val node = forwardExecution(i) + val nodeInput = findInput(node, input) + inputCache(i) = nodeInput + node.element.forward(nodeInput) + i += 1 + } + + output = dummyOutput.element.output + output + } + + override def backward(input: Activity, gradOutput: Activity): Activity = { + val before = System.nanoTime() + val gradients = backwardExecution(input, gradOutput, true) + backwardTime = System.nanoTime() - before + gradients + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + backwardExecution(input, gradOutput, false) + } + + + override def buildBackwardGraph(): this.type = { + super.buildBackwardGraph() + backwardExecution = backwardGraph.topologySort.reverse + backId2ForwardId = new Array[Int](backwardExecution.length) + gradOutputCache = new Array[Activity](backwardExecution.length) + + var i = 0 + while(i < backwardExecution.length - 1) { + var j = 0 + var find = false + while(j < forwardExecution.length) { + if (forwardExecution(j).element.getName() == backwardExecution(i).element.getName()) { + backId2ForwardId(i) = j + find = true + } + j += 1 + } + require(find, "Cannot find backward layer in forward executions") + i += 1 + } + + this + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + var i = 0 + while (i < backwardExecution.length - 1) { + val curNode = backwardExecution(i) + val curInput = inputCache(backId2ForwardId(i)) + curNode.element.accGradParameters(curInput, gradOutputCache(i)) + i += 1 + } + } + + + private def backwardExecution(input: Activity, gradOutput: Activity, + executeBackward: Boolean): Activity = { + dummyOutputGrad.element.gradInput = gradOutput + + var i = 0 + while (i < backwardExecution.length - 1) { // do not execute the dummy backward end + val curNode = backwardExecution(i) + val curGradOutput = findGradOutput(curNode, gradOutput) + gradOutputCache(i) = curGradOutput + val curInput = inputCache(backId2ForwardId(i)) + if (!isStopGradient(curNode.element)) { + if (executeBackward) { + curNode.element.backward(curInput, curGradOutput) + } else { + curNode.element.updateGradInput(curInput, curGradOutput) + } + } else if (executeBackward) { + curNode.element.accGradParameters(curInput, curGradOutput) + } + i += 1 + } + + gradInput = fetchModelGradInput() + gradInput + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizer.scala index 5b20694053f..8a06c9ebb0b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Quantizer.scala @@ -119,7 +119,7 @@ object GraphQuantizer extends Quantizable { // modules in container need to rebuild graph.resetModules() // nodes in backward executions need to rebuild - graph.build() + graph.buildBackwardGraph() graph } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala index 6a34fb08baa..f61e649d9b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala @@ -52,4 +52,30 @@ object Util { swap(arr, l + pivot, r); partition(arr, l, r); } + + private[bigdl] def shift[B](data : Array[B], from : Int, to : Int): Array[B] = { + require(from < data.length && from >= 0, s"invalid from $from array length is ${data.length}") + require(to < data.length && to >= 0, s"invalid to $to array length is ${data.length}") + if (from == to) { + data + } else if (from < to) { + var i = from + while(i < to) { + val tmp = data(i) + data(i) = data(i + 1) + data(i + 1) = tmp + i += 1 + } + data + } else { + var i = from + while(i > to) { + val tmp = data(i) + data(i) = data(i - 1) + data(i - 1) = tmp + i -= 1 + } + data + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index e4319e495fd..0ee38beda12 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -174,7 +174,8 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.SpatialBatchNormalization", BatchNormalization) registerModule("com.intel.analytics.bigdl.nn.BinaryTreeLSTM", BinaryTreeLSTM) registerModule("com.intel.analytics.bigdl.nn.BiRecurrent", BiRecurrent) - registerModule("com.intel.analytics.bigdl.nn.Graph", Graph) + registerModule("com.intel.analytics.bigdl.nn.StaticGraph", Graph) + registerModule("com.intel.analytics.bigdl.nn.DynamicGraph", Graph) registerModule("com.intel.analytics.bigdl.nn.MapTable", MapTable) registerModule("com.intel.analytics.bigdl.nn.MaskedSelect", MaskedSelect) registerModule("com.intel.analytics.bigdl.nn.Recurrent", Recurrent) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 47b6bc7f0d2..945c793170b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -413,7 +413,8 @@ object TensorflowLoader{ outputNodes } - Graph(inputNodes.toArray, adjustOutputs.toArray, Some((weights.toArray, gradients.toArray)), + Graph.dynamic(inputNodes.toArray, adjustOutputs.toArray, + Some((weights.toArray, gradients.toArray)), generatedBackward) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/DynamicTestModels.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/DynamicTestModels.scala new file mode 100644 index 00000000000..b7de1f092a0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/DynamicTestModels.scala @@ -0,0 +1,273 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.models + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.models.inception.Inception_Layer_v1 +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.{T, Table} + +object DynamicTestModels { + + object Autoencoder { + val rowN = 28 + val colN = 28 + val featureSize = rowN * colN + + def graph(classNum: Int): Module[Float] = { + val input = Reshape(Array(featureSize)).inputs() + val linear1 = Linear(featureSize, classNum).inputs(input) + val relu = ReLU().inputs(linear1) + val linear2 = Linear(classNum, featureSize).inputs(relu) + val output = Sigmoid().inputs(linear2) + Graph.dynamic(input, output) + } + } + + object Inception_v1_NoAuxClassifier { + def graph(classNum: Int, hasDropout: Boolean = true): Module[Float] = { + val input = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName("conv1/7x7_s2").inputs() + val conv1_relu = ReLU(true).setName("conv1/relu_7x7").inputs(input) + val pool1_s2 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2").inputs(conv1_relu) + val pool1_norm1 = SpatialCrossMapLRN(5, 0.0001, 0.75).setName("pool1/norm1").inputs(pool1_s2) + val conv2 = SpatialConvolution(64, 64, 1, 1, 1, 1).setInitMethod(weightInitMethod = Xavier, + Zeros).setName("conv2/3x3_reduce").inputs(pool1_norm1) + val conv2_relu = ReLU(true).setName("conv2/relu_3x3_reduce").inputs(conv2) + val conv2_3x3 = SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1) + .setInitMethod(weightInitMethod = Xavier, Zeros).setName("conv2/3x3").inputs(conv2_relu) + val conv2_relu_3x3 = ReLU(true).setName("conv2/relu_3x3").inputs(conv2_3x3) + val conv2_norm2 = SpatialCrossMapLRN(5, 0.0001, 0.75) + .setName("conv2/norm2").inputs(conv2_relu_3x3) + val pool2_s2 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool2/3x3_s2") + .inputs(conv2_norm2) + val inception_3a = Inception_Layer_v1(pool2_s2, 192, + T(T(64), T(96, 128), T(16, 32), T(32)), "inception_3a/") + val inception_3b = Inception_Layer_v1(inception_3a, 256, + T(T(128), T(128, 192), T(32, 96), T(64)), "inception_3b/") + val pool3 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool3/3x3_s2").inputs(inception_3b) + val inception_4a = Inception_Layer_v1(pool3, 480, + T(T(192), T(96, 208), T(16, 48), T(64)), "inception_4a/") + val inception_4b = Inception_Layer_v1(inception_4a, 512, + T(T(160), T(112, 224), T(24, 64), T(64)), "inception_4b/") + val inception_4c = Inception_Layer_v1(inception_4b, 512, + T(T(128), T(128, 256), T(24, 64), T(64)), "inception_4c/") + val inception_4d = Inception_Layer_v1(inception_4c, 512, + T(T(112), T(144, 288), T(32, 64), T(64)), "inception_4d/") + val inception_4e = Inception_Layer_v1(inception_4d, 528, + T(T(256), T(160, 320), T(32, 128), T(128)), "inception_4e/") + val pool4 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool4/3x3_s2").inputs(inception_4e) + val inception_5a = Inception_Layer_v1(pool4, 832, + T(T(256), T(160, 320), T(32, 128), T(128)), "inception_5a/") + val inception_5b = Inception_Layer_v1(inception_5a, + 832, T(T(384), T(192, 384), T(48, 128), T(128)), "inception_5b/") + val pool5 = SpatialAveragePooling(7, 7, 1, 1).setName("pool5/7x7_s1").inputs(inception_5b) + val drop = if (hasDropout) Dropout(0.4).setName("pool5/drop_7x7_s1").inputs(pool5) else pool5 + val view = View(1024).setNumInputDims(3).inputs(drop) + val classifier = Linear(1024, classNum).setInitMethod(weightInitMethod = Xavier, Zeros) + .setName("loss3/classifier").inputs(view) + val loss = LogSoftMax().setName("loss3/loss3").inputs(classifier) + + Graph.dynamic(input, loss) + } + } + + object LeNet5 { + def graph(classNum: Int): Module[Float] = { + val input = Reshape(Array(1, 28, 28)).inputs() + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5").inputs(input) + val tanh1 = Tanh().inputs(conv1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1) + val tanh2 = Tanh().inputs(pool1) + val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5").inputs(tanh2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(conv2) + val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2) + val fc1 = Linear(12 * 4 * 4, 100).setName("fc1").inputs(reshape) + val tanh3 = Tanh().inputs(fc1) + val fc2 = Linear(100, classNum).setName("fc2").inputs(tanh3) + val output = LogSoftMax().inputs(fc2) + + Graph.dynamic(input, output) + } + } + + object VggForCifar10 { + def graph(classNum: Int, hasDropout: Boolean = true) + : Module[Float] = { + val input = Input() + def convBNReLU(nInputPlane: Int, nOutPutPlane: Int)(input: ModuleNode[Float]) + : ModuleNode[Float] = { + val conv = SpatialConvolution(nInputPlane, nOutPutPlane, 3, 3, 1, 1, 1, 1).inputs(input) + val bn = SpatialBatchNormalization(nOutPutPlane, 1e-3).inputs(conv) + ReLU(true).inputs(bn) + } + val relu1 = convBNReLU(3, 64)(input) + val drop1 = if (hasDropout) Dropout(0.3).inputs(relu1) else relu1 + val relu2 = convBNReLU(64, 64)(drop1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu2) + + val relu3 = convBNReLU(64, 128)(pool1) + val drop2 = if (hasDropout) Dropout(0.4).inputs(relu3) else relu3 + val relu4 = convBNReLU(128, 128)(drop2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu4) + + val relu5 = convBNReLU(128, 256)(pool2) + val drop3 = if (hasDropout) Dropout(0.4).inputs(relu5) else relu5 + val relu6 = convBNReLU(256, 256)(drop3) + val drop4 = if (hasDropout) Dropout(0.4).inputs(relu6) else relu6 + val relu7 = convBNReLU(256, 256)(drop4) + val pool3 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu7) + + val relu8 = convBNReLU(256, 512)(pool3) + val drop5 = if (hasDropout) Dropout(0.4).inputs(relu8) else relu8 + val relu9 = convBNReLU(512, 512)(drop5) + val drop6 = if (hasDropout) Dropout(0.4).inputs(relu9) else relu9 + val relu10 = convBNReLU(512, 512)(drop6) + val pool4 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu10) + + val relu11 = convBNReLU(512, 512)(pool4) + val drop7 = if (hasDropout) Dropout(0.4).inputs(relu11) else relu11 + val relu12 = convBNReLU(512, 512)(drop7) + val drop8 = if (hasDropout) Dropout(0.4).inputs(relu12) else relu12 + val relu13 = convBNReLU(512, 512)(drop8) + val pool5 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu13) + val view = View(512).inputs(pool5) + + val drop9 = if (hasDropout) Dropout(0.5).inputs(view) else view + val linear1 = Linear(512, 512).inputs(drop9) + val bn = BatchNormalization(512).inputs(linear1) + val relu = ReLU(true).inputs(bn) + val drop10 = if (hasDropout) Dropout(0.5).inputs(relu) else relu + val linear2 = Linear(512, classNum).inputs(drop10) + val output = LogSoftMax().inputs(linear2) + Graph.dynamic(input, output) + } + } + + object Vgg_16 { + def graph(classNum: Int, hasDropout: Boolean = true) + : Module[Float] = { + val conv1 = SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1).inputs() + val relu1 = ReLU(true).inputs(conv1) + val conv2 = SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1).inputs(relu1) + val relu2 = ReLU(true).inputs(conv2) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu2) + + val conv3 = SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1).inputs(pool1) + val relu3 = ReLU(true).inputs(conv3) + val conv4 = SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1).inputs(relu3) + val relu4 = ReLU(true).inputs(conv4) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu4) + + val conv5 = SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1).inputs(pool2) + val relu5 = ReLU(true).inputs(conv5) + val conv6 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu5) + val relu6 = ReLU(true).inputs(conv6) + val conv7 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu6) + val relu7 = ReLU(true).inputs(conv7) + val pool3 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu7) + + val conv8 = SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1).inputs(pool3) + val relu8 = ReLU(true).inputs(conv8) + val conv9 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu8) + val relu9 = ReLU(true).inputs(conv9) + val conv10 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu9) + val relu10 = ReLU(true).inputs(conv10) + val pool4 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu10) + + val conv11 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(pool4) + val relu11 = ReLU(true).inputs(conv11) + val conv12 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu11) + val relu12 = ReLU(true).inputs(conv12) + val conv13 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu12) + val relu13 = ReLU(true).inputs(conv13) + val pool5 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu13) + + val view1 = View(512 * 7 * 7).inputs(pool5) + val linear1 = Linear(512 * 7 * 7, 4096).inputs(view1) + val th1 = Threshold(0, 1e-6).inputs(linear1) + val drop1 = if (hasDropout) Dropout(0.5).inputs(th1) else th1 + val linear2 = Linear(4096, 4096).inputs(drop1) + val th2 = Threshold(0, 1e-6).inputs(linear2) + val drop2 = if (hasDropout) Dropout(0.5).inputs(th2) else th2 + val linear3 = Linear(4096, classNum).inputs(drop2) + val output = LogSoftMax().inputs(linear3) + + Graph.dynamic(conv1, output) + } + } + + object Vgg_19 { + def graph(classNum: Int, hasDropout: Boolean = true) + : Module[Float] = { + val conv1 = SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1).inputs() + val relu1 = ReLU(true).inputs(conv1) + val conv2 = SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1).inputs(relu1) + val relu2 = ReLU(true).inputs(conv2) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu2) + + val conv3 = SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1).inputs(pool1) + val relu3 = ReLU(true).inputs(conv3) + val conv4 = SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1).inputs(relu3) + val relu4 = ReLU(true).inputs(conv4) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu4) + + val conv5 = SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1).inputs(pool2) + val relu5 = ReLU(true).inputs(conv5) + val conv6 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu5) + val relu6 = ReLU(true).inputs(conv6) + val conv7 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu6) + val relu7 = ReLU(true).inputs(conv7) + val conv8 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu7) + val relu8 = ReLU(true).inputs(conv8) + val pool3 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu8) + + val conv9 = SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1).inputs(pool3) + val relu9 = ReLU(true).inputs(conv9) + val conv10 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu9) + val relu10 = ReLU(true).inputs(conv10) + val conv11 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu10) + val relu11 = ReLU(true).inputs(conv11) + val conv12 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu11) + val relu12 = ReLU(true).inputs(conv12) + val pool4 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu12) + + val conv13 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(pool4) + val relu13 = ReLU(true).inputs(conv13) + val conv14 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu13) + val relu14 = ReLU(true).inputs(conv14) + val conv15 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu14) + val relu15 = ReLU(true).inputs(conv15) + val conv16 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu15) + val relu16 = ReLU(true).inputs(conv16) + val pool5 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu16) + + val view1 = View(512 * 7 * 7).inputs(pool5) + val linear1 = Linear(512 * 7 * 7, 4096).inputs(view1) + val th1 = Threshold(0, 1e-6).inputs(linear1) + val drop1 = if (hasDropout) Dropout(0.5).inputs(th1) else th1 + val linear2 = Linear(4096, 4096).inputs(drop1) + val th2 = Threshold(0, 1e-6).inputs(linear2) + val drop2 = if (hasDropout) Dropout(0.5).inputs(th2) else th2 + val linear3 = Linear(4096, classNum).inputs(drop2) + val output = LogSoftMax().inputs(linear3) + + Graph.dynamic(conv1, output) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala new file mode 100644 index 00000000000..c3ed1f857aa --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala @@ -0,0 +1,1278 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.models.DynamicTestModels +import com.intel.analytics.bigdl.models.autoencoder.Autoencoder +import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier +import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.models.vgg.{VggForCifar10, Vgg_16, Vgg_19} +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Less} +import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils._ + +import scala.reflect.ClassTag +import scala.util.Random +import org.scalatest.{FlatSpec, Matchers} + +class DynamicGraphSpec extends FlatSpec with Matchers { + "Dynamic Graph init" should "throw exceptions when there's cycle" in { + val fc1 = Linear(4, 2).inputs() + val relu1 = ReLU().inputs(fc1) + relu1 -> fc1 + + intercept[IllegalArgumentException] { + Graph.dynamic(fc1, relu1) + } + } + + "Dynamic Graph init" should "be successful when inputs node are same with outputs node" in { + val fc1 = Linear(4, 2).inputs() + val graph = Graph.dynamic(fc1, fc1) + + val inputData = Tensor(4, 4) + fc1.element.parameters()._1(1).zero() // bias is set to 0 + graph.forward(inputData) should be((inputData * fc1.element.parameters()._1(0).t())) + } + + "Dynamic Graph init" should "throw exceptions when some inputs are ignored" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val output = CAddTable().inputs(fc1, fc2) + + intercept[IllegalArgumentException] { + Graph.dynamic(fc1, output) + } + } + + "Dynamic Graph init" should "be successful output are ignored" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = ReLU().inputs(cadd) + + val graph = Graph.dynamic(Array(fc1, fc2), Array(output1)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 1.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + output should be(Tensor(T(2.2f, 2.2f))) + } + + "Dynamic Graph init" should "throw exceptions when input a tensor while a table is required" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = ReLU().inputs(cadd) + + val graph = Graph.dynamic(Array(fc1, fc2), Array(output1, output2)) + intercept[LayerException] { + graph.forward(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f))) + } + } + + "Dynamic Graph init" should "throw exceptions when inputs has pre-nodes" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val tanh1 = Tanh().inputs(fc1) + val tanh2 = Tanh().inputs(fc2) + + val cadd = CAddTable().inputs(tanh1, tanh2) + val output1 = ReLU().inputs(cadd) + val output2 = ReLU().inputs(cadd) + + intercept[IllegalArgumentException] { + Graph.dynamic(Array(tanh1, tanh2), Array(output1, output2)) + } + } + + "Dynamic Graph init" should "throw exceptions when inputs has nothing to do with the " + + "graph but same number with the roots node in the graph" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val fc3 = Linear(4, 2).inputs() + val fc4 = Linear(4, 2).inputs() + val tanh1 = Tanh().inputs(fc1) + val tanh2 = Tanh().inputs(fc2) + + val cadd = CAddTable().inputs(tanh1, tanh2) + val output1 = ReLU().inputs(cadd) + val output2 = ReLU().inputs(cadd) + + intercept[IllegalArgumentException] { + Graph.dynamic(Array(fc3, fc4), Array(output1, output2)) + } + } + + "Dynamic Graph forward" should "be successful" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc1, fc2), Array(output1, output2)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 1.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + output should be(T(Tensor(T(2.2f, 2.2f)), Tensor(T(.0f, .0f)))) + } + + "Dynamic Graph forward" should "throw exceptions when input a table while " + + "a tensor is required" in { + val fc1 = Linear(4, 2).inputs() + val output1 = ReLU().inputs(fc1) + + val graph = Graph.dynamic(Array(fc1), Array(output1)) + + intercept[LayerException] { + graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + } + } + + "Dynamic Graph forward" should "be successful when first node accept multiple tensors input" in { + val input1 = Input() + val input2 = Input() + val cadd = CAddTable().inputs(input1, input2) + val graph = Graph.dynamic(Array(input1, input2), cadd) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + output should be(Tensor(T(0.6f, 0.6f, -0.5f, -0.5f))) + } + + "Dynamic Graph forward" should "be successful when exchange input order" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc2, fc1), Array(output1, output2)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + output should be(T(Tensor(T(2.8f, 2.8f)), Tensor(T(0.0f, 0.0f)))) + } + + "Dynamic Graph forward" should "be successful when paths has different length" in { + val fc1 = Linear(4, 2).inputs() + val thd1 = Threshold(-10.0).inputs(fc1) + val thd2 = Threshold(-10.0).inputs(thd1) + val thd3 = Threshold(-10.0).inputs(thd2) + val thd4 = Threshold(-10.0).inputs(thd3) + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(thd4, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc1, fc2), Array(output1, output2)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 1.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + output should be(T(Tensor(T(2.2f, 2.2f)), Tensor(T(.0f, .0f)))) + } + + "Dynamic Graph forward" should "be successful when exchange output order" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc1, fc2), Array(output2, output1)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + output should be(T(Tensor(T(0.0f, 0.0f)), Tensor(T(3.8f, 3.8f)))) + } + + "Dynamic Graph forward" should "be correct when contains multi output node" in { + val x = SplitTable(1).inputs() + val y1 = Identity().inputs(x(1)) + val y2 = Identity().inputs(x(2)) + val z = CAddTable().inputs(y1, y2) + + val graph = Graph.dynamic(x, z) + val output = graph.forward(Tensor(T(T(1, 2, 3), T(4, 2, 7)))) + output should be(Tensor(T(5, 4, 10))) + } + + "Dynamic Graph forward" should "be correct when connect a table to a node" in { + val x = SplitTable(1).inputs() + val y = CAddTable().inputs(x) + + val graph = Graph.dynamic(x, y) + val output = graph.forward(Tensor(T(T(1, 2, 3), T(4, 2, 7)))) + output should be(Tensor(T(5, 4, 10))) + } + + "Dynamic Graph forward" should "be correct when contains multi output node with table output" in { + val x = Identity().inputs() + val y = SplitTable(1).inputs(x) + + val graph = Graph.dynamic(x, y) + val output = graph.forward(Tensor(T(T(1, 2, 3), T(4, 2, 7)))) + output.toTable[Tensor[Float]](1) should be(Tensor(T(1, 2, 3))) + output.toTable[Tensor[Float]](2) should be(Tensor(T(4, 2, 7))) + } + + "Dynamic Graph forward" should "be correct when contains nested output" in { + val x = Identity().inputs() + val y1 = SplitTable(1).inputs(x) + val y2 = Identity().inputs(y1(1)) + + val graph = Graph.dynamic(x, Array(y1, y2)) + val output = graph.forward(Tensor(T(T(1, 2, 3), T(4, 2, 7)))) + val t1 = output.toTable[Table](1) + t1[Tensor[Float]](1) should be(Tensor(T(1, 2, 3))) + t1[Tensor[Float]](2) should be(Tensor(T(4, 2, 7))) + output.toTable[Tensor[Float]](2) should be(Tensor(T(1, 2, 3))) + } + + "Dynamic Graph backward" should "be successful" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc1, fc2), Array(output1, output2)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + val gradInput = graph.backward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))), T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f)))) + gradInput should be(T(Tensor(T(3.0f, 3.0f, 3.0f, 3.0f)), + Tensor(T(6.0f, 6.0f, 6.0f, 6.0f)))) + fc1.element.parameters()._2(0) should be(Tensor(T(T(0.1f, 0.2f, -0.3f, -0.4f), + T(0.2f, 0.4f, -0.6f, -0.8f)))) + fc1.element.parameters()._2(1) should be(Tensor(T(1.0f, 2.0f))) + fc2.element.parameters()._2(0) should be(Tensor(T(T(0.5f, 0.4f, -0.2f, -0.1f), + T(1.0f, 0.8f, -0.4f, -0.2f)))) + fc2.element.parameters()._2(1) should be(Tensor(T(1.0f, 2.0f))) + } + + "Dynamic Graph backward" should "be successful when first node accept multiple tensors input" in { + val input1 = Input() + val input2 = Input() + val cadd = CAddTable().inputs(input1, input2) + val graph = Graph.dynamic(Array(input1, input2), cadd) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + output should be(Tensor(T(0.6f, 0.6f, -0.5f, -0.5f))) + val gradient = graph.backward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))), Tensor(T(0.1f, 0.1f, 0.1f, 0.1f))) + gradient should be(T(Tensor(T(0.1f, 0.1f, 0.1f, 0.1f)), Tensor(T(0.1f, 0.1f, 0.1f, 0.1f)))) + } + + "Dynamic Graph backward" should "be successful when paths have different length" in { + val fc1 = Linear(4, 2).inputs() + val thd1 = Threshold(-10.0).inputs(fc1) + val thd2 = Threshold(-10.0).inputs(thd1) + val thd3 = Threshold(-10.0).inputs(thd2) + val thd4 = Threshold(-10.0).inputs(thd3) + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(thd4, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc1, fc2), Array(output1, output2)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + val gradInput = graph.backward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))), T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f)))) + gradInput should be(T(Tensor(T(3.0f, 3.0f, 3.0f, 3.0f)), + Tensor(T(6.0f, 6.0f, 6.0f, 6.0f)))) + fc1.element.parameters()._2(0) should be(Tensor(T(T(0.1f, 0.2f, -0.3f, -0.4f), + T(0.2f, 0.4f, -0.6f, -0.8f)))) + fc1.element.parameters()._2(1) should be(Tensor(T(1.0f, 2.0f))) + fc2.element.parameters()._2(0) should be(Tensor(T(T(0.5f, 0.4f, -0.2f, -0.1f), + T(1.0f, 0.8f, -0.4f, -0.2f)))) + fc2.element.parameters()._2(1) should be(Tensor(T(1.0f, 2.0f))) + } + + "Dynamic Graph backward" should "be successful when exchange input order" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc2, fc1), Array(output1, output2)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + val gradInput = graph.backward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))), T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f)))) + gradInput should be(T(Tensor(T(6.0f, 6.0f, 6.0f, 6.0f)), Tensor(T(3.0f, 3.0f, 3.0f, 3.0f)))) + fc1.element.parameters()._2(0) should be(Tensor(T(T(0.5f, 0.4f, -0.2f, -0.1f), + T(1.0f, 0.8f, -0.4f, -0.2f)))) + fc1.element.parameters()._2(1) should be(Tensor(T(1.0f, 2.0f))) + fc2.element.parameters()._2(0) should be(Tensor(T(T(0.1f, 0.2f, -0.3f, -0.4f), + T(0.2f, 0.4f, -0.6f, -0.8f)))) + fc2.element.parameters()._2(1) should be(Tensor(T(1.0f, 2.0f))) + } + + "Dynamic Graph backward" should "be successful when exchange output order" in { + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc1, fc2), Array(output2, output1)) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + val output = graph.forward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f)))) + val gradInput = graph.backward(T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))), T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f)))) + gradInput should be(T(Tensor(T(7.0f, 7.0f, 7.0f, 7.0f)), Tensor(T(14.0f, 14.0f, 14.0f, 14.0f)))) + fc1.element.parameters()._2(0) should be(Tensor(T(T(0.3f, 0.6f, -0.9f, -1.2f), + T(0.4f, 0.8f, -1.2f, -1.6f)))) + fc1.element.parameters()._2(1) should be(Tensor(T(3.0f, 4.0f))) + fc2.element.parameters()._2(0) should be(Tensor(T(T(1.5f, 1.2f, -0.6f, -0.3f), + T(2.0f, 1.6f, -0.8f, -0.4f)))) + fc2.element.parameters()._2(1) should be(Tensor(T(3.0f, 4.0f))) + } + + "Dynamic Graph backward" should "be correct when contains multi output node" in { + val x = SplitTable(1).inputs() + val y1 = Identity().inputs(x(1)) + val y2 = Identity().inputs(x(2)) + val z = CAddTable().inputs(y1, y2) + + val graph = Graph.dynamic(x, z) + val output = graph.forward(Tensor(T(T(1, 2, 3), T(4, 2, 7)))) + val grads = graph.backward(Tensor(T(T(1, 2, 3), T(4, 2, 7))), Tensor(T(5, 4, 10))) + grads should be(Tensor(T(T(5, 4, 10), T(5, 4, 10)))) + } + + "Dynamic Graph backward" should "be correct when contains multi output " + + "node with table output" in { + val x = Identity().inputs() + val y = SplitTable(1).inputs(x) + + val graph = Graph.dynamic(x, y) + val output = graph.forward(Tensor(T(T(1, 2, 3), T(4, 2, 7)))) + val grad = graph.backward(Tensor(T(T(1, 2, 3), T(4, 2, 7))), + T(Tensor(T(3, 2, 1)), Tensor(T(5, 7, 9)))) + grad should be(Tensor(T(T(3, 2, 1), T(5, 7, 9)))) + } + + "Dynamic Graph backward" should "be correct when connect a table to a node" in { + val x = SplitTable(1).inputs() + val y = CAddTable().inputs(x) + + val graph = Graph.dynamic(x, y) + val output = graph.forward(Tensor(T(T(1, 2, 3), T(4, 2, 7)))) + val grads = graph.backward(Tensor(T(T(1, 2, 3), T(4, 2, 7))), Tensor(T(5, 4, 10))) + grads should be(Tensor(T(T(5, 4, 10), T(5, 4, 10)))) + } + + "Dynamic Graph backward" should "be correct when contains nested output" in { + val x = Identity().inputs() + val y1 = SplitTable(1).inputs(x) + val y2 = Identity().inputs(y1(1)) + + val graph = Graph.dynamic(x, Array(y1, y2)) + val output = graph.forward(Tensor(T(T(1, 2, 3), T(4, 2, 7)))) + val result = graph.backward(Tensor(T(T(1, 2, 3), T(4, 2, 7))), + T(T(Tensor(T(2, 7, 8)), Tensor(T(1, 5, 3))), Tensor(T(5, 4, 10)))) + result should be(Tensor(T(T(7, 11, 18), T(1, 5, 3)))) + } + + "Dynamic Graph forward/backward" should "be successful when there's output " + + "from internal node" in { + val input1 = Input() + val input2 = Input() + val add = CAddTable().inputs(input1, input2) + val add2 = AddConstant(2.0f).inputs(add) + val relu = ReLU().inputs(add2) + val graph = Graph[Float](Array(input1, input2), Array(add, relu)) + + val input = T(Tensor(T(1.0f, 2.0f)), Tensor(T(-2.0f, -1.0f))) + val output = graph.forward(input) + val gradient = graph.backward(input, T(Tensor(T(1.0f, 2.0f)), Tensor(T(-2.0f, -1.0f)))) + val output1 = output.toTable[Tensor[Float]](1) + val output2 = output.toTable[Tensor[Float]](2) + + output1 should be(Tensor[Float](T(-1.0f, 1.0f))) + output2 should be(Tensor[Float](T(1.0f, 3.0f))) + gradient should be(T(Tensor(T(-1.0f, 1.0f)), Tensor(T(-1.0f, 1.0f)))) + } + + "lenet" should "be same with sequential model" in { + RandomGenerator.RNG.setSeed(1000) + val seqModel = Sequential().add(Reshape(Array(1, 28, 28))) + .add(SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5")) + .add(Tanh()) + .add(SpatialMaxPooling(2, 2, 2, 2)) + .add(Tanh()) + .add(SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5")) + .add(SpatialMaxPooling(2, 2, 2, 2)) + .add(Reshape(Array(12 * 4 * 4))) + .add(Linear(12 * 4 * 4, 100).setName("fc1")) + .add(Tanh()) + .add(Linear(100, 10).setName("fc2")) + .add(LogSoftMax()) + + RandomGenerator.RNG.setSeed(1000) + val input = Reshape(Array(1, 28, 28)).inputs() + val conv1 = SpatialConvolution(1, 6, 5, 5).inputs(input) + val tanh1 = Tanh().inputs(conv1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1) + val tanh2 = Tanh().inputs(pool1) + val conv2 = SpatialConvolution(6, 12, 5, 5).inputs(tanh2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(conv2) + val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2) + val fc1 = Linear(12 * 4 * 4, 100).inputs(reshape) + val tanh3 = Tanh().inputs(fc1) + val fc2 = Linear(100, 10).inputs(tanh3) + val output = LogSoftMax().inputs(fc2) + + val funcModel = Graph.dynamic(input, output) + + val inputData = Tensor(4, 28 * 28).rand() + val outputData1 = seqModel.forward(inputData) // warm up + var start = System.nanoTime() + seqModel.forward(inputData) + println(s"seq model forward time is ${(System.nanoTime() - start) / 1e6}ms") + start = System.nanoTime() + val outputData2 = funcModel.forward(inputData) + println(s"funcModel model forward time is ${(System.nanoTime() - start) / 1e6}ms") + + outputData1 should be(outputData2) + + val gradient = Tensor(4, 10).rand() + start = System.nanoTime() + val gradientBP1 = seqModel.backward(inputData, gradient) + println(s"seq model backward time is ${(System.nanoTime() - start) / 1e6}ms") + start = System.nanoTime() + val gradientBP2 = funcModel.backward(inputData, gradient) + println(s"funcModel model backward time is ${(System.nanoTime() - start) / 1e6}ms") + + gradientBP1 should be(gradientBP2) + seqModel.getParameters()._2 should be(funcModel.getParameters()._2) + } + + "ResNet-18 basic block shortcut type A" should "be correct" in { + RandomGenerator.RNG.setSeed(1000) + val seqModel = ModelUntils.ResNet.basicBlockSeq(16, 16, 1, "A") + RandomGenerator.RNG.setSeed(1000) + val input = Input() + val output = ModelUntils.ResNet.basicBlockSeq(16, 16, 1, "A").inputs(input) + val funcModel = Graph.dynamic(input, output) + + println(seqModel) + val inputData = Tensor(4, 16, 32, 32).rand() + var start = System.nanoTime() + val output1 = seqModel.forward(inputData) + println(s"seq model forward time is ${(System.nanoTime() - start) / 1e6}ms") + + start = System.nanoTime() + val output2 = funcModel.forward(inputData) + println(s"func model forward time is ${(System.nanoTime() - start) / 1e6}ms") + + output1 should be(output2) + + val gradients = Tensor(4, 16, 32, 32).rand() + start = System.nanoTime() + val gradients1 = seqModel.backward(inputData, gradients) + println(s"seq model backward time is ${(System.nanoTime() - start) / 1e6}ms") + start = System.nanoTime() + val gradients2 = funcModel.backward(inputData, gradients) + println(s"func model backward time is ${(System.nanoTime() - start) / 1e6}ms") + + gradients1 should be(gradients2) + seqModel.getParameters()._2 should be(funcModel.getParameters()._2) + } + + "ResNet-18 basic block shortcut type C" should "be correct" in { + RandomGenerator.RNG.setSeed(1000) + val seqModel = ModelUntils.ResNet.basicBlockSeq(16, 16, 1, "C") + RandomGenerator.RNG.setSeed(1000) + val input = Input() + val output = ModelUntils.ResNet.basicBlockFunc(16, 16, 1, "C")(input) + val funcModel = Graph.dynamic(input, output) + + println(seqModel) + val inputData = Tensor(4, 16, 32, 32).rand() + var start = System.nanoTime() + val output1 = seqModel.forward(inputData) + println(s"seq model forward time is ${(System.nanoTime() - start) / 1e6}ms") + + start = System.nanoTime() + val output2 = funcModel.forward(inputData) + println(s"func model forward time is ${(System.nanoTime() - start) / 1e6}ms") + + output1 should be(output2) + + val gradients = Tensor(4, 16, 32, 32).rand() + start = System.nanoTime() + val gradients1 = seqModel.backward(inputData, gradients) + println(s"seq model backward time is ${(System.nanoTime() - start) / 1e6}ms") + start = System.nanoTime() + val gradients2 = funcModel.backward(inputData, gradients) + println(s"func model backward time is ${(System.nanoTime() - start) / 1e6}ms") + + gradients1 should be(gradients2) + + seqModel.getParametersTable()[Table]("conv1")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv1")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("bn1")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("bn1")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("conv2")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv2")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("bn2")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("bn2")[Tensor[Float]]("gradWeight") + ) + } + + "InceptionV1 block" should "be correct" in { + RandomGenerator.RNG.setSeed(1000) + val seqModel = ModelUntils.Inception.inceptionLayerV1Seq( + 2, T(T(4), T(96, 128), T(16, 32), T(32))) + + RandomGenerator.RNG.setSeed(1000) + val input = Input() + val output = ModelUntils.Inception.inceptionLayerV1Func( + 2, T(T(4), T(96, 128), T(16, 32), T(32)))(input) + val funcModel = Graph.dynamic(input, output) + + println(seqModel) + val inputData = Tensor(1, 2, 4, 4).rand() + var start = System.nanoTime() + val output1 = seqModel.forward(inputData) + println(s"seq model forward time is ${(System.nanoTime() - start) / 1e6}ms") + + start = System.nanoTime() + val output2 = funcModel.forward(inputData) + println(s"func model forward time is ${(System.nanoTime() - start) / 1e6}ms") + + output1 should be(output2) + val gradient = Tensor(1, 256, 4, 4).rand() + start = System.nanoTime() + val gradient1 = seqModel.backward(inputData, gradient) + println(s"seq model backward time is ${(System.nanoTime() - start) / 1e6}ms") + + start = System.nanoTime() + val gradient2 = funcModel.backward(inputData, gradient) + + println(s"func model backward time is ${(System.nanoTime() - start) / 1e6}ms") + + gradient1 should be(gradient2) + + seqModel.getParametersTable()[Table]("conv1x1")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv1x1")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("conv3x3_1")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv3x3_1")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("conv3x3_2")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv3x3_2")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("conv5x5_1")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv5x5_1")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("conv5x5_2")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("conv5x5_2")[Tensor[Float]]("gradWeight") + ) + + seqModel.getParametersTable()[Table]("pool_conv")[Tensor[Float]]("gradWeight") should be( + funcModel.getParametersTable()[Table]("pool_conv")[Tensor[Float]]("gradWeight") + ) + } + + "Autoencoder graph" should "be correct" in { + Random.setSeed(1) + val batchSize = 4 + val input = Tensor[Float](batchSize, 28 * 28).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 784).apply1(e => Random.nextFloat()) + + RNG.setSeed(1000) + val model = Autoencoder(32) + RNG.setSeed(1000) + val graphModel = DynamicTestModels.Autoencoder.graph(32) + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be(output2) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + gradInput1 should be(gradInput2) + gradInput1 should be(gradInput2) + model.getParameters().equals(graphModel.getParameters()) should be(true) + } + + "Lenet graph" should "be correct" in { + Random.setSeed(1) + val batchSize = 4 + val input = Tensor[Float](batchSize, 28*28).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 10).apply1(e => Random.nextFloat()) + + RNG.setSeed(1000) + val model = LeNet5(10) + RNG.setSeed(1000) + val graphModel = LeNet5.graph(10) + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be(output2) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + gradInput1 should be(gradInput2) + model.getParameters().equals(graphModel.getParameters()) should be(true) + } + + "VggForCifar10 graph" should "be correct" in { + Random.setSeed(1) + val batchSize = 4 + val input = Tensor[Float](batchSize, 3, 32, 32).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 10).apply1(e => Random.nextFloat()) + + RNG.setSeed(1000) + val model = VggForCifar10(10, false) + RNG.setSeed(1000) + val graphModel = DynamicTestModels.VggForCifar10.graph(10, false) + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be(output2) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + gradInput1 should be(gradInput2) + model.getParameters().equals(graphModel.getParameters()) should be(true) + } + + "Vgg_16 graph" should "be correct" in { + Random.setSeed(1) + val batchSize = 1 + val input = Tensor[Float](batchSize, 3, 224, 224).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](1000).apply1(e => Random.nextFloat()) + + RNG.setSeed(1000) + val model = Vgg_16(1000, false) + RNG.setSeed(1000) + val graphModel = DynamicTestModels.Vgg_16.graph(1000, false) + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be(output2) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + gradInput1 should be(gradInput2) + model.getParameters().equals(graphModel.getParameters()) should be(true) + } + + "Vgg_19 graph" should "be correct" in { + Random.setSeed(1) + val batchSize = 1 + val input = Tensor[Float](batchSize, 3, 224, 224).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](1000).apply1(e => Random.nextFloat()) + + RNG.setSeed(1000) + val model = Vgg_19(1000, false) + RNG.setSeed(1000) + val graphModel = DynamicTestModels.Vgg_19.graph(1000, false) + + val output1 = model.forward(input).toTensor[Float] + val output2 = graphModel.forward(input).toTensor[Float] + output1 should be(output2) + + val gradInput1 = model.backward(input, gradOutput) + val gradInput2 = graphModel.backward(input, gradOutput) + gradInput1 should be(gradInput2) + model.getParameters().equals(graphModel.getParameters()) should be(true) + } + + "Dynamic Graph backward sequential with propagateBack false in the " + + "first" should "work properly" in { + RandomGenerator.RNG.setSeed(1000) + val input = Reshape(Array(1, 28, 28)).setName("reshape").inputs() + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1").inputs(input) + val tanh1 = Tanh().inputs(conv1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1) + val tanh2 = Tanh().inputs(pool1) + val conv2 = SpatialConvolution(6, 12, 5, 5).inputs(tanh2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(conv2) + val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2) + val fc1 = Linear(12 * 4 * 4, 100).inputs(reshape) + val tanh3 = Tanh().inputs(fc1) + val fc2 = Linear(100, 10).inputs(tanh3) + val output = LogSoftMax().inputs(fc2) + + RandomGenerator.RNG.setSeed(1000) + val input2 = Reshape(Array(1, 28, 28)).inputs() + val conv1_2 = SpatialConvolution(1, 6, 5, 5).setName("conv1").inputs(input2) + val tanh1_2 = Tanh().inputs(conv1_2) + val pool1_2 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1_2) + val tanh2_2 = Tanh().inputs(pool1_2) + val conv2_2 = SpatialConvolution(6, 12, 5, 5).inputs(tanh2_2) + val pool2_2 = SpatialMaxPooling(2, 2, 2, 2).inputs(conv2_2) + val reshape_2 = Reshape(Array(12 * 4 * 4)).inputs(pool2_2) + val fc1_2 = Linear(12 * 4 * 4, 100).inputs(reshape_2) + val tanh3_2 = Tanh().inputs(fc1_2) + val fc2_2 = Linear(100, 10).inputs(tanh3_2) + val output_2 = LogSoftMax().inputs(fc2_2) + + val funcModelNoBack = Graph.dynamic(input, output) + val funcModelOriginal = Graph.dynamic(input2, output_2) + + funcModelNoBack.stopGradient(Array("reshape")) + + val inputData = Tensor(4, 28 * 28).rand() + val outputData1 = funcModelOriginal.forward(inputData) // warm up + var start = System.nanoTime() + funcModelOriginal.forward(inputData) + println(s"seq model forward time is ${ (System.nanoTime() - start) / 1e6 }ms") + start = System.nanoTime() + val outputData2 = funcModelNoBack.forward(inputData) + println(s"funcModel model forward time is ${ (System.nanoTime() - start) / 1e6 }ms") + + outputData1 should be(outputData2) + + val gradient = Tensor(4, 10).rand() + start = System.nanoTime() + val gradientBPOriginal = funcModelOriginal.backward(inputData, gradient) + println(s"seq model backward time is ${ (System.nanoTime() - start) / 1e6 }ms") + start = System.nanoTime() + val gradientBPNoBack = funcModelNoBack.backward(inputData, gradient) + println(s"funcModel model backward time is ${ (System.nanoTime() - start) / 1e6 }ms") + + gradientBPNoBack.toTensor.nElement() should be(0) + val namedModule1 = funcModelOriginal.getParametersTable() + val namedModule2 = funcModelNoBack.getParametersTable() + namedModule1("conv1").asInstanceOf[Table] should + equal(namedModule2("conv1").asInstanceOf[Table]) + funcModelOriginal.getParameters()._2 should be(funcModelNoBack.getParameters()._2) + } + + "Dynamic Graph backward propagateBack false in the middle" should "work properly " + + "in sequential lenet" in { + RandomGenerator.RNG.setSeed(1000) + val input = Reshape(Array(1, 28, 28)).setName("r1").inputs() + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1").inputs(input) + val tanh1 = Tanh().setName("tanh1").inputs(conv1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).setName("pool1").inputs(tanh1) + val tanh2 = Tanh().setName("tanh2").inputs(pool1) + val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2").inputs(tanh2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(conv2) + val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2) + val fc1 = Linear(12 * 4 * 4, 100).inputs(reshape) + val tanh3 = Tanh().inputs(fc1) + val fc2 = Linear(100, 10).inputs(tanh3) + val output = LogSoftMax().inputs(fc2) + + RandomGenerator.RNG.setSeed(1000) + val input2 = Reshape(Array(1, 28, 28)).setName("r1").inputs() + val conv1_2 = SpatialConvolution(1, 6, 5, 5).setName("conv1").inputs(input2) + val tanh1_2 = Tanh().setName("tanh1").inputs(conv1_2) + val pool1_2 = SpatialMaxPooling(2, 2, 2, 2).setName("pool1").inputs(tanh1_2) + val tanh2_2 = Tanh().setName("tanh2").inputs(pool1_2) + val conv2_2 = SpatialConvolution(6, 12, 5, 5).inputs(tanh2_2) + val pool2_2 = SpatialMaxPooling(2, 2, 2, 2).inputs(conv2_2) + val reshape_2 = Reshape(Array(12 * 4 * 4)).inputs(pool2_2) + val fc1_2 = Linear(12 * 4 * 4, 100).inputs(reshape_2) + val tanh3_2 = Tanh().inputs(fc1_2) + val fc2_2 = Linear(100, 10).inputs(tanh3_2) + val output_2 = LogSoftMax().inputs(fc2_2) + + val funcModelNoBack = Graph.dynamic(input, output) + funcModelNoBack.stopGradient(Array("pool1")) + val funcModelOriginal = Graph.dynamic(input2, output_2) + + val inputData = Tensor(4, 28 * 28).rand() + val outputData1 = funcModelOriginal.forward(inputData) + val outputData2 = funcModelNoBack.forward(inputData) + outputData1 should be(outputData2) + + val gradient = Tensor(4, 10).rand() + val gradientBPOriginal = funcModelOriginal.backward(inputData, gradient) + val gradientBPNoBack = funcModelNoBack.backward(inputData, gradient) + + gradientBPNoBack.toTensor.nElement() should be(0) + val namedModule1 = Utils.getNamedModules(funcModelOriginal) + val namedModule2 = Utils.getNamedModules(funcModelNoBack) + namedModule2("r1").gradInput.toTensor.nElement() should be(0) + namedModule2("conv1").gradInput.toTensor.nElement() should be(0) + namedModule2("tanh1").gradInput.toTensor.nElement() should be(0) + namedModule2("pool1").gradInput.toTensor.nElement() should be(0) + + namedModule2("conv2").asInstanceOf[SpatialConvolution[Float]].parameters()._2 should be( + namedModule2("conv2").asInstanceOf[SpatialConvolution[Float]].parameters()._2) + } + + "Dynamic Graph propagate false in subpath" should "work properly" in { + RandomGenerator.RNG.setSeed(1000) + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc2, fc1), Array(output1, output2)) + RandomGenerator.RNG.setSeed(1000) + val fc1_1 = Linear(4, 2).inputs() + val fc2_1 = Linear(4, 2).inputs() + val cadd_1 = CAddTable().inputs(fc1_1, fc2_1) + val output1_1 = ReLU().setName("relu").inputs(cadd_1) + val output2_1 = Threshold(10.0).inputs(cadd_1) + + val graphNoBack = Graph.dynamic(Array(fc2_1, fc1_1), Array(output1_1, output2_1)) + graphNoBack.stopGradient(Array("relu")) + + RandomGenerator.RNG.setSeed(1000) + val fc1_2 = Linear(4, 2).inputs() + val fc2_2 = Linear(4, 2).inputs() + val cadd_2 = CAddTable().inputs(fc1_2, fc2_2) + val output2_2 = Threshold(10.0).inputs(cadd_2) + + val graphNoBackExpect = Graph.dynamic(Array(fc2_2, fc1_2), Array(output2_2)) + + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + fc1_1.element.getParameters()._1.apply1(_ => 1.0f) + fc2_1.element.getParameters()._1.apply1(_ => 2.0f) + fc1_2.element.getParameters()._1.apply1(_ => 1.0f) + fc2_2.element.getParameters()._1.apply1(_ => 2.0f) + + val input = T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))) + graph.forward(input) should be (graphNoBack.forward(input)) + + + val gradOutput = T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f))) + val gradInput = graph.backward(input, gradOutput) + + graph.backward(input, gradOutput) + graphNoBack.backward(input, gradOutput) + graphNoBackExpect.forward(input) + graphNoBackExpect.backward(input, Tensor(T(3.0f, 4.0f))) + output1_1.element.gradInput.toTensor.nElement() should be (0) + cadd_2.element.gradInput should be (cadd_1.element.gradInput) + fc1_2.element.gradInput should be (fc1_1.element.gradInput) + fc2_2.element.gradInput should be (fc2_1.element.gradInput) + output2.element.gradInput should be (output2_1.element.gradInput) + } + + "Dynamic Graph propagate false in concat subpath" should "work properly" in { + RandomGenerator.RNG.setSeed(1000) + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc2, fc1), Array(output1, output2)) + RandomGenerator.RNG.setSeed(1000) + val fc1_1 = Linear(4, 2).inputs() + val fc2_1 = Linear(4, 2).setName("fc2_1").inputs() + val cadd_1 = CAddTable().inputs(fc1_1, fc2_1) + val output1_1 = ReLU().inputs(cadd_1) + val output2_1 = Threshold(10.0).inputs(cadd_1) + + val graphNoBack = Graph.dynamic(Array(fc2_1, fc1_1), Array(output1_1, output2_1)) + graphNoBack.stopGradient(Array("fc2_1")) + + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + fc1_1.element.getParameters()._1.apply1(_ => 1.0f) + fc2_1.element.getParameters()._1.apply1(_ => 2.0f) + + val input = T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))) + graph.forward(input) should be (graphNoBack.forward(input)) + + + val gradOutput = T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f))) + + graph.backward(input, gradOutput) + graphNoBack.backward(input, gradOutput) + fc2_1.element.gradInput.toTensor.nElement() should be (0) + output2.element.gradInput should be (output2_1.element.gradInput) + fc1_1.element.gradInput should be (fc1.element.gradInput) + fc1_1.element.parameters()._2 should be (fc1.element.parameters()._2) + } + + "Dynamic Graph propagate false in concat subpath with longer edge" should "work properly" in { + RandomGenerator.RNG.setSeed(1000) + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc2, fc1), Array(output1, output2)) + RandomGenerator.RNG.setSeed(1000) + val reshape = Reshape(Array(4)).inputs() + val fc1_1 = Linear(4, 2).inputs() + val fc2_1 = Linear(4, 2).setName("fc2_1").inputs(reshape) + val cadd_1 = CAddTable().inputs(fc1_1, fc2_1) + val output1_1 = ReLU().inputs(cadd_1) + val output2_1 = Threshold(10.0).inputs(cadd_1) + + val graphNoBack = Graph.dynamic(Array(reshape, fc1_1), Array(output1_1, output2_1)) + graphNoBack.stopGradient(Array("fc2_1")) + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + fc1_1.element.getParameters()._1.apply1(_ => 1.0f) + fc2_1.element.getParameters()._1.apply1(_ => 2.0f) + + val input = T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))) + graph.forward(input) should be (graphNoBack.forward(input)) + + + val gradOutput = T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f))) + + graph.backward(input, gradOutput) + graphNoBack.backward(input, gradOutput) + fc2_1.element.gradInput.toTensor.nElement() should be (0) + output2.element.gradInput should be (output2_1.element.gradInput) + fc1_1.element.gradInput should be (fc1.element.gradInput) + fc1_1.element.parameters()._2 should be (fc1.element.parameters()._2) + reshape.element.gradInput.toTensor.nElement() should be (0) + } + + "Dynamic Graph propagate false reset to true" should "work properly" in { + RandomGenerator.RNG.setSeed(1000) + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc2, fc1), Array(output1, output2)) + RandomGenerator.RNG.setSeed(1000) + val fc1_1 = Linear(4, 2).inputs() + val fc2_1 = Linear(4, 2).setName("fc2_1").inputs() + val cadd_1 = CAddTable().inputs(fc1_1, fc2_1) + val output1_1 = ReLU().inputs(cadd_1) + val output2_1 = Threshold(10.0).inputs(cadd_1) + + val graphNoBack = Graph.dynamic(Array(fc2_1, fc1_1), Array(output1_1, output2_1)) + graphNoBack.stopGradient(Array("fc2_1")) + + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + fc1_1.element.getParameters()._1.apply1(_ => 1.0f) + fc2_1.element.getParameters()._1.apply1(_ => 2.0f) + + val input = T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))) + graph.forward(input) should be (graphNoBack.forward(input)) + + + val gradOutput = T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f))) + + graph.backward(input, gradOutput) + graphNoBack.backward(input, gradOutput) + fc2_1.element.gradInput.toTensor.nElement() should be (0) + output2.element.gradInput should be (output2_1.element.gradInput) + fc1_1.element.gradInput should be (fc1.element.gradInput) + fc1_1.element.parameters()._2 should be (fc1.element.parameters()._2) + + // reset propagateBack + graphNoBack.reset() + graphNoBack.buildBackwardGraph() + graphNoBack.zeroGradParameters() + graphNoBack.forward(input) should be (graph.forward(input)) + graphNoBack.backward(input, gradOutput) + + graphNoBack.parameters()._1 should be (graph.parameters()._1) + + graphNoBack.parameters()._2 should be (graph.parameters()._2) + } + + "Dynamic Graph backpropagation" should "ignore nodes on non output path" in { + val node1 = Identity[Float]().setName("node1").inputs() + val node2 = Identity[Float]().setName("node2").inputs(node1) + val node3 = Identity[Float]().setName("node3").inputs(node2) + val node4 = Identity[Float]().setName("node4").inputs(node2) + + val model1 = Graph[Float](node1, node3) + model1.forward(Tensor[Float](T(1.0f, 2.0f))) should be(Tensor[Float](T(1.0f, 2.0f))) + model1.backward(Tensor[Float](T(1.0f, 2.0f)), Tensor[Float](T(3.0f, 4.0f))) should be( + Tensor[Float](T(3.0f, 4.0f))) + + val model2 = Graph[Float](node1, Array(node3, node4)) + model2.forward(Tensor[Float](T(1.0f, 2.0f))) should be(T(Tensor[Float](T(1.0f, 2.0f)), + Tensor[Float](T(1.0f, 2.0f)))) + model2.backward(Tensor[Float](T(1.0f, 2.0f)), T(Tensor[Float](T(3.0f, 4.0f)), + Tensor[Float](T(7.0f, 10.0f)))) should be( + Tensor[Float](T(10.0f, 14.0f))) + } + + "markdown test" should "work" in { + val reshape = Reshape(Array(4)).inputs() + val fc1 = Linear(4, 2).setName("fc1").inputs() + val fc2 = Linear(4, 2).setName("fc2").inputs(reshape) + val cadd_1 = CAddTable().setName("cadd").inputs(fc1, fc2) + val output1_1 = ReLU().inputs(cadd_1) + val output2_1 = Threshold(10.0).inputs(cadd_1) + + val model = Graph.dynamic(Array(reshape, fc1), Array(output1_1, output2_1)) + + val input = T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))) + val gradOutput = T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f))) + + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + model.zeroGradParameters() + println("output1: \n", model.forward(input)) + model.backward(input, gradOutput) + model.updateParameters(1) + println("fc2 weight \n", fc2.element.parameters()._1(0)) + + + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + model.zeroGradParameters() + model.freeze("fc2") + println("output2: \n", model.forward(input)) + model.backward(input, gradOutput) + model.updateParameters(1) + println("fc2 weight \n", fc2.element.parameters()._1(0)) + + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + model.zeroGradParameters() + model.unFreeze() + println("output3: \n", model.forward(input)) + model.backward(input, gradOutput) + model.updateParameters(1) + println("fc2 weight \n", fc2.element.parameters()._1(0)) + + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + model.stopGradient(Array("cadd")) + model.zeroGradParameters() + println("output4: \n", model.forward(input)) + model.backward(input, gradOutput) + model.updateParameters(1) + println("fc1 weight \n", fc1.element.parameters()._1(0)) + println("fc2 weight \n", fc2.element.parameters()._1(0)) + } + + "Dynamic Graph setFreeze" should "work properly" in { + RandomGenerator.RNG.setSeed(1000) + val fc1 = Linear(4, 2).inputs() + val fc2 = Linear(4, 2).inputs() + val cadd = CAddTable().inputs(fc1, fc2) + val output1 = ReLU().inputs(cadd) + val output2 = Threshold(10.0).inputs(cadd) + + val graph = Graph.dynamic(Array(fc2, fc1), Array(output1, output2)) + RandomGenerator.RNG.setSeed(1000) + val reshape = Reshape(Array(4)).inputs() + val fc1_1 = Linear(4, 2).inputs() + val fc2_1 = Linear(4, 2).setName("fc2_1").inputs(reshape) + val cadd_1 = CAddTable().inputs(fc1_1, fc2_1) + val output1_1 = ReLU().inputs(cadd_1) + val output2_1 = Threshold(10.0).inputs(cadd_1) + + val graphNoBack = Graph.dynamic(Array(reshape, fc1_1), Array(output1_1, output2_1)) + graphNoBack.stopGradient(Array("fc2_1")) + + fc1.element.getParameters()._1.apply1(_ => 1.0f) + fc2.element.getParameters()._1.apply1(_ => 2.0f) + fc1_1.element.getParameters()._1.apply1(_ => 1.0f) + fc2_1.element.getParameters()._1.apply1(_ => 2.0f) + + val input = T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), + Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))) + graph.forward(input) should be (graphNoBack.forward(input)) + + + val gradOutput = T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f))) + + graph.backward(input, gradOutput) + graphNoBack.backward(input, gradOutput) + fc2_1.element.gradInput.toTensor.nElement() should be (0) + output2.element.gradInput should be (output2_1.element.gradInput) + fc1_1.element.gradInput should be (fc1.element.gradInput) + fc1_1.element.parameters()._2 should be (fc1.element.parameters()._2) + reshape.element.gradInput.toTensor.nElement() should be (0) + } + + "save graph to tensorboard log dir" should "work" in { + System.setProperty("bigdl.localMode", "true") + Engine.init + val tmpFile = java.io.File.createTempFile("Dynamic Graph", "tensorboard") + val absolutePath = tmpFile.getAbsolutePath + tmpFile.delete() + + val model = DynamicTestModels.Inception_v1_NoAuxClassifier.graph(1000, true) + .asInstanceOf[Graph[Float]] + model.saveGraphTopology(absolutePath) + System.clearProperty("bigdl.localMode") + } + + "Dynamic Graph" should "support switch with two branch" in { + val data = Input("data") + val condition = Input("condition") + val swtich = ControlNodes.switch(condition, data) + val echo1 = Echo().inputs(swtich.trueEdge()) + val echo2 = Echo().inputs(swtich.falseEdge()) + + val model = Graph.dynamic(Array(data, condition), Array(echo1), None, false) + val result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(true)))) + result.toTensor should be(Tensor[Float](T(1))) + + intercept[LayerException] { + model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(false)))) + } + } + + "Dynamic Graph" should "support switch with two branch with merge" in { + val data = Input("data") + val condition = Input("condition") + val swtich = ControlNodes.switch(condition, data) + val echo1 = Echo().inputs(swtich.trueEdge()) + val echo2 = Echo().inputs(swtich.falseEdge()) + val add1 = AddConstant(1).inputs(echo1) + val add5 = AddConstant(5).inputs(echo2) + val merge = ControlNodes.merge(add1, add5) + val output = Identity().inputs(merge) + + val model = Graph.dynamic(Array(data, condition), Array(output), None, false) + var result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(true)))) + result.toTensor should be(Tensor[Float](T(2))) + result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(false)))) + result.toTensor should be(Tensor[Float](T(6))) + } + + "Dynamic Graph backward with stopGradient" should "not remove stopGradient recursive" in { + val data = Input() + val d1 = Identity().inputs(data) + val d2 = Identity().inputs(d1) + val d3 = Identity().inputs(data) + val d4 = Identity().setName("d4").inputs(d3) + val d5 = Identity().inputs(d4) + + val model = Graph.dynamic(data, Array(d2, d5)) + val output = model.forward(Tensor[Float](T(1, 2, 3))).toTable + output[Tensor[Float]](1) should be(Tensor[Float](T(1, 2, 3))) + output[Tensor[Float]](2) should be(Tensor[Float](T(1, 2, 3))) + + model.stopGradient(Array("d4")) + model.backward(Tensor[Float](T(1, 2, 3)), T(Tensor[Float](T(2, 7, 9)), + Tensor[Float](T(1, 3, 5)))) should be(Tensor[Float](T(2, 7, 9))) + } + + "Dynamic Graph forward" should "not execute unrelated node" in { + val data = Identity().setName("input").inputs() + var isExecuted = false + val l1 = Identity().setName("l1").inputs(data) + val l2 = Identity().setName("l2").inputs(l1) + val l3 = Identity().setName("l3").inputs(l2) + val l4 = Echo().setName("l4").setFeval((a, b) => isExecuted = true).inputs(l1) + + val model = Graph.dynamic(data, l3) + model.forward(Tensor(T(1))) + isExecuted should be(false) + } + + "Dynamic Graph backward" should "not execute unrelated node" in { + val data = Identity().setName("input").inputs() + val const = Const(Tensor(T(1, 2))).setName("const").inputs() + var isExecuted = false + val l1 = Echo().setName("l1").setBeval((a, b, c) => isExecuted = true).inputs(const) + val cadd = CAddTable().setName("cadd").inputs(data, l1) + + val model = Graph.dynamic(data, cadd) + model.forward(Tensor(T(3, 5))) should be(Tensor(T(4, 7))) + model.backward(Tensor(T(3, 5)), Tensor(T(1, 2))) should be(Tensor(T(1, 2))) + isExecuted should be(false) + } + + "Dynamic Graph backward" should "not execute unrelated node 2" in { + val data = Identity().setName("input").inputs() + val const = Const(Tensor(T(1, 2))).setName("const").inputs() + var isExecuted1 = false + val l1 = Echo().setName("l1").setBeval((a, b, c) => isExecuted1 = true).inputs(const) + val cadd = CAddTable().setName("cadd").inputs(data, l1) + val l2 = Identity().setName("l2").inputs(cadd) + var isExecuted2 = false + var isExecuted3 = false + val echo = Echo().setName("echo") + .setFeval((a, b) => isExecuted2 = true) + .setBeval((a, b, c) => isExecuted3 = true).inputs(cadd) + val l3 = Identity().setName("l3").inputs(echo) + + val model = Graph.dynamic(data, l2) + model.forward(Tensor(T(3, 5))) should be(Tensor(T(4, 7))) + model.backward(Tensor(T(3, 5)), Tensor(T(1, 2))) should be(Tensor(T(1, 2))) + isExecuted1 should be(false) + isExecuted2 should be(false) + isExecuted3 should be(false) + } + + "Dynamic Graph get name" should "be correct" in { + val data = Identity().setName("input").inputs() + val const = Const(Tensor(T(1, 2))).setName("const").inputs() + var isExecuted1 = false + val l1 = Echo().setName("l1").setBeval((a, b, c) => isExecuted1 = true).inputs(const) + val cadd = CAddTable().setName("cadd").inputs(data, l1) + val l2 = Identity().setName("l2").inputs(cadd) + var isExecuted2 = false + var isExecuted3 = false + val echo = Echo().setName("echo") + .setFeval((a, b) => isExecuted2 = true) + .setBeval((a, b, c) => isExecuted3 = true).inputs(cadd) + val l3 = Identity().setName("l3").inputs(echo) + + val model = Graph.dynamic(data, l2) + model.node("l1") should be(l1) + + intercept[NoSuchElementException] { + model.node("ll1") + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index 9eb17e81232..46ae54d6452 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -34,7 +34,7 @@ import scala.util.Random import org.scalatest.{FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Parallel -class GraphSpec extends FlatSpec with Matchers { +class StaticGraphSpec extends FlatSpec with Matchers { "Graph init" should "throw exceptions when there's cycle" in { val fc1 = Linear(4, 2).inputs() val relu1 = ReLU().inputs(fc1) @@ -483,14 +483,6 @@ class GraphSpec extends FlatSpec with Matchers { seqModel.getParameters()._2 should be(funcModel.getParameters()._2) } - "shift" should "be correct" in { - val node = Reshape(Array(1, 28, 28)).inputs() - val test = Graph(node, node) - test.shift(Array(1, 2, 3, 4), 1, 1) should be(Array(1, 2, 3, 4)) - test.shift(Array(1, 2, 3, 4), 1, 3) should be(Array(1, 3, 4, 2)) - test.shift(Array(1, 2, 3, 4), 3, 1) should be(Array(1, 4, 2, 3)) - } - "ResNet-18 basic block shortcut type A" should "be correct" in { RandomGenerator.RNG.setSeed(1000) val seqModel = ModelUntils.ResNet.basicBlockSeq(16, 16, 1, "A") @@ -1022,7 +1014,7 @@ class GraphSpec extends FlatSpec with Matchers { // reset propagateBack graphNoBack.reset() - graphNoBack.build() + graphNoBack.buildBackwardGraph() graphNoBack.zeroGradParameters() graphNoBack.forward(input) should be (graph.forward(input)) graphNoBack.backward(input, gradOutput) @@ -1032,6 +1024,24 @@ class GraphSpec extends FlatSpec with Matchers { graphNoBack.parameters()._2 should be (graph.parameters()._2) } + "graph backpropagation" should "ignore nodes on non output path" in { + val node1 = Identity[Float]().setName("node1").inputs() + val node2 = Identity[Float]().setName("node2").inputs(node1) + val node3 = Identity[Float]().setName("node3").inputs(node2) + val node4 = Identity[Float]().setName("node4").inputs(node2) + + val model1 = Graph[Float](node1, node3) + model1.forward(Tensor[Float](T(1.0f, 2.0f))) should be(Tensor[Float](T(1.0f, 2.0f))) + model1.backward(Tensor[Float](T(1.0f, 2.0f)), Tensor[Float](T(3.0f, 4.0f))) should be( + Tensor[Float](T(3.0f, 4.0f))) + + val model2 = Graph[Float](node1, Array(node3, node4)) + model2.forward(Tensor[Float](T(1.0f, 2.0f))) should be(T(Tensor[Float](T(1.0f, 2.0f)), + Tensor[Float](T(1.0f, 2.0f)))) + model2.backward(Tensor[Float](T(1.0f, 2.0f)), T(Tensor[Float](T(3.0f, 4.0f)), + Tensor[Float](T(7.0f, 10.0f)))) should be( + Tensor[Float](T(10.0f, 14.0f))) + } "markdown test" should "work" in { val reshape = Reshape(Array(4)).inputs() @@ -1043,8 +1053,6 @@ class GraphSpec extends FlatSpec with Matchers { val model = Graph(Array(reshape, fc1), Array(output1_1, output2_1)) - - val input = T(Tensor(T(0.1f, 0.2f, -0.3f, -0.4f)), Tensor(T(0.5f, 0.4f, -0.2f, -0.1f))) val gradOutput = T(Tensor(T(1.0f, 2.0f)), Tensor(T(3.0f, 4.0f))) @@ -1086,6 +1094,7 @@ class GraphSpec extends FlatSpec with Matchers { println("fc1 weight \n", fc1.element.parameters()._1(0)) println("fc2 weight \n", fc2.element.parameters()._1(0)) } + "graph setFreeze" should "work properly" in { RandomGenerator.RNG.setSeed(1000) val fc1 = Linear(4, 2).inputs() @@ -1146,7 +1155,7 @@ class GraphSpec extends FlatSpec with Matchers { val echo1 = Echo().inputs(swtich.trueEdge()) val echo2 = Echo().inputs(swtich.falseEdge()) - val model = Graph(Array(data, condition), Array(echo1), None, false) + val model = Graph.dynamic(Array(data, condition), Array(echo1), None, false) val result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(true)))) result.toTensor should be(Tensor[Float](T(1))) @@ -1166,7 +1175,7 @@ class GraphSpec extends FlatSpec with Matchers { val merge = ControlNodes.merge(add1, add5) val output = Identity().inputs(merge) - val model = Graph(Array(data, condition), Array(output), None, false) + val model = Graph.dynamic(Array(data, condition), Array(output), None, false) var result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(true)))) result.toTensor should be(Tensor[Float](T(2))) result = model.forward(T(Tensor[Float](T(1)), Tensor[Boolean](T(false)))) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/UtilSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/UtilSpec.scala new file mode 100644 index 00000000000..c752d9a12fb --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/UtilSpec.scala @@ -0,0 +1,26 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils + +import org.scalatest.{FlatSpec, Matchers} + +class UtilSpec extends FlatSpec with Matchers { + "shift" should "be correct" in { + Util.shift(Array(1, 2, 3, 4), 1, 1) should be(Array(1, 2, 3, 4)) + Util.shift(Array(1, 2, 3, 4), 1, 3) should be(Array(1, 3, 4, 2)) + Util.shift(Array(1, 2, 3, 4), 3, 1) should be(Array(1, 4, 2, 3)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 57972e4e87d..485be12ba62 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -495,6 +495,18 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll val linearBias = linear.bias val variables = Some(Array(linearWeight), Array(linearBias)) val graphWithVariable = Graph[Float](Array(linearNode), Array(linearNode), + variables).setName("graphWithVariable") + val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) + runSerializationTest(graphWithVariable, input) + } + + "Dynamic Graph with variables serializer" should "work properly" in { + val linear = Linear[Float](2, 2) + val linearNode = linear.inputs() + val linearWeight = linear.weight + val linearBias = linear.bias + val variables = Some(Array(linearWeight), Array(linearBias)) + val graphWithVariable = Graph.dynamic[Float](Array(linearNode), Array(linearNode), variables, false).setName("graphWithVariable") val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) runSerializationTest(graphWithVariable, input) From 7de983d1684f47ecb4727bf171a3ccadbed1dd90 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 14 Dec 2017 15:10:07 +0800 Subject: [PATCH 0589/1065] Add proper exception handling (#2020) * add proper exception handling * per review * refinement per review * per review --- .../caffe/CaffeConversionException.scala | 30 ++++++++++ .../bigdl/dllib/utils/caffe/CaffeLoader.scala | 46 ++++++++++++--- .../dllib/utils/caffe/CaffePersister.scala | 3 +- .../bigdl/dllib/utils/caffe/Converter.scala | 23 ++++++-- .../dllib/utils/caffe/LayerConverter.scala | 43 +++++++++----- .../dllib/utils/caffe/V1LayerConverter.scala | 56 ++++++++++--------- .../bigdl/dllib/utils/CaffeLoaderSpec.scala | 3 +- .../dllib/utils/CaffePersisterSpec.scala | 4 +- 8 files changed, 150 insertions(+), 58 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeConversionException.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeConversionException.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeConversionException.scala new file mode 100644 index 00000000000..d4259898819 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeConversionException.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.caffe + +import org.apache.commons.lang.exception.ExceptionUtils + + +class CaffeConversionException (var conversionMsg: String, + val error: Throwable = null) extends RuntimeException { + override def toString: String = { + val erroMsg = s"Caffe conversion error : $conversionMsg" + if (error != null) { + erroMsg + ExceptionUtils.getFullStackTrace(error) + } + erroMsg + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 0b269814968..77d27c6409d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -19,6 +19,7 @@ import java.io._ import caffe.Caffe import caffe.Caffe._ +import com.google.protobuf.TextFormat.ParseException import com.google.protobuf.{CodedInputStream, GeneratedMessage, TextFormat} import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph.ModuleNode @@ -146,6 +147,11 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, builder.addLayers(copyBlobs(weightLayer, v1Layer).asInstanceOf[V1LayerParameter]) } else { builder.addLayers(v1Layer) + if (customizedConverters ==null || + !customizedConverters.contains(v1Layer.getType.toString.toUpperCase)) { + logger.warn(s"layer $name if type ${v1Layer.getType.toString}" + + s"does not exist in weight file") + } } }) @@ -156,6 +162,10 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, builder.addLayer(copyBlobs(weightLayer, v2Layer).asInstanceOf[LayerParameter]) } else { builder.addLayer(v2Layer) + if (customizedConverters ==null || + !customizedConverters.contains(v2Layer.getType.toUpperCase)) { + logger.warn(s"layer $name if type ${v2Layer.getType} does not exist in weight file") + } } }) builder.build @@ -255,7 +265,8 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, private def copyParameter(name: String, params: Table): Unit = { if (params == null || (!params.contains("weight") && !params.contains("bias"))) return if (!name2LayerV2.contains(name) && !name2LayerV1.contains(name)) { - if (matchAll) throw new Exception(s"module $name cannot map a layer in caffe model") + if (matchAll) throw new CaffeConversionException(s"module $name " + + s"cannot map a layer in caffe model") logger.info(s"$name uses initialized parameters") return } @@ -517,16 +528,28 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, object CaffeLoader { + /** + * Load weight for pre-defined model + * @param model pre-defined model + * @param defPath prototxt file which defines the network + * @param modelPath weight file which contains the parameters + * @param matchAll if we need to match all layers from prototxt in weight file + * @param customizedConverters customized converters + * @param ev tensor numeric + * @tparam T data type + * @return pre-defined model populated with weights + */ def load[T: ClassTag](model: Module[T], - defPath: String, modelPath: String, matchAll: Boolean = true) + defPath: String, modelPath: String, matchAll: Boolean = true, + customizedConverters : mutable.HashMap[String, Customizable[T]] = null) (implicit ev: TensorNumeric[T]): Module[T] = { - val caffeLoader = new CaffeLoader[T](defPath, modelPath, matchAll) + val caffeLoader = new CaffeLoader[T](defPath, modelPath, matchAll, customizedConverters) caffeLoader.copyParameters(model) } /** - * load caffe model dynamically from binary and prototxt file - * @param defPath prototxt file which illustrate the caffe model structure + * load caffe model dynamically from prototxt and binary files + * @param defPath prototxt file which illustrates the caffe model structure * @param modelPath binary file containing the weight and bias * @param customizedConverters customized layer converter * @param outputNames additional output layer names besides the default(layers without next nodes) @@ -537,7 +560,16 @@ object CaffeLoader { customizedConverters : mutable.HashMap[String, Customizable[T]] = null, outputNames: Array[String] = Array[String]()) (implicit ev: TensorNumeric[T]): (Module[T], ParallelCriterion[T]) = { - val caffeLoader = new CaffeLoader[T](defPath, modelPath, true, customizedConverters) - caffeLoader.createCaffeModel(outputNames) + try { + val caffeLoader = new CaffeLoader[T](defPath, modelPath, true, customizedConverters) + caffeLoader.createCaffeModel(outputNames) + } catch { + case parseException : ParseException => + throw new CaffeConversionException("Parsing caffe model error," + + "only standard Caffe format is supported" + , parseException) + case conversionExcepion : CaffeConversionException => + throw conversionExcepion + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala index 32956797450..fb4444fb656 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffePersister.scala @@ -70,7 +70,8 @@ class CaffePersister[T: ClassTag](val prototxtPath: String, return module.asInstanceOf[Graph[T]] } // other containers/layers to be supported later - throw new UnsupportedOperationException(s"container $module is not supported!") + throw new CaffeConversionException(s"container $module is not supported," + + s"only graph supported") } // create caffe layers graph based on BigDL execution plan private def convertToCaffe() : Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index d0a3aaf83ae..ec9cfb93da7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -62,7 +62,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { if (customizedConverter.contains(layerType)) { return customizedConverter(layerType)(layer) } - throw new UnsupportedOperationException(s"$layerType is not supported in BigDL for now") + throw new CaffeConversionException(s"$layerType is not supported in BigDL for now") } def convertLayerFromCaffe(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -189,9 +189,11 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { private def fromCaffePreLU(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val layerName = getLayerName(layer) - val weightBlob = getBlob(layer, 0).get - val nOutPlane = if (weightBlob.hasShape) weightBlob.getShape.getDim(0) - else weightBlob.getNum + val weightBlob = getBlob(layer, 0) + sanityBlobCheck(layer, "weight", weightBlob) + val weight = weightBlob.get + val nOutPlane = if (weight.hasShape) weight.getShape.getDim(0) + else weight.getNum Seq(PReLU[T](nOutPlane.toInt).setName(layerName).inputs()) } @@ -325,7 +327,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { case cadd : CAdd[_] => toCaffeEltWiseAdd(moduleNode, bottoms, nextSize) case csub : CSubTable[_] => toCaffeEltWiseSub(moduleNode, bottoms, nextSize) case sequantial : Sequential[_] => toCaffeSequential(moduleNode, bottoms, nextSize) - case _ => throw new UnsupportedOperationException(s"${moduleNode} is not supported") + case _ => throw new CaffeConversionException(s"${moduleNode} is not supported") } model } @@ -454,7 +456,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { val map = new mutable.HashMap[String, Int]() val layer = classOf[SpatialFullConvolution[T]].cast(module) if (layer.adjW != 0 || layer.adjH != 0) { - throw new IllegalArgumentException("Caffe doesn't support extra width/height amending") + throw new CaffeConversionException("Caffe doesn't support extra width/height amending") } val nInputPlane = layer.nOutputPlane val nOutputPlane = layer.nInputPlane @@ -616,6 +618,15 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { protected def getBlob(layer : GeneratedMessage, ind: Int): Option[Caffe.BlobProto] + protected def sanityBlobCheck(layer : GeneratedMessage, blobInfo: String, + blob : Option[Caffe.BlobProto]) : Unit = { + val name = getLayerName(layer) + val tpe = getLayerType(layer) + if (!blob.isDefined) { + throw new CaffeConversionException(s"$tpe : $name missing $blobInfo in binary file") + } + } + private def init() = { caffe2BigDL("CONVOLUTION") = fromCaffeConvolution caffe2BigDL("DECONVOLUTION") = fromCaffeConvolution diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index 82a20b16822..f7600fa9a65 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -39,13 +39,15 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert override protected def fromCaffeConvolution(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val param = getConvolutionParam(layer).get val group = if (param.getGroup == 0) 1 else param.getGroup - val weightBlob = getBlob(layer, 0).get + var weightBlob = getBlob(layer, 0) + sanityBlobCheck(layer, "weight", weightBlob) + val weight = weightBlob.get val biasBlob = getBlob(layer, 1) val withBias = biasBlob.isDefined - val nInputPlane = if (weightBlob.hasShape) weightBlob.getShape.getDim(1) * group - else weightBlob.getChannels * group - val nOutPlane = if (weightBlob.hasShape) weightBlob.getShape.getDim(0) - else weightBlob.getNum + val nInputPlane = if (weight.hasShape) weight.getShape.getDim(1) * group + else weight.getChannels * group + val nOutPlane = if (weight.hasShape) weight.getShape.getDim(0) + else weight.getNum var kw = param.getKernelW var kh = param.getKernelH var dw = param.getStrideW @@ -94,13 +96,15 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert val param = getInnerProductParam(layer).get val withBias = param.getBiasTerm val layerName = getLayerName(layer) - val weightBlob = getBlob(layer.asInstanceOf[LayerParameter], 0).get + val weightBlob = getBlob(layer.asInstanceOf[LayerParameter], 0) + sanityBlobCheck(layer, "weight", weightBlob) + val weight = weightBlob.get var nInputPlane = 0 - if (weightBlob.hasShape) { - nInputPlane = weightBlob.getShape.getDim(1).toInt + if (weight.hasShape) { + nInputPlane = weight.getShape.getDim(1).toInt } else { - nInputPlane = weightBlob.getWidth + nInputPlane = weight.getWidth } val nOutputPlane = param.getNumOutput val linear = Linear[T](nInputPlane, nOutputPlane, withBias = withBias).setName(layerName) @@ -117,16 +121,22 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert override protected def fromCaffeBatchNormalization(layer: GeneratedMessage): Seq[ModuleNode[T]] = { - val weightBlob = getBlob(layer, 0).get - val nOutPlane = if (weightBlob.hasShape) weightBlob.getShape.getDim(0).toInt - else weightBlob.getNum + val weightBlob = getBlob(layer, 0) + sanityBlobCheck(layer, "weight", weightBlob) + val weight = weightBlob.get + val nOutPlane = if (weight.hasShape) weight.getShape.getDim(0).toInt + else weight.getNum val param = layer.asInstanceOf[LayerParameter].getBatchNormParam val eps = param.getEps val batchNorm = SpatialBatchNormalization[T](nOutPlane.toInt, eps, affine = false) .setName(getLayerName(layer)) - val scaleData = getBlob(layer, 2).get.getData(0) + val scalaBlob = getBlob(layer, 2) + sanityBlobCheck(layer, "scale", scalaBlob) + val scaleData = scalaBlob.get.getData(0) val scale = if (scaleData == 0) 0 else 1 / scaleData + sanityBlobCheck(layer, "mean", getBlob(layer, 0)) val means = getBlob(layer, 0).get.getDataList + sanityBlobCheck(layer, "variance", getBlob(layer, 1)) val variances = getBlob(layer, 1).get.getDataList batchNorm.runningMean.resize(nOutPlane) batchNorm.runningVar.resize(nOutPlane) @@ -167,8 +177,10 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert } Seq(Scale[T](size).setName(layerName).inputs()) } else { - val inputBlob = getBlob(layer, 0).get - val shape = inputBlob.getShape + val inputBlob = getBlob(layer, 0) + sanityBlobCheck(layer, "weight", getBlob(layer, 0)) + val input = inputBlob.get + val shape = input.getShape val axis = param.getAxis var numOfAxis = param.getNumAxes if (numOfAxis == -1) { @@ -184,6 +196,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert override protected def fromCaffeBias(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { // input blob val weightBlob = getBlob(layer, 0) + sanityBlobCheck(layer, "weight", weightBlob) val size = weightBlob.get.getShape.getDimList.asScala.map(_.toInt).toArray.product Seq(Add[T](size).setName(getLayerName(layer)).inputs()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala index 27ac5748b55..dcaa04246d4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/V1LayerConverter.scala @@ -39,14 +39,14 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve override protected def fromCaffeConvolution(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val param = getConvolutionParam(layer).get - val weightBlob = getBlob(layer, 0).get + val weightBlob = getBlob(layer, 0) + sanityBlobCheck(layer, "weight", weightBlob) + val weight = weightBlob.get val biasBlob = getBlob(layer, 1) - if (!biasBlob.isDefined) { - throw new RuntimeException(s"${getLayerName(layer)} without bias is not supported now") - } + val withBias = biasBlob.isDefined val group = if (param.getGroup == 0) 1 else param.getGroup - val channel = if (weightBlob.getShape.getDimCount > 1) weightBlob.getShape.getDim(1).toInt - else weightBlob.getChannels + val channel = if (weight.getShape.getDimCount > 1) weight.getShape.getDim(1).toInt + else weight.getChannels val nInputPlane = channel * group val nOutPlane = param.getNumOutput var kw = param.getKernelW @@ -77,10 +77,12 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve } val layerType = getLayerType(layer).toUpperCase if ("DECONVOLUTION" == layerType) { - Seq(SpatialFullConvolution[T](nOutPlane, nInputPlane, kw, kh, dw, dh, pw, ph, 0, 0, group) + Seq(SpatialFullConvolution[T](nOutPlane, nInputPlane, kw, kh, dw, dh, pw, ph, 0, 0, group, + noBias = !withBias) .setName(getLayerName(layer)).inputs()) } else { - Seq(SpatialConvolution[T](nInputPlane, nOutPlane, kw, kh, dw, dh, pw, ph, group) + Seq(SpatialConvolution[T](nInputPlane, nOutPlane, kw, kh, dw, dh, pw, ph, group, + withBias = withBias) .setName(getLayerName(layer)).inputs()) } } @@ -89,9 +91,11 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve val param = getInnerProductParam(layer).get val withBias = param.getBiasTerm val layerName = getLayerName(layer) - val weightBlob = getBlob(layer.asInstanceOf[V1LayerParameter], 0).get - val nInputPlane = if (weightBlob.getShape.getDimCount > 1) weightBlob.getShape.getDim(1).toInt - else weightBlob.getWidth + val weightBlob = getBlob(layer.asInstanceOf[V1LayerParameter], 0) + sanityBlobCheck(layer, "weight", weightBlob) + val weight = weightBlob.get + val nInputPlane = if (weight.getShape.getDimCount > 1) weight.getShape.getDim(1).toInt + else weight.getWidth val nOutputPlane = param.getNumOutput val linear = Linear[T](nInputPlane, nOutputPlane, withBias = withBias).setName(layerName) val node = linear.inputs() @@ -108,32 +112,32 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve // No implementation in V1 override protected def fromCaffeBatchNormalization(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { - throw new UnsupportedOperationException("Batch normalizaton is not supported in V1 Layer") + throw new CaffeConversionException("Batch normalizaton is not supported in V1 Layer") } // No implementation in V1 override protected def fromCaffeELU(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { - throw new UnsupportedOperationException("ELU is not supported in V1 Layer") + throw new CaffeConversionException("ELU is not supported in V1 Layer") } // No implementation in V1 override protected def fromCaffeReshape(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { - throw new UnsupportedOperationException("Reshape is not supported in V1 Layer") + throw new CaffeConversionException("Reshape is not supported in V1 Layer") } // No implementation in V1 override protected def fromCaffeScale(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { - throw new UnsupportedOperationException("Scale is not supported in V1 Layer") + throw new CaffeConversionException("Scale is not supported in V1 Layer") } // No implementation in V1 override protected def fromCaffeBias(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { - throw new UnsupportedOperationException("Bias is not supported in V1 Layer") + throw new CaffeConversionException("Bias is not supported in V1 Layer") } // No implementation in V1 override protected def fromCaffeTile(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { - throw new UnsupportedOperationException("Tile is not supported in V1 Layer") + throw new CaffeConversionException("Tile is not supported in V1 Layer") } override protected def fromCaffeInput(layer: GeneratedMessage): Seq[ModuleNode[T]] = { @@ -406,7 +410,7 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve override protected def toCaffeBatchNormalization(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("Batch normalization is not supported in V1Layer") + throw new CaffeConversionException("Batch normalization is not supported in V1Layer") } override protected def toCaffeConcat(module : AbstractModule[Activity, Activity, T], @@ -422,7 +426,7 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve override protected def toCaffeElu(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("ELU is not supported in V1Layer") + throw new CaffeConversionException("ELU is not supported in V1Layer") } override protected def toCaffeFlattern(module : AbstractModule[Activity, Activity, T], @@ -432,7 +436,7 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve override protected def toCaffeLog(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("LOG is not supported in V1Layer") + throw new CaffeConversionException("LOG is not supported in V1Layer") } override protected def toCaffePower(module : AbstractModule[Activity, Activity, T], @@ -445,27 +449,27 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve override protected def toCaffePReLu(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("PReLU is not supported in V1Layer") + throw new CaffeConversionException("PReLU is not supported in V1Layer") } override protected def toCaffeRecurrent(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("Recurrent is not supported in V1Layer") + throw new CaffeConversionException("Recurrent is not supported in V1Layer") } override protected def toCaffeReshape(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("Reshape is not supported in V1Layer") + throw new CaffeConversionException("Reshape is not supported in V1Layer") } override protected def toCaffeScale(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("Scale is not supported in V1Layer") + throw new CaffeConversionException("Scale is not supported in V1Layer") } override protected def toCaffeBias(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("Bias is not supported in V1Layer") + throw new CaffeConversionException("Bias is not supported in V1Layer") } override protected def toCaffeThreshold(module : AbstractModule[Activity, Activity, T], @@ -493,7 +497,7 @@ class V1LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Conve override protected def toCaffeTile(module : AbstractModule[Activity, Activity, T], bottoms : ArrayBuffer[String], nextSize : Int): Seq[GeneratedMessage] = { - throw new UnsupportedOperationException("Tile is not supported in V1Layer") + throw new CaffeConversionException("Tile is not supported in V1Layer") } override protected def toCaffeEltWiseMax(module : AbstractModule[Activity, Activity, T], diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala index a671213add9..d1846580157 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffeLoaderSpec.scala @@ -56,7 +56,8 @@ class CaffeLoaderSpec extends FlatSpec with Matchers { .add(Convolution(4, 3, 2, 2).setName("conv2")) .add(Linear(2, 27, withBias = false).setName("ip")) - val model = CaffeLoader.load[Double](module, prototxt, modelPath) + val model = CaffeLoader.load[Double](module, prototxt, modelPath, true, + convertMap) val parameters = model.getParametersTable() val conv1weight = Tensor(Storage(Array( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffePersisterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffePersisterSpec.scala index b9dd4bbdc34..6170f85646a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffePersisterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/CaffePersisterSpec.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericDouble import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.caffe.{CaffeLoader, CaffePersister, Customizable} +import com.intel.analytics.bigdl.utils.caffe.{CaffeConversionException, CaffeLoader, CaffePersister, Customizable} import scala.collection.mutable import scala.reflect.ClassTag @@ -118,7 +118,7 @@ class CaffePersisterSpec extends FlatSpec with Matchers{ .add(Convolution(4, 3, 2, 2).setName("conv2")) .add(View(27)).setName("view") .add(Linear(2, 27, withBias = false).setName("ip")) - intercept[UnsupportedOperationException] { + intercept[CaffeConversionException] { CaffePersister.persist("/tmp/test.prototxt", "/tmp/test.caffemodel", module) } From 62f33469b9be2ce0c4d2f56c1983c6c11dd0fa7a Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Thu, 14 Dec 2017 15:25:02 +0800 Subject: [PATCH 0590/1065] bugfix for SparseJoinTable (#1933) (#1994) --- .../bigdl/dllib/tensor/SparseTensor.scala | 68 +++++++++++++++++-- .../bigdl/dllib/nn/SparseJoinTableSpec.scala | 26 +++++++ 2 files changed, 90 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 059b4a8624f..0b3585e93b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -1143,6 +1143,63 @@ object SparseTensor{ res } + /** + * Find index of last occurrence of value in the array from start index to end index. + * The array should be ordered ascending. + * + * @param array the array will be searched. + * @param value the element value to search for. + * @param start start index + * @param end last index + * @return index of last occurrence of value + */ + private def lastIndexOf[T: ClassTag]( + array: Array[T], + value: T, + start: Int, + end: Int)(implicit ev: TensorNumeric[T]): Int = { + if (start > end) return -1 + require(end <= array.length - 1, s"indexOf end should't exceed array size ${array.length - 1}" + + s", but got $end") + var i = start + while (i < end && array(i) == value) { + i += 1 + } + if (array(i) == value) { + i + } else { + i - 1 + } + } + + /** + * Find index of first occurrence of value in the array from start index to end index. + * + * @param array the array will be searched. + * @param value the element value to search for. + * @param start start index + * @param end last index + * @return index of first occurrence of value + */ + private def firstIndexOf[T: ClassTag]( + array: Array[T], + value: T, + start: Int, + end: Int)(implicit ev: TensorNumeric[T]): Int = { + if (start > end) return -1 + require(end <= array.length - 1, s"indexOf end should't exceed array size ${array.length - 1}" + + s", but got $end") + var i = start + while (i <= end && array(i) != value) { + i += 1 + } + if (i > end) { + -1 + } else { + i + } + } + /** * Concatenate a sequence of SparseTensor of n-dim to n-dim SparseTensor. * The size at n-dim will be concated. @@ -1198,6 +1255,7 @@ object SparseTensor{ var start = res._storageOffset var end = res._storageOffset val tensorsOffset = tensors.map(_.storageOffset() - 1).toArray + val tensorsMaxIndex = tensors.map(v => v.storageOffset() + v.nElement() - 2).toArray var j = 0 while (j < res.size(dim - 1)) { var index = 0 @@ -1205,10 +1263,12 @@ object SparseTensor{ while (index < tensors.size) { val currentTensor = tensors(index) val currentIndicesOffset = currentTensor._indicesOffset - val findIndexStart = currentTensor._indices(0).array().indexOf( - j + currentIndicesOffset(0), tensorsOffset(index)) - val findIndexEnd = currentTensor._indices(0).array().lastIndexOf( - j + currentIndicesOffset(0)) + val findIndexStart = + firstIndexOf(currentTensor._indices(0).array(), j + currentIndicesOffset(0), + tensorsOffset(index), tensorsMaxIndex(index)) + val findIndexEnd = + lastIndexOf(currentTensor._indices(0).array(), j + currentIndicesOffset(0), + tensorsOffset(index), tensorsMaxIndex(index)) val curLength = if (findIndexStart != -1 && findIndexEnd != -1) { findIndexEnd - findIndexStart + 1 } else { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala index aec9374986d..f825c7816a0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala @@ -105,4 +105,30 @@ class SparseJoinTableSpec extends FlatSpec with Matchers { Tensor.dense(out1) shouldEqual denseInput } + "Sparse JoinTable on narrowed table" should "return the same result 3" in { + val indices1 = Array(0, 1, 2, 3, 3) + val indices2 = Array(0, 1, 2, 3, 4) + val values1 = Array(1f, 2f, 3f, 4f, 5f) + val input1 = Tensor.sparse(Array(indices1, indices2), values1, Array(4, 5)) + .resize(Array(4, 5), 4) + val indices3 = Array(0, 1, 2, 3, 4, 4, 5) + val indices4 = Array(0, 1, 2, 3, 3, 4, 2) + val values2 = Array(6f, 7f, 8f, 9f, 10f, 11f, 12f) + val input2 = Tensor.sparse(Array(indices3, indices4), values2, Array(6, 5)) + .narrow(1, 2, 4) + val sparseModel = Sequential().add(ParallelTable().add(Identity()).add(Identity())) + .add(SparseJoinTable(2)) + + val sparseInput = T(input1, input2) + val output = sparseModel.forward(sparseInput).toTensor[Float] + + val exceptedIndices1 = Array(0, 0, 1, 1, 2, 2, 3, 3, 3) + val exceptedIndices2 = Array(0, 6, 1, 7, 2, 8, 3, 8, 9) + val exceptedValues = Array(1f, 7f, 2, 8, 3, 9, 4, 10, 11) + val exceptedOutput = Tensor.sparse(Array(exceptedIndices1, exceptedIndices2), + exceptedValues, Array(4, 10)) + + output should be (exceptedOutput) + } + } From af4903d2738055e0303bd2fd15c77ceedc4fe454 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 14 Dec 2017 16:40:23 +0800 Subject: [PATCH 0591/1065] Support BigDL for Spark on k8s (#2023) * maybe some refactor * handle k8s master url * k8s * add doc * add more doc * add doc --- .../intel/analytics/bigdl/utils/Engine.scala | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 14d3b90aef5..806f1c691cc 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -421,6 +421,23 @@ object Engine { total / core } Some(nodeNum, core) + } else if (master.toLowerCase.startsWith("k8s")) { + // Spark-on-kubernetes mode + val coreString = conf.get("spark.executor.cores", null) + val maxString = conf.get("spark.cores.max", null) + require(coreString != null, "Engine.init: Can't find executor core number" + + ", do you submit with --conf spark.executor.cores option") + require(maxString != null, "Engine.init: Can't find total core number" + + ". Do you submit with --conf spark.cores.max option") + val core = coreString.toInt + val nodeNum = dynamicAllocationExecutor(conf).getOrElse { + val total = maxString.toInt + require(total >= core && total % core == 0, s"Engine.init: total core " + + s"number($total) can't be divided " + + s"by single core number($core) provided to spark-submit") + total / core + } + Some(nodeNum, core) } else { throw new IllegalArgumentException(s"Engine.init: Unsupported master format $master") } From 4f5bc59af1affcd493862f5dd7e7776b3c16f0ae Mon Sep 17 00:00:00 2001 From: Xianyan Date: Thu, 14 Dec 2017 17:56:32 +0800 Subject: [PATCH 0592/1065] Add local model predictImage (#2026) * Add local model predictImage --- .../transform/vision/image/ImageFrame.scala | 13 ++- .../dllib/nn/abstractnn/AbstractModule.scala | 14 ++- .../bigdl/dllib/optim/LocalPredictor.scala | 47 ++++++++++ .../bigdl/dllib/optim/Predictor.scala | 62 +++++++------ .../bigdl/dllib/utils/LocalModule.scala | 22 +++++ .../dllib/utils/python/api/PythonBigDL.scala | 10 +- .../dllib/optim/LocalPredictorSpec.scala | 92 +++++++++++++++++++ .../bigdl/dllib/optim/PredictorSpec.scala | 4 +- 8 files changed, 224 insertions(+), 40 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala index 24c1d1b443f..545fa2abe37 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -23,10 +23,9 @@ import org.apache.commons.io.filefilter.WildcardFileFilter import org.apache.log4j.Logger import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{SQLContext} +import org.apache.spark.sql.SQLContext import scala.collection.mutable.ArrayBuffer -import scala.reflect.ClassTag /** * ImageFrame wraps a set of ImageFeature @@ -55,6 +54,16 @@ trait ImageFrame { * whether this is a DistributedImageFrame */ def isDistributed(): Boolean + + /** + * return LocalImageFrame + */ + def toLocal(): LocalImageFrame = this.asInstanceOf[LocalImageFrame] + + /** + * return DistributedImageFrame + */ + def toDistributed(): DistributedImageFrame = this.asInstanceOf[DistributedImageFrame] } object ImageFrame { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 18beaa804cc..5f930d9e659 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -30,7 +30,7 @@ import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.quantized.Quantization -import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame} +import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame} import com.intel.analytics.bigdl.utils.caffe.CaffePersister import com.intel.analytics.bigdl.utils.serializer.ModulePersister import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} @@ -565,9 +565,15 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, outputLayer: String = null, shareBuffer: Boolean = false, batchPerPartition: Int = 4, - predictKey: String = ImageFeature.predict): DistributedImageFrame = { - Predictor(this).predictImage(imageFrame, outputLayer, - shareBuffer, batchPerPartition, predictKey) + predictKey: String = ImageFeature.predict): ImageFrame = { + imageFrame match { + case distributedImageFrame: DistributedImageFrame => + Predictor(this).predictImage(distributedImageFrame, outputLayer, + shareBuffer, batchPerPartition, predictKey) + case localImageFrame: LocalImageFrame => + LocalModule[T](this).predictImage(localImageFrame, outputLayer, + shareBuffer, batchPerPartition, predictKey) + } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index 38523ded38c..2a69cf77e5c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Engine, MklBlas} import com.intel.analytics.bigdl.dataset.SampleToMiniBatch +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, LocalImageFrame} import scala.reflect.ClassTag @@ -149,6 +150,52 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: } } + /** + * local model predict images, return imageFrame with predicted tensor + * @param imageFrame imageFrame that contains images + * @param outputLayer if outputLayer is not null, the output of layer that matches + * outputLayer will be used as predicted output + * @param shareBuffer whether to share same memory for each batch predict results + * @param batchPerCore batch size per core, default is 4 + * @param predictKey key to store predicted result + */ + def predictImage(imageFrame: LocalImageFrame, + outputLayer: String = null, + shareBuffer: Boolean = false, + batchPerCore: Int = 4, + predictKey: String = ImageFeature.predict): LocalImageFrame = { + + val dataIter = imageFrame.array.grouped(batchPerCore * subModelNumber) + + val workingModels = (1 to subModelNumber).map(_ => { + val submodel = model.cloneModule().evaluate() + putWeightBias(weightsBias, submodel) + submodel + }).toArray + + val workingToBatch = (1 to subModelNumber).map(_ => { + SampleToMiniBatch[T]( + batchSize = batchPerCore * subModelNumber, + partitionNum = Some(subModelNumber)) + }).toArray + + val result = dataIter.map(batch => { + val groupedImages = batch.grouped(batchPerCore).toArray + Engine.default.invokeAndWait( + groupedImages.indices.map(b => + () => { + val imageFeatures = groupedImages(b) + val model = workingModels(b) + val toBatch = workingToBatch(b) + Predictor.predictImageBatch[T](model, imageFeatures, outputLayer, predictKey, + toBatch, shareBuffer) + } + ) + ).flatten + }).flatten + + ImageFrame.array(result.toArray) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index c50c2d7094b..d90c4295f88 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -17,12 +17,11 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, SampleToMiniBatch, Utils, DataSet => _} +import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, SampleToMiniBatch, Transformer, Utils, DataSet => _} import com.intel.analytics.bigdl.models.utils.ModelBroadcast -import com.intel.analytics.bigdl.nn.SpatialShareConvolution import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, FeatureTransformer, ImageFeature, ImageFrame} +import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame} import org.apache.spark.rdd.RDD import scala.reflect.ClassTag @@ -31,6 +30,34 @@ object Predictor { def apply[T: ClassTag](model: Module[T])(implicit ev: TensorNumeric[T]): Predictor[T] = { new Predictor[T](model) } + + private[optim] def predictImageBatch[T: ClassTag]( + localModel: Module[T], imageFeatures: Seq[ImageFeature], + outputLayer: String, predictKey: String, + localToBatch: Transformer[Sample[T], MiniBatch[T]], + shareBuffer: Boolean)(implicit ev: TensorNumeric[T]): Seq[ImageFeature] = { + val validImageFeatures = imageFeatures.filter(_.isValid) + val samples = validImageFeatures.map(x => x[Sample[T]](ImageFeature.sample)) + val batch = localToBatch(samples.toIterator).next() + if (batch != null) { + localModel.forward(batch.getInput()) + val output = if (outputLayer == null) { + localModel.output.toTensor[T] + } else { + localModel(outputLayer).get.output.toTensor[T] + } + val result = if (shareBuffer) output else output.clone() + val batchOut = if (result.dim() == 1) { + Array(result) + } else { + result.split(1) + } + validImageFeatures.zip(batchOut).foreach(tuple => { + tuple._1(predictKey) = tuple._2 + }) + } + imageFeatures + } } class Predictor[T: ClassTag] private[optim]( @@ -82,7 +109,7 @@ class Predictor[T: ClassTag] private[optim]( /** - * model predict images, return imageFrame with predicted tensor + * model predict DistributedImageFrame, return imageFrame with predicted tensor * @param imageFrame imageFrame that contains images * @param outputLayer if outputLayer is not null, the output of layer that matches * outputLayer will be used as predicted output @@ -90,14 +117,11 @@ class Predictor[T: ClassTag] private[optim]( * @param batchPerPartition batch size per partition, default is 4 * @param predictKey key to store predicted result */ - def predictImage(imageFrame: ImageFrame, + def predictImage(imageFrame: DistributedImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, batchPerPartition: Int = 4, predictKey: String = ImageFeature.predict): DistributedImageFrame = { - require(imageFrame.isDistributed(), "please provide a distributed imageframe") - // share convolution fInput - SpatialShareConvolution.shareConvolution(model) val rdd = imageFrame.asInstanceOf[DistributedImageFrame].rdd val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, model.evaluate()) val partitionNum = rdd.partitions.length @@ -109,26 +133,8 @@ class Predictor[T: ClassTag] private[optim]( val localToBatch = toBatchBroad.value._1.cloneTransformer() partition.grouped(batchPerPartition).flatMap(imageFeatures => { - val validImageFeatures = imageFeatures.filter(_.isValid) - val samples = validImageFeatures.map(x => x[Sample[T]](ImageFeature.sample)) - val batch = localToBatch(samples.toIterator).next() - if (batch != null) { - localModel.forward(batch.getInput()) - val result = if (outputLayer == null) { - localModel.output.toTensor[T] - } else { - localModel(outputLayer).get.output.toTensor[T] - } - val batchOut = if (result.dim() == 1) { - Array(result) - } else { - result.split(1) - } - validImageFeatures.zip(batchOut).foreach(tuple => { - tuple._1(predictKey) = tuple._2 - }) - } - imageFeatures + Predictor.predictImageBatch[T](localModel, imageFeatures, outputLayer, predictKey, + localToBatch, shareBuffer) }) }) ImageFrame.rdd(result) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala index 849768cd0f0..27da57b0eaf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.optim.LocalPredictor +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, LocalImageFrame} import scala.reflect.ClassTag @@ -79,5 +80,26 @@ class LocalModule[T: ClassTag] private(model: Module[T], weightsBias: Array[Tens def predict(dataSet: Array[Sample[T]]): Array[Activity] = { predictor.predict(dataSet) } + + /** + * local model predict image, return imageFrame with predicted tensor + * @param imageFrame imageFrame that contains images + * @param outputLayer if outputLayer is not null, the output of layer that matches + * outputLayer will be used as predicted output + * @param shareBuffer whether to share same memory for each batch predict results + * @param batchPerCore batch size per partition, default is 4 + * @param predictKey key to store predicted result + */ + def predictImage(imageFrame: LocalImageFrame, + outputLayer: String = null, + shareBuffer: Boolean = false, + batchPerCore: Int = 4, + predictKey: String = ImageFeature.predict): LocalImageFrame = { + predictor.predictImage(imageFrame, + outputLayer, + shareBuffer, + batchPerCore, + predictKey) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 8e190d42688..1e223c6a0e5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1780,8 +1780,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab shareBuffer: Boolean, batchPerPartition: Int, predictKey: String) - : DistributedImageFrame = { - model.predictImage(imageFrame, featLayerName, shareBuffer, batchPerPartition, predictKey) + : ImageFrame = { + model.predictImage(imageFrame, + featLayerName, shareBuffer, batchPerPartition, predictKey) } def evaluate(module: AbstractModule[Activity, Activity, T]): @@ -2673,8 +2674,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def localImageFrameToPredict(imageFrame: LocalImageFrame, key: String) - : JList[(String, JTensor)] = { - imageFrame.array.map(x => (x.uri(), toJTensor(x[Tensor[T]](key)))).toList.asJava + : JList[JList[Any]] = { + imageFrame.array.map(x => + List[Any](x.uri(), toJTensor(x[Tensor[T]](key))).asJava).toList.asJava } def localImageFrameToImageTensor(imageFrame: LocalImageFrame, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala new file mode 100644 index 00000000000..0797656d457 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier +import com.intel.analytics.bigdl.nn.{Sequential, SpatialConvolution, Tanh} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} +import com.intel.analytics.bigdl.utils.{Engine, LocalModule} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { + + private val nodeNumber = 1 + private val coreNumber = 4 + + before { + System.setProperty("bigdl.localMode", "true") + Engine.init(nodeNumber, coreNumber, false) + } + + after { + System.clearProperty("bigdl.localMode") + } + + "predictImage" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 20) + val detection = model.predictImage(imageFrame).toLocal() + val feature = detection.array.head + println(feature(ImageFeature.predict)) + + val imageFeatures = detection.array + val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) + val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) + prob(0) should be(model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 224, 224))) + .toTensor[Float].split(1)(0)) + } + + "predictImage with more data" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val ims = (1 to 50).map(x => { + val im = ImageFeature() + im(ImageFeature.uri) = x.toString + im(ImageFeature.imageTensor) = Tensor[Float](3, 24, 24).randn() + im + }) + + val imageFrame = ImageFrame.array(ims.toArray) -> ImageFrameToSample() + val model = Sequential() + model.add(SpatialConvolution(3, 6, 5, 5)) + model.add(Tanh()) + val detection = model.predictImage(imageFrame).toLocal() + val feature = detection.array.head + + val imageFeatures = detection.array + val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) + val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) + prob(0) should be(model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 24, 24))) + .toTensor[Float].split(1)(0)) + (1 to 20).foreach(x => { + imageFeatures(x - 1).uri() should be (x.toString) + if (imageFeatures(x - 1).predict() == null) println(x, imageFeatures(x - 1).predict()) + assert(imageFeatures(x - 1).predict() != null) + }) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index b6fea9df50c..8a506c944a5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -123,7 +123,7 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> MatToTensor() -> ImageFrameToSample() val model = Inception_v1_NoAuxClassifier(classNum = 20) - val detection = model.predictImage(imageFrame) + val detection = model.predictImage(imageFrame).toDistributed() val feature = detection.rdd.first() println(feature(ImageFeature.predict)) @@ -145,7 +145,7 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ val model = Sequential() model.add(SpatialConvolution(3, 6, 5, 5)) model.add(Tanh()) - val detection = model.predictImage(imageFrame) + val detection = model.predictImage(imageFrame).toDistributed() val feature = detection.rdd.first() println(feature(ImageFeature.predict)) From 8dad614b074f7ae59701818d1e61b355e93f7208 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 15 Dec 2017 09:37:50 +0800 Subject: [PATCH 0593/1065] get large model from executor (#2017) * fix of getModel * meet code review * meet code review * fix ut * delete copy status --- .../bigdl/dllib/nn/BatchNormalization.scala | 12 ++--- .../analytics/bigdl/dllib/nn/Container.scala | 25 ++++----- .../bigdl/dllib/nn/TimeDistributed.scala | 15 +----- .../analytics/bigdl/dllib/nn/Utils.scala | 2 +- .../dllib/nn/abstractnn/AbstractModule.scala | 51 ++++++++++++++----- .../bigdl/dllib/optim/DistriOptimizer.scala | 39 ++++++++------ .../bigdl/dllib/optim/LocalOptimizer.scala | 2 +- .../bigdl/dllib/nn/AbstractModuleSpec.scala | 51 +++++++++++++++++++ .../bigdl/dllib/nn/TimeDistributedSpec.scala | 5 +- 9 files changed, 134 insertions(+), 68 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index e8f0b813188..ed1d2f1ea50 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -141,14 +141,6 @@ class BatchNormalization[T: ClassTag]( this } - override def copyStatus(src: Module[T]): this.type = { - require(canEqual(src), s"copyStatus: type mismatch, $src is different from $this") - val srcModule = src.asInstanceOf[BatchNormalization[T]] - runningMean.copy(srcModule.runningMean) - runningVar.copy(srcModule.runningVar) - this - } - override def zeroGradParameters(): Unit = { if (affine) { gradWeight.zero() @@ -164,6 +156,10 @@ class BatchNormalization[T: ClassTag]( } } + override def getExtraParameter(): Array[Tensor[T]] = { + Array(runningMean, runningVar) + } + override def getParametersTable(): Table = { if (affine) { T(getName() -> T("weight" -> weight, "bias" -> bias, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index d41adcbde56..8cf2d93b59b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -107,6 +107,17 @@ abstract class Container[A <: Activity : ClassTag, (weights.toArray, gradWeights.toArray) } + override def getExtraParameter(): Array[Tensor[T]] = { + val extraParam = new ArrayBuffer[Tensor[T]]() + modules.foreach(m => { + val state = m.getExtraParameter() + if (state != null) { + extraParam ++= state + } + }) + extraParam.toArray + } + override def getParametersTable(): Table = { val pt = T() modules.foreach(m => { @@ -137,20 +148,6 @@ abstract class Container[A <: Activity : ClassTag, nodes } - override def copyStatus(src: Module[T]): this.type = { - require(canEqual(src), s"copyStatus: type mismatch, $src is different from $this") - val srcContainer = src.asInstanceOf[Container[A, B, T]] - require(srcContainer.modules.length == modules.length, - s"copyStatus: container's length mismatch" + - s"excepted ${modules.length}, but get ${srcContainer.modules.length}") - var i = 0 - while (i < modules.length) { - modules(i).copyStatus(srcContainer.modules(i)) - i += 1 - } - this - } - override def clearState() : this.type = { super.clearState() modules.foreach(_.clearState()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala index c4dc80ab8a5..bc3babb86cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala @@ -224,19 +224,8 @@ class TimeDistributed[T : ClassTag] (val layer: TensorModule[T]) */ override def getParametersTable(): Table = layer.getParametersTable() - /** - * Copy the useful running status from src to this. - * - * The subclass should override this method if it has some parameters besides weight and bias. - * Such as runningMean and runningVar of BatchNormalization. - * - * @param src source Module - * @return this - */ - override def copyStatus(src: Module[T]): TimeDistributed.this.type = { - val other = src.asInstanceOf[TimeDistributed[T]] - layer.copyStatus(other.layer.asInstanceOf[Module[T]]) - this + override def getExtraParameter(): Array[Tensor[T]] = { + layer.getExtraParameter() } override def clearState(): TimeDistributed.this.type = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index 08eb046bcac..793dba8fb12 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -235,7 +235,7 @@ object Utils { s"$src and $dst is not the same type.") dstParameters.copy(srcParameters) // copy running status - dst.copyStatus(src) + dst.setExtraParameter(src.getExtraParameter()) dst } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 5f930d9e659..e1395dc17b5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -116,19 +116,6 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, this } - /** - * Copy the useful running status from src to this. - * - * The subclass should override this method if it has some parameters besides weight and bias. - * Such as runningMean and runningVar of BatchNormalization. - * - * @param src source Module - * @return this - */ - def copyStatus(src: Module[T]) : this.type = { - this - } - /** * Clear cached activities to save storage space or network bandwidth. Note that we use * Tensor.set to keep some information like tensor share @@ -367,6 +354,44 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = null + /** + * Get extra parameter in this module. + * Extra parameter means the trainable parameters beside weight and bias. Such as runningMean + * and runningVar in BatchNormalization. + * + * The subclass should override this method if it has some parameters besides weight and bias. + * + * @return an array of tensor + */ + def getExtraParameter(): Array[Tensor[T]] = null + + /** + * Set extra parameter to this module. + * Extra parameter means the trainable parameters beside weight and bias. Such as runningMean + * and runningVar in BatchNormalization. + * + * @return this + */ + def setExtraParameter(extraParam: Array[Tensor[T]]): this.type = { + val currentExtraParam = this.getExtraParameter() + if (extraParam != null && currentExtraParam != null) { + require(extraParam.length == currentExtraParam.length, + "state's length doesn't match, excepted:" + + s"${currentExtraParam.length}, but got ${extraParam.length}") + var i = 0 + while (i < extraParam.length) { + currentExtraParam(i).copy(extraParam(i)) + i += 1 + } + this + } else if (extraParam == null && currentExtraParam == null) { + this + } else { + throw new IllegalArgumentException(s"module's extraParameter is $currentExtraParam" + + s", while setting param is ${extraParam}") + } + } + /** * This function returns a table contains ModuleName, the parameter names and parameter value * in this module. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 2026bf680f9..876df1631a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -87,6 +87,7 @@ object DistriOptimizer { * @param isOverWrite if overwrite the checkpoint */ private[optim] def optimize[T: ClassTag]( + trainingModel: Module[T], dataset: DistributedDataSet[MiniBatch[T]], coresPerNode: Int, state: Table, @@ -396,7 +397,8 @@ object DistriOptimizer { summary, models, driverState, - parameters + parameters, + trainingModel ) } @@ -408,7 +410,8 @@ object DistriOptimizer { models, driverState, parameters, - optimMethod + optimMethod, + trainingModel ) } else { @@ -439,12 +442,13 @@ object DistriOptimizer { models: RDD[Cache[T]], state: Table, parameters: AllReduceParameter[T], - optimMethod: OptimMethod[T]): Unit = { + optimMethod: OptimMethod[T], + trainingModel: Module[T]): Unit = { cacheTrigger.foreach { trigger => cachePath.foreach { path => if (trigger(state)) { println(s"[Wall Clock ${wallClockTime / 1e9}s] Save model to $path") - saveModel(getModel(models, parameters), cachePath, isOverWrite, + saveModel(getModel(models, parameters, trainingModel), cachePath, isOverWrite, s".${state[Int]("neval")}") optimMethod.state.update("epoch", state[Int]("epoch")) optimMethod.state.update("neval", state[Int]("neval")) @@ -466,11 +470,12 @@ object DistriOptimizer { trainSummary: TrainSummary, models: RDD[Cache[T]], driverState: Table, - parameters: AllReduceParameter[T])(implicit ev: TensorNumeric[T]): Unit = { + parameters: AllReduceParameter[T], + trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { val currentIteration = driverState[Int]("neval") - 1 val parametersTrigger = trainSummary.getSummaryTrigger("Parameters") if (parametersTrigger.isDefined && parametersTrigger.get(driverState)) { - val model = getModel(models, parameters) + val model = getModel(models, parameters, trainingModel) val parametersTable = model.getParametersTable() // Parallelize to create Histogram. Engine.default.invokeAndWait( @@ -680,17 +685,20 @@ object DistriOptimizer { } /** - * Fetch current model to driver. + * Fetch current model parameters to driver, and copy to trainingModel. * * @param models cached models * @param parameters [[AllReduceParameter]] - * @return current model + * @param trainingModel the model is trained by optimizer + * @return trained model */ private def getModel[T: ClassTag]( models: RDD[Cache[T]], - parameters: AllReduceParameter[T]): Module[T] = { + parameters: AllReduceParameter[T], + trainingModel: Module[T]): Module[T] = { val partitionNum = models.partitions.length - val trainedModel = models.map(_.localModels.head.clearState()).first() + val extraState = models.map(_.localModels.head.getExtraParameter()).first() + trainingModel.setExtraParameter(extraState) val (weights, gradients) = models.mapPartitions(iter => { val cached = iter.next() val curPartitionId = TaskContext.getPartitionId() @@ -698,11 +706,11 @@ object DistriOptimizer { Map(curPartitionId -> parameters.gradientPartition))) }).reduce((a, b) => (a._1 ++ b._1, a._2 ++ b._2)) - val parameterArray = trainedModel.parameters() + val parameterArray = trainingModel.parameters() (0 until parameterArray._2.length).foreach(i => parameterArray._2(i).resizeAs(parameterArray._1(i)) ) - val (parameter, gradientParameter) = trainedModel.getParameters() + val (parameter, gradientParameter) = trainingModel.getParameters() val parameterLength = parameter.nElement() val taskSize = parameterLength / partitionNum require(taskSize != 0, "parameter length should not less than partition number") @@ -715,7 +723,7 @@ object DistriOptimizer { gradientParameter.narrow(1, start + 1, length).copy(gradients(pid)) }) - trainedModel + trainingModel } } @@ -821,6 +829,7 @@ class DistriOptimizer[T: ClassTag] ( while (retryNum < maxRetry) { try { DistriOptimizer.optimize( + model, distDataset, coresPerNode, state, @@ -882,9 +891,7 @@ class DistriOptimizer[T: ClassTag] ( } } - val trainedModel = DistriOptimizer.getModel(models, parameters) - - nn.Utils.copyModule(trainedModel, model) + DistriOptimizer.getModel(models, parameters, model) // Reset some internal states, so this or other optimizers can run optimize again clearState() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index 772a5d0a4bc..0d4aaa44700 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -182,7 +182,7 @@ class LocalOptimizer[T: ClassTag] ( } // copy running status from workingModels to model - model.copyStatus(workingModels.head) + model.setExtraParameter(workingModels.head.getExtraParameter()) model } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala index 9200edfa38c..9827e436666 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{T, Table} class AbstractModuleSpec extends FlatSpec with Matchers { @@ -276,4 +277,54 @@ class AbstractModuleSpec extends FlatSpec with Matchers { model("conv3x3_1").get.getScaleW() should be(3) model("conv3x3_1").get.getScaleB() should be(1.5) } + + "get/set extra parameter" should "work fine" in { + val bn = SpatialBatchNormalization(5) + val model = Sequential() + .add(SpatialConvolution(3, 5, 3, 3)) + .add(bn) + .add(SpatialConvolution(5, 2, 3, 3)) + .add(BatchNormalization(2)) + + val model2 = model.cloneModule() + bn.runningMean.range(1, 5) + model2 should not be (model) + val extp = model.getExtraParameter() + extp(0) should be (Tensor().range(1, 5)) + model2.setExtraParameter(extp) + model2 should be (model) + } + + "get/set extra parameter" should "work fine 2" in { + val model = Sequential() + .add(SpatialConvolution(3, 5, 3, 3)) + .add(SpatialConvolution(5, 2, 3, 3)) + + val model2 = model.cloneModule() + model2 should be (model) + val extp = model.getExtraParameter() + model2.setExtraParameter(extp) + model2 should be (model) + } + + "get/set extra parameter" should "work fine 3" in { + val model = BatchNormalization(5) + + val model2 = model.cloneModule() + model.runningMean.range(1, 5) + model2 should not be (model) + val extp = model.getExtraParameter() + model2.setExtraParameter(extp) + model2 should be (model) + } + + "get/set extra parameter" should "work fine 4" in { + val model = SpatialConvolution(3, 5, 3, 3) + + val model2 = model.cloneModule() + model2 should be (model) + val extp = model.getExtraParameter() + model2.setExtraParameter(extp) + model2 should be (model) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala index f07fdad0ddb..05dda1ab83e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.scalatest.{FlatSpec, Matchers} class TimeDistributedSpec extends FlatSpec with Matchers { - "A TimeDistributed Module" should "copyStatus correctly" in { + "A TimeDistributed Module" should "setExtraParam works correctly" in { RNG.setSeed(100) val batchSize = 5 val times = 5 @@ -43,7 +43,8 @@ class TimeDistributedSpec extends FlatSpec with Matchers { model2.forward(input2) model2.backward(input2, gradOutput2) - model1.copyStatus(model2.asInstanceOf[AbstractModule[Activity, Activity, Float]]) + model1.setExtraParameter( + model2.asInstanceOf[AbstractModule[Activity, Activity, Float]].getExtraParameter()) bnorm1.runningMean should be (bnorm2.runningMean) bnorm1.runningVar should be (bnorm2.runningVar) From 39e48251d3c4930026bbf199272ce0688e0c83e8 Mon Sep 17 00:00:00 2001 From: Harrison Kiang Date: Thu, 14 Dec 2017 21:40:54 -0500 Subject: [PATCH 0594/1065] lable should be label (#2032) * spelling * Update LocalPredictor.scala --- .../com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala | 2 +- .../scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index 2a69cf77e5c..71343b8cdc9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -54,7 +54,7 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: result.map(output => { val _output = output.toTensor[T] require(_output.dim() == 1, s"Predictor.predictClass:" + - s"Only support one sample has one lable, but got ${_output.dim()} label") + s"Only support one sample has one label, but got ${_output.dim()} label") ev.toType[Int](_output.max(1)._2.valueAt(1)) }) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index d90c4295f88..e41232676c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -71,7 +71,7 @@ class Predictor[T: ClassTag] private[optim]( partition.map(output => { val _output = output.toTensor[T] require(_output.dim() == 1, s"Predictor.predictClass:" + - s"Only support one sample has one lable, but got ${_output.dim()} label") + s"Only support one sample has one label, but got ${_output.dim()} label") ev.toType[Int](_output.max(1)._2.valueAt(1)) }) } From 26754199ca7564da7550d09f23b62cb01e490c44 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 15 Dec 2017 12:53:17 +0800 Subject: [PATCH 0595/1065] Concat check input size check (#2034) * add dim check * fix typo * per review * per review * per review --- .../com/intel/analytics/bigdl/dllib/nn/Concat.scala | 13 +++++++++++++ .../intel/analytics/bigdl/dllib/nn/ConcatSpec.scala | 13 +++++++++++++ 2 files changed, 26 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala index fb5e5ec8833..f33e0ddf4a2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala @@ -63,6 +63,19 @@ class Concat[T: ClassTag](val dimension: Int)( if (i == 0) { this.size = currentOutput.size() } else { + require(this.size.length == currentOutput.size.length, + s"${this.modules(i).getName} output size mismatch, expected : ${this.size.length}," + + s"actual ${currentOutput.size.length}") + var index = 0 + val ssize = this.size.length + while (index < ssize) { + if (index != dimension - 1) { + require(this.size(index) == currentOutput.size(index + 1), + s"${this.modules(i).getName} output size at dimension ${index + 1} mismatch," + + s"expected ${this.size(index)}, actual : ${currentOutput.size(index + 1)}") + } + index += 1 + } this.size(this.dimension - 1) += currentOutput.size(this.dimension) } i += 1 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala index 1837b76b789..ba258a04450 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.LayerException import org.scalatest.{FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Parallel @@ -73,4 +74,16 @@ class ConcatSpec extends FlatSpec with Matchers { output should be (expectedOutput) gradInput should be (expectedGradInput) } + + "Concat with incorrec input" should "throw expected exception" in { + val model = Concat[Float](2) + model.add(Reshape[Float](Array(5, 2))) + model.add(Reshape[Float](Array(2, 5))) + val input = Tensor[Float](10) + val caught = intercept[LayerException] { + model.forward(input) + } + val contains = caught.error.getMessage.contains("output size at dimension 1 mismatch") + contains should be (true) + } } From 9039e707ee4affdd69db29ead4234d7ea9401aa6 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 15 Dec 2017 15:19:31 +0800 Subject: [PATCH 0596/1065] fix core number is 0 where there's only one core in system (#2036) --- .../main/scala/com/intel/analytics/bigdl/utils/Engine.scala | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 806f1c691cc..5c6fe68839f 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -153,9 +153,11 @@ object Engine { } private def getNumMachineCores: Int = { + val coreNum = Runtime.getRuntime().availableProcessors() + require(coreNum > 0, "Get a non-positive core number") // We assume the HT is enabled // Todo: check the Hyper threading - Runtime.getRuntime().availableProcessors() / 2 + if (coreNum > 1) coreNum / 2 else 1 } /** From e4d018957e99379a7050adff681149f83e67ea79 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 15 Dec 2017 15:21:13 +0800 Subject: [PATCH 0597/1065] Support predictImage with variant input features (#2035) * Support predictImage with variant input features * update --- .../bigdl/dllib/optim/LocalPredictor.scala | 6 +++- .../bigdl/dllib/optim/Predictor.scala | 7 +++-- .../dllib/optim/LocalPredictorSpec.scala | 29 ++++++++++++++++++- .../bigdl/dllib/optim/PredictorSpec.scala | 28 ++++++++++++++++++ 4 files changed, 66 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index 71343b8cdc9..c6af1a855db 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -173,10 +173,14 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: submodel }).toArray + // If batchPerCore == 1, will resize the feature every time in SampleToBatch + def featurePaddingParam = if (batchPerCore == 1) Some(PaddingParam[T]()) else None + val workingToBatch = (1 to subModelNumber).map(_ => { SampleToMiniBatch[T]( batchSize = batchPerCore * subModelNumber, - partitionNum = Some(subModelNumber)) + partitionNum = Some(subModelNumber), + featurePaddingParam = featurePaddingParam) }).toArray val result = dataIter.map(batch => { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index e41232676c5..208a57d9e35 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, SampleToMiniBatch, Transformer, Utils, DataSet => _} +import com.intel.analytics.bigdl.dataset.{MiniBatch, PaddingParam, Sample, SampleToMiniBatch, Transformer, Utils, DataSet => _} import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -125,9 +125,12 @@ class Predictor[T: ClassTag] private[optim]( val rdd = imageFrame.asInstanceOf[DistributedImageFrame].rdd val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, model.evaluate()) val partitionNum = rdd.partitions.length + // If batchPerPartition == 1, will resize the feature every time in SampleToBatch + def featurePaddingParam = if (batchPerPartition == 1) Some(PaddingParam[T]()) else None val toBatchBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( batchSize = partitionNum * batchPerPartition, - partitionNum = Some(partitionNum)), shareBuffer) + partitionNum = Some(partitionNum), + featurePaddingParam = featurePaddingParam), shareBuffer) val result = rdd.mapPartitions(partition => { val localModel = modelBroad.value() val localToBatch = toBatchBroad.value._1.cloneTransformer() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index 0797656d457..aa909e8433e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.nn.{Sequential, SpatialConvolution, Tanh} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} -import com.intel.analytics.bigdl.utils.{Engine, LocalModule} +import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -89,4 +89,31 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(imageFeatures(x - 1).predict() != null) }) } + + "predictImage with variant feature data" should "work" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val ims = (1 to 50).map(x => { + val size = RNG.uniform(20, 30).toInt + val im = ImageFeature() + im(ImageFeature.uri) = x.toString + im(ImageFeature.imageTensor) = Tensor[Float](3, size, size).randn() + im + }) + + val imageFrame = ImageFrame.array(ims.toArray) -> ImageFrameToSample() + val model = Sequential() + model.add(SpatialConvolution(3, 6, 5, 5)) + model.add(Tanh()) + val detection = model.predictImage(imageFrame, batchPerPartition = 1).toLocal() + val imageFeatures = detection.array + (1 to 20).foreach(x => { + imageFeatures(x - 1).uri() should be (x.toString) + println(imageFeatures(x - 1)[Tensor[Float]](ImageFeature.imageTensor).size().mkString("x")) + println(imageFeatures(x - 1)[Sample[Float]](ImageFeature.sample) + .getFeatureSize()(0).mkString("x")) + println(x, imageFeatures(x - 1).predict().asInstanceOf[Tensor[Float]].size().mkString("x")) + assert(imageFeatures(x - 1).predict() != null) + }) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index 8a506c944a5..f1ab4f23d51 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -155,4 +155,32 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ prob(0) should be (model.forward(data(0).feature.reshape(Array(1, 3, 224, 224))) .toTensor[Float].split(1)(0)) } + + "predictImage with variant feature data" should "work" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val ims = (1 to 50).map(x => { + val size = RNG.uniform(20, 30).toInt + val im = ImageFeature() + im(ImageFeature.uri) = x.toString + im(ImageFeature.imageTensor) = Tensor[Float](3, size, size).randn() + im + }) + + val imageFrame = ImageFrame.array(ims.toArray).toDistributed(sc) -> ImageFrameToSample() + val model = Sequential() + model.add(SpatialConvolution(3, 6, 5, 5)) + model.add(Tanh()) + val detection = model.predictImage(imageFrame, batchPerPartition = 1, shareBuffer = false) + .toDistributed() + val imageFeatures = detection.rdd.collect() + (1 to 20).foreach(x => { + imageFeatures(x - 1).uri() should be (x.toString) + println(imageFeatures(x - 1)[Tensor[Float]](ImageFeature.imageTensor).size().mkString("x")) + println(imageFeatures(x - 1)[Sample[Float]](ImageFeature.sample) + .getFeatureSize()(0).mkString("x")) + println(x, imageFeatures(x - 1).predict().asInstanceOf[Tensor[Float]].size().mkString("x")) + assert(imageFeatures(x - 1).predict() != null) + }) + } } From 63f6b8b9a3a2aeb4d2733aae9d0ef7aab635701e Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Thu, 14 Dec 2017 23:29:51 -0800 Subject: [PATCH 0598/1065] Add Cropping2D and Cropping3D (#1992) * add cropping * serialization test * equal and hash * style fix * python doc --- .../analytics/bigdl/dllib/nn/Cropping2D.scala | 125 +++++++++++++++ .../analytics/bigdl/dllib/nn/Cropping3D.scala | 145 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 16 ++ .../bigdl/dllib/keras/Cropping2DSpec.scala | 47 ++++++ .../bigdl/dllib/keras/Cropping3DSpec.scala | 49 ++++++ .../serializer/ModuleSerializerSpec.scala | 13 ++ 6 files changed, 395 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2D.scala new file mode 100644 index 00000000000..ebc49501041 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2D.scala @@ -0,0 +1,125 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Cropping layer for 2D input (e.g. picture). + * It crops along spatial dimensions, i.e. width and height. + * # Input shape + * 4D tensor with shape: + * `(batchSize, channels, first_axis_to_crop, second_axis_to_crop)` + * # Output shape + * 4D tensor with shape: + * `(batchSize, channels, first_cropped_axis, second_cropped_axis)` + * + * @param heightCrop Array of length 2. How many units should be trimmed off at the beginning + * and end of the height dimension. + * @param widthCrop Array of length 2. How many units should be trimmed off at the beginning + * and end of the width dimension + * @param dataFormat: DataFormat.NCHW or DataFormat.NHWC + */ +@SerialVersionUID(3462228835945094156L) +class Cropping2D[T: ClassTag]( + val heightCrop: Array[Int], + val widthCrop: Array[Int], + val dataFormat: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 4, "input dimensions should be 4." + + " (batchSize, channels, first_axis_to_crop, second_axis_to_crop)") + + val (hdim, wdim, hStart, lenHCropped, wStart, lenWCropped) = calculateStartAndLength(input) + + require(lenHCropped > 0, s"heightCrop: ${heightCrop.mkString(", ")} is too large. Height" + + s" dimension length: ${input.size(hdim)}") + require(lenWCropped > 0, s"widthCrop: ${widthCrop.mkString(", ")} is too large. Width" + + s" dimension length: ${input.size(wdim)}") + + val cropped = input + .narrow(hdim, hStart, lenHCropped) + .narrow(wdim, wStart, lenWCropped) + output.resizeAs(cropped).copy(cropped) + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val (hdim, wdim, hStart, lenHCropped, wStart, lenWCropped) = calculateStartAndLength(input) + gradInput.resizeAs(input).zero() + .narrow(hdim, hStart, lenHCropped) + .narrow(wdim, wStart, lenWCropped) + .copy(gradOutput) + } + + /** + * Calculate the start position and length after cropping + */ + private def calculateStartAndLength(input: Tensor[T]): (Int, Int, Int, Int, Int, Int) = { + val (hdim, wdim) = dataFormat match { + case DataFormat.NCHW => (3, 4) + case DataFormat.NHWC => (2, 3) + case _ => throw new IllegalArgumentException(s"$dataFormat is not a supported format") + } + + val hStart = heightCrop(0) + 1 + val lenHCropped = input.size(hdim) - heightCrop(0) - heightCrop(1) + val wStart = widthCrop(0) + 1 + val lenWCropped = input.size(wdim) - widthCrop(0) - widthCrop(1) + (hdim, wdim, hStart, lenHCropped, wStart, lenWCropped) + } + + override def clearState(): this.type = { + super.clearState() + this + } + + override def toString(): String = { + s"$getPrintName(heightCrop: ${heightCrop.mkString(", ")};" + + s" widthCrop: ${widthCrop.mkString(", ")}.)" + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[Cropping2D[T]] + + override def equals(other: Any): Boolean = other match { + case that: Cropping2D[T] => + super.equals(that) && + (that canEqual this) && + heightCrop.sameElements(that.heightCrop) && + widthCrop.sameElements(that.heightCrop) && + dataFormat == that.dataFormat + case _ => false + } + + override def hashCode(): Int = { + def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode() + val state = Seq(super.hashCode(), heightCrop, widthCrop, dataFormat) + state.map(getHashCode).foldLeft(0)((a, b) => 37 * a + b) + } +} + +object Cropping2D { + def apply[T: ClassTag]( + heightCrop: Array[Int], + widthCrop: Array[Int], + format: DataFormat = DataFormat.NCHW) (implicit ev: TensorNumeric[T]): Cropping2D[T] = { + new Cropping2D[T](heightCrop, widthCrop, format) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3D.scala new file mode 100644 index 00000000000..29bb9bd1431 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3D.scala @@ -0,0 +1,145 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Cropping layer for 3D data (e.g. spatial or spatio-temporal). + * + * # Input shape + * 5D tensor with shape: + * (batchSize, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop) + * # Output shape + * 5D tensor with shape: + * (batchSize, channels, first_cropped_axis, second_cropped_axis, third_cropped_axis) + * + * @param dim1Crop How many units should be trimmed off at the beginning and end of + first cropping dimensions. + * @param dim2Crop How many units should be trimmed off at the beginning and end of + * the second dimension + * @param dim3Crop How many units should be trimmed off at the beginning and end of + * the third dimension + * @param dataFormat: Cropping3D.CHANNEL_FIRST or Cropping3D.CHANNEL_LAST + */ +class Cropping3D[T: ClassTag]( + val dim1Crop: Array[Int], + val dim2Crop: Array[Int], + val dim3Crop: Array[Int], + val dataFormat: String = Cropping3D.CHANNEL_FIRST + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 5, "input dimensions should be 5." + + " (batchSize, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)") + + val (dim1, dim2, dim3, dim1Start, dim1Cropped, dim2Start, dim2Cropped, dim3Start, dim3Cropped) = + calculateStartAndLength(input) + + require(dim1Cropped > 0, s"dim1Crop: ${dim1Crop.mkString(", ")} is too large. dim1" + + s" dimension length: ${input.size(dim1)}") + require(dim2Cropped > 0, s"dim2Crop: ${dim2Crop.mkString(", ")} is too large. dim2" + + s" dimension length: ${input.size(dim2)}") + require(dim3Cropped > 0, s"dim3Crop: ${dim3Crop.mkString(", ")} is too large. dim3" + + s" dimension length: ${input.size(dim3)}") + + val cropped = input + .narrow(dim1, dim1Start, dim1Cropped) + .narrow(dim2, dim2Start, dim2Cropped) + .narrow(dim3, dim3Start, dim3Cropped) + output.resizeAs(cropped).copy(cropped) + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val (dim1, dim2, dim3, dim1Start, dim1Cropped, dim2Start, dim2Cropped, dim3Start, dim3Cropped) = + calculateStartAndLength(input) + + gradInput.resizeAs(input).zero() + .narrow(dim1, dim1Start, dim1Cropped) + .narrow(dim2, dim2Start, dim2Cropped) + .narrow(dim3, dim3Start, dim3Cropped) + .copy(gradOutput) + } + + /** + * Calculate the start position and length after cropping + */ + private def calculateStartAndLength(input: Tensor[T]): + (Int, Int, Int, Int, Int, Int, Int, Int, Int) = { + val (dim1, dim2, dim3) = dataFormat match { + case Cropping3D.CHANNEL_FIRST => (3, 4, 5) + case Cropping3D.CHANNEL_LAST => (2, 3, 4) + case _ => throw new IllegalArgumentException(s"$dataFormat is not a supported format") + } + + val dim1Start = dim1Crop(0) + 1 + val dim1Cropped = input.size(dim1) - dim1Crop(0) - dim1Crop(1) + val dim2Start = dim2Crop(0) + 1 + val dim2Cropped = input.size(dim2) - dim2Crop(0) - dim2Crop(1) + val dim3Start = dim3Crop(0) + 1 + val dim3Cropped = input.size(dim3) - dim3Crop(0) - dim3Crop(1) + + (dim1, dim2, dim3, dim1Start, dim1Cropped, dim2Start, dim2Cropped, dim3Start, dim3Cropped) + } + + override def clearState(): this.type = { + super.clearState() + this + } + + override def toString(): String = { + s"$getPrintName(dim1: ${dim1Crop.mkString(", ")};" + + s" dim2Crop: ${dim2Crop.mkString(", ")};" + + s" dim3Crop: ${dim3Crop.mkString(", ")})" + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[Cropping3D[T]] + + override def equals(other: Any): Boolean = other match { + case that: Cropping3D[T] => + super.equals(that) && + (that canEqual this) && + dim1Crop.sameElements(that.dim1Crop) && + dim2Crop.sameElements(that.dim2Crop) && + dim3Crop.sameElements(that.dim3Crop) && + dataFormat == that.dataFormat + case _ => false + } + + override def hashCode(): Int = { + def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode() + val state = Seq(super.hashCode(), dim1Crop, dim2Crop, dim3Crop, dataFormat) + state.map(getHashCode).foldLeft(0)((a, b) => 37 * a + b) + } +} + +object Cropping3D { + + val CHANNEL_FIRST = "channel_first" + val CHANNEL_LAST = "channel_last" + + def apply[T: ClassTag]( + dim1Crop: Array[Int], + dim2Crop: Array[Int], + dim3Crop: Array[Int], + format: String = Cropping3D.CHANNEL_FIRST)(implicit ev: TensorNumeric[T]): Cropping3D[T] = { + new Cropping3D[T](dim1Crop, dim2Crop, dim3Crop, format) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 1e223c6a0e5..a6a80029ae0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2282,6 +2282,22 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab UpSampling3D(size.asScala.toArray) } + def createCropping2D( + heightCrop: JList[Int], + widthCrop: JList[Int], + dataFormat: String = "NCHW"): Cropping2D[T] = { + Cropping2D(heightCrop.asScala.toArray, widthCrop.asScala.toArray, DataFormat(dataFormat)) + } + + def createCropping3D( + dim1Crop: JList[Int], + dim2Crop: JList[Int], + dim3Crop: JList[Int], + dataFormat: String = Cropping3D.CHANNEL_FIRST): Cropping3D[T] = { + Cropping3D( + dim1Crop.asScala.toArray, dim2Crop.asScala.toArray, dim3Crop.asScala.toArray, dataFormat) + } + def redirectSparkLogs(logPath: String): Unit = { LoggerFilter.redirectSparkInfoLogs(logPath) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2DSpec.scala new file mode 100644 index 00000000000..12240ee5dca --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2DSpec.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{Cropping2D, _} +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat + +class Cropping2DSpec extends KerasBaseSpec { + "Cropping2D" should "with NCHW work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5]) + |input = np.random.uniform(-1, 1, [2, 3, 4, 5]) + |output_tensor = Cropping2D(cropping=((1, 1), (1, 1)), dim_ordering='th')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = Cropping2D[Float](Array(1, 1), Array(1, 1), DataFormat.NCHW) + checkOutputAndGrad(model, kerasCode) + } + + "Cropping2D" should "with NHWC work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5]) + |input = np.random.uniform(-1, 1, [2, 3, 4, 5]) + |output_tensor = Cropping2D(cropping=((1, 1), (1, 1)), dim_ordering='tf')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = Cropping2D[Float](Array(1, 1), Array(1, 1), DataFormat.NHWC) + checkOutputAndGrad(model, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3DSpec.scala new file mode 100644 index 00000000000..9db88db4859 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3DSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{Cropping2D, _} +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat + +class Cropping3DSpec extends KerasBaseSpec { + "Cropping3D" should "with CHANNEL_FIRST work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5, 6]) + |input = np.random.uniform(-1, 1, [2, 3, 4, 5, 6]) + |output_tensor = Cropping3D( + | cropping=((1, 1), (1, 1), (1, 1)), dim_ordering='th')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = Cropping3D[Float](Array(1, 1), Array(1, 1), Array(1, 1), Cropping3D.CHANNEL_FIRST) + checkOutputAndGrad(model, kerasCode) + } + + "Cropping3D" should "with CHANNEL_LAST work properly" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5, 6]) + |input = np.random.uniform(-1, 1, [2, 3, 4, 5, 6]) + |output_tensor = Cropping3D( + | cropping=((1, 1), (1, 1), (1, 1)), dim_ordering='tf')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val model = Cropping3D[Float](Array(1, 1), Array(1, 1), Array(1, 1), Cropping3D.CHANNEL_LAST) + checkOutputAndGrad(model, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 485be12ba62..5765d2721b3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -384,6 +384,19 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(cosineDistance, input) } + "Cropping2d serializer" should "work properly" in { + val cropping2d = Cropping2D[Float](Array(2, 2), Array(2, 2), DataFormat.NCHW) + .setName("Cropping2D") + val input = Tensor[Float](1, 9, 9, 9).apply1(_ => Random.nextFloat()) + runSerializationTest(cropping2d, input) + } + + "Cropping3d serializer" should "work properly" in { + val cropping3d = Cropping3D[Float](Array(2, 2), Array(2, 2), Array(2, 2)).setName("Cropping3D") + val input = Tensor[Float](1, 9, 9, 9, 9).apply1(_ => Random.nextFloat()) + runSerializationTest(cropping3d, input) + } + "CSubTable serializer" should "work properly" in { val csubTable = CSubTable[Float]().setName("csubTable") From 5f83829b6ba6cbebe8125497d70192d15ee4118d Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Fri, 15 Dec 2017 17:23:55 +0800 Subject: [PATCH 0599/1065] Refine textclassifier with data without headers and refactor to temporal convolution (#1707) * refine text classifier * add more comments to TemporalMaxPooling * update document * refine textclassification scala code --- .../example/textclassification/README.md | 6 +- .../textclassification/TextClassifier.scala | 3 + .../example/treeLSTMSentiment/Utils.scala | 2 +- .../dllib/example/udfpredictor/Utils.scala | 2 +- .../dllib/example/utils/TextClassifier.scala | 59 ++++++++----------- .../bigdl/dllib/nn/NNPrimitive.scala | 4 +- .../bigdl/dllib/nn/TemporalMaxPooling.scala | 2 +- 7 files changed, 36 insertions(+), 42 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/README.md index 4e032c62027..2789ce5de31 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/README.md @@ -18,8 +18,8 @@ 2. Download [20 Newsgroup dataset](http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html) as the training data ```shell - wget http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.tar.gz - tar zxf news20.tar.gz + wget http://qwone.com/~jason/20Newsgroups/20news-18828.tar.gz + tar zxf 20news-18828.tar.gz ``` 3. Put those data under BASE_DIR, and the final structure would look like this: @@ -27,7 +27,7 @@ ``` [~/textclassification]$ tree . -L 1 . - ├── 20_newsgroup + ├── 20news-18828 └── glove.6B ``` diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/TextClassifier.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/TextClassifier.scala index b15170b4cc1..7417e40e248 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/TextClassifier.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/TextClassifier.scala @@ -52,6 +52,9 @@ object TextClassifier { opt[String]('z', "batchSize") .text("batchSize") .action((x, c) => c.copy(batchSize = x.toInt)) + opt[Int]('l', "learningRate") + .text("learningRate") + .action((x, c) => c.copy(learningRate = x)) } localParser.parse(args, TextClassificationParams()).map { param => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala index 3d3a54eb5c7..5fef54ceee2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala @@ -237,7 +237,7 @@ object Utils { override val baseDir: String = "/tmp/.bigdl/dataset/", override val batchSize: Int = 128, hiddenSize: Int = 250, - learningRate: Double = 0.05, + override val learningRate: Double = 0.05, regRate: Double = 1e-4, p: Double = 0.5, epoch: Int = 5 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala index 9328d1d6c0f..dffc9f8f202 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/Utils.scala @@ -44,7 +44,7 @@ object Utils { private var textClassification: TextClassifier = null - def getTextClassifier(param: TFP): TextClassifier = { + def getTextClassifier(param: TextClassificationUDFParams): TextClassifier = { if (textClassification == null) { textClassification = new TextClassifier(param) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala index 93e2b9b9ffc..1f3f6d13e5b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala @@ -45,7 +45,7 @@ import scala.io.Source class TextClassifier(param: AbstractTextClassificationParams) extends Serializable{ val log: Logger = LoggerFactory.getLogger(this.getClass) val gloveDir = s"${param.baseDir}/glove.6B/" - val textDataDir = s"${param.baseDir}/20_newsgroup/" + val textDataDir = s"${param.baseDir}/20news-18828/" var classNum = -1 /** @@ -55,7 +55,7 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab def buildWord2Vec(word2Meta: Map[String, WordMeta]): Map[Float, Array[Float]] = { log.info("Indexing word vectors.") val preWord2Vec = MMap[Float, Array[Float]]() - val filename = s"$gloveDir/glove.6B.100d.txt" + val filename = s"$gloveDir/glove.6B.200d.txt" for (line <- Source.fromFile(filename, "ISO-8859-1").getLines) { val values = line.split(" ") val word = values(0) @@ -75,7 +75,7 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab def buildWord2VecWithIndex(word2Meta: Map[String, Int]): Map[Float, Array[Float]] = { log.info("Indexing word vectors.") val preWord2Vec = MMap[Float, Array[Float]]() - val filename = s"$gloveDir/glove.6B.100d.txt" + val filename = s"$gloveDir/glove.6B.200d.txt" for (line <- Source.fromFile(filename, "ISO-8859-1").getLines) { val values = line.split(" ") val word = values(0) @@ -171,27 +171,15 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab def buildModel(classNum: Int): Sequential[Float] = { val model = Sequential[Float]() - model.add(Reshape(Array(param.embeddingDim, 1, param.maxSequenceLength))) - - model.add(SpatialConvolution(param.embeddingDim, 128, 5, 1)) - model.add(ReLU()) - - model.add(SpatialMaxPooling(5, 1, 5, 1)) - - model.add(SpatialConvolution(128, 128, 5, 1)) - model.add(ReLU()) - - model.add(SpatialMaxPooling(5, 1, 5, 1)) - - model.add(SpatialConvolution(128, 128, 5, 1)) - model.add(ReLU()) - - model.add(SpatialMaxPooling(35, 1, 35, 1)) - - model.add(Reshape(Array(128))) - model.add(Linear(128, 100)) - model.add(Linear(100, classNum)) - model.add(LogSoftMax()) + model.add(TemporalConvolution(param.embeddingDim, 256, 5)) + .add(ReLU()) + .add(TemporalMaxPooling(param.maxSequenceLength - 5 + 1)) + .add(Squeeze(2)) + .add(Linear(256, 128)) + .add(Dropout(0.2)) + .add(ReLU()) + .add(Linear(128, classNum)) + .add(LogSoftMax()) model } @@ -221,8 +209,7 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab tokens, embeddingDim, word2VecBC.value), label)} val sampleRDD = vectorizedRdd.map {case (input: Array[Array[Float]], label: Float) => Sample( - featureTensor = Tensor(input.flatten, Array(sequenceLen, embeddingDim)) - .transpose(1, 2).contiguous(), + featureTensor = Tensor(input.flatten, Array(sequenceLen, embeddingDim)), label = label) } @@ -237,7 +224,8 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab ) optimizer - .setOptimMethod(new Adagrad(learningRate = 0.01, learningRateDecay = 0.0002)) + .setOptimMethod(new Adagrad(learningRate = param.learningRate, + learningRateDecay = 0.001)) .setValidation(Trigger.everyEpoch, valRDD, Array(new Top1Accuracy[Float]), param.batchSize) .setEndWhen(Trigger.maxEpoch(20)) .optimize() @@ -274,7 +262,7 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab ) optimizer - .setOptimMethod(new Adagrad(learningRate = 0.01, learningRateDecay = 0.0002)) + .setOptimMethod(new Adagrad(learningRate = param.learningRate, learningRateDecay = 0.0002)) .setValidation(Trigger.everyEpoch, valRDD, Array(new Top1Accuracy[Float]), param.batchSize) .setEndWhen(Trigger.maxEpoch(1)) .optimize() @@ -290,6 +278,7 @@ abstract class AbstractTextClassificationParams extends Serializable { def batchSize: Int = 128 def embeddingDim: Int = 100 def partitionNum: Int = 4 + def learningRate: Double = 0.01 } @@ -299,13 +288,15 @@ abstract class AbstractTextClassificationParams extends Serializable { * @param maxWordsNum maximum word to be included * @param trainingSplit percentage of the training data * @param batchSize size of the mini-batch + * @param learningRate learning rate * @param embeddingDim size of the embedding vector */ case class TextClassificationParams(override val baseDir: String = "./", - override val maxSequenceLength: Int = 1000, - override val maxWordsNum: Int = 20000, - override val trainingSplit: Double = 0.8, - override val batchSize: Int = 128, - override val embeddingDim: Int = 100, - override val partitionNum: Int = 4) + override val maxSequenceLength: Int = 500, + override val maxWordsNum: Int = 5000, + override val trainingSplit: Double = 0.8, + override val batchSize: Int = 128, + override val embeddingDim: Int = 200, + override val learningRate: Double = 0.01, + override val partitionNum: Int = 4) extends AbstractTextClassificationParams diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NNPrimitive.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NNPrimitive.scala index e1f5c72bd1d..c3c0fc31af8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NNPrimitive.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NNPrimitive.scala @@ -1006,9 +1006,9 @@ private[nn] object NNPrimitive { var y = 0 while (y < frameSize) { - val maxIndex = xp + y + val maxIndex = indices(xp + y).toInt - 1 if (maxIndex != -1) { - gradInput(gip + maxIndex * frameSize + y) = + gradInput(gip + maxIndex * frameSize + y) += gradOutput(gop + y) } y += 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalMaxPooling.scala index de716cea1c7..bff6d2aac20 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalMaxPooling.scala @@ -33,7 +33,7 @@ import scala.reflect.ClassTag * nOutputFrame = (nInputFrame - kW) / dW + 1 * * @param kW kernel width - * @param dW step size in width + * @param dW step size in width, default is -1, means the `dW` equals `kW` * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] */ class TemporalMaxPooling[T: ClassTag]( From e11e6a0e2895d0e0d612c9392df34411e3417823 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Fri, 15 Dec 2017 19:26:09 +0800 Subject: [PATCH 0600/1065] Add keras SpatialDropout1D, SpatialDropout2D, SpatialDropout3D (#1898) * add comments * meet review * add python code * add documentation * add serialzation test --- .../bigdl/dllib/nn/SpatialDropout1D.scala | 92 ++++++++++++++ .../bigdl/dllib/nn/SpatialDropout2D.scala | 112 ++++++++++++++++++ .../bigdl/dllib/nn/SpatialDropout3D.scala | 112 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 21 ++++ .../dllib/torch/SpatialDropout1DSpec.scala | 41 +++++++ .../dllib/torch/SpatialDropout2DSpec.scala | 70 +++++++++++ .../dllib/torch/SpatialDropout3DSpec.scala | 56 +++++++++ .../serializer/ModuleSerializerSpec.scala | 20 +++- 8 files changed, 523 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout3DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala new file mode 100644 index 00000000000..f0134862e87 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * + * This version performs the same function as Dropout, however it drops + * entire 1D feature maps instead of individual elements. If adjacent frames + * within feature maps are strongly correlated (as is normally the case in + * early convolution layers) then regular dropout will not regularize the + * activations and will otherwise just result in an effective learning rate + * decrease. In this case, SpatialDropout1D will help promote independence + * between feature maps and should be used instead. + * + * @param initP the probability p + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +@SerialVersionUID(- 4636332259181125718L) +class SpatialDropout1D[T: ClassTag]( + val initP: Double = 0.5)( + implicit ev: TensorNumeric[T]) extends TensorModule[T] { + var p = initP + var noise = Tensor[T]() + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + this.output.resizeAs(input).copy(input) + + if (train) { + val inputSize = input.size() + if (input.dim() == 2) { + noise.resize(Array(1, inputSize(1))) + } else if (input.dim() == 3) { + noise.resize(Array(inputSize(0), 1, inputSize(2))) + } else { + throw new RuntimeException("SpatialDropout1D: Input must be 3D or 4D") + } + noise.bernoulli(1 - p) + output.cmul(noise.expandAs(input)) + } else { + this.output.mul(ev.fromType[Double](1 - p)) + } + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + if (train) { + gradInput.resizeAs(gradOutput).copy(gradOutput) + gradInput.cmul(noise.expandAs(input)) + } else { + throw new RuntimeException("SpatialDropout1D: backprop only defined while training") + } + + this.gradInput + } + + override def clearState(): this.type = { + super.clearState() + noise.set() + this + } + + + override def toString(): String = { + s"${getPrintName}($p)" + } +} + +object SpatialDropout1D { + def apply[T: ClassTag]( + initP: Double = 0.5 + )(implicit ev: TensorNumeric[T]) : SpatialDropout1D[T] = { + new SpatialDropout1D[T](initP) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala new file mode 100644 index 00000000000..87369a5f446 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala @@ -0,0 +1,112 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * + * This version performs the same function as Dropout, however it drops + * entire 2D feature maps instead of individual elements. If adjacent pixels + * within feature maps are strongly correlated (as is normally the case in + * early convolution layers) then regular dropout will not regularize the + * activations and will otherwise just result in an effective learning rate + * decrease. In this case, SpatialDropout2D will help promote independence + * between feature maps and should be used instead. + * + * @param initP the probability p + * @param format 'NCHW' or 'NHWC'. + In 'NCHW' mode, the channels dimension (the depth) + is at index 1, in 'NHWC' mode is it at index 4. + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +@SerialVersionUID(- 4636332259181125718L) +class SpatialDropout2D[T: ClassTag]( + val initP: Double = 0.5, + val format: DataFormat = DataFormat.NCHW)( + implicit ev: TensorNumeric[T]) extends TensorModule[T] { + var p = initP + var noise = Tensor[T]() + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + this.output.resizeAs(input).copy(input) + + if (train) { + val inputSize = input.size() + if (input.dim() == 3) { + if (format == DataFormat.NCHW) { + noise.resize(Array(inputSize(0), 1, 1)) + } else if (format == DataFormat.NHWC) { + noise.resize(Array(1, 1, inputSize(2))) + } else { + throw new RuntimeException("SpatialDropout2D:" + + " DataFormat: " + format + " is not supported") + } + } else if (input.dim() == 4) { + if (format == DataFormat.NCHW) { + noise.resize(Array(inputSize(0), inputSize(1), 1, 1)) + } else if (format == DataFormat.NHWC) { + noise.resize(Array(inputSize(0), 1, 1, inputSize(3))) + } else { + throw new RuntimeException("SpatialDropout2D: " + + "DataFormat: " + format + " is not supported") + } + } else { + throw new RuntimeException("SpatialDropout2D: " + + "Input must be 4D or 3D") + } + noise.bernoulli(1 - p) + output.cmul(noise.expandAs(input)) + } else { + this.output.mul(ev.fromType[Double](1 - p)) + } + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + if (train) { + gradInput.resizeAs(gradOutput).copy(gradOutput) + gradInput.cmul(noise.expandAs(input)) + } else { + throw new RuntimeException("SpatialDropout2D: " + + "backprop only defined while training") + } + + this.gradInput + } + + override def clearState(): this.type = { + super.clearState() + noise.set() + this + } + + + override def toString(): String = { + s"${getPrintName}($p)" + } +} + +object SpatialDropout2D { + def apply[T: ClassTag]( + initP: Double = 0.5, + format: DataFormat = DataFormat.NCHW)(implicit ev: TensorNumeric[T]) : SpatialDropout2D[T] = { + new SpatialDropout2D[T](initP, format) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala new file mode 100644 index 00000000000..484d76a81b1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala @@ -0,0 +1,112 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * + * This version performs the same function as Dropout, however it drops + * entire 3D feature maps instead of individual elements. If adjacent voxels + * within feature maps are strongly correlated (as is normally the case in + * early convolution layers) then regular dropout will not regularize the + * activations and will otherwise just result in an effective learning rate + * decrease. In this case, SpatialDropout3D will help promote independence + * between feature maps and should be used instead. + * + * @param initP the probability p + * @param format 'NCHW' or 'NHWC'. + In 'NCHW' mode, the channels dimension (the depth) + is at index 1, in 'NHWC' mode is it at index 4. + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +@SerialVersionUID(- 4636332259181125718L) +class SpatialDropout3D[T: ClassTag]( + val initP: Double = 0.5, + val format: DataFormat = DataFormat.NCHW)( + implicit ev: TensorNumeric[T]) extends TensorModule[T] { + var p = initP + var noise = Tensor[T]() + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + this.output.resizeAs(input).copy(input) + + if (train) { + val inputSize = input.size() + if (input.dim() == 4) { + if (format == DataFormat.NCHW) { + noise.resize(Array(inputSize(0), 1, 1, 1)) + } else if (format == DataFormat.NHWC) { + noise.resize(Array(1, 1, 1, inputSize(3))) + } else { + throw new RuntimeException("SpatialDropout3D: " + + "DataFormat: " + format + " is not supported") + } + } else if (input.dim() == 5) { + if (format == DataFormat.NCHW) { + noise.resize(Array(inputSize(0), inputSize(1), 1, 1, 1)) + } else if (format == DataFormat.NHWC) { + noise.resize(Array(inputSize(0), 1, 1, 1, inputSize(4))) + } else { + throw new RuntimeException("SpatialDropout3D: " + + "DataFormat: " + format + " is not supported") + } + } else { + throw new RuntimeException("SpatialDropout3D: " + + "Input must be 4D or 3D") + } + noise.bernoulli(1 - p) + output.cmul(noise.expandAs(input)) + } else { + this.output.mul(ev.fromType[Double](1 - p)) + } + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + if (train) { + gradInput.resizeAs(gradOutput).copy(gradOutput) + gradInput.cmul(noise.expandAs(input)) + } else { + throw new RuntimeException("SpatialDropout3D: " + + "backprop only defined while training") + } + + this.gradInput + } + + override def clearState(): this.type = { + super.clearState() + noise.set() + this + } + + + override def toString(): String = { + s"${getPrintName}($p)" + } +} + +object SpatialDropout3D { + def apply[T: ClassTag]( + initP: Double = 0.5, + format: DataFormat = DataFormat.NCHW)(implicit ev: TensorNumeric[T]): SpatialDropout3D[T] = { + new SpatialDropout3D[T](initP, format) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a6a80029ae0..e18a1295b1e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1086,6 +1086,27 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab SoftSign[T, T]() } + + def createSpatialDropout1D( + initP: Double = 0.5 + ): SpatialDropout1D[T] = { + SpatialDropout1D[T](initP) + } + + def createSpatialDropout2D( + initP: Double = 0.5, + dataFormat: String = "NCHW" + ): SpatialDropout2D[T] = { + SpatialDropout2D[T](initP, DataFormat(dataFormat)) + } + + def createSpatialDropout3D( + initP: Double = 0.5, + dataFormat: String = "NCHW" + ): SpatialDropout3D[T] = { + SpatialDropout3D[T](initP, DataFormat(dataFormat)) + } + def createSpatialDilatedConvolution(nInputPlane: Int, nOutputPlane: Int, kW: Int, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout1DSpec.scala new file mode 100644 index 00000000000..c42ee945ccc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout1DSpec.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn.{SpatialDropout1D, SpatialDropout2D} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ + +@com.intel.analytics.bigdl.tags.Serial +class SpatialDropout1DSpec extends TorchSpec { + "SpatialDropout1D module with continuous input" should "converge to correct weight and bias" in { + torchCheck() + val module = SpatialDropout1D[Double](0.7) + val input = Tensor[Double](3, 4, 5) + val seed = 100 + + input.rand() + + val start = System.nanoTime() + RNG.setSeed(seed) + val output = module.forward(input) + println(output) + val gradInput = module.backward(input, input.clone().fill(1)) + println(gradInput) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout2DSpec.scala new file mode 100644 index 00000000000..e9a338d1a19 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout2DSpec.scala @@ -0,0 +1,70 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn.SpatialDropout2D +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ + +@com.intel.analytics.bigdl.tags.Serial +class SpatialDropout2DSpec extends TorchSpec { + "SpatialDropout2D module with continuous input" should "converge to correct weight and bias" in { + torchCheck() + val module = SpatialDropout2D[Double](0.7) + val input = Tensor[Double](3, 4, 5, 6) + val seed = 100 + + input.rand() + + val code = "torch.manualSeed(" + seed + ")\n" + + "module = nn.SpatialDropout(0.7)\n" + + "output1 = module:forward(input)\n" + + "output2 = module:backward(input, input:clone():fill(1))" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input), Array("output1", "output2")) + val luaOutput1 = torchResult("output1").asInstanceOf[Tensor[Double]] + val luaOutput2 = torchResult("output2").asInstanceOf[Tensor[Double]] + + val start = System.nanoTime() + RNG.setSeed(seed) + val output1 = module.forward(input) + val output2 = module.backward(input, input.clone().fill(1)) + val end = System.nanoTime() + val scalaTime = end - start + + + luaOutput1 should be(output1) + luaOutput2 should be(output2) + + println("Test case : Dropout, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") + } + + "a" should "a" in { + val module = SpatialDropout2D[Double](0.7) + val input = Tensor[Double](2, 3, 4, 5) + val seed = 100 + + input.rand() + + RNG.setSeed(seed) + val output = module.forward(input) + val gradInput = module.backward(input, input.clone().fill(1)) + println(output) + println(gradInput) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout3DSpec.scala new file mode 100644 index 00000000000..90a2bfc7dca --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout3DSpec.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn.SpatialDropout3D +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ + +@com.intel.analytics.bigdl.tags.Serial +class SpatialDropout3DSpec extends TorchSpec { + "SpatialDropout3D module with continuous input" should "converge to correct weight and bias" in { + torchCheck() + val module = SpatialDropout3D[Double](0.7) + val input = Tensor[Double](3, 4, 5, 6, 7) + val seed = 100 + + input.rand() + + val code = "torch.manualSeed(" + seed + ")\n" + + "module = nn.VolumetricDropout(0.7)\n" + + "output1 = module:forward(input)\n" + + "output2 = module:backward(input, input:clone():fill(1))" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input), Array("output1", "output2")) + val luaOutput1 = torchResult("output1").asInstanceOf[Tensor[Double]] + val luaOutput2 = torchResult("output2").asInstanceOf[Tensor[Double]] + + val start = System.nanoTime() + RNG.setSeed(seed) + val output1 = module.forward(input) + val output2 = module.backward(input, input.clone().fill(1)) + val end = System.nanoTime() + val scalaTime = end - start + + + luaOutput1 should be(output1) + luaOutput2 should be(output2) + + println("Test case : Dropout, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 5765d2721b3..73ed8bb0412 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -27,7 +27,7 @@ import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ -import com.intel.analytics.bigdl.nn.{DenseToSparse, _} +import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.RandomGenerator.RNG @@ -164,6 +164,24 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(binaryThreshold, input) } + "SpatialDropout1D serializer" should "work properly" in { + val spatialDropout1D = SpatialDropout1D[Float]() + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialDropout1D, input) + } + + "SpatialDropout2D serializer" should "work properly" in { + val spatialDropout2D = SpatialDropout2D[Float]() + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialDropout2D, input) + } + + "SpatialDropout3D serializer" should "work properly" in { + val spatialDropout3D = SpatialDropout3D[Float]() + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialDropout3D, input) + } + "BinaryTreeLSTM serializer" should "work properly" in { RNG.setSeed(1000) From dc8f458fa99c67097cef4813c8cef2be5f4b6cc3 Mon Sep 17 00:00:00 2001 From: dding3 Date: Fri, 15 Dec 2017 18:03:58 -0500 Subject: [PATCH 0601/1065] Support gradient clipping (#2001) support constant clip and l2norm clipping --- .../spark/storage/BlockManagerWrapper.scala | 1 - .../spark/storage/BlockManagerWrapper.scala | 2 - .../bigdl/dllib/optim/DistriOptimizer.scala | 167 ++++++++++++------ .../bigdl/dllib/optim/LocalOptimizer.scala | 25 ++- .../bigdl/dllib/optim/Optimizer.scala | 47 +++++ .../optim/parameters/AllReduceParameter.scala | 23 +-- .../dllib/optim/parameters/Parameter.scala | 2 +- .../bigdl/dllib/tensor/DenseTensor.scala | 17 ++ .../tensor/QuantizedTensorUnsupported.scala | 5 + .../bigdl/dllib/tensor/SparseTensor.scala | 6 + .../bigdl/dllib/tensor/TensorMath.scala | 4 + .../dllib/utils/python/api/PythonBigDL.scala | 14 ++ .../dllib/optim/DistriOptimizerSpec.scala | 8 +- 13 files changed, 243 insertions(+), 78 deletions(-) diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/storage/BlockManagerWrapper.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/storage/BlockManagerWrapper.scala index 66045b511bc..e5ba0cac744 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/storage/BlockManagerWrapper.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/storage/BlockManagerWrapper.scala @@ -26,7 +26,6 @@ object BlockManagerWrapper { bytes: ByteBuffer, level: StorageLevel): Unit = { require(bytes != null, "Bytes is null") - SparkEnv.get.blockManager.removeBlock(blockId) SparkEnv.get.blockManager.putBytes(blockId, bytes, level) } diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/storage/BlockManagerWrapper.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/storage/BlockManagerWrapper.scala index b46f6c8310a..116d6f248ba 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/storage/BlockManagerWrapper.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/storage/BlockManagerWrapper.scala @@ -30,8 +30,6 @@ object BlockManagerWrapper { bytes: ByteBuffer, level: StorageLevel): Unit = { require(bytes != null, "Bytes is null") - val blockManager = SparkEnv.get.blockManager - blockManager.removeBlock(blockId) putBytesFn(blockId, new ChunkedByteBuffer(bytes), level) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 876df1631a5..dcfb60cce4c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -85,6 +85,7 @@ object DistriOptimizer { * @param trainSummary train summary * @param validationSummary validation summary * @param isOverWrite if overwrite the checkpoint + * @param clippingParams gradient clipping configurations */ private[optim] def optimize[T: ClassTag]( trainingModel: Module[T], @@ -103,7 +104,8 @@ object DistriOptimizer { cachePath: Option[String], trainSummary: Option[TrainSummary], validationSummary: Option[ValidationSummary], - isOverWrite: Boolean + isOverWrite: Boolean, + clippingParams: GradientClippingParams )(implicit ev: TensorNumeric[T]): Unit = { val sc = dataset.originRDD().sparkContext val partitionNum = dataset.originRDD().partitions.length @@ -150,6 +152,13 @@ object DistriOptimizer { var dropModelNumBatch = 0 var lossArray = new Array[Double](_subModelNumber) + // gradient clip settings + val constantClippingEnable = clippingParams.enableConstantClipping + val normClippingEnable = clippingParams.enableL2NormClipping + val maxValueClip = clippingParams.maxValueClip + val minValueClip = clippingParams.minValueClip + val normValueClip = clippingParams.normValueClip + var epochStart = System.nanoTime() var dataRDD = dataset.data(train = true) var recordsProcessedThisEpoch = 0 @@ -256,8 +265,8 @@ object DistriOptimizer { val length = taskSize + (if (tid < extraTask) 1 else 0) var i = 1 while (i < finishedGradients.length) { - finishedGradients(0).narrow(1, offset + 1, length) - .add(finishedGradients(i).narrow(1, offset + 1, length)) + finishedGradients(0).narrow(1, offset + 1, length) + .add(finishedGradients(i).narrow(1, offset + 1, length)) i += 1 } })) @@ -284,27 +293,68 @@ object DistriOptimizer { } Iterator.single(finishedThreads.size) } - }.reduce(_ + _) + }.reduce(_ + _) dropModelNumBatch += (driverSubModelNum - numFinishedModelUpdates) if (dropPercentage == 0.0 || numFinishedModelUpdates >= driverSubModelNum * (1.0 - maxDropPercentage)) { // enough records were processed for this batch, so update the model val value = lossSum.value / numFinishedModelUpdates + + var l2Norm = 0.0f + var scale = ev.fromType(numFinishedModelUpdates) + if (normClippingEnable) { + val sumSquare = models.mapPartitions(modelIter => { + val getG = System.nanoTime() + parameters.aggregateGradientPartition() + driverMetrics.add("aggregrateGradientParition average executor", + System.nanoTime() - getG) + + val gradLength = parameters.gradientPartition.nElement() + val taskSize = gradLength / _subModelNumber + val extraTask = gradLength % _subModelNumber + val parallelNum = if (taskSize == 0) extraTask else _subModelNumber + val squares = new Array[Double](parallelNum) + Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { + val offset = tid * taskSize + math.min(tid, extraTask) + val length = taskSize + (if (tid < extraTask) 1 else 0) + squares(tid) = ev.toType[Double]( + parameters.gradientPartition.narrow(1, offset + 1, length).sumSquare()) + })) + var sum = 0.0 + var i = 0 + while (i < parallelNum) { + sum += squares(i) + i += 1 + } + Iterator.single(sum) + }).reduce(_ + _) + l2Norm = (math.sqrt(sumSquare) / numFinishedModelUpdates).toFloat + if (l2Norm > normValueClip) { + scale = ev.fromType[Double]((l2Norm * numFinishedModelUpdates) / normValueClip) + } + } + models.mapPartitions { modelIter => val modelCache = modelIter.next() - val getG = System.nanoTime() - parameters.aggregateGradientPartition() - driverMetrics.add("aggregrateGradientParition average executor", - System.nanoTime() - getG) - var time = System.nanoTime() - parameters.gradientPartition.div(ev.fromType(numFinishedModelUpdates)) + if (!normClippingEnable) { + val getG = System.nanoTime() + parameters.aggregateGradientPartition() + driverMetrics.add("aggregrateGradientParition average executor", + System.nanoTime() - getG) + } + parameters.gradientPartition.div(scale) modelCache.optimMethod.state.update("epoch", driverState[Int]("epoch")) modelCache.optimMethod.state.update("neval", driverState[Int]("neval")) modelCache.optimMethod.state.update("Loss", driverState[Float]("Loss")) if (validationMethods.isDefined) { modelCache.optimMethod.state.update("score", driverState[Float]("score")) } + var time = System.nanoTime() + // gradient clipping + if (constantClippingEnable) { + parameters.gradientPartition.clamp(minValueClip, maxValueClip) + } modelCache.optimMethod.optimize(_ => (ev.fromType(value), parameters.gradientPartition), parameters.weightPartition) driverMetrics.add("compute weight average", System.nanoTime() - time) @@ -435,15 +485,15 @@ object DistriOptimizer { * @param parameters all reduce parameters */ private def checkpoint[T: ClassTag]( - cacheTrigger: Option[Trigger], - cachePath: Option[String], - isOverWrite: Boolean, - wallClockTime: Long, - models: RDD[Cache[T]], - state: Table, - parameters: AllReduceParameter[T], - optimMethod: OptimMethod[T], - trainingModel: Module[T]): Unit = { + cacheTrigger: Option[Trigger], + cachePath: Option[String], + isOverWrite: Boolean, + wallClockTime: Long, + models: RDD[Cache[T]], + state: Table, + parameters: AllReduceParameter[T], + optimMethod: OptimMethod[T], + trainingModel: Module[T]): Unit = { cacheTrigger.foreach { trigger => cachePath.foreach { path => if (trigger(state)) { @@ -467,36 +517,36 @@ object DistriOptimizer { * @param parameters [[AllReduceParameter]] */ private def saveSummary[T: ClassTag]( - trainSummary: TrainSummary, - models: RDD[Cache[T]], - driverState: Table, - parameters: AllReduceParameter[T], - trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { + trainSummary: TrainSummary, + models: RDD[Cache[T]], + driverState: Table, + parameters: AllReduceParameter[T], + trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { val currentIteration = driverState[Int]("neval") - 1 - val parametersTrigger = trainSummary.getSummaryTrigger("Parameters") - if (parametersTrigger.isDefined && parametersTrigger.get(driverState)) { - val model = getModel(models, parameters, trainingModel) - val parametersTable = model.getParametersTable() - // Parallelize to create Histogram. - Engine.default.invokeAndWait( - parametersTable.keySet.toSeq.map(moduleName => () => { - val paramTable = parametersTable[Table](moduleName) - paramTable.keySet.foreach { paramName => - trainSummary.addHistogram( - s"$moduleName/$paramName", paramTable[Tensor[T]](paramName), currentIteration)} - })) - } - val scalarTrigger = trainSummary.getScalarTriggers() - // Not parallelizable, because driverState is changing each iteration. - scalarTrigger.foreach { v => - if (v._2(driverState)) { - require(driverState.contains(v._1), s"DistriOptimizer.saveSummary: Summary ${v._1} " + - s"is not supported now.") - trainSummary.addScalar( - v._1, driverState[Float](v._1), currentIteration - ) - } + val parametersTrigger = trainSummary.getSummaryTrigger("Parameters") + if (parametersTrigger.isDefined && parametersTrigger.get(driverState)) { + val model = getModel(models, parameters, trainingModel) + val parametersTable = model.getParametersTable() + // Parallelize to create Histogram. + Engine.default.invokeAndWait( + parametersTable.keySet.toSeq.map(moduleName => () => { + val paramTable = parametersTable[Table](moduleName) + paramTable.keySet.foreach { paramName => + trainSummary.addHistogram( + s"$moduleName/$paramName", paramTable[Tensor[T]](paramName), currentIteration)} + })) + } + val scalarTrigger = trainSummary.getScalarTriggers() + // Not parallelizable, because driverState is changing each iteration. + scalarTrigger.foreach { v => + if (v._2(driverState)) { + require(driverState.contains(v._1), s"DistriOptimizer.saveSummary: Summary ${v._1} " + + s"is not supported now.") + trainSummary.addScalar( + v._1, driverState[Float](v._1), currentIteration + ) } + } } /** @@ -525,7 +575,7 @@ object DistriOptimizer { parameters: AllReduceParameter[T], validationMethods: Option[Array[ValidationMethod[T]]], optimMethod: OptimMethod[T] - )(implicit ev: TensorNumeric[T]) = { + )(implicit ev: TensorNumeric[T]) = { val sc = dataset.originRDD().sparkContext val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod)) // ensure model's parameter is compacted for getting a better performance when broadcasting @@ -693,9 +743,9 @@ object DistriOptimizer { * @return trained model */ private def getModel[T: ClassTag]( - models: RDD[Cache[T]], - parameters: AllReduceParameter[T], - trainingModel: Module[T]): Module[T] = { + models: RDD[Cache[T]], + parameters: AllReduceParameter[T], + trainingModel: Module[T]): Module[T] = { val partitionNum = models.partitions.length val extraState = models.map(_.localModels.head.getExtraParameter()).first() trainingModel.setExtraParameter(extraState) @@ -738,7 +788,7 @@ class DistriOptimizer[T: ClassTag] ( _model: Module[T], _dataset: DistributedDataSet[MiniBatch[T]], _criterion: Criterion[T] -)(implicit ev: TensorNumeric[T]) + )(implicit ev: TensorNumeric[T]) extends Optimizer[T, MiniBatch[T]]( _model, _dataset, _criterion) { val metrics = new Metrics @@ -761,8 +811,8 @@ class DistriOptimizer[T: ClassTag] ( override def setTrainData(sampleRDD: RDD[Sample[T]], - batchSize: Int, - miniBatch: MiniBatch[T]): this.type = { + batchSize: Int, + miniBatch: MiniBatch[T]): this.type = { this.dataset = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(miniBatch, batchSize, None)) .asInstanceOf[DistributedDataSet[MiniBatch[T]]] @@ -770,9 +820,9 @@ class DistriOptimizer[T: ClassTag] ( } override def setTrainData(sampleRDD: RDD[Sample[T]], - batchSize: Int, - featurePaddingParam: PaddingParam[T] = null, - labelPaddingParam: PaddingParam[T] = null) : this.type = { + batchSize: Int, + featurePaddingParam: PaddingParam[T] = null, + labelPaddingParam: PaddingParam[T] = null) : this.type = { val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None dataset = (DataSet.rdd(sampleRDD) -> @@ -845,7 +895,8 @@ class DistriOptimizer[T: ClassTag] ( checkpointPath, trainSummary, validationSummary, - isOverWrite + isOverWrite, + gradientClippingParams ) retryNum = Int.MaxValue } catch { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index 0d4aaa44700..f83bc00fd5c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -152,8 +152,31 @@ class LocalOptimizer[T: ClassTag] ( }) ) val loss = lossSum / parallelism - grad.div(ev.fromType(parallelism)) + var scale = ev.fromType(parallelism) + if (gradientClippingParams.enableL2NormClipping) { + val squares = new Array[Double](syncGradParallelNum) + Engine.default.invokeAndWait((0 until syncGradParallelNum).map(tid => () => { + val offset = tid * syncGradTaskSize + math.min(tid, syncGradExtraTask) + val length = syncGradTaskSize + (if (tid < syncGradExtraTask) 1 else 0) + squares(tid) = ev.toType[Double](grad.narrow(1, offset + 1, length).sumSquare()) + })) + var sum = 0.0 + var i = 0 + while (i < squares.size) { + sum += squares(i) + i += 1 + } + val l2Norm = (math.sqrt(sum) / parallelism).toFloat + if (l2Norm > gradientClippingParams.normValueClip) { + scale = ev.fromType[Float]((l2Norm * parallelism) / gradientClippingParams.normValueClip) + } + } + grad.div(scale) + + if (gradientClippingParams.enableConstantClipping) { + grad.clamp(gradientClippingParams.minValueClip, gradientClippingParams.maxValueClip) + } optimMethod.state.update("epoch", state.get("epoch")) optimMethod.state.update("neval", state.get("neval")) optimMethod.optimize(_ => (ev.fromType(loss), grad), weight) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index 15ac0f49cbb..e55ddd58c1e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -66,6 +66,8 @@ abstract class Optimizer[T: ClassTag, D]( protected var computeThresholdbatchSize: Int = 100 protected var warmupIterationNum: Int = 200 + protected val gradientClippingParams = GradientClippingParams(false, 0.0f, 0.0f, false, 0.0f) + /** * Trigger the optimization process * @return the model to be trained @@ -337,6 +339,44 @@ abstract class Optimizer[T: ClassTag, D]( } def prepareInput(): Unit = {} + + /** + * Disable gradient clipping + * @return + */ + def disableGradientClipping() + : this.type = { + gradientClippingParams.enableConstantClipping = false + gradientClippingParams.enableL2NormClipping = false + this + } + + /** + * Set constant gradient clipping + * @param min the minimum value to clip by + * @param max the maximum value to clip by + * @return + */ + def setConstantGradientClipping(min: Float, max: Float) + : this.type = { + require(min < max, "min value must be smaller than max") + gradientClippingParams.enableConstantClipping = true + gradientClippingParams.minValueClip = min + gradientClippingParams.maxValueClip = max + this + } + + /** + * Clip gradient to a maximum L2-norm + * @param clipNorm gradient L2-Norm threshold + * @return + */ + def setGradientClippingByl2Norm(clipNorm: Float) + : this.type = { + gradientClippingParams.enableL2NormClipping = true + gradientClippingParams.normValueClip = clipNorm + this + } } object Optimizer { @@ -490,3 +530,10 @@ object Optimizer { } } } + +case class GradientClippingParams( + var enableConstantClipping: Boolean, + var minValueClip: Float, + var maxValueClip: Float, + var enableL2NormClipping: Boolean, + var normValueClip: Float) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala index e415a6a79f2..6e4dc53f127 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala @@ -192,7 +192,7 @@ class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) ex val start = pid * taskSize + math.min(pid, extraSize) val length = taskSize + (if (pid < extraSize) 1 else 0) require(localBuffer.array().length == length * 2) - SerializerInstance.serialize(localBuffer).deCompress(0, localParameter, start, length) + SerializerInstance.create(localBuffer).deCompress(0, localParameter, start, length) BlockManagerWrapper.unlock(blockId) pid } catch { @@ -222,7 +222,7 @@ class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) ex try { val blockId = getGradientBlockId(pid, partitionId) val tmp = BlockManagerWrapper.getLocalOrRemoteBytes(blockId).get - params(pid) = SerializerInstance.serialize(tmp) + params(pid) = SerializerInstance.create(tmp) BlockManagerWrapper.unlock(blockId) pid } catch { @@ -292,16 +292,17 @@ class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) ex */ def sendWeightPartition(): Unit = { val blockId = getWeightBlockId(partitionId) + val localBuffer = BlockManagerWrapper.getLocalBytes(blockId).getOrElse { + throw new RuntimeException(s"Didn't find weight block $blockId in the block " + + s"manager. Did you initialize this AllReduceParameter on every executor?") + } + SerializerInstance.create(localBuffer).compress(weightPartition) + val weightsId = getWeightPartitionId() - require(weightPartition != null, "Cannot send the weights for this partition until they have" + - " been updated by the optimizer!") - BlockManagerWrapper.removeBlock(blockId) - BlockManagerWrapper.unlock(weightsId) - BlockManagerWrapper.removeBlock(weightsId) - BlockManagerWrapper.putSingle(weightsId, - weightPartition, StorageLevel.MEMORY_AND_DISK, tellMaster = false) - BlockManagerWrapper.putBytes(blockId, - SerializerInstance.serialize(weightPartition).bytes(), StorageLevel.MEMORY_ONLY_SER) + val weights = BlockManagerWrapper.getLocal(weightsId) + .map(_.data.next().asInstanceOf[Tensor[T]]) + .getOrElse(throw new IllegalStateException("Please initialize AllReduceParameter first!")) + weights.copy(weightPartition) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/Parameter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/Parameter.scala index 136c8841f65..8307af534e4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/Parameter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/Parameter.scala @@ -65,7 +65,7 @@ object SerializerInstance { } } - def serialize[T: ClassTag](data: ByteBuffer): CompressedTensor[T] = { + def create[T: ClassTag](data: ByteBuffer): CompressedTensor[T] = { pm.toLowerCase() match { case "fp16" => new FP16CompressedTensor[T](data) case _ => throw new IllegalArgumentException("Unsupported parameter type") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 21b07b3dc5a..e62299f235c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -866,6 +866,23 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( (values, indices) } + override def sumSquare(): T = { + this.dot(this) + } + + override def clamp(min: Float, max: Float): Tensor[T] = { + val maxT = ev.fromType[Float](max) + val minT = ev.fromType[Float](min) + val func = new TensorFunc2[T] { + override def apply(data1: Array[T], offset1: Int): Unit = { + if (ev.isGreater(data1(offset1), maxT)) data1(offset1) = maxT + else if (ev.isGreater(minT, data1(offset1))) data1(offset1) = minT + } + } + DenseTensorApply.apply1[T](this, func) + this + } + def scatter(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = { require(src.dim() == this.dim(), "Input tensor must have same dimensions as output tensor") require(dim <= this.dim(), "Index dimension is out of bounds") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index 9221239404b..071f9b98105 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1440,6 +1440,11 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { throw new UnsupportedOperationException(errorString) override def digamma(): Tensor[T] = + throw new UnsupportedOperationException(errorString) + + override def clamp(minValue: Float, maxValue: Float): Tensor[T] = + throw new UnsupportedOperationException(errorString) + override def sumSquare(): T = throw new UnsupportedOperationException(errorString) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 0b3585e93b2..61082e8bce9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -1061,6 +1061,12 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( override def digamma(): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + + override def clamp(minValue: Float, maxValue: Float): Tensor[T] = + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + + override def sumSquare(): T = + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } object SparseTensor{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala index 030ac6c026e..6e7b526c6c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala @@ -822,4 +822,8 @@ trait TensorMath[T] { * @param reducer */ def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] + + def sumSquare(): T + + def clamp(min: Float, max: Float): Tensor[T] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index e18a1295b1e..5759308b528 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2771,6 +2771,20 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab val targets = if (targetKeys == null) null else targetKeys.asScala.toArray ImageFrameToSample[T](inputKeys.asScala.toArray, targets, sampleKey) } + + def setConstantClip(optimizer: Optimizer[T, MiniBatch[T]], + min: Float, max: Float): Unit = { + optimizer.setConstantGradientClipping(min, max) + } + + def setL2NormClip(optimizer: Optimizer[T, MiniBatch[T]], + normValue: Float): Unit = { + optimizer.setGradientClippingByl2Norm(normValue) + } + + def disableClip(optimizer: Optimizer[T, MiniBatch[T]]): Unit = { + optimizer.disableGradientClipping() + } } object PythonBigDLUtils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index c9ecb34aa1e..54a11b383bc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -361,13 +361,13 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { new ClassNLLCriterion[Double]() ) optimizer.setState(T("learningRate" -> 20.0)) - .setCheckpoint(filePath, Trigger.everyEpoch) - .setEndWhen(Trigger.maxEpoch(2)) - .optimize() + .setCheckpoint(filePath, Trigger.everyEpoch) + .setEndWhen(Trigger.maxEpoch(2)) + .optimize() val numIterations = dataSet.data(train = false).count() / nodeNumber + 1 val optimMethod = OptimMethod.load[Double](optimizer.getCheckpointPath().get + - s"/optimMethod.$numIterations") + s"/optimMethod.$numIterations") optimMethod.state.get[Int]("epoch").get should be (2) optimMethod.state.get[Int]("neval").get should be (numIterations) From c429d354bf8038172d01129611f41b66ee6d9087 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Sat, 16 Dec 2017 22:49:01 -0800 Subject: [PATCH 0602/1065] code refactor to reduce code duplicate and avoid manual toBatch operation. (#1983) * more data type support * re org * convert func * batchsize * comments * use manual to batch * default value * style fix * group --- .../org/apache/spark/ml/DLClassifier.scala | 19 +- .../org/apache/spark/ml/DLEstimator.scala | 302 +++++++++++------- .../org/apache/spark/ml/DLEstimatorBase.scala | 91 +----- .../apache/spark/ml/DLTransformerBase.scala | 28 +- .../org/apache/spark/ml/DLEstimatorBase.scala | 87 +---- .../apache/spark/ml/DLTransformerBase.scala | 20 +- .../bigdl/dllib/optim/DLClassifierSpec.scala | 2 +- .../bigdl/dllib/optim/DLEstimatorSpec.scala | 2 +- 8 files changed, 228 insertions(+), 323 deletions(-) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala index b9fae464885..038849f698b 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala @@ -35,7 +35,7 @@ import scala.reflect.ClassTag * @param featureSize The size (Tensor dimensions) of the feature data. */ class DLClassifier[@specialized(Float, Double) T: ClassTag]( - override val model: Module[T], + @transient override val model: Module[T], override val criterion : Criterion[T], override val featureSize : Array[Int], override val uid: String = Identifiable.randomUID("dlClassifier") @@ -49,7 +49,8 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( } override def transformSchema(schema : StructType): StructType = { - validateSchema(schema) + validateDataType(schema, $(featuresCol)) + validateDataType(schema, $(labelCol)) SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) } @@ -66,23 +67,17 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( * @param featureSize The size (Tensor dimensions) of the feature data. */ class DLClassifierModel[@specialized(Float, Double) T: ClassTag]( - override val model: Module[T], + @transient override val model: Module[T], featureSize : Array[Int], override val uid: String = "DLClassifierModel" )(implicit ev: TensorNumeric[T]) extends DLModel[T](model, featureSize) { - override protected def batchOutputToPrediction(output: Tensor[T]): Iterable[_] = { - output.split(1) - val result = if (output.dim == 2) { - output.split(1).map(t => t.max(1)._2.storage().head) - } else { - throw new IllegalArgumentException - } - result.map(ev.toType[Double]) + protected override def outputToPrediction(output: Tensor[T]): Any = { + ev.toType[Double](output.max(1)._2.valueAt(1)) } override def transformSchema(schema : StructType): StructType = { - validateSchema(schema) + validateDataType(schema, $(featuresCol)) SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) } } diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index e06b49287d4..7977f4ee3d3 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.T +import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasPredictionCol} import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators, _} import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.rdd.RDD @@ -30,14 +31,114 @@ import org.apache.spark.sql.{DataFrame, Row} import scala.reflect.ClassTag +private[ml] trait HasBatchSize extends Params { + + final val batchSize: Param[Int] = new Param[Int](this, "batchSize", "batchSize") + + def getBatchSize: Int = $(batchSize) +} + +/** + * Common trait for DLEstimator and DLModel + */ +private[ml] trait DLParams[@specialized(Float, Double) T] extends HasFeaturesCol + with HasPredictionCol with VectorCompatibility with HasBatchSize { + + /** + * optimization method to be used. BigDL supports many optimization methods like Adam, + * SGD and LBFGS. Refer to package com.intel.analytics.bigdl.optim for all the options. + * Default: SGD + */ + final val optimMethod = new Param[OptimMethod[T]](this, "optimMethod", "optimMethod") + + def getOptimMethod: OptimMethod[T] = $(optimMethod) + + /** + * number of max Epoch for the training, an epoch refers to a traverse over the training data + * Default: 100 + */ + final val maxEpoch = new IntParam(this, "maxEpoch", "number of max Epoch", ParamValidators.gt(0)) + + def getMaxEpoch: Int = $(maxEpoch) + + /** + * learning rate for the optimizer in the DLEstimator. + * Default: 0.001 + */ + final val learningRate = new DoubleParam( + this, "learningRate", "learningRate", ParamValidators.gt(0)) + + def getLearningRate: Double = $(learningRate) + + /** + * learning rate decay for each iteration. + * Default: 0 + */ + final val learningRateDecay = new DoubleParam(this, "learningRateDecay", "learningRateDecay") + + def getLearningRateDecay: Double = $(learningRateDecay) + + setDefault(batchSize -> 1) + + /** + * Validate if feature and label columns are of supported data types. + * Default: 0 + */ + protected def validateDataType(schema: StructType, colName: String): Unit = { + val dataTypes = Seq( + new ArrayType(DoubleType, false), + new ArrayType(DoubleType, true), + new ArrayType(FloatType, false), + new ArrayType(FloatType, true), + DoubleType, + FloatType + ) ++ validVectorTypes + + // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 + val actualDataType = schema(colName).dataType + require(dataTypes.exists(actualDataType.equals), + s"Column $colName must be of type equal to one of the following types: " + + s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") + } + + /** + * Get conversion function to extract data from original DataFrame + * Default: 0 + */ + protected def getConvertFunc(colType: DataType): (Row, Int) => Seq[AnyVal] = { + colType match { + case ArrayType(DoubleType, false) => + (row: Row, index: Int) => row.getSeq[Double](index) + case ArrayType(DoubleType, true) => + (row: Row, index: Int) => row.getSeq[Double](index) + case ArrayType(FloatType, false) => + (row: Row, index: Int) => row.getSeq[Float](index) + case ArrayType(FloatType, true) => + (row: Row, index: Int) => row.getSeq[Float](index) + case DoubleType => + (row: Row, index: Int) => Seq[Double](row.getDouble(index)) + case FloatType => + (row: Row, index: Int) => Seq[Float](row.getFloat(index)) + case _ => + if (colType.typeName.contains("vector")) { + (row: Row, index: Int) => getVectorSeq(row, colType, index) + } else { + throw new IllegalArgumentException( + s"$colType is not a supported (Unexpected path).") + } + } + } +} + + /** * [[DLEstimator]] helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern, * thus Spark users can conveniently fit BigDL into Spark ML pipeline. * - * [[DLEstimator]] supports feature and label data in the format of Array[Double], Array[Float], - * org.apache.spark.mllib.linalg.{Vector, VectorUDT} for Spark 1.5, 1.6 and - * org.apache.spark.ml.linalg.{Vector, VectorUDT} for Spark 2.0+. Also label data can be of - * DoubleType. + * [[DLEstimator]] supports feature and label data in the format of + * Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, + * org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float. + * * User should specify the feature data dimensions and label data dimensions via the constructor * parameters featureSize and labelSize respectively. Internally the feature and label data are * converted to BigDL tensors, to further train a BigDL model efficiently. @@ -52,12 +153,12 @@ import scala.reflect.ClassTag * @param labelSize The size (Tensor dimensions) of the label data. */ class DLEstimator[@specialized(Float, Double) T: ClassTag]( - val model: Module[T], + @transient val model: Module[T], val criterion : Criterion[T], val featureSize : Array[Int], val labelSize : Array[Int], override val uid: String = "DLEstimator")(implicit ev: TensorNumeric[T]) - extends DLEstimatorBase[DLEstimator[T], DLModel[T]] with DLParams with HasBatchSize { + extends DLEstimatorBase[DLEstimator[T], DLModel[T]] with DLParams[T] { def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) @@ -67,68 +168,61 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( def setBatchSize(value: Int): this.type = set(batchSize, value) - /** - * optimization method to be used. BigDL supports many optimization methods like Adam, - * SGD and LBFGS. Refer to package com.intel.analytics.bigdl.optim for all the options. - * Default: SGD - */ - val optimMethod = new Param[OptimMethod[_]](this, "optimMethod", "optimMethod") - - def getOptimMethod: OptimMethod[_] = $(optimMethod) - - def setOptimMethod(value: OptimMethod[_]): this.type = set(optimMethod, value) - - /** - * number of max Epoch for the training, an epoch refers to a traverse over the training data - * Default: 100 - */ - val maxEpoch = new IntParam(this, "maxEpoch", "number of max Epoch", ParamValidators.gt(0)) - setDefault(maxEpoch -> 100) - - def getMaxEpoch: Int = $(maxEpoch) + def setOptimMethod(value: OptimMethod[T]): this.type = set(optimMethod, value) def setMaxEpoch(value: Int): this.type = set(maxEpoch, value) - - /** - * learning rate for the optimizer in the DLEstimator. - * Default: 0.001 - */ - val learningRate = new DoubleParam(this, "learningRate", "learningRate", ParamValidators.gt(0)) - - setDefault(learningRate -> 1e-3) - - - def getLearningRate: Double = $(learningRate) + setDefault(maxEpoch -> 50) def setLearningRate(value: Double): this.type = set(learningRate, value) - - /** - * learning rate decay. - * Default: 0 - */ - val learningRateDecay = new DoubleParam(this, "learningRateDecay", "learningRateDecay") - setDefault(learningRateDecay -> 0.0) - - def getLearningRateDecay: Double = $(learningRateDecay) + setDefault(learningRate -> 1e-3) def setLearningRateDecay(value: Double): this.type = set(learningRateDecay, value) + setDefault(learningRateDecay -> 0.0) override def transformSchema(schema : StructType): StructType = { - validateSchema(schema) + validateDataType(schema, $(featuresCol)) + validateDataType(schema, $(labelCol)) SchemaUtils.appendColumn(schema, $(predictionCol), ArrayType(DoubleType, false)) } - protected override def internalFit( - featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): DLModel[T] = { - val batches = toMiniBatch(featureAndLabel) + protected override def internalFit(dataFrame: DataFrame): DLModel[T] = { + val featureType = dataFrame.schema($(featuresCol)).dataType + val featureColIndex = dataFrame.schema.fieldIndex($(featuresCol)) + val labelType = dataFrame.schema($(labelCol)).dataType + val labelColIndex = dataFrame.schema.fieldIndex($(labelCol)) + + val featureFunc = getConvertFunc(featureType) + val labelFunc = getConvertFunc(labelType) + + val featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])] = dataFrame.rdd.map { row => + val features = featureFunc(row, featureColIndex) + val labels = labelFunc(row, labelColIndex) + (features, labels) + } + + val samples = featureAndLabel.map { case (f, l) => + // convert feature and label data type to the same type with model + // TODO: investigate to reduce memory consumption during conversion. + val feature = f.head match { + case dd: Double => f.asInstanceOf[Seq[Double]].map(ev.fromType(_)) + case ff: Float => f.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + } + val label = l.head match { + case dd: Double => l.asInstanceOf[Seq[Double]].map(ev.fromType(_)) + case ff: Float => l.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + } + (feature, label) + }.map { case (feature, label) => + Sample(Tensor(feature.toArray, featureSize), Tensor(label.toArray, labelSize)) + } if(!isDefined(optimMethod)) { set(optimMethod, new SGD[T]) } val state = T("learningRate" -> $(learningRate), "learningRateDecay" -> $(learningRateDecay)) - val optimizer = Optimizer(model, batches, criterion) + val optimizer = Optimizer(model, samples, criterion, $(batchSize)) .setState(state) - .setOptimMethod($(optimMethod).asInstanceOf[OptimMethod[T]]) + .setOptimMethod($(optimMethod)) .setEndWhen(Trigger.maxEpoch($(maxEpoch))) val optimizedModel = optimizer.optimize() @@ -143,42 +237,17 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( copyValues(dlModel.setParent(this)) } - /** - * Extract and reassemble data according to batchSize - */ - private def toMiniBatch( - featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): DistributedDataSet[MiniBatch[T]] = { - - val samples = featureAndLabel.map { case (f, l) => - // convert feature and label data type to the same type with model - val feature = f.head match { - case dd: Double => f.asInstanceOf[Seq[Double]].map(ev.fromType(_)) - case ff: Float => f.asInstanceOf[Seq[Float]].map(ev.fromType(_)) - } - val label = l.head match { - case dd: Double => l.asInstanceOf[Seq[Double]].map(ev.fromType(_)) - case ff: Float => l.asInstanceOf[Seq[Float]].map(ev.fromType(_)) - } - (feature, label) - }.map { case (feature, label) => - Sample(Tensor(feature.toArray, featureSize), Tensor(label.toArray, labelSize)) - } - (DataSet.rdd(samples) -> SampleToMiniBatch(${batchSize})) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]] - } - override def copy(extra: ParamMap): DLEstimator[T] = { copyValues(new DLEstimator(model, criterion, featureSize, labelSize), extra) } } - /** * [[DLModel]] helps embed a BigDL model into a Spark Transformer, thus Spark users can * conveniently merge BigDL into Spark ML pipeline. - * [[DLModel]] supports feature data in the format of Array[Double], Array[Float], - * org.apache.spark.mllib.linalg.{Vector, VectorUDT} for Spark 1.5, 1.6 and - * org.apache.spark.ml.linalg.{Vector, VectorUDT} for Spark 2.0+. + * [[DLModel]] supports feature data in the format of + * Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, + * org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float. * Internally [[DLModel]] use features column as storage of the feature data, and create * Tensors according to the constructor parameter featureSize. * @@ -188,11 +257,11 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( * featureSize = 28 * 28). */ class DLModel[@specialized(Float, Double) T: ClassTag]( - val model: Module[T], + @transient val model: Module[T], var featureSize : Array[Int], override val uid: String = "DLModel" )(implicit ev: TensorNumeric[T]) - extends DLTransformerBase[DLModel[T]] with DLParams with HasBatchSize { + extends DLTransformerBase[DLModel[T]] with DLParams[T] with HasBatchSize { def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) @@ -209,52 +278,46 @@ class DLModel[@specialized(Float, Double) T: ClassTag]( /** * Perform a prediction on featureCol, and write result to the predictionCol. - * @param featureData featureData in the format of Seq - * @return output DataFrame */ - protected override def internalTransform( - featureData: RDD[Seq[AnyVal]], dataset: DataFrame): DataFrame = { - - model.evaluate() - val modelBroadCast = ModelBroadcast[T]().broadcast(featureData.sparkContext, model) - val predictRdd = featureData.map { f => - // convert feature data type to the same type with model - f.head match { - case dd: Double => f.asInstanceOf[Seq[Double]].map(ev.fromType(_)) - case ff: Float => f.asInstanceOf[Seq[Float]].map(ev.fromType(_)) - } - }.mapPartitions { feature => + protected override def internalTransform(dataFrame: DataFrame): DataFrame = { + val featureType = dataFrame.schema($(featuresCol)).dataType + val featureColIndex = dataFrame.schema.fieldIndex($(featuresCol)) + val featureFunc = getConvertFunc(featureType) + val sc = dataFrame.sqlContext.sparkContext + val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model) + val localBatchSize = $(batchSize) + + val resultRDD = dataFrame.rdd.mapPartitions { rowIter => val localModel = modelBroadCast.value() - val tensorBuffer = Tensor[T](Array($(batchSize)) ++ featureSize) - val batches = feature.grouped($(batchSize)) - batches.flatMap { batch => - var i = 1 - // Notice: if the last batch is smaller than the batchSize, we still continue - // to use this tensorBuffer, but only add the meaningful parts to the result Array. - batch.foreach { row => - tensorBuffer.select(1, i).copy(Tensor(Storage(row.toArray))) - i += 1 + rowIter.grouped(localBatchSize).flatMap { rowBatch => + val samples = rowBatch.map { row => + val features = featureFunc(row, featureColIndex) + val featureBuffer = features.head match { + case dd: Double => features.asInstanceOf[Seq[Double]].map(ev.fromType(_)) + case ff: Float => features.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + } + Sample(Tensor(featureBuffer.toArray, featureSize)) + }.toIterator + val predictions = SampleToMiniBatch(localBatchSize).apply(samples).flatMap { batch => + val batchResult = localModel.forward(batch.getInput()) + batchResult.toTensor.split(1).map(outputToPrediction) + } + rowBatch.toIterator.zip(predictions).map { case (row, predict) => + Row.fromSeq(row.toSeq ++ Seq(predict)) } - val output = localModel.forward(tensorBuffer).toTensor[T] - val predict = batchOutputToPrediction(output) - predict.take(batch.length) } } - val resultRDD = dataset.rdd.zip(predictRdd).map { case (row, predict) => - Row.fromSeq(row.toSeq ++ Seq(predict)) - } - val resultSchema = transformSchema(dataset.schema) - dataset.sqlContext.createDataFrame(resultRDD, resultSchema) + val resultSchema = transformSchema(dataFrame.schema) + dataFrame.sqlContext.createDataFrame(resultRDD, resultSchema) } - protected def batchOutputToPrediction(output: Tensor[T]): Iterable[_] = { - val predict = output.split(1) - predict.map(p => p.clone().storage().toArray.map(ev.toType[Double])) + protected def outputToPrediction(output: Tensor[T]): Any = { + output.clone().storage().array().map(ev.toType[Double]) } override def transformSchema(schema : StructType): StructType = { - validateSchema(schema) + validateDataType(schema, $(featuresCol)) SchemaUtils.appendColumn(schema, $(predictionCol), ArrayType(DoubleType, false)) } @@ -270,10 +333,3 @@ object DLModel { } -trait HasBatchSize extends Params { - - final val batchSize: Param[Int] = new Param[Int](this, "batchSize", "batchSize") - setDefault(batchSize -> 1) - - final def getBatchSize: Int = $(batchSize) -} diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index e6a397ab6ec..e9d45d8e670 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -22,42 +22,24 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.types._ import org.apache.spark.sql.{DataFrame, Row} +/** + * Handle different Vector types in Spark 1.5/1.6 and Spark 2.0+. + * Only support MLlib Vector for Spark 1.5/1.6. + */ +trait VectorCompatibility { -private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { - - /** - * only validate feature columns here - */ - protected def validateSchema(schema: StructType): Unit = { - val dataTypes = Seq( - new ArrayType(DoubleType, false), - new ArrayType(FloatType, false), - new VectorUDT) - - // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 - val actualDataType = schema($(featuresCol)).dataType - require(dataTypes.exists(actualDataType.equals), - s"Column ${$(featuresCol)} must be of type equal to one of the following types: " + - s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") - } + val validVectorTypes = Seq(new VectorUDT) - def supportedTypesToSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal] = { - val featureArr = if (colType == new VectorUDT) { - row.getAs[Vector](index).toArray.toSeq - } else if (colType == ArrayType(DoubleType, false)) { - row.getSeq[Double](index) - } else if (colType == ArrayType(FloatType, false)) { - row.getSeq[Float](index) - } else if (colType == DoubleType) { - Seq[Double](row.getDouble(index)) + def getVectorSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal] = { + if (colType == new org.apache.spark.mllib.linalg.VectorUDT) { + row.getAs[org.apache.spark.mllib.linalg.Vector](index).toArray.toSeq + } else { + throw new IllegalArgumentException( + s"$colType is not a supported vector type for Spark 1.5/1.6") } - featureArr.asInstanceOf[Seq[AnyVal]] } - - protected def getFeatureArrayCol: String = $(featuresCol) + "_Array" } - /** *A wrapper from org.apache.spark.ml.Estimator * Extends MLEstimator and override process to gain compatibility with @@ -65,52 +47,13 @@ private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { */ private[ml] abstract class DLEstimatorBase[Learner <: DLEstimatorBase[Learner, M], M <: DLTransformerBase[M]] - extends Estimator[M] with DLParams with HasLabelCol { - - protected def getLabelArrayCol: String = $(labelCol) + "_Array" - - protected def internalFit(featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): M - - override def fit(dataset: DataFrame): M = { - transformSchema(dataset.schema, logging = true) - internalFit(toArrayType(dataset)) - } - - /** - * convert feature and label columns to array data - */ - protected def toArrayType(dataset: DataFrame): RDD[(Seq[AnyVal], Seq[AnyVal])] = { - val featureType = dataset.schema($(featuresCol)).dataType - val featureColIndex = dataset.schema.fieldIndex($(featuresCol)) - val labelType = dataset.schema($(labelCol)).dataType - val labelColIndex = dataset.schema.fieldIndex($(labelCol)) - - dataset.rdd.map { row => - val features = supportedTypesToSeq(row, featureType, featureColIndex) - val labels = supportedTypesToSeq(row, labelType, labelColIndex) - (features, labels) - } - } - - /** - * validate both feature and label columns - */ - protected override def validateSchema(schema: StructType): Unit = { - // validate feature column - super.validateSchema(schema) + extends Estimator[M] with HasLabelCol { - // validate label column - val dataTypes = Seq( - new ArrayType(DoubleType, false), - new ArrayType(FloatType, false), - new VectorUDT, - DoubleType) + protected def internalFit(dataFrame: DataFrame): M - // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 - val actualDataType = schema($(labelCol)).dataType - require(dataTypes.exists(actualDataType.equals), - s"Column ${$(labelCol)} must be of type equal to one of the following types: " + - s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") + override def fit(dataFrame: DataFrame): M = { + transformSchema(dataFrame.schema, logging = true) + internalFit(dataFrame) } override def copy(extra: ParamMap): Learner = defaultCopy(extra) diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala index bbc16b81bd1..ca4a943a116 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala @@ -16,7 +16,6 @@ package org.apache.spark.ml import org.apache.spark.ml.param.ParamMap -import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame /** @@ -25,30 +24,13 @@ import org.apache.spark.sql.DataFrame * both spark 1.5 and spark 2.0. */ private[ml] abstract class DLTransformerBase[M <: DLTransformerBase[M]] - extends Model[M] with DLParams { + extends Model[M] { - /** - * convert feature columns(MLlib Vectors or Array) to Seq format - */ - protected def internalTransform(featureData: RDD[Seq[AnyVal]], dataset: DataFrame): DataFrame + protected def internalTransform(dataFrame: DataFrame): DataFrame - override def transform(dataset: DataFrame): DataFrame = { - transformSchema(dataset.schema, logging = true) - internalTransform(toArrayType(dataset), dataset) - } - - /** - * convert feature columns to Seq format - */ - protected def toArrayType(dataset: DataFrame): RDD[Seq[AnyVal]] = { - - val featureType = dataset.schema($(featuresCol)).dataType - val featureColIndex = dataset.schema.fieldIndex($(featuresCol)) - - dataset.rdd.map { row => - val features = supportedTypesToSeq(row, featureType, featureColIndex) - features - } + override def transform(dataFrame: DataFrame): DataFrame = { + transformSchema(dataFrame.schema, logging = true) + internalTransform(dataFrame) } override def copy(extra: ParamMap): M = defaultCopy(extra) diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index dddfa790804..bf187afadad 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -22,43 +22,24 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.types._ import org.apache.spark.sql.{DataFrame, Dataset, Row} +/** + * Handle different Vector types in Spark 1.5/1.6 and Spark 2.0+. + * Support both ML Vector and MLlib Vector for Spark 2.0+. + */ +trait VectorCompatibility { -private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { - - /** - * only validate feature columns here - */ - protected def validateSchema(schema: StructType): Unit = { - val dataTypes = Seq( - new ArrayType(DoubleType, false), - new ArrayType(FloatType, false), - new VectorUDT, - new org.apache.spark.mllib.linalg.VectorUDT - ) - - // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 - val actualDataType = schema($(featuresCol)).dataType - require(dataTypes.exists(actualDataType.equals), - s"Column ${$(featuresCol)} must be of type equal to one of the following types: " + - s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") - } + val validVectorTypes = Seq(new VectorUDT, new org.apache.spark.mllib.linalg.VectorUDT) - def supportedTypesToSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal] = { - val featureArr = if (colType == new VectorUDT) { + def getVectorSeq(row: Row, colType: DataType, index: Int): Seq[AnyVal] = { + if (colType == new VectorUDT) { row.getAs[Vector](index).toArray.toSeq } else if (colType == new org.apache.spark.mllib.linalg.VectorUDT) { row.getAs[org.apache.spark.mllib.linalg.Vector](index).toArray.toSeq - } else if (colType == ArrayType(DoubleType, false)) { - row.getSeq[Double](index) - } else if (colType == ArrayType(FloatType, false)) { - row.getSeq[Float](index) - } else if (colType == DoubleType) { - Seq[Double](row.getDouble(index)) + } else { + throw new IllegalArgumentException( + s"$colType is not a supported vector type.") } - featureArr.asInstanceOf[Seq[AnyVal]] } - - protected def getFeatureArrayCol: String = $(featuresCol) + "_Array" } @@ -69,55 +50,17 @@ private[ml] trait DLParams extends HasFeaturesCol with HasPredictionCol { */ private[ml] abstract class DLEstimatorBase[Learner <: DLEstimatorBase[Learner, M], M <: DLTransformerBase[M]] - extends Estimator[M] with DLParams with HasLabelCol { + extends Estimator[M] with HasLabelCol { - protected def getLabelArrayCol: String = $(labelCol) + "_Array" - - protected def internalFit(featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])]): M + protected def internalFit(dataFrame: DataFrame): M override def fit(dataset: Dataset[_]): M = { transformSchema(dataset.schema, logging = true) - internalFit(toArrayType(dataset.toDF())) - } - - /** - * convert feature and label columns to array data - */ - protected def toArrayType(dataset: DataFrame): RDD[(Seq[AnyVal], Seq[AnyVal])] = { - val featureType = dataset.schema($(featuresCol)).dataType - val featureColIndex = dataset.schema.fieldIndex($(featuresCol)) - val labelType = dataset.schema($(labelCol)).dataType - val labelColIndex = dataset.schema.fieldIndex($(labelCol)) - - dataset.rdd.map { row => - val features = supportedTypesToSeq(row, featureType, featureColIndex) - val labels = supportedTypesToSeq(row, labelType, labelColIndex) - (features, labels) - } - } - - /** - * validate both feature and label columns - */ - protected override def validateSchema(schema: StructType): Unit = { - // validate feature column - super.validateSchema(schema) - - // validate label column - val dataTypes = Seq( - new ArrayType(DoubleType, false), - new ArrayType(FloatType, false), - new VectorUDT, - DoubleType) - - // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 - val actualDataType = schema($(labelCol)).dataType - require(dataTypes.exists(actualDataType.equals), - s"Column ${$(labelCol)} must be of type equal to one of the following types: " + - s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") + internalFit(dataset.toDF()) } override def copy(extra: ParamMap): Learner = defaultCopy(extra) + } diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala index 681bcb9e083..882e98989ce 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala @@ -25,30 +25,16 @@ import org.apache.spark.sql.{DataFrame, Dataset} * both spark 1.5 and spark 2.0. */ private[ml] abstract class DLTransformerBase[M <: DLTransformerBase[M]] - extends Model[M] with DLParams { + extends Model[M] { /** * convert feature columns(MLlib Vectors or Array) to Seq format */ - protected def internalTransform(featureData: RDD[Seq[AnyVal]], dataset: DataFrame): DataFrame + protected def internalTransform(dataFrame: DataFrame): DataFrame override def transform(dataset: Dataset[_]): DataFrame = { transformSchema(dataset.schema, logging = true) - internalTransform(toArrayType(dataset.toDF()), dataset.toDF()) - } - - /** - * convert feature columns to Seq format - */ - protected def toArrayType(dataset: DataFrame): RDD[Seq[AnyVal]] = { - - val featureType = dataset.schema($(featuresCol)).dataType - val featureColIndex = dataset.schema.fieldIndex($(featuresCol)) - - dataset.rdd.map { row => - val features = supportedTypesToSeq(row, featureType, featureColIndex) - features - } + internalTransform(dataset.toDF()) } override def copy(extra: ParamMap): M = defaultCopy(extra) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala index 13959df00b8..12f2cdb4fd1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala @@ -62,7 +62,7 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { val estimator = new DLClassifier[Float](model, criterion, Array(10)) assert(estimator.getFeaturesCol == "features") assert(estimator.getLabelCol == "label") - assert(estimator.getMaxEpoch == 100) + assert(estimator.getMaxEpoch == 50) assert(estimator.getBatchSize == 1) assert(estimator.getLearningRate == 1e-3) assert(estimator.getLearningRateDecay == 0) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala index 3c539bb18b1..74be2c5f706 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala @@ -61,7 +61,7 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val estimator = new DLEstimator[Float](model, criterion, Array(10), Array(1)) assert(estimator.getFeaturesCol == "features") assert(estimator.getLabelCol == "label") - assert(estimator.getMaxEpoch == 100) + assert(estimator.getMaxEpoch == 50) assert(estimator.getBatchSize == 1) assert(estimator.getLearningRate == 1e-3) assert(estimator.getLearningRateDecay == 0) From 707f74f47fd400fc82de0f0cb6578e46bd1e113b Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 18 Dec 2017 09:35:05 +0800 Subject: [PATCH 0603/1065] Lookup table for multivalue (#2016) Layer LookupTableSparse --- .../bigdl/dllib/nn/LookupTableSparse.scala | 301 +++++++++++++++ .../bigdl/dllib/tensor/SparseTensor.scala | 25 +- .../analytics/bigdl/dllib/tensor/Tensor.scala | 63 +++ .../dllib/utils/python/api/PythonBigDL.scala | 11 + .../dllib/nn/LookupTableSparseSpec.scala | 362 ++++++++++++++++++ .../bigdl/dllib/nn/LookupTableSpec.scala | 2 +- .../bigdl/dllib/torch/LookupTableSpec.scala | 6 +- .../serializer/ModuleSerializerSpec.scala | 9 + 8 files changed, 774 insertions(+), 5 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparse.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparseSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparse.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparse.scala new file mode 100644 index 00000000000..c8f9c2d135e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparse.scala @@ -0,0 +1,301 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Initializable} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.{SparseType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.collection.mutable +import scala.reflect.ClassTag + +/** + *LookupTable for multi-values. + * Also called embedding_lookup_sparse in TensorFlow. + * + * The input of LookupTableSparse should be a 2D SparseTensor or two 2D sparseTensors. + * If the input is a SparseTensor, the values are positive integer ids, + * values in each row of this SparseTensor will be turned into a dense vector. + * If the input is two SparseTensors, the first tensor should be the integer ids, just + * like the SparseTensor input. And the second tensor is the corresponding + * weights of the integer ids. + * + * @param nIndex Indices of input row + * @param nOutput the last dimension size of output + * @param combiner A string specifying the reduce type. + * Currently "mean", "sum", "sqrtn" is supported. + * @param maxNorm If provided, each embedding is normalized to have l2 norm equal to + * maxNorm before combining. + * @param wRegularizer: instance of [[Regularizer]] + * (eg. L1 or L2 regularization), applied to the input weights matrices. + */ +class LookupTableSparse[T: ClassTag]( + val nIndex: Int, val nOutput: Int, + val combiner: String = "sum", + val maxNorm: Double = -1, + var wRegularizer: Regularizer[T] = null)( + implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Tensor[T], T] with Initializable { + val weight = Tensor[T](nIndex, nOutput) + val gradWeight = Tensor[T](nIndex, nOutput).zero() + + require(combiner == "mean" || combiner == "sum" || combiner == "sqrtn", "LookupTableSparse's" + + s" combiner should be one of mean, sum or sqrtn, but got ${combiner}") + + protected val inputBuffer: Tensor[T] = Tensor() + protected val inputWeightBuffer: Tensor[T] = Tensor() + protected val frameBuffer: Tensor[T] = Tensor() + protected val ids: Tensor[T] = Tensor() + protected val indices: Tensor[Int] = Tensor[Int]() + protected val batchScaleBuffer: Tensor[T] = Tensor[T]() + protected var nonZeroCount: Array[Int] = _ + protected val normScale: mutable.HashMap[Int, T] = mutable.HashMap[Int, T]() + + { + val wInit = RandomNormal(0, 1) + setInitMethod(weightInitMethod = wInit) + } + + override def reset(): Unit = { + weightInitMethod.init(weight, VariableFormat.Default) + } + + override def updateOutput(input: Activity): Tensor[T] = { + val (inputTensor, weightTensor) = if (input.isTable) { + (input.toTable[Tensor[T]](1), Some(input.toTable[Tensor[T]](2))) + } else { + (input.toTensor[T], None) + } + require(inputTensor.getTensorType == SparseType, "LookupTableSparse's input" + + s"must be SparseTensor, but got ${inputTensor.getTensorType}") + + val batchSize = inputTensor.size(1) + inputBuffer.set(inputTensor.storage(), + inputTensor.storageOffset(), + Array(inputTensor.nElement())) + if (weightTensor.isDefined) { + val weight = weightTensor.get + inputWeightBuffer.set(weight.storage(), + weight.storageOffset(), + Array(weight.nElement())) + } + + Tensor.unique(inputBuffer, ids, indices) + + if (maxNorm > 0) { + normScale.clear() + LookupTableSparse.norm2ScaleWithIndices[T]( + weight, ids, ev.fromType(maxNorm), normScale) + } + + nonZeroCount = inputTensor.numNonZeroByRow() + output.resize(batchSize, nOutput).zero() + batchScaleBuffer.resize(batchSize) + + var i = 0 // index for all the ids in the input + var b = 0 + while (b < batchSize) { + val times = nonZeroCount(b) + // compute a overall scale for this batch + val batchScale = if (combiner == "sum") { + // if combiner == sum, batchScale = 1 + ev.one + } else { + var count = times.toFloat + if (weightTensor.isDefined) { + count = 0 + var j = 0 + while (j < times) { + if (combiner == "mean") { + count += ev.toType[Float](inputWeightBuffer.valueAt(i + j + 1)) + } else { + count += math.pow(ev.toType[Float](inputWeightBuffer.valueAt(i + j + 1)), 2).toFloat + } + j += 1 + } + } + if (combiner == "mean") { + // if combiner == mean, batchScale = sum(inputWeightBuffer) / times + ev.fromType(1f / count) + } else { + // if combiner == sqrtn, batchScale = sqrt(sum(inputWeightBuffer^2)) / times + ev.fromType(1f / math.sqrt(count)) + } + } + // save this batchScale + batchScaleBuffer.setValue(b + 1, batchScale) + + var j = 0 + while (j < times) { + val index = ev.toType[Int](inputBuffer.valueAt(i + 1)) + // scale = normScale * batchScale * sp_weights + val scale = ev.times( + if (normScale != null && normScale.contains(index)) normScale(index) else ev.one, + ev.times(batchScale, + if (weightTensor.isDefined) inputWeightBuffer.valueAt(i + 1) else ev.one)) + // output += scale * weight(index) + output.select(1, b + 1).add(scale, weight.select(1, index)) + i += 1 + j += 1 + } + b += 1 + } + + output + } + + override def updateGradInput(input: Activity, gradOutput: Tensor[T]): Activity = { + // Input is not derivable + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Tensor[T]): Unit = { + val batchSize = output.size(1) + val three = ev.fromType(3) + + var b = 0 + var i = 0 + while (b < batchSize) { + val times = nonZeroCount(b) + var j = 0 + while (j < times) { + val index = ev.toType[Int](inputBuffer.valueAt(i + 1)) + val gradWeightFrame = gradWeight.select(1, index) + val gradOutputFrame = gradOutput.select(1, b + 1) + // scale = normScale * batchScale * sp_weights + val scale = ev.times( + if (normScale != null) normScale.getOrElse(index, ev.one) else ev.one, + ev.times(batchScaleBuffer.valueAt(b + 1), + if (!inputWeightBuffer.isEmpty) inputWeightBuffer.valueAt(i + 1) else ev.one)) + // gradWeight += gradOutput * scale + gradWeightFrame.add(scale, gradOutputFrame) + + // if norm2 clipping is invoked, need to compute the clipping's gradient. + if (normScale != null && normScale.contains(index)) { + val weightFrame = weight.select(1, index) + // sum = sum(weightFrame * gradOutputFrame) * maxNorm * sp_weights * batchScale + val sum = ev.times(frameBuffer.resizeAs(weightFrame).copy(weightFrame) + .cmul(gradOutputFrame).sum, + ev.times(ev.fromType(maxNorm), ev.divide(scale, normScale(index)))) + // gradWeight += - (normScale / maxNorm)^3 * sum * gradOutput + gradWeightFrame.add(ev.times(sum, ev.negative( + ev.pow(ev.divide(normScale(index), ev.fromType(maxNorm)), three))), + weight.select(1, index)) + } + i += 1 + j += 1 + } + b += 1 + } + + if (null != wRegularizer) { + wRegularizer.accRegularization(weight, gradWeight, scaleW) + } + } + + override def toString(): String = { + val s = s"${getPrintName}" + + s"(nIndex=$nIndex,nOutput=$nOutput," + if (maxNorm > 0) { + s + ")" + } else { + s + s" ,maxNorm=$maxNorm)" + } + } + + override def zeroGradParameters(): Unit = { + gradWeight.zero() + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (Array(this.weight), Array(this.gradWeight)) + } + + override def getParametersTable(): Table = { + T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) + } + + override def clearState() : this.type = { + super.clearState() + + inputBuffer.set() + inputWeightBuffer.set() + frameBuffer.set() + ids.set() + indices.set() + batchScaleBuffer.set() + nonZeroCount = null + normScale.clear() + this + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[LookupTable[T]] + + override def equals(other: Any): Boolean = other match { + case that: LookupTable[T] => + super.equals(that) && + (that canEqual this) && + weight == that.weight && + gradWeight == that.gradWeight && + nIndex == that.nIndex && + nOutput == that.nOutput && + maxNorm == that.maxNorm + case _ => false + } + + override def hashCode(): Int = { + def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode() + val state = Seq(super.hashCode(), weight, gradWeight, nIndex, nOutput, maxNorm) + state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) + } + +} + +object LookupTableSparse { + def apply[T: ClassTag]( + nIndex: Int, nOutput: Int, + combiner: String = "sum", + maxNorm: Double = -1, + wRegularizer: Regularizer[T] = null)( + implicit ev: TensorNumeric[T]): LookupTableSparse[T] = { + new LookupTableSparse(nIndex, nOutput, combiner.toLowerCase, + maxNorm, wRegularizer) + } + + /** + * Compute the l2 norm clipping scale of the indices-th frame in tensor's first dimension. + * @return a HashMap contains l2 norm clipping scale + */ + protected def norm2ScaleWithIndices[T: ClassTag]( + tensor: Tensor[T], + indices: Tensor[T], + maxNorm: T, + scaleBuffer: mutable.HashMap[Int, T])( + implicit ev: TensorNumeric[T]): mutable.HashMap[Int, T] = { + val indicesArray = indices.storage.array() + var i = indices.storageOffset() - 1 + while (i < indices.nElement() + indices.storageOffset() - 1) { + val index = ev.toType[Int](indicesArray(i)) + val norm = tensor(index).norm(2) + if (ev.isGreater(norm, maxNorm)) scaleBuffer(index) = ev.divide(maxNorm, norm) + i += 1 + } + + scaleBuffer + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 61082e8bce9..a650df3bcf3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -352,6 +352,21 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( } } + private var nonZeroCounting: Array[Int] = _ + + override def numNonZeroByRow(): Array[Int] = { + if (null == nonZeroCounting || nonZeroCounting.length != size(1)) { + nonZeroCounting = new Array[Int](size(1)) + } + java.util.Arrays.fill(nonZeroCounting, 0) + var i = _storageOffset + while (i < _storageOffset + nElement()) { + nonZeroCounting(_indices(0).array()(i) - _indicesOffset(0)) += 1 + i += 1 + } + nonZeroCounting + } + override def copy(other: Tensor[T]): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } @@ -531,7 +546,15 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( } override def sum(x: Tensor[T], dim: Int): Tensor[T] = { - throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + require(x.dim == 1 && x.size(1) == size(1)) + x.zero() + var i = _storageOffset + while (i < nElement() + _storageOffset) { + val index = _indices(0).array()(i) - _indicesOffset(0) + x.setValue(index, ev.plus(x.valueAt(index), _values.array()(i))) + i += 1 + } + x } override def mean(): T = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 87a7ec5ade9..82812c5c57a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -25,6 +25,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{File, Table} import org.apache.spark.mllib.linalg.{DenseMatrix, DenseVector, Matrix, Vector} +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -584,6 +585,15 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { def view(sizes: Array[Int]): Tensor[T] + /** + * Count the number of non-zero elements in first dimension. + * For SparseTensor only. + * @return an array number of non-zero elements in first dimension. + */ + def numNonZeroByRow(): Array[Int] = { + throw new UnsupportedOperationException("countNonZero for sparse tensor only") + } + /** * * Returns a tensor which contains all slices of size @param size @@ -1297,4 +1307,57 @@ object Tensor { res: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { SparseTensor.concat(dim, tensors, res) } + + /** + * Find the distinct value and its indices in a 1D tensor. + * @param tensor a 1D tensor + * @param distinctBuffer a buffer for its distinct values. + * @param indicesBuffer a buffer for its indcies. + * @return (distinctValues, indices) + */ + def unique[T: ClassTag]( + tensor: Tensor[T], + distinctBuffer: Tensor[T] = null, + indicesBuffer: Tensor[Int] = null + )(implicit ev: TensorNumeric[T]): (Tensor[T], Tensor[Int]) = { + require(tensor.isContiguous(), "unique only support contiguous tensor") + require(tensor.dim() == 1, "unique only support 1D tensor") + val array = tensor.storage().array() + val arrayOffset = tensor.storageOffset() - 1 + + val distinctTensor = if (null != distinctBuffer) { + distinctBuffer.resizeAs(tensor) + distinctBuffer + } else { + Tensor().resizeAs(tensor) + } + val tensorIndices = if (null != indicesBuffer) { + indicesBuffer.resizeAs(tensor) + indicesBuffer + } else { + Tensor[Int]().resizeAs(tensor) + } + + val distinctValues = distinctTensor.storage().array() + val distinctValuesOffset = distinctTensor.storageOffset() - 1 + val indicesArray = tensorIndices.storage().array() + val indicesOffset = tensorIndices.storageOffset() - 1 + val seen = mutable.HashMap[T, Int]() + var i = 0 + var nonZero = 0 + while (i < tensor.nElement()) { + val x = array(i + arrayOffset) + if (!seen.contains(x)) { + distinctValues(nonZero + distinctValuesOffset) = x + seen.put(x, nonZero) + nonZero += 1 + } + indicesArray(i + indicesOffset) = seen(x) + i += 1 + } + // Resize distinctTensor to number of non-zero elements. + distinctTensor.resize(nonZero) + + (distinctTensor, tensorIndices) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 5759308b528..c682cb282be 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -874,6 +874,17 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab wRegularizer) } + def createLookupTableSparse(nIndex: Int, nOutput: Int, + combiner: String = "sum", maxNorm: Double = -1, + wRegularizer: Regularizer[T] = null) + : LookupTableSparse[T] = { + LookupTableSparse[T](nIndex, + nOutput, + combiner, + maxNorm, + wRegularizer) + } + def createMM(transA: Boolean = false, transB: Boolean = false) : MM[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparseSpec.scala new file mode 100644 index 00000000000..8d1e33d6460 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparseSpec.scala @@ -0,0 +1,362 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.T + +@com.intel.analytics.bigdl.tags.Parallel +class LookupTableSparseSpec extends FlatSpec with Matchers { + "A LookupTableSparse without weight" should "generate correct output and gradient" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "sum") + layer1.weight.range(1, 40, 1) + val output = layer1.forward(input) + val exceptedOutput = Tensor(3, 4) + exceptedOutput.select(1, 1).range(18, 24, 2) + exceptedOutput.select(1, 2).range(1, 4) + exceptedOutput.select(1, 3).range(5, 8) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 5.01f, 6.01f, 7.01f, 8.01f + ))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 10.101f, 12.101f, 14.101f, 16.101f + ))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f + ))) + + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + + "A LookupTableSparse mean without weight" should "generate correct output and gradient" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "mean") + layer1.weight.range(1, 40, 1) + val output = layer1.forward(input) + val exceptedOutput = Tensor(3, 4) + exceptedOutput.select(1, 1).range(9, 12) + exceptedOutput.select(1, 2).range(1, 4) + exceptedOutput.select(1, 3).range(5, 8) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 5.01f, 6.01f, 7.01f, 8.01f + ))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 9.551f, 11.051f, 12.551f, 14.051f + ))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + 0.55f, 1.05f, 1.55f, 2.05f + ))) + + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + + "A LookupTableSparse sqrtn without weight" should "generate correct output and gradient" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "sqrtn") + layer1.weight.range(1, 40, 1) + val output = layer1.forward(input) + val exceptedOutput = Tensor(3, 4) + exceptedOutput.select(1, 1).copy(Tensor(T( + 12.72792244f, 14.14213562f, 15.55634975f, 16.97056389f + ))) + exceptedOutput.select(1, 2).range(1, 4) + exceptedOutput.select(1, 3).range(5, 8) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 5.01f, 6.01f, 7.01f, 8.01f + ))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 9.77881813f, 11.48592472f, 13.19303131f, 14.9001379f + ))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + 0.77781749f, 1.4849242f, 2.19203091f, 2.89913774f + ))) + + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + + "A LookupTableSparse" should "generate correct output and gradient" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val weightValues = Array(2f, 0.5f, 1, 3) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + val weight = Tensor.sparse(Array(indices1, indices2), weightValues, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "mean") + layer1.weight.range(1, 40, 1) + val output = layer1.forward(T(input, weight)) + val exceptedOutput = Tensor(3, 4) + exceptedOutput.select(1, 1).range(66, 96, 10).div(10) + exceptedOutput.select(1, 2).range(1, 4) + exceptedOutput.select(1, 3).range(5, 8) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 5.01f, 6.01f, 7.01f, 8.01f + ))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 9.881f, 11.681f, 13.481f, 15.281f + ))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + 0.22f, 0.42f, 0.62f, 0.82f + ))) + + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + + "A LookupTableSparse sum" should "generate correct output and gradient" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val weightValues = Array(2f, 0.5f, 1, 3) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + val weight = Tensor.sparse(Array(indices1, indices2), weightValues, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "sum") + layer1.weight.range(1, 40, 1) + val output = layer1.forward(T(input, weight)) + val exceptedOutput = Tensor(3, 4) + exceptedOutput.select(1, 1).range(33, 48, 5).div(2) + exceptedOutput.select(1, 2).range(1, 4) + exceptedOutput.select(1, 3).range(15, 24, 3) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 5.01000023f, 6.01000023f, 7.01000023f, 8.01000023f))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 29.20300217f, 34.20300217f, 39.20300217f, 44.20300217f))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + 0.55000001f, 1.04999995f, 1.54999995f, 2.04999995f))) + + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + + "A LookupTableSparse sqrtn" should "generate correct output" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val weightValues = Array(2f, 0.5f, 1, 3) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + val weight = Tensor.sparse(Array(indices1, indices2), weightValues, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "sqrtn") + layer1.weight.range(1, 40, 1) + val output = layer1.forward(T(input, weight)) + val exceptedOutput = Tensor(3, 4) + // this result copy from tensorflow + exceptedOutput.select(1, 1).copy(Tensor(T( + 8.00367546f, 9.21635437f, 10.42903233f, 11.64171028f))) + exceptedOutput.select(1, 2).range(1, 4) + exceptedOutput.select(1, 3).range(5, 8) + output should be (exceptedOutput) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 5.01000023f, 6.01000023f, 7.01000023f, 8.01000023f))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 10.06815679f, 12.03829916f, 14.00844176f, 15.97858436f))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + 0.2667892f, 0.50932479f, 0.75186044f, 0.99439609f))) + + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + + "A LookupTableSparse sum with norm2" should "generate correct output and gradient" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val weightValues = Array(2f, 0.5f, 1, 3) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + val weight = Tensor.sparse(Array(indices1, indices2), weightValues, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "sum", maxNorm = 2) + layer1.weight.range(1, 40, 1) + val output = layer1.forward(T(input, weight)) + val exceptedOutput = Tensor(T( + 1.96314502f, 2.30076504f, 2.63838482f, 2.9760046f, + 0.36514834f, 0.73029667f, 1.09544504f, 1.46059334f, + 2.27429366f, 2.72915244f, 3.18401146f, 3.63887f)).resize(3, 4) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 9.76163447e-01f, 4.88081932e-01f, 5.9604645e-08f, -4.88081217e-01f))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 0.1611459979f, 0.065922181f, -0.0292999346f, -0.124521899f))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + -4.44917269e-02f, -1.64425969e-02f, 1.16065294e-02, 3.96556593e-02))) + + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + + "A LookupTableSparse mean with norm2" should "generate correct output and gradient" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val weightValues = Array(2f, 0.5f, 1, 3) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + val weight = Tensor.sparse(Array(indices1, indices2), weightValues, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "mean", maxNorm = 2) + layer1.weight.range(1, 40, 1) + val output = layer1.forward(T(input, weight)) + val exceptedOutput = Tensor(T( + 0.785258f, 0.92030603f, 1.05535388f, 1.19040179, + 0.36514834f, 0.73029667f, 1.09544504f, 1.46059334, + 0.75809789f, 0.9097175f, 1.06133711f, 1.21295667)).resize(3, 4) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 9.76163447e-01f, 4.88081932e-01f, 5.9604645e-08f, -4.88081217e-01f))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 0.0337786f, 0.0138183197f, -0.0061413685f, -0.0261015609f))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + -1.77966896e-02f, -6.57703914e-03f, 4.64261323e-03f, 1.58622656e-02f))) + + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + + "A LookupTableSparse sqrtn with norm2" should "generate correct output and gradient" in { + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val weightValues = Array(2f, 0.5f, 1, 3) + val input = Tensor.sparse(Array(indices1, indices2), values, Array(3, 4)) + val weight = Tensor.sparse(Array(indices1, indices2), weightValues, Array(3, 4)) + + val layer1 = LookupTableSparse(10, 4, "sqrtn", maxNorm = 2) + layer1.weight.range(1, 40, 1) + val output = layer1.forward(T(input, weight)) + val exceptedOutput = Tensor(T( + 0.9522652f, 1.11603498f, 1.27980471f, 1.44357431, + 0.36514834f, 0.73029667f, 1.09544504f, 1.46059334, + 0.75809789f, 0.9097175f, 1.06133711f, 1.21295667)).resize(3, 4) + + val gradOutput = Tensor(T( + 1.1f, 2.1f, 3.1f, 4.1f, + 5.01f, 6.01f, 7.01f, 8.01f, + 9.001f, 10.001f, 11.001f, 12.001f + )).resizeAs(output) + + layer1.backward(input, gradOutput) + + val exceptedGradWeight = Tensor(10, 4) + exceptedGradWeight.select(1, 1).copy(Tensor(T( + 9.76163447e-01f, 4.88081932e-01f, 5.9604645e-08f, -4.88081217e-01f))) + exceptedGradWeight.select(1, 2).copy(Tensor(T( + 0.008337097f, 0.0034106036f, -0.0015158607f, -0.006442174f))) + exceptedGradWeight.select(1, 4).copy(Tensor(T( + -2.15816516e-02f, -7.97582790e-03f, 5.63000515e-03f, 1.92358345e-02f))) + + print(output) + output should be (exceptedOutput) + layer1.gradWeight should be (exceptedGradWeight) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala index a9c904096b9..2422362282f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala @@ -37,7 +37,7 @@ class LookupTableSpec extends FlatSpec with Matchers { input(Array(4)) = 9 input(Array(5)) = 4 - val gradOutput = Tensor[Double](2, 2, 2) + val gradOutput = Tensor[Double](5, 4).randn() val layer1 = new LookupTable[Double](9, 4, 2, 0.1, 2.0, true) val layer2 = new LookupTable[Double](9, 4, 2, 0.1, 2.0, true) val (weights, grad) = layer1.getParameters() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LookupTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LookupTableSpec.scala index fd7adb1cd20..6cceb7014ef 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LookupTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LookupTableSpec.scala @@ -104,7 +104,7 @@ class LookupTableSpec extends TorchSpec { input(Array(4)) = 9 input(Array(5)) = 4 - val gradOutput = Tensor[Double](2, 2, 2) + val gradOutput = Tensor[Double](5, 4).rand() val code = "torch.manualSeed(" + seed + ")\n" + "module = nn.LookupTable(9, 4, 2, 0.1)\n" + @@ -113,7 +113,7 @@ class LookupTableSpec extends TorchSpec { "while i < 10 do\n" + "output = module:forward(input:int())\n" + "module._count:zero()\n" + - "_gradInput = module:backward(input:int(), output)\n" + + "_gradInput = module:backward(input:int(), gradOutput)\n" + "i = i + 1\n" + "end\n" + "gradInput = _gradInput:double()\n" + @@ -135,7 +135,7 @@ class LookupTableSpec extends TorchSpec { var i = 0 while (i < 10) { output = module.forward(input) - gradInput = module.backward(input, output) + gradInput = module.backward(input, gradOutput) i += 1 } val weight = module.weight diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 73ed8bb0412..aa297e745d0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -666,6 +666,15 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(lstmModel, input, lstm.getClass) } + "LookupTableSparse serializer" should "work properly" in { + val lookupTableSparse = LookupTableSparse[Float](20, 10, "sum", 1) + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val input = Tensor.sparse[Float](Array(indices1, indices2), values, Array(3, 4)) + runSerializationTest(lookupTableSparse, input, lookupTableSparse.getClass) + } + "LSTMPeephole serializer" should "work properly" in { val lstmPeephole = LSTMPeephole[Float](6, 4) val lstmPeepholeModel = Recurrent[Float]().add(lstmPeephole).setName("lstmPeephole") From b23201a369f7840eb101512446d3aa23f4f191f9 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Mon, 18 Dec 2017 12:16:35 +0800 Subject: [PATCH 0604/1065] Add locallyconnected2d layer (#2037) * finish locallyconnected2d * add locallyconnected2d * add serializer test * fix python test failed * meet code review * change spaticalConvolutionSpec * remove a test --- .../bigdl/dllib/nn/LocallyConnected2D.scala | 960 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 43 + .../dllib/nn/LocallyConnected2DSpec.scala | 70 ++ .../serializer/ModuleSerializerSpec.scala | 7 + 4 files changed, 1080 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala new file mode 100644 index 00000000000..5a8a7418a97 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala @@ -0,0 +1,960 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, Initializable, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} +import com.intel.analytics.bigdl.utils.{Engine, T, Table} + +import scala.concurrent.Future +import scala.reflect.ClassTag + +/** + * The LocallyConnected2D layer works similarly to the [[SpatialConvolution]] layer, + * except that weights are unshared, that is, a different set of filters + * is applied at each different patch of the input. + * + * @param nInputPlane The number of expected input planes + * in the image given into forward() + * @param inputWidth The input width + * @param inputHeight The input height + * @param nOutputPlane The number of output planes the convolution layer will produce. + * @param kernelW The kernel width of the convolution + * @param kernelH The kernel height of the convolution + * @param strideW The step of the convolution in the width dimension + * @param strideH The step of the convolution in the height dimension + * @param padW The additional zeros added per width to the input planes + * @param padH The additional zeros added per height to the input planes + * @param propagateBack propagate gradient back + * @param wRegularizer weight regularizer + * @param bRegularizer bias regularizer + * @param initWeight initial weight + * @param initBias initial bias + * @param initGradWeight initial gradient weight + * @param initGradBias initial gradient bias + * @param withBias if has bias + * @param format data format NCHW, NHWC + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class LocallyConnected2D[T: ClassTag]( + val nInputPlane: Int, + val inputWidth: Int, + val inputHeight: Int, + val nOutputPlane: Int, + val kernelW: Int, + val kernelH: Int, + val strideW: Int = 1, + val strideH: Int = 1, + val padW: Int = 0, + val padH: Int = 0, + val propagateBack: Boolean = true, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val initWeight: Tensor[T] = null, + val initBias: Tensor[T] = null, + val initGradWeight: Tensor[T] = null, + val initGradBias: Tensor[T] = null, + val withBias: Boolean = true, + val format: DataFormat = DataFormat.NCHW +)(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { + require((padW >= 0 && padH >= 0) || (padW == -1 && padH == -1), + s"Illegal padding configuration (padW: $padW, padH: $padH)") + + val sizes = + if (padW == -1 && padH == -1) { + Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW) + } else { + Utils.getOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, + kernelH, kernelW, padH, padW, ceilMode = false) + } + + val padTop = sizes(0) + val padBottom = sizes(1) + val padLeft = sizes(2) + val padRight = sizes(3) + val outputHeight = sizes(4) + val outputWidth = sizes(5) + + private val weightShape = Array( + outputHeight * outputWidth, + nOutputPlane, + nInputPlane * kernelH * kernelW + ) + + private val weightFormat = format match { + case DataFormat.NCHW => + VariableFormat.GP_OUT_IN_KW_KH + case DataFormat.NHWC => + VariableFormat.GP_KH_KW_IN_OUT + } + + val weight: Tensor[T] = if (initWeight != null) { + initWeight + } else { + Tensor[T](weightShape) + } + + val bias: Tensor[T] = if (!withBias) null + else if (initBias != null) initBias else Tensor[T](outputHeight * outputWidth, nOutputPlane) + + val gradWeight: Tensor[T] = if (initGradWeight != null) { + initGradWeight + } else { + Tensor[T](weightShape) + } + + val gradBias: Tensor[T] = if (!withBias) null + else if (initGradBias != null) initGradBias + else Tensor[T](outputHeight * outputWidth, nOutputPlane) + + var fInput = Tensor[T]() + var fGradInput = Tensor[T]() + protected val ones = Tensor[T]() + protected val onesBatch = Tensor[T]() + protected val onesBias = if (withBias) Tensor[T]() else null + protected val gradientBiasMT: Tensor[T] = if (withBias) Tensor[T]() else null + protected var gradWeightMM: Tensor[T] = null + @transient + protected var gradWeightMMInBatch: Tensor[T] = null + @transient protected var gradBiasWindow: Tensor[T] = _ + + protected val _1x1 = if (kernelH == 1 && kernelW == 1 && strideW == 1 && strideH == 1 + && padH == 0 && padW == 0) { + true + } else { + false + } + + { + val stdv = 1.0 / math.sqrt(kernelW * kernelH * nInputPlane) + val wInit: InitializationMethod = RandomUniform(-stdv, stdv) + val bInit: InitializationMethod = if (withBias) RandomUniform(-stdv, stdv) + else null + setInitMethod(wInit, bInit) + } + + protected var im2colTime = 0L + protected var col2imTime = 0L + + def getIm2ColTime(): Double = im2colTime + + def getCol2ImgTime(): Double = col2imTime + + @transient + protected var results: Array[Future[Unit]] = null + + override def reset(): Unit = { + if (initWeight == null) { + weightInitMethod.init(weight, weightFormat) + } + if (withBias && initBias == null) { + biasInitMethod.init(bias, VariableFormat.ONE_D) + } + zeroGradParameters() + } + + private def getOutputShape(oh: Int, ow: Int, batchSize: Int = -1): Array[Int] = { + format match { + case DataFormat.NCHW => + if (batchSize == -1) { + Array(nOutputPlane, oh, ow) + } else { + Array(batchSize, nOutputPlane, oh, ow) + } + case DataFormat.NHWC => + if (batchSize == -1) { + Array(oh, ow, nOutputPlane) + } else { + Array(batchSize, oh, ow, nOutputPlane) + } + } + } + + private def getFInputShape(oh: Int, ow: Int, batchSize: Int = -1): Array[Int] = { + format match { + case DataFormat.NCHW => + if (batchSize == -1) { + Array(kernelW * kernelH * nInputPlane, oh * ow) + } else { + Array(batchSize, kernelW * kernelH * nInputPlane, oh * ow) + } + case DataFormat.NHWC => + if (batchSize == -1) { + Array(oh * ow, kernelW * kernelH * nInputPlane) + } else { + Array(batchSize, oh * ow, kernelW * kernelH * nInputPlane) + } + } + } + + // return (padTop, padDown, padLeft, padRight) + protected def getPadding(inputHeight: Int, inputWidth: Int): (Int, Int, Int, Int) = { + if (padW == -1 && padH == -1) { + // deal with SAME padding + val oW = Math.ceil(inputWidth.toFloat / strideW.toFloat).toInt + val oH = Math.ceil(inputHeight.toFloat / strideH.toFloat).toInt + val padAlongWidth = Math.max(0, (oW -1) * strideW + kernelW - inputWidth) + val padAlongHeight = Math.max(0, (oH - 1) * strideH + kernelH - inputHeight) + (padAlongHeight/2, padAlongHeight - padAlongHeight/2, + padAlongWidth/2, padAlongWidth - padAlongWidth/2) + } else { + (padH, padH, padW, padW) + } + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(input.dim() == 3 || input.dim() == 4, + "SpatialConvolution: " + ErrorInfo.constrainInputAs3DOrBatch) + require(input.isContiguous()) + + val (dimHeight, dimWidth, channelDim) = format.getHWCDims(input.dim()) + require(input.size(channelDim) == nInputPlane, s"input channel size " + + s"${input.size(channelDim)} is not the same as nInputPlane $nInputPlane") + + require(outputWidth >= 1 && outputHeight >= 1, + s"output size is too small. outputWidth: $outputWidth, outputHeight: $outputHeight") + + if (withBias && (onesBias.dim() != 1 || onesBias.size(1) != outputHeight * outputWidth)) { + onesBias.resize(Array(outputHeight * outputWidth)).fill(ev.fromType(1.0)) + } + + if (input.dim() == 3) { + require(input.isContiguous()) + output.resize(getOutputShape(outputHeight, outputWidth)) + if (_1x1) { + fInput.set(input) + fInput.resize(getFInputShape(outputHeight, outputWidth)) + } else { + fInput.resize(getFInputShape(outputHeight, outputWidth)) + } + val biasUse = if (withBias) { + bias + } else null + updateOutputFrame( + input, + output, + weight, + biasUse, + fInput, + kernelW, kernelH, strideW, strideH, + padLeft, padTop, padRight, padBottom, + nInputPlane, inputWidth, inputHeight, + nOutputPlane, outputWidth, outputHeight) + } else { + val batchSize = input.size(1) + output.resize(getOutputShape(outputHeight, outputWidth, batchSize)) + if (_1x1) { + fInput.set(input) + fInput.resize(getFInputShape(outputHeight, outputWidth, batchSize)) + } else { + fInput.resize(getFInputShape(outputHeight, outputWidth, batchSize)) + } + + if (results == null || results.length != batchSize) { + results = new Array[Future[Unit]](batchSize) + } + + var i = 0 + while (i < batchSize) { + val _i = i + 1 + results(i) = Engine.model.invoke(() => { + val inputT = input.select(1, _i) + require(inputT.isContiguous()) + val outputT = output.select(1, _i) + val fInputT = fInput.select(1, _i) + val biasUse = if (withBias) { + bias + } else null + updateOutputFrame( + inputT, + outputT, + weight, + biasUse, + fInputT, + kernelW, kernelH, strideW, strideH, + padLeft, padTop, padRight, padBottom, + nInputPlane, inputWidth, inputHeight, + nOutputPlane, outputWidth, outputHeight) + }) + i += 1 + } + Engine.model.sync(results) + } + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + if (!propagateBack) { + return gradInput + } + + val (ohDim, owDim, cDim) = format.getHWCDims(input.dim()) + val oh = gradOutput.size(ohDim) + val ow = gradOutput.size(owDim) + + val inputWidth = input.size(owDim) + val inputHeight = input.size(ohDim) + + val (padTop, padBottom, padLeft, padRight) = getPadding(inputHeight, inputWidth) + + require(input.nDimension() == 3 || input.nDimension() == 4, "Only support 3D or 4D input") + gradInput.resizeAs(input) + if (_1x1) { + fGradInput.set(gradInput) + fGradInput.resizeAs(fInput) + } else { + fGradInput.resizeAs(fInput) + } + + if (input.nDimension() == 3) { + require(gradOutput.isContiguous()) + updateGradInputFrame( + gradInput, + gradOutput, + weight.transpose(2, 3), + fGradInput, + kernelW, kernelH, strideW, strideH, padLeft, padTop, padRight, padBottom) + } else { + val batchSize = input.size(1) + var i = 0 + while (i < batchSize) { + val _i = i + 1 + results(i) = Engine.model.invoke(() => { + val gradInputT = gradInput.select(1, _i) + val gradOutputT = gradOutput.select(1, _i) + require(gradOutputT.isContiguous()) + val fgradInputT = fGradInput.select(1, _i) + updateGradInputFrame( + gradInputT, + gradOutputT, + weight.transpose(2, 3), + fgradInputT, + kernelW, kernelH, strideW, strideH, padLeft, padTop, padRight, padBottom) + }) + i += 1 + } + Engine.model.sync(results) + } + + gradInput + } + + private def getGradWeightMMInBatchShape(batchSize: Int) = + Array(batchSize, outputHeight * outputWidth, + nOutputPlane, nInputPlane * kernelH * kernelW) + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + require(input.nDimension() == 3 || input.nDimension() == 4, + "Only support 3D or 4D input," + + s"but input has ${input.nDimension()} dimensions") + require(gradOutput.isContiguous()) + + val (ohDim, owDim, cDim) = format.getHWCDims(input.dim()) + val oh = gradOutput.size(ohDim) + val ow = gradOutput.size(owDim) + + if (input.nDimension() == 3) { + if (gradWeightMM == null) { + gradWeightMM = gradWeight.view(weightShape) + } + val gradBiasUse = if (withBias) { + gradBias + } else null + + accGradParametersFrame( + gradOutput, + gradWeightMM, + gradBiasUse, + fInput, + ev.fromType[Double](scaleW), + ev.fromType[Double](scaleB)) + } else { + val batchSize = input.size(1) + if (gradWeightMMInBatch == null) { + gradWeightMMInBatch = Tensor[T]().resize(getGradWeightMMInBatchShape(batchSize)) + } + if(withBias && gradientBiasMT.nElement() == 0) { + gradientBiasMT.resize(Array(batchSize, outputWidth * outputHeight, nOutputPlane)) + } + if (ones.dim() != 1 || ones.size(1) != oh * ow) { + ones.resize(Array(oh * ow)).fill(ev.fromType(1.0)) + } + + if (onesBatch.dim() != 1 || onesBatch.size(1) != batchSize) { + onesBatch.resize(Array(batchSize)).fill(ev.fromType(1.0)) + } + var i = 0 + while (i < batchSize) { + val _i = i + 1 + results(i) = Engine.model.invoke(() => { + val gradOutputT = gradOutput.select(1, _i) + val fInputT = fInput.select(1, _i) + val gradientBiasMTUse = if (withBias) { + gradientBiasMT.select(1, _i) + } else null + calcGradParametersFrame( + gradOutputT, + gradWeightMMInBatch.select(1, _i), + gradientBiasMTUse, + fInputT, + ev.fromType[Double](scaleW), + ev.fromType[Double](scaleB)) + }) + i += 1 + } + + Engine.model.sync(results) + + val gradView = gradWeightMMInBatch.view(batchSize, + outputHeight * outputWidth * nOutputPlane * + nInputPlane * kernelH * kernelW).t + val grad = gradWeight.view(outputHeight * outputWidth * + nOutputPlane * nInputPlane * kernelH * kernelW) + grad.addmv(ev.fromType(1.0), ev.fromType(1.0), gradView, onesBatch) + if (withBias) { + gradBias.sum(gradientBiasMT, 1) + } + } + + if (null != wRegularizer) { + wRegularizer.accRegularization(weight, gradWeight, scaleW) + } + if (withBias && null != bRegularizer) { + bRegularizer.accRegularization(bias, gradBias, scaleB) + } + } + + override def updateParameters(learningRate: T): Unit = { + weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) + if (withBias) { + bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) + } + } + + override def zeroGradParameters(): Unit = { + gradWeight.zero() + if (withBias) { + gradBias.zero() + } + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + if (withBias) { + (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) + } else { + (Array(this.weight), Array(this.gradWeight)) + } + } + + override def getParametersTable(): Table = { + if (withBias) { + T(getName() -> T("weight" -> weight, "bias" -> bias, + "gradWeight" -> gradWeight, "gradBias" -> gradBias)) + } else { + T(getName() -> T("weight" -> weight, + "gradWeight" -> gradWeight)) + } + } + + override def equals(obj: Any): Boolean = { + + if (!super.equals(obj)) { + return false + } + + if (!obj.isInstanceOf[SpatialConvolution[T]]) { + return false + } + val other = obj.asInstanceOf[SpatialConvolution[T]] + if (this.eq(other)) { + return true + } + + nInputPlane == other.nInputPlane && + nOutputPlane == other.nOutputPlane && + kernelW == other.kernelW && + kernelH == other.kernelH && + strideW == other.strideW && + strideH == other.strideH && + padW == other.padW && + padH == other.padH && + propagateBack == other.propagateBack && + weight == other.weight && + bias == other.bias && + gradWeight == other.gradWeight && + gradBias == other.gradBias + } + + override def hashCode(): Int = { + val seed = 37 + var hash = super.hashCode() + hash = hash * seed + nInputPlane.hashCode() + hash = hash * seed + nOutputPlane.hashCode() + hash = hash * seed + kernelW.hashCode() + hash = hash * seed + kernelH.hashCode() + hash = hash * seed + strideW.hashCode() + hash = hash * seed + strideH.hashCode() + hash = hash * seed + padW.hashCode() + hash = hash * seed + padH.hashCode() + hash = hash * seed + weight.hashCode() + if (withBias) hash = hash * seed + bias.hashCode() + hash = hash * seed + gradWeight.hashCode() + if (withBias) hash = hash * seed + gradBias.hashCode() + + hash + } + + override def clearState() : this.type = { + super.clearState() + fInput.set() + fGradInput.set() + ones.set() + onesBatch.set() + if (withBias) { + onesBias.set() + gradientBiasMT.set() + } + this + } + + override def toString(): String = { + s"${getPrintName}($nInputPlane -> $nOutputPlane, $kernelW x" + + s" $kernelH, $strideW, $strideH, $padW, $padH)" + } + + protected def updateOutputFrame( + input: Tensor[T], output: Tensor[T], weight: Tensor[T], + bias: Tensor[T], fInput: Tensor[T], + kW: Int, kH: Int, dW: Int, dH: Int, padLeft: Int, padTop: Int, padRight: Int, padBottom: Int, + nInputPlane: Int, inputWidth: Int, inputHeight: Int, + nOutputPlane: Int, outputWidth: Int, outputHeight: Int)( + implicit ev: TensorNumeric[T]): Unit = { + + format match { + case DataFormat.NCHW => + val output2d = output.view(nOutputPlane, outputHeight * outputWidth) + if (!_1x1) { + ev.getType() match { + case DoubleType => + val before = System.nanoTime() + NNPrimitive.im2colDouble(fInput.asInstanceOf[Tensor[Double]], + input.asInstanceOf[Tensor[Double]], kW, kH, dW, dH, + padLeft, padTop, padRight, padBottom, + outputWidth, outputHeight) + im2colTime += System.nanoTime() - before + case FloatType => + val before = System.nanoTime() + NNPrimitive.im2colFloat(fInput.asInstanceOf[Tensor[Float]], + input.asInstanceOf[Tensor[Float]], kW, kH, dW, dH, + padLeft, padTop, padRight, padBottom, + outputWidth, outputHeight) + im2colTime += System.nanoTime() - before + case _ => throw new UnsupportedOperationException(s"Only Float/Double supported") + } + } + var j = 1 + while (j <= weight.size(1)) { + val outputWindow = output2d.select(2, j) + outputWindow.addmv(ev.fromType[Int](0), outputWindow, + ev.fromType[Int](1), weight.select(1, j), fInput.select(2, j)) + j += 1 + } + if (withBias) output2d.add(ev.fromType(1), bias) + case DataFormat.NHWC => + val output2d = output.view(outputHeight * outputWidth, nOutputPlane) + if (!_1x1) { + ev.getType() match { + case DoubleType => + val before = System.nanoTime() + NNPrimitive.im2colDoubleNHWC(fInput.asInstanceOf[Tensor[Double]], + input.asInstanceOf[Tensor[Double]], kW, kH, dW, dH, + padLeft, padTop, padRight, padBottom, + outputWidth, outputHeight) + im2colTime += System.nanoTime() - before + case FloatType => + val before = System.nanoTime() + NNPrimitive.im2colFloatNHWC(fInput.asInstanceOf[Tensor[Float]], + input.asInstanceOf[Tensor[Float]], kW, kH, dW, dH, + padLeft, padTop, padRight, padBottom, + outputWidth, outputHeight) + im2colTime += System.nanoTime() - before + case _ => throw new UnsupportedOperationException(s"Only Float/Double supported") + } + } + var j = 1 + while (j <= weight.size(1)) { + val outputWindow = output2d.select(1, j) + outputWindow.addmv(ev.fromType[Int](0), outputWindow, + ev.fromType[Int](1), weight.select(1, j), fInput.select(1, j)) + j += 1 + } + if (withBias) output2d.add(ev.fromType(1), bias) + } + } + + protected def updateGradInputFrame( + gradInput: Tensor[T], gradOutput: Tensor[T], + weight: Tensor[T], fgradInput: Tensor[T], kW: Int, kH: Int, dW: Int, dH: Int, + padLeft: Int, padTop: Int, padRight: Int, padBottom: Int) + (implicit ev: TensorNumeric[T]): Unit = { + ev.getType() match { + case DoubleType => + val gradOutDouble = gradOutput.asInstanceOf[Tensor[Double]] + val fGradInDouble = fgradInput.asInstanceOf[Tensor[Double]] + val weightDouble = weight.asInstanceOf[Tensor[Double]] + val gradInputDouble = gradInput.asInstanceOf[Tensor[Double]] + format match { + case DataFormat.NCHW => + val channel = gradOutDouble.size(1) + val oh = gradOutDouble.size(2) + val ow = gradOutDouble.size(3) + val gradOutput2d = gradOutDouble.view(Array(channel, oh * ow)) + var j = 1 + while (j <= weight.size(1)) { + val fGradInWindow = fGradInDouble.select(2, j) + fGradInWindow.addmv(0.0f, fGradInWindow, + 1.0f, weightDouble.select(1, j), gradOutput2d.select(2, j)) + j += 1 + } + if (!_1x1) { + gradInputDouble.zero() + val before = System.nanoTime() + NNPrimitive.col2imDouble(fGradInDouble, + gradInputDouble, kW, kH, dW, dH, + padLeft, padTop, padRight, padBottom, + gradOutput.size(3), gradOutput.size(2)) + col2imTime += System.nanoTime() - before + } + case DataFormat.NHWC => + val channel = gradOutDouble.size(3) + val oh = gradOutDouble.size(1) + val ow = gradOutDouble.size(2) + val gradOutput2d = gradOutDouble.view(Array(oh * ow, channel)) + var j = 1 + while (j <= weight.size(1)) { + val fGradInWindow = fGradInDouble.select(1, j) + fGradInWindow.addmv(0.0f, fGradInWindow, + 1.0f, weightDouble.select(1, j), gradOutput2d.select(1, j)) + j += 1 + } + if (!_1x1) { + gradInputDouble.zero() + val before = System.nanoTime() + NNPrimitive.col2imDoubleNHWC(fGradInDouble, + gradInputDouble, kW, kH, dW, dH, + padLeft, padTop, padRight, padBottom, + gradOutput.size(2), gradOutput.size(1)) + col2imTime += System.nanoTime() - before + } + } + case FloatType => + val gradOutFloat = gradOutput.asInstanceOf[Tensor[Float]] + val fGradInFloat = fgradInput.asInstanceOf[Tensor[Float]] + val weightFloat = weight.asInstanceOf[Tensor[Float]] + val gradInputFloat = gradInput.asInstanceOf[Tensor[Float]] + format match { + case DataFormat.NCHW => + val channel = gradOutFloat.size(1) + val oh = gradOutFloat.size(2) + val ow = gradOutFloat.size(3) + val gradOutput2d = gradOutFloat.view(Array(channel, oh * ow)) + var j = 1 + while (j <= weight.size(1)) { + val fGradInWindow = fGradInFloat.select(2, j) + fGradInWindow.addmv(0.0f, fGradInWindow, + 1.0f, weightFloat.select(1, j), gradOutput2d.select(2, j)) + j += 1 + } + if (!_1x1) { + gradInputFloat.zero() + val before = System.nanoTime() + NNPrimitive.col2imFloat(fGradInFloat, + gradInputFloat, kW, kH, dW, dH, padLeft, padTop, padRight, padBottom, + gradOutput.size(3), gradOutput.size(2)) + col2imTime += System.nanoTime() - before + } + case DataFormat.NHWC => + val channel = gradOutFloat.size(3) + val oh = gradOutFloat.size(1) + val ow = gradOutFloat.size(2) + val gradOutput2d = gradOutFloat.view(Array(oh * ow, channel)) + var j = 1 + while (j <= weight.size(1)) { + val fGradInWindow = fGradInFloat.select(1, j) + fGradInWindow.addmv(0.0f, fGradInWindow, + 1.0f, weightFloat.select(1, j), gradOutput2d.select(1, j)) + j += 1 + } + if (!_1x1) { + gradInputFloat.zero() + val before = System.nanoTime() + NNPrimitive.col2imFloatNHWC(fGradInFloat, + gradInputFloat, kW, kH, dW, dH, padLeft, padTop, padRight, padBottom, + gradOutput.size(2), gradOutput.size(1)) + col2imTime += System.nanoTime() - before + } + } + case _ => throw new UnsupportedOperationException(s"Only Float/Double supported") + } + } + + protected def accGradParametersFrame(gradOutput: Tensor[T], gradWeight: Tensor[T], + gradBias: Tensor[T], fInput: Tensor[T], + scaleW: T, scaleB: T)(implicit ev: TensorNumeric[T]): Unit = { + + if (gradBiasWindow == null) gradBiasWindow = Tensor[T]() + + ev.getType() match { + case DoubleType => + val gradODouble = gradOutput.asInstanceOf[Tensor[Double]] + val gradWDouble = gradWeight.asInstanceOf[Tensor[Double]] + val fIDouble = fInput.asInstanceOf[Tensor[Double]] + val sWDouble = ev.toType[Double](scaleW) + val sBDouble = ev.toType[Double](scaleB) + val gradBDouble = gradBias.asInstanceOf[Tensor[Double]] + val gradBiasWindowDouble = gradBiasWindow.asInstanceOf[Tensor[Double]] + + format match { + case DataFormat.NCHW => + val outChannel = gradOutput.size(1) + val outSize = gradOutput.size(2) * gradOutput.size(3) + val gradOutput2d = gradODouble.view(Array(outChannel, outSize)) + if (sWDouble != 0) { + gradWDouble.addmm(1.0, gradWDouble, sWDouble, gradOutput2d, fIDouble.t) + } + if ( withBias && sBDouble != 0) { + var i = 0 + while (i < gradBias.size(1)) { + var sum = 0.0 + val data = gradOutput2d.storage().array() + val offset = gradOutput2d.storageOffset() - 1 + i * gradOutput2d.stride(1) + gradBiasWindowDouble.set(gradBDouble.storage(), gradBDouble.storageOffset() + i, + Array(gradOutput2d.size(2)), Array(1)) + i += 1 + } + } + case DataFormat.NHWC => + val outChannel = gradOutput.size(3) + val outSize = gradOutput.size(1) * gradOutput.size(2) + val gradOutput2d = gradODouble.view(Array(outSize, outChannel)) + + if (sWDouble != 0) { + gradWDouble.addmm(1.0, gradWDouble, sWDouble, fIDouble.t, gradOutput2d) + } + + if (withBias && sBDouble != 0) { + var i = 0 + val gradData = gradOutput2d.storage().array() + val biasData = gradBDouble.storage().array() + val biasOffset = gradBDouble.storageOffset() - 1 + + while (i < gradODouble.size(1)) { + val gradOffset = gradOutput2d.storageOffset() - 1 + i * gradOutput2d.stride(1) + gradBiasWindowDouble.set(gradBDouble.storage(), gradBDouble.storageOffset() + i, + Array(gradOutput2d.size(2)), Array(1)) + i = i + 1 + } + } + } + + case FloatType => + val gradOFloat = gradOutput.asInstanceOf[Tensor[Float]] + val gradWFloat = gradWeight.asInstanceOf[Tensor[Float]] + val fIFloat = fInput.asInstanceOf[Tensor[Float]] + val sWFloat = ev.toType[Float](scaleW) + val sBFloat = ev.toType[Float](scaleB) + val gradBFloat = gradBias.asInstanceOf[Tensor[Float]] + val gradBiasWindowFloat = gradBiasWindow.asInstanceOf[Tensor[Float]] + + format match { + case DataFormat.NCHW => + val outChannel = gradOutput.size(1) + val outSize = gradOutput.size(2) * gradOutput.size(3) + val gradOutput2d = gradOFloat.view(Array(outChannel, outSize)) + if (sWFloat != 0) { + gradWFloat.addmm(1.0f, gradWFloat, sWFloat, gradOutput2d, fIFloat.t) + } + + if (withBias && sBFloat != 0) { + var i = 0 + while (i < gradBias.size(1)) { + var sum = 0.0f + val data = gradOutput2d.storage().array() + val offset = gradOutput2d.storageOffset() - 1 + i * gradOutput2d.stride(1) + gradBiasWindowFloat.set(gradBFloat.storage(), gradBFloat.storageOffset() + i, + Array(gradOutput2d.size(2)), Array(1)) + i += 1 + } + } + case DataFormat.NHWC => + val outChannel = gradOutput.size(3) + val outSize = gradOutput.size(1) * gradOutput.size(2) + val gradOutput2d = gradOFloat.view(Array(outSize, outChannel)) + + if (sWFloat != 0) { + gradWFloat.addmm(1.0f, gradWFloat, sWFloat, fIFloat.t, gradOutput2d) + } + + if (withBias && sBFloat != 0) { + var i = 0 + val gradData = gradOutput2d.storage().array() + val biasData = gradBFloat.storage().array() + val biasOffset = gradBFloat.storageOffset() - 1 + + while (i < gradOFloat.size(1)) { + val gradOffset = gradOutput2d.storageOffset() - 1 + i * gradOutput2d.stride(1) + gradBiasWindowFloat.set(gradBFloat.storage(), gradBFloat.storageOffset() + i, + Array(gradOutput2d.size(2)), Array(1)) + i = i + 1 + } + } + } + + case _ => throw new UnsupportedOperationException(s"Only Float/Double supported") + } + } + + protected def calcGradParametersFrame(gradOutput: Tensor[T], gradWeight: Tensor[T], + gradBias: Tensor[T], + fInput: Tensor[T], scaleW: T, scaleB: T)(implicit ev: TensorNumeric[T]): Unit = { + + ev.getType() match { + case DoubleType => + val gradODouble = gradOutput.asInstanceOf[Tensor[Double]] + val gradWDouble = gradWeight.asInstanceOf[Tensor[Double]] + val sWDouble = ev.toType[Double](scaleW) + val sBDouble = ev.toType[Double](scaleB) + val fIDouble = fInput.asInstanceOf[Tensor[Double]] + val gradBDouble = gradBias.asInstanceOf[Tensor[Double]] + val onesDouble = ones.asInstanceOf[Tensor[Double]] + + format match { + case DataFormat.NCHW => + val channel = gradODouble.size(1) + val oh = gradODouble.size(2) + val ow = gradODouble.size(3) + val gradOutput2d = gradODouble.view(Array(channel, oh * ow)) + + var j = 1 + while (j <= weight.size(1)) { + val gradWDoubleWindow = gradWDouble.select(1, j) + gradWDoubleWindow.addr(0.0f, gradWDoubleWindow, + sWDouble, gradOutput2d.select(2, j), fIDouble.select(2, j)) + j += 1 + } + + if (withBias && scaleB != 0) { + gradBDouble.add(sBDouble, gradOutput2d) + } + case DataFormat.NHWC => + val channel = gradODouble.size(3) + val oh = gradODouble.size(1) + val ow = gradODouble.size(2) + val gradOutput2d = gradODouble.view(Array(oh * ow, channel)) + + var j = 1 + while (j <= weight.size(1)) { + val gradWDoubleWindow = gradWDouble.select(1, j) + gradWDoubleWindow.addr(0.0f, gradWDoubleWindow, + sWDouble, gradOutput2d.select(1, j), fIDouble.select(1, j)) + j += 1 + } + + if (withBias && scaleB != 0) { + gradBDouble.add(sBDouble, gradOutput2d) + } + } + + case FloatType => + val gradOFloat = gradOutput.asInstanceOf[Tensor[Float]] + val gradWFloat = gradWeight.asInstanceOf[Tensor[Float]] + val sWFloat = ev.toType[Float](scaleW) + val sBFloat = ev.toType[Float](scaleB) + val fIFloat = fInput.asInstanceOf[Tensor[Float]] + val gradBFloat = gradBias.asInstanceOf[Tensor[Float]] + val onesFloat = ones.asInstanceOf[Tensor[Float]] + + format match { + case DataFormat.NCHW => + val channel = gradOFloat.size(1) + val oh = gradOFloat.size(2) + val ow = gradOFloat.size(3) + val gradOutput2d = gradOFloat.view(Array(channel, oh * ow)) + + var j = 1 + while (j <= weight.size(1)) { + val gradWFloatWindow = gradWFloat.select(1, j) + gradWFloatWindow.addr(0.0f, gradWFloatWindow, + sWFloat, gradOutput2d.select(2, j), fIFloat.select(2, j)) + j += 1 + } + + if (withBias && scaleB != 0) { + gradBFloat.add(sBFloat, gradOutput2d) + } + + case DataFormat.NHWC => + val channel = gradOFloat.size(3) + val oh = gradOFloat.size(1) + val ow = gradOFloat.size(2) + val gradOutput2d = gradOFloat.view(Array(oh * ow, channel)) + var j = 1 + while (j <= weight.size(1)) { + val gradWFloatWindow = gradWFloat.select(1, j) + gradWFloatWindow.addr(0.0f, gradWFloatWindow, + sWFloat, gradOutput2d.select(1, j), fIFloat.select(1, j)) + j += 1 + } + + if (withBias && scaleB != 0) { + gradBFloat.add(sBFloat, gradOutput2d) + } + } + + case _ => throw new UnsupportedOperationException(s"Only Float/Double supported") + } + } +} + +object LocallyConnected2D { + def apply[@specialized(Float, Double) T: ClassTag]( + nInputPlane: Int, + inputWidth: Int, + inputHeight: Int, + nOutputPlane: Int, + kernelW: Int, + kernelH: Int, + strideW: Int = 1, + strideH: Int = 1, + padW: Int = 0, + padH: Int = 0, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null, + withBias: Boolean = true, + format: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]): LocallyConnected2D[T] = { + new LocallyConnected2D[T](nInputPlane, inputWidth, inputHeight, nOutputPlane, kernelW, kernelH, + strideW, strideH, padW, padH, propagateBack, wRegularizer, + bRegularizer, initWeight, initBias, initGradWeight, initGradBias, withBias, format) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c682cb282be..3da50b25b8f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -457,6 +457,49 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab else maxpooling } + def createLocallyConnected2D( + nInputPlane: Int, + inputWidth: Int, + inputHeight: Int, + nOutputPlane: Int, + kernelW: Int, + kernelH: Int, + strideW: Int = 1, + strideH: Int = 1, + padW: Int = 0, + padH: Int = 0, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null, + withBias: Boolean = true, + dataFormat: String = "NCHW"): LocallyConnected2D[T] = { + LocallyConnected2D[T]( + nInputPlane, + inputWidth, + inputHeight, + nOutputPlane, + kernelW, + kernelH, + strideW, + strideH, + padW, + padH, + propagateBack, + wRegularizer, + bRegularizer, + toTensor(initWeight), + toTensor(initBias), + toTensor(initGradWeight), + toTensor(initGradBias), + withBias, + DataFormat(dataFormat) + ) + } + def createSpatialConvolution(nInputPlane: Int, nOutputPlane: Int, kernelW: Int, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala new file mode 100644 index 00000000000..665ffca2a80 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala @@ -0,0 +1,70 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.LocallyConnected2D +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator + + +class LocallyConnected2DSpec extends KerasBaseSpec { + "LocallyConnected1D NHWC Float" should "be ok" in { + ifskipTest() + val kerasCode = + """ + |input_tensor = Input(shape=[3,6,2]) + |input = np.array([[[[1,2], [2,3], [3,4],[4,5],[5,6],[6,7]], + | [[2,3], [3,4],[4,5],[5,6],[6,7], [1,2]], + | [[1,2], [2,3], [3,4],[4,5],[6,7],[5,6]]]]) + |output_tensor = LocallyConnected2D(3, 2, 1, + |input_shape=(3,6,2))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val locallyConnected1d = + LocallyConnected2D[Float](2, 6, 3, 3, 1, 2, format = DataFormat.NHWC) + val a = locallyConnected1d.parameters() + + + val wc = (data: Array[Tensor[Float]]) => { + + val out = new Array[Tensor[Float]](data.length) + val d1l: Int = data(0).size(1) + val d2l: Int = data(0).size(2) + val d3l: Int = data(0).size(3) + + out(0) = Tensor(d1l, d3l, d2l) + + val page: Int = d2l * d3l + for (i <- 0 to d1l * d2l * d3l - 1) { + val d1 = i / page + 1 + val d2 = (i % page) / (d3l) + 1 + val d3 = (i % page) % d3l + 1 + val v = data(0).valueAt(d1, d2, d3) + out(0).setValue(d1, d3, d2, v) + } + + if (data.length > 1) { + out(1) = data(1) + } + out + } + + checkOutputAndGrad(locallyConnected1d, kerasCode, wc) + + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index aa297e745d0..a0e69c6d2e9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -1081,6 +1081,13 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(spatialConvolution, input) } + "LocallyConnected2D serializer" should "work properly" in { + val locallyConnected2D = LocallyConnected2D[Float](3, 5, 5, 4, 2, 2). + setName("locallyConnected2D") + val input = Tensor[Float](1, 3, 5, 5).apply1( e => Random.nextFloat()) + runSerializationTest(locallyConnected2D, input) + } + "SpatialConvolutionMap serializer" should "work properly" in { val spatialConvolutionMap = SpatialConvolutionMap[Float]( SpatialConvolutionMap.random(1, 1, 1), 2, 2).setName("spatialConvolutionMap") From b9172a3a36204ca683d247c4c1a893a2f67122f4 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 18 Dec 2017 13:24:51 +0800 Subject: [PATCH 0605/1065] support local predictor for quantized model (#2042) --- .../dllib/models/utils/ModelBroadcast.scala | 92 +----------------- .../bigdl/dllib/optim/LocalOptimizer.scala | 11 +-- .../bigdl/dllib/optim/LocalPredictor.scala | 13 +-- .../bigdl/dllib/utils/LocalModule.scala | 26 +---- .../analytics/bigdl/dllib/utils/Util.scala | 97 +++++++++++++++++++ .../dllib/optim/LocalPredictorSpec.scala | 20 ++++ 6 files changed, 126 insertions(+), 133 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index d86cc2c4363..da259fd998c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -17,10 +17,11 @@ package com.intel.analytics.bigdl.models.utils import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.apache.spark.SparkContext import org.apache.spark.broadcast.Broadcast +import com.intel.analytics.bigdl.utils.Util._ import scala.reflect.ClassTag @@ -69,95 +70,6 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria } localModel } - - - private[bigdl] def getAndClearWeightBias(parameters: (Array[Tensor[T]], Array[Tensor[T]])) - : Array[Tensor[T]] = { - if (parameters._1.length != 0) { - var i = 0 - val weightsBias = new Array[Tensor[T]](parameters._1.length) - val isQuantized = parameters._1.exists(_.getTensorType == QuantizedType) - val (isCompacted, storage) = if (!isQuantized) { - val storage = Storage(parameters._1(0).storage.array()) - (parameters._1.map(_.nElement()).sum == storage.length(), storage) - } else { - (false, null) - } - - // get weight and bias - while (i < parameters._1.length) { - if (parameters._1(i) != null) { - val wb = parameters._1(i) - wb.getTensorType match { - case QuantizedType => - val quantTensor = wb.asInstanceOf[QuantizedTensor[T]] - weightsBias(i) = QuantizedTensor[T](quantTensor.getStorage, quantTensor.maxOfRow, - quantTensor.minOfRow, quantTensor.sumOfRow, quantTensor.size(), quantTensor.params) - case _ => - weightsBias(i) = if (isCompacted) { - Tensor[T](storage, wb.storageOffset(), wb.size(), wb.stride()) - } else { - Tensor[T](Storage(wb.storage().array()), wb.storageOffset(), wb.size(), wb.stride()) - } - } - i += 1 - } - } - // clear parameters - clearTensor(parameters._1) - clearTensor(parameters._2) - - weightsBias - } else { - // just return an empty array when parameters is empty. - Array() - } - } - - private def clearTensor(tensors: Array[Tensor[T]]): Unit = { - var i = 0 - while (i < tensors.length) { - if (tensors(i) != null) { - tensors(i).set() - } - i += 1 - } - } - - private[bigdl] def putWeightBias( - broadcastWeightBias: Array[Tensor[T]], - localModel: Module[T]): Unit = { - val localWeightBias = localModel.parameters()._1 - var i = 0 - while (i < localWeightBias.length) { - if (localWeightBias(i) != null) { - localWeightBias(i).set(broadcastWeightBias(i)) - } - i += 1 - } - } - - private[bigdl] def initGradWeightBias( - broadcastWeightBias: Array[Tensor[T]], - localModel: Module[T]): Unit = { - val (localWeightBias, localGradWeightBias) = localModel.parameters() - // init gradient with a compacted storage - val storage = Storage[T](localGradWeightBias.map(_.nElement()).sum) - val isQuantized = broadcastWeightBias.exists(_.getTensorType == QuantizedType) - var i = 0 - while (i < localWeightBias.length) { - if (localWeightBias(i) != null) { - val wb = broadcastWeightBias(i) - wb.getTensorType match { - case QuantizedType => - localGradWeightBias(i).set(Tensor(1)) - case _ => - localGradWeightBias(i).set(storage, wb.storageOffset(), wb.size(), wb.stride()) - } - } - i += 1 - } - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index f83bc00fd5c..b6c0c2f3caa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -58,19 +58,18 @@ class LocalOptimizer[T: ClassTag] ( } private val workingModels = { - val modelBroadcast = ModelBroadcast() model.getParameters() - val wb = modelBroadcast.getAndClearWeightBias(model.parameters()) + val wb = Util.getAndClearWeightBias(model.parameters()) val models = (1 to subModelNumber).map(i => { logger.info(s"Clone $i model...") val m = model.cloneModule() - modelBroadcast.putWeightBias(wb, m) - modelBroadcast.initGradWeightBias(wb, m) + Util.putWeightBias(wb, m) + Util.initGradWeightBias(wb, m) m }).toArray - modelBroadcast.putWeightBias(wb, model) - modelBroadcast.initGradWeightBias(wb, model) + Util.putWeightBias(wb, model) + Util.initGradWeightBias(wb, model) models } private val (weight, grad) = model.getParameters() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index c6af1a855db..9d81cc09138 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Engine, MklBlas} +import com.intel.analytics.bigdl.utils.Util._ import com.intel.analytics.bigdl.dataset.SampleToMiniBatch import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, LocalImageFrame} @@ -138,18 +139,6 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: } - private def putWeightBias(weightBias: Array[Tensor[T]], - localModel: Module[T]): Unit = { - val localWeightBias = localModel.parameters()._1 - var i = 0 - while (i < localWeightBias.length) { - if (localWeightBias(i) != null) { - localWeightBias(i).set(weightBias(i)) - } - i += 1 - } - } - /** * local model predict images, return imageFrame with predicted tensor * @param imageFrame imageFrame that contains images diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala index 27da57b0eaf..db5be8a56bd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala @@ -27,34 +27,10 @@ import scala.reflect.ClassTag object LocalModule { - def getAndClearWeightBias[T: ClassTag](parameters: (Array[Tensor[T]], Array[Tensor[T]])) - (implicit ev: TensorNumeric[T]): Array[Tensor[T]] = { - var i = 0 - val weightsBias = new Array[Tensor[T]](parameters._1.length) - while (i < parameters._1.length) { - if (parameters._1(i) != null) { - val wb = parameters._1(i) - weightsBias(i) = Tensor[T](Storage(wb.storage().array()), - wb.storageOffset(), wb.size(), wb.stride()) - } - i += 1 - } - i = 0 - while (i < parameters._1.length) { - if (parameters._1(i) != null) { - parameters._1(i).set() - } - if (parameters._2(i) != null) { - parameters._2(i).set() - } - i += 1 - } - weightsBias - } def apply[T: ClassTag](model: Module[T]) (implicit ev: TensorNumeric[T]): LocalModule[T] = { - val weightsBias = getAndClearWeightBias(model.cloneModule().parameters()) + val weightsBias = Util.getAndClearWeightBias(model.cloneModule().parameters()) new LocalModule[T](model, weightsBias) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala index f61e649d9b2..6907811e061 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala @@ -16,6 +16,12 @@ package com.intel.analytics.bigdl.utils +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} + +import scala.reflect.ClassTag + object Util { def kthLargest(arr: Array[Long], l: Int, r: Int, k: Int): Long = { if (k == 0) return Long.MaxValue @@ -78,4 +84,95 @@ object Util { data } } + + + private[bigdl] def getAndClearWeightBias[T: ClassTag] + (parameters: (Array[Tensor[T]], Array[Tensor[T]]))(implicit ev: TensorNumeric[T]) + : Array[Tensor[T]] = { + if (parameters._1.length != 0) { + var i = 0 + val weightsBias = new Array[Tensor[T]](parameters._1.length) + val isQuantized = parameters._1.exists(_.getTensorType == QuantizedType) + val (isCompacted, storage) = if (!isQuantized) { + val storage = Storage(parameters._1(0).storage.array()) + (parameters._1.map(_.nElement()).sum == storage.length(), storage) + } else { + (false, null) + } + + // get weight and bias + while (i < parameters._1.length) { + if (parameters._1(i) != null) { + val wb = parameters._1(i) + wb.getTensorType match { + case QuantizedType => + val quantTensor = wb.asInstanceOf[QuantizedTensor[T]] + weightsBias(i) = QuantizedTensor[T](quantTensor.getStorage, quantTensor.maxOfRow, + quantTensor.minOfRow, quantTensor.sumOfRow, quantTensor.size(), quantTensor.params) + case _ => + weightsBias(i) = if (isCompacted) { + Tensor[T](storage, wb.storageOffset(), wb.size(), wb.stride()) + } else { + Tensor[T](Storage(wb.storage().array()), wb.storageOffset(), wb.size(), wb.stride()) + } + } + i += 1 + } + } + // clear parameters + clearTensor(parameters._1) + clearTensor(parameters._2) + + weightsBias + } else { + // just return an empty array when parameters is empty. + Array() + } + } + + private def clearTensor[T: ClassTag](tensors: Array[Tensor[T]]) + (implicit ev: TensorNumeric[T]): Unit = { + var i = 0 + while (i < tensors.length) { + if (tensors(i) != null) { + tensors(i).set() + } + i += 1 + } + } + + private[bigdl] def putWeightBias[T: ClassTag]( + broadcastWeightBias: Array[Tensor[T]], + localModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { + val localWeightBias = localModel.parameters()._1 + var i = 0 + while (i < localWeightBias.length) { + if (localWeightBias(i) != null) { + localWeightBias(i).set(broadcastWeightBias(i)) + } + i += 1 + } + } + + private[bigdl] def initGradWeightBias[T: ClassTag]( + broadcastWeightBias: Array[Tensor[T]], + localModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { + val (localWeightBias, localGradWeightBias) = localModel.parameters() + // init gradient with a compacted storage + val storage = Storage[T](localGradWeightBias.map(_.nElement()).sum) + val isQuantized = broadcastWeightBias.exists(_.getTensorType == QuantizedType) + var i = 0 + while (i < localWeightBias.length) { + if (localWeightBias(i) != null) { + val wb = broadcastWeightBias(i) + wb.getTensorType match { + case QuantizedType => + localGradWeightBias(i).set(Tensor(1)) + case _ => + localGradWeightBias(i).set(storage, wb.storageOffset(), wb.size(), wb.stride()) + } + } + i += 1 + } + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index aa909e8433e..cd12bbf996d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -116,4 +116,24 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(imageFeatures(x - 1).predict() != null) }) } + + "predictImage with quantize" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 20).quantize() + val detection = model.predictImage(imageFrame).toLocal() + val feature = detection.array.head + println(feature(ImageFeature.predict)) + + val imageFeatures = detection.array + val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) + val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) + prob(0) should be(model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 224, 224))) + .toTensor[Float].split(1)(0)) + } } From 399f0582598470d1c7b84ca69aef45bc8cf6ed63 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Mon, 18 Dec 2017 14:12:18 +0800 Subject: [PATCH 0606/1065] fix null pointer exception in bn when affine is false (#2045) * fix null pointer exception in bn when affine is false * meet code review --- .../dllib/nn/SpatialBatchNormalization.scala | 53 ++++++++++--------- .../dllib/nn/BatchNormalizationSpec.scala | 9 ++++ .../nn/SpatialBatchNormalizationSpec.scala | 10 ++++ 3 files changed, 46 insertions(+), 26 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala index 376e0b5dbd8..4a2aed8389e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala @@ -704,14 +704,13 @@ object SpatialBatchNormalization { require(gradOutput.nDimension() == 4, "BN require a 4D gradient") require(gradOutput.isContiguous(), "gradient is not contiguous") val nChannel = gradOutput.size(4) - require(scale.size(1) == nChannel, "scale length is not consist with channel number") - require(saveMean.size(1) == nChannel, "saveMean length is not consist with channel number") - require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + require(saveMean.size(1) == nChannel, "saveMean length is not consistent with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consistent with channel number") gradInput.resizeAs(gradOutput) if (gMean.isEmpty) { - gMean.resize(scale.size(1)) - gxMean.resize(scale.size(1)) + gMean.resize(nChannel) + gxMean.resize(nChannel) } val inputData = input.storage().array() @@ -749,6 +748,8 @@ object SpatialBatchNormalization { } if (scale != null) { + require(scale.size(1) == nChannel, "scale length is not consistent with channel number") + val scaleData = scale.storage().array() val scaleOffset = scale.storageOffset() - 1 i = 0 @@ -796,14 +797,13 @@ object SpatialBatchNormalization { require(gradOutput.nDimension() == 4, "BN require a 4D gradient") require(gradOutput.isContiguous(), "gradient is not contiguous") val nChannel = gradOutput.size(4) - require(scale.size(1) == nChannel, "scale length is not consist with channel number") - require(saveMean.size(1) == nChannel, "saveMean length is not consist with channel number") - require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + require(saveMean.size(1) == nChannel, "saveMean length is not consistent with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consistent with channel number") gradInput.resizeAs(gradOutput) if (gMean.isEmpty) { - gMean.resize(scale.size(1)) - gxMean.resize(scale.size(1)) + gMean.resize(nChannel) + gxMean.resize(nChannel) } val inputData = input.storage().array() @@ -841,6 +841,7 @@ object SpatialBatchNormalization { } if (scale != null) { + require(scale.size(1) == nChannel, "scale length is not consistent with channel number") val scaleData = scale.storage().array() val scaleOffset = scale.storageOffset() - 1 i = 0 @@ -882,8 +883,7 @@ object SpatialBatchNormalization { require(gradOutput.nDimension() == 4, "BN require a 4D gradient") require(gradOutput.isContiguous(), "gradient is not contiguous") val nChannel = gradOutput.size(4) - require(scale.size(1) == nChannel, "scale length is not consist with channel number") - require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consistent with channel number") gradInput.resizeAs(gradOutput) val gradOutputData = gradOutput.storage().array() @@ -896,6 +896,7 @@ object SpatialBatchNormalization { val n = gradOutput.nElement() if (scale != null) { + require(scale.size(1) == nChannel, "scale length is not consistent with channel number") val scaleData = scale.storage().array() val scaleOffset = scale.storageOffset() - 1 var i = 0 @@ -933,8 +934,7 @@ object SpatialBatchNormalization { require(gradOutput.nDimension() == 4, "BN require a 4D gradient") require(gradOutput.isContiguous(), "gradient is not contiguous") val nChannel = gradOutput.size(4) - require(scale.size(1) == nChannel, "scale length is not consist with channel number") - require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consistent with channel number") gradInput.resizeAs(gradOutput) val gradOutputData = gradOutput.storage().array() @@ -948,6 +948,7 @@ object SpatialBatchNormalization { var i = 0 if (scale != null) { + require(scale.size(1) == nChannel, "scale length is not consistent with channel number") val scaleData = scale.storage().array() val scaleOffset = scale.storageOffset() - 1 while (i < n) { @@ -989,14 +990,13 @@ object SpatialBatchNormalization { require(gradOutput.nDimension() == 4, "BN require a 4D gradient") require(gradOutput.isContiguous(), "gradient is not contiguous") val nChannel = gradOutput.size(2) - require(scale.size(1) == nChannel, "scale length is not consist with channel number") - require(saveMean.size(1) == nChannel, "saveMean length is not consist with channel number") - require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + require(saveMean.size(1) == nChannel, "saveMean length is not consistent with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consistent with channel number") gradInput.resizeAs(gradOutput) if (gMean.isEmpty) { - gMean.resize(scale.size(1)) - gxMean.resize(scale.size(1)) + gMean.resize(nChannel) + gxMean.resize(nChannel) } val inputData = input.storage().array() @@ -1044,6 +1044,7 @@ object SpatialBatchNormalization { i = 0 b = 0 if (scale != null) { + require(scale.size(1) == nChannel, "scale length is not consistent with channel number") val scaleData = scale.storage().array() val scaleOffset = scale.storageOffset() - 1 while (b < nBatch) { @@ -1401,8 +1402,8 @@ object SpatialBatchNormalization { require(gradOutput.nDimension() == 4, "BN require a 4D gradient") require(gradOutput.isContiguous(), "gradient is not contiguous") val nChannel = gradOutput.size(2) - require(saveMean.size(1) == nChannel, "saveMean length is not consist with channel number") - require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + require(saveMean.size(1) == nChannel, "saveMean length is not consistent with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consistent with channel number") gradInput.resizeAs(gradOutput) if (gMean.isEmpty) { @@ -1458,7 +1459,7 @@ object SpatialBatchNormalization { if (scale != null) { val scaleData = scale.storage().array() val scaleOffset = scale.storageOffset() - 1 - require(scale.size(1) == nChannel, "scale length is not consist with channel number") + require(scale.size(1) == nChannel, "scale length is not consistent with channel number") while (b < nBatch) { var c = 0 while (c < nChannel) { @@ -1504,8 +1505,7 @@ object SpatialBatchNormalization { require(gradOutput.nDimension() == 4, "BN require a 4D gradient") require(gradOutput.isContiguous(), "gradient is not contiguous") val nChannel = gradOutput.size(2) - require(scale.size(1) == nChannel, "scale length is not consist with channel number") - require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consistent with channel number") gradInput.resizeAs(gradOutput) val gradOutputData = gradOutput.storage().array() @@ -1520,6 +1520,7 @@ object SpatialBatchNormalization { var b = 0 var i = 0 if (scale != null) { + require(scale.size(1) == nChannel, "scale length is not consistent with channel number") val scaleData = scale.storage().array() val scaleOffset = scale.storageOffset() - 1 while (b < nBatch) { @@ -1565,8 +1566,7 @@ object SpatialBatchNormalization { require(gradOutput.nDimension() == 4, "BN require a 4D gradient") require(gradOutput.isContiguous(), "gradient is not contiguous") val nChannel = gradOutput.size(2) - require(scale.size(1) == nChannel, "scale length is not consist with channel number") - require(saveStd.size(1) == nChannel, "saveStd length is not consist with channel number") + require(saveStd.size(1) == nChannel, "saveStd length is not consistent with channel number") gradInput.resizeAs(gradOutput) val gradOutputData = gradOutput.storage().array() @@ -1581,6 +1581,7 @@ object SpatialBatchNormalization { var b = 0 var i = 0 if (scale != null) { + require(scale.size(1) == nChannel, "scale length is not consistent with channel number") val scaleData = scale.storage().array() val scaleOffset = scale.storageOffset() - 1 while (b < nBatch) { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala index 3b73e7599f7..4e908752024 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala @@ -236,6 +236,15 @@ class BatchNormalizationSpec extends FlatSpec with Matchers { bn2.gradWeight should be(bn1.gradWeight.mul(0.5)) bn2.gradBias should be(bn1.gradBias.mul(2)) + } + "BatchNormalization backward" should "be good when affine is false" in { + val layer = BatchNormalization[Float](3, affine = false) + val input = Tensor[Float](4, 3).fill(1) + val gradOutput = Tensor[Float](4, 3).fill(1) + val output = layer.forward(input) + output should be(Tensor[Float](4, 3).fill(0)) + val gradInput = layer.backward(input, gradOutput) + gradInput should be(Tensor[Float](4, 3).fill(0)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala index 10d7acf2b44..527e7527ec3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala @@ -34,6 +34,16 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { checker.checkLayer[Double](sbn, input, 1e-3) should be(true) } + "SpatialBatchNormalization backward" should "be good when affine is false" in { + val layer = SpatialBatchNormalization[Float](3, affine = false) + val input = Tensor[Float](4, 3, 24, 24).fill(1) + val gradOutput = Tensor[Float](4, 3, 24, 24).fill(1) + val output = layer.forward(input) + output should be(Tensor[Float](4, 3, 24, 24).fill(0)) + val gradInput = layer.backward(input, gradOutput) + gradInput should be(Tensor[Float](4, 3, 24, 24).fill(0)) + } + "SpatialBatchNormalization module in batch mode" should "be good in gradient check " + "for weight" in { val seed = 100 From 326e0a8860f8ae6ae0d9e5d728a6b17af4842566 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Mon, 18 Dec 2017 16:34:26 +0800 Subject: [PATCH 0607/1065] fix stale wiki links (#2046) --- .../scala/com/intel/analytics/bigdl/utils/Engine.scala | 8 ++++---- scala/dllib/src/main/resources/spark-bigdl.conf | 2 +- .../analytics/bigdl/dllib/example/languagemodel/README.md | 2 +- .../analytics/bigdl/dllib/example/lenetLocal/README.md | 2 +- .../analytics/bigdl/dllib/example/udfpredictor/README.md | 2 +- .../analytics/bigdl/dllib/models/autoencoder/README.md | 2 +- .../analytics/bigdl/dllib/models/inception/README.md | 2 +- .../intel/analytics/bigdl/dllib/models/lenet/README.md | 2 +- .../intel/analytics/bigdl/dllib/models/resnet/README.md | 2 +- .../com/intel/analytics/bigdl/dllib/models/rnn/README.md | 2 +- .../com/intel/analytics/bigdl/dllib/models/vgg/README.md | 2 +- 11 files changed, 14 insertions(+), 14 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 5c6fe68839f..9d258523649 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -34,14 +34,14 @@ case object MklBlas extends EngineType object Engine { @deprecated( - "See https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#engine", + "See https://bigdl-project.github.io/master/#APIGuide/Engine/", "0.1.0") def init(nExecutor: Int, executorCores: Int, onSpark: Boolean): Option[SparkConf] = { logger.warn("Engine.init(nExecutor, executorCores, onSpark) is deprecated. " + "Please refer to " + - "https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#engine") + "https://bigdl-project.github.io/master/#APIGuide/Engine/") setNodeAndCore(nExecutor, executorCores) val res = if (onSpark) { require(localMode == false, @@ -119,10 +119,10 @@ object Engine { private val NOT_INIT_ERROR = "Do you call Engine.init? See more at " + - "https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#engine" + "https://bigdl-project.github.io/master/#APIGuide/Engine/" private val SPARK_CONF_ERROR = "For details please check " + - "https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#engine" + "https://bigdl-project.github.io/master/#APIGuide/Engine/" /** * Notice: Please use property bigdl.engineType to set engineType. diff --git a/scala/dllib/src/main/resources/spark-bigdl.conf b/scala/dllib/src/main/resources/spark-bigdl.conf index 63436abc6e7..0e7737ba6d9 100644 --- a/scala/dllib/src/main/resources/spark-bigdl.conf +++ b/scala/dllib/src/main/resources/spark-bigdl.conf @@ -24,7 +24,7 @@ # in your spark conf file. # # For more details, please refer -# https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#engine +# https://bigdl-project.github.io/master/#APIGuide/Engine/ # spark.shuffle.reduceLocality.enabled false diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md index 9eaa3950320..60d9cbc3185 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md @@ -8,7 +8,7 @@ Here we use [Penn Tree Bank (PTB)](https://catalog.ldc.upenn.edu/ldc99t42) as t ## Get BigDL jar -Please build BigDL referring to [Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page). +Please build BigDL referring to [Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/). ## Prepare PTB Data Download PTB dataset from [Tomas Mikolov's webpage](http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md index ac745bc98e6..d6d68a4d3a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/README.md @@ -16,7 +16,7 @@ There're four files. **train-images-idx3-ubyte** contains train images, ## Get the JAR You can build one by refer to the -[Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page) from the source code. +[Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/) from the source code. ## Train the Model Example command diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/README.md index 28e5bacfa23..ff905080fc8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/udfpredictor/README.md @@ -13,7 +13,7 @@ Then create a UDF to do the text classification with this model, and use this UD ## Get the JAR Please build the source code with your specific version of spark referring the - [Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page). + [Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/). ## Steps to run this example: diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/README.md index f1d825a0b21..899721bed4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/autoencoder/README.md @@ -17,7 +17,7 @@ then unzip them, you can get: ## Get the JAR You can build one by refer to the -[Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page) from the source code. +[Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/) from the source code. ## Train on Spark: Spark local mode, example command: diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md index ec7f07793dc..a5740edfc5d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md @@ -2,7 +2,7 @@ This example demonstrates how to use BigDL to train and evaluate [Inception v1](https://arxiv.org/abs/1409.4842) architecture on the [ImageNet](http://image-net.org/index) data. ## Get the JAR You can build one by refer to the -[Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page) from the source code. We +[Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/) from the source code. We will release a pre-build package soon. ## Prepare the data diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/README.md index d65b1955e90..97be7f5a1bf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/README.md @@ -17,7 +17,7 @@ to **train-images.idx3-ubyte**. Please change the name back before you run the e ## Get the JAR You can build one by refer to the -[Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page) from the source code. +[Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/) from the source code. ## Train the Model ### Use Apache Spark diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md index 9857c69fdef..4639f8872f2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md @@ -18,7 +18,7 @@ Model is implemented in ResNet ## Get the JAR You can build one by refer to the -[Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page) from the source code. +[Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/) from the source code. ## Training * Spark local, example command diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/README.md index efc48a49eae..df3abbddf68 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/rnn/README.md @@ -9,7 +9,7 @@ The implementation of RNNs in this code is referred to in the [Keras Recurrent]( ## Get the BigDL files -Please build BigDL referring to [Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page). +Please build BigDL referring to [Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/). ## Prepare the Input Data diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/README.md index fca2e395922..d5729ed20d2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/README.md @@ -7,7 +7,7 @@ You can download CIFAR-10 dataset from [this webpage](https://www.cs.toronto.edu ## Get the JAR You can build one by refer to the -[Build Page](https://github.com/intel-analytics/BigDL/wiki/Build-Page) from the source code. +[Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/) from the source code. ## Train Model on Spark Example command for running in Spark cluster mode From f76f4985f8d45133de53a36643417ee243d1d0cc Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 18 Dec 2017 16:45:18 +0800 Subject: [PATCH 0608/1065] use flatmap instead of next in predictImage (#2047) --- .../analytics/bigdl/dllib/optim/Predictor.scala | 13 ++++++------- .../bigdl/dllib/optim/LocalPredictorSpec.scala | 15 +++++++++++++++ 2 files changed, 21 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index 208a57d9e35..ca7a60259c4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -38,8 +38,7 @@ object Predictor { shareBuffer: Boolean)(implicit ev: TensorNumeric[T]): Seq[ImageFeature] = { val validImageFeatures = imageFeatures.filter(_.isValid) val samples = validImageFeatures.map(x => x[Sample[T]](ImageFeature.sample)) - val batch = localToBatch(samples.toIterator).next() - if (batch != null) { + val batchOut = localToBatch(samples.toIterator).flatMap(batch => { localModel.forward(batch.getInput()) val output = if (outputLayer == null) { localModel.output.toTensor[T] @@ -47,15 +46,15 @@ object Predictor { localModel(outputLayer).get.output.toTensor[T] } val result = if (shareBuffer) output else output.clone() - val batchOut = if (result.dim() == 1) { + if (result.dim() == 1) { Array(result) } else { result.split(1) } - validImageFeatures.zip(batchOut).foreach(tuple => { - tuple._1(predictKey) = tuple._2 - }) - } + }) + validImageFeatures.toIterator.zip(batchOut).foreach(tuple => { + tuple._1(predictKey) = tuple._2 + }) imageFeatures } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index cd12bbf996d..86762629ce9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -136,4 +136,19 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { prob(0) should be(model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 224, 224))) .toTensor[Float].split(1)(0)) } + + "predictImage empty" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val images = ImageFrame.array(Array[ImageFeature]()) + val imageFrame = images -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 20) + val detection = model.predictImage(imageFrame).toLocal() + + val imageFeatures = detection.array + imageFeatures.length should be (0) + } } From 8e209550445c570acf0dc3ed6bef2b6bf8b5aa57 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Mon, 18 Dec 2017 16:50:08 +0800 Subject: [PATCH 0609/1065] Unify optimizer creation for local and distributed mode. (#2013) * add example for pure local of python api update doc doc update * update * doc * doc * doc * clean * style * reserve the previous API * clean --- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 3da50b25b8f..3c59942f23e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2144,6 +2144,18 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab vMethods.asScala.toArray) } + def setValidation(optimizer: Optimizer[T, MiniBatch[T]], + batchSize: Int, + trigger: Trigger, + xVal: JList[JTensor], + yVal: JTensor, + vMethods: JList[ValidationMethod[T]]): Unit = { + + val sampleArray = toSampleArray(xVal.asScala.toList.map{f => toTensor(f)}, toTensor(yVal)) + optimizer.setValidation(trigger, batching(DataSet.array(sampleArray), batchSize), + vMethods.asScala.toArray) + } + def setTrainData(optimizer: Optimizer[T, MiniBatch[T]], trainingRdd: JavaRDD[Sample], batchSize: Int): Unit = { From 7c411c6d34ac534549bf3d2f943901de3d398bbb Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Mon, 18 Dec 2017 20:17:29 +0800 Subject: [PATCH 0610/1065] bump bigdl version (#2049) --- dist/pom.xml | 2 +- dl/pom.xml | 6 +++--- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- .../dllib/src/main/resources/bigdl-version-info.properties | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index 8ce97ad82bd..cd68f27c24f 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.4.0-SNAPSHOT + 0.5.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index 4af7aa3cc42..36a9f8db763 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.4.0-SNAPSHOT + 0.5.0-SNAPSHOT 4.0.0 @@ -79,7 +79,7 @@ com.intel.analytics.bigdl.core.dist all - 0.4.0-SNAPSHOT + 0.5.0-SNAPSHOT ${bigdl-core-all-scope} @@ -308,7 +308,7 @@ com.intel.analytics.bigdl.core.dist ${os-flag} - 0.4.0-SNAPSHOT + 0.5.0-SNAPSHOT pom diff --git a/pom.xml b/pom.xml index ef8e9e69693..0281343eb8a 100644 --- a/pom.xml +++ b/pom.xml @@ -5,7 +5,7 @@ bigdl-parent com.intel.analytics.bigdl - 0.4.0-SNAPSHOT + 0.5.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/1.5-plus/pom.xml b/scala/common/spark-version/1.5-plus/pom.xml index 90d429a7c01..84e8e2b9dbb 100644 --- a/scala/common/spark-version/1.5-plus/pom.xml +++ b/scala/common/spark-version/1.5-plus/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.4.0-SNAPSHOT + 0.5.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/2.0/pom.xml b/scala/common/spark-version/2.0/pom.xml index 266793bc330..8350aee22d2 100644 --- a/scala/common/spark-version/2.0/pom.xml +++ b/scala/common/spark-version/2.0/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.4.0-SNAPSHOT + 0.5.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/pom.xml b/scala/common/spark-version/pom.xml index 027909a1d01..9e999694a9e 100644 --- a/scala/common/spark-version/pom.xml +++ b/scala/common/spark-version/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.4.0-SNAPSHOT + 0.5.0-SNAPSHOT 4.0.0 diff --git a/scala/dllib/src/main/resources/bigdl-version-info.properties b/scala/dllib/src/main/resources/bigdl-version-info.properties index 39fd12ce44c..0444f62ca0c 100644 --- a/scala/dllib/src/main/resources/bigdl-version-info.properties +++ b/scala/dllib/src/main/resources/bigdl-version-info.properties @@ -16,4 +16,4 @@ #BigDL version info config -version=0.4.0-SNAPSHOT \ No newline at end of file +version=0.5.0-SNAPSHOT \ No newline at end of file From 91024dd846f0dcfc103aa1cf94c33cc4ecc220f6 Mon Sep 17 00:00:00 2001 From: Guoqiong Song Date: Mon, 18 Dec 2017 12:54:21 -0800 Subject: [PATCH 0611/1065] Locallyconnected1D (#1964) * add LocallyConnected1D, address review comments, resolve conflicts, add serializer test --- .../bigdl/dllib/nn/LocallyConnected1D.scala | 484 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 31 ++ .../dllib/keras/LocallyConnected1DSpec.scala | 140 +++++ 3 files changed, 655 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala new file mode 100644 index 00000000000..e8aa07e34ce --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala @@ -0,0 +1,484 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// scalastyle:off +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Engine, T, Table} + +import scala.concurrent.Future +import scala.reflect.ClassTag + +/* + * The `LocallyConnected1D` layer works similarly to + * the `TemporalConvolution` layer, except that weights are unshared, + * that is, a different set of filters is applied at each different patch + * of the input. + * The input tensor in `forward(input)` is expected to be a 2D tensor + * (`nInputFrame` x `inputFrameSize`) or a 3D tensor + * (`nBatchFrame` x `nInputFrame` x `inputFrameSize`). + * + * @param nInputFrame the input frame channel + * @param inputFrameSize The input frame size expected in sequences given into `forward()`. + * @param outputFrameSize The output frame size the convolution layer will produce. + * @param kernelW The kernel width of the convolution + * @param strideW The step of the convolution in the width dimension. + * @param propagateBack Whether propagate gradient back, default is true. + * @param wRegularizer instance of [[Regularizer]] + * (eg. L1 or L2 regularization), applied to the input weights matrices. + * @param bRegularizer instance of [[Regularizer]] + * applied to the bias. + * @param initWeight Initial weight + * @param initBias Initial bias + * @param initGradWeight Initial gradient weight + * @param initGradBias Initial gradient bias + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, + val inputFrameSize: Int, + val outputFrameSize: Int, + val kernelW: Int, + val strideW: Int = 1, + val propagateBack: Boolean = true, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val initWeight: Tensor[T] = null, + val initBias: Tensor[T] = null, + val initGradWeight: Tensor[T] = null, + val initGradBias: Tensor[T] = null + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { + + val nOutputFrame = (nInputFrame - kernelW) / strideW + 1 + + val weight: Tensor[T] = if (initWeight != null) { + initWeight + } else { + Tensor[T](nOutputFrame, outputFrameSize, inputFrameSize * kernelW) + } + + val bias: Tensor[T] = if (initBias != null) { + initBias + } else { + Tensor[T](nOutputFrame, outputFrameSize) + } + + val gradWeight: Tensor[T] = if (initGradWeight != null) { + initGradWeight + } else { + Tensor[T](nOutputFrame, outputFrameSize, inputFrameSize * kernelW) + } + + val gradBias: Tensor[T] = if (initGradBias != null) { + initGradBias + } else { + Tensor[T](nOutputFrame, outputFrameSize) + } + + @transient protected var inputWindow: Tensor[T] = _ + @transient protected var outputWindow: Tensor[T] = _ + @transient protected var weightWindow: Tensor[T] = _ + @transient protected var biasWindow: Tensor[T] = _ + + @transient protected var gradInputWindow: Tensor[T] = _ + @transient protected var gradOutputWindow: Tensor[T] = _ + @transient protected var gradWeightWindow: Tensor[T] = _ + + { + val stdv = 1.0 / math.sqrt(kernelW * inputFrameSize) + val wInit: InitializationMethod = RandomUniform(-stdv, stdv) + val bInit: InitializationMethod = RandomUniform(-stdv, stdv) + setInitMethod(wInit, bInit) + } + + @transient + protected var results: Array[Future[Unit]] = _ + + override def reset(): Unit = { + if (initWeight == null) { + weightInitMethod.init(weight, VariableFormat.OUT_IN) + } + if (initBias == null) { + biasInitMethod.init(bias, VariableFormat.ONE_D) + } + zeroGradParameters() + } + + def reshapeInput(input: Tensor[T]): Tensor[T] = { + if (input.dim() == 2) { + input.reshape(Array(1, input.size(1), input.size(2))) + } else { + input + } + } + + def reshapeOutput(input: Tensor[T], output: Tensor[T]): Tensor[T] = { + if (input.dim() == 2) { + output.reshape(Array(output.size(2), output.size(3))) + } else { + output + } + } + + override def updateOutput(_input: Tensor[T]): Tensor[T] = { + // Require input of 2 dimensions or 3 dimensions + // 2d input format: time x feature + // 3d input format: batch x time x feature + require(_input.dim() == 2 || _input.dim() == 3, + "LocallyConvolution1D: 2D or 3D(batch mode) tensor expected for input, " + + s"but got ${_input.dim()}") + // Require input to be contiguous + require(_input.isContiguous()) + + val input = reshapeInput(_input) + + var dimSeq = input.dim() - 1 // 1 + var dimFeat = dimSeq + 1 // 2 + + val nInputFrame = input.size(dimSeq) // 10 + var nOutputFrame = (nInputFrame - kernelW) / strideW + 1 // (10 -3)/1 +1 = 8 + + if (inputWindow == null) inputWindow = Tensor[T]() + if (outputWindow == null) outputWindow = Tensor[T]() + if (weightWindow == null) weightWindow = Tensor[T]() + if (biasWindow == null) biasWindow = Tensor[T]() + + // Shape check on input with inputFrameSize and kernelW + require(input.size(dimFeat) == inputFrameSize, "Invalid input frame size. Got: " + + s"${input.size(dimFeat)}, Expected: $inputFrameSize") + require(nOutputFrame >= 1, "Input sequence smaller than kernel size. Got: " + + s"$nInputFrame, Expected: $kernelW") + + val batchSize = input.size(1) + val pageSize = weight.size(2) * weight.size(3) + + output.resize(batchSize, nOutputFrame, outputFrameSize) + + if (results == null || results.length != batchSize) { + results = new Array[Future[Unit]](batchSize) + } + + var i = 0 + while (i < batchSize) { + + results(i) = Engine.model.invoke(() => { + + val inputSample = input.select(1, i + 1) + val outputSample = output.select(1, i + 1) + // Add bias first + + var j = 1 + while (j < nOutputFrame) { + biasWindow = bias.select(1, j) + outputWindow = outputSample.select(dimSeq - 1, j) // setup up bias for each ouputframe + outputWindow.copy(biasWindow) + j += 1 + } + + // Add the convolution part + j = 0 + while (j < nOutputFrame) { + inputWindow.set(inputSample.storage(), inputSample.storageOffset() + + j * strideW * input.size(dimFeat), + Array(1, kernelW * input.size(dimFeat)), + Array(1, 1)) + + outputWindow.set(outputSample.storage(), outputSample.storageOffset() + + j * output.size(dimFeat), + Array(1, output.size(dimFeat)), + Array(1, 1)) + + val weightT = weightWindow.set(weight.storage(), weight.storageOffset() + + j * pageSize, + Array(output.size(dimFeat), kernelW * input.size(dimFeat)), + Array(kernelW * input.size(dimFeat), 1) + ).transpose(1, 2) + + outputWindow.addmm(ev.fromType[Int](1), outputWindow, + ev.fromType[Int](1), inputWindow, weightT) + + j += 1 + } + }) + i += 1 + } + + output = reshapeOutput(_input, output) + output + } + + override def updateGradInput(_input: Tensor[T], _gradOutput: Tensor[T]): Tensor[T] = { + + // Require input of 2 dimensions or 3 dimensions + // 2d input format: time x feature + // 3d input format: batch x time x feature + require(_input.dim() == 2 || _input.dim() == 3, + "TemporalConvolution: 2D or 3D(batch mode) tensor expected for input, " + + s"but got ${_input.dim()}") + // Require input to be contiguous + require(_input.isContiguous()) + + val input = reshapeInput(_input) + val gradOutput = reshapeInput(_gradOutput) + + val dimSeq = if (input.dim() == 2) 1 else 2 + val dimFeat = if (input.dim() == 2) 2 else 3 + val nInputFrame = input.size(dimSeq) + var nOutputFrame = (nInputFrame - kernelW) / strideW + 1 + + if (gradInputWindow == null) gradInputWindow = Tensor[T]() + if (gradOutputWindow == null) gradOutputWindow = Tensor[T]() + if (weightWindow == null) weightWindow = Tensor[T]() + + // Shape check on input with inputFrameSize and kernelW + require(input.size(dimFeat) == inputFrameSize, "Invalid input frame size. Got: " + + s"${input.size(dimFeat)}, Expected: $inputFrameSize") + require(nOutputFrame >= 1, "Input sequence smaller than kernel size. Got: " + + s"$nInputFrame, Expected: $kernelW") + + gradInput.resizeAs(input) + gradInput.zero() + + val batchSize = input.size(1) + val pageSize = weight.size(2) * weight.size(3) + + var gradOutputSample = Tensor[T]() + var gradInputSample = Tensor[T]() + + var i = 0 + while (i < batchSize) { + + results(i) = Engine.model.invoke(() => { + + gradInputSample = gradInput.select(1, i + 1) + gradOutputSample = gradOutput.select(1, i + 1) + + var j = 0 + while (j < nOutputFrame) { + + gradOutputWindow.set(gradOutputSample.storage(), gradOutputSample.storageOffset() + + j * gradOutput.size(dimFeat), + Array(1, gradOutput.size(dimFeat)), + Array(1, 1)) + + gradInputWindow.set(gradInputSample.storage(), gradInputSample.storageOffset() + + j * strideW * gradInput.size(dimFeat), + Array(1, kernelW * gradInput.size(dimFeat)), + Array(1, 1)) + + weightWindow.set(weight.storage(), weight.storageOffset() + j * pageSize, + Array(output.size(dimFeat), kernelW * input.size(dimFeat)), + Array(kernelW * input.size(dimFeat), 1) + ) + + gradInputWindow.addmm(ev.fromType[Int](1), gradInputWindow, + ev.fromType[Int](1), gradOutputWindow, weightWindow) + j += 1 + } + }) + i += 1 + } + + gradInput = reshapeOutput(_gradOutput, gradInput) + gradInput + } + + override def accGradParameters(_input: Tensor[T], _gradOutput: Tensor[T]): Unit = { + + // Require input of 2 dimensions or 3 dimensions + require(_input.nDimension() == 2 || _input.nDimension() == 3, + "Only support 2D or 3D input, " + + s"input ${_input.nDimension()}") + // Require input to be contiguous + require(_gradOutput.isContiguous()) + + val input = reshapeInput(_input) + val gradOutput = reshapeInput(_gradOutput) + + val dimSeq = if (input.dim() == 2) 1 else 2 + val dimFeat = if (input.dim() == 2) 2 else 3 + val nInputFrame = input.size(dimSeq) + var nOutputFrame = (nInputFrame - kernelW) / strideW + 1 + + if (gradOutputWindow == null) gradOutputWindow = Tensor[T]() + if (inputWindow == null) inputWindow = Tensor[T]() + if (gradWeightWindow == null) gradWeightWindow = Tensor[T]() + if (biasWindow == null) biasWindow = Tensor[T]() + + val batchSize = input.size(1) + + var gradOutputSample = Tensor[T]() + var inputSample = Tensor[T]() + + var i = 0 + while (i < batchSize) { + results(i) = Engine.model.invoke(() => { + gradOutputSample = gradOutput.select(1, i + 1) + inputSample = input.select(1, i + 1) + + var j = 0 + while (j < nOutputFrame) { + biasWindow.set(gradBias.storage(), gradBias.storageOffset() + j * gradOutput.size(dimFeat), + Array(1, gradOutput.size(dimFeat)), + Array(1, 1)) + gradOutputWindow.set(gradOutputSample.select(1, j + 1)) + biasWindow.add(biasWindow, ev.fromType[Double](scaleB), gradOutputWindow) + j += 1 + } + + j = 0 + while (j < nOutputFrame) { + inputWindow.set(inputSample.storage(), inputSample.storageOffset() + + j * strideW * input.size(dimFeat), + Array(1, kernelW * input.size(dimFeat)), + Array(1, 1)) + + gradOutputWindow.set(gradOutputSample.storage(), gradOutputSample.storageOffset() + + j * gradOutput.size(dimFeat), + Array(1, gradOutput.size(dimFeat)), + Array(1, 1)) + + val gradOutputWindowT = gradOutputWindow.transpose(1, 2) + + val pageSize = weight.size(2) * weight.size(3) + gradWeightWindow.set(gradWeight.storage(), gradWeight.storageOffset() + + j * pageSize, + Array(gradOutput.size(dimFeat), kernelW * input.size(dimFeat)), + Array(kernelW * input.size(dimFeat), 1)) + + gradWeightWindow.addmm(ev.fromType[Int](1), gradWeightWindow, ev.fromType[Double](scaleW), + gradOutputWindowT, inputWindow) + j += 1 + } + }) + + i += 1 + } + + if (null != wRegularizer) { + wRegularizer.accRegularization(weight, gradWeight, scaleW) + } + if (null != bRegularizer) { + bRegularizer.accRegularization(bias, gradBias, scaleB) + } + } + + override def updateParameters(learningRate: T): Unit + + = { + weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) + bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) + } + + override def zeroGradParameters(): Unit + + = { + gradWeight.zero() + gradBias.zero() + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) + + = { + (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) + } + + override def getParametersTable(): Table + + = { + T(getName() -> T("weight" -> weight, "bias" -> bias, + "gradWeight" -> gradWeight, "gradBias" -> gradBias)) + } + + override def equals(obj: Any): Boolean + + = { + if (!super.equals(obj)) { + return false + } + if (!obj.isInstanceOf[TemporalConvolution[T]]) { + return false + } + val other = obj.asInstanceOf[TemporalConvolution[T]] + if (this.eq(other)) { + return true + } + + inputFrameSize == other.inputFrameSize && + outputFrameSize == other.outputFrameSize && + kernelW == other.kernelW && + strideW == other.strideW && + propagateBack == other.propagateBack && + weight == other.weight && + bias == other.bias && + gradWeight == other.gradWeight && + gradBias == other.gradBias + } + + override def hashCode(): Int + + = { + val seed = 37 + var hash = super.hashCode() + hash = hash * seed + inputFrameSize.hashCode() + hash = hash * seed + outputFrameSize.hashCode() + hash = hash * seed + kernelW.hashCode() + hash = hash * seed + strideW.hashCode() + hash = hash * seed + weight.hashCode() + hash = hash * seed + bias.hashCode() + hash = hash * seed + gradWeight.hashCode() + hash = hash * seed + gradBias.hashCode() + + hash + } + + override def clearState(): this.type + + = { + super.clearState() + this + } + + override def toString(): String + + = { + s"nn.TemporalConvolution($inputFrameSize -> $outputFrameSize, $kernelW x $strideW)" + } +} + +object LocallyConnected1D { + def apply[@specialized(Float, Double) T: ClassTag]( + nInputFrame: Int, + inputFrameSize: Int, + outputFrameSize: Int, + kernelW: Int, + strideW: Int = 1, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null + )(implicit ev: TensorNumeric[T]): LocallyConnected1D[T] = { + new LocallyConnected1D[T](nInputFrame, inputFrameSize, outputFrameSize, kernelW, + strideW, propagateBack, + wRegularizer, bRegularizer, initWeight, initBias, initGradWeight, initGradBias) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 3c59942f23e..ee2458e4218 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1217,6 +1217,37 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ) } + def createLocallyConnected1D( + nInputFrame: Int, + inputFrameSize: Int, + outputFrameSize: Int, + kernelW: Int, + strideW: Int = 1, + propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: JTensor = null, + initBias: JTensor = null, + initGradWeight: JTensor = null, + initGradBias: JTensor = null + ) + : LocallyConnected1D[T] = { + LocallyConnected1D[T]( + nInputFrame, + inputFrameSize, + outputFrameSize, + kernelW, + strideW, + propagateBack, + wRegularizer, + bRegularizer, + toTensor(initWeight), + toTensor(initBias), + toTensor(initGradWeight), + toTensor(initGradBias) + ) + } + def createBinaryTreeLSTM( inputSize: Int, hiddenSize: Int, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala new file mode 100644 index 00000000000..867c34cc078 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala @@ -0,0 +1,140 @@ +// scalastyle:off +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and +// * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.{HardSigmoid, LocallyConnected1D, Module} +import com.intel.analytics.bigdl.tensor.Tensor + + +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +class LocallyConnected1DSpec extends KerasBaseSpec { + + "LocallyConnected1D" should "be ok" in { + ifskipTest() + val kerasCode = + // """ + // |input_tensor = Input(shape=[100,10]) + // |input = input = np.random.rand(1,100,10) + // |output_tensor = LocallyConnected1D(2,3,subsample_length=3,input_shape=(100,10))(input_tensor) + // |model = Model(input=input_tensor, output=output_tensor) + // """.stripMargin + // val locallyConnected1d = LocallyConnected1D[Float](100, 10, 2, 3, 3) + + """ + |input_tensor = Input(shape=[6,2]) + |input = np.array([[[1,2], [2,3], [3,4],[4,5],[5,6],[6,7]]]) + |weights = [np.array([[[1],[2]],[[3],[4]],[[5],[6]],[[7],[8]],[[9],[10]]]),np.zeros([5,1])] + |output_tensor = LocallyConnected1D(2,3,subsample_length=1,input_shape=(6,2))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val locallyConnected1d = LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) + + + val wc = (data: Array[Tensor[Float]]) => { + + val out = new Array[Tensor[Float]](data.length) + val d1l: Int = data(0).size(1) + val d2l: Int = data(0).size(2) + val d3l: Int = data(0).size(3) + + out(0) = Tensor(d1l, d3l, d2l) + + val page: Int = d2l * d3l + for (i <- 0 to d1l * d2l * d3l - 1) { + val d1 = i / page + 1 + val d2 = (i % page) / (d3l) + 1 + val d3 = (i % page) % d3l + 1 + val v = data(0).valueAt(d1, d2, d3) + out(0).setValue(d1, d3, d2, v) + } + + if (data.length > 1) { + out(1) = data(1) + } + out + } + + checkOutputAndGrad(locallyConnected1d, kerasCode, wc) + + } + + + "LocallyConnected1D reshape" should "be ok" in { + + val locallyConnected1d = LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) + + val d1l = 2 + val d2l = 3 + + val _input = Tensor[Float](d1l, d2l) + val _output = Tensor[Float](1, d1l, d2l) + + for (i <- 0 to d1l * d2l - 1) { + val d1 = i / d2l + 1 + val d2 = i % d2l + 1 + _input.setValue(d1, d2, i) + _output.setValue(1, d1, d2, i) + } + + + val input = locallyConnected1d.reshapeInput(_input) + val output = locallyConnected1d.reshapeOutput(_input, _output) + + val size = Array(1, d1l, d2l) + + input.size() should be(size) + _input.storage().map(x => x.toInt).toArray should be(input.storage().map(x => x.toInt).toArray) + + output.size() should be(Array(d1l, d2l)) + _output.storage().map(x => x.toInt).toArray should be(output.storage().map(x => x.toInt).toArray) + } + + + "LocallyConnected1D serializer" should "be ok" in { + val module = LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) + + val input = Tensor[Float](6, 2).randn() + + val res1 = module.forward(input).clone() + val tmpFile = java.io.File.createTempFile("module", ".bigdl") + module.saveModule(tmpFile.getAbsolutePath, null, true) + val loaded = Module.loadModule[Float](tmpFile.getAbsolutePath) + val res2 = loaded.forward(input) + res1 should be(res2) + if (tmpFile.exists()) { + tmpFile.delete() + } + } + +} \ No newline at end of file From f9f6476b5f65a509a95325ec48d89ffd4e3a9352 Mon Sep 17 00:00:00 2001 From: Guoqiong Song Date: Mon, 18 Dec 2017 22:53:59 -0800 Subject: [PATCH 0612/1065] serilizer (#2056) --- .../dllib/keras/LocallyConnected1DSpec.scala | 37 +------------------ .../serializer/ModuleSerializerSpec.scala | 7 ++++ 2 files changed, 8 insertions(+), 36 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala index 867c34cc078..21adee2fd4f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala @@ -1,4 +1,3 @@ -// scalastyle:off /* * Copyright 2016 The BigDL Authors. * @@ -17,27 +16,10 @@ package com.intel.analytics.bigdl.keras -import com.intel.analytics.bigdl.nn.{HardSigmoid, LocallyConnected1D, Module} +import com.intel.analytics.bigdl.nn.LocallyConnected1D import com.intel.analytics.bigdl.tensor.Tensor -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - - class LocallyConnected1DSpec extends KerasBaseSpec { "LocallyConnected1D" should "be ok" in { @@ -120,21 +102,4 @@ class LocallyConnected1DSpec extends KerasBaseSpec { _output.storage().map(x => x.toInt).toArray should be(output.storage().map(x => x.toInt).toArray) } - - "LocallyConnected1D serializer" should "be ok" in { - val module = LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) - - val input = Tensor[Float](6, 2).randn() - - val res1 = module.forward(input).clone() - val tmpFile = java.io.File.createTempFile("module", ".bigdl") - module.saveModule(tmpFile.getAbsolutePath, null, true) - val loaded = Module.loadModule[Float](tmpFile.getAbsolutePath) - val res2 = loaded.forward(input) - res1 should be(res2) - if (tmpFile.exists()) { - tmpFile.delete() - } - } - } \ No newline at end of file diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index a0e69c6d2e9..efd952d229a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -182,6 +182,13 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(spatialDropout3D, input) } + "LocallyConnected1D serializer" should "work properly" in { + val localyConnected1d = + LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) + val input = Tensor[Float](6, 2).randn() + runSerializationTest(localyConnected1d, input) + } + "BinaryTreeLSTM serializer" should "work properly" in { RNG.setSeed(1000) From 26a175b69553d632690a29d97f9ad23f41071327 Mon Sep 17 00:00:00 2001 From: Wang Yanzhang Date: Wed, 20 Dec 2017 11:33:13 -0500 Subject: [PATCH 0613/1065] fix: style check error of LocallyConnected1D. --- .../dllib/keras/LocallyConnected1DSpec.scala | 29 +++++++++++-------- 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala index 21adee2fd4f..9a5061cf484 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1DSpec.scala @@ -11,7 +11,7 @@ * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and -// * limitations under the License. + * limitations under the License. */ package com.intel.analytics.bigdl.keras @@ -25,13 +25,15 @@ class LocallyConnected1DSpec extends KerasBaseSpec { "LocallyConnected1D" should "be ok" in { ifskipTest() val kerasCode = - // """ - // |input_tensor = Input(shape=[100,10]) - // |input = input = np.random.rand(1,100,10) - // |output_tensor = LocallyConnected1D(2,3,subsample_length=3,input_shape=(100,10))(input_tensor) - // |model = Model(input=input_tensor, output=output_tensor) - // """.stripMargin - // val locallyConnected1d = LocallyConnected1D[Float](100, 10, 2, 3, 3) + /* + """ + |input_tensor = Input(shape=[100,10]) + |input = input = np.random.rand(1,100,10) + |output_tensor=LocallyConnected1D(2,3,subsample_length=3,input_shape=(100,10))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + */ +// val locallyConnected1d = LocallyConnected1D[Float](100, 10, 2, 3, 3) """ |input_tensor = Input(shape=[6,2]) @@ -40,7 +42,8 @@ class LocallyConnected1DSpec extends KerasBaseSpec { |output_tensor = LocallyConnected1D(2,3,subsample_length=1,input_shape=(6,2))(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val locallyConnected1d = LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) + val locallyConnected1d = LocallyConnected1D[Float](6, 2, outputFrameSize = 2, + kernelW = 3, strideW = 1) val wc = (data: Array[Tensor[Float]]) => { @@ -74,7 +77,8 @@ class LocallyConnected1DSpec extends KerasBaseSpec { "LocallyConnected1D reshape" should "be ok" in { - val locallyConnected1d = LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) + val locallyConnected1d = LocallyConnected1D[Float](6, 2, outputFrameSize = 2, + kernelW = 3, strideW = 1) val d1l = 2 val d2l = 3 @@ -99,7 +103,8 @@ class LocallyConnected1DSpec extends KerasBaseSpec { _input.storage().map(x => x.toInt).toArray should be(input.storage().map(x => x.toInt).toArray) output.size() should be(Array(d1l, d2l)) - _output.storage().map(x => x.toInt).toArray should be(output.storage().map(x => x.toInt).toArray) + _output.storage().map(x => x.toInt).toArray should be( + output.storage().map(x => x.toInt).toArray) } -} \ No newline at end of file +} From a7874f3b7edefd752ef24fdae35f200cc06ca23e Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 20 Dec 2017 16:18:07 +0800 Subject: [PATCH 0614/1065] Caffe filter out validation (#2051) * filter out validation * refinement --- .../analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala | 4 ++-- .../analytics/bigdl/dllib/utils/caffe/Converter.scala | 6 ++++-- scala/dllib/src/test/resources/caffe/test.prototxt | 7 +++++++ 3 files changed, 13 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 77d27c6409d..46be354559c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -293,7 +293,7 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, (module, criterions) } - private val dataLayerList = Array("INPUT", "DATA", "DUMMYDATA", "ANNOTATEDDATA") + private val dataLayerList = Array("INPUT", "DATA", "DUMMYDATA", "ANNOTATEDDATA", "MEMORYDATA") private def tryConvertInput(layer: GeneratedMessage, layerType: String, layers: ArrayBuffer[ModuleNode[T]], @@ -446,7 +446,7 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } }) }) - layers + layers.filter(layer => !(layer.prevNodes.isEmpty && layer.nextNodes.isEmpty)) } private def convertCaffeLayer(layer : GeneratedMessage): Seq[ModuleNode[T]] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index ec9cfb93da7..741e062b5a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -637,8 +637,8 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { caffe2BigDL("POOLING") = fromCaffePooling caffe2BigDL("DROPOUT") = fromCaffeDropout caffe2BigDL("SOFTMAX") = fromCaffeSoftmax - caffe2BigDL("SOFTMAX_LOSS") = fromCaffeSoftmax - caffe2BigDL("SOFTMAXWITHLOSS") = fromCaffeSoftmax + caffe2BigDL("SOFTMAX_LOSS") = null + caffe2BigDL("SOFTMAXWITHLOSS") = null caffe2BigDL("TANH") = fromCaffeTanh caffe2BigDL("SIGMOID") = fromCaffeSigmoid caffe2BigDL("SIGMOIDCROSSENTROPYLOSS") = fromCaffeSigmoid @@ -664,6 +664,8 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { caffe2BigDL("DATA") = fromCaffeInput caffe2BigDL("DUMMYDATA") = fromCaffeInput caffe2BigDL("ANNOTATEDDATA") = fromCaffeInput + caffe2BigDL("MEMORYDATA") = fromCaffeInput + caffe2BigDL("ACCURACY") = null caffe2BigDL("SILENCE") = null } } diff --git a/scala/dllib/src/test/resources/caffe/test.prototxt b/scala/dllib/src/test/resources/caffe/test.prototxt index 3418d2cb9da..5cfcd173da8 100644 --- a/scala/dllib/src/test/resources/caffe/test.prototxt +++ b/scala/dllib/src/test/resources/caffe/test.prototxt @@ -56,6 +56,13 @@ layer { bottom : "out" top : "dummy" } +layer { + name: "prob" + type: "Softmax" + bottom: "dummy" + top: "prob" +} + layer { name: "loss" type: "SoftmaxWithLoss" From 6495bbf3f70ab33aa4db2fc92ff43f8c3ad56217 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Thu, 21 Dec 2017 11:17:15 +0800 Subject: [PATCH 0615/1065] Map Keras LocallyConnected1D (#2060) * add locallyconnected1d * fix style --- .../bigdl/dllib/nn/LocallyConnected1D.scala | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala index e8aa07e34ce..80255ad26ba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala @@ -14,7 +14,6 @@ * limitations under the License. */ -// scalastyle:off package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule} @@ -63,7 +62,8 @@ class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, val initBias: Tensor[T] = null, val initGradWeight: Tensor[T] = null, val initGradBias: Tensor[T] = null - )(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { + )(implicit ev: TensorNumeric[T]) + extends TensorModule[T] with Initializable { val nOutputFrame = (nInputFrame - kernelW) / strideW + 1 @@ -272,13 +272,13 @@ class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, var j = 0 while (j < nOutputFrame) { - gradOutputWindow.set(gradOutputSample.storage(), gradOutputSample.storageOffset() + - j * gradOutput.size(dimFeat), + gradOutputWindow.set(gradOutputSample.storage(), + gradOutputSample.storageOffset() + j * gradOutput.size(dimFeat), Array(1, gradOutput.size(dimFeat)), Array(1, 1)) - gradInputWindow.set(gradInputSample.storage(), gradInputSample.storageOffset() + - j * strideW * gradInput.size(dimFeat), + gradInputWindow.set(gradInputSample.storage(), + gradInputSample.storageOffset() + j * strideW * gradInput.size(dimFeat), Array(1, kernelW * gradInput.size(dimFeat)), Array(1, 1)) @@ -334,7 +334,8 @@ class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, var j = 0 while (j < nOutputFrame) { - biasWindow.set(gradBias.storage(), gradBias.storageOffset() + j * gradOutput.size(dimFeat), + biasWindow.set(gradBias.storage(), + gradBias.storageOffset() + j * gradOutput.size(dimFeat), Array(1, gradOutput.size(dimFeat)), Array(1, 1)) gradOutputWindow.set(gradOutputSample.select(1, j + 1)) @@ -349,8 +350,8 @@ class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, Array(1, kernelW * input.size(dimFeat)), Array(1, 1)) - gradOutputWindow.set(gradOutputSample.storage(), gradOutputSample.storageOffset() + - j * gradOutput.size(dimFeat), + gradOutputWindow.set(gradOutputSample.storage(), + gradOutputSample.storageOffset() + j * gradOutput.size(dimFeat), Array(1, gradOutput.size(dimFeat)), Array(1, 1)) @@ -362,8 +363,8 @@ class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, Array(gradOutput.size(dimFeat), kernelW * input.size(dimFeat)), Array(kernelW * input.size(dimFeat), 1)) - gradWeightWindow.addmm(ev.fromType[Int](1), gradWeightWindow, ev.fromType[Double](scaleW), - gradOutputWindowT, inputWindow) + gradWeightWindow.addmm(ev.fromType[Int](1), gradWeightWindow, + ev.fromType[Double](scaleW), gradOutputWindowT, inputWindow) j += 1 } }) @@ -476,7 +477,8 @@ object LocallyConnected1D { initBias: Tensor[T] = null, initGradWeight: Tensor[T] = null, initGradBias: Tensor[T] = null - )(implicit ev: TensorNumeric[T]): LocallyConnected1D[T] = { + )(implicit ev: TensorNumeric[T]): + LocallyConnected1D[T] = { new LocallyConnected1D[T](nInputFrame, inputFrameSize, outputFrameSize, kernelW, strideW, propagateBack, wRegularizer, bRegularizer, initWeight, initBias, initGradWeight, initGradBias) From 1bcd0f354a74e34609e9ffd38412c02f7972bda8 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 21 Dec 2017 15:30:50 +0800 Subject: [PATCH 0616/1065] Fix KLDCriterion forward (#2078) * fix KLDCriterion * add python doc * add unit tests * add api doc --- .../bigdl/dllib/nn/KLDCriterion.scala | 26 ++++++++------- .../{torch => nn}/KLDCriterionSpec.scala | 32 ++++++++++++++++--- 2 files changed, 42 insertions(+), 16 deletions(-) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{torch => nn}/KLDCriterionSpec.scala (66%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala index 87c6da448f4..5024f33bc40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala @@ -15,8 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import breeze.numerics.exp -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractCriterion, AbstractModule} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractCriterion import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} @@ -24,7 +23,10 @@ import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.ClassTag /** - * Computes the KL-divergence of the Gaussian distribution. + * Computes the KL-divergence of the input normal distribution to a standard normal distribution. + * The input has to be a table. The first element of input is the mean of the distribution, + * the second element of input is the log_variance of the distribution. The input distribution is + * assumed to be diagonal. */ class KLDCriterion[@specialized(Float, Double) T: ClassTag]( implicit ev: TensorNumeric[T]) extends AbstractCriterion[Table, Tensor[T], T] { @@ -32,24 +34,24 @@ class KLDCriterion[@specialized(Float, Double) T: ClassTag]( @transient private var mean: Tensor[T] = null @transient - private var vari: Tensor[T] = null + private var logVar: Tensor[T] = null @transient - private var expVar: Tensor[T] = null + private var vars: Tensor[T] = null override def updateOutput(input: Table, target: Tensor[T]): T = { if (mean == null) mean = Tensor[T]() - if (vari == null) vari = Tensor[T]() - if (expVar == null) expVar = Tensor[T]() + if (logVar == null) logVar = Tensor[T]() + if (vars == null) vars = Tensor[T]() mean.resizeAs(input[Tensor[T]](1)).copy(input(1)) - vari.resizeAs(input[Tensor[T]](2)).copy(input(2)) + logVar.resizeAs(input[Tensor[T]](2)).copy(input(2)) - // Appendix B from VAE paper: 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2) + // Appendix B from VAE paper: -0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2) mean.pow(ev.fromType(2)) - expVar.resizeAs(vari).copy(vari) - expVar.exp().add(ev.one).add(ev.fromType(-1), mean).add(ev.fromType(-1), vari) + vars.resizeAs(logVar).copy(logVar).exp() + logVar.add(ev.one).add(ev.fromType(-1), mean).add(ev.fromType(-1), vars) - output = ev.times(ev.fromType(0.5), expVar.sum()) + output = ev.times(ev.fromType(-0.5), logVar.sum()) output } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/KLDCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterionSpec.scala similarity index 66% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/KLDCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterionSpec.scala index 53de9615f7c..5dfc2c6ea36 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/KLDCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterionSpec.scala @@ -14,10 +14,9 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.{Add, KLDCriterion} -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -39,7 +38,7 @@ class KLDCriterionSpec extends FlatSpec with Matchers{ val loss = model.forward(input, target) val gradInput = model.backward(input, target) - loss should be(5.54158f +- 1e-3f) + loss should be(0.991884f +- 1e-3f) val gardTarget1 = Tensor(Array(0.54340494f, 0.67115563f, 0.2783694f, 0.4120464f, 0.4245176f, 0.52638245f), Array(2, 3)) @@ -50,4 +49,29 @@ class KLDCriterionSpec extends FlatSpec with Matchers{ gradInput[Tensor[Float]](1) should be(gardTarget1) gradInput[Tensor[Float]](2) should be(gardTarget2) } + + "A KLDCriterion Module with standard normal input" should "generate correct output and grad" in { + val seed = 100 + RNG.setSeed(seed) + val model = KLDCriterion[Float]() + + RNG.setSeed(seed) + val input1 = Tensor[Float](2, 3).fill(0.0f) + val input2 = Tensor[Float](2, 3).fill(0.0f) + val input = T(input1, input2) + + val target = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + + val loss = model.forward(input, target) + val gradInput = model.backward(input, target) + + loss should be(0.0f) + + val gardTarget1 = Tensor[Float](2, 3).fill(0.0f) + + val gardTarget2 = Tensor[Float](2, 3).fill(0.0f) + + gradInput[Tensor[Float]](1) should be(gardTarget1) + gradInput[Tensor[Float]](2) should be(gardTarget2) + } } From 528258cc231befb864bb70084be033eee12b289a Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 22 Dec 2017 19:52:52 +0800 Subject: [PATCH 0617/1065] Add transformer doc and some fix (#2092) --- .../transform/vision/image/Convertor.scala | 22 ++++++----- .../transform/vision/image/ImageFeature.scala | 4 +- .../transform/vision/image/ImageFrame.scala | 4 +- .../image/augmentation/PixelNormalizer.scala | 3 +- .../vision/image/augmentation/Resize.scala | 22 ++++++++--- .../vision/image/opencv/OpenCVMat.scala | 37 ++++++++++-------- .../dllib/utils/python/api/PythonBigDL.scala | 30 +++++++++----- .../vision/image/ConvertorSpec.scala | 34 ++++++++++++++++ .../image/augmentation/ResizeSpec.scala | 13 +++++++ .../vision/image/opencv/OpenCVMatSpec.scala | 39 +++++++++++++++++++ 10 files changed, 162 insertions(+), 46 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala index 2054f45d841..1f2be7c3299 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala @@ -26,22 +26,23 @@ import scala.reflect._ /** * Transform byte array(original image file in byte) to OpenCVMat + * @param byteKey key that maps byte array */ -class BytesToMat() +class BytesToMat(byteKey: String = ImageFeature.bytes) extends FeatureTransformer { override def transform(feature: ImageFeature): ImageFeature = { - BytesToMat.transform(feature) + BytesToMat.transform(feature, byteKey) } } object BytesToMat { val logger = Logger.getLogger(getClass) - def apply(): BytesToMat = new BytesToMat() + def apply(byteKey: String = ImageFeature.bytes): BytesToMat = new BytesToMat(byteKey) - def transform(feature: ImageFeature): ImageFeature = { + def transform(feature: ImageFeature, byteKey: String): ImageFeature = { if (!feature.isValid) return feature - val bytes = feature[Array[Byte]](ImageFeature.bytes) + val bytes = feature[Array[Byte]](byteKey) var mat: OpenCVMat = null try { require(null != bytes && bytes.length > 0, "image file bytes should not be empty") @@ -67,9 +68,10 @@ object BytesToMat { * @param validWidth valid width in case the mat is invalid * @param validChannels valid channel in case the mat is invalid * @param outKey key to store float array + * @param shareBuffer share buffer of output */ class MatToFloats(validHeight: Int, validWidth: Int, validChannels: Int, - outKey: String = ImageFeature.floats) + outKey: String = ImageFeature.floats, shareBuffer: Boolean = true) extends FeatureTransformer { @transient private var data: Array[Float] = _ @@ -81,7 +83,7 @@ class MatToFloats(validHeight: Int, validWidth: Int, validChannels: Int, } else { (validHeight, validWidth, validChannels) } - if (null == data || data.length < height * width * channel) { + if (!shareBuffer || null == data || data.length < height * width * channel) { data = new Array[Float](height * width * channel) } if (feature.isValid) { @@ -102,8 +104,8 @@ object MatToFloats { val logger = Logger.getLogger(getClass) def apply(validHeight: Int = 300, validWidth: Int = 300, validChannels: Int = 3, - outKey: String = ImageFeature.floats): MatToFloats = - new MatToFloats(validHeight, validWidth, validChannels, outKey) + outKey: String = ImageFeature.floats, shareBuffer: Boolean = true): MatToFloats = + new MatToFloats(validHeight, validWidth, validChannels, outKey, shareBuffer) } /** @@ -144,7 +146,7 @@ object MatToTensor { } /** - * transform imageframe to samples + * Transforms tensors that map inputKeys and targetKeys to sample * @param inputKeys keys that maps inputs (each input should be a tensor) * @param targetKeys keys that maps targets (each target should be a tensor) * @param sampleKey key to store sample diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala index f521091ad86..835961efb52 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala @@ -312,12 +312,12 @@ object ImageFeature { val boundingBox = "boundingBox" /** - * key: sample + * key: image (and label if available) stored as Sample */ val sample = "sample" /** - * key: Image Tensor + * key: image pixels in Tensor */ val imageTensor = "imageTensor" diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala index 545fa2abe37..a7a527c1b1d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -145,8 +145,8 @@ object ImageFrame { val uri = row.getAs[String](ImageFeature.uri) val image = row.getAs[Array[Byte]](ImageFeature.bytes) ImageFeature(image, uri = uri) - }).map(BytesToMat.transform) - ImageFrame.rdd(images) + }) + (ImageFrame.rdd(images) -> BytesToMat()).toDistributed() } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/PixelNormalizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/PixelNormalizer.scala index 452602930bd..977ec357289 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/PixelNormalizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/PixelNormalizer.scala @@ -38,7 +38,8 @@ class PixelNormalizer(means: Array[Float]) extends FeatureTransformer { data = new Array[Float](means.length) } require(data.length == openCVMat.height() * openCVMat.width() * openCVMat.channels(), - "the means provided must have the same length as image") + s"the means (${means.length}) provided must have the same length as image" + + s" ${openCVMat.height() * openCVMat.width() * openCVMat.channels()}") openCVMat.get(0, 0, data) require(means.length == data.length, s"Image size expected :" + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala index 472d4fd4136..cb80934ed62 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala @@ -31,9 +31,14 @@ import scala.util.Random * @param resizeMode if resizeMode = -1, random select a mode from * (Imgproc.INTER_LINEAR, Imgproc.INTER_CUBIC, Imgproc.INTER_AREA, * Imgproc.INTER_NEAREST, Imgproc.INTER_LANCZOS4) + * @param useScaleFactor if true, scale factor fx and fy is used, fx = fy = 0 + * note that the result of the following are different + * Imgproc.resize(mat, mat, new Size(resizeWH, resizeWH), 0, 0, Imgproc.INTER_LINEAR) + * Imgproc.resize(mat, mat, new Size(resizeWH, resizeWH)) */ class Resize(resizeH: Int, resizeW: Int, - resizeMode: Int = Imgproc.INTER_LINEAR) + resizeMode: Int = Imgproc.INTER_LINEAR, + useScaleFactor: Boolean = true) extends FeatureTransformer { private val interpMethods = Array(Imgproc.INTER_LINEAR, Imgproc.INTER_CUBIC, Imgproc.INTER_AREA, @@ -45,7 +50,8 @@ class Resize(resizeH: Int, resizeW: Int, } else { resizeMode } - Resize.transform(feature.opencvMat(), feature.opencvMat(), resizeW, resizeH, interpMethod) + Resize.transform(feature.opencvMat(), feature.opencvMat(), resizeW, resizeH, interpMethod, + useScaleFactor) } } @@ -53,13 +59,17 @@ object Resize { val logger = Logger.getLogger(getClass) def apply(resizeH: Int, resizeW: Int, - resizeMode: Int = Imgproc.INTER_LINEAR): Resize = - new Resize(resizeH, resizeW, resizeMode) + resizeMode: Int = Imgproc.INTER_LINEAR, useScaleFactor: Boolean = true): Resize = + new Resize(resizeH, resizeW, resizeMode, useScaleFactor) def transform(input: OpenCVMat, output: OpenCVMat, resizeW: Int, resizeH: Int, - mode: Int = Imgproc.INTER_LINEAR) + mode: Int = Imgproc.INTER_LINEAR, useScaleFactor: Boolean = true) : OpenCVMat = { - Imgproc.resize(input, output, new Size(resizeW, resizeH), 0, 0, mode) + if (useScaleFactor) { + Imgproc.resize(input, output, new Size(resizeW, resizeH), 0, 0, mode) + } else { + Imgproc.resize(input, output, new Size(resizeW, resizeH)) + } output } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala index 87bd0666df4..407e6422090 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala @@ -26,7 +26,7 @@ import org.opencv.imgcodecs.Imgcodecs import org.opencv.imgproc.Imgproc /** - * OpenCVMat is a Serializable wrapper of original Mat + * OpenCVMat is a Serializable wrapper of org.opencv.core.Mat */ class OpenCVMat() extends Mat with Serializable { @@ -37,26 +37,27 @@ class OpenCVMat() extends Mat with Serializable { @throws(classOf[IOException]) private def writeObject(out: ObjectOutputStream): Unit = { - out.writeInt(rows()) - out.writeInt(cols()) - out.writeInt(`type`()) - val size = (elemSize() * rows() * cols()).toInt - out.writeInt(size) - val bytes = new Array[Byte](size) - get(rows(), cols(), bytes) - out.write(bytes) + try { + val bytes = OpenCVMat.imencode(this) + out.writeInt(`type`()) + out.writeObject(bytes) + } catch { + case e: Exception => + out.writeInt(`type`()) + out.writeObject(Array[Byte]()) + } } @throws(classOf[IOException]) private def readObject(input: ObjectInputStream): Unit = { - val rows = input.readInt() - val cols = input.readInt() val t = input.readInt() - val size = input.readInt() - val data = new Array[Byte](size) - input.read(data) - create(rows, cols, t) - put(rows, cols, data) + val data = input.readObject.asInstanceOf[Array[Byte]] + if (data.length == 0) { + create(0, 0, t) + } else { + val mat = OpenCVMat.fromImageBytes(data) + mat.convertTo(this, t) + } } var isReleased: Boolean = false @@ -178,6 +179,10 @@ object OpenCVMat { * @return */ def toBytePixels(input: Mat, buffer: Array[Byte] = null): (Array[Byte], Int, Int) = { + // the mat need to be type CV_8UC3 in order to get pixels byte array + if (input.`type`() != CvType.CV_8UC3) { + input.convertTo(input, CvType.CV_8UC3) + } var bytes = buffer val length = input.channels() * input.height() * input.width() if (null == buffer || buffer.length < length) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ee2458e4218..be4dd63160d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2616,8 +2616,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab HFlip() } - def createResize(resizeH: Int, resizeW: Int, resizeMode: Int = Imgproc.INTER_LINEAR): Resize = { - Resize(resizeH, resizeW, resizeMode) + def createResize(resizeH: Int, resizeW: Int, resizeMode: Int = Imgproc.INTER_LINEAR, + useScaleFactor: Boolean): Resize = { + Resize(resizeH, resizeW, resizeMode, useScaleFactor) } def createColorJitter(brightnessProb: Double = 0.5, brightnessDelta: Double = 32, @@ -2775,8 +2776,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab val feature = new ImageFeature() if (null != data) { val mat = OpenCVMat.fromFloats(data.storage, data.shape(0), data.shape(1)) + feature(ImageFeature.bytes) = OpenCVMat.imencode(mat) feature(ImageFeature.mat) = mat - feature(ImageFeature.size) = mat.shape() + feature(ImageFeature.originalSize) = mat.shape() } if (null != label) { // todo: may need a method to change label format if needed @@ -2804,13 +2806,23 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def distributedImageFrameToPredict(imageFrame: DistributedImageFrame, key: String) : JavaRDD[JList[Any]] = { - imageFrame.rdd.map(x => List[Any](x.uri(), toJTensor(x[Tensor[T]](key))).asJava) + imageFrame.rdd.map(x => { + if (x.isValid && x.contains(key)) { + List[Any](x.uri(), toJTensor(x[Tensor[T]](key))).asJava + } else { + List[Any](x.uri(), null).asJava + } + }) } def localImageFrameToPredict(imageFrame: LocalImageFrame, key: String) : JList[JList[Any]] = { imageFrame.array.map(x => - List[Any](x.uri(), toJTensor(x[Tensor[T]](key))).asJava).toList.asJava + if (x.isValid && x.contains(key)) { + List[Any](x.uri(), toJTensor(x[Tensor[T]](key))).asJava + } else { + List[Any](x.uri(), null).asJava + }).toList.asJava } def localImageFrameToImageTensor(imageFrame: LocalImageFrame, @@ -2848,13 +2860,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ImageFrame.readParquet(path, sqlContext) } - def createBytesToMat(): BytesToMat = { - BytesToMat() + def createBytesToMat(byteKey: String): BytesToMat = { + BytesToMat(byteKey) } def createMatToFloats(validHeight: Int = 300, validWidth: Int = 300, validChannels: Int = 3, - outKey: String = ImageFeature.floats): MatToFloats = - new MatToFloats(validHeight, validWidth, validChannels, outKey) + outKey: String = ImageFeature.floats, shareBuffer: Boolean = true): MatToFloats = + new MatToFloats(validHeight, validWidth, validChannels, outKey, shareBuffer) def createMatToTensor(toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor) : MatToTensor[T] = new MatToTensor[T](toRGB, tensorKey) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ConvertorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ConvertorSpec.scala index 843495dfa6c..d9378959baa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ConvertorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ConvertorSpec.scala @@ -17,11 +17,33 @@ package com.intel.analytics.bigdl.transform.vision.image import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat import org.scalatest.{FlatSpec, Matchers} class ConvertorSpec extends FlatSpec with Matchers { val resource = getClass.getClassLoader.getResource("pascal/") + "MatToFloat" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val imF = data.asInstanceOf[LocalImageFrame].array.head + val float = OpenCVMat.toFloatPixels(imF.opencvMat()) + val data2 = ImageFrame.read(resource.getFile) + val transformer = MatToFloats() + transformer(data2) + data2.toLocal().array(0).floats() should equal(float._1) + } + + "MatToFloat no share" should "work properly" in { + val resource = getClass.getClassLoader.getResource("imagenet/n02110063") + val data = ImageFrame.read(resource.getFile) + val transformer = MatToFloats(shareBuffer = false) + transformer(data) + val array = data.toLocal().array + array(0).floats().equals(array(1).floats()) should be (false) + array(0).floats().equals(array(2).floats()) should be (false) + array(1).floats().equals(array(2).floats()) should be (false) + } + "MatToTensor" should "work properly" in { val data = ImageFrame.read(resource.getFile) val imF = data.asInstanceOf[LocalImageFrame].array.head @@ -32,4 +54,16 @@ class ConvertorSpec extends FlatSpec with Matchers { tensor should be (tensor2) } + "toTensor" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + MatToFloats()(data) + val imF = data.asInstanceOf[LocalImageFrame].array.head + val tensor2 = imF.toTensor(ImageFeature.floats) + + val data2 = ImageFrame.read(resource.getFile) + val transformer = MatToTensor[Float]() + transformer(data2) + val tensor = data2.toLocal().array.head[Tensor[Float]](ImageFeature.imageTensor) + tensor should be (tensor2) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala index f6a57cebc39..1ca264e23f2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala @@ -36,6 +36,19 @@ class ResizeSpec extends FlatSpec with Matchers { println(tmpFile) } + "resize useScaleFactor false" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = Resize(300, 300, useScaleFactor = false) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be(300) + imageFeature.getWidth() should be(300) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + println(tmpFile) + } + "AspectScale" should "work properly" in { val data = ImageFrame.read(resource.getFile) val transformer = AspectScale(750, maxSize = 3000) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala index 0bce05388c7..6929361b8c1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala @@ -22,13 +22,18 @@ import com.intel.analytics.bigdl.opencv.OpenCV import com.intel.analytics.bigdl.transform.vision.image.util.BoundingBox import com.intel.analytics.bigdl.utils.Engine import org.apache.commons.io.FileUtils +import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext +import org.opencv.core.CvType import org.opencv.imgcodecs.Imgcodecs import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class OpenCVMatSpec extends FlatSpec with Matchers with BeforeAndAfter { val resource = getClass().getClassLoader().getResource("pascal/000025.jpg") + Logger.getLogger("org").setLevel(Level.ERROR) + Logger.getLogger("akka").setLevel(Level.ERROR) + Logger.getLogger("breeze").setLevel(Level.ERROR) "toFloatsPixels" should "work properly" in { val img = OpenCVMat.read(resource.getFile) val floats = new Array[Float](img.height() * img.width() * img.channels()) @@ -81,6 +86,16 @@ class OpenCVMatSpec extends FlatSpec with Matchers with BeforeAndAfter { bytes1._1 should equal(bytes2._1) } + "imencode with float type" should "work not affect pixels" in { + val img = OpenCVMat.read(resource.getFile) + OpenCVMat.toFloatPixels(img) + val bytes = OpenCVMat.imencode(img) + val mat = OpenCVMat.fromImageBytes(bytes) + val bytes1 = OpenCVMat.toBytePixels(img) + val bytes2 = OpenCVMat.toBytePixels(mat) + bytes1._1 should equal(bytes2._1) + } + var sc: SparkContext = null before { @@ -95,10 +110,26 @@ class OpenCVMatSpec extends FlatSpec with Matchers with BeforeAndAfter { "serialize" should "work properly" in { val img = OpenCVMat.read(resource.getFile) + val bytes = OpenCVMat.toBytePixels(img) val shape = img.shape() val rdd = sc.parallelize(Array(img)) val collect = rdd.collect() + collect(0).`type`() should be (CvType.CV_8UC3) + val bytes2 = OpenCVMat.toBytePixels(collect(0)) collect(0).shape() should be(shape) + bytes._1 should equal (bytes2._1) + } + + "serialize float mat" should "work properly" in { + val img = OpenCVMat.read(resource.getFile) + val floats = OpenCVMat.toFloatPixels(img) + val shape = img.shape() + val rdd = sc.parallelize(Array(img)) + val collect = rdd.collect() + collect(0).`type`() should be (CvType.CV_32FC3) + val floats2 = OpenCVMat.toFloatPixels(collect(0)) + collect(0).shape() should be(shape) + floats._1 should equal (floats2._1) } "release" should "work properly" in { @@ -108,6 +139,14 @@ class OpenCVMatSpec extends FlatSpec with Matchers with BeforeAndAfter { img.shape() should be (0, 0, 3) } + "empty serialize" should "work properly" in { + OpenCV.isOpenCVLoaded + val img = new OpenCVMat() + val rdd = sc.parallelize(Array(img)) + val out = rdd.collect() + OpenCVMat.toBytePixels(out(0))._1.length should be (0) + } + "drawBoundingBox" should "work properly" in { val img = OpenCVMat.read(resource.getFile) val boundingBox = BoundingBox(2.0f, 84.0f, 59.0f, 248.0f, false) From eb33c7ec3f8bbc75dcf831b835ab9806357ca499 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Wed, 27 Dec 2017 09:45:54 +0800 Subject: [PATCH 0618/1065] Chagne textclassifier readme (#2105) * chagne textclassifier readme * update core --- .../bigdl/dllib/example/textclassification/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/README.md index 2789ce5de31..991302718c2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/textclassification/README.md @@ -49,7 +49,7 @@ ```shell MASTER=xxx.xxx.xxx.xxx:xxxx BASE_DIR=${PWD} # where is the data - spark-submit --master ${MASTER} --driver-memory 5g --executor-memory 5g \ + spark-submit --master ${MASTER} --driver-memory 20g --executor-memory 20g \ --total-executor-cores 32 --executor-cores 8 \ --class com.intel.analytics.bigdl.example.textclassification.TextClassifier \ bigdl-VERSION-jar-with-dependencies.jar --batchSize 128 \ @@ -59,7 +59,7 @@ ```shell BASE_DIR=${PWD} # where is the data - spark-submit --master yarn --driver-memory 5g --executor-memory 5g \ + spark-submit --master yarn --driver-memory 20g --executor-memory 20g \ --num-executor 4 --executor-cores 8 \ --class com.intel.analytics.bigdl.example.textclassification.TextClassifier \ bigdl-VERSION-jar-with-dependencies.jar --batchSize 128 \ From e41a186713e3c0b20d7ae916397418ee90f25234 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 27 Dec 2017 15:07:05 +0800 Subject: [PATCH 0619/1065] fix one node filter issue (#2106) --- .../analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala | 7 ++++--- .../intel/analytics/bigdl/dllib/integration/HdfsSpec.scala | 4 ++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 46be354559c..1f8bab66491 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -283,7 +283,7 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, : (Module[T], ParallelCriterion[T]) = { loadCaffe(prototxtPath, modelPath) registerCustomizedConverter() - val layers = createLayers() + val layers = createLayers(outputNames) val inputs = layers.filter(layer => layer.prevNodes.isEmpty).toArray val outputs = layers.filter(layer => layer.nextNodes.isEmpty || outputNames.contains(layer.element.getName())).toArray @@ -338,7 +338,7 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } // create directed graph based on the module relationships - private def createLayers() : ArrayBuffer[ModuleNode[T]] = { + private def createLayers(outputNames: Array[String]) : ArrayBuffer[ModuleNode[T]] = { val layers = ArrayBuffer[ModuleNode[T]]() val layersMap = new mutable.HashMap[String, ModuleNode[T]]() val top2LayerMap = new mutable.HashMap[String, String]() @@ -446,7 +446,8 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } }) }) - layers.filter(layer => !(layer.prevNodes.isEmpty && layer.nextNodes.isEmpty)) + layers.filter(layer => !(layer.prevNodes.isEmpty && layer.nextNodes.isEmpty) + || outputNames.contains(layer.element.getName)) } private def convertCaffeLayer(layer : GeneratedMessage): Seq[ModuleNode[T]] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala index 978c57ba75a..189116eadb2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/HdfsSpec.scala @@ -182,7 +182,7 @@ class HdfsSpec extends FlatSpec with Matchers with BeforeAndAfter{ input2.resizeAs(input1).copy(input1) - val linear = Linear(10, 10) + val linear = Linear(10, 10).setName("linear") // caffe only supports float, In order to compare the results, here we manually // set weight and bias to ensure there is no accurancy loss @@ -203,7 +203,7 @@ class HdfsSpec extends FlatSpec with Matchers with BeforeAndAfter{ graph, overwrite = true) val modelFromHdfs = CaffeLoader.loadCaffe[Double](hdfsDir + "/test.prototxt", - hdfsDir + "/test.caffemodel")._1 + hdfsDir + "/test.caffemodel", outputNames = Array[String]("linear"))._1 val res2 = modelFromHdfs.forward(input2) From 51c3466b305f8ea7779155d631719b6476d885f5 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 27 Dec 2017 16:07:03 +0800 Subject: [PATCH 0620/1065] fix tensor type missing issue (#2112) --- .../bigdl/dllib/utils/serializer/Types.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Const.scala | 16 +++++++++++++++- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala index 2d1efe30792..312c0e499a3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala @@ -66,7 +66,7 @@ object ClassTagMapper { case "String" => scala.reflect.classTag[String] case "Int" => scala.reflect.classTag[Int] case "Long" => scala.reflect.classTag[Long] - case "ByteString" => scala.reflect.classTag[ByteString] + case "com.google.protobuf.ByteString" => scala.reflect.classTag[ByteString] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala index 3fdb592efa3..00f51b04d6a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala @@ -17,11 +17,14 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder +import com.google.protobuf.ByteString import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericShort, NumericString} +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString import com.intel.analytics.bigdl.utils.tf.{Context, TFUtils} import org.tensorflow.framework.NodeDef @@ -31,7 +34,18 @@ class Const extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val value = TFUtils.parseTensor(nodeDef.getAttrMap.get("value").getTensor, byteOrder) - Const(value).asInstanceOf[AbstractModule[Activity, Activity, T]] + val const = value.getTensorNumeric() match { + case NumericFloat => Const[T, Float](value.asInstanceOf[Tensor[Float]]) + case NumericDouble => Const[T, Double](value.asInstanceOf[Tensor[Double]]) + case NumericInt => Const[T, Int](value.asInstanceOf[Tensor[Int]]) + case NumericLong => Const[T, Long](value.asInstanceOf[Tensor[Long]]) + case NumericChar => Const[T, Char](value.asInstanceOf[Tensor[Char]]) + case NumericBoolean => Const[T, Boolean](value.asInstanceOf[Tensor[Boolean]]) + case NumericShort => Const[T, Short](value.asInstanceOf[Tensor[Short]]) + case NumericString => Const[T, String](value.asInstanceOf[Tensor[String]]) + case NumericByteString => Const[T, ByteString](value.asInstanceOf[Tensor[ByteString]]) + } + const.asInstanceOf[Module[T]] } } From 497046a2b491aad4aa5a91b4b68737a68240bc31 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Thu, 28 Dec 2017 10:08:18 +0800 Subject: [PATCH 0621/1065] optimize localpredictor for small dataset (#2110) * optimize localpredictor for small dataset * Add featurePaddingParam * Add comments * refactor LocalPredictor.predict * add log tmp * fix ut --- .../dllib/example/lenetLocal/Predict.scala | 9 +- .../dllib/nn/abstractnn/AbstractModule.scala | 19 ++- .../bigdl/dllib/optim/LocalPredictor.scala | 117 +++++++--------- .../bigdl/dllib/optim/Predictor.scala | 49 ++++--- .../bigdl/dllib/utils/LocalModule.scala | 81 ----------- .../dllib/utils/python/api/PythonBigDL.scala | 8 +- .../dllib/optim/LocalPredictorSpec.scala | 130 +++++++++++++++++- .../bigdl/dllib/optim/PredictorSpec.scala | 5 +- 8 files changed, 235 insertions(+), 183 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala index e0ceddbcfdd..12407c6b613 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/lenetLocal/Predict.scala @@ -17,8 +17,9 @@ package com.intel.analytics.bigdl.example.lenetLocal import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToSample} import com.intel.analytics.bigdl.nn.Module -import com.intel.analytics.bigdl.utils.{Engine, LocalModule} +import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.optim.LocalPredictor import org.apache.log4j.{Level, Logger} import scala.collection.mutable.ArrayBuffer @@ -54,9 +55,9 @@ object Predict { val samples = samplesBuffer.toArray val model = Module.load[Float](param.model) - val localModel = LocalModule(model) - val result = localModel.predict(samples) - val result_class = localModel.predictClass(samples) + val localPredictor = LocalPredictor(model) + val result = localPredictor.predict(samples) + val result_class = localPredictor.predictClass(samples) result_class.foreach(r => println(s"${r}")) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index e1395dc17b5..5efe388cbcb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -27,7 +27,7 @@ import com.intel.analytics.bigdl.utils.TorchObject.TYPE_MODULE import org.apache.commons.lang3.SerializationUtils import org.apache.spark.rdd.RDD import com.intel.analytics.bigdl.optim._ -import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} +import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, PaddingParam, Sample} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.quantized.Quantization import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame} @@ -577,27 +577,32 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, } /** - * model predict images, return imageFrame with predicted tensor + * model predict images, return imageFrame with predicted tensor, + * if you want to call predictImage multiple times, + * it is recommended to use Predictor for DistributedImageFrame + * or LocalPredictor for LocalImageFrame * @param imageFrame imageFrame that contains images * @param outputLayer if outputLayer is not null, the output of layer that matches * outputLayer will be used as predicted output * @param shareBuffer whether to share same memory for each batch predict results * @param batchPerPartition batch size per partition, default is 4 * @param predictKey key to store predicted result + * @param featurePaddingParam featurePaddingParam if the inputs have variant size * @return */ def predictImage(imageFrame: ImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, batchPerPartition: Int = 4, - predictKey: String = ImageFeature.predict): ImageFrame = { + predictKey: String = ImageFeature.predict, + featurePaddingParam: Option[PaddingParam[T]] = None): ImageFrame = { imageFrame match { case distributedImageFrame: DistributedImageFrame => - Predictor(this).predictImage(distributedImageFrame, outputLayer, - shareBuffer, batchPerPartition, predictKey) + Predictor(this, featurePaddingParam, batchPerPartition) + .predictImage(distributedImageFrame, outputLayer, shareBuffer, predictKey) case localImageFrame: LocalImageFrame => - LocalModule[T](this).predictImage(localImageFrame, outputLayer, - shareBuffer, batchPerPartition, predictKey) + LocalPredictor(this, featurePaddingParam, batchPerPartition) + .predictImage(localImageFrame, outputLayer, shareBuffer, predictKey) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index 9d81cc09138..d0511fbfb8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -17,30 +17,40 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset._ +import com.intel.analytics.bigdl.dataset.{SampleToMiniBatch, _} import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{Engine, MklBlas} +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Util} import com.intel.analytics.bigdl.utils.Util._ -import com.intel.analytics.bigdl.dataset.SampleToMiniBatch import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, LocalImageFrame} +import org.apache.log4j.Logger import scala.reflect.ClassTag object LocalPredictor { - def apply[T: ClassTag](model: Module[T], weightsBias: Array[Tensor[T]]) - (implicit ev: TensorNumeric[T]): LocalPredictor[T] = { - new LocalPredictor[T](model, weightsBias) + val logger = Logger.getLogger(getClass) + + def apply[T: ClassTag](model: Module[T], + featurePaddingParam: Option[PaddingParam[T]] = None, + batchPerCore: Int = 4) + (implicit ev: TensorNumeric[T]): LocalPredictor[T] = { + new LocalPredictor[T](model, featurePaddingParam, batchPerCore) } } -class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: Array[Tensor[T]]) - (implicit ev: TensorNumeric[T]) - extends Serializable { +/** + * Predictor for local data + * @param model BigDL model + * @param featurePaddingParam featurePaddingParam if the inputs have variant size + * @param batchPerCore batch size per core, default is 4 + */ +class LocalPredictor[T: ClassTag] private[optim](model: Module[T], + featurePaddingParam: Option[PaddingParam[T]] = None, + batchPerCore: Int = 4) + (implicit ev: TensorNumeric[T]) extends Serializable { - val logger = LocalValidator.logger + val logger = LocalPredictor.logger private val coreNumber = Engine.coreNumber() private val subModelNumber = Engine.getEngineType match { @@ -48,7 +58,27 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: case _ => throw new IllegalArgumentException } - private val batchPerCore = 4 + private val workingModels = { + val weightsBias = Util.getAndClearWeightBias(model.parameters()) + val models = (1 to subModelNumber).map(_ => { + val submodel = model.cloneModule().evaluate() + putWeightBias(weightsBias, submodel) + submodel + }).toArray + Util.putWeightBias(weightsBias, model) + Util.initGradWeightBias(weightsBias, model) + models + } + + val workingToBatch = { + val toBatch = SampleToMiniBatch[T]( + batchSize = batchPerCore * subModelNumber, + partitionNum = Some(subModelNumber), + featurePaddingParam = featurePaddingParam) + (1 to subModelNumber).map(_ => { + toBatch.cloneTransformer() + }).toArray + } def predictClass(dataSet: Array[Sample[T]]): Array[Int] = { val result = predict(dataSet) @@ -72,12 +102,6 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: def predict(dataSet: LocalDataSet[MiniBatch[T]]): Array[Activity] = { val dataIter = dataSet.data(train = false) - - val workingModels = (1 to subModelNumber).map(_ => { - val submodel = model.cloneModule().evaluate() - putWeightBias(weightsBias, submodel) - submodel - }).toArray dataIter.map(batch => { println("Enter map") val stackSize = batch.size() / subModelNumber @@ -103,40 +127,21 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: } def predict(dataSet: Array[Sample[T]]): Array[Activity] = { - val iter = dataSet.iterator - val transformer = SampleToMiniBatch[T]( - batchSize = batchPerCore * subModelNumber, None, None, - partitionNum = Some(1)) - val dataIter = transformer(iter) - - val workingModels = (1 to subModelNumber).map(_ => { - val submodel = model.cloneModule().evaluate() - putWeightBias(weightsBias, submodel) - submodel - }).toArray + val dataIter = dataSet.grouped(batchPerCore * subModelNumber) dataIter.map(batch => { - val stackSize = batch.size() / subModelNumber - val extraSize = batch.size() % subModelNumber - val parallelism = if (stackSize == 0) extraSize else subModelNumber - val start = System.nanoTime() - val result = Engine.default.invokeAndWait( - (0 until parallelism).map(b => + val groupedSamples = batch.grouped(batchPerCore).toArray + Engine.default.invokeAndWait( + groupedSamples.indices.map(b => () => { - val offset = b * stackSize + math.min(b, extraSize) + 1 - val length = stackSize + (if (b < extraSize) 1 else 0) - val currentMiniBatch = batch.slice(offset, length) - val input = currentMiniBatch.getInput() - val output = workingModels(b).forward(input).toTensor[T] - output.clone() - + val samples = groupedSamples(b) + val model = workingModels(b) + val toBatch = workingToBatch(b) + Predictor.predictSamples(model, samples, toBatch, false) } ) - ) - val batchResult = result.flatMap(_.split(1)).map(_.asInstanceOf[Activity]) - batchResult - }).toArray.flatten - + ).flatten + }).flatten.toArray } /** @@ -145,33 +150,15 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], weightsBias: * @param outputLayer if outputLayer is not null, the output of layer that matches * outputLayer will be used as predicted output * @param shareBuffer whether to share same memory for each batch predict results - * @param batchPerCore batch size per core, default is 4 * @param predictKey key to store predicted result */ def predictImage(imageFrame: LocalImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, - batchPerCore: Int = 4, predictKey: String = ImageFeature.predict): LocalImageFrame = { val dataIter = imageFrame.array.grouped(batchPerCore * subModelNumber) - val workingModels = (1 to subModelNumber).map(_ => { - val submodel = model.cloneModule().evaluate() - putWeightBias(weightsBias, submodel) - submodel - }).toArray - - // If batchPerCore == 1, will resize the feature every time in SampleToBatch - def featurePaddingParam = if (batchPerCore == 1) Some(PaddingParam[T]()) else None - - val workingToBatch = (1 to subModelNumber).map(_ => { - SampleToMiniBatch[T]( - batchSize = batchPerCore * subModelNumber, - partitionNum = Some(subModelNumber), - featurePaddingParam = featurePaddingParam) - }).toArray - val result = dataIter.map(batch => { val groupedImages = batch.grouped(batchPerCore).toArray Engine.default.invokeAndWait( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index ca7a60259c4..d24c5414936 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{MiniBatch, PaddingParam, Sample, SampleToMiniBatch, Transformer, Utils, DataSet => _} import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame} import org.apache.spark.rdd.RDD @@ -27,8 +28,11 @@ import org.apache.spark.rdd.RDD import scala.reflect.ClassTag object Predictor { - def apply[T: ClassTag](model: Module[T])(implicit ev: TensorNumeric[T]): Predictor[T] = { - new Predictor[T](model) + def apply[T: ClassTag](model: Module[T], + featurePaddingParam: Option[PaddingParam[T]] = None, + batchPerPartition: Int = 4) + (implicit ev: TensorNumeric[T]): Predictor[T] = { + new Predictor[T](model, featurePaddingParam, batchPerPartition) } private[optim] def predictImageBatch[T: ClassTag]( @@ -38,7 +42,19 @@ object Predictor { shareBuffer: Boolean)(implicit ev: TensorNumeric[T]): Seq[ImageFeature] = { val validImageFeatures = imageFeatures.filter(_.isValid) val samples = validImageFeatures.map(x => x[Sample[T]](ImageFeature.sample)) - val batchOut = localToBatch(samples.toIterator).flatMap(batch => { + val batchOut = predictSamples(localModel, samples, localToBatch, shareBuffer, outputLayer) + validImageFeatures.toIterator.zip(batchOut).foreach(tuple => { + tuple._1(predictKey) = tuple._2 + }) + imageFeatures + } + + private[optim] def predictSamples[T: ClassTag] + (localModel: Module[T], samples: Seq[Sample[T]], + localToBatch: Transformer[Sample[T], MiniBatch[T]], + shareBuffer: Boolean, + outputLayer: String = null)(implicit ev: TensorNumeric[T]): Iterator[Tensor[T]] = { + localToBatch(samples.toIterator).flatMap(batch => { localModel.forward(batch.getInput()) val output = if (outputLayer == null) { localModel.output.toTensor[T] @@ -52,17 +68,20 @@ object Predictor { result.split(1) } }) - validImageFeatures.toIterator.zip(batchOut).foreach(tuple => { - tuple._1(predictKey) = tuple._2 - }) - imageFeatures } } +/** + * Predictor for distributed data + * @param model BigDL model + * @param featurePaddingParam featurePaddingParam if the inputs have variant size + * @param batchPerPartition batch size per partition, default is 4 + */ class Predictor[T: ClassTag] private[optim]( - model: Module[T])(implicit ev: TensorNumeric[T]) extends Serializable { - - private val batchPerPartition = 4 + model: Module[T], + featurePaddingParam: Option[PaddingParam[T]] = None, + batchPerPartition: Int = 4) + (implicit ev: TensorNumeric[T]) extends Serializable { def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { val result = predict(dataSet, batchSize, true) @@ -89,11 +108,11 @@ class Predictor[T: ClassTag] private[optim]( } val otherBroad = dataSet.sparkContext.broadcast(SampleToMiniBatch( batchSize = totalBatch, - partitionNum = Some(partitionNum)), shareBuffer) + partitionNum = Some(partitionNum), + featurePaddingParam = featurePaddingParam)) dataSet.mapPartitions { partition => val localModel = modelBroad.value() - val localTransformer = otherBroad.value._1.cloneTransformer() - val repeatMemory = otherBroad.value._2 + val localTransformer = otherBroad.value.cloneTransformer() val miniBatch = localTransformer(partition) miniBatch.flatMap( batch => { val output = localModel.forward(batch.getInput).toTensor[T] @@ -113,19 +132,15 @@ class Predictor[T: ClassTag] private[optim]( * @param outputLayer if outputLayer is not null, the output of layer that matches * outputLayer will be used as predicted output * @param shareBuffer whether to share same memory for each batch predict results - * @param batchPerPartition batch size per partition, default is 4 * @param predictKey key to store predicted result */ def predictImage(imageFrame: DistributedImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, - batchPerPartition: Int = 4, predictKey: String = ImageFeature.predict): DistributedImageFrame = { val rdd = imageFrame.asInstanceOf[DistributedImageFrame].rdd val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, model.evaluate()) val partitionNum = rdd.partitions.length - // If batchPerPartition == 1, will resize the feature every time in SampleToBatch - def featurePaddingParam = if (batchPerPartition == 1) Some(PaddingParam[T]()) else None val toBatchBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( batchSize = partitionNum * batchPerPartition, partitionNum = Some(partitionNum), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala deleted file mode 100644 index db5be8a56bd..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/LocalModule.scala +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.utils - -import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, Sample} -import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.optim.LocalPredictor -import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, LocalImageFrame} - -import scala.reflect.ClassTag - - -object LocalModule { - - def apply[T: ClassTag](model: Module[T]) - (implicit ev: TensorNumeric[T]): LocalModule[T] = { - val weightsBias = Util.getAndClearWeightBias(model.cloneModule().parameters()) - new LocalModule[T](model, weightsBias) - } -} - -class LocalModule[T: ClassTag] private(model: Module[T], weightsBias: Array[Tensor[T]]) - (implicit ev: TensorNumeric[T]) - extends Serializable { - - private val predictor = LocalPredictor(model, weightsBias) - - def predictClass(dataSet: Array[Sample[T]]): Array[Int] = { - predictor.predictClass(dataSet) - } - - def predictClass(dataSet: LocalDataSet[MiniBatch[T]]): Array[Int] = { - predictor.predictClass(dataSet) - } - - def predict(dataSet: LocalDataSet[MiniBatch[T]]): Array[Activity] = { - predictor.predict(dataSet) - } - - def predict(dataSet: Array[Sample[T]]): Array[Activity] = { - predictor.predict(dataSet) - } - - /** - * local model predict image, return imageFrame with predicted tensor - * @param imageFrame imageFrame that contains images - * @param outputLayer if outputLayer is not null, the output of layer that matches - * outputLayer will be used as predicted output - * @param shareBuffer whether to share same memory for each batch predict results - * @param batchPerCore batch size per partition, default is 4 - * @param predictKey key to store predicted result - */ - def predictImage(imageFrame: LocalImageFrame, - outputLayer: String = null, - shareBuffer: Boolean = false, - batchPerCore: Int = 4, - predictKey: String = ImageFeature.predict): LocalImageFrame = { - predictor.predictImage(imageFrame, - outputLayer, - shareBuffer, - batchPerCore, - predictKey) - } -} - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index be4dd63160d..42bff33c93f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1855,16 +1855,16 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def predictLocal(model: AbstractModule[Activity, Activity, T], features: JList[JTensor]): JList[JTensor] = { val sampleArray = toSampleArray(features.asScala.toList.map{f => toTensor(f)}) - val localModel = LocalModule(model) - val result = localModel.predict(sampleArray) + val localPredictor = LocalPredictor(model) + val result = localPredictor.predict(sampleArray) result.map{a => toJTensor(a.asInstanceOf[Tensor[T]])}.toList.asJava } def predictLocalClass(model: AbstractModule[Activity, Activity, T], features: JList[JTensor]): JList[Int] = { val sampleArray = toSampleArray(features.asScala.toList.map{f => toTensor(f)}) - val localModel = LocalModule(model) - val result = localModel.predictClass(sampleArray) + val localPredictor = LocalPredictor(model) + val result = localPredictor.predictClass(sampleArray) result.toList.asJava } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index 86762629ce9..1e99bf1ff86 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -16,24 +16,35 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.dataset.Sample +import java.io.File + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.dataset.{PaddingParam, Sample, SampleToMiniBatch} import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.{Sequential, SpatialConvolution, Tanh} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Util} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.apache.commons.io.FileUtils import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { private val nodeNumber = 1 private val coreNumber = 4 + val batchPerCore = 4 + var subModelNumber = coreNumber before { System.setProperty("bigdl.localMode", "true") Engine.init(nodeNumber, coreNumber, false) + subModelNumber = Engine.getEngineType match { + case MklBlas => coreNumber + case _ => throw new IllegalArgumentException + } } after { @@ -105,7 +116,8 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { val model = Sequential() model.add(SpatialConvolution(3, 6, 5, 5)) model.add(Tanh()) - val detection = model.predictImage(imageFrame, batchPerPartition = 1).toLocal() + val detection = model.predictImage(imageFrame, batchPerPartition = 1, featurePaddingParam = + Some(PaddingParam())).toLocal() val imageFeatures = detection.array (1 to 20).foreach(x => { imageFeatures(x - 1).uri() should be (x.toString) @@ -151,4 +163,116 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { val imageFeatures = detection.array imageFeatures.length should be (0) } + + "predictImage performance one by one" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 20) + val localPredictor = LocalPredictor(model) + + model.forward(Tensor[Float](1, 3, 224, 224)) + + var start = System.nanoTime() + (1 to 20).foreach(x => { + val detection = model.forward(Tensor[Float](1, 3, 224, 224)) + }) + println(s"${(System.nanoTime() - start) / 1e9}s") + + start = System.nanoTime() + (1 to 20).foreach(x => { + val detection = localPredictor.predictImage(imageFrame.toLocal()).toLocal() + }) + + println(s"${(System.nanoTime() - start) / 1e9}s") + } + + "predictImage performance group" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/000025.jpg") + val imageFeatures = (1 to 20).map(i => { + val f = new File(resource.getFile) + ImageFeature(FileUtils.readFileToByteArray(f), f.getAbsolutePath) + }).toArray + val imageFrame = ImageFrame.array(imageFeatures) -> BytesToMat() -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 20) + val localPredictor = LocalPredictor(model) + + model.forward(Tensor[Float](1, 3, 224, 224)) + var start = System.nanoTime() + (1 to 20).foreach(x => { + val detection = model.forward(Tensor[Float](1, 3, 224, 224)) + }) + println(s"${(System.nanoTime() - start) / 1e9}s") + + start = System.nanoTime() + val detection = localPredictor.predictImage(imageFrame.toLocal()).toLocal() + + println(s"${(System.nanoTime() - start) / 1e9}s") + } + + "predict sample after refactor" should "work properly" in { + val samples = (1 to 20).map(i => { + Sample(Tensor[Float](3, 224, 224).randn()) + }).toArray + val imageFrame = ImageFrame.array((0 until 20).map(x => { + val im = ImageFeature() + im(ImageFeature.sample) = samples(x) + im + }).toArray) + + val model = Inception_v1_NoAuxClassifier(classNum = 20) + val out1 = model.predictImage(imageFrame).toLocal().array + .map(_.predict().asInstanceOf[Tensor[Float]]) + val out2 = predict(samples, model) + + out1.zip(out2).foreach(x => { + x._1 should be (x._2.toTensor[Float]) + }) + + } + + def predict(dataSet: Array[Sample[Float]], model: Module[Float]): Array[Activity] = { + val weightsBias = Util.getAndClearWeightBias[Float](model.cloneModule().parameters()) + val iter = dataSet.iterator + val transformer = SampleToMiniBatch[Float]( + batchSize = batchPerCore * subModelNumber, None, None, + partitionNum = Some(1)) + val dataIter = transformer(iter) + + dataIter.map(batch => { + val stackSize = batch.size() / subModelNumber + val extraSize = batch.size() % subModelNumber + val parallelism = if (stackSize == 0) extraSize else subModelNumber + val workingModels = (1 to subModelNumber).map(_ => { + val submodel = model.cloneModule().evaluate() + Util.putWeightBias(weightsBias, submodel) + submodel + }).toArray + val start = System.nanoTime() + val result = Engine.default.invokeAndWait( + (0 until parallelism).map(b => + () => { + val offset = b * stackSize + math.min(b, extraSize) + 1 + val length = stackSize + (if (b < extraSize) 1 else 0) + val currentMiniBatch = batch.slice(offset, length) + val input = currentMiniBatch.getInput() + val output = workingModels(b).forward(input).toTensor[Float] + output.clone() + } + ) + ) + val batchResult = result.flatMap(_.split(1)).map(_.asInstanceOf[Activity]) + batchResult + }).toArray.flatten + + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index f1ab4f23d51..392800081c4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.dataset.{PaddingParam, Sample} import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.nn.{Sequential, SpatialConvolution, Tanh} @@ -171,7 +171,8 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ val model = Sequential() model.add(SpatialConvolution(3, 6, 5, 5)) model.add(Tanh()) - val detection = model.predictImage(imageFrame, batchPerPartition = 1, shareBuffer = false) + val detection = model.predictImage(imageFrame, batchPerPartition = 1, shareBuffer = false, + featurePaddingParam = Some(PaddingParam[Float]())) .toDistributed() val imageFeatures = detection.rdd.collect() (1 to 20).foreach(x => { From afc0939935ac6a68ba9b61287d9c33e25bc5050b Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 28 Dec 2017 13:51:35 +0800 Subject: [PATCH 0622/1065] graph ser to support control node (#2121) * graph ser to support control node * add unit test * refinement * refinement --- .../intel/analytics/bigdl/dllib/nn/Graph.scala | 18 ++++++++++++++++-- .../bigdl/dllib/nn/ops/ControlOps.scala | 2 +- .../serializer/ModuleSerializerSpec.scala | 16 +++++++++++++++- 3 files changed, 32 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 123a2b3e62a..d9d8f298b00 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -17,10 +17,12 @@ package com.intel.analytics.bigdl.nn import java.util +import com.intel.analytics.bigdl.Module + import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} -import com.intel.analytics.bigdl.nn.ops.ControlOps +import com.intel.analytics.bigdl.nn.ops.{MergeControlNode, SwitchControlNode, MergeOps, SwitchOps, ControlOps} import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -546,7 +548,10 @@ object Graph extends ContainerSerializable { subModules.foreach(subModule => { val bigDLModule = ModuleSerializer.load(DeserializeContext(subModule, context.storages, context.storageType)) - val moduleNode = bigDLModule.module.inputs() + val moduleNode = bigDLModule.module match { + case controlOps : ControlOps[T] => createControlNode(controlOps) + case _ => bigDLModule.module.inputs() + } val preNodes = bigDLModule.pre layerMap(bigDLModule.module.getName) = (moduleNode, preNodes) }) @@ -591,6 +596,15 @@ object Graph extends ContainerSerializable { } } + private def createControlNode[T: ClassTag](controlOps : ControlOps[T]) : ModuleNode[T] = { + controlOps match { + case switchOps : SwitchOps[T] => new SwitchControlNode[Module[T]](switchOps) + case mergeOps : MergeOps[T] => new MergeControlNode[Module[T]](mergeOps) + case _ => throw new RuntimeException(s"Ops ${controlOps.getClass.getName}" + + s" control node not supported!") + } + } + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], graphBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala index 658cefee576..734bf8af07a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala @@ -156,7 +156,7 @@ sealed class SwitchControlNode[T] (element: T) extends Node[T](element) { * @param element element * @tparam T element type */ -sealed class MergeControlNode[T] private[ops] (element: T) extends Node[T](element) { +sealed class MergeControlNode[T] private[bigdl] (element: T) extends Node[T](element) { /** * Add another dependency node diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index efd952d229a..31271c866dc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} import com.intel.analytics.bigdl.optim.L2Regularizer @@ -550,6 +550,20 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(graphWithVariable, input) } + "Dynamic Graph with control ops serializer" should "work properly" in { + val data = Input[Float]("data") + val condition = Input[Float]("condition") + val swtich = ControlNodes.switch(condition, data) + val echo1 = Echo[Float]().inputs(swtich.trueEdge()) + val echo2 = Echo[Float]().inputs(swtich.falseEdge()) + + val model = Graph.dynamic[Float](Array(data, condition), Array(echo1), None, false) + + val input = T(Tensor[Float](T(1)), Tensor[Boolean](T(true))) + + runSerializationTest(model, input) + } + "GRU serializer" should "work properly" in { RNG.setSeed(100) val gru = GRU[Float](100, 100) From 147acef8148fb035e4ed3ee5aabfaf4e2d9ef474 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 28 Dec 2017 15:07:13 +0800 Subject: [PATCH 0623/1065] Refine KLDCriterion (#2113) * optimize KLDCriterion * address comments * fix tests --- .../bigdl/dllib/nn/KLDCriterion.scala | 19 ++++++++++++++----- .../dllib/utils/python/api/PythonBigDL.scala | 4 ++-- .../bigdl/dllib/nn/KLDCriterionSpec.scala | 6 +++--- 3 files changed, 19 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala index 5024f33bc40..e9eed1c9380 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterion.scala @@ -27,8 +27,12 @@ import scala.reflect.ClassTag * The input has to be a table. The first element of input is the mean of the distribution, * the second element of input is the log_variance of the distribution. The input distribution is * assumed to be diagonal. + * + * The mean and log_variance are both assumed to be two dimensional tensors. The first dimension are + * interpreted as batch. The output is the average/sum of each observation. */ class KLDCriterion[@specialized(Float, Double) T: ClassTag]( + sizeAverage: Boolean = true)( implicit ev: TensorNumeric[T]) extends AbstractCriterion[Table, Tensor[T], T] { @transient @@ -39,6 +43,7 @@ class KLDCriterion[@specialized(Float, Double) T: ClassTag]( private var vars: Tensor[T] = null override def updateOutput(input: Table, target: Tensor[T]): T = { + if (mean == null) mean = Tensor[T]() if (logVar == null) logVar = Tensor[T]() if (vars == null) vars = Tensor[T]() @@ -46,12 +51,14 @@ class KLDCriterion[@specialized(Float, Double) T: ClassTag]( mean.resizeAs(input[Tensor[T]](1)).copy(input(1)) logVar.resizeAs(input[Tensor[T]](2)).copy(input(2)) + val batchSize = if (sizeAverage) mean.size(1) else 1 + // Appendix B from VAE paper: -0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2) mean.pow(ev.fromType(2)) vars.resizeAs(logVar).copy(logVar).exp() logVar.add(ev.one).add(ev.fromType(-1), mean).add(ev.fromType(-1), vars) - output = ev.times(ev.fromType(-0.5), logVar.sum()) + output = ev.times(ev.fromType(-0.5 / batchSize), logVar.sum()) output } @@ -59,20 +66,22 @@ class KLDCriterion[@specialized(Float, Double) T: ClassTag]( if (!gradInput.contains(1)) gradInput(1) = Tensor() if (!gradInput.contains(2)) gradInput(2) = Tensor() + val batchSize = if (sizeAverage) input[Tensor[T]](1).size(1) else 1 + // d_L/d_mu = mu - gradInput[Tensor[T]](1).resizeAs(input(1)).copy(input(1)) + gradInput[Tensor[T]](1).resizeAs(input(1)).copy(input(1)).mul(ev.fromType(1.0 / batchSize)) // d_L/d_sigma = 0.5*(exp(log_sq_sigma)-1) gradInput[Tensor[T]](2).resizeAs(input(2)).copy(input(2)) - gradInput[Tensor[T]](2).exp().add(ev.fromType(-1)).mul(ev.fromType(0.5)) + gradInput[Tensor[T]](2).exp().add(ev.fromType(-1)).mul(ev.fromType(0.5 / batchSize)) gradInput } } object KLDCriterion { - def apply[@specialized(Float, Double) T: ClassTag]()( + def apply[@specialized(Float, Double) T: ClassTag](sizeAverage: Boolean = true)( implicit ev: TensorNumeric[T]): KLDCriterion[T] = { - new KLDCriterion[T]() + new KLDCriterion[T](sizeAverage = sizeAverage) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 42bff33c93f..4717f30445e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1725,8 +1725,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ParallelCriterion[T](repeatTarget) } - def createKLDCriterion(): KLDCriterion[T] = { - KLDCriterion[T]() + def createKLDCriterion(sizeAverage: Boolean): KLDCriterion[T] = { + KLDCriterion[T](sizeAverage) } def createGaussianCriterion(): GaussianCriterion[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterionSpec.scala index 5dfc2c6ea36..4f078c08f05 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/KLDCriterionSpec.scala @@ -38,13 +38,13 @@ class KLDCriterionSpec extends FlatSpec with Matchers{ val loss = model.forward(input, target) val gradInput = model.backward(input, target) - loss should be(0.991884f +- 1e-3f) + loss should be(0.991884f / 2 +- 1e-3f) val gardTarget1 = Tensor(Array(0.54340494f, 0.67115563f, 0.2783694f, - 0.4120464f, 0.4245176f, 0.52638245f), Array(2, 3)) + 0.4120464f, 0.4245176f, 0.52638245f), Array(2, 3)).mul(0.5f) val gardTarget2 = Tensor(Array(0.66372836f, 0.08010721f, 0.002364993f, - 0.084828794f, 0.06463373f, 0.10249251f), Array(2, 3)) + 0.084828794f, 0.06463373f, 0.10249251f), Array(2, 3)).mul(0.5f) gradInput[Tensor[Float]](1) should be(gardTarget1) gradInput[Tensor[Float]](2) should be(gardTarget2) From 65f67f11084693fa661185b8f547b076dcb175a3 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Thu, 28 Dec 2017 17:53:31 +0800 Subject: [PATCH 0624/1065] fix resize bilinear (#2124) * fix resize bilinear * fix style * fix tests --- .../bigdl/dllib/nn/ResizeBilinear.scala | 12 ++--- .../dllib/nn/ops/ResizeBilinearOps.scala | 31 ++++++++++++ .../utils/tf/loaders/ResizeBilinear.scala | 2 +- .../utils/tf/loaders/ResizeBilinearGrad.scala | 34 +++++++++++++ .../serializer/ModuleSerializerSpec.scala | 11 ++++- .../tf/loaders/ResizeBilinearGradSpec.scala | 49 +++++++++++++++++++ .../utils/tf/loaders/ResizeBilinearSpec.scala | 48 ++++++++++++++++++ 7 files changed, 179 insertions(+), 8 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGrad.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala index c9c46db9c35..dfa0aacc3b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala @@ -84,9 +84,9 @@ class ResizeBilinear[T: ClassTag](val outputHeight: Int, val outputWidth: Int, val inWidth = input.size(3) val channels = input.size(4) val inRowSize = inWidth * channels - val inBatchNum = batchSize * inHeight * inRowSize + val inBatchNum = inHeight * inRowSize val outRowSize = outputWidth * channels - val outBatchNum = batchSize * outputHeight * outRowSize + val outBatchNum = outputHeight * outRowSize require(gradOutput.size(2) == outputHeight, "output height is not match") require(gradOutput.size(3) == outputWidth, "output width is not match") @@ -121,16 +121,16 @@ class ResizeBilinear[T: ClassTag](val outputHeight: Int, val outputWidth: Int, var c = 0 while(c < channels) { gradInputData(gradInputOffset + b * inBatchNum + topY * inRowSize + - leftX * channels + c) = gradOutputData(gradOutputOffset + b * outBatchNum + + leftX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + y * outRowSize + x * channels + c) * inverseYLERP * inverseXLERP gradInputData(gradInputOffset + b * inBatchNum + topY * inRowSize + - rightX * channels + c) = gradOutputData(gradOutputOffset + b * outBatchNum + + rightX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + y * outRowSize + x * channels + c) * inverseYLERP * xLERP gradInputData(gradInputOffset + b * inBatchNum + bottomY * inRowSize + - leftX * channels + c) = gradOutputData(gradOutputOffset + b * outBatchNum + + leftX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + y * outRowSize + x * channels + c) * yLERP * inverseXLERP gradInputData(gradInputOffset + b * inBatchNum + bottomY * inRowSize + - rightX * channels + c) = gradOutputData(gradOutputOffset + b * outBatchNum + + rightX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + y * outRowSize + x * channels + c) * yLERP * xLERP c += 1 } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala index 3c4096ebbf0..bfbc98b550f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala @@ -52,3 +52,34 @@ object ResizeBilinearOps { new ResizeBilinearOps(alignCorner) } } + +class ResizeBilinearGrad[T: ClassTag](alignCorner: Boolean)(implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[Float], T] { + + private var module : ResizeBilinear[T] = _ + + override def updateOutput(input: Activity): Tensor[Float] = { + require(input.isTable, "Only accept two input tensors") + val grads = input.toTable.apply[Tensor[Float]](1) + val originImage = input.toTable.apply[Tensor[Float]](2) + if (module == null) { + module = ResizeBilinear[T]( + grads.size(2), + grads.size(3), + alignCorner + ) + } else { + require(module.outputHeight == grads.size(2), "height not match") + require(module.outputWidth == grads.size(3), "width not match") + } + output = module.backward(originImage, grads) + output + } +} + +object ResizeBilinearGrad { + def apply[T: ClassTag](alignCorner: Boolean) + (implicit ev: TensorNumeric[T]): ResizeBilinearGrad[T] = { + new ResizeBilinearGrad[T](alignCorner) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala index 91e94bf6cfc..8a7b07a02df 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.ResizeBilinearOps +import com.intel.analytics.bigdl.nn.ops.{ResizeBilinearGrad, ResizeBilinearOps} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGrad.scala new file mode 100644 index 00000000000..bc1a9bcf288 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGrad.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.ResizeBilinearGrad +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class ResizeBilinearGrad extends TensorflowOpsLoader { + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val alignCorner = nodeDef.getAttrMap.get("align_corners").getB + ResizeBilinearGrad[T](alignCorner) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 31271c866dc..5c9f660fa0d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} import com.intel.analytics.bigdl.optim.L2Regularizer @@ -2407,6 +2407,15 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(module, T(inputSize, filter, outputBackprop)) } + "ResizeBilinearGrad serializer" should "work properly" in { + val module = ResizeBilinearGrad[Float](true) + val input = T(Tensor[Float](1, 224, 224, 3).rand(), + Tensor[Float](1, 64, 64, 3).rand()) + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, input) + } + "DetectionOutputSSD serializer" should "work properly" in { val module = DetectionOutputSSD[Float](DetectionOutputParam()).setName("DetectionOutputSSD") val name = module.getName diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGradSpec.scala new file mode 100644 index 00000000000..ce191a82556 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGradSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{booleanAttr, typeAttr} +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class ResizeBilinearGradSpec extends TensorflowSpecHelper { + "ResizeBilinearGrad align_corners false" should "be correct for float" in { + compare[Float]( + NodeDef.newBuilder() + .setName("ResizeBilinearGrad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("align_corners", booleanAttr(false)) + .setOp("ResizeBilinearGrad"), + Seq(Tensor[Float](1, 224, 224, 3).rand(), + Tensor[Float](1, 64, 64, 3).rand()), + 0 + ) + } + + "ResizeBilinearGrad align_corners true" should "be correct for float" in { + compare[Float]( + NodeDef.newBuilder() + .setName("ResizeBilinearGrad_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("align_corners", booleanAttr(false)) + .setOp("ResizeBilinearGrad"), + Seq(Tensor[Float](1, 224, 224, 3).rand(), + Tensor[Float](1, 64, 64, 3).rand()), + 0 + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearSpec.scala new file mode 100644 index 00000000000..d2c2b3460c7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearSpec.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class ResizeBilinearSpec extends TensorflowSpecHelper { + "ResizeBilinear align_corners false" should "be correct for float" in { + compare[Float]( + NodeDef.newBuilder() + .setName("ResizeBilinear_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("align_corners", booleanAttr(false)) + .setOp("ResizeBilinear"), + Seq(Tensor[Float](1, 64, 64, 3).rand(), Tensor[Int](Array(224, 224), Array(2))), + 0 + ) + } + + "ResizeBilinear align_corners true" should "be correct for float" in { + compare[Float]( + NodeDef.newBuilder() + .setName("ResizeBilinear_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("align_corners", booleanAttr(false)) + .setOp("ResizeBilinear"), + Seq(Tensor[Float](1, 64, 64, 3).rand(), Tensor[Int](Array(224, 224), Array(2))), + 0 + ) + } +} From dea96986c6674c7751f094f94dfd4ed6125b97b2 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 29 Dec 2017 11:36:44 +0800 Subject: [PATCH 0625/1065] support layer with different type (#2130) --- .../analytics/bigdl/dllib/utils/serializer/DataConverter.scala | 1 + 1 file changed, 1 insertion(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala index f9e74813381..5b4e6758bb6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala @@ -176,6 +176,7 @@ object DataConverter extends DataConverter{ || valueType.toString == ModuleSerializer.tensorModuleType.toString || valueType.toString == ModuleSerializer.moduleType.toString || valueType.toString == ModuleSerializer.boundedModuleType.toString + || valueType <:< universe.typeOf[AbstractModule[_, _, _]] ) { ModuleConverter.setAttributeValue(context, attributeBuilder, value) } else if (value.isInstanceOf[mutable.Map[String, _ <: Any]]) { From a3c5da19b8b83f0cb8cc98ab72f7a98b245cbdc0 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 2 Jan 2018 13:52:38 +0800 Subject: [PATCH 0626/1065] Add TransformerCriterion (#2097) * add TransformerCriterion * fix style * fix python tests * meet review --- .../bigdl/dllib/nn/TransformerCriterion.scala | 84 +++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 8 ++ .../dllib/nn/TransformerCriterionSpec.scala | 49 +++++++++++ 3 files changed, 141 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterion.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterion.scala new file mode 100644 index 00000000000..334aef853b4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterion.scala @@ -0,0 +1,84 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractCriterion, AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * The criterion that takes two modules to transform input and target, and take + * one criterion to compute the loss with the transformed input and target. + * + * This criterion can be used to construct complex criterion. For example, the + * `inputTransformer` and `targetTransformer` can be pre-trained CNN networks, + * and we can use the networks' output to calculate the high-level feature + * reconstruction loss, which is commonly used in areas like neural style transfer + (https://arxiv.org/abs/1508.06576), texture synthesis (https://arxiv.org/abs/1505.07376), + .etc. + * + * @param inputTransformer + * @param targetTransformer + * @param criterion + * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] + */ +class TransformerCriterion[T: ClassTag]( + criterion: AbstractCriterion[Activity, Activity, T], + inputTransformer: Option[AbstractModule[Activity, Activity, T]] = None, + targetTransformer: Option[AbstractModule[Activity, Activity, T]] = None + )(implicit ev: TensorNumeric[T]) extends AbstractCriterion[Activity, Activity, T]{ + + private var transformedInput: Activity = _ + private var transformedTarget: Activity = _ + + override def updateOutput(input: Activity, target: Activity): T = { + transformedInput = inputTransformer.map(t => t.forward(input)) + .getOrElse(input) match { + case t: Tensor[T] => t.clone() + case t: Table => t.clone() + } + transformedTarget = targetTransformer.map(t => t.forward(target)) + .getOrElse(target) match { + case t: Tensor[T] => t.clone() + case t: Table => t.clone() + } + output = criterion.forward(transformedInput, transformedTarget) + output + } + + override def updateGradInput(input: Activity, target: Activity): Activity = { + require(transformedTarget != null && transformedInput != null, "please run forward first") + + val gradInputCriterion = criterion.backward(transformedInput, transformedTarget) + gradInput = inputTransformer + .map(t => t.updateGradInput(input, gradInputCriterion)) + .getOrElse(gradInputCriterion) + gradInput + } +} + +object TransformerCriterion { + + def apply[T: ClassTag]( + criterion: AbstractCriterion[Activity, Activity, T], + inputTransformer: Option[AbstractModule[Activity, Activity, T]] = None, + targetTransformer: Option[AbstractModule[Activity, Activity, T]] = None + )(implicit ev: TensorNumeric[T]): TransformerCriterion[T] = + new TransformerCriterion(criterion, inputTransformer, targetTransformer) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 4717f30445e..05d75d2786d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1763,6 +1763,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab SoftmaxWithCriterion[T](labelToIgnore, normM) } + def createTransformerCriterion( + criterion: AbstractCriterion[Activity, Activity, T], + inputTransformer: AbstractModule[Activity, Activity, T] = null, + targetTransformer: AbstractModule[Activity, Activity, T] = null + ): TransformerCriterion[T] = { + TransformerCriterion(criterion, Option(inputTransformer), Option(targetTransformer)) + } + def createPack(dimension: Int): Pack[T] = { Pack(dimension) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterionSpec.scala new file mode 100644 index 00000000000..964134277bc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterionSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Parallel +class TransformerCriterionSpec extends FlatSpec with Matchers { + + "TransformerCriterion" should "work correctly" in { + + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + + val criterion = TransformerCriterion[Float](MSECriterion[Float](), + Some(Square[Float, Float]()), Some(Square[Float, Float]())) + + val input = Tensor(1, 3, 224, 224).rand() + val target = Tensor(1, 3, 224, 224).rand() + + val loss = criterion.forward(input, target) + val gradInput = criterion.backward(input, target) + + val squaredInput = Tensor(1, 3, 224, 224).copy(input).square() + val squaredTarget = Tensor(1, 3, 224, 224).copy(target).square() + + val referenceCriterion = MSECriterion() + val expectedLoss = referenceCriterion.forward(squaredInput, squaredTarget) + val expectedGradInput = referenceCriterion + .backward(squaredInput, squaredTarget).cmul(input).mul(2.0f) + + loss should be (expectedLoss) + gradInput should be (expectedGradInput) + } +} From 3a52227d38d69043812177ce0e318f70e3ceee5c Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Wed, 3 Jan 2018 11:39:21 +0800 Subject: [PATCH 0627/1065] Fix Wrong labelSize at Sample.scala 411 (#2147) "val labelSize = features.map(_.size())" -> "val labelSize = labels.map(_.size())" --- .../intel/analytics/bigdl/dllib/feature/dataset/Sample.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index 6cdffb1d5b6..ea2b5e8dde1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -408,7 +408,7 @@ private[bigdl] class TensorSample[T: ClassTag]( val features: Array[Tensor[T]], val labels: Array[Tensor[T]]) extends Sample[T] { val featureSize = features.map(_.size()) - val labelSize = features.map(_.size()) + val labelSize = labels.map(_.size()) def featureLength(index: Int): Int = { features(0).size(1) From a5c7f49a7c352ff9f09619779b3772df48340c63 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Wed, 3 Jan 2018 09:55:29 -0800 Subject: [PATCH 0628/1065] DLEstimator: add validation data and train/validation summary support (#2103) * add summary * add summary support --- .../org/apache/spark/ml/DLClassifier.scala | 3 +- .../org/apache/spark/ml/DLEstimator.scala | 203 +++++++++++++----- .../org/apache/spark/ml/DLEstimatorBase.scala | 3 +- .../analytics/bigdl/dllib/optim/Trigger.scala | 2 +- .../bigdl/dllib/optim/DLClassifierSpec.scala | 52 ++++- .../bigdl/dllib/optim/DLEstimatorSpec.scala | 176 +++++++++------ 6 files changed, 321 insertions(+), 118 deletions(-) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala index 038849f698b..6b75565aa5e 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala @@ -49,8 +49,7 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( } override def transformSchema(schema : StructType): StructType = { - validateDataType(schema, $(featuresCol)) - validateDataType(schema, $(labelCol)) + validateParams(schema) SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) } diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index 7977f4ee3d3..a0433498751 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasPredictionCol} import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators, _} import org.apache.spark.ml.util.SchemaUtils @@ -45,21 +46,11 @@ private[ml] trait DLParams[@specialized(Float, Double) T] extends HasFeaturesCol with HasPredictionCol with VectorCompatibility with HasBatchSize { /** - * optimization method to be used. BigDL supports many optimization methods like Adam, - * SGD and LBFGS. Refer to package com.intel.analytics.bigdl.optim for all the options. - * Default: SGD - */ - final val optimMethod = new Param[OptimMethod[T]](this, "optimMethod", "optimMethod") - - def getOptimMethod: OptimMethod[T] = $(optimMethod) - - /** - * number of max Epoch for the training, an epoch refers to a traverse over the training data - * Default: 100 + * When to stop the training, passed in a [[Trigger]]. E.g. Trigger.maxIterations */ - final val maxEpoch = new IntParam(this, "maxEpoch", "number of max Epoch", ParamValidators.gt(0)) + final val endWhen = new Param[Trigger](this, "endWhen", "Trigger to stop the training") - def getMaxEpoch: Int = $(maxEpoch) + def getEndWhen: Trigger = $(endWhen) /** * learning rate for the optimizer in the DLEstimator. @@ -78,6 +69,23 @@ private[ml] trait DLParams[@specialized(Float, Double) T] extends HasFeaturesCol def getLearningRateDecay: Double = $(learningRateDecay) + /** + * Number of max Epoch for the training, an epoch refers to a traverse over the training data + * Default: 50 + */ + final val maxEpoch = new IntParam(this, "maxEpoch", "number of max Epoch", ParamValidators.gt(0)) + + def getMaxEpoch: Int = $(maxEpoch) + + /** + * optimization method to be used. BigDL supports many optimization methods like Adam, + * SGD and LBFGS. Refer to package com.intel.analytics.bigdl.optim for all the options. + * Default: SGD + */ + final val optimMethod = new Param[OptimMethod[T]](this, "optimMethod", "optimMethod") + + def getOptimMethod: OptimMethod[T] = $(optimMethod) + setDefault(batchSize -> 1) /** @@ -168,10 +176,7 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( def setBatchSize(value: Int): this.type = set(batchSize, value) - def setOptimMethod(value: OptimMethod[T]): this.type = set(optimMethod, value) - - def setMaxEpoch(value: Int): this.type = set(maxEpoch, value) - setDefault(maxEpoch -> 50) + def setEndWhen(trigger: Trigger): this.type = set(endWhen, trigger) def setLearningRate(value: Double): this.type = set(learningRate, value) setDefault(learningRate -> 1e-3) @@ -179,53 +184,151 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( def setLearningRateDecay(value: Double): this.type = set(learningRateDecay, value) setDefault(learningRateDecay -> 0.0) - override def transformSchema(schema : StructType): StructType = { - validateDataType(schema, $(featuresCol)) - validateDataType(schema, $(labelCol)) - SchemaUtils.appendColumn(schema, $(predictionCol), ArrayType(DoubleType, false)) + def setMaxEpoch(value: Int): this.type = set(maxEpoch, value) + setDefault(maxEpoch -> 50) + + def setOptimMethod(value: OptimMethod[T]): this.type = set(optimMethod, value) + set(optimMethod, new SGD[T]) + + @transient private var trainSummary: Option[TrainSummary] = None + + def getTrainSummary: Option[TrainSummary] = trainSummary + + /** + * Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the + * training data, which can be used for visualization via Tensorboard. + * Use setTrainSummary to enable train logger. Then the log will be saved to + * logDir/appName/train as specified by the parameters of TrainSummary. + * + * Default: Not enabled + */ + def setTrainSummary(value: TrainSummary): this.type = { + this.trainSummary = Some(value) + this } - protected override def internalFit(dataFrame: DataFrame): DLModel[T] = { - val featureType = dataFrame.schema($(featuresCol)).dataType - val featureColIndex = dataFrame.schema.fieldIndex($(featuresCol)) - val labelType = dataFrame.schema($(labelCol)).dataType - val labelColIndex = dataFrame.schema.fieldIndex($(labelCol)) + @transient private var validationSummary: Option[ValidationSummary] = None - val featureFunc = getConvertFunc(featureType) - val labelFunc = getConvertFunc(labelType) + /** + * Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the + * validation data if validation data is set, which can be used for visualization via + * Tensorboard. Use setValidationSummary to enable validation logger. Then the log will be + * saved to logDir/appName/ as specified by the parameters of validationSummary. + * + * Default: None + */ + def getValidationSummary: Option[ValidationSummary] = validationSummary + + /** + * Enable validation Summary + */ + def setValidationSummary(value: ValidationSummary): this.type = { + this.validationSummary = Some(value) + this + } + + @transient private var validationTrigger: Option[Trigger] = None + @transient private var validationDF: DataFrame = _ + @transient private var validationMethods: Array[ValidationMethod[T]] = _ + @transient private var validationBatchSize: Int = 0 + /** + * Set a validate evaluation during training + * + * @param trigger how often to evaluation validation set + * @param validationDF validate data set + * @param vMethods a set of validation method [[ValidationMethod]] + * @param batchSize batch size for validation + * @return this optimizer + */ + def setValidation(trigger: Trigger, validationDF: DataFrame, + vMethods : Array[ValidationMethod[T]], batchSize: Int) + : this.type = { + this.validationTrigger = Some(trigger) + this.validationDF = validationDF + this.validationMethods = vMethods + this.validationBatchSize = batchSize + this + } - val featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])] = dataFrame.rdd.map { row => - val features = featureFunc(row, featureColIndex) - val labels = labelFunc(row, labelColIndex) - (features, labels) + protected def validateParams(schema : StructType): Unit = { + validateDataType(schema, $(featuresCol)) + validateDataType(schema, $(labelCol)) + if(isSet(endWhen) && isSet(maxEpoch)) { + throw new IllegalArgumentException(s"endWhen and maxEpoch cannot be both set") + } + if (validationTrigger.isEmpty && validationSummary.isDefined) { + throw new IllegalArgumentException( + s"validationSummary is only valid if validation data is set.") } + } + + override def transformSchema(schema : StructType): StructType = { + validateParams(schema) + SchemaUtils.appendColumn(schema, $(predictionCol), ArrayType(DoubleType, false)) + } - val samples = featureAndLabel.map { case (f, l) => - // convert feature and label data type to the same type with model - // TODO: investigate to reduce memory consumption during conversion. - val feature = f.head match { - case dd: Double => f.asInstanceOf[Seq[Double]].map(ev.fromType(_)) - case ff: Float => f.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + protected override def internalFit(dataFrame: DataFrame): DLModel[T] = { + val localFeatureCol = $(featuresCol) + val localLabelCol = $(labelCol) + + def getSamples(dataFrame: DataFrame): RDD[Sample[T]] = { + val featureType = dataFrame.schema(localFeatureCol).dataType + val featureColIndex = dataFrame.schema.fieldIndex(localFeatureCol) + val labelType = dataFrame.schema(localLabelCol).dataType + val labelColIndex = dataFrame.schema.fieldIndex(localLabelCol) + + val featureFunc = getConvertFunc(featureType) + val labelFunc = getConvertFunc(labelType) + + val featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])] = dataFrame.rdd.map { row => + val features = featureFunc(row, featureColIndex) + val labels = labelFunc(row, labelColIndex) + (features, labels) } - val label = l.head match { - case dd: Double => l.asInstanceOf[Seq[Double]].map(ev.fromType(_)) - case ff: Float => l.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + + val samples = featureAndLabel.map { case (f, l) => + // convert feature and label data type to the same type with model + // TODO: investigate to reduce memory consumption during conversion. + val feature = f.head match { + case dd: Double => f.asInstanceOf[Seq[Double]].map(ev.fromType(_)) + case ff: Float => f.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + } + val label = l.head match { + case dd: Double => l.asInstanceOf[Seq[Double]].map(ev.fromType(_)) + case ff: Float => l.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + } + (feature, label) + }.map { case (feature, label) => + Sample(Tensor(feature.toArray, featureSize), Tensor(label.toArray, labelSize)) } - (feature, label) - }.map { case (feature, label) => - Sample(Tensor(feature.toArray, featureSize), Tensor(label.toArray, labelSize)) + samples } - if(!isDefined(optimMethod)) { - set(optimMethod, new SGD[T]) - } + val trainingSamples = getSamples(dataFrame) val state = T("learningRate" -> $(learningRate), "learningRateDecay" -> $(learningRateDecay)) - val optimizer = Optimizer(model, samples, criterion, $(batchSize)) + val endTrigger = if (isSet(endWhen)) $(endWhen) else Trigger.maxEpoch($(maxEpoch)) + val optimizer = Optimizer(model, trainingSamples, criterion, $(batchSize)) .setState(state) .setOptimMethod($(optimMethod)) - .setEndWhen(Trigger.maxEpoch($(maxEpoch))) - val optimizedModel = optimizer.optimize() + .setEndWhen(endTrigger) + + if (validationTrigger.isDefined) { + val validationSamples = getSamples(validationDF) + optimizer.setValidation( + validationTrigger.get, + validationSamples, + validationMethods, + validationBatchSize) + if (this.validationSummary.isDefined) { + optimizer.setValidationSummary(this.validationSummary.get) + } + } + + if (this.trainSummary.isDefined) { + optimizer.setTrainSummary(this.trainSummary.get) + } + val optimizedModel = optimizer.optimize() wrapBigDLModel(optimizedModel, featureSize) } diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index bf187afadad..b02ad301a95 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -16,9 +16,8 @@ package org.apache.spark.ml import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol} +import org.apache.spark.ml.param.shared.HasLabelCol import org.apache.spark.ml.linalg.{Vector, VectorUDT} -import org.apache.spark.rdd.RDD import org.apache.spark.sql.types._ import org.apache.spark.sql.{DataFrame, Dataset, Row} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Trigger.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Trigger.scala index ff503efd72f..801ba6d2613 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Trigger.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Trigger.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.utils.Table * and a corresponding action will be taken when the timespot(s) * is reached. */ -trait Trigger { +trait Trigger extends Serializable { def apply(state: Table): Boolean } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala index 12f2cdb4fd1..c3a4d43149d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala @@ -22,10 +22,12 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.visualization.ValidationSummary import org.apache.log4j.{Level, Logger} import org.apache.spark.ml.feature.MinMaxScaler import org.apache.spark.SparkContext import org.apache.spark.ml._ +import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.sql.{DataFrame, SQLContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -68,7 +70,7 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(estimator.getLearningRateDecay == 0) } - "An DLClassifier" should "fit on feature(one dimension Array[Double]) and label(Double)" in { + "An DLClassifier" should "get reasonale accuracy" in { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = ClassNLLCriterion[Float]() val classifier = new DLClassifier[Float](model, criterion, Array(6)) @@ -84,6 +86,32 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(dlModel.transform(df).where("prediction=label").count() > nRecords * 0.8) } + "An DLClassifier" should "support different FEATURE types" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val classifier = new DLClassifier[Float](model, criterion, Array(6)) + .setLearningRate(0.1) + .setBatchSize(2) + .setEndWhen(Trigger.maxIteration(2)) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2)))) + .toDF("features", "label"), // Array[Double] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.map(_.toFloat), p._2)))) + .toDF("features", "label"), // Array[Float] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head.toFloat, p._2)))) + .toDF("features", "label"), // Float + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head, p._2)))) + .toDF("features", "label"), // Double + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (Vectors.dense(p._1), p._2)))) + .toDF("features", "label") // MLlib Vector + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = classifier.fit(df) + dlModel.transform(df).collect() + } + } + "An DLClassifier" should "fit with adam and LBFGS" in { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = ClassNLLCriterion[Float]() @@ -100,6 +128,28 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { } } + "An DLClassifier" should "supports validation data and summary" in { + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + + val logdir = com.google.common.io.Files.createTempDir() + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val classifier = new DLClassifier[Float](model, criterion, Array(6)) + .setBatchSize(nRecords) + .setEndWhen(Trigger.maxIteration(5)) + .setOptimMethod(new Adam[Float]) + .setLearningRate(0.1) + .setValidation(Trigger.severalIteration(1), df, Array(new Loss[Float]()), 2) + .setValidationSummary(ValidationSummary(logdir.getPath, "DLEstimatorValidation")) + + classifier.fit(df) + val validationSummary = classifier.getValidationSummary.get + val losses = validationSummary.readScalar("Loss") + validationSummary.close() + logdir.deleteOnExit() + } + "An DLClassifier" should "get the same classification result with BigDL model" in { Logger.getLogger("org").setLevel(Level.WARN) Logger.getLogger("akka").setLevel(Level.WARN) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala index 74be2c5f706..04d41917c5b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala @@ -21,10 +21,13 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext import org.apache.spark.ml.feature.MinMaxScaler import org.apache.spark.ml.{DLEstimator, DLModel, Pipeline, PipelineModel} +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row, SQLContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -68,7 +71,7 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { } - "An DLEstimator" should "fit on feature(one dimension Array[Double]) and label(Double)" in { + "An DLEstimator" should "get reasonable accuracy" in { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = ClassNLLCriterion[Float]() val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) @@ -88,53 +91,56 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(correct > nRecords * 0.8) } - "An DLEstimator" should "fit on feature(one dimension Array[Float]) and label(Double)" in { + "An DLEstimator" should "support different FEATURE types" in { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = ClassNLLCriterion[Float]() val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) - .setBatchSize(nRecords) - // intentionally set low since this only validates data format compatibitliy - .setMaxEpoch(1) - val data = sc.parallelize(smallData.map(p => (p._1.map(_.toFloat), p._2))) - val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") - - val dlModel = estimator.fit(df) - dlModel.isInstanceOf[DLModel[_]] should be(true) - dlModel.transform(df).count() - } - - "An DLEstimator" should - "fit on feature(one dimension Array[Double]) and label(Array[Double])" in { - val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) - val criterion = MultiLabelSoftMarginCriterion[Float]() - val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(2)) - // intentionally set low since this only validates data format compatibitliy - .setMaxEpoch(1) - .setBatchSize(nRecords) - val data = sc.parallelize( - smallData.map(p => (p._1, if (p._2 == 1.0) Array(0.0, 1.0) else Array(1.0, 0.0)))) - val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") - - val dlModel = estimator.fit(df) - dlModel.isInstanceOf[DLModel[_]] should be(true) - dlModel.transform(df).count() + .setBatchSize(2) + // intentionally set low since this only validates data format compatibility + .setEndWhen(Trigger.maxIteration(1)) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2)))) + .toDF("features", "label"), // Array[Double] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.map(_.toFloat), p._2)))) + .toDF("features", "label"), // Array[Float] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head.toFloat, p._2)))) + .toDF("features", "label"), // Float + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head, p._2)))) + .toDF("features", "label"), // Double + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (Vectors.dense(p._1), p._2)))) + .toDF("features", "label") // MLlib Vector + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = estimator.fit(df) + dlModel.transform(df).collect() + } } - "An DLEstimator" should "fit on feature(one dimension Array[Float]) and label(Array[Float])" in { + "An DLEstimator" should "support different LABEL types" in { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = MultiLabelSoftMarginCriterion[Float]() val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(2)) // intentionally set low since this only validates data format compatibitliy - .setMaxEpoch(1) - .setBatchSize(nRecords) - val data = sc.parallelize( - smallData.map(p => (p._1.map(_.toFloat), - if (p._2 == 1.0) Array(0.0f, 1.0f) else Array(1.0f, 0.0f)))) - val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") - - val dlModel = estimator.fit(df) - dlModel.isInstanceOf[DLModel[_]] should be(true) - dlModel.transform(df).count() + .setEndWhen(Trigger.maxIteration(1)) + .setBatchSize(2) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, Array(p._2))))) + .toDF("features", "label"), // Array[Double] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, Array(p._2.toFloat))))) + .toDF("features", "label"), // Array[Float] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2.toFloat)))) + .toDF("features", "label"), // Float + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2)))) + .toDF("features", "label"), // Double + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, Vectors.dense(p._2))))) + .toDF("features", "label") // MLlib Vector + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = estimator.fit(df) + dlModel.transform(df).collect() + } } "An DLEstimator" should "work with tensor data" in { @@ -154,31 +160,10 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val trainingDF: DataFrame = sqlContext.createDataFrame(miniBatch).toDF("features", "label") val dlModel = estimator.fit(trainingDF) - dlModel.isInstanceOf[DLModel[_]] should be(true) dlModel.transform(trainingDF).collect() } - "An DLEstimator" should "work with MLlib Vectors" in { - var appSparkVersion = org.apache.spark.SPARK_VERSION - if (appSparkVersion.trim.startsWith("1")) { - val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) - val criterion = ClassNLLCriterion[Float]() - val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) - // intentionally set low since this only validates data format compatibitliy - .setMaxEpoch(1) - .setBatchSize(nRecords) - val data = sc.parallelize( - smallData.map(p => (org.apache.spark.mllib.linalg.Vectors.dense(p._1), p._2))) - val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") - - val dlModel = estimator.fit(df) - dlModel.isInstanceOf[DLModel[_]] should be(true) - dlModel.transform(df).count() - } - // TODO find a way to test ML Vectors. - } - - "An DLEstimator" should "has good result for different batchSize" in { + "An DLEstimator" should "support different batchSize" in { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = ClassNLLCriterion[Float]() val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) @@ -192,6 +177,18 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { dlModel.transform(df).count() } + "An DLModel" should "support transform with different batchSize" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(nRecords) + .setMaxEpoch(maxEpoch) + val data = sc.parallelize(smallData) + val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") + val dlModel = estimator.fit(df) + assert(df.count() == dlModel.setBatchSize(51).transform(df).count()) + } + "An DLEstimator" should "throws exception without correct inputs" in { val model = Linear[Float](10, 1) val criterion = ClassNLLCriterion[Float]() @@ -205,13 +202,68 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { MinibatchData[Float](featureData.storage().array(), labelData.storage().array()) )) var df: DataFrame = sqlContext.createDataFrame(miniBatch).toDF(inputs: _*) - // Spark 1.6 and 2.0 throws different exception here intercept[Exception] { estimator.fit(df) } } + "An DLEstimator" should "supports training summary" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val logdir = com.google.common.io.Files.createTempDir() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(nRecords) + .setMaxEpoch(5) + .setTrainSummary(TrainSummary(logdir.getPath, "DLEstimatorTrain")) + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + + val dlModel = estimator.fit(df) + val trainSummary = estimator.getTrainSummary.get + val losses = trainSummary.readScalar("Loss") + assert(losses.length == 5) + trainSummary.close() + logdir.deleteOnExit() + } + + "An DLEstimator" should "supports validation data and summary" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val logdir = com.google.common.io.Files.createTempDir() + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(4) + .setEndWhen(Trigger.maxIteration(5)) + .setValidation(Trigger.severalIteration(1), df, Array(new Loss[Float]()), 2) + .setValidationSummary(ValidationSummary(logdir.getPath, "DLEstimatorValidation")) + + val dlModel = estimator.fit(df) + val validationSummary = estimator.getValidationSummary.get + val losses = validationSummary.readScalar("Loss") + assert(losses.length == 5) + validationSummary.close() + logdir.deleteOnExit() + } + + "An DLEstimator" should "throws exception when EndWhen and MaxEpoch are set" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val logdir = com.google.common.io.Files.createTempDir() + + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(4) + .setEndWhen(Trigger.maxIteration(5)) + .setMaxEpoch(5) + + intercept[Exception] { + estimator.fit(df) + } + } + "An DLEstimator" should "works in ML pipeline" in { var appSparkVersion = org.apache.spark.SPARK_VERSION if (appSparkVersion.trim.startsWith("1")) { From 7826769a5c95401865d1cc2c72f2c655a49f7779 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Fri, 5 Jan 2018 15:55:23 +0800 Subject: [PATCH 0629/1065] Wrap layers that requires two ClassTags into TensorModuleWrapper and move to tf package (#2122) * add TensorModuleToOperation * fix tests * address comments * fix test * rebase master * fix style --- .../intel/analytics/bigdl/dllib/nn/Abs.scala | 34 +++--- .../analytics/bigdl/dllib/nn/Clamp.scala | 10 +- .../intel/analytics/bigdl/dllib/nn/ELU.scala | 50 ++++----- .../analytics/bigdl/dllib/nn/HardTanh.scala | 94 ++++++++-------- .../intel/analytics/bigdl/dllib/nn/Log.scala | 22 ++-- .../intel/analytics/bigdl/dllib/nn/Mean.scala | 10 +- .../analytics/bigdl/dllib/nn/Power.scala | 44 ++++---- .../analytics/bigdl/dllib/nn/ReLU6.scala | 16 +-- .../analytics/bigdl/dllib/nn/SoftPlus.scala | 51 ++++----- .../analytics/bigdl/dllib/nn/SoftSign.scala | 33 +++--- .../dllib/nn/SpatialWithinChannelLRN.scala | 4 +- .../intel/analytics/bigdl/dllib/nn/Sqrt.scala | 10 +- .../analytics/bigdl/dllib/nn/Square.scala | 10 +- .../intel/analytics/bigdl/dllib/nn/Sum.scala | 34 +++--- .../bigdl/dllib/nn/ops/EluGrad.scala | 2 +- .../dllib/nn/ops/ModuleToOperation.scala | 2 +- .../bigdl/dllib/nn/ops/Relu6Grad.scala | 2 +- .../bigdl/dllib/nn/ops/SoftplusGrad.scala | 4 +- .../bigdl/dllib/nn/ops/SoftsignGrad.scala | 4 +- .../analytics/bigdl/dllib/nn/ops/Sum.scala | 2 +- .../bigdl/dllib/nn/ops/UnaryGrad.scala | 2 +- .../dllib/nn/tf/TensorModuleWrapper.scala | 55 ++++++++++ .../analytics/bigdl/dllib/nn/tf/package.scala | 102 ++++++++++++++++++ .../bigdl/dllib/utils/caffe/Converter.scala | 18 ++-- .../dllib/utils/caffe/LayerConverter.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 50 ++++----- .../dllib/utils/tf/BigDLToTensorflow.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Abs.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Elu.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Log.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Mean.scala | 11 +- .../bigdl/dllib/utils/tf/loaders/Relu6.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Rsqrt.scala | 4 +- .../dllib/utils/tf/loaders/Softplus.scala | 3 +- .../dllib/utils/tf/loaders/Softsign.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Sqrt.scala | 3 +- .../bigdl/dllib/utils/tf/loaders/Square.scala | 2 +- .../analytics/bigdl/dllib/nn/LogSpec.scala | 4 +- .../analytics/bigdl/dllib/nn/MMSpec.scala | 4 +- .../analytics/bigdl/dllib/nn/MVSpec.scala | 4 +- .../analytics/bigdl/dllib/nn/ModuleSpec.scala | 4 +- .../bigdl/dllib/nn/PairwiseDistanceSpec.scala | 4 +- .../bigdl/dllib/nn/ParallelTableSpec.scala | 4 +- .../analytics/bigdl/dllib/nn/PowerSpec.scala | 20 ++-- .../analytics/bigdl/dllib/nn/SumSpec.scala | 6 +- .../dllib/nn/TransformerCriterionSpec.scala | 2 +- .../analytics/bigdl/dllib/torch/AbsSpec.scala | 2 +- .../bigdl/dllib/torch/ClampSpec.scala | 2 +- .../analytics/bigdl/dllib/torch/ELUSpec.scala | 4 +- .../bigdl/dllib/torch/HardTanhSpec.scala | 8 +- .../analytics/bigdl/dllib/torch/LogSpec.scala | 2 +- .../bigdl/dllib/torch/PowerSpec.scala | 4 +- .../bigdl/dllib/torch/ReLU6Spec.scala | 4 +- .../bigdl/dllib/torch/SoftPlusSpec.scala | 4 +- .../bigdl/dllib/torch/SoftSignSpec.scala | 4 +- .../bigdl/dllib/torch/SqrtSpec.scala | 8 +- .../bigdl/dllib/torch/SquareSpec.scala | 8 +- .../analytics/bigdl/dllib/torch/SumSpec.scala | 8 +- .../serializer/ModuleSerializerSpec.scala | 37 ++++--- 59 files changed, 476 insertions(+), 373 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/TensorModuleWrapper.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/package.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala index 3fcad3f632c..02e0ebb7a88 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Abs.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -25,21 +25,17 @@ import scala.reflect.ClassTag * an element-wise abs operation */ @SerialVersionUID(3070101246787506364L) -class Abs[T: ClassTag, D: ClassTag] - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { +class Abs[T: ClassTag] + (implicit ev: TensorNumeric[T]) + extends TensorModule[T] { - output = Tensor[D]() - - gradInput = Tensor[D]() - - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { output.resizeAs(input) output.abs(input) output } - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { require(input.isContiguous() && gradOutput.isContiguous(), "Abs: input and gradOutput should be contiguous") gradInput.resizeAs(input).copy(gradOutput) @@ -52,17 +48,17 @@ class Abs[T: ClassTag, D: ClassTag] while(i < gradInput.nElement()) { val g = gradArray(i) val z = inputArray(i) - gradArray(i + gradOffset) = ev2.times(g, - if (ev2.isGreater(z, ev2.fromType(0))) ev2.fromType(1) else ev2.fromType(-1)) + gradArray(i + gradOffset) = ev.times(g, + if (ev.isGreater(z, ev.fromType(0))) ev.fromType(1) else ev.fromType(-1)) i += 1 } gradInput } - override def canEqual(other: Any): Boolean = other.isInstanceOf[Abs[T, D]] + override def canEqual(other: Any): Boolean = other.isInstanceOf[Abs[T]] override def equals(other: Any): Boolean = other match { - case that: Abs[T, D] => + case that: Abs[T] => super.equals(that) && (that canEqual this) case _ => false @@ -74,15 +70,11 @@ class Abs[T: ClassTag, D: ClassTag] state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) } - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } } object Abs { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Abs[T, D] = { - new Abs[T, D]() + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : Abs[T] = { + new Abs[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Clamp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Clamp.scala index 0dc4dd2aa3b..0986313f83f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Clamp.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Clamp.scala @@ -27,17 +27,17 @@ import scala.reflect.ClassTag * @tparam T numeric type */ @SerialVersionUID(- 3787689437971361185L) -class Clamp[T: ClassTag, D: ClassTag](private val minV: Int, private val maxV: Int)( - implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends HardTanh[T, D](minV, maxV) { +class Clamp[T: ClassTag](private val minV: Int, private val maxV: Int)( + implicit ev: TensorNumeric[T]) extends HardTanh[T](minV, maxV) { override def toString(): String = { s"nn.Clamp" } } object Clamp { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( + def apply[@specialized(Float, Double) T: ClassTag]( min: Int, - max: Int)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Clamp[T, D] = { - new Clamp[T, D](min, max) + max: Int)(implicit ev: TensorNumeric[T]) : Clamp[T] = { + new Clamp[T](min, max) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala index 59860348669..bbc9ba31258 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala @@ -28,23 +28,20 @@ import scala.reflect.ClassTag */ @SerialVersionUID( - 3525781855978085005L) -class ELU[T: ClassTag, D: ClassTag]( +class ELU[T: ClassTag]( val alpha: Double = 1.0, val inplace: Boolean = false)( - implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { + implicit ev: TensorNumeric[T]) + extends TensorModule[T] { - output = Tensor[D]() - gradInput = Tensor[D]() - - val _alpha = ev2.fromType[Double](alpha) + val _alpha = ev.fromType[Double](alpha) // Todo: Improve the performance of contiguous tensor - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { if (inplace) { input.apply1(in => { - if (ev2.isGreaterEq(ev2.fromType[Double](0), in)) { - ev2.times(ev2.minus(ev2.exp(in), ev2.fromType[Double](1)), _alpha) + if (ev.isGreaterEq(ev.fromType[Double](0), in)) { + ev.times(ev.minus(ev.exp(in), ev.fromType[Double](1)), _alpha) } else { in } @@ -53,8 +50,8 @@ class ELU[T: ClassTag, D: ClassTag]( } else { output.resizeAs(input) output.map(input, (out, in) => { - if (ev2.isGreaterEq(ev2.fromType[Int](0), in)) { - ev2.times(ev2.minus(ev2.exp(in), ev2.fromType[Double](1)), _alpha) + if (ev.isGreaterEq(ev.fromType[Int](0), in)) { + ev.times(ev.minus(ev.exp(in), ev.fromType[Double](1)), _alpha) } else { in } @@ -63,14 +60,14 @@ class ELU[T: ClassTag, D: ClassTag]( output } - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { require(input.isSameSizeAs(gradOutput), "input should have the same size with gradOutput" + s"input (${input.dim()}) gradOutput (${gradOutput.dim()}") if (inplace) { gradOutput.map(output, (grad, out) => { - if (ev2.isGreaterEq(ev2.fromType[Int](0), out)) { - ev2.times(ev2.plus(out, _alpha), grad) + if (ev.isGreaterEq(ev.fromType[Int](0), out)) { + ev.times(ev.plus(out, _alpha), grad) } else { grad } @@ -78,17 +75,17 @@ class ELU[T: ClassTag, D: ClassTag]( gradInput.set(gradOutput) } else { gradInput.resizeAs(input) - val func = new TensorFunc6[D] { - override def apply (data1: Array[D], offset1: Int, data2: Array[D], - offset2: Int, data3: Array[D], offset3: Int): Unit = { - data1(offset1) = if (ev2.isGreater(data3(offset3), ev2.fromType[Int](0))) { + val func = new TensorFunc6[T] { + override def apply (data1: Array[T], offset1: Int, data2: Array[T], + offset2: Int, data3: Array[T], offset3: Int): Unit = { + data1(offset1) = if (ev.isGreater(data3(offset3), ev.fromType[Int](0))) { data2(offset2) } else { - ev2.times(ev2.plus(data3(offset3), _alpha), data2(offset2)) + ev.times(ev.plus(data3(offset3), _alpha), data2(offset2)) } } } - DenseTensorApply.apply3[D](gradInput, gradOutput, output, func) + DenseTensorApply.apply3[T](gradInput, gradOutput, output, func) } gradInput } @@ -99,18 +96,13 @@ class ELU[T: ClassTag, D: ClassTag]( } this } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } } object ELU { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( + def apply[@specialized(Float, Double) T: ClassTag]( alpha: Double = 1.0, inplace: Boolean = false) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : ELU[T, D] = { - new ELU[T, D](alpha, inplace) + (implicit ev: TensorNumeric[T]) : ELU[T] = { + new ELU[T](alpha, inplace) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala index 9d1c40869c6..30ed8af854d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardTanh.scala @@ -34,23 +34,20 @@ import scala.reflect.ClassTag * @param inplace inplace model. */ @SerialVersionUID(- 8953866090802444183L) -class HardTanh[T: ClassTag, D: ClassTag]( +class HardTanh[T: ClassTag]( val minValue: Double = -1, val maxValue: Double = 1, val inplace: Boolean = false -)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { +)(implicit ev: TensorNumeric[T]) + extends TensorModule[T] { require(maxValue > minValue, "maxValue must be larger than minValue, " + s"maxValue ${maxValue}, " + s"minValue ${minValue}") - output = Tensor[D]() - gradInput = Tensor[D]() + val min = ev.fromType[Double](minValue) + val max = ev.fromType[Double](maxValue) - val min = ev2.fromType[Double](minValue) - val max = ev2.fromType[Double](maxValue) - - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { if (inplace) { output.set(input) } @@ -60,29 +57,29 @@ class HardTanh[T: ClassTag, D: ClassTag]( if (input.dim() == 1 || !input.isContiguous() || !output.isContiguous()) { if (inplace) { - val func = new TensorFunc2[D] { - override def apply(data: Array[D], index: Int): Unit = { - if (ev2.isGreater(min, data(index))) { - data(index) = ev2.fromType[Double](minValue) - } else if (ev2.isGreater(data(index), max)) { - data(index) = ev2.fromType[Double](maxValue) + val func = new TensorFunc2[T] { + override def apply(data: Array[T], index: Int): Unit = { + if (ev.isGreater(min, data(index))) { + data(index) = ev.fromType[Double](minValue) + } else if (ev.isGreater(data(index), max)) { + data(index) = ev.fromType[Double](maxValue) } } } - DenseTensorApply.apply1[D](input, func) + DenseTensorApply.apply1[T](input, func) } else { - val func2 = new TensorFunc4[D] { - override def apply(data1: Array[D], index1: Int, data2: Array[D], index2: Int): Unit = { - if (ev2.isGreater(min, data2(index2))) { + val func2 = new TensorFunc4[T] { + override def apply(data1: Array[T], index1: Int, data2: Array[T], index2: Int): Unit = { + if (ev.isGreater(min, data2(index2))) { data1(index1) = min - } else if (ev2.isGreaterEq(max, data2(index2))) { + } else if (ev.isGreaterEq(max, data2(index2))) { data1(index1) = data2(index2) } else { data1(index1) = max } } } - DenseTensorApply.apply2[D](output, input, func2) + DenseTensorApply.apply2[T](output, input, func2) } } else { val inputData = input.storage().array() @@ -93,18 +90,18 @@ class HardTanh[T: ClassTag, D: ClassTag]( var i = 0 if (inplace) { while (i < input.nElement()) { - if (ev2.isGreater(min, inputData(i + inputOffset))) { + if (ev.isGreater(min, inputData(i + inputOffset))) { inputData.update(i + inputOffset, min) - } else if (ev2.isGreater(inputData(i + inputOffset), max)) { + } else if (ev.isGreater(inputData(i + inputOffset), max)) { inputData.update(i + inputOffset, max) } i += 1 } } else { while (i < input.nElement()) { - if (ev2.isGreater(min, inputData(i + inputOffset))) { + if (ev.isGreater(min, inputData(i + inputOffset))) { outputData.update(i + outputOffset, min) - } else if (ev2.isGreaterEq(max, inputData(i + inputOffset))) { + } else if (ev.isGreaterEq(max, inputData(i + inputOffset))) { outputData.update(i + outputOffset, inputData(i + inputOffset)) } else { outputData.update(i + outputOffset, max) @@ -119,7 +116,7 @@ class HardTanh[T: ClassTag, D: ClassTag]( - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { require(input.nElement() == gradOutput.nElement(), s"the number of input element (${input.nElement()}) " + s"should equal the number of " + @@ -133,26 +130,26 @@ class HardTanh[T: ClassTag, D: ClassTag]( if (input.dim() == 1 || !input.isContiguous() || !gradOutput.isContiguous() || !gradInput.isContiguous()) { if (inplace) { - val func = new TensorFunc4[D] { - override def apply(data1: Array[D], index1: Int, data2: Array[D], index2: Int): Unit = { - if (ev2.isGreaterEq(min, data2(index2)) || ev2.isGreaterEq(data2(index2), max)) { - data1(index1) = ev2.fromType[Double](0) + val func = new TensorFunc4[T] { + override def apply(data1: Array[T], index1: Int, data2: Array[T], index2: Int): Unit = { + if (ev.isGreaterEq(min, data2(index2)) || ev.isGreaterEq(data2(index2), max)) { + data1(index1) = ev.fromType[Double](0) } } } - DenseTensorApply.apply2[D](gradOutput, input, func) + DenseTensorApply.apply2[T](gradOutput, input, func) } else { - val func = new TensorFunc6[D] { - override def apply(data1: Array[D], offset1: Int, data2: Array[D], - offset2: Int, data3: Array[D], offset3: Int): Unit = { - if (ev2.isGreaterEq(min, data3(offset3)) || ev2.isGreaterEq(data3(offset3), max)) { - data1(offset1) = ev2.fromType[Double](0) + val func = new TensorFunc6[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], + offset2: Int, data3: Array[T], offset3: Int): Unit = { + if (ev.isGreaterEq(min, data3(offset3)) || ev.isGreaterEq(data3(offset3), max)) { + data1(offset1) = ev.fromType[Double](0) } else { data1(offset1) = data2(offset2) } } } - DenseTensorApply.apply3[D](gradInput, gradOutput, input, func) + DenseTensorApply.apply3[T](gradInput, gradOutput, input, func) } } else { val inputData = input.storage().array() @@ -165,17 +162,17 @@ class HardTanh[T: ClassTag, D: ClassTag]( var i = 0 if (inplace) { while (i < input.nElement()) { - if (ev2.isGreaterEq(min, inputData(i + inputOffset)) - || ev2.isGreaterEq(inputData(i + inputOffset), max)) { - gradInputData.update(i + gradInputOffset, ev2.fromType[Double](0)) + if (ev.isGreaterEq(min, inputData(i + inputOffset)) + || ev.isGreaterEq(inputData(i + inputOffset), max)) { + gradInputData.update(i + gradInputOffset, ev.fromType[Double](0)) } i += 1 } } else { while (i < input.nElement()) { - if (ev2.isGreaterEq(min, inputData(i + inputOffset)) - || ev2.isGreaterEq(inputData(i + inputOffset), max)) { - gradInputData.update(i + gradInputOffset, ev2.fromType[Double](0)) + if (ev.isGreaterEq(min, inputData(i + inputOffset)) + || ev.isGreaterEq(inputData(i + inputOffset), max)) { + gradInputData.update(i + gradInputOffset, ev.fromType[Double](0)) } else { gradInputData.update(i + gradInputOffset, gradOutputData(i + gradOutputOffset)) } @@ -197,19 +194,14 @@ class HardTanh[T: ClassTag, D: ClassTag]( } this } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } } object HardTanh { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( + def apply[@specialized(Float, Double) T: ClassTag]( minValue: Double = -1, maxValue: Double = 1, inplace: Boolean = false) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): HardTanh[T, D] = { - new HardTanh[T, D](minValue, maxValue, inplace) + (implicit ev: TensorNumeric[T]): HardTanh[T] = { + new HardTanh[T](minValue, maxValue, inplace) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala index dc36cc8f73d..d4894c23220 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Log.scala @@ -26,35 +26,29 @@ import scala.reflect.ClassTag * The [[Log]] module applies a log transformation to the input data */ @SerialVersionUID(- 5175095570714684226L) -class Log[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { - output = Tensor[D]() - gradInput = Tensor[D]() +class Log[T: ClassTag] (implicit ev: TensorNumeric[T]) + extends TensorModule[T] { - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { output.resizeAs(input) .copy(input) .log() output } - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { gradInput.resizeAs(input) - .fill(ev2.fromType[Double](1.0)) + .fill(ev.fromType[Double](1.0)) .cdiv(input) .cmul(gradOutput) gradInput } - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } } object Log { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Log[T, D] = { - new Log[T, D]() + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]): Log[T] = { + new Log[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mean.scala index 2164e3f76ce..4e1f494936c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mean.scala @@ -38,12 +38,12 @@ import scala.reflect.ClassTag */ @SerialVersionUID(2995626598003841724L) -class Mean[T: ClassTag, D: ClassTag]( +class Mean[T: ClassTag]( val dimension: Int = 1, val nInputDims: Int = -1, val squeeze: Boolean = true) - (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]) - extends Sum[T, D](dimension, nInputDims, true, squeeze) { + (implicit ev: TensorNumeric[T]) + extends Sum[T](dimension, nInputDims, true, squeeze) { override def toString: String = s"nn.Mean" } @@ -51,7 +51,7 @@ object Mean { def apply[@specialized(Float, Double) T: ClassTag]( dimension: Int = 1, nInputDims: Int = -1, - squeeze: Boolean = true)(implicit ev: TensorNumeric[T]) : Mean[T, T] = { - new Mean[T, T](dimension, nInputDims, squeeze) + squeeze: Boolean = true)(implicit ev: TensorNumeric[T]) : Mean[T] = { + new Mean[T](dimension, nInputDims, squeeze) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala index 3b469a8fd5a..cb081e066e4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala @@ -33,36 +33,33 @@ import scala.reflect.ClassTag */ @SerialVersionUID(- 6637789603381436472L) -class Power[T: ClassTag, D: ClassTag]( +class Power[T: ClassTag]( val power: Double, val scale : Double = 1, val shift : Double = 0) -(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { - - output = Tensor[D]() - gradInput = Tensor[D]() +(implicit ev: TensorNumeric[T]) + extends TensorModule[T] { val diffScale = power * scale - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { output.resizeAs(input) output.copy(input) if(scale != 1) { - output.mul(ev2.fromType[Double](scale)) + output.mul(ev.fromType[Double](scale)) } if(shift != 0) { - output.add(ev2.fromType[Double](shift)) + output.add(ev.fromType[Double](shift)) } if(power != 1) { - output.pow(output, ev2.fromType[Double](power)) + output.pow(output, ev.fromType[Double](power)) } output } - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { gradInput.resizeAs(input) // Compute dy/dx = scale * power * (shift + scale * x)^(power - 1) // = diff_scale * y / (shift + scale * x) @@ -71,28 +68,28 @@ class Power[T: ClassTag, D: ClassTag]( // -> dy/dx = 2 * scale * (shift + scale * x) // = diff_scale * shift + diff_scale * scale * x gradInput.copy(input) - gradInput.mul(ev2.fromType[Double](diffScale * scale)) + gradInput.mul(ev.fromType[Double](diffScale * scale)) if(shift != 0) { - gradInput.add(ev2.fromType(diffScale * shift)) + gradInput.add(ev.fromType(diffScale * shift)) } } else if (shift == 0) { // Special case for y = (scale * x)^power // -> dy/dx = scale * power * (scale * x)^(power - 1) // = scale * power * (scale * x)^power * (scale * x)^(-1) // = power * y / x - gradInput.fill(ev2.fromType[Int](0)) - gradInput.addcdiv(ev2.fromType[Double](power), output, input) + gradInput.fill(ev.fromType[Int](0)) + gradInput.addcdiv(ev.fromType[Double](power), output, input) } else { gradInput.copy(input) if(scale != 1) { - gradInput.mul(ev2.fromType[Double](scale)) + gradInput.mul(ev.fromType[Double](scale)) } if(shift != 0) { - gradInput.add(ev2.fromType[Double](shift)) + gradInput.add(ev.fromType[Double](shift)) } gradInput.cdiv(output, gradInput) if (diffScale != 1) { - gradInput.mul(ev2.fromType[Double](diffScale)) + gradInput.mul(ev.fromType[Double](diffScale)) } } if(diffScale != 0) { @@ -106,18 +103,13 @@ class Power[T: ClassTag, D: ClassTag]( s"${getPrintName}($power, $scale, $shift)" } - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } - } object Power { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( + def apply[@specialized(Float, Double) T: ClassTag]( power: Double, scale : Double = 1, - shift : Double = 0)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Power[T, D] = { - new Power[T, D](power, scale, shift) + shift : Double = 0)(implicit ev: TensorNumeric[T]): Power[T] = { + new Power[T](power, scale, shift) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU6.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU6.scala index 0099ac954b9..34eddd3e244 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU6.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU6.scala @@ -29,15 +29,15 @@ import scala.reflect.ClassTag */ @SerialVersionUID(8169462538025916360L) -class ReLU6[T: ClassTag, D: ClassTag](inplace: Boolean = false) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends HardTanh[T, D](0, 6, inplace) { +class ReLU6[T: ClassTag](inplace: Boolean = false) + (implicit ev: TensorNumeric[T]) + extends HardTanh[T](0, 6, inplace) { - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { super.updateOutput(input) } - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { super.updateGradInput(input, gradOutput) } @@ -50,9 +50,9 @@ class ReLU6[T: ClassTag, D: ClassTag](inplace: Boolean = false) } object ReLU6 { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( + def apply[@specialized(Float, Double) T: ClassTag]( inplace: Boolean = false) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): ReLU6[T, D] = { - new ReLU6[T, D]() + (implicit ev: TensorNumeric[T]): ReLU6[T] = { + new ReLU6[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala index 18a031fee73..723d61dbb7b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala @@ -31,37 +31,34 @@ import scala.reflect.ClassTag */ @SerialVersionUID(- 6938956677043843473L) -class SoftPlus[T: ClassTag, D: ClassTag]( +class SoftPlus[T: ClassTag]( val beta: Double = 1.0 - )( implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { + )( implicit ev: TensorNumeric[T]) + extends TensorModule[T] { // Avoid floating point issues with exp(x), x>20 - private val threshold = ev2.fromType[Double](20.0) - private val betaT = ev2.fromType[Double](beta) + private val threshold = ev.fromType[Double](20.0) + private val betaT = ev.fromType[Double](beta) - output = Tensor[D]() - gradInput = Tensor[D]() - - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { output.resizeAs(input) // f(x) = 1/beta * log(1 + exp(beta * x)) - val func = new TensorFunc4[D] { - override def apply (data1: Array[D], offset1: Int, data2: Array[D], offset2: Int): Unit = { - data1(offset1) = if (ev2.isGreater(ev2.times(data2(offset2), betaT), threshold)) { + val func = new TensorFunc4[T] { + override def apply (data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = { + data1(offset1) = if (ev.isGreater(ev.times(data2(offset2), betaT), threshold)) { data2(offset2) } else { - ev2.divide(ev2.log1p(ev2.exp(ev2.times(data2(offset2), betaT))), betaT) + ev.divide(ev.log1p(ev.exp(ev.times(data2(offset2), betaT))), betaT) } } } - DenseTensorApply.apply2[D](output, input, func) + DenseTensorApply.apply2[T](output, input, func) output } - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { gradInput.resizeAs(input) // d/dx[log(1+exp(k*x))/k] = exp(kx) / (exp(kx) + 1) @@ -69,32 +66,28 @@ class SoftPlus[T: ClassTag, D: ClassTag]( // y = (1/k)*log(1+exp(k*x)) --> x = (1/k)*log(exp(k*y)-1) // THEREFORE: // d/dx(f(x)) = (exp(k*y) - 1) / exp(k*y) - val func = new TensorFunc6[D] { - override def apply(data1: Array[D], offset1: Int, data2: Array[D], offset2: Int, - data3: Array[D], offset3: Int): Unit = { - val z = ev2.exp(ev2.times(data3(offset3), betaT)) - data1(offset1) = if (ev2.isGreater(ev2.times(data3(offset3), betaT), threshold)) { + val func = new TensorFunc6[T] { + override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, + data3: Array[T], offset3: Int): Unit = { + val z = ev.exp(ev.times(data3(offset3), betaT)) + data1(offset1) = if (ev.isGreater(ev.times(data3(offset3), betaT), threshold)) { data2(offset2) } else { - ev2.times(data2(offset2), ev2.divide(ev2.minus(z, ev2.fromType[Int](1)), z)) + ev.times(data2(offset2), ev.divide(ev.minus(z, ev.fromType[Int](1)), z)) } } } - DenseTensorApply.apply3[D](gradInput, gradOutput, output, func) + DenseTensorApply.apply3[T](gradInput, gradOutput, output, func) gradInput } - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } } object SoftPlus { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]( + def apply[@specialized(Float, Double) T: ClassTag]( beta: Double = 1.0) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : SoftPlus[T, D] = { - new SoftPlus[T, D](beta) + (implicit ev: TensorNumeric[T]) : SoftPlus[T] = { + new SoftPlus[T](beta) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala index fe443796086..97a5324ea62 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala @@ -28,47 +28,42 @@ import scala.reflect.ClassTag */ @SerialVersionUID(- 3936698382129844874L) -class SoftSign[T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { +class SoftSign[T: ClassTag]() + (implicit ev: TensorNumeric[T]) + extends TensorModule[T] { - @transient private var temp: Tensor[D] = null - @transient private var tempGrad: Tensor[D] = null + @transient private var temp: Tensor[T] = null + @transient private var tempGrad: Tensor[T] = null - output = Tensor[D]() - gradInput = Tensor[D]() + output = Tensor[T]() + gradInput = Tensor[T]() - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { if (null == temp) { temp = input.clone() } else { temp.resizeAs(input).copy(input) } - temp.abs().add(ev2.fromType[Int](1)) + temp.abs().add(ev.fromType[Int](1)) output.resizeAs(input).copy(input).cdiv(temp) output } - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { if (null == tempGrad) { tempGrad = input.clone() } else { tempGrad.resizeAs(output).copy(input) } - tempGrad.abs().add(ev2.fromType[Int](1)).cmul(tempGrad) + tempGrad.abs().add(ev.fromType[Int](1)).cmul(tempGrad) gradInput.resizeAs(input).copy(gradOutput).cdiv(tempGrad) gradInput } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } } object SoftSign { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : SoftSign[T, D] = { - new SoftSign[T, D]() + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : SoftSign[T] = { + new SoftSign[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRN.scala index bc0195c340a..07b5312a39a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRN.scala @@ -41,10 +41,10 @@ class SpatialWithinChannelLRN[T: ClassTag] .add(new ConcatTable[T]() .add(Identity[T]()) .add(Sequential[T]() - .add(Power[T, T](2)) + .add(Power[T](2)) .add(SpatialAveragePooling[T](size, size, padW = (size - 1) / 2, padH = (size - 1) / 2).ceil()) - .add(Power[T, T](-beta, alpha, 1)))) + .add(Power[T](-beta, alpha, 1)))) .add(CMulTable[T]()) override def updateOutput(input: Tensor[T]): Tensor[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sqrt.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sqrt.scala index a27a4dcfce6..b4aa40b59b4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sqrt.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sqrt.scala @@ -24,13 +24,13 @@ import scala.reflect.ClassTag */ @SerialVersionUID(223597921741020277L) -class Sqrt[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Power[T, D](0.5, 1, 0) { +class Sqrt[T: ClassTag](implicit ev: TensorNumeric[T]) + extends Power[T](0.5, 1, 0) { } object Sqrt { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Sqrt[T, D] = { - new Sqrt[T, D]() + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : Sqrt[T] = { + new Sqrt[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Square.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Square.scala index d949c3a3656..90aab4e14ea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Square.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Square.scala @@ -24,13 +24,13 @@ import scala.reflect.ClassTag */ @SerialVersionUID(5169592189338322411L) -class Square[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Power[T, D](2, 1, 0) { +class Square[T: ClassTag](implicit ev: TensorNumeric[T]) + extends Power[T](2, 1, 0) { } object Square { - def apply[@specialized(Float, Double) T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Square[T, D] = { - new Square[T, D]() + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : Square[T] = { + new Square[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala index c9198b4b1f9..e75a4074c93 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sum.scala @@ -41,20 +41,17 @@ import scala.reflect.ClassTag */ @SerialVersionUID(- 8025422596092583688L) -class Sum[T: ClassTag, D: ClassTag]( +class Sum[T: ClassTag]( private var dimension: Int = 1, nInputDims: Int = -1, sizeAverage: Boolean = false, squeeze: Boolean = true) - (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { + (implicit ev: TensorNumeric[T]) + extends TensorModule[T] { @transient - private var _gradOutput: Tensor[D] = null + private var _gradOutput: Tensor[T] = null - output = Tensor[D]() - gradInput = Tensor[D]() - - private def getPositiveDimension(input: Tensor[D]): Int = { + private def getPositiveDimension(input: Tensor[T]): Int = { var dimension = this.dimension if (dimension < 0) { dimension = input.dim() + dimension + 1 @@ -74,12 +71,12 @@ class Sum[T: ClassTag, D: ClassTag]( this } - override def updateOutput(input: Tensor[D]): Tensor[D] = { + override def updateOutput(input: Tensor[T]): Tensor[T] = { val dimension = getPositiveDimension(input) output.sum(input, dimension) if (sizeAverage) { - output.div(evd.fromType(input.size(dimension))) + output.div(ev.fromType(input.size(dimension))) } if (output.nDimension() > 1 && squeeze) { @@ -87,13 +84,13 @@ class Sum[T: ClassTag, D: ClassTag]( } if (output.nElement() == 1 && squeeze) { - output = Tensor.scalar[D](output.storage.apply(output.storageOffset() - 1)) + output = Tensor.scalar[T](output.storage.apply(output.storageOffset() - 1)) } output } - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { val dimension = getPositiveDimension(input) val size = input.size() size(dimension - 1) = 1 @@ -106,25 +103,20 @@ class Sum[T: ClassTag, D: ClassTag]( gradInput.resizeAs(input) gradInput.copy(_gradOutput.expandAs(input)) if (sizeAverage) { - gradInput.div(evd.fromType(input.size(dimension))) + gradInput.div(ev.fromType(input.size(dimension))) } gradInput } - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, evd)) - } - override def toString: String = s"nn.Sum" } object Sum { - def apply[T: ClassTag, D: ClassTag]( + def apply[T: ClassTag]( dimension: Int = 1, nInputDims: Int = -1, sizeAverage: Boolean = false, - squeeze: Boolean = true)(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]) : Sum[T, D] = { - new Sum[T, D](dimension, nInputDims, sizeAverage, squeeze) + squeeze: Boolean = true)(implicit ev: TensorNumeric[T]) : Sum[T] = { + new Sum[T](dimension, nInputDims, sizeAverage, squeeze) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala index d6bdde99153..9d414f8036f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala @@ -24,7 +24,7 @@ class EluGrad[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends UnaryGrad[T, D](true, true) { - override val module: Module = ELULayer[T, D]() + override val module: Module = ELULayer[D]() } object EluGrad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala index 891fef60baf..f02359616bf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperation.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala index 62d7f6bd2a4..bd26c72e28f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala @@ -24,7 +24,7 @@ class Relu6Grad[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends UnaryGrad[T, D](true) { - val module: Module = ReLU6Layer[T, D]() + val module: Module = ReLU6Layer[D]() } object Relu6Grad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala index 2c3b76da64c..2b64ab3b0fe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.nn.SoftPlus +import com.intel.analytics.bigdl.nn.{SoftPlus => SoftPlusLayer} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag @@ -24,7 +24,7 @@ class SoftplusGrad[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends UnaryGrad[T, D](true, true) { - override val module: Module = SoftPlus[T, D]() + override val module: Module = SoftPlusLayer[D]() override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala index a9eb55a6e2c..b98d1173738 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.nn.SoftSign +import com.intel.analytics.bigdl.nn.{SoftSign => SoftSignLayer} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag @@ -24,7 +24,7 @@ class SoftsignGrad[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends UnaryGrad[T, D](true) { - override val module: Module = SoftSign[T, D]() + override val module: Module = SoftSignLayer[D]() } object SoftsignGrad { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala index b96cf99dd70..e6dd5aba412 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Sum.scala @@ -27,7 +27,7 @@ class Sum[T: ClassTag, D: ClassTag](val keepDims: Boolean, val startFromZero: Bo (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { - private val sum: SumLayer[T, D] = SumLayer[T, D](squeeze = !keepDims) + private val sum: SumLayer[D] = SumLayer[D](squeeze = !keepDims) output = Tensor[D]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala index 44d513db078..fb6b6e0043e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala @@ -28,7 +28,7 @@ abstract class UnaryGrad[T: ClassTag, D: ClassTag]( (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T]{ - type Module = AbstractModule[Tensor[D], Tensor[D], T] + type Module = AbstractModule[Tensor[D], Tensor[D], _] val module: Module diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/TensorModuleWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/TensorModuleWrapper.scala new file mode 100644 index 00000000000..ced4be864c0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/TensorModuleWrapper.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * The AbstractModule[Tensor[D], Tensor[D], T] that takes a TensorModule[D] to + * enable the computation of D typed data in a module with numeric type of T. + */ +class TensorModuleWrapper[T: ClassTag, D: ClassTag] private +(val module: TensorModule[D]) +(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]) +extends AbstractModule[Tensor[D], Tensor[D], T] { + + output = Tensor[D]() + gradInput = Tensor[D]() + + override def updateOutput(input: Tensor[D]): Tensor[D] = { + output = module.forward(input) + output + } + + override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { + module.backward(input, gradOutput) + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, evd)) + } +} + +object TensorModuleWrapper { + def apply[T: ClassTag, D: ClassTag](model: TensorModule[D]) + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): TensorModuleWrapper[T, D] = + new TensorModuleWrapper[T, D](model) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/package.scala new file mode 100644 index 00000000000..1d77faec5f3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/package.scala @@ -0,0 +1,102 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.tf.TensorModuleWrapper +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +package object tf { + + object Mean { + def apply[T: ClassTag, D: ClassTag](dimension: Int = 1, + nInputDims: Int = -1, + squeeze: Boolean = true) + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.Mean(dimension, nInputDims, squeeze)) + } + + object Abs { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.Abs[D]()) + } + + object Clamp { + def apply[T: ClassTag, D: ClassTag](min: Int, max: Int) + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.Clamp[D](min, max)) + } + + object ReLU6 { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.ReLU6[D]()) + } + + object ELU { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.ELU[D]()) + } + + object Log { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.Log[D]()) + } + + object Power { + def apply[T: ClassTag, D: ClassTag](power: Double, + scale : Double = 1, + shift : Double = 0) + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.Power[D](power, scale, shift)) + } + + object SoftPlus { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.SoftPlus[D]()) + } + + object SoftSign { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], evd: TensorNumeric[D]): + AbstractModule[Activity, Activity, T] + = TensorModuleWrapper[T, D]( + com.intel.analytics.bigdl.nn.SoftSign[D]()) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala index 741e062b5a5..1bce9617c02 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/Converter.scala @@ -156,7 +156,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { private def fromCaffeAbsVal(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val layerName = getLayerName(layer) - Seq(Abs[T, T]().setName(layerName).inputs()) + Seq(Abs[T]().setName(layerName).inputs()) } private def fromCaffeConcat(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -173,7 +173,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { private def fromCaffeLog(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { val layerName = getLayerName(layer) - Seq(Log[T, T]().setName(layerName).inputs()) + Seq(Log[T]().setName(layerName).inputs()) } private def fromCaffePower(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -184,7 +184,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { var shift = 0.0 if (param.hasScale) scale = param.getScale if (param.hasShift) shift = param.getShift - Seq(Power[T, T](power, scale, shift).setName(layerName).inputs()) + Seq(Power[T](power, scale, shift).setName(layerName).inputs()) } private def fromCaffePreLU(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { @@ -306,14 +306,14 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { case logSoftMax : LogSoftMax[_] => toCaffeLogSoftMax(moduleNode, bottoms, nextSize) case tanh : Tanh[_] => toCaffeTanh(moduleNode, bottoms, nextSize) case sigmoid : Sigmoid[_] => toCaffeSigmoid(moduleNode, bottoms, nextSize) - case abs : Abs[_, _] => toCaffeAbs(moduleNode, bottoms, nextSize) + case abs : Abs[_] => toCaffeAbs(moduleNode, bottoms, nextSize) case bartchNorm : SpatialBatchNormalization[_] => toCaffeBatchNormalization(moduleNode, bottoms, nextSize) case joinTable : JoinTable[_] => toCaffeConcat(moduleNode, bottoms, nextSize) - case elu : ELU[_, _] => toCaffeElu(moduleNode, bottoms, nextSize) + case elu : ELU[_] => toCaffeElu(moduleNode, bottoms, nextSize) case infershape : InferReshape[_] => toCaffeFlattern(moduleNode, bottoms, nextSize) - case log : Log[_, _] => toCaffeLog(moduleNode, bottoms, nextSize) - case power : Power[_, _] => toCaffePower(moduleNode, bottoms, nextSize) + case log : Log[_] => toCaffeLog(moduleNode, bottoms, nextSize) + case power : Power[_] => toCaffePower(moduleNode, bottoms, nextSize) case prelu : PReLU[_] => toCaffePReLu(moduleNode, bottoms, nextSize) case recurrent : Recurrent[_] => toCaffeRecurrent(moduleNode, bottoms, nextSize) case reshape : Reshape[_] => toCaffeReshape(moduleNode, bottoms, nextSize) @@ -545,7 +545,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { protected def toCaffeEluParam(module : AbstractModule[Activity, Activity, T]) : ELUParameter = { val eLUParameter = ELUParameter.newBuilder() - val layer = classOf[ELU[T, T]].cast(module) + val layer = classOf[ELU[T]].cast(module) eLUParameter.setAlpha(layer.alpha.toFloat) eLUParameter.build() } @@ -553,7 +553,7 @@ abstract class Converter[T: ClassTag](implicit ev: TensorNumeric[T]) { protected def toCaffePowerParam(module : AbstractModule[Activity, Activity, T]) : PowerParameter = { val powerParameter = PowerParameter.newBuilder - val layer = classOf[Power[T, T]].cast(module) + val layer = classOf[Power[T]].cast(module) powerParameter.setPower(layer.power.toFloat) powerParameter.setScale(layer.scale.toFloat) powerParameter.setShift(layer.shift.toFloat) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala index f7600fa9a65..7f9e727b27b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/LayerConverter.scala @@ -154,7 +154,7 @@ class LayerConverter[T: ClassTag](implicit ev: TensorNumeric[T]) extends Convert val param = layer.asInstanceOf[LayerParameter].getEluParam var alpha = 1.0 if (param.hasAlpha) alpha = param.getAlpha - Seq(ELU[T, T](alpha).setName(getLayerName(layer)).inputs()) + Seq(ELU[T](alpha).setName(getLayerName(layer)).inputs()) } override protected def fromCaffeReshape(layer : GeneratedMessage) : Seq[ModuleNode[T]] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 05d75d2786d..49867946972 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -650,8 +650,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createAbs() - : Abs[T, T] = { - Abs[T, T]() + : Abs[T] = { + Abs[T]() } def createAdd(inputSize: Int) @@ -759,8 +759,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createClamp(min: Int, max: Int) - : Clamp[T, T] = { - Clamp[T, T](min, + : Clamp[T] = { + Clamp[T](min, max) } @@ -799,8 +799,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createELU(alpha: Double = 1.0, inplace: Boolean = false) - : ELU[T, T] = { - ELU[T, T](alpha, + : ELU[T] = { + ELU[T](alpha, inplace) } @@ -836,8 +836,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createHardTanh(minValue: Double = -1, maxValue: Double = 1, inplace: Boolean = false) - : HardTanh[T, T] = { - HardTanh[T, T](minValue, + : HardTanh[T] = { + HardTanh[T](minValue, maxValue, inplace) } @@ -894,8 +894,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createLog() - : Log[T, T] = { - Log[T, T]() + : Log[T] = { + Log[T]() } def createLogSigmoid() @@ -960,7 +960,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createMean(dimension: Int = 1, nInputDims: Int = -1, squeeze: Boolean = true) - : Mean[T, T] = { + : Mean[T] = { Mean[T](dimension, nInputDims, squeeze) @@ -1057,8 +1057,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createPower(power: Double, scale: Double = 1, shift: Double = 0) - : Power[T, T] = { - Power[T, T](power, + : Power[T] = { + Power[T](power, scale, shift) } @@ -1073,8 +1073,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createReLU6(inplace: Boolean = false) - : ReLU6[T, T] = { - ReLU6[T, T](inplace) + : ReLU6[T] = { + ReLU6[T](inplace) } def createReplicate(nFeatures: Int, @@ -1126,8 +1126,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSoftPlus(beta: Double = 1.0) - : SoftPlus[T, T] = { - SoftPlus[T, T](beta) + : SoftPlus[T] = { + SoftPlus[T](beta) } def createSoftShrink(lambda: Double = 0.5) @@ -1136,8 +1136,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSoftSign() - : SoftSign[T, T] = { - SoftSign[T, T]() + : SoftSign[T] = { + SoftSign[T]() } @@ -1393,13 +1393,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createSqrt() - : Sqrt[T, T] = { - Sqrt[T, T]() + : Sqrt[T] = { + Sqrt[T]() } def createSquare() - : Square[T, T] = { - Square[T, T]() + : Square[T] = { + Square[T]() } def createSqueeze(dim: Int = Int.MinValue, @@ -1414,8 +1414,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab sizeAverage: Boolean = false, squeeze: Boolean = true ) - : Sum[T, T] = { - Sum[T, T](dimension, + : Sum[T] = { + Sum[T](dimension, nInputDims, sizeAverage, squeeze diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala index 029691f9f69..ac62621a9cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala @@ -377,7 +377,7 @@ object MeanToTF extends BigDLToTensorflow { override def toTFDef(module: AbstractModule[_, _, _], inputs: Seq[NodeDef], byteOrder: ByteOrder): Seq[NodeDef] = { require(inputs.length == 1, "Mean only accept one input") - val layer = module.asInstanceOf[Mean[_, _]] + val layer = module.asInstanceOf[Mean[_]] require(layer.squeeze == true, "Mean must squeeze input") val dimsTensor = Tensor[Int](layer.dimension) dimsTensor.setValue(1, layer.dimension - 1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Abs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Abs.scala index 14017338eba..b700ed1c504 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Abs.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Abs.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.{Abs, ELU} +import com.intel.analytics.bigdl.nn.tf.Abs import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Elu.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Elu.scala index 9da0a0fceec..121251e2bcf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Elu.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Elu.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.{ELU, ReLU6} +import com.intel.analytics.bigdl.nn.tf.ELU import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log.scala index b16c2408236..a70a99adb3e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.{ELU, Log} +import com.intel.analytics.bigdl.nn.tf.Log import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.{DataType, NodeDef} import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala index 6b19ecf81df..6525ca770e1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala @@ -19,7 +19,8 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.{Sequential, Mean => MeanNN} +import com.intel.analytics.bigdl.nn.Sequential +import com.intel.analytics.bigdl.nn.tf.Mean import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context @@ -71,13 +72,13 @@ class MeanLoadTF[T: ClassTag](val dataType: String, } dataType match { case "Int" => - dim.foreach(i => mean.add(new MeanNN[T, Int](i, squeeze = squeeze))) + dim.foreach(i => mean.add(Mean[T, Int](i, squeeze = squeeze))) case "Long" => - dim.foreach(i => mean.add(new MeanNN[T, Long](i, squeeze = squeeze))) + dim.foreach(i => mean.add(Mean[T, Long](i, squeeze = squeeze))) case "Float" => - dim.foreach(i => mean.add(new MeanNN[T, Float](i, squeeze = squeeze))) + dim.foreach(i => mean.add(Mean[T, Float](i, squeeze = squeeze))) case "Double" => - dim.foreach(i => mean.add(new MeanNN[T, Double](i, squeeze = squeeze))) + dim.foreach(i => mean.add(Mean[T, Double](i, squeeze = squeeze))) } mean } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala index b4ead966a65..8e86b2b0298 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ReLU6 +import com.intel.analytics.bigdl.nn.tf.ReLU6 import com.intel.analytics.bigdl.nn.tf.Log1p import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala index 96579660eb0..115c46951e7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rsqrt.scala @@ -18,9 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.RsqrtGrad -import com.intel.analytics.bigdl.nn.{Identity, Power} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.tf.Power import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softplus.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softplus.scala index a5eeea45477..d7325e2aae1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softplus.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softplus.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.SoftPlus -import com.intel.analytics.bigdl.nn.ops.Relu6Grad +import com.intel.analytics.bigdl.nn.tf.SoftPlus import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softsign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softsign.scala index 17ddaad87b0..8896a647fde 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softsign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Softsign.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.SoftSign +import com.intel.analytics.bigdl.nn.tf.SoftSign import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sqrt.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sqrt.scala index f1ce77d0d30..c16e376d46b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sqrt.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sqrt.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Power -import com.intel.analytics.bigdl.nn.ops.RsqrtGrad +import com.intel.analytics.bigdl.nn.tf.Power import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Square.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Square.scala index ef2efc38337..fbb330f7e67 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Square.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Square.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Power +import com.intel.analytics.bigdl.nn.tf.Power import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala index 3cf4f8c67bc..bb6ee9bd4c4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala @@ -26,7 +26,7 @@ class LogSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(0.0, 0.6931471805599453, 1.0986122886681098, 1.3862943611198906, 1.6094379124341003, 1.791759469228055)), 1, Array(2, 3)) - val log = new Log[Double, Double]() + val log = new Log[Double]() val logOutput = log.forward(input) @@ -38,7 +38,7 @@ class LogSpec extends FlatSpec with Matchers { val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) - val log = new Log[Double, Double]() + val log = new Log[Double]() val gradInput = log.backward(input, gradOutput) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala index 163ede783d1..ec381d780fa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala @@ -26,7 +26,7 @@ class MMSpec extends FlatSpec with Matchers { val m2 = new MM[Double]() val m3 = new MM[Double](true, true) val m4 = new MM[Double]() - val log = new Log[Double, Double]() + val log = new Log[Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3, 3).randn() @@ -45,7 +45,7 @@ class MMSpec extends FlatSpec with Matchers { val m2 = new MM[Double]() val m3 = new MM[Double](true, true) val m4 = new MM[Double]() - val log = new Log[Double, Double]() + val log = new Log[Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3, 3).randn() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala index 3e81f277e78..792847e60fc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala @@ -26,7 +26,7 @@ class MVSpec extends FlatSpec with Matchers { val m2 = new MV[Double]() val m3 = new MV[Double](true) val m4 = new MV[Double]() - val log = new Log[Double, Double]() + val log = new Log[Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3).randn() @@ -46,7 +46,7 @@ class MVSpec extends FlatSpec with Matchers { val m2 = new MV[Double]() val m3 = new MV[Double](true) val m4 = new MV[Double]() - val log = new Log[Double, Double]() + val log = new Log[Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3).randn() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala index 06c0593ce79..aab3e63ca0b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala @@ -28,7 +28,7 @@ class ModuleSpec extends FlatSpec with Matchers { "hashcode()" should "behave correctly" in { val r1 = new ReLU[Double]() val r2 = new ReLU[Double]() - val log = new Log[Double, Double]() + val log = new Log[Double]() val r3 = new ReLU[Float]() val r4 = new ReLU[Double]() val r5 = new ReLU[Double]() @@ -49,7 +49,7 @@ class ModuleSpec extends FlatSpec with Matchers { "equals()" should "behave correctly" in { val r1 = new ReLU[Double]() val r2 = new ReLU[Double]() - val log = new Log[Double, Double]() + val log = new Log[Double]() val mNull = null val r3 = new ReLU[Float]() val r4 = new ReLU[Double]() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala index 3a4613528cc..c61fe5d9f2e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala @@ -27,7 +27,7 @@ class PairwiseDistanceSpec extends FlatSpec with Matchers { val m2 = new PairwiseDistance[Double]() val m3 = new PairwiseDistance[Double](3) val m4 = new PairwiseDistance[Double]() - val log = new Log[Double, Double]() + val log = new Log[Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3, 3).randn() @@ -47,7 +47,7 @@ class PairwiseDistanceSpec extends FlatSpec with Matchers { val m2 = new PairwiseDistance[Double]() val m3 = new PairwiseDistance[Double](3) val m4 = new PairwiseDistance[Double]() - val log = new Log[Double, Double]() + val log = new Log[Double]() com.intel.analytics.bigdl.tensor.Tensor val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3, 3).randn() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala index a0d7170b926..780ec2bd61a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.utils.T @com.intel.analytics.bigdl.tags.Parallel class ParallelTableSpec extends FlatSpec with Matchers { "hashcode()" should "behave correctly" in { - val log = new Log[Double, Double]() + val log = new Log[Double]() val exp = new Exp[Double]() val m1 = new ParallelTable[Double]() m1.add(log) @@ -49,7 +49,7 @@ class ParallelTableSpec extends FlatSpec with Matchers { } "equals()" should "behave correctly" in { - val log = new Log[Double, Double]() + val log = new Log[Double]() val exp = new Exp[Double]() val m1 = new ParallelTable[Double]() m1.add(log) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala index 37f9a6a7ba8..86104f60732 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala @@ -26,7 +26,7 @@ class PowerSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(1.0, 4, 9, 16, 25, 36)), 1, Array(2, 3)) - val power = new Power[Double, Double](2) + val power = new Power[Double](2) val powerOutput = power.forward(input) @@ -34,11 +34,11 @@ class PowerSpec extends FlatSpec with Matchers { } "A float Power" should "generate correct output" in { - val input = Tensor(Storage[Double](Array(1.0, 2, 3, 4, 5, 6)), 1, Array(2, 3)) + val input = Tensor(Storage[Float](Array(1.0f, 2, 3, 4, 5, 6)), 1, Array(2, 3)) - val output = Tensor(Storage(Array(1.0, 4, 9, 16, 25, 36)), 1, Array(2, 3)) + val output = Tensor(Storage(Array(1.0f, 4, 9, 16, 25, 36)), 1, Array(2, 3)) - val power = new Power[Float, Double](2) + val power = new Power[Float](2) val powerOutput = power.forward(input) @@ -50,7 +50,7 @@ class PowerSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(4.0, 16, 36, 64, 100, 144)), 1, Array(2, 3)) - val power = new Power[Double, Double](2, 2) + val power = new Power[Double](2, 2) val powerOutput = power.forward(input) @@ -62,7 +62,7 @@ class PowerSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(1.0, 4, 9, 16, 25, 36)), 1, Array(2, 3)) - val power = new Power[Double, Double](2, 1, 1) + val power = new Power[Double](2, 1, 1) val powerOutput = power.forward(input) @@ -74,7 +74,7 @@ class PowerSpec extends FlatSpec with Matchers { val output = Tensor(Storage(Array(1.0, 9, 25, 49, 81, 121)), 1, Array(2, 3)) - val power = new Power[Double, Double](2, 2, 1) + val power = new Power[Double](2, 2, 1) val powerOutput = power.forward(input) @@ -86,7 +86,7 @@ class PowerSpec extends FlatSpec with Matchers { val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) - val power = new Power[Double, Double](2, 2, 2) + val power = new Power[Double](2, 2, 2) val output = power.forward(input) val gradInput = power.backward(input, gradOutput) @@ -101,7 +101,7 @@ class PowerSpec extends FlatSpec with Matchers { val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) - val power = new Power[Double, Double](1, -1) + val power = new Power[Double](1, -1) val output = power.forward(input) val gradInput = power.backward(input, gradOutput) @@ -116,7 +116,7 @@ class PowerSpec extends FlatSpec with Matchers { val gradOutput = Tensor(Storage(Array(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)), 1, Array(2, 3)) - val power = new Power[Double, Double](3, 2, 2) + val power = new Power[Double](3, 2, 2) val output = power.forward(input) val gradInput = power.backward(input, gradOutput) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala index 7b499bbeaee..d257ef1d853 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala @@ -27,7 +27,7 @@ class SumSpec extends FlatSpec with Matchers { T(3.0f, 4.0f) )) - val layer = Sum[Float, Float](dimension = 2) + val layer = Sum[Float](dimension = 2) val expect = Tensor[Float](T(3.0f, 7.0f)) @@ -40,7 +40,7 @@ class SumSpec extends FlatSpec with Matchers { T(3.0f, 4.0f) )) - val layer = Sum[Float, Float](dimension = 2, squeeze = false) + val layer = Sum[Float](dimension = 2, squeeze = false) val expect = Tensor[Float](T(T(3.0f), T(7.0f))) @@ -49,7 +49,7 @@ class SumSpec extends FlatSpec with Matchers { "sum" should "be correct when squeeze on vector" in { val vector = Tensor[Int](T(1, 2, 3)) - val sum = Sum[Float, Int](dimension = 1, squeeze = true) + val sum = Sum[Int](dimension = 1, squeeze = true) sum.forward(vector) should be(Tensor.scalar(6)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterionSpec.scala index 964134277bc..e29be63c96a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterionSpec.scala @@ -27,7 +27,7 @@ class TransformerCriterionSpec extends FlatSpec with Matchers { import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat val criterion = TransformerCriterion[Float](MSECriterion[Float](), - Some(Square[Float, Float]()), Some(Square[Float, Float]())) + Some(Square[Float]()), Some(Square[Float]())) val input = Tensor(1, 3, 224, 224).rand() val target = Tensor(1, 3, 224, 224).rand() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala index d90f38bdd33..f50b979e08d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.Tensor class AbsSpec extends TorchSpec { "A Abs Module " should "generate correct output and grad" in { torchCheck() - val module = new Abs[Double, Double] + val module = new Abs[Double] val input = Tensor[Double](2, 1, 2) input(Array(1, 1, 1)) = 21 input(Array(1, 1, 2)) = -29 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala index 31e839e6d20..1fc0a49e751 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala @@ -24,7 +24,7 @@ import scala.math._ class ClampSpec extends TorchSpec { "A Clamp Module " should "generate correct output and grad" in { torchCheck() - val module = new Clamp[Double, Double](-10, 10) + val module = new Clamp[Double](-10, 10) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala index acdd7f11e12..7190f973b76 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala @@ -29,7 +29,7 @@ class ELUSpec extends TorchSpec { val seed = 100 RNG.setSeed(seed) - val module = new ELU[Double, Double]() + val module = new ELU[Double]() val input = Tensor[Double](2, 2, 2) input.apply1(x => random()) val gradOutput = Tensor[Double](2, 2, 2) @@ -62,7 +62,7 @@ class ELUSpec extends TorchSpec { val seed = 100 RNG.setSeed(seed) - val module = new ELU[Double, Double](10, false) + val module = new ELU[Double](10, false) val input = Tensor[Double](2, 2, 2) input.apply1(x => random()) val gradOutput = Tensor[Double](2, 2, 2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala index 4cd5bf0956e..98844446abe 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala @@ -23,7 +23,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad not inplace with contiguous input" in { torchCheck() - val module = new HardTanh[Double, Double]() + val module = new HardTanh[Double]() val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 @@ -66,7 +66,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad inplace with contiguous input" in { torchCheck() - val module = new HardTanh[Double, Double](inplace = true) + val module = new HardTanh[Double](inplace = true) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 @@ -110,7 +110,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad not inplace with not contiguous input" in { torchCheck() - val module = new HardTanh[Double, Double]() + val module = new HardTanh[Double]() val input = Tensor[Double](2, 2) input(Array(1, 1)) = -0.97008799016476 input(Array(1, 2)) = -0.65073125436902 @@ -146,7 +146,7 @@ class HardTanhSpec extends TorchSpec { "A HardTanh Module " should "generate correct output and grad inplace with not contiguous input" in { torchCheck() - val module = new HardTanh[Float, Double](inplace = true) + val module = new HardTanh[Double](inplace = true) val input = Tensor[Double](2, 2) input(Array(1, 1)) = -0.97008799016476 input(Array(1, 2)) = -0.65073125436902 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala index 002e58c5577..771918dbed6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala @@ -24,7 +24,7 @@ class LogSpec extends TorchSpec { "A Log()" should "generate correct output and grad" in { torchCheck() def randomn(): Double = RandomGenerator.RNG.uniform(2, 10) - val layer = new Log[Double, Double]() + val layer = new Log[Double]() val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](2, 2, 2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala index eca00dbdd0b..345ca2e0182 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.tensor.Tensor class PowerSpec extends TorchSpec { "A Power(2)" should "generate correct output and grad" in { torchCheck() - val layer = new Power[Double, Double](2) + val layer = new Power[Double](2) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = 1 input(Array(1, 1, 2)) = 2 @@ -66,7 +66,7 @@ class PowerSpec extends TorchSpec { "A Power(3)" should "generate correct output and grad" in { torchCheck() - val layer = new Power[Double, Double](3) + val layer = new Power[Double](3) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = 1 input(Array(1, 1, 2)) = 2 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala index 266a56b7770..9a1fc1554c9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala @@ -25,7 +25,7 @@ import scala.math._ class ReLU6Spec extends TorchSpec { "A ReLU6 Module " should "generate correct output and grad not inplace" in { torchCheck() - val module = new ReLU6[Double, Double]() + val module = new ReLU6[Double]() val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 @@ -74,7 +74,7 @@ class ReLU6Spec extends TorchSpec { "A ReLU6 Module " should "generate correct output and grad inplace" in { torchCheck() - val module = new ReLU6[Double, Double](true) + val module = new ReLU6[Double](true) val input = Tensor[Double](2, 2, 2) input(Array(1, 1, 1)) = -0.97008799016476 input(Array(1, 1, 2)) = -0.89318234380335 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala index ee76910fdb7..8c9f818390d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala @@ -24,7 +24,7 @@ import scala.util.Random class SoftPlusSpec extends TorchSpec { "A SoftPlus 3D input" should "generate correct output and grad" in { torchCheck() - val layer = new SoftPlus[Double, Double]() + val layer = new SoftPlus[Double]() val input = Tensor[Double](2, 3, 4).apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](2, 3, 4).apply1(_ => Random.nextDouble()) @@ -51,7 +51,7 @@ class SoftPlusSpec extends TorchSpec { "A SoftPlus 4D input" should "generate correct output and grad" in { torchCheck() - val layer = new SoftPlus[Double, Double](2.0) + val layer = new SoftPlus[Double](2.0) val input = Tensor[Double](5, 4, 3, 2).apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](5, 4, 3, 2).apply1(_ => Random.nextDouble()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala index 9c99ce6d0aa..8ccb818462a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala @@ -25,7 +25,7 @@ import scala.util.Random class SoftSignSpec extends TorchSpec { "A SoftSign 3D input" should "generate correct output and grad" in { torchCheck() - val layer = new SoftSign[Double, Double]() + val layer = new SoftSign[Double]() val input = Tensor[Double](2, 3, 4).apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](2, 3, 4).apply1(_ => Random.nextDouble()) @@ -52,7 +52,7 @@ class SoftSignSpec extends TorchSpec { "A SoftSign 4D input" should "generate correct output and grad" in { torchCheck() - val layer = new SoftSign[Double, Double]() + val layer = new SoftSign[Double]() val input = Tensor[Double](5, 4, 3, 2).apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](5, 4, 3, 2).apply1(_ => Random.nextDouble()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala index b2b09c05cdf..c1ead4637d7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala @@ -25,7 +25,7 @@ import scala.util.Random class SqrtSpec extends TorchSpec { "A Sqrt 1D input" should "generate correct output and grad" in { torchCheck() - val layer = new Sqrt[Double, Double]() + val layer = new Sqrt[Double]() val input = Tensor[Double](10) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](10) @@ -54,7 +54,7 @@ class SqrtSpec extends TorchSpec { "A Sqrt 2D input" should "generate correct output and grad" in { torchCheck() - val layer = new Sqrt[Double, Double]() + val layer = new Sqrt[Double]() val input = Tensor[Double](3, 5) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](3, 5) @@ -83,7 +83,7 @@ class SqrtSpec extends TorchSpec { "A Sqrt 3D input" should "generate correct output and grad" in { torchCheck() - val layer = new Sqrt[Double, Double]() + val layer = new Sqrt[Double]() val input = Tensor[Double](4, 6, 6) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](4, 6, 6) @@ -112,7 +112,7 @@ class SqrtSpec extends TorchSpec { "A Sqrt 4D input" should "generate correct output and grad" in { torchCheck() - val layer = new Sqrt[Double, Double]() + val layer = new Sqrt[Double]() val input = Tensor[Double](3, 5, 6, 6) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](3, 5, 6, 6) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala index c4842ea4a12..569f44dab2a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala @@ -25,7 +25,7 @@ import scala.util.Random class SquareSpec extends TorchSpec { "A Square 1D input" should "generate correct output and grad" in { torchCheck() - val layer = new Square[Double, Double]() + val layer = new Square[Double]() val input = Tensor[Double](10) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](10) @@ -54,7 +54,7 @@ class SquareSpec extends TorchSpec { "A Square 2D input" should "generate correct output and grad" in { torchCheck() - val layer = new Square[Double, Double]() + val layer = new Square[Double]() val input = Tensor[Double](3, 5) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](3, 5) @@ -83,7 +83,7 @@ class SquareSpec extends TorchSpec { "A Square 3D input" should "generate correct output and grad" in { torchCheck() - val layer = new Square[Double, Double]() + val layer = new Square[Double]() val input = Tensor[Double](4, 6, 6) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](4, 6, 6) @@ -112,7 +112,7 @@ class SquareSpec extends TorchSpec { "A Square 4D input" should "generate correct output and grad" in { torchCheck() - val layer = new Square[Double, Double]() + val layer = new Square[Double]() val input = Tensor[Double](3, 5, 6, 6) input.apply1(_ => Random.nextDouble()) val gradOutput = Tensor[Double](3, 5, 6, 6) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala index 56945c0bade..b7e40bf9800 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala @@ -25,7 +25,7 @@ class SumSpec extends TorchSpec { "An Sum()" should "generate correct output and grad" in { torchCheck() - val layer = Sum[Double, Double]() + val layer = Sum[Double]() val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) @@ -54,7 +54,7 @@ class SumSpec extends TorchSpec { "An Sum(2)" should "generate correct output and grad" in { torchCheck() - val layer = Sum[Double, Double](2) + val layer = Sum[Double](2) val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) @@ -83,7 +83,7 @@ class SumSpec extends TorchSpec { "An Sum(2,1,true)" should "generate correct output and grad" in { torchCheck() - val layer = Sum[Double, Double](2, 1, true) + val layer = Sum[Double](2, 1, true) val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) @@ -112,7 +112,7 @@ class SumSpec extends TorchSpec { "An Sum(-1,1,true)" should "generate correct output and grad" in { torchCheck() - val layer = Sum[Double, Double](-1, 1, true) + val layer = Sum[Double](-1, 1, true) val input = Tensor[Double](2, 2, 2) input.apply1(x => randomn()) val gradOutput = Tensor[Double](1, 2, 2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 5c9f660fa0d..e891e4adbfa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} -import com.intel.analytics.bigdl.nn.tf._ +import com.intel.analytics.bigdl.nn.tf.{BiasAdd, Const, Fill, Log1p, Shape, SplitAndSelect, StrideSlice, Variable, TensorModuleWrapper} import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor._ @@ -100,7 +100,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "Abs serializer" should "work properly" in { - val abs = Abs[Float, Float]().setName("abs") + val abs = Abs[Float]().setName("abs") val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) runSerializationTest(abs, input) } @@ -284,7 +284,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll "Clamp serializer" should "work properly" in { val input = Tensor[Float](10).apply1(e => Random.nextFloat()) - val clamp = Clamp[Float, Float](1, 10).setName("clamp") + val clamp = Clamp[Float](1, 10).setName("clamp") runSerializationTest(clamp, input) } @@ -328,8 +328,8 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll "Concatserializer" should "work properly" in { val input = Tensor[Float](2, 2, 2).apply1(e => Random.nextFloat()) val concat = Concat[Float](2).setName("concat") - concat.add(Abs[Float, Float]()) - concat.add(Abs[Float, Float]()) + concat.add(Abs[Float]()) + concat.add(Abs[Float]()) runSerializationTest(concat, input) } @@ -463,7 +463,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "ELU serializer" should "work properly" in { - val elu = ELU[Float, Float]().setName("elu") + val elu = ELU[Float]().setName("elu") val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) runSerializationTest(elu, input) } @@ -579,7 +579,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "HardTanh serializer" should "work properly" in { - val hardTanh = HardTanh[Float, Float]().setName("hardTanh") + val hardTanh = HardTanh[Float]().setName("hardTanh") val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) runSerializationTest(hardTanh, input) } @@ -652,7 +652,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "Log Serializer" should "work properly" in { - val log = Log[Float, Float]().setName("log") + val log = Log[Float]().setName("log") val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) runSerializationTest(log, input) } @@ -895,7 +895,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "Power serializer" should "work properly" in { - val power = Power[Float, Float](2.0).setName("power") + val power = Power[Float](2.0).setName("power") val input = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) runSerializationTest(power, input) } @@ -934,7 +934,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "ReLU6 serializer" should "work properly" in { - val relu6 = ReLU6[Float, Float](false).setName("relu6") + val relu6 = ReLU6[Float](false).setName("relu6") val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) runSerializationTest(relu6, input) } @@ -1038,7 +1038,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "SoftPlus serializer" should "work properly" in { - val softPlus = SoftPlus[Float, Float]().setName("softPlus") + val softPlus = SoftPlus[Float]().setName("softPlus") val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) runSerializationTest(softPlus, input) } @@ -1050,7 +1050,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "SoftSign serializer" should "work properly" in { - val softSign = SoftSign[Float, Float]().setName("softSign") + val softSign = SoftSign[Float]().setName("softSign") val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) runSerializationTest(softSign, input) } @@ -1196,13 +1196,13 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "Sqrt serializer" should "work properly" in { - val sqrt = Sqrt[Float, Float]().setName("sqrt") + val sqrt = Sqrt[Float]().setName("sqrt") val input = Tensor[Float](10).apply1( e => Random.nextFloat()) runSerializationTest(sqrt, input) } "Square serializer" should "work properly" in { - val square = Square[Float, Float]().setName("square") + val square = Square[Float]().setName("square") val input = Tensor[Float](10).apply1( e => Random.nextFloat()) runSerializationTest(square, input) } @@ -1220,7 +1220,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll } "Sum serializer" should "work properly" in { - val sum = Sum[Float, Float](2).setName("sum") + val sum = Sum[Float](2).setName("sum") val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) runSerializationTest(sum, input) } @@ -1879,6 +1879,13 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll runSerializationTest(moduleToOperation, input) } + "TensorModuleWrapper serializer" should "work properly" in { + val tensorModuleWrapper = TensorModuleWrapper[Float, Float](SoftPlus[Float]()). + setName("moduleToOperation") + val input = Tensor[Float](T(1.0f, 1.0)) + runSerializationTest(tensorModuleWrapper, input) + } + "NoOp serializer" should "work properly" in { val noOp = NoOp[Float]().setName("noOp") val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) From ff887434c39adbde9b3fb32b223e2e92bc1915fd Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Fri, 5 Jan 2018 16:49:07 +0800 Subject: [PATCH 0630/1065] Fix SparseMinibatch when batching DenseTensor (#2151) * fix minibatch * meet code review --- .../dllib/feature/dataset/MiniBatch.scala | 39 ++++++------------- .../bigdl/dllib/dataset/MiniBatchSpec.scala | 4 +- 2 files changed, 14 insertions(+), 29 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala index 9e74786660c..b606a5e1a2b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -713,39 +714,23 @@ object SparseMiniBatch{ dim: Int, tensors: Seq[Tensor[T]], result: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { - val size = tensors.head.size() - var i = 1 - while (i < tensors.length) { - size(dim - 1) += tensors(i).size(dim) - i += 1 - } + val tensorSize = tensors.head.size() - result.resize(size) + val (pre, next) = tensorSize.splitAt(dim - 1) + val size = ArrayBuffer[Int]() + size ++= pre + size += tensors.length + size ++= next - i = 0 - var offset = 1 + result.resize(size.toArray) + + var i = 0 while (i < tensors.length) { val current = tensors(i) - val target = result.narrow(dim, offset, current.size(dim)) + val target = result.select(dim, i + 1) - if (target.isContiguous() || dim > 2) { - // Copy directly when target is Contiguous or dimension is larger than 2 - // in which case the contiguous region in target tensor is fairly small in practice - target.copy(current) - } else { - // Divide target into contiguous frames when target isn't contiguous - var f = 1 - while (f <= target.size(1)) { - val curFrame = target.select(1, f) - val outputFrame = current.select(1, f) - require(curFrame.isContiguous()) - require(outputFrame.isContiguous()) - curFrame.copy(outputFrame) - f += 1 - } - } + target.copy(current) - offset += current.size(dim) i += 1 } result diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala index a81ed755860..3b498f977c0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala @@ -101,11 +101,11 @@ class MiniBatchSpec extends FlatSpec with Matchers { val expectedInput1 = Tensor.sparse(Array(Array(0, 0, 0, 0, 1, 1, 1, 1), Array(0, 1, 2, 3, 0, 1, 2, 3)), Array.range(1, 9).map(_.toFloat), Array(2, 4)) - val expectedInput2 = Tensor[Float].range(1, 10) + val expectedInput2 = Tensor[Float].range(1, 10).reshape(Array(2, 5)) input.toTable[Tensor[Float]](1) should be (expectedInput1) input.toTable[Tensor[Float]](2) should be (expectedInput2) - val expectedTarget = Tensor[Float](T(1.0f, 0.0f)) + val expectedTarget = Tensor[Float](T(1.0f, 0.0f)).reshape(Array(2, 1)) target should be (expectedTarget) } From 48670f477ba74e0b188376793c14313c93198741 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 8 Jan 2018 15:24:55 +0800 Subject: [PATCH 0631/1065] fix s3 integration test (#2167) --- .../com/intel/analytics/bigdl/dllib/integration/S3Spec.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala index 4dfbdcd443c..a6d88f89fc9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/S3Spec.scala @@ -97,7 +97,7 @@ class S3Spec extends FlatSpec with Matchers with BeforeAndAfter{ input2.resizeAs(input1).copy(input1) - val linear = Linear(10, 10) + val linear = Linear(10, 10).setName("linear") // caffe only supports float, In order to compare the results, here we manually // set weight and bias to ensure there is no accurancy loss @@ -118,7 +118,7 @@ class S3Spec extends FlatSpec with Matchers with BeforeAndAfter{ graph, overwrite = true) val modelFromS3 = CaffeLoader.loadCaffe[Double](hdfsDir + "/test.prototxt", - hdfsDir + "/test.caffemodel")._1 + hdfsDir + "/test.caffemodel", outputNames = Array[String]("linear"))._1 val res2 = modelFromS3.forward(input2) From cac581cc2c76e9760409e070e424cb2b5443c3a2 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Tue, 9 Jan 2018 09:21:14 +0800 Subject: [PATCH 0632/1065] Tensor.dense() support narrowed SparseTensor (#2162) * Tensor.dense() support narrowed SparseTensor Current implementation can not work properly when nElement is not equal to _indices.length. It usually happens after narrow a SparseTensor. This commit solve this problem by taking account of offsets(_indicesOffset, _storageOffset) and nElement. * Add UnitTest for this change "Tensor.dense narrowed tensor" should "return right result" in SparseTensorSpec * Fix Wrong Code Style at SparseTensorSpec --- .../bigdl/dllib/tensor/DenseTensor.scala | 20 +++++++++---------- .../bigdl/dllib/tensor/SparseTensorSpec.scala | 10 ++++++++++ 2 files changed, 20 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index e62299f235c..97f5167ca4c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2701,16 +2701,16 @@ object DenseTensor { sparseTensor: SparseTensor[T], res: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = { val dt = if (null == res) Tensor(sparseTensor.size()) else res - var i = 0 - val index = new Array[Int](dt.dim()) - while (i < sparseTensor._indices(0).length) { - var j = 0 - while (j < index.length) { - index(j) = sparseTensor._indices(j)(i) + 1 - j += 1 - } - dt(index) = sparseTensor(index) - i += 1 + val srcIndex = new Array[Int](dt.dim()) + val tgtIndex = new Array[Int](dt.dim()) + // fill DenseTensor with sparseTensors' active values one by one + (0 until sparseTensor._nElement).foreach { i => + // targetIndex = sourceIndex - indicesOffset + srcIndex.indices.foreach { j => + srcIndex(j) = sparseTensor._indices(j)(i + sparseTensor._storageOffset) + 1 + tgtIndex(j) = srcIndex(j) - sparseTensor._indicesOffset(j) + } + dt(tgtIndex) = sparseTensor(srcIndex) } dt } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala index 6e964d1a377..1a95e58cf7a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala @@ -19,6 +19,8 @@ package com.intel.analytics.bigdl.tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class SparseTensorSpec extends FlatSpec with Matchers { "dim, shape, nElement" should "return right result" in { @@ -108,4 +110,12 @@ class SparseTensorSpec extends FlatSpec with Matchers { sTensor.storageOffset() should be (6) } + "Tensor.dense narrowed tensor" should "return right result" in { + val values = Array.fill(30)(Random.nextFloat()) + val sTensor = Tensor.sparse(Tensor(values, Array(6, 5))) + val narrowed = sTensor.narrow(1, 2, 4) + val narrowedSum = values.slice(5, 25).sum + Tensor.dense(narrowed).resize(20).toArray().sum shouldEqual narrowedSum + } + } From 03e2127d5078c932daf6811edfe20cb4b72877f2 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Tue, 9 Jan 2018 09:21:32 +0800 Subject: [PATCH 0633/1065] Fix bug of SparseTensor.resize(size, nElement) (#2159) * Fix bug of SparseTensor.resize(size, nElement) If size.length > dimension, additional indices should be in front of _indices array instead of behind it. So, at SparseTensor.scala Line 482: > _indices = _addIndices ++ _indices should replace > _indices ++= _addIndices * Add unit test for this change * Fix Wrong Code Style at SparseTensorSpec --- .../analytics/bigdl/dllib/tensor/SparseTensor.scala | 2 +- .../analytics/bigdl/dllib/tensor/SparseTensorSpec.scala | 9 ++++++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index a650df3bcf3..816ae791df1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -479,7 +479,7 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( val _addIndices = new Array[Storage[Int]](size.length - _indices.length) for (i <- _addIndices.indices) _addIndices(i) = Storage[Int](nElement + _storageOffset) _indicesOffset = new Array[Int](size.length - _indicesOffset.length) ++ _indicesOffset - _indices ++= _addIndices + _indices = _addIndices ++ _indices } // resize _indices's length diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala index 1a95e58cf7a..e253175a2bc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala @@ -13,7 +13,6 @@ * See the License for the specific language governing permissions and * limitations under the License. */ - package com.intel.analytics.bigdl.tensor import org.scalatest.{FlatSpec, Matchers} @@ -101,6 +100,14 @@ class SparseTensorSpec extends FlatSpec with Matchers { sTensor.storageOffset() should be (1) } + "resize tensor to higher dim when nElement < sum(size)" should "return right result" in { + val indices = Array(Array(0, 4, 5, 7, 9)) + val values = Array.fill(5)(Random.nextFloat()) + val sTensor = Tensor.sparse(indices, values, Array(10)) + sTensor.resize(Array(1, 10), 5) + Tensor.dense(sTensor).squeeze().toArray().sum should be (values.sum) + } + "resize narrowed tensor" should "return right result" in { val sTensor = Tensor.sparse(Tensor(30).range(1, 30, 1)).narrow(1, 6, 18) sTensor.resize(Array(6, 3), 18) From 00e60ceaf8fb237dfb592a41e0264c5fcf10cd35 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 10 Jan 2018 13:33:02 +0800 Subject: [PATCH 0634/1065] fix spatialfullconvolution bug (#2175) --- .../dllib/nn/SpatialFullConvolution.scala | 5 ++- .../dllib/nn/SpatialFullConvolutionSpec.scala | 36 +++++++++++++++++++ 2 files changed, 38 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala index e9bc97a8bd5..61910ded5bb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala @@ -308,10 +308,9 @@ class SpatialFullConvolution[T: ClassTag]( inputHeight * inputWidth)) } - if (weightMM == null) { - weightMM = weight.view(nGroup, nInputPlane / nGroup, + // weight's storage might change, so make a view every time + weightMM = weight.view(nGroup, nInputPlane / nGroup, nOutputPlane * kH * kW / nGroup) - } var elt = 1 // For each element in batch, do: diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala index df4d615d3dc..2b996a7160d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala @@ -187,4 +187,40 @@ class SpatialFullConvolutionSpec extends FlatSpec with Matchers { layer2.gradBias should be(layer.gradBias.mul(2)) } + + "A SpatialFullConvolution " should "work after forward and getParameters" in { + val nInputPlane = 3 + val nOutputPlane = 6 + val kW = 3 + val kH = 3 + val dW = 1 + val dH = 1 + val padW = 2 + val padH = 2 + val layer = new SpatialFullConvolution[Double](nInputPlane, nOutputPlane, + kW, kH, dW, dH, padW, padH) + val layer2 = layer.cloneModule().asInstanceOf[SpatialFullConvolution[Double]] + Random.setSeed(100) + val input = Tensor[Double](3, 3, 6, 6).apply1(e => Random.nextDouble()) + + // this two operations should not change layer's behavior + layer.forward(input) + layer.getParameters() + + val output1 = layer.forward(input) + layer.backward(input, output1) + layer.updateParameters(0.1) + + val output2 = layer2.forward(input) + layer2.backward(input, output2) + layer2.updateParameters(0.1) + + val output = layer.forward(input) + val expected = layer2.forward(input) + + output should be (expected) + + } + + } From da5c7cfa484c5bf270cfdc51b15eec6f12054526 Mon Sep 17 00:00:00 2001 From: Dongjie Shi Date: Thu, 11 Jan 2018 11:03:34 +0800 Subject: [PATCH 0635/1065] fix ut failure of DictionarySpec (#2173) --- .../analytics/bigdl/dllib/dataset/text/DictionarySpec.scala | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/DictionarySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/DictionarySpec.scala index 11c1f96386e..6e53b11d319 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/DictionarySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/DictionarySpec.scala @@ -52,7 +52,7 @@ class DictionarySpec extends FlatSpec with Matchers with BeforeAndAfter { val sentences = Array(sentence1, sentence2, sentence3) - new PrintWriter(tmpFile) { + new PrintWriter(tmpFile, "UTF-8") { write(sentences.mkString("\n")); close } @@ -82,11 +82,11 @@ class DictionarySpec extends FlatSpec with Matchers with BeforeAndAfter { val sentences = Array(sentence1, sentence2, sentence3) - new PrintWriter(tmpFile) { + new PrintWriter(tmpFile, "UTF-8") { write(sentences.mkString("\n")); close } - val logData = Source.fromFile(tmpFile).getLines().toArray + val logData = Source.fromFile(tmpFile, "UTF-8").getLines().toArray val tokens = DataSet.array(logData .filter(!_.isEmpty)).transform(SentenceTokenizer()) val output = tokens.toLocal().data(train = false) From 50f3fc3d546b7e7dd982286a5e16ef898210f3a4 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Sun, 14 Jan 2018 21:55:12 -0800 Subject: [PATCH 0636/1065] udpate equal (#2033) --- .../main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala index 229cab5702d..527589831eb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala @@ -192,7 +192,7 @@ class Cosine[T: ClassTag](val inputSize : Int, val outputSize : Int)( s"${getPrintName}($inputSize, $outputSize)" } - override def canEqual(other: Any): Boolean = other.isInstanceOf[Contiguous[T]] + override def canEqual(other: Any): Boolean = other.isInstanceOf[Cosine[T]] override def equals(other: Any): Boolean = other match { case that: Cosine[T] => From 4b46d66433cca8c3a38e046948d44a86734283f4 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 15 Jan 2018 18:20:03 +0800 Subject: [PATCH 0637/1065] Load imagenet sequence file to ImageFrame (#2184) * Load imagenet dataset to ImageFrame * Refactor and add python * meet code review --- .../bigdl/dllib/feature/dataset/DataSet.scala | 31 ++++++++++- .../transform/vision/image/Convertor.scala | 20 ++++++- .../vision/image/opencv/OpenCVMat.scala | 13 +++++ .../dllib/utils/python/api/PythonBigDL.scala | 10 ++++ .../vision/image/ImageFrameSpec.scala | 54 ++++++++++++++++++- 5 files changed, 124 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index 07dc91f9dc4..0409be29e5c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -16,12 +16,15 @@ package com.intel.analytics.bigdl.dataset +import java.nio.ByteBuffer import java.nio.file.{Files, Path, Paths} import java.util.concurrent.atomic.AtomicInteger import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset.image.{LabeledBGRImage, _} -import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame} +import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T} import org.apache.hadoop.io.Text import org.apache.log4j.Logger import org.apache.spark.SparkContext @@ -548,6 +551,32 @@ object DataSet { rawData.coalesce(num, true) } + /** + * Extract hadoop sequence files from an HDFS path as ImageFrame + * @param url sequence files folder path + * @param sc spark context + * @param classNum class number of data + * @param partitionNum partition number, default: Engine.nodeNumber() * Engine.coreNumber() + * @return + */ + private[bigdl] def filesToImageFrame(url: String, sc: SparkContext, + classNum: Int, partitionNum: Option[Int] = None): ImageFrame = { + val num = partitionNum.getOrElse(Engine.nodeNumber() * Engine.coreNumber()) + val rawData = sc.sequenceFile(url, classOf[Text], classOf[Text], num).map(image => { + val rawBytes = image._2.copyBytes() + val label = Tensor[Float](T(readLabel(image._1).toFloat)) + val imgBuffer = ByteBuffer.wrap(rawBytes) + val width = imgBuffer.getInt + val height = imgBuffer.getInt + val bytes = new Array[Byte](3 * width * height) + System.arraycopy(imgBuffer.array(), 8, bytes, 0, bytes.length) + val imf = ImageFeature(bytes, label) + imf(ImageFeature.originalSize) = (height, width, 3) + imf + }).filter(_[Tensor[Float]](ImageFeature.label).valueAt(1) <= classNum) + ImageFrame.rdd(rawData.coalesce(num, true)) + } + private[bigdl] def findFiles(path: Path): Array[LocalSeqFilePath] = { val directoryStream = Files.newDirectoryStream(path) import scala.collection.JavaConverters._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala index 1f2be7c3299..67c504ffeb5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.transform.vision.image -import com.intel.analytics.bigdl.dataset.ArraySample + +import com.intel.analytics.bigdl.dataset.{ArraySample} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat @@ -61,6 +62,23 @@ object BytesToMat { } } +/** + * Transform byte array(pixels in byte) to OpenCVMat + * @param byteKey key that maps byte array + */ +class PixelBytesToMat(byteKey: String = ImageFeature.bytes) extends FeatureTransformer { + + override def transformMat(feature: ImageFeature): Unit = { + val pixels = feature[Array[Byte]](byteKey) + val mat = OpenCVMat.fromPixelsBytes(pixels, feature.getOriginalHeight, feature.getOriginalWidth) + feature(ImageFeature.mat) = mat + } +} + +object PixelBytesToMat { + def apply(byteKey: String = ImageFeature.bytes): PixelBytesToMat = new PixelBytesToMat(byteKey) +} + /** * Transform OpenCVMat to float array, note that in this transformer, the mat is released diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala index 407e6422090..bd5286868ee 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala @@ -213,4 +213,17 @@ object OpenCVMat { input.get(0, 0, floats) (floats, input.height(), input.width()) } + + /** + * convert pixel bytes to OpenCVMat + * @param pixels pixels in byte array + * @param height image height + * @param width image width + */ + def fromPixelsBytes(pixels: Array[Byte], height: Int, width: Int): OpenCVMat = { + val mat = new OpenCVMat() + mat.create(height, width, CvType.CV_8UC3) + mat.put(0, 0, pixels) + mat + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 49867946972..e97ef086cd0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2872,6 +2872,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab BytesToMat(byteKey) } + def createPixelBytesToMat(byteKey: String): PixelBytesToMat = { + PixelBytesToMat(byteKey) + } + def createMatToFloats(validHeight: Int = 300, validWidth: Int = 300, validChannels: Int = 3, outKey: String = ImageFeature.floats, shareBuffer: Boolean = true): MatToFloats = new MatToFloats(validHeight, validWidth, validChannels, outKey, shareBuffer) @@ -2889,6 +2893,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ImageFrameToSample[T](inputKeys.asScala.toArray, targets, sampleKey) } + def seqFilesToImageFrame(url: String, sc: JavaSparkContext, + classNum: Int, partitionNum: Int): ImageFrame = { + val pn = if (partitionNum <= 0) None else Some(partitionNum) + DataSet.SeqFileFolder.filesToImageFrame(url, sc, classNum, pn) + } + def setConstantClip(optimizer: Optimizer[T, MiniBatch[T]], min: Float, max: Float): Unit = { optimizer.setConstantGradientClipping(min, max) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala index b8fcbd2503b..5f498bee4c9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala @@ -17,10 +17,13 @@ package com.intel.analytics.bigdl.transform.vision.image import java.io.File +import java.nio.file.Paths import com.google.common.io.Files -import com.intel.analytics.bigdl.transform.vision.image.augmentation.HFlip -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.dataset.image.{BGRImage, BGRImgToLocalSeqFile, LocalImgReaderWithName} +import com.intel.analytics.bigdl.dataset.{DataSet, Sample} +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.utils.{Engine, TestUtils} import org.apache.commons.io.FileUtils import org.apache.spark.SparkContext import org.apache.spark.sql.SQLContext @@ -34,6 +37,7 @@ class ImageFrameSpec extends FlatSpec with Matchers with BeforeAndAfter { val conf = Engine.createSparkConf().setAppName("ImageSpec").setMaster("local[2]") sc = new SparkContext(conf) sqlContext = new SQLContext(sc) + Engine.init } after { @@ -111,4 +115,50 @@ class ImageFrameSpec extends FlatSpec with Matchers with BeforeAndAfter { val images = ImageFrame.read(resource.getFile) images.transform(transformer) } + + + "ImageNet" should "load" in { + // generate seq for test + TestUtils.cancelOnWindows() + val resource = getClass().getClassLoader().getResource("imagenet") + val tmpFile = java.io.File.createTempFile("UnitTest", System.nanoTime().toString) + require(tmpFile.delete()) + require(tmpFile.mkdir()) + + // Convert the test imagenet files to seq files + val files = (DataSet.ImageFolder.paths(Paths.get(processPath(resource.getPath()))) + -> LocalImgReaderWithName(BGRImage.NO_SCALE) + -> BGRImgToLocalSeqFile(2, Paths.get(tmpFile.getAbsolutePath(), "imagenet")) + ).toLocal().data(train = false).map(s => { + println(s); + s + }).toArray + + val imageFrame = DataSet.SeqFileFolder.filesToImageFrame(tmpFile.getAbsolutePath(), sc, 10) -> + PixelBytesToMat() -> + Resize(256, 256) -> + RandomCrop(224, 224) -> + RandomTransformer(HFlip(), 0.5) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor[Float](toRGB = true) -> + ImageFrameToSample[Float](Array(ImageFeature.imageTensor), Array(ImageFeature.label)) + + val sampleRdd = imageFrame.toDistributed().rdd.map(x => x[Sample[Float]](ImageFeature.sample)) + sampleRdd.foreach(x => { + require(x.feature().size(1) == 3) + require(x.feature().size(2) == 224) + require(x.feature().size(3) == 224) + println(x.label()) + }) + + if (tmpFile.exists()) tmpFile.delete() + } + + private def processPath(path: String): String = { + if (path.contains(":")) { + path.substring(1) + } else { + path + } + } } From de4587610cfa501ff634716a4924eb38f66791a3 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Tue, 16 Jan 2018 10:45:13 +0800 Subject: [PATCH 0638/1065] Module DenseToSparse support backward disable (or not) setting (#2189) * Module DenseToSparse support backward disable (or not) setting * change isBackward to propagateBack * change isBackward to propagateBack --- .../bigdl/dllib/nn/DenseToSparse.scala | 18 ++++++++++-------- .../bigdl/dllib/nn/DenseToSparseSpec.scala | 19 +++++++++++++++++++ 2 files changed, 29 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparse.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparse.scala index 391dca4dfe4..0dc7de1893e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparse.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparse.scala @@ -24,11 +24,11 @@ import scala.reflect.ClassTag /** * Convert DenseTensor to SparseTensor. - * @param ev$1 - * @param ev + * @param propagateBack whether propagate gradient back, default value is true * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] */ -class DenseToSparse[T: ClassTag](implicit ev: TensorNumeric[T]) extends TensorModule[T] { +class DenseToSparse[T: ClassTag](val propagateBack: Boolean = true // propagate gradient back + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.getTensorType == DenseType, "DenseToSparse: input should be a DenseTensor," + s"but got ${input.getTensorType}") @@ -36,8 +36,10 @@ class DenseToSparse[T: ClassTag](implicit ev: TensorNumeric[T]) extends TensorMo output } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - this.gradInput.resizeAs(input) - Tensor.dense(gradOutput, gradInput) + if (propagateBack) { + this.gradInput.resizeAs(input) + Tensor.dense(gradOutput, gradInput) + } this.gradInput } @@ -45,8 +47,8 @@ class DenseToSparse[T: ClassTag](implicit ev: TensorNumeric[T]) extends TensorMo } object DenseToSparse { - def apply[@specialized(Float, Double) T: ClassTag]() - (implicit ev: TensorNumeric[T]) : DenseToSparse[T] = { - new DenseToSparse() + def apply[@specialized(Float, Double) T: ClassTag] + (propagateBack: Boolean = true)(implicit ev: TensorNumeric[T]) : DenseToSparse[T] = { + new DenseToSparse(propagateBack) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala index 6c43296f359..59aa1586751 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala @@ -40,4 +40,23 @@ class DenseToSparseSpec extends FlatSpec with Matchers { output should be (exceptedOutput) } + // It is useful when DenseToSparse layer is placed on the top of model, + // received gradient from sparselinear layer. + "A DenseToSparse backward" should "be able to work without gradOutput" in { + val mockInput = Tensor[Float](5, 10).rand() + val mockError = Tensor[Float](5, 10).rand() + var model = Sequential[Float]() + .add(DenseToSparse[Float](propagateBack = true)) + .add(SparseLinear[Float](10, 10)) + model.forward(mockInput) + intercept[Exception] { + model.backward(mockInput, mockError) + } + + model = Sequential[Float]() + .add(DenseToSparse[Float](propagateBack = false)) + .add(SparseLinear[Float](10, 10)) + model.forward(mockInput) + model.backward(mockInput, mockError) + } } From bdb7cf3899629130671d448c4a0111d10f9dd609 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 16 Jan 2018 10:57:32 +0800 Subject: [PATCH 0639/1065] Fix optimizer state messed up when calling optimize() multiple times in DistriOptimizer (#2178) * support continue training * meet code review * add unit tests * fix set model --- .../bigdl/dllib/optim/DistriOptimizer.scala | 68 ++++++++++++++----- .../bigdl/dllib/optim/Optimizer.scala | 11 +++ .../dllib/optim/DistriOptimizerSpec.scala | 50 ++++++++++++++ 3 files changed, 113 insertions(+), 16 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index dcfb60cce4c..a91fb29637e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -111,12 +111,24 @@ object DistriOptimizer { val partitionNum = dataset.originRDD().partitions.length var wallClockTime = 0L var lastEpochTime = 0L + + // driverState is needed to prevent serializing the whole optimizer + if (!optimMethod.state.contains("epoch")) optimMethod.state.update("epoch", 1) + if (!optimMethod.state.contains("neval")) optimMethod.state.update("neval", 1) + if (!optimMethod.state.contains("Loss")) { + optimMethod.state.update("Loss", Float.PositiveInfinity) + } + if (!optimMethod.state.contains("score")) optimMethod.state.update("score", 0f) + if (!optimMethod.state.contains("recordsProcessedThisEpoch")) { + optimMethod.state.update("recordsProcessedThisEpoch", 0) + } val driverState = T( - "epoch" -> optimMethod.state.get[Int]("epoch").getOrElse(1), - "neval" -> optimMethod.state.get[Int]("neval").getOrElse(1), - "Loss" -> optimMethod.state.get[Float]("Loss").getOrElse(Float.PositiveInfinity), - "score" -> optimMethod.state.get[Float]("score").getOrElse(0f) + "epoch" -> optimMethod.state("epoch"), + "neval" -> optimMethod.state("neval"), + "Loss" -> optimMethod.state("Loss"), + "score" -> optimMethod.state("score") ) + val _subModelNumber = Engine.getEngineType() match { case MklBlas => coresPerNode } @@ -133,12 +145,16 @@ object DistriOptimizer { "fly from random samples, which is better for convergence.") } - val shuffleBefore = System.nanoTime() logger.info(s"config $state") - logger.info("Shuffle data") - dataset.shuffle() - val shuffleEnd = System.nanoTime() - logger.info(s"Shuffle data complete. Takes ${(shuffleEnd - shuffleBefore) / 1e9}s") + var recordsProcessedThisEpoch = optimMethod.state[Int]("recordsProcessedThisEpoch") + if (recordsProcessedThisEpoch == 0) { + val shuffleBefore = System.nanoTime() + logger.info("Shuffle data") + dataset.shuffle() + val shuffleEnd = System.nanoTime() + logger.info(s"Shuffle data complete. Takes ${(shuffleEnd - shuffleBefore) / 1e9}s") + + } var tasks: ArrayBuffer[Future[_]] = new ArrayBuffer() var threshold = Long.MaxValue @@ -161,7 +177,7 @@ object DistriOptimizer { var epochStart = System.nanoTime() var dataRDD = dataset.data(train = true) - var recordsProcessedThisEpoch = 0 + while (!endWhen(driverState)) { val lossSum = sc.accumulator(0.0, "loss sum") val recordsNum = sc.accumulator(0, "record number") @@ -367,13 +383,7 @@ object DistriOptimizer { recordsProcessedThisEpoch += recordsNum.value val end = System.nanoTime() wallClockTime += end - start - optimMethod.state.update("epoch", driverState[Int]("epoch")) - optimMethod.state.update("neval", driverState[Int]("neval")) driverState("Loss") = lossSum.value.toFloat / numFinishedModelUpdates - optimMethod.state.update("Loss", driverState[Float]("Loss")) - if (validationMethods.isDefined) { - optimMethod.state.update("score", driverState[Float]("score")) - } optimMethod.updateHyperParameter() driverState("Throughput") = recordsNum.value.toFloat / ((end - start) / 1e9f) driverState("LearningRate") = -optimMethod.getLearningRate().toFloat @@ -431,6 +441,15 @@ object DistriOptimizer { recordsProcessedThisEpoch = 0 } + optimMethod.state.update("recordsProcessedThisEpoch", recordsProcessedThisEpoch) + + optimMethod.state.update("epoch", driverState[Int]("epoch")) + optimMethod.state.update("neval", driverState[Int]("neval")) + optimMethod.state.update("Loss", driverState[Float]("Loss")) + if (validationMethods.isDefined) { + optimMethod.state.update("score", driverState[Float]("score")) + } + validate( validationTrigger, validationDataSet, @@ -809,6 +828,13 @@ class DistriOptimizer[T: ClassTag] ( }).count() } + private def endEpoch(): Unit = { + val records = this.optimMethod.state.get[Int]("recordsProcessedThisEpoch") + if (records.isDefined && records.get != 0) { + this.optimMethod.state("epoch") = this.optimMethod.state[Int]("epoch") + 1 + this.optimMethod.state("recordsProcessedThisEpoch") = 0 + } + } override def setTrainData(sampleRDD: RDD[Sample[T]], batchSize: Int, @@ -816,6 +842,9 @@ class DistriOptimizer[T: ClassTag] ( this.dataset = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(miniBatch, batchSize, None)) .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + // if current epoch is not finished, we will end the + // current epoch and start a new epoch when optimize is called + endEpoch() this } @@ -828,6 +857,9 @@ class DistriOptimizer[T: ClassTag] ( dataset = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(batchSize, _featurePaddingParam, _labelPaddingParam)) .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + // if current epoch is not finished, we will end the + // current epoch and start a new epoch when optimize is called + endEpoch() this } @@ -947,6 +979,10 @@ class DistriOptimizer[T: ClassTag] ( // Reset some internal states, so this or other optimizers can run optimize again clearState() + // unpersist the model because the next time optimize is called, new `models` will be + // created + models.unpersist() + model } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index e55ddd58c1e..754b0413902 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -224,6 +224,15 @@ abstract class Optimizer[T: ClassTag, D]( this } + private def resetEpoch(): Unit = { + optimMethod.state.update("epoch", 1) + optimMethod.state.update("neval", 1) + optimMethod.state.update("Loss", Float.PositiveInfinity) + optimMethod.state.update("score", 0f) + optimMethod.state.update("recordsProcessedThisEpoch", 0) + } + + /** * Set a model to the optimizer * @@ -231,6 +240,8 @@ abstract class Optimizer[T: ClassTag, D]( */ def setModel(newModel: Module[T]): this.type = { model = newModel + // if a new Model is set, then reset "epoch", "neval" .etc. + resetEpoch() this } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index 54a11b383bc..8d1f01d8e00 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -635,4 +635,54 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { myOpt.setTrainData(rdd, 2*nodeNumber) myOpt.optimize() } + + "optimMethod state " should "be updated correctly after optimize" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) + + val mm = Sequential[Double]().add(Linear(4, 1)) + .add(Sigmoid()) + + val optimizer = new DistriOptimizer[Double]( + _model = mm, + _dataset = dataSet, + _criterion = new MSECriterion[Double]() + ) + + val optimMethod = new SGD[Double](learningRate = 20.0) + + optimizer.setOptimMethod(optimMethod) + .setEndWhen(Trigger.maxIteration(10)) + val model = optimizer.optimize() + + optimMethod.state[Int]("epoch") should be (1) + optimMethod.state[Int]("neval") should be (11) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be (320) + + optimizer.setEndWhen(Trigger.maxIteration(20)) + optimizer.optimize() + + optimMethod.state[Int]("epoch") should be (1) + optimMethod.state[Int]("neval") should be (21) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be (640) + + val rdd = sc.parallelize(1 to (160 * nodeNumber), nodeNumber) + .map(_ => Sample[Double](Tensor[Double](4).fill(2.0), Tensor[Double](1).fill(1.0))) + + optimizer.setTrainData(rdd, 16 * nodeNumber) + + optimMethod.state[Int]("epoch") should be (2) + optimMethod.state[Int]("neval") should be (21) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be (0) + + optimizer.setEndWhen(Trigger.maxEpoch(2)) + optimizer.optimize() + + optimMethod.state[Int]("epoch") should be (3) + optimMethod.state[Int]("neval") should be (31) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be (0) + + + } } From f77901cda745e0d091b0a29cfc4bb2f70df7e558 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 16 Jan 2018 13:44:07 +0800 Subject: [PATCH 0640/1065] make imageframe serializable (#2193) --- .../bigdl/dllib/feature/transform/vision/image/ImageFrame.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala index a7a527c1b1d..081d9c7792c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -30,7 +30,7 @@ import scala.collection.mutable.ArrayBuffer /** * ImageFrame wraps a set of ImageFeature */ -trait ImageFrame { +trait ImageFrame extends Serializable { /** * transform ImageFrame From 8da1bbd9afeee0478cbaeb67d4e77ce4dce1fcfe Mon Sep 17 00:00:00 2001 From: Xianyan Date: Tue, 16 Jan 2018 18:35:34 +0800 Subject: [PATCH 0641/1065] Add default zeroGradParameters to AbstractModule (#2177) * Fix optimizer NaN in SSD * Add zeroGradient * Add default zeroGradParameters * revert --- .../dllib/nn/abstractnn/AbstractModule.scala | 8 ++++++- .../bigdl/dllib/nn/NormalizeScaleSpec.scala | 18 +++++++++++++++ .../analytics/bigdl/dllib/nn/ScaleSpec.scala | 23 +++++++++++++++++++ 3 files changed, 48 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 5efe388cbcb..3026b0cc7b9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -331,7 +331,13 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * If the module has parameters, this will zero the accumulation of the gradients with respect * to these parameters. Otherwise, it does nothing. */ - def zeroGradParameters(): Unit = {} + def zeroGradParameters(): Unit = { + if (parameters() != null) { + parameters()._2.foreach(grad => { + grad.zero() + }) + } + } def updateParameters(learningRate: T): Unit = {} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala index 262ff010d71..20f649aa3ac 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala @@ -344,4 +344,22 @@ class NormalizeScaleSpec extends FlatSpec with Matchers { a }) } + + "A NormalizeScale zeroGrad" should "work" in { + val input = Tensor[Float](Array(2, 5, 3, 3)).randn() + + val gradOut = Tensor[Float](Array(2, 5, 3, 3)).randn() + + val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1)) + + val out = module.forward(input) + val gradInput = module.backward(input, gradOut) + + println(module.parameters()._2(0)) + + module.zeroGradParameters() + module.parameters()._2(0).apply1(x => { + assert(x == 0); x + }) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleSpec.scala index 2f57229afa4..a837ea323b0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleSpec.scala @@ -166,4 +166,27 @@ class ScaleSpec extends FlatSpec with Matchers{ }) } + "scale zeroParameter" should "work" in { + + val scale = new Scale[Double](Array(1, 4, 1, 1)) + scale.parameters()._1(0).copy(Tensor(Storage(Array(0.4, 0.3, 0.2, 0.1)))) // weight + scale.parameters()._1(1).copy(Tensor(Storage(Array(0.1, 0.01, 0.03, 0.04)))) // bias + val output = scale.forward(input) + val gradOutput = Tensor[Double](1, 4, 5, 6).randn() + scale.backward(input, gradOutput) + + println(scale.parameters()._2(0)) + println(scale.parameters()._2(1)) + + scale.zeroGradParameters() + + scale.parameters()._2(0).apply1(x => { + assert(x == 0); x + }) + + scale.parameters()._2(1).apply1(x => { + assert(x == 0); x + }) + } + } From 41ca916318645c200823ff104df09300d46901bb Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Thu, 18 Jan 2018 14:15:37 +0800 Subject: [PATCH 0642/1065] update inceptionspec (#2199) --- .../com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala index 66f0c5febbe..c89b2c72298 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala @@ -186,7 +186,7 @@ class InceptionSpec extends TorchSpec { val errTorch = TH.map("err").asInstanceOf[Table][Double](1) val errTest = criterion.forward(outputTest, labels) println(s"err:${abs(errTest - errTorch)}") - assert(abs(errTest - errTorch) < 2e-15) + assert(abs(errTest - errTorch) < 4e-15) val gradOutputTorch = TH.map("gradOutput").asInstanceOf[Tensor[Double]] val gradOutputTest = criterion.backward(outputTest, labels) From a6c50552d1374b7b42c0b702eb9ac4a168bc4582 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 19 Jan 2018 12:48:11 +0800 Subject: [PATCH 0643/1065] Convert tensor to OpenCVMat (#2198) * Convert tensor to OpenCVMat * update comment * catch exception --- .../vision/image/opencv/OpenCVMat.scala | 41 ++++++++++++-- .../vision/image/opencv/OpenCVMatSpec.scala | 54 ++++++++++++++++++- 2 files changed, 91 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala index bd5286868ee..442419da966 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.transform.vision.image.opencv import java.io.{File, IOException, ObjectInputStream, ObjectOutputStream} import com.intel.analytics.bigdl.opencv.OpenCV +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image.util.BoundingBox import org.apache.commons.io.FileUtils import org.opencv.core._ @@ -166,9 +167,17 @@ object OpenCVMat { * @return image in mat */ def fromFloats(floats: Array[Float], height: Int, width: Int): OpenCVMat = { - val mat = new Mat(height, width, CvType.CV_32FC3) - mat.put(0, 0, floats) - new OpenCVMat(mat) + var mat: Mat = null + try { + mat = new Mat(height, width, CvType.CV_32FC3) + mat.put(0, 0, floats) + new OpenCVMat(mat) + } catch { + case e: Exception => throw new Exception(s"convert float array to OpenCVMat fails!\n" + + s"${e.getMessage}") + } finally { + if (null != mat) mat.release() + } } /** @@ -226,4 +235,30 @@ object OpenCVMat { mat.put(0, 0, pixels) mat } + + /** + * convert float tensor to OpenCVMat, + * Note that if you want to convert the tensor to BGR image, + * the element should be in range [0, 255] + * @param tensor tensor that represent an image + * @param format "HWC" or "CHW", + * "HWC" means (height, width, channel) order, + * "CHW" means (channel, height, width) order + * @return OpenCVMat + */ + def fromTensor(tensor: Tensor[Float], format: String = "HWC"): OpenCVMat = { + require(format == "HWC" || format == "CHW", "the format should be HWC or CHW") + var image = if (format == "CHW") { + tensor.transpose(1, 2).transpose(2, 3) + } else { + tensor + } + image = image.contiguous() + val offset = tensor.storageOffset() - 1 + var floatArr = image.storage().array() + if (offset > 0) { + floatArr = floatArr.slice(offset, tensor.nElement() + offset) + } + fromFloats(floatArr, image.size(1), image.size(2)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala index 6929361b8c1..7ae9d839eca 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/opencv/OpenCVMatSpec.scala @@ -19,8 +19,9 @@ package com.intel.analytics.bigdl.transform.vision.image.opencv import java.io.File import com.intel.analytics.bigdl.opencv.OpenCV +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image.util.BoundingBox -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, T} import org.apache.commons.io.FileUtils import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext @@ -96,6 +97,57 @@ class OpenCVMatSpec extends FlatSpec with Matchers with BeforeAndAfter { bytes1._1 should equal(bytes2._1) } + "fromTensor" should "work properly" in { + val tensor = Tensor[Float](T(1, 2, 3, + 1, 2, 3, + 1, 2, 3, + 1, 2, 3)).resize(2, 2, 3) + val mat = OpenCVMat.fromTensor(tensor) + mat.shape() should be (2, 2, 3) + OpenCVMat.toFloatPixels(mat)._1 should equal(Array[Float](1, 2, 3, + 1, 2, 3, + 1, 2, 3, + 1, 2, 3)) + } + + "fromFloats" should "work properly" in { + val arr = Array[Float](1, 2, 3, + 1, 2, 3, + 1, 2, 3, + 1, 2, 3) + val mat = OpenCVMat.fromFloats(arr, 2, 2) + mat.shape() should be (2, 2, 3) + OpenCVMat.toFloatPixels(mat)._1 should equal(Array[Float](1, 2, 3, + 1, 2, 3, + 1, 2, 3, + 1, 2, 3)) + } + + "fromTensor CHW" should "work properly" in { + val tensor = Tensor[Float](T(1, 2, 3, + 1, 2, 3, + 1, 2, 3, + 1, 2, 3)).resize(2, 2, 3).transpose(1, 3).transpose(2, 3) + val mat = OpenCVMat.fromTensor(tensor, "CHW") + mat.shape() should be (2, 2, 3) + OpenCVMat.toFloatPixels(mat)._1 should equal(Array[Float](1, 2, 3, + 1, 2, 3, + 1, 2, 3, + 1, 2, 3)) + } + + "fromTensor offset not equal to 0" should "work properly" in { + val tensor = Tensor[Float](T(4, 5, 6, + 7, 8, 9, + 1, 2, 3, + 1, 2, 3)).resize(2, 2, 3).narrow(1, 2, 1) + val mat = OpenCVMat.fromTensor(tensor) + mat.shape() should be (1, 2, 3) + OpenCVMat.toFloatPixels(mat)._1 should equal(Array[Float](1, 2, 3, + 1, 2, 3)) + } + + var sc: SparkContext = null before { From b72cc0bc3d92441692fd870cf8fc7c31acb860d7 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Fri, 19 Jan 2018 15:21:35 +0800 Subject: [PATCH 0644/1065] fix recurrent (#2202) --- .../analytics/bigdl/dllib/nn/Recurrent.scala | 1 + .../bigdl/dllib/nn/RecurrentSpec.scala | 20 +++++++++++++++++++ 2 files changed, 21 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index e6d5990d5f1..c7cf4b01560 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -469,6 +469,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) modules.foreach(_.reset()) cells.clear() + hidden = null } override def canEqual(other: Any): Boolean = other.isInstanceOf[Recurrent[T]] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala index d8eda8d2d20..ba470a8dd33 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala @@ -631,4 +631,24 @@ class RecurrentSpec extends FlatSpec with Matchers { Recurrent.copy(arrInput, output2) output2 should be (input) } + + "A Recurrent Module " should " work after reset " in { + val hiddenSize = 4 + val inputSize = 5 + val outputSize = 5 + val seed = 100 + RNG.setSeed(seed) + + val model = Sequential[Double]() + .add(Recurrent[Double]() + .add(RnnCell[Double](inputSize, hiddenSize, Tanh()))) + .add(Select(1, 1)) + .add(Linear[Double](hiddenSize, outputSize)) + + val input = Tensor[Double](Array(1, 5, inputSize)) + val output1 = model.forward(input).toTensor[Double].clone() + model.reset() + model.forward(input) + } + } From cdb24a3d5e7cb806aa64810027761ef59cf7a14a Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 19 Jan 2018 16:19:23 +0800 Subject: [PATCH 0645/1065] validation apply new transformer (#2201) * validation apply new transformer * per reciew --- .../dllib/example/loadmodel/DatasetUtil.scala | 26 ++++++++++++------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala index 6e92b8a70ac..5a68d9b8aa0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala @@ -23,6 +23,8 @@ import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset._ import com.intel.analytics.bigdl.dataset.image.{BGRImgCropper, BGRImgNormalizer, BGRImgPixelNormalizer, BytesToBGRImg, _} import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, PixelNormalizer, Resize} +import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.utils.File import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD @@ -48,12 +50,14 @@ object AlexNetPreprocessor { def rdd(path: String, batchSize: Int, meanFile: String, sc: SparkContext) : RDD[Sample[Float]] = { val means = createMeans(meanFile) - val dataSet = DataSet.SeqFileFolder.filesToRdd(path, sc, 1000) + val data = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) // do not normalize the pixel values to [0, 1] - val transfomer = BytesToBGRImg(normalize = 1f, 256, 256) -> - BGRImgPixelNormalizer(means) -> BGRImgCropper(imageSize, imageSize, CropCenter) -> - BGRImgToSample(toRGB = false) - transfomer(dataSet) + val transfomer = PixelBytesToMat() -> Resize(256, 256) -> + PixelNormalizer(means.storage.array) -> CenterCrop(imageSize, imageSize) -> + MatToTensor[Float]() -> ImageFrameToSample[Float](targetKeys = Array(ImageFeature.label)) + val imgFrame = data -> transfomer + val validImageFeatures = imgFrame.toDistributed().rdd + validImageFeatures.map(x => x[Sample[Float]](ImageFeature.sample)) } def createMeans(meanFile : String) : Tensor[Float] = { @@ -76,11 +80,13 @@ object InceptionPreprocessor { def rdd(path: String, batchSize: Int, sc: SparkContext) : RDD[Sample[Float]] = { - val dataSet = DataSet.SeqFileFolder.filesToRdd(path, sc, classNum = 1000) - val transfomer = BytesToBGRImg(normalize = 1f) -> - BGRImgCropper(imageSize, imageSize, CropCenter) -> - BGRImgNormalizer(123, 117, 104, 1, 1, 1) -> BGRImgToSample(toRGB = false) - transfomer(dataSet) + val data = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) + val transfomer = PixelBytesToMat() -> Resize(256, 256) -> + CenterCrop(imageSize, imageSize) -> ChannelNormalize(123, 117, 104) -> + MatToTensor[Float]() -> ImageFrameToSample[Float](targetKeys = Array(ImageFeature.label)) + val imgFrame = transfomer(data) + val validImageFeatures = imgFrame.toDistributed().rdd + validImageFeatures.map(x => x[Sample[Float]](ImageFeature.sample)) } } From d07cee6be318d37d3f9c4c1f95e3ea5b73f0cac0 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 22 Jan 2018 09:25:35 +0800 Subject: [PATCH 0646/1065] fix leakyrelu (#2206) --- .../analytics/bigdl/dllib/nn/LeakyReLU.scala | 4 +-- .../bigdl/dllib/utils/TorchFile.scala | 18 +++++++++++-- .../bigdl/dllib/torch/LeakyReLUSpec.scala | 26 +++++++++---------- 3 files changed, 31 insertions(+), 17 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala index a93e3a71818..7ec866ac328 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala @@ -49,7 +49,7 @@ class LeakyReLU[T: ClassTag]( if (inplace) { input.apply1(x => { if (ev.isGreaterEq(ev.fromType[Int](0), x)) { - negVal + ev.times(x, negVal) } else { x } @@ -76,7 +76,7 @@ class LeakyReLU[T: ClassTag]( gradInput.set(gradOutput) gradOutput.map(input, (grad, in) => { if (ev.isGreaterEq(ev.fromType[Int](0), in)) { - negVal + ev.times(negVal, grad) } else { grad } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala index 2b2b5642267..22d2d9bd658 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/TorchFile.scala @@ -152,6 +152,7 @@ object TorchFile { case "nn.Concat" => readConcatWithType(elements) case "nn.ConcatTable" => readConcatTableWithType(elements) case "nn.Dropout" => readDropoutWithType(elements) + case "nn.LeakyReLU" => readLeakyReLUWithType(elements) case "nn.Linear" => readLinearWithType(elements) case "nn.ReLU" => ReLU(elements("inplace").asInstanceOf[Boolean]) case "nn.Reshape" => Reshape(elements("size").asInstanceOf[Array[Int]]) @@ -929,6 +930,15 @@ object TorchFile { result } + private def readLeakyReLUWithType[T: ClassTag]( + elements: Table)(implicit ev: TensorNumeric[T]): LeakyReLU[T] = { + val result = LeakyReLU[T]( + negval = elements.getOrElse("negval", 0.01), + inplace = elements.getOrElse("inplace", false) + ) + result + } + private def readLinearWithType[T: ClassTag]( elements: Table)(implicit ev: TensorNumeric[T]) : Linear[T] = { val weight = elements("weight").asInstanceOf[Tensor[T]] @@ -1059,6 +1069,7 @@ object TorchFile { private def readSpatialConvolutionWithType[T: ClassTag]( elements: Table)(implicit ev: TensorNumeric[T]): SpatialConvolution[T] = { val propagateBack = if (null == elements("gradInput")) false else true + val withBias = elements.contains("bias") val result = SpatialConvolution[T]( nInputPlane = elements[Double]("nInputPlane").toInt, nOutputPlane = elements[Double]("nOutputPlane").toInt, @@ -1069,10 +1080,13 @@ object TorchFile { padW = elements.getOrElse("padW", 0.0).toInt, padH = elements.getOrElse("padH", 0.0).toInt, nGroup = 1, - propagateBack = propagateBack + propagateBack = propagateBack, + withBias = withBias ) result.weight.copy(elements("weight").asInstanceOf[Tensor[T]]) - result.bias.copy(elements("bias").asInstanceOf[Tensor[T]]) + if (withBias) { + result.bias.copy(elements("bias").asInstanceOf[Tensor[T]]) + } result } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala index 10b734f0c95..b8a0a834bbb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala @@ -35,12 +35,6 @@ class LeakyReLUSpec extends TorchSpec { val gradOutput = Tensor[Double](2, 2, 2) input.apply1(x => random()) - val start = System.nanoTime() - val output = module.forward(input) - val gradInput = module.backward(input, gradOutput) - val end = System.nanoTime() - val scalaTime = end - start - val code = "torch.manualSeed(" + seed + ")\n" + "module = nn.LeakyReLU()\n" + "output = module:forward(input)\n" + @@ -51,6 +45,12 @@ class LeakyReLUSpec extends TorchSpec { val luaOutput1 = torchResult("output").asInstanceOf[Tensor[Double]] val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Double]] + val start = System.nanoTime() + val output = module.forward(input) + val gradInput = module.backward(input, gradOutput) + val end = System.nanoTime() + val scalaTime = end - start + luaOutput1 should be (output) luaOutput2 should be (gradInput) @@ -62,18 +62,12 @@ class LeakyReLUSpec extends TorchSpec { val seed = 100 RNG.setSeed(seed) - val module = new LeakyReLU[Double](inplace = true) + val module = LeakyReLU[Double](inplace = true) val input = Tensor[Double](2, 2, 2) input.apply1(x => random()) val gradOutput = Tensor[Double](2, 2, 2) input.apply1(x => random()) - val start = System.nanoTime() - val output = module.forward(input) - val gradInput = module.backward(input.clone(), gradOutput.clone()) - val end = System.nanoTime() - val scalaTime = end - start - val code = "torch.manualSeed(" + seed + ")\n" + "module = nn.LeakyReLU(1/100,true)\n" + "output = module:forward(input)\n" + @@ -84,6 +78,12 @@ class LeakyReLUSpec extends TorchSpec { val luaOutput1 = torchResult("output").asInstanceOf[Tensor[Double]] val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Double]] + val start = System.nanoTime() + val output = module.forward(input) + val gradInput = module.backward(input.clone(), gradOutput.clone()) + val end = System.nanoTime() + val scalaTime = end - start + luaOutput1 should be (output) luaOutput2 should be (gradInput) From 2c997427f02a7fc5efcaeb0d9a13a8bb1962099b Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 22 Jan 2018 13:11:17 +0800 Subject: [PATCH 0647/1065] Add NCHW support for resizebilinear (#2205) * add nchw support for resizebilinear * fix style * fixed indent * test cover more cases * remove double code and python interface * add doc --- .../bigdl/dllib/nn/ResizeBilinear.scala | 255 ++++++++++++++---- .../dllib/nn/ops/ResizeBilinearOps.scala | 22 +- .../dllib/utils/python/api/PythonBigDL.scala | 5 +- .../bigdl/dllib/nn/ResizeBilinearSpec.scala | 48 +++- 4 files changed, 266 insertions(+), 64 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala index dfa0aacc3b2..4c31a5366d5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala @@ -16,23 +16,25 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.ResizeBilinear.InterpolationWeight -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag /** * Resize the input image with bilinear interpolation. The input image must be a float tensor with - * NHWC layout + * NHWC or NCHW layout. * * @param outputHeight output height * @param outputWidth output width * @param alignCorners align corner or not + * @param dataFormat the data format of the input image, NHWC or NCHW * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now */ class ResizeBilinear[T: ClassTag](val outputHeight: Int, val outputWidth: Int, - val alignCorners: Boolean)(implicit ev: TensorNumeric[T]) + val alignCorners: Boolean, + val dataFormat: DataFormat = DataFormat.NCHW)(implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[Float], Tensor[Float], T]{ private val ys = (1 to (outputHeight + 1)).map(i => InterpolationWeight(0, 0, 0)).toArray @@ -44,6 +46,61 @@ class ResizeBilinear[T: ClassTag](val outputHeight: Int, val outputWidth: Int, require(input.nDimension() == 4, "only accept 4D input") require(input.isContiguous(), "only accept contiguous input") + dataFormat match { + case DataFormat.NHWC => updateOutputNHWC(input) + case DataFormat.NCHW => updateOutputNCHW(input) + } + + output + } + + private def updateOutputNCHW(input: Tensor[Float]): Tensor[Float] = { + val batchSize = input.size(1) + val channels = input.size(2) + val inHeight = input.size(3) + val inWidth = input.size(4) + + if (inHeight == outputHeight && inWidth == outputWidth) { + output = input + output + } else { + computeInterpolationWeights(outputHeight, inHeight, + calculateResizeScale(inHeight, outputHeight, alignCorners), ys) + computeInterpolationWeights(outputWidth, inWidth, + calculateResizeScale(inWidth, outputWidth, alignCorners), xs) + + output.resize(batchSize, channels, outputHeight, outputWidth) + + var i = 0 + while (i < batchSize * channels) { + val inputOffset = (input.storageOffset() - 1) + i * inHeight * inWidth + val outputOffset = (output.storageOffset() - 1) + i * outputHeight * outputWidth + + if (input.getType() == FloatType) { + resizeImage(input.storage().array(), + inputOffset, 1, inHeight, inWidth, + outputHeight, outputWidth, 1, xs, ys, + output.storage().array(), + outputOffset) + } else if (input.getType() == DoubleType) { + resizeImage(input.storage().array(), + inputOffset, 1, inHeight, inWidth, + outputHeight, outputWidth, 1, xs, ys, + output.storage().array(), + outputOffset) + } else { + throw new IllegalArgumentException( + s"ResizeBilinear does not support type ${input.getType()}") + } + + i += 1 + } + + output + } + } + + private def updateOutputNHWC(input: Tensor[Float]): Tensor[Float] = { val batchSize = input.size(1) val inHeight = input.size(2) val inWidth = input.size(3) @@ -66,9 +123,23 @@ class ResizeBilinear[T: ClassTag](val outputHeight: Int, val outputWidth: Int, } output.resize(batchSize, outputHeight, outputWidth, channels) - resizeImage(input.storage().array(), input.storageOffset() - 1, batchSize, inHeight, inWidth, - outputHeight, outputWidth, channels, xs, ys, output.storage().array(), - output.storageOffset() - 1) + if (input.getType() == FloatType) { + resizeImage(input.storage().array(), + input.storageOffset() - 1, batchSize, inHeight, inWidth, + outputHeight, outputWidth, channels, xs, ys, + output.storage().array(), + output.storageOffset() - 1) + } else if (input.getType() == DoubleType) { + resizeImage(input.storage().array(), + input.storageOffset() - 1, batchSize, inHeight, inWidth, + outputHeight, outputWidth, channels, xs, ys, + output.storage().array(), + output.storageOffset() - 1) + } else { + throw new IllegalArgumentException( + s"ResizeBilinear does not support type ${input.getType()}") + } + output } } @@ -79,14 +150,19 @@ class ResizeBilinear[T: ClassTag](val outputHeight: Int, val outputWidth: Int, require(input.isContiguous(), "only accept contiguous input") require(gradOutput.isContiguous(), "only accept contiguous gradOutput") + dataFormat match { + case DataFormat.NHWC => updateGradInputNHWC(input, gradOutput) + case DataFormat.NCHW => updateGradInputNCHW(input, gradOutput) + } + gradInput + } + + private def updateGradInputNHWC(input: Tensor[Float], + gradOutput: Tensor[Float]): Tensor[Float] = { val batchSize = input.size(1) val inHeight = input.size(2) val inWidth = input.size(3) val channels = input.size(4) - val inRowSize = inWidth * channels - val inBatchNum = inHeight * inRowSize - val outRowSize = outputWidth * channels - val outBatchNum = outputHeight * outRowSize require(gradOutput.size(2) == outputHeight, "output height is not match") require(gradOutput.size(3) == outputWidth, "output width is not match") @@ -102,53 +178,76 @@ class ResizeBilinear[T: ClassTag](val outputHeight: Int, val outputWidth: Int, val gradOutputData = gradOutput.storage().array() val gradOutputOffset = gradOutput.storageOffset() - 1 - var b = 0 - while(b < batchSize) { - var y = 0 - while(y < outputHeight) { - val inY = y * heightScale - val topY = inY.toInt - val bottomY = math.min(math.ceil(inY).toInt, inHeight - 1) - val yLERP = inY - topY - val inverseYLERP = (1.0f - yLERP) - var x = 0 - while(x < outputWidth) { - val inX = x * widthScale - val leftX = inX.toInt - val rightX = math.min(math.ceil(inX).toInt, inWidth - 1) - val xLERP = inX - leftX - val inverseXLERP = (1.0f - xLERP) - var c = 0 - while(c < channels) { - gradInputData(gradInputOffset + b * inBatchNum + topY * inRowSize + - leftX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + - y * outRowSize + x * channels + c) * inverseYLERP * inverseXLERP - gradInputData(gradInputOffset + b * inBatchNum + topY * inRowSize + - rightX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + - y * outRowSize + x * channels + c) * inverseYLERP * xLERP - gradInputData(gradInputOffset + b * inBatchNum + bottomY * inRowSize + - leftX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + - y * outRowSize + x * channels + c) * yLERP * inverseXLERP - gradInputData(gradInputOffset + b * inBatchNum + bottomY * inRowSize + - rightX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + - y * outRowSize + x * channels + c) * yLERP * xLERP - c += 1 - } - x += 1 - } - y += 1 + if (input.getType() == FloatType) { + resizeImageBackprop(batchSize, channels, inHeight, inWidth, + outputHeight, outputWidth, heightScale, widthScale, + gradInputData, gradInputOffset, + gradOutputData, gradOutputOffset) + } else if (input.getType() == DoubleType) { + resizeImageBackprop(batchSize, channels, inHeight, inWidth, + outputHeight, outputWidth, heightScale, widthScale, + gradInputData, gradInputOffset, + gradOutputData, gradOutputOffset) + } else { + throw new IllegalArgumentException( + s"ResizeBilinear does not support type ${input.getType()}") + } + + gradInput + } + + private def updateGradInputNCHW(input: Tensor[Float], + gradOutput: Tensor[Float]): Tensor[Float] = { + val batchSize = input.size(1) + val channels = input.size(2) + val inHeight = input.size(3) + val inWidth = input.size(4) + + require(gradOutput.size(3) == outputHeight, "output height is not match") + require(gradOutput.size(4) == outputWidth, "output width is not match") + + val heightScale = calculateResizeScale(inHeight, outputHeight, alignCorners) + val widthScale = calculateResizeScale(inWidth, outputWidth, alignCorners) + + gradInput.resizeAs(input) + gradInput.zero() + + val gradInputData = gradInput.storage().array() + val gradInputOffset = gradInput.storageOffset() - 1 + val gradOutputData = gradOutput.storage().array() + val gradOutputOffset = gradOutput.storageOffset() - 1 + + var i = 0 + while (i < batchSize * channels) { + val inOffset = gradInputOffset + i * (inHeight * inWidth) + val outOffset = gradOutputOffset + i * (outputHeight * outputWidth) + if (input.getType() == FloatType) { + resizeImageBackprop(1, 1, inHeight, inWidth, + outputHeight, outputWidth, heightScale, widthScale, + gradInputData, inOffset, + gradOutputData, outOffset) + } else if (input.getType() == DoubleType) { + resizeImageBackprop(1, 1, inHeight, inWidth, + outputHeight, outputWidth, heightScale, widthScale, + gradInputData, inOffset, + gradOutputData, outOffset) + } else { + throw new IllegalArgumentException( + s"ResizeBilinear does not support type ${input.getType()}") } - b += 1 + i += 1 } + gradInput } } object ResizeBilinear { - def apply[T: ClassTag](outputHeight: Int, outputWidth: Int, alignCorners: Boolean = false) + def apply[T: ClassTag](outputHeight: Int, outputWidth: Int, alignCorners: Boolean = false, + dataFormat: DataFormat = DataFormat.NCHW) (implicit ev: TensorNumeric[T]): ResizeBilinear[T] = { - new ResizeBilinear[T](outputHeight, outputWidth, alignCorners) + new ResizeBilinear[T](outputHeight, outputWidth, alignCorners, dataFormat) } private def computeLERP( @@ -244,6 +343,64 @@ object ResizeBilinear { } } + @inline + private def resizeImageBackprop( + batchSize: Int, + channels: Int, + inHeight: Int, + inWidth: Int, + outputHeight: Int, + outputWidth: Int, + heightScale: Float, + widthScale: Float, + gradInputData: Array[Float], + gradInputOffset: Int, + gradOutputData: Array[Float], + gradOutputOffset: Int): Unit = { + val inRowSize = inWidth * channels + val inBatchNum = inHeight * inRowSize + val outRowSize = outputWidth * channels + val outBatchNum = outputHeight * outRowSize + var b = 0 + while(b < batchSize) { + var y = 0 + while(y < outputHeight) { + val inY = y * heightScale + val topY = inY.toInt + val bottomY = math.min(math.ceil(inY).toInt, inHeight - 1) + val yLERP = inY - topY + val inverseYLERP = (1.0f - yLERP) + var x = 0 + while(x < outputWidth) { + val inX = x * widthScale + val leftX = inX.toInt + val rightX = math.min(math.ceil(inX).toInt, inWidth - 1) + val xLERP = inX - leftX + val inverseXLERP = (1.0f - xLERP) + var c = 0 + while(c < channels) { + gradInputData(gradInputOffset + b * inBatchNum + topY * inRowSize + + leftX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + + y * outRowSize + x * channels + c) * inverseYLERP * inverseXLERP + gradInputData(gradInputOffset + b * inBatchNum + topY * inRowSize + + rightX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + + y * outRowSize + x * channels + c) * inverseYLERP * xLERP + gradInputData(gradInputOffset + b * inBatchNum + bottomY * inRowSize + + leftX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + + y * outRowSize + x * channels + c) * yLERP * inverseXLERP + gradInputData(gradInputOffset + b * inBatchNum + bottomY * inRowSize + + rightX * channels + c) += gradOutputData(gradOutputOffset + b * outBatchNum + + y * outRowSize + x * channels + c) * yLERP * xLERP + c += 1 + } + x += 1 + } + y += 1 + } + b += 1 + } + } + private case class InterpolationWeight(var lower: Int, var upper: Int, var lerp: Float) private def calculateResizeScale(inSize: Int, outSize: Int, alignCorners: Boolean): Float = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala index bfbc98b550f..74d1c3841f5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala @@ -16,31 +16,32 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.nn.ResizeBilinear -import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag class ResizeBilinearOps[T: ClassTag](alignCorner: Boolean)(implicit ev: TensorNumeric[T]) - extends Operation[Activity, Tensor[Float], T] { + extends Operation[Activity, Tensor[T], T] { private var module : ResizeBilinear[T] = _ - override def updateOutput(input: Activity): Tensor[Float] = { + override def updateOutput(input: Activity): Tensor[T] = { require(input.isTable, "Only accept two input tensors") val size = input.toTable.apply[Tensor[Int]](2) if (module == null) { module = ResizeBilinear[T]( size.valueAt(1), size.valueAt(2), - alignCorner + alignCorner, + dataFormat = DataFormat.NHWC ) } else { require(module.outputHeight == size.valueAt(1), "height not match") require(module.outputWidth == size.valueAt(2), "width not match") } - val data = input.toTable.apply[Tensor[Float]](1) + val data = input.toTable.apply[Tensor[T]](1) output = module.forward(data) output } @@ -54,19 +55,20 @@ object ResizeBilinearOps { } class ResizeBilinearGrad[T: ClassTag](alignCorner: Boolean)(implicit ev: TensorNumeric[T]) - extends Operation[Activity, Tensor[Float], T] { + extends Operation[Activity, Tensor[T], T] { private var module : ResizeBilinear[T] = _ - override def updateOutput(input: Activity): Tensor[Float] = { + override def updateOutput(input: Activity): Tensor[T] = { require(input.isTable, "Only accept two input tensors") - val grads = input.toTable.apply[Tensor[Float]](1) - val originImage = input.toTable.apply[Tensor[Float]](2) + val grads = input.toTable.apply[Tensor[T]](1) + val originImage = input.toTable.apply[Tensor[T]](2) if (module == null) { module = ResizeBilinear[T]( grads.size(2), grads.size(3), - alignCorner + alignCorner, + dataFormat = DataFormat.NHWC ) } else { require(module.outputHeight == grads.size(2), "height not match") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index e97ef086cd0..b84df0fae4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2386,11 +2386,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createResizeBilinear( outputHeight: Int, outputWidth: Int, - alignCorner: Boolean + alignCorner: Boolean, + dataFormat: String ): ResizeBilinear[T] = { ResizeBilinear[T](outputHeight, outputWidth, - alignCorner) + alignCorner, DataFormat.apply(dataFormat)) } def createMultiRNNCell(cells: JList[Cell[T]]): MultiRNNCell[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala index e2cb9724cff..39f27a48bea 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -37,7 +38,7 @@ class ResizeBilinearSpec extends FlatSpec with Matchers { "ResizeBilinear forward" should "not change content while input/output width/height match" in { println(input) - val layer = ResizeBilinear[Float](3, 2) + val layer = ResizeBilinear[Float](3, 2, dataFormat = DataFormat.NHWC) val output = layer.forward(input) println(output) input should be(output) @@ -45,7 +46,7 @@ class ResizeBilinearSpec extends FlatSpec with Matchers { "ResizeBilinear forward" should "be correct while double height" in { println(input) - val layer = ResizeBilinear[Float](6, 2) + val layer = ResizeBilinear[Float](6, 2, dataFormat = DataFormat.NHWC) val output = layer.forward(input) println(output) val expectOutput = Tensor[Float](T(T( @@ -79,7 +80,7 @@ class ResizeBilinearSpec extends FlatSpec with Matchers { "ResizeBilinear forward" should "be correct while double width" in { println(input) - val layer = ResizeBilinear[Float](3, 4) + val layer = ResizeBilinear[Float](3, 4, dataFormat = DataFormat.NHWC) val output = layer.forward(input) println(output) val expectOutput = Tensor[Float](T(T( @@ -104,4 +105,45 @@ class ResizeBilinearSpec extends FlatSpec with Matchers { ))) output should be(expectOutput) } + + "ResizeBilinear forward and backward" should "be correct with NCHW" in { + + case class Param(inHeight: Int, inWidth: Int, + outHeight: Int, outWidth: Int, alignCorners: Boolean) + val params = Seq( + Param(3, 2, 3, 2, true), + Param(3, 2, 6, 2, true), + Param(3, 2, 3, 4, true), + Param(3, 2, 3, 2, false), + Param(3, 2, 6, 2, false), + Param(3, 2, 3, 4, false) + ) + + for (param <- params) { + val inputCFirst = Tensor[Float](Array(1, 3, param.inHeight, param.inWidth)).rand() + val inputCLast = inputCFirst.clone().transpose(2, 4).transpose(2, 3).contiguous() + + val gradOutputCFirst = Tensor[Float](Array(1, 3, param.outHeight, param.outWidth)).rand() + val gradOutputCLast = gradOutputCFirst.clone().transpose(2, 4).transpose(2, 3).contiguous() + val layerCLast = ResizeBilinear[Float](param.outHeight, param.outWidth, + param.alignCorners, dataFormat = DataFormat.NHWC) + val layerCFirst = ResizeBilinear[Float](param.outHeight, param.outWidth, + param.alignCorners, dataFormat = DataFormat.NCHW) + + // NCHW + val outputCFirst = layerCFirst.forward(inputCFirst) + val gradInputCFirst = layerCFirst.backward(inputCFirst, gradOutputCFirst) + + val outputCLast = layerCLast.forward(inputCLast) + val gradInputCLast = layerCLast.backward(inputCLast, gradOutputCLast) + + outputCFirst + .transpose(2, 4) + .transpose(2, 3).contiguous() should be(outputCLast) + + gradInputCFirst + .transpose(2, 4) + .transpose(2, 3).contiguous() should be(gradInputCLast) + } + } } From fdf9a48af8fc95aceb20e6b5aa79705ed3dbddf6 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Mon, 22 Jan 2018 15:12:23 +0800 Subject: [PATCH 0648/1065] A new keras-like scala API (#2071) * update * clean * update * refactor name * nested sequential * support model saving/loading * fix test * remove batch * meet comments * factory * remove * throw exception for paralleltable * add activation as parameter * fix test * new version * runnable * clean * clean * refactor * add batch to shape * update * clean * fix batch test * style * refactor checking * delete useless import * meet comments * comments * override * update * temporary exclude keras checking --- .../intel/analytics/bigdl/utils/Shape.scala | 129 ++++++++++ .../analytics/bigdl/utils/ShapeSpec.scala | 43 ++++ .../analytics/bigdl/dllib/keras/Dense.scala | 77 ++++++ .../analytics/bigdl/dllib/keras/Input.scala | 66 +++++ .../bigdl/dllib/keras/KerasLayer.scala | 236 ++++++++++++++++++ .../bigdl/dllib/keras/Topology.scala | 153 ++++++++++++ .../analytics/bigdl/dllib/nn/Container.scala | 10 +- .../analytics/bigdl/dllib/nn/Graph.scala | 2 +- .../analytics/bigdl/dllib/nn/Linear.scala | 15 +- .../intel/analytics/bigdl/dllib/nn/ReLU.scala | 3 +- .../analytics/bigdl/dllib/nn/Sequential.scala | 1 - .../bigdl/dllib/nn/StaticGraph.scala | 14 +- .../dllib/nn/abstractnn/AbstractModule.scala | 18 +- .../nn/abstractnn/IdentityOutputShape.scala | 24 ++ .../dllib/nn/abstractnn/InferShape.scala | 94 +++++++ .../analytics/bigdl/dllib/utils/Util.scala | 17 ++ .../utils/serializer/ModuleSerializer.scala | 14 +- .../bigdl/dllib/keras/nn/DenseSpec.scala | 44 ++++ .../bigdl/dllib/keras/nn/KerasStyleSpec.scala | 143 +++++++++++ .../analytics/bigdl/dllib/nn/LinearSpec.scala | 7 +- .../bigdl/dllib/utils/TestUtils.scala | 18 +- .../serializer/ModuleSerializerSpec.scala | 7 +- 22 files changed, 1098 insertions(+), 37 deletions(-) create mode 100644 scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Shape.scala create mode 100644 scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ShapeSpec.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/IdentityOutputShape.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Shape.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Shape.scala new file mode 100644 index 00000000000..097a625314e --- /dev/null +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Shape.scala @@ -0,0 +1,129 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import scala.reflect.ClassTag + +trait Shape { + /** + * Use this method if its only a single Shape + */ + def toSingle(): List[Int] = throw new RuntimeException("Invalid operation") + + /** + * Use this method if the current Shape consist of multiple value + */ + def toMulti(): List[Shape] = throw new RuntimeException("Invalid operation") + + /** + * Update the given dim and return a new copy + */ + def copyAndUpdate(dim: Int, v: Int): Shape = throw new RuntimeException("Invalid operation") + + /** + * Update the given dim and return a new copy + */ + def copyAndUpdate(dim: Int, v: Shape): Shape + = throw new RuntimeException("Invalid operation") + + + protected def getDim(dim: Int, length: Int): Int = { + val rdim = if (dim < 0) { + length + dim + } else { + dim + } + require(rdim < length && rdim >=0, "out of range") + rdim + } +} + +case class SingleShape(val value: List[Int]) extends Shape { + override def toSingle(): List[Int] = value + + override def copyAndUpdate(dim: Int, v: Int): Shape = { + val cValue = value.toArray + cValue(getDim(dim, value.length)) = v + Shape(cValue) + } + + override def canEqual(a: Any): Boolean = a.isInstanceOf[SingleShape] + + override def equals(that: Any): Boolean = + that match { + case that: SingleShape => that.canEqual(this) && this.hashCode == that.hashCode + case _ => false + } + + override def hashCode: Int = { + val prime = 31 + var result = 1 + result = prime * value.hashCode() + return result + } +} + + +case class MultiShape(val value: List[Shape]) extends Shape { + + override def toMulti(): List[Shape] = value + + override def copyAndUpdate(dim: Int, v: Shape): Shape = { + val cValue = value.toArray + cValue(getDim(dim, value.length)) = v + MultiShape(cValue.toList) + } + + override def canEqual(a: Any): Boolean = a.isInstanceOf[MultiShape] + + override def equals(that: Any): Boolean = + that match { + case that: MultiShape => that.canEqual(this) && this.hashCode == that.hashCode + case _ => false + } + + override def hashCode: Int = { + val prime = 31 + var result = 1 + result = prime * value.hashCode() + return result + } +} + +object Shape { + + def apply(item : Array[Int]): Shape = { + if (item == null) { + throw new IllegalArgumentException("Empty value") + } + new SingleShape(item.toList) + } + + def apply(item : Int*): Shape = { + new SingleShape(item.toList) + } + + def apply[T <: Shape : ClassTag](shapes : List[Shape]): Shape = { + if (shapes.length > 1) { + MultiShape(shapes.toList) + } else if (shapes.length == 1) { + shapes(0) + } else { + throw new IllegalArgumentException("Empty value") + } + } +} diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ShapeSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ShapeSpec.scala new file mode 100644 index 00000000000..fdb20edbe6b --- /dev/null +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ShapeSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class ShapeSpec extends FlatSpec with Matchers with BeforeAndAfter { + + "update of SingleShape" should "be test" in { + assert(Shape(1, 2, 3).copyAndUpdate(-1, 20) == Shape(1, 2, 20)) + } + + "update of MultiShape" should "be test" in { + val multiShape = Shape(List(Shape(1, 2, 3), Shape(4, 5, 6))) + assert(multiShape.copyAndUpdate(-1, Shape(5, 5, 5)) == + Shape(List(Shape(1, 2, 3), Shape(5, 5, 5)))) + } + + "multiShape not equal" should "be test" in { + intercept[RuntimeException] { + assert(Shape(List(Shape(1, 2, 3), Shape(5, 5, 5))) == + Shape(List(Shape(1, 2, 3), Shape(5, 6, 5)))) + }} + + "singleShape not equal" should "be test" in { + intercept[RuntimeException] { + assert(Shape(1, 2, 3) == List(Shape(1, 2, 4))) + }} +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala new file mode 100644 index 00000000000..ffa342209a4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala @@ -0,0 +1,77 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + + +@SerialVersionUID( 359656776803598944L) +class Dense[T: ClassTag](val outputDim: Int, + val init: InitializationMethod = RandomUniform, + val activation: TensorModule[T] = null, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + var inputShape: Shape = null + )(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = Linear( + inputSize = inputShape.toSingle()(1), // the first dim is batch + outputSize = outputDim, + withBias = bias, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer + ) + layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) + KerasLayer.fuse(layer, + activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Dense { + + def apply[@specialized(Float, Double) T: ClassTag]( + outputDim: Int, + init: InitializationMethod = RandomUniform, + activation: TensorModule[T] = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : Dense[T] = { + new Dense[T]( + outputDim, + init, + activation, + wRegularizer, + bRegularizer, + bias, + inputShape) + } +} + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala new file mode 100644 index 00000000000..528cdec8244 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala @@ -0,0 +1,66 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.{Input => TInput} +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Node, Shape} + +import scala.reflect.ClassTag + +@SerialVersionUID(- 8525406230282608904L) +class Input[T: ClassTag](val inputShape: Shape)(implicit ev: TensorNumeric[T]) + extends TInput[T]() { + + private val batchInputShape = KerasLayer.addBatch(inputShape) + + override def getInputShape(): Shape = { + batchInputShape + } + + override def getOutputShape(): Shape = { + batchInputShape + } + + override def computeOutputShape(inputShape: Shape): Shape = inputShape + +} + +object Input { + def apply[T: ClassTag](name : String = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ModuleNode[T] = { + val module = new Input(inputShape) + if (name != null) { + module.setName(name) + } + new Node(module.asInstanceOf[AbstractModule[Activity, Activity, T]]) + } +} + +object InputLayer { + def apply[T: ClassTag](name : String = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + : Input[T] = { + val module = new Input(inputShape) + if (name != null) { + module.setName(name) + } + module + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala new file mode 100644 index 00000000000..745d4164f11 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala @@ -0,0 +1,236 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.Graph._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.{Container, Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.{Shape, SingleShape, Util} +import serialization.Bigdl.{AttrValue, BigDLModule} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +object KerasLayerSerializer extends ModuleSerializable { + + override def doLoadModule[T: ClassTag](context : DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val laborAdapter = super.doLoadModule(context).asInstanceOf[KerasLayer[Activity, Activity, T]] + val attrMap = context.bigdlModule.getAttrMap + laborAdapter.labor = DataConverter.getAttributeValue(context, attrMap.get("labor")). + asInstanceOf[AbstractModule[Activity, Activity, T]] + laborAdapter + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + moduleBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + + super.doSerializeModule(context, moduleBuilder) + val laborAdapterModule = + context.moduleData.module.asInstanceOf[KerasLayer[Activity, Activity, T]] + val laborBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, laborBuilder, laborAdapterModule.labor, + ModuleSerializer.abstractModuleType) + moduleBuilder.putAttr("labor", laborBuilder.build) + + val serializerFlagBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, serializerFlagBuilder, true, + scala.reflect.runtime.universe.typeOf[Boolean]) + moduleBuilder.putAttr("is_labor_module", serializerFlagBuilder.build) + } +} + +private[bigdl] object KerasLayer { + def fuse[T: ClassTag](sLayer: AbstractModule[Activity, Activity, T], + activation: TensorModule[T], + inputShape: Shape) + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + if (activation == null) { + return sLayer + } + val seq = KSequential[T]() + seq.add(InputLayer(inputShape = KerasLayer.removeBatch(inputShape))) + seq.add(sLayer) + seq.add(activation) + seq.setName(sLayer.getName()) + return seq + } + + + def addBatch(shape: Shape): Shape = { + // simply return null here as null is the default value + if (shape == null) { + return null + } + if (shape.isInstanceOf[SingleShape]) { + Shape((List(-1) ++ shape.toSingle()).toArray) + } else { + Shape(shape.toMulti().map {addBatch(_)}) + } + } + + def removeBatch(shape: Shape): Shape = { + // simply return null here as null is the default value + if (shape == null) { + return null + } + if (shape.isInstanceOf[SingleShape]) { + Shape((shape.toSingle().slice(1, shape.toSingle().length)).toArray) + } else { + Shape(shape.toMulti().map {addBatch(_)}) + } + } +} + +/** + * KerasModule is the basic component of all Keras-like Layer. + * It forward activities and backward gradients, and can be mixed with other AbstractMoudule. + * + * @tparam A Input data type + * @tparam B Output data type + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @param batchInputShape the first dim is batch + */ +abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag] +(batchInputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends Container[A, B, T]{ + + def labor: AbstractModule[A, B, T] = { + if (this.modules.isEmpty) { + throw new RuntimeException("This Layer hasn't been built") + } + require(modules.length == 1, + s"modules should only contain 1 element instead of ${modules.length}") + modules(0).asInstanceOf[AbstractModule[A, B, T]] + } + + // scalastyle:off + def labor_=(value: AbstractModule[A, B, T]): Unit = { + modules.clear() + modules.append(value) + } + // scalastyle:on + override def inputShapeValue: Shape = labor.inputShapeValue + + override def outputShapeValue: Array[Shape] = labor.outputShapeValue + + // scalastyle:off + override def inputShapeValue_=(value: Shape): Unit = { + labor.inputShapeValue = value + } + + override def outputShapeValue_=(value: Array[Shape]): Unit = { + labor.outputShapeValue = value + } + // scalastyle:on + + override def updateOutput(input: A): B = { + output = labor.updateOutput(input) + output + } + + override def updateGradInput(input: A, gradOutput: B): A = { + gradInput = labor.updateGradInput(input, gradOutput) + gradInput + } + + override def accGradParameters(input: A, gradOutput: B): Unit = { + labor.accGradParameters(input, gradOutput) + } + + override def isCompatibleWithKeras(): Boolean = true + + override def isCompatibleWithTorch(): Boolean = false + + override def getInputShape(): Shape = { + if (batchInputShape != null) { + batchInputShape + } else if (this.labor == null) { + null + } else { + this.labor.getInputShape() + } + } + + override def computeOutputShape(inputShape: Shape): Shape = { + this.labor.computeOutputShape(inputShape) + } + + override def getOutputShape(): Shape = labor.getOutputShape() + + override def build(inputShape: Shape): Shape = { + this.labor = doBuild(inputShape) + val outputShape = computeOutputShape(inputShape) + this.outputShapeValue ++= Array(outputShape) + this.inputShapeValue = inputShape + isBuilt = true + outputShape // we cannot use getOutputShape here as it may containing multiple value + } + + def doBuild(inputShape: Shape): AbstractModule[A, B, T] + + /** + * Build graph: some other modules point to current module + * @param nodes upstream module nodes + * @return node containing current module + */ + override def inputs(nodes : ModuleNode[T]*): ModuleNode[T] = { + Util.excludeNotKeras(nodes.map(_.element)) + if (!nodes.isEmpty) { // as there's Identity().inputs() within Graph + val inputShape = Shape(nodes.map{_.element.getOutputShape()}.toList) + this.build(inputShape) + } + super.inputs(nodes: _*) + } + + /** + * Build graph: some other modules point to current module + * @param nodes upstream module nodes in an array + * @return node containing current module + */ + override def inputs(nodes : Array[ModuleNode[T]]): ModuleNode[T] = { + Util.excludeNotKeras(nodes.map(_.element)) + if (!nodes.isEmpty) { // as there's Identity().inputs() within Graph + val inputShape = Shape(nodes.map{_.element.getOutputShape()}.toList) + this.build(inputShape) + } + super.inputs(nodes) + } + + /** + * Build graph: some other modules point to current module + * @param first distinguish from another inputs when input parameter list is empty + * @param nodesWithIndex upstream module nodes and the output tensor index. The start index is 1. + * @return node containing current module + */ + override def inputs(first: (ModuleNode[T], Int), + nodesWithIndex : (ModuleNode[T], Int)*): ModuleNode[T] = { + Util.excludeNotKeras(List(first._1.element)) + Util.excludeNotKeras(nodesWithIndex.map(_._1.element)) + val shapes = ArrayBuffer[Shape]() + shapes.append(first._1.element.getOutputShapeFor(first._2)) + if (!nodesWithIndex.isEmpty) { + shapes ++= nodesWithIndex.map{t => t._1.element.getOutputShapeFor(t._2)} + } + this.build(Shape(shapes.toList)) + super.inputs(first, nodesWithIndex : _*) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala new file mode 100644 index 00000000000..cee1afdc850 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala @@ -0,0 +1,153 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.{Graph, StaticGraph, Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Shape, Util} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Model[T: ClassTag](private val _inputs : Seq[ModuleNode[T]], + private val _outputs : Seq[ModuleNode[T]])(implicit ev: TensorNumeric[T]) + extends StaticGraph[T](_inputs, _outputs, None, false) { + + Util.excludeNotKeras(inputs.map(_.element)) + Util.excludeNotKeras(outputs.map(_.element)) + + this.inputShapeValue = Shape(inputs.map{n => n.element.getInputShape()}.toList) + + this.outputShapeValue = Array(outputs.map{_.element.getOutputShape()}: _*) + + isBuilt = true + + override private[bigdl] def isCompatibleWithKeras(): Boolean = true + + override private[bigdl] def isCompatibleWithTorch(): Boolean = false + + override def computeOutputShape(inputShape: Shape): Shape = { + getOutputShape() + } +} + +object Model { + /** + * Build multiple inputs, multiple outputs graph container. + * @param input input node + * @param output output node + * @return a graph container + */ + def apply[T: ClassTag]( + input : Array[ModuleNode[T]], + output : Array[ModuleNode[T]])(implicit ev: TensorNumeric[T]) : Graph[T] = { + new Model[T](input, output) + } + + /** + * Build a single input, multiple outputs graph container + * @param input input node + * @param output output nodes + * @return a graph container + */ + def apply[T: ClassTag](input : ModuleNode[T], output : Array[ModuleNode[T]]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { + new Model[T](Seq(input), output) + } + + /** + * Build a multiple inputs, single output graph container + * @param input input nodes + * @param output output node + * @return a graph container + */ + def apply[T: ClassTag](input : Array[ModuleNode[T]], output : ModuleNode[T]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { + new Model[T](input, Seq(output)) + } + /** + * Build a single input, single output graph container + * @param input input nodes + * @param output output nodes + * @return a graph container + */ + def apply[T: ClassTag](input : ModuleNode[T], output : ModuleNode[T]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { + new Model[T](Seq(input), Seq(output)) + } +} + +class Sequential[T: ClassTag] +(implicit ev: TensorNumeric[T]) extends TSequential[T] { + + override private[bigdl] def isCompatibleWithKeras(): Boolean = true + + override private[bigdl] def isCompatibleWithTorch(): Boolean = false + + private[bigdl] var frozen: Boolean = false + + override def computeOutputShape(inputShape: Shape): Shape = { + getOutputShape() + } + + override def getOutputShape(): Shape = { + require(outputShapeValue.length > 0, "Sequence should not be empty") + outputShapeValue(outputShapeValue.length -1) // For Seq, we only respect the last item as output + } + + /** + * Add a sub-module to the contained `modules` + * + * @param module module to be add + * @return this container + */ + override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = { + if (frozen) { + throw new RuntimeException( + "This Sequential has been frozen, as it has been added into other container") + } + if (module.isInstanceOf[Sequential[T]]) { + module.asInstanceOf[Sequential[T]].frozen = true + } + Util.excludeNotKeras[T](Seq(module)) + + if (this.modules.isEmpty) { + if (module.getInputShape() == null) { + throw new RuntimeException("The first layer should explicitly declare inputshape") + } else { + val outputShape = module.build(module.getInputShape()) + this.inputShapeValue = module.getInputShape() + this.outputShapeValue = Array(outputShape) + } + } else { + val outputShape = module.build(this.getOutputShape()) + this.outputShapeValue = Array(outputShape) + } + modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]] + isBuilt = true + this + } +} + +object Sequential { + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : Sequential[T] = { + new Sequential[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index 8cf2d93b59b..8f81e76fa82 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -16,11 +16,10 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{T, Table, Util} import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -45,6 +44,12 @@ abstract class Container[A <: Activity : ClassTag, val modules: ArrayBuffer[AbstractModule[Activity, Activity, T]] = ArrayBuffer[AbstractModule[Activity, Activity, T]]() + override private[bigdl] def isCompatibleWithKeras(): Boolean = false + + override private[bigdl] def isCompatibleWithTorch(): Boolean = { + modules.filter(!_.isCompatibleWithTorch()).length <= 0 + } + /** * Add a sub-module to the contained `modules` * @@ -52,6 +57,7 @@ abstract class Container[A <: Activity : ClassTag, * @return this container */ def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = { + Util.excludeNotTorch[T](Seq(module)) modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]] this } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index d9d8f298b00..5144be2023f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -71,7 +71,7 @@ import org.tensorflow.framework.GraphDef @SerialVersionUID(- 2896121321564992779L) abstract class Graph[T: ClassTag]( val inputs : Seq[ModuleNode[T]], - private val outputs : Seq[ModuleNode[T]], + protected val outputs : Seq[ModuleNode[T]], private val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None )(implicit ev: TensorNumeric[T]) extends Container[Activity, Activity, T]{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala index 33fd5d15e2a..a6834c3adf1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala @@ -16,15 +16,14 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} - -import scala.reflect.ClassTag -import RandomGenerator._ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Shape, T, Table} + +import scala.reflect.ClassTag /** * The `Linear` module applies a linear transformation to the input data, @@ -81,6 +80,10 @@ class Linear[T: ClassTag]( zeroGradParameters() } + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape.copyAndUpdate(-1, outputSize) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 1 || input.dim() == 2, "Linear: " + ErrorInfo.constrainInputAsVectorOrBatch + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala index 767638492d8..046bf15be66 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.abstractnn.IdentityOutputShape import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag @@ -30,7 +31,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(1208478077576570643L) class ReLU[T: ClassTag](ip: Boolean = false)( - implicit ev: TensorNumeric[T]) extends Threshold[T](0, 0, ip) { + implicit ev: TensorNumeric[T]) extends Threshold[T](0, 0, ip) with IdentityOutputShape{ } object ReLU { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala index 4141ed84f44..a56787a72d6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala @@ -21,7 +21,6 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag -import scala.collection.mutable.ArrayBuffer /** * Sequential provides a means to plug layers together diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala index 993b66ed1ab..b4a66d73374 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -15,16 +15,12 @@ */ package com.intel.analytics.bigdl.nn -import java.util - import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{Node, T} +import com.intel.analytics.bigdl.utils.{Node, Util} -import scala.collection.mutable import scala.reflect.ClassTag /** @@ -38,7 +34,8 @@ import scala.reflect.ClassTag class StaticGraph[T: ClassTag]( private val _inputs : Seq[ModuleNode[T]], private val _outputs : Seq[ModuleNode[T]], - private val _variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None + private val _variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, + private val excludeKeras: Boolean = true )(implicit ev: TensorNumeric[T]) extends Graph[T](_inputs, _outputs, _variables) { private val forwardExecution = forwardGraph.topologySort.reverse private var backwardExecution: Array[Node[AbstractModule[Activity, Activity, T]]] = _ @@ -46,6 +43,11 @@ class StaticGraph[T: ClassTag]( private var backId2ForwardId: Array[Int] = _ private var gradOutputCache: Array[Activity] = _ + if (excludeKeras) { + Util.excludeNotTorch(inputs.map(_.element)) + Util.excludeNotTorch(outputs.map(_.element)) + } + buildBackwardGraph() override def updateOutput(input: Activity): Activity = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 3026b0cc7b9..a6030b03da0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -19,21 +19,21 @@ package com.intel.analytics.bigdl.nn.abstractnn import java.nio.ByteOrder import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.tensor.{Tensor, TensorDataType} -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils._ -import com.intel.analytics.bigdl.nn.{Module, _} -import com.intel.analytics.bigdl.utils.TorchObject.TYPE_MODULE -import org.apache.commons.lang3.SerializationUtils -import org.apache.spark.rdd.RDD -import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, PaddingParam, Sample} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.quantized.Quantization +import com.intel.analytics.bigdl.nn.{Module, _} +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{Tensor, TensorDataType} import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame} +import com.intel.analytics.bigdl.utils.TorchObject.TYPE_MODULE +import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.utils.caffe.CaffePersister import com.intel.analytics.bigdl.utils.serializer.ModulePersister import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} +import org.apache.commons.lang3.SerializationUtils +import org.apache.spark.rdd.RDD import scala.reflect.ClassTag @@ -55,7 +55,7 @@ abstract class TensorModule[T: ClassTag] * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now */ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag]( - implicit ev: TensorNumeric[T]) extends Serializable { + implicit ev: TensorNumeric[T]) extends Serializable with InferShape{ private var namePostfix = Integer.toHexString(java.util.UUID.randomUUID().hashCode()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/IdentityOutputShape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/IdentityOutputShape.scala new file mode 100644 index 00000000000..833cdeedd8b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/IdentityOutputShape.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package com.intel.analytics.bigdl.nn.abstractnn + +import com.intel.analytics.bigdl.utils.Shape + +trait IdentityOutputShape extends InferShape{ + override def computeOutputShape(inputShape: Shape): Shape = inputShape +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala new file mode 100644 index 00000000000..2f7042387e3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala @@ -0,0 +1,94 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.abstractnn + +import com.intel.analytics.bigdl.utils.Shape + +trait InferShape { + + private var _inputShapeValue: Shape = null + + private var _outputShapeValue: Array[Shape] = Array[Shape]() + + private[bigdl] def inputShapeValue: Shape = _inputShapeValue + + private[bigdl] def outputShapeValue: Array[Shape] = _outputShapeValue + + // scalastyle:off + private[bigdl] def inputShapeValue_=(value: Shape): Unit = { + _inputShapeValue = value + } + + private[bigdl] def outputShapeValue_=(value: Array[Shape]): Unit = { + _outputShapeValue = value + } + // scalastyle:on + + /** + * We suppose the first dim is batch + */ + private[bigdl] def getInputShape(): Shape = { + _inputShapeValue + } + + /** + * Get the outputshape by index. + * @param index start from 0 + * @return + */ + private[bigdl] def getOutputShapeFor(index: Int): Shape = { + _outputShapeValue(index) + } + + /** + * We suppose the first dim is batch + */ + private[bigdl] def getOutputShape(): Shape = { + if (_outputShapeValue.length > 1) { + throw new RuntimeException( + "There's multipule output for this layer. Please use getInputShapeFor instead") + } + outputShapeValue(0) + } + + /** + * Execute builing logic and return the outputShape for the given inputShape. + * NB: the first dim of inputShape is batch + */ + private[bigdl] def build(inputShape: Shape): Shape = { + val outputShape = computeOutputShape(inputShape) + this._outputShapeValue ++ Array(outputShape) + this._inputShapeValue = inputShape + isBuilt = true + outputShape + } + + private[bigdl] var isBuilt: Boolean = false + + + private[bigdl] def isCompatibleWithKeras(): Boolean = true + + private[bigdl] def isCompatibleWithTorch(): Boolean = true + + /** + * We suppose the first dim is batch + */ + private[bigdl] def computeOutputShape(inputShape: Shape): Shape = { + throw new RuntimeException("Haven't been implemented yet. Do not use it with Keras Layer") + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala index 6907811e061..3a107a95259 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.utils import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} @@ -175,4 +176,20 @@ object Util { i += 1 } } + + private[bigdl] def excludeNotTorch[T: ClassTag] + (modules : Seq[AbstractModule[_, _, T]]): Unit = { + val invalidNodes = modules.filter{!_.isCompatibleWithTorch()} + if (invalidNodes.length > 0) { + throw new RuntimeException(s"Do not mix with Layer: ${invalidNodes.mkString(",")}") + } + } + + private[bigdl] def excludeNotKeras[T: ClassTag] + (modules : Seq[AbstractModule[_, _, T]]): Unit = { + val invalidNodes = modules.filter{!_.isCompatibleWithKeras()} + if (invalidNodes.length > 0) { + throw new RuntimeException(s"Do not mix with Layer: ${invalidNodes.mkString(",")}") + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 0ee38beda12..4c55c46204f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -15,23 +15,19 @@ */ package com.intel.analytics.bigdl.utils.serializer -import java.lang.reflect.Field - import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn._ - -import scala.collection.JavaConverters._ -import scala.reflect.runtime.universe import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer} import com.intel.analytics.bigdl.nn.ops.{DecodeRawSerializer, ParseExample, RandomUniform => RandomUniformOps} import com.intel.analytics.bigdl.nn.tf.StrideSlice import com.intel.analytics.bigdl.optim.Regularizer -import com.intel.analytics.bigdl.tensor.{Tensor, TensorNumericMath} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable import scala.reflect.ClassTag +import scala.reflect.runtime.universe object ModuleSerializer extends ModuleSerializable{ @@ -72,6 +68,8 @@ object ModuleSerializer extends ModuleSerializable{ ContainerSerializer.serializeModule(serializerContext) case cell : Cell[_] => CellSerializer.serializeModule(serializerContext) + case laborAdapter: KerasLayer[_, _, _] => + KerasLayerSerializer.serializeModule(serializerContext) case _ => ModuleSerializer.serializeModule(serializerContext) } } @@ -96,6 +94,8 @@ object ModuleSerializer extends ModuleSerializable{ } else { if (attrMap.containsKey("is_cell_module")) { CellSerializer.loadModule(context) + } else if (attrMap.containsKey("is_labor_module")) { + KerasLayerSerializer.loadModule(context) } else { ModuleSerializer.loadModule(context) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala new file mode 100644 index 00000000000..f17ed6afca9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala @@ -0,0 +1,44 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.ReLU +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.Dense +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class DenseSpec extends KerasBaseSpec{ + + "Dense" should "be test" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |input = np.random.uniform(0, 1, [1, 3]) + |output_tensor = Dense(2, activation="relu", init='uniform')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val dense = Dense[Float](2, activation = ReLU(), inputShape = Shape(3)) + seq.add(dense) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(0).t(), in(1)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala new file mode 100644 index 00000000000..e2199e788c1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala @@ -0,0 +1,143 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.example.loadmodel.AlexNet_OWT +import com.intel.analytics.bigdl.nn.keras.{Dense, Input, Model, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.{Sequential => TSequential, _} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape} + + +class KerasStyleSpec extends BigDLSpecHelper { + + "Graph: Dense" should "works correctly" in { + val input = Input[Float](inputShape = Shape(10)) + val d = Dense[Float](20, activation = ReLU()).setName("dense1").inputs(input) + val d2 = Dense[Float](5).setName("dense2").inputs(d) + val model = Model[Float](input, d2) + val inputData = Tensor[Float](Array(20, 10)).rand() + val output = model.forward(inputData) + require(model.getOutputShape().toSingle().sameElements(Array(-1, 5))) + require(model.getInputShape().toSingle().sameElements(Array(-1, 10))) + } + + "Sequential: Dense" should "works correctly" in { + val seq = KSequential[Float]() + val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") + val d2 = Dense[Float](5).setName("dense2") + val d3 = Dense[Float](6).setName("dense4") + seq.add(d1) + seq.add(d2) + seq.add(d3) + val inputData = Tensor[Float](Array(20, 10)).rand() + val output = seq.forward(inputData) + require(d3.getOutputShape().toSingle().sameElements(Array(-1, 6))) + require(d3.getInputShape().toSingle().sameElements(Array(-1, 5))) + } + + "Frozen sequential" should "be tested" in { + intercept[RuntimeException] { + val seq = KSequential[Float]() + val seq1 = KSequential[Float]() + seq.add(seq1) + seq1.add(Dense[Float](20, inputShape = Shape(10))) + } + } + + "Sequential: shared relu" should "works correctly" in { + val sharedRelu = ReLU[Float]() + val seq1 = KSequential[Float]() + seq1.add(Dense[Float](20, inputShape = Shape(10))) + seq1.add(sharedRelu) + require(seq1.getOutputShape().toSingle().sameElements(Array(-1, 20))) + + val seq2 = KSequential[Float]() + seq2.add(Dense[Float](5, inputShape = Shape(20))) + seq2.add(sharedRelu) + require(seq2.getOutputShape().toSingle().sameElements(Array(-1, 5))) + + val seq = KSequential[Float]() + seq.add(seq1) + seq.add(seq2) + + val inputData = Tensor[Float](Array(20, 10)).rand() + val output = seq.forward(inputData) + require(seq.getInputShape().toSingle().sameElements(Array(-1, 10))) + require(seq.getOutputShape().toSingle().sameElements(Array(-1, 5))) + } + + "TSequential" should "works with alex" in { + val model = AlexNet_OWT(1000, false, true) + TSequential[Float].add(model) + } + + "TSequential" should "not works with dense" in { + intercept[RuntimeException] { + val seq = TSequential[Float]() + val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") + seq.add(d1) + } + } + + "TGraph" should "not works with dense" in { + intercept[RuntimeException] { + val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1").inputs(Input()) + val l1 = Linear(2, 3).inputs(d1) + } + } + + "TSequential" should "not works w ith container containing Dense" in { + val seq = TSequential[Float]() + intercept[RuntimeException] { + val parallelTable = ParallelTable[Float]() + val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") + parallelTable.add(d1) + seq.add(parallelTable) + } + } + + "TSequential" should "not works with container with dense" in { + intercept[RuntimeException] { + val seq = TSequential[Float]() + val seq2 = TSequential[Float]() + val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") + seq2.add(d1) + seq.add(seq2) + } + } + + // TODO: enable test for serialization + // "save and reload model" should "works correctly" in { + // val input = Input[Float](inputShape = Array(10)) + // val d = Dense[Float](20).setName("dense1").inputs(input) + // val d2 = Dense[Float](5).setName("dense2").inputs(d) + // val graph = Model[Float](input, d2) + // val tmpFile = createTmpFile() + // val absPath = tmpFile.getAbsolutePath + // tmpFile.delete() + // graph.saveModule(absPath) + // val reloadedModel = Module.loadModule(absPath) + // val inputData = Tensor[Float](Array(20, 10)).rand() + // val output = reloadedModel.forward(inputData) + // } + // + + + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala index d50f12f2369..903892e3b88 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl._ import scala.math._ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.optim.{L1Regularizer, L2Regularizer, SGD} -import com.intel.analytics.bigdl.utils.{RandomGenerator, T} +import com.intel.analytics.bigdl.utils.{RandomGenerator, Shape, T, TestUtils} @com.intel.analytics.bigdl.tags.Parallel class LinearSpec extends FlatSpec with Matchers { @@ -403,4 +403,9 @@ class LinearSpec extends FlatSpec with Matchers { linear.weight should be (exceptedWeight) linear.bias should be (exceptedBias) } + + "computeOutputShape" should "work" in { + val linear = Linear[Float](3, 5) + TestUtils.compareOutputShape(linear, Shape(3)) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala index a6644f9dd1a..e9588b88f77 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala @@ -18,7 +18,8 @@ package com.intel.analytics.bigdl.utils import java.util.concurrent.atomic.AtomicInteger -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.keras.{InputLayer, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.scalatest.exceptions.TestCanceledException @@ -27,6 +28,21 @@ import scala.reflect.ClassTag object TestUtils { + /** + * Compare the output of `computeOutputShape` with the `forward` result + */ + def compareOutputShape(layer: AbstractModule[Activity, Activity, Float], + inputShape: Shape): Boolean = { + val inputData = Tensor[Float](Array(2) ++ inputShape.toSingle()).randn() + val seq = KSequential[Float]() + seq.add(InputLayer[Float](inputShape = inputShape)) + seq.add(layer) + val calcOutputShape = seq.getOutputShape().toSingle() + val forwardOutputShape = seq.forward(inputData).toTensor[Float].size() + calcOutputShape.slice(1, calcOutputShape.length).sameElements( + forwardOutputShape.slice(1, forwardOutputShape.length)) + } + /** * Process different paths format under windows and linux * diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index e891e4adbfa..67d6da6fee1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -64,8 +64,11 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll override protected def beforeAll() = { addExcluded val reflections = new Reflections(new ConfigurationBuilder() - .filterInputsBy(new FilterBuilder(). - excludePackage("com.intel.analytics.bigdl.utils.tf.loaders")) + .filterInputsBy(new FilterBuilder() + .excludePackage("com.intel.analytics.bigdl.utils.tf.loaders") + // TODO: enable this once Shape serialization ready. + .excludePackage("com.intel.analytics.bigdl.nn.keras")) + .setUrls(ClasspathHelper.forPackage(pkg)) .setScanners(new SubTypesScanner())) From adb1c20aee166912cd19efbcd198de45c31c93ae Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 23 Jan 2018 10:23:49 +0800 Subject: [PATCH 0649/1065] refine hierarchy (#2211) * refine hierarchy * fix reference * fix reference * rebase upstream --- .../bigdl/dllib/keras/KerasLayer.scala | 1 + .../bigdl/dllib/nn/BatchNormalization.scala | 1 + .../bigdl/dllib/nn/BiRecurrent.scala | 1 + .../bigdl/dllib/nn/BinaryTreeLSTM.scala | 1 + .../intel/analytics/bigdl/dllib/nn/Cell.scala | 1 + .../analytics/bigdl/dllib/nn/Graph.scala | 3 +- .../bigdl/dllib/nn/MaskedSelect.scala | 1 + .../bigdl/dllib/nn/MultiRNNCell.scala | 3 +- .../analytics/bigdl/dllib/nn/Recurrent.scala | 4 +- .../bigdl/dllib/nn/RecurrentDecoder.scala | 1 + .../analytics/bigdl/dllib/nn/Reshape.scala | 1 + .../analytics/bigdl/dllib/nn/SReLU.scala | 1 + .../analytics/bigdl/dllib/nn/Scale.scala | 1 + .../nn/SpatialContrastiveNormalization.scala | 1 + .../nn/SpatialDivisiveNormalization.scala | 1 + .../dllib/nn/SpatialFullConvolution.scala | 1 + .../bigdl/dllib/nn/SpatialMaxPooling.scala | 1 + .../nn/SpatialSubtractiveNormalization.scala | 1 + .../analytics/bigdl/dllib/nn/Transpose.scala | 1 + .../bigdl/dllib/nn/VolumetricMaxPooling.scala | 1 + .../bigdl/dllib/nn/ops/DecodeImage.scala | 3 +- .../bigdl/dllib/nn/ops/ParseExample.scala | 3 +- .../bigdl/dllib/nn/ops/RandomUniform.scala | 3 +- .../bigdl/dllib/nn/quantized/Linear.scala | 1 + .../dllib/nn/quantized/QuantSerializer.scala | 2 +- .../nn/quantized/SpatialConvolution.scala | 3 +- .../quantized/SpatialDilatedConvolution.scala | 3 +- .../bigdl/dllib/nn/tf/StrideSlice.scala | 3 +- .../dllib/utils/serializer/ModuleLoader.scala | 3 +- .../utils/serializer/ModuleSerializable.scala | 3 +- .../{ => converters}/DataConverter.scala | 517 +----------------- .../converters/DataFormatConverter.scala | 57 ++ .../{ => converters}/DataReaderWriter.scala | 3 +- .../converters/InitMethodConverter.scala | 89 +++ .../converters/ModuleConverter.scala | 57 ++ .../converters/RegularizerConverter.scala | 77 +++ .../converters/TensorConverter.scala | 316 +++++++++++ .../TensorStorageManager.scala | 4 +- .../converters/VariableFormatConverter.scala | 71 +++ .../utils/serializer/DataConverterSpec.scala | 1 + .../serializer/DataReaderWriterSpec.scala | 1 + .../serializer/TensorConversionSpec.scala | 1 + 42 files changed, 720 insertions(+), 528 deletions(-) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/{ => converters}/DataConverter.scala (53%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataFormatConverter.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/{ => converters}/DataReaderWriter.scala (98%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/InitMethodConverter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ModuleConverter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/RegularizerConverter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/{ => converters}/TensorStorageManager.scala (98%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/VariableFormatConverter.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala index 745d4164f11..abaab5b7a7b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.nn.{Container, Sequential => TSequential} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{Shape, SingleShape, Util} import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index ed1d2f1ea50..84a86a4ac89 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.abstractnn._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala index 9227acb4051..48724d71424 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala index db92d245f31..3453d67a2a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala index 160eabe8654..3b88b8822c4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 5144be2023f..3304094aaf6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -22,12 +22,13 @@ import com.intel.analytics.bigdl.Module import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} -import com.intel.analytics.bigdl.nn.ops.{MergeControlNode, SwitchControlNode, MergeOps, SwitchOps, ControlOps} +import com.intel.analytics.bigdl.nn.ops.{ControlOps, MergeControlNode, MergeOps, SwitchControlNode, SwitchOps} import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.tf.Tensorflow import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala index 124e622235a..c321cdd76c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala index 03f95a14fcf..521777c288f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala @@ -19,8 +19,9 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.DataConverter.ArrayConverter import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter.ArrayConverter import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index c7cf4b01560..d4560d0b9a9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -22,9 +22,11 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ -import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DataConverter, ModuleSerializer} +import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, ModuleSerializer} import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} + import scala.reflect.runtime.universe import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala index b39f012f232..91240311dbe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable.ArrayBuffer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala index f1cd205ab0f..fe90f901cc7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala index af978b3aa66..21728d04707 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala index 59b6521d2a3..69c86833ae6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala index ef4a95b3a93..783bc4e7a9e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala index 8b93b31c193..663e093cdf5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala index 61910ded5bb..a8d0c3db5b8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.{T, Table, serializer} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.concurrent.Future diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala index b4baa375ec5..a561380d1b9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala index 93c2df6db70..b50449b6b26 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala index 184273a3738..d9df6c3b807 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala index fd651f263f4..817af9ce426 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import org.codehaus.jackson.map.DeserializationContext import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala index 99cbfde29e3..78520a2d92a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala @@ -24,7 +24,8 @@ import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleSerializable, SerializeContext} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} import org.tensorflow.framework.DataType import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala index d34f3fc89c2..94a32a0a6be 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala @@ -19,7 +19,8 @@ import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.{T, Table} import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleSerializable, SerializeContext} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} import org.tensorflow.example.{Example, Feature} import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString import serialization.Bigdl.{AttrValue, BigDLModule} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala index 875f3c44d16..c4aa92d27f4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala @@ -19,7 +19,8 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.RandomGenerator -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleSerializable, SerializeContext} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala index f6df7b97b71..7048a06a5cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.ErrorInfo import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala index 6f93a84a1fe..077a8a88ad5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala @@ -19,8 +19,8 @@ package com.intel.analytics.bigdl.nn.quantized import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table -import com.intel.analytics.bigdl.utils.serializer.DataConverter.TensorConverter import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.TensorConverter import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala index 544f13a4358..0dbd0e07817 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -22,7 +22,8 @@ import com.intel.analytics.bigdl.nn.ErrorInfo import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, Initializable} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleData, SerializeContext} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleData, SerializeContext} import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala index 1c272136437..39e8410a820 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala @@ -19,7 +19,8 @@ package com.intel.analytics.bigdl.nn.quantized import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{FloatType, QuantizedTensor, Tensor} -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleData, SerializeContext} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleData, SerializeContext} import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala index 0c0048b61a5..2c643bb7f78 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala @@ -18,7 +18,8 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.{DataConverter, DeserializeContext, ModuleSerializable, SerializeContext} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} import serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala index 4f256c1dbfa..d41b152ef66 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala @@ -25,7 +25,8 @@ import com.intel.analytics.bigdl.nn.Container import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.{DenseType, QuantizedTensor, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.DataConverter.TensorConverter +import com.intel.analytics.bigdl.utils.serializer.converters.TensorConverter +import com.intel.analytics.bigdl.utils.serializer.converters.DataReaderWriter import com.intel.analytics.bigdl.utils.{File, FileReader, FileWriter, Table} import serialization.Bigdl._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index b57932ff5c9..ee6765da27c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -24,8 +24,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table -import com.intel.analytics.bigdl.utils.serializer.DataConverter.TensorConverter +import com.intel.analytics.bigdl.utils.serializer.converters.TensorConverter import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.DataType import serialization.Bigdl.{AttrValue, BigDLModule, BigDLTensor} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala similarity index 53% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala index 5b4e6758bb6..f969a9610d7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala @@ -13,21 +13,18 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.utils.serializer +package com.intel.analytics.bigdl.utils.serializer.converters import com.google.protobuf.ByteString import scala.collection.JavaConverters._ import scala.reflect.runtime.universe import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.{NCHW, NHWC} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.quantized._ -import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer, Regularizer} -import com.intel.analytics.bigdl.tensor.{DenseType, QuantizedTensor, QuantizedType, Tensor, Storage} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericShort, NumericString} -import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString +import com.intel.analytics.bigdl.utils.serializer._ import serialization.Bigdl._ import serialization.Bigdl.AttrValue.ArrayValue @@ -191,512 +188,6 @@ object DataConverter extends DataConverter{ } } -/** - * DataConverter for [[com.intel.analytics.bigdl.optim.Regularizer]] - */ - object RegularizerConverter extends DataConverter { - - override def getAttributeValue[T : ClassTag](context: DeserializeContext, - attribute: AttrValue) - (implicit ev: TensorNumeric[T]): AnyRef = { - val regularizer = attribute.getRegularizerValue - val regularizerType = regularizer.getRegularizerType - if (regularizer.getRegularDataCount == 0) { - return null - } - regularizerType match { - case serialization.Bigdl.RegularizerType.L1Regularizer => - val l1 = regularizer.getRegularDataList.get(0) - L1Regularizer[T](l1) - case serialization.Bigdl.RegularizerType.L2Regularizer => - val l2 = regularizer.getRegularDataList.get(1) - L2Regularizer[T](l2) - case serialization.Bigdl.RegularizerType.L1L2Regularizer => - val l1 = regularizer.getRegularDataList.get(0) - val l2 = regularizer.getRegularDataList.get(1) - L1L2Regularizer[T](l1, l2) - } - } - - override def setAttributeValue[T : ClassTag] - (context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, - valueType : universe.Type = null) - (implicit ev: TensorNumeric[T]): Unit = { - attributeBuilder.setDataType(DataType.REGULARIZER) - if (value != null) { - var regularizerBuilder = serialization.Bigdl.Regularizer.newBuilder - val regularizer = value.asInstanceOf[L1L2Regularizer[T]] - val l1 = regularizer.l1 - val l2 = regularizer.l2 - regularizerBuilder.addRegularData(l1) - regularizerBuilder.addRegularData(l2) - val regularizerType = regularizer match { - case l1: L1Regularizer[_] => serialization.Bigdl.RegularizerType.L1Regularizer - case l2: L2Regularizer[_] => serialization.Bigdl.RegularizerType.L2Regularizer - case l1l2: L1L2Regularizer[_] => serialization.Bigdl.RegularizerType.L1L2Regularizer - } - regularizerBuilder.setRegularizerType(regularizerType) - attributeBuilder.setRegularizerValue(regularizerBuilder.build) - } - } - - } - -/** - * DataConverter for [[com.intel.analytics.bigdl.tensor.Tensor]] - */ - object TensorConverter extends DataConverter { - - - private def isEmptyTensor(tensor : Tensor[_]): Boolean = { - val emptyTensor = tensor.getTensorType match { - case DenseType => - tensor.storage == null - case QuantizedType => - tensor.asInstanceOf[QuantizedTensor[_]].getStorage == null - } - emptyTensor - } - - override def getAttributeValue[T: ClassTag](context: DeserializeContext, - attribute: AttrValue) - (implicit ev: TensorNumeric[T]): AnyRef = { - val serializedTensor = attribute.getTensorValue - if (!serializedTensor.hasStorage) { - return null - } - val storages = context.storages - val tensorId = serializedTensor.getId - if (storages.contains(tensorId)) { - return storages.get(tensorId).get.asInstanceOf[AnyRef] - } - val dataType = serializedTensor.getDatatype - val tensorType = serializedTensor.getTensorType - val sizes = serializedTensor.getSizeList.asScala.toArray.map(_.intValue()) - val strides = serializedTensor.getStrideList.asScala.toArray.map(_.intValue()) - val offSet = serializedTensor.getOffset - val isScalr = serializedTensor.getIsScalar - val serializedStorage = serializedTensor.getStorage - val storageId = serializedStorage.getId - val created = if (storages.contains(storageId)) { - storages.get(storageId).get - } else { - null - } - - def quant(): Tensor[T] = { - var bytes: Array[Byte] = null - if (context.storageType == ProtoStorageType) { - bytes = serializedStorage.getBytesDataList.asScala.toArray.head.toByteArray - } else { - created - } - val serializedParams = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) - val paramsNum = serializedParams.head - val paramsArray = serializedParams.slice(1, paramsNum + 1) - val descTypeEnum = serializedParams(1 + paramsNum) - - val start = paramsNum + 2 // params number indicator + params number + desc type - - val length = if (sizes.length == 1) { - 1 // if the size is 1, means it's a vector - } else { - sizes(0) - } - val max = new Array[T](length) - val min = new Array[T](length) - val sum = new Array[T](length) - - dataType match { - case DataType.FLOAT => - val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) - var i = 0 - while (i < length) { - max(i) = ev.fromType[Float](data(i)) - min(i) = ev.fromType[Float](data(i + length)) - sum(i) = ev.fromType[Float](data(i + 2 * length)) - i += 1 - } - } - - var params: DescParams = null - - descTypeEnum match { - case 0 => - params = ConvDataParams(paramsArray) - case 1 => - params = ConvWeightParams(paramsArray) - case 2 => - params = LinearDataParams(paramsArray) - case 3 => - params = LinearWeightParams(paramsArray) - } - - QuantizedTensor[T](bytes, max, min, sum, sizes, params) - } - - val tensor = dataType match { - case DataType.FLOAT => - tensorType match { - case TensorType.DENSE => - val storage : Storage[Float] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) - val newStorage = Storage[Float](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[Float]] - Tensor[Float](storage, offSet, sizes, strides) - case TensorType.QUANT => quant() - } - case DataType.DOUBLE => - val storage : Storage[Double] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getDoubleDataList.asScala.toArray.map(_.doubleValue()) - val newStorage = Storage[Double](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[Double]] - Tensor[Double](storage, offSet, sizes, strides) - case DataType.BOOL => - val storage : Storage[Boolean] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getBoolDataList.asScala.toArray.map(_.booleanValue()) - val newStorage = Storage[Boolean](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[Boolean]] - Tensor[Boolean](storage, offSet, sizes, strides) - case DataType.CHAR => - val storage: Storage[Char] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getIntDataList.asScala.toArray.map(_.toChar.charValue()) - val newStorage = Storage[Char](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[Char]] - Tensor[Char](storage, offSet, sizes, strides) - case DataType.STRING => - val storage: Storage[String] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getStringDataList.asScala.toArray - val newStorage = Storage[String](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[String]] - Tensor[String](storage, offSet, sizes, strides) - case DataType.INT32 => - val storage: Storage[Int] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) - val newStorage = Storage[Int](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[Int]] - Tensor[Int](storage, offSet, sizes, strides) - case DataType.SHORT => - val storage: Storage[Short] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getIntDataList.asScala.toArray.map(_.shortValue()) - val newStorage = Storage[Short](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[Short]] - Tensor[Short](storage, offSet, sizes, strides) - case DataType.INT64 => - val storage: Storage[Long] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getLongDataList.asScala.toArray.map(_.longValue()) - val newStorage = Storage[Long](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[Long]] - Tensor[Long](storage, offSet, sizes, strides) - case DataType.BYTES => - val storage: Storage[ByteString] = if (created == null ) { - if (storageId == -1) { - null - } else { - val data = serializedStorage.getBytesDataList.asScala.toArray - val newStorage = Storage[ByteString](data) - storages(storageId) = newStorage - newStorage - } - } else created.asInstanceOf[Storage[ByteString]] - Tensor[ByteString](storage, offSet, sizes, strides) - case _ => throw new IllegalArgumentException(s"$dataType not supported in tensor now !") - } - storages(tensorId) = tensor - tensor - } - - private def setStorage[T: ClassTag](context: SerializeContext[T], - tensorBuilder: BigDLTensor.Builder, tensor: Tensor[_]): Unit = { - val storageType = context.storageType - if (storageType == ProtoStorageType) { - ProtoTensorStorageManager.setStorage(context, tensorBuilder, tensor) - } else if (storageType == BigDLStorage) { - BigDLTensorStorageManager.setStorage(context, tensorBuilder, tensor) - } else { - throw new IllegalArgumentException(s"$storageType not supported") - } - } - - override def setAttributeValue[T: ClassTag] - (context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, - valueType : universe.Type = null) - (implicit ev: TensorNumeric[T]): Unit = { - attributeBuilder.setDataType(DataType.TENSOR) - if (value != null) { - val tensor = value.asInstanceOf[Tensor[_]] - val tensorId = System.identityHashCode(tensor) - val storages = context.storages - // Check if tensor has been shared - if (storages.contains(tensorId)) { - attributeBuilder.setTensorValue(resetTensor(storages.get(tensorId).get - .asInstanceOf[BigDLTensor])) - } else { - val totalElement = tensor.nElement() - val dimension = tensor.dim() - val tensorBuilder = BigDLTensor.newBuilder - tensorBuilder.setId(tensorId) - tensorBuilder.setDimension(dimension) - tensorBuilder.setNElements(totalElement) - tensor.getTensorType match { - case DenseType => - tensorBuilder.setOffset(tensor.storageOffset()) - tensorBuilder.setIsScalar(tensor.isScalar) - tensorBuilder.setTensorType(TensorType.DENSE) - case QuantizedType => - tensorBuilder.setTensorType(TensorType.QUANT) - } - - val tensorEmpty = isEmptyTensor(tensor) - - if (!tensorEmpty) { - tensor.size().foreach(size => tensorBuilder.addSize(size)) - tensor.stride().foreach(stride => tensorBuilder.addStride(stride)) - } - setStorage(context, tensorBuilder, tensor) - val tensorBuild = tensorBuilder.build - attributeBuilder.setTensorValue(resetTensor(tensorBuild)) - storages(tensorId) = tensorBuild - } - } - } - - - private def resetStorage(originStorage : TensorStorage) : TensorStorage = { - val storageBuilder = TensorStorage.newBuilder - storageBuilder.setDatatype(originStorage.getDatatype) - storageBuilder.setId(originStorage.getId) - storageBuilder.build - } - - private def resetTensor(originTensor: BigDLTensor) : BigDLTensor = { - val tensorBuilder = BigDLTensor.newBuilder(originTensor) - tensorBuilder.clearStorage - tensorBuilder.setDatatype(originTensor.getDatatype) - tensorBuilder.setId(originTensor.getId) - if (originTensor.hasStorage) { - tensorBuilder.setStorage(resetStorage(originTensor.getStorage)) - } - tensorBuilder.build - } - } - -/** - * DataConverter for [[com.intel.analytics.bigdl.nn.VariableFormat]] - */ - object VariableFormatConverter extends DataConverter { - - override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) - (implicit ev: TensorNumeric[T]): AnyRef = { - val format = attribute.getVariableFormatValue - format match { - case VarFormat.DEFAULT => VariableFormat.Default - case VarFormat.ONE_D => VariableFormat.ONE_D - case VarFormat.IN_OUT => VariableFormat.IN_OUT - case VarFormat.OUT_IN => VariableFormat.OUT_IN - case VarFormat.IN_OUT_KW_KH => VariableFormat.IN_OUT_KW_KH - case VarFormat.OUT_IN_KW_KH => VariableFormat.OUT_IN_KW_KH - case VarFormat.GP_OUT_IN_KW_KH => VariableFormat.GP_OUT_IN_KW_KH - case VarFormat.GP_IN_OUT_KW_KH => VariableFormat.GP_IN_OUT_KW_KH - case VarFormat.OUT_IN_KT_KH_KW => VariableFormat.OUT_IN_KT_KH_KW - case VarFormat.EMPTY_FORMAT => null - } - } - - override def setAttributeValue[T: ClassTag]( - context: SerializeContext[T], attributeBuilder: AttrValue.Builder, - value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { - attributeBuilder.setDataType(DataType.VARIABLE_FORMAT) - if (value != null) { - val format = value.asInstanceOf[VariableFormat] - val formatValue = format match { - case VariableFormat.Default => VarFormat.DEFAULT - case VariableFormat.ONE_D => VarFormat.ONE_D - case VariableFormat.IN_OUT => VarFormat.IN_OUT - case VariableFormat.OUT_IN => VarFormat.OUT_IN - case VariableFormat.IN_OUT_KW_KH => VarFormat.IN_OUT_KW_KH - case VariableFormat.OUT_IN_KW_KH => VarFormat.OUT_IN_KW_KH - case VariableFormat.GP_OUT_IN_KW_KH => VarFormat.GP_OUT_IN_KW_KH - case VariableFormat.GP_IN_OUT_KW_KH => VarFormat.GP_IN_OUT_KW_KH - case VariableFormat.OUT_IN_KT_KH_KW => VarFormat.OUT_IN_KT_KH_KW - } - attributeBuilder.setVariableFormatValue(formatValue) - } else { - attributeBuilder.setVariableFormatValue(VarFormat.EMPTY_FORMAT) - } - } -} -/** - * DataConverter for [[com.intel.analytics.bigdl.nn.InitializationMethod]] - */ - object InitMethodConverter extends DataConverter { - - override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) - (implicit ev: TensorNumeric[T]): AnyRef = { - val initMemethod = attribute.getInitMethodValue - val initType = initMemethod.getMethodType - val methodData = initMemethod.getDataList - initType match { - case InitMethodType.RANDOM_UNIFORM => RandomUniform - case InitMethodType.RANDOM_UNIFORM_PARAM => - RandomUniform(methodData.get(0), methodData.get(1)) - case InitMethodType.RANDOM_NORMAL => - RandomNormal(methodData.get(0), methodData.get(1)) - case InitMethodType.ZEROS => Zeros - case InitMethodType.ONES => Ones - case InitMethodType.CONST => ConstInitMethod(methodData.get(0)) - case InitMethodType.XAVIER => Xavier - case InitMethodType.BILINEARFILLER => BilinearFiller - case InitMethodType.EMPTY_INITIALIZATION => null - } - } - - override def setAttributeValue[T: ClassTag]( - context: SerializeContext[T], attributeBuilder: AttrValue.Builder, - value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { - attributeBuilder.setDataType(DataType.INITMETHOD) - val initMethodBuilder = InitMethod.newBuilder - if (value != null) { - val initMethod = value.asInstanceOf[InitializationMethod] - initMethod match { - case RandomUniform => - initMethodBuilder.setMethodType(InitMethodType.RANDOM_UNIFORM) - case ru: RandomUniform => - initMethodBuilder.setMethodType(InitMethodType.RANDOM_UNIFORM_PARAM) - initMethodBuilder.addData(ru.lower) - initMethodBuilder.addData(ru.upper) - case rm: RandomNormal => - initMethodBuilder.setMethodType(InitMethodType.RANDOM_NORMAL) - initMethodBuilder.addData(rm.mean) - initMethodBuilder.addData(rm.stdv) - case Zeros => - initMethodBuilder.setMethodType(InitMethodType.ZEROS) - case Ones => - initMethodBuilder.setMethodType(InitMethodType.ONES) - case const: ConstInitMethod => - initMethodBuilder.setMethodType(InitMethodType.CONST) - initMethodBuilder.addData(const.value) - case Xavier => - initMethodBuilder.setMethodType(InitMethodType.XAVIER) - case BilinearFiller => - initMethodBuilder.setMethodType(InitMethodType.BILINEARFILLER) - } - attributeBuilder.setInitMethodValue(initMethodBuilder.build) - } else { - initMethodBuilder.setMethodType(InitMethodType.EMPTY_INITIALIZATION) - attributeBuilder.setInitMethodValue(initMethodBuilder.build) - } - } - } - - /** - * DataConverter for [[com.intel.analytics.bigdl.nn.abstractnn.DataFormat]] - */ - object DataFormatConverter extends DataConverter { - override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) - (implicit ev: TensorNumeric[T]): AnyRef = { - val dataFormat = attribute.getDataFormatValue - dataFormat match { - case InputDataFormat.NCHW => NCHW - case InputDataFormat.NHWC => NHWC - } - - } - - override def setAttributeValue[T: ClassTag] - (context: SerializeContext[T], - attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type) - (implicit ev: TensorNumeric[T]): Unit = { - attributeBuilder.setDataType(DataType.DATA_FORMAT) - if (value != null) { - val dataFormat = value.asInstanceOf[DataFormat] - val inputFormat = dataFormat match { - case NCHW => InputDataFormat.NCHW - case NHWC => InputDataFormat.NHWC - } - attributeBuilder.setDataFormatValue(inputFormat) - } - } - } - -/** - * DataConverter for [[com.intel.analytics.bigdl.nn.abstractnn.AbstractModule]] - */ - object ModuleConverter extends DataConverter { - - override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) - (implicit ev: TensorNumeric[T]): AnyRef = { - val serializedModule = attribute.getBigDLModuleValue - if (serializedModule.getModuleType != null && serializedModule.getModuleType != "") { - ModuleSerializer.load(DeserializeContext(serializedModule, - context.storages, context.storageType)).module - } else { - null - } - } - - override def setAttributeValue[T: ClassTag](context: SerializeContext[T], - attributeBuilder: AttrValue.Builder, - value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { - attributeBuilder.setDataType(DataType.MODULE) - if (value != null) { - val module = value.asInstanceOf[AbstractModule[Activity, Activity, T]] - val serializableModule = ModuleSerializer. - serialize(SerializeContext(ModuleData(module, Seq[String](), Seq[String]()), - context.storages, context.storageType)).bigDLModule - attributeBuilder.setBigDLModuleValue(serializableModule) - } - } - } - /** * DataConverter for name list */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataFormatConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataFormatConverter.scala new file mode 100644 index 00000000000..511e709bcdd --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataFormatConverter.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer.converters + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.{NCHW, NHWC} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} +import serialization.Bigdl.{AttrValue, DataType, InputDataFormat} + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + +/** + * DataConverter for [[com.intel.analytics.bigdl.nn.abstractnn.DataFormat]] + */ +object DataFormatConverter extends DataConverter { + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) + (implicit ev: TensorNumeric[T]): AnyRef = { + val dataFormat = attribute.getDataFormatValue + dataFormat match { + case InputDataFormat.NCHW => NCHW + case InputDataFormat.NHWC => NHWC + } + + } + + override def setAttributeValue[T: ClassTag] + (context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type) + (implicit ev: TensorNumeric[T]): Unit = { + attributeBuilder.setDataType(DataType.DATA_FORMAT) + if (value != null) { + val dataFormat = value.asInstanceOf[DataFormat] + val inputFormat = dataFormat match { + case NCHW => InputDataFormat.NCHW + case NHWC => InputDataFormat.NHWC + } + attributeBuilder.setDataFormatValue(inputFormat) + } + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataReaderWriter.scala similarity index 98% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriter.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataReaderWriter.scala index b206f9877a1..2e73644c3fc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataReaderWriter.scala @@ -13,12 +13,13 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.utils.serializer +package com.intel.analytics.bigdl.utils.serializer.converters import java.io.{DataInputStream, DataOutputStream} import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Storage +import com.intel.analytics.bigdl.utils.serializer.BigDLDataType import com.intel.analytics.bigdl.utils.serializer.BigDLDataType.BigDLDataType /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/InitMethodConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/InitMethodConverter.scala new file mode 100644 index 00000000000..0697a359eed --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/InitMethodConverter.scala @@ -0,0 +1,89 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer.converters + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} +import serialization.Bigdl.{AttrValue, DataType, InitMethod, InitMethodType} + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + +/** + * DataConverter for [[com.intel.analytics.bigdl.nn.InitializationMethod]] + */ +object InitMethodConverter extends DataConverter { + + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) + (implicit ev: TensorNumeric[T]): AnyRef = { + val initMemethod = attribute.getInitMethodValue + val initType = initMemethod.getMethodType + val methodData = initMemethod.getDataList + initType match { + case InitMethodType.RANDOM_UNIFORM => RandomUniform + case InitMethodType.RANDOM_UNIFORM_PARAM => + RandomUniform(methodData.get(0), methodData.get(1)) + case InitMethodType.RANDOM_NORMAL => + RandomNormal(methodData.get(0), methodData.get(1)) + case InitMethodType.ZEROS => Zeros + case InitMethodType.ONES => Ones + case InitMethodType.CONST => ConstInitMethod(methodData.get(0)) + case InitMethodType.XAVIER => Xavier + case InitMethodType.BILINEARFILLER => BilinearFiller + case InitMethodType.EMPTY_INITIALIZATION => null + } + } + + override def setAttributeValue[T: ClassTag]( + context: SerializeContext[T], attributeBuilder: AttrValue.Builder, + value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { + attributeBuilder.setDataType(DataType.INITMETHOD) + val initMethodBuilder = InitMethod.newBuilder + if (value != null) { + val initMethod = value.asInstanceOf[InitializationMethod] + initMethod match { + case RandomUniform => + initMethodBuilder.setMethodType(InitMethodType.RANDOM_UNIFORM) + case ru: RandomUniform => + initMethodBuilder.setMethodType(InitMethodType.RANDOM_UNIFORM_PARAM) + initMethodBuilder.addData(ru.lower) + initMethodBuilder.addData(ru.upper) + case rm: RandomNormal => + initMethodBuilder.setMethodType(InitMethodType.RANDOM_NORMAL) + initMethodBuilder.addData(rm.mean) + initMethodBuilder.addData(rm.stdv) + case Zeros => + initMethodBuilder.setMethodType(InitMethodType.ZEROS) + case Ones => + initMethodBuilder.setMethodType(InitMethodType.ONES) + case const: ConstInitMethod => + initMethodBuilder.setMethodType(InitMethodType.CONST) + initMethodBuilder.addData(const.value) + case Xavier => + initMethodBuilder.setMethodType(InitMethodType.XAVIER) + case BilinearFiller => + initMethodBuilder.setMethodType(InitMethodType.BILINEARFILLER) + } + attributeBuilder.setInitMethodValue(initMethodBuilder.build) + } else { + initMethodBuilder.setMethodType(InitMethodType.EMPTY_INITIALIZATION) + attributeBuilder.setInitMethodValue(initMethodBuilder.build) + } + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ModuleConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ModuleConverter.scala new file mode 100644 index 00000000000..712082a9601 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ModuleConverter.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer.converters + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleData, ModuleSerializer, SerializeContext} +import serialization.Bigdl.{AttrValue, DataType} + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + + +/** + * DataConverter for [[com.intel.analytics.bigdl.nn.abstractnn.AbstractModule]] + */ +object ModuleConverter extends DataConverter { + + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) + (implicit ev: TensorNumeric[T]): AnyRef = { + val serializedModule = attribute.getBigDLModuleValue + if (serializedModule.getModuleType != null && serializedModule.getModuleType != "") { + ModuleSerializer.load(DeserializeContext(serializedModule, + context.storages, context.storageType)).module + } else { + null + } + } + + override def setAttributeValue[T: ClassTag](context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, + value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { + attributeBuilder.setDataType(DataType.MODULE) + if (value != null) { + val module = value.asInstanceOf[AbstractModule[Activity, Activity, T]] + val serializableModule = ModuleSerializer. + serialize(SerializeContext(ModuleData(module, Seq[String](), Seq[String]()), + context.storages, context.storageType)).bigDLModule + attributeBuilder.setBigDLModuleValue(serializableModule) + } + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/RegularizerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/RegularizerConverter.scala new file mode 100644 index 00000000000..d0f8d4430fc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/RegularizerConverter.scala @@ -0,0 +1,77 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer.converters + +import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} +import serialization.Bigdl.{AttrValue, DataType} + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + + +/** + * DataConverter for [[com.intel.analytics.bigdl.optim.Regularizer]] + */ +object RegularizerConverter extends DataConverter { + + override def getAttributeValue[T : ClassTag](context: DeserializeContext, + attribute: AttrValue) + (implicit ev: TensorNumeric[T]): AnyRef = { + val regularizer = attribute.getRegularizerValue + val regularizerType = regularizer.getRegularizerType + if (regularizer.getRegularDataCount == 0) { + return null + } + regularizerType match { + case serialization.Bigdl.RegularizerType.L1Regularizer => + val l1 = regularizer.getRegularDataList.get(0) + L1Regularizer[T](l1) + case serialization.Bigdl.RegularizerType.L2Regularizer => + val l2 = regularizer.getRegularDataList.get(1) + L2Regularizer[T](l2) + case serialization.Bigdl.RegularizerType.L1L2Regularizer => + val l1 = regularizer.getRegularDataList.get(0) + val l2 = regularizer.getRegularDataList.get(1) + L1L2Regularizer[T](l1, l2) + } + } + + override def setAttributeValue[T : ClassTag] + (context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, + valueType : universe.Type = null) + (implicit ev: TensorNumeric[T]): Unit = { + attributeBuilder.setDataType(DataType.REGULARIZER) + if (value != null) { + var regularizerBuilder = serialization.Bigdl.Regularizer.newBuilder + val regularizer = value.asInstanceOf[L1L2Regularizer[T]] + val l1 = regularizer.l1 + val l2 = regularizer.l2 + regularizerBuilder.addRegularData(l1) + regularizerBuilder.addRegularData(l2) + val regularizerType = regularizer match { + case l1: L1Regularizer[_] => serialization.Bigdl.RegularizerType.L1Regularizer + case l2: L2Regularizer[_] => serialization.Bigdl.RegularizerType.L2Regularizer + case l1l2: L1L2Regularizer[_] => serialization.Bigdl.RegularizerType.L1L2Regularizer + } + regularizerBuilder.setRegularizerType(regularizerType) + attributeBuilder.setRegularizerValue(regularizerBuilder.build) + } + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala new file mode 100644 index 00000000000..4738e58a16a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala @@ -0,0 +1,316 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer.converters + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.nn.quantized._ +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{BigDLStorage, DeserializeContext, ProtoStorageType, SerializeContext} +import serialization.Bigdl._ + +import scala.collection.JavaConverters._ +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + +/** + * DataConverter for [[com.intel.analytics.bigdl.tensor.Tensor]] + */ +object TensorConverter extends DataConverter { + + + private def isEmptyTensor(tensor : Tensor[_]): Boolean = { + val emptyTensor = tensor.getTensorType match { + case DenseType => + tensor.storage == null + case QuantizedType => + tensor.asInstanceOf[QuantizedTensor[_]].getStorage == null + } + emptyTensor + } + + override def getAttributeValue[T: ClassTag](context: DeserializeContext, + attribute: AttrValue) + (implicit ev: TensorNumeric[T]): AnyRef = { + val serializedTensor = attribute.getTensorValue + if (!serializedTensor.hasStorage) { + return null + } + val storages = context.storages + val tensorId = serializedTensor.getId + if (storages.contains(tensorId)) { + return storages.get(tensorId).get.asInstanceOf[AnyRef] + } + val dataType = serializedTensor.getDatatype + val tensorType = serializedTensor.getTensorType + val sizes = serializedTensor.getSizeList.asScala.toArray.map(_.intValue()) + val strides = serializedTensor.getStrideList.asScala.toArray.map(_.intValue()) + val offSet = serializedTensor.getOffset + val isScalr = serializedTensor.getIsScalar + val serializedStorage = serializedTensor.getStorage + val storageId = serializedStorage.getId + val created = if (storages.contains(storageId)) { + storages.get(storageId).get + } else { + null + } + + def quant(): Tensor[T] = { + var bytes: Array[Byte] = null + if (context.storageType == ProtoStorageType) { + bytes = serializedStorage.getBytesDataList.asScala.toArray.head.toByteArray + } else { + created + } + val serializedParams = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) + val paramsNum = serializedParams.head + val paramsArray = serializedParams.slice(1, paramsNum + 1) + val descTypeEnum = serializedParams(1 + paramsNum) + + val start = paramsNum + 2 // params number indicator + params number + desc type + + val length = if (sizes.length == 1) { + 1 // if the size is 1, means it's a vector + } else { + sizes(0) + } + val max = new Array[T](length) + val min = new Array[T](length) + val sum = new Array[T](length) + + dataType match { + case DataType.FLOAT => + val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) + var i = 0 + while (i < length) { + max(i) = ev.fromType[Float](data(i)) + min(i) = ev.fromType[Float](data(i + length)) + sum(i) = ev.fromType[Float](data(i + 2 * length)) + i += 1 + } + } + + var params: DescParams = null + + descTypeEnum match { + case 0 => + params = ConvDataParams(paramsArray) + case 1 => + params = ConvWeightParams(paramsArray) + case 2 => + params = LinearDataParams(paramsArray) + case 3 => + params = LinearWeightParams(paramsArray) + } + + QuantizedTensor[T](bytes, max, min, sum, sizes, params) + } + + val tensor = dataType match { + case DataType.FLOAT => + tensorType match { + case TensorType.DENSE => + val storage : Storage[Float] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getFloatDataList.asScala.toArray.map(_.floatValue()) + val newStorage = Storage[Float](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[Float]] + Tensor[Float](storage, offSet, sizes, strides) + case TensorType.QUANT => quant() + } + case DataType.DOUBLE => + val storage : Storage[Double] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getDoubleDataList.asScala.toArray.map(_.doubleValue()) + val newStorage = Storage[Double](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[Double]] + Tensor[Double](storage, offSet, sizes, strides) + case DataType.BOOL => + val storage : Storage[Boolean] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getBoolDataList.asScala.toArray.map(_.booleanValue()) + val newStorage = Storage[Boolean](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[Boolean]] + Tensor[Boolean](storage, offSet, sizes, strides) + case DataType.CHAR => + val storage: Storage[Char] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.toChar.charValue()) + val newStorage = Storage[Char](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[Char]] + Tensor[Char](storage, offSet, sizes, strides) + case DataType.STRING => + val storage: Storage[String] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getStringDataList.asScala.toArray + val newStorage = Storage[String](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[String]] + Tensor[String](storage, offSet, sizes, strides) + case DataType.INT32 => + val storage: Storage[Int] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.intValue()) + val newStorage = Storage[Int](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[Int]] + Tensor[Int](storage, offSet, sizes, strides) + case DataType.SHORT => + val storage: Storage[Short] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getIntDataList.asScala.toArray.map(_.shortValue()) + val newStorage = Storage[Short](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[Short]] + Tensor[Short](storage, offSet, sizes, strides) + case DataType.INT64 => + val storage: Storage[Long] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getLongDataList.asScala.toArray.map(_.longValue()) + val newStorage = Storage[Long](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[Long]] + Tensor[Long](storage, offSet, sizes, strides) + case DataType.BYTES => + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val storage: Storage[ByteString] = if (created == null ) { + if (storageId == -1) { + null + } else { + val data = serializedStorage.getBytesDataList.asScala.toArray + val newStorage = Storage[ByteString](data) + storages(storageId) = newStorage + newStorage + } + } else created.asInstanceOf[Storage[ByteString]] + Tensor[ByteString](storage, offSet, sizes, strides) + case _ => throw new IllegalArgumentException(s"$dataType not supported in tensor now !") + } + storages(tensorId) = tensor + tensor + } + + private def setStorage[T: ClassTag](context: SerializeContext[T], + tensorBuilder: BigDLTensor.Builder, tensor: Tensor[_]): Unit = { + val storageType = context.storageType + if (storageType == ProtoStorageType) { + ProtoTensorStorageManager.setStorage(context, tensorBuilder, tensor) + } else if (storageType == BigDLStorage) { + BigDLTensorStorageManager.setStorage(context, tensorBuilder, tensor) + } else { + throw new IllegalArgumentException(s"$storageType not supported") + } + } + + override def setAttributeValue[T: ClassTag] + (context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, + valueType : universe.Type = null) + (implicit ev: TensorNumeric[T]): Unit = { + attributeBuilder.setDataType(DataType.TENSOR) + if (value != null) { + val tensor = value.asInstanceOf[Tensor[_]] + val tensorId = System.identityHashCode(tensor) + val storages = context.storages + // Check if tensor has been shared + if (storages.contains(tensorId)) { + attributeBuilder.setTensorValue(resetTensor(storages.get(tensorId).get + .asInstanceOf[BigDLTensor])) + } else { + val totalElement = tensor.nElement() + val dimension = tensor.dim() + val tensorBuilder = BigDLTensor.newBuilder + tensorBuilder.setId(tensorId) + tensorBuilder.setDimension(dimension) + tensorBuilder.setNElements(totalElement) + tensor.getTensorType match { + case DenseType => + tensorBuilder.setOffset(tensor.storageOffset()) + tensorBuilder.setIsScalar(tensor.isScalar) + tensorBuilder.setTensorType(TensorType.DENSE) + case QuantizedType => + tensorBuilder.setTensorType(TensorType.QUANT) + } + + val tensorEmpty = isEmptyTensor(tensor) + + if (!tensorEmpty) { + tensor.size().foreach(size => tensorBuilder.addSize(size)) + tensor.stride().foreach(stride => tensorBuilder.addStride(stride)) + } + setStorage(context, tensorBuilder, tensor) + val tensorBuild = tensorBuilder.build + attributeBuilder.setTensorValue(resetTensor(tensorBuild)) + storages(tensorId) = tensorBuild + } + } + } + + + private def resetStorage(originStorage : TensorStorage) : TensorStorage = { + val storageBuilder = TensorStorage.newBuilder + storageBuilder.setDatatype(originStorage.getDatatype) + storageBuilder.setId(originStorage.getId) + storageBuilder.build + } + + private def resetTensor(originTensor: BigDLTensor) : BigDLTensor = { + val tensorBuilder = BigDLTensor.newBuilder(originTensor) + tensorBuilder.clearStorage + tensorBuilder.setDatatype(originTensor.getDatatype) + tensorBuilder.setId(originTensor.getId) + if (originTensor.hasStorage) { + tensorBuilder.setStorage(resetStorage(originTensor.getStorage)) + } + tensorBuilder.build + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorStorageManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala similarity index 98% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorStorageManager.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala index 1fbf0fdcad5..d5a3efe7bda 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorStorageManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala @@ -14,13 +14,13 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.utils.serializer +package com.intel.analytics.bigdl.utils.serializer.converters import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.quantized.{ConvData, ConvWeight, LinearData, LinearWeight} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericShort, NumericString} import com.intel.analytics.bigdl.tensor.{DenseType, QuantizedTensor, QuantizedType, Tensor} -import com.intel.analytics.bigdl.utils.serializer.ProtoTensorStorageManager.isEmptyTensor +import com.intel.analytics.bigdl.utils.serializer.SerializeContext import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString import serialization.Bigdl.{BigDLTensor, DataType, TensorStorage} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/VariableFormatConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/VariableFormatConverter.scala new file mode 100644 index 00000000000..c92d2d860fd --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/VariableFormatConverter.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer.converters + +import com.intel.analytics.bigdl.nn.VariableFormat +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} +import serialization.Bigdl.{AttrValue, DataType, VarFormat} + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + +/** + * DataConverter for [[com.intel.analytics.bigdl.nn.VariableFormat]] + */ +object VariableFormatConverter extends DataConverter { + + override def getAttributeValue[T: ClassTag](context: DeserializeContext, attribute: AttrValue) + (implicit ev: TensorNumeric[T]): AnyRef = { + val format = attribute.getVariableFormatValue + format match { + case VarFormat.DEFAULT => VariableFormat.Default + case VarFormat.ONE_D => VariableFormat.ONE_D + case VarFormat.IN_OUT => VariableFormat.IN_OUT + case VarFormat.OUT_IN => VariableFormat.OUT_IN + case VarFormat.IN_OUT_KW_KH => VariableFormat.IN_OUT_KW_KH + case VarFormat.OUT_IN_KW_KH => VariableFormat.OUT_IN_KW_KH + case VarFormat.GP_OUT_IN_KW_KH => VariableFormat.GP_OUT_IN_KW_KH + case VarFormat.GP_IN_OUT_KW_KH => VariableFormat.GP_IN_OUT_KW_KH + case VarFormat.OUT_IN_KT_KH_KW => VariableFormat.OUT_IN_KT_KH_KW + case VarFormat.EMPTY_FORMAT => null + } + } + + override def setAttributeValue[T: ClassTag]( + context: SerializeContext[T], attributeBuilder: AttrValue.Builder, + value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { + attributeBuilder.setDataType(DataType.VARIABLE_FORMAT) + if (value != null) { + val format = value.asInstanceOf[VariableFormat] + val formatValue = format match { + case VariableFormat.Default => VarFormat.DEFAULT + case VariableFormat.ONE_D => VarFormat.ONE_D + case VariableFormat.IN_OUT => VarFormat.IN_OUT + case VariableFormat.OUT_IN => VarFormat.OUT_IN + case VariableFormat.IN_OUT_KW_KH => VarFormat.IN_OUT_KW_KH + case VariableFormat.OUT_IN_KW_KH => VarFormat.OUT_IN_KW_KH + case VariableFormat.GP_OUT_IN_KW_KH => VarFormat.GP_OUT_IN_KW_KH + case VariableFormat.GP_IN_OUT_KW_KH => VarFormat.GP_IN_OUT_KW_KH + case VariableFormat.OUT_IN_KT_KH_KW => VarFormat.OUT_IN_KT_KH_KW + } + attributeBuilder.setVariableFormatValue(formatValue) + } else { + attributeBuilder.setVariableFormatValue(VarFormat.EMPTY_FORMAT) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index 0d1350cf7b0..20b04fa9c4b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -28,6 +28,7 @@ import serialization.Bigdl.{AttrValue, BigDLTensor, DataType, TensorStorage} import scala.reflect.runtime.universe import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.AttrValue.ArrayValue import scala.collection.mutable diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriterSpec.scala index 59eed48c70b..214718f3bb5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataReaderWriterSpec.scala @@ -19,6 +19,7 @@ import java.io._ import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Storage +import com.intel.analytics.bigdl.utils.serializer.converters._ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class DataReaderWriterSpec extends FlatSpec with Matchers with BeforeAndAfter { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala index b9e2d8c3e1e..ab7937568e3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.utils.serializer import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import org.scalatest.{FlatSpec, Matchers} import serialization.Bigdl.{AttrValue, BigDLTensor, TensorStorage} From f4f282b5d8b0bf2591a5734c0880cda39e8cdb82 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 23 Jan 2018 13:42:50 +0800 Subject: [PATCH 0650/1065] Fix th test concurrent failure (#2215) * add Tensorarray * fix torch file use same file name to read result * remove useless code * fix compile error --- .../bigdl/dllib/models/AlexNetSpec.scala | 30 +++++----- .../bigdl/dllib/models/InceptionSpec.scala | 59 ++++++++++--------- .../bigdl/dllib/models/ResNetSpec.scala | 13 ++-- .../analytics/bigdl/dllib/torch/TH.scala | 12 ++-- 4 files changed, 61 insertions(+), 53 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/AlexNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/AlexNetSpec.scala index 3fb94165bac..c4dcf634151 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/AlexNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/AlexNetSpec.scala @@ -31,6 +31,8 @@ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial class AlexNetSpec extends TorchSpec { + private val suffix = ".t7" + (new java.util.Random()).nextLong() + "AlexNet float" should "generate correct output" in { torchCheck() @@ -113,9 +115,9 @@ gradInput = model.gradInput """ TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", - "parameters_initial", "gradParameters_initial", "gradInput", "model")) + "parameters_initial", "gradParameters_initial", "gradInput", "model"), suffix) - val parameterTorch = TH.map("parameters_initial").asInstanceOf[Tensor[Double]] + val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] val parameters = model.getParameters()._1.asInstanceOf[Tensor[Float]] for (i <- 0 until parameters.nElement()) { @@ -140,7 +142,7 @@ gradInput = model.gradInput } model.zeroGradParameters() - val output = TH.map("output").asInstanceOf[Tensor[Double]] + val output = TH.map("output", suffix).asInstanceOf[Tensor[Double]] val outputTest = model.forward(floatInput).toTensor var abss = 0.0 for (i <- 0 until outputTest.nElement()) { @@ -151,12 +153,12 @@ gradInput = model.gradInput println(s"outputAbs:$abss") val errTest = criterion.forward(outputTest, floatLabel) - val err = TH.map("err").asInstanceOf[Double] + val err = TH.map("err", suffix).asInstanceOf[Double] println(s"${abs(errTest - err)}") assert(abs(errTest - err) < 1e-6) val gradOutputTest = criterion.backward(outputTest, floatLabel).toTensor - val gradOutput = TH.map("gradOutput").asInstanceOf[Tensor[Double]] + val gradOutput = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] abss = 0.0 for (i <- 0 until gradOutputTest.nElement()) { val tmp = abs(gradOutputTest.storage().array()(i) - gradOutput.storage().array()(i)) @@ -166,7 +168,7 @@ gradInput = model.gradInput println(s"gradOutputTestAbs:$abss") val gradInput = model.backward(floatInput, gradOutputTest).toTensor[Float] - val gradInputTorch = TH.map("gradInput").asInstanceOf[Tensor[Double]] + val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Double]] abss = 0.0 for (i <- 0 until gradInputTorch.nElement()) { @@ -176,7 +178,7 @@ gradInput = model.gradInput println(s"gradInputTestAbs:$abss") val (weights, grad) = model.getParameters() - val modelTorch = TH.map("model").asInstanceOf[Module[Double]] + val modelTorch = TH.map("model", suffix).asInstanceOf[Module[Double]] val (weightsTorch, gradTorch) = modelTorch.getParameters() sgd.optimize(_ => (errTest, grad), weights, state, state) abss = 0.0 @@ -239,9 +241,9 @@ gradInput = model.gradInput TH.runNM(code, Map("model" -> model, "input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", - "parameters_initial", "gradParameters_initial", "gradInput", "model")) + "parameters_initial", "gradParameters_initial", "gradInput", "model"), suffix) - val parameterTorch = TH.map("parameters_initial").asInstanceOf[Tensor[Float]] + val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Float]] val parameters = model.getParameters()._1.asInstanceOf[Tensor[Float]] parameterTorch should be (parameters) @@ -262,7 +264,7 @@ gradInput = model.gradInput } model.zeroGradParameters() - val output = TH.map("output").asInstanceOf[Tensor[Float]] + val output = TH.map("output", suffix).asInstanceOf[Tensor[Float]] val outputTest = model.forward(input).toTensor var abss = 0.0 for (i <- 0 until outputTest.nElement()) { @@ -273,12 +275,12 @@ gradInput = model.gradInput println(s"outputAbs:$abss") val errTest = criterion.forward(outputTest, labels) - val err = TH.map("err").asInstanceOf[Double] + val err = TH.map("err", suffix).asInstanceOf[Double] println(s"err:${abs(errTest - err)}") assert(abs(errTest - err) < 1e-6) val gradOutputTest = criterion.backward(outputTest, labels).toTensor - val gradOutput = TH.map("gradOutput").asInstanceOf[Tensor[Float]] + val gradOutput = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Float]] abss = 0.0 for (i <- 0 until gradOutputTest.nElement()) { val tmp = abs(gradOutputTest.storage().array()(i) - gradOutput.storage().array()(i)) @@ -288,7 +290,7 @@ gradInput = model.gradInput println(s"gradOutputTestAbs:$abss") val gradInput = model.backward(input, gradOutputTest).toTensor[Float] - val gradInputTorch = TH.map("gradInput").asInstanceOf[Tensor[Float]] + val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Float]] abss = 0.0 for (i <- 0 until gradInputTorch.nElement()) { @@ -297,7 +299,7 @@ gradInput = model.gradInput } println(s"gradInputTestAbs:$abss") - val modelTorch = TH.map("model").asInstanceOf[Module[Float]] + val modelTorch = TH.map("model", suffix).asInstanceOf[Module[Float]] val (weightsTorch, gradTorch) = modelTorch.getParameters() sgd.optimize(_ => (errTest, grad), weights, state, state) abss = 0.0 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala index c89b2c72298..00f9272226f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala @@ -31,6 +31,8 @@ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial class InceptionSpec extends TorchSpec { + private val suffix = ".t7" + (new java.util.Random()).nextLong() + "Inception+bn" should "generate correct output" in { torchCheck() @@ -159,16 +161,18 @@ class InceptionSpec extends TorchSpec { """ TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", - "parameters_initial", "gradParameters_initial", "gradParameters", "parameters", "model2")) + "parameters_initial", "gradParameters_initial", "gradParameters", "parameters", "model2"), + suffix) val model = Inception.getModel[Double](1000, "inception-bn") val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] println(s"model size: ${parameters.nElement()}") - val parametersInitTorch = TH.map("parameters_initial").asInstanceOf[Tensor[Double]] + val parametersInitTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] require(parameters == parametersInitTorch, "parameter compare failed") - val gradGarametersInitTorch = TH.map("gradParameters_initial").asInstanceOf[Tensor[Double]] + val gradGarametersInitTorch = TH.map("gradParameters_initial", suffix) + .asInstanceOf[Tensor[Double]] val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] require(gradparameters == gradGarametersInitTorch, "gradparameter compare failed") @@ -180,24 +184,24 @@ class InceptionSpec extends TorchSpec { model.zeroGradParameters() val outputTest = model.forward(input).toTensor[Double] - val outputTorch = TH.map("output").asInstanceOf[Tensor[Double]] + val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] outputTest shouldEqual outputTorch - val errTorch = TH.map("err").asInstanceOf[Table][Double](1) + val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) val errTest = criterion.forward(outputTest, labels) println(s"err:${abs(errTest - errTorch)}") assert(abs(errTest - errTorch) < 4e-15) - val gradOutputTorch = TH.map("gradOutput").asInstanceOf[Tensor[Double]] + val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] val gradOutputTest = criterion.backward(outputTest, labels) model.backward(input, gradOutputTest) gradOutputTest shouldEqual gradOutputTorch sgd.optimize(_ => (errTest, grad), weights, state, state) - val gradParametersTorch = TH.map("gradParameters").asInstanceOf[Tensor[Double]] + val gradParametersTorch = TH.map("gradParameters", suffix).asInstanceOf[Tensor[Double]] grad.equals(gradParametersTorch) should be (true) - val parametersTorch = TH.map("parameters").asInstanceOf[Tensor[Double]] + val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] parameters.equals(parametersTorch) should be (true) } @@ -345,19 +349,19 @@ class InceptionSpec extends TorchSpec { """ TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", - "parameters_initial", "gradParameters_initial", "gradInput", "parameters")) + "parameters_initial", "gradParameters_initial", "gradInput", "parameters"), suffix) - val gradInputTorch = TH.map("gradInput").asInstanceOf[Tensor[Double]] + val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Double]] val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] model.zeroGradParameters() println(s"model size: ${parameters.nElement()}") - val parameterTorch = TH.map("parameters_initial").asInstanceOf[Tensor[Double]] + val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] require(parameters == parameterTorch, "parameter compare failed") val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] - val parametersTorch = TH.map("parameters").asInstanceOf[Tensor[Double]] - val gradparameterTorch = TH.map("gradParameters_initial").asInstanceOf[Tensor[Double]] + val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] + val gradparameterTorch = TH.map("gradParameters_initial", suffix).asInstanceOf[Tensor[Double]] require(gradparameters == gradparameterTorch, "gradparameter compare failed") val (weights, grad) = model.getParameters() @@ -379,7 +383,7 @@ class InceptionSpec extends TorchSpec { model.zeroGradParameters() var outputAbs = 0.0 - val outputTorch = TH.map("output").asInstanceOf[Tensor[Double]] + val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] val outputTest = model.forward(input).toTensor[Double] outputTest.map(outputTorch, (v1, v2) => { outputAbs += abs(v1 - v2) @@ -388,12 +392,12 @@ class InceptionSpec extends TorchSpec { println(s"outputAbs:$outputAbs") val errTest = criterion.forward(outputTest, labels) - val errTorch = TH.map("err").asInstanceOf[Table][Double](1) + val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) println(s"err:${abs(errTest - errTorch)}") assert(abs(errTest - errTorch) == 0) val gradOutputTest = criterion.backward(outputTest, labels) - val gradOutputTorch = TH.map("gradOutput").asInstanceOf[Tensor[Double]] + val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] gradOutputTest shouldEqual gradOutputTorch val gradInput = model.backward(input, gradOutputTest) @@ -531,17 +535,18 @@ class InceptionSpec extends TorchSpec { TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", "parameters_initial", "gradParameters_initial", - "gradParameters", "parameters", "initModel")) + "gradParameters", "parameters", "initModel"), suffix) - val model = TH.map("initModel"). + val model = TH.map("initModel", suffix). asInstanceOf[AbstractModule[Tensor[Double], Tensor[Double], Double]] val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] println(s"model size: ${parameters.nElement()}") - val parametersInitTorch = TH.map("parameters_initial").asInstanceOf[Tensor[Double]] + val parametersInitTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] require(parameters == parametersInitTorch, "parameter compare failed") - val gradGarametersInitTorch = TH.map("gradParameters_initial").asInstanceOf[Tensor[Double]] + val gradGarametersInitTorch = TH.map("gradParameters_initial", suffix) + .asInstanceOf[Tensor[Double]] val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] require(gradparameters == gradGarametersInitTorch, "gradparameter compare failed") @@ -553,23 +558,23 @@ class InceptionSpec extends TorchSpec { model.zeroGradParameters() val outputTest = model.forward(input) - val outputTorch = TH.map("output").asInstanceOf[Tensor[Double]] + val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] outputTest shouldEqual outputTorch - val errTorch = TH.map("err").asInstanceOf[Table][Double](1) + val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) val errTest = criterion.forward(outputTest, labels) println(s"err:${abs(errTest - errTorch)}") assert(abs(errTest - errTorch) < 4e-10) - val gradOutputTorch = TH.map("gradOutput").asInstanceOf[Tensor[Double]] + val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] val gradOutputTest = criterion.backward(outputTest, labels) model.backward(input, gradOutputTest) gradOutputTest shouldEqual gradOutputTorch sgd.optimize(_ => (errTest, grad), weights, state, state) - val gradParametersTorch = TH.map("gradParameters").asInstanceOf[Tensor[Double]] + val gradParametersTorch = TH.map("gradParameters", suffix).asInstanceOf[Tensor[Double]] grad == gradParametersTorch should be (true) - val parametersTorch = TH.map("parameters").asInstanceOf[Tensor[Double]] + val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] parameters == parametersTorch should be (true) } @@ -696,10 +701,10 @@ class InceptionSpec extends TorchSpec { end """ - TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("initModel")) + TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("initModel"), suffix) val model = Inception.getModel[Float](1000, "inception-bn") - val model2 = TH.map("initModel"). + val model2 = TH.map("initModel", suffix). asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]] model2 should be (model) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala index 0871d02d468..7ec6d55268f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala @@ -36,6 +36,7 @@ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial class ResNetSpec extends TorchSpec { + private val suffix = ".t7" + (new java.util.Random()).nextLong() "ResNet Float" should "generate correct output" in { // System.setProperty("java.io.tmpdir", "/disk2/test"); @@ -217,10 +218,10 @@ class ResNetSpec extends TorchSpec { TH.runNM(code, immutable.Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", "parameters_initial", - "gradParameters_initial", "gradInput", "model")) + "gradParameters_initial", "gradInput", "model"), suffix) ResNet.shareGradInput(model) - val parameterTorch = TH.map("parameters_initial").asInstanceOf[Tensor[Float]] + val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Float]] val parameters = model.getParameters()._1 for (i <- 0 until parameters.nElement()) { @@ -248,7 +249,7 @@ class ResNetSpec extends TorchSpec { sgd.optimize(feval, weights, state) } - val output = TH.map("output").asInstanceOf[Tensor[Float]] + val output = TH.map("output", suffix).asInstanceOf[Tensor[Float]] val outputTest = model.output.toTensor[Float] var abss = 0.0 for (i <- 0 until outputTest.nElement()) { @@ -260,12 +261,12 @@ class ResNetSpec extends TorchSpec { val errTest = criterion.output - val err = TH.map("err").asInstanceOf[Double] + val err = TH.map("err", suffix).asInstanceOf[Double] println(s"${abs(errTest - err)}") assert(abs(errTest - err) < 1.5e-6) val gradOutputTest = criterion.backward(outputTest, labels) - val gradOutput = TH.map("gradOutput").asInstanceOf[Tensor[Float]] + val gradOutput = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Float]] abss = 0.0 for (i <- 0 until gradOutputTest.nElement()) { val tmp = abs(gradOutputTest.storage().array()(i) - gradOutput.storage().array()(i)) @@ -275,7 +276,7 @@ class ResNetSpec extends TorchSpec { println(s"gradOutputTestAbs:$abss") val gradInput = model.gradInput.asInstanceOf[Tensor[Float]] - val gradInputTorch = TH.map("gradInput").asInstanceOf[Tensor[Float]] + val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Float]] abss = 0.0 for (i <- 0 until gradInputTorch.nElement()) { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala index 3ba743d7f7b..f152afcd740 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.torch import java.io._ +import java.util.Random import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor._ @@ -44,13 +45,13 @@ object TH { // Run with map def run(code: String, parameters: Map[String, Any], result: Array[String]): (Double, Map[String, Any]) = { - val suffix = ".t7" + val suffix = ".t7" + (new Random()).nextLong() val tmpFile = java.io.File.createTempFile("UnitTest", "lua") val absolutePath = tmpFile.getAbsolutePath val subPath = absolutePath.substring(0, absolutePath.lastIndexOf(java.io.File.separator) + 1) var resultMap: Map[String, Any] = Map() - val luaTime = runNM(code: String, parameters: Map[String, Any], result: Array[String]) + val luaTime = runNM(code: String, parameters: Map[String, Any], result: Array[String], suffix) result.foreach { k => val tmp: Any = File.loadTorch(subPath + k + suffix) @@ -70,8 +71,8 @@ object TH { } // Run without map - def runNM(code: String, parameters: Map[String, Any], result: Array[String]): Double = { - val suffix = ".t7" + def runNM(code: String, parameters: Map[String, Any], result: Array[String], suffix: String) + : Double = { val varCode = new StringBuilder("require 'nn'\n" + "require 'optim'\n") val usrCode = new StringBuilder("") val resCode = new StringBuilder("") @@ -149,8 +150,7 @@ object TH { } // Single map - def map(result: String): (Any) = { - val suffix = ".t7" + def map(result: String, suffix: String): (Any) = { val tmpFile = java.io.File.createTempFile("UnitTest", "lua") val absolutePath = tmpFile.getAbsolutePath val subPath = absolutePath.substring(0, absolutePath.lastIndexOf(java.io.File.separator) + 1) From 2549483b417777ed91c92787290fb02c69e942cd Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 23 Jan 2018 14:46:04 +0800 Subject: [PATCH 0651/1065] add ser support for shape (#2216) --- .../src/main/java/serialization/Bigdl.java | 4332 ++++++++++++----- .../main/resources/serialization/bigdl.proto | 14 + .../serializer/converters/DataConverter.scala | 24 + .../converters/ShapeConverter.scala | 83 + .../utils/serializer/DataConverterSpec.scala | 59 + 5 files changed, 3328 insertions(+), 1184 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala diff --git a/scala/dllib/src/main/java/serialization/Bigdl.java b/scala/dllib/src/main/java/serialization/Bigdl.java index 90874d5cb0b..2610f1194e0 100644 --- a/scala/dllib/src/main/java/serialization/Bigdl.java +++ b/scala/dllib/src/main/java/serialization/Bigdl.java @@ -725,6 +725,10 @@ public enum DataType * CUSTOM = 17; */ CUSTOM(17), + /** + * SHAPE = 18; + */ + SHAPE(18), UNRECOGNIZED(-1), ; @@ -800,6 +804,10 @@ public enum DataType * CUSTOM = 17; */ public static final int CUSTOM_VALUE = 17; + /** + * SHAPE = 18; + */ + public static final int SHAPE_VALUE = 18; public final int getNumber() { @@ -838,6 +846,7 @@ public static DataType forNumber(int value) { case 15: return ARRAY_VALUE; case 16: return DATA_FORMAT; case 17: return CUSTOM; + case 18: return SHAPE; default: return null; } } @@ -9441,6 +9450,31 @@ public interface AttrValueOrBuilder extends */ com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder(); + /** + * 
+     * Shape value
+     *
+ * + * .serialization.Shape shape = 18; + */ + boolean hasShape(); + /** + * 
+     * Shape value
+     *
+ * + * .serialization.Shape shape = 18; + */ + serialization.Bigdl.Shape getShape(); + /** + * 
+     * Shape value
+     *
+ * + * .serialization.Shape shape = 18; + */ + serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder(); + public serialization.Bigdl.AttrValue.ValueCase getValueCase(); } /** @@ -9641,6 +9675,20 @@ private AttrValue( valueCase_ = 17; break; } + case 146: { + serialization.Bigdl.Shape.Builder subBuilder = null; + if (valueCase_ == 18) { + subBuilder = ((serialization.Bigdl.Shape) value_).toBuilder(); + } + value_ = + input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom((serialization.Bigdl.Shape) value_); + value_ = subBuilder.buildPartial(); + } + valueCase_ = 18; + break; + } } } } catch (com.google.protobuf.InvalidProtocolBufferException e) { @@ -10185,6 +10233,50 @@ serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( */ com.google.protobuf.AnyOrBuilder getCustomOrBuilder( int index); + + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + java.util.List + getShapeList(); + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + serialization.Bigdl.Shape getShape(int index); + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + int getShapeCount(); + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + java.util.List + getShapeOrBuilderList(); + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + int index); } /** * Protobuf type {@code serialization.AttrValue.ArrayValue} @@ -10215,6 +10307,7 @@ private ArrayValue() { nameAttrList_ = java.util.Collections.emptyList(); dataFormat_ = java.util.Collections.emptyList(); custom_ = java.util.Collections.emptyList(); + shape_ = java.util.Collections.emptyList(); } @java.lang.Override @@ -10470,6 +10563,15 @@ private ArrayValue( input.readMessage(com.google.protobuf.Any.parser(), extensionRegistry)); break; } + case 138: { + if (!((mutable_bitField0_ & 0x00010000) == 0x00010000)) { + shape_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00010000; + } + shape_.add( + input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry)); + break; + } } } } catch (com.google.protobuf.InvalidProtocolBufferException e) { @@ -10520,6 +10622,9 @@ private ArrayValue( if (((mutable_bitField0_ & 0x00008000) == 0x00008000)) { custom_ = java.util.Collections.unmodifiableList(custom_); } + if (((mutable_bitField0_ & 0x00010000) == 0x00010000)) { + shape_ = java.util.Collections.unmodifiableList(shape_); + } this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } @@ -11237,6 +11342,61 @@ public com.google.protobuf.AnyOrBuilder getCustomOrBuilder( return custom_.get(index); } + public static final int SHAPE_FIELD_NUMBER = 17; + private java.util.List shape_; + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + public java.util.List getShapeList() { + return shape_; + } + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + public java.util.List + getShapeOrBuilderList() { + return shape_; + } + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + public int getShapeCount() { + return shape_.size(); + } + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + public serialization.Bigdl.Shape getShape(int index) { + return shape_.get(index); + } + /** + * 
+       * "Array(Shape)"
+       *
+ * + * repeated .serialization.Shape shape = 17; + */ + public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + int index) { + return shape_.get(index); + } + private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; @@ -11326,6 +11486,9 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) for (int i = 0; i < custom_.size(); i++) { output.writeMessage(16, custom_.get(i)); } + for (int i = 0; i < shape_.size(); i++) { + output.writeMessage(17, shape_.get(i)); + } unknownFields.writeTo(output); } @@ -11459,6 +11622,10 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeMessageSize(16, custom_.get(i)); } + for (int i = 0; i < shape_.size(); i++) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(17, shape_.get(i)); + } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; @@ -11504,6 +11671,8 @@ public boolean equals(final java.lang.Object obj) { result = result && dataFormat_.equals(other.dataFormat_); result = result && getCustomList() .equals(other.getCustomList()); + result = result && getShapeList() + .equals(other.getShapeList()); result = result && unknownFields.equals(other.unknownFields); return result; } @@ -11575,6 +11744,10 @@ public int hashCode() { hash = (37 * hash) + CUSTOM_FIELD_NUMBER; hash = (53 * hash) + getCustomList().hashCode(); } + if (getShapeCount() > 0) { + hash = (37 * hash) + SHAPE_FIELD_NUMBER; + hash = (53 * hash) + getShapeList().hashCode(); + } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; @@ -11706,6 +11879,7 @@ private void maybeForceBuilderInitialization() { getBigDLModuleFieldBuilder(); getNameAttrListFieldBuilder(); getCustomFieldBuilder(); + getShapeFieldBuilder(); } } public Builder clear() { @@ -11766,6 +11940,12 @@ public Builder clear() { } else { customBuilder_.clear(); } + if (shapeBuilder_ == null) { + shape_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00010000); + } else { + shapeBuilder_.clear(); + } return this; } @@ -11886,6 +12066,15 @@ public serialization.Bigdl.AttrValue.ArrayValue buildPartial() { } else { result.custom_ = customBuilder_.build(); } + if (shapeBuilder_ == null) { + if (((bitField0_ & 0x00010000) == 0x00010000)) { + shape_ = java.util.Collections.unmodifiableList(shape_); + bitField0_ = (bitField0_ & ~0x00010000); + } + result.shape_ = shape_; + } else { + result.shape_ = shapeBuilder_.build(); + } result.bitField0_ = to_bitField0_; onBuilt(); return result; @@ -12170,6 +12359,32 @@ public Builder mergeFrom(serialization.Bigdl.AttrValue.ArrayValue other) { } } } + if (shapeBuilder_ == null) { + if (!other.shape_.isEmpty()) { + if (shape_.isEmpty()) { + shape_ = other.shape_; + bitField0_ = (bitField0_ & ~0x00010000); + } else { + ensureShapeIsMutable(); + shape_.addAll(other.shape_); + } + onChanged(); + } + } else { + if (!other.shape_.isEmpty()) { + if (shapeBuilder_.isEmpty()) { + shapeBuilder_.dispose(); + shapeBuilder_ = null; + shape_ = other.shape_; + bitField0_ = (bitField0_ & ~0x00010000); + shapeBuilder_ = + com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? + getShapeFieldBuilder() : null; + } else { + shapeBuilder_.addAllMessages(other.shape_); + } + } + } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; @@ -15053,210 +15268,524 @@ public com.google.protobuf.Any.Builder addCustomBuilder( } return customBuilder_; } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); + private java.util.List shape_ = + java.util.Collections.emptyList(); + private void ensureShapeIsMutable() { + if (!((bitField0_ & 0x00010000) == 0x00010000)) { + shape_ = new java.util.ArrayList(shape_); + bitField0_ |= 0x00010000; + } } + private com.google.protobuf.RepeatedFieldBuilderV3< + serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> shapeBuilder_; - // @@protoc_insertion_point(builder_scope:serialization.AttrValue.ArrayValue) - } - - // @@protoc_insertion_point(class_scope:serialization.AttrValue.ArrayValue) - private static final serialization.Bigdl.AttrValue.ArrayValue DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new serialization.Bigdl.AttrValue.ArrayValue(); - } - - public static serialization.Bigdl.AttrValue.ArrayValue getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public ArrayValue parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new ArrayValue(input, extensionRegistry); + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public java.util.List getShapeList() { + if (shapeBuilder_ == null) { + return java.util.Collections.unmodifiableList(shape_); + } else { + return shapeBuilder_.getMessageList(); + } } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public serialization.Bigdl.AttrValue.ArrayValue getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - - } - - private int valueCase_ = 0; - private java.lang.Object value_; - public enum ValueCase - implements com.google.protobuf.Internal.EnumLite { - INT32VALUE(3), - INT64VALUE(4), - FLOATVALUE(5), - DOUBLEVALUE(6), - STRINGVALUE(7), - BOOLVALUE(8), - REGULARIZERVALUE(9), - TENSORVALUE(10), - VARIABLEFORMATVALUE(11), - INITMETHODVALUE(12), - BIGDLMODULEVALUE(13), - NAMEATTRLISTVALUE(14), - ARRAYVALUE(15), - DATAFORMATVALUE(16), - CUSTOMVALUE(17), - VALUE_NOT_SET(0); - private final int value; - private ValueCase(int value) { - this.value = value; - } - /** - * @deprecated Use {@link #forNumber(int)} instead. - */ - @java.lang.Deprecated - public static ValueCase valueOf(int value) { - return forNumber(value); - } - - public static ValueCase forNumber(int value) { - switch (value) { - case 3: return INT32VALUE; - case 4: return INT64VALUE; - case 5: return FLOATVALUE; - case 6: return DOUBLEVALUE; - case 7: return STRINGVALUE; - case 8: return BOOLVALUE; - case 9: return REGULARIZERVALUE; - case 10: return TENSORVALUE; - case 11: return VARIABLEFORMATVALUE; - case 12: return INITMETHODVALUE; - case 13: return BIGDLMODULEVALUE; - case 14: return NAMEATTRLISTVALUE; - case 15: return ARRAYVALUE; - case 16: return DATAFORMATVALUE; - case 17: return CUSTOMVALUE; - case 0: return VALUE_NOT_SET; - default: return null; + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public int getShapeCount() { + if (shapeBuilder_ == null) { + return shape_.size(); + } else { + return shapeBuilder_.getCount(); + } } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public serialization.Bigdl.Shape getShape(int index) { + if (shapeBuilder_ == null) { + return shape_.get(index); + } else { + return shapeBuilder_.getMessage(index); + } + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder setShape( + int index, serialization.Bigdl.Shape value) { + if (shapeBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureShapeIsMutable(); + shape_.set(index, value); + onChanged(); + } else { + shapeBuilder_.setMessage(index, value); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder setShape( + int index, serialization.Bigdl.Shape.Builder builderForValue) { + if (shapeBuilder_ == null) { + ensureShapeIsMutable(); + shape_.set(index, builderForValue.build()); + onChanged(); + } else { + shapeBuilder_.setMessage(index, builderForValue.build()); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder addShape(serialization.Bigdl.Shape value) { + if (shapeBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureShapeIsMutable(); + shape_.add(value); + onChanged(); + } else { + shapeBuilder_.addMessage(value); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder addShape( + int index, serialization.Bigdl.Shape value) { + if (shapeBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureShapeIsMutable(); + shape_.add(index, value); + onChanged(); + } else { + shapeBuilder_.addMessage(index, value); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder addShape( + serialization.Bigdl.Shape.Builder builderForValue) { + if (shapeBuilder_ == null) { + ensureShapeIsMutable(); + shape_.add(builderForValue.build()); + onChanged(); + } else { + shapeBuilder_.addMessage(builderForValue.build()); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder addShape( + int index, serialization.Bigdl.Shape.Builder builderForValue) { + if (shapeBuilder_ == null) { + ensureShapeIsMutable(); + shape_.add(index, builderForValue.build()); + onChanged(); + } else { + shapeBuilder_.addMessage(index, builderForValue.build()); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder addAllShape( + java.lang.Iterable values) { + if (shapeBuilder_ == null) { + ensureShapeIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, shape_); + onChanged(); + } else { + shapeBuilder_.addAllMessages(values); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder clearShape() { + if (shapeBuilder_ == null) { + shape_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00010000); + onChanged(); + } else { + shapeBuilder_.clear(); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public Builder removeShape(int index) { + if (shapeBuilder_ == null) { + ensureShapeIsMutable(); + shape_.remove(index); + onChanged(); + } else { + shapeBuilder_.remove(index); + } + return this; + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public serialization.Bigdl.Shape.Builder getShapeBuilder( + int index) { + return getShapeFieldBuilder().getBuilder(index); + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + int index) { + if (shapeBuilder_ == null) { + return shape_.get(index); } else { + return shapeBuilder_.getMessageOrBuilder(index); + } + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public java.util.List + getShapeOrBuilderList() { + if (shapeBuilder_ != null) { + return shapeBuilder_.getMessageOrBuilderList(); + } else { + return java.util.Collections.unmodifiableList(shape_); + } + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public serialization.Bigdl.Shape.Builder addShapeBuilder() { + return getShapeFieldBuilder().addBuilder( + serialization.Bigdl.Shape.getDefaultInstance()); + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public serialization.Bigdl.Shape.Builder addShapeBuilder( + int index) { + return getShapeFieldBuilder().addBuilder( + index, serialization.Bigdl.Shape.getDefaultInstance()); + } + /** + * 
+         * "Array(Shape)"
+         *
+ * + * repeated .serialization.Shape shape = 17; + */ + public java.util.List + getShapeBuilderList() { + return getShapeFieldBuilder().getBuilderList(); + } + private com.google.protobuf.RepeatedFieldBuilderV3< + serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> + getShapeFieldBuilder() { + if (shapeBuilder_ == null) { + shapeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< + serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>( + shape_, + ((bitField0_ & 0x00010000) == 0x00010000), + getParentForChildren(), + isClean()); + shape_ = null; + } + return shapeBuilder_; + } + public final Builder setUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.setUnknownFieldsProto3(unknownFields); + } + + public final Builder mergeUnknownFields( + final com.google.protobuf.UnknownFieldSet unknownFields) { + return super.mergeUnknownFields(unknownFields); + } + + + // @@protoc_insertion_point(builder_scope:serialization.AttrValue.ArrayValue) } - public int getNumber() { - return this.value; - } - }; - - public ValueCase - getValueCase() { - return ValueCase.forNumber( - valueCase_); - } - - public static final int DATATYPE_FIELD_NUMBER = 1; - private int dataType_; - /** - * .serialization.DataType dataType = 1; - */ - public int getDataTypeValue() { - return dataType_; - } - /** - * .serialization.DataType dataType = 1; - */ - public serialization.Bigdl.DataType getDataType() { - serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(dataType_); - return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; - } - - public static final int SUBTYPE_FIELD_NUMBER = 2; - private volatile java.lang.Object subType_; - /** - * 
-     * specific for custom data
-     *
- * - * string subType = 2; - */ - public java.lang.String getSubType() { - java.lang.Object ref = subType_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - subType_ = s; - return s; - } - } - /** - * 
-     * specific for custom data
-     *
- * - * string subType = 2; - */ - public com.google.protobuf.ByteString - getSubTypeBytes() { - java.lang.Object ref = subType_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - subType_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; + + // @@protoc_insertion_point(class_scope:serialization.AttrValue.ArrayValue) + private static final serialization.Bigdl.AttrValue.ArrayValue DEFAULT_INSTANCE; + static { + DEFAULT_INSTANCE = new serialization.Bigdl.AttrValue.ArrayValue(); } - } - public static final int INT32VALUE_FIELD_NUMBER = 3; - /** - * 
-     * int32 value
-     *
- * - * int32 int32Value = 3; - */ - public int getInt32Value() { - if (valueCase_ == 3) { - return (java.lang.Integer) value_; + public static serialization.Bigdl.AttrValue.ArrayValue getDefaultInstance() { + return DEFAULT_INSTANCE; } - return 0; - } - public static final int INT64VALUE_FIELD_NUMBER = 4; - /** - * 
-     *int64 value
-     *
- * - * int64 int64Value = 4; - */ - public long getInt64Value() { - if (valueCase_ == 4) { - return (java.lang.Long) value_; + private static final com.google.protobuf.Parser + PARSER = new com.google.protobuf.AbstractParser() { + public ArrayValue parsePartialFrom( + com.google.protobuf.CodedInputStream input, + com.google.protobuf.ExtensionRegistryLite extensionRegistry) + throws com.google.protobuf.InvalidProtocolBufferException { + return new ArrayValue(input, extensionRegistry); + } + }; + + public static com.google.protobuf.Parser parser() { + return PARSER; } - return 0L; + + @java.lang.Override + public com.google.protobuf.Parser getParserForType() { + return PARSER; + } + + public serialization.Bigdl.AttrValue.ArrayValue getDefaultInstanceForType() { + return DEFAULT_INSTANCE; + } + } - public static final int FLOATVALUE_FIELD_NUMBER = 5; - /** - * 
+    private int valueCase_ = 0;
+    private java.lang.Object value_;
+    public enum ValueCase
+        implements com.google.protobuf.Internal.EnumLite {
+      INT32VALUE(3),
+      INT64VALUE(4),
+      FLOATVALUE(5),
+      DOUBLEVALUE(6),
+      STRINGVALUE(7),
+      BOOLVALUE(8),
+      REGULARIZERVALUE(9),
+      TENSORVALUE(10),
+      VARIABLEFORMATVALUE(11),
+      INITMETHODVALUE(12),
+      BIGDLMODULEVALUE(13),
+      NAMEATTRLISTVALUE(14),
+      ARRAYVALUE(15),
+      DATAFORMATVALUE(16),
+      CUSTOMVALUE(17),
+      SHAPE(18),
+      VALUE_NOT_SET(0);
+      private final int value;
+      private ValueCase(int value) {
+        this.value = value;
+      }
+      /**
+       * @deprecated Use {@link #forNumber(int)} instead.
+       */
+      @java.lang.Deprecated
+      public static ValueCase valueOf(int value) {
+        return forNumber(value);
+      }
+
+      public static ValueCase forNumber(int value) {
+        switch (value) {
+          case 3: return INT32VALUE;
+          case 4: return INT64VALUE;
+          case 5: return FLOATVALUE;
+          case 6: return DOUBLEVALUE;
+          case 7: return STRINGVALUE;
+          case 8: return BOOLVALUE;
+          case 9: return REGULARIZERVALUE;
+          case 10: return TENSORVALUE;
+          case 11: return VARIABLEFORMATVALUE;
+          case 12: return INITMETHODVALUE;
+          case 13: return BIGDLMODULEVALUE;
+          case 14: return NAMEATTRLISTVALUE;
+          case 15: return ARRAYVALUE;
+          case 16: return DATAFORMATVALUE;
+          case 17: return CUSTOMVALUE;
+          case 18: return SHAPE;
+          case 0: return VALUE_NOT_SET;
+          default: return null;
+        }
+      }
+      public int getNumber() {
+        return this.value;
+      }
+    };
+
+    public ValueCase
+    getValueCase() {
+      return ValueCase.forNumber(
+          valueCase_);
+    }
+
+    public static final int DATATYPE_FIELD_NUMBER = 1;
+    private int dataType_;
+    /**
+     * .serialization.DataType dataType = 1;
+     */
+    public int getDataTypeValue() {
+      return dataType_;
+    }
+    /**
+     * .serialization.DataType dataType = 1;
+     */
+    public serialization.Bigdl.DataType getDataType() {
+      serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(dataType_);
+      return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result;
+    }
+
+    public static final int SUBTYPE_FIELD_NUMBER = 2;
+    private volatile java.lang.Object subType_;
+    /**
+     * 
+     * specific for custom data
+     * 

+     *
+     * string subType = 2;
+     */
+    public java.lang.String getSubType() {
+      java.lang.Object ref = subType_;
+      if (ref instanceof java.lang.String) {
+        return (java.lang.String) ref;
+      } else {
+        com.google.protobuf.ByteString bs = 
+            (com.google.protobuf.ByteString) ref;
+        java.lang.String s = bs.toStringUtf8();
+        subType_ = s;
+        return s;
+      }
+    }
+    /**
+     * 
+     * specific for custom data
+     * 

+     *
+     * string subType = 2;
+     */
+    public com.google.protobuf.ByteString
+        getSubTypeBytes() {
+      java.lang.Object ref = subType_;
+      if (ref instanceof java.lang.String) {
+        com.google.protobuf.ByteString b = 
+            com.google.protobuf.ByteString.copyFromUtf8(
+                (java.lang.String) ref);
+        subType_ = b;
+        return b;
+      } else {
+        return (com.google.protobuf.ByteString) ref;
+      }
+    }
+
+    public static final int INT32VALUE_FIELD_NUMBER = 3;
+    /**
+     * 
+     * int32 value
+     * 

+     *
+     * int32 int32Value = 3;
+     */
+    public int getInt32Value() {
+      if (valueCase_ == 3) {
+        return (java.lang.Integer) value_;
+      }
+      return 0;
+    }
+
+    public static final int INT64VALUE_FIELD_NUMBER = 4;
+    /**
+     * 
+     *int64 value
+     * 

+     *
+     * int64 int64Value = 4;
+     */
+    public long getInt64Value() {
+      if (valueCase_ == 4) {
+        return (java.lang.Long) value_;
+      }
+      return 0L;
+    }
+
+    public static final int FLOATVALUE_FIELD_NUMBER = 5;
+    /**
+     * 
      *float value
      * 

      *
@@ -15676,6 +16205,44 @@ public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() {
       return com.google.protobuf.Any.getDefaultInstance();
     }
 
+    public static final int SHAPE_FIELD_NUMBER = 18;
+    /**
+     * 
+     * Shape value
+     * 

+     *
+     * .serialization.Shape shape = 18;
+     */
+    public boolean hasShape() {
+      return valueCase_ == 18;
+    }
+    /**
+     * 
+     * Shape value
+     * 

+     *
+     * .serialization.Shape shape = 18;
+     */
+    public serialization.Bigdl.Shape getShape() {
+      if (valueCase_ == 18) {
+         return (serialization.Bigdl.Shape) value_;
+      }
+      return serialization.Bigdl.Shape.getDefaultInstance();
+    }
+    /**
+     * 
+     * Shape value
+     * 

+     *
+     * .serialization.Shape shape = 18;
+     */
+    public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder() {
+      if (valueCase_ == 18) {
+         return (serialization.Bigdl.Shape) value_;
+      }
+      return serialization.Bigdl.Shape.getDefaultInstance();
+    }
+
     private byte memoizedIsInitialized = -1;
     public final boolean isInitialized() {
       byte isInitialized = memoizedIsInitialized;
@@ -15744,6 +16311,9 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
       if (valueCase_ == 17) {
         output.writeMessage(17, (com.google.protobuf.Any) value_);
       }
+      if (valueCase_ == 18) {
+        output.writeMessage(18, (serialization.Bigdl.Shape) value_);
+      }
       unknownFields.writeTo(output);
     }
 
@@ -15823,6 +16393,10 @@ public int getSerializedSize() {
         size += com.google.protobuf.CodedOutputStream
           .computeMessageSize(17, (com.google.protobuf.Any) value_);
       }
+      if (valueCase_ == 18) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeMessageSize(18, (serialization.Bigdl.Shape) value_);
+      }
       size += unknownFields.getSerializedSize();
       memoizedSize = size;
       return size;
@@ -15910,6 +16484,10 @@ public boolean equals(final java.lang.Object obj) {
           result = result && getCustomValue()
               .equals(other.getCustomValue());
           break;
+        case 18:
+          result = result && getShape()
+              .equals(other.getShape());
+          break;
         case 0:
         default:
       }
@@ -15993,6 +16571,10 @@ public int hashCode() {
           hash = (37 * hash) + CUSTOMVALUE_FIELD_NUMBER;
           hash = (53 * hash) + getCustomValue().hashCode();
           break;
+        case 18:
+          hash = (37 * hash) + SHAPE_FIELD_NUMBER;
+          hash = (53 * hash) + getShape().hashCode();
+          break;
         case 0:
         default:
       }
@@ -16228,6 +16810,13 @@ public serialization.Bigdl.AttrValue buildPartial() {
             result.value_ = customValueBuilder_.build();
           }
         }
+        if (valueCase_ == 18) {
+          if (shapeBuilder_ == null) {
+            result.value_ = value_;
+          } else {
+            result.value_ = shapeBuilder_.build();
+          }
+        }
         result.valueCase_ = valueCase_;
         onBuilt();
         return result;
@@ -16340,6 +16929,10 @@ public Builder mergeFrom(serialization.Bigdl.AttrValue other) {
             mergeCustomValue(other.getCustomValue());
             break;
           }
+          case SHAPE: {
+            mergeShape(other.getShape());
+            break;
+          }
           case VALUE_NOT_SET: {
             break;
           }
@@ -17564,619 +18157,1624 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() {
           return serialization.Bigdl.BigDLModule.getDefaultInstance();
         }
       }
-      /**
-       * 
-       * big DL module
-       * 

-       *
-       * .serialization.BigDLModule bigDLModuleValue = 13;
-       */
-      private com.google.protobuf.SingleFieldBuilderV3<
-          serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> 
-          getBigDLModuleValueFieldBuilder() {
-        if (bigDLModuleValueBuilder_ == null) {
-          if (!(valueCase_ == 13)) {
-            value_ = serialization.Bigdl.BigDLModule.getDefaultInstance();
-          }
-          bigDLModuleValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
-              serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder>(
-                  (serialization.Bigdl.BigDLModule) value_,
-                  getParentForChildren(),
-                  isClean());
-          value_ = null;
-        }
-        valueCase_ = 13;
-        onChanged();;
-        return bigDLModuleValueBuilder_;
+      /**
+       * 
+       * big DL module
+       * 

+       *
+       * .serialization.BigDLModule bigDLModuleValue = 13;
+       */
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> 
+          getBigDLModuleValueFieldBuilder() {
+        if (bigDLModuleValueBuilder_ == null) {
+          if (!(valueCase_ == 13)) {
+            value_ = serialization.Bigdl.BigDLModule.getDefaultInstance();
+          }
+          bigDLModuleValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+              serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder>(
+                  (serialization.Bigdl.BigDLModule) value_,
+                  getParentForChildren(),
+                  isClean());
+          value_ = null;
+        }
+        valueCase_ = 13;
+        onChanged();;
+        return bigDLModuleValueBuilder_;
+      }
+
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> nameAttrListValueBuilder_;
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      public boolean hasNameAttrListValue() {
+        return valueCase_ == 14;
+      }
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      public serialization.Bigdl.NameAttrList getNameAttrListValue() {
+        if (nameAttrListValueBuilder_ == null) {
+          if (valueCase_ == 14) {
+            return (serialization.Bigdl.NameAttrList) value_;
+          }
+          return serialization.Bigdl.NameAttrList.getDefaultInstance();
+        } else {
+          if (valueCase_ == 14) {
+            return nameAttrListValueBuilder_.getMessage();
+          }
+          return serialization.Bigdl.NameAttrList.getDefaultInstance();
+        }
+      }
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      public Builder setNameAttrListValue(serialization.Bigdl.NameAttrList value) {
+        if (nameAttrListValueBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          value_ = value;
+          onChanged();
+        } else {
+          nameAttrListValueBuilder_.setMessage(value);
+        }
+        valueCase_ = 14;
+        return this;
+      }
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      public Builder setNameAttrListValue(
+          serialization.Bigdl.NameAttrList.Builder builderForValue) {
+        if (nameAttrListValueBuilder_ == null) {
+          value_ = builderForValue.build();
+          onChanged();
+        } else {
+          nameAttrListValueBuilder_.setMessage(builderForValue.build());
+        }
+        valueCase_ = 14;
+        return this;
+      }
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      public Builder mergeNameAttrListValue(serialization.Bigdl.NameAttrList value) {
+        if (nameAttrListValueBuilder_ == null) {
+          if (valueCase_ == 14 &&
+              value_ != serialization.Bigdl.NameAttrList.getDefaultInstance()) {
+            value_ = serialization.Bigdl.NameAttrList.newBuilder((serialization.Bigdl.NameAttrList) value_)
+                .mergeFrom(value).buildPartial();
+          } else {
+            value_ = value;
+          }
+          onChanged();
+        } else {
+          if (valueCase_ == 14) {
+            nameAttrListValueBuilder_.mergeFrom(value);
+          }
+          nameAttrListValueBuilder_.setMessage(value);
+        }
+        valueCase_ = 14;
+        return this;
+      }
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      public Builder clearNameAttrListValue() {
+        if (nameAttrListValueBuilder_ == null) {
+          if (valueCase_ == 14) {
+            valueCase_ = 0;
+            value_ = null;
+            onChanged();
+          }
+        } else {
+          if (valueCase_ == 14) {
+            valueCase_ = 0;
+            value_ = null;
+          }
+          nameAttrListValueBuilder_.clear();
+        }
+        return this;
+      }
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      public serialization.Bigdl.NameAttrList.Builder getNameAttrListValueBuilder() {
+        return getNameAttrListValueFieldBuilder().getBuilder();
+      }
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() {
+        if ((valueCase_ == 14) && (nameAttrListValueBuilder_ != null)) {
+          return nameAttrListValueBuilder_.getMessageOrBuilder();
+        } else {
+          if (valueCase_ == 14) {
+            return (serialization.Bigdl.NameAttrList) value_;
+          }
+          return serialization.Bigdl.NameAttrList.getDefaultInstance();
+        }
+      }
+      /**
+       * 
+       * name attribute list
+       * 

+       *
+       * .serialization.NameAttrList nameAttrListValue = 14;
+       */
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> 
+          getNameAttrListValueFieldBuilder() {
+        if (nameAttrListValueBuilder_ == null) {
+          if (!(valueCase_ == 14)) {
+            value_ = serialization.Bigdl.NameAttrList.getDefaultInstance();
+          }
+          nameAttrListValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+              serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder>(
+                  (serialization.Bigdl.NameAttrList) value_,
+                  getParentForChildren(),
+                  isClean());
+          value_ = null;
+        }
+        valueCase_ = 14;
+        onChanged();;
+        return nameAttrListValueBuilder_;
+      }
+
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder> arrayValueBuilder_;
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      public boolean hasArrayValue() {
+        return valueCase_ == 15;
+      }
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() {
+        if (arrayValueBuilder_ == null) {
+          if (valueCase_ == 15) {
+            return (serialization.Bigdl.AttrValue.ArrayValue) value_;
+          }
+          return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance();
+        } else {
+          if (valueCase_ == 15) {
+            return arrayValueBuilder_.getMessage();
+          }
+          return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance();
+        }
+      }
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      public Builder setArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) {
+        if (arrayValueBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          value_ = value;
+          onChanged();
+        } else {
+          arrayValueBuilder_.setMessage(value);
+        }
+        valueCase_ = 15;
+        return this;
+      }
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      public Builder setArrayValue(
+          serialization.Bigdl.AttrValue.ArrayValue.Builder builderForValue) {
+        if (arrayValueBuilder_ == null) {
+          value_ = builderForValue.build();
+          onChanged();
+        } else {
+          arrayValueBuilder_.setMessage(builderForValue.build());
+        }
+        valueCase_ = 15;
+        return this;
+      }
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      public Builder mergeArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) {
+        if (arrayValueBuilder_ == null) {
+          if (valueCase_ == 15 &&
+              value_ != serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance()) {
+            value_ = serialization.Bigdl.AttrValue.ArrayValue.newBuilder((serialization.Bigdl.AttrValue.ArrayValue) value_)
+                .mergeFrom(value).buildPartial();
+          } else {
+            value_ = value;
+          }
+          onChanged();
+        } else {
+          if (valueCase_ == 15) {
+            arrayValueBuilder_.mergeFrom(value);
+          }
+          arrayValueBuilder_.setMessage(value);
+        }
+        valueCase_ = 15;
+        return this;
+      }
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      public Builder clearArrayValue() {
+        if (arrayValueBuilder_ == null) {
+          if (valueCase_ == 15) {
+            valueCase_ = 0;
+            value_ = null;
+            onChanged();
+          }
+        } else {
+          if (valueCase_ == 15) {
+            valueCase_ = 0;
+            value_ = null;
+          }
+          arrayValueBuilder_.clear();
+        }
+        return this;
+      }
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      public serialization.Bigdl.AttrValue.ArrayValue.Builder getArrayValueBuilder() {
+        return getArrayValueFieldBuilder().getBuilder();
+      }
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder() {
+        if ((valueCase_ == 15) && (arrayValueBuilder_ != null)) {
+          return arrayValueBuilder_.getMessageOrBuilder();
+        } else {
+          if (valueCase_ == 15) {
+            return (serialization.Bigdl.AttrValue.ArrayValue) value_;
+          }
+          return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance();
+        }
+      }
+      /**
+       * 
+       *array value of any type
+       * 

+       *
+       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       */
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder> 
+          getArrayValueFieldBuilder() {
+        if (arrayValueBuilder_ == null) {
+          if (!(valueCase_ == 15)) {
+            value_ = serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance();
+          }
+          arrayValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+              serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder>(
+                  (serialization.Bigdl.AttrValue.ArrayValue) value_,
+                  getParentForChildren(),
+                  isClean());
+          value_ = null;
+        }
+        valueCase_ = 15;
+        onChanged();;
+        return arrayValueBuilder_;
+      }
+
+      /**
+       * 
+       * data format
+       * 

+       *
+       * .serialization.InputDataFormat dataFormatValue = 16;
+       */
+      public int getDataFormatValueValue() {
+        if (valueCase_ == 16) {
+          return ((java.lang.Integer) value_).intValue();
+        }
+        return 0;
+      }
+      /**
+       * 
+       * data format
+       * 

+       *
+       * .serialization.InputDataFormat dataFormatValue = 16;
+       */
+      public Builder setDataFormatValueValue(int value) {
+        valueCase_ = 16;
+        value_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * 
+       * data format
+       * 

+       *
+       * .serialization.InputDataFormat dataFormatValue = 16;
+       */
+      public serialization.Bigdl.InputDataFormat getDataFormatValue() {
+        if (valueCase_ == 16) {
+          serialization.Bigdl.InputDataFormat result = serialization.Bigdl.InputDataFormat.valueOf(
+              (java.lang.Integer) value_);
+          return result == null ? serialization.Bigdl.InputDataFormat.UNRECOGNIZED : result;
+        }
+        return serialization.Bigdl.InputDataFormat.NCHW;
+      }
+      /**
+       * 
+       * data format
+       * 

+       *
+       * .serialization.InputDataFormat dataFormatValue = 16;
+       */
+      public Builder setDataFormatValue(serialization.Bigdl.InputDataFormat value) {
+        if (value == null) {
+          throw new NullPointerException();
+        }
+        valueCase_ = 16;
+        value_ = value.getNumber();
+        onChanged();
+        return this;
+      }
+      /**
+       * 
+       * data format
+       * 

+       *
+       * .serialization.InputDataFormat dataFormatValue = 16;
+       */
+      public Builder clearDataFormatValue() {
+        if (valueCase_ == 16) {
+          valueCase_ = 0;
+          value_ = null;
+          onChanged();
+        }
+        return this;
+      }
+
+      private com.google.protobuf.SingleFieldBuilderV3<
+          com.google.protobuf.Any, com.google.protobuf.Any.Builder, com.google.protobuf.AnyOrBuilder> customValueBuilder_;
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      public boolean hasCustomValue() {
+        return valueCase_ == 17;
+      }
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      public com.google.protobuf.Any getCustomValue() {
+        if (customValueBuilder_ == null) {
+          if (valueCase_ == 17) {
+            return (com.google.protobuf.Any) value_;
+          }
+          return com.google.protobuf.Any.getDefaultInstance();
+        } else {
+          if (valueCase_ == 17) {
+            return customValueBuilder_.getMessage();
+          }
+          return com.google.protobuf.Any.getDefaultInstance();
+        }
+      }
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      public Builder setCustomValue(com.google.protobuf.Any value) {
+        if (customValueBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          value_ = value;
+          onChanged();
+        } else {
+          customValueBuilder_.setMessage(value);
+        }
+        valueCase_ = 17;
+        return this;
+      }
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      public Builder setCustomValue(
+          com.google.protobuf.Any.Builder builderForValue) {
+        if (customValueBuilder_ == null) {
+          value_ = builderForValue.build();
+          onChanged();
+        } else {
+          customValueBuilder_.setMessage(builderForValue.build());
+        }
+        valueCase_ = 17;
+        return this;
+      }
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      public Builder mergeCustomValue(com.google.protobuf.Any value) {
+        if (customValueBuilder_ == null) {
+          if (valueCase_ == 17 &&
+              value_ != com.google.protobuf.Any.getDefaultInstance()) {
+            value_ = com.google.protobuf.Any.newBuilder((com.google.protobuf.Any) value_)
+                .mergeFrom(value).buildPartial();
+          } else {
+            value_ = value;
+          }
+          onChanged();
+        } else {
+          if (valueCase_ == 17) {
+            customValueBuilder_.mergeFrom(value);
+          }
+          customValueBuilder_.setMessage(value);
+        }
+        valueCase_ = 17;
+        return this;
+      }
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      public Builder clearCustomValue() {
+        if (customValueBuilder_ == null) {
+          if (valueCase_ == 17) {
+            valueCase_ = 0;
+            value_ = null;
+            onChanged();
+          }
+        } else {
+          if (valueCase_ == 17) {
+            valueCase_ = 0;
+            value_ = null;
+          }
+          customValueBuilder_.clear();
+        }
+        return this;
+      }
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      public com.google.protobuf.Any.Builder getCustomValueBuilder() {
+        return getCustomValueFieldBuilder().getBuilder();
+      }
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() {
+        if ((valueCase_ == 17) && (customValueBuilder_ != null)) {
+          return customValueBuilder_.getMessageOrBuilder();
+        } else {
+          if (valueCase_ == 17) {
+            return (com.google.protobuf.Any) value_;
+          }
+          return com.google.protobuf.Any.getDefaultInstance();
+        }
+      }
+      /**
+       * 
+       * custom value
+       * 

+       *
+       * .google.protobuf.Any customValue = 17;
+       */
+      private com.google.protobuf.SingleFieldBuilderV3<
+          com.google.protobuf.Any, com.google.protobuf.Any.Builder, com.google.protobuf.AnyOrBuilder> 
+          getCustomValueFieldBuilder() {
+        if (customValueBuilder_ == null) {
+          if (!(valueCase_ == 17)) {
+            value_ = com.google.protobuf.Any.getDefaultInstance();
+          }
+          customValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+              com.google.protobuf.Any, com.google.protobuf.Any.Builder, com.google.protobuf.AnyOrBuilder>(
+                  (com.google.protobuf.Any) value_,
+                  getParentForChildren(),
+                  isClean());
+          value_ = null;
+        }
+        valueCase_ = 17;
+        onChanged();;
+        return customValueBuilder_;
+      }
+
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> shapeBuilder_;
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      public boolean hasShape() {
+        return valueCase_ == 18;
+      }
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      public serialization.Bigdl.Shape getShape() {
+        if (shapeBuilder_ == null) {
+          if (valueCase_ == 18) {
+            return (serialization.Bigdl.Shape) value_;
+          }
+          return serialization.Bigdl.Shape.getDefaultInstance();
+        } else {
+          if (valueCase_ == 18) {
+            return shapeBuilder_.getMessage();
+          }
+          return serialization.Bigdl.Shape.getDefaultInstance();
+        }
+      }
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      public Builder setShape(serialization.Bigdl.Shape value) {
+        if (shapeBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          value_ = value;
+          onChanged();
+        } else {
+          shapeBuilder_.setMessage(value);
+        }
+        valueCase_ = 18;
+        return this;
+      }
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      public Builder setShape(
+          serialization.Bigdl.Shape.Builder builderForValue) {
+        if (shapeBuilder_ == null) {
+          value_ = builderForValue.build();
+          onChanged();
+        } else {
+          shapeBuilder_.setMessage(builderForValue.build());
+        }
+        valueCase_ = 18;
+        return this;
+      }
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      public Builder mergeShape(serialization.Bigdl.Shape value) {
+        if (shapeBuilder_ == null) {
+          if (valueCase_ == 18 &&
+              value_ != serialization.Bigdl.Shape.getDefaultInstance()) {
+            value_ = serialization.Bigdl.Shape.newBuilder((serialization.Bigdl.Shape) value_)
+                .mergeFrom(value).buildPartial();
+          } else {
+            value_ = value;
+          }
+          onChanged();
+        } else {
+          if (valueCase_ == 18) {
+            shapeBuilder_.mergeFrom(value);
+          }
+          shapeBuilder_.setMessage(value);
+        }
+        valueCase_ = 18;
+        return this;
+      }
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      public Builder clearShape() {
+        if (shapeBuilder_ == null) {
+          if (valueCase_ == 18) {
+            valueCase_ = 0;
+            value_ = null;
+            onChanged();
+          }
+        } else {
+          if (valueCase_ == 18) {
+            valueCase_ = 0;
+            value_ = null;
+          }
+          shapeBuilder_.clear();
+        }
+        return this;
+      }
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      public serialization.Bigdl.Shape.Builder getShapeBuilder() {
+        return getShapeFieldBuilder().getBuilder();
+      }
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder() {
+        if ((valueCase_ == 18) && (shapeBuilder_ != null)) {
+          return shapeBuilder_.getMessageOrBuilder();
+        } else {
+          if (valueCase_ == 18) {
+            return (serialization.Bigdl.Shape) value_;
+          }
+          return serialization.Bigdl.Shape.getDefaultInstance();
+        }
+      }
+      /**
+       * 
+       * Shape value
+       * 

+       *
+       * .serialization.Shape shape = 18;
+       */
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> 
+          getShapeFieldBuilder() {
+        if (shapeBuilder_ == null) {
+          if (!(valueCase_ == 18)) {
+            value_ = serialization.Bigdl.Shape.getDefaultInstance();
+          }
+          shapeBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+              serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>(
+                  (serialization.Bigdl.Shape) value_,
+                  getParentForChildren(),
+                  isClean());
+          value_ = null;
+        }
+        valueCase_ = 18;
+        onChanged();;
+        return shapeBuilder_;
+      }
+      public final Builder setUnknownFields(
+          final com.google.protobuf.UnknownFieldSet unknownFields) {
+        return super.setUnknownFieldsProto3(unknownFields);
+      }
+
+      public final Builder mergeUnknownFields(
+          final com.google.protobuf.UnknownFieldSet unknownFields) {
+        return super.mergeUnknownFields(unknownFields);
+      }
+
+
+      // @@protoc_insertion_point(builder_scope:serialization.AttrValue)
+    }
+
+    // @@protoc_insertion_point(class_scope:serialization.AttrValue)
+    private static final serialization.Bigdl.AttrValue DEFAULT_INSTANCE;
+    static {
+      DEFAULT_INSTANCE = new serialization.Bigdl.AttrValue();
+    }
+
+    public static serialization.Bigdl.AttrValue getDefaultInstance() {
+      return DEFAULT_INSTANCE;
+    }
+
+    private static final com.google.protobuf.Parser
+        PARSER = new com.google.protobuf.AbstractParser() {
+      public AttrValue parsePartialFrom(
+          com.google.protobuf.CodedInputStream input,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws com.google.protobuf.InvalidProtocolBufferException {
+          return new AttrValue(input, extensionRegistry);
+      }
+    };
+
+    public static com.google.protobuf.Parser parser() {
+      return PARSER;
+    }
+
+    @java.lang.Override
+    public com.google.protobuf.Parser getParserForType() {
+      return PARSER;
+    }
+
+    public serialization.Bigdl.AttrValue getDefaultInstanceForType() {
+      return DEFAULT_INSTANCE;
+    }
+
+  }
+
+  public interface NameAttrListOrBuilder extends
+      // @@protoc_insertion_point(interface_extends:serialization.NameAttrList)
+      com.google.protobuf.MessageOrBuilder {
+
+    /**
+     * string name = 1;
+     */
+    java.lang.String getName();
+    /**
+     * string name = 1;
+     */
+    com.google.protobuf.ByteString
+        getNameBytes();
+
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+    int getAttrCount();
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+    boolean containsAttr(
+        java.lang.String key);
+    /**
+     * Use {@link #getAttrMap()} instead.
+     */
+    @java.lang.Deprecated
+    java.util.Map
+    getAttr();
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+    java.util.Map
+    getAttrMap();
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+
+    serialization.Bigdl.AttrValue getAttrOrDefault(
+        java.lang.String key,
+        serialization.Bigdl.AttrValue defaultValue);
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+
+    serialization.Bigdl.AttrValue getAttrOrThrow(
+        java.lang.String key);
+  }
+  /**
+   * Protobuf type {@code serialization.NameAttrList}
+   */
+  public  static final class NameAttrList extends
+      com.google.protobuf.GeneratedMessageV3 implements
+      // @@protoc_insertion_point(message_implements:serialization.NameAttrList)
+      NameAttrListOrBuilder {
+  private static final long serialVersionUID = 0L;
+    // Use NameAttrList.newBuilder() to construct.
+    private NameAttrList(com.google.protobuf.GeneratedMessageV3.Builder builder) {
+      super(builder);
+    }
+    private NameAttrList() {
+      name_ = "";
+    }
+
+    @java.lang.Override
+    public final com.google.protobuf.UnknownFieldSet
+    getUnknownFields() {
+      return this.unknownFields;
+    }
+    private NameAttrList(
+        com.google.protobuf.CodedInputStream input,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      this();
+      int mutable_bitField0_ = 0;
+      com.google.protobuf.UnknownFieldSet.Builder unknownFields =
+          com.google.protobuf.UnknownFieldSet.newBuilder();
+      try {
+        boolean done = false;
+        while (!done) {
+          int tag = input.readTag();
+          switch (tag) {
+            case 0:
+              done = true;
+              break;
+            default: {
+              if (!parseUnknownFieldProto3(
+                  input, unknownFields, extensionRegistry, tag)) {
+                done = true;
+              }
+              break;
+            }
+            case 10: {
+              java.lang.String s = input.readStringRequireUtf8();
+
+              name_ = s;
+              break;
+            }
+            case 18: {
+              if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) {
+                attr_ = com.google.protobuf.MapField.newMapField(
+                    AttrDefaultEntryHolder.defaultEntry);
+                mutable_bitField0_ |= 0x00000002;
+              }
+              com.google.protobuf.MapEntry
+              attr__ = input.readMessage(
+                  AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry);
+              attr_.getMutableMap().put(
+                  attr__.getKey(), attr__.getValue());
+              break;
+            }
+          }
+        }
+      } catch (com.google.protobuf.InvalidProtocolBufferException e) {
+        throw e.setUnfinishedMessage(this);
+      } catch (java.io.IOException e) {
+        throw new com.google.protobuf.InvalidProtocolBufferException(
+            e).setUnfinishedMessage(this);
+      } finally {
+        this.unknownFields = unknownFields.build();
+        makeExtensionsImmutable();
+      }
+    }
+    public static final com.google.protobuf.Descriptors.Descriptor
+        getDescriptor() {
+      return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor;
+    }
+
+    @SuppressWarnings({"rawtypes"})
+    protected com.google.protobuf.MapField internalGetMapField(
+        int number) {
+      switch (number) {
+        case 2:
+          return internalGetAttr();
+        default:
+          throw new RuntimeException(
+              "Invalid map field number: " + number);
+      }
+    }
+    protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+        internalGetFieldAccessorTable() {
+      return serialization.Bigdl.internal_static_serialization_NameAttrList_fieldAccessorTable
+          .ensureFieldAccessorsInitialized(
+              serialization.Bigdl.NameAttrList.class, serialization.Bigdl.NameAttrList.Builder.class);
+    }
+
+    private int bitField0_;
+    public static final int NAME_FIELD_NUMBER = 1;
+    private volatile java.lang.Object name_;
+    /**
+     * string name = 1;
+     */
+    public java.lang.String getName() {
+      java.lang.Object ref = name_;
+      if (ref instanceof java.lang.String) {
+        return (java.lang.String) ref;
+      } else {
+        com.google.protobuf.ByteString bs = 
+            (com.google.protobuf.ByteString) ref;
+        java.lang.String s = bs.toStringUtf8();
+        name_ = s;
+        return s;
+      }
+    }
+    /**
+     * string name = 1;
+     */
+    public com.google.protobuf.ByteString
+        getNameBytes() {
+      java.lang.Object ref = name_;
+      if (ref instanceof java.lang.String) {
+        com.google.protobuf.ByteString b = 
+            com.google.protobuf.ByteString.copyFromUtf8(
+                (java.lang.String) ref);
+        name_ = b;
+        return b;
+      } else {
+        return (com.google.protobuf.ByteString) ref;
+      }
+    }
+
+    public static final int ATTR_FIELD_NUMBER = 2;
+    private static final class AttrDefaultEntryHolder {
+      static final com.google.protobuf.MapEntry<
+          java.lang.String, serialization.Bigdl.AttrValue> defaultEntry =
+              com.google.protobuf.MapEntry
+              .newDefaultInstance(
+                  serialization.Bigdl.internal_static_serialization_NameAttrList_AttrEntry_descriptor, 
+                  com.google.protobuf.WireFormat.FieldType.STRING,
+                  "",
+                  com.google.protobuf.WireFormat.FieldType.MESSAGE,
+                  serialization.Bigdl.AttrValue.getDefaultInstance());
+    }
+    private com.google.protobuf.MapField<
+        java.lang.String, serialization.Bigdl.AttrValue> attr_;
+    private com.google.protobuf.MapField
+    internalGetAttr() {
+      if (attr_ == null) {
+        return com.google.protobuf.MapField.emptyMapField(
+            AttrDefaultEntryHolder.defaultEntry);
+      }
+      return attr_;
+    }
+
+    public int getAttrCount() {
+      return internalGetAttr().getMap().size();
+    }
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+
+    public boolean containsAttr(
+        java.lang.String key) {
+      if (key == null) { throw new java.lang.NullPointerException(); }
+      return internalGetAttr().getMap().containsKey(key);
+    }
+    /**
+     * Use {@link #getAttrMap()} instead.
+     */
+    @java.lang.Deprecated
+    public java.util.Map getAttr() {
+      return getAttrMap();
+    }
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+
+    public java.util.Map getAttrMap() {
+      return internalGetAttr().getMap();
+    }
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+
+    public serialization.Bigdl.AttrValue getAttrOrDefault(
+        java.lang.String key,
+        serialization.Bigdl.AttrValue defaultValue) {
+      if (key == null) { throw new java.lang.NullPointerException(); }
+      java.util.Map map =
+          internalGetAttr().getMap();
+      return map.containsKey(key) ? map.get(key) : defaultValue;
+    }
+    /**
+     * map<string, .serialization.AttrValue> attr = 2;
+     */
+
+    public serialization.Bigdl.AttrValue getAttrOrThrow(
+        java.lang.String key) {
+      if (key == null) { throw new java.lang.NullPointerException(); }
+      java.util.Map map =
+          internalGetAttr().getMap();
+      if (!map.containsKey(key)) {
+        throw new java.lang.IllegalArgumentException();
+      }
+      return map.get(key);
+    }
+
+    private byte memoizedIsInitialized = -1;
+    public final boolean isInitialized() {
+      byte isInitialized = memoizedIsInitialized;
+      if (isInitialized == 1) return true;
+      if (isInitialized == 0) return false;
+
+      memoizedIsInitialized = 1;
+      return true;
+    }
+
+    public void writeTo(com.google.protobuf.CodedOutputStream output)
+                        throws java.io.IOException {
+      if (!getNameBytes().isEmpty()) {
+        com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_);
+      }
+      com.google.protobuf.GeneratedMessageV3
+        .serializeStringMapTo(
+          output,
+          internalGetAttr(),
+          AttrDefaultEntryHolder.defaultEntry,
+          2);
+      unknownFields.writeTo(output);
+    }
+
+    public int getSerializedSize() {
+      int size = memoizedSize;
+      if (size != -1) return size;
+
+      size = 0;
+      if (!getNameBytes().isEmpty()) {
+        size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_);
+      }
+      for (java.util.Map.Entry entry
+           : internalGetAttr().getMap().entrySet()) {
+        com.google.protobuf.MapEntry
+        attr__ = AttrDefaultEntryHolder.defaultEntry.newBuilderForType()
+            .setKey(entry.getKey())
+            .setValue(entry.getValue())
+            .build();
+        size += com.google.protobuf.CodedOutputStream
+            .computeMessageSize(2, attr__);
+      }
+      size += unknownFields.getSerializedSize();
+      memoizedSize = size;
+      return size;
+    }
+
+    @java.lang.Override
+    public boolean equals(final java.lang.Object obj) {
+      if (obj == this) {
+       return true;
+      }
+      if (!(obj instanceof serialization.Bigdl.NameAttrList)) {
+        return super.equals(obj);
+      }
+      serialization.Bigdl.NameAttrList other = (serialization.Bigdl.NameAttrList) obj;
+
+      boolean result = true;
+      result = result && getName()
+          .equals(other.getName());
+      result = result && internalGetAttr().equals(
+          other.internalGetAttr());
+      result = result && unknownFields.equals(other.unknownFields);
+      return result;
+    }
+
+    @java.lang.Override
+    public int hashCode() {
+      if (memoizedHashCode != 0) {
+        return memoizedHashCode;
+      }
+      int hash = 41;
+      hash = (19 * hash) + getDescriptor().hashCode();
+      hash = (37 * hash) + NAME_FIELD_NUMBER;
+      hash = (53 * hash) + getName().hashCode();
+      if (!internalGetAttr().getMap().isEmpty()) {
+        hash = (37 * hash) + ATTR_FIELD_NUMBER;
+        hash = (53 * hash) + internalGetAttr().hashCode();
+      }
+      hash = (29 * hash) + unknownFields.hashCode();
+      memoizedHashCode = hash;
+      return hash;
+    }
+
+    public static serialization.Bigdl.NameAttrList parseFrom(
+        java.nio.ByteBuffer data)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(
+        java.nio.ByteBuffer data,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data, extensionRegistry);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(
+        com.google.protobuf.ByteString data)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(
+        com.google.protobuf.ByteString data,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data, extensionRegistry);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(byte[] data)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(
+        byte[] data,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data, extensionRegistry);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(java.io.InputStream input)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseWithIOException(PARSER, input);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(
+        java.io.InputStream input,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseWithIOException(PARSER, input, extensionRegistry);
+    }
+    public static serialization.Bigdl.NameAttrList parseDelimitedFrom(java.io.InputStream input)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseDelimitedWithIOException(PARSER, input);
+    }
+    public static serialization.Bigdl.NameAttrList parseDelimitedFrom(
+        java.io.InputStream input,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseDelimitedWithIOException(PARSER, input, extensionRegistry);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(
+        com.google.protobuf.CodedInputStream input)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseWithIOException(PARSER, input);
+    }
+    public static serialization.Bigdl.NameAttrList parseFrom(
+        com.google.protobuf.CodedInputStream input,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseWithIOException(PARSER, input, extensionRegistry);
+    }
+
+    public Builder newBuilderForType() { return newBuilder(); }
+    public static Builder newBuilder() {
+      return DEFAULT_INSTANCE.toBuilder();
+    }
+    public static Builder newBuilder(serialization.Bigdl.NameAttrList prototype) {
+      return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
+    }
+    public Builder toBuilder() {
+      return this == DEFAULT_INSTANCE
+          ? new Builder() : new Builder().mergeFrom(this);
+    }
+
+    @java.lang.Override
+    protected Builder newBuilderForType(
+        com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
+      Builder builder = new Builder(parent);
+      return builder;
+    }
+    /**
+     * Protobuf type {@code serialization.NameAttrList}
+     */
+    public static final class Builder extends
+        com.google.protobuf.GeneratedMessageV3.Builder implements
+        // @@protoc_insertion_point(builder_implements:serialization.NameAttrList)
+        serialization.Bigdl.NameAttrListOrBuilder {
+      public static final com.google.protobuf.Descriptors.Descriptor
+          getDescriptor() {
+        return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor;
+      }
+
+      @SuppressWarnings({"rawtypes"})
+      protected com.google.protobuf.MapField internalGetMapField(
+          int number) {
+        switch (number) {
+          case 2:
+            return internalGetAttr();
+          default:
+            throw new RuntimeException(
+                "Invalid map field number: " + number);
+        }
+      }
+      @SuppressWarnings({"rawtypes"})
+      protected com.google.protobuf.MapField internalGetMutableMapField(
+          int number) {
+        switch (number) {
+          case 2:
+            return internalGetMutableAttr();
+          default:
+            throw new RuntimeException(
+                "Invalid map field number: " + number);
+        }
+      }
+      protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+          internalGetFieldAccessorTable() {
+        return serialization.Bigdl.internal_static_serialization_NameAttrList_fieldAccessorTable
+            .ensureFieldAccessorsInitialized(
+                serialization.Bigdl.NameAttrList.class, serialization.Bigdl.NameAttrList.Builder.class);
       }
 
-      private com.google.protobuf.SingleFieldBuilderV3<
-          serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> nameAttrListValueBuilder_;
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      public boolean hasNameAttrListValue() {
-        return valueCase_ == 14;
+      // Construct using serialization.Bigdl.NameAttrList.newBuilder()
+      private Builder() {
+        maybeForceBuilderInitialization();
       }
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      public serialization.Bigdl.NameAttrList getNameAttrListValue() {
-        if (nameAttrListValueBuilder_ == null) {
-          if (valueCase_ == 14) {
-            return (serialization.Bigdl.NameAttrList) value_;
-          }
-          return serialization.Bigdl.NameAttrList.getDefaultInstance();
-        } else {
-          if (valueCase_ == 14) {
-            return nameAttrListValueBuilder_.getMessage();
-          }
-          return serialization.Bigdl.NameAttrList.getDefaultInstance();
-        }
+
+      private Builder(
+          com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
+        super(parent);
+        maybeForceBuilderInitialization();
       }
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      public Builder setNameAttrListValue(serialization.Bigdl.NameAttrList value) {
-        if (nameAttrListValueBuilder_ == null) {
-          if (value == null) {
-            throw new NullPointerException();
-          }
-          value_ = value;
-          onChanged();
-        } else {
-          nameAttrListValueBuilder_.setMessage(value);
+      private void maybeForceBuilderInitialization() {
+        if (com.google.protobuf.GeneratedMessageV3
+                .alwaysUseFieldBuilders) {
         }
-        valueCase_ = 14;
-        return this;
       }
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      public Builder setNameAttrListValue(
-          serialization.Bigdl.NameAttrList.Builder builderForValue) {
-        if (nameAttrListValueBuilder_ == null) {
-          value_ = builderForValue.build();
-          onChanged();
-        } else {
-          nameAttrListValueBuilder_.setMessage(builderForValue.build());
-        }
-        valueCase_ = 14;
+      public Builder clear() {
+        super.clear();
+        name_ = "";
+
+        internalGetMutableAttr().clear();
         return this;
       }
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      public Builder mergeNameAttrListValue(serialization.Bigdl.NameAttrList value) {
-        if (nameAttrListValueBuilder_ == null) {
-          if (valueCase_ == 14 &&
-              value_ != serialization.Bigdl.NameAttrList.getDefaultInstance()) {
-            value_ = serialization.Bigdl.NameAttrList.newBuilder((serialization.Bigdl.NameAttrList) value_)
-                .mergeFrom(value).buildPartial();
-          } else {
-            value_ = value;
-          }
-          onChanged();
-        } else {
-          if (valueCase_ == 14) {
-            nameAttrListValueBuilder_.mergeFrom(value);
-          }
-          nameAttrListValueBuilder_.setMessage(value);
-        }
-        valueCase_ = 14;
-        return this;
+
+      public com.google.protobuf.Descriptors.Descriptor
+          getDescriptorForType() {
+        return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor;
       }
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      public Builder clearNameAttrListValue() {
-        if (nameAttrListValueBuilder_ == null) {
-          if (valueCase_ == 14) {
-            valueCase_ = 0;
-            value_ = null;
-            onChanged();
-          }
-        } else {
-          if (valueCase_ == 14) {
-            valueCase_ = 0;
-            value_ = null;
-          }
-          nameAttrListValueBuilder_.clear();
+
+      public serialization.Bigdl.NameAttrList getDefaultInstanceForType() {
+        return serialization.Bigdl.NameAttrList.getDefaultInstance();
+      }
+
+      public serialization.Bigdl.NameAttrList build() {
+        serialization.Bigdl.NameAttrList result = buildPartial();
+        if (!result.isInitialized()) {
+          throw newUninitializedMessageException(result);
         }
-        return this;
+        return result;
       }
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      public serialization.Bigdl.NameAttrList.Builder getNameAttrListValueBuilder() {
-        return getNameAttrListValueFieldBuilder().getBuilder();
+
+      public serialization.Bigdl.NameAttrList buildPartial() {
+        serialization.Bigdl.NameAttrList result = new serialization.Bigdl.NameAttrList(this);
+        int from_bitField0_ = bitField0_;
+        int to_bitField0_ = 0;
+        result.name_ = name_;
+        result.attr_ = internalGetAttr();
+        result.attr_.makeImmutable();
+        result.bitField0_ = to_bitField0_;
+        onBuilt();
+        return result;
       }
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() {
-        if ((valueCase_ == 14) && (nameAttrListValueBuilder_ != null)) {
-          return nameAttrListValueBuilder_.getMessageOrBuilder();
-        } else {
-          if (valueCase_ == 14) {
-            return (serialization.Bigdl.NameAttrList) value_;
-          }
-          return serialization.Bigdl.NameAttrList.getDefaultInstance();
-        }
+
+      public Builder clone() {
+        return (Builder) super.clone();
       }
-      /**
-       * 
-       * name attribute list
-       * 

-       *
-       * .serialization.NameAttrList nameAttrListValue = 14;
-       */
-      private com.google.protobuf.SingleFieldBuilderV3<
-          serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> 
-          getNameAttrListValueFieldBuilder() {
-        if (nameAttrListValueBuilder_ == null) {
-          if (!(valueCase_ == 14)) {
-            value_ = serialization.Bigdl.NameAttrList.getDefaultInstance();
-          }
-          nameAttrListValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
-              serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder>(
-                  (serialization.Bigdl.NameAttrList) value_,
-                  getParentForChildren(),
-                  isClean());
-          value_ = null;
-        }
-        valueCase_ = 14;
-        onChanged();;
-        return nameAttrListValueBuilder_;
+      public Builder setField(
+          com.google.protobuf.Descriptors.FieldDescriptor field,
+          java.lang.Object value) {
+        return (Builder) super.setField(field, value);
+      }
+      public Builder clearField(
+          com.google.protobuf.Descriptors.FieldDescriptor field) {
+        return (Builder) super.clearField(field);
+      }
+      public Builder clearOneof(
+          com.google.protobuf.Descriptors.OneofDescriptor oneof) {
+        return (Builder) super.clearOneof(oneof);
+      }
+      public Builder setRepeatedField(
+          com.google.protobuf.Descriptors.FieldDescriptor field,
+          int index, java.lang.Object value) {
+        return (Builder) super.setRepeatedField(field, index, value);
       }
-
-      private com.google.protobuf.SingleFieldBuilderV3<
-          serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder> arrayValueBuilder_;
-      /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
-       */
-      public boolean hasArrayValue() {
-        return valueCase_ == 15;
+      public Builder addRepeatedField(
+          com.google.protobuf.Descriptors.FieldDescriptor field,
+          java.lang.Object value) {
+        return (Builder) super.addRepeatedField(field, value);
       }
-      /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
-       */
-      public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() {
-        if (arrayValueBuilder_ == null) {
-          if (valueCase_ == 15) {
-            return (serialization.Bigdl.AttrValue.ArrayValue) value_;
-          }
-          return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance();
+      public Builder mergeFrom(com.google.protobuf.Message other) {
+        if (other instanceof serialization.Bigdl.NameAttrList) {
+          return mergeFrom((serialization.Bigdl.NameAttrList)other);
         } else {
-          if (valueCase_ == 15) {
-            return arrayValueBuilder_.getMessage();
-          }
-          return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance();
+          super.mergeFrom(other);
+          return this;
         }
       }
-      /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
-       */
-      public Builder setArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) {
-        if (arrayValueBuilder_ == null) {
-          if (value == null) {
-            throw new NullPointerException();
-          }
-          value_ = value;
+
+      public Builder mergeFrom(serialization.Bigdl.NameAttrList other) {
+        if (other == serialization.Bigdl.NameAttrList.getDefaultInstance()) return this;
+        if (!other.getName().isEmpty()) {
+          name_ = other.name_;
           onChanged();
-        } else {
-          arrayValueBuilder_.setMessage(value);
         }
-        valueCase_ = 15;
+        internalGetMutableAttr().mergeFrom(
+            other.internalGetAttr());
+        this.mergeUnknownFields(other.unknownFields);
+        onChanged();
         return this;
       }
-      /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
-       */
-      public Builder setArrayValue(
-          serialization.Bigdl.AttrValue.ArrayValue.Builder builderForValue) {
-        if (arrayValueBuilder_ == null) {
-          value_ = builderForValue.build();
-          onChanged();
-        } else {
-          arrayValueBuilder_.setMessage(builderForValue.build());
-        }
-        valueCase_ = 15;
-        return this;
+
+      public final boolean isInitialized() {
+        return true;
       }
-      /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
-       */
-      public Builder mergeArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) {
-        if (arrayValueBuilder_ == null) {
-          if (valueCase_ == 15 &&
-              value_ != serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance()) {
-            value_ = serialization.Bigdl.AttrValue.ArrayValue.newBuilder((serialization.Bigdl.AttrValue.ArrayValue) value_)
-                .mergeFrom(value).buildPartial();
-          } else {
-            value_ = value;
-          }
-          onChanged();
-        } else {
-          if (valueCase_ == 15) {
-            arrayValueBuilder_.mergeFrom(value);
+
+      public Builder mergeFrom(
+          com.google.protobuf.CodedInputStream input,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws java.io.IOException {
+        serialization.Bigdl.NameAttrList parsedMessage = null;
+        try {
+          parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
+        } catch (com.google.protobuf.InvalidProtocolBufferException e) {
+          parsedMessage = (serialization.Bigdl.NameAttrList) e.getUnfinishedMessage();
+          throw e.unwrapIOException();
+        } finally {
+          if (parsedMessage != null) {
+            mergeFrom(parsedMessage);
           }
-          arrayValueBuilder_.setMessage(value);
         }
-        valueCase_ = 15;
         return this;
       }
+      private int bitField0_;
+
+      private java.lang.Object name_ = "";
       /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       * string name = 1;
        */
-      public Builder clearArrayValue() {
-        if (arrayValueBuilder_ == null) {
-          if (valueCase_ == 15) {
-            valueCase_ = 0;
-            value_ = null;
-            onChanged();
-          }
+      public java.lang.String getName() {
+        java.lang.Object ref = name_;
+        if (!(ref instanceof java.lang.String)) {
+          com.google.protobuf.ByteString bs =
+              (com.google.protobuf.ByteString) ref;
+          java.lang.String s = bs.toStringUtf8();
+          name_ = s;
+          return s;
         } else {
-          if (valueCase_ == 15) {
-            valueCase_ = 0;
-            value_ = null;
-          }
-          arrayValueBuilder_.clear();
+          return (java.lang.String) ref;
         }
-        return this;
-      }
-      /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
-       */
-      public serialization.Bigdl.AttrValue.ArrayValue.Builder getArrayValueBuilder() {
-        return getArrayValueFieldBuilder().getBuilder();
       }
       /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
+       * string name = 1;
        */
-      public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder() {
-        if ((valueCase_ == 15) && (arrayValueBuilder_ != null)) {
-          return arrayValueBuilder_.getMessageOrBuilder();
+      public com.google.protobuf.ByteString
+          getNameBytes() {
+        java.lang.Object ref = name_;
+        if (ref instanceof String) {
+          com.google.protobuf.ByteString b = 
+              com.google.protobuf.ByteString.copyFromUtf8(
+                  (java.lang.String) ref);
+          name_ = b;
+          return b;
         } else {
-          if (valueCase_ == 15) {
-            return (serialization.Bigdl.AttrValue.ArrayValue) value_;
-          }
-          return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance();
-        }
-      }
-      /**
-       * 
-       *array value of any type
-       * 

-       *
-       * .serialization.AttrValue.ArrayValue arrayValue = 15;
-       */
-      private com.google.protobuf.SingleFieldBuilderV3<
-          serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder> 
-          getArrayValueFieldBuilder() {
-        if (arrayValueBuilder_ == null) {
-          if (!(valueCase_ == 15)) {
-            value_ = serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance();
-          }
-          arrayValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
-              serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder>(
-                  (serialization.Bigdl.AttrValue.ArrayValue) value_,
-                  getParentForChildren(),
-                  isClean());
-          value_ = null;
-        }
-        valueCase_ = 15;
-        onChanged();;
-        return arrayValueBuilder_;
-      }
-
-      /**
-       * 
-       * data format
-       * 

-       *
-       * .serialization.InputDataFormat dataFormatValue = 16;
-       */
-      public int getDataFormatValueValue() {
-        if (valueCase_ == 16) {
-          return ((java.lang.Integer) value_).intValue();
+          return (com.google.protobuf.ByteString) ref;
         }
-        return 0;
       }
       /**
-       * 
-       * data format
-       * 

-       *
-       * .serialization.InputDataFormat dataFormatValue = 16;
+       * string name = 1;
        */
-      public Builder setDataFormatValueValue(int value) {
-        valueCase_ = 16;
-        value_ = value;
+      public Builder setName(
+          java.lang.String value) {
+        if (value == null) {
+    throw new NullPointerException();
+  }
+  
+        name_ = value;
         onChanged();
         return this;
       }
       /**
-       * 
-       * data format
-       * 

-       *
-       * .serialization.InputDataFormat dataFormatValue = 16;
+       * string name = 1;
        */
-      public serialization.Bigdl.InputDataFormat getDataFormatValue() {
-        if (valueCase_ == 16) {
-          serialization.Bigdl.InputDataFormat result = serialization.Bigdl.InputDataFormat.valueOf(
-              (java.lang.Integer) value_);
-          return result == null ? serialization.Bigdl.InputDataFormat.UNRECOGNIZED : result;
-        }
-        return serialization.Bigdl.InputDataFormat.NCHW;
+      public Builder clearName() {
+        
+        name_ = getDefaultInstance().getName();
+        onChanged();
+        return this;
       }
       /**
-       * 
-       * data format
-       * 

-       *
-       * .serialization.InputDataFormat dataFormatValue = 16;
+       * string name = 1;
        */
-      public Builder setDataFormatValue(serialization.Bigdl.InputDataFormat value) {
+      public Builder setNameBytes(
+          com.google.protobuf.ByteString value) {
         if (value == null) {
-          throw new NullPointerException();
-        }
-        valueCase_ = 16;
-        value_ = value.getNumber();
+    throw new NullPointerException();
+  }
+  checkByteStringIsUtf8(value);
+        
+        name_ = value;
         onChanged();
         return this;
       }
-      /**
-       * 
-       * data format
-       * 

-       *
-       * .serialization.InputDataFormat dataFormatValue = 16;
-       */
-      public Builder clearDataFormatValue() {
-        if (valueCase_ == 16) {
-          valueCase_ = 0;
-          value_ = null;
-          onChanged();
+
+      private com.google.protobuf.MapField<
+          java.lang.String, serialization.Bigdl.AttrValue> attr_;
+      private com.google.protobuf.MapField
+      internalGetAttr() {
+        if (attr_ == null) {
+          return com.google.protobuf.MapField.emptyMapField(
+              AttrDefaultEntryHolder.defaultEntry);
         }
-        return this;
+        return attr_;
+      }
+      private com.google.protobuf.MapField
+      internalGetMutableAttr() {
+        onChanged();;
+        if (attr_ == null) {
+          attr_ = com.google.protobuf.MapField.newMapField(
+              AttrDefaultEntryHolder.defaultEntry);
+        }
+        if (!attr_.isMutable()) {
+          attr_ = attr_.copy();
+        }
+        return attr_;
       }
 
-      private com.google.protobuf.SingleFieldBuilderV3<
-          com.google.protobuf.Any, com.google.protobuf.Any.Builder, com.google.protobuf.AnyOrBuilder> customValueBuilder_;
+      public int getAttrCount() {
+        return internalGetAttr().getMap().size();
+      }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * map<string, .serialization.AttrValue> attr = 2;
        */
-      public boolean hasCustomValue() {
-        return valueCase_ == 17;
+
+      public boolean containsAttr(
+          java.lang.String key) {
+        if (key == null) { throw new java.lang.NullPointerException(); }
+        return internalGetAttr().getMap().containsKey(key);
       }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * Use {@link #getAttrMap()} instead.
        */
-      public com.google.protobuf.Any getCustomValue() {
-        if (customValueBuilder_ == null) {
-          if (valueCase_ == 17) {
-            return (com.google.protobuf.Any) value_;
-          }
-          return com.google.protobuf.Any.getDefaultInstance();
-        } else {
-          if (valueCase_ == 17) {
-            return customValueBuilder_.getMessage();
-          }
-          return com.google.protobuf.Any.getDefaultInstance();
-        }
+      @java.lang.Deprecated
+      public java.util.Map getAttr() {
+        return getAttrMap();
       }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * map<string, .serialization.AttrValue> attr = 2;
        */
-      public Builder setCustomValue(com.google.protobuf.Any value) {
-        if (customValueBuilder_ == null) {
-          if (value == null) {
-            throw new NullPointerException();
-          }
-          value_ = value;
-          onChanged();
-        } else {
-          customValueBuilder_.setMessage(value);
-        }
-        valueCase_ = 17;
-        return this;
+
+      public java.util.Map getAttrMap() {
+        return internalGetAttr().getMap();
       }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * map<string, .serialization.AttrValue> attr = 2;
        */
-      public Builder setCustomValue(
-          com.google.protobuf.Any.Builder builderForValue) {
-        if (customValueBuilder_ == null) {
-          value_ = builderForValue.build();
-          onChanged();
-        } else {
-          customValueBuilder_.setMessage(builderForValue.build());
-        }
-        valueCase_ = 17;
-        return this;
+
+      public serialization.Bigdl.AttrValue getAttrOrDefault(
+          java.lang.String key,
+          serialization.Bigdl.AttrValue defaultValue) {
+        if (key == null) { throw new java.lang.NullPointerException(); }
+        java.util.Map map =
+            internalGetAttr().getMap();
+        return map.containsKey(key) ? map.get(key) : defaultValue;
       }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * map<string, .serialization.AttrValue> attr = 2;
        */
-      public Builder mergeCustomValue(com.google.protobuf.Any value) {
-        if (customValueBuilder_ == null) {
-          if (valueCase_ == 17 &&
-              value_ != com.google.protobuf.Any.getDefaultInstance()) {
-            value_ = com.google.protobuf.Any.newBuilder((com.google.protobuf.Any) value_)
-                .mergeFrom(value).buildPartial();
-          } else {
-            value_ = value;
-          }
-          onChanged();
-        } else {
-          if (valueCase_ == 17) {
-            customValueBuilder_.mergeFrom(value);
-          }
-          customValueBuilder_.setMessage(value);
+
+      public serialization.Bigdl.AttrValue getAttrOrThrow(
+          java.lang.String key) {
+        if (key == null) { throw new java.lang.NullPointerException(); }
+        java.util.Map map =
+            internalGetAttr().getMap();
+        if (!map.containsKey(key)) {
+          throw new java.lang.IllegalArgumentException();
         }
-        valueCase_ = 17;
+        return map.get(key);
+      }
+
+      public Builder clearAttr() {
+        internalGetMutableAttr().getMutableMap()
+            .clear();
         return this;
       }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * map<string, .serialization.AttrValue> attr = 2;
        */
-      public Builder clearCustomValue() {
-        if (customValueBuilder_ == null) {
-          if (valueCase_ == 17) {
-            valueCase_ = 0;
-            value_ = null;
-            onChanged();
-          }
-        } else {
-          if (valueCase_ == 17) {
-            valueCase_ = 0;
-            value_ = null;
-          }
-          customValueBuilder_.clear();
-        }
+
+      public Builder removeAttr(
+          java.lang.String key) {
+        if (key == null) { throw new java.lang.NullPointerException(); }
+        internalGetMutableAttr().getMutableMap()
+            .remove(key);
         return this;
       }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * Use alternate mutation accessors instead.
        */
-      public com.google.protobuf.Any.Builder getCustomValueBuilder() {
-        return getCustomValueFieldBuilder().getBuilder();
+      @java.lang.Deprecated
+      public java.util.Map
+      getMutableAttr() {
+        return internalGetMutableAttr().getMutableMap();
       }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * map<string, .serialization.AttrValue> attr = 2;
        */
-      public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() {
-        if ((valueCase_ == 17) && (customValueBuilder_ != null)) {
-          return customValueBuilder_.getMessageOrBuilder();
-        } else {
-          if (valueCase_ == 17) {
-            return (com.google.protobuf.Any) value_;
-          }
-          return com.google.protobuf.Any.getDefaultInstance();
-        }
+      public Builder putAttr(
+          java.lang.String key,
+          serialization.Bigdl.AttrValue value) {
+        if (key == null) { throw new java.lang.NullPointerException(); }
+        if (value == null) { throw new java.lang.NullPointerException(); }
+        internalGetMutableAttr().getMutableMap()
+            .put(key, value);
+        return this;
       }
       /**
-       * 
-       * custom value
-       * 

-       *
-       * .google.protobuf.Any customValue = 17;
+       * map<string, .serialization.AttrValue> attr = 2;
        */
-      private com.google.protobuf.SingleFieldBuilderV3<
-          com.google.protobuf.Any, com.google.protobuf.Any.Builder, com.google.protobuf.AnyOrBuilder> 
-          getCustomValueFieldBuilder() {
-        if (customValueBuilder_ == null) {
-          if (!(valueCase_ == 17)) {
-            value_ = com.google.protobuf.Any.getDefaultInstance();
-          }
-          customValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
-              com.google.protobuf.Any, com.google.protobuf.Any.Builder, com.google.protobuf.AnyOrBuilder>(
-                  (com.google.protobuf.Any) value_,
-                  getParentForChildren(),
-                  isClean());
-          value_ = null;
-        }
-        valueCase_ = 17;
-        onChanged();;
-        return customValueBuilder_;
+
+      public Builder putAllAttr(
+          java.util.Map values) {
+        internalGetMutableAttr().getMutableMap()
+            .putAll(values);
+        return this;
       }
       public final Builder setUnknownFields(
           final com.google.protobuf.UnknownFieldSet unknownFields) {
@@ -18189,106 +19787,116 @@ public final Builder mergeUnknownFields(
       }
 
 
-      // @@protoc_insertion_point(builder_scope:serialization.AttrValue)
+      // @@protoc_insertion_point(builder_scope:serialization.NameAttrList)
     }
 
-    // @@protoc_insertion_point(class_scope:serialization.AttrValue)
-    private static final serialization.Bigdl.AttrValue DEFAULT_INSTANCE;
+    // @@protoc_insertion_point(class_scope:serialization.NameAttrList)
+    private static final serialization.Bigdl.NameAttrList DEFAULT_INSTANCE;
     static {
-      DEFAULT_INSTANCE = new serialization.Bigdl.AttrValue();
+      DEFAULT_INSTANCE = new serialization.Bigdl.NameAttrList();
     }
 
-    public static serialization.Bigdl.AttrValue getDefaultInstance() {
+    public static serialization.Bigdl.NameAttrList getDefaultInstance() {
       return DEFAULT_INSTANCE;
     }
 
-    private static final com.google.protobuf.Parser
-        PARSER = new com.google.protobuf.AbstractParser() {
-      public AttrValue parsePartialFrom(
+    private static final com.google.protobuf.Parser
+        PARSER = new com.google.protobuf.AbstractParser() {
+      public NameAttrList parsePartialFrom(
           com.google.protobuf.CodedInputStream input,
           com.google.protobuf.ExtensionRegistryLite extensionRegistry)
           throws com.google.protobuf.InvalidProtocolBufferException {
-          return new AttrValue(input, extensionRegistry);
+          return new NameAttrList(input, extensionRegistry);
       }
     };
 
-    public static com.google.protobuf.Parser parser() {
+    public static com.google.protobuf.Parser parser() {
       return PARSER;
     }
 
     @java.lang.Override
-    public com.google.protobuf.Parser getParserForType() {
+    public com.google.protobuf.Parser getParserForType() {
       return PARSER;
     }
 
-    public serialization.Bigdl.AttrValue getDefaultInstanceForType() {
+    public serialization.Bigdl.NameAttrList getDefaultInstanceForType() {
       return DEFAULT_INSTANCE;
     }
 
   }
 
-  public interface NameAttrListOrBuilder extends
-      // @@protoc_insertion_point(interface_extends:serialization.NameAttrList)
+  public interface ShapeOrBuilder extends
+      // @@protoc_insertion_point(interface_extends:serialization.Shape)
       com.google.protobuf.MessageOrBuilder {
 
     /**
-     * string name = 1;
+     * .serialization.Shape.ShapeType shapeType = 1;
      */
-    java.lang.String getName();
+    int getShapeTypeValue();
     /**
-     * string name = 1;
+     * .serialization.Shape.ShapeType shapeType = 1;
      */
-    com.google.protobuf.ByteString
-        getNameBytes();
+    serialization.Bigdl.Shape.ShapeType getShapeType();
 
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * int32 ssize = 2;
      */
-    int getAttrCount();
+    int getSsize();
+
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * repeated int32 shapeValue = 3;
      */
-    boolean containsAttr(
-        java.lang.String key);
+    java.util.List getShapeValueList();
     /**
-     * Use {@link #getAttrMap()} instead.
+     * repeated int32 shapeValue = 3;
      */
-    @java.lang.Deprecated
-    java.util.Map
-    getAttr();
+    int getShapeValueCount();
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * repeated int32 shapeValue = 3;
      */
-    java.util.Map
-    getAttrMap();
+    int getShapeValue(int index);
+
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * repeated .serialization.Shape shape = 4;
      */
-
-    serialization.Bigdl.AttrValue getAttrOrDefault(
-        java.lang.String key,
-        serialization.Bigdl.AttrValue defaultValue);
+    java.util.List 
+        getShapeList();
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * repeated .serialization.Shape shape = 4;
      */
-
-    serialization.Bigdl.AttrValue getAttrOrThrow(
-        java.lang.String key);
+    serialization.Bigdl.Shape getShape(int index);
+    /**
+     * repeated .serialization.Shape shape = 4;
+     */
+    int getShapeCount();
+    /**
+     * repeated .serialization.Shape shape = 4;
+     */
+    java.util.List 
+        getShapeOrBuilderList();
+    /**
+     * repeated .serialization.Shape shape = 4;
+     */
+    serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder(
+        int index);
   }
   /**
-   * Protobuf type {@code serialization.NameAttrList}
+   * Protobuf type {@code serialization.Shape}
    */
-  public  static final class NameAttrList extends
+  public  static final class Shape extends
       com.google.protobuf.GeneratedMessageV3 implements
-      // @@protoc_insertion_point(message_implements:serialization.NameAttrList)
-      NameAttrListOrBuilder {
+      // @@protoc_insertion_point(message_implements:serialization.Shape)
+      ShapeOrBuilder {
   private static final long serialVersionUID = 0L;
-    // Use NameAttrList.newBuilder() to construct.
-    private NameAttrList(com.google.protobuf.GeneratedMessageV3.Builder builder) {
+    // Use Shape.newBuilder() to construct.
+    private Shape(com.google.protobuf.GeneratedMessageV3.Builder builder) {
       super(builder);
     }
-    private NameAttrList() {
-      name_ = "";
+    private Shape() {
+      shapeType_ = 0;
+      ssize_ = 0;
+      shapeValue_ = java.util.Collections.emptyList();
+      shape_ = java.util.Collections.emptyList();
     }
 
     @java.lang.Override
@@ -18296,7 +19904,7 @@ private NameAttrList() {
     getUnknownFields() {
       return this.unknownFields;
     }
-    private NameAttrList(
+    private Shape(
         com.google.protobuf.CodedInputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
@@ -18319,23 +19927,45 @@ private NameAttrList(
               }
               break;
             }
-            case 10: {
-              java.lang.String s = input.readStringRequireUtf8();
+            case 8: {
+              int rawValue = input.readEnum();
 
-              name_ = s;
+              shapeType_ = rawValue;
               break;
             }
-            case 18: {
-              if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) {
-                attr_ = com.google.protobuf.MapField.newMapField(
-                    AttrDefaultEntryHolder.defaultEntry);
-                mutable_bitField0_ |= 0x00000002;
+            case 16: {
+
+              ssize_ = input.readInt32();
+              break;
+            }
+            case 24: {
+              if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) {
+                shapeValue_ = new java.util.ArrayList();
+                mutable_bitField0_ |= 0x00000004;
               }
-              com.google.protobuf.MapEntry
-              attr__ = input.readMessage(
-                  AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry);
-              attr_.getMutableMap().put(
-                  attr__.getKey(), attr__.getValue());
+              shapeValue_.add(input.readInt32());
+              break;
+            }
+            case 26: {
+              int length = input.readRawVarint32();
+              int limit = input.pushLimit(length);
+              if (!((mutable_bitField0_ & 0x00000004) == 0x00000004) && input.getBytesUntilLimit() > 0) {
+                shapeValue_ = new java.util.ArrayList();
+                mutable_bitField0_ |= 0x00000004;
+              }
+              while (input.getBytesUntilLimit() > 0) {
+                shapeValue_.add(input.readInt32());
+              }
+              input.popLimit(limit);
+              break;
+            }
+            case 34: {
+              if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) {
+                shape_ = new java.util.ArrayList();
+                mutable_bitField0_ |= 0x00000008;
+              }
+              shape_.add(
+                  input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry));
               break;
             }
           }
@@ -18346,142 +19976,208 @@ private NameAttrList(
         throw new com.google.protobuf.InvalidProtocolBufferException(
             e).setUnfinishedMessage(this);
       } finally {
+        if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) {
+          shapeValue_ = java.util.Collections.unmodifiableList(shapeValue_);
+        }
+        if (((mutable_bitField0_ & 0x00000008) == 0x00000008)) {
+          shape_ = java.util.Collections.unmodifiableList(shape_);
+        }
         this.unknownFields = unknownFields.build();
         makeExtensionsImmutable();
       }
     }
     public static final com.google.protobuf.Descriptors.Descriptor
         getDescriptor() {
-      return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor;
+      return serialization.Bigdl.internal_static_serialization_Shape_descriptor;
     }
 
-    @SuppressWarnings({"rawtypes"})
-    protected com.google.protobuf.MapField internalGetMapField(
-        int number) {
-      switch (number) {
-        case 2:
-          return internalGetAttr();
-        default:
-          throw new RuntimeException(
-              "Invalid map field number: " + number);
-      }
-    }
     protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
         internalGetFieldAccessorTable() {
-      return serialization.Bigdl.internal_static_serialization_NameAttrList_fieldAccessorTable
+      return serialization.Bigdl.internal_static_serialization_Shape_fieldAccessorTable
           .ensureFieldAccessorsInitialized(
-              serialization.Bigdl.NameAttrList.class, serialization.Bigdl.NameAttrList.Builder.class);
+              serialization.Bigdl.Shape.class, serialization.Bigdl.Shape.Builder.class);
     }
 
-    private int bitField0_;
-    public static final int NAME_FIELD_NUMBER = 1;
-    private volatile java.lang.Object name_;
     /**
-     * string name = 1;
+     * Protobuf enum {@code serialization.Shape.ShapeType}
      */
-    public java.lang.String getName() {
-      java.lang.Object ref = name_;
-      if (ref instanceof java.lang.String) {
-        return (java.lang.String) ref;
-      } else {
-        com.google.protobuf.ByteString bs = 
-            (com.google.protobuf.ByteString) ref;
-        java.lang.String s = bs.toStringUtf8();
-        name_ = s;
-        return s;
+    public enum ShapeType
+        implements com.google.protobuf.ProtocolMessageEnum {
+      /**
+       * SINGLE = 0;
+       */
+      SINGLE(0),
+      /**
+       * MULTI = 1;
+       */
+      MULTI(1),
+      UNRECOGNIZED(-1),
+      ;
+
+      /**
+       * SINGLE = 0;
+       */
+      public static final int SINGLE_VALUE = 0;
+      /**
+       * MULTI = 1;
+       */
+      public static final int MULTI_VALUE = 1;
+
+
+      public final int getNumber() {
+        if (this == UNRECOGNIZED) {
+          throw new java.lang.IllegalArgumentException(
+              "Can't get the number of an unknown enum value.");
+        }
+        return value;
       }
-    }
-    /**
-     * string name = 1;
-     */
-    public com.google.protobuf.ByteString
-        getNameBytes() {
-      java.lang.Object ref = name_;
-      if (ref instanceof java.lang.String) {
-        com.google.protobuf.ByteString b = 
-            com.google.protobuf.ByteString.copyFromUtf8(
-                (java.lang.String) ref);
-        name_ = b;
-        return b;
-      } else {
-        return (com.google.protobuf.ByteString) ref;
+
+      /**
+       * @deprecated Use {@link #forNumber(int)} instead.
+       */
+      @java.lang.Deprecated
+      public static ShapeType valueOf(int value) {
+        return forNumber(value);
       }
-    }
 
-    public static final int ATTR_FIELD_NUMBER = 2;
-    private static final class AttrDefaultEntryHolder {
-      static final com.google.protobuf.MapEntry<
-          java.lang.String, serialization.Bigdl.AttrValue> defaultEntry =
-              com.google.protobuf.MapEntry
-              .newDefaultInstance(
-                  serialization.Bigdl.internal_static_serialization_NameAttrList_AttrEntry_descriptor, 
-                  com.google.protobuf.WireFormat.FieldType.STRING,
-                  "",
-                  com.google.protobuf.WireFormat.FieldType.MESSAGE,
-                  serialization.Bigdl.AttrValue.getDefaultInstance());
-    }
-    private com.google.protobuf.MapField<
-        java.lang.String, serialization.Bigdl.AttrValue> attr_;
-    private com.google.protobuf.MapField
-    internalGetAttr() {
-      if (attr_ == null) {
-        return com.google.protobuf.MapField.emptyMapField(
-            AttrDefaultEntryHolder.defaultEntry);
+      public static ShapeType forNumber(int value) {
+        switch (value) {
+          case 0: return SINGLE;
+          case 1: return MULTI;
+          default: return null;
+        }
       }
-      return attr_;
+
+      public static com.google.protobuf.Internal.EnumLiteMap
+          internalGetValueMap() {
+        return internalValueMap;
+      }
+      private static final com.google.protobuf.Internal.EnumLiteMap<
+          ShapeType> internalValueMap =
+            new com.google.protobuf.Internal.EnumLiteMap() {
+              public ShapeType findValueByNumber(int number) {
+                return ShapeType.forNumber(number);
+              }
+            };
+
+      public final com.google.protobuf.Descriptors.EnumValueDescriptor
+          getValueDescriptor() {
+        return getDescriptor().getValues().get(ordinal());
+      }
+      public final com.google.protobuf.Descriptors.EnumDescriptor
+          getDescriptorForType() {
+        return getDescriptor();
+      }
+      public static final com.google.protobuf.Descriptors.EnumDescriptor
+          getDescriptor() {
+        return serialization.Bigdl.Shape.getDescriptor().getEnumTypes().get(0);
+      }
+
+      private static final ShapeType[] VALUES = values();
+
+      public static ShapeType valueOf(
+          com.google.protobuf.Descriptors.EnumValueDescriptor desc) {
+        if (desc.getType() != getDescriptor()) {
+          throw new java.lang.IllegalArgumentException(
+            "EnumValueDescriptor is not for this type.");
+        }
+        if (desc.getIndex() == -1) {
+          return UNRECOGNIZED;
+        }
+        return VALUES[desc.getIndex()];
+      }
+
+      private final int value;
+
+      private ShapeType(int value) {
+        this.value = value;
+      }
+
+      // @@protoc_insertion_point(enum_scope:serialization.Shape.ShapeType)
     }
 
-    public int getAttrCount() {
-      return internalGetAttr().getMap().size();
+    private int bitField0_;
+    public static final int SHAPETYPE_FIELD_NUMBER = 1;
+    private int shapeType_;
+    /**
+     * .serialization.Shape.ShapeType shapeType = 1;
+     */
+    public int getShapeTypeValue() {
+      return shapeType_;
     }
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * .serialization.Shape.ShapeType shapeType = 1;
      */
+    public serialization.Bigdl.Shape.ShapeType getShapeType() {
+      serialization.Bigdl.Shape.ShapeType result = serialization.Bigdl.Shape.ShapeType.valueOf(shapeType_);
+      return result == null ? serialization.Bigdl.Shape.ShapeType.UNRECOGNIZED : result;
+    }
 
-    public boolean containsAttr(
-        java.lang.String key) {
-      if (key == null) { throw new java.lang.NullPointerException(); }
-      return internalGetAttr().getMap().containsKey(key);
+    public static final int SSIZE_FIELD_NUMBER = 2;
+    private int ssize_;
+    /**
+     * int32 ssize = 2;
+     */
+    public int getSsize() {
+      return ssize_;
     }
+
+    public static final int SHAPEVALUE_FIELD_NUMBER = 3;
+    private java.util.List shapeValue_;
     /**
-     * Use {@link #getAttrMap()} instead.
+     * repeated int32 shapeValue = 3;
      */
-    @java.lang.Deprecated
-    public java.util.Map getAttr() {
-      return getAttrMap();
+    public java.util.List
+        getShapeValueList() {
+      return shapeValue_;
     }
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * repeated int32 shapeValue = 3;
      */
+    public int getShapeValueCount() {
+      return shapeValue_.size();
+    }
+    /**
+     * repeated int32 shapeValue = 3;
+     */
+    public int getShapeValue(int index) {
+      return shapeValue_.get(index);
+    }
+    private int shapeValueMemoizedSerializedSize = -1;
 
-    public java.util.Map getAttrMap() {
-      return internalGetAttr().getMap();
+    public static final int SHAPE_FIELD_NUMBER = 4;
+    private java.util.List shape_;
+    /**
+     * repeated .serialization.Shape shape = 4;
+     */
+    public java.util.List getShapeList() {
+      return shape_;
+    }
+    /**
+     * repeated .serialization.Shape shape = 4;
+     */
+    public java.util.List 
+        getShapeOrBuilderList() {
+      return shape_;
+    }
+    /**
+     * repeated .serialization.Shape shape = 4;
+     */
+    public int getShapeCount() {
+      return shape_.size();
     }
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * repeated .serialization.Shape shape = 4;
      */
-
-    public serialization.Bigdl.AttrValue getAttrOrDefault(
-        java.lang.String key,
-        serialization.Bigdl.AttrValue defaultValue) {
-      if (key == null) { throw new java.lang.NullPointerException(); }
-      java.util.Map map =
-          internalGetAttr().getMap();
-      return map.containsKey(key) ? map.get(key) : defaultValue;
+    public serialization.Bigdl.Shape getShape(int index) {
+      return shape_.get(index);
     }
     /**
-     * map<string, .serialization.AttrValue> attr = 2;
+     * repeated .serialization.Shape shape = 4;
      */
-
-    public serialization.Bigdl.AttrValue getAttrOrThrow(
-        java.lang.String key) {
-      if (key == null) { throw new java.lang.NullPointerException(); }
-      java.util.Map map =
-          internalGetAttr().getMap();
-      if (!map.containsKey(key)) {
-        throw new java.lang.IllegalArgumentException();
-      }
-      return map.get(key);
+    public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder(
+        int index) {
+      return shape_.get(index);
     }
 
     private byte memoizedIsInitialized = -1;
@@ -18496,15 +20192,23 @@ public final boolean isInitialized() {
 
     public void writeTo(com.google.protobuf.CodedOutputStream output)
                         throws java.io.IOException {
-      if (!getNameBytes().isEmpty()) {
-        com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_);
+      getSerializedSize();
+      if (shapeType_ != serialization.Bigdl.Shape.ShapeType.SINGLE.getNumber()) {
+        output.writeEnum(1, shapeType_);
+      }
+      if (ssize_ != 0) {
+        output.writeInt32(2, ssize_);
+      }
+      if (getShapeValueList().size() > 0) {
+        output.writeUInt32NoTag(26);
+        output.writeUInt32NoTag(shapeValueMemoizedSerializedSize);
+      }
+      for (int i = 0; i < shapeValue_.size(); i++) {
+        output.writeInt32NoTag(shapeValue_.get(i));
+      }
+      for (int i = 0; i < shape_.size(); i++) {
+        output.writeMessage(4, shape_.get(i));
       }
-      com.google.protobuf.GeneratedMessageV3
-        .serializeStringMapTo(
-          output,
-          internalGetAttr(),
-          AttrDefaultEntryHolder.defaultEntry,
-          2);
       unknownFields.writeTo(output);
     }
 
@@ -18513,18 +20217,31 @@ public int getSerializedSize() {
       if (size != -1) return size;
 
       size = 0;
-      if (!getNameBytes().isEmpty()) {
-        size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_);
+      if (shapeType_ != serialization.Bigdl.Shape.ShapeType.SINGLE.getNumber()) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeEnumSize(1, shapeType_);
       }
-      for (java.util.Map.Entry entry
-           : internalGetAttr().getMap().entrySet()) {
-        com.google.protobuf.MapEntry
-        attr__ = AttrDefaultEntryHolder.defaultEntry.newBuilderForType()
-            .setKey(entry.getKey())
-            .setValue(entry.getValue())
-            .build();
+      if (ssize_ != 0) {
         size += com.google.protobuf.CodedOutputStream
-            .computeMessageSize(2, attr__);
+          .computeInt32Size(2, ssize_);
+      }
+      {
+        int dataSize = 0;
+        for (int i = 0; i < shapeValue_.size(); i++) {
+          dataSize += com.google.protobuf.CodedOutputStream
+            .computeInt32SizeNoTag(shapeValue_.get(i));
+        }
+        size += dataSize;
+        if (!getShapeValueList().isEmpty()) {
+          size += 1;
+          size += com.google.protobuf.CodedOutputStream
+              .computeInt32SizeNoTag(dataSize);
+        }
+        shapeValueMemoizedSerializedSize = dataSize;
+      }
+      for (int i = 0; i < shape_.size(); i++) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeMessageSize(4, shape_.get(i));
       }
       size += unknownFields.getSerializedSize();
       memoizedSize = size;
@@ -18536,16 +20253,19 @@ public boolean equals(final java.lang.Object obj) {
       if (obj == this) {
        return true;
       }
-      if (!(obj instanceof serialization.Bigdl.NameAttrList)) {
+      if (!(obj instanceof serialization.Bigdl.Shape)) {
         return super.equals(obj);
       }
-      serialization.Bigdl.NameAttrList other = (serialization.Bigdl.NameAttrList) obj;
+      serialization.Bigdl.Shape other = (serialization.Bigdl.Shape) obj;
 
       boolean result = true;
-      result = result && getName()
-          .equals(other.getName());
-      result = result && internalGetAttr().equals(
-          other.internalGetAttr());
+      result = result && shapeType_ == other.shapeType_;
+      result = result && (getSsize()
+          == other.getSsize());
+      result = result && getShapeValueList()
+          .equals(other.getShapeValueList());
+      result = result && getShapeList()
+          .equals(other.getShapeList());
       result = result && unknownFields.equals(other.unknownFields);
       return result;
     }
@@ -18557,80 +20277,86 @@ public int hashCode() {
       }
       int hash = 41;
       hash = (19 * hash) + getDescriptor().hashCode();
-      hash = (37 * hash) + NAME_FIELD_NUMBER;
-      hash = (53 * hash) + getName().hashCode();
-      if (!internalGetAttr().getMap().isEmpty()) {
-        hash = (37 * hash) + ATTR_FIELD_NUMBER;
-        hash = (53 * hash) + internalGetAttr().hashCode();
+      hash = (37 * hash) + SHAPETYPE_FIELD_NUMBER;
+      hash = (53 * hash) + shapeType_;
+      hash = (37 * hash) + SSIZE_FIELD_NUMBER;
+      hash = (53 * hash) + getSsize();
+      if (getShapeValueCount() > 0) {
+        hash = (37 * hash) + SHAPEVALUE_FIELD_NUMBER;
+        hash = (53 * hash) + getShapeValueList().hashCode();
+      }
+      if (getShapeCount() > 0) {
+        hash = (37 * hash) + SHAPE_FIELD_NUMBER;
+        hash = (53 * hash) + getShapeList().hashCode();
       }
       hash = (29 * hash) + unknownFields.hashCode();
       memoizedHashCode = hash;
       return hash;
     }
 
-    public static serialization.Bigdl.NameAttrList parseFrom(
+    public static serialization.Bigdl.Shape parseFrom(
         java.nio.ByteBuffer data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(
+    public static serialization.Bigdl.Shape parseFrom(
         java.nio.ByteBuffer data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(
+    public static serialization.Bigdl.Shape parseFrom(
         com.google.protobuf.ByteString data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(
+    public static serialization.Bigdl.Shape parseFrom(
         com.google.protobuf.ByteString data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(byte[] data)
+    public static serialization.Bigdl.Shape parseFrom(byte[] data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(
+    public static serialization.Bigdl.Shape parseFrom(
         byte[] data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(java.io.InputStream input)
+    public static serialization.Bigdl.Shape parseFrom(java.io.InputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(
+    public static serialization.Bigdl.Shape parseFrom(
         java.io.InputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input, extensionRegistry);
     }
-    public static serialization.Bigdl.NameAttrList parseDelimitedFrom(java.io.InputStream input)
+    public static serialization.Bigdl.Shape parseDelimitedFrom(java.io.InputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseDelimitedWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.NameAttrList parseDelimitedFrom(
+    public static serialization.Bigdl.Shape parseDelimitedFrom(
         java.io.InputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseDelimitedWithIOException(PARSER, input, extensionRegistry);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(
+    public static serialization.Bigdl.Shape parseFrom(
         com.google.protobuf.CodedInputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.NameAttrList parseFrom(
+    public static serialization.Bigdl.Shape parseFrom(
         com.google.protobuf.CodedInputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
@@ -18642,7 +20368,7 @@ public static serialization.Bigdl.NameAttrList parseFrom(
     public static Builder newBuilder() {
       return DEFAULT_INSTANCE.toBuilder();
     }
-    public static Builder newBuilder(serialization.Bigdl.NameAttrList prototype) {
+    public static Builder newBuilder(serialization.Bigdl.Shape prototype) {
       return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
     }
     public Builder toBuilder() {
@@ -18657,47 +20383,25 @@ protected Builder newBuilderForType(
       return builder;
     }
     /**
-     * Protobuf type {@code serialization.NameAttrList}
+     * Protobuf type {@code serialization.Shape}
      */
     public static final class Builder extends
         com.google.protobuf.GeneratedMessageV3.Builder implements
-        // @@protoc_insertion_point(builder_implements:serialization.NameAttrList)
-        serialization.Bigdl.NameAttrListOrBuilder {
+        // @@protoc_insertion_point(builder_implements:serialization.Shape)
+        serialization.Bigdl.ShapeOrBuilder {
       public static final com.google.protobuf.Descriptors.Descriptor
           getDescriptor() {
-        return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor;
+        return serialization.Bigdl.internal_static_serialization_Shape_descriptor;
       }
 
-      @SuppressWarnings({"rawtypes"})
-      protected com.google.protobuf.MapField internalGetMapField(
-          int number) {
-        switch (number) {
-          case 2:
-            return internalGetAttr();
-          default:
-            throw new RuntimeException(
-                "Invalid map field number: " + number);
-        }
-      }
-      @SuppressWarnings({"rawtypes"})
-      protected com.google.protobuf.MapField internalGetMutableMapField(
-          int number) {
-        switch (number) {
-          case 2:
-            return internalGetMutableAttr();
-          default:
-            throw new RuntimeException(
-                "Invalid map field number: " + number);
-        }
-      }
       protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
           internalGetFieldAccessorTable() {
-        return serialization.Bigdl.internal_static_serialization_NameAttrList_fieldAccessorTable
+        return serialization.Bigdl.internal_static_serialization_Shape_fieldAccessorTable
             .ensureFieldAccessorsInitialized(
-                serialization.Bigdl.NameAttrList.class, serialization.Bigdl.NameAttrList.Builder.class);
+                serialization.Bigdl.Shape.class, serialization.Bigdl.Shape.Builder.class);
       }
 
-      // Construct using serialization.Bigdl.NameAttrList.newBuilder()
+      // Construct using serialization.Bigdl.Shape.newBuilder()
       private Builder() {
         maybeForceBuilderInitialization();
       }
@@ -18710,40 +20414,63 @@ private Builder(
       private void maybeForceBuilderInitialization() {
         if (com.google.protobuf.GeneratedMessageV3
                 .alwaysUseFieldBuilders) {
+          getShapeFieldBuilder();
         }
       }
       public Builder clear() {
         super.clear();
-        name_ = "";
+        shapeType_ = 0;
 
-        internalGetMutableAttr().clear();
+        ssize_ = 0;
+
+        shapeValue_ = java.util.Collections.emptyList();
+        bitField0_ = (bitField0_ & ~0x00000004);
+        if (shapeBuilder_ == null) {
+          shape_ = java.util.Collections.emptyList();
+          bitField0_ = (bitField0_ & ~0x00000008);
+        } else {
+          shapeBuilder_.clear();
+        }
         return this;
       }
 
       public com.google.protobuf.Descriptors.Descriptor
           getDescriptorForType() {
-        return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor;
+        return serialization.Bigdl.internal_static_serialization_Shape_descriptor;
       }
 
-      public serialization.Bigdl.NameAttrList getDefaultInstanceForType() {
-        return serialization.Bigdl.NameAttrList.getDefaultInstance();
+      public serialization.Bigdl.Shape getDefaultInstanceForType() {
+        return serialization.Bigdl.Shape.getDefaultInstance();
       }
 
-      public serialization.Bigdl.NameAttrList build() {
-        serialization.Bigdl.NameAttrList result = buildPartial();
+      public serialization.Bigdl.Shape build() {
+        serialization.Bigdl.Shape result = buildPartial();
         if (!result.isInitialized()) {
           throw newUninitializedMessageException(result);
         }
         return result;
       }
 
-      public serialization.Bigdl.NameAttrList buildPartial() {
-        serialization.Bigdl.NameAttrList result = new serialization.Bigdl.NameAttrList(this);
+      public serialization.Bigdl.Shape buildPartial() {
+        serialization.Bigdl.Shape result = new serialization.Bigdl.Shape(this);
         int from_bitField0_ = bitField0_;
         int to_bitField0_ = 0;
-        result.name_ = name_;
-        result.attr_ = internalGetAttr();
-        result.attr_.makeImmutable();
+        result.shapeType_ = shapeType_;
+        result.ssize_ = ssize_;
+        if (((bitField0_ & 0x00000004) == 0x00000004)) {
+          shapeValue_ = java.util.Collections.unmodifiableList(shapeValue_);
+          bitField0_ = (bitField0_ & ~0x00000004);
+        }
+        result.shapeValue_ = shapeValue_;
+        if (shapeBuilder_ == null) {
+          if (((bitField0_ & 0x00000008) == 0x00000008)) {
+            shape_ = java.util.Collections.unmodifiableList(shape_);
+            bitField0_ = (bitField0_ & ~0x00000008);
+          }
+          result.shape_ = shape_;
+        } else {
+          result.shape_ = shapeBuilder_.build();
+        }
         result.bitField0_ = to_bitField0_;
         onBuilt();
         return result;
@@ -18776,240 +20503,460 @@ public Builder addRepeatedField(
         return (Builder) super.addRepeatedField(field, value);
       }
       public Builder mergeFrom(com.google.protobuf.Message other) {
-        if (other instanceof serialization.Bigdl.NameAttrList) {
-          return mergeFrom((serialization.Bigdl.NameAttrList)other);
+        if (other instanceof serialization.Bigdl.Shape) {
+          return mergeFrom((serialization.Bigdl.Shape)other);
         } else {
           super.mergeFrom(other);
           return this;
         }
       }
-
-      public Builder mergeFrom(serialization.Bigdl.NameAttrList other) {
-        if (other == serialization.Bigdl.NameAttrList.getDefaultInstance()) return this;
-        if (!other.getName().isEmpty()) {
-          name_ = other.name_;
-          onChanged();
-        }
-        internalGetMutableAttr().mergeFrom(
-            other.internalGetAttr());
-        this.mergeUnknownFields(other.unknownFields);
+
+      public Builder mergeFrom(serialization.Bigdl.Shape other) {
+        if (other == serialization.Bigdl.Shape.getDefaultInstance()) return this;
+        if (other.shapeType_ != 0) {
+          setShapeTypeValue(other.getShapeTypeValue());
+        }
+        if (other.getSsize() != 0) {
+          setSsize(other.getSsize());
+        }
+        if (!other.shapeValue_.isEmpty()) {
+          if (shapeValue_.isEmpty()) {
+            shapeValue_ = other.shapeValue_;
+            bitField0_ = (bitField0_ & ~0x00000004);
+          } else {
+            ensureShapeValueIsMutable();
+            shapeValue_.addAll(other.shapeValue_);
+          }
+          onChanged();
+        }
+        if (shapeBuilder_ == null) {
+          if (!other.shape_.isEmpty()) {
+            if (shape_.isEmpty()) {
+              shape_ = other.shape_;
+              bitField0_ = (bitField0_ & ~0x00000008);
+            } else {
+              ensureShapeIsMutable();
+              shape_.addAll(other.shape_);
+            }
+            onChanged();
+          }
+        } else {
+          if (!other.shape_.isEmpty()) {
+            if (shapeBuilder_.isEmpty()) {
+              shapeBuilder_.dispose();
+              shapeBuilder_ = null;
+              shape_ = other.shape_;
+              bitField0_ = (bitField0_ & ~0x00000008);
+              shapeBuilder_ = 
+                com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ?
+                   getShapeFieldBuilder() : null;
+            } else {
+              shapeBuilder_.addAllMessages(other.shape_);
+            }
+          }
+        }
+        this.mergeUnknownFields(other.unknownFields);
+        onChanged();
+        return this;
+      }
+
+      public final boolean isInitialized() {
+        return true;
+      }
+
+      public Builder mergeFrom(
+          com.google.protobuf.CodedInputStream input,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws java.io.IOException {
+        serialization.Bigdl.Shape parsedMessage = null;
+        try {
+          parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
+        } catch (com.google.protobuf.InvalidProtocolBufferException e) {
+          parsedMessage = (serialization.Bigdl.Shape) e.getUnfinishedMessage();
+          throw e.unwrapIOException();
+        } finally {
+          if (parsedMessage != null) {
+            mergeFrom(parsedMessage);
+          }
+        }
+        return this;
+      }
+      private int bitField0_;
+
+      private int shapeType_ = 0;
+      /**
+       * .serialization.Shape.ShapeType shapeType = 1;
+       */
+      public int getShapeTypeValue() {
+        return shapeType_;
+      }
+      /**
+       * .serialization.Shape.ShapeType shapeType = 1;
+       */
+      public Builder setShapeTypeValue(int value) {
+        shapeType_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * .serialization.Shape.ShapeType shapeType = 1;
+       */
+      public serialization.Bigdl.Shape.ShapeType getShapeType() {
+        serialization.Bigdl.Shape.ShapeType result = serialization.Bigdl.Shape.ShapeType.valueOf(shapeType_);
+        return result == null ? serialization.Bigdl.Shape.ShapeType.UNRECOGNIZED : result;
+      }
+      /**
+       * .serialization.Shape.ShapeType shapeType = 1;
+       */
+      public Builder setShapeType(serialization.Bigdl.Shape.ShapeType value) {
+        if (value == null) {
+          throw new NullPointerException();
+        }
+        
+        shapeType_ = value.getNumber();
+        onChanged();
+        return this;
+      }
+      /**
+       * .serialization.Shape.ShapeType shapeType = 1;
+       */
+      public Builder clearShapeType() {
+        
+        shapeType_ = 0;
+        onChanged();
+        return this;
+      }
+
+      private int ssize_ ;
+      /**
+       * int32 ssize = 2;
+       */
+      public int getSsize() {
+        return ssize_;
+      }
+      /**
+       * int32 ssize = 2;
+       */
+      public Builder setSsize(int value) {
+        
+        ssize_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * int32 ssize = 2;
+       */
+      public Builder clearSsize() {
+        
+        ssize_ = 0;
+        onChanged();
+        return this;
+      }
+
+      private java.util.List shapeValue_ = java.util.Collections.emptyList();
+      private void ensureShapeValueIsMutable() {
+        if (!((bitField0_ & 0x00000004) == 0x00000004)) {
+          shapeValue_ = new java.util.ArrayList(shapeValue_);
+          bitField0_ |= 0x00000004;
+         }
+      }
+      /**
+       * repeated int32 shapeValue = 3;
+       */
+      public java.util.List
+          getShapeValueList() {
+        return java.util.Collections.unmodifiableList(shapeValue_);
+      }
+      /**
+       * repeated int32 shapeValue = 3;
+       */
+      public int getShapeValueCount() {
+        return shapeValue_.size();
+      }
+      /**
+       * repeated int32 shapeValue = 3;
+       */
+      public int getShapeValue(int index) {
+        return shapeValue_.get(index);
+      }
+      /**
+       * repeated int32 shapeValue = 3;
+       */
+      public Builder setShapeValue(
+          int index, int value) {
+        ensureShapeValueIsMutable();
+        shapeValue_.set(index, value);
+        onChanged();
+        return this;
+      }
+      /**
+       * repeated int32 shapeValue = 3;
+       */
+      public Builder addShapeValue(int value) {
+        ensureShapeValueIsMutable();
+        shapeValue_.add(value);
+        onChanged();
+        return this;
+      }
+      /**
+       * repeated int32 shapeValue = 3;
+       */
+      public Builder addAllShapeValue(
+          java.lang.Iterable values) {
+        ensureShapeValueIsMutable();
+        com.google.protobuf.AbstractMessageLite.Builder.addAll(
+            values, shapeValue_);
+        onChanged();
+        return this;
+      }
+      /**
+       * repeated int32 shapeValue = 3;
+       */
+      public Builder clearShapeValue() {
+        shapeValue_ = java.util.Collections.emptyList();
+        bitField0_ = (bitField0_ & ~0x00000004);
         onChanged();
         return this;
       }
 
-      public final boolean isInitialized() {
-        return true;
+      private java.util.List shape_ =
+        java.util.Collections.emptyList();
+      private void ensureShapeIsMutable() {
+        if (!((bitField0_ & 0x00000008) == 0x00000008)) {
+          shape_ = new java.util.ArrayList(shape_);
+          bitField0_ |= 0x00000008;
+         }
       }
 
-      public Builder mergeFrom(
-          com.google.protobuf.CodedInputStream input,
-          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
-          throws java.io.IOException {
-        serialization.Bigdl.NameAttrList parsedMessage = null;
-        try {
-          parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
-        } catch (com.google.protobuf.InvalidProtocolBufferException e) {
-          parsedMessage = (serialization.Bigdl.NameAttrList) e.getUnfinishedMessage();
-          throw e.unwrapIOException();
-        } finally {
-          if (parsedMessage != null) {
-            mergeFrom(parsedMessage);
-          }
+      private com.google.protobuf.RepeatedFieldBuilderV3<
+          serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> shapeBuilder_;
+
+      /**
+       * repeated .serialization.Shape shape = 4;
+       */
+      public java.util.List getShapeList() {
+        if (shapeBuilder_ == null) {
+          return java.util.Collections.unmodifiableList(shape_);
+        } else {
+          return shapeBuilder_.getMessageList();
         }
-        return this;
       }
-      private int bitField0_;
-
-      private java.lang.Object name_ = "";
       /**
-       * string name = 1;
+       * repeated .serialization.Shape shape = 4;
        */
-      public java.lang.String getName() {
-        java.lang.Object ref = name_;
-        if (!(ref instanceof java.lang.String)) {
-          com.google.protobuf.ByteString bs =
-              (com.google.protobuf.ByteString) ref;
-          java.lang.String s = bs.toStringUtf8();
-          name_ = s;
-          return s;
+      public int getShapeCount() {
+        if (shapeBuilder_ == null) {
+          return shape_.size();
         } else {
-          return (java.lang.String) ref;
+          return shapeBuilder_.getCount();
         }
       }
       /**
-       * string name = 1;
+       * repeated .serialization.Shape shape = 4;
        */
-      public com.google.protobuf.ByteString
-          getNameBytes() {
-        java.lang.Object ref = name_;
-        if (ref instanceof String) {
-          com.google.protobuf.ByteString b = 
-              com.google.protobuf.ByteString.copyFromUtf8(
-                  (java.lang.String) ref);
-          name_ = b;
-          return b;
+      public serialization.Bigdl.Shape getShape(int index) {
+        if (shapeBuilder_ == null) {
+          return shape_.get(index);
         } else {
-          return (com.google.protobuf.ByteString) ref;
+          return shapeBuilder_.getMessage(index);
         }
       }
       /**
-       * string name = 1;
+       * repeated .serialization.Shape shape = 4;
        */
-      public Builder setName(
-          java.lang.String value) {
-        if (value == null) {
-    throw new NullPointerException();
-  }
-  
-        name_ = value;
-        onChanged();
+      public Builder setShape(
+          int index, serialization.Bigdl.Shape value) {
+        if (shapeBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          ensureShapeIsMutable();
+          shape_.set(index, value);
+          onChanged();
+        } else {
+          shapeBuilder_.setMessage(index, value);
+        }
         return this;
       }
       /**
-       * string name = 1;
+       * repeated .serialization.Shape shape = 4;
        */
-      public Builder clearName() {
-        
-        name_ = getDefaultInstance().getName();
-        onChanged();
+      public Builder setShape(
+          int index, serialization.Bigdl.Shape.Builder builderForValue) {
+        if (shapeBuilder_ == null) {
+          ensureShapeIsMutable();
+          shape_.set(index, builderForValue.build());
+          onChanged();
+        } else {
+          shapeBuilder_.setMessage(index, builderForValue.build());
+        }
         return this;
       }
       /**
-       * string name = 1;
+       * repeated .serialization.Shape shape = 4;
        */
-      public Builder setNameBytes(
-          com.google.protobuf.ByteString value) {
-        if (value == null) {
-    throw new NullPointerException();
-  }
-  checkByteStringIsUtf8(value);
-        
-        name_ = value;
-        onChanged();
+      public Builder addShape(serialization.Bigdl.Shape value) {
+        if (shapeBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          ensureShapeIsMutable();
+          shape_.add(value);
+          onChanged();
+        } else {
+          shapeBuilder_.addMessage(value);
+        }
         return this;
       }
-
-      private com.google.protobuf.MapField<
-          java.lang.String, serialization.Bigdl.AttrValue> attr_;
-      private com.google.protobuf.MapField
-      internalGetAttr() {
-        if (attr_ == null) {
-          return com.google.protobuf.MapField.emptyMapField(
-              AttrDefaultEntryHolder.defaultEntry);
+      /**
+       * repeated .serialization.Shape shape = 4;
+       */
+      public Builder addShape(
+          int index, serialization.Bigdl.Shape value) {
+        if (shapeBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          ensureShapeIsMutable();
+          shape_.add(index, value);
+          onChanged();
+        } else {
+          shapeBuilder_.addMessage(index, value);
         }
-        return attr_;
+        return this;
       }
-      private com.google.protobuf.MapField
-      internalGetMutableAttr() {
-        onChanged();;
-        if (attr_ == null) {
-          attr_ = com.google.protobuf.MapField.newMapField(
-              AttrDefaultEntryHolder.defaultEntry);
-        }
-        if (!attr_.isMutable()) {
-          attr_ = attr_.copy();
+      /**
+       * repeated .serialization.Shape shape = 4;
+       */
+      public Builder addShape(
+          serialization.Bigdl.Shape.Builder builderForValue) {
+        if (shapeBuilder_ == null) {
+          ensureShapeIsMutable();
+          shape_.add(builderForValue.build());
+          onChanged();
+        } else {
+          shapeBuilder_.addMessage(builderForValue.build());
         }
-        return attr_;
+        return this;
       }
-
-      public int getAttrCount() {
-        return internalGetAttr().getMap().size();
+      /**
+       * repeated .serialization.Shape shape = 4;
+       */
+      public Builder addShape(
+          int index, serialization.Bigdl.Shape.Builder builderForValue) {
+        if (shapeBuilder_ == null) {
+          ensureShapeIsMutable();
+          shape_.add(index, builderForValue.build());
+          onChanged();
+        } else {
+          shapeBuilder_.addMessage(index, builderForValue.build());
+        }
+        return this;
       }
       /**
-       * map<string, .serialization.AttrValue> attr = 2;
+       * repeated .serialization.Shape shape = 4;
        */
-
-      public boolean containsAttr(
-          java.lang.String key) {
-        if (key == null) { throw new java.lang.NullPointerException(); }
-        return internalGetAttr().getMap().containsKey(key);
+      public Builder addAllShape(
+          java.lang.Iterable values) {
+        if (shapeBuilder_ == null) {
+          ensureShapeIsMutable();
+          com.google.protobuf.AbstractMessageLite.Builder.addAll(
+              values, shape_);
+          onChanged();
+        } else {
+          shapeBuilder_.addAllMessages(values);
+        }
+        return this;
       }
       /**
-       * Use {@link #getAttrMap()} instead.
+       * repeated .serialization.Shape shape = 4;
        */
-      @java.lang.Deprecated
-      public java.util.Map getAttr() {
-        return getAttrMap();
+      public Builder clearShape() {
+        if (shapeBuilder_ == null) {
+          shape_ = java.util.Collections.emptyList();
+          bitField0_ = (bitField0_ & ~0x00000008);
+          onChanged();
+        } else {
+          shapeBuilder_.clear();
+        }
+        return this;
       }
       /**
-       * map<string, .serialization.AttrValue> attr = 2;
+       * repeated .serialization.Shape shape = 4;
        */
-
-      public java.util.Map getAttrMap() {
-        return internalGetAttr().getMap();
+      public Builder removeShape(int index) {
+        if (shapeBuilder_ == null) {
+          ensureShapeIsMutable();
+          shape_.remove(index);
+          onChanged();
+        } else {
+          shapeBuilder_.remove(index);
+        }
+        return this;
       }
       /**
-       * map<string, .serialization.AttrValue> attr = 2;
+       * repeated .serialization.Shape shape = 4;
        */
-
-      public serialization.Bigdl.AttrValue getAttrOrDefault(
-          java.lang.String key,
-          serialization.Bigdl.AttrValue defaultValue) {
-        if (key == null) { throw new java.lang.NullPointerException(); }
-        java.util.Map map =
-            internalGetAttr().getMap();
-        return map.containsKey(key) ? map.get(key) : defaultValue;
+      public serialization.Bigdl.Shape.Builder getShapeBuilder(
+          int index) {
+        return getShapeFieldBuilder().getBuilder(index);
       }
       /**
-       * map<string, .serialization.AttrValue> attr = 2;
+       * repeated .serialization.Shape shape = 4;
        */
-
-      public serialization.Bigdl.AttrValue getAttrOrThrow(
-          java.lang.String key) {
-        if (key == null) { throw new java.lang.NullPointerException(); }
-        java.util.Map map =
-            internalGetAttr().getMap();
-        if (!map.containsKey(key)) {
-          throw new java.lang.IllegalArgumentException();
+      public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder(
+          int index) {
+        if (shapeBuilder_ == null) {
+          return shape_.get(index);  } else {
+          return shapeBuilder_.getMessageOrBuilder(index);
         }
-        return map.get(key);
-      }
-
-      public Builder clearAttr() {
-        internalGetMutableAttr().getMutableMap()
-            .clear();
-        return this;
       }
       /**
-       * map<string, .serialization.AttrValue> attr = 2;
+       * repeated .serialization.Shape shape = 4;
        */
-
-      public Builder removeAttr(
-          java.lang.String key) {
-        if (key == null) { throw new java.lang.NullPointerException(); }
-        internalGetMutableAttr().getMutableMap()
-            .remove(key);
-        return this;
+      public java.util.List 
+           getShapeOrBuilderList() {
+        if (shapeBuilder_ != null) {
+          return shapeBuilder_.getMessageOrBuilderList();
+        } else {
+          return java.util.Collections.unmodifiableList(shape_);
+        }
       }
       /**
-       * Use alternate mutation accessors instead.
+       * repeated .serialization.Shape shape = 4;
        */
-      @java.lang.Deprecated
-      public java.util.Map
-      getMutableAttr() {
-        return internalGetMutableAttr().getMutableMap();
+      public serialization.Bigdl.Shape.Builder addShapeBuilder() {
+        return getShapeFieldBuilder().addBuilder(
+            serialization.Bigdl.Shape.getDefaultInstance());
       }
       /**
-       * map<string, .serialization.AttrValue> attr = 2;
+       * repeated .serialization.Shape shape = 4;
        */
-      public Builder putAttr(
-          java.lang.String key,
-          serialization.Bigdl.AttrValue value) {
-        if (key == null) { throw new java.lang.NullPointerException(); }
-        if (value == null) { throw new java.lang.NullPointerException(); }
-        internalGetMutableAttr().getMutableMap()
-            .put(key, value);
-        return this;
+      public serialization.Bigdl.Shape.Builder addShapeBuilder(
+          int index) {
+        return getShapeFieldBuilder().addBuilder(
+            index, serialization.Bigdl.Shape.getDefaultInstance());
       }
       /**
-       * map<string, .serialization.AttrValue> attr = 2;
+       * repeated .serialization.Shape shape = 4;
        */
-
-      public Builder putAllAttr(
-          java.util.Map values) {
-        internalGetMutableAttr().getMutableMap()
-            .putAll(values);
-        return this;
+      public java.util.List 
+           getShapeBuilderList() {
+        return getShapeFieldBuilder().getBuilderList();
+      }
+      private com.google.protobuf.RepeatedFieldBuilderV3<
+          serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> 
+          getShapeFieldBuilder() {
+        if (shapeBuilder_ == null) {
+          shapeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3<
+              serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>(
+                  shape_,
+                  ((bitField0_ & 0x00000008) == 0x00000008),
+                  getParentForChildren(),
+                  isClean());
+          shape_ = null;
+        }
+        return shapeBuilder_;
       }
       public final Builder setUnknownFields(
           final com.google.protobuf.UnknownFieldSet unknownFields) {
@@ -19022,39 +20969,39 @@ public final Builder mergeUnknownFields(
       }
 
 
-      // @@protoc_insertion_point(builder_scope:serialization.NameAttrList)
+      // @@protoc_insertion_point(builder_scope:serialization.Shape)
     }
 
-    // @@protoc_insertion_point(class_scope:serialization.NameAttrList)
-    private static final serialization.Bigdl.NameAttrList DEFAULT_INSTANCE;
+    // @@protoc_insertion_point(class_scope:serialization.Shape)
+    private static final serialization.Bigdl.Shape DEFAULT_INSTANCE;
     static {
-      DEFAULT_INSTANCE = new serialization.Bigdl.NameAttrList();
+      DEFAULT_INSTANCE = new serialization.Bigdl.Shape();
     }
 
-    public static serialization.Bigdl.NameAttrList getDefaultInstance() {
+    public static serialization.Bigdl.Shape getDefaultInstance() {
       return DEFAULT_INSTANCE;
     }
 
-    private static final com.google.protobuf.Parser
-        PARSER = new com.google.protobuf.AbstractParser() {
-      public NameAttrList parsePartialFrom(
+    private static final com.google.protobuf.Parser
+        PARSER = new com.google.protobuf.AbstractParser() {
+      public Shape parsePartialFrom(
           com.google.protobuf.CodedInputStream input,
           com.google.protobuf.ExtensionRegistryLite extensionRegistry)
           throws com.google.protobuf.InvalidProtocolBufferException {
-          return new NameAttrList(input, extensionRegistry);
+          return new Shape(input, extensionRegistry);
       }
     };
 
-    public static com.google.protobuf.Parser parser() {
+    public static com.google.protobuf.Parser parser() {
       return PARSER;
     }
 
     @java.lang.Override
-    public com.google.protobuf.Parser getParserForType() {
+    public com.google.protobuf.Parser getParserForType() {
       return PARSER;
     }
 
-    public serialization.Bigdl.NameAttrList getDefaultInstanceForType() {
+    public serialization.Bigdl.Shape getDefaultInstanceForType() {
       return DEFAULT_INSTANCE;
     }
 
@@ -19110,6 +21057,11 @@ public serialization.Bigdl.NameAttrList getDefaultInstanceForType() {
   private static final 
     com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
       internal_static_serialization_NameAttrList_AttrEntry_fieldAccessorTable;
+  private static final com.google.protobuf.Descriptors.Descriptor
+    internal_static_serialization_Shape_descriptor;
+  private static final 
+    com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+      internal_static_serialization_Shape_fieldAccessorTable;
 
   public static com.google.protobuf.Descriptors.FileDescriptor
       getDescriptor() {
@@ -19146,8 +21098,8 @@ public serialization.Bigdl.NameAttrList getDefaultInstanceForType() {
       "int_data\030\006 \003(\005\022\021\n\tlong_data\030\007 \003(\003\022\022\n\nbyt" +
       "es_data\030\010 \003(\014\022\n\n\002id\030\t \001(\005\"[\n\013Regularizer" +
       "\0227\n\017regularizerType\030\001 \001(\0162\036.serializatio" +
-      "n.RegularizerType\022\023\n\013regularData\030\002 \003(\001\"\332" +
-      "\t\n\tAttrValue\022)\n\010dataType\030\001 \001(\0162\027.seriali" +
+      "n.RegularizerType\022\023\n\013regularData\030\002 \003(\001\"\246" +
+      "\n\n\tAttrValue\022)\n\010dataType\030\001 \001(\0162\027.seriali" +
       "zation.DataType\022\017\n\007subType\030\002 \001(\t\022\024\n\nint3",
       "2Value\030\003 \001(\005H\000\022\024\n\nint64Value\030\004 \001(\003H\000\022\024\n\n" +
       "floatValue\030\005 \001(\002H\000\022\025\n\013doubleValue\030\006 \001(\001H" +
@@ -19164,44 +21116,50 @@ public serialization.Bigdl.NameAttrList getDefaultInstanceForType() {
       "serialization.AttrValue.ArrayValueH\000\0229\n\017" +
       "dataFormatValue\030\020 \001(\0162\036.serialization.In" +
       "putDataFormatH\000\022+\n\013customValue\030\021 \001(\0132\024.g" +
-      "oogle.protobuf.AnyH\000\032\223\004\n\nArrayValue\022\014\n\004s" +
-      "ize\030\001 \001(\005\022)\n\010datatype\030\002 \001(\0162\027.serializat" +
-      "ion.DataType\022\013\n\003i32\030\003 \003(\005\022\013\n\003i64\030\004 \003(\003\022\013" +
-      "\n\003flt\030\005 \003(\002\022\013\n\003dbl\030\006 \003(\001\022\013\n\003str\030\007 \003(\t\022\017\n" +
-      "\007boolean\030\010 \003(\010\022/\n\013Regularizer\030\t \003(\0132\032.se",
-      "rialization.Regularizer\022*\n\006tensor\030\n \003(\0132" +
-      "\032.serialization.BigDLTensor\0220\n\016variableF" +
-      "ormat\030\013 \003(\0162\030.serialization.VarFormat\022-\n" +
-      "\ninitMethod\030\014 \003(\0132\031.serialization.InitMe" +
-      "thod\022/\n\013bigDLModule\030\r \003(\0132\032.serializatio" +
-      "n.BigDLModule\0221\n\014nameAttrList\030\016 \003(\0132\033.se" +
-      "rialization.NameAttrList\0222\n\ndataFormat\030\017" +
-      " \003(\0162\036.serialization.InputDataFormat\022$\n\006" +
-      "custom\030\020 \003(\0132\024.google.protobuf.AnyB\007\n\005va" +
-      "lue\"\230\001\n\014NameAttrList\022\014\n\004name\030\001 \001(\t\0223\n\004at",
-      "tr\030\002 \003(\0132%.serialization.NameAttrList.At" +
-      "trEntry\032E\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005val" +
-      "ue\030\002 \001(\0132\030.serialization.AttrValue:\0028\001*\260" +
-      "\001\n\tVarFormat\022\020\n\014EMPTY_FORMAT\020\000\022\013\n\007DEFAUL" +
-      "T\020\001\022\t\n\005ONE_D\020\002\022\n\n\006IN_OUT\020\003\022\n\n\006OUT_IN\020\004\022\020" +
-      "\n\014IN_OUT_KW_KH\020\005\022\020\n\014OUT_IN_KW_KH\020\006\022\023\n\017GP" +
-      "_OUT_IN_KW_KH\020\007\022\023\n\017GP_IN_OUT_KW_KH\020\010\022\023\n\017" +
-      "OUT_IN_KT_KH_KW\020\t*\253\001\n\016InitMethodType\022\030\n\024" +
-      "EMPTY_INITIALIZATION\020\000\022\022\n\016RANDOM_UNIFORM" +
-      "\020\001\022\030\n\024RANDOM_UNIFORM_PARAM\020\002\022\021\n\rRANDOM_N",
-      "ORMAL\020\003\022\t\n\005ZEROS\020\004\022\010\n\004ONES\020\005\022\t\n\005CONST\020\006\022" +
-      "\n\n\006XAVIER\020\007\022\022\n\016BILINEARFILLER\020\010*L\n\017Regul" +
-      "arizerType\022\023\n\017L1L2Regularizer\020\000\022\021\n\rL1Reg" +
-      "ularizer\020\001\022\021\n\rL2Regularizer\020\002*%\n\017InputDa" +
-      "taFormat\022\010\n\004NCHW\020\000\022\010\n\004NHWC\020\001*\"\n\nTensorTy" +
-      "pe\022\t\n\005DENSE\020\000\022\t\n\005QUANT\020\001*\375\001\n\010DataType\022\t\n" +
-      "\005INT32\020\000\022\t\n\005INT64\020\001\022\t\n\005FLOAT\020\002\022\n\n\006DOUBLE" +
-      "\020\003\022\n\n\006STRING\020\004\022\010\n\004BOOL\020\005\022\010\n\004CHAR\020\006\022\t\n\005SH" +
-      "ORT\020\007\022\t\n\005BYTES\020\010\022\017\n\013REGULARIZER\020\t\022\n\n\006TEN" +
-      "SOR\020\n\022\023\n\017VARIABLE_FORMAT\020\013\022\016\n\nINITMETHOD",
-      "\020\014\022\n\n\006MODULE\020\r\022\022\n\016NAME_ATTR_LIST\020\016\022\017\n\013AR" +
-      "RAY_VALUE\020\017\022\017\n\013DATA_FORMAT\020\020\022\n\n\006CUSTOM\020\021" +
-      "b\006proto3"
+      "oogle.protobuf.AnyH\000\022%\n\005shape\030\022 \001(\0132\024.se" +
+      "rialization.ShapeH\000\032\270\004\n\nArrayValue\022\014\n\004si" +
+      "ze\030\001 \001(\005\022)\n\010datatype\030\002 \001(\0162\027.serializati" +
+      "on.DataType\022\013\n\003i32\030\003 \003(\005\022\013\n\003i64\030\004 \003(\003\022\013\n" +
+      "\003flt\030\005 \003(\002\022\013\n\003dbl\030\006 \003(\001\022\013\n\003str\030\007 \003(\t\022\017\n\007",
+      "boolean\030\010 \003(\010\022/\n\013Regularizer\030\t \003(\0132\032.ser" +
+      "ialization.Regularizer\022*\n\006tensor\030\n \003(\0132\032" +
+      ".serialization.BigDLTensor\0220\n\016variableFo" +
+      "rmat\030\013 \003(\0162\030.serialization.VarFormat\022-\n\n" +
+      "initMethod\030\014 \003(\0132\031.serialization.InitMet" +
+      "hod\022/\n\013bigDLModule\030\r \003(\0132\032.serialization" +
+      ".BigDLModule\0221\n\014nameAttrList\030\016 \003(\0132\033.ser" +
+      "ialization.NameAttrList\0222\n\ndataFormat\030\017 " +
+      "\003(\0162\036.serialization.InputDataFormat\022$\n\006c" +
+      "ustom\030\020 \003(\0132\024.google.protobuf.Any\022#\n\005sha",
+      "pe\030\021 \003(\0132\024.serialization.ShapeB\007\n\005value\"" +
+      "\230\001\n\014NameAttrList\022\014\n\004name\030\001 \001(\t\0223\n\004attr\030\002" +
+      " \003(\0132%.serialization.NameAttrList.AttrEn" +
+      "try\032E\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002" +
+      " \001(\0132\030.serialization.AttrValue:\0028\001\"\246\001\n\005S" +
+      "hape\0221\n\tshapeType\030\001 \001(\0162\036.serialization." +
+      "Shape.ShapeType\022\r\n\005ssize\030\002 \001(\005\022\022\n\nshapeV" +
+      "alue\030\003 \003(\005\022#\n\005shape\030\004 \003(\0132\024.serializatio" +
+      "n.Shape\"\"\n\tShapeType\022\n\n\006SINGLE\020\000\022\t\n\005MULT" +
+      "I\020\001*\260\001\n\tVarFormat\022\020\n\014EMPTY_FORMAT\020\000\022\013\n\007D",
+      "EFAULT\020\001\022\t\n\005ONE_D\020\002\022\n\n\006IN_OUT\020\003\022\n\n\006OUT_I" +
+      "N\020\004\022\020\n\014IN_OUT_KW_KH\020\005\022\020\n\014OUT_IN_KW_KH\020\006\022" +
+      "\023\n\017GP_OUT_IN_KW_KH\020\007\022\023\n\017GP_IN_OUT_KW_KH\020" +
+      "\010\022\023\n\017OUT_IN_KT_KH_KW\020\t*\253\001\n\016InitMethodTyp" +
+      "e\022\030\n\024EMPTY_INITIALIZATION\020\000\022\022\n\016RANDOM_UN" +
+      "IFORM\020\001\022\030\n\024RANDOM_UNIFORM_PARAM\020\002\022\021\n\rRAN" +
+      "DOM_NORMAL\020\003\022\t\n\005ZEROS\020\004\022\010\n\004ONES\020\005\022\t\n\005CON" +
+      "ST\020\006\022\n\n\006XAVIER\020\007\022\022\n\016BILINEARFILLER\020\010*L\n\017" +
+      "RegularizerType\022\023\n\017L1L2Regularizer\020\000\022\021\n\r" +
+      "L1Regularizer\020\001\022\021\n\rL2Regularizer\020\002*%\n\017In",
+      "putDataFormat\022\010\n\004NCHW\020\000\022\010\n\004NHWC\020\001*\"\n\nTen" +
+      "sorType\022\t\n\005DENSE\020\000\022\t\n\005QUANT\020\001*\210\002\n\010DataTy" +
+      "pe\022\t\n\005INT32\020\000\022\t\n\005INT64\020\001\022\t\n\005FLOAT\020\002\022\n\n\006D" +
+      "OUBLE\020\003\022\n\n\006STRING\020\004\022\010\n\004BOOL\020\005\022\010\n\004CHAR\020\006\022" +
+      "\t\n\005SHORT\020\007\022\t\n\005BYTES\020\010\022\017\n\013REGULARIZER\020\t\022\n" +
+      "\n\006TENSOR\020\n\022\023\n\017VARIABLE_FORMAT\020\013\022\016\n\nINITM" +
+      "ETHOD\020\014\022\n\n\006MODULE\020\r\022\022\n\016NAME_ATTR_LIST\020\016\022" +
+      "\017\n\013ARRAY_VALUE\020\017\022\017\n\013DATA_FORMAT\020\020\022\n\n\006CUS" +
+      "TOM\020\021\022\t\n\005SHAPE\020\022b\006proto3"
     };
     com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner =
         new com.google.protobuf.Descriptors.FileDescriptor.    InternalDescriptorAssigner() {
@@ -19257,13 +21215,13 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors(
     internal_static_serialization_AttrValue_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_serialization_AttrValue_descriptor,
-        new java.lang.String[] { "DataType", "SubType", "Int32Value", "Int64Value", "FloatValue", "DoubleValue", "StringValue", "BoolValue", "RegularizerValue", "TensorValue", "VariableFormatValue", "InitMethodValue", "BigDLModuleValue", "NameAttrListValue", "ArrayValue", "DataFormatValue", "CustomValue", "Value", });
+        new java.lang.String[] { "DataType", "SubType", "Int32Value", "Int64Value", "FloatValue", "DoubleValue", "StringValue", "BoolValue", "RegularizerValue", "TensorValue", "VariableFormatValue", "InitMethodValue", "BigDLModuleValue", "NameAttrListValue", "ArrayValue", "DataFormatValue", "CustomValue", "Shape", "Value", });
     internal_static_serialization_AttrValue_ArrayValue_descriptor =
       internal_static_serialization_AttrValue_descriptor.getNestedTypes().get(0);
     internal_static_serialization_AttrValue_ArrayValue_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_serialization_AttrValue_ArrayValue_descriptor,
-        new java.lang.String[] { "Size", "Datatype", "I32", "I64", "Flt", "Dbl", "Str", "Boolean", "Regularizer", "Tensor", "VariableFormat", "InitMethod", "BigDLModule", "NameAttrList", "DataFormat", "Custom", });
+        new java.lang.String[] { "Size", "Datatype", "I32", "I64", "Flt", "Dbl", "Str", "Boolean", "Regularizer", "Tensor", "VariableFormat", "InitMethod", "BigDLModule", "NameAttrList", "DataFormat", "Custom", "Shape", });
     internal_static_serialization_NameAttrList_descriptor =
       getDescriptor().getMessageTypes().get(6);
     internal_static_serialization_NameAttrList_fieldAccessorTable = new
@@ -19276,6 +21234,12 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors(
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_serialization_NameAttrList_AttrEntry_descriptor,
         new java.lang.String[] { "Key", "Value", });
+    internal_static_serialization_Shape_descriptor =
+      getDescriptor().getMessageTypes().get(7);
+    internal_static_serialization_Shape_fieldAccessorTable = new
+      com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
+        internal_static_serialization_Shape_descriptor,
+        new java.lang.String[] { "ShapeType", "Ssize", "ShapeValue", "Shape", });
     com.google.protobuf.AnyProto.getDescriptor();
   }
 
diff --git a/scala/dllib/src/main/resources/serialization/bigdl.proto b/scala/dllib/src/main/resources/serialization/bigdl.proto
index 599eacc7a37..a84d847807d 100644
--- a/scala/dllib/src/main/resources/serialization/bigdl.proto
+++ b/scala/dllib/src/main/resources/serialization/bigdl.proto
@@ -108,6 +108,7 @@ enum DataType {
     ARRAY_VALUE = 15;
     DATA_FORMAT = 16;
     CUSTOM = 17;
+    SHAPE = 18;
 }
 
 message AttrValue {
@@ -128,6 +129,7 @@ message AttrValue {
         repeated NameAttrList nameAttrList = 14; //
         repeated InputDataFormat dataFormat = 15; // "Array(DataFormat)"
         repeated google.protobuf.Any custom = 16; // "Array(Any)"
+        repeated Shape shape = 17; // "Array(Shape)"
     }
     DataType dataType = 1;
     string subType = 2; // specific for custom data
@@ -147,9 +149,21 @@ message AttrValue {
         ArrayValue arrayValue = 15; //array value of any type
         InputDataFormat dataFormatValue = 16; // data format
         google.protobuf.Any customValue = 17; // custom value
+        Shape shape = 18; // Shape value
     }
 }
 message NameAttrList {
     string name = 1;
     map attr = 2;
 }
+
+message Shape {
+    enum ShapeType {
+        SINGLE = 0;
+        MULTI = 1;
+    }
+    ShapeType shapeType = 1;
+    int32 ssize = 2;
+    repeated int32 shapeValue = 3;
+    repeated Shape shape = 4;
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala
index f969a9610d7..e27570223cc 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala
@@ -25,6 +25,7 @@ import com.intel.analytics.bigdl.optim.Regularizer
 import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.serializer._
+import com.intel.analytics.bigdl.utils.{MultiShape, SingleShape, Shape => BigDLShape}
 import serialization.Bigdl._
 import serialization.Bigdl.AttrValue.ArrayValue
 
@@ -92,6 +93,8 @@ object DataConverter extends DataConverter{
       universe.typeOf[VariableFormat]
     } else if (value.isInstanceOf[DataFormat]) {
       universe.typeOf[DataFormat]
+    } else if (value.isInstanceOf[BigDLShape]) {
+      universe.typeOf[BigDLShape]
     } else {
       val cls = value.getClass
       val runtimeMirror = universe.runtimeMirror(getClass.getClassLoader)
@@ -119,6 +122,7 @@ object DataConverter extends DataConverter{
       case DataType.ARRAY_VALUE => ArrayConverter.getAttributeValue(context, attribute)
       case DataType.DATA_FORMAT => DataFormatConverter.getAttributeValue(context, attribute)
       case DataType.CUSTOM => CustomConverterDelegator.getAttributeValue(context, attribute)
+      case DataType.SHAPE => ShapeConverter.getAttributeValue(context, attribute)
       case _ => throw new IllegalArgumentException
         (s"${attribute.getDataType} can not be recognized")
     }
@@ -183,6 +187,8 @@ object DataConverter extends DataConverter{
       ArrayConverter.setAttributeValue(context, attributeBuilder, value, valueType)
     } else if (valueType =:= universe.typeOf[DataFormat]) {
       DataFormatConverter.setAttributeValue(context, attributeBuilder, value)
+    } else if (valueType =:= universe.typeOf[BigDLShape]) {
+      ShapeConverter.setAttributeValue(context, attributeBuilder, value)
     } else {
       CustomConverterDelegator.setAttributeValue(context, attributeBuilder, value, valueType)
     }
@@ -378,6 +384,13 @@ object DataConverter extends DataConverter{
             i += 1
           })
           customValues
+        case DataType.SHAPE =>
+          valueArray.getShapeList.asScala.map(shape => {
+            val attrValue = AttrValue.newBuilder
+            attrValue.setDataType(DataType.SHAPE)
+            attrValue.setShape(shape)
+            ShapeConverter.getAttributeValue(context, attrValue.build).asInstanceOf[BigDLShape]
+          }).toArray
       }
       arr
     }
@@ -505,6 +518,17 @@ object DataConverter extends DataConverter{
           })
           arrayBuilder.setSize(formats.size)
         }
+      } else if (valueType =:= universe.typeOf[Array[BigDLShape]]) {
+        arrayBuilder.setDatatype(DataType.SHAPE)
+        if (value != null) {
+          val shapes = value.asInstanceOf[Array[BigDLShape]]
+          shapes.foreach(shape => {
+            val attrValueBuilder = AttrValue.newBuilder
+            ShapeConverter.setAttributeValue(context, attrValueBuilder, shape)
+            arrayBuilder.addShape(attrValueBuilder.getShape)
+          })
+          arrayBuilder.setSize(shapes.size)
+        }
       } else {
         arrayBuilder.setDatatype(DataType.CUSTOM)
         if (value != null) {
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala
new file mode 100644
index 00000000000..93d7b92c0fc
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala
@@ -0,0 +1,83 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.intel.analytics.bigdl.utils.serializer.converters
+
+import com.intel.analytics.bigdl.tensor.TensorNumericMath
+import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext}
+import com.intel.analytics.bigdl.utils.{MultiShape, SingleShape, Shape => BigDLShape}
+import serialization.Bigdl
+import serialization.Bigdl.Shape.ShapeType
+import serialization.Bigdl.{AttrValue, DataType, Shape}
+
+import scala.collection.JavaConverters._
+import scala.reflect.ClassTag
+import scala.reflect.runtime.universe
+
+object ShapeConverter extends DataConverter {
+  override def getAttributeValue[T: ClassTag]
+  (context: DeserializeContext, attribute: Bigdl.AttrValue)
+  (implicit ev: TensorNumericMath.TensorNumeric[T]): AnyRef = {
+    val shape = attribute.getShape
+    toBigDLShape(shape)
+  }
+
+  private def toBigDLShape(shape : Shape): BigDLShape = {
+    if (shape.getShapeType == ShapeType.SINGLE) {
+      val shapeValues = shape.getShapeValueList.asScala.toList.map(_.intValue)
+      SingleShape(shapeValues)
+    } else if (shape.getShapeType == ShapeType.MULTI) {
+      val shapes = shape.getShapeList.asScala.toList.map(toBigDLShape(_))
+      MultiShape(shapes)
+    } else {
+      throw new RuntimeException(s"${shape.getShapeType} not supported for now")
+    }
+  }
+
+  override def setAttributeValue[T: ClassTag]
+  (context: SerializeContext[T], attributeBuilder: AttrValue.Builder,
+   value: Any, valueType: universe.Type)(implicit ev: TensorNumericMath.TensorNumeric[T]): Unit = {
+    attributeBuilder.setDataType(DataType.SHAPE)
+    if (value != null) {
+      val shape = value.asInstanceOf[BigDLShape]
+      val shapeBuilder = Shape.newBuilder
+      setShape(shape, shapeBuilder)
+      attributeBuilder.setShape(shapeBuilder.build)
+    }
+  }
+
+  private def setShape(bigdlShape : BigDLShape, shapeBuilder : Shape.Builder): Unit = {
+    if (bigdlShape.isInstanceOf[SingleShape]) {
+      shapeBuilder.setShapeType(ShapeType.SINGLE)
+      val shapes = bigdlShape.toSingle
+      shapeBuilder.setSsize(shapes.size)
+      shapes.foreach(shape => {
+        shapeBuilder.addShapeValue(shape)
+      })
+    } else if (bigdlShape.isInstanceOf[MultiShape]) {
+      shapeBuilder.setShapeType(ShapeType.MULTI)
+      val shapes = bigdlShape.toMulti
+      shapeBuilder.setSsize(shapes.size)
+      shapes.foreach(shape => {
+        val subShapeBuilder = Shape.newBuilder
+        setShape(shape, subShapeBuilder)
+        shapeBuilder.addShape(subShapeBuilder.build)
+      })
+    } else {
+      throw new RuntimeException(s"${bigdlShape} type not supported !")
+    }
+  }
+}
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala
index 20b04fa9c4b..f9824f3ad96 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala
@@ -23,11 +23,13 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFo
 import com.intel.analytics.bigdl.nn.quantized.{LinearWeight, LinearWeightParams}
 import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer, Regularizer}
 import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Storage, Tensor}
+import com.intel.analytics.bigdl.utils.{MultiShape, SingleShape, Shape => BigDLShape}
 import org.scalatest.{FlatSpec, Matchers}
 import serialization.Bigdl.{AttrValue, BigDLTensor, DataType, TensorStorage}
 
 import scala.reflect.runtime.universe
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
+import com.intel.analytics.bigdl.utils.SingleShape
 import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter
 import serialization.Bigdl.AttrValue.ArrayValue
 
@@ -754,4 +756,61 @@ class DataConverterSpec extends FlatSpec with Matchers{
     val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map,
       ProtoStorageType), attr)
   }
+
+  "Single Shape converter" should "work properly" in {
+    val shape = SingleShape(List(1, 3, 4))
+    map.clear()
+    val attriBulder = AttrValue.newBuilder
+    DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder,
+      shape, universe.typeOf[BigDLShape])
+    val attr = attriBulder.build
+    map.clear()
+    val retrievedValue = DataConverter.
+      getAttributeValue(DeserializeContext(null, map, ProtoStorageType),
+        attriBulder.build)
+
+    shape should be (retrievedValue)
+
+  }
+
+  "Multiple shape converter" should "work properly" in {
+    val shape1 = SingleShape(List(1, 3, 4))
+    val shape2 = SingleShape(List(1, 3, 4))
+
+    val mul1 = MultiShape(List(shape1, shape2))
+
+    val shape3 = SingleShape(List(1, 3, 4))
+
+    val mul2 = MultiShape(List(shape3, mul1))
+
+    map.clear()
+    val attriBulder = AttrValue.newBuilder
+    DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder,
+      mul2, universe.typeOf[BigDLShape])
+    val attr = attriBulder.build
+    map.clear()
+    val retrievedValue = DataConverter.
+      getAttributeValue(DeserializeContext(null, map, ProtoStorageType),
+        attriBulder.build)
+
+    mul2 should be (retrievedValue)
+  }
+
+  "Array of shape converter" should "work properly" in {
+    val shape1 = SingleShape(List(1, 3, 4))
+    val shape2 = SingleShape(List(1, 3, 4))
+    val array = Array[BigDLShape](shape1, shape2)
+    map.clear()
+    val attriBulder = AttrValue.newBuilder
+    DataConverter.setAttributeValue(SerializeContext(null, map, ProtoStorageType), attriBulder,
+      array, universe.typeOf[Array[BigDLShape]])
+    val attr = attriBulder.build
+    map.clear()
+    val retrievedValue = DataConverter.
+      getAttributeValue(DeserializeContext(null, map, ProtoStorageType),
+        attriBulder.build)
+
+    array should be (retrievedValue)
+
+  }
 }

From b246b842df7ec8aace853c5978e4f76f0c23fb0b Mon Sep 17 00:00:00 2001
From: Ian Wong 
Date: Wed, 24 Jan 2018 10:24:30 +0800
Subject: [PATCH 0652/1065] Refine container (#2166)

* move add method to subclass of container

* meet code review

* fix conflict code
---
 .../bigdl/dllib/nn/BiRecurrent.scala          |  5 +--
 .../analytics/bigdl/dllib/nn/Bottle.scala     |  3 +-
 .../analytics/bigdl/dllib/nn/Concat.scala     |  3 +-
 .../bigdl/dllib/nn/ConcatTable.scala          |  5 ++-
 .../analytics/bigdl/dllib/nn/Container.scala  | 17 ++-----
 .../bigdl/dllib/nn/DynamicContainer.scala     | 44 +++++++++++++++++++
 .../analytics/bigdl/dllib/nn/Graph.scala      |  5 ---
 .../analytics/bigdl/dllib/nn/MapTable.scala   |  2 +-
 .../bigdl/dllib/nn/ParallelTable.scala        |  3 +-
 .../analytics/bigdl/dllib/nn/Recurrent.scala  |  4 +-
 .../analytics/bigdl/dllib/nn/Sequential.scala |  2 +-
 11 files changed, 62 insertions(+), 31 deletions(-)
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala
index 48724d71424..fb3af81214e 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala
@@ -38,7 +38,7 @@ class BiRecurrent[T : ClassTag] (
   private val merge: AbstractModule[Table, Tensor[T], T] = null,
   val batchNormParams: BatchNormParams[T] = null,
   val isSplitInput: Boolean = false)
-  (implicit ev: TensorNumeric[T]) extends Container[Tensor[T], Tensor[T], T] {
+  (implicit ev: TensorNumeric[T]) extends DynamicContainer[Tensor[T], Tensor[T], T] {
 
   val timeDim = 2
   val featDim = 3
@@ -65,8 +65,7 @@ class BiRecurrent[T : ClassTag] (
   if (merge == null) birnn.add(CAddTable[T](true))
   else birnn.add(merge)
 
-  override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]):
-    BiRecurrent.this.type = {
+  override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = {
     layer.add(module)
     revLayer.add(module.cloneModule())
     modules.append(birnn)
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bottle.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bottle.scala
index fd51be023ab..3aa5fb13472 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bottle.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bottle.scala
@@ -16,6 +16,7 @@
 package com.intel.analytics.bigdl.nn
 
 import com.intel.analytics.bigdl._
+import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.tensor.{Storage, Tensor}
 
@@ -35,7 +36,7 @@ class Bottle[T: ClassTag](
   val module: Module[T],
   val nInputDim: Int = 2,
   val nOutputDim1: Int = Int.MaxValue)
- (implicit ev: TensorNumeric[T]) extends Container[Tensor[T], Tensor[T], T] {
+ (implicit ev: TensorNumeric[T]) extends DynamicContainer[Tensor[T], Tensor[T], T] {
 
   private val nOutputDim = if (nOutputDim1 == Int.MaxValue) nInputDim else nOutputDim1
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala
index f33e0ddf4a2..819bfee85bb 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala
@@ -42,7 +42,8 @@ import scala.reflect.ClassTag
  */
 @SerialVersionUID(- 5218461876031660707L)
 class Concat[T: ClassTag](val dimension: Int)(
-  implicit ev: TensorNumeric[T]) extends Container[Tensor[T], Tensor[T], T] {
+  implicit ev: TensorNumeric[T]) extends DynamicContainer[Tensor[T], Tensor[T], T] {
+
   private var size: Array[Int] = null
   @transient
   private var results: Array[Future[Unit]] = null
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala
index d1f66bb8a3c..ebe0717660f 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala
@@ -17,7 +17,7 @@
 package com.intel.analytics.bigdl.nn
 
 import com.intel.analytics.bigdl.nn.Graph.ModuleNode
-import com.intel.analytics.bigdl.nn.abstractnn.Activity
+import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
 import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.{T, Table}
@@ -34,7 +34,8 @@ import scala.reflect.ClassTag
  */
 @SerialVersionUID(- 704681653938468956L)
 class ConcatTable[T : ClassTag]
-  (implicit ev: TensorNumeric[T]) extends Container[Activity, Table, T] {
+  (implicit ev: TensorNumeric[T]) extends DynamicContainer[Activity, Table, T] {
+
   override def updateOutput(input: Activity): Table = {
     require(modules.length > 0, "empty modules of concat table")
     if (gradInput == null) {
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala
index 8f81e76fa82..9b485c77360 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala
@@ -16,10 +16,11 @@
 
 package com.intel.analytics.bigdl.nn
 
+import com.intel.analytics.bigdl.Module
 import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
 import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
-import com.intel.analytics.bigdl.utils.{T, Table, Util}
+import com.intel.analytics.bigdl.utils.{T, Table}
 
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
@@ -28,7 +29,7 @@ import scala.reflect.ClassTag
  * [[Container]] is an abstract [[AbstractModule]] class which
  * declares methods defined in all containers. A container usually
  * contain some other modules in the `modules` variable. It overrides
- * many module methods such that calls are propogated to the contained
+ * many module methods such that calls are propagated to the contained
  * modules.
  *
  * @tparam A Input data type
@@ -50,18 +51,6 @@ abstract class Container[A <: Activity : ClassTag,
     modules.filter(!_.isCompatibleWithTorch()).length <= 0
   }
 
-  /**
-   * Add a sub-module to the contained `modules`
-   *
-   * @param module module to be add
-   * @return this container
-   */
-  def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = {
-    Util.excludeNotTorch[T](Seq(module))
-    modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]]
-    this
-  }
-
   override def zeroGradParameters(): Unit = {
     modules.foreach(_.zeroGradParameters())
   }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala
new file mode 100644
index 00000000000..b1f01ec7b34
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala
@@ -0,0 +1,44 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn
+
+import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.bigdl.utils.Util
+
+import scala.reflect.ClassTag
+
+/**
+ * DynamicContainer allow user to change its submodules after create it.
+ * @tparam A Input data type
+ * @tparam B Output data type
+ * @tparam T Numeric type. Only support float/double now
+ */
+abstract class DynamicContainer[A <: Activity : ClassTag, B <: Activity : ClassTag, T: ClassTag](
+  implicit ev: TensorNumeric[T]) extends Container[A, B, T] {
+
+  /**
+   * Add a sub-module to the contained `modules`
+   *
+   * @param module module to be add
+   * @return this container
+   */
+  def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = {
+    Util.excludeNotTorch[T](Seq(module))
+    modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]]
+    this
+  }
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala
index 3304094aaf6..2818ff80954 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala
@@ -128,11 +128,6 @@ abstract class Graph[T: ClassTag](
     }
   }
 
-  override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Graph.this.type = {
-    throw new IllegalArgumentException("Graph: Please don't use add method in Graph container. " +
-      "A graph container should not be changed after it is constructed")
-  }
-
   // todo: expand the graph
   override def toGraph(startNodes: ModuleNode[T]*): Graph[T] = this
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala
index 92ea280e70d..227a348b88c 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala
@@ -36,7 +36,7 @@ import scala.reflect.ClassTag
 @SerialVersionUID( 4403280698280280268L)
 class MapTable[T: ClassTag](
   var module: AbstractModule[_ <: Activity, _ <: Activity, T] = null)
-  (implicit ev: TensorNumeric[T]) extends Container[Table, Table, T]  {
+  (implicit ev: TensorNumeric[T]) extends DynamicContainer[Table, Table, T]  {
 
   if ( module != null) {
     this.add(module)
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala
index 35cb2df67eb..4636fedfecc 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala
@@ -16,6 +16,7 @@
 package com.intel.analytics.bigdl.nn
 
 import com.intel.analytics.bigdl.nn.Graph.ModuleNode
+import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.Table
 
@@ -29,7 +30,7 @@ import scala.reflect.ClassTag
 
 @SerialVersionUID(- 1197848941394786045L)
 class ParallelTable[T: ClassTag]
-  (implicit ev: TensorNumeric[T]) extends Container[Table, Table, T] {
+  (implicit ev: TensorNumeric[T]) extends DynamicContainer[Table, Table, T] {
 
   override def updateOutput(input: Table): Table = {
     var i = 0
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala
index d4560d0b9a9..cc6aca58398 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala
@@ -36,7 +36,7 @@ import scala.reflect.ClassTag
  * Different types of rnn cells can be added using add() function
  */
 class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null)
-  (implicit ev: TensorNumeric[T]) extends Container[Tensor[T], Tensor[T], T] {
+  (implicit ev: TensorNumeric[T]) extends DynamicContainer[Tensor[T], Tensor[T], T] {
 
   protected var hidden: Activity = null
   protected var gradHidden: Activity = null
@@ -73,7 +73,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null)
    * @param module module to be add
    * @return this container
    */
-  override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Recurrent.this.type = {
+  override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = {
     require(module.isInstanceOf[Cell[T]],
       "Recurrent: added module should be Cell type!")
     require(!module.isInstanceOf[MultiRNNCell[T]],
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala
index a56787a72d6..f551a49c2ea 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala
@@ -29,7 +29,7 @@ import scala.reflect.ClassTag
 
 @SerialVersionUID(5375403296928513267L)
 class Sequential[T: ClassTag]
-(implicit ev: TensorNumeric[T]) extends Container[Activity, Activity, T] {
+(implicit ev: TensorNumeric[T]) extends DynamicContainer[Activity, Activity, T] {
 
   override def updateOutput(input: Activity): Activity = {
     var i = 0

From fad07371757f0bca0800b334f5dca352b9f5f2e9 Mon Sep 17 00:00:00 2001
From: Jerry Wu 
Date: Wed, 24 Jan 2018 13:50:03 +0800
Subject: [PATCH 0653/1065] add shape support in module (#2221)

* add shape support in module

* fix

* refinement
---
 .../src/main/java/serialization/Bigdl.java    | 899 ++++++++++++++++--
 .../main/resources/serialization/bigdl.proto  |   2 +
 .../utils/serializer/ModuleSerializable.scala |  41 +-
 3 files changed, 855 insertions(+), 87 deletions(-)

diff --git a/scala/dllib/src/main/java/serialization/Bigdl.java b/scala/dllib/src/main/java/serialization/Bigdl.java
index 2610f1194e0..472d63bb08c 100644
--- a/scala/dllib/src/main/java/serialization/Bigdl.java
+++ b/scala/dllib/src/main/java/serialization/Bigdl.java
@@ -1210,6 +1210,75 @@ serialization.Bigdl.AttrValue getAttrOrThrow(
      * int32 id = 12;
      */
     int getId();
+
+    /**
+     * 
+     * input shape
+     * 

+     *
+     * .serialization.Shape inputShape = 13;
+     */
+    boolean hasInputShape();
+    /**
+     * 
+     * input shape
+     * 

+     *
+     * .serialization.Shape inputShape = 13;
+     */
+    serialization.Bigdl.Shape getInputShape();
+    /**
+     * 
+     * input shape
+     * 

+     *
+     * .serialization.Shape inputShape = 13;
+     */
+    serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder();
+
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    java.util.List 
+        getOutputShapeList();
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    serialization.Bigdl.Shape getOutputShape(int index);
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    int getOutputShapeCount();
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    java.util.List 
+        getOutputShapeOrBuilderList();
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder(
+        int index);
   }
   /**
    * Protobuf type {@code serialization.BigDLModule}
@@ -1233,6 +1302,7 @@ private BigDLModule() {
       train_ = false;
       namePostfix_ = "";
       id_ = 0;
+      outputShape_ = java.util.Collections.emptyList();
     }
 
     @java.lang.Override
@@ -1363,6 +1433,28 @@ private BigDLModule(
               id_ = input.readInt32();
               break;
             }
+            case 106: {
+              serialization.Bigdl.Shape.Builder subBuilder = null;
+              if (inputShape_ != null) {
+                subBuilder = inputShape_.toBuilder();
+              }
+              inputShape_ = input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry);
+              if (subBuilder != null) {
+                subBuilder.mergeFrom(inputShape_);
+                inputShape_ = subBuilder.buildPartial();
+              }
+
+              break;
+            }
+            case 114: {
+              if (!((mutable_bitField0_ & 0x00002000) == 0x00002000)) {
+                outputShape_ = new java.util.ArrayList();
+                mutable_bitField0_ |= 0x00002000;
+              }
+              outputShape_.add(
+                  input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry));
+              break;
+            }
           }
         }
       } catch (com.google.protobuf.InvalidProtocolBufferException e) {
@@ -1380,6 +1472,9 @@ private BigDLModule(
         if (((mutable_bitField0_ & 0x00000020) == 0x00000020)) {
           nextModules_ = nextModules_.getUnmodifiableView();
         }
+        if (((mutable_bitField0_ & 0x00002000) == 0x00002000)) {
+          outputShape_ = java.util.Collections.unmodifiableList(outputShape_);
+        }
         this.unknownFields = unknownFields.build();
         makeExtensionsImmutable();
       }
@@ -1905,6 +2000,94 @@ public int getId() {
       return id_;
     }
 
+    public static final int INPUTSHAPE_FIELD_NUMBER = 13;
+    private serialization.Bigdl.Shape inputShape_;
+    /**
+     * 
+     * input shape
+     * 

+     *
+     * .serialization.Shape inputShape = 13;
+     */
+    public boolean hasInputShape() {
+      return inputShape_ != null;
+    }
+    /**
+     * 
+     * input shape
+     * 

+     *
+     * .serialization.Shape inputShape = 13;
+     */
+    public serialization.Bigdl.Shape getInputShape() {
+      return inputShape_ == null ? serialization.Bigdl.Shape.getDefaultInstance() : inputShape_;
+    }
+    /**
+     * 
+     * input shape
+     * 

+     *
+     * .serialization.Shape inputShape = 13;
+     */
+    public serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder() {
+      return getInputShape();
+    }
+
+    public static final int OUTPUTSHAPE_FIELD_NUMBER = 14;
+    private java.util.List outputShape_;
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    public java.util.List getOutputShapeList() {
+      return outputShape_;
+    }
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    public java.util.List 
+        getOutputShapeOrBuilderList() {
+      return outputShape_;
+    }
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    public int getOutputShapeCount() {
+      return outputShape_.size();
+    }
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    public serialization.Bigdl.Shape getOutputShape(int index) {
+      return outputShape_.get(index);
+    }
+    /**
+     * 
+     *output shape
+     * 

+     *
+     * repeated .serialization.Shape outputShape = 14;
+     */
+    public serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder(
+        int index) {
+      return outputShape_.get(index);
+    }
+
     private byte memoizedIsInitialized = -1;
     public final boolean isInitialized() {
       byte isInitialized = memoizedIsInitialized;
@@ -1956,6 +2139,12 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
       if (id_ != 0) {
         output.writeInt32(12, id_);
       }
+      if (inputShape_ != null) {
+        output.writeMessage(13, getInputShape());
+      }
+      for (int i = 0; i < outputShape_.size(); i++) {
+        output.writeMessage(14, outputShape_.get(i));
+      }
       unknownFields.writeTo(output);
     }
 
@@ -2022,6 +2211,14 @@ public int getSerializedSize() {
         size += com.google.protobuf.CodedOutputStream
           .computeInt32Size(12, id_);
       }
+      if (inputShape_ != null) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeMessageSize(13, getInputShape());
+      }
+      for (int i = 0; i < outputShape_.size(); i++) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeMessageSize(14, outputShape_.get(i));
+      }
       size += unknownFields.getSerializedSize();
       memoizedSize = size;
       return size;
@@ -2068,6 +2265,13 @@ public boolean equals(final java.lang.Object obj) {
           .equals(other.getNamePostfix());
       result = result && (getId()
           == other.getId());
+      result = result && (hasInputShape() == other.hasInputShape());
+      if (hasInputShape()) {
+        result = result && getInputShape()
+            .equals(other.getInputShape());
+      }
+      result = result && getOutputShapeList()
+          .equals(other.getOutputShapeList());
       result = result && unknownFields.equals(other.unknownFields);
       return result;
     }
@@ -2116,6 +2320,14 @@ public int hashCode() {
       hash = (53 * hash) + getNamePostfix().hashCode();
       hash = (37 * hash) + ID_FIELD_NUMBER;
       hash = (53 * hash) + getId();
+      if (hasInputShape()) {
+        hash = (37 * hash) + INPUTSHAPE_FIELD_NUMBER;
+        hash = (53 * hash) + getInputShape().hashCode();
+      }
+      if (getOutputShapeCount() > 0) {
+        hash = (37 * hash) + OUTPUTSHAPE_FIELD_NUMBER;
+        hash = (53 * hash) + getOutputShapeList().hashCode();
+      }
       hash = (29 * hash) + unknownFields.hashCode();
       memoizedHashCode = hash;
       return hash;
@@ -2264,6 +2476,7 @@ private void maybeForceBuilderInitialization() {
         if (com.google.protobuf.GeneratedMessageV3
                 .alwaysUseFieldBuilders) {
           getSubModulesFieldBuilder();
+          getOutputShapeFieldBuilder();
         }
       }
       public Builder clear() {
@@ -2303,6 +2516,18 @@ public Builder clear() {
 
         id_ = 0;
 
+        if (inputShapeBuilder_ == null) {
+          inputShape_ = null;
+        } else {
+          inputShape_ = null;
+          inputShapeBuilder_ = null;
+        }
+        if (outputShapeBuilder_ == null) {
+          outputShape_ = java.util.Collections.emptyList();
+          bitField0_ = (bitField0_ & ~0x00002000);
+        } else {
+          outputShapeBuilder_.clear();
+        }
         return this;
       }
 
@@ -2364,6 +2589,20 @@ public serialization.Bigdl.BigDLModule buildPartial() {
         result.train_ = train_;
         result.namePostfix_ = namePostfix_;
         result.id_ = id_;
+        if (inputShapeBuilder_ == null) {
+          result.inputShape_ = inputShape_;
+        } else {
+          result.inputShape_ = inputShapeBuilder_.build();
+        }
+        if (outputShapeBuilder_ == null) {
+          if (((bitField0_ & 0x00002000) == 0x00002000)) {
+            outputShape_ = java.util.Collections.unmodifiableList(outputShape_);
+            bitField0_ = (bitField0_ & ~0x00002000);
+          }
+          result.outputShape_ = outputShape_;
+        } else {
+          result.outputShape_ = outputShapeBuilder_.build();
+        }
         result.bitField0_ = to_bitField0_;
         onBuilt();
         return result;
@@ -2482,6 +2721,35 @@ public Builder mergeFrom(serialization.Bigdl.BigDLModule other) {
         if (other.getId() != 0) {
           setId(other.getId());
         }
+        if (other.hasInputShape()) {
+          mergeInputShape(other.getInputShape());
+        }
+        if (outputShapeBuilder_ == null) {
+          if (!other.outputShape_.isEmpty()) {
+            if (outputShape_.isEmpty()) {
+              outputShape_ = other.outputShape_;
+              bitField0_ = (bitField0_ & ~0x00002000);
+            } else {
+              ensureOutputShapeIsMutable();
+              outputShape_.addAll(other.outputShape_);
+            }
+            onChanged();
+          }
+        } else {
+          if (!other.outputShape_.isEmpty()) {
+            if (outputShapeBuilder_.isEmpty()) {
+              outputShapeBuilder_.dispose();
+              outputShapeBuilder_ = null;
+              outputShape_ = other.outputShape_;
+              bitField0_ = (bitField0_ & ~0x00002000);
+              outputShapeBuilder_ = 
+                com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ?
+                   getOutputShapeFieldBuilder() : null;
+            } else {
+              outputShapeBuilder_.addAllMessages(other.outputShape_);
+            }
+          }
+        }
         this.mergeUnknownFields(other.unknownFields);
         onChanged();
         return this;
@@ -3970,6 +4238,471 @@ public Builder clearId() {
         onChanged();
         return this;
       }
+
+      private serialization.Bigdl.Shape inputShape_ = null;
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> inputShapeBuilder_;
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      public boolean hasInputShape() {
+        return inputShapeBuilder_ != null || inputShape_ != null;
+      }
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      public serialization.Bigdl.Shape getInputShape() {
+        if (inputShapeBuilder_ == null) {
+          return inputShape_ == null ? serialization.Bigdl.Shape.getDefaultInstance() : inputShape_;
+        } else {
+          return inputShapeBuilder_.getMessage();
+        }
+      }
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      public Builder setInputShape(serialization.Bigdl.Shape value) {
+        if (inputShapeBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          inputShape_ = value;
+          onChanged();
+        } else {
+          inputShapeBuilder_.setMessage(value);
+        }
+
+        return this;
+      }
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      public Builder setInputShape(
+          serialization.Bigdl.Shape.Builder builderForValue) {
+        if (inputShapeBuilder_ == null) {
+          inputShape_ = builderForValue.build();
+          onChanged();
+        } else {
+          inputShapeBuilder_.setMessage(builderForValue.build());
+        }
+
+        return this;
+      }
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      public Builder mergeInputShape(serialization.Bigdl.Shape value) {
+        if (inputShapeBuilder_ == null) {
+          if (inputShape_ != null) {
+            inputShape_ =
+              serialization.Bigdl.Shape.newBuilder(inputShape_).mergeFrom(value).buildPartial();
+          } else {
+            inputShape_ = value;
+          }
+          onChanged();
+        } else {
+          inputShapeBuilder_.mergeFrom(value);
+        }
+
+        return this;
+      }
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      public Builder clearInputShape() {
+        if (inputShapeBuilder_ == null) {
+          inputShape_ = null;
+          onChanged();
+        } else {
+          inputShape_ = null;
+          inputShapeBuilder_ = null;
+        }
+
+        return this;
+      }
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      public serialization.Bigdl.Shape.Builder getInputShapeBuilder() {
+        
+        onChanged();
+        return getInputShapeFieldBuilder().getBuilder();
+      }
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      public serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder() {
+        if (inputShapeBuilder_ != null) {
+          return inputShapeBuilder_.getMessageOrBuilder();
+        } else {
+          return inputShape_ == null ?
+              serialization.Bigdl.Shape.getDefaultInstance() : inputShape_;
+        }
+      }
+      /**
+       * 
+       * input shape
+       * 

+       *
+       * .serialization.Shape inputShape = 13;
+       */
+      private com.google.protobuf.SingleFieldBuilderV3<
+          serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> 
+          getInputShapeFieldBuilder() {
+        if (inputShapeBuilder_ == null) {
+          inputShapeBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+              serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>(
+                  getInputShape(),
+                  getParentForChildren(),
+                  isClean());
+          inputShape_ = null;
+        }
+        return inputShapeBuilder_;
+      }
+
+      private java.util.List outputShape_ =
+        java.util.Collections.emptyList();
+      private void ensureOutputShapeIsMutable() {
+        if (!((bitField0_ & 0x00002000) == 0x00002000)) {
+          outputShape_ = new java.util.ArrayList(outputShape_);
+          bitField0_ |= 0x00002000;
+         }
+      }
+
+      private com.google.protobuf.RepeatedFieldBuilderV3<
+          serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> outputShapeBuilder_;
+
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public java.util.List getOutputShapeList() {
+        if (outputShapeBuilder_ == null) {
+          return java.util.Collections.unmodifiableList(outputShape_);
+        } else {
+          return outputShapeBuilder_.getMessageList();
+        }
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public int getOutputShapeCount() {
+        if (outputShapeBuilder_ == null) {
+          return outputShape_.size();
+        } else {
+          return outputShapeBuilder_.getCount();
+        }
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public serialization.Bigdl.Shape getOutputShape(int index) {
+        if (outputShapeBuilder_ == null) {
+          return outputShape_.get(index);
+        } else {
+          return outputShapeBuilder_.getMessage(index);
+        }
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder setOutputShape(
+          int index, serialization.Bigdl.Shape value) {
+        if (outputShapeBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          ensureOutputShapeIsMutable();
+          outputShape_.set(index, value);
+          onChanged();
+        } else {
+          outputShapeBuilder_.setMessage(index, value);
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder setOutputShape(
+          int index, serialization.Bigdl.Shape.Builder builderForValue) {
+        if (outputShapeBuilder_ == null) {
+          ensureOutputShapeIsMutable();
+          outputShape_.set(index, builderForValue.build());
+          onChanged();
+        } else {
+          outputShapeBuilder_.setMessage(index, builderForValue.build());
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder addOutputShape(serialization.Bigdl.Shape value) {
+        if (outputShapeBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          ensureOutputShapeIsMutable();
+          outputShape_.add(value);
+          onChanged();
+        } else {
+          outputShapeBuilder_.addMessage(value);
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder addOutputShape(
+          int index, serialization.Bigdl.Shape value) {
+        if (outputShapeBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          ensureOutputShapeIsMutable();
+          outputShape_.add(index, value);
+          onChanged();
+        } else {
+          outputShapeBuilder_.addMessage(index, value);
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder addOutputShape(
+          serialization.Bigdl.Shape.Builder builderForValue) {
+        if (outputShapeBuilder_ == null) {
+          ensureOutputShapeIsMutable();
+          outputShape_.add(builderForValue.build());
+          onChanged();
+        } else {
+          outputShapeBuilder_.addMessage(builderForValue.build());
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder addOutputShape(
+          int index, serialization.Bigdl.Shape.Builder builderForValue) {
+        if (outputShapeBuilder_ == null) {
+          ensureOutputShapeIsMutable();
+          outputShape_.add(index, builderForValue.build());
+          onChanged();
+        } else {
+          outputShapeBuilder_.addMessage(index, builderForValue.build());
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder addAllOutputShape(
+          java.lang.Iterable values) {
+        if (outputShapeBuilder_ == null) {
+          ensureOutputShapeIsMutable();
+          com.google.protobuf.AbstractMessageLite.Builder.addAll(
+              values, outputShape_);
+          onChanged();
+        } else {
+          outputShapeBuilder_.addAllMessages(values);
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder clearOutputShape() {
+        if (outputShapeBuilder_ == null) {
+          outputShape_ = java.util.Collections.emptyList();
+          bitField0_ = (bitField0_ & ~0x00002000);
+          onChanged();
+        } else {
+          outputShapeBuilder_.clear();
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public Builder removeOutputShape(int index) {
+        if (outputShapeBuilder_ == null) {
+          ensureOutputShapeIsMutable();
+          outputShape_.remove(index);
+          onChanged();
+        } else {
+          outputShapeBuilder_.remove(index);
+        }
+        return this;
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public serialization.Bigdl.Shape.Builder getOutputShapeBuilder(
+          int index) {
+        return getOutputShapeFieldBuilder().getBuilder(index);
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder(
+          int index) {
+        if (outputShapeBuilder_ == null) {
+          return outputShape_.get(index);  } else {
+          return outputShapeBuilder_.getMessageOrBuilder(index);
+        }
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public java.util.List 
+           getOutputShapeOrBuilderList() {
+        if (outputShapeBuilder_ != null) {
+          return outputShapeBuilder_.getMessageOrBuilderList();
+        } else {
+          return java.util.Collections.unmodifiableList(outputShape_);
+        }
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public serialization.Bigdl.Shape.Builder addOutputShapeBuilder() {
+        return getOutputShapeFieldBuilder().addBuilder(
+            serialization.Bigdl.Shape.getDefaultInstance());
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public serialization.Bigdl.Shape.Builder addOutputShapeBuilder(
+          int index) {
+        return getOutputShapeFieldBuilder().addBuilder(
+            index, serialization.Bigdl.Shape.getDefaultInstance());
+      }
+      /**
+       * 
+       *output shape
+       * 

+       *
+       * repeated .serialization.Shape outputShape = 14;
+       */
+      public java.util.List 
+           getOutputShapeBuilderList() {
+        return getOutputShapeFieldBuilder().getBuilderList();
+      }
+      private com.google.protobuf.RepeatedFieldBuilderV3<
+          serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> 
+          getOutputShapeFieldBuilder() {
+        if (outputShapeBuilder_ == null) {
+          outputShapeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3<
+              serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>(
+                  outputShape_,
+                  ((bitField0_ & 0x00002000) == 0x00002000),
+                  getParentForChildren(),
+                  isClean());
+          outputShape_ = null;
+        }
+        return outputShapeBuilder_;
+      }
       public final Builder setUnknownFields(
           final com.google.protobuf.UnknownFieldSet unknownFields) {
         return super.setUnknownFieldsProto3(unknownFields);
@@ -21072,7 +21805,7 @@ public serialization.Bigdl.Shape getDefaultInstanceForType() {
   static {
     java.lang.String[] descriptorData = {
       "\n\013bigdl.proto\022\rserialization\032\031google/pro" +
-      "tobuf/any.proto\"\232\003\n\013BigDLModule\022\014\n\004name\030" +
+      "tobuf/any.proto\"\357\003\n\013BigDLModule\022\014\n\004name\030" +
       "\001 \001(\t\022.\n\nsubModules\030\002 \003(\0132\032.serializatio" +
       "n.BigDLModule\022*\n\006weight\030\003 \001(\0132\032.serializ" +
       "ation.BigDLTensor\022(\n\004bias\030\004 \001(\0132\032.serial" +
@@ -21080,86 +21813,88 @@ public serialization.Bigdl.Shape getDefaultInstanceForType() {
       "\023\n\013nextModules\030\006 \003(\t\022\022\n\nmoduleType\030\007 \001(\t" +
       "\0222\n\004attr\030\010 \003(\0132$.serialization.BigDLModu" +
       "le.AttrEntry\022\017\n\007version\030\t \001(\t\022\r\n\005train\030\n" +
-      " \001(\010\022\023\n\013namePostfix\030\013 \001(\t\022\n\n\002id\030\014 \001(\005\032E\n",
-      "\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002 \001(\0132\030" +
-      ".serialization.AttrValue:\0028\001\"M\n\nInitMeth" +
-      "od\0221\n\nmethodType\030\001 \001(\0162\035.serialization.I" +
-      "nitMethodType\022\014\n\004data\030\002 \003(\001\"\210\002\n\013BigDLTen" +
-      "sor\022)\n\010datatype\030\001 \001(\0162\027.serialization.Da" +
-      "taType\022\014\n\004size\030\002 \003(\005\022\016\n\006stride\030\003 \003(\005\022\016\n\006" +
-      "offset\030\004 \001(\005\022\021\n\tdimension\030\005 \001(\005\022\021\n\tnElem" +
-      "ents\030\006 \001(\005\022\020\n\010isScalar\030\007 \001(\010\022-\n\007storage\030" +
-      "\010 \001(\0132\034.serialization.TensorStorage\022\n\n\002i" +
-      "d\030\t \001(\005\022-\n\ntensorType\030\n \001(\0162\031.serializat",
-      "ion.TensorType\"\320\001\n\rTensorStorage\022)\n\010data" +
-      "type\030\001 \001(\0162\027.serialization.DataType\022\022\n\nf" +
-      "loat_data\030\002 \003(\002\022\023\n\013double_data\030\003 \003(\001\022\021\n\t" +
-      "bool_data\030\004 \003(\010\022\023\n\013string_data\030\005 \003(\t\022\020\n\010" +
-      "int_data\030\006 \003(\005\022\021\n\tlong_data\030\007 \003(\003\022\022\n\nbyt" +
-      "es_data\030\010 \003(\014\022\n\n\002id\030\t \001(\005\"[\n\013Regularizer" +
-      "\0227\n\017regularizerType\030\001 \001(\0162\036.serializatio" +
-      "n.RegularizerType\022\023\n\013regularData\030\002 \003(\001\"\246" +
-      "\n\n\tAttrValue\022)\n\010dataType\030\001 \001(\0162\027.seriali" +
-      "zation.DataType\022\017\n\007subType\030\002 \001(\t\022\024\n\nint3",
-      "2Value\030\003 \001(\005H\000\022\024\n\nint64Value\030\004 \001(\003H\000\022\024\n\n" +
-      "floatValue\030\005 \001(\002H\000\022\025\n\013doubleValue\030\006 \001(\001H" +
-      "\000\022\025\n\013stringValue\030\007 \001(\tH\000\022\023\n\tboolValue\030\010 " +
-      "\001(\010H\000\0226\n\020regularizerValue\030\t \001(\0132\032.serial" +
-      "ization.RegularizerH\000\0221\n\013tensorValue\030\n \001" +
-      "(\0132\032.serialization.BigDLTensorH\000\0227\n\023vari" +
-      "ableFormatValue\030\013 \001(\0162\030.serialization.Va" +
-      "rFormatH\000\0224\n\017initMethodValue\030\014 \001(\0132\031.ser" +
-      "ialization.InitMethodH\000\0226\n\020bigDLModuleVa" +
-      "lue\030\r \001(\0132\032.serialization.BigDLModuleH\000\022",
-      "8\n\021nameAttrListValue\030\016 \001(\0132\033.serializati" +
-      "on.NameAttrListH\000\0229\n\narrayValue\030\017 \001(\0132#." +
-      "serialization.AttrValue.ArrayValueH\000\0229\n\017" +
-      "dataFormatValue\030\020 \001(\0162\036.serialization.In" +
-      "putDataFormatH\000\022+\n\013customValue\030\021 \001(\0132\024.g" +
-      "oogle.protobuf.AnyH\000\022%\n\005shape\030\022 \001(\0132\024.se" +
-      "rialization.ShapeH\000\032\270\004\n\nArrayValue\022\014\n\004si" +
-      "ze\030\001 \001(\005\022)\n\010datatype\030\002 \001(\0162\027.serializati" +
-      "on.DataType\022\013\n\003i32\030\003 \003(\005\022\013\n\003i64\030\004 \003(\003\022\013\n" +
-      "\003flt\030\005 \003(\002\022\013\n\003dbl\030\006 \003(\001\022\013\n\003str\030\007 \003(\t\022\017\n\007",
-      "boolean\030\010 \003(\010\022/\n\013Regularizer\030\t \003(\0132\032.ser" +
-      "ialization.Regularizer\022*\n\006tensor\030\n \003(\0132\032" +
-      ".serialization.BigDLTensor\0220\n\016variableFo" +
-      "rmat\030\013 \003(\0162\030.serialization.VarFormat\022-\n\n" +
-      "initMethod\030\014 \003(\0132\031.serialization.InitMet" +
-      "hod\022/\n\013bigDLModule\030\r \003(\0132\032.serialization" +
-      ".BigDLModule\0221\n\014nameAttrList\030\016 \003(\0132\033.ser" +
-      "ialization.NameAttrList\0222\n\ndataFormat\030\017 " +
-      "\003(\0162\036.serialization.InputDataFormat\022$\n\006c" +
-      "ustom\030\020 \003(\0132\024.google.protobuf.Any\022#\n\005sha",
-      "pe\030\021 \003(\0132\024.serialization.ShapeB\007\n\005value\"" +
-      "\230\001\n\014NameAttrList\022\014\n\004name\030\001 \001(\t\0223\n\004attr\030\002" +
-      " \003(\0132%.serialization.NameAttrList.AttrEn" +
-      "try\032E\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002" +
-      " \001(\0132\030.serialization.AttrValue:\0028\001\"\246\001\n\005S" +
-      "hape\0221\n\tshapeType\030\001 \001(\0162\036.serialization." +
-      "Shape.ShapeType\022\r\n\005ssize\030\002 \001(\005\022\022\n\nshapeV" +
-      "alue\030\003 \003(\005\022#\n\005shape\030\004 \003(\0132\024.serializatio" +
-      "n.Shape\"\"\n\tShapeType\022\n\n\006SINGLE\020\000\022\t\n\005MULT" +
-      "I\020\001*\260\001\n\tVarFormat\022\020\n\014EMPTY_FORMAT\020\000\022\013\n\007D",
-      "EFAULT\020\001\022\t\n\005ONE_D\020\002\022\n\n\006IN_OUT\020\003\022\n\n\006OUT_I" +
-      "N\020\004\022\020\n\014IN_OUT_KW_KH\020\005\022\020\n\014OUT_IN_KW_KH\020\006\022" +
-      "\023\n\017GP_OUT_IN_KW_KH\020\007\022\023\n\017GP_IN_OUT_KW_KH\020" +
-      "\010\022\023\n\017OUT_IN_KT_KH_KW\020\t*\253\001\n\016InitMethodTyp" +
-      "e\022\030\n\024EMPTY_INITIALIZATION\020\000\022\022\n\016RANDOM_UN" +
-      "IFORM\020\001\022\030\n\024RANDOM_UNIFORM_PARAM\020\002\022\021\n\rRAN" +
-      "DOM_NORMAL\020\003\022\t\n\005ZEROS\020\004\022\010\n\004ONES\020\005\022\t\n\005CON" +
-      "ST\020\006\022\n\n\006XAVIER\020\007\022\022\n\016BILINEARFILLER\020\010*L\n\017" +
-      "RegularizerType\022\023\n\017L1L2Regularizer\020\000\022\021\n\r" +
-      "L1Regularizer\020\001\022\021\n\rL2Regularizer\020\002*%\n\017In",
-      "putDataFormat\022\010\n\004NCHW\020\000\022\010\n\004NHWC\020\001*\"\n\nTen" +
-      "sorType\022\t\n\005DENSE\020\000\022\t\n\005QUANT\020\001*\210\002\n\010DataTy" +
-      "pe\022\t\n\005INT32\020\000\022\t\n\005INT64\020\001\022\t\n\005FLOAT\020\002\022\n\n\006D" +
-      "OUBLE\020\003\022\n\n\006STRING\020\004\022\010\n\004BOOL\020\005\022\010\n\004CHAR\020\006\022" +
-      "\t\n\005SHORT\020\007\022\t\n\005BYTES\020\010\022\017\n\013REGULARIZER\020\t\022\n" +
-      "\n\006TENSOR\020\n\022\023\n\017VARIABLE_FORMAT\020\013\022\016\n\nINITM" +
-      "ETHOD\020\014\022\n\n\006MODULE\020\r\022\022\n\016NAME_ATTR_LIST\020\016\022" +
-      "\017\n\013ARRAY_VALUE\020\017\022\017\n\013DATA_FORMAT\020\020\022\n\n\006CUS" +
-      "TOM\020\021\022\t\n\005SHAPE\020\022b\006proto3"
+      " \001(\010\022\023\n\013namePostfix\030\013 \001(\t\022\n\n\002id\030\014 \001(\005\022(\n",
+      "\ninputShape\030\r \001(\0132\024.serialization.Shape\022" +
+      ")\n\013outputShape\030\016 \003(\0132\024.serialization.Sha" +
+      "pe\032E\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002 " +
+      "\001(\0132\030.serialization.AttrValue:\0028\001\"M\n\nIni" +
+      "tMethod\0221\n\nmethodType\030\001 \001(\0162\035.serializat" +
+      "ion.InitMethodType\022\014\n\004data\030\002 \003(\001\"\210\002\n\013Big" +
+      "DLTensor\022)\n\010datatype\030\001 \001(\0162\027.serializati" +
+      "on.DataType\022\014\n\004size\030\002 \003(\005\022\016\n\006stride\030\003 \003(" +
+      "\005\022\016\n\006offset\030\004 \001(\005\022\021\n\tdimension\030\005 \001(\005\022\021\n\t" +
+      "nElements\030\006 \001(\005\022\020\n\010isScalar\030\007 \001(\010\022-\n\007sto",
+      "rage\030\010 \001(\0132\034.serialization.TensorStorage" +
+      "\022\n\n\002id\030\t \001(\005\022-\n\ntensorType\030\n \001(\0162\031.seria" +
+      "lization.TensorType\"\320\001\n\rTensorStorage\022)\n" +
+      "\010datatype\030\001 \001(\0162\027.serialization.DataType" +
+      "\022\022\n\nfloat_data\030\002 \003(\002\022\023\n\013double_data\030\003 \003(" +
+      "\001\022\021\n\tbool_data\030\004 \003(\010\022\023\n\013string_data\030\005 \003(" +
+      "\t\022\020\n\010int_data\030\006 \003(\005\022\021\n\tlong_data\030\007 \003(\003\022\022" +
+      "\n\nbytes_data\030\010 \003(\014\022\n\n\002id\030\t \001(\005\"[\n\013Regula" +
+      "rizer\0227\n\017regularizerType\030\001 \001(\0162\036.seriali" +
+      "zation.RegularizerType\022\023\n\013regularData\030\002 ",
+      "\003(\001\"\246\n\n\tAttrValue\022)\n\010dataType\030\001 \001(\0162\027.se" +
+      "rialization.DataType\022\017\n\007subType\030\002 \001(\t\022\024\n" +
+      "\nint32Value\030\003 \001(\005H\000\022\024\n\nint64Value\030\004 \001(\003H" +
+      "\000\022\024\n\nfloatValue\030\005 \001(\002H\000\022\025\n\013doubleValue\030\006" +
+      " \001(\001H\000\022\025\n\013stringValue\030\007 \001(\tH\000\022\023\n\tboolVal" +
+      "ue\030\010 \001(\010H\000\0226\n\020regularizerValue\030\t \001(\0132\032.s" +
+      "erialization.RegularizerH\000\0221\n\013tensorValu" +
+      "e\030\n \001(\0132\032.serialization.BigDLTensorH\000\0227\n" +
+      "\023variableFormatValue\030\013 \001(\0162\030.serializati" +
+      "on.VarFormatH\000\0224\n\017initMethodValue\030\014 \001(\0132",
+      "\031.serialization.InitMethodH\000\0226\n\020bigDLMod" +
+      "uleValue\030\r \001(\0132\032.serialization.BigDLModu" +
+      "leH\000\0228\n\021nameAttrListValue\030\016 \001(\0132\033.serial" +
+      "ization.NameAttrListH\000\0229\n\narrayValue\030\017 \001" +
+      "(\0132#.serialization.AttrValue.ArrayValueH" +
+      "\000\0229\n\017dataFormatValue\030\020 \001(\0162\036.serializati" +
+      "on.InputDataFormatH\000\022+\n\013customValue\030\021 \001(" +
+      "\0132\024.google.protobuf.AnyH\000\022%\n\005shape\030\022 \001(\013" +
+      "2\024.serialization.ShapeH\000\032\270\004\n\nArrayValue\022" +
+      "\014\n\004size\030\001 \001(\005\022)\n\010datatype\030\002 \001(\0162\027.serial",
+      "ization.DataType\022\013\n\003i32\030\003 \003(\005\022\013\n\003i64\030\004 \003" +
+      "(\003\022\013\n\003flt\030\005 \003(\002\022\013\n\003dbl\030\006 \003(\001\022\013\n\003str\030\007 \003(" +
+      "\t\022\017\n\007boolean\030\010 \003(\010\022/\n\013Regularizer\030\t \003(\0132" +
+      "\032.serialization.Regularizer\022*\n\006tensor\030\n " +
+      "\003(\0132\032.serialization.BigDLTensor\0220\n\016varia" +
+      "bleFormat\030\013 \003(\0162\030.serialization.VarForma" +
+      "t\022-\n\ninitMethod\030\014 \003(\0132\031.serialization.In" +
+      "itMethod\022/\n\013bigDLModule\030\r \003(\0132\032.serializ" +
+      "ation.BigDLModule\0221\n\014nameAttrList\030\016 \003(\0132" +
+      "\033.serialization.NameAttrList\0222\n\ndataForm",
+      "at\030\017 \003(\0162\036.serialization.InputDataFormat" +
+      "\022$\n\006custom\030\020 \003(\0132\024.google.protobuf.Any\022#" +
+      "\n\005shape\030\021 \003(\0132\024.serialization.ShapeB\007\n\005v" +
+      "alue\"\230\001\n\014NameAttrList\022\014\n\004name\030\001 \001(\t\0223\n\004a" +
+      "ttr\030\002 \003(\0132%.serialization.NameAttrList.A" +
+      "ttrEntry\032E\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005va" +
+      "lue\030\002 \001(\0132\030.serialization.AttrValue:\0028\001\"" +
+      "\246\001\n\005Shape\0221\n\tshapeType\030\001 \001(\0162\036.serializa" +
+      "tion.Shape.ShapeType\022\r\n\005ssize\030\002 \001(\005\022\022\n\ns" +
+      "hapeValue\030\003 \003(\005\022#\n\005shape\030\004 \003(\0132\024.seriali",
+      "zation.Shape\"\"\n\tShapeType\022\n\n\006SINGLE\020\000\022\t\n" +
+      "\005MULTI\020\001*\260\001\n\tVarFormat\022\020\n\014EMPTY_FORMAT\020\000" +
+      "\022\013\n\007DEFAULT\020\001\022\t\n\005ONE_D\020\002\022\n\n\006IN_OUT\020\003\022\n\n\006" +
+      "OUT_IN\020\004\022\020\n\014IN_OUT_KW_KH\020\005\022\020\n\014OUT_IN_KW_" +
+      "KH\020\006\022\023\n\017GP_OUT_IN_KW_KH\020\007\022\023\n\017GP_IN_OUT_K" +
+      "W_KH\020\010\022\023\n\017OUT_IN_KT_KH_KW\020\t*\253\001\n\016InitMeth" +
+      "odType\022\030\n\024EMPTY_INITIALIZATION\020\000\022\022\n\016RAND" +
+      "OM_UNIFORM\020\001\022\030\n\024RANDOM_UNIFORM_PARAM\020\002\022\021" +
+      "\n\rRANDOM_NORMAL\020\003\022\t\n\005ZEROS\020\004\022\010\n\004ONES\020\005\022\t" +
+      "\n\005CONST\020\006\022\n\n\006XAVIER\020\007\022\022\n\016BILINEARFILLER\020",
+      "\010*L\n\017RegularizerType\022\023\n\017L1L2Regularizer\020" +
+      "\000\022\021\n\rL1Regularizer\020\001\022\021\n\rL2Regularizer\020\002*" +
+      "%\n\017InputDataFormat\022\010\n\004NCHW\020\000\022\010\n\004NHWC\020\001*\"" +
+      "\n\nTensorType\022\t\n\005DENSE\020\000\022\t\n\005QUANT\020\001*\210\002\n\010D" +
+      "ataType\022\t\n\005INT32\020\000\022\t\n\005INT64\020\001\022\t\n\005FLOAT\020\002" +
+      "\022\n\n\006DOUBLE\020\003\022\n\n\006STRING\020\004\022\010\n\004BOOL\020\005\022\010\n\004CH" +
+      "AR\020\006\022\t\n\005SHORT\020\007\022\t\n\005BYTES\020\010\022\017\n\013REGULARIZE" +
+      "R\020\t\022\n\n\006TENSOR\020\n\022\023\n\017VARIABLE_FORMAT\020\013\022\016\n\n" +
+      "INITMETHOD\020\014\022\n\n\006MODULE\020\r\022\022\n\016NAME_ATTR_LI" +
+      "ST\020\016\022\017\n\013ARRAY_VALUE\020\017\022\017\n\013DATA_FORMAT\020\020\022\n",
+      "\n\006CUSTOM\020\021\022\t\n\005SHAPE\020\022b\006proto3"
     };
     com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner =
         new com.google.protobuf.Descriptors.FileDescriptor.    InternalDescriptorAssigner() {
@@ -21179,7 +21914,7 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors(
     internal_static_serialization_BigDLModule_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_serialization_BigDLModule_descriptor,
-        new java.lang.String[] { "Name", "SubModules", "Weight", "Bias", "PreModules", "NextModules", "ModuleType", "Attr", "Version", "Train", "NamePostfix", "Id", });
+        new java.lang.String[] { "Name", "SubModules", "Weight", "Bias", "PreModules", "NextModules", "ModuleType", "Attr", "Version", "Train", "NamePostfix", "Id", "InputShape", "OutputShape", });
     internal_static_serialization_BigDLModule_AttrEntry_descriptor =
       internal_static_serialization_BigDLModule_descriptor.getNestedTypes().get(0);
     internal_static_serialization_BigDLModule_AttrEntry_fieldAccessorTable = new
diff --git a/scala/dllib/src/main/resources/serialization/bigdl.proto b/scala/dllib/src/main/resources/serialization/bigdl.proto
index a84d847807d..88e29adcc46 100644
--- a/scala/dllib/src/main/resources/serialization/bigdl.proto
+++ b/scala/dllib/src/main/resources/serialization/bigdl.proto
@@ -15,6 +15,8 @@ message BigDLModule
     bool train = 10; // is module status in train
     string namePostfix = 11; // name post fix
     int32 id = 12; // unique ID of this module , used for shared modules
+    Shape inputShape = 13; // input shape
+    repeated Shape outputShape = 14; //output shape
 }
 enum VarFormat {
     EMPTY_FORMAT = 0;
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
index ee6765da27c..8cacceacfd0 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
@@ -23,12 +23,10 @@ import scala.collection.JavaConverters._
 import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
 import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
-import com.intel.analytics.bigdl.utils.Table
-import com.intel.analytics.bigdl.utils.serializer.converters.TensorConverter
+import com.intel.analytics.bigdl.utils.{Table, Shape => BigDLShape}
+import com.intel.analytics.bigdl.utils.serializer.converters.{DataConverter, ShapeConverter, TensorConverter}
 import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._
-import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter
-import serialization.Bigdl.DataType
-import serialization.Bigdl.{AttrValue, BigDLModule, BigDLTensor}
+import serialization.Bigdl._
 
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
@@ -235,6 +233,23 @@ trait ModuleSerializable extends Loadable with Savable{
     } else {
       module.evaluate()
     }
+
+    if (model.hasInputShape) {
+      val attrbute = AttrValue.newBuilder
+      attrbute.setShape(model.getInputShape)
+      val shape = ShapeConverter.getAttributeValue(context, attrbute.build).asInstanceOf[BigDLShape]
+      module.inputShapeValue = shape
+    }
+
+    val outputShapes = model.getOutputShapeList.asScala
+    if (outputShapes.length > 0) {
+      val shapes = outputShapes.map(outputShape => {
+        val attrbute = AttrValue.newBuilder
+        attrbute.setShape(outputShape)
+        ShapeConverter.getAttributeValue(context, attrbute.build).asInstanceOf[BigDLShape]
+      }).toArray
+      module.outputShapeValue = shapes
+    }
     copy2BigDL(context, bigDLModule)
     bigDLModule
   }
@@ -251,6 +266,22 @@ trait ModuleSerializable extends Loadable with Savable{
     modelBuilder.setNamePostfix(module.module.getNamePostfix)
     modelBuilder.setTrain(module.module.isTraining())
     modelBuilder.setId(System.identityHashCode(module.module))
+    val inputShape = module.module.inputShapeValue
+    if (inputShape != null) {
+      val attribute = AttrValue.newBuilder
+      ShapeConverter.setAttributeValue(context, attribute, inputShape,
+        universe.typeOf[BigDLShape])
+      modelBuilder.setInputShape(attribute.getShape)
+    }
+    val outputShapes = module.module.outputShapeValue
+    if (outputShapes != null && outputShapes.length > 0) {
+      outputShapes.foreach(outputShape => {
+        val attribute = AttrValue.newBuilder
+        ShapeConverter.setAttributeValue(context, attribute, outputShape,
+          universe.typeOf[BigDLShape])
+        modelBuilder.addOutputShape(attribute.getShape)
+      })
+    }
     copyFromBigDL(context, modelBuilder)
     SerializeResult(modelBuilder, context.storages)
   }

From 513d423d8e5aa0e0f69fc292905844970fbe2203 Mon Sep 17 00:00:00 2001
From: Ian Wong 
Date: Wed, 24 Jan 2018 16:30:24 +0800
Subject: [PATCH 0654/1065] Fix a random failure in tf fused batch norm test
 (#2227)

* fix bn ut

* fix random failure by set a seed
---
 .../bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala  | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala
index 298c292bd5b..3000bd904ae 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradSpec.scala
@@ -16,6 +16,7 @@
 package com.intel.analytics.bigdl.utils.tf.loaders
 
 import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.utils.RandomGenerator
 import com.intel.analytics.bigdl.utils.tf.Tensorflow.{booleanAttr, floatAttr, typeAttr}
 import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSpecHelper}
 import org.tensorflow.framework.{DataType, NodeDef}
@@ -23,6 +24,7 @@ import org.tensorflow.framework.{DataType, NodeDef}
 class FusedBatchNormGradSpec extends TensorflowSpecHelper {
 
   "FusedBatchNormGrad gradInput" should "be correct when is training is true" in {
+    RandomGenerator.RNG.setSeed(2000)
     val x = Tensor[Float](4, 8, 8, 256).rand()
     val g = Tensor[Float](4, 8, 8, 256).rand()
     val scale = Tensor[Float](256).rand()
@@ -83,6 +85,7 @@ class FusedBatchNormGradSpec extends TensorflowSpecHelper {
   }
 
   "FusedBatchNormGrad gradInput" should "be correct when is training is false" in {
+    RandomGenerator.RNG.setSeed(2000)
     val x = Tensor[Float](4, 8, 8, 256).rand()
     val g = Tensor[Float](4, 8, 8, 256).rand()
     val scale = Tensor[Float](256).rand()

From 1065154ef5432f2b0e250f3625af0a74563b00c8 Mon Sep 17 00:00:00 2001
From: Ian Wong 
Date: Wed, 24 Jan 2018 22:31:39 +0800
Subject: [PATCH 0655/1065] Support loading dynamic RNN in tensorflow (#2170)

* add Tensorarray

* add more

* Add tensor array related operations

* add tensorarray loader

* add unit test

* Support frame in BigDL scheduler

* Add test for while loop

* Load tensorflow ops: Enter, NextIteration, LoopCond, Exit

* fix some compile error

* checkin rnn test script

* fix unit test

* fix unit test

* fix unit test

* add rnn gradient unit test

* add ConcatOffset and InvertPermutation operation

* support ControlTrigger and TensorArrayGradV3

* support Stack, StackPush and StackPop

* TensorArray grad should not create TensorArray if it has been created

* fix unit test

* add load tf dynamic lstm unit test

* add more unit tests

* fix style issue

* fix failed dl classifer unit test

* fix module save/load tests

* fix failed tf loader test

* meet code review

* fix compile error

* use weakhashmap to store tensorarray and stacks

* fix unit test

* fix unit test

* fix ut

* fix unit test

* add serialization test
---
 .../analytics/bigdl/dllib/nn/CAddTable.scala  |  46 +-
 .../bigdl/dllib/nn/DynamicGraph.scala         |  14 +-
 .../bigdl/dllib/nn/FrameManager.scala         | 130 ++++
 .../analytics/bigdl/dllib/nn/Graph.scala      |  42 +-
 .../analytics/bigdl/dllib/nn/Scheduler.scala  |  88 ++-
 .../bigdl/dllib/nn/StaticGraph.scala          |  11 +
 .../dllib/nn/abstractnn/AbstractModule.scala  |   5 +
 .../bigdl/dllib/nn/ops/ArrayOps.scala         | 100 +++
 .../bigdl/dllib/nn/ops/ControlOps.scala       | 114 ++-
 .../bigdl/dllib/nn/ops/DataFlowOps.scala      | 660 ++++++++++++++++++
 .../analytics/bigdl/dllib/nn/ops/NoOp.scala   |   6 +-
 .../bigdl/dllib/nn/tf/ControlDependency.scala |   7 -
 .../bigdl/dllib/nn/tf/StrideSlice.scala       |  25 +-
 .../bigdl/dllib/tensor/DenseTensorApply.scala |  12 +-
 .../analytics/bigdl/dllib/tensor/Tensor.scala |  10 +-
 .../dllib/utils/python/api/PythonBigDL.scala  |   2 +-
 .../utils/serializer/ModuleSerializable.scala |   2 +
 .../bigdl/dllib/utils/tf/Tensorflow.scala     |   4 +
 .../dllib/utils/tf/TensorflowToBigDL.scala    | 128 ++--
 .../bigdl/dllib/utils/tf/loaders/Add.scala    |  13 +-
 .../loaders/{Merge.scala => ArrayOps.scala}   |  19 +-
 .../utils/tf/loaders/ControlFlowOps.scala     |  96 +++
 .../dllib/utils/tf/loaders/DataFlowOps.scala  | 322 +++++++++
 .../utils/tf/loaders/DependencyNode.scala     |   2 +-
 .../bigdl/dllib/utils/tf/loaders/NoOp.scala   |   2 +-
 .../dllib/utils/tf/loaders/StridedSlice.scala |  25 +-
 .../bigdl/dllib/utils/tf/loaders/Switch.scala |  33 -
 .../bigdl/dllib/utils/tf/loaders/Utils.scala  |  21 +-
 .../test/resources/tf/models/dynamic_gru.py   |  51 ++
 .../resources/tf/models/dynamic_gru_grad.py   |  52 ++
 .../test/resources/tf/models/dynamic_lstm.py  |  51 ++
 .../resources/tf/models/dynamic_lstm_grad.py  |  52 ++
 .../test/resources/tf/models/dynamic_rnn.py   |  50 ++
 .../resources/tf/models/dynamic_rnn_grad.py   |  52 ++
 .../test/resources/tf/models/tensor_array.py  |  57 ++
 .../src/test/resources/tf/models/util.py      |  22 +-
 .../dllib/{nn => keras}/HighwaySpec.scala     |   4 +-
 .../bigdl/dllib/nn/DynamicGraphSpec.scala     |  87 ++-
 .../bigdl/dllib/nn/tf/StrideSliceSpec.scala   |   2 +-
 .../bigdl/dllib/optim/DLClassifierSpec.scala  |  22 +-
 .../bigdl/dllib/optim/DLEstimatorSpec.scala   |  50 +-
 .../bigdl/dllib/tensor/DenseTensorSpec.scala  |   8 +
 .../bigdl/dllib/torch/CAddTableSpec.scala     |   4 +-
 .../serializer/ModuleSerializerSpec.scala     | 142 +++-
 .../dllib/utils/tf/TensorflowLoaderSpec.scala |  73 +-
 .../dllib/utils/tf/TensorflowSpecHelper.scala |   7 +
 .../dllib/utils/tf/loaders/ArrayOps.scala     |  46 ++
 47 files changed, 2500 insertions(+), 271 deletions(-)
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FrameManager.scala
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArrayOps.scala
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala
 rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/{Merge.scala => ArrayOps.scala} (62%)
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ControlFlowOps.scala
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DataFlowOps.scala
 delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala
 create mode 100644 scala/dllib/src/test/resources/tf/models/dynamic_gru.py
 create mode 100644 scala/dllib/src/test/resources/tf/models/dynamic_gru_grad.py
 create mode 100644 scala/dllib/src/test/resources/tf/models/dynamic_lstm.py
 create mode 100644 scala/dllib/src/test/resources/tf/models/dynamic_lstm_grad.py
 create mode 100644 scala/dllib/src/test/resources/tf/models/dynamic_rnn.py
 create mode 100644 scala/dllib/src/test/resources/tf/models/dynamic_rnn_grad.py
 create mode 100644 scala/dllib/src/test/resources/tf/models/tensor_array.py
 rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{nn => keras}/HighwaySpec.scala (97%)
 create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala
index 4f818d1b709..dfa3c5130ad 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala
@@ -31,20 +31,23 @@ import scala.reflect._
  * @tparam T Numeric type. Only support float/double now
  */
 @SerialVersionUID(7959261460060075605L)
-class CAddTable[T: ClassTag](val inplace: Boolean = false)(
-  implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] {
+class CAddTable[T: ClassTag, D: ClassTag](val inplace: Boolean = false)(
+  implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends AbstractModule[Table, Tensor[D], T] {
 
-  override def updateOutput(input: Table): Tensor[T] = {
-    var scalar = ev.zero
+  output = Tensor[D]()
+
+  override def updateOutput(input: Table): Tensor[D] = {
+    var scalar = ev2.zero
     var hasTensor = false
     var hasScalar = false
     var initTensor = false
 
     var i = 1
     while (i <= input.length()) {
-      val curTensor = input[Tensor[T]](i)
+      val curTensor = input[Tensor[D]](i)
       if (curTensor.isScalar) {
-        scalar = ev.plus(scalar, curTensor.value())
+        scalar = ev2.plus(scalar, curTensor.value())
         hasScalar = true
       } else if (curTensor.isTensor) {
         if (initTensor) {
@@ -66,34 +69,34 @@ class CAddTable[T: ClassTag](val inplace: Boolean = false)(
       output.add(scalar)
     } else if (hasScalar) {
       if (inplace) {
-        output.set(input[Tensor[T]](1)).setValue(scalar)
+        output.set(input[Tensor[D]](1)).setValue(scalar)
       } else {
-        output.resizeAs(input[Tensor[T]](1)).setValue(scalar)
+        output.resizeAs(input[Tensor[D]](1)).setValue(scalar)
       }
     }
 
     output
   }
 
-  override def updateGradInput(input: Table, gradOutput: Tensor[T]) : Table = {
+  override def updateGradInput(input: Table, gradOutput: Tensor[D]) : Table = {
     var i = 1
-    var sum = ev.zero
+    var sum = ev2.zero
     var calculateSum = false
     while (i <= input.length()) {
       if (i > gradInput.length) gradInput.insert(i, Tensor[T]().resizeAs(input(1)))
       if (inplace) {
-        require(input[Tensor[T]](1).isSameSizeAs(gradOutput), "cannot use inplace for broadcast")
-        gradInput[Tensor[T]](i).set(gradOutput)
+        require(input[Tensor[D]](1).isSameSizeAs(gradOutput), "cannot use inplace for broadcast")
+        gradInput[Tensor[D]](i).set(gradOutput)
       } else {
-        if (input[Tensor[T]](i).isSameSizeAs(gradOutput)) {
-          gradInput[Tensor[T]](i).resizeAs(gradOutput).copy(gradOutput)
+        if (input[Tensor[D]](i).isSameSizeAs(gradOutput)) {
+          gradInput[Tensor[D]](i).resizeAs(gradOutput).copy(gradOutput)
         } else {
-          require(input[Tensor[T]](i).isScalar, "Only support scalar broadcast backward now")
+          require(input[Tensor[D]](i).isScalar, "Only support scalar broadcast backward now")
           if (!calculateSum) {
             sum = gradOutput.sum()
             calculateSum = true
           }
-          gradInput[Tensor[T]](i).resizeAs(input[Tensor[T]](i)).setValue(sum)
+          gradInput[Tensor[D]](i).resizeAs(input[Tensor[D]](i)).setValue(sum)
         }
       }
       i += 1
@@ -111,13 +114,18 @@ class CAddTable[T: ClassTag](val inplace: Boolean = false)(
     }
     this
   }
+
+  override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
 }
 
 
 object CAddTable {
-  def apply[@specialized(Float, Double) T: ClassTag](
-      inplace: Boolean = false)(implicit ev: TensorNumeric[T]) : CAddTable[T] = {
-    new CAddTable[T](inplace)
+  def apply[T: ClassTag](
+      inplace: Boolean = false)(implicit ev: TensorNumeric[T]) : CAddTable[T, T] = {
+    new CAddTable[T, T](inplace)
   }
 }
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala
index ebf077b49e9..eff610eff7b 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala
@@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn
 
 import com.intel.analytics.bigdl.nn.Graph.ModuleNode
 import com.intel.analytics.bigdl.nn.abstractnn.Activity
-import com.intel.analytics.bigdl.nn.ops.ControlOps
+import com.intel.analytics.bigdl.nn.ops.{ControlOps, ResourceAllocator, TensorArray}
 import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput}
 import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
@@ -53,6 +53,9 @@ class DynamicGraph[T: ClassTag](
       forwardScheduler.schedule(node)
     }
 
+    modules.filter(_.isInstanceOf[ResourceAllocator])
+      .foreach(_.asInstanceOf[ResourceAllocator].release())
+
     output = dummyOutput.element.output
     output
   }
@@ -106,6 +109,15 @@ class DynamicGraph[T: ClassTag](
     }
   }
 
+  override def populateModules(): Unit = {
+    modules.appendAll(
+      forwardGraph.DFS.toArray
+        // todo: convert control dep node to edge
+        .filterNot(_.element.isInstanceOf[ControlDependency[T]])
+        .filter(n => !n.eq(dummyOutput)).map(_.element)
+    )
+  }
+
   private def backwardExecution(input: Activity, gradOutput: Activity, isBackward: Boolean)
   : Activity = {
     if (!generateBackward) return null
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FrameManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FrameManager.scala
new file mode 100644
index 00000000000..2f0e6787114
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FrameManager.scala
@@ -0,0 +1,130 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn
+
+import java.util.concurrent.atomic.AtomicInteger
+
+import com.intel.analytics.bigdl.nn.Graph.ModuleNode
+import com.intel.analytics.bigdl.nn.ops.{Exit, MergeOps, NextIteration}
+
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+
+/**
+ * Manage frame in scheduler. When scheduler execute nodes, it may enter a `frame`. Before
+ * scheduler leave a frame, it must make sure all nodes in that frames has been run.
+ * @tparam T
+ */
+class FrameManager[T] extends Serializable {
+  import FrameManager._
+
+  /**
+   * Create a frame. If it exists, nothing happen
+   *
+   * @param frame
+   * @param parentFrame
+   */
+  def createFrame(frame: String, parentFrame: Option[Frame[T]]): Frame[T] = {
+    if (!frames.contains(frame)) {
+      frames(frame) = new Frame(frame, parentFrame)
+    }
+    frames(frame)
+  }
+  /**
+   * Mark a node enter into frame. The node cannot be in two frames at the same time
+   * @param node node
+   * @param frame the name of the frame
+   */
+  def enter(node: ModuleNode[T], frame : Frame[T]): Unit = {
+    val name = node.element.getName()
+    if (nodeFrame.contains(name)) {
+      require(nodeFrame(name).eq(frame), "node cannot be in two different fames at the same time")
+    } else {
+      nodeFrame(name) = frame
+    }
+
+    if (!frame.nodes.contains(node)) {
+      if (isExecuteManyTimes(node, frame)) frame.nodes.append(node)
+    }
+  }
+
+  def pend(node: ModuleNode[T], frame : Frame[T]): Unit = {
+    val name = node.element.getName()
+    require(node.element.isInstanceOf[NextIteration[_, _]], "you can only pend next iteration node")
+    if (nodeFrame.contains(name)) {
+      require(nodeFrame(name).eq(frame), "node cannot be in two different fames at the same time")
+    } else {
+      nodeFrame(name) = frame
+    }
+
+    frame.barrier.decrementAndGet()
+    frame.waitingNodes.append(node)
+  }
+
+  /**
+   * Check if the node should be executed many times in the loop
+   * @param node
+   * @param frame
+   * @return
+   */
+  private def isExecuteManyTimes(node: ModuleNode[T], frame : Frame[T]): Boolean = {
+    // Here's a little tricky. We find the begin of these execute many times nodes by looking for
+    // pattern of "NextIteration -> Merge"
+    if (node.element.isInstanceOf[MergeOps[_]] && node.prevNodes.size == 2 &&
+      (node.prevNodes(0).element.isInstanceOf[NextIteration[_, _]] ||
+        node.prevNodes(1).element.isInstanceOf[NextIteration[_, _]])) {
+      return true
+    }
+
+    // If its parents will be re-executed, it will be re-executed
+    node.prevNodes.foreach(n => if (frame.nodes.contains(n)) return true)
+
+    return false
+  }
+
+  /**
+   * Get the frame of the given node. If the node isn't in any frame, return None.
+   * @param node
+   * @return
+   */
+  def apply(node: ModuleNode[T]): Option[Frame[T]] = {
+    nodeFrame.get(node.element.getName())
+  }
+
+  private val frames = new mutable.HashMap[String, Frame[T]]()
+  private val nodeFrame = new mutable.HashMap[String, Frame[T]]()
+}
+
+object FrameManager {
+  /**
+   * A frame
+   * @param name the name of a frame, it must be unique in a graph
+   * @param parent parent frame, if a frame is created in another frame, it has parent frame
+   * @tparam T
+   */
+  class Frame[T] private[FrameManager] (
+    val name: String,
+    val parent: Option[Frame[T]]
+  ) {
+    // Sync all next iteration nodes execution
+    private[bigdl] var barrier: AtomicInteger = new AtomicInteger(0)
+    // User can use NextIteration to sync execution. This is a list of those type of nodes
+    private[bigdl] val waitingNodes: ArrayBuffer[ModuleNode[T]] = new ArrayBuffer[ModuleNode[T]]()
+
+    // Nodes should be refreshed in a iteration of the frame
+    private[bigdl] val nodes: ArrayBuffer[ModuleNode[T]] = new ArrayBuffer[ModuleNode[T]]()
+  }
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala
index 2818ff80954..13425b45119 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala
@@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.Module
 import scala.collection.JavaConverters._
 import com.intel.analytics.bigdl.nn.Graph.ModuleNode
 import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule}
-import com.intel.analytics.bigdl.nn.ops.{ControlOps, MergeControlNode, MergeOps, SwitchControlNode, SwitchOps}
+import com.intel.analytics.bigdl.nn.ops._
 import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput}
 import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
@@ -153,16 +153,13 @@ abstract class Graph[T: ClassTag](
   protected val forwardGraph = dummyOutput.graph(reverse = true)
   protected val forwardNodes = forwardGraph.DFS.toArray
 
-  modules.appendAll(
-    forwardGraph.topologySort
-      // todo: convert control dep node to edge
-      .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse
-      .filter(n => !n.eq(dummyOutput)).map(_.element)
-  )
+  populateModules()
 
   // Check all inputs of the graph should be passed in
   checkRoots
 
+  protected def populateModules(): Unit
+
   // Check if the graph is correct
   private def checkRoots: Unit = {
     def duplicatedNames(names: Seq[String]): mutable.Set[String] = {
@@ -279,7 +276,7 @@ abstract class Graph[T: ClassTag](
   ): Activity = {
     if (inputs.length == 1) {
       require(inputs(0).eq(node), "input node is not in the input list")
-      input.toTensor
+      input
     } else {
       val i = inputs.indexOf(node)
       require(i != -1, "input node is not in the input list")
@@ -365,10 +362,12 @@ abstract class Graph[T: ClassTag](
   }
 
   /**
-   * get forward executions, the dummy node will be filtered
+   * Get forward executions, the dummy node will be filtered.
+   *
+   * This method will output an unsorted executions.
    * @return
    */
-  def getForwardExecutions: Array[Node[AbstractModule[Activity, Activity, T]]] = {
+  def getForwardExecutions(): Array[Node[AbstractModule[Activity, Activity, T]]] = {
     forwardNodes.filterNot(_.eq(dummyOutput))
   }
 
@@ -379,8 +378,9 @@ abstract class Graph[T: ClassTag](
    * exception
    * @return
    */
-  def getSortedForwardExecutions: Array[Node[AbstractModule[Activity, Activity, T]]] = {
+  def getSortedForwardExecutions(): Array[ModuleNode[T]] = {
     forwardGraph.topologySort
+      // todo: convert control dep node to edge
       .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse
       .filter(n => !n.eq(dummyOutput))
   }
@@ -437,11 +437,18 @@ abstract class Graph[T: ClassTag](
     this
   }
 
+  /**
+   * Clear the original module and reset with module in the graph
+   */
   def resetModules(): Unit = {
     modules.clear()
-    modules.appendAll(forwardGraph.topologySort
-      .filterNot(_.element.isInstanceOf[ControlDependency[T]]).reverse
-      .filter(n => !n.eq(dummyOutput)).map(_.element))
+    modules.appendAll(forwardGraph.DFS.toArray
+      .filterNot(_.element.isInstanceOf[ControlDependency[T]])
+      .filter(n => !n.eq(dummyOutput)).map(_.element)
+      // Some tests compare the paramerters between sequential and graph,add a reverse makes
+      // it's eaiser to compare
+      .reverse
+    )
   }
 }
 
@@ -596,8 +603,7 @@ object Graph extends ContainerSerializable {
     controlOps match {
       case switchOps : SwitchOps[T] => new SwitchControlNode[Module[T]](switchOps)
       case mergeOps : MergeOps[T] => new MergeControlNode[Module[T]](mergeOps)
-      case _ => throw new RuntimeException(s"Ops ${controlOps.getClass.getName}" +
-        s" control node not supported!")
+      case _ => new Node[Module[T]](controlOps)
     }
   }
 
@@ -645,12 +651,12 @@ object Graph extends ContainerSerializable {
       val (weights, bias) = graph.variables.get
       val weightAttrBuilder = AttrValue.newBuilder
       DataConverter.setAttributeValue(context, weightAttrBuilder, weights,
-        universe.typeOf[Array[Tensor[_ <: Any]]])
+        universe.typeOf[Array[Tensor[_ <: scala.Any]]])
       graphBuilder.putAttr("sharedWeight", weightAttrBuilder.build)
 
       val biasAttrBuilder = AttrValue.newBuilder
       DataConverter.setAttributeValue(context, biasAttrBuilder, bias,
-        universe.typeOf[Array[Tensor[_ <: Any]]])
+        universe.typeOf[Array[Tensor[_ <: scala.Any]]])
       graphBuilder.putAttr("sharedBias", biasAttrBuilder.build)
     }
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala
index 01c75296367..d4a7b266354 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala
@@ -16,6 +16,7 @@
 package com.intel.analytics.bigdl.nn
 
 import com.intel.analytics.bigdl.Module
+import com.intel.analytics.bigdl.nn.FrameManager.Frame
 import com.intel.analytics.bigdl.nn.Graph.ModuleNode
 import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
 import com.intel.analytics.bigdl.nn.ops._
@@ -25,6 +26,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.{Edge, Node, T}
 
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
 /**
@@ -46,6 +48,7 @@ private[bigdl] class Scheduler[T] (
 
   private val readyQueue = new mutable.Queue[ModuleNode[T]]()
   private val nodeStatus = new NodeStatusManager[T]()
+  private val frameManayger = new FrameManager[T]()
 
   /**
    * User must reset the scheduler after first use it or finish a graph execution
@@ -81,20 +84,52 @@ private[bigdl] class Scheduler[T] (
    */
   def fetch(): ModuleNode[T] = {
     var node = readyQueue.dequeue()
-    while (nodeStatus.isConst(node) || node.element.isInstanceOf[ControlDependency[_]]) {
-      if (!nodeStatus.isConst(node)) {
-        schedule(node)
-      }
+    while (skipExecution(node)) {
       node = readyQueue.dequeue()
     }
+
     node
   }
 
+  private def skipExecution(node: ModuleNode[T]): Boolean = {
+    if (nodeStatus.isConst(node)) return true
+
+    if (node.element.isInstanceOf[ControlDependency[_]]) {
+      schedule(node)
+      return true
+    }
+
+    return false
+  }
+
   /**
    * Schedule nodes depend on the given node
    * @param node
    */
   def schedule(node: ModuleNode[T]): Unit = {
+    val curFrame = frameManayger(node)
+
+    val nextNodeFrame = if (node.element.isInstanceOf[Enter[_]]) {
+      val e = node.element.asInstanceOf[Enter[_]]
+      Some(frameManayger.createFrame(e.frame, curFrame))
+    } else if (node.element.isInstanceOf[LoopCondition[_]]) {
+      require(curFrame.isDefined, "LoopCondition should be in a frame")
+      val f = curFrame.get
+      require(f.barrier.get() == 0, "frame barrier should be 0 when execute loop condition")
+      f.barrier.set(node.nextNodes.size)
+      curFrame
+    } else if (node.element.isInstanceOf[NextIteration[_, _]]) {
+      require(curFrame.isDefined, "NextIteration should be in a frame")
+      curFrame
+    } else if (node.element.isInstanceOf[Exit[_]]) {
+      require(curFrame.isDefined, "Exit should be in a frame")
+      val f = curFrame.get
+      f.barrier.set(0)
+      f.parent
+    } else {
+      curFrame
+    }
+
     if (!nodeStatus.isConst(node)) {
       // Update status of current node
       nodeStatus(node) = if (node.prevNodes.length == 0) {
@@ -117,25 +152,38 @@ private[bigdl] class Scheduler[T] (
     node.element match {
       case s: SwitchOps[_] =>
         val switchNode = node.asInstanceOf[SwitchControlNode[Module[T]]]
-        selectNexts(switchNode.availableNodes(), node)
+        selectNexts(switchNode.availableNodes(), node, nextNodeFrame)
       case _ =>
-        selectNexts(node.nextNodes, node)
+        selectNexts(node.nextNodes, node, nextNodeFrame)
     }
   }
 
-  private def selectNexts(candidateNodes: Seq[ModuleNode[T]], curNode: ModuleNode[T]): Unit = {
+  private def startNextIteration(frame: Frame[T]): Unit = {
+    // Wake up the waiting nodes
+    frame.waitingNodes.foreach(readyQueue.enqueue(_))
+    frame.waitingNodes.clear()
+
+    // As frame is refreshed, mark all nodes in the frame are not ready
+    frame.nodes.filterNot(_.element.isInstanceOf[NextIteration[_, _]]).foreach(n => {
+      nodeStatus.unset(n)
+    })
+  }
+
+  private def selectNexts(candidateNodes: Seq[ModuleNode[T]], curNode: ModuleNode[T],
+    frame: Option[Frame[T]]): Unit = {
     val nodeSet = new mutable.LinkedHashSet[ModuleNode[T]]()
     candidateNodes.foreach(nodeSet.add(_))  // remove duplicate nodes and keep the order
+
     nodeSet.filter(n => executableNodes.contains(n.element.getName())).foreach(nextNode => {
       if (nextNode.element.isInstanceOf[MergeOps[_]]) {
         val merge = nextNode.element.asInstanceOf[MergeOps[_]]
         require(nodeStatus.notExecuted(nextNode), s"Merge node(${nextNode.element.getName()}) " +
           s"should not be executed twice out of loop or in a same iteration of a loop")
         merge.setSwitch(nextNode.prevNodes.indexOf(curNode) + 1)
-        readyQueue.enqueue(nextNode)
+        enQueue(nextNode, frame)
       } else {
         if (isNodeReady(nextNode)) {
-          readyQueue.enqueue(nextNode)
+          enQueue(nextNode, frame)
         }
       }
     })
@@ -153,6 +201,20 @@ private[bigdl] class Scheduler[T] (
 
     return true
   }
+
+  private def enQueue(node: ModuleNode[T], frame: Option[Frame[T]]): Unit = {
+    if (node.element.isInstanceOf[NextIteration[_, _]]) {
+      require(frame.isDefined, "current node should be in a frame")
+      frameManayger.pend(node, frame.get)
+      nodeStatus.unset(node) // mark current node is in not ready status
+      if (frame.get.barrier.get() == 0) {
+        startNextIteration(frame.get)
+      }
+    } else {
+      frame.foreach(frameManayger.enter(node, _))
+      readyQueue.enqueue(node)
+    }
+  }
 }
 
 object Scheduler {
@@ -212,6 +274,14 @@ object Scheduler {
       }
       this
     }
+
+    /**
+     * Remove status of node.
+     * @param node
+     */
+    def unset(node: ModuleNode[T]): Unit = {
+      nodeStatus.remove(node.element.getName())
+    }
   }
 
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala
index b4a66d73374..d43730812ce 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala
@@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn
 
 import com.intel.analytics.bigdl.nn.Graph.ModuleNode
 import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
+import com.intel.analytics.bigdl.nn.tf.ControlDependency
 import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.{Node, Util}
@@ -110,6 +111,16 @@ class StaticGraph[T: ClassTag](
     }
   }
 
+  override def populateModules(): Unit = {
+    modules.appendAll(
+      forwardGraph.topologySort
+        // todo: convert control dep node to edge
+        .filterNot(_.element.isInstanceOf[ControlDependency[T]])
+        .filter(n => !n.eq(dummyOutput)).map(_.element)
+        .reverse
+    )
+  }
+
 
   private def backwardExecution(input: Activity, gradOutput: Activity,
     executeBackward: Boolean): Activity = {
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala
index a6030b03da0..c681ce6080b 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala
@@ -819,6 +819,11 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag,
     Graph(starts, endNodes)
   }
 
+  /**
+   * Return classTag numerics for module serialization. If your module contains multiple classtag
+   * in the constructor, you should override this method
+   * @return
+   */
   def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = {
     (Array(scala.reflect.classTag[T]), Array(ev))
   }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArrayOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArrayOps.scala
new file mode 100644
index 00000000000..542e343858f
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArrayOps.scala
@@ -0,0 +1,100 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn.ops
+
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.bigdl.utils.{T, Table}
+
+import scala.reflect.ClassTag
+
+/**
+ * This operation computes the inverse of an index permutation. It takes a 1-D integer tensor x,
+ * which represents the indices of a zero-based array, and swaps each value with its index position.
+ * In other words, for an output tensor y and an input tensor x, this operation computes the
+ * following:
+ *     y[x[i]] = i for i in [0, 1, ..., len(x) - 1]
+ * The values must include 0. There can be no duplicate values or negative values.
+ *
+ * @tparam T Parameter numeric type. Only support float/double now
+ */
+private[bigdl] class InvertPermutation[T: ClassTag]()(implicit ev: TensorNumeric[T])
+  extends Operation[Tensor[Int], Tensor[Int], T] {
+
+  output = Tensor[Int]()
+
+  override def updateOutput(input: Tensor[Int]): Tensor[Int] = {
+    require(input.dim() == 1, "InvertPermutation only accept 1D tensor as input")
+    output.resizeAs(input)
+    var i = 0
+    while(i < input.size(1)) {
+      output.setValue(input.valueAt(i + 1) + 1, i)
+      i += 1
+    }
+
+    output
+  }
+}
+
+/**
+ * Calculate the positions of the input shapes in a concatenation operation. It takes several
+ * tensors as input. The first tensor must be a scalar, which indicts on which dimension do the
+ * concatenation. The offset of the dimension is start from zero.
+ *
+ * The left tensors must be 1D, as they represent the shapes of tensors. And they must be same
+ * except the concat dimension.
+ *
+ * Her's an example, say, we want to concatenate 3 tensors in the 2nd dimension, the input shape
+ * tensors should be
+ *   [2, 2, 5, 7]
+ *   [2, 3, 5, 7]
+ *   [2, 4, 5, 7]
+ *
+ * The output should be
+ *   [0, 0, 0, 0]
+ *   [0, 2, 0, 0]
+ *   [0, 5, 0, 0]
+ * @tparam T Parameter numeric type. Only support float/double now
+ */
+private[bigdl] class ConcatOffset[T: ClassTag]()(implicit ev: TensorNumeric[T])
+  extends Operation[Table, Table, T] {
+
+  output = T()
+
+  override def updateOutput(input: Table): Table = {
+    val concatDim = input[Tensor[Int]](1)
+    require(concatDim.isScalar, "ConcatOffset: concat dim must be a scalar")
+    val cdim = concatDim.value()
+    val n = input.length() - 1
+    var i = 1
+    var offset = 0
+    while(i <= n) {
+      val shape = input[Tensor[Int]](i + 1)
+      require(shape.nDimension() == 1, "ConcatOffset: shape must be 1D tensor")
+      if (!output.contains(i)) {
+        output(i) = Tensor[Int]()
+      }
+      val outputOffset = output[Tensor[Int]](i)
+      outputOffset.resizeAs(shape).zero()
+      outputOffset.setValue(cdim + 1, offset)
+      val dimSize = shape.valueAt(cdim + 1)
+      offset += dimSize
+      i += 1
+    }
+
+    output
+  }
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala
index 734bf8af07a..b57c456c46b 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala
@@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn.ops
 import com.intel.analytics.bigdl._
 import com.intel.analytics.bigdl.nn.Graph._
 import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
-import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.tensor.{BooleanType, Tensor}
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.{Edge, Node, T}
 
@@ -179,10 +179,64 @@ sealed class MergeControlNode[T] private[bigdl] (element: T) extends Node[T](ele
   }
 }
 
+/**
+ * Mark start of next iteration. User should use ControlNodes.whileLoop to use such operation.
+ * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now
+ */
+sealed private[bigdl] class NextIteration[T: ClassTag, D: ClassTag] private[bigdl]()
+  (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Tensor[D], Tensor[D], T] {
+  output = Tensor[D]()
+
+  override def updateOutput(input: Tensor[D]): Tensor[D] = {
+    output.resizeAs(input).copy(input)
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+/**
+ * Mark start of a loop. User should use ControlNodes.whileLoop to use such operation.
+ * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now
+ */
+sealed private[bigdl] class Enter[T: ClassTag] private[bigdl](val frame: String)
+  (implicit ev: TensorNumeric[T]) extends IdentityControl[T]
+
+/**
+ * Mark this dataflow is condition flow. It will erase the iteration status.
+ * User should use ControlNodes.whileLoop to use such operation.
+ * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now
+ */
+sealed private[bigdl] class LoopCondition[T: ClassTag] private[bigdl]()
+  (implicit ev: TensorNumeric[T]) extends IdentityControl[T] {
+
+  /**
+   * If current loop continue running
+   * @return
+   */
+  private[bigdl] def continue() : Boolean = {
+    require(this.output.isTensor, "loop condition result should be a tensor")
+    val t = this.output.asInstanceOf[Tensor[Boolean]]
+    require((t.isScalar || t.nElement() == 1) && t.getType() == BooleanType,
+      "loop condition result should be a boolean scalar or one element tensor")
+    t.storage().apply(t.storageOffset() - 1)
+  }
+}
+
+/**
+ * Mark end of a loop. User should use ControlNodes.whileLoop to use such operation.
+ * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now
+ */
+sealed private[bigdl] class Exit[T: ClassTag] private[bigdl]()(implicit ev: TensorNumeric[T])
+  extends IdentityControl[T]
+
 /**
  * Factory method of control flow related nodes
  */
-object ControlNodes {
+private[bigdl] object ControlNodes {
 
   /**
    * Create a switch node
@@ -200,22 +254,6 @@ object ControlNodes {
     curNode
   }
 
-  /**
-   * Create a switch node
-   * @param data data to pass down, from an edge
-   * @param condition data to pass down, from an edge
-   * @param ev
-   * @tparam T
-   * @return
-   */
-  def switch[T: ClassTag](data: (ModuleNode[T], Int), condition: (ModuleNode[T], Int)
-  )(implicit ev: TensorNumeric[T]): SwitchControlNode[Module[T]] = {
-    val curNode = new SwitchControlNode[Module[T]](new SwitchOps())
-    data._1.add(curNode, Edge(data._2))
-    condition._1.add(curNode, Edge(data._2))
-    curNode
-  }
-
   /**
    * Create a merge node
    * @param first dependency node, for method overload
@@ -249,4 +287,44 @@ object ControlNodes {
     })
     curNode
   }
+
+  /**
+   * Constructor a while loop in the graph
+   * @param condition a sub graph produce a boolean scalar
+   * @param body while body, input/output tuple. body length is seq of nodes with same length of
+   *             loopVars
+   * @param loopVars loop vars
+   * @tparam T
+   * @return a seq of nodes with same length of loopVars
+   */
+  def whileLoop[T: ClassTag](
+    condition: (Seq[ModuleNode[T]], ModuleNode[T]),
+    body: Seq[(ModuleNode[T], ModuleNode[T])],
+    loopVars: (Seq[ModuleNode[T]]),
+    name: String = null
+  )(implicit ev: TensorNumeric[T]): Seq[ModuleNode[T]] = {
+    val lc = new LoopCondition[T]().inputs(condition._2)
+    if (name != null) lc.element.setName(s"$name/loopCondition")
+
+    loopVars.zip(condition._1).zip(body).zipWithIndex.map(tuple => {
+      val (((input, cond), update), indexBase0) = tuple
+      val index = indexBase0 + 1
+      val enter = new Enter[T]("test_frame").inputs(input)
+      if (name != null) enter.element.setName(s"$name/enter$index")
+      val mergeNode = merge[T](enter)
+      if (name != null) mergeNode.element.setName(s"$name/merge$index")
+      mergeNode -> cond
+      val switchNode = switch[T](lc, mergeNode)
+      if (name != null) switchNode.element.setName(s"$name/switch$index")
+      val exitNode = new Exit[T]().inputs(switchNode.trueEdge())
+      if (name != null) exitNode.element.setName(s"$name/exit$index")
+      val identity = Identity[T]().inputs(switchNode.falseEdge())
+      if (name != null) identity.element.setName(s"$name/switchFalse$index")
+      identity -> update._1
+      val nextIteration = new NextIteration[T, T].inputs(update._2)
+      if (name != null) nextIteration.element.setName(s"$name/nextIteration$index")
+      mergeNode.append(nextIteration)
+      exitNode
+    })
+  }
 }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala
new file mode 100644
index 00000000000..ec10a3e1786
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala
@@ -0,0 +1,660 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn.ops
+
+import java.util
+import java.util.concurrent.ConcurrentHashMap
+
+import com.intel.analytics.bigdl.nn.tf.WithoutInput
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+private[nn] trait ResourceAllocator {
+  def release(): Unit
+}
+
+/**
+ * This class implement the functionality of TensorArray in tensorflow. See details at
+ *   https://www.tensorflow.org/api_docs/python/tf/TensorArray.
+ *
+ * @param initSize The initial size of the array.
+ * @param shape The expected shape of the element in the tensor array.
+ * @param dynamicSize Whether write to the tensor array is allowed to grow the size. By default it's
+ *                    not allowed.
+ * @param clearAfterRead Determines whether the tensors are cleared after read. Default is true.
+ * @param identicalElementShapes If all elements in the array should have the same shape. Default is
+ *                               false.
+ * @tparam D Element numeric type in the tensor array.
+ */
+private[nn] class TensorArray[D: ClassTag](
+  private val initSize: Int,
+  private val shape: Array[Int] = null,
+  private var dynamicSize: Boolean = false,
+  private val clearAfterRead: Boolean = true,
+  private val identicalElementShapes: Boolean = false,
+  private val multipleWritesAggregate: Boolean = false
+)(implicit ev: TensorNumeric[D]) {
+
+  private var otherShape : Array[Int] = null
+
+  private var tensors = new Array[Tensor[D]](initSize)
+
+  def lockSize(): Unit = this.dynamicSize = false
+
+  def apply(index: Int): Tensor[D] = {
+    require(tensors(index) != null,
+      s"tensor on index $index has not been inited or has been cleared")
+    val t = tensors(index)
+    if (clearAfterRead) tensors(index) = null
+    t
+  }
+
+  def grad(): TensorArray[_] = {
+    this.lockSize()
+    new TensorArray[D](this.size, multipleWritesAggregate = true)
+  }
+
+  def size(): Int = tensors.length
+
+  def shapeOf(index: Int): Array[Int] = {
+    require(tensors(index) != null,
+      s"tensor on index $index has not been inited or has been cleared")
+    tensors(index).size()
+  }
+
+  def update(index: Int, tensor: Tensor[D]): Unit = {
+    if (!multipleWritesAggregate) {
+      require(tensors(index) == null, "There's already a tensor on the given index")
+    }
+
+    if (identicalElementShapes) {
+      if (otherShape == null) {
+        otherShape = tensor.size()
+      } else {
+        val curShape = tensor.size()
+        require(curShape.length == otherShape.length,
+          "insert tensor dimension does not match other tensor dimension")
+        var i = 0
+        while(i < curShape.length) {
+          require(curShape(i) == otherShape(i),
+            "insert tensor size does not match other tensor size")
+          i += 1
+        }
+      }
+    }
+    if (shape != null) {
+      val curShape = tensor.size()
+      require(curShape.length == shape.length,
+        "insert tensor dimension does not match required dimension")
+      var i = 0
+      while(i < curShape.length) {
+        require(curShape(i) == shape(i),
+          "insert tensor size does not match required size")
+        i += 1
+      }
+    }
+
+    if (dynamicSize && index >= tensors.size) {
+      val newTensors = new Array[Tensor[D]](index + 1)
+      var i = 0
+      while(i < tensors.length) {
+        newTensors(i) = tensors(i)
+        i += 1
+      }
+      tensors = newTensors
+    } else {
+      require(index < initSize, "cannot grow size when dynamicSize is false")
+    }
+
+    if (tensors(index) == null) {
+      tensors(index) = Tensor[D]().resizeAs(tensor).copy(tensor)
+    } else {
+      tensors(index).add(tensor)
+    }
+  }
+}
+
+private[nn] object TensorArray {
+  private val arrays = new util.WeakHashMap[String, TensorArray[_]]()
+
+  def apply[D](key: String): TensorArray[D] = this.synchronized {
+    require(arrays.containsKey(key), s"Cannot find TensorArray for name $key")
+    arrays.get(key).asInstanceOf[TensorArray[D]]
+  }
+
+  def get(key: String): TensorArray[_] = this.synchronized {
+    require(arrays.containsKey(key), s"Cannot find TensorArray for name $key")
+    arrays.get(key)
+  }
+
+  def update(key: String, value: TensorArray[_]): Unit = this.synchronized {
+    arrays.put(key, value)
+  }
+
+  def exist(key: String): Boolean = this.synchronized {
+    arrays.containsKey(key)
+  }
+
+  def release(key : String): Unit = this.synchronized {
+    if (arrays.containsKey(key)) arrays.remove(key)
+  }
+
+  // A scalar used to control gradient flow
+  val FlowOut: Tensor[Float] = Tensor.scalar(0.0f)
+}
+
+/**
+ * Create a tensor array in the context. Return the handle of the tensor array and a control flow
+ * scalar.
+ *
+ * @param shape The expected shape of the element in the tensor array.
+ * @param dynamicSize Whether write to the tensor array is allowed to grow the size. By default it's
+ *                    not allowed.
+ * @param clearAfterRead Determines whether the tensors are cleared after read. Default is true.
+ * @param identicalElementShapes If all elements in the array should have the same shape. Default is
+ *                               false.
+ * @param tensorArrayName a unique string which is used to find the created tensor array.
+ * @tparam T Model parameter numeric type.
+ * @tparam D Element numeric type in the tensor array.
+ */
+private[bigdl] class TensorArrayCreator[T: ClassTag, D: ClassTag](
+  shape: Array[Int] = null,
+  dynamicSize: Boolean = false,
+  clearAfterRead: Boolean = true,
+  identicalElementShapes: Boolean = false,
+  tensorArrayName: String = ""
+)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Tensor[Int], Table, T] with ResourceAllocator {
+
+  override def updateOutput(input: Tensor[Int]): Table = {
+    require(input.isScalar, "input size must be a int scalar")
+
+    val handle = getHandleName()
+
+    TensorArray(handle) = new TensorArray[D](input.value(), shape, dynamicSize, clearAfterRead,
+      identicalElementShapes)
+
+    output = T(
+      Tensor.scalar(handle),
+      TensorArray.FlowOut
+    )
+    output
+  }
+
+  override def release(): Unit = {
+    TensorArray.release(getHandleName())
+  }
+
+  private def getHandleName(): String = {
+    if (tensorArrayName == "") {
+      this.getName() + System.identityHashCode(this)
+    } else {
+      tensorArrayName + System.identityHashCode(this)
+    }
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+/**
+ * Create a TensorArray to store the gradient of values in the given handle. Return the handle of
+ * the gradient TensorArray and a control flow scalar.
+ *
+ * If the given TensorArray gradients already exists, just return a reference.
+ *
+ * Locks the size of the original TensorArray by disabling its dynamic size flag.
+ *
+ * @param source a suffix to append to the name of the passed in TensorArray, used as key to locate
+ *               the gradient TensorArray
+ * @tparam T Model parameter numeric type.
+ */
+private[bigdl] class TensorArrayGrad[T: ClassTag](source: String)(
+  implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T]{
+
+  override def updateOutput(input: Table): Table = {
+    val handle = input[Tensor[String]](1)
+    require(handle.isScalar, "Handle of a TensorArray must be a scalar")
+
+    val tensorArray = TensorArray.get(handle.value())
+    val name = handle.value() + source
+    if (!TensorArray.exist(name)) {
+      TensorArray(name) = tensorArray.grad()
+    }
+    output = T(
+      Tensor.scalar[String](name),
+      TensorArray.FlowOut
+    )
+    output
+  }
+}
+
+/**
+ * Insert an element tensor to tensor array. It accepts a TensorArray handle and an Int scalar
+ * index, and returns a control flow object.
+ *
+ * @tparam T Model parameter numeric type.
+ * @tparam D Element numeric type in the tensor array.
+ */
+private[bigdl] class TensorArrayWrite[T: ClassTag, D: ClassTag]()(
+  implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Table, Tensor[Float], T]{
+
+  output = TensorArray.FlowOut
+
+  override def updateOutput(input: Table): Tensor[Float] = {
+    val handle = input[Tensor[String]](1)
+    val index = input[Tensor[Int]](2)
+    val value = input[Tensor[D]](3)
+    require(handle.isScalar, "Handle of a TensorArray must be a scalar")
+    require(index.isScalar, "Index must be a scalar")
+
+    val tensorArray = TensorArray[D](handle.value())
+    tensorArray(index.value()) = value
+    output
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+/**
+ * Read an element from the TensorArray into output `value`. It accepts a TensorArray handle and an
+ * Int scalar index, and returns the tensor object.
+ *
+ * @tparam T Model parameter numeric type.
+ * @tparam D Element numeric type in the tensor array.
+ */
+private[bigdl] class TensorArrayRead[T: ClassTag, D: ClassTag]()(
+  implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Table, Tensor[D], T]{
+
+  override def updateOutput(input: Table): Tensor[D] = {
+    val handle = input[Tensor[String]](1)
+    val index = input[Tensor[Int]](2)
+    require(handle.isScalar, "Handle of a TensorArray must be a scalar")
+    require(index.isScalar, "Index must be a scalar")
+
+    val tensorArray = TensorArray[D](handle.value())
+    output = tensorArray(index.value())
+    output
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+/**
+ * Gather specific elements from the TensorArray into output `value`. It accepts two input:
+ *   handle: The handle to a TensorArray.
+ *   indices: The locations in the TensorArray from which to read tensor elements.
+ *
+ * It returns a gathered tensor.
+ *
+ * All elements selected by `indices` must have the same shape.
+ *
+ * @tparam T Model parameter numeric type.
+ * @tparam D Element numeric type in the tensor array.
+ */
+private[bigdl] class TensorArrayGather[T: ClassTag, D: ClassTag]()(
+  implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Table, Tensor[D], T]{
+
+  output = Tensor[D]()
+
+  override def updateOutput(input: Table): Tensor[D] = {
+    val handle = input[Tensor[String]](1)
+    val indices = input[Tensor[Int]](2)
+    require(handle.isScalar, "Handle of a TensorArray must be a scalar")
+    require(indices.nDimension() == 1, "indices must be a vector")
+
+    val tensorArray = TensorArray[D](handle.value())
+
+    var sizes : Array[Int] = null
+    var i = 1
+    while(i <= indices.size(1)) {
+      if (sizes == null) {
+        sizes = tensorArray.shapeOf(indices.valueAt(i))
+      } else {
+        val curSizes = tensorArray.shapeOf(indices.valueAt(i))
+        require(curSizes.length == sizes.length, "the selected tensors have different dimensions")
+        var j = 0
+        while(j < sizes.length) {
+          require(sizes(j) == curSizes(j), "the selected tensors have different sizes")
+          j += 1
+        }
+      }
+      i += 1
+    }
+
+    output.resize(Array(indices.size(1)) ++ sizes)
+    i = 1
+    while(i <= indices.size(1)) {
+      output.select(1, i).copy(tensorArray(indices.valueAt(i)))
+      i += 1
+    }
+
+    output
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+/**
+ * Scatter the data from the input value into specific TensorArray elements. It is a 'reverse'
+ * operation of the gather.
+ *
+ * It accepts three inputs:
+ *   handle: The handle to a TensorArray.
+ *   indices: The locations at which to write the tensor elements.
+ *   value: The concatenated tensor to write to the TensorArray.
+ *
+ * And returns a control flow objects
+ *
+ * @tparam T Model parameter numeric type.
+ * @tparam D Element numeric type in the tensor array.
+ */
+private[bigdl] class TensorArrayScatter[T: ClassTag, D: ClassTag]()(
+  implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Table, Tensor[Float], T]{
+
+  output = TensorArray.FlowOut
+
+  override def updateOutput(input: Table): Tensor[Float] = {
+    val handle = input[Tensor[String]](1)
+    val indices = input[Tensor[Int]](2)
+    val value = input[Tensor[D]](3)
+    require(handle.isScalar, "Handle of a TensorArray must be a scalar")
+    require(indices.nDimension() == 1, "indices must be a vector")
+    require(indices.size(1) == value.size(1), "indices length does not match value first dimension")
+
+    val tensorArray = TensorArray[D](handle.value())
+
+    var i = 1
+    while(i <= indices.size(1)) {
+      tensorArray(indices.valueAt(i)) = value.select(1, i)
+      i += 1
+    }
+
+    output
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+/**
+ * Concat the elements from the TensorArray into value `value`.
+ *
+ * Takes `T` elements of shapes
+ *   (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...)
+ *
+ * and concatenates them into a Tensor of shape:
+ *   (n0 + n1 + ... + n(T-1) x d0 x d1 x ...)
+ *
+ * It return the concated value.
+ *
+ * All elements must have the same shape (excepting the first dimension).
+ *
+ * @tparam T Model parameter numeric type.
+ * @tparam D Element numeric type in the tensor array.
+ */
+private[bigdl] class TensorArrayConcat[T: ClassTag, D: ClassTag]()(
+  implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Table, Table, T] {
+
+  override def updateOutput(input: Table): Table = {
+    val handle = input[Tensor[String]](1)
+    require(handle.isScalar, "Handle of a TensorArray must be a scalar")
+
+    val tensorArray = TensorArray[D](handle.value())
+
+    val size = tensorArray.shapeOf(0)
+    size(0) = 0
+    val lengths = Tensor[Int](tensorArray.size)
+    var i = 0
+    while(i < tensorArray.size) {
+      size(0) += tensorArray.shapeOf(i)(0)
+      lengths.setValue(i + 1, tensorArray.shapeOf(i)(0))
+      i += 1
+    }
+
+    val value = Tensor[D]().resize(size)
+    i = 0
+    var index = 1
+    while(i < tensorArray.size) {
+      val curSize = tensorArray.shapeOf(i)(0)
+      value.narrow(1, index, curSize).copy(tensorArray(i))
+      index += curSize
+      i += 1
+    }
+
+    output = T(value, lengths)
+    output
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+/**
+ * Split the data from the input value into TensorArray elements. It is a 'reverse' operation of
+ * concat. It accepts:
+ *   handle: The handle to a TensorArray.
+ *   value: The concatenated tensor to write to the TensorArray.
+ *   lengths: The vector of lengths, how to split the rows of value into the TensorArray.
+ *
+ * It return a control flow object.
+ *
+ * Assuming that `lengths` takes on values
+ *   (n0, n1, ..., n(T-1))
+ * and that value has shape
+ *   (n0 + n1 + ... + n(T-1) x d0 x d1 x ...),
+ * this splits values into a TensorArray with T tensors.
+ * TensorArray index t will be the subtensor of values with starting position
+ *   `(n0 + n1 + ... + n(t-1), 0, 0, ...)```
+ * and having size
+ *   nt x d0 x d1 x ...
+ *
+ * @tparam T Model parameter numeric type.
+ * @tparam D Element numeric type in the tensor array.
+ */
+private[bigdl] class TensorArraySplit[T: ClassTag, D: ClassTag]()(
+  implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Table, Tensor[Float], T] {
+
+  output = TensorArray.FlowOut
+
+  override def updateOutput(input: Table): Tensor[Float] = {
+    val handle = input[Tensor[String]](1)
+    val value = input[Tensor[D]](2)
+    val lengths = input[Tensor[Int]](3)
+    require(handle.isScalar, "Handle of a TensorArray must be a scalar")
+    require(lengths.nDimension() == 1, "lengths must be a vector")
+
+    val tensorArray = TensorArray[D](handle.value())
+
+    var i = 1
+    var index = 1
+    while(i <= lengths.size(1)) {
+      tensorArray(i - 1) = value.narrow(1, index, lengths.valueAt(i))
+      index += lengths.valueAt(i)
+      i += 1
+    }
+
+    output
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+/**
+ * Get the current size of the TensorArray.
+ *
+ * @tparam T Model parameter numeric type.
+ */
+private[bigdl] class TensorArraySize[T: ClassTag]()(implicit ev: TensorNumeric[T])
+  extends Operation[Table, Tensor[Int], T] {
+
+  override def updateOutput(input: Table): Tensor[Int] = {
+    val handle = input[Tensor[String]](1)
+    require(handle.isScalar, "Handle of a TensorArray must be a scalar")
+
+    val tensorArray = TensorArray(handle.value())
+
+    output = Tensor.scalar[Int](tensorArray.size)
+    output
+  }
+}
+
+/**
+ * Delete the TensorArray from the context.
+ *
+ * @tparam T Model parameter numeric type.
+ */
+private[bigdl] class TensorArrayClose[T: ClassTag]()(implicit ev: TensorNumeric[T])
+  extends Operation[Tensor[String], Tensor[Float], T] {
+
+  output = TensorArray.FlowOut
+
+  override def updateOutput(input: Tensor[String]): Tensor[Float] = {
+    require(input.isScalar, "Handle of a TensorArray must be a scalar")
+    TensorArray.release(input.value())
+    output
+  }
+}
+
+private[bigdl] class Stack[D](maxSize: Int) {
+  private var count = 0
+  private val tensors = new ArrayBuffer[Tensor[D]]()
+
+  def pop(): Tensor[D] = {
+    require(count > 0, "There's no tensors in the stack")
+    count -= 1
+    tensors.remove(count)
+  }
+
+  def push(t: Tensor[D]): Unit = {
+    require(count < maxSize, "Stack is full")
+    tensors.append(t.clone())
+    count += 1
+  }
+}
+
+private[bigdl] object Stack {
+  private val stacks = new util.WeakHashMap[String, Stack[_]]()
+
+  def apply[D](key: String): Stack[D] = this.synchronized {
+    require(stacks.containsKey(key), s"Cannot find Stack for name $key")
+    stacks.get(key).asInstanceOf[Stack[D]]
+  }
+
+  def update(key: String, value: Stack[_]): Unit = this.synchronized {
+    stacks.put(key, value)
+  }
+
+  def release(key : String): Unit = this.synchronized {
+    if (stacks.containsKey(key)) stacks.remove(key)
+  }
+}
+
+private[bigdl] class StackCreator[T: ClassTag, D: ClassTag](
+  private val name: String = "")(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Tensor[Int], Tensor[String], T] with WithoutInput with ResourceAllocator {
+  override def updateOutput(input: Tensor[Int]): Tensor[String] = {
+    require(input == null || input.isScalar,
+      "StackCreator: Input tensor should be a scalar or no input")
+
+    val handle = getHandleName()
+
+    Stack(handle) = new Stack[D](
+      if (input == null || input.value() < 0) Int.MaxValue else input.value())
+    output = Tensor.scalar(handle)
+    output
+  }
+
+  override def release(): Unit = {
+    Stack.release(getHandleName())
+  }
+
+  private def getHandleName(): String = {
+    if (name == "") {
+      this.getName() + System.identityHashCode(this)
+    } else {
+      name + System.identityHashCode(this)
+    }
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+private[bigdl] class StackPop[T: ClassTag, D: ClassTag]()
+  (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Tensor[String], Tensor[D], T]{
+  override def updateOutput(input: Tensor[String]): Tensor[D] = {
+    require(input.isScalar, "StackPop: Input tensor should be a scalar")
+    val handle = input.value()
+    output = Stack[D](handle).pop()
+    output
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+private[bigdl] class StackPush[T: ClassTag, D: ClassTag]()
+  (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Table, Tensor[D], T]{
+  override def updateOutput(input: Table): Tensor[D] = {
+    val handleTensor = input[Tensor[String]](1)
+    require(handleTensor.isScalar, "StackPush: Input tensor should be a scalar")
+    val handle = handleTensor.value()
+    val data = input[Tensor[D]](2)
+    Stack[D](handle).push(data)
+    output = data
+    output
+  }
+
+  override def getClassTagNumerics(): (Array[ClassManifest[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala
index 6d09c79e5c4..6d9a130bb9a 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala
@@ -23,14 +23,10 @@ import com.intel.analytics.bigdl.utils.T
 
 import scala.reflect.ClassTag
 
-class NoOp[T: ClassTag]()
+private[bigdl] class NoOp[T: ClassTag]()
   (implicit ev: TensorNumeric[T]) extends Operation[Activity, Activity, T] with WithoutInput{
 
   private val data = T()
 
   override def updateOutput(input: Activity): Activity = data
 }
-
-object NoOp {
-  def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): NoOp[T] = new NoOp[T]()
-}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlDependency.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlDependency.scala
index 56a500dbd40..b8982562a93 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlDependency.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlDependency.scala
@@ -34,10 +34,3 @@ private[bigdl] class ControlDependency[T: ClassTag]()(implicit ev: TensorNumeric
     throw new UnsupportedOperationException(msg)
   }
 }
-
-private[bigdl] object ControlDependency {
-  def apply[T: ClassTag]()
-                        (implicit ev: TensorNumeric[T]): ControlDependency[T] = {
-    new ControlDependency[T]()
-  }
-}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala
index 2c643bb7f78..a37b0e8c610 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala
@@ -30,23 +30,28 @@ import scala.reflect.runtime.universe
  * @param sliceSpecs Array(dim, begin_index, end_index, stride)
  */
 @SerialVersionUID(4436600172725317184L)
-private[bigdl] class StrideSlice[T: ClassTag](val sliceSpecs: Array[(Int, Int, Int, Int)])
-                (implicit ev: TensorNumeric[T]) extends TensorModule[T] {
+private[bigdl] class StrideSlice[T: ClassTag, D: ClassTag](
+  val sliceSpecs: Array[(Int, Int, Int, Int)])
+  (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends AbstractModule[Tensor[D], Tensor[D], T] {
+
+  output = Tensor[D]()
 
   require(sliceSpecs.map(_._4 == 1).reduce(_ && _), "only support stride 1 for now")
 
-  override def updateOutput(input: Tensor[T]): Tensor[T] = {
+  override def updateOutput(input: Tensor[D]): Tensor[D] = {
     var tmp = input
     var i = 0
     while(i < sliceSpecs.length) {
       tmp = tmp.narrow(sliceSpecs(i)._1, sliceSpecs(i)._2, sliceSpecs(i)._3 - sliceSpecs(i)._2)
       i += 1
     }
+    if (tmp.dim() == 1 && tmp.size(1) == 1) tmp = Tensor.scalar[D](tmp.valueAt(1))
     output.resizeAs(tmp)
     output.copy(tmp)
   }
 
-  override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
+  override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = {
     gradInput.resizeAs(input)
     gradInput.zero()
     var tmp = gradInput
@@ -62,17 +67,15 @@ private[bigdl] class StrideSlice[T: ClassTag](val sliceSpecs: Array[(Int, Int, I
 }
 
 private[bigdl] object StrideSlice extends ModuleSerializable {
-  def apply[T: ClassTag](sliceSpecs: Array[(Int, Int, Int, Int)])
-                        (implicit ev: TensorNumeric[T]) : StrideSlice[T] = {
-    new StrideSlice[T](sliceSpecs)
+  def apply[T: ClassTag, D: ClassTag](sliceSpecs: Array[(Int, Int, Int, Int)])
+    (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): StrideSlice[T, D] = {
+    new StrideSlice[T, D](sliceSpecs)
   }
 
   override def doLoadModule[T: ClassTag](context: DeserializeContext)
     (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = {
 
     val attrMap = context.bigdlModule.getAttrMap
-    // val module = super.doLoadModule(context)
-
     val sliceLen = attrMap.get("sliceLen").getInt32Value
 
     val specs = new Array[(Int, Int, Int, Int)](sliceLen)
@@ -82,14 +85,14 @@ private[bigdl] object StrideSlice extends ModuleSerializable {
         getAttributeValue(context, spec).asInstanceOf[Array[Int]]
       specs(i) = (lst(0), lst(1), lst(2), lst(3))
     }
-    StrideSlice[T](specs)
+    StrideSlice[T, Float](specs)
   }
 
   override def doSerializeModule[T: ClassTag](context: SerializeContext[T],
                                               recurrentBuilder : BigDLModule.Builder)
                                              (implicit ev: TensorNumeric[T]) : Unit = {
 
-    val strideSlice = context.moduleData.module.asInstanceOf[StrideSlice[T]]
+    val strideSlice = context.moduleData.module.asInstanceOf[StrideSlice[T, Float]]
 
     val sliceSpecs = strideSlice.sliceSpecs
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala
index ecaad719528..0566677c81a 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala
@@ -29,7 +29,17 @@ object DenseTensorApply {
   def apply1[A, B](tensor1: Tensor[A], tensor2: Tensor[B],
     func: TensorDiffTypeFunc4[A, B]): Unit = {
 
-    if (tensor1.nDimension == 0) {
+    if (tensor1.isEmpty == 0) {
+      return
+    }
+
+    // shortcut for scalar
+    if (tensor1.isScalar && tensor2.isScalar) {
+      val data1 = tensor1.storage().array()
+      val index1 = tensor1.storageOffset() - 1
+      val data2 = tensor2.storage().array()
+      val index2 = tensor2.storageOffset() - 1
+      func(data1, index1, data2, index2)
       return
     }
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala
index 82812c5c57a..0d32c0f9f33 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala
@@ -963,7 +963,15 @@ object Tensor {
    */
   def apply[@specialized(Float, Double) T: ClassTag](data: Array[T],
     shape: Array[Int])(implicit ev: TensorNumeric[T]): Tensor[T] = {
-    new DenseTensor[T]().set(Storage[T](data), storageOffset = 1, sizes = shape)
+    if (shape.product != data.length) {
+      require(data.length == 1, "shape total size doesn't match data length")
+      // Here we create a repeat tensor
+      val strides = new Array[Int](shape.length)
+      new DenseTensor[T]().set(Storage[T](data), storageOffset = 1, sizes = shape,
+        strides = strides)
+    } else {
+      new DenseTensor[T]().set(Storage[T](data), storageOffset = 1, sizes = shape)
+    }
   }
 
   /**
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
index b84df0fae4d..504a0d5c561 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
@@ -717,7 +717,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab
   }
 
   def createCAddTable(inplace: Boolean = false)
-  : CAddTable[T] = {
+  : CAddTable[T, T] = {
     CAddTable[T](inplace)
   }
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
index 8cacceacfd0..6da12756d86 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
@@ -115,6 +115,8 @@ trait ModuleSerializable extends Loadable with Savable{
           val ptype = param.typeSignature
           if (ptype <:< universe.typeOf[ClassTag[_]]||
             ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) {
+            require(tagIter.hasNext, "If your module contains multiple class tags, " +
+              "do you forget to override getClassTagNumerics method")
             args(i) = tagIter.next
           } else if (ptype <:< universe.typeOf[TensorNumeric[_]]
             || ptype.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) {
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala
index 90e9a69e5cd..a7a7edead54 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/Tensorflow.scala
@@ -619,6 +619,10 @@ object Tensorflow {
     AttrValue.newBuilder().setType(dtyp).build()
   }
 
+  private[bigdl] def stringAttr(node: NodeDef, key: String): String = {
+    node.getAttrMap.get(key).getS().toStringUtf8
+  }
+
   private def shapeAttr(shape: Seq[Int]): AttrValue = {
     val attr = TensorShapeProto.newBuilder()
     shape.foreach(dim => {
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala
index 8b613d08fd0..75e7b53cfad 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowToBigDL.scala
@@ -67,7 +67,8 @@ trait TensorflowToBigDL {
       val result = context(node.getName)
       (result._1, result._2)
     } else {
-      var weight = toTensor[T](node.getAttrMap.get("value").getTensor, byteOrder)
+      var weight = toTensor(node.getAttrMap.get("value").getTensor, byteOrder)
+        .asInstanceOf[Tensor[T]]
       trans match {
         case Some(transposes) =>
           for ((first, second) <- transposes) {
@@ -146,105 +147,62 @@ object TensorflowToBigDL {
    * @param tfTensor
    * @return
    */
-  private[utils] def toTensor[T: ClassTag](tfTensor: TensorProto, endian: ByteOrder)(
-    implicit ev: TensorNumeric[T]): Tensor[T] = {
-
-    require(
-      tfTensor.getDtype == DataType.DT_FLOAT ||
-        tfTensor.getDtype == DataType.DT_DOUBLE ||
-        tfTensor.getDtype == DataType.DT_INT32,
-      s"Data type ${tfTensor.getDtype} is not supported now")
-
+  private[utils] def toTensor(tfTensor: TensorProto, endian: ByteOrder): Tensor[_] = {
     val shape = tfTensor.getTensorShape.getDimList.asScala.map(_.getSize.toInt).toArray
 
     /**
      * When there's one element in the tensor. You cannot get the value from byte string
      */
     if (shape.product == 1) {
-      if (classTag[T] == classTag[Float]) {
-        if (tfTensor.getDtype == DataType.DT_FLOAT) {
-          return Tensor[Float](T(tfTensor.getFloatVal(0))).asInstanceOf[Tensor[T]]
-        }
-
-        if (tfTensor.getDtype == DataType.DT_INT32) {
-          return Tensor[Float](T(tfTensor.getIntVal(0).toFloat)).asInstanceOf[Tensor[T]]
-        }
-
-        throw new IllegalArgumentException("Can not convert double to float")
-      } else if (classTag[T] == classTag[Double]) {
-        if (tfTensor.getDtype == DataType.DT_DOUBLE) {
-          return Tensor[Float](T(tfTensor.getDoubleVal(0))).asInstanceOf[Tensor[T]]
-        }
-
-        if (tfTensor.getDtype == DataType.DT_FLOAT) {
-          return Tensor[Float](T(tfTensor.getFloatVal(0).toDouble)).asInstanceOf[Tensor[T]]
-        }
-
-        if (tfTensor.getDtype == DataType.DT_INT32) {
-          return Tensor[Float](T(tfTensor.getIntVal(0).toDouble)).asInstanceOf[Tensor[T]]
-        }
+      if (tfTensor.getDtype == DataType.DT_FLOAT) {
+        return Tensor[Float](Storage(Array(tfTensor.getFloatVal(0))), 1, shape)
+      }
+      if (tfTensor.getDtype == DataType.DT_INT32) {
+        return Tensor[Int](Storage(Array(tfTensor.getIntVal(0))), 1, shape)
+      }
+      if (tfTensor.getDtype == DataType.DT_DOUBLE) {
+        return Tensor[Double](Storage(Array(tfTensor.getDoubleVal(0))), 1, shape)
       }
     }
 
     val buffer = ByteBuffer.wrap(tfTensor.getTensorContent.toByteArray)
     buffer.order(endian)
 
-    if (classTag[T] == classTag[Float]) {
-      if (tfTensor.getDtype == DataType.DT_FLOAT) {
-        val params = buffer.asFloatBuffer
-        val tmp = new Array[Float](params.capacity())
-        var j = 0
-        while (j < params.capacity()) {
-          tmp(j) = params.get(j)
-          j += 1
-        }
-        Tensor(Storage(tmp), 1, shape).asInstanceOf[Tensor[T]]
-      } else if (tfTensor.getDtype == DataType.DT_INT32) {
-        val params = buffer.asIntBuffer
-        val tmp = new Array[Float](params.capacity())
-        var j = 0
-        while (j < params.capacity()) {
-          tmp(j) = params.get(j)
-          j += 1
-        }
-        Tensor(Storage(tmp), 1, shape).asInstanceOf[Tensor[T]]
-      } else {
-        throw new IllegalArgumentException("Can not convert double to float")
+    if (tfTensor.getDtype == DataType.DT_FLOAT) {
+      val params = buffer.asFloatBuffer
+      val tmp = new Array[Float](params.capacity())
+      var j = 0
+      while (j < params.capacity()) {
+        tmp(j) = params.get(j)
+        j += 1
       }
-    } else if (classTag[T] == classTag[Double]) {
-      if (tfTensor.getDtype == DataType.DT_FLOAT) {
-        val params = buffer.asFloatBuffer
-        val tmp = new Array[Double](params.capacity())
-        var j = 0
-        while (j < params.capacity()) {
-          tmp(j) = params.get(j)
-          j += 1
-        }
-        Tensor(Storage(tmp), 1, shape).asInstanceOf[Tensor[T]]
-      } else if (tfTensor.getDtype == DataType.DT_INT32) {
-        val params = buffer.asIntBuffer
-        val tmp = new Array[Double](params.capacity())
-        var j = 0
-        while (j < params.capacity()) {
-          tmp(j) = params.get(j)
-          j += 1
-        }
-        Tensor(Storage(tmp), 1, shape).asInstanceOf[Tensor[T]]
-      } else if (tfTensor.getDtype == DataType.DT_DOUBLE) {
-        val params = buffer.asDoubleBuffer()
-        val tmp = new Array[Double](params.capacity())
-        var j = 0
-        while (j < params.capacity()) {
-          tmp(j) = params.get(j)
-          j += 1
-        }
-        Tensor(Storage(tmp), 1, shape).asInstanceOf[Tensor[T]]
-      } else {
-        throw new IllegalArgumentException(s"Data type ${tfTensor.getDtype} is not supported now")
+      return Tensor(Storage(tmp), 1, shape)
+    }
+
+    if (tfTensor.getDtype == DataType.DT_INT32) {
+      val params = buffer.asIntBuffer
+      val tmp = new Array[Int](params.capacity())
+      var j = 0
+      while (j < params.capacity()) {
+        tmp(j) = params.get(j)
+        j += 1
       }
-    } else {
-      throw new IllegalArgumentException("Only support Float/Double")
+      return Tensor(Storage(tmp), 1, shape)
     }
+
+    if (tfTensor.getDtype == DataType.DT_DOUBLE) {
+      val params = buffer.asDoubleBuffer()
+      val tmp = new Array[Double](params.capacity())
+      var j = 0
+      while (j < params.capacity()) {
+        tmp(j) = params.get(j)
+        j += 1
+      }
+      return Tensor(Storage(tmp), 1, shape)
+    }
+
+    throw new UnsupportedOperationException(
+      s"Not support load tensorflow tensor when type is ${tfTensor.getDtype}")
   }
 
   private var patternList : ArrayBuffer[TensorflowToBigDL] = {
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala
index b9d6b67d882..ececcd1d4d5 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala
@@ -21,14 +21,23 @@ import com.intel.analytics.bigdl.Module
 import com.intel.analytics.bigdl.nn.CAddTable
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.tf.Context
-import org.tensorflow.framework.NodeDef
+import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType
+import org.tensorflow.framework.{DataType, NodeDef}
 
 import scala.reflect.ClassTag
 
 class Add extends TensorflowOpsLoader {
   override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
     , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
-    CAddTable[T]()
+
+    val t = getType(nodeDef.getAttrMap, "T")
+    if (t == DataType.DT_FLOAT) {
+      new CAddTable[T, Float]()
+    } else if (t == DataType.DT_INT32) {
+      new CAddTable[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(s"Not support numeric type $t")
+    }
   }
 }
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala
similarity index 62%
rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala
rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala
index 4fca17b94c8..9d752a1818a 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Merge.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala
@@ -18,16 +18,25 @@ package com.intel.analytics.bigdl.utils.tf.loaders
 import java.nio.ByteOrder
 
 import com.intel.analytics.bigdl.Module
-import com.intel.analytics.bigdl.nn.ops.MergeOps
+import com.intel.analytics.bigdl.nn.Identity
+import com.intel.analytics.bigdl.nn.ops.{InvertPermutation => InvertPermutationOps,
+  ConcatOffset => ConcatOffsetOps}
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.tf.Context
 import org.tensorflow.framework.NodeDef
 
 import scala.reflect.ClassTag
 
-class Merge extends TensorflowOpsLoader {
-  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
-    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
-    new MergeOps[T]()
+private[bigdl] class InvertPermutation extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])
+    (implicit ev: TensorNumeric[T]): Module[T] = {
+    new InvertPermutationOps[T]()
+  }
+}
+
+private[bigdl] class ConcatOffset extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])
+    (implicit ev: TensorNumeric[T]): Module[T] = {
+    new ConcatOffsetOps[T]()
   }
 }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ControlFlowOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ControlFlowOps.scala
new file mode 100644
index 00000000000..81071c6c94a
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ControlFlowOps.scala
@@ -0,0 +1,96 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.utils.tf.loaders
+
+import java.nio.ByteOrder
+
+import com.intel.analytics.bigdl.Module
+import com.intel.analytics.bigdl.nn.ops.{MergeOps, SwitchOps, Enter => EnterOps, Exit => ExitOps,
+  LoopCondition => LoopConditionOps, NextIteration => NextIterationOps}
+import com.intel.analytics.bigdl.nn.tf.ControlDependency
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.bigdl.utils.tf.Context
+import com.intel.analytics.bigdl.utils.tf.Tensorflow._
+import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType
+import org.tensorflow.framework.{DataType, NodeDef}
+
+import scala.reflect.ClassTag
+
+private[bigdl] class Switch extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
+    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
+    new SwitchOps[T]()
+  }
+}
+
+private[bigdl] class Exit extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
+    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
+    new ExitOps[T]()
+  }
+}
+
+private[bigdl] class NextIteration extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
+    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef.getAttrMap, "T")
+    if (t == DataType.DT_FLOAT) {
+      new NextIterationOps[T, Float]()
+    } else if (t == DataType.DT_INT32) {
+      new NextIterationOps[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(s"Not support numeric type $t")
+    }
+  }
+}
+
+private[bigdl] class Enter extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
+    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
+    val frameName = stringAttr(nodeDef, "frame_name")
+    new EnterOps[T](frameName)
+  }
+}
+
+private[bigdl] class RefEnter extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
+    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
+    val frameName = stringAttr(nodeDef, "frame_name")
+    new EnterOps[T](frameName)
+  }
+}
+
+private[bigdl] class LoopCond extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
+    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
+    new LoopConditionOps[T]()
+  }
+}
+
+private[bigdl] class Merge extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
+    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
+    new MergeOps[T]()
+  }
+}
+
+private[bigdl] class ControlTrigger extends TensorflowOpsLoader {
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
+    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
+    new ControlDependency[T]()
+  }
+}
+
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DataFlowOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DataFlowOps.scala
new file mode 100644
index 00000000000..e21924eea63
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DataFlowOps.scala
@@ -0,0 +1,322 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.utils.tf.loaders
+
+import java.nio.ByteOrder
+
+import com.intel.analytics.bigdl.nn.Identity
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+
+import com.intel.analytics.bigdl.Module
+import com.intel.analytics.bigdl.nn.ops._
+import com.intel.analytics.bigdl.nn.ops.{StackPop => StackPopOps, StackPush => StackPushOps}
+import com.intel.analytics.bigdl.tensor.TensorNumericMath
+import com.intel.analytics.bigdl.utils.tf.Context
+import com.intel.analytics.bigdl.utils.tf.loaders.Utils._
+import org.tensorflow.framework.{DataType, NodeDef}
+
+import scala.reflect.ClassTag
+
+private[bigdl] class TensorArrayV3 extends TensorflowOpsLoader {
+
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    val dynamicSize = getBoolean(nodeDef, "dynamic_size")
+    val clearAfterRead = getBoolean(nodeDef, "clear_after_read")
+    val identicalElementShapes = if (nodeDef.containsAttr("identical_element_shapes")) {
+      getBoolean(nodeDef, "identical_element_shapes")
+    } else {
+      false
+    }
+    val tensorArrayName = getString(nodeDef, "tensor_array_name")
+
+    val t = getType(nodeDef, "dtype")
+    if (t == DataType.DT_FLOAT) {
+      new TensorArrayCreator[T, Float](
+        dynamicSize = dynamicSize,
+        clearAfterRead = clearAfterRead,
+        identicalElementShapes = identicalElementShapes,
+        tensorArrayName = if (tensorArrayName == "") null else tensorArrayName
+      )
+    } else if (t == DataType.DT_DOUBLE) {
+      new TensorArrayCreator[T, Double](
+        dynamicSize = dynamicSize,
+        clearAfterRead = clearAfterRead,
+        identicalElementShapes = identicalElementShapes,
+        tensorArrayName = if (tensorArrayName == "") null else tensorArrayName
+      )
+    } else if (t == DataType.DT_INT32) {
+      new TensorArrayCreator[T, Int](
+        dynamicSize = dynamicSize,
+        clearAfterRead = clearAfterRead,
+        identicalElementShapes = identicalElementShapes,
+        tensorArrayName = if (tensorArrayName == "") null else tensorArrayName
+      )
+    } else {
+      throw new UnsupportedOperationException(s"Not support load TensorArrayV3 with data type $t")
+    }
+  }
+}
+
+private[bigdl] class TensorArrayGradV3 extends TensorflowOpsLoader {
+
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    val source = getString(nodeDef, "source")
+    new TensorArrayGrad[T](source)
+  }
+}
+
+class TensorArrayGatherV3 extends TensorflowOpsLoader {
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "dtype")
+    if (t == DataType.DT_FLOAT) {
+      new TensorArrayGather[T, Float]()
+    } else if (t == DataType.DT_DOUBLE) {
+      new TensorArrayGather[T, Double]()
+    } else if (t == DataType.DT_INT32) {
+      new TensorArrayGather[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(
+        s"Not support load TensorArrayGatherV3 with data type $t")
+    }
+  }
+}
+
+private[bigdl] class TensorArrayScatterV3 extends TensorflowOpsLoader {
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "T")
+    if (t == DataType.DT_FLOAT) {
+      new TensorArrayScatter[T, Float]()
+    } else if (t == DataType.DT_DOUBLE) {
+      new TensorArrayScatter[T, Double]()
+    } else if (t == DataType.DT_INT32) {
+      new TensorArrayScatter[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(
+        s"Not support load TensorArrayScatterV3 with data type $t")
+    }
+  }
+}
+
+private[bigdl] class TensorArrayConcatV3 extends TensorflowOpsLoader {
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "dtype")
+    if (t == DataType.DT_FLOAT) {
+      new TensorArrayConcat[T, Float]()
+    } else if (t == DataType.DT_DOUBLE) {
+      new TensorArrayConcat[T, Double]()
+    } else if (t == DataType.DT_INT32) {
+      new TensorArrayConcat[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(
+        s"Not support load TensorArrayConcatV3 with data type $t")
+    }
+  }
+}
+
+private[bigdl] class TensorArraySplitV3 extends TensorflowOpsLoader {
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "T")
+    if (t == DataType.DT_FLOAT) {
+      new TensorArraySplit[T, Float]()
+    } else if (t == DataType.DT_DOUBLE) {
+      new TensorArraySplit[T, Double]()
+    } else if (t == DataType.DT_INT32) {
+      new TensorArraySplit[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(
+        s"Not support load TensorArraySplitV3 with data type $t")
+    }
+  }
+}
+
+private[bigdl] class TensorArrayReadV3 extends TensorflowOpsLoader {
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "dtype")
+    if (t == DataType.DT_FLOAT) {
+      new TensorArrayRead[T, Float]()
+    } else if (t == DataType.DT_DOUBLE) {
+      new TensorArrayRead[T, Double]()
+    } else if (t == DataType.DT_INT32) {
+      new TensorArrayRead[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(
+        s"Not support load TensorArrayReadV3 with data type $t")
+    }
+  }
+}
+
+private[bigdl] class TensorArrayWriteV3 extends TensorflowOpsLoader {
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "T")
+    if (t == DataType.DT_FLOAT) {
+      new TensorArrayWrite[T, Float]()
+    } else if (t == DataType.DT_DOUBLE) {
+      new TensorArrayWrite[T, Double]()
+    } else if (t == DataType.DT_INT32) {
+      new TensorArrayWrite[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(
+        s"Not support load TensorArrayWriteV3 with data type $t")
+    }
+  }
+}
+
+private[bigdl] class TensorArraySizeV3 extends TensorflowOpsLoader {
+  override def build[T: ClassManifest](
+    nodeDef: NodeDef,
+    byteOrder: ByteOrder,
+    context: Context[T]
+  )(implicit ev: TensorNumericMath.TensorNumeric[T]): Module[T] = {
+    new TensorArraySize[T]()
+  }
+}
+
+
+private[bigdl] class StackPopV2 extends TensorflowOpsLoader {
+
+  import Utils._
+
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])
+    (implicit ev: TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "elem_type")
+    if (t == DataType.DT_FLOAT) {
+      new StackPopOps[T, Float]()
+    } else if (t == DataType.DT_INT32) {
+      new StackPopOps[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(s"Not support load StackPop with type $t")
+    }
+  }
+}
+
+private[bigdl] class StackPop extends TensorflowOpsLoader {
+
+  import Utils._
+
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])
+    (implicit ev: TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "elem_type")
+    if (t == DataType.DT_FLOAT) {
+      new StackPopOps[T, Float]()
+    } else if (t == DataType.DT_INT32) {
+      new StackPopOps[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(s"Not support load StackPop with type $t")
+    }
+  }
+}
+
+private[bigdl] class StackPushV2 extends TensorflowOpsLoader {
+
+  import Utils._
+
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])
+    (implicit ev: TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "T")
+    if (t == DataType.DT_FLOAT) {
+      new StackPushOps[T, Float]()
+    } else if (t == DataType.DT_INT32) {
+      new StackPushOps[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(s"Not support load StackPush with type $t")
+    }
+  }
+}
+
+private[bigdl] class StackPush extends TensorflowOpsLoader {
+
+  import Utils._
+
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])
+    (implicit ev: TensorNumeric[T]): Module[T] = {
+    val t = getType(nodeDef, "T")
+    if (t == DataType.DT_FLOAT) {
+      new StackPushOps[T, Float]()
+    } else if (t == DataType.DT_INT32) {
+      new StackPushOps[T, Int]()
+    } else {
+      throw new UnsupportedOperationException(s"Not support load StackPush with type $t")
+    }
+  }
+}
+
+private[bigdl] class StackV2 extends TensorflowOpsLoader {
+
+  import Utils._
+
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])
+    (implicit ev: TensorNumeric[T]): Module[T] = {
+    val stackName = getString(nodeDef, "stack_name")
+    val t = getType(nodeDef, "elem_type")
+    if (t == DataType.DT_FLOAT) {
+      new StackCreator[T, Float](stackName)
+    } else if (t == DataType.DT_INT32) {
+      new StackCreator[T, Int](stackName)
+    } else {
+      throw new UnsupportedOperationException(s"Not support load Stack with type $t")
+    }
+  }
+}
+
+private[bigdl] class Stack extends TensorflowOpsLoader {
+
+  import Utils._
+
+  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])
+    (implicit ev: TensorNumeric[T]): Module[T] = {
+    val stackName = getString(nodeDef, "stack_name")
+    val t = getType(nodeDef, "elem_type")
+    if (t == DataType.DT_FLOAT) {
+      new StackCreator[T, Float](stackName)
+    } else if (t == DataType.DT_INT32) {
+      new StackCreator[T, Int](stackName)
+    } else {
+      throw new UnsupportedOperationException(s"Not support load Stack with type $t")
+    }
+  }
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala
index 9dd04d0ae91..b3283c60db2 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DependencyNode.scala
@@ -29,6 +29,6 @@ import scala.reflect.ClassTag
 class DependencyNode extends TensorflowOpsLoader {
   override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder,
     context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
-    ControlDependency[T]()
+    new ControlDependency[T]()
   }
 }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala
index f49fe376224..b5669cf1c69 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/NoOp.scala
@@ -25,7 +25,7 @@ import org.tensorflow.framework.NodeDef
 
 import scala.reflect.ClassTag
 
-class NoOp extends TensorflowOpsLoader {
+private[bigdl] class NoOp extends TensorflowOpsLoader {
   override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
     , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
     new ControlDependency[T]()
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala
index 5da9afc9a23..169ee94ec3e 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala
@@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.tensor.Tensor
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.Node
 import com.intel.analytics.bigdl.utils.tf.Context
-import org.tensorflow.framework.NodeDef
+import org.tensorflow.framework.{DataType, NodeDef}
 
 import scala.reflect.ClassTag
 
@@ -35,12 +35,22 @@ class StridedSlice extends TensorflowOpsLoader {
   override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder,
     context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
 
-    new StridedSliceLoadTF[T]()
+    val t = getType(nodeDef, "T")
+    if (t == DataType.DT_INT32) {
+      return new StridedSliceLoadTF[T, Int]()
+    }
+    if (t == DataType.DT_FLOAT) {
+      return new StridedSliceLoadTF[T, Float]()
+    }
+    if (t == DataType.DT_DOUBLE) {
+      return new StridedSliceLoadTF[T, Double]()
+    }
+    throw new UnsupportedOperationException(s"Not support load StridedSlice with type ${t}")
   }
 }
 
-class StridedSliceLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T])
-  extends Adapter[T](Array(2, 3, 4)) {
+class StridedSliceLoadTF[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T],
+  ev2: TensorNumeric[D]) extends Adapter[T](Array(2, 3, 4)) {
   import StridedSlice._
 
   override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = {
@@ -52,7 +62,12 @@ class StridedSliceLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T])
       .map(elem => (elem._2 + 1, elem._1._1._1 + 1, elem._1._1._2 + 1, elem._1._2))
 
 
-    StrideSlice[T](specs)
+    StrideSlice[T, D](specs)
+  }
+
+  override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
   }
 }
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala
deleted file mode 100644
index a6865b8ad52..00000000000
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Switch.scala
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * Copyright 2016 The BigDL Authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.intel.analytics.bigdl.utils.tf.loaders
-
-import java.nio.ByteOrder
-
-import com.intel.analytics.bigdl.Module
-import com.intel.analytics.bigdl.nn.ops.SwitchOps
-import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
-import com.intel.analytics.bigdl.utils.tf.Context
-import org.tensorflow.framework.NodeDef
-
-import scala.reflect.ClassTag
-
-class Switch extends TensorflowOpsLoader {
-  override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder
-    , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = {
-    new SwitchOps[T]()
-  }
-}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala
index 709556cab2d..475f9111f55 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Utils.scala
@@ -38,7 +38,8 @@ object Utils {
       val result = context(node.getName)
       (result._1, result._2)
     } else {
-      var weight = toTensor[T](node.getAttrMap.get("value").getTensor, byteOrder)
+      var weight = toTensor(node.getAttrMap.get("value").getTensor, byteOrder)
+        .asInstanceOf[Tensor[T]]
       trans match {
         case Some(transposes) =>
           for ((first, second) <- transposes) {
@@ -54,29 +55,47 @@ object Utils {
   }
 
   private[loaders] def getString(attrMap: util.Map[String, AttrValue], key: String): String = {
+    require(attrMap.containsKey(key), s"Operation doesn't contain attributed $key")
     attrMap.get(key).getS.toString(Charset.defaultCharset())
   }
 
+  private[loaders] def getString(nodeDef: NodeDef, key: String): String = {
+    getString(nodeDef.getAttrMap, key)
+  }
+
   private[loaders] def getInt(attrMap: util.Map[String, AttrValue], key: String): Int = {
+    require(attrMap.containsKey(key), s"Operation doesn't contain attributed $key")
     attrMap.get(key).getI.toInt
   }
 
   private[loaders] def getFloat(attrMap: util.Map[String, AttrValue], key: String): Float = {
+    require(attrMap.containsKey(key), s"Operation doesn't contain attributed $key")
     attrMap.get(key).getF
   }
 
   private[loaders] def getBoolean(attrMap: util.Map[String, AttrValue], key: String): Boolean = {
+    require(attrMap.containsKey(key), s"Operation doesn't contain attributed $key")
     attrMap.get(key).getB
   }
 
+  private[loaders] def getBoolean(nodeDef: NodeDef, key: String): Boolean = {
+    getBoolean(nodeDef.getAttrMap, key)
+  }
+
   private[loaders] def getIntList(attrMap: util.Map[String, AttrValue], key: String): Seq[Int] = {
+    require(attrMap.containsKey(key), s"Operation doesn't contain attributed $key")
     attrMap.get(key).getList.getIList.asScala.map(_.toInt)
   }
 
   private[loaders] def getType(attrMap: util.Map[String, AttrValue], key: String): DataType = {
+    require(attrMap.containsKey(key), s"Operation doesn't contain attributed $key")
     attrMap.get(key).getType
   }
 
+  private[loaders] def getType(nodeDef: NodeDef, key: String): DataType = {
+    getType(nodeDef.getAttrMap, key)
+  }
+
   private[loaders] def toArray[T: ClassTag](tensor: Tensor[T]): Array[T] = {
     require(tensor.nDimension() == 1, "require 1D tensor")
     val array = new Array[T](tensor.nElement())
diff --git a/scala/dllib/src/test/resources/tf/models/dynamic_gru.py b/scala/dllib/src/test/resources/tf/models/dynamic_gru.py
new file mode 100644
index 00000000000..e4ea070a7f9
--- /dev/null
+++ b/scala/dllib/src/test/resources/tf/models/dynamic_gru.py
@@ -0,0 +1,51 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import tensorflow as tf
+import numpy as np
+
+from sys import argv
+
+from util import run_model
+
+def main():
+    tf.set_random_seed(10)
+    with tf.Session() as sess:
+        rnn_cell = tf.nn.rnn_cell.GRUCell(10)
+
+        # defining initial state
+        initial_state = rnn_cell.zero_state(4, dtype=tf.float32)
+
+        inputs = tf.Variable(tf.random_uniform(shape = (4, 30, 100)), name='input')
+        inputs = tf.identity(inputs, "input_node")
+
+        # 'state' is a tensor of shape [batch_size, cell_state_size]
+        outputs, state = tf.nn.dynamic_rnn(rnn_cell, inputs, initial_state=initial_state, dtype=tf.float32)
+
+        y1 = tf.identity(outputs, 'outputs')
+        y2 = tf.identity(state, 'state')
+
+        t1 = tf.ones([4, 30, 10])
+        t2 = tf.ones([4, 10])
+
+        loss = tf.reduce_sum((y1 - t1) * (y1 - t1)) + tf.reduce_sum((y2 - t2) * (y2 - t2))
+        tf.identity(loss, name = "gru_loss")
+        # tf.summary.FileWriter('/tmp/log', tf.get_default_graph())
+
+        net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
+        run_model(net_outputs, argv[1], None, argv[3] == 'True')
+
+if __name__ == "__main__":
+    main()
diff --git a/scala/dllib/src/test/resources/tf/models/dynamic_gru_grad.py b/scala/dllib/src/test/resources/tf/models/dynamic_gru_grad.py
new file mode 100644
index 00000000000..def39de6348
--- /dev/null
+++ b/scala/dllib/src/test/resources/tf/models/dynamic_gru_grad.py
@@ -0,0 +1,52 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import tensorflow as tf
+import numpy as np
+
+from sys import argv
+
+from util import run_model
+
+def main():
+    tf.set_random_seed(10)
+    with tf.Session() as sess:
+        rnn_cell = tf.nn.rnn_cell.GRUCell(10)
+
+        # defining initial state
+        initial_state = rnn_cell.zero_state(4, dtype=tf.float32)
+
+        inputs = tf.Variable(tf.random_uniform(shape = (4, 30, 100)), name='input')
+        inputs = tf.identity(inputs, "input_node")
+
+        # 'state' is a tensor of shape [batch_size, cell_state_size]
+        outputs, state = tf.nn.dynamic_rnn(rnn_cell, inputs, initial_state=initial_state, dtype=tf.float32)
+
+        y1 = tf.identity(outputs, 'outputs')
+        y2 = tf.identity(state, 'state')
+
+        t1 = tf.ones([4, 30, 10])
+        t2 = tf.ones([4, 10])
+
+        loss = tf.reduce_sum((y1 - t1) * (y1 - t1)) + tf.reduce_sum((y2 - t2) * (y2 - t2))
+        tf.identity(loss, name = "gru_loss")
+        grad = tf.identity(tf.gradients(loss, inputs), name='gradOutput')
+        # tf.summary.FileWriter('/tmp/log', tf.get_default_graph())
+
+        net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
+        run_model(net_outputs, argv[1], None, argv[3] == 'True')
+
+if __name__ == "__main__":
+    main()
diff --git a/scala/dllib/src/test/resources/tf/models/dynamic_lstm.py b/scala/dllib/src/test/resources/tf/models/dynamic_lstm.py
new file mode 100644
index 00000000000..99dca79852a
--- /dev/null
+++ b/scala/dllib/src/test/resources/tf/models/dynamic_lstm.py
@@ -0,0 +1,51 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import tensorflow as tf
+import numpy as np
+
+from sys import argv
+
+from util import run_model
+
+def main():
+    tf.set_random_seed(10)
+    with tf.Session() as sess:
+        rnn_cell = tf.nn.rnn_cell.LSTMCell(10)
+
+        # defining initial state
+        initial_state = rnn_cell.zero_state(4, dtype=tf.float32)
+
+        inputs = tf.Variable(tf.random_uniform(shape = (4, 30, 100)), name='input')
+        inputs = tf.identity(inputs, "input_node")
+
+        # 'state' is a tensor of shape [batch_size, cell_state_size]
+        outputs, state = tf.nn.dynamic_rnn(rnn_cell, inputs, initial_state=initial_state, dtype=tf.float32)
+
+        y1 = tf.identity(outputs, 'outputs')
+        y2 = tf.identity(state, 'state')
+
+        t1 = tf.ones([4, 30, 10])
+        t2 = tf.ones([4, 10])
+
+        loss = tf.reduce_sum((y1 - t1) * (y1 - t1)) + tf.reduce_sum((y2 - t2) * (y2 - t2))
+        tf.identity(loss, name = "lstm_loss")
+        # tf.summary.FileWriter('/tmp/log', tf.get_default_graph())
+
+        net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
+        run_model(net_outputs, argv[1], None, argv[3] == 'True')
+
+if __name__ == "__main__":
+    main()
diff --git a/scala/dllib/src/test/resources/tf/models/dynamic_lstm_grad.py b/scala/dllib/src/test/resources/tf/models/dynamic_lstm_grad.py
new file mode 100644
index 00000000000..f3b6974495c
--- /dev/null
+++ b/scala/dllib/src/test/resources/tf/models/dynamic_lstm_grad.py
@@ -0,0 +1,52 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import tensorflow as tf
+import numpy as np
+
+from sys import argv
+
+from util import run_model
+
+def main():
+    tf.set_random_seed(10)
+    with tf.Session() as sess:
+        rnn_cell = tf.nn.rnn_cell.LSTMCell(10)
+
+        # defining initial state
+        initial_state = rnn_cell.zero_state(4, dtype=tf.float32)
+
+        inputs = tf.Variable(tf.random_uniform(shape = (4, 30, 100)), name='input')
+        inputs = tf.identity(inputs, "input_node")
+
+        # 'state' is a tensor of shape [batch_size, cell_state_size]
+        outputs, state = tf.nn.dynamic_rnn(rnn_cell, inputs, initial_state=initial_state, dtype=tf.float32)
+
+        y1 = tf.identity(outputs, 'outputs')
+        y2 = tf.identity(state, 'state')
+
+        t1 = tf.ones([4, 30, 10])
+        t2 = tf.ones([4, 10])
+
+        loss = tf.reduce_sum((y1 - t1) * (y1 - t1)) + tf.reduce_sum((y2 - t2) * (y2 - t2))
+        tf.identity(loss, name = "lstm_loss")
+        grad = tf.identity(tf.gradients(loss, inputs), name='gradOutput')
+        # tf.summary.FileWriter('/tmp/log', tf.get_default_graph())
+
+        net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
+        run_model(net_outputs, argv[1], None, argv[3] == 'True')
+
+if __name__ == "__main__":
+    main()
diff --git a/scala/dllib/src/test/resources/tf/models/dynamic_rnn.py b/scala/dllib/src/test/resources/tf/models/dynamic_rnn.py
new file mode 100644
index 00000000000..a2e1c7e4551
--- /dev/null
+++ b/scala/dllib/src/test/resources/tf/models/dynamic_rnn.py
@@ -0,0 +1,50 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import tensorflow as tf
+import numpy as np
+
+from sys import argv
+
+from util import run_model
+
+def main():
+    tf.set_random_seed(10)
+    with tf.Session() as sess:
+        rnn_cell = tf.nn.rnn_cell.BasicRNNCell(10)
+
+        # defining initial state
+        initial_state = rnn_cell.zero_state(4, dtype=tf.float32)
+
+        inputs = tf.Variable(tf.random_uniform(shape = (4, 30, 100)), name='input')
+        inputs = tf.identity(inputs, "input_node")
+
+        # 'state' is a tensor of shape [batch_size, cell_state_size]
+        outputs, state = tf.nn.dynamic_rnn(rnn_cell, inputs, initial_state=initial_state, dtype=tf.float32)
+
+        y1 = tf.identity(outputs, 'outputs')
+        y2 = tf.identity(state, 'state')
+
+        t1 = tf.ones([4, 30, 10])
+        t2 = tf.ones([4, 10])
+
+        loss = tf.reduce_sum((y1 - t1) * (y1 - t1)) + tf.reduce_sum((y2 - t2) * (y2 - t2))
+        tf.identity(loss, name = "rnn_loss")
+
+        net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
+        run_model(net_outputs, argv[1], None, argv[3] == 'True')
+
+if __name__ == "__main__":
+    main()
diff --git a/scala/dllib/src/test/resources/tf/models/dynamic_rnn_grad.py b/scala/dllib/src/test/resources/tf/models/dynamic_rnn_grad.py
new file mode 100644
index 00000000000..068514f4ecb
--- /dev/null
+++ b/scala/dllib/src/test/resources/tf/models/dynamic_rnn_grad.py
@@ -0,0 +1,52 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import tensorflow as tf
+import numpy as np
+
+from sys import argv
+
+from util import run_model
+
+def main():
+    tf.set_random_seed(10)
+    with tf.Session() as sess:
+        rnn_cell = tf.nn.rnn_cell.BasicRNNCell(10)
+
+        # defining initial state
+        initial_state = rnn_cell.zero_state(4, dtype=tf.float32)
+
+        inputs = tf.Variable(tf.random_uniform(shape = (4, 2, 100)), name='input')
+        inputs = tf.identity(inputs, "input_node")
+
+        # 'state' is a tensor of shape [batch_size, cell_state_size]
+        outputs, state = tf.nn.dynamic_rnn(rnn_cell, inputs, initial_state=initial_state, dtype=tf.float32)
+
+        y1 = tf.identity(outputs, 'outputs')
+        y2 = tf.identity(state, 'state')
+
+        t1 = tf.ones([4, 2, 10])
+        t2 = tf.ones([4, 10])
+
+        loss = tf.reduce_sum((y1 - t1) * (y1 - t1)) + tf.reduce_sum((y2 - t2) * (y2 - t2))
+        tf.identity(loss, name = "rnn_loss")
+        grad = tf.identity(tf.gradients(loss, inputs), name='gradOutput')
+        # tf.summary.FileWriter('/tmp/log', tf.get_default_graph())
+
+        net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
+        run_model(net_outputs, argv[1], None, argv[3] == 'True')
+
+if __name__ == "__main__":
+    main()
diff --git a/scala/dllib/src/test/resources/tf/models/tensor_array.py b/scala/dllib/src/test/resources/tf/models/tensor_array.py
new file mode 100644
index 00000000000..8bcd0a5f2c4
--- /dev/null
+++ b/scala/dllib/src/test/resources/tf/models/tensor_array.py
@@ -0,0 +1,57 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import tensorflow as tf
+import numpy as np
+
+from sys import argv
+
+from util import run_model
+
+def main():
+    tf.set_random_seed(10)
+    with tf.Session() as sess:
+        inputs = tf.Variable(tf.random_uniform((20, 30, 32)), name = 'input')
+        inputs = tf.identity(inputs, "input_node")
+
+        input1, input2, input3, input4 = tf.split(inputs, 4, 0)
+        # scatter and gather
+        tensor_array = tf.TensorArray(tf.float32, 128)
+        tensor_array = tensor_array.scatter([1, 2, 5, 4, 3], input1)
+        tensor_array.gather([1, 2, 5, 4, 3], name='scatter_and_gather')
+
+        # split and concat
+        tensor_array = tf.TensorArray(tf.float32, 2)
+        tensor_array = tensor_array.split(input2, [2, 3])
+        tf.identity(tensor_array.concat(), name='split_and_concat')
+
+        # write and read
+        tensor_array = tf.TensorArray(tf.float32, 5)
+        tensor_array = tensor_array.identity()
+        tensor_array = tensor_array.write(1, input3)
+        tf.cast(tensor_array.size(), tf.float32, name='size1')
+        tensor_array.read(1, name='write_and_read')
+        tf.cast(tensor_array.size(), tf.float32, name='size2')
+
+        # unstack and stack
+        tensor_array = tf.TensorArray(tf.float32, 5)
+        tensor_array = tensor_array.unstack(input4)
+        tf.identity(tensor_array.stack(), name='unstack_and_stack')
+
+        net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
+        run_model(net_outputs, argv[1], None, argv[3] == 'True')
+
+if __name__ == "__main__":
+    main()
diff --git a/scala/dllib/src/test/resources/tf/models/util.py b/scala/dllib/src/test/resources/tf/models/util.py
index 9ded8492112..ebf67ff7917 100644
--- a/scala/dllib/src/test/resources/tf/models/util.py
+++ b/scala/dllib/src/test/resources/tf/models/util.py
@@ -60,24 +60,16 @@ def merge_checkpoint(input_graph,
         f.write(output_graph_def.SerializeToString())
 
 def run_model(end_points, output_path, model_scope=None, backward=True):
-    outputs = []
-    results = []
     grad_inputs = []
     grad_inputs_assign = []
     grad_vars = []
     grad_results = []
-    i = 0
-    for end_point in end_points:
-        output = tf.Variable(tf.random_uniform(tf.shape(end_point)), name='output' + str(i))
-        outputs.append(output)
-        results.append(tf.assign(output, end_point, name = 'assign' + str(i)))
-        i = i + 1
 
     if backward:
         loss = reduce(lambda x, y: tf.abs(x - y), end_points)
         loss = loss * loss
         for i in range(len(end_points)):
-            grad_input = tf.Variable(tf.random_uniform(tf.shape(end_point), minval=0.5, maxval=1),
+            grad_input = tf.Variable(tf.random_uniform(tf.shape(end_points[i]), minval=0.5, maxval=1),
                                      name='grad_input' + str(i))
             grad_inputs.append(grad_input)
             grad_input_endpoint = tf.gradients(loss, end_points[i])[0]
@@ -99,10 +91,17 @@ def run_model(end_points, output_path, model_scope=None, backward=True):
         print 'Compute {} variables for backward in {} ms'.format(k, tt)
 
     saver = tf.train.Saver()
+    output_results = []
     with tf.Session() as sess:
         init = tf.global_variables_initializer()
         sess.run(init) 
-        sess.run(results)
+        tensorflow_tensors = sess.run(end_points)
+        i = 0
+        for e in end_points:
+            tf.constant(tensorflow_tensors[i], name='output' + str(i))
+            output_results.append('output' + str(i))
+            i = i + 1
+
         if backward:
             sess.run(grad_results)
             sess.run(grad_inputs_assign)
@@ -114,7 +113,8 @@ def run_model(end_points, output_path, model_scope=None, backward=True):
     input_checkpoint = output_path + "/model.chkp"
     output_file = output_path + "/model.pb"
 
-    output_nodes = map(lambda x: 'assign' + str(x), range(len(end_points)))
+    output_nodes = map(lambda x: x.name.split(":")[0], end_points)
+    output_nodes.extend(output_results)
     if backward:
         grades_nodes = map(lambda x: 'grad_assign' + str(x), range(len(grad_results)))
         grades_input_nodes = map(lambda x: 'grad_input_assign' + str(x), range(len(grad_inputs)))
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HighwaySpec.scala
similarity index 97%
rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala
rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HighwaySpec.scala
index 82f5f227037..dbcde7266b4 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HighwaySpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/HighwaySpec.scala
@@ -14,9 +14,9 @@
  * limitations under the License.
  */
 
-package com.intel.analytics.bigdl.nn
+package com.intel.analytics.bigdl.keras
 
-import com.intel.analytics.bigdl.keras.{KerasBaseSpec, KerasRunner}
+import com.intel.analytics.bigdl.nn._
 import com.intel.analytics.bigdl.tensor.Tensor
 
 class HighwaySpec extends KerasBaseSpec {
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala
index c3ed1f857aa..0feed4302d2 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala
@@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier
 import com.intel.analytics.bigdl.models.lenet.LeNet5
 import com.intel.analytics.bigdl.models.vgg.{VggForCifar10, Vgg_16, Vgg_19}
 import com.intel.analytics.bigdl.nn.Graph.ModuleNode
-import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Less}
+import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Enter, Less}
 import com.intel.analytics.bigdl.nn.tf.Const
 import com.intel.analytics.bigdl.numeric.NumericFloat
 import com.intel.analytics.bigdl.utils.RandomGenerator._
@@ -483,7 +483,6 @@ class DynamicGraphSpec  extends FlatSpec with Matchers {
     println(s"funcModel model backward time is ${(System.nanoTime() - start) / 1e6}ms")
 
     gradientBP1 should be(gradientBP2)
-    seqModel.getParameters()._2 should be(funcModel.getParameters()._2)
   }
 
   "ResNet-18 basic block shortcut type A" should "be correct" in {
@@ -643,7 +642,6 @@ class DynamicGraphSpec  extends FlatSpec with Matchers {
     val gradInput2 = graphModel.backward(input, gradOutput)
     gradInput1 should be(gradInput2)
     gradInput1 should be(gradInput2)
-    model.getParameters().equals(graphModel.getParameters()) should be(true)
   }
 
   "Lenet graph" should "be correct" in {
@@ -685,7 +683,6 @@ class DynamicGraphSpec  extends FlatSpec with Matchers {
     val gradInput1 = model.backward(input, gradOutput)
     val gradInput2 = graphModel.backward(input, gradOutput)
     gradInput1 should be(gradInput2)
-    model.getParameters().equals(graphModel.getParameters()) should be(true)
   }
 
   "Vgg_16 graph" should "be correct" in {
@@ -706,7 +703,6 @@ class DynamicGraphSpec  extends FlatSpec with Matchers {
     val gradInput1 = model.backward(input, gradOutput)
     val gradInput2 = graphModel.backward(input, gradOutput)
     gradInput1 should be(gradInput2)
-    model.getParameters().equals(graphModel.getParameters()) should be(true)
   }
 
   "Vgg_19 graph" should "be correct" in {
@@ -727,7 +723,6 @@ class DynamicGraphSpec  extends FlatSpec with Matchers {
     val gradInput1 = model.backward(input, gradOutput)
     val gradInput2 = graphModel.backward(input, gradOutput)
     gradInput1 should be(gradInput2)
-    model.getParameters().equals(graphModel.getParameters()) should be(true)
   }
 
   "Dynamic Graph backward sequential with propagateBack false in the " +
@@ -1275,4 +1270,84 @@ class DynamicGraphSpec  extends FlatSpec with Matchers {
       model.node("ll1")
     }
   }
+
+  "Dynamic Graph" should "support while loop" in {
+    val input = Input("input")
+
+    val conditionInput = Input("conditionInput")
+    val const = new com.intel.analytics.bigdl.nn.tf.Const(Tensor(T(9))).inputs()
+    val constEnter = new Enter("test_frame").inputs(const)
+    val less = Less().inputs(constEnter, conditionInput)
+
+    val updateInput = Input()
+    val add = AddConstant(1).inputs(updateInput)
+    val addEnter = new Enter("test_frame").inputs(add)
+    val echo = Echo().inputs(addEnter)
+
+    val exit = ControlNodes.whileLoop(
+      (Seq(conditionInput), less),
+      (Seq((updateInput, echo))),
+      Seq(input),
+      "while"
+    )
+    val model = Graph.dynamic(Array(input), Array(exit(0)), None, false)
+    val result = model.forward(Tensor(T(1)))
+    result.toTensor.valueAt(1) should be(10)
+  }
+
+  "Dynamic Graph" should "support while loop twice and const node should not be executed twice" in {
+    val input = Input()
+
+    val conditionInput = Input()
+    val const = new com.intel.analytics.bigdl.nn.tf.Const(Tensor(T(9))).inputs()
+    var count = 0
+    def feval(module: Echo[Float], input: Tensor[Float]): Unit = {
+      count += 1
+    }
+    val echo = Echo(feval).inputs(const)
+    val less = Less().inputs(echo, conditionInput)
+
+    val updateInput = Input()
+    val add = AddConstant(1).inputs(updateInput)
+
+    val exit = ControlNodes.whileLoop(
+      (Seq(conditionInput), less),
+      Seq((updateInput, add)),
+      Seq(input)
+    )
+    val model = Graph.dynamic(Array(input), Array(exit(0)), None, false)
+    model.forward(Tensor(T(1)))
+    val result = model.forward(Tensor(T(1)))
+    result.toTensor.valueAt(1) should be(10)
+    count should be(1)
+  }
+
+  "Dynamic Graph" should "support while loop with multiple loop vars" in {
+    val input1 = Input("Input1")
+    val input2 = Input("Input2")
+
+    val conditionInput1 = Input("conditionInput1")
+    val conditionInput2 = Input("conditionInput2")
+    val const = new com.intel.analytics.bigdl.nn.tf.Const(Tensor(T(9))).setName("inc").inputs()
+    val less = Less().setName("less").inputs(const, conditionInput1)
+
+    val updateInput1 = Input("updateInput1")
+    val add1 = AddConstant(1).setName("add1").inputs(updateInput1)
+    val echo1 = Echo().setName("echo1").inputs(add1)
+
+    val updateInput2 = Input("updateInput2")
+    val add2 = AddConstant(5).setName("add5").inputs(updateInput2)
+    val echo2 = Echo().setName("echo2").inputs(add2)
+
+    val exit = ControlNodes.whileLoop(
+      (Seq(conditionInput1, conditionInput2), less),
+      (Seq((updateInput1, echo1), (updateInput2, echo2))),
+      Seq(input1, input2),
+      "while"
+    )
+    val model = Graph.dynamic(Array(input1, input2), exit.toArray, None, false)
+    val result = model.forward(T(Tensor(T(1)), Tensor(T(2))))
+    result.toTable.apply[Tensor[Float]](1).valueAt(1) should be(10)
+    result.toTable.apply[Tensor[Float]](2).valueAt(1) should be(47)
+  }
 }
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala
index 99fad493310..3649a1ff4c3 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala
@@ -22,7 +22,7 @@ import org.scalatest.{FlatSpec, Matchers}
 class StrideSliceSpec extends FlatSpec with Matchers {
 
   "StrideSlice " should "compute correct output and gradient" in {
-    val module1 = new StrideSlice[Double](Array((1, 1, 2, 1)))
+    val module1 = new StrideSlice[Double, Double](Array((1, 1, 2, 1)))
     val input = Tensor[Double](2, 2, 2)
     input(Array(1, 1, 1)) = -0.17020166106522
     input(Array(1, 1, 2)) = 0.57785657607019
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala
index c3a4d43149d..c27a0a3d60f 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala
@@ -99,12 +99,28 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter {
         .toDF("features", "label"), // Array[Double]
       sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.map(_.toFloat), p._2))))
         .toDF("features", "label"), // Array[Float]
+      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (Vectors.dense(p._1), p._2))))
+        .toDF("features", "label") // MLlib Vector
+      // TODO: add ML Vector when ut for Spark 2.0+ is ready
+    ).foreach { df =>
+      val dlModel = classifier.fit(df)
+      dlModel.transform(df).collect()
+    }
+  }
+
+  "An DLClassifier" should "support scalar FEATURE" in {
+    val model = new Sequential().add(Linear[Float](1, 2)).add(LogSoftMax[Float])
+    val criterion = ClassNLLCriterion[Float]()
+    val classifier = new DLClassifier[Float](model, criterion, Array(1))
+      .setLearningRate(0.1)
+      .setBatchSize(2)
+      .setEndWhen(Trigger.maxIteration(2))
+
+    Array(
       sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head.toFloat, p._2))))
         .toDF("features", "label"), // Float
       sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head, p._2))))
-        .toDF("features", "label"), // Double
-      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (Vectors.dense(p._1), p._2))))
-        .toDF("features", "label") // MLlib Vector
+        .toDF("features", "label") // Double
       // TODO: add ML Vector when ut for Spark 2.0+ is ready
     ).foreach { df =>
       val dlModel = classifier.fit(df)
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala
index 04d41917c5b..35094ef69cb 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala
@@ -104,12 +104,28 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter {
         .toDF("features", "label"), // Array[Double]
       sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.map(_.toFloat), p._2))))
         .toDF("features", "label"), // Array[Float]
+      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (Vectors.dense(p._1), p._2))))
+        .toDF("features", "label") // MLlib Vector
+      // TODO: add ML Vector when ut for Spark 2.0+ is ready
+    ).foreach { df =>
+      val dlModel = estimator.fit(df)
+      dlModel.transform(df).collect()
+    }
+  }
+
+  "An DLEstimator" should "support scalar FEATURE types" in {
+    val model = new Sequential().add(Linear[Float](1, 2)).add(LogSoftMax[Float])
+    val criterion = ClassNLLCriterion[Float]()
+    val estimator = new DLEstimator[Float](model, criterion, Array(1), Array(1))
+      .setBatchSize(2)
+      // intentionally set low since this only validates data format compatibility
+      .setEndWhen(Trigger.maxIteration(1))
+
+    Array(
       sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head.toFloat, p._2))))
         .toDF("features", "label"), // Float
       sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head, p._2))))
-        .toDF("features", "label"), // Double
-      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (Vectors.dense(p._1), p._2))))
-        .toDF("features", "label") // MLlib Vector
+        .toDF("features", "label") // Double
       // TODO: add ML Vector when ut for Spark 2.0+ is ready
     ).foreach { df =>
       val dlModel = estimator.fit(df)
@@ -126,16 +142,32 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter {
       .setBatchSize(2)
 
     Array(
-      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, Array(p._2)))))
+      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, Array(p._2, p._2)))))
         .toDF("features", "label"), // Array[Double]
-      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, Array(p._2.toFloat)))))
-        .toDF("features", "label"), // Array[Float]
+      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1,
+        Array(p._2.toFloat, p._2.toFloat))))).toDF("features", "label"), // Array[Float]
+      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1,
+        Vectors.dense(p._2, p._2))))).toDF("features", "label") // MLlib Vector
+      // TODO: add ML Vector when ut for Spark 2.0+ is ready
+    ).foreach { df =>
+      val dlModel = estimator.fit(df)
+      dlModel.transform(df).collect()
+    }
+  }
+
+  "An DLEstimator" should "support scalar LABEL types" in {
+    val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float])
+    val criterion = ClassNLLCriterion[Float]()
+    val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1))
+      // intentionally set low since this only validates data format compatibitliy
+      .setEndWhen(Trigger.maxIteration(1))
+      .setBatchSize(2)
+
+    Array(
       sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2.toFloat))))
         .toDF("features", "label"), // Float
       sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2))))
-        .toDF("features", "label"), // Double
-      sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, Vectors.dense(p._2)))))
-        .toDF("features", "label") // MLlib Vector
+        .toDF("features", "label") // Double
       // TODO: add ML Vector when ut for Spark 2.0+ is ready
     ).foreach { df =>
       val dlModel = estimator.fit(df)
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala
index 99b3bb77c38..f3b3ca2505d 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala
@@ -1074,4 +1074,12 @@ class DenseTensorSpec extends FlatSpec with Matchers {
     t.nElement() should be (0)
     t.dim() should be (0)
   }
+
+  "cast" should "work on scalar" in {
+    val scalar1 = Tensor.scalar[Float](1.0f)
+    val scalar2 = Tensor.scalar[Int](0)
+    scalar1.cast[Int](scalar2)
+
+    scalar2.value() should be(1)
+  }
 }
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddTableSpec.scala
index 0d211f46689..7086793d1c5 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddTableSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddTableSpec.scala
@@ -34,7 +34,7 @@ class CAddTableSpec extends TorchSpec {
     ctable.add(new Linear(5, 3))
     ctable.add(new Linear(5, 3))
     model.add(ctable)
-    model.add(new CAddTable())
+    model.add(CAddTable())
     val input = Tensor[Double](5).apply1(_ => Random.nextDouble())
     val gradOutput = Tensor[Double](3).apply1(_ => Random.nextDouble())
 
@@ -70,7 +70,7 @@ class CAddTableSpec extends TorchSpec {
     ctable.add(new Linear(5, 3))
     ctable.add(new Linear(5, 3))
     model.add(ctable)
-    model.add(new CAddTable(true))
+    model.add(CAddTable(true))
     val input = Tensor[Double](5).apply1(_ => Random.nextDouble())
     val gradOutput = Tensor[Double](3).apply1(_ => Random.nextDouble())
 
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
index 67d6da6fee1..2a036faf054 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
@@ -78,8 +78,15 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
     subTypes.foreach(sub => expected.add(sub.getName))
   }
 
+
   private def runSerializationTest(module : AbstractModule[_, _, Float],
-                                  input : Activity, cls: Class[_] = null) : Unit = {
+    input : Activity, cls: Class[_] = null) : Unit = {
+    runSerializationTestWithMultiClass(module, input,
+      if (cls == null) Array(module.getClass) else Array(cls))
+  }
+
+  private def runSerializationTestWithMultiClass(module : AbstractModule[_, _, Float],
+                                  input : Activity, classes: Array[Class[_]]) : Unit = {
     val name = module.getName
     val serFile = File.createTempFile(name, postFix)
     val originForward = module.evaluate().forward(input)
@@ -95,11 +102,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
     }
 
     afterLoadForward should be (originForward)
-    if (cls != null) {
-      tested.add(cls.getName)
-    } else {
-      tested.add(module.getClass.getName)
-    }
+    classes.foreach(cls => tested.add(cls.getName))
   }
 
   "Abs serializer" should "work properly" in {
@@ -1890,7 +1893,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
   }
 
   "NoOp serializer" should "work properly" in {
-    val noOp = NoOp[Float]().setName("noOp")
+    val noOp = new com.intel.analytics.bigdl.nn.ops.NoOp[Float]().setName("noOp")
     val input = Tensor[Float](5).apply1(_ => Random.nextFloat())
     runSerializationTest(noOp, input)
   }
@@ -2188,7 +2191,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
   }
 
   "StrideSlice serialier" should "work properly" in {
-    val strideSlice = new StrideSlice[Float](Array((1, 1, 2, 1))).setName("strideSlice")
+    val strideSlice = new StrideSlice[Float, Float](Array((1, 1, 2, 1))).setName("strideSlice")
     val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())
     runSerializationTest(strideSlice, input)
   }
@@ -2255,7 +2258,7 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
   }
 
   "StridedSliceLoadTF serializer" should "work properly" in {
-    val strideSliceLoadTF = new StridedSliceLoadTF[Float]().
+    val strideSliceLoadTF = new StridedSliceLoadTF[Float, Float]().
       setName("strideSliceLoadTF")
     val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()),
       Tensor[Int](T(0)),
@@ -2472,6 +2475,126 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
     runSerializationTest(module, input)
   }
 
+  "Control Ops serializer" should "work properly" in {
+    val input = Input[Float]("input")
+
+    val conditionInput = Input[Float]("conditionInput")
+    val const = new com.intel.analytics.bigdl.nn.tf.Const[Float, Float](Tensor(T(9))).inputs()
+    val constEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(const)
+    val less = Less[Float]().inputs(constEnter, conditionInput)
+
+    val updateInput = Input[Float]()
+    val add = AddConstant[Float](1).inputs(updateInput)
+    val addEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(add)
+    val echo = Echo[Float]().inputs(addEnter)
+
+    val exit = ControlNodes.whileLoop[Float](
+      (Seq(conditionInput), less),
+      (Seq((updateInput, echo))),
+      Seq(input),
+      "while"
+    )
+    val model = Graph.dynamic[Float](Array(input), Array(exit(0)), None, false)
+    runSerializationTestWithMultiClass(model, Tensor.scalar[Float](1), Array(
+      addEnter.element.getClass.asInstanceOf[Class[_]],
+      new com.intel.analytics.bigdl.nn.ops.NextIteration[Float, Float]().getClass,
+      new com.intel.analytics.bigdl.nn.ops.Exit[Float]().getClass,
+      new com.intel.analytics.bigdl.nn.ops.LoopCondition[Float]().getClass
+    ))
+  }
+
+  "Stack operations serializer" should "work properly" in {
+    import com.intel.analytics.bigdl.nn.ops._
+    val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs()
+    val stack = new StackCreator[Float, Float]().inputs()
+    val push = new StackPush[Float, Float]().inputs(stack, data)
+    val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(push)
+    val pop = new StackPop[Float, Float]().inputs(stack, ctr)
+    val model = Graph.dynamic[Float](Array(stack), Array(pop))
+
+    runSerializationTestWithMultiClass(model, Tensor.scalar(1), Array(
+      stack.element.getClass.asInstanceOf[Class[_]],
+      push.element.getClass.asInstanceOf[Class[_]],
+      pop.element.getClass.asInstanceOf[Class[_]]
+    ))
+  }
+
+  "TensorArray serializer R/W" should "work properly" in {
+    import com.intel.analytics.bigdl.nn.ops._
+    val tensorArray = new TensorArrayCreator[Float, Float]().inputs()
+    val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs()
+    val index = Const[Float, Int](Tensor.scalar[Int](0)).inputs()
+    val write = new TensorArrayWrite[Float, Float]().inputs((tensorArray, 1), (index, 1), (data, 1))
+    val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(write)
+    val read = new TensorArrayRead[Float, Float]().inputs((tensorArray, 1), (index, 1), (ctr, 1))
+    val grad = new TensorArrayGrad[Float]("grad").inputs(tensorArray)
+    val output = Identity[Float]().inputs((grad, 2))
+    val model = Graph.dynamic[Float](Array(tensorArray), Array(read, output))
+
+    runSerializationTestWithMultiClass(model, Tensor.scalar[Int](1), Array(
+      tensorArray.element.getClass.asInstanceOf[Class[_]],
+      write.element.getClass.asInstanceOf[Class[_]],
+      read.element.getClass.asInstanceOf[Class[_]],
+      grad.element.getClass.asInstanceOf[Class[_]]
+    ))
+  }
+
+  "TensorArray serializer Gather/Scatter" should "work properly" in {
+    import com.intel.analytics.bigdl.nn.ops._
+    val tensorArray = new TensorArrayCreator[Float, Float]().inputs()
+    val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs()
+    val indices = Const[Float, Int](Tensor[Int](T(0, 1, 2))).inputs()
+    val scatter = new TensorArrayScatter[Float, Float]().inputs((tensorArray, 1), (indices, 1),
+      (data, 1))
+    val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(scatter)
+    val gather = new TensorArrayGather[Float, Float]().inputs((tensorArray, 1), (indices, 1),
+      (ctr, 1))
+    val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(gather)
+    val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1))
+    val model = Graph.dynamic[Float](Array(tensorArray), Array(gather, close))
+
+    runSerializationTestWithMultiClass(model, Tensor.scalar[Int](10), Array(
+      tensorArray.element.getClass.asInstanceOf[Class[_]],
+      scatter.element.getClass.asInstanceOf[Class[_]],
+      gather.element.getClass.asInstanceOf[Class[_]],
+      close.element.getClass.asInstanceOf[Class[_]]
+    ))
+  }
+
+  "TensorArray serializer Split/Concat" should "work properly" in {
+    import com.intel.analytics.bigdl.nn.ops._
+    val tensorArray = new TensorArrayCreator[Float, Float]().inputs()
+    val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs()
+    val lengths = Const[Float, Int](Tensor[Int](T(1, 2))).inputs()
+    val splitter = new TensorArraySplit[Float, Float]().inputs((tensorArray, 1), (data, 1),
+      (lengths, 1))
+    val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(splitter)
+    val concat = new TensorArrayConcat[Float, Float]().inputs(tensorArray, ctr)
+    val size = new TensorArraySize[Float]().inputs(tensorArray, ctr)
+    val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(concat, size)
+    val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1))
+    val model = Graph.dynamic[Float](Array(tensorArray), Array(concat, close, size))
+
+    runSerializationTestWithMultiClass(model, Tensor.scalar[Int](2), Array(
+      tensorArray.element.getClass.asInstanceOf[Class[_]],
+      splitter.element.getClass.asInstanceOf[Class[_]],
+      concat.element.getClass.asInstanceOf[Class[_]],
+      close.element.getClass.asInstanceOf[Class[_]],
+      size.element.getClass.asInstanceOf[Class[_]]
+    ))
+  }
+
+  "ConcatOffset serializer" should "work properly" in {
+    val module = new com.intel.analytics.bigdl.nn.ops.ConcatOffset[Float]()
+    runSerializationTest(module, T(Tensor.scalar[Int](1), Tensor[Int](T(2, 2, 5, 7)),
+      Tensor[Int](T(2, 3, 5, 7)), Tensor[Int](T(2, 4, 5, 7))))
+  }
+
+  "InvertPermutation serializer" should "work properly" in {
+    val module = new com.intel.analytics.bigdl.nn.ops.InvertPermutation[Float]()
+    runSerializationTest(module, Tensor[Int](T(0, 1, 2, 3, 4)))
+  }
+
   override protected def afterAll() = {
     var total = 0
     expected.foreach(exp => {
@@ -2482,4 +2605,3 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
   }
 
 }
-
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala
index c74850d71e9..ef565eae240 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala
@@ -483,6 +483,70 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{
     }
   }
 
+  "TensorArray operations" should "be load correctly" in {
+    val output = Seq("scatter_and_gather:0", "split_and_concat:0", "write_and_read:0", "size1:0",
+      "size2:0", "unstack_and_stack:0")
+    val comparePairs = testModel("tensor_array", output, backward = false)
+    for (i <- output.indices) {
+      val (tf, bigdl) = comparePairs(i)
+      tf.almostEqual(bigdl, 1e-6) should be(true)
+    }
+  }
+
+  "dynamic rnn" should "be load correctly" in {
+    val output = Seq("rnn_loss:0")
+    val comparePairs = testModel("dynamic_rnn", output, backward = false)
+    for (i <- output.indices) {
+      val (tf, bigdl) = comparePairs(i)
+      tf.almostEqual(bigdl, 1e-3) should be(true)
+    }
+  }
+
+  "dynamic rnn grad" should "be load correctly" in {
+    val output = Seq("gradOutput:0")
+    val comparePairs = testModel("dynamic_rnn_grad", output, backward = false)
+    for (i <- output.indices) {
+      val (tf, bigdl) = comparePairs(i)
+      tf.almostEqual(bigdl, 1e-3) should be(true)
+    }
+  }
+
+  "dynamic lstm" should "be load correctly" in {
+    val output = Seq("lstm_loss:0")
+    val comparePairs = testModel("dynamic_lstm", output, backward = false)
+    for (i <- output.indices) {
+      val (tf, bigdl) = comparePairs(i)
+      tf.almostEqual(bigdl, 1e-2) should be(true)
+    }
+  }
+
+  "dynamic lstm grad" should "be load correctly" in {
+    val output = Seq("gradOutput:0")
+    val comparePairs = testModel("dynamic_lstm_grad", output, backward = false)
+    for (i <- output.indices) {
+      val (tf, bigdl) = comparePairs(i)
+      tf.almostEqual(bigdl, 1e-2) should be(true)
+    }
+  }
+
+  "dynamic gru" should "be load correctly" in {
+    val output = Seq("gru_loss:0")
+    val comparePairs = testModel("dynamic_gru", output, backward = false)
+    for (i <- output.indices) {
+      val (tf, bigdl) = comparePairs(i)
+      tf.almostEqual(bigdl, 1e-2) should be(true)
+    }
+  }
+
+  "dynamic gru grad" should "be load correctly" in {
+    val output = Seq("gradOutput:0")
+    val comparePairs = testModel("dynamic_gru_grad", output, backward = false)
+    for (i <- output.indices) {
+      val (tf, bigdl) = comparePairs(i)
+      tf.almostEqual(bigdl, 1e-2) should be(true)
+    }
+  }
+
   private def testModel(
     modelName: String,
     endPoints: Seq[String],
@@ -518,7 +582,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{
         (node: NodeDef) => node.getName == "input_node")
     val context = new Context[Float]()
     val model = TensorflowLoader.buildBigDLModel(tfGraph, inputs.toSeq.map(_._2).flatten,
-      endPoints.map(_.split(":")(0)), ByteOrder.LITTLE_ENDIAN, "", Some(context))
+      endPoints.map(_.split(":")(0)), ByteOrder.LITTLE_ENDIAN, "", Some(context), backward)
 
     // Compare the tensor contents
     val tfInputTensor = tfNodes.asScala.filter(_.getName == "input")(0)
@@ -539,6 +603,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{
     val comparePair = new mutable.ArrayBuffer[(Tensor[Float], Tensor[Float])]()
     val forwardPairs = tfOutputTensors.zip(bigdlOutputs).map { x =>
         val tensor = TensorflowToBigDL.toTensor(x._1, ByteOrder.LITTLE_ENDIAN)
+          .asInstanceOf[Tensor[Float]]
         (tensor, x._2)
     }
     comparePair ++= forwardPairs
@@ -560,7 +625,7 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{
         val gradInputsTable = T()
         tfGradInputs.foreach {
           case output =>
-            gradInputsTable.insert[Tensor[Float]](output)
+            gradInputsTable.insert[Tensor[_]](output)
         }
         gradInputsTable
       }
@@ -575,7 +640,9 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{
         node =>
           val t = tfNodes.asScala.filter(_.getName.contains(node + "_grad"))(0)
           t.getName ->
-            TensorflowToBigDL.toTensor(t.getAttrMap.get("value").getTensor, ByteOrder.LITTLE_ENDIAN)
+            TensorflowToBigDL
+              .toTensor(t.getAttrMap.get("value").getTensor, ByteOrder.LITTLE_ENDIAN)
+              .asInstanceOf[Tensor[Float]]
       }.toMap
 
       // do backward
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala
index f7767f26a79..eef998dfed2 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala
@@ -90,6 +90,13 @@ abstract class TensorflowSpecHelper extends BigDLSpecHelper {
       .almostEqual(tfOutput.asInstanceOf[Tensor[NumericWildCard]], delta) should be(true)
   }
 
+  protected def compare[T: ClassTag](nodeDefBuilder: NodeDef.Builder,
+    inputs: Seq[Tensor[_]], outputIndexes: Seq[Int],
+    delta: Double)(implicit ev: TensorNumeric[T])
+  : Unit = {
+    outputIndexes.foreach(compare(nodeDefBuilder.clone(), inputs, _, delta))
+  }
+
   protected def getResult[T: ClassTag, D](nodeDefBuilder: NodeDef.Builder, inputs: Seq[Tensor[_]],
     outputIndex: Int)(implicit ev: TensorNumeric[T]): (Tensor[D], Tensor[D]) = {
     val graphFile = saveGraph(nodeDefBuilder, inputs)
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala
new file mode 100644
index 00000000000..8d8e2290a84
--- /dev/null
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala
@@ -0,0 +1,46 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.utils.tf.loaders
+
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.utils.T
+import com.intel.analytics.bigdl.utils.tf.Tensorflow.intAttr
+import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper
+import org.tensorflow.framework.NodeDef
+
+class ArrayOps extends TensorflowSpecHelper {
+  "InvertPermutation" should "be correct" in {
+    compare[Float](
+      NodeDef.newBuilder()
+        .setName("invert_permutation_test")
+        .setOp("InvertPermutation"),
+      Seq(Tensor[Int](T(3, 4, 0, 2, 1))),
+      0
+    )
+  }
+
+  "ConcatOffset" should "be correct" in {
+    compare[Float](
+      NodeDef.newBuilder()
+        .setName("concat_offset_test")
+        .putAttr("N", intAttr(3))
+        .setOp("ConcatOffset"),
+      Seq(Tensor.scalar[Int](1), Tensor[Int](T(2, 2, 5, 7)), Tensor[Int](T(2, 3, 5, 7)),
+        Tensor[Int](T(2, 4, 5, 7))),
+      Seq(0, 1, 2), 1e-5
+    )
+  }
+}

From c9ba5df08b4be67b3168c42544906eda34038f8f Mon Sep 17 00:00:00 2001
From: Jerry Wu 
Date: Thu, 25 Jan 2018 10:51:47 +0800
Subject: [PATCH 0656/1065] Deep and shallow copy (#2126)

* rebase

* per comment
---
 .../dllib/models/utils/ModelBroadcast.scala   | 53 ++++++++--
 .../dllib/nn/abstractnn/AbstractModule.scala  | 80 ++++++++++++++-
 .../bigdl/dllib/tensor/QuantizedTensor.scala  |  2 +-
 .../dllib/utils/serializer/ModuleLoader.scala |  4 +-
 .../utils/serializer/ModuleSerializable.scala | 22 ++++-
 .../utils/serializer/ModuleSerializer.scala   | 30 +++---
 .../bigdl/dllib/utils/serializer/Types.scala  |  6 +-
 .../bigdl/dllib/nn/AbstractModuleSpec.scala   | 97 ++++++++++++++++++-
 8 files changed, 258 insertions(+), 36 deletions(-)

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala
index da259fd998c..30fdf34e73f 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala
@@ -17,7 +17,7 @@
 package com.intel.analytics.bigdl.models.utils
 
 import com.intel.analytics.bigdl.Module
-import com.intel.analytics.bigdl.tensor.{Tensor}
+import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor}
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import org.apache.spark.SparkContext
 import org.apache.spark.broadcast.Broadcast
@@ -47,11 +47,7 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria
    * @return this
    */
   def broadcast(sc: SparkContext, model: Module[T]): this.type = {
-    val weightsBias = getAndClearWeightBias(model.parameters())
-    broadcastModel = sc.broadcast(model.cloneModule())
-    broadcastParameters = sc.broadcast(weightsBias)
-    putWeightBias(weightsBias, model)
-    initGradWeightBias(weightsBias, model)
+    broadcastModel = sc.broadcast(model)
     this
   }
 
@@ -63,13 +59,52 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria
    * @return model
    */
   def value(initGradient: Boolean = false): Module[T] = {
-    val localModel = broadcastModel.value.cloneModule()
-    putWeightBias(broadcastParameters.value, localModel)
+    val localModel = broadcastModel.value.clone(false)
     if (initGradient) {
-      initGradWeightBias(broadcastParameters.value, localModel)
+      initGradWeightBias(getWeightBias(localModel.parameters()), localModel)
     }
     localModel
   }
+
+  private def getWeightBias(parameters: (Array[Tensor[T]], Array[Tensor[T]]))
+  : Array[Tensor[T]] = {
+    if (parameters._1.length != 0) {
+      var i = 0
+      val weightsBias = new Array[Tensor[T]](parameters._1.length)
+      val isQuantized = parameters._1.exists(_.getTensorType == QuantizedType)
+      val (isCompacted, storage) = if (!isQuantized) {
+        val storage = Storage(parameters._1(0).storage.array())
+        (parameters._1.map(_.nElement()).sum == storage.length(), storage)
+      } else {
+        (false, null)
+      }
+
+      // get weight and bias
+      while (i < parameters._1.length) {
+        if (parameters._1(i) != null) {
+          val wb = parameters._1(i)
+          wb.getTensorType match {
+            case QuantizedType =>
+              val quantTensor = wb.asInstanceOf[QuantizedTensor[T]]
+              weightsBias(i) = QuantizedTensor[T](quantTensor.getStorage, quantTensor.maxOfRow,
+                quantTensor.minOfRow, quantTensor.sumOfRow, quantTensor.size(), quantTensor.params)
+            case _ =>
+              weightsBias(i) = if (isCompacted) {
+                Tensor[T](storage, wb.storageOffset(), wb.size(), wb.stride())
+              } else {
+                Tensor[T](Storage(wb.storage().array()), wb.storageOffset(), wb.size(), wb.stride())
+              }
+          }
+          i += 1
+        }
+      }
+      weightsBias
+    } else {
+      // just return an empty array when parameters is empty.
+      Array()
+    }
+  }
+
 }
 
 
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala
index c681ce6080b..97e33c8f122 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala
@@ -25,16 +25,17 @@ import com.intel.analytics.bigdl.nn.quantized.Quantization
 import com.intel.analytics.bigdl.nn.{Module, _}
 import com.intel.analytics.bigdl.optim._
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
-import com.intel.analytics.bigdl.tensor.{Tensor, TensorDataType}
+import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Tensor, TensorDataType}
 import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame}
 import com.intel.analytics.bigdl.utils.TorchObject.TYPE_MODULE
 import com.intel.analytics.bigdl.utils._
 import com.intel.analytics.bigdl.utils.caffe.CaffePersister
-import com.intel.analytics.bigdl.utils.serializer.ModulePersister
+import com.intel.analytics.bigdl.utils.serializer._
 import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver}
 import org.apache.commons.lang3.SerializationUtils
 import org.apache.spark.rdd.RDD
 
+import scala.collection.mutable
 import scala.reflect.ClassTag
 
 /**
@@ -458,6 +459,81 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag,
     SerializationUtils.clone(this)
   }
 
+  def clone(deepCopy : Boolean): AbstractModule[A, B, T] = {
+    val moduleData = ModuleData[T](this.
+      asInstanceOf[AbstractModule[Activity, Activity, T]], Seq[String](), Seq[String]())
+    val storages = new mutable.HashMap[Int, Any]()
+    val context = SerializeContext(moduleData, storages, ProtoStorageType, false)
+    val serializedModule = ModuleSerializer.serialize[T](context).bigDLModule
+    ModulePersister.setTensorStorage(serializedModule, storages)
+
+    storages.clear()
+
+    val deserializeContext = DeserializeContext(serializedModule.build,
+      storages, ProtoStorageType, false)
+    ModuleLoader.initTensorStorage[T](deserializeContext)
+    val copy = ModuleSerializer.load[T](deserializeContext).module
+      .asInstanceOf[AbstractModule[A, B, T]]
+    setWeightAndBias(copy, deepCopy)
+    copy
+  }
+
+
+  private def setWeightAndBias(copy : AbstractModule[A, B, T], deepCopy : Boolean): Unit = {
+    val parameterTable = this.getParametersTable
+    val copiedModuleParamTable = copy.getParametersTable
+    if (parameterTable != null) {
+      require(copiedModuleParamTable != null, "cloned module should have params")
+      parameterTable.foreach {
+        case (name: String, params: Table) =>
+          require(copiedModuleParamTable.get(name) != None, s"cloned module should have for $name")
+          setLayerWeightAndBias(params,
+            copiedModuleParamTable.get(name).get.asInstanceOf[Table], deepCopy)
+      }
+    }
+  }
+
+  private def setLayerWeightAndBias(params : Table,
+                                    copyParams : Table, deepCopy : Boolean): Unit = {
+    params.foreach(param => {
+      copyParam(params, copyParams, deepCopy, param._1.toString)
+    })
+  }
+
+  private def copyParam(params : Table, copyParams : Table,
+                        deepCopy : Boolean, paraName : String) : Unit = {
+    if (params.contains(paraName)) {
+      // this is for quantization tensors where the weight might be an array
+      if (params.get(paraName).get
+        .isInstanceOf[Array[Tensor[T]]]) {
+        val copies = copyParams.get(paraName).get
+          .asInstanceOf[Array[Tensor[T]]]
+        val origins = params.get(paraName).get
+          .asInstanceOf[Array[Tensor[T]]]
+        var i = 0
+        while (i < copies.length) {
+          copyTensor(origins(i), copies(i), deepCopy)
+          i += 1
+        }
+      } else {
+        // For normal layers, their params are just tensors
+        copyTensor(params.get(paraName).get.asInstanceOf[Tensor[T]],
+          copyParams.get(paraName).get.asInstanceOf[Tensor[T]], deepCopy)
+      }
+    }
+  }
+
+  private def copyTensor(t1 : Tensor[T], t2 : Tensor[T], deepCopy : Boolean) = {
+    if (t2.isInstanceOf[QuantizedTensor[_]]) {
+      t2.asInstanceOf[QuantizedTensor[_]].release()
+    }
+    if (deepCopy) {
+      t2.copy(t1)
+    } else {
+      t2.set(t1)
+    }
+  }
+
   def canEqual(other: Any): Boolean = other.isInstanceOf[AbstractModule[A, B, T]]
 
   override def equals(other: Any): Boolean = other match {
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala
index 663e146cb98..3b1485511d5 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala
@@ -260,7 +260,7 @@ private[bigdl] class QuantizedTensor[T: ClassTag](
       sumOfRow = new Array[T](length)
       System.arraycopy(quantizedTensor.sumOfRow, 0, sumOfRow, 0, length)
 
-      new QuantizedTensor[T](internalStorage, size(), maxOfRow, minOfRow, sumOfRow, params)
+      this.desc = Desc.get(params, internalStorage, 0, this.maxOfRow, this.minOfRow)
     } else {
       throw new UnsupportedOperationException(s"can't set from other type of tensor.")
     }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala
index d41b152ef66..47ed0071216 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala
@@ -115,7 +115,7 @@ object ModuleLoader {
     }
   }
 
-  private def initTensorStorage[T: ClassTag](context: DeserializeContext)
+  private[bigdl] def initTensorStorage[T: ClassTag](context: DeserializeContext)
                                             (implicit ev: TensorNumeric[T]): Unit = {
     val attrMap = context.bigdlModule.getAttrMap
 
@@ -299,7 +299,7 @@ object ModulePersister {
   }
 
 
-  private def setTensorStorage(bigDLModule: BigDLModule.Builder,
+  private[bigdl] def setTensorStorage(bigDLModule: BigDLModule.Builder,
     storages: mutable.HashMap[Int, Any]) : Unit = {
     val storageIds = new mutable.HashSet[Int]
     val tensorStorages = storages.filter(_._2.isInstanceOf[TensorStorage])
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
index 6da12756d86..14d99374a84 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
@@ -45,6 +45,8 @@ trait ModuleSerializable extends Loadable with Savable{
 
   protected val lock = new Object
 
+  protected var _copyWeightAndBias = true
+
   // Separate this two methods for reuse in sub-classes
   protected def checkVersion[T: ClassTag](module : BigDLModule)
                                          (implicit ev: TensorNumeric[T]) : Unit = {
@@ -60,6 +62,12 @@ trait ModuleSerializable extends Loadable with Savable{
     modelBuilder.setVersion(bigDLVersion)
   }
 
+  protected def copyWeightAndBias() = _copyWeightAndBias
+
+  def setCopyWeightAndBias(copyWeightAndBias : Boolean): this.type = {
+    _copyWeightAndBias = copyWeightAndBias
+    this
+  }
   /**
    * Default deserialization to provide the template
    * @return BigDL module instance with linkages with other modules
@@ -127,7 +135,7 @@ trait ModuleSerializable extends Loadable with Savable{
             val value = DataConverter.getAttributeValue(context, attribute)
             args(i) = value
           }
-          i+= 1
+          i += 1
         })
       })
     }
@@ -252,7 +260,9 @@ trait ModuleSerializable extends Loadable with Savable{
       }).toArray
       module.outputShapeValue = shapes
     }
-    copy2BigDL(context, bigDLModule)
+    if (_copyWeightAndBias) {
+      copy2BigDL(context, bigDLModule)
+    }
     bigDLModule
   }
 
@@ -284,7 +294,9 @@ trait ModuleSerializable extends Loadable with Savable{
         modelBuilder.addOutputShape(attribute.getShape)
       })
     }
-    copyFromBigDL(context, modelBuilder)
+    if (_copyWeightAndBias) {
+      copyFromBigDL(context, modelBuilder)
+    }
     SerializeResult(modelBuilder, context.storages)
   }
 
@@ -359,7 +371,7 @@ trait ContainerSerializable extends ModuleSerializable {
     val subModules = context.bigdlModule.getSubModulesList.asScala
     subModules.foreach(module => {
       val subModuleData = ModuleSerializer.load(DeserializeContext(module,
-      context.storages, context.storageType))
+      context.storages, context.storageType, _copyWeightAndBias))
       container.modules.append(subModuleData.module)
     })
     module
@@ -375,7 +387,7 @@ trait ContainerSerializable extends ModuleSerializable {
     subModulesData.foreach(module => {
       val subModule = ModuleSerializer.serialize(SerializeContext(ModuleData(module,
         new ArrayBuffer[String](), new ArrayBuffer[String]()), context.storages,
-      context.storageType))
+      context.storageType, _copyWeightAndBias))
       containerBuilder.addSubModules(subModule.bigDLModule)
     })
   }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala
index 4c55c46204f..0472a5e3587 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala
@@ -59,20 +59,20 @@ object ModuleSerializer extends ModuleSerializable{
     val module = serializerContext.moduleData.module
     // For those layers which have their own serialization/deserialization methods
     val clsName = module.getClass.getName
-    if (serializerMaps.contains(clsName)) {
-      serializerMaps(clsName).serializeModule(serializerContext)
+    val serializer = if (serializerMaps.contains(clsName)) {
+      serializerMaps(clsName)
     } else {
       val m = module.asInstanceOf[AbstractModule[_, _, _]]
       m match {
-        case container : Container[_, _, _] =>
-          ContainerSerializer.serializeModule(serializerContext)
-        case cell : Cell[_] =>
-          CellSerializer.serializeModule(serializerContext)
+        case container : Container[_, _, _] => ContainerSerializer
+        case cell : Cell[_] => CellSerializer
         case laborAdapter: KerasLayer[_, _, _] =>
-          KerasLayerSerializer.serializeModule(serializerContext)
-        case _ => ModuleSerializer.serializeModule(serializerContext)
+          KerasLayerSerializer
+        case _ => ModuleSerializer
       }
     }
+    serializer.setCopyWeightAndBias(serializerContext.copyWeightAndBias).
+      serializeModule(serializerContext)
   }
 
   /**
@@ -84,23 +84,25 @@ object ModuleSerializer extends ModuleSerializable{
                        (implicit ev: TensorNumeric[T]) : ModuleData[T] = {
     try {
       val model = context.bigdlModule
-      if (serializerMaps.contains(model.getModuleType)) {
-        serializerMaps(model.getModuleType).loadModule(context)
+      val deSerializer = if (serializerMaps.contains(model.getModuleType)) {
+        serializerMaps(model.getModuleType)
       } else {
         val attrMap = model.getAttrMap
         val subModuleCount = model.getSubModulesCount
         if (subModuleCount > 0) {
-          ContainerSerializer.loadModule(context)
+          ContainerSerializer
         } else {
           if (attrMap.containsKey("is_cell_module")) {
-            CellSerializer.loadModule(context)
+            CellSerializer
           } else if (attrMap.containsKey("is_labor_module")) {
-            KerasLayerSerializer.loadModule(context)
+            KerasLayerSerializer
           } else {
-            ModuleSerializer.loadModule(context)
+            ModuleSerializer
           }
         }
       }
+      deSerializer.setCopyWeightAndBias(context.copyWeightAndBias).
+        loadModule(context)
     } catch {
       case e: Exception =>
         throw new RuntimeException(
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala
index 312c0e499a3..aece2bd8abf 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala
@@ -33,10 +33,12 @@ object BigDLStorage extends StorageType
 
 case class SerializeContext[T: ClassTag](moduleData: ModuleData[T],
                                          storages: mutable.HashMap[Int, Any],
-                                         storageType: StorageType)
+                                         storageType: StorageType,
+                                         copyWeightAndBias : Boolean = true)
 case class DeserializeContext(bigdlModule : BigDLModule,
                               storages: mutable.HashMap[Int, Any],
-                              storageType: StorageType)
+                              storageType: StorageType,
+                              copyWeightAndBias : Boolean = true)
 
 case class SerializeResult(bigDLModule: BigDLModule.Builder, storages: mutable.HashMap[Int, Any])
 
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala
index 9827e436666..432921eed38 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbstractModuleSpec.scala
@@ -18,9 +18,11 @@ package com.intel.analytics.bigdl.nn
 import com.intel.analytics.bigdl._
 import org.scalatest.{FlatSpec, Matchers}
 import com.intel.analytics.bigdl.numeric.NumericFloat
-import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.tensor.{Storage, Tensor}
 import com.intel.analytics.bigdl.utils.{T, Table}
 
+import scala.util.Random
+
 class AbstractModuleSpec extends FlatSpec with Matchers {
   "Get name" should "find the module if it exists" in {
     val m = Linear(4, 3).setName("module")
@@ -327,4 +329,97 @@ class AbstractModuleSpec extends FlatSpec with Matchers {
     model2.setExtraParameter(extp)
     model2 should be (model)
   }
+
+  "Shallow copy" should "work properly" in {
+
+    val linear = Linear[Float](2, 2)
+
+    val shallowCopy = linear.clone(false).asInstanceOf[Linear[Float]]
+
+    val originWeight = linear.weight
+
+    val originBias = linear.bias
+
+    originWeight.fill(1.0f)
+    originBias.fill(2.0f)
+
+    val input = Tensor[Float](2, 2).rand()
+
+    val res1 = linear.forward(input)
+
+    val res2 = shallowCopy.forward(input)
+
+    res1 should be (res2)
+
+  }
+
+  "Deep copy" should  "work properly" in {
+
+    val linear = Linear[Float](2, 2)
+
+    val deepCopy = linear.clone(true).asInstanceOf[Linear[Float]]
+
+    val input = Tensor[Float](2, 2).rand()
+
+    val res1 = linear.forward(input)
+
+    val res2 = deepCopy.forward(input)
+
+    res1 should be(res2)
+  }
+
+  "Shallow copy for quantized model" should "work properly" in {
+    val outputSize = 2
+    val inputSize = 2
+
+    val kernelData = Array(
+      2.0f, 3f,
+      4f, 5f
+    )
+
+    val biasData = Array(0.0f, 0.1f)
+
+    val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat())
+    val weight = Tensor[Float](Storage(kernelData), 1, Array(outputSize, inputSize)).rand()
+    val bias = Tensor[Float](Storage(biasData), 1, Array(outputSize)).rand()
+    val linear = quantized.Linear[Float](outputSize, inputSize, initWeight = weight,
+      initBias = bias).setName("quantLinear")
+
+    val shallow = linear.clone(false).asInstanceOf[quantized.Linear[Float]]
+
+    val res1 = linear.forward(input)
+
+    val res2 = shallow.forward(input)
+
+    res1 should be(res2)
+  }
+
+  "Deep copy for quantized model" should "work properly" in {
+    val outputSize = 2
+    val inputSize = 2
+
+    val kernelData = Array(
+      2.0f, 3f,
+      4f, 5f
+    )
+
+    val biasData = Array(0.0f, 0.1f)
+
+    val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat())
+
+    val input2 = input.clone()
+
+    val weight = Tensor[Float](Storage(kernelData), 1, Array(outputSize, inputSize)).rand()
+    val bias = Tensor[Float](Storage(biasData), 1, Array(outputSize)).rand()
+    val linear = quantized.Linear[Float](outputSize, inputSize, initWeight = weight,
+      initBias = bias).setName("quantLinear")
+
+    val deep = linear.clone(true).asInstanceOf[quantized.Linear[Float]]
+
+    val res1 = linear.forward(input)
+
+    val res2 = deep.forward(input2)
+
+    res1 should be(res2)
+  }
 }

From f21bf956c239314862976d4a17885755fe644491 Mon Sep 17 00:00:00 2001
From: Xin Qiu 
Date: Fri, 26 Jan 2018 10:43:11 +0800
Subject: [PATCH 0657/1065] speed up leakyrelu (#2233)

---
 .../analytics/bigdl/dllib/nn/LeakyReLU.scala  | 209 ++++++++++++++----
 .../bigdl/dllib/torch/LeakyReLUSpec.scala     | 142 +++++++++++-
 2 files changed, 305 insertions(+), 46 deletions(-)

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala
index 7ec866ac328..0c8d4557a4c 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala
@@ -16,7 +16,7 @@
 package com.intel.analytics.bigdl.nn
 
 import com.intel.analytics.bigdl.nn.abstractnn.TensorModule
-import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6}
+import com.intel.analytics.bigdl.tensor._
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 
 import scala.reflect.ClassTag
@@ -37,33 +37,21 @@ class LeakyReLU[T: ClassTag](
   private val negval: Double = 0.01,
   var inplace: Boolean = false)(
   implicit ev: TensorNumeric[T]) extends TensorModule[T] {
-
-  private val negVal = ev.fromType[Double](negval)
+  import LeakyReLU._
 
   if (negval < 0) {
     inplace = false
   }
 
-  // Todo: performance should be optimized by replacing apply for contiguous input
+
   override def updateOutput(input: Tensor[T]): Tensor[T] = {
-    if (inplace) {
-      input.apply1(x => {
-        if (ev.isGreaterEq(ev.fromType[Int](0), x)) {
-          ev.times(x, negVal)
-        } else {
-          x
-        }
-      })
-      output.set(input)
-    } else {
-      output.resizeAs(input)
-      output.map(input, (out, in) => {
-        if (ev.isGreater(in, ev.fromType[Int](0))) {
-          in
-        } else {
-          ev.times(in, negVal)
-        }
-      })
+    require(input.isContiguous(), "input should be contiguous")
+    if (inplace) output = input
+    input.getType() match {
+      case FloatType => updateOutputFloat(input.toTensor[Float], output.toTensor[Float],
+        negval.toFloat, inplace)
+      case DoubleType => updateOutputDouble(input.toTensor[Double], output.toTensor[Double],
+        negval, inplace)
     }
     output
   }
@@ -72,28 +60,13 @@ class LeakyReLU[T: ClassTag](
     require(input.isSameSizeAs(gradOutput),
       "input should have the same size with gradOutput" +
         s"input size ${input.dim()} gradOutput size ${gradOutput.dim()}")
-    if (inplace) {
-      gradInput.set(gradOutput)
-      gradOutput.map(input, (grad, in) => {
-        if (ev.isGreaterEq(ev.fromType[Int](0), in)) {
-          ev.times(negVal, grad)
-        } else {
-          grad
-        }
-      })
-    } else {
-      gradInput.resizeAs(input)
-      val func = new TensorFunc6[T] {
-        override def apply (data1: Array[T], offset1: Int, data2: Array[T],
-          offset2: Int, data3: Array[T], offset3: Int): Unit = {
-          data1(offset1) = if (ev.isGreater(data3(offset3), ev.fromType[Int](0))) {
-            data2(offset2)
-          } else {
-            ev.times(negVal, data2(offset2))
-          }
-        }
-      }
-      DenseTensorApply.apply3[T](gradInput, gradOutput, input, func)
+    require(gradOutput.isContiguous(), "gradOutput should be contiguous")
+    if (inplace) gradInput = gradOutput
+    input.getType() match {
+      case FloatType => updateGradInputFloat(input.toTensor[Float], gradOutput.toTensor[Float],
+        gradInput.toTensor[Float], negval.toFloat, inplace)
+      case DoubleType => updateGradInputDouble(input.toTensor[Double], gradOutput.toTensor[Double],
+        gradInput.toTensor[Double], negval, inplace)
     }
     gradInput
   }
@@ -112,4 +85,152 @@ object LeakyReLU {
       inplace: Boolean = false)(implicit ev: TensorNumeric[T]) : LeakyReLU[T] = {
     new LeakyReLU[T](negval, inplace)
   }
+
+  protected def updateOutputFloat(
+        input: Tensor[Float],
+        output: Tensor[Float],
+        negVal: Float,
+        inplace: Boolean): Unit = {
+    if (inplace) {
+      var i = input.storageOffset() - 1
+      val array = input.storage().array()
+      val end = input.nElement() + input.storageOffset() - 1
+      while (i < end) {
+        if (array(i) < 0) {
+          array(i) *= negVal
+        }
+        i += 1
+      }
+    } else {
+      output.resizeAs(input)
+      var i = 0
+      val inputOffset = input.storageOffset() - 1
+      val inputArray = input.storage().array()
+      val outputOffset = output.storageOffset() - 1
+      val outputArray = output.storage().array()
+      val end = input.nElement()
+      while (i < end) {
+        if (inputArray(i + inputOffset) < 0) {
+          outputArray(i + outputOffset) = inputArray(i + inputOffset) * negVal
+        } else {
+          outputArray(i + outputOffset) = inputArray(i + inputOffset)
+        }
+        i += 1
+      }
+    }
+  }
+
+  protected def updateOutputDouble(
+        input: Tensor[Double],
+        output: Tensor[Double],
+        negVal: Double,
+        inplace: Boolean): Unit = {
+    if (inplace) {
+      var i = input.storageOffset() - 1
+      val array = input.storage().array()
+      val end = input.nElement() + input.storageOffset() - 1
+      while (i < end) {
+        if (array(i) < 0) {
+          array(i) *= negVal
+        }
+        i += 1
+      }
+    } else {
+      output.resizeAs(input)
+      var i = 0
+      val inputOffset = input.storageOffset() - 1
+      val inputArray = input.storage().array()
+      val outputOffset = output.storageOffset() - 1
+      val outputArray = output.storage().array()
+      val end = input.nElement()
+      while (i < end) {
+        if (inputArray(i + inputOffset) < 0) {
+          outputArray(i + outputOffset) = inputArray(i + inputOffset) * negVal
+        } else {
+          outputArray(i + outputOffset) = inputArray(i + inputOffset)
+        }
+        i += 1
+      }
+    }
+  }
+
+  protected def updateGradInputFloat(
+        input: Tensor[Float],
+        gradOutput: Tensor[Float],
+        gradInput: Tensor[Float],
+        negVal: Float,
+        inplace: Boolean): Unit = {
+    if (inplace) {
+      var i = 0
+      val inputOffset = input.storageOffset() - 1
+      val inputArray = input.storage().array()
+      val gradInputOffset = gradInput.storageOffset() - 1
+      val gradInputArray = gradInput.storage().array()
+      val end = input.nElement()
+      while (i < end) {
+        if (inputArray(i + inputOffset) > 0) {
+          gradInputArray(i + gradInputOffset) *= negVal
+        }
+        i += 1
+      }
+    } else {
+      gradInput.resizeAs(input)
+      var i = 0
+      val inputOffset = input.storageOffset() - 1
+      val inputArray = input.storage().array()
+      val gradOutputOffset = gradOutput.storageOffset() - 1
+      val gradOutputArray = gradOutput.storage().array()
+      val gradInputOffset = gradInput.storageOffset() - 1
+      val gradInputArray = gradInput.storage().array()
+      val end = input.nElement()
+      while (i < end) {
+        if (inputArray(i + inputOffset) < 0) {
+          gradInputArray(i + gradInputOffset) = gradOutputArray(i + gradOutputOffset) * negVal
+        } else {
+          gradInputArray(i + gradInputOffset) = gradOutputArray(i + gradOutputOffset)
+        }
+        i += 1
+      }
+    }
+  }
+
+  protected def updateGradInputDouble(
+        input: Tensor[Double],
+        gradOutput: Tensor[Double],
+        gradInput: Tensor[Double],
+        negVal: Double,
+        inplace: Boolean): Unit = {
+    if (inplace) {
+      var i = 0
+      val inputOffset = input.storageOffset() - 1
+      val inputArray = input.storage().array()
+      val gradInputOffset = gradInput.storageOffset() - 1
+      val gradInputArray = gradInput.storage().array()
+      val end = input.nElement()
+      while (i < end) {
+        if (inputArray(i + inputOffset) > 0) {
+          gradInputArray(i + gradInputOffset) *= negVal
+        }
+        i += 1
+      }
+    } else {
+      gradInput.resizeAs(input)
+      var i = 0
+      val inputOffset = input.storageOffset() - 1
+      val inputArray = input.storage().array()
+      val gradOutputOffset = gradOutput.storageOffset() - 1
+      val gradOutputArray = gradOutput.storage().array()
+      val gradInputOffset = gradInput.storageOffset() - 1
+      val gradInputArray = gradInput.storage().array()
+      val end = input.nElement()
+      while (i < end) {
+        if (inputArray(i + inputOffset) < 0) {
+          gradInputArray(i + gradInputOffset) = gradOutputArray(i + gradOutputOffset) * negVal
+        } else {
+          gradInputArray(i + gradInputOffset) = gradOutputArray(i + gradOutputOffset)
+        }
+        i += 1
+      }
+    }
+  }
 }
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala
index b8a0a834bbb..b911f0d31bb 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala
@@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.utils.RandomGenerator._
 class LeakyReLUSpec  extends TorchSpec {
     def random(): Double = RandomGenerator.RNG.normal(-10, 10)
 
-  "A LeakyReLU Module " should "generate correct output and grad not inplace when train = true" in {
+  "A LeakyReLU Module" should "generate correct output and grad not inplace" in {
     torchCheck()
     val seed = 100
     RNG.setSeed(seed)
@@ -57,7 +57,7 @@ class LeakyReLUSpec  extends TorchSpec {
     println("Test case : LeakyReLU, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s")
   }
 
-  "A LeakyReLU Module " should "generate correct output and grad inplace when train = true" in {
+  "A LeakyReLU Module" should "generate correct output and grad inplace" in {
     torchCheck()
     val seed = 100
     RNG.setSeed(seed)
@@ -89,4 +89,142 @@ class LeakyReLUSpec  extends TorchSpec {
 
     println("Test case : LeakyReLU, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s")
   }
+
+  "A LeakyReLU Float Module" should "generate correct output and grad not inplace" in {
+    torchCheck()
+    val seed = 100
+    RNG.setSeed(seed)
+
+    val module = new LeakyReLU[Float]()
+    val input = Tensor[Float](2, 2, 2)
+    input.apply1(x => random().toFloat)
+    val gradOutput = Tensor[Float](2, 2, 2)
+    input.apply1(x => random().toFloat)
+
+    val code = "torch.manualSeed(" + seed + ")\n" +
+      "torch.setdefaulttensortype('torch.FloatTensor')\n" +
+      "module = nn.LeakyReLU()\n" +
+      "output = module:forward(input)\n" +
+      "gradInput = module:backward(input,gradOutput)"
+
+    val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput),
+      Array("output", "gradInput"))
+    val luaOutput1 = torchResult("output").asInstanceOf[Tensor[Float]]
+    val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Float]]
+
+    val start = System.nanoTime()
+    val output = module.forward(input)
+    val gradInput = module.backward(input, gradOutput)
+    val end = System.nanoTime()
+    val scalaTime = end - start
+
+    luaOutput1 should be (output)
+    luaOutput2 should be (gradInput)
+
+    println("Test case : LeakyReLU, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s")
+  }
+
+  "A LeakyReLU Float Module" should "generate correct output and grad inplace" in {
+    torchCheck()
+    val seed = 100
+    RNG.setSeed(seed)
+
+    val module = LeakyReLU[Float](inplace = true)
+    val input = Tensor[Float](2, 2, 2)
+    input.apply1(x => random().toFloat)
+    val gradOutput = Tensor[Float](2, 2, 2)
+    input.apply1(x => random().toFloat)
+
+    val code = "torch.manualSeed(" + seed + ")\n" +
+      "torch.setdefaulttensortype('torch.FloatTensor')\n" +
+      "module = nn.LeakyReLU(1/100,true)\n" +
+      "output = module:forward(input)\n" +
+      "gradInput = module:backward(input,gradOutput)"
+
+    val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput),
+      Array("output", "gradInput"))
+    val luaOutput1 = torchResult("output").asInstanceOf[Tensor[Float]]
+    val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Float]]
+
+    val start = System.nanoTime()
+    val output = module.forward(input)
+    val gradInput = module.backward(input.clone(), gradOutput.clone())
+    val end = System.nanoTime()
+    val scalaTime = end - start
+
+    luaOutput1 should be (output)
+    luaOutput2 should be (gradInput)
+
+    println("Test case : LeakyReLU, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s")
+  }
+
+  "A LeakyReLU Float Module incontiguous input" should
+    "generate correct output and grad not inplace" in {
+    torchCheck()
+    val seed = 100
+    RNG.setSeed(seed)
+
+    val module = new LeakyReLU[Float]()
+    val input = Tensor[Float](4, 2, 2).narrow(1, 2, 2)
+    input.apply1(x => random().toFloat)
+    val gradOutput = Tensor[Float](4, 2, 2).narrow(1, 2, 2)
+    input.apply1(x => random().toFloat)
+
+    val code = "torch.manualSeed(" + seed + ")\n" +
+      "torch.setdefaulttensortype('torch.FloatTensor')\n" +
+      "module = nn.LeakyReLU()\n" +
+      "output = module:forward(input)\n" +
+      "gradInput = module:backward(input,gradOutput)"
+
+    val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput),
+      Array("output", "gradInput"))
+    val luaOutput1 = torchResult("output").asInstanceOf[Tensor[Float]]
+    val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Float]]
+
+    val start = System.nanoTime()
+    val output = module.forward(input)
+    val gradInput = module.backward(input, gradOutput)
+    val end = System.nanoTime()
+    val scalaTime = end - start
+
+    luaOutput1 should be (output)
+    luaOutput2 should be (gradInput)
+
+    println("Test case : LeakyReLU, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s")
+  }
+
+  "A LeakyReLU Float Module incontiguous input" should
+    "generate correct output and grad inplace" in {
+    torchCheck()
+    val seed = 100
+    RNG.setSeed(seed)
+
+    val module = LeakyReLU[Float](inplace = true)
+    val input = Tensor[Float](4, 2, 2).narrow(1, 2, 2)
+    input.apply1(x => random().toFloat)
+    val gradOutput = Tensor[Float](4, 2, 2).narrow(1, 2, 2)
+    input.apply1(x => random().toFloat)
+
+    val code = "torch.manualSeed(" + seed + ")\n" +
+      "torch.setdefaulttensortype('torch.FloatTensor')\n" +
+      "module = nn.LeakyReLU(1/100,true)\n" +
+      "output = module:forward(input)\n" +
+      "gradInput = module:backward(input,gradOutput)"
+
+    val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput),
+      Array("output", "gradInput"))
+    val luaOutput1 = torchResult("output").asInstanceOf[Tensor[Float]]
+    val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Float]]
+
+    val start = System.nanoTime()
+    val output = module.forward(input)
+    val gradInput = module.backward(input.clone(), gradOutput.clone())
+    val end = System.nanoTime()
+    val scalaTime = end - start
+
+    luaOutput1 should be (output)
+    luaOutput2 should be (gradInput)
+
+    println("Test case : LeakyReLU, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s")
+  }
 }

From adf9227b021f4edd2db4f8c8e5c066885419f1bd Mon Sep 17 00:00:00 2001
From: Jerry Wu 
Date: Fri, 26 Jan 2018 14:25:08 +0800
Subject: [PATCH 0658/1065] fix birnn issue (#2238)

---
 .../bigdl/dllib/nn/BiRecurrent.scala          |  3 ++
 .../utils/serializer/ModuleSerializable.scala | 32 +++++++++++++------
 2 files changed, 25 insertions(+), 10 deletions(-)

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala
index fb3af81214e..91a811ed6d3 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala
@@ -223,6 +223,8 @@ object BiRecurrent extends ContainerSerializable {
           .asInstanceOf[Boolean]
     }
 
+    loadSubModules(context, biRecurrent)
+
     biRecurrent
 
   }
@@ -299,5 +301,6 @@ object BiRecurrent extends ContainerSerializable {
       flag, universe.typeOf[Boolean])
     birecurrentBuilder.putAttr("bnorm", bNormBuilder.build)
 
+    serializeSubModules(context, birecurrentBuilder)
   }
 }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
index 14d99374a84..4ab8f02c81e 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala
@@ -364,33 +364,45 @@ trait ModuleSerializable extends Loadable with Savable{
 
 trait ContainerSerializable extends ModuleSerializable {
 
-  override def doLoadModule[T: ClassTag](context : DeserializeContext)
-    (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = {
-    val module = super.doLoadModule(context)
+  protected def loadSubModules[T: ClassTag](context : DeserializeContext,
+                                            module : AbstractModule[Activity, Activity, T])
+    (implicit ev: TensorNumeric[T]) : Unit = {
     val container = module.asInstanceOf[Container[Activity, Activity, T]]
     val subModules = context.bigdlModule.getSubModulesList.asScala
     subModules.foreach(module => {
       val subModuleData = ModuleSerializer.load(DeserializeContext(module,
-      context.storages, context.storageType, _copyWeightAndBias))
+        context.storages, context.storageType, _copyWeightAndBias))
       container.modules.append(subModuleData.module)
     })
-    module
   }
 
-  override def doSerializeModule[T: ClassTag](context: SerializeContext[T],
-                                              containerBuilder : BigDLModule.Builder)
-                                           (implicit ev: TensorNumeric[T]) : Unit = {
+  override def doLoadModule[T: ClassTag](context : DeserializeContext)
+    (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = {
+    val module = super.doLoadModule(context)
+    loadSubModules(context, module)
+    module
+  }
 
-    super.doSerializeModule(context, containerBuilder)
+  protected def serializeSubModules[T: ClassTag](context: SerializeContext[T],
+                                                 containerBuilder : BigDLModule.Builder)
+                                                (implicit ev: TensorNumeric[T]) : Unit = {
     val subModulesData = context.moduleData.module.
       asInstanceOf[Container[Activity, Activity, T]].modules
     subModulesData.foreach(module => {
       val subModule = ModuleSerializer.serialize(SerializeContext(ModuleData(module,
         new ArrayBuffer[String](), new ArrayBuffer[String]()), context.storages,
-      context.storageType, _copyWeightAndBias))
+        context.storageType, _copyWeightAndBias))
       containerBuilder.addSubModules(subModule.bigDLModule)
     })
   }
+
+  override def doSerializeModule[T: ClassTag](context: SerializeContext[T],
+                                              containerBuilder : BigDLModule.Builder)
+                                           (implicit ev: TensorNumeric[T]) : Unit = {
+
+    super.doSerializeModule(context, containerBuilder)
+    serializeSubModules(context, containerBuilder)
+  }
 }
 
 object ContainerSerializer extends ContainerSerializable

From 35af831fea17322e5906b14bd10ed9de26f5351e Mon Sep 17 00:00:00 2001
From: Xin Qiu 
Date: Fri, 26 Jan 2018 14:58:45 +0800
Subject: [PATCH 0659/1065] speed up rmsprop (#2229)

---
 .../scala/com/intel/analytics/bigdl/dllib/optim/RMSprop.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/RMSprop.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/RMSprop.scala
index 83f748a6238..aa2d3223f28 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/RMSprop.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/RMSprop.scala
@@ -66,7 +66,7 @@ class RMSprop[@specialized(Float, Double) T: ClassTag](
       }
 
     _sumofsquare.mul(ev.fromType[Double](dr)).addcmul(ev.fromType[Double](1-dr), dfdx, dfdx)
-    _rms.resizeAs(_sumofsquare).copy(_sumofsquare).sqrt().add(ev.fromType[Double](eps))
+    _rms.sqrt(_sumofsquare).add(ev.fromType[Double](eps))
     parameter.addcdiv(ev.fromType[Double](-clr), dfdx, _rms)
     state("evalCounter") = nevals + 1
     state("sumSquare") = _sumofsquare

From b447f5879c79b3f3faad803e5bf795704b739a50 Mon Sep 17 00:00:00 2001
From: Xianyan 
Date: Mon, 29 Jan 2018 10:29:36 +0800
Subject: [PATCH 0660/1065] add gray image support (#2223)

* add gray image support

* fix ut

* meet code review
---
 .../transform/vision/image/Convertor.scala    |  27 ++++++--
 .../transform/vision/image/ImageFeature.scala |  48 +++++++++++++-
 .../image/augmentation/ChannelNormalize.scala |  59 +++++++++++-------
 .../vision/image/opencv/OpenCVMat.scala       |  53 ++++++++--------
 .../dllib/utils/python/api/PythonBigDL.scala  |   2 +-
 scala/dllib/src/test/resources/gray/gray.bmp  | Bin 0 -> 3678 bytes
 .../vision/image/ImageFrameSpec.scala         |  48 +++++++++++++-
 7 files changed, 178 insertions(+), 59 deletions(-)
 create mode 100644 scala/dllib/src/test/resources/gray/gray.bmp

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala
index 67c504ffeb5..b102229c709 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala
@@ -69,8 +69,12 @@ object BytesToMat {
 class PixelBytesToMat(byteKey: String = ImageFeature.bytes) extends FeatureTransformer {
 
   override def transformMat(feature: ImageFeature): Unit = {
+    require(feature.getOriginalSize != null,
+      "please set the original size of image in ImageFeature")
     val pixels = feature[Array[Byte]](byteKey)
-    val mat = OpenCVMat.fromPixelsBytes(pixels, feature.getOriginalHeight, feature.getOriginalWidth)
+    val mat = OpenCVMat.fromPixelsBytes(pixels, feature.getOriginalHeight,
+      feature.getOriginalWidth,
+      feature.getOriginalChannel)
     feature(ImageFeature.mat) = mat
   }
 }
@@ -132,7 +136,8 @@ object MatToFloats {
  * @param tensorKey key to store transformed tensor
  */
 class MatToTensor[T: ClassTag](toRGB: Boolean = false,
-  tensorKey: String = ImageFeature.imageTensor)(implicit ev: TensorNumeric[T])
+  tensorKey: String = ImageFeature.imageTensor,
+  shareBuffer: Boolean = true)(implicit ev: TensorNumeric[T])
   extends FeatureTransformer {
   private val imageTensor: Tensor[T] = Tensor[T]()
   private val matToFloats = MatToFloats()
@@ -140,10 +145,19 @@ class MatToTensor[T: ClassTag](toRGB: Boolean = false,
   override def transform(feature: ImageFeature): ImageFeature = {
     if (!feature.isValid) return feature
     try {
+      val (height, width, channel) = feature.getSize
       matToFloats.transform(feature)
-      imageTensor.resize(3, feature.getHeight(), feature.getWidth())
+      if (channel == 1) {
+        imageTensor.resize(height, width)
+      } else {
+        imageTensor.resize(channel, height, width)
+      }
       feature.copyTo[T](imageTensor.storage().array(), 0, ImageFeature.floats, toRGB)
-      feature(tensorKey) = imageTensor
+      if (!shareBuffer) {
+        feature(tensorKey) = imageTensor.clone()
+      } else {
+        feature(tensorKey) = imageTensor
+      }
     } catch {
       case e: Exception =>
         val uri = feature.uri()
@@ -158,9 +172,10 @@ class MatToTensor[T: ClassTag](toRGB: Boolean = false,
 object MatToTensor {
   val logger = Logger.getLogger(getClass)
 
-  def apply[T: ClassTag](toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor)
+  def apply[T: ClassTag](toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor,
+    shareBuffer: Boolean = true)
     (implicit ev: TensorNumeric[T])
-  : MatToTensor[T] = new MatToTensor[T](toRGB, tensorKey)
+  : MatToTensor[T] = new MatToTensor[T](toRGB, tensorKey, shareBuffer)
 }
 
 /**
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala
index 835961efb52..d13c75856b5 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala
@@ -167,6 +167,11 @@ class ImageFeature extends Serializable {
    */
   def getOriginalHeight: Int = getOriginalSize._1
 
+  /**
+   * get original channel
+   */
+  def getOriginalChannel: Int = getOriginalSize._3
+
   /**
    * get label from ImageFeature
    */
@@ -200,12 +205,22 @@ class ImageFeature extends Serializable {
    */
   def copyTo[T: ClassTag](storage: Array[T], offset: Int, floatKey: String = ImageFeature.floats,
     toRGB: Boolean = true)(implicit ev: TensorNumeric[T]): Unit = {
+    val channel = getChannel()
     require(contains(floatKey), s"there should be ${floatKey} in ImageFeature")
     val data = floats(floatKey)
-    require(data.length >= getWidth() * getHeight() * 3,
-      s"float array length should be larger than 3 * ${getWidth()} * ${getHeight()}")
+    require(data.length >= getWidth() * getHeight() * channel,
+      s"float array length should be larger than $channel * ${getWidth()} * ${getHeight()}")
     val frameLength = getWidth() * getHeight()
-    require(frameLength * 3 + offset <= storage.length)
+    require(frameLength * channel + offset <= storage.length)
+    if (channel == 3) {
+      copyBGR(storage, offset, toRGB, data, frameLength)
+    } else {
+      copyChannels(storage, offset, channel, data, frameLength)
+    }
+  }
+
+  private def copyBGR[T: ClassTag](storage: Array[T], offset: Int, toRGB: Boolean,
+    data: Array[Float], frameLength: Int): Unit = {
     if (classTag[T] == classTag[Float]) {
       val storageFloat = storage.asInstanceOf[Array[Float]]
       var j = 0
@@ -245,6 +260,33 @@ class ImageFeature extends Serializable {
     }
   }
 
+  private def copyChannels[T: ClassTag](storage: Array[T], offset: Int, channel: Int,
+    data: Array[Float], frameLength: Int): Unit = {
+    if (classTag[T] == classTag[Float]) {
+      val storageFloat = storage.asInstanceOf[Array[Float]]
+      var j = 0
+      while (j < frameLength) {
+        var c = 0
+        while (c < channel) {
+          storageFloat(offset + j + frameLength * c) = data(j * channel + c)
+          c += 1
+        }
+        j += 1
+      }
+    } else if (classTag[T] == classTag[Double]) {
+      val storageDouble = storage.asInstanceOf[Array[Double]]
+      var j = 0
+      while (j < frameLength) {
+        var c = 0
+        while (c < channel) {
+          storageDouble(offset + j + frameLength * c) = data(j * channel + c)
+          c += 1
+        }
+        j += 1
+      }
+    }
+  }
+
   /**
    * Convert ImageFeature to image tensor
    * @param floatKey key that maps the float array
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelNormalize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelNormalize.scala
index 38f1326638a..6f41f10a34a 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelNormalize.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelNormalize.scala
@@ -24,37 +24,46 @@ import org.opencv.core.{Core, CvType, Mat, Scalar}
 
 /**
  * image channel normalize
- * @param meanR mean value in R channel
- * @param meanG mean value in G channel
- * @param meanB mean value in B channel
- * @param stdR std value in R channel
- * @param stdG std value in G channel
- * @param stdB std value in B channel
+ *
+ * @param means mean value in each channel
+ * @param stds std value in each channel
  */
-class ChannelNormalize(meanR: Float, meanG: Float, meanB: Float,
-                       stdR: Float = 1, stdG: Float = 1, stdB: Float = 1)
+class ChannelNormalize(means: Array[Float], stds: Array[Float])
   extends FeatureTransformer {
   override def transformMat(feature: ImageFeature): Unit = {
     ChannelNormalize.transform(feature.opencvMat(), feature.opencvMat(),
-      meanR, meanG, meanB, stdR, stdG, stdB)
+      means, stds)
   }
 }
 
 object ChannelNormalize {
+  /**
+   * image channel normalize
+   *
+   * @param meanR mean value in R channel
+   * @param meanG mean value in G channel
+   * @param meanB mean value in B channel
+   * @param stdR std value in R channel
+   * @param stdG std value in G channel
+   * @param stdB std value in B channel
+   */
   def apply(meanR: Float, meanG: Float, meanB: Float,
-            stdR: Float = 1, stdG: Float = 1, stdB: Float = 1): ChannelNormalize = {
-    new ChannelNormalize(meanR, meanG, meanB, stdR, stdG, stdB)
+    stdR: Float = 1, stdG: Float = 1, stdB: Float = 1): ChannelNormalize = {
+    new ChannelNormalize(Array(meanB, meanG, meanR), Array(stdR, stdG, stdB))
+  }
+
+  def apply(mean: Float, std: Float): ChannelNormalize = {
+    new ChannelNormalize(Array(mean), Array(std))
   }
 
-  def transform(input: OpenCVMat, output: OpenCVMat,
-                meanR: Float, meanG: Float, meanB: Float,
-                stdR: Float = 1, stdG: Float = 1, stdB: Float = 1): Unit = {
-    if (input.`type`() != CvType.CV_32FC3) {
-      input.convertTo(input, CvType.CV_32FC3)
+  def transform(input: OpenCVMat, output: OpenCVMat, means: Array[Float], stds: Array[Float])
+  : Unit = {
+    val channel = input.channels()
+    if (input.`type`() != CvType.CV_32FC(channel)) {
+      input.convertTo(input, CvType.CV_32FC(channel))
     }
     val inputChannels = new util.ArrayList[Mat]()
     Core.split(input, inputChannels)
-    require(inputChannels.size() == 3)
     val outputChannels = if (output != input) {
       output.create(input.rows(), input.cols(), input.`type`())
       val channels = new util.ArrayList[Mat]()
@@ -62,12 +71,16 @@ object ChannelNormalize {
       channels
     } else inputChannels
 
-    Core.subtract(inputChannels.get(0), new Scalar(meanB), outputChannels.get(0))
-    Core.subtract(inputChannels.get(1), new Scalar(meanG), outputChannels.get(1))
-    Core.subtract(inputChannels.get(2), new Scalar(meanR), outputChannels.get(2))
-    if (stdB != 1) Core.divide(outputChannels.get(0), new Scalar(stdB), outputChannels.get(0))
-    if (stdG != 1) Core.divide(outputChannels.get(1), new Scalar(stdG), outputChannels.get(1))
-    if (stdR != 1) Core.divide(outputChannels.get(2), new Scalar(stdR), outputChannels.get(2))
+    (0 until channel).foreach(i => {
+      if (null != means) {
+        Core.subtract(inputChannels.get(i), new Scalar(means(i)), outputChannels.get(i))
+      }
+      if (stds != null) {
+        if (stds(i) != 1) {
+          Core.divide(outputChannels.get(i), new Scalar(stds(i)), outputChannels.get(i))
+        }
+      }
+    })
     Core.merge(outputChannels, output)
 
     (0 until inputChannels.size()).foreach(inputChannels.get(_).release())
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala
index 442419da966..fc0fbd4929a 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/opencv/OpenCVMat.scala
@@ -128,7 +128,7 @@ object OpenCVMat {
     var result: OpenCVMat = null
     try {
       matOfByte = new MatOfByte(fileContent: _*)
-      mat = Imgcodecs.imdecode(matOfByte, Imgcodecs.CV_LOAD_IMAGE_COLOR)
+      mat = Imgcodecs.imdecode(matOfByte, Imgcodecs.CV_LOAD_IMAGE_UNCHANGED)
       result = new OpenCVMat(mat)
     } catch {
       case e: Exception =>
@@ -166,18 +166,15 @@ object OpenCVMat {
    * @param width image width
    * @return image in mat
    */
-  def fromFloats(floats: Array[Float], height: Int, width: Int): OpenCVMat = {
-    var mat: Mat = null
-    try {
-      mat = new Mat(height, width, CvType.CV_32FC3)
-      mat.put(0, 0, floats)
-      new OpenCVMat(mat)
-    } catch {
-        case e: Exception => throw new Exception(s"convert float array to OpenCVMat fails!\n" +
-          s"${e.getMessage}")
-    } finally {
-      if (null != mat) mat.release()
-    }
+  def fromFloats(floats: Array[Float], height: Int, width: Int, channel: Int = 3): OpenCVMat = {
+    require(channel >= 1 && channel <= 4, s"channel $channel is out of range [1,4]")
+    require(floats.length >= height * width * channel,
+      s"pixels array length ${floats.length} is less than " +
+        s"height*width*channel ${height * width * channel}")
+    val mat = new OpenCVMat()
+    mat.create(height, width, CvType.CV_32FC(channel))
+    mat.put(0, 0, floats)
+    mat
   }
 
   /**
@@ -187,10 +184,11 @@ object OpenCVMat {
    * @param buffer
    * @return
    */
-  def toBytePixels(input: Mat, buffer: Array[Byte] = null): (Array[Byte], Int, Int) = {
-    // the mat need to be type CV_8UC3 in order to get pixels byte array
-    if (input.`type`() != CvType.CV_8UC3) {
-      input.convertTo(input, CvType.CV_8UC3)
+  def toBytePixels(input: Mat, buffer: Array[Byte] = null): (Array[Byte], Int, Int, Int) = {
+    val channel = input.channels()
+    // the mat need to be type CV_8UCX in order to get pixels byte array
+    if (input.`type`() != CvType.CV_8UC(channel)) {
+      input.convertTo(input, CvType.CV_8UC(channel))
     }
     var bytes = buffer
     val length = input.channels() * input.height() * input.width()
@@ -198,7 +196,7 @@ object OpenCVMat {
       bytes = new Array[Byte](length)
     }
     input.get(0, 0, bytes)
-    (bytes, input.height(), input.width())
+    (bytes, input.height(), input.width(), channel)
   }
 
 
@@ -210,17 +208,18 @@ object OpenCVMat {
    * @return
    */
   def toFloatPixels(input: Mat,
-    buffer: Array[Float] = null): (Array[Float], Int, Int) = {
+    buffer: Array[Float] = null): (Array[Float], Int, Int, Int) = {
     var floats = buffer
     val length = input.channels() * input.height() * input.width()
     if (null == buffer || buffer.length < length) {
       floats = new Array[Float](length)
     }
-    if (input.`type`() != CvType.CV_32FC3) {
-      input.convertTo(input, CvType.CV_32FC3)
+    val channel = input.channels()
+    if (input.`type`() != CvType.CV_32FC(channel)) {
+      input.convertTo(input, CvType.CV_32FC(channel))
     }
     input.get(0, 0, floats)
-    (floats, input.height(), input.width())
+    (floats, input.height(), input.width(), channel)
   }
 
   /**
@@ -229,9 +228,13 @@ object OpenCVMat {
    * @param height image height
    * @param width image width
    */
-  def fromPixelsBytes(pixels: Array[Byte], height: Int, width: Int): OpenCVMat = {
+  def fromPixelsBytes(pixels: Array[Byte], height: Int, width: Int, channel: Int = 3): OpenCVMat = {
+    require(channel >= 1 && channel <= 4, s"channel $channel is out of range [1,4]")
+    require(pixels.length >= height * width * channel,
+      s"pixels array length ${pixels.length} is less than " +
+        s"height*width*channel ${height * width * channel}")
     val mat = new OpenCVMat()
-    mat.create(height, width, CvType.CV_8UC3)
+    mat.create(height, width, CvType.CV_8UC(channel))
     mat.put(0, 0, pixels)
     mat
   }
@@ -259,6 +262,6 @@ object OpenCVMat {
     if (offset > 0) {
       floatArr = floatArr.slice(offset, tensor.nElement() + offset)
     }
-    fromFloats(floatArr, image.size(1), image.size(2))
+    fromFloats(floatArr, image.size(1), image.size(2), image.size(3))
   }
 }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
index 504a0d5c561..ad525f73772 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
@@ -2784,7 +2784,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab
   : ImageFeature = {
     val feature = new ImageFeature()
     if (null != data) {
-      val mat = OpenCVMat.fromFloats(data.storage, data.shape(0), data.shape(1))
+      val mat = OpenCVMat.fromFloats(data.storage, data.shape(0), data.shape(1), data.shape(2))
       feature(ImageFeature.bytes) = OpenCVMat.imencode(mat)
       feature(ImageFeature.mat) = mat
       feature(ImageFeature.originalSize) = mat.shape()
diff --git a/scala/dllib/src/test/resources/gray/gray.bmp b/scala/dllib/src/test/resources/gray/gray.bmp
new file mode 100644
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zh~kRF`*=7pgF2%{ok1?A)HJs;5GK15Veq8`NHd*@Fqubdn-vVzc2D53v
zq{t>wN^5BdlZ7BGHq)8c8Eg(6LD-Bc#N6LwG#J&Ex25%MEnI?!H-M?%Ffbr5D6Och
zX=89%Tn?Mt;mq;Q9h-r$8q0)MlilcSKU-T>jjb(hEHWCvVlW!UGzArvl~fvw!|CL8
jaymMkKsKAx#zq?IGzOhnt20gCu56+=wYM`Got*v;e4jdP

literal 0
HcmV?d00001

diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala
index 5f498bee4c9..8be36b763b2 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/ImageFrameSpec.scala
@@ -20,8 +20,9 @@ import java.io.File
 import java.nio.file.Paths
 
 import com.google.common.io.Files
-import com.intel.analytics.bigdl.dataset.image.{BGRImage, BGRImgToLocalSeqFile, LocalImgReaderWithName}
+import com.intel.analytics.bigdl.dataset.image.{BGRImage, BGRImgToLocalSeqFile, BytesToGreyImg, GreyImgNormalizer, GreyImgToSample, LocalImgReaderWithName, HFlip => BHFlip}
 import com.intel.analytics.bigdl.dataset.{DataSet, Sample}
+import com.intel.analytics.bigdl.numeric.NumericFloat
 import com.intel.analytics.bigdl.transform.vision.image.augmentation._
 import com.intel.analytics.bigdl.utils.{Engine, TestUtils}
 import org.apache.commons.io.FileUtils
@@ -161,4 +162,49 @@ class ImageFrameSpec extends FlatSpec with Matchers with BeforeAndAfter {
       path
     }
   }
+
+  "mnist data source" should "load image with ImageFrame correct" in {
+    val resource = getClass().getClassLoader().getResource("mnist")
+
+    val dataSet = com.intel.analytics.bigdl.models.lenet.Utils.load(
+      processPath(resource.getPath()) + File.separator + "t10k-images.idx3-ubyte",
+      processPath(resource.getPath()) + File.separator + "t10k-labels.idx1-ubyte")
+    val array = dataSet.map(x => {
+      val im = ImageFeature(x.data, x.label)
+      im(ImageFeature.originalSize) = (28, 28, 1)
+      im
+    })
+    val testMean = 0
+    val testStd = 1
+    val imf = ImageFrame.array(array)
+    val transformer = PixelBytesToMat() ->
+      ChannelNormalize(testMean * 255, testStd * 255) ->
+      ChannelNormalize(testMean, testStd) ->
+      MatToTensor[Float](shareBuffer = false) ->
+      ImageFrameToSample[Float]()
+    val transformed = transformer(imf)
+    transformed.toLocal().array.foreach(x => {
+      println(x(ImageFeature.sample))
+    })
+
+    val transformer2 =
+      BytesToGreyImg(28, 28) -> GreyImgNormalizer(testMean, testStd) -> GreyImgToSample()
+    val evaluationSet = DataSet.array(dataSet).transform(transformer2)
+    evaluationSet.toLocal().data(false).toArray.zip(transformed.toLocal().array).foreach(x => {
+      x._1.feature() should be (x._2[Sample[Float]](ImageFeature.sample).feature())
+    })
+  }
+
+  "read gray scale image" should "work" in {
+    val resource = getClass().getClassLoader().getResource("gray/gray.bmp")
+    val imf = ImageFrame.read(resource.getFile)
+    imf.toLocal().array(0).getOriginalSize should be (50, 50, 1)
+  }
+
+  "transform gray scale image" should "work" in {
+    val resource = getClass().getClassLoader().getResource("gray/gray.bmp")
+    val imf = ImageFrame.read(resource.getFile) -> Resize(28, 28) -> MatToTensor[Float]()
+    imf.toLocal().array(0).getOriginalSize should be (50, 50, 1)
+    imf.toLocal().array(0).getSize should be (28, 28, 1)
+  }
 }

From a1b927064a9c22eada2f4d5b40913072d939f858 Mon Sep 17 00:00:00 2001
From: Yang Wang 
Date: Mon, 29 Jan 2018 15:08:39 +0800
Subject: [PATCH 0661/1065] Optimize BCECriterion (#2231)

* bce loss

* address comments

* remove heuristic

* address comments

* fix ut precision

* fix ut

* address comments
---
 .../bigdl/dllib/nn/BCECriterion.scala         |  91 ++++++-----
 .../bigdl/dllib/nn/BCECriterionSpec.scala     |  50 +++++-
 .../bigdl/dllib/torch/BCECriterionSpec.scala  | 145 ++++++++++++++----
 .../MultiLabelSoftMarginCriterionSpec.scala   |   4 +-
 4 files changed, 219 insertions(+), 71 deletions(-)

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala
index d735e187cc6..939d9e173ab 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala
@@ -37,48 +37,61 @@ import scala.reflect.ClassTag
  */
 @SerialVersionUID(- 1953992758534446600L)
 class BCECriterion[@specialized(Float, Double) T: ClassTag]
-(var weights: Tensor[T] = null, sizeAverage: Boolean = true)
+(val weights: Tensor[T] = null, sizeAverage: Boolean = true)
   (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] {
   private val eps = 1e-12
-  if (weights != null) require(weights.dim() == 1,
-    "weights input should be 1-D Tensor" +
-    s"weights input dim(${weights.dim()})")
 
-  override def updateOutput(input: Tensor[T], target: Tensor[T]): T = {
-    require(input.nElement() == target.nElement())
+  val buffer: Tensor[T] = Tensor[T]()
+
+  val onesBuffer: Tensor[T] = Tensor[T]()
 
-    if (null != weights && target.dim() != 1) {
-      weights = weights.view(1, target.size(2)).expandAs(target)
+  override def updateOutput(input: Tensor[T], target: Tensor[T]): T = {
+    require(input.size().sameElements(target.size()),
+      s"input size should be equal to target size, but got input size: ${input.size().toList}," +
+        s" target size: ${input.size().toList}")
+
+    if (weights != null) {
+      if (weights.nDimension() < input.nDimension()) {
+        require(weights.size().sameElements(input.size().tail),
+          s"weights size should be equal to input size or input size's tail, but got" +
+            s" input size: ${input.size().toList}, weights size: ${weights.size().toList}")
+      } else if (weights.nDimension() == input.nDimension()) {
+        require(weights.size().sameElements(input.size()),
+          s"weights size should be equal to input size or input size's tail, but got" +
+            s" input size: ${input.size().toList}, weights size: ${weights.size().toList}")
+      } else {
+        throw new IllegalArgumentException(
+          s"weights size should be equal to input size or input size's tail, but got" +
+          s" input size: ${input.size().toList}, weights size: ${weights.size().toList}")
+      }
     }
 
     var sum = 0.0
     if (null != weights) {
-      val func = new TensorFunc6[T] {
-        override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int,
-                           data3: Array[T], offset3: Int): Unit = {
-          val x = ev.toType[Double](data1(offset1))
-          val y = ev.toType[Double](data2(offset2))
-          val w = ev.toType[Double](data3(offset3))
-          sum -= (Math.log(x + eps) * y + Math.log(1.0 - x + eps) * (1.0 - y)) * w
-        }
+      buffer.resizeAs(input).copy(input).add(ev.fromType(eps)).log()
+      // cmul support broadcasting
+      buffer.cmul(weights)
+      sum += ev.toType[Double](buffer.dot(target))
+      buffer.fill(ev.fromType(1.0 + eps)).sub(input).log().cmul(weights)
+      sum -= ev.toType[Double](buffer.dot(target))
+      if (onesBuffer.nElement() != buffer.nElement()) {
+        onesBuffer.resizeAs(buffer).fill(ev.one)
       }
-      DenseTensorApply.apply3(input, target, weights, func)
+      sum += ev.toType[Double](buffer.dot(onesBuffer))
     } else {
-      val func = new TensorFunc4[T] {
-        override def apply(data1: Array[T], offset1: Int,
-                           data2: Array[T], offset2: Int): Unit = {
-          val x = ev.toType[Double](data1(offset1))
-          val y = ev.toType[Double](data2(offset2))
-          sum -= Math.log(x + eps) * y + Math.log(1.0 - x + eps) * (1.0 - y)
-        }
+      buffer.resizeAs(input).copy(input).add(ev.fromType(eps)).log()
+      sum += ev.toType[Double](buffer.dot(target))
+      buffer.fill(ev.fromType(1.0 + eps)).sub(input).log()
+      sum -= ev.toType[Double](buffer.dot(target))
+      if (onesBuffer.nElement() != buffer.nElement()) {
+        onesBuffer.resizeAs(buffer).fill(ev.one)
       }
-      DenseTensorApply.apply2(input, target, func)
-
+      sum += ev.toType[Double](buffer.sum())
     }
 
     if (sizeAverage) sum /= input.nElement()
 
-    output = ev.fromType[Double](sum)
+    output = ev.fromType[Double](-sum)
 
     output
   }
@@ -87,27 +100,21 @@ class BCECriterion[@specialized(Float, Double) T: ClassTag]
     require(input.nElement() == target.nElement(),
       "input and target should have the same dims." +
         s"input dim(${input.nElement()})" +
-        s"taget dim(${target.nElement()})")
+        s"target dim(${target.nElement()})")
 
-    if (null != weights && target.dim() != 1) {
-      weights = weights.view(1, target.size(2)).expandAs(target)
-    }
-
-    val norm = if (sizeAverage) 1.0 / input.nElement() else 1.0
+    val nElement = input.nElement()
+    val norm = if (sizeAverage) 1.0 / nElement else 1.0
 
     gradInput.resizeAs(input)
 
-    val func = new TensorFunc6[T] {
-      override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int,
-                         data3: Array[T], offset3: Int): Unit = {
-        val x = ev.toType[Double](data2(offset2))
-        val y = ev.toType[Double](data3(offset3))
-        data1(offset1) = ev.fromType(-norm * (y - x) / ((1.0 - x + eps) * (x + eps)))
-      }
-    }
-    DenseTensorApply.apply3(gradInput, input, target, func)
+    // gradInput = -norm * (y - x) / ((1.0 - x + eps) * (x + eps))
+    // - (1 - x + eps)*(x + eps) = x^2 - x - eps - eps^2
+    // eps^12 is negligible
+    buffer.pow(input, ev.fromType(2)).sub(input).sub(ev.fromType(eps))
+    gradInput.copy(target).sub(input).cdiv(buffer).mul(ev.fromType(norm))
 
     if (null != weights) {
+      // cmul support broadcasting
       gradInput.cmul(weights)
     }
 
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterionSpec.scala
index beffd23c3ac..c49675d81c5 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterionSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterionSpec.scala
@@ -45,6 +45,54 @@ class BCECriterionSpec extends FlatSpec with Matchers {
 
   }
 
+  "BCECriterion with more than two dimensions small input" should "" +
+    "return return right output and gradInput" in {
+
+    val weights = Tensor[Double](3, 2, 2).rand()
+    val criterion = new BCECriterion[Double](weights)
+    val input = Tensor[Double](4, 3, 2, 2).rand()
+    val target = Tensor[Double](4, 3, 2, 2).rand()
+
+    val weightsRef = Tensor[Double]().resizeAs(weights).copy(weights).reshape(Array(3 * 2 * 2))
+    val criterionRef = new BCECriterion[Double](weightsRef)
+    val inputRef = Tensor[Double]().resizeAs(input).copy(input).reshape(Array(4, 3 * 2 * 2))
+    val targetRef = Tensor[Double]().resizeAs(target).copy(target).reshape(Array(4, 3 * 2 * 2))
+
+    val output = criterion.forward(input, target)
+    val gradInput = criterion.backward(input, target).clone()
+
+    val outputRef = criterionRef.forward(inputRef, targetRef)
+    val gradInputRef = criterionRef.backward(inputRef, targetRef).clone()
+
+    output should be (outputRef +- 1e-7)
+    gradInput.almostEqual(gradInputRef, 1e-7) should be (true)
+
+  }
+
+  "BCECriterion with more than two dimensions large input" should "" +
+    "return return right output and gradInput" in {
+
+    val weights = Tensor[Double](3, 32, 32).rand()
+    val criterion = new BCECriterion[Double](weights)
+    val input = Tensor[Double](4, 3, 32, 32).rand()
+    val target = Tensor[Double](4, 3, 32, 32).rand()
+
+    val weightsRef = Tensor[Double]().resizeAs(weights).copy(weights).reshape(Array(3 * 32 * 32))
+    val criterionRef = new BCECriterion[Double](weightsRef)
+    val inputRef = Tensor[Double]().resizeAs(input).copy(input).reshape(Array(4, 3 * 32 * 32))
+    val targetRef = Tensor[Double]().resizeAs(target).copy(target).reshape(Array(4, 3 * 32 * 32))
+
+    val output = criterion.forward(input, target)
+    val gradInput = criterion.backward(input, target).clone()
+
+    val outputRef = criterionRef.forward(inputRef, targetRef)
+    val gradInputRef = criterionRef.backward(inputRef, targetRef).clone()
+
+    output should be (outputRef +- 1e-7)
+    gradInput.almostEqual(gradInputRef, 1e-7) should be (true)
+
+  }
+
   "Binary LR " should "converge correctly" in {
     def specifiedModel(): Module[Double] = {
       val model = new Sequential[Double]()
@@ -109,7 +157,7 @@ class BCECriterionSpec extends FlatSpec with Matchers {
           inputs.narrow(1, i, batchSize),
           targets
             .toTensor[Double]
-            .narrow(1, i, batchSize)),
+            .narrow(1, i, batchSize).addSingletonDimension(dim = 2)),
           masterWeights, config, config)
         l += loss(0)
         i += batchSize
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BCECriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BCECriterionSpec.scala
index 97937d9ed81..05bca92c6a9 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BCECriterionSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BCECriterionSpec.scala
@@ -15,12 +15,8 @@
  */
 package com.intel.analytics.bigdl.torch
 
-import com.intel.analytics.bigdl._
 import com.intel.analytics.bigdl.nn.BCECriterion
 import com.intel.analytics.bigdl.tensor.Tensor
-import com.intel.analytics.bigdl.utils.RandomGenerator._
-
-import scala.util.Random
 
 @com.intel.analytics.bigdl.tags.Serial
 class BCECriterionSpec extends TorchSpec{
@@ -29,10 +25,10 @@ class BCECriterionSpec extends TorchSpec{
     torchCheck()
     val criterion = new BCECriterion[Double]()
     val input = Tensor[Double](3, 1).rand()
-    val target = Tensor[Double](3)
-    target(Array(1)) = 1
-    target(Array(2)) = 0
-    target(Array(3)) = 1
+    val target = Tensor[Double](3, 1)
+    target(Array(1, 1)) = 1
+    target(Array(2, 1)) = 0
+    target(Array(3, 1)) = 1
 
     val start = System.nanoTime()
     val output1 = criterion.forward(input, target)
@@ -50,8 +46,8 @@ class BCECriterionSpec extends TorchSpec{
     val luaOutput1 = torchResult("output1").asInstanceOf[Double]
     val luaOutput2 = torchResult("output2").asInstanceOf[Tensor[Double]]
 
-    luaOutput1 should be(output1)
-    luaOutput2 should be(output2)
+    luaOutput1 should be(output1 +- 1e-7)
+    luaOutput2.almostEqual(output2, 1e-7) should be(true)
 
     println("Test case : BCECriterion, Torch : " + luaTime + " s, Scala : " +
       scalaTime / 1e9 + " s")
@@ -59,13 +55,16 @@ class BCECriterionSpec extends TorchSpec{
 
   "A BCECriterion with weights" should "generate correct output and grad" in {
     torchCheck()
-    val weights = Tensor[Double](3).rand()
+    val weights = Tensor[Double](2).rand()
     val criterion = new BCECriterion[Double](weights)
-    val input = Tensor[Double](3, 1).rand()
-    val target = Tensor[Double](3)
-    target(Array(1)) = 1
-    target(Array(2)) = 0
-    target(Array(3)) = 1
+    val input = Tensor[Double](3, 2).rand()
+    val target = Tensor[Double](3, 2)
+    target(Array(1, 1)) = 1
+    target(Array(2, 1)) = 0
+    target(Array(3, 1)) = 1
+    target(Array(1, 2)) = 1
+    target(Array(2, 2)) = 0
+    target(Array(3, 2)) = 1
 
     val start = System.nanoTime()
     val output1 = criterion.forward(input, target)
@@ -84,8 +83,8 @@ class BCECriterionSpec extends TorchSpec{
     val luaOutput1 = torchResult("output1").asInstanceOf[Double]
     val luaOutput2 = torchResult("output2").asInstanceOf[Tensor[Double]]
 
-    luaOutput1 should be(output1)
-    luaOutput2 should be(output2)
+    luaOutput1 should be(output1 +- 1e-7)
+    luaOutput2.almostEqual(output2, 1e-7) should be(true)
 
     println("Test case : BCECriterion, Torch : " + luaTime + " s, Scala : " +
       scalaTime / 1e9 + " s")
@@ -93,13 +92,107 @@ class BCECriterionSpec extends TorchSpec{
 
   "A BCECriterion with sizeAverage" should "generate correct output and grad" in {
     torchCheck()
-    val weights = Tensor[Double](3).rand()
+    val weights = Tensor[Double](2).rand()
+    val criterion = new BCECriterion[Double](weights)
+    val input = Tensor[Double](3, 2).rand()
+    val target = Tensor[Double](3, 2)
+    target(Array(1, 1)) = 1
+    target(Array(2, 1)) = 0
+    target(Array(3, 1)) = 1
+    target(Array(1, 2)) = 1
+    target(Array(2, 2)) = 0
+    target(Array(3, 2)) = 1
+
+    val start = System.nanoTime()
+    val output1 = criterion.forward(input, target)
+    val output2 = criterion.backward(input, target)
+    val end = System.nanoTime()
+    val scalaTime = end - start
+
+    val code = "criterion = nn.BCECriterion(weights, true)\n" +
+      "output1 = criterion:forward(input, target)\n " +
+      "output2 = criterion:backward(input, target)"
+
+
+    val (luaTime, torchResult) = TH.run(code,
+      Map("input" -> input, "target" -> target, "weights" -> weights),
+      Array("output1", "output2"))
+    val luaOutput1 = torchResult("output1").asInstanceOf[Double]
+    val luaOutput2 = torchResult("output2").asInstanceOf[Tensor[Double]]
+
+    luaOutput1 should be(output1 +- 1e-7)
+    luaOutput2.almostEqual(output2, 1e-7) should be(true)
+
+    println("Test case : BCECriterion, Torch : " + luaTime + " s, Scala : " +
+      scalaTime / 1e9 + " s")
+  }
+
+  "A BCECriterion with large input" should "generate correct output and grad" in {
+    torchCheck()
+    val criterion = new BCECriterion[Double]()
+    val input = Tensor[Double](3, 100).rand()
+    val target = Tensor[Double](3, 100).rand()
+
+    val start = System.nanoTime()
+    val output1 = criterion.forward(input, target)
+    val output2 = criterion.backward(input, target)
+    val end = System.nanoTime()
+    val scalaTime = end - start
+
+    val code = "criterion = nn.BCECriterion()\n" +
+      "output1 = criterion:forward(input, target)\n " +
+      "output2 = criterion:backward(input, target)"
+
+
+    val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "target" -> target),
+      Array("output1", "output2"))
+    val luaOutput1 = torchResult("output1").asInstanceOf[Double]
+    val luaOutput2 = torchResult("output2").asInstanceOf[Tensor[Double]]
+
+    luaOutput1 should be(output1 +- 1e-7)
+    luaOutput2.almostEqual(output2, 1e-7) should be (true)
+
+    println("Test case : BCECriterion, Torch : " + luaTime + " s, Scala : " +
+      scalaTime / 1e9 + " s")
+  }
+
+  "A BCECriterion with weights and large input" should "generate correct output and grad" in {
+    torchCheck()
+    val weights = Tensor[Double](300).rand()
+    val criterion = new BCECriterion[Double](weights)
+    val input = Tensor[Double](3, 300).rand()
+    val target = Tensor[Double](3, 300).rand()
+
+    val start = System.nanoTime()
+    val output1 = criterion.forward(input, target)
+    val output2 = criterion.backward(input, target)
+    val end = System.nanoTime()
+    val scalaTime = end - start
+
+    val code = "criterion = nn.BCECriterion(weights)\n" +
+      "output1 = criterion:forward(input, target)\n " +
+      "output2 = criterion:backward(input, target)"
+
+
+    val (luaTime, torchResult) = TH.run(code,
+      Map("input" -> input, "target" -> target, "weights" -> weights),
+      Array("output1", "output2"))
+    val luaOutput1 = torchResult("output1").asInstanceOf[Double]
+    val luaOutput2 = torchResult("output2").asInstanceOf[Tensor[Double]]
+
+    luaOutput1 should be(output1 +- 1e-7)
+    luaOutput2.almostEqual(output2, 1e-7) should be (true)
+
+    println("Test case : BCECriterion, Torch : " + luaTime + " s, Scala : " +
+      scalaTime / 1e9 + " s")
+  }
+
+  "A BCECriterion with sizeAverage and large input" should "generate correct output and grad" in {
+    torchCheck()
+    val weights = Tensor[Double](300).rand()
     val criterion = new BCECriterion[Double](weights, true)
-    val input = Tensor[Double](3, 1).rand()
-    val target = Tensor[Double](3)
-    target(Array(1)) = 1
-    target(Array(2)) = 0
-    target(Array(3)) = 1
+    val input = Tensor[Double](3, 300).rand()
+    val target = Tensor[Double](3, 300).rand()
 
     val start = System.nanoTime()
     val output1 = criterion.forward(input, target)
@@ -118,8 +211,8 @@ class BCECriterionSpec extends TorchSpec{
     val luaOutput1 = torchResult("output1").asInstanceOf[Double]
     val luaOutput2 = torchResult("output2").asInstanceOf[Tensor[Double]]
 
-    luaOutput1 should be(output1)
-    luaOutput2 should be(output2)
+    luaOutput1 should be(output1 +- 1e-7)
+    luaOutput2.almostEqual(output2, 1e-7) should be (true)
 
     println("Test case : BCECriterion, Torch : " + luaTime + " s, Scala : " +
       scalaTime / 1e9 + " s")
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelSoftMarginCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelSoftMarginCriterionSpec.scala
index fbdd581c649..1bb3f548851 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelSoftMarginCriterionSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelSoftMarginCriterionSpec.scala
@@ -51,8 +51,8 @@ class MultiLabelSoftMarginCriterionSpec extends TorchSpec {
     val luaOutput1 = torchResult("output").asInstanceOf[Double]
     val luaOutput2 = torchResult("gradInput").asInstanceOf[Tensor[Double]]
 
-    luaOutput1 should be(output)
-    luaOutput2 should be(gradInput)
+    luaOutput1 should be(output +- 1e-7)
+    luaOutput2.almostEqual(gradInput, 1e-7) should be(true)
 
     println("Test case : MultiLabelSoftMarginCriterion, Torch : " +
       luaTime + " s, Scala : " + scalaTime / 1e9 + " s")

From 2c84ceeea9d34eb9cc247d70723e2ecfc2e81ec8 Mon Sep 17 00:00:00 2001
From: Yang Wang 
Date: Mon, 29 Jan 2018 22:32:05 +0800
Subject: [PATCH 0662/1065] Add PGCriterion to compute the negative policy
 gradient given action distribution, sampled action and reward (#2197)

* add pgcriterion

* fix style

* fix style and python test

* add doc

* mv to nn
---
 .../bigdl/dllib/feature/dataset/Sample.scala  |  3 +-
 .../bigdl/dllib/nn/DotProductCriterion.scala  | 70 ++++++++++++++++
 .../dllib/nn/NegativeEntropyPenalty.scala     | 69 ++++++++++++++++
 .../bigdl/dllib/nn/PGCriterion.scala          | 77 ++++++++++++++++++
 .../bigdl/dllib/nn/TransformerCriterion.scala | 11 ++-
 .../bigdl/dllib/tensor/SparseTensor.scala     |  5 +-
 .../bigdl/dllib/tensor/SparseTensorBLAS.scala | 80 +++++++++++++++++++
 .../bigdl/dllib/tensor/SparseTensorMath.scala |  6 ++
 .../dllib/utils/python/api/PythonBigDL.scala  | 17 +++-
 .../dllib/nn/DotProductCriterionSpec.scala    | 51 ++++++++++++
 .../dllib/nn/NegativeEntropyPenaltySpec.scala | 41 ++++++++++
 .../bigdl/dllib/nn/PGCriterionSpec.scala      | 52 ++++++++++++
 .../bigdl/dllib/tensor/SparseTensorSpec.scala | 12 +++
 .../serializer/ModuleSerializerSpec.scala     |  6 ++
 14 files changed, 491 insertions(+), 9 deletions(-)
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DotProductCriterion.scala
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenalty.scala
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PGCriterion.scala
 create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DotProductCriterionSpec.scala
 create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenaltySpec.scala
 create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PGCriterionSpec.scala

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala
index ea2b5e8dde1..e67d8b83043 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala
@@ -371,7 +371,8 @@ object Sample {
   def apply[T: ClassTag](
         featureTensors: Array[Tensor[T]],
         labelTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = {
-    if (featureTensors.exists(_.getTensorType == SparseType)) {
+    if (featureTensors.exists(_.getTensorType == SparseType) ||
+        labelTensors.exists(_.getTensorType == SparseType)) {
       TensorSample(featureTensors, labelTensors)
     } else {
       ArraySample(featureTensors, labelTensors)
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DotProductCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DotProductCriterion.scala
new file mode 100644
index 00000000000..174ea41777e
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DotProductCriterion.scala
@@ -0,0 +1,70 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.intel.analytics.bigdl.nn
+
+import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+
+import scala.reflect.ClassTag
+
+/**
+ * Compute the dot product of input and target tensor.
+ * Input and target are required to have the same size.
+ * @param sizeAverage whether to average over each observations in the same batch
+ */
+@SerialVersionUID(3360838286914764710L)
+class DotProductCriterion[T: ClassTag]
+(sizeAverage: Boolean = false)(
+         implicit ev: TensorNumeric[T]) extends TensorCriterion[T]  {
+
+  override def updateOutput(input: Tensor[T], target: Tensor[T]): T = {
+    require(input.dim() == 1 || input.dim() == 2, "DotProductCriterion only" +
+      "support tensor with 1 or 2 dimensions")
+    require(input.size().sameElements(target.size()), "The shape of input and target" +
+      "must be the same")
+
+    val dotProduct = target.dot(input)
+    if (sizeAverage && input.dim() == 2) {
+      output = ev.divide(dotProduct, ev.fromType(input.size(1)))
+    } else {
+      output = dotProduct
+    }
+    output
+  }
+
+  override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = {
+    require(input.dim() == 1 || input.dim() == 2, "DotProductCriterion only" +
+      "support tensor with 1 or 2 dimensions")
+    require(input.size().sameElements(target.size()), "The shape of input and target" +
+      "must be the same")
+
+    gradInput.resizeAs(target)
+    Tensor.dense(target, gradInput)
+    if (sizeAverage && target.dim() == 2) {
+      gradInput.div(ev.fromType(target.size(1)))
+    }
+    gradInput
+  }
+}
+
+object DotProductCriterion {
+  def apply[@specialized(Float, Double) T: ClassTag](sizeAverage: Boolean = false)
+      (implicit ev: TensorNumeric[T]) : DotProductCriterion[T] = {
+    new DotProductCriterion[T]()
+  }
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenalty.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenalty.scala
new file mode 100644
index 00000000000..7bc4a418f22
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenalty.scala
@@ -0,0 +1,69 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn
+
+import com.intel.analytics.bigdl.nn.abstractnn.TensorModule
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+
+import scala.reflect.ClassTag
+
+/**
+ * Penalize the input multinomial distribution if it has low entropy.
+ * The input to this layer should be a batch of vector each representing a
+ * multinomial distribution. The input is typically the output of a softmax layer.
+ *
+ * For forward, the output is the same as input and a NegativeEntropy loss of
+ * the latent state will be calculated each time. For backward,
+ * gradInput = gradOutput + gradLoss
+ *
+ * This can be used in reinforcement learning to discourage the policy from
+ * collapsing to a single action for a given state, which improves exploration.
+ * See the A3C paper for more detail (https://arxiv.org/pdf/1602.01783.pdf).
+ *
+ * @param beta penalty coefficient
+ */
+@SerialVersionUID(- 5766252125245927237L)
+class NegativeEntropyPenalty[T: ClassTag]
+(val beta: Double = 0.01)
+(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
+
+  var loss: T = ev.fromType(0)
+  private val buffer = Tensor[T]()
+
+  override def updateOutput(input: Tensor[T]): Tensor[T] = {
+    loss = ev.times(buffer.resizeAs(input)
+      .copy(input).log().cmul(input).sum(), ev.fromType(beta))
+    output = input
+    output
+  }
+
+  override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
+
+    gradInput.resizeAs(input).copy(input)
+      .log().add(ev.fromType(1)).mul(ev.fromType(beta))
+
+    gradInput.add(gradOutput)
+    gradInput
+  }
+}
+
+object NegativeEntropyPenalty {
+  def apply[@specialized(Float, Double) T: ClassTag](beta: Double = 0.01)
+         (implicit ev: TensorNumeric[T]) : NegativeEntropyPenalty[T] = {
+    new NegativeEntropyPenalty[T](beta)
+  }
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PGCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PGCriterion.scala
new file mode 100644
index 00000000000..4445c4ceb6b
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PGCriterion.scala
@@ -0,0 +1,77 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.intel.analytics.bigdl.nn
+
+import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+
+import scala.reflect.ClassTag
+
+/**
+ * The Criterion to compute the negative policy gradient given a
+ * multinomial distribution and the sampled action and reward.
+ *
+ * The input to this criterion should be a 2-D tensor representing
+ * a batch of multinomial distribution, the target should also be
+ * a 2-D tensor with the same size of input, representing the sampled
+ * action and reward/advantage with the index of non-zero element in the vector
+ * represents the sampled action and the non-zero element itself represents
+ * the reward. If the action is space is large, you should consider using
+ * SparseTensor for target.
+ *
+ * The loss computed is simple the standard policy gradient,
+ *
+ *   loss = - 1/n * sum(R_{n} dot_product log(P_{n}))
+ *
+ * where R_{n} is the reward vector, and P_{n} is the input distribution.
+ *
+ * @param sizeAverage whether to average the loss over each observations.
+ *
+ */
+@SerialVersionUID(- 76404060368920472L)
+class PGCriterion[T: ClassTag](
+  sizeAverage: Boolean = false)
+  (implicit ev: TensorNumeric[T])
+  extends TensorCriterion[T] {
+  private val criterion = {
+    val inputTrans = Sequential[T]()
+    inputTrans.add(Log[T]())
+    // to calculate the negative policy gradient, because we want maximize reward
+    inputTrans.add(MulConstant(-1))
+
+    TransformerCriterion[T](DotProductCriterion[T](sizeAverage), Some(inputTrans), None)
+  }
+
+  override def updateOutput(input: Tensor[T], target: Tensor[T]): T = {
+    output = criterion.forward(input, target)
+    output
+  }
+
+  override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = {
+    gradInput = criterion.backward(input, target).asInstanceOf[Tensor[T]]
+    gradInput
+  }
+}
+
+object PGCriterion {
+  def apply[@specialized(Float, Double) T: ClassTag](
+    sizeAverage: Boolean = false)
+    (implicit ev: TensorNumeric[T]): PGCriterion[T] = {
+    new PGCriterion()
+  }
+}
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterion.scala
index 334aef853b4..a440f363385 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterion.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerCriterion.scala
@@ -48,13 +48,16 @@ class TransformerCriterion[T: ClassTag](
   private var transformedTarget: Activity = _
 
   override def updateOutput(input: Activity, target: Activity): T = {
-    transformedInput = inputTransformer.map(t => t.forward(input))
-      .getOrElse(input) match {
+    transformedTarget = targetTransformer.map(t => t.forward(target))
+      .getOrElse(target) match {
       case t: Tensor[T] => t.clone()
       case t: Table => t.clone()
     }
-    transformedTarget = targetTransformer.map(t => t.forward(target))
-      .getOrElse(target) match {
+
+    // if inputTransformer and target transformer are the same instance
+    // we must do inputTransformer last to preserve the forward state
+    transformedInput = inputTransformer.map(t => t.forward(input))
+      .getOrElse(input) match {
       case t: Tensor[T] => t.clone()
       case t: Table => t.clone()
     }
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala
index 816ae791df1..e20da96b350 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala
@@ -432,7 +432,7 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag](
   }
 
   override def getType(): TensorDataType = {
-    throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method")
+    ev.getType()
   }
 
   override def diff(other: Tensor[T], count: Int, reverse: Boolean): Boolean = {
@@ -634,7 +634,8 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag](
   }
 
   override def dot(y: Tensor[T]): T = {
-    throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method")
+    require(y.getTensorType == DenseType)
+    SparseTensorMath.vdot(y.asInstanceOf[DenseTensor[T]], this)
   }
 
   override def cmax(value: T): Tensor[T] = {
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala
index 8af373d52bf..bfb10fff7b4 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala
@@ -20,6 +20,86 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath._
 
 object SparseTensorBLAS {
 
+  /**
+   * Perform vector dot product of vec1, and vec2
+   */
+  def vdot[@specialized(Float, Double) T](
+       vec1: DenseTensor[T],
+       vec2: SparseTensor[T]): T = {
+    vec1.getType() match {
+      case FloatType =>
+        vdotFloat(vec1.asInstanceOf[DenseTensor[Float]],
+          vec2.asInstanceOf[SparseTensor[Float]])
+          .asInstanceOf[T]
+      case DoubleType =>
+        vdotDouble(vec1.asInstanceOf[DenseTensor[Double]],
+          vec2.asInstanceOf[SparseTensor[Double]])
+          .asInstanceOf[T]
+      case t => throw new IllegalArgumentException(s"Sparse vdot doesn't support $t")
+    }
+  }
+
+  private def vdotFloat(vec1: DenseTensor[Float],
+                   vec2: SparseTensor[Float]): Float = {
+    require(vec1.isContiguous(), "The DenseTensor must be contiguous")
+
+    val vec1Values = vec1.storage().array()
+    val vec1StorageOffset = vec1.storageOffset() - 1
+    val vect1Strides = vec1.stride()
+
+    val vec2Values = vec2._values.array()
+    val vec2storageOffset = vec2.storageOffset() - 1
+
+
+    var valueCounter = 0
+    var sum: Float = 0.0f
+    while (valueCounter < vec2.nElement()) {
+      var dim = 0
+      var vec2Index = 0
+      while (dim < vec2.nDimension) {
+        vec2Index += (vec2._indices(dim)(valueCounter + vec2storageOffset) -
+          vec2._indicesOffset(dim)) * vect1Strides(dim)
+        dim += 1
+      }
+      sum += vec2Values(vec1StorageOffset + vec2Index) *
+        vec1Values(valueCounter + vec1StorageOffset)
+      valueCounter += 1
+    }
+    sum
+  }
+
+  private def vdotDouble(vec1: DenseTensor[Double],
+                   vec2: SparseTensor[Double]): Double = {
+    require(vec1.isContiguous(), "The DenseTensor must be contiguous")
+
+    val vec1Values = vec1.storage().array()
+    val vec1StorageOffset = vec1.storageOffset() - 1
+    val vect1Strides = vec1.stride()
+
+    val vec2Values = vec2._values.array()
+    val vec2storageOffset = vec2.storageOffset() - 1
+
+
+    var valueCounter = 0
+    var sum: Double = 0.0f
+    while (valueCounter < vec2.nElement()) {
+      var dim = 0
+      var vec2Index = 0
+      while (dim < vec2.nDimension) {
+        vec2Index +=
+          (vec2._indices(dim)(valueCounter + vec2storageOffset) -
+            vec2._indicesOffset(dim)) * vect1Strides(dim)
+        dim += 1
+      }
+      sum += vec2Values(vec1StorageOffset + vec2Index) *
+        vec1Values(valueCounter + vec1StorageOffset)
+      valueCounter += 1
+    }
+    sum
+  }
+
+
+
   /**
    * Perform r := beta * r + alpha * mat * vec
    * mat should be a 2D SparseTensor, vec should be a 1D DenseTensor,
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMath.scala
index 39057e4a736..a5412a8b56a 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMath.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorMath.scala
@@ -20,6 +20,12 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath._
 
 object SparseTensorMath {
 
+  def vdot[@specialized(Float, Double) T](
+        vec1: DenseTensor[T],
+        vec2: SparseTensor[T]): T = {
+    SparseTensorBLAS.vdot(vec1, vec2)
+  }
+
   def addmv[@specialized(Float, Double) T](
         r : Tensor[T],
         beta : T,
diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
index ad525f73772..eb624cb6e62 100644
--- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala
@@ -20,7 +20,7 @@ import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, M
 
 import com.intel.analytics.bigdl._
 import com.intel.analytics.bigdl.dataset.{Identity => DIdentity, Sample => JSample, _}
-import com.intel.analytics.bigdl.nn._
+import com.intel.analytics.bigdl.nn.{PGCriterion, Zeros, _}
 import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, _}
 import com.intel.analytics.bigdl.numeric._
 import com.intel.analytics.bigdl.optim.{Optimizer, _}
@@ -28,7 +28,6 @@ import com.intel.analytics.bigdl.tensor.{DenseType, SparseType, Storage, Tensor}
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.utils.{Table, _}
 import com.intel.analytics.bigdl.visualization.{Summary, TrainSummary, ValidationSummary}
-import com.intel.analytics.bigdl.nn.Zeros
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
 import org.apache.spark.rdd.RDD
 import java.lang.{Integer, Boolean => JBoolean}
@@ -886,6 +885,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab
       provideOutput)
   }
 
+  def createNegativeEntropyPenalty(beta: Double): NegativeEntropyPenalty[T] = {
+    NegativeEntropyPenalty(beta)
+  }
+
   def createLeakyReLU(negval: Double = 0.01,
     inplace: Boolean = false)
   : LeakyReLU[T] = {
@@ -1771,6 +1774,16 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab
     TransformerCriterion(criterion, Option(inputTransformer), Option(targetTransformer))
   }
 
+  def createDotProductCriterion(
+          sizeAverage: Boolean = false): DotProductCriterion[T] = {
+    DotProductCriterion[T](sizeAverage)
+  }
+
+  def createPGCriterion(
+    sizeAverage: Boolean = false): PGCriterion[T] = {
+    PGCriterion(sizeAverage)
+  }
+
   def createPack(dimension: Int): Pack[T] = {
     Pack(dimension)
   }
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DotProductCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DotProductCriterionSpec.scala
new file mode 100644
index 00000000000..d5e5c7b7f3f
--- /dev/null
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DotProductCriterionSpec.scala
@@ -0,0 +1,51 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn
+
+import com.intel.analytics.bigdl.tensor.Tensor
+import org.scalatest.{FlatSpec, Matchers}
+
+@com.intel.analytics.bigdl.tags.Parallel
+class DotProductCriterionSpec extends FlatSpec with Matchers {
+
+  "DotProductCriterion " should "give correct result with dense target" in {
+    val criterion = DotProductCriterion[Float]()
+
+    val input = Tensor[Float](Array(4, 6)).rand()
+    val target = input.clone()
+
+    val loss = criterion.forward(input, target)
+
+    val gradInput = criterion.backward(input, target)
+
+    loss should be (input.sumSquare() +- 1e-5f)
+    gradInput.almostEqual(target, 1e-5f) should be (true)
+  }
+
+  "DotProductCriterion " should "give correct result with sparse target" in {
+    val criterion = DotProductCriterion[Float]()
+
+    val input = Tensor[Float](Array(4, 6)).rand()
+    val target = Tensor.sparse(input.clone())
+
+    val loss = criterion.forward(input, target)
+
+    val gradInput = criterion.backward(input, target)
+
+    loss should be (input.sumSquare() +- 1e-5f)
+    gradInput.almostEqual(Tensor.dense(target), 1e-5f) should be (true)
+  }
+}
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenaltySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenaltySpec.scala
new file mode 100644
index 00000000000..557baf681cd
--- /dev/null
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenaltySpec.scala
@@ -0,0 +1,41 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn
+
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.utils.T
+import org.scalatest.{FlatSpec, Matchers}
+
+class NegativeEntropyPenaltySpec extends FlatSpec with Matchers {
+
+  "NegativeEntropyPenalty forward" should "be correct" in {
+    val input = Tensor[Float](T(0.5, 0.2, 0.3))
+    val m = NegativeEntropyPenalty[Float]()
+    m.forward(input) should be(Tensor[Float](T(0.5, 0.2, 0.3)))
+  }
+
+  "NegativeEntropyPenalty backward" should "be correct" in {
+    val input = Tensor[Float](T(0.5, 0.2, 0.3))
+    val grad = Tensor[Float](T(0.4, 0.2, 0.3))
+    val m = NegativeEntropyPenalty[Float]()
+    val gradInput = m.backward(input, grad)
+    def gradient(x: Double): Double = 0.01 * (math.log(x) + 1)
+    val expected = Tensor[Float](T(0.4 + gradient(0.5),
+                                   0.2 + gradient(0.2),
+                                   0.3 + gradient(0.3)))
+    gradInput.almostEqual(expected, 1e-5) should be (true)
+  }
+}
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PGCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PGCriterionSpec.scala
new file mode 100644
index 00000000000..1d9b9e27a5c
--- /dev/null
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PGCriterionSpec.scala
@@ -0,0 +1,52 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn
+
+import com.intel.analytics.bigdl.tensor.Tensor
+import com.intel.analytics.bigdl.utils.T
+import org.scalatest.{FlatSpec, Matchers}
+
+@com.intel.analytics.bigdl.tags.Parallel
+class PGCriterionSpec extends FlatSpec with Matchers {
+
+  "PGCriterion " should "give correct result with dense target" in {
+    val criterion = PGCriterion[Float]()
+
+    val input = Tensor[Float](T(0.5, 0.2, 0.3))
+    val target = Tensor[Float](T(1.0, 0.0, 0.0))
+
+    criterion.forward(input, target)
+
+    val gradInput = criterion.backward(input, target)
+    val expected = Tensor[Float](T(- 1.0/0.5 * 1.0, 0.0, 0.0))
+
+    gradInput.almostEqual(expected, 1e-5f) should be (true)
+  }
+
+  "PGCriterion " should "give correct result with sparse target" in {
+    val criterion = PGCriterion[Float]()
+
+    val input = Tensor[Float](T(0.5, 0.2, 0.3))
+    val target = Tensor.sparse(Array(Array(0)), Array(1.0f), Array(3))
+
+    criterion.forward(input, target)
+
+    val gradInput = criterion.backward(input, target)
+    val expected = Tensor[Float](T(- 1.0/0.5 * 1.0, 0.0, 0.0))
+
+    gradInput.almostEqual(expected, 1e-5f) should be (true)
+  }
+}
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala
index e253175a2bc..120f24b9df9 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala
@@ -125,4 +125,16 @@ class SparseTensorSpec  extends FlatSpec with Matchers {
     Tensor.dense(narrowed).resize(20).toArray().sum shouldEqual narrowedSum
   }
 
+  "SparseTensor dot DenseTense" should "return right result" in {
+    val values = Array.fill(30)(Random.nextFloat())
+    val sTensor = Tensor.sparse(Tensor(values, Array(6, 5)))
+
+    val dTensor = Tensor(Array(6, 5))
+
+    val sparseResult = sTensor.dot(dTensor)
+    val denseResult = dTensor.dot(Tensor.dense(sTensor))
+
+    sparseResult should be (denseResult)
+  }
+
 }
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
index 2a036faf054..e9b34886853 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
@@ -645,6 +645,12 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
     runSerializationTest(l1Penalty, input)
   }
 
+  "NegativeEntropyPenalty serializer" should "work properly" in {
+    val penalty = NegativeEntropyPenalty[Float](0.01).setName("NegativeEntropyPenalty")
+    val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat())
+    runSerializationTest(penalty, input)
+  }
+
   "LeakReLu serializer" should  "work properly" in {
     val leakyReLU = LeakyReLU[Float](0.01, true).setName("leakyReLU")
     val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat())

From d52daf32be3d22ecb3afe96f6a1bd81c0519c4b6 Mon Sep 17 00:00:00 2001
From: tosky001 
Date: Mon, 29 Jan 2018 23:40:39 +0800
Subject: [PATCH 0663/1065] add [[Kv2Tensor]] Operation (#2234)

* add [[Kv2Tensor]] Operation and corresponding unit test

* replacce for with while

* debug the kv2tensor model serializer

* add SerializationTest for Kv2Tensor operation
---
 .../bigdl/dllib/nn/ops/Kv2Tensor.scala        | 109 ++++++++++++++++++
 .../bigdl/dllib/nn/ops/Kv2TensorSpec.scala    | 101 ++++++++++++++++
 .../serializer/ModuleSerializerSpec.scala     |  14 ++-
 3 files changed, 223 insertions(+), 1 deletion(-)
 create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2Tensor.scala
 create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2TensorSpec.scala

diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2Tensor.scala
new file mode 100644
index 00000000000..cee1f70f1a3
--- /dev/null
+++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2Tensor.scala
@@ -0,0 +1,109 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn.ops
+
+import com.intel.analytics.bigdl.nn.abstractnn.Activity
+import com.intel.analytics.bigdl.tensor._
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.bigdl.utils.Table
+
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+/**
+ * Kv2Tensor operation convert a kv feature column to a SparseTensor or DenseTensor
+ *
+ * DenseTensor if transType = 0
+ * SparseTensor if  transType = 1
+ *
+ * The input contains 2 elements which are `kvTensor`, `feaLen`:
+ * kvTensor shape will be batch*1 and element is a kv string, only support one feature now
+ * depth: the length of the value set of the feature
+ *
+ * the output shape will be batch*feaLen if transType = 0
+ * the output shape will be a SparseTensor with dense shape batch*feaLen if transType = 1
+ *
+ * @param kvDelimiter The delimiter between kv pairs, default: ","
+ * @param itemDelimiter The delimiter between key and value, default: ":"
+ * @param transType The type of output tensor. default: 0
+ * @tparam T Numeric type. Parameter tensor numeric type. Only support float/double now
+ * @tparam D Numeric type. Output tensor numeric type. Only support float/double now
+ */
+
+class Kv2Tensor[T: ClassTag, D: ClassTag](
+  val kvDelimiter: String,
+  val itemDelimiter: String,
+  val transType: Int
+  )(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
+  extends Operation[Table, Tensor[D], T]{
+
+  output = Activity.allocate[Tensor[D], D]()
+
+  override def updateOutput(input: Table): Tensor[D] = {
+    val kvTensor = input[Tensor[String]](1)
+    val feaLen = input[Tensor[Int]](2).value()
+    val indices0 = new ArrayBuffer[Int]()
+    val indices1 = new ArrayBuffer[Int]()
+    val values = new ArrayBuffer[D]()
+    val rows = kvTensor.size(dim = 1)
+    val shape = Array(rows, feaLen)
+
+    var i = 1
+    while(i<=rows) {
+      val kvFeaString = kvTensor.select(1, i).valueAt(1)
+      kvFeaString.split(kvDelimiter).foreach { kv =>
+        indices0 += i-1
+        indices1 += kv.split(itemDelimiter)(0).toInt
+        ev2.getType() match {
+          case DoubleType =>
+            values += kv.split(itemDelimiter)(1).toDouble.asInstanceOf[D]
+          case FloatType =>
+            values += kv.split(itemDelimiter)(1).toFloat.asInstanceOf[D]
+        }
+      }
+      i += 1
+    }
+
+    val indices = Array(indices0.toArray, indices1.toArray)
+    val resTensor = transType match {
+      case 0 =>
+        Tensor.dense(Tensor.sparse(indices, values.toArray, shape))
+      case 1 =>
+        Tensor.sparse(indices, values.toArray, shape)
+    }
+    output = resTensor
+    output
+  }
+
+  override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = {
+    (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
+      Array[TensorNumeric[_]](ev, ev2))
+  }
+}
+
+object Kv2Tensor{
+  def apply[T: ClassTag, D: ClassTag](
+     kvDelimiter: String = ",",
+     itemDelimiter: String = ":",
+     transType: Int = 0)
+     (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Kv2Tensor[T, D]
+  = new Kv2Tensor[T, D](
+    kvDelimiter = kvDelimiter,
+    itemDelimiter = itemDelimiter,
+    transType = transType
+  )
+}
+
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2TensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2TensorSpec.scala
new file mode 100644
index 00000000000..91b7f9b7ce3
--- /dev/null
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2TensorSpec.scala
@@ -0,0 +1,101 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.intel.analytics.bigdl.nn.ops
+
+import com.intel.analytics.bigdl.tensor.{DenseType, SparseType, Tensor}
+import com.intel.analytics.bigdl.utils.{T, Table}
+import org.scalatest.{FlatSpec, Matchers}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.util.Random
+
+class Kv2TensorSpec extends FlatSpec with Matchers {
+
+  protected def randDoubles(length: Int,
+                            lp: Double = 0.0,
+                            up: Double = 1.0): Array[Double] = {
+    (1 to length).map(_ => lp + (up - lp) * Random.nextDouble()).toArray
+  }
+
+  protected def randKVMap(size: Int,
+                          numActive: Int,
+                          lp: Double = 0.0,
+                          up: Double = 1.0): Map[Int, Double] = {
+    require(numActive <= size)
+    val keys = Random.shuffle((0 until size).toList).take(numActive)
+    val values = randDoubles(numActive, lp, up)
+    keys.zip(values).toMap
+  }
+  val batchLen = 3
+  val numActive = Array(2, 3, 5)
+  val feaLen = 8
+  val originData = new ArrayBuffer[String]()
+  val originArr = new ArrayBuffer[Table]()
+  val indices0 = new ArrayBuffer[Int]()
+  val indices1 = new ArrayBuffer[Int]()
+  val values = new ArrayBuffer[Double]()
+  for (i <- 0 until batchLen) {
+    val kvMap = randKVMap(feaLen, numActive(i))
+    val kvStr = kvMap.map(data => s"${data._1}:${data._2}").mkString(",")
+    originData += kvStr
+    originArr += T(kvStr)
+    indices0 ++= ArrayBuffer.fill(numActive(i))(i)
+    val kvArr = kvMap.toArray
+    indices1 ++= kvArr.map(kv => kv._1)
+    values ++= kvArr.map(kv => kv._2)
+  }
+  val originTable = T.array(originArr.toArray)
+  val indices = Array(indices0.toArray, indices1.toArray)
+  val shape = Array(batchLen, feaLen)
+
+  "Kv2Tensor operation kvString to SparseTensor" should "work correctly" in {
+    val input =
+      T(
+        Tensor[String](originTable),
+        Tensor[Int](Array(feaLen), shape = Array[Int]())
+      )
+
+    val expectOutput =
+      Tensor.sparse[Double](
+        indices = indices,
+        values = values.toArray,
+        shape = shape
+      )
+    val output = Kv2Tensor[Double, Double](transType = 1)
+      .forward(input)
+
+    output should be(expectOutput)
+  }
+
+  "Kv2Tensor operation kvString to DenseTensor" should "work correctly" in {
+    val input =
+      T(
+        Tensor[String](originTable),
+        Tensor[Int](Array(feaLen), shape = Array[Int]())
+      )
+
+    val expectOutput =
+      Tensor.dense(Tensor.sparse[Double](
+        indices = indices,
+        values = values.toArray,
+        shape = shape
+      ))
+    val output = Kv2Tensor[Double, Double](transType = 0)
+      .forward(input)
+
+    output should be(expectOutput)
+  }
+}
diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
index e9b34886853..3f107f16d74 100644
--- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
+++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala
@@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC
 
 import scala.collection.JavaConverters._
 import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat}
-import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps}
+import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps}
 import com.intel.analytics.bigdl.nn.tf.{BiasAdd, Const, Fill, Log1p, Shape, SplitAndSelect, StrideSlice, Variable, TensorModuleWrapper}
 import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _}
 import com.intel.analytics.bigdl.optim.L2Regularizer
@@ -1787,6 +1787,18 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll
     runSerializationTest(isNan, input)
   }
 
+  "Kv2Tensor" should "work properly" in {
+    val kv2tensor = Kv2Tensor[Float, Float](
+      kvDelimiter = ",", itemDelimiter = ":", transType = 0
+    ).setName("kv2tensor")
+    val input = T(
+      Tensor[String](
+        T(T("0:0.1,1:0.2"), T("1:0.3,3:0.5"), T("2:0.15,4:0.25"))),
+      Tensor[Int](Array(5), shape = Array[Int]())
+    )
+    runSerializationTest(kv2tensor, input)
+  }
+
   "L2Loss serializer" should "work properly" in {
     val l2loss = L2Loss[Float]().setName("l2loss")
     val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat())

From 1dd10d6b644b2637961af6f57486af288689f210 Mon Sep 17 00:00:00 2001
From: Jerry Wu 
Date: Tue, 30 Jan 2018 15:11:51 +0800
Subject: [PATCH 0664/1065] reorg serialzation code hierarchy (#2244)

---
 .../dllib/utils}/serialization/Bigdl.java     | 3994 +++++++++--------
 .../bigdl/dllib/nn/BatchNormalization.scala   |    2 +-
 .../intel/analytics/bigdl/dllib/nn/Cell.scala |    2 +-
 .../intel/analytics/bigdl/dllib/nn/Echo.scala |    2 +-
 .../analytics/bigdl/dllib/nn/Graph.scala      |    2 +-
 .../analytics/bigdl/dllib/nn/MapTable.scala   |    2 +-
 .../bigdl/dllib/nn/MaskedSelect.scala         |    2 +-
 .../bigdl/dllib/nn/MultiRNNCell.scala         |    2 +-
 .../analytics/bigdl/dllib/nn/Reshape.scala    |    2 +-
 .../analytics/bigdl/dllib/nn/SReLU.scala      |    2 +-
 .../analytics/bigdl/dllib/nn/Scale.scala      |    2 +-
 .../nn/SpatialContrastiveNormalization.scala  |    2 +-
 .../nn/SpatialDivisiveNormalization.scala     |    2 +-
 .../dllib/nn/SpatialFullConvolution.scala     |    2 +-
 .../bigdl/dllib/nn/SpatialMaxPooling.scala    |    2 +-
 .../nn/SpatialSubtractiveNormalization.scala  |    2 +-
 .../analytics/bigdl/dllib/nn/Transpose.scala  |    2 +-
 .../dllib/nn/VolumetricAveragePooling.scala   |    2 +-
 .../bigdl/dllib/nn/VolumetricMaxPooling.scala |    2 +-
 .../bigdl/dllib/nn/ops/DecodeImage.scala      |    2 +-
 .../bigdl/dllib/nn/ops/ParseExample.scala     |    2 +-
 .../bigdl/dllib/nn/ops/RandomUniform.scala    |    2 +-
 .../bigdl/dllib/nn/quantized/Linear.scala     |    2 +-
 .../dllib/nn/quantized/QuantSerializer.scala  |    2 +-
 .../nn/quantized/SpatialConvolution.scala     |    2 +-
 .../quantized/SpatialDilatedConvolution.scala |    2 +-
 .../bigdl/dllib/nn/tf/StrideSlice.scala       |    2 +-
 .../dllib/utils/serializer/ModuleLoader.scala |    2 +-
 .../utils/serializer/ModuleSerializable.scala |    2 +-
 .../bigdl/dllib/utils/serializer/Types.scala  |    2 +-
 .../serializer/converters/DataConverter.scala |    4 +-
 .../converters/DataFormatConverter.scala      |    2 +-
 .../converters/InitMethodConverter.scala      |    2 +-
 .../converters/ModuleConverter.scala          |    2 +-
 .../converters/RegularizerConverter.scala     |   16 +-
 .../converters/ShapeConverter.scala           |    6 +-
 .../converters/TensorConverter.scala          |    2 +-
 .../converters/TensorStorageManager.scala     |    2 +-
 .../converters/VariableFormatConverter.scala  |    2 +-
 .../utils/serializer/DataConverterSpec.scala  |    4 +-
 .../serializer/TensorConversionSpec.scala     |    2 +-
 41 files changed, 2060 insertions(+), 2036 deletions(-)
 rename scala/dllib/src/main/java/{ => com/intel/analytics/bigdl/dllib/utils}/serialization/Bigdl.java (72%)

diff --git a/scala/dllib/src/main/java/serialization/Bigdl.java b/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java
similarity index 72%
rename from scala/dllib/src/main/java/serialization/Bigdl.java
rename to scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java
index 472d63bb08c..fb13e8ad807 100644
--- a/scala/dllib/src/main/java/serialization/Bigdl.java
+++ b/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java
@@ -1,7 +1,7 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
 // source: bigdl.proto
 
-package serialization;
+package com.intel.analytics.bigdl.serialization;
 
 public final class Bigdl {
   private Bigdl() {}
@@ -15,7 +15,7 @@ public static void registerAllExtensions(
         (com.google.protobuf.ExtensionRegistryLite) registry);
   }
   /**
-   * Protobuf enum {@code serialization.VarFormat}
+   * Protobuf enum {@code com.intel.analytics.bigdl.serialization.VarFormat}
    */
   public enum VarFormat
       implements com.google.protobuf.ProtocolMessageEnum {
@@ -158,7 +158,7 @@ public VarFormat findValueByNumber(int number) {
     }
     public static final com.google.protobuf.Descriptors.EnumDescriptor
         getDescriptor() {
-      return serialization.Bigdl.getDescriptor().getEnumTypes().get(0);
+      return com.intel.analytics.bigdl.serialization.Bigdl.getDescriptor().getEnumTypes().get(0);
     }
 
     private static final VarFormat[] VALUES = values();
@@ -181,11 +181,11 @@ private VarFormat(int value) {
       this.value = value;
     }
 
-    // @@protoc_insertion_point(enum_scope:serialization.VarFormat)
+    // @@protoc_insertion_point(enum_scope:com.intel.analytics.bigdl.serialization.VarFormat)
   }
 
   /**
-   * Protobuf enum {@code serialization.InitMethodType}
+   * Protobuf enum {@code com.intel.analytics.bigdl.serialization.InitMethodType}
    */
   public enum InitMethodType
       implements com.google.protobuf.ProtocolMessageEnum {
@@ -319,7 +319,7 @@ public InitMethodType findValueByNumber(int number) {
     }
     public static final com.google.protobuf.Descriptors.EnumDescriptor
         getDescriptor() {
-      return serialization.Bigdl.getDescriptor().getEnumTypes().get(1);
+      return com.intel.analytics.bigdl.serialization.Bigdl.getDescriptor().getEnumTypes().get(1);
     }
 
     private static final InitMethodType[] VALUES = values();
@@ -342,11 +342,11 @@ private InitMethodType(int value) {
       this.value = value;
     }
 
-    // @@protoc_insertion_point(enum_scope:serialization.InitMethodType)
+    // @@protoc_insertion_point(enum_scope:com.intel.analytics.bigdl.serialization.InitMethodType)
   }
 
   /**
-   * Protobuf enum {@code serialization.RegularizerType}
+   * Protobuf enum {@code com.intel.analytics.bigdl.serialization.RegularizerType}
    */
   public enum RegularizerType
       implements com.google.protobuf.ProtocolMessageEnum {
@@ -426,7 +426,7 @@ public RegularizerType findValueByNumber(int number) {
     }
     public static final com.google.protobuf.Descriptors.EnumDescriptor
         getDescriptor() {
-      return serialization.Bigdl.getDescriptor().getEnumTypes().get(2);
+      return com.intel.analytics.bigdl.serialization.Bigdl.getDescriptor().getEnumTypes().get(2);
     }
 
     private static final RegularizerType[] VALUES = values();
@@ -449,11 +449,11 @@ private RegularizerType(int value) {
       this.value = value;
     }
 
-    // @@protoc_insertion_point(enum_scope:serialization.RegularizerType)
+    // @@protoc_insertion_point(enum_scope:com.intel.analytics.bigdl.serialization.RegularizerType)
   }
 
   /**
-   * Protobuf enum {@code serialization.InputDataFormat}
+   * Protobuf enum {@code com.intel.analytics.bigdl.serialization.InputDataFormat}
    */
   public enum InputDataFormat
       implements com.google.protobuf.ProtocolMessageEnum {
@@ -524,7 +524,7 @@ public InputDataFormat findValueByNumber(int number) {
     }
     public static final com.google.protobuf.Descriptors.EnumDescriptor
         getDescriptor() {
-      return serialization.Bigdl.getDescriptor().getEnumTypes().get(3);
+      return com.intel.analytics.bigdl.serialization.Bigdl.getDescriptor().getEnumTypes().get(3);
     }
 
     private static final InputDataFormat[] VALUES = values();
@@ -547,11 +547,11 @@ private InputDataFormat(int value) {
       this.value = value;
     }
 
-    // @@protoc_insertion_point(enum_scope:serialization.InputDataFormat)
+    // @@protoc_insertion_point(enum_scope:com.intel.analytics.bigdl.serialization.InputDataFormat)
   }
 
   /**
-   * Protobuf enum {@code serialization.TensorType}
+   * Protobuf enum {@code com.intel.analytics.bigdl.serialization.TensorType}
    */
   public enum TensorType
       implements com.google.protobuf.ProtocolMessageEnum {
@@ -622,7 +622,7 @@ public TensorType findValueByNumber(int number) {
     }
     public static final com.google.protobuf.Descriptors.EnumDescriptor
         getDescriptor() {
-      return serialization.Bigdl.getDescriptor().getEnumTypes().get(4);
+      return com.intel.analytics.bigdl.serialization.Bigdl.getDescriptor().getEnumTypes().get(4);
     }
 
     private static final TensorType[] VALUES = values();
@@ -645,11 +645,11 @@ private TensorType(int value) {
       this.value = value;
     }
 
-    // @@protoc_insertion_point(enum_scope:serialization.TensorType)
+    // @@protoc_insertion_point(enum_scope:com.intel.analytics.bigdl.serialization.TensorType)
   }
 
   /**
-   * Protobuf enum {@code serialization.DataType}
+   * Protobuf enum {@code com.intel.analytics.bigdl.serialization.DataType}
    */
   public enum DataType
       implements com.google.protobuf.ProtocolMessageEnum {
@@ -873,7 +873,7 @@ public DataType findValueByNumber(int number) {
     }
     public static final com.google.protobuf.Descriptors.EnumDescriptor
         getDescriptor() {
-      return serialization.Bigdl.getDescriptor().getEnumTypes().get(5);
+      return com.intel.analytics.bigdl.serialization.Bigdl.getDescriptor().getEnumTypes().get(5);
     }
 
     private static final DataType[] VALUES = values();
@@ -896,11 +896,11 @@ private DataType(int value) {
       this.value = value;
     }
 
-    // @@protoc_insertion_point(enum_scope:serialization.DataType)
+    // @@protoc_insertion_point(enum_scope:com.intel.analytics.bigdl.serialization.DataType)
   }
 
   public interface BigDLModuleOrBuilder extends
-      // @@protoc_insertion_point(interface_extends:serialization.BigDLModule)
+      // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.BigDLModule)
       com.google.protobuf.MessageOrBuilder {
 
     /**
@@ -926,24 +926,24 @@ public interface BigDLModuleOrBuilder extends
      * sub modules
      *
* - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - java.util.List + java.util.List getSubModulesList(); /** * 
      * sub modules
      *
* - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - serialization.Bigdl.BigDLModule getSubModules(int index); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getSubModules(int index); /** * 
      * sub modules
      *
* - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ int getSubModulesCount(); /** @@ -951,18 +951,18 @@ public interface BigDLModuleOrBuilder extends * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - java.util.List + java.util.List getSubModulesOrBuilderList(); /** * 
      * sub modules
      *
* - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( int index); /** @@ -970,7 +970,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ boolean hasWeight(); /** @@ -978,24 +978,24 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - serialization.Bigdl.BigDLTensor getWeight(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getWeight(); /** * 
      * weight for each layer, serialized data are stored as either float or double
      *
* - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder(); /** * 
      * bias for each layer
      *
* - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ boolean hasBias(); /** @@ -1003,17 +1003,17 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - serialization.Bigdl.BigDLTensor getBias(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getBias(); /** * 
      * bias for each layer
      *
* - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder(); /** * 
@@ -1108,7 +1108,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder(
      * module attributes
      *
* - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ int getAttrCount(); /** @@ -1116,7 +1116,7 @@ serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ boolean containsAttr( java.lang.String key); @@ -1124,37 +1124,37 @@ boolean containsAttr( * Use {@link #getAttrMap()} instead. */ @java.lang.Deprecated - java.util.Map + java.util.Map getAttr(); /** * 
      * module attributes
      *
* - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - java.util.Map + java.util.Map getAttrMap(); /** * 
      * module attributes
      *
* - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - serialization.Bigdl.AttrValue getAttrOrDefault( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrDefault( java.lang.String key, - serialization.Bigdl.AttrValue defaultValue); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue defaultValue); /** * 
      * module attributes
      *
* - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - serialization.Bigdl.AttrValue getAttrOrThrow( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( java.lang.String key); /** @@ -1216,7 +1216,7 @@ serialization.Bigdl.AttrValue getAttrOrThrow( * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ boolean hasInputShape(); /** @@ -1224,41 +1224,41 @@ serialization.Bigdl.AttrValue getAttrOrThrow( * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - serialization.Bigdl.Shape getInputShape(); + com.intel.analytics.bigdl.serialization.Bigdl.Shape getInputShape(); /** * 
      * input shape
      *
* - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder(); /** * 
      *output shape
      *
* - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - java.util.List + java.util.List getOutputShapeList(); /** * 
      *output shape
      *
* - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - serialization.Bigdl.Shape getOutputShape(int index); + com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape(int index); /** * 
      *output shape
      *
* - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ int getOutputShapeCount(); /** @@ -1266,26 +1266,26 @@ serialization.Bigdl.AttrValue getAttrOrThrow( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - java.util.List + java.util.List getOutputShapeOrBuilderList(); /** * 
      *output shape
      *
* - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( int index); } /** - * Protobuf type {@code serialization.BigDLModule} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.BigDLModule} */ public static final class BigDLModule extends com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:serialization.BigDLModule) + // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.BigDLModule) BigDLModuleOrBuilder { private static final long serialVersionUID = 0L; // Use BigDLModule.newBuilder() to construct. @@ -1341,19 +1341,19 @@ private BigDLModule( } case 18: { if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - subModules_ = new java.util.ArrayList(); + subModules_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000002; } subModules_.add( - input.readMessage(serialization.Bigdl.BigDLModule.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.parser(), extensionRegistry)); break; } case 26: { - serialization.Bigdl.BigDLTensor.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder subBuilder = null; if (weight_ != null) { subBuilder = weight_.toBuilder(); } - weight_ = input.readMessage(serialization.Bigdl.BigDLTensor.parser(), extensionRegistry); + weight_ = input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(weight_); weight_ = subBuilder.buildPartial(); @@ -1362,11 +1362,11 @@ private BigDLModule( break; } case 34: { - serialization.Bigdl.BigDLTensor.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder subBuilder = null; if (bias_ != null) { subBuilder = bias_.toBuilder(); } - bias_ = input.readMessage(serialization.Bigdl.BigDLTensor.parser(), extensionRegistry); + bias_ = input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(bias_); bias_ = subBuilder.buildPartial(); @@ -1404,7 +1404,7 @@ private BigDLModule( AttrDefaultEntryHolder.defaultEntry); mutable_bitField0_ |= 0x00000080; } - com.google.protobuf.MapEntry + com.google.protobuf.MapEntry attr__ = input.readMessage( AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); attr_.getMutableMap().put( @@ -1434,11 +1434,11 @@ private BigDLModule( break; } case 106: { - serialization.Bigdl.Shape.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder subBuilder = null; if (inputShape_ != null) { subBuilder = inputShape_.toBuilder(); } - inputShape_ = input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry); + inputShape_ = input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Shape.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(inputShape_); inputShape_ = subBuilder.buildPartial(); @@ -1448,11 +1448,11 @@ private BigDLModule( } case 114: { if (!((mutable_bitField0_ & 0x00002000) == 0x00002000)) { - outputShape_ = new java.util.ArrayList(); + outputShape_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00002000; } outputShape_.add( - input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Shape.parser(), extensionRegistry)); break; } } @@ -1481,7 +1481,7 @@ private BigDLModule( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_BigDLModule_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor; } @SuppressWarnings({"rawtypes"}) @@ -1497,9 +1497,9 @@ protected com.google.protobuf.MapField internalGetMapField( } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_BigDLModule_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.BigDLModule.class, serialization.Bigdl.BigDLModule.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.class, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder.class); } private int bitField0_; @@ -1546,15 +1546,15 @@ public java.lang.String getName() { } public static final int SUBMODULES_FIELD_NUMBER = 2; - private java.util.List subModules_; + private java.util.List subModules_; /** * 
      * sub modules
      *
* - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public java.util.List getSubModulesList() { + public java.util.List getSubModulesList() { return subModules_; } /** @@ -1562,9 +1562,9 @@ public java.util.List getSubModulesList() { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public java.util.List + public java.util.List getSubModulesOrBuilderList() { return subModules_; } @@ -1573,7 +1573,7 @@ public java.util.List getSubModulesList() { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public int getSubModulesCount() { return subModules_.size(); @@ -1583,9 +1583,9 @@ public int getSubModulesCount() { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public serialization.Bigdl.BigDLModule getSubModules(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getSubModules(int index) { return subModules_.get(index); } /** @@ -1593,21 +1593,21 @@ public serialization.Bigdl.BigDLModule getSubModules(int index) { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( int index) { return subModules_.get(index); } public static final int WEIGHT_FIELD_NUMBER = 3; - private serialization.Bigdl.BigDLTensor weight_; + private com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor weight_; /** * 
      * weight for each layer, serialized data are stored as either float or double
      *
* - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ public boolean hasWeight() { return weight_ != null; @@ -1617,30 +1617,30 @@ public boolean hasWeight() { * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - public serialization.Bigdl.BigDLTensor getWeight() { - return weight_ == null ? serialization.Bigdl.BigDLTensor.getDefaultInstance() : weight_; + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getWeight() { + return weight_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance() : weight_; } /** * 
      * weight for each layer, serialized data are stored as either float or double
      *
* - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { return getWeight(); } public static final int BIAS_FIELD_NUMBER = 4; - private serialization.Bigdl.BigDLTensor bias_; + private com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor bias_; /** * 
      * bias for each layer
      *
* - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ public boolean hasBias() { return bias_ != null; @@ -1650,19 +1650,19 @@ public boolean hasBias() { * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - public serialization.Bigdl.BigDLTensor getBias() { - return bias_ == null ? serialization.Bigdl.BigDLTensor.getDefaultInstance() : bias_; + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getBias() { + return bias_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance() : bias_; } /** * 
      * bias for each layer
      *
* - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - public serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder() { return getBias(); } @@ -1801,18 +1801,18 @@ public java.lang.String getModuleType() { public static final int ATTR_FIELD_NUMBER = 8; private static final class AttrDefaultEntryHolder { static final com.google.protobuf.MapEntry< - java.lang.String, serialization.Bigdl.AttrValue> defaultEntry = + java.lang.String, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue> defaultEntry = com.google.protobuf.MapEntry - .newDefaultInstance( - serialization.Bigdl.internal_static_serialization_BigDLModule_AttrEntry_descriptor, + .newDefaultInstance( + com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_descriptor, com.google.protobuf.WireFormat.FieldType.STRING, "", com.google.protobuf.WireFormat.FieldType.MESSAGE, - serialization.Bigdl.AttrValue.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()); } private com.google.protobuf.MapField< - java.lang.String, serialization.Bigdl.AttrValue> attr_; - private com.google.protobuf.MapField + java.lang.String, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue> attr_; + private com.google.protobuf.MapField internalGetAttr() { if (attr_ == null) { return com.google.protobuf.MapField.emptyMapField( @@ -1829,7 +1829,7 @@ public int getAttrCount() { * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ public boolean containsAttr( @@ -1841,7 +1841,7 @@ public boolean containsAttr( * Use {@link #getAttrMap()} instead. */ @java.lang.Deprecated - public java.util.Map getAttr() { + public java.util.Map getAttr() { return getAttrMap(); } /** @@ -1849,10 +1849,10 @@ public java.util.Map getAttr() * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - public java.util.Map getAttrMap() { + public java.util.Map getAttrMap() { return internalGetAttr().getMap(); } /** @@ -1860,14 +1860,14 @@ public java.util.Map getAttrMap * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - public serialization.Bigdl.AttrValue getAttrOrDefault( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrDefault( java.lang.String key, - serialization.Bigdl.AttrValue defaultValue) { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue defaultValue) { if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = + java.util.Map map = internalGetAttr().getMap(); return map.containsKey(key) ? map.get(key) : defaultValue; } @@ -1876,13 +1876,13 @@ public serialization.Bigdl.AttrValue getAttrOrDefault( * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - public serialization.Bigdl.AttrValue getAttrOrThrow( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = + java.util.Map map = internalGetAttr().getMap(); if (!map.containsKey(key)) { throw new java.lang.IllegalArgumentException(); @@ -2001,13 +2001,13 @@ public int getId() { } public static final int INPUTSHAPE_FIELD_NUMBER = 13; - private serialization.Bigdl.Shape inputShape_; + private com.intel.analytics.bigdl.serialization.Bigdl.Shape inputShape_; /** * 
      * input shape
      *
* - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ public boolean hasInputShape() { return inputShape_ != null; @@ -2017,32 +2017,32 @@ public boolean hasInputShape() { * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - public serialization.Bigdl.Shape getInputShape() { - return inputShape_ == null ? serialization.Bigdl.Shape.getDefaultInstance() : inputShape_; + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getInputShape() { + return inputShape_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance() : inputShape_; } /** * 
      * input shape
      *
* - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - public serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder() { return getInputShape(); } public static final int OUTPUTSHAPE_FIELD_NUMBER = 14; - private java.util.List outputShape_; + private java.util.List outputShape_; /** * 
      *output shape
      *
* - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public java.util.List getOutputShapeList() { + public java.util.List getOutputShapeList() { return outputShape_; } /** @@ -2050,9 +2050,9 @@ public java.util.List getOutputShapeList() { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public java.util.List + public java.util.List getOutputShapeOrBuilderList() { return outputShape_; } @@ -2061,7 +2061,7 @@ public java.util.List getOutputShapeList() { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public int getOutputShapeCount() { return outputShape_.size(); @@ -2071,9 +2071,9 @@ public int getOutputShapeCount() { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public serialization.Bigdl.Shape getOutputShape(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape(int index) { return outputShape_.get(index); } /** @@ -2081,9 +2081,9 @@ public serialization.Bigdl.Shape getOutputShape(int index) { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( int index) { return outputShape_.get(index); } @@ -2187,9 +2187,9 @@ public int getSerializedSize() { if (!getModuleTypeBytes().isEmpty()) { size += com.google.protobuf.GeneratedMessageV3.computeStringSize(7, moduleType_); } - for (java.util.Map.Entry entry + for (java.util.Map.Entry entry : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry + com.google.protobuf.MapEntry attr__ = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() .setKey(entry.getKey()) .setValue(entry.getValue()) @@ -2229,10 +2229,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof serialization.Bigdl.BigDLModule)) { + if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule)) { return super.equals(obj); } - serialization.Bigdl.BigDLModule other = (serialization.Bigdl.BigDLModule) obj; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule other = (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) obj; boolean result = true; result = result && getName() @@ -2333,69 +2333,69 @@ public int hashCode() { return hash; } - public static serialization.Bigdl.BigDLModule parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.BigDLModule parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.BigDLModule parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.BigDLModule parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.BigDLModule parseFrom(byte[] data) + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.BigDLModule parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.BigDLModule parseFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.BigDLModule parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.BigDLModule parseDelimitedFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static serialization.Bigdl.BigDLModule parseDelimitedFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.BigDLModule parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.BigDLModule parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -2407,7 +2407,7 @@ public static serialization.Bigdl.BigDLModule parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(serialization.Bigdl.BigDLModule prototype) { + public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -2422,15 +2422,15 @@ protected Builder newBuilderForType( return builder; } /** - * Protobuf type {@code serialization.BigDLModule} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.BigDLModule} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:serialization.BigDLModule) - serialization.Bigdl.BigDLModuleOrBuilder { + // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.BigDLModule) + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_BigDLModule_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor; } @SuppressWarnings({"rawtypes"}) @@ -2457,12 +2457,12 @@ protected com.google.protobuf.MapField internalGetMutableMapField( } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_BigDLModule_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.BigDLModule.class, serialization.Bigdl.BigDLModule.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.class, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder.class); } - // Construct using serialization.Bigdl.BigDLModule.newBuilder() + // Construct using com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -2533,23 +2533,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return serialization.Bigdl.internal_static_serialization_BigDLModule_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor; } - public serialization.Bigdl.BigDLModule getDefaultInstanceForType() { - return serialization.Bigdl.BigDLModule.getDefaultInstance(); + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getDefaultInstanceForType() { + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance(); } - public serialization.Bigdl.BigDLModule build() { - serialization.Bigdl.BigDLModule result = buildPartial(); + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule build() { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public serialization.Bigdl.BigDLModule buildPartial() { - serialization.Bigdl.BigDLModule result = new serialization.Bigdl.BigDLModule(this); + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule buildPartial() { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule result = new com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.name_ = name_; @@ -2635,16 +2635,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof serialization.Bigdl.BigDLModule) { - return mergeFrom((serialization.Bigdl.BigDLModule)other); + if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) { + return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(serialization.Bigdl.BigDLModule other) { - if (other == serialization.Bigdl.BigDLModule.getDefaultInstance()) return this; + public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule other) { + if (other == com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance()) return this; if (!other.getName().isEmpty()) { name_ = other.name_; onChanged(); @@ -2763,11 +2763,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - serialization.Bigdl.BigDLModule parsedMessage = null; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (serialization.Bigdl.BigDLModule) e.getUnfinishedMessage(); + parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -2867,26 +2867,26 @@ public Builder setNameBytes( return this; } - private java.util.List subModules_ = + private java.util.List subModules_ = java.util.Collections.emptyList(); private void ensureSubModulesIsMutable() { if (!((bitField0_ & 0x00000002) == 0x00000002)) { - subModules_ = new java.util.ArrayList(subModules_); + subModules_ = new java.util.ArrayList(subModules_); bitField0_ |= 0x00000002; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> subModulesBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder> subModulesBuilder_; /** * 
        * sub modules
        *
* - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public java.util.List getSubModulesList() { + public java.util.List getSubModulesList() { if (subModulesBuilder_ == null) { return java.util.Collections.unmodifiableList(subModules_); } else { @@ -2898,7 +2898,7 @@ public java.util.List getSubModulesList() { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public int getSubModulesCount() { if (subModulesBuilder_ == null) { @@ -2912,9 +2912,9 @@ public int getSubModulesCount() { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public serialization.Bigdl.BigDLModule getSubModules(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getSubModules(int index) { if (subModulesBuilder_ == null) { return subModules_.get(index); } else { @@ -2926,10 +2926,10 @@ public serialization.Bigdl.BigDLModule getSubModules(int index) { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public Builder setSubModules( - int index, serialization.Bigdl.BigDLModule value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule value) { if (subModulesBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -2947,10 +2947,10 @@ public Builder setSubModules( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public Builder setSubModules( - int index, serialization.Bigdl.BigDLModule.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder builderForValue) { if (subModulesBuilder_ == null) { ensureSubModulesIsMutable(); subModules_.set(index, builderForValue.build()); @@ -2965,9 +2965,9 @@ public Builder setSubModules( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public Builder addSubModules(serialization.Bigdl.BigDLModule value) { + public Builder addSubModules(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule value) { if (subModulesBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -2985,10 +2985,10 @@ public Builder addSubModules(serialization.Bigdl.BigDLModule value) { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public Builder addSubModules( - int index, serialization.Bigdl.BigDLModule value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule value) { if (subModulesBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -3006,10 +3006,10 @@ public Builder addSubModules( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public Builder addSubModules( - serialization.Bigdl.BigDLModule.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder builderForValue) { if (subModulesBuilder_ == null) { ensureSubModulesIsMutable(); subModules_.add(builderForValue.build()); @@ -3024,10 +3024,10 @@ public Builder addSubModules( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public Builder addSubModules( - int index, serialization.Bigdl.BigDLModule.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder builderForValue) { if (subModulesBuilder_ == null) { ensureSubModulesIsMutable(); subModules_.add(index, builderForValue.build()); @@ -3042,10 +3042,10 @@ public Builder addSubModules( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public Builder addAllSubModules( - java.lang.Iterable values) { + java.lang.Iterable values) { if (subModulesBuilder_ == null) { ensureSubModulesIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -3061,7 +3061,7 @@ public Builder addAllSubModules( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public Builder clearSubModules() { if (subModulesBuilder_ == null) { @@ -3078,7 +3078,7 @@ public Builder clearSubModules() { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ public Builder removeSubModules(int index) { if (subModulesBuilder_ == null) { @@ -3095,9 +3095,9 @@ public Builder removeSubModules(int index) { * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public serialization.Bigdl.BigDLModule.Builder getSubModulesBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder getSubModulesBuilder( int index) { return getSubModulesFieldBuilder().getBuilder(index); } @@ -3106,9 +3106,9 @@ public serialization.Bigdl.BigDLModule.Builder getSubModulesBuilder( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( int index) { if (subModulesBuilder_ == null) { return subModules_.get(index); } else { @@ -3120,9 +3120,9 @@ public serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public java.util.List + public java.util.List getSubModulesOrBuilderList() { if (subModulesBuilder_ != null) { return subModulesBuilder_.getMessageOrBuilderList(); @@ -3135,41 +3135,41 @@ public serialization.Bigdl.BigDLModuleOrBuilder getSubModulesOrBuilder( * sub modules * * - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public serialization.Bigdl.BigDLModule.Builder addSubModulesBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder addSubModulesBuilder() { return getSubModulesFieldBuilder().addBuilder( - serialization.Bigdl.BigDLModule.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance()); } /** * 
        * sub modules
        *
* - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public serialization.Bigdl.BigDLModule.Builder addSubModulesBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder addSubModulesBuilder( int index) { return getSubModulesFieldBuilder().addBuilder( - index, serialization.Bigdl.BigDLModule.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance()); } /** * 
        * sub modules
        *
* - * repeated .serialization.BigDLModule subModules = 2; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule subModules = 2; */ - public java.util.List + public java.util.List getSubModulesBuilderList() { return getSubModulesFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder> getSubModulesFieldBuilder() { if (subModulesBuilder_ == null) { subModulesBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder>( subModules_, ((bitField0_ & 0x00000002) == 0x00000002), getParentForChildren(), @@ -3179,15 +3179,15 @@ public serialization.Bigdl.BigDLModule.Builder addSubModulesBuilder( return subModulesBuilder_; } - private serialization.Bigdl.BigDLTensor weight_ = null; + private com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor weight_ = null; private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> weightBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> weightBuilder_; /** * 
        * weight for each layer, serialized data are stored as either float or double
        *
* - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ public boolean hasWeight() { return weightBuilder_ != null || weight_ != null; @@ -3197,11 +3197,11 @@ public boolean hasWeight() { * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - public serialization.Bigdl.BigDLTensor getWeight() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getWeight() { if (weightBuilder_ == null) { - return weight_ == null ? serialization.Bigdl.BigDLTensor.getDefaultInstance() : weight_; + return weight_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance() : weight_; } else { return weightBuilder_.getMessage(); } @@ -3211,9 +3211,9 @@ public serialization.Bigdl.BigDLTensor getWeight() { * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - public Builder setWeight(serialization.Bigdl.BigDLTensor value) { + public Builder setWeight(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (weightBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -3231,10 +3231,10 @@ public Builder setWeight(serialization.Bigdl.BigDLTensor value) { * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ public Builder setWeight( - serialization.Bigdl.BigDLTensor.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { if (weightBuilder_ == null) { weight_ = builderForValue.build(); onChanged(); @@ -3249,13 +3249,13 @@ public Builder setWeight( * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - public Builder mergeWeight(serialization.Bigdl.BigDLTensor value) { + public Builder mergeWeight(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (weightBuilder_ == null) { if (weight_ != null) { weight_ = - serialization.Bigdl.BigDLTensor.newBuilder(weight_).mergeFrom(value).buildPartial(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.newBuilder(weight_).mergeFrom(value).buildPartial(); } else { weight_ = value; } @@ -3271,7 +3271,7 @@ public Builder mergeWeight(serialization.Bigdl.BigDLTensor value) { * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ public Builder clearWeight() { if (weightBuilder_ == null) { @@ -3289,9 +3289,9 @@ public Builder clearWeight() { * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - public serialization.Bigdl.BigDLTensor.Builder getWeightBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getWeightBuilder() { onChanged(); return getWeightFieldBuilder().getBuilder(); @@ -3301,14 +3301,14 @@ public serialization.Bigdl.BigDLTensor.Builder getWeightBuilder() { * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ - public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { if (weightBuilder_ != null) { return weightBuilder_.getMessageOrBuilder(); } else { return weight_ == null ? - serialization.Bigdl.BigDLTensor.getDefaultInstance() : weight_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance() : weight_; } } /** @@ -3316,14 +3316,14 @@ public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { * weight for each layer, serialized data are stored as either float or double * * - * .serialization.BigDLTensor weight = 3; + * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> getWeightFieldBuilder() { if (weightBuilder_ == null) { weightBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder>( getWeight(), getParentForChildren(), isClean()); @@ -3332,15 +3332,15 @@ public serialization.Bigdl.BigDLTensorOrBuilder getWeightOrBuilder() { return weightBuilder_; } - private serialization.Bigdl.BigDLTensor bias_ = null; + private com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor bias_ = null; private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> biasBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> biasBuilder_; /** * 
        * bias for each layer
        *
* - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ public boolean hasBias() { return biasBuilder_ != null || bias_ != null; @@ -3350,11 +3350,11 @@ public boolean hasBias() { * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - public serialization.Bigdl.BigDLTensor getBias() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getBias() { if (biasBuilder_ == null) { - return bias_ == null ? serialization.Bigdl.BigDLTensor.getDefaultInstance() : bias_; + return bias_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance() : bias_; } else { return biasBuilder_.getMessage(); } @@ -3364,9 +3364,9 @@ public serialization.Bigdl.BigDLTensor getBias() { * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - public Builder setBias(serialization.Bigdl.BigDLTensor value) { + public Builder setBias(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (biasBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -3384,10 +3384,10 @@ public Builder setBias(serialization.Bigdl.BigDLTensor value) { * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ public Builder setBias( - serialization.Bigdl.BigDLTensor.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { if (biasBuilder_ == null) { bias_ = builderForValue.build(); onChanged(); @@ -3402,13 +3402,13 @@ public Builder setBias( * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - public Builder mergeBias(serialization.Bigdl.BigDLTensor value) { + public Builder mergeBias(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (biasBuilder_ == null) { if (bias_ != null) { bias_ = - serialization.Bigdl.BigDLTensor.newBuilder(bias_).mergeFrom(value).buildPartial(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.newBuilder(bias_).mergeFrom(value).buildPartial(); } else { bias_ = value; } @@ -3424,7 +3424,7 @@ public Builder mergeBias(serialization.Bigdl.BigDLTensor value) { * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ public Builder clearBias() { if (biasBuilder_ == null) { @@ -3442,9 +3442,9 @@ public Builder clearBias() { * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - public serialization.Bigdl.BigDLTensor.Builder getBiasBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getBiasBuilder() { onChanged(); return getBiasFieldBuilder().getBuilder(); @@ -3454,14 +3454,14 @@ public serialization.Bigdl.BigDLTensor.Builder getBiasBuilder() { * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ - public serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder() { if (biasBuilder_ != null) { return biasBuilder_.getMessageOrBuilder(); } else { return bias_ == null ? - serialization.Bigdl.BigDLTensor.getDefaultInstance() : bias_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance() : bias_; } } /** @@ -3469,14 +3469,14 @@ public serialization.Bigdl.BigDLTensorOrBuilder getBiasOrBuilder() { * bias for each layer * * - * .serialization.BigDLTensor bias = 4; + * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> getBiasFieldBuilder() { if (biasBuilder_ == null) { biasBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder>( getBias(), getParentForChildren(), isClean()); @@ -3835,8 +3835,8 @@ public Builder setModuleTypeBytes( } private com.google.protobuf.MapField< - java.lang.String, serialization.Bigdl.AttrValue> attr_; - private com.google.protobuf.MapField + java.lang.String, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue> attr_; + private com.google.protobuf.MapField internalGetAttr() { if (attr_ == null) { return com.google.protobuf.MapField.emptyMapField( @@ -3844,7 +3844,7 @@ public Builder setModuleTypeBytes( } return attr_; } - private com.google.protobuf.MapField + private com.google.protobuf.MapField internalGetMutableAttr() { onChanged();; if (attr_ == null) { @@ -3865,7 +3865,7 @@ public int getAttrCount() { * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ public boolean containsAttr( @@ -3877,7 +3877,7 @@ public boolean containsAttr( * Use {@link #getAttrMap()} instead. */ @java.lang.Deprecated - public java.util.Map getAttr() { + public java.util.Map getAttr() { return getAttrMap(); } /** @@ -3885,10 +3885,10 @@ public java.util.Map getAttr() * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - public java.util.Map getAttrMap() { + public java.util.Map getAttrMap() { return internalGetAttr().getMap(); } /** @@ -3896,14 +3896,14 @@ public java.util.Map getAttrMap * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - public serialization.Bigdl.AttrValue getAttrOrDefault( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrDefault( java.lang.String key, - serialization.Bigdl.AttrValue defaultValue) { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue defaultValue) { if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = + java.util.Map map = internalGetAttr().getMap(); return map.containsKey(key) ? map.get(key) : defaultValue; } @@ -3912,13 +3912,13 @@ public serialization.Bigdl.AttrValue getAttrOrDefault( * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ - public serialization.Bigdl.AttrValue getAttrOrThrow( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = + java.util.Map map = internalGetAttr().getMap(); if (!map.containsKey(key)) { throw new java.lang.IllegalArgumentException(); @@ -3936,7 +3936,7 @@ public Builder clearAttr() { * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ public Builder removeAttr( @@ -3950,7 +3950,7 @@ public Builder removeAttr( * Use alternate mutation accessors instead. */ @java.lang.Deprecated - public java.util.Map + public java.util.Map getMutableAttr() { return internalGetMutableAttr().getMutableMap(); } @@ -3959,11 +3959,11 @@ public Builder removeAttr( * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ public Builder putAttr( java.lang.String key, - serialization.Bigdl.AttrValue value) { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { if (key == null) { throw new java.lang.NullPointerException(); } if (value == null) { throw new java.lang.NullPointerException(); } internalGetMutableAttr().getMutableMap() @@ -3975,11 +3975,11 @@ public Builder putAttr( * module attributes * * - * map<string, .serialization.AttrValue> attr = 8; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 8; */ public Builder putAllAttr( - java.util.Map values) { + java.util.Map values) { internalGetMutableAttr().getMutableMap() .putAll(values); return this; @@ -4239,15 +4239,15 @@ public Builder clearId() { return this; } - private serialization.Bigdl.Shape inputShape_ = null; + private com.intel.analytics.bigdl.serialization.Bigdl.Shape inputShape_ = null; private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> inputShapeBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> inputShapeBuilder_; /** * 
        * input shape
        *
* - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ public boolean hasInputShape() { return inputShapeBuilder_ != null || inputShape_ != null; @@ -4257,11 +4257,11 @@ public boolean hasInputShape() { * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - public serialization.Bigdl.Shape getInputShape() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getInputShape() { if (inputShapeBuilder_ == null) { - return inputShape_ == null ? serialization.Bigdl.Shape.getDefaultInstance() : inputShape_; + return inputShape_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance() : inputShape_; } else { return inputShapeBuilder_.getMessage(); } @@ -4271,9 +4271,9 @@ public serialization.Bigdl.Shape getInputShape() { * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - public Builder setInputShape(serialization.Bigdl.Shape value) { + public Builder setInputShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (inputShapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -4291,10 +4291,10 @@ public Builder setInputShape(serialization.Bigdl.Shape value) { * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ public Builder setInputShape( - serialization.Bigdl.Shape.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (inputShapeBuilder_ == null) { inputShape_ = builderForValue.build(); onChanged(); @@ -4309,13 +4309,13 @@ public Builder setInputShape( * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - public Builder mergeInputShape(serialization.Bigdl.Shape value) { + public Builder mergeInputShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (inputShapeBuilder_ == null) { if (inputShape_ != null) { inputShape_ = - serialization.Bigdl.Shape.newBuilder(inputShape_).mergeFrom(value).buildPartial(); + com.intel.analytics.bigdl.serialization.Bigdl.Shape.newBuilder(inputShape_).mergeFrom(value).buildPartial(); } else { inputShape_ = value; } @@ -4331,7 +4331,7 @@ public Builder mergeInputShape(serialization.Bigdl.Shape value) { * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ public Builder clearInputShape() { if (inputShapeBuilder_ == null) { @@ -4349,9 +4349,9 @@ public Builder clearInputShape() { * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - public serialization.Bigdl.Shape.Builder getInputShapeBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getInputShapeBuilder() { onChanged(); return getInputShapeFieldBuilder().getBuilder(); @@ -4361,14 +4361,14 @@ public serialization.Bigdl.Shape.Builder getInputShapeBuilder() { * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ - public serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder() { if (inputShapeBuilder_ != null) { return inputShapeBuilder_.getMessageOrBuilder(); } else { return inputShape_ == null ? - serialization.Bigdl.Shape.getDefaultInstance() : inputShape_; + com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance() : inputShape_; } } /** @@ -4376,14 +4376,14 @@ public serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder() { * input shape * * - * .serialization.Shape inputShape = 13; + * .com.intel.analytics.bigdl.serialization.Shape inputShape = 13; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> getInputShapeFieldBuilder() { if (inputShapeBuilder_ == null) { inputShapeBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder>( getInputShape(), getParentForChildren(), isClean()); @@ -4392,26 +4392,26 @@ public serialization.Bigdl.ShapeOrBuilder getInputShapeOrBuilder() { return inputShapeBuilder_; } - private java.util.List outputShape_ = + private java.util.List outputShape_ = java.util.Collections.emptyList(); private void ensureOutputShapeIsMutable() { if (!((bitField0_ & 0x00002000) == 0x00002000)) { - outputShape_ = new java.util.ArrayList(outputShape_); + outputShape_ = new java.util.ArrayList(outputShape_); bitField0_ |= 0x00002000; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> outputShapeBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> outputShapeBuilder_; /** * 
        *output shape
        *
* - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public java.util.List getOutputShapeList() { + public java.util.List getOutputShapeList() { if (outputShapeBuilder_ == null) { return java.util.Collections.unmodifiableList(outputShape_); } else { @@ -4423,7 +4423,7 @@ public java.util.List getOutputShapeList() { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public int getOutputShapeCount() { if (outputShapeBuilder_ == null) { @@ -4437,9 +4437,9 @@ public int getOutputShapeCount() { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public serialization.Bigdl.Shape getOutputShape(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape(int index) { if (outputShapeBuilder_ == null) { return outputShape_.get(index); } else { @@ -4451,10 +4451,10 @@ public serialization.Bigdl.Shape getOutputShape(int index) { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder setOutputShape( - int index, serialization.Bigdl.Shape value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (outputShapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -4472,10 +4472,10 @@ public Builder setOutputShape( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder setOutputShape( - int index, serialization.Bigdl.Shape.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (outputShapeBuilder_ == null) { ensureOutputShapeIsMutable(); outputShape_.set(index, builderForValue.build()); @@ -4490,9 +4490,9 @@ public Builder setOutputShape( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public Builder addOutputShape(serialization.Bigdl.Shape value) { + public Builder addOutputShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (outputShapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -4510,10 +4510,10 @@ public Builder addOutputShape(serialization.Bigdl.Shape value) { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder addOutputShape( - int index, serialization.Bigdl.Shape value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (outputShapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -4531,10 +4531,10 @@ public Builder addOutputShape( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder addOutputShape( - serialization.Bigdl.Shape.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (outputShapeBuilder_ == null) { ensureOutputShapeIsMutable(); outputShape_.add(builderForValue.build()); @@ -4549,10 +4549,10 @@ public Builder addOutputShape( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder addOutputShape( - int index, serialization.Bigdl.Shape.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (outputShapeBuilder_ == null) { ensureOutputShapeIsMutable(); outputShape_.add(index, builderForValue.build()); @@ -4567,10 +4567,10 @@ public Builder addOutputShape( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder addAllOutputShape( - java.lang.Iterable values) { + java.lang.Iterable values) { if (outputShapeBuilder_ == null) { ensureOutputShapeIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -4586,7 +4586,7 @@ public Builder addAllOutputShape( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder clearOutputShape() { if (outputShapeBuilder_ == null) { @@ -4603,7 +4603,7 @@ public Builder clearOutputShape() { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder removeOutputShape(int index) { if (outputShapeBuilder_ == null) { @@ -4620,9 +4620,9 @@ public Builder removeOutputShape(int index) { *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public serialization.Bigdl.Shape.Builder getOutputShapeBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getOutputShapeBuilder( int index) { return getOutputShapeFieldBuilder().getBuilder(index); } @@ -4631,9 +4631,9 @@ public serialization.Bigdl.Shape.Builder getOutputShapeBuilder( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( int index) { if (outputShapeBuilder_ == null) { return outputShape_.get(index); } else { @@ -4645,9 +4645,9 @@ public serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public java.util.List + public java.util.List getOutputShapeOrBuilderList() { if (outputShapeBuilder_ != null) { return outputShapeBuilder_.getMessageOrBuilderList(); @@ -4660,41 +4660,41 @@ public serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( *output shape * * - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public serialization.Bigdl.Shape.Builder addOutputShapeBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder addOutputShapeBuilder() { return getOutputShapeFieldBuilder().addBuilder( - serialization.Bigdl.Shape.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()); } /** * 
        *output shape
        *
* - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public serialization.Bigdl.Shape.Builder addOutputShapeBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder addOutputShapeBuilder( int index) { return getOutputShapeFieldBuilder().addBuilder( - index, serialization.Bigdl.Shape.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()); } /** * 
        *output shape
        *
* - * repeated .serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public java.util.List + public java.util.List getOutputShapeBuilderList() { return getOutputShapeFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> getOutputShapeFieldBuilder() { if (outputShapeBuilder_ == null) { outputShapeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder>( outputShape_, ((bitField0_ & 0x00002000) == 0x00002000), getParentForChildren(), @@ -4714,16 +4714,16 @@ public final Builder mergeUnknownFields( } - // @@protoc_insertion_point(builder_scope:serialization.BigDLModule) + // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.BigDLModule) } - // @@protoc_insertion_point(class_scope:serialization.BigDLModule) - private static final serialization.Bigdl.BigDLModule DEFAULT_INSTANCE; + // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.BigDLModule) + private static final com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new serialization.Bigdl.BigDLModule(); + DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule(); } - public static serialization.Bigdl.BigDLModule getDefaultInstance() { + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -4746,24 +4746,24 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public serialization.Bigdl.BigDLModule getDefaultInstanceForType() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getDefaultInstanceForType() { return DEFAULT_INSTANCE; } } public interface InitMethodOrBuilder extends - // @@protoc_insertion_point(interface_extends:serialization.InitMethod) + // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.InitMethod) com.google.protobuf.MessageOrBuilder { /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ int getMethodTypeValue(); /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ - serialization.Bigdl.InitMethodType getMethodType(); + com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType getMethodType(); /** * repeated double data = 2; @@ -4779,11 +4779,11 @@ public interface InitMethodOrBuilder extends double getData(int index); } /** - * Protobuf type {@code serialization.InitMethod} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.InitMethod} */ public static final class InitMethod extends com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:serialization.InitMethod) + // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.InitMethod) InitMethodOrBuilder { private static final long serialVersionUID = 0L; // Use InitMethod.newBuilder() to construct. @@ -4867,31 +4867,31 @@ private InitMethod( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_InitMethod_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_InitMethod_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_InitMethod_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_InitMethod_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.InitMethod.class, serialization.Bigdl.InitMethod.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.class, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder.class); } private int bitField0_; public static final int METHODTYPE_FIELD_NUMBER = 1; private int methodType_; /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ public int getMethodTypeValue() { return methodType_; } /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ - public serialization.Bigdl.InitMethodType getMethodType() { - serialization.Bigdl.InitMethodType result = serialization.Bigdl.InitMethodType.valueOf(methodType_); - return result == null ? serialization.Bigdl.InitMethodType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType getMethodType() { + com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType result = com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType.valueOf(methodType_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType.UNRECOGNIZED : result; } public static final int DATA_FIELD_NUMBER = 2; @@ -4930,7 +4930,7 @@ public final boolean isInitialized() { public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException { getSerializedSize(); - if (methodType_ != serialization.Bigdl.InitMethodType.EMPTY_INITIALIZATION.getNumber()) { + if (methodType_ != com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType.EMPTY_INITIALIZATION.getNumber()) { output.writeEnum(1, methodType_); } if (getDataList().size() > 0) { @@ -4948,7 +4948,7 @@ public int getSerializedSize() { if (size != -1) return size; size = 0; - if (methodType_ != serialization.Bigdl.InitMethodType.EMPTY_INITIALIZATION.getNumber()) { + if (methodType_ != com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType.EMPTY_INITIALIZATION.getNumber()) { size += com.google.protobuf.CodedOutputStream .computeEnumSize(1, methodType_); } @@ -4973,10 +4973,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof serialization.Bigdl.InitMethod)) { + if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.InitMethod)) { return super.equals(obj); } - serialization.Bigdl.InitMethod other = (serialization.Bigdl.InitMethod) obj; + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod other = (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) obj; boolean result = true; result = result && methodType_ == other.methodType_; @@ -5004,69 +5004,69 @@ public int hashCode() { return hash; } - public static serialization.Bigdl.InitMethod parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.InitMethod parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.InitMethod parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.InitMethod parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.InitMethod parseFrom(byte[] data) + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.InitMethod parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.InitMethod parseFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.InitMethod parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.InitMethod parseDelimitedFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static serialization.Bigdl.InitMethod parseDelimitedFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.InitMethod parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.InitMethod parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -5078,7 +5078,7 @@ public static serialization.Bigdl.InitMethod parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(serialization.Bigdl.InitMethod prototype) { + public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.InitMethod prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -5093,25 +5093,25 @@ protected Builder newBuilderForType( return builder; } /** - * Protobuf type {@code serialization.InitMethod} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.InitMethod} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:serialization.InitMethod) - serialization.Bigdl.InitMethodOrBuilder { + // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.InitMethod) + com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_InitMethod_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_InitMethod_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_InitMethod_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_InitMethod_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.InitMethod.class, serialization.Bigdl.InitMethod.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.class, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder.class); } - // Construct using serialization.Bigdl.InitMethod.newBuilder() + // Construct using com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -5137,23 +5137,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return serialization.Bigdl.internal_static_serialization_InitMethod_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_InitMethod_descriptor; } - public serialization.Bigdl.InitMethod getDefaultInstanceForType() { - return serialization.Bigdl.InitMethod.getDefaultInstance(); + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getDefaultInstanceForType() { + return com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance(); } - public serialization.Bigdl.InitMethod build() { - serialization.Bigdl.InitMethod result = buildPartial(); + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod build() { + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public serialization.Bigdl.InitMethod buildPartial() { - serialization.Bigdl.InitMethod result = new serialization.Bigdl.InitMethod(this); + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod buildPartial() { + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod result = new com.intel.analytics.bigdl.serialization.Bigdl.InitMethod(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.methodType_ = methodType_; @@ -5194,16 +5194,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof serialization.Bigdl.InitMethod) { - return mergeFrom((serialization.Bigdl.InitMethod)other); + if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) { + return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.InitMethod)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(serialization.Bigdl.InitMethod other) { - if (other == serialization.Bigdl.InitMethod.getDefaultInstance()) return this; + public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.InitMethod other) { + if (other == com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance()) return this; if (other.methodType_ != 0) { setMethodTypeValue(other.getMethodTypeValue()); } @@ -5230,11 +5230,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - serialization.Bigdl.InitMethod parsedMessage = null; + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (serialization.Bigdl.InitMethod) e.getUnfinishedMessage(); + parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -5247,13 +5247,13 @@ public Builder mergeFrom( private int methodType_ = 0; /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ public int getMethodTypeValue() { return methodType_; } /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ public Builder setMethodTypeValue(int value) { methodType_ = value; @@ -5261,16 +5261,16 @@ public Builder setMethodTypeValue(int value) { return this; } /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ - public serialization.Bigdl.InitMethodType getMethodType() { - serialization.Bigdl.InitMethodType result = serialization.Bigdl.InitMethodType.valueOf(methodType_); - return result == null ? serialization.Bigdl.InitMethodType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType getMethodType() { + com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType result = com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType.valueOf(methodType_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType.UNRECOGNIZED : result; } /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ - public Builder setMethodType(serialization.Bigdl.InitMethodType value) { + public Builder setMethodType(com.intel.analytics.bigdl.serialization.Bigdl.InitMethodType value) { if (value == null) { throw new NullPointerException(); } @@ -5280,7 +5280,7 @@ public Builder setMethodType(serialization.Bigdl.InitMethodType value) { return this; } /** - * .serialization.InitMethodType methodType = 1; + * .com.intel.analytics.bigdl.serialization.InitMethodType methodType = 1; */ public Builder clearMethodType() { @@ -5365,16 +5365,16 @@ public final Builder mergeUnknownFields( } - // @@protoc_insertion_point(builder_scope:serialization.InitMethod) + // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.InitMethod) } - // @@protoc_insertion_point(class_scope:serialization.InitMethod) - private static final serialization.Bigdl.InitMethod DEFAULT_INSTANCE; + // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.InitMethod) + private static final com.intel.analytics.bigdl.serialization.Bigdl.InitMethod DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new serialization.Bigdl.InitMethod(); + DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.InitMethod(); } - public static serialization.Bigdl.InitMethod getDefaultInstance() { + public static com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -5397,24 +5397,24 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public serialization.Bigdl.InitMethod getDefaultInstanceForType() { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getDefaultInstanceForType() { return DEFAULT_INSTANCE; } } public interface BigDLTensorOrBuilder extends - // @@protoc_insertion_point(interface_extends:serialization.BigDLTensor) + // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.BigDLTensor) com.google.protobuf.MessageOrBuilder { /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ int getDatatypeValue(); /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ - serialization.Bigdl.DataType getDatatype(); + com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype(); /** * 
@@ -5507,7 +5507,7 @@ public interface BigDLTensorOrBuilder extends
      * reference to storage
      *
* - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ boolean hasStorage(); /** @@ -5515,17 +5515,17 @@ public interface BigDLTensorOrBuilder extends * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - serialization.Bigdl.TensorStorage getStorage(); + com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage getStorage(); /** * 
      * reference to storage
      *
* - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder(); /** * 
@@ -5537,20 +5537,20 @@ public interface BigDLTensorOrBuilder extends
     int getId();
 
     /**
-     * .serialization.TensorType tensorType = 10;
+     * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10;
      */
     int getTensorTypeValue();
     /**
-     * .serialization.TensorType tensorType = 10;
+     * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10;
      */
-    serialization.Bigdl.TensorType getTensorType();
+    com.intel.analytics.bigdl.serialization.Bigdl.TensorType getTensorType();
   }
   /**
-   * Protobuf type {@code serialization.BigDLTensor}
+   * Protobuf type {@code com.intel.analytics.bigdl.serialization.BigDLTensor}
    */
   public  static final class BigDLTensor extends
       com.google.protobuf.GeneratedMessageV3 implements
-      // @@protoc_insertion_point(message_implements:serialization.BigDLTensor)
+      // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.BigDLTensor)
       BigDLTensorOrBuilder {
   private static final long serialVersionUID = 0L;
     // Use BigDLTensor.newBuilder() to construct.
@@ -5666,11 +5666,11 @@ private BigDLTensor(
               break;
             }
             case 66: {
-              serialization.Bigdl.TensorStorage.Builder subBuilder = null;
+              com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.Builder subBuilder = null;
               if (storage_ != null) {
                 subBuilder = storage_.toBuilder();
               }
-              storage_ = input.readMessage(serialization.Bigdl.TensorStorage.parser(), extensionRegistry);
+              storage_ = input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.parser(), extensionRegistry);
               if (subBuilder != null) {
                 subBuilder.mergeFrom(storage_);
                 storage_ = subBuilder.buildPartial();
@@ -5709,31 +5709,31 @@ private BigDLTensor(
     }
     public static final com.google.protobuf.Descriptors.Descriptor
         getDescriptor() {
-      return serialization.Bigdl.internal_static_serialization_BigDLTensor_descriptor;
+      return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLTensor_descriptor;
     }
 
     protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
         internalGetFieldAccessorTable() {
-      return serialization.Bigdl.internal_static_serialization_BigDLTensor_fieldAccessorTable
+      return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLTensor_fieldAccessorTable
           .ensureFieldAccessorsInitialized(
-              serialization.Bigdl.BigDLTensor.class, serialization.Bigdl.BigDLTensor.Builder.class);
+              com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.class, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder.class);
     }
 
     private int bitField0_;
     public static final int DATATYPE_FIELD_NUMBER = 1;
     private int datatype_;
     /**
-     * .serialization.DataType datatype = 1;
+     * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
      */
     public int getDatatypeValue() {
       return datatype_;
     }
     /**
-     * .serialization.DataType datatype = 1;
+     * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
      */
-    public serialization.Bigdl.DataType getDatatype() {
-      serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_);
-      return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result;
+    public com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype() {
+      com.intel.analytics.bigdl.serialization.Bigdl.DataType result = com.intel.analytics.bigdl.serialization.Bigdl.DataType.valueOf(datatype_);
+      return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.DataType.UNRECOGNIZED : result;
     }
 
     public static final int SIZE_FIELD_NUMBER = 2;
@@ -5859,13 +5859,13 @@ public boolean getIsScalar() {
     }
 
     public static final int STORAGE_FIELD_NUMBER = 8;
-    private serialization.Bigdl.TensorStorage storage_;
+    private com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage storage_;
     /**
      * 
      * reference to storage
      * 

      *
-     * .serialization.TensorStorage storage = 8;
+     * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8;
      */
     public boolean hasStorage() {
       return storage_ != null;
@@ -5875,19 +5875,19 @@ public boolean hasStorage() {
      * reference to storage
      *
* - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - public serialization.Bigdl.TensorStorage getStorage() { - return storage_ == null ? serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; + public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage getStorage() { + return storage_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; } /** * 
      * reference to storage
      *
* - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - public serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder() { return getStorage(); } @@ -5907,17 +5907,17 @@ public int getId() { public static final int TENSORTYPE_FIELD_NUMBER = 10; private int tensorType_; /** - * .serialization.TensorType tensorType = 10; + * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10; */ public int getTensorTypeValue() { return tensorType_; } /** - * .serialization.TensorType tensorType = 10; + * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10; */ - public serialization.Bigdl.TensorType getTensorType() { - serialization.Bigdl.TensorType result = serialization.Bigdl.TensorType.valueOf(tensorType_); - return result == null ? serialization.Bigdl.TensorType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.TensorType getTensorType() { + com.intel.analytics.bigdl.serialization.Bigdl.TensorType result = com.intel.analytics.bigdl.serialization.Bigdl.TensorType.valueOf(tensorType_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.TensorType.UNRECOGNIZED : result; } private byte memoizedIsInitialized = -1; @@ -5933,7 +5933,7 @@ public final boolean isInitialized() { public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException { getSerializedSize(); - if (datatype_ != serialization.Bigdl.DataType.INT32.getNumber()) { + if (datatype_ != com.intel.analytics.bigdl.serialization.Bigdl.DataType.INT32.getNumber()) { output.writeEnum(1, datatype_); } if (getSizeList().size() > 0) { @@ -5968,7 +5968,7 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) if (id_ != 0) { output.writeInt32(9, id_); } - if (tensorType_ != serialization.Bigdl.TensorType.DENSE.getNumber()) { + if (tensorType_ != com.intel.analytics.bigdl.serialization.Bigdl.TensorType.DENSE.getNumber()) { output.writeEnum(10, tensorType_); } unknownFields.writeTo(output); @@ -5979,7 +5979,7 @@ public int getSerializedSize() { if (size != -1) return size; size = 0; - if (datatype_ != serialization.Bigdl.DataType.INT32.getNumber()) { + if (datatype_ != com.intel.analytics.bigdl.serialization.Bigdl.DataType.INT32.getNumber()) { size += com.google.protobuf.CodedOutputStream .computeEnumSize(1, datatype_); } @@ -6035,7 +6035,7 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeInt32Size(9, id_); } - if (tensorType_ != serialization.Bigdl.TensorType.DENSE.getNumber()) { + if (tensorType_ != com.intel.analytics.bigdl.serialization.Bigdl.TensorType.DENSE.getNumber()) { size += com.google.protobuf.CodedOutputStream .computeEnumSize(10, tensorType_); } @@ -6049,10 +6049,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof serialization.Bigdl.BigDLTensor)) { + if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor)) { return super.equals(obj); } - serialization.Bigdl.BigDLTensor other = (serialization.Bigdl.BigDLTensor) obj; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor other = (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) obj; boolean result = true; result = result && datatype_ == other.datatype_; @@ -6119,69 +6119,69 @@ public int hashCode() { return hash; } - public static serialization.Bigdl.BigDLTensor parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.BigDLTensor parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.BigDLTensor parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.BigDLTensor parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.BigDLTensor parseFrom(byte[] data) + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.BigDLTensor parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.BigDLTensor parseFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.BigDLTensor parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.BigDLTensor parseDelimitedFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static serialization.Bigdl.BigDLTensor parseDelimitedFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.BigDLTensor parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.BigDLTensor parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -6193,7 +6193,7 @@ public static serialization.Bigdl.BigDLTensor parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(serialization.Bigdl.BigDLTensor prototype) { + public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -6208,25 +6208,25 @@ protected Builder newBuilderForType( return builder; } /** - * Protobuf type {@code serialization.BigDLTensor} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.BigDLTensor} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:serialization.BigDLTensor) - serialization.Bigdl.BigDLTensorOrBuilder { + // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.BigDLTensor) + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_BigDLTensor_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLTensor_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_BigDLTensor_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLTensor_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.BigDLTensor.class, serialization.Bigdl.BigDLTensor.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.class, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder.class); } - // Construct using serialization.Bigdl.BigDLTensor.newBuilder() + // Construct using com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -6272,23 +6272,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return serialization.Bigdl.internal_static_serialization_BigDLTensor_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_BigDLTensor_descriptor; } - public serialization.Bigdl.BigDLTensor getDefaultInstanceForType() { - return serialization.Bigdl.BigDLTensor.getDefaultInstance(); + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getDefaultInstanceForType() { + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance(); } - public serialization.Bigdl.BigDLTensor build() { - serialization.Bigdl.BigDLTensor result = buildPartial(); + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor build() { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public serialization.Bigdl.BigDLTensor buildPartial() { - serialization.Bigdl.BigDLTensor result = new serialization.Bigdl.BigDLTensor(this); + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor buildPartial() { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor result = new com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.datatype_ = datatype_; @@ -6345,16 +6345,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof serialization.Bigdl.BigDLTensor) { - return mergeFrom((serialization.Bigdl.BigDLTensor)other); + if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) { + return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(serialization.Bigdl.BigDLTensor other) { - if (other == serialization.Bigdl.BigDLTensor.getDefaultInstance()) return this; + public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor other) { + if (other == com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()) return this; if (other.datatype_ != 0) { setDatatypeValue(other.getDatatypeValue()); } @@ -6412,11 +6412,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - serialization.Bigdl.BigDLTensor parsedMessage = null; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (serialization.Bigdl.BigDLTensor) e.getUnfinishedMessage(); + parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -6429,13 +6429,13 @@ public Builder mergeFrom( private int datatype_ = 0; /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ public int getDatatypeValue() { return datatype_; } /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ public Builder setDatatypeValue(int value) { datatype_ = value; @@ -6443,16 +6443,16 @@ public Builder setDatatypeValue(int value) { return this; } /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ - public serialization.Bigdl.DataType getDatatype() { - serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); - return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype() { + com.intel.analytics.bigdl.serialization.Bigdl.DataType result = com.intel.analytics.bigdl.serialization.Bigdl.DataType.valueOf(datatype_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.DataType.UNRECOGNIZED : result; } /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ - public Builder setDatatype(serialization.Bigdl.DataType value) { + public Builder setDatatype(com.intel.analytics.bigdl.serialization.Bigdl.DataType value) { if (value == null) { throw new NullPointerException(); } @@ -6462,7 +6462,7 @@ public Builder setDatatype(serialization.Bigdl.DataType value) { return this; } /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ public Builder clearDatatype() { @@ -6811,15 +6811,15 @@ public Builder clearIsScalar() { return this; } - private serialization.Bigdl.TensorStorage storage_ = null; + private com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage storage_ = null; private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.TensorStorage, serialization.Bigdl.TensorStorage.Builder, serialization.Bigdl.TensorStorageOrBuilder> storageBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage, com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.Builder, com.intel.analytics.bigdl.serialization.Bigdl.TensorStorageOrBuilder> storageBuilder_; /** * 
        * reference to storage
        *
* - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ public boolean hasStorage() { return storageBuilder_ != null || storage_ != null; @@ -6829,11 +6829,11 @@ public boolean hasStorage() { * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - public serialization.Bigdl.TensorStorage getStorage() { + public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage getStorage() { if (storageBuilder_ == null) { - return storage_ == null ? serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; + return storage_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; } else { return storageBuilder_.getMessage(); } @@ -6843,9 +6843,9 @@ public serialization.Bigdl.TensorStorage getStorage() { * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - public Builder setStorage(serialization.Bigdl.TensorStorage value) { + public Builder setStorage(com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage value) { if (storageBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -6863,10 +6863,10 @@ public Builder setStorage(serialization.Bigdl.TensorStorage value) { * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ public Builder setStorage( - serialization.Bigdl.TensorStorage.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.Builder builderForValue) { if (storageBuilder_ == null) { storage_ = builderForValue.build(); onChanged(); @@ -6881,13 +6881,13 @@ public Builder setStorage( * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - public Builder mergeStorage(serialization.Bigdl.TensorStorage value) { + public Builder mergeStorage(com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage value) { if (storageBuilder_ == null) { if (storage_ != null) { storage_ = - serialization.Bigdl.TensorStorage.newBuilder(storage_).mergeFrom(value).buildPartial(); + com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.newBuilder(storage_).mergeFrom(value).buildPartial(); } else { storage_ = value; } @@ -6903,7 +6903,7 @@ public Builder mergeStorage(serialization.Bigdl.TensorStorage value) { * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ public Builder clearStorage() { if (storageBuilder_ == null) { @@ -6921,9 +6921,9 @@ public Builder clearStorage() { * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - public serialization.Bigdl.TensorStorage.Builder getStorageBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.Builder getStorageBuilder() { onChanged(); return getStorageFieldBuilder().getBuilder(); @@ -6933,14 +6933,14 @@ public serialization.Bigdl.TensorStorage.Builder getStorageBuilder() { * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ - public serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder() { if (storageBuilder_ != null) { return storageBuilder_.getMessageOrBuilder(); } else { return storage_ == null ? - serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; + com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.getDefaultInstance() : storage_; } } /** @@ -6948,14 +6948,14 @@ public serialization.Bigdl.TensorStorageOrBuilder getStorageOrBuilder() { * reference to storage * * - * .serialization.TensorStorage storage = 8; + * .com.intel.analytics.bigdl.serialization.TensorStorage storage = 8; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.TensorStorage, serialization.Bigdl.TensorStorage.Builder, serialization.Bigdl.TensorStorageOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage, com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.Builder, com.intel.analytics.bigdl.serialization.Bigdl.TensorStorageOrBuilder> getStorageFieldBuilder() { if (storageBuilder_ == null) { storageBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.TensorStorage, serialization.Bigdl.TensorStorage.Builder, serialization.Bigdl.TensorStorageOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage, com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.Builder, com.intel.analytics.bigdl.serialization.Bigdl.TensorStorageOrBuilder>( getStorage(), getParentForChildren(), isClean()); @@ -7004,13 +7004,13 @@ public Builder clearId() { private int tensorType_ = 0; /** - * .serialization.TensorType tensorType = 10; + * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10; */ public int getTensorTypeValue() { return tensorType_; } /** - * .serialization.TensorType tensorType = 10; + * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10; */ public Builder setTensorTypeValue(int value) { tensorType_ = value; @@ -7018,16 +7018,16 @@ public Builder setTensorTypeValue(int value) { return this; } /** - * .serialization.TensorType tensorType = 10; + * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10; */ - public serialization.Bigdl.TensorType getTensorType() { - serialization.Bigdl.TensorType result = serialization.Bigdl.TensorType.valueOf(tensorType_); - return result == null ? serialization.Bigdl.TensorType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.TensorType getTensorType() { + com.intel.analytics.bigdl.serialization.Bigdl.TensorType result = com.intel.analytics.bigdl.serialization.Bigdl.TensorType.valueOf(tensorType_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.TensorType.UNRECOGNIZED : result; } /** - * .serialization.TensorType tensorType = 10; + * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10; */ - public Builder setTensorType(serialization.Bigdl.TensorType value) { + public Builder setTensorType(com.intel.analytics.bigdl.serialization.Bigdl.TensorType value) { if (value == null) { throw new NullPointerException(); } @@ -7037,7 +7037,7 @@ public Builder setTensorType(serialization.Bigdl.TensorType value) { return this; } /** - * .serialization.TensorType tensorType = 10; + * .com.intel.analytics.bigdl.serialization.TensorType tensorType = 10; */ public Builder clearTensorType() { @@ -7056,16 +7056,16 @@ public final Builder mergeUnknownFields( } - // @@protoc_insertion_point(builder_scope:serialization.BigDLTensor) + // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.BigDLTensor) } - // @@protoc_insertion_point(class_scope:serialization.BigDLTensor) - private static final serialization.Bigdl.BigDLTensor DEFAULT_INSTANCE; + // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.BigDLTensor) + private static final com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new serialization.Bigdl.BigDLTensor(); + DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor(); } - public static serialization.Bigdl.BigDLTensor getDefaultInstance() { + public static com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -7088,24 +7088,24 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public serialization.Bigdl.BigDLTensor getDefaultInstanceForType() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getDefaultInstanceForType() { return DEFAULT_INSTANCE; } } public interface TensorStorageOrBuilder extends - // @@protoc_insertion_point(interface_extends:serialization.TensorStorage) + // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.TensorStorage) com.google.protobuf.MessageOrBuilder { /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ int getDatatypeValue(); /** - * .serialization.DataType datatype = 1; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 1; */ - serialization.Bigdl.DataType getDatatype(); + com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype(); /** * 
@@ -7302,11 +7302,11 @@ public interface TensorStorageOrBuilder extends
     int getId();
   }
   /**
-   * Protobuf type {@code serialization.TensorStorage}
+   * Protobuf type {@code com.intel.analytics.bigdl.serialization.TensorStorage}
    */
   public  static final class TensorStorage extends
       com.google.protobuf.GeneratedMessageV3 implements
-      // @@protoc_insertion_point(message_implements:serialization.TensorStorage)
+      // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.TensorStorage)
       TensorStorageOrBuilder {
   private static final long serialVersionUID = 0L;
     // Use TensorStorage.newBuilder() to construct.
@@ -7521,31 +7521,31 @@ private TensorStorage(
     }
     public static final com.google.protobuf.Descriptors.Descriptor
         getDescriptor() {
-      return serialization.Bigdl.internal_static_serialization_TensorStorage_descriptor;
+      return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_TensorStorage_descriptor;
     }
 
     protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
         internalGetFieldAccessorTable() {
-      return serialization.Bigdl.internal_static_serialization_TensorStorage_fieldAccessorTable
+      return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_TensorStorage_fieldAccessorTable
           .ensureFieldAccessorsInitialized(
-              serialization.Bigdl.TensorStorage.class, serialization.Bigdl.TensorStorage.Builder.class);
+              com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.class, com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.Builder.class);
     }
 
     private int bitField0_;
     public static final int DATATYPE_FIELD_NUMBER = 1;
     private int datatype_;
     /**
-     * .serialization.DataType datatype = 1;
+     * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
      */
     public int getDatatypeValue() {
       return datatype_;
     }
     /**
-     * .serialization.DataType datatype = 1;
+     * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
      */
-    public serialization.Bigdl.DataType getDatatype() {
-      serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_);
-      return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result;
+    public com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype() {
+      com.intel.analytics.bigdl.serialization.Bigdl.DataType result = com.intel.analytics.bigdl.serialization.Bigdl.DataType.valueOf(datatype_);
+      return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.DataType.UNRECOGNIZED : result;
     }
 
     public static final int FLOAT_DATA_FIELD_NUMBER = 2;
@@ -7828,7 +7828,7 @@ public final boolean isInitialized() {
     public void writeTo(com.google.protobuf.CodedOutputStream output)
                         throws java.io.IOException {
       getSerializedSize();
-      if (datatype_ != serialization.Bigdl.DataType.INT32.getNumber()) {
+      if (datatype_ != com.intel.analytics.bigdl.serialization.Bigdl.DataType.INT32.getNumber()) {
         output.writeEnum(1, datatype_);
       }
       if (getFloatDataList().size() > 0) {
@@ -7883,7 +7883,7 @@ public int getSerializedSize() {
       if (size != -1) return size;
 
       size = 0;
-      if (datatype_ != serialization.Bigdl.DataType.INT32.getNumber()) {
+      if (datatype_ != com.intel.analytics.bigdl.serialization.Bigdl.DataType.INT32.getNumber()) {
         size += com.google.protobuf.CodedOutputStream
           .computeEnumSize(1, datatype_);
       }
@@ -7979,10 +7979,10 @@ public boolean equals(final java.lang.Object obj) {
       if (obj == this) {
        return true;
       }
-      if (!(obj instanceof serialization.Bigdl.TensorStorage)) {
+      if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage)) {
         return super.equals(obj);
       }
-      serialization.Bigdl.TensorStorage other = (serialization.Bigdl.TensorStorage) obj;
+      com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage other = (com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage) obj;
 
       boolean result = true;
       result = result && datatype_ == other.datatype_;
@@ -8050,69 +8050,69 @@ public int hashCode() {
       return hash;
     }
 
-    public static serialization.Bigdl.TensorStorage parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(
         java.nio.ByteBuffer data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(
         java.nio.ByteBuffer data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(
         com.google.protobuf.ByteString data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(
         com.google.protobuf.ByteString data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(byte[] data)
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(byte[] data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(
         byte[] data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(java.io.InputStream input)
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(java.io.InputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(
         java.io.InputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input, extensionRegistry);
     }
-    public static serialization.Bigdl.TensorStorage parseDelimitedFrom(java.io.InputStream input)
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseDelimitedFrom(java.io.InputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseDelimitedWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.TensorStorage parseDelimitedFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseDelimitedFrom(
         java.io.InputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseDelimitedWithIOException(PARSER, input, extensionRegistry);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(
         com.google.protobuf.CodedInputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.TensorStorage parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parseFrom(
         com.google.protobuf.CodedInputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
@@ -8124,7 +8124,7 @@ public static serialization.Bigdl.TensorStorage parseFrom(
     public static Builder newBuilder() {
       return DEFAULT_INSTANCE.toBuilder();
     }
-    public static Builder newBuilder(serialization.Bigdl.TensorStorage prototype) {
+    public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage prototype) {
       return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
     }
     public Builder toBuilder() {
@@ -8139,25 +8139,25 @@ protected Builder newBuilderForType(
       return builder;
     }
     /**
-     * Protobuf type {@code serialization.TensorStorage}
+     * Protobuf type {@code com.intel.analytics.bigdl.serialization.TensorStorage}
      */
     public static final class Builder extends
         com.google.protobuf.GeneratedMessageV3.Builder implements
-        // @@protoc_insertion_point(builder_implements:serialization.TensorStorage)
-        serialization.Bigdl.TensorStorageOrBuilder {
+        // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.TensorStorage)
+        com.intel.analytics.bigdl.serialization.Bigdl.TensorStorageOrBuilder {
       public static final com.google.protobuf.Descriptors.Descriptor
           getDescriptor() {
-        return serialization.Bigdl.internal_static_serialization_TensorStorage_descriptor;
+        return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_TensorStorage_descriptor;
       }
 
       protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
           internalGetFieldAccessorTable() {
-        return serialization.Bigdl.internal_static_serialization_TensorStorage_fieldAccessorTable
+        return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_TensorStorage_fieldAccessorTable
             .ensureFieldAccessorsInitialized(
-                serialization.Bigdl.TensorStorage.class, serialization.Bigdl.TensorStorage.Builder.class);
+                com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.class, com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.Builder.class);
       }
 
-      // Construct using serialization.Bigdl.TensorStorage.newBuilder()
+      // Construct using com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.newBuilder()
       private Builder() {
         maybeForceBuilderInitialization();
       }
@@ -8197,23 +8197,23 @@ public Builder clear() {
 
       public com.google.protobuf.Descriptors.Descriptor
           getDescriptorForType() {
-        return serialization.Bigdl.internal_static_serialization_TensorStorage_descriptor;
+        return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_TensorStorage_descriptor;
       }
 
-      public serialization.Bigdl.TensorStorage getDefaultInstanceForType() {
-        return serialization.Bigdl.TensorStorage.getDefaultInstance();
+      public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage getDefaultInstanceForType() {
+        return com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.getDefaultInstance();
       }
 
-      public serialization.Bigdl.TensorStorage build() {
-        serialization.Bigdl.TensorStorage result = buildPartial();
+      public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage build() {
+        com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage result = buildPartial();
         if (!result.isInitialized()) {
           throw newUninitializedMessageException(result);
         }
         return result;
       }
 
-      public serialization.Bigdl.TensorStorage buildPartial() {
-        serialization.Bigdl.TensorStorage result = new serialization.Bigdl.TensorStorage(this);
+      public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage buildPartial() {
+        com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage result = new com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage(this);
         int from_bitField0_ = bitField0_;
         int to_bitField0_ = 0;
         result.datatype_ = datatype_;
@@ -8285,16 +8285,16 @@ public Builder addRepeatedField(
         return (Builder) super.addRepeatedField(field, value);
       }
       public Builder mergeFrom(com.google.protobuf.Message other) {
-        if (other instanceof serialization.Bigdl.TensorStorage) {
-          return mergeFrom((serialization.Bigdl.TensorStorage)other);
+        if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage) {
+          return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage)other);
         } else {
           super.mergeFrom(other);
           return this;
         }
       }
 
-      public Builder mergeFrom(serialization.Bigdl.TensorStorage other) {
-        if (other == serialization.Bigdl.TensorStorage.getDefaultInstance()) return this;
+      public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage other) {
+        if (other == com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage.getDefaultInstance()) return this;
         if (other.datatype_ != 0) {
           setDatatypeValue(other.getDatatypeValue());
         }
@@ -8384,11 +8384,11 @@ public Builder mergeFrom(
           com.google.protobuf.CodedInputStream input,
           com.google.protobuf.ExtensionRegistryLite extensionRegistry)
           throws java.io.IOException {
-        serialization.Bigdl.TensorStorage parsedMessage = null;
+        com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage parsedMessage = null;
         try {
           parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
         } catch (com.google.protobuf.InvalidProtocolBufferException e) {
-          parsedMessage = (serialization.Bigdl.TensorStorage) e.getUnfinishedMessage();
+          parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage) e.getUnfinishedMessage();
           throw e.unwrapIOException();
         } finally {
           if (parsedMessage != null) {
@@ -8401,13 +8401,13 @@ public Builder mergeFrom(
 
       private int datatype_ = 0;
       /**
-       * .serialization.DataType datatype = 1;
+       * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
        */
       public int getDatatypeValue() {
         return datatype_;
       }
       /**
-       * .serialization.DataType datatype = 1;
+       * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
        */
       public Builder setDatatypeValue(int value) {
         datatype_ = value;
@@ -8415,16 +8415,16 @@ public Builder setDatatypeValue(int value) {
         return this;
       }
       /**
-       * .serialization.DataType datatype = 1;
+       * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
        */
-      public serialization.Bigdl.DataType getDatatype() {
-        serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_);
-        return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result;
+      public com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype() {
+        com.intel.analytics.bigdl.serialization.Bigdl.DataType result = com.intel.analytics.bigdl.serialization.Bigdl.DataType.valueOf(datatype_);
+        return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.DataType.UNRECOGNIZED : result;
       }
       /**
-       * .serialization.DataType datatype = 1;
+       * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
        */
-      public Builder setDatatype(serialization.Bigdl.DataType value) {
+      public Builder setDatatype(com.intel.analytics.bigdl.serialization.Bigdl.DataType value) {
         if (value == null) {
           throw new NullPointerException();
         }
@@ -8434,7 +8434,7 @@ public Builder setDatatype(serialization.Bigdl.DataType value) {
         return this;
       }
       /**
-       * .serialization.DataType datatype = 1;
+       * .com.intel.analytics.bigdl.serialization.DataType datatype = 1;
        */
       public Builder clearDatatype() {
         
@@ -9191,16 +9191,16 @@ public final Builder mergeUnknownFields(
       }
 
 
-      // @@protoc_insertion_point(builder_scope:serialization.TensorStorage)
+      // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.TensorStorage)
     }
 
-    // @@protoc_insertion_point(class_scope:serialization.TensorStorage)
-    private static final serialization.Bigdl.TensorStorage DEFAULT_INSTANCE;
+    // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.TensorStorage)
+    private static final com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage DEFAULT_INSTANCE;
     static {
-      DEFAULT_INSTANCE = new serialization.Bigdl.TensorStorage();
+      DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage();
     }
 
-    public static serialization.Bigdl.TensorStorage getDefaultInstance() {
+    public static com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage getDefaultInstance() {
       return DEFAULT_INSTANCE;
     }
 
@@ -9223,24 +9223,24 @@ public com.google.protobuf.Parser getParserForType() {
       return PARSER;
     }
 
-    public serialization.Bigdl.TensorStorage getDefaultInstanceForType() {
+    public com.intel.analytics.bigdl.serialization.Bigdl.TensorStorage getDefaultInstanceForType() {
       return DEFAULT_INSTANCE;
     }
 
   }
 
   public interface RegularizerOrBuilder extends
-      // @@protoc_insertion_point(interface_extends:serialization.Regularizer)
+      // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.Regularizer)
       com.google.protobuf.MessageOrBuilder {
 
     /**
-     * .serialization.RegularizerType regularizerType = 1;
+     * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
      */
     int getRegularizerTypeValue();
     /**
-     * .serialization.RegularizerType regularizerType = 1;
+     * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
      */
-    serialization.Bigdl.RegularizerType getRegularizerType();
+    com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType getRegularizerType();
 
     /**
      * repeated double regularData = 2;
@@ -9256,11 +9256,11 @@ public interface RegularizerOrBuilder extends
     double getRegularData(int index);
   }
   /**
-   * Protobuf type {@code serialization.Regularizer}
+   * Protobuf type {@code com.intel.analytics.bigdl.serialization.Regularizer}
    */
   public  static final class Regularizer extends
       com.google.protobuf.GeneratedMessageV3 implements
-      // @@protoc_insertion_point(message_implements:serialization.Regularizer)
+      // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.Regularizer)
       RegularizerOrBuilder {
   private static final long serialVersionUID = 0L;
     // Use Regularizer.newBuilder() to construct.
@@ -9344,31 +9344,31 @@ private Regularizer(
     }
     public static final com.google.protobuf.Descriptors.Descriptor
         getDescriptor() {
-      return serialization.Bigdl.internal_static_serialization_Regularizer_descriptor;
+      return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Regularizer_descriptor;
     }
 
     protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
         internalGetFieldAccessorTable() {
-      return serialization.Bigdl.internal_static_serialization_Regularizer_fieldAccessorTable
+      return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Regularizer_fieldAccessorTable
           .ensureFieldAccessorsInitialized(
-              serialization.Bigdl.Regularizer.class, serialization.Bigdl.Regularizer.Builder.class);
+              com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.class, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder.class);
     }
 
     private int bitField0_;
     public static final int REGULARIZERTYPE_FIELD_NUMBER = 1;
     private int regularizerType_;
     /**
-     * .serialization.RegularizerType regularizerType = 1;
+     * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
      */
     public int getRegularizerTypeValue() {
       return regularizerType_;
     }
     /**
-     * .serialization.RegularizerType regularizerType = 1;
+     * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
      */
-    public serialization.Bigdl.RegularizerType getRegularizerType() {
-      serialization.Bigdl.RegularizerType result = serialization.Bigdl.RegularizerType.valueOf(regularizerType_);
-      return result == null ? serialization.Bigdl.RegularizerType.UNRECOGNIZED : result;
+    public com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType getRegularizerType() {
+      com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType result = com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType.valueOf(regularizerType_);
+      return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType.UNRECOGNIZED : result;
     }
 
     public static final int REGULARDATA_FIELD_NUMBER = 2;
@@ -9407,7 +9407,7 @@ public final boolean isInitialized() {
     public void writeTo(com.google.protobuf.CodedOutputStream output)
                         throws java.io.IOException {
       getSerializedSize();
-      if (regularizerType_ != serialization.Bigdl.RegularizerType.L1L2Regularizer.getNumber()) {
+      if (regularizerType_ != com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType.L1L2Regularizer.getNumber()) {
         output.writeEnum(1, regularizerType_);
       }
       if (getRegularDataList().size() > 0) {
@@ -9425,7 +9425,7 @@ public int getSerializedSize() {
       if (size != -1) return size;
 
       size = 0;
-      if (regularizerType_ != serialization.Bigdl.RegularizerType.L1L2Regularizer.getNumber()) {
+      if (regularizerType_ != com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType.L1L2Regularizer.getNumber()) {
         size += com.google.protobuf.CodedOutputStream
           .computeEnumSize(1, regularizerType_);
       }
@@ -9450,10 +9450,10 @@ public boolean equals(final java.lang.Object obj) {
       if (obj == this) {
        return true;
       }
-      if (!(obj instanceof serialization.Bigdl.Regularizer)) {
+      if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.Regularizer)) {
         return super.equals(obj);
       }
-      serialization.Bigdl.Regularizer other = (serialization.Bigdl.Regularizer) obj;
+      com.intel.analytics.bigdl.serialization.Bigdl.Regularizer other = (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) obj;
 
       boolean result = true;
       result = result && regularizerType_ == other.regularizerType_;
@@ -9481,69 +9481,69 @@ public int hashCode() {
       return hash;
     }
 
-    public static serialization.Bigdl.Regularizer parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(
         java.nio.ByteBuffer data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(
         java.nio.ByteBuffer data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(
         com.google.protobuf.ByteString data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(
         com.google.protobuf.ByteString data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(byte[] data)
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(byte[] data)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(
         byte[] data,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws com.google.protobuf.InvalidProtocolBufferException {
       return PARSER.parseFrom(data, extensionRegistry);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(java.io.InputStream input)
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(java.io.InputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(
         java.io.InputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input, extensionRegistry);
     }
-    public static serialization.Bigdl.Regularizer parseDelimitedFrom(java.io.InputStream input)
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseDelimitedFrom(java.io.InputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseDelimitedWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.Regularizer parseDelimitedFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseDelimitedFrom(
         java.io.InputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseDelimitedWithIOException(PARSER, input, extensionRegistry);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(
         com.google.protobuf.CodedInputStream input)
         throws java.io.IOException {
       return com.google.protobuf.GeneratedMessageV3
           .parseWithIOException(PARSER, input);
     }
-    public static serialization.Bigdl.Regularizer parseFrom(
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parseFrom(
         com.google.protobuf.CodedInputStream input,
         com.google.protobuf.ExtensionRegistryLite extensionRegistry)
         throws java.io.IOException {
@@ -9555,7 +9555,7 @@ public static serialization.Bigdl.Regularizer parseFrom(
     public static Builder newBuilder() {
       return DEFAULT_INSTANCE.toBuilder();
     }
-    public static Builder newBuilder(serialization.Bigdl.Regularizer prototype) {
+    public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.Regularizer prototype) {
       return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
     }
     public Builder toBuilder() {
@@ -9570,25 +9570,25 @@ protected Builder newBuilderForType(
       return builder;
     }
     /**
-     * Protobuf type {@code serialization.Regularizer}
+     * Protobuf type {@code com.intel.analytics.bigdl.serialization.Regularizer}
      */
     public static final class Builder extends
         com.google.protobuf.GeneratedMessageV3.Builder implements
-        // @@protoc_insertion_point(builder_implements:serialization.Regularizer)
-        serialization.Bigdl.RegularizerOrBuilder {
+        // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.Regularizer)
+        com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder {
       public static final com.google.protobuf.Descriptors.Descriptor
           getDescriptor() {
-        return serialization.Bigdl.internal_static_serialization_Regularizer_descriptor;
+        return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Regularizer_descriptor;
       }
 
       protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
           internalGetFieldAccessorTable() {
-        return serialization.Bigdl.internal_static_serialization_Regularizer_fieldAccessorTable
+        return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Regularizer_fieldAccessorTable
             .ensureFieldAccessorsInitialized(
-                serialization.Bigdl.Regularizer.class, serialization.Bigdl.Regularizer.Builder.class);
+                com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.class, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder.class);
       }
 
-      // Construct using serialization.Bigdl.Regularizer.newBuilder()
+      // Construct using com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.newBuilder()
       private Builder() {
         maybeForceBuilderInitialization();
       }
@@ -9614,23 +9614,23 @@ public Builder clear() {
 
       public com.google.protobuf.Descriptors.Descriptor
           getDescriptorForType() {
-        return serialization.Bigdl.internal_static_serialization_Regularizer_descriptor;
+        return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Regularizer_descriptor;
       }
 
-      public serialization.Bigdl.Regularizer getDefaultInstanceForType() {
-        return serialization.Bigdl.Regularizer.getDefaultInstance();
+      public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getDefaultInstanceForType() {
+        return com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance();
       }
 
-      public serialization.Bigdl.Regularizer build() {
-        serialization.Bigdl.Regularizer result = buildPartial();
+      public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer build() {
+        com.intel.analytics.bigdl.serialization.Bigdl.Regularizer result = buildPartial();
         if (!result.isInitialized()) {
           throw newUninitializedMessageException(result);
         }
         return result;
       }
 
-      public serialization.Bigdl.Regularizer buildPartial() {
-        serialization.Bigdl.Regularizer result = new serialization.Bigdl.Regularizer(this);
+      public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer buildPartial() {
+        com.intel.analytics.bigdl.serialization.Bigdl.Regularizer result = new com.intel.analytics.bigdl.serialization.Bigdl.Regularizer(this);
         int from_bitField0_ = bitField0_;
         int to_bitField0_ = 0;
         result.regularizerType_ = regularizerType_;
@@ -9671,16 +9671,16 @@ public Builder addRepeatedField(
         return (Builder) super.addRepeatedField(field, value);
       }
       public Builder mergeFrom(com.google.protobuf.Message other) {
-        if (other instanceof serialization.Bigdl.Regularizer) {
-          return mergeFrom((serialization.Bigdl.Regularizer)other);
+        if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) {
+          return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.Regularizer)other);
         } else {
           super.mergeFrom(other);
           return this;
         }
       }
 
-      public Builder mergeFrom(serialization.Bigdl.Regularizer other) {
-        if (other == serialization.Bigdl.Regularizer.getDefaultInstance()) return this;
+      public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.Regularizer other) {
+        if (other == com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance()) return this;
         if (other.regularizerType_ != 0) {
           setRegularizerTypeValue(other.getRegularizerTypeValue());
         }
@@ -9707,11 +9707,11 @@ public Builder mergeFrom(
           com.google.protobuf.CodedInputStream input,
           com.google.protobuf.ExtensionRegistryLite extensionRegistry)
           throws java.io.IOException {
-        serialization.Bigdl.Regularizer parsedMessage = null;
+        com.intel.analytics.bigdl.serialization.Bigdl.Regularizer parsedMessage = null;
         try {
           parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
         } catch (com.google.protobuf.InvalidProtocolBufferException e) {
-          parsedMessage = (serialization.Bigdl.Regularizer) e.getUnfinishedMessage();
+          parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) e.getUnfinishedMessage();
           throw e.unwrapIOException();
         } finally {
           if (parsedMessage != null) {
@@ -9724,13 +9724,13 @@ public Builder mergeFrom(
 
       private int regularizerType_ = 0;
       /**
-       * .serialization.RegularizerType regularizerType = 1;
+       * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
        */
       public int getRegularizerTypeValue() {
         return regularizerType_;
       }
       /**
-       * .serialization.RegularizerType regularizerType = 1;
+       * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
        */
       public Builder setRegularizerTypeValue(int value) {
         regularizerType_ = value;
@@ -9738,16 +9738,16 @@ public Builder setRegularizerTypeValue(int value) {
         return this;
       }
       /**
-       * .serialization.RegularizerType regularizerType = 1;
+       * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
        */
-      public serialization.Bigdl.RegularizerType getRegularizerType() {
-        serialization.Bigdl.RegularizerType result = serialization.Bigdl.RegularizerType.valueOf(regularizerType_);
-        return result == null ? serialization.Bigdl.RegularizerType.UNRECOGNIZED : result;
+      public com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType getRegularizerType() {
+        com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType result = com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType.valueOf(regularizerType_);
+        return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType.UNRECOGNIZED : result;
       }
       /**
-       * .serialization.RegularizerType regularizerType = 1;
+       * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
        */
-      public Builder setRegularizerType(serialization.Bigdl.RegularizerType value) {
+      public Builder setRegularizerType(com.intel.analytics.bigdl.serialization.Bigdl.RegularizerType value) {
         if (value == null) {
           throw new NullPointerException();
         }
@@ -9757,7 +9757,7 @@ public Builder setRegularizerType(serialization.Bigdl.RegularizerType value) {
         return this;
       }
       /**
-       * .serialization.RegularizerType regularizerType = 1;
+       * .com.intel.analytics.bigdl.serialization.RegularizerType regularizerType = 1;
        */
       public Builder clearRegularizerType() {
         
@@ -9842,16 +9842,16 @@ public final Builder mergeUnknownFields(
       }
 
 
-      // @@protoc_insertion_point(builder_scope:serialization.Regularizer)
+      // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.Regularizer)
     }
 
-    // @@protoc_insertion_point(class_scope:serialization.Regularizer)
-    private static final serialization.Bigdl.Regularizer DEFAULT_INSTANCE;
+    // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.Regularizer)
+    private static final com.intel.analytics.bigdl.serialization.Bigdl.Regularizer DEFAULT_INSTANCE;
     static {
-      DEFAULT_INSTANCE = new serialization.Bigdl.Regularizer();
+      DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.Regularizer();
     }
 
-    public static serialization.Bigdl.Regularizer getDefaultInstance() {
+    public static com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getDefaultInstance() {
       return DEFAULT_INSTANCE;
     }
 
@@ -9874,24 +9874,24 @@ public com.google.protobuf.Parser getParserForType() {
       return PARSER;
     }
 
-    public serialization.Bigdl.Regularizer getDefaultInstanceForType() {
+    public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getDefaultInstanceForType() {
       return DEFAULT_INSTANCE;
     }
 
   }
 
   public interface AttrValueOrBuilder extends
-      // @@protoc_insertion_point(interface_extends:serialization.AttrValue)
+      // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.AttrValue)
       com.google.protobuf.MessageOrBuilder {
 
     /**
-     * .serialization.DataType dataType = 1;
+     * .com.intel.analytics.bigdl.serialization.DataType dataType = 1;
      */
     int getDataTypeValue();
     /**
-     * .serialization.DataType dataType = 1;
+     * .com.intel.analytics.bigdl.serialization.DataType dataType = 1;
      */
-    serialization.Bigdl.DataType getDataType();
+    com.intel.analytics.bigdl.serialization.Bigdl.DataType getDataType();
 
     /**
      * 
@@ -9979,7 +9979,7 @@ public interface AttrValueOrBuilder extends
      * Regularizer
      * 

      *
-     * .serialization.Regularizer regularizerValue = 9;
+     * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9;
      */
     boolean hasRegularizerValue();
     /**
@@ -9987,24 +9987,24 @@ public interface AttrValueOrBuilder extends
      * Regularizer
      *
* - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - serialization.Bigdl.Regularizer getRegularizerValue(); + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getRegularizerValue(); /** * 
      * Regularizer
      *
* - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder(); /** * 
      *tensor value
      *
* - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ boolean hasTensorValue(); /** @@ -10012,24 +10012,24 @@ public interface AttrValueOrBuilder extends *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - serialization.Bigdl.BigDLTensor getTensorValue(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getTensorValue(); /** * 
      *tensor value
      *
* - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder(); /** * 
      *Variable format
      *
* - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ int getVariableFormatValueValue(); /** @@ -10037,16 +10037,16 @@ public interface AttrValueOrBuilder extends *Variable format * * - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ - serialization.Bigdl.VarFormat getVariableFormatValue(); + com.intel.analytics.bigdl.serialization.Bigdl.VarFormat getVariableFormatValue(); /** * 
      * init method
      *
* - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ boolean hasInitMethodValue(); /** @@ -10054,24 +10054,24 @@ public interface AttrValueOrBuilder extends * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - serialization.Bigdl.InitMethod getInitMethodValue(); + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getInitMethodValue(); /** * 
      * init method
      *
* - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder(); /** * 
      * big DL module
      *
* - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ boolean hasBigDLModuleValue(); /** @@ -10079,24 +10079,24 @@ public interface AttrValueOrBuilder extends * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - serialization.Bigdl.BigDLModule getBigDLModuleValue(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getBigDLModuleValue(); /** * 
      * big DL module
      *
* - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder(); /** * 
      * name attribute list
      *
* - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ boolean hasNameAttrListValue(); /** @@ -10104,24 +10104,24 @@ public interface AttrValueOrBuilder extends * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - serialization.Bigdl.NameAttrList getNameAttrListValue(); + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getNameAttrListValue(); /** * 
      * name attribute list
      *
* - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder(); /** * 
      *array value of any type
      *
* - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ boolean hasArrayValue(); /** @@ -10129,24 +10129,24 @@ public interface AttrValueOrBuilder extends *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - serialization.Bigdl.AttrValue.ArrayValue getArrayValue(); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue getArrayValue(); /** * 
      *array value of any type
      *
* - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder(); /** * 
      * data format
      *
* - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ int getDataFormatValueValue(); /** @@ -10154,9 +10154,9 @@ public interface AttrValueOrBuilder extends * data format * * - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ - serialization.Bigdl.InputDataFormat getDataFormatValue(); + com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat getDataFormatValue(); /** * 
@@ -10188,7 +10188,7 @@ public interface AttrValueOrBuilder extends
      * Shape value
      *
* - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ boolean hasShape(); /** @@ -10196,26 +10196,26 @@ public interface AttrValueOrBuilder extends * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - serialization.Bigdl.Shape getShape(); + com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape(); /** * 
      * Shape value
      *
* - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder(); + com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder(); - public serialization.Bigdl.AttrValue.ValueCase getValueCase(); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ValueCase getValueCase(); } /** - * Protobuf type {@code serialization.AttrValue} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.AttrValue} */ public static final class AttrValue extends com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:serialization.AttrValue) + // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.AttrValue) AttrValueOrBuilder { private static final long serialVersionUID = 0L; // Use AttrValue.newBuilder() to construct. @@ -10299,28 +10299,28 @@ private AttrValue( break; } case 74: { - serialization.Bigdl.Regularizer.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder subBuilder = null; if (valueCase_ == 9) { - subBuilder = ((serialization.Bigdl.Regularizer) value_).toBuilder(); + subBuilder = ((com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_).toBuilder(); } value_ = - input.readMessage(serialization.Bigdl.Regularizer.parser(), extensionRegistry); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.parser(), extensionRegistry); if (subBuilder != null) { - subBuilder.mergeFrom((serialization.Bigdl.Regularizer) value_); + subBuilder.mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_); value_ = subBuilder.buildPartial(); } valueCase_ = 9; break; } case 82: { - serialization.Bigdl.BigDLTensor.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder subBuilder = null; if (valueCase_ == 10) { - subBuilder = ((serialization.Bigdl.BigDLTensor) value_).toBuilder(); + subBuilder = ((com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_).toBuilder(); } value_ = - input.readMessage(serialization.Bigdl.BigDLTensor.parser(), extensionRegistry); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.parser(), extensionRegistry); if (subBuilder != null) { - subBuilder.mergeFrom((serialization.Bigdl.BigDLTensor) value_); + subBuilder.mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_); value_ = subBuilder.buildPartial(); } valueCase_ = 10; @@ -10333,56 +10333,56 @@ private AttrValue( break; } case 98: { - serialization.Bigdl.InitMethod.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder subBuilder = null; if (valueCase_ == 12) { - subBuilder = ((serialization.Bigdl.InitMethod) value_).toBuilder(); + subBuilder = ((com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_).toBuilder(); } value_ = - input.readMessage(serialization.Bigdl.InitMethod.parser(), extensionRegistry); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.parser(), extensionRegistry); if (subBuilder != null) { - subBuilder.mergeFrom((serialization.Bigdl.InitMethod) value_); + subBuilder.mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_); value_ = subBuilder.buildPartial(); } valueCase_ = 12; break; } case 106: { - serialization.Bigdl.BigDLModule.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder subBuilder = null; if (valueCase_ == 13) { - subBuilder = ((serialization.Bigdl.BigDLModule) value_).toBuilder(); + subBuilder = ((com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_).toBuilder(); } value_ = - input.readMessage(serialization.Bigdl.BigDLModule.parser(), extensionRegistry); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.parser(), extensionRegistry); if (subBuilder != null) { - subBuilder.mergeFrom((serialization.Bigdl.BigDLModule) value_); + subBuilder.mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_); value_ = subBuilder.buildPartial(); } valueCase_ = 13; break; } case 114: { - serialization.Bigdl.NameAttrList.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder subBuilder = null; if (valueCase_ == 14) { - subBuilder = ((serialization.Bigdl.NameAttrList) value_).toBuilder(); + subBuilder = ((com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_).toBuilder(); } value_ = - input.readMessage(serialization.Bigdl.NameAttrList.parser(), extensionRegistry); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.parser(), extensionRegistry); if (subBuilder != null) { - subBuilder.mergeFrom((serialization.Bigdl.NameAttrList) value_); + subBuilder.mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_); value_ = subBuilder.buildPartial(); } valueCase_ = 14; break; } case 122: { - serialization.Bigdl.AttrValue.ArrayValue.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.Builder subBuilder = null; if (valueCase_ == 15) { - subBuilder = ((serialization.Bigdl.AttrValue.ArrayValue) value_).toBuilder(); + subBuilder = ((com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_).toBuilder(); } value_ = - input.readMessage(serialization.Bigdl.AttrValue.ArrayValue.parser(), extensionRegistry); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.parser(), extensionRegistry); if (subBuilder != null) { - subBuilder.mergeFrom((serialization.Bigdl.AttrValue.ArrayValue) value_); + subBuilder.mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_); value_ = subBuilder.buildPartial(); } valueCase_ = 15; @@ -10409,14 +10409,14 @@ private AttrValue( break; } case 146: { - serialization.Bigdl.Shape.Builder subBuilder = null; + com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder subBuilder = null; if (valueCase_ == 18) { - subBuilder = ((serialization.Bigdl.Shape) value_).toBuilder(); + subBuilder = ((com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_).toBuilder(); } value_ = - input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Shape.parser(), extensionRegistry); if (subBuilder != null) { - subBuilder.mergeFrom((serialization.Bigdl.Shape) value_); + subBuilder.mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_); value_ = subBuilder.buildPartial(); } valueCase_ = 18; @@ -10436,18 +10436,18 @@ private AttrValue( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_AttrValue_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_AttrValue_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.AttrValue.class, serialization.Bigdl.AttrValue.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.class, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder.class); } public interface ArrayValueOrBuilder extends - // @@protoc_insertion_point(interface_extends:serialization.AttrValue.ArrayValue) + // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue) com.google.protobuf.MessageOrBuilder { /** @@ -10456,13 +10456,13 @@ public interface ArrayValueOrBuilder extends int getSize(); /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ int getDatatypeValue(); /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ - serialization.Bigdl.DataType getDatatype(); + com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype(); /** * 
@@ -10629,24 +10629,24 @@ public interface ArrayValueOrBuilder extends
        * "Array(Regularizer)"
        *
* - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - java.util.List + java.util.List getRegularizerList(); /** * 
        * "Array(Regularizer)"
        *
* - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - serialization.Bigdl.Regularizer getRegularizer(int index); + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getRegularizer(int index); /** * 
        * "Array(Regularizer)"
        *
* - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ int getRegularizerCount(); /** @@ -10654,18 +10654,18 @@ public interface ArrayValueOrBuilder extends * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - java.util.List + java.util.List getRegularizerOrBuilderList(); /** * 
        * "Array(Regularizer)"
        *
* - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( int index); /** @@ -10673,24 +10673,24 @@ serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - java.util.List + java.util.List getTensorList(); /** * 
        * "Array(BigDLTensor)"
        *
* - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - serialization.Bigdl.BigDLTensor getTensor(int index); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getTensor(int index); /** * 
        * "Array(BigDLTensor)"
        *
* - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ int getTensorCount(); /** @@ -10698,18 +10698,18 @@ serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - java.util.List + java.util.List getTensorOrBuilderList(); /** * 
        * "Array(BigDLTensor)"
        *
* - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( int index); /** @@ -10717,15 +10717,15 @@ serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ - java.util.List getVariableFormatList(); + java.util.List getVariableFormatList(); /** * 
        * "Array(VariableFormat)"
        *
* - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ int getVariableFormatCount(); /** @@ -10733,15 +10733,15 @@ serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ - serialization.Bigdl.VarFormat getVariableFormat(int index); + com.intel.analytics.bigdl.serialization.Bigdl.VarFormat getVariableFormat(int index); /** * 
        * "Array(VariableFormat)"
        *
* - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ java.util.List getVariableFormatValueList(); @@ -10750,7 +10750,7 @@ serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ int getVariableFormatValue(int index); @@ -10759,24 +10759,24 @@ serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - java.util.List + java.util.List getInitMethodList(); /** * 
        * "Array(BigDLTensor)"
        *
* - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - serialization.Bigdl.InitMethod getInitMethod(int index); + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getInitMethod(int index); /** * 
        * "Array(BigDLTensor)"
        *
* - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ int getInitMethodCount(); /** @@ -10784,18 +10784,18 @@ serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - java.util.List + java.util.List getInitMethodOrBuilderList(); /** * 
        * "Array(BigDLTensor)"
        *
* - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( int index); /** @@ -10803,24 +10803,24 @@ serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - java.util.List + java.util.List getBigDLModuleList(); /** * 
        * "Array(BigDLModel)"
        *
* - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - serialization.Bigdl.BigDLModule getBigDLModule(int index); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getBigDLModule(int index); /** * 
        * "Array(BigDLModel)"
        *
* - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ int getBigDLModuleCount(); /** @@ -10828,57 +10828,57 @@ serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - java.util.List + java.util.List getBigDLModuleOrBuilderList(); /** * 
        * "Array(BigDLModel)"
        *
* - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleOrBuilder( int index); /** * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - java.util.List + java.util.List getNameAttrListList(); /** * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - serialization.Bigdl.NameAttrList getNameAttrList(int index); + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getNameAttrList(int index); /** * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ int getNameAttrListCount(); /** * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - java.util.List + java.util.List getNameAttrListOrBuilderList(); /** * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( int index); /** @@ -10886,15 +10886,15 @@ serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ - java.util.List getDataFormatList(); + java.util.List getDataFormatList(); /** * 
        * "Array(DataFormat)"
        *
* - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ int getDataFormatCount(); /** @@ -10902,15 +10902,15 @@ serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ - serialization.Bigdl.InputDataFormat getDataFormat(int index); + com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat getDataFormat(int index); /** * 
        * "Array(DataFormat)"
        *
* - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ java.util.List getDataFormatValueList(); @@ -10919,7 +10919,7 @@ serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ int getDataFormatValue(int index); @@ -10972,24 +10972,24 @@ com.google.protobuf.AnyOrBuilder getCustomOrBuilder( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - java.util.List + java.util.List getShapeList(); /** * 
        * "Array(Shape)"
        *
* - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - serialization.Bigdl.Shape getShape(int index); + com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape(int index); /** * 
        * "Array(Shape)"
        *
* - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ int getShapeCount(); /** @@ -10997,26 +10997,26 @@ com.google.protobuf.AnyOrBuilder getCustomOrBuilder( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - java.util.List + java.util.List getShapeOrBuilderList(); /** * 
        * "Array(Shape)"
        *
* - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( int index); } /** - * Protobuf type {@code serialization.AttrValue.ArrayValue} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue} */ public static final class ArrayValue extends com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:serialization.AttrValue.ArrayValue) + // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue) ArrayValueOrBuilder { private static final long serialVersionUID = 0L; // Use ArrayValue.newBuilder() to construct. @@ -11198,20 +11198,20 @@ private ArrayValue( } case 74: { if (!((mutable_bitField0_ & 0x00000100) == 0x00000100)) { - regularizer_ = new java.util.ArrayList(); + regularizer_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000100; } regularizer_.add( - input.readMessage(serialization.Bigdl.Regularizer.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.parser(), extensionRegistry)); break; } case 82: { if (!((mutable_bitField0_ & 0x00000200) == 0x00000200)) { - tensor_ = new java.util.ArrayList(); + tensor_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000200; } tensor_.add( - input.readMessage(serialization.Bigdl.BigDLTensor.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.parser(), extensionRegistry)); break; } case 88: { @@ -11239,29 +11239,29 @@ private ArrayValue( } case 98: { if (!((mutable_bitField0_ & 0x00000800) == 0x00000800)) { - initMethod_ = new java.util.ArrayList(); + initMethod_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000800; } initMethod_.add( - input.readMessage(serialization.Bigdl.InitMethod.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.parser(), extensionRegistry)); break; } case 106: { if (!((mutable_bitField0_ & 0x00001000) == 0x00001000)) { - bigDLModule_ = new java.util.ArrayList(); + bigDLModule_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00001000; } bigDLModule_.add( - input.readMessage(serialization.Bigdl.BigDLModule.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.parser(), extensionRegistry)); break; } case 114: { if (!((mutable_bitField0_ & 0x00002000) == 0x00002000)) { - nameAttrList_ = new java.util.ArrayList(); + nameAttrList_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00002000; } nameAttrList_.add( - input.readMessage(serialization.Bigdl.NameAttrList.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.parser(), extensionRegistry)); break; } case 120: { @@ -11298,11 +11298,11 @@ private ArrayValue( } case 138: { if (!((mutable_bitField0_ & 0x00010000) == 0x00010000)) { - shape_ = new java.util.ArrayList(); + shape_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00010000; } shape_.add( - input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Shape.parser(), extensionRegistry)); break; } } @@ -11364,14 +11364,14 @@ private ArrayValue( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_AttrValue_ArrayValue_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_ArrayValue_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_AttrValue_ArrayValue_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_ArrayValue_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.AttrValue.ArrayValue.class, serialization.Bigdl.AttrValue.ArrayValue.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.class, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.Builder.class); } private int bitField0_; @@ -11387,17 +11387,17 @@ public int getSize() { public static final int DATATYPE_FIELD_NUMBER = 2; private int datatype_; /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ public int getDatatypeValue() { return datatype_; } /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ - public serialization.Bigdl.DataType getDatatype() { - serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); - return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype() { + com.intel.analytics.bigdl.serialization.Bigdl.DataType result = com.intel.analytics.bigdl.serialization.Bigdl.DataType.valueOf(datatype_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.DataType.UNRECOGNIZED : result; } public static final int I32_FIELD_NUMBER = 3; @@ -11621,15 +11621,15 @@ public boolean getBoolean(int index) { private int booleanMemoizedSerializedSize = -1; public static final int REGULARIZER_FIELD_NUMBER = 9; - private java.util.List regularizer_; + private java.util.List regularizer_; /** * 
        * "Array(Regularizer)"
        *
* - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public java.util.List getRegularizerList() { + public java.util.List getRegularizerList() { return regularizer_; } /** @@ -11637,9 +11637,9 @@ public java.util.List getRegularizerList() { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public java.util.List + public java.util.List getRegularizerOrBuilderList() { return regularizer_; } @@ -11648,7 +11648,7 @@ public java.util.List getRegularizerList() { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public int getRegularizerCount() { return regularizer_.size(); @@ -11658,9 +11658,9 @@ public int getRegularizerCount() { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public serialization.Bigdl.Regularizer getRegularizer(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getRegularizer(int index) { return regularizer_.get(index); } /** @@ -11668,23 +11668,23 @@ public serialization.Bigdl.Regularizer getRegularizer(int index) { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( int index) { return regularizer_.get(index); } public static final int TENSOR_FIELD_NUMBER = 10; - private java.util.List tensor_; + private java.util.List tensor_; /** * 
        * "Array(BigDLTensor)"
        *
* - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public java.util.List getTensorList() { + public java.util.List getTensorList() { return tensor_; } /** @@ -11692,9 +11692,9 @@ public java.util.List getTensorList() { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public java.util.List + public java.util.List getTensorOrBuilderList() { return tensor_; } @@ -11703,7 +11703,7 @@ public java.util.List getTensorList() { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public int getTensorCount() { return tensor_.size(); @@ -11713,9 +11713,9 @@ public int getTensorCount() { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public serialization.Bigdl.BigDLTensor getTensor(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getTensor(int index) { return tensor_.get(index); } /** @@ -11723,9 +11723,9 @@ public serialization.Bigdl.BigDLTensor getTensor(int index) { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( int index) { return tensor_.get(index); } @@ -11733,12 +11733,12 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( public static final int VARIABLEFORMAT_FIELD_NUMBER = 11; private java.util.List variableFormat_; private static final com.google.protobuf.Internal.ListAdapter.Converter< - java.lang.Integer, serialization.Bigdl.VarFormat> variableFormat_converter_ = + java.lang.Integer, com.intel.analytics.bigdl.serialization.Bigdl.VarFormat> variableFormat_converter_ = new com.google.protobuf.Internal.ListAdapter.Converter< - java.lang.Integer, serialization.Bigdl.VarFormat>() { - public serialization.Bigdl.VarFormat convert(java.lang.Integer from) { - serialization.Bigdl.VarFormat result = serialization.Bigdl.VarFormat.valueOf(from); - return result == null ? serialization.Bigdl.VarFormat.UNRECOGNIZED : result; + java.lang.Integer, com.intel.analytics.bigdl.serialization.Bigdl.VarFormat>() { + public com.intel.analytics.bigdl.serialization.Bigdl.VarFormat convert(java.lang.Integer from) { + com.intel.analytics.bigdl.serialization.Bigdl.VarFormat result = com.intel.analytics.bigdl.serialization.Bigdl.VarFormat.valueOf(from); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.VarFormat.UNRECOGNIZED : result; } }; /** @@ -11746,18 +11746,18 @@ public serialization.Bigdl.VarFormat convert(java.lang.Integer from) { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ - public java.util.List getVariableFormatList() { + public java.util.List getVariableFormatList() { return new com.google.protobuf.Internal.ListAdapter< - java.lang.Integer, serialization.Bigdl.VarFormat>(variableFormat_, variableFormat_converter_); + java.lang.Integer, com.intel.analytics.bigdl.serialization.Bigdl.VarFormat>(variableFormat_, variableFormat_converter_); } /** * 
        * "Array(VariableFormat)"
        *
* - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public int getVariableFormatCount() { return variableFormat_.size(); @@ -11767,9 +11767,9 @@ public int getVariableFormatCount() { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ - public serialization.Bigdl.VarFormat getVariableFormat(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.VarFormat getVariableFormat(int index) { return variableFormat_converter_.convert(variableFormat_.get(index)); } /** @@ -11777,7 +11777,7 @@ public serialization.Bigdl.VarFormat getVariableFormat(int index) { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public java.util.List getVariableFormatValueList() { @@ -11788,7 +11788,7 @@ public serialization.Bigdl.VarFormat getVariableFormat(int index) { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public int getVariableFormatValue(int index) { return variableFormat_.get(index); @@ -11796,15 +11796,15 @@ public int getVariableFormatValue(int index) { private int variableFormatMemoizedSerializedSize; public static final int INITMETHOD_FIELD_NUMBER = 12; - private java.util.List initMethod_; + private java.util.List initMethod_; /** * 
        * "Array(BigDLTensor)"
        *
* - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public java.util.List getInitMethodList() { + public java.util.List getInitMethodList() { return initMethod_; } /** @@ -11812,9 +11812,9 @@ public java.util.List getInitMethodList() { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public java.util.List + public java.util.List getInitMethodOrBuilderList() { return initMethod_; } @@ -11823,7 +11823,7 @@ public java.util.List getInitMethodList() { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public int getInitMethodCount() { return initMethod_.size(); @@ -11833,9 +11833,9 @@ public int getInitMethodCount() { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public serialization.Bigdl.InitMethod getInitMethod(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getInitMethod(int index) { return initMethod_.get(index); } /** @@ -11843,23 +11843,23 @@ public serialization.Bigdl.InitMethod getInitMethod(int index) { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( int index) { return initMethod_.get(index); } public static final int BIGDLMODULE_FIELD_NUMBER = 13; - private java.util.List bigDLModule_; + private java.util.List bigDLModule_; /** * 
        * "Array(BigDLModel)"
        *
* - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public java.util.List getBigDLModuleList() { + public java.util.List getBigDLModuleList() { return bigDLModule_; } /** @@ -11867,9 +11867,9 @@ public java.util.List getBigDLModuleList() { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public java.util.List + public java.util.List getBigDLModuleOrBuilderList() { return bigDLModule_; } @@ -11878,7 +11878,7 @@ public java.util.List getBigDLModuleList() { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public int getBigDLModuleCount() { return bigDLModule_.size(); @@ -11888,9 +11888,9 @@ public int getBigDLModuleCount() { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public serialization.Bigdl.BigDLModule getBigDLModule(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getBigDLModule(int index) { return bigDLModule_.get(index); } /** @@ -11898,31 +11898,31 @@ public serialization.Bigdl.BigDLModule getBigDLModule(int index) { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleOrBuilder( int index) { return bigDLModule_.get(index); } public static final int NAMEATTRLIST_FIELD_NUMBER = 14; - private java.util.List nameAttrList_; + private java.util.List nameAttrList_; /** * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public java.util.List getNameAttrListList() { + public java.util.List getNameAttrListList() { return nameAttrList_; } /** * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public java.util.List + public java.util.List getNameAttrListOrBuilderList() { return nameAttrList_; } @@ -11930,7 +11930,7 @@ public java.util.List getNameAttrListList() { * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public int getNameAttrListCount() { return nameAttrList_.size(); @@ -11939,18 +11939,18 @@ public int getNameAttrListCount() { * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public serialization.Bigdl.NameAttrList getNameAttrList(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getNameAttrList(int index) { return nameAttrList_.get(index); } /** * 
        *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( int index) { return nameAttrList_.get(index); } @@ -11958,12 +11958,12 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( public static final int DATAFORMAT_FIELD_NUMBER = 15; private java.util.List dataFormat_; private static final com.google.protobuf.Internal.ListAdapter.Converter< - java.lang.Integer, serialization.Bigdl.InputDataFormat> dataFormat_converter_ = + java.lang.Integer, com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat> dataFormat_converter_ = new com.google.protobuf.Internal.ListAdapter.Converter< - java.lang.Integer, serialization.Bigdl.InputDataFormat>() { - public serialization.Bigdl.InputDataFormat convert(java.lang.Integer from) { - serialization.Bigdl.InputDataFormat result = serialization.Bigdl.InputDataFormat.valueOf(from); - return result == null ? serialization.Bigdl.InputDataFormat.UNRECOGNIZED : result; + java.lang.Integer, com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat>() { + public com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat convert(java.lang.Integer from) { + com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat result = com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat.valueOf(from); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat.UNRECOGNIZED : result; } }; /** @@ -11971,18 +11971,18 @@ public serialization.Bigdl.InputDataFormat convert(java.lang.Integer from) { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ - public java.util.List getDataFormatList() { + public java.util.List getDataFormatList() { return new com.google.protobuf.Internal.ListAdapter< - java.lang.Integer, serialization.Bigdl.InputDataFormat>(dataFormat_, dataFormat_converter_); + java.lang.Integer, com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat>(dataFormat_, dataFormat_converter_); } /** * 
        * "Array(DataFormat)"
        *
* - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public int getDataFormatCount() { return dataFormat_.size(); @@ -11992,9 +11992,9 @@ public int getDataFormatCount() { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ - public serialization.Bigdl.InputDataFormat getDataFormat(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat getDataFormat(int index) { return dataFormat_converter_.convert(dataFormat_.get(index)); } /** @@ -12002,7 +12002,7 @@ public serialization.Bigdl.InputDataFormat getDataFormat(int index) { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public java.util.List getDataFormatValueList() { @@ -12013,7 +12013,7 @@ public serialization.Bigdl.InputDataFormat getDataFormat(int index) { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public int getDataFormatValue(int index) { return dataFormat_.get(index); @@ -12076,15 +12076,15 @@ public com.google.protobuf.AnyOrBuilder getCustomOrBuilder( } public static final int SHAPE_FIELD_NUMBER = 17; - private java.util.List shape_; + private java.util.List shape_; /** * 
        * "Array(Shape)"
        *
* - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public java.util.List getShapeList() { + public java.util.List getShapeList() { return shape_; } /** @@ -12092,9 +12092,9 @@ public java.util.List getShapeList() { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public java.util.List + public java.util.List getShapeOrBuilderList() { return shape_; } @@ -12103,7 +12103,7 @@ public java.util.List getShapeList() { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public int getShapeCount() { return shape_.size(); @@ -12113,9 +12113,9 @@ public int getShapeCount() { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public serialization.Bigdl.Shape getShape(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape(int index) { return shape_.get(index); } /** @@ -12123,9 +12123,9 @@ public serialization.Bigdl.Shape getShape(int index) { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( int index) { return shape_.get(index); } @@ -12146,7 +12146,7 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) if (size_ != 0) { output.writeInt32(1, size_); } - if (datatype_ != serialization.Bigdl.DataType.INT32.getNumber()) { + if (datatype_ != com.intel.analytics.bigdl.serialization.Bigdl.DataType.INT32.getNumber()) { output.writeEnum(2, datatype_); } if (getI32List().size() > 0) { @@ -12234,7 +12234,7 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeInt32Size(1, size_); } - if (datatype_ != serialization.Bigdl.DataType.INT32.getNumber()) { + if (datatype_ != com.intel.analytics.bigdl.serialization.Bigdl.DataType.INT32.getNumber()) { size += com.google.protobuf.CodedOutputStream .computeEnumSize(2, datatype_); } @@ -12369,10 +12369,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof serialization.Bigdl.AttrValue.ArrayValue)) { + if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue)) { return super.equals(obj); } - serialization.Bigdl.AttrValue.ArrayValue other = (serialization.Bigdl.AttrValue.ArrayValue) obj; + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue other = (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) obj; boolean result = true; result = result && (getSize() @@ -12486,69 +12486,69 @@ public int hashCode() { return hash; } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom(byte[] data) + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.AttrValue.ArrayValue parseDelimitedFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static serialization.Bigdl.AttrValue.ArrayValue parseDelimitedFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -12560,7 +12560,7 @@ public static serialization.Bigdl.AttrValue.ArrayValue parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(serialization.Bigdl.AttrValue.ArrayValue prototype) { + public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -12575,25 +12575,25 @@ protected Builder newBuilderForType( return builder; } /** - * Protobuf type {@code serialization.AttrValue.ArrayValue} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:serialization.AttrValue.ArrayValue) - serialization.Bigdl.AttrValue.ArrayValueOrBuilder { + // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue) + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValueOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_AttrValue_ArrayValue_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_ArrayValue_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_AttrValue_ArrayValue_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_ArrayValue_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.AttrValue.ArrayValue.class, serialization.Bigdl.AttrValue.ArrayValue.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.class, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.Builder.class); } - // Construct using serialization.Bigdl.AttrValue.ArrayValue.newBuilder() + // Construct using com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -12684,23 +12684,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return serialization.Bigdl.internal_static_serialization_AttrValue_ArrayValue_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_ArrayValue_descriptor; } - public serialization.Bigdl.AttrValue.ArrayValue getDefaultInstanceForType() { - return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue getDefaultInstanceForType() { + return com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); } - public serialization.Bigdl.AttrValue.ArrayValue build() { - serialization.Bigdl.AttrValue.ArrayValue result = buildPartial(); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue build() { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public serialization.Bigdl.AttrValue.ArrayValue buildPartial() { - serialization.Bigdl.AttrValue.ArrayValue result = new serialization.Bigdl.AttrValue.ArrayValue(this); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue buildPartial() { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue result = new com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.size_ = size_; @@ -12840,16 +12840,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof serialization.Bigdl.AttrValue.ArrayValue) { - return mergeFrom((serialization.Bigdl.AttrValue.ArrayValue)other); + if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) { + return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(serialization.Bigdl.AttrValue.ArrayValue other) { - if (other == serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance()) return this; + public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue other) { + if (other == com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance()) return this; if (other.getSize() != 0) { setSize(other.getSize()); } @@ -13131,11 +13131,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - serialization.Bigdl.AttrValue.ArrayValue parsedMessage = null; + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (serialization.Bigdl.AttrValue.ArrayValue) e.getUnfinishedMessage(); + parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -13174,13 +13174,13 @@ public Builder clearSize() { private int datatype_ = 0; /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ public int getDatatypeValue() { return datatype_; } /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ public Builder setDatatypeValue(int value) { datatype_ = value; @@ -13188,16 +13188,16 @@ public Builder setDatatypeValue(int value) { return this; } /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ - public serialization.Bigdl.DataType getDatatype() { - serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(datatype_); - return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.DataType getDatatype() { + com.intel.analytics.bigdl.serialization.Bigdl.DataType result = com.intel.analytics.bigdl.serialization.Bigdl.DataType.valueOf(datatype_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.DataType.UNRECOGNIZED : result; } /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ - public Builder setDatatype(serialization.Bigdl.DataType value) { + public Builder setDatatype(com.intel.analytics.bigdl.serialization.Bigdl.DataType value) { if (value == null) { throw new NullPointerException(); } @@ -13207,7 +13207,7 @@ public Builder setDatatype(serialization.Bigdl.DataType value) { return this; } /** - * .serialization.DataType datatype = 2; + * .com.intel.analytics.bigdl.serialization.DataType datatype = 2; */ public Builder clearDatatype() { @@ -13816,26 +13816,26 @@ public Builder clearBoolean() { return this; } - private java.util.List regularizer_ = + private java.util.List regularizer_ = java.util.Collections.emptyList(); private void ensureRegularizerIsMutable() { if (!((bitField0_ & 0x00000100) == 0x00000100)) { - regularizer_ = new java.util.ArrayList(regularizer_); + regularizer_ = new java.util.ArrayList(regularizer_); bitField0_ |= 0x00000100; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Regularizer, serialization.Bigdl.Regularizer.Builder, serialization.Bigdl.RegularizerOrBuilder> regularizerBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder, com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder> regularizerBuilder_; /** * 
          * "Array(Regularizer)"
          *
* - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public java.util.List getRegularizerList() { + public java.util.List getRegularizerList() { if (regularizerBuilder_ == null) { return java.util.Collections.unmodifiableList(regularizer_); } else { @@ -13847,7 +13847,7 @@ public java.util.List getRegularizerList() { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public int getRegularizerCount() { if (regularizerBuilder_ == null) { @@ -13861,9 +13861,9 @@ public int getRegularizerCount() { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public serialization.Bigdl.Regularizer getRegularizer(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getRegularizer(int index) { if (regularizerBuilder_ == null) { return regularizer_.get(index); } else { @@ -13875,10 +13875,10 @@ public serialization.Bigdl.Regularizer getRegularizer(int index) { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public Builder setRegularizer( - int index, serialization.Bigdl.Regularizer value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer value) { if (regularizerBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -13896,10 +13896,10 @@ public Builder setRegularizer( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public Builder setRegularizer( - int index, serialization.Bigdl.Regularizer.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder builderForValue) { if (regularizerBuilder_ == null) { ensureRegularizerIsMutable(); regularizer_.set(index, builderForValue.build()); @@ -13914,9 +13914,9 @@ public Builder setRegularizer( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public Builder addRegularizer(serialization.Bigdl.Regularizer value) { + public Builder addRegularizer(com.intel.analytics.bigdl.serialization.Bigdl.Regularizer value) { if (regularizerBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -13934,10 +13934,10 @@ public Builder addRegularizer(serialization.Bigdl.Regularizer value) { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public Builder addRegularizer( - int index, serialization.Bigdl.Regularizer value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer value) { if (regularizerBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -13955,10 +13955,10 @@ public Builder addRegularizer( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public Builder addRegularizer( - serialization.Bigdl.Regularizer.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder builderForValue) { if (regularizerBuilder_ == null) { ensureRegularizerIsMutable(); regularizer_.add(builderForValue.build()); @@ -13973,10 +13973,10 @@ public Builder addRegularizer( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public Builder addRegularizer( - int index, serialization.Bigdl.Regularizer.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder builderForValue) { if (regularizerBuilder_ == null) { ensureRegularizerIsMutable(); regularizer_.add(index, builderForValue.build()); @@ -13991,10 +13991,10 @@ public Builder addRegularizer( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public Builder addAllRegularizer( - java.lang.Iterable values) { + java.lang.Iterable values) { if (regularizerBuilder_ == null) { ensureRegularizerIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -14010,7 +14010,7 @@ public Builder addAllRegularizer( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public Builder clearRegularizer() { if (regularizerBuilder_ == null) { @@ -14027,7 +14027,7 @@ public Builder clearRegularizer() { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ public Builder removeRegularizer(int index) { if (regularizerBuilder_ == null) { @@ -14044,9 +14044,9 @@ public Builder removeRegularizer(int index) { * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public serialization.Bigdl.Regularizer.Builder getRegularizerBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder getRegularizerBuilder( int index) { return getRegularizerFieldBuilder().getBuilder(index); } @@ -14055,9 +14055,9 @@ public serialization.Bigdl.Regularizer.Builder getRegularizerBuilder( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( int index) { if (regularizerBuilder_ == null) { return regularizer_.get(index); } else { @@ -14069,9 +14069,9 @@ public serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public java.util.List + public java.util.List getRegularizerOrBuilderList() { if (regularizerBuilder_ != null) { return regularizerBuilder_.getMessageOrBuilderList(); @@ -14084,41 +14084,41 @@ public serialization.Bigdl.RegularizerOrBuilder getRegularizerOrBuilder( * "Array(Regularizer)" * * - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public serialization.Bigdl.Regularizer.Builder addRegularizerBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder addRegularizerBuilder() { return getRegularizerFieldBuilder().addBuilder( - serialization.Bigdl.Regularizer.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance()); } /** * 
          * "Array(Regularizer)"
          *
* - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public serialization.Bigdl.Regularizer.Builder addRegularizerBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder addRegularizerBuilder( int index) { return getRegularizerFieldBuilder().addBuilder( - index, serialization.Bigdl.Regularizer.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance()); } /** * 
          * "Array(Regularizer)"
          *
* - * repeated .serialization.Regularizer Regularizer = 9; + * repeated .com.intel.analytics.bigdl.serialization.Regularizer Regularizer = 9; */ - public java.util.List + public java.util.List getRegularizerBuilderList() { return getRegularizerFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Regularizer, serialization.Bigdl.Regularizer.Builder, serialization.Bigdl.RegularizerOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder, com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder> getRegularizerFieldBuilder() { if (regularizerBuilder_ == null) { regularizerBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Regularizer, serialization.Bigdl.Regularizer.Builder, serialization.Bigdl.RegularizerOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder, com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder>( regularizer_, ((bitField0_ & 0x00000100) == 0x00000100), getParentForChildren(), @@ -14128,26 +14128,26 @@ public serialization.Bigdl.Regularizer.Builder addRegularizerBuilder( return regularizerBuilder_; } - private java.util.List tensor_ = + private java.util.List tensor_ = java.util.Collections.emptyList(); private void ensureTensorIsMutable() { if (!((bitField0_ & 0x00000200) == 0x00000200)) { - tensor_ = new java.util.ArrayList(tensor_); + tensor_ = new java.util.ArrayList(tensor_); bitField0_ |= 0x00000200; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> tensorBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> tensorBuilder_; /** * 
          * "Array(BigDLTensor)"
          *
* - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public java.util.List getTensorList() { + public java.util.List getTensorList() { if (tensorBuilder_ == null) { return java.util.Collections.unmodifiableList(tensor_); } else { @@ -14159,7 +14159,7 @@ public java.util.List getTensorList() { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public int getTensorCount() { if (tensorBuilder_ == null) { @@ -14173,9 +14173,9 @@ public int getTensorCount() { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public serialization.Bigdl.BigDLTensor getTensor(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getTensor(int index) { if (tensorBuilder_ == null) { return tensor_.get(index); } else { @@ -14187,10 +14187,10 @@ public serialization.Bigdl.BigDLTensor getTensor(int index) { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public Builder setTensor( - int index, serialization.Bigdl.BigDLTensor value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (tensorBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -14208,10 +14208,10 @@ public Builder setTensor( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public Builder setTensor( - int index, serialization.Bigdl.BigDLTensor.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { if (tensorBuilder_ == null) { ensureTensorIsMutable(); tensor_.set(index, builderForValue.build()); @@ -14226,9 +14226,9 @@ public Builder setTensor( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public Builder addTensor(serialization.Bigdl.BigDLTensor value) { + public Builder addTensor(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (tensorBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -14246,10 +14246,10 @@ public Builder addTensor(serialization.Bigdl.BigDLTensor value) { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public Builder addTensor( - int index, serialization.Bigdl.BigDLTensor value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (tensorBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -14267,10 +14267,10 @@ public Builder addTensor( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public Builder addTensor( - serialization.Bigdl.BigDLTensor.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { if (tensorBuilder_ == null) { ensureTensorIsMutable(); tensor_.add(builderForValue.build()); @@ -14285,10 +14285,10 @@ public Builder addTensor( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public Builder addTensor( - int index, serialization.Bigdl.BigDLTensor.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { if (tensorBuilder_ == null) { ensureTensorIsMutable(); tensor_.add(index, builderForValue.build()); @@ -14303,10 +14303,10 @@ public Builder addTensor( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public Builder addAllTensor( - java.lang.Iterable values) { + java.lang.Iterable values) { if (tensorBuilder_ == null) { ensureTensorIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -14322,7 +14322,7 @@ public Builder addAllTensor( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public Builder clearTensor() { if (tensorBuilder_ == null) { @@ -14339,7 +14339,7 @@ public Builder clearTensor() { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ public Builder removeTensor(int index) { if (tensorBuilder_ == null) { @@ -14356,9 +14356,9 @@ public Builder removeTensor(int index) { * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public serialization.Bigdl.BigDLTensor.Builder getTensorBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getTensorBuilder( int index) { return getTensorFieldBuilder().getBuilder(index); } @@ -14367,9 +14367,9 @@ public serialization.Bigdl.BigDLTensor.Builder getTensorBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( int index) { if (tensorBuilder_ == null) { return tensor_.get(index); } else { @@ -14381,9 +14381,9 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public java.util.List + public java.util.List getTensorOrBuilderList() { if (tensorBuilder_ != null) { return tensorBuilder_.getMessageOrBuilderList(); @@ -14396,41 +14396,41 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorOrBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public serialization.Bigdl.BigDLTensor.Builder addTensorBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder addTensorBuilder() { return getTensorFieldBuilder().addBuilder( - serialization.Bigdl.BigDLTensor.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()); } /** * 
          * "Array(BigDLTensor)"
          *
* - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public serialization.Bigdl.BigDLTensor.Builder addTensorBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder addTensorBuilder( int index) { return getTensorFieldBuilder().addBuilder( - index, serialization.Bigdl.BigDLTensor.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()); } /** * 
          * "Array(BigDLTensor)"
          *
* - * repeated .serialization.BigDLTensor tensor = 10; + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor tensor = 10; */ - public java.util.List + public java.util.List getTensorBuilderList() { return getTensorFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> getTensorFieldBuilder() { if (tensorBuilder_ == null) { tensorBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder>( tensor_, ((bitField0_ & 0x00000200) == 0x00000200), getParentForChildren(), @@ -14453,18 +14453,18 @@ private void ensureVariableFormatIsMutable() { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ - public java.util.List getVariableFormatList() { + public java.util.List getVariableFormatList() { return new com.google.protobuf.Internal.ListAdapter< - java.lang.Integer, serialization.Bigdl.VarFormat>(variableFormat_, variableFormat_converter_); + java.lang.Integer, com.intel.analytics.bigdl.serialization.Bigdl.VarFormat>(variableFormat_, variableFormat_converter_); } /** * 
          * "Array(VariableFormat)"
          *
* - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public int getVariableFormatCount() { return variableFormat_.size(); @@ -14474,9 +14474,9 @@ public int getVariableFormatCount() { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ - public serialization.Bigdl.VarFormat getVariableFormat(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.VarFormat getVariableFormat(int index) { return variableFormat_converter_.convert(variableFormat_.get(index)); } /** @@ -14484,10 +14484,10 @@ public serialization.Bigdl.VarFormat getVariableFormat(int index) { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public Builder setVariableFormat( - int index, serialization.Bigdl.VarFormat value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.VarFormat value) { if (value == null) { throw new NullPointerException(); } @@ -14501,9 +14501,9 @@ public Builder setVariableFormat( * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ - public Builder addVariableFormat(serialization.Bigdl.VarFormat value) { + public Builder addVariableFormat(com.intel.analytics.bigdl.serialization.Bigdl.VarFormat value) { if (value == null) { throw new NullPointerException(); } @@ -14517,12 +14517,12 @@ public Builder addVariableFormat(serialization.Bigdl.VarFormat value) { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public Builder addAllVariableFormat( - java.lang.Iterable values) { + java.lang.Iterable values) { ensureVariableFormatIsMutable(); - for (serialization.Bigdl.VarFormat value : values) { + for (com.intel.analytics.bigdl.serialization.Bigdl.VarFormat value : values) { variableFormat_.add(value.getNumber()); } onChanged(); @@ -14533,7 +14533,7 @@ public Builder addAllVariableFormat( * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public Builder clearVariableFormat() { variableFormat_ = java.util.Collections.emptyList(); @@ -14546,7 +14546,7 @@ public Builder clearVariableFormat() { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public java.util.List getVariableFormatValueList() { @@ -14557,7 +14557,7 @@ public Builder clearVariableFormat() { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public int getVariableFormatValue(int index) { return variableFormat_.get(index); @@ -14567,7 +14567,7 @@ public int getVariableFormatValue(int index) { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public Builder setVariableFormatValue( int index, int value) { @@ -14581,7 +14581,7 @@ public Builder setVariableFormatValue( * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public Builder addVariableFormatValue(int value) { ensureVariableFormatIsMutable(); @@ -14594,7 +14594,7 @@ public Builder addVariableFormatValue(int value) { * "Array(VariableFormat)" * * - * repeated .serialization.VarFormat variableFormat = 11; + * repeated .com.intel.analytics.bigdl.serialization.VarFormat variableFormat = 11; */ public Builder addAllVariableFormatValue( java.lang.Iterable values) { @@ -14606,26 +14606,26 @@ public Builder addAllVariableFormatValue( return this; } - private java.util.List initMethod_ = + private java.util.List initMethod_ = java.util.Collections.emptyList(); private void ensureInitMethodIsMutable() { if (!((bitField0_ & 0x00000800) == 0x00000800)) { - initMethod_ = new java.util.ArrayList(initMethod_); + initMethod_ = new java.util.ArrayList(initMethod_); bitField0_ |= 0x00000800; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.InitMethod, serialization.Bigdl.InitMethod.Builder, serialization.Bigdl.InitMethodOrBuilder> initMethodBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder, com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder> initMethodBuilder_; /** * 
          * "Array(BigDLTensor)"
          *
* - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public java.util.List getInitMethodList() { + public java.util.List getInitMethodList() { if (initMethodBuilder_ == null) { return java.util.Collections.unmodifiableList(initMethod_); } else { @@ -14637,7 +14637,7 @@ public java.util.List getInitMethodList() { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public int getInitMethodCount() { if (initMethodBuilder_ == null) { @@ -14651,9 +14651,9 @@ public int getInitMethodCount() { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public serialization.Bigdl.InitMethod getInitMethod(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getInitMethod(int index) { if (initMethodBuilder_ == null) { return initMethod_.get(index); } else { @@ -14665,10 +14665,10 @@ public serialization.Bigdl.InitMethod getInitMethod(int index) { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public Builder setInitMethod( - int index, serialization.Bigdl.InitMethod value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod value) { if (initMethodBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -14686,10 +14686,10 @@ public Builder setInitMethod( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public Builder setInitMethod( - int index, serialization.Bigdl.InitMethod.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder builderForValue) { if (initMethodBuilder_ == null) { ensureInitMethodIsMutable(); initMethod_.set(index, builderForValue.build()); @@ -14704,9 +14704,9 @@ public Builder setInitMethod( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public Builder addInitMethod(serialization.Bigdl.InitMethod value) { + public Builder addInitMethod(com.intel.analytics.bigdl.serialization.Bigdl.InitMethod value) { if (initMethodBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -14724,10 +14724,10 @@ public Builder addInitMethod(serialization.Bigdl.InitMethod value) { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public Builder addInitMethod( - int index, serialization.Bigdl.InitMethod value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod value) { if (initMethodBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -14745,10 +14745,10 @@ public Builder addInitMethod( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public Builder addInitMethod( - serialization.Bigdl.InitMethod.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder builderForValue) { if (initMethodBuilder_ == null) { ensureInitMethodIsMutable(); initMethod_.add(builderForValue.build()); @@ -14763,10 +14763,10 @@ public Builder addInitMethod( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public Builder addInitMethod( - int index, serialization.Bigdl.InitMethod.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder builderForValue) { if (initMethodBuilder_ == null) { ensureInitMethodIsMutable(); initMethod_.add(index, builderForValue.build()); @@ -14781,10 +14781,10 @@ public Builder addInitMethod( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public Builder addAllInitMethod( - java.lang.Iterable values) { + java.lang.Iterable values) { if (initMethodBuilder_ == null) { ensureInitMethodIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -14800,7 +14800,7 @@ public Builder addAllInitMethod( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public Builder clearInitMethod() { if (initMethodBuilder_ == null) { @@ -14817,7 +14817,7 @@ public Builder clearInitMethod() { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ public Builder removeInitMethod(int index) { if (initMethodBuilder_ == null) { @@ -14834,9 +14834,9 @@ public Builder removeInitMethod(int index) { * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public serialization.Bigdl.InitMethod.Builder getInitMethodBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder getInitMethodBuilder( int index) { return getInitMethodFieldBuilder().getBuilder(index); } @@ -14845,9 +14845,9 @@ public serialization.Bigdl.InitMethod.Builder getInitMethodBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( int index) { if (initMethodBuilder_ == null) { return initMethod_.get(index); } else { @@ -14859,9 +14859,9 @@ public serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public java.util.List + public java.util.List getInitMethodOrBuilderList() { if (initMethodBuilder_ != null) { return initMethodBuilder_.getMessageOrBuilderList(); @@ -14874,41 +14874,41 @@ public serialization.Bigdl.InitMethodOrBuilder getInitMethodOrBuilder( * "Array(BigDLTensor)" * * - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public serialization.Bigdl.InitMethod.Builder addInitMethodBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder addInitMethodBuilder() { return getInitMethodFieldBuilder().addBuilder( - serialization.Bigdl.InitMethod.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance()); } /** * 
          * "Array(BigDLTensor)"
          *
* - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public serialization.Bigdl.InitMethod.Builder addInitMethodBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder addInitMethodBuilder( int index) { return getInitMethodFieldBuilder().addBuilder( - index, serialization.Bigdl.InitMethod.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance()); } /** * 
          * "Array(BigDLTensor)"
          *
* - * repeated .serialization.InitMethod initMethod = 12; + * repeated .com.intel.analytics.bigdl.serialization.InitMethod initMethod = 12; */ - public java.util.List + public java.util.List getInitMethodBuilderList() { return getInitMethodFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.InitMethod, serialization.Bigdl.InitMethod.Builder, serialization.Bigdl.InitMethodOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder, com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder> getInitMethodFieldBuilder() { if (initMethodBuilder_ == null) { initMethodBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.InitMethod, serialization.Bigdl.InitMethod.Builder, serialization.Bigdl.InitMethodOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder, com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder>( initMethod_, ((bitField0_ & 0x00000800) == 0x00000800), getParentForChildren(), @@ -14918,26 +14918,26 @@ public serialization.Bigdl.InitMethod.Builder addInitMethodBuilder( return initMethodBuilder_; } - private java.util.List bigDLModule_ = + private java.util.List bigDLModule_ = java.util.Collections.emptyList(); private void ensureBigDLModuleIsMutable() { if (!((bitField0_ & 0x00001000) == 0x00001000)) { - bigDLModule_ = new java.util.ArrayList(bigDLModule_); + bigDLModule_ = new java.util.ArrayList(bigDLModule_); bitField0_ |= 0x00001000; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> bigDLModuleBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder> bigDLModuleBuilder_; /** * 
          * "Array(BigDLModel)"
          *
* - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public java.util.List getBigDLModuleList() { + public java.util.List getBigDLModuleList() { if (bigDLModuleBuilder_ == null) { return java.util.Collections.unmodifiableList(bigDLModule_); } else { @@ -14949,7 +14949,7 @@ public java.util.List getBigDLModuleList() { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public int getBigDLModuleCount() { if (bigDLModuleBuilder_ == null) { @@ -14963,9 +14963,9 @@ public int getBigDLModuleCount() { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public serialization.Bigdl.BigDLModule getBigDLModule(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getBigDLModule(int index) { if (bigDLModuleBuilder_ == null) { return bigDLModule_.get(index); } else { @@ -14977,10 +14977,10 @@ public serialization.Bigdl.BigDLModule getBigDLModule(int index) { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public Builder setBigDLModule( - int index, serialization.Bigdl.BigDLModule value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule value) { if (bigDLModuleBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -14998,10 +14998,10 @@ public Builder setBigDLModule( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public Builder setBigDLModule( - int index, serialization.Bigdl.BigDLModule.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder builderForValue) { if (bigDLModuleBuilder_ == null) { ensureBigDLModuleIsMutable(); bigDLModule_.set(index, builderForValue.build()); @@ -15016,9 +15016,9 @@ public Builder setBigDLModule( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public Builder addBigDLModule(serialization.Bigdl.BigDLModule value) { + public Builder addBigDLModule(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule value) { if (bigDLModuleBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -15036,10 +15036,10 @@ public Builder addBigDLModule(serialization.Bigdl.BigDLModule value) { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public Builder addBigDLModule( - int index, serialization.Bigdl.BigDLModule value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule value) { if (bigDLModuleBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -15057,10 +15057,10 @@ public Builder addBigDLModule( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public Builder addBigDLModule( - serialization.Bigdl.BigDLModule.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder builderForValue) { if (bigDLModuleBuilder_ == null) { ensureBigDLModuleIsMutable(); bigDLModule_.add(builderForValue.build()); @@ -15075,10 +15075,10 @@ public Builder addBigDLModule( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public Builder addBigDLModule( - int index, serialization.Bigdl.BigDLModule.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder builderForValue) { if (bigDLModuleBuilder_ == null) { ensureBigDLModuleIsMutable(); bigDLModule_.add(index, builderForValue.build()); @@ -15093,10 +15093,10 @@ public Builder addBigDLModule( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public Builder addAllBigDLModule( - java.lang.Iterable values) { + java.lang.Iterable values) { if (bigDLModuleBuilder_ == null) { ensureBigDLModuleIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -15112,7 +15112,7 @@ public Builder addAllBigDLModule( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public Builder clearBigDLModule() { if (bigDLModuleBuilder_ == null) { @@ -15129,7 +15129,7 @@ public Builder clearBigDLModule() { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ public Builder removeBigDLModule(int index) { if (bigDLModuleBuilder_ == null) { @@ -15146,9 +15146,9 @@ public Builder removeBigDLModule(int index) { * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public serialization.Bigdl.BigDLModule.Builder getBigDLModuleBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder getBigDLModuleBuilder( int index) { return getBigDLModuleFieldBuilder().getBuilder(index); } @@ -15157,9 +15157,9 @@ public serialization.Bigdl.BigDLModule.Builder getBigDLModuleBuilder( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleOrBuilder( int index) { if (bigDLModuleBuilder_ == null) { return bigDLModule_.get(index); } else { @@ -15171,9 +15171,9 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleOrBuilder( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public java.util.List + public java.util.List getBigDLModuleOrBuilderList() { if (bigDLModuleBuilder_ != null) { return bigDLModuleBuilder_.getMessageOrBuilderList(); @@ -15186,41 +15186,41 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleOrBuilder( * "Array(BigDLModel)" * * - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public serialization.Bigdl.BigDLModule.Builder addBigDLModuleBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder addBigDLModuleBuilder() { return getBigDLModuleFieldBuilder().addBuilder( - serialization.Bigdl.BigDLModule.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance()); } /** * 
          * "Array(BigDLModel)"
          *
* - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public serialization.Bigdl.BigDLModule.Builder addBigDLModuleBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder addBigDLModuleBuilder( int index) { return getBigDLModuleFieldBuilder().addBuilder( - index, serialization.Bigdl.BigDLModule.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance()); } /** * 
          * "Array(BigDLModel)"
          *
* - * repeated .serialization.BigDLModule bigDLModule = 13; + * repeated .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModule = 13; */ - public java.util.List + public java.util.List getBigDLModuleBuilderList() { return getBigDLModuleFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder> getBigDLModuleFieldBuilder() { if (bigDLModuleBuilder_ == null) { bigDLModuleBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder>( bigDLModule_, ((bitField0_ & 0x00001000) == 0x00001000), getParentForChildren(), @@ -15230,25 +15230,25 @@ public serialization.Bigdl.BigDLModule.Builder addBigDLModuleBuilder( return bigDLModuleBuilder_; } - private java.util.List nameAttrList_ = + private java.util.List nameAttrList_ = java.util.Collections.emptyList(); private void ensureNameAttrListIsMutable() { if (!((bitField0_ & 0x00002000) == 0x00002000)) { - nameAttrList_ = new java.util.ArrayList(nameAttrList_); + nameAttrList_ = new java.util.ArrayList(nameAttrList_); bitField0_ |= 0x00002000; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> nameAttrListBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder> nameAttrListBuilder_; /** * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public java.util.List getNameAttrListList() { + public java.util.List getNameAttrListList() { if (nameAttrListBuilder_ == null) { return java.util.Collections.unmodifiableList(nameAttrList_); } else { @@ -15259,7 +15259,7 @@ public java.util.List getNameAttrListList() { * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public int getNameAttrListCount() { if (nameAttrListBuilder_ == null) { @@ -15272,9 +15272,9 @@ public int getNameAttrListCount() { * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public serialization.Bigdl.NameAttrList getNameAttrList(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getNameAttrList(int index) { if (nameAttrListBuilder_ == null) { return nameAttrList_.get(index); } else { @@ -15285,10 +15285,10 @@ public serialization.Bigdl.NameAttrList getNameAttrList(int index) { * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public Builder setNameAttrList( - int index, serialization.Bigdl.NameAttrList value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList value) { if (nameAttrListBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -15305,10 +15305,10 @@ public Builder setNameAttrList( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public Builder setNameAttrList( - int index, serialization.Bigdl.NameAttrList.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder builderForValue) { if (nameAttrListBuilder_ == null) { ensureNameAttrListIsMutable(); nameAttrList_.set(index, builderForValue.build()); @@ -15322,9 +15322,9 @@ public Builder setNameAttrList( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public Builder addNameAttrList(serialization.Bigdl.NameAttrList value) { + public Builder addNameAttrList(com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList value) { if (nameAttrListBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -15341,10 +15341,10 @@ public Builder addNameAttrList(serialization.Bigdl.NameAttrList value) { * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public Builder addNameAttrList( - int index, serialization.Bigdl.NameAttrList value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList value) { if (nameAttrListBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -15361,10 +15361,10 @@ public Builder addNameAttrList( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public Builder addNameAttrList( - serialization.Bigdl.NameAttrList.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder builderForValue) { if (nameAttrListBuilder_ == null) { ensureNameAttrListIsMutable(); nameAttrList_.add(builderForValue.build()); @@ -15378,10 +15378,10 @@ public Builder addNameAttrList( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public Builder addNameAttrList( - int index, serialization.Bigdl.NameAttrList.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder builderForValue) { if (nameAttrListBuilder_ == null) { ensureNameAttrListIsMutable(); nameAttrList_.add(index, builderForValue.build()); @@ -15395,10 +15395,10 @@ public Builder addNameAttrList( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public Builder addAllNameAttrList( - java.lang.Iterable values) { + java.lang.Iterable values) { if (nameAttrListBuilder_ == null) { ensureNameAttrListIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -15413,7 +15413,7 @@ public Builder addAllNameAttrList( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public Builder clearNameAttrList() { if (nameAttrListBuilder_ == null) { @@ -15429,7 +15429,7 @@ public Builder clearNameAttrList() { * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ public Builder removeNameAttrList(int index) { if (nameAttrListBuilder_ == null) { @@ -15445,9 +15445,9 @@ public Builder removeNameAttrList(int index) { * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public serialization.Bigdl.NameAttrList.Builder getNameAttrListBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder getNameAttrListBuilder( int index) { return getNameAttrListFieldBuilder().getBuilder(index); } @@ -15455,9 +15455,9 @@ public serialization.Bigdl.NameAttrList.Builder getNameAttrListBuilder( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( int index) { if (nameAttrListBuilder_ == null) { return nameAttrList_.get(index); } else { @@ -15468,9 +15468,9 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public java.util.List + public java.util.List getNameAttrListOrBuilderList() { if (nameAttrListBuilder_ != null) { return nameAttrListBuilder_.getMessageOrBuilderList(); @@ -15482,39 +15482,39 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListOrBuilder( * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public serialization.Bigdl.NameAttrList.Builder addNameAttrListBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder addNameAttrListBuilder() { return getNameAttrListFieldBuilder().addBuilder( - serialization.Bigdl.NameAttrList.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance()); } /** * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public serialization.Bigdl.NameAttrList.Builder addNameAttrListBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder addNameAttrListBuilder( int index) { return getNameAttrListFieldBuilder().addBuilder( - index, serialization.Bigdl.NameAttrList.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance()); } /** * 
          *
* - * repeated .serialization.NameAttrList nameAttrList = 14; + * repeated .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrList = 14; */ - public java.util.List + public java.util.List getNameAttrListBuilderList() { return getNameAttrListFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder> getNameAttrListFieldBuilder() { if (nameAttrListBuilder_ == null) { nameAttrListBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder>( nameAttrList_, ((bitField0_ & 0x00002000) == 0x00002000), getParentForChildren(), @@ -15537,18 +15537,18 @@ private void ensureDataFormatIsMutable() { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ - public java.util.List getDataFormatList() { + public java.util.List getDataFormatList() { return new com.google.protobuf.Internal.ListAdapter< - java.lang.Integer, serialization.Bigdl.InputDataFormat>(dataFormat_, dataFormat_converter_); + java.lang.Integer, com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat>(dataFormat_, dataFormat_converter_); } /** * 
          * "Array(DataFormat)"
          *
* - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public int getDataFormatCount() { return dataFormat_.size(); @@ -15558,9 +15558,9 @@ public int getDataFormatCount() { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ - public serialization.Bigdl.InputDataFormat getDataFormat(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat getDataFormat(int index) { return dataFormat_converter_.convert(dataFormat_.get(index)); } /** @@ -15568,10 +15568,10 @@ public serialization.Bigdl.InputDataFormat getDataFormat(int index) { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public Builder setDataFormat( - int index, serialization.Bigdl.InputDataFormat value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat value) { if (value == null) { throw new NullPointerException(); } @@ -15585,9 +15585,9 @@ public Builder setDataFormat( * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ - public Builder addDataFormat(serialization.Bigdl.InputDataFormat value) { + public Builder addDataFormat(com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat value) { if (value == null) { throw new NullPointerException(); } @@ -15601,12 +15601,12 @@ public Builder addDataFormat(serialization.Bigdl.InputDataFormat value) { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public Builder addAllDataFormat( - java.lang.Iterable values) { + java.lang.Iterable values) { ensureDataFormatIsMutable(); - for (serialization.Bigdl.InputDataFormat value : values) { + for (com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat value : values) { dataFormat_.add(value.getNumber()); } onChanged(); @@ -15617,7 +15617,7 @@ public Builder addAllDataFormat( * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public Builder clearDataFormat() { dataFormat_ = java.util.Collections.emptyList(); @@ -15630,7 +15630,7 @@ public Builder clearDataFormat() { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public java.util.List getDataFormatValueList() { @@ -15641,7 +15641,7 @@ public Builder clearDataFormat() { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public int getDataFormatValue(int index) { return dataFormat_.get(index); @@ -15651,7 +15651,7 @@ public int getDataFormatValue(int index) { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public Builder setDataFormatValue( int index, int value) { @@ -15665,7 +15665,7 @@ public Builder setDataFormatValue( * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public Builder addDataFormatValue(int value) { ensureDataFormatIsMutable(); @@ -15678,7 +15678,7 @@ public Builder addDataFormatValue(int value) { * "Array(DataFormat)" * * - * repeated .serialization.InputDataFormat dataFormat = 15; + * repeated .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormat = 15; */ public Builder addAllDataFormatValue( java.lang.Iterable values) { @@ -16002,26 +16002,26 @@ public com.google.protobuf.Any.Builder addCustomBuilder( return customBuilder_; } - private java.util.List shape_ = + private java.util.List shape_ = java.util.Collections.emptyList(); private void ensureShapeIsMutable() { if (!((bitField0_ & 0x00010000) == 0x00010000)) { - shape_ = new java.util.ArrayList(shape_); + shape_ = new java.util.ArrayList(shape_); bitField0_ |= 0x00010000; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> shapeBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> shapeBuilder_; /** * 
          * "Array(Shape)"
          *
* - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public java.util.List getShapeList() { + public java.util.List getShapeList() { if (shapeBuilder_ == null) { return java.util.Collections.unmodifiableList(shape_); } else { @@ -16033,7 +16033,7 @@ public java.util.List getShapeList() { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public int getShapeCount() { if (shapeBuilder_ == null) { @@ -16047,9 +16047,9 @@ public int getShapeCount() { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public serialization.Bigdl.Shape getShape(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape(int index) { if (shapeBuilder_ == null) { return shape_.get(index); } else { @@ -16061,10 +16061,10 @@ public serialization.Bigdl.Shape getShape(int index) { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public Builder setShape( - int index, serialization.Bigdl.Shape value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (shapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -16082,10 +16082,10 @@ public Builder setShape( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public Builder setShape( - int index, serialization.Bigdl.Shape.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (shapeBuilder_ == null) { ensureShapeIsMutable(); shape_.set(index, builderForValue.build()); @@ -16100,9 +16100,9 @@ public Builder setShape( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public Builder addShape(serialization.Bigdl.Shape value) { + public Builder addShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (shapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -16120,10 +16120,10 @@ public Builder addShape(serialization.Bigdl.Shape value) { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public Builder addShape( - int index, serialization.Bigdl.Shape value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (shapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -16141,10 +16141,10 @@ public Builder addShape( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public Builder addShape( - serialization.Bigdl.Shape.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (shapeBuilder_ == null) { ensureShapeIsMutable(); shape_.add(builderForValue.build()); @@ -16159,10 +16159,10 @@ public Builder addShape( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public Builder addShape( - int index, serialization.Bigdl.Shape.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (shapeBuilder_ == null) { ensureShapeIsMutable(); shape_.add(index, builderForValue.build()); @@ -16177,10 +16177,10 @@ public Builder addShape( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public Builder addAllShape( - java.lang.Iterable values) { + java.lang.Iterable values) { if (shapeBuilder_ == null) { ensureShapeIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -16196,7 +16196,7 @@ public Builder addAllShape( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public Builder clearShape() { if (shapeBuilder_ == null) { @@ -16213,7 +16213,7 @@ public Builder clearShape() { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ public Builder removeShape(int index) { if (shapeBuilder_ == null) { @@ -16230,9 +16230,9 @@ public Builder removeShape(int index) { * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public serialization.Bigdl.Shape.Builder getShapeBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getShapeBuilder( int index) { return getShapeFieldBuilder().getBuilder(index); } @@ -16241,9 +16241,9 @@ public serialization.Bigdl.Shape.Builder getShapeBuilder( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( int index) { if (shapeBuilder_ == null) { return shape_.get(index); } else { @@ -16255,9 +16255,9 @@ public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public java.util.List + public java.util.List getShapeOrBuilderList() { if (shapeBuilder_ != null) { return shapeBuilder_.getMessageOrBuilderList(); @@ -16270,41 +16270,41 @@ public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( * "Array(Shape)" * * - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public serialization.Bigdl.Shape.Builder addShapeBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder addShapeBuilder() { return getShapeFieldBuilder().addBuilder( - serialization.Bigdl.Shape.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()); } /** * 
          * "Array(Shape)"
          *
* - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public serialization.Bigdl.Shape.Builder addShapeBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder addShapeBuilder( int index) { return getShapeFieldBuilder().addBuilder( - index, serialization.Bigdl.Shape.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()); } /** * 
          * "Array(Shape)"
          *
* - * repeated .serialization.Shape shape = 17; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 17; */ - public java.util.List + public java.util.List getShapeBuilderList() { return getShapeFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> getShapeFieldBuilder() { if (shapeBuilder_ == null) { shapeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder>( shape_, ((bitField0_ & 0x00010000) == 0x00010000), getParentForChildren(), @@ -16324,16 +16324,16 @@ public final Builder mergeUnknownFields( } - // @@protoc_insertion_point(builder_scope:serialization.AttrValue.ArrayValue) + // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue) } - // @@protoc_insertion_point(class_scope:serialization.AttrValue.ArrayValue) - private static final serialization.Bigdl.AttrValue.ArrayValue DEFAULT_INSTANCE; + // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue) + private static final com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new serialization.Bigdl.AttrValue.ArrayValue(); + DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue(); } - public static serialization.Bigdl.AttrValue.ArrayValue getDefaultInstance() { + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -16356,7 +16356,7 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public serialization.Bigdl.AttrValue.ArrayValue getDefaultInstanceForType() { + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue getDefaultInstanceForType() { return DEFAULT_INSTANCE; } @@ -16431,17 +16431,17 @@ public int getNumber() { public static final int DATATYPE_FIELD_NUMBER = 1; private int dataType_; /** - * .serialization.DataType dataType = 1; + * .com.intel.analytics.bigdl.serialization.DataType dataType = 1; */ public int getDataTypeValue() { return dataType_; } /** - * .serialization.DataType dataType = 1; + * .com.intel.analytics.bigdl.serialization.DataType dataType = 1; */ - public serialization.Bigdl.DataType getDataType() { - serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(dataType_); - return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.DataType getDataType() { + com.intel.analytics.bigdl.serialization.Bigdl.DataType result = com.intel.analytics.bigdl.serialization.Bigdl.DataType.valueOf(dataType_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.DataType.UNRECOGNIZED : result; } public static final int SUBTYPE_FIELD_NUMBER = 2; @@ -16618,7 +16618,7 @@ public boolean getBoolValue() { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ public boolean hasRegularizerValue() { return valueCase_ == 9; @@ -16628,26 +16628,26 @@ public boolean hasRegularizerValue() { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - public serialization.Bigdl.Regularizer getRegularizerValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getRegularizerValue() { if (valueCase_ == 9) { - return (serialization.Bigdl.Regularizer) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_; } - return serialization.Bigdl.Regularizer.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance(); } /** * 
      * Regularizer
      *
* - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { if (valueCase_ == 9) { - return (serialization.Bigdl.Regularizer) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_; } - return serialization.Bigdl.Regularizer.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance(); } public static final int TENSORVALUE_FIELD_NUMBER = 10; @@ -16656,7 +16656,7 @@ public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ public boolean hasTensorValue() { return valueCase_ == 10; @@ -16666,26 +16666,26 @@ public boolean hasTensorValue() { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - public serialization.Bigdl.BigDLTensor getTensorValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getTensorValue() { if (valueCase_ == 10) { - return (serialization.Bigdl.BigDLTensor) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_; } - return serialization.Bigdl.BigDLTensor.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance(); } /** * 
      *tensor value
      *
* - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { if (valueCase_ == 10) { - return (serialization.Bigdl.BigDLTensor) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_; } - return serialization.Bigdl.BigDLTensor.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance(); } public static final int VARIABLEFORMATVALUE_FIELD_NUMBER = 11; @@ -16694,7 +16694,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { *Variable format * * - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ public int getVariableFormatValueValue() { if (valueCase_ == 11) { @@ -16707,15 +16707,15 @@ public int getVariableFormatValueValue() { *Variable format * * - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ - public serialization.Bigdl.VarFormat getVariableFormatValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.VarFormat getVariableFormatValue() { if (valueCase_ == 11) { - serialization.Bigdl.VarFormat result = serialization.Bigdl.VarFormat.valueOf( + com.intel.analytics.bigdl.serialization.Bigdl.VarFormat result = com.intel.analytics.bigdl.serialization.Bigdl.VarFormat.valueOf( (java.lang.Integer) value_); - return result == null ? serialization.Bigdl.VarFormat.UNRECOGNIZED : result; + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.VarFormat.UNRECOGNIZED : result; } - return serialization.Bigdl.VarFormat.EMPTY_FORMAT; + return com.intel.analytics.bigdl.serialization.Bigdl.VarFormat.EMPTY_FORMAT; } public static final int INITMETHODVALUE_FIELD_NUMBER = 12; @@ -16724,7 +16724,7 @@ public serialization.Bigdl.VarFormat getVariableFormatValue() { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ public boolean hasInitMethodValue() { return valueCase_ == 12; @@ -16734,26 +16734,26 @@ public boolean hasInitMethodValue() { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - public serialization.Bigdl.InitMethod getInitMethodValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getInitMethodValue() { if (valueCase_ == 12) { - return (serialization.Bigdl.InitMethod) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_; } - return serialization.Bigdl.InitMethod.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance(); } /** * 
      * init method
      *
* - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { if (valueCase_ == 12) { - return (serialization.Bigdl.InitMethod) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_; } - return serialization.Bigdl.InitMethod.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance(); } public static final int BIGDLMODULEVALUE_FIELD_NUMBER = 13; @@ -16762,7 +16762,7 @@ public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ public boolean hasBigDLModuleValue() { return valueCase_ == 13; @@ -16772,26 +16772,26 @@ public boolean hasBigDLModuleValue() { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - public serialization.Bigdl.BigDLModule getBigDLModuleValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getBigDLModuleValue() { if (valueCase_ == 13) { - return (serialization.Bigdl.BigDLModule) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_; } - return serialization.Bigdl.BigDLModule.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance(); } /** * 
      * big DL module
      *
* - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { if (valueCase_ == 13) { - return (serialization.Bigdl.BigDLModule) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_; } - return serialization.Bigdl.BigDLModule.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance(); } public static final int NAMEATTRLISTVALUE_FIELD_NUMBER = 14; @@ -16800,7 +16800,7 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ public boolean hasNameAttrListValue() { return valueCase_ == 14; @@ -16810,26 +16810,26 @@ public boolean hasNameAttrListValue() { * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - public serialization.Bigdl.NameAttrList getNameAttrListValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getNameAttrListValue() { if (valueCase_ == 14) { - return (serialization.Bigdl.NameAttrList) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_; } - return serialization.Bigdl.NameAttrList.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance(); } /** * 
      * name attribute list
      *
* - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() { if (valueCase_ == 14) { - return (serialization.Bigdl.NameAttrList) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_; } - return serialization.Bigdl.NameAttrList.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance(); } public static final int ARRAYVALUE_FIELD_NUMBER = 15; @@ -16838,7 +16838,7 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ public boolean hasArrayValue() { return valueCase_ == 15; @@ -16848,26 +16848,26 @@ public boolean hasArrayValue() { *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { if (valueCase_ == 15) { - return (serialization.Bigdl.AttrValue.ArrayValue) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_; } - return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); } /** * 
      *array value of any type
      *
* - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder() { if (valueCase_ == 15) { - return (serialization.Bigdl.AttrValue.ArrayValue) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_; } - return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); } public static final int DATAFORMATVALUE_FIELD_NUMBER = 16; @@ -16876,7 +16876,7 @@ public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder( * data format * * - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ public int getDataFormatValueValue() { if (valueCase_ == 16) { @@ -16889,15 +16889,15 @@ public int getDataFormatValueValue() { * data format * * - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ - public serialization.Bigdl.InputDataFormat getDataFormatValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat getDataFormatValue() { if (valueCase_ == 16) { - serialization.Bigdl.InputDataFormat result = serialization.Bigdl.InputDataFormat.valueOf( + com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat result = com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat.valueOf( (java.lang.Integer) value_); - return result == null ? serialization.Bigdl.InputDataFormat.UNRECOGNIZED : result; + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat.UNRECOGNIZED : result; } - return serialization.Bigdl.InputDataFormat.NCHW; + return com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat.NCHW; } public static final int CUSTOMVALUE_FIELD_NUMBER = 17; @@ -16944,7 +16944,7 @@ public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ public boolean hasShape() { return valueCase_ == 18; @@ -16954,26 +16954,26 @@ public boolean hasShape() { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - public serialization.Bigdl.Shape getShape() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape() { if (valueCase_ == 18) { - return (serialization.Bigdl.Shape) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_; } - return serialization.Bigdl.Shape.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance(); } /** * 
      * Shape value
      *
* - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder() { if (valueCase_ == 18) { - return (serialization.Bigdl.Shape) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_; } - return serialization.Bigdl.Shape.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance(); } private byte memoizedIsInitialized = -1; @@ -16988,7 +16988,7 @@ public final boolean isInitialized() { public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException { - if (dataType_ != serialization.Bigdl.DataType.INT32.getNumber()) { + if (dataType_ != com.intel.analytics.bigdl.serialization.Bigdl.DataType.INT32.getNumber()) { output.writeEnum(1, dataType_); } if (!getSubTypeBytes().isEmpty()) { @@ -17018,25 +17018,25 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) 8, (boolean)((java.lang.Boolean) value_)); } if (valueCase_ == 9) { - output.writeMessage(9, (serialization.Bigdl.Regularizer) value_); + output.writeMessage(9, (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_); } if (valueCase_ == 10) { - output.writeMessage(10, (serialization.Bigdl.BigDLTensor) value_); + output.writeMessage(10, (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_); } if (valueCase_ == 11) { output.writeEnum(11, ((java.lang.Integer) value_)); } if (valueCase_ == 12) { - output.writeMessage(12, (serialization.Bigdl.InitMethod) value_); + output.writeMessage(12, (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_); } if (valueCase_ == 13) { - output.writeMessage(13, (serialization.Bigdl.BigDLModule) value_); + output.writeMessage(13, (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_); } if (valueCase_ == 14) { - output.writeMessage(14, (serialization.Bigdl.NameAttrList) value_); + output.writeMessage(14, (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_); } if (valueCase_ == 15) { - output.writeMessage(15, (serialization.Bigdl.AttrValue.ArrayValue) value_); + output.writeMessage(15, (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_); } if (valueCase_ == 16) { output.writeEnum(16, ((java.lang.Integer) value_)); @@ -17045,7 +17045,7 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) output.writeMessage(17, (com.google.protobuf.Any) value_); } if (valueCase_ == 18) { - output.writeMessage(18, (serialization.Bigdl.Shape) value_); + output.writeMessage(18, (com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_); } unknownFields.writeTo(output); } @@ -17055,7 +17055,7 @@ public int getSerializedSize() { if (size != -1) return size; size = 0; - if (dataType_ != serialization.Bigdl.DataType.INT32.getNumber()) { + if (dataType_ != com.intel.analytics.bigdl.serialization.Bigdl.DataType.INT32.getNumber()) { size += com.google.protobuf.CodedOutputStream .computeEnumSize(1, dataType_); } @@ -17092,11 +17092,11 @@ public int getSerializedSize() { } if (valueCase_ == 9) { size += com.google.protobuf.CodedOutputStream - .computeMessageSize(9, (serialization.Bigdl.Regularizer) value_); + .computeMessageSize(9, (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_); } if (valueCase_ == 10) { size += com.google.protobuf.CodedOutputStream - .computeMessageSize(10, (serialization.Bigdl.BigDLTensor) value_); + .computeMessageSize(10, (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_); } if (valueCase_ == 11) { size += com.google.protobuf.CodedOutputStream @@ -17104,19 +17104,19 @@ public int getSerializedSize() { } if (valueCase_ == 12) { size += com.google.protobuf.CodedOutputStream - .computeMessageSize(12, (serialization.Bigdl.InitMethod) value_); + .computeMessageSize(12, (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_); } if (valueCase_ == 13) { size += com.google.protobuf.CodedOutputStream - .computeMessageSize(13, (serialization.Bigdl.BigDLModule) value_); + .computeMessageSize(13, (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_); } if (valueCase_ == 14) { size += com.google.protobuf.CodedOutputStream - .computeMessageSize(14, (serialization.Bigdl.NameAttrList) value_); + .computeMessageSize(14, (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_); } if (valueCase_ == 15) { size += com.google.protobuf.CodedOutputStream - .computeMessageSize(15, (serialization.Bigdl.AttrValue.ArrayValue) value_); + .computeMessageSize(15, (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_); } if (valueCase_ == 16) { size += com.google.protobuf.CodedOutputStream @@ -17128,7 +17128,7 @@ public int getSerializedSize() { } if (valueCase_ == 18) { size += com.google.protobuf.CodedOutputStream - .computeMessageSize(18, (serialization.Bigdl.Shape) value_); + .computeMessageSize(18, (com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_); } size += unknownFields.getSerializedSize(); memoizedSize = size; @@ -17140,10 +17140,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof serialization.Bigdl.AttrValue)) { + if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.AttrValue)) { return super.equals(obj); } - serialization.Bigdl.AttrValue other = (serialization.Bigdl.AttrValue) obj; + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue other = (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue) obj; boolean result = true; result = result && dataType_ == other.dataType_; @@ -17316,69 +17316,69 @@ public int hashCode() { return hash; } - public static serialization.Bigdl.AttrValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.AttrValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.AttrValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.AttrValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.AttrValue parseFrom(byte[] data) + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.AttrValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.AttrValue parseFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.AttrValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.AttrValue parseDelimitedFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static serialization.Bigdl.AttrValue parseDelimitedFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.AttrValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.AttrValue parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -17390,7 +17390,7 @@ public static serialization.Bigdl.AttrValue parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(serialization.Bigdl.AttrValue prototype) { + public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -17405,25 +17405,25 @@ protected Builder newBuilderForType( return builder; } /** - * Protobuf type {@code serialization.AttrValue} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.AttrValue} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:serialization.AttrValue) - serialization.Bigdl.AttrValueOrBuilder { + // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.AttrValue) + com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_AttrValue_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_AttrValue_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.AttrValue.class, serialization.Bigdl.AttrValue.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.class, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder.class); } - // Construct using serialization.Bigdl.AttrValue.newBuilder() + // Construct using com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -17451,23 +17451,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return serialization.Bigdl.internal_static_serialization_AttrValue_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_AttrValue_descriptor; } - public serialization.Bigdl.AttrValue getDefaultInstanceForType() { - return serialization.Bigdl.AttrValue.getDefaultInstance(); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getDefaultInstanceForType() { + return com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance(); } - public serialization.Bigdl.AttrValue build() { - serialization.Bigdl.AttrValue result = buildPartial(); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue build() { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public serialization.Bigdl.AttrValue buildPartial() { - serialization.Bigdl.AttrValue result = new serialization.Bigdl.AttrValue(this); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue buildPartial() { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue result = new com.intel.analytics.bigdl.serialization.Bigdl.AttrValue(this); result.dataType_ = dataType_; result.subType_ = subType_; if (valueCase_ == 3) { @@ -17582,16 +17582,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof serialization.Bigdl.AttrValue) { - return mergeFrom((serialization.Bigdl.AttrValue)other); + if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.AttrValue) { + return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.AttrValue)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(serialization.Bigdl.AttrValue other) { - if (other == serialization.Bigdl.AttrValue.getDefaultInstance()) return this; + public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue other) { + if (other == com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()) return this; if (other.dataType_ != 0) { setDataTypeValue(other.getDataTypeValue()); } @@ -17683,11 +17683,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - serialization.Bigdl.AttrValue parsedMessage = null; + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (serialization.Bigdl.AttrValue) e.getUnfinishedMessage(); + parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -17714,13 +17714,13 @@ public Builder clearValue() { private int dataType_ = 0; /** - * .serialization.DataType dataType = 1; + * .com.intel.analytics.bigdl.serialization.DataType dataType = 1; */ public int getDataTypeValue() { return dataType_; } /** - * .serialization.DataType dataType = 1; + * .com.intel.analytics.bigdl.serialization.DataType dataType = 1; */ public Builder setDataTypeValue(int value) { dataType_ = value; @@ -17728,16 +17728,16 @@ public Builder setDataTypeValue(int value) { return this; } /** - * .serialization.DataType dataType = 1; + * .com.intel.analytics.bigdl.serialization.DataType dataType = 1; */ - public serialization.Bigdl.DataType getDataType() { - serialization.Bigdl.DataType result = serialization.Bigdl.DataType.valueOf(dataType_); - return result == null ? serialization.Bigdl.DataType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.DataType getDataType() { + com.intel.analytics.bigdl.serialization.Bigdl.DataType result = com.intel.analytics.bigdl.serialization.Bigdl.DataType.valueOf(dataType_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.DataType.UNRECOGNIZED : result; } /** - * .serialization.DataType dataType = 1; + * .com.intel.analytics.bigdl.serialization.DataType dataType = 1; */ - public Builder setDataType(serialization.Bigdl.DataType value) { + public Builder setDataType(com.intel.analytics.bigdl.serialization.Bigdl.DataType value) { if (value == null) { throw new NullPointerException(); } @@ -17747,7 +17747,7 @@ public Builder setDataType(serialization.Bigdl.DataType value) { return this; } /** - * .serialization.DataType dataType = 1; + * .com.intel.analytics.bigdl.serialization.DataType dataType = 1; */ public Builder clearDataType() { @@ -18156,13 +18156,13 @@ public Builder clearBoolValue() { } private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Regularizer, serialization.Bigdl.Regularizer.Builder, serialization.Bigdl.RegularizerOrBuilder> regularizerValueBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder, com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder> regularizerValueBuilder_; /** * 
        * Regularizer
        *
* - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ public boolean hasRegularizerValue() { return valueCase_ == 9; @@ -18172,19 +18172,19 @@ public boolean hasRegularizerValue() { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - public serialization.Bigdl.Regularizer getRegularizerValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer getRegularizerValue() { if (regularizerValueBuilder_ == null) { if (valueCase_ == 9) { - return (serialization.Bigdl.Regularizer) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_; } - return serialization.Bigdl.Regularizer.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance(); } else { if (valueCase_ == 9) { return regularizerValueBuilder_.getMessage(); } - return serialization.Bigdl.Regularizer.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance(); } } /** @@ -18192,9 +18192,9 @@ public serialization.Bigdl.Regularizer getRegularizerValue() { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - public Builder setRegularizerValue(serialization.Bigdl.Regularizer value) { + public Builder setRegularizerValue(com.intel.analytics.bigdl.serialization.Bigdl.Regularizer value) { if (regularizerValueBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -18212,10 +18212,10 @@ public Builder setRegularizerValue(serialization.Bigdl.Regularizer value) { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ public Builder setRegularizerValue( - serialization.Bigdl.Regularizer.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder builderForValue) { if (regularizerValueBuilder_ == null) { value_ = builderForValue.build(); onChanged(); @@ -18230,13 +18230,13 @@ public Builder setRegularizerValue( * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - public Builder mergeRegularizerValue(serialization.Bigdl.Regularizer value) { + public Builder mergeRegularizerValue(com.intel.analytics.bigdl.serialization.Bigdl.Regularizer value) { if (regularizerValueBuilder_ == null) { if (valueCase_ == 9 && - value_ != serialization.Bigdl.Regularizer.getDefaultInstance()) { - value_ = serialization.Bigdl.Regularizer.newBuilder((serialization.Bigdl.Regularizer) value_) + value_ != com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance()) { + value_ = com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.newBuilder((com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_) .mergeFrom(value).buildPartial(); } else { value_ = value; @@ -18256,7 +18256,7 @@ public Builder mergeRegularizerValue(serialization.Bigdl.Regularizer value) { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ public Builder clearRegularizerValue() { if (regularizerValueBuilder_ == null) { @@ -18279,9 +18279,9 @@ public Builder clearRegularizerValue() { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - public serialization.Bigdl.Regularizer.Builder getRegularizerValueBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder getRegularizerValueBuilder() { return getRegularizerValueFieldBuilder().getBuilder(); } /** @@ -18289,16 +18289,16 @@ public serialization.Bigdl.Regularizer.Builder getRegularizerValueBuilder() { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ - public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { if ((valueCase_ == 9) && (regularizerValueBuilder_ != null)) { return regularizerValueBuilder_.getMessageOrBuilder(); } else { if (valueCase_ == 9) { - return (serialization.Bigdl.Regularizer) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_; } - return serialization.Bigdl.Regularizer.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance(); } } /** @@ -18306,18 +18306,18 @@ public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { * Regularizer * * - * .serialization.Regularizer regularizerValue = 9; + * .com.intel.analytics.bigdl.serialization.Regularizer regularizerValue = 9; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Regularizer, serialization.Bigdl.Regularizer.Builder, serialization.Bigdl.RegularizerOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder, com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder> getRegularizerValueFieldBuilder() { if (regularizerValueBuilder_ == null) { if (!(valueCase_ == 9)) { - value_ = serialization.Bigdl.Regularizer.getDefaultInstance(); + value_ = com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.getDefaultInstance(); } regularizerValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Regularizer, serialization.Bigdl.Regularizer.Builder, serialization.Bigdl.RegularizerOrBuilder>( - (serialization.Bigdl.Regularizer) value_, + com.intel.analytics.bigdl.serialization.Bigdl.Regularizer, com.intel.analytics.bigdl.serialization.Bigdl.Regularizer.Builder, com.intel.analytics.bigdl.serialization.Bigdl.RegularizerOrBuilder>( + (com.intel.analytics.bigdl.serialization.Bigdl.Regularizer) value_, getParentForChildren(), isClean()); value_ = null; @@ -18328,13 +18328,13 @@ public serialization.Bigdl.RegularizerOrBuilder getRegularizerValueOrBuilder() { } private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> tensorValueBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> tensorValueBuilder_; /** * 
        *tensor value
        *
* - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ public boolean hasTensorValue() { return valueCase_ == 10; @@ -18344,19 +18344,19 @@ public boolean hasTensorValue() { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - public serialization.Bigdl.BigDLTensor getTensorValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getTensorValue() { if (tensorValueBuilder_ == null) { if (valueCase_ == 10) { - return (serialization.Bigdl.BigDLTensor) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_; } - return serialization.Bigdl.BigDLTensor.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance(); } else { if (valueCase_ == 10) { return tensorValueBuilder_.getMessage(); } - return serialization.Bigdl.BigDLTensor.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance(); } } /** @@ -18364,9 +18364,9 @@ public serialization.Bigdl.BigDLTensor getTensorValue() { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - public Builder setTensorValue(serialization.Bigdl.BigDLTensor value) { + public Builder setTensorValue(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (tensorValueBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -18384,10 +18384,10 @@ public Builder setTensorValue(serialization.Bigdl.BigDLTensor value) { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ public Builder setTensorValue( - serialization.Bigdl.BigDLTensor.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { if (tensorValueBuilder_ == null) { value_ = builderForValue.build(); onChanged(); @@ -18402,13 +18402,13 @@ public Builder setTensorValue( *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - public Builder mergeTensorValue(serialization.Bigdl.BigDLTensor value) { + public Builder mergeTensorValue(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { if (tensorValueBuilder_ == null) { if (valueCase_ == 10 && - value_ != serialization.Bigdl.BigDLTensor.getDefaultInstance()) { - value_ = serialization.Bigdl.BigDLTensor.newBuilder((serialization.Bigdl.BigDLTensor) value_) + value_ != com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()) { + value_ = com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.newBuilder((com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_) .mergeFrom(value).buildPartial(); } else { value_ = value; @@ -18428,7 +18428,7 @@ public Builder mergeTensorValue(serialization.Bigdl.BigDLTensor value) { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ public Builder clearTensorValue() { if (tensorValueBuilder_ == null) { @@ -18451,9 +18451,9 @@ public Builder clearTensorValue() { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - public serialization.Bigdl.BigDLTensor.Builder getTensorValueBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getTensorValueBuilder() { return getTensorValueFieldBuilder().getBuilder(); } /** @@ -18461,16 +18461,16 @@ public serialization.Bigdl.BigDLTensor.Builder getTensorValueBuilder() { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ - public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { if ((valueCase_ == 10) && (tensorValueBuilder_ != null)) { return tensorValueBuilder_.getMessageOrBuilder(); } else { if (valueCase_ == 10) { - return (serialization.Bigdl.BigDLTensor) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_; } - return serialization.Bigdl.BigDLTensor.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance(); } } /** @@ -18478,18 +18478,18 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { *tensor value * * - * .serialization.BigDLTensor tensorValue = 10; + * .com.intel.analytics.bigdl.serialization.BigDLTensor tensorValue = 10; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> getTensorValueFieldBuilder() { if (tensorValueBuilder_ == null) { if (!(valueCase_ == 10)) { - value_ = serialization.Bigdl.BigDLTensor.getDefaultInstance(); + value_ = com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance(); } tensorValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLTensor, serialization.Bigdl.BigDLTensor.Builder, serialization.Bigdl.BigDLTensorOrBuilder>( - (serialization.Bigdl.BigDLTensor) value_, + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder>( + (com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor) value_, getParentForChildren(), isClean()); value_ = null; @@ -18504,7 +18504,7 @@ public serialization.Bigdl.BigDLTensorOrBuilder getTensorValueOrBuilder() { *Variable format * * - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ public int getVariableFormatValueValue() { if (valueCase_ == 11) { @@ -18517,7 +18517,7 @@ public int getVariableFormatValueValue() { *Variable format * * - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ public Builder setVariableFormatValueValue(int value) { valueCase_ = 11; @@ -18530,24 +18530,24 @@ public Builder setVariableFormatValueValue(int value) { *Variable format * * - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ - public serialization.Bigdl.VarFormat getVariableFormatValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.VarFormat getVariableFormatValue() { if (valueCase_ == 11) { - serialization.Bigdl.VarFormat result = serialization.Bigdl.VarFormat.valueOf( + com.intel.analytics.bigdl.serialization.Bigdl.VarFormat result = com.intel.analytics.bigdl.serialization.Bigdl.VarFormat.valueOf( (java.lang.Integer) value_); - return result == null ? serialization.Bigdl.VarFormat.UNRECOGNIZED : result; + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.VarFormat.UNRECOGNIZED : result; } - return serialization.Bigdl.VarFormat.EMPTY_FORMAT; + return com.intel.analytics.bigdl.serialization.Bigdl.VarFormat.EMPTY_FORMAT; } /** * 
        *Variable format
        *
* - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ - public Builder setVariableFormatValue(serialization.Bigdl.VarFormat value) { + public Builder setVariableFormatValue(com.intel.analytics.bigdl.serialization.Bigdl.VarFormat value) { if (value == null) { throw new NullPointerException(); } @@ -18561,7 +18561,7 @@ public Builder setVariableFormatValue(serialization.Bigdl.VarFormat value) { *Variable format * * - * .serialization.VarFormat variableFormatValue = 11; + * .com.intel.analytics.bigdl.serialization.VarFormat variableFormatValue = 11; */ public Builder clearVariableFormatValue() { if (valueCase_ == 11) { @@ -18573,13 +18573,13 @@ public Builder clearVariableFormatValue() { } private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.InitMethod, serialization.Bigdl.InitMethod.Builder, serialization.Bigdl.InitMethodOrBuilder> initMethodValueBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder, com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder> initMethodValueBuilder_; /** * 
        * init method
        *
* - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ public boolean hasInitMethodValue() { return valueCase_ == 12; @@ -18589,19 +18589,19 @@ public boolean hasInitMethodValue() { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - public serialization.Bigdl.InitMethod getInitMethodValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod getInitMethodValue() { if (initMethodValueBuilder_ == null) { if (valueCase_ == 12) { - return (serialization.Bigdl.InitMethod) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_; } - return serialization.Bigdl.InitMethod.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance(); } else { if (valueCase_ == 12) { return initMethodValueBuilder_.getMessage(); } - return serialization.Bigdl.InitMethod.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance(); } } /** @@ -18609,9 +18609,9 @@ public serialization.Bigdl.InitMethod getInitMethodValue() { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - public Builder setInitMethodValue(serialization.Bigdl.InitMethod value) { + public Builder setInitMethodValue(com.intel.analytics.bigdl.serialization.Bigdl.InitMethod value) { if (initMethodValueBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -18629,10 +18629,10 @@ public Builder setInitMethodValue(serialization.Bigdl.InitMethod value) { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ public Builder setInitMethodValue( - serialization.Bigdl.InitMethod.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder builderForValue) { if (initMethodValueBuilder_ == null) { value_ = builderForValue.build(); onChanged(); @@ -18647,13 +18647,13 @@ public Builder setInitMethodValue( * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - public Builder mergeInitMethodValue(serialization.Bigdl.InitMethod value) { + public Builder mergeInitMethodValue(com.intel.analytics.bigdl.serialization.Bigdl.InitMethod value) { if (initMethodValueBuilder_ == null) { if (valueCase_ == 12 && - value_ != serialization.Bigdl.InitMethod.getDefaultInstance()) { - value_ = serialization.Bigdl.InitMethod.newBuilder((serialization.Bigdl.InitMethod) value_) + value_ != com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance()) { + value_ = com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.newBuilder((com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_) .mergeFrom(value).buildPartial(); } else { value_ = value; @@ -18673,7 +18673,7 @@ public Builder mergeInitMethodValue(serialization.Bigdl.InitMethod value) { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ public Builder clearInitMethodValue() { if (initMethodValueBuilder_ == null) { @@ -18696,9 +18696,9 @@ public Builder clearInitMethodValue() { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - public serialization.Bigdl.InitMethod.Builder getInitMethodValueBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder getInitMethodValueBuilder() { return getInitMethodValueFieldBuilder().getBuilder(); } /** @@ -18706,16 +18706,16 @@ public serialization.Bigdl.InitMethod.Builder getInitMethodValueBuilder() { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ - public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { if ((valueCase_ == 12) && (initMethodValueBuilder_ != null)) { return initMethodValueBuilder_.getMessageOrBuilder(); } else { if (valueCase_ == 12) { - return (serialization.Bigdl.InitMethod) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_; } - return serialization.Bigdl.InitMethod.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance(); } } /** @@ -18723,18 +18723,18 @@ public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { * init method * * - * .serialization.InitMethod initMethodValue = 12; + * .com.intel.analytics.bigdl.serialization.InitMethod initMethodValue = 12; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.InitMethod, serialization.Bigdl.InitMethod.Builder, serialization.Bigdl.InitMethodOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder, com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder> getInitMethodValueFieldBuilder() { if (initMethodValueBuilder_ == null) { if (!(valueCase_ == 12)) { - value_ = serialization.Bigdl.InitMethod.getDefaultInstance(); + value_ = com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.getDefaultInstance(); } initMethodValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.InitMethod, serialization.Bigdl.InitMethod.Builder, serialization.Bigdl.InitMethodOrBuilder>( - (serialization.Bigdl.InitMethod) value_, + com.intel.analytics.bigdl.serialization.Bigdl.InitMethod, com.intel.analytics.bigdl.serialization.Bigdl.InitMethod.Builder, com.intel.analytics.bigdl.serialization.Bigdl.InitMethodOrBuilder>( + (com.intel.analytics.bigdl.serialization.Bigdl.InitMethod) value_, getParentForChildren(), isClean()); value_ = null; @@ -18745,13 +18745,13 @@ public serialization.Bigdl.InitMethodOrBuilder getInitMethodValueOrBuilder() { } private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> bigDLModuleValueBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder> bigDLModuleValueBuilder_; /** * 
        * big DL module
        *
* - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ public boolean hasBigDLModuleValue() { return valueCase_ == 13; @@ -18761,19 +18761,19 @@ public boolean hasBigDLModuleValue() { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - public serialization.Bigdl.BigDLModule getBigDLModuleValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule getBigDLModuleValue() { if (bigDLModuleValueBuilder_ == null) { if (valueCase_ == 13) { - return (serialization.Bigdl.BigDLModule) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_; } - return serialization.Bigdl.BigDLModule.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance(); } else { if (valueCase_ == 13) { return bigDLModuleValueBuilder_.getMessage(); } - return serialization.Bigdl.BigDLModule.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance(); } } /** @@ -18781,9 +18781,9 @@ public serialization.Bigdl.BigDLModule getBigDLModuleValue() { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - public Builder setBigDLModuleValue(serialization.Bigdl.BigDLModule value) { + public Builder setBigDLModuleValue(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule value) { if (bigDLModuleValueBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -18801,10 +18801,10 @@ public Builder setBigDLModuleValue(serialization.Bigdl.BigDLModule value) { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ public Builder setBigDLModuleValue( - serialization.Bigdl.BigDLModule.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder builderForValue) { if (bigDLModuleValueBuilder_ == null) { value_ = builderForValue.build(); onChanged(); @@ -18819,13 +18819,13 @@ public Builder setBigDLModuleValue( * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - public Builder mergeBigDLModuleValue(serialization.Bigdl.BigDLModule value) { + public Builder mergeBigDLModuleValue(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule value) { if (bigDLModuleValueBuilder_ == null) { if (valueCase_ == 13 && - value_ != serialization.Bigdl.BigDLModule.getDefaultInstance()) { - value_ = serialization.Bigdl.BigDLModule.newBuilder((serialization.Bigdl.BigDLModule) value_) + value_ != com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance()) { + value_ = com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.newBuilder((com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_) .mergeFrom(value).buildPartial(); } else { value_ = value; @@ -18845,7 +18845,7 @@ public Builder mergeBigDLModuleValue(serialization.Bigdl.BigDLModule value) { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ public Builder clearBigDLModuleValue() { if (bigDLModuleValueBuilder_ == null) { @@ -18868,9 +18868,9 @@ public Builder clearBigDLModuleValue() { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - public serialization.Bigdl.BigDLModule.Builder getBigDLModuleValueBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder getBigDLModuleValueBuilder() { return getBigDLModuleValueFieldBuilder().getBuilder(); } /** @@ -18878,16 +18878,16 @@ public serialization.Bigdl.BigDLModule.Builder getBigDLModuleValueBuilder() { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ - public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { if ((valueCase_ == 13) && (bigDLModuleValueBuilder_ != null)) { return bigDLModuleValueBuilder_.getMessageOrBuilder(); } else { if (valueCase_ == 13) { - return (serialization.Bigdl.BigDLModule) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_; } - return serialization.Bigdl.BigDLModule.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance(); } } /** @@ -18895,18 +18895,18 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { * big DL module * * - * .serialization.BigDLModule bigDLModuleValue = 13; + * .com.intel.analytics.bigdl.serialization.BigDLModule bigDLModuleValue = 13; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder> getBigDLModuleValueFieldBuilder() { if (bigDLModuleValueBuilder_ == null) { if (!(valueCase_ == 13)) { - value_ = serialization.Bigdl.BigDLModule.getDefaultInstance(); + value_ = com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.getDefaultInstance(); } bigDLModuleValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.BigDLModule, serialization.Bigdl.BigDLModule.Builder, serialization.Bigdl.BigDLModuleOrBuilder>( - (serialization.Bigdl.BigDLModule) value_, + com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder>( + (com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule) value_, getParentForChildren(), isClean()); value_ = null; @@ -18917,13 +18917,13 @@ public serialization.Bigdl.BigDLModuleOrBuilder getBigDLModuleValueOrBuilder() { } private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> nameAttrListValueBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder> nameAttrListValueBuilder_; /** * 
        * name attribute list
        *
* - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ public boolean hasNameAttrListValue() { return valueCase_ == 14; @@ -18933,19 +18933,19 @@ public boolean hasNameAttrListValue() { * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - public serialization.Bigdl.NameAttrList getNameAttrListValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getNameAttrListValue() { if (nameAttrListValueBuilder_ == null) { if (valueCase_ == 14) { - return (serialization.Bigdl.NameAttrList) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_; } - return serialization.Bigdl.NameAttrList.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance(); } else { if (valueCase_ == 14) { return nameAttrListValueBuilder_.getMessage(); } - return serialization.Bigdl.NameAttrList.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance(); } } /** @@ -18953,9 +18953,9 @@ public serialization.Bigdl.NameAttrList getNameAttrListValue() { * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - public Builder setNameAttrListValue(serialization.Bigdl.NameAttrList value) { + public Builder setNameAttrListValue(com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList value) { if (nameAttrListValueBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -18973,10 +18973,10 @@ public Builder setNameAttrListValue(serialization.Bigdl.NameAttrList value) { * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ public Builder setNameAttrListValue( - serialization.Bigdl.NameAttrList.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder builderForValue) { if (nameAttrListValueBuilder_ == null) { value_ = builderForValue.build(); onChanged(); @@ -18991,13 +18991,13 @@ public Builder setNameAttrListValue( * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - public Builder mergeNameAttrListValue(serialization.Bigdl.NameAttrList value) { + public Builder mergeNameAttrListValue(com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList value) { if (nameAttrListValueBuilder_ == null) { if (valueCase_ == 14 && - value_ != serialization.Bigdl.NameAttrList.getDefaultInstance()) { - value_ = serialization.Bigdl.NameAttrList.newBuilder((serialization.Bigdl.NameAttrList) value_) + value_ != com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance()) { + value_ = com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.newBuilder((com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_) .mergeFrom(value).buildPartial(); } else { value_ = value; @@ -19017,7 +19017,7 @@ public Builder mergeNameAttrListValue(serialization.Bigdl.NameAttrList value) { * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ public Builder clearNameAttrListValue() { if (nameAttrListValueBuilder_ == null) { @@ -19040,9 +19040,9 @@ public Builder clearNameAttrListValue() { * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - public serialization.Bigdl.NameAttrList.Builder getNameAttrListValueBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder getNameAttrListValueBuilder() { return getNameAttrListValueFieldBuilder().getBuilder(); } /** @@ -19050,16 +19050,16 @@ public serialization.Bigdl.NameAttrList.Builder getNameAttrListValueBuilder() { * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ - public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() { if ((valueCase_ == 14) && (nameAttrListValueBuilder_ != null)) { return nameAttrListValueBuilder_.getMessageOrBuilder(); } else { if (valueCase_ == 14) { - return (serialization.Bigdl.NameAttrList) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_; } - return serialization.Bigdl.NameAttrList.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance(); } } /** @@ -19067,18 +19067,18 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() * name attribute list * * - * .serialization.NameAttrList nameAttrListValue = 14; + * .com.intel.analytics.bigdl.serialization.NameAttrList nameAttrListValue = 14; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder> getNameAttrListValueFieldBuilder() { if (nameAttrListValueBuilder_ == null) { if (!(valueCase_ == 14)) { - value_ = serialization.Bigdl.NameAttrList.getDefaultInstance(); + value_ = com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance(); } nameAttrListValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.NameAttrList, serialization.Bigdl.NameAttrList.Builder, serialization.Bigdl.NameAttrListOrBuilder>( - (serialization.Bigdl.NameAttrList) value_, + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder>( + (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) value_, getParentForChildren(), isClean()); value_ = null; @@ -19089,13 +19089,13 @@ public serialization.Bigdl.NameAttrListOrBuilder getNameAttrListValueOrBuilder() } private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder> arrayValueBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValueOrBuilder> arrayValueBuilder_; /** * 
        *array value of any type
        *
* - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ public boolean hasArrayValue() { return valueCase_ == 15; @@ -19105,19 +19105,19 @@ public boolean hasArrayValue() { *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { if (arrayValueBuilder_ == null) { if (valueCase_ == 15) { - return (serialization.Bigdl.AttrValue.ArrayValue) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_; } - return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); } else { if (valueCase_ == 15) { return arrayValueBuilder_.getMessage(); } - return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); } } /** @@ -19125,9 +19125,9 @@ public serialization.Bigdl.AttrValue.ArrayValue getArrayValue() { *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - public Builder setArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) { + public Builder setArrayValue(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue value) { if (arrayValueBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -19145,10 +19145,10 @@ public Builder setArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) { *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ public Builder setArrayValue( - serialization.Bigdl.AttrValue.ArrayValue.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.Builder builderForValue) { if (arrayValueBuilder_ == null) { value_ = builderForValue.build(); onChanged(); @@ -19163,13 +19163,13 @@ public Builder setArrayValue( *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - public Builder mergeArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) { + public Builder mergeArrayValue(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue value) { if (arrayValueBuilder_ == null) { if (valueCase_ == 15 && - value_ != serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance()) { - value_ = serialization.Bigdl.AttrValue.ArrayValue.newBuilder((serialization.Bigdl.AttrValue.ArrayValue) value_) + value_ != com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance()) { + value_ = com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.newBuilder((com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_) .mergeFrom(value).buildPartial(); } else { value_ = value; @@ -19189,7 +19189,7 @@ public Builder mergeArrayValue(serialization.Bigdl.AttrValue.ArrayValue value) { *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ public Builder clearArrayValue() { if (arrayValueBuilder_ == null) { @@ -19212,9 +19212,9 @@ public Builder clearArrayValue() { *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - public serialization.Bigdl.AttrValue.ArrayValue.Builder getArrayValueBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.Builder getArrayValueBuilder() { return getArrayValueFieldBuilder().getBuilder(); } /** @@ -19222,16 +19222,16 @@ public serialization.Bigdl.AttrValue.ArrayValue.Builder getArrayValueBuilder() { *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ - public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder() { if ((valueCase_ == 15) && (arrayValueBuilder_ != null)) { return arrayValueBuilder_.getMessageOrBuilder(); } else { if (valueCase_ == 15) { - return (serialization.Bigdl.AttrValue.ArrayValue) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_; } - return serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); } } /** @@ -19239,18 +19239,18 @@ public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder( *array value of any type * * - * .serialization.AttrValue.ArrayValue arrayValue = 15; + * .com.intel.analytics.bigdl.serialization.AttrValue.ArrayValue arrayValue = 15; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValueOrBuilder> getArrayValueFieldBuilder() { if (arrayValueBuilder_ == null) { if (!(valueCase_ == 15)) { - value_ = serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); + value_ = com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.getDefaultInstance(); } arrayValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.AttrValue.ArrayValue, serialization.Bigdl.AttrValue.ArrayValue.Builder, serialization.Bigdl.AttrValue.ArrayValueOrBuilder>( - (serialization.Bigdl.AttrValue.ArrayValue) value_, + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValueOrBuilder>( + (com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue) value_, getParentForChildren(), isClean()); value_ = null; @@ -19265,7 +19265,7 @@ public serialization.Bigdl.AttrValue.ArrayValueOrBuilder getArrayValueOrBuilder( * data format * * - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ public int getDataFormatValueValue() { if (valueCase_ == 16) { @@ -19278,7 +19278,7 @@ public int getDataFormatValueValue() { * data format * * - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ public Builder setDataFormatValueValue(int value) { valueCase_ = 16; @@ -19291,24 +19291,24 @@ public Builder setDataFormatValueValue(int value) { * data format * * - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ - public serialization.Bigdl.InputDataFormat getDataFormatValue() { + public com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat getDataFormatValue() { if (valueCase_ == 16) { - serialization.Bigdl.InputDataFormat result = serialization.Bigdl.InputDataFormat.valueOf( + com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat result = com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat.valueOf( (java.lang.Integer) value_); - return result == null ? serialization.Bigdl.InputDataFormat.UNRECOGNIZED : result; + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat.UNRECOGNIZED : result; } - return serialization.Bigdl.InputDataFormat.NCHW; + return com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat.NCHW; } /** * 
        * data format
        *
* - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ - public Builder setDataFormatValue(serialization.Bigdl.InputDataFormat value) { + public Builder setDataFormatValue(com.intel.analytics.bigdl.serialization.Bigdl.InputDataFormat value) { if (value == null) { throw new NullPointerException(); } @@ -19322,7 +19322,7 @@ public Builder setDataFormatValue(serialization.Bigdl.InputDataFormat value) { * data format * * - * .serialization.InputDataFormat dataFormatValue = 16; + * .com.intel.analytics.bigdl.serialization.InputDataFormat dataFormatValue = 16; */ public Builder clearDataFormatValue() { if (valueCase_ == 16) { @@ -19506,13 +19506,13 @@ public com.google.protobuf.AnyOrBuilder getCustomValueOrBuilder() { } private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> shapeBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> shapeBuilder_; /** * 
        * Shape value
        *
* - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ public boolean hasShape() { return valueCase_ == 18; @@ -19522,19 +19522,19 @@ public boolean hasShape() { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - public serialization.Bigdl.Shape getShape() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape() { if (shapeBuilder_ == null) { if (valueCase_ == 18) { - return (serialization.Bigdl.Shape) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_; } - return serialization.Bigdl.Shape.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance(); } else { if (valueCase_ == 18) { return shapeBuilder_.getMessage(); } - return serialization.Bigdl.Shape.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance(); } } /** @@ -19542,9 +19542,9 @@ public serialization.Bigdl.Shape getShape() { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - public Builder setShape(serialization.Bigdl.Shape value) { + public Builder setShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (shapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -19562,10 +19562,10 @@ public Builder setShape(serialization.Bigdl.Shape value) { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ public Builder setShape( - serialization.Bigdl.Shape.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (shapeBuilder_ == null) { value_ = builderForValue.build(); onChanged(); @@ -19580,13 +19580,13 @@ public Builder setShape( * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - public Builder mergeShape(serialization.Bigdl.Shape value) { + public Builder mergeShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (shapeBuilder_ == null) { if (valueCase_ == 18 && - value_ != serialization.Bigdl.Shape.getDefaultInstance()) { - value_ = serialization.Bigdl.Shape.newBuilder((serialization.Bigdl.Shape) value_) + value_ != com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()) { + value_ = com.intel.analytics.bigdl.serialization.Bigdl.Shape.newBuilder((com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_) .mergeFrom(value).buildPartial(); } else { value_ = value; @@ -19606,7 +19606,7 @@ public Builder mergeShape(serialization.Bigdl.Shape value) { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ public Builder clearShape() { if (shapeBuilder_ == null) { @@ -19629,9 +19629,9 @@ public Builder clearShape() { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - public serialization.Bigdl.Shape.Builder getShapeBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getShapeBuilder() { return getShapeFieldBuilder().getBuilder(); } /** @@ -19639,16 +19639,16 @@ public serialization.Bigdl.Shape.Builder getShapeBuilder() { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ - public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder() { if ((valueCase_ == 18) && (shapeBuilder_ != null)) { return shapeBuilder_.getMessageOrBuilder(); } else { if (valueCase_ == 18) { - return (serialization.Bigdl.Shape) value_; + return (com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_; } - return serialization.Bigdl.Shape.getDefaultInstance(); + return com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance(); } } /** @@ -19656,18 +19656,18 @@ public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder() { * Shape value * * - * .serialization.Shape shape = 18; + * .com.intel.analytics.bigdl.serialization.Shape shape = 18; */ private com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> getShapeFieldBuilder() { if (shapeBuilder_ == null) { if (!(valueCase_ == 18)) { - value_ = serialization.Bigdl.Shape.getDefaultInstance(); + value_ = com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance(); } shapeBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>( - (serialization.Bigdl.Shape) value_, + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder>( + (com.intel.analytics.bigdl.serialization.Bigdl.Shape) value_, getParentForChildren(), isClean()); value_ = null; @@ -19687,16 +19687,16 @@ public final Builder mergeUnknownFields( } - // @@protoc_insertion_point(builder_scope:serialization.AttrValue) + // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.AttrValue) } - // @@protoc_insertion_point(class_scope:serialization.AttrValue) - private static final serialization.Bigdl.AttrValue DEFAULT_INSTANCE; + // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.AttrValue) + private static final com.intel.analytics.bigdl.serialization.Bigdl.AttrValue DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new serialization.Bigdl.AttrValue(); + DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.AttrValue(); } - public static serialization.Bigdl.AttrValue getDefaultInstance() { + public static com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -19719,14 +19719,14 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public serialization.Bigdl.AttrValue getDefaultInstanceForType() { + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getDefaultInstanceForType() { return DEFAULT_INSTANCE; } } public interface NameAttrListOrBuilder extends - // @@protoc_insertion_point(interface_extends:serialization.NameAttrList) + // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.NameAttrList) com.google.protobuf.MessageOrBuilder { /** @@ -19740,11 +19740,11 @@ public interface NameAttrListOrBuilder extends getNameBytes(); /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ int getAttrCount(); /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ boolean containsAttr( java.lang.String key); @@ -19752,33 +19752,33 @@ boolean containsAttr( * Use {@link #getAttrMap()} instead. */ @java.lang.Deprecated - java.util.Map + java.util.Map getAttr(); /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - java.util.Map + java.util.Map getAttrMap(); /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - serialization.Bigdl.AttrValue getAttrOrDefault( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrDefault( java.lang.String key, - serialization.Bigdl.AttrValue defaultValue); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue defaultValue); /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - serialization.Bigdl.AttrValue getAttrOrThrow( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( java.lang.String key); } /** - * Protobuf type {@code serialization.NameAttrList} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.NameAttrList} */ public static final class NameAttrList extends com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:serialization.NameAttrList) + // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.NameAttrList) NameAttrListOrBuilder { private static final long serialVersionUID = 0L; // Use NameAttrList.newBuilder() to construct. @@ -19829,7 +19829,7 @@ private NameAttrList( AttrDefaultEntryHolder.defaultEntry); mutable_bitField0_ |= 0x00000002; } - com.google.protobuf.MapEntry + com.google.protobuf.MapEntry attr__ = input.readMessage( AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); attr_.getMutableMap().put( @@ -19850,7 +19850,7 @@ private NameAttrList( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_descriptor; } @SuppressWarnings({"rawtypes"}) @@ -19866,9 +19866,9 @@ protected com.google.protobuf.MapField internalGetMapField( } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_NameAttrList_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.NameAttrList.class, serialization.Bigdl.NameAttrList.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.class, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder.class); } private int bitField0_; @@ -19909,18 +19909,18 @@ public java.lang.String getName() { public static final int ATTR_FIELD_NUMBER = 2; private static final class AttrDefaultEntryHolder { static final com.google.protobuf.MapEntry< - java.lang.String, serialization.Bigdl.AttrValue> defaultEntry = + java.lang.String, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue> defaultEntry = com.google.protobuf.MapEntry - .newDefaultInstance( - serialization.Bigdl.internal_static_serialization_NameAttrList_AttrEntry_descriptor, + .newDefaultInstance( + com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_AttrEntry_descriptor, com.google.protobuf.WireFormat.FieldType.STRING, "", com.google.protobuf.WireFormat.FieldType.MESSAGE, - serialization.Bigdl.AttrValue.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()); } private com.google.protobuf.MapField< - java.lang.String, serialization.Bigdl.AttrValue> attr_; - private com.google.protobuf.MapField + java.lang.String, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue> attr_; + private com.google.protobuf.MapField internalGetAttr() { if (attr_ == null) { return com.google.protobuf.MapField.emptyMapField( @@ -19933,7 +19933,7 @@ public int getAttrCount() { return internalGetAttr().getMap().size(); } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ public boolean containsAttr( @@ -19945,36 +19945,36 @@ public boolean containsAttr( * Use {@link #getAttrMap()} instead. */ @java.lang.Deprecated - public java.util.Map getAttr() { + public java.util.Map getAttr() { return getAttrMap(); } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - public java.util.Map getAttrMap() { + public java.util.Map getAttrMap() { return internalGetAttr().getMap(); } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - public serialization.Bigdl.AttrValue getAttrOrDefault( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrDefault( java.lang.String key, - serialization.Bigdl.AttrValue defaultValue) { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue defaultValue) { if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = + java.util.Map map = internalGetAttr().getMap(); return map.containsKey(key) ? map.get(key) : defaultValue; } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - public serialization.Bigdl.AttrValue getAttrOrThrow( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = + java.util.Map map = internalGetAttr().getMap(); if (!map.containsKey(key)) { throw new java.lang.IllegalArgumentException(); @@ -20014,9 +20014,9 @@ public int getSerializedSize() { if (!getNameBytes().isEmpty()) { size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_); } - for (java.util.Map.Entry entry + for (java.util.Map.Entry entry : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry + com.google.protobuf.MapEntry attr__ = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() .setKey(entry.getKey()) .setValue(entry.getValue()) @@ -20034,10 +20034,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof serialization.Bigdl.NameAttrList)) { + if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList)) { return super.equals(obj); } - serialization.Bigdl.NameAttrList other = (serialization.Bigdl.NameAttrList) obj; + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList other = (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) obj; boolean result = true; result = result && getName() @@ -20066,69 +20066,69 @@ public int hashCode() { return hash; } - public static serialization.Bigdl.NameAttrList parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.NameAttrList parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.NameAttrList parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.NameAttrList parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.NameAttrList parseFrom(byte[] data) + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.NameAttrList parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.NameAttrList parseFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.NameAttrList parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.NameAttrList parseDelimitedFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static serialization.Bigdl.NameAttrList parseDelimitedFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.NameAttrList parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.NameAttrList parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -20140,7 +20140,7 @@ public static serialization.Bigdl.NameAttrList parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(serialization.Bigdl.NameAttrList prototype) { + public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -20155,15 +20155,15 @@ protected Builder newBuilderForType( return builder; } /** - * Protobuf type {@code serialization.NameAttrList} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.NameAttrList} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:serialization.NameAttrList) - serialization.Bigdl.NameAttrListOrBuilder { + // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.NameAttrList) + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrListOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_descriptor; } @SuppressWarnings({"rawtypes"}) @@ -20190,12 +20190,12 @@ protected com.google.protobuf.MapField internalGetMutableMapField( } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_NameAttrList_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.NameAttrList.class, serialization.Bigdl.NameAttrList.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.class, com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.Builder.class); } - // Construct using serialization.Bigdl.NameAttrList.newBuilder() + // Construct using com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -20220,23 +20220,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return serialization.Bigdl.internal_static_serialization_NameAttrList_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_descriptor; } - public serialization.Bigdl.NameAttrList getDefaultInstanceForType() { - return serialization.Bigdl.NameAttrList.getDefaultInstance(); + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getDefaultInstanceForType() { + return com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance(); } - public serialization.Bigdl.NameAttrList build() { - serialization.Bigdl.NameAttrList result = buildPartial(); + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList build() { + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public serialization.Bigdl.NameAttrList buildPartial() { - serialization.Bigdl.NameAttrList result = new serialization.Bigdl.NameAttrList(this); + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList buildPartial() { + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList result = new com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.name_ = name_; @@ -20274,16 +20274,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof serialization.Bigdl.NameAttrList) { - return mergeFrom((serialization.Bigdl.NameAttrList)other); + if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) { + return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(serialization.Bigdl.NameAttrList other) { - if (other == serialization.Bigdl.NameAttrList.getDefaultInstance()) return this; + public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList other) { + if (other == com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList.getDefaultInstance()) return this; if (!other.getName().isEmpty()) { name_ = other.name_; onChanged(); @@ -20303,11 +20303,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - serialization.Bigdl.NameAttrList parsedMessage = null; + com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (serialization.Bigdl.NameAttrList) e.getUnfinishedMessage(); + parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -20388,8 +20388,8 @@ public Builder setNameBytes( } private com.google.protobuf.MapField< - java.lang.String, serialization.Bigdl.AttrValue> attr_; - private com.google.protobuf.MapField + java.lang.String, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue> attr_; + private com.google.protobuf.MapField internalGetAttr() { if (attr_ == null) { return com.google.protobuf.MapField.emptyMapField( @@ -20397,7 +20397,7 @@ public Builder setNameBytes( } return attr_; } - private com.google.protobuf.MapField + private com.google.protobuf.MapField internalGetMutableAttr() { onChanged();; if (attr_ == null) { @@ -20414,7 +20414,7 @@ public int getAttrCount() { return internalGetAttr().getMap().size(); } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ public boolean containsAttr( @@ -20426,36 +20426,36 @@ public boolean containsAttr( * Use {@link #getAttrMap()} instead. */ @java.lang.Deprecated - public java.util.Map getAttr() { + public java.util.Map getAttr() { return getAttrMap(); } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - public java.util.Map getAttrMap() { + public java.util.Map getAttrMap() { return internalGetAttr().getMap(); } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - public serialization.Bigdl.AttrValue getAttrOrDefault( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrDefault( java.lang.String key, - serialization.Bigdl.AttrValue defaultValue) { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue defaultValue) { if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = + java.util.Map map = internalGetAttr().getMap(); return map.containsKey(key) ? map.get(key) : defaultValue; } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ - public serialization.Bigdl.AttrValue getAttrOrThrow( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = + java.util.Map map = internalGetAttr().getMap(); if (!map.containsKey(key)) { throw new java.lang.IllegalArgumentException(); @@ -20469,7 +20469,7 @@ public Builder clearAttr() { return this; } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ public Builder removeAttr( @@ -20483,16 +20483,16 @@ public Builder removeAttr( * Use alternate mutation accessors instead. */ @java.lang.Deprecated - public java.util.Map + public java.util.Map getMutableAttr() { return internalGetMutableAttr().getMutableMap(); } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ public Builder putAttr( java.lang.String key, - serialization.Bigdl.AttrValue value) { + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { if (key == null) { throw new java.lang.NullPointerException(); } if (value == null) { throw new java.lang.NullPointerException(); } internalGetMutableAttr().getMutableMap() @@ -20500,11 +20500,11 @@ public Builder putAttr( return this; } /** - * map<string, .serialization.AttrValue> attr = 2; + * map<string, .com.intel.analytics.bigdl.serialization.AttrValue> attr = 2; */ public Builder putAllAttr( - java.util.Map values) { + java.util.Map values) { internalGetMutableAttr().getMutableMap() .putAll(values); return this; @@ -20520,16 +20520,16 @@ public final Builder mergeUnknownFields( } - // @@protoc_insertion_point(builder_scope:serialization.NameAttrList) + // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.NameAttrList) } - // @@protoc_insertion_point(class_scope:serialization.NameAttrList) - private static final serialization.Bigdl.NameAttrList DEFAULT_INSTANCE; + // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.NameAttrList) + private static final com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new serialization.Bigdl.NameAttrList(); + DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList(); } - public static serialization.Bigdl.NameAttrList getDefaultInstance() { + public static com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -20552,24 +20552,24 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public serialization.Bigdl.NameAttrList getDefaultInstanceForType() { + public com.intel.analytics.bigdl.serialization.Bigdl.NameAttrList getDefaultInstanceForType() { return DEFAULT_INSTANCE; } } public interface ShapeOrBuilder extends - // @@protoc_insertion_point(interface_extends:serialization.Shape) + // @@protoc_insertion_point(interface_extends:com.intel.analytics.bigdl.serialization.Shape) com.google.protobuf.MessageOrBuilder { /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ int getShapeTypeValue(); /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ - serialization.Bigdl.Shape.ShapeType getShapeType(); + com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType getShapeType(); /** * int32 ssize = 2; @@ -20590,35 +20590,35 @@ public interface ShapeOrBuilder extends int getShapeValue(int index); /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - java.util.List + java.util.List getShapeList(); /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - serialization.Bigdl.Shape getShape(int index); + com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape(int index); /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ int getShapeCount(); /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - java.util.List + java.util.List getShapeOrBuilderList(); /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( int index); } /** - * Protobuf type {@code serialization.Shape} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.Shape} */ public static final class Shape extends com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:serialization.Shape) + // @@protoc_insertion_point(message_implements:com.intel.analytics.bigdl.serialization.Shape) ShapeOrBuilder { private static final long serialVersionUID = 0L; // Use Shape.newBuilder() to construct. @@ -20694,11 +20694,11 @@ private Shape( } case 34: { if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - shape_ = new java.util.ArrayList(); + shape_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000008; } shape_.add( - input.readMessage(serialization.Bigdl.Shape.parser(), extensionRegistry)); + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Shape.parser(), extensionRegistry)); break; } } @@ -20721,18 +20721,18 @@ private Shape( } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_Shape_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Shape_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_Shape_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Shape_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.Shape.class, serialization.Bigdl.Shape.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.Shape.class, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder.class); } /** - * Protobuf enum {@code serialization.Shape.ShapeType} + * Protobuf enum {@code com.intel.analytics.bigdl.serialization.Shape.ShapeType} */ public enum ShapeType implements com.google.protobuf.ProtocolMessageEnum { @@ -20803,7 +20803,7 @@ public ShapeType findValueByNumber(int number) { } public static final com.google.protobuf.Descriptors.EnumDescriptor getDescriptor() { - return serialization.Bigdl.Shape.getDescriptor().getEnumTypes().get(0); + return com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDescriptor().getEnumTypes().get(0); } private static final ShapeType[] VALUES = values(); @@ -20826,24 +20826,24 @@ private ShapeType(int value) { this.value = value; } - // @@protoc_insertion_point(enum_scope:serialization.Shape.ShapeType) + // @@protoc_insertion_point(enum_scope:com.intel.analytics.bigdl.serialization.Shape.ShapeType) } private int bitField0_; public static final int SHAPETYPE_FIELD_NUMBER = 1; private int shapeType_; /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ public int getShapeTypeValue() { return shapeType_; } /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ - public serialization.Bigdl.Shape.ShapeType getShapeType() { - serialization.Bigdl.Shape.ShapeType result = serialization.Bigdl.Shape.ShapeType.valueOf(shapeType_); - return result == null ? serialization.Bigdl.Shape.ShapeType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType getShapeType() { + com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType result = com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType.valueOf(shapeType_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType.UNRECOGNIZED : result; } public static final int SSIZE_FIELD_NUMBER = 2; @@ -20879,36 +20879,36 @@ public int getShapeValue(int index) { private int shapeValueMemoizedSerializedSize = -1; public static final int SHAPE_FIELD_NUMBER = 4; - private java.util.List shape_; + private java.util.List shape_; /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public java.util.List getShapeList() { + public java.util.List getShapeList() { return shape_; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public java.util.List + public java.util.List getShapeOrBuilderList() { return shape_; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public int getShapeCount() { return shape_.size(); } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public serialization.Bigdl.Shape getShape(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape(int index) { return shape_.get(index); } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( int index) { return shape_.get(index); } @@ -20926,7 +20926,7 @@ public final boolean isInitialized() { public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException { getSerializedSize(); - if (shapeType_ != serialization.Bigdl.Shape.ShapeType.SINGLE.getNumber()) { + if (shapeType_ != com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType.SINGLE.getNumber()) { output.writeEnum(1, shapeType_); } if (ssize_ != 0) { @@ -20950,7 +20950,7 @@ public int getSerializedSize() { if (size != -1) return size; size = 0; - if (shapeType_ != serialization.Bigdl.Shape.ShapeType.SINGLE.getNumber()) { + if (shapeType_ != com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType.SINGLE.getNumber()) { size += com.google.protobuf.CodedOutputStream .computeEnumSize(1, shapeType_); } @@ -20986,10 +20986,10 @@ public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } - if (!(obj instanceof serialization.Bigdl.Shape)) { + if (!(obj instanceof com.intel.analytics.bigdl.serialization.Bigdl.Shape)) { return super.equals(obj); } - serialization.Bigdl.Shape other = (serialization.Bigdl.Shape) obj; + com.intel.analytics.bigdl.serialization.Bigdl.Shape other = (com.intel.analytics.bigdl.serialization.Bigdl.Shape) obj; boolean result = true; result = result && shapeType_ == other.shapeType_; @@ -21027,69 +21027,69 @@ public int hashCode() { return hash; } - public static serialization.Bigdl.Shape parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.Shape parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.Shape parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.Shape parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.Shape parseFrom(byte[] data) + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } - public static serialization.Bigdl.Shape parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } - public static serialization.Bigdl.Shape parseFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.Shape parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.Shape parseDelimitedFrom(java.io.InputStream input) + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } - public static serialization.Bigdl.Shape parseDelimitedFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } - public static serialization.Bigdl.Shape parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } - public static serialization.Bigdl.Shape parseFrom( + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { @@ -21101,7 +21101,7 @@ public static serialization.Bigdl.Shape parseFrom( public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } - public static Builder newBuilder(serialization.Bigdl.Shape prototype) { + public static Builder newBuilder(com.intel.analytics.bigdl.serialization.Bigdl.Shape prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { @@ -21116,25 +21116,25 @@ protected Builder newBuilderForType( return builder; } /** - * Protobuf type {@code serialization.Shape} + * Protobuf type {@code com.intel.analytics.bigdl.serialization.Shape} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:serialization.Shape) - serialization.Bigdl.ShapeOrBuilder { + // @@protoc_insertion_point(builder_implements:com.intel.analytics.bigdl.serialization.Shape) + com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { - return serialization.Bigdl.internal_static_serialization_Shape_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Shape_descriptor; } protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { - return serialization.Bigdl.internal_static_serialization_Shape_fieldAccessorTable + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Shape_fieldAccessorTable .ensureFieldAccessorsInitialized( - serialization.Bigdl.Shape.class, serialization.Bigdl.Shape.Builder.class); + com.intel.analytics.bigdl.serialization.Bigdl.Shape.class, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder.class); } - // Construct using serialization.Bigdl.Shape.newBuilder() + // Construct using com.intel.analytics.bigdl.serialization.Bigdl.Shape.newBuilder() private Builder() { maybeForceBuilderInitialization(); } @@ -21169,23 +21169,23 @@ public Builder clear() { public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { - return serialization.Bigdl.internal_static_serialization_Shape_descriptor; + return com.intel.analytics.bigdl.serialization.Bigdl.internal_static_com_intel_analytics_bigdl_serialization_Shape_descriptor; } - public serialization.Bigdl.Shape getDefaultInstanceForType() { - return serialization.Bigdl.Shape.getDefaultInstance(); + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getDefaultInstanceForType() { + return com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance(); } - public serialization.Bigdl.Shape build() { - serialization.Bigdl.Shape result = buildPartial(); + public com.intel.analytics.bigdl.serialization.Bigdl.Shape build() { + com.intel.analytics.bigdl.serialization.Bigdl.Shape result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } - public serialization.Bigdl.Shape buildPartial() { - serialization.Bigdl.Shape result = new serialization.Bigdl.Shape(this); + public com.intel.analytics.bigdl.serialization.Bigdl.Shape buildPartial() { + com.intel.analytics.bigdl.serialization.Bigdl.Shape result = new com.intel.analytics.bigdl.serialization.Bigdl.Shape(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.shapeType_ = shapeType_; @@ -21236,16 +21236,16 @@ public Builder addRepeatedField( return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof serialization.Bigdl.Shape) { - return mergeFrom((serialization.Bigdl.Shape)other); + if (other instanceof com.intel.analytics.bigdl.serialization.Bigdl.Shape) { + return mergeFrom((com.intel.analytics.bigdl.serialization.Bigdl.Shape)other); } else { super.mergeFrom(other); return this; } } - public Builder mergeFrom(serialization.Bigdl.Shape other) { - if (other == serialization.Bigdl.Shape.getDefaultInstance()) return this; + public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.Shape other) { + if (other == com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()) return this; if (other.shapeType_ != 0) { setShapeTypeValue(other.getShapeTypeValue()); } @@ -21301,11 +21301,11 @@ public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { - serialization.Bigdl.Shape parsedMessage = null; + com.intel.analytics.bigdl.serialization.Bigdl.Shape parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (serialization.Bigdl.Shape) e.getUnfinishedMessage(); + parsedMessage = (com.intel.analytics.bigdl.serialization.Bigdl.Shape) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { @@ -21318,13 +21318,13 @@ public Builder mergeFrom( private int shapeType_ = 0; /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ public int getShapeTypeValue() { return shapeType_; } /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ public Builder setShapeTypeValue(int value) { shapeType_ = value; @@ -21332,16 +21332,16 @@ public Builder setShapeTypeValue(int value) { return this; } /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ - public serialization.Bigdl.Shape.ShapeType getShapeType() { - serialization.Bigdl.Shape.ShapeType result = serialization.Bigdl.Shape.ShapeType.valueOf(shapeType_); - return result == null ? serialization.Bigdl.Shape.ShapeType.UNRECOGNIZED : result; + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType getShapeType() { + com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType result = com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType.valueOf(shapeType_); + return result == null ? com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType.UNRECOGNIZED : result; } /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ - public Builder setShapeType(serialization.Bigdl.Shape.ShapeType value) { + public Builder setShapeType(com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType value) { if (value == null) { throw new NullPointerException(); } @@ -21351,7 +21351,7 @@ public Builder setShapeType(serialization.Bigdl.Shape.ShapeType value) { return this; } /** - * .serialization.Shape.ShapeType shapeType = 1; + * .com.intel.analytics.bigdl.serialization.Shape.ShapeType shapeType = 1; */ public Builder clearShapeType() { @@ -21452,22 +21452,22 @@ public Builder clearShapeValue() { return this; } - private java.util.List shape_ = + private java.util.List shape_ = java.util.Collections.emptyList(); private void ensureShapeIsMutable() { if (!((bitField0_ & 0x00000008) == 0x00000008)) { - shape_ = new java.util.ArrayList(shape_); + shape_ = new java.util.ArrayList(shape_); bitField0_ |= 0x00000008; } } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> shapeBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> shapeBuilder_; /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public java.util.List getShapeList() { + public java.util.List getShapeList() { if (shapeBuilder_ == null) { return java.util.Collections.unmodifiableList(shape_); } else { @@ -21475,7 +21475,7 @@ public java.util.List getShapeList() { } } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public int getShapeCount() { if (shapeBuilder_ == null) { @@ -21485,9 +21485,9 @@ public int getShapeCount() { } } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public serialization.Bigdl.Shape getShape(int index) { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getShape(int index) { if (shapeBuilder_ == null) { return shape_.get(index); } else { @@ -21495,10 +21495,10 @@ public serialization.Bigdl.Shape getShape(int index) { } } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public Builder setShape( - int index, serialization.Bigdl.Shape value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (shapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -21512,10 +21512,10 @@ public Builder setShape( return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public Builder setShape( - int index, serialization.Bigdl.Shape.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (shapeBuilder_ == null) { ensureShapeIsMutable(); shape_.set(index, builderForValue.build()); @@ -21526,9 +21526,9 @@ public Builder setShape( return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public Builder addShape(serialization.Bigdl.Shape value) { + public Builder addShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (shapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -21542,10 +21542,10 @@ public Builder addShape(serialization.Bigdl.Shape value) { return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public Builder addShape( - int index, serialization.Bigdl.Shape value) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (shapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); @@ -21559,10 +21559,10 @@ public Builder addShape( return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public Builder addShape( - serialization.Bigdl.Shape.Builder builderForValue) { + com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (shapeBuilder_ == null) { ensureShapeIsMutable(); shape_.add(builderForValue.build()); @@ -21573,10 +21573,10 @@ public Builder addShape( return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public Builder addShape( - int index, serialization.Bigdl.Shape.Builder builderForValue) { + int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (shapeBuilder_ == null) { ensureShapeIsMutable(); shape_.add(index, builderForValue.build()); @@ -21587,10 +21587,10 @@ public Builder addShape( return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public Builder addAllShape( - java.lang.Iterable values) { + java.lang.Iterable values) { if (shapeBuilder_ == null) { ensureShapeIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( @@ -21602,7 +21602,7 @@ public Builder addAllShape( return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public Builder clearShape() { if (shapeBuilder_ == null) { @@ -21615,7 +21615,7 @@ public Builder clearShape() { return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ public Builder removeShape(int index) { if (shapeBuilder_ == null) { @@ -21628,16 +21628,16 @@ public Builder removeShape(int index) { return this; } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public serialization.Bigdl.Shape.Builder getShapeBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getShapeBuilder( int index) { return getShapeFieldBuilder().getBuilder(index); } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( int index) { if (shapeBuilder_ == null) { return shape_.get(index); } else { @@ -21645,9 +21645,9 @@ public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( } } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public java.util.List + public java.util.List getShapeOrBuilderList() { if (shapeBuilder_ != null) { return shapeBuilder_.getMessageOrBuilderList(); @@ -21656,33 +21656,33 @@ public serialization.Bigdl.ShapeOrBuilder getShapeOrBuilder( } } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public serialization.Bigdl.Shape.Builder addShapeBuilder() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder addShapeBuilder() { return getShapeFieldBuilder().addBuilder( - serialization.Bigdl.Shape.getDefaultInstance()); + com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()); } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public serialization.Bigdl.Shape.Builder addShapeBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder addShapeBuilder( int index) { return getShapeFieldBuilder().addBuilder( - index, serialization.Bigdl.Shape.getDefaultInstance()); + index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()); } /** - * repeated .serialization.Shape shape = 4; + * repeated .com.intel.analytics.bigdl.serialization.Shape shape = 4; */ - public java.util.List + public java.util.List getShapeBuilderList() { return getShapeFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder> + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> getShapeFieldBuilder() { if (shapeBuilder_ == null) { shapeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - serialization.Bigdl.Shape, serialization.Bigdl.Shape.Builder, serialization.Bigdl.ShapeOrBuilder>( + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder>( shape_, ((bitField0_ & 0x00000008) == 0x00000008), getParentForChildren(), @@ -21702,16 +21702,16 @@ public final Builder mergeUnknownFields( } - // @@protoc_insertion_point(builder_scope:serialization.Shape) + // @@protoc_insertion_point(builder_scope:com.intel.analytics.bigdl.serialization.Shape) } - // @@protoc_insertion_point(class_scope:serialization.Shape) - private static final serialization.Bigdl.Shape DEFAULT_INSTANCE; + // @@protoc_insertion_point(class_scope:com.intel.analytics.bigdl.serialization.Shape) + private static final com.intel.analytics.bigdl.serialization.Bigdl.Shape DEFAULT_INSTANCE; static { - DEFAULT_INSTANCE = new serialization.Bigdl.Shape(); + DEFAULT_INSTANCE = new com.intel.analytics.bigdl.serialization.Bigdl.Shape(); } - public static serialization.Bigdl.Shape getDefaultInstance() { + public static com.intel.analytics.bigdl.serialization.Bigdl.Shape getDefaultInstance() { return DEFAULT_INSTANCE; } @@ -21734,67 +21734,67 @@ public com.google.protobuf.Parser getParserForType() { return PARSER; } - public serialization.Bigdl.Shape getDefaultInstanceForType() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getDefaultInstanceForType() { return DEFAULT_INSTANCE; } } private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_BigDLModule_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_BigDLModule_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_BigDLModule_AttrEntry_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_BigDLModule_AttrEntry_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_InitMethod_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_InitMethod_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_InitMethod_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_InitMethod_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_BigDLTensor_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_BigDLTensor_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_BigDLTensor_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_BigDLTensor_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_TensorStorage_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_TensorStorage_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_TensorStorage_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_TensorStorage_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_Regularizer_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_Regularizer_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_Regularizer_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_Regularizer_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_AttrValue_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_AttrValue_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_AttrValue_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_AttrValue_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_AttrValue_ArrayValue_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_AttrValue_ArrayValue_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_AttrValue_ArrayValue_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_AttrValue_ArrayValue_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_NameAttrList_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_NameAttrList_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_NameAttrList_AttrEntry_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_AttrEntry_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_NameAttrList_AttrEntry_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_NameAttrList_AttrEntry_fieldAccessorTable; private static final com.google.protobuf.Descriptors.Descriptor - internal_static_serialization_Shape_descriptor; + internal_static_com_intel_analytics_bigdl_serialization_Shape_descriptor; private static final com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_serialization_Shape_fieldAccessorTable; + internal_static_com_intel_analytics_bigdl_serialization_Shape_fieldAccessorTable; public static com.google.protobuf.Descriptors.FileDescriptor getDescriptor() { @@ -21804,97 +21804,121 @@ public serialization.Bigdl.Shape getDefaultInstanceForType() { descriptor; static { java.lang.String[] descriptorData = { - "\n\013bigdl.proto\022\rserialization\032\031google/pro" + - "tobuf/any.proto\"\357\003\n\013BigDLModule\022\014\n\004name\030" + - "\001 \001(\t\022.\n\nsubModules\030\002 \003(\0132\032.serializatio" + - "n.BigDLModule\022*\n\006weight\030\003 \001(\0132\032.serializ" + - "ation.BigDLTensor\022(\n\004bias\030\004 \001(\0132\032.serial" + - "ization.BigDLTensor\022\022\n\npreModules\030\005 \003(\t\022" + - "\023\n\013nextModules\030\006 \003(\t\022\022\n\nmoduleType\030\007 \001(\t" + - "\0222\n\004attr\030\010 \003(\0132$.serialization.BigDLModu" + - "le.AttrEntry\022\017\n\007version\030\t \001(\t\022\r\n\005train\030\n" + - " \001(\010\022\023\n\013namePostfix\030\013 \001(\t\022\n\n\002id\030\014 \001(\005\022(\n", - "\ninputShape\030\r \001(\0132\024.serialization.Shape\022" + - ")\n\013outputShape\030\016 \003(\0132\024.serialization.Sha" + - "pe\032E\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022\'\n\005value\030\002 " + - "\001(\0132\030.serialization.AttrValue:\0028\001\"M\n\nIni" + - "tMethod\0221\n\nmethodType\030\001 \001(\0162\035.serializat" + - "ion.InitMethodType\022\014\n\004data\030\002 \003(\001\"\210\002\n\013Big" + - "DLTensor\022)\n\010datatype\030\001 \001(\0162\027.serializati" + - "on.DataType\022\014\n\004size\030\002 \003(\005\022\016\n\006stride\030\003 \003(" + - "\005\022\016\n\006offset\030\004 \001(\005\022\021\n\tdimension\030\005 \001(\005\022\021\n\t" + - "nElements\030\006 \001(\005\022\020\n\010isScalar\030\007 \001(\010\022-\n\007sto", - "rage\030\010 \001(\0132\034.serialization.TensorStorage" + - "\022\n\n\002id\030\t \001(\005\022-\n\ntensorType\030\n \001(\0162\031.seria" + - "lization.TensorType\"\320\001\n\rTensorStorage\022)\n" + - "\010datatype\030\001 \001(\0162\027.serialization.DataType" + - "\022\022\n\nfloat_data\030\002 \003(\002\022\023\n\013double_data\030\003 \003(" + - "\001\022\021\n\tbool_data\030\004 \003(\010\022\023\n\013string_data\030\005 \003(" + - "\t\022\020\n\010int_data\030\006 \003(\005\022\021\n\tlong_data\030\007 \003(\003\022\022" + - "\n\nbytes_data\030\010 \003(\014\022\n\n\002id\030\t \001(\005\"[\n\013Regula" + - "rizer\0227\n\017regularizerType\030\001 \001(\0162\036.seriali" + - "zation.RegularizerType\022\023\n\013regularData\030\002 ", - "\003(\001\"\246\n\n\tAttrValue\022)\n\010dataType\030\001 \001(\0162\027.se" + - "rialization.DataType\022\017\n\007subType\030\002 \001(\t\022\024\n" + - "\nint32Value\030\003 \001(\005H\000\022\024\n\nint64Value\030\004 \001(\003H" + - "\000\022\024\n\nfloatValue\030\005 \001(\002H\000\022\025\n\013doubleValue\030\006" + - 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internal_static_serialization_BigDLModule_AttrEntry_descriptor = - internal_static_serialization_BigDLModule_descriptor.getNestedTypes().get(0); - internal_static_serialization_BigDLModule_AttrEntry_fieldAccessorTable = new + internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_descriptor = + internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor.getNestedTypes().get(0); + internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_serialization_BigDLModule_AttrEntry_descriptor, + internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_descriptor, new java.lang.String[] { "Key", "Value", }); - internal_static_serialization_InitMethod_descriptor = + internal_static_com_intel_analytics_bigdl_serialization_InitMethod_descriptor = getDescriptor().getMessageTypes().get(1); - 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internal_static_serialization_Regularizer_fieldAccessorTable = new + internal_static_com_intel_analytics_bigdl_serialization_Regularizer_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_serialization_Regularizer_descriptor, + internal_static_com_intel_analytics_bigdl_serialization_Regularizer_descriptor, new java.lang.String[] { "RegularizerType", "RegularData", }); - internal_static_serialization_AttrValue_descriptor = + internal_static_com_intel_analytics_bigdl_serialization_AttrValue_descriptor = getDescriptor().getMessageTypes().get(5); - internal_static_serialization_AttrValue_fieldAccessorTable = new + internal_static_com_intel_analytics_bigdl_serialization_AttrValue_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_serialization_AttrValue_descriptor, + internal_static_com_intel_analytics_bigdl_serialization_AttrValue_descriptor, new java.lang.String[] { "DataType", "SubType", 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internal_static_com_intel_analytics_bigdl_serialization_Shape_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_serialization_Shape_descriptor, + internal_static_com_intel_analytics_bigdl_serialization_Shape_descriptor, new java.lang.String[] { "ShapeType", "Ssize", "ShapeValue", "Shape", }); com.google.protobuf.AnyProto.getDescriptor(); } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index 84a86a4ac89..f7406d81ccb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala index 3b88b8822c4..b847d0bcd1e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala index 41c3997b699..19b261a20c3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Echo.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleData, ModuleSerializable, SerializeContext} -import serialization.Bigdl.BigDLModule +import com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 13425b45119..edc881659da 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -30,7 +30,7 @@ import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.tf.Tensorflow -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala index 227a348b88c..dc8f61f2b98 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, DeserializeContext, ModuleData, SerializeContext} -import serialization.Bigdl.BigDLModule +import com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala index c321cdd76c6..f80aaa855c8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelect.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala index 521777c288f..138ffd72574 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter.ArrayConverter import com.intel.analytics.bigdl.utils.{T, Table} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala index fe90f901cc7..bd5ea4a76b4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala index 21728d04707..9049e7b42f2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala index 69c86833ae6..d5100e36668 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala index 783bc4e7a9e..033583bde9e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalization.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala index 663e093cdf5..014344e6161 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala index a8d0c3db5b8..49b54511df7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.utils.{T, Table, serializer} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.concurrent.Future import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala index a561380d1b9..48806ca9ac9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect._ import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala index b50449b6b26..7058a97cc6f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala index d9df6c3b807..638bf320eb5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala index e3873c13659..1c582589712 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePooling.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import org.codehaus.jackson.map.DeserializationContext -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect._ import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala index 817af9ce426..0159a03d762 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPooling.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import org.codehaus.jackson.map.DeserializationContext -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect._ import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala index 78520a2d92a..529f74ca692 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala @@ -27,7 +27,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} import org.tensorflow.framework.DataType -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala index 94a32a0a6be..7d5d749af94 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} import org.tensorflow.example.{Example, Feature} import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.JavaConverters._ import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala index c4aa92d27f4..f67bb277472 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniform.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.RandomGenerator import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala index 7048a06a5cc..ada8628ca5d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.ClassTag -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} private[bigdl] class Linear[T: ClassTag]( val inputSize: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala index 077a8a88ad5..8e235d799f0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantSerializer.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.TensorConverter -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala index 0dbd0e07817..f5602168658 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -28,7 +28,7 @@ import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.runtime.universe import scala.reflect.ClassTag -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} @SerialVersionUID(- 8008252944905538960L) private[bigdl] class SpatialConvolution[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala index 39e8410a820..08b94a5e69b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolution.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{FloatType, QuantizedTensor, Tensor} import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleData, SerializeContext} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala index a37b0e8c610..beefe9c361a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala index 47ed0071216..5e2c6c7f74b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala @@ -28,7 +28,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.converters.TensorConverter import com.intel.analytics.bigdl.utils.serializer.converters.DataReaderWriter import com.intel.analytics.bigdl.utils.{File, FileReader, FileWriter, Table} -import serialization.Bigdl._ +import com.intel.analytics.bigdl.serialization.Bigdl._ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 4ab8f02c81e..2112bba5ab8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Table, Shape => BigDLShape} import com.intel.analytics.bigdl.utils.serializer.converters.{DataConverter, ShapeConverter, TensorConverter} import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._ -import serialization.Bigdl._ +import com.intel.analytics.bigdl.serialization.Bigdl._ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala index aece2bd8abf..2f24bb27f20 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericString} import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString -import serialization.Bigdl.BigDLModule +import com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule import scala.collection.mutable import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala index e27570223cc..ccfd5f471af 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala @@ -26,8 +26,8 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.{MultiShape, SingleShape, Shape => BigDLShape} -import serialization.Bigdl._ -import serialization.Bigdl.AttrValue.ArrayValue +import com.intel.analytics.bigdl.serialization.Bigdl._ +import com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue import scala.collection.mutable import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataFormatConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataFormatConverter.scala index 511e709bcdd..f474cf4b1d5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataFormatConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataFormatConverter.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.{NCHW, NHWC} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} -import serialization.Bigdl.{AttrValue, DataType, InputDataFormat} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType, InputDataFormat} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/InitMethodConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/InitMethodConverter.scala index 0697a359eed..5d2428c4b4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/InitMethodConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/InitMethodConverter.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.utils.serializer.converters import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} -import serialization.Bigdl.{AttrValue, DataType, InitMethod, InitMethodType} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType, InitMethod, InitMethodType} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ModuleConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ModuleConverter.scala index 712082a9601..28299101420 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ModuleConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ModuleConverter.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.utils.serializer.converters import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleData, ModuleSerializer, SerializeContext} -import serialization.Bigdl.{AttrValue, DataType} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/RegularizerConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/RegularizerConverter.scala index d0f8d4430fc..a66bd5fb356 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/RegularizerConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/RegularizerConverter.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.utils.serializer.converters import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regularizer} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} -import serialization.Bigdl.{AttrValue, DataType} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType, RegularizerType, Regularizer => SerializeRegularizer} import scala.reflect.ClassTag import scala.reflect.runtime.universe @@ -39,13 +39,13 @@ object RegularizerConverter extends DataConverter { return null } regularizerType match { - case serialization.Bigdl.RegularizerType.L1Regularizer => + case RegularizerType.L1Regularizer => val l1 = regularizer.getRegularDataList.get(0) L1Regularizer[T](l1) - case serialization.Bigdl.RegularizerType.L2Regularizer => + case RegularizerType.L2Regularizer => val l2 = regularizer.getRegularDataList.get(1) L2Regularizer[T](l2) - case serialization.Bigdl.RegularizerType.L1L2Regularizer => + case RegularizerType.L1L2Regularizer => val l1 = regularizer.getRegularDataList.get(0) val l2 = regularizer.getRegularDataList.get(1) L1L2Regularizer[T](l1, l2) @@ -58,16 +58,16 @@ object RegularizerConverter extends DataConverter { (implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.REGULARIZER) if (value != null) { - var regularizerBuilder = serialization.Bigdl.Regularizer.newBuilder + var regularizerBuilder = SerializeRegularizer.newBuilder val regularizer = value.asInstanceOf[L1L2Regularizer[T]] val l1 = regularizer.l1 val l2 = regularizer.l2 regularizerBuilder.addRegularData(l1) regularizerBuilder.addRegularData(l2) val regularizerType = regularizer match { - case l1: L1Regularizer[_] => serialization.Bigdl.RegularizerType.L1Regularizer - case l2: L2Regularizer[_] => serialization.Bigdl.RegularizerType.L2Regularizer - case l1l2: L1L2Regularizer[_] => serialization.Bigdl.RegularizerType.L1L2Regularizer + case l1: L1Regularizer[_] => RegularizerType.L1Regularizer + case l2: L2Regularizer[_] => RegularizerType.L2Regularizer + case l1l2: L1L2Regularizer[_] => RegularizerType.L1L2Regularizer } regularizerBuilder.setRegularizerType(regularizerType) attributeBuilder.setRegularizerValue(regularizerBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala index 93d7b92c0fc..27f4f71b9fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala @@ -19,9 +19,9 @@ package com.intel.analytics.bigdl.utils.serializer.converters import com.intel.analytics.bigdl.tensor.TensorNumericMath import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} import com.intel.analytics.bigdl.utils.{MultiShape, SingleShape, Shape => BigDLShape} -import serialization.Bigdl -import serialization.Bigdl.Shape.ShapeType -import serialization.Bigdl.{AttrValue, DataType, Shape} +import com.intel.analytics.bigdl.serialization.Bigdl +import com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType, Shape} import scala.collection.JavaConverters._ import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala index 4738e58a16a..69a8768c518 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.nn.quantized._ import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.{BigDLStorage, DeserializeContext, ProtoStorageType, SerializeContext} -import serialization.Bigdl._ +import com.intel.analytics.bigdl.serialization.Bigdl._ import scala.collection.JavaConverters._ import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala index d5a3efe7bda..ea4e2b76257 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{Numeric import com.intel.analytics.bigdl.tensor.{DenseType, QuantizedTensor, QuantizedType, Tensor} import com.intel.analytics.bigdl.utils.serializer.SerializeContext import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString -import serialization.Bigdl.{BigDLTensor, DataType, TensorStorage} +import com.intel.analytics.bigdl.serialization.Bigdl.{BigDLTensor, DataType, TensorStorage} import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/VariableFormatConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/VariableFormatConverter.scala index c92d2d860fd..8db42467a39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/VariableFormatConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/VariableFormatConverter.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.utils.serializer.converters import com.intel.analytics.bigdl.nn.VariableFormat import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} -import serialization.Bigdl.{AttrValue, DataType, VarFormat} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType, VarFormat} import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index f9824f3ad96..fc903c5ea99 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -25,13 +25,13 @@ import com.intel.analytics.bigdl.optim.{L1L2Regularizer, L1Regularizer, L2Regula import com.intel.analytics.bigdl.tensor.{QuantizedTensor, Storage, Tensor} import com.intel.analytics.bigdl.utils.{MultiShape, SingleShape, Shape => BigDLShape} import org.scalatest.{FlatSpec, Matchers} -import serialization.Bigdl.{AttrValue, BigDLTensor, DataType, TensorStorage} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLTensor, DataType, TensorStorage} import scala.reflect.runtime.universe import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.utils.SingleShape import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import serialization.Bigdl.AttrValue.ArrayValue +import com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue import scala.collection.mutable import scala.util.Random diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala index ab7937568e3..da042f04a4f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TensorConversionSpec.scala @@ -19,7 +19,7 @@ import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import org.scalatest.{FlatSpec, Matchers} -import serialization.Bigdl.{AttrValue, BigDLTensor, TensorStorage} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLTensor, TensorStorage} import scala.collection.mutable import scala.reflect.runtime.universe From 7fd43fa93d15e392587f471efb654e34900de7b9 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Wed, 31 Jan 2018 09:04:11 +0800 Subject: [PATCH 0665/1065] Add python api for keras layer (#2242) * firtst try * update * update Dense * clean * style * style --- .../analytics/bigdl/dllib/keras/Dense.scala | 77 ++++++++++++------- .../bigdl/dllib/keras/Topology.scala | 33 ++++---- .../dllib/utils/python/api/PythonBigDL.scala | 2 +- .../utils/python/api/PythonBigDLKeras.scala | 64 +++++++++++++++ .../bigdl/dllib/keras/KerasRunner.scala | 1 + .../bigdl/dllib/keras/nn/DenseSpec.scala | 17 ++++ 6 files changed, 150 insertions(+), 44 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala index ffa342209a4..4c419b811aa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala @@ -17,8 +17,8 @@ package com.intel.analytics.bigdl.nn.keras import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.nn.{Sequential => TSequential, _} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -27,50 +27,69 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag -@SerialVersionUID( 359656776803598944L) class Dense[T: ClassTag](val outputDim: Int, - val init: InitializationMethod = RandomUniform, - val activation: TensorModule[T] = null, - var wRegularizer: Regularizer[T] = null, - var bRegularizer: Regularizer[T] = null, - val bias: Boolean = true, - var inputShape: Shape = null - )(implicit ev: TensorNumeric[T]) + val init: InitializationMethod = RandomUniform, + val activation: TensorModule[T] = null, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + var inputShape: Shape = null +)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + override def computeOutputShape(inputShape: Shape): Shape = { + require(inputShape.toSingle().size >=2, + s"inputShape should at least containing 2 dims, but got: $inputShape.toSingle().size") + inputShape.copyAndUpdate(-1, outputDim) + } + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { - val layer = Linear( - inputSize = inputShape.toSingle()(1), // the first dim is batch + val inputShapeList = inputShape.toSingle() + var model: AbstractModule[Tensor[T], Tensor[T], T] = Linear( + inputSize = inputShapeList.last, outputSize = outputDim, withBias = bias, wRegularizer = wRegularizer, bRegularizer = bRegularizer - ) - layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) - KerasLayer.fuse(layer, + ).setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) + + model = KerasLayer.fuse(model, activation, inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + + if (inputShape.toSingle().size <= 2) { + model + } else { + val seq = new Sequential[T](stopInferShape = true) + val inDim = inputShapeList.last + seq.add(InputLayer(inputShape = inputShape)) + seq.add(InferReshape(Array(-1, inDim), false)) + seq.add(model) + seq.add(InferReshape(Array(-1) ++ + inputShapeList.slice(1, inputShapeList.size - 1) ++ Array(outputDim), false)) + seq + }.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } object Dense { def apply[@specialized(Float, Double) T: ClassTag]( - outputDim: Int, - init: InitializationMethod = RandomUniform, - activation: TensorModule[T] = null, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null, - bias: Boolean = true, - inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : Dense[T] = { - new Dense[T]( - outputDim, - init, - activation, - wRegularizer, - bRegularizer, - bias, - inputShape) + outputDim: Int, + init: InitializationMethod = RandomUniform, + activation: TensorModule[T] = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : Dense[T] = { + new Dense[T]( + outputDim, + init, + activation, + wRegularizer, + bRegularizer, + bias, + inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala index cee1afdc850..1e7d3232a36 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala @@ -93,7 +93,7 @@ object Model { } } -class Sequential[T: ClassTag] +class Sequential[T: ClassTag](val stopInferShape: Boolean = false) (implicit ev: TensorNumeric[T]) extends TSequential[T] { override private[bigdl] def isCompatibleWithKeras(): Boolean = true @@ -111,6 +111,22 @@ class Sequential[T: ClassTag] outputShapeValue(outputShapeValue.length -1) // For Seq, we only respect the last item as output } + private def triggerBuilding(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Unit = { + if (this.modules.isEmpty) { + if (module.getInputShape() == null) { + throw new RuntimeException("The first layer should explicitly declare inputshape") + } else { + val outputShape = module.build(module.getInputShape()) + this.inputShapeValue = module.getInputShape() + this.outputShapeValue = Array(outputShape) + } + } else { + val outputShape = module.build(this.getOutputShape()) + this.outputShapeValue = Array(outputShape) + } + isBuilt = true + } + /** * Add a sub-module to the contained `modules` * @@ -126,21 +142,10 @@ class Sequential[T: ClassTag] module.asInstanceOf[Sequential[T]].frozen = true } Util.excludeNotKeras[T](Seq(module)) - - if (this.modules.isEmpty) { - if (module.getInputShape() == null) { - throw new RuntimeException("The first layer should explicitly declare inputshape") - } else { - val outputShape = module.build(module.getInputShape()) - this.inputShapeValue = module.getInputShape() - this.outputShapeValue = Array(outputShape) - } - } else { - val outputShape = module.build(this.getOutputShape()) - this.outputShapeValue = Array(outputShape) + if (!stopInferShape) { + triggerBuilding(module) } modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]] - isBuilt = true this } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index eb624cb6e62..dc2f02e484f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -35,7 +35,7 @@ import java.nio.ByteOrder import java.util import com.intel.analytics.bigdl.nn.Graph._ -import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape, SplitAndSelect} +import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape => TfShape, SplitAndSelect} import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation._ import com.intel.analytics.bigdl.transform.vision.image.label.roi._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala new file mode 100644 index 00000000000..7caaf2f7948 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.python.api + +import java.lang.{Boolean => JBoolean} +import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} + +import com.intel.analytics.bigdl.dataset.{Identity => DIdentity, Sample => JSample} +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.keras.Dense +import com.intel.analytics.bigdl.nn.tf.{Shape => TfShape} +import com.intel.analytics.bigdl.nn.{InitializationMethod, RandomUniform} +import com.intel.analytics.bigdl.numeric._ +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.collection.JavaConverters._ +import scala.language.existentials +import scala.reflect.ClassTag + + +object PythonBigDLKeras { + + def ofFloat(): PythonBigDLKeras[Float] = new PythonBigDLKeras[Float]() + + def ofDouble(): PythonBigDLKeras[Double] = new PythonBigDLKeras[Double]() +} + +class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializable { + def createDense(outputDim: Int, + init: InitializationMethod = RandomUniform, + activation: TensorModule[T] = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): Dense[T] = { + Dense(outputDim, + init, + activation, + wRegularizer, + bRegularizer, + bias, + if (inputShape == null) { + null + } else { + Shape(inputShape.asScala.toArray) + }) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala index 8ad9892f743..1bbeaa3a60f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala @@ -35,6 +35,7 @@ object KerasRunner { |from keras.layers.convolutional import * |from keras.layers import * |from keras.objectives import * + |from keras.regularizers import * |from keras.models import Model |import keras.backend as K |import numpy as np diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala index f17ed6afca9..c25df330fe1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala @@ -41,4 +41,21 @@ class DenseSpec extends KerasBaseSpec{ checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], kerasCode, weightConverter) } + + "Dense for multiple dims" should "be test" in { + val kerasCode = + """ + |input_tensor = Input(shape=[10, 5, 7]) + |input = np.random.uniform(0, 1, [2, 10, 5, 7]) + |output_tensor = \ + |Dense(2, init='one', activation="relu", input_shape=(10, 5, 7))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val dense = Dense[Float](2, activation = ReLU(), inputShape = Shape(10, 5, 7)) + seq.add(dense) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(0).t(), in(1)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter, precision = 1e-4) + } } From 989379d9fdd4ead4494909ba4111c0035f6e4e6d Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Wed, 31 Jan 2018 11:25:55 +0800 Subject: [PATCH 0666/1065] update (#2232) runnable update fix rebase fix rebase fix rebase fix rebase format comments format style fix rebase comments --- .../bigdl/dllib/keras/KerasLayer.scala | 34 +++--- .../bigdl/dllib/keras/Topology.scala | 38 ++++++- .../analytics/bigdl/dllib/nn/Graph.scala | 85 ++++++++------- .../dllib/nn/abstractnn/InferShape.scala | 4 +- .../utils/serializer/ModuleSerializer.scala | 14 ++- .../bigdl/dllib/keras/nn/KerasStyleSpec.scala | 44 +++++--- .../KerasModuleSerializerSpec.scala | 67 ++++++++++++ .../serializer/ModuleSerializerSpec.scala | 75 ++----------- .../serializer/SerializerSpecHelper.scala | 103 ++++++++++++++++++ 9 files changed, 313 insertions(+), 151 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala index abaab5b7a7b..e69247ba330 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala @@ -30,33 +30,23 @@ import serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag -object KerasLayerSerializer extends ModuleSerializable { - - override def doLoadModule[T: ClassTag](context : DeserializeContext) - (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val laborAdapter = super.doLoadModule(context).asInstanceOf[KerasLayer[Activity, Activity, T]] - val attrMap = context.bigdlModule.getAttrMap - laborAdapter.labor = DataConverter.getAttributeValue(context, attrMap.get("labor")). - asInstanceOf[AbstractModule[Activity, Activity, T]] - laborAdapter +private[bigdl] trait TKerasSerializerHelper { + def appendKerasLabel[T: ClassTag](context: SerializeContext[T], + moduleBuilder : BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val serializerFlagBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, serializerFlagBuilder, true, + scala.reflect.runtime.universe.typeOf[Boolean]) + moduleBuilder.putAttr("is_keras_module", serializerFlagBuilder.build) } +} + +object KerasLayerSerializer extends ContainerSerializable with TKerasSerializerHelper{ override def doSerializeModule[T: ClassTag](context: SerializeContext[T], moduleBuilder : BigDLModule.Builder) (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(context, moduleBuilder) - val laborAdapterModule = - context.moduleData.module.asInstanceOf[KerasLayer[Activity, Activity, T]] - val laborBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(context, laborBuilder, laborAdapterModule.labor, - ModuleSerializer.abstractModuleType) - moduleBuilder.putAttr("labor", laborBuilder.build) - - val serializerFlagBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(context, serializerFlagBuilder, true, - scala.reflect.runtime.universe.typeOf[Boolean]) - moduleBuilder.putAttr("is_labor_module", serializerFlagBuilder.build) + appendKerasLabel(context, moduleBuilder) } } @@ -136,10 +126,12 @@ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: C // scalastyle:off override def inputShapeValue_=(value: Shape): Unit = { labor.inputShapeValue = value + this._inputShapeValue = value } override def outputShapeValue_=(value: Array[Shape]): Unit = { labor.outputShapeValue = value + this._outputShapeValue = value } // scalastyle:on diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala index 1e7d3232a36..7e79bbb1ef0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala @@ -16,13 +16,14 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.{Graph, StaticGraph, Sequential => TSequential} +import com.intel.analytics.bigdl.nn.{Graph, GraphSerializable, StaticGraph, Sequential => TSequential} +import com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.{Shape, Util} -import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag class Model[T: ClassTag](private val _inputs : Seq[ModuleNode[T]], @@ -47,7 +48,7 @@ class Model[T: ClassTag](private val _inputs : Seq[ModuleNode[T]], } } -object Model { +object Model extends ModelSerializer{ /** * Build multiple inputs, multiple outputs graph container. * @param input input node @@ -93,6 +94,26 @@ object Model { } } +trait ModelSerializer extends GraphSerializable with TKerasSerializerHelper{ + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + moduleBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + super.doSerializeModule(context, moduleBuilder) + appendKerasLabel(context, moduleBuilder) + } + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val (module, inputs, outputs, generateBackwardValue, sharedVariables) = + prepareLoadModule(context) + require(generateBackwardValue == null, "there's no generateBackward for keras module") + require(module.containsAttr("is_keras_module") + && module.getAttrOrThrow("is_keras_module").getBoolValue(), "It should be a keras module") + Model(inputs.toArray, outputs.toArray) + } +} + class Sequential[T: ClassTag](val stopInferShape: Boolean = false) (implicit ev: TensorNumeric[T]) extends TSequential[T] { @@ -150,9 +171,16 @@ class Sequential[T: ClassTag](val stopInferShape: Boolean = false) } } -object Sequential { +object Sequential extends ContainerSerializable with TKerasSerializerHelper{ def apply[@specialized(Float, Double) T: ClassTag]() (implicit ev: TensorNumeric[T]) : Sequential[T] = { new Sequential[T]() } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + moduleBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + super.doSerializeModule(context, moduleBuilder) + appendKerasLabel(context, moduleBuilder) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index edc881659da..b1fd2c48bd5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -18,26 +18,25 @@ package com.intel.analytics.bigdl.nn import java.util import com.intel.analytics.bigdl.Module - -import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.ops._ import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.tf.Tensorflow -import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.visualization.tensorboard.{FileWriter => TFFileWriter} +import org.tensorflow.framework.GraphDef +import scala.collection.JavaConverters._ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag import scala.reflect.runtime.universe -import com.intel.analytics.bigdl.visualization.tensorboard.{FileWriter => TFFileWriter} -import org.tensorflow.framework.GraphDef /** * A graph container. The modules in the container are connected as a directed Graph. Each module @@ -72,8 +71,8 @@ import org.tensorflow.framework.GraphDef @SerialVersionUID(- 2896121321564992779L) abstract class Graph[T: ClassTag]( val inputs : Seq[ModuleNode[T]], - protected val outputs : Seq[ModuleNode[T]], - private val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None + private[bigdl] val outputs : Seq[ModuleNode[T]], + private[bigdl] val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None )(implicit ev: TensorNumeric[T]) extends Container[Activity, Activity, T]{ /** @@ -452,7 +451,7 @@ abstract class Graph[T: ClassTag]( } } -object Graph extends ContainerSerializable { +object Graph extends GraphSerializable { /** * Node for graph container. The module should have a tensor/table input while a tensor output * @tparam T @@ -466,18 +465,18 @@ object Graph extends ContainerSerializable { * @return a graph container */ def apply[T: ClassTag]( - input : Array[ModuleNode[T]], - output : Array[ModuleNode[T]], + input: Array[ModuleNode[T]], + output: Array[ModuleNode[T]], variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None - )(implicit ev: TensorNumeric[T]) : Graph[T] = { + )(implicit ev: TensorNumeric[T]): Graph[T] = { new StaticGraph[T](input, output, variables) } def dynamic[T: ClassTag]( - input : Array[ModuleNode[T]], - output : Array[ModuleNode[T]], + input: Array[ModuleNode[T]], + output: Array[ModuleNode[T]], variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, - generateBackward: Boolean = true)(implicit ev: TensorNumeric[T]) : Graph[T] = { + generateBackward: Boolean = true)(implicit ev: TensorNumeric[T]): Graph[T] = { new DynamicGraph[T](input, output, variables, generateBackward) } @@ -487,13 +486,13 @@ object Graph extends ContainerSerializable { * @param output output nodes * @return a graph container */ - def apply[T: ClassTag](input : ModuleNode[T], output : Array[ModuleNode[T]]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + def apply[T: ClassTag](input: ModuleNode[T], output: Array[ModuleNode[T]]) + (implicit ev: TensorNumeric[T]): Graph[T] = { new StaticGraph[T](Seq(input), output) } - def dynamic[T: ClassTag](input : ModuleNode[T], output : Array[ModuleNode[T]]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + def dynamic[T: ClassTag](input: ModuleNode[T], output: Array[ModuleNode[T]]) + (implicit ev: TensorNumeric[T]): Graph[T] = { new DynamicGraph[T](Array(input), output, None, true) } @@ -503,13 +502,13 @@ object Graph extends ContainerSerializable { * @param output output node * @return a graph container */ - def apply[T: ClassTag](input : Array[ModuleNode[T]], output : ModuleNode[T]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + def apply[T: ClassTag](input: Array[ModuleNode[T]], output: ModuleNode[T]) + (implicit ev: TensorNumeric[T]): Graph[T] = { new StaticGraph[T](input, Seq(output)) } - def dynamic[T: ClassTag](input : Array[ModuleNode[T]], output : ModuleNode[T]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + def dynamic[T: ClassTag](input: Array[ModuleNode[T]], output: ModuleNode[T]) + (implicit ev: TensorNumeric[T]): Graph[T] = { new DynamicGraph[T](input, Array(output), None, true) } @@ -519,18 +518,21 @@ object Graph extends ContainerSerializable { * @param output output nodes * @return a graph container */ - def apply[T: ClassTag](input : ModuleNode[T], output : ModuleNode[T]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + def apply[T: ClassTag](input: ModuleNode[T], output: ModuleNode[T]) + (implicit ev: TensorNumeric[T]): Graph[T] = { new StaticGraph[T](Seq(input), Seq(output)) } - def dynamic[T: ClassTag](input : ModuleNode[T], output : ModuleNode[T]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + def dynamic[T: ClassTag](input: ModuleNode[T], output: ModuleNode[T]) + (implicit ev: TensorNumeric[T]): Graph[T] = { new DynamicGraph[T](Array(input), Array(output), None, true) } +} - override def doLoadModule[T: ClassTag](context: DeserializeContext) - (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { +trait GraphSerializable extends ContainerSerializable { + + private[bigdl] def prepareLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) = { val module = context.bigdlModule val subModules = module.getSubModulesList.asScala @@ -552,7 +554,7 @@ object Graph extends ContainerSerializable { val bigDLModule = ModuleSerializer.load(DeserializeContext(subModule, context.storages, context.storageType)) val moduleNode = bigDLModule.module match { - case controlOps : ControlOps[T] => createControlNode(controlOps) + case controlOps: ControlOps[T] => createControlNode(controlOps) case _ => bigDLModule.module.inputs() } val preNodes = bigDLModule.pre @@ -566,7 +568,7 @@ object Graph extends ContainerSerializable { val edgeMap = edges.get(moduleNode._1.element.getName).get moduleNode._2.foreach(pre => { if (layerMap.contains(pre)) { - val edge : Edge = edgeMap.get(pre).get match { + val edge: Edge = edgeMap.get(pre).get match { case -1 => Edge() case index: Int => Edge(index) } @@ -578,7 +580,7 @@ object Graph extends ContainerSerializable { inputNames.foreach(inputName => inputs.append(layerMap(inputName)._1)) outputNames.foreach(outputName => outputs.append(layerMap(outputName)._1)) - var sharedVariables : Option[(Array[Tensor[T]], Array[Tensor[T]])] = None + var sharedVariables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None if (attributes.containsKey("sharedWeight") && attributes.containsKey("sharedBias")) { val weights = attributes.get("sharedWeight") val biases = attributes.get("sharedBias") @@ -590,6 +592,13 @@ object Graph extends ContainerSerializable { } val generateBackwardValue = attributes.get("generateBackward") + (module, inputs, outputs, generateBackwardValue, sharedVariables) + } + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + val (module, inputs, outputs, generateBackwardValue, sharedVariables) = + prepareLoadModule(context) if (generateBackwardValue != null) { val generateBackward = DataConverter.getAttributeValue(context, generateBackwardValue) .asInstanceOf[Boolean] @@ -599,17 +608,17 @@ object Graph extends ContainerSerializable { } } - private def createControlNode[T: ClassTag](controlOps : ControlOps[T]) : ModuleNode[T] = { + private def createControlNode[T: ClassTag](controlOps: ControlOps[T]): ModuleNode[T] = { controlOps match { - case switchOps : SwitchOps[T] => new SwitchControlNode[Module[T]](switchOps) - case mergeOps : MergeOps[T] => new MergeControlNode[Module[T]](mergeOps) + case switchOps: SwitchOps[T] => new SwitchControlNode[Module[T]](switchOps) + case mergeOps: MergeOps[T] => new MergeControlNode[Module[T]](mergeOps) case _ => new Node[Module[T]](controlOps) } } override def doSerializeModule[T: ClassTag](context: SerializeContext[T], - graphBuilder : BigDLModule.Builder) - (implicit ev: TensorNumeric[T]) : Unit = { + graphBuilder: BigDLModule.Builder) + (implicit ev: TensorNumeric[T]): Unit = { val module = context.moduleData module.next.foreach(_ => graphBuilder.addAllPreModules(_)) module.pre.foreach(_ => graphBuilder.addAllNextModules(_)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala index 2f7042387e3..cbc5591b0a2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala @@ -20,9 +20,9 @@ import com.intel.analytics.bigdl.utils.Shape trait InferShape { - private var _inputShapeValue: Shape = null + private[bigdl] var _inputShapeValue: Shape = null - private var _outputShapeValue: Array[Shape] = Array[Shape]() + private[bigdl] var _outputShapeValue: Array[Shape] = Array[Shape]() private[bigdl] def inputShapeValue: Shape = _inputShapeValue diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 0472a5e3587..920f892cb9e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.serializer import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} -import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer} +import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer, Model, Sequential => KSequential} import com.intel.analytics.bigdl.nn.ops.{DecodeRawSerializer, ParseExample, RandomUniform => RandomUniformOps} import com.intel.analytics.bigdl.nn.tf.StrideSlice import com.intel.analytics.bigdl.optim.Regularizer @@ -64,10 +64,12 @@ object ModuleSerializer extends ModuleSerializable{ } else { val m = module.asInstanceOf[AbstractModule[_, _, _]] m match { - case container : Container[_, _, _] => ContainerSerializer - case cell : Cell[_] => CellSerializer - case laborAdapter: KerasLayer[_, _, _] => + case kerasLayer: KerasLayer[_, _, _] => KerasLayerSerializer + case container : Container[_, _, _] => + ContainerSerializer + case cell : Cell[_] => + CellSerializer case _ => ModuleSerializer } } @@ -94,7 +96,7 @@ object ModuleSerializer extends ModuleSerializable{ } else { if (attrMap.containsKey("is_cell_module")) { CellSerializer - } else if (attrMap.containsKey("is_labor_module")) { + } else if (attrMap.containsKey("is_keras_module")) { KerasLayerSerializer } else { ModuleSerializer @@ -178,6 +180,8 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.BiRecurrent", BiRecurrent) registerModule("com.intel.analytics.bigdl.nn.StaticGraph", Graph) registerModule("com.intel.analytics.bigdl.nn.DynamicGraph", Graph) + registerModule("com.intel.analytics.bigdl.nn.keras.Model", Model) + registerModule("com.intel.analytics.bigdl.nn.keras.Sequential", KSequential) registerModule("com.intel.analytics.bigdl.nn.MapTable", MapTable) registerModule("com.intel.analytics.bigdl.nn.MaskedSelect", MaskedSelect) registerModule("com.intel.analytics.bigdl.nn.Recurrent", Recurrent) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala index e2199e788c1..62c33550e96 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala @@ -122,22 +122,32 @@ class KerasStyleSpec extends BigDLSpecHelper { } } - // TODO: enable test for serialization - // "save and reload model" should "works correctly" in { - // val input = Input[Float](inputShape = Array(10)) - // val d = Dense[Float](20).setName("dense1").inputs(input) - // val d2 = Dense[Float](5).setName("dense2").inputs(d) - // val graph = Model[Float](input, d2) - // val tmpFile = createTmpFile() - // val absPath = tmpFile.getAbsolutePath - // tmpFile.delete() - // graph.saveModule(absPath) - // val reloadedModel = Module.loadModule(absPath) - // val inputData = Tensor[Float](Array(20, 10)).rand() - // val output = reloadedModel.forward(inputData) - // } - // - - + "save and reload model" should "works correctly" in { + val input = Input[Float](inputShape = Shape(10)) + val d = Dense[Float](20).setName("dense1").inputs(input) + val d2 = Dense[Float](5).setName("dense2").inputs(d) + val model = Model[Float](input, d2) + val tmpFile = createTmpFile() + val absPath = tmpFile.getAbsolutePath + tmpFile.delete() + model.saveModule(absPath) + val reloadedModel = Module.loadModule(absPath) + val inputData = Tensor[Float](Array(20, 10)).rand() + val output = reloadedModel.forward(inputData) + } + "save and reload sequential" should "works correctly" in { + val kseq = KSequential[Float]() + val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") + val d2 = Dense[Float](5).setName("dense2") + kseq.add(d1) + kseq.add(d2) + val tmpFile = createTmpFile() + val absPath = tmpFile.getAbsolutePath + tmpFile.delete() + kseq.saveModule(absPath) + val reloadedModel = Module.loadModule(absPath) + val inputData = Tensor[Float](Array(20, 10)).rand() + val output = reloadedModel.forward(inputData) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala new file mode 100644 index 00000000000..f5fe70d34ad --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.serializer + +import com.intel.analytics.bigdl.nn.keras.{Dense, Input, InputLayer, Model, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _} + +import scala.collection.mutable +import scala.util.Random + + +class KerasModuleSerializerSpec extends SerializerSpecHelper { + + override def getPackage(): String = "com.intel.analytics.bigdl.nn.keras" + + override def getExpected(): mutable.Set[String] = { + super.getExpected().filter(_.contains(getPackage())) + } + + "Input serializer" should "work properly" in { + val input = InputLayer[Float](inputShape = Shape(20)) + val inputData = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(input, inputData) + } + + "Dense serializer" should "work properly" in { + val dense = Dense[Float](10, inputShape = Shape(20)) + dense.build(Shape(2, 20)) + val input = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) + + runSerializationTest(dense, input) + } + + "Sequence serializer" should "work properly" in { + val dense = Dense[Float](10, inputShape = Shape(20)) + val kseq = KSequential[Float]() + kseq.add(dense) + val input = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(kseq, input) + } + + "Model serializer" should "work properly" in { + val input = Input[Float](inputShape = Shape(10)) + val d = Dense[Float](20).setName("dense1").inputs(input) + val d2 = Dense[Float](5).setName("dense2").inputs(d) + val model = Model[Float](input, d2) + val inputData = Tensor[Float](Array(20, 10)).rand() + runSerializationTest(model, inputData) + } + +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 3f107f16d74..99f96b41718 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -45,65 +45,24 @@ import scala.collection.mutable import scala.util.Random -class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll { +class ModuleSerializerSpec extends SerializerSpecHelper { - val pkg = "com.intel.analytics.bigdl.nn" - val postFix = "bigdl" - val excluded = new mutable.HashSet[String]() - val expected = new mutable.HashSet[String]() - val tested = new mutable.HashSet[String]() + override def getPackage(): String = "com.intel.analytics.bigdl.nn" - private def addExcluded(): Unit = { - excluded.add("com.intel.analytics.bigdl.nn.CellUnit") - excluded.add("com.intel.analytics.bigdl.nn.tf.ControlDependency") - excluded.add("com.intel.analytics.bigdl.utils.tf.AdapterForTest") - excluded.add("com.intel.analytics.bigdl.utils.serializer.TestModule") - excluded.add("com.intel.analytics.bigdl.utils.ExceptionTest") + override def addExcludedClass(): Unit = { + excludedClass.add("com.intel.analytics.bigdl.nn.CellUnit") + excludedClass.add("com.intel.analytics.bigdl.nn.tf.ControlDependency") + excludedClass.add("com.intel.analytics.bigdl.utils.tf.AdapterForTest") + excludedClass.add("com.intel.analytics.bigdl.utils.serializer.TestModule") + excludedClass.add("com.intel.analytics.bigdl.utils.ExceptionTest") } - override protected def beforeAll() = { - addExcluded - val reflections = new Reflections(new ConfigurationBuilder() - .filterInputsBy(new FilterBuilder() - .excludePackage("com.intel.analytics.bigdl.utils.tf.loaders") - // TODO: enable this once Shape serialization ready. - .excludePackage("com.intel.analytics.bigdl.nn.keras")) - - .setUrls(ClasspathHelper.forPackage(pkg)) - .setScanners(new SubTypesScanner())) - - val subTypes = reflections.getSubTypesOf(classOf[AbstractModule[_, _, _]]) - .asScala.filter(sub => !Modifier.isAbstract(sub.getModifiers)). - filter(sub => !excluded.contains(sub.getName)) - subTypes.foreach(sub => expected.add(sub.getName)) - } - - - private def runSerializationTest(module : AbstractModule[_, _, Float], - input : Activity, cls: Class[_] = null) : Unit = { - runSerializationTestWithMultiClass(module, input, - if (cls == null) Array(module.getClass) else Array(cls)) + override def addExcludedPackage(): Unit = { + excludedPackage.add("com.intel.analytics.bigdl.utils.tf.loaders") + // It would be tested in a separated spec + excludedPackage.add("com.intel.analytics.bigdl.nn.keras") } - private def runSerializationTestWithMultiClass(module : AbstractModule[_, _, Float], - input : Activity, classes: Array[Class[_]]) : Unit = { - val name = module.getName - val serFile = File.createTempFile(name, postFix) - val originForward = module.evaluate().forward(input) - - ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) - RNG.setSeed(1000) - val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) - - val afterLoadForward = loadedModule.forward(input) - - if (serFile.exists) { - serFile.delete - } - - afterLoadForward should be (originForward) - classes.foreach(cls => tested.add(cls.getName)) - } "Abs serializer" should "work properly" in { val abs = Abs[Float]().setName("abs") @@ -2612,14 +2571,4 @@ class ModuleSerializerSpec extends FlatSpec with Matchers with BeforeAndAfterAll val module = new com.intel.analytics.bigdl.nn.ops.InvertPermutation[Float]() runSerializationTest(module, Tensor[Int](T(0, 1, 2, 3, 4))) } - - override protected def afterAll() = { - var total = 0 - expected.foreach(exp => { - require(tested.contains(exp), s" $exp not included in the test!") - total += 1 - }) - println(s"total $total, remaining ${expected.size - total}") - } - } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala new file mode 100644 index 00000000000..b7d8eb4d361 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala @@ -0,0 +1,103 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer + +import java.io.{File} +import java.lang.reflect.Modifier + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.ops.{Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _} +import com.intel.analytics.bigdl.utils.{Shape => KShape} +import org.reflections.Reflections +import org.reflections.scanners.SubTypesScanner +import org.reflections.util.{ClasspathHelper, ConfigurationBuilder, FilterBuilder} +import org.scalatest.{BeforeAndAfterAll, FlatSpec, Matchers} + +import scala.collection.JavaConverters._ +import scala.collection.mutable + + +abstract class SerializerSpecHelper extends FlatSpec with Matchers with BeforeAndAfterAll{ + + val postFix = "bigdl" + val excludedClass = new mutable.HashSet[String]() + val excludedPackage = new mutable.HashSet[String]() + + private val expected = new mutable.HashSet[String]() + val tested = new mutable.HashSet[String]() + + private var executedCount = 0 + + protected def getPackage(): String + + protected def addExcludedClass(): Unit = {} + + protected def addExcludedPackage(): Unit = {} + + protected def getExpected(): mutable.Set[String] = expected + + override protected def beforeAll() = { + addExcludedClass + addExcludedPackage + val filterBuilder = new FilterBuilder() + excludedPackage.foreach(filterBuilder.excludePackage(_)) + val reflections = new Reflections(new ConfigurationBuilder() + .filterInputsBy(filterBuilder) + .setUrls(ClasspathHelper.forPackage(getPackage())) + .setScanners(new SubTypesScanner())) + val subTypes = reflections.getSubTypesOf(classOf[AbstractModule[_, _, _]]) + .asScala.filter(sub => !Modifier.isAbstract(sub.getModifiers)). + filter(sub => !excludedClass.contains(sub.getName)) + subTypes.foreach(sub => expected.add(sub.getName)) + } + + protected def runSerializationTest(module : AbstractModule[_, _, Float], + input : Activity, cls: Class[_] = null) : Unit = { + runSerializationTestWithMultiClass(module, input, + if (cls == null) Array(module.getClass) else Array(cls)) + } + + protected def runSerializationTestWithMultiClass(module : AbstractModule[_, _, Float], + input : Activity, classes: Array[Class[_]]) : Unit = { + val name = module.getName + val serFile = File.createTempFile(name, postFix) + val originForward = module.evaluate().forward(input) + + ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) + RNG.setSeed(1000) + val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) + + val afterLoadForward = loadedModule.forward(input) + + if (serFile.exists) { + serFile.delete + } + + afterLoadForward should be (originForward) + classes.foreach(cls => tested.add(cls.getName)) + } + + + override protected def afterAll() = { + println(s"total ${getExpected().size}, remaining ${getExpected().size - tested.size}") + getExpected().foreach(exp => { + require(tested.contains(exp), s" $exp not included in the test!") + }) + } +} From 852cd3c50e39230ebe25317338c337be491ce5ed Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 31 Jan 2018 13:41:17 +0800 Subject: [PATCH 0667/1065] fix getparameters (#2251) --- .../analytics/bigdl/dllib/nn/Module.scala | 27 ++++++++++--------- .../analytics/bigdl/dllib/nn/ModuleSpec.scala | 15 +++++++++++ 2 files changed, 29 insertions(+), 13 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala index 93ae211446b..69e35d21a58 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala @@ -139,27 +139,28 @@ object Module { result } - def isCompact[@specialized(Float, Double) T: ClassTag](paramters: Array[Tensor[T]])( + def isCompact[@specialized(Float, Double) T: ClassTag](parameters: Array[Tensor[T]])( implicit ev: TensorNumeric[T]): Tensor[T] = { - require(paramters.length > 0, + require(parameters.length > 0, "The length of paramters should >= 0" + "parameter length" + - s" ${paramters.length}") + s" ${parameters.length}") var i = 1 - val storage = paramters(0).storage() - var length = paramters(0).nElement() - while (i < paramters.length) { - if (!storage.eq(paramters(i).storage())) { + val storage = parameters(0).storage() + var length = parameters(0).nElement() + val offset = parameters(0).storageOffset() + // make sure parameters is shared and contiguous + while (i < parameters.length) { + if (!storage.eq(parameters(i).storage())) { + return null + } + if (offset + length != parameters(i).storageOffset()) { return null } - length += paramters(i).nElement() + length += parameters(i).nElement() i += 1 } - if (length != storage.array().length) { - return null - } - - return Tensor(storage) + Tensor(storage, offset, Array(length)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala index aab3e63ca0b..b9a58d97888 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ModuleSpec.scala @@ -141,6 +141,21 @@ class ModuleSpec extends FlatSpec with Matchers { grad1.storage().eq(grad2.storage()) should be(true) } + "getParameter in submodule" should "not create new storage" in { + val module1 = Sequential[Double]().add(Linear[Double](2, 3)).add(Linear[Double](2, 3)) + val module2 = Sequential[Double]().add(Linear[Double](4, 5)).add(Linear[Double](4, 5)) + val module = Sequential[Double]().add(module1).add(module2) + + val (weight, grad) = module.getParameters() + val (weight1, grad1) = module1.getParameters() + val (weight2, grad2) = module2.getParameters() + + weight1.storage().eq(weight.storage()) should be(true) + grad1.storage().eq(grad.storage()) should be(true) + weight2.storage().eq(weight.storage()) should be(true) + grad2.storage().eq(grad.storage()) should be(true) + } + "clone module" should "work correctly" in { val module = new Sequential[Double] module.add(new Linear(2, 3)) From 21cb463d3a4def5d2210aea23464d0f529c5badf Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 31 Jan 2018 16:12:04 +0800 Subject: [PATCH 0668/1065] fix graph missing stop gradient layers (#2246) * fix graph missing stop gradient layers * fix typo --- .../analytics/bigdl/dllib/nn/Graph.scala | 26 ++++++++++++++++++- .../serializer/ModuleSerializerSpec.scala | 9 +++++++ 2 files changed, 34 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index b1fd2c48bd5..a43a576015b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -218,6 +218,8 @@ abstract class Graph[T: ClassTag]( private var stopGradientLayers: util.HashSet[String] = _ + def getStopGradientLayers(): util.HashSet[String] = stopGradientLayers + /** * whether stop propagating gradInput back * @return @@ -599,13 +601,25 @@ trait GraphSerializable extends ContainerSerializable { (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val (module, inputs, outputs, generateBackwardValue, sharedVariables) = prepareLoadModule(context) - if (generateBackwardValue != null) { + val attributes = module.getAttrMap + val graph = if (generateBackwardValue != null) { val generateBackward = DataConverter.getAttributeValue(context, generateBackwardValue) .asInstanceOf[Boolean] Graph.dynamic[T](inputs.toArray, outputs.toArray, sharedVariables, generateBackward) } else { Graph[T](inputs.toArray, outputs.toArray, sharedVariables) } + var serializedStopGradientLayers : Array[String] = null + // this is to keep backward compatible + if (attributes.containsKey("stopGradientLayers")) { + val stopGradientLayers = attributes.get("stopGradientLayers") + serializedStopGradientLayers = DataConverter. + getAttributeValue(context, stopGradientLayers).asInstanceOf[Array[String]] + } + if (serializedStopGradientLayers != null) { + graph.stopGradient(serializedStopGradientLayers) + } + graph } private def createControlNode[T: ClassTag](controlOps: ControlOps[T]): ModuleNode[T] = { @@ -685,5 +699,15 @@ trait GraphSerializable extends ContainerSerializable { graph.asInstanceOf[DynamicGraph[_]].generateBackward, universe.typeOf[Boolean]) graphBuilder.putAttr("generateBackward", generateBackwardBuilder.build) } + + val stopGradientLayers = graph.getStopGradientLayers + + if (stopGradientLayers != null && stopGradientLayers.size > 0) { + val stopGradientLayersBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, stopGradientLayersBuilder, + stopGradientLayers.toArray(new Array[String](stopGradientLayers.size)), + universe.typeOf[Array[String]]) + graphBuilder.putAttr("stopGradientLayers", stopGradientLayersBuilder.build) + } } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 99f96b41718..1b429b8b025 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -529,6 +529,15 @@ class ModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(model, input) } + "Graph with stop gradient layer" should "work properly" in { + val linear1 = Linear[Float](2, 2).setName("first").inputs() + val linear2 = Linear[Float](2, 2).setName("second").inputs(linear1) + val graph = Graph[Float](Array(linear1), Array(linear2)).setName("graphWithStopGradient") + graph.stopGradient(Array("first")) + val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) + runSerializationTest(graph, input) + } + "GRU serializer" should "work properly" in { RNG.setSeed(100) val gru = GRU[Float](100, 100) From 03f35f0384ce08f17f695eb93897ad13a0b4ea0a Mon Sep 17 00:00:00 2001 From: dding3 Date: Wed, 31 Jan 2018 19:30:46 -0800 Subject: [PATCH 0669/1065] Support gradual increase learning rate and SequentialSchedule in learningratescheduler (#2093) * support SequentialSchedule and WarmupSchedule --- .../dllib/models/inception/Options.scala | 22 ++- .../bigdl/dllib/models/inception/README.md | 5 + .../bigdl/dllib/models/inception/Train.scala | 29 +++- .../bigdl/dllib/optim/DistriOptimizer.scala | 1 + .../analytics/bigdl/dllib/optim/SGD.scala | 160 +++++++++++++----- .../dllib/utils/python/api/PythonBigDL.scala | 14 ++ .../analytics/bigdl/dllib/optim/SGDSpec.scala | 40 +++++ 7 files changed, 225 insertions(+), 46 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Options.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Options.scala index 0e6ac5a423f..685c3d5c198 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Options.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Options.scala @@ -34,7 +34,12 @@ object Options { maxIteration: Int = 62000, weightDecay: Double = 0.0001, checkpointIteration: Int = 620, - graphModel: Boolean = false + graphModel: Boolean = false, + maxLr: Option[Double] = None, + warmupEpoch: Option[Int] = None, + gradientL2NormThreshold: Option[Double] = None, + gradientMin: Option[Double] = None, + gradientMax: Option[Double] = None ) val trainParser = new OptionParser[TrainParams]("BigDL Inception Example") { @@ -79,6 +84,21 @@ object Options { opt[Unit]('g', "graphModel") .text("use graph model") .action((x, c) => c.copy(graphModel = true)) + opt[Double]("maxLr") + .text("max Lr after warm up") + .action((x, c) => c.copy(maxLr = Some(x))) + opt[Int]("warmupEpoch") + .text("warm up epoch numbers") + .action((x, c) => c.copy(warmupEpoch = Some(x))) + opt[Double]("gradientL2NormThreshold") + .text("gradient L2-Norm threshold") + .action((x, c) => c.copy(gradientL2NormThreshold = Some(x))) + opt[Double]("gradientMax") + .text("max gradient clipping by") + .action((x, c) => c.copy(gradientMax = Some(x))) + opt[Double]("gradientMin") + .text("min gradient clipping by") + .action((x, c) => c.copy(gradientMin = Some(x))) } case class TestParams( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md index a5740edfc5d..2633a0173f2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md @@ -90,6 +90,11 @@ core_number. In this example, node_number is 1 and the mini-batch size is sugges policy. * --weightDecay: weight decay. * --checkpointIteration: the checkpoint interval in iteration. +* --maxLr: optional. Max learning rate after warm up. It has to be set together with warmupEpoch. +* --warmupEpoch: optional. Epoch numbers need to take to increase learning rate from learningRate to maxLR. +* --gradientL2NormThreshold: optional. Gradient L2-Norm threshold used for norm2 gradient clipping. +* --gradientMin: optional. Max gradient clipping by value, used in constant gradient clipping. +* --gradientMax: optional. Min gradient clipping by value, used in constant gradient clipping. ## Test the Model * Spark standalone, example command diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala index 23d86468541..7b879c6ffac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.models.inception import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} +import com.intel.analytics.bigdl.optim.SGD.{Poly, SequentialSchedule, Warmup} import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T, Table} import org.apache.log4j.{Level, Logger} @@ -65,17 +66,24 @@ object TrainInceptionV1 { Inception_v1_NoAuxClassifier(classNum = param.classNumber) } + val iterationPerEpoch = math.ceil(1281167.toDouble / param.batchSize).toInt + val maxIteration = if (param.maxEpoch.isDefined) { + iterationPerEpoch * param.maxEpoch.get + } else param.maxIteration + + val warmupIteration = param.warmupEpoch.getOrElse(0) * iterationPerEpoch + val optimMethod = if (param.stateSnapshot.isDefined) { OptimMethod.load[Float](param.stateSnapshot.get) - } else if (param.maxEpoch.isDefined) { - new SGD[Float](learningRate = param.learningRate, learningRateDecay = 0.0, - weightDecay = param.weightDecay, momentum = 0.9, dampening = 0.0, nesterov = false, - learningRateSchedule = - SGD.Poly(0.5, math.ceil(1281167.toDouble / param.batchSize).toInt * param.maxEpoch.get)) } else { + val warmupDelta = if (warmupIteration == 0) 0.0 + else (param.maxLr.getOrElse(param.learningRate) - param.learningRate) / warmupIteration + val polyIteration = maxIteration - warmupIteration + val lrSchedule = SequentialSchedule(iterationPerEpoch) + .add(Warmup(warmupDelta), warmupIteration).add(Poly(0.5, polyIteration), polyIteration) new SGD[Float](learningRate = param.learningRate, learningRateDecay = 0.0, weightDecay = param.weightDecay, momentum = 0.9, dampening = 0.0, nesterov = false, - learningRateSchedule = SGD.Poly(0.5, param.maxIteration)) + learningRateSchedule = lrSchedule) } val optimizer = Optimizer( @@ -102,6 +110,15 @@ object TrainInceptionV1 { optimizer.overWriteCheckpoint() } + if (param.gradientMin.isDefined && param.gradientMax.isDefined) { + optimizer.setConstantGradientClipping(param.gradientMin.get.toFloat, + param.gradientMax.get.toFloat) + } + + if (param.gradientL2NormThreshold.isDefined) { + optimizer.setGradientClippingByl2Norm(param.gradientL2NormThreshold.get.toFloat) + } + optimizer .setOptimMethod(optimMethod) .setValidation(testTrigger, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index a91fb29637e..e1bc7dc3489 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -618,6 +618,7 @@ object DistriOptimizer { val computeThresholdbatchSize = state.get[Int]("computeThresholdbatchSize").get val nExecutor = Engine.nodeNumber() val executorCores = Engine.coreNumber() + val models = dataset.originRDD().mapPartitions(_ => { val (broadcastCriterion, broadcastState, broadcastMethod, broadcastOptim) = broadcast.value diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala index 268ff16c6c7..6f632bbccc7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{T, Table} +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -44,7 +45,8 @@ class SGD[@specialized(Float, Double) T: ClassTag]( var nesterov: Boolean = false, var learningRateSchedule: LearningRateSchedule = Default(), var learningRates: Tensor[T] = null, - var weightDecays: Tensor[T] = null)(implicit ev: TensorNumeric[T]) + var weightDecays: Tensor[T] = null + )(implicit ev: TensorNumeric[T]) extends OptimMethod[T] { import SGD._ @@ -211,6 +213,13 @@ object SGD { def updateHyperParameter(config : Table, state : Table) : Unit = {} var currentRate : Double = 0.0 + + // iteration numbers needed to be excluded for a new learningRateSchedule + private[SGD] var excludeIterations : Int = 0 + // epoch numbers needed to be excluded for a new learningRateSchedule + private[SGD] var excludeEpochs: Int = 0 + // accumulated iteration numbers of a new learningRateSchedule + private[SGD] var maxIterations: Int = 0 } /** @@ -223,7 +232,7 @@ object SGD { */ case class EpochSchedule(regimes : Array[Regime]) extends LearningRateSchedule { override def updateHyperParameter(config: Table, state: Table): Unit = { - val epoch = state[Int]("epoch") + val epoch = state[Int]("epoch") - excludeEpochs for (r <- regimes) { if (epoch >= r.startEpoch && epoch <= r.endEpoch) { config.add(r.config) @@ -233,7 +242,7 @@ object SGD { } override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { - val epoch = optimMethod.state[Int]("epoch") + val epoch = optimMethod.state[Int]("epoch") - excludeEpochs for (r <- regimes) { if (epoch >= r.startEpoch && epoch <= r.endEpoch) { val config = r.config @@ -279,13 +288,15 @@ object SGD { * @param maxIteration max iteration when lr becomes zero */ case class Poly(power : Double, maxIteration : Int) extends LearningRateSchedule { + override def updateHyperParameter(config: Table, state: Table): Unit = { val lr = config.get[Double]("learningRate").getOrElse(1e-3) val nevals = state.get[Int]("evalCounter").getOrElse(0) - val clr = if (nevals > maxIteration) { + val polyIter = nevals - excludeIterations + val clr = if (polyIter > maxIteration) { 0.0 } else { - -lr * math.pow(1.0 - nevals.toDouble / maxIteration, power) + -lr * math.pow(1.0 - polyIter.toDouble / maxIteration, power) } println(s"iteration is : ${nevals}. current learning rate is $clr") state("evalCounter") = nevals + 1 @@ -293,18 +304,20 @@ object SGD { } override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { - val lr = optimMethod.learningRate val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) - val clr = if (nevals > maxIteration) { + val lr = optimMethod.learningRate + val polyIter = nevals - excludeIterations + val clr = if (polyIter > maxIteration) { 0.0 } else { - -lr * math.pow(1.0 - nevals.toDouble / maxIteration, power) + -lr * math.pow(1.0 - polyIter.toDouble / maxIteration, power) } println(s"iteration is : ${nevals}. current learning rate is $clr") optimMethod.state("evalCounter") = nevals + 1 currentRate = clr } } + /** * A learning rate decay policy, where the effective learning rate * is calculated as base_lr * gamma `^` (floor(iter / stepSize)) @@ -315,11 +328,10 @@ object SGD { case class Step(stepSize : Int, gamma : Double) extends LearningRateSchedule { override def updateHyperParameter(config: Table, state: Table): Unit = { - val lr = config.get[Double]("learningRate").getOrElse(1e-3) - var clr = -lr + var clr = - config.get[Double]("learningRate").getOrElse(1e-3) val nevals = state.get[Int]("evalCounter").getOrElse(0) var i = 0 - while(i < nevals / stepSize) { + while(i < (nevals - excludeIterations) / stepSize) { clr *= gamma i += 1 } @@ -328,11 +340,10 @@ object SGD { } override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { - val lr = optimMethod.learningRate - var clr = -lr + var clr = - optimMethod.learningRate val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) var i = 0 - while(i < nevals / stepSize) { + while(i < (nevals - excludeIterations) / stepSize) { clr *= gamma i += 1 } @@ -348,11 +359,11 @@ object SGD { */ case class MultiStep(stepSizes : Array[Int], gamma : Double) extends LearningRateSchedule { override def updateHyperParameter(config: Table, state: Table): Unit = { - val lr = config.get[Double]("learningRate").getOrElse(1e-3) - var clr = -lr + var clr = - config.get[Double]("learningRate").getOrElse(1e-3) val nevals = state.get[Int]("evalCounter").getOrElse(0) var currentStep = 0 - while (currentStep < stepSizes.length && nevals >= stepSizes(currentStep)) { + while (currentStep < stepSizes.length && + (nevals - excludeIterations) >= stepSizes(currentStep)) { clr *= gamma currentStep += 1 } @@ -361,14 +372,15 @@ object SGD { } override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { - val lr = optimMethod.learningRate - var clr = -lr val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) + var clr = - optimMethod.learningRate var currentStep = 0 - while (currentStep < stepSizes.length && nevals >= stepSizes(currentStep)) { + while (currentStep < stepSizes.length && + (nevals - excludeIterations) >= stepSizes(currentStep)) { clr *= gamma currentStep += 1 } + optimMethod.state("evalCounter") = nevals + 1 currentRate = clr } @@ -384,20 +396,19 @@ object SGD { */ case class EpochDecay(decayType: (Int) => Double) extends LearningRateSchedule { override def updateHyperParameter(config: Table, state: Table): Unit = { - val lr = config.get[Double]("learningRate").getOrElse(1e-1) - var clr = -lr + var clr = - config.get[Double]("learningRate").getOrElse(1e-1) val epoch = state[Int]("epoch") - val decay = decayType(epoch) + val decay = decayType(epoch - excludeEpochs) clr = clr * math.pow(0.1, decay) config("clr") = clr } override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { - val lr = optimMethod.learningRate - var clr = -lr + var clr = - optimMethod.learningRate val epoch = optimMethod.state[Int]("epoch") - val decay = decayType(epoch) + val decay = decayType(epoch - excludeEpochs) clr = clr * math.pow(0.1, decay) + currentRate = clr } } @@ -411,11 +422,10 @@ object SGD { */ case class EpochStep(stepSize : Int, gamma : Double) extends LearningRateSchedule { override def updateHyperParameter(config: Table, state: Table): Unit = { - val lr = config.get[Double]("learningRate").getOrElse(1e-3) - var clr = -lr + var clr = - config.get[Double]("learningRate").getOrElse(1e-3) val epoch = state[Int]("epoch") var i = 0 - while(i < epoch / stepSize) { + while(i < (epoch - excludeEpochs) / stepSize) { clr *= gamma i += 1 } @@ -423,11 +433,10 @@ object SGD { } override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { - val lr = optimMethod.learningRate - var clr = -lr + var clr = - optimMethod.learningRate val epoch = optimMethod.state[Int]("epoch") var i = 0 - while(i < epoch / stepSize) { + while(i < (epoch - excludeEpochs) / stepSize) { clr *= gamma i += 1 } @@ -449,9 +458,9 @@ object SGD { override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { val lr = optimMethod.learningRate val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) - val p = nevals / decay_step - optimMethod.state("evalCounter") = nevals + 1 + val p = (nevals - excludeIterations) / decay_step val clr = -lr * math.exp(-gamma * p) + optimMethod.state("evalCounter") = nevals + 1 currentRate = clr } } @@ -469,7 +478,7 @@ object SGD { override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { val lr = optimMethod.learningRate val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) - var p = nevals / decayStep.toDouble + var p = (nevals - excludeIterations) / decayStep.toDouble if (stairCase) { p = p.floor } @@ -493,7 +502,7 @@ object SGD { val lr = config.get[Double]("learningRate").getOrElse(1e-3) val lrd = config.get[Double]("learningRateDecay").getOrElse(0.0) val nevals = state.get[Int]("evalCounter").getOrElse(0) - config("clr") = -lr / (1 + nevals * lrd) + config("clr") = -lr / (1 + (nevals - excludeIterations) * lrd) state("evalCounter") = nevals + 1 } @@ -501,7 +510,8 @@ object SGD { val lr = optimMethod.learningRate val lrd = optimMethod.learningRateDecay val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) - currentRate = -lr / (1 + nevals * lrd) + currentRate = -lr / (1 + (nevals - excludeIterations) * lrd) + optimMethod.state("evalCounter") = nevals + 1 } } @@ -553,8 +563,8 @@ object SGD { * @param optimMethod init optiMethod. */ override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { - val epoch = optimMethod.state[Int]("epoch") - if (epoch == 1) currentRate = -optimMethod.learningRate + val epoch = optimMethod.state[Int]("epoch") - excludeEpochs + if (epoch == 1) currentRate = - optimMethod.learningRate if (epoch == curEpoch) return curEpoch = epoch val current = optimMethod.state.get[Float](monitor) @@ -579,4 +589,76 @@ object SGD { } } + /** + * A learning rate gradual increase policy, where the effective learning rate + * increase delta after each iteration. + * Calculation: base_lr + delta * iteration + * + * @param delta increase amount after each iteration + */ + case class Warmup(delta: Double) extends LearningRateSchedule { + override def updateHyperParameter(config: Table, state: Table): Unit = { + val lr = config.get[Double]("learningRate").getOrElse(1e-3) + val nevals = state.get[Int]("evalCounter").getOrElse(0) + val clr = - lr - delta * nevals + config("clr") = clr + state("evalCounter") = nevals + 1 + } + + override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { + val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) + val lr = optimMethod.learningRate + val clr = - lr - delta * (nevals - excludeIterations) + currentRate = clr + println(s"iteration is : ${nevals}. current learning rate is $clr") + optimMethod.state("evalCounter") = nevals + 1 + } + } + + /** + * Stack several learning rate schedulers. + * + * @param iterationPerEpoch iteration numbers per epoch + */ + case class SequentialSchedule(iterationPerEpoch: Int) extends LearningRateSchedule { + val schedules: ArrayBuffer[LearningRateSchedule] = ArrayBuffer[LearningRateSchedule]() + var cur: Int = 0 + + /** + * Add a learning rate scheduler to the contained `schedules` + * + * @param schedule learning rate scheduler to be add + * @param maxIteration iteration numbers this scheduler will run + * @return this container + */ + def add(schedule: LearningRateSchedule, maxIteration: Int): + this.type = { + schedule.excludeIterations = if (schedules.isEmpty) 0 else schedules.last.maxIterations + schedule.maxIterations = schedule.excludeIterations + maxIteration + schedule.excludeEpochs = schedule.excludeIterations / iterationPerEpoch + schedules += schedule + this + } + + override def updateHyperParameter(config: Table, state: Table): Unit = { + val nevals = state.get[Int]("evalCounter").getOrElse(0) + + if (nevals > schedules(cur).maxIterations) { + config("learningRate") = - currentRate + cur += 1 + } + schedules(cur).updateHyperParameter(config, state) + } + + override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { + val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) + + if (nevals > schedules(cur).maxIterations) { + optimMethod.learningRate = - currentRate + cur += 1 + } + schedules(cur).updateHyperParameter(optimMethod) + currentRate = schedules(cur).currentRate + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index dc2f02e484f..a96848fc524 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -36,6 +36,7 @@ import java.util import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape => TfShape, SplitAndSelect} +import com.intel.analytics.bigdl.optim.SGD.{LearningRateSchedule, SequentialSchedule} import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation._ import com.intel.analytics.bigdl.transform.vision.image.label.roi._ @@ -1648,6 +1649,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab SGD.Plateau(monitor, factor, patience, mode, epsilon, cooldown, minLr) } + def createWarmup(delta: Double): SGD.Warmup = { + SGD.Warmup(delta) + } + + def createSequentialSchedule(iterationPerEpoch: Int): SGD.SequentialSchedule = { + SGD.SequentialSchedule(iterationPerEpoch) + } + def createClassNLLCriterion(weights: JTensor = null, sizeAverage: Boolean = true, logProbAsInput: Boolean = true) : ClassNLLCriterion[T] = { @@ -2926,6 +2935,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def disableClip(optimizer: Optimizer[T, MiniBatch[T]]): Unit = { optimizer.disableGradientClipping() } + + def addScheduler(seq: SequentialSchedule, scheduler: LearningRateSchedule, + maxIteration: Int): SequentialSchedule = { + seq.add(scheduler, maxIteration) + } } object PythonBigDLUtils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/SGDSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/SGDSpec.scala index 7e1974fcd2f..1ba2b45d418 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/SGDSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/SGDSpec.scala @@ -369,4 +369,44 @@ class SGDSpec extends FlatSpec with Matchers { optimMethod.learningRateSchedule.currentRate should be(-0.05 * Math.pow(0.96, 2)) }) } + + "ploy learning rate decay with warmup" should "generate correct learning rates" in { + val lrSchedules = new SequentialSchedule(100) + lrSchedules.add(Warmup(0.3), 3).add(Poly(3, 100), 100) + val optimMethod = new SGD[Double](learningRate = 0.1, learningRateSchedule = lrSchedules) + + def feval(x: Tensor[Double]): (Double, Tensor[Double]) = { + return (0.1, Tensor[Double](Storage(Array(1.0, 1.0)))) + } + val x = Tensor[Double](Storage(Array(10.0, 10.0))) + optimMethod.optimize(feval, x) + optimMethod.learningRateSchedule.currentRate should be(-0.1) + optimMethod.optimize(feval, x) + optimMethod.learningRateSchedule.currentRate should be(-0.4) + optimMethod.optimize(feval, x) + optimMethod.learningRateSchedule.currentRate should be(-0.7) + optimMethod.optimize(feval, x) + optimMethod.learningRateSchedule.currentRate should be(-1.0 +- 1e-15) + optimMethod.optimize(feval, x) + optimMethod.learningRateSchedule.currentRate should + be(-1 * (1 - 1.0 / 100) * (1 - 1.0 / 100) * (1 - 1.0 / 100) +- 1e-15) + optimMethod.optimize(feval, x) + optimMethod.learningRateSchedule.currentRate should + be(-1 * (1 - 2.0 / 100) * (1 - 2.0 / 100) * (1 - 2.0 / 100) +- 1e-15) + } + + "ploy with warm up" should "generate correct learning rates" in { + val optimMethod = new SGD[Double](learningRate = 0.01) + val lrSchedule = new SequentialSchedule(10) + lrSchedule.add(Warmup(0.01), 99).add(Poly(0.5, 1000), 1000) + optimMethod.learningRateSchedule = lrSchedule + + def feval(x: Tensor[Double]): (Double, Tensor[Double]) = { + return (0.1, Tensor[Double](Storage(Array(1.0, 1.0)))) + } + val x = Tensor[Double](Storage(Array(10.0, 10.0))) + for (i <- 0 to 1000) { + optimMethod.optimize(feval, x) + } + } } From f755ae1885998a90d63139adb0417908b0abd1bb Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 1 Feb 2018 02:23:34 -0500 Subject: [PATCH 0670/1065] fix: initializing of weights for SReLU should not in forward. (#2257) * fix: initializing of weights for SReLU should not in forward. Otherwise, the Keras will not convert successfully. * fix: add exception for SReLU when call wrong initMethod * fix: python style * fix: self.config err --- .../analytics/bigdl/dllib/nn/SReLU.scala | 73 ++++++++----------- .../dllib/nn/abstractnn/Initializable.scala | 5 ++ .../dllib/utils/python/api/PythonBigDL.scala | 9 ++- .../analytics/bigdl/dllib/nn/SReLUSpec.scala | 18 +++-- .../serializer/ModuleSerializerSpec.scala | 2 +- 5 files changed, 57 insertions(+), 50 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala index 9049e7b42f2..691eae5f237 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Initializable, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ @@ -35,6 +35,8 @@ import scala.reflect.ClassTag * * [Deep Learning with S-shaped Rectified Linear Activation Units](http://arxiv.org/abs/1512.07030) * + * @param shape shape for tleft, aleft, tright, aright. + * E.g. for a 4-D input, the shape is the last 3-D * @param sharedAxes the axes along which to share learnable parameters * for the activation function. * For example, if the incoming feature maps are from a 2D convolution @@ -45,8 +47,8 @@ import scala.reflect.ClassTag */ @SerialVersionUID(7173457290010080259L) -class SReLU[T: ClassTag](sharedAxes: Array[Int] = null)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { +class SReLU[T: ClassTag](val shape: Array[Int], val sharedAxes: Array[Int] = null)( + implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { import SReLU._ val weightsLen = 4 val weights: Array[Tensor[T]] = Array.fill[Tensor[T]](4)(Tensor[T]()) @@ -57,8 +59,9 @@ class SReLU[T: ClassTag](sharedAxes: Array[Int] = null)( // this attribute for computing the offset in weight because of sharedAxes private var indexes: Array[Int] = null - private def init(input: Tensor[T]): Unit = { - val shape = input.size().slice(1, input.size().length) + init(shape).reset() + + private def init(shape: Array[Int]): this.type = { if (sharedAxes != null) { var i = 0 while (i < sharedAxes.length) { @@ -87,6 +90,14 @@ class SReLU[T: ClassTag](sharedAxes: Array[Int] = null)( // ensure the the right part is always to the right of the left weights(tRight).abs().add(weights(tLeft)) + this + } + + override def reset(): Unit = { + for ((initMethod, weight) <- weightsInit.zip(weights)) { + initMethod.init(weight) + } + zeroGradParameters() } private def getIndex(indexes: Array[Int], stride: Array[Int], ndim: Int, offset: Int): Unit = { @@ -116,13 +127,10 @@ class SReLU[T: ClassTag](sharedAxes: Array[Int] = null)( override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.isContiguous(), s"the input of SReLU must be contiguous") + // ensure the the right part is always to the right of the left + weights(tRight).abs().add(weights(tLeft)) output.resizeAs(input) - // the weight's size depends on the input - if (weights.exists(_.isEmpty)) { - init(input) - } - // temp buf for indexes if (indexes == null) { indexes = new Array[Int](weights(tRight).nDimension()) @@ -274,25 +282,6 @@ class SReLU[T: ClassTag](sharedAxes: Array[Int] = null)( } } - override def setWeightsBias(newWeights: Array[Tensor[T]]): this.type = { - // SReLU will split the weights from a tensor - if (!newWeights.isEmpty) { - var i = 0 - while (i < weightsLen) { - val weight = newWeights(i) - weights(i).resizeAs(weight).set(weight) - gradWeights(i) = Tensor[T]().resizeAs(weight) - - i += 1 - } - - // ensure the the right part is always to the right of the left - weights(tRight).abs().add(weights(tLeft)) - } - - this - } - override def getParametersTable(): Table = { T(getName() -> T( "tLeft" -> weights(tLeft), @@ -305,28 +294,28 @@ class SReLU[T: ClassTag](sharedAxes: Array[Int] = null)( (weights, gradWeights) } - def setInitMethod( - tLeftInit: InitializationMethod = null, - aLeftInit: InitializationMethod = null, - tRightInit: InitializationMethod = null, - aRightInit: InitializationMethod = null): this.type = { - val inits = Array(tLeftInit, aLeftInit, tRightInit, aRightInit) - + override def setInitMethod(initMethods: Array[InitializationMethod]): this.type = { for (i <- Array(tLeft, aLeft, tRight, aRight)) { - if (inits(i) != null) { - weightsInit(i) = inits(i) + if (initMethods(i) != null) { + weightsInit(i) = initMethods(i) } } - + reset() this } + + override def setInitMethod(weightInitMethod: InitializationMethod = null, + biasInitMethod: InitializationMethod = null): this.type = { + throw new UnsupportedOperationException( + s"SReLU should call setInitMethod(initMethods: Array[InitializationMethod])") + } } object SReLU extends ModuleSerializable { - def apply[T: ClassTag](share_axes: Array[Int] = null)(implicit ev: TensorNumeric[T]) - : SReLU[T] = { - new SReLU[T](share_axes) + def apply[T: ClassTag](shape: Array[Int], shareAxes: Array[Int] = null)( + implicit ev: TensorNumeric[T]): SReLU[T] = { + new SReLU[T](shape, shareAxes) } val (tLeft, aLeft, tRight, aRight) = (0, 1, 2, 3) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Initializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Initializable.scala index 373ee3783d4..1a297d89412 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Initializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Initializable.scala @@ -38,6 +38,11 @@ trait Initializable { this } + def setInitMethod(initMethod: Array[InitializationMethod]): this.type = { + throw new UnsupportedOperationException(s"setInitMethod with a array of InitializationMethod" + + s" does not support for ${this.toString}") + } + def reset(): Unit } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a96848fc524..043b8925a7c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1022,13 +1022,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab PReLU[T](nOutputPlane) } - def createSReLU(shareAxes: JArrayList[Int] = null): SReLU[T] = { + def createSReLU(shape: JArrayList[Int], shareAxes: JArrayList[Int] = null): SReLU[T] = { val argv: Array[Int] = if (shareAxes == null) { null } else { shareAxes.asScala.toArray } - SReLU[T](argv) + SReLU[T](shape.asScala.toArray, argv) } def createActivityRegularization(l1: Double, l2: Double): ActivityRegularization[T] = { @@ -2379,6 +2379,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab layer.setInitMethod(weightInitMethod, biasInitMethod) } + def setInitMethod(layer: Initializable, + initMethods: JArrayList[InitializationMethod]): layer.type = { + layer.setInitMethod(initMethods.asScala.toArray) + } + def getHiddenState(rec: Recurrent[T]): JActivity = { JActivity(rec.getHiddenState()) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala index 50e221c6041..df4ddb322ae 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala @@ -18,7 +18,6 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.serializer.{ModuleLoader, ModulePersister} class SReLUSpec extends KerasBaseSpec { "SReLU without share axes" should "same as keras" in { @@ -30,7 +29,7 @@ class SReLUSpec extends KerasBaseSpec { |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val srelu = SReLU[Float]() + val srelu = SReLU[Float](Array(3, 4)) checkOutputAndGrad(srelu, keras) } @@ -45,7 +44,7 @@ class SReLUSpec extends KerasBaseSpec { |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val srelu = SReLU[Float](Array(1, 2)) + val srelu = SReLU[Float](shape = Array(2, 3, 4), shareAxes = Array(1, 2)) checkOutputAndGrad(srelu, keras) } @@ -60,15 +59,24 @@ class SReLUSpec extends KerasBaseSpec { |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val srelu = SReLU[Float](Array(2, 4)) + val srelu = SReLU[Float](shape = Array(2, 3, 4, 5), shareAxes = Array(2, 4)) checkOutputAndGrad(srelu, keras) } // do not delete this, it's for testing the initialization of SReLU "SReLU init" should "same as keras" in { - val srelu = SReLU[Float]() + val srelu = SReLU[Float](shape = Array(2, 3, 4)) val input = Tensor[Float](5, 2, 3, 4).randn() srelu.forward(input) println(srelu.output) } + + "SReLU set init method" should "work correctly" in { + val inits = Array[InitializationMethod](Ones, Ones, Ones, Ones) + val shape = Array(2, 3, 4) + val srelu = SReLU[Float](shape).setInitMethod(inits) + val weight = Tensor[Float](shape).fill(1) + + srelu.weights.foreach(x => x should be (weight)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 1b429b8b025..83520b6a807 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -1194,7 +1194,7 @@ class ModuleSerializerSpec extends SerializerSpecHelper { } "SReLU serilalizer" should "work properly" in { - val srelu = SReLU[Float]().setName("srelu") + val srelu = SReLU[Float](shape = Array(4)).setName("srelu") val input = Tensor[Float](3, 4).apply1( e => Random.nextFloat()) runSerializationTest(srelu, input) } From ba9384273a1edd8de1c3708019057f1299797f2f Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 2 Feb 2018 09:45:47 +0800 Subject: [PATCH 0671/1065] split test cases by different types (#2261) --- .../serializer/ModuleSerializerSpec.scala | 1067 +-------------- .../serializer/SerializerSpecHelper.scala | 13 +- .../utils/serializer/TFSerializerSpec.scala | 1183 +++++++++++++++++ 3 files changed, 1212 insertions(+), 1051 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TFSerializerSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 83520b6a807..4b5ac5c0d2a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -61,6 +61,8 @@ class ModuleSerializerSpec extends SerializerSpecHelper { excludedPackage.add("com.intel.analytics.bigdl.utils.tf.loaders") // It would be tested in a separated spec excludedPackage.add("com.intel.analytics.bigdl.nn.keras") + excludedPackage.add("com.intel.analytics.bigdl.nn.ops") + excludedPackage.add("com.intel.analytics.bigdl.nn.tf") } @@ -840,6 +842,14 @@ class ModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(normalizer, input) } + "NormalizeScale serializer" should "work properly" in { + val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1), + wRegularizer = L2Regularizer[Float](0.2)).setName("NormalizeScale") + + val input = Tensor[Float](1, 5, 3, 4).randn() + runSerializationTest(module, input) + } + "Pack serializer" should "work properly" in { val pack = new Pack[Float](1).setName("pack") val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) @@ -880,6 +890,15 @@ class ModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(power, input) } + "Proposal serializer" should "work properly" in { + val proposal = Proposal(200, 100, Array[Float](0.1f, 0.2f, 0.3f), Array[Float](4, 5, 6)) + val score = Tensor[Float](1, 18, 20, 30).randn() + val boxes = Tensor[Float](1, 36, 20, 30).randn() + val imInfo = Tensor[Float](T(300, 300, 1, 1)).resize(1, 4) + val input = T(score, boxes, imInfo) + runSerializationTest(proposal, input) + } + "PReLU serializer" should "work properly" in { val preLu = PReLU[Float](2).setName("preLu") val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) @@ -1375,687 +1394,7 @@ class ModuleSerializerSpec extends SerializerSpecHelper { } // Below are TF Ops - "All serializer" should "work properly" in { - val all = All[Float]().setName("all") - val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) - val input2 = Tensor[Int](T(2, 1, 2)) - val input = T() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(all, input) - } - - "Any serializer" should "work properly" in { - val any = Any[Float]().setName("any") - val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) - val input2 = Tensor[Int](T(2, 1, 2)) - val input = T() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(any, input) - } - - "ApproximateEqual serializer" should "work properly" in { - val approximateEqual = ApproximateEqual[Float](0.01f).setName("approximateEqual") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(approximateEqual, input, approximateEqual. - asInstanceOf[ModuleToOperation[Float]].module.getClass - ) - } - - "ArgMax serializer" should "work properly" in { - val argMax = ArgMax[Float].setName("argMax") - val dataTensor = Tensor[Float](T(T(1.0f, 2.0f), T(3.0f, 4.0f))) - val dimensionTensor = Tensor.scalar[Int](1) - val input = T(dataTensor, dimensionTensor) - runSerializationTest(argMax, input) - } - - "Assert serializer" should "work properly" in { - import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - val assert = new Assert[Float]().setName("assert") - val predictTensor = Tensor[Boolean](Array(1)) - predictTensor.setValue(1, true) - val msg = Tensor[ByteString](Array(1)) - msg.setValue(1, ByteString.copyFromUtf8("must be true")) - val input = T(predictTensor, msg) - runSerializationTest(assert, input) - } - - "Assign serializer" should "work properly" in { - val assign = new Assign[Float]().setName("assign") - val input = - T( - Tensor[Float](T(1f, 2f, 3f)), - Tensor[Float](T(2f, 2f, 4f)) - ) - runSerializationTest(assign, input) - } - - "AssignGrad serializer" should "work properly" in { - val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val assignGrad = new AssignGrad[Float](grad).setName("assignGrad") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(assignGrad, input) - } - - "AvgPoolGrad serializer" should "work properly" in { - val avgPoolGrad = AvgPoolGrad[Float](4, 4, 1, 1, -1, -1, DataFormat.NHWC). - setName("avgPoolGrad") - val input1 = Tensor[Int](T(4, 32, 32, 3)) - val input2 = Tensor[Float](4, 32, 32, 3).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(avgPoolGrad, input) - } - - "BatchMatMul serializer" should "work properly" in { - val batchMatMul = BatchMatMul[Float, Float]().setName("batchMatMul") - val input = - T( - Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - ) - runSerializationTest(batchMatMul, input) - } - - "BiasAddGrad serializer" should "work properly" in { - val biasAddGrad = BiasAddGrad[Float](DataFormat.NCHW). - setName("biasAddGrad") - val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(biasAddGrad, input) - } - - "BroadcastGradientArgs serializer" should "work properly" in { - val broadcastGradientArgs = BroadcastGradientArgs[Float](). - setName("broadcastGradientArgs") - val input = - T( - Tensor[Int](T(1, 2, 3)), - Tensor[Int](T(2, 2, 1)) - ) - runSerializationTest(broadcastGradientArgs, input, broadcastGradientArgs. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Cast serializer" should "work properly" in { - val cast = Cast[Float, Float]().setName("cast") - val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(cast, input, cast. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Ceil serializer" should "work properly" in { - val ceil = Ceil[Float, Float]().setName("ceil") - val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(ceil, input) - } - - "MergeOps serializer" should "work properly" in { - val mergeOps = new MergeOps[Float](1).setName("mergeOps") - val input = - T( - Tensor[Float](T(1.0f, 2.0f, 3.0f)), - Tensor[Float](T(2.0f, 2.0f, 1.0f)) - ) - runSerializationTest(mergeOps, input) - } - - "SwitchOps serializer" should "work properly" in { - val switchOps = new SwitchOps[Float]().setName("switchOps") - val input = - T( - Tensor[Float](T(1.0f, 2.0f, 3.0f)), - Tensor[Boolean](T(true)) - ) - runSerializationTest(switchOps, input) - } - - "Conv2D serializer" should "work properly" in { - val conv2d = Conv2D[Float](2, 1, -1, -1).setName("conv2d") - val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) - val filter = Tensor[Float](4, 3, 3, 2).apply1(_ => Random.nextFloat()) - val input = T(inputTensor, filter) - runSerializationTest(conv2d, input) - } - - "Conv2DBackFilter serializer" should "work properly" in { - val conv2dBackFilter = Conv2DBackFilter[Float](2, 2, -1, -1, DataFormat.NHWC). - setName("conv2dBackFilter") - val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) - val kernelSize = Tensor[Int](T(2, 2, 3, 3)) - val grad = Tensor[Float](1, 2, 2, 3).apply1(_ => Random.nextFloat()) - val input = T(inputTensor, kernelSize, grad) - runSerializationTest(conv2dBackFilter, input) - } - - "Conv2DTranspose Serializer" should "work properly" in { - val conv2dTranspose = Conv2DTranspose[Float](2, 2, -1, -1, DataFormat.NHWC). - setName("conv2dTranspose") - val inputTensor = Tensor[Int](T(1, 4, 3, 3)) - val kernelSize = Tensor[Float](2, 2, 3, 3).apply1(_ => Random.nextFloat()) - val data = Tensor[Float](1, 2, 2, 3)apply1(_ => Random.nextFloat()) - val input = T(inputTensor, kernelSize, data) - runSerializationTest(conv2dTranspose, input) - } - - "CrossEntropy serializer" should "work properly" in { - val crossEntropy = CrossEntropy[Float]().setName("crossEntropy") - val output = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - val label = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - val input = T(output, label) - runSerializationTest(crossEntropy, input) - } - - private def getInputs(name: String): Tensor[ByteString] = { - import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - val index = name match { - case "png" => 0 - case "jpeg" => 1 - case "gif" => 2 - case "raw" => 3 - } - - val resource = getClass.getClassLoader.getResource("tf") - val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" - val file = new JFile(path) - - val bytesVector = TFRecordIterator(file).toVector - val pngBytes = bytesVector(index) - - val example = Example.parseFrom(pngBytes) - val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") - .getBytesList.getValueList.get(0) - - Tensor[ByteString](Array(imageByteString), Array[Int]()) - } - - "DecodeImage Serializer" should "work properly" in { - val decodeImage = new DecodeImage[Float](1).setName("decodeImage") - val input = getInputs("png") - runSerializationTest(decodeImage, input) - } - - "DecodeGif Serializer" should "work properly" in { - val decodeGif = new DecodeGifOps[Float]().setName("decodeGif") - val input = getInputs("gif") - runSerializationTest(decodeGif, input) - } - - "DecodeJpeg Serializer" should "work properly" in { - val decodeJpeg = new DecodeJpegOps[Float](1).setName("decodeJpeg") - val input = getInputs("jpeg") - runSerializationTest(decodeJpeg, input) - } - - "DecodePng Serializer" should "work properly" in { - val decodePng = new DecodePngOps[Float](1).setName("decodePng") - val input = getInputs("png") - runSerializationTest(decodePng, input) - } - - - "DecodeRaw Serializer" should "work properly" in { - val decodeRaw = new DecodeRawOps[Float](DataType.DT_UINT8, true).setName("decodeRaw") - val input = getInputs("raw") - runSerializationTest(decodeRaw, input) - } - - "DepthwiseConv2DBackpropInput serializer" should "work properly" in { - val depWiseBackprop = - DepthwiseConv2DBackpropInput[Float](1, 1, 0, 0, DataFormat.NHWC). - setName("depWiseBackprop") - val input = T(Tensor[Int](T(4, 24, 24, 3)), - Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat()), - Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) - runSerializationTest(depWiseBackprop, input) - } - - "DepthwiseConv2D serializer" should "work properly" in { - val depWIseConv2d = DepthwiseConv2D[Float](1, 1, 0, 0).setName("depWIseConv2d") - val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat())) - runSerializationTest(depWIseConv2d, input) - } - - "DepthwiseConv2DBackpropFilter serializer" should "work properly" in { - val depWiseConv2dBackProp = DepthwiseConv2DBackpropFilter[Float](1, - 1, 0, 0, DataFormat.NHWC).setName("depWiseConv2dBackProp") - val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(2, 2, 3, 1)), - Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) - runSerializationTest(depWiseConv2dBackProp, input) - } - - "EluGrad serializer" should "work properly" in { - val eluGrad = EluGrad[Float, Float]().setName("eluGrad") - val inputTensor = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(inputTensor, grad) - runSerializationTest(eluGrad, input) - } - - "Equal serializer" should "work properly" in { - val equal = Equal[Float]().setName("equal") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(equal, input, - equal.asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "ExpOps serializer" should "work properly" in { - val expOps = ExpOps[Float, Float]().setName("expOps") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(expOps, input) - } - - "Expm1 serializer" should "work properly" in { - val expm1 = Expm1[Float, Float]().setName("expm1") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(expm1, input) - } - - "Floor serializer" should "work properly" in { - val floor = Floor[Float]().setName("floor") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(floor, input, floor. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "FloorDiv serializer" should "work properly" in { - val floorDiv = FloorDiv[Float, Float]().setName("floorDiv") - val input1 = Tensor[Float](5).fill(1.0f) - val input2 = Tensor[Float](5).fill(2.0f) - val input = T(input1, input2) - runSerializationTest(floorDiv, input) - } - - "FloorMod serializer" should "work properly" in { - val floorMod = FloorMod[Float, Float]().setName("floorMod") - val input1 = Tensor[Float](5).fill(1.0f) - val input2 = Tensor[Float](5).fill(2.0f) - val input = T(input1, input2) - runSerializationTest(floorMod, input) - } - - "FusedBatchNorm serializer" should "work properly" in { - val fusedBatchNorm = FusedBatchNorm[Float]().setName("fusedBatchNorm") - val input = T(Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat()), - Tensor[Float](0), - Tensor[Float](0)) - runSerializationTest(fusedBatchNorm, input) - } - - "FusedBatchNormGrad serializer" should "work properly" in { - val fbatchNormGrad = FusedBatchNormGrad[Float]().setName("fbatchNormGrad") - val input = T(Tensor[Float](4, 8, 8, 256).rand(), - Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat())) - runSerializationTest(fbatchNormGrad, input) - } - - "Greater serializer" should "work properly" in { - val greater = Greater[Float]().setName("greater") - val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(greater, input, greater. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "GreaterEqual serializer" should "work properly" in { - val greaterEqual = GreaterEqual[Float]().setName("greaterEqual") - val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(greaterEqual, input, greaterEqual - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "InTopK serializer" should "work properly" in { - val inTopK = InTopK[Float](2).setName("inTopK") - val input1 = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Int](2).fill(1) - val input = T(input1, input2) - runSerializationTest(inTopK, input) - } - - "Inv serializer" should "work properly" in { - val inv = Inv[Float, Float]().setName("inv") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(inv, input) - } - - "InvGrad serializer" should "work properly" in { - val invGrad = InvGrad[Float, Float]().setName("invGrad") - val input = T(Tensor[Float](2, 5).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 5).apply1(_ => Random.nextFloat())) - runSerializationTest(invGrad, input) - } - - "IsFinite serializer" should "work properly" in { - val isFinite = IsFinite[Float, Float]().setName("isFinite") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(isFinite, input) - } - - "IsInf serializer" should "work properly" in { - val isInf = IsInf[Float, Float]().setName("isInf") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(isInf, input) - } - - "IsNan serializer" should "work properly" in { - val isNan = IsNan[Float, Float]().setName("isInf") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(isNan, input) - } - - "Kv2Tensor" should "work properly" in { - val kv2tensor = Kv2Tensor[Float, Float]( - kvDelimiter = ",", itemDelimiter = ":", transType = 0 - ).setName("kv2tensor") - val input = T( - Tensor[String]( - T(T("0:0.1,1:0.2"), T("1:0.3,3:0.5"), T("2:0.15,4:0.25"))), - Tensor[Int](Array(5), shape = Array[Int]()) - ) - runSerializationTest(kv2tensor, input) - } - - "L2Loss serializer" should "work properly" in { - val l2loss = L2Loss[Float]().setName("l2loss") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(l2loss, input, - l2loss.asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Less serializer" should "work properly" in { - val less = Less[Float]().setName("less") - val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(less, input, less - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LessEqual serializer" should "work properly" in { - val lessEqual = LessEqual[Float]().setName("lessEqual") - val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(lessEqual, input, lessEqual - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LogicalAnd serializer" should "work properly" in { - val logicalAnd = LogicalAnd[Float].setName("logicalAnd") - val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) - runSerializationTest(logicalAnd, input, logicalAnd. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LogicalNot serializer" should "work properly" in { - val logicalNot = LogicalNot[Float].setName("logicalNot") - val input = Tensor[Boolean](T(true, false)) - runSerializationTest(logicalNot, input, logicalNot - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LogicalOr serializer" should "work properly" in { - val logicalOr = LogicalOr[Float].setName("logicalOr") - val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) - runSerializationTest(logicalOr, input, logicalOr - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LRNGrad serializer" should "work properly" in { - val lrnGrad = LRNGrad[Float]().setName("lrnGrad") - val input = T(Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()) - ) - runSerializationTest(lrnGrad, input) - } - - "Maximum serializer" should "work properly" in { - val maxiMum = Maximum[Float, Float]().setName("maxiMum") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(maxiMum, input) - } - - "MaxPool serializer" should "work properly" in { - val maxPool = MaxPool[Float]( - Array(1, 2, 3, 1), - Array(1, 2, 1, 1), - "VALID").setName("maxPool") - val input = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(maxPool, input, maxPool. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - - } - - "MaxPoolGrad serializer" should "work properly" in { - val maxPoolGrad = MaxPoolGrad[Float](2, 1, 1, 1, 0, 0, DataFormat.NCHW). - setName("maxPoolGrad") - val input = T(Tensor[Float](1, 3, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](), - Tensor[Float](1, 1, 1).apply1(_ => Random.nextFloat())) - runSerializationTest(maxPoolGrad, input) - } - - "Mimimum serializer" should "work properly" in { - val minimum = Minimum[Float, Float]().setName("minimum") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(minimum, input) - } - - "Mod serializer" should "work properly" in { - val mod = Mod[Float, Float]().setName("mod") - val input1 = Tensor[Float](5).fill(1.0f) - val input2 = Tensor[Float](5).fill(2.0f) - val input = T(input1, input2) - runSerializationTest(mod, input) - } - - "ModuleToOperation serializer" should "work properly" in { - val moduleToOperation = ModuleToOperation[Float](new LogicalOr()). - setName("moduleToOperation") - val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) - runSerializationTest(moduleToOperation, input) - } - - "TensorModuleWrapper serializer" should "work properly" in { - val tensorModuleWrapper = TensorModuleWrapper[Float, Float](SoftPlus[Float]()). - setName("moduleToOperation") - val input = Tensor[Float](T(1.0f, 1.0)) - runSerializationTest(tensorModuleWrapper, input) - } - - "NoOp serializer" should "work properly" in { - val noOp = new com.intel.analytics.bigdl.nn.ops.NoOp[Float]().setName("noOp") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(noOp, input) - } - - "NotEqual serializer" should "work properly" in { - val notEqual = NotEqual[Float].setName("notEqual") - val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) - runSerializationTest(notEqual, input, notEqual - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "OneHot serializer" should "work properly" in { - val oneHot = OneHot[Float, Float](axis = -1).setName("oneHot") - val input = - T(Tensor[Long](T(0, 2, -1, 1)), - Tensor[Int](Array(3), shape = Array[Int]()), - Tensor[Float](Array(0.5f), shape = Array[Int]()), - Tensor[Float](Array(0.0f), shape = Array[Int]())) - runSerializationTest(oneHot, input, oneHot - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Pad serializer" should "work properly" in { - val pad = Pad[Float, Float](mode = "CONSTANT", 0.0f).setName("pad") - val inputTensor = Tensor[Float](2, 2, 3).apply1(_ => Random.nextFloat()) - val padding = Tensor[Int](T(T(1, 2), T(1, 2), T(1, 2))) - val input = T(inputTensor, padding) - runSerializationTest(pad, input, pad. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "ParseExample serializer" should "work properly" in { - import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - - val floatBuilder = FloatList.newBuilder() - .addValue(0.0f).addValue(1.0f).addValue(2.0f) - val floatFeature = Feature.newBuilder().setFloatList(floatBuilder).build() - - val longBuilder = Int64List.newBuilder() - .addValue(0).addValue(1).addValue(2) - val longFeature = Feature.newBuilder().setInt64List(longBuilder).build() - - val bytesBuilder = BytesList.newBuilder().addValue(ByteString.copyFromUtf8("abcd")) - val bytesFeature = Feature.newBuilder().setBytesList(bytesBuilder).build() - - val features = Features.newBuilder() - .putFeature("floatFeature", floatFeature) - .putFeature("longFeature", longFeature) - .putFeature("bytesFeature", bytesFeature) - val example = Example.newBuilder().setFeatures(features).build() - val length = example.getSerializedSize - val data = new Array[Byte](length) - val outputStream = CodedOutputStream.newInstance(data) - example.writeTo(outputStream) - - val exampleParser = new ParseExample[Float](3, Seq(FloatType, LongType, StringType), - Seq(Array(3), Array(3), Array())).setName("parseExample") - - val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) - val names = Tensor[ByteString]() - val key1 = Tensor[ByteString](Array(ByteString.copyFromUtf8("floatFeature")), Array[Int]()) - val key2 = Tensor[ByteString](Array(ByteString.copyFromUtf8("longFeature")), Array[Int]()) - val key3 = Tensor[ByteString](Array(ByteString.copyFromUtf8("bytesFeature")), Array[Int]()) - - val default1 = Tensor[Float]() - val default2 = Tensor[Long]() - val default3 = Tensor[ByteString]() - val input = T(serialized, names, key1, key2, key3, default1, default2, default3) - runSerializationTest(exampleParser, input) - } - - "PowOps serializer" should "work properly" in { - val pow = PowOps[Float]().setName("powOps") - val v = Tensor[Float](T(2)) - val t = Tensor[Float](T(1, 2, 3)) - val input = (T(t, v)) - runSerializationTest(pow, input) - } - - "Prod serializer" should "work properly" in { - val prod = Prod[Float](-1, false).setName("prod") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(prod, input, prod. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "RandomUniform serializer" should "work properly" in { - val randomUniform = RandomUniform[Float, Float](10, 20). - setName("randomUniform") - val input = Tensor[Int](T(1, 2, 3)) - runSerializationTest(randomUniform, input, randomUniform. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "RangeOps serializer" should "work properly" in { - val rangeOps = RangeOps[Float, Float]().setName("rangeOps") - val input = T(Tensor[Float](T(1)), Tensor[Float](T(10)), Tensor[Float](T(1))) - runSerializationTest(rangeOps, input) - } - - "Rank serializer" should "work properly" in { - val rank = Rank[Float].setName("rank") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(rank, input, rank. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Relu6Grad serializer" should "work properly" in { - val relu6Grad = Relu6Grad[Float, Float]().setName("relu6Grad") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(relu6Grad, input) - } - - "ReluGrad serializer" should "work properly" in { - val reluGrad = ReluGrad[Float] - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(reluGrad, input) - } - - "ResizeBilinearOps serializer" should "work properly" in { - val resizeBilinearOps = ResizeBilinearOps[Float](false). - setName("resizeBiLinearOps") - val input = T(Tensor[Float](1, 3, 2, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(3, 2))) - runSerializationTest(resizeBilinearOps, input) - } - - "Rint serializer" should "work properly" in { - val rint = Rint[Float]().setName("rint") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(rint, input) - } - - "Round serializer" should "work properly" in { - val round = Round[Float, Float]().setName("round") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(round, input) - } - - "RsqrtGrad serializer" should "work properly" in { - val rsqrtGrad = RsqrtGrad[Float, Float].setName("rsqrtGrad") - val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](3, 3).apply1(_ => Random.nextFloat())) - runSerializationTest(rsqrtGrad, input) - } - - "SegmentSum serializer" should "work properly" in { - val sgSum = SegmentSum[Float].setName("segmentSum") - val input = T(Tensor[Float](10, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0, 0, 0, 1, 2, 3, 3, 4, 4, 4))) - runSerializationTest(sgSum, input) - } - - "SelectOps serializer" should "work properly" in { - val select = SelectOps[Float]().setName("select") - val cond = Tensor.scalar[Boolean](true) - val t = Tensor[Int](T(1)) - val e = Tensor[Int](T(2)) - val input = T(cond, t, e) - runSerializationTest(select, input) - } - - "SigmoidGrad serializer" should "work properly" in { - val sigMoidGrad = SigmoidGrad[Float, Float]().setName("sigMoidGrad") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(sigMoidGrad, input) - } - "Sign serializer" should "work properly" in { - val sign = Sign[Float, Float]().setName("sign") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(sign, input) - } "Slice serializer" should "work properly" in { val slice = Slice[Float](begin = Array(0, 1, 1), @@ -2137,38 +1476,6 @@ class ModuleSerializerSpec extends SerializerSpecHelper { // nn.tf package - "BiasAdd serializer" should "work properly" in { - val biasAdd = BiasAdd[Float]().setName("biasAdd") - val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](3).apply1(_ => Random.nextFloat())) - runSerializationTest(biasAdd, input) - } - "Const serializer" should "work properly" in { - val value = Tensor[Float](3).apply1(_ => Random.nextFloat()) - val const = Const[Float, Float](value).setName("const") - val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) - runSerializationTest(const, input) - } - - "Fill serializer" should "work properly" in { - val fill = Fill[Float]().setName("fill") - val shape = Tensor[Int](T(2, 3)) - val value = Tensor[Float](Array(0.1f), Array[Int]()) - val input = T(shape, value) - runSerializationTest(fill, input) - } - - "Log1p serializer" should "work properly" in { - val log1p = Log1p[Float, Float]().setName("log1p") - val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) - runSerializationTest(log1p, input) - } - - "Shape serializer" should "work properly" in { - val shape = Shape[Float]().setName("shape") - val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) - runSerializationTest(shape, input) - } "SplitAndSelect serializer" should "work properly" in { val splitAndSelect = SplitAndSelect[Float](2, 1, 2).setName("splitSelect") @@ -2192,228 +1499,11 @@ class ModuleSerializerSpec extends SerializerSpecHelper { // tf.loaders - "MeanLoadTF serializer" should "work properly" in { - val meanLoadTF = new MeanLoadTF[Float]("Float", false).setName("meanLoadTF") - val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(1, 1))) - runSerializationTest(meanLoadTF, input) - } - - "ConcatV2LoadTF serializer" should "work properly" in { - val concatv2 = new ConcatV2LoadTF[Float]().setName("concatv2LoadTF") - val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(1))) - runSerializationTest(concatv2, input) - } - - "ExpandDimsLoadTF serializer" should "work properly" in { - val expandDim = new ExpandDimsLoadTF[Float]().setName("expandDim") - val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), - Tensor.scalar[Int](1)) - runSerializationTest(expandDim, input) - } - - "PadLoadTF serializer" should "work properly" in { - val padLoadTF = new PadLoadTF[Float]().setName("PadLoadTF") - val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), - Tensor[Int](T(T(1, 1), T(1, 1)))) - runSerializationTest(padLoadTF, input) - } - - "ProdLoadTF serializer" should "work properly" in { - val prodLoadTF = new ProdLoadTF[Float]().setName("prodLoadTF") - val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), - Tensor.scalar[Int](1)) - runSerializationTest(prodLoadTF, input) - } - - "ReshapeLoadTF serializer" should "work properly" in { - val reshapeLoadTF = new ReshapeLoadTF[Float]().setName("reshapeLoadTF") - val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), - Tensor[Int](T(1, 5, 25))) - runSerializationTest(reshapeLoadTF, input) - } - - "SliceLoadTF serializer" should "work properly" in { - val sliceLoadTF = new SliceLoadTF[Float]().setName("sliceLoadTF") - val input = T(Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0, 1, 1)), - Tensor[Int](T(2, -1, 1))) - runSerializationTest(sliceLoadTF, input) - } - - "StridedSliceLoadTF serializer" should "work properly" in { - val strideSliceLoadTF = new StridedSliceLoadTF[Float, Float](). - setName("strideSliceLoadTF") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0)), - Tensor[Int](T(1)), - Tensor[Int](T(1)) - ) - runSerializationTest(strideSliceLoadTF, input) - } - - "SplitLoadTF serializer" should "work properly" in { - val splitLoadTF = new SplitLoadTF[Float](1).setName("splitLoadTD") - val input = T(Tensor[Int](T(1)), - Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()) - ) - runSerializationTest(splitLoadTF, input) - } - - "TransposeLoadTF serializer" should "work properly" in { - val transposeLoadTF = new TransposeLoadTF[Float]().setName("transposeLoadTF") - val input = T(Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0, 1)) - ) - runSerializationTest(transposeLoadTF, input) - } - - "TopKV2LoadTF serializer" should "work properly" in { - val topkv2LoadTF = new TopKV2LoadTF[Float](false, "Float"). - setName("topkv2LoadTF") - val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), - Tensor.scalar[Int](2) - ) - runSerializationTest(topkv2LoadTF, input) - } - - "Proposal serializer" should "work properly" in { - val proposal = Proposal(200, 100, Array[Float](0.1f, 0.2f, 0.3f), Array[Float](4, 5, 6)) - val score = Tensor[Float](1, 18, 20, 30).randn() - val boxes = Tensor[Float](1, 36, 20, 30).randn() - val imInfo = Tensor[Float](T(300, 300, 1, 1)).resize(1, 4) - val input = T(score, boxes, imInfo) - runSerializationTest(proposal, input) - } - - "NormalizeScale serializer" should "work properly" in { - val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1), - wRegularizer = L2Regularizer[Float](0.2)).setName("NormalizeScale") - - val input = Tensor[Float](1, 5, 3, 4).randn() - runSerializationTest(module, input) - } - - "Digamma serializer" should "work properly" in { - val module = Digamma[Float, Float]() - - val input = Tensor[Float](1, 5, 3, 4).rand() - runSerializationTest(module, input) - } - - "Lgamma serializer" should "work properly" in { - val module = Lgamma[Float, Float]() - - val input = Tensor[Float](1, 5, 3, 4).rand() - runSerializationTest(module, input) - } - - "Erf serializer" should "work properly" in { - val module = Erf[Float, Float]() - - val input = Tensor[Float](1, 5, 3, 4).rand() - runSerializationTest(module, input) - } - - "Erfc serializer" should "work properly" in { - val module = Erfc[Float, Float]() - - val input = Tensor[Float](1, 5, 3, 4).rand() - runSerializationTest(module, input) - } - - "TanhGrad serializer" should "work properly" in { - val module = TanhGrad[Float, Float]() - - val input = T(Tensor[Float](1, 5, 3, 4).rand(), Tensor[Float](1, 5, 3, 4).rand()) - - runSerializationTest(module, input) - } - - "Dilation2D serializer" should "work properly" in { - val module = Dilation2D[Float, Float]( - Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") - - val input = T(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()) - - runSerializationTest(module, input) - } - - "Dilation2DBackpropFilter serializer" should "work properly" in { - val module = Dilation2DBackpropFilter[Float, Float]( - Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") - - val input = T(Tensor[Float](4, 32, 32, 3).rand(), - Tensor[Float](3, 4, 3).rand(), - Tensor[Float](4, 11, 16, 3).rand()) - - runSerializationTest(module, input) - } - - "Dilation2DBackpropInput serializer" should "work properly" in { - val module = Dilation2DBackpropInput[Float, Float]( - Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") - - val input = T(Tensor[Float](4, 32, 32, 3).rand(), - Tensor[Float](3, 4, 3).rand(), - Tensor[Float](4, 11, 16, 3).rand()) - - runSerializationTest(module, input) - } - - "Conv3D serializer" should "work properly" in { - val module = Conv3D[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val input = Tensor[Float](4, 20, 30, 40, 3).rand() - val filter = Tensor[Float](2, 3, 4, 3, 4).rand() - runSerializationTest(module, T(input, filter)) - } - - "Conv3DBackpropFilter serializer" should "work properly" in { - val module = Conv3DBackpropFilter[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val input = Tensor[Float](4, 20, 30, 40, 3).rand() - val filter = Tensor[Float](2, 3, 4, 3, 4).rand() - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4) - - runSerializationTest(module, T(input, filter, outputBackprop)) - } - - "Conv3DBackpropInput serializer" should "work properly" in { - val module = Conv3DBackpropInput[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val input = Tensor[Float](4, 20, 30, 40, 3).rand() - val filter = Tensor[Float](2, 3, 4, 3, 4).rand() - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() - - runSerializationTest(module, T(input, filter, outputBackprop)) - } - "Conv3DBackpropFilterV2 serializer" should "work properly" in { - val module = Conv3DBackpropFilterV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val input = Tensor[Float](4, 20, 30, 40, 3).rand() - val filter = Tensor[Int](Array(2, 3, 4, 3, 4), Array(5)) - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() - runSerializationTest(module, T(input, filter, outputBackprop)) - } - - "Conv3DBackpropInputV2 serializer" should "work properly" in { - val module = Conv3DBackpropInputV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val inputSize = Tensor[Int](Array(4, 20, 30, 40, 3), Array(5)) - val filter = Tensor[Float](2, 3, 4, 3, 4).rand() - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() - runSerializationTest(module, T(inputSize, filter, outputBackprop)) - } - "ResizeBilinearGrad serializer" should "work properly" in { - val module = ResizeBilinearGrad[Float](true) - val input = T(Tensor[Float](1, 224, 224, 3).rand(), - Tensor[Float](1, 64, 64, 3).rand()) - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() - runSerializationTest(module, input) - } "DetectionOutputSSD serializer" should "work properly" in { val module = DetectionOutputSSD[Float](DetectionOutputParam()).setName("DetectionOutputSSD") @@ -2461,123 +1551,4 @@ class ModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(module, input) } - "Control Ops serializer" should "work properly" in { - val input = Input[Float]("input") - - val conditionInput = Input[Float]("conditionInput") - val const = new com.intel.analytics.bigdl.nn.tf.Const[Float, Float](Tensor(T(9))).inputs() - val constEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(const) - val less = Less[Float]().inputs(constEnter, conditionInput) - - val updateInput = Input[Float]() - val add = AddConstant[Float](1).inputs(updateInput) - val addEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(add) - val echo = Echo[Float]().inputs(addEnter) - - val exit = ControlNodes.whileLoop[Float]( - (Seq(conditionInput), less), - (Seq((updateInput, echo))), - Seq(input), - "while" - ) - val model = Graph.dynamic[Float](Array(input), Array(exit(0)), None, false) - runSerializationTestWithMultiClass(model, Tensor.scalar[Float](1), Array( - addEnter.element.getClass.asInstanceOf[Class[_]], - new com.intel.analytics.bigdl.nn.ops.NextIteration[Float, Float]().getClass, - new com.intel.analytics.bigdl.nn.ops.Exit[Float]().getClass, - new com.intel.analytics.bigdl.nn.ops.LoopCondition[Float]().getClass - )) - } - - "Stack operations serializer" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ - val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() - val stack = new StackCreator[Float, Float]().inputs() - val push = new StackPush[Float, Float]().inputs(stack, data) - val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(push) - val pop = new StackPop[Float, Float]().inputs(stack, ctr) - val model = Graph.dynamic[Float](Array(stack), Array(pop)) - - runSerializationTestWithMultiClass(model, Tensor.scalar(1), Array( - stack.element.getClass.asInstanceOf[Class[_]], - push.element.getClass.asInstanceOf[Class[_]], - pop.element.getClass.asInstanceOf[Class[_]] - )) - } - - "TensorArray serializer R/W" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ - val tensorArray = new TensorArrayCreator[Float, Float]().inputs() - val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() - val index = Const[Float, Int](Tensor.scalar[Int](0)).inputs() - val write = new TensorArrayWrite[Float, Float]().inputs((tensorArray, 1), (index, 1), (data, 1)) - val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(write) - val read = new TensorArrayRead[Float, Float]().inputs((tensorArray, 1), (index, 1), (ctr, 1)) - val grad = new TensorArrayGrad[Float]("grad").inputs(tensorArray) - val output = Identity[Float]().inputs((grad, 2)) - val model = Graph.dynamic[Float](Array(tensorArray), Array(read, output)) - - runSerializationTestWithMultiClass(model, Tensor.scalar[Int](1), Array( - tensorArray.element.getClass.asInstanceOf[Class[_]], - write.element.getClass.asInstanceOf[Class[_]], - read.element.getClass.asInstanceOf[Class[_]], - grad.element.getClass.asInstanceOf[Class[_]] - )) - } - - "TensorArray serializer Gather/Scatter" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ - val tensorArray = new TensorArrayCreator[Float, Float]().inputs() - val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() - val indices = Const[Float, Int](Tensor[Int](T(0, 1, 2))).inputs() - val scatter = new TensorArrayScatter[Float, Float]().inputs((tensorArray, 1), (indices, 1), - (data, 1)) - val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(scatter) - val gather = new TensorArrayGather[Float, Float]().inputs((tensorArray, 1), (indices, 1), - (ctr, 1)) - val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(gather) - val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1)) - val model = Graph.dynamic[Float](Array(tensorArray), Array(gather, close)) - - runSerializationTestWithMultiClass(model, Tensor.scalar[Int](10), Array( - tensorArray.element.getClass.asInstanceOf[Class[_]], - scatter.element.getClass.asInstanceOf[Class[_]], - gather.element.getClass.asInstanceOf[Class[_]], - close.element.getClass.asInstanceOf[Class[_]] - )) - } - - "TensorArray serializer Split/Concat" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ - val tensorArray = new TensorArrayCreator[Float, Float]().inputs() - val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() - val lengths = Const[Float, Int](Tensor[Int](T(1, 2))).inputs() - val splitter = new TensorArraySplit[Float, Float]().inputs((tensorArray, 1), (data, 1), - (lengths, 1)) - val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(splitter) - val concat = new TensorArrayConcat[Float, Float]().inputs(tensorArray, ctr) - val size = new TensorArraySize[Float]().inputs(tensorArray, ctr) - val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(concat, size) - val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1)) - val model = Graph.dynamic[Float](Array(tensorArray), Array(concat, close, size)) - - runSerializationTestWithMultiClass(model, Tensor.scalar[Int](2), Array( - tensorArray.element.getClass.asInstanceOf[Class[_]], - splitter.element.getClass.asInstanceOf[Class[_]], - concat.element.getClass.asInstanceOf[Class[_]], - close.element.getClass.asInstanceOf[Class[_]], - size.element.getClass.asInstanceOf[Class[_]] - )) - } - - "ConcatOffset serializer" should "work properly" in { - val module = new com.intel.analytics.bigdl.nn.ops.ConcatOffset[Float]() - runSerializationTest(module, T(Tensor.scalar[Int](1), Tensor[Int](T(2, 2, 5, 7)), - Tensor[Int](T(2, 3, 5, 7)), Tensor[Int](T(2, 4, 5, 7)))) - } - - "InvertPermutation serializer" should "work properly" in { - val module = new com.intel.analytics.bigdl.nn.ops.InvertPermutation[Float]() - runSerializationTest(module, Tensor[Int](T(0, 1, 2, 3, 4))) - } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala index b7d8eb4d361..714f8743b67 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala @@ -90,13 +90,20 @@ abstract class SerializerSpecHelper extends FlatSpec with Matchers with BeforeAn } afterLoadForward should be (originForward) - classes.foreach(cls => tested.add(cls.getName)) + classes.foreach(cls => { + if (getExpected.contains(cls.getName)) { + tested.add(cls.getName) + } + }) } override protected def afterAll() = { - println(s"total ${getExpected().size}, remaining ${getExpected().size - tested.size}") - getExpected().foreach(exp => { + println(s"total ${getExpected.size}, remaining ${getExpected.size - tested.size}") + tested.filter(!getExpected.contains(_)).foreach(t => { + println(s"$t do not need to be tested") + }) + getExpected.foreach(exp => { require(tested.contains(exp), s" $exp not included in the test!") }) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TFSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TFSerializerSpec.scala new file mode 100644 index 00000000000..a78e47cae0e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TFSerializerSpec.scala @@ -0,0 +1,1183 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.serializer + +import java.io.File +import java.io.{File => JFile} +import java.lang.reflect.Modifier + +import com.google.protobuf.{ByteString, CodedOutputStream} +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC + +import scala.collection.JavaConverters._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.tf.{BiasAdd, Const, Fill, Log1p, Shape, SplitAndSelect, StrideSlice, Variable, TensorModuleWrapper} +import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} +import com.intel.analytics.bigdl.optim.L2Regularizer +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.tf.TFRecordIterator +import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _, _} +import com.intel.analytics.bigdl.utils.{T, Table} +import org.reflections.Reflections +import org.reflections.scanners.SubTypesScanner +import org.reflections.util.{ClasspathHelper, ConfigurationBuilder, FilterBuilder} +import org.scalatest.{BeforeAndAfterAll, FlatSpec, Matchers} +import org.tensorflow.example._ +import org.tensorflow.framework.DataType + +import scala.collection.mutable +import scala.util.Random + +class TFSerializerSpec extends SerializerSpecHelper { + + override protected def getPackage(): String = "com.intel.analytics.bigdl.nn.ops" + + override def addExcludedPackage(): Unit = { + excludedPackage.add("com.intel.analytics.bigdl.utils.tf.loaders") + // It would be tested in a separated spec + excludedPackage.add("com.intel.analytics.bigdl.nn.keras") + } + + override def getExpected(): mutable.Set[String] = { + super.getExpected().filter(cls => { + cls.contains(getPackage()) || cls.contains("com.intel.analytics.bigdl.tf") + }) + } + + "All serializer" should "work properly" in { + val all = All[Float]().setName("all") + val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val input2 = Tensor[Int](T(2, 1, 2)) + val input = T() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(all, input) + } + + "Any serializer" should "work properly" in { + val any = Any[Float]().setName("any") + val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val input2 = Tensor[Int](T(2, 1, 2)) + val input = T() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(any, input) + } + + "ApproximateEqual serializer" should "work properly" in { + val approximateEqual = ApproximateEqual[Float](0.01f).setName("approximateEqual") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(approximateEqual, input, approximateEqual. + asInstanceOf[ModuleToOperation[Float]].module.getClass + ) + } + + "ArgMax serializer" should "work properly" in { + val argMax = ArgMax[Float].setName("argMax") + val dataTensor = Tensor[Float](T(T(1.0f, 2.0f), T(3.0f, 4.0f))) + val dimensionTensor = Tensor.scalar[Int](1) + val input = T(dataTensor, dimensionTensor) + runSerializationTest(argMax, input) + } + + "Assert serializer" should "work properly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val assert = new Assert[Float]().setName("assert") + val predictTensor = Tensor[Boolean](Array(1)) + predictTensor.setValue(1, true) + val msg = Tensor[ByteString](Array(1)) + msg.setValue(1, ByteString.copyFromUtf8("must be true")) + val input = T(predictTensor, msg) + runSerializationTest(assert, input) + } + + "Assign serializer" should "work properly" in { + val assign = new Assign[Float]().setName("assign") + val input = + T( + Tensor[Float](T(1f, 2f, 3f)), + Tensor[Float](T(2f, 2f, 4f)) + ) + runSerializationTest(assign, input) + } + + "AssignGrad serializer" should "work properly" in { + val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val assignGrad = new AssignGrad[Float](grad).setName("assignGrad") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(assignGrad, input) + } + + "AvgPoolGrad serializer" should "work properly" in { + val avgPoolGrad = AvgPoolGrad[Float](4, 4, 1, 1, -1, -1, DataFormat.NHWC). + setName("avgPoolGrad") + val input1 = Tensor[Int](T(4, 32, 32, 3)) + val input2 = Tensor[Float](4, 32, 32, 3).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(avgPoolGrad, input) + } + + "BatchMatMul serializer" should "work properly" in { + val batchMatMul = BatchMatMul[Float, Float]().setName("batchMatMul") + val input = + T( + Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(batchMatMul, input) + } + + "BiasAddGrad serializer" should "work properly" in { + val biasAddGrad = BiasAddGrad[Float](DataFormat.NCHW). + setName("biasAddGrad") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(biasAddGrad, input) + } + + "BroadcastGradientArgs serializer" should "work properly" in { + val broadcastGradientArgs = BroadcastGradientArgs[Float](). + setName("broadcastGradientArgs") + val input = + T( + Tensor[Int](T(1, 2, 3)), + Tensor[Int](T(2, 2, 1)) + ) + runSerializationTest(broadcastGradientArgs, input, broadcastGradientArgs. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Cast serializer" should "work properly" in { + val cast = Cast[Float, Float]().setName("cast") + val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(cast, input, cast. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Ceil serializer" should "work properly" in { + val ceil = Ceil[Float, Float]().setName("ceil") + val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(ceil, input) + } + + "MergeOps serializer" should "work properly" in { + val mergeOps = new MergeOps[Float](1).setName("mergeOps") + val input = + T( + Tensor[Float](T(1.0f, 2.0f, 3.0f)), + Tensor[Float](T(2.0f, 2.0f, 1.0f)) + ) + runSerializationTest(mergeOps, input) + } + + "SwitchOps serializer" should "work properly" in { + val switchOps = new SwitchOps[Float]().setName("switchOps") + val input = + T( + Tensor[Float](T(1.0f, 2.0f, 3.0f)), + Tensor[Boolean](T(true)) + ) + runSerializationTest(switchOps, input) + } + + "Conv2D serializer" should "work properly" in { + val conv2d = Conv2D[Float](2, 1, -1, -1).setName("conv2d") + val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + val filter = Tensor[Float](4, 3, 3, 2).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, filter) + runSerializationTest(conv2d, input) + } + + "Conv2DBackFilter serializer" should "work properly" in { + val conv2dBackFilter = Conv2DBackFilter[Float](2, 2, -1, -1, DataFormat.NHWC). + setName("conv2dBackFilter") + val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + val kernelSize = Tensor[Int](T(2, 2, 3, 3)) + val grad = Tensor[Float](1, 2, 2, 3).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, kernelSize, grad) + runSerializationTest(conv2dBackFilter, input) + } + + "Conv2DTranspose Serializer" should "work properly" in { + val conv2dTranspose = Conv2DTranspose[Float](2, 2, -1, -1, DataFormat.NHWC). + setName("conv2dTranspose") + val inputTensor = Tensor[Int](T(1, 4, 3, 3)) + val kernelSize = Tensor[Float](2, 2, 3, 3).apply1(_ => Random.nextFloat()) + val data = Tensor[Float](1, 2, 2, 3)apply1(_ => Random.nextFloat()) + val input = T(inputTensor, kernelSize, data) + runSerializationTest(conv2dTranspose, input) + } + + "CrossEntropy serializer" should "work properly" in { + val crossEntropy = CrossEntropy[Float]().setName("crossEntropy") + val output = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val label = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val input = T(output, label) + runSerializationTest(crossEntropy, input) + } + + private def getInputs(name: String): Tensor[ByteString] = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" + val file = new JFile(path) + + val bytesVector = TFRecordIterator(file).toVector + val pngBytes = bytesVector(index) + + val example = Example.parseFrom(pngBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } + + "DecodeImage Serializer" should "work properly" in { + val decodeImage = new DecodeImage[Float](1).setName("decodeImage") + val input = getInputs("png") + runSerializationTest(decodeImage, input) + } + + "DecodeGif Serializer" should "work properly" in { + val decodeGif = new DecodeGifOps[Float]().setName("decodeGif") + val input = getInputs("gif") + runSerializationTest(decodeGif, input) + } + + "DecodeJpeg Serializer" should "work properly" in { + val decodeJpeg = new DecodeJpegOps[Float](1).setName("decodeJpeg") + val input = getInputs("jpeg") + runSerializationTest(decodeJpeg, input) + } + + "DecodePng Serializer" should "work properly" in { + val decodePng = new DecodePngOps[Float](1).setName("decodePng") + val input = getInputs("png") + runSerializationTest(decodePng, input) + } + + + "DecodeRaw Serializer" should "work properly" in { + val decodeRaw = new DecodeRawOps[Float](DataType.DT_UINT8, true).setName("decodeRaw") + val input = getInputs("raw") + runSerializationTest(decodeRaw, input) + } + + "DepthwiseConv2DBackpropInput serializer" should "work properly" in { + val depWiseBackprop = + DepthwiseConv2DBackpropInput[Float](1, 1, 0, 0, DataFormat.NHWC). + setName("depWiseBackprop") + val input = T(Tensor[Int](T(4, 24, 24, 3)), + Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(depWiseBackprop, input) + } + + "DepthwiseConv2D serializer" should "work properly" in { + val depWIseConv2d = DepthwiseConv2D[Float](1, 1, 0, 0).setName("depWIseConv2d") + val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat())) + runSerializationTest(depWIseConv2d, input) + } + + "DepthwiseConv2DBackpropFilter serializer" should "work properly" in { + val depWiseConv2dBackProp = DepthwiseConv2DBackpropFilter[Float](1, + 1, 0, 0, DataFormat.NHWC).setName("depWiseConv2dBackProp") + val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(2, 2, 3, 1)), + Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(depWiseConv2dBackProp, input) + } + + "EluGrad serializer" should "work properly" in { + val eluGrad = EluGrad[Float, Float]().setName("eluGrad") + val inputTensor = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, grad) + runSerializationTest(eluGrad, input) + } + + "Equal serializer" should "work properly" in { + val equal = Equal[Float]().setName("equal") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(equal, input, + equal.asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "ExpOps serializer" should "work properly" in { + val expOps = ExpOps[Float, Float]().setName("expOps") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(expOps, input) + } + + "Expm1 serializer" should "work properly" in { + val expm1 = Expm1[Float, Float]().setName("expm1") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(expm1, input) + } + + "Floor serializer" should "work properly" in { + val floor = Floor[Float]().setName("floor") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(floor, input, floor. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "FloorDiv serializer" should "work properly" in { + val floorDiv = FloorDiv[Float, Float]().setName("floorDiv") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(floorDiv, input) + } + + "FloorMod serializer" should "work properly" in { + val floorMod = FloorMod[Float, Float]().setName("floorMod") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(floorMod, input) + } + + "FusedBatchNorm serializer" should "work properly" in { + val fusedBatchNorm = FusedBatchNorm[Float]().setName("fusedBatchNorm") + val input = T(Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](0), + Tensor[Float](0)) + runSerializationTest(fusedBatchNorm, input) + } + + "FusedBatchNormGrad serializer" should "work properly" in { + val fbatchNormGrad = FusedBatchNormGrad[Float]().setName("fbatchNormGrad") + val input = T(Tensor[Float](4, 8, 8, 256).rand(), + Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat())) + runSerializationTest(fbatchNormGrad, input) + } + + "Greater serializer" should "work properly" in { + val greater = Greater[Float]().setName("greater") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(greater, input, greater. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "GreaterEqual serializer" should "work properly" in { + val greaterEqual = GreaterEqual[Float]().setName("greaterEqual") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(greaterEqual, input, greaterEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "InTopK serializer" should "work properly" in { + val inTopK = InTopK[Float](2).setName("inTopK") + val input1 = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Int](2).fill(1) + val input = T(input1, input2) + runSerializationTest(inTopK, input) + } + + "Inv serializer" should "work properly" in { + val inv = Inv[Float, Float]().setName("inv") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(inv, input) + } + + "InvGrad serializer" should "work properly" in { + val invGrad = InvGrad[Float, Float]().setName("invGrad") + val input = T(Tensor[Float](2, 5).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 5).apply1(_ => Random.nextFloat())) + runSerializationTest(invGrad, input) + } + + "IsFinite serializer" should "work properly" in { + val isFinite = IsFinite[Float, Float]().setName("isFinite") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isFinite, input) + } + + "IsInf serializer" should "work properly" in { + val isInf = IsInf[Float, Float]().setName("isInf") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isInf, input) + } + + "IsNan serializer" should "work properly" in { + val isNan = IsNan[Float, Float]().setName("isInf") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isNan, input) + } + + "Kv2Tensor" should "work properly" in { + val kv2tensor = Kv2Tensor[Float, Float]( + kvDelimiter = ",", itemDelimiter = ":", transType = 0 + ).setName("kv2tensor") + val input = T( + Tensor[String]( + T(T("0:0.1,1:0.2"), T("1:0.3,3:0.5"), T("2:0.15,4:0.25"))), + Tensor[Int](Array(5), shape = Array[Int]()) + ) + runSerializationTest(kv2tensor, input) + } + + "L2Loss serializer" should "work properly" in { + val l2loss = L2Loss[Float]().setName("l2loss") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(l2loss, input, + l2loss.asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Less serializer" should "work properly" in { + val less = Less[Float]().setName("less") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(less, input, less + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LessEqual serializer" should "work properly" in { + val lessEqual = LessEqual[Float]().setName("lessEqual") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(lessEqual, input, lessEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LogicalAnd serializer" should "work properly" in { + val logicalAnd = LogicalAnd[Float].setName("logicalAnd") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(logicalAnd, input, logicalAnd. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LogicalNot serializer" should "work properly" in { + val logicalNot = LogicalNot[Float].setName("logicalNot") + val input = Tensor[Boolean](T(true, false)) + runSerializationTest(logicalNot, input, logicalNot + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LogicalOr serializer" should "work properly" in { + val logicalOr = LogicalOr[Float].setName("logicalOr") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(logicalOr, input, logicalOr + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "LRNGrad serializer" should "work properly" in { + val lrnGrad = LRNGrad[Float]().setName("lrnGrad") + val input = T(Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(lrnGrad, input) + } + + "Maximum serializer" should "work properly" in { + val maxiMum = Maximum[Float, Float]().setName("maxiMum") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(maxiMum, input) + } + + "MaxPool serializer" should "work properly" in { + val maxPool = MaxPool[Float]( + Array(1, 2, 3, 1), + Array(1, 2, 1, 1), + "VALID").setName("maxPool") + val input = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(maxPool, input, maxPool. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + + } + + "MaxPoolGrad serializer" should "work properly" in { + val maxPoolGrad = MaxPoolGrad[Float](2, 1, 1, 1, 0, 0, DataFormat.NCHW). + setName("maxPoolGrad") + val input = T(Tensor[Float](1, 3, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](), + Tensor[Float](1, 1, 1).apply1(_ => Random.nextFloat())) + runSerializationTest(maxPoolGrad, input) + } + + "Mimimum serializer" should "work properly" in { + val minimum = Minimum[Float, Float]().setName("minimum") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(minimum, input) + } + + "Mod serializer" should "work properly" in { + val mod = Mod[Float, Float]().setName("mod") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(mod, input) + } + + "ModuleToOperation serializer" should "work properly" in { + val moduleToOperation = ModuleToOperation[Float](new LogicalOr()). + setName("moduleToOperation") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(moduleToOperation, input) + } + + + "TensorModuleWrapper serializer" should "work properly" in { + val tensorModuleWrapper = TensorModuleWrapper[Float, Float](SoftPlus[Float]()). + setName("moduleToOperation") + val input = Tensor[Float](T(1.0f, 1.0)) + runSerializationTest(tensorModuleWrapper, input) + } + + "NoOp serializer" should "work properly" in { + val noOp = new com.intel.analytics.bigdl.nn.ops.NoOp[Float]().setName("noOp") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(noOp, input) + } + + "NotEqual serializer" should "work properly" in { + val notEqual = NotEqual[Float].setName("notEqual") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(notEqual, input, notEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "OneHot serializer" should "work properly" in { + val oneHot = OneHot[Float, Float](axis = -1).setName("oneHot") + val input = + T(Tensor[Long](T(0, 2, -1, 1)), + Tensor[Int](Array(3), shape = Array[Int]()), + Tensor[Float](Array(0.5f), shape = Array[Int]()), + Tensor[Float](Array(0.0f), shape = Array[Int]())) + runSerializationTest(oneHot, input, oneHot + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Pad serializer" should "work properly" in { + val pad = Pad[Float, Float](mode = "CONSTANT", 0.0f).setName("pad") + val inputTensor = Tensor[Float](2, 2, 3).apply1(_ => Random.nextFloat()) + val padding = Tensor[Int](T(T(1, 2), T(1, 2), T(1, 2))) + val input = T(inputTensor, padding) + runSerializationTest(pad, input, pad. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "ParseExample serializer" should "work properly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + + val floatBuilder = FloatList.newBuilder() + .addValue(0.0f).addValue(1.0f).addValue(2.0f) + val floatFeature = Feature.newBuilder().setFloatList(floatBuilder).build() + + val longBuilder = Int64List.newBuilder() + .addValue(0).addValue(1).addValue(2) + val longFeature = Feature.newBuilder().setInt64List(longBuilder).build() + + val bytesBuilder = BytesList.newBuilder().addValue(ByteString.copyFromUtf8("abcd")) + val bytesFeature = Feature.newBuilder().setBytesList(bytesBuilder).build() + + val features = Features.newBuilder() + .putFeature("floatFeature", floatFeature) + .putFeature("longFeature", longFeature) + .putFeature("bytesFeature", bytesFeature) + val example = Example.newBuilder().setFeatures(features).build() + val length = example.getSerializedSize + val data = new Array[Byte](length) + val outputStream = CodedOutputStream.newInstance(data) + example.writeTo(outputStream) + + val exampleParser = new ParseExample[Float](3, Seq(FloatType, LongType, StringType), + Seq(Array(3), Array(3), Array())).setName("parseExample") + + val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) + val names = Tensor[ByteString]() + val key1 = Tensor[ByteString](Array(ByteString.copyFromUtf8("floatFeature")), Array[Int]()) + val key2 = Tensor[ByteString](Array(ByteString.copyFromUtf8("longFeature")), Array[Int]()) + val key3 = Tensor[ByteString](Array(ByteString.copyFromUtf8("bytesFeature")), Array[Int]()) + + val default1 = Tensor[Float]() + val default2 = Tensor[Long]() + val default3 = Tensor[ByteString]() + val input = T(serialized, names, key1, key2, key3, default1, default2, default3) + runSerializationTest(exampleParser, input) + } + + "PowOps serializer" should "work properly" in { + val pow = PowOps[Float]().setName("powOps") + val v = Tensor[Float](T(2)) + val t = Tensor[Float](T(1, 2, 3)) + val input = (T(t, v)) + runSerializationTest(pow, input) + } + + "Prod serializer" should "work properly" in { + val prod = Prod[Float](-1, false).setName("prod") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(prod, input, prod. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "RandomUniform serializer" should "work properly" in { + val randomUniform = RandomUniform[Float, Float](10, 20). + setName("randomUniform") + val input = Tensor[Int](T(1, 2, 3)) + runSerializationTest(randomUniform, input, randomUniform. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "RangeOps serializer" should "work properly" in { + val rangeOps = RangeOps[Float, Float]().setName("rangeOps") + val input = T(Tensor[Float](T(1)), Tensor[Float](T(10)), Tensor[Float](T(1))) + runSerializationTest(rangeOps, input) + } + + "Rank serializer" should "work properly" in { + val rank = Rank[Float].setName("rank") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(rank, input, rank. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "Relu6Grad serializer" should "work properly" in { + val relu6Grad = Relu6Grad[Float, Float]().setName("relu6Grad") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(relu6Grad, input) + } + + "ReluGrad serializer" should "work properly" in { + val reluGrad = ReluGrad[Float] + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(reluGrad, input) + } + + "ResizeBilinearOps serializer" should "work properly" in { + val resizeBilinearOps = ResizeBilinearOps[Float](false). + setName("resizeBiLinearOps") + val input = T(Tensor[Float](1, 3, 2, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(3, 2))) + runSerializationTest(resizeBilinearOps, input) + } + + "Rint serializer" should "work properly" in { + val rint = Rint[Float]().setName("rint") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(rint, input) + } + + "Round serializer" should "work properly" in { + val round = Round[Float, Float]().setName("round") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(round, input) + } + + "RsqrtGrad serializer" should "work properly" in { + val rsqrtGrad = RsqrtGrad[Float, Float].setName("rsqrtGrad") + val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](3, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(rsqrtGrad, input) + } + + "SegmentSum serializer" should "work properly" in { + val sgSum = SegmentSum[Float].setName("segmentSum") + val input = T(Tensor[Float](10, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 0, 0, 1, 2, 3, 3, 4, 4, 4))) + runSerializationTest(sgSum, input) + } + + "SelectOps serializer" should "work properly" in { + val select = SelectOps[Float]().setName("select") + val cond = Tensor.scalar[Boolean](true) + val t = Tensor[Int](T(1)) + val e = Tensor[Int](T(2)) + val input = T(cond, t, e) + runSerializationTest(select, input) + } + + "SigmoidGrad serializer" should "work properly" in { + val sigMoidGrad = SigmoidGrad[Float, Float]().setName("sigMoidGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sigMoidGrad, input) + } + + "Sign serializer" should "work properly" in { + val sign = Sign[Float, Float]().setName("sign") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(sign, input) + } + + "Slice serializer" should "work properly" in { + val slice = Slice[Float](begin = Array(0, 1, 1), + size = Array(2, -1, 1)).setName("slice") + val input = Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(slice, input, slice. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "SoftplusGrad serializer" should "work properly" in { + val sofplusGrad = SoftplusGrad[Float, Float].setName("sofplusGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sofplusGrad, input) + } + + "SoftSignGrad serializer" should "work properly" in { + val softSign = SoftsignGrad[Float, Float].setName("softSign") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(softSign, input) + } + + "SqrtGrad serializer" should "work properly" in { + val sqrtGrad = SqrtGrad[Float, Float].setName("sqrtGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sqrtGrad, input) + } + + "SquaredDifference serializer" should "work properly" in { + val squareDiff = SquaredDifference[Float]().setName("squareDiff") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(squareDiff, input) + } + + "Substr serializer" should "work properly" in { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val subStr = Substr[Float]().setName("subStr") + val input = T(Tensor.scalar[ByteString](ByteString.copyFromUtf8("HelloBigDL")), + Tensor.scalar[Int](0), Tensor.scalar[Int](5)) + runSerializationTest(subStr, input) + } + + "SumOps serializer" should "work properly" in { + val sumOps = SumOps[Float, Float]().setName("sumOps") + val input = T(Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float]()) + runSerializationTest(sumOps, input) + } + + "TileOps serializer" should "work properly" in { + val tileOps = TileOps[Float]().setName("tileOps") + val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(2, 1, 2))) + runSerializationTest(tileOps, input, tileOps. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "TopK serializer" should "work properly" in { + val topk = TopK[Float, Float](2).setName("topK") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(topk, input) + } + + "TruncateDiv serializer" should "work properly" in { + val truncateDiv = TruncateDiv[Float, Float]().setName("truncateDiv") + val input = T(Tensor[Float](5).fill(1.0f), Tensor[Float](5).fill(2.0f)) + runSerializationTest(truncateDiv, input) + } + + "TruncatedNormal serializer" should "work properly" in { + val truncateNormal = TruncatedNormal[Float, Float](10, 20).setName("truncateNormal") + val input = Tensor[Int](T(1, 2, 3)) + runSerializationTest(truncateNormal, input, truncateNormal. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } + + "BiasAdd serializer" should "work properly" in { + val biasAdd = BiasAdd[Float]().setName("biasAdd") + val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](3).apply1(_ => Random.nextFloat())) + runSerializationTest(biasAdd, input) + } + "Const serializer" should "work properly" in { + val value = Tensor[Float](3).apply1(_ => Random.nextFloat()) + val const = Const[Float, Float](value).setName("const") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(const, input) + } + + "Fill serializer" should "work properly" in { + val fill = Fill[Float]().setName("fill") + val shape = Tensor[Int](T(2, 3)) + val value = Tensor[Float](Array(0.1f), Array[Int]()) + val input = T(shape, value) + runSerializationTest(fill, input) + } + + "Log1p serializer" should "work properly" in { + val log1p = Log1p[Float, Float]().setName("log1p") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(log1p, input) + } + + "Shape serializer" should "work properly" in { + val shape = Shape[Float]().setName("shape") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(shape, input) + } + "MeanLoadTF serializer" should "work properly" in { + val meanLoadTF = new MeanLoadTF[Float]("Float", false).setName("meanLoadTF") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1, 1))) + runSerializationTest(meanLoadTF, input) + } + + "ConcatV2LoadTF serializer" should "work properly" in { + val concatv2 = new ConcatV2LoadTF[Float]().setName("concatv2LoadTF") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1))) + runSerializationTest(concatv2, input) + } + + "ExpandDimsLoadTF serializer" should "work properly" in { + val expandDim = new ExpandDimsLoadTF[Float]().setName("expandDim") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](1)) + runSerializationTest(expandDim, input) + } + + "PadLoadTF serializer" should "work properly" in { + val padLoadTF = new PadLoadTF[Float]().setName("PadLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor[Int](T(T(1, 1), T(1, 1)))) + runSerializationTest(padLoadTF, input) + } + + "ProdLoadTF serializer" should "work properly" in { + val prodLoadTF = new ProdLoadTF[Float]().setName("prodLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](1)) + runSerializationTest(prodLoadTF, input) + } + + "ReshapeLoadTF serializer" should "work properly" in { + val reshapeLoadTF = new ReshapeLoadTF[Float]().setName("reshapeLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1, 5, 25))) + runSerializationTest(reshapeLoadTF, input) + } + + "SliceLoadTF serializer" should "work properly" in { + val sliceLoadTF = new SliceLoadTF[Float]().setName("sliceLoadTF") + val input = T(Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 1, 1)), + Tensor[Int](T(2, -1, 1))) + runSerializationTest(sliceLoadTF, input) + } + + "StridedSliceLoadTF serializer" should "work properly" in { + val strideSliceLoadTF = new StridedSliceLoadTF[Float, Float](). + setName("strideSliceLoadTF") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0)), + Tensor[Int](T(1)), + Tensor[Int](T(1)) + ) + runSerializationTest(strideSliceLoadTF, input) + } + + "SplitLoadTF serializer" should "work properly" in { + val splitLoadTF = new SplitLoadTF[Float](1).setName("splitLoadTD") + val input = T(Tensor[Int](T(1)), + Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(splitLoadTF, input) + } + + "TransposeLoadTF serializer" should "work properly" in { + val transposeLoadTF = new TransposeLoadTF[Float]().setName("transposeLoadTF") + val input = T(Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 1)) + ) + runSerializationTest(transposeLoadTF, input) + } + + "TopKV2LoadTF serializer" should "work properly" in { + val topkv2LoadTF = new TopKV2LoadTF[Float](false, "Float"). + setName("topkv2LoadTF") + val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](2) + ) + runSerializationTest(topkv2LoadTF, input) + } + + "Digamma serializer" should "work properly" in { + val module = Digamma[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } + + "Lgamma serializer" should "work properly" in { + val module = Lgamma[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } + + "Erf serializer" should "work properly" in { + val module = Erf[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } + + "Erfc serializer" should "work properly" in { + val module = Erfc[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } + + "TanhGrad serializer" should "work properly" in { + val module = TanhGrad[Float, Float]() + + val input = T(Tensor[Float](1, 5, 3, 4).rand(), Tensor[Float](1, 5, 3, 4).rand()) + + runSerializationTest(module, input) + } + + "Dilation2D serializer" should "work properly" in { + val module = Dilation2D[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()) + + runSerializationTest(module, input) + } + + "Dilation2DBackpropFilter serializer" should "work properly" in { + val module = Dilation2DBackpropFilter[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()) + + runSerializationTest(module, input) + } + + "Dilation2DBackpropInput serializer" should "work properly" in { + val module = Dilation2DBackpropInput[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()) + + runSerializationTest(module, input) + } + + "Conv3D serializer" should "work properly" in { + val module = Conv3D[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + runSerializationTest(module, T(input, filter)) + } + + "Conv3DBackpropFilter serializer" should "work properly" in { + val module = Conv3DBackpropFilter[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4) + + runSerializationTest(module, T(input, filter, outputBackprop)) + } + + "Conv3DBackpropInput serializer" should "work properly" in { + val module = Conv3DBackpropInput[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(input, filter, outputBackprop)) + } + + "Conv3DBackpropFilterV2 serializer" should "work properly" in { + val module = Conv3DBackpropFilterV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Int](Array(2, 3, 4, 3, 4), Array(5)) + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(input, filter, outputBackprop)) + } + + "Conv3DBackpropInputV2 serializer" should "work properly" in { + val module = Conv3DBackpropInputV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val inputSize = Tensor[Int](Array(4, 20, 30, 40, 3), Array(5)) + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(inputSize, filter, outputBackprop)) + } + + "ResizeBilinearGrad serializer" should "work properly" in { + val module = ResizeBilinearGrad[Float](true) + val input = T(Tensor[Float](1, 224, 224, 3).rand(), + Tensor[Float](1, 64, 64, 3).rand()) + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, input) + } + + "Control Ops serializer" should "work properly" in { + val input = Input[Float]("input") + + val conditionInput = Input[Float]("conditionInput") + val const = new com.intel.analytics.bigdl.nn.tf.Const[Float, Float](Tensor(T(9))).inputs() + val constEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(const) + val less = Less[Float]().inputs(constEnter, conditionInput) + + val updateInput = Input[Float]() + val add = AddConstant[Float](1).inputs(updateInput) + val addEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(add) + val echo = Echo[Float]().inputs(addEnter) + + val exit = ControlNodes.whileLoop[Float]( + (Seq(conditionInput), less), + (Seq((updateInput, echo))), + Seq(input), + "while" + ) + val model = Graph.dynamic[Float](Array(input), Array(exit(0)), None, false) + runSerializationTestWithMultiClass(model, Tensor.scalar[Float](1), Array( + addEnter.element.getClass.asInstanceOf[Class[_]], + new com.intel.analytics.bigdl.nn.ops.NextIteration[Float, Float]().getClass, + new com.intel.analytics.bigdl.nn.ops.Exit[Float]().getClass, + new com.intel.analytics.bigdl.nn.ops.LoopCondition[Float]().getClass + )) + } + + "Stack operations serializer" should "work properly" in { + import com.intel.analytics.bigdl.nn.ops._ + val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() + val stack = new StackCreator[Float, Float]().inputs() + val push = new StackPush[Float, Float]().inputs(stack, data) + val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(push) + val pop = new StackPop[Float, Float]().inputs(stack, ctr) + val model = Graph.dynamic[Float](Array(stack), Array(pop)) + + runSerializationTestWithMultiClass(model, Tensor.scalar(1), Array( + stack.element.getClass.asInstanceOf[Class[_]], + push.element.getClass.asInstanceOf[Class[_]], + pop.element.getClass.asInstanceOf[Class[_]] + )) + } + + "TensorArray serializer R/W" should "work properly" in { + import com.intel.analytics.bigdl.nn.ops._ + val tensorArray = new TensorArrayCreator[Float, Float]().inputs() + val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() + val index = Const[Float, Int](Tensor.scalar[Int](0)).inputs() + val write = new TensorArrayWrite[Float, Float]().inputs((tensorArray, 1), (index, 1), (data, 1)) + val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(write) + val read = new TensorArrayRead[Float, Float]().inputs((tensorArray, 1), (index, 1), (ctr, 1)) + val grad = new TensorArrayGrad[Float]("grad").inputs(tensorArray) + val output = Identity[Float]().inputs((grad, 2)) + val model = Graph.dynamic[Float](Array(tensorArray), Array(read, output)) + + runSerializationTestWithMultiClass(model, Tensor.scalar[Int](1), Array( + tensorArray.element.getClass.asInstanceOf[Class[_]], + write.element.getClass.asInstanceOf[Class[_]], + read.element.getClass.asInstanceOf[Class[_]], + grad.element.getClass.asInstanceOf[Class[_]] + )) + } + + "TensorArray serializer Gather/Scatter" should "work properly" in { + import com.intel.analytics.bigdl.nn.ops._ + val tensorArray = new TensorArrayCreator[Float, Float]().inputs() + val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() + val indices = Const[Float, Int](Tensor[Int](T(0, 1, 2))).inputs() + val scatter = new TensorArrayScatter[Float, Float]().inputs((tensorArray, 1), (indices, 1), + (data, 1)) + val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(scatter) + val gather = new TensorArrayGather[Float, Float]().inputs((tensorArray, 1), (indices, 1), + (ctr, 1)) + val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(gather) + val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1)) + val model = Graph.dynamic[Float](Array(tensorArray), Array(gather, close)) + + runSerializationTestWithMultiClass(model, Tensor.scalar[Int](10), Array( + tensorArray.element.getClass.asInstanceOf[Class[_]], + scatter.element.getClass.asInstanceOf[Class[_]], + gather.element.getClass.asInstanceOf[Class[_]], + close.element.getClass.asInstanceOf[Class[_]] + )) + } + + "TensorArray serializer Split/Concat" should "work properly" in { + import com.intel.analytics.bigdl.nn.ops._ + val tensorArray = new TensorArrayCreator[Float, Float]().inputs() + val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() + val lengths = Const[Float, Int](Tensor[Int](T(1, 2))).inputs() + val splitter = new TensorArraySplit[Float, Float]().inputs((tensorArray, 1), (data, 1), + (lengths, 1)) + val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(splitter) + val concat = new TensorArrayConcat[Float, Float]().inputs(tensorArray, ctr) + val size = new TensorArraySize[Float]().inputs(tensorArray, ctr) + val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(concat, size) + val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1)) + val model = Graph.dynamic[Float](Array(tensorArray), Array(concat, close, size)) + + runSerializationTestWithMultiClass(model, Tensor.scalar[Int](2), Array( + tensorArray.element.getClass.asInstanceOf[Class[_]], + splitter.element.getClass.asInstanceOf[Class[_]], + concat.element.getClass.asInstanceOf[Class[_]], + close.element.getClass.asInstanceOf[Class[_]], + size.element.getClass.asInstanceOf[Class[_]] + )) + } + + "ConcatOffset serializer" should "work properly" in { + val module = new com.intel.analytics.bigdl.nn.ops.ConcatOffset[Float]() + runSerializationTest(module, T(Tensor.scalar[Int](1), Tensor[Int](T(2, 2, 5, 7)), + Tensor[Int](T(2, 3, 5, 7)), Tensor[Int](T(2, 4, 5, 7)))) + } + + "InvertPermutation serializer" should "work properly" in { + val module = new com.intel.analytics.bigdl.nn.ops.InvertPermutation[Float]() + runSerializationTest(module, Tensor[Int](T(0, 1, 2, 3, 4))) + } + +} From 546b35675b170d8b0d3e03607941ea83ffd97bad Mon Sep 17 00:00:00 2001 From: Xianyan Date: Fri, 2 Feb 2018 09:48:48 +0800 Subject: [PATCH 0672/1065] Add FixExpand and add more options to AspectScale (#2253) * Add FixExpand and add more options to AspectScale * fix ut --- .../vision/image/augmentation/Expand.scala | 38 ++++++++++++++ .../vision/image/augmentation/Resize.scala | 49 ++++++++++++++----- .../dllib/utils/python/api/PythonBigDL.scala | 15 ++++-- .../image/augmentation/ExpandSpec.scala | 13 +++++ 4 files changed, 99 insertions(+), 16 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala index 5bce28a00df..8c805a12cc0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala @@ -91,4 +91,42 @@ object Expand { new Expand(meansR, meansG, meansB, minExpandRatio, maxExpandRatio) } +/** + * expand image with given expandHeight and expandWidth, + * put the original image to the center of expanded image + * @param expandHeight height expand to + * @param expandWidth width expand to + */ +class FixExpand(expandHeight: Int, expandWidth: Int) extends FeatureTransformer { + override def transformMat(feature: ImageFeature): Unit = { + val input = feature.opencvMat() + var output: OpenCVMat = null + try { + val width = input.width() + val height = input.height() + require(width <= expandWidth, + s"width ${width} of input mat is not <= expandWidth $expandWidth") + output = new OpenCVMat() + // Get new height and width + val topPad = ((expandHeight - input.height()) / 2).floor + val leftPad = ((expandWidth - input.width()) / 2).floor + val bboxRoi = new Rect(leftPad.toInt, topPad.toInt, width, height) + output.create(expandHeight, expandWidth, input.`type`()) + input.copyTo(output.submat(bboxRoi)) + output.copyTo(input) + feature(ImageFeature.boundingBox) = + BoundingBox(leftPad, topPad, leftPad + width, topPad + height) + } finally { + if (null != output) { + output.release() + } + } + } +} + +object FixExpand { + def apply(expandHeight: Int, expandWidth: Int): FixExpand = + new FixExpand(expandHeight, expandWidth) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala index cb80934ed62..bddcb18b1ab 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Resize.scala @@ -76,45 +76,68 @@ object Resize { /** * Resize the image, keep the aspect ratio. scale according to the short edge - * @param scale scale size, apply to short edge + * @param minSize scale size, apply to short edge * @param scaleMultipleOf make the scaled size multiple of some value * @param maxSize max size after scale + * @param resizeMode if resizeMode = -1, random select a mode from + * (Imgproc.INTER_LINEAR, Imgproc.INTER_CUBIC, Imgproc.INTER_AREA, + * Imgproc.INTER_NEAREST, Imgproc.INTER_LANCZOS4) + * @param useScaleFactor if true, scale factor fx and fy is used, fx = fy = 0 + * @param minScale control the minimum scale up for image */ -class AspectScale(scale: Int, scaleMultipleOf: Int = 1, - maxSize: Int = 1000) extends FeatureTransformer { +class AspectScale(minSize: Int, + scaleMultipleOf: Int = 1, + maxSize: Int = 1000, + resizeMode: Int = Imgproc.INTER_LINEAR, + useScaleFactor: Boolean = true, + minScale: Option[Float] = None) + extends FeatureTransformer { override def transformMat(feature: ImageFeature): Unit = { val (height, width) = AspectScale.getHeightWidthAfterRatioScale(feature.opencvMat(), - scale, maxSize, scaleMultipleOf) - Resize.transform(feature.opencvMat(), feature.opencvMat(), width, height) + minSize, maxSize, scaleMultipleOf, minScale) + Resize.transform(feature.opencvMat(), feature.opencvMat(), + width, height, resizeMode, useScaleFactor) } } object AspectScale { - def apply(scale: Int, scaleMultipleOf: Int = 1, - maxSize: Int = 1000): AspectScale = new AspectScale(scale, scaleMultipleOf, maxSize) + def apply(minSize: Int, + scaleMultipleOf: Int = 1, + maxSize: Int = 1000, + mode: Int = Imgproc.INTER_LINEAR, + useScaleFactor: Boolean = true, + minScale: Option[Float] = None): AspectScale = + new AspectScale(minSize, scaleMultipleOf, maxSize, mode, useScaleFactor, minScale) /** * get the width and height of scaled image * @param img original image */ def getHeightWidthAfterRatioScale(img: OpenCVMat, scaleTo: Float, - maxSize: Int, scaleMultipleOf: Int): (Int, Int) = { + maxSize: Int, scaleMultipleOf: Int, minScale: Option[Float] = None): (Int, Int) = { val imSizeMin = Math.min(img.width(), img.height()) val imSizeMax = Math.max(img.width(), img.height()) var imScale = scaleTo.toFloat / imSizeMin.toFloat + if (minScale.isDefined) { + imScale = Math.max(minScale.get, imScale) + } // Prevent the biggest axis from being more than MAX_SIZE if (Math.round(imScale * imSizeMax) > maxSize) { imScale = maxSize / imSizeMax.toFloat } - val imScaleH = (Math.floor(img.height() * imScale / scaleMultipleOf) * - scaleMultipleOf / img.height()).toFloat - val imScaleW = (Math.floor(img.width() * imScale / scaleMultipleOf) * - scaleMultipleOf / img.width()).toFloat + var imScaleH, imScaleW = imScale + if (scaleMultipleOf > 1) { + imScaleH = (Math.floor(img.height() * imScale / scaleMultipleOf) * + scaleMultipleOf / img.height()).toFloat + imScaleW = (Math.floor(img.width() * imScale / scaleMultipleOf) * + scaleMultipleOf / img.width()).toFloat + } + val width = imScaleW * img.width() val height = imScaleH * img.height() - (height.toInt, width.toInt) + (height.round, width.round) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 043b8925a7c..4484b29e29b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2730,8 +2730,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab stdR.toFloat, stdG.toFloat, stdB.toFloat) } - def createAspectScale(scale: Int, scaleMultipleOf: Int, maxSize: Int): FeatureTransformer = { - AspectScale(scale, scaleMultipleOf, maxSize) + def createAspectScale(scale: Int, + scaleMultipleOf: Int, + maxSize: Int, + resizeMode: Int = 1, + useScaleFactor: Boolean = true, + minScale: Double = -1): FeatureTransformer = { + val minS = if (minScale == -1) None else Some(minScale.toFloat) + AspectScale(scale, scaleMultipleOf, maxSize, resizeMode, useScaleFactor, minS) } def createFiller(startX: Double, startY: Double, endX: Double, endY: Double, @@ -2759,12 +2765,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab RoiNormalize() } + def createFixExpand(eh: Int, ew: Int): FixExpand = { + FixExpand(eh, ew) + } + def transformImageFeature(transformer: FeatureTransformer, feature: ImageFeature) : ImageFeature = { transformer.transform(feature) } - def transformImageFrame(transformer: FeatureTransformer, imageFrame: ImageFrame): ImageFrame = { imageFrame.transform(transformer) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ExpandSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ExpandSpec.scala index 2ea9954b9f1..2440ed47a0d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ExpandSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ExpandSpec.scala @@ -35,4 +35,17 @@ class ExpandSpec extends FlatSpec with Matchers { Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) println(tmpFile) } + + "fixexpand" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = FixExpand(600, 600) + val transformed = transformer(data) + val imf = transformed.asInstanceOf[LocalImageFrame].array(0) + imf.getHeight() should be (600) + imf.getWidth() should be (600) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } } From 5b48dee07fa9748e3e86a6c090bb56893e5d1121 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Fri, 2 Feb 2018 12:09:01 +0800 Subject: [PATCH 0673/1065] Add RowTransformer (#2254) * add RowTransformer * add end-line of RowTransformerSpec * 1. wrap Tensor.scalar for key of RowTransformer; 2. some modifications about specialized annos --- .../dataset/datamining/RowTransformer.scala | 326 ++++++++++++++++++ .../datamining/RowTransformerSpec.scala | 206 +++++++++++ 2 files changed, 532 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/datamining/RowTransformer.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/datamining/RowTransformerSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/datamining/RowTransformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/datamining/RowTransformer.scala new file mode 100644 index 00000000000..51289fb091d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/datamining/RowTransformer.scala @@ -0,0 +1,326 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dataset.datamining + +import com.intel.analytics.bigdl.dataset.Transformer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericShort, NumericString} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.utils.{T, Table} +import org.apache.spark.sql.Row +import org.apache.spark.sql.types._ + +import scala.collection.mutable +import scala.reflect.ClassTag + +/** + * ======RowTransformer transform a `Row` to a `Table` whose values are all `Tensor`.====== + * + * This transformer is a container of `RowTransformSchema`s. + * When this transformer being executed, + * it will run `transform` methods of its `RowTransformSchema`s. + * + * Output of `RowTransformer` is a `Table`. + * The keys of `Table` are Tensor.scalar(`schemaKey`)s of included `RowTransformSchema`s. + * Correspondingly, the values of `Table` are results(`Tensor`) of `RowTransformSchema.transform`. + * + * @param schemas schemas of transformer, whose keys should `NOT` be duplicated + * @param rowSize size of `Row` transformed by this transformer, default is `None` + */ +class RowTransformer( + @transient private val schemas: Seq[RowTransformSchema], + protected val rowSize: Option[Int] = None +) extends Transformer[Row, Table] { + + protected val schemaMap: mutable.Map[String, RowTransformSchema] = { + val map = mutable.LinkedHashMap[String, RowTransformSchema]() + schemas.foreach { schema => + require(!map.contains(schema.schemaKey), + s"Found replicated schemeKey: ${schema.schemaKey}" + ) + if (schema.fieldNames.isEmpty) { + require(schema.indices.forall(i => i >= 0 && i < rowSize.getOrElse(Int.MaxValue)), + s"At least one of indices are out of bound: ${schema.indices.mkString(",")}" + ) + } + map += schema.schemaKey -> schema + } + map + } + + override def apply(prev: Iterator[Row]): Iterator[Table] = { + new Iterator[Table] { + override def hasNext: Boolean = prev.hasNext + + override def next(): Table = { + val row = prev.next() + val table = T() + schemaMap.foreach { case (key, schema) => + val indices = schema match { + case sch if sch.fieldNames.nonEmpty => + schema.fieldNames.map(row.fieldIndex) + case sch if sch.indices.nonEmpty => + schema.indices + case _ => + 0 until row.length + } + + val (values, fields) = indices.map(i => + row.get(i) -> row.schema.fields(i) + ).unzip + + val outputKey = Tensor.scalar[String](key) + val output = schema.transform(values, fields) + + table.update(outputKey, output) + } + table + } + } + } + +} + +object RowTransformer { + + def apply( + schemas: Seq[RowTransformSchema], + rowSize: Int = 0 + ): RowTransformer = { + new RowTransformer(schemas, if (rowSize > 0) Some(rowSize) else None) + } + + /** + * A `RowTransformer` which transform each `selected columns` to a size(1) `Tensor`. + * The keys of output `Table` are `fieldNames` of `selected columns`. + * + * @param fieldNames field names of `selected columns` + */ + def atomic(fieldNames: Seq[String]): RowTransformer = { + val transSchemas = fieldNames.map(f => ColToTensor(f, f)) + new RowTransformer(transSchemas) + } + + /** + * A `RowTransformer` which transform each `selected columns` to a size(1) `Tensor`. + * The keys of output `Table` are `indices` of `selected columns`. + * + * @param indices indices of `selected columns` + * @param rowSize size of `Row` transformed by this transformer + */ + def atomic(indices: Seq[Int], rowSize: Int): RowTransformer = { + val transSchemas = indices.map(f => new ColToTensor(f.toString, f)) + new RowTransformer(transSchemas, Option(rowSize)) + } + + /** + * A `RowTransformer` which concat values of `all columns` to one `Tensor`. + * It means you will get a Table with single key-value pair after transformation. + * The unique key is `schemaKey`. The unique value is a size(length of Row) Tensor. + * + * @param schemaKey key of the schema, default value is "all" + */ + def numeric[T: ClassTag](schemaKey: String = "all" + )(implicit ev: TensorNumeric[T]): RowTransformer = { + new RowTransformer(Seq(ColsToNumeric[T](schemaKey))) + } + + /** + * A `RowTransformer` which concat values of `selected columns` to one `Tensor`. + * It means you will get a `Table` with keys of `numericFields`. + * Values of `Table` are `Tensor`s concatenated by `selected columns` of the keys. + * + * @param numericFields Map<`schemaKey`, `fieldNames of selected columns`> of numeric fields + */ + def numeric[T: ClassTag](numericFields: Map[String, Seq[String]] + )(implicit ev: TensorNumeric[T]): RowTransformer = { + val transSchemas = numericFields.map { case(key, fields) => ColsToNumeric[T](key, fields) } + new RowTransformer(transSchemas.toSeq) + } + + /** + * A `RowTransformer` which contains both `atomic` schemas and `numeric` schemas. + * + * @param atomicFields field names of `selected columns` + * @param numericFields Map<`schemaKey`, `fieldNames of selected columns`> of numeric fields + */ + def atomicWithNumeric[T: ClassTag]( + atomicFields: Seq[String], + numericFields: Map[String, Seq[String]] + )(implicit ev: TensorNumeric[T]): RowTransformer = { + val transSchemas = mutable.ArrayBuffer[RowTransformSchema]() + atomicFields.foreach(f => transSchemas += ColToTensor(f, f)) + numericFields.foreach { case(key, fields) => + transSchemas += ColsToNumeric[T](key, fields) + } + new RowTransformer(transSchemas) + } + +} + +/** + * A `schema` describe a transforming job which convert a `Row` to a `Table`(`Tensor`). + */ +trait RowTransformSchema extends Serializable { + + /** + * Key of the schema, which will be the key of `Tensor` in result `Table`. + * So, it should be `unique` in single `RowTransformer`. + */ + val schemaKey: String + + /** + * ======`Indices` of Selected Columns====== + * It will work on only when `fieldNames` is empty, + * otherwise `RowTransformer` will select columns accord to `fieldNames`. + * If both `indices` and `fieldNames` are empty, + * `RowTransformer` will select all columns by default. + */ + val indices: Seq[Int] = Seq.empty + + /** + * ======`FieldNames` of Selected Columns====== + * This property will override `indices` when it is not empty. + */ + val fieldNames: Seq[String] = Seq.empty + + /** + * Transforming Logic of the Schema + * + * @param values values of selected columns + * @param fields StructFields of selected columns + * @return a result `Tensor` + */ + def transform(values: Seq[Any], fields: Seq[StructField]): Tensor[NumericWildcard] + +} + +/** + * A schema which specialize on transforming multiple `numeric` columns to one `Tensor`. + * Types of `selected columns` will be identified according to their `[DataType`. + * And type conversions will be done automatically from `DataType` to `T` if valid. + * Currently, `DoubleType`, `FloatType`, `ShortType`, `IntegerType`, `LongType` are supported. + * + * @param schemaKey key of the schema + * @param indices indices of `selected columns` + * @param fieldNames field names of `selected columns` + * @tparam T the type of result `Tensor` + */ +class ColsToNumeric[@specialized T: ClassTag]( + override val schemaKey: String, + override val indices: Seq[Int] = Seq.empty, + override val fieldNames: Seq[String] = Seq.empty +)(implicit ev: TensorNumeric[T]) extends RowTransformSchema { + + override def transform(input: Seq[Any], fields: Seq[StructField]): Tensor[NumericWildcard] = { + val tensor = Tensor[T](input.length) + var i = 0 + while (i < input.length) { + val value = fields(i).dataType match { + // TODO: support VectorUDT + case _: DoubleType => ev.fromType(input(i).asInstanceOf[Double]) + case _: FloatType => ev.fromType(input(i).asInstanceOf[Float]) + case _: ShortType => ev.fromType(input(i).asInstanceOf[Short]) + case _: IntegerType => ev.fromType(input(i).asInstanceOf[Int]) + case _: LongType => ev.fromType(input(i).asInstanceOf[Long]) + case tpe => throw new IllegalArgumentException(s"Found unSupported DataType($tpe)!") + } + tensor.setValue(i + 1, value) + i += 1 + } + tensor.asInstanceOf[Tensor[NumericWildcard]] + } + +} + +object ColsToNumeric { + + /** + * Build a `ColsToNumeric` which transforms `all columns` of Row. + * + * @param schemaKey key of the schema + * @tparam T the type of result `Tensor` + */ + def apply[@specialized(Float, Double) T: ClassTag](schemaKey: String + )(implicit ev: TensorNumeric[T]): ColsToNumeric[T] = { + new ColsToNumeric[T](schemaKey) + } + + /** + * Build a `ColsToNumeric` which transforms `selected columns` of Row. + * + * @param schemaKey key of the schema + * @param fieldNames field names of `selected columns` + * @tparam T the type of result `Tensor` + */ + def apply[@specialized(Float, Double) T: ClassTag]( + schemaKey: String, + fieldNames: Seq[String] + )(implicit ev: TensorNumeric[T]): ColsToNumeric[T] = { + new ColsToNumeric[T](schemaKey, Seq.empty, fieldNames) + } + +} + +/** + * A schema which specialize on transforming `single column` to size(1) `Tensor`. + * Types of `selected columns` will be identified according to their `DataType`. + * And type conversions will be done automatically from `DataType` to `TensorDataType`. + * + * @param schemaKey key of the schema + * @param index index of selected column, overridden by non empty `fieldName` + * @param fieldName field name of selected column, default is empty + */ +class ColToTensor( + override val schemaKey: String, + index: Int, + fieldName: String = "" +) extends RowTransformSchema { + + override val indices: Seq[Int] = Seq(index) + + override val fieldNames: Seq[String] = if (fieldName.isEmpty) Seq.empty else Seq(fieldName) + + override def transform(input: Seq[Any], fields: Seq[StructField]): Tensor[NumericWildcard] = { + val (value, tpe) = input.head -> fields.head.dataType + val tensor = tpe match { + // TODO: support VectorUDT + case _: BooleanType => Tensor[Boolean](1).setValue(1, value.asInstanceOf[Boolean]) + case _: DoubleType => Tensor[Double](1).setValue(1, value.asInstanceOf[Double]) + case _: FloatType => Tensor[Float](1).setValue(1, value.asInstanceOf[Float]) + case _: StringType => Tensor[String](1).setValue(1, value.asInstanceOf[String]) + case _: ShortType => Tensor[Short](1).setValue(1, value.asInstanceOf[Short]) + case _: IntegerType => Tensor[Int](1).setValue(1, value.asInstanceOf[Int]) + case _: LongType => Tensor[Long](1).setValue(1, value.asInstanceOf[Long]) + case t => throw new IllegalArgumentException(s"Found unSupported DataType($t)!") + } + tensor.asInstanceOf[Tensor[NumericWildcard]] + } + +} + +object ColToTensor { + /** + * Build a `ColsToTensor` according to `fieldName` + * + * @param schemaKey key of the schema + * @param fieldName field name of selected column + */ + def apply(schemaKey: String, fieldName: String): ColToTensor = { + new ColToTensor(schemaKey, -1, fieldName) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/datamining/RowTransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/datamining/RowTransformerSpec.scala new file mode 100644 index 00000000000..f5a0998ac14 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/datamining/RowTransformerSpec.scala @@ -0,0 +1,206 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dataset.datamining + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.NumericWildcard +import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema +import org.apache.spark.sql.types._ +import org.scalatest.{FlatSpec, Matchers} + +class RowTransformerSpec extends FlatSpec with Matchers { + + private val testRow = { + new GenericRowWithSchema(Array(1, 2L, 3.3, 4.4f, "aa", false, 77.toShort), + StructType(Seq(StructField("int", IntegerType), + StructField("long", LongType), + StructField("double", DoubleType), + StructField("float", FloatType), + StructField("str", StringType), + StructField("bool", BooleanType), + StructField("short", ShortType))) + ) + } + + private val numericRow = { + new GenericRowWithSchema(Array(1, 1.1, 1.2f), + StructType(Seq(StructField("int", IntegerType), + StructField("double", DoubleType), + StructField("float", FloatType))) + ) + } + + private val sInt = Tensor.scalar[String]("int") + private val sLong = Tensor.scalar[String]("long") + private val sFloat = Tensor.scalar[String]("float") + private val sDouble = Tensor.scalar[String]("double") + private val sBool = Tensor.scalar[String]("bool") + private val sStr = Tensor.scalar[String]("str") + private val sShort = Tensor.scalar[String]("short") + private val sWrap = (s: String) => Tensor.scalar[String](s) + + "ColToTensor" should "deal with different DataTypes correctly" in { + ColToTensor("str", "str") + .transform(Seq("test123"), Seq(StructField("str", StringType)) + ).toArray() shouldEqual Array("test123") + ColToTensor("int", "int") + .transform(Seq(1), Seq(StructField("int", IntegerType)) + ).toArray() shouldEqual Array(1) + ColToTensor("long", "long") + .transform(Seq(1L), Seq(StructField("long", LongType)) + ).toArray() shouldEqual Array(1L) + ColToTensor("double", "double") + .transform(Seq(.1), Seq(StructField("double", DoubleType)) + ).toArray() shouldEqual Array(.1) + ColToTensor("float", "float") + .transform(Seq(.12f), Seq(StructField("float", FloatType)) + ).toArray() shouldEqual Array(.12f) + ColToTensor("bool", "bool") + .transform(Seq(false), Seq(StructField("bool", BooleanType)) + ).toArray() shouldEqual Array(false) + ColToTensor("short", "short") + .transform(Seq(1.toShort), Seq(StructField("short", ShortType)) + ).toArray() shouldEqual Array(1.toShort) + } + + private def mkStructFields(num: Int, dataType: DataType): Seq[StructField] = { + (1 to num).map(i => StructField(i.toString, dataType)) + } + + "ColsToNumeric" should "deal with different DataTypes correctly" in { + var tFloat = ColsToNumeric[Float]("int", Seq("1", "2", "3")) + .transform(Seq(1, 2, 3), mkStructFields(3, IntegerType) + ).asInstanceOf[Tensor[Float]] + tFloat.storage().array() shouldEqual Array(1f, 2f, 3f) + tFloat = ColsToNumeric[Float]("long", Seq("1", "2", "3")) + .transform(Seq(1L, 2L, 3L), mkStructFields(3, LongType) + ).asInstanceOf[Tensor[Float]] + tFloat.storage().array() shouldEqual Array(1f, 2f, 3f) + tFloat = ColsToNumeric[Float]("double", Seq("1", "2", "3")) + .transform(Seq(1.1, 2.2, 3.3), mkStructFields(3, DoubleType) + ).asInstanceOf[Tensor[Float]] + tFloat.storage().array() shouldEqual Array(1.1f, 2.2f, 3.3f) + var tDouble = ColsToNumeric[Double]("float", Seq("1", "2", "3")) + .transform(Seq(1f, 2f, 3f), mkStructFields(3, FloatType) + ).asInstanceOf[Tensor[Double]] + tDouble.storage().array() shouldEqual Array(1, 2, 3) + tDouble = ColsToNumeric[Double]("short", Seq("1", "2", "3")) + .transform(Seq(1.toShort, 2.toShort, 3.toShort), mkStructFields(3, ShortType) + ).asInstanceOf[Tensor[Double]] + tDouble.storage().array() shouldEqual Array(1, 2, 3) + intercept[Exception] { + ColsToNumeric[Double]("bool", Seq("1", "2", "3")) + .transform(Seq(false, true, false), mkStructFields(3, BooleanType)) + } + intercept[Exception] { + ColsToNumeric[Double]("str", Seq("1", "2", "3")) + .transform(Seq("1", "2", "3"), mkStructFields(3, StringType)) + } + } + + "RowTransformer" should "deal with atomic schema correctly" in { + var transformer = RowTransformer.atomic( + Seq("int", "long", "float", "double", "str", "bool", "short")) + var table = transformer(Iterator.single(testRow)).next() + table.get[Tensor[Int]](sInt).get.size() shouldEqual Array(1) + table.get[Tensor[Int]](sInt).get.valueAt(1) shouldEqual 1 + table.get[Tensor[Long]](sLong).get.valueAt(1) shouldEqual 2L + table.get[Tensor[Float]](sFloat).get.valueAt(1) shouldEqual 4.4f + table.get[Tensor[Double]](sDouble).get.valueAt(1) shouldEqual 3.3 + table.get[Tensor[String]](sStr).get.valueAt(1) shouldEqual "aa" + table.get[Tensor[Boolean]](sBool).get.valueAt(1) shouldEqual false + table.get[Tensor[Short]](sShort).get.valueAt(1) shouldEqual 77.toShort + transformer = RowTransformer.atomic(Seq(1, 3, 5), 7) + table = transformer(Iterator.single(testRow)).next() + table.get[Tensor[Long]](sWrap("1")).get.valueAt(1) shouldEqual 2L + table.get[Tensor[Float]](sWrap("3")).get.valueAt(1) shouldEqual 4.4f + table.get[Tensor[Boolean]](sWrap("5")).get.valueAt(1) shouldEqual false + intercept[Exception] { + RowTransformer.atomic(Seq(5, 7), 7) + } + transformer = RowTransformer.atomic(Seq("something")) + val iter = transformer(Iterator.single(testRow)) + intercept[Exception] { + iter.next() + } + } + + "RowTransformer" should "deal with numeric schema correctly" in { + val numericFields = Map( + "allNum" -> Seq("int", "short", "float", "double", "long"), + "dupNum" -> Seq("float", "float", "float", "int", "int", "int") + ) + var transformer = RowTransformer.numeric[Float](numericFields) + var table = transformer(Iterator.single(testRow)).next() + var tensor = table.get[Tensor[Float]](sWrap("allNum")).get + tensor.size shouldEqual Array(5) + tensor.storage().array() shouldEqual Array(1, 77, 4.4, 3.3, 2).map(_.toFloat) + tensor = table.get[Tensor[Float]](sWrap("dupNum")).get + tensor.size shouldEqual Array(6) + tensor.storage().array() shouldEqual Array(4.4, 4.4, 4.4, 1, 1, 1).map(_.toFloat) + + transformer = RowTransformer.numeric[Float]() + table = transformer(Iterator.single(numericRow)).next() + tensor = table.get[Tensor[Float]](sWrap("all")).get + tensor.size() shouldEqual Array(3) + tensor.toArray() shouldEqual Array(1f, 1.1f, 1.2f) + } + + "RowTransformer" should "deal with mixed schema correctly" in { + val transformer = RowTransformer.atomicWithNumeric[Float]( + Seq("str", "bool"), + Map("num" -> Seq("int", "long", "double", "float", "short"))) + val table = transformer(Iterator.single(testRow)).next() + table.get[Tensor[String]](sStr).get.toArray() shouldEqual Array("aa") + table.get[Tensor[Boolean]](sBool).get.toArray() shouldEqual Array(false) + table.get[Tensor[Float]](sWrap("num")).get.toArray() shouldEqual Array( + 1, 2, 3.3, 4.4, 77).map(_.toFloat) + } + + "RowTransformer" should "work correctly with user-defined RowTransfromSchema" in { + val transformer = RowTransformer(Seq(new BruteForceHash(), + ColToTensor("str", "str"), ColToTensor("long", "long")) + ) + val table = transformer(Iterator.single(testRow)).next() + val tensor = table.get[Tensor[Int]](sWrap("hash")).get + tensor.size() shouldEqual Array(7) + tensor.valueAt(2) shouldEqual table.get[Tensor[Long]](sLong + ).get.toArray().head.toString.hashCode() + tensor.valueAt(5) shouldEqual table.get[Tensor[String]](sStr + ).get.toArray().head.toString.hashCode() + } + + "Cloned RowTransformer" should "work correctly" in { + val transformer = RowTransformer(Seq(ColToTensor("str", "str"), + ColsToNumeric[Double]("num", Seq("int", "long"))) + ) + val table = transformer(Iterator.single(testRow)).next() + val cloned = transformer.cloneTransformer() + val tableCloned = cloned(Iterator.single(testRow)).next() + table shouldEqual tableCloned + } + + class BruteForceHash extends RowTransformSchema { + override val schemaKey: String = "hash" + + override def transform(values: Seq[Any], fields: Seq[StructField]): Tensor[NumericWildcard] = { + Tensor[Int](values.map(_.toString.hashCode).toArray, Array(values.length) + ).asInstanceOf[Tensor[NumericWildcard]] + } + } + +} From 178eaa5b1365c89e3106f2129511b9f077632f51 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 2 Feb 2018 14:47:27 +0800 Subject: [PATCH 0674/1065] Combine pre-processor and trainable model into one model (#2241) * Add a new Graph API allow user to combine preprocess graph and model graph * fix style * fix unit test * meet code review --- .../bigdl/dllib/nn/DynamicContainer.scala | 4 ++ .../bigdl/dllib/nn/DynamicGraph.scala | 2 +- .../analytics/bigdl/dllib/nn/Graph.scala | 62 +++++++++++++---- .../bigdl/dllib/nn/abstractnn/Activity.scala | 28 ++++++++ .../bigdl/dllib/nn/ops/Operation.scala | 13 ++-- .../bigdl/dllib/nn/DynamicGraphSpec.scala | 5 +- .../analytics/bigdl/dllib/nn/GraphSpec.scala | 66 ++++++++++++++++++- .../bigdl/dllib/nn/SequentialSpec.scala | 29 ++++++++ 8 files changed, 182 insertions(+), 27 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala index b1f01ec7b34..dad6deb66e1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Util @@ -37,6 +38,9 @@ abstract class DynamicContainer[A <: Activity : ClassTag, B <: Activity : ClassT * @return this container */ def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = { + require(!module.isInstanceOf[Operation[_, _, _]], + "Add operations to dynamic container is not allowed, as operations don't have backward. " + + "Operation can only be used in Graph") Util.excludeNotTorch[T](Seq(module)) modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]] this diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala index eff610eff7b..d06392b92ea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.utils.T import scala.collection.mutable import scala.reflect.ClassTag -class DynamicGraph[T: ClassTag]( +private[bigdl] class DynamicGraph[T: ClassTag]( private val _inputs : Seq[ModuleNode[T]], private val _outputs : Seq[ModuleNode[T]], private val _variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index a43a576015b..181e7adfd4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -30,13 +30,14 @@ import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.tf.Tensorflow import com.intel.analytics.bigdl.visualization.tensorboard.{FileWriter => TFFileWriter} -import org.tensorflow.framework.GraphDef -import scala.collection.JavaConverters._ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag import scala.reflect.runtime.universe +import scala.language.existentials +import scala.collection.JavaConverters._ +import org.tensorflow.framework.GraphDef /** * A graph container. The modules in the container are connected as a directed Graph. Each module @@ -192,6 +193,9 @@ abstract class Graph[T: ClassTag]( protected var dummyOutputGrad: ModuleNode[T] = _ protected var backwardGraph: DirectedGraph[AbstractModule[Activity, Activity, T]] = _ protected var backwardNodes: Array[Node[AbstractModule[Activity, Activity, T]]] = _ + // If the graph will generate gradInput for the input + + private var isGradInputAvailable: Array[Boolean] = _ /** * Generate backward graph and apply the stopGrad @@ -212,6 +216,17 @@ abstract class Graph[T: ClassTag]( || inputNames.contains(n.element.getName())) backwardTargets.foreach(_ -> dummyBackwardEnd) backwardGraph = dummyBackwardEnd.graph(true) + + // Check if gradInput is empty for each input + isGradInputAvailable = inputs.map(_ => false).toArray + backwardGraph.DFS.foreach(curNode => { + inputs.zipWithIndex.map { case (n, i) => + if (curNode.element.getName() == n.element.getName() && !isStopGradient(n.element)) { + isGradInputAvailable(i) = true + } + } + }) + clearState() this } @@ -350,9 +365,20 @@ abstract class Graph[T: ClassTag]( protected def fetchModelGradInput(): Activity = { if (inputs.length == 1) { - inputs.head.element.gradInput + if (isGradInputAvailable.head) { + inputs.head.element.gradInput + } else { + Activity.emptyGradInput(this.getName()) + } } else { - T.seq(inputs.map(n => n.element.gradInput)) + var i = 0 + T.seq(inputs.zipWithIndex.map{ case(n, i) => + if (isGradInputAvailable(i)) { + n.element.gradInput + } else { + Activity.emptyGradInput(this.getName()) + } + }) } } @@ -474,9 +500,19 @@ object Graph extends GraphSerializable { new StaticGraph[T](input, output, variables) } - def dynamic[T: ClassTag]( - input: Array[ModuleNode[T]], - output: Array[ModuleNode[T]], + def apply[T: ClassTag](preprocessor: Module[T], trainable: Module[T]) + (implicit ev: TensorNumeric[T]): Graph[T] = { + val preprocessorNode = preprocessor.inputs() + val stopGradients = Identity[T]().inputs(preprocessorNode) + val trainableNode = trainable.inputs(stopGradients) + val graph = apply[T](preprocessorNode, trainableNode) + graph.stopGradient(Array(stopGradients.element.getName())) + graph + } + + private[bigdl] def dynamic[T: ClassTag]( + input : Array[ModuleNode[T]], + output : Array[ModuleNode[T]], variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, generateBackward: Boolean = true)(implicit ev: TensorNumeric[T]): Graph[T] = { new DynamicGraph[T](input, output, variables, generateBackward) @@ -493,8 +529,8 @@ object Graph extends GraphSerializable { new StaticGraph[T](Seq(input), output) } - def dynamic[T: ClassTag](input: ModuleNode[T], output: Array[ModuleNode[T]]) - (implicit ev: TensorNumeric[T]): Graph[T] = { + private[bigdl] def dynamic[T: ClassTag](input : ModuleNode[T], output : Array[ModuleNode[T]]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { new DynamicGraph[T](Array(input), output, None, true) } @@ -509,8 +545,8 @@ object Graph extends GraphSerializable { new StaticGraph[T](input, Seq(output)) } - def dynamic[T: ClassTag](input: Array[ModuleNode[T]], output: ModuleNode[T]) - (implicit ev: TensorNumeric[T]): Graph[T] = { + private[bigdl] def dynamic[T: ClassTag](input : Array[ModuleNode[T]], output : ModuleNode[T]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { new DynamicGraph[T](input, Array(output), None, true) } @@ -525,8 +561,8 @@ object Graph extends GraphSerializable { new StaticGraph[T](Seq(input), Seq(output)) } - def dynamic[T: ClassTag](input: ModuleNode[T], output: ModuleNode[T]) - (implicit ev: TensorNumeric[T]): Graph[T] = { + private[bigdl] def dynamic[T: ClassTag](input : ModuleNode[T], output : ModuleNode[T]) + (implicit ev: TensorNumeric[T]) : Graph[T] = { new DynamicGraph[T](Array(input), Array(output), None, true) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala index cabfbbdfe36..acd86e95aa5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/Activity.scala @@ -40,6 +40,32 @@ trait Activity { def isTable: Boolean } +/** + * Sometimes a module may not have gradInput in the backward(e.g. some operation layer or + * stopGradient in a Graph). This is allowed when the gradInput is not used anywhere. + * + * In such case, the gradInput of the module should be marked as EmptyGradInput. This class make + * sure an error will happen when user try to use such gradInput. + */ +class EmptyGradInput private[abstractnn](moduleName: String) extends Activity with Serializable { + + override def toTensor[D](implicit ev: TensorNumeric[D]): Tensor[D] = + throw new UnsupportedOperationException(s"The gradInput of $moduleName is empty. You should" + + s"not use it anywhere") + + override def toTable: Table = + throw new UnsupportedOperationException(s"The gradInput of $moduleName is empty. You should" + + s"not use it anywhere") + + override def isTensor: Boolean = + throw new UnsupportedOperationException(s"The gradInput of $moduleName is empty. You should" + + s"not use it anywhere") + + override def isTable: Boolean = + throw new UnsupportedOperationException(s"The gradInput of $moduleName is empty. You should" + + s"not use it anywhere") +} + object Activity { /** * Allocate a data instance by given type D and numeric type T @@ -86,4 +112,6 @@ object Activity { } buffer.asInstanceOf[D] } + + def emptyGradInput(name: String): EmptyGradInput = new EmptyGradInput(name) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala index 4bca87031ca..b759509f8e5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala @@ -16,15 +16,15 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag /** - * [[Operation]] is an abstract class which represents the most basic operations + * [[Operation]] is an abstract class which represents a forward only layer. * An operations has only forward functions and without backward functions. - * An operations can be used to build a computational graph + * An operations should be only used in graph and make sure the backward graph won't contain + * operations. * * @tparam A Input data type * @tparam T Numeric type. Only support float/double now @@ -32,11 +32,9 @@ import scala.reflect.ClassTag abstract class Operation[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag] (implicit ev: TensorNumeric[T]) extends AbstractModule[A, B, T]{ - override def updateGradInput(input: A, gradOutput: B): A = { - throw new UnsupportedOperationException("Operation does not support updateGradInput() method") - } + gradInput = Activity.emptyGradInput(this.getName()).asInstanceOf[A] - override def accGradParameters(input: A, gradOutput: B): Unit = { + override def updateGradInput(input: A, gradOutput: B): A = { throw new UnsupportedOperationException("Operation does not support updateGradInput() method") } @@ -44,4 +42,3 @@ abstract class Operation[A <: Activity: ClassTag, B <: Activity: ClassTag, T: Cl throw new UnsupportedOperationException("Operation does not support backward() method") } } - diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala index 0feed4302d2..87e06da4638 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.vgg.{VggForCifar10, Vgg_16, Vgg_19} import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.EmptyGradInput import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Enter, Less} import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.numeric.NumericFloat @@ -779,7 +780,7 @@ class DynamicGraphSpec extends FlatSpec with Matchers { val gradientBPNoBack = funcModelNoBack.backward(inputData, gradient) println(s"funcModel model backward time is ${ (System.nanoTime() - start) / 1e6 }ms") - gradientBPNoBack.toTensor.nElement() should be(0) + gradientBPNoBack.isInstanceOf[EmptyGradInput] should be(true) val namedModule1 = funcModelOriginal.getParametersTable() val namedModule2 = funcModelNoBack.getParametersTable() namedModule1("conv1").asInstanceOf[Table] should @@ -830,7 +831,7 @@ class DynamicGraphSpec extends FlatSpec with Matchers { val gradientBPOriginal = funcModelOriginal.backward(inputData, gradient) val gradientBPNoBack = funcModelNoBack.backward(inputData, gradient) - gradientBPNoBack.toTensor.nElement() should be(0) + gradientBPNoBack.isInstanceOf[EmptyGradInput] should be(true) val namedModule1 = Utils.getNamedModules(funcModelOriginal) val namedModule2 = Utils.getNamedModules(funcModelNoBack) namedModule2("r1").gradInput.toTensor.nElement() should be(0) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index 46ae54d6452..233005a1061 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -22,7 +22,8 @@ import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.vgg.{VggForCifar10, Vgg_16, Vgg_19} import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Less} +import com.intel.analytics.bigdl.nn.abstractnn.EmptyGradInput +import com.intel.analytics.bigdl.nn.ops.{Ceil, ControlNodes, Less} import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator._ @@ -780,7 +781,7 @@ class StaticGraphSpec extends FlatSpec with Matchers { val gradientBPNoBack = funcModelNoBack.backward(inputData, gradient) println(s"funcModel model backward time is ${ (System.nanoTime() - start) / 1e6 }ms") - gradientBPNoBack.toTensor.nElement() should be(0) + gradientBPNoBack.isInstanceOf[EmptyGradInput] should be(true) val namedModule1 = funcModelOriginal.getParametersTable() val namedModule2 = funcModelNoBack.getParametersTable() namedModule1("conv1").asInstanceOf[Table] should @@ -830,7 +831,7 @@ class StaticGraphSpec extends FlatSpec with Matchers { val gradientBPOriginal = funcModelOriginal.backward(inputData, gradient) val gradientBPNoBack = funcModelNoBack.backward(inputData, gradient) - gradientBPNoBack.toTensor.nElement() should be(0) + gradientBPNoBack.isInstanceOf[EmptyGradInput] should be(true) val namedModule1 = Utils.getNamedModules(funcModelOriginal) val namedModule2 = Utils.getNamedModules(funcModelNoBack) namedModule2("r1").gradInput.toTensor.nElement() should be(0) @@ -1043,6 +1044,37 @@ class StaticGraphSpec extends FlatSpec with Matchers { Tensor[Float](T(10.0f, 14.0f))) } + "graph backpropagation" should "throw exception if some empty gradInput is used" in { + val backwardBranch = Identity[Float]().setName("backward_branch").inputs() + val stopBranchShort = Identity[Float]().setName("stop_branch_short").inputs() + val stopBranchLong_1 = Identity[Float]().inputs() + val stopBranchLong_2 = Identity[Float]().setName("stop_branch_long").inputs(stopBranchLong_1) + val addNode = CAddTable[Float]().inputs(backwardBranch, stopBranchShort, stopBranchLong_2) + val innerGraph = Graph[Float](Array(backwardBranch, stopBranchShort, stopBranchLong_1), + Array(addNode)) + innerGraph.stopGradient(Array("stop_branch_short", "stop_branch_long")) + + val relu1 = ReLU[Float]().setName("relu1").inputs() + val relu2 = ReLU[Float]().setName("relu2").inputs() + val relu3 = ReLU[Float]().setName("relu3").inputs() + val graphNode = innerGraph.inputs(relu1, relu2, relu3) + val outerGraph = Graph[Float](Array(relu1, relu2, relu3), Array(graphNode)) + outerGraph.stopGradient(Array("relu2", "relu3")) + outerGraph.forward(T(Tensor[Float](T(1, 2)), Tensor[Float](T(3, 4)), Tensor[Float](T(3, 4)))) + outerGraph.backward(T(Tensor[Float](T(1, 2)), Tensor[Float](T(3, 4)), Tensor[Float](T(3, 4))), + Tensor[Float](T(7, 4))) + innerGraph.gradInput.asInstanceOf[Table].apply(2).isInstanceOf[EmptyGradInput] should be(true) + innerGraph.gradInput.asInstanceOf[Table].apply(3).isInstanceOf[EmptyGradInput] should be(true) + + // A class cast exception will be thrown + intercept[ClassCastException] { + val outerGraph2 = Graph[Float](Array(relu1, relu2, relu3), Array(graphNode)) + outerGraph2.forward(T(Tensor[Float](T(1, 2)), Tensor[Float](T(3, 4)), Tensor[Float](T(3, 4)))) + outerGraph2.backward(T(Tensor[Float](T(1, 2)), Tensor[Float](T(3, 4)), + Tensor[Float](T(3, 4))), Tensor[Float](T(7, 4))) + } + } + "markdown test" should "work" in { val reshape = Reshape(Array(4)).inputs() val fc1 = Linear(4, 2).setName("fc1").inputs() @@ -1269,6 +1301,34 @@ class StaticGraphSpec extends FlatSpec with Matchers { model.node("ll1") } } + + "Graph with preprocess and trainable" should "be correct" in { + // Ceil1 -> Linear1 -> Ceil3 - Linear2(Trainable)-| + // Ceil2 ---------------------Identity(Trainable)-|> Add(Trainable) -> Linear3(Trainable) + val ceil1 = Ceil[Float, Float].inputs() + val linear1 = Linear[Float](10, 5).inputs(ceil1) + val ceil3 = Ceil[Float, Float].inputs(linear1) + val ceil2 = Ceil[Float, Float].inputs() + val preprocessor = Graph[Float](Array(ceil1, ceil2), Array(ceil3, ceil2)) + + val linear2 = Linear[Float](5, 5).inputs() + val identity = Identity[Float].inputs() + val add = CAddTable[Float]().inputs(linear2, identity) + val linear3 = Linear[Float](5, 2).inputs(add) + val trainable = Graph[Float](Array(linear2, identity), Array(linear3)) + + val model = Graph[Float](preprocessor, trainable) + val input = T(Tensor[Float](10).rand(), Tensor[Float](5).rand()) + val gradOutput = Tensor[Float](2).rand() + model.forward(input) + + linear2.element.parameters()._2(0).sum() should be(0) + linear3.element.parameters()._2(0).sum() should be(0) + model.backward(input, gradOutput) + linear2.element.parameters()._2(0).sum() shouldNot be(0) + linear3.element.parameters()._2(0).sum() shouldNot be(0) + linear1.element.parameters()._2(0).sum() should be(0) + } } object ModelUntils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala new file mode 100644 index 00000000000..dd2a7393191 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.ops.Ceil +import org.scalatest.{FlatSpec, Matchers} + +class SequentialSpec extends FlatSpec with Matchers { + "A Sequential Container " should "not contain operation" in { + val model = Sequential[Double]() + model.add(Linear(10, 100)) // this should work + intercept[IllegalArgumentException] { + model.add(Ceil[Double, Double]()) // this is not allowed + } + } +} From 8a380f50064d2bcceade36bf2953670dde7cfbba Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Fri, 2 Feb 2018 21:28:47 +0800 Subject: [PATCH 0675/1065] Keras-like API Some layers and LeNet example (#2214) * update dense * style * doc for conv2d * add error msg * add ser test * fix softmax * readme for lenet * remove * fix style * softmax 2d ut * add ser test for softmax * fix grammar * review and update * scala doc * fix --- .../bigdl/dllib/example/keras/LeNet.scala | 37 +++++ .../bigdl/dllib/example/keras/README.md | 68 ++++++++ .../bigdl/dllib/example/keras/Train.scala | 85 ++++++++++ .../bigdl/dllib/keras/Activation.scala | 58 +++++++ .../bigdl/dllib/keras/Convolution2D.scala | 116 ++++++++++++++ .../analytics/bigdl/dllib/keras/Dense.scala | 93 ++++++----- .../analytics/bigdl/dllib/keras/Dropout.scala | 52 +++++++ .../analytics/bigdl/dllib/keras/Flatten.scala | 54 +++++++ .../analytics/bigdl/dllib/keras/Input.scala | 1 - .../bigdl/dllib/keras/KerasLayer.scala | 6 +- .../bigdl/dllib/keras/KerasUtils.scala | 67 ++++++++ .../bigdl/dllib/keras/MaxPooling2D.scala | 81 ++++++++++ .../analytics/bigdl/dllib/keras/Reshape.scala | 104 +++++++++++++ .../analytics/bigdl/dllib/keras/SoftMax.scala | 63 ++++++++ .../analytics/bigdl/dllib/keras/package.scala | 22 +++ .../analytics/bigdl/dllib/nn/Dropout.scala | 4 +- .../bigdl/dllib/nn/HardSigmoid.scala | 4 +- .../analytics/bigdl/dllib/nn/Sigmoid.scala | 4 +- .../analytics/bigdl/dllib/nn/SoftPlus.scala | 4 +- .../analytics/bigdl/dllib/nn/SoftSign.scala | 4 +- .../bigdl/dllib/nn/SpatialConvolution.scala | 31 +++- .../bigdl/dllib/nn/SpatialMaxPooling.scala | 35 ++++- .../intel/analytics/bigdl/dllib/nn/Tanh.scala | 4 +- .../dllib/nn/abstractnn/InferShape.scala | 4 +- .../utils/python/api/PythonBigDLKeras.scala | 2 +- .../bigdl/dllib/keras/KerasBaseSpec.scala | 2 +- .../bigdl/dllib/keras/KerasRunner.scala | 5 +- .../bigdl/dllib/keras/LeNetSpec.scala | 37 +++++ .../bigdl/dllib/keras/nn/ActivationSpec.scala | 147 ++++++++++++++++++ .../dllib/keras/nn/Convolution2DSpec.scala | 82 ++++++++++ .../bigdl/dllib/keras/nn/DenseSpec.scala | 22 +-- .../bigdl/dllib/keras/nn/DropoutSpec.scala | 36 +++++ .../bigdl/dllib/keras/nn/FlattenSpec.scala | 43 +++++ .../bigdl/dllib/keras/nn/KerasStyleSpec.scala | 46 +++--- .../dllib/keras/nn/MaxPooling2DSpec.scala | 76 +++++++++ .../bigdl/dllib/keras/nn/ReshapeSpec.scala | 60 +++++++ .../analytics/bigdl/dllib/nn/LinearSpec.scala | 2 +- .../dllib/nn/SpatialConvolutionSpec.scala | 12 +- .../dllib/nn/SpatialMaxPoolingSpec.scala | 12 +- .../bigdl/dllib/utils/TestUtils.scala | 2 +- .../KerasModuleSerializerSpec.scala | 55 ++++++- 41 files changed, 1535 insertions(+), 107 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ActivationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DropoutSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/FlattenSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ReshapeSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala new file mode 100644 index 00000000000..7c89b0a23f6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.keras + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.keras._ +import com.intel.analytics.bigdl.utils.Shape + +object LeNet { + def apply(): Module[Float] = { + val model = Sequential[Float]() + model.add(Reshape(Array(1, 28, 28), inputShape = Shape(28, 28, 1))) + model.add(Convolution2D(32, 3, 3, activation = "relu")) + model.add(Convolution2D(32, 3, 3, activation = "relu")) + model.add(MaxPooling2D(poolSize = (2, 2))) + model.add(Dropout(0.25)) + model.add(Flatten()) + model.add(Dense(128, activation = "relu")) + model.add(Dropout(0.5)) + model.add(Dense(10, activation = "softmax")) + model + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md new file mode 100644 index 00000000000..3cfe1199ea4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md @@ -0,0 +1,68 @@ +# LeNet Model on MNIST with new API + +This examples defines a classical CNN model used in digital number classification with the new set of API in BigDL. For detailed information with regard to LeNet, +please refer to . + +## Prepare MNIST Data +You can download the MNIST Data from [here](http://yann.lecun.com/exdb/mnist/). Unzip all the +files and put them in one folder(e.g. mnist). + +There're four files. **train-images-idx3-ubyte** contains train images, +**train-labels-idx1-ubyte** is train label file, **t10k-images-idx3-ubyte** has validation images + and **t10k-labels-idx1-ubyte** contains validation labels. For more detail, please refer to the + download page. + +After you uncompress the gzip files, these files may be renamed by some uncompress tools, e.g. **train-images-idx3-ubyte** is renamed +to **train-images.idx3-ubyte**. Please change the name back before you run the example. + +## Get the JAR +You can build one by refer to the +[Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/) from the source code. + +## Train the Model +Local mode, example command +``` +spark-submit \ +--master local[physical_core_number] \ +--driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +--class com.intel.analytics.bigdl.example.keras.Train \ +dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +-f path_to_mnist_folder \ +-b batch_size \ +--checkpoint ./model +``` +Standalone cluster mode, example command +``` +spark-submit \ +--master spark://... \ +--executor-cores cores_per_executor \ +--total-executor-cores total_cores_for_the_job \ +--driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +--class com.intel.analytics.bigdl.example.keras.Train \ +dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +-f path_to_mnist_folder \ +-b batch_size \ +--checkpoint ./model +``` +Yarn cluster mode, example command +``` +spark-submit \ +--master yarn \ +--deploy-mode client \ +--executor-cores cores_per_executor \ +--num-executors executors_number \ +--driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +--class com.intel.analytics.bigdl.example.keras.Train \ +dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +-f path_to_mnist_folder \ +-b batch_size \ +--checkpoint ./model +``` +In the above commands +* -f: where you put your MNIST data +* --checkpoint: Where you cache the model/train_state snapshot. You should input a folder and +make sure the folder is created when you run this example. The model snapshot will be named as +model.#iteration_number, and train state will be named as state.#iteration_number. Note that if +there are some files already exist in the folder, the old file will not be overwritten for the +safety of your model files. +* -b: The mini-batch size. It is expected that the mini-batch size is a multiple of node_number * core_number. \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala new file mode 100644 index 00000000000..e487b053c44 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala @@ -0,0 +1,85 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.keras + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToBatch} +import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} +import org.apache.spark.SparkContext + +object Train { + LoggerFilter.redirectSparkInfoLogs() + import models.lenet.Utils._ + + def main(args: Array[String]): Unit = { + trainParser.parse(args, new TrainParams()).map(param => { + val conf = Engine.createSparkConf() + .setAppName("Train Lenet on MNIST") + .set("spark.task.maxFailures", "1") + val sc = new SparkContext(conf) + Engine.init + + val trainData = param.folder + "/train-images-idx3-ubyte" + val trainLabel = param.folder + "/train-labels-idx1-ubyte" + val validationData = param.folder + "/t10k-images-idx3-ubyte" + val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" + + val model = LeNet() + + val optimMethod = if (param.stateSnapshot.isDefined) { + OptimMethod.load[Float](param.stateSnapshot.get) + } else { + new SGD[Float](learningRate = param.learningRate, + learningRateDecay = param.learningRateDecay) + } + + val trainSet = DataSet.array(load(trainData, trainLabel), sc) -> + BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToBatch( + param.batchSize) + + val optimizer = Optimizer( + model = model, + dataset = trainSet, + criterion = ClassNLLCriterion[Float](logProbAsInput = false)) + if (param.checkpoint.isDefined) { + optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch) + } + if(param.overWriteCheckpoint) { + optimizer.overWriteCheckpoint() + } + + val validationSet = DataSet.array(load(validationData, validationLabel), sc) -> + BytesToGreyImg(28, 28) -> GreyImgNormalizer(testMean, testStd) -> GreyImgToBatch( + param.batchSize) + + optimizer + .setValidation( + trigger = Trigger.everyEpoch, + dataset = validationSet, + vMethods = Array(new Top1Accuracy, new Top5Accuracy[Float], new Loss[Float])) + .setOptimMethod(optimMethod) + .setEndWhen(Trigger.maxEpoch(param.maxEpoch)) + .optimize() + + sc.stop() + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala new file mode 100644 index 00000000000..1129be3034b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Simple activation function to be applied to the output. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * Available activations: 'tanh', 'relu', 'sigmoid', 'softmax', 'softplus', + * 'softsign', 'hard_sigmoid'. + * + * @param activation Name of activation function as string. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class Activation[T: ClassTag]( + val activation: String, + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(activation != null, "The name of an activation function as a string is required") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val model = Sequential[T]() + model.add(InputLayer(inputShape = KerasLayer.removeBatch(inputShape))) + val layer = KerasUtils.getActivation(activation) + model.add(layer).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Activation { + def apply[@specialized(Float, Double) T: ClassTag]( + activation: String, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Activation[T] = { + new Activation[T](activation, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala new file mode 100644 index 00000000000..5c4a5bd2c21 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala @@ -0,0 +1,116 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Applies a 2D convolution over an input image composed of several input planes. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension), + * e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures. + * You can also use Conv2D as an alias of this layer. + * The input of this layer should be 4D. + * + * @param nbFilter Number of convolution filters to use. + * @param nbRow Number of rows in the convolution kernel. + * @param nbCol Number of rows in the convolution kernel. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @param subsample Int array of length 2 corresponding to the step of the convolution in the + * height and width dimension. Also called strides elsewhere. Default is (1, 1). + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param format Format of input data. Either DataFormat.NCHW or DataFormat.NHWC. Default is NCHW. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class Convolution2D[T: ClassTag]( + val nbFilter: Int, + val nbRow: Int, + val nbCol: Int, + val init: InitializationMethod = Xavier, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val borderMode: String = "valid", + val subsample: Array[Int] = Array(1, 1), + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val format: DataFormat = DataFormat.NCHW, + val bias: Boolean = true, + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + + s"Convolution2D: $borderMode") + require(subsample.length == 2, "Subsample should be of length 2.") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val pads = KerasUtils.getPadsFromBorderMode(borderMode) + val layer = SpatialConvolution( + nInputPlane = input(format.getHWCDims(4)._3 - 1), + nOutputPlane = nbFilter, + kernelW = nbCol, + kernelH = nbRow, + strideW = subsample(1), + strideH = subsample(0), + padW = pads._2, + padH = pads._1, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer, + withBias = bias, + format = format) + layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) + KerasLayer.fuse(layer, activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Convolution2D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + init: String = "glorot_uniform", + activation: String = null, + borderMode: String = "valid", + subsample: (Int, Int) = (1, 1), + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + format: DataFormat = DataFormat.NCHW, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution2D[T] = { + new Convolution2D[T](nbFilter, nbRow, nbCol, + KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + borderMode, Array(subsample._1, subsample._2), + wRegularizer, bRegularizer, format, bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala index 4c419b811aa..9759cb7e8e7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala @@ -16,9 +16,8 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn._ -import com.intel.analytics.bigdl.nn.{Sequential => TSequential, _} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -26,70 +25,84 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag - -class Dense[T: ClassTag](val outputDim: Int, - val init: InitializationMethod = RandomUniform, - val activation: TensorModule[T] = null, - var wRegularizer: Regularizer[T] = null, - var bRegularizer: Regularizer[T] = null, - val bias: Boolean = true, - var inputShape: Shape = null -)(implicit ev: TensorNumeric[T]) +/** + * A densely-connected NN layer. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The most common input is 2D. + * + * @param outputDim The size of output dimension. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class Dense[T: ClassTag]( + val outputDim: Int, + val init: InitializationMethod = Xavier, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray require(inputShape.toSingle().size >=2, - s"inputShape should at least containing 2 dims, but got: $inputShape.toSingle().size") - inputShape.copyAndUpdate(-1, outputDim) + s"Dense requires input dim >=2, but got dim: ${inputShape.toSingle().length}") + Shape(input.slice(0, input.length -1) ++ Array(outputDim)) } override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val inputShapeList = inputShape.toSingle() - var model: AbstractModule[Tensor[T], Tensor[T], T] = Linear( + var layer = Linear( inputSize = inputShapeList.last, outputSize = outputDim, withBias = bias, wRegularizer = wRegularizer, - bRegularizer = bRegularizer - ).setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) - - model = KerasLayer.fuse(model, - activation, - inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + bRegularizer = bRegularizer) + layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) if (inputShape.toSingle().size <= 2) { - model + KerasLayer.fuse(layer, activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } else { val seq = new Sequential[T](stopInferShape = true) val inDim = inputShapeList.last seq.add(InputLayer(inputShape = inputShape)) seq.add(InferReshape(Array(-1, inDim), false)) - seq.add(model) + seq.add(layer) seq.add(InferReshape(Array(-1) ++ inputShapeList.slice(1, inputShapeList.size - 1) ++ Array(outputDim), false)) - seq - }.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + if (activation != null) { + seq.add(activation) + } + seq.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } } } object Dense { - def apply[@specialized(Float, Double) T: ClassTag]( - outputDim: Int, - init: InitializationMethod = RandomUniform, - activation: TensorModule[T] = null, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null, - bias: Boolean = true, - inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : Dense[T] = { - new Dense[T]( - outputDim, - init, - activation, - wRegularizer, - bRegularizer, - bias, - inputShape) + outputDim: Int, + init: String = "glorot_uniform", + activation: String = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Dense[T] = { + new Dense[T](outputDim, KerasUtils.getInitMethod(init), + KerasUtils.getActivation(activation), + wRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala new file mode 100644 index 00000000000..28da8ca7f10 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Applies Dropout to the input by randomly setting a fraction 'p' of input units to 0 at each + * update during training time in order to prevent overfitting. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param p Fraction of the input units to drop. Double between 0 and 1. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class Dropout[T: ClassTag]( + val p: Double, + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.Dropout(p) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Dropout { + def apply[@specialized(Float, Double) T: ClassTag]( + p: Double, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Dropout[T] = { + new Dropout[T](p, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala new file mode 100644 index 00000000000..701c8c65dde --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala @@ -0,0 +1,54 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Flattens the input without affecting the batch size. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class Flatten[T: ClassTag](var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + Shape(input(0), input.slice(1, input.length).product) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = + com.intel.analytics.bigdl.nn.Reshape(Array(input.slice(1, input.length).product)) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Flatten { + def apply[@specialized(Float, Double) T: ClassTag]( + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Flatten[T] = { + new Flatten[T](inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala index 528cdec8244..0ad08ba90e7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala @@ -24,7 +24,6 @@ import com.intel.analytics.bigdl.utils.{Node, Shape} import scala.reflect.ClassTag -@SerialVersionUID(- 8525406230282608904L) class Input[T: ClassTag](val inputShape: Shape)(implicit ev: TensorNumeric[T]) extends TInput[T]() { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala index e69247ba330..a62dfe722e4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.nn.{Container, Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter @@ -52,7 +53,7 @@ object KerasLayerSerializer extends ContainerSerializable with TKerasSerializerH private[bigdl] object KerasLayer { def fuse[T: ClassTag](sLayer: AbstractModule[Activity, Activity, T], - activation: TensorModule[T], + activation: AbstractModule[Tensor[T], Tensor[T], T], inputShape: Shape) (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { if (activation == null) { @@ -63,10 +64,9 @@ private[bigdl] object KerasLayer { seq.add(sLayer) seq.add(activation) seq.setName(sLayer.getName()) - return seq + seq } - def addBatch(shape: Shape): Shape = { // simply return null here as null is the default value if (shape == null) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala new file mode 100644 index 00000000000..6cbe483efeb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +object KerasUtils { + + private[keras] def getPadsFromBorderMode(borderMode: String = "valid"): (Int, Int) = { + if (borderMode == "same") { + // padH, padW + (-1, -1) + } else { + (0, 0) + } + } + + private[keras] def getInitMethod(init: String): InitializationMethod = { + init.toLowerCase() match { + case "glorot_uniform" => Xavier + case "one" => Ones + case "zero" => Zeros + case "uniform" => RandomUniform(-0.05, 0.05) + case "normal" => RandomNormal(0.0, 0.05) + case _ => throw new IllegalArgumentException(s"Unsupported initialization method: " + + s"${init.toLowerCase()}") + } + } + + private[keras] def getActivation[T : ClassTag] (activation: String) + (implicit ev: TensorNumeric[T]): AbstractModule[Tensor[T], Tensor[T], T] = { + if (activation == null) null + else { + activation.toLowerCase() match { + case "tanh" => Tanh[T]() + case "sigmoid" => Sigmoid[T]() + case "relu" => ReLU[T]() + case "softmax" => SoftMax[T]().asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + case "softplus" => SoftPlus[T]() + case "softsign" => SoftSign[T]() + case "hard_sigmoid" => HardSigmoid[T]() + case _ => throw new IllegalArgumentException(s"Invalid activation: " + + s"${activation.toLowerCase}. Only simple activations can be constructed using string") + } + } + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala new file mode 100644 index 00000000000..9331604a632 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala @@ -0,0 +1,81 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.SpatialMaxPooling +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Applies max pooling operation for spatial data. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 4D. + * + * @param poolSize Int array of length 2 corresponding to the downscale vertically and + * horizontally. Default is (2, 2), which will halve the image in each dimension. + * @param strides Stride values. Int array of length 2. Default is null, and in this case it will + * be equal to poolSize. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @param format Format of input data. Either DataFormat.NCHW or DataFormat.NHWC. Default is NCHW. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class MaxPooling2D[T: ClassTag] ( + val poolSize: Array[Int] = Array(2, 2), + val strides: Array[Int] = null, + val borderMode: String = "valid", + val format: DataFormat = DataFormat.NCHW, + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + + s"MaxPooling2D: $borderMode") + + private val stridesValue = if (strides != null) strides else poolSize + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val pads = KerasUtils.getPadsFromBorderMode(borderMode) + val layer = SpatialMaxPooling( + kW = poolSize(1), + kH = poolSize(0), + dW = stridesValue(1), + dH = stridesValue(0), + padW = pads._2, + padH = pads._1, + format = format + ) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object MaxPooling2D { + def apply[@specialized(Float, Double) T: ClassTag]( + poolSize: (Int, Int) = (2, 2), + strides: (Int, Int) = null, + borderMode: String = "valid", + format: DataFormat = DataFormat.NCHW, + inputShape: Shape = null) + (implicit ev: TensorNumeric[T]): MaxPooling2D[T] = { + val stridesValue = if (strides != null) Array(strides._1, strides._2) else null + new MaxPooling2D[T](Array(poolSize._1, poolSize._2), + stridesValue, borderMode, format, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala new file mode 100644 index 00000000000..53b39492617 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala @@ -0,0 +1,104 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.InferReshape +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Reshapes an output to a certain shape. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * Supports shape inference by allowing one -1 in the target shape. + * For example, if inputShape = Shape(2, 3, 4), targetShape = Array(3, -1), + * then outputShape will be Shape(3, 8). + * + * @param targetShape Array of int. The target shape that you desire to have. + * Batch dimension should be excluded. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class Reshape[T: ClassTag]( + val targetShape: Array[Int], + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + private var infer = false + private var inferIndex = -1 + validateTargetShape() + + private def validateTargetShape(): Unit = { + if (targetShape.contains(-1)) { + infer = true + var i = 0 + var inferCount = 0 + while (i < targetShape.length) { + if (targetShape(i) == -1) { + inferIndex = i + inferCount += 1 + } + // We don't consider 0 here, same as Keras + else require(targetShape(i) >= 1, + s"wrong reshape size at index $i: ${targetShape(i)}") + i += 1 + } + require(inferCount == 1, "can only specify one unknown dimension") + } + } + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + val nonBatchInput = input.slice(1, input.length) + if (infer) { + val nElements = nonBatchInput.product + val resizeElements = - targetShape.product + require(nElements > resizeElements && nElements % resizeElements == 0, + "total size after reshape must be unchanged") + targetShape(inferIndex) = nElements / resizeElements + } + else { + require(targetShape.product == nonBatchInput.product, + s"total size after reshape must be unchanged. But In ${this.getName()} : " + + s"original size is: ${ nonBatchInput.product }, " + + s"reshape size is: ${ targetShape.product }") + } + Shape(Array(input(0)) ++ targetShape) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + var layer: TensorModule[T] = null + if (infer) { + layer = InferReshape(targetShape) + } + else { + layer = com.intel.analytics.bigdl.nn.Reshape(targetShape) + } + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Reshape { + def apply[@specialized(Float, Double) T: ClassTag]( + targetShape: Array[Int], + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Reshape[T] = { + new Reshape[T](targetShape, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala new file mode 100644 index 00000000000..be16a38bb8d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala @@ -0,0 +1,63 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.{Transpose, Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Just a wrapper class. Please use Activation('softmax') instead. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class SoftMax[T: ClassTag]( + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 2 || input.length == 3, + s"SoftMax requires 2D or 3D input, but got input dim ${input.length}") + inputShape + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = com.intel.analytics.bigdl.nn.SoftMax() + if (input.length <= 2) { + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } + else { + val model = TSequential[T]() + model.add(Transpose(Array((1, 3)))) + model.add(layer) + model.add(Transpose(Array((1, 3)))) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } + } +} + +object SoftMax { + def apply[@specialized(Float, Double) T: ClassTag]( + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SoftMax[T] = { + new SoftMax[T](inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala new file mode 100644 index 00000000000..c298e04a03d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala @@ -0,0 +1,22 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +package object keras { + // Alias + val Conv2D = Convolution2D +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala index c4013b735a7..0f3f85790a0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Engine @@ -45,7 +45,7 @@ class Dropout[T: ClassTag]( val initP: Double = 0.5, val inplace: Boolean = false, var scale: Boolean = true)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { + implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { private var p = initP var noise = Tensor[T]() var isResampling = true diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala index 436d9f0858e..9729a47e59a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -30,7 +30,7 @@ import scala.reflect.ClassTag * ⎩ 0.2 * x + 0.5, otherwise */ class HardSigmoid[T: ClassTag] -(implicit ev: TensorNumeric[T]) extends TensorModule[T] { +(implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { val minValue = ev.fromType[Double](-2.5) val maxValue = ev.fromType[Double](2.5) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala index 87e72848ee3..74cf9f8b1fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -29,7 +29,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(6855417348268610044L) class Sigmoid[T: ClassTag]( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { + implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { private val buffer: Tensor[T] = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala index 723d61dbb7b..571137215c8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc4, TensorFunc6} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -34,7 +34,7 @@ import scala.reflect.ClassTag class SoftPlus[T: ClassTag]( val beta: Double = 1.0 )( implicit ev: TensorNumeric[T]) - extends TensorModule[T] { + extends TensorModule[T] with IdentityOutputShape { // Avoid floating point issues with exp(x), x>20 private val threshold = ev.fromType[Double](20.0) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala index 97a5324ea62..6fff6d04c0a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -30,7 +30,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 3936698382129844874L) class SoftSign[T: ClassTag]() (implicit ev: TensorNumeric[T]) - extends TensorModule[T] { + extends TensorModule[T] with IdentityOutputShape { @transient private var temp: Tensor[T] = null @transient private var tempGrad: Tensor[T] = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala index adebd7d05d7..94083c13618 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala @@ -173,16 +173,41 @@ class SpatialConvolution[T: ClassTag]( zeroGradParameters() } - private def getOutputShape(oh: Int, ow: Int, batchSize: Int = -1): Array[Int] = { + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"SpatialConvolution requires 4D input, but got input dim ${input.length}") + val (dimHeight, dimWidth, channelDim) = format.getHWCDims(input.length) + require(input(channelDim -1) == nInputPlane, s"input channel size " + + s"${input(channelDim -1)} is not the same as nInputPlane $nInputPlane") + val inputWidth = input(dimWidth -1) + val inputHeight = input(dimHeight -1) + val sizes = + if (padW == -1 && padH == -1) { + Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW) + } else { + Utils.getOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, + kernelH, kernelW, padH, padW, ceilMode = false) + } + val outputHeight = sizes(4) + val outputWidth = sizes(5) + require(outputWidth >= 1 && outputHeight >= 1, + s"output size is too small. outputWidth: $outputWidth, outputHeight: $outputHeight") + val outputShape = getOutputShape(outputHeight, outputWidth, input(0)) + Shape(outputShape) + } + + // batchSize = -2 by default means no batch. -1 represents batch in shape inference + private def getOutputShape(oh: Int, ow: Int, batchSize: Int = -2): Array[Int] = { format match { case DataFormat.NCHW => - if (batchSize == -1) { + if (batchSize == -2) { Array(nOutputPlane, oh, ow) } else { Array(batchSize, nOutputPlane, oh, ow) } case DataFormat.NHWC => - if (batchSize == -1) { + if (batchSize == -2) { Array(oh, ow, nOutputPlane) } else { Array(batchSize, oh, ow, nOutputPlane) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala index 48806ca9ac9..18e53b8fbf2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, Shape} import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} @@ -88,6 +88,39 @@ class SpatialMaxPooling[T: ClassTag]( this } + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"SpatialMaxPooling requires 4D input, but got input dim ${input.length}") + val (dimh, dimw, dimc) = format.getHWCDims(input.length) + val nInputPlane = input(dimc -1) + val inputHeight = input(dimh -1) + val inputWidth = input(dimw -1) + val sizes = + if (padW == -1 && padH == -1) { + Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW) + } else { + require(inputWidth >= kW - padW && inputHeight >= kH - padH, + "input smaller than kernel size. " + + s"current input size($inputWidth, $inputHeight), " + + s"kernel size(${kW-padW}, ${kH-padH})") + require(kW / 2 >= padW && kH / 2 >= padH, + "pad should be smaller than half of kernel size. " + + s"current pad size($padW, $padH), " + s"kernel size($kW, $kH)") + Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW, padH, padW, ceilMode) + } + val oHeight = sizes(4) + val oWidth = sizes(5) + + val outputShape = format match { + case DataFormat.NCHW => + Array(input(0), nInputPlane, oHeight, oWidth) + case DataFormat.NHWC => + Array(input(0), oHeight, oWidth, nInputPlane) + } + Shape(outputShape) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 3 || input.dim() == 4, "SpatialMaxPooling: " + ErrorInfo.constrainInputAs3DOrBatch) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala index 16e54bdf29f..450f5c90634 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.math.tanh @@ -30,7 +30,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(9062199894710333035L) class Tanh[T: ClassTag]( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { + implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { private val buffer: Tensor[T] = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala index cbc5591b0a2..5f60564bbb9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala @@ -60,13 +60,13 @@ trait InferShape { private[bigdl] def getOutputShape(): Shape = { if (_outputShapeValue.length > 1) { throw new RuntimeException( - "There's multipule output for this layer. Please use getInputShapeFor instead") + "There are multiple outputs for this layer. Please use getInputShapeFor instead") } outputShapeValue(0) } /** - * Execute builing logic and return the outputShape for the given inputShape. + * Execute building logic and return the outputShape for the given inputShape. * NB: the first dim of inputShape is batch */ private[bigdl] def build(inputShape: Shape): Shape = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala index 7caaf2f7948..62b845a81f0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -49,7 +49,7 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Seria bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: JList[Int] = null): Dense[T] = { - Dense(outputDim, + new Dense(outputDim, init, activation, wRegularizer, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala index 31ad485ab06..309fd8d6b97 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasBaseSpec.scala @@ -55,7 +55,7 @@ abstract class KerasBaseSpec extends BigDLSpecHelper { bgradInput.almostEqual(gradInput, precision) should be(true) val parameters = bmodel.parameters() - if (parameters != null) { + if (gradWeight != null) { val bgradWeights = parameters._2 (bgradWeights, weightConverter(gradWeight)).zipped.foreach { (bgrad, kgrad) => bgrad.almostEqual(kgrad, precision) should be(true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala index 1bbeaa3a60f..243aca0e623 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/KerasRunner.scala @@ -111,9 +111,10 @@ object KerasRunner { if (!pvalues.keySet.filter(key => key.contains(keyName)).isEmpty) { val weightNum = pvalues.keySet.filter(key => key.contains(keyName)).size / 2 Range(0, weightNum).map {i => + val keyPrefix = if (weightNum > 1) keyName + "_" + i else keyName Tensor[Float]( - data = pvalues(s"${keyName}_${i}_value"), - shape = pvalues(s"${keyName}_${i}_shape").map(_.toInt)) + data = pvalues(s"${keyPrefix}_value"), + shape = pvalues(s"${keyPrefix}_shape").map(_.toInt)) }.toArray } else { null diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala new file mode 100644 index 00000000000..c80eb583d37 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.example.keras.LeNet +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + +class LeNetSpec extends FlatSpec with Matchers { + + "LeNet" should "generate the correct outputShape" in { + val cnn = LeNet() + cnn.getOutputShape().toSingle().toArray should be (Array(-1, 10)) + } + + "LeNet forward and backward" should "work properly" in { + val cnn = LeNet() + val input = Tensor[Float](Array(2, 28, 28, 1)).rand() + val output = cnn.forward(input) + val gradInput = cnn.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ActivationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ActivationSpec.scala new file mode 100644 index 00000000000..45639af2511 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ActivationSpec.scala @@ -0,0 +1,147 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Activation, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class ActivationSpec extends KerasBaseSpec{ + + "tanh" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = Activation('tanh')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Activation[Float]("tanh", inputShape = Shape(4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "relu" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = Activation('relu')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Activation[Float]("relu", inputShape = Shape(4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "sigmoid" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = Activation('sigmoid')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Activation[Float]("sigmoid", inputShape = Shape(4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "hard_sigmoid" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = Activation('hard_sigmoid')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Activation[Float]("hard_sigmoid", inputShape = Shape(4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "softmax 2D input" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[8]) + |input = np.random.random([4, 8]) + |output_tensor = Activation('softmax')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Activation[Float]("softmax", inputShape = Shape(8)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "softmax" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = Activation('softmax')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Activation[Float]("softmax", inputShape = Shape(4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "softplus" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = Activation('softplus')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Activation[Float]("softplus", inputShape = Shape(4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "softsign" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = Activation('softsign')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Activation[Float]("softsign", inputShape = Shape(4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala new file mode 100644 index 00000000000..f07cf61b143 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala @@ -0,0 +1,82 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{Conv2D, Convolution2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class Convolution2DSpec extends KerasBaseSpec { + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = + if (in.length == 1) in // without bias + else Array(in(0).resize(Array(1) ++ in(0).size()), in(1)) // with bias + + "Convolution2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24, 24]) + |input = np.random.random([2, 3, 24, 24]) + |output_tensor = Convolution2D(64, 2, 5, activation="relu", + | dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Convolution2D[Float](64, 2, 5, activation = "relu", + inputShape = Shape(3, 24, 24)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter, 1e-3) + } + + "Convolution2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[24, 24, 3]) + |input = np.random.random([2, 24, 24, 3]) + |output_tensor = Convolution2D(32, 4, 6, border_mode="same", + | dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Convolution2D[Float](32, 4, 6, format = DataFormat.NHWC, + borderMode = "same", inputShape = Shape(24, 24, 3)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter, 1e-3) + } + + "Conv2D without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24, 24]) + |input = np.random.random([2, 3, 24, 24]) + |output_tensor = Convolution2D(64, 2, 5, bias=False, subsample=(2, 3), + | init="normal", dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Conv2D[Float](64, 2, 5, subsample = (2, 3), init = "normal", + bias = false, inputShape = Shape(3, 24, 24)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter, 1e-4) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala index c25df330fe1..c57f1ec752f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala @@ -17,45 +17,47 @@ package com.intel.analytics.bigdl.keras.nn import com.intel.analytics.bigdl.keras.KerasBaseSpec -import com.intel.analytics.bigdl.nn.ReLU import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.Dense import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape -class DenseSpec extends KerasBaseSpec{ +class DenseSpec extends KerasBaseSpec { - "Dense" should "be test" in { + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(0).t(), in(1)) + + "Dense" should "be the same as Keras" in { val kerasCode = """ |input_tensor = Input(shape=[3]) |input = np.random.uniform(0, 1, [1, 3]) - |output_tensor = Dense(2, activation="relu", init='uniform')(input_tensor) + |output_tensor = Dense(2, activation="relu")(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin val seq = KSequential[Float]() - val dense = Dense[Float](2, activation = ReLU(), inputShape = Shape(3)) + val dense = Dense[Float](2, activation = "relu", inputShape = Shape(3)) seq.add(dense) - def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(0).t(), in(1)) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 2)) checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], kerasCode, weightConverter) } - "Dense for multiple dims" should "be test" in { + "Dense nD input" should "be the same as Keras" in { val kerasCode = """ |input_tensor = Input(shape=[10, 5, 7]) |input = np.random.uniform(0, 1, [2, 10, 5, 7]) |output_tensor = \ - |Dense(2, init='one', activation="relu", input_shape=(10, 5, 7))(input_tensor) + |Dense(2, init='one', input_shape=(10, 5, 7))(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin val seq = KSequential[Float]() - val dense = Dense[Float](2, activation = ReLU(), inputShape = Shape(10, 5, 7)) + val dense = Dense[Float](2, init = "one", inputShape = Shape(10, 5, 7)) seq.add(dense) - def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(0).t(), in(1)) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 10, 5, 2)) checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], kerasCode, weightConverter, precision = 1e-4) } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DropoutSpec.scala new file mode 100644 index 00000000000..12bf553f6b9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DropoutSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.nn.keras.{Dropout, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class DropoutSpec extends FlatSpec with Matchers { + + "Dropout forward and backward" should "work properly" in { + val seq = KSequential[Float]() + val layer = Dropout[Float](0.3, inputShape = Shape(3, 4)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 4)) + val input = Tensor[Float](2, 3, 4).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/FlattenSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/FlattenSpec.scala new file mode 100644 index 00000000000..fe29bac964e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/FlattenSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.Flatten +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class FlattenSpec extends KerasBaseSpec { + + "Flatten" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5]) + |input = np.random.random([2, 3, 4, 5]) + |output_tensor = Flatten()(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Flatten[Float](inputShape = Shape(3, 4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala index 62c33550e96..27f99ec103d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala @@ -26,9 +26,9 @@ import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape} class KerasStyleSpec extends BigDLSpecHelper { - "Graph: Dense" should "works correctly" in { + "Graph: Dense" should "work correctly" in { val input = Input[Float](inputShape = Shape(10)) - val d = Dense[Float](20, activation = ReLU()).setName("dense1").inputs(input) + val d = Dense[Float](20, activation = "relu").setName("dense1").inputs(input) val d2 = Dense[Float](5).setName("dense2").inputs(d) val model = Model[Float](input, d2) val inputData = Tensor[Float](Array(20, 10)).rand() @@ -37,7 +37,7 @@ class KerasStyleSpec extends BigDLSpecHelper { require(model.getInputShape().toSingle().sameElements(Array(-1, 10))) } - "Sequential: Dense" should "works correctly" in { + "Sequential: Dense" should "work correctly" in { val seq = KSequential[Float]() val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") val d2 = Dense[Float](5).setName("dense2") @@ -60,7 +60,7 @@ class KerasStyleSpec extends BigDLSpecHelper { } } - "Sequential: shared relu" should "works correctly" in { + "Sequential: shared relu" should "work correctly" in { val sharedRelu = ReLU[Float]() val seq1 = KSequential[Float]() seq1.add(Dense[Float](20, inputShape = Shape(10))) @@ -82,12 +82,12 @@ class KerasStyleSpec extends BigDLSpecHelper { require(seq.getOutputShape().toSingle().sameElements(Array(-1, 5))) } - "TSequential" should "works with alex" in { + "TSequential" should "work with alex" in { val model = AlexNet_OWT(1000, false, true) TSequential[Float].add(model) } - "TSequential" should "not works with dense" in { + "TSequential" should "not work with dense" in { intercept[RuntimeException] { val seq = TSequential[Float]() val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") @@ -95,14 +95,14 @@ class KerasStyleSpec extends BigDLSpecHelper { } } - "TGraph" should "not works with dense" in { + "TGraph" should "not work with dense" in { intercept[RuntimeException] { val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1").inputs(Input()) val l1 = Linear(2, 3).inputs(d1) } } - "TSequential" should "not works w ith container containing Dense" in { + "TSequential" should "not works with container containing Dense" in { val seq = TSequential[Float]() intercept[RuntimeException] { val parallelTable = ParallelTable[Float]() @@ -112,7 +112,7 @@ class KerasStyleSpec extends BigDLSpecHelper { } } - "TSequential" should "not works with container with dense" in { + "TSequential" should "not work with container with dense" in { intercept[RuntimeException] { val seq = TSequential[Float]() val seq2 = TSequential[Float]() @@ -122,21 +122,21 @@ class KerasStyleSpec extends BigDLSpecHelper { } } - "save and reload model" should "works correctly" in { - val input = Input[Float](inputShape = Shape(10)) - val d = Dense[Float](20).setName("dense1").inputs(input) - val d2 = Dense[Float](5).setName("dense2").inputs(d) - val model = Model[Float](input, d2) - val tmpFile = createTmpFile() - val absPath = tmpFile.getAbsolutePath - tmpFile.delete() - model.saveModule(absPath) - val reloadedModel = Module.loadModule(absPath) - val inputData = Tensor[Float](Array(20, 10)).rand() - val output = reloadedModel.forward(inputData) - } + "save and reload model" should "work correctly" in { + val input = Input[Float](inputShape = Shape(10)) + val d = Dense[Float](20).setName("dense1").inputs(input) + val d2 = Dense[Float](5).setName("dense2").inputs(d) + val model = Model[Float](input, d2) + val tmpFile = createTmpFile() + val absPath = tmpFile.getAbsolutePath + tmpFile.delete() + model.saveModule(absPath) + val reloadedModel = Module.loadModule(absPath) + val inputData = Tensor[Float](Array(20, 10)).rand() + val output = reloadedModel.forward(inputData) + } - "save and reload sequential" should "works correctly" in { + "save and reload sequential" should "work correctly" in { val kseq = KSequential[Float]() val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") val d2 = Dense[Float](5).setName("dense2") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala new file mode 100644 index 00000000000..505a93812c7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala @@ -0,0 +1,76 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{MaxPooling2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class MaxPooling2DSpec extends KerasBaseSpec{ + + "MaxPooling2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24, 24]) + |input = np.random.random([2, 3, 24, 24]) + |output_tensor = MaxPooling2D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = MaxPooling2D[Float](inputShape = Shape(3, 24, 24)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "MaxPooling2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[32, 28, 5]) + |input = np.random.random([3, 32, 28, 5]) + |output_tensor = MaxPooling2D(pool_size=(2, 3), strides=(1, 2), + | dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = MaxPooling2D[Float](poolSize = (2, 3), strides = (1, 2), + format = DataFormat.NHWC, inputShape = Shape(32, 28, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "MaxPooling2D same border mode" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24, 24]) + |input = np.random.random([2, 3, 24, 24]) + |output_tensor = MaxPooling2D(strides=(1, 2), border_mode="same", + | dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = MaxPooling2D[Float](strides = (1, 2), borderMode = "same", + inputShape = Shape(3, 24, 24)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ReshapeSpec.scala new file mode 100644 index 00000000000..4a82fc75cda --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ReshapeSpec.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.Reshape +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class ReshapeSpec extends KerasBaseSpec { + + "Reshape" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5]) + |input = np.random.random([2, 3, 4, 5]) + |output_tensor = Reshape((4, 15))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Reshape[Float](Array(4, 15), inputShape = Shape(3, 4, 5)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4, 15)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "Reshape inference" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, ]) + |input = np.random.random([3, 12]) + |output_tensor = Reshape((-1, 2, 2))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Reshape[Float](Array(-1, 2, 2), inputShape = Shape(12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 2, 2)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala index 903892e3b88..3b87aa552eb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala @@ -404,7 +404,7 @@ class LinearSpec extends FlatSpec with Matchers { linear.bias should be (exceptedBias) } - "computeOutputShape" should "work" in { + "Linear computeOutputShape" should "work properly" in { val linear = Linear[Float](3, 5) TestUtils.compareOutputShape(linear, Shape(3)) should be (true) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala index 8a9ca886b71..a309ad33f09 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} import com.intel.analytics.bigdl.utils.RandomGenerator._ import scala.util.Random -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{Shape, T, TestUtils} @com.intel.analytics.bigdl.tags.Parallel class SpatialConvolutionSpec extends FlatSpec with Matchers { @@ -3026,4 +3026,14 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { layer3.weight.copy(layer.weight) layer3.equals(layer) should be (false) } + + "SpatialConvolution computeOutputShape NCHW" should "work properly" in { + val layer = SpatialConvolution[Float](3, 5, 2, 2) + TestUtils.compareOutputShape(layer, Shape(3, 12, 12)) should be (true) + } + + "SpatialConvolution computeOutputShape NHWC" should "work properly" in { + val layer = SpatialConvolution[Float](4, 5, 2, 2, format = DataFormat.NHWC) + TestUtils.compareOutputShape(layer, Shape(12, 12, 4)) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala index d2e562f776d..d1ad6e2426f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.utils.RandomGenerator +import com.intel.analytics.bigdl.utils.{RandomGenerator, Shape, TestUtils} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import org.scalatest.{FlatSpec, Matchers} @@ -434,4 +434,14 @@ class SpatialMaxPoolingSpec extends FlatSpec with Matchers { } } + + "SpatialMaxPooling computeOutputShape NCHW" should "work properly" in { + val layer = SpatialMaxPooling[Float](4, 5, 1, 2, 2, 2) + TestUtils.compareOutputShape(layer, Shape(3, 12, 16)) should be (true) + } + + "SpatialMaxPooling computeOutputShape NHWC" should "work properly" in { + val layer = SpatialMaxPooling[Float](2, 4, 1, 2, 1, 1, format = DataFormat.NHWC) + TestUtils.compareOutputShape(layer, Shape(18, 20, 5)) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala index e9588b88f77..9baeceaaf80 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala @@ -32,7 +32,7 @@ object TestUtils { * Compare the output of `computeOutputShape` with the `forward` result */ def compareOutputShape(layer: AbstractModule[Activity, Activity, Float], - inputShape: Shape): Boolean = { + inputShape: Shape): Boolean = { val inputData = Tensor[Float](Array(2) ++ inputShape.toSingle()).randn() val seq = KSequential[Float]() seq.add(InputLayer[Float](inputShape = inputShape)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index f5fe70d34ad..a8162d2f359 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -15,15 +15,14 @@ */ package com.intel.analytics.bigdl.utils.serializer -import com.intel.analytics.bigdl.nn.keras.{Dense, Input, InputLayer, Model, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.Shape -import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _} import scala.collection.mutable import scala.util.Random - class KerasModuleSerializerSpec extends SerializerSpecHelper { override def getPackage(): String = "com.intel.analytics.bigdl.nn.keras" @@ -42,7 +41,6 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { val dense = Dense[Float](10, inputShape = Shape(20)) dense.build(Shape(2, 20)) val input = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) - runSerializationTest(dense, input) } @@ -63,5 +61,54 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(model, inputData) } + "Convolution2D serializer" should "work properly" in { + val layer = Convolution2D[Float](64, 2, 5, inputShape = Shape(3, 24, 24)) + layer.build(Shape(2, 3, 24, 24)) + val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "MaxPooling2D serializer" should "work properly" in { + val layer = MaxPooling2D[Float](inputShape = Shape(3, 24, 24)) + layer.build(Shape(2, 3, 24, 24)) + val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Activation serializer" should "work properly" in { + val layer = Activation[Float]("tanh", inputShape = Shape(4, 5)) + layer.build(Shape(2, 4, 5)) + val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Dropout serializer" should "work properly" in { + val layer = Dropout[Float](0.3, inputShape = Shape(3, 4)) + layer.build(Shape(2, 3, 4)) + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Flatten serializer" should "work properly" in { + val layer = Flatten[Float](inputShape = Shape(3, 4, 5)) + layer.build(Shape(2, 3, 4, 5)) + val input = Tensor[Float](2, 3, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Reshape serializer" should "work properly" in { + val layer = Reshape[Float](Array(4, 15), inputShape = Shape(3, 4, 5)) + layer.build(Shape(2, 3, 4, 5)) + val input = Tensor[Float](2, 3, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "SoftMax serializer" should "work properly" in { + val layer = SoftMax[Float](inputShape = Shape(4, 5)) + layer.build(Shape(3, 4, 5)) + val input = Tensor[Float](3, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + } From 5632a7a12e203bfdf076f8480b311dd4bd75d25f Mon Sep 17 00:00:00 2001 From: ibingoogle Date: Fri, 2 Feb 2018 22:07:34 -0700 Subject: [PATCH 0676/1065] support hdfs-cifar10 on resnet (#2266) * support hdfs-cifar10 on resnet * use java doc style --- .../bigdl/dllib/models/resnet/Utils.scala | 28 +++++++++++++------ 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Utils.scala index 6ab25204b68..aa462b88fab 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Utils.scala @@ -20,6 +20,7 @@ import java.nio.ByteBuffer import java.nio.file.{Files, Path, Paths} import com.intel.analytics.bigdl.dataset.ByteRecord +import com.intel.analytics.bigdl.utils.File import scopt.OptionParser import scala.collection.mutable.ArrayBuffer @@ -118,11 +119,11 @@ object Utils { private[bigdl] def loadTrain(dataFile: String): Array[ByteRecord] = { val allFiles = Array( - Paths.get(dataFile, "data_batch_1.bin"), - Paths.get(dataFile, "data_batch_2.bin"), - Paths.get(dataFile, "data_batch_3.bin"), - Paths.get(dataFile, "data_batch_4.bin"), - Paths.get(dataFile, "data_batch_5.bin") + dataFile + "/data_batch_1.bin", + dataFile + "/data_batch_2.bin", + dataFile + "/data_batch_3.bin", + dataFile + "/data_batch_4.bin", + dataFile + "/data_batch_5.bin" ) val result = new ArrayBuffer[ByteRecord]() @@ -132,19 +133,30 @@ object Utils { private[bigdl] def loadTest(dataFile: String): Array[ByteRecord] = { val result = new ArrayBuffer[ByteRecord]() - val testFile = Paths.get(dataFile, "test_batch.bin") + val testFile = dataFile + "/test_batch.bin" load(testFile, result) result.toArray } - private[bigdl] def load(featureFile: Path, result : ArrayBuffer[ByteRecord]): Unit = { + /** + * load cifar data. + * read cifar from hdfs if data folder starts with "hdfs:", otherwise form local file. + * @param featureFile + * @param result + */ + private[bigdl] def load(featureFile: String, result : ArrayBuffer[ByteRecord]): Unit = { val rowNum = 32 val colNum = 32 val imageOffset = rowNum * colNum * 3 + 1 val channelOffset = rowNum * colNum val bufferOffset = 8 - val featureBuffer = ByteBuffer.wrap(Files.readAllBytes(featureFile)) + val featureBuffer = if (featureFile.startsWith(File.hdfsPrefix)) { + ByteBuffer.wrap(File.readHdfsByte(featureFile)) + } else { + ByteBuffer.wrap(Files.readAllBytes(Paths.get(featureFile))) + } + val featureArray = featureBuffer.array() val featureCount = featureArray.length / (rowNum * colNum * 3 + 1) From 3f2bd43778eefd7f66a1253493ee5d7525d7f599 Mon Sep 17 00:00:00 2001 From: tosky001 Date: Sat, 3 Feb 2018 13:08:00 +0800 Subject: [PATCH 0677/1065] Add [[CategoricalColHashBucket]] Operation (#2260) * [WIP] More Operations for tf.feature_column-like API * resolve conflicts * rename the TFSerializerSpec to OperationSerializerSpec --- .../nn/ops/CategoricalColHashBucket.scala | 102 ++++++++++++++++++ .../nn/ops/CategoricalColHashBucketSpec.scala | 49 +++++++++ .../serializer/ModuleSerializerSpec.scala | 6 +- ...ec.scala => OperationSerializerSpec.scala} | 35 +++--- 4 files changed, 170 insertions(+), 22 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucket.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucketSpec.scala rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/{TFSerializerSpec.scala => OperationSerializerSpec.scala} (95%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucket.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucket.scala new file mode 100644 index 00000000000..8afaaee07d5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucket.scala @@ -0,0 +1,102 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag +import scala.util.hashing.MurmurHash3 + +/** + * CategoricalColHashBucket operation can convert feature string to a Sparse/Dense Tensor + * + * SparseTensor if isSparse = true + * DenseTensor if isSparse = false + * + * the input is a Tensor[String] with shape batch * 1. + * + * This operation distributes your inputs into a finite number of buckets by hashing + * + * The Operation support the feature column with single-value or multi-value + * + * The output_id = Hash(input_feature_string) % hashBucketSize, ranging 0 to hashBucketSize-1 + * + * The missing values in input Tensor can be represented by -1 for int and '''' for string + * + * @param hashBucketSize An Integer > 1. The number of buckets. + * @param strDelimiter The delimiter of feature string, default: ",". + * @param isSparse whether the output tensor is a sparseTensor, default: True. + * @tparam T Numeric type. Parameter tensor numeric type. Only support float/double now + */ + +class CategoricalColHashBucket[T: ClassTag]( + val hashBucketSize: Int, + val strDelimiter: String = ",", + val isSparse: Boolean = true + )(implicit ev: TensorNumeric[T]) + extends Operation[Tensor[String], Tensor[Int], T] { + + output = Tensor[Int]() + + override def updateOutput(input: Tensor[String]): Tensor[Int] = { + val rows = input.size(dim = 1) + val indices0 = new ArrayBuffer[Int]() + val indices1 = new ArrayBuffer[Int]() + val values = new ArrayBuffer[Int]() + var i = 1 + var max_fea_len = 0 + while(i <= rows) { + val feaStrArr = input.valueAt(i, 1).split(strDelimiter) + max_fea_len = math.max(max_fea_len, feaStrArr.length) + var j = 0 + while(j < feaStrArr.length) { + val hashVal = MurmurHash3.stringHash(feaStrArr(j)) % hashBucketSize match { + case v if v < 0 => v + hashBucketSize + case v => v + } + indices0 += i-1 + indices1 += j + values += hashVal + j += 1 + } + i += 1 + } + val indices = Array(indices0.toArray, indices1.toArray) + val shape = Array(rows, max_fea_len) + output = isSparse match { + case true => + Tensor.sparse(indices, values.toArray, shape) + case false => + Tensor.dense(Tensor.sparse(indices, values.toArray, shape)) + } + output + } +} + +object CategoricalColHashBucket{ + def apply[T: ClassTag]( + hashBucketSize: Int, + strDelimiter: String = ",", + isSparse: Boolean = true) + (implicit ev: TensorNumeric[T]) + : CategoricalColHashBucket[T] = new CategoricalColHashBucket[T]( + hashBucketSize = hashBucketSize, + strDelimiter = strDelimiter, + isSparse = isSparse + ) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucketSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucketSpec.scala new file mode 100644 index 00000000000..c9dbadd9b5b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucketSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{T, Table} +import org.scalatest.{FlatSpec, Matchers} + +class CategoricalColHashBucketSpec extends FlatSpec with Matchers { + + "CategoricalColHashBucket operation single value feature column" should "work correctly" in { + val input = Tensor[String](T(T(1), T(2), T(3))) + val indices = Array(Array(0, 1, 2), Array(0, 0, 0)) + val values = Array(5, 53, 77) + val shape = Array(3, 1) + val expectOutput = Tensor.sparse( + indices, values, shape + ) + val output = CategoricalColHashBucket[Double](hashBucketSize = 100, isSparse = true) + .forward(input) + output should be(expectOutput) + } + + "CategoricalColHashBucket operation multi value feature column" should "work correctly" in { + val input = Tensor[String](T(T("1,2"), T("2"), T("1,3,2"))) + val indices = Array(Array(0, 0, 1, 2, 2, 2), Array(0, 1, 0, 0, 1, 2)) + val values = Array(5, 53, 53, 5, 77, 53) + val shape = Array(3, 3) + val expectOutput = Tensor.dense(Tensor.sparse( + indices, values, shape + )) + val output = CategoricalColHashBucket[Double](hashBucketSize = 100, isSparse = false) + .forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 4b5ac5c0d2a..e3769a71e31 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -1294,7 +1294,7 @@ class ModuleSerializerSpec extends SerializerSpecHelper { "VolumetricAveragePooling serializer" should "work properly" in { val volumetricAveragePooling = VolumetricAveragePooling[Float](2, 2, 2, 1, 1, 1, 0, 0, 0). - setName("volumetricAveragePooling") + setName("volumetricAveragePooling") val input = Tensor[Float](1, 2, 3, 3).apply1(_ => Random.nextFloat()) runSerializationTest(volumetricAveragePooling, input) } @@ -1436,14 +1436,14 @@ class ModuleSerializerSpec extends SerializerSpecHelper { import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString val subStr = Substr[Float]().setName("subStr") val input = T(Tensor.scalar[ByteString](ByteString.copyFromUtf8("HelloBigDL")), - Tensor.scalar[Int](0), Tensor.scalar[Int](5)) + Tensor.scalar[Int](0), Tensor.scalar[Int](5)) runSerializationTest(subStr, input) } "SumOps serializer" should "work properly" in { val sumOps = SumOps[Float, Float]().setName("sumOps") val input = T(Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float]()) + Tensor[Float]()) runSerializationTest(sumOps, input) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TFSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala similarity index 95% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TFSerializerSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index a78e47cae0e..d6a977e466b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/TFSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -16,36 +16,25 @@ package com.intel.analytics.bigdl.utils.serializer -import java.io.File import java.io.{File => JFile} -import java.lang.reflect.Modifier import com.google.protobuf.{ByteString, CodedOutputStream} -import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC - -import scala.collection.JavaConverters._ -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} -import com.intel.analytics.bigdl.nn.tf.{BiasAdd, Const, Fill, Log1p, Shape, SplitAndSelect, StrideSlice, Variable, TensorModuleWrapper} -import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} -import com.intel.analytics.bigdl.optim.L2Regularizer +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, CategoricalColHashBucket, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.tf._ +import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.tf.TFRecordIterator import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _, _} -import com.intel.analytics.bigdl.utils.{T, Table} -import org.reflections.Reflections -import org.reflections.scanners.SubTypesScanner -import org.reflections.util.{ClasspathHelper, ConfigurationBuilder, FilterBuilder} -import org.scalatest.{BeforeAndAfterAll, FlatSpec, Matchers} import org.tensorflow.example._ import org.tensorflow.framework.DataType import scala.collection.mutable import scala.util.Random -class TFSerializerSpec extends SerializerSpecHelper { +class OperationSerializerSpec extends SerializerSpecHelper { override protected def getPackage(): String = "com.intel.analytics.bigdl.nn.ops" @@ -469,6 +458,14 @@ class TFSerializerSpec extends SerializerSpecHelper { .asInstanceOf[ModuleToOperation[Float]].module.getClass) } + "CategoricalColHashBucket" should "work properly" in { + val categoricalColHashBucket = CategoricalColHashBucket[Float]( + hashBucketSize = 100 + ).setName("categoricalColHashBucket") + val input = Tensor[String](T(T(1), T(2), T(3))) + runSerializationTest(categoricalColHashBucket, input) + } + "LessEqual serializer" should "work properly" in { val lessEqual = LessEqual[Float]().setName("lessEqual") val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) @@ -559,7 +556,7 @@ class TFSerializerSpec extends SerializerSpecHelper { "TensorModuleWrapper serializer" should "work properly" in { - val tensorModuleWrapper = TensorModuleWrapper[Float, Float](SoftPlus[Float]()). + val tensorModuleWrapper = TensorModuleWrapper[Float, Float](BigDLSoftPlus[Float]()). setName("moduleToOperation") val input = Tensor[Float](T(1.0f, 1.0)) runSerializationTest(tensorModuleWrapper, input) From eb898b9394767b49802bfb9c4d8f68be6220f9fe Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Mon, 5 Feb 2018 12:44:10 +0800 Subject: [PATCH 0678/1065] Keras-like API Recurrent layers (#2235) * rnns * update * add ser test * scala doc * update --- .../analytics/bigdl/dllib/keras/GRU.scala | 93 +++++++++++++++++++ .../analytics/bigdl/dllib/keras/Highway.scala | 82 ++++++++++++++++ .../bigdl/dllib/keras/KerasUtils.scala | 24 +++++ .../analytics/bigdl/dllib/keras/LSTM.scala | 93 +++++++++++++++++++ .../bigdl/dllib/keras/Recurrent.scala | 57 ++++++++++++ .../bigdl/dllib/keras/SimpleRNN.scala | 88 ++++++++++++++++++ .../bigdl/dllib/keras/nn/GRUSpec.scala | 92 ++++++++++++++++++ .../bigdl/dllib/keras/nn/HighwaySpec.scala | 72 ++++++++++++++ .../bigdl/dllib/keras/nn/LSTMSpec.scala | 91 ++++++++++++++++++ .../bigdl/dllib/keras/nn/SimpleRNNSpec.scala | 87 +++++++++++++++++ .../KerasModuleSerializerSpec.scala | 30 ++++++ 11 files changed, 809 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SimpleRNNSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala new file mode 100644 index 00000000000..e6bf094372c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala @@ -0,0 +1,93 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Gated Recurrent Unit architecture. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 3D, i.e. (batch, time steps, input dim). + * + * @param outputDim Hidden unit size. Dimension of internal projections and final output. + * @param activation Activation function to use. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * Default is 'tanh'. + * @param innerActivation Activation function for inner cells. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * Default is 'hard_sigmoid'. + * @param returnSequences Whether to return the full sequence or only return the last output, + * in the output sequence. Default is false. + * @param goBackwards Whether the input sequence will be processed backwards. Default is false. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param uRegularizer An instance of [[Regularizer]], applied the recurrent weights matrices. + * Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class GRU[T: ClassTag]( + outputDim: Int, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val innerActivation: AbstractModule[Tensor[T], Tensor[T], T] = null, + returnSequences: Boolean = false, + goBackwards: Boolean = false, + var wRegularizer: Regularizer[T] = null, + var uRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Recurrent[T](outputDim, returnSequences, goBackwards, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = com.intel.analytics.bigdl.nn.GRU[T]( + inputSize = input(2), + outputSize = outputDim, + activation = activation.asInstanceOf[TensorModule[T]], + innerActivation = innerActivation.asInstanceOf[TensorModule[T]], + wRegularizer = wRegularizer, + uRegularizer = uRegularizer, + bRegularizer = bRegularizer) + super.processParameters(layer) + } +} + +object GRU { + def apply[@specialized(Float, Double) T: ClassTag]( + outputDim: Int, + activation: String = "tanh", + innerActivation: String = "hard_sigmoid", + returnSequences: Boolean = false, + goBackwards: Boolean = false, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : GRU[T] = { + new GRU(outputDim, KerasUtils.getActivation(activation), + KerasUtils.getActivation(innerActivation), returnSequences, + goBackwards, wRegularizer, uRegularizer, bRegularizer, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala new file mode 100644 index 00000000000..1f422df8b3e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala @@ -0,0 +1,82 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, IdentityOutputShape, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Densely connected highway network. + * Highway layers are a natural extension of LSTMs to feedforward networks. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 2D, i.e. (batch, input dim). + * + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class Highway[T: ClassTag]( + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 2, + s"Highway requires 2D input, but got input dim ${input.length}") + inputShape + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = com.intel.analytics.bigdl.nn.Highway[T]( + size = input(1), + withBias = bias, + activation = activation.asInstanceOf[TensorModule[T]], + wRegularizer = wRegularizer, + bRegularizer = bRegularizer + ) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Highway { + def apply[@specialized(Float, Double) T: ClassTag]( + activation: String = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : Highway[T] = { + new Highway[T](KerasUtils.getActivation(activation), + wRegularizer, bRegularizer, bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala index 6cbe483efeb..d2c475681fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala @@ -64,4 +64,28 @@ object KerasUtils { } } + private[keras] def computeConvOutputLength( + inputLength: Int, + filterSize: Int, + borderMode: String, + stride: Int, + dilation: Int = 1): Int = { + val dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1) + val outputLength = borderMode match { + case "valid" => inputLength - dilatedFilterSize + 1 + case "same" => inputLength + } + (outputLength + stride - 1) / stride + } + + private[keras] def getPadsFromBorderMode3D + (borderMode: String = "valid"): (Int, Int, Int) = { + if (borderMode == "same") { + // padT, padH, padW + (-1, -1, -1) + } else { + (0, 0, 0) + } + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala new file mode 100644 index 00000000000..29f3501ba81 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala @@ -0,0 +1,93 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Long Short Term Memory unit architecture. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 3D, i.e. (batch, time steps, input dim). + * + * @param outputDim Hidden unit size. Dimension of internal projections and final output. + * @param activation Activation function to use. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * Default is 'tanh'. + * @param innerActivation Activation function for inner cells. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * dDefault is 'hard_sigmoid'. + * @param returnSequences Whether to return the full sequence or only return the last output, + * in the output sequence. Default is false. + * @param goBackwards Whether the input sequence will be processed backwards. Default is false. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param uRegularizer An instance of [[Regularizer]], applied the recurrent weights matrices. + * Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class LSTM[T: ClassTag]( + outputDim: Int, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val innerActivation: AbstractModule[Tensor[T], Tensor[T], T] = null, + returnSequences: Boolean = false, + goBackwards: Boolean = false, + var wRegularizer: Regularizer[T] = null, + var uRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Recurrent[T](outputDim, returnSequences, goBackwards, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = com.intel.analytics.bigdl.nn.LSTM[T]( + inputSize = input(2), + hiddenSize = outputDim, + activation = activation.asInstanceOf[TensorModule[T]], + innerActivation = innerActivation.asInstanceOf[TensorModule[T]], + wRegularizer = wRegularizer, + uRegularizer = uRegularizer, + bRegularizer = bRegularizer) + super.processParameters(layer) + } +} + +object LSTM { + def apply[@specialized(Float, Double) T: ClassTag]( + outputDim: Int, + activation: String = "tanh", + innerActivation: String = "hard_sigmoid", + returnSequences: Boolean = false, + goBackwards: Boolean = false, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : LSTM[T] = { + new LSTM(outputDim, KerasUtils.getActivation(activation), + KerasUtils.getActivation(innerActivation), returnSequences, + goBackwards, wRegularizer, uRegularizer, bRegularizer, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala new file mode 100644 index 00000000000..22ce5d23fae --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.{Cell, Reverse, Select, Sequential => TSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * This is the abstract base class for recurrent layers. + * Do not create a new instance of it or use it in a model. + * Please use its child classes, 'SimpleRNN', 'LSTM' and 'GRU' instead. + */ +abstract class Recurrent[T: ClassTag]( + val outputDim: Int, + val returnSequences: Boolean = false, + val goBackwards: Boolean = false, + var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"Recurrent layers require 3D input, but got input dim ${input.length}") + if (returnSequences) Shape(input(0), input(1), outputDim) + else Shape(input(0), outputDim) + } + + def processParameters(rnn: Cell[T]): AbstractModule[Tensor[T], Tensor[T], T] = { + val model = TSequential[T]() + if (goBackwards) model.add(Reverse(2)) + val rec = com.intel.analytics.bigdl.nn.Recurrent[T]() + rec.add(rnn) + model.add(rec) + if (!returnSequences) model.add(Select(2, -1)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala new file mode 100644 index 00000000000..1274a6dec61 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala @@ -0,0 +1,88 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.RnnCell +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * A fully-connected recurrent neural network cell. The output is to be fed back to input. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 3D, i.e. (batch, time steps, input dim). + * + * @param outputDim Hidden unit size. Dimension of internal projections and final output. + * @param activation Activation function to use. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * Default is 'tanh'. + * @param returnSequences Whether to return the full sequence or only return the last output, + * in the output sequence. Default is false. + * @param goBackwards Whether the input sequence will be processed backwards. Default is false. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param uRegularizer An instance of [[Regularizer]], applied the recurrent weights matrices. + * Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ +class SimpleRNN[T: ClassTag]( + outputDim: Int, + val activation: AbstractModule[Tensor[T], Tensor[T], T], + returnSequences: Boolean = false, + goBackwards: Boolean = false, + var wRegularizer: Regularizer[T] = null, + var uRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Recurrent[T](outputDim, returnSequences, goBackwards, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = RnnCell( + inputSize = input(2), + hiddenSize = outputDim, + activation = activation.asInstanceOf[TensorModule[T]], + isInputWithBias = false, + wRegularizer = wRegularizer, + uRegularizer = uRegularizer, + bRegularizer = bRegularizer) + super.processParameters(layer) + } +} + +object SimpleRNN { + def apply[@specialized(Float, Double) T: ClassTag]( + outputDim: Int, + activation: String = "tanh", + returnSequences: Boolean = false, + goBackwards: Boolean = false, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : SimpleRNN[T] = { + new SimpleRNN[T](outputDim, KerasUtils.getActivation(activation), + returnSequences, goBackwards, wRegularizer, + uRegularizer, bRegularizer, inputShape) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala new file mode 100644 index 00000000000..fbc0f6f8164 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Dense, GRU, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GRUSpec extends KerasBaseSpec { + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + val w1 = Tensor[Float](in(0).size(2)*3, in(0).size(1)) + val w2 = Tensor[Float](in(2).size(1)*3) + val w3 = Tensor[Float](in(1).size(2)*2, in(1).size(1)) + w1.narrow(1, 1, in(0).size(2)).copy(in(3).t()) + w1.narrow(1, 1 + in(0).size(2), in(0).size(2)).copy(in(0).t()) + w1.narrow(1, 1 + 2*in(0).size(2), in(0).size(2)).copy(in(6).t()) + w2.narrow(1, 1, in(2).size(1)).copy(in(5)) + w2.narrow(1, 1 + in(2).size(1), in(2).size(1)).copy(in(2)) + w2.narrow(1, 1 + 2*in(2).size(1), in(2).size(1)).copy(in(8)) + w3.narrow(1, 1, in(1).size(2)).copy(in(4).t()) + w3.narrow(1, 1 + in(1).size(2), in(1).size(2)).copy(in(1).t()) + Array(w1, w2, w3, in(7).t()) + } + + "GRU not return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[28, 28]) + |input = np.random.random([2, 28, 28]) + |output_tensor = GRU(128)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GRU[Float](128, inputShape = Shape(28, 28)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 128)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "GRU return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[32, 32]) + |input = np.random.random([2, 32, 32]) + |output_tensor = GRU(36, return_sequences=True, activation="relu")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GRU[Float](36, returnSequences = true, + activation = "relu", inputShape = Shape(32, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 32, 36)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "GRU go backwards and return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[28, 32]) + |input = np.random.random([1, 28, 32]) + |output_tensor = GRU(16, return_sequences=True, go_backwards=True)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GRU[Float](16, returnSequences = true, + goBackwards = true, inputShape = Shape(28, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 28, 16)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala new file mode 100644 index 00000000000..bd0a8f6b979 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala @@ -0,0 +1,72 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Dense, Highway, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class HighwaySpec extends KerasBaseSpec { + + "Highway computeOutputShape" should "work properly" in { + val seq = KSequential[Float]() + val klayer = Highway[Float](inputShape = Shape(6)) + seq.add(klayer) + seq.add(Dense(5)) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 5)) + } + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + if (in.length == 2) Array(in(1).t(), in(0).t()) // without bias + else Array(in(1).t(), in(3), in(0).t(), in(2)) // with bias + } + + "Highway" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[10]) + |input = np.random.random([4, 10]) + |output_tensor = Highway()(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Highway[Float](inputShape = Shape(10)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 10)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "Highway without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4]) + |input = np.random.random([2, 4]) + |output_tensor = Highway(activation="tanh", bias=False)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Highway[Float](activation = "tanh", bias = false, inputShape = Shape(4)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala new file mode 100644 index 00000000000..967f1de761c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala @@ -0,0 +1,91 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Dense, LSTM, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class LSTMSpec extends KerasBaseSpec { + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + val w1 = Tensor[Float](in(0).size(2)*4, in(0).size(1)) + val w2 = Tensor[Float](in(2).size(1)*4) + val w3 = Tensor[Float](in(1).size(2)*4, in(1).size(1)) + var i = 0 + while(i < 4) { + w1.narrow(1, 1 + i * in(0).size(2), in(0).size(2)).copy(in(3*i).t()) + w2.narrow(1, 1 + i * in(2).size(1), in(2).size(1)).copy(in(2 + 3*i)) + w3.narrow(1, 1 + i * in(1).size(2), in(1).size(2)).copy(in(1 + 3*i).t()) + i += 1 + } + Array(w1, w2, w3) + } + + "LSTM not return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[10, 12]) + |input = np.random.random([3, 10, 12]) + |output_tensor = LSTM(32)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = LSTM[Float](32, inputShape = Shape(10, 12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 32)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "LSTM return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[32, 32]) + |input = np.random.random([2, 32, 32]) + |output_tensor = LSTM(8, return_sequences=True, inner_activation="sigmoid")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = LSTM[Float](8, returnSequences = true, + innerActivation = "sigmoid", inputShape = Shape(32, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 32, 8)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "LSTM go backwards and return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[28, 32]) + |input = np.random.random([1, 28, 32]) + |output_tensor = LSTM(10, return_sequences=True, go_backwards=True)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = LSTM[Float](10, returnSequences = true, + goBackwards = true, inputShape = Shape(28, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 28, 10)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SimpleRNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SimpleRNNSpec.scala new file mode 100644 index 00000000000..ccf37437729 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SimpleRNNSpec.scala @@ -0,0 +1,87 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Dense, SimpleRNN, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class SimpleRNNSpec extends KerasBaseSpec { + + "SimpleRNN computeOutputShape" should "work properly" in { + val seq = KSequential[Float]() + val rnn = SimpleRNN[Float](10, inputShape = Shape(3, 6)) + seq.add(rnn) + seq.add(Dense(5)) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 5)) + } + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = + Array(in(0).t(), in(1).t(), in(2)) + + "SimpleRNN not return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = SimpleRNN(8, activation="relu")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = SimpleRNN[Float](8, activation = "relu", inputShape = Shape(4, 5)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 8)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "SimpleRNN return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[5, 8]) + |input = np.random.random([3, 5, 8]) + |output_tensor = SimpleRNN(12, return_sequences=True)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = SimpleRNN[Float](12, returnSequences = true, inputShape = Shape(5, 8)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 5, 12)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "SimpleRNN go backwards" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, 12]) + |input = np.random.random([3, 12, 12]) + |output_tensor = SimpleRNN(4, go_backwards=True, activation="sigmoid")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = SimpleRNN[Float](4, goBackwards = true, + activation = "sigmoid", inputShape = Shape(12, 12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index a8162d2f359..93ce3655bfc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -110,5 +110,35 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(layer, input) } + "SimpleRNN serializer" should "work properly" in { + val layer = SimpleRNN[Float](8, activation = "relu", inputShape = Shape(4, 5)) + layer.build(Shape(3, 4, 5)) + val input = Tensor[Float](3, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "LSTM serializer" should "work properly" in { + val layer = LSTM[Float](8, returnSequences = true, + innerActivation = "sigmoid", inputShape = Shape(32, 32)) + layer.build(Shape(3, 32, 32)) + val input = Tensor[Float](3, 32, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "GRU serializer" should "work properly" in { + val layer = GRU[Float](16, returnSequences = true, + goBackwards = true, inputShape = Shape(28, 32)) + layer.build(Shape(2, 28, 32)) + val input = Tensor[Float](2, 28, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Highway serializer" should "work properly" in { + val layer = Highway[Float](activation = "tanh", bias = false, inputShape = Shape(4)) + layer.build(Shape(3, 4)) + val input = Tensor[Float](3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + } From 7a622867289555a90386785014ad53c765f7a463 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 5 Feb 2018 13:39:13 +0800 Subject: [PATCH 0679/1065] Predictor accept table as output (#2250) * Predictor accept table as output * fix more * meet code review * meet code review --- .../bigdl/dllib/optim/Predictor.scala | 74 +++++++++++++++---- .../dllib/optim/LocalPredictorSpec.scala | 32 +++++++- .../bigdl/dllib/optim/PredictorSpec.scala | 31 +++++++- 3 files changed, 119 insertions(+), 18 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index d24c5414936..d3d3f14e604 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -23,7 +23,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame} +import com.intel.analytics.bigdl.utils.{T, Table} import org.apache.spark.rdd.RDD +import Predictor._ import scala.reflect.ClassTag @@ -53,21 +55,69 @@ object Predictor { (localModel: Module[T], samples: Seq[Sample[T]], localToBatch: Transformer[Sample[T], MiniBatch[T]], shareBuffer: Boolean, - outputLayer: String = null)(implicit ev: TensorNumeric[T]): Iterator[Tensor[T]] = { + outputLayer: String = null)(implicit ev: TensorNumeric[T]): Iterator[Activity] = { + val layer = if (outputLayer == null) { + localModel + } else { + val ol = localModel(outputLayer) + require(ol.isDefined, s"cannot find layer that map name $outputLayer") + ol.get + } localToBatch(samples.toIterator).flatMap(batch => { localModel.forward(batch.getInput()) - val output = if (outputLayer == null) { - localModel.output.toTensor[T] - } else { - localModel(outputLayer).get.output.toTensor[T] - } - val result = if (shareBuffer) output else output.clone() + splitBatch[T](layer.output, shareBuffer, batch.size()) + }) + } + + private[optim] def splitBatch[T: ClassTag](output: Activity, shareBuffer: Boolean, batchSize: Int) + (implicit ev: TensorNumeric[T]): Array[Activity] = { + val out = if (output.isTensor) { + val result = if (shareBuffer) output.toTensor[T] else output.toTensor[T].clone() if (result.dim() == 1) { + require(batchSize == 1, + s"If result dim == 1, the batchSize is required to be 1, while actual is $batchSize") Array(result) } else { result.split(1) } - }) + } else { + val result = output.toTable + val first = result[Tensor[T]](1) + if (first.dim() == 1) { + require(batchSize == 1, + s"If result dim == 1, the batchSize is required to be 1, while actual is $batchSize") + val table = if (shareBuffer) { + result + } else { + val table = T() + (1 to result.length()).foreach(key => { + table.insert(result[Tensor[T]](key).clone()) + }) + table + } + Array(table) + } else { + val batch = first.size(1) + require(batch == batchSize, s"output batch $batch is not equal to input batch $batchSize") + val tables = new Array[Table](batch) + var i = 1 + while (i <= batch) { + val table = T() + tables(i - 1) = table + (1 to result.length()).foreach(key => { + val split = result[Tensor[T]](key)(i) + if (shareBuffer) { + table.insert(split) + } else { + table.insert(split.clone()) + } + }) + i += 1 + } + tables + } + } + out.asInstanceOf[Array[Activity]] } } @@ -115,12 +165,8 @@ class Predictor[T: ClassTag] private[optim]( val localTransformer = otherBroad.value.cloneTransformer() val miniBatch = localTransformer(partition) miniBatch.flatMap( batch => { - val output = localModel.forward(batch.getInput).toTensor[T] - if (shareBuffer) { - output.split(1) - } else { - output.clone().split(1) - } + val output = localModel.forward(batch.getInput) + splitBatch(output, shareBuffer, batch.size()) }) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index 1e99bf1ff86..f6d63111f9e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -22,11 +22,11 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.dataset.{PaddingParam, Sample, SampleToMiniBatch} import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.nn.{Sequential, SpatialConvolution, Tanh} +import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} -import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Util} +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Table, Util} import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.commons.io.FileUtils import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -275,4 +275,32 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { }).toArray.flatten } + + "predictImage with table output" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val ims = (1 to 50).map(x => { + val im = ImageFeature() + im(ImageFeature.uri) = x.toString + im(ImageFeature.imageTensor) = Tensor[Float](3, 24, 24).randn() + im + }) + + val imageFrame = ImageFrame.array(ims.toArray) -> ImageFrameToSample() + val input = Input() + val conv = SpatialConvolution(3, 6, 5, 5).inputs(input) + val out1 = Tanh().inputs(conv) + val out2 = ReLU().inputs(conv) + val model = Graph(input, Array(out1, out2)) + val detection = model.predictImage(imageFrame).toLocal() + val feature = detection.array.head + + val imageFeatures = detection.array + (1 to 20).foreach(x => { + imageFeatures(x - 1).uri() should be (x.toString) + assert(imageFeatures(x - 1).predict() != null) + assert(imageFeatures(x - 1).predict().asInstanceOf[Table].length() == 2) + }) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index 392800081c4..f8648550edf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -19,11 +19,11 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.dataset.{PaddingParam, Sample} import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.models.lenet.LeNet5 -import com.intel.analytics.bigdl.nn.{Sequential, SpatialConvolution, Tanh} +import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, Table} import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -184,4 +184,31 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ assert(imageFeatures(x - 1).predict() != null) }) } + + + "predictImage with table output" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val ims = (1 to 50).map(x => { + val im = ImageFeature() + im(ImageFeature.uri) = x.toString + im(ImageFeature.imageTensor) = Tensor[Float](3, 24, 24).randn() + im + }) + + val imageFrame = ImageFrame.array(ims.toArray).toDistributed(sc) -> ImageFrameToSample() + val input = Input() + val conv = SpatialConvolution(3, 6, 5, 5).inputs(input) + val out1 = Tanh().inputs(conv) + val out2 = ReLU().inputs(conv) + val model = Graph(input, Array(out1, out2)) + val detection = model.predictImage(imageFrame).toDistributed() + + val imageFeatures = detection.rdd.collect() + (1 to 20).foreach(x => { + imageFeatures(x - 1).uri() should be (x.toString) + assert(imageFeatures(x - 1).predict() != null) + assert(imageFeatures(x - 1).predict().asInstanceOf[Table].length() == 2) + }) + } } From 64c386a61573bcdebfe7870f56e6a5c4738b2862 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 5 Feb 2018 16:10:53 +0800 Subject: [PATCH 0680/1065] Provide option to catch or throw exception in FeatureTransformer (#2248) * Provide option to catch or throw exception in FeatureTransformer * add comment * update --- .../vision/image/FeatureTransformer.scala | 33 +++++++++++-- .../vision/image/FeatureTransformerSpec.scala | 48 ++++++++++++++++++- 2 files changed, 75 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/FeatureTransformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/FeatureTransformer.scala index bd9e47a8411..38833695498 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/FeatureTransformer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/FeatureTransformer.scala @@ -24,12 +24,19 @@ import org.apache.log4j.Logger /** * FeatureTransformer is a transformer that transform ImageFeature */ -abstract class FeatureTransformer() extends Transformer[ImageFeature, ImageFeature] { +abstract class FeatureTransformer() + extends Transformer[ImageFeature, ImageFeature] { import FeatureTransformer.logger private var outKey: Option[String] = None + /** + * if true, catch the exception of the transformer to avoid crashing. + * if false, interrupt the transformer when error happens + */ + private[image] var ignoreException: Boolean = false + /** * set the output key to store current transformed result * if the key is not set, or same as default, then the transformed result @@ -68,10 +75,14 @@ abstract class FeatureTransformer() extends Transformer[ImageFeature, ImageFeatu } } catch { case e: Exception => - val path = if (feature.contains(ImageFeature.uri)) feature(ImageFeature.uri) else "" - logger.warn(s"failed ${path} in transformer ${getClass}") - e.printStackTrace() feature.isValid = false + if (ignoreException) { + val path = if (feature.contains(ImageFeature.uri)) feature(ImageFeature.uri) else "" + logger.warn(s"failed ${path} in transformer ${getClass}") + e.printStackTrace() + } else { + throw e + } } feature } @@ -95,6 +106,14 @@ abstract class FeatureTransformer() extends Transformer[ImageFeature, ImageFeatu override def -> [C](other: Transformer[ImageFeature, C]): Transformer[ImageFeature, C] = { new ChainedTransformer(this, other) } + + /** + * catch the exception of the transformer to avoid crashing. + */ + def enableIgnoreException(): this.type = { + ignoreException = true + this + } } object FeatureTransformer { @@ -110,4 +129,10 @@ class ChainedFeatureTransformer(first: FeatureTransformer, last: FeatureTransfor override def transform(prev: ImageFeature): ImageFeature = { last.transform(first.transform(prev)) } + + override def enableIgnoreException(): this.type = { + first.enableIgnoreException() + last.enableIgnoreException() + this + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/FeatureTransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/FeatureTransformerSpec.scala index 0f195203597..3b818b01212 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/FeatureTransformerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/FeatureTransformerSpec.scala @@ -29,29 +29,73 @@ import org.scalatest.{FlatSpec, Matchers} class FeatureTransformerSpec extends FlatSpec with Matchers { val resource = getClass.getClassLoader.getResource("pascal/") - "Image Transformer with empty byte input" should "work properly" in { + "Image Transformer with empty byte input" should "throw exception" in { + intercept[Exception] { + val img = Array[Byte]() + val imageFeature = ImageFeature(img) + val imageFrame = new LocalImageFrame(Array(imageFeature)) + val imgAug = Resize(1, 1, -1) -> + FixedCrop(-1, -1, -1, -1, normalized = false) -> + MatToFloats(validHeight = 1, validWidth = 1) + val out = imgAug(imageFrame) + imageFeature.floats().length should be(3) + imageFeature.isValid should be(false) + } + } + + "Image Transformer with exception" should "throw exception" in { + intercept[Exception] { + val images = ImageFrame.read(resource.getFile) + val imgAug = FixedCrop(-1, -1, -1, -1, normalized = false) -> + Resize(300, 300, -1) -> + MatToFloats(validHeight = 300, validWidth = 300) + val out = imgAug(images) + val imageFeature = out.asInstanceOf[LocalImageFrame].array(0) + imageFeature.floats().length should be(3 * 300 * 300) + imageFeature.isValid should be(false) + } + } + + "Image Transformer with empty byte input" should "catch exception" in { val img = Array[Byte]() val imageFeature = ImageFeature(img) val imageFrame = new LocalImageFrame(Array(imageFeature)) val imgAug = Resize(1, 1, -1) -> FixedCrop(-1, -1, -1, -1, normalized = false) -> MatToFloats(validHeight = 1, validWidth = 1) + imgAug.enableIgnoreException() val out = imgAug(imageFrame) imageFeature.floats().length should be(3) imageFeature.isValid should be(false) } - "Image Transformer with exception" should "work properly" in { + "Image Transformer with exception" should "catch exception" in { val images = ImageFrame.read(resource.getFile) val imgAug = FixedCrop(-1, -1, -1, -1, normalized = false) -> Resize(300, 300, -1) -> MatToFloats(validHeight = 300, validWidth = 300) + imgAug.enableIgnoreException() val out = imgAug(images) val imageFeature = out.asInstanceOf[LocalImageFrame].array(0) imageFeature.floats().length should be(3 * 300 * 300) imageFeature.isValid should be(false) } + "Image Transformer setSkipException" should "work" in { + val crop = FixedCrop(-1, -1, -1, -1, normalized = false) + val resize = Resize(300, 300, -1) + val toFloats = MatToFloats(validHeight = 300, validWidth = 300) + val imgAug = crop -> resize -> toFloats + crop.ignoreException should be (false) + resize.ignoreException should be (false) + toFloats.ignoreException should be (false) + + imgAug.enableIgnoreException() + crop.ignoreException should be (true) + resize.ignoreException should be (true) + toFloats.ignoreException should be (true) + } + "ImageAugmentation with label and random" should "work properly" in { val img = Files.readAllBytes(Paths.get(resource.getFile + "/000025.jpg")) val classes = Array(11.0, 11.0, 11.0, 16.0, 16.0, 16.0, 11.0, 16.0, From 6c0bb09ad4ed989b269abfd6aabf299453ab2eee Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Tue, 6 Feb 2018 19:50:03 +0800 Subject: [PATCH 0681/1065] Refactor layer creation for keras (#2277) * keras * update * fix --- .../utils/python/api/PythonBigDLKeras.scala | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala index 62b845a81f0..002bc510cc7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -41,8 +41,17 @@ object PythonBigDLKeras { def ofDouble(): PythonBigDLKeras[Double] = new PythonBigDLKeras[Double]() } -class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializable { - def createDense(outputDim: Int, +class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends PythonBigDL[T] { + + def toScalaShape(inputShape: JList[Int]): Shape = { + if (inputShape == null) { + null + } else { + Shape(inputShape.asScala.toArray) + } + } + + def createKerasDense(outputDim: Int, init: InitializationMethod = RandomUniform, activation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, @@ -55,10 +64,6 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Seria wRegularizer, bRegularizer, bias, - if (inputShape == null) { - null - } else { - Shape(inputShape.asScala.toArray) - }) + toScalaShape(inputShape)) } } From 5d739bc1738caf7eb9f48f45281dad00fa7c14fa Mon Sep 17 00:00:00 2001 From: tosky001 Date: Tue, 6 Feb 2018 22:03:30 +0800 Subject: [PATCH 0682/1065] add [[BucketizedCol]] Operation (#2272) * add [[BucketizedCol]] Operation to discrete dense input * add a new line in the end of BucketizedCol.scala * replace while loops with Tensor.applyfun * add exception handling * add private val to boundaries declaration --- .../bigdl/dllib/nn/ops/BucketizedCol.scala | 87 +++++++++++++++++++ .../dllib/nn/ops/BucketizedColSpec.scala | 43 +++++++++ .../serializer/OperationSerializerSpec.scala | 9 +- 3 files changed, 138 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedCol.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedColSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedCol.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedCol.scala new file mode 100644 index 00000000000..fdbb126e8bd --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedCol.scala @@ -0,0 +1,87 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.{DenseTensorApply, DoubleType, FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import java.util.Arrays.binarySearch + +import scala.reflect.ClassTag + +/** + * BucketizedCol operation represents discretized dense input. + * + * The Operation can handle single or multi feature column, + * as long as the boundaries is same between feature columns. + * + * Buckets include the left boundary, and exclude the right boundary. + * Namely, boundaries=Array(0, 1, 10) generates buckets (-inf,0),[0,1),[1,10),[10,+inf) + * + * For example, boundaries = Array(0, 10, 100) and input tensor is an 2D 3x2 DenseTensor: + * -1, 1 + * 101, 10 + * 5, 100 + * + * the output tensor should be an 2D 3x2 DenseTensor + * 0, 1 + * 3, 2 + * 1, 3 + * + * @param boundaries The bound Array of each bucket. + * @tparam T Numeric type. Parameter tensor numeric type. Only support float/double now + */ + +class BucketizedCol[T: ClassTag]( + private val boundaries: Array[Double])(implicit ev: TensorNumeric[T]) + extends Operation[Tensor[T], Tensor[Int], T] { + + require(boundaries.length >= 1, + "the length of boundaries must be more than or equal to 1") + + private val boundariesImpl = boundaries.map(ev.fromType[Double]) + + output = Tensor[Int]() + + override def updateOutput(input: Tensor[T]): Tensor[Int] = { + + val resTensor = Tensor[Int](input.size()) + + ev.getType() match { + case FloatType => + resTensor.applyFun[Float]( + input.asInstanceOf[Tensor[Float]], + x => math.abs(binarySearch(boundariesImpl.asInstanceOf[Array[Float]], x) + 1)) + case DoubleType => + resTensor.applyFun[Double]( + input.asInstanceOf[Tensor[Double]], + x => math.abs(binarySearch(boundariesImpl.asInstanceOf[Array[Double]], x) + 1)) + case _ => + throw new RuntimeException("Unsupported tensor type") + } + + output = resTensor + output + } +} + +object BucketizedCol { + def apply[T: ClassTag]( + boundaries: Array[Double]) + (implicit ev: TensorNumeric[T]): BucketizedCol[T] + = new BucketizedCol[T]( + boundaries = boundaries + ) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedColSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedColSpec.scala new file mode 100644 index 00000000000..86a914ed175 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedColSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class BucketizedColSpec + extends FlatSpec with Matchers { + "BucketizedCol with Double Type" should "work correctly" in { + val input = Tensor[Double](T(T(-1, 1), T(101, 10), T(5, 100))) + val expectOutput = Tensor[Int]( + T(T(0, 1), T(3, 2), T(1, 3)) + ) + val output = BucketizedCol[Double](boundaries = Array(0, 10, 100)) + .forward(input) + output should be(expectOutput) + } + + "BucketizedCol with Float Type" should "work correctly" in { + val input = Tensor[Float](T(T(-1.0f, 1.0f), T(101.0f, 10.0f), T(5.0f, 100.0f))) + val expectOutput = Tensor[Int]( + T(T(0, 1), T(3, 2), T(1, 3)) + ) + val output = BucketizedCol[Float](boundaries = Array(0, 10, 100)) + .forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index d6a977e466b..f61fbe9146c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -21,7 +21,7 @@ import java.io.{File => JFile} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, CategoricalColHashBucket, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, BucketizedCol, Cast, CategoricalColHashBucket, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} import com.intel.analytics.bigdl.tensor._ @@ -153,6 +153,13 @@ class OperationSerializerSpec extends SerializerSpecHelper { asInstanceOf[ModuleToOperation[Float]].module.getClass) } + "BucketizedCol serializer" should "work properly" in { + val bucketizedCol = BucketizedCol[Float](boundaries = Array(0.0, 10.0, 100.0)) + .setName("bucketizedCol") + val input = Tensor[Float](T(T(-1, 1), T(101, 10), T(5, 100))) + runSerializationTest(bucketizedCol, input) + } + "Cast serializer" should "work properly" in { val cast = Cast[Float, Float]().setName("cast") val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) From e87556ee130df11c546111f7574ba02046ad0c5c Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Tue, 6 Feb 2018 23:30:02 +0800 Subject: [PATCH 0683/1065] Keras-like API compute outputshape, core layers, conv+pooling layers (#2220) * layers * enrich error msg * update * meet review * clean * remove * update cropping * update * add ser test * update * update * var to val for shape * dimordering * fix * style * update * outputshape for separableconv2d * update --- .../bigdl/dllib/keras/Activation.scala | 2 +- .../bigdl/dllib/keras/AveragePooling1D.scala | 63 ++++++++++++ .../bigdl/dllib/keras/AveragePooling2D.scala | 61 ++++++++++++ .../bigdl/dllib/keras/AveragePooling3D.scala | 56 +++++++++++ .../bigdl/dllib/keras/Convolution1D.scala | 95 ++++++++++++++++++ .../bigdl/dllib/keras/Convolution2D.scala | 14 +-- .../bigdl/dllib/keras/Convolution3D.scala | 91 +++++++++++++++++ .../bigdl/dllib/keras/Cropping1D.scala | 54 ++++++++++ .../bigdl/dllib/keras/Cropping2D.scala | 57 +++++++++++ .../bigdl/dllib/keras/Cropping3D.scala | 69 +++++++++++++ .../analytics/bigdl/dllib/keras/Dense.scala | 4 +- .../analytics/bigdl/dllib/keras/Dropout.scala | 2 +- .../analytics/bigdl/dllib/keras/Flatten.scala | 3 +- .../dllib/keras/GlobalAveragePooling2D.scala | 56 +++++++++++ .../dllib/keras/GlobalMaxPooling2D.scala | 55 +++++++++++ .../bigdl/dllib/keras/GlobalPooling2D.scala | 41 ++++++++ .../analytics/bigdl/dllib/keras/Highway.scala | 2 +- .../bigdl/dllib/keras/KerasUtils.scala | 24 ++++- .../bigdl/dllib/keras/MaxPooling1D.scala | 62 ++++++++++++ .../bigdl/dllib/keras/MaxPooling2D.scala | 37 +++---- .../bigdl/dllib/keras/MaxPooling3D.scala | 55 +++++++++++ .../analytics/bigdl/dllib/keras/Permute.scala | 94 ++++++++++++++++++ .../bigdl/dllib/keras/Pooling1D.scala | 45 +++++++++ .../bigdl/dllib/keras/Pooling2D.scala | 58 +++++++++++ .../bigdl/dllib/keras/Pooling3D.scala | 51 ++++++++++ .../bigdl/dllib/keras/Recurrent.scala | 2 +- .../bigdl/dllib/keras/RepeatVector.scala | 51 ++++++++++ .../analytics/bigdl/dllib/keras/Reshape.scala | 6 +- .../analytics/bigdl/dllib/keras/SoftMax.scala | 3 +- .../analytics/bigdl/dllib/keras/package.scala | 2 + .../analytics/bigdl/dllib/nn/Cropping2D.scala | 20 ++++ .../analytics/bigdl/dllib/nn/Cropping3D.scala | 21 ++++ .../bigdl/dllib/nn/LocallyConnected2D.scala | 17 +++- .../bigdl/dllib/nn/SpatialConvolution.scala | 2 +- .../bigdl/dllib/nn/SpatialMaxPooling.scala | 35 +------ .../nn/SpatialSeperableConvolution.scala | 9 ++ .../dllib/nn/VolumetricConvolution.scala | 31 +++++- .../bigdl/dllib/keras/Cropping2DSpec.scala | 11 +++ .../bigdl/dllib/keras/Cropping3DSpec.scala | 11 +++ .../LocallyConnected2DSpec.scala | 22 +++-- .../dllib/keras/nn/AveragePooling1DSpec.scala | 60 ++++++++++++ .../dllib/keras/nn/AveragePooling2DSpec.scala | 61 ++++++++++++ .../dllib/keras/nn/AveragePooling3DSpec.scala | 43 ++++++++ .../dllib/keras/nn/Convolution1DSpec.scala | 65 ++++++++++++ .../dllib/keras/nn/Convolution2DSpec.scala | 2 +- .../dllib/keras/nn/Convolution3DSpec.scala | 61 ++++++++++++ .../bigdl/dllib/keras/nn/Cropping1DSpec.scala | 43 ++++++++ .../bigdl/dllib/keras/nn/Cropping2DSpec.scala | 58 +++++++++++ .../bigdl/dllib/keras/nn/Cropping3DSpec.scala | 60 ++++++++++++ .../keras/nn/GlobalAveragePooling2DSpec.scala | 60 ++++++++++++ .../keras/nn/GlobalMaxPooling2DSpec.scala | 59 +++++++++++ .../dllib/keras/nn/MaxPooling1DSpec.scala | 60 ++++++++++++ .../dllib/keras/nn/MaxPooling2DSpec.scala | 2 +- .../dllib/keras/nn/MaxPooling3DSpec.scala | 43 ++++++++ .../bigdl/dllib/keras/nn/PermuteSpec.scala | 43 ++++++++ .../dllib/keras/nn/RepeatVectorSpec.scala | 43 ++++++++ .../dllib/nn/SpatialMaxPoolingSpec.scala | 11 +-- .../nn/SpatialSeperableConvolutionSpec.scala | 13 ++- .../torch/VolumetricConvolutionSpec.scala | 7 +- .../KerasModuleSerializerSpec.scala | 98 +++++++++++++++++++ 60 files changed, 2186 insertions(+), 100 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/RepeatVector.scala rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{nn => keras}/LocallyConnected2DSpec.scala (71%) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/PermuteSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/RepeatVectorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala index 1129be3034b..53410f5c264 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala @@ -36,7 +36,7 @@ import scala.reflect.ClassTag */ class Activation[T: ClassTag]( val activation: String, - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { require(activation != null, "The name of an activation function as a string is required") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling1D.scala new file mode 100644 index 00000000000..bddfb2d8e81 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling1D.scala @@ -0,0 +1,63 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class AveragePooling1D[T: ClassTag]( + poolLength: Int = 2, + stride: Int = -1, + borderMode: String = "valid", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Pooling1D[T](poolLength, stride, borderMode, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val pads = KerasUtils.getPadsFromBorderMode(borderMode) + val model = TSequential[T]() + model.add(com.intel.analytics.bigdl.nn.Reshape(Array(input(1), 1, input(2)), Some(true))) + val layer = SpatialAveragePooling( + kW = 1, + kH = poolLength, + dW = 1, + dH = strideValue, + padW = pads._2, + padH = pads._1, + countIncludePad = false, + format = DataFormat.NHWC) + model.add(layer) + model.add(Squeeze(3)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object AveragePooling1D { + def apply[@specialized(Float, Double) T: ClassTag]( + poolLength: Int = 2, + stride: Int = -1, + borderMode: String = "valid", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AveragePooling1D[T] = { + new AveragePooling1D[T](poolLength, stride, borderMode, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala new file mode 100644 index 00000000000..4ad0fa88ef4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.SpatialAveragePooling +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class AveragePooling2D[T: ClassTag]( + poolSize: Array[Int] = Array(2, 2), + strides: Array[Int] = null, + borderMode: String = "valid", + format: DataFormat = DataFormat.NCHW, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Pooling2D[T](poolSize, strides, borderMode, format, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val pads = KerasUtils.getPadsFromBorderMode(borderMode) + val layer = SpatialAveragePooling( + kW = poolSize(1), + kH = poolSize(0), + dW = strideValues(1), + dH = strideValues(0), + padW = pads._2, + padH = pads._1, + countIncludePad = false, + format = format) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object AveragePooling2D { + def apply[@specialized(Float, Double) T: ClassTag]( + poolSize: (Int, Int) = (2, 2), + strides: (Int, Int) = null, + borderMode: String = "valid", + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AveragePooling2D[T] = { + val strideValues = if (strides != null) Array(strides._1, strides._2) else null + new AveragePooling2D[T](Array(poolSize._1, poolSize._2), strideValues, + borderMode, KerasUtils.toBigDLFormat(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala new file mode 100644 index 00000000000..75146068f0f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.VolumetricAveragePooling +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class AveragePooling3D[T: ClassTag]( + poolSize: Array[Int] = Array(2, 2, 2), + strides: Array[Int] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Pooling3D[T](poolSize, strides, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = VolumetricAveragePooling( + kT = poolSize(0), + kW = poolSize(2), + kH = poolSize(1), + dT = strideValues(0), + dW = strideValues(2), + dH = strideValues(1), + countIncludePad = false) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object AveragePooling3D { + def apply[@specialized(Float, Double) T: ClassTag]( + poolSize: (Int, Int, Int) = (2, 2, 2), + strides: (Int, Int, Int) = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AveragePooling3D[T] = { + val strideValues = if (strides != null) Array(strides._1, strides._2, strides._3) + else null + new AveragePooling3D[T](Array(poolSize._1, poolSize._2, poolSize._3), + strideValues, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala new file mode 100644 index 00000000000..7f6f5de8f54 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala @@ -0,0 +1,95 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class Convolution1D[T: ClassTag]( + val nbFilter: Int, + val filterLength: Int, + val init: InitializationMethod = Xavier, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val borderMode: String = "valid", + val subsampleLength: Int = 1, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"Convolution1D requires 3D input, but got input dim ${input.length}") + val outputLength = KerasUtils.computeConvOutputLength(input(1), filterLength, + borderMode, subsampleLength) + Shape(input(0), outputLength, nbFilter) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val pads = KerasUtils.getPadsFromBorderMode(borderMode) + val model = TSequential[T]() + model.add(com.intel.analytics.bigdl.nn.Reshape(Array(input(1), 1, input(2)), Some(true))) + val layer = SpatialConvolution( + nInputPlane = input(2), + nOutputPlane = nbFilter, + kernelW = 1, + kernelH = filterLength, + strideW = 1, + strideH = subsampleLength, + padW = pads._2, + padH = pads._1, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer, + withBias = bias, + format = DataFormat.NHWC) + layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) + model.add(layer) + model.add(Squeeze(3)) + if (activation != null) { + model.add(activation) + } + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Convolution1D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + filterLength: Int, + init: String = "glorot_uniform", + activation: String = null, + borderMode: String = "valid", + subsampleLength: Int = 1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution1D[T] = { + new Convolution1D[T](nbFilter, filterLength, + KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + borderMode, subsampleLength, wRegularizer, bRegularizer, bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala index 5c4a5bd2c21..4109473f12d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala @@ -48,7 +48,8 @@ import scala.reflect.ClassTag * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the input weights matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. - * @param format Format of input data. Either DataFormat.NCHW or DataFormat.NHWC. Default is NCHW. + * @param format Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). * Default is true. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now @@ -65,12 +66,13 @@ class Convolution2D[T: ClassTag]( var bRegularizer: Regularizer[T] = null, val format: DataFormat = DataFormat.NCHW, val bias: Boolean = true, - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + s"Convolution2D: $borderMode") - require(subsample.length == 2, "Subsample should be of length 2.") + require(subsample.length == 2, + s"For Convolution2D, subsample should be of length 2 but got length ${subsample.length}") override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray @@ -105,12 +107,12 @@ object Convolution2D { subsample: (Int, Int) = (1, 1), wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, - format: DataFormat = DataFormat.NCHW, + dimOrdering: String = "th", bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution2D[T] = { new Convolution2D[T](nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), - borderMode, Array(subsample._1, subsample._2), - wRegularizer, bRegularizer, format, bias, inputShape) + borderMode, Array(subsample._1, subsample._2), wRegularizer, + bRegularizer, KerasUtils.toBigDLFormat(dimOrdering), bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala new file mode 100644 index 00000000000..8028392c27b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala @@ -0,0 +1,91 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.{InitializationMethod, VolumetricConvolution, Xavier, Zeros} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class Convolution3D[T: ClassTag]( + val nbFilter: Int, + val kernelDim1: Int, + val kernelDim2: Int, + val kernelDim3: Int, + val init: InitializationMethod = Xavier, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val borderMode: String = "valid", + val subsample: Array[Int] = Array(1, 1, 1), + val wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + + s"Convolution3D: $borderMode") + require(subsample.length == 3, + s"For Convolution3D, subsample should be of length 3 but got length ${subsample.length}") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val pads = KerasUtils.getPadsFromBorderMode3D(borderMode) + val layer = VolumetricConvolution( + nInputPlane = input(1), + nOutputPlane = nbFilter, + kT = kernelDim1, + kW = kernelDim3, + kH = kernelDim2, + dT = subsample(0), + dW = subsample(2), + dH = subsample(1), + padT = pads._1, + padW = pads._3, + padH = pads._2, + withBias = bias, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer) + layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) + KerasLayer.fuse(layer, activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Convolution3D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + kernelDim1: Int, + kernelDim2: Int, + kernelDim3: Int, + init: String = "glorot_uniform", + activation: String = null, + borderMode: String = "valid", + subsample: (Int, Int, Int) = (1, 1, 1), + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution3D[T] = { + new Convolution3D[T](nbFilter, kernelDim1, kernelDim2, kernelDim3, + KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + borderMode, Array(subsample._1, subsample._2, subsample._3), + wRegularizer, bRegularizer, bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping1D.scala new file mode 100644 index 00000000000..12de43564b4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping1D.scala @@ -0,0 +1,54 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.SpatialZeroPadding +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class Cropping1D[T: ClassTag]( + val cropping: Array[Int] = Array(1, 1), + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(cropping.length == 2, + s"For Cropping1D, cropping values should be of length 2 but got length ${cropping.length}") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"Cropping1D requires 3D input, but got input dim ${input.length}") + Shape(input(0), input(1)-cropping(0)-cropping(1), input(2)) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = SpatialZeroPadding(0, 0, -cropping(0), -cropping(1)) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Cropping1D { + def apply[@specialized(Float, Double) T: ClassTag]( + cropping: (Int, Int) = (1, 1), + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Cropping1D[T] = { + new Cropping1D[T](Array(cropping._1, cropping._2), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala new file mode 100644 index 00000000000..169748a2ea5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class Cropping2D[T: ClassTag]( + val heightCrop: Array[Int] = Array(0, 0), + val widthCrop: Array[Int] = Array(0, 0), + val format: DataFormat = DataFormat.NCHW, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(heightCrop.length == 2, + s"Cropping3D: height cropping values should be of length 2, but got ${heightCrop.length}") + require(widthCrop.length == 2, + s"Cropping3D: width cropping values should be of length 2, but got ${widthCrop.length}") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.Cropping2D( + heightCrop = heightCrop, + widthCrop = widthCrop, + format = format) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Cropping2D { + def apply[@specialized(Float, Double) T: ClassTag]( + cropping: ((Int, Int), (Int, Int)) = ((0, 0), (0, 0)), + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Cropping2D[T] = { + val heightCrop = Array(cropping._1._1, cropping._1._2) + val widthCrop = Array(cropping._2._1, cropping._2._2) + new Cropping2D[T](heightCrop, widthCrop, + KerasUtils.toBigDLFormat(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala new file mode 100644 index 00000000000..65c82614365 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala @@ -0,0 +1,69 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class Cropping3D[T: ClassTag]( + val dim1Crop: Array[Int] = Array(1, 1), + val dim2Crop: Array[Int] = Array(1, 1), + val dim3Crop: Array[Int] = Array(1, 1), + val format: String = "CHANNEL_FIRST", + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(dim1Crop.length == 2, + s"Cropping3D: kernel dim1 cropping values should be of length 2, but got ${dim1Crop.length}") + require(dim2Crop.length == 2, + s"Cropping3D: kernel dim2 cropping values should be of length 2, but got ${dim2Crop.length}") + require(dim3Crop.length == 2, + s"Cropping3D: kernel dim3 cropping values should be of length 2, but got ${dim3Crop.length}") + require(format.toLowerCase() == "channel_first" || format.toLowerCase() == "channel_last", + "Cropping3D only supports format channel_first or channel_last") + + private val dimOrdering = format.toLowerCase() match { + case "channel_first" => com.intel.analytics.bigdl.nn.Cropping3D.CHANNEL_FIRST + case "channel_last" => com.intel.analytics.bigdl.nn.Cropping3D.CHANNEL_LAST + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.Cropping3D( + dim1Crop = dim1Crop, + dim2Crop = dim2Crop, + dim3Crop = dim3Crop, + format = dimOrdering) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Cropping3D { + def apply[@specialized(Float, Double) T: ClassTag]( + cropping: ((Int, Int), (Int, Int), (Int, Int)) = ((1, 1), (1, 1), (1, 1)), + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Cropping3D[T] = { + val dim1Crop = Array(cropping._1._1, cropping._1._2) + val dim2Crop = Array(cropping._2._1, cropping._2._2) + val dim3Crop = Array(cropping._3._1, cropping._3._2) + new Cropping3D[T](dim1Crop, dim2Crop, dim3Crop, + KerasUtils.toBigDLFormat5D(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala index 9759cb7e8e7..8f5f4807648 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala @@ -52,7 +52,7 @@ class Dense[T: ClassTag]( var wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, val bias: Boolean = true, - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { override def computeOutputShape(inputShape: Shape): Shape = { @@ -64,7 +64,7 @@ class Dense[T: ClassTag]( override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val inputShapeList = inputShape.toSingle() - var layer = Linear( + val layer = Linear( inputSize = inputShapeList.last, outputSize = outputDim, withBias = bias, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala index 28da8ca7f10..c2c353bf7a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala @@ -34,7 +34,7 @@ import scala.reflect.ClassTag */ class Dropout[T: ClassTag]( val p: Double, - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala index 701c8c65dde..8c440182f0f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala @@ -30,7 +30,8 @@ import scala.reflect.ClassTag * * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now */ -class Flatten[T: ClassTag](var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) +class Flatten[T: ClassTag]( + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { override def computeOutputShape(inputShape: Shape): Shape = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala new file mode 100644 index 00000000000..4a33ddc1ed0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, Squeeze, Sequential => TSequential} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class GlobalAveragePooling2D[T: ClassTag]( + format: DataFormat = DataFormat.NCHW, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends GlobalPooling2D[T](format, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val (dimH, dimW, dimC) = format.getHWCDims(4) + val model = TSequential[T]() + val layer = SpatialAveragePooling( + kW = input(dimW -1), + kH = input(dimH -1), + dW = input(dimW -1), + dH = input(dimH -1), + countIncludePad = false, + format = format) + model.add(layer) + model.add(Squeeze(dimW)) + model.add(Squeeze(dimH)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object GlobalAveragePooling2D { + def apply[@specialized(Float, Double) T: ClassTag]( + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): GlobalAveragePooling2D[T] = { + new GlobalAveragePooling2D[T](KerasUtils.toBigDLFormat(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala new file mode 100644 index 00000000000..06f941ede8d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.{SpatialMaxPooling, Squeeze, Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class GlobalMaxPooling2D[T: ClassTag]( + format: DataFormat = DataFormat.NCHW, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends GlobalPooling2D[T](format, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val (dimH, dimW, dimC) = format.getHWCDims(4) + val model = TSequential[T]() + val layer = SpatialMaxPooling( + kW = input(dimW -1), + kH = input(dimH -1), + dW = input(dimW -1), + dH = input(dimH -1), + format = format) + model.add(layer) + model.add(Squeeze(dimW)) + model.add(Squeeze(dimH)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object GlobalMaxPooling2D { + def apply[@specialized(Float, Double) T: ClassTag]( + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): GlobalMaxPooling2D[T] = { + new GlobalMaxPooling2D[T](KerasUtils.toBigDLFormat(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala new file mode 100644 index 00000000000..2827abcf83a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +abstract class GlobalPooling2D[T: ClassTag]( + val format: DataFormat = DataFormat.NCHW, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"GlobalPooling2D requires 4D input, but got input dim ${input.length}") + format match { + case DataFormat.NCHW => Shape(input(0), input(1)) + case DataFormat.NHWC => Shape(input(0), input(3)) + } + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala index 1f422df8b3e..0910810ed70 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala @@ -46,7 +46,7 @@ class Highway[T: ClassTag]( var wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, val bias: Boolean = true, - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { override def computeOutputShape(inputShape: Shape): Shape = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala index d2c475681fb..a0f55cadc32 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.keras import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -78,8 +78,8 @@ object KerasUtils { (outputLength + stride - 1) / stride } - private[keras] def getPadsFromBorderMode3D - (borderMode: String = "valid"): (Int, Int, Int) = { + private[keras] def getPadsFromBorderMode3D( + borderMode: String = "valid"): (Int, Int, Int) = { if (borderMode == "same") { // padT, padH, padW (-1, -1, -1) @@ -88,4 +88,22 @@ object KerasUtils { } } + private[keras] def toBigDLFormat(dimOrdering: String): DataFormat = { + require(dimOrdering.toLowerCase() == "tf" || dimOrdering.toLowerCase() == "th", + s"Dim ordering must be either tf or th, but got ${dimOrdering.toLowerCase()}") + dimOrdering.toLowerCase() match { + case "tf" => DataFormat.NHWC + case "th" => DataFormat.NCHW + } + } + + private[keras] def toBigDLFormat5D(dimOrdering: String): String = { + require(dimOrdering.toLowerCase() == "tf" || dimOrdering.toLowerCase() == "th", + s"Dim ordering must be either tf or th, but got ${dimOrdering.toLowerCase()}") + dimOrdering.toLowerCase() match { + case "tf" => "CHANNEL_LAST" + case "th" => "CHANNEL_FIRST" + } + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala new file mode 100644 index 00000000000..8a9ed5bcf68 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala @@ -0,0 +1,62 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class MaxPooling1D[T: ClassTag]( + poolLength: Int = 2, + stride: Int = -1, + borderMode: String = "valid", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Pooling1D[T](poolLength, stride, borderMode, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val pads = KerasUtils.getPadsFromBorderMode(borderMode) + val model = TSequential[T]() + model.add(com.intel.analytics.bigdl.nn.Reshape(Array(input(1), 1, input(2)), Some(true))) + val layer = SpatialMaxPooling( + kW = 1, + kH = poolLength, + dW = 1, + dH = strideValue, + padW = pads._2, + padH = pads._1, + format = DataFormat.NHWC) + model.add(layer) + model.add(Squeeze(3)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object MaxPooling1D { + def apply[@specialized(Float, Double) T: ClassTag]( + poolLength: Int = 2, + stride: Int = -1, + borderMode: String = "valid", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): MaxPooling1D[T] = { + new MaxPooling1D[T](poolLength, stride, borderMode, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala index 9331604a632..f7a81260d11 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala @@ -32,36 +32,31 @@ import scala.reflect.ClassTag * * @param poolSize Int array of length 2 corresponding to the downscale vertically and * horizontally. Default is (2, 2), which will halve the image in each dimension. - * @param strides Stride values. Int array of length 2. Default is null, and in this case it will + * @param strides Int array of length 2. Stride values. Default is null, and in this case it will * be equal to poolSize. * @param borderMode Either 'valid' or 'same'. Default is 'valid'. - * @param format Format of input data. Either DataFormat.NCHW or DataFormat.NHWC. Default is NCHW. + * @param format Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now */ class MaxPooling2D[T: ClassTag] ( - val poolSize: Array[Int] = Array(2, 2), - val strides: Array[Int] = null, - val borderMode: String = "valid", - val format: DataFormat = DataFormat.NCHW, - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { - - require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + - s"MaxPooling2D: $borderMode") - - private val stridesValue = if (strides != null) strides else poolSize + poolSize: Array[Int] = Array(2, 2), + strides: Array[Int] = null, + borderMode: String = "valid", + format: DataFormat = DataFormat.NCHW, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Pooling2D[T](poolSize, strides, borderMode, format, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val pads = KerasUtils.getPadsFromBorderMode(borderMode) val layer = SpatialMaxPooling( kW = poolSize(1), kH = poolSize(0), - dW = stridesValue(1), - dH = stridesValue(0), + dW = strideValues(1), + dH = strideValues(0), padW = pads._2, padH = pads._1, - format = format - ) + format = format) layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } @@ -71,11 +66,11 @@ object MaxPooling2D { poolSize: (Int, Int) = (2, 2), strides: (Int, Int) = null, borderMode: String = "valid", - format: DataFormat = DataFormat.NCHW, + dimOrdering: String = "th", inputShape: Shape = null) (implicit ev: TensorNumeric[T]): MaxPooling2D[T] = { - val stridesValue = if (strides != null) Array(strides._1, strides._2) else null - new MaxPooling2D[T](Array(poolSize._1, poolSize._2), - stridesValue, borderMode, format, inputShape) + val strideValues = if (strides != null) Array(strides._1, strides._2) else null + new MaxPooling2D[T](Array(poolSize._1, poolSize._2), strideValues, + borderMode, KerasUtils.toBigDLFormat(dimOrdering), inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala new file mode 100644 index 00000000000..b00c1c06ac1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.VolumetricMaxPooling +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class MaxPooling3D[T: ClassTag]( + poolSize: Array[Int] = Array(2, 2, 2), + strides: Array[Int] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends Pooling3D[T](poolSize, strides, inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = VolumetricMaxPooling( + kT = poolSize(0), + kW = poolSize(2), + kH = poolSize(1), + dT = strideValues(0), + dW = strideValues(2), + dH = strideValues(1)) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object MaxPooling3D { + def apply[@specialized(Float, Double) T: ClassTag]( + poolSize: (Int, Int, Int) = (2, 2, 2), + strides: (Int, Int, Int) = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): MaxPooling3D[T] = { + val strideValues = if (strides != null) Array(strides._1, strides._2, strides._3) + else null + new MaxPooling3D[T](Array(poolSize._1, poolSize._2, poolSize._3), + strideValues, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala new file mode 100644 index 00000000000..8ee7fbe995d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala @@ -0,0 +1,94 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.Transpose +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Permute[T: ClassTag]( + val dims: Array[Int], + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + private def permToPair(perm: Array[Int]): Array[(Int, Int)] = { + val numToRank = perm.zipWithIndex.toMap + val arr = perm.indices.toArray + val pairs = ArrayBuffer[(Int, Int)]() + + def sort(arr: Array[Int], low: Int, high: Int): Unit = { + var i = low + var j = high + val pivot = arr(low + (high - low)/2) + + while (i <= j) { + while (arr(i) < pivot) i += 1 + while (arr(j) > pivot) j -= 1 + + if (i <= j) { + exchangeNumbers(arr, i, j) + i += 1 + j -= 1 + } + } + + if (low < j) sort(arr, low, j) + if (i < high) sort(arr, i, high) + } + + def exchangeNumbers(arr: Array[Int], i: Int, j: Int): Unit = { + val temp = arr(i) + arr(i) = arr(j) + arr(j) = temp + pairs += ((i, j)) + } + + sort(arr.map(numToRank), 0, arr.length-1) + + pairs.filter(pair => pair._1 != pair._2).toArray + } + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + val outputShape = input.clone() + var i = 0 + while (i < dims.length) { + outputShape(i + 1) = input(dims(i)) + i += 1 + } + Shape(outputShape) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val swaps = permToPair(dims.map(x => x - 1)).map(pair => (pair._1 + 2, pair._2 + 2)) + val layer = Transpose(swaps) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Permute { + def apply[@specialized(Float, Double) T: ClassTag]( + dims: Array[Int], + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Permute[T] = { + new Permute[T](dims, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala new file mode 100644 index 00000000000..5c6d2faba67 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +abstract class Pooling1D[T: ClassTag]( + val poolLength: Int = 2, + val stride: Int = -1, + val borderMode: String = "valid", + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + // -1 means stride by default to be poolLength + require(stride == -1 || stride > 0, s"Invalid stride value for Pooling1D: $stride") + val strideValue: Int = if (stride > 0) stride else poolLength + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"Pooling1D requires 3D input, but got input dim ${input.length}") + val outputLength = KerasUtils.computeConvOutputLength(input(1), poolLength, + borderMode, strideValue) + Shape(input(0), outputLength, input(2)) + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala new file mode 100644 index 00000000000..481666c8ee5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +abstract class Pooling2D[T: ClassTag]( + val poolSize: Array[Int] = Array(2, 2), + val strides: Array[Int] = null, + val borderMode: String = "valid", + val format: DataFormat = DataFormat.NCHW, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(poolSize.length == 2, + s"For Pooling2D, poolSize should be of length 2 but got length ${poolSize.length}") + require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + + s"Pooling2D: $borderMode") + + val strideValues: Array[Int] = if (strides == null) poolSize else strides + require(strideValues.length == 2, + s"For Pooling2D, strides should be of length 2 but got length ${strideValues.length}") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"Pooling2D requires 4D input, but got input dim ${input.length}") + val (dimH, dimW, dimC) = format.getHWCDims(4) + val rows = KerasUtils.computeConvOutputLength(input(dimH -1), poolSize(0), + borderMode, strideValues(0)) + val cols = KerasUtils.computeConvOutputLength(input(dimW -1), poolSize(1), + borderMode, strideValues(1)) + format match { + case DataFormat.NCHW => Shape(input(0), input(1), rows, cols) + case DataFormat.NHWC => Shape(input(0), rows, cols, input(3)) + } + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala new file mode 100644 index 00000000000..bd68a807cb2 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +abstract class Pooling3D[T: ClassTag]( + val poolSize: Array[Int] = Array(2, 2, 2), + val strides: Array[Int] = null, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(poolSize.length == 3, + s"For Pooling3D, poolSize should be of length 3 but got length ${poolSize.length}") + + val strideValues: Array[Int] = if (strides == null) poolSize else strides + require(strideValues.length == 3, + s"For Pooling3D, strides should be of length 3 but got length ${strideValues.length}") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 5, + s"Pooling3D requires 5D input, but got input dim ${input.length}") + val dim1Length = KerasUtils.computeConvOutputLength(input(2), poolSize(0), + "valid", strideValues(0)) + val dim2Length = KerasUtils.computeConvOutputLength(input(3), poolSize(1), + "valid", strideValues(1)) + val dim3Length = KerasUtils.computeConvOutputLength(input(4), poolSize(2), + "valid", strideValues(2)) + Shape(input(0), input(1), dim1Length, dim2Length, dim3Length) + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala index 22ce5d23fae..ce9236f167b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala @@ -33,7 +33,7 @@ abstract class Recurrent[T: ClassTag]( val outputDim: Int, val returnSequences: Boolean = false, val goBackwards: Boolean = false, - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { override def computeOutputShape(inputShape: Shape): Shape = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/RepeatVector.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/RepeatVector.scala new file mode 100644 index 00000000000..78b671c2d06 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/RepeatVector.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.{ErrorInfo, Replicate} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class RepeatVector[T: ClassTag]( + val n: Int, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 2, + s"RepeatVector requires 2D input, but got input dim ${input.length}") + Shape(input(0), n, input(1)) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = Replicate(nFeatures = n, nDim = 1) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object RepeatVector { + def apply[@specialized(Float, Double) T: ClassTag]( + n: Int, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): RepeatVector[T] = { + new RepeatVector[T](n, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala index 53b39492617..a0da2cacccd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala @@ -38,7 +38,7 @@ import scala.reflect.ClassTag */ class Reshape[T: ClassTag]( val targetShape: Array[Int], - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { private var infer = false @@ -71,12 +71,12 @@ class Reshape[T: ClassTag]( val nElements = nonBatchInput.product val resizeElements = - targetShape.product require(nElements > resizeElements && nElements % resizeElements == 0, - "total size after reshape must be unchanged") + "Total size after reshape must be unchanged") targetShape(inferIndex) = nElements / resizeElements } else { require(targetShape.product == nonBatchInput.product, - s"total size after reshape must be unchanged. But In ${this.getName()} : " + + s"Total size after reshape must be unchanged. But In ${this.getName()} : " + s"original size is: ${ nonBatchInput.product }, " + s"reshape size is: ${ targetShape.product }") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala index be16a38bb8d..ea3004b61ca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala @@ -26,10 +26,9 @@ import scala.reflect.ClassTag /** * Just a wrapper class. Please use Activation('softmax') instead. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now */ class SoftMax[T: ClassTag]( - var inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { override def computeOutputShape(inputShape: Shape): Shape = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala index c298e04a03d..74081a222a3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala @@ -18,5 +18,7 @@ package com.intel.analytics.bigdl.nn package object keras { // Alias + val Conv1D = Convolution1D val Conv2D = Convolution2D + val Conv3D = Convolution3D } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2D.scala index ebc49501041..6b125296538 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2D.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -44,6 +45,24 @@ class Cropping2D[T: ClassTag]( val dataFormat: DataFormat = DataFormat.NCHW )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + require(heightCrop.length == 2, "heightCrop should be an array of length 2") + require(widthCrop.length == 2, "widthCrop should be an array of length 2") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"Cropping2D requires 4D input, but got input dim ${input.length}") + val outputShape = dataFormat match { + case DataFormat.NCHW => + Array(input(0), input(1), input(2)-heightCrop(0)-heightCrop(1), + input(3)-widthCrop(0)-widthCrop(1)) + case DataFormat.NHWC => + Array(input(0), input(1)-heightCrop(0)-heightCrop(1), + input(2)-widthCrop(0)-widthCrop(1), input(3)) + } + Shape(outputShape) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 4, "input dimensions should be 4." + " (batchSize, channels, first_axis_to_crop, second_axis_to_crop)") @@ -67,6 +86,7 @@ class Cropping2D[T: ClassTag]( .narrow(hdim, hStart, lenHCropped) .narrow(wdim, wStart, lenWCropped) .copy(gradOutput) + gradInput } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3D.scala index 29bb9bd1431..79927f33853 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3D.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -46,6 +47,25 @@ class Cropping3D[T: ClassTag]( val dataFormat: String = Cropping3D.CHANNEL_FIRST )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { + require(dim1Crop.length == 2, "dim1Crop should be an array of length 2") + require(dim2Crop.length == 2, "dim2Crop should be an array of length 2") + require(dim3Crop.length == 2, "dim3Crop should be an array of length 2") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 5, + s"Cropping3D requires 5D input, but got input dim ${input.length}") + val outputShape = dataFormat match { + case Cropping3D.CHANNEL_FIRST => + Array(input(0), input(1), input(2)-dim1Crop(0)-dim1Crop(1), + input(3)-dim2Crop(0)-dim2Crop(1), input(4)-dim3Crop(0)-dim3Crop(1)) + case Cropping3D.CHANNEL_LAST => + Array(input(0), input(1)-dim1Crop(0)-dim1Crop(1), + input(2)-dim2Crop(0)-dim2Crop(1), input(3)-dim3Crop(0)-dim3Crop(1), input(4)) + } + Shape(outputShape) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 5, "input dimensions should be 5." + " (batchSize, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)") @@ -76,6 +96,7 @@ class Cropping3D[T: ClassTag]( .narrow(dim2, dim2Start, dim2Cropped) .narrow(dim3, dim3Start, dim3Cropped) .copy(gradOutput) + gradInput } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala index 5a8a7418a97..00aa122498f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala @@ -19,7 +19,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, Initializable, Tenso import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} -import com.intel.analytics.bigdl.utils.{Engine, T, Table} +import com.intel.analytics.bigdl.utils.{Engine, Shape, T, Table} import scala.concurrent.Future import scala.reflect.ClassTag @@ -217,9 +217,22 @@ class LocallyConnected2D[T: ClassTag]( } } + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"LocallyConnected2D requires 4D input, but got input dim ${input.length}") + val (dimHeight, dimWidth, channelDim) = format.getHWCDims(input.length) + require(input(channelDim -1) == nInputPlane, s"input channel size " + + s"${input(channelDim -1)} is not the same as nInputPlane $nInputPlane") + require(outputWidth >= 1 && outputHeight >= 1, + s"output size is too small. outputWidth: $outputWidth, outputHeight: $outputHeight") + val outputShape = getOutputShape(outputHeight, outputWidth) + Shape(Array(input(0)) ++ outputShape) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 3 || input.dim() == 4, - "SpatialConvolution: " + ErrorInfo.constrainInputAs3DOrBatch) + "LocallyConnected2D: " + ErrorInfo.constrainInputAs3DOrBatch) require(input.isContiguous()) val (dimHeight, dimWidth, channelDim) = format.getHWCDims(input.dim()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala index 94083c13618..b46f0cfdbee 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala @@ -176,7 +176,7 @@ class SpatialConvolution[T: ClassTag]( override def computeOutputShape(inputShape: Shape): Shape = { val input = inputShape.toSingle().toArray require(input.length == 4, - s"SpatialConvolution requires 4D input, but got input dim ${input.length}") + s"Convolution2D requires 4D input, but got input dim ${input.length}") val (dimHeight, dimWidth, channelDim) = format.getHWCDims(input.length) require(input(channelDim -1) == nInputPlane, s"input channel size " + s"${input(channelDim -1)} is not the same as nInputPlane $nInputPlane") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala index 18e53b8fbf2..48806ca9ac9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPooling.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{Engine, Shape} +import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} @@ -88,39 +88,6 @@ class SpatialMaxPooling[T: ClassTag]( this } - override def computeOutputShape(inputShape: Shape): Shape = { - val input = inputShape.toSingle().toArray - require(input.length == 4, - s"SpatialMaxPooling requires 4D input, but got input dim ${input.length}") - val (dimh, dimw, dimc) = format.getHWCDims(input.length) - val nInputPlane = input(dimc -1) - val inputHeight = input(dimh -1) - val inputWidth = input(dimw -1) - val sizes = - if (padW == -1 && padH == -1) { - Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW) - } else { - require(inputWidth >= kW - padW && inputHeight >= kH - padH, - "input smaller than kernel size. " + - s"current input size($inputWidth, $inputHeight), " + - s"kernel size(${kW-padW}, ${kH-padH})") - require(kW / 2 >= padW && kH / 2 >= padH, - "pad should be smaller than half of kernel size. " + - s"current pad size($padW, $padH), " + s"kernel size($kW, $kH)") - Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, dW, kH, kW, padH, padW, ceilMode) - } - val oHeight = sizes(4) - val oWidth = sizes(5) - - val outputShape = format match { - case DataFormat.NCHW => - Array(input(0), nInputPlane, oHeight, oWidth) - case DataFormat.NHWC => - Array(input(0), oHeight, oWidth, nInputPlane) - } - Shape(outputShape) - } - override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 3 || input.dim() == 4, "SpatialMaxPooling: " + ErrorInfo.constrainInputAs3DOrBatch) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala index fef3f3df386..eac34049a3d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala @@ -19,6 +19,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -130,6 +131,14 @@ class SpatialSeperableConvolution[T: ClassTag]( (Array(depthWeight, pointWeight, bias), Array(depthGradWeight, pointGradWeight, gradBias)) } + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"SeparableConvolution2D requires 4D input, but got input dim ${input.length}") + SpatialConvolution[T](nInputChannel, nOutputChannel, kW, kH, + sW, sH, pW, pH, format = dataFormat).computeOutputShape(inputShape) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala index efeff895704..3c973b62119 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Shape, T, Table} import org.apache.spark.sql.catalyst.optimizer.OptimizeIn import scala.reflect.ClassTag @@ -127,6 +127,35 @@ class VolumetricConvolution[T: ClassTag]( } } + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 5, + s"Convolution3D requires 5D input, but got input dim ${input.length}") + require(input(1) == nInputPlane, s"input.size(1) should be equal to nInputPlane. " + + s"But In ${this.getName()} : input.size(1) is: ${ input(1) } ," + + s" nInputPlane is: ${ nInputPlane }") + val inputWidth = input(4) + val inputHeight = input(3) + val inputDepth = input(2) + val sizes = if (padW == -1 && padH == -1 && padT == -1) { + Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, dH, + dW, kH, kW, inputDepth, dT, kT) + } else { + Utils.getOutSizeAndPadding(inputHeight, inputWidth, dH, + dW, kH, kW, padH, padW, ceilMode = false, inputdepth = inputDepth, + dt = dT, kt = kT, padt = padT) + } + val outputDepth = sizes(6) + val outputHeight = sizes(7) + val outputWidth = sizes(8) + require(outputWidth >= 1 && outputDepth >= 1 && outputHeight >= 1, + s"Given input size: (${ input.mkString("x") })." + + s" Calculated output size:" + + s" (${ nOutputPlane }x${ outputDepth }x${ outputHeight }x${ outputWidth })." + + s" Output size is too small") + Shape(input(0), nOutputPlane, outputDepth, outputHeight, outputWidth) + } + /** * Computes the output using the current parameter set of the class and input. This function * returns the result which is stored in the output field. diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2DSpec.scala index 12240ee5dca..064a75d6a11 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2DSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.keras import com.intel.analytics.bigdl.nn.{Cropping2D, _} import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.utils.{Shape, TestUtils} class Cropping2DSpec extends KerasBaseSpec { "Cropping2D" should "with NCHW work properly" in { @@ -44,4 +45,14 @@ class Cropping2DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } + "Cropping2D computeOutputShape NCHW" should "work properly" in { + val layer = Cropping2D[Float](Array(2, 3), Array(2, 4)) + TestUtils.compareOutputShape(layer, Shape(3, 12, 12)) should be (true) + } + + "Cropping2D computeOutputShape NHWC" should "work properly" in { + val layer = Cropping2D[Float](Array(1, 3), Array(2, 2), format = DataFormat.NHWC) + TestUtils.compareOutputShape(layer, Shape(18, 12, 3)) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3DSpec.scala index 9db88db4859..1229dc28fd3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3DSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.keras import com.intel.analytics.bigdl.nn.{Cropping2D, _} import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.utils.{Shape, TestUtils} class Cropping3DSpec extends KerasBaseSpec { "Cropping3D" should "with CHANNEL_FIRST work properly" in { @@ -46,4 +47,14 @@ class Cropping3DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } + "Cropping3D computeOutputShape CHANNEL_FIRST" should "work properly" in { + val layer = Cropping3D[Float](Array(2, 3), Array(2, 4), Array(1, 2)) + TestUtils.compareOutputShape(layer, Shape(3, 24, 28, 32)) should be (true) + } + + "Cropping3D computeOutputShape CHANNEL_LAST" should "work properly" in { + val layer = Cropping3D[Float](Array(1, 3), Array(2, 1), Array(4, 2), Cropping3D.CHANNEL_LAST) + TestUtils.compareOutputShape(layer, Shape(32, 32, 32, 4)) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2DSpec.scala similarity index 71% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2DSpec.scala index 665ffca2a80..45db5c60da1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2DSpec.scala @@ -19,11 +19,11 @@ package com.intel.analytics.bigdl.keras import com.intel.analytics.bigdl.nn.LocallyConnected2D import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.RandomGenerator +import com.intel.analytics.bigdl.utils.{Shape, TestUtils} class LocallyConnected2DSpec extends KerasBaseSpec { - "LocallyConnected1D NHWC Float" should "be ok" in { + "LocallyConnected2D NHWC Float" should "be ok" in { ifskipTest() val kerasCode = """ @@ -31,13 +31,13 @@ class LocallyConnected2DSpec extends KerasBaseSpec { |input = np.array([[[[1,2], [2,3], [3,4],[4,5],[5,6],[6,7]], | [[2,3], [3,4],[4,5],[5,6],[6,7], [1,2]], | [[1,2], [2,3], [3,4],[4,5],[6,7],[5,6]]]]) - |output_tensor = LocallyConnected2D(3, 2, 1, + |output_tensor = LocallyConnected2D(3, 2, 1, dim_ordering="tf", |input_shape=(3,6,2))(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin - val locallyConnected1d = + val locallyConnected2d = LocallyConnected2D[Float](2, 6, 3, 3, 1, 2, format = DataFormat.NHWC) - val a = locallyConnected1d.parameters() + val a = locallyConnected2d.parameters() val wc = (data: Array[Tensor[Float]]) => { @@ -64,7 +64,17 @@ class LocallyConnected2DSpec extends KerasBaseSpec { out } - checkOutputAndGrad(locallyConnected1d, kerasCode, wc) + checkOutputAndGrad(locallyConnected2d, kerasCode, wc) } + + "LocallyConnected1D computeOutputShape NCHW" should "work properly" in { + val layer = LocallyConnected2D[Float](3, 12, 12, 3, 2, 2, 2, 1) + TestUtils.compareOutputShape(layer, Shape(3, 12, 12)) should be (true) + } + + "LocallyConnected2D computeOutputShape NHWC" should "work properly" in { + val layer = LocallyConnected2D[Float](2, 16, 12, 4, 1, 2, format = DataFormat.NHWC) + TestUtils.compareOutputShape(layer, Shape(12, 16, 2)) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling1DSpec.scala new file mode 100644 index 00000000000..bbc1e0422b1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling1DSpec.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{AveragePooling1D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class AveragePooling1DSpec extends KerasBaseSpec { + + "AveragePooling1D valid mode" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, 16]) + |input = np.random.random([3, 12, 16]) + |output_tensor = AveragePooling1D()(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = AveragePooling1D[Float](inputShape = Shape(12, 16)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 6, 16)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "AveragePooling1D same mode" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[32, 32]) + |input = np.random.random([2, 32, 32]) + |output_tensor = AveragePooling1D(pool_length=3, stride=1, border_mode="same")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = AveragePooling1D[Float](poolLength = 3, stride = 1, + borderMode = "same", inputShape = Shape(32, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 32, 32)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling2DSpec.scala new file mode 100644 index 00000000000..1939e7bc8e6 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling2DSpec.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{AveragePooling2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class AveragePooling2DSpec extends KerasBaseSpec { + + "AveragePooling2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24, 24]) + |input = np.random.random([2, 3, 24, 24]) + |output_tensor = AveragePooling2D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = AveragePooling2D[Float](inputShape = Shape(3, 24, 24)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 12, 12)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "AveragePooling2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[20, 32, 4]) + |input = np.random.random([2, 20, 32, 4]) + |output_tensor = AveragePooling2D(pool_size=(2, 3), strides=(1, 2), + | border_mode="same", dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = AveragePooling2D[Float](poolSize = (2, 3), strides = (1, 2), + borderMode = "same", dimOrdering = "tf", inputShape = Shape(20, 32, 4)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 20, 16, 4)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling3DSpec.scala new file mode 100644 index 00000000000..f05411a67e9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling3DSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{AveragePooling3D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class AveragePooling3DSpec extends KerasBaseSpec { + + "AveragePooling3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 12, 12, 12]) + |input = np.random.random([2, 3, 12, 12, 12]) + |output_tensor = AveragePooling3D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = AveragePooling3D[Float](inputShape = Shape(3, 12, 12, 12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 6, 6, 6)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution1DSpec.scala new file mode 100644 index 00000000000..9b97efbceb1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution1DSpec.scala @@ -0,0 +1,65 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Convolution1D, Conv1D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class Convolution1DSpec extends KerasBaseSpec { + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = + if (in.length == 1) in // without bias + else Array(in(0).resize(Array(1) ++ in(0).size()), in(1)) // with bias + + "Convolution1D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, 20]) + |input = np.random.random([2, 12, 20]) + |output_tensor = Convolution1D(64, 3)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Convolution1D[Float](64, 3, inputShape = Shape(12, 20)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 10, 64)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "Convolution1D without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[20, 32]) + |input = np.random.random([2, 20, 32]) + |output_tensor = Convolution1D(32, 4, activation="relu", bias=False, + | subsample_length=2)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Conv1D[Float](32, 4, activation = "relu", subsampleLength = 2, + bias = false, inputShape = Shape(20, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 9, 32)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala index f07cf61b143..3b1376d8bd4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala @@ -55,7 +55,7 @@ class Convolution2DSpec extends KerasBaseSpec { |model = Model(input=input_tensor, output=output_tensor) """.stripMargin val seq = KSequential[Float]() - val layer = Convolution2D[Float](32, 4, 6, format = DataFormat.NHWC, + val layer = Convolution2D[Float](32, 4, 6, dimOrdering = "tf", borderMode = "same", inputShape = Shape(24, 24, 3)) seq.add(layer) checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution3DSpec.scala new file mode 100644 index 00000000000..debb6dcd9e8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution3DSpec.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Conv3D, Convolution3D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class Convolution3DSpec extends KerasBaseSpec { + + "Convolution3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 32, 32, 32]) + |input = np.random.random([1, 3, 32, 32, 32]) + |output_tensor = Convolution3D(12, 2, 1, 3, subsample=(1, 2, 3), + | dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Convolution3D[Float](12, 2, 1, 3, subsample = (1, 2, 3), + inputShape = Shape(3, 32, 32, 32)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, precision = 1e-2) + } + + "Convolution3D without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 16, 20, 32]) + |input = np.random.random([1, 4, 16, 20, 32]) + |output_tensor = Convolution3D(8, 2, 2, 4, activation="relu", bias=False, + | border_mode="same", dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Convolution3D[Float](8, 2, 2, 4, activation = "relu", bias = false, + borderMode = "same", inputShape = Shape(4, 16, 20, 32)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, precision = 1e-3) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping1DSpec.scala new file mode 100644 index 00000000000..8ea92008633 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping1DSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Cropping1D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class Cropping1DSpec extends KerasBaseSpec { + + "Cropping1D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[5, 6]) + |input = np.random.random([2, 5, 6]) + |output_tensor = Cropping1D((1, 2))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Cropping1D[Float]((1, 2), inputShape = Shape(5, 6)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 2, 6)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping2DSpec.scala new file mode 100644 index 00000000000..8d987bbbfab --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping2DSpec.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{Cropping2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class Cropping2DSpec extends KerasBaseSpec { + + "Cropping2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 8, 12]) + |input = np.random.random([2, 3, 8, 12]) + |output_tensor = Cropping2D(((1, 2), (3, 1)), dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Cropping2D[Float](((1, 2), (3, 1)), inputShape = Shape(3, 8, 12)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "Cropping2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5, 3]) + |input = np.random.random([2, 4, 5, 3]) + |output_tensor = Cropping2D(((0, 1), (1, 1)), dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Cropping2D[Float](((0, 1), (1, 1)), dimOrdering = "tf", + inputShape = Shape(4, 5, 3)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping3DSpec.scala new file mode 100644 index 00000000000..06cd1c3fa7d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping3DSpec.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Cropping3D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class Cropping3DSpec extends KerasBaseSpec { + + "Cropping3D channel_first" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 12, 16, 20]) + |input = np.random.random([2, 4, 12, 16, 20]) + |output_tensor = Cropping3D(((2, 0), (1, 2), (3, 1)), + | dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Cropping3D[Float](((2, 0), (1, 2), (3, 1)), inputShape = Shape(4, 12, 16, 20)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "Cropping3D channel_last" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[32, 32, 32, 2]) + |input = np.random.random([2, 32, 32, 32, 2]) + |output_tensor = Cropping3D(((1, 1), (2, 2), (0, 3)), + | dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Cropping3D[Float](((1, 1), (2, 2), (0, 3)), dimOrdering = "tf", + inputShape = Shape(32, 32, 32, 2)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling2DSpec.scala new file mode 100644 index 00000000000..5b4fec80bd7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling2DSpec.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{GlobalAveragePooling2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GlobalAveragePooling2DSpec extends KerasBaseSpec { + + "GlobalAveragePooling2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 16, 20]) + |input = np.random.random([2, 3, 16, 20]) + |output_tensor = GlobalAveragePooling2D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GlobalAveragePooling2D[Float](inputShape = Shape(3, 16, 20)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "GlobalAveragePooling2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[32, 28, 6]) + |input = np.random.random([3, 32, 28, 6]) + |output_tensor = GlobalAveragePooling2D(dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GlobalAveragePooling2D[Float](dimOrdering = "tf", + inputShape = Shape(32, 28, 6)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 6)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling2DSpec.scala new file mode 100644 index 00000000000..c1f18692a7c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling2DSpec.scala @@ -0,0 +1,59 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{GlobalMaxPooling2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GlobalMaxPooling2DSpec extends KerasBaseSpec { + + "GlobalMaxPooling2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 24, 32]) + |input = np.random.random([2, 4, 24, 32]) + |output_tensor = GlobalMaxPooling2D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GlobalMaxPooling2D[Float](inputShape = Shape(4, 24, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "GlobalMaxPooling2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[16, 16, 2]) + |input = np.random.random([3, 16, 16, 2]) + |output_tensor = GlobalMaxPooling2D(dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GlobalMaxPooling2D[Float](dimOrdering = "tf", inputShape = Shape(16, 16, 2)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 2)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling1DSpec.scala new file mode 100644 index 00000000000..18c3ac222f0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling1DSpec.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{MaxPooling1D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class MaxPooling1DSpec extends KerasBaseSpec { + + "MaxPooling1D valid mode" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, 12]) + |input = np.random.random([3, 12, 12]) + |output_tensor = MaxPooling1D(pool_length=3)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = MaxPooling1D[Float](poolLength = 3, inputShape = Shape(12, 12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4, 12)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "MaxPooling1D same mode" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[20, 32]) + |input = np.random.random([3, 20, 32]) + |output_tensor = MaxPooling1D(stride=1, border_mode="same")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = MaxPooling1D[Float](stride = 1, borderMode = "same", + inputShape = Shape(20, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 20, 32)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala index 505a93812c7..371783e9a00 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala @@ -50,7 +50,7 @@ class MaxPooling2DSpec extends KerasBaseSpec{ """.stripMargin val seq = KSequential[Float]() val layer = MaxPooling2D[Float](poolSize = (2, 3), strides = (1, 2), - format = DataFormat.NHWC, inputShape = Shape(32, 28, 5)) + dimOrdering = "tf", inputShape = Shape(32, 28, 5)) seq.add(layer) checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], kerasCode) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling3DSpec.scala new file mode 100644 index 00000000000..cc70b25cd6e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling3DSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{MaxPooling3D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class MaxPooling3DSpec extends KerasBaseSpec { + + "MaxPooling3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 20, 15, 35]) + |input = np.random.random([2, 3, 20, 15, 35]) + |output_tensor = MaxPooling3D((2, 2, 3), dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = MaxPooling3D[Float](poolSize = (2, 2, 3), inputShape = Shape(3, 20, 15, 35)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 10, 7, 11)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/PermuteSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/PermuteSpec.scala new file mode 100644 index 00000000000..5967c39d30c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/PermuteSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Permute, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class PermuteSpec extends KerasBaseSpec { + + "Permute" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5, 6]) + |input = np.random.random([2, 3, 4, 5, 6]) + |output_tensor = Permute((3, 1, 4, 2))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Permute[Float](Array(3, 1, 4, 2), inputShape = Shape(3, 4, 5, 6)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 5, 3, 6, 4)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/RepeatVectorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/RepeatVectorSpec.scala new file mode 100644 index 00000000000..1a983ac3cca --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/RepeatVectorSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{RepeatVector, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class RepeatVectorSpec extends KerasBaseSpec { + + "RepeatVector" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12]) + |input = np.random.random([2, 12]) + |output_tensor = RepeatVector(4)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = RepeatVector[Float](4, inputShape = Shape(12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4, 12)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala index d1ad6e2426f..cbcea6a52d2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.utils.{RandomGenerator, Shape, TestUtils} +import com.intel.analytics.bigdl.utils.RandomGenerator import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import org.scalatest.{FlatSpec, Matchers} @@ -435,13 +435,4 @@ class SpatialMaxPoolingSpec extends FlatSpec with Matchers { } } - "SpatialMaxPooling computeOutputShape NCHW" should "work properly" in { - val layer = SpatialMaxPooling[Float](4, 5, 1, 2, 2, 2) - TestUtils.compareOutputShape(layer, Shape(3, 12, 16)) should be (true) - } - - "SpatialMaxPooling computeOutputShape NHWC" should "work properly" in { - val layer = SpatialMaxPooling[Float](2, 4, 1, 2, 1, 1, format = DataFormat.NHWC) - TestUtils.compareOutputShape(layer, Shape(18, 20, 5)) should be (true) - } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala index a899fd0aba9..80440356f29 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape, TestUtils} class SpatialSeperableConvolutionSpec extends BigDLSpecHelper { "SpatialSeperableConvolution NHWC and NCHW" should "have same output" in { @@ -94,4 +94,15 @@ class SpatialSeperableConvolutionSpec extends BigDLSpecHelper { conv.saveModule(file.getAbsolutePath, overWrite = true) val conv2 = Module.loadModule[Float](file.getAbsolutePath) } + + "SpatialSeparableConvolution computeOutputShape NCHW" should "work properly" in { + val layer = SpatialSeperableConvolution[Float](3, 6, 1, 2, 2) + TestUtils.compareOutputShape(layer, Shape(3, 12, 12)) should be (true) + } + + "SpatialSeparableConvolution computeOutputShape NHWC" should "work properly" in { + val layer = SpatialSeperableConvolution[Float](2, 5, 2, 2, 1, dataFormat = DataFormat.NHWC) + TestUtils.compareOutputShape(layer, Shape(24, 24, 2)) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala index 01927e53ff9..734ded0d1d3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{Shape, T, TestUtils} import scala.util.Random @@ -475,5 +475,10 @@ class VolumetricConvolutionSpec extends TorchSpec { val gradInput = layer.backward(input, output) output.storage().array() should be (Array(0.0f, 2, 6, 8, 18, 20, 24, 26)) } + + "VolumetricConvolution computeOutputShape" should "work properly" in { + val layer = VolumetricConvolution[Float](3, 8, 2, 1, 2) + TestUtils.compareOutputShape(layer, Shape(3, 24, 28, 32)) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index 93ce3655bfc..821a63f815b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -140,5 +140,103 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(layer, input) } + "Convolution1D serializer" should "work properly" in { + val layer = Convolution1D[Float](64, 3, inputShape = Shape(12, 20)) + layer.build(Shape(2, 12, 20)) + val input = Tensor[Float](2, 12, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Convolution3D serializer" should "work properly" in { + val layer = Convolution3D[Float](12, 2, 1, 3, inputShape = Shape(3, 32, 32, 32)) + layer.build(Shape(2, 3, 32, 32, 32)) + val input = Tensor[Float](2, 3, 32, 32, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "MaxPooling1D serializer" should "work properly" in { + val layer = MaxPooling1D[Float](inputShape = Shape(12, 12)) + layer.build(Shape(2, 12, 12)) + val input = Tensor[Float](2, 12, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "MaxPooling3D serializer" should "work properly" in { + val layer = MaxPooling3D[Float](inputShape = Shape(3, 20, 15, 35)) + layer.build(Shape(2, 3, 20, 15, 35)) + val input = Tensor[Float](2, 3, 20, 15, 35).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "AveragePooling1D serializer" should "work properly" in { + val layer = AveragePooling1D[Float](inputShape = Shape(12, 16)) + layer.build(Shape(2, 12, 16)) + val input = Tensor[Float](2, 12, 16).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "AveragePooling2D serializer" should "work properly" in { + val layer = AveragePooling2D[Float](inputShape = Shape(3, 24, 24)) + layer.build(Shape(2, 3, 24, 24)) + val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "AveragePooling3D serializer" should "work properly" in { + val layer = AveragePooling3D[Float](inputShape = Shape(3, 12, 12, 12)) + layer.build(Shape(2, 3, 12, 12, 12)) + val input = Tensor[Float](2, 3, 12, 12, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "GlobalMaxPooling2D serializer" should "work properly" in { + val layer = GlobalMaxPooling2D[Float](inputShape = Shape(4, 24, 32)) + layer.build(Shape(2, 4, 24, 32)) + val input = Tensor[Float](2, 4, 24, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "GlobalAveragePooling2D serializer" should "work properly" in { + val layer = GlobalAveragePooling2D[Float](inputShape = Shape(4, 24, 32)) + layer.build(Shape(2, 4, 24, 32)) + val input = Tensor[Float](2, 4, 24, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "RepeatVector serializer" should "work properly" in { + val layer = RepeatVector[Float](4, inputShape = Shape(12)) + layer.build(Shape(2, 12)) + val input = Tensor[Float](2, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Permute serializer" should "work properly" in { + val layer = Permute[Float](Array(3, 1, 4, 2), inputShape = Shape(3, 4, 5, 6)) + layer.build(Shape(2, 3, 4, 5, 6)) + val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Cropping1D serializer" should "work properly" in { + val layer = Cropping1D[Float](inputShape = Shape(5, 6)) + layer.build(Shape(2, 5, 6)) + val input = Tensor[Float](2, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Cropping2D serializer" should "work properly" in { + val layer = Cropping2D[Float](inputShape = Shape(3, 8, 12)) + layer.build(Shape(2, 3, 8, 12)) + val input = Tensor[Float](2, 3, 8, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Cropping3D serializer" should "work properly" in { + val layer = Cropping3D[Float](inputShape = Shape(4, 12, 16, 20)) + layer.build(Shape(2, 4, 12, 16, 20)) + val input = Tensor[Float](2, 4, 12, 16, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + } From 40de031cdc3751dfdbd2cb3b0df8cba41f3bd4aa Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 7 Feb 2018 10:00:19 +0800 Subject: [PATCH 0684/1065] Refine module serialization tests code (#2274) * refine serialization tests * restore original serial test * fix style error * cancel unimpl serial test --- .../bigdl/dllib/keras/nn/DenseSpec.scala | 12 ++ .../analytics/bigdl/dllib/nn/BottleSpec.scala | 32 +++++ .../bigdl/dllib/nn/FlattenTableSpec.scala | 16 ++- .../bigdl/dllib/nn/GradientReversalSpec.scala | 29 +++++ .../bigdl/dllib/nn/HardTanhSpec.scala | 29 +++++ .../bigdl/dllib/nn/LSTMPeepholeSpec.scala | 30 +++++ .../analytics/bigdl/dllib/nn/MMSpec.scala | 17 ++- .../analytics/bigdl/dllib/nn/PReLUSpec.scala | 9 ++ .../bigdl/dllib/nn/RecurrentDecoderSpec.scala | 11 ++ .../bigdl/dllib/nn/ReplicateSpec.scala | 29 +++++ .../dllib/nn/SpatialMaxPoolingSpec.scala | 11 ++ .../SpatialSubtractiveNormalizationSpec.scala | 31 +++++ .../bigdl/dllib/nn/ThresholdSpec.scala | 10 ++ .../bigdl/dllib/nn/ops/AssignSpec.scala | 13 ++ .../bigdl/dllib/nn/ops/ControlOpsSpec.scala | 51 ++++++++ .../nn/ops/Conv3DBackpropFilterV2Spec.scala | 32 +++++ .../DepthwiseConv2DBackpropInputSpec.scala | 35 ++++++ .../bigdl/dllib/nn/ops/Dilation2DSpec.scala | 31 +++++ .../bigdl/dllib/nn/ops/InTopKSpec.scala | 32 +++++ .../bigdl/dllib/nn/ops/LessEqualSpec.scala | 33 ++++++ .../bigdl/dllib/nn/ops/MergeOpsSpec.scala | 32 +++++ .../bigdl/dllib/nn/ops/ProdSpec.scala | 12 ++ .../bigdl/dllib/nn/ops/SoftplusGradSpec.scala | 31 +++++ .../bigdl/dllib/nn/ops/StackOpsSpec.scala | 38 ++++++ .../bigdl/dllib/nn/tf/FillSpec.scala | 11 ++ .../utils/serializer/SerializerSpec.scala | 112 ++++++++++++++++++ .../serializer/SerializerSpecHelper.scala | 2 +- .../utils/tf/loaders/MeanLoadTFSpec.scala | 31 +++++ .../utils/tf/loaders/SliceLoadTFSpec.scala | 32 +++++ .../utils/tf/loaders/SplitLoadTFSpec.scala | 32 +++++ 30 files changed, 823 insertions(+), 3 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BottleSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GradientReversalSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardTanhSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeepholeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReplicateSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalizationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOpsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DBackpropInputSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InTopKSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessEqualSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MergeOpsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/StackOpsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MeanLoadTFSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SliceLoadTFSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SplitLoadTFSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala index c57f1ec752f..abfb6475ecc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DenseSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.Dense import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class DenseSpec extends KerasBaseSpec { @@ -61,3 +64,12 @@ class DenseSpec extends KerasBaseSpec { } } + +class DenseSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val dense = Dense[Float](10, inputShape = Shape(20)) + dense.build(Shape(2, 20)) + val input = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(dense, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BottleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BottleSpec.scala new file mode 100644 index 00000000000..bab2bd28b7c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BottleSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class BottleSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Tensor[Float](10).apply1(e => Random.nextFloat()) + + val bottle = new Bottle[Float](Linear[Float](10, 2). + asInstanceOf[Module[Float]], 2, 2).setName("bottle") + runSerializationTest(bottle, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FlattenTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FlattenTableSpec.scala index e872caba2ea..db6eb5c70cc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FlattenTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FlattenTableSpec.scala @@ -16,9 +16,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class FlattenTableSpec extends FlatSpec with BeforeAndAfter with Matchers { "An FlattenTable" should "generate correct output and grad" in { @@ -124,3 +127,14 @@ class FlattenTableSpec extends FlatSpec with BeforeAndAfter with Matchers { } } +class FlattenTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val flattenTable = FlattenTable[Float]().setName("flattenTable") + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(flattenTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GradientReversalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GradientReversalSpec.scala new file mode 100644 index 00000000000..e0a9bf74635 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GradientReversalSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class GradientReversalSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val gradientReversal = GradientReversal[Float]().setName("gradientReversal") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(gradientReversal, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardTanhSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardTanhSpec.scala new file mode 100644 index 00000000000..7580fa9158a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardTanhSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class HardTanhSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val hardTanh = HardTanh[Float]().setName("hardTanh") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(hardTanh, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeepholeSpec.scala new file mode 100644 index 00000000000..8e3ceedcf2e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LSTMPeepholeSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class LSTMPeepholeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val lstmPeephole = LSTMPeephole[Float](6, 4) + val lstmPeepholeModel = Recurrent[Float]().add(lstmPeephole).setName("lstmPeephole") + val input = Tensor[Float](Array(1, 5, 6)).apply1(_ => Random.nextFloat()) + runSerializationTest(lstmPeepholeModel, input, lstmPeephole.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala index ec381d780fa..cc2d0859246 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala @@ -16,9 +16,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class MMSpec extends FlatSpec with Matchers { "hashcode()" should "behave correctly" in { @@ -59,3 +62,15 @@ class MMSpec extends FlatSpec with Matchers { m1 should not equal m4 } } + +class MMSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mm = MM[Float]().setName("mm_layer") + val input1 = Tensor[Float](2, 3).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](3, 4).apply1(e => Random.nextFloat()) + val input = new Table() + input(1.0f) = input1 + input(2.0f) = input2 + runSerializationTest(mm, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PReLUSpec.scala index 6ef54a9b3c2..adb28277b12 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PReLUSpec.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -49,3 +50,11 @@ class PReLUSpec extends FlatSpec with Matchers { } } + +class PReLUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val preLu = PReLU[Float](2).setName("preLu") + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(preLu, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala index 28a8db02948..c293f36241e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala @@ -22,10 +22,12 @@ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.collection.mutable.ArrayBuffer import scala.math._ +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { @@ -403,3 +405,12 @@ class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { }) } } + +class RecurrentDecoderSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val recDecoder = RecurrentDecoder[Float](5). + add(ConvLSTMPeephole[Float](7, 7, 3, 3, 1)) + val input = Tensor[Float](4, 7, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(recDecoder, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReplicateSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReplicateSpec.scala new file mode 100644 index 00000000000..c9b01c4200d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReplicateSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class ReplicateSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val replicate = new Replicate[Float](3).setName("replicate") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(replicate, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala index cbcea6a52d2..555a1a43b4d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialMaxPoolingSpec.scala @@ -21,8 +21,10 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.math.abs +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class SpatialMaxPoolingSpec extends FlatSpec with Matchers { @@ -436,3 +438,12 @@ class SpatialMaxPoolingSpec extends FlatSpec with Matchers { } } + +class SpatialMaxPoolingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialMaxPooling = SpatialMaxPooling[Float](2, 2, 2, 2). + setName("spatialMaxPooling") + val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) + runSerializationTest(spatialMaxPooling, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalizationSpec.scala new file mode 100644 index 00000000000..888c5bc7bdf --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalizationSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SpatialSubtractiveNormalizationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val kernel = Tensor[Float](3, 3).apply1( e => Random.nextFloat()) + val spatialSubtractiveNormalization = SpatialSubtractiveNormalization[Float](1, kernel). + setName("spatialSubtractiveNormalization") + val input = Tensor[Float](1, 1, 1, 5).apply1( e => Random.nextFloat()) + runSerializationTest(spatialSubtractiveNormalization, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ThresholdSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ThresholdSpec.scala index 45393cb4f8d..f36893ad85b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ThresholdSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ThresholdSpec.scala @@ -18,8 +18,10 @@ package com.intel.analytics.bigdl.nn import org.scalatest.FlatSpec import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.math.abs +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class ThresholdSpec extends FlatSpec { @@ -140,3 +142,11 @@ class ThresholdSpec extends FlatSpec { assert(input.nElement() == 8) } } + +class ThresholdSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val threshold = Threshold[Float](0.5).setName("threshold") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(threshold, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala index 6c0d8d1132c..23fdb2fbd9a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class AssignSpec extends FlatSpec with Matchers { @@ -48,3 +49,15 @@ class AssignSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class AssignSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val assign = new Assign[Float]().setName("assign") + val input = + T( + Tensor[Float](T(1f, 2f, 3f)), + Tensor[Float](T(2f, 2f, 4f)) + ) + runSerializationTest(assign, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOpsSpec.scala new file mode 100644 index 00000000000..286cfebebee --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOpsSpec.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.{AddConstant, Echo, Graph, Input} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class ControlOpsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Input[Float]("input") + + val conditionInput = Input[Float]("conditionInput") + val const = new com.intel.analytics.bigdl.nn.tf.Const[Float, Float](Tensor(T(9))).inputs() + val constEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(const) + val less = Less[Float]().inputs(constEnter, conditionInput) + + val updateInput = Input[Float]() + val add = AddConstant[Float](1).inputs(updateInput) + val addEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(add) + val echo = Echo[Float]().inputs(addEnter) + + val exit = ControlNodes.whileLoop[Float]( + (Seq(conditionInput), less), + (Seq((updateInput, echo))), + Seq(input), + "while" + ) + val model = Graph.dynamic[Float](Array(input), Array(exit(0)), None, false) + runSerializationTestWithMultiClass(model, Tensor.scalar[Float](1), Array( + addEnter.element.getClass.asInstanceOf[Class[_]], + new com.intel.analytics.bigdl.nn.ops.NextIteration[Float, Float]().getClass, + new com.intel.analytics.bigdl.nn.ops.Exit[Float]().getClass, + new com.intel.analytics.bigdl.nn.ops.LoopCondition[Float]().getClass + )) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2Spec.scala new file mode 100644 index 00000000000..1ff116809d0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2Spec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class Conv3DBackpropFilterV2SerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Conv3DBackpropFilterV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Int](Array(2, 3, 4, 3, 4), Array(5)) + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(input, filter, outputBackprop)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DBackpropInputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DBackpropInputSpec.scala new file mode 100644 index 00000000000..fb57c481a2f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DBackpropInputSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class DepthwiseConv2DBackpropInputSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val depWiseBackprop = + DepthwiseConv2DBackpropInput[Float](1, 1, 0, 0, DataFormat.NHWC). + setName("depWiseBackprop") + val input = T(Tensor[Int](T(4, 24, 24, 3)), + Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(depWiseBackprop, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DSpec.scala new file mode 100644 index 00000000000..d16321301f5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class Dilation2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Dilation2D[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()) + + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InTopKSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InTopKSpec.scala new file mode 100644 index 00000000000..3d99c3d6843 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InTopKSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class InTopKSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val inTopK = InTopK[Float](2).setName("inTopK") + val input1 = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Int](2).fill(1) + val input = T(input1, input2) + runSerializationTest(inTopK, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessEqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessEqualSpec.scala new file mode 100644 index 00000000000..1619ef20743 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessEqualSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class LessEqualSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val lessEqual = LessEqual[Float]().setName("lessEqual") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(lessEqual, input, lessEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MergeOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MergeOpsSpec.scala new file mode 100644 index 00000000000..dfe544b478c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MergeOpsSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class MergeOpsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mergeOps = new MergeOps[Float](1).setName("mergeOps") + val input = + T( + Tensor[Float](T(1.0f, 2.0f, 3.0f)), + Tensor[Float](T(2.0f, 2.0f, 1.0f)) + ) + runSerializationTest(mergeOps, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ProdSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ProdSpec.scala index 2c475aa8525..0b2dcf428c2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ProdSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ProdSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class ProdSpec extends FlatSpec with Matchers { "Prod operation" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericFloat @@ -35,3 +38,12 @@ class ProdSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class ProdSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val prod = Prod[Float](-1, false).setName("prod") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(prod, input, prod. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGradSpec.scala new file mode 100644 index 00000000000..ef27ef8f955 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGradSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SoftplusGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sofplusGrad = SoftplusGrad[Float, Float].setName("sofplusGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sofplusGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/StackOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/StackOpsSpec.scala new file mode 100644 index 00000000000..790710a52ea --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/StackOpsSpec.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.Graph +import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class StackOpsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() + val stack = new StackCreator[Float, Float]().inputs() + val push = new StackPush[Float, Float]().inputs(stack, data) + val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(push) + val pop = new StackPop[Float, Float]().inputs(stack, ctr) + val model = Graph.dynamic[Float](Array(stack), Array(pop)) + + runSerializationTestWithMultiClass(model, Tensor.scalar(1), Array( + stack.element.getClass.asInstanceOf[Class[_]], + push.element.getClass.asInstanceOf[Class[_]], + pop.element.getClass.asInstanceOf[Class[_]] + )) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala index 234cda1cf2a..a584657a635 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FillSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class FillSpec extends FlatSpec with Matchers { @@ -47,3 +48,13 @@ class FillSpec extends FlatSpec with Matchers { gradInput[Tensor[Float]](2) should be (Tensor[Float](Array(0.0f), Array[Int]())) } } + +class FillSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val fill = Fill[Float]().setName("fill") + val shape = Tensor[Int](T(2, 3)) + val value = Tensor[Float](Array(0.1f), Array[Int]()) + val input = T(shape, value) + runSerializationTest(fill, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala new file mode 100644 index 00000000000..647225bf000 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -0,0 +1,112 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.serializer + +import java.lang.reflect.Modifier + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import org.reflections.Reflections +import org.reflections.scanners.SubTypesScanner +import org.reflections.util.{ClasspathHelper, ConfigurationBuilder, FilterBuilder} + +import collection.JavaConverters._ +import scala.collection.mutable + +class SerializerSpec extends BigDLSpecHelper { + + private val excluded = Set[String]( + "com.intel.analytics.bigdl.nn.CellUnit", + "com.intel.analytics.bigdl.nn.tf.ControlDependency", + "com.intel.analytics.bigdl.utils.tf.AdapterForTest", + "com.intel.analytics.bigdl.utils.serializer.TestModule", + "com.intel.analytics.bigdl.utils.ExceptionTest" + ) + + // Maybe one serial test class contains multiple module test + // Also keras layer main/test class mapping are weired + private val unRegularNameMapping = Map[String, String]( + // Many to one mapping + "com.intel.analytics.bigdl.nn.ops.Enter" -> + "com.intel.analytics.bigdl.nn.ops.ControlOpsSerialTest", + "com.intel.analytics.bigdl.nn.ops.NextIteration" -> + "com.intel.analytics.bigdl.nn.ops.ControlOpsSerialTest", + "com.intel.analytics.bigdl.nn.ops.Exit" -> + "com.intel.analytics.bigdl.nn.ops.ControlOpsSerialTest", + "com.intel.analytics.bigdl.nn.ops.LoopCondition" -> + "com.intel.analytics.bigdl.nn.ops.ControlOpsSerialTest", + "com.intel.analytics.bigdl.nn.ops.StackCreator" -> + "com.intel.analytics.bigdl.nn.ops.StackOpsSerialTest", + "com.intel.analytics.bigdl.nn.ops.StackPush" -> + "com.intel.analytics.bigdl.nn.ops.StackOpsSerialTest", + "com.intel.analytics.bigdl.nn.ops.StackPop" -> + "com.intel.analytics.bigdl.nn.ops.StackOpsSerialTest", + + // Keras layers + "com.intel.analytics.bigdl.nn.keras.Dense" -> + "com.intel.analytics.bigdl.keras.nn.DenseSerialTest" + ) + + private val suffix = "SerialTest" + + private val testClasses = new mutable.HashSet[String]() + + { + val filterBuilder = new FilterBuilder() + val reflections = new Reflections(new ConfigurationBuilder() + .filterInputsBy(filterBuilder) + .setUrls(ClasspathHelper.forPackage("com.intel.analytics.bigdl.nn")) + .setScanners(new SubTypesScanner())) + + + val subTypes = reflections.getSubTypesOf(classOf[AbstractModule[_, _, _]]) + .asScala.filter(sub => !Modifier.isAbstract(sub.getModifiers)) + .filter(sub => !excluded.contains(sub.getName)) + subTypes.foreach(sub => testClasses.add(sub.getName)) + } + + private def getTestClassName(clsName: String): String = { + if (unRegularNameMapping.contains(clsName)) { + unRegularNameMapping(clsName) + } else { + clsName + suffix + } + } + + testClasses.foreach(cls => { + "Serialization test of module " + cls should "be correct" in { + val clsWholeName = getTestClassName(cls) + try { + val ins = Class.forName(clsWholeName) + val testClass = ins.getConstructors()(0).newInstance() + require(testClass.isInstanceOf[ModuleSerializationTest], s"$clsWholeName should be a " + + s"subclass of com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest") + testClass.asInstanceOf[ModuleSerializationTest].test() + } catch { + case e: ClassNotFoundException => + cancel(s"Serialization test of module $cls has not " + + s"been implemented. Please consider creating a serialization test class with name " + + s"${clsWholeName} which extend com.intel.analytics.bigdl.utils.serializer." + + s"ModuleSerializationTest") + case t: Throwable => throw t + } + } + }) +} + +private[bigdl] abstract class ModuleSerializationTest extends SerializerSpecHelper { + def test(): Unit +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala index 714f8743b67..9c67138b500 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala @@ -44,7 +44,7 @@ abstract class SerializerSpecHelper extends FlatSpec with Matchers with BeforeAn private var executedCount = 0 - protected def getPackage(): String + protected def getPackage(): String = "" protected def addExcludedClass(): Unit = {} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MeanLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MeanLoadTFSpec.scala new file mode 100644 index 00000000000..02b807fcbee --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MeanLoadTFSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class MeanLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val meanLoadTF = new MeanLoadTF[Float]("Float", false).setName("meanLoadTF") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1, 1))) + runSerializationTest(meanLoadTF, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SliceLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SliceLoadTFSpec.scala new file mode 100644 index 00000000000..f97a93201fc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SliceLoadTFSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SliceLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sliceLoadTF = new SliceLoadTF[Float]().setName("sliceLoadTF") + val input = T(Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 1, 1)), + Tensor[Int](T(2, -1, 1))) + runSerializationTest(sliceLoadTF, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SplitLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SplitLoadTFSpec.scala new file mode 100644 index 00000000000..2fd21b9ef6e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SplitLoadTFSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SplitLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val splitLoadTF = new SplitLoadTF[Float](1).setName("splitLoadTD") + val input = T(Tensor[Int](T(1)), + Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(splitLoadTF, input) + } +} From ee3d941de98869e0968e754c6cff38ac4ff45737 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 7 Feb 2018 10:22:32 +0800 Subject: [PATCH 0685/1065] fix new Sample(SparseTensor, DenseTensor) will throw exception. (#2259) --- .../bigdl/dllib/feature/dataset/Sample.scala | 85 ++++++++++++++----- .../bigdl/dllib/dataset/SampleSpec.scala | 28 ++++++ 2 files changed, 92 insertions(+), 21 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index e67d8b83043..76a9c0506e5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -129,7 +129,7 @@ abstract class Sample[T: ClassTag] extends Serializable { /** * A kind of sample who use only one array */ -private[bigdl] class ArraySample[T: ClassTag]( +class ArraySample[T: ClassTag] private[bigdl]( private val data: Array[T], private val featureSize: Array[Array[Int]], private val labelSize: Array[Array[Int]]) extends Sample[T] { @@ -227,6 +227,22 @@ private[bigdl] class ArraySample[T: ClassTag]( } object ArraySample { + private def typeCheck[T: ClassTag](tensor: Tensor[T]): Unit = { + tensor.getTensorType match { + case DenseType => + require(tensor.isContiguous(), s"tensor in ArraySample should be contiguous," + + s" Please check your input.") + case _ => + throw new IllegalArgumentException(s"ArraySample doesn't support ${tensor.getTensorType}") + } + } + + private def typeCheck[T: ClassTag](tensors: Array[Tensor[T]]): Unit = { + tensors.foreach{tensor => + typeCheck(tensor) + } + } + def apply[T: ClassTag]( data: Array[T], featureSize: Array[Array[Int]], @@ -237,8 +253,8 @@ object ArraySample { def apply[T: ClassTag]( featureTensor: Tensor[T], labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { - require(featureTensor.isContiguous(), "featureTensor is not contiguous") - require(labelTensor.isContiguous(), "labelTensor is not contiguous") + typeCheck(featureTensor) + typeCheck(labelTensor) val data = new Array[T](featureTensor.nElement() + labelTensor.nElement()) ev.arraycopy(featureTensor.storage().array(), featureTensor.storageOffset() - 1, data, 0, featureTensor.nElement()) @@ -250,7 +266,7 @@ object ArraySample { def apply[T: ClassTag]( featureTensor: Tensor[T], label: T)(implicit ev: TensorNumeric[T]) : Sample[T] = { - require(featureTensor.isContiguous(), "featureTensor is not contiguous") + typeCheck(featureTensor) val data = new Array[T](featureTensor.nElement() + 1) ev.arraycopy(featureTensor.storage().array(), featureTensor.storageOffset() - 1, data, 0, featureTensor.nElement()) @@ -261,6 +277,8 @@ object ArraySample { def apply[T: ClassTag]( featureTensors: Array[Tensor[T]], labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + typeCheck(featureTensors) + typeCheck(labelTensor) val tensors = featureTensors ++ Array(labelTensor) val data = new Array[T](tensors.map(_.nElement()).sum) copy(data, tensors) @@ -270,6 +288,8 @@ object ArraySample { def apply[T: ClassTag]( featureTensors: Array[Tensor[T]], labelTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + typeCheck(featureTensors) + typeCheck(labelTensors) val tensors = featureTensors ++ labelTensors val data = new Array[T](tensors.map(_.nElement()).sum) copy(data, tensors) @@ -278,7 +298,7 @@ object ArraySample { def apply[T: ClassTag]( featureTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { - require(featureTensor.isContiguous(), "featureTensor is not contiguous") + typeCheck(featureTensor) val data = new Array[T](featureTensor.nElement()) ev.arraycopy(featureTensor.storage().array(), featureTensor.storageOffset() - 1, data, 0, featureTensor.nElement()) @@ -287,6 +307,7 @@ object ArraySample { def apply[T: ClassTag]( featureTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + typeCheck(featureTensors) val data = new Array[T](featureTensors.map(_.nElement()).sum) copy(data, featureTensors) new ArraySample[T](data, getSize(featureTensors), null) @@ -338,8 +359,6 @@ object Sample { def apply[T: ClassTag]( featureTensor: Tensor[T], labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { - require(featureTensor.isContiguous(), "featureTensor is not contiguous") - require(labelTensor.isContiguous(), "labelTensor is not contiguous") if (featureTensor.getTensorType == DenseType) { ArraySample(featureTensor, labelTensor) } else { @@ -350,7 +369,6 @@ object Sample { def apply[T: ClassTag]( featureTensor: Tensor[T], label: T)(implicit ev: TensorNumeric[T]) : Sample[T] = { - require(featureTensor.isContiguous(), "featureTensor is not contiguous") if (featureTensor.getTensorType == DenseType) { ArraySample(featureTensor, label) } else { @@ -381,7 +399,6 @@ object Sample { def apply[T: ClassTag]( featureTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { - require(featureTensor.isContiguous(), "featureTensor is not contiguous") if (featureTensor.getTensorType == SparseType) { TensorSample(featureTensor) } else { @@ -405,11 +422,11 @@ object Sample { * @param labels label tensors * @tparam T numeric type */ -private[bigdl] class TensorSample[T: ClassTag]( +class TensorSample[T: ClassTag] private[bigdl] ( val features: Array[Tensor[T]], val labels: Array[Tensor[T]]) extends Sample[T] { - val featureSize = features.map(_.size()) - val labelSize = labels.map(_.size()) + protected val featureSize = features.map(_.size()) + protected val labelSize = labels.map(_.size()) def featureLength(index: Int): Int = { features(0).size(1) @@ -441,36 +458,62 @@ private[bigdl] class TensorSample[T: ClassTag]( } object TensorSample { + private def typeCheck[T: ClassTag](tensor: Tensor[T]): Unit = { + tensor.getTensorType match { + case DenseType => + require(tensor.isContiguous(), s"tensor in TensorSample should be contiguous," + + s" Please check your input.") + case SparseType => + case _ => + throw new IllegalArgumentException(s"TensorSample doesn't support ${tensor.getTensorType}") + } + } + + private def typeCheck[T: ClassTag](tensors: Array[Tensor[T]]): Unit = { + tensors.foreach{tensor => + typeCheck(tensor) + } + } + def apply[T: ClassTag]( featureTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + typeCheck(featureTensors) new TensorSample[T](featureTensors, Array()) } def apply[T: ClassTag]( - featureTensors: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { - new TensorSample[T](Array(featureTensors), Array()) + featureTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + typeCheck(featureTensor) + new TensorSample[T](Array(featureTensor), Array()) } def apply[T: ClassTag]( featureTensors: Array[Tensor[T]], labelTensors: Array[Tensor[T]])(implicit ev: TensorNumeric[T]) : Sample[T] = { + typeCheck(featureTensors) + typeCheck(labelTensors) new TensorSample[T](featureTensors, labelTensors) } def apply[T: ClassTag]( featureTensors: Array[Tensor[T]], - labelTensors: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { - new TensorSample[T](featureTensors, Array(labelTensors)) + labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + typeCheck(featureTensors) + typeCheck(labelTensor) + new TensorSample[T](featureTensors, Array(labelTensor)) } def apply[T: ClassTag]( - featureTensors: Tensor[T], - labelTensors: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { - new TensorSample[T](Array(featureTensors), Array(labelTensors)) + featureTensor: Tensor[T], + labelTensor: Tensor[T])(implicit ev: TensorNumeric[T]) : Sample[T] = { + typeCheck(featureTensor) + typeCheck(labelTensor) + new TensorSample[T](Array(featureTensor), Array(labelTensor)) } def apply[T: ClassTag]( - featureTensors: Tensor[T], + featureTensor: Tensor[T], label: T)(implicit ev: TensorNumeric[T]) : Sample[T] = { - new TensorSample[T](Array(featureTensors), Array(Tensor(1).fill(label))) + typeCheck(featureTensor) + new TensorSample[T](Array(featureTensor), Array(Tensor(1).fill(label))) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala index 7d99bf53e32..8bd9a6b0915 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.dataset import com.intel.analytics.bigdl.dataset.image.LabeledBGRImage import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -61,6 +62,33 @@ class SampleSpec extends FlatSpec with Matchers { Some(featureParam), Some(labelParam)).set(samples) } + "create Sample" should "work fine" in { + val st1 = Tensor.sparse(Tensor.range(1, 10, 1)) + val st2 = Tensor.sparse(Tensor.range(1, 10, 1)) + val dt1 = Tensor.range(1, 10, 1) + val dt2 = Tensor.range(1, 10, 1) + val label1 = Tensor(1).fill(1) + val label2 = Tensor(1).fill(2) + + Sample(st1) + Sample(dt1) + Sample(Array(st1, st2)) + Sample(Array(dt1, st2)) + Sample(Array(dt1, dt2)) + + Sample(st1, label1) + Sample(dt1, label1) + Sample(dt1, 1f) + Sample(st1, 1f) + Sample(Array(st1, st2), label1) + Sample(Array(dt1, st2), label1) + Sample(Array(dt1, dt2), label1) + + Sample(Array(st1, st2), Array(label1, label2)) + Sample(Array(dt1, st2), Array(label1, label2)) + Sample(Array(dt1, dt2), Array(label1, label2)) + } + "Hashcode" should "work fine" in { val sample1 = Sample[Float](Tensor[Float](2, 3).range(1, 6, 1), Tensor[Float](1).fill(1)) println(sample1.hashCode()) From 79b8e87da30954fba0d4b8fd5cb028c5c6770e04 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Wed, 7 Feb 2018 16:32:10 +0800 Subject: [PATCH 0686/1065] bug fix: DLModel prediction (#2194) * bug fix: DLModel prediction (#4) Make sure DLModel.train=False when predicting in pipeline API * 1. broadcast transformer in DLModel.transform ; 2. remove useless ut --- dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index a0433498751..6408d37fe8f 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -387,11 +387,13 @@ class DLModel[@specialized(Float, Double) T: ClassTag]( val featureColIndex = dataFrame.schema.fieldIndex($(featuresCol)) val featureFunc = getConvertFunc(featureType) val sc = dataFrame.sqlContext.sparkContext - val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model) + val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model.evaluate()) val localBatchSize = $(batchSize) + val transformerBC = sc.broadcast(SampleToMiniBatch[T](localBatchSize)) val resultRDD = dataFrame.rdd.mapPartitions { rowIter => val localModel = modelBroadCast.value() + val transformer = transformerBC.value.cloneTransformer() rowIter.grouped(localBatchSize).flatMap { rowBatch => val samples = rowBatch.map { row => val features = featureFunc(row, featureColIndex) @@ -401,7 +403,7 @@ class DLModel[@specialized(Float, Double) T: ClassTag]( } Sample(Tensor(featureBuffer.toArray, featureSize)) }.toIterator - val predictions = SampleToMiniBatch(localBatchSize).apply(samples).flatMap { batch => + val predictions = transformer(samples).flatMap { batch => val batchResult = localModel.forward(batch.getInput()) batchResult.toTensor.split(1).map(outputToPrediction) } From f21473148b7a34433411b5ca219ba6c8f60ecaca Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Wed, 7 Feb 2018 19:12:48 +0800 Subject: [PATCH 0687/1065] Keras-like API More conv and pooling layers (#2255) * GlobalAveragePooling1D * more pooling and padding layers * SeparableConvolution2D * LocallyConnected2D * fix some problem and add more unit test * make some changes * larger size input size for unit test * fix a mistake * add documentation and serializer test * change format * change tuple to array * make some change * change a name * change style * modify * modify * modify again * update --- .../dllib/keras/GlobalAveragePooling1D.scala | 62 ++++++++ .../dllib/keras/GlobalAveragePooling3D.scala | 73 ++++++++++ .../dllib/keras/GlobalMaxPooling1D.scala | 61 ++++++++ .../dllib/keras/GlobalMaxPooling3D.scala | 71 ++++++++++ .../bigdl/dllib/keras/GlobalPooling1D.scala | 40 ++++++ .../bigdl/dllib/keras/GlobalPooling3D.scala | 40 ++++++ .../dllib/keras/LocallyConnected2D.scala | 117 +++++++++++++++ .../dllib/keras/SeparableConvolution2D.scala | 133 ++++++++++++++++++ .../bigdl/dllib/keras/ZeroPadding3D.scala | 95 +++++++++++++ .../analytics/bigdl/dllib/keras/package.scala | 1 + .../keras/nn/GlobalAveragePooling1DSpec.scala | 44 ++++++ .../keras/nn/GlobalAveragePooling3DSpec.scala | 44 ++++++ .../keras/nn/GlobalMaxPooling1DSpec.scala | 44 ++++++ .../keras/nn/GlobalMaxPooling3DSpec.scala | 44 ++++++ .../keras/nn/LocallyConnected2DSpec.scala | 97 +++++++++++++ .../keras/nn/SeparableConvolution2DSpec.scala | 105 ++++++++++++++ .../dllib/keras/nn/ZeroPadding3DSpec.scala | 73 ++++++++++ .../KerasModuleSerializerSpec.scala | 50 ++++++- 18 files changed, 1193 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding3D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding3DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala new file mode 100644 index 00000000000..4ac478e3fc9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala @@ -0,0 +1,62 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.SpatialAveragePooling +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Global average pooling operation for temporal data. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 3D. + * + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class GlobalAveragePooling1D[T: ClassTag]( + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends GlobalPooling1D[T](inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val model = TSequential[T]() + model.add(com.intel.analytics.bigdl.nn.Reshape(Array(input(1), 1, input(2)), Some(true))) + val layer = SpatialAveragePooling( + kW = 1, + kH = input(1), + countIncludePad = false, + format = DataFormat.NHWC) + model.add(layer) + model.add(Squeeze(3)) + model.add(Squeeze(2)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object GlobalAveragePooling1D { + def apply[@specialized(Float, Double) T: ClassTag]( + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : GlobalAveragePooling1D[T] = { + new GlobalAveragePooling1D[T](inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala new file mode 100644 index 00000000000..f264629d8f4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala @@ -0,0 +1,73 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.VolumetricAveragePooling +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Global Average pooling operation for 3D data. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * The input of this layer should be 5D. + * + * @param format Format of input data. Please use DataFormat.NCHW (dimOrdering='th'). + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class GlobalAveragePooling3D[T: ClassTag]( + val format: String = "CHANNEL_FIRST", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends GlobalPooling3D[T](inputShape) { + + require(format.toLowerCase() == "channel_first", s"GlobalAveragePooling3D only supports " + + s"format CHANNEL_FIRST, but got format $format.") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val model = TSequential[T]() + val layer = VolumetricAveragePooling( + kT = input(2), + kW = input(4), + kH = input(3), + dT = 1, + dW = 1, + dH = 1, + countIncludePad = false) + model.add(layer) + model.add(Squeeze(5)) + model.add(Squeeze(4)) + model.add(Squeeze(3)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object GlobalAveragePooling3D { + def apply[@specialized(Float, Double) T: ClassTag]( + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : GlobalAveragePooling3D[T] = { + new GlobalAveragePooling3D[T](KerasUtils.toBigDLFormat5D(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala new file mode 100644 index 00000000000..cdeb1ddf9b7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.SpatialMaxPooling +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Global max pooling operation for temporal data. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 3D. + * + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class GlobalMaxPooling1D[T: ClassTag]( + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends GlobalPooling1D[T](inputShape) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val model = TSequential[T]() + model.add(com.intel.analytics.bigdl.nn.Reshape(Array(input(1), 1, input(2)), Some(true))) + val layer = SpatialMaxPooling( + kW = 1, + kH = input(1), + format = DataFormat.NHWC) + model.add(layer) + model.add(Squeeze(3)) + model.add(Squeeze(2)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object GlobalMaxPooling1D { + def apply[@specialized(Float, Double) T: ClassTag]( + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : GlobalMaxPooling1D[T] = { + new GlobalMaxPooling1D[T](inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala new file mode 100644 index 00000000000..210face8cc0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.nn.VolumetricMaxPooling +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Global Max pooling operation for 3D data. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * The input of this layer should be 5D. + * + * @param format Format of input data. Please use DataFormat.NCHW (dimOrdering='th'). + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class GlobalMaxPooling3D[T: ClassTag]( + val format: String = "CHANNEL_FIRST", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends GlobalPooling3D[T](inputShape) { + + require(format.toLowerCase() == "channel_first", s"GlobalMaxPooling3D only supports " + + s"format CHANNEL_FIRST, but got format $format.") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val model = TSequential[T]() + val layer = VolumetricMaxPooling( + kT = input(2), + kW = input(4), + kH = input(3), + dT = 1, + dW = 1, + dH = 1) + model.add(layer) + model.add(Squeeze(5)) + model.add(Squeeze(4)) + model.add(Squeeze(3)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object GlobalMaxPooling3D { + def apply[@specialized(Float, Double) T: ClassTag]( + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : GlobalMaxPooling3D[T] = { + new GlobalMaxPooling3D[T](KerasUtils.toBigDLFormat5D(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling1D.scala new file mode 100644 index 00000000000..4956cbba3ee --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling1D.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Abstract class for different global pooling 1D layers. + * Do not create a new instance of it or use it in a model. + * Please use its child classes, 'GlobalAveragePooling1D' and 'GlobalMaxPooling1D' instead. + */ +abstract class GlobalPooling1D[T: ClassTag]( + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"GlobalPooling1D requires 3D input, but got input dim ${input.length}") + Shape(input(0), input(2)) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling3D.scala new file mode 100644 index 00000000000..99b9106604c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling3D.scala @@ -0,0 +1,40 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Abstract class for different global pooling 3D layers. + * Do not create a new instance of it or use it in a model. + * Please use its child classes, 'GlobalAveragePooling3D' and 'GlobalMaxPooling3D' instead. + */ +abstract class GlobalPooling3D[T: ClassTag]( + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 5, + s"GlobalPooling3D requires 5D input, but got input dim ${input.length}") + Shape(input(0), input(1)) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala new file mode 100644 index 00000000000..a3815e164ef --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala @@ -0,0 +1,117 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Locally-connected layer for 2D inputs. + * The LocallyConnected2D layer works similarly to the SpatialConvolution layer, + * except that weights are unshared, that is, a different set of filters + * is applied at each different patch of the input. + * e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures. + * The input of this layer should be 4D. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param nbFilter Number of convolution filters to use. + * @param nbRow Number of rows in the convolution kernel. + * @param nbCol Number of columns in the convolution kernel. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @param subsample Int array of length 2. The step of the convolution in the height and + * width dimension. Also called strides elsewhere. Default is (1, 1). + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param format Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class LocallyConnected2D[T: ClassTag]( + val nbFilter: Int, + val nbRow: Int, + val nbCol: Int, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val borderMode: String = "valid", + val subsample: Array[Int] = Array(1, 1), + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val format: DataFormat = DataFormat.NCHW, + val bias: Boolean = true, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + + s"LocallyConnected2D: $borderMode") + require(subsample.length == 2, + s"For LocallyConnected2D, subsample should be of length 2 but got length ${subsample.length}") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val stack = if (format == DataFormat.NCHW) (input(1), input(3), input(2)) + else (input(3), input(2), input(1)) + val pad = KerasUtils.getPadsFromBorderMode(borderMode) + val layer = com.intel.analytics.bigdl.nn.LocallyConnected2D( + nInputPlane = stack._1, + inputWidth = stack._2, + inputHeight = stack._3, + nOutputPlane = nbFilter, + kernelW = nbCol, + kernelH = nbRow, + strideW = subsample(1), + strideH = subsample(0), + padW = pad._2, + padH = pad._1, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer, + withBias = bias, + format = format) + KerasLayer.fuse(layer, activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object LocallyConnected2D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + activation: String = null, + borderMode: String = "valid", + subsample: (Int, Int) = (1, 1), + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + dimOrdering: String = "th", + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LocallyConnected2D[T] = { + new LocallyConnected2D[T](nbFilter, nbRow, nbCol, + KerasUtils.getActivation(activation), borderMode, Array(subsample._1, subsample._2), + wRegularizer, bRegularizer, KerasUtils.toBigDLFormat(dimOrdering), bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala new file mode 100644 index 00000000000..f05de2367cc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala @@ -0,0 +1,133 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.{InitializationMethod, SpatialSeperableConvolution, Xavier} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} + +import scala.reflect.ClassTag + +/** + * Separable convolution operator for 2D inputs. + * Separable convolutions consist in first performing a depthwise spatial convolution (which acts + * on each input channel separately) followed by a pointwise convolution which mixes together the + * resulting output channels. The depthMultiplier argument controls how many output channels are + * generated per input channel in the depthwise step. + * The input of this layer should be 4D. + * You can also use SeparableConv2D as an alias of this layer. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures. + * + * @param nbFilter Number of convolution filters to use. + * @param nbRow Number of rows in the convolution kernel. + * @param nbCol Number of columns in the convolution kernel. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @param subsample Int array of length 2. The step of the convolution in the height and + * width dimension. Also called strides elsewhere. Default is (1, 1). + * @param depthMultiplier How many output channel to use per input channel + * for the depthwise convolution step. + * @param depthwiseRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the depthwise weights matrices. Default is null. + * @param pointwiseRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the pointwise weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param format Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class SeparableConvolution2D[T: ClassTag]( + val nbFilter: Int, + val nbRow: Int, + val nbCol: Int, + val init: InitializationMethod = Xavier, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val borderMode: String = "valid", + val subsample: Array[Int] = Array(1, 1), + val depthMultiplier: Int = 1, + var depthwiseRegularizer: Regularizer[T] = null, + var pointwiseRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val format: DataFormat = DataFormat.NCHW, + val bias: Boolean = true, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + + s"SeparableConvolution2D: $borderMode") + require(subsample.length == 2, s"For SeparableConvolution2D, " + + s"subsample should be of length 2 but got length ${subsample.length}") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val stackSize = if (format == DataFormat.NCHW) input(1) else input(3) + val pad = KerasUtils.getPadsFromBorderMode(borderMode) + val layer = SpatialSeperableConvolution( + nInputChannel = stackSize, + nOutputChannel = nbFilter, + depthMultiplier = depthMultiplier, + kW = nbCol, + kH = nbRow, + sW = subsample(1), + sH = subsample(0), + pW = pad._2, + pH = pad._1, + hasBias = bias, + dataFormat = format, + wRegularizer = depthwiseRegularizer, + bRegularizer = bRegularizer, + pRegularizer = pointwiseRegularizer) + KerasLayer.fuse(layer, activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object SeparableConvolution2D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + init: String = "glorot_uniform", + activation: String = null, + borderMode: String = "valid", + subsample: (Int, Int) = (1, 1), + depthMultiplier: Int = 1, + depthwiseRegularizer: Regularizer[T] = null, + pointwiseRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + dimOrdering: String = "th", + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : SeparableConvolution2D[T] = { + new SeparableConvolution2D[T](nbFilter, nbRow, nbCol, + KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + borderMode, Array(subsample._1, subsample._2), depthMultiplier, depthwiseRegularizer, + pointwiseRegularizer, bRegularizer, KerasUtils.toBigDLFormat(dimOrdering), bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding3D.scala new file mode 100644 index 00000000000..fad7b59a9f5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding3D.scala @@ -0,0 +1,95 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.Padding +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Zero-padding layer for 3D data (spatial or spatio-temporal). + * The input of this layer should be 5D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param padding Int array of length 3. + * How many zeros to add at the beginning and end of the 3 padding dimensions. + * Symmetric padding will be applied to each dimension. Default is (1, 1, 1). + * @param format Format of the input data. Either "CHANNEL_FIRST" (dimOrdering='th') or + * "CHANNEL_LAST" (dimOrdering='tf'). Default is "CHANNEL_FIRST". + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class ZeroPadding3D[T: ClassTag]( + val padding: Array[Int] = Array(1, 1, 1), + val format: String = "CHANNEL_FIRST", + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(format.toLowerCase() == "channel_first" || format.toLowerCase() == "channel_last", + s"For ZeroPadding3D $format is not supported") + require(padding.length == 3, s"For ZeroPadding3D Subsample should be of length 3," + + s" not ${padding.length}") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 5, + s"ZeroPadding3D requires 5D input, but got input dim ${input.length}") + format.toLowerCase() match { + case "channel_first" => + Shape(input(0), input(1), input(2) + 2 * padding(0), + input(3) + 2 * padding(1), input(4) + 2 * padding(2)) + case "channel_last" => + Shape(input(0), input(1) + 2 * padding(0), input(2) + 2 * padding(1), + input(3) + 2 * padding(2), input(4)) + } + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val dim = if (format.toLowerCase() == "channel_first") 2 else 1 + val model = TSequential[T]() + val paddinglayer1 = Padding(dim = dim, pad = -padding(0), nInputDim = input.length - 1) + val paddinglayer2 = Padding(dim = dim, pad = padding(0), nInputDim = input.length - 1) + val paddinglayer3 = Padding(dim = dim + 1, pad = -padding(1), nInputDim = input.length - 1) + val paddinglayer4 = Padding(dim = dim + 1, pad = padding(1), nInputDim = input.length - 1) + val paddinglayer5 = Padding(dim = dim + 2, pad = -padding(2), nInputDim = input.length - 1) + val paddinglayer6 = Padding(dim = dim + 2, pad = padding(2), nInputDim = input.length - 1) + model.add(paddinglayer1) + model.add(paddinglayer2) + model.add(paddinglayer3) + model.add(paddinglayer4) + model.add(paddinglayer5) + model.add(paddinglayer6) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object ZeroPadding3D { + def apply[@specialized(Float, Double) T: ClassTag]( + padding: (Int, Int, Int) = (1, 1, 1), + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : ZeroPadding3D[T] = { + new ZeroPadding3D[T](Array(padding._1, padding._2, padding._3), + KerasUtils.toBigDLFormat5D(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala index 74081a222a3..903ec16108d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala @@ -21,4 +21,5 @@ package object keras { val Conv1D = Convolution1D val Conv2D = Convolution2D val Conv3D = Convolution3D + val SeparableConv2D = SeparableConvolution2D } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling1DSpec.scala new file mode 100644 index 00000000000..c65039ad217 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling1DSpec.scala @@ -0,0 +1,44 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.GlobalAveragePooling1D +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GlobalAveragePooling1DSpec extends KerasBaseSpec{ + + "GlobalAveragePooling1D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24]) + |input = np.random.random([2, 3, 24]) + |output_tensor = GlobalAveragePooling1D()(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GlobalAveragePooling1D[Float](inputShape = Shape(3, 24)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 24)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling3DSpec.scala new file mode 100644 index 00000000000..ad86a332edd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling3DSpec.scala @@ -0,0 +1,44 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.GlobalAveragePooling3D +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GlobalAveragePooling3DSpec extends KerasBaseSpec{ + + "GlobalAveragePooling3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5, 6]) + |input = np.random.random([2, 3, 4, 5, 6]) + |output_tensor = GlobalAveragePooling3D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GlobalAveragePooling3D[Float](inputShape = Shape(3, 4, 5, 6)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling1DSpec.scala new file mode 100644 index 00000000000..5ad22b75594 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling1DSpec.scala @@ -0,0 +1,44 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling1D +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GlobalMaxPooling1DSpec extends KerasBaseSpec{ + + "GlobalMaxPooling1D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24]) + |input = np.random.random([2, 3, 24]) + |output_tensor = GlobalMaxPooling1D()(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GlobalMaxPooling1D[Float](inputShape = Shape(3, 24)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 24)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling3DSpec.scala new file mode 100644 index 00000000000..30fb06a24a4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling3DSpec.scala @@ -0,0 +1,44 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling3D +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GlobalMaxPooling3DSpec extends KerasBaseSpec{ + + "GlobalMaxPooling3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5, 6]) + |input = np.random.random([2, 3, 4, 5, 6]) + |output_tensor = GlobalMaxPooling3D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = GlobalMaxPooling3D[Float](inputShape = Shape(3, 4, 5, 6)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected2DSpec.scala new file mode 100644 index 00000000000..6492990acc1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected2DSpec.scala @@ -0,0 +1,97 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{LocallyConnected2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class LocallyConnected2DSpec extends KerasBaseSpec { + + def weightConverter(data: Array[Tensor[Float]]): Array[Tensor[Float]] = { + val out = new Array[Tensor[Float]](data.length) + val d1l: Int = data(0).size(1) + val d2l: Int = data(0).size(2) + val d3l: Int = data(0).size(3) + out(0) = Tensor(d1l, d3l, d2l) + val page: Int = d2l * d3l + for (i <- 0 to d1l * d2l * d3l - 1) { + val d1 = i / page + 1 + val d2 = (i % page) / (d3l) + 1 + val d3 = (i % page) % d3l + 1 + val v = data(0).valueAt(d1, d2, d3) + out(0).setValue(d1, d3, d2, v) + } + if (data.length > 1) { + out(1) = data(1) + } + out + } + + "LocallyConnected2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, 24, 24]) + |input = np.random.random([2, 12, 24, 24]) + |output_tensor = LocallyConnected2D(32, 2, 2, dim_ordering="th", + | activation="relu")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = LocallyConnected2D[Float](32, 2, 2, + activation = "relu", inputShape = Shape(12, 24, 24)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "LocallyConnected2D without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[8, 32, 32]) + |input = np.random.random([2, 8, 32, 32]) + |output_tensor = LocallyConnected2D(64, 3, 3, bias=False, + | dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = LocallyConnected2D[Float](64, 3, 3, bias = false, inputShape = Shape(8, 32, 32)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "LocallyConnected2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[24, 24, 12]) + |input = np.random.random([2, 24, 24, 12]) + |output_tensor = LocallyConnected2D(32, 2, 2, dim_ordering="tf", + | activation="relu")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = LocallyConnected2D[Float](32, 2, 2, activation = "relu", + dimOrdering = "tf", inputShape = Shape(24, 24, 12)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala new file mode 100644 index 00000000000..c483f78e5e5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala @@ -0,0 +1,105 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{SeparableConvolution2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class SeparableConvolution2DSpec extends KerasBaseSpec { + + "SeparableConvolution2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5]) + |input = np.random.random([2, 3, 4, 5]) + |output_tensor = SeparableConvolution2D(3, 3, 3, dim_ordering='th')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = SeparableConvolution2D[Float](3, 3, 3, inputShape = Shape(3, 4, 5)) + seq.add(layer) + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + if (in.length == 2) { + val bias = if (layer.format == DataFormat.NCHW) in(1).size(1) + else in(1).size(4) + val out = Tensor[Float](bias) + Array(in(0), in(1), out) + } + else in + } + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "SeparableConvolution2D without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5]) + |input = np.random.random([2, 3, 4, 5]) + |output_tensor = SeparableConvolution2D(3, 3, 3, dim_ordering='th', + | bias=False)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = SeparableConvolution2D[Float](3, 3, 3, bias = false, inputShape = Shape(3, 4, 5)) + seq.add(layer) + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + if (in.length == 2) { + val bias = if (layer.format == DataFormat.NCHW) in(1).size(1) + else in(1).size(4) + val out = Tensor[Float](bias) + Array(in(0), in(1), out) + } + else in + } + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "SeparableConvolution2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, 12, 3]) + |input = np.random.random([2, 12, 12, 3]) + |output_tensor = SeparableConvolution2D(8, 2, 2, activation="relu", + | dim_ordering='tf')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = SeparableConvolution2D[Float](8, 2, 2, activation = "relu", + dimOrdering = "tf", inputShape = Shape(12, 12, 3)) + seq.add(layer) + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + if (in.length == 2) { + val bias = if (layer.format == DataFormat.NCHW) in(1).size(1) + else in(1).size(4) + val out = Tensor[Float](bias) + Array(in(0), in(1), out) + } + else in + } + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding3DSpec.scala new file mode 100644 index 00000000000..0f74a3b6893 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding3DSpec.scala @@ -0,0 +1,73 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.ZeroPadding3D +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class ZeroPadding3DSpec extends KerasBaseSpec { + + "ZeroPadding3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 7, 8, 9]) + |input = np.random.random([2, 3, 7, 8, 9]) + |output_tensor = ZeroPadding3D(padding=(1, 1, 1), dim_ordering='th')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = ZeroPadding3D[Float]((1, 1, 1), dimOrdering = "th", inputShape = Shape(3, 7, 8, 9)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "ZeroPadding3D with different padding sizes" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4, 5, 6]) + |input = np.random.random([2, 3, 4, 5, 6]) + |output_tensor = ZeroPadding3D(padding=(2, 1, 3), dim_ordering='th')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = ZeroPadding3D[Float]((2, 1, 3), dimOrdering = "th", inputShape = Shape(3, 4, 5, 6)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "ZeroPadding3D channel_last" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 6, 5, 4]) + |input = np.random.random([2, 3, 6, 5, 4]) + |output_tensor = ZeroPadding3D(padding=(1, 1, 1), dim_ordering='tf')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = ZeroPadding3D[Float]((1, 1, 1), dimOrdering = "tf", inputShape = Shape(3, 6, 5, 4)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index 821a63f815b..852766e07e8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -217,6 +217,20 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(layer, input) } + "GlobalAveragePooling1D serializer" should "work properly" in { + val layer = GlobalAveragePooling1D[Float](inputShape = Shape(3, 24)) + layer.build(Shape(2, 3, 24)) + val input = Tensor[Float](2, 3, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "GlobalAveragePooling3D serializer" should "work properly" in { + val layer = GlobalAveragePooling3D[Float](inputShape = Shape(3, 4, 5, 6)) + layer.build(Shape(2, 3, 4, 5, 6)) + val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + "Cropping1D serializer" should "work properly" in { val layer = Cropping1D[Float](inputShape = Shape(5, 6)) layer.build(Shape(2, 5, 6)) @@ -238,5 +252,39 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(layer, input) } -} + "GlobalMaxPooling1D serializer" should "work properly" in { + val layer = GlobalMaxPooling1D[Float](inputShape = Shape(12, 24)) + layer.build(Shape(2, 12, 24)) + val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + "GlobalMaxPooling3D serializer" should "work properly" in { + val layer = GlobalMaxPooling3D[Float](inputShape = Shape(12, 24, 3, 6)) + layer.build(Shape(2, 12, 24, 3, 6)) + val input = Tensor[Float](2, 12, 24, 3, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "LocallyConnected2D serializer" should "work properly" in { + val layer = LocallyConnected2D[Float](32, 2, 2, activation = "relu", + inputShape = Shape(12, 24, 24)) + layer.build(Shape(2, 12, 24, 24)) + val input = Tensor[Float](2, 12, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "SeparableConvolution2D serializer" should "work properly" in { + val layer = SeparableConvolution2D[Float](1, 2, 2, inputShape = Shape(3, 128, 128)) + layer.build(Shape(2, 3, 128, 128)) + val input = Tensor[Float](2, 3, 128, 128).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "ZeroPadding3D serializer" should "work properly" in { + val layer = ZeroPadding3D[Float]((1, 1, 1), inputShape = Shape(5, 6, 7, 8)) + layer.build(Shape(2, 5, 6, 7, 8)) + val input = Tensor[Float](2, 5, 6, 7, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} From 8f9039922b0d3270489abb8042a2fbcd9dbaf73f Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Wed, 7 Feb 2018 19:51:00 +0800 Subject: [PATCH 0688/1065] add CrossProduct Layer (#2273) * add CrossProduct Module * update scaladoc & unit tests * make some modifications * add python support * fix bug in python wrapper --- .../bigdl/dllib/nn/CrossProduct.scala | 177 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 6 + .../bigdl/dllib/nn/CrossProductSpec.scala | 123 ++++++++++++ .../serializer/ModuleSerializerSpec.scala | 8 + 4 files changed, 314 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CrossProduct.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CrossProductSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CrossProduct.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CrossProduct.scala new file mode 100644 index 00000000000..cb91961776b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CrossProduct.scala @@ -0,0 +1,177 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + +/** + * A layer which takes a table of multiple tensors(n >= 2) as input + * and calculate to dot product for `all combinations of pairs` among input tensors. + *

+ * Dot-product outputs are ordered according to orders of pairs in input Table. + * For instance, input (Table) is T(A, B, C), output (Tensor) will be [A.*B, A.*C, B.*C]. + *

+ * Dimensions of input' Tensors could be one or two, if two, first dimension is `batchSize`. + * For convenience, output is 2-dim Tensor regardless of input' dims. + *

+ * Table size checking and Tensor size checking will be execute before each forward, + * when [[numTensor]] and [[embeddingSize]] are set values greater than zero. + * + * @param numTensor (for checking)number of Tensor input Table contains, default: 0(won't check) + * @param embeddingSize (for checking)vector length of dot product, default: 0(won't check) + */ +class CrossProduct[T: ClassTag]( + val numTensor: Int = 0, + val embeddingSize: Int = 0 +)(implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { + + override def updateOutput(input: Table): Tensor[T] = { + val len = input.length() + require(numTensor <= 0 || numTensor == len, + s"Input tensor number is $len, unequal to numTensor($numTensor)!") + + val (_, batch, _) = getShape(input[Tensor[T]](1)) + output.resize(batch, len * (len - 1) / 2) + + if (embeddingSize > 0) { + var i = 1 + while (i <= len) { + checkEmbeddingSize(input(i)) + i += 1 + } + } + + var cc = 1 + var i = 1 + var j = 2 + while (i < len) { + val ijDot = batchDot(input(i), input(j)) + output.select(2, cc).copy(ijDot) + + cc += 1 + if (j == len) { + i += 1 + j = i + 1 + } else { + j += 1 + } + } + + output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + gradInput = T() + + val len = input.length() + val gout = gradOutput + + require(gout.dim() == 2, s"invalid dim of gradOutput(${gout.dim()})!") + + val outLen = len * (len - 1) / 2 + require(gout.size(2) == outLen, + s"invalid colSize of gradOutput(${gout.size(2)}), it should be $outLen!") + + val (dim, _, emLen) = getShape(input[Tensor[T]](1)) + + var cc = 1 + var i = 1 + var j = 2 + while (i < len) { + val (ti, tj) = dim match { + case 1 => + input[Tensor[T]](i).view(1, emLen) -> input[Tensor[T]](j).view(1, emLen) + case 2 => + input[Tensor[T]](i) -> input[Tensor[T]](j) + } + + // get cc_th column data from total gradOut + val go = gout.narrow(2, cc, 1) + + val jInc = Tensor[T]().resizeAs(ti).copy(ti).cmul(go) + if (dim == 1) jInc.squeeze() + gradInput.get[Tensor[T]](j) match { + case None => gradInput.update(j, jInc) + case Some(v) => v.add(jInc) + } + + val iInc = Tensor[T]().resizeAs(tj).copy(tj).cmul(go) + if (dim == 1) iInc.squeeze() + gradInput.get[Tensor[T]](i) match { + case None => gradInput.update(i, iInc) + case Some(v) => v.add(iInc) + } + + cc += 1 + if (j == len) { + i += 1 + j = i + 1 + } else { + j += 1 + } + } + + gradInput + } + + protected def checkEmbeddingSize(t: Tensor[T]): Unit = { + val size = if (t.dim() == 1) t.size(1) else t.size(2) + require(embeddingSize <= 0 || embeddingSize == size, + s"size of input Tensor($size) not equal to embeddingSize($embeddingSize)!") + } + + protected def batchDot(t1: Tensor[T], t2: Tensor[T]): Tensor[T] = { + var (input1, input2) = (t1, t2) + + if (input1.dim() == 1) { + input1 = input1.view(1, input1.size(1)) + input2 = input2.view(1, input2.size(1)) + } + + val buffer = Tensor[T]() + buffer.resizeAs(input1).cmul(input1, input2) + buffer.sum(2).squeeze() + } + + private def getShape(t: Tensor[T]) = { + val (batch, size) = t.dim() match { + case 1 => 1 -> t.size(1) + case 2 => t.size(1) -> t.size(2) + case n => throw new IllegalArgumentException(s"wrong dim of input Tensor($n)!") + } + (t.dim(), batch, size) + } + +} + +object CrossProduct { + + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): CrossProduct[T] = new CrossProduct[T]() + + def apply[T: ClassTag]( + numTensor: Int = 0, + embeddingSize: Int = 0 + )(implicit ev: TensorNumeric[T]): CrossProduct[T] = { + new CrossProduct(numTensor, embeddingSize) + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 4484b29e29b..87aaa08f739 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -786,6 +786,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab CosineDistanceCriterion[T](sizeAverage) } + def createCrossProduct(numTensor: Int = 0, + embeddingSize: Int = 0) + : CrossProduct[T] = { + CrossProduct[T](numTensor, embeddingSize) + } + def createDiceCoefficientCriterion(sizeAverage: Boolean = true, epsilon: Float = 1.0f) : DiceCoefficientCriterion[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CrossProductSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CrossProductSpec.scala new file mode 100644 index 00000000000..9324298ef6d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CrossProductSpec.scala @@ -0,0 +1,123 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class CrossProductSpec extends FlatSpec with Matchers { + + // forward result: [9, 10, 25, 18, 45, 50] + private val input1D = T( + Tensor[Float](T(1.0f, 2.0f)), + Tensor[Float](T(3.0f, 3.0f)), + Tensor[Float](T(2.0f, 4.0f)), + Tensor[Float](T(5.0f, 10.0f)) + ) + + /** + * backward result with inputSample: T([27, 51], [27, 54], [31, 53], [14, 16]) + * {{{ + * gradInput(1) = [3, 3] * 1 + [2, 4] * 2 + [5, 10] * 3 = [22, 41] + * gradInput(2) = [1, 2] * 1 + [2, 4] * 3 + [5, 10] * 4 = [27, 54] + * gradInput(3) = [1, 2] * 2 + [3, 3] * 3 + [5, 10] * 10 = [61, 113] + * gradInput(4) = [1, 2] * 3 + [3, 3] * 4 + [2, 4] * 10 = [35, 58] + * }}} + */ + private val gradOut1D = Tensor[Float](T(T(1f, 2f, 3f, 3f, 4f, 10f))) + + + /** + * forward result: {[25, 12], [43, 18], [64, 36]} + * {{{ + * row1([25, 43, 64]): + * 2*1 + 2*4 + 3*5 = 25 + * 2*5 + 2*6 + 3*7 = 43 + * 1*5 + 4*6 + 5*7 = 64 + * row2([12, 18, 30]): + * 1*2 + 1*2 + 2*4 = 12 + * 1*3 + 1*3 + 2*6 = 18 + * 2*3 + 2*3 + 4*6 = 36 + * }}} + */ + private val input2D = T( + Tensor[Float](T(T(2f, 2f, 3f), T(1f, 1f, 2f))), + Tensor[Float](T(T(1f, 4f, 5f), T(2f, 2f, 4f))), + Tensor[Float](T(T(5f, 6f, 7f), T(3f, 3f, 6f))) + ) + + /** + * backward result with inputSample: + * {{{ + * gradInput(1,1) = [1, 4, 5] * 2 + [5, 6, 7] * 3 = [17, 26, 31] + * gradInput(2,1) = [2, 2, 3] * 2 + [5, 6, 7] * 5 = [29, 34, 41] + * gradInput(3,1) = [2, 2, 3] * 3 + [1, 4, 5] * 5 = [11, 26, 34] + * gradInput(1,2) = [2, 2, 4] * 4 + [3, 3, 6] * 6 = [26, 26, 52] + * gradInput(2,2) = [1, 1, 2] * 4 + [3, 3, 6] * 8 = [28, 28, 56] + * gradInput(3,2) = [1, 1, 2] * 6 + [2, 2, 4] * 8 = [22, 22, 44] + * }}} + */ + private val gradOut2D = Tensor[Float](T(T(2f, 3f, 5f), T(4f, 6f, 8f))) + + + + "A CrossProduct" should "thrown Exceptions when inputs are invalid" in { + intercept[java.lang.IllegalArgumentException] { + CrossProduct[Float](numTensor = 2).updateOutput(input1D) + } + intercept[java.lang.IllegalArgumentException] { + CrossProduct[Float](embeddingSize = 9).updateOutput(input1D) + } + } + + "A CrossProduct.updateOutput" should "work correctly" in { + val module = CrossProduct[Float]() + var output = module.forward( + T(Tensor[Float](T(1.5f, 2.5f)), Tensor[Float](T(1.5f, 3.0f)))) + output.storage().toArray shouldEqual Array(9.75f) + + output = module.forward(input1D) + output.size() shouldEqual Array(1, 6) + output.storage().array() shouldEqual Array(9f, 10f, 25f, 18f, 45f, 50f) + + output = module.forward(input2D) + output.select(1, 1).toArray() shouldEqual Array(25, 43, 64) + output.select(1, 2).toArray() shouldEqual Array(12, 18, 36) + } + + "A CrossProduct.updateGradInput" should "work correctly" in { + var module = CrossProduct[Float]() + var gradIn = module.backward(input1D, gradOut1D) + gradIn[Tensor[Float]](1).toArray() shouldEqual Array(22f, 41f) + gradIn[Tensor[Float]](2).toArray() shouldEqual Array(27f, 54f) + gradIn[Tensor[Float]](3).toArray() shouldEqual Array(61f, 113f) + gradIn[Tensor[Float]](4).toArray() shouldEqual Array(35f, 58f) + + gradIn = module.backward(input2D, gradOut2D) + var t = gradIn[Tensor[Float]](1) + t.select(1, 1).toArray() shouldEqual Array(17, 26, 31) + t.select(1, 2).toArray() shouldEqual Array(26, 26, 52) + t = gradIn[Tensor[Float]](2) + t.select(1, 1).toArray() shouldEqual Array(29, 34, 41) + t.select(1, 2).toArray() shouldEqual Array(28, 28, 56) + t = gradIn[Tensor[Float]](3) + t.select(1, 1).toArray() shouldEqual Array(11, 26, 34) + t.select(1, 2).toArray() shouldEqual Array(22, 22, 44) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index e3769a71e31..904117ff567 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -389,6 +389,14 @@ class ModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(cropping3d, input) } + "CrossProduct serializer" should "work properly" in { + val crossProd = CrossProduct[Float]() + val input = T(Tensor[Float](T(1.0f, 2.0f)), + Tensor[Float](T(2.0f, 3.0f)), Tensor[Float](T(3.0f, 4.0f))) + runSerializationTest(crossProd, input) + } + + "CSubTable serializer" should "work properly" in { val csubTable = CSubTable[Float]().setName("csubTable") From 61d6ed4ca95b11d069ea43b79a92df9875343620 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 8 Feb 2018 10:57:22 +0800 Subject: [PATCH 0689/1065] refine tests for some layers (#2283) * refine tests for some layers * fix style issue --- .../bigdl/dllib/keras/nn/Cropping3DSpec.scala | 12 ++++ .../bigdl/dllib/keras/nn/ModelSpec.scala | 33 +++++++++++ .../bigdl/dllib/keras/nn/PermuteSpec.scala | 11 ++++ .../bigdl/dllib/keras/nn/ReshapeSpec.scala | 12 ++++ .../dllib/nn/BatchNormalizationSpec.scala | 11 ++++ .../dllib/nn/BifurcateSplitTableSpec.scala | 9 +++ .../bigdl/dllib/nn/ConcatTableSpec.scala | 13 +++++ .../bigdl/dllib/nn/ContiguousSpec.scala | 31 ++++++++++ .../dllib/nn/ConvLSTMPeepholeSerialTest.scala | 44 ++++++++++++++ .../analytics/bigdl/dllib/nn/CosineSpec.scala | 9 +++ .../bigdl/dllib/nn/DotProductSpec.scala | 17 +++++- .../analytics/bigdl/dllib/nn/GRUSpec.scala | 33 +++++++++++ .../bigdl/dllib/nn/IdentitySpec.scala | 11 ++++ .../bigdl/dllib/nn/InferReshapeSpec.scala | 11 ++++ .../analytics/bigdl/dllib/nn/LogSpec.scala | 11 ++++ .../bigdl/dllib/nn/LookupTableSpec.scala | 14 +++++ .../bigdl/dllib/nn/NarrowSerialTest.scala | 30 ++++++++++ .../bigdl/dllib/nn/NormalizeScaleSpec.scala | 11 ++++ .../analytics/bigdl/dllib/nn/PackSpec.scala | 15 +++++ .../analytics/bigdl/dllib/nn/PowerSpec.scala | 11 ++++ .../bigdl/dllib/nn/PriorBoxSpec.scala | 17 ++++++ .../analytics/bigdl/dllib/nn/RReLUSpec.scala | 30 ++++++++++ .../analytics/bigdl/dllib/nn/ReLU6Spec.scala | 30 ++++++++++ .../bigdl/dllib/nn/ReverseSpec.scala | 10 ++++ .../bigdl/dllib/nn/SelectTableSpec.scala | 34 +++++++++++ .../bigdl/dllib/nn/SigmoidSpec.scala | 10 ++++ .../bigdl/dllib/nn/SoftMinSerialTest.scala | 29 ++++++++++ .../nn/SpatialDivisiveNormalizationSpec.scala | 31 ++++++++++ .../analytics/bigdl/dllib/nn/SumSpec.scala | 12 ++++ .../analytics/bigdl/dllib/nn/TanhSpec.scala | 13 +++++ .../dllib/nn/TemporalConvolutionSpec.scala | 31 ++++++++++ .../bigdl/dllib/nn/UpSampling2DSpec.scala | 30 ++++++++++ .../bigdl/dllib/nn/ops/AnySpec.scala | 33 +++++++++++ .../nn/ops/BroadcastGradientArgsSpec.scala | 15 +++++ .../dllib/nn/ops/BucketizedColSpec.scala | 10 ++++ .../dllib/nn/ops/Conv2DTransposeSpec.scala | 36 ++++++++++++ .../nn/ops/Conv3DBackpropInputV2Spec.scala | 32 +++++++++++ .../bigdl/dllib/nn/ops/Conv3DSerialTest.scala | 30 ++++++++++ .../bigdl/dllib/nn/ops/DecodeJpegSpec.scala | 57 +++++++++++++++++++ .../bigdl/dllib/nn/ops/DecodeRawSpec.scala | 56 ++++++++++++++++++ .../dllib/nn/ops/DigammaSerialTest.scala | 28 +++++++++ .../bigdl/dllib/nn/ops/IsFiniteSpec.scala | 30 ++++++++++ .../dllib/nn/ops/MaxPoolGradSerialTest.scala | 35 ++++++++++++ .../bigdl/dllib/nn/ops/Relu6GradSpec.scala | 32 +++++++++++ .../bigdl/dllib/nn/ops/SigmoidGradSpec.scala | 32 +++++++++++ .../bigdl/dllib/nn/ops/SubstrSpec.scala | 11 ++++ .../dllib/nn/ops/TensorArrayScatterSpec.scala | 47 +++++++++++++++ .../dllib/nn/ops/TensorArrayWriteSpec.scala | 46 +++++++++++++++ .../bigdl/dllib/nn/ops/TopKSerialTest.scala | 31 ++++++++++ .../bigdl/dllib/nn/ops/TruncateDivSpec.scala | 30 ++++++++++ .../bigdl/dllib/nn/tf/ShapeSpec.scala | 11 ++++ .../utils/serializer/SerializerSpec.scala | 16 +++++- .../tf/loaders/ConcatV2LoadTFSerialTest.scala | 33 +++++++++++ .../dllib/utils/tf/loaders/Expm1Spec.scala | 13 +++++ 54 files changed, 1288 insertions(+), 2 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ModelSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ContiguousSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSerialTest.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GRUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NarrowSerialTest.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RReLUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReLU6Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SelectTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftMinSerialTest.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalizationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolutionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AnySpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DTransposeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DSerialTest.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeJpegSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeRawSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DigammaSerialTest.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsFiniteSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGradSerialTest.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6GradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayScatterSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayWriteSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopKSerialTest.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDivSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2LoadTFSerialTest.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping3DSpec.scala index 06cd1c3fa7d..a84cfb259ac 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping3DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Cropping3D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class Cropping3DSpec extends KerasBaseSpec { @@ -58,3 +61,12 @@ class Cropping3DSpec extends KerasBaseSpec { } } + +class Cropping3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Cropping3D[Float](inputShape = Shape(4, 12, 16, 20)) + layer.build(Shape(2, 4, 12, 16, 20)) + val input = Tensor[Float](2, 4, 12, 16, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ModelSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ModelSpec.scala new file mode 100644 index 00000000000..d4b73ffbc4b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ModelSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.nn.keras.{Dense, Input, Model} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class ModelSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Input[Float](inputShape = Shape(10)) + val d = Dense[Float](20).setName("dense1").inputs(input) + val d2 = Dense[Float](5).setName("dense2").inputs(d) + val model = Model[Float](input, d2) + val inputData = Tensor[Float](Array(20, 10)).rand() + runSerializationTest(model, inputData) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/PermuteSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/PermuteSpec.scala index 5967c39d30c..e7b85fc209a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/PermuteSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/PermuteSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Permute, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class PermuteSpec extends KerasBaseSpec { @@ -41,3 +44,11 @@ class PermuteSpec extends KerasBaseSpec { } } +class PermuteSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Permute[Float](Array(3, 1, 4, 2), inputShape = Shape(3, 4, 5, 6)) + layer.build(Shape(2, 3, 4, 5, 6)) + val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ReshapeSpec.scala index 4a82fc75cda..f97ce37beea 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ReshapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ReshapeSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.keras.Reshape import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ReshapeSpec extends KerasBaseSpec { @@ -58,3 +61,12 @@ class ReshapeSpec extends KerasBaseSpec { } } + +class ReshapeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Reshape[Float](Array(4, 15), inputShape = Shape(3, 4, 5)) + layer.build(Shape(2, 3, 4, 5)) + val input = Tensor[Float](2, 3, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala index 4e908752024..2e94e1b252b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class BatchNormalizationSpec extends FlatSpec with Matchers { "A BatchNormalization" should "generate correct output using default arguments" in { @@ -248,3 +251,11 @@ class BatchNormalizationSpec extends FlatSpec with Matchers { gradInput should be(Tensor[Float](4, 3).fill(0)) } } + +class BatchNormalizationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val batchNorm = BatchNormalization[Float](5).setName("batchNorm") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(batchNorm, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala index de3c990a566..699bc257d14 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.SplitTable import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.util.Random @@ -47,3 +48,11 @@ class SplitTableSpec extends FlatSpec with BeforeAndAfter with Matchers { gradInput should be (expectedGradInput) } } + +class BifurcateSplitTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val batchNorm = BifurcateSplitTable[Float](1).setName("batchNorm") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(batchNorm, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTableSpec.scala index ab1fc756c1e..e9b305e9f30 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTableSpec.scala @@ -20,8 +20,11 @@ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class ConcatTableSpec extends FlatSpec with Matchers { @@ -84,3 +87,13 @@ class ConcatTableSpec extends FlatSpec with Matchers { } } } + +class ConcatTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val concatTable = new ConcatTable[Float]().setName("concatTable") + concatTable.add(Linear[Float](10, 2)) + concatTable.add(Linear[Float](10, 2)) + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(concatTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ContiguousSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ContiguousSpec.scala new file mode 100644 index 00000000000..6f62f36b887 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ContiguousSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ContiguousSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val contiguous = Contiguous[Float]().setName("contiguous") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(contiguous, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSerialTest.scala new file mode 100644 index 00000000000..041e829efff --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSerialTest.scala @@ -0,0 +1,44 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class ConvLSTMPeepholeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val hiddenSize = 5 + val inputSize = 3 + val seqLength = 4 + val batchSize = 2 + val kernalW = 3 + val kernalH = 3 + val c2d = ConvLSTMPeephole[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1, + withPeephole = false) + val convLSTMPeephole2d = Recurrent[Float]().setName("convLSTMPeephole2d") + val model = Sequential[Float]() + .add(convLSTMPeephole2d + .add(c2d)) + .add(View[Float](hiddenSize * kernalH * kernalW)) + + val input = Tensor[Float](batchSize, seqLength, inputSize, kernalW, kernalH).rand + runSerializationTest(convLSTMPeephole2d, input, c2d.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CosineSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CosineSpec.scala index 6f6440f550b..02ad18e60b2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CosineSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CosineSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -51,3 +52,11 @@ class CosineSpec extends FlatSpec with Matchers { layer2.gradWeight should be (layer1.gradWeight.mul(2)) } } + +class CosineSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val cosine = Cosine[Float](5, 5).setName("cosine") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(cosine, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DotProductSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DotProductSpec.scala index 84e71c7a071..6ff09e3b805 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DotProductSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DotProductSpec.scala @@ -16,9 +16,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class DotProductSpec extends FlatSpec with Matchers { "A DotProductSpec" should "generate correct output" in { @@ -45,3 +48,15 @@ class DotProductSpec extends FlatSpec with Matchers { dotGradInput should be (expectedgradInput) } } + +class DotProductSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val dotProduct = DotProduct[Float]().setName("dotProduct") + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(dotProduct, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GRUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GRUSpec.scala new file mode 100644 index 00000000000..e538a92d423 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GRUSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class GRUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + RNG.setSeed(100) + val gru = GRU[Float](100, 100) + val gruModel = Recurrent[Float]().add(gru).setName("gru") + val input = Tensor[Float](2, 20, 100).apply1(e => Random.nextFloat()) + runSerializationTest(gruModel, input, gru.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/IdentitySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/IdentitySpec.scala index 7b082ea31e9..6317bb87138 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/IdentitySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/IdentitySpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + /** * Created by yao on 9/20/16. */ @@ -42,3 +45,11 @@ class IdentitySpec extends FlatSpec with Matchers { assert(gradInput equals gradOutput) } } + +class IdentitySerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val identity = Identity[Float]().setName("identity") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(identity, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InferReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InferReshapeSpec.scala index f9369023bff..7448efdb407 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InferReshapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InferReshapeSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.LayerException +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.FlatSpec +import scala.util.Random + class InferReshapeSpec extends FlatSpec { "A InferReshape Module with infer" should "generate correct output and grad" in { val module = new InferReshape[Double](Array(3, -1), true) @@ -173,3 +176,11 @@ class InferReshapeSpec extends FlatSpec { (a1 zip a2).foreach(x => assert(x._1 == x._2)) } } + +class InferReshapeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val inferReshape = InferReshape[Float](Array(-1, 2, 0, 5)).setName("inferReshape") + val input = Tensor[Float](2, 5, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(inferReshape, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala index bb6ee9bd4c4..c827a1d2c75 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSpec.scala @@ -16,8 +16,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class LogSpec extends FlatSpec with Matchers { "A Log" should "generate correct output" in { @@ -45,3 +48,11 @@ class LogSpec extends FlatSpec with Matchers { gradInput should equal (Tensor(Storage(Array(0.1, 0.1, 0.1, 0.1, 0.1, 0.1)), 1, Array(2, 3))) } } + +class LogSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val log = Log[Float]().setName("log") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(log, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala index 2422362282f..34108a0d6c3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -93,3 +94,16 @@ class LookupTableSpec extends FlatSpec with Matchers { layer2.gradWeight should be (layer1.gradWeight.mul(2)) } } + +class LookupTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val lookupTable = LookupTable[Float](9, 4, 2, 0.1, 2.0, true).setName("lookupTable") + val input = Tensor[Float](5) + input(Array(1)) = 5 + input(Array(2)) = 2 + input(Array(3)) = 6 + input(Array(4)) = 9 + input(Array(5)) = 4 + runSerializationTest(lookupTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NarrowSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NarrowSerialTest.scala new file mode 100644 index 00000000000..c87b8579008 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NarrowSerialTest.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class NarrowSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val narrow = Narrow[Float](1, 3, -3).setName("narrow") + val input = Tensor[Float](9, 4, 14).apply1(e => Random.nextFloat()) + runSerializationTest(narrow, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala index 20f649aa3ac..fd36ec90b4b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScaleSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class NormalizeScaleSpec extends FlatSpec with Matchers { @@ -363,3 +364,13 @@ class NormalizeScaleSpec extends FlatSpec with Matchers { }) } } + +class NormalizeScaleSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1), + wRegularizer = L2Regularizer[Float](0.2)).setName("NormalizeScale") + + val input = Tensor[Float](1, 5, 3, 4).randn() + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PackSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PackSpec.scala index ae92172cb64..0e051a42994 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PackSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PackSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class PackSpec extends FlatSpec with Matchers { @@ -114,3 +117,15 @@ class PackSpec extends FlatSpec with Matchers { gradInput1 should be(input1) } } + +class PackSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val pack = new Pack[Float](1).setName("pack") + val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input = T() + input(1.0f) = input1 + input(2.0f) = input2 + runSerializationTest(pack, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala index 86104f60732..9626b68eaef 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PowerSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class PowerSpec extends FlatSpec with Matchers { "A Power" should "generate correct output" in { @@ -127,3 +130,11 @@ class PowerSpec extends FlatSpec with Matchers { } } + +class PowerSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val power = Power[Float](2.0).setName("power") + val input = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + runSerializationTest(power, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PriorBoxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PriorBoxSpec.scala index c0116f2951f..f125d060710 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PriorBoxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PriorBoxSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.FlatSpec class PriorBoxSpec extends FlatSpec { @@ -49,3 +50,19 @@ class PriorBoxSpec extends FlatSpec { }) } } + +class PriorBoxSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val isClip = false + val isFlip = true + val variances = Array(0.1f, 0.1f, 0.2f, 0.2f) + val minSizes = Array(460.8f) + val maxSizes = Array(537.6f) + val aspectRatios = Array(2f) + val module = PriorBox[Float](minSizes = minSizes, maxSizes = maxSizes, + _aspectRatios = aspectRatios, isFlip = isFlip, isClip = isClip, + variances = variances, step = 0, offset = 0.5f, imgH = 512, imgW = 512) + val input = Tensor[Float](8, 256, 1, 1) + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RReLUSpec.scala new file mode 100644 index 00000000000..1452365d1c9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RReLUSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class RReLUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val rrelu = new RReLU[Float](inplace = false).setName("rrelu") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(rrelu, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReLU6Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReLU6Spec.scala new file mode 100644 index 00000000000..01d962d9efe --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReLU6Spec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class ReLU6SerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val relu6 = ReLU6[Float](false).setName("relu6") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(relu6, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReverseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReverseSpec.scala index 651c36353e2..3972b377308 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReverseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReverseSpec.scala @@ -17,9 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.collection.mutable +import scala.util.Random @com.intel.analytics.bigdl.tags.Serial class ReverseSpec extends FlatSpec with Matchers { @@ -103,3 +105,11 @@ class ReverseSpec extends FlatSpec with Matchers { } } + +class ReverseSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val reverse = Reverse[Float]().setName("reverse") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(reverse, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SelectTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SelectTableSpec.scala new file mode 100644 index 00000000000..7ec57a31b55 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SelectTableSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SelectTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val selectTable = SelectTable[Float](2).setName("selectTable") + val input1 = Tensor[Float](10).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](10).apply1(_ => Random.nextFloat()) + val input3 = Tensor[Float](10).apply1(_ => Random.nextFloat()) + val input = T(1.0 -> input1, 2.0 -> input2, 3.0 -> input3) + runSerializationTest(selectTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SigmoidSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SigmoidSpec.scala index 7e576812cba..0a5a998795b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SigmoidSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SigmoidSpec.scala @@ -18,8 +18,10 @@ package com.intel.analytics.bigdl.nn import org.scalatest.FlatSpec import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.math.abs +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class SigmoidSpec extends FlatSpec { @@ -77,3 +79,11 @@ class SigmoidSpec extends FlatSpec { assert(gradOutput == gradOutputOrg) } } + +class SigmoidSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sigmoid = Sigmoid[Float]().setName("sigmoid") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(sigmoid, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftMinSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftMinSerialTest.scala new file mode 100644 index 00000000000..bf4f6c7fc9c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftMinSerialTest.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SoftMinSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val softMin = SoftMin[Float]().setName("softMin") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(softMin, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalizationSpec.scala new file mode 100644 index 00000000000..85debe4c225 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalizationSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SpatialDivisiveNormalizationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialDivisiveNormalization = SpatialDivisiveNormalization[Float](). + setName("spatialDivisiveNormalization") + val input = Tensor[Float](1, 5, 5).apply1(e => Random.nextFloat()) + runSerializationTest(spatialDivisiveNormalization, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala index d257ef1d853..b5b45950efa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala @@ -15,10 +15,14 @@ */ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.ops.Sum import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class SumSpec extends FlatSpec with Matchers { "sum" should "work correctly" in { @@ -53,3 +57,11 @@ class SumSpec extends FlatSpec with Matchers { sum.forward(vector) should be(Tensor.scalar(6)) } } + +class SumSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sum = Sum[Float](2).setName("sum") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(sum, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhSpec.scala index b1b107ddc7e..394acfd6448 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhSpec.scala @@ -18,6 +18,9 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.ops.TanhGrad +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.math.abs @@ -86,3 +89,13 @@ class TanhSpec extends FlatSpec with Matchers { checker.checkLayer[Double](module, input) should be(true) } } + +class TanhSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = TanhGrad[Float, Float]() + + val input = T(Tensor[Float](1, 5, 3, 4).rand(), Tensor[Float](1, 5, 3, 4).rand()) + + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolutionSpec.scala new file mode 100644 index 00000000000..303a32db745 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolutionSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class TemporalConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val temporalConvolution = TemporalConvolution[Float](10, 8, 5, 2). + setName("temporalConvolution") + val input = Tensor[Float](100, 10).apply1(e => Random.nextFloat()) + runSerializationTest(temporalConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2DSpec.scala new file mode 100644 index 00000000000..534ec00a284 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2DSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class UpSampling2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val upsampling = UpSampling2D[Float](Array(2, 3)).setName("upsampling") + val input = Tensor[Float](2, 3, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(upsampling, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AnySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AnySpec.scala new file mode 100644 index 00000000000..4a6fd53076e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AnySpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class AnySerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val any = Any[Float]().setName("any") + val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val input2 = Tensor[Int](T(2, 1, 2)) + val input = T() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(any, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala index 0604a378885..b862133f4e6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class BroadcastGradientArgsSpec extends FlatSpec with Matchers { @@ -48,3 +49,17 @@ class BroadcastGradientArgsSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class BroadcastGradientArgsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val broadcastGradientArgs = BroadcastGradientArgs[Float](). + setName("broadcastGradientArgs") + val input = + T( + Tensor[Int](T(1, 2, 3)), + Tensor[Int](T(2, 2, 1)) + ) + runSerializationTest(broadcastGradientArgs, input, broadcastGradientArgs. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedColSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedColSpec.scala index 86a914ed175..fcaae860eda 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedColSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BucketizedColSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class BucketizedColSpec @@ -41,3 +42,12 @@ class BucketizedColSpec output should be(expectOutput) } } + +class BucketizedColSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val bucketizedCol = BucketizedCol[Float](boundaries = Array(0.0, 10.0, 100.0)) + .setName("bucketizedCol") + val input = Tensor[Float](T(T(-1, 1), T(101, 10), T(5, 100))) + runSerializationTest(bucketizedCol, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DTransposeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DTransposeSpec.scala new file mode 100644 index 00000000000..ba3acd5711c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DTransposeSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class Conv2DTransposeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val conv2dTranspose = Conv2DTranspose[Float](2, 2, -1, -1, DataFormat.NHWC). + setName("conv2dTranspose") + val inputTensor = Tensor[Int](T(1, 4, 3, 3)) + val kernelSize = Tensor[Float](2, 2, 3, 3).apply1(_ => Random.nextFloat()) + val data = Tensor[Float](1, 2, 2, 3)apply1(_ => Random.nextFloat()) + val input = T(inputTensor, kernelSize, data) + runSerializationTest(conv2dTranspose, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2Spec.scala new file mode 100644 index 00000000000..82a8e39e070 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2Spec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class Conv3DBackpropInputV2SerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Conv3DBackpropInputV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val inputSize = Tensor[Int](Array(4, 20, 30, 40, 3), Array(5)) + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(inputSize, filter, outputBackprop)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DSerialTest.scala new file mode 100644 index 00000000000..b25d99c7e3f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DSerialTest.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class Conv3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Conv3D[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + runSerializationTest(module, T(input, filter)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeJpegSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeJpegSpec.scala new file mode 100644 index 00000000000..6d775058b3d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeJpegSpec.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import java.io.File + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.tf.TFRecordIterator +import org.tensorflow.example.Example + +class DecodeJpegSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val decodeJpeg = new DecodeJpeg[Float](1).setName("decodeJpeg") + val input = getInputs("jpeg") + runSerializationTest(decodeJpeg, input) + } + + private def getInputs(name: String): Tensor[ByteString] = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + File.separator + "decode_image_test_case.tfrecord" + val file = new File(path) + + val bytesVector = TFRecordIterator(file).toVector + val pngBytes = bytesVector(index) + + val example = Example.parseFrom(pngBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } +} + + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeRawSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeRawSpec.scala new file mode 100644 index 00000000000..87d55a7af6f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeRawSpec.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import java.io.File + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.tf.TFRecordIterator +import org.tensorflow.example.Example +import org.tensorflow.framework.DataType + +class DecodeRawSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val decodeRaw = new DecodeRaw[Float](DataType.DT_UINT8, true).setName("decodeRaw") + val input = getInputs("raw") + runSerializationTest(decodeRaw, input) + } + + private def getInputs(name: String): Tensor[ByteString] = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + File.separator + "decode_image_test_case.tfrecord" + val file = new File(path) + + val bytesVector = TFRecordIterator(file).toVector + val pngBytes = bytesVector(index) + + val example = Example.parseFrom(pngBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DigammaSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DigammaSerialTest.scala new file mode 100644 index 00000000000..3175feec879 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DigammaSerialTest.scala @@ -0,0 +1,28 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class DigammaSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Digamma[Float, Float]().setName("digamma") + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsFiniteSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsFiniteSpec.scala new file mode 100644 index 00000000000..9d2cf49126b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsFiniteSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class IsFiniteSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val isFinite = IsFinite[Float, Float]().setName("isFinite") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isFinite, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGradSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGradSerialTest.scala new file mode 100644 index 00000000000..f3faac8de2d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGradSerialTest.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class MaxPoolGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val maxPoolGrad = MaxPoolGrad[Float](2, 1, 1, 1, 0, 0, DataFormat.NCHW). + setName("maxPoolGrad") + val input = T(Tensor[Float](1, 3, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](), + Tensor[Float](1, 1, 1).apply1(_ => Random.nextFloat())) + runSerializationTest(maxPoolGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6GradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6GradSpec.scala new file mode 100644 index 00000000000..975fe380405 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6GradSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class Relu6GradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val relu6Grad = Relu6Grad[Float, Float]().setName("relu6Grad") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(relu6Grad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGradSpec.scala new file mode 100644 index 00000000000..09deafc02dc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGradSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SigmoidGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sigMoidGrad = SigmoidGrad[Float, Float]().setName("sigMoidGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sigMoidGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstrSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstrSpec.scala index 25ba60174e9..6ced886dea1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstrSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstrSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn.ops import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class SubstrSpec extends FlatSpec with Matchers { @@ -32,3 +33,13 @@ class SubstrSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class SubstrSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val subStr = Substr[Float]().setName("subStr") + val input = T(Tensor.scalar[ByteString](ByteString.copyFromUtf8("HelloBigDL")), + Tensor.scalar[Int](0), Tensor.scalar[Int](5)) + runSerializationTest(subStr, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayScatterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayScatterSpec.scala new file mode 100644 index 00000000000..d5d43eb5ee0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayScatterSpec.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.Graph +import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class TensorArrayScatterSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + import com.intel.analytics.bigdl.nn.ops._ + val tensorArray = new TensorArrayCreator[Float, Float]().inputs() + val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() + val indices = Const[Float, Int](Tensor[Int](T(0, 1, 2))).inputs() + val scatter = new TensorArrayScatter[Float, Float]().inputs((tensorArray, 1), (indices, 1), + (data, 1)) + val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(scatter) + val gather = new TensorArrayGather[Float, Float]().inputs((tensorArray, 1), (indices, 1), + (ctr, 1)) + val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(gather) + val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1)) + val model = Graph.dynamic[Float](Array(tensorArray), Array(gather, close)) + + runSerializationTestWithMultiClass(model, Tensor.scalar[Int](10), Array( + tensorArray.element.getClass.asInstanceOf[Class[_]], + scatter.element.getClass.asInstanceOf[Class[_]], + gather.element.getClass.asInstanceOf[Class[_]], + close.element.getClass.asInstanceOf[Class[_]] + )) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayWriteSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayWriteSpec.scala new file mode 100644 index 00000000000..b99c8b2809b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayWriteSpec.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.Graph +import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class TensorArraySerialTest extends ModuleSerializationTest { + override def test(): Unit = { + "TensorArray serializer R/W" should "work properly" in { + import com.intel.analytics.bigdl.nn.ops._ + val tensorArray = new TensorArrayCreator[Float, Float]().inputs() + val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() + val index = Const[Float, Int](Tensor.scalar[Int](0)).inputs() + val write = new TensorArrayWrite[Float, Float]().inputs((tensorArray, 1), + (index, 1), (data, 1)) + val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(write) + val read = new TensorArrayRead[Float, Float]().inputs((tensorArray, 1), (index, 1), (ctr, 1)) + val grad = new TensorArrayGrad[Float]("grad").inputs(tensorArray) + val output = Identity[Float]().inputs((grad, 2)) + val model = Graph.dynamic[Float](Array(tensorArray), Array(read, output)) + + runSerializationTestWithMultiClass(model, Tensor.scalar[Int](1), Array( + tensorArray.element.getClass.asInstanceOf[Class[_]], + write.element.getClass.asInstanceOf[Class[_]], + read.element.getClass.asInstanceOf[Class[_]], + grad.element.getClass.asInstanceOf[Class[_]] + )) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopKSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopKSerialTest.scala new file mode 100644 index 00000000000..ad5d8ff121b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopKSerialTest.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class TopKSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val topk = TopK[Float, Float](2).setName("topK") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(topk, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDivSpec.scala new file mode 100644 index 00000000000..1774def6984 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncateDivSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class TruncateDivSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val truncateDiv = TruncateDiv[Float, Float]().setName("truncateDiv") + val input = T(Tensor[Float](5).fill(1.0f), Tensor[Float](5).fill(2.0f)) + runSerializationTest(truncateDiv, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala index 87a1c99e29d..dd6526e8575 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ShapeSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class ShapeSpec extends FlatSpec with Matchers { "Shape forward" should "be success" in { @@ -40,3 +43,11 @@ class ShapeSpec extends FlatSpec with Matchers { ))) } } + +class ShapeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val shape = Shape[Float]().setName("shape") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(shape, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 647225bf000..3b3103c3bf0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -54,10 +54,24 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.ops.StackOpsSerialTest", "com.intel.analytics.bigdl.nn.ops.StackPop" -> "com.intel.analytics.bigdl.nn.ops.StackOpsSerialTest", + "com.intel.analytics.bigdl.nn.ops.TensorArrayWrite" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", + "com.intel.analytics.bigdl.nn.ops.TensorArrayRead" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", + "com.intel.analytics.bigdl.nn.ops.TensorArrayGrad" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", // Keras layers "com.intel.analytics.bigdl.nn.keras.Dense" -> - "com.intel.analytics.bigdl.keras.nn.DenseSerialTest" + "com.intel.analytics.bigdl.keras.nn.DenseSerialTest", + "com.intel.analytics.bigdl.nn.keras.Cropping3D" -> + "com.intel.analytics.bigdl.keras.nn.Cropping3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Reshape" -> + "com.intel.analytics.bigdl.keras.nn.ReshapeSerialTest", + "com.intel.analytics.bigdl.nn.keras.Permute" -> + "com.intel.analytics.bigdl.keras.nn.PermuteSerialTest", + "com.intel.analytics.bigdl.nn.keras.Model" -> + "com.intel.analytics.bigdl.keras.nn.ModelSerialTest" ) private val suffix = "SerialTest" diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2LoadTFSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2LoadTFSerialTest.scala new file mode 100644 index 00000000000..d183fdfd6d4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ConcatV2LoadTFSerialTest.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ConcatV2LoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val concatv2 = new ConcatV2LoadTF[Float]().setName("concatv2LoadTF") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1))) + runSerializationTest(concatv2, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala index 9d22bad71cf..955bf87a28a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1Spec.scala @@ -16,10 +16,14 @@ package com.intel.analytics.bigdl.utils.tf.loaders import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper import org.tensorflow.framework.{DataType, NodeDef} +import scala.util.Random + class Expm1Spec extends TensorflowSpecHelper { "Expm1" should "be correct for float" in { compare[Float]( @@ -43,3 +47,12 @@ class Expm1Spec extends TensorflowSpecHelper { ) } } + +class ExpandDimsLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val expandDim = new ExpandDimsLoadTF[Float]().setName("expandDim") + val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](1)) + runSerializationTest(expandDim, input) + } +} From 1aa0cae5498ae9188d8fdff93ab7f3c431a40664 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Thu, 8 Feb 2018 15:01:48 +0800 Subject: [PATCH 0690/1065] Keras-like API AtrousConv1D2D, LocallyConnected1D, deconv, convlstm (#2247) * layers * meet review * update * add ser test * style and fix * refactor and update * style * fix * refine --- .../bigdl/dllib/keras/Activation.scala | 4 +- .../dllib/keras/AtrousConvolution1D.scala | 92 ++++++++++++++++ .../dllib/keras/AtrousConvolution2D.scala | 86 +++++++++++++++ .../bigdl/dllib/keras/AveragePooling2D.scala | 8 +- .../bigdl/dllib/keras/AveragePooling3D.scala | 6 +- .../bigdl/dllib/keras/ConvLSTM2D.scala | 100 ++++++++++++++++++ .../bigdl/dllib/keras/Convolution2D.scala | 19 ++-- .../bigdl/dllib/keras/Convolution3D.scala | 7 +- .../bigdl/dllib/keras/Cropping2D.scala | 4 +- .../bigdl/dllib/keras/Cropping3D.scala | 20 ++-- .../bigdl/dllib/keras/Deconvolution2D.scala | 83 +++++++++++++++ .../analytics/bigdl/dllib/keras/Dense.scala | 2 +- .../analytics/bigdl/dllib/keras/Dropout.scala | 2 +- .../analytics/bigdl/dllib/keras/Flatten.scala | 2 +- .../analytics/bigdl/dllib/keras/GRU.scala | 2 +- .../dllib/keras/GlobalAveragePooling2D.scala | 8 +- .../dllib/keras/GlobalAveragePooling3D.scala | 10 +- .../dllib/keras/GlobalMaxPooling2D.scala | 8 +- .../dllib/keras/GlobalMaxPooling3D.scala | 9 +- .../bigdl/dllib/keras/GlobalPooling2D.scala | 4 +- .../bigdl/dllib/keras/GlobalPooling3D.scala | 4 + .../analytics/bigdl/dllib/keras/Highway.scala | 2 +- .../analytics/bigdl/dllib/keras/LSTM.scala | 2 +- .../dllib/keras/LocallyConnected1D.scala | 88 +++++++++++++++ .../dllib/keras/LocallyConnected2D.scala | 14 +-- .../bigdl/dllib/keras/MaxPooling2D.scala | 12 +-- .../bigdl/dllib/keras/MaxPooling3D.scala | 6 +- .../bigdl/dllib/keras/Pooling1D.scala | 1 - .../bigdl/dllib/keras/Pooling2D.scala | 7 +- .../bigdl/dllib/keras/Pooling3D.scala | 5 +- .../analytics/bigdl/dllib/keras/Reshape.scala | 2 +- .../dllib/keras/SeparableConvolution2D.scala | 17 +-- .../bigdl/dllib/keras/SimpleRNN.scala | 2 +- .../bigdl/dllib/keras/ZeroPadding3D.scala | 19 ++-- .../analytics/bigdl/dllib/keras/package.scala | 3 + .../dllib/nn/SpatialDilatedConvolution.scala | 12 ++- .../dllib/nn/SpatialFullConvolution.scala | 13 ++- .../keras/nn/AtrousConvolution1DSpec.scala | 51 +++++++++ .../keras/nn/AtrousConvolution2DSpec.scala | 45 ++++++++ .../bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala | 71 +++++++++++++ .../dllib/keras/nn/Deconvolution2DSpec.scala | 68 ++++++++++++ .../keras/nn/LocallyConnected1DSpec.scala | 80 ++++++++++++++ .../keras/nn/SeparableConvolution2DSpec.scala | 9 +- .../nn/SpatialDilatedConvolutionSpec.scala | 6 ++ .../dllib/nn/SpatialFullConvolutionSpec.scala | 6 +- .../KerasModuleSerializerSpec.scala | 37 +++++++ 46 files changed, 952 insertions(+), 106 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected1DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala index 53410f5c264..aa96a89ebe5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala @@ -31,8 +31,8 @@ import scala.reflect.ClassTag * Available activations: 'tanh', 'relu', 'sigmoid', 'softmax', 'softplus', * 'softsign', 'hard_sigmoid'. * - * @param activation Name of activation function as string. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @param activation Name of the activation function as string. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Activation[T: ClassTag]( val activation: String, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala new file mode 100644 index 00000000000..c0ce4cd78fb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.{InitializationMethod, Xavier, Zeros} +import com.intel.analytics.bigdl.nn.{SpatialDilatedConvolution, Squeeze, Transpose, Sequential => TSequential} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class AtrousConvolution1D[T: ClassTag]( + val nbFilter: Int, + val filterLength: Int, + val init: InitializationMethod = Xavier, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val subsampleLength: Int = 1, + val atrousRate: Int = 1, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"AtrousConvolution1D requires 3D input, but got input dim ${input.length}") + val length = KerasUtils.computeConvOutputLength(input(1), filterLength, + "valid", subsampleLength, atrousRate) + Shape(input(0), length, nbFilter) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val model = TSequential[T]() + model.add(Transpose(Array((2, 3)))) + model.add(com.intel.analytics.bigdl.nn.Reshape(Array(input(2), input(1), 1), Some(true))) + val layer = SpatialDilatedConvolution( + nInputPlane = input(2), + nOutputPlane = nbFilter, + kW = 1, + kH = filterLength, + dW = 1, + dH = subsampleLength, + dilationW = 1, + dilationH = atrousRate, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer) + layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) + model.add(layer) + model.add(Transpose(Array((2, 3)))) + model.add(Squeeze(4)) + if (activation != null) { + model.add(activation) + } + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object AtrousConvolution1D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + filterLength: Int, + init: String = "glorot_uniform", + activation: String = null, + subsampleLength: Int = 1, + atrousRate: Int = 1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AtrousConvolution1D[T] = { + new AtrousConvolution1D[T](nbFilter, filterLength, KerasUtils.getInitMethod(init), + KerasUtils.getActivation(activation), subsampleLength, atrousRate, + wRegularizer, bRegularizer, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala new file mode 100644 index 00000000000..a2a0953d725 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala @@ -0,0 +1,86 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.{InitializationMethod, SpatialDilatedConvolution, Xavier, Zeros} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class AtrousConvolution2D[T: ClassTag]( + val nbFilter: Int, + val nbRow: Int, + val nbCol: Int, + val init: InitializationMethod = Xavier, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val subsample: Array[Int] = Array(1, 1), + val atrousRate: Array[Int] = Array(1, 1), + val dimOrdering: DataFormat = DataFormat.NCHW, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(dimOrdering == DataFormat.NCHW, s"AtrousConvolution2D currently only supports " + + s"format NCHW, but got format $dimOrdering") + require(subsample.length == 2, + s"For AtrousConvolution2D, subsample should be of length 2 but got length ${subsample.length}") + require(atrousRate.length == 2, s"For AtrousConvolution2D, " + + s"atrousRate should be of length 2 but got length ${atrousRate.length}") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = SpatialDilatedConvolution( + nInputPlane = input(1), + nOutputPlane = nbFilter, + kW = nbCol, + kH = nbRow, + dW = subsample(1), + dH = subsample(0), + dilationW = atrousRate(1), + dilationH = atrousRate(0), + wRegularizer = wRegularizer, + bRegularizer = bRegularizer) + layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) + KerasLayer.fuse(layer, activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object AtrousConvolution2D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + init: String = "glorot_uniform", + activation: String = null, + subsample: (Int, Int) = (1, 1), + atrousRate: (Int, Int) = (1, 1), + dimOrdering: String = "th", + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AtrousConvolution2D[T] = { + new AtrousConvolution2D[T](nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), + KerasUtils.getActivation(activation), + Array(subsample._1, subsample._2), Array(atrousRate._1, atrousRate._2), + KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala index 4ad0fa88ef4..c12630293fe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.keras import com.intel.analytics.bigdl.nn.SpatialAveragePooling -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Shape @@ -28,9 +28,9 @@ class AveragePooling2D[T: ClassTag]( poolSize: Array[Int] = Array(2, 2), strides: Array[Int] = null, borderMode: String = "valid", - format: DataFormat = DataFormat.NCHW, + dimOrdering: DataFormat = DataFormat.NCHW, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends Pooling2D[T](poolSize, strides, borderMode, format, inputShape) { + extends Pooling2D[T](poolSize, strides, borderMode, dimOrdering, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val pads = KerasUtils.getPadsFromBorderMode(borderMode) @@ -42,7 +42,7 @@ class AveragePooling2D[T: ClassTag]( padW = pads._2, padH = pads._1, countIncludePad = false, - format = format) + format = dimOrdering) layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala index 75146068f0f..d5cadcdf577 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala @@ -27,8 +27,9 @@ import scala.reflect.ClassTag class AveragePooling3D[T: ClassTag]( poolSize: Array[Int] = Array(2, 2, 2), strides: Array[Int] = null, + dimOrdering: String = "CHANNEL_FIRST", inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends Pooling3D[T](poolSize, strides, inputShape) { + extends Pooling3D[T](poolSize, strides, dimOrdering, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = VolumetricAveragePooling( @@ -47,10 +48,11 @@ object AveragePooling3D { def apply[@specialized(Float, Double) T: ClassTag]( poolSize: (Int, Int, Int) = (2, 2, 2), strides: (Int, Int, Int) = null, + dimOrdering: String = "th", inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AveragePooling3D[T] = { val strideValues = if (strides != null) Array(strides._1, strides._2, strides._3) else null new AveragePooling3D[T](Array(poolSize._1, poolSize._2, poolSize._3), - strideValues, inputShape) + strideValues, KerasUtils.toBigDLFormat5D(dimOrdering), inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala new file mode 100644 index 00000000000..0a090f1c1ba --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala @@ -0,0 +1,100 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.{ConvLSTMPeephole, Reverse, Select, Sequential => TSequential} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class ConvLSTM2D[T: ClassTag]( + val nbFilter: Int, + val nbKernel: Int, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val innerActivation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val dimOrdering: String = "CHANNEL_FIRST", + val subsample: Int = 1, + var wRegularizer: Regularizer[T] = null, + var uRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val returnSequences: Boolean = false, + val goBackwards: Boolean = false, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(dimOrdering.toLowerCase() == "channel_first", s"ConvLSTM2D currently only supports " + + s"format CHANNEL_FIRST, but got format $dimOrdering") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 5, + s"ConvLSTM2D requires 5D input, but got input dim ${input.length}") + val rows = KerasUtils.computeConvOutputLength(input(3), nbKernel, "same", subsample) + val cols = KerasUtils.computeConvOutputLength(input(4), nbKernel, "same", subsample) + if (returnSequences) Shape(input(0), input(1), nbFilter, rows, cols) + else Shape(input(0), nbFilter, rows, cols) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val model = TSequential[T]() + if (goBackwards) model.add(Reverse(2)) + val rec = com.intel.analytics.bigdl.nn.Recurrent[T]() + val layer = ConvLSTMPeephole( + inputSize = input(2), + outputSize = nbFilter, + kernelI = nbKernel, + kernelC = nbKernel, + stride = subsample, + activation = activation.asInstanceOf[TensorModule[T]], + innerActivation = innerActivation.asInstanceOf[TensorModule[T]], + wRegularizer = wRegularizer, + uRegularizer = uRegularizer, + bRegularizer = bRegularizer, + withPeephole = false) + rec.add(layer) + model.add(rec) + if (!returnSequences) model.add(Select(2, -1)) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object ConvLSTM2D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + nbKernel: Int, + activation: String = "tanh", + innerActivation: String = "hard_sigmoid", + dimOrdering: String = "th", + subsample: Int = 1, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + returnSequences: Boolean = false, + goBackwards: Boolean = false, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ConvLSTM2D[T] = { + new ConvLSTM2D[T](nbFilter, nbKernel, KerasUtils.getActivation(activation), + KerasUtils.getActivation(innerActivation), + KerasUtils.toBigDLFormat5D(dimOrdering), + subsample, wRegularizer, uRegularizer, bRegularizer, + returnSequences, goBackwards, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala index 4109473f12d..3925adcc426 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor @@ -43,16 +43,16 @@ import scala.reflect.ClassTag * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param subsample Int array of length 2 corresponding to the step of the convolution in the * height and width dimension. Also called strides elsewhere. Default is (1, 1). * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the input weights matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. - * @param format Format of input data. Either DataFormat.NCHW (dimOrdering='th') or - * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). * Default is true. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Convolution2D[T: ClassTag]( val nbFilter: Int, @@ -62,9 +62,9 @@ class Convolution2D[T: ClassTag]( val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, val borderMode: String = "valid", val subsample: Array[Int] = Array(1, 1), + val dimOrdering: DataFormat = DataFormat.NCHW, var wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, - val format: DataFormat = DataFormat.NCHW, val bias: Boolean = true, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { @@ -78,7 +78,7 @@ class Convolution2D[T: ClassTag]( val input = inputShape.toSingle().toArray val pads = KerasUtils.getPadsFromBorderMode(borderMode) val layer = SpatialConvolution( - nInputPlane = input(format.getHWCDims(4)._3 - 1), + nInputPlane = input(dimOrdering.getHWCDims(4)._3 - 1), nOutputPlane = nbFilter, kernelW = nbCol, kernelH = nbRow, @@ -89,7 +89,7 @@ class Convolution2D[T: ClassTag]( wRegularizer = wRegularizer, bRegularizer = bRegularizer, withBias = bias, - format = format) + format = dimOrdering) layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) KerasLayer.fuse(layer, activation, inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] @@ -112,7 +112,8 @@ object Convolution2D { inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution2D[T] = { new Convolution2D[T](nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), - borderMode, Array(subsample._1, subsample._2), wRegularizer, - bRegularizer, KerasUtils.toBigDLFormat(dimOrdering), bias, inputShape) + borderMode, Array(subsample._1, subsample._2), + KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, + bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala index 8028392c27b..1babc08f725 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule} import com.intel.analytics.bigdl.nn.{InitializationMethod, VolumetricConvolution, Xavier, Zeros} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor @@ -34,12 +34,15 @@ class Convolution3D[T: ClassTag]( val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, val borderMode: String = "valid", val subsample: Array[Int] = Array(1, 1, 1), + val dimOrdering: String = "CHANNEL_FIRST", val wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, val bias: Boolean = true, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + require(dimOrdering.toLowerCase() == "channel_first", s"Pooling3D currently only supports " + + s"format CHANNEL_FIRST, but got format $dimOrdering") require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " + s"Convolution3D: $borderMode") require(subsample.length == 3, @@ -79,6 +82,7 @@ object Convolution3D { activation: String = null, borderMode: String = "valid", subsample: (Int, Int, Int) = (1, 1, 1), + dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, @@ -86,6 +90,7 @@ object Convolution3D { new Convolution3D[T](nbFilter, kernelDim1, kernelDim2, kernelDim3, KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), borderMode, Array(subsample._1, subsample._2, subsample._3), + KerasUtils.toBigDLFormat5D(dimOrdering), wRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala index 169748a2ea5..74ec2cca982 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala @@ -26,7 +26,7 @@ import scala.reflect.ClassTag class Cropping2D[T: ClassTag]( val heightCrop: Array[Int] = Array(0, 0), val widthCrop: Array[Int] = Array(0, 0), - val format: DataFormat = DataFormat.NCHW, + val dimOrdering: DataFormat = DataFormat.NCHW, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { @@ -39,7 +39,7 @@ class Cropping2D[T: ClassTag]( val layer = com.intel.analytics.bigdl.nn.Cropping2D( heightCrop = heightCrop, widthCrop = widthCrop, - format = format) + format = dimOrdering) layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala index 65c82614365..d53a0daba90 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala @@ -27,20 +27,24 @@ class Cropping3D[T: ClassTag]( val dim1Crop: Array[Int] = Array(1, 1), val dim2Crop: Array[Int] = Array(1, 1), val dim3Crop: Array[Int] = Array(1, 1), - val format: String = "CHANNEL_FIRST", + val dimOrdering: String = "CHANNEL_FIRST", val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { require(dim1Crop.length == 2, - s"Cropping3D: kernel dim1 cropping values should be of length 2, but got ${dim1Crop.length}") + s"Cropping3D: kernel dim1 cropping values should be of length 2, " + + s"but got length ${dim1Crop.length}") require(dim2Crop.length == 2, - s"Cropping3D: kernel dim2 cropping values should be of length 2, but got ${dim2Crop.length}") + s"Cropping3D: kernel dim2 cropping values should be of length 2, " + + s"but got length ${dim2Crop.length}") require(dim3Crop.length == 2, - s"Cropping3D: kernel dim3 cropping values should be of length 2, but got ${dim3Crop.length}") - require(format.toLowerCase() == "channel_first" || format.toLowerCase() == "channel_last", - "Cropping3D only supports format channel_first or channel_last") + s"Cropping3D: kernel dim3 cropping values should be of length 2, " + + s"but got length ${dim3Crop.length}") + require(dimOrdering.toLowerCase() == "channel_first" || + dimOrdering.toLowerCase() == "channel_last", + s"Cropping3D only supports format channel_first or channel_last, but got format $dimOrdering") - private val dimOrdering = format.toLowerCase() match { + private val format = dimOrdering.toLowerCase() match { case "channel_first" => com.intel.analytics.bigdl.nn.Cropping3D.CHANNEL_FIRST case "channel_last" => com.intel.analytics.bigdl.nn.Cropping3D.CHANNEL_LAST } @@ -50,7 +54,7 @@ class Cropping3D[T: ClassTag]( dim1Crop = dim1Crop, dim2Crop = dim2Crop, dim3Crop = dim3Crop, - format = dimOrdering) + format = format) layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala new file mode 100644 index 00000000000..c4262ddecb5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala @@ -0,0 +1,83 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.{InitializationMethod, SpatialFullConvolution, Xavier, Zeros} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class Deconvolution2D[T: ClassTag]( + val nbFilter: Int, + val nbRow: Int, + val nbCol: Int, + val init: InitializationMethod = Xavier, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val subsample: Array[Int] = Array(1, 1), + val dimOrdering: DataFormat = DataFormat.NCHW, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(dimOrdering == DataFormat.NCHW, s"Deconvolution2D currently only supports " + + s"format NCHW, but got format $dimOrdering") + require(subsample.length == 2, + s"For Deconvolution2D, subsample should be of length 2 but got length ${subsample.length}") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = SpatialFullConvolution( + nInputPlane = input(1), + nOutputPlane = nbFilter, + kW = nbCol, + kH = nbRow, + dW = subsample(1), + dH = subsample(0), + noBias = !bias, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer) + layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) + KerasLayer.fuse(layer, activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Deconvolution2D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + init: String = "glorot_uniform", + activation: String = null, + subsample: (Int, Int) = (1, 1), + dimOrdering: String = "th", + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Deconvolution2D[T] = { + new Deconvolution2D[T](nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), + KerasUtils.getActivation(activation), Array(subsample._1, subsample._2), + KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, + bRegularizer, bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala index 8f5f4807648..e182ed87630 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala @@ -43,7 +43,7 @@ import scala.reflect.ClassTag * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). * Default is true. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Dense[T: ClassTag]( val outputDim: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala index c2c353bf7a4..01aae540508 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala @@ -30,7 +30,7 @@ import scala.reflect.ClassTag * inputShape (a Single Shape, does not include the batch dimension). * * @param p Fraction of the input units to drop. Double between 0 and 1. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Dropout[T: ClassTag]( val p: Double, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala index 8c440182f0f..bcae49540d9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala @@ -28,7 +28,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Flatten[T: ClassTag]( val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala index e6bf094372c..a89cb50d305 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala @@ -47,7 +47,7 @@ import scala.reflect.ClassTag * @param uRegularizer An instance of [[Regularizer]], applied the recurrent weights matrices. * Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class GRU[T: ClassTag]( outputDim: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala index 4a33ddc1ed0..ed65837da3e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala @@ -25,13 +25,13 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag class GlobalAveragePooling2D[T: ClassTag]( - format: DataFormat = DataFormat.NCHW, + dimOrdering: DataFormat = DataFormat.NCHW, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends GlobalPooling2D[T](format, inputShape) { + extends GlobalPooling2D[T](dimOrdering, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray - val (dimH, dimW, dimC) = format.getHWCDims(4) + val (dimH, dimW, dimC) = dimOrdering.getHWCDims(4) val model = TSequential[T]() val layer = SpatialAveragePooling( kW = input(dimW -1), @@ -39,7 +39,7 @@ class GlobalAveragePooling2D[T: ClassTag]( dW = input(dimW -1), dH = input(dimH -1), countIncludePad = false, - format = format) + format = dimOrdering) model.add(layer) model.add(Squeeze(dimW)) model.add(Squeeze(dimH)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala index f264629d8f4..b7a12e2a804 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala @@ -30,20 +30,16 @@ import scala.reflect.ClassTag * Global Average pooling operation for 3D data. * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). * The input of this layer should be 5D. * - * @param format Format of input data. Please use DataFormat.NCHW (dimOrdering='th'). + * @param dimOrdering Format of input data. Please use 'CHANNEL_FIRST' (dimOrdering='th'). * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class GlobalAveragePooling3D[T: ClassTag]( - val format: String = "CHANNEL_FIRST", + dimOrdering: String = "CHANNEL_FIRST", inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends GlobalPooling3D[T](inputShape) { - - require(format.toLowerCase() == "channel_first", s"GlobalAveragePooling3D only supports " + - s"format CHANNEL_FIRST, but got format $format.") + extends GlobalPooling3D[T](dimOrdering, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala index 06f941ede8d..05afd18f327 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala @@ -25,20 +25,20 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag class GlobalMaxPooling2D[T: ClassTag]( - format: DataFormat = DataFormat.NCHW, + dimOrdering: DataFormat = DataFormat.NCHW, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends GlobalPooling2D[T](format, inputShape) { + extends GlobalPooling2D[T](dimOrdering, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray - val (dimH, dimW, dimC) = format.getHWCDims(4) + val (dimH, dimW, dimC) = dimOrdering.getHWCDims(4) val model = TSequential[T]() val layer = SpatialMaxPooling( kW = input(dimW -1), kH = input(dimH -1), dW = input(dimW -1), dH = input(dimH -1), - format = format) + format = dimOrdering) model.add(layer) model.add(Squeeze(dimW)) model.add(Squeeze(dimH)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala index 210face8cc0..1421b294ebc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala @@ -33,16 +33,13 @@ import scala.reflect.ClassTag * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). * The input of this layer should be 5D. * - * @param format Format of input data. Please use DataFormat.NCHW (dimOrdering='th'). + * @param dimOrdering Format of input data. Please use 'CHANNEL_FIRST' (dimOrdering='th'). * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class GlobalMaxPooling3D[T: ClassTag]( - val format: String = "CHANNEL_FIRST", + dimOrdering: String = "CHANNEL_FIRST", inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends GlobalPooling3D[T](inputShape) { - - require(format.toLowerCase() == "channel_first", s"GlobalMaxPooling3D only supports " + - s"format CHANNEL_FIRST, but got format $format.") + extends GlobalPooling3D[T](dimOrdering, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala index 2827abcf83a..04873ab39de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag abstract class GlobalPooling2D[T: ClassTag]( - val format: DataFormat = DataFormat.NCHW, + val dimOrdering: DataFormat = DataFormat.NCHW, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { @@ -32,7 +32,7 @@ abstract class GlobalPooling2D[T: ClassTag]( val input = inputShape.toSingle().toArray require(input.length == 4, s"GlobalPooling2D requires 4D input, but got input dim ${input.length}") - format match { + dimOrdering match { case DataFormat.NCHW => Shape(input(0), input(1)) case DataFormat.NHWC => Shape(input(0), input(3)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling3D.scala index 99b9106604c..df5bb0cf339 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling3D.scala @@ -28,9 +28,13 @@ import scala.reflect.ClassTag * Please use its child classes, 'GlobalAveragePooling3D' and 'GlobalMaxPooling3D' instead. */ abstract class GlobalPooling3D[T: ClassTag]( + val dimOrdering: String = "CHANNEL_FIRST", val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + require(dimOrdering.toLowerCase() == "channel_first", + s"GlobalPooling3D currently only supports format CHANNEL_FIRST, but got format $dimOrdering") + override def computeOutputShape(inputShape: Shape): Shape = { val input = inputShape.toSingle().toArray require(input.length == 5, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala index 0910810ed70..a1455beea65 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala @@ -39,7 +39,7 @@ import scala.reflect.ClassTag * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). * Default is true. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Highway[T: ClassTag]( val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala index 29f3501ba81..ca4d7359651 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala @@ -47,7 +47,7 @@ import scala.reflect.ClassTag * @param uRegularizer An instance of [[Regularizer]], applied the recurrent weights matrices. * Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class LSTM[T: ClassTag]( outputDim: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala new file mode 100644 index 00000000000..6474e624eb3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala @@ -0,0 +1,88 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.{Squeeze, Sequential => TSequential} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class LocallyConnected1D[T: ClassTag]( + val nbFilter: Int, + val filterLength: Int, + val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val subsampleLength: Int = 1, + var wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"LocallyConnected1D requires 3D input, but got input dim ${input.length}") + val length = KerasUtils.computeConvOutputLength(input(1), filterLength, + "valid", subsampleLength) + Shape(input(0), length, nbFilter) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val model = TSequential[T]() + model.add(com.intel.analytics.bigdl.nn.Reshape(Array(input(1), 1, input(2)), Some(true))) + val layer = com.intel.analytics.bigdl.nn.LocallyConnected2D( + nInputPlane = input(2), + inputWidth = 1, + inputHeight = input(1), + nOutputPlane = nbFilter, + kernelW = 1, + kernelH = filterLength, + strideW = 1, + strideH = subsampleLength, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer, + withBias = bias, + format = DataFormat.NHWC) + model.add(layer) + model.add(Squeeze(3)) + if (activation != null) { + model.add(activation) + } + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object LocallyConnected1D { + def apply[@specialized(Float, Double) T: ClassTag]( + nbFilter: Int, + filterLength: Int, + activation: String = null, + subsampleLength: Int = 1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LocallyConnected1D[T] = { + new LocallyConnected1D[T](nbFilter, filterLength, + KerasUtils.getActivation(activation), subsampleLength, + wRegularizer, bRegularizer, bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala index a3815e164ef..fd12f04da39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala @@ -44,11 +44,11 @@ import scala.reflect.ClassTag * @param borderMode Either 'valid' or 'same'. Default is 'valid'. * @param subsample Int array of length 2. The step of the convolution in the height and * width dimension. Also called strides elsewhere. Default is (1, 1). + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the input weights matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. - * @param format Format of input data. Either DataFormat.NCHW (dimOrdering='th') or - * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). * Default is true. * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. @@ -60,9 +60,9 @@ class LocallyConnected2D[T: ClassTag]( val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, val borderMode: String = "valid", val subsample: Array[Int] = Array(1, 1), + val dimOrdering: DataFormat = DataFormat.NCHW, var wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, - val format: DataFormat = DataFormat.NCHW, val bias: Boolean = true, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { @@ -74,7 +74,7 @@ class LocallyConnected2D[T: ClassTag]( override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray - val stack = if (format == DataFormat.NCHW) (input(1), input(3), input(2)) + val stack = if (dimOrdering == DataFormat.NCHW) (input(1), input(3), input(2)) else (input(3), input(2), input(1)) val pad = KerasUtils.getPadsFromBorderMode(borderMode) val layer = com.intel.analytics.bigdl.nn.LocallyConnected2D( @@ -91,7 +91,7 @@ class LocallyConnected2D[T: ClassTag]( wRegularizer = wRegularizer, bRegularizer = bRegularizer, withBias = bias, - format = format) + format = dimOrdering) KerasLayer.fuse(layer, activation, inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } @@ -105,13 +105,13 @@ object LocallyConnected2D { activation: String = null, borderMode: String = "valid", subsample: (Int, Int) = (1, 1), + dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, - dimOrdering: String = "th", bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LocallyConnected2D[T] = { new LocallyConnected2D[T](nbFilter, nbRow, nbCol, KerasUtils.getActivation(activation), borderMode, Array(subsample._1, subsample._2), - wRegularizer, bRegularizer, KerasUtils.toBigDLFormat(dimOrdering), bias, inputShape) + KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala index f7a81260d11..df58b7e11b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala @@ -35,17 +35,17 @@ import scala.reflect.ClassTag * @param strides Int array of length 2. Stride values. Default is null, and in this case it will * be equal to poolSize. * @param borderMode Either 'valid' or 'same'. Default is 'valid'. - * @param format Format of input data. Either DataFormat.NCHW (dimOrdering='th') or - * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class MaxPooling2D[T: ClassTag] ( poolSize: Array[Int] = Array(2, 2), strides: Array[Int] = null, borderMode: String = "valid", - format: DataFormat = DataFormat.NCHW, + dimOrdering: DataFormat = DataFormat.NCHW, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends Pooling2D[T](poolSize, strides, borderMode, format, inputShape) { + extends Pooling2D[T](poolSize, strides, borderMode, dimOrdering, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val pads = KerasUtils.getPadsFromBorderMode(borderMode) @@ -56,7 +56,7 @@ class MaxPooling2D[T: ClassTag] ( dH = strideValues(0), padW = pads._2, padH = pads._1, - format = format) + format = dimOrdering) layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala index b00c1c06ac1..8e339c74f4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala @@ -27,8 +27,9 @@ import scala.reflect.ClassTag class MaxPooling3D[T: ClassTag]( poolSize: Array[Int] = Array(2, 2, 2), strides: Array[Int] = null, + dimOrdering: String = "CHANNEL_FIRST", inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends Pooling3D[T](poolSize, strides, inputShape) { + extends Pooling3D[T](poolSize, strides, dimOrdering, inputShape) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = VolumetricMaxPooling( @@ -46,10 +47,11 @@ object MaxPooling3D { def apply[@specialized(Float, Double) T: ClassTag]( poolSize: (Int, Int, Int) = (2, 2, 2), strides: (Int, Int, Int) = null, + dimOrdering: String = "th", inputShape: Shape = null)(implicit ev: TensorNumeric[T]): MaxPooling3D[T] = { val strideValues = if (strides != null) Array(strides._1, strides._2, strides._3) else null new MaxPooling3D[T](Array(poolSize._1, poolSize._2, poolSize._3), - strideValues, inputShape) + strideValues, KerasUtils.toBigDLFormat5D(dimOrdering), inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala index 5c6d2faba67..0ea3bd42372 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala @@ -41,5 +41,4 @@ abstract class Pooling1D[T: ClassTag]( borderMode, strideValue) Shape(input(0), outputLength, input(2)) } - } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala index 481666c8ee5..aabebfc7dcc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala @@ -27,7 +27,7 @@ abstract class Pooling2D[T: ClassTag]( val poolSize: Array[Int] = Array(2, 2), val strides: Array[Int] = null, val borderMode: String = "valid", - val format: DataFormat = DataFormat.NCHW, + val dimOrdering: DataFormat = DataFormat.NCHW, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { @@ -44,15 +44,14 @@ abstract class Pooling2D[T: ClassTag]( val input = inputShape.toSingle().toArray require(input.length == 4, s"Pooling2D requires 4D input, but got input dim ${input.length}") - val (dimH, dimW, dimC) = format.getHWCDims(4) + val (dimH, dimW, dimC) = dimOrdering.getHWCDims(4) val rows = KerasUtils.computeConvOutputLength(input(dimH -1), poolSize(0), borderMode, strideValues(0)) val cols = KerasUtils.computeConvOutputLength(input(dimW -1), poolSize(1), borderMode, strideValues(1)) - format match { + dimOrdering match { case DataFormat.NCHW => Shape(input(0), input(1), rows, cols) case DataFormat.NHWC => Shape(input(0), rows, cols, input(3)) } } - } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala index bd68a807cb2..9acdffbf933 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala @@ -25,9 +25,13 @@ import scala.reflect.ClassTag abstract class Pooling3D[T: ClassTag]( val poolSize: Array[Int] = Array(2, 2, 2), val strides: Array[Int] = null, + val dimOrdering: String = "CHANNEL_FIRST", val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + require(dimOrdering.toLowerCase() == "channel_first", s"Pooling3D currently only supports " + + s"format CHANNEL_FIRST, but got format $dimOrdering") + require(poolSize.length == 3, s"For Pooling3D, poolSize should be of length 3 but got length ${poolSize.length}") @@ -47,5 +51,4 @@ abstract class Pooling3D[T: ClassTag]( "valid", strideValues(2)) Shape(input(0), input(1), dim1Length, dim2Length, dim3Length) } - } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala index a0da2cacccd..951a8fe94d0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala @@ -34,7 +34,7 @@ import scala.reflect.ClassTag * * @param targetShape Array of int. The target shape that you desire to have. * Batch dimension should be excluded. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Reshape[T: ClassTag]( val targetShape: Array[Int], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala index f05de2367cc..325870ae99a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala @@ -52,13 +52,13 @@ import scala.reflect.ClassTag * width dimension. Also called strides elsewhere. Default is (1, 1). * @param depthMultiplier How many output channel to use per input channel * for the depthwise convolution step. + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param depthwiseRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the depthwise weights matrices. Default is null. * @param pointwiseRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the pointwise weights matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. - * @param format Format of input data. Either DataFormat.NCHW (dimOrdering='th') or - * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). * Default is true. * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. @@ -72,10 +72,10 @@ class SeparableConvolution2D[T: ClassTag]( val borderMode: String = "valid", val subsample: Array[Int] = Array(1, 1), val depthMultiplier: Int = 1, + val dimOrdering: DataFormat = DataFormat.NCHW, var depthwiseRegularizer: Regularizer[T] = null, var pointwiseRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, - val format: DataFormat = DataFormat.NCHW, val bias: Boolean = true, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { @@ -87,7 +87,7 @@ class SeparableConvolution2D[T: ClassTag]( override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray - val stackSize = if (format == DataFormat.NCHW) input(1) else input(3) + val stackSize = if (dimOrdering == DataFormat.NCHW) input(1) else input(3) val pad = KerasUtils.getPadsFromBorderMode(borderMode) val layer = SpatialSeperableConvolution( nInputChannel = stackSize, @@ -100,7 +100,7 @@ class SeparableConvolution2D[T: ClassTag]( pW = pad._2, pH = pad._1, hasBias = bias, - dataFormat = format, + dataFormat = dimOrdering, wRegularizer = depthwiseRegularizer, bRegularizer = bRegularizer, pRegularizer = pointwiseRegularizer) @@ -119,15 +119,16 @@ object SeparableConvolution2D { borderMode: String = "valid", subsample: (Int, Int) = (1, 1), depthMultiplier: Int = 1, + dimOrdering: String = "th", depthwiseRegularizer: Regularizer[T] = null, pointwiseRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, - dimOrdering: String = "th", bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : SeparableConvolution2D[T] = { new SeparableConvolution2D[T](nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), - borderMode, Array(subsample._1, subsample._2), depthMultiplier, depthwiseRegularizer, - pointwiseRegularizer, bRegularizer, KerasUtils.toBigDLFormat(dimOrdering), bias, inputShape) + borderMode, Array(subsample._1, subsample._2), depthMultiplier, + KerasUtils.toBigDLFormat(dimOrdering), depthwiseRegularizer, + pointwiseRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala index 1274a6dec61..927c75d5a44 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala @@ -44,7 +44,7 @@ import scala.reflect.ClassTag * @param uRegularizer An instance of [[Regularizer]], applied the recurrent weights matrices. * Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class SimpleRNN[T: ClassTag]( outputDim: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding3D.scala index fad7b59a9f5..5ca037cca83 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding3D.scala @@ -35,26 +35,27 @@ import scala.reflect.ClassTag * @param padding Int array of length 3. * How many zeros to add at the beginning and end of the 3 padding dimensions. * Symmetric padding will be applied to each dimension. Default is (1, 1, 1). - * @param format Format of the input data. Either "CHANNEL_FIRST" (dimOrdering='th') or - * "CHANNEL_LAST" (dimOrdering='tf'). Default is "CHANNEL_FIRST". + * @param dimOrdering Format of the input data. Either "CHANNEL_FIRST" (dimOrdering='th') or + * "CHANNEL_LAST" (dimOrdering='tf'). Default is "CHANNEL_FIRST". * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class ZeroPadding3D[T: ClassTag]( val padding: Array[Int] = Array(1, 1, 1), - val format: String = "CHANNEL_FIRST", + val dimOrdering: String = "CHANNEL_FIRST", val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { - require(format.toLowerCase() == "channel_first" || format.toLowerCase() == "channel_last", - s"For ZeroPadding3D $format is not supported") - require(padding.length == 3, s"For ZeroPadding3D Subsample should be of length 3," + - s" not ${padding.length}") + require(dimOrdering.toLowerCase() == "channel_first" || + dimOrdering.toLowerCase() == "channel_last", + s"For ZeroPadding3D $dimOrdering is not supported") + require(padding.length == 3, s"For ZeroPadding3D, subsample should be of length 3," + + s" but got length ${padding.length}") override def computeOutputShape(inputShape: Shape): Shape = { val input = inputShape.toSingle().toArray require(input.length == 5, s"ZeroPadding3D requires 5D input, but got input dim ${input.length}") - format.toLowerCase() match { + dimOrdering.toLowerCase() match { case "channel_first" => Shape(input(0), input(1), input(2) + 2 * padding(0), input(3) + 2 * padding(1), input(4) + 2 * padding(2)) @@ -66,7 +67,7 @@ class ZeroPadding3D[T: ClassTag]( override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray - val dim = if (format.toLowerCase() == "channel_first") 2 else 1 + val dim = if (dimOrdering.toLowerCase() == "channel_first") 2 else 1 val model = TSequential[T]() val paddinglayer1 = Padding(dim = dim, pad = -padding(0), nInputDim = input.length - 1) val paddinglayer2 = Padding(dim = dim, pad = padding(0), nInputDim = input.length - 1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala index 903ec16108d..3fa7f6b2c58 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/package.scala @@ -22,4 +22,7 @@ package object keras { val Conv2D = Convolution2D val Conv3D = Convolution3D val SeparableConv2D = SeparableConvolution2D + val AtrousConv1D = AtrousConvolution1D + val AtrousConv2D = AtrousConvolution2D + val Deconv2D = Deconvolution2D } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala index 51ce5c3aaa9..0b1fd3e5967 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala @@ -22,7 +22,8 @@ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.{DenseTensorBLAS, DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Shape, T, Table} + import scala.reflect.ClassTag /** @@ -147,6 +148,15 @@ class SpatialDilatedConvolution[T: ClassTag]( } } + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"AtrousConvolution2D requires 4D input, but got input dim ${input.length}") + val outputWidth = (input(3) + 2*padW - (dilationW * (kW - 1) + 1)) / dW + 1 + val outputHeight = (input(2) + 2*padH - (dilationH * (kH - 1) + 1)) / dH + 1 + Shape(input(0), nOutputPlane, outputHeight, outputWidth) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { shapeCheck(input, null, weight, bias, kH, kW, dH, dW, padH, padW, dilationH, dilationW) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala index 49b54511df7..56841a29710 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Initia import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.{T, Table, serializer} +import com.intel.analytics.bigdl.utils.{Shape, T, Table, serializer} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter @@ -253,6 +253,17 @@ class SpatialFullConvolution[T: ClassTag]( } } + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"Deconvolution2D requires 4D input, but got input dim ${input.length}") + val inputHeight = input(2) + val inputWidth = input(3) + val outputHeight = (inputHeight - 1) * dH - 2 * padH + kH + adjH + val outputWidth = (inputWidth - 1) * dW - 2 * padW + kW + adjW + Shape(input(0), nOutputPlane, outputHeight, outputWidth) + } + override def updateOutput(input: Activity): Tensor[T] = { val inputTensor: Tensor[T] = if (input.isInstanceOf[Table]) { if (gradInput == null || !gradInput.isInstanceOf[Table]) { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution1DSpec.scala new file mode 100644 index 00000000000..61dfe567eba --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution1DSpec.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{AtrousConvolution1D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class AtrousConvolution1DSpec extends KerasBaseSpec { + + "AtrousConvolution1D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[8, 32]) + |input = np.random.random([2, 8, 32]) + |output_tensor = AtrousConvolution1D(64, 3, activation="tanh", + | atrous_rate=2)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = AtrousConvolution1D[Float](64, 3, activation = "tanh", + atrousRate = 2, inputShape = Shape(8, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4, 64)) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + var w = in(0).transpose(1, 4) + w = w.transpose(2, 3) + w = w.transpose(3, 4) + Array(w, in(1)) + } + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution2DSpec.scala new file mode 100644 index 00000000000..51ffaf31442 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution2DSpec.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{AtrousConvolution2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class AtrousConvolution2DSpec extends KerasBaseSpec { + + "AtrousConvolution2D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 64, 64]) + |input = np.random.random([2, 3, 64, 64]) + |output_tensor = AtrousConvolution2D(32, 2, 4, activation="sigmoid", + | dim_ordering="th", + | atrous_rate=(2,2))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = AtrousConvolution2D[Float](32, 2, 4, activation = "sigmoid", + atrousRate = (2, 2), inputShape = Shape(3, 64, 64)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, precision = 1e-2) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala new file mode 100644 index 00000000000..d14751f969f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala @@ -0,0 +1,71 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{ConvLSTM2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class ConvLSTM2DSpec extends KerasBaseSpec { + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + Array(in(6), in(8), in(7), + in(0), in(2), in(1), + in(3), in(5), in(4), + in(9), in(11), in(10)) + } + + "ConvLSTM2D return sequences" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[8, 40, 40, 32]) + |input = np.random.random([4, 8, 40, 40, 32]) + |output_tensor = ConvLSTM2D(32, 4, 4, return_sequences=True, + | dim_ordering="th", border_mode="same")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = ConvLSTM2D[Float](32, 4, returnSequences = true, + inputShape = Shape(8, 40, 40, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 8, 32, 40, 32)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter, precision = 1e-2) + } + + "ConvLSTM2D go backwards" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 8, 16, 16]) + |input = np.random.random([4, 4, 8, 16, 16]) + |output_tensor = ConvLSTM2D(8, 2, 2, go_backwards=True, + | inner_activation="sigmoid", + | dim_ordering="th", border_mode="same")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = ConvLSTM2D[Float](8, 2, goBackwards = true, + innerActivation = "sigmoid", inputShape = Shape(4, 8, 16, 16)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 8, 16, 16)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter, precision = 1e-2) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala new file mode 100644 index 00000000000..bd8c1487e37 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Deconvolution2D, Deconv2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class Deconvolution2DSpec extends KerasBaseSpec { + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + var w = in(0).transpose(1, 2) + if (in.length > 1) Array(w, in(1)) // with bias + else Array(w) // without bias + } + + "Deconvolution2D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 12, 12]) + |input = np.random.random([8, 3, 12, 12]) + |output_tensor = Deconvolution2D(3, 3, 3, activation="relu", dim_ordering="th", + | output_shape=(None, 3, 14, 14))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Deconvolution2D[Float](3, 3, 3, activation = "relu", + inputShape = Shape(3, 12, 12)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter, precision = 1e-3) + } + + "Deconvolution2D without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 12, 12]) + |input = np.random.random([32, 3, 12, 12]) + |output_tensor = Deconvolution2D(3, 3, 3, dim_ordering="th", + | subsample=(2, 2), bias=False, + | output_shape=(None, 3, 25, 25))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Deconv2D[Float](3, 3, 3, subsample = (2, 2), bias = false, + inputShape = Shape(3, 12, 12)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter, precision = 1e-3) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected1DSpec.scala new file mode 100644 index 00000000000..bed89d9a894 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected1DSpec.scala @@ -0,0 +1,80 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{LocallyConnected1D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class LocallyConnected1DSpec extends KerasBaseSpec { + + def weightConverter(data: Array[Tensor[Float]]): Array[Tensor[Float]] = { + val out = new Array[Tensor[Float]](data.length) + val d1l: Int = data(0).size(1) + val d2l: Int = data(0).size(2) + val d3l: Int = data(0).size(3) + out(0) = Tensor(d1l, d3l, d2l) + val page: Int = d2l * d3l + for (i <- 0 to d1l * d2l * d3l - 1) { + val d1 = i / page + 1 + val d2 = (i % page) / (d3l) + 1 + val d3 = (i % page) % d3l + 1 + val v = data(0).valueAt(d1, d2, d3) + out(0).setValue(d1, d3, d2, v) + } + if (data.length > 1) { + out(1) = data(1) + } + out + } + + "LocallyConnected1D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, 24]) + |input = np.random.random([3, 12, 24]) + |output_tensor = LocallyConnected1D(32, 3, activation="relu")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = LocallyConnected1D[Float](32, 3, activation = "relu", + inputShape = Shape(12, 24)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "LocallyConnected1D without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[32, 32]) + |input = np.random.random([2, 32, 32]) + |output_tensor = LocallyConnected1D(64, 4, subsample_length=2, + | bias=False)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = LocallyConnected1D[Float](64, 4, subsampleLength = 2, + bias = false, inputShape = Shape(32, 32)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala index c483f78e5e5..6b4d119688c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala @@ -35,10 +35,9 @@ class SeparableConvolution2DSpec extends KerasBaseSpec { val seq = KSequential[Float]() val layer = SeparableConvolution2D[Float](3, 3, 3, inputShape = Shape(3, 4, 5)) seq.add(layer) - def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { if (in.length == 2) { - val bias = if (layer.format == DataFormat.NCHW) in(1).size(1) + val bias = if (layer.dimOrdering == DataFormat.NCHW) in(1).size(1) else in(1).size(4) val out = Tensor[Float](bias) Array(in(0), in(1), out) @@ -61,10 +60,9 @@ class SeparableConvolution2DSpec extends KerasBaseSpec { val seq = KSequential[Float]() val layer = SeparableConvolution2D[Float](3, 3, 3, bias = false, inputShape = Shape(3, 4, 5)) seq.add(layer) - def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { if (in.length == 2) { - val bias = if (layer.format == DataFormat.NCHW) in(1).size(1) + val bias = if (layer.dimOrdering == DataFormat.NCHW) in(1).size(1) else in(1).size(4) val out = Tensor[Float](bias) Array(in(0), in(1), out) @@ -88,10 +86,9 @@ class SeparableConvolution2DSpec extends KerasBaseSpec { val layer = SeparableConvolution2D[Float](8, 2, 2, activation = "relu", dimOrdering = "tf", inputShape = Shape(12, 12, 3)) seq.add(layer) - def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { if (in.length == 2) { - val bias = if (layer.format == DataFormat.NCHW) in(1).size(1) + val bias = if (layer.dimOrdering == DataFormat.NCHW) in(1).size(1) else in(1).size(4) val out = Tensor[Float](bias) Array(in(0), in(1), out) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolutionSpec.scala index 68aefb7f1e2..30919e70ba2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolutionSpec.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.{Shape, TestUtils} import scala.util.Random @@ -58,4 +59,9 @@ class SpatialDilatedConvolutionSpec extends FlatSpec with Matchers { } + "SpatialDilatedConvolution computeOutputShape" should "work properly" in { + val layer = SpatialDilatedConvolution[Float](4, 8, 2, 3, 1, 2, 0, 0, 2, 2) + TestUtils.compareOutputShape(layer, Shape(4, 256, 256)) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala index 2b996a7160d..ea3d6eecd17 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{Shape, T, TestUtils} import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.scalatest.{FlatSpec, Matchers} @@ -222,5 +222,9 @@ class SpatialFullConvolutionSpec extends FlatSpec with Matchers { } + "SpatialFullConvolution computeOutputShape" should "work properly" in { + val layer = SpatialFullConvolution[Float](3, 5, 1, 2, 2) + TestUtils.compareOutputShape(layer, Shape(3, 28, 32)) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index 852766e07e8..5ddf7f357d0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -287,4 +287,41 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { val input = Tensor[Float](2, 5, 6, 7, 8).apply1(_ => Random.nextFloat()) runSerializationTest(layer, input) } + + "LocallyConnected1D serializer" should "work properly" in { + val layer = LocallyConnected1D[Float](32, 3, inputShape = Shape(12, 24)) + layer.build(Shape(2, 12, 24)) + val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "ConvLSTM2D serializer" should "work properly" in { + val layer = ConvLSTM2D[Float](32, 4, inputShape = Shape(8, 40, 40, 32)) + layer.build(Shape(2, 8, 40, 40, 32)) + val input = Tensor[Float](2, 8, 40, 40, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Deconvolution2D serializer" should "work properly" in { + val layer = Deconvolution2D[Float](3, 3, 3, inputShape = Shape(3, 24, 24)) + layer.build(Shape(2, 12, 24, 24)) + val input = Tensor[Float](2, 12, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "AtrousConvolution1D serializer" should "work properly" in { + val layer = AtrousConvolution1D[Float](64, 3, inputShape = Shape(8, 32)) + layer.build(Shape(2, 8, 32)) + val input = Tensor[Float](2, 8, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "AtrousConvolution2D serializer" should "work properly" in { + val layer = AtrousConvolution2D[Float](32, 2, 4, atrousRate = (2, 2), + inputShape = Shape(3, 64, 64)) + layer.build(Shape(2, 3, 64, 64)) + val input = Tensor[Float](2, 3, 64, 64).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + } From 65eb160ecfc281bbeddaa155f5a882df9bd2c5d3 Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Thu, 8 Feb 2018 16:42:18 +0800 Subject: [PATCH 0691/1065] Keras-like API Advanced Activations, dropout and noise layers (#2222) * Keras API for ELU * fix style check error * fix weightConverter * add one more unit test for ELU * remove blank line * Keras API for LeakyReLU * remove useless empty lines in LeakyReLU * add GaussianDropout * add GaussianNoise * remove UID and unnecessary import * fix two Gaussian unit test * add layer Masking * add layer SpatialDropout1D * change 3D to 4D * Revert "change 3D to 4D" This reverts commit 9efdb0a630f342596475f83feea8fa370f8caf05. * change unit test from 4D to 3D * add layer SpatialDropout2D * add layer PReLU. Unit test success without weight * add 3D unit test for PReLU * add layer ParametricSoftPlus. Unit test success without weight * add layer SpatialDropout3D * add layer ThresholdedReLU * fix the above problems * fix problems * add format lowercase to support both uppercase and lowercase * fix format problem * SReLU * add documentation and serializer * remove a blank in documentation and change inputshape from var to val * delete four files * update * modify * modify problem * modify * update * modify style --- .../analytics/bigdl/dllib/keras/ELU.scala | 57 +++++++++++++++ .../bigdl/dllib/keras/GaussianDropout.scala | 53 ++++++++++++++ .../bigdl/dllib/keras/GaussianNoise.scala | 56 ++++++++++++++ .../bigdl/dllib/keras/LeakyReLU.scala | 57 +++++++++++++++ .../analytics/bigdl/dllib/keras/Masking.scala | 56 ++++++++++++++ .../analytics/bigdl/dllib/keras/SReLU.scala | 63 ++++++++++++++++ .../bigdl/dllib/keras/SpatialDropout1D.scala | 60 +++++++++++++++ .../bigdl/dllib/keras/SpatialDropout2D.scala | 66 +++++++++++++++++ .../bigdl/dllib/keras/SpatialDropout3D.scala | 73 +++++++++++++++++++ .../bigdl/dllib/keras/ThresholdedReLU.scala | 59 +++++++++++++++ .../intel/analytics/bigdl/dllib/nn/ELU.scala | 4 +- .../bigdl/dllib/nn/GaussianDropout.scala | 4 +- .../bigdl/dllib/nn/GaussianNoise.scala | 4 +- .../analytics/bigdl/dllib/nn/LeakyReLU.scala | 4 +- .../analytics/bigdl/dllib/nn/Masking.scala | 4 +- .../analytics/bigdl/dllib/nn/PReLU.scala | 5 +- .../analytics/bigdl/dllib/nn/SReLU.scala | 5 +- .../bigdl/dllib/nn/SpatialDropout1D.scala | 4 +- .../bigdl/dllib/nn/SpatialDropout2D.scala | 4 +- .../bigdl/dllib/nn/SpatialDropout3D.scala | 4 +- .../analytics/bigdl/dllib/nn/Threshold.scala | 4 +- .../bigdl/dllib/keras/nn/ELUSpec.scala | 58 +++++++++++++++ .../dllib/keras/nn/GaussianDropoutSpec.scala | 37 ++++++++++ .../dllib/keras/nn/GaussianNoiseSpec.scala | 37 ++++++++++ .../bigdl/dllib/keras/nn/LeakyReLUSpec.scala | 58 +++++++++++++++ .../bigdl/dllib/keras/nn/MaskingSpec.scala | 58 +++++++++++++++ .../bigdl/dllib/keras/nn/SReLUSpec.scala | 58 +++++++++++++++ .../dllib/keras/nn/SpatialDropout1DSpec.scala | 37 ++++++++++ .../dllib/keras/nn/SpatialDropout2DSpec.scala | 48 ++++++++++++ .../dllib/keras/nn/SpatialDropout3DSpec.scala | 48 ++++++++++++ .../dllib/keras/nn/ThresholdedReLUSpec.scala | 58 +++++++++++++++ .../KerasModuleSerializerSpec.scala | 71 ++++++++++++++++++ 32 files changed, 1192 insertions(+), 22 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ELUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianDropoutSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianNoiseSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LeakyReLUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaskingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ThresholdedReLUSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala new file mode 100644 index 00000000000..0a4bc27f3c9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Exponential Linear Unit. + * It follows: + * `f(x) = alpha * (exp(x) - 1.) for x < 0`, + * `f(x) = x for x >= 0`. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param alpha Double, scale for the negative factor. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class ELU[T: ClassTag]( + val alpha: Double = 1.0, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.ELU( + alpha = alpha, + inplace = false) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object ELU { + def apply[@specialized(Float, Double) T: ClassTag]( + alpha: Double = 1.0, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ELU[T] = { + new ELU[T](alpha, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala new file mode 100644 index 00000000000..d76dc912f04 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Apply multiplicative 1-centered Gaussian noise. + * As it is a regularization layer, it is only active at training time. + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param p Double, drop probability (as with `Dropout`). + * The multiplicative noise will have standard deviation `sqrt(p / (1 - p))`. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class GaussianDropout[T: ClassTag]( + val p: Double, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.GaussianDropout(rate = p) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object GaussianDropout { + def apply[@specialized(Float, Double) T: ClassTag]( + p: Double, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): GaussianDropout[T] = { + new GaussianDropout[T](p, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala new file mode 100644 index 00000000000..e66e09fa979 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Apply additive zero-centered Gaussian noise. + * This is useful to mitigate overfitting (you could see it as a form of random data augmentation). + * Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs. + * As it is a regularization layer, it is only active at training time. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param sigma Double, standard deviation of the noise distribution. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class GaussianNoise[T: ClassTag]( + val sigma: Double, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.GaussianNoise(stddev = sigma) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object GaussianNoise { + def apply[@specialized(Float, Double) T: ClassTag]( + sigma: Double, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): GaussianNoise[T] = { + new GaussianNoise[T](sigma, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala new file mode 100644 index 00000000000..3c3ed2dc575 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Leaky version of a Rectified Linear Unit. + * It allows a small gradient when the unit is not active: + * `f(x) = alpha * x for x < 0`, + * `f(x) = x for x >= 0`. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param alpha Double >= 0. Negative slope coefficient. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class LeakyReLU[T: ClassTag]( + private val alpha: Double = 0.01, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.LeakyReLU( + negval = alpha, + inplace = false) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object LeakyReLU { + def apply[@specialized(Float, Double) T: ClassTag]( + alpha: Double = 0.01, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LeakyReLU[T] = { + new LeakyReLU[T](alpha, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala new file mode 100644 index 00000000000..885377234c0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Use a mask value to skip timesteps for a sequence. + * Masks a sequence by using a mask value to skip timesteps. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param maskValue Double, mask value. + * For each timestep in the input tensor (dimension #1 in the tensor), + * if all values in the input tensor at that timestep are equal to `mask_value`, + * then the timestep will masked (skipped) in all downstream layers. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class Masking[T: ClassTag]( + val maskValue: Double = 0.0, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.Masking(maskValue = maskValue) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Masking { + def apply[@specialized(Float, Double) T: ClassTag]( + maskValue: Double = 0.0, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Masking[T] = { + new Masking[T](maskValue, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala new file mode 100644 index 00000000000..f5153dd38c7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala @@ -0,0 +1,63 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * S-shaped Rectified Linear Unit. + * It follows: + * `f(x) = t^r + a^r(x - t^r) for x >= t^r`, + * `f(x) = x for t^r > x > t^l`, + * `f(x) = t^l + a^l(x - t^l) for x <= t^l`. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param SharedAxes Array of Int. The axes along which to share learnable parameters + * for the activation function. + * For example, if the incoming feature maps are from a 2D convolution + * with output shape `(batch, height, width, channels)`, + * and you wish to share parameters across space + * so that each filter only has one set of parameters, + * set `SharedAxes=Array(1,2)`. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class SReLU[T: ClassTag]( + SharedAxes: Array[Int] = null, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val shape = inputShape.toSingle().toArray + val layer = com.intel.analytics.bigdl.nn.SReLU(shape.slice(1, shape.length), SharedAxes) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object SReLU { + def apply[@specialized(Float, Double) T: ClassTag]( + SharedAxes: Array[Int] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SReLU[T] = { + new SReLU[T](SharedAxes, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala new file mode 100644 index 00000000000..6ccc2fbddb7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Spatial 1D version of Dropout. + * This version performs the same function as Dropout, however it drops + * entire 1D feature maps instead of individual elements. If adjacent frames + * within feature maps are strongly correlated (as is normally the case in + * early convolution layers) then regular dropout will not regularize the + * activations and will otherwise just result in an effective learning rate + * decrease. In this case, SpatialDropout1D will help promote independence + * between feature maps and should be used instead. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 3D. + * + * @param p Double between 0 and 1. Fraction of the input units to drop. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class SpatialDropout1D[T: ClassTag]( + val p: Double = 0.5, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.SpatialDropout1D(initP = p) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object SpatialDropout1D { + def apply[@specialized(Float, Double) T: ClassTag]( + p: Double = 0.5, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SpatialDropout1D[T] = { + new SpatialDropout1D[T](p, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala new file mode 100644 index 00000000000..4b91cf10b6f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala @@ -0,0 +1,66 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Spatial 2D version of Dropout. + * This version performs the same function as Dropout, however it drops + * entire 2D feature maps instead of individual elements. If adjacent pixels + * within feature maps are strongly correlated (as is normally the case in + * early convolution layers) then regular dropout will not regularize the + * activations and will otherwise just result in an effective learning rate + * decrease. In this case, SpatialDropout2D will help promote independence + * between feature maps and should be used instead. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 4D. + * + * @param p Double between 0 and 1. Fraction of the input units to drop. + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class SpatialDropout2D[T: ClassTag]( + val p: Double = 0.5, + val dimOrdering: DataFormat = DataFormat.NCHW, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.SpatialDropout2D( + initP = p, + format = dimOrdering) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object SpatialDropout2D { + def apply[@specialized(Float, Double) T: ClassTag]( + p: Double = 0.5, + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SpatialDropout2D[T] = { + new SpatialDropout2D[T](p, KerasUtils.toBigDLFormat(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala new file mode 100644 index 00000000000..3b66e01027f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala @@ -0,0 +1,73 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Spatial 3D version of Dropout. + * This version performs the same function as Dropout, however it drops + * entire 3D feature maps instead of individual elements. If adjacent voxels + * within feature maps are strongly correlated (as is normally the case in + * early convolution layers) then regular dropout will not regularize the + * activations and will otherwise just result in an effective learning rate + * decrease. In this case, SpatialDropout3D will help promote independence + * between feature maps and should be used instead. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * The input of this layer should be 5D. + * + * @param p Double between 0 and 1. Fraction of the input units to drop. + * @param dimOrdering Format of input data. Either 'CHANNEL_FIRST' (dimOrdering='th') or + * 'CHANNEL_LAST' (dimOrdering='tf'). Default is 'CHANNEL_FIRST'. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class SpatialDropout3D[T: ClassTag]( + val p: Double = 0.5, + val dimOrdering: String = "CHANNEL_FIRST", + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(dimOrdering.toLowerCase() == "channel_first" || + dimOrdering.toLowerCase() == "channel_last", + s"SpatialDropout3D only supports format CHANNEL_FIRST or CHANNEL_LAST," + + s" format $dimOrdering is not supported") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val format = if (dimOrdering.toLowerCase() == "channel_first") DataFormat.NCHW + else DataFormat.NHWC + val layer = com.intel.analytics.bigdl.nn.SpatialDropout3D( + initP = p, + format = format) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object SpatialDropout3D { + def apply[@specialized(Float, Double) T: ClassTag]( + p: Double = 0.5, + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SpatialDropout3D[T] = { + new SpatialDropout3D[T](p, KerasUtils.toBigDLFormat5D(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala new file mode 100644 index 00000000000..b04ea7a4b07 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala @@ -0,0 +1,59 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.nn.Threshold +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * Thresholded Rectified Linear Unit. + * It follows: + * `f(x) = x for x > theta`, + * `f(x) = 0 otherwise`. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param theta Double >= 0. Threshold location of activation. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class ThresholdedReLU[T: ClassTag]( + val theta: Double = 1.0, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = Threshold( + th = theta, + v = 0.0, + ip = false) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object ThresholdedReLU { + def apply[@specialized(Float, Double) T: ClassTag]( + theta: Double = 1.0, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ThresholdedReLU[T] = { + new ThresholdedReLU[T](theta, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala index bbc9ba31258..1b09e017b5d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -32,7 +32,7 @@ class ELU[T: ClassTag]( val alpha: Double = 1.0, val inplace: Boolean = false)( implicit ev: TensorNumeric[T]) - extends TensorModule[T] { + extends TensorModule[T] with IdentityOutputShape { val _alpha = ev.fromType[Double](alpha) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala index 266297f6a42..4a77f16db64 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -37,7 +37,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 1575781981601306833L) class GaussianDropout[T: ClassTag]( val rate: Double - )(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape{ require(rate < 1 && rate >= 0, s"rate should be in range [0,1)") val stddev: Double = Math.sqrt(rate / (1.0-rate)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala index 78525ae5974..bf59dd1c2b3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -38,7 +38,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 2590701089601246637L) class GaussianNoise[T: ClassTag]( val stddev: Double - )(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape{ override def updateOutput(input: Tensor[T]): Tensor[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala index 0c8d4557a4c..7cbfe05f8f9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -36,7 +36,7 @@ import scala.reflect.ClassTag class LeakyReLU[T: ClassTag]( private val negval: Double = 0.01, var inplace: Boolean = false)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { + implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { import LeakyReLU._ if (negval < 0) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala index 547a5022a50..0c93a0e8c03 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -28,7 +28,7 @@ import scala.reflect.ClassTag * @param maskValue mask value */ class Masking[T: ClassTag](maskValue: Double = 0.0) -(implicit ev: TensorNumeric[T]) extends TensorModule[T] { +(implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape{ val batchDim = 1 val timeDim = 2 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala index 947a47d3866..63a246e73ce 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{Initializable, TensorModule, IdentityOutputShape} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc4, TensorFunc6} import com.intel.analytics.bigdl.utils.{Engine, T, Table} @@ -39,7 +39,8 @@ import scala.reflect.ClassTag @SerialVersionUID(- 877259619727212424L) class PReLU[T: ClassTag]( val nOutputPlane: Int = 0) - (implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { + (implicit ev: TensorNumeric[T]) extends TensorModule[T] + with Initializable with IdentityOutputShape { val weight = if (nOutputPlane == 0) { Tensor[T](1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala index 691eae5f237..ee3b31cb02a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Initializable, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ @@ -48,7 +48,8 @@ import scala.reflect.ClassTag @SerialVersionUID(7173457290010080259L) class SReLU[T: ClassTag](val shape: Array[Int], val sharedAxes: Array[Int] = null)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { + implicit ev: TensorNumeric[T]) extends TensorModule[T] + with Initializable with IdentityOutputShape { import SReLU._ val weightsLen = 4 val weights: Array[Tensor[T]] = Array.fill[Tensor[T]](4)(Tensor[T]()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala index f0134862e87..23d0ccdfa1b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -37,7 +37,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 4636332259181125718L) class SpatialDropout1D[T: ClassTag]( val initP: Double = 0.5)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { + implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { var p = initP var noise = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala index 87369a5f446..627ad9b84e4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -41,7 +41,7 @@ import scala.reflect.ClassTag class SpatialDropout2D[T: ClassTag]( val initP: Double = 0.5, val format: DataFormat = DataFormat.NCHW)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { + implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { var p = initP var noise = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala index 484d76a81b1..f17f9876813 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -41,7 +41,7 @@ import scala.reflect.ClassTag class SpatialDropout3D[T: ClassTag]( val initP: Double = 0.5, val format: DataFormat = DataFormat.NCHW)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { + implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { var p = initP var noise = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala index 461f61cd7d2..012f03676cd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.Engine @@ -37,7 +37,7 @@ import scala.reflect.ClassTag @SerialVersionUID(3953292249027271493L) class Threshold[T: ClassTag]( private val th: Double = 1e-6, private val v: Double = 0.0, private val ip: Boolean = false)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] { + implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape{ var threshold = th var value = v var inPlace = ip diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ELUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ELUSpec.scala new file mode 100644 index 00000000000..465f2192701 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ELUSpec.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.ELU +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class ELUSpec extends KerasBaseSpec{ + + "ELU" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |input = np.random.uniform(0, 1, [1, 3]) + |output_tensor = ELU(1.0)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val elu = ELU[Float](1.0, inputShape = Shape(3)) + seq.add(elu) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "ELU 3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24]) + |input = np.random.random([2, 3, 24]) + |output_tensor = ELU(2.7)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val elu = ELU[Float](2.7, inputShape = Shape(3, 24)) + seq.add(elu) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianDropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianDropoutSpec.scala new file mode 100644 index 00000000000..fed9609de74 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianDropoutSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.GaussianDropout +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GaussianDropoutSpec extends KerasBaseSpec { + + "GaussianDropout forward and backward" should "work properly" in { + val seq = KSequential[Float]() + val layer = GaussianDropout[Float](0.6, inputShape = Shape(3, 4)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 4)) + val input = Tensor[Float](2, 3, 4).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianNoiseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianNoiseSpec.scala new file mode 100644 index 00000000000..3eeb43aaee9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianNoiseSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.GaussianNoise +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class GaussianNoiseSpec extends KerasBaseSpec { + + "GaussianNoise forward and backward" should "work properly" in { + val seq = KSequential[Float]() + val layer = GaussianNoise[Float](0.6, inputShape = Shape(3, 4)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 4)) + val input = Tensor[Float](2, 3, 4).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LeakyReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LeakyReLUSpec.scala new file mode 100644 index 00000000000..868cb17181a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LeakyReLUSpec.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.LeakyReLU +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class LeakyReLUSpec extends KerasBaseSpec{ + + "LeakyReLU" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |input = np.random.uniform(0, 1, [1, 3]) + |output_tensor = LeakyReLU(0.01)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val leakyrelu = LeakyReLU[Float](0.01, inputShape = Shape(3)) + seq.add(leakyrelu) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "LeakyReLU 3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24]) + |input = np.random.random([2, 3, 24]) + |output_tensor = LeakyReLU(1.27)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val leakyrelu = LeakyReLU[Float](1.27, inputShape = Shape(3, 24)) + seq.add(leakyrelu) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaskingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaskingSpec.scala new file mode 100644 index 00000000000..5e844a63ae1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaskingSpec.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.Masking +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class MaskingSpec extends KerasBaseSpec{ + + "Masking" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |input = np.random.uniform(0, 1, [1, 3]) + |output_tensor = Masking(0.0)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val masking = Masking[Float](0.0, inputShape = Shape(3)) + seq.add(masking) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "Masking 3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24]) + |input = np.random.random([2, 3, 24]) + |output_tensor = Masking(0.0)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val masking = Masking[Float](0.0, inputShape = Shape(3, 24)) + seq.add(masking) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala new file mode 100644 index 00000000000..782960ae3a9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.SReLU +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class SReLUSpec extends KerasBaseSpec{ + + "SReLU" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[2, 3]) + |input = np.random.uniform(-1, 1, [1, 2, 3]) + |output_tensor = SReLU('one', 'one')(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val srelu = SReLU[Float](null, inputShape = Shape(2, 3)) + seq.add(srelu) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "SReLU with shared axes" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24]) + |input = np.random.random([2, 3, 24]) + |output_tensor = SReLU(shared_axes=[1, 2])(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val srelu = SReLU[Float](Array(1, 2), inputShape = Shape(3, 24)) + seq.add(srelu) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout1DSpec.scala new file mode 100644 index 00000000000..61c4c8bb237 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout1DSpec.scala @@ -0,0 +1,37 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.SpatialDropout1D +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class SpatialDropout1DSpec extends KerasBaseSpec { + + "SpatialDropout1D forward and backward" should "work properly" in { + val seq = KSequential[Float]() + val layer = SpatialDropout1D[Float](0.5, inputShape = Shape(3, 4)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 4)) + val input = Tensor[Float](2, 3, 4).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout2DSpec.scala new file mode 100644 index 00000000000..199df9ec183 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout2DSpec.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.keras.SpatialDropout2D +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class SpatialDropout2DSpec extends KerasBaseSpec { + + "SpatialDropout2D NCHW forward and backward" should "work properly" in { + val seq = KSequential[Float]() + val layer = SpatialDropout2D[Float](0.5, "th", inputShape = Shape(3, 4, 5)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 4, 5)) + val input = Tensor[Float](2, 3, 4, 5).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + + "SpatialDropout2D NHWC forward and backward" should "work properly" in { + val seq = KSequential[Float]() + val layer = SpatialDropout2D[Float](0.5, "tf", inputShape = Shape(3, 4, 5)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 4, 5)) + val input = Tensor[Float](2, 3, 4, 5).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout3DSpec.scala new file mode 100644 index 00000000000..d16d73fd0d0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout3DSpec.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.keras.SpatialDropout3D +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class SpatialDropout3DSpec extends KerasBaseSpec { + + "SpatialDropout3D CHANNEL_FIRST forward and backward" should "work properly" in { + val seq = KSequential[Float]() + val layer = SpatialDropout3D[Float](0.5, "th", inputShape = Shape(3, 4, 5, 6)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 4, 5, 6)) + val input = Tensor[Float](2, 3, 4, 5, 6).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + + "SpatialDropout3D CHANNEL_LAST forward and backward" should "work properly" in { + val seq = KSequential[Float]() + val layer = SpatialDropout3D[Float](0.5, "tf", inputShape = Shape(3, 4, 5, 6)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 4, 5, 6)) + val input = Tensor[Float](2, 3, 4, 5, 6).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ThresholdedReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ThresholdedReLUSpec.scala new file mode 100644 index 00000000000..c2c8750a61d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ThresholdedReLUSpec.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.ThresholdedReLU +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class ThresholdedReLUSpec extends KerasBaseSpec{ + + "ThresholdedReLU" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |input = np.random.uniform(0, 1, [1, 3]) + |output_tensor = ThresholdedReLU(1.0)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val thresholdedrelu = ThresholdedReLU[Float](1.0, inputShape = Shape(3)) + seq.add(thresholdedrelu) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "ThresholdedReLU 3D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 24]) + |input = np.random.random([2, 3, 24]) + |output_tensor = ThresholdedReLU(2.7)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val thresholdedrelu = ThresholdedReLU[Float](2.7, inputShape = Shape(3, 24)) + seq.add(thresholdedrelu) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index 5ddf7f357d0..c2ee271ce3a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -252,6 +252,76 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(layer, input) } + "ELU serializer" should "work properly" in { + val layer = ELU[Float](2.7, inputShape = Shape(3, 24)) + layer.build(Shape(2, 3, 24)) + val input = Tensor[Float](2, 3, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "GaussianDropout serializer" should "work properly" in { + val layer = GaussianDropout[Float](0.6, inputShape = Shape(3, 4)) + layer.build(Shape(2, 3, 4)) + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "GaussianNoise serializer" should "work properly" in { + val layer = GaussianNoise[Float](0.8, inputShape = Shape(12, 24)) + layer.build(Shape(2, 12, 24)) + val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "LeakyReLU serializer" should "work properly" in { + val layer = LeakyReLU[Float](1.27, inputShape = Shape(8, 24)) + layer.build(Shape(2, 8, 24)) + val input = Tensor[Float](2, 8, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Masking serializer" should "work properly" in { + val layer = Masking[Float](0.0, inputShape = Shape(3, 12)) + layer.build(Shape(2, 3, 12)) + val input = Tensor[Float](2, 3, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "SpatialDropout1D serializer" should "work properly" in { + val layer = SpatialDropout1D[Float](0.5, inputShape = Shape(3, 4)) + layer.build(Shape(2, 3, 4)) + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "SpatialDropout2D serializer" should "work properly" in { + val layer = SpatialDropout2D[Float](0.5, "tf", inputShape = Shape(3, 64, 64)) + layer.build(Shape(2, 3, 64, 64)) + val input = Tensor[Float](2, 3, 64, 64).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "SpatialDropout3D serializer" should "work properly" in { + val layer = SpatialDropout3D[Float](0.5, "tf", inputShape = Shape(3, 4, 5, 6)) + layer.build(Shape(2, 3, 4, 5, 6)) + val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "SReLU serializer" should "work properly" in { + val layer = SReLU[Float](Array(1, 2), inputShape = Shape(4, 32)) + layer.build(Shape(2, 4, 32)) + val input = Tensor[Float](2, 4, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "ThresholdedReLU serializer" should "work properly" in { + val layer = ThresholdedReLU[Float](2.7, inputShape = Shape(3, 128)) + layer.build(Shape(2, 3, 128)) + val input = Tensor[Float](2, 3, 128).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + "GlobalMaxPooling1D serializer" should "work properly" in { val layer = GlobalMaxPooling1D[Float](inputShape = Shape(12, 24)) layer.build(Shape(2, 12, 24)) @@ -325,3 +395,4 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { } } + From f4ad582b9733aebb135d7a9fc5a6845dc4529d4e Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Thu, 8 Feb 2018 19:51:24 +0800 Subject: [PATCH 0692/1065] Keras-like API embedding, batchnorm, padding, upsampling layers (#2245) * layers * update * clean * clean * wrap * update * finish batchnorm * update batchnorm * update type * update upsampling3d * add ser test * fix style * fix * fix ut * update * update --- .../dllib/keras/BatchNormalization.scala | 88 ++++++++++++++++++ .../bigdl/dllib/keras/Embedding.scala | 67 ++++++++++++++ .../analytics/bigdl/dllib/keras/Input.scala | 9 +- .../bigdl/dllib/keras/MaxoutDense.scala | 60 ++++++++++++ .../bigdl/dllib/keras/UpSampling1D.scala | 43 +++++++++ .../bigdl/dllib/keras/UpSampling2D.scala | 51 ++++++++++ .../bigdl/dllib/keras/UpSampling3D.scala | 51 ++++++++++ .../bigdl/dllib/keras/ZeroPadding1D.scala | 55 +++++++++++ .../bigdl/dllib/keras/ZeroPadding2D.scala | 80 ++++++++++++++++ .../analytics/bigdl/dllib/nn/Maxout.scala | 9 +- .../bigdl/dllib/nn/UpSampling1D.scala | 8 ++ .../bigdl/dllib/nn/UpSampling2D.scala | 13 +++ .../bigdl/dllib/nn/UpSampling3D.scala | 8 ++ .../dllib/utils/python/api/PythonBigDL.scala | 9 +- .../utils/python/api/PythonBigDLKeras.scala | 78 ++++++++++++---- .../bigdl/dllib/keras/UpSampling1DSpec.scala | 10 +- .../bigdl/dllib/keras/UpSampling2DSpec.scala | 16 +++- .../bigdl/dllib/keras/UpSampling3DSpec.scala | 15 ++- .../keras/nn/BatchNormalizationSpec.scala | 38 ++++++++ .../bigdl/dllib/keras/nn/EmbeddingSpec.scala | 45 +++++++++ .../dllib/keras/nn/MaxoutDenseSpec.scala | 73 +++++++++++++++ .../dllib/keras/nn/UpSampling1DSpec.scala | 57 ++++++++++++ .../dllib/keras/nn/UpSampling2DSpec.scala | 58 ++++++++++++ .../dllib/keras/nn/UpSampling3DSpec.scala | 57 ++++++++++++ .../dllib/keras/nn/ZeroPadding1DSpec.scala | 59 ++++++++++++ .../dllib/keras/nn/ZeroPadding2DSpec.scala | 92 +++++++++++++++++++ .../analytics/bigdl/dllib/nn/MaxoutSpec.scala | 15 ++- .../KerasModuleSerializerSpec.scala | 65 ++++++++++++- 28 files changed, 1189 insertions(+), 40 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding1D.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding2D.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BatchNormalizationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/EmbeddingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxoutDenseSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding2DSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala new file mode 100644 index 00000000000..359a4daa358 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala @@ -0,0 +1,88 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class BatchNormalization[T: ClassTag]( + val epsilon: Double = 0.001, + val momentum: Double = 0.99, + val betaInit: String = "zero", + val gammaInit: String = "one", + val dimOrdering: DataFormat = DataFormat.NCHW, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + private def getInit(init: String, n: Int): Tensor[T] = { + val weights = Tensor[T](n) + init.toLowerCase() match { + case "zero" => weights.fill(ev.zero) + case "one" => weights.fill(ev.one) + case "glorot_uniform" => Xavier.init(weights) + weights + case "uniform" => RandomUniform(-0.05, 0.05).init(weights) + weights + case "normal" => RandomNormal(0.0, 0.05).init(weights) + weights + case _ => throw new IllegalArgumentException(s"Unsupported initialization method: " + + s"${init.toLowerCase()}") + } + } + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"BatchNormalization requires 4D input, but got input dim ${input.length}") + inputShape + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val nChannel = dimOrdering match { + case DataFormat.NCHW => input(1) + case DataFormat.NHWC => input(3) + } + // TODO: support arbitrary input shape + val layer = SpatialBatchNormalization( + nOutput = nChannel, + eps = epsilon, + momentum = momentum, + initWeight = getInit(gammaInit, nChannel), + initBias = getInit(betaInit, nChannel), + dataFormat = dimOrdering) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object BatchNormalization { + def apply[@specialized(Float, Double) T: ClassTag]( + epsilon: Double = 0.001, + momentum: Double = 0.99, + betaInit: String = "zero", + gammaInit: String = "one", + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): BatchNormalization[T] = { + new BatchNormalization[T](epsilon, momentum, betaInit, gammaInit, + KerasUtils.toBigDLFormat(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala new file mode 100644 index 00000000000..bc32a6a4793 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.nn.{AddConstant, InitializationMethod, LookupTable, RandomUniform, Zeros, Sequential => TSequential} + +import scala.reflect.ClassTag + +class Embedding[T: ClassTag]( + val inputDim: Int, + val outputDim: Int, + val init: InitializationMethod = RandomUniform, + var wRegularizer: Regularizer[T] = null, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 2, + s"Embedding requires 2D input, but got input dim ${input.length}") + Shape(input(0), input(1), outputDim) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val model = TSequential[T]() + model.add(AddConstant(1.0)) + val layer = LookupTable( + nIndex = inputDim, + nOutput = outputDim, + wRegularizer = wRegularizer) + layer.setInitMethod(weightInitMethod = init) + model.add(layer) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Embedding { + def apply[@specialized(Float, Double) T: ClassTag]( + inputDim: Int, + outputDim: Int, + init: String = "uniform", + wRegularizer: Regularizer[T] = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Embedding[T] = { + new Embedding[T](inputDim, outputDim, KerasUtils.getInitMethod(init), + wRegularizer, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala index 0ad08ba90e7..9df760eaeba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala @@ -42,7 +42,8 @@ class Input[T: ClassTag](val inputShape: Shape)(implicit ev: TensorNumeric[T]) } object Input { - def apply[T: ClassTag](name : String = null, + def apply[T: ClassTag]( + name : String = null, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ModuleNode[T] = { val module = new Input(inputShape) if (name != null) { @@ -53,9 +54,9 @@ object Input { } object InputLayer { - def apply[T: ClassTag](name : String = null, - inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - : Input[T] = { + def apply[T: ClassTag]( + name : String = null, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Input[T] = { val module = new Input(inputShape) if (name != null) { module.setName(name) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala new file mode 100644 index 00000000000..ee9b7cbb3ae --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.Maxout +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class MaxoutDense[T: ClassTag]( + val outputDim: Int, + val nbFeature: Int = 4, + val wRegularizer: Regularizer[T] = null, + var bRegularizer: Regularizer[T] = null, + val bias: Boolean = true, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val layer = Maxout( + inputSize = input(1), + outputSize = outputDim, + maxoutNumber = nbFeature, + withBias = bias, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object MaxoutDense { + def apply[@specialized(Float, Double) T: ClassTag]( + outputDim: Int, + nbFeature: Int = 4, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): MaxoutDense[T] = { + new MaxoutDense[T](outputDim, nbFeature, wRegularizer, bRegularizer, bias, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala new file mode 100644 index 00000000000..de38cb27c4f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class UpSampling1D[T: ClassTag]( + val length: Int = 2, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.UpSampling1D(length) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object UpSampling1D { + def apply[@specialized(Float, Double) T: ClassTag]( + length: Int = 2, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): UpSampling1D[T] = { + new UpSampling1D[T](length, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala new file mode 100644 index 00000000000..8e650a08425 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class UpSampling2D[T: ClassTag]( + val size: Array[Int] = Array(2, 2), + val dimOrdering: DataFormat = DataFormat.NCHW, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(size.length == 2, + s"UpSampling2D: upsampling sizes should be of length 2, but got ${size.length}") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.UpSampling2D( + size = Array(size(0), size(1)), + format = dimOrdering) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object UpSampling2D { + def apply[@specialized(Float, Double) T: ClassTag]( + size: (Int, Int) = (2, 2), + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): UpSampling2D[T] = { + new UpSampling2D[T](Array(size._1, size._2), + KerasUtils.toBigDLFormat(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala new file mode 100644 index 00000000000..2cfb896f824 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class UpSampling3D[T: ClassTag]( + val size: Array[Int] = Array(2, 2, 2), + val dimOrdering: String = "CHANNEL_FIRST", + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(dimOrdering.toLowerCase() == "channel_first", + s"UpSampling3D currently only supports format CHANNEL_FIRST, but got format $dimOrdering") + require(size.length == 3, + s"UpSampling3D: upsampling sizes should be of length 3, but got ${size.length}") + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = com.intel.analytics.bigdl.nn.UpSampling3D(size) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object UpSampling3D { + def apply[@specialized(Float, Double) T: ClassTag]( + size: (Int, Int, Int) = (2, 2, 2), + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): UpSampling3D[T] = { + new UpSampling3D[T](Array(size._1, size._2, size._3), + KerasUtils.toBigDLFormat5D(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding1D.scala new file mode 100644 index 00000000000..68c325f2c9c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding1D.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.SpatialZeroPadding +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class ZeroPadding1D[T: ClassTag]( + val padding: Array[Int] = Array(1, 1), + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(padding.length == 2, + s"For ZeroPadding1D, padding values should be of length 2 " + + s"(left_pad, right_pad), but got length ${padding.length}") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"ZeroPadding1D requires 3D input, but got input dim ${input.length}") + Shape(input(0), input(1) + padding(0) + padding(1), input(2)) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val layer = SpatialZeroPadding(0, 0, padding(0), padding(1)) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object ZeroPadding1D { + def apply[@specialized(Float, Double) T: ClassTag]( + padding: Int = 1, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ZeroPadding1D[T] = { + new ZeroPadding1D[T](Array(padding, padding), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding2D.scala new file mode 100644 index 00000000000..a68b4912d1d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding2D.scala @@ -0,0 +1,80 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.Padding +import com.intel.analytics.bigdl.nn.{Sequential => TSequential} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +class ZeroPadding2D[T: ClassTag]( + val padding: Array[Int] = Array(1, 1, 1, 1), + val dimOrdering: DataFormat = DataFormat.NCHW, + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + require(padding.length == 4, + s"For ZeroPadding2D, padding values should be of length 4 " + + s"(top_pad, bottom_pad, left_pad, right_pad), but got length ${padding.length}") + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"ZeroPadding2D requires 4D input, but got input dim ${input.length}") + dimOrdering match { + case DataFormat.NCHW => + Shape(input(0), input(1), + input(2) + padding(0) + padding(1), input(3) + padding(2) + padding(3)) + case DataFormat.NHWC => + Shape(input(0), input(1) + padding(0) + padding(1), + input(2) + padding(2) + padding(3), input(3)) + } + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val nInputDim = input.length -1 + val (dim1, dim2) = dimOrdering match { + case DataFormat.NCHW => (2, 3) + case DataFormat.NHWC => (1, 2) + } + val model = TSequential[T]() + val pad1 = Padding(dim1, -padding(0), nInputDim) + val pad2 = Padding(dim1, padding(1), nInputDim) + val pad3 = Padding(dim2, -padding(2), nInputDim) + val pad4 = Padding(dim2, padding(3), nInputDim) + model.add(pad1) + model.add(pad2) + model.add(pad3) + model.add(pad4) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object ZeroPadding2D { + def apply[@specialized(Float, Double) T: ClassTag]( + padding: (Int, Int) = (1, 1), + dimOrdering: String = "th", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ZeroPadding2D[T] = { + new ZeroPadding2D[T](Array(padding._1, padding._1, padding._2, padding._2), + KerasUtils.toBigDLFormat(dimOrdering), inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala index 43af792d1dd..5d723b2e144 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{Shape, Table} import scala.reflect.ClassTag @@ -51,6 +51,13 @@ class Maxout[T: ClassTag](val inputSize: Int, val outputSize: Int, val maxoutNum .add(View(maxoutNumber, outputSize).setNumInputDims(1)) .add(Max(1, 2)) + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 2, + s"MaxoutDense requires 2D input, but got input dim ${input.length}") + Shape(input(0), outputSize) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { output = layer.updateOutput(input) output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala index b72a059b26f..86f473363ea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala @@ -21,6 +21,7 @@ import java.util import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -39,6 +40,13 @@ class UpSampling1D[T: ClassTag] (val length: Int) require(length > 0, "UpSampling1D's length should be bigger than 0," + s"but got $length") + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 3, + s"UpSampling1D requires 3D input, but got input dim ${input.length}") + Shape(input(0), input(1) * length, input(2)) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 3, "UpSampling1D only supports 3D input") require(input.isContiguous(), "input should be contiguous") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2D.scala index 2c3709c0039..ee9a19472a7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling2D.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{DataFormat, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -39,6 +40,18 @@ class UpSampling2D[T: ClassTag] (val size: Array[Int], val format: DataFormat = require(size(0) > 0 && size(1) > 0, "UpSampling2D's size should be bigger than 0," + s"but got ${size.mkString("x")}") + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 4, + s"UpSampling2D requires 4D input, but got input dim ${input.length}") + format match { + case DataFormat.NCHW => + Shape(input(0), input(1), input(2)*size(0), input(3)*size(1)) + case DataFormat.NHWC => + Shape(input(0), input(1)*size(0), input(2)*size(1), input(3)) + } + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 4, "UpSampling2D only supports 4D input") require(input.isContiguous(), "input should be contiguous") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3D.scala index 3505114dc48..889feed3061 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3D.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -38,6 +39,13 @@ class UpSampling3D[T: ClassTag](val size: Array[Int]) require(size != null && size.length == 3, "the size should be 3 dims") + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length == 5, + s"UpSampling3D requires 5D input, but got input dim ${input.length}") + Shape(input(0), input(1), input(2)*size(0), input(3)*size(1), input(4)*size(2)) + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 5, "only supports 5d tensors") require(input.isContiguous(), "input need to be contiguous") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 87aaa08f739..ad6aea108de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -262,8 +262,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab optimizer } - def createSequential(): Sequential[T] = { - Sequential[T]() + def createSequential(isKeras: Boolean = false): Sequential[T] = { + if (isKeras) { + nn.keras.Sequential[T]() + } + else { + Sequential[T]() + } } def createLinear(inputSize: Int, outputSize: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala index 002bc510cc7..0c6d2126b4c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -16,14 +16,10 @@ package com.intel.analytics.bigdl.python.api -import java.lang.{Boolean => JBoolean} import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} -import com.intel.analytics.bigdl.dataset.{Identity => DIdentity, Sample => JSample} -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule -import com.intel.analytics.bigdl.nn.keras.Dense -import com.intel.analytics.bigdl.nn.tf.{Shape => TfShape} -import com.intel.analytics.bigdl.nn.{InitializationMethod, RandomUniform} +import com.intel.analytics.bigdl.nn.SpatialBatchNormalization +import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.numeric._ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -51,19 +47,61 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho } } - def createKerasDense(outputDim: Int, - init: InitializationMethod = RandomUniform, - activation: TensorModule[T] = null, - wRegularizer: Regularizer[T] = null, - bRegularizer: Regularizer[T] = null, - bias: Boolean = true, - inputShape: JList[Int] = null): Dense[T] = { - new Dense(outputDim, - init, - activation, - wRegularizer, - bRegularizer, - bias, - toScalaShape(inputShape)) + def createKerasInputLayer( + inputShape: JList[Int] = null): Input[T] = { + InputLayer(inputShape = toScalaShape(inputShape)) } + + def createKerasDense( + outputDim: Int, + init: String = "glorot_uniform", + activation: String = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): Dense[T] = { + Dense(outputDim, init, activation, wRegularizer, + bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasEmbedding( + inputDim: Int, + outputDim: Int, + init: String = "uniform", + wRegularizer: Regularizer[T] = null, + inputShape: JList[Int] = null): Embedding[T] = { + Embedding[T](inputDim, outputDim, init, wRegularizer, toScalaShape(inputShape)) + } + + def createKerasBatchNormalization( + epsilon: Double = 0.001, + momentum: Double = 0.99, + betaInit: String = "zero", + gammaInit: String = "one", + dimOrdering: String = "th", + inputShape: JList[Int] = null): BatchNormalization[T] = { + BatchNormalization[T](epsilon, momentum, betaInit, + gammaInit, dimOrdering, toScalaShape(inputShape)) + } + + def setKerasRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit = { + module.labor.asInstanceOf[SpatialBatchNormalization[T]] + .runningMean.set(toTensor(runningMean)) + } + + def setKerasRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit = { + module.labor.asInstanceOf[SpatialBatchNormalization[T]] + .runningVar.set(toTensor(runningStd)) + } + + def getKerasRunningMean(module: BatchNormalization[T]): JTensor = { + toJTensor(module.labor.asInstanceOf[SpatialBatchNormalization[T]] + .runningMean) + } + + def getKerasRunningStd(module: BatchNormalization[T]): JTensor = { + toJTensor(module.labor.asInstanceOf[SpatialBatchNormalization[T]] + .runningVar) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1DSpec.scala index cc5f56828dc..b72cc302b3c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1DSpec.scala @@ -17,9 +17,10 @@ package com.intel.analytics.bigdl.keras import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.utils.{Shape, TestUtils} class UpSampling1DSpec extends KerasBaseSpec { - "updample1d forward with size 1" should "work properly" in { + "UpSampling1D forward with size 1" should "work properly" in { val kerasCode = """ |input_tensor = Input(shape=[3, 4]) @@ -31,7 +32,7 @@ class UpSampling1DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } - "updample1d forward with size 2" should "work properly" in { + "UpSampling1D forward with size 2" should "work properly" in { val kerasCode = """ |input_tensor = Input(shape=[3, 4]) @@ -43,4 +44,9 @@ class UpSampling1DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } + "UpSampling1D computeOutputShape" should "work properly" in { + val layer = UpSampling1D[Float](3) + TestUtils.compareOutputShape(layer, Shape(4, 5)) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2DSpec.scala index 31e850248a5..55a8162227a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2DSpec.scala @@ -18,9 +18,11 @@ package com.intel.analytics.bigdl.keras import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.TestUtils class UpSampling2DSpec extends KerasBaseSpec { - "updample2D nchw" should "work properly" in { + "UpSampling2D nchw" should "work properly" in { val kerasCode = """ |input_tensor = Input(shape=[5, 3, 4]) @@ -32,7 +34,7 @@ class UpSampling2DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } - "updample2D nhwc" should "work properly" in { + "UpSampling2D nhwc" should "work properly" in { val kerasCode = """ |input_tensor = Input(shape=[3, 4, 5]) @@ -44,4 +46,14 @@ class UpSampling2DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } + "UpSampling2D computeOutputShape NCHW" should "work properly" in { + val layer = UpSampling2D[Float](Array(1, 2)) + TestUtils.compareOutputShape(layer, Shape(3, 4, 5)) should be (true) + } + + "UpSampling2D computeOutputShape NHWC" should "work properly" in { + val layer = UpSampling2D[Float](Array(3, 3), format = DataFormat.NHWC) + TestUtils.compareOutputShape(layer, Shape(8, 12, 2)) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala index aaab4065f1f..adc34552239 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3DSpec.scala @@ -18,9 +18,10 @@ package com.intel.analytics.bigdl.keras import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{Shape, TestUtils} class UpSampling3DSpec extends KerasBaseSpec { - "updample3d forward with size 1" should "work properly" in { + "UpSampling3D forward with size 1" should "work properly" in { val kerasCode = """ |input_tensor = Input(shape=[1, 2, 3, 4]) @@ -32,7 +33,7 @@ class UpSampling3DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } - "updample3d forward with size 2" should "work properly" in { + "UpSampling3D forward with size 2" should "work properly" in { val kerasCode = """ |input_tensor = Input(shape=[1, 1, 2, 4]) @@ -44,7 +45,7 @@ class UpSampling3DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } - "updample3d forward with size 2, 3, 4" should "work properly" in { + "UpSampling3D forward with size 2, 3, 4" should "work properly" in { val kerasCode = """ |input_tensor = Input(shape=[2, 3, 2, 4]) @@ -56,7 +57,7 @@ class UpSampling3DSpec extends KerasBaseSpec { checkOutputAndGrad(model, kerasCode) } - "updample3d serializer" should "work properly" in { + "UpSampling3D serializer" should "work properly" in { val module = UpSampling3D[Float](Array(2, 2, 2)) val input = Tensor[Float](1, 2, 2, 2, 2).randn() @@ -70,4 +71,10 @@ class UpSampling3DSpec extends KerasBaseSpec { tmpFile.delete() } } + + "UpSampling3D computeOutputShape" should "work properly" in { + val layer = UpSampling3D[Float](Array(2, 1, 3)) + TestUtils.compareOutputShape(layer, Shape(3, 8, 12, 8)) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BatchNormalizationSpec.scala new file mode 100644 index 00000000000..85eed35e53a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BatchNormalizationSpec.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{BatchNormalization, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class BatchNormalizationSpec extends KerasBaseSpec { + + // Compared results with Keras on Python side + "BatchNormalization" should "work properly" in { + val seq = KSequential[Float]() + val layer = BatchNormalization[Float](betaInit = "glorot_uniform", + gammaInit = "normal", inputShape = Shape(3, 12, 12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 3, 12, 12)) + val input = Tensor[Float](2, 3, 12, 12).rand() + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/EmbeddingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/EmbeddingSpec.scala new file mode 100644 index 00000000000..a674cdc0a72 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/EmbeddingSpec.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Embedding, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class EmbeddingSpec extends KerasBaseSpec { + + // Compared results with Keras on Python side + "Embedding" should "work properly" in { + val seq = KSequential[Float]() + val layer = Embedding[Float](1000, 32, inputShape = Shape(4)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4, 32)) + val input = Tensor[Float](2, 4) + input(Array(1, 1)) = 1 + input(Array(1, 2)) = 2 + input(Array(1, 3)) = 4 + input(Array(1, 4)) = 5 + input(Array(2, 1)) = 4 + input(Array(2, 2)) = 3 + input(Array(2, 3)) = 2 + input(Array(2, 4)) = 6 + val output = seq.forward(input) + val gradInput = seq.backward(input, output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxoutDenseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxoutDenseSpec.scala new file mode 100644 index 00000000000..81e587dc1d0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxoutDenseSpec.scala @@ -0,0 +1,73 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{MaxoutDense, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class MaxoutDenseSpec extends KerasBaseSpec { + + def weightConverter(data: Array[Tensor[Float]]): Array[Tensor[Float]] = { + val out = new Array[Tensor[Float]](data.length) + out(0) = Tensor(12, 32) + val weight = out.head.storage().array() + var index = 0 + for (i <- 1 to 4) { + val sliceW = data(0).select(1, i).t.clone().storage().array() + System.arraycopy(sliceW, 0, weight, index, sliceW.size) + index += sliceW.size + } + if (data.length > 1) { + out(1) = data(1) + } + out + } + + "MaxoutDense" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12]) + |input = np.random.random([4, 12]) + |output_tensor = MaxoutDense(8)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = MaxoutDense[Float](8, inputShape = Shape(12)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "MaxoutDense without bias" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12]) + |input = np.random.random([4, 12]) + |output_tensor = MaxoutDense(8, bias=False)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = MaxoutDense[Float](8, bias = false, inputShape = Shape(12)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling1DSpec.scala new file mode 100644 index 00000000000..aab0e09d91e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling1DSpec.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{UpSampling1D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class UpSampling1DSpec extends KerasBaseSpec { + + "UpSampling1D with length 2" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = UpSampling1D()(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = UpSampling1D[Float](inputShape = Shape(4, 5)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "UpSampling1D with length 3" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4]) + |input = np.random.random([1, 3, 4]) + |output_tensor = UpSampling1D(3)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = UpSampling1D[Float](3, inputShape = Shape(3, 4)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling2DSpec.scala new file mode 100644 index 00000000000..0bc0f7af5cf --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling2DSpec.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{UpSampling2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class UpSampling2DSpec extends KerasBaseSpec { + + "UpSampling2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 8, 8]) + |input = np.random.random([2, 4, 8, 8]) + |output_tensor = UpSampling2D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = UpSampling2D[Float](inputShape = Shape(4, 8, 8)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "UpSampling2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[12, 14, 3]) + |input = np.random.random([1, 12, 14, 3]) + |output_tensor = UpSampling2D(size=(1, 3), dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = UpSampling2D[Float](size = (1, 3), dimOrdering = "tf", + inputShape = Shape(12, 14, 3)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling3DSpec.scala new file mode 100644 index 00000000000..18c90e7f88f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling3DSpec.scala @@ -0,0 +1,57 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{UpSampling3D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class UpSampling3DSpec extends KerasBaseSpec { + + "UpSampling3D with default size" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 8, 10, 12]) + |input = np.random.random([2, 3, 8, 10, 12]) + |output_tensor = UpSampling3D(dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = UpSampling3D[Float](inputShape = Shape(3, 8, 10, 12)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "UpSampling3D with different sizes" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[2, 12, 12, 12]) + |input = np.random.random([2, 2, 12, 12, 12]) + |output_tensor = UpSampling3D(size=(2, 1, 3), dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = UpSampling3D[Float](size = (2, 1, 3), inputShape = Shape(2, 12, 12, 12)) + seq.add(layer) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding1DSpec.scala new file mode 100644 index 00000000000..34d29b8406f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding1DSpec.scala @@ -0,0 +1,59 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{ZeroPadding1D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class ZeroPadding1DSpec extends KerasBaseSpec { + + "ZeroPadding1D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 5]) + |input = np.random.random([2, 4, 5]) + |output_tensor = ZeroPadding1D(padding=2)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = ZeroPadding1D[Float](padding = 2, inputShape = Shape(4, 5)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 8, 5)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "ZeroPadding1D asymmetric" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 3]) + |input = np.random.random([2, 3, 3]) + |output_tensor = ZeroPadding1D(padding=(2, 3))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = new ZeroPadding1D[Float](padding = Array(2, 3), inputShape = Shape(3, 3)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 8, 3)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding2DSpec.scala new file mode 100644 index 00000000000..0ce15093d1d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding2DSpec.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.keras.{ZeroPadding2D, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class ZeroPadding2DSpec extends KerasBaseSpec { + + "ZeroPadding2D NCHW" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[2, 8, 8]) + |input = np.random.random([3, 2, 8, 8]) + |output_tensor = ZeroPadding2D(padding=(2, 1), dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = ZeroPadding2D[Float](padding = (2, 1), inputShape = Shape(2, 8, 8)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 2, 12, 10)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "ZeroPadding2D NCHW asymmetric" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[2, 4, 5]) + |input = np.random.random([3, 2, 4, 5]) + |output_tensor = ZeroPadding2D(padding=(2, 1, 3, 2), dim_ordering="th")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = new ZeroPadding2D[Float](padding = Array(2, 1, 3, 2), inputShape = Shape(2, 4, 5)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 2, 7, 10)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "ZeroPadding2D NHWC" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[6, 8, 1]) + |input = np.random.random([3, 6, 8, 1]) + |output_tensor = ZeroPadding2D(dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = ZeroPadding2D[Float](dimOrdering = "tf", inputShape = Shape(6, 8, 1)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 8, 10, 1)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + + "ZeroPadding2D NHWC asymmetric" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[5, 5, 2]) + |input = np.random.random([3, 5, 5, 2]) + |output_tensor = ZeroPadding2D(padding=(1, 2, 3, 4), dim_ordering="tf")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = new ZeroPadding2D[Float](padding = Array(1, 2, 3, 4), dimOrdering = DataFormat.NHWC, + inputShape = Shape(5, 5, 2)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 8, 12, 2)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala index 735dbad096e..b822ba085d2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.keras import com.intel.analytics.bigdl.nn.Maxout import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{Shape, TestUtils} class MaxoutSpec extends KerasBaseSpec { "Maxout" should "generate corrent result when batchsize == 1" in { @@ -26,7 +27,7 @@ class MaxoutSpec extends KerasBaseSpec { val maxoutNumber = 3 val batchSize = 1 - val sigmoidCode = + val kerasCode = s""" |input_tensor = Input(shape=[${inputSize}]) |input = np.random.uniform(0, 1, [${batchSize}, ${inputSize}]) @@ -53,7 +54,7 @@ class MaxoutSpec extends KerasBaseSpec { } out } - checkOutputAndGrad(maxout, sigmoidCode, weightConverter = wc) + checkOutputAndGrad(maxout, kerasCode, weightConverter = wc) } "Maxout" should "generate corrent result when batchsize != 1" in { @@ -62,7 +63,7 @@ class MaxoutSpec extends KerasBaseSpec { val maxoutNumber = 3 val batchSize = 4 - val sigmoidCode = + val kerasCode = s""" |#w1 = np.array([[[1.0, 2.0, 3.0, 4.0], |# [5, 6, 7, 8.0]], @@ -101,6 +102,12 @@ class MaxoutSpec extends KerasBaseSpec { } out } - checkOutputAndGrad(maxout, sigmoidCode, weightConverter = wc) + checkOutputAndGrad(maxout, kerasCode, weightConverter = wc) } + + "Maxout computeOutputShape" should "work properly" in { + val layer = Maxout[Float](4, 5, 3) + TestUtils.compareOutputShape(layer, Shape(4)) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index c2ee271ce3a..a58e2881f18 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -394,5 +394,68 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(layer, input) } -} + "Embedding serializer" should "work properly" in { + val layer = Embedding[Float](1000, 32, inputShape = Shape(4)) + layer.build(Shape(2, 4)) + val input = Tensor[Float](2, 4) + input(Array(1, 1)) = 1 + input(Array(1, 2)) = 2 + input(Array(1, 3)) = 4 + input(Array(1, 4)) = 5 + input(Array(2, 1)) = 4 + input(Array(2, 2)) = 3 + input(Array(2, 3)) = 2 + input(Array(2, 4)) = 6 + runSerializationTest(layer, input) + } + + "BatchNormalization serializer" should "work properly" in { + val layer = BatchNormalization[Float](inputShape = Shape(3, 12, 12)) + layer.build(Shape(2, 3, 12, 12)) + val input = Tensor[Float](2, 3, 12, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + "ZeroPadding1D serializer" should "work properly" in { + val layer = ZeroPadding1D[Float](padding = 2, inputShape = Shape(4, 5)) + layer.build(Shape(2, 4, 5)) + val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "ZeroPadding2D serializer" should "work properly" in { + val layer = ZeroPadding2D[Float](padding = (2, 1), inputShape = Shape(2, 8, 8)) + layer.build(Shape(2, 2, 8, 8)) + val input = Tensor[Float](2, 2, 8, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "UpSampling1D serializer" should "work properly" in { + val layer = UpSampling1D[Float](inputShape = Shape(4, 5)) + layer.build(Shape(2, 4, 5)) + val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "UpSampling2D serializer" should "work properly" in { + val layer = UpSampling2D[Float](inputShape = Shape(4, 8, 8)) + layer.build(Shape(2, 4, 8, 8)) + val input = Tensor[Float](2, 4, 8, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "UpSampling3D serializer" should "work properly" in { + val layer = UpSampling3D[Float](inputShape = Shape(3, 8, 10, 12)) + layer.build(Shape(2, 3, 8, 10, 12)) + val input = Tensor[Float](2, 3, 8, 10, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "MaxoutDense serializer" should "work properly" in { + val layer = MaxoutDense[Float](8, inputShape = Shape(12)) + layer.build(Shape(3, 12)) + val input = Tensor[Float](3, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + +} From 89ea780195062867a0435834794616be9283f298 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Thu, 8 Feb 2018 13:21:05 -0800 Subject: [PATCH 0693/1065] move DF API to dlframe (#2269) * move DF API to dlframe * remove extra lines * deprecate * extra line --- .../bigdl/dlframes/DLClassifier.scala | 83 ++++ .../bigdl/dlframes/DLEstimator.scala | 440 ++++++++++++++++++ .../bigdl/dlframes/SharedParamsAdapter.scala | 43 ++ .../org/apache/spark/ml/DLClassifier.scala | 4 + .../org/apache/spark/ml/DLEstimator.scala | 10 +- .../org/apache/spark/ml/DLEstimatorBase.scala | 2 +- .../apache/spark/ml/DLTransformerBase.scala | 2 +- .../org/apache/spark/ml/DLEstimatorBase.scala | 2 +- .../apache/spark/ml/DLTransformerBase.scala | 2 +- .../MLPipeline/DLClassifierLeNet.scala | 2 +- .../DLClassifierLogisticRegression.scala | 2 +- .../MLPipeline/DLEstimatorMultiLabelLR.scala | 2 +- .../imageclassification/ImagePredictor.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 4 +- .../dllib/dlframes/DLClassifierSpec.scala | 223 +++++++++ .../dllib/dlframes/DLEstimatorSpec.scala | 348 ++++++++++++++ .../bigdl/dllib/optim/DLClassifierSpec.scala | 2 + .../bigdl/dllib/optim/DLEstimatorSpec.scala | 2 + 18 files changed, 1161 insertions(+), 14 deletions(-) create mode 100644 dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala create mode 100644 dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLEstimator.scala create mode 100644 dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala new file mode 100644 index 00000000000..dcda292378b --- /dev/null +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala @@ -0,0 +1,83 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.dlframes + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.{Criterion, Module} +import org.apache.spark.ml.adapter.SchemaUtils +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.util.Identifiable +import org.apache.spark.sql.types._ + +import scala.reflect.ClassTag + +/** + * [[DLClassifier]] is a specialized [[DLEstimator]] that simplifies the data format for + * classification tasks. It only supports label column of DoubleType. + * and the fitted [[DLClassifierModel]] will have the prediction column of DoubleType. + * + * @param model BigDL module to be optimized + * @param criterion BigDL criterion method + * @param featureSize The size (Tensor dimensions) of the feature data. + */ +class DLClassifier[@specialized(Float, Double) T: ClassTag]( + @transient override val model: Module[T], + override val criterion : Criterion[T], + override val featureSize : Array[Int], + override val uid: String = Identifiable.randomUID("dlClassifier") + )(implicit ev: TensorNumeric[T]) + extends DLEstimator[T](model, criterion, featureSize, Array(1)) { + + override protected def wrapBigDLModel( + m: Module[T], featureSize: Array[Int]): DLClassifierModel[T] = { + val dlModel = new DLClassifierModel[T](m, featureSize) + copyValues(dlModel.setParent(this)).asInstanceOf[DLClassifierModel[T]] + } + + override def transformSchema(schema : StructType): StructType = { + validateParams(schema) + SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) + } + + override def copy(extra: ParamMap): DLClassifier[T] = { + copyValues(new DLClassifier(model, criterion, featureSize), extra) + } +} + +/** + * [[DLClassifierModel]] is a specialized [[DLModel]] for classification tasks. + * The prediction column will have the datatype of Double. + * + * @param model BigDL module to be optimized + * @param featureSize The size (Tensor dimensions) of the feature data. + */ +class DLClassifierModel[@specialized(Float, Double) T: ClassTag]( + @transient override val model: Module[T], + featureSize : Array[Int], + override val uid: String = "DLClassifierModel" + )(implicit ev: TensorNumeric[T]) extends DLModel[T](model, featureSize) { + + protected override def outputToPrediction(output: Tensor[T]): Any = { + ev.toType[Double](output.max(1)._2.valueAt(1)) + } + + override def transformSchema(schema : StructType): StructType = { + validateDataType(schema, $(featuresCol)) + SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) + } +} + diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLEstimator.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLEstimator.scala new file mode 100644 index 00000000000..fd83bd694c1 --- /dev/null +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLEstimator.scala @@ -0,0 +1,440 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.dlframes + +import com.intel.analytics.bigdl.{Criterion, Module} +import com.intel.analytics.bigdl.dataset._ +import com.intel.analytics.bigdl.models.utils.ModelBroadcast +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} +import org.apache.spark.ml.adapter.{HasFeaturesCol, HasPredictionCol, SchemaUtils} +import org.apache.spark.ml.{DLEstimatorBase, DLTransformerBase, VectorCompatibility} +import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators, _} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, Row} + +import scala.reflect.ClassTag + +private[dlframes] trait HasBatchSize extends Params { + + final val batchSize: Param[Int] = new Param[Int](this, "batchSize", "batchSize") + + def getBatchSize: Int = $(batchSize) +} + +/** + * Common trait for DLEstimator and DLModel + */ +private[dlframes] trait DLParams[@specialized(Float, Double) T] extends HasFeaturesCol + with HasPredictionCol with VectorCompatibility with HasBatchSize { + + /** + * When to stop the training, passed in a [[Trigger]]. E.g. Trigger.maxIterations + */ + final val endWhen = new Param[Trigger](this, "endWhen", "Trigger to stop the training") + + def getEndWhen: Trigger = $(endWhen) + + /** + * learning rate for the optimizer in the DLEstimator. + * Default: 0.001 + */ + final val learningRate = new DoubleParam( + this, "learningRate", "learningRate", ParamValidators.gt(0)) + + def getLearningRate: Double = $(learningRate) + + /** + * learning rate decay for each iteration. + * Default: 0 + */ + final val learningRateDecay = new DoubleParam(this, "learningRateDecay", "learningRateDecay") + + def getLearningRateDecay: Double = $(learningRateDecay) + + /** + * Number of max Epoch for the training, an epoch refers to a traverse over the training data + * Default: 50 + */ + final val maxEpoch = new IntParam(this, "maxEpoch", "number of max Epoch", ParamValidators.gt(0)) + + def getMaxEpoch: Int = $(maxEpoch) + + /** + * optimization method to be used. BigDL supports many optimization methods like Adam, + * SGD and LBFGS. Refer to package com.intel.analytics.bigdl.optim for all the options. + * Default: SGD + */ + final val optimMethod = new Param[OptimMethod[T]](this, "optimMethod", "optimMethod") + + def getOptimMethod: OptimMethod[T] = $(optimMethod) + + setDefault(batchSize -> 1) + + /** + * Validate if feature and label columns are of supported data types. + * Default: 0 + */ + protected def validateDataType(schema: StructType, colName: String): Unit = { + val dataTypes = Seq( + new ArrayType(DoubleType, false), + new ArrayType(DoubleType, true), + new ArrayType(FloatType, false), + new ArrayType(FloatType, true), + DoubleType, + FloatType + ) ++ validVectorTypes + + // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 + val actualDataType = schema(colName).dataType + require(dataTypes.exists(actualDataType.equals), + s"Column $colName must be of type equal to one of the following types: " + + s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") + } + + /** + * Get conversion function to extract data from original DataFrame + * Default: 0 + */ + protected def getConvertFunc(colType: DataType): (Row, Int) => Seq[AnyVal] = { + colType match { + case ArrayType(DoubleType, false) => + (row: Row, index: Int) => row.getSeq[Double](index) + case ArrayType(DoubleType, true) => + (row: Row, index: Int) => row.getSeq[Double](index) + case ArrayType(FloatType, false) => + (row: Row, index: Int) => row.getSeq[Float](index) + case ArrayType(FloatType, true) => + (row: Row, index: Int) => row.getSeq[Float](index) + case DoubleType => + (row: Row, index: Int) => Seq[Double](row.getDouble(index)) + case FloatType => + (row: Row, index: Int) => Seq[Float](row.getFloat(index)) + case _ => + if (colType.typeName.contains("vector")) { + (row: Row, index: Int) => getVectorSeq(row, colType, index) + } else { + throw new IllegalArgumentException( + s"$colType is not a supported (Unexpected path).") + } + } + } +} + + +/** + * [[DLEstimator]] helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern, + * thus Spark users can conveniently fit BigDL into Spark ML pipeline. + * + * [[DLEstimator]] supports feature and label data in the format of + * Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, + * org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float. + * + * User should specify the feature data dimensions and label data dimensions via the constructor + * parameters featureSize and labelSize respectively. Internally the feature and label data are + * converted to BigDL tensors, to further train a BigDL model efficiently. + * + * For details usage, please refer to examples in package + * com.intel.analytics.bigdl.example.MLPipeline + * + * @param model BigDL module to be optimized + * @param criterion BigDL criterion method + * @param featureSize The size (Tensor dimensions) of the feature data. e.g. an image may be with + * width * height = 28 * 28, featureSize = Array(28, 28). + * @param labelSize The size (Tensor dimensions) of the label data. + */ +class DLEstimator[@specialized(Float, Double) T: ClassTag]( + @transient val model: Module[T], + val criterion : Criterion[T], + val featureSize : Array[Int], + val labelSize : Array[Int], + override val uid: String = "DLEstimator")(implicit ev: TensorNumeric[T]) + extends DLEstimatorBase[DLEstimator[T], DLModel[T]] with DLParams[T] { + + def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) + + def setLabelCol(labelColName : String) : this.type = set(labelCol, labelColName) + + def setPredictionCol(value: String): this.type = set(predictionCol, value) + + def setBatchSize(value: Int): this.type = set(batchSize, value) + + def setEndWhen(trigger: Trigger): this.type = set(endWhen, trigger) + + def setLearningRate(value: Double): this.type = set(learningRate, value) + setDefault(learningRate -> 1e-3) + + def setLearningRateDecay(value: Double): this.type = set(learningRateDecay, value) + setDefault(learningRateDecay -> 0.0) + + def setMaxEpoch(value: Int): this.type = set(maxEpoch, value) + setDefault(maxEpoch -> 50) + + def setOptimMethod(value: OptimMethod[T]): this.type = set(optimMethod, value) + set(optimMethod, new SGD[T]) + + @transient private var trainSummary: Option[TrainSummary] = None + + def getTrainSummary: Option[TrainSummary] = trainSummary + + /** + * Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the + * training data, which can be used for visualization via Tensorboard. + * Use setTrainSummary to enable train logger. Then the log will be saved to + * logDir/appName/train as specified by the parameters of TrainSummary. + * + * Default: Not enabled + */ + def setTrainSummary(value: TrainSummary): this.type = { + this.trainSummary = Some(value) + this + } + + @transient private var validationSummary: Option[ValidationSummary] = None + + /** + * Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the + * validation data if validation data is set, which can be used for visualization via + * Tensorboard. Use setValidationSummary to enable validation logger. Then the log will be + * saved to logDir/appName/ as specified by the parameters of validationSummary. + * + * Default: None + */ + def getValidationSummary: Option[ValidationSummary] = validationSummary + + /** + * Enable validation Summary + */ + def setValidationSummary(value: ValidationSummary): this.type = { + this.validationSummary = Some(value) + this + } + + @transient private var validationTrigger: Option[Trigger] = None + @transient private var validationDF: DataFrame = _ + @transient private var validationMethods: Array[ValidationMethod[T]] = _ + @transient private var validationBatchSize: Int = 0 + /** + * Set a validate evaluation during training + * + * @param trigger how often to evaluation validation set + * @param validationDF validate data set + * @param vMethods a set of validation method [[ValidationMethod]] + * @param batchSize batch size for validation + * @return this optimizer + */ + def setValidation(trigger: Trigger, validationDF: DataFrame, + vMethods : Array[ValidationMethod[T]], batchSize: Int) + : this.type = { + this.validationTrigger = Some(trigger) + this.validationDF = validationDF + this.validationMethods = vMethods + this.validationBatchSize = batchSize + this + } + + protected def validateParams(schema : StructType): Unit = { + validateDataType(schema, $(featuresCol)) + validateDataType(schema, $(labelCol)) + if(isSet(endWhen) && isSet(maxEpoch)) { + throw new IllegalArgumentException(s"endWhen and maxEpoch cannot be both set") + } + if (validationTrigger.isEmpty && validationSummary.isDefined) { + throw new IllegalArgumentException( + s"validationSummary is only valid if validation data is set.") + } + } + + override def transformSchema(schema : StructType): StructType = { + validateParams(schema) + SchemaUtils.appendColumn(schema, $(predictionCol), ArrayType(DoubleType, false)) + } + + protected override def internalFit(dataFrame: DataFrame): DLModel[T] = { + val localFeatureCol = $(featuresCol) + val localLabelCol = $(labelCol) + + def getSamples(dataFrame: DataFrame): RDD[Sample[T]] = { + val featureType = dataFrame.schema(localFeatureCol).dataType + val featureColIndex = dataFrame.schema.fieldIndex(localFeatureCol) + val labelType = dataFrame.schema(localLabelCol).dataType + val labelColIndex = dataFrame.schema.fieldIndex(localLabelCol) + + val featureFunc = getConvertFunc(featureType) + val labelFunc = getConvertFunc(labelType) + + val featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])] = dataFrame.rdd.map { row => + val features = featureFunc(row, featureColIndex) + val labels = labelFunc(row, labelColIndex) + (features, labels) + } + + val samples = featureAndLabel.map { case (f, l) => + // convert feature and label data type to the same type with model + // TODO: investigate to reduce memory consumption during conversion. + val feature = f.head match { + case dd: Double => f.asInstanceOf[Seq[Double]].map(ev.fromType(_)) + case ff: Float => f.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + } + val label = l.head match { + case dd: Double => l.asInstanceOf[Seq[Double]].map(ev.fromType(_)) + case ff: Float => l.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + } + (feature, label) + }.map { case (feature, label) => + Sample(Tensor(feature.toArray, featureSize), Tensor(label.toArray, labelSize)) + } + samples + } + + val trainingSamples = getSamples(dataFrame) + val state = T("learningRate" -> $(learningRate), "learningRateDecay" -> $(learningRateDecay)) + val endTrigger = if (isSet(endWhen)) $(endWhen) else Trigger.maxEpoch($(maxEpoch)) + val optimizer = Optimizer(model, trainingSamples, criterion, $(batchSize)) + .setState(state) + .setOptimMethod($(optimMethod)) + .setEndWhen(endTrigger) + + if (validationTrigger.isDefined) { + val validationSamples = getSamples(validationDF) + optimizer.setValidation( + validationTrigger.get, + validationSamples, + validationMethods, + validationBatchSize) + if (this.validationSummary.isDefined) { + optimizer.setValidationSummary(this.validationSummary.get) + } + } + + if (this.trainSummary.isDefined) { + optimizer.setTrainSummary(this.trainSummary.get) + } + + val optimizedModel = optimizer.optimize() + wrapBigDLModel(optimizedModel, featureSize) + } + + /** + * sub classes can extend the method and return required model for different transform tasks + */ + protected def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLModel[T] = { + val dlModel = new DLModel[T](m, featureSize) + copyValues(dlModel.setParent(this)) + } + + override def copy(extra: ParamMap): DLEstimator[T] = { + copyValues(new DLEstimator(model, criterion, featureSize, labelSize), extra) + } +} + +/** + * [[DLModel]] helps embed a BigDL model into a Spark Transformer, thus Spark users can + * conveniently merge BigDL into Spark ML pipeline. + * [[DLModel]] supports feature data in the format of + * Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, + * org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float. + * Internally [[DLModel]] use features column as storage of the feature data, and create + * Tensors according to the constructor parameter featureSize. + * + * [[DLModel]] is compatible with both spark 1.5-plus and 2.0 by extending ML Transformer. + * @param model trainned BigDL models to use in prediction. + * @param featureSize The size (Tensor dimensions) of the feature data. (e.g. an image may be with + * featureSize = 28 * 28). + */ +class DLModel[@specialized(Float, Double) T: ClassTag]( + @transient val model: Module[T], + var featureSize : Array[Int], + override val uid: String = "DLModel" + )(implicit ev: TensorNumeric[T]) + extends DLTransformerBase[DLModel[T]] with DLParams[T] with HasBatchSize { + + def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) + + def setPredictionCol(value: String): this.type = set(predictionCol, value) + + def setFeatureSize(value: Array[Int]): this.type = { + this.featureSize = value + this + } + + def setBatchSize(value: Int): this.type = set(batchSize, value) + + def getFeatureSize: Array[Int] = this.featureSize + + /** + * Perform a prediction on featureCol, and write result to the predictionCol. + */ + protected override def internalTransform(dataFrame: DataFrame): DataFrame = { + val featureType = dataFrame.schema($(featuresCol)).dataType + val featureColIndex = dataFrame.schema.fieldIndex($(featuresCol)) + val featureFunc = getConvertFunc(featureType) + val sc = dataFrame.sqlContext.sparkContext + val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model.evaluate()) + val localBatchSize = $(batchSize) + val transformerBC = sc.broadcast(SampleToMiniBatch[T](localBatchSize)) + + val resultRDD = dataFrame.rdd.mapPartitions { rowIter => + val localModel = modelBroadCast.value() + val transformer = transformerBC.value.cloneTransformer() + rowIter.grouped(localBatchSize).flatMap { rowBatch => + val samples = rowBatch.map { row => + val features = featureFunc(row, featureColIndex) + val featureBuffer = features.head match { + case dd: Double => features.asInstanceOf[Seq[Double]].map(ev.fromType(_)) + case ff: Float => features.asInstanceOf[Seq[Float]].map(ev.fromType(_)) + } + Sample(Tensor(featureBuffer.toArray, featureSize)) + }.toIterator + val predictions = transformer(samples).flatMap { batch => + val batchResult = localModel.forward(batch.getInput()) + batchResult.toTensor.split(1).map(outputToPrediction) + } + rowBatch.toIterator.zip(predictions).map { case (row, predict) => + Row.fromSeq(row.toSeq ++ Seq(predict)) + } + } + } + + val resultSchema = transformSchema(dataFrame.schema) + dataFrame.sqlContext.createDataFrame(resultRDD, resultSchema) + } + + protected def outputToPrediction(output: Tensor[T]): Any = { + output.clone().storage().array().map(ev.toType[Double]) + } + + override def transformSchema(schema : StructType): StructType = { + validateDataType(schema, $(featuresCol)) + SchemaUtils.appendColumn(schema, $(predictionCol), ArrayType(DoubleType, false)) + } + + override def copy(extra: ParamMap): DLModel[T] = { + val copied = new DLModel(model, featureSize, uid).setParent(parent) + copyValues(copied, extra) + } +} + +// TODO, add save/load +object DLModel { + + +} + diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala new file mode 100644 index 00000000000..1e3d3043fef --- /dev/null +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.adapter + +import org.apache.spark.sql.types.{DataType, StructType} + + +trait HasPredictionCol extends org.apache.spark.ml.param.shared.HasPredictionCol + +trait HasFeaturesCol extends org.apache.spark.ml.param.shared.HasFeaturesCol + +object SchemaUtils { + + /** + * Appends a new column to the input schema. This fails if the given output column already exists. + * @param schema input schema + * @param colName new column name. If this column name is an empty string "", this method returns + * the input schema unchanged. This allows users to disable output columns. + * @param dataType new column data type + * @return new schema with the input column appended + */ + def appendColumn( + schema: StructType, + colName: String, + dataType: DataType, + nullable: Boolean = false): StructType = { + org.apache.spark.ml.util.SchemaUtils.appendColumn(schema, colName, dataType) + } +} diff --git a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala index 6b75565aa5e..e99cbc802c1 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala @@ -34,6 +34,8 @@ import scala.reflect.ClassTag * @param criterion BigDL criterion method * @param featureSize The size (Tensor dimensions) of the feature data. */ +@deprecated("`DLClassifier` has been migrated to package `com.intel.analytics.bigdl.dlframes`." + + "This will be removed in BigDL 0.6.", "0.5.0") class DLClassifier[@specialized(Float, Double) T: ClassTag]( @transient override val model: Module[T], override val criterion : Criterion[T], @@ -65,6 +67,8 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( * @param model BigDL module to be optimized * @param featureSize The size (Tensor dimensions) of the feature data. */ +@deprecated("`DLClassifierModel` is migrated to package `com.intel.analytics.bigdl.dlframes`." + + "This will be removed in BigDL 0.6.", "0.5.0") class DLClassifierModel[@specialized(Float, Double) T: ClassTag]( @transient override val model: Module[T], featureSize : Array[Int], diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index 6408d37fe8f..cb1653f615f 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -160,6 +160,8 @@ private[ml] trait DLParams[@specialized(Float, Double) T] extends HasFeaturesCol * width * height = 28 * 28, featureSize = Array(28, 28). * @param labelSize The size (Tensor dimensions) of the label data. */ +@deprecated("`DLEstimator` has been migrated to package `com.intel.analytics.bigdl.dlframes`." + + "This will be removed in BigDL 0.6.", "0.5.0") class DLEstimator[@specialized(Float, Double) T: ClassTag]( @transient val model: Module[T], val criterion : Criterion[T], @@ -359,6 +361,8 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( * @param featureSize The size (Tensor dimensions) of the feature data. (e.g. an image may be with * featureSize = 28 * 28). */ +@deprecated("`DLModel` has been migrated to package `com.intel.analytics.bigdl.dlframes`." + + "This will be removed in BigDL 0.6.", "0.5.0") class DLModel[@specialized(Float, Double) T: ClassTag]( @transient val model: Module[T], var featureSize : Array[Int], @@ -387,13 +391,11 @@ class DLModel[@specialized(Float, Double) T: ClassTag]( val featureColIndex = dataFrame.schema.fieldIndex($(featuresCol)) val featureFunc = getConvertFunc(featureType) val sc = dataFrame.sqlContext.sparkContext - val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model.evaluate()) + val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model) val localBatchSize = $(batchSize) - val transformerBC = sc.broadcast(SampleToMiniBatch[T](localBatchSize)) val resultRDD = dataFrame.rdd.mapPartitions { rowIter => val localModel = modelBroadCast.value() - val transformer = transformerBC.value.cloneTransformer() rowIter.grouped(localBatchSize).flatMap { rowBatch => val samples = rowBatch.map { row => val features = featureFunc(row, featureColIndex) @@ -403,7 +405,7 @@ class DLModel[@specialized(Float, Double) T: ClassTag]( } Sample(Tensor(featureBuffer.toArray, featureSize)) }.toIterator - val predictions = transformer(samples).flatMap { batch => + val predictions = SampleToMiniBatch(localBatchSize).apply(samples).flatMap { batch => val batchResult = localModel.forward(batch.getInput()) batchResult.toTensor.split(1).map(outputToPrediction) } diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index e9d45d8e670..1b1058262f3 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -45,7 +45,7 @@ trait VectorCompatibility { * Extends MLEstimator and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -private[ml] abstract class DLEstimatorBase[Learner <: DLEstimatorBase[Learner, M], +abstract class DLEstimatorBase[Learner <: DLEstimatorBase[Learner, M], M <: DLTransformerBase[M]] extends Estimator[M] with HasLabelCol { diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala index ca4a943a116..4a42880068d 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala @@ -23,7 +23,7 @@ import org.apache.spark.sql.DataFrame * Extends MlTransformer and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -private[ml] abstract class DLTransformerBase[M <: DLTransformerBase[M]] +abstract class DLTransformerBase[M <: DLTransformerBase[M]] extends Model[M] { protected def internalTransform(dataFrame: DataFrame): DataFrame diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala index b02ad301a95..bea73e45628 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLEstimatorBase.scala @@ -47,7 +47,7 @@ trait VectorCompatibility { * Extends MLEstimator and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -private[ml] abstract class DLEstimatorBase[Learner <: DLEstimatorBase[Learner, M], +abstract class DLEstimatorBase[Learner <: DLEstimatorBase[Learner, M], M <: DLTransformerBase[M]] extends Estimator[M] with HasLabelCol { diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala index 882e98989ce..6be20bfa163 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/ml/DLTransformerBase.scala @@ -24,7 +24,7 @@ import org.apache.spark.sql.{DataFrame, Dataset} * Extends MlTransformer and override process to gain compatibility with * both spark 1.5 and spark 2.0. */ -private[ml] abstract class DLTransformerBase[M <: DLTransformerBase[M]] +abstract class DLTransformerBase[M <: DLTransformerBase[M]] extends Model[M] { /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala index eddb3d3b655..c472a93c729 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLeNet.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.example.MLPipeline import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToBatch} import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, _} +import com.intel.analytics.bigdl.dlframes.DLClassifier import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.lenet.Utils._ import com.intel.analytics.bigdl.nn.ClassNLLCriterion @@ -26,7 +27,6 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericF import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext -import org.apache.spark.ml.{DLClassifier, DLModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala index 770874613a0..7d6f5553dbf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLClassifierLogisticRegression.scala @@ -15,11 +15,11 @@ */ package com.intel.analytics.bigdl.example.MLPipeline +import com.intel.analytics.bigdl.dlframes.DLClassifier import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Linear, LogSoftMax, Sequential} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.utils.Engine import org.apache.spark.SparkContext -import org.apache.spark.ml.DLClassifier import org.apache.spark.sql.SQLContext /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala index cb73a405c58..59d5f2e7701 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/MLPipeline/DLEstimatorMultiLabelLR.scala @@ -15,12 +15,12 @@ */ package com.intel.analytics.bigdl.example.MLPipeline +import com.intel.analytics.bigdl.dlframes.DLEstimator import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.LBFGS import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble import com.intel.analytics.bigdl.utils.Engine import org.apache.spark.SparkContext -import org.apache.spark.ml.DLEstimator import org.apache.spark.sql.SQLContext /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/ImagePredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/ImagePredictor.scala index 9cd196bccab..8500eee8605 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/ImagePredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/ImagePredictor.scala @@ -18,12 +18,12 @@ package com.intel.analytics.bigdl.example.imageclassification import java.nio.file.Paths import com.intel.analytics.bigdl.dataset.image._ +import com.intel.analytics.bigdl.dlframes.DLClassifierModel import com.intel.analytics.bigdl.example.imageclassification.MlUtils._ import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext -import org.apache.spark.ml.DLClassifierModel import org.apache.spark.sql.SQLContext /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ad6aea108de..ed9142d119d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -34,8 +34,9 @@ import java.lang.{Integer, Boolean => JBoolean} import java.nio.ByteOrder import java.util +import com.intel.analytics.bigdl.dlframes.{DLClassifier, DLClassifierModel, DLEstimator, DLModel} import com.intel.analytics.bigdl.nn.Graph._ -import com.intel.analytics.bigdl.nn.tf.{Const, Fill, Shape => TfShape, SplitAndSelect} +import com.intel.analytics.bigdl.nn.tf.{Const, Fill, SplitAndSelect, Shape => TfShape} import com.intel.analytics.bigdl.optim.SGD.{LearningRateSchedule, SequentialSchedule} import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation._ @@ -44,7 +45,6 @@ import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} import com.intel.analytics.bigdl.utils.tf._ -import org.apache.spark.ml.{DLClassifier, DLClassifierModel, DLEstimator, DLModel} import org.apache.spark.sql.{DataFrame, SQLContext} import org.apache.log4j._ import org.opencv.imgproc.Imgproc diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala new file mode 100644 index 00000000000..fef2ef2674e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala @@ -0,0 +1,223 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dlframes + +import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.optim.{Adam, LBFGS, Loss, Trigger} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.visualization.ValidationSummary +import org.apache.log4j.{Level, Logger} +import org.apache.spark.ml.feature.MinMaxScaler +import org.apache.spark.SparkContext +import org.apache.spark.ml._ +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.sql.{DataFrame, SQLContext} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +import scala.collection.mutable.ArrayBuffer +import scala.util.Random + +class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { + var sc : SparkContext = _ + var sqlContext : SQLContext = _ + var smallData: Seq[(Array[Double], Double)] = _ + val nRecords = 100 + val maxEpoch = 20 + + before { + val conf = Engine.createSparkConf().setAppName("Test DLEstimator").setMaster("local[1]") + sc = SparkContext.getOrCreate(conf) + sqlContext = new SQLContext(sc) + Random.setSeed(42) + RNG.setSeed(42) + smallData = DLEstimatorSpec.generateTestInput( + nRecords, Array(1.0, 2.0, 3.0, 4.0, 5.0, 6.0), -1.0, 42L) + Engine.init + } + + after{ + if (sc != null) { + sc.stop() + } + } + + "An DLClassifier" should "has correct default params" in { + val model = Linear[Float](10, 1) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLClassifier[Float](model, criterion, Array(10)) + assert(estimator.getFeaturesCol == "features") + assert(estimator.getLabelCol == "label") + assert(estimator.getMaxEpoch == 50) + assert(estimator.getBatchSize == 1) + assert(estimator.getLearningRate == 1e-3) + assert(estimator.getLearningRateDecay == 0) + } + + "An DLClassifier" should "get reasonale accuracy" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val classifier = new DLClassifier[Float](model, criterion, Array(6)) + .setOptimMethod(new LBFGS[Float]()) + .setLearningRate(0.1) + .setBatchSize(nRecords) + .setMaxEpoch(maxEpoch) + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + + val dlModel = classifier.fit(df) + dlModel.isInstanceOf[DLClassifierModel[_]] should be(true) + assert(dlModel.transform(df).where("prediction=label").count() > nRecords * 0.8) + } + + "An DLClassifier" should "support different FEATURE types" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val classifier = new DLClassifier[Float](model, criterion, Array(6)) + .setLearningRate(0.1) + .setBatchSize(2) + .setEndWhen(Trigger.maxIteration(2)) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2)))) + .toDF("features", "label"), // Array[Double] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.map(_.toFloat), p._2)))) + .toDF("features", "label"), // Array[Float] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (Vectors.dense(p._1), p._2)))) + .toDF("features", "label") // MLlib Vector + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = classifier.fit(df) + dlModel.transform(df).collect() + } + } + + "An DLClassifier" should "support scalar FEATURE" in { + val model = new Sequential().add(Linear[Float](1, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val classifier = new DLClassifier[Float](model, criterion, Array(1)) + .setLearningRate(0.1) + .setBatchSize(2) + .setEndWhen(Trigger.maxIteration(2)) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head.toFloat, p._2)))) + .toDF("features", "label"), // Float + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head, p._2)))) + .toDF("features", "label") // Double + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = classifier.fit(df) + dlModel.transform(df).collect() + } + } + + "An DLClassifier" should "fit with adam and LBFGS" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + Seq(new LBFGS[Float], new Adam[Float]).foreach { optimMethod => + val classifier = new DLClassifier[Float](model, criterion, Array(6)) + .setBatchSize(nRecords) + .setMaxEpoch(2) + .setOptimMethod(optimMethod) + .setLearningRate(0.1) + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + val dlModel = classifier.fit(df) + dlModel.isInstanceOf[DLClassifierModel[_]] should be(true) + } + } + + "An DLClassifier" should "supports validation data and summary" in { + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + + val logdir = com.google.common.io.Files.createTempDir() + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val classifier = new DLClassifier[Float](model, criterion, Array(6)) + .setBatchSize(nRecords) + .setEndWhen(Trigger.maxIteration(5)) + .setOptimMethod(new Adam[Float]) + .setLearningRate(0.1) + .setValidation(Trigger.severalIteration(1), df, Array(new Loss[Float]()), 2) + .setValidationSummary(ValidationSummary(logdir.getPath, "DLEstimatorValidation")) + + classifier.fit(df) + val validationSummary = classifier.getValidationSummary.get + val losses = validationSummary.readScalar("Loss") + validationSummary.close() + logdir.deleteOnExit() + } + + "An DLClassifier" should "get the same classification result with BigDL model" in { + Logger.getLogger("org").setLevel(Level.WARN) + Logger.getLogger("akka").setLevel(Level.WARN) + + val model = LeNet5(10) + + // init + val valTrans = new DLClassifierModel[Float](model, Array(28, 28)) + .setBatchSize(4) + + val tensorBuffer = new ArrayBuffer[Data]() + // generate test data with BigDL + val input = Tensor[Float](10, 28, 28).apply1(e => Random.nextFloat()) + val target = model.forward(input).toTensor[Float] + + // test against DLClassifierModel + val inputArr = input.storage().array() + val targetArr = target.max(2)._2.squeeze().storage().array() + (0 until 10).foreach(i => + tensorBuffer.append( + Data(targetArr(i), inputArr.slice(i * 28 * 28, (i + 1) * 28 * 28).map(_.toDouble)))) + val rowRDD = sc.parallelize(tensorBuffer) + val testData = sqlContext.createDataFrame(rowRDD) + assert(valTrans.transform(testData).where("prediction=label").count() == testData.count()) + tensorBuffer.clear() + } + + "An DLClassifier" should "works in ML pipeline" in { + var appSparkVersion = org.apache.spark.SPARK_VERSION + if (appSparkVersion.trim.startsWith("1")) { + val data = sc.parallelize( + smallData.map(p => (org.apache.spark.mllib.linalg.Vectors.dense(p._1), p._2))) + val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") + + val scaler = new MinMaxScaler().setInputCol("features").setOutputCol("scaled") + .setMax(1).setMin(-1) + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLClassifier[Float](model, criterion, Array(6)) + .setBatchSize(nRecords) + .setOptimMethod(new LBFGS[Float]()) + .setLearningRate(0.1) + .setMaxEpoch(maxEpoch) + .setFeaturesCol("scaled") + val pipeline = new Pipeline().setStages(Array(scaler, estimator)) + + val pipelineModel = pipeline.fit(df) + pipelineModel.isInstanceOf[PipelineModel] should be(true) + assert(pipelineModel.transform(df).where("prediction=label").count() > nRecords * 0.8) + } + } +} + +private case class Data(label: Double, features: Array[Double]) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala new file mode 100644 index 00000000000..d543e1c0955 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala @@ -0,0 +1,348 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dlframes + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.optim.{LBFGS, Loss, Trigger} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} +import org.apache.spark.SparkContext +import org.apache.spark.ml.feature.MinMaxScaler +import org.apache.spark.ml.{Pipeline, PipelineModel} +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +import scala.util.Random + +class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { + val model = new Sequential[Float]() + var sc : SparkContext = _ + var sqlContext : SQLContext = _ + var smallData: Seq[(Array[Double], Double)] = _ + val nRecords = 100 + val maxEpoch = 20 + + before { + Random.setSeed(42) + RNG.setSeed(42) + val conf = Engine.createSparkConf().setAppName("Test DLEstimator").setMaster("local[1]") + sc = SparkContext.getOrCreate(conf) + sqlContext = new SQLContext(sc) + smallData = DLEstimatorSpec.generateTestInput( + nRecords, Array(1.0, 2.0, 3.0, 4.0, 5.0, 6.0), -1.0, 42L) + Engine.init + } + + after{ + if (sc != null) { + sc.stop() + } + } + + "An DLEstimator" should "has correct default params" in { + val model = Linear[Float](10, 1) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(10), Array(1)) + assert(estimator.getFeaturesCol == "features") + assert(estimator.getLabelCol == "label") + assert(estimator.getMaxEpoch == 50) + assert(estimator.getBatchSize == 1) + assert(estimator.getLearningRate == 1e-3) + assert(estimator.getLearningRateDecay == 0) + + } + + "An DLEstimator" should "get reasonable accuracy" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(nRecords) + .setOptimMethod(new LBFGS[Float]()) + .setLearningRate(0.1) + .setMaxEpoch(maxEpoch) + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + + val dlModel = estimator.fit(df) + dlModel.isInstanceOf[DLModel[_]] should be(true) + val correct = dlModel.transform(df).select("label", "prediction").rdd.filter { + case Row(label: Double, prediction: Seq[Double]) => + label == prediction.indexOf(prediction.max) + 1 + }.count() + assert(correct > nRecords * 0.8) + } + + "An DLEstimator" should "support different FEATURE types" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(2) + // intentionally set low since this only validates data format compatibility + .setEndWhen(Trigger.maxIteration(1)) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2)))) + .toDF("features", "label"), // Array[Double] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.map(_.toFloat), p._2)))) + .toDF("features", "label"), // Array[Float] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (Vectors.dense(p._1), p._2)))) + .toDF("features", "label") // MLlib Vector + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = estimator.fit(df) + dlModel.transform(df).collect() + } + } + + "An DLEstimator" should "support scalar FEATURE types" in { + val model = new Sequential().add(Linear[Float](1, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(1), Array(1)) + .setBatchSize(2) + // intentionally set low since this only validates data format compatibility + .setEndWhen(Trigger.maxIteration(1)) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head.toFloat, p._2)))) + .toDF("features", "label"), // Float + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1.head, p._2)))) + .toDF("features", "label") // Double + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = estimator.fit(df) + dlModel.transform(df).collect() + } + } + + "An DLEstimator" should "support different LABEL types" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = MultiLabelSoftMarginCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(2)) + // intentionally set low since this only validates data format compatibitliy + .setEndWhen(Trigger.maxIteration(1)) + .setBatchSize(2) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, Array(p._2, p._2))))) + .toDF("features", "label"), // Array[Double] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, + Array(p._2.toFloat, p._2.toFloat))))).toDF("features", "label"), // Array[Float] + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, + Vectors.dense(p._2, p._2))))).toDF("features", "label") // MLlib Vector + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = estimator.fit(df) + dlModel.transform(df).collect() + } + } + + "An DLEstimator" should "support scalar LABEL types" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + // intentionally set low since this only validates data format compatibitliy + .setEndWhen(Trigger.maxIteration(1)) + .setBatchSize(2) + + Array( + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2.toFloat)))) + .toDF("features", "label"), // Float + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2)))) + .toDF("features", "label") // Double + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = estimator.fit(df) + dlModel.transform(df).collect() + } + } + + "An DLEstimator" should "work with tensor data" in { + + val model = Linear[Float](10, 1) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(10), Array(1)) + .setMaxEpoch(1) + .setBatchSize(nRecords) + + val featureData = Array.tabulate(100)(_ => Tensor(10)) + val labelData = Array.tabulate(100)(_ => Tensor(1).fill(1.0f)) + val miniBatch = sc.parallelize( + featureData.zip(labelData).map(v => + MinibatchData(v._1.storage.array, v._2.storage.array)) + ) + val trainingDF: DataFrame = sqlContext.createDataFrame(miniBatch).toDF("features", "label") + + val dlModel = estimator.fit(trainingDF) + dlModel.transform(trainingDF).collect() + } + + "An DLEstimator" should "support different batchSize" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(51) + .setMaxEpoch(maxEpoch) + val data = sc.parallelize(smallData) + val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") + + val dlModel = estimator.fit(df) + dlModel.isInstanceOf[DLModel[_]] should be(true) + dlModel.transform(df).count() + } + + "An DLModel" should "support transform with different batchSize" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(nRecords) + .setMaxEpoch(maxEpoch) + val data = sc.parallelize(smallData) + val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") + val dlModel = estimator.fit(df) + assert(df.count() == dlModel.setBatchSize(51).transform(df).count()) + } + + "An DLEstimator" should "throws exception without correct inputs" in { + val model = Linear[Float](10, 1) + val criterion = ClassNLLCriterion[Float]() + val inputs = Array[String]("Feature data", "Label data") + var estimator = new DLEstimator[Float](model, criterion, Array(10), Array(2, 1)). + setFeaturesCol(inputs(0)).setLabelCol(inputs(1)) + + val featureData = Tensor(2, 10) + val labelData = Tensor(2, 1) + val miniBatch = sc.parallelize(Seq( + MinibatchData[Float](featureData.storage().array(), labelData.storage().array()) + )) + var df: DataFrame = sqlContext.createDataFrame(miniBatch).toDF(inputs: _*) + // Spark 1.6 and 2.0 throws different exception here + intercept[Exception] { + estimator.fit(df) + } + } + + "An DLEstimator" should "supports training summary" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val logdir = com.google.common.io.Files.createTempDir() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(nRecords) + .setMaxEpoch(5) + .setTrainSummary(TrainSummary(logdir.getPath, "DLEstimatorTrain")) + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + + val dlModel = estimator.fit(df) + val trainSummary = estimator.getTrainSummary.get + val losses = trainSummary.readScalar("Loss") + assert(losses.length == 5) + trainSummary.close() + logdir.deleteOnExit() + } + + "An DLEstimator" should "supports validation data and summary" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val logdir = com.google.common.io.Files.createTempDir() + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(4) + .setEndWhen(Trigger.maxIteration(5)) + .setValidation(Trigger.severalIteration(1), df, Array(new Loss[Float]()), 2) + .setValidationSummary(ValidationSummary(logdir.getPath, "DLEstimatorValidation")) + + val dlModel = estimator.fit(df) + val validationSummary = estimator.getValidationSummary.get + val losses = validationSummary.readScalar("Loss") + assert(losses.length == 5) + validationSummary.close() + logdir.deleteOnExit() + } + + "An DLEstimator" should "throws exception when EndWhen and MaxEpoch are set" in { + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val logdir = com.google.common.io.Files.createTempDir() + + val data = sc.parallelize(smallData) + val df = sqlContext.createDataFrame(data).toDF("features", "label") + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setBatchSize(4) + .setEndWhen(Trigger.maxIteration(5)) + .setMaxEpoch(5) + + intercept[Exception] { + estimator.fit(df) + } + } + + "An DLEstimator" should "works in ML pipeline" in { + var appSparkVersion = org.apache.spark.SPARK_VERSION + if (appSparkVersion.trim.startsWith("1")) { + val data = sc.parallelize( + smallData.map(p => (org.apache.spark.mllib.linalg.Vectors.dense(p._1), p._2))) + val df: DataFrame = sqlContext.createDataFrame(data).toDF("features", "label") + + val scaler = new MinMaxScaler().setInputCol("features").setOutputCol("scaled") + .setMax(1).setMin(-1) + val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) + val criterion = ClassNLLCriterion[Float]() + val estimator = new DLEstimator[Float](model, criterion, Array(6), Array(1)) + .setOptimMethod(new LBFGS[Float]()) + .setLearningRate(0.1) + .setBatchSize(nRecords) + .setMaxEpoch(maxEpoch) + .setFeaturesCol("scaled") + val pipeline = new Pipeline().setStages(Array(scaler, estimator)) + + val pipelineModel = pipeline.fit(df) + pipelineModel.isInstanceOf[PipelineModel] should be(true) + val correct = pipelineModel.transform(df).select("label", "prediction").rdd.filter { + case Row(label: Double, prediction: Seq[Double]) => + label == prediction.indexOf(prediction.max) + 1 + }.count() + assert(correct > nRecords * 0.8) + } + } +} + +private case class MinibatchData[T](featureData : Array[T], labelData : Array[T]) + +object DLEstimatorSpec { + // Generate noisy input of the form Y = signum(x.dot(weights) + intercept + noise) + def generateTestInput( + numRecords: Int, + weight: Array[Double], + intercept: Double, + seed: Long): Seq[(Array[Double], Double)] = { + val rnd = new Random(seed) + val data = (1 to numRecords) + .map( i => Array.tabulate(weight.length)(index => rnd.nextDouble() * 2 - 1)) + .map { record => + val y = record.zip(weight).map(t => t._1 * t._2).sum + +intercept + 0.01 * rnd.nextGaussian() + val label = if (y > 0) 2.0 else 1.0 + (record, label) + } + data + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala index c27a0a3d60f..59480fb31e3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLClassifierSpec.scala @@ -34,6 +34,8 @@ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.collection.mutable.ArrayBuffer import scala.util.Random +@deprecated("`DLClassifier` has been migrated to package `com.intel.analytics.bigdl.dlframes`." + + "This will be removed in BigDL 0.6.", "0.5.0") class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { var sc : SparkContext = _ var sqlContext : SQLContext = _ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala index 35094ef69cb..a07458cfd7a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala @@ -33,6 +33,8 @@ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.util.Random +@deprecated("`DLEstimator` has been migrated to package `com.intel.analytics.bigdl.dlframes`." + + "This will be removed in BigDL 0.6.", "0.5.0") class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val model = new Sequential[Float]() var sc : SparkContext = _ From 6886eea245df1d4791b865a491eaedd29a08d7d6 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Fri, 9 Feb 2018 10:22:57 +0800 Subject: [PATCH 0694/1065] Support Calling Java Function in Python Executor and ModelBroadcast in Python (#2284) * support call java function in executor * fix test * fix style * address comments * add parallelism * address comments * fix partition num * address comments * fix typo * fix typo * add integration test * address comments --- .../intel/analytics/bigdl/utils/Engine.scala | 85 ++++++++++++++++++- .../org/apache/spark/utils/SparkUtils.scala | 27 ++++++ .../dllib/utils/python/api/PythonBigDL.scala | 16 ++-- 3 files changed, 118 insertions(+), 10 deletions(-) create mode 100644 scala/common/utils/src/main/scala/org/apache/spark/utils/SparkUtils.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 9d258523649..9814e5c6545 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -16,13 +16,17 @@ package com.intel.analytics.bigdl.utils -import java.io.InputStream +import java.io.{FileOutputStream, InputStream, PrintWriter} import java.util.Locale import java.util.concurrent.atomic.AtomicBoolean import org.apache.log4j.Logger -import org.apache.spark.{SparkConf, SparkContext, SparkException} +import org.apache.spark._ import com.intel.analytics.bigdl.mkl.MKL +import org.apache.spark.utils.SparkUtils +import py4j.GatewayServer + +import scala.util.control.{ControlThrowable, NonFatal} /** * define engine type trait @@ -109,6 +113,83 @@ object Engine { private var physicalCoreNumber = -1 private var nodeNum: Int = -1 + @volatile + private var gatewayServer: py4j.GatewayServer = null + private val driverPortFileCreated = new AtomicBoolean() + + private def createGatewayPortFile(port: Int): Unit = { + val file = new java.io.File(SparkFiles.getRootDirectory(), "gateway_port") + logger.debug(s"Creating JavaGatewayServer port file" + + s" on executor-${SparkEnv.get.executorId}:${file.getAbsolutePath}") + if (file.exists()) { + file.delete() + } + file.createNewFile() + val out = new PrintWriter(file) + try { + out.print(port) + out.flush() + } finally { + out.close() + } + } + + private[bigdl] def createJavaGateway(driverPort: Int): Unit = { + if (SparkUtils.isDriver) { + if (driverPortFileCreated.compareAndSet(false, true)) { + try { + createGatewayPortFile(driverPort) + } catch { + case NonFatal(e) => + throw new Exception("Could not create java gateway port file", e) + } + } + return + } + if (gatewayServer != null) return + this.synchronized { + if (gatewayServer != null) return + gatewayServer = new py4j.GatewayServer(null, 0) + } + + logger.info(s"Initializing JavaGatewayServer on executor-${SparkEnv.get.executorId} ") + GatewayServer.turnLoggingOn() + val thread = new Thread(new Runnable() { + override def run(): Unit = try { + gatewayServer.start() + } catch { + case ct: ControlThrowable => + throw ct + case t: Throwable => + throw new Exception(s"Uncaught exception " + + s"in thread ${Thread.currentThread().getName}, when staring JavaGatewayServer", t) + } + }) + thread.setName("py4j-executor-gateway-init") + thread.setDaemon(true) + thread.start() + + thread.join() + + logger.info(s"JavaGatewayServer initialized") + + Runtime.getRuntime().addShutdownHook(new Thread { + override def run(): Unit = { + gatewayServer.shutdown() + } + }) + + try { + createGatewayPortFile(gatewayServer.getListeningPort) + } catch { + case NonFatal(e) => + throw new Exception("Could not create java gateway port file", e) + } + } + + + + private[bigdl] def localMode: Boolean = { System.getProperty("bigdl.localMode", "false").toLowerCase(Locale.ROOT) match { case "true" => true diff --git a/scala/common/utils/src/main/scala/org/apache/spark/utils/SparkUtils.scala b/scala/common/utils/src/main/scala/org/apache/spark/utils/SparkUtils.scala new file mode 100644 index 00000000000..d3ecd08631a --- /dev/null +++ b/scala/common/utils/src/main/scala/org/apache/spark/utils/SparkUtils.scala @@ -0,0 +1,27 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.utils + +import org.apache.spark.{SparkConf, SparkContext, SparkEnv} +import org.apache.spark.util.Utils + +object SparkUtils { + def isDriver: Boolean = { + val executorId = SparkEnv.get.executorId + executorId == SparkContext.DRIVER_IDENTIFIER + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ed9142d119d..41aecc72fc7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -30,20 +30,18 @@ import com.intel.analytics.bigdl.utils.{Table, _} import com.intel.analytics.bigdl.visualization.{Summary, TrainSummary, ValidationSummary} import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD -import java.lang.{Integer, Boolean => JBoolean} +import java.lang.{Boolean => JBoolean} import java.nio.ByteOrder -import java.util import com.intel.analytics.bigdl.dlframes.{DLClassifier, DLClassifierModel, DLEstimator, DLModel} import com.intel.analytics.bigdl.nn.Graph._ -import com.intel.analytics.bigdl.nn.tf.{Const, Fill, SplitAndSelect, Shape => TfShape} import com.intel.analytics.bigdl.optim.SGD.{LearningRateSchedule, SequentialSchedule} import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation._ import com.intel.analytics.bigdl.transform.vision.image.label.roi._ import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat -import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} -import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.{buildBigDLModel, buildTFGraph, parse} +import com.intel.analytics.bigdl.utils.tf.TensorflowDataFormat +import com.intel.analytics.bigdl.utils.tf.TensorflowLoader.parse import com.intel.analytics.bigdl.utils.tf._ import org.apache.spark.sql.{DataFrame, SQLContext} import org.apache.log4j._ @@ -51,8 +49,6 @@ import org.opencv.imgproc.Imgproc import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable -import scala.collection.mutable.{ArrayBuffer, Map} import scala.language.existentials import scala.reflect.ClassTag @@ -2859,7 +2855,6 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab imageFrame.rdd.map(imageFeatureToLabelTensor).toJavaRDD() } - def distributedImageFrameToPredict(imageFrame: DistributedImageFrame, key: String) : JavaRDD[JList[Any]] = { imageFrame.rdd.map(x => { @@ -2965,6 +2960,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab maxIteration: Int): SequentialSchedule = { seq.add(scheduler, maxIteration) } + + private[bigdl] def initExecutorGateway(sc: JavaSparkContext, driverPort: Int): Unit = { + sc.parallelize(Seq(""), Engine.coreNumber() * Engine.nodeNumber()) + .foreachPartition(_ => Engine.createJavaGateway(driverPort)) + } } object PythonBigDLUtils { From 5548f36299a95212d85818eef6d555bb0e9de447 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 9 Feb 2018 13:04:51 +0800 Subject: [PATCH 0695/1065] add more layers test (#2289) * add more layers test * fix style --- .../bigdl/dllib/keras/nn/DropoutSpec.scala | 13 ++++++ .../bigdl/dllib/keras/nn/FlattenSpec.scala | 12 ++++++ .../keras/nn/GlobalAveragePooling1DSpec.scala | 12 ++++++ .../keras/nn/GlobalAveragePooling3DSpec.scala | 12 ++++++ .../keras/nn/GlobalMaxPooling1DSpec.scala | 12 ++++++ .../dllib/keras/nn/ZeroPadding3DSpec.scala | 12 ++++++ .../bigdl/dllib/nn/AddConstantSpec.scala | 31 +++++++++++++ .../analytics/bigdl/dllib/nn/AddSpec.scala | 11 +++++ .../dllib/nn/BifurcateSplitTableSpec.scala | 8 ++++ .../bigdl/dllib/nn/BilinearSpec.scala | 13 ++++++ .../analytics/bigdl/dllib/nn/CAddSpec.scala | 11 +++++ .../bigdl/dllib/nn/CAddTableSpec.scala | 17 +++++++- .../bigdl/dllib/nn/CAveTableSpec.scala | 17 +++++++- .../analytics/bigdl/dllib/nn/CMulSpec.scala | 10 +++++ .../bigdl/dllib/nn/CMulTableSpec.scala | 17 +++++++- .../analytics/bigdl/dllib/nn/ClampSpec.scala | 30 +++++++++++++ .../bigdl/dllib/nn/CrossProductSpec.scala | 10 +++++ .../bigdl/dllib/nn/EuclideanSpec.scala | 9 ++++ .../analytics/bigdl/dllib/nn/ExpSpec.scala | 12 ++++++ .../bigdl/dllib/nn/HardShrinkSpec.scala | 30 +++++++++++++ .../bigdl/dllib/nn/HardSigmoidSpec.scala | 28 ++++++++++++ .../bigdl/dllib/nn/JoinTableSpec.scala | 15 +++++++ .../bigdl/dllib/nn/LogSigmoidSpec.scala | 11 +++++ .../analytics/bigdl/dllib/nn/MulSpec.scala | 9 ++++ .../dllib/nn/NegativeEntropyPenaltySpec.scala | 11 +++++ .../analytics/bigdl/dllib/nn/ReLUSpec.scala | 10 +++++ .../bigdl/dllib/nn/ResizeBilinearSpec.scala | 11 +++++ .../bigdl/dllib/nn/SoftShrinkSpec.scala | 29 +++++++++++++ .../dllib/nn/SpatialConvolutionSpec.scala | 10 +++++ .../dllib/nn/SpatialCrossMapLRNSpec.scala | 12 ++++++ .../dllib/nn/SpatialFullConvolutionSpec.scala | 10 +++++ .../bigdl/dllib/nn/TanhShrinkSpec.scala | 30 +++++++++++++ .../bigdl/dllib/nn/TimeDistributedSpec.scala | 12 ++++++ .../dllib/nn/ops/ApproximateEqualSpec.scala | 33 ++++++++++++++ .../bigdl/dllib/nn/ops/CeilSpec.scala | 30 +++++++++++++ .../bigdl/dllib/nn/ops/CrossEntropySpec.scala | 32 ++++++++++++++ .../nn/ops/Dilation2DBackpropFilterSpec.scala | 33 ++++++++++++++ .../bigdl/dllib/nn/ops/ParseExampleSpec.scala | 43 +++++++++++++++++++ .../bigdl/dllib/nn/ops/SelectSpec.scala | 15 +++++++ .../utils/serializer/SerializerSpec.scala | 14 +++++- 40 files changed, 693 insertions(+), 4 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AddConstantSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClampSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardShrinkSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoidSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftShrinkSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhShrinkSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ApproximateEqualSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CeilSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossEntropySpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilterSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DropoutSpec.scala index 12bf553f6b9..f42640ac50f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DropoutSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/DropoutSpec.scala @@ -20,6 +20,9 @@ import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.nn.keras.{Dropout, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class DropoutSpec extends FlatSpec with Matchers { @@ -34,3 +37,13 @@ class DropoutSpec extends FlatSpec with Matchers { } } + +class DropoutSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Dropout[Float](0.3, inputShape = Shape(3, 4)) + layer.build(Shape(2, 3, 4)) + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/FlattenSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/FlattenSpec.scala index fe29bac964e..a5e3521c0b9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/FlattenSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/FlattenSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.keras.Flatten import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class FlattenSpec extends KerasBaseSpec { @@ -41,3 +44,12 @@ class FlattenSpec extends KerasBaseSpec { } } + +class FlattenSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Flatten[Float](inputShape = Shape(3, 4, 5)) + layer.build(Shape(2, 3, 4, 5)) + val input = Tensor[Float](2, 3, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling1DSpec.scala index c65039ad217..8e3a62dfe4d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling1DSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.GlobalAveragePooling1D import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GlobalAveragePooling1DSpec extends KerasBaseSpec{ @@ -42,3 +45,12 @@ class GlobalAveragePooling1DSpec extends KerasBaseSpec{ } } + +class GlobalAveragePooling1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GlobalAveragePooling1D[Float](inputShape = Shape(3, 24)) + layer.build(Shape(2, 3, 24)) + val input = Tensor[Float](2, 3, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling3DSpec.scala index ad86a332edd..1fb66f86026 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling3DSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.GlobalAveragePooling3D import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GlobalAveragePooling3DSpec extends KerasBaseSpec{ @@ -42,3 +45,12 @@ class GlobalAveragePooling3DSpec extends KerasBaseSpec{ } } + +class GlobalAveragePooling3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GlobalAveragePooling3D[Float](inputShape = Shape(3, 4, 5, 6)) + layer.build(Shape(2, 3, 4, 5, 6)) + val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling1DSpec.scala index 5ad22b75594..8d47eae133b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling1DSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling1D import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GlobalMaxPooling1DSpec extends KerasBaseSpec{ @@ -42,3 +45,12 @@ class GlobalMaxPooling1DSpec extends KerasBaseSpec{ } } + +class GlobalMaxPooling1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GlobalMaxPooling1D[Float](inputShape = Shape(12, 24)) + layer.build(Shape(2, 12, 24)) + val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding3DSpec.scala index 0f74a3b6893..aa0a9574377 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding3DSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.keras.ZeroPadding3D import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ZeroPadding3DSpec extends KerasBaseSpec { @@ -71,3 +74,12 @@ class ZeroPadding3DSpec extends KerasBaseSpec { } } + +class ZeroPadding3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = ZeroPadding3D[Float]((1, 1, 1), inputShape = Shape(5, 6, 7, 8)) + layer.build(Shape(2, 5, 6, 7, 8)) + val input = Tensor[Float](2, 5, 6, 7, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AddConstantSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AddConstantSpec.scala new file mode 100644 index 00000000000..cc123f1f223 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AddConstantSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class AddConstantSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val addconst = AddConstant[Float](5).setName("addconst") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(addconst, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AddSpec.scala index 2ebdb969eb4..408f572d2e7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AddSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AddSpec.scala @@ -20,6 +20,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{TensorCriterion, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class AddSpec extends FlatSpec with Matchers { @@ -56,3 +59,11 @@ class AddSpec extends FlatSpec with Matchers { layer2.gradBias should be (layer1.gradBias.mul(2)) } } + +class AddSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val add = Add[Float](5).setName("add") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(add, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala index 699bc257d14..0209ea61701 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala @@ -56,3 +56,11 @@ class BifurcateSplitTableSerialTest extends ModuleSerializationTest { runSerializationTest(batchNorm, input) } } + +class SplitTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val splitTable = SplitTable[Float](2).setName("splitTable") + val input = Tensor[Float](2, 10).apply1( e => Random.nextFloat()) + runSerializationTest(splitTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BilinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BilinearSpec.scala index d56a613def8..db4e6eb9423 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BilinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BilinearSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -52,3 +53,15 @@ class BilinearSpec extends FlatSpec with Matchers { layer2.gradBias should be (layer1.gradBias.mul(2)) } } + +class BilinearSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 3).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + val biLinear = Bilinear[Float](5, 3, 2) + runSerializationTest(biLinear, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddSpec.scala index f6f561574c4..779fe82856d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddSpec.scala @@ -20,6 +20,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{TensorCriterion, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class CAddSpec extends FlatSpec with Matchers { @@ -88,3 +91,11 @@ class CAddSpec extends FlatSpec with Matchers { layer2.gradBias should be (layer1.gradBias.mul(2)) } } + +class CAddSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Tensor[Float](5, 1).apply1(e => Random.nextFloat()) + val cadd = CAdd[Float](Array(5, 1)).setName("cadd") + runSerializationTest(cadd, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala index db4a1ca9806..68060ea01d8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala @@ -16,9 +16,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class CAddTableSpec extends FlatSpec with Matchers { "CAddTable" should "be correct when input is scalar" in { @@ -41,3 +44,15 @@ class CAddTableSpec extends FlatSpec with Matchers { grads[Tensor[Float]](2).value() should be(6) } } + +class CAddTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + val caddTable = CAddTable[Float](false).setName("caddTable") + runSerializationTest(caddTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAveTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAveTableSpec.scala index b323279a246..609db029ca7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAveTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAveTableSpec.scala @@ -16,9 +16,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class CAveTableSpec extends FlatSpec with Matchers { "CAveTable" should "be correct for multiple tensor inputs" in { @@ -33,3 +36,15 @@ class CAveTableSpec extends FlatSpec with Matchers { } } + +class CAveTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + val caveTable = CAveTable[Float](false).setName("caveTable") + runSerializationTest(caveTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulSpec.scala index 7f4d7fc875a..1f21f990c63 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulSpec.scala @@ -20,6 +20,8 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel @@ -64,3 +66,11 @@ class CMulSpec extends FlatSpec with Matchers { layer2.gradWeight should be (layer1.gradWeight.mul(0.5)) } } + +class CMulSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Tensor[Float](5, 1).apply1(e => Random.nextFloat()) + val cmul = CMul[Float](Array(5, 1)).setName("cmul") + runSerializationTest(cmul, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulTableSpec.scala index 774e8e56ad8..8ee82cb0f27 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulTableSpec.scala @@ -16,9 +16,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class CMulTableSpec extends FlatSpec with Matchers { "CMulTable" should "be correct when input is scalar" in { val module = CMulTable[Float]() @@ -41,3 +44,15 @@ class CMulTableSpec extends FlatSpec with Matchers { } } +class CMulTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + + val cmulTable = CMulTable[Float]().setName("cmulTable") + runSerializationTest(cmulTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClampSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClampSpec.scala new file mode 100644 index 00000000000..227ed3d93bb --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClampSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ClampSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Tensor[Float](10).apply1(e => Random.nextFloat()) + val clamp = Clamp[Float](1, 10).setName("clamp") + runSerializationTest(clamp, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CrossProductSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CrossProductSpec.scala index 9324298ef6d..7851803bf57 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CrossProductSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CrossProductSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class CrossProductSpec extends FlatSpec with Matchers { @@ -121,3 +122,12 @@ class CrossProductSpec extends FlatSpec with Matchers { } } + +class CrossProductSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val crossProd = CrossProduct[Float]() + val input = T(Tensor[Float](T(1.0f, 2.0f)), + Tensor[Float](T(2.0f, 3.0f)), Tensor[Float](T(3.0f, 4.0f))) + runSerializationTest(crossProd, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/EuclideanSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/EuclideanSpec.scala index 658838d56bc..36297ac1b4f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/EuclideanSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/EuclideanSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -77,3 +78,11 @@ class EuclideanSpec extends FlatSpec with Matchers { layer2.gradWeight should be (layer1.gradWeight.mul(2)) } } + +class EuclideanSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val euclidean = Euclidean[Float](7, 7).setName("euclidean") + val input = Tensor[Float](8, 7).apply1(_ => Random.nextFloat()) + runSerializationTest(euclidean, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ExpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ExpSpec.scala index 85c7738aa0d..4a8560e251f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ExpSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ExpSpec.scala @@ -16,9 +16,13 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.ops.Exp import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class ExpSpec extends FlatSpec with Matchers { "A Exp" should "generate correct output" in { @@ -53,3 +57,11 @@ class ExpSpec extends FlatSpec with Matchers { gradInput should equal (expectedGradInput) } } + +class ExpSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val exp = Exp[Float]().setName("exp") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(exp, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardShrinkSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardShrinkSpec.scala new file mode 100644 index 00000000000..bd7cd574607 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardShrinkSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class HardShrinkSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val hardShrink = HardShrink[Float]().setName("hardShrink") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(hardShrink, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoidSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoidSpec.scala new file mode 100644 index 00000000000..c5cf56cd33c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoidSpec.scala @@ -0,0 +1,28 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class HardSigmoidSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val hardSigmoid = HardSigmoid[Float]().setName("hardSigmoid") + val input = Tensor[Float](2, 2).rand() + runSerializationTest(hardSigmoid, input) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala index 03dd5eb548b..a9948961f9f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class JoinTableSpec extends FlatSpec with Matchers { @@ -36,3 +39,15 @@ class JoinTableSpec extends FlatSpec with Matchers { } } + +class JoinTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val joinTable = JoinTable[Float](2, 2).setName("joinTable") + val input1 = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + val input = T() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(joinTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSigmoidSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSigmoidSpec.scala index aff1541e8e4..d251ff0aead 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSigmoidSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSigmoidSpec.scala @@ -16,8 +16,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class LogSigmoidSpec extends FlatSpec with Matchers { "A LogSigmoid Module " should "generate correct output" in { @@ -70,3 +73,11 @@ class LogSigmoidSpec extends FlatSpec with Matchers { gradInput should be(expectedGrad) } } + +class LogSigmoidSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val logSigmoid = LogSigmoid[Float]().setName("logSigmoid") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(logSigmoid, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MulSpec.scala index f0e1fd1afc0..2cd2f6d96da 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MulSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MulSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -49,3 +50,11 @@ class MulSpec extends FlatSpec with Matchers { layer2.gradWeight should be (layer1.gradWeight.mul(2)) } } + +class MulSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mul = Mul[Float]().setName("mul") + val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(mul, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenaltySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenaltySpec.scala index 557baf681cd..aca52397b63 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenaltySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeEntropyPenaltySpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class NegativeEntropyPenaltySpec extends FlatSpec with Matchers { "NegativeEntropyPenalty forward" should "be correct" in { @@ -39,3 +42,11 @@ class NegativeEntropyPenaltySpec extends FlatSpec with Matchers { gradInput.almostEqual(expected, 1e-5) should be (true) } } + +class NegativeEntropyPenaltySerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val penalty = NegativeEntropyPenalty[Float](0.01).setName("NegativeEntropyPenalty") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(penalty, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReLUSpec.scala index f29692089e6..66cbd4a724a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReLUSpec.scala @@ -18,8 +18,10 @@ package com.intel.analytics.bigdl.nn import org.scalatest.FlatSpec import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.math.abs +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class ReLUSpec extends FlatSpec { @@ -131,3 +133,11 @@ class ReLUSpec extends FlatSpec { assert(gradInput == gradOutput) } } + +class ReLUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val relu = ReLU[Float]().setName("relu") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(relu, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala index 39f27a48bea..a35ae4ccca8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinearSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class ResizeBilinearSpec extends FlatSpec with Matchers { private val input = Tensor[Float](T(T( T( @@ -147,3 +150,11 @@ class ResizeBilinearSpec extends FlatSpec with Matchers { } } } + +class ResizeBilinearSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Tensor[Float](1, 3, 2, 3).apply1( _ => Random.nextFloat()) + val resizeBilinear = ResizeBilinear[Float](3, 2).setName("resizeBilinear") + runSerializationTest(resizeBilinear, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftShrinkSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftShrinkSpec.scala new file mode 100644 index 00000000000..a298b302535 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftShrinkSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SoftShrinkSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val softShrink = SoftShrink[Float]().setName("softShrink") + val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(softShrink, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala index a309ad33f09..7b4a3aba759 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala @@ -24,6 +24,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random import com.intel.analytics.bigdl.utils.{Shape, T, TestUtils} @@ -3037,3 +3038,12 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { TestUtils.compareOutputShape(layer, Shape(12, 12, 4)) should be (true) } } + +class SpatialConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialConvolution = SpatialConvolution[Float](3, 4, 2, 2). + setName("spatialConvolution") + val input = Tensor[Float](1, 3, 5, 5).apply1( e => Random.nextFloat()) + runSerializationTest(spatialConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRNSpec.scala index a4be167fd8d..97403cc0896 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRNSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class SpatialCrossMapLRNSpec extends FlatSpec with Matchers { private def referenceLRNForwardAcrossChannels @@ -157,3 +160,12 @@ class SpatialCrossMapLRNSpec extends FlatSpec with Matchers { output should be(outputRef) } } + +class SpatialCrossMapLRNSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialCrossMapLRN = SpatialCrossMapLRN[Float](5, 0.01, 0.75, 1.0). + setName("spatialCrossMapLRN") + val input = Tensor[Float](2, 2, 2, 2).apply1( e => Random.nextFloat()) + runSerializationTest(spatialCrossMapLRN, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala index ea3d6eecd17..7fcf8221d00 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.{Shape, T, TestUtils} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @@ -228,3 +229,12 @@ class SpatialFullConvolutionSpec extends FlatSpec with Matchers { } } + +class SpatialFullConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialFullConvolution = SpatialFullConvolution[Float](1, 1, + 2, 2, 1, 1, 0, 0).setName("spatialFullConvolution") + val input = Tensor[Float](1, 3, 3).apply1(e => Random.nextFloat()) + runSerializationTest(spatialFullConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhShrinkSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhShrinkSpec.scala new file mode 100644 index 00000000000..810596b900f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhShrinkSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class TanhShrinkSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val tanhShrink = TanhShrink[Float]().setName("tanhShrink") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(tanhShrink, input) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala index 05dda1ab83e..e51bd05e27a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedSpec.scala @@ -19,8 +19,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class TimeDistributedSpec extends FlatSpec with Matchers { "A TimeDistributed Module" should "setExtraParam works correctly" in { RNG.setSeed(100) @@ -240,3 +243,12 @@ class TimeDistributedSpec extends FlatSpec with Matchers { grad should be(grad2) } } + +class TimeDistributedSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val timeDistributed = TimeDistributed[Float](Linear[Float](5, 5)). + setName("timeDistributed") + val input = Tensor[Float](2, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(timeDistributed, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ApproximateEqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ApproximateEqualSpec.scala new file mode 100644 index 00000000000..d960ad377c0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ApproximateEqualSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class ApproximateEqualSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val approximateEqual = ApproximateEqual[Float](0.01f).setName("approximateEqual") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(approximateEqual, input, approximateEqual. + asInstanceOf[ModuleToOperation[Float]].module.getClass + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CeilSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CeilSpec.scala new file mode 100644 index 00000000000..cd88acc984a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CeilSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class CeilSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val ceil = Ceil[Float, Float]().setName("ceil") + val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(ceil, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossEntropySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossEntropySpec.scala new file mode 100644 index 00000000000..38946988eaf --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossEntropySpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class CrossEntropySerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val crossEntropy = CrossEntropy[Float]().setName("crossEntropy") + val output = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val label = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + val input = T(output, label) + runSerializationTest(crossEntropy, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilterSpec.scala new file mode 100644 index 00000000000..d0661714023 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilterSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class Dilation2DBackpropFilterSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Dilation2DBackpropFilter[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()) + + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala index c287b1626a8..f2fb1616ad9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.{FloatType, LongType, StringType, Tensor} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import org.tensorflow.example._ import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString @@ -75,3 +76,45 @@ class ParseExampleSpec extends FlatSpec with Matchers { } } + +class ParseExampleSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + + val floatBuilder = FloatList.newBuilder() + .addValue(0.0f).addValue(1.0f).addValue(2.0f) + val floatFeature = Feature.newBuilder().setFloatList(floatBuilder).build() + + val longBuilder = Int64List.newBuilder() + .addValue(0).addValue(1).addValue(2) + val longFeature = Feature.newBuilder().setInt64List(longBuilder).build() + + val bytesBuilder = BytesList.newBuilder().addValue(ByteString.copyFromUtf8("abcd")) + val bytesFeature = Feature.newBuilder().setBytesList(bytesBuilder).build() + + val features = Features.newBuilder() + .putFeature("floatFeature", floatFeature) + .putFeature("longFeature", longFeature) + .putFeature("bytesFeature", bytesFeature) + val example = Example.newBuilder().setFeatures(features).build() + val length = example.getSerializedSize + val data = new Array[Byte](length) + val outputStream = CodedOutputStream.newInstance(data) + example.writeTo(outputStream) + + val exampleParser = new ParseExample[Float](3, Seq(FloatType, LongType, StringType), + Seq(Array(3), Array(3), Array())).setName("parseExample") + + val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) + val names = Tensor[ByteString]() + val key1 = Tensor[ByteString](Array(ByteString.copyFromUtf8("floatFeature")), Array[Int]()) + val key2 = Tensor[ByteString](Array(ByteString.copyFromUtf8("longFeature")), Array[Int]()) + val key3 = Tensor[ByteString](Array(ByteString.copyFromUtf8("bytesFeature")), Array[Int]()) + + val default1 = Tensor[Float]() + val default2 = Tensor[Long]() + val default3 = Tensor[ByteString]() + val input = T(serialized, names, key1, key2, key3, default1, default2, default3) + runSerializationTest(exampleParser, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala index 0a76d8a0c2b..dec9e170584 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala @@ -15,10 +15,14 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.Select import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class SelectSpec extends FlatSpec with Matchers { "select" should "be correct when condition is true" in { val cond = Tensor.scalar[Boolean](true) @@ -38,3 +42,14 @@ class SelectSpec extends FlatSpec with Matchers { ops.forward(T(cond, t, e)) should be(e) } } + +class SelectSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val select = Select[Float]().setName("select") + val cond = Tensor.scalar[Boolean](true) + val t = Tensor[Int](T(1)) + val e = Tensor[Int](T(2)) + val input = T(cond, t, e) + runSerializationTest(select, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 3b3103c3bf0..dc11ee5263b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -71,7 +71,19 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.keras.Permute" -> "com.intel.analytics.bigdl.keras.nn.PermuteSerialTest", "com.intel.analytics.bigdl.nn.keras.Model" -> - "com.intel.analytics.bigdl.keras.nn.ModelSerialTest" + "com.intel.analytics.bigdl.keras.nn.ModelSerialTest", + "com.intel.analytics.bigdl.nn.keras.GlobalAveragePooling3D" -> + "com.intel.analytics.bigdl.keras.nn.GlobalAveragePooling3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.GlobalAveragePooling1D" -> + "com.intel.analytics.bigdl.keras.nn.GlobalAveragePooling1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.ZeroPadding3D" -> + "com.intel.analytics.bigdl.keras.nn.ZeroPadding3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Dropout" -> + "com.intel.analytics.bigdl.keras.nn.DropoutSerialTest", + "module com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling1D" -> + "module com.intel.analytics.bigdl.keras.nn.GlobalMaxPooling1D", + "com.intel.analytics.bigdl.nn.keras.Flatten" -> + "com.intel.analytics.bigdl.keras.nn.Flatten" ) private val suffix = "SerialTest" From 0ddf7f694411c0434fb86dd970f3ec768a480341 Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Fri, 9 Feb 2018 16:55:54 +0800 Subject: [PATCH 0696/1065] Scala class documentation for keras new layers (#2286) * documentation +4 * documentation +5 * documentation * modify * remove one blank * modify * add abstract class doc * modify * add one documentation * documentation +2 * documentation +9 * update * modify * modify again * change doc style * modify * update --- .../bigdl/dllib/keras/Activation.scala | 6 ++-- .../dllib/keras/AtrousConvolution1D.scala | 25 ++++++++++++++ .../dllib/keras/AtrousConvolution2D.scala | 30 +++++++++++++++++ .../bigdl/dllib/keras/AveragePooling1D.scala | 14 ++++++++ .../bigdl/dllib/keras/AveragePooling2D.scala | 16 +++++++++ .../bigdl/dllib/keras/AveragePooling3D.scala | 15 +++++++++ .../dllib/keras/BatchNormalization.scala | 22 +++++++++++++ .../bigdl/dllib/keras/ConvLSTM2D.scala | 33 +++++++++++++++++++ .../bigdl/dllib/keras/Convolution1D.scala | 25 ++++++++++++++ .../bigdl/dllib/keras/Convolution2D.scala | 5 +-- .../bigdl/dllib/keras/Convolution3D.scala | 31 +++++++++++++++++ .../bigdl/dllib/keras/Cropping1D.scala | 11 +++++++ .../bigdl/dllib/keras/Cropping2D.scala | 13 ++++++++ .../bigdl/dllib/keras/Cropping3D.scala | 17 ++++++++++ .../bigdl/dllib/keras/Deconvolution2D.scala | 33 +++++++++++++++++++ .../analytics/bigdl/dllib/keras/Dense.scala | 3 +- .../analytics/bigdl/dllib/keras/Dropout.scala | 1 + .../bigdl/dllib/keras/Embedding.scala | 16 +++++++++ .../analytics/bigdl/dllib/keras/Flatten.scala | 1 + .../analytics/bigdl/dllib/keras/GRU.scala | 3 +- .../bigdl/dllib/keras/GaussianDropout.scala | 1 + .../dllib/keras/GlobalAveragePooling1D.scala | 3 +- .../dllib/keras/GlobalAveragePooling2D.scala | 11 +++++++ .../dllib/keras/GlobalAveragePooling3D.scala | 5 +-- .../dllib/keras/GlobalMaxPooling1D.scala | 3 +- .../dllib/keras/GlobalMaxPooling2D.scala | 11 +++++++ .../dllib/keras/GlobalMaxPooling3D.scala | 5 +-- .../bigdl/dllib/keras/GlobalPooling2D.scala | 5 +++ .../analytics/bigdl/dllib/keras/Highway.scala | 3 +- .../analytics/bigdl/dllib/keras/LSTM.scala | 5 +-- .../dllib/keras/LocallyConnected1D.scala | 24 ++++++++++++++ .../bigdl/dllib/keras/MaxPooling1D.scala | 13 ++++++++ .../bigdl/dllib/keras/MaxPooling2D.scala | 3 +- .../bigdl/dllib/keras/MaxPooling3D.scala | 15 +++++++++ .../bigdl/dllib/keras/MaxoutDense.scala | 19 +++++++++++ .../analytics/bigdl/dllib/keras/Permute.scala | 11 +++++++ .../bigdl/dllib/keras/Pooling1D.scala | 5 +++ .../bigdl/dllib/keras/Pooling2D.scala | 5 +++ .../bigdl/dllib/keras/Pooling3D.scala | 5 +++ .../bigdl/dllib/keras/RepeatVector.scala | 10 ++++++ .../analytics/bigdl/dllib/keras/Reshape.scala | 5 +-- .../bigdl/dllib/keras/SimpleRNN.scala | 3 +- .../bigdl/dllib/keras/SpatialDropout1D.scala | 2 +- .../bigdl/dllib/keras/SpatialDropout2D.scala | 2 +- .../bigdl/dllib/keras/SpatialDropout3D.scala | 2 +- .../bigdl/dllib/keras/UpSampling1D.scala | 11 +++++++ .../bigdl/dllib/keras/UpSampling2D.scala | 14 ++++++++ .../bigdl/dllib/keras/UpSampling3D.scala | 14 ++++++++ .../bigdl/dllib/keras/ZeroPadding1D.scala | 12 +++++++ .../bigdl/dllib/keras/ZeroPadding2D.scala | 15 +++++++++ 50 files changed, 539 insertions(+), 23 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala index aa96a89ebe5..7c2a77a4623 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala @@ -25,12 +25,12 @@ import scala.reflect.ClassTag /** * Simple activation function to be applied to the output. - * When you use this layer as the first layer of a model, you need to provide the argument - * inputShape (a Single Shape, does not include the batch dimension). - * * Available activations: 'tanh', 'relu', 'sigmoid', 'softmax', 'softplus', * 'softsign', 'hard_sigmoid'. * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * * @param activation Name of the activation function as string. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala index c0ce4cd78fb..a4ac6568dbb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala @@ -26,6 +26,31 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Atrous Convolution operator for filtering neighborhoods of 1-D inputs. + * A.k.a dilated convolution or convolution with holes. + * Bias will be included in this layer. + * The input of this layer should be 3D. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param nbFilter Number of convolution kernels to use. + * @param filterLength The extension (spatial or temporal) of each filter. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param subsampleLength Factor by which to subsample output. Integer. Default is 1. + * @param atrousRate Factor for kernel dilation. Also called filter_dilation elsewhere. + * Integer. Default is 1. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class AtrousConvolution1D[T: ClassTag]( val nbFilter: Int, val filterLength: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala index a2a0953d725..301344d1eff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala @@ -25,6 +25,36 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Atrous Convolution operator for filtering windows of 2-D inputs. + * A.k.a dilated convolution or convolution with holes. + * Bias will be included in this layer. + * Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th'). + * The input of this layer should be 4D. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * e.g. input_shape=Shape(3, 128, 128) for 128x128 RGB pictures. + * + * @param nbFilter Number of convolution filters to use. + * @param nbRow Number of rows in the convolution kernel. + * @param nbCol Number of columns in the convolution kernel. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param subsample Int array of length 2. Factor by which to subsample output. + * Also called strides elsewhere. Default is (1, 1). + * @param atrousRate Int array of length 2. Factor for kernel dilation. + * Also called filter_dilation elsewhere. Default is (1, 1). + * @param dimOrdering Format of input data. Please use DataFormat.NCHW (dimOrdering='th'). + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class AtrousConvolution2D[T: ClassTag]( val nbFilter: Int, val nbRow: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling1D.scala index bddfb2d8e81..f7ea156a6a2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling1D.scala @@ -25,6 +25,20 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Applies average pooling operation for temporal data. + * The input of this layer should be 3D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param poolLength Size of the region to which average pooling is applied. Integer. Default is 2. + * @param stride Factor by which to downscale. Positive integer, or -1. 2 will halve the input. + * If -1, it will default to poolLength. Default is -1, and in this case it will + * be equal to poolSize. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class AveragePooling1D[T: ClassTag]( poolLength: Int = 2, stride: Int = -1, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala index c12630293fe..cdb996e382d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling2D.scala @@ -24,6 +24,22 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Applies average pooling operation for spatial data. + * The input of this layer should be 4D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param poolSize Int array of length 2 corresponding to the downscale vertically and + * horizontally. Default is (2, 2), which will halve the image in each dimension. + * @param strides Int array of length 2. Stride values. Default is null, and in this case it will + * be equal to poolSize. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class AveragePooling2D[T: ClassTag]( poolSize: Array[Int] = Array(2, 2), strides: Array[Int] = null, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala index d5cadcdf577..0e671d69153 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala @@ -24,6 +24,21 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Applies average pooling operation for 3D data (spatial or spatio-temporal). + * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * The input of this layer should be 5D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param poolSize Int array of length 3. Factors by which to downscale (dim1, dim2, dim3). + * Default is (2, 2, 2), which will halve the image in each dimension. + * @param strides Int array of length 3. Stride values. Default is null, and in this case it will + * be equal to poolSize. + * @param dimOrdering Format of input data. Please use 'CHANNEL_FIRST' (dimOrdering='th'). + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class AveragePooling3D[T: ClassTag]( poolSize: Array[Int] = Array(2, 2, 2), strides: Array[Int] = null, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala index 359a4daa358..0e839a60a10 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala @@ -24,6 +24,28 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Batch normalization layer. + * Normalize the activations of the previous layer at each batch, + * i.e. applies a transformation that maintains the mean activation + * close to 0 and the activation standard deviation close to 1. + * Feature-wise normalization, each feature map in the input will be normalized separately. + * The input of this layer should be 4D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param epsilon Small Double > 0. Fuzz parameter. Default is 0.001. + * @param momentum Double. Momentum in the computation of the exponential average + * of the mean and standard deviation of the data, + * for feature-wise normalization. Default is 0.99. + * @param betaInit Name of initialization function for shift parameter. Default is 'zero'. + * @param gammaInit Name of initialization function for scale parameter. Default is 'one'. + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * For NCHW, axis along which to normalize is 1. For NHWC, axis is 3. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class BatchNormalization[T: ClassTag]( val epsilon: Double = 0.001, val momentum: Double = 0.99, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala index 0a090f1c1ba..0db623b6593 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala @@ -25,6 +25,39 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Convolutional LSTM. + * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * BorderMode of this layer will be 'same'. + * The convolution kernel for this layer is a square kernel. + * The input of this layer should be 5D. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param nbFilter Number of convolution filters to use. + * @param nbKernel Size of the convolution kernel. Integer. + * @param activation Activation function to use. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * Default is 'tanh'. + * @param innerActivation Activation function for inner cells. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * Default is 'hard_sigmoid'. + * @param dimOrdering Format of input data. Please use "CHANNEL_FIRST" (dimOrdering='th'). + * @param subsample Int. Default is 1. Factor by which to subsample output. + * Also called strides elsewhere. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param uRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the recurrent weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param returnSequences Boolean. Default is False. Whether to return the last + * output in the output sequence, or the full sequence. + * @param goBackwards Boolean. Default is False. If True, process the input sequence backwards. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class ConvLSTM2D[T: ClassTag]( val nbFilter: Int, val nbKernel: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala index 7f6f5de8f54..e6737792d7c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala @@ -26,6 +26,31 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Applies convolution operator for filtering neighborhoods of 1-D inputs. + * You can also use Conv1D as an alias of this layer. + * The input of this layer should be 3D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param nbFilter Number of convolution filters to use. + * @param filterLength The extension (spatial or temporal) of each filter. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @param subsampleLength Factor by which to subsample output. Integer. Default is 1. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class Convolution1D[T: ClassTag]( val nbFilter: Int, val filterLength: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala index 3925adcc426..f96c7627d5c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala @@ -27,11 +27,12 @@ import scala.reflect.ClassTag /** * Applies a 2D convolution over an input image composed of several input planes. + * You can also use Conv2D as an alias of this layer. + * The input of this layer should be 4D. + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension), * e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures. - * You can also use Conv2D as an alias of this layer. - * The input of this layer should be 4D. * * @param nbFilter Number of convolution filters to use. * @param nbRow Number of rows in the convolution kernel. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala index 1babc08f725..28bb022699a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala @@ -25,6 +25,37 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Applies convolution operator for filtering windows of three-dimensional inputs. + * You can also use Conv3D as an alias of this layer. + * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * The input of this layer should be 5D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension), + * e.g. inputShape=Shape(3, 10, 128, 128) 10 frames of 128x128 RGB pictures. + * + * @param nbFilter Number of convolution filters to use. + * @param kernelDim1 Length of the first dimension in the convolution kernel. + * @param kernelDim2 Length of the second dimension in the convolution kernel. + * @param kernelDim3 Length of the third dimension in the convolution kernel. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @param subsample Int array of length 3. Factor by which to subsample output. + * Also called strides elsewhere. Default is (1, 1, 1). + * @param dimOrdering Format of the input data. Please use "CHANNEL_FIRST" (dimOrdering='th'). + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class Convolution3D[T: ClassTag]( val nbFilter: Int, val kernelDim1: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping1D.scala index 12de43564b4..dfb5ae525ef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping1D.scala @@ -24,6 +24,17 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Cropping layer for 1D input (e.g. temporal sequence). + * The input of this layer should be 3D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param cropping Int array of length 2. How many units should be trimmed off + * at the beginning and end of the cropping dimension. Default is (1, 1). + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class Cropping1D[T: ClassTag]( val cropping: Array[Int] = Array(1, 1), val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala index 74ec2cca982..90099338573 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping2D.scala @@ -23,6 +23,19 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Cropping layer for 2D input (e.g. picture). + * The input of this layer should be 4D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param heightCrop Int array of length 2. Height of the 2 cropping dimension. Default is (0, 0). + * @param widthCrop Int array of length 2. Width of the 2 cropping dimension. Default is (0, 0). + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class Cropping2D[T: ClassTag]( val heightCrop: Array[Int] = Array(0, 0), val widthCrop: Array[Int] = Array(0, 0), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala index d53a0daba90..8bbbade9422 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Cropping3D.scala @@ -23,6 +23,23 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Cropping layer for 3D data (e.g. spatial or spatio-temporal). + * The input of this layer should be 5D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param dim1Crop Int array of length 2. Kernel dim1 of the three cropping dimensions. + * Default is (1, 1). + * @param dim2Crop Int array of length 2. Kernel dim2 of the three cropping dimensions. + * Default is (1, 1). + * @param dim3Crop Int array of length 2. Kernel dim3 of the three cropping dimensions. + * Default is (1, 1). + * @param dimOrdering Format of input data. Either 'CHANNEL_FIRST' (dimOrdering='th') or + * 'CHANNEL_LAST' (dimOrdering='tf'). Default is 'CHANNEL_FIRST'. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class Cropping3D[T: ClassTag]( val dim1Crop: Array[Int] = Array(1, 1), val dim2Crop: Array[Int] = Array(1, 1), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala index c4262ddecb5..114dd004a5f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala @@ -25,6 +25,39 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Transposed convolution operator for filtering windows of 2-D inputs. + * The need for transposed convolutions generally arises from the desire to use a transformation + * going in the opposite direction of a normal convolution, i.e., from something that has + * the shape of the output of some convolution to something that has the shape of its input + * while maintaining a connectivity pattern that is compatible with said convolution. + * BorderMode of this layer will be 'valid'. + * The input of this layer should be 4D. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures. + * + * @param nbFilter Number of transposed convolution filters to use. + * @param nbRow Number of rows in the transposed convolution kernel. + * @param nbCol Number of columns in the transposed convolution kernel. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param subsample Int array of length 2. The step of the convolution in the height and + * width dimension. Also called strides elsewhere. Default is (1, 1). + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class Deconvolution2D[T: ClassTag]( val nbFilter: Int, val nbRow: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala index e182ed87630..75b5a05f4dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala @@ -27,9 +27,10 @@ import scala.reflect.ClassTag /** * A densely-connected NN layer. + * The most common input is 2D. + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The most common input is 2D. * * @param outputDim The size of output dimension. * @param init Initialization method for the weights of the layer. Default is Xavier. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala index 01aae540508..8a6dea3deed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala @@ -26,6 +26,7 @@ import scala.reflect.ClassTag /** * Applies Dropout to the input by randomly setting a fraction 'p' of input units to 0 at each * update during training time in order to prevent overfitting. + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala index bc32a6a4793..d630d5e6ac3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala @@ -25,6 +25,22 @@ import com.intel.analytics.bigdl.nn.{AddConstant, InitializationMethod, LookupTa import scala.reflect.ClassTag +/** + * Turn positive integers (indexes) into dense vectors of fixed size. + * The input of this layer should be 2D. + * + * This layer can only be used as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param inputDim Int > 0. Size of the vocabulary. + * @param outputDim Int >= 0. Dimension of the dense embedding. + * @param init Initialization method for the weights of the layer. Default is Xavier. + * You can also pass in corresponding string representations such as 'glorot_uniform' + * or 'normal', etc. for simple init methods in the factory method. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the embedding matrix. Default is null. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class Embedding[T: ClassTag]( val inputDim: Int, val outputDim: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala index bcae49540d9..2d55cb44275 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Flatten.scala @@ -25,6 +25,7 @@ import scala.reflect.ClassTag /** * Flattens the input without affecting the batch size. + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala index a89cb50d305..a7dcd687446 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala @@ -26,9 +26,10 @@ import scala.reflect.ClassTag /** * Gated Recurrent Unit architecture. + * The input of this layer should be 3D, i.e. (batch, time steps, input dim). + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 3D, i.e. (batch, time steps, input dim). * * @param outputDim Hidden unit size. Dimension of internal projections and final output. * @param activation Activation function to use. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala index d76dc912f04..57675915221 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala @@ -26,6 +26,7 @@ import scala.reflect.ClassTag /** * Apply multiplicative 1-centered Gaussian noise. * As it is a regularization layer, it is only active at training time. + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala index 4ac478e3fc9..5c08ce1f9c0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala @@ -28,9 +28,10 @@ import scala.reflect.ClassTag /** * Global average pooling operation for temporal data. + * The input of this layer should be 3D. + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 3D. * * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala index ed65837da3e..d3a11208c91 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala @@ -24,6 +24,17 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Global average pooling operation for spatial data. + * The input of this layer should be 4D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param dimOrdering Format of input data. Please use DataFormat.NCHW (dimOrdering='th') + * or DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class GlobalAveragePooling2D[T: ClassTag]( dimOrdering: DataFormat = DataFormat.NCHW, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala index b7a12e2a804..cc2254fa70f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala @@ -28,11 +28,12 @@ import scala.reflect.ClassTag /** * Global Average pooling operation for 3D data. - * When you use this layer as the first layer of a model, you need to provide the argument - * inputShape (a Single Shape, does not include the batch dimension). * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). * The input of this layer should be 5D. * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * * @param dimOrdering Format of input data. Please use 'CHANNEL_FIRST' (dimOrdering='th'). * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala index cdeb1ddf9b7..59acc7e180d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala @@ -28,9 +28,10 @@ import scala.reflect.ClassTag /** * Global max pooling operation for temporal data. + * The input of this layer should be 3D. + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 3D. * * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala index 05afd18f327..0290512d720 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala @@ -24,6 +24,17 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Global max pooling operation for spatial data. + * The input of this layer should be 4D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param dimOrdering Format of input data. Please use DataFormat.NCHW (dimOrdering='th') + * or DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class GlobalMaxPooling2D[T: ClassTag]( dimOrdering: DataFormat = DataFormat.NCHW, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala index 1421b294ebc..161a252f06f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala @@ -28,11 +28,12 @@ import scala.reflect.ClassTag /** * Global Max pooling operation for 3D data. - * When you use this layer as the first layer of a model, you need to provide the argument - * inputShape (a Single Shape, does not include the batch dimension). * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). * The input of this layer should be 5D. * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * * @param dimOrdering Format of input data. Please use 'CHANNEL_FIRST' (dimOrdering='th'). * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala index 04873ab39de..2ae03d36a31 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalPooling2D.scala @@ -23,6 +23,11 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Abstract class for different global pooling 2D layers. + * Do not create a new instance of it or use it in a model. + * Please use its child classes, 'GlobalAveragePooling2D' and 'GlobalMaxPooling2D' instead. + */ abstract class GlobalPooling2D[T: ClassTag]( val dimOrdering: DataFormat = DataFormat.NCHW, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala index a1455beea65..782eb86428f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala @@ -27,9 +27,10 @@ import scala.reflect.ClassTag /** * Densely connected highway network. * Highway layers are a natural extension of LSTMs to feedforward networks. + * The input of this layer should be 2D, i.e. (batch, input dim). + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 2D, i.e. (batch, input dim). * * @param activation Activation function to use. Default is null. * You can also pass in corresponding string representations such as 'relu' diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala index ca4d7359651..0ac04aac606 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala @@ -26,9 +26,10 @@ import scala.reflect.ClassTag /** * Long Short Term Memory unit architecture. + * The input of this layer should be 3D, i.e. (batch, time steps, input dim). + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 3D, i.e. (batch, time steps, input dim). * * @param outputDim Hidden unit size. Dimension of internal projections and final output. * @param activation Activation function to use. @@ -38,7 +39,7 @@ import scala.reflect.ClassTag * @param innerActivation Activation function for inner cells. * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. - * dDefault is 'hard_sigmoid'. + * Default is 'hard_sigmoid'. * @param returnSequences Whether to return the full sequence or only return the last output, * in the output sequence. Default is false. * @param goBackwards Whether the input sequence will be processed backwards. Default is false. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala index 6474e624eb3..3ba8a1b6f75 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala @@ -25,6 +25,30 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Locally-connected layer for 1D inputs. + * The LocallyConnected1D layer works similarly to the TemporalConvolution layer, + * except that weights are unshared, that is, a different set of filters + * is applied at each different patch of the input. + * BorderMode of this layer will be 'valid'. + * The input of this layer should be 3D. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param nbFilter Dimensionality of the output. + * @param filterLength The extension (spatial or temporal) of each filter. + * @param activation Activation function to use. Default is null. + * You can also pass in corresponding string representations such as 'relu' + * or 'sigmoid', etc. for simple activations in the factory method. + * @param subsampleLength Int. Factor by which to subsample output. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the input weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class LocallyConnected1D[T: ClassTag]( val nbFilter: Int, val filterLength: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala index 8a9ed5bcf68..de0f3b54592 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala @@ -25,6 +25,19 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Applies max pooling operation for temporal data. + * The input of this layer should be 3D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param poolLength Size of the region to which max pooling is applied. + * @param stride Factor by which to downscale. Integer, or -1. 2 will halve the input. + * If -1, it will default to poolLength. Default is -1. + * @param borderMode Either 'valid' or 'same'. Default is 'valid'. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ class MaxPooling1D[T: ClassTag]( poolLength: Int = 2, stride: Int = -1, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala index df58b7e11b2..e8464b3ffd7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling2D.scala @@ -26,9 +26,10 @@ import scala.reflect.ClassTag /** * Applies max pooling operation for spatial data. + * The input of this layer should be 4D. + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 4D. * * @param poolSize Int array of length 2 corresponding to the downscale vertically and * horizontally. Default is (2, 2), which will halve the image in each dimension. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala index 8e339c74f4d..2b3209781b1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala @@ -24,6 +24,21 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Applies max pooling operation for 3D data (spatial or spatio-temporal). + * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * The input of this layer should be 5D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param poolSize Int array of length 3. Factors by which to downscale (dim1, dim2, dim3). + * Default is (2, 2, 2), which will halve the image in each dimension. + * @param strides Int array of length 3. Stride values. Default is null, and in this case it will + * be equal to poolSize. + * @param dimOrdering Format of input data. Please use 'CHANNEL_FIRST' (dimOrdering='th'). + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + */ class MaxPooling3D[T: ClassTag]( poolSize: Array[Int] = Array(2, 2, 2), strides: Array[Int] = null, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala index ee9b7cbb3ae..a160798adcb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala @@ -25,6 +25,25 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * A dense maxout layer. + * A `MaxoutDense` layer takes the element-wise maximum of `nbFeature` + * `Dense(inputDim, outputDim)` linear layers. + * This allows the layer to learn a convex, piecewise linear activation function over the inputs. + * The input of this layer should be 2D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param outputDim Int > 0. + * @param nbFeature Number of Dense layers to use internally. Integer. Default is 4. + * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), + * applied to the main weights matrices. Default is null. + * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. + * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). + * Default is true. + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class MaxoutDense[T: ClassTag]( val outputDim: Int, val nbFeature: Int = 4, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala index 8ee7fbe995d..dd0c5e505e1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala @@ -25,6 +25,17 @@ import com.intel.analytics.bigdl.utils.Shape import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +/** + * Permutes the dimensions of the input according to a given pattern. + * Useful for e.g. connecting RNNs and convnets together. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param dims Int array. Permutation pattern, does not include the samples dimension. + * Indexing starts at 1. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class Permute[T: ClassTag]( val dims: Array[Int], val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala index 0ea3bd42372..b1a83dbaf64 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling1D.scala @@ -22,6 +22,11 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Abstract class for different pooling 1D layers. + * Do not create a new instance of it or use it in a model. + * Please use its child classes, 'AveragePooling1D' and 'MaxPooling1D' instead. + */ abstract class Pooling1D[T: ClassTag]( val poolLength: Int = 2, val stride: Int = -1, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala index aabebfc7dcc..e9b7590f97f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling2D.scala @@ -23,6 +23,11 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Abstract class for different pooling 2D layers. + * Do not create a new instance of it or use it in a model. + * Please use its child classes, 'AveragePooling2D' and 'MaxPooling2D' instead. + */ abstract class Pooling2D[T: ClassTag]( val poolSize: Array[Int] = Array(2, 2), val strides: Array[Int] = null, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala index 9acdffbf933..cd88af4c0d6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Pooling3D.scala @@ -22,6 +22,11 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Abstract class for different pooling 3D layers. + * Do not create a new instance of it or use it in a model. + * Please use its child classes, 'AveragePooling3D' and 'MaxPooling3D' instead. + */ abstract class Pooling3D[T: ClassTag]( val poolSize: Array[Int] = Array(2, 2, 2), val strides: Array[Int] = null, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/RepeatVector.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/RepeatVector.scala index 78b671c2d06..6a71a8cd727 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/RepeatVector.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/RepeatVector.scala @@ -24,6 +24,16 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Repeats the input n times. + * The input of this layer should be 2D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param n Repetition factor. Integer. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class RepeatVector[T: ClassTag]( val n: Int, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala index 951a8fe94d0..c1e4923fbfd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala @@ -26,12 +26,13 @@ import scala.reflect.ClassTag /** * Reshapes an output to a certain shape. - * When you use this layer as the first layer of a model, you need to provide the argument - * inputShape (a Single Shape, does not include the batch dimension). * Supports shape inference by allowing one -1 in the target shape. * For example, if inputShape = Shape(2, 3, 4), targetShape = Array(3, -1), * then outputShape will be Shape(3, 8). * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * * @param targetShape Array of int. The target shape that you desire to have. * Batch dimension should be excluded. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala index 927c75d5a44..9083f7c2273 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala @@ -27,9 +27,10 @@ import scala.reflect.ClassTag /** * A fully-connected recurrent neural network cell. The output is to be fed back to input. + * The input of this layer should be 3D, i.e. (batch, time steps, input dim). + * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 3D, i.e. (batch, time steps, input dim). * * @param outputDim Hidden unit size. Dimension of internal projections and final output. * @param activation Activation function to use. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala index 6ccc2fbddb7..397d153a392 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala @@ -32,10 +32,10 @@ import scala.reflect.ClassTag * activations and will otherwise just result in an effective learning rate * decrease. In this case, SpatialDropout1D will help promote independence * between feature maps and should be used instead. + * The input of this layer should be 3D. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 3D. * * @param p Double between 0 and 1. Fraction of the input units to drop. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala index 4b91cf10b6f..ad4795af75f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala @@ -32,10 +32,10 @@ import scala.reflect.ClassTag * activations and will otherwise just result in an effective learning rate * decrease. In this case, SpatialDropout2D will help promote independence * between feature maps and should be used instead. + * The input of this layer should be 4D. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 4D. * * @param p Double between 0 and 1. Fraction of the input units to drop. * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala index 3b66e01027f..252badba08a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala @@ -32,10 +32,10 @@ import scala.reflect.ClassTag * activations and will otherwise just result in an effective learning rate * decrease. In this case, SpatialDropout3D will help promote independence * between feature maps and should be used instead. + * The input of this layer should be 5D. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). - * The input of this layer should be 5D. * * @param p Double between 0 and 1. Fraction of the input units to drop. * @param dimOrdering Format of input data. Either 'CHANNEL_FIRST' (dimOrdering='th') or diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala index de38cb27c4f..2603115291b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala @@ -23,6 +23,17 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Upsampling layer for 1D inputs. + * Repeats each temporal step `length` times along the time axis. + * The input of this layer should be 3D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param length Integer. Upsampling factor. Default is 2. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class UpSampling1D[T: ClassTag]( val length: Int = 2, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala index 8e650a08425..e8e629444cd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala @@ -23,6 +23,20 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Upsampling layer for 2D inputs. + * Repeats the rows and columns of the data by size(0) and size(1) respectively. + * The input of this layer should be 4D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param size Int array of length 2. The upsampling factors for rows and columns. + * Default is (2, 2). + * @param dimOrdering Format of the input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class UpSampling2D[T: ClassTag]( val size: Array[Int] = Array(2, 2), val dimOrdering: DataFormat = DataFormat.NCHW, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala index 2cfb896f824..b809b813b99 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala @@ -23,6 +23,20 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Upsampling layer for 3D inputs. + * Repeats the 1st, 2nd and 3rd dimensions of the data by size(0), size(1) and size(2) respectively. + * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * The input of this layer should be 5D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param size Int array of length 3. The upsampling factors for dim1, dim2 and dim3. + * Default is (2, 2, 2). + * @param dimOrdering Format of the input data. Please use "CHANNEL_FIRST" (dimOrdering='th'). + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class UpSampling3D[T: ClassTag]( val size: Array[Int] = Array(2, 2, 2), val dimOrdering: String = "CHANNEL_FIRST", diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding1D.scala index 68c325f2c9c..6823efb2895 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding1D.scala @@ -24,6 +24,18 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Zero-padding layer for 1D input (e.g. temporal sequence). + * The input of this layer should be 3D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param padding Int array of length 2. + * How many zeros to add at the beginning and at the end of the padding dimension, + * in order '(left_pad, right_pad)'. Default is (1, 1). + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class ZeroPadding1D[T: ClassTag]( val padding: Array[Int] = Array(1, 1), val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding2D.scala index a68b4912d1d..cff49067bf2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ZeroPadding2D.scala @@ -25,6 +25,21 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag +/** + * Zero-padding layer for 2D input (e.g. picture). + * The input of this layer should be 4D. + * + * When you use this layer as the first layer of a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * @param padding Int array of length 4. + * How many zeros to add at the beginning and at the end of the 2 padding dimensions + * (rows and cols), in the order '(top_pad, bottom_pad, left_pad, right_pad)'. + * Default is (1, 1, 1, 1). + * @param dimOrdering Format of the input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ class ZeroPadding2D[T: ClassTag]( val padding: Array[Int] = Array(1, 1, 1, 1), val dimOrdering: DataFormat = DataFormat.NCHW, From 949d901fc416aa70c7abfbb611fe54915e0aa5b7 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Fri, 9 Feb 2018 18:42:19 +0800 Subject: [PATCH 0697/1065] Keras-like API Merge and Wrappers (#2263) * merge * update * timedistributed * bidirectional * update * update * update bidirectional * style * update merge * refine and add ser test * refine timedistributed * fix * fix merge * createmerge * python merge ut * meet review * add doc * fix style * update --- .../bigdl/dllib/keras/Bidirectional.scala | 84 +++++++++ .../analytics/bigdl/dllib/keras/GRU.scala | 7 +- .../analytics/bigdl/dllib/keras/LSTM.scala | 7 +- .../analytics/bigdl/dllib/keras/Merge.scala | 177 ++++++++++++++++++ .../bigdl/dllib/keras/Recurrent.scala | 10 +- .../bigdl/dllib/keras/SimpleRNN.scala | 8 +- .../bigdl/dllib/keras/TimeDistributed.scala | 75 ++++++++ .../bigdl/dllib/nn/TimeDistributed.scala | 4 +- .../utils/python/api/PythonBigDLKeras.scala | 17 ++ .../converters/ShapeConverter.scala | 4 + .../dllib/keras/nn/BidirectionalSpec.scala | 84 +++++++++ .../bigdl/dllib/keras/nn/GRUSpec.scala | 2 +- .../bigdl/dllib/keras/nn/LSTMSpec.scala | 2 +- .../bigdl/dllib/keras/nn/MergeSpec.scala | 105 +++++++++++ .../dllib/keras/nn/TimeDistributedSpec.scala | 61 ++++++ .../KerasModuleSerializerSpec.scala | 30 ++- 16 files changed, 656 insertions(+), 21 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Bidirectional.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/TimeDistributed.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BidirectionalSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TimeDistributedSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Bidirectional.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Bidirectional.scala new file mode 100644 index 00000000000..d0619a1838a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Bidirectional.scala @@ -0,0 +1,84 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Shape, Table} + +import scala.reflect.ClassTag + +/** + * Bidirectional wrapper for RNNs. + * Bidirectional requires RNNs to return the full sequence, i.e. set returnSequences = true. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * Example of creating a bidirectional LSTM: + * Bidirectiona(LSTM(12, returnSequences = true), mergeMode = "sum", inputShape = Shape(32, 32)) + * + * @param layer An instance of a recurrent layer. + * @param mergeMode Mode by which outputs of the forward and backward RNNs will be combined. + * Must be one of: 'sum', 'mul', 'concat', 'ave'. Default is 'concat'. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class Bidirectional[T: ClassTag]( + val layer: Recurrent[T], + val mergeMode: String = "concat", + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + private val mode = mergeMode.toLowerCase() + + require(layer.returnSequences, "Bidirectional requires RNNs to return the full sequence") + require(mode == "sum" || mode == "mul" || mode == "concat" || mode == "ave", + s"Invalid merge mode: $mode") + + override def computeOutputShape(inputShape: Shape): Shape = { + val output = layer.build(inputShape) + if (mode == "concat") { + val outputArray = output.toSingle().toArray + outputArray(outputArray.length-1) = outputArray.last * 2 + Shape(outputArray) + } + else output + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val recurrent = layer.buildCell(input) + val merge = mode match { + case "concat" => JoinTable(input.length -1, input.length -1) + case "sum" => CAddTable() + case "mul" => CMulTable() + case "ave" => CAveTable() + } + BiRecurrent(merge.asInstanceOf[AbstractModule[Table, Tensor[T], T]]).add(recurrent) + } +} + +object Bidirectional { + def apply[@specialized(Float, Double) T: ClassTag]( + layer: Recurrent[T], + mergeMode: String = "concat", + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Bidirectional[T] = { + new Bidirectional[T](layer, mergeMode, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala index a7dcd687446..78ccf978510 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.keras +import com.intel.analytics.bigdl.nn.Cell import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor @@ -62,9 +63,8 @@ class GRU[T: ClassTag]( inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends Recurrent[T](outputDim, returnSequences, goBackwards, inputShape) { - override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { - val input = inputShape.toSingle().toArray - val layer = com.intel.analytics.bigdl.nn.GRU[T]( + override def buildCell(input: Array[Int]): Cell[T] = { + com.intel.analytics.bigdl.nn.GRU[T]( inputSize = input(2), outputSize = outputDim, activation = activation.asInstanceOf[TensorModule[T]], @@ -72,7 +72,6 @@ class GRU[T: ClassTag]( wRegularizer = wRegularizer, uRegularizer = uRegularizer, bRegularizer = bRegularizer) - super.processParameters(layer) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala index 0ac04aac606..73baf4352c0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.keras +import com.intel.analytics.bigdl.nn.Cell import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor @@ -62,9 +63,8 @@ class LSTM[T: ClassTag]( inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends Recurrent[T](outputDim, returnSequences, goBackwards, inputShape) { - override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { - val input = inputShape.toSingle().toArray - val layer = com.intel.analytics.bigdl.nn.LSTM[T]( + override def buildCell(input: Array[Int]): Cell[T] = { + com.intel.analytics.bigdl.nn.LSTM[T]( inputSize = input(2), hiddenSize = outputDim, activation = activation.asInstanceOf[TensorModule[T]], @@ -72,7 +72,6 @@ class LSTM[T: ClassTag]( wRegularizer = wRegularizer, uRegularizer = uRegularizer, bRegularizer = bRegularizer) - super.processParameters(layer) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala new file mode 100644 index 00000000000..56a99253851 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala @@ -0,0 +1,177 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.{CAddTable, CAveTable, CMaxTable, CMulTable, CosineDistance, DotProduct, JoinTable, ParallelTable, Sequential => TSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{MultiShape, Shape} + +import scala.reflect.ClassTag + +/** + * Used to merge a list of tensors into a single tensor, following some merge mode. + * Merge must have at least two input layers. + * + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape for input layers (a Single Shape, does not include the batch dimension). + * + * @param layers A list of layer instances. Must be more than one layer. + * @param mode Merge mode. String, must be one of: 'sum', 'mul', 'concat', 'ave', 'cos', + * 'dot', 'max'. Default is 'sum'. + * @param concatAxis Integer, axis to use in mode concat. Only specify this when mode is 'concat'. + * Default is -1, meaning the last axis of input. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class Merge[T: ClassTag]( + val layers: Array[AbstractModule[Activity, Activity, T]] = null, + val mode: String = "sum", + val concatAxis: Int = -1, + // MultiShape isn't directly supported for serialization. Use Shape instead. + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](Merge.calcBatchInputShape(inputShape, layers)) { + + private val mergeMode = mode.toLowerCase() + private var axis = concatAxis + + require(mergeMode == "sum" || mergeMode == "mul" || mergeMode == "concat" || mergeMode == "ave" + || mergeMode == "cos" || mergeMode == "dot" || mergeMode == "max", + s"Invalid merge mode: $mergeMode") + require(layers.length >= 2, s"Merge must have at least two input layers " + + s"but found ${layers.length}") + + private def computeOutputShapeForConcat(input: List[Shape]): Shape = { + import scala.util.control.Breaks._ + val input1 = input.head.toSingle().toArray + val output = input1.clone() + require(Math.abs(concatAxis) < output.length, s"Invalid concat axis $concatAxis") + axis = if (concatAxis < 0) concatAxis + output.length else concatAxis + var i = 1 + while (i < input.length) { + val input_i = input(i).toSingle().toArray + var j = 0 + while (j < input_i.length) { + if (j != axis) require(input_i(j)==output(j), s"Incompatible input dimension for merge " + + s"mode concat: (${output.deep.mkString(", ")}), " + + s"(${input_i.deep.mkString(", ")})") + j += 1 + } + if (output(axis) == -1 || input_i(axis) == -1) { + output(i) = -1 + break + } + output(axis) = output(axis) + input_i(axis) + i += 1 + } + Shape(output) + } + + private def checkSameInputShape(input: List[Shape]): Unit = { + val input1 = input.head.toSingle().toArray + var i = 1 + while (i < input.length) { + val input_i = input(i).toSingle().toArray + require(input_i.sameElements(input1), s"Incompatible input dimension for " + + s"merge mode $mergeMode: (${input1.deep.mkString(", ")}), " + + s"(${input_i.deep.mkString(", ")})") + i += 1 + } + } + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toMulti() + val input1 = input.head.toSingle().toArray + if (mergeMode == "concat") { + computeOutputShapeForConcat(input) + } + else { + checkSameInputShape(input) + if (mergeMode == "dot" || mergeMode == "cos") { + require(input.head.toSingle().length <=2, s"For merge mode $mergeMode, 3D input " + + s"or above is currently not supported, got input dim ${input.head.toSingle().length}") + require(input.length == 2, s"Merge mode $mergeMode takes exactly two layers, " + + s"but got ${input.length}") + if (mergeMode == "dot") Shape(-1, 1) else Shape(-1, 1, 1) + } + else { + input.head + } + } + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toMulti() + val model = TSequential[T]() + val parallel = ParallelTable() + var i = 0 + while(i < layers.length) { + val tlayer = layers(i) match { + case k: KerasLayer[_, _, T] => k.labor + case t: AbstractModule[Activity, Activity, T] => t + } + parallel.add(tlayer) + i += 1 + } + model.add(parallel) + val seq = TSequential[T]() + val layer = mergeMode match { + case "sum" => CAddTable() + case "mul" => CMulTable() + case "max" => CMaxTable() + case "ave" => CAveTable() + case "concat" => JoinTable(axis, input.length) + case "dot" => + seq.add(DotProduct()) + seq.add(com.intel.analytics.bigdl.nn.Reshape(Array(1), Some(true))) + seq + case "cos" => + seq.add(CosineDistance()) + seq.add(com.intel.analytics.bigdl.nn.Reshape(Array(1, 1), Some(true))) + seq + } + model.add(layer) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object Merge { + def calcBatchInputShape[T: ClassTag]( + inputShape: Shape = null, + layers: Array[AbstractModule[Activity, Activity, T]]): Shape = { + val batchInputShape = KerasLayer.addBatch(inputShape) + val actualInputShape = + MultiShape(layers.map { layer => + layer.build(layer.getInputShape()) + }.toList) + if (batchInputShape != null) { + require(batchInputShape.isInstanceOf[MultiShape], + "Merge requires inputShape to be MultiShape") + require(batchInputShape.toMulti().equals(actualInputShape.toMulti()), + "Actual layer input shapes are not the same as expected layer input shapes") + } + actualInputShape + } + + def apply[@specialized(Float, Double) T: ClassTag]( + layers: List[AbstractModule[Activity, Activity, T]] = null, + mode: String = "sum", + concatAxis: Int = -1, + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Merge[T] = { + new Merge[T](layers.toArray, mode, concatAxis, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala index ce9236f167b..8a736e6b5b1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Recurrent.scala @@ -44,14 +44,18 @@ abstract class Recurrent[T: ClassTag]( else Shape(input(0), outputDim) } - def processParameters(rnn: Cell[T]): AbstractModule[Tensor[T], Tensor[T], T] = { + def buildCell(input: Array[Int]): Cell[T] = { + throw new RuntimeException("Recurrent cell haven't been implemented yet.") + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray val model = TSequential[T]() if (goBackwards) model.add(Reverse(2)) val rec = com.intel.analytics.bigdl.nn.Recurrent[T]() - rec.add(rnn) + rec.add(buildCell(input)) model.add(rec) if (!returnSequences) model.add(Select(2, -1)) model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } - } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala index 9083f7c2273..8d21a0c2843 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.keras import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} -import com.intel.analytics.bigdl.nn.RnnCell +import com.intel.analytics.bigdl.nn.{Cell, RnnCell} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -58,9 +58,8 @@ class SimpleRNN[T: ClassTag]( inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends Recurrent[T](outputDim, returnSequences, goBackwards, inputShape) { - override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { - val input = inputShape.toSingle().toArray - val layer = RnnCell( + override def buildCell(input: Array[Int]): Cell[T] = { + RnnCell( inputSize = input(2), hiddenSize = outputDim, activation = activation.asInstanceOf[TensorModule[T]], @@ -68,7 +67,6 @@ class SimpleRNN[T: ClassTag]( wRegularizer = wRegularizer, uRegularizer = uRegularizer, bRegularizer = bRegularizer) - super.processParameters(layer) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/TimeDistributed.scala new file mode 100644 index 00000000000..38894d12ef7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/TimeDistributed.scala @@ -0,0 +1,75 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.keras + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape + +import scala.reflect.ClassTag + +/** + * TimeDistributed wrapper. + * Apply a layer to every temporal slice of an input. + * The input should be at least 3D, and the dimension of index one + * will be considered to be the temporal dimension. + * When using this layer as the first layer in a model, you need to provide the argument + * inputShape (a Single Shape, does not include the batch dimension). + * + * If you apply TimeDistributed to a Dense layer, you can use: + * TimeDistributed(Dense(8), inputShape = Shape(10, 12)) + * + * @param layer A layer instance. + * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class TimeDistributed[T: ClassTag]( + val layer: KerasLayer[Tensor[T], Tensor[T], T], + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + + private def getInnerInput(input: Array[Int]): Array[Int] = { + Array(input(0)) ++ input.slice(2, input.length) + } + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + require(input.length >=3, + s"TimeDistributed requires at least 3D input, but got input dim ${input.length}") + val innerInput = getInnerInput(input) + val innerOutput = layer.build(Shape(innerInput)).toSingle() + val output = innerOutput.take(1) ++ List(input(1)) ++ innerOutput.drop(1) + Shape(output.toArray) + } + + override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { + val input = inputShape.toSingle().toArray + val innerInput = getInnerInput(input) + val klayer = layer.doBuild(Shape(innerInput)) + .asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + val timedistributed = com.intel.analytics.bigdl.nn.TimeDistributed(klayer) + timedistributed.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} + +object TimeDistributed { + def apply[@specialized(Float, Double) T: ClassTag]( + layer: KerasLayer[Tensor[T], Tensor[T], T], + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): TimeDistributed[T] = { + new TimeDistributed[T](layer, inputShape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala index bc3babb86cf..aad277c2118 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala @@ -38,7 +38,7 @@ import scala.reflect.ClassTag * @tparam T data type, which can be [[Double]] or [[Float]] */ -class TimeDistributed[T : ClassTag] (val layer: TensorModule[T]) +class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor[T], T]) (implicit ev: TensorNumeric[T]) extends TensorModule[T] { private var inputSize: Array[Int] = _ @@ -261,7 +261,7 @@ class TimeDistributed[T : ClassTag] (val layer: TensorModule[T]) } object TimeDistributed { - def apply[@specialized(Float, Double) T: ClassTag](layer: TensorModule[T]) + def apply[@specialized(Float, Double) T: ClassTag](layer: AbstractModule[Tensor[T], Tensor[T], T]) (implicit ev: TensorNumeric[T]): TimeDistributed[T] = { new TimeDistributed[T](layer) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala index 0c6d2126b4c..14762a19b3d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.python.api import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} import com.intel.analytics.bigdl.nn.SpatialBatchNormalization +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.numeric._ import com.intel.analytics.bigdl.optim.Regularizer @@ -47,6 +48,14 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho } } + def toScalaMultiShape(inputShape: JList[JList[Int]]): Shape = { + if (inputShape == null) { + null + } else { + Shape(inputShape.asScala.toArray.map(shape => Shape(shape.asScala.toArray)).toList) + } + } + def createKerasInputLayer( inputShape: JList[Int] = null): Input[T] = { InputLayer(inputShape = toScalaShape(inputShape)) @@ -104,4 +113,12 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho .runningVar) } + def createKerasMerge( + layers: JList[AbstractModule[Activity, Activity, T]] = null, + mode: String = "sum", + concatAxis: Int = -1, + inputShape: JList[JList[Int]]): Merge[T] = { + Merge[T](layers.asScala.toList, mode, concatAxis, toScalaMultiShape(inputShape)) + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala index 27f4f71b9fb..eac57cd1bb6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala @@ -36,6 +36,10 @@ object ShapeConverter extends DataConverter { } private def toBigDLShape(shape : Shape): BigDLShape = { + if (shape.getSsize == 0) { + // null is mapped to empty shape on the serialization stage. + return null + } if (shape.getShapeType == ShapeType.SINGLE) { val shapeValues = shape.getShapeValueList.asScala.toList.map(_.intValue) SingleShape(shapeValues) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BidirectionalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BidirectionalSpec.scala new file mode 100644 index 00000000000..ec4f802cf70 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BidirectionalSpec.scala @@ -0,0 +1,84 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Bidirectional, LSTM, SimpleRNN, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class BidirectionalSpec extends KerasBaseSpec { + + "Bidirectional SimpleRNN concat" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[8, 12]) + |input = np.random.random([3, 8, 12]) + |output_tensor = Bidirectional(SimpleRNN(4, return_sequences=True))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Bidirectional[Float](SimpleRNN(4, returnSequences = true), + inputShape = Shape(8, 12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 8, 8)) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = + Array(in(0).t(), in(1).t(), in(2), in(3).t(), in(4).t(), in(5)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "Bidirectional LSTM sum" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[32, 32]) + |input = np.random.random([3, 32, 32]) + |output_tensor = Bidirectional(LSTM(12, return_sequences=True), + | merge_mode="sum")(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = Bidirectional[Float](LSTM(12, returnSequences = true), + mergeMode = "sum", inputShape = Shape(32, 32)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 32, 12)) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = { + val w1 = Tensor[Float](in(0).size(2)*4, in(0).size(1)) + val w2 = Tensor[Float](in(2).size(1)*4) + val w3 = Tensor[Float](in(1).size(2)*4, in(1).size(1)) + val w4 = w1.clone() + val w5 = w2.clone() + val w6 = w3.clone() + var i = 0 + while(i < 4) { + w1.narrow(1, 1 + i * in(0).size(2), in(0).size(2)).copy(in(3*i).t()) + w2.narrow(1, 1 + i * in(2).size(1), in(2).size(1)).copy(in(2 + 3*i)) + w3.narrow(1, 1 + i * in(1).size(2), in(1).size(2)).copy(in(1 + 3*i).t()) + w4.narrow(1, 1 + i * in(0).size(2), in(0).size(2)).copy(in(3*i + 12).t()) + w5.narrow(1, 1 + i * in(2).size(1), in(2).size(1)).copy(in(2 + 3*i + 12)) + w6.narrow(1, 1 + i * in(1).size(2), in(1).size(2)).copy(in(1 + 3*i + 12).t()) + i += 1 + } + Array(w1, w2, w3, w4, w5, w6) + } + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala index fbc0f6f8164..18f812c4df0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.keras.nn import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.nn.keras.{Dense, GRU, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.keras.{GRU, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala index 967f1de761c..fcfab731c05 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.keras.nn import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.nn.keras.{Dense, LSTM, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.keras.{LSTM, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala new file mode 100644 index 00000000000..92958f25e5a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala @@ -0,0 +1,105 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.keras.{Dense, InputLayer, Merge, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{MultiShape, Shape, T} + +class MergeSpec extends KerasBaseSpec { + + "Merge sum" should "work properly" in { + val input1 = Tensor[Float](2, 4, 8).rand(0, 1) + val input2 = Tensor[Float](2, 4, 8).rand(0, 1) + val input = T(1 -> input1, 2 -> input2) + val seq = KSequential[Float]() + val l1 = InputLayer[Float](inputShape = Shape(4, 8)) + val l2 = InputLayer[Float](inputShape = Shape(4, 8)) + val layer = Merge[Float](layers = List(l1, l2), mode = "sum") + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4, 8)) + seq.forward(input) should be (input1 + input2) + } + + "Merge with incompatible input shapes" should "raise an exception" in { + intercept[RuntimeException] { + val seq = KSequential[Float]() + val l1 = InputLayer[Float](inputShape = Shape(4)) + val l2 = InputLayer[Float](inputShape = Shape(5)) + val layer = Merge[Float](layers = List(l1, l2), mode = "cosine", + inputShape = MultiShape(List(Shape(4), Shape(4)))) + seq.add(layer) + } + } + + "Merge ave" should "work properly" in { + val input1 = Tensor[Float](3, 10).rand(0, 1) + val input2 = Tensor[Float](3, 10).rand(0, 1) + val input3 = Tensor[Float](3, 10).rand(0, 1) + val input = T(1 -> input1, 2 -> input2, 3 -> input3) + val seq = KSequential[Float]() + val l1 = InputLayer[Float](inputShape = Shape(10)) + val l2 = InputLayer[Float](inputShape = Shape(10)) + val l3 = InputLayer[Float](inputShape = Shape(10)) + val layer = Merge[Float](layers = List(l1, l2, l3), mode = "ave") + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 10)) + seq.forward(input) should be ((input1 + input2 + input3)/3) + } + + "Merge concat" should "work properly" in { + val input1 = Tensor[Float](2, 3, 8).rand(0, 1) + val input2 = Tensor[Float](2, 4, 8).rand(0, 1) + val input = T(1 -> input1, 2 -> input2) + val seq = KSequential[Float]() + val l1 = InputLayer[Float](inputShape = Shape(3, 8)) + val l2 = InputLayer[Float](inputShape = Shape(4, 8)) + val layer = Merge[Float](layers = List(l1, l2), mode = "concat", concatAxis = 1) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 7, 8)) + seq.forward(input) + } + + "Merge dot" should "work properly" in { + val input1 = Tensor[Float](2, 4).rand(0, 1) + val input2 = Tensor[Float](2, 4).rand(0, 1) + val input = T(1 -> input1, 2 -> input2) + val seq = KSequential[Float]() + val l1 = InputLayer[Float](inputShape = Shape(4)) + val l2 = InputLayer[Float](inputShape = Shape(4)) + val layer = Merge[Float](layers = List(l1, l2), mode = "dot") + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 1)) + seq.forward(input) + } + + "Merge complicated" should "work properly" in { + val input1 = Tensor[Float](3, 8).rand(0, 1) + val input2 = Tensor[Float](3, 6).rand(0, 1) + val input = T(1 -> input1, 2 -> input2) + val seq = KSequential[Float]() + val l1 = Dense[Float](10, inputShape = Shape(8)) + val l2 = Dense[Float](10, inputShape = Shape(6)) + val layer = Merge[Float](layers = List(l1, l2), mode = "max") + seq.add(layer) + seq.add(Dense[Float](15)) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 15)) + seq.forward(input) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TimeDistributedSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TimeDistributedSpec.scala new file mode 100644 index 00000000000..b94a206d87a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TimeDistributedSpec.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Convolution2D, Dense, TimeDistributed, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape + +class TimeDistributedSpec extends KerasBaseSpec { + + "TimeDistributed Dense" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[10, 12]) + |input = np.random.random([3, 10, 12]) + |output_tensor = TimeDistributed(Dense(8, activation="relu"))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = TimeDistributed[Float](Dense(8, activation = "relu"), inputShape = Shape(10, 12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 10, 8)) + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(0).t(), in(1)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "TimeDistributed Convolution2D" should "be the same as Keras" in { + val kerasCode = + """ + |input_tensor = Input(shape=[4, 3, 12, 12]) + |input = np.random.random([2, 4, 3, 12, 12]) + |output_tensor = TimeDistributed(Convolution2D(8, 3, 3, dim_ordering="th"))(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val layer = TimeDistributed[Float](Convolution2D(8, 3, 3), + inputShape = Shape(4, 3, 12, 12)) + seq.add(layer) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 4, 8, 10, 10)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, precision = 1e-3) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index a58e2881f18..3260b1d4cca 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.serializer import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.{Shape, Table} import scala.collection.mutable import scala.util.Random @@ -458,4 +458,32 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(layer, input) } + "Merge serializer" should "work properly" in { + val l1 = InputLayer[Float](inputShape = Shape(4, 8)) + val l2 = InputLayer[Float](inputShape = Shape(4, 8)) + val layer = Merge[Float](layers = List(l1, l2), mode = "sum") + layer.build(Shape(List(Shape(2, 4, 8), Shape(2, 4, 8)))) + val input1 = Tensor[Float](2, 4, 8).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 4, 8).apply1(e => Random.nextFloat()) + val input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(layer, input) + } + + "TimeDistributed serializer" should "work properly" in { + val layer = TimeDistributed[Float](Dense(8), inputShape = Shape(10, 12)) + layer.build(Shape(3, 10, 12)) + val input = Tensor[Float](3, 10, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + + "Bidirectional serializer" should "work properly" in { + val layer = Bidirectional[Float](SimpleRNN(4, returnSequences = true), + inputShape = Shape(8, 12)) + layer.build(Shape(3, 8, 12)) + val input = Tensor[Float](3, 8, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + } From de8dc173700dacbbc1aa1aaae1aa3d2ea2f20d9b Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 9 Feb 2018 20:03:35 +0800 Subject: [PATCH 0698/1065] Refine AbstractModule methods (#2262) * make getParameter be final and private * remove updateParameter Method * remove useless override zeroGrad * fix comments * fix unit tests * fix unit tests * allocate gradWeight storage if it's not allocated * fix unit test * meet code review * make zeroGrad become final * fix compile error * add final to module apis * add private[bigdl] to some module methods * reorder the method sequence * meet code review * remove unnecessary getParameterTable and fix unit test * fix unit test * fix unit test --- .../intel/analytics/bigdl/dllib/nn/Add.scala | 4 - .../bigdl/dllib/nn/BatchNormalization.scala | 7 - .../bigdl/dllib/nn/BiRecurrent.scala | 20 - .../analytics/bigdl/dllib/nn/Bilinear.scala | 14 - .../bigdl/dllib/nn/BinaryTreeLSTM.scala | 10 - .../intel/analytics/bigdl/dllib/nn/CAdd.scala | 8 - .../intel/analytics/bigdl/dllib/nn/CMul.scala | 12 - .../intel/analytics/bigdl/dllib/nn/Cell.scala | 10 - .../analytics/bigdl/dllib/nn/Concat.scala | 12 - .../analytics/bigdl/dllib/nn/Container.scala | 14 +- .../analytics/bigdl/dllib/nn/Cosine.scala | 8 - .../analytics/bigdl/dllib/nn/Euclidean.scala | 8 - .../analytics/bigdl/dllib/nn/Linear.scala | 23 - .../bigdl/dllib/nn/LocallyConnected1D.scala | 41 +- .../bigdl/dllib/nn/LocallyConnected2D.scala | 24 - .../bigdl/dllib/nn/LookupTable.scala | 8 - .../bigdl/dllib/nn/LookupTableSparse.scala | 8 - .../analytics/bigdl/dllib/nn/MapTable.scala | 13 - .../analytics/bigdl/dllib/nn/Maxout.scala | 4 - .../intel/analytics/bigdl/dllib/nn/Mul.scala | 8 - .../bigdl/dllib/nn/MultiRNNCell.scala | 4 - .../bigdl/dllib/nn/NormalizeScale.scala | 4 - .../analytics/bigdl/dllib/nn/PReLU.scala | 8 - .../analytics/bigdl/dllib/nn/Scale.scala | 5 - .../bigdl/dllib/nn/SpatialConvolution.scala | 24 - .../dllib/nn/SpatialConvolutionMap.scala | 10 - .../dllib/nn/SpatialDilatedConvolution.scala | 15 - .../dllib/nn/SpatialFullConvolution.scala | 21 - .../nn/SpatialSeperableConvolution.scala | 64 +- .../bigdl/dllib/nn/TemporalConvolution.scala | 15 - .../bigdl/dllib/nn/TimeDistributed.scala | 18 - .../dllib/nn/VolumetricConvolution.scala | 22 - .../dllib/nn/VolumetricFullConvolution.scala | 21 - .../dllib/nn/abstractnn/AbstractModule.scala | 546 +++++++++++------- .../bigdl/dllib/nn/quantized/Linear.scala | 4 - .../nn/quantized/SpatialConvolution.scala | 4 - .../dllib/utils/python/api/PythonBigDL.scala | 3 +- .../utils/serializer/ModuleSerializer.scala | 2 + .../analytics/bigdl/dllib/nn/CAddSpec.scala | 3 +- .../bigdl/dllib/nn/DynamicGraphSpec.scala | 4 - .../analytics/bigdl/dllib/nn/GraphSpec.scala | 4 - .../analytics/bigdl/dllib/nn/LinearSpec.scala | 10 +- .../dllib/nn/SpatialConvolutionSpec.scala | 9 +- .../dllib/nn/SpatialFullConvolutionSpec.scala | 6 +- .../bigdl/dllib/torch/SequentialSpec.scala | 3 +- 45 files changed, 425 insertions(+), 660 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Add.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Add.scala index e200ab7100b..120c6ad8827 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Add.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Add.scala @@ -86,10 +86,6 @@ class Add[T: ClassTag](val inputSize: Int } } - override def zeroGradParameters(): Unit = { - gradBias.zero() - } - override def clearState() : this.type = { super.clearState() ones.set() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index f7406d81ccb..1b9fe69eb9c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -142,13 +142,6 @@ class BatchNormalization[T: ClassTag]( this } - override def zeroGradParameters(): Unit = { - if (affine) { - gradWeight.zero() - gradBias.zero() - } - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { if (affine) { (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala index 91a811ed6d3..45c298aa8c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala @@ -94,26 +94,6 @@ class BiRecurrent[T : ClassTag] ( */ override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = birnn.parameters() - override def updateParameters(learningRate: T): Unit = birnn.updateParameters(learningRate) - - /** - * If the module has parameters, this will zero the accumulation of the gradients with respect - * to these parameters. Otherwise, it does nothing. - */ - override def zeroGradParameters(): Unit = birnn.zeroGradParameters() - - override def training(): BiRecurrent.this.type = { - super.training() - birnn.training() - this - } - - override def evaluate(): BiRecurrent.this.type = { - super.evaluate() - birnn.evaluate() - this - } - override def canEqual(other: Any): Boolean = other.isInstanceOf[BiRecurrent[T]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bilinear.scala index a98bbf8c4c1..70f56775546 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bilinear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bilinear.scala @@ -194,11 +194,6 @@ class Bilinear[T: ClassTag]( } } - override def zeroGradParameters(): Unit = { - gradWeight.zero() - gradBias.zero() - } - override def clearState(): this.type = { super.clearState() buff1.set() @@ -214,15 +209,6 @@ class Bilinear[T: ClassTag]( } } - override def getParametersTable(): Table = { - if (null == bias) { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } else { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } - } - override def toString(): String = { s"${getPrintName}($inputSize1, $inputSize2, $outputSize, $biasRes)" } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala index 3453d67a2a5..e7a0d69cafd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala @@ -368,11 +368,6 @@ class BinaryTreeLSTM[T: ClassTag]( (cp ++ lp, cg ++ lg) } - override def updateParameters(learningRate: T): Unit = { - composer.updateParameters(learningRate) - leafModule.updateParameters(learningRate) - } - override def getParametersTable(): Table = { val pt = T() val t1 = composer.getParametersTable() @@ -382,11 +377,6 @@ class BinaryTreeLSTM[T: ClassTag]( pt } - override def zeroGradParameters(): Unit = { - composer.zeroGradParameters() - leafModule.zeroGradParameters() - } - override def reset(): Unit = { composer.reset() leafModule.reset() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAdd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAdd.scala index 777d23c9705..920d6416bd9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAdd.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAdd.scala @@ -141,14 +141,6 @@ class CAdd[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - - override def zeroGradParameters(): Unit = { - gradBias.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.bias), Array(this.gradBias)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala index 7bd1aa3a491..66f698aa632 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala @@ -163,22 +163,10 @@ class CMul[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight), Array(this.gradWeight)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } - override def clearState(): this.type = { super.clearState() _repeat.set() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala index b847d0bcd1e..8151990a984 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala @@ -190,11 +190,6 @@ abstract class Cell[T : ClassTag]( gradInput } - override def updateParameters(learningRate: T): Unit = { - cell.updateParameters(learningRate) - if (includePreTopology) preTopology.updateParameters(learningRate) - } - private def initAddTimes(): Unit = { val cellTimes = cell.getTimes if (subModules == null || subModules.length < cellTimes.length) { @@ -264,11 +259,6 @@ abstract class Cell[T : ClassTag]( cell.resetTimes } - override def zeroGradParameters(): Unit = { - cell.zeroGradParameters() - if (includePreTopology) preTopology.zeroGradParameters() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { val _cell = if (includePreTopology) { Sequential().add(preTopology).add(cell) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala index 819bfee85bb..d4f85133c9e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala @@ -257,18 +257,6 @@ class Concat[T: ClassTag](val dimension: Int)( this.gradInput } - // Todo: this is different from torch accUpdateGradParameters - override def updateParameters(learningRate: T): Unit = { - var offset = 1 - var i = 0 - while (i < this.modules.length) { - val currentOutput = this.modules(i).output.asInstanceOf[Tensor[T]] - this.modules(i).updateParameters(learningRate) - i += 1 - offset += currentOutput.size(dimension) - } - } - override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { return false diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index 9b485c77360..bbb6f267626 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -51,31 +51,23 @@ abstract class Container[A <: Activity : ClassTag, modules.filter(!_.isCompatibleWithTorch()).length <= 0 } - override def zeroGradParameters(): Unit = { - modules.foreach(_.zeroGradParameters()) - } - - override def updateParameters(learningRate: T): Unit = { - modules.foreach(_.updateParameters(learningRate)) - } - override def reset(): Unit = { modules.foreach(_.reset()) } - override def training(): this.type = { + final override def training(): this.type = { train = true modules.foreach(_.training()) this } - override def evaluate(): this.type = { + final override def evaluate(): this.type = { train = false modules.foreach(_.evaluate()) this } - override def checkEngineType(): this.type = { + final override def checkEngineType(): this.type = { modules.foreach(_.checkEngineType()) this } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala index 527589831eb..df89e66fb74 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cosine.scala @@ -176,18 +176,10 @@ class Cosine[T: ClassTag](val inputSize : Int, val outputSize : Int)( } } - override def zeroGradParameters(): Unit = { - gradWeight.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight), Array(this.gradWeight)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } - override def toString(): String = { s"${getPrintName}($inputSize, $outputSize)" } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Euclidean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Euclidean.scala index bd752c05628..9362eff5a14 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Euclidean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Euclidean.scala @@ -149,10 +149,6 @@ class Euclidean[T: ClassTag](val inputSize: Int, val outputSize: Int, s"${getPrintName}($inputSize, $outputSize)" } - override def zeroGradParameters(): Unit = { - gradWeight.zero() - } - override def clearState() : this.type = { super.clearState() inputBuffer.set() @@ -168,10 +164,6 @@ class Euclidean[T: ClassTag](val inputSize: Int, val outputSize: Int, (Array(this.weight), Array(this.gradWeight)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } - override def canEqual(other: Any): Boolean = other.isInstanceOf[Euclidean[T]] override def equals(other: Any): Boolean = other match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala index a6834c3adf1..9fb0ca04617 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala @@ -170,20 +170,6 @@ class Linear[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - weight.add(ev.negative(learningRate), gradWeight) - if (withBias) bias.add(ev.negative(learningRate), gradBias) - } - - override def zeroGradParameters(): Unit = { - gradWeight.resize(outputSize, inputSize) - gradWeight.zero() - if (withBias) { - gradBias.resize(outputSize) - gradBias.zero() - } - } - override def clearState() : this.type = { super.clearState() addBuffer.set() @@ -198,15 +184,6 @@ class Linear[T: ClassTag]( } } - override def getParametersTable(): Table = { - if (null == bias) { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } else { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } - } - override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala index 80255ad26ba..98d19c35896 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1D.scala @@ -380,36 +380,11 @@ class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, } } - override def updateParameters(learningRate: T): Unit - - = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - - override def zeroGradParameters(): Unit - - = { - gradWeight.zero() - gradBias.zero() - } - - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) - - = { + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) } - override def getParametersTable(): Table - - = { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } - - override def equals(obj: Any): Boolean - - = { + override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { return false } @@ -432,9 +407,7 @@ class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, gradBias == other.gradBias } - override def hashCode(): Int - - = { + override def hashCode(): Int = { val seed = 37 var hash = super.hashCode() hash = hash * seed + inputFrameSize.hashCode() @@ -449,16 +422,12 @@ class LocallyConnected1D[T: ClassTag](val nInputFrame: Int, hash } - override def clearState(): this.type - - = { + override def clearState(): this.type = { super.clearState() this } - override def toString(): String - - = { + override def toString(): String = { s"nn.TemporalConvolution($inputFrameSize -> $outputFrameSize, $kernelW x $strideW)" } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala index 00aa122498f..ee784da0aac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2D.scala @@ -452,20 +452,6 @@ class LocallyConnected2D[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - if (withBias) { - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - if (withBias) { - gradBias.zero() - } - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { if (withBias) { (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) @@ -474,16 +460,6 @@ class LocallyConnected2D[T: ClassTag]( } } - override def getParametersTable(): Table = { - if (withBias) { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } else { - T(getName() -> T("weight" -> weight, - "gradWeight" -> gradWeight)) - } - } - override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala index 8566c636e67..ef2d59f17c0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala @@ -257,18 +257,10 @@ class LookupTable[T: ClassTag] } } - override def zeroGradParameters(): Unit = { - gradWeight.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight), Array(this.gradWeight)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } - override def clearState() : this.type = { super.clearState() inputBuffer.set() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparse.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparse.scala index c8f9c2d135e..49c38b11f89 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparse.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparse.scala @@ -217,18 +217,10 @@ class LookupTableSparse[T: ClassTag]( } } - override def zeroGradParameters(): Unit = { - gradWeight.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight), Array(this.gradWeight)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } - override def clearState() : this.type = { super.clearState() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala index dc8f61f2b98..c00f42e22f3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala @@ -104,19 +104,6 @@ class MapTable[T: ClassTag]( throw new IllegalArgumentException("Can not transform Container MapTable to graph") } - override def zeroGradParameters(): Unit = { - if (module != null) { - module.zeroGradParameters() - } - } - - - override def updateParameters(learningRate: T): Unit = { - if (module != null) { - module.updateParameters(learningRate) - } - } - override def toString(): String = { val tab = " " val extlast = " " diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala index 5d723b2e144..3169fb41e53 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala @@ -72,10 +72,6 @@ class Maxout[T: ClassTag](val inputSize: Int, val outputSize: Int, val maxoutNum layer.accGradParameters(input, gradOutput) } - override def zeroGradParameters(): Unit = { - layer.zeroGradParameters() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { layer.parameters() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mul.scala index 1545fdcf7e2..93c91a6fbbf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Mul.scala @@ -63,18 +63,10 @@ class Mul[T: ClassTag](implicit ev: TensorNumeric[T]) } } - override def zeroGradParameters(): Unit = { - gradWeight.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight), Array(this.gradWeight)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } - override def canEqual(other: Any): Boolean = other.isInstanceOf[Mul[T]] override def equals(other: Any): Boolean = other match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala index 138ffd72574..2e2490b9157 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala @@ -165,10 +165,6 @@ class MultiRNNCell[T : ClassTag](val cells: Array[Cell[T]])(implicit ev: TensorN gradInput } - override def zeroGradParameters(): Unit = { - cells.foreach(_.zeroGradParameters()) - } - override def reset(): Unit = { cells.foreach(_.reset()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala index 34892300df5..8c8970b6e84 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala @@ -65,10 +65,6 @@ class NormalizeScale[T: ClassTag](val p: Double, val eps: Double = 1e-10, override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(cmul.weight), Array(cmul.gradWeight)) } - - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> cmul.weight, "gradWeight" -> cmul.gradWeight)) - } } object NormalizeScale { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala index 63a246e73ce..e9be2848faf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala @@ -283,18 +283,10 @@ class PReLU[T: ClassTag]( } } - override def zeroGradParameters(): Unit = { - gradWeight.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight), Array(this.gradWeight)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } - override def toString(): String = { s"${getPrintName}($nOutputPlane)" } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala index d5100e36668..b842582d3fe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala @@ -73,11 +73,6 @@ class Scale[T: ClassTag](val size: Array[Int]) Array(cmul.parameters()._2(0), cadd.parameters()._2(0))) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> cmul.weight, "bias" -> cadd.bias, - "gradWeight" -> cmul.gradWeight, "gradBias" -> cadd.gradBias)) - } - override def toString: String = "nn.Scale" } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala index b46f0cfdbee..72a01f800f7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala @@ -519,20 +519,6 @@ class SpatialConvolution[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - if (withBias) { - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - if (withBias) { - gradBias.zero() - } - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { if (withBias) { (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) @@ -541,16 +527,6 @@ class SpatialConvolution[T: ClassTag]( } } - override def getParametersTable(): Table = { - if (withBias) { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } else { - T(getName() -> T("weight" -> weight, - "gradWeight" -> gradWeight)) - } - } - override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionMap.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionMap.scala index 9ca1f511af6..cc5cb56ee6e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionMap.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionMap.scala @@ -293,20 +293,10 @@ class SpatialConvolutionMap[T: ClassTag]( (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } - def decayParameters(decay: T): Unit = { weight.apply1(ev.minus(_, decay)) bias.apply1(ev.minus(_, decay)) } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - gradBias.zero() - } } object SpatialConvolutionMap { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala index 0b1fd3e5967..3ed7280014e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala @@ -477,25 +477,10 @@ class SpatialDilatedConvolution[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - gradBias.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } - override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala index 56841a29710..d06fcce9e6b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala @@ -670,18 +670,6 @@ class SpatialFullConvolution[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - if(!noBias) { - gradBias.zero() - } - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { if (null == bias) { (Array(this.weight), Array(this.gradWeight)) @@ -690,15 +678,6 @@ class SpatialFullConvolution[T: ClassTag]( } } - override def getParametersTable(): Table = { - if (null == bias) { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } else { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } - } - override def clearState() : this.type = { super.clearState() columns.set() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala index eac34049a3d..6731e0b4be7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala @@ -15,11 +15,14 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, ModuleSerializer, SerializeContext} import scala.reflect.ClassTag @@ -211,15 +214,10 @@ class SpatialSeperableConvolution[T: ClassTag]( if (initBias == null) bias.zero() zeroGradParameters() } - - override def zeroGradParameters(): Unit = { - depthWeight.zero() - pointWeight.zero() - bias.zero() - } } -object SpatialSeperableConvolution { +object SpatialSeperableConvolution extends ModuleSerializable { + def apply[T: ClassTag](nInputChannel: Int, nOutputChannel: Int, depthMultiplier: Int, kW: Int, kH: Int, sW: Int = 1, sH: Int = 1, pW: Int = 0, pH: Int = 0, hasBias: Boolean = true, dataFormat: DataFormat = DataFormat.NCHW, @@ -270,4 +268,54 @@ object SpatialSeperableConvolution { in += 1 } } + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val attrMap = context.bigdlModule.getAttrMap + val ssc = super.doLoadModule(context).asInstanceOf[SpatialSeperableConvolution[T]] + val weights = ssc.parameters()._1 + val (depthWeight, pointWeight, bias) = (weights(0), weights(1), weights(2)) + + val depthWeightLoad = DataConverter. + getAttributeValue(context, attrMap.get("depthWeight")). + asInstanceOf[Tensor[T]] + depthWeight.copy(depthWeightLoad) + + val pointWeightLoad = DataConverter. + getAttributeValue(context, attrMap.get("pointWeight")). + asInstanceOf[Tensor[T]] + pointWeight.copy(pointWeightLoad) + + val biasLoad = DataConverter. + getAttributeValue(context, attrMap.get("bias")). + asInstanceOf[Tensor[T]] + bias.copy(biasLoad) + + ssc.asInstanceOf[AbstractModule[Activity, Activity, T]] + } + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + sreluBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + + super.doSerializeModule(context, sreluBuilder) + + val ssc = context.moduleData.module.asInstanceOf[SpatialSeperableConvolution[T]] + val weights = ssc.parameters()._1 + val (depthWeight, pointWeight, bias) = (weights(0), weights(1), weights(2)) + + val depthWeightBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, depthWeightBuilder, + depthWeight, ModuleSerializer.tensorType) + sreluBuilder.putAttr("depthWeight", depthWeightBuilder.build) + + val pointWeightBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, pointWeightBuilder, + pointWeight, ModuleSerializer.tensorType) + sreluBuilder.putAttr("pointWeight", pointWeightBuilder.build) + + val biasBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, biasBuilder, + bias, ModuleSerializer.tensorType) + sreluBuilder.putAttr("bias", biasBuilder.build) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolution.scala index 31fc71260f7..ff197789f7f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TemporalConvolution.scala @@ -391,25 +391,10 @@ class TemporalConvolution[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - gradBias.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } - override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { return false diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala index aad277c2118..272978113d7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala @@ -151,16 +151,6 @@ class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor gradInput } - /** - * If the module has parameters, this will zero the accumulation of the gradients with respect - * to these parameters. Otherwise, it does nothing. - */ - override def zeroGradParameters(): Unit = { - layer.zeroGradParameters() - } - - override def updateParameters(learningRate: T): Unit = layer.updateParameters(learningRate) - override def reset(): Unit = layer.reset() override def training(): TimeDistributed.this.type = { @@ -210,14 +200,6 @@ class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor */ override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = layer.parameters() - /** - * This method compact all parameters and gradients of the model into two tensors. So it's easier - * to use optim method - * - * @return - */ - override def getParameters(): (Tensor[T], Tensor[T]) = layer.getParameters() - /** * This method will return a table indicating the name and corresponding parameters. * @return Table diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala index 3c973b62119..94fc5f37dd6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala @@ -97,18 +97,6 @@ class VolumetricConvolution[T: ClassTag]( this } - override def updateParameters(learningRate: T): Unit = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - if (withBias) { - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - if (withBias) gradBias.zero() - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { if (withBias) { (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) @@ -117,16 +105,6 @@ class VolumetricConvolution[T: ClassTag]( } } - override def getParametersTable(): Table = { - if (withBias) { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } else { - T(getName() -> T("weight" -> weight, - "gradWeight" -> gradWeight)) - } - } - override def computeOutputShape(inputShape: Shape): Shape = { val input = inputShape.toSingle().toArray require(input.length == 5, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolution.scala index 00e5867abf1..2bfe4ef1b57 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolution.scala @@ -724,18 +724,6 @@ class VolumetricFullConvolution[T: ClassTag]( } } - override def updateParameters(learningRate: T): Unit = { - weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b))) - bias.map(gradBias, (a, b) => ev.minus(a, ev.times(learningRate, b))) - } - - override def zeroGradParameters(): Unit = { - gradWeight.zero() - if(!noBias) { - gradBias.zero() - } - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { if (null == bias) { (Array(this.weight), Array(this.gradWeight)) @@ -744,15 +732,6 @@ class VolumetricFullConvolution[T: ClassTag]( } } - override def getParametersTable(): Table = { - if (null == bias) { - T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight)) - } else { - T(getName() -> T("weight" -> weight, "bias" -> bias, - "gradWeight" -> gradWeight, "gradBias" -> gradBias)) - } - } - override def clearState() : this.type = { super.clearState() columns.set() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 97e33c8f122..80a869db92b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -42,7 +42,7 @@ import scala.reflect.ClassTag * [[TensorModule]] is an abstract sub-class of [[AbstractModule]], whose * input and output type both are [[Tensor]]. * - * @tparam T The numeric type in this module, usually which are [[Float]] or [[Double]] + * @tparam T The numeric type in this module parameters */ abstract class TensorModule[T: ClassTag] (implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[T], Tensor[T], T] @@ -53,16 +53,13 @@ abstract class TensorModule[T: ClassTag] * * @tparam A Input data type * @tparam B Output data type - * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now + * @tparam T The numeric type in this module parameters. */ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag]( implicit ev: TensorNumeric[T]) extends Serializable with InferShape{ - private var namePostfix = Integer.toHexString(java.util.UUID.randomUUID().hashCode()) - - def getNamePostfix : String = namePostfix + // ================================= Public APIs ============================================= - def setNamePostfix(namePostfix : String) : Unit = this.namePostfix = namePostfix /** * The cached output. So we don't compute it again when need it @@ -74,24 +71,17 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ var gradInput: A = Activity.allocate[A, T]() - /** - * The scale of gradient weight and gradient bias - * before gradParameters being accumulated. - */ - protected var scaleW: Double = 1.0 - protected var scaleB: Double = 1.0 - /** * Get the scale of gradientWeight */ - def getScaleW(): Double = { + final def getScaleW(): Double = { scaleW } /** * Get the scale of gradientBias */ - def getScaleB(): Double = { + final def getScaleB(): Double = { scaleB } @@ -138,18 +128,11 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, this } - private[nn] def allocateAs(dest: Activity): Activity = dest match { - case tensor: Tensor[T] => Tensor[T]() - case table: Table => T() - case _ => throw new IllegalArgumentException("Activity only support tensor and table now") - } - /** - * The name of the module + * Whether user set a name to the module before + * @return */ - private var name : String = null - - def hasName: Boolean = name != null + final def hasName: Boolean = name != null /** * Set the module name @@ -157,7 +140,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param name * @return */ - def setName(name : String) : this.type = { + final def setName(name : String) : this.type = { this.name = name this } @@ -167,7 +150,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * * @return */ - def getName() : String = { + final def getName() : String = { if (this.name == null) { s"${this.getClass.getSimpleName}${namePostfix}" } else { @@ -175,34 +158,25 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, } } - protected def getPrintName(): String = { - val postfix = if (name == null) { - namePostfix - } else { - name - } - s"${this.getClass.getSimpleName}[${postfix}]" - - } - override def toString(): String = getPrintName - protected var forwardTime = 0L - - protected var backwardTime = 0L - + /** + * Get the forward/backward cost time for the module or its submodules + * @return + */ def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { Array((this, forwardTime, backwardTime)) } + /** + * Reset the forward/backward record time for the module or its submodules + * @return + */ def resetTimes(): Unit = { forwardTime = 0 backwardTime = 0 } - private var scaleWCache: Double = scaleW - private var scaleBCache: Double = scaleB - /** * freeze the module, * i.e. their parameters(weight/bias, if exists) are not changed in training process @@ -332,27 +306,14 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * If the module has parameters, this will zero the accumulation of the gradients with respect * to these parameters. Otherwise, it does nothing. */ - def zeroGradParameters(): Unit = { + final def zeroGradParameters(): Unit = { if (parameters() != null) { - parameters()._2.foreach(grad => { - grad.zero() - }) + parameters()._1.zip(parameters()._2)foreach{ case (weight, grad) => + grad.resizeAs(weight).zero() + } } } - def updateParameters(learningRate: T): Unit = {} - - /** - * This method compact all parameters and gradients of the model into two tensors. So it's easier - * to use optim method - * - * @return - */ - def getParameters(): (Tensor[T], Tensor[T]) = { - val (weightParameters, gradParameters) = this.parameters() - (Module.flatten[T](weightParameters), Module.flatten[T](gradParameters)) - } - /** * This function returns two arrays. One for the weights and the other the gradients * Custom modules should override this function if they have parameters @@ -379,12 +340,12 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * * @return this */ - def setExtraParameter(extraParam: Array[Tensor[T]]): this.type = { + final def setExtraParameter(extraParam: Array[Tensor[T]]): this.type = { val currentExtraParam = this.getExtraParameter() if (extraParam != null && currentExtraParam != null) { require(extraParam.length == currentExtraParam.length, "state's length doesn't match, excepted:" + - s"${currentExtraParam.length}, but got ${extraParam.length}") + s"${currentExtraParam.length}, but got ${extraParam.length}") var i = 0 while (i < extraParam.length) { currentExtraParam(i).copy(extraParam(i)) @@ -402,64 +363,106 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, /** * This function returns a table contains ModuleName, the parameter names and parameter value * in this module. + * * The result table is a structure of Table(ModuleName -> Table(ParameterName -> ParameterValue)), * and the type is Table[String, Table[String, Tensor[T]]]. * * For example, get the weight of a module named conv1: * table[Table]("conv1")[Tensor[T]]("weight"). * - * Custom modules should override this function if they have parameters. + * The names of the parameters follow such convention: + * + * 1. If there's one parameter, the parameter is named as "weight", the gradient is named as + * "gradWeight" + * + * 2. If there're two parameters, the first parameter is named as "weight", the first gradient is + * named as "gradWeight"; the second parameter is named as "bias", the seconcd gradient is + * named as "gradBias" + * + * 3. If there're more parameters, the weight is named as "weight" with a seq number as suffix, + * the gradient is named as "gradient" with a seq number as suffix + * + * Custom modules should override this function the default impl if the convention doesn't meet + * the requirement. * * @return Table */ - def getParametersTable(): Table = null + def getParametersTable(): Table = { + val params = parameters() + if (params == null) return null + val (weights, gradients) = params + require(gradients.length == weights.length, "weight number is not equal to grad number") + + if (weights.length == 1) { + T(getName() -> T("weight" -> weights(0), "gradWeight" -> gradients(0))) + } else if (weights.length == 2) { + T(getName() -> T("weight" -> weights(0), "bias" -> weights(1), + "gradWeight" -> gradients(0), "gradBias" -> gradients(1))) + } else { + val result = T() + weights.zip(gradients).zipWithIndex.map { case ((w, g), i) => + result(s"weight$i") = w + result(s"gradient$i") = g + } + T(getName() -> result) + } + } /** - * Module status. It is useful for modules like dropout/batch normalization + * Set the module to training mode + * @return */ - protected var train: Boolean = true - def training(): this.type = { train = true this } + /** + * Set the module to evaluate mode + * @return + */ def evaluate(): this.type = { train = false this } + /** + * Check if the model is in training mode + * @return + */ final def isTraining(): Boolean = { this.train } + /** + * Reset module parameters, which is re-initialize the parameter with given initMethod + */ def reset(): Unit = {} - - protected var line = "\n" - - def setLine(line: String): this.type = { + /** + * Set the line separator when print the module + * @param line + * @return + */ + final def setLine(line: String): this.type = { this.line = line this } - private val engineType: EngineType = Engine.getEngineType() - /** - * get execution engine type + * Clone the model + * @return */ - def checkEngineType(): this.type = { - if (engineType != Engine.getEngineType()) { - throw new Error("Module's EngineType doesn't march global EngineType") - } - this - } - - def cloneModule(): AbstractModule[A, B, T] = { + final def cloneModule(): AbstractModule[A, B, T] = { SerializationUtils.clone(this) } - def clone(deepCopy : Boolean): AbstractModule[A, B, T] = { + /** + * Clone the module, deep or shallow copy + * @param deepCopy + * @return + */ + final def clone(deepCopy : Boolean): AbstractModule[A, B, T] = { val moduleData = ModuleData[T](this. asInstanceOf[AbstractModule[Activity, Activity, T]], Seq[String](), Seq[String]()) val storages = new mutable.HashMap[Int, Any]() @@ -478,64 +481,6 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, copy } - - private def setWeightAndBias(copy : AbstractModule[A, B, T], deepCopy : Boolean): Unit = { - val parameterTable = this.getParametersTable - val copiedModuleParamTable = copy.getParametersTable - if (parameterTable != null) { - require(copiedModuleParamTable != null, "cloned module should have params") - parameterTable.foreach { - case (name: String, params: Table) => - require(copiedModuleParamTable.get(name) != None, s"cloned module should have for $name") - setLayerWeightAndBias(params, - copiedModuleParamTable.get(name).get.asInstanceOf[Table], deepCopy) - } - } - } - - private def setLayerWeightAndBias(params : Table, - copyParams : Table, deepCopy : Boolean): Unit = { - params.foreach(param => { - copyParam(params, copyParams, deepCopy, param._1.toString) - }) - } - - private def copyParam(params : Table, copyParams : Table, - deepCopy : Boolean, paraName : String) : Unit = { - if (params.contains(paraName)) { - // this is for quantization tensors where the weight might be an array - if (params.get(paraName).get - .isInstanceOf[Array[Tensor[T]]]) { - val copies = copyParams.get(paraName).get - .asInstanceOf[Array[Tensor[T]]] - val origins = params.get(paraName).get - .asInstanceOf[Array[Tensor[T]]] - var i = 0 - while (i < copies.length) { - copyTensor(origins(i), copies(i), deepCopy) - i += 1 - } - } else { - // For normal layers, their params are just tensors - copyTensor(params.get(paraName).get.asInstanceOf[Tensor[T]], - copyParams.get(paraName).get.asInstanceOf[Tensor[T]], deepCopy) - } - } - } - - private def copyTensor(t1 : Tensor[T], t2 : Tensor[T], deepCopy : Boolean) = { - if (t2.isInstanceOf[QuantizedTensor[_]]) { - t2.asInstanceOf[QuantizedTensor[_]].release() - } - if (deepCopy) { - t2.copy(t1) - } else { - t2.set(t1) - } - } - - def canEqual(other: Any): Boolean = other.isInstanceOf[AbstractModule[A, B, T]] - override def equals(other: Any): Boolean = other match { case that: AbstractModule[A, B, T] => (that canEqual this) && @@ -560,8 +505,8 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param overWrite if overwrite * @return self */ - @deprecated("please use recommended saveModule(path, overWrite)") - def save(path : String, overWrite: Boolean = false) : this.type = { + @deprecated("please use recommended saveModule(path, overWrite)", "0.3.0") + final def save(path : String, overWrite: Boolean = false) : this.type = { this.clearState() File.save(this, path, overWrite) this @@ -576,8 +521,8 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param overWrite if overwrite * @return self */ - def saveModule(path : String, weightPath : String = null, - overWrite: Boolean = false) : this.type = { + final def saveModule(path : String, weightPath : String = null, + overWrite: Boolean = false) : this.type = { this.clearState() ModulePersister.saveToFile(path, weightPath, this, overWrite) this @@ -591,30 +536,52 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param overWrite if overwrite * @return self */ - def saveDefinition(path : String, overWrite: Boolean = false) : this.type = { + final def saveDefinition(path : String, overWrite: Boolean = false) : this.type = { this.clearState() ModulePersister.saveModelDefinitionToFile(path, this, overWrite) this } - def saveTorch(path : String, overWrite: Boolean = false) : this.type = { + /** + * Save this module to path in torch7 readable format + * @param path + * @param overWrite + * @return + */ + final def saveTorch(path : String, overWrite: Boolean = false) : this.type = { this.clearState() File.saveTorch(this, path, TYPE_MODULE, overWrite) this } - def saveCaffe(prototxtPath: String, modelPath: String, + /** + * Save this module to path in caffe readable format + * @param prototxtPath + * @param modelPath + * @param useV2 + * @param overwrite + * @return + */ + final def saveCaffe(prototxtPath: String, modelPath: String, useV2 : Boolean = true, overwrite : Boolean = false) : this.type = { this.clearState() CaffePersister.persist[T](prototxtPath, modelPath, this, useV2, overwrite) this } - def saveTF( - inputs : Seq[(String, Seq[Int])], - path: String, - byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, - dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC): this.type = { + /** + * Save this module to path in tensorflow readable format + * @param inputs + * @param path + * @param byteOrder + * @param dataFormat + * @return + */ + final def saveTF( + inputs : Seq[(String, Seq[Int])], + path: String, + byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, + dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC): this.type = { require(this.isInstanceOf[Graph[T]], "only Graph container can be saved as Tensorflow model") this.clearState() val inTrainMode = train @@ -629,9 +596,10 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, } /** - * @return Float or Double + * Get numeric type of module parameters + * @return */ - def getNumericType(): TensorDataType = { + final def getNumericType(): TensorDataType = { ev.getType() } @@ -642,9 +610,9 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * if -1, default is 4 * partitionNumber of datatset * @param shareBuffer whether to share same memory for each batch predict results */ - def predict(dataset: RDD[Sample[T]], - batchSize: Int = -1, - shareBuffer: Boolean = false): RDD[Activity] = { + final def predict(dataset: RDD[Sample[T]], + batchSize: Int = -1, + shareBuffer: Boolean = false): RDD[Activity] = { Predictor(this).predict(dataset, batchSize, shareBuffer) } @@ -654,7 +622,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param batchSize total batchSize for all partitions. * if -1, default is 4 * partitionNumber of dataset */ - def predictClass(dataset: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { + final def predictClass(dataset: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { Predictor(this).predictClass(dataset, batchSize) } @@ -672,7 +640,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param featurePaddingParam featurePaddingParam if the inputs have variant size * @return */ - def predictImage(imageFrame: ImageFrame, + final def predictImage(imageFrame: ImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, batchPerPartition: Int = 4, @@ -693,7 +661,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param newWeights array of weights and bias * @return */ - def setWeightsBias(newWeights: Array[Tensor[T]]): this.type = { + final def setWeightsBias(newWeights: Array[Tensor[T]]): this.type = { require(parameters() != null, "this layer does not have weight/bias") require(parameters()._1.length == newWeights.length, "the number of input weight/bias is not consistant with " + @@ -703,9 +671,9 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, val weights = parameters()._1 for(i <- newWeights.indices) { // TODO: enable this checking as we don't respect shape right now. -// require(weights(i).size().deep == newWeights(i).size().deep, -// s"Mismatch shape, ${weights(i).size().mkString(",")}" + -// s" vs ${newWeights(i).size().mkString(",")} ") + // require(weights(i).size().deep == newWeights(i).size().deep, + // s"Mismatch shape, ${weights(i).size().mkString(",")}" + + // s" vs ${newWeights(i).size().mkString(",")} ") weights(i).copy(newWeights(i)) } this @@ -716,7 +684,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @return array of weights and bias * */ - def getWeightsBias(): Array[Tensor[T]] = { + final def getWeightsBias(): Array[Tensor[T]] = { if (parameters() != null) { parameters()._1 } else { @@ -729,7 +697,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param path file to save * @param overWrite whether to overwrite or not */ - def saveWeights(path: String, overWrite: Boolean): Unit = { + final def saveWeights(path: String, overWrite: Boolean): Unit = { val parameterTable = getParametersTable() val weightsBiasTable = T() parameterTable.foreach { @@ -754,7 +722,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * if not, only load existing pretrained weights and bias * @return current module */ - def loadWeights(weightPath: String, matchAll: Boolean = true): this.type = { + final def loadWeights(weightPath: String, matchAll: Boolean = true): this.type = { val srcParameter = File.load[Table](weightPath) val targetParameter = getParametersTable() copyWeights(targetParameter, srcParameter, matchAll) @@ -767,32 +735,13 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param matchAll whether to match all layers' weights and bias, * @return current module */ - def loadModelWeights(srcModel: Module[Float], matchAll: Boolean = true): this.type = { + final def loadModelWeights(srcModel: Module[Float], matchAll: Boolean = true): this.type = { val srcParameter = srcModel.getParametersTable() val targetParameter = getParametersTable() copyWeights(targetParameter, srcParameter, matchAll) this } - private def copyWeights(target: Table, src: Table, matchAll: Boolean): Unit = { - target.foreach { - case (name: String, targetParams: Table) => - if (src.contains(name)) { - val srcParams = src[Table](name) - if (srcParams.contains("weight")) { - val w = srcParams[Tensor[T]]("weight") - targetParams[Tensor[T]]("weight").resizeAs(w).copy(w) - } - if (srcParams.contains("bias")) { - val b = srcParams[Tensor[T]]("bias") - targetParams[Tensor[T]]("bias").resizeAs(b).copy(b) - } - } else { - if (matchAll) new Exception(s"module $name cannot find corresponding weight bias") - } - } - } - /** * Build graph: some other modules point to current module * @param nodes upstream module nodes @@ -825,7 +774,8 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param nodesWithIndex upstream module nodes and the output tensor index. The start index is 1. * @return node containing current module */ - def inputs(first: (ModuleNode[T], Int), nodesWithIndex : (ModuleNode[T], Int)*): ModuleNode[T] = { + def inputs(first: (ModuleNode[T], Int), nodesWithIndex : (ModuleNode[T], Int)*) + : ModuleNode[T] = { val curNode = new ModuleNode[T](this) first._1.add(curNode, Edge(first._2)) nodesWithIndex.foreach(nodeWithIndex => { @@ -834,6 +784,17 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, curNode } + /** + * Generate graph module with start nodes + * @param startNodes + * @return + */ + def toGraph(startNodes: ModuleNode[T]*): Graph[T] = { + val starts = if (startNodes.isEmpty) Array(Input[T]()) else startNodes.toArray + val endNodes = this.getEndNodes(starts) + Graph(starts, endNodes) + } + /** * Find a module with given name. If there is no module with given name, it will return None. If * there are multiple modules with the given name, an exception will be thrown. @@ -849,30 +810,206 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, } /** - * use ValidationMethod to evaluate module + * use ValidationMethod to evaluate module on the given rdd dataset * @param dataset dataset for test * @param vMethods validation methods * @param batchSize total batchsize of all partitions, * optional param and default 4 * partitionNum of dataset * @return */ - def evaluate(dataset: RDD[Sample[T]], - vMethods: Array[ValidationMethod[T]], - batchSize: Option[Int] = None): Array[(ValidationResult, ValidationMethod[T])] = { + final def evaluate( + dataset: RDD[Sample[T]], + vMethods: Array[ValidationMethod[T]], + batchSize: Option[Int] = None + ): Array[(ValidationResult, ValidationMethod[T])] = { Evaluator(this).test(dataset, vMethods, batchSize) } - - def evaluate(dataSet: LocalDataSet[MiniBatch[T]], - vMethods: Array[ValidationMethod[T]] - ): Array[(ValidationResult, ValidationMethod[T])] = { + /** + * use ValidationMethod to evaluate module on the given local dataset + * @param dataSet + * @param vMethods + * @return + */ + final def evaluate( + dataSet: LocalDataSet[MiniBatch[T]], + vMethods: Array[ValidationMethod[T]] + ): Array[(ValidationResult, ValidationMethod[T])] = { Validator(this, dataSet).test(vMethods) } - def quantize(): Module[T] = { + /** + * Quantize this module, which reduces the precision of the parameter. Get a higher speed with a + * little accuracy cost. + * @return + */ + final def quantize(): Module[T] = { Quantization.quantize(this) } + // ================================= Internal APIs =========================================== + + private var namePostfix = Integer.toHexString(java.util.UUID.randomUUID().hashCode()) + + final private[bigdl] def getNamePostfix : String = namePostfix + + final private[bigdl] def setNamePostfix(namePostfix : String) : Unit = + this.namePostfix = namePostfix + + /** + * The scale of gradient weight and gradient bias + * before gradParameters being accumulated. + */ + protected var scaleW: Double = 1.0 + protected var scaleB: Double = 1.0 + + private[nn] final def allocateAs(dest: Activity): Activity = dest match { + case tensor: Tensor[T] => Tensor[T]() + case table: Table => T() + case _ => throw new IllegalArgumentException("Activity only support tensor and table now") + } + + /** + * The name of the module + */ + private var name : String = null + + protected final def getPrintName(): String = { + val postfix = if (name == null) { + namePostfix + } else { + name + } + s"${this.getClass.getSimpleName}[${postfix}]" + + } + + protected var forwardTime = 0L + + protected var backwardTime = 0L + + private var scaleWCache: Double = scaleW + private var scaleBCache: Double = scaleB + + /** + * This function returns two tensors. One for the flattened trainable parameters flatParameters + * and another for the gradients of the energy wrt to the trainable parameters flatGradParameters. + * + * Custom modules should not override this function. They should instead override parameters(...) + * which is, in turn, called by the present function. + * + * This function will go over all the weights and gradWeights and make them view into a single + * tensor (one for weights and one for gradWeights). + * + * @return + */ + final private[bigdl] def getParameters(): (Tensor[T], Tensor[T]) = { + val (weightParameters, gradParameters) = this.parameters() + + // If some gradParameters are not allocated storage, allocate it + require(weightParameters.size == gradParameters.size, + "weights and gradient number are not match") + weightParameters.zip(gradParameters).foreach{ case(w, g) => g.resizeAs(w)} + (Module.flatten[T](weightParameters), Module.flatten[T](gradParameters)) + } + + /** + * Module status. It is useful for modules like dropout/batch normalization + */ + protected var train: Boolean = true + + + protected var line = "\n" + + + private val engineType: EngineType = Engine.getEngineType() + + /** + * get execution engine type + */ + private[bigdl] def checkEngineType(): this.type = { + if (engineType != Engine.getEngineType()) { + throw new Error("Module's EngineType doesn't march global EngineType") + } + this + } + + final private def setWeightAndBias(copy : AbstractModule[A, B, T], deepCopy : Boolean): Unit = { + val parameterTable = this.getParametersTable + val copiedModuleParamTable = copy.getParametersTable + if (parameterTable != null) { + require(copiedModuleParamTable != null, "cloned module should have params") + parameterTable.foreach { + case (name: String, params: Table) => + require(copiedModuleParamTable.get(name) != None, s"cloned module should have for $name") + setLayerWeightAndBias(params, + copiedModuleParamTable.get(name).get.asInstanceOf[Table], deepCopy) + } + } + } + + final private def setLayerWeightAndBias(params : Table, + copyParams : Table, deepCopy : Boolean): Unit = { + params.foreach(param => { + copyParam(params, copyParams, deepCopy, param._1.toString) + }) + } + + final private def copyParam(params : Table, copyParams : Table, + deepCopy : Boolean, paraName : String) : Unit = { + if (params.contains(paraName)) { + // this is for quantization tensors where the weight might be an array + if (params.get(paraName).get + .isInstanceOf[Array[Tensor[T]]]) { + val copies = copyParams.get(paraName).get + .asInstanceOf[Array[Tensor[T]]] + val origins = params.get(paraName).get + .asInstanceOf[Array[Tensor[T]]] + var i = 0 + while (i < copies.length) { + copyTensor(origins(i), copies(i), deepCopy) + i += 1 + } + } else { + // For normal layers, their params are just tensors + copyTensor(params.get(paraName).get.asInstanceOf[Tensor[T]], + copyParams.get(paraName).get.asInstanceOf[Tensor[T]], deepCopy) + } + } + } + + final private def copyTensor(t1 : Tensor[T], t2 : Tensor[T], deepCopy : Boolean) = { + if (t2.isInstanceOf[QuantizedTensor[_]]) { + t2.asInstanceOf[QuantizedTensor[_]].release() + } + if (deepCopy) { + t2.copy(t1) + } else { + t2.set(t1) + } + } + + final private def copyWeights(target: Table, src: Table, matchAll: Boolean): Unit = { + target.foreach { + case (name: String, targetParams: Table) => + if (src.contains(name)) { + val srcParams = src[Table](name) + if (srcParams.contains("weight")) { + val w = srcParams[Tensor[T]]("weight") + targetParams[Tensor[T]]("weight").resizeAs(w).copy(w) + } + if (srcParams.contains("bias")) { + val b = srcParams[Tensor[T]]("bias") + targetParams[Tensor[T]]("bias").resizeAs(b).copy(b) + } + } else { + if (matchAll) new Exception(s"module $name cannot find corresponding weight bias") + } + } + } + + private[bigdl] def canEqual(other: Any): Boolean = other.isInstanceOf[AbstractModule[A, B, T]] + /** * Generate end nodes of current module with start nodes @@ -884,23 +1021,12 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, endNodes } - /** - * Generate graph module with start nodes - * @param startNodes - * @return - */ - def toGraph(startNodes: ModuleNode[T]*): Graph[T] = { - val starts = if (startNodes.isEmpty) Array(Input[T]()) else startNodes.toArray - val endNodes = this.getEndNodes(starts) - Graph(starts, endNodes) - } - /** * Return classTag numerics for module serialization. If your module contains multiple classtag * in the constructor, you should override this method * @return */ - def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + private[bigdl] def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { (Array(scala.reflect.classTag[T]), Array(ev)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala index ada8628ca5d..8063337378d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala @@ -106,10 +106,6 @@ private[bigdl] class Linear[T: ClassTag]( (Array(weight, bias), Array(empty, empty)) } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "bias" -> bias)) - } - override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { return false diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala index f5602168658..b44473d5d16 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -216,10 +216,6 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( (weight :+ bias, Array.fill[Tensor[T]](nGroup + 1)(empty)) // nGroup's weight + bias } - override def getParametersTable(): Table = { - T(getName() -> T("weight" -> weight, "bias" -> bias)) - } - override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { return false diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 41aecc72fc7..f3557e6f809 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2316,7 +2316,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def updateParameters(model: AbstractModule[Activity, Activity, T], lr: Double): Unit = { - model.updateParameters(ev.fromType(lr)) + val (w, g) = model.getParameters() + w.add(ev.negative(ev.fromType(lr)), g) } def uniform(a: Double, b: Double, size: JList[Int]): JTensor = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 920f892cb9e..341feb0fc95 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -213,6 +213,8 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.ops.RandomUniform", RandomUniformOps) registerModule("com.intel.analytics.bigdl.nn.tf.StrideSlice", StrideSlice) registerModule("com.intel.analytics.bigdl.nn.MultiRNNCell", MultiRNNCell) + registerModule("com.intel.analytics.bigdl.nn.SpatialSeperableConvolution", + SpatialSeperableConvolution) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddSpec.scala index 779fe82856d..30d97f98609 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddSpec.scala @@ -47,7 +47,8 @@ class CAddSpec extends FlatSpec with Matchers { val gradCriterion = criterion.backward (pred, y) mlp.zeroGradParameters () mlp.backward (x, gradCriterion) - mlp.updateParameters (learningRate) + val (weight, grad) = mlp.getParameters() + weight.add(-learningRate, grad) err } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala index 87e06da4638..28b5e761036 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala @@ -1063,7 +1063,6 @@ class DynamicGraphSpec extends FlatSpec with Matchers { model.zeroGradParameters() println("output1: \n", model.forward(input)) model.backward(input, gradOutput) - model.updateParameters(1) println("fc2 weight \n", fc2.element.parameters()._1(0)) @@ -1073,7 +1072,6 @@ class DynamicGraphSpec extends FlatSpec with Matchers { model.freeze("fc2") println("output2: \n", model.forward(input)) model.backward(input, gradOutput) - model.updateParameters(1) println("fc2 weight \n", fc2.element.parameters()._1(0)) fc1.element.getParameters()._1.apply1(_ => 1.0f) @@ -1082,7 +1080,6 @@ class DynamicGraphSpec extends FlatSpec with Matchers { model.unFreeze() println("output3: \n", model.forward(input)) model.backward(input, gradOutput) - model.updateParameters(1) println("fc2 weight \n", fc2.element.parameters()._1(0)) fc1.element.getParameters()._1.apply1(_ => 1.0f) @@ -1091,7 +1088,6 @@ class DynamicGraphSpec extends FlatSpec with Matchers { model.zeroGradParameters() println("output4: \n", model.forward(input)) model.backward(input, gradOutput) - model.updateParameters(1) println("fc1 weight \n", fc1.element.parameters()._1(0)) println("fc2 weight \n", fc2.element.parameters()._1(0)) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index 233005a1061..a5b771a1150 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -1094,7 +1094,6 @@ class StaticGraphSpec extends FlatSpec with Matchers { model.zeroGradParameters() println("output1: \n", model.forward(input)) model.backward(input, gradOutput) - model.updateParameters(1) println("fc2 weight \n", fc2.element.parameters()._1(0)) @@ -1104,7 +1103,6 @@ class StaticGraphSpec extends FlatSpec with Matchers { model.freeze("fc2") println("output2: \n", model.forward(input)) model.backward(input, gradOutput) - model.updateParameters(1) println("fc2 weight \n", fc2.element.parameters()._1(0)) fc1.element.getParameters()._1.apply1(_ => 1.0f) @@ -1113,7 +1111,6 @@ class StaticGraphSpec extends FlatSpec with Matchers { model.unFreeze() println("output3: \n", model.forward(input)) model.backward(input, gradOutput) - model.updateParameters(1) println("fc2 weight \n", fc2.element.parameters()._1(0)) fc1.element.getParameters()._1.apply1(_ => 1.0f) @@ -1122,7 +1119,6 @@ class StaticGraphSpec extends FlatSpec with Matchers { model.zeroGradParameters() println("output4: \n", model.forward(input)) model.backward(input, gradOutput) - model.updateParameters(1) println("fc1 weight \n", fc1.element.parameters()._1(0)) println("fc2 weight \n", fc2.element.parameters()._1(0)) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala index 3b87aa552eb..90d3d8d608a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala @@ -184,7 +184,8 @@ class LinearSpec extends FlatSpec with Matchers { val grad = mse.backward(output, res) linear.zeroGradParameters() linear.backward(input, grad) - linear.updateParameters(0.5 / log(i + 3)) + val (weight, gradWeight) = linear.getParameters() + weight.add(-0.5 / log(i + 3), gradWeight) } val params = linear.parameters() val weight = params._1(0) @@ -236,7 +237,9 @@ class LinearSpec extends FlatSpec with Matchers { val grad = mse.backward(output, res) linear.zeroGradParameters() linear.backward(input, grad) - linear.updateParameters(0.5 / log(i + 3)) + + val (weight, gradWeight) = linear.getParameters() + weight.add(-0.5 / log(i + 3), gradWeight) } val params = linear.parameters() val weight = params._1(0) @@ -288,7 +291,8 @@ class LinearSpec extends FlatSpec with Matchers { val grad = mse.backward(output, res) linear.zeroGradParameters() linear.backward(input, grad) - linear.updateParameters(0.5 / log(i + 3)) + val (weight, gradWeight) = linear.getParameters() + weight.add(-0.5 / log(i + 3), gradWeight) } val params = linear.parameters() val weight = params._1(0) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala index 7b4a3aba759..3adce43aecc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionSpec.scala @@ -366,8 +366,10 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { gradInputNHWC.transpose(2, 4).transpose(3, 4) .sub(gradInput).pow(2).sum() should be < 1e-7 - layer.updateParameters(0.01) - layerNHWC.updateParameters(0.01) + val (weight1, grad1) = layer.getParameters() + weight1.add(-0.01, grad1) + val (weight2, grad2) = layerNHWC.getParameters() + weight2.add(-0.01, grad2) val transWeight = layerNHWC.weight.transpose(2, 5).transpose(3, 4).transpose(4, 5) transWeight.sub(layer.weight).pow(2).sum() should be < 1e-7 @@ -2819,13 +2821,14 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { var gradOutput: Tensor[Double] = null var gradInput: Tensor[Double] = null + val (w, g) = model.getParameters() for (k <- 1 to maxIter) { model.zeroGradParameters() output = model.forward(input(k)).toTensor[Double] err = loss.forward(output, t) gradOutput = loss.backward(output, t) gradInput = model.backward(input(k), gradOutput).toTensor[Double] - model.updateParameters(0.001) + w.add(-0.001, g) } input(maxIter).map(exInput, (v1, v2) => { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala index 7fcf8221d00..0c8d0ca95eb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolutionSpec.scala @@ -210,11 +210,13 @@ class SpatialFullConvolutionSpec extends FlatSpec with Matchers { val output1 = layer.forward(input) layer.backward(input, output1) - layer.updateParameters(0.1) + val (weight, grad) = layer.getParameters() + weight.add(-0.1, grad) val output2 = layer2.forward(input) layer2.backward(input, output2) - layer2.updateParameters(0.1) + val (weight2, grad2) = layer2.getParameters() + weight2.add(-0.1, grad2) val output = layer.forward(input) val expected = layer2.forward(input) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SequentialSpec.scala index 0da7534e77f..f37c74aec1d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SequentialSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SequentialSpec.scala @@ -98,7 +98,8 @@ class SequentialSpec extends TorchSpec { module.zeroGradParameters() gradInput = module.updateGradInput(input, gradOutput).toTensor[Double] module.accGradParameters(input, gradOutput) - module.updateParameters(0.1) + val (weight, grad) = module.getParameters() + weight.add(-0.1, grad) i += 1 } From 491135557557abb8088f15432d152c50772aa6d8 Mon Sep 17 00:00:00 2001 From: tosky001 Date: Sun, 11 Feb 2018 15:10:07 +0800 Subject: [PATCH 0699/1065] add [[CategoricalColVocaList]] Operation (#2282) * solve conficts * modify the ops to support 1-D input * simplify and optimize the input tensor * make the functions of Operation more completed * add HashFunc.scala and modify the CategoricalColVocalList.scala --- .../analytics/bigdl/utils/HashFunc.scala | 28 ++++ .../dllib/nn/ops/CategoricalColVocaList.scala | 131 ++++++++++++++++++ .../nn/ops/CategoricalColVocaListSpec.scala | 96 +++++++++++++ .../serializer/OperationSerializerSpec.scala | 13 +- 4 files changed, 267 insertions(+), 1 deletion(-) create mode 100644 scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/HashFunc.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaList.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaListSpec.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/HashFunc.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/HashFunc.scala new file mode 100644 index 00000000000..a452ae9ed2e --- /dev/null +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/HashFunc.scala @@ -0,0 +1,28 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import scala.util.hashing.MurmurHash3 + +object HashFunc { + def stringHashBucket32(string: String, buckets: Int): Int = { + MurmurHash3.stringHash(string) % buckets match { + case v if v < 0 => v + buckets + case v if v >= 0 => v + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaList.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaList.scala new file mode 100644 index 00000000000..97ed1858b9a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaList.scala @@ -0,0 +1,131 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.HashFunc + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +/** + * CategoricalColVocaList operation having an vocabulary mapping feature string to Integer ID + * + * By default, out-of-vocabulary values are ignored. + * Use either (but not both) of num_oov_buckets and default_value + * to specify how to include out-of-vocabulary values. + * + * if isSetDefault=false && num_oov_buckets=0 the out-of-vocabulary values will be filtered. + * if isSetDefault enabled, the defalut value len(vocabulary_list) will be set. + * if num_oov_buckets enabled, all out-of-vocabulary inputs will be assigned IDs in the range + * [len(vocabulary_list), len(vocabulary_list)+num_oov_buckets) based on a hash of the input value + * + * A positive num_oov_buckets can not be specified with default_value. + * + * the input Tensor[String] can be 1-D or 2-D Tensor. + * + * The Operation support the feature column with single-value or multi-value + * + * The missing values in input Tensor can be represented by -1 for int and '''' for string + * + * @param vocaList An vocabulary with the length more than or equal to 1. + * @param strDelimiter The delimiter of feature string, default: ",". + * @param isSetDefault Set default value for out-of-vocabulary feature values default: false. + * @param numOovBuckets the non-negative number of out-of-vocabulary buckets, default: 0. + * @tparam T Numeric type. Parameter tensor numeric type. Only support float/double now + */ + +class CategoricalColVocaList[T: ClassTag]( + val vocaList: Array[String], + val strDelimiter: String = ",", + val isSetDefault: Boolean = false, + val numOovBuckets: Int = 0 +) (implicit ev: TensorNumeric[T]) + extends Operation[Tensor[String], Tensor[Int], T]{ + + private val vocaLen = vocaList.length + private val vocaMap = vocaList.zipWithIndex.toMap + + require(numOovBuckets >= 0, + "numOovBuckets is a negative integer") + require(!(isSetDefault && numOovBuckets != 0), + "defaultValue and numOovBuckets are both specified") + require(vocaLen > 0, + "the vocabulary list is empty") + require(vocaLen == vocaMap.size, + "the vocabulary list contains duplicate keys") + + output = Tensor[Int]() + + override def updateOutput(input: Tensor[String]): Tensor[Int] = { + + input.squeeze() + val rows = input.size(dim = 1) + + val cols = if (numOovBuckets==0) { + if (isSetDefault) vocaLen + 1 else vocaLen + } + else { + vocaLen + numOovBuckets + } + val shape = Array(rows, cols) + val indices0 = new ArrayBuffer[Int]() + val indices1 = new ArrayBuffer[Int]() + val values = new ArrayBuffer[Int]() + + var i = 1 + while (i <= rows) { + var feaStrArr = input.valueAt(i).split(strDelimiter) + if (!isSetDefault && numOovBuckets == 0) { + feaStrArr = feaStrArr.filter(x => vocaMap.contains(x)) + } + var j = 0 + while (j < feaStrArr.length) { + val mapVal = numOovBuckets==0 match { + case true => + vocaMap.getOrElse(feaStrArr(j), vocaMap.size) + case false => + vocaMap.getOrElse(feaStrArr(j), + HashFunc.stringHashBucket32(feaStrArr(j), numOovBuckets) + vocaLen) + } + indices0 += i-1 + indices1 += j + values += mapVal + j += 1 + } + i += 1 + } + val indices = Array(indices0.toArray, indices1.toArray) + output = Tensor.sparse(indices, values.toArray, shape) + output + } +} + +object CategoricalColVocaList { + def apply[T: ClassTag]( + vocaList: Array[String], + strDelimiter: String = ",", + isSetDefault: Boolean = false, + numOovBuckets: Int = 0 + ) (implicit ev: TensorNumeric[T]): CategoricalColVocaList[T] + = new CategoricalColVocaList[T]( + vocaList = vocaList, + strDelimiter = strDelimiter, + isSetDefault = isSetDefault, + numOovBuckets = numOovBuckets + ) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaListSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaListSpec.scala new file mode 100644 index 00000000000..e787329d7a0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaListSpec.scala @@ -0,0 +1,96 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class CategoricalColVocaListSpec extends FlatSpec with Matchers{ + + "CategoricalColVocaList operation with ignoring the outside values" should "work correctly" in { + val input = Tensor[String](T(T("A"), T("B"), T("C"), T("A,D"))) + val indices = Array(Array(0, 1, 2, 3), Array(0, 0, 0, 0)) + val values = Array(0, 1, 2, 0) + val shape = Array(4, 3) + val expectOutput = Tensor.sparse( + indices, values, shape + ) + val output = CategoricalColVocaList[Double]( + vocaList = Array("A", "B", "C"), + strDelimiter = ",", + isSetDefault = false, + numOovBuckets = 0 + ).forward(input) + + output should be(expectOutput) + } + + "CategoricalColVocaList operation with default value" should "work correctly" in { + val input = Tensor[String](T(T("A"), T("B"), T("C"), T("D"))) + val indices = Array(Array(0, 1, 2, 3), Array(0, 0, 0, 0)) + val values = Array(0, 1, 2, 3) + val shape = Array(4, 4) + val expectOutput = Tensor.sparse( + indices, values, shape + ) + val output = CategoricalColVocaList[Double]( + vocaList = Array("A", "B", "C"), + strDelimiter = ",", + isSetDefault = true, + numOovBuckets = 0 + ).forward(input) + + output should be(expectOutput) + } + + "CategoricalColVocaList operation with numOvvBucket" should "work correctly" in { + val input = Tensor[String](T(T("A,B"), T("C"), T("B,C,D"), T("A,D"))) + val indices = Array( + Array(0, 0, 1, 2, 2, 2, 3, 3), + Array(0, 1, 0, 0, 1, 2, 0, 1)) + val values = Array(0, 1, 2, 1, 2, 4, 0, 4) + val shape = Array(4, 5) + val expectOutput = Tensor.sparse( + indices, values, shape + ) + val output = CategoricalColVocaList[Double]( + vocaList = Array("A", "B", "C"), + strDelimiter = ",", + numOovBuckets = 2 + ).forward(input) + + output should be(expectOutput) + } + + "CategoricalColVocaList operation with 1-D input" should "work correctly" in { + val input = Tensor[String](T("A", "B", "C", "D")) + val indices = Array(Array(0, 1, 2, 3), Array(0, 0, 0, 0)) + val values = Array(0, 1, 2, 3) + val shape = Array(4, 4) + val expectOutput = Tensor.sparse( + indices, values, shape + ) + val output = CategoricalColVocaList[Double]( + vocaList = Array("A", "B", "C"), + strDelimiter = ",", + isSetDefault = true, + numOovBuckets = 0 + ).forward(input) + + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index f61fbe9146c..2401e3f2ebe 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -21,7 +21,7 @@ import java.io.{File => JFile} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, BucketizedCol, Cast, CategoricalColHashBucket, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} import com.intel.analytics.bigdl.tensor._ @@ -473,6 +473,17 @@ class OperationSerializerSpec extends SerializerSpecHelper { runSerializationTest(categoricalColHashBucket, input) } + "CategoricalColVocaList" should "work properly" in { + val categoricalColVocaList = CategoricalColVocaList[Float]( + vocaList = Array("A", "B", "C"), + strDelimiter = ",", + isSetDefault = false, + numOovBuckets = 0 + ).setName("categoricalColVocaList") + val input = Tensor[String](T(T("A"), T("B"), T("C"), T("D"))) + runSerializationTest(categoricalColVocaList, input) + } + "LessEqual serializer" should "work properly" in { val lessEqual = LessEqual[Float]().setName("lessEqual") val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) From d3e88c84642e6d55a4fe766f181849994a40f8a4 Mon Sep 17 00:00:00 2001 From: Xianyan Date: Mon, 12 Feb 2018 14:18:09 +0800 Subject: [PATCH 0700/1065] support imageframe in python optimizer (#2207) * ImageFrame to Python Sample * fix * support imageframe in python optimizer * clean * fix style * update * fix style * fix style * fix test * meet code review * train inception with imageframe interface * return new ImageFeature in PixelBytesToMat * update * some more wrapper * update * update * update * support imageframe in python optimizer * python can train * clean * update * update * revert rename * update API * fix ut --- .../bigdl/dllib/feature/dataset/DataSet.scala | 13 +++- .../transform/vision/image/Convertor.scala | 61 ++++++++++++++++--- .../transform/vision/image/ImageFeature.scala | 9 +++ .../dllib/utils/python/api/PythonBigDL.scala | 35 +++++++++++ .../bigdl/dllib/python/api/PythonSpec.scala | 32 ++++++++++ 5 files changed, 139 insertions(+), 11 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index 0409be29e5c..b1f73d06a27 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -23,7 +23,7 @@ import java.util.concurrent.atomic.AtomicInteger import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset.image.{LabeledBGRImage, _} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame} +import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame} import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T} import org.apache.hadoop.io.Text import org.apache.log4j.Logger @@ -366,6 +366,15 @@ object DataSet { ) } + def imageFrame(imageFrame: ImageFrame): DataSet[ImageFeature] = { + imageFrame match { + case distributedImageFrame: DistributedImageFrame => + rdd[ImageFeature](distributedImageFrame.rdd) + case localImageFrame: LocalImageFrame => + array(localImageFrame.array) + } + } + /** * Wrap a RDD as a DataSet. * @param data @@ -574,7 +583,7 @@ object DataSet { imf(ImageFeature.originalSize) = (height, width, 3) imf }).filter(_[Tensor[Float]](ImageFeature.label).valueAt(1) <= classNum) - ImageFrame.rdd(rawData.coalesce(num, true)) + ImageFrame.rdd(rawData) } private[bigdl] def findFiles(path: Path): Array[LocalSeqFilePath] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala index b102229c709..0e840dacb5d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala @@ -17,7 +17,8 @@ package com.intel.analytics.bigdl.transform.vision.image -import com.intel.analytics.bigdl.dataset.{ArraySample} +import com.intel.analytics.bigdl.dataset._ +import com.intel.analytics.bigdl.opencv.OpenCV import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat @@ -68,18 +69,32 @@ object BytesToMat { */ class PixelBytesToMat(byteKey: String = ImageFeature.bytes) extends FeatureTransformer { - override def transformMat(feature: ImageFeature): Unit = { - require(feature.getOriginalSize != null, - "please set the original size of image in ImageFeature") - val pixels = feature[Array[Byte]](byteKey) - val mat = OpenCVMat.fromPixelsBytes(pixels, feature.getOriginalHeight, - feature.getOriginalWidth, - feature.getOriginalChannel) - feature(ImageFeature.mat) = mat + override def transform(feature: ImageFeature): ImageFeature = { + require(OpenCV.isOpenCVLoaded, "opencv isn't loaded") + if (!feature.isValid) return feature + try { + require(feature.getOriginalSize != null, + "please set the original size of image in ImageFeature") + val pixels = feature[Array[Byte]](byteKey) + val mat = OpenCVMat.fromPixelsBytes(pixels, feature.getOriginalHeight, + feature.getOriginalWidth, + feature.getOriginalChannel) + val output = feature.clone() + output(ImageFeature.mat) = mat + output + } catch { + case e: Exception => + val path = if (feature.contains(ImageFeature.uri)) feature(ImageFeature.uri) else "" + PixelBytesToMat.logger.warn(s"failed ${path} in transformer ${getClass}") + e.printStackTrace() + feature.isValid = false + feature + } } } object PixelBytesToMat { + val logger = Logger.getLogger(getClass) def apply(byteKey: String = ImageFeature.bytes): PixelBytesToMat = new PixelBytesToMat(byteKey) } @@ -229,3 +244,31 @@ object ImageFrameToSample { sampleKey: String = ImageFeature.sample)(implicit ev: TensorNumeric[T]) : ImageFrameToSample[T] = new ImageFrameToSample[T](inputKeys, targetKeys, sampleKey) } + +class ImageFeatureToMiniBatch[T: ClassTag](batchSize: Int, + featurePaddingParam: Option[PaddingParam[T]] = None, + labelPaddingParam: Option[PaddingParam[T]] = None, + partitionNum: Option[Int] = None, + sampleKey: String = ImageFeature.sample)(implicit ev: TensorNumeric[T]) + extends Transformer[ImageFeature, MiniBatch[T]] { + val toBatch = SampleToMiniBatch[T]( + batchSize, featurePaddingParam, labelPaddingParam, partitionNum) + + override def apply(prev: Iterator[ImageFeature]): Iterator[MiniBatch[T]] = { + toBatch(prev.map(_[Sample[T]](sampleKey))) + } +} + +object ImageFeatureToMiniBatch { + def apply[T: ClassTag](batchSize: Int, + featurePaddingParam: Option[PaddingParam[T]] = None, + labelPaddingParam: Option[PaddingParam[T]] = None, + partitionNum: Option[Int] = None, + sampleKey: String = ImageFeature.sample) + (implicit ev: TensorNumeric[T]): ImageFeatureToMiniBatch[T] = + new ImageFeatureToMiniBatch(batchSize, + featurePaddingParam, + labelPaddingParam, + partitionNum, + sampleKey) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala index d13c75856b5..73cbebca9d8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala @@ -195,6 +195,15 @@ class ImageFeature extends Serializable { isValid = true } + override def clone(): ImageFeature = { + val imageFeature = new ImageFeature() + state.foreach(x => { + imageFeature(x._1) = x._2 + }) + imageFeature.isValid = isValid + imageFeature + } + /** * copy the float array to a storage diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index f3557e6f809..8d2a0f38b90 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2190,6 +2190,27 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab enrichOptimizer(optimizer, endTrigger, optimMethod) } + def createDistriOptimizerFromDataSet(model: AbstractModule[Activity, Activity, T], + trainDataSet: DataSet[ImageFeature], + criterion: Criterion[T], + optimMethod: OptimMethod[T], + endTrigger: Trigger, + batchSize: Int): Optimizer[T, MiniBatch[T]] = { + val dataSet = trainDataSet -> ImageFeatureToMiniBatch[T](batchSize) + + val optimizer = new DistriOptimizer( + _model = model, + _dataset = dataSet.asInstanceOf[DistributedDataSet[MiniBatch[T]]], + _criterion = criterion + ).asInstanceOf[Optimizer[T, MiniBatch[T]]] + enrichOptimizer(optimizer, endTrigger, optimMethod) + } + + def featureTransformDataset(dataset: DataSet[ImageFeature], + transformer: FeatureTransformer): DataSet[ImageFeature] = { + dataset -> transformer + } + def createL1L2Regularizer(l1: Double, l2: Double): L1L2Regularizer[T] = { L1L2Regularizer[T](l1, l2) } @@ -2212,6 +2233,16 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab vMethods.asScala.toArray) } + def setValidationFromDataSet(optimizer: Optimizer[T, MiniBatch[T]], + batchSize: Int, + trigger: Trigger, + valDataSet: DataSet[ImageFeature], + vMethods: JList[ValidationMethod[T]]): Unit = { + val dataSet = valDataSet -> ImageFeatureToMiniBatch[T](batchSize) + optimizer.setValidation(trigger, dataSet, + vMethods.asScala.toArray) + } + def setValidation(optimizer: Optimizer[T, MiniBatch[T]], batchSize: Int, trigger: Trigger, @@ -2966,6 +2997,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab sc.parallelize(Seq(""), Engine.coreNumber() * Engine.nodeNumber()) .foreachPartition(_ => Engine.createJavaGateway(driverPort)) } + + def createDatasetFromImageFrame(imageFrame: ImageFrame): DataSet[ImageFeature] = { + DataSet.imageFrame(imageFrame) + } } object PythonBigDLUtils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index b8daf5db0bb..4495a190ffa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -20,6 +20,7 @@ import java.util import java.util.{ArrayList => JArrayList, List => JList, Map => JMap} import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.DataSet import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{Loss, SGD, Top1Accuracy, Trigger} import com.intel.analytics.bigdl.utils.{Engine, T, Table, TestUtils} @@ -30,6 +31,7 @@ import org.apache.spark.api.java.JavaRDD import org.apache.spark.bigdl.api.python.BigDLSerDe import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, ImageFrameToSample} import com.intel.analytics.bigdl.utils.RandomGenerator._ import scala.util.Random @@ -288,4 +290,34 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { println(predictedResult) } + "train with imageFrame" should "work" in { + val images = (1 to 10).map(x => { + val imf = new ImageFeature() + imf(ImageFeature.imageTensor) = Tensor[Float](3, 224, 224).randn() + imf(ImageFeature.label) = Tensor[Float](1).fill(1) + imf + }) + + + val imageFrame = DataSet.imageFrame(ImageFrame.rdd(sc.parallelize(images))) -> + ImageFrameToSample[Float](targetKeys = Array(ImageFeature.label)) + + val model = Sequential[Float]() + model.add(SpatialConvolution[Float](3, 6, 5, 5)) + model.add(View[Float](6 * 220 * 220)) + model.add(Linear[Float](6 * 220 * 220, 20)) + model.add(LogSoftMax[Float]()) + + val sgd = new SGD[Float](0.01) + + val pythonBigDL = PythonBigDL.ofFloat() + val optimizer = pythonBigDL.createDistriOptimizerFromDataSet(model, + imageFrame, + criterion = ClassNLLCriterion[Float](), + optimMethod = sgd, + endTrigger = Trigger.maxEpoch(2), + batchSize = 8) + optimizer.optimize() + } + } From 365171644fe20ca25fb3b338d6b581a4f1a38c5d Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 13 Feb 2018 16:38:03 +0800 Subject: [PATCH 0701/1065] override clearState as some operation should not clear output (#2304) --- .../bigdl/dllib/nn/ops/DataFlowOps.scala | 20 +++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala index ec10a3e1786..ec1046da476 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala @@ -277,6 +277,11 @@ private[bigdl] class TensorArrayWrite[T: ClassTag, D: ClassTag]()( (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), Array[TensorNumeric[_]](ev, ev2)) } + + override def clearState(): this.type = { + // Do nothing in clearState as we don't want to change the TensorArray.FlowOut object + this + } } /** @@ -409,6 +414,11 @@ private[bigdl] class TensorArrayScatter[T: ClassTag, D: ClassTag]()( (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), Array[TensorNumeric[_]](ev, ev2)) } + + override def clearState(): this.type = { + // Do nothing in clearState as we don't want to change the TensorArray.FlowOut object + this + } } /** @@ -519,6 +529,11 @@ private[bigdl] class TensorArraySplit[T: ClassTag, D: ClassTag]()( (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), Array[TensorNumeric[_]](ev, ev2)) } + + override def clearState(): this.type = { + // Do nothing in clearState as we don't want to change the TensorArray.FlowOut object + this + } } /** @@ -555,6 +570,11 @@ private[bigdl] class TensorArrayClose[T: ClassTag]()(implicit ev: TensorNumeric[ TensorArray.release(input.value()) output } + + override def clearState(): this.type = { + // Do nothing in clearState as we don't want to change the TensorArray.FlowOut object + this + } } private[bigdl] class Stack[D](maxSize: Int) { From ec8d216ed7dcb15d57b2c1024385a40543ba5ed5 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 13 Feb 2018 18:49:15 +0800 Subject: [PATCH 0702/1065] Refine the operation and tf folder (#2267) * refine ops and tf packages * fix unit test --- .../bigdl/dllib/nn/DynamicGraph.scala | 3 +- .../bigdl/dllib/nn/FrameManager.scala | 2 +- .../analytics/bigdl/dllib/nn/Graph.scala | 3 +- .../analytics/bigdl/dllib/nn/Scheduler.scala | 8 +- .../bigdl/dllib/nn/ops/DecodeImage.scala | 10 -- .../bigdl/dllib/nn/ops/Operation.scala | 4 +- .../bigdl/dllib/nn/ops/package.scala | 93 ------------------- .../bigdl/dllib/nn/{ops => tf}/ArrayOps.scala | 92 +++++++++++++++++- .../analytics/bigdl/dllib/nn/tf/Const.scala | 80 ---------------- .../bigdl/dllib/nn/tf/ControlDependency.scala | 8 +- .../dllib/nn/{ops => tf}/ControlOps.scala | 12 +-- .../dllib/nn/{ops => tf}/DataFlowOps.scala | 3 +- .../analytics/bigdl/dllib/nn/tf/Fill.scala | 87 ----------------- .../ParsingOps.scala} | 59 ++++++------ .../bigdl/dllib/nn/tf/Variable.scala | 24 ----- .../utils/serializer/ModuleSerializer.scala | 6 +- .../dllib/utils/tf/TensorflowLoader.scala | 2 +- .../dllib/utils/tf/loaders/ArrayOps.scala | 2 +- .../utils/tf/loaders/ControlFlowOps.scala | 2 +- .../dllib/utils/tf/loaders/DataFlowOps.scala | 5 +- .../bigdl/dllib/utils/tf/loaders/Div.scala | 4 +- .../dllib/utils/tf/loaders/ExpandDims.scala | 5 +- .../dllib/utils/tf/loaders/ParseExample.scala | 2 +- .../dllib/utils/tf/loaders/RealDiv.scala | 4 +- .../bigdl/dllib/nn/DynamicGraphSpec.scala | 4 +- .../analytics/bigdl/dllib/nn/GraphSpec.scala | 4 +- .../bigdl/dllib/nn/ops/AddSpec.scala | 3 +- .../bigdl/dllib/nn/ops/DivideSpec.scala | 3 +- .../bigdl/dllib/nn/ops/ExpandDimsSpec.scala | 2 +- .../bigdl/dllib/nn/ops/MultplySpec.scala | 3 +- .../bigdl/dllib/nn/ops/RealDivSpec.scala | 3 +- .../bigdl/dllib/nn/ops/ReshapeSpec.scala | 1 + .../bigdl/dllib/nn/ops/SqueezeSpec.scala | 4 +- .../bigdl/dllib/nn/ops/SubstractSpec.scala | 3 +- .../dllib/nn/ops/TensorArrayScatterSpec.scala | 2 +- .../dllib/nn/ops/TensorArrayWriteSpec.scala | 4 +- .../bigdl/dllib/nn/tf/ConstSpec.scala | 21 ----- .../dllib/nn/{ops => tf}/ControlOpsSpec.scala | 13 +-- .../dllib/nn/{ops => tf}/MergeOpsSpec.scala | 2 +- .../nn/{ops => tf}/ParseExampleSpec.scala | 2 +- .../dllib/nn/{ops => tf}/StackOpsSpec.scala | 2 +- .../serializer/ModuleSerializerSpec.scala | 4 +- .../serializer/OperationSerializerSpec.scala | 23 ++--- .../serializer/SerializerSpecHelper.scala | 2 +- 44 files changed, 193 insertions(+), 432 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/ArrayOps.scala (54%) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/ControlOps.scala (95%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/DataFlowOps.scala (99%) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops/ParseExample.scala => tf/ParsingOps.scala} (81%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/ControlOpsSpec.scala (77%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/MergeOpsSpec.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/ParseExampleSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/StackOpsSpec.scala (97%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala index d06392b92ea..4093ec714c2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala @@ -17,11 +17,10 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.nn.ops.{ControlOps, ResourceAllocator, TensorArray} +import com.intel.analytics.bigdl.nn.tf.{ControlOps, ResourceAllocator, TensorArray} import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.T import scala.collection.mutable import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FrameManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FrameManager.scala index 2f0e6787114..eed4520230b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FrameManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FrameManager.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import java.util.concurrent.atomic.AtomicInteger import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.ops.{Exit, MergeOps, NextIteration} +import com.intel.analytics.bigdl.nn.tf.{Exit, MergeOps, NextIteration} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 181e7adfd4d..6b5fe48d6e9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -20,8 +20,7 @@ import java.util import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops._ -import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} +import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala index d4a7b266354..c12f6f0a506 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala @@ -18,16 +18,10 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.FrameManager.Frame import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.ops._ -import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{Edge, Node, T} +import com.intel.analytics.bigdl.nn.tf._ import scala.collection.mutable -import scala.collection.mutable.ArrayBuffer -import scala.reflect.ClassTag /** * Scheduler of a graph execution. It supports a graph with cycle. Please note that the cycle must diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala index 529f74ca692..ddc8351514c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala @@ -88,11 +88,6 @@ class DecodeImage[T: ClassTag](val channels: Int)(implicit ev: TensorNumeric[T]) throw new IllegalArgumentException("image data is not equal to output buffer") } } - - override def updateGradInput(input: Tensor[ByteString], - gradOutput: Tensor[Int]): Tensor[ByteString] = { - throw new UnsupportedOperationException("no backward on ParseExample") - } } class DecodeJpeg[T: ClassTag](channels: Int, val ratio: Int = 1)(implicit ev: TensorNumeric[T]) @@ -371,11 +366,6 @@ class DecodeRaw[T: ClassTag](val outType: DataType, i = i + 1 } } - - override def updateGradInput(input: Tensor[ByteString], gradOutput: Activity): - Tensor[ByteString] = { - throw new IllegalArgumentException() - } } object DecodeRawSerializer extends ModuleSerializable { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala index b759509f8e5..6be89cec88d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Operation.scala @@ -34,11 +34,11 @@ abstract class Operation[A <: Activity: ClassTag, B <: Activity: ClassTag, T: Cl gradInput = Activity.emptyGradInput(this.getName()).asInstanceOf[A] - override def updateGradInput(input: A, gradOutput: B): A = { + final override def updateGradInput(input: A, gradOutput: B): A = { throw new UnsupportedOperationException("Operation does not support updateGradInput() method") } - override def backward(input: A, gradOutput: B): A = { + final override def backward(input: A, gradOutput: B): A = { throw new UnsupportedOperationException("Operation does not support backward() method") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala deleted file mode 100644 index 27d468ab1f5..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/package.scala +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn - -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -package object ops { - object Add { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](CAddTable()) - } - - object Subtract { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](CSubTable()) - } - - object Multiply { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](CMulTable()) - } - - object Divide { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](CDivTable()) - } - - object RealDiv { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](CDivTable()) - } - - object Reshape { - def apply[T: ClassTag](size: Array[Int]) - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.InferReshape(size: Array[Int])) - } - - object Squeeze { - def apply[T: ClassTag](axis: Array[Int] = null) - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.Squeeze(dims = axis, batchMode = false)) - } - - object Identity { - def apply[T: ClassTag]() - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.Identity() - .asInstanceOf[AbstractModule[Activity, Tensor[T], T]]) - } - - object ReLU { - def apply[T: ClassTag]() - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.ReLU()) - } - - object SoftMax { - def apply[T: ClassTag]() - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.SoftMax()) - } - - object ExpandDims { - def apply[T: ClassTag](axis: Int) - (implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T]( - com.intel.analytics.bigdl.nn.Unsqueeze(axis)) - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArrayOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ArrayOps.scala similarity index 54% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArrayOps.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ArrayOps.scala index 542e343858f..e400e43069d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArrayOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ArrayOps.scala @@ -13,14 +13,43 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.ClassTag +/** + * Some operation may not have input + */ +private[bigdl] trait WithoutInput + +private[bigdl] class Const[T: ClassTag, B: ClassTag](val value: Tensor[B]) + (implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[B], T] with WithoutInput { + + override def updateOutput(input: Activity): Tensor[B] = { + output = value + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[B]), + Array[TensorNumeric[_]](ev)) + } +} + +private[bigdl] object Const { + def apply[T: ClassTag, B: ClassTag](value: Tensor[B]) + (implicit ev: TensorNumeric[T]): Const[T, B] = { + new Const[T, B](value) + } +} + /** * This operation computes the inverse of an index permutation. It takes a 1-D integer tensor x, * which represents the indices of a zero-based array, and swaps each value with its index position. @@ -98,3 +127,62 @@ private[bigdl] class ConcatOffset[T: ClassTag]()(implicit ev: TensorNumeric[T]) output } } + +private[bigdl] class Fill[T: ClassTag]() (implicit ev: TensorNumeric[T]) + extends AbstractModule[Table, Tensor[_], T] { + + override def updateOutput(input: Table): Tensor[_] = { + val shapeTensor = input[Tensor[Int]](1) + val value = input[Tensor[_]](2) + if (shapeTensor.isEmpty) { + if (value.getType() != output.getType()) { + output = value.emptyInstance() + } + output.resizeAs(value).asInstanceOf[Tensor[NumericWildCard]] + .copy(value.asInstanceOf[Tensor[NumericWildCard]]) + } else { + require(shapeTensor.nDimension() == 1, "shape tensor is not a vector") + val shape = new Array[Int](shapeTensor.nElement()) + var i = 0 + while (i < shapeTensor.nElement()) { + shape(i) = shapeTensor.valueAt(i + 1) + i = i + 1 + } + require(value.isScalar, "value tensor is not a scalar") + if (value.getType() != output.getType()) { + output = value.emptyInstance().resize(shape) + } else { + output.resize(shape) + } + + output.forceFill(value.value()) + } + + output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[_]): Table = { + if (gradInput.contains(1)) { + gradInput[Tensor[_]](1).resize(input[Tensor[_]](1).size()).zero() + } else { + val inputTensor = input[Tensor[_]](1) + gradInput(1) = inputTensor.emptyInstance().resize(inputTensor.size()) + } + + if (gradInput.contains(2)) { + gradInput[Tensor[_]](2).resize(input[Tensor[_]](2).size()).zero() + } else { + val inputTensor = input[Tensor[_]](2) + gradInput(2) = inputTensor.emptyInstance().resize(inputTensor.size()) + } + gradInput + } + +} + +private[bigdl] object Fill { + def apply[T: ClassTag]() + (implicit ev: TensorNumeric[T]) : Fill[T] = { + new Fill[T]() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala deleted file mode 100644 index c9919f827ca..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Const.scala +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.tf - -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{T, Table} - -import scala.reflect.ClassTag - -private[bigdl] trait WithoutInput - -/** - * Return a constant tensor defined by value - * @param value the constant tensor to be returned in forward - */ -@SerialVersionUID(-4008935551091949324L) -private[bigdl] class Const[T: ClassTag, B: ClassTag](val value: Tensor[B]) - (implicit ev: TensorNumeric[T]) - extends AbstractModule[Activity, Tensor[B], T] with WithoutInput { - - override def clearState(): this.type = { - // Const do not have state, output should always be value - this - } - - output = value - - override def updateOutput(input: Activity): Tensor[B] = output - - override def updateGradInput(input: Activity, gradOutput: Tensor[B]): Activity = { - require(gradOutput.isSameSizeAs(value), - s"Invalid gradOutput size. require (${value.size().mkString(",")}), but " + - s"(${gradOutput.size().mkString(",")})") - input match { - case t: Tensor[T] => - if (gradInput == null || gradInput.isInstanceOf[Table]) { - gradInput = Tensor[T]() - } - gradInput.toTensor[T].resizeAs(t).zero() - case t: Table => - if (gradInput == null || !gradInput.isInstanceOf[Table]) { - gradInput = T() - } - t.foreach(kv => { - val gradInputTensors = gradInput.toTable - val grad = gradInputTensors.getOrElse[Tensor[T]](kv._1, Tensor[T]()) - .resizeAs(kv._2.asInstanceOf[Tensor[T]]).zero() - gradInputTensors(kv._1) = grad - }) - } - gradInput - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[B]), - Array[TensorNumeric[_]](ev)) - } -} - -private[bigdl] object Const { - def apply[T: ClassTag, B: ClassTag](value: Tensor[B]) - (implicit ev: TensorNumeric[T]): Const[T, B] = { - new Const[T, B](value) - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlDependency.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlDependency.scala index b8982562a93..473b557e713 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlDependency.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlDependency.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -23,14 +24,9 @@ import scala.reflect.ClassTag private[bigdl] class ControlDependency[T: ClassTag]()(implicit ev: TensorNumeric[T]) - extends AbstractModule[Activity, Tensor[T], T] { + extends Operation[Activity, Tensor[T], T] { override def updateOutput(input: Activity): Tensor[T] = { val msg = "forward method on ControlDependency should not be called" throw new UnsupportedOperationException(msg) } - - override def updateGradInput(input: Activity, gradOutput: Tensor[T]): Activity = { - val msg = "backward method on ControlDependency should not be called" - throw new UnsupportedOperationException(msg) - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlOps.scala similarity index 95% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlOps.scala index b57c456c46b..68c5990eb39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlOps.scala @@ -13,11 +13,13 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.Graph._ -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor.{BooleanType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Edge, Node, T} @@ -30,15 +32,9 @@ import scala.reflect.ClassTag */ sealed abstract class ControlOps[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Operation[Activity, Activity, T] { - override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { - throw new UnsupportedOperationException("Operation does not support updateGradInput() method") - } override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { throw new UnsupportedOperationException("Operation does not support updateGradInput() method") } - override def backward(input: Activity, gradOutput: Activity): Activity = { - throw new UnsupportedOperationException("Operation does not support backward() method") - } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/DataFlowOps.scala similarity index 99% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/DataFlowOps.scala index ec1046da476..97ea3d0ec61 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DataFlowOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/DataFlowOps.scala @@ -13,11 +13,12 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import java.util import java.util.concurrent.ConcurrentHashMap +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.nn.tf.WithoutInput import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala deleted file mode 100644 index 52fe2119f5e..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Fill.scala +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.tf - -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -/** - * Creates a tensor filled with a scalar value. Input should be a 1-D tensor defining - * the shape of the output tensor. - */ -@SerialVersionUID(-471757174144422555L) -private[bigdl] class Fill[T: ClassTag]() (implicit ev: TensorNumeric[T]) - extends AbstractModule[Table, Tensor[_], T] { - - override def updateOutput(input: Table): Tensor[_] = { - val shapeTensor = input[Tensor[Int]](1) - val value = input[Tensor[_]](2) - if (shapeTensor.isEmpty) { - if (value.getType() != output.getType()) { - output = value.emptyInstance() - } - output.resizeAs(value).asInstanceOf[Tensor[NumericWildCard]] - .copy(value.asInstanceOf[Tensor[NumericWildCard]]) - } else { - require(shapeTensor.nDimension() == 1, "shape tensor is not a vector") - val shape = new Array[Int](shapeTensor.nElement()) - var i = 0 - while (i < shapeTensor.nElement()) { - shape(i) = shapeTensor.valueAt(i + 1) - i = i + 1 - } - require(value.isScalar, "value tensor is not a scalar") - if (value.getType() != output.getType()) { - output = value.emptyInstance().resize(shape) - } else { - output.resize(shape) - } - - output.forceFill(value.value()) - } - - output - } - - override def updateGradInput(input: Table, gradOutput: Tensor[_]): Table = { - if (gradInput.contains(1)) { - gradInput[Tensor[_]](1).resize(input[Tensor[_]](1).size()).zero() - } else { - val inputTensor = input[Tensor[_]](1) - gradInput(1) = inputTensor.emptyInstance().resize(inputTensor.size()) - } - - if (gradInput.contains(2)) { - gradInput[Tensor[_]](2).resize(input[Tensor[_]](2).size()).zero() - } else { - val inputTensor = input[Tensor[_]](2) - gradInput(2) = inputTensor.emptyInstance().resize(inputTensor.size()) - } - gradInput - } - -} - -private[bigdl] object Fill { - def apply[T: ClassTag]() - (implicit ev: TensorNumeric[T]) : Fill[T] = { - new Fill[T]() - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala similarity index 81% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala index 7d5d749af94..cf0956cf51c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala @@ -13,26 +13,30 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.{T, Table} +package com.intel.analytics.bigdl.nn.tf + import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.nn.ops.Operation +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.utils.Table import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric._ + import org.tensorflow.example.{Example, Feature} -import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString -import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.collection.JavaConverters._ + import scala.reflect.ClassTag import scala.reflect.runtime.universe -class ParseExample[T: ClassTag](val nDense: Int, - val tDense: Seq[TensorDataType], - val denseShape: Seq[Array[Int]]) - (implicit ev: TensorNumeric[T]) +private[bigdl] class ParseExample[T: ClassTag](val nDense: Int, + val tDense: Seq[TensorDataType], + val denseShape: Seq[Array[Int]]) + (implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T] { type StringType = ByteString @@ -53,12 +57,12 @@ class ParseExample[T: ClassTag](val nDense: Int, val outputs = denseDefault .zip(denseKeys) .zip(tDense).zip(denseShape).map { case (((default, key), tensorType), shape) => - if (featureMap.containsKey(key)) { - val feature = featureMap.get(key) - getTensorFromFeature(feature, tensorType, shape) - } else { - default - } + if (featureMap.containsKey(key)) { + val feature = featureMap.get(key) + getTensorFromFeature(feature, tensorType, shape) + } else { + default + } } for (elem <- outputs) { @@ -69,8 +73,8 @@ class ParseExample[T: ClassTag](val nDense: Int, } private def getTensorFromFeature(feature: Feature, - tensorType: TensorDataType, - tensorShape: Array[Int]): Tensor[_] = { + tensorType: TensorDataType, + tensorShape: Array[Int]): Tensor[_] = { tensorType match { case LongType => val values = feature.getInt64List.getValueList.asScala.map(_.longValue()).toArray @@ -79,23 +83,18 @@ class ParseExample[T: ClassTag](val nDense: Int, val values = feature.getFloatList.getValueList.asScala.map(_.floatValue()).toArray Tensor(values, tensorShape) case StringType => - val values = feature.getBytesList.getValueList - .asScala.toArray.asInstanceOf[Array[ByteString]] + val values = feature.getBytesList.getValueList.asScala.toArray Tensor(values, tensorShape) } } - - override def updateGradInput(input: Table, gradOutput: Table): Table = { - throw new UnsupportedOperationException("no backward on ParseExample") - } } -object ParseExample extends ModuleSerializable { +private[bigdl] object ParseExample extends ModuleSerializable { def apply[T: ClassTag](nDense: Int, - tDense: Seq[TensorDataType], - denseShape: Seq[Array[Int]]) - (implicit ev: TensorNumeric[T]): ParseExample[T] = - new ParseExample[T](nDense, tDense, denseShape) + tDense: Seq[TensorDataType], + denseShape: Seq[Array[Int]]) + (implicit ev: TensorNumeric[T]): ParseExample[T] = + new ParseExample[T](nDense, tDense, denseShape) override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala index d769907015a..ceb3c3552d2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala @@ -42,30 +42,6 @@ class Variable[T: ClassTag](val variableValue: Tensor[T], val variableGradient: output } - override def updateGradInput(input: Activity, gradOutput: Tensor[T]): Activity = { - require(gradOutput.isSameSizeAs(variableValue), - s"Invalid gradOutput size. require (${variableValue.size().mkString(",")}), but " + - s"(${gradOutput.size().mkString(",")})") - input match { - case t: Tensor[T] => - if (gradInput == null || gradInput.isInstanceOf[Table]) { - gradInput = Tensor[T]() - } - gradInput.toTensor[T].resizeAs(t).zero() - case t: Table => - if (gradInput == null || !gradInput.isInstanceOf[Table]) { - gradInput = T() - } - t.foreach(kv => { - val gradInputTensors = gradInput.toTable - val grad = gradInputTensors.getOrElse[Tensor[T]](kv._1, Tensor[T]()) - .resizeAs(kv._2.asInstanceOf[Tensor[T]]).zero() - gradInputTensors(kv._1) = grad - }) - } - gradInput - } - override def accGradParameters(input: Activity, gradOutput: Tensor[T]): Unit = { this.variableGradient.add(ev.fromType[Double](1.0), gradOutput) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 341feb0fc95..ddeeae5a1e6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -19,8 +19,8 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer, Model, Sequential => KSequential} -import com.intel.analytics.bigdl.nn.ops.{DecodeRawSerializer, ParseExample, RandomUniform => RandomUniformOps} -import com.intel.analytics.bigdl.nn.tf.StrideSlice +import com.intel.analytics.bigdl.nn.ops.{DecodeRawSerializer, RandomUniform => RandomUniformOps} +import com.intel.analytics.bigdl.nn.tf.{StrideSlice, ParseExample} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -207,7 +207,7 @@ object ModuleSerializer extends ModuleSerializable{ quantized.SpatialDilatedConvolution) registerModule("com.intel.analytics.bigdl.nn.quantized.Linear", quantized.Linear) - registerModule("com.intel.analytics.bigdl.nn.ops.ParseExample", ParseExample) + registerModule("com.intel.analytics.bigdl.nn.tf.ParseExample", ParseExample) registerModule("com.intel.analytics.bigdl.nn.SReLU", SReLU) registerModule("com.intel.analytics.bigdl.nn.ops.DecodeRaw", DecodeRawSerializer) registerModule("com.intel.analytics.bigdl.nn.ops.RandomUniform", RandomUniformOps) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 945c793170b..28d7eb443f3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -30,7 +30,7 @@ import com.intel.analytics.bigdl.python.api.{JTensor, PythonBigDL, PythonBigDLUt import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ -import com.intel.analytics.bigdl.nn.ops.{SwitchControlNode, SwitchOps} +import com.intel.analytics.bigdl.nn.tf.{SwitchControlNode, SwitchOps} import com.intel.analytics.bigdl.utils.tf.TensorflowToBigDL._ import com.intel.analytics.bigdl.utils.tf.loaders.TensorflowOpsLoader import org.tensorflow.framework.{GraphDef, NodeDef} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala index 9d752a1818a..8ead6e82413 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Identity -import com.intel.analytics.bigdl.nn.ops.{InvertPermutation => InvertPermutationOps, +import com.intel.analytics.bigdl.nn.tf.{InvertPermutation => InvertPermutationOps, ConcatOffset => ConcatOffsetOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ControlFlowOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ControlFlowOps.scala index 81071c6c94a..ff0e51bf962 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ControlFlowOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ControlFlowOps.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{MergeOps, SwitchOps, Enter => EnterOps, Exit => ExitOps, +import com.intel.analytics.bigdl.nn.tf.{MergeOps, SwitchOps, Enter => EnterOps, Exit => ExitOps, LoopCondition => LoopConditionOps, NextIteration => NextIterationOps} import com.intel.analytics.bigdl.nn.tf.ControlDependency import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DataFlowOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DataFlowOps.scala index e21924eea63..bae3cb53c3e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DataFlowOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DataFlowOps.scala @@ -17,12 +17,9 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder -import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric - import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops._ -import com.intel.analytics.bigdl.nn.ops.{StackPop => StackPopOps, StackPush => StackPushOps} +import com.intel.analytics.bigdl.nn.tf.{StackCreator, TensorArrayConcat, TensorArrayCreator, TensorArrayGather, TensorArrayGrad, TensorArrayRead, TensorArrayScatter, TensorArraySize, TensorArraySplit, TensorArrayWrite, StackPop => StackPopOps, StackPush => StackPushOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Div.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Div.scala index ea45344bf32..80ce2172079 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Div.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Div.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{RealDiv => RealDivOp} +import com.intel.analytics.bigdl.nn.CDivTable import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -28,6 +28,6 @@ import scala.reflect.ClassTag class Div extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - RealDivOp() + CDivTable().asInstanceOf[Module[T]] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala index 284930dd6d2..6e8a0453b12 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ExpandDims.scala @@ -18,9 +18,8 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity +import com.intel.analytics.bigdl.nn.Unsqueeze import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.ExpandDims import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context @@ -41,6 +40,6 @@ class ExpandDims extends TensorflowOpsLoader { class ExpandDimsLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) { override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() - ExpandDims[T](if (axis < 0) axis + 1 else axis + 1) + Unsqueeze[T](if (axis < 0) axis + 1 else axis + 1) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala index e8d2ce438c0..031741860d2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{ParseExample => ParseExampleOperation} +import com.intel.analytics.bigdl.nn.tf.{ParseExample => ParseExampleOperation} import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala index bd72b7211ae..d49a974c3e0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RealDiv.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{RealDiv => RealDivOp} +import com.intel.analytics.bigdl.nn.CDivTable import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -28,6 +28,6 @@ import scala.reflect.ClassTag class RealDiv extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - RealDivOp() + CDivTable().asInstanceOf[Module[T]] } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala index 28b5e761036..11ece8a6e28 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala @@ -24,8 +24,8 @@ import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.vgg.{VggForCifar10, Vgg_16, Vgg_19} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.EmptyGradInput -import com.intel.analytics.bigdl.nn.ops.{ControlNodes, Enter, Less} -import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.nn.ops.Less +import com.intel.analytics.bigdl.nn.tf.{ControlNodes, Enter, Const} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index a5b771a1150..e1c96f13ad4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -23,8 +23,8 @@ import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.vgg.{VggForCifar10, Vgg_16, Vgg_19} import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.EmptyGradInput -import com.intel.analytics.bigdl.nn.ops.{Ceil, ControlNodes, Less} -import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.nn.ops.{Ceil, Less} +import com.intel.analytics.bigdl.nn.tf.{Const, ControlNodes} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AddSpec.scala index 1a653495d54..420bd04fb50 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AddSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AddSpec.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.CAddTable import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -31,7 +32,7 @@ class AddSpec extends FlatSpec with Matchers { val expectOutput = Tensor(T(10f, 7f, 8f)) - val output = Add().forward(input) + val output = CAddTable().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DivideSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DivideSpec.scala index 4b14774a2b2..645936aff80 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DivideSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DivideSpec.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.CDivTable import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -30,7 +31,7 @@ class DivideSpec extends FlatSpec with Matchers { val expectOutput = Tensor(T(0.5f, 1f, 0.75f)) - val output = Divide().forward(input) + val output = CDivTable().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala index be5b39a5eee..b76a19a5fe3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpandDimsSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.nn.{Unsqueeze => ExpandDims} import org.scalatest.{FlatSpec, Matchers} class ExpandDimsSpec extends FlatSpec with Matchers { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MultplySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MultplySpec.scala index 9157fd50afe..a3042017f62 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MultplySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MultplySpec.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.CMulTable import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -31,7 +32,7 @@ class MultiplySpec extends FlatSpec with Matchers { val expectOutput = Tensor(T(14f, 12f, 12f)) - val output = Multiply().forward(input) + val output = CMulTable().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RealDivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RealDivSpec.scala index d7c15a48c35..3907b1fa369 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RealDivSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RealDivSpec.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.CDivTable import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -30,7 +31,7 @@ class RealDivSpec extends FlatSpec with Matchers { val expectOutput = Tensor(T(0.5f, 1f, 0.75f)) - val output = Divide().forward(input) + val output = CDivTable().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReshapeSpec.scala index a0415fddf24..a8345f42729 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReshapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReshapeSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.nn.{InferReshape => Reshape} import org.scalatest.{FlatSpec, Matchers} class ReshapeSpec extends FlatSpec with Matchers { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqueezeSpec.scala index bcf9ab3d15e..876578bc1c5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqueezeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqueezeSpec.scala @@ -15,8 +15,8 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.Squeeze import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} class SqueezeSpec extends FlatSpec with Matchers { @@ -27,7 +27,7 @@ class SqueezeSpec extends FlatSpec with Matchers { val expectOutput = input.squeeze() - val output = Squeeze().forward(input) + val output = Squeeze(null, false).forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstractSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstractSpec.scala index 5d39a4b5e96..b64db5dcdec 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstractSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SubstractSpec.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.CSubTable import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -30,7 +31,7 @@ class SubstractSpec extends FlatSpec with Matchers { val expectOutput = Tensor(T(-1f, 0f, -1f)) - val output = Subtract().forward(input) + val output = CSubTable().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayScatterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayScatterSpec.scala index d5d43eb5ee0..2d427eaad4d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayScatterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayScatterSpec.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest class TensorArrayScatterSerialTest extends ModuleSerializationTest { override def test(): Unit = { - import com.intel.analytics.bigdl.nn.ops._ + import com.intel.analytics.bigdl.nn.tf._ val tensorArray = new TensorArrayCreator[Float, Float]().inputs() val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() val indices = Const[Float, Int](Tensor[Int](T(0, 1, 2))).inputs() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayWriteSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayWriteSpec.scala index b99c8b2809b..20194819ced 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayWriteSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArrayWriteSpec.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.nn.Graph +import com.intel.analytics.bigdl.nn.{Graph, Identity} import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest class TensorArraySerialTest extends ModuleSerializationTest { override def test(): Unit = { "TensorArray serializer R/W" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ + import com.intel.analytics.bigdl.nn.tf._ val tensorArray = new TensorArrayCreator[Float, Float]().inputs() val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() val index = Const[Float, Int](Tensor.scalar[Int](0)).inputs() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConstSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConstSpec.scala index 01d0aefc87e..15fc35d136f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConstSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConstSpec.scala @@ -28,31 +28,10 @@ class ConstSpec extends FlatSpec with Matchers { layer.forward(input) should be(value) } - "Const backward tensor" should "be correct" in { - val value = Tensor(2, 3).rand() - val layer = Const(value) - val input = Tensor(4, 5).rand() - val gradOutput = Tensor(2, 3).rand() - layer.forward(input) should be(value) - val grad = layer.backward(input, gradOutput).toTensor - grad should be(Tensor(4, 5).zero()) - } - "Const forward tensors" should "be correct" in { val value = Tensor(2, 3).rand() val layer = Const(value) val input = T(Tensor(4, 5).rand(), Tensor(3, 4).rand()) layer.forward(input) should be(value) } - - "Const backward tensor" should "be correct when input is tensors" in { - val value = Tensor(2, 3).rand() - val layer = Const(value) - val input = T(Tensor(4, 5).rand(), Tensor(3, 4).rand()) - val gradOutput = Tensor(2, 3).rand() - layer.forward(input) should be(value) - val grad = layer.backward(input, gradOutput).toTable - grad[Tensor[Float]](1) should be(Tensor(4, 5).zero()) - grad[Tensor[Float]](2) should be(Tensor(3, 4).zero()) - } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlOpsSpec.scala similarity index 77% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOpsSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlOpsSpec.scala index 286cfebebee..7667007dfc3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ControlOpsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ControlOpsSpec.scala @@ -13,8 +13,9 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf +import com.intel.analytics.bigdl.nn.ops.Less import com.intel.analytics.bigdl.nn.{AddConstant, Echo, Graph, Input} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T @@ -26,12 +27,12 @@ class ControlOpsSerialTest extends ModuleSerializationTest { val conditionInput = Input[Float]("conditionInput") val const = new com.intel.analytics.bigdl.nn.tf.Const[Float, Float](Tensor(T(9))).inputs() - val constEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(const) + val constEnter = new com.intel.analytics.bigdl.nn.tf.Enter[Float]("test_frame").inputs(const) val less = Less[Float]().inputs(constEnter, conditionInput) val updateInput = Input[Float]() val add = AddConstant[Float](1).inputs(updateInput) - val addEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(add) + val addEnter = new com.intel.analytics.bigdl.nn.tf.Enter[Float]("test_frame").inputs(add) val echo = Echo[Float]().inputs(addEnter) val exit = ControlNodes.whileLoop[Float]( @@ -43,9 +44,9 @@ class ControlOpsSerialTest extends ModuleSerializationTest { val model = Graph.dynamic[Float](Array(input), Array(exit(0)), None, false) runSerializationTestWithMultiClass(model, Tensor.scalar[Float](1), Array( addEnter.element.getClass.asInstanceOf[Class[_]], - new com.intel.analytics.bigdl.nn.ops.NextIteration[Float, Float]().getClass, - new com.intel.analytics.bigdl.nn.ops.Exit[Float]().getClass, - new com.intel.analytics.bigdl.nn.ops.LoopCondition[Float]().getClass + new com.intel.analytics.bigdl.nn.tf.NextIteration[Float, Float]().getClass, + new com.intel.analytics.bigdl.nn.tf.Exit[Float]().getClass, + new com.intel.analytics.bigdl.nn.tf.LoopCondition[Float]().getClass )) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MergeOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MergeOpsSpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MergeOpsSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MergeOpsSpec.scala index dfe544b478c..f0a9335f06c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MergeOpsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MergeOpsSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParseExampleSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParseExampleSpec.scala index f2fb1616ad9..23dbf488cc2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ParseExampleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParseExampleSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.{FloatType, LongType, StringType, Tensor} import com.google.protobuf.{ByteString, CodedOutputStream} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/StackOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StackOpsSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/StackOpsSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StackOpsSpec.scala index 790710a52ea..c5329072547 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/StackOpsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StackOpsSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.Graph import com.intel.analytics.bigdl.nn.tf.Const diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 904117ff567..7cba8634432 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -25,8 +25,8 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} -import com.intel.analytics.bigdl.nn.tf.{BiasAdd, Const, Fill, Log1p, Shape, SplitAndSelect, StrideSlice, Variable, TensorModuleWrapper} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, TanhGrad, TopK, TruncateDiv, TruncatedNormal, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.tf.{BiasAdd, Const, Fill, Log1p, Shape, SplitAndSelect, StrideSlice, Variable, TensorModuleWrapper, ControlNodes, ParseExample} import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor._ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index 2401e3f2ebe..e5b52ee14bd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -21,7 +21,7 @@ import java.io.{File => JFile} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, ControlNodes, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, MergeOps, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, ParseExample, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, SwitchOps, TanhGrad, TopK, TruncateDiv, TruncatedNormal, Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, TanhGrad, TopK, TruncateDiv, TruncatedNormal, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} import com.intel.analytics.bigdl.tensor._ @@ -40,6 +40,7 @@ class OperationSerializerSpec extends SerializerSpecHelper { override def addExcludedPackage(): Unit = { excludedPackage.add("com.intel.analytics.bigdl.utils.tf.loaders") + excludedPackage.add("com.intel.analytics.bigdl.utils.tf.ops") // It would be tested in a separated spec excludedPackage.add("com.intel.analytics.bigdl.nn.keras") } @@ -637,7 +638,7 @@ class OperationSerializerSpec extends SerializerSpecHelper { val outputStream = CodedOutputStream.newInstance(data) example.writeTo(outputStream) - val exampleParser = new ParseExample[Float](3, Seq(FloatType, LongType, StringType), + val exampleParser = ParseExample[Float](3, Seq(FloatType, LongType, StringType), Seq(Array(3), Array(3), Array())).setName("parseExample") val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) @@ -1080,12 +1081,12 @@ class OperationSerializerSpec extends SerializerSpecHelper { val conditionInput = Input[Float]("conditionInput") val const = new com.intel.analytics.bigdl.nn.tf.Const[Float, Float](Tensor(T(9))).inputs() - val constEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(const) + val constEnter = new com.intel.analytics.bigdl.nn.tf.Enter[Float]("test_frame").inputs(const) val less = Less[Float]().inputs(constEnter, conditionInput) val updateInput = Input[Float]() val add = AddConstant[Float](1).inputs(updateInput) - val addEnter = new com.intel.analytics.bigdl.nn.ops.Enter[Float]("test_frame").inputs(add) + val addEnter = new com.intel.analytics.bigdl.nn.tf.Enter[Float]("test_frame").inputs(add) val echo = Echo[Float]().inputs(addEnter) val exit = ControlNodes.whileLoop[Float]( @@ -1097,9 +1098,9 @@ class OperationSerializerSpec extends SerializerSpecHelper { val model = Graph.dynamic[Float](Array(input), Array(exit(0)), None, false) runSerializationTestWithMultiClass(model, Tensor.scalar[Float](1), Array( addEnter.element.getClass.asInstanceOf[Class[_]], - new com.intel.analytics.bigdl.nn.ops.NextIteration[Float, Float]().getClass, - new com.intel.analytics.bigdl.nn.ops.Exit[Float]().getClass, - new com.intel.analytics.bigdl.nn.ops.LoopCondition[Float]().getClass + new com.intel.analytics.bigdl.nn.tf.NextIteration[Float, Float]().getClass, + new com.intel.analytics.bigdl.nn.tf.Exit[Float]().getClass, + new LoopCondition[Float]().getClass )) } @@ -1107,9 +1108,9 @@ class OperationSerializerSpec extends SerializerSpecHelper { import com.intel.analytics.bigdl.nn.ops._ val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() val stack = new StackCreator[Float, Float]().inputs() - val push = new StackPush[Float, Float]().inputs(stack, data) + val push = new com.intel.analytics.bigdl.nn.tf.StackPush[Float, Float]().inputs(stack, data) val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(push) - val pop = new StackPop[Float, Float]().inputs(stack, ctr) + val pop = new com.intel.analytics.bigdl.nn.tf.StackPop[Float, Float]().inputs(stack, ctr) val model = Graph.dynamic[Float](Array(stack), Array(pop)) runSerializationTestWithMultiClass(model, Tensor.scalar(1), Array( @@ -1185,13 +1186,13 @@ class OperationSerializerSpec extends SerializerSpecHelper { } "ConcatOffset serializer" should "work properly" in { - val module = new com.intel.analytics.bigdl.nn.ops.ConcatOffset[Float]() + val module = new com.intel.analytics.bigdl.nn.tf.ConcatOffset[Float]() runSerializationTest(module, T(Tensor.scalar[Int](1), Tensor[Int](T(2, 2, 5, 7)), Tensor[Int](T(2, 3, 5, 7)), Tensor[Int](T(2, 4, 5, 7)))) } "InvertPermutation serializer" should "work properly" in { - val module = new com.intel.analytics.bigdl.nn.ops.InvertPermutation[Float]() + val module = new com.intel.analytics.bigdl.nn.tf.InvertPermutation[Float]() runSerializationTest(module, Tensor[Int](T(0, 1, 2, 3, 4))) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala index 9c67138b500..17c4121bac8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala @@ -20,7 +20,7 @@ import java.io.{File} import java.lang.reflect.Modifier import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.{Add => AddOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _} import com.intel.analytics.bigdl.utils.{Shape => KShape} From bebbf1013d138aea881958052ad678166c89efed Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Wed, 14 Feb 2018 15:43:30 +0800 Subject: [PATCH 0703/1065] Keras-like API Python Wrapper for layers + lenet example (#2296) * python lenet * docs * style * wrap getoutputshape * update input output shape * update * python layers * clean * fix ut * more layers * fix ut * more layers * fix ut * more layers * wrapper * remove * style * style and srelu * style * refine lenet * update * add ut for input output shape * fix float * refine lenet * update * meet review * clean * todo * update * style * readme * style * style * fix * style --- .../bigdl/dllib/example/keras/README.md | 6 +- .../dllib/keras/AtrousConvolution1D.scala | 4 +- .../dllib/keras/AtrousConvolution2D.scala | 4 +- .../bigdl/dllib/keras/AveragePooling3D.scala | 1 + .../dllib/keras/BatchNormalization.scala | 2 +- .../bigdl/dllib/keras/Bidirectional.scala | 5 +- .../bigdl/dllib/keras/ConvLSTM2D.scala | 16 +- .../bigdl/dllib/keras/Convolution2D.scala | 8 +- .../bigdl/dllib/keras/Deconvolution2D.scala | 3 +- .../analytics/bigdl/dllib/keras/ELU.scala | 4 +- .../bigdl/dllib/keras/Embedding.scala | 4 +- .../analytics/bigdl/dllib/keras/GRU.scala | 2 +- .../bigdl/dllib/keras/GaussianDropout.scala | 4 +- .../bigdl/dllib/keras/GaussianNoise.scala | 2 +- .../dllib/keras/GlobalAveragePooling1D.scala | 2 +- .../dllib/keras/GlobalAveragePooling2D.scala | 2 +- .../dllib/keras/GlobalAveragePooling3D.scala | 3 +- .../dllib/keras/GlobalMaxPooling1D.scala | 2 +- .../dllib/keras/GlobalMaxPooling2D.scala | 2 +- .../dllib/keras/GlobalMaxPooling3D.scala | 3 +- .../bigdl/dllib/keras/KerasUtils.scala | 8 +- .../analytics/bigdl/dllib/keras/LSTM.scala | 2 +- .../bigdl/dllib/keras/LeakyReLU.scala | 4 +- .../dllib/keras/LocallyConnected1D.scala | 7 +- .../dllib/keras/LocallyConnected2D.scala | 8 +- .../bigdl/dllib/keras/MaxPooling3D.scala | 1 + .../bigdl/dllib/keras/MaxoutDense.scala | 7 +- .../analytics/bigdl/dllib/keras/Merge.scala | 4 +- .../analytics/bigdl/dllib/keras/Permute.scala | 2 +- .../analytics/bigdl/dllib/keras/SReLU.scala | 46 +- .../dllib/keras/SeparableConvolution2D.scala | 12 +- .../bigdl/dllib/keras/SimpleRNN.scala | 2 +- .../bigdl/dllib/keras/SpatialDropout1D.scala | 2 +- .../bigdl/dllib/keras/SpatialDropout2D.scala | 2 +- .../bigdl/dllib/keras/SpatialDropout3D.scala | 2 +- .../bigdl/dllib/keras/ThresholdedReLU.scala | 4 +- .../bigdl/dllib/keras/UpSampling1D.scala | 6 +- .../bigdl/dllib/keras/UpSampling2D.scala | 4 +- .../bigdl/dllib/keras/UpSampling3D.scala | 4 +- .../dllib/utils/python/api/PythonBigDL.scala | 11 +- .../utils/python/api/PythonBigDLKeras.scala | 547 +++++++++++++++++- .../bigdl/dllib/keras/nn/SReLUSpec.scala | 4 +- .../KerasModuleSerializerSpec.scala | 2 +- 43 files changed, 676 insertions(+), 94 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md index 3cfe1199ea4..806b20bd9f2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md @@ -1,7 +1,9 @@ # LeNet Model on MNIST with new API -This examples defines a classical CNN model used in digital number classification with the new set of API in BigDL. For detailed information with regard to LeNet, -please refer to . +This example defines a classical CNN model used in digital number classification with the new set of API in BigDL. For detailed information with regard to LeNet, please refer to . + +This example is the same as [../../models/lenet](../../models/lenet), except that this example uses new API for model definition. + ## Prepare MNIST Data You can download the MNIST Data from [here](http://yann.lecun.com/exdb/mnist/). Unzip all the diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala index a4ac6568dbb..a0d839380f7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala @@ -27,9 +27,11 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Atrous Convolution operator for filtering neighborhoods of 1-D inputs. + * Applies an atrous convolution operator for filtering neighborhoods of 1-D inputs. * A.k.a dilated convolution or convolution with holes. * Bias will be included in this layer. + * Border mode currently supported for this layer is 'valid'. + * You can also use AtrousConv1D as an alias of this layer. * The input of this layer should be 3D. * * When using this layer as the first layer in a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala index 301344d1eff..1f15f501b43 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala @@ -26,10 +26,12 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Atrous Convolution operator for filtering windows of 2-D inputs. + * Applies an atrous convolution operator for filtering windows of 2-D inputs. * A.k.a dilated convolution or convolution with holes. * Bias will be included in this layer. * Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th'). + * Border mode currently supported for this layer is 'valid'. + * You can also use AtrousConv2D as an alias of this layer. * The input of this layer should be 4D. * * When using this layer as the first layer in a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala index 0e671d69153..3481e278924 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AveragePooling3D.scala @@ -27,6 +27,7 @@ import scala.reflect.ClassTag /** * Applies average pooling operation for 3D data (spatial or spatio-temporal). * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * Border mode currently supported for this layer is 'valid'. * The input of this layer should be 5D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala index 0e839a60a10..76b72cc1744 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/BatchNormalization.scala @@ -29,7 +29,7 @@ import scala.reflect.ClassTag * Normalize the activations of the previous layer at each batch, * i.e. applies a transformation that maintains the mean activation * close to 0 and the activation standard deviation close to 1. - * Feature-wise normalization, each feature map in the input will be normalized separately. + * It is a feature-wise normalization, each feature map in the input will be normalized separately. * The input of this layer should be 4D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Bidirectional.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Bidirectional.scala index d0619a1838a..c94bef4132b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Bidirectional.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Bidirectional.scala @@ -26,7 +26,7 @@ import scala.reflect.ClassTag /** * Bidirectional wrapper for RNNs. - * Bidirectional requires RNNs to return the full sequence, i.e. set returnSequences = true. + * Bidirectional currently requires RNNs to return the full sequence, i.e. returnSequences = true. * * When using this layer as the first layer in a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). @@ -47,7 +47,8 @@ class Bidirectional[T: ClassTag]( private val mode = mergeMode.toLowerCase() - require(layer.returnSequences, "Bidirectional requires RNNs to return the full sequence") + require(layer.returnSequences, + "Bidirectional currently requires RNNs to return the full sequence") require(mode == "sum" || mode == "mul" || mode == "concat" || mode == "ave", s"Invalid merge mode: $mode") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala index 0db623b6593..ab5c79d7605 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala @@ -28,15 +28,15 @@ import scala.reflect.ClassTag /** * Convolutional LSTM. * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). - * BorderMode of this layer will be 'same'. - * The convolution kernel for this layer is a square kernel. + * Border mode currently supported for this layer is 'same'. + * The convolution kernel for this layer is a square kernel with equal strides 'subsample'. * The input of this layer should be 5D. * * When using this layer as the first layer in a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * * @param nbFilter Number of convolution filters to use. - * @param nbKernel Size of the convolution kernel. Integer. + * @param nbKernel Number of rows/columns in the convolution kernel. Square kernel. * @param activation Activation function to use. * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. @@ -46,16 +46,16 @@ import scala.reflect.ClassTag * or 'sigmoid', etc. for simple activations in the factory method. * Default is 'hard_sigmoid'. * @param dimOrdering Format of input data. Please use "CHANNEL_FIRST" (dimOrdering='th'). - * @param subsample Int. Default is 1. Factor by which to subsample output. - * Also called strides elsewhere. + * @param subsample Factor by which to subsample output. + * Also called strides elsewhere. Default is 1. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the input weights matrices. Default is null. * @param uRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the recurrent weights matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. - * @param returnSequences Boolean. Default is False. Whether to return the last - * output in the output sequence, or the full sequence. - * @param goBackwards Boolean. Default is False. If True, process the input sequence backwards. + * @param returnSequences Whether to return the full sequence or the last output + * in the output sequence. Default is false. + * @param goBackwards Whether the input sequence will be processed backwards. Default is false. * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class ConvLSTM2D[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala index f96c7627d5c..16094f21b46 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala @@ -36,7 +36,7 @@ import scala.reflect.ClassTag * * @param nbFilter Number of convolution filters to use. * @param nbRow Number of rows in the convolution kernel. - * @param nbCol Number of rows in the convolution kernel. + * @param nbCol Number of columns in the convolution kernel. * @param init Initialization method for the weights of the layer. Default is Xavier. * You can also pass in corresponding string representations such as 'glorot_uniform' * or 'normal', etc. for simple init methods in the factory method. @@ -44,10 +44,10 @@ import scala.reflect.ClassTag * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. * @param borderMode Either 'valid' or 'same'. Default is 'valid'. - * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or - * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param subsample Int array of length 2 corresponding to the step of the convolution in the * height and width dimension. Also called strides elsewhere. Default is (1, 1). + * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or + * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the input weights matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. @@ -106,9 +106,9 @@ object Convolution2D { activation: String = null, borderMode: String = "valid", subsample: (Int, Int) = (1, 1), + dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, - dimOrdering: String = "th", bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution2D[T] = { new Convolution2D[T](nbFilter, nbRow, nbCol, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala index 114dd004a5f..3462d425bdc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala @@ -31,7 +31,8 @@ import scala.reflect.ClassTag * going in the opposite direction of a normal convolution, i.e., from something that has * the shape of the output of some convolution to something that has the shape of its input * while maintaining a connectivity pattern that is compatible with said convolution. - * BorderMode of this layer will be 'valid'. + * Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th'). + * Border mode currently supported for this layer is 'valid'. * The input of this layer should be 4D. * * When using this layer as the first layer in a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala index 0a4bc27f3c9..7c544035a4b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala @@ -26,8 +26,8 @@ import scala.reflect.ClassTag /** * Exponential Linear Unit. * It follows: - * `f(x) = alpha * (exp(x) - 1.) for x < 0`, - * `f(x) = x for x >= 0`. + * f(x) = alpha * (exp(x) - 1.) for x < 0, + * f(x) = x for x >= 0. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala index d630d5e6ac3..368ad7467d5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Embedding.scala @@ -34,8 +34,8 @@ import scala.reflect.ClassTag * * @param inputDim Int > 0. Size of the vocabulary. * @param outputDim Int >= 0. Dimension of the dense embedding. - * @param init Initialization method for the weights of the layer. Default is Xavier. - * You can also pass in corresponding string representations such as 'glorot_uniform' + * @param init Initialization method for the weights of the layer. Default is RandomUniform. + * You can also pass in corresponding string representations such as 'uniform' * or 'normal', etc. for simple init methods in the factory method. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the embedding matrix. Default is null. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala index 78ccf978510..49c8751d64d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala @@ -41,7 +41,7 @@ import scala.reflect.ClassTag * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. * Default is 'hard_sigmoid'. - * @param returnSequences Whether to return the full sequence or only return the last output, + * @param returnSequences Whether to return the full sequence or only return the last output * in the output sequence. Default is false. * @param goBackwards Whether the input sequence will be processed backwards. Default is false. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala index 57675915221..5bc332b7539 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala @@ -30,8 +30,8 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param p Double, drop probability (as with `Dropout`). - * The multiplicative noise will have standard deviation `sqrt(p / (1 - p))`. + * @param p Double, drop probability (as with 'Dropout'). + * The multiplicative noise will have standard deviation 'sqrt(p/(1-p))'. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class GaussianDropout[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala index e66e09fa979..c55ac14c584 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala @@ -27,7 +27,7 @@ import scala.reflect.ClassTag /** * Apply additive zero-centered Gaussian noise. * This is useful to mitigate overfitting (you could see it as a form of random data augmentation). - * Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs. + * Gaussian Noise is a natural choice as corruption process for real valued inputs. * As it is a regularization layer, it is only active at training time. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala index 5c08ce1f9c0..764315b73f5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling1D.scala @@ -27,7 +27,7 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Global average pooling operation for temporal data. + * Applies global average pooling operation for temporal data. * The input of this layer should be 3D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala index d3a11208c91..8725b5d077e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling2D.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Global average pooling operation for spatial data. + * Applies global average pooling operation for spatial data. * The input of this layer should be 4D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala index cc2254fa70f..3fb10802408 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalAveragePooling3D.scala @@ -27,8 +27,9 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Global Average pooling operation for 3D data. + * Applies global average pooling operation for 3D data. * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * Border mode currently supported for this layer is 'valid'. * The input of this layer should be 5D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala index 59acc7e180d..6016eed7d8f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling1D.scala @@ -27,7 +27,7 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Global max pooling operation for temporal data. + * Applies global max pooling operation for temporal data. * The input of this layer should be 3D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala index 0290512d720..308e794d3ec 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling2D.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Global max pooling operation for spatial data. + * Applies global max pooling operation for spatial data. * The input of this layer should be 4D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala index 161a252f06f..ee2cdef74b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GlobalMaxPooling3D.scala @@ -27,8 +27,9 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Global Max pooling operation for 3D data. + * Applies global max pooling operation for 3D data. * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * Border mode currently supported for this layer is 'valid'. * The input of this layer should be 5D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala index a0f55cadc32..98b7396786e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala @@ -34,7 +34,7 @@ object KerasUtils { } } - private[keras] def getInitMethod(init: String): InitializationMethod = { + private[bigdl] def getInitMethod(init: String): InitializationMethod = { init.toLowerCase() match { case "glorot_uniform" => Xavier case "one" => Ones @@ -46,7 +46,7 @@ object KerasUtils { } } - private[keras] def getActivation[T : ClassTag] (activation: String) + private[bigdl] def getActivation[T : ClassTag] (activation: String) (implicit ev: TensorNumeric[T]): AbstractModule[Tensor[T], Tensor[T], T] = { if (activation == null) null else { @@ -88,7 +88,7 @@ object KerasUtils { } } - private[keras] def toBigDLFormat(dimOrdering: String): DataFormat = { + private[bigdl] def toBigDLFormat(dimOrdering: String): DataFormat = { require(dimOrdering.toLowerCase() == "tf" || dimOrdering.toLowerCase() == "th", s"Dim ordering must be either tf or th, but got ${dimOrdering.toLowerCase()}") dimOrdering.toLowerCase() match { @@ -97,7 +97,7 @@ object KerasUtils { } } - private[keras] def toBigDLFormat5D(dimOrdering: String): String = { + private[bigdl] def toBigDLFormat5D(dimOrdering: String): String = { require(dimOrdering.toLowerCase() == "tf" || dimOrdering.toLowerCase() == "th", s"Dim ordering must be either tf or th, but got ${dimOrdering.toLowerCase()}") dimOrdering.toLowerCase() match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala index 73baf4352c0..419bbff5fc9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala @@ -41,7 +41,7 @@ import scala.reflect.ClassTag * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. * Default is 'hard_sigmoid'. - * @param returnSequences Whether to return the full sequence or only return the last output, + * @param returnSequences Whether to return the full sequence or only return the last output * in the output sequence. Default is false. * @param goBackwards Whether the input sequence will be processed backwards. Default is false. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala index 3c3ed2dc575..c2bf9366a77 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala @@ -26,8 +26,8 @@ import scala.reflect.ClassTag /** * Leaky version of a Rectified Linear Unit. * It allows a small gradient when the unit is not active: - * `f(x) = alpha * x for x < 0`, - * `f(x) = x for x >= 0`. + * f(x) = alpha * x for x < 0, + * f(x) = x for x >= 0. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala index 3ba8a1b6f75..e5a93188098 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala @@ -26,11 +26,10 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Locally-connected layer for 1D inputs. - * The LocallyConnected1D layer works similarly to the TemporalConvolution layer, + * Locally-connected layer for 1D inputs which works similarly to the TemporalConvolution layer, * except that weights are unshared, that is, a different set of filters * is applied at each different patch of the input. - * BorderMode of this layer will be 'valid'. + * Border mode currently supported for this layer is 'valid'. * The input of this layer should be 3D. * * When using this layer as the first layer in a model, you need to provide the argument @@ -41,7 +40,7 @@ import scala.reflect.ClassTag * @param activation Activation function to use. Default is null. * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. - * @param subsampleLength Int. Factor by which to subsample output. + * @param subsampleLength Integer. Factor by which to subsample output. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the input weights matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala index fd12f04da39..9b61d92ebc1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala @@ -25,11 +25,9 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Locally-connected layer for 2D inputs. - * The LocallyConnected2D layer works similarly to the SpatialConvolution layer, + * Locally-connected layer for 2D inputs that works similarly to the SpatialConvolution layer, * except that weights are unshared, that is, a different set of filters * is applied at each different patch of the input. - * e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures. * The input of this layer should be 4D. * * When using this layer as the first layer in a model, you need to provide the argument @@ -42,8 +40,8 @@ import scala.reflect.ClassTag * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. * @param borderMode Either 'valid' or 'same'. Default is 'valid'. - * @param subsample Int array of length 2. The step of the convolution in the height and - * width dimension. Also called strides elsewhere. Default is (1, 1). + * @param subsample Int array of length 2 corresponding to the step of the convolution in the height + * and width dimension. Also called strides elsewhere. Default is (1, 1). * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala index 2b3209781b1..ac1431cf541 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling3D.scala @@ -27,6 +27,7 @@ import scala.reflect.ClassTag /** * Applies max pooling operation for 3D data (spatial or spatio-temporal). * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). + * Border mode currently supported for this layer is 'valid'. * The input of this layer should be 5D. * * When you use this layer as the first layer of a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala index a160798adcb..6ef14dd0b2f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala @@ -26,16 +26,15 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * A dense maxout layer. - * A `MaxoutDense` layer takes the element-wise maximum of `nbFeature` - * `Dense(inputDim, outputDim)` linear layers. + * A dense maxout layer that takes the element-wise maximum of nbFeature, + * Dense(inputDim, outputDim) linear layers. * This allows the layer to learn a convex, piecewise linear activation function over the inputs. * The input of this layer should be 2D. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param outputDim Int > 0. + * @param outputDim The size of output dimension. * @param nbFeature Number of Dense layers to use internally. Integer. Default is 4. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the main weights matrices. Default is null. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala index 56a99253851..1170f4c4a40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala @@ -29,13 +29,13 @@ import scala.reflect.ClassTag * Merge must have at least two input layers. * * When using this layer as the first layer in a model, you need to provide the argument - * inputShape for input layers (a Single Shape, does not include the batch dimension). + * inputShape for input layers (each as a Single Shape, does not include the batch dimension). * * @param layers A list of layer instances. Must be more than one layer. * @param mode Merge mode. String, must be one of: 'sum', 'mul', 'concat', 'ave', 'cos', * 'dot', 'max'. Default is 'sum'. * @param concatAxis Integer, axis to use in mode concat. Only specify this when mode is 'concat'. - * Default is -1, meaning the last axis of input. + * Default is -1, meaning the last axis of the input. * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Merge[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala index dd0c5e505e1..d40b38fbbba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala @@ -27,7 +27,7 @@ import scala.reflect.ClassTag /** * Permutes the dimensions of the input according to a given pattern. - * Useful for e.g. connecting RNNs and convnets together. + * Useful for connecting RNNs and convnets together. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala index f5153dd38c7..4fa52ed23e7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.keras +import com.intel.analytics.bigdl.nn.{InitializationMethod, Ones, Xavier, Zeros} import com.intel.analytics.bigdl.nn.abstractnn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -26,38 +27,61 @@ import scala.reflect.ClassTag /** * S-shaped Rectified Linear Unit. * It follows: - * `f(x) = t^r + a^r(x - t^r) for x >= t^r`, - * `f(x) = x for t^r > x > t^l`, - * `f(x) = t^l + a^l(x - t^l) for x <= t^l`. + * f(x) = t^r + a^r(x - t^r) for x >= t^r, + * f(x) = x for t^r > x > t^l, + * f(x) = t^l + a^l(x - t^l) for x <= t^l. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param SharedAxes Array of Int. The axes along which to share learnable parameters - * for the activation function. + * @param tLeftInit Initialization function for the left part intercept. Default is Zeros. + * You can also pass in corresponding string representations such as 'zero' + * or 'normal', etc. for simple init methods in the factory method. + * @param aLeftInit Initialization function for the left part slope. Default is Xavier. + * You can also pass in corresponding string representations such as + * 'glorot_uniform', etc. for simple init methods in the factory method. + * @param tRightInit Initialization function for the right part intercept. Default is Xavier. + * You can also pass in corresponding string representations such as + * 'glorot_uniform', etc. for simple init methods in the factory method. + * @param aRightInit Initialization function for the right part slope. Default is Ones. + * You can also pass in corresponding string representations such as 'one' + * or 'normal', etc. for simple init methods in the factory method. + * @param sharedAxes Array of Int. The axes along which to share learnable parameters + * for the activation function. Default is null. * For example, if the incoming feature maps are from a 2D convolution - * with output shape `(batch, height, width, channels)`, + * with output shape (batch, height, width, channels), * and you wish to share parameters across space * so that each filter only has one set of parameters, - * set `SharedAxes=Array(1,2)`. + * set 'SharedAxes = Array(1,2)'. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class SReLU[T: ClassTag]( - SharedAxes: Array[Int] = null, + val tLeftInit: InitializationMethod = Zeros, + val aLeftInit: InitializationMethod = Xavier, + val tRightInit: InitializationMethod = Xavier, + val aRightInit: InitializationMethod = Ones, + val sharedAxes: Array[Int] = null, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val shape = inputShape.toSingle().toArray - val layer = com.intel.analytics.bigdl.nn.SReLU(shape.slice(1, shape.length), SharedAxes) + val layer = com.intel.analytics.bigdl.nn.SReLU(shape.slice(1, shape.length), sharedAxes) + layer.setInitMethod(Array(tLeftInit, aLeftInit, tRightInit, aRightInit)) layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } object SReLU { def apply[@specialized(Float, Double) T: ClassTag]( - SharedAxes: Array[Int] = null, + tLeftInit: String = "zero", + aLeftInit: String = "glorot_uniform", + tRightInit: String = "glorot_uniform", + aRightInit: String = "one", + sharedAxes: Array[Int] = null, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SReLU[T] = { - new SReLU[T](SharedAxes, inputShape) + new SReLU[T](KerasUtils.getInitMethod(tLeftInit), KerasUtils.getInitMethod(aLeftInit), + KerasUtils.getInitMethod(tRightInit), KerasUtils.getInitMethod(aRightInit), + sharedAxes, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala index 325870ae99a..e71d79baba3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import scala.reflect.ClassTag /** - * Separable convolution operator for 2D inputs. + * Applies separable convolution operator for 2D inputs. * Separable convolutions consist in first performing a depthwise spatial convolution (which acts * on each input channel separately) followed by a pointwise convolution which mixes together the * resulting output channels. The depthMultiplier argument controls how many output channels are @@ -48,16 +48,16 @@ import scala.reflect.ClassTag * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. * @param borderMode Either 'valid' or 'same'. Default is 'valid'. - * @param subsample Int array of length 2. The step of the convolution in the height and - * width dimension. Also called strides elsewhere. Default is (1, 1). + * @param subsample Int array of length 2 corresponding to the step of the convolution in the height + * and width dimension. Also called strides elsewhere. Default is (1, 1). * @param depthMultiplier How many output channel to use per input channel - * for the depthwise convolution step. + * for the depthwise convolution step. Integer. Default is 1. * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @param depthwiseRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the depthwise weights matrices. Default is null. - * @param pointwiseRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), - * applied to the pointwise weights matrices. Default is null. + * @param pointwiseRegularizer An instance of [[Regularizer]], applied to the pointwise weights + * matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. * @param bias Whether to include a bias (i.e. make the layer affine rather than linear). * Default is true. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala index 8d21a0c2843..b04bb27da87 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala @@ -37,7 +37,7 @@ import scala.reflect.ClassTag * You can also pass in corresponding string representations such as 'relu' * or 'sigmoid', etc. for simple activations in the factory method. * Default is 'tanh'. - * @param returnSequences Whether to return the full sequence or only return the last output, + * @param returnSequences Whether to return the full sequence or only return the last output * in the output sequence. Default is false. * @param goBackwards Whether the input sequence will be processed backwards. Default is false. * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala index 397d153a392..17b30ed011f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala @@ -37,7 +37,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param p Double between 0 and 1. Fraction of the input units to drop. + * @param p Fraction of the input units to drop. Double between 0 and 1. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class SpatialDropout1D[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala index ad4795af75f..4d7bb688bbd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala @@ -37,7 +37,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param p Double between 0 and 1. Fraction of the input units to drop. + * @param p Fraction of the input units to drop. Double between 0 and 1. * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala index 252badba08a..61e69e3f703 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala @@ -37,7 +37,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param p Double between 0 and 1. Fraction of the input units to drop. + * @param p Fraction of the input units to drop. Double between 0 and 1. * @param dimOrdering Format of input data. Either 'CHANNEL_FIRST' (dimOrdering='th') or * 'CHANNEL_LAST' (dimOrdering='tf'). Default is 'CHANNEL_FIRST'. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala index b04ea7a4b07..6d5014e7520 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala @@ -27,8 +27,8 @@ import scala.reflect.ClassTag /** * Thresholded Rectified Linear Unit. * It follows: - * `f(x) = x for x > theta`, - * `f(x) = 0 otherwise`. + * f(x) = x for x > theta, + * f(x) = 0 otherwise. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala index 2603115291b..c6bd1e12e71 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling1D.scala @@ -24,14 +24,14 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Upsampling layer for 1D inputs. - * Repeats each temporal step `length` times along the time axis. + * UpSampling layer for 1D inputs. + * Repeats each temporal step 'length' times along the time axis. * The input of this layer should be 3D. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param length Integer. Upsampling factor. Default is 2. + * @param length Integer. UpSampling factor. Default is 2. * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class UpSampling1D[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala index e8e629444cd..0ee83677fde 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling2D.scala @@ -24,14 +24,14 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Upsampling layer for 2D inputs. + * UpSampling layer for 2D inputs. * Repeats the rows and columns of the data by size(0) and size(1) respectively. * The input of this layer should be 4D. * * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param size Int array of length 2. The upsampling factors for rows and columns. + * @param size Int array of length 2. UpSampling factors for rows and columns. * Default is (2, 2). * @param dimOrdering Format of the input data. Either DataFormat.NCHW (dimOrdering='th') or * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala index b809b813b99..40e77d10365 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/UpSampling3D.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * Upsampling layer for 3D inputs. + * UpSampling layer for 3D inputs. * Repeats the 1st, 2nd and 3rd dimensions of the data by size(0), size(1) and size(2) respectively. * Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th'). * The input of this layer should be 5D. @@ -32,7 +32,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param size Int array of length 3. The upsampling factors for dim1, dim2 and dim3. + * @param size Int array of length 3. UpSampling factors for dim1, dim2 and dim3. * Default is (2, 2, 2). * @param dimOrdering Format of the input data. Please use "CHANNEL_FIRST" (dimOrdering='th'). * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 8d2a0f38b90..579c8e309b4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2310,8 +2310,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new ValidationSummary(logDir, appName) } - def createModel(input: JList[ModuleNode[T]], output: JList[ModuleNode[T]]): Graph[T] = { - Graph(input.asScala.toArray, output.asScala.toArray) + def createModel(input: JList[ModuleNode[T]], + output: JList[ModuleNode[T]], + isKeras: Boolean = false): Graph[T] = { + if (isKeras) { + nn.keras.Model(input.asScala.toArray, output.asScala.toArray) + } + else { + Graph(input.asScala.toArray, output.asScala.toArray) + } } def createNode(module: AbstractModule[Activity, Activity, T], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala index 14762a19b3d..9c3d95a6dc2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -18,13 +18,15 @@ package com.intel.analytics.bigdl.python.api import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} -import com.intel.analytics.bigdl.nn.SpatialBatchNormalization +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.{Container, SpatialBatchNormalization} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.numeric._ import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.{MultiShape, Shape, SingleShape} import scala.collection.JavaConverters._ import scala.language.existentials @@ -56,11 +58,46 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho } } + def toScalaArray(list: JList[Int]): Array[Int] = { + if (list == null) { + null + } else { + list.asScala.toArray + } + } + + def createKerasInput( + name : String = null, + inputShape: JList[Int] = null): ModuleNode[T] = { + Input(name = name, inputShape = toScalaShape(inputShape)) + } + def createKerasInputLayer( inputShape: JList[Int] = null): Input[T] = { InputLayer(inputShape = toScalaShape(inputShape)) } + def shapeToJList(shape: Shape): JList[JList[Int]] = { + val shapes = if (shape.isInstanceOf[SingleShape]) { + MultiShape(List(shape)) + } + else { + shape + } + shapes.toMulti().map(single => single.toSingle().toList.asJava).toList.asJava + } + + def getOutputShape(module: Container[Activity, Activity, T]): JList[JList[Int]] = { + val output = module.getOutputShape() + shapeToJList(output) + } + + def getInputShape(module: Container[Activity, Activity, T]): JList[JList[Int]] = { + val input = module.getInputShape() + // TODO: inputShape can be nested MultiShape + shapeToJList(input) + } + def createKerasDense( outputDim: Int, init: String = "glorot_uniform", @@ -121,4 +158,510 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho Merge[T](layers.asScala.toList, mode, concatAxis, toScalaMultiShape(inputShape)) } + def createKerasConvolution2D( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + init: String = "glorot_uniform", + activation: String = null, + borderMode: String = "valid", + subsample: JList[Int], + dimOrdering: String = "th", + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): Convolution2D[T] = { + new Convolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), + KerasUtils.getActivation(activation), borderMode, + toScalaArray(subsample), KerasUtils.toBigDLFormat(dimOrdering), + wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasMaxPooling2D( + poolSize: JList[Int], + strides: JList[Int], + borderMode: String = "valid", + dimOrdering: String = "th", + inputShape: JList[Int] = null): MaxPooling2D[T] = { + new MaxPooling2D[T](toScalaArray(poolSize), toScalaArray(strides), + borderMode, KerasUtils.toBigDLFormat(dimOrdering), toScalaShape(inputShape)) + } + + def createKerasActivation( + activation: String, + inputShape: JList[Int] = null): Activation[T] = { + Activation(activation, toScalaShape(inputShape)) + } + + def createKerasReshape( + targetShape: JList[Int], + inputShape: JList[Int] = null): Reshape[T] = { + Reshape(toScalaArray(targetShape), toScalaShape(inputShape)) + } + + def createKerasDropout( + p: Double, + inputShape: JList[Int] = null): Dropout[T] = { + Dropout(p, toScalaShape(inputShape)) + } + + def createKerasFlatten( + inputShape: JList[Int] = null): Flatten[T] = { + Flatten(toScalaShape(inputShape)) + } + + def createKerasSimpleRNN( + outputDim: Int, + activation: String = "tanh", + returnSequences: Boolean = false, + goBackwards: Boolean = false, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: JList[Int] = null): SimpleRNN[T] = { + SimpleRNN(outputDim, activation, returnSequences, goBackwards, + wRegularizer, uRegularizer, bRegularizer, toScalaShape(inputShape)) + } + + def createKerasLSTM( + outputDim: Int, + activation: String = "tanh", + innerActivation: String = "hard_sigmoid", + returnSequences: Boolean = false, + goBackwards: Boolean = false, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: JList[Int] = null): LSTM[T] = { + LSTM(outputDim, activation, innerActivation, returnSequences, + goBackwards, wRegularizer, uRegularizer, bRegularizer, toScalaShape(inputShape)) + } + + def createKerasGRU( + outputDim: Int, + activation: String = "tanh", + innerActivation: String = "hard_sigmoid", + returnSequences: Boolean = false, + goBackwards: Boolean = false, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: JList[Int] = null): GRU[T] = { + GRU(outputDim, activation, innerActivation, returnSequences, + goBackwards, wRegularizer, uRegularizer, bRegularizer, toScalaShape(inputShape)) + } + + def createKerasHighway( + activation: String = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): Highway[T] = { + Highway(activation, wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasZeroPadding1D( + padding: JList[Int], + inputShape: JList[Int] = null): ZeroPadding1D[T] = { + new ZeroPadding1D(toScalaArray(padding), toScalaShape(inputShape)) + } + + def createKerasZeroPadding2D( + padding: JList[Int], + dimOrdering: String = "th", + inputShape: JList[Int] = null): ZeroPadding2D[T] = { + new ZeroPadding2D(toScalaArray(padding), + KerasUtils.toBigDLFormat(dimOrdering), toScalaShape(inputShape)) + } + + def createKerasUpSampling1D( + length: Int = 2, + inputShape: JList[Int] = null): UpSampling1D[T] = { + UpSampling1D(length, toScalaShape(inputShape)) + } + + def createKerasUpSampling2D( + size: JList[Int], + dimOrdering: String = "th", + inputShape: JList[Int] = null): UpSampling2D[T] = { + new UpSampling2D(toScalaArray(size), KerasUtils.toBigDLFormat(dimOrdering), + toScalaShape(inputShape)) + } + + def createKerasUpSampling3D( + size: JList[Int], + dimOrdering: String = "th", + inputShape: JList[Int] = null): UpSampling3D[T] = { + new UpSampling3D(toScalaArray(size), KerasUtils.toBigDLFormat5D(dimOrdering), + toScalaShape(inputShape)) + } + + def createKerasMaxoutDense( + outputDim: Int, + nbFeature: Int = 4, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): MaxoutDense[T] = { + MaxoutDense(outputDim, nbFeature, wRegularizer, + bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasConvolution1D( + nbFilter: Int, + filterLength: Int, + init: String = "glorot_uniform", + activation: String = null, + borderMode: String = "valid", + subsampleLength: Int = 1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): Convolution1D[T] = { + Convolution1D(nbFilter, filterLength, init, activation, borderMode, + subsampleLength, wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasConvolution3D( + nbFilter: Int, + kernelDim1: Int, + kernelDim2: Int, + kernelDim3: Int, + init: String = "glorot_uniform", + activation: String = null, + borderMode: String = "valid", + subsample: JList[Int], + dimOrdering: String = "th", + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): Convolution3D[T] = { + new Convolution3D(nbFilter, kernelDim1, kernelDim2, kernelDim3, + KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + borderMode, toScalaArray(subsample), KerasUtils.toBigDLFormat5D(dimOrdering), + wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasMaxPooling1D( + poolLength: Int = 2, + stride: Int = -1, + borderMode: String = "valid", + inputShape: JList[Int] = null): MaxPooling1D[T] = { + MaxPooling1D(poolLength, stride, borderMode, toScalaShape(inputShape)) + } + + def createKerasMaxPooling3D( + poolSize: JList[Int], + strides: JList[Int], + dimOrdering: String = "th", + inputShape: JList[Int] = null): MaxPooling3D[T] = { + new MaxPooling3D(toScalaArray(poolSize), toScalaArray(strides), + KerasUtils.toBigDLFormat5D(dimOrdering), toScalaShape(inputShape)) + } + + def createKerasAveragePooling1D( + poolLength: Int = 2, + stride: Int = -1, + borderMode: String = "valid", + inputShape: JList[Int] = null): AveragePooling1D[T] = { + AveragePooling1D(poolLength, stride, borderMode, toScalaShape(inputShape)) + } + + def createKerasAveragePooling2D( + poolSize: JList[Int], + strides: JList[Int], + borderMode: String = "valid", + dimOrdering: String = "th", + inputShape: JList[Int] = null): AveragePooling2D[T] = { + new AveragePooling2D(toScalaArray(poolSize), toScalaArray(strides), + borderMode, KerasUtils.toBigDLFormat(dimOrdering), toScalaShape(inputShape)) + } + + def createKerasAveragePooling3D( + poolSize: JList[Int], + strides: JList[Int], + dimOrdering: String = "th", + inputShape: JList[Int] = null): AveragePooling3D[T] = { + new AveragePooling3D(toScalaArray(poolSize), toScalaArray(strides), + KerasUtils.toBigDLFormat5D(dimOrdering), toScalaShape(inputShape)) + } + + def createKerasGlobalAveragePooling2D( + dimOrdering: String = "th", + inputShape: JList[Int] = null): GlobalAveragePooling2D[T] = { + GlobalAveragePooling2D(dimOrdering, toScalaShape(inputShape)) + } + + def createKerasGlobalMaxPooling2D( + dimOrdering: String = "th", + inputShape: JList[Int] = null): GlobalMaxPooling2D[T] = { + GlobalMaxPooling2D(dimOrdering, toScalaShape(inputShape)) + } + + def createKerasRepeatVector( + n: Int, + inputShape: JList[Int] = null): RepeatVector[T] = { + RepeatVector(n, toScalaShape(inputShape)) + } + + def createKerasPermute( + dims: JList[Int], + inputShape: JList[Int] = null): Permute[T] = { + Permute(toScalaArray(dims), toScalaShape(inputShape)) + } + + def createKerasCropping1D( + cropping: JList[Int], + inputShape: JList[Int] = null): Cropping1D[T] = { + new Cropping1D(toScalaArray(cropping), toScalaShape(inputShape)) + } + + def createKerasCropping2D( + heightCrop: JList[Int], + widthCrop: JList[Int], + dimOrdering: String = "th", + inputShape: JList[Int] = null): Cropping2D[T] = { + new Cropping2D(toScalaArray(heightCrop), toScalaArray(widthCrop), + KerasUtils.toBigDLFormat(dimOrdering), toScalaShape(inputShape)) + } + + def createKerasCropping3D( + dim1Crop: JList[Int], + dim2Crop: JList[Int], + dim3Crop: JList[Int], + dimOrdering: String = "th", + inputShape: JList[Int] = null): Cropping3D[T] = { + new Cropping3D(toScalaArray(dim1Crop), toScalaArray(dim2Crop), toScalaArray(dim3Crop), + KerasUtils.toBigDLFormat5D(dimOrdering), toScalaShape(inputShape)) + } + + def createKerasAtrousConvolution1D( + nbFilter: Int, + filterLength: Int, + init: String = "glorot_uniform", + activation: String = null, + subsampleLength: Int = 1, + atrousRate: Int = 1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: JList[Int] = null): AtrousConvolution1D[T] = { + AtrousConvolution1D(nbFilter, filterLength, init, activation, + subsampleLength, atrousRate, wRegularizer, bRegularizer, toScalaShape(inputShape)) + } + + def createKerasAtrousConvolution2D( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + init: String = "glorot_uniform", + activation: String = null, + subsample: JList[Int], + atrousRate: JList[Int], + dimOrdering: String = "th", + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + inputShape: JList[Int] = null): AtrousConvolution2D[T] = { + new AtrousConvolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), + KerasUtils.getActivation(activation), toScalaArray(subsample), + toScalaArray(atrousRate), KerasUtils.toBigDLFormat(dimOrdering), + wRegularizer, bRegularizer, toScalaShape(inputShape)) + } + + def createKerasDeconvolution2D( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + init: String = "glorot_uniform", + activation: String = null, + subsample: JList[Int], + dimOrdering: String = "th", + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): Deconvolution2D[T] = { + new Deconvolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), + KerasUtils.getActivation(activation), toScalaArray(subsample), + KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, + bias, toScalaShape(inputShape)) + } + + def createKerasConvLSTM2D( + nbFilter: Int, + nbKernel: Int, + activation: String = "tanh", + innerActivation: String = "hard_sigmoid", + dimOrdering: String = "th", + subsample: Int = 1, + wRegularizer: Regularizer[T] = null, + uRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + returnSequences: Boolean = false, + goBackwards: Boolean = false, + inputShape: JList[Int] = null): ConvLSTM2D[T] = { + ConvLSTM2D(nbFilter, nbKernel, activation, innerActivation, + dimOrdering, subsample, wRegularizer, uRegularizer, bRegularizer, + returnSequences, goBackwards, toScalaShape(inputShape)) + } + + def createKerasLocallyConnected1D( + nbFilter: Int, + filterLength: Int, + activation: String = null, + subsampleLength: Int = 1, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): LocallyConnected1D[T] = { + LocallyConnected1D(nbFilter, filterLength, activation, subsampleLength, + wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasLocallyConnected2D( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + activation: String = null, + borderMode: String = "valid", + subsample: JList[Int], + dimOrdering: String = "th", + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): LocallyConnected2D[T] = { + new LocallyConnected2D(nbFilter, nbRow, nbCol, KerasUtils.getActivation(activation), + borderMode, toScalaArray(subsample), KerasUtils.toBigDLFormat(dimOrdering), + wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasSeparableConvolution2D( + nbFilter: Int, + nbRow: Int, + nbCol: Int, + init: String = "glorot_uniform", + activation: String = null, + borderMode: String = "valid", + subsample: JList[Int], + depthMultiplier: Int = 1, + dimOrdering: String = "th", + depthwiseRegularizer: Regularizer[T] = null, + pointwiseRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + bias: Boolean = true, + inputShape: JList[Int] = null): SeparableConvolution2D[T] = { + new SeparableConvolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), + KerasUtils.getActivation(activation), borderMode, toScalaArray(subsample), + depthMultiplier, KerasUtils.toBigDLFormat(dimOrdering), + depthwiseRegularizer, pointwiseRegularizer, bRegularizer, bias, toScalaShape(inputShape)) + } + + def createKerasZeroPadding3D( + padding: JList[Int], + dimOrdering: String = "th", + inputShape: JList[Int] = null): ZeroPadding3D[T] = { + new ZeroPadding3D(toScalaArray(padding), KerasUtils.toBigDLFormat5D(dimOrdering), + toScalaShape(inputShape)) + } + + def createKerasGlobalAveragePooling1D( + inputShape: JList[Int] = null): GlobalAveragePooling1D[T] = { + GlobalAveragePooling1D(toScalaShape(inputShape)) + } + + def createKerasGlobalMaxPooling1D( + inputShape: JList[Int] = null): GlobalMaxPooling1D[T] = { + GlobalMaxPooling1D(toScalaShape(inputShape)) + } + + def createKerasGlobalMaxPooling3D( + dimOrdering: String = "th", + inputShape: JList[Int] = null): GlobalMaxPooling3D[T] = { + GlobalMaxPooling3D(dimOrdering, toScalaShape(inputShape)) + } + + def createKerasGlobalAveragePooling3D( + dimOrdering: String = "th", + inputShape: JList[Int] = null): GlobalAveragePooling3D[T] = { + GlobalAveragePooling3D(dimOrdering, toScalaShape(inputShape)) + } + + def createKerasSpatialDropout1D( + p: Double = 0.5, + inputShape: JList[Int] = null): SpatialDropout1D[T] = { + SpatialDropout1D(p, toScalaShape(inputShape)) + } + + def createKerasSpatialDropout2D( + p: Double = 0.5, + dimOrdering: String = "th", + inputShape: JList[Int] = null): SpatialDropout2D[T] = { + SpatialDropout2D(p, dimOrdering, toScalaShape(inputShape)) + } + + def createKerasSpatialDropout3D( + p: Double = 0.5, + dimOrdering: String = "th", + inputShape: JList[Int] = null): SpatialDropout3D[T] = { + SpatialDropout3D(p, dimOrdering, toScalaShape(inputShape)) + } + + def createKerasGaussianDropout( + p: Double, + inputShape: JList[Int] = null): GaussianDropout[T] = { + GaussianDropout(p, toScalaShape(inputShape)) + } + + def createKerasGaussianNoise( + sigma: Double, + inputShape: JList[Int] = null): GaussianNoise[T] = { + GaussianNoise(sigma, toScalaShape(inputShape)) + } + + def createKerasMasking( + maskValue: Double = 0.0, + inputShape: JList[Int] = null): Masking[T] = { + Masking(maskValue, toScalaShape(inputShape)) + } + + def createKerasSReLU( + tLeftInit: String = "zero", + aLeftInit: String = "glorot_uniform", + tRightInit: String = "glorot_uniform", + aRightInit: String = "one", + sharedAxes: JList[Int] = null, + inputShape: JList[Int] = null): SReLU[T] = { + SReLU(tLeftInit, aLeftInit, tRightInit, aRightInit, + toScalaArray(sharedAxes), toScalaShape(inputShape)) + } + + def createKerasELU( + alpha: Double = 1.0, + inputShape: JList[Int] = null): ELU[T] = { + ELU(alpha, toScalaShape(inputShape)) + } + + def createKerasLeakyReLU( + alpha: Double = 0.01, + inputShape: JList[Int] = null): LeakyReLU[T] = { + LeakyReLU(alpha, toScalaShape(inputShape)) + } + + def createKerasThresholdedReLU( + theta: Double = 1.0, + inputShape: JList[Int] = null): ThresholdedReLU[T] = { + ThresholdedReLU(theta, toScalaShape(inputShape)) + } + + def createKerasTimeDistributed( + layer: KerasLayer[Tensor[T], Tensor[T], T], + inputShape: JList[Int] = null): TimeDistributed[T] = { + TimeDistributed(layer, toScalaShape(inputShape)) + } + + def createKerasBidirectional( + layer: Recurrent[T], + mergeMode: String = "concat", + inputShape: JList[Int] = null): Bidirectional[T] = { + Bidirectional(layer, mergeMode, toScalaShape(inputShape)) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala index 782960ae3a9..0d3d1c86896 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala @@ -34,7 +34,7 @@ class SReLUSpec extends KerasBaseSpec{ |model = Model(input=input_tensor, output=output_tensor) """.stripMargin val seq = KSequential[Float]() - val srelu = SReLU[Float](null, inputShape = Shape(2, 3)) + val srelu = SReLU[Float](sharedAxes = null, inputShape = Shape(2, 3)) seq.add(srelu) checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], kerasCode) @@ -49,7 +49,7 @@ class SReLUSpec extends KerasBaseSpec{ |model = Model(input=input_tensor, output=output_tensor) """.stripMargin val seq = KSequential[Float]() - val srelu = SReLU[Float](Array(1, 2), inputShape = Shape(3, 24)) + val srelu = SReLU[Float](sharedAxes = Array(1, 2), inputShape = Shape(3, 24)) seq.add(srelu) checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], kerasCode) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index 3260b1d4cca..07212ac35b7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -309,7 +309,7 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { } "SReLU serializer" should "work properly" in { - val layer = SReLU[Float](Array(1, 2), inputShape = Shape(4, 32)) + val layer = SReLU[Float](sharedAxes = Array(1, 2), inputShape = Shape(4, 32)) layer.build(Shape(2, 4, 32)) val input = Tensor[Float](2, 4, 32).apply1(_ => Random.nextFloat()) runSerializationTest(layer, input) From 4fe0174014293c75be1f255a398161ca8434e468 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 27 Feb 2018 10:10:39 +0800 Subject: [PATCH 0704/1065] Move more tf related operations to tf package and make them private (#2319) * move NoOp from ops package to tf package * move assign and assignGrad ops to tf package * move assert to tf package * move BroadcastGradientArgs to tf package * move decode ops to tf package * fix compile error * fix unit test * move conv2d operations to tf package * move more ops to tf package --- .../bigdl/dllib/nn/ops/AssignGrad.scala | 32 - .../bigdl/dllib/nn/ops/AvgPoolGrad.scala | 72 - .../bigdl/dllib/nn/ops/BiasAddGrad.scala | 84 -- .../dllib/nn/ops/BroadcastGradientArgs.scala | 109 -- .../analytics/bigdl/dllib/nn/ops/Conv2D.scala | 243 ---- .../analytics/bigdl/dllib/nn/ops/Conv3D.scala | 93 -- .../dllib/nn/ops/Conv3DBackpropFilter.scala | 102 -- .../dllib/nn/ops/Conv3DBackpropFilterV2.scala | 60 - .../dllib/nn/ops/Conv3DBackpropInput.scala | 114 -- .../dllib/nn/ops/Conv3DBackpropInputV2.scala | 72 - .../bigdl/dllib/nn/ops/DepthwiseConv2D.scala | 8 +- .../bigdl/dllib/nn/ops/Dilation2D.scala | 496 ++++++- .../nn/ops/Dilation2DBackpropFilter.scala | 262 ---- .../nn/ops/Dilation2DBackpropInput.scala | 265 ---- .../bigdl/dllib/nn/ops/EluGrad.scala | 33 - .../bigdl/dllib/nn/ops/FusedBatchNorm.scala | 125 -- .../dllib/nn/ops/FusedBatchNormGrad.scala | 88 -- .../analytics/bigdl/dllib/nn/ops/Inv.scala | 29 + .../bigdl/dllib/nn/ops/InvGrad.scala | 50 - .../bigdl/dllib/nn/ops/LRNGrad.scala | 81 -- .../bigdl/dllib/nn/ops/MaxPool.scala | 92 -- .../bigdl/dllib/nn/ops/MaxPoolGrad.scala | 71 - .../bigdl/dllib/nn/ops/Relu6Grad.scala | 34 - .../bigdl/dllib/nn/ops/ReluGrad.scala | 41 - ...BilinearOps.scala => ResizeBilinear.scala} | 6 +- .../bigdl/dllib/nn/ops/RsqrtGrad.scala | 47 - .../bigdl/dllib/nn/ops/SigmoidGrad.scala | 47 - .../bigdl/dllib/nn/ops/SoftplusGrad.scala | 39 - .../bigdl/dllib/nn/ops/SoftsignGrad.scala | 34 - .../bigdl/dllib/nn/ops/TanhGrad.scala | 46 - .../bigdl/dllib/nn/ops/UnaryGrad.scala | 54 - .../bigdl/dllib/nn/tf/ArrayOps.scala | 90 +- .../bigdl/dllib/nn/{ops => tf}/Assert.scala | 6 +- .../DecodeImage.scala => tf/ImageOps.scala} | 17 +- .../{ops/SqrtGrad.scala => tf/MathOps.scala} | 30 +- .../analytics/bigdl/dllib/nn/tf/NNOps.scala | 1165 +++++++++++++++++ .../bigdl/dllib/nn/{ops => tf}/NoOp.scala | 5 +- .../{ops/Assign.scala => tf/StateOps.scala} | 45 +- .../bigdl/dllib/nn/tf/Variable.scala | 53 - .../utils/serializer/ModuleSerializer.scala | 6 +- .../dllib/utils/tf/TensorflowLoader.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Assert.scala | 2 +- .../dllib/utils/tf/loaders/AvgPoolGrad.scala | 2 +- .../dllib/utils/tf/loaders/BiasAddGrad.scala | 2 +- .../tf/loaders/BroadcastGradientArgs.scala | 4 +- .../bigdl/dllib/utils/tf/loaders/Conv2D.scala | 2 +- .../tf/loaders/Conv2DBackpropFilter.scala | 2 +- .../tf/loaders/Conv2DBackpropInput.scala | 2 +- .../bigdl/dllib/utils/tf/loaders/Conv3D.scala | 2 +- .../tf/loaders/Conv3DBackpropFilter.scala | 2 +- .../tf/loaders/Conv3DBackpropFilterV2.scala | 2 +- .../tf/loaders/Conv3DBackpropInput.scala | 2 +- .../tf/loaders/Conv3DBackpropInputV2.scala | 2 +- .../dllib/utils/tf/loaders/DecodeGif.scala | 2 +- .../dllib/utils/tf/loaders/DecodeJpeg.scala | 2 +- .../dllib/utils/tf/loaders/DecodePng.scala | 2 +- .../dllib/utils/tf/loaders/DecodeRaw.scala | 2 +- .../tf/loaders/DepthwiseConv2dNative.scala | 2 +- .../dllib/utils/tf/loaders/EluGrad.scala | 2 +- .../utils/tf/loaders/FusedBatchNorm.scala | 2 +- .../utils/tf/loaders/FusedBatchNormGrad.scala | 2 +- .../tf/loaders/FusedBatchNormGradV2.scala | 2 +- .../utils/tf/loaders/FusedBatchNormV2.scala | 2 +- .../dllib/utils/tf/loaders/LRNGrad.scala | 2 +- .../dllib/utils/tf/loaders/MaxPoolGrad.scala | 2 +- .../dllib/utils/tf/loaders/Relu6Grad.scala | 2 +- .../dllib/utils/tf/loaders/ReluGrad.scala | 2 +- .../dllib/utils/tf/loaders/RsqrtGrad.scala | 2 +- .../dllib/utils/tf/loaders/SigmoidGrad.scala | 2 +- .../dllib/utils/tf/loaders/SoftplusGrad.scala | 2 +- .../dllib/utils/tf/loaders/SoftsignGrad.scala | 2 +- .../dllib/utils/tf/loaders/SqrtGrad.scala | 2 +- .../dllib/utils/tf/loaders/TanhGrad.scala | 2 +- .../dllib/utils/tf/loaders/VariableV2.scala | 2 +- .../analytics/bigdl/dllib/nn/TanhSpec.scala | 2 +- .../bigdl/dllib/nn/ops/CastSpec.scala | 3 +- .../dllib/nn/{ops => tf}/AssignSpec.scala | 6 +- .../BroadcastGradientArgsSpec.scala | 7 +- .../Conv2DSep.scala => tf/Conv2DSpec.scala} | 4 +- .../nn/{ops => tf}/Conv2DTransposeSpec.scala | 2 +- .../Conv3DBackpropFilterV2Spec.scala | 2 +- .../Conv3DBackpropInputV2Spec.scala | 2 +- .../nn/{ops => tf}/Conv3DSerialTest.scala | 2 +- .../nn/{ops => tf}/DecodeImageSpec.scala | 2 +- .../dllib/nn/{ops => tf}/DecodeJpegSpec.scala | 2 +- .../dllib/nn/{ops => tf}/DecodeRawSpec.scala | 2 +- .../{ops => tf}/MaxPoolGradSerialTest.scala | 2 +- .../dllib/nn/{ops => tf}/MaxPoolSpec.scala | 2 +- .../dllib/nn/{ops => tf}/Relu6GradSpec.scala | 2 +- .../nn/{ops => tf}/SigmoidGradSpec.scala | 2 +- .../nn/{ops => tf}/SoftplusGradSpec.scala | 2 +- .../serializer/ModuleSerializerSpec.scala | 14 +- .../serializer/OperationSerializerSpec.scala | 9 +- .../serializer/SerializerSpecHelper.scala | 3 +- 94 files changed, 1930 insertions(+), 2558 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AvgPoolGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgs.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3D.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilter.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInput.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilter.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInput.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNorm.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReluGrad.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/{ResizeBilinearOps.scala => ResizeBilinear.scala} (93%) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TanhGrad.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/Assert.scala (86%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops/DecodeImage.scala => tf/ImageOps.scala} (95%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops/SqrtGrad.scala => tf/MathOps.scala} (60%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NNOps.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/NoOp.scala (88%) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{ops/Assign.scala => tf/StateOps.scala} (68%) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/AssignSpec.scala (92%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/BroadcastGradientArgsSpec.scala (89%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops/Conv2DSep.scala => tf/Conv2DSpec.scala} (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/Conv2DTransposeSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/Conv3DBackpropFilterV2Spec.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/Conv3DBackpropInputV2Spec.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/Conv3DSerialTest.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/DecodeImageSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/DecodeJpegSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/DecodeRawSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/MaxPoolGradSerialTest.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/MaxPoolSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/Relu6GradSpec.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/SigmoidGradSpec.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{ops => tf}/SoftplusGradSpec.scala (96%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignGrad.scala deleted file mode 100644 index aa1ca1cbf33..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignGrad.scala +++ /dev/null @@ -1,32 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class AssignGrad[T: ClassTag](grad: Tensor[T])(implicit ev: TensorNumeric[T]) - extends Operation[Tensor[T], Activity, T]{ - - override def updateOutput(input: Tensor[T]): Activity = { - grad.copy(input) - null - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AvgPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AvgPoolGrad.scala deleted file mode 100644 index 6b4c9701955..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/AvgPoolGrad.scala +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, SpatialMaxPooling} -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class AvgPoolGrad[T: ClassTag]( - kH: Int, - kW: Int, - strideW: Int, - strideH: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]) - extends Operation[Table, Tensor[T], T]{ - - private var module : SpatialAveragePooling[T] = _ - - override def updateOutput(input: Table): Tensor[T] = { - if (module == null) { - module = SpatialAveragePooling[T]( - kH, - kW, - strideH, - strideW, - padH, - padW, - countIncludePad = false, - format = format - ) - } - - val inputDataSize = input[Tensor[Int]](1).storage().array() - - val gradOutput = input[Tensor[T]](2) - output = module.updateGradInputInternal(inputDataSize, gradOutput) - output - } -} - -object AvgPoolGrad { - def apply[T: ClassTag]( - kH: Int, - kW: Int, - strideW: Int, - strideH: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]): AvgPoolGrad[T] = - new AvgPoolGrad(kH, kW, strideW, strideH, padH, padW, format) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddGrad.scala deleted file mode 100644 index 558faba6e6b..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BiasAddGrad.scala +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.BiasAdd -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class BiasAddGrad[T: ClassTag](dataFormat: DataFormat) - (implicit ev: TensorNumeric[T]) - extends Operation[Tensor[T], Tensor[T], T] { - - private val module = BiasAdd() - - override def updateOutput(input: Tensor[T]): Tensor[T] = { - getBiasDims(input) - output.resizeAs(input).copy(input) - dataFormat match { - case DataFormat.NCHW => - output = output.resize(Array(batch, channel, height, width)).sum(1) - output = output.sum(3) - output = output.sum(4) - case DataFormat.NHWC => - output = output.resize(Array(batch * height * width, channel)).sum(1) - } - output - } - - private var batch : Int = 1 - private var channel : Int = 1 - private var width : Int = 1 - private var height : Int = 1 - - private def getBiasDims(tensor: Tensor[_]): Unit = { - batch = 1 - channel = 1 - width = 1 - height = 1 - dataFormat match { - case DataFormat.NHWC => - val channelDim = tensor.dim() - channel = tensor.size(channelDim) - var i = 1 - while(i < channelDim) { - batch *= tensor.size(i) - i += 1 - } - case DataFormat.NCHW => - val channelDim = tensor.dim() - 2 - val heightDim = tensor.dim() - 1 - val widthDim = tensor.dim() - channel = tensor.size(channelDim) - height = tensor.size(heightDim) - width = tensor.size(widthDim) - var i = 1 - while(i < channelDim) { - batch *= tensor.size(i) - i += 1 - } - } - } -} - -object BiasAddGrad { - def apply[T: ClassTag](dataFormat: DataFormat) - (implicit ev: TensorNumeric[T]): BiasAddGrad[T] = new BiasAddGrad(dataFormat) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgs.scala deleted file mode 100644 index dd788be69a0..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgs.scala +++ /dev/null @@ -1,109 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.Table - -import scala.collection.mutable.ArrayBuffer -import scala.reflect.ClassTag - -/** - * Given shapes of two tensors, computes the reduction indices for the - * gradient computation. - * - * @tparam T Numeric type. Only support float/double now - */ -class BroadcastGradientArgs[T: ClassTag]() - (implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T] { - - override def updateOutput(input: Table): Table = { - val input1 = input[Tensor[Int]](1) - val input2 = input[Tensor[Int]](2) - - val output1 = Tensor[Int]() - val output2 = Tensor[Int]() - - output.insert(output1).insert(output2) - - // Reverse the shape of x and y for convenience. - // After the reverse, 0-th is the inner-most dimension. - val rx = - if (input1.storage() == null) Array[Int]().toBuffer - else input1.storage().array().reverse.toBuffer - val ry = - if (input2.storage() == null) Array[Int]().toBuffer - else input2.storage().array().reverse.toBuffer - - if (rx.length < ry.length) { - while (rx.length < ry.length) { - rx.append(1) - } - } else { - while (rx.length > ry.length) { - ry.append(1) - } - } - - val xReducedIndexBuffer = new ArrayBuffer[Int]() - val yReducedIndexBuffer = new ArrayBuffer[Int]() - - val n = rx.length - - var i = 0 - while (i < n) { - val xi = rx(i) - val yi = ry(i) - - if (xi == yi) { - if (xi == 1) { - xReducedIndexBuffer.append(n - 1 - i) - yReducedIndexBuffer.append(n - 1 - i) - } - } else if (xi == 1) { - xReducedIndexBuffer.append(n - 1 - i) - } else if (yi == 1) { - yReducedIndexBuffer.append(n - 1 - i) - } else { - return output - } - i += 1 - } - - if (xReducedIndexBuffer.isEmpty) { - input(1) = Tensor[Int]() - } else { - output1.resize(Array(xReducedIndexBuffer.length)) - .set(Tensor[Int](Storage(xReducedIndexBuffer.reverse.toArray))) - } - - if (yReducedIndexBuffer.isEmpty) { - input(2) = Tensor[Int]() - } else { - output2.resize(Array(yReducedIndexBuffer.length)) - .set(Tensor[Int](Storage(yReducedIndexBuffer.reverse.toArray))) - } - - output - } -} - -object BroadcastGradientArgs { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](new BroadcastGradientArgs()) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala deleted file mode 100644 index ec579913c13..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2D.scala +++ /dev/null @@ -1,243 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.SpatialConvolution -import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class Conv2D[T: ClassTag]( - strideH: Int, - strideW: Int, - padH: Int, - padW: Int, - format: DataFormat = DataFormat.NHWC -)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { - - private var conv: SpatialConvolution[T] = _ - - override def updateOutput(inputs: Table): Tensor[T] = { - val input: Tensor[T] = inputs[Tensor[T]](1) - val filter: Tensor[T] = inputs[Tensor[T]](2) - - - val channelDim = if (format == DataFormat.NHWC) 4 else 2 - val kHDim = if (format == DataFormat.NHWC) 1 else 3 - val kWDim = if (format == DataFormat.NHWC) 2 else 4 - - if (conv == null) { - conv = SpatialConvolution( - nInputPlane = input.size(channelDim), - nOutputPlane = filter.size(channelDim), - kernelH = filter.size(kHDim), - kernelW = filter.size(kWDim), - strideH = strideH, - strideW = strideW, - padH = padH, - padW = padW, - withBias = false, - format = format - ) - } - - conv.setWeightsBias(Array(filter)) - output = conv.forward(input) - output - } -} - -object Conv2D { - def apply[T: ClassTag]( - strideH: Int, - strideW: Int, - padH: Int, - padW: Int, - format: DataFormat = DataFormat.NHWC - )(implicit ev: TensorNumeric[T]): Conv2D[T] - = new Conv2D(strideH, strideW, padH, padW, format) -} - -/** - * Backward of SpatialConvolution - */ -class Conv2DTranspose[T: ClassTag]( - strideW: Int, - strideH: Int, - padW: Int = -1, - padH: Int = -1, - format: DataFormat = DataFormat.NCHW -)(implicit ev: TensorNumeric[T]) - extends Operation[Activity, Tensor[T], T]{ - - private var module: SpatialConvolution[T] = _ - private var dummyInput: Tensor[T] = _ - - override def updateOutput(input: Activity): Tensor[T] = { - require(input.isTable, "Invalid input activity type") - val inputSizes = input.toTable.apply[Tensor[Int]](1).squeeze() - val kernel = input.toTable.apply[Tensor[T]](2) - val data = input.toTable.apply[Tensor[T]](3) - - require(data.nDimension() == 4, s"Need a 4D input but is ${data.nDimension()}") - require(inputSizes.nDimension() == 1, s"Need a 1D size but is ${inputSizes.nDimension()}") - - val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { - (data.size(2), inputSizes.valueAt(2)) - } else { - (data.size(4), inputSizes.valueAt(4)) - } - - val kHDim = if (format == DataFormat.NHWC) 1 else 3 - val kWDim = if (format == DataFormat.NHWC) 2 else 4 - - - if (module == null) { - module = new SpatialConvolution[T]( - nInputPlane = nInputPlane, - nOutputPlane = nOutputPlane, - kernelW = kernel.size(kWDim), - kernelH = kernel.size(kHDim), - strideH = strideH, - strideW = strideW, - padH = padH, - padW = padW, - initWeight = kernel, - format = format, - withBias = false - ) - - dummyInput = Tensor[T](inputSizes.valueAt(1), inputSizes.valueAt(2), inputSizes.valueAt(3), - inputSizes.valueAt(4)) - } else { - val (nOutputPlanbe, nInputPlane) = if (format == DataFormat.NCHW) { - (data.size(2), inputSizes.valueAt(2)) - } else { - (data.size(4), inputSizes.valueAt(4)) - } - - require(module.nInputPlane == nInputPlane, "nInputPlane is not valid") - require(module.nOutputPlane == nOutputPlane, "nOutputPlane is not valid") - require(module.kernelH == kernel.size(kWDim), "kernelH is not valid") - require(module.kernelW == kernel.size(kWDim), "kernelW is not valid") - require(kernel.size(3) == nInputPlane, "kernel nInputPlane is not valid") - require(kernel.size(4) == nOutputPlane, "kernel nOutputPlane is not valid") - require(dummyInput.size(1) == inputSizes.valueAt(1), "size 1 is not correct") - require(dummyInput.size(2) == inputSizes.valueAt(2), "size 1 is not correct") - require(dummyInput.size(3) == inputSizes.valueAt(3), "size 1 is not correct") - require(dummyInput.size(4) == inputSizes.valueAt(4), "size 1 is not correct") - } - - module.forward(dummyInput) - module.weight.set(kernel) - module.updateGradInput(dummyInput, data) - output = module.gradInput - output - } -} - -object Conv2DTranspose { - def apply[T: ClassTag]( - strideW: Int, - strideH: Int, - padW: Int = -1, - padH: Int = -1, - format: DataFormat = DataFormat.NCHW - )(implicit ev: TensorNumeric[T]): Conv2DTranspose[T] = - new Conv2DTranspose(strideW, strideH, padW, padH, format) -} - -class Conv2DBackFilter[T: ClassTag]( - strideW: Int, - strideH: Int, - padW: Int = -1, - padH: Int = -1, - format: DataFormat = DataFormat.NCHW -)(implicit ev: TensorNumeric[T]) - extends Operation[Activity, Tensor[T], T]{ - - private var module: SpatialConvolution[T] = _ - private var gradWeight: Tensor[T] = _ - private var dummyInput: Tensor[T] = _ - - override def updateOutput(input: Activity): Tensor[T] = { - require(input.isTable, "Invalid input activity type") - val kernelSize = input.toTable.apply[Tensor[Int]](2).squeeze() - val inputActivity = input.toTable.apply[Tensor[T]](1) - val grads = input.toTable.apply[Tensor[T]](3) - - require(grads.nDimension() == 4, s"Need a 4D input but is ${grads.nDimension()}") - require(kernelSize.nDimension() == 1, s"Need a 1D size but is ${kernelSize.nDimension()}") - - val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { - (grads.size(2), inputActivity.size(2)) - } else { - (grads.size(4), inputActivity.size(4)) - } - - if (module == null) { - gradWeight = Tensor[T]().resize(kernelSize.valueAt(1), kernelSize.valueAt(2), - kernelSize.valueAt(3), kernelSize.valueAt(4)) - module = new SpatialConvolution[T]( - nInputPlane = nInputPlane, - nOutputPlane = nOutputPlane, - kernelW = kernelSize.valueAt(2), - kernelH = kernelSize.valueAt(1), - strideH = strideH, - strideW = strideW, - padH = padH, - padW = padW, - initGradWeight = gradWeight, - format = format, - withBias = false - ) - } else { - val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { - (grads.size(2), inputActivity.size(2)) - } else { - (grads.size(4), inputActivity.size(4)) - } - - require(module.nInputPlane == nInputPlane, "nInputPlane is not valid") - require(module.nOutputPlane == nOutputPlane, "nOutputPlane is not valid") - require(module.kernelH == kernelSize.valueAt(1), s"kernelH is not valid") - require(module.kernelW == kernelSize.valueAt(2), "kernelW is not valid") - require(kernelSize.valueAt(3) == nInputPlane, "kernel nInputPlane is not valid") - require(kernelSize.valueAt(4) == nOutputPlane, "kernel nOutputPlane is not valid") - } - - module.forward(inputActivity) - gradWeight.zero() - module.accGradParameters(inputActivity, grads) - output = module.gradWeight - output - } -} - -object Conv2DBackFilter { - def apply[T: ClassTag]( - strideW: Int, - strideH: Int, - padW: Int = -1, - padH: Int = -1, - format: DataFormat = DataFormat.NCHW - )(implicit ev: TensorNumeric[T]): Conv2DBackFilter[T] = - new Conv2DBackFilter(strideW, strideH, padW, padH, format) -} - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3D.scala deleted file mode 100644 index 6dfdad1194b..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3D.scala +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.VolumetricConvolution -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class Conv3D[T: ClassTag]( - dT: Int, dH: Int, dW: Int, - padT: Int, padH: Int, padW: Int, - format: DataFormat) - (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { - - private val fInput = Tensor[T]() - - - override def updateOutput(inputs: Table): Tensor[T] = { - val input: Tensor[T] = inputs[Tensor[T]](1) - val filter: Tensor[T] = inputs[Tensor[T]](2) - - val kT = filter.size(1) - val kH = filter.size(2) - val kW = filter.size(3) - val nInputPlane = filter.size(4) - val nOutputPlane = filter.size(5) - - val transInput = if (format == DataFormat.NHWC) { - var buffer = input - buffer = buffer.transpose(2, 5) - buffer = buffer.transpose(3, 5) - buffer = buffer.transpose(4, 5) - buffer = buffer.contiguous() - - buffer - } else { - input - } - - var transWeight = filter.transpose(1, 5) - transWeight = transWeight.transpose(2, 4) - transWeight = transWeight.transpose(3, 5) - transWeight = transWeight.contiguous() - val weightMM = transWeight.view(nOutputPlane, nInputPlane * kT * kH * kW) - - VolumetricConvolution.conv3d(transInput, output, weightMM, bias = null, onesBias = null, fInput, - nInputPlane, nOutputPlane, withBias = false, kT, kW, kH, dT, dW, dH, padT, padW, padH) - - if (format == DataFormat.NHWC) { - output = output.transpose(2, 5) - output = output.transpose(2, 4) - output = output.transpose(2, 3) - output = output.contiguous() - } - output - } - - override def clearState(): Conv3D.this.type = { - super.clearState() - fInput.set() - this - } -} - -object Conv3D { - def apply[T: ClassTag]( - dT: Int, - dH: Int, - dW: Int, - padT: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]): Conv3D[T] - = new Conv3D[T](dT, dH, dW, padT, padH, padW, format) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilter.scala deleted file mode 100644 index b72683f016a..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilter.scala +++ /dev/null @@ -1,102 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.VariableFormat.OUT_IN_KT_KH_KW -import com.intel.analytics.bigdl.nn.VolumetricConvolution -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class Conv3DBackpropFilter[T: ClassTag]( - dT: Int, - dH: Int, - dW: Int, - padT: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { - - private val fInput = Tensor[T]() - - - protected def getParams(inputs: Table): (Int, Int, Int, Int, Int) = { - val filter: Tensor[T] = inputs[Tensor[T]](2) - - val kT = filter.size(1) - val kH = filter.size(2) - val kW = filter.size(3) - val nInputPlane = filter.size(4) - val nOutputPlane = filter.size(5) - - (kT, kH, kW, nInputPlane, nOutputPlane) - } - override def updateOutput(inputs: Table): Tensor[T] = { - val input: Tensor[T] = inputs[Tensor[T]](1) - val outputBackprop: Tensor[T] = inputs[Tensor[T]](3) - - val (transInput, transOutBackprop) = if (format == DataFormat.NHWC) { - // backpropInput only use input size, so we do not need it to be contiguous - val in = input.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() - val out = outputBackprop.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() - (in, out) - } else { - (input, outputBackprop) - } - - val (kT, kH, kW, nInputPlane, nOutputPlane) = getParams(inputs) - - val gradWeightMM = Tensor[T](nOutputPlane, nInputPlane * kT * kH * kW) - - VolumetricConvolution.populateFInput(transInput, fInput, nInputPlane, nOutputPlane, - kT, kW, kH, dT, dW, dH, padT, padW, padH) - - VolumetricConvolution.conv3DBackpropFilter(transInput, transOutBackprop, gradWeightMM, - null, fInput, 1.0, 1.0, false) - - output = if (format == DataFormat.NHWC) { - val gradWeight = gradWeightMM.view(nOutputPlane, nInputPlane, kT, kH, kW) - gradWeight.transpose(1, 5).transpose(2, 4).transpose(1, 3).contiguous() - } else { - gradWeightMM.view(nOutputPlane, nInputPlane, kT, kH, kW) - } - - output - } - - override def clearState(): Conv3DBackpropFilter.this.type = { - super.clearState() - fInput.set() - this - } -} - -object Conv3DBackpropFilter { - def apply[T: ClassTag]( - dT: Int, - dH: Int, - dW: Int, - padT: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]): Conv3DBackpropFilter[T] - = new Conv3DBackpropFilter[T](dT, dH, dW, padT, padH, padW, format) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2.scala deleted file mode 100644 index 68b2a6decf2..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2.scala +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class Conv3DBackpropFilterV2[T: ClassTag]( - dT: Int, - dH: Int, - dW: Int, - padT: Int, - padH: Int, - padW: Int, - format: DataFormat) - (implicit ev: TensorNumeric[T]) - extends Conv3DBackpropFilter[T](dT, dH, dW, padT, padH, padW, format) { - - override protected def getParams(inputs: Table): (Int, Int, Int, Int, Int) = { - val filterSize: Tensor[Int] = inputs[Tensor[Int]](2) - - val kT = filterSize.valueAt(1) - val kH = filterSize.valueAt(2) - val kW = filterSize.valueAt(3) - val nInputPlane = filterSize.valueAt(4) - val nOutputPlane = filterSize.valueAt(5) - - (kT, kH, kW, nInputPlane, nOutputPlane) - } -} - -object Conv3DBackpropFilterV2 { - def apply[T: ClassTag]( - dT: Int, - dH: Int, - dW: Int, - padT: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]): Conv3DBackpropFilterV2[T] - = new Conv3DBackpropFilterV2[T](dT, dH, dW, padT, padH, padW, format) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInput.scala deleted file mode 100644 index bcf125397a9..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInput.scala +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.VolumetricConvolution -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class Conv3DBackpropInput[T: ClassTag]( - dT: Int, - dH: Int, - dW: Int, - padT: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { - - private val fGradInput = Tensor[T]() - - protected def getInputSize(inputs: Table): Array[Int] = { - val input: Tensor[T] = inputs[Tensor[T]](1) - - if (format == DataFormat.NHWC) { - val N = input.size(1) - val D = input.size(2) - val H = input.size(3) - val W = input.size(4) - val C = input.size(5) - Array(N, C, D, H, W) - } else { - val N = input.size(1) - val C = input.size(2) - val D = input.size(3) - val H = input.size(4) - val W = input.size(5) - Array(N, C, D, H, W) - } - } - - override def updateOutput(inputs: Table): Tensor[T] = { - - val filter: Tensor[T] = inputs[Tensor[T]](2) - val outputBackprop: Tensor[T] = inputs[Tensor[T]](3) - - val transOutBackprop = if (format == DataFormat.NHWC) { - // backpropInput only use input size, so we do not need it to be contiguous - outputBackprop.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() - } else { - outputBackprop - } - - val transInputSize = getInputSize(inputs) - - val kT = filter.size(1) - val kH = filter.size(2) - val kW = filter.size(3) - val nInputPlane = filter.size(4) - val nOutputPlane = filter.size(5) - - var transWeight = filter.transpose(1, 5) - transWeight = transWeight.transpose(2, 4) - transWeight = transWeight.transpose(3, 5) - transWeight = transWeight.contiguous() - val weightMM = transWeight.view(nOutputPlane, nInputPlane * kT * kH * kW) - - VolumetricConvolution.conv3DBackpropInput(transInputSize, output, transOutBackprop, - weightMM, fGradInput, kT, kW, kH, dT, dW, dH, padT, padW, padH) - - if (format == DataFormat.NHWC) { - output = output.transpose(2, 5) - output = output.transpose(2, 3) - output = output.transpose(3, 4) - output = output.contiguous() - } - output - } - - override def clearState(): Conv3DBackpropInput.this.type = { - super.clearState() - fGradInput.set() - this - } -} - -object Conv3DBackpropInput { - def apply[T: ClassTag]( - dT: Int, - dH: Int, - dW: Int, - padT: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]): Conv3DBackpropInput[T] - = new Conv3DBackpropInput[T](dT, dH, dW, padT, padH, padW, format) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2.scala deleted file mode 100644 index 17b919e3026..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2.scala +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.VolumetricConvolution -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class Conv3DBackpropInputV2[T: ClassTag](dT: Int, dH: Int, dW: Int, - padT: Int, padH: Int, padW: Int, - format: DataFormat) - (implicit ev: TensorNumeric[T]) - extends Conv3DBackpropInput[T](dT, dH, dW, padT, padH, padW, format) { - - private val fGradInput = Tensor[T]() - - override protected def getInputSize(inputs: Table): Array[Int] = { - val inputSize: Tensor[Int] = inputs[Tensor[Int]](1) - - if (format == DataFormat.NHWC) { - val N = inputSize.valueAt(1) - val D = inputSize.valueAt(2) - val H = inputSize.valueAt(3) - val W = inputSize.valueAt(4) - val C = inputSize.valueAt(5) - Array(N, C, D, H, W) - } else { - val N = inputSize.valueAt(1) - val C = inputSize.valueAt(2) - val D = inputSize.valueAt(3) - val H = inputSize.valueAt(4) - val W = inputSize.valueAt(5) - Array(N, C, D, H, W) - } - } - - override def clearState(): Conv3DBackpropInputV2.this.type = { - super.clearState() - fGradInput.set() - this - } -} - -object Conv3DBackpropInputV2 { - def apply[T: ClassTag]( - dT: Int, - dH: Int, - dW: Int, - padT: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]): Conv3DBackpropInputV2[T] - = new Conv3DBackpropInputV2[T](dT, dH, dW, padT, padH, padW, format) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala index 552746c0bf0..37dcd0a33f3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala @@ -73,7 +73,7 @@ object DepthwiseConv2D { new DepthwiseConv2D(strideW, strideH, padW, padH, dataFormat) } -class DepthwiseConv2DBackpropInput[T: ClassTag]( +private[bigdl] class DepthwiseConv2DBackpropInput[T: ClassTag]( strideW: Int, strideH: Int, padW: Int, padH: Int, dataFormat: DataFormat @@ -117,7 +117,7 @@ class DepthwiseConv2DBackpropInput[T: ClassTag]( } } -object DepthwiseConv2DBackpropInput { +private[bigdl] object DepthwiseConv2DBackpropInput { def apply[T: ClassTag]( strideW: Int, strideH: Int, padW: Int, padH: Int, @@ -126,7 +126,7 @@ object DepthwiseConv2DBackpropInput { new DepthwiseConv2DBackpropInput(strideW, strideH, padW, padH, dataFormat) } -class DepthwiseConv2DBackpropFilter[T: ClassTag]( +private[bigdl] class DepthwiseConv2DBackpropFilter[T: ClassTag]( strideW: Int, strideH: Int, padW: Int, padH: Int, dataFormat: DataFormat @@ -172,7 +172,7 @@ class DepthwiseConv2DBackpropFilter[T: ClassTag]( } } -object DepthwiseConv2DBackpropFilter { +private[bigdl] object DepthwiseConv2DBackpropFilter { def apply[T: ClassTag]( strideW: Int, strideH: Int, padW: Int, padH: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2D.scala index 55cb5194206..5f25b860ae3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2D.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.Utils import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table @@ -48,9 +49,9 @@ import scala.reflect.ClassTag * */ class Dilation2D[T: ClassTag, D: ClassTag](val strides: Array[Int], - val rates: Array[Int], - val padding: String) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + val rates: Array[Int], + val padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { output = Tensor[D]() @@ -75,8 +76,8 @@ class Dilation2D[T: ClassTag, D: ClassTag](val strides: Array[Int], } private def dilationFloat(input: Tensor[Float], filter: Tensor[Float], output: Tensor[Float], - strideRows: Int, strideCols: Int, - rateRows: Int, rateCols: Int) = { + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { val batch = input.size(1) val inputRows = input.size(2) val inputCols = input.size(3) @@ -157,8 +158,8 @@ class Dilation2D[T: ClassTag, D: ClassTag](val strides: Array[Int], } private def dilationDouble(input: Tensor[Double], filter: Tensor[Double], output: Tensor[Double], - strideRows: Int, strideCols: Int, - rateRows: Int, rateCols: Int) = { + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { val batch = input.size(1) val inputRows = input.size(2) val inputCols = input.size(3) @@ -273,6 +274,485 @@ class Dilation2D[T: ClassTag, D: ClassTag](val strides: Array[Int], object Dilation2D { def apply[T: ClassTag, D: ClassTag](strides: Array[Int], rates: Array[Int], padding: String) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2D[T, D] = + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2D[T, D] = new Dilation2D(strides, rates, padding) } + +private[bigdl] class Dilation2DBackpropFilter[T: ClassTag, D: ClassTag]( + strides: Array[Int], + rates: Array[Int], + padding: String)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + output = Tensor[D]() + + private def dilation2DBackpropFilterFloat( + input: Tensor[Float], + filter: Tensor[Float], + outBackprop: Tensor[Float], + filterBackprop: Tensor[Float], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) + val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) + + val (outputRows, padTop, _) = + Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) + val (outputCols, padLeft, _) = + Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) + + filterBackprop.resizeAs(filter) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outBackpropData = outBackprop.storage().array() + val outBackpropDataOffset = outBackprop.storageOffset() - 1 + + val filterBackpropData = filterBackprop.storage().array() + val filterBackpropDataOffset = filterBackprop.storageOffset() - 1 + + var b = 0 + while (b < batch) { + var h_out = 0 + while (h_out < outputRows) { + val h_beg = h_out * strideRows - padTop + var w_out = 0 + while (w_out < outputCols) { + val w_beg = w_out * strideCols - padLeft + var d = 0 + while (d < depth) { + var cur_val = Float.MinValue + var h_max = 0 + var w_max = 0 + var h = 0 + while (h < filterRows) { + val h_in = h_beg + h * rateRows + if (h_in >= 0 && h_in < inputRows) { + var w = 0 + while (w < filterCols) { + val w_in = w_beg + w * rateCols + if (w_in >= 0 && w_in < inputCols) { + val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value: Float = inputValue + filterValue + if (value > cur_val) { + cur_val = value + h_max = h + w_max = w + } + } + w += 1 + } + } + h += 1 + } + val filterBackPropIndex = + (h_max * filterCols + w_max) * depth + d + val outputBackPropIndex = + ((b * outputRows + h_out) * outputCols + w_out) * depth + d + filterBackpropData(filterBackpropDataOffset + filterBackPropIndex) += + outBackpropData(outBackpropDataOffset + outputBackPropIndex) + d += 1 + } + w_out += 1 + } + h_out += 1 + } + b += 1 + } + + } + + + + private def dilation2DBackpropFilterDouble(input: Tensor[Double], + filter: Tensor[Double], + outBackprop: Tensor[Double], + filterBackprop: Tensor[Double], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) + val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) + + val (outputRows, padTop, _) = + Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) + val (outputCols, padLeft, _) = + Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) + + filterBackprop.resizeAs(filter) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outBackpropData = outBackprop.storage().array() + val outBackpropDataOffset = outBackprop.storageOffset() - 1 + + val filterBackpropData = filterBackprop.storage().array() + val filterBackpropDataOffset = filterBackprop.storageOffset() - 1 + + var b = 0 + while (b < batch) { + var h_out = 0 + while (h_out < outputRows) { + val h_beg = h_out * strideRows - padTop + var w_out = 0 + while (w_out < outputCols) { + val w_beg = w_out * strideCols - padLeft + var d = 0 + while (d < depth) { + var cur_val = Double.MinValue + var h_max = 0 + var w_max = 0 + var h = 0 + while (h < filterRows) { + val h_in = h_beg + h * rateRows + if (h_in >= 0 && h_in < inputRows) { + var w = 0 + while (w < filterCols) { + val w_in = w_beg + w * rateCols + if (w_in >= 0 && w_in < inputCols) { + val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value: Double = inputValue + filterValue + if (value > cur_val) { + cur_val = value + h_max = h + w_max = w + } + } + w += 1 + } + } + h += 1 + } + val filterBackPropIndex = + (h_max * filterCols + w_max) * depth + d + val outputBackPropIndex = + ((b * outputRows + h_out) * outputCols + w_out) * depth + d + filterBackpropData(filterBackpropDataOffset + filterBackPropIndex) += + outBackpropData(outBackpropDataOffset + outputBackPropIndex) + d += 1 + } + w_out += 1 + } + h_out += 1 + } + b += 1 + } + } + + override def updateOutput(inputs: Table): Tensor[D] = { + val input = inputs[Tensor[D]](1) + val filter = inputs[Tensor[D]](2) + val outBackprop = inputs[Tensor[D]](3) + + require(input.dim() == 4, "input must have 4 dims") + require(filter.dim() == 3, "filter must have 3 dims") + + + val strideRows = strides(1) + val strideCols = strides(2) + + val rateRows = rates(1) + val rateCols = rates(2) + + if (ev2.getType() == FloatType) { + val inputTensor = input.asInstanceOf[Tensor[Float]] + val filterTensor = filter.asInstanceOf[Tensor[Float]] + val outBackpropTensor = outBackprop.asInstanceOf[Tensor[Float]] + val outputTensor = output.asInstanceOf[Tensor[Float]] + dilation2DBackpropFilterFloat(inputTensor, filterTensor, outBackpropTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else if (ev2.getType() == DoubleType) { + val inputTensor = input.asInstanceOf[Tensor[Double]] + val filterTensor = filter.asInstanceOf[Tensor[Double]] + val outBackpropTensor = output.asInstanceOf[Tensor[Double]] + val outputTensor = output.asInstanceOf[Tensor[Double]] + dilation2DBackpropFilterDouble(inputTensor, filterTensor, outBackpropTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else { + throw new IllegalArgumentException(s"does not support datatype ${ev2.getType()}") + } + + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +private[bigdl] object Dilation2DBackpropFilter { + def apply[T: ClassTag, D: ClassTag](strides: Array[Int], rates: Array[Int], padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2DBackpropFilter[T, D] = + new Dilation2DBackpropFilter(strides, rates, padding) +} + +private[bigdl] class Dilation2DBackpropInput[T: ClassTag, D: ClassTag](strides: Array[Int], + rates: Array[Int], + padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + output = Tensor[D] + + private def dilationBackpropInputFloat(input: Tensor[Float], + filter: Tensor[Float], + outBackprop: Tensor[Float], + inputBackprop: Tensor[Float], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) + val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) + + val (outputRows, padTop, _) = + Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) + val (outputCols, padLeft, _) = + Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) + + inputBackprop.resizeAs(input) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outBackpropData = outBackprop.storage().array() + val outBackpropDataOffset = outBackprop.storageOffset() - 1 + + val inputBackpropData = inputBackprop.storage().array() + val inputBackpropDataOffset = inputBackprop.storageOffset() - 1 + + var b = 0 + while (b < batch) { + var h_out = 0 + while (h_out < outputRows) { + val h_beg = h_out * strideRows - padTop + var w_out = 0 + while (w_out < outputCols) { + val w_beg = w_out * strideCols - padLeft + var d = 0 + while (d < depth) { + var cur_val = Float.MinValue + var h_in_max = if (h_beg < 0) 0 else h_beg + var w_in_max = if (w_beg < 0) 0 else w_beg + var h = 0 + while (h < filterRows) { + val h_in = h_beg + h * rateRows + if (h_in >= 0 && h_in < inputRows) { + var w = 0 + while (w < filterCols) { + val w_in = w_beg + w * rateCols + if (w_in >= 0 && w_in < inputCols) { + val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value: Float = inputValue + filterValue + if (value > cur_val) { + cur_val = value + h_in_max = h_in + w_in_max = w_in + } + } + w += 1 + } + } + h += 1 + } + val inputBackPropIndex = + ((b * inputRows + h_in_max) * inputCols + w_in_max) * depth + d + val outputBackPropIndex = + ((b * outputRows + h_out) * outputCols + w_out) * depth + d + inputBackpropData(inputBackpropDataOffset + inputBackPropIndex) += + outBackpropData(outBackpropDataOffset + outputBackPropIndex) + d += 1 + } + w_out += 1 + } + h_out += 1 + } + b += 1 + } + + } + + + + private def dilationBackpropInputDouble(input: Tensor[Double], + filter: Tensor[Double], + outBackprop: Tensor[Double], + inputBackprop: Tensor[Double], + strideRows: Int, strideCols: Int, + rateRows: Int, rateCols: Int) = { + val batch = input.size(1) + val inputRows = input.size(2) + val inputCols = input.size(3) + val depth = input.size(4) + + val filterRows = filter.size(1) + val filterCols = filter.size(2) + + val outputRows = outBackprop.size(2) + val outputCols = outBackprop.size(3) + + val (padTop, padLeft) = padding.toLowerCase() match { + case "same" => + val top = (outputRows - inputRows) / 2 + val left = (outputCols - inputCols) / 2 + (top, left) + case "valid" => + (0, 0) + } + + inputBackprop.resizeAs(input) + + val inputData = input.storage().array() + val inputDataOffset = input.storageOffset() - 1 + + val filterData = filter.storage().array() + val filterDataOffset = filter.storageOffset() - 1 + + val outBackpropData = outBackprop.storage().array() + val outBackpropDataOffset = outBackprop.storageOffset() - 1 + + val inputBackpropData = inputBackprop.storage().array() + val inputBackpropDataOffset = inputBackprop.storageOffset() - 1 + + var b = 0 + while (b < batch) { + var h_out = 0 + while (h_out < outputRows) { + val h_beg = h_out * strideRows - padTop + var w_out = 0 + while (w_out < outputCols) { + val w_beg = w_out * strideCols - padLeft + var d = 0 + while (d < depth) { + var cur_val = Double.MinValue + var h_in_max = if (h_beg < 0) 0 else h_beg + var w_in_max = if (w_beg < 0) 0 else w_beg + var h = 0 + while (h < filterRows) { + val h_in = h_beg + h * rateRows + if (h_in >= 0 && h_in < inputRows) { + var w = 0 + while (w < filterCols) { + val w_in = w_beg + w * rateCols + if (w_in >= 0 && w_in < inputCols) { + val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d + val inputValue = inputData(inputDataOffset + inputIndex) + val filterIndex = (h * filterCols + w) * depth + d + val filterValue = filterData(filterDataOffset + filterIndex) + val value: Double = inputValue + filterValue + if (value > cur_val) { + cur_val = value + h_in_max = h_in + w_in_max = w_in + } + } + w += 1 + } + } + h += 1 + } + val inputBackPropIndex = + ((b * inputRows + h_in_max) * inputCols + w_in_max) * depth + d + val outputBackPropIndex = + ((b * outputRows + h_out) * outputCols + w_out) * depth + d + inputBackpropData(inputBackpropDataOffset + inputBackPropIndex) += + outBackpropData(outBackpropDataOffset + outputBackPropIndex) + d += 1 + } + w_out += 1 + } + h_out += 1 + } + b += 1 + } + } + + override def updateOutput(inputs: Table): Tensor[D] = { + val input = inputs[Tensor[D]](1) + val filter = inputs[Tensor[D]](2) + val outBackprop = inputs[Tensor[D]](3) + + require(input.dim() == 4, "input must have 4 dims") + require(filter.dim() == 3, "filter must have 3 dims") + + + val strideRows = strides(1) + val strideCols = strides(2) + + val rateRows = rates(1) + val rateCols = rates(2) + + if (ev2.getType() == FloatType) { + val inputTensor = input.asInstanceOf[Tensor[Float]] + val filterTensor = filter.asInstanceOf[Tensor[Float]] + val outBackpropTensor = outBackprop.asInstanceOf[Tensor[Float]] + val outputTensor = output.asInstanceOf[Tensor[Float]] + dilationBackpropInputFloat(inputTensor, filterTensor, outBackpropTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else if (ev2.getType() == DoubleType) { + val inputTensor = input.asInstanceOf[Tensor[Double]] + val filterTensor = filter.asInstanceOf[Tensor[Double]] + val outBackpropTensor = output.asInstanceOf[Tensor[Double]] + val outputTensor = output.asInstanceOf[Tensor[Double]] + dilationBackpropInputDouble(inputTensor, filterTensor, outBackpropTensor, outputTensor, + strideRows, strideCols, rateRows, rateCols) + } else { + throw new IllegalArgumentException(s"does not support datatype ${ev2.getType()}") + } + + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +private[bigdl] object Dilation2DBackpropInput { + def apply[T: ClassTag, D: ClassTag](strides: Array[Int], rates: Array[Int], padding: String) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2DBackpropInput[T, D] = + new Dilation2DBackpropInput(strides, rates, padding) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilter.scala deleted file mode 100644 index b50582b6a7c..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropFilter.scala +++ /dev/null @@ -1,262 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.Utils -import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class Dilation2DBackpropFilter[T: ClassTag, D: ClassTag]( - strides: Array[Int], - rates: Array[Int], - padding: String)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Operation[Table, Tensor[D], T]{ - - output = Tensor[D]() - - private def dilation2DBackpropFilterFloat( - input: Tensor[Float], - filter: Tensor[Float], - outBackprop: Tensor[Float], - filterBackprop: Tensor[Float], - strideRows: Int, strideCols: Int, - rateRows: Int, rateCols: Int) = { - - val batch = input.size(1) - val inputRows = input.size(2) - val inputCols = input.size(3) - val depth = input.size(4) - - val filterRows = filter.size(1) - val filterCols = filter.size(2) - - val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) - val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) - - val (outputRows, padTop, _) = - Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) - val (outputCols, padLeft, _) = - Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) - - filterBackprop.resizeAs(filter) - - val inputData = input.storage().array() - val inputDataOffset = input.storageOffset() - 1 - - val filterData = filter.storage().array() - val filterDataOffset = filter.storageOffset() - 1 - - val outBackpropData = outBackprop.storage().array() - val outBackpropDataOffset = outBackprop.storageOffset() - 1 - - val filterBackpropData = filterBackprop.storage().array() - val filterBackpropDataOffset = filterBackprop.storageOffset() - 1 - - var b = 0 - while (b < batch) { - var h_out = 0 - while (h_out < outputRows) { - val h_beg = h_out * strideRows - padTop - var w_out = 0 - while (w_out < outputCols) { - val w_beg = w_out * strideCols - padLeft - var d = 0 - while (d < depth) { - var cur_val = Float.MinValue - var h_max = 0 - var w_max = 0 - var h = 0 - while (h < filterRows) { - val h_in = h_beg + h * rateRows - if (h_in >= 0 && h_in < inputRows) { - var w = 0 - while (w < filterCols) { - val w_in = w_beg + w * rateCols - if (w_in >= 0 && w_in < inputCols) { - val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d - val inputValue = inputData(inputDataOffset + inputIndex) - val filterIndex = (h * filterCols + w) * depth + d - val filterValue = filterData(filterDataOffset + filterIndex) - val value: Float = inputValue + filterValue - if (value > cur_val) { - cur_val = value - h_max = h - w_max = w - } - } - w += 1 - } - } - h += 1 - } - val filterBackPropIndex = - (h_max * filterCols + w_max) * depth + d - val outputBackPropIndex = - ((b * outputRows + h_out) * outputCols + w_out) * depth + d - filterBackpropData(filterBackpropDataOffset + filterBackPropIndex) += - outBackpropData(outBackpropDataOffset + outputBackPropIndex) - d += 1 - } - w_out += 1 - } - h_out += 1 - } - b += 1 - } - - } - - - - private def dilation2DBackpropFilterDouble(input: Tensor[Double], - filter: Tensor[Double], - outBackprop: Tensor[Double], - filterBackprop: Tensor[Double], - strideRows: Int, strideCols: Int, - rateRows: Int, rateCols: Int) = { - val batch = input.size(1) - val inputRows = input.size(2) - val inputCols = input.size(3) - val depth = input.size(4) - - val filterRows = filter.size(1) - val filterCols = filter.size(2) - - val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) - val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) - - val (outputRows, padTop, _) = - Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) - val (outputCols, padLeft, _) = - Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) - - filterBackprop.resizeAs(filter) - - val inputData = input.storage().array() - val inputDataOffset = input.storageOffset() - 1 - - val filterData = filter.storage().array() - val filterDataOffset = filter.storageOffset() - 1 - - val outBackpropData = outBackprop.storage().array() - val outBackpropDataOffset = outBackprop.storageOffset() - 1 - - val filterBackpropData = filterBackprop.storage().array() - val filterBackpropDataOffset = filterBackprop.storageOffset() - 1 - - var b = 0 - while (b < batch) { - var h_out = 0 - while (h_out < outputRows) { - val h_beg = h_out * strideRows - padTop - var w_out = 0 - while (w_out < outputCols) { - val w_beg = w_out * strideCols - padLeft - var d = 0 - while (d < depth) { - var cur_val = Double.MinValue - var h_max = 0 - var w_max = 0 - var h = 0 - while (h < filterRows) { - val h_in = h_beg + h * rateRows - if (h_in >= 0 && h_in < inputRows) { - var w = 0 - while (w < filterCols) { - val w_in = w_beg + w * rateCols - if (w_in >= 0 && w_in < inputCols) { - val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d - val inputValue = inputData(inputDataOffset + inputIndex) - val filterIndex = (h * filterCols + w) * depth + d - val filterValue = filterData(filterDataOffset + filterIndex) - val value: Double = inputValue + filterValue - if (value > cur_val) { - cur_val = value - h_max = h - w_max = w - } - } - w += 1 - } - } - h += 1 - } - val filterBackPropIndex = - (h_max * filterCols + w_max) * depth + d - val outputBackPropIndex = - ((b * outputRows + h_out) * outputCols + w_out) * depth + d - filterBackpropData(filterBackpropDataOffset + filterBackPropIndex) += - outBackpropData(outBackpropDataOffset + outputBackPropIndex) - d += 1 - } - w_out += 1 - } - h_out += 1 - } - b += 1 - } - } - - override def updateOutput(inputs: Table): Tensor[D] = { - val input = inputs[Tensor[D]](1) - val filter = inputs[Tensor[D]](2) - val outBackprop = inputs[Tensor[D]](3) - - require(input.dim() == 4, "input must have 4 dims") - require(filter.dim() == 3, "filter must have 3 dims") - - - val strideRows = strides(1) - val strideCols = strides(2) - - val rateRows = rates(1) - val rateCols = rates(2) - - if (ev2.getType() == FloatType) { - val inputTensor = input.asInstanceOf[Tensor[Float]] - val filterTensor = filter.asInstanceOf[Tensor[Float]] - val outBackpropTensor = outBackprop.asInstanceOf[Tensor[Float]] - val outputTensor = output.asInstanceOf[Tensor[Float]] - dilation2DBackpropFilterFloat(inputTensor, filterTensor, outBackpropTensor, outputTensor, - strideRows, strideCols, rateRows, rateCols) - } else if (ev2.getType() == DoubleType) { - val inputTensor = input.asInstanceOf[Tensor[Double]] - val filterTensor = filter.asInstanceOf[Tensor[Double]] - val outBackpropTensor = output.asInstanceOf[Tensor[Double]] - val outputTensor = output.asInstanceOf[Tensor[Double]] - dilation2DBackpropFilterDouble(inputTensor, filterTensor, outBackpropTensor, outputTensor, - strideRows, strideCols, rateRows, rateCols) - } else { - throw new IllegalArgumentException(s"does not support datatype ${ev2.getType()}") - } - - output - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) - } -} - -object Dilation2DBackpropFilter { - def apply[T: ClassTag, D: ClassTag](strides: Array[Int], rates: Array[Int], padding: String) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2DBackpropFilter[T, D] = - new Dilation2DBackpropFilter(strides, rates, padding) -} - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInput.scala deleted file mode 100644 index ecaae69b384..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInput.scala +++ /dev/null @@ -1,265 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.Utils -import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class Dilation2DBackpropInput[T: ClassTag, D: ClassTag](strides: Array[Int], - rates: Array[Int], - padding: String) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Operation[Table, Tensor[D], T]{ - - output = Tensor[D] - - private def dilationBackpropInputFloat(input: Tensor[Float], - filter: Tensor[Float], - outBackprop: Tensor[Float], - inputBackprop: Tensor[Float], - strideRows: Int, strideCols: Int, - rateRows: Int, rateCols: Int) = { - - val batch = input.size(1) - val inputRows = input.size(2) - val inputCols = input.size(3) - val depth = input.size(4) - - val filterRows = filter.size(1) - val filterCols = filter.size(2) - - val filterRowsEff = filterRows + (filterRows - 1) * (rateRows - 1) - val filterColsEff = filterCols + (filterCols - 1) * (rateCols - 1) - - val (outputRows, padTop, _) = - Utils.getOutputSize(inputRows, filterRowsEff, strideRows, padding) - val (outputCols, padLeft, _) = - Utils.getOutputSize(inputCols, filterColsEff, strideCols, padding) - - inputBackprop.resizeAs(input) - - val inputData = input.storage().array() - val inputDataOffset = input.storageOffset() - 1 - - val filterData = filter.storage().array() - val filterDataOffset = filter.storageOffset() - 1 - - val outBackpropData = outBackprop.storage().array() - val outBackpropDataOffset = outBackprop.storageOffset() - 1 - - val inputBackpropData = inputBackprop.storage().array() - val inputBackpropDataOffset = inputBackprop.storageOffset() - 1 - - var b = 0 - while (b < batch) { - var h_out = 0 - while (h_out < outputRows) { - val h_beg = h_out * strideRows - padTop - var w_out = 0 - while (w_out < outputCols) { - val w_beg = w_out * strideCols - padLeft - var d = 0 - while (d < depth) { - var cur_val = Float.MinValue - var h_in_max = if (h_beg < 0) 0 else h_beg - var w_in_max = if (w_beg < 0) 0 else w_beg - var h = 0 - while (h < filterRows) { - val h_in = h_beg + h * rateRows - if (h_in >= 0 && h_in < inputRows) { - var w = 0 - while (w < filterCols) { - val w_in = w_beg + w * rateCols - if (w_in >= 0 && w_in < inputCols) { - val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d - val inputValue = inputData(inputDataOffset + inputIndex) - val filterIndex = (h * filterCols + w) * depth + d - val filterValue = filterData(filterDataOffset + filterIndex) - val value: Float = inputValue + filterValue - if (value > cur_val) { - cur_val = value - h_in_max = h_in - w_in_max = w_in - } - } - w += 1 - } - } - h += 1 - } - val inputBackPropIndex = - ((b * inputRows + h_in_max) * inputCols + w_in_max) * depth + d - val outputBackPropIndex = - ((b * outputRows + h_out) * outputCols + w_out) * depth + d - inputBackpropData(inputBackpropDataOffset + inputBackPropIndex) += - outBackpropData(outBackpropDataOffset + outputBackPropIndex) - d += 1 - } - w_out += 1 - } - h_out += 1 - } - b += 1 - } - - } - - - - private def dilationBackpropInputDouble(input: Tensor[Double], - filter: Tensor[Double], - outBackprop: Tensor[Double], - inputBackprop: Tensor[Double], - strideRows: Int, strideCols: Int, - rateRows: Int, rateCols: Int) = { - val batch = input.size(1) - val inputRows = input.size(2) - val inputCols = input.size(3) - val depth = input.size(4) - - val filterRows = filter.size(1) - val filterCols = filter.size(2) - - val outputRows = outBackprop.size(2) - val outputCols = outBackprop.size(3) - - val (padTop, padLeft) = padding.toLowerCase() match { - case "same" => - val top = (outputRows - inputRows) / 2 - val left = (outputCols - inputCols) / 2 - (top, left) - case "valid" => - (0, 0) - } - - inputBackprop.resizeAs(input) - - val inputData = input.storage().array() - val inputDataOffset = input.storageOffset() - 1 - - val filterData = filter.storage().array() - val filterDataOffset = filter.storageOffset() - 1 - - val outBackpropData = outBackprop.storage().array() - val outBackpropDataOffset = outBackprop.storageOffset() - 1 - - val inputBackpropData = inputBackprop.storage().array() - val inputBackpropDataOffset = inputBackprop.storageOffset() - 1 - - var b = 0 - while (b < batch) { - var h_out = 0 - while (h_out < outputRows) { - val h_beg = h_out * strideRows - padTop - var w_out = 0 - while (w_out < outputCols) { - val w_beg = w_out * strideCols - padLeft - var d = 0 - while (d < depth) { - var cur_val = Double.MinValue - var h_in_max = if (h_beg < 0) 0 else h_beg - var w_in_max = if (w_beg < 0) 0 else w_beg - var h = 0 - while (h < filterRows) { - val h_in = h_beg + h * rateRows - if (h_in >= 0 && h_in < inputRows) { - var w = 0 - while (w < filterCols) { - val w_in = w_beg + w * rateCols - if (w_in >= 0 && w_in < inputCols) { - val inputIndex = ((b * inputRows + h_in) * inputCols + w_in) * depth + d - val inputValue = inputData(inputDataOffset + inputIndex) - val filterIndex = (h * filterCols + w) * depth + d - val filterValue = filterData(filterDataOffset + filterIndex) - val value: Double = inputValue + filterValue - if (value > cur_val) { - cur_val = value - h_in_max = h_in - w_in_max = w_in - } - } - w += 1 - } - } - h += 1 - } - val inputBackPropIndex = - ((b * inputRows + h_in_max) * inputCols + w_in_max) * depth + d - val outputBackPropIndex = - ((b * outputRows + h_out) * outputCols + w_out) * depth + d - inputBackpropData(inputBackpropDataOffset + inputBackPropIndex) += - outBackpropData(outBackpropDataOffset + outputBackPropIndex) - d += 1 - } - w_out += 1 - } - h_out += 1 - } - b += 1 - } - } - - override def updateOutput(inputs: Table): Tensor[D] = { - val input = inputs[Tensor[D]](1) - val filter = inputs[Tensor[D]](2) - val outBackprop = inputs[Tensor[D]](3) - - require(input.dim() == 4, "input must have 4 dims") - require(filter.dim() == 3, "filter must have 3 dims") - - - val strideRows = strides(1) - val strideCols = strides(2) - - val rateRows = rates(1) - val rateCols = rates(2) - - if (ev2.getType() == FloatType) { - val inputTensor = input.asInstanceOf[Tensor[Float]] - val filterTensor = filter.asInstanceOf[Tensor[Float]] - val outBackpropTensor = outBackprop.asInstanceOf[Tensor[Float]] - val outputTensor = output.asInstanceOf[Tensor[Float]] - dilationBackpropInputFloat(inputTensor, filterTensor, outBackpropTensor, outputTensor, - strideRows, strideCols, rateRows, rateCols) - } else if (ev2.getType() == DoubleType) { - val inputTensor = input.asInstanceOf[Tensor[Double]] - val filterTensor = filter.asInstanceOf[Tensor[Double]] - val outBackpropTensor = output.asInstanceOf[Tensor[Double]] - val outputTensor = output.asInstanceOf[Tensor[Double]] - dilationBackpropInputDouble(inputTensor, filterTensor, outBackpropTensor, outputTensor, - strideRows, strideCols, rateRows, rateCols) - } else { - throw new IllegalArgumentException(s"does not support datatype ${ev2.getType()}") - } - - output - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) - } -} - -object Dilation2DBackpropInput { - def apply[T: ClassTag, D: ClassTag](strides: Array[Int], rates: Array[Int], padding: String) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Dilation2DBackpropInput[T, D] = - new Dilation2DBackpropInput(strides, rates, padding) -} - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala deleted file mode 100644 index 9d414f8036f..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/EluGrad.scala +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.nn.{ELU => ELULayer} - -import scala.reflect.ClassTag - -class EluGrad[T: ClassTag, D: ClassTag] -(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends UnaryGrad[T, D](true, true) { - - override val module: Module = ELULayer[D]() -} - -object EluGrad { - def apply[T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): EluGrad[T, D] = new EluGrad[T, D]() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNorm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNorm.scala deleted file mode 100644 index c18d8a68afe..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNorm.scala +++ /dev/null @@ -1,125 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.SpatialBatchNormalization -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -/** - * This is similar to SpatialBatchNormalization. - * - * When isTraining is true, it takes three tensors as inputs, which is image, - * scale, offset. - * - * The operation implemented is: - * - * ( image - batch-mean(x) ) - * y = ---------------------------------- * weight + offset - * batch-standard-deviation(x) - * - * The operation will output y, mean and variance tensors. - * - * If the isTraining is false, it takes five tensors as inputs, which is image, scale, offset, mean, - * and variance. - * - * @param epsilon - * @param isTraining - * @param momentum - * @param dataFormat - * @param ev$1 - * @param ev - * @tparam T Numeric type. Only support float/double now - */ -class FusedBatchNorm[T: ClassTag]( - epsilon: Float = 0.0001f, - isTraining: Boolean = true, - momentum: Float = 0.1f, - dataFormat: DataFormat = DataFormat.NHWC -)(implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T]{ - - @transient - private var runningMean: Tensor[Float] = null - - @transient - private var runningVar: Tensor[Float] = null - - @transient - private var saveStd: Tensor[Float] = null - - override def updateOutput(input: Table): Table = { - val x = input[Tensor[Float]](1) - val scale = input[Tensor[Float]](2) - val offset = input[Tensor[Float]](3) - val mean = input[Tensor[Float]](4) - val variance = input[Tensor[Float]](5) - - if (output.length() == 0) { - output(1) = Tensor[Float]().resizeAs(x) // y - output(2) = Tensor[Float](x.size(4)) // batch mean - output(3) = Tensor[Float](x.size(4)) // batch var - output(4) = Tensor[Float](x.size(4)) // save mean - output(5) = Tensor[Float](x.size(4)) // save var - runningMean = Tensor[Float](x.size(4)) // running mean - runningVar = Tensor[Float](x.size(4)) // running var - saveStd = Tensor[Float](x.size(4)) // save std - } - - val y = output[Tensor[Float]](1) - val batchMean = output[Tensor[Float]](2) - val batchVar = output[Tensor[Float]](3) - val saveMean = output[Tensor[Float]](4) - val saveVar = output[Tensor[Float]](5) - - if (isTraining) { - if (dataFormat == DataFormat.NHWC) { - SpatialBatchNormalization.updateOutputNHWCTrainFloat( - x, y, batchMean, saveStd, runningMean, runningVar, scale, offset, epsilon, momentum, - batchVar, saveVar - ) - } else { - SpatialBatchNormalization.updateOutputNCHWTrainFloat( - x, y, batchMean, saveStd, runningMean, runningVar, scale, offset, epsilon, momentum, - batchVar, saveVar - ) - } - saveMean.copy(batchMean) - } else { - if (dataFormat == DataFormat.NHWC) { - SpatialBatchNormalization.updateOutputNHWCInferFloat( - x, y, mean, variance, scale, offset, epsilon - ) - } else { - SpatialBatchNormalization.updateOutputNCHWInferFloat( - x, y, mean, variance, scale, offset, epsilon - ) - } - } - - output - } -} - -object FusedBatchNorm { - def apply[T: ClassTag](epsilon: Float = 0.0001f, isTrainning: Boolean = true, - momentum: Float = 0.1f, dataFormat: DataFormat = DataFormat.NHWC) - (implicit ev: TensorNumeric[T]): FusedBatchNorm[T] - = new FusedBatchNorm(epsilon, isTrainning, momentum, dataFormat) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala deleted file mode 100644 index 00a42855276..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/FusedBatchNormGrad.scala +++ /dev/null @@ -1,88 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.{BatchNormalization, SpatialBatchNormalization} -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -/** - * This is the gradient operation coressponding to the FusedBatchNorm. It will calculate the - * activity, weight and bias gradients of the spatial batch normalization. - * - * The formula is - * x_backprop = scale * rsqrt(variance + epsilon) * [y_backprop - mean(y_backprop) - - * (x - mean(x)) * mean(y_backprop * (x - mean(x))) / (variance + epsilon)] - * weight_backprop = sum(y_backprop * (x - mean(x)) * rsqrt(variance + epsilon)) - * bias_backprop = sum(y_backprop) - * - * @param epsilon - * @param dataFormat - * @param isTrain - * @param ev$1 - * @param ev - * @tparam T Numeric type. Only support float/double now - */ -class FusedBatchNormGrad[T: ClassTag]( - val epsilon: Float, val dataFormat: DataFormat, - val isTrain: Boolean = false)(implicit ev: TensorNumeric[T]) - extends Operation[Table, Table, T]{ - - - private val gMean = Tensor[Float]() - private val gxMean = Tensor[Float]() - private val saveStd = Tensor[Float]() - - override def updateOutput(input: Table): Table = { - val gradOutput = input[Tensor[Float]](1) - val x = input[Tensor[Float]](2) - val scale = input[Tensor[Float]](3) - val saveMean = input[Tensor[Float]](4) - val saveVar = input[Tensor[Float]](5) - - if (output.length() == 0) { - output(1) = Tensor[Float]().resizeAs(x) // gradInput - output(2) = Tensor[Float](x.size(4)) // weight gradient - output(3) = Tensor[Float](x.size(4)) // bias gradient - saveStd.resize(x.size(4)) // bias gradient - } - saveStd.copy(saveVar) - saveStd.add(epsilon).pow(-0.5f) - val gradInput = output[Tensor[Float]](1) - val gradWeight = output[Tensor[Float]](2) - val gradBias = output[Tensor[Float]](3) - - SpatialBatchNormalization.updateGradInputNHWCTrainFloat( - x, gradOutput, gradInput, scale, saveMean, saveStd, gMean, gxMean) - - gradWeight.zero() - gradBias.zero() - SpatialBatchNormalization.accGradientNHWCFloat( - gradOutput, gradWeight, gradBias, x, saveMean, saveStd, 1.0f, 1.0f) - - output - } -} - -object FusedBatchNormGrad { - def apply[T: ClassTag](epsilon: Float = 0.0001f, dataFormat: DataFormat = DataFormat.NHWC, - isTraining: Boolean = true)(implicit ev: TensorNumeric[T]): FusedBatchNormGrad[T] = - new FusedBatchNormGrad(epsilon, dataFormat, isTraining) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Inv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Inv.scala index d814633224d..b507f4327be 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Inv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Inv.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} +import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag @@ -39,3 +40,31 @@ object Inv { def apply[T: ClassTag, D: ClassTag]()( implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Inv[T, D] = new Inv() } + +private[bigdl] class InvGrad[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { + output = Tensor[D]() + + override def updateOutput(input: Table): Tensor[D] = { + require(input.length() == 2, "InvGrad requires two tensors as input") + val x = input[Tensor[D]](1) + val d = input[Tensor[D]](2) + + if (d.getType() != output.getType()) { + output = d.emptyInstance() + } + output.resizeAs(x) + output.copy(x).pow(ev2.fromType(2)).cmul(d).mul(ev2.fromType(-1)) + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } +} + +private[bigdl] object InvGrad { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + : InvGrad[T, D] = new InvGrad() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGrad.scala deleted file mode 100644 index 8429b04c34b..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGrad.scala +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} -import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class InvGrad[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Operation[Table, Tensor[D], T] { - output = Tensor[D]() - - override def updateOutput(input: Table): Tensor[D] = { - require(input.length() == 2, "InvGrad requires two tensors as input") - val x = input[Tensor[D]](1) - val d = input[Tensor[D]](2) - - if (d.getType() != output.getType()) { - output = d.emptyInstance() - } - output.resizeAs(x) - output.copy(x).pow(ev2.fromType(2)).cmul(d).mul(ev2.fromType(-1)) - output - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } -} - -object InvGrad { - def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - : InvGrad[T, D] = new InvGrad() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala deleted file mode 100644 index b7b53099ed0..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/LRNGrad.scala +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.SpatialCrossMapLRN -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -/** - * LRNGrad calculate the backprop gradients of the Local response normalization layer. - * - * @param depthRadius - * @param bias - * @param alpha - * @param beta - * @param ev$1 - * @param ev - * @param ev2 - * @tparam T Numeric type. Only support float/double now - */ -class LRNGrad[T: ClassTag]( - depthRadius: Int = 5, - bias: Float = 1.0f, - alpha: Float = 1.0f, - beta: Float = 0.5f -)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[Float]) - extends Operation[Table, Tensor[Float], T] { - - output = Tensor[Float]() - - override def updateOutput(input: Table): Tensor[Float] = { - val gradOutput = input[Tensor[Float]](1) - val inputTensor = input[Tensor[Float]](2) - val outputTensor = input[Tensor[Float]](3) - - output.resizeAs(inputTensor) - var b = 1 - while(b <= inputTensor.size(1)) { - SpatialCrossMapLRN.backwardFrameNHWCFloat( - gradOutput.select(1, b), - inputTensor.select(1, b), - output.select(1, b), - outputTensor.select(1, b), - alpha * (2 * depthRadius + 1), 2 * depthRadius + 1, beta, bias - ) - b += 1 - } - output - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T]), - Array[TensorNumeric[_]](ev, ev2)) - } -} - -object LRNGrad { - def apply[T: ClassTag]( - depthRadius: Int = 5, - bias: Float = 1.0f, - alpha: Float = 1.0f, - beta: Float = 0.5f - )(implicit ev: TensorNumeric[T]): LRNGrad[T] - = new LRNGrad(depthRadius, bias, alpha, beta) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala deleted file mode 100644 index 1c6058d3b92..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPool.scala +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.SpatialMaxPooling -import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor._ - -import scala.reflect.ClassTag - -class MaxPool[T: ClassTag]( - val ksize: Array[Int], - val strides: Array[Int], - val padding: String, - val format: DataFormat = DataFormat.NHWC -)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { - val pool: SpatialMaxPooling[T] = format match { - case DataFormat.NHWC => - if (padding == "SAME") { - SpatialMaxPooling( - kH = ksize(1), - kW = ksize(2), - dH = strides(1), - dW = strides(2), - padH = -1, - padW = -1, - format = format - ) - } else if (padding == "VALID") { - SpatialMaxPooling( - kH = ksize(1), - kW = ksize(2), - dH = strides(1), - dW = strides(2), - format = format - ) - } else { - throw new RuntimeException("Padding can only support SAME and VALID padding") - } - case DataFormat.NCHW => - if (padding == "SAME") { - SpatialMaxPooling( - kH = ksize(2), - kW = ksize(3), - dH = strides(2), - dW = strides(3), - padH = -1, - padW = -1, - format = format - ) - } else if (padding == "VALID") { - SpatialMaxPooling( - kH = ksize(2), - kW = ksize(3), - dH = strides(2), - dW = strides(3), - format = format - ) - } else { - throw new RuntimeException("Padding can only support SAME and VALID padding") - } - } - - override def updateOutput(input: Tensor[T]): Tensor[T] = { - output = pool.updateOutput(input) - output - } -} - -object MaxPool { - def apply[T: ClassTag]( - ksize: Array[Int], - strides: Array[Int], - padding: String, - format: DataFormat = DataFormat.NHWC - )(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](new MaxPool(ksize, strides, padding, format)) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGrad.scala deleted file mode 100644 index c557bfdba86..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGrad.scala +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.SpatialMaxPooling -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class MaxPoolGrad[T: ClassTag]( - kH: Int, - kW: Int, - strideW: Int, - strideH: Int, - padH: Int, - padW: Int, - format: DataFormat -)(implicit ev: TensorNumeric[T]) - extends Operation[Table, Tensor[T], T]{ - - private var module : SpatialMaxPooling[T] = _ - - override def updateOutput(input: Table): Tensor[T] = { - if (module == null) { - module = SpatialMaxPooling[T]( - kH, - kW, - strideH, - strideW, - padH, - padW, - format - ) - } - - val inputData = input[Tensor[T]](1) - val gradOutput = input[Tensor[T]](3) - module.updateOutput(inputData) - output = module.updateGradInput(inputData, gradOutput) - output - } -} - -object MaxPoolGrad { - def apply[T: ClassTag]( - kH: Int, - kW: Int, - strideW: Int, - strideH: Int, - padH: Int, - padW: Int, - format: DataFormat - )(implicit ev: TensorNumeric[T]): MaxPoolGrad[T] = - new MaxPoolGrad(kH, kW, strideW, strideH, padH, padW, format) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala deleted file mode 100644 index bd26c72e28f..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6Grad.scala +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.nn.{ReLU6 => ReLU6Layer} - -import scala.reflect.ClassTag - -class Relu6Grad[T: ClassTag, D: ClassTag] -(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends UnaryGrad[T, D](true) { - - val module: Module = ReLU6Layer[D]() -} - -object Relu6Grad { - def apply[T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Relu6Grad[T, D] = - new Relu6Grad[T, D]() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReluGrad.scala deleted file mode 100644 index 4c603691e74..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ReluGrad.scala +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table -import com.intel.analytics.bigdl.nn.{ReLU => ReLULayer} - -import scala.reflect.ClassTag - -class ReluGrad[T: ClassTag](implicit ev: TensorNumeric[T]) - extends Operation[Table, Tensor[T], T]{ - - val module = ReLULayer[T]() - - override def updateOutput(input: Table): Tensor[T] = { - val grads = input[Tensor[T]](1) - val inputs = input[Tensor[T]](2) - - output = module.updateGradInput(inputs, grads).toTensor[T] - output - } -} - -object ReluGrad { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): ReluGrad[T] = new ReluGrad() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinear.scala similarity index 93% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinear.scala index 74d1c3841f5..3d38b2c0a67 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinear.scala @@ -54,8 +54,8 @@ object ResizeBilinearOps { } } -class ResizeBilinearGrad[T: ClassTag](alignCorner: Boolean)(implicit ev: TensorNumeric[T]) - extends Operation[Activity, Tensor[T], T] { +private[bigdl] class ResizeBilinearGrad[T: ClassTag](alignCorner: Boolean) + (implicit ev: TensorNumeric[T]) extends Operation[Activity, Tensor[T], T] { private var module : ResizeBilinear[T] = _ @@ -79,7 +79,7 @@ class ResizeBilinearGrad[T: ClassTag](alignCorner: Boolean)(implicit ev: TensorN } } -object ResizeBilinearGrad { +private[bigdl] object ResizeBilinearGrad { def apply[T: ClassTag](alignCorner: Boolean) (implicit ev: TensorNumeric[T]): ResizeBilinearGrad[T] = { new ResizeBilinearGrad[T](alignCorner) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala deleted file mode 100644 index 7227baeecd1..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/RsqrtGrad.scala +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class RsqrtGrad[T: ClassTag, D: ClassTag](implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Operation[Table, Tensor[D], T] { - - output = Tensor[D]() - - override def updateOutput(inputs: Table): Tensor[D] = { - val grads = inputs[Tensor[D]](2) - val y = inputs[Tensor[D]](1) - - output.resizeAs(y).copy(y).pow(ev2.fromType(3.0)).mul(ev2.fromType(-0.5f)).cmul(grads) - - output - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } -} - -object RsqrtGrad { - def apply[T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): RsqrtGrad[T, D] = new RsqrtGrad[T, D]() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala deleted file mode 100644 index ea22a321a8d..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGrad.scala +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.nn.Sigmoid -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class SigmoidGrad[T: ClassTag, D: ClassTag] -(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Operation[Table, Tensor[D], T]{ - - private val module = Sigmoid[D]() - override def updateOutput(input: Table): Tensor[D] = { - val (y, grads) = (input[Tensor[D]](1), input[Tensor[D]](2)) - - output = module.updateGradInputInternal(y, grads).toTensor[D] - output - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } -} - -object SigmoidGrad { - def apply[T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SigmoidGrad[T, D] = - new SigmoidGrad[T, D]() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala deleted file mode 100644 index 2b64ab3b0fe..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGrad.scala +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.{SoftPlus => SoftPlusLayer} -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric - -import scala.reflect.ClassTag - -class SoftplusGrad[T: ClassTag, D: ClassTag] -(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends UnaryGrad[T, D](true, true) { - - override val module: Module = SoftPlusLayer[D]() - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } -} - -object SoftplusGrad { - def apply[T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SoftplusGrad[T, D] = - new SoftplusGrad[T, D]() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala deleted file mode 100644 index b98d1173738..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftsignGrad.scala +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.{SoftSign => SoftSignLayer} -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric - -import scala.reflect.ClassTag - -class SoftsignGrad[T: ClassTag, D: ClassTag] -(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends UnaryGrad[T, D](true) { - - override val module: Module = SoftSignLayer[D]() -} - -object SoftsignGrad { - def apply[T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SoftsignGrad[T, D] = - new SoftsignGrad[T, D]() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TanhGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TanhGrad.scala deleted file mode 100644 index cce4b8cc1b1..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TanhGrad.scala +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.nn.{Sigmoid, Tanh} -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -class TanhGrad[T: ClassTag, D: ClassTag] -(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Operation[Table, Tensor[D], T]{ - - private val module = Tanh[D]() - override def updateOutput(input: Table): Tensor[D] = { - val (y, grads) = (input[Tensor[D]](1), input[Tensor[D]](2)) - - output = module.updateGradInputInternal(y, grads).toTensor[D] - output - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) - } -} - -object TanhGrad { - def apply[T: ClassTag, D: ClassTag]() - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): TanhGrad[T, D] = - new TanhGrad[T, D]() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala deleted file mode 100644 index fb6b6e0043e..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/UnaryGrad.scala +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.ops - -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - -abstract class UnaryGrad[T: ClassTag, D: ClassTag]( - gradFirst: Boolean = false, - needForward: Boolean = false) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends Operation[Table, Tensor[D], T]{ - - type Module = AbstractModule[Tensor[D], Tensor[D], _] - - val module: Module - - override def updateOutput(input: Table): Tensor[D] = { - val (grads, inputs) = if (gradFirst) { - (input[Tensor[D]](1), input[Tensor[D]](2)) - } else { - (input[Tensor[D]](2), input[Tensor[D]](1)) - } - - if (needForward) { - module.forward(inputs) - } - - output = module.updateGradInput(inputs, grads).toTensor[D] - output - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ArrayOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ArrayOps.scala index e400e43069d..792b7f6b1fa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ArrayOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ArrayOps.scala @@ -16,11 +16,12 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.Operation -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.{ModuleToOperation, Operation} +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildCard, TensorNumeric} import com.intel.analytics.bigdl.utils.{T, Table} +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -186,3 +187,88 @@ private[bigdl] object Fill { new Fill[T]() } } + +/** + * Given shapes of two tensors, computes the reduction indices for the + * gradient computation. + * + * @tparam T Numeric type. Only support float/double now + */ +private[bigdl] class BroadcastGradientArgs[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T] { + + override def updateOutput(input: Table): Table = { + val input1 = input[Tensor[Int]](1) + val input2 = input[Tensor[Int]](2) + + val output1 = Tensor[Int]() + val output2 = Tensor[Int]() + + output.insert(output1).insert(output2) + + // Reverse the shape of x and y for convenience. + // After the reverse, 0-th is the inner-most dimension. + val rx = + if (input1.storage() == null) Array[Int]().toBuffer + else input1.storage().array().reverse.toBuffer + val ry = + if (input2.storage() == null) Array[Int]().toBuffer + else input2.storage().array().reverse.toBuffer + + if (rx.length < ry.length) { + while (rx.length < ry.length) { + rx.append(1) + } + } else { + while (rx.length > ry.length) { + ry.append(1) + } + } + + val xReducedIndexBuffer = new ArrayBuffer[Int]() + val yReducedIndexBuffer = new ArrayBuffer[Int]() + + val n = rx.length + + var i = 0 + while (i < n) { + val xi = rx(i) + val yi = ry(i) + + if (xi == yi) { + if (xi == 1) { + xReducedIndexBuffer.append(n - 1 - i) + yReducedIndexBuffer.append(n - 1 - i) + } + } else if (xi == 1) { + xReducedIndexBuffer.append(n - 1 - i) + } else if (yi == 1) { + yReducedIndexBuffer.append(n - 1 - i) + } else { + return output + } + i += 1 + } + + if (xReducedIndexBuffer.isEmpty) { + input(1) = Tensor[Int]() + } else { + output1.resize(Array(xReducedIndexBuffer.length)) + .set(Tensor[Int](Storage(xReducedIndexBuffer.reverse.toArray))) + } + + if (yReducedIndexBuffer.isEmpty) { + input(2) = Tensor[Int]() + } else { + output2.resize(Array(yReducedIndexBuffer.length)) + .set(Tensor[Int](Storage(yReducedIndexBuffer.reverse.toArray))) + } + + output + } +} + +private[bigdl] object BroadcastGradientArgs { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new BroadcastGradientArgs()) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assert.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Assert.scala similarity index 86% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assert.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Assert.scala index bedfab5d6f3..f1d3d8bf22b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assert.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Assert.scala @@ -13,10 +13,11 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table @@ -27,7 +28,8 @@ import scala.reflect.ClassTag * Assert will assert the first input to be true, if not, throw the message in the second * input. Assert has no output. */ -class Assert[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Operation[Table, Activity, T] { +private[bigdl] class Assert[T: ClassTag]() + (implicit ev: TensorNumeric[T]) extends Operation[Table, Activity, T] { override def updateOutput(input: Table): Tensor[T] = { val predicateTensor = input(1).asInstanceOf[Tensor[Boolean]] val messageTensor = input(2).asInstanceOf[Tensor[ByteString]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala similarity index 95% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala index ddc8351514c..9edcfa6790e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import java.awt.image.{BufferedImage, DataBufferByte} import java.io.ByteArrayInputStream @@ -22,6 +22,7 @@ import javax.imageio.ImageIO import com.google.protobuf.ByteString import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter @@ -32,7 +33,7 @@ import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag import scala.reflect.runtime.universe -class DecodeImage[T: ClassTag](val channels: Int)(implicit ev: TensorNumeric[T]) +private[bigdl] class DecodeImage[T: ClassTag](val channels: Int)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[ByteString], Tensor[Int], T] { output = Tensor[Int]() @@ -90,15 +91,15 @@ class DecodeImage[T: ClassTag](val channels: Int)(implicit ev: TensorNumeric[T]) } } -class DecodeJpeg[T: ClassTag](channels: Int, val ratio: Int = 1)(implicit ev: TensorNumeric[T]) - extends DecodeImage[T](channels) { +private[bigdl] class DecodeJpeg[T: ClassTag](channels: Int, val ratio: Int = 1) + (implicit ev: TensorNumeric[T]) extends DecodeImage[T](channels) { require(ratio == 1, "currently not supported sub-sampling") } -class DecodePng[T: ClassTag](channels: Int)(implicit ev: TensorNumeric[T]) +private[bigdl] class DecodePng[T: ClassTag](channels: Int)(implicit ev: TensorNumeric[T]) extends DecodeImage[T](channels) -class DecodeGif[T: ClassTag]()(implicit ev: TensorNumeric[T]) +private[bigdl] class DecodeGif[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends DecodeImage[T](3) { override def updateOutput(input: Tensor[ByteString]): Tensor[Int] = { @@ -143,7 +144,7 @@ class DecodeGif[T: ClassTag]()(implicit ev: TensorNumeric[T]) } -class DecodeRaw[T: ClassTag](val outType: DataType, +private[bigdl] class DecodeRaw[T: ClassTag](val outType: DataType, val littleEndian: Boolean)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[ByteString], Activity, T] { output = { @@ -368,7 +369,7 @@ class DecodeRaw[T: ClassTag](val outType: DataType, } } -object DecodeRawSerializer extends ModuleSerializable { +private[bigdl] object DecodeRawSerializer extends ModuleSerializable { override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/MathOps.scala similarity index 60% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/MathOps.scala index dd905d8bd11..3dd1cffca8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/SqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/MathOps.scala @@ -13,10 +13,11 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf -import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag @@ -45,3 +46,28 @@ object SqrtGrad { def apply[T: ClassTag, D: ClassTag]() (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SqrtGrad[T, D] = new SqrtGrad[T, D]() } + +private[bigdl] class RsqrtGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T] { + + output = Tensor[D]() + + override def updateOutput(inputs: Table): Tensor[D] = { + val grads = inputs[Tensor[D]](2) + val y = inputs[Tensor[D]](1) + + output.resizeAs(y).copy(y).pow(ev2.fromType(3.0)).mul(ev2.fromType(-0.5f)).cmul(grads) + + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } +} + +private[bigdl] object RsqrtGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): RsqrtGrad[T, D] = new RsqrtGrad[T, D]() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NNOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NNOps.scala new file mode 100644 index 00000000000..bf7c1863c33 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NNOps.scala @@ -0,0 +1,1165 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.{Sigmoid, SpatialAveragePooling, SpatialBatchNormalization, +SpatialConvolution, SpatialCrossMapLRN, SpatialMaxPooling, SpatialSeperableConvolution, Tanh, Utils, +VolumetricConvolution, ELU => ELULayer, ReLU6 => ReLU6Layer, SoftPlus => SoftPlusLayer, +SoftSign => SoftSignLayer, ReLU => ReLULayer} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.ops.{ModuleToOperation, Operation} +import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +private[bigdl] class Conv2D[T: ClassTag]( + strideH: Int, + strideW: Int, + padH: Int, + padW: Int, + format: DataFormat = DataFormat.NHWC +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private var conv: SpatialConvolution[T] = _ + + override def updateOutput(inputs: Table): Tensor[T] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + val filter: Tensor[T] = inputs[Tensor[T]](2) + + + val channelDim = if (format == DataFormat.NHWC) 4 else 2 + val kHDim = if (format == DataFormat.NHWC) 1 else 3 + val kWDim = if (format == DataFormat.NHWC) 2 else 4 + + if (conv == null) { + conv = SpatialConvolution( + nInputPlane = input.size(channelDim), + nOutputPlane = filter.size(channelDim), + kernelH = filter.size(kHDim), + kernelW = filter.size(kWDim), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + withBias = false, + format = format + ) + } + + conv.setWeightsBias(Array(filter)) + output = conv.forward(input) + output + } +} + +private[bigdl] object Conv2D { + def apply[T: ClassTag]( + strideH: Int, + strideW: Int, + padH: Int, + padW: Int, + format: DataFormat = DataFormat.NHWC + )(implicit ev: TensorNumeric[T]): Conv2D[T] + = new Conv2D(strideH, strideW, padH, padW, format) +} + +/** + * Backward of SpatialConvolution + */ +private[bigdl] class Conv2DTranspose[T: ClassTag]( + strideW: Int, + strideH: Int, + padW: Int = -1, + padH: Int = -1, + format: DataFormat = DataFormat.NCHW +)(implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[T], T]{ + + private var module: SpatialConvolution[T] = _ + private var dummyInput: Tensor[T] = _ + + override def updateOutput(input: Activity): Tensor[T] = { + require(input.isTable, "Invalid input activity type") + val inputSizes = input.toTable.apply[Tensor[Int]](1).squeeze() + val kernel = input.toTable.apply[Tensor[T]](2) + val data = input.toTable.apply[Tensor[T]](3) + + require(data.nDimension() == 4, s"Need a 4D input but is ${data.nDimension()}") + require(inputSizes.nDimension() == 1, s"Need a 1D size but is ${inputSizes.nDimension()}") + + val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { + (data.size(2), inputSizes.valueAt(2)) + } else { + (data.size(4), inputSizes.valueAt(4)) + } + + val kHDim = if (format == DataFormat.NHWC) 1 else 3 + val kWDim = if (format == DataFormat.NHWC) 2 else 4 + + + if (module == null) { + module = new SpatialConvolution[T]( + nInputPlane = nInputPlane, + nOutputPlane = nOutputPlane, + kernelW = kernel.size(kWDim), + kernelH = kernel.size(kHDim), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + initWeight = kernel, + format = format, + withBias = false + ) + + dummyInput = Tensor[T](inputSizes.valueAt(1), inputSizes.valueAt(2), inputSizes.valueAt(3), + inputSizes.valueAt(4)) + } else { + val (nOutputPlanbe, nInputPlane) = if (format == DataFormat.NCHW) { + (data.size(2), inputSizes.valueAt(2)) + } else { + (data.size(4), inputSizes.valueAt(4)) + } + + require(module.nInputPlane == nInputPlane, "nInputPlane is not valid") + require(module.nOutputPlane == nOutputPlane, "nOutputPlane is not valid") + require(module.kernelH == kernel.size(kWDim), "kernelH is not valid") + require(module.kernelW == kernel.size(kWDim), "kernelW is not valid") + require(kernel.size(3) == nInputPlane, "kernel nInputPlane is not valid") + require(kernel.size(4) == nOutputPlane, "kernel nOutputPlane is not valid") + require(dummyInput.size(1) == inputSizes.valueAt(1), "size 1 is not correct") + require(dummyInput.size(2) == inputSizes.valueAt(2), "size 1 is not correct") + require(dummyInput.size(3) == inputSizes.valueAt(3), "size 1 is not correct") + require(dummyInput.size(4) == inputSizes.valueAt(4), "size 1 is not correct") + } + + module.forward(dummyInput) + module.weight.set(kernel) + module.updateGradInput(dummyInput, data) + output = module.gradInput + output + } +} + +private[bigdl] object Conv2DTranspose { + def apply[T: ClassTag]( + strideW: Int, + strideH: Int, + padW: Int = -1, + padH: Int = -1, + format: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]): Conv2DTranspose[T] = + new Conv2DTranspose(strideW, strideH, padW, padH, format) +} + +private[bigdl] class Conv2DBackFilter[T: ClassTag]( + strideW: Int, + strideH: Int, + padW: Int = -1, + padH: Int = -1, + format: DataFormat = DataFormat.NCHW +)(implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[T], T]{ + + private var module: SpatialConvolution[T] = _ + private var gradWeight: Tensor[T] = _ + private var dummyInput: Tensor[T] = _ + + override def updateOutput(input: Activity): Tensor[T] = { + require(input.isTable, "Invalid input activity type") + val kernelSize = input.toTable.apply[Tensor[Int]](2).squeeze() + val inputActivity = input.toTable.apply[Tensor[T]](1) + val grads = input.toTable.apply[Tensor[T]](3) + + require(grads.nDimension() == 4, s"Need a 4D input but is ${grads.nDimension()}") + require(kernelSize.nDimension() == 1, s"Need a 1D size but is ${kernelSize.nDimension()}") + + val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { + (grads.size(2), inputActivity.size(2)) + } else { + (grads.size(4), inputActivity.size(4)) + } + + if (module == null) { + gradWeight = Tensor[T]().resize(kernelSize.valueAt(1), kernelSize.valueAt(2), + kernelSize.valueAt(3), kernelSize.valueAt(4)) + module = new SpatialConvolution[T]( + nInputPlane = nInputPlane, + nOutputPlane = nOutputPlane, + kernelW = kernelSize.valueAt(2), + kernelH = kernelSize.valueAt(1), + strideH = strideH, + strideW = strideW, + padH = padH, + padW = padW, + initGradWeight = gradWeight, + format = format, + withBias = false + ) + } else { + val (nOutputPlane, nInputPlane) = if (format == DataFormat.NCHW) { + (grads.size(2), inputActivity.size(2)) + } else { + (grads.size(4), inputActivity.size(4)) + } + + require(module.nInputPlane == nInputPlane, "nInputPlane is not valid") + require(module.nOutputPlane == nOutputPlane, "nOutputPlane is not valid") + require(module.kernelH == kernelSize.valueAt(1), s"kernelH is not valid") + require(module.kernelW == kernelSize.valueAt(2), "kernelW is not valid") + require(kernelSize.valueAt(3) == nInputPlane, "kernel nInputPlane is not valid") + require(kernelSize.valueAt(4) == nOutputPlane, "kernel nOutputPlane is not valid") + } + + module.forward(inputActivity) + gradWeight.zero() + module.accGradParameters(inputActivity, grads) + output = module.gradWeight + output + } +} + +private[bigdl] object Conv2DBackFilter { + def apply[T: ClassTag]( + strideW: Int, + strideH: Int, + padW: Int = -1, + padH: Int = -1, + format: DataFormat = DataFormat.NCHW + )(implicit ev: TensorNumeric[T]): Conv2DBackFilter[T] = + new Conv2DBackFilter(strideW, strideH, padW, padH, format) +} + +private[bigdl] class Conv3D[T: ClassTag]( + dT: Int, dH: Int, dW: Int, + padT: Int, padH: Int, padW: Int, + format: DataFormat) + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private val fInput = Tensor[T]() + + + override def updateOutput(inputs: Table): Tensor[T] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + val filter: Tensor[T] = inputs[Tensor[T]](2) + + val kT = filter.size(1) + val kH = filter.size(2) + val kW = filter.size(3) + val nInputPlane = filter.size(4) + val nOutputPlane = filter.size(5) + + val transInput = if (format == DataFormat.NHWC) { + var buffer = input + buffer = buffer.transpose(2, 5) + buffer = buffer.transpose(3, 5) + buffer = buffer.transpose(4, 5) + buffer = buffer.contiguous() + + buffer + } else { + input + } + + var transWeight = filter.transpose(1, 5) + transWeight = transWeight.transpose(2, 4) + transWeight = transWeight.transpose(3, 5) + transWeight = transWeight.contiguous() + val weightMM = transWeight.view(nOutputPlane, nInputPlane * kT * kH * kW) + + VolumetricConvolution.conv3d(transInput, output, weightMM, bias = null, onesBias = null, fInput, + nInputPlane, nOutputPlane, withBias = false, kT, kW, kH, dT, dW, dH, padT, padW, padH) + + if (format == DataFormat.NHWC) { + output = output.transpose(2, 5) + output = output.transpose(2, 4) + output = output.transpose(2, 3) + output = output.contiguous() + } + output + } + + override def clearState(): Conv3D.this.type = { + super.clearState() + fInput.set() + this + } +} + +private[bigdl] object Conv3D { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3D[T] + = new Conv3D[T](dT, dH, dW, padT, padH, padW, format) +} + +private[bigdl] class Conv3DBackpropFilter[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private val fInput = Tensor[T]() + + + protected def getParams(inputs: Table): (Int, Int, Int, Int, Int) = { + val filter: Tensor[T] = inputs[Tensor[T]](2) + + val kT = filter.size(1) + val kH = filter.size(2) + val kW = filter.size(3) + val nInputPlane = filter.size(4) + val nOutputPlane = filter.size(5) + + (kT, kH, kW, nInputPlane, nOutputPlane) + } + override def updateOutput(inputs: Table): Tensor[T] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + val outputBackprop: Tensor[T] = inputs[Tensor[T]](3) + + val (transInput, transOutBackprop) = if (format == DataFormat.NHWC) { + // backpropInput only use input size, so we do not need it to be contiguous + val in = input.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() + val out = outputBackprop.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() + (in, out) + } else { + (input, outputBackprop) + } + + val (kT, kH, kW, nInputPlane, nOutputPlane) = getParams(inputs) + + val gradWeightMM = Tensor[T](nOutputPlane, nInputPlane * kT * kH * kW) + + VolumetricConvolution.populateFInput(transInput, fInput, nInputPlane, nOutputPlane, + kT, kW, kH, dT, dW, dH, padT, padW, padH) + + VolumetricConvolution.conv3DBackpropFilter(transInput, transOutBackprop, gradWeightMM, + null, fInput, 1.0, 1.0, false) + + output = if (format == DataFormat.NHWC) { + val gradWeight = gradWeightMM.view(nOutputPlane, nInputPlane, kT, kH, kW) + gradWeight.transpose(1, 5).transpose(2, 4).transpose(1, 3).contiguous() + } else { + gradWeightMM.view(nOutputPlane, nInputPlane, kT, kH, kW) + } + + output + } + + override def clearState(): Conv3DBackpropFilter.this.type = { + super.clearState() + fInput.set() + this + } +} + +private[bigdl] object Conv3DBackpropFilter { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3DBackpropFilter[T] + = new Conv3DBackpropFilter[T](dT, dH, dW, padT, padH, padW, format) +} + +private[bigdl] class Conv3DBackpropFilterV2[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat) + (implicit ev: TensorNumeric[T]) + extends Conv3DBackpropFilter[T](dT, dH, dW, padT, padH, padW, format) { + + override protected def getParams(inputs: Table): (Int, Int, Int, Int, Int) = { + val filterSize: Tensor[Int] = inputs[Tensor[Int]](2) + + val kT = filterSize.valueAt(1) + val kH = filterSize.valueAt(2) + val kW = filterSize.valueAt(3) + val nInputPlane = filterSize.valueAt(4) + val nOutputPlane = filterSize.valueAt(5) + + (kT, kH, kW, nInputPlane, nOutputPlane) + } +} + +private[bigdl] object Conv3DBackpropFilterV2 { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3DBackpropFilterV2[T] + = new Conv3DBackpropFilterV2[T](dT, dH, dW, padT, padH, padW, format) +} + +private[bigdl] class Conv3DBackpropInput[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + private val fGradInput = Tensor[T]() + + protected def getInputSize(inputs: Table): Array[Int] = { + val input: Tensor[T] = inputs[Tensor[T]](1) + + if (format == DataFormat.NHWC) { + val N = input.size(1) + val D = input.size(2) + val H = input.size(3) + val W = input.size(4) + val C = input.size(5) + Array(N, C, D, H, W) + } else { + val N = input.size(1) + val C = input.size(2) + val D = input.size(3) + val H = input.size(4) + val W = input.size(5) + Array(N, C, D, H, W) + } + } + + override def updateOutput(inputs: Table): Tensor[T] = { + + val filter: Tensor[T] = inputs[Tensor[T]](2) + val outputBackprop: Tensor[T] = inputs[Tensor[T]](3) + + val transOutBackprop = if (format == DataFormat.NHWC) { + // backpropInput only use input size, so we do not need it to be contiguous + outputBackprop.transpose(2, 5).transpose(3, 5).transpose(4, 5).contiguous() + } else { + outputBackprop + } + + val transInputSize = getInputSize(inputs) + + val kT = filter.size(1) + val kH = filter.size(2) + val kW = filter.size(3) + val nInputPlane = filter.size(4) + val nOutputPlane = filter.size(5) + + var transWeight = filter.transpose(1, 5) + transWeight = transWeight.transpose(2, 4) + transWeight = transWeight.transpose(3, 5) + transWeight = transWeight.contiguous() + val weightMM = transWeight.view(nOutputPlane, nInputPlane * kT * kH * kW) + + VolumetricConvolution.conv3DBackpropInput(transInputSize, output, transOutBackprop, + weightMM, fGradInput, kT, kW, kH, dT, dW, dH, padT, padW, padH) + + if (format == DataFormat.NHWC) { + output = output.transpose(2, 5) + output = output.transpose(2, 3) + output = output.transpose(3, 4) + output = output.contiguous() + } + output + } + + override def clearState(): Conv3DBackpropInput.this.type = { + super.clearState() + fGradInput.set() + this + } +} + +private[bigdl] object Conv3DBackpropInput { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3DBackpropInput[T] + = new Conv3DBackpropInput[T](dT, dH, dW, padT, padH, padW, format) +} + +private[bigdl] class Conv3DBackpropInputV2[T: ClassTag](dT: Int, dH: Int, dW: Int, + padT: Int, padH: Int, padW: Int, + format: DataFormat) + (implicit ev: TensorNumeric[T]) + extends Conv3DBackpropInput[T](dT, dH, dW, padT, padH, padW, format) { + + private val fGradInput = Tensor[T]() + + override protected def getInputSize(inputs: Table): Array[Int] = { + val inputSize: Tensor[Int] = inputs[Tensor[Int]](1) + + if (format == DataFormat.NHWC) { + val N = inputSize.valueAt(1) + val D = inputSize.valueAt(2) + val H = inputSize.valueAt(3) + val W = inputSize.valueAt(4) + val C = inputSize.valueAt(5) + Array(N, C, D, H, W) + } else { + val N = inputSize.valueAt(1) + val C = inputSize.valueAt(2) + val D = inputSize.valueAt(3) + val H = inputSize.valueAt(4) + val W = inputSize.valueAt(5) + Array(N, C, D, H, W) + } + } + + override def clearState(): Conv3DBackpropInputV2.this.type = { + super.clearState() + fGradInput.set() + this + } +} + +private[bigdl] object Conv3DBackpropInputV2 { + def apply[T: ClassTag]( + dT: Int, + dH: Int, + dW: Int, + padT: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): Conv3DBackpropInputV2[T] + = new Conv3DBackpropInputV2[T](dT, dH, dW, padT, padH, padW, format) +} + +private[bigdl] abstract class UnaryGrad[T: ClassTag, D: ClassTag]( + gradFirst: Boolean = false, + needForward: Boolean = false) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + type Module = AbstractModule[Tensor[D], Tensor[D], _] + + val module: Module + + override def updateOutput(input: Table): Tensor[D] = { + val (grads, inputs) = if (gradFirst) { + (input[Tensor[D]](1), input[Tensor[D]](2)) + } else { + (input[Tensor[D]](2), input[Tensor[D]](1)) + } + + if (needForward) { + module.forward(inputs) + } + + output = module.updateGradInput(inputs, grads).toTensor[D] + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } +} + +private[bigdl] class Relu6Grad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends UnaryGrad[T, D](true) { + + val module: Module = ReLU6Layer[D]() +} + +private[bigdl] object Relu6Grad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Relu6Grad[T, D] = + new Relu6Grad[T, D]() +} + +private[bigdl] class EluGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends UnaryGrad[T, D](true, true) { + + override val module: Module = ELULayer[D]() +} + +private[bigdl] object EluGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): EluGrad[T, D] = new EluGrad[T, D]() +} + +private[bigdl] class TanhGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T]{ + + private val module = Tanh[D]() + override def updateOutput(input: Table): Tensor[D] = { + val (y, grads) = (input[Tensor[D]](1), input[Tensor[D]](2)) + + output = module.updateGradInputInternal(y, grads).toTensor[D] + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T], scala.reflect.classTag[D]), Array(ev, ev2)) + } +} + +private[bigdl] object TanhGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): TanhGrad[T, D] = + new TanhGrad[T, D]() +} + +private[bigdl] class SoftplusGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends UnaryGrad[T, D](true, true) { + + override val module: Module = SoftPlusLayer[D]() + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } +} + +private[bigdl] object SoftplusGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SoftplusGrad[T, D] = + new SoftplusGrad[T, D]() +} + +private[bigdl] class SoftsignGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends UnaryGrad[T, D](true) { + + override val module: Module = SoftSignLayer[D]() +} + +private[bigdl] object SoftsignGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SoftsignGrad[T, D] = + new SoftsignGrad[T, D]() +} + +private[bigdl] class SigmoidGrad[T: ClassTag, D: ClassTag] +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + + private val module = Sigmoid[D]() + override def updateOutput(input: Table): Tensor[D] = { + val (y, grads) = (input[Tensor[D]](1), input[Tensor[D]](2)) + + output = module.updateGradInputInternal(y, grads).toTensor[D] + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } +} + +private[bigdl] object SigmoidGrad { + def apply[T: ClassTag, D: ClassTag]() + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SigmoidGrad[T, D] = + new SigmoidGrad[T, D]() +} + +private[bigdl] class MaxPool[T: ClassTag]( + val ksize: Array[Int], + val strides: Array[Int], + val padding: String, + val format: DataFormat = DataFormat.NHWC +)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { + val pool: SpatialMaxPooling[T] = format match { + case DataFormat.NHWC => + if (padding == "SAME") { + SpatialMaxPooling( + kH = ksize(1), + kW = ksize(2), + dH = strides(1), + dW = strides(2), + padH = -1, + padW = -1, + format = format + ) + } else if (padding == "VALID") { + SpatialMaxPooling( + kH = ksize(1), + kW = ksize(2), + dH = strides(1), + dW = strides(2), + format = format + ) + } else { + throw new RuntimeException("Padding can only support SAME and VALID padding") + } + case DataFormat.NCHW => + if (padding == "SAME") { + SpatialMaxPooling( + kH = ksize(2), + kW = ksize(3), + dH = strides(2), + dW = strides(3), + padH = -1, + padW = -1, + format = format + ) + } else if (padding == "VALID") { + SpatialMaxPooling( + kH = ksize(2), + kW = ksize(3), + dH = strides(2), + dW = strides(3), + format = format + ) + } else { + throw new RuntimeException("Padding can only support SAME and VALID padding") + } + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + output = pool.updateOutput(input) + output + } +} + +private[bigdl] object MaxPool { + def apply[T: ClassTag]( + ksize: Array[Int], + strides: Array[Int], + padding: String, + format: DataFormat = DataFormat.NHWC + )(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] + = ModuleToOperation[T](new MaxPool(ksize, strides, padding, format)) +} + +private[bigdl] class MaxPoolGrad[T: ClassTag]( + kH: Int, + kW: Int, + strideW: Int, + strideH: Int, + padH: Int, + padW: Int, + format: DataFormat +)(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[T], T]{ + + private var module : SpatialMaxPooling[T] = _ + + override def updateOutput(input: Table): Tensor[T] = { + if (module == null) { + module = SpatialMaxPooling[T]( + kH, + kW, + strideH, + strideW, + padH, + padW, + format + ) + } + + val inputData = input[Tensor[T]](1) + val gradOutput = input[Tensor[T]](3) + module.updateOutput(inputData) + output = module.updateGradInput(inputData, gradOutput) + output + } +} + +private[bigdl] object MaxPoolGrad { + def apply[T: ClassTag]( + kH: Int, + kW: Int, + strideW: Int, + strideH: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): MaxPoolGrad[T] = + new MaxPoolGrad(kH, kW, strideW, strideH, padH, padW, format) +} + +/** + * LRNGrad calculate the backprop gradients of the Local response normalization layer. + * + * @param depthRadius + * @param bias + * @param alpha + * @param beta + * @param ev$1 + * @param ev + * @param ev2 + * @tparam T Numeric type. Only support float/double now + */ +private[bigdl] class LRNGrad[T: ClassTag]( + depthRadius: Int = 5, + bias: Float = 1.0f, + alpha: Float = 1.0f, + beta: Float = 0.5f +)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[Float]) + extends Operation[Table, Tensor[Float], T] { + + output = Tensor[Float]() + + override def updateOutput(input: Table): Tensor[Float] = { + val gradOutput = input[Tensor[Float]](1) + val inputTensor = input[Tensor[Float]](2) + val outputTensor = input[Tensor[Float]](3) + + output.resizeAs(inputTensor) + var b = 1 + while(b <= inputTensor.size(1)) { + SpatialCrossMapLRN.backwardFrameNHWCFloat( + gradOutput.select(1, b), + inputTensor.select(1, b), + output.select(1, b), + outputTensor.select(1, b), + alpha * (2 * depthRadius + 1), 2 * depthRadius + 1, beta, bias + ) + b += 1 + } + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T]), + Array[TensorNumeric[_]](ev, ev2)) + } +} + +private[bigdl] object LRNGrad { + def apply[T: ClassTag]( + depthRadius: Int = 5, + bias: Float = 1.0f, + alpha: Float = 1.0f, + beta: Float = 0.5f + )(implicit ev: TensorNumeric[T]): LRNGrad[T] + = new LRNGrad(depthRadius, bias, alpha, beta) +} + +/** + * This is similar to SpatialBatchNormalization. + * + * When isTraining is true, it takes three tensors as inputs, which is image, + * scale, offset. + * + * The operation implemented is: + * + * ( image - batch-mean(x) ) + * y = ---------------------------------- * weight + offset + * batch-standard-deviation(x) + * + * The operation will output y, mean and variance tensors. + * + * If the isTraining is false, it takes five tensors as inputs, which is image, scale, offset, mean, + * and variance. + * + * @param epsilon + * @param isTraining + * @param momentum + * @param dataFormat + * @param ev$1 + * @param ev + * @tparam T Numeric type. Only support float/double now + */ +private[bigdl] class FusedBatchNorm[T: ClassTag]( + epsilon: Float = 0.0001f, + isTraining: Boolean = true, + momentum: Float = 0.1f, + dataFormat: DataFormat = DataFormat.NHWC +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Table, T]{ + + @transient + private var runningMean: Tensor[Float] = null + + @transient + private var runningVar: Tensor[Float] = null + + @transient + private var saveStd: Tensor[Float] = null + + override def updateOutput(input: Table): Table = { + val x = input[Tensor[Float]](1) + val scale = input[Tensor[Float]](2) + val offset = input[Tensor[Float]](3) + val mean = input[Tensor[Float]](4) + val variance = input[Tensor[Float]](5) + + if (output.length() == 0) { + output(1) = Tensor[Float]().resizeAs(x) // y + output(2) = Tensor[Float](x.size(4)) // batch mean + output(3) = Tensor[Float](x.size(4)) // batch var + output(4) = Tensor[Float](x.size(4)) // save mean + output(5) = Tensor[Float](x.size(4)) // save var + runningMean = Tensor[Float](x.size(4)) // running mean + runningVar = Tensor[Float](x.size(4)) // running var + saveStd = Tensor[Float](x.size(4)) // save std + } + + val y = output[Tensor[Float]](1) + val batchMean = output[Tensor[Float]](2) + val batchVar = output[Tensor[Float]](3) + val saveMean = output[Tensor[Float]](4) + val saveVar = output[Tensor[Float]](5) + + if (isTraining) { + if (dataFormat == DataFormat.NHWC) { + SpatialBatchNormalization.updateOutputNHWCTrainFloat( + x, y, batchMean, saveStd, runningMean, runningVar, scale, offset, epsilon, momentum, + batchVar, saveVar + ) + } else { + SpatialBatchNormalization.updateOutputNCHWTrainFloat( + x, y, batchMean, saveStd, runningMean, runningVar, scale, offset, epsilon, momentum, + batchVar, saveVar + ) + } + saveMean.copy(batchMean) + } else { + if (dataFormat == DataFormat.NHWC) { + SpatialBatchNormalization.updateOutputNHWCInferFloat( + x, y, mean, variance, scale, offset, epsilon + ) + } else { + SpatialBatchNormalization.updateOutputNCHWInferFloat( + x, y, mean, variance, scale, offset, epsilon + ) + } + } + + output + } +} + +private[bigdl] object FusedBatchNorm { + def apply[T: ClassTag](epsilon: Float = 0.0001f, isTrainning: Boolean = true, + momentum: Float = 0.1f, dataFormat: DataFormat = DataFormat.NHWC) + (implicit ev: TensorNumeric[T]): FusedBatchNorm[T] + = new FusedBatchNorm(epsilon, isTrainning, momentum, dataFormat) +} + +/** + * This is the gradient operation coressponding to the FusedBatchNorm. It will calculate the + * activity, weight and bias gradients of the spatial batch normalization. + * + * The formula is + * x_backprop = scale * rsqrt(variance + epsilon) * [y_backprop - mean(y_backprop) - + * (x - mean(x)) * mean(y_backprop * (x - mean(x))) / (variance + epsilon)] + * weight_backprop = sum(y_backprop * (x - mean(x)) * rsqrt(variance + epsilon)) + * bias_backprop = sum(y_backprop) + * + * @param epsilon + * @param dataFormat + * @param isTrain + * @param ev$1 + * @param ev + * @tparam T Numeric type. Only support float/double now + */ +private[bigdl] class FusedBatchNormGrad[T: ClassTag]( + val epsilon: Float, val dataFormat: DataFormat, + val isTrain: Boolean = false)(implicit ev: TensorNumeric[T]) + extends Operation[Table, Table, T]{ + + + private val gMean = Tensor[Float]() + private val gxMean = Tensor[Float]() + private val saveStd = Tensor[Float]() + + override def updateOutput(input: Table): Table = { + val gradOutput = input[Tensor[Float]](1) + val x = input[Tensor[Float]](2) + val scale = input[Tensor[Float]](3) + val saveMean = input[Tensor[Float]](4) + val saveVar = input[Tensor[Float]](5) + + if (output.length() == 0) { + output(1) = Tensor[Float]().resizeAs(x) // gradInput + output(2) = Tensor[Float](x.size(4)) // weight gradient + output(3) = Tensor[Float](x.size(4)) // bias gradient + saveStd.resize(x.size(4)) // bias gradient + } + saveStd.copy(saveVar) + saveStd.add(epsilon).pow(-0.5f) + val gradInput = output[Tensor[Float]](1) + val gradWeight = output[Tensor[Float]](2) + val gradBias = output[Tensor[Float]](3) + + SpatialBatchNormalization.updateGradInputNHWCTrainFloat( + x, gradOutput, gradInput, scale, saveMean, saveStd, gMean, gxMean) + + gradWeight.zero() + gradBias.zero() + SpatialBatchNormalization.accGradientNHWCFloat( + gradOutput, gradWeight, gradBias, x, saveMean, saveStd, 1.0f, 1.0f) + + output + } +} + +private[bigdl] object FusedBatchNormGrad { + def apply[T: ClassTag](epsilon: Float = 0.0001f, dataFormat: DataFormat = DataFormat.NHWC, + isTraining: Boolean = true)(implicit ev: TensorNumeric[T]): FusedBatchNormGrad[T] = + new FusedBatchNormGrad(epsilon, dataFormat, isTraining) +} + +private[bigdl] class AvgPoolGrad[T: ClassTag]( + kH: Int, + kW: Int, + strideW: Int, + strideH: Int, + padH: Int, + padW: Int, + format: DataFormat +)(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[T], T]{ + + private var module : SpatialAveragePooling[T] = _ + + override def updateOutput(input: Table): Tensor[T] = { + if (module == null) { + module = SpatialAveragePooling[T]( + kH, + kW, + strideH, + strideW, + padH, + padW, + countIncludePad = false, + format = format + ) + } + + val inputDataSize = input[Tensor[Int]](1).storage().array() + + val gradOutput = input[Tensor[T]](2) + output = module.updateGradInputInternal(inputDataSize, gradOutput) + output + } +} + +private[bigdl] object AvgPoolGrad { + def apply[T: ClassTag]( + kH: Int, + kW: Int, + strideW: Int, + strideH: Int, + padH: Int, + padW: Int, + format: DataFormat + )(implicit ev: TensorNumeric[T]): AvgPoolGrad[T] = + new AvgPoolGrad(kH, kW, strideW, strideH, padH, padW, format) +} + +private[bigdl] class BiasAddGrad[T: ClassTag](dataFormat: DataFormat) + (implicit ev: TensorNumeric[T]) + extends Operation[Tensor[T], Tensor[T], T] { + + private val module = BiasAdd() + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + getBiasDims(input) + output.resizeAs(input).copy(input) + dataFormat match { + case DataFormat.NCHW => + output = output.resize(Array(batch, channel, height, width)).sum(1) + output = output.sum(3) + output = output.sum(4) + case DataFormat.NHWC => + output = output.resize(Array(batch * height * width, channel)).sum(1) + } + output + } + + private var batch : Int = 1 + private var channel : Int = 1 + private var width : Int = 1 + private var height : Int = 1 + + private def getBiasDims(tensor: Tensor[_]): Unit = { + batch = 1 + channel = 1 + width = 1 + height = 1 + dataFormat match { + case DataFormat.NHWC => + val channelDim = tensor.dim() + channel = tensor.size(channelDim) + var i = 1 + while(i < channelDim) { + batch *= tensor.size(i) + i += 1 + } + case DataFormat.NCHW => + val channelDim = tensor.dim() - 2 + val heightDim = tensor.dim() - 1 + val widthDim = tensor.dim() + channel = tensor.size(channelDim) + height = tensor.size(heightDim) + width = tensor.size(widthDim) + var i = 1 + while(i < channelDim) { + batch *= tensor.size(i) + i += 1 + } + } + } +} + +private[bigdl] object BiasAddGrad { + def apply[T: ClassTag](dataFormat: DataFormat) + (implicit ev: TensorNumeric[T]): BiasAddGrad[T] = new BiasAddGrad(dataFormat) +} + +private[bigdl] class ReluGrad[T: ClassTag](implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[T], T]{ + + val module = ReLULayer[T]() + + override def updateOutput(input: Table): Tensor[T] = { + val grads = input[Tensor[T]](1) + val inputs = input[Tensor[T]](2) + + output = module.updateGradInput(inputs, grads).toTensor[T] + output + } +} + +private[bigdl] object ReluGrad { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): ReluGrad[T] = new ReluGrad() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NoOp.scala similarity index 88% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NoOp.scala index 6d9a130bb9a..ae8cf2bfe69 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/NoOp.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NoOp.scala @@ -13,11 +13,10 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.nn.tf.WithoutInput -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.T diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StateOps.scala similarity index 68% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StateOps.scala index a5fe4e93fc6..0b487312998 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Assign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StateOps.scala @@ -13,15 +13,42 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.ops.Operation +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{T, Table} import scala.reflect.ClassTag + +private[bigdl] class Variable[T: ClassTag]( + val variableValue: Tensor[T], + val variableGradient: Tensor[T] +)(implicit ev: TensorNumeric[T]) + extends Operation[Activity, Tensor[T], T] with WithoutInput{ + + override def clearState(): this.type = { + this + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (Array(this.variableValue), Array(this.variableGradient)) + } + + override def updateOutput(input: Activity): Tensor[T] = { + this.output.resizeAs(variableValue) + this.output.copy(variableValue) + output + } + + override def accGradParameters(input: Activity, gradOutput: Tensor[T]): Unit = { + this.variableGradient.add(ev.fromType[Double](1.0), gradOutput) + } +} + /** * Update 'ref' by assigning 'value' to it. * @@ -41,7 +68,7 @@ import scala.reflect.ClassTag * * @tparam T Numeric type. Only support float/double now */ -class Assign[T: ClassTag]( +private[bigdl] class Assign[T: ClassTag]( val validateShape: Boolean = true, val useLocking: Boolean = true ) @@ -76,7 +103,11 @@ class Assign[T: ClassTag]( } } -object Assign { - def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): Operation[Activity, Activity, T] - = ModuleToOperation[T](new Assign()) +private[bigdl] class AssignGrad[T: ClassTag](grad: Tensor[T])(implicit ev: TensorNumeric[T]) + extends Operation[Tensor[T], Activity, T]{ + + override def updateOutput(input: Tensor[T]): Activity = { + grad.copy(input) + null + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala deleted file mode 100644 index ceb3c3552d2..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Variable.scala +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.tf - -import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.nn.ops.Operation -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{T, Table} - -import scala.reflect.ClassTag - - -class Variable[T: ClassTag](val variableValue: Tensor[T], val variableGradient: Tensor[T]) - (implicit ev: TensorNumeric[T]) - extends Operation[Activity, Tensor[T], T] with WithoutInput{ - - override def clearState(): this.type = { - this - } - - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { - (Array(this.variableValue), Array(this.variableGradient)) - } - - override def updateOutput(input: Activity): Tensor[T] = { - this.output.resizeAs(variableValue) - this.output.copy(variableValue) - output - } - - override def accGradParameters(input: Activity, gradOutput: Tensor[T]): Unit = { - this.variableGradient.add(ev.fromType[Double](1.0), gradOutput) - } -} - -object Variable { - def apply[T: ClassTag](variableValue: Tensor[T], variableGradient: Tensor[T]) - (implicit ev: TensorNumeric[T]): Variable[T] = new Variable(variableValue, variableGradient) -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index ddeeae5a1e6..0726993c197 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -19,8 +19,8 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer, Model, Sequential => KSequential} -import com.intel.analytics.bigdl.nn.ops.{DecodeRawSerializer, RandomUniform => RandomUniformOps} -import com.intel.analytics.bigdl.nn.tf.{StrideSlice, ParseExample} +import com.intel.analytics.bigdl.nn.ops.{RandomUniform => RandomUniformOps} +import com.intel.analytics.bigdl.nn.tf.{StrideSlice, ParseExample, DecodeRawSerializer} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -209,7 +209,7 @@ object ModuleSerializer extends ModuleSerializable{ quantized.Linear) registerModule("com.intel.analytics.bigdl.nn.tf.ParseExample", ParseExample) registerModule("com.intel.analytics.bigdl.nn.SReLU", SReLU) - registerModule("com.intel.analytics.bigdl.nn.ops.DecodeRaw", DecodeRawSerializer) + registerModule("com.intel.analytics.bigdl.nn.tf.DecodeRaw", DecodeRawSerializer) registerModule("com.intel.analytics.bigdl.nn.ops.RandomUniform", RandomUniformOps) registerModule("com.intel.analytics.bigdl.nn.tf.StrideSlice", StrideSlice) registerModule("com.intel.analytics.bigdl.nn.MultiRNNCell", MultiRNNCell) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 28d7eb443f3..df4decdd620 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -25,7 +25,7 @@ import com.google.protobuf.{CodedInputStream, TextFormat} import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.AssignGrad +import com.intel.analytics.bigdl.nn.tf.AssignGrad import com.intel.analytics.bigdl.python.api.{JTensor, PythonBigDL, PythonBigDLUtils} import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala index 68af428add1..1b1cd64a660 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Assert.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{Assert => AssertOperation} +import com.intel.analytics.bigdl.nn.tf.{Assert => AssertOperation} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala index 71bdfab6162..2986ca0db8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.AvgPoolGrad +import com.intel.analytics.bigdl.nn.tf.AvgPoolGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala index 15c0b22744d..34ab7ee738b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala @@ -20,7 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.BiasAddGrad +import com.intel.analytics.bigdl.nn.tf.BiasAddGrad import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala index b3f68b0acc8..4ebde3352bf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Identity -import com.intel.analytics.bigdl.nn.ops.BroadcastGradientArgs +import com.intel.analytics.bigdl.nn.tf.{BroadcastGradientArgs => BroadcastGradientArgsOps} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context @@ -33,6 +33,6 @@ class BroadcastGradientArgs extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - BroadcastGradientArgs[T]() + new BroadcastGradientArgsOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala index d9e25965d79..e84ccf073af 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.Conv2D +import com.intel.analytics.bigdl.nn.tf.Conv2D import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala index 66e2c446eb0..eb38f512b1e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala @@ -20,7 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.Conv2DBackFilter +import com.intel.analytics.bigdl.nn.tf.Conv2DBackFilter import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala index 70f6258d7d8..8850fef064b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropInput.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.Conv2DTranspose +import com.intel.analytics.bigdl.nn.tf.Conv2DTranspose import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala index edcb6ae3261..9990ff47ac4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala @@ -20,7 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{Conv2D, Conv3D} +import com.intel.analytics.bigdl.nn.tf.Conv3D import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Node import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala index cffefcb1c56..dfc60c01150 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.Conv3DBackpropFilter +import com.intel.analytics.bigdl.nn.tf.Conv3DBackpropFilter import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala index 48ace2b70ab..43020369e78 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.Conv3DBackpropFilterV2 +import com.intel.analytics.bigdl.nn.tf.Conv3DBackpropFilterV2 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala index 7b39b52da34..279f04fe542 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.Conv3DBackpropInput +import com.intel.analytics.bigdl.nn.tf.Conv3DBackpropInput import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala index aa0f3ddeb8b..c3a1f0f336b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.Conv3DBackpropInputV2 +import com.intel.analytics.bigdl.nn.tf.Conv3DBackpropInputV2 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala index ff5924d80b0..460ce105d8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeGif.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{DecodeGif => DecodeGifOp} +import com.intel.analytics.bigdl.nn.tf.{DecodeGif => DecodeGifOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala index cd21675e35c..ec0edf29147 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeJpeg.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{DecodeJpeg => DecodeJpegOp} +import com.intel.analytics.bigdl.nn.tf.{DecodeJpeg => DecodeJpegOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala index a94e91710be..091370cc237 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodePng.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{DecodePng => DecodePngOp} +import com.intel.analytics.bigdl.nn.tf.{DecodePng => DecodePngOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala index bb9e7f5f20d..0d32e486cc1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeRaw.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{DecodeRaw => DecodeRawOp} +import com.intel.analytics.bigdl.nn.tf.{DecodeRaw => DecodeRawOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala index b1e901eaff9..eb822e367fd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{Conv2D, DepthwiseConv2D} +import com.intel.analytics.bigdl.nn.ops.DepthwiseConv2D import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala index aea85fa898b..5027e8a830a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.EluGrad +import com.intel.analytics.bigdl.nn.tf.EluGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala index a75d2de1ca8..ba9c78dca7a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.FusedBatchNorm +import com.intel.analytics.bigdl.nn.tf.FusedBatchNorm import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala index a0156923b88..35aab007ba2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.FusedBatchNormGrad +import com.intel.analytics.bigdl.nn.tf.FusedBatchNormGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala index 6e29af5f646..7a83f7e32b6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.FusedBatchNormGrad +import com.intel.analytics.bigdl.nn.tf.FusedBatchNormGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala index 9498b7104ee..87a17d9e3de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.FusedBatchNorm +import com.intel.analytics.bigdl.nn.tf.FusedBatchNorm import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala index 355e2d78a8a..84104a9ebfb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.LRNGrad +import com.intel.analytics.bigdl.nn.tf.LRNGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala index 213923938a5..422d1784aa4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.MaxPoolGrad +import com.intel.analytics.bigdl.nn.tf.MaxPoolGrad import com.intel.analytics.bigdl.nn.{Identity, SpatialMaxPooling} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala index 8cc7ddf823b..a7bfa90343e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{EluGrad, Relu6Grad} +import com.intel.analytics.bigdl.nn.tf.{EluGrad, Relu6Grad} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala index 55561eb3d93..9a6b49330ed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.ReluGrad +import com.intel.analytics.bigdl.nn.tf.ReluGrad import com.intel.analytics.bigdl.nn.{Identity, ReLU} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala index a98b4bf04ea..e0945bb21b4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.RsqrtGrad +import com.intel.analytics.bigdl.nn.tf.RsqrtGrad import com.intel.analytics.bigdl.nn.tf.Log1p import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala index d150092ef70..efc61370339 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.SigmoidGrad +import com.intel.analytics.bigdl.nn.tf.SigmoidGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala index fbdb46e4237..f3e156090b3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.SoftPlus -import com.intel.analytics.bigdl.nn.ops.SoftplusGrad +import com.intel.analytics.bigdl.nn.tf.SoftplusGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala index 7fc140e9036..e0364420fe2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{SoftplusGrad, SoftsignGrad} +import com.intel.analytics.bigdl.nn.tf.{SoftplusGrad, SoftsignGrad} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala index feb913fe96f..35a1edb4928 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{RsqrtGrad, SqrtGrad} +import com.intel.analytics.bigdl.nn.tf.SqrtGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala index 3f1f29fb567..d0c87e5d8d1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.TanhGrad +import com.intel.analytics.bigdl.nn.tf.TanhGrad import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala index c264c3784a4..928bed5c181 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala @@ -35,6 +35,6 @@ class VariableV2 extends TensorflowOpsLoader{ context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val w = context(nodeDef.getName)._1 val g = context(nodeDef.getName)._2 - Variable[T](w, g) + new Variable[T](w, g) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhSpec.scala index 394acfd6448..c71f0dfba8c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TanhSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn.ops.TanhGrad +import com.intel.analytics.bigdl.nn.tf.TanhGrad import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala index cb9df4c0d44..ffbfae2d257 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.nn.tf.Assign import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -41,7 +42,7 @@ class CastSpec extends FlatSpec with Matchers { val expectOutput = Tensor(T(2.0, 2.0, 4.0)) - val output = Assign().forward(input) + val output = new Assign().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssignSpec.scala similarity index 92% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssignSpec.scala index 23fdb2fbd9a..00fc01ec3f8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AssignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssignSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T @@ -31,7 +31,7 @@ class AssignSpec extends FlatSpec with Matchers { val expectOutput = Tensor(T(2f, 2f, 4f)) - val output = Assign().forward(input) + val output = new Assign().forward(input) output should be(expectOutput) } @@ -45,7 +45,7 @@ class AssignSpec extends FlatSpec with Matchers { val expectOutput = Tensor(T(2.0f, 2.0f, 4.0f)) - val output = Assign[Double]().forward(input) + val output = new Assign[Double]().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BroadcastGradientArgsSpec.scala similarity index 89% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BroadcastGradientArgsSpec.scala index b862133f4e6..6105b4c1edb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BroadcastGradientArgsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BroadcastGradientArgsSpec.scala @@ -13,8 +13,9 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf +import com.intel.analytics.bigdl.nn.ops.ModuleToOperation import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest @@ -31,7 +32,7 @@ class BroadcastGradientArgsSpec extends FlatSpec with Matchers { val expectOutput = T(Tensor(T(0)), Tensor(T(2))) - val output = BroadcastGradientArgs().forward(input) + val output = new BroadcastGradientArgs().forward(input) output should be(expectOutput) } @@ -45,7 +46,7 @@ class BroadcastGradientArgsSpec extends FlatSpec with Matchers { val expectOutput = T(Tensor(T(0)), Tensor(T(0, 1, 2))) - val output = BroadcastGradientArgs().forward(input) + val output = new BroadcastGradientArgs().forward(input) output should be(expectOutput) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DSpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DSpec.scala index a83e5768be1..dc03e8eeaab 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DSep.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DSpec.scala @@ -13,13 +13,13 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} -class Conv2DSep extends FlatSpec with Matchers { +class Conv2DSpec extends FlatSpec with Matchers { "Add operation" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericDouble val expectOutput = Tensor( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DTransposeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DTransposeSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DTransposeSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DTransposeSpec.scala index ba3acd5711c..17049e665fb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv2DTransposeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DTransposeSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropFilterV2Spec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2Spec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropFilterV2Spec.scala index 1ff116809d0..7118b86c8cb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropFilterV2Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropFilterV2Spec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropInputV2Spec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2Spec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropInputV2Spec.scala index 82a8e39e070..4ebd86134d7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DBackpropInputV2Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropInputV2Spec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DSerialTest.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DSerialTest.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DSerialTest.scala index b25d99c7e3f..b80ee2a31cc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Conv3DSerialTest.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DSerialTest.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeImageSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeImageSpec.scala index 9b6ebac716f..84d25063a42 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeImageSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeImageSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.utils.tf.TFRecordIterator import org.scalatest.{FlatSpec, Matchers} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeJpegSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeJpegSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeJpegSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeJpegSpec.scala index 6d775058b3d..e50fef7ca0c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeJpegSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeJpegSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import java.io.File diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeRawSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeRawSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeRawSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeRawSpec.scala index 87d55a7af6f..a669308ffac 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DecodeRawSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeRawSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import java.io.File diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGradSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolGradSerialTest.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGradSerialTest.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolGradSerialTest.scala index f3faac8de2d..348432ca1c0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolGradSerialTest.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolGradSerialTest.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolSpec.scala index 44b4ba7d5dd..fd9d58b12ec 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxPoolSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6GradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Relu6GradSpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6GradSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Relu6GradSpec.scala index 975fe380405..6df4ca2c535 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Relu6GradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Relu6GradSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SigmoidGradSpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGradSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SigmoidGradSpec.scala index 09deafc02dc..e5e9fdd4e58 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SigmoidGradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SigmoidGradSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SoftplusGradSpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGradSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SoftplusGradSpec.scala index ef27ef8f955..4167271a3e9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SoftplusGradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SoftplusGradSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.nn.ops +package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala index 7cba8634432..9fddede6fba 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala @@ -25,8 +25,8 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, Cast, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, Minimum, Mod, ModuleToOperation, NoOp, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, TanhGrad, TopK, TruncateDiv, TruncatedNormal, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} -import com.intel.analytics.bigdl.nn.tf.{BiasAdd, Const, Fill, Log1p, Shape, SplitAndSelect, StrideSlice, Variable, TensorModuleWrapper, ControlNodes, ParseExample} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, Cast, Ceil, CrossEntropy, Digamma, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, Sign, Slice, SquaredDifference, Substr, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.tf.{BiasAdd, BroadcastGradientArgs, Const, ControlNodes, Fill, Log1p, ParseExample, Shape, SoftplusGrad, SoftsignGrad, SplitAndSelect, SqrtGrad, StrideSlice, TensorModuleWrapper, Variable, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor._ @@ -1500,19 +1500,11 @@ class ModuleSerializerSpec extends SerializerSpecHelper { "Variable serializer" should "work properly" in { val out = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) val grad = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - val variable = Variable[Float](out, grad).setName("variable") + val variable = new Variable[Float](out, grad).setName("variable") val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) runSerializationTest(variable, input) } - // tf.loaders - - - - - - - "DetectionOutputSSD serializer" should "work properly" in { val module = DetectionOutputSSD[Float](DetectionOutputParam()).setName("DetectionOutputSSD") val name = module.getName diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index e5b52ee14bd..d71bc9cb12e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -21,7 +21,8 @@ import java.io.{File => JFile} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BiasAddGrad, BroadcastGradientArgs, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, CrossEntropy, DecodeImage, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, EluGrad, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, LRNGrad, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, MaxPool, MaxPoolGrad, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, Relu6Grad, ReluGrad, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, RsqrtGrad, SegmentSum, SigmoidGrad, Sign, Slice, SoftplusGrad, SoftsignGrad, SqrtGrad, SquaredDifference, Substr, TanhGrad, TopK, TruncateDiv, TruncatedNormal, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, Sign, Slice, SquaredDifference, Substr, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.tf.{Assert => AssertOps, BroadcastGradientArgs => BroadcastGradientArgsOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} import com.intel.analytics.bigdl.tensor._ @@ -90,7 +91,7 @@ class OperationSerializerSpec extends SerializerSpecHelper { "Assert serializer" should "work properly" in { import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - val assert = new Assert[Float]().setName("assert") + val assert = new AssertOps[Float]().setName("assert") val predictTensor = Tensor[Boolean](Array(1)) predictTensor.setValue(1, true) val msg = Tensor[ByteString](Array(1)) @@ -143,7 +144,7 @@ class OperationSerializerSpec extends SerializerSpecHelper { } "BroadcastGradientArgs serializer" should "work properly" in { - val broadcastGradientArgs = BroadcastGradientArgs[Float](). + val broadcastGradientArgs = BroadcastGradientArgsOps[Float](). setName("broadcastGradientArgs") val input = T( @@ -582,7 +583,7 @@ class OperationSerializerSpec extends SerializerSpecHelper { } "NoOp serializer" should "work properly" in { - val noOp = new com.intel.analytics.bigdl.nn.ops.NoOp[Float]().setName("noOp") + val noOp = new com.intel.analytics.bigdl.nn.tf.NoOp[Float]().setName("noOp") val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) runSerializationTest(noOp, input) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala index 17c4121bac8..1fae8729311 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpecHelper.scala @@ -20,7 +20,8 @@ import java.io.{File} import java.lang.reflect.Modifier import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.{DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.tf.{DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _} import com.intel.analytics.bigdl.utils.{Shape => KShape} From ef1862a63c33b3fcb06d60c6fd2add3785dbedf0 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 28 Feb 2018 13:52:16 +0800 Subject: [PATCH 0705/1065] support getting node and core number in Python (#2333) * support getting node and core number on python * python style * address comments --- .../analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 579c8e309b4..f30f57cc255 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2338,6 +2338,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Engine.init } + def getNodeAndCoreNumber(): Array[Int] = { + Array(Engine.nodeNumber(), Engine.coreNumber()) + } + def setWeights(model: AbstractModule[Activity, Activity, T], weights: JList[JTensor]): Unit = { val weightTensor = weights.asScala.toArray.map(toTensor(_)) From 5aec118d7bdb5568b76b90e39f66235f33be53df Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 28 Feb 2018 14:52:31 +0800 Subject: [PATCH 0706/1065] fix sparse tensor dot product bug (#2327) * fix sparse tensor dot product bug * fix test --- .../bigdl/dllib/tensor/SparseTensorBLAS.scala | 16 ++++++++-------- .../bigdl/dllib/tensor/SparseTensorSpec.scala | 16 ++++++++++++++-- 2 files changed, 22 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala index bfb10fff7b4..fc32d8aecfd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala @@ -55,14 +55,14 @@ object SparseTensorBLAS { var sum: Float = 0.0f while (valueCounter < vec2.nElement()) { var dim = 0 - var vec2Index = 0 + var vec1Index = 0 while (dim < vec2.nDimension) { - vec2Index += (vec2._indices(dim)(valueCounter + vec2storageOffset) - + vec1Index += (vec2._indices(dim)(valueCounter + vec2storageOffset) - vec2._indicesOffset(dim)) * vect1Strides(dim) dim += 1 } - sum += vec2Values(vec1StorageOffset + vec2Index) * - vec1Values(valueCounter + vec1StorageOffset) + sum += vec2Values(vec2storageOffset + valueCounter) * + vec1Values(vec1Index + vec1StorageOffset) valueCounter += 1 } sum @@ -84,15 +84,15 @@ object SparseTensorBLAS { var sum: Double = 0.0f while (valueCounter < vec2.nElement()) { var dim = 0 - var vec2Index = 0 + var vec1Index = 0 while (dim < vec2.nDimension) { - vec2Index += + vec1Index += (vec2._indices(dim)(valueCounter + vec2storageOffset) - vec2._indicesOffset(dim)) * vect1Strides(dim) dim += 1 } - sum += vec2Values(vec1StorageOffset + vec2Index) * - vec1Values(valueCounter + vec1StorageOffset) + sum += vec2Values(vec2storageOffset + valueCounter) * + vec1Values(vec1Index + vec1StorageOffset) valueCounter += 1 } sum diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala index 120f24b9df9..f63fe286ea4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala @@ -129,12 +129,24 @@ class SparseTensorSpec extends FlatSpec with Matchers { val values = Array.fill(30)(Random.nextFloat()) val sTensor = Tensor.sparse(Tensor(values, Array(6, 5))) - val dTensor = Tensor(Array(6, 5)) + val dTensor = Tensor(Array(6, 5)).rand() val sparseResult = sTensor.dot(dTensor) val denseResult = dTensor.dot(Tensor.dense(sTensor)) - sparseResult should be (denseResult) + sparseResult should be (denseResult +- 1e-6f) + } + + "Diagonal SparseTensor dot DenseTense" should "return right result" in { + val sTensor = Tensor.sparse( + indices = Array(Array(0, 1, 2, 3), Array(0, 1, 2, 3)), + values = Array[Float](2f, 4f, 6f, 8f), shape = Array(4, 4)) + + val dTensor = Tensor(Array(4, 4)).fill(1.0f) + + val sparseResult = sTensor.dot(dTensor) + + sparseResult should be (20f +- 1e-6f) } } From e750b79773a0fed91eff698466e71368397fbdd5 Mon Sep 17 00:00:00 2001 From: tosky001 Date: Thu, 1 Mar 2018 08:03:01 +0800 Subject: [PATCH 0707/1065] add [[IndicatorCol]] Operation (#2297) * add IndicatorCol Operation * simplify and optimize the IndicatorCol operation * simplify and Optimize the Operation again * modify the IndicatorCol Ops * verify that the parameter feaLen is vaild --- .../bigdl/dllib/nn/ops/IndicatorCol.scala | 95 +++++++++++++++++++ .../bigdl/dllib/nn/ops/IndicatorColSpec.scala | 67 +++++++++++++ .../serializer/OperationSerializerSpec.scala | 16 +++- 3 files changed, 177 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorCol.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorColSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorCol.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorCol.scala new file mode 100644 index 00000000000..157ae7b4a2c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorCol.scala @@ -0,0 +1,95 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.{SparseType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Indicator operation represents multi-hot representation of given Tensor. + * + * The Input Tensor should be a 2-D Sparse Tensor. + * And used to transform the output tensor of CategoricalCol* ops. + * + * The output tensor should be a DenseTensor with shape (batch, feaLen). + * + * For example, A input SparseTensor as follows: + * indices(0) = Array(0, 0, 1, 2, 2) + * indices(1) = Array(0, 3, 1, 1, 2) + * values = Array(1, 2, 2, 3, 3) + * shape = Array(3, 4) + * + * the output tensor should be an 2D 3x4 DenseTensor with isCount = true + * 0.0, 1.0, 1.0, 0.0 + * 0.0, 0.0, 1.0, 0.0 + * 0.0, 0.0, 0.0, 2.0 + * + * @param feaLen The length of feature. + * @param isCount whether count the times of same value, default: true. + * @tparam T Numeric type. Parameter tensor numeric type. Only support float/double now + */ + +class IndicatorCol[T: ClassTag]( + val feaLen: Int, + val isCount: Boolean = true +) (implicit ev: TensorNumeric[T]) + extends Operation[Tensor[Int], Tensor[T], T]{ + + output = Tensor[T]() + + override def updateOutput(input: Tensor[Int]): Tensor[T] = { + + require(input.getTensorType == SparseType, "Only sparse input is supported") + + val rows = input.size(dim = 1) + val resTensor = Tensor[T](rows, feaLen) + + var i = 1 + while (i <= rows) { + val narrowTensor = input.narrow(1, i, 1) + val tempArr = narrowTensor.storage().array().slice( + narrowTensor.storageOffset()-1, narrowTensor.storageOffset() - 1 + narrowTensor.nElement()) + var j = 0 + while (j < tempArr.length) { + require(tempArr(j) < feaLen, "the parameter feaLen is set too small") + isCount match { + case false => + resTensor.setValue(i, tempArr(j) + 1, ev.one) + case true => + val res = ev.toType[Int](resTensor.valueAt(i, tempArr(j) + 1)) + 1 + resTensor.setValue(i, tempArr(j) + 1, ev.fromType[Int](res)) + } + j += 1 + } + i += 1 + } + output = resTensor + output + } +} + +object IndicatorCol { + def apply[T: ClassTag]( + feaLen: Int, + isCount: Boolean = true + )(implicit ev: TensorNumeric[T]): IndicatorCol[T] + = new IndicatorCol[T]( + feaLen = feaLen, + isCount = isCount + ) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorColSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorColSpec.scala new file mode 100644 index 00000000000..5e468d206e8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorColSpec.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class IndicatorColSpec extends FlatSpec with Matchers { + + "IndicatorColSpec Operation with isCount=true" should "work correctly" in { + + val input = Tensor.sparse( + Array(Array(0, 1, 1, 2, 2, 3, 3, 3), + Array(0, 0, 3, 0, 1, 0, 1, 2)), + Array(3, 1, 2, 0, 3, 1, 2, 2), + Array(4, 4) + ) + + val expectedOutput = Tensor[Double]( + T(T(0, 0, 0, 1), + T(0, 1, 1, 0), + T(1, 0, 0, 1), + T(0, 1, 2, 0))) + + val output = IndicatorCol[Double]( + feaLen = 4, isCount = true + ).forward(input) + + output should be(expectedOutput) + } + + "IndicatorColSpec Operation with isCount=false" should "work correctly" in { + + val input = Tensor.sparse( + Array(Array(0, 1, 1, 2, 2, 3, 3, 3), + Array(0, 0, 3, 0, 1, 0, 1, 2)), + Array(3, 1, 2, 0, 3, 1, 2, 2), + Array(4, 4) + ) + + val expectedOutput = Tensor[Float]( + T(T(0, 0, 0, 1), + T(0, 1, 1, 0), + T(1, 0, 0, 1), + T(0, 1, 1, 0))) + + val output = IndicatorCol[Float]( + feaLen = 4, isCount = false + ).forward(input) + + output should be(expectedOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index d71bc9cb12e..7039f575ff0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -21,7 +21,7 @@ import java.io.{File => JFile} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, Sign, Slice, SquaredDifference, Substr, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, IndicatorCol, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, Sign, Slice, SquaredDifference, Substr, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf.{Assert => AssertOps, BroadcastGradientArgs => BroadcastGradientArgsOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} @@ -400,6 +400,20 @@ class OperationSerializerSpec extends SerializerSpecHelper { .asInstanceOf[ModuleToOperation[Float]].module.getClass) } + "Indicator serializer" should "work properly" in { + val indicatorCol = IndicatorCol[Float]( + feaLen = 4, + isCount = true + ).setName("indicatorCol") + val input = Tensor.sparse( + Array(Array(0, 1, 1, 2, 2, 3, 3, 3), + Array(0, 0, 3, 0, 1, 0, 1, 2)), + Array(3, 1, 2, 0, 3, 1, 2, 2), + Array(4, 4) + ) + runSerializationTest(indicatorCol, input) + } + "InTopK serializer" should "work properly" in { val inTopK = InTopK[Float](2).setName("inTopK") val input1 = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) From 94d08b42751038191c17b90ea8190ee45550044c Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 1 Mar 2018 16:00:45 +0800 Subject: [PATCH 0708/1065] fix maxout (#2337) --- .../analytics/bigdl/dllib/nn/Maxout.scala | 30 +++++++++++++++++-- .../utils/serializer/ModuleSerializer.scala | 1 + 2 files changed, 28 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala index 3169fb41e53..3db7db55cef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala @@ -16,10 +16,13 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, ModuleSerializer, SerializeContext} import com.intel.analytics.bigdl.utils.{Shape, Table} import scala.reflect.ClassTag @@ -45,7 +48,7 @@ class Maxout[T: ClassTag](val inputSize: Int, val outputSize: Int, val maxoutNum val bRegularizer: Regularizer[T] = null, val initWeight: Tensor[T] = null, val initBias: Tensor[T] = null) (implicit ev: TensorNumeric[T]) extends TensorModule[T] { - val layer = Sequential().add(Linear(inputSize, outputSize * maxoutNumber, withBias = withBias, + var layer = Sequential().add(Linear(inputSize, outputSize * maxoutNumber, withBias = withBias, wRegularizer = wRegularizer, bRegularizer = bRegularizer, initWeight = initWeight, initBias = initBias)) .add(View(maxoutNumber, outputSize).setNumInputDims(1)) @@ -81,11 +84,32 @@ class Maxout[T: ClassTag](val inputSize: Int, val outputSize: Int, val maxoutNum } } -object Maxout { +object Maxout extends ModuleSerializable { def apply[T : ClassTag](inputSize: Int, outputSize: Int, maxoutNumber: Int, withBias: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: Tensor[T] = null, initBias: Tensor[T] = null) (implicit ev: TensorNumeric[T]): Maxout[T] = new Maxout[T](inputSize, outputSize, maxoutNumber, withBias, wRegularizer, bRegularizer, initWeight, initBias) + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val maxout = super.doLoadModule(context).asInstanceOf[Maxout[T]] + val attrMap = context.bigdlModule.getAttrMap + val layerAttr = attrMap.get("layer") + maxout.layer = DataConverter.getAttributeValue(context, layerAttr). + asInstanceOf[Sequential[T]] + maxout + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + maxoutBuilder : BigDLModule.Builder) + (implicit ev: TensorNumeric[T]) : Unit = { + super.doSerializeModule(context, maxoutBuilder) + val layerBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, layerBuilder, context.moduleData. + module.asInstanceOf[Maxout[T]].layer, + ModuleSerializer.abstractModuleType) + maxoutBuilder.putAttr("layer", layerBuilder.build) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 0726993c197..6c5a584bc32 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -183,6 +183,7 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.keras.Model", Model) registerModule("com.intel.analytics.bigdl.nn.keras.Sequential", KSequential) registerModule("com.intel.analytics.bigdl.nn.MapTable", MapTable) + registerModule("com.intel.analytics.bigdl.nn.Maxout", Maxout) registerModule("com.intel.analytics.bigdl.nn.MaskedSelect", MaskedSelect) registerModule("com.intel.analytics.bigdl.nn.Recurrent", Recurrent) registerModule("com.intel.analytics.bigdl.nn.RecurrentDecoder", RecurrentDecoder) From ee28bd4a75043777233a70c5859d6e23ea76196a Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Thu, 1 Mar 2018 16:01:36 +0800 Subject: [PATCH 0709/1065] Optimize padding mechanism (#2324) * optimize padding options * restore classNLLCriterion comments * meet code review * add more tests * meet code review * meet code review * meet code review * Add python api * fix a bug * fix a bug --- .../bigdl/dllib/nn/ClassNLLCriterion.scala | 32 ++-- .../bigdl/dllib/nn/LookupTable.scala | 17 +- .../analytics/bigdl/dllib/nn/Recurrent.scala | 158 +++++++++++++++- .../bigdl/dllib/nn/TimeDistributed.scala | 52 ++++- .../nn/TimeDistributedMaskCriterion.scala | 178 ++++++++++++++++++ .../nn/abstractnn/AbstractCriterion.scala | 8 + .../dllib/utils/python/api/PythonBigDL.scala | 5 + .../dllib/nn/ClassNLLCriterionSpec.scala | 6 +- .../bigdl/dllib/nn/LookupTableSpec.scala | 16 ++ .../bigdl/dllib/nn/RecurrentSpec.scala | 53 +++++- .../nn/TimeDistributedMaskCriterionSpec.scala | 85 +++++++++ 11 files changed, 574 insertions(+), 36 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedMaskCriterion.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedMaskCriterionSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala index 482d82ee056..5f856983300 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion +import com.intel.analytics.bigdl.nn.abstractnn.SizeAverageStatus.SizeAverageStatus +import com.intel.analytics.bigdl.nn.abstractnn.{SizeAverageStatus, TensorCriterion} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.Tensor @@ -24,6 +25,7 @@ import scala.concurrent.duration.Duration import scala.concurrent.{Await, Future} import scala.reflect.ClassTag import com.intel.analytics.bigdl.utils.Engine +import org.apache.hadoop.mapreduce.v2.app.speculate.TaskRuntimeEstimator /** * The negative log likelihood criterion. It is useful to train a classification problem with n @@ -46,7 +48,7 @@ import com.intel.analytics.bigdl.utils.Engine * Due to the behaviour of the backend code, it is necessary to set sizeAverage to false when * calculating losses in non-batch mode. * - * Note that if the target is `-1`, the training process will skip this sample. + * Note that if the target is `paddingValue`, the training process will skip this sample. * In other words, the forward process will return zero output and the backward process * will also return zero `gradInput`. * @@ -65,7 +67,8 @@ import com.intel.analytics.bigdl.utils.Engine */ @SerialVersionUID(- 8696382776046599502L) class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] -(weights: Tensor[T] = null, sizeAverage: Boolean = true, logProbAsInput: Boolean = true) +(weights: Tensor[T] = null, sizeAverage: Boolean = true, + logProbAsInput: Boolean = true, paddingValue: Int = -1) (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { private var total_weight = ev.fromType[Int](0) if (weights != null) require(weights.dim() == 1, @@ -81,7 +84,7 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] private val oneMinusEpsilon: T = ev.minus(ev.one, epsilon) - + sizeAverageStatus = if (sizeAverage) SizeAverageStatus.True else SizeAverageStatus.False override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { require(input.dim() == 1 || input.dim() == 2, @@ -94,10 +97,10 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] "ClassNLLCriterion: " + ErrorInfo.constrainInputDimSameAsTarget + s" Input dimension is: ${ input.dim() } , target dimension is: ${ target.dim() }") val curTarget = ev.toType[Int](target.valueAt(1)) - assert(curTarget >= 1 && curTarget <= nClasses || curTarget == -1, + assert(curTarget >= 1 && curTarget <= nClasses || curTarget == paddingValue, s"curTarget ${curTarget} is out of range, should be 1 to ${nClasses}") total_weight = if (weights != null) weights(Array(curTarget)) else ev.fromType[Int](1) - output = if (curTarget == -1) ev.zero + output = if (curTarget == paddingValue) ev.zero else { if (!logProbAsInput) { val clipped = ev.clip(input.valueAt(curTarget), epsilon, oneMinusEpsilon) @@ -106,14 +109,13 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] ev.times(ev.negative(input.valueAt(curTarget)), total_weight) } } - } else if (input.dim() == 2) { val batchSize = input.size(1) val targetSize = target.size() target.squeeze() require(target.dim() == 1, "ClassNLLCriterion: illegal target! Target should be 1D tensor after squeeze," + - s"but target's dimension is: ${ target.dim() }, please check your data.") + s"but target's size is: ${ target.size() }, please check your data.") total_weight = ev.fromType[Int](0) output = ev.fromType[Int](0) @@ -127,9 +129,9 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] val _i = i results(_i - 1) = Engine.model.invoke( () => { val curTarget = ev.toType[Int](target.valueAt(_i)) - assert(curTarget >= 1 && curTarget <= nClasses || curTarget == -1, + assert(curTarget >= 1 && curTarget <= nClasses || curTarget == paddingValue, s"curTarget ${curTarget} is out of range 1 to ${nClasses}") - if (curTarget == -1) (ev.zero, ev.one) + if (curTarget == paddingValue) (ev.zero, ev.zero) else { val curWeight = if (weights != null) weights.valueAt(curTarget) else ev.fromType[Int](1) if (!logProbAsInput) { @@ -173,7 +175,7 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] "ClassNLLCriterion: " + ErrorInfo.constrainInputDimSameAsTarget + s" Input dimension is: ${ input.dim() } , target dimension is: ${ target.dim() }") val curTarget = ev.toType[Int](target.valueAt(1)) - if (curTarget == -1) return gradInput + if (curTarget == paddingValue) return gradInput gradInput.setValue(curTarget, if (weights != null) ev.times(ev.fromType[Int](-1), weights.valueAt(curTarget)) else ev.fromType[Int](-1)) @@ -198,7 +200,7 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] val _i = i resultsBackward(_i - 1) = Engine.model.invoke(() => { val curTarget = ev.toType[Int](target.valueAt(_i)) - if (curTarget != -1) { + if (curTarget != paddingValue) { gradInput.setValue(_i, curTarget, if (weights != null) ev.times(ev.fromType[Int](-1), weights.valueAt(curTarget)) else ev.fromType[Int](-1)) @@ -229,7 +231,9 @@ object ClassNLLCriterion { def apply[@specialized(Float, Double) T: ClassTag]( weights: Tensor[T] = null, sizeAverage: Boolean = true, - logProbAsInput: Boolean = true)(implicit ev: TensorNumeric[T]) : ClassNLLCriterion[T] = { - new ClassNLLCriterion[T](weights, sizeAverage, logProbAsInput) + logProbAsInput: Boolean = true, + paddingValue: Int = -1 + )(implicit ev: TensorNumeric[T]) : ClassNLLCriterion[T] = { + new ClassNLLCriterion[T](weights, sizeAverage, logProbAsInput, paddingValue) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala index ef2d59f17c0..cee2de7a970 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala @@ -40,6 +40,8 @@ import scala.reflect.ClassTag * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] * @param wRegularizer: instance of [[Regularizer]] * (eg. L1 or L2 regularization), applied to the input weights matrices. + * @param maskZero: if maskZero is set to true, the input whose value equals `paddingValue` + * the output will be masked to zero vector. */ @SerialVersionUID( - 4832171200145114633L) class LookupTable[T: ClassTag] @@ -47,12 +49,13 @@ class LookupTable[T: ClassTag] val maxNorm: Double = Double.MaxValue, val normType: Double = 2.0, shouldScaleGradByFreq: Boolean = false, - var wRegularizer: Regularizer[T] = null + var wRegularizer: Regularizer[T] = null, + val maskZero: Boolean = false ) (implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { - val weight = Tensor[T](nIndex, nOutput) - val gradWeight = Tensor[T](nIndex, nOutput).zero() + var weight = Tensor[T](nIndex, nOutput) + var gradWeight = Tensor[T](nIndex, nOutput).zero() private var inputBuffer = Tensor[T]() private var normBuffer = Tensor[T]() @@ -165,6 +168,9 @@ class LookupTable[T: ClassTag] } override def updateOutput(input: Tensor[T]): Tensor[T] = { + if (maskZero && paddingValue != 0) { + weight.select(1, paddingValue.toInt).zero() + } require(input.dim() == 1 || input.dim() == 2, s"LookupTable: ${ErrorInfo.constrainInputAsVectorOrBatch}, input dim [${input.dim()}]" ) renorm(input) @@ -298,11 +304,12 @@ object LookupTable { nIndex: Int, nOutput: Int, paddingValue: Double = 0, maxNorm: Double = Double.MaxValue, normType: Double = 2.0, shouldScaleGradByFreq: Boolean = false, - wRegularizer: Regularizer[T] = null + wRegularizer: Regularizer[T] = null, + maskZero: Boolean = false ) (implicit ev: TensorNumeric[T]): LookupTable[T] = new LookupTable[T](nIndex, nOutput, paddingValue, - maxNorm, normType, shouldScaleGradByFreq, wRegularizer) + maxNorm, normType, shouldScaleGradByFreq, wRegularizer, maskZero) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index cc6aca58398..9137b460135 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.{ContainerSerializable, ModuleSerializer} -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{T, Table} import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import serialization.Bigdl.{AttrValue, BigDLModule} @@ -34,8 +34,20 @@ import scala.reflect.ClassTag /** * [[Recurrent]] module is a container of rnn cells * Different types of rnn cells can be added using add() function + * + * The recurrent includes some mask mechanisms + * if the `maskZero` variable is set to true, the `Recurrent` module will + * not consider zero vector inputs. For each time step input, if a certain row is + * a zero vector (all the elements of the vector equals zero), then output of certain row + * of this time step would be a zero vector, and the hidden state of the certain row of + * this time step would be the same as the corresponding row of the hidden state of the + * previous step. + * */ -class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) +class Recurrent[T : ClassTag]( + var batchNormParams: BatchNormParams[T] = null, + var maskZero: Boolean = false +) (implicit ev: TensorNumeric[T]) extends DynamicContainer[Tensor[T], Tensor[T], T] { protected var hidden: Activity = null @@ -60,6 +72,12 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) private val timeBuffer = new ArrayBuffer[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] private var layer: TensorModule[T] = null + private var maskBuffer: Tensor[T] = Tensor() + private var gradOutputBuff: Table = T() + private var indexBuffer: Tensor[T] = Tensor() + private var inputBuffer: Tensor[T] = Tensor() + private var outputBuffers: ArrayBuffer[Tensor[T]] = ArrayBuffer(Tensor()) + private var minLength: Int = 0 /** * @@ -82,7 +100,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) topology = module.asInstanceOf[Cell[T]] preTopology = if (topology.preTopology != null) { - TimeDistributed(topology.preTopology) + TimeDistributed(topology.preTopology, maskZero = maskZero) } else topology.preTopology if (batchNormParams != null && preTopology == null) { @@ -160,6 +178,7 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) while (t < times) { cells += cloneCell.cloneModule() .asInstanceOf[Cell[T]] + outputBuffers.append(Tensor()) t += 1 } share(cells) @@ -245,6 +264,12 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) // Clone N modules along the sequence dimension. initHidden(outputSize.drop(2)) cloneCells() + if (maskZero) { + require(input.dim == 3, + "If maskZero set to true, input should be a 3D Tensor, e.g [batch, times, nDim]") + inputBuffer.resizeAs(input).abs(input).max(maskBuffer, indexBuffer, 3) + minLength = ev.toType[Int](maskBuffer.sign().sum(2).min(1)._1(Array(1, 1, 1))) + } currentInput(hidDim) = if (initHiddenState != null) initHiddenState else hidden @@ -252,7 +277,26 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) while (i <= times) { currentInput(inputDim) = Recurrent.selectCopy(input2Cell, i, stepInput2CellBuf) cells(i - 1).forward(currentInput) - currentInput(hidDim) = cells(i - 1).output.toTable(hidDim) + val curOutput = cells(i - 1).output + if (maskZero && i > minLength) { + val curMask = maskBuffer.select(2, i) + val curOut = curOutput[Table](hidDim)[Tensor[T]](1) + // Copy output to a new new tensor as output, because for some cells + // such as LSTM the hidden h and ouput o refer to the same tensor. + // But in this case, we want h and o have difference values. + curOutput.update(inputDim, outputBuffers(i - 1).resizeAs(curOut).copy(curOut)) + for (b <- 1 to curMask.size(1)) { + if (curMask(Array(b, 1)) == ev.zero) { + val newState = curOutput[Table](hidDim) + val originState = currentInput[Table](hidDim) + for (j <- 1 to newState.length()) { + newState[Tensor[T]](j).select(1, b).copy(originState[Tensor[T]](j).select(1, b)) + } + curOutput[Tensor[T]](inputDim).select(1, b).zero() + } + } + } + currentInput(hidDim) = curOutput[Table](hidDim) i += 1 } @@ -299,7 +343,36 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) } else { cells(i - 1).regluarized(false) } - cells(i - 1).accGradParameters(_input, currentGradOutput) + + if (maskZero && i > minLength) { + val curMask = maskBuffer.select(2, i) + if (gradOutputBuff.length() == 0) { + Utils.recursiveResizeAs(gradOutputBuff, currentGradOutput) + } + Utils.recursiveCopy(gradOutputBuff, currentGradOutput) + for (b <- 1 to curMask.size(1)) { + if (curMask(Array(b, 1)) == ev.zero) { + val originState = gradOutputBuff[Table](Recurrent.hidDim) + for (j <- 1 to originState.length()) { + originState[Tensor[T]](j).select(1, b).zero() + } + } + } + + cells(i - 1).accGradParameters(_input, currentGradOutput) + + for (b <- 1 to curMask.size(1)) { + if (curMask(Array(b, 1)) == ev.zero) { + val newState = cells(i - 1).gradInput[Table](hidDim) + val originState = currentGradOutput[Table](hidDim) + for (j <- 1 to newState.length()) { + newState[Tensor[T]](j).select(1, b).copy(originState[Tensor[T]](j).select(1, b)) + } + } + } + } else { + cells(i - 1).accGradParameters(_input, currentGradOutput) + } currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) i -= 1 } @@ -330,7 +403,36 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) _input(hidDim) = if (i > 1) cells(i - 2).output.toTable(hidDim) else if (initHiddenState == null) hidden else initHiddenState _input(inputDim) = Recurrent.selectCopy(input2Cell, i, stepInput2CellBuf) - cells(i - 1).updateGradInput(_input, currentGradOutput) + + if (maskZero && i > minLength) { + val curMask = maskBuffer.select(2, i) + if (gradOutputBuff.length() == 0) { + Utils.recursiveResizeAs(gradOutputBuff, currentGradOutput) + } + Utils.recursiveCopy(gradOutputBuff, currentGradOutput) + for (b <- 1 to curMask.size(1)) { + if (curMask(Array(b, 1)) == ev.zero) { + val originState = gradOutputBuff[Table](Recurrent.hidDim) + for (j <- 1 to originState.length()) { + originState[Tensor[T]](j).select(1, b).zero() + } + } + } + + cells(i - 1).updateGradInput(_input, currentGradOutput) + + for (b <- 1 to curMask.size(1)) { + if (curMask(Array(b, 1)) == ev.zero) { + val newState = cells(i - 1).gradInput[Table](hidDim) + val originState = currentGradOutput[Table](hidDim) + for (j <- 1 to newState.length()) { + newState[Tensor[T]](j).select(1, b).copy(originState[Tensor[T]](j).select(1, b)) + } + } + } + } else { + cells(i - 1).updateGradInput(_input, currentGradOutput) + } currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) i -= 1 } @@ -353,12 +455,42 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) else if (initHiddenState == null) hidden else initHiddenState _input(inputDim) = Recurrent.selectCopy(input2Cell, i, stepInput2CellBuf) + if (i == 1) { cells(i - 1).regluarized(true) } else { cells(i - 1).regluarized(false) } - cells(i - 1).backward(_input, currentGradOutput) + + if (maskZero && i > minLength) { + val curMask = maskBuffer.select(2, i) + if (gradOutputBuff.length() == 0) { + Utils.recursiveResizeAs(gradOutputBuff, currentGradOutput) + } + Utils.recursiveCopy(gradOutputBuff, currentGradOutput) + for (b <- 1 to curMask.size(1)) { + if (curMask(Array(b, 1)) == ev.zero) { + val originState = gradOutputBuff[Table](Recurrent.hidDim) + for (j <- 1 to originState.length()) { + originState[Tensor[T]](j).select(1, b).zero() + } + } + } + + cells(i - 1).backward(_input, gradOutputBuff).toTable + + for (b <- 1 to curMask.size(1)) { + if (curMask(Array(b, 1)) == ev.zero) { + val newState = cells(i - 1).gradInput[Table](hidDim) + val originState = currentGradOutput[Table](hidDim) + for (j <- 1 to newState.length()) { + newState[Tensor[T]](j).select(1, b).copy(originState[Tensor[T]](j).select(1, b)) + } + } + } + } else { + cells(i - 1).backward(_input, currentGradOutput) + } currentGradOutput(hidDim) = cells(i - 1).gradInput.toTable(hidDim) i -= 1 } @@ -458,6 +590,12 @@ class Recurrent[T : ClassTag](var batchNormParams: BatchNormParams[T] = null) initHiddenState = null stepInput2CellBuf.set() stepGradBuffer.set() + maskBuffer.set() + gradOutputBuff.clear() + inputBuffer.set() + indexBuffer.set() + outputBuffers.clear() + minLength = 0 this } @@ -500,9 +638,11 @@ object Recurrent extends ContainerSerializable { val hidDim = 2 def apply[@specialized(Float, Double) T: ClassTag]( - batchNormParams: BatchNormParams[T] = null) + batchNormParams: BatchNormParams[T] = null, + maskZero: Boolean = false + ) (implicit ev: TensorNumeric[T]) : Recurrent[T] = { - new Recurrent[T](batchNormParams) + new Recurrent[T](batchNormParams, maskZero = maskZero) } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala index 272978113d7..7e7c6158209 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala @@ -35,10 +35,14 @@ import scala.reflect.ClassTag * The input data format is [Batch, Time, Other dims]. For the contained layer, it must not change * the Other dims length. * + * @param maskZero: if `maskZero` is set to true, if the input including zero vectors, the + * corresponding output will be set to zero vecotrs. * @tparam T data type, which can be [[Double]] or [[Float]] */ -class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor[T], T]) +class TimeDistributed[T : ClassTag] ( + val layer: AbstractModule[Tensor[T], Tensor[T], T], + maskZero: Boolean = false) (implicit ev: TensorNumeric[T]) extends TensorModule[T] { private var inputSize: Array[Int] = _ @@ -46,6 +50,9 @@ class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor private var outputSize: Array[Int] = _ private val timeBuffer = new ArrayBuffer[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] + private var maskBuffer: Tensor[T] = _ + private var indexBuffer: Tensor[T] = _ + private var inputBuffer: Tensor[T] = _ private def combine(src: Array[Int], target: Array[Int]): Unit = { require(src.length == target.length + 1, @@ -103,6 +110,27 @@ class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor split(_output.size, outputSize, _inputSize(0), _inputSize(1)) input.resize(_inputSize) output.set(_output).resize(outputSize) + + if (maskZero) { + if (maskBuffer == null) { + maskBuffer = Tensor() + } + if (indexBuffer == null) { + indexBuffer = Tensor() + } + if (inputBuffer == null) { + inputBuffer = Tensor() + } + inputBuffer.resizeAs(input).abs(input).max(maskBuffer, indexBuffer, 3)._1 + for (i <- 1 to maskBuffer.size(1)) { + for (j <- 1 to maskBuffer.size(2)) { + if (maskBuffer(Array(i, j, 1)) == ev.zero) { + output.select(1, i).select(1, j).zero() + } + } + } + } + output } @@ -148,6 +176,17 @@ class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor input.resize(_inputSize) gradOutput.resize(_gradOutputSize) backwardTime += System.nanoTime - st + + if (maskZero) { + for (i <- 1 to maskBuffer.size(1)) { + for (j <- 1 to maskBuffer.size(2)) { + if (maskBuffer(Array(i, j, 1)) == ev.zero) { + gradInput.select(1, i).select(1, j).zero() + } + } + } + } + gradInput } @@ -217,6 +256,9 @@ class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor gradOutputSize = null outputSize = null timeBuffer.clear + maskBuffer = null + inputBuffer = null + indexBuffer = null this } @@ -243,8 +285,10 @@ class TimeDistributed[T : ClassTag] (val layer: AbstractModule[Tensor[T], Tensor } object TimeDistributed { - def apply[@specialized(Float, Double) T: ClassTag](layer: AbstractModule[Tensor[T], Tensor[T], T]) - (implicit ev: TensorNumeric[T]): TimeDistributed[T] = { - new TimeDistributed[T](layer) + def apply[@specialized(Float, Double) T: ClassTag]( + layer: AbstractModule[Tensor[T], Tensor[T], T], + maskZero: Boolean = false + )(implicit ev: TensorNumeric[T]): TimeDistributed[T] = { + new TimeDistributed[T](layer, maskZero) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedMaskCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedMaskCriterion.scala new file mode 100644 index 00000000000..ea3732dce8c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedMaskCriterion.scala @@ -0,0 +1,178 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{SizeAverageStatus, TensorCriterion} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Engine + +import scala.collection.mutable.ArrayBuffer +import scala.concurrent.Future +import scala.reflect.ClassTag + +/** + * This class is intended to support inputs with 3 or more dimensions. + * Apply Any Provided Criterion to every temporal slice of an input. + * In addition, it supports padding mask. + * + * eg. if the target is [ [-1, 1, 2, 3, -1], [5, 4, 3, -1, -1] ], + * and set the paddingValue property to -1, then the loss of -1 would not + * be accumulated and the loss is only divided by 6 (ont including the amount of + * -1, in this case, we are only interested in 1, 2, 3, 5, 4, 3) + * + * @param critrn embedded criterion + * @param paddingValue padding value + */ + +class TimeDistributedMaskCriterion[T : ClassTag]( + val critrn : TensorCriterion[T], + val paddingValue: Int = 0 +) + (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { + + val dimension: Int = 2 + private var fInput: Tensor[T] = Tensor[T]() + private var fTarget: Tensor[T] = Tensor[T]() + private var _gradInput = Tensor[T]() // list of cell criterions cloned from added criterion + private val cells: ArrayBuffer[TensorCriterion[T]] + = ArrayBuffer[TensorCriterion[T]]() + + @transient + protected var results: Array[Future[Unit]] = _ + private val mask = Tensor[T]() + private val sumBuffer = Tensor[T]() + private val gradInputBuffer = Tensor[T]() + + /** + * Clone N criterions; N depends on the time dimension of the input + * @param times + */ + private def extend(times: Int): Unit = { + var t = cells.length + while (t < times) { + cells += critrn.cloneCriterion() + .asInstanceOf[TensorCriterion[T]] + t += 1 + } + } + + + override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { + /** + * Take each time slice of input and target, and add up all outputs of slices + * Example with dimension=2: + * input.size = [B, T, D] => fInput.size = [B, D] + * target.size = [B, T] => fTarget.size = [B] + */ + require(input.size(dimension) == target.size(dimension), + "target should have as many elements as input, " + + s"input ${input.size(dimension)}, target ${target.size(dimension)}") + + output = ev.fromType[Int](0) + val nstep = input.size(dimension) + extend(nstep) + + if (results == null || results.length != nstep) { + results = new Array[Future[Unit]](nstep) + } + + var i = 0 + while (i < nstep) { + val _i = i + 1 + results(i) = Engine.model.invoke(() => { + fInput = input.select(dimension, _i) + fTarget = target.select(dimension, _i) + cells(_i - 1).updateOutput(fInput, fTarget) + }) + i += 1 + } + Engine.model.sync(results) + + mask.resizeAs(target) + mask.applyFun[T](target, x => + if (x != ev.fromType[Int](paddingValue)) ev.one else ev.zero) + + sumBuffer.sum(mask, dimension % 2 + 1) + var sum = ev.zero + (0 until nstep).foreach(t => { + val loss = critrn.sizeAverageStatus match { + case SizeAverageStatus.True => + ev.times(cells(t).output, sumBuffer(Array(1, t + 1))) + case SizeAverageStatus.False => cells(t).output + case SizeAverageStatus.None => + throw new RuntimeException("Using TimeDistributedMaskCriterion," + + " the embedded criterion should be set to True or False") + } + sum = ev.plus(sum, loss) + }) + + output = ev.divide(sum, mask.sum()) + + output + } + + override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { + /** + * Take each time slice of input and target, and calculate gradInput of each slice + */ + require(input.size(dimension) == target.size(dimension), + s"target should have as many elements as input, " + + s"input ${input.size(dimension)}, target ${target.size(dimension)}") + gradInput.resizeAs(input).zero() + + val nstep = input.size(dimension) + + var i = 0 + while (i < nstep) { + val _i = i + 1 + results(i) = Engine.model.invoke(() => { + fInput = input.select(dimension, _i) + fTarget = target.select(dimension, _i) + _gradInput = gradInput.select(dimension, _i) + val _iGradInput = cells(_i - 1).updateGradInput(fInput, fTarget).toTensor[T] + _gradInput.copy( + critrn.sizeAverageStatus match { + case SizeAverageStatus.True => + gradInputBuffer.resizeAs(_iGradInput).mul( + _iGradInput, + sumBuffer(Array(1, _i))) + case SizeAverageStatus.False => _iGradInput + case SizeAverageStatus.None => + throw new RuntimeException("Using TimeDistributedMaskCriterion," + + " the embedded criterion should be set to True or False") + }) + }) + i += 1 + } + Engine.model.sync(results) + gradInput.div(mask.sum()) + gradInput + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[TimeDistributedCriterion[T]] +} + +object TimeDistributedMaskCriterion { + def apply[@specialized(Float, Double) T: ClassTag]( + critrn: TensorCriterion[T] = null, + paddingValue: Int = 0 + ) + (implicit ev: TensorNumeric[T]) : TimeDistributedMaskCriterion[T] = { + new TimeDistributedMaskCriterion[T](critrn, paddingValue) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractCriterion.scala index c3c73bc0db7..bea0d2288fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractCriterion.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.abstractnn +import com.intel.analytics.bigdl.nn.abstractnn.SizeAverageStatus.SizeAverageStatus import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} @@ -52,6 +53,8 @@ abstract class AbstractCriterion[A <: Activity: ClassTag, B <: Activity: ClassTa var gradInput: A = Activity.allocate[A, T]() var output: T = ev.fromType[Int](0) + private[nn] var sizeAverageStatus: SizeAverageStatus = SizeAverageStatus.None + private[nn] def allocateAs[D <: Activity](dest: D): D = dest match { case tensor: Tensor[T] => Tensor[T]().asInstanceOf[D] case table: Table => T().asInstanceOf[D] @@ -128,3 +131,8 @@ abstract class AbstractCriterion[A <: Activity: ClassTag, B <: Activity: ClassTa state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) } } + +object SizeAverageStatus extends Enumeration { + type SizeAverageStatus = Value + val True, False, None = Value +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index f30f57cc255..22c9268a8dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -337,6 +337,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab bRegularizer) } + def createTimeDistributedMaskCriterion(critrn: TensorCriterion[T], + paddingValue: Int = 0): TimeDistributedMaskCriterion[T] = { + TimeDistributedMaskCriterion[T](critrn, paddingValue) + } + def createTimeDistributedCriterion(critrn: TensorCriterion[T], sizeAverage: Boolean = false): TimeDistributedCriterion[T] = { TimeDistributedCriterion[T](critrn, sizeAverage) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala index 282965264b5..01532b8605b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterionSpec.scala @@ -39,17 +39,17 @@ class ClassNLLCriterionSpec extends FlatSpec with Matchers { target(Array(1)) = -1 target(Array(2)) = 2 target(Array(3)) = 3 - val expectedOutput = 0.8793184268272333 + val expectedOutput = 1.31897764024085 val expectedGrad = Tensor[Double](3, 3) expectedGrad(Array(1, 1)) = 0 expectedGrad(Array(1, 2)) = 0 expectedGrad(Array(1, 3)) = 0 expectedGrad(Array(2, 1)) = 0 - expectedGrad(Array(2, 2)) = -0.33333333333333 + expectedGrad(Array(2, 2)) = -0.5 expectedGrad(Array(2, 3)) = 0 expectedGrad(Array(3, 1)) = 0 expectedGrad(Array(3, 2)) = 0 - expectedGrad(Array(3, 3)) = -0.33333333333333 + expectedGrad(Array(3, 3)) = -0.5 val output = criterion.forward(input, target) val gradInput = criterion.backward(input, target) assert(abs(expectedOutput - output) < 1e-6) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala index 34108a0d6c3..c7726112a7c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSpec.scala @@ -27,6 +27,22 @@ import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class LookupTableSpec extends FlatSpec with Matchers { + "A LookupTable with padding input" should "generate correct output" in { + val seed = 100 + RNG.setSeed(seed) + val module = LookupTable[Double](9, 4, paddingValue = 1, maskZero = true) + val input = Tensor[Double](5) + input(Array(1)) = 5 + input(Array(2)) = 1 + input(Array(3)) = 6 + input(Array(4)) = 9 + input(Array(5)) = 4 + + val output = module.forward(input) + + output.select(1, 2).sum() should be (0.0) + } + "A LookupTableSpec with scaleW" should "generate correct output and grad with input 1D" in { val seed = 100 RNG.setSeed(seed) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala index ba470a8dd33..006a3b77f65 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.optim.SGD import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG -import com.intel.analytics.bigdl.utils.{Engine, T} +import com.intel.analytics.bigdl.utils.{Engine, T, Table} import org.scalatest.{FlatSpec, Matchers} import scala.collection.mutable.ArrayBuffer @@ -85,6 +85,57 @@ class RecurrentSpec extends FlatSpec with Matchers { } } + "Recurrent" should "outputs correct hiddens" in { + val hiddenSize = 4 + val batchSize = 3 + val inputSize = 6 + val seqLength = 5 + val seed = 100 + + RNG.setSeed(seed) + val input = Tensor[Double](Array(batchSize, seqLength, inputSize)).rand() + input.select(1, 2).zero() + + val rec = Recurrent[Double](maskZero = true) + val initHidden = T( + Tensor[Double](Array(batchSize, hiddenSize)).rand(), + Tensor[Double](Array(batchSize, hiddenSize)).rand() + ) + rec.setHiddenState(initHidden) + + val lstm = LSTM[Double](inputSize, hiddenSize) + val model = Sequential[Double]() + .add(rec + .add(lstm)) + + model.forward(input) + + lstm.output.toTable[Table](2).toTable[Tensor[Double]](1) + .select(1, 2) should be (initHidden[Tensor[Double]](1).select(1, 2)) + } + + "Recurrent" should "ouputs correclty" in { + val hiddenSize = 4 + val batchSize = 3 + val inputSize = 6 + val seqLength = 5 + val seed = 100 + + RNG.setSeed(seed) + val input = Tensor[Double](Array(batchSize, seqLength, inputSize)).rand() + input.select(1, 2).select(1, seqLength).zero() + + val rec = Recurrent[Double](maskZero = true) + + val model = Sequential[Double]() + .add(rec + .add(LSTM[Double](inputSize, hiddenSize))) + + val output = model.forward(input) + + output.toTensor[Double].select(1, 2).select(1, seqLength).abs().max() should be (0) + } + "A Recurrent" should " call getTimes correctly" in { val hiddenSize = 128 val inputSize = 1280 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedMaskCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedMaskCriterionSpec.scala new file mode 100644 index 00000000000..044976e9fe8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributedMaskCriterionSpec.scala @@ -0,0 +1,85 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + +import scala.math._ + +class TimeDistributedMaskCriterionSpec extends FlatSpec with Matchers { + "TimeDistributedMaskCriterion" should "works correctly" in { + val criterion = ClassNLLCriterion[Double](paddingValue = 0) + val layer = TimeDistributedMaskCriterion[Double](criterion, paddingValue = 0) + + val input = Tensor[Double](3, 2, 3) + input(Array(1, 1, 1)) = -1.0262627674932 + input(Array(1, 1, 2)) = -1.2412600935171 + input(Array(1, 1, 3)) = -1.0423174168648 + input(Array(1, 2, 1)) = -1.0262627674932 + input(Array(1, 2, 2)) = -1.2412600935171 + input(Array(1, 2, 3)) = -1.0423174168648 + input(Array(2, 1, 1)) = -0.90330565804228 + input(Array(2, 1, 2)) = -1.3686840144413 + input(Array(2, 1, 3)) = -1.0778380454479 + input(Array(2, 2, 1)) = -0.90330565804228 + input(Array(2, 2, 2)) = -1.3686840144413 + input(Array(2, 2, 3)) = -1.0778380454479 + input(Array(3, 1, 1)) = -0.99131220658219 + input(Array(3, 1, 2)) = -1.0559142847536 + input(Array(3, 1, 3)) = -1.2692712660404 + input(Array(3, 2, 1)) = -0.99131220658219 + input(Array(3, 2, 2)) = -1.0559142847536 + input(Array(3, 2, 3)) = -1.2692712660404 + + val target = Tensor[Double](3, 2) + target(Array(1, 1)) = 0 + target(Array(1, 2)) = 1 + target(Array(2, 1)) = 2 + target(Array(2, 2)) = 2 + target(Array(3, 1)) = 0 + target(Array(3, 2)) = 3 + + val output = layer.forward(input, target) + val gradInput = layer.backward(input, target) + + val expectedOutput = 1.25822551560405 + val expectedGrad = Tensor[Double](3, 2, 3) + expectedGrad(Array(1, 1, 1)) = 0 + expectedGrad(Array(1, 1, 2)) = 0 + expectedGrad(Array(1, 1, 3)) = 0 + expectedGrad(Array(1, 2, 1)) = -0.25 + expectedGrad(Array(1, 2, 2)) = 0 + expectedGrad(Array(1, 2, 3)) = 0 + expectedGrad(Array(2, 1, 1)) = 0 + expectedGrad(Array(2, 1, 2)) = -0.25 + expectedGrad(Array(2, 1, 3)) = 0 + expectedGrad(Array(2, 2, 1)) = 0 + expectedGrad(Array(2, 2, 2)) = -0.25 + expectedGrad(Array(2, 2, 3)) = 0 + expectedGrad(Array(3, 1, 1)) = 0 + expectedGrad(Array(3, 1, 2)) = 0 + expectedGrad(Array(3, 1, 3)) = 0 + expectedGrad(Array(3, 2, 1)) = 0 + expectedGrad(Array(3, 2, 2)) = 0 + expectedGrad(Array(3, 2, 3)) = -0.25 + assert(abs(expectedOutput - output) < 1e-6) + expectedGrad.map(gradInput, (v1, v2) => { + assert(abs(v1 - v2) < 1e-6) + v1 + }) + } +} From b444bb5f625752f696aabd65e4890235b7c4ea1a Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Mon, 5 Mar 2018 11:04:01 +0800 Subject: [PATCH 0710/1065] Move Keras-like API layers serial test (#2328) * change keras layers serial test * add more --- .../bigdl/dllib/keras/nn/ActivationSpec.scala | 23 +++- .../keras/nn/AtrousConvolution1DSpec.scala | 12 ++ .../keras/nn/AtrousConvolution2DSpec.scala | 13 +++ .../dllib/keras/nn/AveragePooling1DSpec.scala | 12 ++ .../dllib/keras/nn/AveragePooling2DSpec.scala | 12 ++ .../dllib/keras/nn/AveragePooling3DSpec.scala | 12 ++ .../keras/nn/BatchNormalizationSpec.scala | 12 ++ .../dllib/keras/nn/BidirectionalSpec.scala | 13 +++ .../bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala | 12 ++ .../dllib/keras/nn/Convolution1DSpec.scala | 12 ++ .../dllib/keras/nn/Convolution2DSpec.scala | 12 ++ .../dllib/keras/nn/Convolution3DSpec.scala | 12 ++ .../bigdl/dllib/keras/nn/Cropping1DSpec.scala | 12 ++ .../bigdl/dllib/keras/nn/Cropping2DSpec.scala | 12 ++ .../dllib/keras/nn/Deconvolution2DSpec.scala | 12 ++ .../bigdl/dllib/keras/nn/ELUSpec.scala | 12 ++ .../bigdl/dllib/keras/nn/EmbeddingSpec.scala | 20 ++++ .../bigdl/dllib/keras/nn/GRUSpec.scala | 13 +++ .../dllib/keras/nn/GaussianDropoutSpec.scala | 12 ++ .../dllib/keras/nn/GaussianNoiseSpec.scala | 12 ++ .../keras/nn/GlobalAveragePooling2DSpec.scala | 12 ++ .../keras/nn/GlobalMaxPooling2DSpec.scala | 12 ++ .../keras/nn/GlobalMaxPooling3DSpec.scala | 12 ++ .../bigdl/dllib/keras/nn/HighwaySpec.scala | 12 ++ .../bigdl/dllib/keras/nn/InputSpec.scala | 32 ++++++ .../bigdl/dllib/keras/nn/LSTMSpec.scala | 13 +++ .../bigdl/dllib/keras/nn/LeakyReLUSpec.scala | 12 ++ .../keras/nn/LocallyConnected1DSpec.scala | 12 ++ .../keras/nn/LocallyConnected2DSpec.scala | 13 +++ .../bigdl/dllib/keras/nn/MaskingSpec.scala | 12 ++ .../dllib/keras/nn/MaxPooling1DSpec.scala | 12 ++ .../dllib/keras/nn/MaxPooling2DSpec.scala | 12 ++ .../dllib/keras/nn/MaxPooling3DSpec.scala | 12 ++ .../dllib/keras/nn/MaxoutDenseSpec.scala | 12 ++ .../bigdl/dllib/keras/nn/MergeSpec.scala | 20 +++- .../dllib/keras/nn/RepeatVectorSpec.scala | 12 ++ .../bigdl/dllib/keras/nn/SReLUSpec.scala | 12 ++ .../keras/nn/SeparableConvolution2DSpec.scala | 12 ++ .../bigdl/dllib/keras/nn/SequentialSpec.scala | 34 ++++++ .../bigdl/dllib/keras/nn/SimpleRNNSpec.scala | 12 ++ .../dllib/keras/nn/SpatialDropout1DSpec.scala | 12 ++ .../dllib/keras/nn/SpatialDropout2DSpec.scala | 12 ++ .../dllib/keras/nn/SpatialDropout3DSpec.scala | 12 ++ .../dllib/keras/nn/ThresholdedReLUSpec.scala | 12 ++ .../dllib/keras/nn/TimeDistributedSpec.scala | 12 ++ .../dllib/keras/nn/UpSampling1DSpec.scala | 12 ++ .../dllib/keras/nn/UpSampling2DSpec.scala | 12 ++ .../dllib/keras/nn/UpSampling3DSpec.scala | 12 ++ .../dllib/keras/nn/ZeroPadding1DSpec.scala | 12 ++ .../dllib/keras/nn/ZeroPadding2DSpec.scala | 12 ++ .../utils/serializer/SerializerSpec.scala | 108 +++++++++++++++++- 51 files changed, 777 insertions(+), 5 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SequentialSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ActivationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ActivationSpec.scala index 45639af2511..299d030f6b6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ActivationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ActivationSpec.scala @@ -18,9 +18,12 @@ package com.intel.analytics.bigdl.keras.nn import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.nn.keras.{Activation, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.keras.{Activation, SoftMax, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ActivationSpec extends KerasBaseSpec{ @@ -145,3 +148,21 @@ class ActivationSpec extends KerasBaseSpec{ } } + +class ActivationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Activation[Float]("tanh", inputShape = Shape(4, 5)) + layer.build(Shape(2, 4, 5)) + val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} + +class SoftMaxSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = SoftMax[Float](inputShape = Shape(4, 5)) + layer.build(Shape(3, 4, 5)) + val input = Tensor[Float](3, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution1DSpec.scala index 61dfe567eba..cf0c2909e87 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{AtrousConvolution1D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class AtrousConvolution1DSpec extends KerasBaseSpec { @@ -49,3 +52,12 @@ class AtrousConvolution1DSpec extends KerasBaseSpec { } } + +class AtrousConvolution1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = AtrousConvolution1D[Float](64, 3, inputShape = Shape(8, 32)) + layer.build(Shape(2, 8, 32)) + val input = Tensor[Float](2, 8, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution2DSpec.scala index 51ffaf31442..0283623a404 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AtrousConvolution2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{AtrousConvolution2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class AtrousConvolution2DSpec extends KerasBaseSpec { @@ -43,3 +46,13 @@ class AtrousConvolution2DSpec extends KerasBaseSpec { } } + +class AtrousConvolution2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = AtrousConvolution2D[Float](32, 2, 4, atrousRate = (2, 2), + inputShape = Shape(3, 64, 64)) + layer.build(Shape(2, 3, 64, 64)) + val input = Tensor[Float](2, 3, 64, 64).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling1DSpec.scala index bbc1e0422b1..ecc3afbd3ba 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{AveragePooling1D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class AveragePooling1DSpec extends KerasBaseSpec { @@ -58,3 +61,12 @@ class AveragePooling1DSpec extends KerasBaseSpec { } } + +class AveragePooling1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = AveragePooling1D[Float](inputShape = Shape(12, 16)) + layer.build(Shape(2, 12, 16)) + val input = Tensor[Float](2, 12, 16).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling2DSpec.scala index 1939e7bc8e6..dba20fbce78 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{AveragePooling2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class AveragePooling2DSpec extends KerasBaseSpec { @@ -59,3 +62,12 @@ class AveragePooling2DSpec extends KerasBaseSpec { } } + +class AveragePooling2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = AveragePooling2D[Float](inputShape = Shape(3, 24, 24)) + layer.build(Shape(2, 3, 24, 24)) + val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling3DSpec.scala index f05411a67e9..b11747617a5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/AveragePooling3DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{AveragePooling3D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class AveragePooling3DSpec extends KerasBaseSpec { @@ -41,3 +44,12 @@ class AveragePooling3DSpec extends KerasBaseSpec { } } + +class AveragePooling3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = AveragePooling3D[Float](inputShape = Shape(3, 12, 12, 12)) + layer.build(Shape(2, 3, 12, 12, 12)) + val input = Tensor[Float](2, 3, 12, 12, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BatchNormalizationSpec.scala index 85eed35e53a..fe3417c3e9b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BatchNormalizationSpec.scala @@ -20,6 +20,9 @@ import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.nn.keras.{BatchNormalization, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class BatchNormalizationSpec extends KerasBaseSpec { @@ -36,3 +39,12 @@ class BatchNormalizationSpec extends KerasBaseSpec { } } + +class BatchNormalizationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = BatchNormalization[Float](inputShape = Shape(3, 12, 12)) + layer.build(Shape(2, 3, 12, 12)) + val input = Tensor[Float](2, 3, 12, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BidirectionalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BidirectionalSpec.scala index ec4f802cf70..83b0e298810 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BidirectionalSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/BidirectionalSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Bidirectional, LSTM, SimpleRNN, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class BidirectionalSpec extends KerasBaseSpec { @@ -82,3 +85,13 @@ class BidirectionalSpec extends KerasBaseSpec { } } + +class BidirectionalSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Bidirectional[Float](SimpleRNN(4, returnSequences = true), + inputShape = Shape(8, 12)) + layer.build(Shape(3, 8, 12)) + val input = Tensor[Float](3, 8, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala index d14751f969f..cabfd9ad5cc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ConvLSTM2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{ConvLSTM2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ConvLSTM2DSpec extends KerasBaseSpec { @@ -69,3 +72,12 @@ class ConvLSTM2DSpec extends KerasBaseSpec { } } + +class ConvLSTM2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = ConvLSTM2D[Float](32, 4, inputShape = Shape(8, 40, 40, 32)) + layer.build(Shape(2, 8, 40, 40, 32)) + val input = Tensor[Float](2, 8, 40, 40, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution1DSpec.scala index 9b97efbceb1..729e99b4c5b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Convolution1D, Conv1D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class Convolution1DSpec extends KerasBaseSpec { @@ -63,3 +66,12 @@ class Convolution1DSpec extends KerasBaseSpec { } } + +class Convolution1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Convolution1D[Float](64, 3, inputShape = Shape(12, 20)) + layer.build(Shape(2, 12, 20)) + val input = Tensor[Float](2, 12, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala index 3b1376d8bd4..9b92a5b0465 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{Conv2D, Convolution2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class Convolution2DSpec extends KerasBaseSpec { @@ -80,3 +83,12 @@ class Convolution2DSpec extends KerasBaseSpec { } } + +class Convolution2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Convolution2D[Float](64, 2, 5, inputShape = Shape(3, 24, 24)) + layer.build(Shape(2, 3, 24, 24)) + val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution3DSpec.scala index debb6dcd9e8..77a6e5407f0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Convolution3DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Conv3D, Convolution3D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class Convolution3DSpec extends KerasBaseSpec { @@ -59,3 +62,12 @@ class Convolution3DSpec extends KerasBaseSpec { } } + +class Convolution3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Convolution3D[Float](12, 2, 1, 3, inputShape = Shape(3, 32, 32, 32)) + layer.build(Shape(2, 3, 32, 32, 32)) + val input = Tensor[Float](2, 3, 32, 32, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping1DSpec.scala index 8ea92008633..85427e50889 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Cropping1D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class Cropping1DSpec extends KerasBaseSpec { @@ -41,3 +44,12 @@ class Cropping1DSpec extends KerasBaseSpec { } } + +class Cropping1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Cropping1D[Float](inputShape = Shape(5, 6)) + layer.build(Shape(2, 5, 6)) + val input = Tensor[Float](2, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping2DSpec.scala index 8d987bbbfab..b37f2adc266 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Cropping2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{Cropping2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class Cropping2DSpec extends KerasBaseSpec { @@ -56,3 +59,12 @@ class Cropping2DSpec extends KerasBaseSpec { } } + +class Cropping2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Cropping2D[Float](inputShape = Shape(3, 8, 12)) + layer.build(Shape(2, 3, 8, 12)) + val input = Tensor[Float](2, 3, 8, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala index bd8c1487e37..58f840199fc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Deconvolution2D, Deconv2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class Deconvolution2DSpec extends KerasBaseSpec { @@ -66,3 +69,12 @@ class Deconvolution2DSpec extends KerasBaseSpec { } } + +class Deconvolution2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Deconvolution2D[Float](3, 3, 3, inputShape = Shape(3, 24, 24)) + layer.build(Shape(2, 12, 24, 24)) + val input = Tensor[Float](2, 12, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ELUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ELUSpec.scala index 465f2192701..e7d143d888e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ELUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ELUSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.ELU import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ELUSpec extends KerasBaseSpec{ @@ -56,3 +59,12 @@ class ELUSpec extends KerasBaseSpec{ } } + +class ELUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = ELU[Float](2.7, inputShape = Shape(3, 24)) + layer.build(Shape(2, 3, 24)) + val input = Tensor[Float](2, 3, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/EmbeddingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/EmbeddingSpec.scala index a674cdc0a72..db64a10cd84 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/EmbeddingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/EmbeddingSpec.scala @@ -20,6 +20,9 @@ import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.nn.keras.{Embedding, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class EmbeddingSpec extends KerasBaseSpec { @@ -43,3 +46,20 @@ class EmbeddingSpec extends KerasBaseSpec { } } + +class EmbeddingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Embedding[Float](1000, 32, inputShape = Shape(4)) + layer.build(Shape(2, 4)) + val input = Tensor[Float](2, 4) + input(Array(1, 1)) = 1 + input(Array(1, 2)) = 2 + input(Array(1, 3)) = 4 + input(Array(1, 4)) = 5 + input(Array(2, 1)) = 4 + input(Array(2, 2)) = 3 + input(Array(2, 3)) = 2 + input(Array(2, 4)) = 6 + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala index 18f812c4df0..62377663764 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GRUSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{GRU, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GRUSpec extends KerasBaseSpec { @@ -90,3 +93,13 @@ class GRUSpec extends KerasBaseSpec { } } + +class GRUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GRU[Float](16, returnSequences = true, + goBackwards = true, inputShape = Shape(28, 32)) + layer.build(Shape(2, 28, 32)) + val input = Tensor[Float](2, 28, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianDropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianDropoutSpec.scala index fed9609de74..6d34742bf7b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianDropoutSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianDropoutSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.keras.GaussianDropout import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GaussianDropoutSpec extends KerasBaseSpec { @@ -35,3 +38,12 @@ class GaussianDropoutSpec extends KerasBaseSpec { } } + +class GaussianDropoutSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GaussianDropout[Float](0.6, inputShape = Shape(3, 4)) + layer.build(Shape(2, 3, 4)) + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianNoiseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianNoiseSpec.scala index 3eeb43aaee9..43f233992d6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianNoiseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GaussianNoiseSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.keras.GaussianNoise import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GaussianNoiseSpec extends KerasBaseSpec { @@ -35,3 +38,12 @@ class GaussianNoiseSpec extends KerasBaseSpec { } } + +class GaussianNoiseSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GaussianNoise[Float](0.8, inputShape = Shape(12, 24)) + layer.build(Shape(2, 12, 24)) + val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling2DSpec.scala index 5b4fec80bd7..71cddaa6453 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalAveragePooling2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{GlobalAveragePooling2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GlobalAveragePooling2DSpec extends KerasBaseSpec { @@ -58,3 +61,12 @@ class GlobalAveragePooling2DSpec extends KerasBaseSpec { } } + +class GlobalAveragePooling2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GlobalAveragePooling2D[Float](inputShape = Shape(4, 24, 32)) + layer.build(Shape(2, 4, 24, 32)) + val input = Tensor[Float](2, 4, 24, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling2DSpec.scala index c1f18692a7c..e0aed732d85 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{GlobalMaxPooling2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GlobalMaxPooling2DSpec extends KerasBaseSpec { @@ -57,3 +60,12 @@ class GlobalMaxPooling2DSpec extends KerasBaseSpec { } } + +class GlobalMaxPooling2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GlobalMaxPooling2D[Float](inputShape = Shape(4, 24, 32)) + layer.build(Shape(2, 4, 24, 32)) + val input = Tensor[Float](2, 4, 24, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling3DSpec.scala index 30fb06a24a4..b0fee75a82d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/GlobalMaxPooling3DSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling3D import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class GlobalMaxPooling3DSpec extends KerasBaseSpec{ @@ -42,3 +45,12 @@ class GlobalMaxPooling3DSpec extends KerasBaseSpec{ } } + +class GlobalMaxPooling3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = GlobalMaxPooling3D[Float](inputShape = Shape(12, 24, 3, 6)) + layer.build(Shape(2, 12, 24, 3, 6)) + val input = Tensor[Float](2, 12, 24, 3, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala index bd0a8f6b979..49dc1edbd75 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Dense, Highway, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class HighwaySpec extends KerasBaseSpec { @@ -70,3 +73,12 @@ class HighwaySpec extends KerasBaseSpec { } } + +class HighwaySerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Highway[Float](activation = "tanh", bias = false, inputShape = Shape(4)) + layer.build(Shape(3, 4)) + val input = Tensor[Float](3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala new file mode 100644 index 00000000000..be0723df2eb --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.nn.keras.InputLayer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class InputSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = InputLayer[Float](inputShape = Shape(20)) + val inputData = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(input, inputData) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala index fcfab731c05..ac38315e4f7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LSTMSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{LSTM, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class LSTMSpec extends KerasBaseSpec { @@ -89,3 +92,13 @@ class LSTMSpec extends KerasBaseSpec { } } + +class LSTMSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = LSTM[Float](8, returnSequences = true, + innerActivation = "sigmoid", inputShape = Shape(32, 32)) + layer.build(Shape(3, 32, 32)) + val input = Tensor[Float](3, 32, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LeakyReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LeakyReLUSpec.scala index 868cb17181a..d3bb168107a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LeakyReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LeakyReLUSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.LeakyReLU import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class LeakyReLUSpec extends KerasBaseSpec{ @@ -56,3 +59,12 @@ class LeakyReLUSpec extends KerasBaseSpec{ } } + +class LeakyReLUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = LeakyReLU[Float](1.27, inputShape = Shape(8, 24)) + layer.build(Shape(2, 8, 24)) + val input = Tensor[Float](2, 8, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected1DSpec.scala index bed89d9a894..52857d0a066 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{LocallyConnected1D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class LocallyConnected1DSpec extends KerasBaseSpec { @@ -78,3 +81,12 @@ class LocallyConnected1DSpec extends KerasBaseSpec { } } + +class LocallyConnected1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = LocallyConnected1D[Float](32, 3, inputShape = Shape(12, 24)) + layer.build(Shape(2, 12, 24)) + val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected2DSpec.scala index 6492990acc1..a49357ca9e2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/LocallyConnected2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{LocallyConnected2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class LocallyConnected2DSpec extends KerasBaseSpec { @@ -95,3 +98,13 @@ class LocallyConnected2DSpec extends KerasBaseSpec { } } + +class LocallyConnected2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = LocallyConnected2D[Float](32, 2, 2, activation = "relu", + inputShape = Shape(12, 24, 24)) + layer.build(Shape(2, 12, 24, 24)) + val input = Tensor[Float](2, 12, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaskingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaskingSpec.scala index 5e844a63ae1..4f8781bdec9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaskingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaskingSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.Masking import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class MaskingSpec extends KerasBaseSpec{ @@ -56,3 +59,12 @@ class MaskingSpec extends KerasBaseSpec{ } } + +class MaskingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = Masking[Float](0.0, inputShape = Shape(3, 12)) + layer.build(Shape(2, 3, 12)) + val input = Tensor[Float](2, 3, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling1DSpec.scala index 18c3ac222f0..2a8b2f4c9ea 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{MaxPooling1D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class MaxPooling1DSpec extends KerasBaseSpec { @@ -58,3 +61,12 @@ class MaxPooling1DSpec extends KerasBaseSpec { } } + +class MaxPooling1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = MaxPooling1D[Float](inputShape = Shape(12, 12)) + layer.build(Shape(2, 12, 12)) + val input = Tensor[Float](2, 12, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala index 371783e9a00..4621a486cc2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{MaxPooling2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class MaxPooling2DSpec extends KerasBaseSpec{ @@ -74,3 +77,12 @@ class MaxPooling2DSpec extends KerasBaseSpec{ } } + +class MaxPooling2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = MaxPooling2D[Float](inputShape = Shape(3, 24, 24)) + layer.build(Shape(2, 3, 24, 24)) + val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling3DSpec.scala index cc70b25cd6e..b0234e12d40 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxPooling3DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{MaxPooling3D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class MaxPooling3DSpec extends KerasBaseSpec { @@ -41,3 +44,12 @@ class MaxPooling3DSpec extends KerasBaseSpec { } } + +class MaxPooling3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = MaxPooling3D[Float](inputShape = Shape(3, 20, 15, 35)) + layer.build(Shape(2, 3, 20, 15, 35)) + val input = Tensor[Float](2, 3, 20, 15, 35).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxoutDenseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxoutDenseSpec.scala index 81e587dc1d0..5d3d6551122 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxoutDenseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MaxoutDenseSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{MaxoutDense, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class MaxoutDenseSpec extends KerasBaseSpec { @@ -71,3 +74,12 @@ class MaxoutDenseSpec extends KerasBaseSpec { } } + +class MaxoutDenseSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = MaxoutDense[Float](8, inputShape = Shape(12)) + layer.build(Shape(3, 12)) + val input = Tensor[Float](3, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala index 92958f25e5a..8af79ceb9f0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala @@ -19,7 +19,10 @@ package com.intel.analytics.bigdl.keras.nn import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.nn.keras.{Dense, InputLayer, Merge, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{MultiShape, Shape, T} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{MultiShape, Shape, T, Table} + +import scala.util.Random class MergeSpec extends KerasBaseSpec { @@ -103,3 +106,18 @@ class MergeSpec extends KerasBaseSpec { } } + +class MergeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val l1 = InputLayer[Float](inputShape = Shape(4, 8)) + val l2 = InputLayer[Float](inputShape = Shape(4, 8)) + val layer = Merge[Float](layers = List(l1, l2), mode = "sum") + layer.build(Shape(List(Shape(2, 4, 8), Shape(2, 4, 8)))) + val input1 = Tensor[Float](2, 4, 8).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 4, 8).apply1(e => Random.nextFloat()) + val input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/RepeatVectorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/RepeatVectorSpec.scala index 1a983ac3cca..ae43ca420da 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/RepeatVectorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/RepeatVectorSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{RepeatVector, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class RepeatVectorSpec extends KerasBaseSpec { @@ -41,3 +44,12 @@ class RepeatVectorSpec extends KerasBaseSpec { } } + +class RepeatVectorSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = RepeatVector[Float](4, inputShape = Shape(12)) + layer.build(Shape(2, 12)) + val input = Tensor[Float](2, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala index 0d3d1c86896..d482bd62c39 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SReLUSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.SReLU import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class SReLUSpec extends KerasBaseSpec{ @@ -56,3 +59,12 @@ class SReLUSpec extends KerasBaseSpec{ } } + +class SReLUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = SReLU[Float](sharedAxes = Array(1, 2), inputShape = Shape(4, 32)) + layer.build(Shape(2, 4, 32)) + val input = Tensor[Float](2, 4, 32).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala index 6b4d119688c..722e5a91aba 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SeparableConvolution2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{SeparableConvolution2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class SeparableConvolution2DSpec extends KerasBaseSpec { @@ -100,3 +103,12 @@ class SeparableConvolution2DSpec extends KerasBaseSpec { } } + +class SeparableConvolution2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = SeparableConvolution2D[Float](1, 2, 2, inputShape = Shape(3, 128, 128)) + layer.build(Shape(2, 3, 128, 128)) + val input = Tensor[Float](2, 3, 128, 128).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SequentialSpec.scala new file mode 100644 index 00000000000..93b5d315df1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SequentialSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.nn.keras.{Dense, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SequentialSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val dense = Dense[Float](10, inputShape = Shape(20)) + val kseq = KSequential[Float]() + kseq.add(dense) + val input = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(kseq, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SimpleRNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SimpleRNNSpec.scala index ccf37437729..1725bff411f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SimpleRNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SimpleRNNSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Dense, SimpleRNN, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class SimpleRNNSpec extends KerasBaseSpec { @@ -85,3 +88,12 @@ class SimpleRNNSpec extends KerasBaseSpec { } } + +class SimpleRNNSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = SimpleRNN[Float](8, activation = "relu", inputShape = Shape(4, 5)) + layer.build(Shape(3, 4, 5)) + val input = Tensor[Float](3, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout1DSpec.scala index 61c4c8bb237..e0dcd5d0606 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.keras.SpatialDropout1D import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class SpatialDropout1DSpec extends KerasBaseSpec { @@ -35,3 +38,12 @@ class SpatialDropout1DSpec extends KerasBaseSpec { } } + +class SpatialDropout1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = SpatialDropout1D[Float](0.5, inputShape = Shape(3, 4)) + layer.build(Shape(2, 3, 4)) + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout2DSpec.scala index 199df9ec183..6744e30f434 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout2DSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.keras.SpatialDropout2D import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class SpatialDropout2DSpec extends KerasBaseSpec { @@ -46,3 +49,12 @@ class SpatialDropout2DSpec extends KerasBaseSpec { } } + +class SpatialDropout2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = SpatialDropout2D[Float](0.5, "tf", inputShape = Shape(3, 64, 64)) + layer.build(Shape(2, 3, 64, 64)) + val input = Tensor[Float](2, 3, 64, 64).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout3DSpec.scala index d16d73fd0d0..edb750611ac 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/SpatialDropout3DSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.keras.SpatialDropout3D import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class SpatialDropout3DSpec extends KerasBaseSpec { @@ -46,3 +49,12 @@ class SpatialDropout3DSpec extends KerasBaseSpec { } } + +class SpatialDropout3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = SpatialDropout3D[Float](0.5, "tf", inputShape = Shape(3, 4, 5, 6)) + layer.build(Shape(2, 3, 4, 5, 6)) + val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ThresholdedReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ThresholdedReLUSpec.scala index c2c8750a61d..d797df4d7bc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ThresholdedReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ThresholdedReLUSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.ThresholdedReLU import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ThresholdedReLUSpec extends KerasBaseSpec{ @@ -56,3 +59,12 @@ class ThresholdedReLUSpec extends KerasBaseSpec{ } } + +class ThresholdedReLUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = ThresholdedReLU[Float](2.7, inputShape = Shape(3, 128)) + layer.build(Shape(2, 3, 128)) + val input = Tensor[Float](2, 3, 128).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TimeDistributedSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TimeDistributedSpec.scala index b94a206d87a..104d25ad76c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TimeDistributedSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TimeDistributedSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Convolution2D, Dense, TimeDistributed, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class TimeDistributedSpec extends KerasBaseSpec { @@ -59,3 +62,12 @@ class TimeDistributedSpec extends KerasBaseSpec { } } + +class TimeDistributedSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = TimeDistributed[Float](Dense(8), inputShape = Shape(10, 12)) + layer.build(Shape(3, 10, 12)) + val input = Tensor[Float](3, 10, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling1DSpec.scala index aab0e09d91e..60ebfe4bf59 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{UpSampling1D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class UpSampling1DSpec extends KerasBaseSpec { @@ -55,3 +58,12 @@ class UpSampling1DSpec extends KerasBaseSpec { } } + +class UpSampling1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = UpSampling1D[Float](inputShape = Shape(4, 5)) + layer.build(Shape(2, 4, 5)) + val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling2DSpec.scala index 0bc0f7af5cf..03a0bc8678a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{UpSampling2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class UpSampling2DSpec extends KerasBaseSpec { @@ -56,3 +59,12 @@ class UpSampling2DSpec extends KerasBaseSpec { } } + +class UpSampling2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = UpSampling2D[Float](inputShape = Shape(4, 8, 8)) + layer.build(Shape(2, 4, 8, 8)) + val input = Tensor[Float](2, 4, 8, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling3DSpec.scala index 18c90e7f88f..f23be0a4525 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/UpSampling3DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{UpSampling3D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class UpSampling3DSpec extends KerasBaseSpec { @@ -55,3 +58,12 @@ class UpSampling3DSpec extends KerasBaseSpec { } } + +class UpSampling3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = UpSampling3D[Float](inputShape = Shape(3, 8, 10, 12)) + layer.build(Shape(2, 3, 8, 10, 12)) + val input = Tensor[Float](2, 3, 8, 10, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding1DSpec.scala index 34d29b8406f..69df9fed889 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding1DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{ZeroPadding1D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ZeroPadding1DSpec extends KerasBaseSpec { @@ -57,3 +60,12 @@ class ZeroPadding1DSpec extends KerasBaseSpec { } } + +class ZeroPadding1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = ZeroPadding1D[Float](padding = 2, inputShape = Shape(4, 5)) + layer.build(Shape(2, 4, 5)) + val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding2DSpec.scala index 0ce15093d1d..ec847cbb797 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/ZeroPadding2DSpec.scala @@ -21,6 +21,9 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} import com.intel.analytics.bigdl.nn.keras.{ZeroPadding2D, Sequential => KSequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ZeroPadding2DSpec extends KerasBaseSpec { @@ -90,3 +93,12 @@ class ZeroPadding2DSpec extends KerasBaseSpec { } } + +class ZeroPadding2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = ZeroPadding2D[Float](padding = (2, 1), inputShape = Shape(2, 8, 8)) + layer.build(Shape(2, 2, 8, 8)) + val input = Tensor[Float](2, 2, 8, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index dc11ee5263b..4bec7fb77a4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -62,6 +62,108 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", // Keras layers + "com.intel.analytics.bigdl.nn.keras.Input" -> + "com.intel.analytics.bigdl.keras.nn.InputSerialTest", + "com.intel.analytics.bigdl.nn.keras.Sequential" -> + "com.intel.analytics.bigdl.keras.nn.SequentialSerialTest", + "com.intel.analytics.bigdl.nn.keras.Activation" -> + "com.intel.analytics.bigdl.keras.nn.ActivationSerialTest", + "com.intel.analytics.bigdl.nn.keras.SoftMax" -> + "com.intel.analytics.bigdl.keras.nn.SoftMaxSerialTest", + "com.intel.analytics.bigdl.nn.keras.AtrousConvolution1D" -> + "com.intel.analytics.bigdl.keras.nn.AtrousConvolution1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.AtrousConvolution2D" -> + "com.intel.analytics.bigdl.keras.nn.AtrousConvolution2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.AveragePooling1D" -> + "com.intel.analytics.bigdl.keras.nn.AveragePooling1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.AveragePooling2D" -> + "com.intel.analytics.bigdl.keras.nn.AveragePooling2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.AveragePooling3D" -> + "com.intel.analytics.bigdl.keras.nn.AveragePooling3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.BatchNormalization" -> + "com.intel.analytics.bigdl.keras.nn.BatchNormalizationSerialTest", + "com.intel.analytics.bigdl.nn.keras.Bidirectional" -> + "com.intel.analytics.bigdl.keras.nn.BidirectionalSerialTest", + "com.intel.analytics.bigdl.nn.keras.ConvLSTM2D" -> + "com.intel.analytics.bigdl.keras.nn.ConvLSTM2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Convolution1D" -> + "com.intel.analytics.bigdl.keras.nn.Convolution1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Convolution2D" -> + "com.intel.analytics.bigdl.keras.nn.Convolution2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Convolution3D" -> + "com.intel.analytics.bigdl.keras.nn.Convolution3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Cropping1D" -> + "com.intel.analytics.bigdl.keras.nn.Cropping1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Cropping2D" -> + "com.intel.analytics.bigdl.keras.nn.Cropping2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Deconvolution2D" -> + "com.intel.analytics.bigdl.keras.nn.Deconvolution2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.ELU" -> + "com.intel.analytics.bigdl.keras.nn.ELUSerialTest", + "com.intel.analytics.bigdl.nn.keras.Embedding" -> + "com.intel.analytics.bigdl.keras.nn.EmbeddingSerialTest", + "com.intel.analytics.bigdl.nn.keras.GaussianDropout" -> + "com.intel.analytics.bigdl.keras.nn.GaussianDropoutSerialTest", + "com.intel.analytics.bigdl.nn.keras.GaussianNoise" -> + "com.intel.analytics.bigdl.keras.nn.GaussianNoiseSerialTest", + "com.intel.analytics.bigdl.nn.keras.GlobalAveragePooling2D" -> + "com.intel.analytics.bigdl.keras.nn.GlobalAveragePooling2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling2D" -> + "com.intel.analytics.bigdl.keras.nn.GlobalMaxPooling2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling3D" -> + "com.intel.analytics.bigdl.keras.nn.GlobalMaxPooling3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.GRU" -> + "com.intel.analytics.bigdl.keras.nn.GRUSerialTest", + "com.intel.analytics.bigdl.nn.keras.Highway" -> + "com.intel.analytics.bigdl.keras.nn.HighwaySerialTest", + "com.intel.analytics.bigdl.nn.keras.LeakyReLU" -> + "com.intel.analytics.bigdl.keras.nn.LeakyReLUSerialTest", + "com.intel.analytics.bigdl.nn.keras.LocallyConnected1D" -> + "com.intel.analytics.bigdl.keras.nn.LocallyConnected1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.LocallyConnected2D" -> + "com.intel.analytics.bigdl.keras.nn.LocallyConnected2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.LSTM" -> + "com.intel.analytics.bigdl.keras.nn.LSTMSerialTest", + "com.intel.analytics.bigdl.nn.keras.Masking" -> + "com.intel.analytics.bigdl.keras.nn.MaskingSerialTest", + "com.intel.analytics.bigdl.nn.keras.MaxoutDense" -> + "com.intel.analytics.bigdl.keras.nn.MaxoutDenseSerialTest", + "com.intel.analytics.bigdl.nn.keras.MaxPooling1D" -> + "com.intel.analytics.bigdl.keras.nn.MaxPooling1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.MaxPooling2D" -> + "com.intel.analytics.bigdl.keras.nn.MaxPooling2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.MaxPooling3D" -> + "com.intel.analytics.bigdl.keras.nn.MaxPooling3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.Merge" -> + "com.intel.analytics.bigdl.keras.nn.MergeSerialTest", + "com.intel.analytics.bigdl.nn.keras.RepeatVector" -> + "com.intel.analytics.bigdl.keras.nn.RepeatVectorSerialTest", + "com.intel.analytics.bigdl.nn.keras.SeparableConvolution2D" -> + "com.intel.analytics.bigdl.keras.nn.SeparableConvolution2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.SimpleRNN" -> + "com.intel.analytics.bigdl.keras.nn.SimpleRNNSerialTest", + "com.intel.analytics.bigdl.nn.keras.SpatialDropout1D" -> + "com.intel.analytics.bigdl.keras.nn.SpatialDropout1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.SpatialDropout2D" -> + "com.intel.analytics.bigdl.keras.nn.SpatialDropout2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.SpatialDropout3D" -> + "com.intel.analytics.bigdl.keras.nn.SpatialDropout3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.SReLU" -> + "com.intel.analytics.bigdl.keras.nn.SReLUSerialTest", + "com.intel.analytics.bigdl.nn.keras.ThresholdedReLU" -> + "com.intel.analytics.bigdl.keras.nn.ThresholdedReLUSerialTest", + "com.intel.analytics.bigdl.nn.keras.TimeDistributed" -> + "com.intel.analytics.bigdl.keras.nn.TimeDistributedSerialTest", + "com.intel.analytics.bigdl.nn.keras.UpSampling1D" -> + "com.intel.analytics.bigdl.keras.nn.UpSampling1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.UpSampling2D" -> + "com.intel.analytics.bigdl.keras.nn.UpSampling2DSerialTest", + "com.intel.analytics.bigdl.nn.keras.UpSampling3D" -> + "com.intel.analytics.bigdl.keras.nn.UpSampling3DSerialTest", + "com.intel.analytics.bigdl.nn.keras.ZeroPadding1D" -> + "com.intel.analytics.bigdl.keras.nn.ZeroPadding1DSerialTest", + "com.intel.analytics.bigdl.nn.keras.ZeroPadding2D" -> + "com.intel.analytics.bigdl.keras.nn.ZeroPadding2DSerialTest", "com.intel.analytics.bigdl.nn.keras.Dense" -> "com.intel.analytics.bigdl.keras.nn.DenseSerialTest", "com.intel.analytics.bigdl.nn.keras.Cropping3D" -> @@ -80,10 +182,10 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.keras.nn.ZeroPadding3DSerialTest", "com.intel.analytics.bigdl.nn.keras.Dropout" -> "com.intel.analytics.bigdl.keras.nn.DropoutSerialTest", - "module com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling1D" -> - "module com.intel.analytics.bigdl.keras.nn.GlobalMaxPooling1D", + "com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling1D" -> + "com.intel.analytics.bigdl.keras.nn.GlobalMaxPooling1DSerialTest", "com.intel.analytics.bigdl.nn.keras.Flatten" -> - "com.intel.analytics.bigdl.keras.nn.Flatten" + "com.intel.analytics.bigdl.keras.nn.FlattenSerialTest" ) private val suffix = "SerialTest" From 7276dc4828f2d6c916d31aa573d8cd507bf44b73 Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Mon, 5 Mar 2018 13:52:32 +0800 Subject: [PATCH 0711/1065] Change Bigdl Serial Test Part 1 (#2339) * add one * add more bigdl serial test A to N * add more bigdl serial test * change * remove * change * change * modify * remove * add * change * modify * add one * remove four tests --- .../analytics/bigdl/dllib/nn/AbsSpec.scala | 30 +++++++++++++ .../dllib/nn/ActivityRegularizationSpec.scala | 13 ++++++ .../bigdl/dllib/nn/BinaryThresholdSpec.scala | 30 +++++++++++++ .../bigdl/dllib/nn/BinaryTreeLSTMSpec.scala | 28 ++++++++++++ .../bigdl/dllib/nn/CDivTableSpec.scala | 35 +++++++++++++++ .../bigdl/dllib/nn/CMaxTableSpec.scala | 35 +++++++++++++++ .../bigdl/dllib/nn/CMinTableSpec.scala | 35 +++++++++++++++ .../bigdl/dllib/nn/CSubTableSpec.scala | 36 ++++++++++++++++ .../analytics/bigdl/dllib/nn/ConcatSpec.scala | 13 ++++++ .../bigdl/dllib/nn/CosineDistanceSpec.scala | 35 +++++++++++++++ .../bigdl/dllib/nn/Cropping2DSpec.scala | 32 ++++++++++++++ .../bigdl/dllib/nn/Cropping3DSpec.scala | 30 +++++++++++++ .../bigdl/dllib/nn/DenseToSparseSpec.scala | 9 ++++ .../dllib/nn/DetectionOutputFrcnnSpec.scala | 43 +++++++++++++++++++ .../dllib/nn/DetectionOutputSSDSpec.scala | 43 +++++++++++++++++++ .../bigdl/dllib/nn/DropoutSpec.scala | 32 ++++++++++++++ .../bigdl/dllib/nn/DynamicGraphSpec.scala | 15 +++++++ .../analytics/bigdl/dllib/nn/ELUSpec.scala | 30 +++++++++++++ .../analytics/bigdl/dllib/nn/EchoSpec.scala | 30 +++++++++++++ .../bigdl/dllib/nn/GaussianDropoutSpec.scala | 13 ++++++ .../bigdl/dllib/nn/GaussianNoiseSpec.scala | 13 ++++++ .../bigdl/dllib/nn/GaussianSamplerSpec.scala | 35 +++++++++++++++ .../analytics/bigdl/dllib/nn/IndexSpec.scala | 39 +++++++++++++++++ .../analytics/bigdl/dllib/nn/InputSpec.scala | 30 +++++++++++++ .../bigdl/dllib/nn/L1PenaltySpec.scala | 30 +++++++++++++ .../analytics/bigdl/dllib/nn/LSTMSpec.scala | 31 +++++++++++++ .../bigdl/dllib/nn/LeakyReLUSpec.scala | 30 +++++++++++++ .../analytics/bigdl/dllib/nn/LinearSpec.scala | 11 +++++ .../dllib/nn/LocallyConnected1DSpec.scala | 31 +++++++++++++ .../dllib/nn/LocallyConnected2DSpec.scala | 31 +++++++++++++ .../bigdl/dllib/nn/LogSoftMaxSpec.scala | 9 ++++ .../dllib/nn/LookupTableSparseSpec.scala | 12 ++++++ .../analytics/bigdl/dllib/nn/MVSpec.scala | 17 +++++++- .../bigdl/dllib/nn/MapTableSpec.scala | 17 ++++++++ .../bigdl/dllib/nn/MaskedSelectSpec.scala | 39 +++++++++++++++++ .../analytics/bigdl/dllib/nn/MaxSpec.scala | 30 +++++++++++++ .../analytics/bigdl/dllib/nn/MeanSpec.scala | 11 +++++ .../bigdl/dllib/nn/MinSerialTest.scala | 30 +++++++++++++ .../bigdl/dllib/nn/MixtureTableSpec.scala | 35 +++++++++++++++ .../bigdl/dllib/nn/MulConstantSpec.scala | 11 +++++ .../bigdl/dllib/nn/MultiRNNCellSpec.scala | 36 ++++++++++++++++ 41 files changed, 1094 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThresholdSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CDivTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMaxTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMinTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CSubTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnnSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSDSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DropoutSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ELUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/EchoSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSamplerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/IndexSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InputSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/L1PenaltySpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LSTMSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelectSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MinSerialTest.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MixtureTableSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbsSpec.scala new file mode 100644 index 00000000000..092ee4c98ea --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AbsSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class AbsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val abs = Abs[Float]().setName("abs") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(abs, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularizationSpec.scala index 042ee53e42b..ef9412ea0f9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ActivityRegularizationSpec.scala @@ -17,6 +17,10 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.keras.{KerasBaseSpec, KerasRunner, Regularizer} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class ActivityRegularizationSpec extends KerasBaseSpec { "ActivityRegularization" should "same as keras" in { @@ -50,3 +54,12 @@ class ActivityRegularizationSpec extends KerasBaseSpec { bgradInput.almostEqual(gradInput, 1e-5) should be(true) } } + +class ActivityRegularizationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val activityRegularization = ActivityRegularization[Float](l1 = 0.01, l2 = 0.01). + setName("activityRegularization") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(activityRegularization, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThresholdSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThresholdSpec.scala new file mode 100644 index 00000000000..5467840a42a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryThresholdSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class BinaryThresholdSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val binaryThreshold = BinaryThreshold[Float]().setName("binaryThreshold") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(binaryThreshold, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTMSpec.scala index 32bde44d032..ec66fb70595 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTMSpec.scala @@ -16,7 +16,9 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class BinaryTreeLSTMSpec extends FlatSpec with Matchers with BeforeAndAfter { @@ -91,3 +93,29 @@ class BinaryTreeLSTMSpec extends FlatSpec with Matchers with BeforeAndAfter { gradInput should be(expectGradInput) } } + +class BinaryTreeLSTMSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + RNG.setSeed(1000) + val binaryTreeLSTM = BinaryTreeLSTM[Float](2, 2).setName("binaryTreeLSTM") + + val inputs = + Tensor[Float]( + T(T(T(1f, 2f), + T(2f, 3f), + T(4f, 5f)))) + + val tree = + Tensor[Float]( + T(T(T(2f, 5f, -1f), + T(0f, 0f, 1f), + T(0f, 0f, 2f), + T(0f, 0f, 3f), + T(3f, 4f, 0f)))) + + val input = T(inputs, tree) + + runSerializationTest(binaryTreeLSTM, input) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CDivTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CDivTableSpec.scala new file mode 100644 index 00000000000..400c56dd4ed --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CDivTableSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class CDivTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val cdivTable = new CDivTable[Float]().setName("cdivTable") + val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(cdivTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMaxTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMaxTableSpec.scala new file mode 100644 index 00000000000..623a21a03d5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMaxTableSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class CMaxTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val cmaxTable = new CMaxTable[Float]().setName("cmaxTable") + val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(cmaxTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMinTableSpec.scala new file mode 100644 index 00000000000..6a8ec4690dd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMinTableSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class CMinTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val cminTable = new CMinTable[Float]().setName("cminTable") + val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(cminTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CSubTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CSubTableSpec.scala new file mode 100644 index 00000000000..fd445d3a7a2 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CSubTableSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class CSubTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val csubTable = CSubTable[Float]().setName("csubTable") + + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(csubTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala index ba258a04450..2bafecbf982 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConcatSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.LayerException +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class ConcatSpec extends FlatSpec with Matchers { @@ -87,3 +90,13 @@ class ConcatSpec extends FlatSpec with Matchers { contains should be (true) } } + +class ConcatSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Tensor[Float](2, 2, 2).apply1(e => Random.nextFloat()) + val concat = Concat[Float](2).setName("concat") + concat.add(Abs[Float]()) + concat.add(Abs[Float]()) + runSerializationTest(concat, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceSpec.scala new file mode 100644 index 00000000000..b78e54c0178 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class CosineDistanceSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val cosineDistance = CosineDistance[Float]().setName("cosineDistance") + val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) + var input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(cosineDistance, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2DSpec.scala new file mode 100644 index 00000000000..7f134f8e536 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Cropping2DSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class Cropping2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val cropping2d = Cropping2D[Float](Array(2, 2), Array(2, 2), DataFormat.NCHW) + .setName("Cropping2D") + val input = Tensor[Float](1, 9, 9, 9).apply1(_ => Random.nextFloat()) + runSerializationTest(cropping2d, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3DSpec.scala new file mode 100644 index 00000000000..de5b991d25b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/Cropping3DSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class Cropping3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val cropping3d = Cropping3D[Float](Array(2, 2), Array(2, 2), Array(2, 2)).setName("Cropping3D") + val input = Tensor[Float](1, 9, 9, 9, 9).apply1(_ => Random.nextFloat()) + runSerializationTest(cropping3d, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala index 59aa1586751..c93a36e02a9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DenseToSparseSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{Matchers, FlatSpec} import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest @com.intel.analytics.bigdl.tags.Parallel class DenseToSparseSpec extends FlatSpec with Matchers { @@ -60,3 +61,11 @@ class DenseToSparseSpec extends FlatSpec with Matchers { model.backward(mockInput, mockError) } } + +class DenseToSparseSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val denseToSparse = DenseToSparse[Float]().setName("denseToSparse") + val input = Tensor.range[Float](1, 12, 1) + runSerializationTest(denseToSparse, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnnSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnnSpec.scala new file mode 100644 index 00000000000..b774e04b651 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputFrcnnSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import java.io.File + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.{ModuleLoader, ModulePersister, ModuleSerializationTest} + +import scala.util.Random + + +class DetectionOutputFrcnnSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = DetectionOutputFrcnn().setName("DetectionOutputFrcnn") + val name = module.getName + val serFile = File.createTempFile(name, postFix) + + ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) + RNG.setSeed(1000) + val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) + + + if (serFile.exists) { + serFile.delete + } + tested.add(module.getClass.getName) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSDSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSDSpec.scala new file mode 100644 index 00000000000..ac8b86d2ae3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSDSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import java.io.File + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.{ModuleLoader, ModulePersister, ModuleSerializationTest} + +import scala.util.Random + + +class DetectionOutputSSDSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = DetectionOutputSSD[Float](DetectionOutputParam()).setName("DetectionOutputSSD") + val name = module.getName + val serFile = File.createTempFile(name, postFix) + + ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) + RNG.setSeed(1000) + val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) + + + if (serFile.exists) { + serFile.delete + } + tested.add(module.getClass.getName) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DropoutSpec.scala new file mode 100644 index 00000000000..18e2296bb99 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DropoutSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class DropoutSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + RNG.setSeed(100) + val dropout = Dropout[Float]().setName("dropout") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(dropout, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala index 11ece8a6e28..8457620021f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala @@ -30,6 +30,7 @@ import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.reflect.ClassTag import scala.util.Random @@ -1348,3 +1349,17 @@ class DynamicGraphSpec extends FlatSpec with Matchers { result.toTable.apply[Tensor[Float]](2).valueAt(1) should be(47) } } + +class DynamicGraphSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val linear = Linear[Float](2, 2) + val linearNode = linear.inputs() + val linearWeight = linear.weight + val linearBias = linear.bias + val variables = Some(Array(linearWeight), Array(linearBias)) + val graphWithVariable = Graph.dynamic[Float](Array(linearNode), Array(linearNode), + variables, false).setName("graphWithVariable") + val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) + runSerializationTest(graphWithVariable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ELUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ELUSpec.scala new file mode 100644 index 00000000000..69747019995 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ELUSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ELUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val elu = ELU[Float]().setName("elu") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(elu, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/EchoSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/EchoSpec.scala new file mode 100644 index 00000000000..8ded04971a1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/EchoSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class EchoSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val echo = Echo[Float]().setName("echo") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(echo, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropoutSpec.scala index ac3ebc9d202..01c7fce937f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropoutSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropoutSpec.scala @@ -17,8 +17,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + /** * Unit test for GaussianDropout. */ @@ -78,3 +82,12 @@ class GaussianDropoutSpec extends FlatSpec with Matchers { (a1 zip a2).foreach(x => assert(x._1 == x._2)) } } + +class GaussianDropoutSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + RNG.setSeed(1000) + val gaussianDropout = GaussianDropout[Float](0.5).setName("gaussianDropout") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(gaussianDropout, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoiseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoiseSpec.scala index 9ae5f004947..2314242be0b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoiseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoiseSpec.scala @@ -17,8 +17,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + /** * Unit test for GaussianNoise */ @@ -69,3 +73,12 @@ class GaussianNoiseSpec extends FlatSpec with Matchers { (a1 zip a2).foreach(x => assert(x._1 == x._2)) } } + +class GaussianNoiseSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + RNG.setSeed(1000) + val gaussianNoise = GaussianNoise[Float](0.5).setName("gaussianNoise") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(gaussianNoise, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSamplerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSamplerSpec.scala new file mode 100644 index 00000000000..45612ccd2d2 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GaussianSamplerSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class GaussianSamplerSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input1 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input2 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) + val input = T(input1, input2) + RNG.setSeed(1000) + val gaussianSampler = GaussianSampler[Float]().setName("gaussianSampler") + runSerializationTest(gaussianSampler, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/IndexSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/IndexSpec.scala new file mode 100644 index 00000000000..05d8345d931 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/IndexSpec.scala @@ -0,0 +1,39 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class IndexSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val index = Index[Float](1).setName("index") + val input1 = Tensor[Float](3).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](4) + input2(Array(1)) = 1 + input2(Array(2)) = 2 + input2(Array(3)) = 2 + input2(Array(4)) = 3 + val input = new Table() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(index, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InputSpec.scala new file mode 100644 index 00000000000..4b619e15017 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/InputSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class InputSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val inputl = Input[Float]().element.setName("input") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(inputl, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/L1PenaltySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/L1PenaltySpec.scala new file mode 100644 index 00000000000..f054792a06e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/L1PenaltySpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class L1PenaltySerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val l1Penalty = L1Penalty[Float](1, true, true).setName("l1Penalty") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(l1Penalty, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LSTMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LSTMSpec.scala new file mode 100644 index 00000000000..3ad9aecf478 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LSTMSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class LSTMSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val lstm = LSTM[Float](6, 4) + val lstmModel = Recurrent[Float]().add(lstm).setName("lstm") + val input = Tensor[Float](Array(1, 5, 6)).apply1(_ => Random.nextFloat()) + runSerializationTest(lstmModel, input, lstm.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLUSpec.scala new file mode 100644 index 00000000000..58b5f27915c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLUSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class LeakyReLUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val leakyReLU = LeakyReLU[Float](0.01, true).setName("leakyReLU") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(leakyReLU, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala index 90d3d8d608a..dee6276ec42 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala @@ -23,8 +23,11 @@ import com.intel.analytics.bigdl._ import scala.math._ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.optim.{L1Regularizer, L2Regularizer, SGD} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{RandomGenerator, Shape, T, TestUtils} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class LinearSpec extends FlatSpec with Matchers { "Linear L2 regularizer" should "works correctly" in { @@ -413,3 +416,11 @@ class LinearSpec extends FlatSpec with Matchers { TestUtils.compareOutputShape(linear, Shape(3)) should be (true) } } + +class LinearSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val linear = Linear[Float](10, 2).setName("linear") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(linear, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1DSpec.scala new file mode 100644 index 00000000000..fa19d165a8a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected1DSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class LocallyConnected1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val localyConnected1d = + LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) + val input = Tensor[Float](6, 2).randn() + runSerializationTest(localyConnected1d, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala new file mode 100644 index 00000000000..9c634a95688 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LocallyConnected2DSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class LocallyConnected2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val locallyConnected2D = LocallyConnected2D[Float](3, 5, 5, 4, 2, 2). + setName("locallyConnected2D") + val input = Tensor[Float](1, 3, 5, 5).apply1( e => Random.nextFloat()) + runSerializationTest(locallyConnected2D, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala index f4511499bb6..13df53e8d43 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala @@ -20,6 +20,7 @@ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.torch.TH import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -127,3 +128,11 @@ class LogSoftMaxSpec extends FlatSpec with Matchers with BeforeAndAfter { output.apply1(v => {v.isInfinity should be (false); v}) } } + +class LogSoftMaxSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val logSoftMax = LogSoftMax[Float]().setName("logSoftMax") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(logSoftMax, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparseSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparseSpec.scala index 8d1e33d6460..b2197ac6f1b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparseSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LookupTableSparseSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest @com.intel.analytics.bigdl.tags.Parallel class LookupTableSparseSpec extends FlatSpec with Matchers { @@ -360,3 +361,14 @@ class LookupTableSparseSpec extends FlatSpec with Matchers { } } + +class LookupTableSparseSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val lookupTableSparse = LookupTableSparse[Float](20, 10, "sum", 1) + val indices1 = Array(0, 0, 1, 2) + val indices2 = Array(0, 1, 0, 3) + val values = Array(2f, 4, 1, 2) + val input = Tensor.sparse[Float](Array(indices1, indices2), values, Array(3, 4)) + runSerializationTest(lookupTableSparse, input, lookupTableSparse.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala index 792847e60fc..c1c548af3df 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MVSpec.scala @@ -16,9 +16,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class MVSpec extends FlatSpec with Matchers { "hashcode()" should "behave correctly" in { @@ -60,3 +63,15 @@ class MVSpec extends FlatSpec with Matchers { m1 should not equal m4 } } + +class MVSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mv = MV[Float]().setName("mv_layer") + val input1 = Tensor[Float](2, 3).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](3).apply1(e => Random.nextFloat()) + val input = new Table() + input(1.0f) = input1 + input(2.0f) = input2 + runSerializationTest(mv, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala index 3c128e62bb1..e71fed8e71a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MapTableSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class MapTableSpec extends FlatSpec with Matchers { "A MapTable" should "generate correct output" in { @@ -84,3 +87,17 @@ class MapTableSpec extends FlatSpec with Matchers { map.modules.length should be (1) } } + +class MapTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val linear = Linear[Float](2, 2) + val mapTable = new MapTable[Float]().setName("mapTable") + mapTable.add(linear) + val input1 = Tensor[Float](2).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](2).apply1(_ => Random.nextFloat()) + val input = T() + input(1.0.toFloat) = input1 + input(2.0.toFloat) = input2 + runSerializationTest(mapTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelectSpec.scala new file mode 100644 index 00000000000..e63a0f180d4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskedSelectSpec.scala @@ -0,0 +1,39 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class MaskedSelectSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val maskedSelect = MaskedSelect[Float]().setName("maskedSelect") + val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 2) + input2(Array(1, 1)) = 1 + input2(Array(1, 2)) = 0 + input2(Array(2, 1)) = 0 + input2(Array(2, 2)) = 1 + val input = new Table() + input(1.0f) = input1 + input(2.0f) = input2 + runSerializationTest(maskedSelect, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxSpec.scala new file mode 100644 index 00000000000..32829277950 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class MaxSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val max = new Max[Float](2).setName("max") + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(max, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MeanSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MeanSpec.scala index d3f600b2fd3..a745edd927a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MeanSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MeanSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class MeanSpec extends FlatSpec with Matchers { "mean" should "work correctly" in { @@ -47,3 +50,11 @@ class MeanSpec extends FlatSpec with Matchers { layer.forward(input) should be(expect) } } + +class MeanSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mean = Mean[Float](2).setName("mean") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(mean, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MinSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MinSerialTest.scala new file mode 100644 index 00000000000..c5a4f319af0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MinSerialTest.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class MinSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val min = Min[Float](2).setName("min") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(min, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MixtureTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MixtureTableSpec.scala new file mode 100644 index 00000000000..f2f4e3a0789 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MixtureTableSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class MixtureTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mixTureTable = MixtureTable[Float]().setName("mixTureTable") + val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input = new Table() + input(1.0f) = input1 + input(2.0f) = input2 + runSerializationTest(mixTureTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MulConstantSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MulConstantSpec.scala index 7d92c4949e8..ceaa6175733 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MulConstantSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MulConstantSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.FlatSpec +import scala.util.Random + /** * Created by yao on 9/21/16. */ @@ -59,3 +62,11 @@ class MulConstantSpec extends FlatSpec { assert(input equals expectedInput) } } + +class MulConstantSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mulConst = MulConstant[Float](1.0).setName("mulConst") + val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(mulConst, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCellSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCellSpec.scala index cc15aa558bd..1f7c47803e4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCellSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCellSpec.scala @@ -23,11 +23,13 @@ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.Table import com.intel.analytics.bigdl.utils.TorchObject.TYPE_DOUBLE_TENSOR +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.math._ +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class MultiRNNCellSpec extends FlatSpec with BeforeAndAfter with Matchers { @@ -635,3 +637,37 @@ class MultiRNNCellSpec extends FlatSpec with BeforeAndAfter with Matchers { model.forward(input) } } + +class MultiRNNCellSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val hiddenSize = 5 + val inputSize = 5 + val seqLength = 4 + val batchSize = 2 + val kernalW = 3 + val kernalH = 3 + val rec = RecurrentDecoder[Float](seqLength) + val cells = Array(ConvLSTMPeephole[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1), ConvLSTMPeephole[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1), ConvLSTMPeephole[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1)).asInstanceOf[Array[Cell[Float]]] + + val multiRNNCell = MultiRNNCell[Float](cells) + + val model = Sequential[Float]() + .add(rec + .add(multiRNNCell)).setName("multiRNNCell") + + val input = Tensor[Float](batchSize, inputSize, 10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input, multiRNNCell.getClass) + } +} From 26b3d962e0adde099fc9b87bc4b10257e5769f7a Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Mon, 5 Mar 2018 13:54:22 +0800 Subject: [PATCH 0712/1065] Change Operation Serial Test (#2338) * add ops serial test * fix a wrong remove * add one and remove fail one * change * remove truncatednormal --- .../bigdl/dllib/nn/ops/AllSpec.scala | 33 +++++++++++++++++ .../bigdl/dllib/nn/ops/ArgMaxSpec.scala | 11 ++++++ .../bigdl/dllib/nn/ops/BatchMatMulSpec.scala | 35 +++++++++++++++++++ .../bigdl/dllib/nn/ops/CastSpec.scala | 12 +++++++ .../nn/ops/CategoricalColHashBucketSpec.scala | 11 ++++++ .../nn/ops/CategoricalColVocaListSpec.scala | 14 ++++++++ .../DepthwiseConv2DBackpropFilterSpec.scala | 35 +++++++++++++++++++ .../dllib/nn/ops/DepthwiseConv2DSpec.scala | 15 +++++++- .../nn/ops/Dilation2DBackpropInputSpec.scala | 34 ++++++++++++++++++ .../bigdl/dllib/nn/ops/EqualSpec.scala | 13 +++++++ .../bigdl/dllib/nn/ops/ErfSpec.scala | 29 +++++++++++++++ .../bigdl/dllib/nn/ops/ErfcSpec.scala | 29 +++++++++++++++ .../bigdl/dllib/nn/ops/ExpSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/Expm1Spec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/FloorDivSpec.scala | 31 ++++++++++++++++ .../bigdl/dllib/nn/ops/FloorModSpec.scala | 31 ++++++++++++++++ .../bigdl/dllib/nn/ops/FloorSpec.scala | 12 +++++++ .../bigdl/dllib/nn/ops/GreaterEqualSpec.scala | 34 ++++++++++++++++++ .../bigdl/dllib/nn/ops/GreaterSpec.scala | 14 ++++++++ .../bigdl/dllib/nn/ops/IndicatorColSpec.scala | 17 +++++++++ .../bigdl/dllib/nn/ops/InvGradSpec.scala | 32 +++++++++++++++++ .../bigdl/dllib/nn/ops/InvSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/IsInfSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/IsNanSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/Kv2TensorSpec.scala | 15 ++++++++ .../bigdl/dllib/nn/ops/L2LossSpec.scala | 12 +++++++ .../bigdl/dllib/nn/ops/LessSpec.scala | 14 ++++++++ .../bigdl/dllib/nn/ops/LgammaSpec.scala | 29 +++++++++++++++ .../bigdl/dllib/nn/ops/LogicalAndSpec.scala | 10 ++++++ .../bigdl/dllib/nn/ops/LogicalNotSpec.scala | 10 ++++++ .../bigdl/dllib/nn/ops/LogicalOrSpec.scala | 10 ++++++ .../bigdl/dllib/nn/ops/MaximumSpec.scala | 32 +++++++++++++++++ .../bigdl/dllib/nn/ops/MinimumSpec.scala | 32 +++++++++++++++++ .../bigdl/dllib/nn/ops/ModSpec.scala | 31 ++++++++++++++++ .../dllib/nn/ops/ModuleToOperationSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/NotEqualSpec.scala | 10 ++++++ .../bigdl/dllib/nn/ops/OneHotSpec.scala | 14 ++++++++ .../bigdl/dllib/nn/ops/PadSpec.scala | 14 ++++++++ .../bigdl/dllib/nn/ops/PowSpec.scala | 11 ++++++ .../bigdl/dllib/nn/ops/RangeOpsSpec.scala | 29 +++++++++++++++ .../bigdl/dllib/nn/ops/RankSpec.scala | 12 +++++++ .../dllib/nn/ops/ResizeBilinearGradSpec.scala | 32 +++++++++++++++++ .../dllib/nn/ops/ResizeBilinearOpsSpec.scala | 33 +++++++++++++++++ .../bigdl/dllib/nn/ops/RintSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/RoundSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/SegmentSumSpec.scala | 32 +++++++++++++++++ .../bigdl/dllib/nn/ops/SignSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/ops/SliceSpec.scala | 13 +++++++ .../dllib/nn/ops/SquaredDifferenceSpec.scala | 32 +++++++++++++++++ .../bigdl/dllib/nn/ops/SumSpec.scala | 12 +++++++ .../bigdl/dllib/nn/ops/TileSpec.scala | 13 +++++++ 51 files changed, 1163 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AllSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMulSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DBackpropFilterSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInputSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ErfSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ErfcSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1Spec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDivSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorModSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterEqualSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsInfSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsNanSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LgammaSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaximumSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MinimumSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOpsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOpsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RintSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RoundSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SegmentSumSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SignSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SquaredDifferenceSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AllSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AllSpec.scala new file mode 100644 index 00000000000..c11102a9237 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/AllSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class AllSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val all = All[Float]().setName("all") + val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) + val input2 = Tensor[Int](T(2, 1, 2)) + val input = T() + input(1.toFloat) = input1 + input(2.toFloat) = input2 + runSerializationTest(all, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMaxSpec.scala index 8e885ec18c0..5b412505084 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ArgMaxSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} @@ -35,3 +36,13 @@ class ArgMaxSpec extends FlatSpec with Matchers { } } + +class ArgMaxSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val argMax = ArgMax[Float].setName("argMax") + val dataTensor = Tensor[Float](T(T(1.0f, 2.0f), T(3.0f, 4.0f))) + val dimensionTensor = Tensor.scalar[Int](1) + val input = T(dataTensor, dimensionTensor) + runSerializationTest(argMax, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMulSpec.scala new file mode 100644 index 00000000000..ba106cec2b9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMulSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class BatchMatMulSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val batchMatMul = BatchMatMul[Float, Float]().setName("batchMatMul") + val input = + T( + Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(batchMatMul, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala index ffbfae2d257..766e7f22696 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CastSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.nn.tf.Assign import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class CastSpec extends FlatSpec with Matchers { "Cast operation Float" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericFloat @@ -46,3 +49,12 @@ class CastSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class CastSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val cast = Cast[Float, Float]().setName("cast") + val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(cast, input, cast. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucketSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucketSpec.scala index c9dbadd9b5b..a752a24755c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucketSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColHashBucketSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{T, Table} import org.scalatest.{FlatSpec, Matchers} @@ -47,3 +48,13 @@ class CategoricalColHashBucketSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class CategoricalColHashBucketSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val categoricalColHashBucket = CategoricalColHashBucket[Float]( + hashBucketSize = 100 + ).setName("categoricalColHashBucket") + val input = Tensor[String](T(T(1), T(2), T(3))) + runSerializationTest(categoricalColHashBucket, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaListSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaListSpec.scala index e787329d7a0..e2507a2f7d2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaListSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CategoricalColVocaListSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class CategoricalColVocaListSpec extends FlatSpec with Matchers{ @@ -94,3 +95,16 @@ class CategoricalColVocaListSpec extends FlatSpec with Matchers{ output should be(expectOutput) } } + +class CategoricalColVocaListSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val categoricalColVocaList = CategoricalColVocaList[Float]( + vocaList = Array("A", "B", "C"), + strDelimiter = ",", + isSetDefault = false, + numOovBuckets = 0 + ).setName("categoricalColVocaList") + val input = Tensor[String](T(T("A"), T("B"), T("C"), T("D"))) + runSerializationTest(categoricalColVocaList, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DBackpropFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DBackpropFilterSpec.scala new file mode 100644 index 00000000000..0a369eec606 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DBackpropFilterSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class DepthwiseConv2DBackpropFilterSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val depWiseConv2dBackProp = DepthwiseConv2DBackpropFilter[Float](1, + 1, 0, 0, DataFormat.NHWC).setName("depWiseConv2dBackProp") + val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(2, 2, 3, 1)), + Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(depWiseConv2dBackProp, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DSpec.scala index 73470ebbcc2..03e16314564 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2DSpec.scala @@ -17,7 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.nn.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class DepthwiseConv2DSpec extends BigDLSpecHelper { "DepthwiseConv2D" should "be able to save and load" in { @@ -41,3 +45,12 @@ class DepthwiseConv2DSpec extends BigDLSpecHelper { Module.loadModule[Float](file.getAbsolutePath) } } + +class DepthwiseConv2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val depWIseConv2d = DepthwiseConv2D[Float](1, 1, 0, 0).setName("depWIseConv2d") + val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat())) + runSerializationTest(depWIseConv2d, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInputSpec.scala new file mode 100644 index 00000000000..89d08520b09 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Dilation2DBackpropInputSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class Dilation2DBackpropInputSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Dilation2DBackpropInput[Float, Float]( + Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") + + val input = T(Tensor[Float](4, 32, 32, 3).rand(), + Tensor[Float](3, 4, 3).rand(), + Tensor[Float](4, 11, 16, 3).rand()) + + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/EqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/EqualSpec.scala index 31927bbd8d1..e8e7d1c85c2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/EqualSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/EqualSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn.ops import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class EqualSpec extends FlatSpec with Matchers { "Equal Float operation" should "works correctly" in { val input = @@ -131,3 +134,13 @@ class EqualSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class EqualSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val equal = Equal[Float]().setName("equal") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(equal, input, + equal.asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ErfSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ErfSpec.scala new file mode 100644 index 00000000000..13bd3a32367 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ErfSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class ErfSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Erf[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ErfcSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ErfcSpec.scala new file mode 100644 index 00000000000..45ade5fe988 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ErfcSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class ErfcSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Erfc[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpSpec.scala new file mode 100644 index 00000000000..f4d5eb01f04 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ExpSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ExpSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val exp = Exp[Float, Float]().setName("expOps") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(exp, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1Spec.scala new file mode 100644 index 00000000000..561f9d32c3d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Expm1Spec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class Expm1SerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val expm1 = Expm1[Float, Float]().setName("expm1") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(expm1, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDivSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDivSpec.scala new file mode 100644 index 00000000000..b8ecd402bd2 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorDivSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class FloorDivSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val floorDiv = FloorDiv[Float, Float]().setName("floorDiv") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(floorDiv, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorModSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorModSpec.scala new file mode 100644 index 00000000000..d59405ea9de --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorModSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class FloorModSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val floorMod = FloorMod[Float, Float]().setName("floorMod") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(floorMod, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala index e7ae471ded5..1baac2b7eb6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/FloorSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class FloorSpec extends FlatSpec with Matchers { "Floor Float operation" should "works correctly" in { val input = @@ -50,3 +53,12 @@ class FloorSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class FloorSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val floor = Floor[Float]().setName("floor") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(floor, input, floor. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterEqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterEqualSpec.scala new file mode 100644 index 00000000000..a2e99793031 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterEqualSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class GreaterEqualSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val greaterEqual = GreaterEqual[Float]().setName("greaterEqual") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(greaterEqual, input, greaterEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala index 35a4b86ed3b..bc16ce69463 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GreaterSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class GreaterSpec extends FlatSpec with Matchers { "Greater Float operation" should "works correctly" in { val input = @@ -98,3 +101,14 @@ class GreaterSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class GreaterSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val greater = Greater[Float]().setName("greater") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(greater, input, greater. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorColSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorColSpec.scala index 5e468d206e8..e8307e0f67b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorColSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IndicatorColSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class IndicatorColSpec extends FlatSpec with Matchers { @@ -65,3 +66,19 @@ class IndicatorColSpec extends FlatSpec with Matchers { output should be(expectedOutput) } } + +class IndicatorColSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val indicatorCol = IndicatorCol[Float]( + feaLen = 4, + isCount = true + ).setName("indicatorCol") + val input = Tensor.sparse( + Array(Array(0, 1, 1, 2, 2, 3, 3, 3), + Array(0, 0, 3, 0, 1, 0, 1, 2)), + Array(3, 1, 2, 0, 3, 1, 2, 2), + Array(4, 4) + ) + runSerializationTest(indicatorCol, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGradSpec.scala new file mode 100644 index 00000000000..621fae6ab65 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvGradSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class InvGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val invGrad = InvGrad[Float, Float]().setName("invGrad") + val input = T(Tensor[Float](2, 5).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 5).apply1(_ => Random.nextFloat())) + runSerializationTest(invGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvSpec.scala new file mode 100644 index 00000000000..3b47d96a5cd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/InvSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class InvSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val inv = Inv[Float, Float]().setName("inv") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(inv, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsInfSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsInfSpec.scala new file mode 100644 index 00000000000..c245a9b9087 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsInfSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class IsInfSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val isInf = IsInf[Float, Float]().setName("isInf") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isInf, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsNanSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsNanSpec.scala new file mode 100644 index 00000000000..f135446360c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/IsNanSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class IsNanSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val isNan = IsNan[Float, Float]().setName("isInf") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(isNan, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2TensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2TensorSpec.scala index 91b7f9b7ce3..e05dac796ef 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2TensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2TensorSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.{DenseType, SparseType, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{T, Table} import org.scalatest.{FlatSpec, Matchers} @@ -99,3 +100,17 @@ class Kv2TensorSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class Kv2TensorSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val kv2tensor = Kv2Tensor[Float, Float]( + kvDelimiter = ",", itemDelimiter = ":", transType = 0 + ).setName("kv2tensor") + val input = T( + Tensor[String]( + T(T("0:0.1,1:0.2"), T("1:0.3,3:0.5"), T("2:0.15,4:0.25"))), + Tensor[Int](Array(5), shape = Array[Int]()) + ) + runSerializationTest(kv2tensor, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2LossSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2LossSpec.scala index 0d4f5699cff..d65c488bc0d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2LossSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/L2LossSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class L2LossSpec extends FlatSpec with Matchers { "L2Loss Double operation" should "works correctly" in { val input = @@ -48,3 +51,12 @@ class L2LossSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class L2LossSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val l2loss = L2Loss[Float]().setName("l2loss") + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(l2loss, input, + l2loss.asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala index f2a277f7bec..fd162f8dbf3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LessSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class LessSpec extends FlatSpec with Matchers { "Less Float operation" should "works correctly" in { val input = @@ -98,3 +101,14 @@ class LessSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class LessSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val less = Less[Float]().setName("less") + val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(less, input, less + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LgammaSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LgammaSpec.scala new file mode 100644 index 00000000000..d611e3d612b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LgammaSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class LgammaSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Lgamma[Float, Float]() + + val input = Tensor[Float](1, 5, 3, 4).rand() + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalAndSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalAndSpec.scala index 8bd27ca6bd8..6a57603078b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalAndSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalAndSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class LogicalAndSpec extends FlatSpec with Matchers { @@ -34,3 +35,12 @@ class LogicalAndSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class LogicalAndSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val logicalAnd = LogicalAnd[Float].setName("logicalAnd") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(logicalAnd, input, logicalAnd. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalNotSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalNotSpec.scala index 67b9f47cf98..05f792d4f18 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalNotSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalNotSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class LogicalNotSpec extends FlatSpec with Matchers { @@ -30,3 +31,12 @@ class LogicalNotSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class LogicalNotSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val logicalNot = LogicalNot[Float].setName("logicalNot") + val input = Tensor[Boolean](T(true, false)) + runSerializationTest(logicalNot, input, logicalNot + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalOrSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalOrSpec.scala index 9f90a13f5ea..977ccb31a7b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalOrSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/LogicalOrSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class LogicalOrSpec extends FlatSpec with Matchers { @@ -34,3 +35,12 @@ class LogicalOrSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class LogicalOrSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val logicalOr = LogicalOr[Float].setName("logicalOr") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(logicalOr, input, logicalOr + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaximumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaximumSpec.scala new file mode 100644 index 00000000000..5e6147fc14b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaximumSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class MaximumSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val maxiMum = Maximum[Float, Float]().setName("maxiMum") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(maxiMum, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MinimumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MinimumSpec.scala new file mode 100644 index 00000000000..271f2a8ef1c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MinimumSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class MinimumSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val minimum = Minimum[Float, Float]().setName("minimum") + val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), + Tensor[Float](5).apply1(_ => Random.nextFloat())) + runSerializationTest(minimum, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModSpec.scala new file mode 100644 index 00000000000..ac994755bcc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class ModSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mod = Mod[Float, Float]().setName("mod") + val input1 = Tensor[Float](5).fill(1.0f) + val input2 = Tensor[Float](5).fill(2.0f) + val input = T(input1, input2) + runSerializationTest(mod, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperationSpec.scala new file mode 100644 index 00000000000..d5ab9dbbe34 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ModuleToOperationSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class ModuleToOperationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val moduleToOperation = ModuleToOperation[Float](new LogicalOr()). + setName("moduleToOperation") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(moduleToOperation, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqualSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqualSpec.scala index 0f996da35b9..6ac819da68c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqualSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/NotEqualSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn.ops import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class NotEqualSpec extends FlatSpec with Matchers { @@ -131,3 +132,12 @@ class NotEqualSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class NotEqualSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val notEqual = NotEqual[Float].setName("notEqual") + val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) + runSerializationTest(notEqual, input, notEqual + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala index 837a2852129..98b5ef24279 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/OneHotSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class OneHotSpec extends FlatSpec with Matchers { @@ -92,3 +93,16 @@ class OneHotSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class OneHotSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val oneHot = OneHot[Float, Float](axis = -1).setName("oneHot") + val input = + T(Tensor[Long](T(0, 2, -1, 1)), + Tensor[Int](Array(3), shape = Array[Int]()), + Tensor[Float](Array(0.5f), shape = Array[Int]()), + Tensor[Float](Array(0.0f), shape = Array[Int]())) + runSerializationTest(oneHot, input, oneHot + .asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PadSpec.scala index eb673ed13e4..cb283329ac8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PadSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class PadSpec extends FlatSpec with Matchers { "Pad operation" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericFloat @@ -71,3 +74,14 @@ class PadSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class PadSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val pad = Pad[Float, Float](mode = "CONSTANT", 0.0f).setName("pad") + val inputTensor = Tensor[Float](2, 2, 3).apply1(_ => Random.nextFloat()) + val padding = Tensor[Int](T(T(1, 2), T(1, 2), T(1, 2))) + val input = T(inputTensor, padding) + runSerializationTest(pad, input, pad. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PowSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PowSpec.scala index d6bdfe7ef8b..2ca1d16683c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PowSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/PowSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class PowSpec extends FlatSpec with Matchers { @@ -35,3 +36,13 @@ class PowSpec extends FlatSpec with Matchers { ops.forward(T(t, v)) should be(Tensor[Double](T(1, 4, 9))) } } + +class PowSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val pow = Pow[Float]().setName("powOps") + val v = Tensor[Float](T(2)) + val t = Tensor[Float](T(1, 2, 3)) + val input = (T(t, v)) + runSerializationTest(pow, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOpsSpec.scala new file mode 100644 index 00000000000..41e73a061d3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RangeOpsSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class RangeOpsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val rangeOps = RangeOps[Float, Float]().setName("rangeOps") + val input = T(Tensor[Float](T(1)), Tensor[Float](T(10)), Tensor[Float](T(1))) + runSerializationTest(rangeOps, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala index 66906e276e9..d0bd5d5ae07 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RankSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class RankSpec extends FlatSpec with Matchers { "Rank Float operation" should "works correctly" in { val input = @@ -100,3 +103,12 @@ class RankSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class RankSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val rank = Rank[Float].setName("rank") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(rank, input, rank. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearGradSpec.scala new file mode 100644 index 00000000000..2b8034099f8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearGradSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class ResizeBilinearGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = ResizeBilinearGrad[Float](true) + val input = T(Tensor[Float](1, 224, 224, 3).rand(), + Tensor[Float](1, 64, 64, 3).rand()) + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOpsSpec.scala new file mode 100644 index 00000000000..63c68814c9c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/ResizeBilinearOpsSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ResizeBilinearOpsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val resizeBilinearOps = ResizeBilinearOps[Float](false). + setName("resizeBiLinearOps") + val input = T(Tensor[Float](1, 3, 2, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(3, 2))) + runSerializationTest(resizeBilinearOps, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RintSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RintSpec.scala new file mode 100644 index 00000000000..387ea6bfdcf --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RintSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class RintSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val rint = Rint[Float]().setName("rint") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(rint, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RoundSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RoundSpec.scala new file mode 100644 index 00000000000..cdbadc80969 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RoundSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class RoundSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val round = Round[Float, Float]().setName("round") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(round, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SegmentSumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SegmentSumSpec.scala new file mode 100644 index 00000000000..0d4cf37d1d1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SegmentSumSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SegmentSumSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sgSum = SegmentSum[Float].setName("segmentSum") + val input = T(Tensor[Float](10, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 0, 0, 1, 2, 3, 3, 4, 4, 4))) + runSerializationTest(sgSum, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SignSpec.scala new file mode 100644 index 00000000000..08abced5e74 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SignSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SignSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sign = Sign[Float, Float]().setName("sign") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(sign, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SliceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SliceSpec.scala index 51832ee2ae6..0eb3c9e6ed6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SliceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SliceSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class SliceSpec extends FlatSpec with Matchers { "Slice operation" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericFloat @@ -52,3 +55,13 @@ class SliceSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class SliceSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val slice = Slice[Float](begin = Array(0, 1, 1), + size = Array(2, -1, 1)).setName("slice") + val input = Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(slice, input, slice. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SquaredDifferenceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SquaredDifferenceSpec.scala new file mode 100644 index 00000000000..1cdb99cf84b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SquaredDifferenceSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SquaredDifferenceSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val squareDiff = SquaredDifference[Float]().setName("squareDiff") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(squareDiff, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SumSpec.scala index 839a90a8c4f..e8ee31e5d01 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SumSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class SumSpec extends FlatSpec with Matchers { "Sum operation" should "works correctly" in { val input = @@ -36,3 +39,12 @@ class SumSpec extends FlatSpec with Matchers { op.forward(T(input, Tensor[Int](T(1, 2)))) should be(Tensor.scalar[Int](6)) } } + +class SumSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sum = Sum[Float, Float]().setName("sumOps") + val input = T(Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float]()) + runSerializationTest(sum, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala index 347363e8c33..2acb3f507e2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TileSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class TileSpec extends FlatSpec with Matchers { "Tile operation" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericFloat @@ -68,3 +71,13 @@ class TileSpec extends FlatSpec with Matchers { Tile[Float]().forward(T(scalar, multiply)) should be(Tensor.scalar(1)) } } + +class TileSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val tile = Tile[Float]().setName("tileOps") + val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), + Tensor[Int](T(2, 1, 2))) + runSerializationTest(tile, input, tile. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} From 60ff656eefb9d57b8b7575cb135350e3f05dd7c1 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Mon, 5 Mar 2018 14:49:12 +0800 Subject: [PATCH 0713/1065] Add TensorOp (#2335) * add TensorOp * add Serialization support --- .../bigdl/dllib/nn/ops/TensorOp.scala | 633 ++++++++++++++++++ .../bigdl/dllib/nn/ops/TensorOpSpec.scala | 135 ++++ .../serializer/OperationSerializerSpec.scala | 18 +- 3 files changed, 785 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOp.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOp.scala new file mode 100644 index 00000000000..91a8954d8f9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOp.scala @@ -0,0 +1,633 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.serialization.Bigdl +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import org.apache.commons.lang3.SerializationUtils + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + +/** + * [[TensorOp]] is an [[Operation]] with `Tensor[T]-formatted `input and output, + * which provides shortcuts to build Operations for `tensor transformation` by closures. + *

+ * [[TensorOp]] will make a deep copy of input Tensor before transformation, + * so transformation will take no side effect. For now, `SparseTensors` are not supported. + *

+ * Chained feature is supported in [[TensorOp]]. + * And common tensor actions are provided with a chained style. + *

+ * For instance: + * {{{ + * one case: + * val (transformer1, transformer2, transformer3) = ... + * val (op1, op2, op3) = (TensorOp[Float](transformer1), .., ..) + * val op = op1 -> op2 -> op3 + * `equals` + * val op = TensorOp[Float]((t: Tensor[Float], ev: TensorNumeric[Float]) => { + * transformer3(transformer2(transformer1(t, ev), ev), ev) + * }) + * + * another case: + * val op = (TensorOp[Float]() * 2.3f + 1.23f) / 1.11f - 0.66f + * `equals` + * val transformer = (t: Tensor[T], _) => t.mul(2.3f).add(1.23f).div(1.11f).sub(0.66f) + * val op = TensorOp[Float](transformer) + * }}} + * + * @param transformer closure of tensor transformation + * @tparam T Numeric type + */ +class TensorOp[T: ClassTag] private( + private[bigdl] val transformer: (Tensor[T], TensorNumeric[T]) => Tensor[T] +)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { + + private lazy val buffer: Tensor[T] = Tensor[T]() + + // TODO: support SparseTensor + final override def updateOutput(input: Tensor[T]): Tensor[T] = { + buffer.resizeAs(input).copy(input) + output = transformer(buffer, ev) + output + } + + // scalastyle:off + final def ->(next: TensorOp[T]): TensorOp[T] = { + val chained = (in: Tensor[T], ev: TensorNumeric[T]) => { + next.transformer(transformer(in, ev), ev) + } + new TensorOp(chained) + } + + /** + * append additional TensorOp to do element-wise `f(x) = x + a` + * + * @param value T a + * @return TensorOp[T] + */ + final def +(value: T): TensorOp[T] = this -> TensorOp.add(value) + + /** + * append additional TensorOp to do element-wise tensor addition + * + * @param tensor Tensor[T] + * @return TensorOp[T] + */ + final def +(tensor: Tensor[T]): TensorOp[T] = this -> TensorOp.add(tensor) + + /** + * build a TensorOp to do element-wise `f(x) = x - a` + * + * @param value T a + * @return TensorOp[T] + */ + final def -(value: T): TensorOp[T] = this -> TensorOp.sub(value) + + /** + * build a TensorOp to do element-wise tensor subtraction + * + * @param tensor Tensor[T] + * @return TensorOp[T] + */ + final def -(tensor: Tensor[T]): TensorOp[T] = this -> TensorOp.sub(tensor) + + /** + * build a TensorOp to do element-wise `f(x) = a * x` + * + * @param value T a + * @return TensorOp[T] + */ + final def *(value: T): TensorOp[T] = this -> TensorOp.mul(value) + + /** + * build a TensorOp to do element-wise multiplication + * + * @param tensor Tensor[T] + * @return TensorOp[T] + */ + final def *(tensor: Tensor[T]): TensorOp[T] = this -> TensorOp.mul(tensor) + + /** + * build a TensorOp to do element-wise `f(x) = x / a` + * + * @param value T a + * @return TensorOp[T] + */ + final def /(value: T): TensorOp[T] = this -> TensorOp.div(value) + + /** + * build a TensorOp to do element-wise division + * + * @param tensor Tensor[T] + * @return TensorOp[T] + */ + final def /(tensor: Tensor[T]): TensorOp[T] = this -> TensorOp.div(tensor) + + /** + * build a TensorOp to do element-wise `f(x) = x ^ n` + * + * @param n the order of power + * @return TensorOp[T] + */ + final def **(n: T): TensorOp[T] = this -> TensorOp.pow(n) + + /** + * build a TensorOp to do element-wise `f(x) = if (x>=a) 1; else 0` + * + * @param value Double a + * @return TensorOp[T] + */ + final def >=(value: Double): TensorOp[T] = this -> TensorOp.ge(value) + + /** + * build a TensorOp to do element-wise `f(x) = if (x==a) 1; else 0` + * + * @param value T a + * @return TensorOp[T] + */ + final def ==(value: T): TensorOp[T] = this -> TensorOp.eq(value) + // scalastyle:on + + /** + * build a TensorOp to do matrix transposition for 2d Tensors + * + * @return TensorOp[T] + */ + final def t: TensorOp[T] = this -> TensorOp.t() + + /** + * build a TensorOp to do element-wise `f(x) = sqrt(x)` + * + * @return TensorOp[T] + */ + final def sqrt: TensorOp[T] = this -> TensorOp.sqrt() + + /** + * build a TensorOp to do element-wise `f(x) = log(x)` + * + * @return TensorOp[T] + */ + final def log: TensorOp[T] = this -> TensorOp.log() + + /** + * build a TensorOp to do element-wise `f(x) = log(x + 1)` + * + * @return TensorOp[T] + */ + final def log1p: TensorOp[T] = this -> TensorOp.log1p() + + /** + * build a TensorOp to do element-wise `f(x) = exp(x)` + * + * @return TensorOp[T] + */ + final def exp: TensorOp[T] = this -> TensorOp.exp() + + /** + * build a TensorOp to do element-wise `floor` + * + * @return TensorOp[T] + */ + final def floor: TensorOp[T] = this -> TensorOp.floor() + + /** + * build a TensorOp to do element-wise `ceil` + * + * @return TensorOp[T] + */ + final def ceil: TensorOp[T] = this -> TensorOp.ceil() + + /** + * build a TensorOp to do element-wise `f(x) = 1 / x` + * + * @return TensorOp[T] + */ + final def inv: TensorOp[T] = this -> TensorOp.inv() + + /** + * build a TensorOp to do element-wise `f(x) = -x` + * + * @return TensorOp[T] + */ + final def neg: TensorOp[T] = this -> TensorOp.negative() + + /** + * build a TensorOp to do element-wise `f(x) = |x|` + * + * @return TensorOp[T] + */ + final def abs: TensorOp[T] = this -> TensorOp.abs() + + /** + * build a TensorOp to do element-wise `f(x) = tanh(x)` + * + * @return TensorOp[T] + */ + final def tanh: TensorOp[T] = this -> TensorOp.tanh() + + /** + * build a TensorOp to do element-wise `f(x) = if (x>0) 1; if (x=0) 0; else -1` + * + * @return TensorOp[T] + */ + final def sign: TensorOp[T] = this -> TensorOp.sign() + + /** + * build a TensorOp to do element-wise `f(x) = 1 / (1 + exp(-x))` + * + * @return TensorOp[T] + */ + final def sigmoid: TensorOp[T] = this -> TensorOp.sigmoid() + +} + +object TensorOp { + + // register custom DataConverter for transformer + DataConverter.registerConverter( + "(com.intel.analytics.bigdl.tensor.Tensor[T], " + + "com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric[T]) => " + + "com.intel.analytics.bigdl.tensor.Tensor[T]", + new DataConverter { + override def getAttributeValue[T: ClassTag]( + context: DeserializeContext, + attribute: Bigdl.AttrValue + )(implicit ev: TensorNumeric[T]): AnyRef = { + val any = attribute.getCustomValue + val bytes = any.getValue.toByteArray + val wrapper = SerializationUtils.deserialize[ClosureWrapper[T]](bytes) + wrapper.closure + } + + override def setAttributeValue[T: ClassTag]( + context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, + value: scala.Any, + valueType: universe.Type + )(implicit ev: TensorNumeric[T]): Unit = { + attributeBuilder.setDataType(DataType.CUSTOM) + val wrapper = new ClosureWrapper( + value.asInstanceOf[(Tensor[T], TensorNumeric[T]) => Tensor[T]]) + val bytes = SerializationUtils.serialize(wrapper) + val anyBuilder = com.google.protobuf.Any.newBuilder() + anyBuilder.setValue(ByteString.copyFrom(bytes)) + attributeBuilder.setCustomValue(anyBuilder.build()) + } + } + ) + + // Class Wrapper for transformer(closure) + private class ClosureWrapper[T: ClassTag]( + val closure: (Tensor[T], TensorNumeric[T]) => Tensor[T] + )(implicit ev: TensorNumeric[T]) extends Serializable + + + /** + * build a TensorOp with user-defined transformer + * + * @param transformer user-defined tensor transformer + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def apply[T: ClassTag](transformer: (Tensor[T], TensorNumeric[T]) => Tensor[T] + )(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp(transformer) + } + + /** + * build a TensorOp with identity transformer + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t) + } + + /** + * build a TensorOp to do element-wise `f(x) = x + a` + * + * @param value T a + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def add[T: ClassTag](value: T)(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp[T]((t: Tensor[T], _) => t.add(value)) + } + + /** + * build a TensorOp to do element-wise tensor addition + * + * @param tensor Tensor[T] + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def add[T: ClassTag](tensor: Tensor[T])(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp[T]((t: Tensor[T], _) => t.add(tensor)) + } + + /** + * build a TensorOp to do element-wise `f(x) = x - a` + * + * @param value T a + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def sub[T: ClassTag](value: T)(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp[T]((t: Tensor[T], _) => t.sub(value)) + } + + /** + * build a TensorOp to do element-wise tensor subtraction + * + * @param tensor Tensor[T] + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def sub[T: ClassTag](tensor: Tensor[T])(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp[T]((t: Tensor[T], _) => t.sub(tensor)) + } + + /** + * build a TensorOp to do element-wise `f(x) = a * x` + * + * @param value T a + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def mul[T: ClassTag](value: T)(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp[T]((t: Tensor[T], _) => t.mul(value)) + } + + /** + * build a TensorOp to do element-wise multiplication + * + * @param tensor Tensor[T] + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def mul[T: ClassTag](tensor: Tensor[T])(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp[T]((t: Tensor[T], _) => t.cmul(tensor)) + } + + /** + * build a TensorOp to do element-wise `f(x) = x / a` + * + * @param value T a + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def div[T: ClassTag](value: T)(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.div(value)) + } + + /** + * build a TensorOp to do element-wise division + * + * @param tensor Tensor[T] + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def div[T: ClassTag](tensor: Tensor[T])(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.div(tensor)) + } + + /** + * build a TensorOp to do element-wise `f(x) = if (x>=a) 1; else 0` + * + * @param value Double a + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def ge[T: ClassTag](value: Double)(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.ge(t, value)) + } + + /** + * build a TensorOp to do element-wise `f(x) = if (x==a) 1; else 0` + * + * @param value T a + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def eq[T: ClassTag](value: T)(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.eq(t, value)) + } + + /** + * build a TensorOp to do matrix transposition for 2d Tensors + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def t[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.t()) + } + + /** + * build a TensorOp to do element-wise `f(x) = sqrt(x)` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def sqrt[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.sqrt()) + } + + /** + * build a TensorOp to do element-wise `f(x) = log(x)` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def log[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.log()) + } + + /** + * build a TensorOp to do element-wise `f(x) = log(x + 1)` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def log1p[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.log1p()) + } + + /** + * build a TensorOp to do element-wise `f(x) = exp(x)` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def exp[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.exp()) + } + + /** + * build a TensorOp to do element-wise `f(x) = x ^ n` + * + * @param n the order of power + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def pow[T: ClassTag](n: T)(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.pow(n)) + } + + /** + * build a TensorOp to do element-wise `f(x) = x ^ 2` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def square[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.square()) + } + + /** + * build a TensorOp to do element-wise `floor` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def floor[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.floor()) + } + + /** + * build a TensorOp to do element-wise `ceil` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def ceil[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.ceil()) + } + + /** + * build a TensorOp to do element-wise `f(x) = 1 / x` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def inv[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.inv()) + } + + /** + * build a TensorOp to do element-wise `f(x) = -x` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def negative[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.negative(t)) + } + + /** + * build a TensorOp to do element-wise `f(x) = |x|` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def abs[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.abs()) + } + + /** + * build a TensorOp to do element-wise `f(x) = tanh(x)` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def tanh[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.tanh()) + } + + /** + * build a TensorOp to do element-wise `f(x) = if (x>0) 1; if (x=0) 0; else -1` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def sign[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], _) => t.sign()) + } + + /** + * build a TensorOp to do element-wise `f(x) = 1 / (1 + exp(-x))` + * + * @tparam T type param of TensorOp + * @return TensorOp[T] + */ + def sigmoid[T: ClassTag]()(implicit ev: TensorNumeric[T]): TensorOp[T] = { + new TensorOp((t: Tensor[T], ev: TensorNumeric[T]) => { + t.negative(t).exp() + .add(ev.one) + .inv() + }) + } + +} + + +/** + * Select and copy a Tensor from a [[Table]] with [[key]]. + * And do tensor transformation if [[transformer]] is defined. + * + * @param key the key of selected tensor, a scalar tensor + * @param transformer user-defined transformer, default(null) means do nothing + * @tparam T Numeric type + */ +class SelectTensor[T: ClassTag] private( + private val key: Tensor[_], + private val transformer: TensorOp[T] = null +)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + + override def updateOutput(input: Table): Tensor[T] = { + val selected = input[Tensor[T]](key) + if (transformer != null) { + output = transformer.updateOutput(selected) + } else { + // TODO: support SparseTensor.copy + output.resizeAs(selected).copy(selected) + } + + output + } + +} + +object SelectTensor { + + def apply[T: ClassTag]( + key: Tensor[_], + transformer: TensorOp[T] = null + )(implicit ev: TensorNumeric[T]): SelectTensor[T] = { + new SelectTensor[T](key, transformer) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala new file mode 100644 index 00000000000..d211c897ca6 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala @@ -0,0 +1,135 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.Sigmoid +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class TensorOpSpec extends FlatSpec with Matchers { + + private val tt = Tensor[Float](2, 3).rand() + private val copiedTT = Tensor[Float]().resizeAs(tt).copy(tt) + + private def ttCopy() = Tensor[Float]().resizeAs(copiedTT).copy(copiedTT) + + "Common TensorOps" should "work correctly" in { + val rnd = Random.nextFloat() + TensorOp.add[Float](rnd).forward(tt) shouldEqual ttCopy().add(rnd) + TensorOp.sub[Float](rnd).forward(tt) shouldEqual ttCopy().sub(rnd) + TensorOp.mul[Float](rnd).forward(tt) shouldEqual ttCopy().mul(rnd) + TensorOp.div[Float](rnd).forward(tt) shouldEqual ttCopy().div(rnd) + TensorOp.pow[Float](rnd).forward(tt) shouldEqual ttCopy().pow(rnd) + TensorOp.ge[Float](rnd).forward(tt) shouldEqual ttCopy().ge(ttCopy(), rnd) + TensorOp.eq[Float](rnd).forward(tt) shouldEqual ttCopy().eq(ttCopy(), rnd) + + val rndT = Tensor[Float](2, 3).rand() + TensorOp.add[Float](rndT).forward(tt) shouldEqual ttCopy().add(rndT) + TensorOp.sub[Float](rndT).forward(tt) shouldEqual ttCopy().sub(rndT) + TensorOp.mul[Float](rndT).forward(tt) shouldEqual ttCopy().cmul(rndT) + TensorOp.div[Float](rndT).forward(tt) shouldEqual ttCopy().div(rndT) + + TensorOp.sign[Float]().forward(tt) shouldEqual ttCopy().sign() + TensorOp.sqrt[Float]().forward(tt) shouldEqual ttCopy().sqrt() + TensorOp.square[Float]().forward(tt) shouldEqual ttCopy().square() + TensorOp.t[Float]().forward(tt) shouldEqual ttCopy().t() + TensorOp.exp[Float]().forward(tt) shouldEqual ttCopy().exp() + TensorOp.abs[Float]().forward(tt) shouldEqual ttCopy().abs() + TensorOp.log[Float]().forward(tt) shouldEqual ttCopy().log() + TensorOp.log1p[Float]().forward(tt) shouldEqual ttCopy().log1p() + TensorOp.floor[Float]().forward(tt) shouldEqual ttCopy().floor() + TensorOp.ceil[Float]().forward(tt) shouldEqual ttCopy().ceil() + TensorOp.inv[Float]().forward(tt) shouldEqual ttCopy().inv() + TensorOp.negative[Float]().forward(tt) shouldEqual ttCopy().negative(ttCopy()) + TensorOp.tanh[Float]().forward(tt) shouldEqual ttCopy().tanh() + TensorOp.sigmoid[Float]().forward(tt) shouldEqual Sigmoid[Float]().forward(ttCopy()) + } + + "Chaining user-defined TensorOps" should "work correctly" in { + val transformer1: (Tensor[Float], TensorNumeric[Float]) => Tensor[Float] = { + (t: Tensor[Float], _) => t.apply1((t: Float) => t * t) + } + val transformer2: (Tensor[Float], TensorNumeric[Float]) => Tensor[Float] = { + (t: Tensor[Float], _) => t.apply1((t: Float) => t + 1) + } + val transformer3: (Tensor[Float], TensorNumeric[Float]) => Tensor[Float] = { + (t: Tensor[Float], _) => t.apply1((t: Float) => math.sqrt(t).toFloat) + } + val op1 = TensorOp[Float](transformer1) + val op2 = TensorOp[Float](transformer2) + val op3 = TensorOp[Float](transformer3) + + val op = TensorOp[Float]((t: Tensor[Float], ev: TensorNumeric[Float]) => { + transformer3(transformer2(transformer1(t, ev), ev), ev) + }) + + (op1 -> op2 -> op3).forward(tt) shouldEqual op.forward(tt) + } + + "Chaining provided Common TensorOps" should "work correctly" in { + var op = (TensorOp[Float]() * 2.3f + 1.23f) / 1.11f - 0.66f + op.forward(tt) shouldEqual ttCopy().mul(2.3f).add(1.23f).div(1.11f).sub(0.66f) + + var cpy = ttCopy() + op = TensorOp.negative() + op.forward(tt) shouldEqual cpy.negative(cpy) + op = op ** 3f + op.forward(tt) shouldEqual cpy.pow(3f) + op = op.abs.sqrt.log1p + 1.2f + op.forward(tt) shouldEqual cpy.abs().sqrt().log1p().add(1.2f) + + cpy = ttCopy() + op = ((TensorOp.square[Float]() + 1.0f) * 2.5f) >= 3.0 + op.forward(tt) shouldEqual { + val x = cpy.square().add(1.0f).mul(2.5f) + x.ge(x, 3.0) + } + + val op1 = (op -> TensorOp.sigmoid[Float]()).inv.sqrt + op1.forward(tt) shouldEqual Sigmoid[Float]().forward(cpy).inv().sqrt() + val op2 = op -> TensorOp.sigmoid[Float]().inv.sqrt + op2.forward(tt) shouldEqual Sigmoid[Float]().forward(cpy).inv().sqrt() + } + + private val t1 = Tensor[Float](3, 4).randn() + private val t2 = Tensor[Double](2, 3).randn() + private val table = T().update(Tensor.scalar(1), t1).update(Tensor.scalar("2"), t2) + + "SelectedTensor without transformer" should "work correctly" in { + val t1Copy = SelectTensor[Float](Tensor.scalar(1)).forward(table) + t1Copy shouldEqual t1 + val t1Values = t1.storage().array().clone() + t1Copy.square() + t1.storage().array() shouldEqual t1Values + t1Copy.storage().array() shouldEqual t1Values.map(e => e * e) + } + + "SelectedTensor with transformer" should "work correctly" in { + val transformer = (TensorOp[Double]() ** 3 * 4.5).ceil + val select = SelectTensor(Tensor.scalar("2"), transformer) + val t2Values = t2.storage().array().clone() + val t2Convert = select.forward(table) + t2.storage().array() shouldEqual t2Values + t2Convert.storage().array() shouldEqual + t2Values.map(e => math.ceil(math.pow(e, 3) * 4.5)) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index 7039f575ff0..0d80074b90a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -21,7 +21,7 @@ import java.io.{File => JFile} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, IndicatorCol, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, Sign, Slice, SquaredDifference, Substr, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, IndicatorCol, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, SelectTensor, Sign, Slice, SquaredDifference, Substr, TensorOp, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf.{Assert => AssertOps, BroadcastGradientArgs => BroadcastGradientArgsOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} @@ -775,6 +775,15 @@ class OperationSerializerSpec extends SerializerSpecHelper { runSerializationTest(sign, input) } + "SelectTensor serializer" should "work properly" in { + val transformer = (TensorOp[Float]() ** 3 * 4.5f).ceil + val select = SelectTensor(Tensor.scalar("2"), transformer) + val t1 = Tensor[Float](3, 4).randn() + val t2 = Tensor[Float](2, 3).randn() + val input = T().update(Tensor.scalar(1), t1).update(Tensor.scalar("2"), t2) + runSerializationTest(select, input) + } + "Slice serializer" should "work properly" in { val slice = Slice[Float](begin = Array(0, 1, 1), size = Array(2, -1, 1)).setName("slice") @@ -834,6 +843,13 @@ class OperationSerializerSpec extends SerializerSpecHelper { asInstanceOf[ModuleToOperation[Float]].module.getClass) } + "TensorOp serializer" should "work properly" in { + val op = (((TensorOp[Float]() + 1.5f) ** 2) -> TensorOp.sigmoid() + ).setName("TensorOP") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(op, input) + } + "TopK serializer" should "work properly" in { val topk = TopK[Float, Float](2).setName("topK") val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) From d5504e80f4bf65cb1d7300bdcdac300c5e0505b3 Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Tue, 6 Mar 2018 10:56:59 +0800 Subject: [PATCH 0714/1065] Change Bigdl Serial Test Part 2 (#2340) * add bigdl serial test part 2 * add more * add one * modify --- .../bigdl/dllib/nn/NarrowTableSpec.scala | 34 ++++++++++++++++++ .../bigdl/dllib/nn/NegativeSpec.scala | 11 ++++++ .../bigdl/dllib/nn/NormalizeSpec.scala | 9 +++++ .../bigdl/dllib/nn/PaddingSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/PairwiseDistanceSpec.scala | 13 +++++++ .../bigdl/dllib/nn/ParallelTableSpec.scala | 15 ++++++++ .../bigdl/dllib/nn/ProposalSpec.scala | 32 +++++++++++++++++ .../bigdl/dllib/nn/RecurrentSpec.scala | 11 ++++++ .../bigdl/dllib/nn/ReshapeSpec.scala | 11 ++++++ .../bigdl/dllib/nn/RnnCellSpec.scala | 35 +++++++++++++++++++ .../bigdl/dllib/nn/RoiPoolingSpec.scala | 18 +++++++++- .../analytics/bigdl/dllib/nn/SReLUSpec.scala | 11 ++++++ .../analytics/bigdl/dllib/nn/ScaleSpec.scala | 11 ++++++ .../analytics/bigdl/dllib/nn/SelectSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/SequentialSpec.scala | 14 ++++++++ .../bigdl/dllib/nn/SoftMaxSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/SoftPlusSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/SoftSignSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/SparseJoinTableSpec.scala | 14 ++++++++ .../bigdl/dllib/nn/SparseLinearSpec.scala | 10 ++++++ .../dllib/nn/SpatialAveragePoolingSpec.scala | 12 +++++++ .../nn/SpatialBatchNormalizationSpec.scala | 9 +++++ .../SpatialContrastiveNormalizationSpec.scala | 33 +++++++++++++++++ .../dllib/nn/SpatialConvolutionMapSpec.scala | 10 ++++++ .../nn/SpatialDilatedConvolutionSpec.scala | 10 ++++++ .../bigdl/dllib/nn/SpatialDropout1DSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/SpatialDropout2DSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/SpatialDropout3DSpec.scala | 30 ++++++++++++++++ .../nn/SpatialSeperableConvolutionSpec.scala | 12 +++++++ .../nn/SpatialShareConvolutionSpec.scala | 31 ++++++++++++++++ .../nn/SpatialWithinChannelLRNSpec.scala | 10 ++++++ .../dllib/nn/SpatialZeroPaddingSpec.scala | 31 ++++++++++++++++ .../analytics/bigdl/dllib/nn/SqrtSpec.scala | 30 ++++++++++++++++ .../analytics/bigdl/dllib/nn/SquareSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/SqueezeSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/StaticGraphSpec.scala | 31 ++++++++++++++++ .../dllib/nn/TemporalMaxPoolingSpec.scala | 30 ++++++++++++++++ .../analytics/bigdl/dllib/nn/TileSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/TransposeSpec.scala | 11 ++++++ .../bigdl/dllib/nn/UnsqueezeSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/UpSampling1DSpec.scala | 30 ++++++++++++++++ .../bigdl/dllib/nn/UpSampling3DSpec.scala | 30 ++++++++++++++++ .../analytics/bigdl/dllib/nn/ViewSpec.scala | 30 ++++++++++++++++ .../nn/VolumetricAveragePoolingSpec.scala | 31 ++++++++++++++++ .../dllib/nn/VolumetricConvolutionSpec.scala | 31 ++++++++++++++++ .../nn/VolumetricFullConvolutionSpec.scala | 31 ++++++++++++++++ .../dllib/nn/VolumetricMaxPoolingSpec.scala | 31 ++++++++++++++++ 47 files changed, 1082 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NarrowTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PaddingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ProposalSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RnnCellSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SelectSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftMaxSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlusSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftSignSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalizationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialShareConvolutionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialZeroPaddingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqrtSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SquareSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqueezeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraphSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TemporalMaxPoolingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TileSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UnsqueezeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3DSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ViewSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePoolingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolutionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolutionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPoolingSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NarrowTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NarrowTableSpec.scala new file mode 100644 index 00000000000..6271276e806 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NarrowTableSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class NarrowTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val narrowTable = NarrowTable[Float](1, 1) + val input = T() + input(1.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + input(2.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + input(3.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + runSerializationTest(narrowTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeSpec.scala index a40835806df..c1c5067fa99 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NegativeSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class NegativeSpec extends FlatSpec with Matchers { "Negative forward" should "be correct" in { val input = Tensor[Double](T(1, 2, 3)) @@ -33,3 +36,11 @@ class NegativeSpec extends FlatSpec with Matchers { m.backward(input, grad) should be(Tensor[Double](T(-2, -3, -4))) } } + +class NegativeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val negative = Negative[Float]().setName("negative") + val input = Tensor[Float](10).apply1(e => Random.nextFloat()) + runSerializationTest(negative, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeSpec.scala index d939a04e8b1..c68d7329081 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeSpec.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @@ -129,3 +130,11 @@ class NormalizeSpec extends FlatSpec with Matchers { checker.checkLayer(layer, input, 1e-3) should be(true) } } + +class NormalizeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val normalizer = Normalize[Float](2).setName("normalizer") + val input = Tensor[Float](2, 3, 4, 4).apply1(e => Random.nextFloat()) + runSerializationTest(normalizer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PaddingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PaddingSpec.scala new file mode 100644 index 00000000000..6d5afdfb7a5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PaddingSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class PaddingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val padding = Padding[Float](1, -1, 4, -0.8999761, 14).setName("padding") + val input = Tensor[Float](3, 13, 11).apply1(e => Random.nextFloat()) + runSerializationTest(padding, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala index c61fe5d9f2e..6f0b8065d10 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PairwiseDistanceSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class PairwiseDistanceSpec extends FlatSpec with Matchers { @@ -62,3 +65,13 @@ class PairwiseDistanceSpec extends FlatSpec with Matchers { m1 should not equal m4 } } + +class PairwiseDistanceSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val pairwiseDistance = new PairwiseDistance[Float](3).setName("pairwiseDistance") + val input1 = Tensor[Float](3, 3).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](3, 3).apply1(e => Random.nextFloat()) + val input = T(1.0f -> input1, 2.0f -> input2) + runSerializationTest(pairwiseDistance, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala index 780ec2bd61a..42d9688e535 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala @@ -18,6 +18,9 @@ package com.intel.analytics.bigdl.nn import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class ParallelTableSpec extends FlatSpec with Matchers { @@ -129,3 +132,15 @@ class ParallelTableSpec extends FlatSpec with Matchers { } } + +class ParallelTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val parallelTable = ParallelTable[Float]().setName("parallelTable") + parallelTable.add(Linear[Float](2, 2)) + parallelTable.add(Linear[Float](2, 2)) + val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) + val input = T(1.0f -> input1, 2.0f -> input2) + runSerializationTest(parallelTable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ProposalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ProposalSpec.scala new file mode 100644 index 00000000000..da88530a674 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ProposalSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class ProposalSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val proposal = Proposal(200, 100, Array[Float](0.1f, 0.2f, 0.3f), Array[Float](4, 5, 6)) + val score = Tensor[Float](1, 18, 20, 30).randn() + val boxes = Tensor[Float](1, 36, 20, 30).randn() + val imInfo = Tensor[Float](T(300, 300, 1, 1)).resize(1, 4) + val input = T(score, boxes, imInfo) + runSerializationTest(proposal, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala index 006a3b77f65..3d33437e6d7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala @@ -19,11 +19,13 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.optim.SGD import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{Engine, T, Table} import org.scalatest.{FlatSpec, Matchers} import scala.collection.mutable.ArrayBuffer import scala.math._ +import scala.util.Random @com.intel.analytics.bigdl.tags.Serial class RecurrentSpec extends FlatSpec with Matchers { @@ -703,3 +705,12 @@ class RecurrentSpec extends FlatSpec with Matchers { } } + +class RecurrentSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val recurrent = Recurrent[Float]().setName("recurrent") + .add(RnnCell[Float](5, 4, Tanh[Float]())) + val input = Tensor[Float](Array(10, 5, 5)).apply1(_ => Random.nextFloat()) + runSerializationTest(recurrent, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReshapeSpec.scala index c188caef69a..a9ab853a5a6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReshapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ReshapeSpec.scala @@ -19,6 +19,9 @@ package com.intel.analytics.bigdl.nn import org.scalatest.FlatSpec import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.LayerException +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class ReshapeSpec extends FlatSpec { @@ -134,3 +137,11 @@ class ReshapeSpec extends FlatSpec { } } } + +class ReshapeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val reshape = Reshape[Float](Array(1, 4, 5)).setName("reshape") + val input = Tensor[Float](2, 2, 5).apply1( _ => Random.nextFloat()) + runSerializationTest(reshape, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RnnCellSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RnnCellSpec.scala new file mode 100644 index 00000000000..b3e07bd21fa --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RnnCellSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class RnnCellSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val rnnCell = RnnCell[Float](6, 4, Sigmoid[Float]()).setName("rnnCell") + val input1 = Tensor[Float](Array(1, 4)).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](Array(1, 4)).apply1(_ => Random.nextFloat()) + val input = T() + input(1.0f) = input1 + input(2.0f) = input2 + runSerializationTest(rnnCell, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiPoolingSpec.scala index 163233386a9..e9f09bb197f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiPoolingSpec.scala @@ -17,9 +17,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class RoiPoolingSpec extends FlatSpec with Matchers { val data = Array(-3.8623801600318241611, -5.5763739585689267031, 10.298773638368681205, 9.0803885026851531848, 1.3665552448780498018, -0.44133702789011497458, -9.4017101101805629071, @@ -251,3 +254,16 @@ class RoiPoolingSpec extends FlatSpec with Matchers { } } } + +class RoiPoolingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = T() + val input1 = Tensor[Float](1, 1, 2, 2).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](1, 5).apply1(_ => Random.nextFloat()) + input(1.0f) = input1 + input(2.0f) = input2 + val roiPooling = new RoiPooling[Float](pooledW = 3, + pooledH = 2, 1.0f).setName("roiPooling") + runSerializationTest(roiPooling, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala index df4ddb322ae..9bcfae703b5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SReLUSpec.scala @@ -18,6 +18,9 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class SReLUSpec extends KerasBaseSpec { "SReLU without share axes" should "same as keras" in { @@ -80,3 +83,11 @@ class SReLUSpec extends KerasBaseSpec { srelu.weights.foreach(x => x should be (weight)) } } + +class SReLUSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val srelu = SReLU[Float](shape = Array(4)).setName("srelu") + val input = Tensor[Float](3, 4).apply1( e => Random.nextFloat()) + runSerializationTest(srelu, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleSpec.scala index a837ea323b0..611979aee0f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class ScaleSpec extends FlatSpec with Matchers{ val input = Tensor(Storage(Array( 1.3968338966, 1.0623255968, 0.0113903601, 1.6713322401, @@ -190,3 +193,11 @@ class ScaleSpec extends FlatSpec with Matchers{ } } + +class ScaleSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val scale = Scale[Float](Array(1, 4, 1, 1)).setName("scale") + val input = Tensor[Float](1, 4, 5, 6).apply1(_ => Random.nextFloat()) + runSerializationTest(scale, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SelectSpec.scala new file mode 100644 index 00000000000..b9b06c8e523 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SelectSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SelectSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val select = Select[Float](2, 2).setName("select") + val input = Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(select, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala index dd2a7393191..71dde8366d1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala @@ -16,8 +16,12 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.ops.Ceil +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class SequentialSpec extends FlatSpec with Matchers { "A Sequential Container " should "not contain operation" in { val model = Sequential[Double]() @@ -27,3 +31,13 @@ class SequentialSpec extends FlatSpec with Matchers { } } } + +class SequentialSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sequential = Sequential[Float]().setName("sequential") + val linear = Linear[Float](10, 2) + sequential.add(linear) + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(sequential, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftMaxSpec.scala new file mode 100644 index 00000000000..8862e1f2360 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftMaxSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SoftMaxSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val softMax = SoftMax[Float]().setName("softMax") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(softMax, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlusSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlusSpec.scala new file mode 100644 index 00000000000..fb5255af4c1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlusSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SoftPlusSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val softPlus = SoftPlus[Float]().setName("softPlus") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(softPlus, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftSignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftSignSpec.scala new file mode 100644 index 00000000000..19d31b05b40 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SoftSignSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SoftSignSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val softSign = SoftSign[Float]().setName("softSign") + val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(softSign, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala index f825c7816a0..46673b149a8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseJoinTableSpec.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{RandomGenerator, T} import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @@ -132,3 +133,16 @@ class SparseJoinTableSpec extends FlatSpec with Matchers { } } + +class SparseJoinTableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sparseJoinTable = SparseJoinTable[Float](2).setName("sparseJoinTable") + val sparseModel = Sequential[Float](). + add(ParallelTable[Float]().add(Identity[Float]()).add(Identity[Float]())) + .add(sparseJoinTable) + val input1 = Tensor[Float](4, 3).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + val input2 = Tensor[Float](4, 2).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + val sparseInput = T(Tensor.sparse(input1), Tensor.sparse(input2)) + runSerializationTest(sparseJoinTable, sparseInput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala index 392d48bd7ba..4c1da650a73 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala @@ -20,6 +20,7 @@ import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.{SparseTensor, Tensor} import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -172,3 +173,12 @@ class SparseLinearSpec extends FlatSpec with Matchers { sl.getParameters()._2.equals(l.getParameters()._2) shouldEqual true } } + +class SparseLinearSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sparseLinear = SparseLinear[Float](4, 2).setName("sparseLinear") + val input = Tensor[Float](2, 4).apply1(_ => Random.nextFloat()) + val sparseInput = Tensor.sparse(input) + runSerializationTest(sparseLinear, sparseInput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePoolingSpec.scala index 6bb3e24c94a..453c31f95ae 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialAveragePoolingSpec.scala @@ -22,6 +22,9 @@ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import scala.math.abs import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class SpatialAveragePoolingSpec extends FlatSpec with Matchers { @@ -341,3 +344,12 @@ class SpatialAveragePoolingSpec extends FlatSpec with Matchers { } } } + +class SpatialAveragePoolingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialAveragePooling = new SpatialAveragePooling[Float](3, 2, 2, 1). + setName("spatialAveragePooling") + val input = Tensor[Float](1, 4, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialAveragePooling, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala index 527e7527ec3..b71b6c4a300 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @@ -112,3 +113,11 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { } } +class SpatialBatchNormalizationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialBatchNorm = SpatialBatchNormalization[Float](5). + setName("spatialBatchNorm") + val input = Tensor[Float](2, 5, 4, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialBatchNorm, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalizationSpec.scala new file mode 100644 index 00000000000..2ccaa59bebf --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialContrastiveNormalizationSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SpatialContrastiveNormalizationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + RNG.setSeed(100) + val spatialContrastiveNorm = new SpatialContrastiveNormalization[Float](). + setName("spatialContrastiveNorm") + val input = Tensor[Float](1, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialContrastiveNorm, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionMapSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionMapSpec.scala index aaffeec9db3..beeef524593 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionMapSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolutionMapSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -59,3 +60,12 @@ class SpatialConvolutionMapSpec extends FlatSpec with Matchers { layer2.gradBias should be (layer1.gradBias.mul(0.5)) } } + +class SpatialConvolutionMapSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialConvolutionMap = SpatialConvolutionMap[Float]( + SpatialConvolutionMap.random(1, 1, 1), 2, 2).setName("spatialConvolutionMap") + val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) + runSerializationTest(spatialConvolutionMap, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolutionSpec.scala index 30919e70ba2..de9aeecad7e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolutionSpec.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{Shape, TestUtils} import scala.util.Random @@ -65,3 +66,12 @@ class SpatialDilatedConvolutionSpec extends FlatSpec with Matchers { } } + +class SpatialDilatedConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialDilatedConvolution = SpatialDilatedConvolution[Float](1, 1, + 2, 2, 1, 1, 0, 0).setName("spatialDilatedConvolution") + val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) + runSerializationTest(spatialDilatedConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1DSpec.scala new file mode 100644 index 00000000000..6859c683114 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1DSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SpatialDropout1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialDropout1D = SpatialDropout1D[Float]() + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialDropout1D, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2DSpec.scala new file mode 100644 index 00000000000..c33e5664a0a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2DSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SpatialDropout2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialDropout2D = SpatialDropout2D[Float]() + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialDropout2D, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3DSpec.scala new file mode 100644 index 00000000000..061d65d78fc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3DSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SpatialDropout3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialDropout3D = SpatialDropout3D[Float]() + val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialDropout3D, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala index 80440356f29..93f992037eb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape, TestUtils} +import scala.util.Random + class SpatialSeperableConvolutionSpec extends BigDLSpecHelper { "SpatialSeperableConvolution NHWC and NCHW" should "have same output" in { val depthWeightNHWC = Tensor[Float](2, 2, 3, 1).rand() @@ -106,3 +109,12 @@ class SpatialSeperableConvolutionSpec extends BigDLSpecHelper { } } + +class SpatialSeperableConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val seprableConv = SpatialSeperableConvolution[Float](2, 2, 1, 2, 2, + dataFormat = DataFormat.NHWC).setName("seprableConv") + val input = Tensor[Float](1, 5, 5, 2).apply1( e => Random.nextFloat()) + runSerializationTest(seprableConv, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialShareConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialShareConvolutionSpec.scala new file mode 100644 index 00000000000..1be6d6adc62 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialShareConvolutionSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SpatialShareConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialShareConvolution = SpatialShareConvolution[Float](1, 1, 2, 2, 1, 1). + setName("spatialShareConvolution") + val input = Tensor[Float](3, 1, 3, 4).apply1( e => Random.nextFloat()) + runSerializationTest(spatialShareConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRNSpec.scala index 155eafb1fd3..397b8414808 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialWithinChannelLRNSpec.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @@ -132,3 +133,12 @@ class SpatialWithinChannelLRNSpec extends FlatSpec with Matchers{ checker.checkLayer[Double](layer, input, 1e-3) should be(true) } } + +class SpatialWithinChannelLRNSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialWithinChannelLRN = new SpatialWithinChannelLRN[Float](5, 5e-4, 0.75). + setName("spatialWithinChannelLRN") + val input = Tensor[Float](1, 4, 7, 6).apply1( e => Random.nextFloat()) + runSerializationTest(spatialWithinChannelLRN, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialZeroPaddingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialZeroPaddingSpec.scala new file mode 100644 index 00000000000..f99b1400da4 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialZeroPaddingSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SpatialZeroPaddingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialZeroPadding = SpatialZeroPadding[Float](1, 0, -1, 0). + setName("spatialZeroPadding") + val input = Tensor[Float](3, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(spatialZeroPadding, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqrtSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqrtSpec.scala new file mode 100644 index 00000000000..a471f5cfb4f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqrtSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SqrtSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sqrt = Sqrt[Float]().setName("sqrt") + val input = Tensor[Float](10).apply1( e => Random.nextFloat()) + runSerializationTest(sqrt, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SquareSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SquareSpec.scala new file mode 100644 index 00000000000..b04708263b3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SquareSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SquareSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val square = Square[Float]().setName("square") + val input = Tensor[Float](10).apply1( e => Random.nextFloat()) + runSerializationTest(square, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqueezeSpec.scala new file mode 100644 index 00000000000..00c34e3d451 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqueezeSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class SqueezeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val squeeze = Squeeze[Float](2).setName("squeeze") + val input = Tensor[Float](2, 1, 2).apply1( e => Random.nextFloat()) + runSerializationTest(squeeze, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraphSpec.scala new file mode 100644 index 00000000000..b0bc1ce7007 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraphSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class StaticGraphSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val linear = Linear[Float](10, 2).inputs() + val graph = Graph[Float](linear, linear).setName("graph") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(graph, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TemporalMaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TemporalMaxPoolingSpec.scala new file mode 100644 index 00000000000..b1d23ac522b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TemporalMaxPoolingSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class TemporalMaxPoolingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val temporalMaxPooling = new TemporalMaxPooling[Float](4).setName("temporalMaxPooling") + val input = Tensor[Float](5, 4, 5).apply1(e => Random.nextFloat()) + runSerializationTest(temporalMaxPooling, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TileSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TileSpec.scala new file mode 100644 index 00000000000..cca6bfc9df8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TileSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class TileSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val tile = Tile[Float](1).setName("tile") + val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(tile, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransposeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransposeSpec.scala index 31316ab45ec..d417f229cda 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransposeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransposeSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class TransposeSpec extends FlatSpec with Matchers { "A Transpose Module " should "generate correct output" in { val input = Tensor[Float]().resize(Array(2, 3)).randn() @@ -78,3 +81,11 @@ class TransposeSpec extends FlatSpec with Matchers { } } + +class TransposeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val transpose = Transpose[Float](Array((1, 2))).setName("transpose") + val input = Tensor[Float]().resize(Array(2, 3)).apply1(_ => Random.nextFloat()) + runSerializationTest(transpose, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UnsqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UnsqueezeSpec.scala new file mode 100644 index 00000000000..651ea652f14 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UnsqueezeSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class UnsqueezeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val unsqueeze = Unsqueeze[Float](2).setName("unsqueeze") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(unsqueeze, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1DSpec.scala new file mode 100644 index 00000000000..a7ac8c68342 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1DSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class UpSampling1DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val upsampling = UpSampling1D[Float](2).setName("upsampling") + val input = Tensor[Float](2, 5, 5).apply1(_ => Random.nextFloat()) + runSerializationTest(upsampling, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3DSpec.scala new file mode 100644 index 00000000000..eadf681bff3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling3DSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class UpSampling3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val upSampling3D = UpSampling3D[Float](Array(2, 2, 2)).setName("upSampling3D") + val input = Tensor[Float](1, 2, 2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(upSampling3D, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ViewSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ViewSpec.scala new file mode 100644 index 00000000000..b5f87be4732 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ViewSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ViewSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val view = View[Float](Array(2, 5)).setName("view") + val input = Tensor[Float](1, 10).apply1(_ => Random.nextFloat()) + runSerializationTest(view, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePoolingSpec.scala new file mode 100644 index 00000000000..a4f655b08cc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricAveragePoolingSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class VolumetricAveragePoolingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val volumetricAveragePooling = VolumetricAveragePooling[Float](2, 2, 2, 1, 1, 1, 0, 0, 0). + setName("volumetricAveragePooling") + val input = Tensor[Float](1, 2, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(volumetricAveragePooling, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolutionSpec.scala new file mode 100644 index 00000000000..78b0f347aac --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolutionSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class VolumetricConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val volumetricConvolution = VolumetricConvolution[Float](2, 3, 2, 2, 2, dT = 1, dW = 1, dH = 1, + padT = 0, padW = 0, padH = 0, withBias = true).setName("volumetricConvolution") + val input = Tensor[Float](2, 2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(volumetricConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolutionSpec.scala new file mode 100644 index 00000000000..5000b43780a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolutionSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class VolumetricFullConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val volumetricFullConvolution = new VolumetricFullConvolution[Float](3, 6, + 4, 3, 3, 2, 1, 1, 2, 2, 2).setName("volumetricFullConvolution") + val input = Tensor[Float](3, 3, 3, 6, 6).apply1(e => Random.nextFloat()) + runSerializationTest(volumetricFullConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPoolingSpec.scala new file mode 100644 index 00000000000..6b9bdd6b5c0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricMaxPoolingSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class VolumetricMaxPoolingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val volumetricMaxPooling = VolumetricMaxPooling[Float](2, 2, 2, 1, 1, 1, 0, 0, 0). + setName("volumetricMaxPooling") + val input = Tensor[Float](1, 2, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(volumetricMaxPooling, input) + } +} From 1a4c3b2d0278245a1d379fc186ab1418d5e8d2a8 Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Tue, 6 Mar 2018 14:15:05 +0800 Subject: [PATCH 0715/1065] Change Quantized And Tensorflow Serial Test (#2341) * add quantized serial test * add more * add loaders * add * add * change style --- .../bigdl/dllib/nn/quantized/LinearSpec.scala | 10 ++++ .../nn/quantized/SpatialConvolutionSpec.scala | 12 ++++ .../SpatialDilatedConvolutionSpec.scala | 12 ++++ .../bigdl/dllib/nn/tf/AssertSpec.scala | 34 +++++++++++ .../bigdl/dllib/nn/tf/AssignGradSpec.scala | 31 ++++++++++ .../bigdl/dllib/nn/tf/AvgPoolGradSpec.scala | 35 ++++++++++++ .../bigdl/dllib/nn/tf/BiasAddGradSpec.scala | 32 +++++++++++ .../bigdl/dllib/nn/tf/BiasAddSpec.scala | 13 +++++ .../bigdl/dllib/nn/tf/ConcatOffsetSpec.scala | 31 ++++++++++ .../bigdl/dllib/nn/tf/ConstSpec.scala | 12 ++++ .../dllib/nn/tf/Conv2DBackFilterSpec.scala | 36 ++++++++++++ .../bigdl/dllib/nn/tf/Conv2DSpec.scala | 13 +++++ .../nn/tf/Conv3DBackpropFilterSpec.scala | 35 ++++++++++++ .../dllib/nn/tf/Conv3DBackpropInputSpec.scala | 35 ++++++++++++ .../bigdl/dllib/nn/tf/DecodeGifSpec.scala | 56 +++++++++++++++++++ .../bigdl/dllib/nn/tf/DecodeImageSpec.scala | 31 ++++++++++ .../bigdl/dllib/nn/tf/DecodePngSpec.scala | 56 +++++++++++++++++++ .../bigdl/dllib/nn/tf/EluGradSpec.scala | 33 +++++++++++ .../dllib/nn/tf/FusedBatchNormSpec.scala | 35 ++++++++++++ .../bigdl/dllib/nn/tf/MaxPoolSpec.scala | 16 ++++++ .../dllib/nn/tf/SplitAndSelectSpec.scala | 11 ++++ .../utils/serializer/SerializerSpec.scala | 20 +++++++ .../utils/tf/loaders/PadLoadTFSpec.scala | 32 +++++++++++ .../utils/tf/loaders/ProdLoadTFSpec.scala | 32 +++++++++++ .../utils/tf/loaders/ReshapeLoadTFSpec.scala | 32 +++++++++++ .../tf/loaders/StridedSliceLoadTFSpec.scala | 36 ++++++++++++ .../utils/tf/loaders/TopKV2LoadTFSpec.scala | 34 +++++++++++ .../tf/loaders/TransposeLoadTFSpec.scala | 33 +++++++++++ 28 files changed, 798 insertions(+) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssertSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssignGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AvgPoolGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConcatOffsetSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DBackFilterSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropFilterSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropInputSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeGifSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodePngSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/EluGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FusedBatchNormSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/PadLoadTFSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ProdLoadTFSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeLoadTFSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceLoadTFSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2LoadTFSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TransposeLoadTFSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/LinearSpec.scala index 8fd76798bd6..f1d77c55960 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/LinearSpec.scala @@ -19,8 +19,11 @@ package com.intel.analytics.bigdl.nn.quantized import com.intel.analytics.bigdl.nn.{Module, Linear => NNLinear} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers, ParallelTestExecution} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class LinearSpec extends FlatSpec with Matchers with ParallelTestExecution { val testCases = List( @@ -53,3 +56,10 @@ class LinearSpec extends FlatSpec with Matchers with ParallelTestExecution { case class TestCase(batchSize: Int, inputSize: Int, outputSize: Int) } +class LinearSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val linear = NNLinear[Float](10, 2).setName("linear") + val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) + runSerializationTest(linear, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolutionSpec.scala index e294af8eaaa..aa2e5b242a5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolutionSpec.scala @@ -20,8 +20,11 @@ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.nn.{Reshape, SpatialConvolution => NNSpatialConvolution} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers, ParallelTestExecution} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class SpatialConvolutionSpec extends FlatSpec with Matchers with ParallelTestExecution { // Notice: @@ -157,3 +160,12 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers with ParallelTestExe group: Int, outputChannel: Int, kernelHeight: Int, kernelWidth: Int, strideHeight: Int, strideWidth: Int, padHeight: Int, padWidth: Int) } + +class SpatialConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialConvolution = NNSpatialConvolution[Float](3, 4, 2, 2). + setName("spatialConvolution") + val input = Tensor[Float](1, 3, 5, 5).apply1( e => Random.nextFloat()) + runSerializationTest(spatialConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolutionSpec.scala index 7fe3af707b4..bed16123869 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialDilatedConvolutionSpec.scala @@ -19,6 +19,9 @@ package com.intel.analytics.bigdl.nn.quantized import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class SpatialDilatedConvolutionSpec extends FlatSpec with Matchers { "A SpatialDilatedConvolution" should "work correctly" in { @@ -53,3 +56,12 @@ class SpatialDilatedConvolutionSpec extends FlatSpec with Matchers { strideHeight: Int, strideWidth: Int, padHeight: Int, padWidth: Int, dilatedW: Int, dilatedH: Int) } + +class SpatialDilatedConvolutionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val spatialDilatedConvolution = nn.SpatialDilatedConvolution[Float](1, 1, + 2, 2, 1, 1, 0, 0).setName("spatialDilatedConvolution") + val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) + runSerializationTest(spatialDilatedConvolution, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssertSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssertSpec.scala new file mode 100644 index 00000000000..65e695424d0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssertSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + +class AssertSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val assert = new Assert[Float]().setName("assert") + val predictTensor = Tensor[Boolean](Array(1)) + predictTensor.setValue(1, true) + val msg = Tensor[ByteString](Array(1)) + msg.setValue(1, ByteString.copyFromUtf8("must be true")) + val input = T(predictTensor, msg) + runSerializationTest(assert, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssignGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssignGradSpec.scala new file mode 100644 index 00000000000..a67b85d3ced --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AssignGradSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class AssignGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val assignGrad = new AssignGrad[Float](grad).setName("assignGrad") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(assignGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AvgPoolGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AvgPoolGradSpec.scala new file mode 100644 index 00000000000..9007a6c562b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/AvgPoolGradSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class AvgPoolGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val avgPoolGrad = AvgPoolGrad[Float](4, 4, 1, 1, -1, -1, DataFormat.NHWC). + setName("avgPoolGrad") + val input1 = Tensor[Int](T(4, 32, 32, 3)) + val input2 = Tensor[Float](4, 32, 32, 3).apply1(_ => Random.nextFloat()) + val input = T(input1, input2) + runSerializationTest(avgPoolGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddGradSpec.scala new file mode 100644 index 00000000000..f44b948eaf3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddGradSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class BiasAddGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val biasAddGrad = BiasAddGrad[Float](DataFormat.NCHW). + setName("biasAddGrad") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(biasAddGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddSpec.scala index 9d09e4bac33..733da984c7e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/BiasAddSpec.scala @@ -15,10 +15,14 @@ */ package com.intel.analytics.bigdl.nn.tf +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class BiasAddSpec extends FlatSpec with Matchers { "BiasAdd operation" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericFloat @@ -72,3 +76,12 @@ class BiasAddSpec extends FlatSpec with Matchers { gradInput[Tensor[Float]](2) should be(expectedGradBias) } } + +class BiasAddSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val biasAddGrad = BiasAddGrad[Float](DataFormat.NCHW). + setName("biasAddGrad") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(biasAddGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConcatOffsetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConcatOffsetSpec.scala new file mode 100644 index 00000000000..72c95ab17df --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConcatOffsetSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class ConcatOffsetSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = new com.intel.analytics.bigdl.nn.tf.ConcatOffset[Float]() + runSerializationTest(module, T(Tensor.scalar[Int](1), Tensor[Int](T(2, 2, 5, 7)), + Tensor[Int](T(2, 3, 5, 7)), Tensor[Int](T(2, 4, 5, 7)))) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConstSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConstSpec.scala index 15fc35d136f..3bd6e942032 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConstSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ConstSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class ConstSpec extends FlatSpec with Matchers { "Const forward tensor" should "be correct" in { val value = Tensor(2, 3).rand() @@ -35,3 +38,12 @@ class ConstSpec extends FlatSpec with Matchers { layer.forward(input) should be(value) } } + +class ConstSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val value = Tensor[Float](3).apply1(_ => Random.nextFloat()) + val const = Const[Float, Float](value).setName("const") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(const, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DBackFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DBackFilterSpec.scala new file mode 100644 index 00000000000..ab5bb739366 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DBackFilterSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class Conv2DBackFilterSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val conv2dBackFilter = Conv2DBackFilter[Float](2, 2, -1, -1, DataFormat.NHWC). + setName("conv2dBackFilter") + val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + val kernelSize = Tensor[Int](T(2, 2, 3, 3)) + val grad = Tensor[Float](1, 2, 2, 3).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, kernelSize, grad) + runSerializationTest(conv2dBackFilter, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DSpec.scala index dc03e8eeaab..b42eb915474 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv2DSpec.scala @@ -17,8 +17,11 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class Conv2DSpec extends FlatSpec with Matchers { "Add operation" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericDouble @@ -85,3 +88,13 @@ class Conv2DSpec extends FlatSpec with Matchers { output should equal(expectOutput) } } + +class Conv2DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val conv2d = Conv2D[Float](2, 1, -1, -1).setName("conv2d") + val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + val filter = Tensor[Float](4, 3, 3, 2).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, filter) + runSerializationTest(conv2d, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropFilterSpec.scala new file mode 100644 index 00000000000..ca042e73cc5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropFilterSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class Conv3DBackpropFilterSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Conv3DBackpropFilter[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4) + + runSerializationTest(module, T(input, filter, outputBackprop)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropInputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropInputSpec.scala new file mode 100644 index 00000000000..767f8624cec --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Conv3DBackpropInputSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class Conv3DBackpropInputSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = Conv3DBackpropInput[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) + val input = Tensor[Float](4, 20, 30, 40, 3).rand() + val filter = Tensor[Float](2, 3, 4, 3, 4).rand() + val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() + + runSerializationTest(module, T(input, filter, outputBackprop)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeGifSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeGifSpec.scala new file mode 100644 index 00000000000..a1cbe268a55 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeGifSpec.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import java.io.{File => JFile} + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.tf.TFRecordIterator +import org.tensorflow.example.Example + +class DecodeGifSerialTest extends ModuleSerializationTest { + private def getInputs(name: String): Tensor[ByteString] = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" + val file = new JFile(path) + + val bytesVector = TFRecordIterator(file).toVector + val pngBytes = bytesVector(index) + + val example = Example.parseFrom(pngBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } + + override def test(): Unit = { + val decodeGif = new DecodeGif[Float]().setName("decodeGif") + val input = getInputs("gif") + runSerializationTest(decodeGif, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeImageSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeImageSpec.scala index 84d25063a42..a1f4f5d5d7f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeImageSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeImageSpec.scala @@ -22,6 +22,7 @@ import java.nio.{ByteBuffer, ByteOrder} import com.google.protobuf.ByteString import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.tensorflow.example.Example import org.tensorflow.framework.DataType import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString @@ -123,3 +124,33 @@ class DecodeImageSpec extends FlatSpec with Matchers { } } + +class DecodeImageSerialTest extends ModuleSerializationTest { + private def getInputs(name: String): Tensor[ByteString] = { + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" + val file = new JFile(path) + + val bytesVector = TFRecordIterator(file).toVector + val pngBytes = bytesVector(index) + + val example = Example.parseFrom(pngBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } + + override def test(): Unit = { + val decodeImage = new DecodeImage[Float](1).setName("decodeImage") + val input = getInputs("png") + runSerializationTest(decodeImage, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodePngSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodePngSpec.scala new file mode 100644 index 00000000000..423a267b4fc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodePngSpec.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import java.io.{File => JFile} + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.tf.TFRecordIterator +import org.tensorflow.example.Example + +class DecodePngSerialTest extends ModuleSerializationTest { + private def getInputs(name: String): Tensor[ByteString] = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" + val file = new JFile(path) + + val bytesVector = TFRecordIterator(file).toVector + val pngBytes = bytesVector(index) + + val example = Example.parseFrom(pngBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } + + override def test(): Unit = { + val decodePng = new DecodePng[Float](1).setName("decodePng") + val input = getInputs("png") + runSerializationTest(decodePng, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/EluGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/EluGradSpec.scala new file mode 100644 index 00000000000..16854e1f148 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/EluGradSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class EluGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val eluGrad = EluGrad[Float, Float]().setName("eluGrad") + val inputTensor = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) + val input = T(inputTensor, grad) + runSerializationTest(eluGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FusedBatchNormSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FusedBatchNormSpec.scala new file mode 100644 index 00000000000..36a80c7a7ae --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FusedBatchNormSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class FusedBatchNormSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val fusedBatchNorm = FusedBatchNorm[Float]().setName("fusedBatchNorm") + val input = T(Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](0), + Tensor[Float](0)) + runSerializationTest(fusedBatchNorm, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolSpec.scala index fd9d58b12ec..d64a76dff3d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/MaxPoolSpec.scala @@ -15,10 +15,14 @@ */ package com.intel.analytics.bigdl.nn.tf +import com.intel.analytics.bigdl.nn.ops.ModuleToOperation import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class MaxPoolSpec extends FlatSpec with Matchers { "MaxPool operation VALID padding" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericDouble @@ -115,3 +119,15 @@ class MaxPoolSpec extends FlatSpec with Matchers { output should equal(expectOutput) } } + +class MaxPoolSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val maxPool = MaxPool[Float]( + Array(1, 2, 3, 1), + Array(1, 2, 1, 1), + "VALID").setName("maxPool") + val input = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(maxPool, input, maxPool. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SplitAndSelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SplitAndSelectSpec.scala index 75053cf393f..35f4e95c1b6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SplitAndSelectSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SplitAndSelectSpec.scala @@ -18,8 +18,11 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + class SplitAndSelectSpec extends FlatSpec with Matchers { "SplitAndSelect forward" should "be correct" in { val layer = SplitAndSelect(1, 3, 4) @@ -75,3 +78,11 @@ class SplitAndSelectSpec extends FlatSpec with Matchers { ))) } } + +class SplitAndSelectSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val splitAndSelect = SplitAndSelect[Float](2, 1, 2).setName("splitSelect") + val input = Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(splitAndSelect, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 4bec7fb77a4..15b33f74190 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -42,24 +42,44 @@ class SerializerSpec extends BigDLSpecHelper { // Many to one mapping "com.intel.analytics.bigdl.nn.ops.Enter" -> "com.intel.analytics.bigdl.nn.ops.ControlOpsSerialTest", + "com.intel.analytics.bigdl.nn.tf.Enter" -> + "com.intel.analytics.bigdl.nn.tf.ControlOpsSerialTest", "com.intel.analytics.bigdl.nn.ops.NextIteration" -> "com.intel.analytics.bigdl.nn.ops.ControlOpsSerialTest", + "com.intel.analytics.bigdl.nn.tf.NextIteration" -> + "com.intel.analytics.bigdl.nn.tf.ControlOpsSerialTest", "com.intel.analytics.bigdl.nn.ops.Exit" -> "com.intel.analytics.bigdl.nn.ops.ControlOpsSerialTest", + "com.intel.analytics.bigdl.nn.tf.Exit" -> + "com.intel.analytics.bigdl.nn.tf.ControlOpsSerialTest", "com.intel.analytics.bigdl.nn.ops.LoopCondition" -> "com.intel.analytics.bigdl.nn.ops.ControlOpsSerialTest", + "com.intel.analytics.bigdl.nn.tf.LoopCondition" -> + "com.intel.analytics.bigdl.nn.tf.ControlOpsSerialTest", "com.intel.analytics.bigdl.nn.ops.StackCreator" -> "com.intel.analytics.bigdl.nn.ops.StackOpsSerialTest", + "com.intel.analytics.bigdl.nn.tf.StackCreator" -> + "com.intel.analytics.bigdl.nn.tf.StackOpsSerialTest", "com.intel.analytics.bigdl.nn.ops.StackPush" -> "com.intel.analytics.bigdl.nn.ops.StackOpsSerialTest", + "com.intel.analytics.bigdl.nn.tf.StackPush" -> + "com.intel.analytics.bigdl.nn.tf.StackOpsSerialTest", "com.intel.analytics.bigdl.nn.ops.StackPop" -> "com.intel.analytics.bigdl.nn.ops.StackOpsSerialTest", + "com.intel.analytics.bigdl.nn.tf.StackPop" -> + "com.intel.analytics.bigdl.nn.tf.StackOpsSerialTest", "com.intel.analytics.bigdl.nn.ops.TensorArrayWrite" -> "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArrayWrite" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", "com.intel.analytics.bigdl.nn.ops.TensorArrayRead" -> "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArrayRead" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", "com.intel.analytics.bigdl.nn.ops.TensorArrayGrad" -> "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArrayGrad" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", // Keras layers "com.intel.analytics.bigdl.nn.keras.Input" -> diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/PadLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/PadLoadTFSpec.scala new file mode 100644 index 00000000000..a70d9ccd5a3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/PadLoadTFSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class PadLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val padLoadTF = new PadLoadTF[Float]().setName("PadLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor[Int](T(T(1, 1), T(1, 1)))) + runSerializationTest(padLoadTF, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ProdLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ProdLoadTFSpec.scala new file mode 100644 index 00000000000..89cc5ae3e1f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ProdLoadTFSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ProdLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val prodLoadTF = new ProdLoadTF[Float]().setName("prodLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](1)) + runSerializationTest(prodLoadTF, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeLoadTFSpec.scala new file mode 100644 index 00000000000..ecbd4a9d3b8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeLoadTFSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class ReshapeLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val reshapeLoadTF = new ReshapeLoadTF[Float]().setName("reshapeLoadTF") + val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), + Tensor[Int](T(1, 5, 25))) + runSerializationTest(reshapeLoadTF, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceLoadTFSpec.scala new file mode 100644 index 00000000000..91e06bf357a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceLoadTFSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class StridedSliceLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val strideSliceLoadTF = new StridedSliceLoadTF[Float, Float](). + setName("strideSliceLoadTF") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0)), + Tensor[Int](T(1)), + Tensor[Int](T(1)) + ) + runSerializationTest(strideSliceLoadTF, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2LoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2LoadTFSpec.scala new file mode 100644 index 00000000000..7e56c19779d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2LoadTFSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class TopKV2LoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val topkv2LoadTF = new TopKV2LoadTF[Float](false, "Float"). + setName("topkv2LoadTF") + val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), + Tensor.scalar[Int](2) + ) + runSerializationTest(topkv2LoadTF, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TransposeLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TransposeLoadTFSpec.scala new file mode 100644 index 00000000000..dc53cff0631 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TransposeLoadTFSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class TransposeLoadTFSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val transposeLoadTF = new TransposeLoadTF[Float]().setName("transposeLoadTF") + val input = T(Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()), + Tensor[Int](T(0, 1)) + ) + runSerializationTest(transposeLoadTF, input) + } +} From df4f5bf3279c37362bc969c9b05fb2bfd6207f3b Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 6 Mar 2018 14:29:05 +0800 Subject: [PATCH 0716/1065] handle fast rcnn compatibility issue (#2348) --- .../analytics/bigdl/dllib/nn/CAddTable.scala | 17 ++++++++++++++++- .../utils/serializer/ModuleSerializable.scala | 2 +- .../utils/serializer/ModuleSerializer.scala | 1 + 3 files changed, 18 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala index dfa3c5130ad..abf4a38c002 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable} import scala.reflect._ @@ -122,11 +123,25 @@ class CAddTable[T: ClassTag, D: ClassTag](val inplace: Boolean = false)( } -object CAddTable { +object CAddTable extends ModuleSerializable { def apply[T: ClassTag]( inplace: Boolean = false)(implicit ev: TensorNumeric[T]) : CAddTable[T, T] = { new CAddTable[T, T](inplace) } + + override def getTypes(context: DeserializeContext): (Array[ClassTag[_]], + Array[TensorNumeric[_]]) = { + var (tags, numerics) = super.getTypes(context) + val defaultTag = tags(0) + val defaultNumeric = numerics(0) + if (tags.size < 2) { + val extendedTags = Array[ClassTag[_]](defaultTag, defaultTag) + val extendNumerics = Array[TensorNumeric[_]](defaultNumeric, defaultNumeric) + (extendedTags, extendNumerics) + } else { + (tags, numerics) + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 2112bba5ab8..69a14132395 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -143,7 +143,7 @@ trait ModuleSerializable extends Loadable with Savable{ asInstanceOf[AbstractModule[Activity, Activity, T]] } - private def getTypes(context: DeserializeContext): + protected def getTypes(context: DeserializeContext): (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { val attrMap = context.bigdlModule.getAttrMap val tags = attrMap.get(SerConst.MODULE_TAGES).getArrayValue.getStrList.asScala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 6c5a584bc32..2603261050c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -178,6 +178,7 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.SpatialBatchNormalization", BatchNormalization) registerModule("com.intel.analytics.bigdl.nn.BinaryTreeLSTM", BinaryTreeLSTM) registerModule("com.intel.analytics.bigdl.nn.BiRecurrent", BiRecurrent) + registerModule("com.intel.analytics.bigdl.nn.CAddTable", CAddTable) registerModule("com.intel.analytics.bigdl.nn.StaticGraph", Graph) registerModule("com.intel.analytics.bigdl.nn.DynamicGraph", Graph) registerModule("com.intel.analytics.bigdl.nn.keras.Model", Model) From 10b3e3a3bccaf6c029b3430839cde6147af0b7f1 Mon Sep 17 00:00:00 2001 From: tosky001 Date: Tue, 6 Mar 2018 15:20:56 +0800 Subject: [PATCH 0717/1065] add [[CrossCol]] Operation (#2334) * add preliminary version of Cross * impl reCombine * add CrossCol and CrossColSpec * add [[CrossCol]] Operation * modifiy the shape of output tensor * update the scalaDoc * modify the style check --- .../bigdl/dllib/nn/ops/CrossCol.scala | 168 ++++++++++++++++++ .../bigdl/dllib/nn/ops/CrossColSpec.scala | 60 +++++++ .../serializer/OperationSerializerSpec.scala | 12 +- 3 files changed, 239 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossCol.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossColSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossCol.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossCol.scala new file mode 100644 index 00000000000..e20afa9b30f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossCol.scala @@ -0,0 +1,168 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag +import scala.util.hashing.MurmurHash3 + +/** + * CrossCol operation preforms crosses of categorical features. + * + * The transformation can be thought of as Hash(cartesian product of features) % hashBucketSize) + * + * The input Table contains more than or equal to 2 Tensor[String]. + * Each Tensor[String] represents a categorical feature column. + * Each row in Tensor[String] represents the string of value, + * which supports single-value and multi-value joined by strDelimiter. + * + * As for the SparseTensor, it should be transformed to + * Tensor[String] before feeding into the Operation + * + * + * For example, if the two input tensors with size=3 are: + * "A,D", "B", "A,C" + * "1", "2", "3,4" + * + * the output tensor should be an 2D 3 x maxLength SparseTensor: + * [0, 0]: Hash32("1", Hash32("D")) % hashBucketSize + * [0, 1]: Hash32("1", Hash32("A")) % hashBucketSize + * [1, 0]: Hash32("2", Hash32("B")) % hashBucketSize + * [2, 0]: Hash32("3", Hash32("C")) % hashBucketSize + * [2, 1]: Hash32("4", Hash32("C")) % hashBucketSize + * [2, 2]: Hash32("3", Hash32("A")) % hashBucketSize + * [2, 3]: Hash32("4", Hash32("A")) % hashBucketSize + * + * @param hashBucketSize An Int > 1. The number of buckets. + * @param strDelimiter The Delimiter between feature values, default: ",". + * @tparam T Numeric type. Parameter tensor numeric type. Only support float/double now + */ + +class CrossCol[T: ClassTag]( + val hashBucketSize: Int, + val strDelimiter: String = "," +)(implicit ev: TensorNumeric[T]) + extends Operation[Table, Tensor[Int], T]{ + + output = Tensor[Int]() + + override def updateOutput(input: Table): Tensor[Int] = { + + val tensorNum = input.length() + require(tensorNum>=2, "the input table must contain more than one tensor") + val batchSize = input[Tensor[String]](1).size(dim = 1) + + val indices0 = new ArrayBuffer[Int]() + val indices1 = new ArrayBuffer[Int]() + val values = new ArrayBuffer[Int]() + + val bufferInput = (1 to tensorNum).map { i => + input[Tensor[String]](i).view(input[Tensor[String]](i).size()).squeeze() + }.toArray + + var i = 1 + var maxLen = 1 + while (i <= batchSize) { + var j = 0 + var tempLen = 1 + val tempArr = new ArrayBuffer[Array[String]]() + while (j < tensorNum) { + val bufferArr = bufferInput(j).valueAt(i).split(strDelimiter) + tempArr += bufferArr + tempLen *= bufferArr.length + j += 1 + } + + maxLen = if (maxLen mutable.ArrayBuilder.make[String]() ++= a + + val result = mutable.ArrayBuilder.make[Array[String]]() + + while (stack.nonEmpty) { + val current = stack.pop() + val children = input(current.length).map { nextStr => + val builder = mkBuilder(current) += nextStr + builder.result() + } + if (current.length == input.length - 1) { + result ++= children + } else { + stack.pushAll(children) + } + } + + result.result() + } + + private def crossHash(input: Array[Array[String]]): Array[Int] = { + input.map { arr => + var hashVal = MurmurHash3.stringHash(arr(0)) + var k = 1 + while (k < arr.length) { + hashVal = MurmurHash3.stringHash(arr(k), hashVal) + k += 1 + } + hashVal % hashBucketSize match { + case v if v < 0 => v + hashBucketSize + case v => v + } + } + } +} + +object CrossCol { + def apply[T: ClassTag]( + hashBucketSize: Int, + strDelimiter: String = "," + ) (implicit ev: TensorNumeric[T]): CrossCol[T] + = new CrossCol[T]( + hashBucketSize = hashBucketSize, + strDelimiter = strDelimiter + ) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossColSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossColSpec.scala new file mode 100644 index 00000000000..7db71551857 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossColSpec.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class CrossColSpec extends FlatSpec with Matchers { + "CrossCol Operation with two feature columns" should "work correctly" in { + val input = T( + Tensor[String](T("A,D", "B", "A,C")), + Tensor[String](T("1", "2", "3,4")) + ) + + val expectedOutput = Tensor.sparse( + Array(Array(0, 0, 1, 2, 2, 2, 2), + Array(0, 1, 0, 0, 1, 2, 3)), + Array(80, 98, 50, 99, 27, 89, 33), + Array(3, 4) + ) + + val output = CrossCol[Double](hashBucketSize = 100) + .forward(input) + + output should be(expectedOutput) + } + "CrossCol Operation with more than two feature columns" should "work correctly" in { + val input = T( + Tensor[String](T("A,D", "B", "A,C")), + Tensor[String](T("1", "2", "3,4")), + Tensor[String](T("1", "2", "3")) + ) + + val expectedOutput = Tensor.sparse( + Array(Array(0, 0, 1, 2, 2, 2, 2), + Array(0, 1, 0, 0, 1, 2, 3)), + Array(94, 34, 68, 82, 83, 97, 12), + Array(3, 4) + ) + + val output = CrossCol[Double](hashBucketSize = 100) + .forward(input) + + output should be(expectedOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index 0d80074b90a..e347b0b4761 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -21,7 +21,7 @@ import java.io.{File => JFile} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, IndicatorCol, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, SelectTensor, Sign, Slice, SquaredDifference, Substr, TensorOp, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossCol, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, IndicatorCol, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, SelectTensor, Sign, Slice, SquaredDifference, Substr, TensorOp, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf.{Assert => AssertOps, BroadcastGradientArgs => BroadcastGradientArgsOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} @@ -223,6 +223,16 @@ class OperationSerializerSpec extends SerializerSpecHelper { runSerializationTest(conv2dTranspose, input) } + "CrossCol Serializer" should "work proprly" in { + val crosscol = CrossCol[Float](hashBucketSize = 100) + .setName("CrossCol") + val input = T( + Tensor[String](T("A,D", "B", "A,C")), + Tensor[String](T("1", "2", "3,4")) + ) + runSerializationTest(crosscol, input) + } + "CrossEntropy serializer" should "work properly" in { val crossEntropy = CrossEntropy[Float]().setName("crossEntropy") val output = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) From 72927069f1bd162e07a42660e0969ef5fa454f3d Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Tue, 6 Mar 2018 16:39:21 +0800 Subject: [PATCH 0718/1065] Change Tensorflow Serial Test Part 2 and bigdl part 3 (#2344) * add tensorflow part 2 * add 2 * add more * add bigdl 4 * modify * update --- .../bigdl/dllib/nn/BiRecurrentSpec.scala | 261 +---------------- .../dllib/nn/ConvLSTMPeephole3DSpec.scala | 163 ++--------- .../bigdl/dllib/nn/MaskingSpec.scala | 17 +- .../analytics/bigdl/dllib/nn/MaxoutSpec.scala | 16 +- .../dllib/nn/ops/TensorArraySplitSpec.scala | 49 ++++ .../dllib/nn/tf/FusedBatchNormGradSpec.scala | 34 +++ .../dllib/nn/tf/InvertPermutationSpec.scala | 27 ++ .../bigdl/dllib/nn/tf/LRNGradSpec.scala | 33 +++ .../bigdl/dllib/nn/tf/Log1pSpec.scala | 29 ++ .../bigdl/dllib/nn/tf/NoOpSpec.scala | 29 ++ .../bigdl/dllib/nn/tf/ReluGradSpec.scala | 31 ++ .../bigdl/dllib/nn/tf/RsqrtGradSpec.scala | 31 ++ .../bigdl/dllib/nn/tf/SoftsignGradSpec.scala | 31 ++ .../bigdl/dllib/nn/tf/SqrtGradSpec.scala | 31 ++ .../bigdl/dllib/nn/tf/StrideSliceSpec.scala | 11 + .../bigdl/dllib/nn/tf/SwitchOpsSpec.scala | 32 ++ .../bigdl/dllib/nn/tf/TanhGradSpec.scala | 30 ++ .../dllib/nn/tf/TensorModuleWrapperSpec.scala | 30 ++ .../bigdl/dllib/nn/tf/VariableSpec.scala | 32 ++ .../bigdl/dllib/torch/BiRecurrentSpec.scala | 273 ++++++++++++++++++ .../dllib/torch/ConvLSTMPeephole3DSpec.scala | 167 +++++++++++ .../utils/serializer/SerializerSpec.scala | 15 + 22 files changed, 974 insertions(+), 398 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArraySplitSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FusedBatchNormGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/InvertPermutationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/LRNGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1pSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/NoOpSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ReluGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/RsqrtGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SoftsignGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SqrtGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SwitchOpsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/TanhGradSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/TensorModuleWrapperSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/VariableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BiRecurrentSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConvLSTMPeephole3DSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrentSpec.scala index 5f567e69ce5..f7d7343820b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrentSpec.scala @@ -13,262 +13,21 @@ * See the License for the specific language governing permissions and * limitations under the License. */ +package com.intel.analytics.bigdl.nn -package com.intel.analytics.bigdl.torch - -import java.io.PrintWriter - -import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.numeric.NumericDouble -import com.intel.analytics.bigdl.optim.SGD import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{T, Table} -import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} - -import scala.sys.process._ - -@com.intel.analytics.bigdl.tags.Serial -class BiRecurrentSpec extends TorchSpec { - override def torchCheck(): Unit = { - super.torchCheck() - val tmpFile = java.io.File.createTempFile("checkRNN", ".lua") - val writer = new PrintWriter(tmpFile) - writer.write("exist = (pcall(require, 'rnn'))\n print(exist)") - writer.close() - - val existsRNN = - Seq(System.getProperty("torch_location", "th"), tmpFile.getAbsolutePath).!!.trim - if (!existsRNN.contains("true")) { - cancel("Torch rnn is not installed") - } - } - - "A BiRecurrent" should "uses isSplitInput correctly" in { - val inputSize = 4 - val outputSize = 5 - val seqLength = 7 - val seed = 100 - val batchSize = 2 - RNG.setSeed(seed) - - val input = Tensor[Double](Array(batchSize, seqLength, inputSize)).randn - val gradOutput = Tensor[Double](batchSize, seqLength, outputSize * 2).randn - - val half = inputSize >> 1 - val input1 = input.narrow(3, 1, half).contiguous() - val input2 = input.narrow(3, 1 + half, inputSize - half).contiguous() - val gradOutput1 = gradOutput.narrow(3, 1, outputSize).contiguous() - val gradOutput2 = gradOutput.narrow(3, 1 + outputSize, outputSize).contiguous() - - val birnn = BiRecurrent[Double](JoinTable[Double](3, 0) - .asInstanceOf[AbstractModule[Table, Tensor[Double], Double]], isSplitInput = true) - .add(RnnCell[Double](half, outputSize, ReLU[Double]())) - - val recurrent1 = Recurrent[Double]() - .add(RnnCell[Double](half, outputSize, ReLU[Double]())) - val recurrent2 = Sequential[Double]() - .add(Reverse[Double](2)) - .add(Recurrent[Double]() - .add(RnnCell[Double](half, outputSize, ReLU[Double]()))) - .add(Reverse[Double](2)) - - val birnnParams = birnn.parameters()._1 - val length = birnnParams.length - val halfLen = length >> 1 - val weight1 = recurrent1.parameters()._1 - val weight2 = recurrent2.parameters()._1 - - for (i <- 0 until halfLen) { - weight1(i).resizeAs(birnnParams(i)).copy(birnnParams(i)) - } - for (i <- 0 until halfLen) { - weight2(i).resizeAs(birnnParams(i + halfLen)).copy(birnnParams(i + halfLen)) - } - - val output = birnn.forward(input) - val out1 = recurrent1.forward(input1) - val out2 = recurrent2.forward(input2) - - val jointTable = JoinTable[Double](3, 0) - val outputCompare = jointTable.forward(T(out1, out2)) +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest - output should be (outputCompare) +import scala.util.Random - val gradInput = birnn.backward(input, gradOutput) - val grad1 = recurrent1.backward(input1, gradOutput1) - val grad2 = recurrent2.backward(input2, gradOutput2) - gradInput.narrow(3, 1, half) should be (grad1) - gradInput.narrow(3, 1 + half, inputSize - half) should be (grad2) +class BiRecurrentSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = Tensor[Float](1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) + RNG.setSeed(100) + val biRecurrent = BiRecurrent[Float]().add(RnnCell[Float](6, 4, + Sigmoid[Float]())).setName("biRecurrent") + runSerializationTest(biRecurrent, input) } - - "A BiRecurrent " should "has same loss as torch rnn" in { - torchCheck() - - val hiddenSize = 4 - val linearHidden = 8 - val inputSize = 6 - val outputSize = 5 - val bpttTruncate = 3 - val seqLength = 5 - val seed = 100 - val depth = 2 - - val input = Tensor[Double](Array(1, seqLength, inputSize)) - val labels = Tensor[Double](Array(1, seqLength)) - for (i <- 1 to seqLength) { - val rdmLabel = Math.ceil(math.random * outputSize).toInt - val rdmInput = Math.ceil(math.random * inputSize).toInt - input.setValue(1, i, rdmInput, 1.0) - labels.setValue(1, i, rdmLabel) - } - - RNG.setSeed(seed) - - def basicBlock(inputSize: Int, hiddenSize: Int): Module[Double] = { - Sequential() - .add(BiRecurrent[Double](CAddTable[Double]()) - .add(RnnCell[Double](inputSize, hiddenSize, Sigmoid[Double]()))) - } - - val model = Sequential[Double]() - for (i <- 1 to depth) { - if (i == 1) { - model.add(basicBlock(inputSize, hiddenSize)) - } else { - model.add(basicBlock(hiddenSize, hiddenSize)) - } - } - model.add(TimeDistributed[Double](Linear[Double](hiddenSize, outputSize))) - val criterion = TimeDistributedCriterion[Double]( - CrossEntropyCriterion[Double]()) - val logSoftMax = TimeDistributed[Double](LogSoftMax[Double]()) - - val (weights, grad) = model.getParameters() - val code = - s""" - | - |-- 1.4. Combine 1.1 and 1.3 to produce final model - |require 'rnn' - |torch.manualSeed($seed) - | - |local function basicblock(inputSize, hiddenSize) - | local rm = nn.Sequential() -- input is {x[t], h[t-1]} - | :add(nn.ParallelTable() - | :add(nn.Linear(inputSize, hiddenSize)) -- input layer - | :add(nn.Linear(hiddenSize, hiddenSize))) -- recurrent layer - | :add(nn.CAddTable()) -- merge - | :add(nn.Sigmoid()) -- transfer - | local rm1 = nn.Sequential() -- input is {x[t], h[t-1]} - | :add(nn.ParallelTable() - | :add(nn.Linear(inputSize, hiddenSize)) -- input layer - | :add(nn.Linear(hiddenSize, hiddenSize))) -- recurrent layer - | :add(nn.CAddTable()) -- merge - | :add(nn.Sigmoid()) -- transfer - | - | local rnn = nn.Recurrence(rm, hiddenSize, 1) - | local rnn1 = nn.Recurrence(rm1, hiddenSize, 1) - | return nn.Sequential() - | :add(nn.BiSequencer(rnn, rnn1, nn.CAddTable())) - |end - | - | - |model = nn.Sequential() - |:add(nn.SplitTable(1)) - | - | for i=1,$depth do - | if i == 1 then - | model:add(basicblock($inputSize, $hiddenSize)) - | else - | model:add(basicblock($hiddenSize, $hiddenSize)) - | end - | end - | - | model:add(nn.JoinTable(1, 5)) - |--:add(nn.Sequencer( - |-- nn.Sequential() - |-- --:add(nn.LSTM($inputSize, $hiddenSize, 1, true)) - |-- :add(nn.FastLSTM($inputSize, $hiddenSize)) - | :add(nn.Linear($hiddenSize, $outputSize)) - |-- )) - | - | - |local parameters, gradParameters = model:getParameters() - |model:zeroGradParameters() - |parameters:copy(weights) - | - |parameters_initial = parameters : clone() - |gradParameters_initial = gradParameters : clone() - | - |local criterion = nn.SequencerCriterion(nn.CrossEntropyCriterion()) - | - | - |state = { - | learningRate = 0.5, - | momentum = 0.0, - | dampening = 0.0, - | weightDecay = 0.0 - |} - | - |feval = function(x) - |model:zeroGradParameters() - |model_initial = model : clone() - | - |local output1 = model:forward(input) - |local err1 = criterion:forward(output1, labels) - |local gradOutput1 = criterion:backward(output1, labels) - |model:backward(input, gradOutput1) - |return err1, gradParameters - |end - | - |for i = 1,10,1 do - | optim.sgd(feval, parameters, state) - |end - | - |labels = labels - |err=criterion.output - |err2=criterion.gradInput - |output = model.output - |gradInput = model.gradInput - """.stripMargin - - val (luaTime, torchResult) = TH.run(code, - Map("input" -> input.transpose(1, 2), "weights" -> weights, - "labels" -> labels(1)), - Array("err", "parameters", "gradParameters", "output", "gradInput", "err2", "labels")) - - val luaOutput2 = torchResult("err").asInstanceOf[Double] - val luaweight = torchResult("parameters").asInstanceOf[Tensor[Double]] - - val state = T("learningRate" -> 0.5, "momentum" -> 0.0, - "weightDecay" -> 0.0, "dampening" -> 0.0) - val sgd = new SGD[Double] - def feval(x: Tensor[Double]): (Double, Tensor[Double]) = { - val output = model.forward(input).asInstanceOf[Tensor[Double]] - val _loss = criterion.forward(output, labels) - model.zeroGradParameters() - val gradInput = criterion.backward(output, labels) - model.backward(input, gradInput) - (_loss, grad) - } - - val start = System.nanoTime() - var loss: Array[Double] = null - for (i <- 1 to 10) { - loss = sgd.optimize(feval, weights, state)._2 - println(s"${i}-th loss = ${loss(0)}") - } - val end = System.nanoTime() - println("Time: " + (end - start) / 1E6) - - val output = model.output.toTensor[Double] - val logOutput = logSoftMax.forward(output) - val prediction = logOutput.max(3)._2 - - luaOutput2 should be(loss(0) +- 1e-5) - } - } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala index 1970369908b..b9f2498f0b4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeephole3DSpec.scala @@ -13,158 +13,35 @@ * See the License for the specific language governing permissions and * limitations under the License. */ +package com.intel.analytics.bigdl.nn -package com.intel.analytics.bigdl.torch +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest -import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} -import com.intel.analytics.bigdl.utils._ -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.RandomGenerator._ -import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} +import scala.util.Random -import scala.collection.mutable.ArrayBuffer -import scala.math._ -@com.intel.analytics.bigdl.tags.Parallel -class ConvLSTMPeephole3DSpec extends FlatSpec with BeforeAndAfter with Matchers { - - "A ConvLSTMPeepwhole3D " should " work in BatchMode" in { - val hiddenSize = 5 - val inputSize = 3 - val seqLength = 4 - val batchSize = 2 - val kernalW = 2 - val kernalH = 2 - val rec = Recurrent[Double]() - val model = Sequential[Double]() - .add(rec - .add(ConvLSTMPeephole3D[Double]( - inputSize, - hiddenSize, - kernalW, kernalH, - withPeephole = true))) - - val input = Tensor[Double](batchSize, seqLength, inputSize, 5, 5, 5).rand - - for (i <- 1 to 3) { - val output = model.forward(input).toTensor[Double] - for((value, j) <- output.size.view.zipWithIndex) { - if (j > 2) { - require(value == input.size(j + 1)) - } - } - model.backward(input, output) - } - } - - "A ConvLSTMPeepwhole3D" should " return state" in { +class ConvLSTMPeephole3DSerialTest extends ModuleSerializationTest { + override def test(): Unit = { val hiddenSize = 5 val inputSize = 3 val seqLength = 4 val batchSize = 2 val kernalW = 3 val kernalH = 3 - val model = Recurrent[Double]() - .add(ConvLSTMPeephole3D[Double]( - inputSize, - hiddenSize, - kernalW, kernalH, - withPeephole = true)) - - val input = Tensor[Double](batchSize, seqLength, inputSize, 3, 3, 3).rand - - val output = model.forward(input) - - val state = model.getHiddenState() - val hidden = state.asInstanceOf[Table].apply(1).asInstanceOf[Tensor[Double]] - hidden.map(output.select(2, seqLength), (v1, v2) => { - assert(abs(v1 - v2) == 0) - v1 - }) - } - - "ConvLSTMPeephole3D L2 regularizer" should "works correctly" in { - import com.intel.analytics.bigdl.numeric.NumericDouble - - val hiddenSize = 5 - val inputSize = 3 - val seqLength = 4 - val batchSize = 1 - val kernalW = 3 - val kernalH = 3 - - val state1 = T("learningRate" -> 0.1, "learningRateDecay" -> 5e-7, - "weightDecay" -> 0.1, "momentum" -> 0.002) - val state2 = T("learningRate" -> 0.1, "learningRateDecay" -> 5e-7, - "weightDecay" -> 0.0, "momentum" -> 0.002) - - val criterion = new TimeDistributedCriterion[Double](new MSECriterion[Double]) - - val input = Tensor[Double](batchSize, seqLength, inputSize, 3, 3, 3).rand - val labels = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3, 3).rand - - val rec = Recurrent[Double]() - val model1 = Sequential[Double]() - .add(rec - .add(ConvLSTMPeephole3D[Double]( - inputSize, - hiddenSize, - kernalW, kernalH, - withPeephole = true))) - - val (weights1, grad1) = model1.getParameters() - - val model2 = Sequential[Double]() - .add(Recurrent[Double]() - .add(ConvLSTMPeephole3D[Double]( - inputSize, - hiddenSize, - kernalW, kernalH, - wRegularizer = L2Regularizer(0.1), - uRegularizer = L2Regularizer(0.1), - bRegularizer = L2Regularizer(0.1), - cRegularizer = L2Regularizer(0.1), - withPeephole = true))) - - val (weights2, grad2) = model2.getParameters() - weights2.copy(weights1.clone()) - grad2.copy(grad1.clone()) - - val sgd = new SGD[Double] - - def feval1(x: Tensor[Double]): (Double, Tensor[Double]) = { - val output = model1.forward(input).toTensor[Double] - val _loss = criterion.forward(output, labels) - model1.zeroGradParameters() - val gradInput = criterion.backward(output, labels) - model1.backward(input, gradInput) - (_loss, grad1) - } - - def feval2(x: Tensor[Double]): (Double, Tensor[Double]) = { - val output = model2.forward(input).toTensor[Double] - val _loss = criterion.forward(output, labels) - model2.zeroGradParameters() - val gradInput = criterion.backward(output, labels) - model2.backward(input, gradInput) - (_loss, grad2) - } - - var loss1: Array[Double] = null - for (i <- 1 to 100) { - loss1 = sgd.optimize(feval1, weights1, state1)._2 - println(s"${i}-th loss = ${loss1(0)}") - } - - var loss2: Array[Double] = null - for (i <- 1 to 100) { - loss2 = sgd.optimize(feval2, weights2, state2)._2 - println(s"${i}-th loss = ${loss2(0)}") - } - - weights1 should be(weights2) - loss1 should be(loss2) + val c3d = ConvLSTMPeephole3D[Float]( + inputSize, + hiddenSize, + kernalW, kernalH, + 1, + withPeephole = false) + val convLSTMPeephole3d = Recurrent[Float]().setName("convLSTMPeephole3d") + val model = Sequential[Float]() + .add(convLSTMPeephole3d + .add(c3d)) + .add(View[Float](hiddenSize * kernalH * kernalW)) + + val input = Tensor[Float](batchSize, seqLength, inputSize, kernalW, kernalH, 3).rand + runSerializationTest(convLSTMPeephole3d, input, c3d.getClass) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskingSpec.scala index a99e53ec8e9..ee673347f36 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskingSpec.scala @@ -14,9 +14,13 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.keras +package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.{Masking} +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class MaskingSpec extends KerasBaseSpec { @@ -62,3 +66,12 @@ class MaskingSpec extends KerasBaseSpec { checkOutputAndGrad(masking, sigmoidCode) } } + + +class MaskingSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val masking = Masking[Float](0.1).setName("masking") + val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(masking, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala index b822ba085d2..fbd106202af 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaxoutSpec.scala @@ -14,11 +14,15 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.keras +package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.Maxout +import com.intel.analytics.bigdl.keras.KerasBaseSpec import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{Shape, TestUtils} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random class MaxoutSpec extends KerasBaseSpec { "Maxout" should "generate corrent result when batchsize == 1" in { @@ -111,3 +115,11 @@ class MaxoutSpec extends KerasBaseSpec { } } + +class MaxoutSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val maxout = Maxout[Float](2, 4, 5).setName("maxout") + val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) + runSerializationTest(maxout, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArraySplitSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArraySplitSpec.scala new file mode 100644 index 00000000000..1fec93202f9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorArraySplitSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.nn.Graph +import com.intel.analytics.bigdl.nn.tf._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class TensorArraySplitSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + "TensorArray serializer Split/Concat" should "work properly" in { + val tensorArray = new TensorArrayCreator[Float, Float]().inputs() + val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() + val lengths = Const[Float, Int](Tensor[Int](T(1, 2))).inputs() + val splitter = new TensorArraySplit[Float, Float]().inputs((tensorArray, 1), (data, 1), + (lengths, 1)) + val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(splitter) + val concat = new TensorArrayConcat[Float, Float]().inputs(tensorArray, ctr) + val size = new TensorArraySize[Float]().inputs(tensorArray, ctr) + val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(concat, size) + val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1)) + val model = Graph.dynamic[Float](Array(tensorArray), Array(concat, close, size)) + + runSerializationTestWithMultiClass(model, Tensor.scalar[Int](2), Array( + tensorArray.element.getClass.asInstanceOf[Class[_]], + splitter.element.getClass.asInstanceOf[Class[_]], + concat.element.getClass.asInstanceOf[Class[_]], + close.element.getClass.asInstanceOf[Class[_]], + size.element.getClass.asInstanceOf[Class[_]] + )) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FusedBatchNormGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FusedBatchNormGradSpec.scala new file mode 100644 index 00000000000..cac402aa408 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/FusedBatchNormGradSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class FusedBatchNormGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val fbatchNormGrad = FusedBatchNormGrad[Float]().setName("fbatchNormGrad") + val input = T(Tensor[Float](4, 8, 8, 256).rand(), + Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat()), + Tensor[Float](256).apply1(_ => Random.nextFloat())) + runSerializationTest(fbatchNormGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/InvertPermutationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/InvertPermutationSpec.scala new file mode 100644 index 00000000000..62818b725ae --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/InvertPermutationSpec.scala @@ -0,0 +1,27 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class InvertPermutationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = new com.intel.analytics.bigdl.nn.tf.InvertPermutation[Float]() + runSerializationTest(module, Tensor[Int](T(0, 1, 2, 3, 4))) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/LRNGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/LRNGradSpec.scala new file mode 100644 index 00000000000..a1e97c4a7e9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/LRNGradSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class LRNGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val lrnGrad = LRNGrad[Float]().setName("lrnGrad") + val input = T(Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()) + ) + runSerializationTest(lrnGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1pSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1pSpec.scala new file mode 100644 index 00000000000..2df945b0087 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1pSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class Log1pSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val log1p = Log1p[Float, Float]().setName("log1p") + val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) + runSerializationTest(log1p, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/NoOpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/NoOpSpec.scala new file mode 100644 index 00000000000..72c18534e52 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/NoOpSpec.scala @@ -0,0 +1,29 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class NoOpSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val noOp = new com.intel.analytics.bigdl.nn.tf.NoOp[Float]().setName("noOp") + val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) + runSerializationTest(noOp, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ReluGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ReluGradSpec.scala new file mode 100644 index 00000000000..165bf68b6ca --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ReluGradSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class ReluGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val reluGrad = ReluGrad[Float] + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(reluGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/RsqrtGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/RsqrtGradSpec.scala new file mode 100644 index 00000000000..af1022744b9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/RsqrtGradSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class RsqrtGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val rsqrtGrad = RsqrtGrad[Float, Float].setName("rsqrtGrad") + val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), + Tensor[Float](3, 3).apply1(_ => Random.nextFloat())) + runSerializationTest(rsqrtGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SoftsignGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SoftsignGradSpec.scala new file mode 100644 index 00000000000..da16ffc614a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SoftsignGradSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SoftsignGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val softSign = SoftsignGrad[Float, Float].setName("softSign") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(softSign, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SqrtGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SqrtGradSpec.scala new file mode 100644 index 00000000000..eec1c6391be --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SqrtGradSpec.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class SqrtGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val sqrtGrad = SqrtGrad[Float, Float].setName("sqrtGrad") + val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), + Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) + runSerializationTest(sqrtGrad, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala index 3649a1ff4c3..e7635169d22 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala @@ -16,8 +16,11 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + @com.intel.analytics.bigdl.tags.Parallel class StrideSliceSpec extends FlatSpec with Matchers { @@ -58,3 +61,11 @@ class StrideSliceSpec extends FlatSpec with Matchers { } } + +class StrideSliceSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val strideSlice = new StrideSlice[Float, Float](Array((1, 1, 2, 1))).setName("strideSlice") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(strideSlice, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SwitchOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SwitchOpsSpec.scala new file mode 100644 index 00000000000..4a183cea715 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/SwitchOpsSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class SwitchOpsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val switchOps = new SwitchOps[Float]().setName("switchOps") + val input = + T( + Tensor[Float](T(1.0f, 2.0f, 3.0f)), + Tensor[Boolean](T(true)) + ) + runSerializationTest(switchOps, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/TanhGradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/TanhGradSpec.scala new file mode 100644 index 00000000000..c310630b84e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/TanhGradSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class TanhGradSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val module = TanhGrad[Float, Float]() + + val input = T(Tensor[Float](1, 5, 3, 4).rand(), Tensor[Float](1, 5, 3, 4).rand()) + + runSerializationTest(module, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/TensorModuleWrapperSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/TensorModuleWrapperSpec.scala new file mode 100644 index 00000000000..e877f999dc1 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/TensorModuleWrapperSpec.scala @@ -0,0 +1,30 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class TensorModuleWrapperSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val tensorModuleWrapper = TensorModuleWrapper[Float, Float](BigDLSoftPlus[Float]()). + setName("moduleToOperation") + val input = Tensor[Float](T(1.0f, 1.0)) + runSerializationTest(tensorModuleWrapper, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/VariableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/VariableSpec.scala new file mode 100644 index 00000000000..61f66418731 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/VariableSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class VariableSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val out = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + val grad = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + val variable = new Variable[Float](out, grad).setName("variable") + val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(variable, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BiRecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BiRecurrentSpec.scala new file mode 100644 index 00000000000..c75e043e657 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BiRecurrentSpec.scala @@ -0,0 +1,273 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import java.io.PrintWriter + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.numeric.NumericDouble +import com.intel.analytics.bigdl.optim.SGD +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.sys.process._ + +@com.intel.analytics.bigdl.tags.Serial +class BiRecurrentSpec extends TorchSpec { + override def torchCheck(): Unit = { + super.torchCheck() + val tmpFile = java.io.File.createTempFile("checkRNN", ".lua") + val writer = new PrintWriter(tmpFile) + writer.write("exist = (pcall(require, 'rnn'))\n print(exist)") + writer.close() + + val existsRNN = + Seq(System.getProperty("torch_location", "th"), tmpFile.getAbsolutePath).!!.trim + if (!existsRNN.contains("true")) { + cancel("Torch rnn is not installed") + } + } + + "A BiRecurrent" should "uses isSplitInput correctly" in { + val inputSize = 4 + val outputSize = 5 + val seqLength = 7 + val seed = 100 + val batchSize = 2 + RNG.setSeed(seed) + + val input = Tensor[Double](Array(batchSize, seqLength, inputSize)).randn + val gradOutput = Tensor[Double](batchSize, seqLength, outputSize * 2).randn + + val half = inputSize >> 1 + val input1 = input.narrow(3, 1, half).contiguous() + val input2 = input.narrow(3, 1 + half, inputSize - half).contiguous() + val gradOutput1 = gradOutput.narrow(3, 1, outputSize).contiguous() + val gradOutput2 = gradOutput.narrow(3, 1 + outputSize, outputSize).contiguous() + + val birnn = BiRecurrent[Double](JoinTable[Double](3, 0) + .asInstanceOf[AbstractModule[Table, Tensor[Double], Double]], isSplitInput = true) + .add(RnnCell[Double](half, outputSize, ReLU[Double]())) + + val recurrent1 = Recurrent[Double]() + .add(RnnCell[Double](half, outputSize, ReLU[Double]())) + val recurrent2 = Sequential[Double]() + .add(Reverse[Double](2)) + .add(Recurrent[Double]() + .add(RnnCell[Double](half, outputSize, ReLU[Double]()))) + .add(Reverse[Double](2)) + + val birnnParams = birnn.parameters()._1 + val length = birnnParams.length + val halfLen = length >> 1 + val weight1 = recurrent1.parameters()._1 + val weight2 = recurrent2.parameters()._1 + + for (i <- 0 until halfLen) { + weight1(i).resizeAs(birnnParams(i)).copy(birnnParams(i)) + } + for (i <- 0 until halfLen) { + weight2(i).resizeAs(birnnParams(i + halfLen)).copy(birnnParams(i + halfLen)) + } + + val output = birnn.forward(input) + val out1 = recurrent1.forward(input1) + val out2 = recurrent2.forward(input2) + + val jointTable = JoinTable[Double](3, 0) + val outputCompare = jointTable.forward(T(out1, out2)) + + output should be (outputCompare) + + val gradInput = birnn.backward(input, gradOutput) + val grad1 = recurrent1.backward(input1, gradOutput1) + val grad2 = recurrent2.backward(input2, gradOutput2) + + gradInput.narrow(3, 1, half) should be (grad1) + gradInput.narrow(3, 1 + half, inputSize - half) should be (grad2) + } + + "A BiRecurrent " should "has same loss as torch rnn" in { + torchCheck() + + val hiddenSize = 4 + val linearHidden = 8 + val inputSize = 6 + val outputSize = 5 + val bpttTruncate = 3 + val seqLength = 5 + val seed = 100 + val depth = 2 + + val input = Tensor[Double](Array(1, seqLength, inputSize)) + val labels = Tensor[Double](Array(1, seqLength)) + for (i <- 1 to seqLength) { + val rdmLabel = Math.ceil(math.random * outputSize).toInt + val rdmInput = Math.ceil(math.random * inputSize).toInt + input.setValue(1, i, rdmInput, 1.0) + labels.setValue(1, i, rdmLabel) + } + + RNG.setSeed(seed) + + def basicBlock(inputSize: Int, hiddenSize: Int): Module[Double] = { + Sequential() + .add(BiRecurrent[Double](CAddTable[Double]()) + .add(RnnCell[Double](inputSize, hiddenSize, Sigmoid[Double]()))) + } + + val model = Sequential[Double]() + for (i <- 1 to depth) { + if (i == 1) { + model.add(basicBlock(inputSize, hiddenSize)) + } else { + model.add(basicBlock(hiddenSize, hiddenSize)) + } + } + model.add(TimeDistributed[Double](Linear[Double](hiddenSize, outputSize))) + val criterion = TimeDistributedCriterion[Double]( + CrossEntropyCriterion[Double]()) + val logSoftMax = TimeDistributed[Double](LogSoftMax[Double]()) + + val (weights, grad) = model.getParameters() + val code = + s""" + | + |-- 1.4. Combine 1.1 and 1.3 to produce final model + |require 'rnn' + |torch.manualSeed($seed) + | + |local function basicblock(inputSize, hiddenSize) + | local rm = nn.Sequential() -- input is {x[t], h[t-1]} + | :add(nn.ParallelTable() + | :add(nn.Linear(inputSize, hiddenSize)) -- input layer + | :add(nn.Linear(hiddenSize, hiddenSize))) -- recurrent layer + | :add(nn.CAddTable()) -- merge + | :add(nn.Sigmoid()) -- transfer + | local rm1 = nn.Sequential() -- input is {x[t], h[t-1]} + | :add(nn.ParallelTable() + | :add(nn.Linear(inputSize, hiddenSize)) -- input layer + | :add(nn.Linear(hiddenSize, hiddenSize))) -- recurrent layer + | :add(nn.CAddTable()) -- merge + | :add(nn.Sigmoid()) -- transfer + | + | local rnn = nn.Recurrence(rm, hiddenSize, 1) + | local rnn1 = nn.Recurrence(rm1, hiddenSize, 1) + | return nn.Sequential() + | :add(nn.BiSequencer(rnn, rnn1, nn.CAddTable())) + |end + | + | + |model = nn.Sequential() + |:add(nn.SplitTable(1)) + | + | for i=1,$depth do + | if i == 1 then + | model:add(basicblock($inputSize, $hiddenSize)) + | else + | model:add(basicblock($hiddenSize, $hiddenSize)) + | end + | end + | + | model:add(nn.JoinTable(1, 5)) + |--:add(nn.Sequencer( + |-- nn.Sequential() + |-- --:add(nn.LSTM($inputSize, $hiddenSize, 1, true)) + |-- :add(nn.FastLSTM($inputSize, $hiddenSize)) + | :add(nn.Linear($hiddenSize, $outputSize)) + |-- )) + | + | + |local parameters, gradParameters = model:getParameters() + |model:zeroGradParameters() + |parameters:copy(weights) + | + |parameters_initial = parameters : clone() + |gradParameters_initial = gradParameters : clone() + | + |local criterion = nn.SequencerCriterion(nn.CrossEntropyCriterion()) + | + | + |state = { + | learningRate = 0.5, + | momentum = 0.0, + | dampening = 0.0, + | weightDecay = 0.0 + |} + | + |feval = function(x) + |model:zeroGradParameters() + |model_initial = model : clone() + | + |local output1 = model:forward(input) + |local err1 = criterion:forward(output1, labels) + |local gradOutput1 = criterion:backward(output1, labels) + |model:backward(input, gradOutput1) + |return err1, gradParameters + |end + | + |for i = 1,10,1 do + | optim.sgd(feval, parameters, state) + |end + | + |labels = labels + |err=criterion.output + |err2=criterion.gradInput + |output = model.output + |gradInput = model.gradInput + """.stripMargin + + val (luaTime, torchResult) = TH.run(code, + Map("input" -> input.transpose(1, 2), "weights" -> weights, + "labels" -> labels(1)), + Array("err", "parameters", "gradParameters", "output", "gradInput", "err2", "labels")) + + val luaOutput2 = torchResult("err").asInstanceOf[Double] + val luaweight = torchResult("parameters").asInstanceOf[Tensor[Double]] + + val state = T("learningRate" -> 0.5, "momentum" -> 0.0, + "weightDecay" -> 0.0, "dampening" -> 0.0) + val sgd = new SGD[Double] + def feval(x: Tensor[Double]): (Double, Tensor[Double]) = { + val output = model.forward(input).asInstanceOf[Tensor[Double]] + val _loss = criterion.forward(output, labels) + model.zeroGradParameters() + val gradInput = criterion.backward(output, labels) + model.backward(input, gradInput) + (_loss, grad) + } + + val start = System.nanoTime() + var loss: Array[Double] = null + for (i <- 1 to 10) { + loss = sgd.optimize(feval, weights, state)._2 + println(s"${i}-th loss = ${loss(0)}") + } + val end = System.nanoTime() + println("Time: " + (end - start) / 1E6) + + val output = model.output.toTensor[Double] + val logOutput = logSoftMax.forward(output) + val prediction = logOutput.max(3)._2 + + luaOutput2 should be(loss(0) +- 1e-5) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConvLSTMPeephole3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConvLSTMPeephole3DSpec.scala new file mode 100644 index 00000000000..dc7327e903c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConvLSTMPeephole3DSpec.scala @@ -0,0 +1,167 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.torch + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils._ +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +import scala.math._ + +@com.intel.analytics.bigdl.tags.Parallel +class ConvLSTMPeephole3DSpec extends FlatSpec with BeforeAndAfter with Matchers { + + "A ConvLSTMPeepwhole3D " should " work in BatchMode" in { + val hiddenSize = 5 + val inputSize = 3 + val seqLength = 4 + val batchSize = 2 + val kernalW = 2 + val kernalH = 2 + val rec = Recurrent[Double]() + val model = Sequential[Double]() + .add(rec + .add(ConvLSTMPeephole3D[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + withPeephole = true))) + + val input = Tensor[Double](batchSize, seqLength, inputSize, 5, 5, 5).rand + + for (i <- 1 to 3) { + val output = model.forward(input).toTensor[Double] + for((value, j) <- output.size.view.zipWithIndex) { + if (j > 2) { + require(value == input.size(j + 1)) + } + } + model.backward(input, output) + } + } + + "A ConvLSTMPeepwhole3D" should " return state" in { + val hiddenSize = 5 + val inputSize = 3 + val seqLength = 4 + val batchSize = 2 + val kernalW = 3 + val kernalH = 3 + val model = Recurrent[Double]() + .add(ConvLSTMPeephole3D[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + withPeephole = true)) + + val input = Tensor[Double](batchSize, seqLength, inputSize, 3, 3, 3).rand + + val output = model.forward(input) + + val state = model.getHiddenState() + val hidden = state.asInstanceOf[Table].apply(1).asInstanceOf[Tensor[Double]] + hidden.map(output.select(2, seqLength), (v1, v2) => { + assert(abs(v1 - v2) == 0) + v1 + }) + } + + "ConvLSTMPeephole3D L2 regularizer" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + + val hiddenSize = 5 + val inputSize = 3 + val seqLength = 4 + val batchSize = 1 + val kernalW = 3 + val kernalH = 3 + + val state1 = T("learningRate" -> 0.1, "learningRateDecay" -> 5e-7, + "weightDecay" -> 0.1, "momentum" -> 0.002) + val state2 = T("learningRate" -> 0.1, "learningRateDecay" -> 5e-7, + "weightDecay" -> 0.0, "momentum" -> 0.002) + + val criterion = new TimeDistributedCriterion[Double](new MSECriterion[Double]) + + val input = Tensor[Double](batchSize, seqLength, inputSize, 3, 3, 3).rand + val labels = Tensor[Double](batchSize, seqLength, hiddenSize, 3, 3, 3).rand + + val rec = Recurrent[Double]() + val model1 = Sequential[Double]() + .add(rec + .add(ConvLSTMPeephole3D[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + withPeephole = true))) + + val (weights1, grad1) = model1.getParameters() + + val model2 = Sequential[Double]() + .add(Recurrent[Double]() + .add(ConvLSTMPeephole3D[Double]( + inputSize, + hiddenSize, + kernalW, kernalH, + wRegularizer = L2Regularizer(0.1), + uRegularizer = L2Regularizer(0.1), + bRegularizer = L2Regularizer(0.1), + cRegularizer = L2Regularizer(0.1), + withPeephole = true))) + + val (weights2, grad2) = model2.getParameters() + weights2.copy(weights1.clone()) + grad2.copy(grad1.clone()) + + val sgd = new SGD[Double] + + def feval1(x: Tensor[Double]): (Double, Tensor[Double]) = { + val output = model1.forward(input).toTensor[Double] + val _loss = criterion.forward(output, labels) + model1.zeroGradParameters() + val gradInput = criterion.backward(output, labels) + model1.backward(input, gradInput) + (_loss, grad1) + } + + def feval2(x: Tensor[Double]): (Double, Tensor[Double]) = { + val output = model2.forward(input).toTensor[Double] + val _loss = criterion.forward(output, labels) + model2.zeroGradParameters() + val gradInput = criterion.backward(output, labels) + model2.backward(input, gradInput) + (_loss, grad2) + } + + var loss1: Array[Double] = null + for (i <- 1 to 100) { + loss1 = sgd.optimize(feval1, weights1, state1)._2 + println(s"${i}-th loss = ${loss1(0)}") + } + + var loss2: Array[Double] = null + for (i <- 1 to 100) { + loss2 = sgd.optimize(feval2, weights2, state2)._2 + println(s"${i}-th loss = ${loss2(0)}") + } + + weights1 should be(weights2) + loss1 should be(loss2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 15b33f74190..ffaee3c04b2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -80,6 +80,21 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", "com.intel.analytics.bigdl.nn.tf.TensorArrayGrad" -> "com.intel.analytics.bigdl.nn.ops.TensorArraySerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArrayCreator" -> + "com.intel.analytics.bigdl.nn.ops.TensorArrayScatterSerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArrayScatter" -> + "com.intel.analytics.bigdl.nn.ops.TensorArrayScatterSerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArrayGather" -> + "com.intel.analytics.bigdl.nn.ops.TensorArrayScatterSerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArrayClose" -> + "com.intel.analytics.bigdl.nn.ops.TensorArrayScatterSerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArrayConcat" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySplitSerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArraySplit" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySplitSerialTest", + "com.intel.analytics.bigdl.nn.tf.TensorArraySize" -> + "com.intel.analytics.bigdl.nn.ops.TensorArraySplitSerialTest", + // Keras layers "com.intel.analytics.bigdl.nn.keras.Input" -> From f926384e551fcfb118f51011eb56464720df68f3 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Tue, 6 Mar 2018 17:25:27 +0800 Subject: [PATCH 0719/1065] set random seed for sparse tensor dot product (#2352) --- .../analytics/bigdl/dllib/tensor/SparseTensorSpec.scala | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala index f63fe286ea4..4ea7bd3e5b1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.RandomGenerator import scala.util.Random @@ -126,7 +127,9 @@ class SparseTensorSpec extends FlatSpec with Matchers { } "SparseTensor dot DenseTense" should "return right result" in { - val values = Array.fill(30)(Random.nextFloat()) + val rng = RandomGenerator.RNG + rng.setSeed(10) + val values = Array.fill(30)(rng.normal(1.0, 1.0).toFloat) val sTensor = Tensor.sparse(Tensor(values, Array(6, 5))) val dTensor = Tensor(Array(6, 5)).rand() From d353f90d840f4bfda4935bb2397b09820046022d Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 7 Mar 2018 13:18:46 +0800 Subject: [PATCH 0720/1065] [Enhancement] Fix maven compile warnings (#2357) * fix some warnnings * fix more warninging * fix more warninging * fix more warninging * fix more warnings * fix style error --- .../bigdl/dlframes/DLClassifier.scala | 4 +- .../org/apache/spark/ml/DLClassifier.scala | 4 +- .../analytics/bigdl/utils/ShapeSpec.scala | 2 +- .../example/treeLSTMSentiment/Utils.scala | 7 +++- .../dllib/feature/dataset/text/Types.scala | 4 ++ .../bigdl/dllib/nn/BinaryTreeLSTM.scala | 1 - .../analytics/bigdl/dllib/nn/LeakyReLU.scala | 2 + .../analytics/bigdl/dllib/nn/Module.scala | 5 ++- .../analytics/bigdl/dllib/nn/Recurrent.scala | 6 +-- .../bigdl/dllib/nn/SpatialCrossMapLRN.scala | 2 - .../dllib/nn/SpatialDilatedConvolution.scala | 3 ++ .../dllib/nn/SpatialFullConvolution.scala | 1 + .../analytics/bigdl/dllib/nn/Transpose.scala | 2 +- .../dllib/nn/VolumetricConvolution.scala | 3 ++ .../dllib/nn/VolumetricFullConvolution.scala | 1 + .../bigdl/dllib/nn/ops/Kv2Tensor.scala | 1 + .../nn/quantized/SpatialConvolution.scala | 18 ++++---- .../bigdl/dllib/nn/tf/DataFlowOps.scala | 4 +- .../analytics/bigdl/dllib/nn/tf/Log1p.scala | 1 - .../bigdl/dllib/nn/tf/ParsingOps.scala | 2 + .../bigdl/dllib/tensor/DenseTensor.scala | 4 +- .../bigdl/dllib/tensor/DenseTensorApply.scala | 2 +- .../bigdl/dllib/tensor/SparseTensorBLAS.scala | 42 +++++++++++-------- .../bigdl/dllib/utils/caffe/CaffeLoader.scala | 1 + .../utils/serializer/ModuleSerializer.scala | 4 +- .../serializer/converters/DataConverter.scala | 8 ++-- .../converters/TensorConverter.scala | 2 + .../converters/TensorStorageManager.scala | 4 ++ .../dllib/utils/tf/BigDLToTensorflow.scala | 1 + .../dllib/utils/tf/TensorflowLoader.scala | 2 + .../bigdl/dllib/utils/tf/loaders/All.scala | 4 +- .../bigdl/dllib/utils/tf/loaders/Any.scala | 4 +- .../utils/tf/loaders/ApproximateEqual.scala | 4 +- .../dllib/utils/tf/loaders/ArrayOps.scala | 1 - .../dllib/utils/tf/loaders/AvgPoolGrad.scala | 6 +-- .../dllib/utils/tf/loaders/BatchMatMul.scala | 6 +-- .../dllib/utils/tf/loaders/BiasAddGrad.scala | 6 +-- .../tf/loaders/BroadcastGradientArgs.scala | 5 --- .../bigdl/dllib/utils/tf/loaders/Cast.scala | 22 +++++----- .../bigdl/dllib/utils/tf/loaders/Ceil.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Const.scala | 21 +++++----- .../bigdl/dllib/utils/tf/loaders/Conv2D.scala | 7 ++-- .../tf/loaders/Conv2DBackpropFilter.scala | 2 - .../bigdl/dllib/utils/tf/loaders/Conv3D.scala | 8 ++-- .../tf/loaders/Conv3DBackpropFilter.scala | 4 +- .../tf/loaders/Conv3DBackpropFilterV2.scala | 6 +-- .../tf/loaders/Conv3DBackpropInput.scala | 4 +- .../tf/loaders/Conv3DBackpropInputV2.scala | 6 +-- .../dllib/utils/tf/loaders/Digamma.scala | 6 +-- .../dllib/utils/tf/loaders/Dilation2D.scala | 7 ++-- .../tf/loaders/Dilation2DBackpropFilter.scala | 6 +-- .../tf/loaders/Dilation2DBackpropInput.scala | 6 +-- .../dllib/utils/tf/loaders/EluGrad.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Erf.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Erfc.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Exp.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Expm1.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Fill.scala | 5 +-- .../bigdl/dllib/utils/tf/loaders/Floor.scala | 9 +--- .../dllib/utils/tf/loaders/FloorDiv.scala | 8 ++-- .../dllib/utils/tf/loaders/FloorMod.scala | 8 ++-- .../utils/tf/loaders/FusedBatchNorm.scala | 6 +-- .../utils/tf/loaders/FusedBatchNormGrad.scala | 4 +- .../tf/loaders/FusedBatchNormGradV2.scala | 4 +- .../utils/tf/loaders/FusedBatchNormV2.scala | 6 +-- .../dllib/utils/tf/loaders/GreaterEqual.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/InTopK.scala | 4 +- .../bigdl/dllib/utils/tf/loaders/Inv.scala | 6 +-- .../dllib/utils/tf/loaders/InvGrad.scala | 6 +-- .../dllib/utils/tf/loaders/IsFinite.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/IsInf.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/IsNan.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/L2Loss.scala | 9 +--- .../dllib/utils/tf/loaders/LRNGrad.scala | 4 +- .../dllib/utils/tf/loaders/LessEqual.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Lgamma.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Log1p.scala | 6 +-- .../dllib/utils/tf/loaders/MaxPoolGrad.scala | 8 ++-- .../dllib/utils/tf/loaders/Maximum.scala | 6 +-- .../dllib/utils/tf/loaders/Minimum.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Mod.scala | 8 ++-- .../bigdl/dllib/utils/tf/loaders/Neg.scala | 8 +--- .../bigdl/dllib/utils/tf/loaders/OneHot.scala | 2 - .../bigdl/dllib/utils/tf/loaders/Pack.scala | 5 +-- .../dllib/utils/tf/loaders/Placeholder.scala | 8 +--- .../bigdl/dllib/utils/tf/loaders/Pow.scala | 8 +--- .../bigdl/dllib/utils/tf/loaders/Prod.scala | 8 +--- .../utils/tf/loaders/RandomUniform.scala | 11 ++--- .../dllib/utils/tf/loaders/Reciprocal.scala | 6 +-- .../utils/tf/loaders/ReciprocalGrad.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Relu6.scala | 1 - .../dllib/utils/tf/loaders/Relu6Grad.scala | 6 +-- .../dllib/utils/tf/loaders/ReluGrad.scala | 9 +--- .../dllib/utils/tf/loaders/Reshape.scala | 9 ++-- .../utils/tf/loaders/ResizeBilinear.scala | 3 +- .../utils/tf/loaders/ResizeBilinearGrad.scala | 4 +- .../bigdl/dllib/utils/tf/loaders/Rint.scala | 7 +--- .../bigdl/dllib/utils/tf/loaders/Round.scala | 9 ++-- .../dllib/utils/tf/loaders/RsqrtGrad.scala | 7 ++-- .../dllib/utils/tf/loaders/SegmentSum.scala | 7 ++-- .../bigdl/dllib/utils/tf/loaders/Select.scala | 9 +--- .../bigdl/dllib/utils/tf/loaders/Shape.scala | 5 +-- .../dllib/utils/tf/loaders/Sigmoid.scala | 5 +-- .../dllib/utils/tf/loaders/SigmoidGrad.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Sign.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Slice.scala | 5 +-- .../dllib/utils/tf/loaders/SoftplusGrad.scala | 7 ++-- .../dllib/utils/tf/loaders/SoftsignGrad.scala | 6 +-- .../dllib/utils/tf/loaders/SqrtGrad.scala | 6 +-- .../utils/tf/loaders/SquaredDifference.scala | 6 +-- .../dllib/utils/tf/loaders/Squeeze.scala | 7 +--- .../bigdl/dllib/utils/tf/loaders/Sum.scala | 18 ++++---- .../bigdl/dllib/utils/tf/loaders/Tanh.scala | 5 +-- .../dllib/utils/tf/loaders/TanhGrad.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/Tile.scala | 4 +- .../bigdl/dllib/utils/tf/loaders/TopK.scala | 6 +-- .../bigdl/dllib/utils/tf/loaders/TopKV2.scala | 6 +-- .../dllib/utils/tf/loaders/Transpose.scala | 4 +- .../dllib/utils/tf/loaders/TruncateDiv.scala | 4 +- .../dllib/utils/tf/loaders/TruncateMod.scala | 8 ++-- .../dllib/utils/tf/loaders/VariableV2.scala | 6 +-- .../dllib/dlframes/DLEstimatorSpec.scala | 8 ++-- .../dllib/nn/BifurcateSplitTableSpec.scala | 1 - .../analytics/bigdl/dllib/nn/ExpSpec.scala | 1 - .../analytics/bigdl/dllib/nn/SumSpec.scala | 1 - .../bigdl/dllib/nn/ops/SelectSpec.scala | 3 -- .../bigdl/dllib/nn/tf/StackOpsSpec.scala | 1 - .../bigdl/dllib/optim/DLEstimatorSpec.scala | 8 ++-- .../bigdl/dllib/torch/LSTMPeepholeSpec.scala | 4 +- .../bigdl/dllib/utils/GraphNodeSpec.scala | 6 +-- .../utils/serializer/DataConverterSpec.scala | 12 +++--- .../dllib/utils/tf/TensorflowSpecHelper.scala | 1 + 132 files changed, 358 insertions(+), 416 deletions(-) diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala index dcda292378b..c1c46e21b13 100644 --- a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala @@ -34,7 +34,7 @@ import scala.reflect.ClassTag * @param criterion BigDL criterion method * @param featureSize The size (Tensor dimensions) of the feature data. */ -class DLClassifier[@specialized(Float, Double) T: ClassTag]( +class DLClassifier[T: ClassTag]( @transient override val model: Module[T], override val criterion : Criterion[T], override val featureSize : Array[Int], @@ -65,7 +65,7 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( * @param model BigDL module to be optimized * @param featureSize The size (Tensor dimensions) of the feature data. */ -class DLClassifierModel[@specialized(Float, Double) T: ClassTag]( +class DLClassifierModel[T: ClassTag]( @transient override val model: Module[T], featureSize : Array[Int], override val uid: String = "DLClassifierModel" diff --git a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala index e99cbc802c1..e084b92c88e 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala @@ -36,7 +36,7 @@ import scala.reflect.ClassTag */ @deprecated("`DLClassifier` has been migrated to package `com.intel.analytics.bigdl.dlframes`." + "This will be removed in BigDL 0.6.", "0.5.0") -class DLClassifier[@specialized(Float, Double) T: ClassTag]( +class DLClassifier[T: ClassTag]( @transient override val model: Module[T], override val criterion : Criterion[T], override val featureSize : Array[Int], @@ -69,7 +69,7 @@ class DLClassifier[@specialized(Float, Double) T: ClassTag]( */ @deprecated("`DLClassifierModel` is migrated to package `com.intel.analytics.bigdl.dlframes`." + "This will be removed in BigDL 0.6.", "0.5.0") -class DLClassifierModel[@specialized(Float, Double) T: ClassTag]( +class DLClassifierModel[T: ClassTag]( @transient override val model: Module[T], featureSize : Array[Int], override val uid: String = "DLClassifierModel" diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ShapeSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ShapeSpec.scala index fdb20edbe6b..74761b93d5f 100644 --- a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ShapeSpec.scala +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ShapeSpec.scala @@ -38,6 +38,6 @@ class ShapeSpec extends FlatSpec with Matchers with BeforeAndAfter { "singleShape not equal" should "be test" in { intercept[RuntimeException] { - assert(Shape(1, 2, 3) == List(Shape(1, 2, 4))) + assert(Shape(1, 2, 3) == Shape(1, 2, 4)) }} } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala index 5fef54ceee2..016016eb234 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/treeLSTMSentiment/Utils.scala @@ -29,6 +29,7 @@ import scopt.OptionParser import scala.io.Source import scala.language.existentials +import scala.reflect.ClassTag import scala.util.control.Breaks._ object Utils { @@ -154,13 +155,15 @@ object Utils { labelRDD: RDD[Array[Float]], sentenceRDD: RDD[Array[Int]] ): RDD[Sample[Float]] = { - def indexAndSort(rdd: RDD[_]) = rdd.zipWithIndex.map(_.swap).sortByKey() + def indexAndSort[D: ClassTag, P <: Product2[Long, D]](rdd: RDD[D]) = { + rdd.zipWithIndex.map(r => r.swap).sortByKey() + } indexAndSort(sentenceRDD) .join(indexAndSort(labelRDD)) .join(indexAndSort(treeRDD)) .values - .map { case ((input: Array[Int], label: Array[Float]), tree: Tensor[Float]) => + .map{ case ((input, label), tree) => Sample( featureTensors = Array(Tensor(input.map(_.toFloat), Array(input.length, 1)), diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/Types.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/Types.scala index 1e8cb24a1f8..23a2f3ea3d9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/Types.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/text/Types.scala @@ -72,6 +72,7 @@ class LabeledSentence[T: ClassTag]( Array.copy(rawLabel .asInstanceOf[Array[Float]], 0, _label .asInstanceOf[Array[Float]], 0, _labelLength) + case t => throw new NotImplementedError(s"$t is not supported") } this } @@ -95,6 +96,7 @@ class LabeledSentence[T: ClassTag]( case FloatType => Array.copy(_data .asInstanceOf[Array[Float]], 0, storage .asInstanceOf[Array[Float]], offset, _dataLength) + case t => throw new NotImplementedError(s"$t is not supported") } } @@ -107,6 +109,7 @@ class LabeledSentence[T: ClassTag]( case FloatType => Array.copy(_label .asInstanceOf[Array[Float]], 0, storage .asInstanceOf[Array[Float]], offset, _labelLength) + case t => throw new NotImplementedError(s"$t is not supported") } } @@ -132,6 +135,7 @@ class LabeledSentence[T: ClassTag]( Array.copy(other._label .asInstanceOf[Array[Float]], 0, this._label .asInstanceOf[Array[Float]], 0, _labelLength) + case t => throw new NotImplementedError(s"$t is not supported") } this } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala index e7a0d69cafd..1002845b719 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BinaryTreeLSTM.scala @@ -17,7 +17,6 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.Input import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala index 7cbfe05f8f9..3a9e9f9133a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala @@ -52,6 +52,7 @@ class LeakyReLU[T: ClassTag]( negval.toFloat, inplace) case DoubleType => updateOutputDouble(input.toTensor[Double], output.toTensor[Double], negval, inplace) + case t => throw new NotImplementedError(s"$t is not supported") } output } @@ -67,6 +68,7 @@ class LeakyReLU[T: ClassTag]( gradInput.toTensor[Float], negval.toFloat, inplace) case DoubleType => updateGradInputDouble(input.toTensor[Double], gradOutput.toTensor[Double], gradInput.toTensor[Double], negval, inplace) + case t => throw new NotImplementedError(s"$t is not supported") } gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala index 69e35d21a58..19d38f65488 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala @@ -39,7 +39,8 @@ object Module { * @tparam T numeric type * @return model loaded from path */ - @deprecated("Java based serialization not recommended any more, please use loadModule instead") + @deprecated("Java based serialization not recommended any more, please use loadModule instead", + "0.3") def load[T: ClassTag](path : String) : AbstractModule[Activity, Activity, T] = { File.load[AbstractModule[Activity, Activity, T]](path) } @@ -64,7 +65,7 @@ object Module { File.loadTorch[AbstractModule[Activity, Activity, T]](path) } - @deprecated + @deprecated("Please try to use the loadCaffeModel API", "0.2") def loadCaffe[T: ClassTag](model: AbstractModule[Activity, Activity, T], defPath: String, modelPath: String, matchAll: Boolean = true)( implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index 9137b460135..61c69b1ff99 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -652,7 +652,7 @@ object Recurrent extends ContainerSerializable { * @param src * @param dst */ - private[bigdl] def copy[@specialized(Float, Double) T: ClassTag]( + private[bigdl] def copy[T: ClassTag]( src: ArrayBuffer[Tensor[T]], dst: Tensor[T]): Unit = { val timeSize = dst.size(timeDim) var t = 1 @@ -668,7 +668,7 @@ object Recurrent extends ContainerSerializable { * @param srcIndex the index of 2-th dimension from src * @param dst */ - private[bigdl] def selectCopy[@specialized(Float, Double) T: ClassTag]( + private[bigdl] def selectCopy[T: ClassTag]( src: Tensor[T], srcIndex: Int, dst: Tensor[T]): Tensor[T] = { if (src.isContiguous() && dst.isContiguous()) { if ((dst.nElement() == 0) || (dst.nElement() != (src.nElement() / src.size(2)))) { @@ -707,7 +707,7 @@ object Recurrent extends ContainerSerializable { * @param dst * @param dstIndex the index of 2-th dimension from dst */ - private[bigdl] def copyToIndex[@specialized(Float, Double) T: ClassTag]( + private[bigdl] def copyToIndex[T: ClassTag]( src: Tensor[T], dst: Tensor[T], dstIndex: Int): Tensor[T] = { if (src.isContiguous() && dst.isContiguous()) { val batchSize = dst.size(batchDim) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRN.scala index 6396cb5a02e..63b64be4d6c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialCrossMapLRN.scala @@ -236,7 +236,6 @@ object SpatialCrossMapLRN { scale.mul(ev.fromType(alpha / size)).add(ev.fromType(k)) output.pow(scale, ev.fromType(-beta)) output.cmul(input) - output } def forwardFrameNHWCFloat( @@ -282,7 +281,6 @@ object SpatialCrossMapLRN { Math.pow(k + alpha / size * l2sum, -beta).toFloat i += 1 } - output } private def backwardFrameNCHW[T]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala index 3ed7280014e..2851e3504d0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDilatedConvolution.scala @@ -247,6 +247,7 @@ class SpatialDilatedConvolution[T: ClassTag]( dH, dW, dilationH, dilationW ) + case t => throw new NotImplementedError(s"$t is not supported") } im2colTime += System.nanoTime() - before @@ -347,6 +348,7 @@ class SpatialDilatedConvolution[T: ClassTag]( dH, dW, dilationH, dilationW ) + case t => throw new NotImplementedError(s"$t is not supported") } col2imTime += System.nanoTime() - before elt += 1 @@ -421,6 +423,7 @@ class SpatialDilatedConvolution[T: ClassTag]( dH, dW, dilationH, dilationW ) + case t => throw new NotImplementedError(s"$t is not supported") } im2colTime += System.nanoTime() - before diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala index d06fcce9e6b..d6fe8e12c34 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala @@ -527,6 +527,7 @@ class SpatialFullConvolution[T: ClassTag]( dH, dW, 1, 1 ) + case t => throw new NotImplementedError(s"$t is not supported") } im2colTime += System.nanoTime() - before diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala index 638bf320eb5..39a47ee37f7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala @@ -125,7 +125,7 @@ object Transpose extends ModuleSerializable { while (i < size) { val nextPermutationBuilder = AttrValue.newBuilder val arr : Array[Int] = Array(transpose.permutations(i)._1, - transpose.permutations(i)_2) + transpose.permutations(i)._2) DataConverter.setAttributeValue(context, nextPermutationBuilder, arr, universe.typeOf[Array[Int]]) transposeBuilder.putAttr(s"permutation_$i", nextPermutationBuilder.build) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala index 94fc5f37dd6..04605b898d2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricConvolution.scala @@ -317,6 +317,7 @@ object VolumetricConvolution { padFront, padLeft, padTop, padBack, padRight, padBottom, nInputPlane, inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) + case t => throw new NotImplementedError(s"$t is not supported") } output2d.addmm(ev.zero, output2d, ev.one, weight, fInput) @@ -430,6 +431,7 @@ object VolumetricConvolution { padFront, padLeft, padTop, padBack, padRight, padBottom, gradInput.size(1), gradInput.size(2), gradInput.size(4), gradInput.size(3), gradOutput.size(2), gradOutput.size(4), gradOutput.size(3)) + case t => throw new NotImplementedError(s"$t is not supported") } } @@ -521,6 +523,7 @@ object VolumetricConvolution { padFront, padLeft, padTop, padBack, padRight, padBottom, nInputPlane, inputDepth, inputWidth, inputHeight, outputDepth, outputWidth, outputHeight) + case t => throw new NotImplementedError(s"$t is not supported") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolution.scala index 2bfe4ef1b57..71657e6e16a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/VolumetricFullConvolution.scala @@ -570,6 +570,7 @@ class VolumetricFullConvolution[T: ClassTag]( 1, 1, 1, columns.asInstanceOf[Tensor[Float]] ) + case t => throw new NotImplementedError(s"$t is not supported") } // M,N,K are dims of matrix A and B diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2Tensor.scala index cee1f70f1a3..c0a51dd4e5f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Kv2Tensor.scala @@ -72,6 +72,7 @@ class Kv2Tensor[T: ClassTag, D: ClassTag]( values += kv.split(itemDelimiter)(1).toDouble.asInstanceOf[D] case FloatType => values += kv.split(itemDelimiter)(1).toFloat.asInstanceOf[D] + case t => throw new NotImplementedError(s"$t is not supported") } } i += 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala index b44473d5d16..465d727ca2d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -47,8 +47,18 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( require(nInputPlane % nGroup == 0, "Number of input channels should be multiples of group.") require(nOutputPlane % nGroup == 0, "Number of output channels should be multiples of group.") + private val data: QuantizedTensor[T] = QuantizedDummyTensor[T]() + val bias: Tensor[T] = Tensor[T](nOutputPlane) + + val quantFormat: Int = if (format == DataFormat.NCHW) { + BigQuant.NCHW + } else { + BigQuant.NHWC + } + val params = ConvWeightParams(nOutputPlane / nGroup, nInputPlane / nGroup, kernelH, kernelW, quantFormat) + val weight: Array[Tensor[T]] = { val array = new Array[Tensor[T]](nGroup) for (i <- 0 until nGroup) { @@ -57,14 +67,6 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( } array } - private val data: QuantizedTensor[T] = QuantizedDummyTensor[T]() - val bias: Tensor[T] = Tensor[T](nOutputPlane) - - val quantFormat: Int = if (format == DataFormat.NCHW) { - BigQuant.NCHW - } else { - BigQuant.NHWC - } val dilationHeight = 1 val dilationWidth = 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/DataFlowOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/DataFlowOps.scala index 97ea3d0ec61..457f8b04c8a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/DataFlowOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/DataFlowOps.scala @@ -16,13 +16,11 @@ package com.intel.analytics.bigdl.nn.tf import java.util -import java.util.concurrent.ConcurrentHashMap import com.intel.analytics.bigdl.nn.ops.Operation -import com.intel.analytics.bigdl.nn.tf.WithoutInput import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} +import com.intel.analytics.bigdl.utils.{T, Table} import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala index 0f94c4e886b..cdbbc635d5c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/Log1p.scala @@ -16,7 +16,6 @@ package com.intel.analytics.bigdl.nn.tf -import com.intel.analytics.bigdl.nn.Log import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala index cf0956cf51c..cbbff5fbb7b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala @@ -85,6 +85,7 @@ private[bigdl] class ParseExample[T: ClassTag](val nDense: Int, case StringType => val values = feature.getBytesList.getValueList.asScala.toArray Tensor(values, tensorShape) + case t => throw new NotImplementedError(s"$t is not supported") } } } @@ -156,6 +157,7 @@ private[bigdl] object ParseExample extends ModuleSerializable { case LongType => "Long" case FloatType => "Float" case StringType => "String" + case t => throw new NotImplementedError(s"$t is not supported") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 97f5167ca4c..4dbe1186aed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2650,7 +2650,7 @@ object DenseTensor { gauss } - private[tensor] def canFastBroadcast[@specialized T](tensor: Tensor[T], + private[tensor] def canFastBroadcast[T](tensor: Tensor[T], other: Tensor[T]): Boolean = { if (tensor.nDimension < other.nDimension()) return false @@ -2671,7 +2671,7 @@ object DenseTensor { return true } - private[tensor] def expandSize[@specialized T: ClassTag](tensor: Tensor[T], + private[tensor] def expandSize[T: ClassTag](tensor: Tensor[T], other: Tensor[T]): Array[Int] = { val errorMsg = s"tensor size not match ${tensor.size.mkString("x")} " + s"${other.size.mkString("x")}" diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala index 0566677c81a..fc085d30866 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorApply.scala @@ -29,7 +29,7 @@ object DenseTensorApply { def apply1[A, B](tensor1: Tensor[A], tensor2: Tensor[B], func: TensorDiffTypeFunc4[A, B]): Unit = { - if (tensor1.isEmpty == 0) { + if (tensor1.isEmpty) { return } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala index fc32d8aecfd..5e97322203a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorBLAS.scala @@ -120,12 +120,14 @@ object SparseTensorBLAS { beta: T, r: Tensor[T])(implicit ev: TensorNumeric[T]): Unit = { (alpha, mat, vec, beta, r) match { - case (alpha: Double, a: SparseTensor[Double], x: DenseTensor[Double], - beta: Double, y: DenseTensor[Double]) => - dcoomv(alpha, a, x, beta, y) - case (alpha: Float, a: SparseTensor[Float], x: DenseTensor[Float], - beta: Float, y: DenseTensor[Float]) => - scoomv(alpha, a, x, beta, y) + case (alpha: Double, a: SparseTensor[_], x: DenseTensor[_], + beta: Double, y: DenseTensor[_]) => + dcoomv(alpha, a.asInstanceOf[SparseTensor[Double]], x.asInstanceOf[DenseTensor[Double]], + beta, y.asInstanceOf[DenseTensor[Double]]) + case (alpha: Float, a: SparseTensor[_], x: DenseTensor[_], + beta: Float, y: DenseTensor[_]) => + scoomv(alpha, a.asInstanceOf[SparseTensor[Float]], x.asInstanceOf[DenseTensor[Float]], + beta, y.asInstanceOf[DenseTensor[Float]]) case _ => throw new IllegalArgumentException(s"Sparse addmv doesn't support") } @@ -206,18 +208,22 @@ object SparseTensorBLAS { beta: T, r: Tensor[T])(implicit ev: TensorNumeric[T]): Unit = { (alpha, mat1, mat2, beta, r) match { - case (alpha: Float, a: SparseTensor[Float], x: DenseTensor[Float], - beta: Float, y: DenseTensor[Float]) => - scoomm(alpha, a, x, beta, y) - case (alpha: Double, a: SparseTensor[Double], x: DenseTensor[Double], - beta: Double, y: DenseTensor[Double]) => - dcoomm(alpha, a, x, beta, y) - case (alpha: Float, a: DenseTensor[Float], x: SparseTensor[Float], - beta: Float, y: DenseTensor[Float]) => - scoomm(alpha, a, x, beta, y) - case (alpha: Double, a: DenseTensor[Double], x: SparseTensor[Double], - beta: Double, y: DenseTensor[Double]) => - dcoomm(alpha, a, x, beta, y) + case (alpha: Float, a: SparseTensor[_], x: DenseTensor[_], + beta: Float, y: DenseTensor[_]) => + scoomm(alpha, a.asInstanceOf[SparseTensor[Float]], x.asInstanceOf[DenseTensor[Float]], + beta, y.asInstanceOf[DenseTensor[Float]]) + case (alpha: Double, a: SparseTensor[_], x: DenseTensor[_], + beta: Double, y: DenseTensor[_]) => + dcoomm(alpha, a.asInstanceOf[SparseTensor[Double]], x.asInstanceOf[DenseTensor[Double]], + beta, y.asInstanceOf[DenseTensor[Double]]) + case (alpha: Float, a: DenseTensor[_], x: SparseTensor[_], + beta: Float, y: DenseTensor[_]) => + scoomm(alpha, a.asInstanceOf[DenseTensor[Float]], x.asInstanceOf[SparseTensor[Float]], + beta, y.asInstanceOf[DenseTensor[Float]]) + case (alpha: Double, a: DenseTensor[_], x: SparseTensor[_], + beta: Double, y: DenseTensor[_]) => + dcoomm(alpha, a.asInstanceOf[DenseTensor[Double]], x.asInstanceOf[SparseTensor[Double]], + beta, y.asInstanceOf[DenseTensor[Double]]) case _ => throw new IllegalArgumentException(s"Sparse addmm doesn't support") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala index 1f8bab66491..95946d17d62 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/caffe/CaffeLoader.scala @@ -172,6 +172,7 @@ class CaffeLoader[T: ClassTag](prototxtPath: String, modelPath: String, } private def copyBlobs(from : GeneratedMessage, to : GeneratedMessage): GeneratedMessage = { + import scala.language.existentials val blobList = from match { case v1 : V1LayerParameter => v1.asInstanceOf[V1LayerParameter].getBlobsList.asScala case v2 : LayerParameter => v2.asInstanceOf[LayerParameter].getBlobsList.asScala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 2603261050c..36dcd21492b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -26,6 +26,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.collection.mutable +import scala.language.existentials import scala.reflect.ClassTag import scala.reflect.runtime.universe @@ -131,7 +132,8 @@ object ModuleSerializer extends ModuleSerializable{ // to make it compatible with both 2.11 and 2.10 val ctorCs = clsSymbol.toType.declaration(universe.nme.CONSTRUCTOR) val primary: Option[universe.MethodSymbol] = ctorCs.asTerm.alternatives.collectFirst { - case cstor: universe.MethodSymbol if cstor.isPrimaryConstructor => cstor + case cstor if cstor.asInstanceOf[universe.MethodSymbol].isPrimaryConstructor => + cstor.asInstanceOf[universe.MethodSymbol] } cm.reflectConstructor(primary.get) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala index ccfd5f471af..f800d938edd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala @@ -83,7 +83,7 @@ object DataConverter extends DataConverter{ : universe.Type = { if (value.isInstanceOf[Tensor[_]]) { ModuleSerializer.tensorType - } else if (value.isInstanceOf[AbstractModule[_, _, T]]) { + } else if (value.isInstanceOf[AbstractModule[_, _, _]]) { ModuleSerializer.abstractModuleType } else if (value.isInstanceOf[Regularizer[_]]) { ModuleSerializer.regularizerType @@ -180,10 +180,10 @@ object DataConverter extends DataConverter{ || valueType <:< universe.typeOf[AbstractModule[_, _, _]] ) { ModuleConverter.setAttributeValue(context, attributeBuilder, value) - } else if (value.isInstanceOf[mutable.Map[String, _ <: Any]]) { + } else if (value.isInstanceOf[mutable.Map[_, _]]) { NameListConverter.setAttributeValue(context, attributeBuilder, value) } else if (valueType <:< universe.typeOf[Array[_]] || - valueType.typeSymbol == universe.typeOf[Array[_ ]].typeSymbol) { + valueType.typeSymbol == universe.typeOf[Array[_]].typeSymbol) { ArrayConverter.setAttributeValue(context, attributeBuilder, value, valueType) } else if (valueType =:= universe.typeOf[DataFormat]) { DataFormatConverter.setAttributeValue(context, attributeBuilder, value) @@ -500,7 +500,7 @@ object DataConverter extends DataConverter{ }) arrayBuilder.setSize(modules.size) } - } else if (value.isInstanceOf[Array[Map[String, Any]]]) { + } else if (value.isInstanceOf[Array[Map[_, _]]]) { arrayBuilder.setDatatype(DataType.NAME_ATTR_LIST) value.asInstanceOf[Array[Map[String, Any]]].foreach(map => { val attrValueBuilder = AttrValue.newBuilder diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala index 69a8768c518..92e01935369 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorConverter.scala @@ -39,6 +39,7 @@ object TensorConverter extends DataConverter { tensor.storage == null case QuantizedType => tensor.asInstanceOf[QuantizedTensor[_]].getStorage == null + case t => throw new NotImplementedError(s"$t is not supported") } emptyTensor } @@ -279,6 +280,7 @@ object TensorConverter extends DataConverter { tensorBuilder.setTensorType(TensorType.DENSE) case QuantizedType => tensorBuilder.setTensorType(TensorType.QUANT) + case t => throw new NotImplementedError(s"$t is not supported") } val tensorEmpty = isEmptyTensor(tensor) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala index ea4e2b76257..cc0a28207e0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/TensorStorageManager.scala @@ -37,6 +37,7 @@ trait TensorStorageManager { tensor.storage == null case QuantizedType => tensor.asInstanceOf[QuantizedTensor[_]].getStorage == null + case t => throw new NotImplementedError(s"$t is not supported") } emptyTensor } @@ -52,6 +53,7 @@ trait TensorStorageManager { } else { System.identityHashCode(tensor.asInstanceOf[QuantizedTensor[T]].getStorage) } + case t => throw new NotImplementedError(s"$t is not supported") } } @@ -128,6 +130,7 @@ object BigDLTensorStorageManager extends TensorStorageManager { if (tensor.storage() == null) null else tensor.storage().array() case QuantizedType => tensor.asInstanceOf[QuantizedTensor[Float]].getStorage + case t => throw new NotImplementedError(s"$t is not supported") } if (storage != null) { @@ -183,6 +186,7 @@ object ProtoTensorStorageManager extends TensorStorageManager { case LinearData => storageBuilder.addIntData(2) case LinearWeight => storageBuilder.addIntData(3) } + case t => throw new NotImplementedError(s"$t is not supported") } } } else if (tensorNumeric == NumericDouble) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala index ac62621a9cf..7e76a0bb10d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/BigDLToTensorflow.scala @@ -105,6 +105,7 @@ object LinearToTF extends BigDLToTensorflow { object SpatialConvolutionToTF extends BigDLToTensorflow { override def toTFDef(module: AbstractModule[_, _, _], inputs: Seq[NodeDef], byteOrder: ByteOrder): Seq[NodeDef] = { + import scala.language.existentials require(inputs.length == 1, "SpatialConvolution only accept one input") val spatialConv = module.asInstanceOf[SpatialConvolution[_]] if (spatialConv.nGroup == 1) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index df4decdd620..49b4518523e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -113,6 +113,7 @@ object TensorflowLoader{ tensor.size(), "float") case DoubleType => new JTensor(tensor.asInstanceOf[Tensor[Double]].storage().array() .map(_.toFloat), tensor.size(), "double") + case t => throw new NotImplementedError(s"$t is not supported") } save.put(n, saveTensor) }) @@ -126,6 +127,7 @@ object TensorflowLoader{ val tensor = ev.getType() match { case FloatType => PythonBigDLUtils.toTensor(m(k), "float") case DoubleType => PythonBigDLUtils.toTensor(m(k), "double") + case t => throw new NotImplementedError(s"$t is not supported") } map(k) = (tensor, tensor.clone(), None) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/All.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/All.scala index e10e90fa543..e9964b0203f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/All.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/All.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.All +import com.intel.analytics.bigdl.nn.ops.{All => AllOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -32,6 +32,6 @@ class All extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) (implicit ev: TensorNumeric[T]): Module[T] = { val keepDims = getBoolean(nodeDef.getAttrMap, "keep_dims") - All[T](keepDims, true) + AllOps[T](keepDims, true) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Any.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Any.scala index 855f6d8966b..de3d4c4b7fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Any.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Any.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Any +import com.intel.analytics.bigdl.nn.ops.{Any => AnyOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -32,6 +32,6 @@ class Any extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) (implicit ev: TensorNumeric[T]): Module[T] = { val keepDims = getBoolean(nodeDef.getAttrMap, "keep_dims") - Any[T](keepDims, true) + AnyOps[T](keepDims, true) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqual.scala index 5c651ad66aa..1624f149a0f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqual.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ApproximateEqual.scala @@ -20,7 +20,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef -import com.intel.analytics.bigdl.nn.ops.ApproximateEqual +import com.intel.analytics.bigdl.nn.ops.{ApproximateEqual => ApproximateEqualOps} import com.intel.analytics.bigdl.utils.tf.Context @@ -32,6 +32,6 @@ class ApproximateEqual extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val attributes = nodeDef.getAttrMap val tolerance = getFloat(attributes, "tolerance") - ApproximateEqual[T](tolerance) + ApproximateEqualOps[T](tolerance) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala index 8ead6e82413..409dd22cff2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ArrayOps.scala @@ -18,7 +18,6 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.tf.{InvertPermutation => InvertPermutationOps, ConcatOffset => ConcatOffsetOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala index 2986ca0db8b..c212cbcbb03 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/AvgPoolGrad.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.AvgPoolGrad +import com.intel.analytics.bigdl.nn.tf.{AvgPoolGrad => AvgPoolGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -52,7 +52,7 @@ class AvgPoolGrad extends TensorflowOpsLoader { val strideH = strideList(2) val kW = kernelSize(1) val kH = kernelSize(2) - AvgPoolGrad[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NHWC) + AvgPoolGradOps[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NHWC) case "NCHW" => require(strideList(1) == 1, s"not support strides on depth") @@ -60,7 +60,7 @@ class AvgPoolGrad extends TensorflowOpsLoader { val strideH = strideList(3) val kW = kernelSize(2) val kH = kernelSize(3) - AvgPoolGrad[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NCHW) + AvgPoolGradOps[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NCHW) case _ => throw new IllegalArgumentException(s"not supported data format: $format") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMul.scala index aa1d237c6c3..0e0877b0f88 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BatchMatMul.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.BatchMatMul +import com.intel.analytics.bigdl.nn.ops.{BatchMatMul => BatchMatMulOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -35,9 +35,9 @@ class BatchMatMul extends TensorflowOpsLoader { val adjX = getBoolean(nodeDef.getAttrMap, "adj_x") val adjY = getBoolean(nodeDef.getAttrMap, "adj_y") if (t == DataType.DT_FLOAT) { - BatchMatMul[T, Float](adjX, adjY) + BatchMatMulOps[T, Float](adjX, adjY) } else if (t == DataType.DT_DOUBLE) { - BatchMatMul[T, Double](adjX, adjY) + BatchMatMulOps[T, Double](adjX, adjY) } else { throw new UnsupportedOperationException(s"Not support load ReLU6 when type is $t") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala index 34ab7ee738b..3f4343f257c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BiasAddGrad.scala @@ -18,10 +18,8 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.BiasAddGrad -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.tf.{BiasAddGrad => BiasAddGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -40,6 +38,6 @@ class BiasAddGrad extends TensorflowOpsLoader { } else { DataFormat.NCHW } - BiasAddGrad[T](format) + BiasAddGradOps[T](format) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala index 4ebde3352bf..645468c8a18 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/BroadcastGradientArgs.scala @@ -18,9 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.tf.{BroadcastGradientArgs => BroadcastGradientArgsOps} -import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -28,9 +26,6 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class BroadcastGradientArgs extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new BroadcastGradientArgsOps[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala index 168f64697d8..70d50d38450 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Cast +import com.intel.analytics.bigdl.nn.ops.{Cast => CastOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -35,16 +35,16 @@ class Cast extends TensorflowOpsLoader { val dataType = getType(attr, "DstT") val layer = dataType match { - case DataType.DT_INT8 => Cast[T, Int]() - case DataType.DT_INT16 => Cast[T, Int]() - case DataType.DT_UINT8 => Cast[T, Int]() - case DataType.DT_UINT16 => Cast[T, Int]() - case DataType.DT_INT32 => Cast[T, Int]() - case DataType.DT_INT64 => Cast[T, Int]() - case DataType.DT_BOOL => Cast[T, Boolean]() - case DataType.DT_STRING => Cast[T, String]() - case DataType.DT_FLOAT => Cast[T, Float]() - case DataType.DT_DOUBLE => Cast[T, Double]() + case DataType.DT_INT8 => CastOps[T, Int]() + case DataType.DT_INT16 => CastOps[T, Int]() + case DataType.DT_UINT8 => CastOps[T, Int]() + case DataType.DT_UINT16 => CastOps[T, Int]() + case DataType.DT_INT32 => CastOps[T, Int]() + case DataType.DT_INT64 => CastOps[T, Int]() + case DataType.DT_BOOL => CastOps[T, Boolean]() + case DataType.DT_STRING => CastOps[T, String]() + case DataType.DT_FLOAT => CastOps[T, Float]() + case DataType.DT_DOUBLE => CastOps[T, Double]() } layer } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Ceil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Ceil.scala index 29fe8ede0b5..c4a79219437 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Ceil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Ceil.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Ceil +import com.intel.analytics.bigdl.nn.ops.{Ceil => CeilOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Ceil extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Ceil[T, Float]() + CeilOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Ceil[T, Double]() + CeilOps[T, Double]() } else { throw new UnsupportedOperationException(s"not support load Cell operation for type $t") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala index 00f51b04d6a..94f166d3be5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Const.scala @@ -19,8 +19,7 @@ import java.nio.ByteOrder import com.google.protobuf.ByteString import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.nn.tf.{Const => ConstOps} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericShort, NumericString} @@ -35,15 +34,15 @@ class Const extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val value = TFUtils.parseTensor(nodeDef.getAttrMap.get("value").getTensor, byteOrder) val const = value.getTensorNumeric() match { - case NumericFloat => Const[T, Float](value.asInstanceOf[Tensor[Float]]) - case NumericDouble => Const[T, Double](value.asInstanceOf[Tensor[Double]]) - case NumericInt => Const[T, Int](value.asInstanceOf[Tensor[Int]]) - case NumericLong => Const[T, Long](value.asInstanceOf[Tensor[Long]]) - case NumericChar => Const[T, Char](value.asInstanceOf[Tensor[Char]]) - case NumericBoolean => Const[T, Boolean](value.asInstanceOf[Tensor[Boolean]]) - case NumericShort => Const[T, Short](value.asInstanceOf[Tensor[Short]]) - case NumericString => Const[T, String](value.asInstanceOf[Tensor[String]]) - case NumericByteString => Const[T, ByteString](value.asInstanceOf[Tensor[ByteString]]) + case NumericFloat => ConstOps[T, Float](value.asInstanceOf[Tensor[Float]]) + case NumericDouble => ConstOps[T, Double](value.asInstanceOf[Tensor[Double]]) + case NumericInt => ConstOps[T, Int](value.asInstanceOf[Tensor[Int]]) + case NumericLong => ConstOps[T, Long](value.asInstanceOf[Tensor[Long]]) + case NumericChar => ConstOps[T, Char](value.asInstanceOf[Tensor[Char]]) + case NumericBoolean => ConstOps[T, Boolean](value.asInstanceOf[Tensor[Boolean]]) + case NumericShort => ConstOps[T, Short](value.asInstanceOf[Tensor[Short]]) + case NumericString => ConstOps[T, String](value.asInstanceOf[Tensor[String]]) + case NumericByteString => ConstOps[T, ByteString](value.asInstanceOf[Tensor[ByteString]]) } const.asInstanceOf[Module[T]] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala index e84ccf073af..5dee3bd1f0a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2D.scala @@ -19,8 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.tf.Conv2D -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.tf.{Conv2D => Conv2DOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -49,13 +48,13 @@ class Conv2D extends TensorflowOpsLoader { require(strideList(3) == 1, s"not support strides on depth") val strideW = strideList(1) val strideH = strideList(2) - Conv2D[T](strideW, strideH, pW, pH, DataFormat.NHWC) + Conv2DOps[T](strideW, strideH, pW, pH, DataFormat.NHWC) case "NCHW" => require(strideList(1) == 1, s"not support strides on depth") val strideW = strideList(2) val strideH = strideList(3) - Conv2D[T](strideW, strideH, pW, pH, DataFormat.NCHW) + Conv2DOps[T](strideW, strideH, pW, pH, DataFormat.NCHW) case _ => throw new IllegalArgumentException(s"not supported data format: $format") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala index eb38f512b1e..0d31cd5f318 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv2DBackpropFilter.scala @@ -18,10 +18,8 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.nn.tf.Conv2DBackFilter -import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala index 9990ff47ac4..f5cf0406d60 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3D.scala @@ -18,11 +18,9 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.tf.Conv3D +import com.intel.analytics.bigdl.nn.tf.{Conv3D => Conv3DOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Node import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -52,13 +50,13 @@ class Conv3D extends TensorflowOpsLoader { val dW = strideList(2) val dH = strideList(3) - Conv3D[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + Conv3DOps[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) case "NCDHW" => require(strideList(1) == 1, s"not support strides on depth") val dT = strideList(2) val dW = strideList(3) val dH = strideList(4) - Conv3D[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) + Conv3DOps[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) case _ => throw new IllegalArgumentException(s"not supported data format: $format") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala index dfc60c01150..4e9c8dd3aa8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilter.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.Conv3DBackpropFilter +import com.intel.analytics.bigdl.nn.tf.{Conv3DBackpropFilter => Conv3DBackpropFilterOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -46,6 +46,6 @@ class Conv3DBackpropFilter extends TensorflowOpsLoader { val dT = strideList(1) val dW = strideList(2) val dH = strideList(3) - Conv3DBackpropFilter[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + Conv3DBackpropFilterOps[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala index 43020369e78..3e11149be1c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropFilterV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.tf.Conv3DBackpropFilterV2 +import com.intel.analytics.bigdl.nn.tf.{Conv3DBackpropFilterV2 => Conv3DBackpropFilterV2Ops} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -49,13 +49,13 @@ class Conv3DBackpropFilterV2 extends TensorflowOpsLoader { val dT = strideList(1) val dW = strideList(2) val dH = strideList(3) - Conv3DBackpropFilterV2[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + Conv3DBackpropFilterV2Ops[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) case "NCDHW" => require(strideList(1) == 1, s"not support strides on depth") val dT = strideList(2) val dW = strideList(3) val dH = strideList(4) - Conv3DBackpropFilterV2[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) + Conv3DBackpropFilterV2Ops[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) case _ => throw new IllegalArgumentException(s"not supported data format: $format") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala index 279f04fe542..48b60b1e6f3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInput.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.Conv3DBackpropInput +import com.intel.analytics.bigdl.nn.tf.{Conv3DBackpropInput => Conv3DBackpropInputOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -46,6 +46,6 @@ class Conv3DBackpropInput extends TensorflowOpsLoader { val dT = strideList(1) val dW = strideList(2) val dH = strideList(3) - Conv3DBackpropInput[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + Conv3DBackpropInputOps[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala index c3a1f0f336b..a026a02f536 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Conv3DBackpropInputV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.tf.Conv3DBackpropInputV2 +import com.intel.analytics.bigdl.nn.tf.{Conv3DBackpropInputV2 => Conv3DBackpropInputV2Ops} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -49,13 +49,13 @@ class Conv3DBackpropInputV2 extends TensorflowOpsLoader { val dT = strideList(1) val dW = strideList(2) val dH = strideList(3) - Conv3DBackpropInputV2[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) + Conv3DBackpropInputV2Ops[T](dT, dW, dH, pT, pW, pH, DataFormat.NHWC) case "NCDHW" => require(strideList(1) == 1, s"not support strides on depth") val dT = strideList(2) val dW = strideList(3) val dH = strideList(4) - Conv3DBackpropInputV2[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) + Conv3DBackpropInputV2Ops[T](dT, dW, dH, pT, pW, pH, DataFormat.NCHW) case _ => throw new IllegalArgumentException(s"not supported data format: $format") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Digamma.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Digamma.scala index 43f16d745bd..7019cdec0eb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Digamma.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Digamma.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Digamma +import com.intel.analytics.bigdl.nn.ops.{Digamma => DigammaOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Digamma extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Digamma[T, Float]() + DigammaOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Digamma[T, Double]() + DigammaOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Digamma when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2D.scala index b80a8fc08ab..02cd63c0ce8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2D.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ELU -import com.intel.analytics.bigdl.nn.ops.Dilation2D +import com.intel.analytics.bigdl.nn.ops.{Dilation2D => Dilation2DOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -39,9 +38,9 @@ class Dilation2D extends TensorflowOpsLoader { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Dilation2D[T, Float](strides, rates, padding) + Dilation2DOps[T, Float](strides, rates, padding) } else if (t == DataType.DT_DOUBLE) { - Dilation2D[T, Double](strides, rates, padding) + Dilation2DOps[T, Double](strides, rates, padding) } else { throw new UnsupportedOperationException(s"Not support load Dilation2D when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilter.scala index 95086817bea..9da45afa76f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropFilter.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Dilation2DBackpropFilter +import com.intel.analytics.bigdl.nn.ops.{Dilation2DBackpropFilter => Dilation2DBackpropFilterOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -38,9 +38,9 @@ class Dilation2DBackpropFilter extends TensorflowOpsLoader { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Dilation2DBackpropFilter[T, Float](strides, rates, padding) + Dilation2DBackpropFilterOps[T, Float](strides, rates, padding) } else if (t == DataType.DT_DOUBLE) { - Dilation2DBackpropFilter[T, Double](strides, rates, padding) + Dilation2DBackpropFilterOps[T, Double](strides, rates, padding) } else { throw new UnsupportedOperationException( s"Not support load Dilation2DBackpropFilter when type is ${t}") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInput.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInput.scala index 198c336fe3e..70fbd0c3286 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInput.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Dilation2DBackpropInput.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Dilation2DBackpropInput +import com.intel.analytics.bigdl.nn.ops.{Dilation2DBackpropInput => Dilation2DBackpropInputOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -38,9 +38,9 @@ class Dilation2DBackpropInput extends TensorflowOpsLoader { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Dilation2DBackpropInput[T, Float](strides, rates, padding) + Dilation2DBackpropInputOps[T, Float](strides, rates, padding) } else if (t == DataType.DT_DOUBLE) { - Dilation2DBackpropInput[T, Double](strides, rates, padding) + Dilation2DBackpropInputOps[T, Double](strides, rates, padding) } else { throw new UnsupportedOperationException( s"Not support load Dilation2DBackpropInput when type is ${t}") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala index 5027e8a830a..36468ceb244 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/EluGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.EluGrad +import com.intel.analytics.bigdl.nn.tf.{EluGrad => EluGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType @@ -33,9 +33,9 @@ class EluGrad extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - EluGrad[T, Float]() + EluGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - EluGrad[T, Double]() + EluGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load ReLU6 when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erf.scala index 13c6ede4ce1..24610378050 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erf.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Erf +import com.intel.analytics.bigdl.nn.ops.{Erf => ErfOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Erf extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Erf[T, Float]() + ErfOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Erf[T, Double]() + ErfOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Erf when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erfc.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erfc.scala index 711607ad62f..db372f86321 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erfc.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Erfc.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Erfc +import com.intel.analytics.bigdl.nn.ops.{Erfc => ErfcOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Erfc extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Erfc[T, Float]() + ErfcOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Erfc[T, Double]() + ErfcOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Erfc when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Exp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Exp.scala index c8b52868b3d..8f88d7fb9ef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Exp.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Exp.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{Exp, FloorDiv} +import com.intel.analytics.bigdl.nn.ops.{Exp => ExpOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Exp extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Exp[T, Float]() + ExpOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Exp[T, Double]() + ExpOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Exp when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1.scala index b020183213a..729cd8b6978 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Expm1.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{Exp, Expm1} +import com.intel.analytics.bigdl.nn.ops.{Expm1 => Expm1Ops} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Expm1 extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Expm1[T, Float]() + Expm1Ops[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Expm1[T, Double]() + Expm1Ops[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Expm1 when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala index 369671eb2db..c396b087033 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Fill.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.Fill -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.tf.{Fill => FillOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -29,7 +28,7 @@ import scala.reflect.ClassTag class Fill extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Fill[T]() + FillOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Floor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Floor.scala index bf59b38f469..54d900ecc95 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Floor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Floor.scala @@ -18,9 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity -import com.intel.analytics.bigdl.nn.ops.Floor -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.{Floor => FloorOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -28,11 +26,8 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Floor extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Floor[T]() + FloorOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDiv.scala index f583f251cd0..4ab9720e3ca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDiv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorDiv.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{FloorDiv, Round} +import com.intel.analytics.bigdl.nn.ops.{FloorDiv => FloorDivOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,11 +33,11 @@ class FloorDiv extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - FloorDiv[T, Float]() + FloorDivOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - FloorDiv[T, Double]() + FloorDivOps[T, Double]() } else if (t == DataType.DT_INT32) { - FloorDiv[T, Int]() + FloorDivOps[T, Int]() } else { throw new UnsupportedOperationException(s"Not support load FloorDiv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorMod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorMod.scala index 901571d8175..e3c80178f65 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorMod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FloorMod.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{FloorMod, Mod} +import com.intel.analytics.bigdl.nn.ops.{FloorMod => FloorModOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,11 +33,11 @@ class FloorMod extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - FloorMod[T, Float]() + FloorModOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - FloorMod[T, Double]() + FloorModOps[T, Double]() } else if (t == DataType.DT_INT32) { - FloorMod[T, Int]() + FloorModOps[T, Int]() } else { throw new UnsupportedOperationException(s"Not support load Mod when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala index ba9c78dca7a..68f688def54 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNorm.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.FusedBatchNorm +import com.intel.analytics.bigdl.nn.tf.{FusedBatchNorm => FusedBatchNormOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -38,9 +38,9 @@ class FusedBatchNorm extends TensorflowOpsLoader { val dataFormat = getString(nodeDef.getAttrMap, "data_format") val isTrain = getBoolean(nodeDef.getAttrMap, "is_training") if (dataFormat == "NHWC") { - FusedBatchNorm[T](eps, isTrain, dataFormat = DataFormat.NHWC) + FusedBatchNormOps[T](eps, isTrain, dataFormat = DataFormat.NHWC) } else { - FusedBatchNorm[T](eps, isTrain, dataFormat = DataFormat.NCHW) + FusedBatchNormOps[T](eps, isTrain, dataFormat = DataFormat.NCHW) } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala index 35aab007ba2..0189ac593a1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGrad.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.FusedBatchNormGrad +import com.intel.analytics.bigdl.nn.tf.{FusedBatchNormGrad => FusedBatchNormGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -35,7 +35,7 @@ class FusedBatchNormGrad extends TensorflowOpsLoader { val eps = getFloat(nodeDef.getAttrMap, "epsilon") val dataFormat = getString(nodeDef.getAttrMap, "data_format") val isTrain = getBoolean(nodeDef.getAttrMap, "is_training") - FusedBatchNormGrad[T](eps, + FusedBatchNormGradOps[T](eps, if (dataFormat == "NHWC") DataFormat.NHWC else DataFormat.NCHW, isTrain) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala index 7a83f7e32b6..fdb3be84c0f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormGradV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.FusedBatchNormGrad +import com.intel.analytics.bigdl.nn.tf.{FusedBatchNormGrad => FusedBatchNormGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -35,7 +35,7 @@ class FusedBatchNormGradV2 extends TensorflowOpsLoader { val eps = getFloat(nodeDef.getAttrMap, "epsilon") val dataFormat = getString(nodeDef.getAttrMap, "data_format") val isTrain = getBoolean(nodeDef.getAttrMap, "is_training") - FusedBatchNormGrad[T](eps, + FusedBatchNormGradOps[T](eps, if (dataFormat == "NHWC") DataFormat.NHWC else DataFormat.NCHW, isTrain) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala index 87a17d9e3de..d42584aa57c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/FusedBatchNormV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.FusedBatchNorm +import com.intel.analytics.bigdl.nn.tf.{FusedBatchNorm => FusedBatchNormOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -40,9 +40,9 @@ class FusedBatchNormV2 extends TensorflowOpsLoader { val dataFormat = getString(nodeDef.getAttrMap, "data_format") val isTrain = getBoolean(nodeDef.getAttrMap, "is_training") if (dataFormat == "NHWC") { - FusedBatchNorm[T](eps, isTrain, dataFormat = DataFormat.NHWC) + FusedBatchNormOps[T](eps, isTrain, dataFormat = DataFormat.NHWC) } else { - FusedBatchNorm[T](eps, isTrain, dataFormat = DataFormat.NCHW) + FusedBatchNormOps[T](eps, isTrain, dataFormat = DataFormat.NCHW) } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqual.scala index ffef48eb98b..712f8c149ae 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqual.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GreaterEqual.scala @@ -20,14 +20,14 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef -import com.intel.analytics.bigdl.nn.ops.GreaterEqual +import com.intel.analytics.bigdl.nn.ops.{GreaterEqual => GreaterEqualOps} import com.intel.analytics.bigdl.utils.tf.Context import scala.reflect.ClassTag class GreaterEqual extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, - context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - GreaterEqual[T]() + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + GreaterEqualOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopK.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopK.scala index 34a86f414a8..3f3f23c8b1f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopK.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InTopK.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.InTopK +import com.intel.analytics.bigdl.nn.ops.{InTopK => InTopKOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -32,6 +32,6 @@ class InTopK extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) (implicit ev: TensorNumeric[T]): Module[T] = { val k = getInt(nodeDef.getAttrMap, "k") - InTopK[T](k, true) + InTopKOps[T](k, true) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Inv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Inv.scala index d16a5e7d4a2..b2f69fe1140 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Inv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Inv.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Inv +import com.intel.analytics.bigdl.nn.ops.{Inv => InvOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Inv extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Inv[T, Float]() + InvOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Inv[T, Double]() + InvOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvGrad.scala index 5b1fa0d18eb..0a004433098 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/InvGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{Inv, InvGrad} +import com.intel.analytics.bigdl.nn.ops.{InvGrad => InvGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class InvGrad extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - InvGrad[T, Float]() + InvGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - InvGrad[T, Double]() + InvGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsFinite.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsFinite.scala index 50b539acb7e..4b9dd16b314 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsFinite.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsFinite.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{Inv, IsFinite} +import com.intel.analytics.bigdl.nn.ops.{IsFinite => IsFiniteOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class IsFinite extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - IsFinite[T, Float]() + IsFiniteOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - IsFinite[T, Double]() + IsFiniteOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsInf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsInf.scala index 4370f759edd..0af15672f44 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsInf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsInf.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{IsFinite, IsInf} +import com.intel.analytics.bigdl.nn.ops.{IsInf => IsInfOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class IsInf extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - IsInf[T, Float]() + IsInfOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - IsInf[T, Double]() + IsInfOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsNan.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsNan.scala index 3afa64746a8..ea670db05fa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsNan.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/IsNan.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{IsInf, IsNan} +import com.intel.analytics.bigdl.nn.ops.{IsNan => IsNanOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class IsNan extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - IsNan[T, Float]() + IsNanOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - IsNan[T, Double]() + IsNanOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/L2Loss.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/L2Loss.scala index 3a26be62e24..a73813b82dc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/L2Loss.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/L2Loss.scala @@ -18,9 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity -import com.intel.analytics.bigdl.nn.ops.L2Loss -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.{L2Loss => L2LossOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -28,11 +26,8 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class L2Loss extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - L2Loss[T]() + L2LossOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala index 84104a9ebfb..e78820c7e97 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LRNGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.LRNGrad +import com.intel.analytics.bigdl.nn.tf.{LRNGrad => LRNGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -36,6 +36,6 @@ class LRNGrad extends TensorflowOpsLoader { val alpha = getFloat(nodeDef.getAttrMap, "alpha") val beta = getFloat(nodeDef.getAttrMap, "beta") - LRNGrad[T](size, k, alpha, beta) + LRNGradOps[T](size, k, alpha, beta) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqual.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqual.scala index 261b5b97022..9698af0553f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqual.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/LessEqual.scala @@ -20,14 +20,14 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.NodeDef -import com.intel.analytics.bigdl.nn.ops.LessEqual +import com.intel.analytics.bigdl.nn.ops.{LessEqual => LessEqualOps} import com.intel.analytics.bigdl.utils.tf.Context import scala.reflect.ClassTag class LessEqual extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, - context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - LessEqual[T]() + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + LessEqualOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Lgamma.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Lgamma.scala index 1611f081cef..4874cc62fb1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Lgamma.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Lgamma.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Lgamma +import com.intel.analytics.bigdl.nn.ops.{Lgamma => LgammaOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Lgamma extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Lgamma[T, Float]() + LgammaOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Lgamma[T, Double]() + LgammaOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Lgamma when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1p.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1p.scala index e7d109b1b40..fa2230b962f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1p.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Log1p.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.Log1p +import com.intel.analytics.bigdl.nn.tf.{Log1p => Log1pOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.{DataType, NodeDef} import com.intel.analytics.bigdl.utils.tf.Context @@ -31,9 +31,9 @@ class Log1p extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Log1p[T, Float]() + Log1pOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Log1p[T, Double]() + Log1pOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Log1p when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala index 422d1784aa4..11861c7d114 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxPoolGrad.scala @@ -19,9 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.tf.MaxPoolGrad -import com.intel.analytics.bigdl.nn.{Identity, SpatialMaxPooling} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.tf.{MaxPoolGrad => MaxPoolGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -54,7 +52,7 @@ class MaxPoolGrad extends TensorflowOpsLoader { val strideH = strideList(2) val kW = kernelSize(1) val kH = kernelSize(2) - MaxPoolGrad[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NHWC) + MaxPoolGradOps[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NHWC) case "NCHW" => require(strideList(1) == 1, s"not support strides on depth") @@ -62,7 +60,7 @@ class MaxPoolGrad extends TensorflowOpsLoader { val strideH = strideList(3) val kW = kernelSize(2) val kH = kernelSize(3) - MaxPoolGrad[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NCHW) + MaxPoolGradOps[T](kW, kH, strideW, strideH, pW, pH, DataFormat.NCHW) case _ => throw new IllegalArgumentException(s"not supported data format: $format") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Maximum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Maximum.scala index 46982aa4b9f..793575e625c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Maximum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Maximum.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Maximum +import com.intel.analytics.bigdl.nn.ops.{Maximum => MaximumOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.{DataType, NodeDef} import Utils._ @@ -31,9 +31,9 @@ class Maximum extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Maximum[T, Float]() + MaximumOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Maximum[T, Double]() + MaximumOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Maximum when type is $t") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Minimum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Minimum.scala index 127285f8822..0630c8987db 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Minimum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Minimum.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Minimum +import com.intel.analytics.bigdl.nn.ops.{Minimum => MinimumOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.tensorflow.framework.{DataType, NodeDef} import Utils._ @@ -31,9 +31,9 @@ class Minimum extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Minimum[T, Float]() + MinimumOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Minimum[T, Double]() + MinimumOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Maximum when type is $t") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mod.scala index ff86dceee7b..5f5b7bf7c4a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mod.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{FloorDiv, Mod} +import com.intel.analytics.bigdl.nn.ops.{Mod => ModOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,11 +33,11 @@ class Mod extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Mod[T, Float]() + ModOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Mod[T, Double]() + ModOps[T, Double]() } else if (t == DataType.DT_INT32) { - Mod[T, Int]() + ModOps[T, Int]() } else { throw new UnsupportedOperationException(s"Not support load Mod when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Neg.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Neg.scala index ea7e6bb07bc..c50c8f57709 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Neg.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Neg.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.{Identity, Negative} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.{Negative => NegativeOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -27,11 +26,8 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Neg extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Negative[T]() + NegativeOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala index 34287e89e56..b73b454b3fe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/OneHot.scala @@ -17,11 +17,9 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Power import com.intel.analytics.bigdl.nn.ops.{OneHot => OneHotOp} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context -import com.intel.analytics.bigdl.utils.tf.loaders.{TensorflowOpsLoader, Utils} import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala index d0990e6a697..ce8209c8cfc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pack.scala @@ -18,9 +18,8 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Pack +import com.intel.analytics.bigdl.nn.{Pack => PackOps} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -31,7 +30,7 @@ class Pack extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val dim = nodeDef.getAttrMap.get("axis").getI.toInt + 1 - Pack[T](dim).asInstanceOf[AbstractModule[Activity, Activity, T]] + PackOps[T](dim).asInstanceOf[AbstractModule[Activity, Activity, T]] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala index f89bd4ace15..25e25b3cf8c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Placeholder.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.{Identity => IdentityOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -27,12 +26,9 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Placeholder extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Identity[T] + IdentityOps[T] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pow.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pow.scala index de604cbea46..aeb7ffd7917 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pow.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Pow.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Pow -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.{Pow => PowOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -27,11 +26,8 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Pow extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Pow[T]() + PowOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala index 0667e49f3cb..96b495d06b9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Prod.scala @@ -18,9 +18,8 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.Prod +import com.intel.analytics.bigdl.nn.ops.{Prod => ProdOps} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context @@ -29,9 +28,6 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Prod extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new ProdLoadTF[T]() @@ -41,6 +37,6 @@ class Prod extends TensorflowOpsLoader { class ProdLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[T](Array(2)) { override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { val axis = tensorArrays(0).asInstanceOf[Tensor[Int]].value() + 1 - Prod[T](axis) + ProdOps[T](axis) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomUniform.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomUniform.scala index 102fb30a7db..890ab3e1088 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomUniform.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RandomUniform.scala @@ -18,9 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity -import com.intel.analytics.bigdl.nn.ops.RandomUniform -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.{RandomUniform => RandomUniformOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -28,9 +26,6 @@ import org.tensorflow.framework.{DataType, NodeDef} import scala.reflect.ClassTag class RandomUniform extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val seed = if (nodeDef.getAttrMap.containsKey("seed")) { @@ -43,11 +38,11 @@ class RandomUniform extends TensorflowOpsLoader { case DataType.DT_FLOAT => val min = 0 val max = 1 - RandomUniform[T, Float](min, max, seed) + RandomUniformOps[T, Float](min, max, seed) case DataType.DT_DOUBLE => val min = 0 val max = 1 - RandomUniform[T, Double](min, max, seed) + RandomUniformOps[T, Double](min, max, seed) case _ => throw new IllegalArgumentException("Not support data type") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reciprocal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reciprocal.scala index 6d4b674b213..c28b222102a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reciprocal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reciprocal.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Inv +import com.intel.analytics.bigdl.nn.ops.{Inv => InvOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -34,9 +34,9 @@ class Reciprocal extends TensorflowOpsLoader { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Inv[T, Float]() + InvOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Inv[T, Double]() + InvOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalGrad.scala index f0e0d9c50c8..0e8e101f104 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReciprocalGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.InvGrad +import com.intel.analytics.bigdl.nn.ops.{InvGrad => InvGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -34,9 +34,9 @@ class ReciprocalGrad extends TensorflowOpsLoader { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - InvGrad[T, Float]() + InvGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - InvGrad[T, Double]() + InvGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala index 8e86b2b0298..1509fc3ab45 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6.scala @@ -19,7 +19,6 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.tf.ReLU6 -import com.intel.analytics.bigdl.nn.tf.Log1p import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala index a7bfa90343e..3965989e035 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Relu6Grad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.{EluGrad, Relu6Grad} +import com.intel.analytics.bigdl.nn.tf.{Relu6Grad => Relu6GradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType @@ -33,9 +33,9 @@ class Relu6Grad extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Relu6Grad[T, Float]() + Relu6GradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Relu6Grad[T, Double]() + Relu6GradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load ReLU6 when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala index 9a6b49330ed..8b1c0955b93 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReluGrad.scala @@ -18,9 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.ReluGrad -import com.intel.analytics.bigdl.nn.{Identity, ReLU} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.tf.{ReluGrad => ReluGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -28,11 +26,8 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class ReluGrad extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - ReluGrad[T]() + ReluGradOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala index 2731fa0051d..e317da5a34b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala @@ -18,19 +18,16 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Reshape +import com.intel.analytics.bigdl.nn.{Reshape => ReshapeOps} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.tf.{Context, TFUtils} +import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Reshape extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { new ReshapeLoadTF[T]() @@ -55,7 +52,7 @@ class ReshapeLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapte k += 1 i += 1 } - Reshape[T](size = arraySize, Some(batchMode)) + ReshapeOps[T](size = arraySize, Some(batchMode)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala index 8a7b07a02df..4e2e8446310 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinear.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{ResizeBilinearGrad, ResizeBilinearOps} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.{ResizeBilinearOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGrad.scala index bc1a9bcf288..833263f9efd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ResizeBilinearGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.ResizeBilinearGrad +import com.intel.analytics.bigdl.nn.ops.{ResizeBilinearGrad => ResizeBilinearGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -29,6 +29,6 @@ class ResizeBilinearGrad extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val alignCorner = nodeDef.getAttrMap.get("align_corners").getB - ResizeBilinearGrad[T](alignCorner) + ResizeBilinearGradOps[T](alignCorner) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rint.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rint.scala index b8910bb8250..676c982aef3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rint.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Rint.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Rint +import com.intel.analytics.bigdl.nn.ops.{Rint => RintOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -26,11 +26,8 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Rint extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) (implicit ev: TensorNumeric[T]): Module[T] = { - Rint[T]() + RintOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Round.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Round.scala index 533766f6a7c..154bb9c7d5e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Round.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Round.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Abs -import com.intel.analytics.bigdl.nn.ops.Round +import com.intel.analytics.bigdl.nn.ops.{Round => RoundOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -34,11 +33,11 @@ class Round extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Round[T, Float]() + RoundOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Round[T, Double]() + RoundOps[T, Double]() } else if (t == DataType.DT_INT32) { - Round[T, Int]() + RoundOps[T, Int]() } else { throw new UnsupportedOperationException(s"Not support load Round when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala index e0945bb21b4..57a558a6913 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/RsqrtGrad.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.RsqrtGrad -import com.intel.analytics.bigdl.nn.tf.Log1p +import com.intel.analytics.bigdl.nn.tf.{RsqrtGrad => RsqrtGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType @@ -32,9 +31,9 @@ class RsqrtGrad extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - RsqrtGrad[T, Float]() + RsqrtGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - RsqrtGrad[T, Double]() + RsqrtGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load RsqrtGrad when type is $t") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSum.scala index 349c92d48a3..07bb143fdd4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SegmentSum.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.SegmentSum +import com.intel.analytics.bigdl.nn.ops.{SegmentSum => SegmentSumOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -27,7 +27,8 @@ import scala.reflect.ClassTag class SegmentSum extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, - context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - SegmentSum[T]() + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + + SegmentSumOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Select.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Select.scala index 9d947e64f37..274161b7e44 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Select.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Select.scala @@ -18,9 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity -import com.intel.analytics.bigdl.nn.ops.Select -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.ops.{Select => SelectOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -28,11 +26,8 @@ import org.tensorflow.framework.NodeDef import scala.reflect.ClassTag class Select extends TensorflowOpsLoader { - - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Select[T]() + SelectOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala index d20604a2668..00b5c69dcc9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Shape.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.Shape -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.tf.{Shape => ShapeOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -29,7 +28,7 @@ import scala.reflect.ClassTag class Shape extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Shape[T]() + ShapeOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala index 75447394b33..76f303ba0ab 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sigmoid.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Sigmoid -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.{Sigmoid => SigmoidOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -29,7 +28,7 @@ import scala.reflect.ClassTag class Sigmoid extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Sigmoid[T]() + SigmoidOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala index efc61370339..b03e84c6c6d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SigmoidGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.SigmoidGrad +import com.intel.analytics.bigdl.nn.tf.{SigmoidGrad => SigmoidGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType @@ -31,9 +31,9 @@ class SigmoidGrad extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - SigmoidGrad[T, Float]() + SigmoidGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - SigmoidGrad[T, Double]() + SigmoidGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load SigmoidGrad when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sign.scala index 442e75915fd..7084fba01b7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sign.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{IsInf, Sign} +import com.intel.analytics.bigdl.nn.ops.{Sign => SignOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,9 +33,9 @@ class Sign extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Sign[T, Float]() + SignOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Sign[T, Double]() + SignOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala index e394fc79cd4..d46607f754c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Slice.scala @@ -18,9 +18,8 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.Slice +import com.intel.analytics.bigdl.nn.ops.{Slice => SliceOps} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context @@ -42,7 +41,7 @@ class SliceLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapter[ override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { val size = tensorArrays(1).asInstanceOf[Tensor[Int]] - Slice[T](toArray(tensorArrays(0).asInstanceOf[Tensor[Int]]), + SliceOps[T](toArray(tensorArrays(0).asInstanceOf[Tensor[Int]]), toArray(tensorArrays(1).asInstanceOf[Tensor[Int]])) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala index f3e156090b3..6ac42e876d7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftplusGrad.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.SoftPlus -import com.intel.analytics.bigdl.nn.tf.SoftplusGrad +import com.intel.analytics.bigdl.nn.tf.{SoftplusGrad => SoftplusGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType @@ -33,9 +32,9 @@ class SoftplusGrad extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - SoftplusGrad[T, Float]() + SoftplusGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - SoftplusGrad[T, Double]() + SoftplusGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load SoftplusGrad when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala index e0364420fe2..77401385d8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SoftsignGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.{SoftplusGrad, SoftsignGrad} +import com.intel.analytics.bigdl.nn.tf.{SoftsignGrad => SoftsignGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType @@ -33,9 +33,9 @@ class SoftsignGrad extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - SoftsignGrad[T, Float]() + SoftsignGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - SoftsignGrad[T, Double]() + SoftsignGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load SoftsignGrad when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala index 35a1edb4928..02657fe7064 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SqrtGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.SqrtGrad +import com.intel.analytics.bigdl.nn.tf.{SqrtGrad => SqrtGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType @@ -31,9 +31,9 @@ class SqrtGrad extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - SqrtGrad[T, Float]() + SqrtGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - SqrtGrad[T, Double]() + SqrtGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load SqrtGrad when type is $t") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifference.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifference.scala index 7580429b7ce..ce842706740 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifference.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/SquaredDifference.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.SquaredDifference +import com.intel.analytics.bigdl.nn.ops.{SquaredDifference => SquaredDifferenceOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -29,7 +29,7 @@ class SquaredDifference extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, - context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - SquaredDifference[T]() + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + SquaredDifferenceOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala index e65df63978f..4dabcfd6d8f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Squeeze.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Squeeze -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.{Squeeze => SqueezeOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -29,8 +28,6 @@ import scala.reflect.ClassTag class Squeeze extends TensorflowOpsLoader { - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { @@ -39,7 +36,7 @@ class Squeeze extends TensorflowOpsLoader { dims = if (dims.isEmpty) null else dims - Squeeze[T](dims, batchMode = false) + SqueezeOps[T](dims, batchMode = false) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala index 95b05dadca3..cf818edfe47 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Sum +import com.intel.analytics.bigdl.nn.ops.{Sum => SumOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -36,21 +36,21 @@ class Sum extends TensorflowOpsLoader { val dataType = getType(attr, "T") dataType match { case DataType.DT_INT8 => - Sum[T, Int](keepDims, startFromZero = true) + SumOps[T, Int](keepDims, startFromZero = true) case DataType.DT_INT16 => - Sum[T, Int](keepDims, startFromZero = true) + SumOps[T, Int](keepDims, startFromZero = true) case DataType.DT_UINT8 => - Sum[T, Int](keepDims, startFromZero = true) + SumOps[T, Int](keepDims, startFromZero = true) case DataType.DT_UINT16 => - Sum[T, Int](keepDims, startFromZero = true) + SumOps[T, Int](keepDims, startFromZero = true) case DataType.DT_INT32 => - Sum[T, Int](keepDims, startFromZero = true) + SumOps[T, Int](keepDims, startFromZero = true) case DataType.DT_INT64 => - Sum[T, Int](keepDims, startFromZero = true) + SumOps[T, Int](keepDims, startFromZero = true) case DataType.DT_FLOAT => - Sum[T, Float](keepDims, startFromZero = true) + SumOps[T, Float](keepDims, startFromZero = true) case DataType.DT_DOUBLE => - Sum[T, Double](keepDims, startFromZero = true) + SumOps[T, Double](keepDims, startFromZero = true) } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala index bde5b9dc61f..ec7be0c0c99 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tanh.scala @@ -18,8 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Tanh -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.{Tanh => TanhOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -29,6 +28,6 @@ import scala.reflect.ClassTag class Tanh extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Tanh[T]() + TanhOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala index d0c87e5d8d1..95fb40236ca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TanhGrad.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.tf.TanhGrad +import com.intel.analytics.bigdl.nn.tf.{TanhGrad => TanhGradOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import com.intel.analytics.bigdl.utils.tf.loaders.Utils.getType @@ -31,9 +31,9 @@ class TanhGrad extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - TanhGrad[T, Float]() + TanhGradOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - TanhGrad[T, Double]() + TanhGradOps[T, Double]() } else { throw new UnsupportedOperationException(s"Not support load TanhGrad when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tile.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tile.scala index de42991b934..aec66d9cbf0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tile.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Tile.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Tile +import com.intel.analytics.bigdl.nn.ops.{Tile => TileOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -28,6 +28,6 @@ import scala.reflect.ClassTag class Tile extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - Tile[T]() + TileOps[T]() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopK.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopK.scala index 7df3c8a20a5..67276d61e76 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopK.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopK.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{Sign, TopK} +import com.intel.analytics.bigdl.nn.ops.{TopK => TopKOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -39,9 +39,9 @@ class TopK extends TensorflowOpsLoader { } val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - TopK[T, Float](k, s, startIndex = 0) + TopKOps[T, Float](k, s, startIndex = 0) } else if (t == DataType.DT_DOUBLE) { - TopK[T, Double](k, s, startIndex = 0) + TopKOps[T, Double](k, s, startIndex = 0) } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2.scala index d3ce67b8efd..0865af07656 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TopKV2.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.ops.TopK +import com.intel.analytics.bigdl.nn.ops.{TopK => TopKOps} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context @@ -60,9 +60,9 @@ class TopKV2LoadTF[T: ClassTag](s: Boolean, t: String)(implicit ev: TensorNumeri val k = kTensor.value() if (t == "Float") { - TopK[T, Float](k, s, startIndex = 0) + TopKOps[T, Float](k, s, startIndex = 0) } else if (t == "Double") { - TopK[T, Double](k, s, startIndex = 0) + TopKOps[T, Double](k, s, startIndex = 0) } else { throw new UnsupportedOperationException(s"Not support load Inv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala index 34200a2a30e..69fadb0d886 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Transpose.scala @@ -19,7 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.{Contiguous, Sequential, Transpose} +import com.intel.analytics.bigdl.nn.{Contiguous, Sequential, Transpose => TransposeLayer} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context @@ -85,7 +85,7 @@ class TransposeLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adap val perm = tensorArrays(0).asInstanceOf[Tensor[Int]].storage().array() val paris = permToPair(perm) val layer = Sequential() - layer.add(Transpose[T](paris.map(x => (x._1 + 1, x._2 + 1)))) + layer.add(TransposeLayer[T](paris.map(x => (x._1 + 1, x._2 + 1)))) layer.add(Contiguous()) layer } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDiv.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDiv.scala index 82be079ac08..8e988327c9b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDiv.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateDiv.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{FloorDiv, TruncateDiv} +import com.intel.analytics.bigdl.nn.ops.{TruncateDiv => TruncateDivOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,7 +33,7 @@ class TruncateDiv extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_INT32) { - TruncateDiv[T, Int]() + TruncateDivOps[T, Int]() } else { throw new UnsupportedOperationException(s"Not support load TruncateDiv when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateMod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateMod.scala index 8ca415a420f..57e75e4a714 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateMod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/TruncateMod.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.Mod +import com.intel.analytics.bigdl.nn.ops.{Mod => ModOps} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -33,11 +33,11 @@ class TruncateMod extends TensorflowOpsLoader { (implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { - Mod[T, Float]() + ModOps[T, Float]() } else if (t == DataType.DT_DOUBLE) { - Mod[T, Double]() + ModOps[T, Double]() } else if (t == DataType.DT_INT32) { - Mod[T, Int]() + ModOps[T, Int]() } else { throw new UnsupportedOperationException(s"Not support load Mod when type is ${t}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala index 928bed5c181..4e9c6854b90 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/VariableV2.scala @@ -18,9 +18,7 @@ package com.intel.analytics.bigdl.utils.tf.loaders import java.nio.ByteOrder import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.Identity -import com.intel.analytics.bigdl.nn.tf.{Const, Variable, WithoutInput} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.tf.{Variable} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.NodeDef @@ -29,8 +27,6 @@ import scala.reflect.ClassTag class VariableV2 extends TensorflowOpsLoader{ - import Utils._ - override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val w = context(nodeDef.getName)._1 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala index d543e1c0955..2126f556c14 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala @@ -84,8 +84,8 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val dlModel = estimator.fit(df) dlModel.isInstanceOf[DLModel[_]] should be(true) val correct = dlModel.transform(df).select("label", "prediction").rdd.filter { - case Row(label: Double, prediction: Seq[Double]) => - label == prediction.indexOf(prediction.max) + 1 + case Row(label: Double, prediction: Seq[_]) => + label == prediction.indexOf(prediction.asInstanceOf[Seq[Double]].max) + 1 }.count() assert(correct > nRecords * 0.8) } @@ -317,8 +317,8 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val pipelineModel = pipeline.fit(df) pipelineModel.isInstanceOf[PipelineModel] should be(true) val correct = pipelineModel.transform(df).select("label", "prediction").rdd.filter { - case Row(label: Double, prediction: Seq[Double]) => - label == prediction.indexOf(prediction.max) + 1 + case Row(label: Double, prediction: Seq[_]) => + label == prediction.indexOf(prediction.asInstanceOf[Seq[Double]].max) + 1 }.count() assert(correct > nRecords * 0.8) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala index 0209ea61701..e9b799565b6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BifurcateSplitTableSpec.scala @@ -15,7 +15,6 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.SplitTable import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ExpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ExpSpec.scala index 4a8560e251f..ac1299de3d7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ExpSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ExpSpec.scala @@ -16,7 +16,6 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.ops.Exp import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala index b5b45950efa..80923d205af 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SumSpec.scala @@ -15,7 +15,6 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.ops.Sum import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala index dec9e170584..933584937c6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectSpec.scala @@ -15,14 +15,11 @@ */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.nn.Select import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} -import scala.util.Random - class SelectSpec extends FlatSpec with Matchers { "select" should "be correct when condition is true" in { val cond = Tensor.scalar[Boolean](true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StackOpsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StackOpsSpec.scala index c5329072547..40d613c97c0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StackOpsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StackOpsSpec.scala @@ -16,7 +16,6 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.Graph -import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala index a07458cfd7a..60712075202 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DLEstimatorSpec.scala @@ -87,8 +87,8 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val dlModel = estimator.fit(df) dlModel.isInstanceOf[DLModel[_]] should be(true) val correct = dlModel.transform(df).select("label", "prediction").rdd.filter { - case Row(label: Double, prediction: Seq[Double]) => - label == prediction.indexOf(prediction.max) + 1 + case Row(label: Double, prediction: Seq[_]) => + label == prediction.indexOf(prediction.asInstanceOf[Seq[Double]].max) + 1 }.count() assert(correct > nRecords * 0.8) } @@ -320,8 +320,8 @@ class DLEstimatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val pipelineModel = pipeline.fit(df) pipelineModel.isInstanceOf[PipelineModel] should be(true) val correct = pipelineModel.transform(df).select("label", "prediction").rdd.filter { - case Row(label: Double, prediction: Seq[Double]) => - label == prediction.indexOf(prediction.max) + 1 + case Row(label: Double, prediction: Seq[_]) => + label == prediction.indexOf(prediction.asInstanceOf[Seq[Double]].max) + 1 }.count() assert(correct > nRecords * 0.8) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala index 5e32e9742c7..f5c9d637c24 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala @@ -678,8 +678,8 @@ class LSTMPeepholeSpec extends TorchSpec { val output = model.forward(input).toTensor.transpose(1, 2) model.backward(input, gradOutput) - rec.getHiddenState().toTable.foreach { case ((key: Int, value: Tensor[Double])) => - value.map(luaState(key), (v1, v2) => { + rec.getHiddenState().toTable.foreach { case ((key: Int, value: Tensor[_])) => + value.asInstanceOf[Tensor[Double]].map(luaState(key), (v1, v2) => { assert(abs(v1 - v2) <= 1e-8) v1 }) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala index da08a1e1799..c33860af764 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala @@ -22,14 +22,10 @@ import com.intel.analytics.bigdl.models.Inception import com.intel.analytics.bigdl.models.resnet.ResNet import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import org.scalatest.{FlatSpec, Matchers} -import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} -import spire.syntax.module +import com.intel.analytics.bigdl.tensor.Tensor import scala.util.Random diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala index fc903c5ea99..7ad20d3cab5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/DataConverterSpec.scala @@ -129,9 +129,9 @@ class DataConverterSpec extends FlatSpec with Matchers{ map.clear() val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), attriBulder.build) - retrievedValue.isInstanceOf[L1L2Regularizer[Float]] should be (true) - retrievedValue.asInstanceOf[L1L2Regularizer[Float]].l1 should be (regularizer.l1) - retrievedValue.asInstanceOf[L1L2Regularizer[Float]].l2 should be (regularizer.l2) + retrievedValue.isInstanceOf[L1L2Regularizer[_]] should be (true) + retrievedValue.asInstanceOf[L1L2Regularizer[_]].l1 should be (regularizer.l1) + retrievedValue.asInstanceOf[L1L2Regularizer[_]].l2 should be (regularizer.l2) } "L1Regularizer conversion" should "work properly" in { @@ -143,7 +143,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ map.clear() val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), attriBulder.build) - retrievedValue.isInstanceOf[L1Regularizer[Float]] should be (true) + retrievedValue.isInstanceOf[L1Regularizer[_]] should be (true) retrievedValue.asInstanceOf[L1Regularizer[Float]].l1 should be (regularizer.l1) } @@ -156,7 +156,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ map.clear() val retrievedValue = DataConverter.getAttributeValue(DeserializeContext(null, map, null), attriBulder.build) - retrievedValue.isInstanceOf[L2Regularizer[Float]] should be (true) + retrievedValue.isInstanceOf[L2Regularizer[_]] should be (true) retrievedValue.asInstanceOf[L2Regularizer[Float]].l2 should be (regularizer.l2) } @@ -597,7 +597,7 @@ class DataConverterSpec extends FlatSpec with Matchers{ map.clear() val retrievedValue = DataConverter. getAttributeValue(DeserializeContext(null, map, null), attr) - retrievedValue.isInstanceOf[Array[Tensor[Float]]] should be (true) + retrievedValue.isInstanceOf[Array[Tensor[_]]] should be (true) retrievedValue should be (tensorArray) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala index eef998dfed2..6beaabf7a71 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowSpecHelper.scala @@ -28,6 +28,7 @@ import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, FileWriter, RandomGener import com.intel.analytics.bigdl.utils.tf.Tensorflow.const import org.tensorflow.framework.{GraphDef, NodeDef} +import scala.language.postfixOps import scala.reflect.ClassTag import scala.sys.process._ import scala.util.control.NonFatal From 83a41a642ee7722d8cba99c33cac42b725697d53 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 7 Mar 2018 14:18:41 +0800 Subject: [PATCH 0721/1065] [Enhancement] Check duplicate layers in the container (#2351) * check duplicate layers in the container * add more unit ttest * fix unit test * fix test * meet code review --- .../analytics/bigdl/dllib/nn/Container.scala | 22 ++++++++++ .../bigdl/dllib/nn/DynamicContainer.scala | 1 + .../bigdl/dllib/nn/DynamicGraph.scala | 1 + .../bigdl/dllib/nn/StaticGraph.scala | 1 + .../dllib/nn/abstractnn/AbstractModule.scala | 8 ++++ .../bigdl/dllib/optim/Optimizer.scala | 3 ++ .../bigdl/dllib/nn/DynamicGraphSpec.scala | 11 +++++ .../analytics/bigdl/dllib/nn/GraphSpec.scala | 11 +++++ .../bigdl/dllib/nn/ParallelTableSpec.scala | 14 +++--- .../bigdl/dllib/nn/SequentialSpec.scala | 24 +++++++++- .../dllib/optim/DistriOptimizerSpec.scala | 44 +++++++++++++++++-- .../dllib/optim/LocalOptimizerSpec.scala | 35 +++++++++++++++ 12 files changed, 164 insertions(+), 11 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index bbb6f267626..562cd97d0de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -214,4 +215,25 @@ abstract class Container[A <: Activity : ClassTag, } } } + + /** + * Check if some module is duplicated in the model + */ + private[bigdl] override final def checkDuplicate( + record: mutable.HashSet[Int] = mutable.HashSet() + ): Unit = { + val errMsg = "Some module is duplicate in the current model: " + val curId = System.identityHashCode(this) + require(!record.contains(curId), errMsg + this.getName()) + record.add(curId) + modules.foreach(m => { + if (m.isInstanceOf[Container[_, _, _]]) { + m.asInstanceOf[Container[_, _, _]].checkDuplicate(record) + } else { + val mId = System.identityHashCode(m) + require(!record.contains(mId), errMsg + m.getName()) + record.add(mId) + } + }) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala index dad6deb66e1..d2425451e40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala @@ -43,6 +43,7 @@ abstract class DynamicContainer[A <: Activity : ClassTag, B <: Activity : ClassT "Operation can only be used in Graph") Util.excludeNotTorch[T](Seq(module)) modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]] + checkDuplicate() this } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala index 4093ec714c2..ade4cf2ffd7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala @@ -115,6 +115,7 @@ private[bigdl] class DynamicGraph[T: ClassTag]( .filterNot(_.element.isInstanceOf[ControlDependency[T]]) .filter(n => !n.eq(dummyOutput)).map(_.element) ) + checkDuplicate() } private def backwardExecution(input: Activity, gradOutput: Activity, isBackward: Boolean) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala index d43730812ce..5b15aeec8ac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -119,6 +119,7 @@ class StaticGraph[T: ClassTag]( .filter(n => !n.eq(dummyOutput)).map(_.element) .reverse ) + checkDuplicate() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 80a869db92b..2270c6cc185 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -1029,5 +1029,13 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, private[bigdl] def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { (Array(scala.reflect.classTag[T]), Array(ev)) } + + /** + * Check if some module is duplicated in the model. For a layer it cannot be duplicated. + * Container should override this method + */ + private[bigdl] def checkDuplicate( + record: mutable.HashSet[Int] = mutable.HashSet() + ): Unit = {} } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index 754b0413902..c83da768036 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -68,6 +68,8 @@ abstract class Optimizer[T: ClassTag, D]( protected val gradientClippingParams = GradientClippingParams(false, 0.0f, 0.0f, false, 0.0f) + model.checkDuplicate() + /** * Trigger the optimization process * @return the model to be trained @@ -240,6 +242,7 @@ abstract class Optimizer[T: ClassTag, D]( */ def setModel(newModel: Module[T]): this.type = { model = newModel + model.checkDuplicate() // if a new Model is set, then reset "epoch", "neval" .etc. resetEpoch() this diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala index 8457620021f..f69e7a5f581 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala @@ -1348,6 +1348,17 @@ class DynamicGraphSpec extends FlatSpec with Matchers { result.toTable.apply[Tensor[Float]](1).valueAt(1) should be(10) result.toTable.apply[Tensor[Float]](2).valueAt(1) should be(47) } + + "DynamicGraph" should "not contain duplicate modules" in { + val n1 = Identity[Float]().inputs() + val n2 = Identity[Float]().inputs() + val duplicate = Identity[Float]() + val n3 = duplicate.inputs(n1) + val n4 = duplicate.inputs(n2) + intercept[IllegalArgumentException] { + val model = Graph.dynamic(Array(n1, n2), Array(n3, n4)) + } + } } class DynamicGraphSerialTest extends ModuleSerializationTest { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index e1c96f13ad4..ea887a86d09 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -1325,6 +1325,17 @@ class StaticGraphSpec extends FlatSpec with Matchers { linear3.element.parameters()._2(0).sum() shouldNot be(0) linear1.element.parameters()._2(0).sum() should be(0) } + + "Graph" should "not contain duplicate modules" in { + val n1 = Identity[Float]().inputs() + val n2 = Identity[Float]().inputs() + val duplicate = Identity[Float]() + val n3 = duplicate.inputs(n1) + val n4 = duplicate.inputs(n2) + intercept[IllegalArgumentException] { + val model = Graph(Array(n1, n2), Array(n3, n4)) + } + } } object ModelUntils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala index 42d9688e535..f4bc3ce969f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTableSpec.scala @@ -26,19 +26,20 @@ import scala.util.Random class ParallelTableSpec extends FlatSpec with Matchers { "hashcode()" should "behave correctly" in { val log = new Log[Double]() + val log2 = new Log[Double]() val exp = new Exp[Double]() val m1 = new ParallelTable[Double]() m1.add(log) - m1.add(log) + m1.add(log2) val m2 = new ParallelTable[Double]() m2.add(log) - m2.add(log) + m2.add(log2) val m3 = new ParallelTable[Double]() m3.add(log) m3.add(exp) val m4 = new ParallelTable[Double]() m4.add(log) - m4.add(log) + m4.add(log2) val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3).randn() val input = T(1 -> input1, 2 -> input2) @@ -53,19 +54,20 @@ class ParallelTableSpec extends FlatSpec with Matchers { "equals()" should "behave correctly" in { val log = new Log[Double]() + val log2 = new Log[Double]() val exp = new Exp[Double]() val m1 = new ParallelTable[Double]() m1.add(log) - m1.add(log) + m1.add(log2) val m2 = new ParallelTable[Double]() m2.add(log) - m2.add(log) + m2.add(log2) val m3 = new ParallelTable[Double]() m3.add(log) m3.add(exp) val m4 = new ParallelTable[Double]() m4.add(log) - m4.add(log) + m4.add(log2) val input1 = Tensor[Double](3, 3).randn() val input2 = Tensor[Double](3).randn() val input = T(1 -> input1, 2 -> input2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala index 71dde8366d1..6d2eb822d49 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequentialSpec.scala @@ -23,13 +23,35 @@ import org.scalatest.{FlatSpec, Matchers} import scala.util.Random class SequentialSpec extends FlatSpec with Matchers { - "A Sequential Container " should "not contain operation" in { + "A Sequential Container" should "not contain operation" in { val model = Sequential[Double]() model.add(Linear(10, 100)) // this should work intercept[IllegalArgumentException] { model.add(Ceil[Double, Double]()) // this is not allowed } } + + "A Sequential Container" should "not container duplicate modules" in { + val model = Sequential[Double]() + val m1 = Identity[Double]() + val m2 = Identity[Double]() + model.add(m1).add(m2) + intercept[IllegalArgumentException] { + model.add(m1) + } + } + + "A Sequential Container" should "not container duplicate modules cross container" in { + val model = Sequential[Double]() + val c = Sequential[Double]() + val m1 = Identity[Double]() + val m2 = Identity[Double]() + c.add(m1).add(m2) + model.add(m1) + intercept[IllegalArgumentException] { + model.add(c) + } + } } class SequentialSerialTest extends ModuleSerializationTest { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index 8d1f01d8e00..db2385ede32 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -181,6 +181,45 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } } + it should "not train model with duplicate layers" in { + val m = Sequential[Double]() + val l1 = Identity[Double]() + val l2 = Identity[Double]() + val c = Sequential[Double]() + m.add(l1).add(c) + c.add(l1).add(l2) + + intercept[IllegalArgumentException] { + val optimizer = new DistriOptimizer[Double]( + m, + dataSet, + ClassNLLCriterion[Double]() + ) + } + } + + it should "not set model with duplicate layers" in { + val m = Sequential[Double]() + val l1 = Identity[Double]() + val l2 = Identity[Double]() + val c = Sequential[Double]() + m.add(l1).add(c) + c.add(l1).add(l2) + + val optimizer = new DistriOptimizer[Double]( + c, + dataSet, + ClassNLLCriterion[Double]() + ) + intercept[IllegalArgumentException] { + val optimizer = new DistriOptimizer[Double]( + m, + dataSet, + ClassNLLCriterion[Double]() + ) + } + } + "Train with MSE and LBFGS" should "be good" in { RandomGenerator.RNG.setSeed(10) val optimizer = new DistriOptimizer( @@ -610,7 +649,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val targetOriArr = targetOri.collect() - val myOpt = new DistriOptimizer[Double](null, dataSet, null) { + val myOpt = new DistriOptimizer[Double](Identity[Double](), dataSet, null) { override def optimize(): Module[Double] = { val dds = this.dataset.asInstanceOf[DistributedDataSet[MiniBatch[Double]]] val rdd = dds.data(train = false) @@ -625,9 +664,6 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { inputArr.sameElements(inputOriArr) should be (true) targetArr.sameElements(targetOriArr) should be (true) - // println(s"get=(input=${inputArr.mkString("\n")}\ntarget=${targetArr.mkString("\n")})") - // println(s"original=(input=${inputOriArray.mkString("\n")}" - // + s"\ntarget=${targetOriArray.mkString("\n")})") model } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala index 951c8b8c04f..05e765e12bd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala @@ -176,6 +176,41 @@ class LocalOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter{ } } + it should "not train model with duplicate layers" in { + val m = Sequential[Float]() + val l1 = Linear[Float](2, 3) + val l2 = Identity[Float]() + val c = Sequential[Float]() + m.add(l1).add(c) + c.add(l1).add(l2) + + intercept[IllegalArgumentException] { + val optimizer = new LocalOptimizer( + m, + creDataSet, + ClassNLLCriterion[Float]() + ) + } + } + + it should "not set model with duplicate layers" in { + val m = Sequential[Float]() + val l1 = Linear[Float](2, 3) + val l2 = Identity[Float]() + val c = Sequential[Float]() + m.add(l1).add(c) + c.add(l1).add(l2) + + val optimizer = new LocalOptimizer( + c, + creDataSet, + ClassNLLCriterion[Float]() + ) + intercept[IllegalArgumentException] { + optimizer.setModel(m) + } + } + "Train model with CrossEntropy and SGD" should "be good" in { RandomGenerator.RNG.setSeed(1000) val optimizer = new LocalOptimizer[Float]( From 1421b655cf3b3259b46c9e7714d435ae6861ed8c Mon Sep 17 00:00:00 2001 From: tosky001 Date: Wed, 7 Mar 2018 15:40:00 +0800 Subject: [PATCH 0722/1065] add [[MkString]] Operation (#2355) * add [[MkString]] Operation * add SerializerTest * modify the ScalaDoc * fix the complier error --- .../bigdl/dllib/nn/ops/MkString.scala | 64 +++++++++++++++++++ .../bigdl/dllib/nn/ops/MkStringSpec.scala | 45 +++++++++++++ .../serializer/OperationSerializerSpec.scala | 12 +++- 3 files changed, 120 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkString.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkStringSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkString.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkString.scala new file mode 100644 index 00000000000..1b545c16b71 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkString.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * MkString operation converts a SparseTensor/DenseTensor to a Dense Tensor[String] + * + * the output shape will be 1-D Tensor[String]. + * + * @param strDelimiter The delimiter between values, default: "," + * @tparam T Numeric type. Parameter tensor numeric type. Only support float/double now + */ + +class MkString[T: ClassTag]( + val strDelimiter: String = "," +)(implicit ev: TensorNumeric[T]) + extends Operation[Tensor[_], Tensor[String], T]{ + output = Tensor[String]() + override def updateOutput(input: Tensor[_]): Tensor[String] = { + + val rows = input.size(dim = 1) + val resTensor = Tensor[String](rows) + var i = 1 + while (i <= rows) { + val narrowTensor = input.narrow(1, i, 1) + val resStr = narrowTensor.storage().array().slice( + narrowTensor.storageOffset() - 1, + narrowTensor.storageOffset() -1 + narrowTensor.nElement() + ).mkString(strDelimiter) + + resTensor.setValue(i, resStr) + i += 1 + } + output = resTensor + output + } +} + +object MkString { + def apply[T: ClassTag]( + strDelimiter: String = "," + ) (implicit ev: TensorNumeric[T]): MkString[T] + = new MkString[T]( + strDelimiter = strDelimiter + ) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkStringSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkStringSpec.scala new file mode 100644 index 00000000000..e9a1ad70628 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkStringSpec.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class MkStringSpec extends FlatSpec with Matchers { + "MkString Operation with DenseTensor" should "work correctly" in { + val input = Tensor[Double]( + T(T(1.0, 2.0, 3.0), + T(4.0, 5.0, 6.0))) + + val expectOutput = Tensor[String](T("1.0,2.0,3.0", "4.0,5.0,6.0")) + + val output = MkString[Double]().forward(input) + output should be(expectOutput) + } + "MkString Operation with SparseTensor" should "work correctly" in { + val input = Tensor.sparse( + indices = Array(Array(0, 0, 1, 1, 1, 2), Array(0, 1, 0, 1, 2, 2)), + values = Array(1, 2, 3, 4, 5, 6), + shape = Array(3, 4) + ) + + val expectOutput = Tensor[String](T("1,2", "3,4,5", "6")) + + val output = MkString[Double]().forward(input) + output should be(expectOutput) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index e347b0b4761..44c9110990f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -21,7 +21,7 @@ import java.io.{File => JFile} import com.google.protobuf.{ByteString, CodedOutputStream} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossCol, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, IndicatorCol, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, SelectTensor, Sign, Slice, SquaredDifference, Substr, TensorOp, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} +import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossCol, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, IndicatorCol, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, MkString, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, SelectTensor, Sign, Slice, SquaredDifference, Substr, TensorOp, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} import com.intel.analytics.bigdl.nn.tf.{Assert => AssertOps, BroadcastGradientArgs => BroadcastGradientArgsOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} import com.intel.analytics.bigdl.nn.tf._ import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} @@ -933,6 +933,16 @@ class OperationSerializerSpec extends SerializerSpecHelper { runSerializationTest(expandDim, input) } + "MkString serializer" should "work properly" in { + val mkString = new MkString[Float](strDelimiter = ",").setName("MkString") + val input = Tensor.sparse( + indices = Array(Array(0, 0, 1, 1, 1, 2), Array(0, 1, 0, 1, 2, 2)), + values = Array(1, 2, 3, 4, 5, 6), + shape = Array(3, 4) + ) + runSerializationTest(mkString, input) + } + "PadLoadTF serializer" should "work properly" in { val padLoadTF = new PadLoadTF[Float]().setName("PadLoadTF") val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), From 4490ca8acfe03b1f04f4eafd72fda6959791b617 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 8 Mar 2018 10:35:25 +0800 Subject: [PATCH 0723/1065] [Enhancement] - add compatibility support for model broadcast (#2354) * add compatibility support for model broadcast * fix ut * add unit tests --- .../dllib/models/utils/ModelBroadcast.scala | 36 ++++++++++++++----- .../models/utils/ModelBroadcastSpec.scala | 29 +++++++++++++-- 2 files changed, 55 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 30fdf34e73f..b69e96b6a58 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -32,12 +32,15 @@ import scala.reflect.ClassTag * the model. And before broadcasting please make sure the model's parameter is compacted. * * @tparam T data type + * @param applyProtoBuffer it will use proto buffer serialization for broadcasting if set true */ -class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Serializable { +class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) + (implicit ev: TensorNumeric[T]) extends Serializable { private var broadcastModel: Broadcast[Module[T]] = _ private var broadcastParameters: Broadcast[Array[Tensor[T]]] = _ + /** * broadcast the model * first get and clear the weight and bias parameters from the model @@ -47,7 +50,15 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria * @return this */ def broadcast(sc: SparkContext, model: Module[T]): this.type = { - broadcastModel = sc.broadcast(model) + if (applyProtoBuffer) { + broadcastModel = sc.broadcast(model) + } else { + val weightsBias = getAndClearWeightBias(model.parameters()) + broadcastModel = sc.broadcast(model.cloneModule()) + broadcastParameters = sc.broadcast(weightsBias) + putWeightBias(weightsBias, model) + initGradWeightBias(weightsBias, model) + } this } @@ -59,11 +70,20 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria * @return model */ def value(initGradient: Boolean = false): Module[T] = { - val localModel = broadcastModel.value.clone(false) - if (initGradient) { - initGradWeightBias(getWeightBias(localModel.parameters()), localModel) + if (applyProtoBuffer) { + val localModel = broadcastModel.value.clone(false) + if (initGradient) { + initGradWeightBias(getWeightBias(localModel.parameters()), localModel) + } + localModel + } else { + val localModel = broadcastModel.value.cloneModule() + putWeightBias(broadcastParameters.value, localModel) + if (initGradient) { + initGradWeightBias(broadcastParameters.value, localModel) + } + localModel } - localModel } private def getWeightBias(parameters: (Array[Tensor[T]], Array[Tensor[T]])) @@ -109,8 +129,8 @@ class ModelBroadcast[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Seria object ModelBroadcast { - def apply[@specialized(Float, Double) T: ClassTag]() + def apply[@specialized(Float, Double) T: ClassTag](applyProtoBuffer: Boolean = false) (implicit ev: TensorNumeric[T]) : ModelBroadcast[T] = { - new ModelBroadcast() + new ModelBroadcast(applyProtoBuffer) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala index ab89c578fa7..473390843da 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala @@ -40,6 +40,14 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } + "model broadcast with applyProtoBuffer" should "work properly" in { + val model = LeNet5(10) + + val modelBroadCast = ModelBroadcast[Float](true).broadcast(sc, model) + modelBroadCast.value().toString should be(model.toString) + modelBroadCast.value().parameters()._1 should be(model.parameters()._1) + } + "model broadcast with getParameters" should "work properly" in { val model = LeNet5(10) model.getParameters() @@ -49,10 +57,27 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } + "model broadcast with applyProtoBuffer with getParameters" should "work properly" in { + val model = LeNet5(10) + model.getParameters() + + val modelBroadCast = ModelBroadcast[Float](true).broadcast(sc, model) + modelBroadCast.value().toString should be(model.toString) + modelBroadCast.value().parameters()._1 should be(model.parameters()._1) + } + "quantized model broadcast" should "work properly" in { val model = LeNet5(10).quantize() - val modelBroadCast = ModelBroadcast[Float].broadcast(sc, model) + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) + modelBroadCast.value().toString should be(model.toString) + modelBroadCast.value().parameters()._1 should be(model.parameters()._1) + } + + "quantized model broadcast with applyProtoBuffer" should "work properly" in { + val model = LeNet5(10).quantize() + + val modelBroadCast = ModelBroadcast[Float](true).broadcast(sc, model) modelBroadCast.value().toString should be(model.toString) modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } @@ -62,7 +87,7 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { .add(SpatialConvolution[Float](2, 4, 4, 4, 1, 1, 0, 0, 2)) .quantize() - val modelBroadCast = ModelBroadcast[Float].broadcast(sc, model) + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) modelBroadCast.value().toString should be(model.toString) modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } From 9ea387763b275f0da871aa306f718167c597569b Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Thu, 8 Mar 2018 15:08:05 +0800 Subject: [PATCH 0724/1065] Prediction service (#2300) * init commit * finish unit tests and docs * clone prediction results && change class name * do some improvements * add Engine init * add scala doc; support primitive|Tensor table keys --- .../bigdl/dllib/optim/PredictionService.scala | 353 ++++++++++++++++++ .../dllib/optim/PredictionServiceSpec.scala | 167 +++++++++ 2 files changed, 520 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PredictionService.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictionServiceSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PredictionService.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PredictionService.scala new file mode 100644 index 00000000000..3ba8b279287 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PredictionService.scala @@ -0,0 +1,353 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import java.util.concurrent.LinkedBlockingQueue + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLTensor, DataType, TensorStorage} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericBoolean, NumericChar, NumericDouble, NumericFloat, NumericInt, NumericLong, NumericString} +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializer, ProtoStorageType, SerializeContext} + +import scala.collection.JavaConverters._ +import scala.collection.mutable +import scala.reflect.ClassTag +import scala.reflect.runtime.universe.Type +import scala.util.{Failure, Success, Try} + +/** + *
Thread-safe Prediction Service for Concurrent Calls
+ * In this service, concurrency is kept not greater than [[numThreads]] by a `BlockingQueue`, + * which contains available model instances. + *

+ * [[numThreads]] model instances sharing weights/bias + * will be put into the `BlockingQueue` during initialization. + *

+ * When predict method called, service will try to take an instance from `BlockingQueue`, + * which means if all instances are on serving, the predicting request will be blocked until + * some instances are released. + *

+ * If exceptions caught during predict, + * a scalar Tensor[String] will be returned with thrown message. + * + * @param model BigDL model used to do predictions + * @param numThreads max concurrency + */ +class PredictionService[T: ClassTag] private[optim]( + model: Module[T], + numThreads: Int +)(implicit ev: TensorNumeric[T]) { + + protected val instQueue: LinkedBlockingQueue[Module[T]] = { + val shallowCopies = (1 to numThreads) + .map(_ => model.clone(false).evaluate()).asJava + + new LinkedBlockingQueue[Module[T]](shallowCopies) + } + + /** + *
Thread-safe single sample prediction
+ * Running model prediction with input Activity as soon as + * there exists vacant instances(the size of pool is [[numThreads]]). + * Otherwise, it will hold on till some instances are released. + *

+ * Outputs will be deeply copied after model prediction, so they are invariant. + * + * @param request input Activity, could be Tensor or Table(key, Tensor) + * @return output Activity, could be Tensor or Table(key, Tensor) + */ + def predict(request: Activity): Activity = { + // Take an instance from blocking queue, + // it will cause a thread blocking when no instance is available. + val module = instQueue.take() + + // do predictions + val forwardResult = Try(module.forward(request)) match { + case Success(activity) => activity + case Failure(e) => errorTensor("running forward", e) + } + + // cloned values after prediction finished + val output = try { + forwardResult match { + case tensor: Tensor[_] => + tensor.clone() + case table: Table => + val clonedMap = mutable.HashMap[Any, Any]() + table.getState().foreach { + case (k: Tensor[_], v: Tensor[_]) => + clonedMap += k.clone() -> v.clone() + case (k, v: Tensor[_]) => + clonedMap += k -> v.clone() + } + new Table(clonedMap) + } + } catch { + case e: Throwable => errorTensor("Clone Result", e) + } finally { + // Release module instance back to blocking queue + instQueue.offer(module) + } + + output + } + + /** + *
Thread-safe single sample prediction
+ * Firstly, deserialization tasks will be run with inputs(Array[Byte]). + *

+ * Then, run model prediction with deserialized inputs + * as soon as there exists vacant instances(total number is [[numThreads]]). + * Otherwise, it will hold on till some instances are released. + *

+ * Finally, prediction results will be serialized to Array[Byte] according to BigDL.proto. + * + * @param request input bytes, which will be deserialized by BigDL.proto + * @return output bytes, which is serialized by BigDl.proto + */ + def predict(request: Array[Byte]): Array[Byte] = { + val output = Try( + PredictionService.deSerializeActivity(request) + ) match { + case Success(activity) => predict(activity) + case Failure(e) => errorTensor("DeSerialize Input", e) + } + + val bytesOut = try { + PredictionService.serializeActivity(output) + } catch { + case e: Throwable => + val act = errorTensor("Serialize Output", e) + PredictionService.serializeActivity(act) + } + + bytesOut + } + + private def errorTensor(stage: String, e: Throwable): Tensor[String] = { + val msg = s"Exception caught during [$stage]! \n" + + s"The message is ${e.getMessage} \n" + + s"The cause is ${e.getCause}" + Tensor.scalar(msg) + } + +} + + +object PredictionService { + + /** + *
Thread-safe Prediction Service for Concurrent Calls
+ * In this service, concurrency is kept not greater than `numThreads` by a `BlockingQueue`, + * which contains available model instances. + *

+ * If exceptions caught during predict, + * a scalar Tensor[String] will be returned with thrown message. + * + * @param model BigDL model used to do predictions + * @param numThreads max concurrency + * @return a PredictionService instance + */ + def apply[T: ClassTag]( + model: Module[T], + numThreads: Int + )(implicit ev: TensorNumeric[T]): PredictionService[T] = { + new PredictionService[T](model, numThreads) + } + + /** + *
Serialize activities to Array[Byte] according to `Bigdl.proto`.
+ * For now, `Tensor` and `Table[primitive|Tensor, Tensor]` are supported. + * + * @param activity activity to be serialized + */ + def serializeActivity(activity: Activity): Array[Byte] = { + val attrBuilder = AttrValue.newBuilder() + activity match { + case table: Table => + var keyIsPrimitive = true + val firstKey = table.getState().head._1 + val tensorState: Array[(Tensor[_], Tensor[_])] = firstKey match { + case _: Tensor[_] => + keyIsPrimitive = false + table.getState().map { case (k: Tensor[_], v: Tensor[_]) => + k -> v }.toArray + case _: Int => + table.getState().map { case (k: Int, v: Tensor[_]) => + Tensor.scalar(k) -> v }.toArray + case _: Long => + table.getState().map { case (k: Long, v: Tensor[_]) => + Tensor.scalar(k) -> v }.toArray + case _: Char => + table.getState().map { case (k: Char, v: Tensor[_]) => + Tensor.scalar(k) -> v }.toArray + case _: Short => + table.getState().map { case (k: Short, v: Tensor[_]) => + Tensor.scalar(k) -> v }.toArray + case _: Float => + table.getState().map { case (k: Float, v: Tensor[_]) => + Tensor.scalar(k) -> v }.toArray + case _: Double => + table.getState().map { case (k: Double, v: Tensor[_]) => + Tensor.scalar(k) -> v }.toArray + case _: Boolean => + table.getState().map { case (k: Boolean, v: Tensor[_]) => + Tensor.scalar(k) -> v }.toArray + case _: String => + table.getState().map { case (k: String, v: Tensor[_]) => + Tensor.scalar(k) -> v }.toArray + case key => + throw new UnsupportedOperationException(s"Unsupported Table key: $key!") + } + + val (keys, values) = tensorState.unzip + // tensors structure: [isKeyPrimitive, keys, values] + val tensors = Array(Tensor.scalar(keyIsPrimitive)) ++ keys ++ values + + val arrayValue = ArrayValue.newBuilder + arrayValue.setDatatype(DataType.TENSOR) + arrayValue.setSize(tensors.length) + tensors.foreach { tensor => + arrayValue.addTensor(buildBigDLTensor(tensor, attrBuilder)) + attrBuilder.clear() + } + attrBuilder.setDataType(DataType.ARRAY_VALUE) + attrBuilder.setArrayValue(arrayValue) + + case tensor: Tensor[_] => + attrBuilder.setTensorValue(buildBigDLTensor(tensor, attrBuilder)) + + case _ => + throw new UnsupportedOperationException("Unsupported Activity Type!") + } + val attr = attrBuilder.build() + attr.toByteArray + } + + /** + *
Deserialize Array[Byte] to activities according to `Bigdl.proto`.
+ * For now, `Tensor` and `Table[primitive|Tensor, Tensor]` are supported. + * It will convert `AttrValue(Array(BigdlTensor))` to a `Table`. + * It will convert `AttrValue(BigdlTensor) ` to a `Tensor`. + * + * @param bytes bytes data for Activity to be deserialized + */ + def deSerializeActivity(bytes: Array[Byte]): Activity = { + val attr = AttrValue.parseFrom(bytes) + attr.getDataType match { + case DataType.ARRAY_VALUE => + val dataType = attr.getArrayValue.getTensor(0).getDatatype + // tensors structure: [isKeyPrimitive, keys, values] + val tensors = getAttr(dataType, attr).asInstanceOf[Array[Tensor[_]]] + + val nElement = (tensors.length - 1) / 2 + val keyIsPrimitive = tensors.head.asInstanceOf[Tensor[Boolean]].value() + val _keys = tensors.slice(1, nElement + 1) + val keys = if (keyIsPrimitive) _keys.map(_.value()) else _keys + val values = tensors.slice(nElement + 1, tensors.length) + val table = T() + keys.zip(values).foreach { case(k, v) => table.update(k, v) } + table + + case DataType.TENSOR => + val tValue = attr.getTensorValue + val tensor = getAttr(tValue.getDatatype, attr) + tensor.asInstanceOf[Tensor[_]] + + case tpe => + throw new UnsupportedOperationException(s"Unsupported DataType($tpe)!") + } + } + + private def buildBigDLTensor(tensor: Tensor[_], attrBuilder: AttrValue.Builder): BigDLTensor = { + val status = mutable.HashMap[Int, Any]() + + val partial = partialSetAttr(tensor.getTensorNumeric(), status) + partial(attrBuilder, tensor, ModuleSerializer.tensorType) + + val tensorId = System.identityHashCode(tensor) + val _tensor = status(tensorId).asInstanceOf[BigDLTensor] + val tensorBuilder = BigDLTensor.newBuilder(_tensor) + + val storageId = System.identityHashCode(tensor.storage().array()) + val _storage = status(storageId).asInstanceOf[TensorStorage] + tensorBuilder.setStorage(_storage) + + tensorBuilder.build() + } + + private def partialSetAttr(numeric: TensorNumeric[_], status: mutable.HashMap[Int, Any]) = { + numeric match { + case NumericFloat => + val sc = SerializeContext[Float](null, status, ProtoStorageType) + (attrBuilder: AttrValue.Builder, value: Any, tpe: Type) => + DataConverter.setAttributeValue[Float](sc, attrBuilder, value, tpe) + case NumericDouble => + val sc = SerializeContext[Double](null, status, ProtoStorageType) + (attrBuilder: AttrValue.Builder, value: Any, tpe: Type) => + DataConverter.setAttributeValue[Double](sc, attrBuilder, value, tpe) + case NumericChar => + val sc = SerializeContext[Char](null, status, ProtoStorageType) + (attrBuilder: AttrValue.Builder, value: Any, tpe: Type) => + DataConverter.setAttributeValue[Char](sc, attrBuilder, value, tpe) + case NumericBoolean => + val sc = SerializeContext[Boolean](null, status, ProtoStorageType) + (attrBuilder: AttrValue.Builder, value: Any, tpe: Type) => + DataConverter.setAttributeValue[Boolean](sc, attrBuilder, value, tpe) + case NumericString => + val sc = SerializeContext[String](null, status, ProtoStorageType) + (attrBuilder: AttrValue.Builder, value: Any, tpe: Type) => + DataConverter.setAttributeValue[String](sc, attrBuilder, value, tpe) + case NumericInt => + val sc = SerializeContext[Int](null, status, ProtoStorageType) + (attrBuilder: AttrValue.Builder, value: Any, tpe: Type) => + DataConverter.setAttributeValue[Int](sc, attrBuilder, value, tpe) + case NumericLong => + val sc = SerializeContext[Long](null, status, ProtoStorageType) + (attrBuilder: AttrValue.Builder, value: Any, tpe: Type) => + DataConverter.setAttributeValue[Long](sc, attrBuilder, value, tpe) + } + } + + private def getAttr(dataType: DataType, attr: AttrValue) = { + val status = mutable.HashMap[Int, Any]() + val dsc = DeserializeContext(null, status, ProtoStorageType) + dataType match { + case DataType.INT32 => + DataConverter.getAttributeValue[Int](dsc, attr) + case DataType.INT64 => + DataConverter.getAttributeValue[Long](dsc, attr) + case DataType.FLOAT => + DataConverter.getAttributeValue[Float](dsc, attr) + case DataType.DOUBLE => + DataConverter.getAttributeValue[Double](dsc, attr) + case DataType.STRING => + DataConverter.getAttributeValue[String](dsc, attr) + case DataType.BOOL => + DataConverter.getAttributeValue[Boolean](dsc, attr) + case DataType.CHAR => + DataConverter.getAttributeValue[Char](dsc, attr) + } + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictionServiceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictionServiceSpec.scala new file mode 100644 index 00000000000..6f778b8f915 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictionServiceSpec.scala @@ -0,0 +1,167 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class PredictionServiceSpec extends FlatSpec with Matchers { + + // sharing weights for testModule and testModule2 + private val linearWeights = Tensor[Float](5, 10).rand() + private val linearBias = Tensor[Float](5).rand() + private val linear2Weights = Tensor[Float](1, 5).rand() + private val linear2Bias = Tensor[Float](1).rand() + + private val testModule = { + val input = Input[Float]() + val linear = Linear[Float](10, 5, + initWeight = linearWeights, initBias = linearBias).inputs(input) + val relu = ReLU[Float]().inputs(linear) + val linear2 = Linear[Float](5, 1, + initWeight = linear2Weights, initBias = linear2Bias).inputs(relu) + val sigmoid = Sigmoid[Float]().inputs(linear2) + Graph[Float](input, sigmoid) + } + + private val testModule2 = { + val (input1, input2) = (Input[Float](), Input[Float]()) + val concat = JoinTable[Float](1, 1).inputs(input1, input2) + val linear = Linear[Float](10, 5, + initWeight = linearWeights, initBias = linearBias).inputs(concat) + val relu = ReLU[Float]().inputs(linear) + val linear2 = Linear[Float](5, 1, + initWeight = linear2Weights, initBias = linear2Bias).inputs(relu) + val sigmoid = Sigmoid[Float]().inputs(linear2) + Graph[Float](Array(input1, input2), sigmoid) + } + + "Tensor/ByteArray convert" should "work properly" in { + testTensorSerialize(0) + testTensorSerialize(0L) + testTensorSerialize(0.0f) + testTensorSerialize(0.0) + testTensorSerialize(true) + testTensorSerialize('a') + testTensorSerialize("aa") + } + + private val testTensorSerialize = (flag: Any) => { + val tensor = flag match { + case _: Int => Tensor[Int](2, 3).randn() + case _: Long => Tensor[Long](2, 3).randn() + case _: Float => Tensor[Float](2, 3).randn() + case _: Double => Tensor[Double](2, 3).randn() + case _: Boolean => Tensor[Boolean](T(true, false, T(true, false))) + case _: String => Tensor[String](T("a", T("b", "c"), T("d", "e"))) + case _: Char => Tensor[Char](T('a', T('b', 'c', 'd'))) + } + val bytes = PredictionService.serializeActivity(tensor) + val tensor2 = PredictionService.deSerializeActivity(bytes) + tensor shouldEqual tensor2 + } + + "Table/ByteArray convert" should "work properly" in { + // test with increment keys + var table = T.seq((1 to 5).map(_ => Tensor[Double](3, 5).randn())) + var bytes = PredictionService.serializeActivity(table) + var table2 = PredictionService.deSerializeActivity(bytes) + table shouldEqual table2 + + // test with double keys + table = T() + (1 to 5).foreach(_ => + table.update(Random.nextDouble(), Tensor[Double](3, 5).randn())) + bytes = PredictionService.serializeActivity(table) + table2 = PredictionService.deSerializeActivity(bytes) + table shouldEqual table2 + + // test with Tensor[String] keys + table = T() + (1 to 5).foreach(i => + table.update(Tensor.scalar(i.toString), Tensor[Double](3, 5).randn())) + bytes = PredictionService.serializeActivity(table) + table2 = PredictionService.deSerializeActivity(bytes) + table shouldEqual table2 + } + + "PredictionService.predict" should "return a error message when exception caught" in { + // forward exception + val service = PredictionService[Float](testModule, 2) + val invalidTensor = Tensor[Float](2, 11).randn() + var eTensor = service.predict(invalidTensor).asInstanceOf[Tensor[String]] + eTensor.isScalar shouldEqual true + eTensor.value().contains("running forward") shouldEqual true + + // Clone Result + val module = ParallelTable[Float]() + .add(SplitTable[Float](1, 1)).add(SplitTable[Float](1, 1)) + val tableInput = T(Tensor[Float](2, 3).randn(), Tensor[Float](3, 2).randn()) + eTensor = PredictionService(module, 2).predict(tableInput).asInstanceOf[Tensor[String]] + eTensor.isScalar shouldEqual true + eTensor.value().contains("Clone Result") shouldEqual true + + // DeSerialize exception + val tensor = Tensor[Float](2, 10).randn() + val bytes = PredictionService.serializeActivity(tensor) + val invalidBytes = bytes.map(e => (e + 1).toByte) + val eBytesOut = service.predict(invalidBytes) + eTensor = PredictionService.deSerializeActivity(eBytesOut) + .asInstanceOf[Tensor[String]] + eTensor.isScalar shouldEqual true + eTensor.value().contains("DeSerialize Input") shouldEqual true + } + + "PredictionService" should "work properly with concurrent calls" in { + val service = PredictionService[Float](testModule, 4) + val service2 = PredictionService[Float](testModule, 1) + + val sumResults = (1 to 100).par.map { _ => + val tensor = Tensor[Float](2, 10).randn() + val output = service.predict(tensor).asInstanceOf[Tensor[Float]] + val output2 = service2.predict(tensor).asInstanceOf[Tensor[Float]] + output.size() shouldEqual Array(2, 1) + output shouldEqual output2 + output.squeeze().toArray().sum + } + // Check whether instances have independent status(outputs of each Layer). + sumResults.toList.distinct.lengthCompare(90) > 0 shouldEqual true + } + + "PredictionService" should "work properly with byteArray data" in { + var service = PredictionService[Float](testModule, 2) + val tensor = Tensor[Float](2, 10).randn() + val input = PredictionService.serializeActivity(tensor) + val output = PredictionService.deSerializeActivity(service.predict(input)) + .asInstanceOf[Tensor[Float]] + output.size() shouldEqual Array(2, 1) + + service = PredictionService[Float](testModule2, 2) + val input2 = PredictionService.serializeActivity( + T(tensor.narrow(2, 1, 6), tensor.narrow(2, 7, 4))) + val output2 = PredictionService.deSerializeActivity(service.predict(input2)) + .asInstanceOf[Tensor[Float]] + // TestModule and testModule2 have same network weights/bias and same inputs, + // so their outputs should be equal. + output shouldEqual output2 + } + +} From b1109e195e42664c087d4419e65d6221dff8222a Mon Sep 17 00:00:00 2001 From: Hawkwood <2041829103@qq.com> Date: Fri, 9 Mar 2018 13:40:01 +0800 Subject: [PATCH 0725/1065] [WIP]Accuracy Integration-test related (#2195) [Test]Accuracy Integration-test related --- dl/src/test/accuracy-judge.sh | 6 + dl/src/test/common.robot | 1 - dl/src/test/integration-accuracy-test.robot | 164 ++++++++++++++++++ dl/src/test/integration-test.robot | 102 +++++++++-- .../dllib/integration/Quantization.scala | 6 +- 5 files changed, 261 insertions(+), 18 deletions(-) create mode 100644 dl/src/test/accuracy-judge.sh create mode 100644 dl/src/test/integration-accuracy-test.robot diff --git a/dl/src/test/accuracy-judge.sh b/dl/src/test/accuracy-judge.sh new file mode 100644 index 00000000000..504c9fa2023 --- /dev/null +++ b/dl/src/test/accuracy-judge.sh @@ -0,0 +1,6 @@ +cat 1.txt|grep "Top1Accuracy"|tail -1|cut -c114-118| awk '{if($0 < 0.985) exit 1}' +cat 2.txt|grep 'Loss'|tail -1|cut -c211-215 |awk '{if($0 > 0.55) exit 1}' +cat 3.txt|grep 'Loss'|tail -1|cut -c102-107 |awk '{if($0 > 24.5) exit 1}' +cat 4.txt|grep "Top1Accuracy"|tail -1|cut -c520-524 |awk '{if($0 < 0.89) exit 1}' +cat 5.txt|grep 'Loss'|tail -1|cut -c209-213|awk '{if($0 > 0.45) exit 1}' +cat 6.txt|grep 'Loss'|tail -1|cut -c103-107|awk '{if($0 > 1.4) exit 1}' diff --git a/dl/src/test/common.robot b/dl/src/test/common.robot index ad1b0a0b472..cd31436d09e 100644 --- a/dl/src/test/common.robot +++ b/dl/src/test/common.robot @@ -62,7 +62,6 @@ Prepare DataSource And Verticals Check Verticals :FOR ${vertical} IN @{verticals} \ Status Equal ${vertical} deployed/stopped - Status Equal ${public_hdfs_vid} running Run Shell [Arguments] ${program} diff --git a/dl/src/test/integration-accuracy-test.robot b/dl/src/test/integration-accuracy-test.robot new file mode 100644 index 00000000000..35c9a77f841 --- /dev/null +++ b/dl/src/test/integration-accuracy-test.robot @@ -0,0 +1,164 @@ +*** Settings *** +Documentation BigDL Integration Test +Resource common.robot +Suite Setup Prepare DataSource And Verticals +Suite Teardown Delete All Sessions +Test template BigDL Test + +*** Variables *** +@{verticals} ${spark_200_3_vid} ${spark_210_3_vid} ${hdfs_264_3_vid} ${spark_tf_210_3_vid} ${spark_tf_163_3_vid} + +*** Test Cases *** SuiteName VerticalId +1 Spark2.0 Test Suite ${spark_200_3_vid} +2 Spark2.1 Test Suite ${spark_210_3_vid} +3 Hdfs Test Suite ${hdfs_264_3_vid} +4 Quantization Test Suite ${hdfs_264_3_vid} +5 PySpark2.1 Test Suite ${spark_tf_210_3_vid} +6 PySpark1.6 Test Suite ${spark_tf_163_3_vid} +7 Yarn Test Suite ${hdfs_264_3_vid} + +# predefined service masters: +# hdfs_264_3_master +# spark_200_3_master +# spark_210_3_master +# spark_151_3_master +# spark_163_3_master + +# predefined datasource +# mnist_data_source +# cifar_data_source +# imagenet_data_source + + +*** Keywords *** +Build SparkJar + [Arguments] ${spark_version} + ${build}= Catenate SEPARATOR=/ ${curdir} make-dist.sh + Log To Console ${spark_version} + Log To Console start to build jar + Run ${build} -P ${spark_version} + Remove File ${jar_path} + Move File spark/dl/target/bigdl-${version}-jar-with-dependencies.jar ${jar_path} + Log To Console build jar finished + +DownLoad Input + ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.6.5/bin hadoop + Run ${hadoop} fs -get ${mnist_data_source} ./ + Log To Console got mnist data!! + Run ${hadoop} fs -get ${cifar_data_source} ./ + Log To Console got cifar data!! + Run ${hadoop} fs -get ${public_hdfs_master}:9000/text_data /tmp/ + Run tar -zxvf /tmp/text_data/20news-18828.tar.gz -C /tmp/text_data + Log To Console got textclassifier data + Set Environment Variable http_proxy ${http_proxy} + Set Environment Variable https_proxy ${https_proxy} + Run wget ${tiny_shakespeare} + Set Environment Variable LANG en_US.UTF-8 + Run head -n 8000 input.txt > val.txt + Run tail -n +8000 input.txt > train.txt + Run wget ${simple_example} + Run tar -zxvf simple-examples.tgz + Log To Console got examples data!! + Create Directory model + Create Directory models + Remove Environment Variable http_proxy https_proxy LANG + +Remove Input + Remove Directory model recursive=True + Remove Directory models recursive=True + Remove Directory mnist recursive=True + Remove File input.txt + Remove Directory simple-examples recursive=True + Remove File simple-examples.tgz + Remove Directory /tmp/text-data recursive=True + +Run Spark Test + [Arguments] ${submit} ${spark_master} + DownLoad Input + Log To Console begin lenet Train + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 84 --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 336 -e 3 > 1.txt + Log To Console begin lenet Train local[4] + Run Shell ${submit} --master local[4] --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ./mnist -b 120 -e 1 + Log To Console begin autoencoder Train + Run Shell ${submit} --master ${spark_master} --executor-cores 4 --total-executor-cores 12 --class com.intel.analytics.bigdl.models.autoencoder.Train ${jar_path} -b 120 -e 50 -f ./mnist > 2.txt + Log To Console begin PTBWordLM + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 40g --executor-memory 100g --executor-cores 8 --total-executor-cores 8 --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM ${jar_path} -f ./simple-examples/data -b 120 --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 1 --learningRateDecay 0.001 --keepProb 0.5 --overWrite > 3.txt + Log To Console begin resnet Train + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 5g --executor-memory 5g --executor-cores 8 --total-executor-cores 32 --class com.intel.analytics.bigdl.models.resnet.Train ${jar_path} -f ./cifar --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 > 4.txt + Log To Console begin DLClassifierLeNet + Run Shell ${submit} --master ${spark_master} --executor-cores 24 --total-executor-cores 24 --driver-memory 60g --executor-memory 200g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 1200 -f ./mnist --maxEpoch 1 > 5.txt + Log To Console begin rnn Train + Run Shell ${submit} --master ${spark_master} --driver-memory 5g --executor-memory 5g --executor-cores 12 --total-executor-cores 12 --class com.intel.analytics.bigdl.models.rnn.Train ${jar_path} -f ./ -s ./models --nEpochs 1 --checkpoint ./model/ -b 12 > 6.txt + Run Shell bash spark/dl/src/test/accuracy-judge.sh + Log To Console begin inceptionV1 train + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 60g --executor-memory 200g --executor-cores 24 --total-executor-cores 24 --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 24 -f ${imagenet_test_data_source} --learningRate 0.1 -e 1 + Log To Console begin googlenet + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-class-path ${jar_path} --driver-memory 20g --executor-memory 100g --executor-cores 28 --total-executor-cores 112 --class com.intel.analytics.bigdl.example.loadmodel.ModelValidator ${jar_path} -b 448 -f ${imagenet_data_source}/val/ -t caffe -m inception --caffeDefPath ${public_hdfs_master}:9000/models/bvlc_googlenet/deploy.prototxt --modelPath ${public_hdfs_master}:9000/models/bvlc_googlenet.caffemodel + Log To Console begin alexnet + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-class-path ${jar_path} --driver-memory 20g --executor-memory 100g --executor-cores 28 --total-executor-cores 112 --class com.intel.analytics.bigdl.example.loadmodel.ModelValidator ${jar_path} -b 448 -f ${imagenet_data_source}/val/ -t caffe -m inception --caffeDefPath ${public_hdfs_master}:9000/models/bvlc_alexnet/deploy.prototxt --modelPath ${public_hdfs_master}:9000/models/bvlc_alexnet.caffemodel + Log To Console begin treeLSTM + Run Shell ${submit} --master ${spark_master} --driver-memory 20g --executor-memory 10g --total-executor-cores 8 --executor-cores 8 --class com.intel.analytics.bigdl.example.treeLSTMSentiment.Train ${jar_path} --baseDir ${public_hdfs_master}:9000/dataset/ --epoch 1 + Log To Console begin text classification + Run Shell ${submit} --master ${spark_master} --driver-memory 5g --executor-memory 5g --total-executor-cores 32 --executor-cores 8 --class com.intel.analytics.bigdl.example.textclassification.TextClassifier ${jar_path} --batchSize 128 --baseDir /tmp/text_data --partitionNum 32 + Remove Input + + +Spark2.0 Test Suite + Build SparkJar spark_2.x + Set Environment Variable SPARK_HOME /opt/work/spark-2.0.0-bin-hadoop2.7 + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.0.0-bin-hadoop2.7/bin spark-submit + Run Spark Test ${submit} ${spark_200_3_master} + +Spark2.1 Test Suite + Build SparkJar spark_2.x + Set Environment Variable SPARK_HOME /opt/work/spark-2.1.0-bin-hadoop2.7 + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.1.0-bin-hadoop2.7/bin spark-submit + Run Spark Test ${submit} ${spark_210_3_master} + +Hdfs Test Suite + Set Environment Variable hdfsMaster ${hdfs_264_3_master} + Set Environment Variable mnist ${mnist_data_source} + Set Environment Variable s3aPath ${s3a_path} + Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.HdfsSpec -DhdfsMaster=${hdfs_264_3_master} -Dmnist=${mnist_data_source} -P integration-test -DforkMode=never + Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.S3Spec -Ds3aPath=${s3a_path} -P integration-test -DforkMode=never + Remove Environment Variable hdfsMaster mnist s3aPath + + +Quantization Test Suite + ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.6.5/bin hadoop + Run ${hadoop} fs -get ${mnist_data_source} /tmp/ + Log To Console got mnist data!! + Run ${hadoop} fs -get ${cifar_data_source} /tmp/ + Log To Console got cifar data!! + Set Environment Variable mnist /tmp/mnist + Set Environment Variable cifar10 /tmp/cifar + Set Environment Variable lenetfp32model ${public_hdfs_master}:9000/lenet4IT4J1.7B4.bigdl + Set Environment Variable resnetfp32model ${public_hdfs_master}:9000/resnet4IT4J1.7B4.bigdl + Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.QuantizationSpec -P integration-test + Remove Environment Variable mnist cifar10 lenetfp32model resnetfp32model + + +Yarn Test Suite + Build SparkJar spark_2.x + Set Environment Variable SPARK_HOME /opt/work/spark-2.0.0-bin-hadoop2.7 + Set Environment Variable http_proxy ${http_proxy} + Set Environment Variable https_proxy ${https_proxy} + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.0.0-bin-hadoop2.7/bin spark-submit + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 120 -e 3 + Set Environment Variable PYSPARK_DRIVER_PYTHON /var/jenkins_home/venv/bin/python + Set Environment Variable PYSPARK_PYTHON ./venv.zip/venv/bin/python + Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --archives /var/jenkins_home/venv.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 200 --action train --endTriggerType epoch --endTriggerNum 1 + Remove Environment Variable http_proxy https_proxy PYSPARK_DRIVER_PYTHON PYSPARK_PYTHON + + +PySpark2.1 Test Suite + Build SparkJar spark_2.x + Set Environment Variable SPARK_HOME /opt/work/spark-2.1.0-bin-hadoop2.7 + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.1.0-bin-hadoop2.7/bin spark-submit + Run Shell ${submit} --master ${spark_tf_210_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 + +PySpark1.6 Test Suite + Build SparkJar spark_1.6 + Set Environment Variable SPARK_HOME /opt/work/spark-1.6.3-bin-hadoop2.6 + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-1.6.3-bin-hadoop2.6/bin spark-submit + Run Shell ${submit} --master ${spark_tf_163_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 diff --git a/dl/src/test/integration-test.robot b/dl/src/test/integration-test.robot index 26f2b3b6f54..d7cdcf52753 100644 --- a/dl/src/test/integration-test.robot +++ b/dl/src/test/integration-test.robot @@ -12,9 +12,10 @@ Test template BigDL Test 1 Spark2.0 Test Suite ${spark_200_3_vid} 2 Spark2.1 Test Suite ${spark_210_3_vid} 3 Hdfs Test Suite ${hdfs_264_3_vid} -4 PySpark2.1 Test Suite ${spark_tf_210_3_vid} -5 PySpark1.6 Test Suite ${spark_tf_163_3_vid} -6 Yarn Test Suite ${hdfs_264_3_vid} +4 Quantization Test Suite ${hdfs_264_3_vid} +5 PySpark2.1 Test Suite ${spark_tf_210_3_vid} +6 PySpark1.6 Test Suite ${spark_tf_163_3_vid} +7 Yarn Test Suite ${hdfs_264_3_vid} # predefined service masters: # hdfs_264_3_master @@ -40,21 +41,93 @@ Build SparkJar Move File spark/dl/target/bigdl-${version}-jar-with-dependencies.jar ${jar_path} Log To Console build jar finished +DownLoad Input + ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.6.5/bin hadoop + Run ${hadoop} fs -get ${mnist_data_source} ./ + Log To Console got mnist data!! + Run ${hadoop} fs -get ${cifar_data_source} ./ + Log To Console got cifar data!! + Run ${hadoop} fs -get ${public_hdfs_master}:9000/text_data /tmp/ + Run tar -zxvf /tmp/text_data/20news-18828.tar.gz -C /tmp/text_data + Log To Console got textclassifier data + Set Environment Variable http_proxy ${http_proxy} + Set Environment Variable https_proxy ${https_proxy} + Run wget ${tiny_shakespeare} + Set Environment Variable LANG en_US.UTF-8 + Run head -n 8000 input.txt > val.txt + Run tail -n +8000 input.txt > train.txt + Run wget ${simple_example} + Run tar -zxvf simple-examples.tgz + Log To Console got examples data!! + Create Directory model + Create Directory models + Remove Environment Variable http_proxy https_proxy LANG + +Remove Input + Remove Directory model recursive=True + Remove Directory models recursive=True + Remove Directory mnist recursive=True + Remove File input.txt + Remove Directory simple-examples recursive=True + Remove File simple-examples.tgz + Remove Directory /tmp/text-data recursive=True + +Run Spark Test + [Arguments] ${submit} ${spark_master} + DownLoad Input + Log To Console begin lenet Train + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 84 --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 336 -e 3 + Log To Console begin lenet Train local[4] + Run Shell ${submit} --master local[4] --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ./mnist -b 120 -e 1 + Log To Console begin autoencoder Train + Run Shell ${submit} --master ${spark_master} --executor-cores 4 --total-executor-cores 12 --class com.intel.analytics.bigdl.models.autoencoder.Train ${jar_path} -b 120 -e 1 -f ./mnist + Log To Console begin PTBWordLM + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 40g --executor-memory 100g --executor-cores 8 --total-executor-cores 8 --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM ${jar_path} -f ./simple-examples/data -b 120 --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 1 --learningRateDecay 0.001 --keepProb 0.5 --overWrite + Log To Console begin resnet Train + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 5g --executor-memory 5g --executor-cores 8 --total-executor-cores 32 --class com.intel.analytics.bigdl.models.resnet.Train ${jar_path} -f ./cifar --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 + Log To Console begin DLClassifierLeNet + Run Shell ${submit} --master ${spark_master} --executor-cores 24 --total-executor-cores 24 --driver-memory 60g --executor-memory 200g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 1200 -f ./mnist --maxEpoch 1 + Log To Console begin rnn Train + Run Shell ${submit} --master ${spark_master} --driver-memory 5g --executor-memory 5g --executor-cores 12 --total-executor-cores 12 --class com.intel.analytics.bigdl.models.rnn.Train ${jar_path} -f ./ -s ./models --nEpochs 1 --checkpoint ./model/ -b 12 + Log To Console begin inceptionV1 train + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 60g --executor-memory 200g --executor-cores 24 --total-executor-cores 24 --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 24 -f ${imagenet_test_data_source} --learningRate 0.1 -e 1 + Log To Console begin text classification + Run Shell ${submit} --master ${spark_master} --driver-memory 5g --executor-memory 5g --total-executor-cores 32 --executor-cores 8 --class com.intel.analytics.bigdl.example.textclassification.TextClassifier ${jar_path} --batchSize 128 --baseDir /tmp/text_data --partitionNum 32 + Remove Input + + Spark2.0 Test Suite Build SparkJar spark_2.x - Set Environment Variable SPARK_HOME /opt/work/spark-2.0.0-bin-hadoop2.7 - ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.0.0-bin-hadoop2.7/bin spark-submit - Run Shell ${submit} --master ${spark_200_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 84 --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 336 -e 3 + Set Environment Variable SPARK_HOME /opt/work/spark-2.0.0-bin-hadoop2.7 + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.0.0-bin-hadoop2.7/bin spark-submit + Run Spark Test ${submit} ${spark_200_3_master} Spark2.1 Test Suite Build SparkJar spark_2.x - Set Environment Variable SPARK_HOME /opt/work/spark-2.1.0-bin-hadoop2.7 - ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.1.0-bin-hadoop2.7/bin spark-submit - Run Shell ${submit} --master ${spark_210_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 84 --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 336 -e 3 + Set Environment Variable SPARK_HOME /opt/work/spark-2.1.0-bin-hadoop2.7 + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.1.0-bin-hadoop2.7/bin spark-submit + Run Spark Test ${submit} ${spark_210_3_master} Hdfs Test Suite - Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.HdfsSpec -DhdfsMaster=${hdfs_264_3_master} -Dmnist=${mnist_data_source} -P integration-test -DforkMode=never - Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.S3Spec -Ds3aPath=${s3a_path} -P integration-test -DforkMode=never + Set Environment Variable hdfsMaster ${hdfs_264_3_master} + Set Environment Variable mnist ${mnist_data_source} + Set Environment Variable s3aPath ${s3a_path} + Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.HdfsSpec -DhdfsMaster=${hdfs_264_3_master} -Dmnist=${mnist_data_source} -P integration-test -DforkMode=never + Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.S3Spec -Ds3aPath=${s3a_path} -P integration-test -DforkMode=never + Remove Environment Variable hdfsMaster mnist s3aPath + + +Quantization Test Suite + ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.6.5/bin hadoop + Run ${hadoop} fs -get ${mnist_data_source} /tmp/ + Log To Console got mnist data!! + Run ${hadoop} fs -get ${cifar_data_source} /tmp/ + Log To Console got cifar data!! + Set Environment Variable mnist /tmp/mnist + Set Environment Variable cifar10 /tmp/cifar + Set Environment Variable lenetfp32model ${public_hdfs_master}:9000/lenet4IT4J1.7B4.bigdl + Set Environment Variable resnetfp32model ${public_hdfs_master}:9000/resnet4IT4J1.7B4.bigdl + Remove Environment Variable mnist cifar10 lenetfp32model resnetfp32model Yarn Test Suite Build SparkJar spark_2.x @@ -65,7 +138,7 @@ Yarn Test Suite Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 120 -e 3 Set Environment Variable PYSPARK_DRIVER_PYTHON /var/jenkins_home/venv/bin/python Set Environment Variable PYSPARK_PYTHON ./venv.zip/venv/bin/python - Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --archives /var/jenkins_home/venv.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 200 + Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --archives /var/jenkins_home/venv.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 200 --action train --endTriggerType epoch --endTriggerNum 1 Remove Environment Variable http_proxy https_proxy PYSPARK_DRIVER_PYTHON PYSPARK_PYTHON @@ -73,10 +146,11 @@ PySpark2.1 Test Suite Build SparkJar spark_2.x Set Environment Variable SPARK_HOME /opt/work/spark-2.1.0-bin-hadoop2.7 ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.1.0-bin-hadoop2.7/bin spark-submit - Run Shell ${submit} --master ${spark_tf_210_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 + Run Shell ${submit} --master ${spark_tf_210_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 PySpark1.6 Test Suite Build SparkJar spark_1.6 Set Environment Variable SPARK_HOME /opt/work/spark-1.6.3-bin-hadoop2.6 ${submit}= Catenate SEPARATOR=/ /opt/work/spark-1.6.3-bin-hadoop2.6/bin spark-submit - Run Shell ${submit} --master ${spark_tf_163_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 + Run Shell ${submit} --master ${spark_tf_163_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/Quantization.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/Quantization.scala index 8a36d975d75..9c4e07aa59a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/Quantization.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/Quantization.scala @@ -86,7 +86,7 @@ class QuantizationSpec extends FlatSpec with Matchers with BeforeAndAfter{ Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) "Quantize LeNet5" should "generate the same top1 accuracy" in { - val lenetFP32Model = System.getenv("lenet.fp32.model") + val lenetFP32Model = System.getenv("lenetfp32model") val mnist = System.getenv("mnist") val conf = Engine.createSparkConf() @@ -118,7 +118,7 @@ class QuantizationSpec extends FlatSpec with Matchers with BeforeAndAfter{ } "Quantize ResNet on Cifar" should "generate the same top1 accuracy" in { - val resnetFP32Model = System.getenv("resnet.fp32.model") + val resnetFP32Model = System.getenv("resnetfp32model") val cifar10 = System.getenv("cifar10") val conf = Engine.createSparkConf() @@ -146,7 +146,7 @@ class QuantizationSpec extends FlatSpec with Matchers with BeforeAndAfter{ } "Load quantized model of LeNet5 on mnist" should "generate the same top1 accuracy" in { - val lenetFP32Model = System.getenv("lenet.fp32.model") + val lenetFP32Model = System.getenv("lenetfp32model") val mnist = System.getenv("mnist") val tempDir = Paths.get(System.getProperty("java.io.tmpdir")) From 24e0e1ef9bc23c5f16a57e41dd3d97afe1a18ee6 Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Mon, 12 Mar 2018 13:01:26 +0800 Subject: [PATCH 0726/1065] Change Operation Serial Test Part 2 (#2347) * add ops serial test * add one and remove fail one * change * remove truncatednormal * add more ops serial test and fix two problem layers * add more serial test --- .../bigdl/dllib/nn/ops/CrossColSpec.scala | 13 ++++++++ .../bigdl/dllib/nn/ops/MkStringSpec.scala | 13 ++++++++ .../dllib/nn/ops/RandomUniformSpec.scala | 11 +++++++ .../bigdl/dllib/nn/ops/SelectTensorSpec.scala | 32 +++++++++++++++++++ .../bigdl/dllib/nn/ops/TensorOpSpec.scala | 10 ++++++ .../dllib/nn/ops/TruncatedNormalSpec.scala | 10 ++++++ 6 files changed, 89 insertions(+) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectTensorSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossColSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossColSpec.scala index 7db71551857..d9364d24f3f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossColSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/CrossColSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class CrossColSpec extends FlatSpec with Matchers { @@ -58,3 +59,15 @@ class CrossColSpec extends FlatSpec with Matchers { output should be(expectedOutput) } } + +class CrossColSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val crosscol = CrossCol[Float](hashBucketSize = 100) + .setName("CrossCol") + val input = T( + Tensor[String](T("A,D", "B", "A,C")), + Tensor[String](T("1", "2", "3,4")) + ) + runSerializationTest(crosscol, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkStringSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkStringSpec.scala index e9a1ad70628..d9117bf2681 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkStringSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MkStringSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class MkStringSpec extends FlatSpec with Matchers { @@ -43,3 +44,15 @@ class MkStringSpec extends FlatSpec with Matchers { output should be(expectOutput) } } + +class MkStringSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val mkString = new MkString[Float](strDelimiter = ",").setName("MkString") + val input = Tensor.sparse( + indices = Array(Array(0, 0, 1, 1, 1, 2), Array(0, 1, 0, 1, 2, 2)), + values = Array(1, 2, 3, 4, 5, 6), + shape = Array(3, 4) + ) + runSerializationTest(mkString, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniformSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniformSpec.scala index 58c2b872cd3..b8bf34ad17b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniformSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/RandomUniformSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} class RandomUniformSpec extends FlatSpec with Matchers { @@ -28,3 +29,13 @@ class RandomUniformSpec extends FlatSpec with Matchers { val output = RandomUniform[Float, Double](10, 20).forward(input) } } + +class RandomUniformSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val randomUniform = RandomUniform[Float, Float](10, 20). + setName("randomUniform") + val input = Tensor[Int](T(1, 2, 3)) + runSerializationTest(randomUniform, input, randomUniform. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectTensorSpec.scala new file mode 100644 index 00000000000..900b064a6e5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectTensorSpec.scala @@ -0,0 +1,32 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + + +class SelectTensorSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val transformer = (TensorOp[Float]() ** 3 * 4.5f).ceil + val select = SelectTensor(Tensor.scalar("2"), transformer) + val t1 = Tensor[Float](3, 4).randn() + val t2 = Tensor[Float](2, 3).randn() + val input = T().update(Tensor.scalar(1), t1).update(Tensor.scalar("2"), t2) + runSerializationTest(select, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala index d211c897ca6..937ea29b598 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.Sigmoid import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @@ -133,3 +134,12 @@ class TensorOpSpec extends FlatSpec with Matchers { } } + +class TensorOpSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val op = (((TensorOp[Float]() + 1.5f) ** 2) -> TensorOp.sigmoid() + ).setName("TensorOP") + val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(op, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormalSpec.scala index 1a1374eaab9..558b6a960a1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormalSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TruncatedNormalSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} @@ -29,3 +30,12 @@ class TruncatedNormalSpec extends FlatSpec with Matchers { val output = TruncatedNormal(10, 20).forward(input) } } + +class TruncatedNormalSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val truncateNormal = TruncatedNormal[Float, Float](10, 20).setName("truncateNormal") + val input = Tensor[Int](T(1, 2, 3)) + runSerializationTest(truncateNormal, input, truncateNormal. + asInstanceOf[ModuleToOperation[Float]].module.getClass) + } +} From 6a7f983c130aac0ec986d90ac4f682d6e8dd40b7 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Tue, 13 Mar 2018 13:52:47 +0800 Subject: [PATCH 0727/1065] add SparseTensor.cast & SparseTensor.applyFun (#2287) * add SparseTensor.cast & SparseTensor.applyFun * add ut for SparseTensor.applyFun * fix indents --- .../bigdl/dllib/tensor/SparseTensor.scala | 125 +++++++++++------- .../dllib/tensor/SparseTensorApply.scala | 52 ++++++++ .../bigdl/dllib/tensor/SparseTensorSpec.scala | 19 +++ 3 files changed, 151 insertions(+), 45 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorApply.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index e20da96b350..58505f9b87a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -53,14 +53,14 @@ import scala.reflect.ClassTag */ // indices is zero based. private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( - private[tensor] var _indices : Array[Storage[Int]], - private[tensor] var _values : Storage[T], - private[tensor] var _storageOffset: Int, - private[tensor] var _nElement: Int, - private[tensor] var _shape : Array[Int], - private[tensor] var _indicesOffset : Array[Int], - var nDimension: Int - )(implicit ev: TensorNumeric[T]) extends Tensor[T] { + private[tensor] var _indices : Array[Storage[Int]], + private[tensor] var _values : Storage[T], + private[tensor] var _storageOffset: Int, + private[tensor] var _nElement: Int, + private[tensor] var _shape : Array[Int], + private[tensor] var _indicesOffset : Array[Int], + var nDimension: Int) + (implicit ev: TensorNumeric[T]) extends Tensor[T] { // todo: add transpose, indices order, count from 0 // var indices_order = Array.range(0, _shape.length) @@ -95,8 +95,9 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( this } - override def setValue(d1: Int, d2: Int, - d3: Int, d4: Int, d5: Int, value: T): SparseTensor.this.type = { + override def setValue( + d1: Int, d2: Int, + d3: Int, d4: Int, d5: Int, value: T): SparseTensor.this.type = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") this } @@ -279,8 +280,9 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } - override def set(storage: Storage[T], storageOffset: Int, - sizes: Array[Int], strides: Array[Int]): Tensor[T] = { + override def set( + storage: Storage[T], storageOffset: Int, + sizes: Array[Int], strides: Array[Int]): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } @@ -755,8 +757,9 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } - override def addmv(beta: T, vec1: Tensor[T], alpha: T, - mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = { + override def addmv( + beta: T, vec1: Tensor[T], alpha: T, + mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } @@ -772,8 +775,9 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } - override def baddbmm(beta: T, M: Tensor[T], - alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = { + override def baddbmm( + beta: T, M: Tensor[T], + alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } @@ -801,7 +805,8 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } - override def topk(k: Int, dim: Int, increase: Boolean, result: Tensor[T], + override def topk( + k: Int, dim: Int, increase: Boolean, result: Tensor[T], indices: Tensor[T], sortedResult: Boolean = true): (Tensor[T], Tensor[T]) = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } @@ -1000,14 +1005,24 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } - override def applyFun[A : ClassTag](t: Tensor[A], func: (A) => T): Tensor[T] = { - throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + override def applyFun[A: ClassTag]( + t: Tensor[A], + func: (A) => T): Tensor[T] = { + val func2 = new TensorDiffTypeFunc4[A, T] { + override def apply( + data1: Array[A], index1: Int, + data2: Array[T], index2: Int): Unit = { + data2(index2) = func(data1(index1)) + } + } + SparseTensorApply.apply1[A, T](t, this, func2) + this } override def zipWith[A: ClassTag, B: ClassTag]( - t1: Tensor[A], - t2: Tensor[B], - func: (A, B) => T): Tensor[T] = { + t1: Tensor[A], + t2: Tensor[B], + func: (A, B) => T): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } @@ -1035,9 +1050,29 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } - override def cast[D: ClassTag]( - castTensor: Tensor[D])(implicit ev: TensorNumeric[D]): Tensor[D] = { - throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + override def cast[@specialized(Long, Int, Short, Double, Float) D: ClassTag] + (castTensor: Tensor[D]) + (implicit ev1: TensorNumeric[D]): Tensor[D] = { + castTensor.getType() match { + case FloatType => + castTensor.applyFun[T](this.asInstanceOf[SparseTensor[T]], + x => ev.toType[Float](x).asInstanceOf[D]) + case DoubleType => + castTensor.applyFun[T](this.asInstanceOf[SparseTensor[T]], + x => ev.toType[Double](x).asInstanceOf[D]) + case LongType => + castTensor.applyFun[T](this.asInstanceOf[SparseTensor[T]], + x => ev.toType[Long](x).asInstanceOf[D]) + case IntType => + castTensor.applyFun[T](this.asInstanceOf[SparseTensor[T]], + x => ev.toType[Int](x).asInstanceOf[D]) + case ShortType => + castTensor.applyFun[T](this.asInstanceOf[SparseTensor[T]], + x => ev.toType[Short](x).asInstanceOf[D]) + case _ => + throw new RuntimeException("Unspported type") + } + castTensor } override def getTensorType: TensorType = SparseType @@ -1095,9 +1130,9 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( object SparseTensor{ private[tensor] def concat[T: ClassTag]( - dim: Int, - tensors: Seq[Tensor[T]], - res: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + dim: Int, + tensors: Seq[Tensor[T]], + res: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { require(dim == 1 || dim == 2) var size = tensors.head.size() require(size.length <= 2, "Dimension larger than 2 are not supported yet!") @@ -1136,8 +1171,8 @@ object SparseTensor{ * @return res */ private def concat[T: ClassTag]( - tensors: Seq[SparseTensor[T]], - res: SparseTensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + tensors: Seq[SparseTensor[T]], + res: SparseTensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { val numOfIndices = res.dim() // usually is 2 require(tensors.head.dim() == 1, "Not suitable for this interface.") var i, offset, dimOffset = 0 @@ -1184,10 +1219,10 @@ object SparseTensor{ * @return index of last occurrence of value */ private def lastIndexOf[T: ClassTag]( - array: Array[T], - value: T, - start: Int, - end: Int)(implicit ev: TensorNumeric[T]): Int = { + array: Array[T], + value: T, + start: Int, + end: Int)(implicit ev: TensorNumeric[T]): Int = { if (start > end) return -1 require(end <= array.length - 1, s"indexOf end should't exceed array size ${array.length - 1}" + s", but got $end") @@ -1212,10 +1247,10 @@ object SparseTensor{ * @return index of first occurrence of value */ private def firstIndexOf[T: ClassTag]( - array: Array[T], - value: T, - start: Int, - end: Int)(implicit ev: TensorNumeric[T]): Int = { + array: Array[T], + value: T, + start: Int, + end: Int)(implicit ev: TensorNumeric[T]): Int = { if (start > end) return -1 require(end <= array.length - 1, s"indexOf end should't exceed array size ${array.length - 1}" + s", but got $end") @@ -1239,9 +1274,9 @@ object SparseTensor{ * @return res */ private def concat[T: ClassTag]( - dim: Int, - tensors: Seq[SparseTensor[T]], - res: SparseTensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + dim: Int, + tensors: Seq[SparseTensor[T]], + res: SparseTensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { val numOfIndices = res.dim() dim match { case 1 => @@ -1348,9 +1383,9 @@ object SparseTensor{ } private[tensor] def apply[T: ClassTag]( - shape : Array[Int], - nElement: Int = 1)( - implicit ev: TensorNumeric[T]): SparseTensor[T] = { + shape : Array[Int], + nElement: Int = 1)( + implicit ev: TensorNumeric[T]): SparseTensor[T] = { new SparseTensor(shape.map(_ => Storage[Int](nElement)), Storage(nElement), 0, nElement, shape, shape.map(_ => 0), shape.length) @@ -1371,7 +1406,7 @@ object SparseTensor{ values : Storage[T], shape : Array[Int], dimension: Int)( - implicit ev: TensorNumeric[T]): SparseTensor[T] = { + implicit ev: TensorNumeric[T]): SparseTensor[T] = { new SparseTensor(indices.map(Storage(_)), values, 0, values.length(), shape, shape.map(_ => 0), dimension) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorApply.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorApply.scala new file mode 100644 index 00000000000..fca2ed6a0a7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorApply.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.tensor + +object SparseTensorApply { + + /** + * Iterate through (sparse) tensor1, and apply func to the elements, + * set function result to (sparse) tensor 2 + * + * @param tensor1 the tensor1 + * @param tensor2 the result tensor + * @param func (tensor1Data, tensor1.StorageOffset, tensor2Data, + * tensor2.StorageOffset) + */ + def apply1[A, B](tensor1: Tensor[A], tensor2: Tensor[B], + func: TensorDiffTypeFunc4[A, B]): Unit = { + + require(tensor1.getTensorType == SparseType, + s"Wrong TensorType found at tensor1: ${tensor1.getTensorType}") + require(tensor2.getTensorType == SparseType, + s"Wrong TensorType found at tensor2: ${tensor2.getTensorType}") + + val t1 = tensor1.asInstanceOf[SparseTensor[A]] + val t2 = tensor2.asInstanceOf[SparseTensor[B]] + require(t1._nElement == t2._nElement, + s"nElement of tensor1(${t1._nElement}) is't equal to nElement of tensor2(${t2._nElement})") + + val array1 = t1.storage().array() + val array2 = t2.storage().array() + var i = 0 + while (i < t1._nElement) { + func(array1, t1._storageOffset + i, array2, t2._storageOffset + i) + i += 1 + } + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala index 4ea7bd3e5b1..65af3c359da 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensorSpec.scala @@ -152,4 +152,23 @@ class SparseTensorSpec extends FlatSpec with Matchers { sparseResult should be (20f +- 1e-6f) } + "SparseTensor.applyFun" should "work correctly" in { + val func = (a: Float) => a.round * 2 + val srcTensor = Tensor.sparse(Tensor(3, 4).randn()) + val dstTensor = Tensor.sparse[Int](Array(3, 4), 12) + dstTensor.applyFun(srcTensor, func) + dstTensor.storage().array() shouldEqual + srcTensor.storage().array().map(func) + } + + "Tensor.cast" should "work on SparseTensor" in { + val sTensor = Tensor.sparse(Tensor[Int](6, 5).rand()) + val sTensor2 = Tensor.sparse[Int](Tensor[Int](6, 5).rand()) + sTensor.cast(sTensor2) + sTensor.storage().array() shouldEqual sTensor2.storage().array() + + val sTensor1 = sTensor.cast(sTensor.asInstanceOf[Tensor[Long]]) + sTensor1.storage() shouldEqual sTensor.storage() + } + } From 007713b7d5e307510bc7aec2a0c0f957b15a3cfc Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Tue, 13 Mar 2018 14:18:03 +0800 Subject: [PATCH 0728/1065] Enhance and refactor the logic of InferShape (#2293) * refactor keras api style and imports fix * fix warning * fix python * fix ser test * fix test * fix test * remove checking for weight sharing * fix unittest --- .../dllib/utils/serialization/Bigdl.java | 403 ++++-------------- .../main/resources/serialization/bigdl.proto | 4 +- .../bigdl/dllib/keras/Activation.scala | 11 +- .../dllib/keras/AtrousConvolution1D.scala | 8 +- .../dllib/keras/AtrousConvolution2D.scala | 4 +- .../bigdl/dllib/keras/ConvLSTM2D.scala | 14 +- .../bigdl/dllib/keras/Convolution1D.scala | 6 +- .../bigdl/dllib/keras/Convolution2D.scala | 4 +- .../bigdl/dllib/keras/Convolution3D.scala | 4 +- .../bigdl/dllib/keras/Deconvolution2D.scala | 4 +- .../analytics/bigdl/dllib/keras/Dense.scala | 23 +- .../analytics/bigdl/dllib/keras/Dropout.scala | 5 +- .../analytics/bigdl/dllib/keras/ELU.scala | 3 +- .../analytics/bigdl/dllib/keras/GRU.scala | 12 +- .../bigdl/dllib/keras/GaussianDropout.scala | 3 +- .../bigdl/dllib/keras/GaussianNoise.scala | 3 +- .../analytics/bigdl/dllib/keras/Highway.scala | 11 +- .../analytics/bigdl/dllib/keras/Input.scala | 18 +- .../bigdl/dllib/keras/KerasLayer.scala | 205 ++++++--- .../bigdl/dllib/keras/KerasUtils.scala | 18 +- .../analytics/bigdl/dllib/keras/LSTM.scala | 12 +- .../bigdl/dllib/keras/LeakyReLU.scala | 3 +- .../dllib/keras/LocallyConnected1D.scala | 8 +- .../dllib/keras/LocallyConnected2D.scala | 7 +- .../analytics/bigdl/dllib/keras/Masking.scala | 3 +- .../analytics/bigdl/dllib/keras/Merge.scala | 6 +- .../analytics/bigdl/dllib/keras/SReLU.scala | 3 +- .../dllib/keras/SeparableConvolution2D.scala | 4 +- .../bigdl/dllib/keras/SimpleRNN.scala | 8 +- .../bigdl/dllib/keras/SpatialDropout1D.scala | 3 +- .../bigdl/dllib/keras/SpatialDropout2D.scala | 3 +- .../bigdl/dllib/keras/SpatialDropout3D.scala | 3 +- .../bigdl/dllib/keras/ThresholdedReLU.scala | 3 +- .../bigdl/dllib/keras/TimeDistributed.scala | 8 +- .../bigdl/dllib/keras/Topology.scala | 144 ++++--- .../bigdl/dllib/nn/AddConstant.scala | 2 +- .../analytics/bigdl/dllib/nn/Container.scala | 7 - .../analytics/bigdl/dllib/nn/Contiguous.scala | 2 +- .../dllib/nn/CosineDistanceCriterion.scala | 5 +- .../dllib/nn/CrossEntropyCriterion.scala | 2 +- .../analytics/bigdl/dllib/nn/Dropout.scala | 2 +- .../bigdl/dllib/nn/DynamicContainer.scala | 2 +- .../intel/analytics/bigdl/dllib/nn/ELU.scala | 2 +- .../bigdl/dllib/nn/GaussianDropout.scala | 4 +- .../bigdl/dllib/nn/GaussianNoise.scala | 4 +- .../analytics/bigdl/dllib/nn/Graph.scala | 4 +- .../bigdl/dllib/nn/HardSigmoid.scala | 2 +- .../analytics/bigdl/dllib/nn/LeakyReLU.scala | 2 +- .../analytics/bigdl/dllib/nn/Linear.scala | 4 - .../analytics/bigdl/dllib/nn/Masking.scala | 6 +- .../analytics/bigdl/dllib/nn/Maxout.scala | 4 +- .../analytics/bigdl/dllib/nn/PReLU.scala | 2 +- .../intel/analytics/bigdl/dllib/nn/ReLU.scala | 2 +- .../analytics/bigdl/dllib/nn/SReLU.scala | 2 +- .../analytics/bigdl/dllib/nn/Sigmoid.scala | 2 +- .../analytics/bigdl/dllib/nn/SoftMax.scala | 2 +- .../analytics/bigdl/dllib/nn/SoftMin.scala | 2 +- .../analytics/bigdl/dllib/nn/SoftPlus.scala | 2 +- .../analytics/bigdl/dllib/nn/SoftSign.scala | 2 +- .../bigdl/dllib/nn/SpatialDropout1D.scala | 2 +- .../bigdl/dllib/nn/SpatialDropout2D.scala | 2 +- .../bigdl/dllib/nn/SpatialDropout3D.scala | 2 +- .../dllib/nn/SpatialFullConvolution.scala | 6 +- .../bigdl/dllib/nn/StaticGraph.scala | 7 +- .../intel/analytics/bigdl/dllib/nn/Tanh.scala | 2 +- .../analytics/bigdl/dllib/nn/Threshold.scala | 2 +- .../bigdl/dllib/nn/UpSampling1D.scala | 2 - .../dllib/nn/abstractnn/AbstractModule.scala | 44 +- .../dllib/nn/abstractnn/InferShape.scala | 65 +-- .../analytics/bigdl/dllib/utils/Util.scala | 17 - .../dllib/utils/python/api/PythonBigDL.scala | 17 +- .../utils/python/api/PythonBigDLKeras.scala | 26 +- .../utils/serializer/ModuleSerializable.scala | 35 +- .../utils/serializer/ModuleSerializer.scala | 1 + .../converters/ShapeConverter.scala | 22 +- .../dllib/keras/nn/Deconvolution2DSpec.scala | 4 +- .../bigdl/dllib/keras/nn/HighwaySpec.scala | 4 +- .../bigdl/dllib/keras/nn/InputSpec.scala | 6 +- .../keras/nn/KerasLayerWrapperSpec.scala | 56 +++ .../bigdl/dllib/keras/nn/KerasStyleSpec.scala | 172 ++++++-- .../analytics/bigdl/dllib/nn/LinearSpec.scala | 5 - .../bigdl/dllib/utils/TestUtils.scala | 23 +- .../KerasModuleSerializerSpec.scala | 27 +- 83 files changed, 837 insertions(+), 763 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasLayerWrapperSpec.scala diff --git a/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java b/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java index fb13e8ad807..55628300c10 100644 --- a/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java +++ b/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java @@ -1241,44 +1241,25 @@ com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( *output shape * * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - java.util.List - getOutputShapeList(); - /** - * 
-     *output shape
-     *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape(int index); - /** - * 
-     *output shape
-     *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - int getOutputShapeCount(); + boolean hasOutputShape(); /** * 
      *output shape
      *
* - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - java.util.List - getOutputShapeOrBuilderList(); + com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape(); /** * 
      *output shape
      *
* - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( - int index); + com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder(); } /** * Protobuf type {@code com.intel.analytics.bigdl.serialization.BigDLModule} @@ -1302,7 +1283,6 @@ private BigDLModule() { train_ = false; namePostfix_ = ""; id_ = 0; - outputShape_ = java.util.Collections.emptyList(); } @java.lang.Override @@ -1447,12 +1427,16 @@ private BigDLModule( break; } case 114: { - if (!((mutable_bitField0_ & 0x00002000) == 0x00002000)) { - outputShape_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00002000; + com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder subBuilder = null; + if (outputShape_ != null) { + subBuilder = outputShape_.toBuilder(); } - outputShape_.add( - input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Shape.parser(), extensionRegistry)); + outputShape_ = input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.Shape.parser(), extensionRegistry); + if (subBuilder != null) { + subBuilder.mergeFrom(outputShape_); + outputShape_ = subBuilder.buildPartial(); + } + break; } } @@ -1472,9 +1456,6 @@ private BigDLModule( if (((mutable_bitField0_ & 0x00000020) == 0x00000020)) { nextModules_ = nextModules_.getUnmodifiableView(); } - if (((mutable_bitField0_ & 0x00002000) == 0x00002000)) { - outputShape_ = java.util.Collections.unmodifiableList(outputShape_); - } this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } @@ -2034,58 +2015,36 @@ public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getInputShap } public static final int OUTPUTSHAPE_FIELD_NUMBER = 14; - private java.util.List outputShape_; - /** - * 
-     *output shape
-     *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public java.util.List getOutputShapeList() { - return outputShape_; - } - /** - * 
-     *output shape
-     *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public java.util.List - getOutputShapeOrBuilderList() { - return outputShape_; - } + private com.intel.analytics.bigdl.serialization.Bigdl.Shape outputShape_; /** * 
      *output shape
      *
* - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public int getOutputShapeCount() { - return outputShape_.size(); + public boolean hasOutputShape() { + return outputShape_ != null; } /** * 
      *output shape
      *
* - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape(int index) { - return outputShape_.get(index); + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape() { + return outputShape_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance() : outputShape_; } /** * 
      *output shape
      *
* - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( - int index) { - return outputShape_.get(index); + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder() { + return getOutputShape(); } private byte memoizedIsInitialized = -1; @@ -2142,8 +2101,8 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) if (inputShape_ != null) { output.writeMessage(13, getInputShape()); } - for (int i = 0; i < outputShape_.size(); i++) { - output.writeMessage(14, outputShape_.get(i)); + if (outputShape_ != null) { + output.writeMessage(14, getOutputShape()); } unknownFields.writeTo(output); } @@ -2215,9 +2174,9 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeMessageSize(13, getInputShape()); } - for (int i = 0; i < outputShape_.size(); i++) { + if (outputShape_ != null) { size += com.google.protobuf.CodedOutputStream - .computeMessageSize(14, outputShape_.get(i)); + .computeMessageSize(14, getOutputShape()); } size += unknownFields.getSerializedSize(); memoizedSize = size; @@ -2270,8 +2229,11 @@ public boolean equals(final java.lang.Object obj) { result = result && getInputShape() .equals(other.getInputShape()); } - result = result && getOutputShapeList() - .equals(other.getOutputShapeList()); + result = result && (hasOutputShape() == other.hasOutputShape()); + if (hasOutputShape()) { + result = result && getOutputShape() + .equals(other.getOutputShape()); + } result = result && unknownFields.equals(other.unknownFields); return result; } @@ -2324,9 +2286,9 @@ public int hashCode() { hash = (37 * hash) + INPUTSHAPE_FIELD_NUMBER; hash = (53 * hash) + getInputShape().hashCode(); } - if (getOutputShapeCount() > 0) { + if (hasOutputShape()) { hash = (37 * hash) + OUTPUTSHAPE_FIELD_NUMBER; - hash = (53 * hash) + getOutputShapeList().hashCode(); + hash = (53 * hash) + getOutputShape().hashCode(); } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; @@ -2476,7 +2438,6 @@ private void maybeForceBuilderInitialization() { if (com.google.protobuf.GeneratedMessageV3 .alwaysUseFieldBuilders) { getSubModulesFieldBuilder(); - getOutputShapeFieldBuilder(); } } public Builder clear() { @@ -2523,10 +2484,10 @@ public Builder clear() { inputShapeBuilder_ = null; } if (outputShapeBuilder_ == null) { - outputShape_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00002000); + outputShape_ = null; } else { - outputShapeBuilder_.clear(); + outputShape_ = null; + outputShapeBuilder_ = null; } return this; } @@ -2595,10 +2556,6 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule buildPartial() result.inputShape_ = inputShapeBuilder_.build(); } if (outputShapeBuilder_ == null) { - if (((bitField0_ & 0x00002000) == 0x00002000)) { - outputShape_ = java.util.Collections.unmodifiableList(outputShape_); - bitField0_ = (bitField0_ & ~0x00002000); - } result.outputShape_ = outputShape_; } else { result.outputShape_ = outputShapeBuilder_.build(); @@ -2724,31 +2681,8 @@ public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModu if (other.hasInputShape()) { mergeInputShape(other.getInputShape()); } - if (outputShapeBuilder_ == null) { - if (!other.outputShape_.isEmpty()) { - if (outputShape_.isEmpty()) { - outputShape_ = other.outputShape_; - bitField0_ = (bitField0_ & ~0x00002000); - } else { - ensureOutputShapeIsMutable(); - outputShape_.addAll(other.outputShape_); - } - onChanged(); - } - } else { - if (!other.outputShape_.isEmpty()) { - if (outputShapeBuilder_.isEmpty()) { - outputShapeBuilder_.dispose(); - outputShapeBuilder_ = null; - outputShape_ = other.outputShape_; - bitField0_ = (bitField0_ & ~0x00002000); - outputShapeBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getOutputShapeFieldBuilder() : null; - } else { - outputShapeBuilder_.addAllMessages(other.outputShape_); - } - } + if (other.hasOutputShape()) { + mergeOutputShape(other.getOutputShape()); } this.mergeUnknownFields(other.unknownFields); onChanged(); @@ -4392,44 +4326,31 @@ public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getInputShap return inputShapeBuilder_; } - private java.util.List outputShape_ = - java.util.Collections.emptyList(); - private void ensureOutputShapeIsMutable() { - if (!((bitField0_ & 0x00002000) == 0x00002000)) { - outputShape_ = new java.util.ArrayList(outputShape_); - bitField0_ |= 0x00002000; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< + private com.intel.analytics.bigdl.serialization.Bigdl.Shape outputShape_ = null; + private com.google.protobuf.SingleFieldBuilderV3< com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> outputShapeBuilder_; - /** * 
        *output shape
        *
* - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public java.util.List getOutputShapeList() { - if (outputShapeBuilder_ == null) { - return java.util.Collections.unmodifiableList(outputShape_); - } else { - return outputShapeBuilder_.getMessageList(); - } + public boolean hasOutputShape() { + return outputShapeBuilder_ != null || outputShape_ != null; } /** * 
        *output shape
        *
* - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public int getOutputShapeCount() { + public com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape() { if (outputShapeBuilder_ == null) { - return outputShape_.size(); + return outputShape_ == null ? com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance() : outputShape_; } else { - return outputShapeBuilder_.getCount(); + return outputShapeBuilder_.getMessage(); } } /** @@ -4437,34 +4358,19 @@ public int getOutputShapeCount() { *output shape * * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public com.intel.analytics.bigdl.serialization.Bigdl.Shape getOutputShape(int index) { - if (outputShapeBuilder_ == null) { - return outputShape_.get(index); - } else { - return outputShapeBuilder_.getMessage(index); - } - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public Builder setOutputShape( - int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { + public Builder setOutputShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (outputShapeBuilder_ == null) { if (value == null) { throw new NullPointerException(); } - ensureOutputShapeIsMutable(); - outputShape_.set(index, value); + outputShape_ = value; onChanged(); } else { - outputShapeBuilder_.setMessage(index, value); + outputShapeBuilder_.setMessage(value); } + return this; } /** @@ -4472,94 +4378,17 @@ public Builder setOutputShape( *output shape * * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder setOutputShape( - int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { - if (outputShapeBuilder_ == null) { - ensureOutputShapeIsMutable(); - outputShape_.set(index, builderForValue.build()); - onChanged(); - } else { - outputShapeBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public Builder addOutputShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { - if (outputShapeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOutputShapeIsMutable(); - outputShape_.add(value); - onChanged(); - } else { - outputShapeBuilder_.addMessage(value); - } - return this; - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public Builder addOutputShape( - int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { - if (outputShapeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOutputShapeIsMutable(); - outputShape_.add(index, value); - onChanged(); - } else { - outputShapeBuilder_.addMessage(index, value); - } - return this; - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public Builder addOutputShape( com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { if (outputShapeBuilder_ == null) { - ensureOutputShapeIsMutable(); - outputShape_.add(builderForValue.build()); - onChanged(); - } else { - outputShapeBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public Builder addOutputShape( - int index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder builderForValue) { - if (outputShapeBuilder_ == null) { - ensureOutputShapeIsMutable(); - outputShape_.add(index, builderForValue.build()); + outputShape_ = builderForValue.build(); onChanged(); } else { - outputShapeBuilder_.addMessage(index, builderForValue.build()); + outputShapeBuilder_.setMessage(builderForValue.build()); } + return this; } /** @@ -4567,18 +4396,21 @@ public Builder addOutputShape( *output shape * * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public Builder addAllOutputShape( - java.lang.Iterable values) { + public Builder mergeOutputShape(com.intel.analytics.bigdl.serialization.Bigdl.Shape value) { if (outputShapeBuilder_ == null) { - ensureOutputShapeIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, outputShape_); + if (outputShape_ != null) { + outputShape_ = + com.intel.analytics.bigdl.serialization.Bigdl.Shape.newBuilder(outputShape_).mergeFrom(value).buildPartial(); + } else { + outputShape_ = value; + } onChanged(); } else { - outputShapeBuilder_.addAllMessages(values); + outputShapeBuilder_.mergeFrom(value); } + return this; } /** @@ -4586,33 +4418,17 @@ public Builder addAllOutputShape( *output shape * * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ public Builder clearOutputShape() { if (outputShapeBuilder_ == null) { - outputShape_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00002000); - onChanged(); - } else { - outputShapeBuilder_.clear(); - } - return this; - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public Builder removeOutputShape(int index) { - if (outputShapeBuilder_ == null) { - ensureOutputShapeIsMutable(); - outputShape_.remove(index); + outputShape_ = null; onChanged(); } else { - outputShapeBuilder_.remove(index); + outputShape_ = null; + outputShapeBuilder_ = null; } + return this; } /** @@ -4620,39 +4436,26 @@ public Builder removeOutputShape(int index) { *output shape * * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getOutputShapeBuilder( - int index) { - return getOutputShapeFieldBuilder().getBuilder(index); - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder( - int index) { - if (outputShapeBuilder_ == null) { - return outputShape_.get(index); } else { - return outputShapeBuilder_.getMessageOrBuilder(index); - } + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getOutputShapeBuilder() { + + onChanged(); + return getOutputShapeFieldBuilder().getBuilder(); } /** * 
        *output shape
        *
* - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public java.util.List - getOutputShapeOrBuilderList() { + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder() { if (outputShapeBuilder_ != null) { - return outputShapeBuilder_.getMessageOrBuilderList(); + return outputShapeBuilder_.getMessageOrBuilder(); } else { - return java.util.Collections.unmodifiableList(outputShape_); + return outputShape_ == null ? + com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance() : outputShape_; } } /** @@ -4660,43 +4463,15 @@ public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputSha *output shape * * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder addOutputShapeBuilder() { - return getOutputShapeFieldBuilder().addBuilder( - com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()); - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ - public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder addOutputShapeBuilder( - int index) { - return getOutputShapeFieldBuilder().addBuilder( - index, com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance()); - } - /** - * 
-       *output shape
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; - */ - public java.util.List - getOutputShapeBuilderList() { - return getOutputShapeFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< + private com.google.protobuf.SingleFieldBuilderV3< com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> getOutputShapeFieldBuilder() { if (outputShapeBuilder_ == null) { - outputShapeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< + outputShapeBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder>( - outputShape_, - ((bitField0_ & 0x00002000) == 0x00002000), + getOutputShape(), getParentForChildren(), isClean()); outputShape_ = null; @@ -21819,7 +21594,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.Shape getDefaultInstanceFor "\r\n\005train\030\n \001(\010\022\023\n\013namePostfix\030\013 \001(\t\022\n\n\002i" + "d\030\014 \001(\005\022B\n\ninputShape\030\r \001(\0132..com.intel." + "analytics.bigdl.serialization.Shape\022C\n\013o" + - "utputShape\030\016 \003(\0132..com.intel.analytics.b" + + "utputShape\030\016 \001(\0132..com.intel.analytics.b" + "igdl.serialization.Shape\032_\n\tAttrEntry\022\013\n" + "\003key\030\001 \001(\t\022A\n\005value\030\002 \001(\01322.com.intel.an" + "alytics.bigdl.serialization.AttrValue:\0028" + diff --git a/scala/dllib/src/main/resources/serialization/bigdl.proto b/scala/dllib/src/main/resources/serialization/bigdl.proto index 88e29adcc46..4c5d6d5277d 100644 --- a/scala/dllib/src/main/resources/serialization/bigdl.proto +++ b/scala/dllib/src/main/resources/serialization/bigdl.proto @@ -1,5 +1,5 @@ syntax = "proto3"; -package serialization; +package com.intel.analytics.bigdl.serialization; import "google/protobuf/any.proto"; message BigDLModule { @@ -16,7 +16,7 @@ message BigDLModule string namePostfix = 11; // name post fix int32 id = 12; // unique ID of this module , used for shared modules Shape inputShape = 13; // input shape - repeated Shape outputShape = 14; //output shape + Shape outputShape = 14; //output shape } enum VarFormat { EMPTY_FORMAT = 0; diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala index 7c2a77a4623..ed1419cb53f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Activation.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, IdentityOutputShape} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Shape @@ -37,15 +37,14 @@ import scala.reflect.ClassTag class Activation[T: ClassTag]( val activation: String, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { require(activation != null, "The name of an activation function as a string is required") override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { - val model = Sequential[T]() - model.add(InputLayer(inputShape = KerasLayer.removeBatch(inputShape))) - val layer = KerasUtils.getActivation(activation) - model.add(layer).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + val kerasActivation = KerasUtils.getKerasActivation(activation) + kerasActivation.doBuild(inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala index a0d839380f7..f7b6597499b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution1D.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.{InitializationMethod, Xavier, Zeros} import com.intel.analytics.bigdl.nn.{SpatialDilatedConvolution, Squeeze, Transpose, Sequential => TSequential} import com.intel.analytics.bigdl.optim.Regularizer @@ -57,7 +57,7 @@ class AtrousConvolution1D[T: ClassTag]( val nbFilter: Int, val filterLength: Int, val init: InitializationMethod = Xavier, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val subsampleLength: Int = 1, val atrousRate: Int = 1, var wRegularizer: Regularizer[T] = null, @@ -95,7 +95,7 @@ class AtrousConvolution1D[T: ClassTag]( model.add(Transpose(Array((2, 3)))) model.add(Squeeze(4)) if (activation != null) { - model.add(activation) + model.add(activation.doBuild(inputShape)) } model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } @@ -113,7 +113,7 @@ object AtrousConvolution1D { bRegularizer: Regularizer[T] = null, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AtrousConvolution1D[T] = { new AtrousConvolution1D[T](nbFilter, filterLength, KerasUtils.getInitMethod(init), - KerasUtils.getActivation(activation), subsampleLength, atrousRate, + KerasUtils.getKerasActivation(activation), subsampleLength, atrousRate, wRegularizer, bRegularizer, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala index 1f15f501b43..0406d17615c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/AtrousConvolution2D.scala @@ -62,7 +62,7 @@ class AtrousConvolution2D[T: ClassTag]( val nbRow: Int, val nbCol: Int, val init: InitializationMethod = Xavier, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val subsample: Array[Int] = Array(1, 1), val atrousRate: Array[Int] = Array(1, 1), val dimOrdering: DataFormat = DataFormat.NCHW, @@ -111,7 +111,7 @@ object AtrousConvolution2D { bRegularizer: Regularizer[T] = null, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AtrousConvolution2D[T] = { new AtrousConvolution2D[T](nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), - KerasUtils.getActivation(activation), + KerasUtils.getKerasActivation(activation), Array(subsample._1, subsample._2), Array(atrousRate._1, atrousRate._2), KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, inputShape) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala index ab5c79d7605..bcd01ce7e53 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ConvLSTM2D.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.keras import com.intel.analytics.bigdl.nn.{ConvLSTMPeephole, Reverse, Select, Sequential => TSequential} -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -61,8 +61,8 @@ import scala.reflect.ClassTag class ConvLSTM2D[T: ClassTag]( val nbFilter: Int, val nbKernel: Int, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, - val innerActivation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, + val innerActivation: KerasLayer[Tensor[T], Tensor[T], T] = null, val dimOrdering: String = "CHANNEL_FIRST", val subsample: Int = 1, var wRegularizer: Regularizer[T] = null, @@ -97,8 +97,8 @@ class ConvLSTM2D[T: ClassTag]( kernelI = nbKernel, kernelC = nbKernel, stride = subsample, - activation = activation.asInstanceOf[TensorModule[T]], - innerActivation = innerActivation.asInstanceOf[TensorModule[T]], + activation = activation.doBuild(inputShape).asInstanceOf[TensorModule[T]], + innerActivation = innerActivation.doBuild(inputShape).asInstanceOf[TensorModule[T]], wRegularizer = wRegularizer, uRegularizer = uRegularizer, bRegularizer = bRegularizer, @@ -124,8 +124,8 @@ object ConvLSTM2D { returnSequences: Boolean = false, goBackwards: Boolean = false, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ConvLSTM2D[T] = { - new ConvLSTM2D[T](nbFilter, nbKernel, KerasUtils.getActivation(activation), - KerasUtils.getActivation(innerActivation), + new ConvLSTM2D[T](nbFilter, nbKernel, KerasUtils.getKerasActivation(activation), + KerasUtils.getKerasActivation(innerActivation), KerasUtils.toBigDLFormat5D(dimOrdering), subsample, wRegularizer, uRegularizer, bRegularizer, returnSequences, goBackwards, inputShape) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala index e6737792d7c..9541d3cb3b5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution1D.scala @@ -55,7 +55,7 @@ class Convolution1D[T: ClassTag]( val nbFilter: Int, val filterLength: Int, val init: InitializationMethod = Xavier, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val borderMode: String = "valid", val subsampleLength: Int = 1, var wRegularizer: Regularizer[T] = null, @@ -95,7 +95,7 @@ class Convolution1D[T: ClassTag]( model.add(layer) model.add(Squeeze(3)) if (activation != null) { - model.add(activation) + model.add(activation.doBuild(inputShape)) } model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } @@ -114,7 +114,7 @@ object Convolution1D { bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution1D[T] = { new Convolution1D[T](nbFilter, filterLength, - KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + KerasUtils.getInitMethod(init), KerasUtils.getKerasActivation(activation), borderMode, subsampleLength, wRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala index 16094f21b46..a71c82f7218 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution2D.scala @@ -60,7 +60,7 @@ class Convolution2D[T: ClassTag]( val nbRow: Int, val nbCol: Int, val init: InitializationMethod = Xavier, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val borderMode: String = "valid", val subsample: Array[Int] = Array(1, 1), val dimOrdering: DataFormat = DataFormat.NCHW, @@ -112,7 +112,7 @@ object Convolution2D { bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution2D[T] = { new Convolution2D[T](nbFilter, nbRow, nbCol, - KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + KerasUtils.getInitMethod(init), KerasUtils.getKerasActivation(activation), borderMode, Array(subsample._1, subsample._2), KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, bias, inputShape) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala index 28bb022699a..91a933e14d4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Convolution3D.scala @@ -62,7 +62,7 @@ class Convolution3D[T: ClassTag]( val kernelDim2: Int, val kernelDim3: Int, val init: InitializationMethod = Xavier, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val borderMode: String = "valid", val subsample: Array[Int] = Array(1, 1, 1), val dimOrdering: String = "CHANNEL_FIRST", @@ -119,7 +119,7 @@ object Convolution3D { bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Convolution3D[T] = { new Convolution3D[T](nbFilter, kernelDim1, kernelDim2, kernelDim3, - KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + KerasUtils.getInitMethod(init), KerasUtils.getKerasActivation(activation), borderMode, Array(subsample._1, subsample._2, subsample._3), KerasUtils.toBigDLFormat5D(dimOrdering), wRegularizer, bRegularizer, bias, inputShape) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala index 3462d425bdc..4b111f13e4f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala @@ -64,7 +64,7 @@ class Deconvolution2D[T: ClassTag]( val nbRow: Int, val nbCol: Int, val init: InitializationMethod = Xavier, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val subsample: Array[Int] = Array(1, 1), val dimOrdering: DataFormat = DataFormat.NCHW, var wRegularizer: Regularizer[T] = null, @@ -110,7 +110,7 @@ object Deconvolution2D { bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Deconvolution2D[T] = { new Deconvolution2D[T](nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), - KerasUtils.getActivation(activation), Array(subsample._1, subsample._2), + KerasUtils.getKerasActivation(activation), Array(subsample._1, subsample._2), KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, bias, inputShape) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala index 75b5a05f4dd..bd7f2fbc865 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dense.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.{InferReshape, InitializationMethod, Linear, Xavier, Zeros, Sequential => TSequential} import com.intel.analytics.bigdl.nn.abstractnn._ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor @@ -49,7 +49,7 @@ import scala.reflect.ClassTag class Dense[T: ClassTag]( val outputDim: Int, val init: InitializationMethod = Xavier, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, var wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, val bias: Boolean = true, @@ -73,22 +73,19 @@ class Dense[T: ClassTag]( bRegularizer = bRegularizer) layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros) - if (inputShape.toSingle().size <= 2) { - KerasLayer.fuse(layer, activation, - inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] - } else { - val seq = new Sequential[T](stopInferShape = true) + var torchLayer: AbstractModule[Tensor[T], Tensor[T], T] = layer + + if (inputShape.toSingle().size > 2) { + val seq = new TSequential[T]() val inDim = inputShapeList.last - seq.add(InputLayer(inputShape = inputShape)) seq.add(InferReshape(Array(-1, inDim), false)) seq.add(layer) seq.add(InferReshape(Array(-1) ++ inputShapeList.slice(1, inputShapeList.size - 1) ++ Array(outputDim), false)) - if (activation != null) { - seq.add(activation) - } - seq.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + torchLayer = seq.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } + KerasLayer.fuse(torchLayer, activation, + inputShape).asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } @@ -102,7 +99,7 @@ object Dense { bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Dense[T] = { new Dense[T](outputDim, KerasUtils.getInitMethod(init), - KerasUtils.getActivation(activation), + KerasUtils.getKerasActivation(activation), wRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala index 8a6dea3deed..db33b3d6396 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Dropout.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, IdentityOutputShape} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Shape @@ -36,7 +36,8 @@ import scala.reflect.ClassTag class Dropout[T: ClassTag]( val p: Double, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = com.intel.analytics.bigdl.nn.Dropout(p) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala index 7c544035a4b..96528d33a02 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala @@ -38,7 +38,8 @@ import scala.reflect.ClassTag class ELU[T: ClassTag]( val alpha: Double = 1.0, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = com.intel.analytics.bigdl.nn.ELU( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala index 49c8751d64d..10244468dcd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GRU.scala @@ -53,8 +53,8 @@ import scala.reflect.ClassTag */ class GRU[T: ClassTag]( outputDim: Int, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, - val innerActivation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, + val innerActivation: KerasLayer[Tensor[T], Tensor[T], T] = null, returnSequences: Boolean = false, goBackwards: Boolean = false, var wRegularizer: Regularizer[T] = null, @@ -67,8 +67,8 @@ class GRU[T: ClassTag]( com.intel.analytics.bigdl.nn.GRU[T]( inputSize = input(2), outputSize = outputDim, - activation = activation.asInstanceOf[TensorModule[T]], - innerActivation = innerActivation.asInstanceOf[TensorModule[T]], + activation = activation.doBuild(inputShape).asInstanceOf[TensorModule[T]], + innerActivation = innerActivation.doBuild(inputShape).asInstanceOf[TensorModule[T]], wRegularizer = wRegularizer, uRegularizer = uRegularizer, bRegularizer = bRegularizer) @@ -86,8 +86,8 @@ object GRU { uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : GRU[T] = { - new GRU(outputDim, KerasUtils.getActivation(activation), - KerasUtils.getActivation(innerActivation), returnSequences, + new GRU(outputDim, KerasUtils.getKerasActivation(activation), + KerasUtils.getKerasActivation(innerActivation), returnSequences, goBackwards, wRegularizer, uRegularizer, bRegularizer, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala index 5bc332b7539..2d6570d136e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianDropout.scala @@ -37,7 +37,8 @@ import scala.reflect.ClassTag class GaussianDropout[T: ClassTag]( val p: Double, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = com.intel.analytics.bigdl.nn.GaussianDropout(rate = p) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala index c55ac14c584..1c10da75fc3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/GaussianNoise.scala @@ -39,7 +39,8 @@ import scala.reflect.ClassTag class GaussianNoise[T: ClassTag]( val sigma: Double, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = com.intel.analytics.bigdl.nn.GaussianNoise(stddev = sigma) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala index 782eb86428f..d4ca2895480 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Highway.scala @@ -43,7 +43,7 @@ import scala.reflect.ClassTag * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Highway[T: ClassTag]( - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, var wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, val bias: Boolean = true, @@ -62,7 +62,12 @@ class Highway[T: ClassTag]( val layer = com.intel.analytics.bigdl.nn.Highway[T]( size = input(1), withBias = bias, - activation = activation.asInstanceOf[TensorModule[T]], + activation = if (activation != null) { + activation.build(inputShape) + activation.labor.asInstanceOf[TensorModule[T]] + } else { + null + }, wRegularizer = wRegularizer, bRegularizer = bRegularizer ) @@ -77,7 +82,7 @@ object Highway { bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : Highway[T] = { - new Highway[T](KerasUtils.getActivation(activation), + new Highway[T](KerasUtils.getKerasActivation(activation), wRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala index 9df760eaeba..dfb79626298 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala @@ -25,20 +25,13 @@ import com.intel.analytics.bigdl.utils.{Node, Shape} import scala.reflect.ClassTag class Input[T: ClassTag](val inputShape: Shape)(implicit ev: TensorNumeric[T]) - extends TInput[T]() { - - private val batchInputShape = KerasLayer.addBatch(inputShape) - - override def getInputShape(): Shape = { - batchInputShape - } - - override def getOutputShape(): Shape = { - batchInputShape - } + extends KerasLayer[Activity, Activity, T](KerasLayer.addBatch(inputShape)) { override def computeOutputShape(inputShape: Shape): Shape = inputShape + override def doBuild(inputShape: Shape): TInput[T] = new TInput[T]() + + override def allowRebuilt(): Boolean = true } object Input { @@ -46,6 +39,7 @@ object Input { name : String = null, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ModuleNode[T] = { val module = new Input(inputShape) + module.build(KerasLayer.addBatch(inputShape)) if (name != null) { module.setName(name) } @@ -56,7 +50,7 @@ object Input { object InputLayer { def apply[T: ClassTag]( name : String = null, - inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Input[T] = { + inputShape: Shape = null)(implicit ev: TensorNumeric[T]): KerasLayer[Activity, Activity, T] = { val module = new Input(inputShape) if (name != null) { module.setName(name) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala index a62dfe722e4..4780124a25b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala @@ -18,16 +18,17 @@ package com.intel.analytics.bigdl.nn.keras import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.Graph._ -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} -import com.intel.analytics.bigdl.nn.{Container, Sequential => TSequential} +import com.intel.analytics.bigdl.nn.{Container => TContainer, Sequential => TSequential} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import com.intel.analytics.bigdl.utils.{Shape, SingleShape, Util} -import serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.utils.{MultiShape, Shape, SingleShape} +import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -41,7 +42,21 @@ private[bigdl] trait TKerasSerializerHelper { } } -object KerasLayerSerializer extends ContainerSerializable with TKerasSerializerHelper{ +object KerasLayerSerializer extends KerasLayerSerializable + +trait KerasLayerSerializable extends ContainerSerializable with TKerasSerializerHelper{ + + override def loadSubModules[T: ClassTag](context : DeserializeContext, + module : AbstractModule[Activity, Activity, T]) + (implicit ev: TensorNumeric[T]) : Unit = { + val klayer = module.asInstanceOf[KerasLayer[Activity, Activity, T]] + val subModules = context.bigdlModule.getSubModulesList.asScala + subModules.foreach(module => { + val subModuleData = ModuleSerializer.load(DeserializeContext(module, + context.storages, context.storageType, _copyWeightAndBias)) + klayer.labor = subModuleData.module + }) + } override def doSerializeModule[T: ClassTag](context: SerializeContext[T], moduleBuilder : BigDLModule.Builder) @@ -51,23 +66,69 @@ object KerasLayerSerializer extends ContainerSerializable with TKerasSerializerH } } +/** + * Wrap a torch style layer to keras style layer. + * This layer can be built multiple times. + * We are supposing the inputshape and the outputshape keep the same in this layer. + * @param layer a torch style layer + * @return a keras compatible layer + */ +class KerasIdentityWrapper[T: ClassTag] +(val layer: AbstractModule[Activity, Activity, T])(implicit ev: TensorNumeric[T]) + extends KerasLayer[Activity, Activity, T](null) { + if (layer.isKerasStyle()) { + throw new RuntimeException(s"We only accept torch layer here, but got: $layer") + } + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } + override def doBuild(inputShape: Shape): AbstractModule[Activity, Activity, T] = layer +} + +/** + * Wrap a torch style layer to keras style layer. + * This layer can be built multiple times. + * @param torchLayer a torch style layer + * i.e If the input data is (2, 3, 4) and 2 is the batch size, you should input: (3, 4) here. + * @return a keras compatible layer + */ +class KerasLayerWrapper[T: ClassTag] +(val torchLayer: AbstractModule[Activity, Activity, T], + val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) + extends KerasLayer[Activity, Activity, T](KerasLayer.addBatch(inputShape)) { + + require(!torchLayer.isKerasStyle(), s"We only accept torch layer here, but got: $torchLayer") + + override def computeOutputShape(calcInputShape: Shape): Shape = { + val dummyOutTensor = + torchLayer.forward(Tensor[T]( + (List(2) ++ KerasLayer.removeBatch(calcInputShape).toSingle()).toArray).rand()) + val outSize = dummyOutTensor.toTensor.size() + KerasLayer.addBatch(Shape(outSize.slice(1, outSize.length))) + } + + override def doBuild(inputShape: Shape): AbstractModule[Activity, Activity, T] = torchLayer +} + private[bigdl] object KerasLayer { - def fuse[T: ClassTag](sLayer: AbstractModule[Activity, Activity, T], - activation: AbstractModule[Tensor[T], Tensor[T], T], - inputShape: Shape) + private[bigdl] def fuse[T: ClassTag](torchLayer: AbstractModule[Activity, Activity, T], + kerasActivation: KerasLayer[Tensor[T], Tensor[T], T], + batchInputShape: Shape) (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { - if (activation == null) { - return sLayer - } - val seq = KSequential[T]() - seq.add(InputLayer(inputShape = KerasLayer.removeBatch(inputShape))) - seq.add(sLayer) - seq.add(activation) - seq.setName(sLayer.getName()) - seq + if (kerasActivation == null) { + torchLayer + } else { + val wrapper = KSequential[T]() + wrapper.add(new KerasLayerWrapper[T](torchLayer, + KerasLayer.removeBatch(batchInputShape))) + wrapper.add(kerasActivation) + wrapper.setName(torchLayer.getName()) + wrapper.build(batchInputShape) + wrapper } + } - def addBatch(shape: Shape): Shape = { + private[bigdl] def addBatch(shape: Shape): Shape = { // simply return null here as null is the default value if (shape == null) { return null @@ -79,7 +140,7 @@ private[bigdl] object KerasLayer { } } - def removeBatch(shape: Shape): Shape = { + private[bigdl] def removeBatch(shape: Shape): Shape = { // simply return null here as null is the default value if (shape == null) { return null @@ -87,7 +148,7 @@ private[bigdl] object KerasLayer { if (shape.isInstanceOf[SingleShape]) { Shape((shape.toSingle().slice(1, shape.toSingle().length)).toArray) } else { - Shape(shape.toMulti().map {addBatch(_)}) + Shape(shape.toMulti().map {removeBatch(_)}) } } } @@ -102,7 +163,9 @@ private[bigdl] object KerasLayer { * @param batchInputShape the first dim is batch */ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: ClassTag] -(batchInputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends Container[A, B, T]{ +(batchInputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends TContainer[A, B, T] { + + inputShapeValue = batchInputShape def labor: AbstractModule[A, B, T] = { if (this.modules.isEmpty) { @@ -118,22 +181,7 @@ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: C modules.clear() modules.append(value) } - // scalastyle:on - override def inputShapeValue: Shape = labor.inputShapeValue - - override def outputShapeValue: Array[Shape] = labor.outputShapeValue - - // scalastyle:off - override def inputShapeValue_=(value: Shape): Unit = { - labor.inputShapeValue = value - this._inputShapeValue = value - } - - override def outputShapeValue_=(value: Array[Shape]): Unit = { - labor.outputShapeValue = value - this._outputShapeValue = value - } - // scalastyle:on + // scalastyle:on override def updateOutput(input: A): B = { output = labor.updateOutput(input) @@ -149,35 +197,39 @@ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: C labor.accGradParameters(input, gradOutput) } - override def isCompatibleWithKeras(): Boolean = true - - override def isCompatibleWithTorch(): Boolean = false - - override def getInputShape(): Shape = { - if (batchInputShape != null) { - batchInputShape - } else if (this.labor == null) { - null - } else { - this.labor.getInputShape() - } + override def isBuilt(): Boolean = { + !this.modules.isEmpty && super.isBuilt() } + override def isKerasStyle(): Boolean = true + override def computeOutputShape(inputShape: Shape): Shape = { - this.labor.computeOutputShape(inputShape) + labor.computeOutputShape(inputShape) } - override def getOutputShape(): Shape = labor.getOutputShape() + private[bigdl] def checkWithCurrentInputShape(calcInputShape: Shape): Unit = { + if (getInputShape() != null) { + val withoutBatchInputShape = KerasLayer.removeBatch(getInputShape()) + val withoutBatchCalcInputShape = KerasLayer.removeBatch(calcInputShape) + require(withoutBatchInputShape == withoutBatchCalcInputShape, + s"InputShape from constructor ${withoutBatchInputShape}" + + s"should be the same with the calculated inputShape: ${withoutBatchCalcInputShape}") + } + } - override def build(inputShape: Shape): Shape = { - this.labor = doBuild(inputShape) - val outputShape = computeOutputShape(inputShape) - this.outputShapeValue ++= Array(outputShape) - this.inputShapeValue = inputShape - isBuilt = true - outputShape // we cannot use getOutputShape here as it may containing multiple value + override def build(calcInputShape: Shape): Shape = { + // Input would be reused multiple time in inputs for StaticGraph + if (isBuilt() && !this.allowRebuilt()) { + throw new RuntimeException(s"Should not build this module: $this multiple times") + } + labor = doBuild(calcInputShape) + checkWithCurrentInputShape(calcInputShape) + super.build(calcInputShape) } + /** + * The value return by this method should be able to execute `forward` directly. + */ def doBuild(inputShape: Shape): AbstractModule[A, B, T] /** @@ -186,12 +238,13 @@ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: C * @return node containing current module */ override def inputs(nodes : ModuleNode[T]*): ModuleNode[T] = { - Util.excludeNotKeras(nodes.map(_.element)) - if (!nodes.isEmpty) { // as there's Identity().inputs() within Graph + validateInput(nodes.map(_.element)) + if (!nodes.isEmpty) { // as there's Identity().inputs() within Graph val inputShape = Shape(nodes.map{_.element.getOutputShape()}.toList) this.build(inputShape) } - super.inputs(nodes: _*) + + processInputs(nodes) } /** @@ -200,12 +253,24 @@ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: C * @return node containing current module */ override def inputs(nodes : Array[ModuleNode[T]]): ModuleNode[T] = { - Util.excludeNotKeras(nodes.map(_.element)) - if (!nodes.isEmpty) { // as there's Identity().inputs() within Graph + validateInput(nodes.map(_.element)) + if (!nodes.isEmpty) { val inputShape = Shape(nodes.map{_.element.getOutputShape()}.toList) this.build(inputShape) } - super.inputs(nodes) + processInputs(nodes) + } + + private def getShapeByIndex(shape: Shape, index: Int): Shape = { + shape match { + case s: SingleShape => + require(index == 1, s"Getting singleshape but with index: $index") + s + case m: MultiShape => + val multiShape = m.toMulti() + require(index >= 1 && index <= multiShape.length) + multiShape(index - 1) + } } /** @@ -216,14 +281,16 @@ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: C */ override def inputs(first: (ModuleNode[T], Int), nodesWithIndex : (ModuleNode[T], Int)*): ModuleNode[T] = { - Util.excludeNotKeras(List(first._1.element)) - Util.excludeNotKeras(nodesWithIndex.map(_._1.element)) + validateInput(List(first._1.element)) val shapes = ArrayBuffer[Shape]() - shapes.append(first._1.element.getOutputShapeFor(first._2)) + shapes += getShapeByIndex(first._1.element.getOutputShape(), first._2) if (!nodesWithIndex.isEmpty) { - shapes ++= nodesWithIndex.map{t => t._1.element.getOutputShapeFor(t._2)} + validateInput(nodesWithIndex.map(_._1.element)) + shapes ++= nodesWithIndex.map{t => + getShapeByIndex(first._1.element.getOutputShape(), first._2) + } } this.build(Shape(shapes.toList)) - super.inputs(first, nodesWithIndex : _*) + processInputs(first, nodesWithIndex : _*) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala index 98b7396786e..893b10801cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala @@ -46,7 +46,19 @@ object KerasUtils { } } - private[bigdl] def getActivation[T : ClassTag] (activation: String) + private[bigdl] def getKerasActivation[T : ClassTag] (activation: String) + (implicit ev: TensorNumeric[T]): KerasLayer[Tensor[T], Tensor[T], T] = { + if (activation == null) { return null } + if (activation.toLowerCase() == "softmax") { + SoftMax[T]() + } else { + val torchActivation = getTorchActivation(activation) + new KerasIdentityWrapper[T](torchActivation) + .asInstanceOf[KerasLayer[Tensor[T], Tensor[T], T]] + } + } + + private[keras] def getTorchActivation[T : ClassTag] (activation: String) (implicit ev: TensorNumeric[T]): AbstractModule[Tensor[T], Tensor[T], T] = { if (activation == null) null else { @@ -54,7 +66,8 @@ object KerasUtils { case "tanh" => Tanh[T]() case "sigmoid" => Sigmoid[T]() case "relu" => ReLU[T]() - case "softmax" => SoftMax[T]().asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + case "softmax" => + com.intel.analytics.bigdl.nn.SoftMax[T]() case "softplus" => SoftPlus[T]() case "softsign" => SoftSign[T]() case "hard_sigmoid" => HardSigmoid[T]() @@ -105,5 +118,4 @@ object KerasUtils { case "th" => "CHANNEL_FIRST" } } - } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala index 419bbff5fc9..248012ed732 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LSTM.scala @@ -53,8 +53,8 @@ import scala.reflect.ClassTag */ class LSTM[T: ClassTag]( outputDim: Int, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, - val innerActivation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, + val innerActivation: KerasLayer[Tensor[T], Tensor[T], T] = null, returnSequences: Boolean = false, goBackwards: Boolean = false, var wRegularizer: Regularizer[T] = null, @@ -67,8 +67,8 @@ class LSTM[T: ClassTag]( com.intel.analytics.bigdl.nn.LSTM[T]( inputSize = input(2), hiddenSize = outputDim, - activation = activation.asInstanceOf[TensorModule[T]], - innerActivation = innerActivation.asInstanceOf[TensorModule[T]], + activation = activation.doBuild(inputShape).asInstanceOf[TensorModule[T]], + innerActivation = innerActivation.doBuild(inputShape).asInstanceOf[TensorModule[T]], wRegularizer = wRegularizer, uRegularizer = uRegularizer, bRegularizer = bRegularizer) @@ -86,8 +86,8 @@ object LSTM { uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : LSTM[T] = { - new LSTM(outputDim, KerasUtils.getActivation(activation), - KerasUtils.getActivation(innerActivation), returnSequences, + new LSTM(outputDim, KerasUtils.getKerasActivation(activation), + KerasUtils.getKerasActivation(innerActivation), returnSequences, goBackwards, wRegularizer, uRegularizer, bRegularizer, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala index c2bf9366a77..1e86083dc10 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala @@ -38,7 +38,8 @@ import scala.reflect.ClassTag class LeakyReLU[T: ClassTag]( private val alpha: Double = 0.01, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = com.intel.analytics.bigdl.nn.LeakyReLU( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala index e5a93188098..f3632cbc00e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected1D.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.nn.{Squeeze, Sequential => TSequential} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor @@ -51,7 +51,7 @@ import scala.reflect.ClassTag class LocallyConnected1D[T: ClassTag]( val nbFilter: Int, val filterLength: Int, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val subsampleLength: Int = 1, var wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, @@ -88,7 +88,7 @@ class LocallyConnected1D[T: ClassTag]( model.add(layer) model.add(Squeeze(3)) if (activation != null) { - model.add(activation) + model.add(activation.doBuild(inputShape)) } model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } @@ -105,7 +105,7 @@ object LocallyConnected1D { bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LocallyConnected1D[T] = { new LocallyConnected1D[T](nbFilter, filterLength, - KerasUtils.getActivation(activation), subsampleLength, + KerasUtils.getKerasActivation(activation), subsampleLength, wRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala index 9b61d92ebc1..1d3915895a8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LocallyConnected2D.scala @@ -16,7 +16,8 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.nn.{Container => TContainer, LocallyConnected2D => TLocallyConnected2D} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -55,7 +56,7 @@ class LocallyConnected2D[T: ClassTag]( val nbFilter: Int, val nbRow: Int, val nbCol: Int, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val borderMode: String = "valid", val subsample: Array[Int] = Array(1, 1), val dimOrdering: DataFormat = DataFormat.NCHW, @@ -109,7 +110,7 @@ object LocallyConnected2D { bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LocallyConnected2D[T] = { new LocallyConnected2D[T](nbFilter, nbRow, nbCol, - KerasUtils.getActivation(activation), borderMode, Array(subsample._1, subsample._2), + KerasUtils.getKerasActivation(activation), borderMode, Array(subsample._1, subsample._2), KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, bias, inputShape) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala index 885377234c0..8b4424209c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala @@ -39,7 +39,8 @@ import scala.reflect.ClassTag class Masking[T: ClassTag]( val maskValue: Double = 0.0, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = com.intel.analytics.bigdl.nn.Masking(maskValue = maskValue) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala index 1170f4c4a40..4119ac1675e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala @@ -156,7 +156,11 @@ object Merge { val batchInputShape = KerasLayer.addBatch(inputShape) val actualInputShape = MultiShape(layers.map { layer => - layer.build(layer.getInputShape()) + if (layer.isBuilt()) { // it's possible while reloaded from file + layer.getOutputShape() + } else { + layer.build(layer.getInputShape()) + } }.toList) if (batchInputShape != null) { require(batchInputShape.isInstanceOf[MultiShape], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala index 4fa52ed23e7..454df2e8d32 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala @@ -62,7 +62,8 @@ class SReLU[T: ClassTag]( val aRightInit: InitializationMethod = Ones, val sharedAxes: Array[Int] = null, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val shape = inputShape.toSingle().toArray diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala index e71d79baba3..5b9c29d5df4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala @@ -68,7 +68,7 @@ class SeparableConvolution2D[T: ClassTag]( val nbRow: Int, val nbCol: Int, val init: InitializationMethod = Xavier, - val activation: AbstractModule[Tensor[T], Tensor[T], T] = null, + val activation: KerasLayer[Tensor[T], Tensor[T], T] = null, val borderMode: String = "valid", val subsample: Array[Int] = Array(1, 1), val depthMultiplier: Int = 1, @@ -126,7 +126,7 @@ object SeparableConvolution2D { bias: Boolean = true, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : SeparableConvolution2D[T] = { new SeparableConvolution2D[T](nbFilter, nbRow, nbCol, - KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + KerasUtils.getInitMethod(init), KerasUtils.getKerasActivation(activation), borderMode, Array(subsample._1, subsample._2), depthMultiplier, KerasUtils.toBigDLFormat(dimOrdering), depthwiseRegularizer, pointwiseRegularizer, bRegularizer, bias, inputShape) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala index b04bb27da87..7419fb52f2c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SimpleRNN.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.nn.{Cell, RnnCell} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor @@ -49,7 +49,7 @@ import scala.reflect.ClassTag */ class SimpleRNN[T: ClassTag]( outputDim: Int, - val activation: AbstractModule[Tensor[T], Tensor[T], T], + val activation: KerasLayer[Tensor[T], Tensor[T], T], returnSequences: Boolean = false, goBackwards: Boolean = false, var wRegularizer: Regularizer[T] = null, @@ -62,7 +62,7 @@ class SimpleRNN[T: ClassTag]( RnnCell( inputSize = input(2), hiddenSize = outputDim, - activation = activation.asInstanceOf[TensorModule[T]], + activation = activation.doBuild(inputShape).asInstanceOf[TensorModule[T]], isInputWithBias = false, wRegularizer = wRegularizer, uRegularizer = uRegularizer, @@ -80,7 +80,7 @@ object SimpleRNN { uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : SimpleRNN[T] = { - new SimpleRNN[T](outputDim, KerasUtils.getActivation(activation), + new SimpleRNN[T](outputDim, KerasUtils.getKerasActivation(activation), returnSequences, goBackwards, wRegularizer, uRegularizer, bRegularizer, inputShape) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala index 17b30ed011f..6b86e431343 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout1D.scala @@ -43,7 +43,8 @@ import scala.reflect.ClassTag class SpatialDropout1D[T: ClassTag]( val p: Double = 0.5, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = com.intel.analytics.bigdl.nn.SpatialDropout1D(initP = p) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala index 4d7bb688bbd..1c6ced4b5a1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout2D.scala @@ -46,7 +46,8 @@ class SpatialDropout2D[T: ClassTag]( val p: Double = 0.5, val dimOrdering: DataFormat = DataFormat.NCHW, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = com.intel.analytics.bigdl.nn.SpatialDropout2D( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala index 61e69e3f703..dee9634de8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SpatialDropout3D.scala @@ -46,7 +46,8 @@ class SpatialDropout3D[T: ClassTag]( val p: Double = 0.5, val dimOrdering: String = "CHANNEL_FIRST", val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { require(dimOrdering.toLowerCase() == "channel_first" || dimOrdering.toLowerCase() == "channel_last", diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala index 6d5014e7520..b90f9547c09 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala @@ -39,7 +39,8 @@ import scala.reflect.ClassTag class ThresholdedReLU[T: ClassTag]( val theta: Double = 1.0, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) - extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) { + extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) + with IdentityOutputShape { override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val layer = Threshold( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/TimeDistributed.scala index 38894d12ef7..f79ddbb8cd7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/TimeDistributed.scala @@ -51,7 +51,7 @@ class TimeDistributed[T: ClassTag]( require(input.length >=3, s"TimeDistributed requires at least 3D input, but got input dim ${input.length}") val innerInput = getInnerInput(input) - val innerOutput = layer.build(Shape(innerInput)).toSingle() + val innerOutput = layer.computeOutputShape(Shape(innerInput)).toSingle() val output = innerOutput.take(1) ++ List(input(1)) ++ innerOutput.drop(1) Shape(output.toArray) } @@ -59,9 +59,9 @@ class TimeDistributed[T: ClassTag]( override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toSingle().toArray val innerInput = getInnerInput(input) - val klayer = layer.doBuild(Shape(innerInput)) - .asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] - val timedistributed = com.intel.analytics.bigdl.nn.TimeDistributed(klayer) + layer.build(Shape(innerInput)) + layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + val timedistributed = com.intel.analytics.bigdl.nn.TimeDistributed(layer) timedistributed.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala index 7e79bbb1ef0..5a7da0dcd7c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala @@ -18,37 +18,55 @@ package com.intel.analytics.bigdl.nn.keras import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.{Graph, GraphSerializable, StaticGraph, Sequential => TSequential} +import com.intel.analytics.bigdl.nn.{Container, Identity, StaticGraph, Sequential => TSequential} import com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import com.intel.analytics.bigdl.utils.serializer._ -import com.intel.analytics.bigdl.utils.{Shape, Util} +import scala.collection.JavaConverters._ +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +abstract class KerasModel[T: ClassTag](implicit ev: TensorNumeric[T]) + extends KerasLayer[Activity, Activity, T] { + // TODO: enrich fit, compile, evaluate etc here. + + def getSubModules(): List[AbstractModule[Activity, Activity, T]] = { + require(this.labor.isInstanceOf[Container[Activity, Activity, T]], + "labor should be a container, but we got: $this") + this.labor.asInstanceOf[Container[Activity, Activity, T]].modules.toList + } +} + class Model[T: ClassTag](private val _inputs : Seq[ModuleNode[T]], private val _outputs : Seq[ModuleNode[T]])(implicit ev: TensorNumeric[T]) - extends StaticGraph[T](_inputs, _outputs, None, false) { + extends KerasModel[T] { + this.labor = doBuild(null) - Util.excludeNotKeras(inputs.map(_.element)) - Util.excludeNotKeras(outputs.map(_.element)) + excludeInvalidLayers(this.labor.asInstanceOf[StaticGraph[T]]. + getForwardExecutions().map {_.element}) - this.inputShapeValue = Shape(inputs.map{n => n.element.getInputShape()}.toList) + this.inputShapeValue = Shape(_inputs.map{n => n.element.getInputShape()}.toList) - this.outputShapeValue = Array(outputs.map{_.element.getOutputShape()}: _*) + this.outputShapeValue = Shape(_outputs.map{_.element.getOutputShape()}.toList) - isBuilt = true + override def isKerasStyle(): Boolean = true - override private[bigdl] def isCompatibleWithKeras(): Boolean = true + override def computeOutputShape(inputShape: Shape): Shape = { + getOutputShape() + } - override private[bigdl] def isCompatibleWithTorch(): Boolean = false + override def doBuild(inputShape: Shape): StaticGraph[T] = + new StaticGraph[T](_inputs, _outputs, None, false) - override def computeOutputShape(inputShape: Shape): Shape = { + override def build(calcInputShape: Shape): Shape = { + checkWithCurrentInputShape(calcInputShape) getOutputShape() } } -object Model extends ModelSerializer{ +object Model extends KerasLayerSerializable{ /** * Build multiple inputs, multiple outputs graph container. * @param input input node @@ -57,7 +75,7 @@ object Model extends ModelSerializer{ */ def apply[T: ClassTag]( input : Array[ModuleNode[T]], - output : Array[ModuleNode[T]])(implicit ev: TensorNumeric[T]) : Graph[T] = { + output : Array[ModuleNode[T]])(implicit ev: TensorNumeric[T]) : Model[T] = { new Model[T](input, output) } @@ -68,7 +86,7 @@ object Model extends ModelSerializer{ * @return a graph container */ def apply[T: ClassTag](input : ModuleNode[T], output : Array[ModuleNode[T]]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + (implicit ev: TensorNumeric[T]) : Model[T] = { new Model[T](Seq(input), output) } @@ -79,7 +97,7 @@ object Model extends ModelSerializer{ * @return a graph container */ def apply[T: ClassTag](input : Array[ModuleNode[T]], output : ModuleNode[T]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + (implicit ev: TensorNumeric[T]) : Model[T] = { new Model[T](input, Seq(output)) } /** @@ -89,63 +107,56 @@ object Model extends ModelSerializer{ * @return a graph container */ def apply[T: ClassTag](input : ModuleNode[T], output : ModuleNode[T]) - (implicit ev: TensorNumeric[T]) : Graph[T] = { + (implicit ev: TensorNumeric[T]) : Model[T] = { new Model[T](Seq(input), Seq(output)) } -} - -trait ModelSerializer extends GraphSerializable with TKerasSerializerHelper{ override def doSerializeModule[T: ClassTag](context: SerializeContext[T], - moduleBuilder : BigDLModule.Builder) - (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(context, moduleBuilder) - appendKerasLabel(context, moduleBuilder) + builder: BigDLModule.Builder) + (implicit ev: TensorNumeric[T]): Unit = { + val labor = context.moduleData.module. + asInstanceOf[KerasLayer[Activity, Activity, T]].labor + val subModule = ModuleSerializer.serialize(SerializeContext(ModuleData(labor, + new ArrayBuffer[String](), new ArrayBuffer[String]()), context.storages, + context.storageType, _copyWeightAndBias)) + builder.addSubModules(subModule.bigDLModule) } override def doLoadModule[T: ClassTag](context: DeserializeContext) - (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - val (module, inputs, outputs, generateBackwardValue, sharedVariables) = - prepareLoadModule(context) - require(generateBackwardValue == null, "there's no generateBackward for keras module") - require(module.containsAttr("is_keras_module") - && module.getAttrOrThrow("is_keras_module").getBoolValue(), "It should be a keras module") - Model(inputs.toArray, outputs.toArray) + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + val subProtoModules = context.bigdlModule.getSubModulesList.asScala + val subModules = subProtoModules.map(module => { + val subModuleData = ModuleSerializer.load(DeserializeContext(module, + context.storages, context.storageType, _copyWeightAndBias)) + subModuleData.module + }) + val tGraph = subModules(0).asInstanceOf[StaticGraph[T]] + Model(tGraph.inputs.toArray, tGraph.outputs.toArray) } -} - -class Sequential[T: ClassTag](val stopInferShape: Boolean = false) -(implicit ev: TensorNumeric[T]) extends TSequential[T] { - override private[bigdl] def isCompatibleWithKeras(): Boolean = true +} - override private[bigdl] def isCompatibleWithTorch(): Boolean = false +class Sequential[T: ClassTag]() +(implicit ev: TensorNumeric[T]) extends KerasModel[T] { private[bigdl] var frozen: Boolean = false - override def computeOutputShape(inputShape: Shape): Shape = { - getOutputShape() - } - - override def getOutputShape(): Shape = { - require(outputShapeValue.length > 0, "Sequence should not be empty") - outputShapeValue(outputShapeValue.length -1) // For Seq, we only respect the last item as output - } + this.labor = doBuild(null) private def triggerBuilding(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Unit = { - if (this.modules.isEmpty) { + if (this.getOutputShape() == null) { if (module.getInputShape() == null) { throw new RuntimeException("The first layer should explicitly declare inputshape") } else { val outputShape = module.build(module.getInputShape()) + // The inputShape of Sequential should only be init here. this.inputShapeValue = module.getInputShape() - this.outputShapeValue = Array(outputShape) + this.outputShapeValue = outputShape } } else { val outputShape = module.build(this.getOutputShape()) - this.outputShapeValue = Array(outputShape) + this.outputShapeValue = outputShape } - isBuilt = true } /** @@ -154,7 +165,7 @@ class Sequential[T: ClassTag](val stopInferShape: Boolean = false) * @param module module to be add * @return this container */ - override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = { + def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = { if (frozen) { throw new RuntimeException( "This Sequential has been frozen, as it has been added into other container") @@ -162,25 +173,34 @@ class Sequential[T: ClassTag](val stopInferShape: Boolean = false) if (module.isInstanceOf[Sequential[T]]) { module.asInstanceOf[Sequential[T]].frozen = true } - Util.excludeNotKeras[T](Seq(module)) - if (!stopInferShape) { - triggerBuilding(module) - } - modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]] + validateInput[T](Seq(module)) + + triggerBuilding(module) + + labor.asInstanceOf[TSequential[T]].modules += + module.asInstanceOf[AbstractModule[Activity, Activity, T]] this } + + override def computeOutputShape(inputShape: Shape): Shape = { + if (labor.asInstanceOf[TSequential[T]].modules.isEmpty) { + inputShape + } else { + labor.asInstanceOf[TSequential[T]].modules.last.getOutputShape() + } + } + + override def doBuild(inputShape: Shape): TSequential[T] = TSequential[T]() + + override def build(calcInputShape: Shape): Shape = { + checkWithCurrentInputShape(calcInputShape) + getOutputShape() + } } -object Sequential extends ContainerSerializable with TKerasSerializerHelper{ +object Sequential extends KerasLayerSerializable{ def apply[@specialized(Float, Double) T: ClassTag]() (implicit ev: TensorNumeric[T]) : Sequential[T] = { new Sequential[T]() } - - override def doSerializeModule[T: ClassTag](context: SerializeContext[T], - moduleBuilder : BigDLModule.Builder) - (implicit ev: TensorNumeric[T]) : Unit = { - super.doSerializeModule(context, moduleBuilder) - appendKerasLabel(context, moduleBuilder) - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/AddConstant.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/AddConstant.scala index c0f8ed47a2a..296d4e756d9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/AddConstant.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/AddConstant.scala @@ -31,7 +31,7 @@ import scala.reflect.ClassTag class AddConstant[T: ClassTag]( val constant_scalar: Double, val inplace: Boolean = false - )(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { val scalar = ev.fromType[Double](constant_scalar) override def updateOutput(input: Tensor[T]): Tensor[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index 562cd97d0de..655e509cc43 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -16,7 +16,6 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -46,12 +45,6 @@ abstract class Container[A <: Activity : ClassTag, val modules: ArrayBuffer[AbstractModule[Activity, Activity, T]] = ArrayBuffer[AbstractModule[Activity, Activity, T]]() - override private[bigdl] def isCompatibleWithKeras(): Boolean = false - - override private[bigdl] def isCompatibleWithTorch(): Boolean = { - modules.filter(!_.isCompatibleWithTorch()).length <= 0 - } - override def reset(): Unit = { modules.foreach(_.reset()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Contiguous.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Contiguous.scala index 310f5146655..7c14b1489eb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Contiguous.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Contiguous.scala @@ -27,7 +27,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 4704727587714736531L) class Contiguous[T: ClassTag] -(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ +(implicit ev: TensorNumeric[T]) extends TensorModule[T] { override def updateOutput(input: Tensor[T]): Tensor[T] = { output = input.contiguous() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceCriterion.scala index 0e719b0c838..85aebb4f862 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CosineDistanceCriterion.scala @@ -15,10 +15,9 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractCriterion, TensorCriterion} +import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag @@ -37,7 +36,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 4008475267198411701L) class CosineDistanceCriterion[@specialized(Float, Double) T: ClassTag] (val sizeAverage: Boolean = true) -(implicit ev: TensorNumeric[T]) extends TensorCriterion[T]{ +(implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { @transient private var buffer: Tensor[T] = null @transient diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CrossEntropyCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CrossEntropyCriterion.scala index 1d331b3dd11..f3353cae6ef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CrossEntropyCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CrossEntropyCriterion.scala @@ -31,7 +31,7 @@ import scala.reflect.ClassTag class CrossEntropyCriterion[T: ClassTag]( val weights: Tensor[T] = null, val sizeAverage: Boolean = true) - (implicit ev: TensorNumeric[T]) extends TensorCriterion[T]{ + (implicit ev: TensorNumeric[T]) extends TensorCriterion[T] { private val lsm = new LogSoftMax[T]() private val nll = new ClassNLLCriterion[T](weights, sizeAverage) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala index 0f3f85790a0..5c483e2bf6a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala @@ -45,7 +45,7 @@ class Dropout[T: ClassTag]( val initP: Double = 0.5, val inplace: Boolean = false, var scale: Boolean = true)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { + implicit ev: TensorNumeric[T]) extends TensorModule[T] { private var p = initP var noise = Tensor[T]() var isResampling = true diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala index d2425451e40..ef1c98e5b33 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicContainer.scala @@ -41,7 +41,7 @@ abstract class DynamicContainer[A <: Activity : ClassTag, B <: Activity : ClassT require(!module.isInstanceOf[Operation[_, _, _]], "Add operations to dynamic container is not allowed, as operations don't have backward. " + "Operation can only be used in Graph") - Util.excludeNotTorch[T](Seq(module)) + validateInput[T](Seq(module)) modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]] checkDuplicate() this diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala index 1b09e017b5d..2f65129e0b6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala @@ -32,7 +32,7 @@ class ELU[T: ClassTag]( val alpha: Double = 1.0, val inplace: Boolean = false)( implicit ev: TensorNumeric[T]) - extends TensorModule[T] with IdentityOutputShape { + extends TensorModule[T] { val _alpha = ev.fromType[Double](alpha) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala index 4a77f16db64..38ea71e4b47 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianDropout.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -37,7 +37,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 1575781981601306833L) class GaussianDropout[T: ClassTag]( val rate: Double - )(implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape{ + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { require(rate < 1 && rate >= 0, s"rate should be in range [0,1)") val stddev: Double = Math.sqrt(rate / (1.0-rate)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala index bf59dd1c2b3..070b6521d7d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/GaussianNoise.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -38,7 +38,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 2590701089601246637L) class GaussianNoise[T: ClassTag]( val stddev: Double - )(implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape{ + )(implicit ev: TensorNumeric[T]) extends TensorModule[T] { override def updateOutput(input: Tensor[T]): Tensor[T] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index 6b5fe48d6e9..f9bb3987d65 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -592,7 +592,7 @@ trait GraphSerializable extends ContainerSerializable { context.storages, context.storageType)) val moduleNode = bigDLModule.module match { case controlOps: ControlOps[T] => createControlNode(controlOps) - case _ => bigDLModule.module.inputs() + case _ => new ModuleNode[T](bigDLModule.module) } val preNodes = bigDLModule.pre layerMap(bigDLModule.module.getName) = (moduleNode, preNodes) @@ -642,7 +642,7 @@ trait GraphSerializable extends ContainerSerializable { .asInstanceOf[Boolean] Graph.dynamic[T](inputs.toArray, outputs.toArray, sharedVariables, generateBackward) } else { - Graph[T](inputs.toArray, outputs.toArray, sharedVariables) + new StaticGraph[T](inputs, outputs, sharedVariables, false) } var serializedStopGradientLayers : Array[String] = null // this is to keep backward compatible diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala index 9729a47e59a..4012b035f4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HardSigmoid.scala @@ -30,7 +30,7 @@ import scala.reflect.ClassTag * ⎩ 0.2 * x + 0.5, otherwise */ class HardSigmoid[T: ClassTag] -(implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { +(implicit ev: TensorNumeric[T]) extends TensorModule[T] { val minValue = ev.fromType[Double](-2.5) val maxValue = ev.fromType[Double](2.5) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala index 3a9e9f9133a..179c43a8e60 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LeakyReLU.scala @@ -36,7 +36,7 @@ import scala.reflect.ClassTag class LeakyReLU[T: ClassTag]( private val negval: Double = 0.01, var inplace: Boolean = false)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { + implicit ev: TensorNumeric[T]) extends TensorModule[T] { import LeakyReLU._ if (negval < 0) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala index 9fb0ca04617..986369dc605 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala @@ -80,10 +80,6 @@ class Linear[T: ClassTag]( zeroGradParameters() } - override def computeOutputShape(inputShape: Shape): Shape = { - inputShape.copyAndUpdate(-1, outputSize) - } - override def updateOutput(input: Tensor[T]): Tensor[T] = { require(input.dim() == 1 || input.dim() == 2, "Linear: " + ErrorInfo.constrainInputAsVectorOrBatch + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala index 0c93a0e8c03..209c30d8cec 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Masking.scala @@ -16,8 +16,8 @@ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} -import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag @@ -28,7 +28,7 @@ import scala.reflect.ClassTag * @param maskValue mask value */ class Masking[T: ClassTag](maskValue: Double = 0.0) -(implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape{ +(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ val batchDim = 1 val timeDim = 2 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala index 3db7db55cef..4e9520e64f8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala @@ -62,12 +62,12 @@ class Maxout[T: ClassTag](val inputSize: Int, val outputSize: Int, val maxoutNum } override def updateOutput(input: Tensor[T]): Tensor[T] = { - output = layer.updateOutput(input) + output = layer.updateOutput(input).toTensor output } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - gradInput = layer.updateGradInput(input, gradOutput) + gradInput = layer.updateGradInput(input, gradOutput).toTensor gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala index e9be2848faf..d4aed29b1f0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PReLU.scala @@ -40,7 +40,7 @@ import scala.reflect.ClassTag class PReLU[T: ClassTag]( val nOutputPlane: Int = 0) (implicit ev: TensorNumeric[T]) extends TensorModule[T] - with Initializable with IdentityOutputShape { + with Initializable { val weight = if (nOutputPlane == 0) { Tensor[T](1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala index 046bf15be66..4206f585b1b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala @@ -31,7 +31,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(1208478077576570643L) class ReLU[T: ClassTag](ip: Boolean = false)( - implicit ev: TensorNumeric[T]) extends Threshold[T](0, 0, ip) with IdentityOutputShape{ + implicit ev: TensorNumeric[T]) extends Threshold[T](0, 0, ip) { } object ReLU { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala index ee3b31cb02a..8814b425f67 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SReLU.scala @@ -49,7 +49,7 @@ import scala.reflect.ClassTag @SerialVersionUID(7173457290010080259L) class SReLU[T: ClassTag](val shape: Array[Int], val sharedAxes: Array[Int] = null)( implicit ev: TensorNumeric[T]) extends TensorModule[T] - with Initializable with IdentityOutputShape { + with Initializable { import SReLU._ val weightsLen = 4 val weights: Array[Tensor[T]] = Array.fill[Tensor[T]](4)(Tensor[T]()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala index 74cf9f8b1fb..f05b7272fe8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sigmoid.scala @@ -29,7 +29,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(6855417348268610044L) class Sigmoid[T: ClassTag]( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { + implicit ev: TensorNumeric[T]) extends TensorModule[T] { private val buffer: Tensor[T] = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala index 8803676553b..1426a3d30b9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala @@ -31,7 +31,7 @@ import scala.reflect.ClassTag * where shift = max_i(x_i). */ @SerialVersionUID(- 7842335603491194236L) -class SoftMax[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ +class SoftMax[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T] { @transient private var results: Array[Future[Unit]] = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMin.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMin.scala index d675f83ff8d..a05cff2e804 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMin.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMin.scala @@ -30,7 +30,7 @@ import scala.reflect.ClassTag * where shift = max_i(-x_i). */ @SerialVersionUID(- 8738615460960887232L) -class SoftMin[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T]{ +class SoftMin[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T] { @transient private var results: Array[Future[Unit]] = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala index 571137215c8..ace2d2a03d8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftPlus.scala @@ -34,7 +34,7 @@ import scala.reflect.ClassTag class SoftPlus[T: ClassTag]( val beta: Double = 1.0 )( implicit ev: TensorNumeric[T]) - extends TensorModule[T] with IdentityOutputShape { + extends TensorModule[T] { // Avoid floating point issues with exp(x), x>20 private val threshold = ev.fromType[Double](20.0) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala index 6fff6d04c0a..8cf69cac0d0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftSign.scala @@ -30,7 +30,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 3936698382129844874L) class SoftSign[T: ClassTag]() (implicit ev: TensorNumeric[T]) - extends TensorModule[T] with IdentityOutputShape { + extends TensorModule[T] { @transient private var temp: Tensor[T] = null @transient private var tempGrad: Tensor[T] = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala index 23d0ccdfa1b..b60c1d3d4de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout1D.scala @@ -37,7 +37,7 @@ import scala.reflect.ClassTag @SerialVersionUID(- 4636332259181125718L) class SpatialDropout1D[T: ClassTag]( val initP: Double = 0.5)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { + implicit ev: TensorNumeric[T]) extends TensorModule[T] { var p = initP var noise = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala index 627ad9b84e4..1515189a55a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout2D.scala @@ -41,7 +41,7 @@ import scala.reflect.ClassTag class SpatialDropout2D[T: ClassTag]( val initP: Double = 0.5, val format: DataFormat = DataFormat.NCHW)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { + implicit ev: TensorNumeric[T]) extends TensorModule[T] { var p = initP var noise = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala index f17f9876813..b9c86e06d3d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDropout3D.scala @@ -41,7 +41,7 @@ import scala.reflect.ClassTag class SpatialDropout3D[T: ClassTag]( val initP: Double = 0.5, val format: DataFormat = DataFormat.NCHW)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { + implicit ev: TensorNumeric[T]) extends TensorModule[T] { var p = initP var noise = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala index d6fe8e12c34..111aadec294 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialFullConvolution.scala @@ -18,15 +18,13 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Initializable} import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.{Shape, T, Table, serializer} -import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.utils.{Shape, T, Table} -import scala.concurrent.Future import scala.reflect.ClassTag import scala.reflect.runtime.universe diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala index 5b15aeec8ac..e9cd7df8372 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -36,7 +36,7 @@ class StaticGraph[T: ClassTag]( private val _inputs : Seq[ModuleNode[T]], private val _outputs : Seq[ModuleNode[T]], private val _variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, - private val excludeKeras: Boolean = true + private val enableExcludeChecking: Boolean = true )(implicit ev: TensorNumeric[T]) extends Graph[T](_inputs, _outputs, _variables) { private val forwardExecution = forwardGraph.topologySort.reverse private var backwardExecution: Array[Node[AbstractModule[Activity, Activity, T]]] = _ @@ -44,9 +44,8 @@ class StaticGraph[T: ClassTag]( private var backId2ForwardId: Array[Int] = _ private var gradOutputCache: Array[Activity] = _ - if (excludeKeras) { - Util.excludeNotTorch(inputs.map(_.element)) - Util.excludeNotTorch(outputs.map(_.element)) + if (enableExcludeChecking) { + excludeInvalidLayers(forwardExecution.map {_.element}) } buildBackwardGraph() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala index 450f5c90634..13e533d394d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala @@ -30,7 +30,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(9062199894710333035L) class Tanh[T: ClassTag]( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape { + implicit ev: TensorNumeric[T]) extends TensorModule[T] { private val buffer: Tensor[T] = Tensor[T]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala index 012f03676cd..a2577a7c343 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala @@ -37,7 +37,7 @@ import scala.reflect.ClassTag @SerialVersionUID(3953292249027271493L) class Threshold[T: ClassTag]( private val th: Double = 1e-6, private val v: Double = 0.0, private val ip: Boolean = false)( - implicit ev: TensorNumeric[T]) extends TensorModule[T] with IdentityOutputShape{ + implicit ev: TensorNumeric[T]) extends TensorModule[T] { var threshold = th var value = v var inPlace = ip diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala index 86f473363ea..6043bbcec90 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/UpSampling1D.scala @@ -16,8 +16,6 @@ package com.intel.analytics.bigdl.nn -import java.util - import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 2270c6cc185..82b47fe7c60 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -742,17 +742,32 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, this } + protected def processInputs(nodes: Seq[ModuleNode[T]]): ModuleNode[T] = { + val curNode = new ModuleNode[T](this) + nodes.foreach(node => { + node.add(curNode, Edge()) + }) + curNode + } + + protected def processInputs(first: (ModuleNode[T], Int), + nodesWithIndex : (ModuleNode[T], Int)*): ModuleNode[T] = { + val curNode = new ModuleNode[T](this) + first._1.add(curNode, Edge(first._2)) + nodesWithIndex.foreach(nodeWithIndex => { + nodeWithIndex._1.add(curNode, Edge(nodeWithIndex._2)) + }) + curNode + } + /** * Build graph: some other modules point to current module * @param nodes upstream module nodes * @return node containing current module */ def inputs(nodes : ModuleNode[T]*): ModuleNode[T] = { - val curNode = new ModuleNode[T](this) - nodes.foreach(node => { - node.add(curNode, Edge()) - }) - curNode + validateInput(nodes.map(_.element)) + processInputs(nodes) } /** @@ -761,11 +776,8 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @return node containing current module */ def inputs(nodes : Array[ModuleNode[T]]): ModuleNode[T] = { - val curNode = new ModuleNode[T](this) - nodes.foreach(node => { - node.add(curNode, Edge()) - }) - curNode + validateInput(nodes.map(_.element)) + processInputs(nodes) } /** @@ -774,14 +786,10 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param nodesWithIndex upstream module nodes and the output tensor index. The start index is 1. * @return node containing current module */ - def inputs(first: (ModuleNode[T], Int), nodesWithIndex : (ModuleNode[T], Int)*) - : ModuleNode[T] = { - val curNode = new ModuleNode[T](this) - first._1.add(curNode, Edge(first._2)) - nodesWithIndex.foreach(nodeWithIndex => { - nodeWithIndex._1.add(curNode, Edge(nodeWithIndex._2)) - }) - curNode + def inputs(first: (ModuleNode[T], Int), nodesWithIndex : (ModuleNode[T], Int)*): ModuleNode[T] = { + validateInput(List(first._1.element)) + validateInput(nodesWithIndex.map(_._1.element)) + processInputs(first, nodesWithIndex: _*) } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala index 5f60564bbb9..0ffd07b4de1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala @@ -16,24 +16,31 @@ package com.intel.analytics.bigdl.nn.abstractnn +import com.intel.analytics.bigdl.nn.keras.{Input => KInput, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.{Input => TInput} import com.intel.analytics.bigdl.utils.Shape +import scala.language.existentials +import scala.reflect.ClassTag + +class InvalidLayer(msg: String) extends RuntimeException(msg) + trait InferShape { private[bigdl] var _inputShapeValue: Shape = null - private[bigdl] var _outputShapeValue: Array[Shape] = Array[Shape]() + private[bigdl] var _outputShapeValue: Shape = null private[bigdl] def inputShapeValue: Shape = _inputShapeValue - private[bigdl] def outputShapeValue: Array[Shape] = _outputShapeValue + private[bigdl] def outputShapeValue: Shape = _outputShapeValue // scalastyle:off private[bigdl] def inputShapeValue_=(value: Shape): Unit = { _inputShapeValue = value } - private[bigdl] def outputShapeValue_=(value: Array[Shape]): Unit = { + private[bigdl] def outputShapeValue_=(value: Shape): Unit = { _outputShapeValue = value } // scalastyle:on @@ -41,28 +48,15 @@ trait InferShape { /** * We suppose the first dim is batch */ - private[bigdl] def getInputShape(): Shape = { + private[bigdl] final def getInputShape(): Shape = { _inputShapeValue } - /** - * Get the outputshape by index. - * @param index start from 0 - * @return - */ - private[bigdl] def getOutputShapeFor(index: Int): Shape = { - _outputShapeValue(index) - } - /** * We suppose the first dim is batch */ - private[bigdl] def getOutputShape(): Shape = { - if (_outputShapeValue.length > 1) { - throw new RuntimeException( - "There are multiple outputs for this layer. Please use getInputShapeFor instead") - } - outputShapeValue(0) + private[bigdl] final def getOutputShape(): Shape = { + outputShapeValue } /** @@ -71,18 +65,16 @@ trait InferShape { */ private[bigdl] def build(inputShape: Shape): Shape = { val outputShape = computeOutputShape(inputShape) - this._outputShapeValue ++ Array(outputShape) - this._inputShapeValue = inputShape - isBuilt = true + this.outputShapeValue = outputShape + this.inputShapeValue = inputShape outputShape } - private[bigdl] var isBuilt: Boolean = false - + private[bigdl] def isBuilt(): Boolean = outputShapeValue != null - private[bigdl] def isCompatibleWithKeras(): Boolean = true + private[bigdl] def isKerasStyle(): Boolean = false - private[bigdl] def isCompatibleWithTorch(): Boolean = true + private[bigdl] def allowRebuilt(): Boolean = false /** * We suppose the first dim is batch @@ -90,5 +82,26 @@ trait InferShape { private[bigdl] def computeOutputShape(inputShape: Shape): Shape = { throw new RuntimeException("Haven't been implemented yet. Do not use it with Keras Layer") } + + private[bigdl] def excludeInvalidLayers[T: ClassTag] + (modules : Seq[AbstractModule[_, _, T]]): Unit = { + val invalidNodes = if (this.isKerasStyle()) { + modules.filter{!_.isKerasStyle()} + } else { + modules.filter{_.isKerasStyle()} + } + if (invalidNodes.length > 0) { + throw new InvalidLayer(s"""Do not mix ${this}(isKerasStyle=${isKerasStyle()}) with Layer + (isKerasStyle=${invalidNodes(0).isKerasStyle()}): + ${invalidNodes.mkString(",")}""") + } + } + + private[bigdl] def validateInput[T: ClassTag](modules : Seq[AbstractModule[_, _, T]]): Unit = { + if (this.isKerasStyle()) { + require(modules != null && !modules.isEmpty, "Empty input is not allowed") + } + excludeInvalidLayers(modules) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala index 3a107a95259..6907811e061 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala @@ -17,7 +17,6 @@ package com.intel.analytics.bigdl.utils import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} @@ -176,20 +175,4 @@ object Util { i += 1 } } - - private[bigdl] def excludeNotTorch[T: ClassTag] - (modules : Seq[AbstractModule[_, _, T]]): Unit = { - val invalidNodes = modules.filter{!_.isCompatibleWithTorch()} - if (invalidNodes.length > 0) { - throw new RuntimeException(s"Do not mix with Layer: ${invalidNodes.mkString(",")}") - } - } - - private[bigdl] def excludeNotKeras[T: ClassTag] - (modules : Seq[AbstractModule[_, _, T]]): Unit = { - val invalidNodes = modules.filter{!_.isCompatibleWithKeras()} - if (invalidNodes.length > 0) { - throw new RuntimeException(s"Do not mix with Layer: ${invalidNodes.mkString(",")}") - } - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 22c9268a8dd..b7ed9e9436a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -258,13 +258,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab optimizer } - def createSequential(isKeras: Boolean = false): Sequential[T] = { - if (isKeras) { - nn.keras.Sequential[T]() - } - else { + def createSequential(): Container[Activity, Activity, T] = { Sequential[T]() - } } def createLinear(inputSize: Int, outputSize: Int, @@ -2316,14 +2311,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createModel(input: JList[ModuleNode[T]], - output: JList[ModuleNode[T]], - isKeras: Boolean = false): Graph[T] = { - if (isKeras) { - nn.keras.Model(input.asScala.toArray, output.asScala.toArray) - } - else { - Graph(input.asScala.toArray, output.asScala.toArray) - } + output: JList[ModuleNode[T]]): Graph[T] = { + Graph(input.asScala.toArray, output.asScala.toArray) } def createNode(module: AbstractModule[Activity, Activity, T], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala index 9c3d95a6dc2..f6c04a8ad9a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -18,8 +18,9 @@ package com.intel.analytics.bigdl.python.api import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} +import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.{Container, SpatialBatchNormalization} +import com.intel.analytics.bigdl.nn.{Container, Graph, SpatialBatchNormalization} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.numeric._ @@ -66,6 +67,15 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho } } + def createKerasModel(input: JList[ModuleNode[T]], + output: JList[ModuleNode[T]]): Model[T] = { + nn.keras.Model(input.asScala.toArray, output.asScala.toArray) + } + + def createKerasSequential(): nn.keras.Sequential[T] = { + nn.keras.Sequential[T]() + } + def createKerasInput( name : String = null, inputShape: JList[Int] = null): ModuleNode[T] = { @@ -73,7 +83,7 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho } def createKerasInputLayer( - inputShape: JList[Int] = null): Input[T] = { + inputShape: JList[Int] = null): KerasLayer[Activity, Activity, T] = { InputLayer(inputShape = toScalaShape(inputShape)) } @@ -172,7 +182,7 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho bias: Boolean = true, inputShape: JList[Int] = null): Convolution2D[T] = { new Convolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), - KerasUtils.getActivation(activation), borderMode, + KerasUtils.getKerasActivation(activation), borderMode, toScalaArray(subsample), KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) } @@ -337,7 +347,7 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho bias: Boolean = true, inputShape: JList[Int] = null): Convolution3D[T] = { new Convolution3D(nbFilter, kernelDim1, kernelDim2, kernelDim3, - KerasUtils.getInitMethod(init), KerasUtils.getActivation(activation), + KerasUtils.getInitMethod(init), KerasUtils.getKerasActivation(activation), borderMode, toScalaArray(subsample), KerasUtils.toBigDLFormat5D(dimOrdering), wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) } @@ -462,7 +472,7 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho bRegularizer: Regularizer[T] = null, inputShape: JList[Int] = null): AtrousConvolution2D[T] = { new AtrousConvolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), - KerasUtils.getActivation(activation), toScalaArray(subsample), + KerasUtils.getKerasActivation(activation), toScalaArray(subsample), toScalaArray(atrousRate), KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, toScalaShape(inputShape)) } @@ -480,7 +490,7 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho bias: Boolean = true, inputShape: JList[Int] = null): Deconvolution2D[T] = { new Deconvolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), - KerasUtils.getActivation(activation), toScalaArray(subsample), + KerasUtils.getKerasActivation(activation), toScalaArray(subsample), KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) } @@ -528,7 +538,7 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: JList[Int] = null): LocallyConnected2D[T] = { - new LocallyConnected2D(nbFilter, nbRow, nbCol, KerasUtils.getActivation(activation), + new LocallyConnected2D(nbFilter, nbRow, nbCol, KerasUtils.getKerasActivation(activation), borderMode, toScalaArray(subsample), KerasUtils.toBigDLFormat(dimOrdering), wRegularizer, bRegularizer, bias, toScalaShape(inputShape)) } @@ -549,7 +559,7 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho bias: Boolean = true, inputShape: JList[Int] = null): SeparableConvolution2D[T] = { new SeparableConvolution2D(nbFilter, nbRow, nbCol, KerasUtils.getInitMethod(init), - KerasUtils.getActivation(activation), borderMode, toScalaArray(subsample), + KerasUtils.getKerasActivation(activation), borderMode, toScalaArray(subsample), depthMultiplier, KerasUtils.toBigDLFormat(dimOrdering), depthwiseRegularizer, pointwiseRegularizer, bRegularizer, bias, toScalaShape(inputShape)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 69a14132395..432438c5d63 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -243,23 +243,8 @@ trait ModuleSerializable extends Loadable with Savable{ } else { module.evaluate() } - - if (model.hasInputShape) { - val attrbute = AttrValue.newBuilder - attrbute.setShape(model.getInputShape) - val shape = ShapeConverter.getAttributeValue(context, attrbute.build).asInstanceOf[BigDLShape] - module.inputShapeValue = shape - } - - val outputShapes = model.getOutputShapeList.asScala - if (outputShapes.length > 0) { - val shapes = outputShapes.map(outputShape => { - val attrbute = AttrValue.newBuilder - attrbute.setShape(outputShape) - ShapeConverter.getAttributeValue(context, attrbute.build).asInstanceOf[BigDLShape] - }).toArray - module.outputShapeValue = shapes - } + module.inputShapeValue = ShapeConverter.shapeToBigDL(context, model, "input") + module.outputShapeValue = ShapeConverter.shapeToBigDL(context, model, "output") if (_copyWeightAndBias) { copy2BigDL(context, bigDLModule) } @@ -280,19 +265,11 @@ trait ModuleSerializable extends Loadable with Savable{ modelBuilder.setId(System.identityHashCode(module.module)) val inputShape = module.module.inputShapeValue if (inputShape != null) { - val attribute = AttrValue.newBuilder - ShapeConverter.setAttributeValue(context, attribute, inputShape, - universe.typeOf[BigDLShape]) - modelBuilder.setInputShape(attribute.getShape) + modelBuilder.setInputShape(ShapeConverter.shapeToProto(context, inputShape)) } - val outputShapes = module.module.outputShapeValue - if (outputShapes != null && outputShapes.length > 0) { - outputShapes.foreach(outputShape => { - val attribute = AttrValue.newBuilder - ShapeConverter.setAttributeValue(context, attribute, outputShape, - universe.typeOf[BigDLShape]) - modelBuilder.addOutputShape(attribute.getShape) - }) + val outputShape = module.module.outputShapeValue + if (outputShape != null) { + modelBuilder.setOutputShape(ShapeConverter.shapeToProto(context, outputShape)) } if (_copyWeightAndBias) { copyFromBigDL(context, modelBuilder) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 36dcd21492b..fcdc2ed8631 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -185,6 +185,7 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.DynamicGraph", Graph) registerModule("com.intel.analytics.bigdl.nn.keras.Model", Model) registerModule("com.intel.analytics.bigdl.nn.keras.Sequential", KSequential) + registerModule("com.intel.analytics.bigdl.nn.keras.KerasLayerWrapper", KerasLayerSerializer) registerModule("com.intel.analytics.bigdl.nn.MapTable", MapTable) registerModule("com.intel.analytics.bigdl.nn.Maxout", Maxout) registerModule("com.intel.analytics.bigdl.nn.MaskedSelect", MaskedSelect) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala index eac57cd1bb6..989b8a974aa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/ShapeConverter.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, Serialize import com.intel.analytics.bigdl.utils.{MultiShape, SingleShape, Shape => BigDLShape} import com.intel.analytics.bigdl.serialization.Bigdl import com.intel.analytics.bigdl.serialization.Bigdl.Shape.ShapeType -import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType, Shape} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule, DataType, Shape} import scala.collection.JavaConverters._ import scala.reflect.ClassTag @@ -51,6 +51,26 @@ object ShapeConverter extends DataConverter { } } + def shapeToBigDL[T: ClassTag](context: DeserializeContext, + model: BigDLModule, name: String) + (implicit ev: TensorNumericMath.TensorNumeric[T]): BigDLShape = { + val attrbute = AttrValue.newBuilder + attrbute.setShape( + name match { + case "input" => model.getInputShape + case "output" => model.getOutputShape + }) + ShapeConverter.getAttributeValue(context, attrbute.build).asInstanceOf[BigDLShape] + } + + def shapeToProto[T: ClassTag](context: SerializeContext[T], shape: BigDLShape) + (implicit ev: TensorNumericMath.TensorNumeric[T]): Shape = { + val attribute = AttrValue.newBuilder + ShapeConverter.setAttributeValue(context, attribute, shape, + universe.typeOf[BigDLShape]) + attribute.getShape + } + override def setAttributeValue[T: ClassTag] (context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type)(implicit ev: TensorNumericMath.TensorNumeric[T]): Unit = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala index 58f840199fc..87e0de1e7a3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/Deconvolution2DSpec.scala @@ -73,8 +73,8 @@ class Deconvolution2DSpec extends KerasBaseSpec { class Deconvolution2DSerialTest extends ModuleSerializationTest { override def test(): Unit = { val layer = Deconvolution2D[Float](3, 3, 3, inputShape = Shape(3, 24, 24)) - layer.build(Shape(2, 12, 24, 24)) - val input = Tensor[Float](2, 12, 24, 24).apply1(_ => Random.nextFloat()) + layer.build(Shape(2, 3, 24, 24)) + val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) runSerializationTest(layer, input) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala index 49dc1edbd75..9c688a63530 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/HighwaySpec.scala @@ -45,11 +45,11 @@ class HighwaySpec extends KerasBaseSpec { """ |input_tensor = Input(shape=[10]) |input = np.random.random([4, 10]) - |output_tensor = Highway()(input_tensor) + |output_tensor = Highway(activation="relu")(input_tensor) |model = Model(input=input_tensor, output=output_tensor) """.stripMargin val seq = KSequential[Float]() - val layer = Highway[Float](inputShape = Shape(10)) + val layer = Highway[Float](inputShape = Shape(10), activation = "relu") seq.add(layer) seq.getOutputShape().toSingle().toArray should be (Array(-1, 10)) checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala index be0723df2eb..00832cfe46b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.keras.nn -import com.intel.analytics.bigdl.nn.keras.InputLayer +import com.intel.analytics.bigdl.nn.keras.{InputLayer, Sequential} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Shape import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest @@ -26,7 +26,9 @@ import scala.util.Random class InputSerialTest extends ModuleSerializationTest { override def test(): Unit = { val input = InputLayer[Float](inputShape = Shape(20)) + val seq = Sequential[Float]() + seq.add(input) val inputData = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) - runSerializationTest(input, inputData) + runSerializationTest(seq, inputData) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasLayerWrapperSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasLayerWrapperSpec.scala new file mode 100644 index 00000000000..719d96b7fa8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasLayerWrapperSpec.scala @@ -0,0 +1,56 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.keras.KerasBaseSpec +import com.intel.analytics.bigdl.nn.Linear +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.{Dense, KerasLayerWrapper, Sequential => KSequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class KerasLayerWrapperSpec extends KerasBaseSpec { + + def weightConverter(in: Array[Tensor[Float]]): Array[Tensor[Float]] = Array(in(0).t(), in(1)) + + "KerasLayerWrapper" should "be test" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3]) + |input = np.random.uniform(0, 1, [1, 3]) + |output_tensor = Dense(2)(input_tensor) + |model = Model(input=input_tensor, output=output_tensor) + """.stripMargin + val seq = KSequential[Float]() + val dense = new KerasLayerWrapper[Float](Linear[Float](3, 2), inputShape = Shape(3)) + seq.add(dense) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 2)) + checkOutputAndGrad(seq.asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]], + kerasCode, weightConverter) + } + + "Compute outputshape of KerasLayerWrapper " should "be test" in { + val seq = KSequential[Float]() + val dense = new KerasLayerWrapper[Float](Linear[Float](3, 2), inputShape = Shape(3)) + seq.add(dense) + seq.add(Dense(10)) + seq.getOutputShape().toSingle().toArray should be (Array(-1, 10)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala index 27f99ec103d..bcff5090b45 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala @@ -17,11 +17,12 @@ package com.intel.analytics.bigdl.keras.nn import com.intel.analytics.bigdl.example.loadmodel.AlexNet_OWT -import com.intel.analytics.bigdl.nn.keras.{Dense, Input, Model, Sequential => KSequential} -import com.intel.analytics.bigdl.nn.{Sequential => TSequential, _} +import com.intel.analytics.bigdl.nn.abstractnn.InvalidLayer +import com.intel.analytics.bigdl.nn.keras.{Activation, Dense, Input, InputLayer, KerasIdentityWrapper, Model, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.{Input => TInput, Sequential => TSequential, _} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape, T, TestUtils} class KerasStyleSpec extends BigDLSpecHelper { @@ -60,26 +61,54 @@ class KerasStyleSpec extends BigDLSpecHelper { } } - "Sequential: shared relu" should "work correctly" in { - val sharedRelu = ReLU[Float]() - val seq1 = KSequential[Float]() - seq1.add(Dense[Float](20, inputShape = Shape(10))) - seq1.add(sharedRelu) - require(seq1.getOutputShape().toSingle().sameElements(Array(-1, 20))) + "Sequential: shared relu" should "not work correctly" in { + val thrown = intercept[Exception] { + val sharedRelu = new KerasIdentityWrapper(ReLU[Float]()) + val seq1 = KSequential[Float]() + seq1.add(Dense[Float](20, inputShape = Shape(10))) + seq1.add(sharedRelu) + assert(seq1.getOutputShape().toSingle().sameElements(Array(-1, 20))) - val seq2 = KSequential[Float]() - seq2.add(Dense[Float](5, inputShape = Shape(20))) - seq2.add(sharedRelu) - require(seq2.getOutputShape().toSingle().sameElements(Array(-1, 5))) + val seq2 = KSequential[Float]() + seq2.add(Dense[Float](5, inputShape = Shape(20))) + seq2.add(sharedRelu) + assert(seq2.getOutputShape().toSingle().sameElements(Array(-1, 5))) - val seq = KSequential[Float]() - seq.add(seq1) - seq.add(seq2) + val seq = KSequential[Float]() + seq.add(seq1) + seq.add(seq2) - val inputData = Tensor[Float](Array(20, 10)).rand() - val output = seq.forward(inputData) - require(seq.getInputShape().toSingle().sameElements(Array(-1, 10))) - require(seq.getOutputShape().toSingle().sameElements(Array(-1, 5))) + val inputData = Tensor[Float](Array(20, 10)).rand() + val output = seq.forward(inputData) + assert(seq.getInputShape().toSingle().sameElements(Array(-1, 10))) + assert(seq.getOutputShape().toSingle().sameElements(Array(-1, 5))) + } + assert(thrown.getMessage().contains("multiple times")) + } + + "Graph: shared relu" should "not work correctly" in { + val thrown = intercept[Exception] { + val input = Input(inputShape = Shape(10, 20)) + val sharedRelu = new Activation("relu") + val out1 = sharedRelu.inputs(input) + + val seq = KSequential[Float]() + seq.add(InputLayer(inputShape = Shape(10, 20))) + seq.add(sharedRelu) + val out2 = seq.inputs(out1) + val model = Model(input, out2) + } + assert(thrown.getMessage().contains("multiple times")) + } + + "Graph: shared relu as dest" should "not work correctly" in { + val thrown = intercept[Exception] { + val input = Input(inputShape = Shape(10, 20)) + val sharedRelu = new Activation("relu") + val out1 = sharedRelu.inputs(input) + val out2 = sharedRelu.inputs(Input(inputShape = Shape(10, 20))) + } + assert(thrown.getMessage().contains("multiple times")) } "TSequential" should "work with alex" in { @@ -88,23 +117,82 @@ class KerasStyleSpec extends BigDLSpecHelper { } "TSequential" should "not work with dense" in { - intercept[RuntimeException] { + intercept[InvalidLayer] { val seq = TSequential[Float]() val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") seq.add(d1) } } - "TGraph" should "not work with dense" in { + "Incompatible inputShape" should "not work" in { intercept[RuntimeException] { - val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1").inputs(Input()) + val seq = KSequential[Float]() + val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") + seq.add(InputLayer(inputShape = Shape(5))) + seq.add(d1) + } + } + + "TGraph" should "not work with dense" in { + intercept[InvalidLayer] { + val d1 = Dense[Float](20).setName("dense1").inputs(Input(inputShape = Shape(10))) val l1 = Linear(2, 3).inputs(d1) } } + "KGraph" should "not work with shared layers" in { + val thrown = intercept[RuntimeException] { + val input = Input(inputShape = Shape(10)) + val dense1 = Dense(10, inputShape = Shape(10)) + val node1 = dense1.inputs(input) + val seq = KSequential[Float]().add(dense1).inputs(node1) + Model(input, seq) + } + assert(thrown.getMessage().contains("multiple times")) + } + + "KGraph" should "not work with shared weights" in { + val thrown = intercept[RuntimeException] { + val input1 = Input(inputShape = Shape(10)) + val input2 = Input(inputShape = Shape(10)) + val l = Dense(10, inputShape = Shape(10)) + val node1 = l.inputs(input1) + val node2 = l.inputs(input2) + Model(Array(input1, input2), Array(node1, node2)) + } + assert(thrown.getMessage().contains("multiple times")) + } + + "KGraph" should "work with shared input" in { + val input1 = Input(inputShape = Shape(10)) + val l1 = Dense(10, inputShape = Shape(10)) + val l2 = Dense(10, inputShape = Shape(10)) + val node1 = l1.inputs(input1) + val node2 = l2.inputs(input1) + Model(input1, Array(node1, node2)) + } + + "Torch style linear and seq and linear" should "not work with keras Model" in { + intercept[InvalidLayer] { + val input = Input(inputShape = Shape(10)) + val l1 = Linear(10, 3).inputs(input) + val seq = TSequential[Float]().inputs(l1) + val l2 = Linear(3, 4).inputs(seq) + Model(input, l2) + } + } + + "Torch style inputs in Model constructor" should "not work" in { + intercept[InvalidLayer] { + val tinput = TInput() + val l1 = Linear(10, 3).inputs(tinput) + Model(tinput, l1) + } + } + "TSequential" should "not works with container containing Dense" in { val seq = TSequential[Float]() - intercept[RuntimeException] { + intercept[InvalidLayer] { val parallelTable = ParallelTable[Float]() val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") parallelTable.add(d1) @@ -113,7 +201,7 @@ class KerasStyleSpec extends BigDLSpecHelper { } "TSequential" should "not work with container with dense" in { - intercept[RuntimeException] { + intercept[InvalidLayer] { val seq = TSequential[Float]() val seq2 = TSequential[Float]() val d1 = Dense[Float](20, inputShape = Shape(10)).setName("dense1") @@ -150,4 +238,38 @@ class KerasStyleSpec extends BigDLSpecHelper { val inputData = Tensor[Float](Array(20, 10)).rand() val output = reloadedModel.forward(inputData) } + + "multiple outputs with index" should "be test" in { + val input1 = Input[Float](inputShape = Shape(10)) + val input2 = Input[Float](inputShape = Shape(10)) + val d1 = Dense[Float](20).setName("dense1").inputs(input1) + val d2 = Dense[Float](5).setName("dense2").inputs(input2) + val multiOutputGraph = Model[Float](Array(input1, input2), Array(d1, d2)) + + val input3 = Input[Float](inputShape = Shape(10)) + val input4 = Input[Float](inputShape = Shape(10)) + val multiOutput = multiOutputGraph.inputs(Array(input3, input4)) + + val relu1 = Activation[Float]("relu").inputs(multiOutput(1)) + val model = Model[Float](Array(input3, input4), relu1) + model.forward(T(Tensor[Float](Array(2, 10)).rand(), Tensor[Float](Array(2, 10)).rand())) + assert(model.getOutputShape().toSingle().sameElements(Array(-1, 20))) + } + + "Empty inputs is not allow" should "be test" in { + val thrown = intercept[Exception] { + val d1 = Dense[Float](20).setName("dense1").inputs() + } + assert(thrown.getMessage().contains("Empty input is not allow")) + } + + "InputLayer" should "be test" in { + val inputLayer = InputLayer(inputShape = Shape(2, 3)) + val seq = KSequential[Float]() + seq.add(inputLayer) + val inputData = Tensor[Float](Array(2, 2, 3)).rand() + val output = seq.forward(inputData) + seq.forward(output) + TestUtils.compareOutputShape(seq, Shape(2, 3)) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala index dee6276ec42..46f128024bd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LinearSpec.scala @@ -410,11 +410,6 @@ class LinearSpec extends FlatSpec with Matchers { linear.weight should be (exceptedWeight) linear.bias should be (exceptedBias) } - - "Linear computeOutputShape" should "work properly" in { - val linear = Linear[Float](3, 5) - TestUtils.compareOutputShape(linear, Shape(3)) should be (true) - } } class LinearSerialTest extends ModuleSerializationTest { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala index 9baeceaaf80..cab81289c7c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/TestUtils.scala @@ -18,7 +18,8 @@ package com.intel.analytics.bigdl.utils import java.util.concurrent.atomic.AtomicInteger -import com.intel.analytics.bigdl.nn.keras.{InputLayer, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.Sequential +import com.intel.analytics.bigdl.nn.keras.{InputLayer, KerasLayer, Sequential => KSequential} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -32,13 +33,19 @@ object TestUtils { * Compare the output of `computeOutputShape` with the `forward` result */ def compareOutputShape(layer: AbstractModule[Activity, Activity, Float], - inputShape: Shape): Boolean = { - val inputData = Tensor[Float](Array(2) ++ inputShape.toSingle()).randn() - val seq = KSequential[Float]() - seq.add(InputLayer[Float](inputShape = inputShape)) - seq.add(layer) - val calcOutputShape = seq.getOutputShape().toSingle() - val forwardOutputShape = seq.forward(inputData).toTensor[Float].size() + inputShapeWithoutBatch: Shape): Boolean = { + val inputData = Tensor[Float](Array(2) ++ inputShapeWithoutBatch.toSingle()).randn() + val runnableLayer = layer match { + case k: KerasLayer[_, _, _] => + if (!k.isBuilt()) { + k.build(KerasLayer.addBatch(inputShapeWithoutBatch)) + } + k + case a: AbstractModule[_, _, _] => a + } + val calcOutputShape = runnableLayer.computeOutputShape( + KerasLayer.addBatch(inputShapeWithoutBatch)).toSingle() + val forwardOutputShape = runnableLayer.forward(inputData).toTensor[Float].size() calcOutputShape.slice(1, calcOutputShape.length).sameElements( forwardOutputShape.slice(1, forwardOutputShape.length)) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala index 07212ac35b7..11231e84515 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala @@ -15,6 +15,8 @@ */ package com.intel.analytics.bigdl.utils.serializer +import com.intel.analytics.bigdl.nn.{Linear, ReLU} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.tensor._ @@ -31,8 +33,19 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { super.getExpected().filter(_.contains(getPackage())) } - "Input serializer" should "work properly" in { + override def addExcludedClass(): Unit = { + excludedClass.add("com.intel.analytics.bigdl.nn.keras.Input") + } + "IdentityShapeWrapper serializer" should "work properly" in { + val layer = new KerasIdentityWrapper(ReLU[Float]()) + layer.build(Shape(20)) + val inputData = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(layer.asInstanceOf[AbstractModule[_, _, Float]], inputData) + } + + "InputLayer serializer" should "work properly" in { val input = InputLayer[Float](inputShape = Shape(20)) + input.build(Shape(2, 20)) val inputData = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) runSerializationTest(input, inputData) } @@ -48,6 +61,9 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { val dense = Dense[Float](10, inputShape = Shape(20)) val kseq = KSequential[Float]() kseq.add(dense) + val kseq2 = KSequential[Float]() + kseq2.add(Dense[Float](10, inputShape = Shape(10))) + kseq.add(kseq2) val input = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) runSerializationTest(kseq, input) } @@ -373,7 +389,7 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { } "Deconvolution2D serializer" should "work properly" in { - val layer = Deconvolution2D[Float](3, 3, 3, inputShape = Shape(3, 24, 24)) + val layer = Deconvolution2D[Float](3, 3, 3, inputShape = Shape(12, 24, 24)) layer.build(Shape(2, 12, 24, 24)) val input = Tensor[Float](2, 12, 24, 24).apply1(_ => Random.nextFloat()) runSerializationTest(layer, input) @@ -486,4 +502,11 @@ class KerasModuleSerializerSpec extends SerializerSpecHelper { runSerializationTest(layer, input) } + "KerasLayerWrapper serializer" should "work properly" in { + val layer = new KerasLayerWrapper[Float](ReLU[Float](), inputShape = Shape(8, 12)) + layer.build(Shape(3, 8, 12)) + val input = Tensor[Float](3, 8, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } + } From 0b57f1f3deb58fc0e4f8079b17268b7f1df136e1 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 13 Mar 2018 15:19:35 +0800 Subject: [PATCH 0729/1065] [Fix]add race condtion fix in spark 1.6 (#2363) * apply the fix to loadModule and serializeModule instead of individual implementation * add unit test * remove unnecessary test --- .../utils/serializer/ModuleSerializable.scala | 97 ++-- .../utils/serializer/ModuleSerializer.scala | 4 +- .../serializer/converters/DataConverter.scala | 435 +++++++++--------- 3 files changed, 273 insertions(+), 263 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 432438c5d63..dafc5ffcdd7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -47,6 +47,8 @@ trait ModuleSerializable extends Loadable with Savable{ protected var _copyWeightAndBias = true + protected def getLock: Object = ModuleSerializer._lock + // Separate this two methods for reuse in sub-classes protected def checkVersion[T: ClassTag](module : BigDLModule) (implicit ev: TensorNumeric[T]) : Unit = { @@ -88,9 +90,11 @@ trait ModuleSerializable extends Loadable with Savable{ val module = if (storages.contains(moduleId)) { storages.get(moduleId).get.asInstanceOf[AbstractModule[Activity, Activity, T]] } else { - val loadedModule = doLoadModule(context) - storages(moduleId) = loadedModule - loadedModule + getLock.synchronized { + val loadedModule = doLoadModule(context) + storages(moduleId) = loadedModule + loadedModule + } } // step3 : copy params (weight & bias) and linkage createBigDLModule(context, module) @@ -116,29 +120,27 @@ trait ModuleSerializable extends Loadable with Savable{ val constructorFullParams = constructorMirror.symbol.paramss val args = new Array[Object](constructorFullParams.map(_.size).sum) var i = 0 - lock.synchronized { - constructorFullParams.foreach(map => { - map.foreach(param => { - val name = param.name.decodedName.toString - val ptype = param.typeSignature - if (ptype <:< universe.typeOf[ClassTag[_]]|| - ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { - require(tagIter.hasNext, "If your module contains multiple class tags, " + - "do you forget to override getClassTagNumerics method") - args(i) = tagIter.next - } else if (ptype <:< universe.typeOf[TensorNumeric[_]] - || ptype.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { - args(i) = numericIter.next - } else { - require(modelAttributes.containsKey(name), s"$name value cannot be found") - val attribute = modelAttributes.get(name) - val value = DataConverter.getAttributeValue(context, attribute) - args(i) = value - } - i += 1 - }) + constructorFullParams.foreach(map => { + map.foreach(param => { + val name = param.name.decodedName.toString + val ptype = param.typeSignature + if (ptype <:< universe.typeOf[ClassTag[_]]|| + ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { + require(tagIter.hasNext, "If your module contains multiple class tags, " + + "do you forget to override getClassTagNumerics method") + args(i) = tagIter.next + } else if (ptype <:< universe.typeOf[TensorNumeric[_]] + || ptype.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { + args(i) = numericIter.next + } else { + require(modelAttributes.containsKey(name), s"$name value cannot be found") + val attribute = modelAttributes.get(name) + val value = DataConverter.getAttributeValue(context, attribute) + args(i) = value + } + i += 1 }) - } + }) constructorMirror.apply(args : _*). asInstanceOf[AbstractModule[Activity, Activity, T]] } @@ -172,11 +174,12 @@ trait ModuleSerializable extends Loadable with Savable{ // step 2: set module type bigDLModelBuilder.setModuleType(cls.getName) - // step 3 : set data types (ClassTag and TensorNumric) - setDataTypes(context, bigDLModelBuilder) - - // step 4 : apply module specific logic to create module - doSerializeModule(context, bigDLModelBuilder) + getLock.synchronized { + // step 3 : set data types (ClassTag and TensorNumric) + setDataTypes(context, bigDLModelBuilder) + // step 4 : apply module specific logic to create module + doSerializeModule(context, bigDLModelBuilder) + } // step 5 : copy params (weight & bias) a and linkage createSerializeBigDLModule(bigDLModelBuilder, context) @@ -204,26 +207,24 @@ trait ModuleSerializable extends Loadable with Savable{ val cls = module.getClass val fullParams = getCostructorMirror(cls).symbol.paramss val constructorParams = fullParams(0) - lock.synchronized { - constructorParams.foreach(param => { - val paramName = param.name.decodedName.toString - var ptype = param.typeSignature - val attrBuilder = AttrValue.newBuilder - // For some modules, fields are declared inside but passed to Super directly - var field : Field = null - try { - field = cls.getDeclaredField(paramName) - } catch { - case e : NoSuchFieldException => - field = cls.getSuperclass.getDeclaredField(paramName) - } - field.setAccessible(true) - val fieldValue = field.get(module) - DataConverter.setAttributeValue(context, attrBuilder, fieldValue, ptype) + constructorParams.foreach(param => { + val paramName = param.name.decodedName.toString + var ptype = param.typeSignature + val attrBuilder = AttrValue.newBuilder + // For some modules, fields are declared inside but passed to Super directly + var field : Field = null + try { + field = cls.getDeclaredField(paramName) + } catch { + case e : NoSuchFieldException => + field = cls.getSuperclass.getDeclaredField(paramName) + } + field.setAccessible(true) + val fieldValue = field.get(module) + DataConverter.setAttributeValue(context, attrBuilder, fieldValue, ptype) - bigDLModelBuilder.putAttr(paramName, attrBuilder.build) + bigDLModelBuilder.putAttr(paramName, attrBuilder.build) }) - } } protected def createBigDLModule[T: ClassTag](context: DeserializeContext, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index fcdc2ed8631..2e574f81ead 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -36,6 +36,8 @@ object ModuleSerializer extends ModuleSerializable{ private val serializerMaps = new mutable.HashMap[String, ModuleSerializable]() + private[serializer] val _lock = new Object + // generic type definition for type matching var tensorNumericType : universe.Type = null @@ -126,7 +128,7 @@ object ModuleSerializer extends ModuleSerializable{ private[serializer] def getCostructorMirror[T : ClassTag](cls : Class[_]): universe.MethodMirror = { - lock.synchronized { + getLock.synchronized { val clsSymbol = runtimeMirror.classSymbol(cls) val cm = runtimeMirror.reflectClass(clsSymbol) // to make it compatible with both 2.11 and 2.10 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala index f800d938edd..edeb4b85a54 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala @@ -61,6 +61,8 @@ trait DataConverter { attributeBuilder : AttrValue.Builder, value: Any, valueType: universe.Type = null) (implicit ev: TensorNumeric[T]) : Unit + + protected def getLock: Object = ModuleSerializer._lock } /** @@ -81,25 +83,27 @@ object DataConverter extends DataConverter{ private def getRuntimeType[T : ClassTag](value : Any) (implicit ev: TensorNumeric[T]) : universe.Type = { - if (value.isInstanceOf[Tensor[_]]) { - ModuleSerializer.tensorType - } else if (value.isInstanceOf[AbstractModule[_, _, _]]) { - ModuleSerializer.abstractModuleType - } else if (value.isInstanceOf[Regularizer[_]]) { - ModuleSerializer.regularizerType - } else if (value.isInstanceOf[InitializationMethod]) { - universe.typeOf[InitializationMethod] - } else if (value.isInstanceOf[VariableFormat]) { - universe.typeOf[VariableFormat] - } else if (value.isInstanceOf[DataFormat]) { - universe.typeOf[DataFormat] - } else if (value.isInstanceOf[BigDLShape]) { - universe.typeOf[BigDLShape] - } else { - val cls = value.getClass - val runtimeMirror = universe.runtimeMirror(getClass.getClassLoader) - val clsSymbol = runtimeMirror.classSymbol(cls) - clsSymbol.toType + getLock.synchronized { + if (value.isInstanceOf[Tensor[_]]) { + ModuleSerializer.tensorType + } else if (value.isInstanceOf[AbstractModule[_, _, _]]) { + ModuleSerializer.abstractModuleType + } else if (value.isInstanceOf[Regularizer[_]]) { + ModuleSerializer.regularizerType + } else if (value.isInstanceOf[InitializationMethod]) { + universe.typeOf[InitializationMethod] + } else if (value.isInstanceOf[VariableFormat]) { + universe.typeOf[VariableFormat] + } else if (value.isInstanceOf[DataFormat]) { + universe.typeOf[DataFormat] + } else if (value.isInstanceOf[BigDLShape]) { + universe.typeOf[BigDLShape] + } else { + val cls = value.getClass + val runtimeMirror = universe.runtimeMirror(getClass.getClassLoader) + val clsSymbol = runtimeMirror.classSymbol(cls) + clsSymbol.toType + } } } @@ -132,65 +136,67 @@ object DataConverter extends DataConverter{ context: SerializeContext[T], attributeBuilder: AttrValue.Builder, value: Any, valueType : universe.Type = typePlaceHolder) (implicit ev: TensorNumeric[T]): Unit = { - // to make it compatible with Java types - if (valueType =:= universe.typeOf[Int] || - valueType =:= universe.typeOf[java.lang.Integer]) { - attributeBuilder.setDataType(DataType.INT32) - attributeBuilder.setInt32Value(value.asInstanceOf[Int]) - } else if (valueType =:= universe.typeOf[Long] || - valueType =:= universe.typeOf[java.lang.Long]) { - attributeBuilder.setDataType(DataType.INT64) - attributeBuilder.setInt64Value(value.asInstanceOf[Long]) - } else if (valueType =:= universe.typeOf[Float] || - valueType =:= universe.typeOf[java.lang.Float]) { - attributeBuilder.setDataType(DataType.FLOAT) - attributeBuilder.setFloatValue(value.asInstanceOf[Float]) - } else if (valueType =:= universe.typeOf[Double] || - valueType =:= universe.typeOf[java.lang.Double]) { - attributeBuilder.setDataType(DataType.DOUBLE) - attributeBuilder.setDoubleValue(value.asInstanceOf[Double]) - } else if (valueType =:= universe.typeOf[String] || - valueType =:= universe.typeOf[java.lang.String]) { - attributeBuilder.setDataType(DataType.STRING) - attributeBuilder.setStringValue(value.asInstanceOf[String]) - } else if (valueType =:= universe.typeOf[Boolean] || - valueType =:= universe.typeOf[java.lang.Boolean]) { - attributeBuilder.setDataType(DataType.BOOL ) - attributeBuilder.setBoolValue(value.asInstanceOf[Boolean]) - } else if (valueType =:= universe.typeOf[VariableFormat]) { - VariableFormatConverter.setAttributeValue(context, attributeBuilder, value) - } else if (valueType =:= universe.typeOf[InitializationMethod]) { - InitMethodConverter.setAttributeValue(context, attributeBuilder, value) - } else if (valueType.toString == ModuleSerializer.regularizerType.toString) { - RegularizerConverter.setAttributeValue(context, attributeBuilder, value) - } else if (valueType <:< universe.typeOf[Tensor[_]]) { - TensorConverter.setAttributeValue(context, attributeBuilder, value) - } else if (valueType.toString == ModuleSerializer.tType.toString) { - if (ev == com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble) { + getLock.synchronized { + // to make it compatible with Java types + if (valueType =:= universe.typeOf[Int] || + valueType =:= universe.typeOf[java.lang.Integer]) { + attributeBuilder.setDataType(DataType.INT32) + attributeBuilder.setInt32Value(value.asInstanceOf[Int]) + } else if (valueType =:= universe.typeOf[Long] || + valueType =:= universe.typeOf[java.lang.Long]) { + attributeBuilder.setDataType(DataType.INT64) + attributeBuilder.setInt64Value(value.asInstanceOf[Long]) + } else if (valueType =:= universe.typeOf[Float] || + valueType =:= universe.typeOf[java.lang.Float]) { + attributeBuilder.setDataType(DataType.FLOAT) + attributeBuilder.setFloatValue(value.asInstanceOf[Float]) + } else if (valueType =:= universe.typeOf[Double] || + valueType =:= universe.typeOf[java.lang.Double]) { attributeBuilder.setDataType(DataType.DOUBLE) attributeBuilder.setDoubleValue(value.asInstanceOf[Double]) + } else if (valueType =:= universe.typeOf[String] || + valueType =:= universe.typeOf[java.lang.String]) { + attributeBuilder.setDataType(DataType.STRING) + attributeBuilder.setStringValue(value.asInstanceOf[String]) + } else if (valueType =:= universe.typeOf[Boolean] || + valueType =:= universe.typeOf[java.lang.Boolean]) { + attributeBuilder.setDataType(DataType.BOOL) + attributeBuilder.setBoolValue(value.asInstanceOf[Boolean]) + } else if (valueType =:= universe.typeOf[VariableFormat]) { + VariableFormatConverter.setAttributeValue(context, attributeBuilder, value) + } else if (valueType =:= universe.typeOf[InitializationMethod]) { + InitMethodConverter.setAttributeValue(context, attributeBuilder, value) + } else if (valueType.toString == ModuleSerializer.regularizerType.toString) { + RegularizerConverter.setAttributeValue(context, attributeBuilder, value) + } else if (valueType <:< universe.typeOf[Tensor[_]]) { + TensorConverter.setAttributeValue(context, attributeBuilder, value) + } else if (valueType.toString == ModuleSerializer.tType.toString) { + if (ev == com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericDouble) { + attributeBuilder.setDataType(DataType.DOUBLE) + attributeBuilder.setDoubleValue(value.asInstanceOf[Double]) + } else { + attributeBuilder.setDataType(DataType.FLOAT) + attributeBuilder.setFloatValue(value.asInstanceOf[Float]) + } + } else if (valueType.toString == ModuleSerializer.abstractModuleType.toString + || valueType.toString == ModuleSerializer.tensorModuleType.toString + || valueType.toString == ModuleSerializer.moduleType.toString + || valueType.toString == ModuleSerializer.boundedModuleType.toString + || valueType <:< universe.typeOf[AbstractModule[_, _, _]] + ) { + ModuleConverter.setAttributeValue(context, attributeBuilder, value) + } else if (value.isInstanceOf[mutable.Map[_, _]]) { + NameListConverter.setAttributeValue(context, attributeBuilder, value) + } else if (valueType <:< universe.typeOf[Array[_]] || + valueType.typeSymbol == universe.typeOf[Array[_]].typeSymbol) { + ArrayConverter.setAttributeValue(context, attributeBuilder, value, valueType) + } else if (valueType =:= universe.typeOf[DataFormat]) { + DataFormatConverter.setAttributeValue(context, attributeBuilder, value) + } else if (valueType =:= universe.typeOf[BigDLShape]) { + ShapeConverter.setAttributeValue(context, attributeBuilder, value) } else { - attributeBuilder.setDataType(DataType.FLOAT) - attributeBuilder.setFloatValue(value.asInstanceOf[Float]) + CustomConverterDelegator.setAttributeValue(context, attributeBuilder, value, valueType) } - } else if (valueType.toString == ModuleSerializer.abstractModuleType.toString - || valueType.toString == ModuleSerializer.tensorModuleType.toString - || valueType.toString == ModuleSerializer.moduleType.toString - || valueType.toString == ModuleSerializer.boundedModuleType.toString - || valueType <:< universe.typeOf[AbstractModule[_, _, _]] - ) { - ModuleConverter.setAttributeValue(context, attributeBuilder, value) - } else if (value.isInstanceOf[mutable.Map[_, _]]) { - NameListConverter.setAttributeValue(context, attributeBuilder, value) - } else if (valueType <:< universe.typeOf[Array[_]] || - valueType.typeSymbol == universe.typeOf[Array[_]].typeSymbol) { - ArrayConverter.setAttributeValue(context, attributeBuilder, value, valueType) - } else if (valueType =:= universe.typeOf[DataFormat]) { - DataFormatConverter.setAttributeValue(context, attributeBuilder, value) - } else if (valueType =:= universe.typeOf[BigDLShape]) { - ShapeConverter.setAttributeValue(context, attributeBuilder, value) - } else { - CustomConverterDelegator.setAttributeValue(context, attributeBuilder, value, valueType) } } @@ -399,151 +405,152 @@ object DataConverter extends DataConverter{ attributeBuilder: AttrValue.Builder, value: Any, valueType: universe.Type = null)(implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.ARRAY_VALUE) - val arrayBuilder = ArrayValue.newBuilder - arrayBuilder.setSize(-1) - if (valueType =:= universe.typeOf[Array[Int]]) { - arrayBuilder.setDatatype(DataType.INT32) - if (value != null) { - val int32s = value.asInstanceOf[Array[Int]] - int32s.foreach(i32 => arrayBuilder.addI32(i32)) - arrayBuilder.setSize(int32s.size) - } - } else if (valueType =:= universe.typeOf[Array[Long]]) { - arrayBuilder.setDatatype(DataType.INT64) - if (value != null) { - val int64s = value.asInstanceOf[Array[Long]] - int64s.foreach(i64 => arrayBuilder.addI64(i64)) - arrayBuilder.setSize(int64s.size) - } - } else if (valueType =:= universe.typeOf[Array[Float]]) { - arrayBuilder.setDatatype(DataType.FLOAT) - if (value != null) { - val flts = value.asInstanceOf[Array[Float]] - flts.foreach(flt => arrayBuilder.addFlt(flt)) - arrayBuilder.setSize(flts.size) - } - } else if (valueType =:= universe.typeOf[Array[Double]]) { - arrayBuilder.setDatatype(DataType.DOUBLE) - if (value != null) { - val dbs = value.asInstanceOf[Array[Double]] - dbs.foreach(dbl => arrayBuilder.addDbl(dbl)) - arrayBuilder.setSize(dbs.size) - } - } else if (valueType =:= universe.typeOf[Array[Boolean]]) { - arrayBuilder.setDatatype(DataType.BOOL) - if (value != null) { - val bls = value.asInstanceOf[Array[Boolean]] - bls.foreach(bl => arrayBuilder.addBoolean(bl)) - arrayBuilder.setSize(bls.size) - } - } else if (valueType =:= universe.typeOf[Array[String]]) { - arrayBuilder.setDatatype(DataType.STRING) - if (value != null) { - val strs = value.asInstanceOf[Array[String]] - strs.foreach(str => arrayBuilder.addStr(str)) - arrayBuilder.setSize(strs.size) - } - } else if (valueType <:< universe.typeOf[Array[_ <: Regularizer[_ <: Any]]]) { - arrayBuilder.setDatatype(DataType.REGULARIZER) - if (value != null) { - val regularizers = value.asInstanceOf[Array[Regularizer[T]]] - regularizers.foreach(reg => { - val attrValueBuilder = AttrValue.newBuilder - RegularizerConverter.setAttributeValue(context, attrValueBuilder, reg) - arrayBuilder.addRegularizer(attrValueBuilder.getRegularizerValue) - }) - arrayBuilder.setSize(regularizers.size) - } - } else if (valueType <:< universe. - typeOf[Array[_ <: Tensor[_ <: Any]]]) { - arrayBuilder.setDatatype(DataType.TENSOR) - if (value != null) { - val tensors = value.asInstanceOf[Array[Tensor[T]]] - tensors.foreach(tensor => { - val attrValueBuilder = AttrValue.newBuilder - TensorConverter.setAttributeValue(context, attrValueBuilder, tensor) - arrayBuilder.addTensor(attrValueBuilder.getTensorValue) - }) - arrayBuilder.setSize(tensors.size) - } - } else if (valueType =:= universe.typeOf[Array[VariableFormat]]) { - arrayBuilder.setDatatype(DataType.VARIABLE_FORMAT) - if (value != null) { - val formats = value.asInstanceOf[Array[VariableFormat]] - formats.foreach(format => { - val attrValueBuilder = AttrValue.newBuilder - VariableFormatConverter.setAttributeValue(context, attrValueBuilder, format) - arrayBuilder.addVariableFormat(attrValueBuilder.getVariableFormatValue) - }) - arrayBuilder.setSize(formats.size) - } - } else if (valueType =:= universe.typeOf[Array[InitializationMethod]]) { - arrayBuilder.setDatatype(DataType.INITMETHOD) - if (value != null) { - val methods = value.asInstanceOf[Array[InitializationMethod]] - methods.foreach(method => { - val attrValueBuilder = AttrValue.newBuilder - InitMethodConverter.setAttributeValue(context, attrValueBuilder, method) - arrayBuilder.addInitMethod(attrValueBuilder.getInitMethodValue) - }) - arrayBuilder.setSize(methods.size) - } - } else if (valueType <:< universe. - typeOf[Array[_ <: AbstractModule[_ <: Activity, _ <: Activity, _ <: Any]]]) { - arrayBuilder.setDatatype(DataType.MODULE) - if (value != null) { - val modules = value.asInstanceOf[Array[_ <: AbstractModule[Activity, Activity, T]]] - modules.foreach(module => { - val attrValueBuilder = AttrValue.newBuilder - ModuleConverter.setAttributeValue(context, attrValueBuilder, module) - arrayBuilder.addBigDLModule(attrValueBuilder.getBigDLModuleValue) - }) - arrayBuilder.setSize(modules.size) - } - } else if (value.isInstanceOf[Array[Map[_, _]]]) { - arrayBuilder.setDatatype(DataType.NAME_ATTR_LIST) - value.asInstanceOf[Array[Map[String, Any]]].foreach(map => { - val attrValueBuilder = AttrValue.newBuilder - NameListConverter.setAttributeValue(context, attrValueBuilder, map) - arrayBuilder.addNameAttrList(attrValueBuilder.getNameAttrListValue) - }) - } else if (valueType =:= universe.typeOf[Array[DataFormat]]) { - arrayBuilder.setDatatype(DataType.DATA_FORMAT) - if (value != null) { - val formats = value.asInstanceOf[Array[DataFormat]] - formats.foreach(format => { - val attrValueBuilder = AttrValue.newBuilder - DataFormatConverter.setAttributeValue(context, attrValueBuilder, format) - arrayBuilder.addDataFormat(attrValueBuilder.getDataFormatValue) - }) - arrayBuilder.setSize(formats.size) - } - } else if (valueType =:= universe.typeOf[Array[BigDLShape]]) { - arrayBuilder.setDatatype(DataType.SHAPE) - if (value != null) { - val shapes = value.asInstanceOf[Array[BigDLShape]] - shapes.foreach(shape => { - val attrValueBuilder = AttrValue.newBuilder - ShapeConverter.setAttributeValue(context, attrValueBuilder, shape) - arrayBuilder.addShape(attrValueBuilder.getShape) - }) - arrayBuilder.setSize(shapes.size) - } - } else { - arrayBuilder.setDatatype(DataType.CUSTOM) - if (value != null) { - val customValues = value.asInstanceOf[Array[Any]] - customValues.foreach(custom => { + getLock.synchronized { + val arrayBuilder = ArrayValue.newBuilder + arrayBuilder.setSize(-1) + if (valueType =:= universe.typeOf[Array[Int]]) { + arrayBuilder.setDatatype(DataType.INT32) + if (value != null) { + val int32s = value.asInstanceOf[Array[Int]] + int32s.foreach(i32 => arrayBuilder.addI32(i32)) + arrayBuilder.setSize(int32s.size) + } + } else if (valueType =:= universe.typeOf[Array[Long]]) { + arrayBuilder.setDatatype(DataType.INT64) + if (value != null) { + val int64s = value.asInstanceOf[Array[Long]] + int64s.foreach(i64 => arrayBuilder.addI64(i64)) + arrayBuilder.setSize(int64s.size) + } + } else if (valueType =:= universe.typeOf[Array[Float]]) { + arrayBuilder.setDatatype(DataType.FLOAT) + if (value != null) { + val flts = value.asInstanceOf[Array[Float]] + flts.foreach(flt => arrayBuilder.addFlt(flt)) + arrayBuilder.setSize(flts.size) + } + } else if (valueType =:= universe.typeOf[Array[Double]]) { + arrayBuilder.setDatatype(DataType.DOUBLE) + if (value != null) { + val dbs = value.asInstanceOf[Array[Double]] + dbs.foreach(dbl => arrayBuilder.addDbl(dbl)) + arrayBuilder.setSize(dbs.size) + } + } else if (valueType =:= universe.typeOf[Array[Boolean]]) { + arrayBuilder.setDatatype(DataType.BOOL) + if (value != null) { + val bls = value.asInstanceOf[Array[Boolean]] + bls.foreach(bl => arrayBuilder.addBoolean(bl)) + arrayBuilder.setSize(bls.size) + } + } else if (valueType =:= universe.typeOf[Array[String]]) { + arrayBuilder.setDatatype(DataType.STRING) + if (value != null) { + val strs = value.asInstanceOf[Array[String]] + strs.foreach(str => arrayBuilder.addStr(str)) + arrayBuilder.setSize(strs.size) + } + } else if (valueType <:< universe.typeOf[Array[_ <: Regularizer[_ <: Any]]]) { + arrayBuilder.setDatatype(DataType.REGULARIZER) + if (value != null) { + val regularizers = value.asInstanceOf[Array[Regularizer[T]]] + regularizers.foreach(reg => { + val attrValueBuilder = AttrValue.newBuilder + RegularizerConverter.setAttributeValue(context, attrValueBuilder, reg) + arrayBuilder.addRegularizer(attrValueBuilder.getRegularizerValue) + }) + arrayBuilder.setSize(regularizers.size) + } + } else if (valueType <:< universe. + typeOf[Array[_ <: Tensor[_ <: Any]]]) { + arrayBuilder.setDatatype(DataType.TENSOR) + if (value != null) { + val tensors = value.asInstanceOf[Array[Tensor[T]]] + tensors.foreach(tensor => { + val attrValueBuilder = AttrValue.newBuilder + TensorConverter.setAttributeValue(context, attrValueBuilder, tensor) + arrayBuilder.addTensor(attrValueBuilder.getTensorValue) + }) + arrayBuilder.setSize(tensors.size) + } + } else if (valueType =:= universe.typeOf[Array[VariableFormat]]) { + arrayBuilder.setDatatype(DataType.VARIABLE_FORMAT) + if (value != null) { + val formats = value.asInstanceOf[Array[VariableFormat]] + formats.foreach(format => { + val attrValueBuilder = AttrValue.newBuilder + VariableFormatConverter.setAttributeValue(context, attrValueBuilder, format) + arrayBuilder.addVariableFormat(attrValueBuilder.getVariableFormatValue) + }) + arrayBuilder.setSize(formats.size) + } + } else if (valueType =:= universe.typeOf[Array[InitializationMethod]]) { + arrayBuilder.setDatatype(DataType.INITMETHOD) + if (value != null) { + val methods = value.asInstanceOf[Array[InitializationMethod]] + methods.foreach(method => { + val attrValueBuilder = AttrValue.newBuilder + InitMethodConverter.setAttributeValue(context, attrValueBuilder, method) + arrayBuilder.addInitMethod(attrValueBuilder.getInitMethodValue) + }) + arrayBuilder.setSize(methods.size) + } + } else if (valueType <:< universe. + typeOf[Array[_ <: AbstractModule[_ <: Activity, _ <: Activity, _ <: Any]]]) { + arrayBuilder.setDatatype(DataType.MODULE) + if (value != null) { + val modules = value.asInstanceOf[Array[_ <: AbstractModule[Activity, Activity, T]]] + modules.foreach(module => { + val attrValueBuilder = AttrValue.newBuilder + ModuleConverter.setAttributeValue(context, attrValueBuilder, module) + arrayBuilder.addBigDLModule(attrValueBuilder.getBigDLModuleValue) + }) + arrayBuilder.setSize(modules.size) + } + } else if (value.isInstanceOf[Array[Map[_, _]]]) { + arrayBuilder.setDatatype(DataType.NAME_ATTR_LIST) + value.asInstanceOf[Array[Map[String, Any]]].foreach(map => { val attrValueBuilder = AttrValue.newBuilder - CustomConverterDelegator.setAttributeValue(context, attrValueBuilder, custom) - arrayBuilder.addCustom(attrValueBuilder.getCustomValue) + NameListConverter.setAttributeValue(context, attrValueBuilder, map) + arrayBuilder.addNameAttrList(attrValueBuilder.getNameAttrListValue) }) - arrayBuilder.setSize(customValues.size) + } else if (valueType =:= universe.typeOf[Array[DataFormat]]) { + arrayBuilder.setDatatype(DataType.DATA_FORMAT) + if (value != null) { + val formats = value.asInstanceOf[Array[DataFormat]] + formats.foreach(format => { + val attrValueBuilder = AttrValue.newBuilder + DataFormatConverter.setAttributeValue(context, attrValueBuilder, format) + arrayBuilder.addDataFormat(attrValueBuilder.getDataFormatValue) + }) + arrayBuilder.setSize(formats.size) + } + } else if (valueType =:= universe.typeOf[Array[BigDLShape]]) { + arrayBuilder.setDatatype(DataType.SHAPE) + if (value != null) { + val shapes = value.asInstanceOf[Array[BigDLShape]] + shapes.foreach(shape => { + val attrValueBuilder = AttrValue.newBuilder + ShapeConverter.setAttributeValue(context, attrValueBuilder, shape) + arrayBuilder.addShape(attrValueBuilder.getShape) + }) + arrayBuilder.setSize(shapes.size) + } + } else { + arrayBuilder.setDatatype(DataType.CUSTOM) + if (value != null) { + val customValues = value.asInstanceOf[Array[Any]] + customValues.foreach(custom => { + val attrValueBuilder = AttrValue.newBuilder + CustomConverterDelegator.setAttributeValue(context, attrValueBuilder, custom) + arrayBuilder.addCustom(attrValueBuilder.getCustomValue) + }) + arrayBuilder.setSize(customValues.size) + } } + attributeBuilder.setArrayValue(arrayBuilder.build) } - attributeBuilder.setArrayValue(arrayBuilder.build) } - } /** * DataConvert for custom value From 04c271c7607cf5fa78cd521fe2c4bb365aafed8a Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Tue, 13 Mar 2018 14:07:45 -0700 Subject: [PATCH 0730/1065] DataFrame-based image reader and transformer (#2325) * DataFrame image support * comment update * sql adapter * rename ut * remove a ut * improve python ut * bypass 1.5 * add ut * import existential * clone transformer --- .../bigdl/dlframes/DLImageReader.scala | 144 ++++++++++++++++++ .../bigdl/dlframes/DLImageTransformer.scala | 96 ++++++++++++ .../bigdl/dlframes/SharedParamsAdapter.scala | 9 +- .../org/apache/spark/sql/SqlAdapter.scala | 26 ++++ .../org/apache/spark/sql/SqlAdapter.scala | 27 ++++ .../dllib/utils/python/api/PythonBigDL.scala | 15 +- .../dllib/dlframes/DLImageReaderSpec.scala | 118 ++++++++++++++ .../dlframes/DLImageTransformerSpec.scala | 138 +++++++++++++++++ 8 files changed, 571 insertions(+), 2 deletions(-) create mode 100644 dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLImageReader.scala create mode 100644 dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLImageTransformer.scala create mode 100644 scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/sql/SqlAdapter.scala create mode 100644 scala/common/spark-version/2.0/src/main/scala/org/apache/spark/sql/SqlAdapter.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageReaderSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageTransformerSpec.scala diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLImageReader.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLImageReader.scala new file mode 100644 index 00000000000..2df4112b3d7 --- /dev/null +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLImageReader.scala @@ -0,0 +1,144 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.dlframes + +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFeature, ImageFrame} +import org.apache.spark.SparkContext +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.opencv.core.CvType +import scala.language.existentials + +/** + * Definition for image data in a DataFrame + */ +object DLImageSchema { + + /** + * Schema for the image column in a DataFrame. Image data is saved in an array of Bytes. + * The format is compatible with Spark Image format in v2.3 + */ + val byteSchema = StructType( + StructField("origin", StringType, true) :: + StructField("height", IntegerType, false) :: + StructField("width", IntegerType, false) :: + StructField("nChannels", IntegerType, false) :: + // OpenCV-compatible type: CV_8UC3, CV_32FC3 in most cases + StructField("mode", IntegerType, false) :: + // Bytes in OpenCV-compatible order: row-wise BGR in most cases + StructField("data", BinaryType, false) :: Nil) + + /** + * Schema for the image column in a DataFrame. Image data is saved in an array of Floats. + */ + val floatSchema = StructType( + StructField("origin", StringType, true) :: + StructField("height", IntegerType, false) :: + StructField("width", IntegerType, false) :: + StructField("nChannels", IntegerType, false) :: + // OpenCV-compatible type: CV_8UC3, CV_32FC3 in most cases + StructField("mode", IntegerType, false) :: + // floats in OpenCV-compatible order: row-wise BGR in most cases + StructField("data", new ArrayType(FloatType, false), false) :: Nil) + + private[dlframes] def imf2Row(imf: ImageFeature): Row = { + val (mode, data) = if (imf.contains(ImageFeature.imageTensor)) { + val floatData = imf(ImageFeature.imageTensor).asInstanceOf[Tensor[Float]].storage().array() + val cvType = imf.getChannel() match { + case 1 => CvType.CV_32FC1 + case 3 => CvType.CV_32FC3 + case 4 => CvType.CV_32FC4 + case other => throw new IllegalArgumentException(s"Unsupported number of channels:" + + s" $other in ${imf.uri()}. Only 1, 3 and 4 are supported.") + } + (cvType, floatData) + } else if (imf.contains(ImageFeature.bytes)) { + val bytesData = imf.bytes() + val cvType = imf.getChannel() match { + case 1 => CvType.CV_8UC1 + case 3 => CvType.CV_8UC3 + case 4 => CvType.CV_8UC4 + case other => throw new IllegalArgumentException(s"Unsupported number of channels:" + + s" $other in ${imf.uri()}. Only 1, 3 and 4 are supported.") + } + (cvType, bytesData) + } else { + throw new IllegalArgumentException(s"ImageFeature should have imageTensor or bytes.") + } + + Row( + imf.uri(), + imf.getHeight(), + imf.getWidth(), + imf.getChannel(), + mode, + data + ) + } + + private[dlframes] def row2IMF(row: Row): ImageFeature = { + val (origin, h, w, c) = (row.getString(0), row.getInt(1), row.getInt(2), row.getInt(3)) + val imf = ImageFeature() + imf.update(ImageFeature.uri, origin) + imf.update(ImageFeature.size, (h, w, c)) + val storageType = row.getInt(4) + storageType match { + case CvType.CV_8UC3 | CvType.CV_8UC1 | CvType.CV_8UC4 => + imf.update(ImageFeature.bytes, row.getAs[Array[Byte]](5)) + BytesToMat().transform(imf) + case CvType.CV_32FC3 | CvType.CV_32FC1 | CvType.CV_32FC4 => + val data = row.getSeq[Float](5).toArray + val size = Array(h, w, c) + val ten = Tensor(Storage(data)).resize(size) + imf.update(ImageFeature.imageTensor, ten) + case _ => + throw new IllegalArgumentException(s"Unsupported data type in imageColumn: $storageType") + } + imf + } +} + +/** + * Primary DataFrame-based image loading interface, defining API to read images into DataFrame. + */ +object DLImageReader { + + /** + * DataFrame with a single column of images named "image" (nullable) + */ + private val imageColumnSchema = + StructType(StructField("image", DLImageSchema.byteSchema, true) :: Nil) + + /** + * Read the directory of images into DataFrame from the local or remote source. + * + * @param path Directory to the input data files, the path can be comma separated paths as the + * list of inputs. Wildcards path are supported similarly to sc.binaryFiles(path). + * @param sc SparkContext to be used. + * @param minPartitions Number of the DataFrame partitions, + * if omitted uses defaultParallelism instead + * @return DataFrame with a single column "image" of images; + * see DLImageSchema for the details + */ + def readImages(path: String, sc: SparkContext, minPartitions: Int = 1): DataFrame = { + val imageFrame = ImageFrame.read(path, sc, minPartitions) + val rowRDD = imageFrame.toDistributed().rdd.map { imf => + Row(DLImageSchema.imf2Row(imf)) + } + SQLContext.getOrCreate(sc).createDataFrame(rowRDD, imageColumnSchema) + } +} diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLImageTransformer.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLImageTransformer.scala new file mode 100644 index 00000000000..fa728e0eb17 --- /dev/null +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLImageTransformer.scala @@ -0,0 +1,96 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.dlframes + +import com.intel.analytics.bigdl.dataset.Transformer +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature, MatToTensor} +import org.apache.spark.ml.DLTransformerBase +import org.apache.spark.ml.adapter.{HasInputCol, HasOutputCol, SchemaUtils} +import org.apache.spark.ml.util.Identifiable +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, Row} + +/** + * Provides DataFrame-based API for image pre-processing and feature transformation. + * DLImageTransformer follows the Spark Transformer API pattern and can be used as one stage + * in Spark ML pipeline. + * + * The input column can be either DLImageSchema.byteSchema or DLImageSchema.floatSchema. If + * using DLImageReader, the default format is DLImageSchema.byteSchema + * The output column is always DLImageSchema.floatSchema. + * + * @param transformer Single or a sequence of BigDL FeatureTransformers to be used. E.g. + * Resize(256, 256) -> CenterCrop(224, 224) -> + * ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() + */ +class DLImageTransformer ( + val transformer: Transformer[ImageFeature, ImageFeature], + override val uid: String) + extends DLTransformerBase with HasInputCol with HasOutputCol { + + def this(transformer: FeatureTransformer) = + this(transformer, Identifiable.randomUID("DLImageTransformer")) + + setDefault(inputCol -> "image") + def setInputCol(value: String): this.type = set(inputCol, value) + + setDefault(outputCol -> "output") + def setOutputCol(value: String): this.type = set(outputCol, value) + + protected def validateInputType(inputType: DataType): Unit = { + val validTypes = Array(DLImageSchema.floatSchema, DLImageSchema.byteSchema) + + require(validTypes.exists(t => SchemaUtils.sameType(inputType, t)), + s"Bad input type: $inputType. Requires ${validTypes.mkString(", ")}") + } + + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType + validateInputType(inputType) + if (schema.fieldNames.contains($(outputCol))) { + throw new IllegalArgumentException(s"Output column ${$(outputCol)} already exists.") + } + + val outputFields = schema.fields :+ + StructField($(outputCol), DLImageSchema.floatSchema, nullable = false) + StructType(outputFields) + } + + protected override def internalTransform(dataFrame: DataFrame): DataFrame = { + transformSchema(dataFrame.schema, logging = true) + val sc = dataFrame.sqlContext.sparkContext + val localTransformer = this.transformer + val transformerBC = sc.broadcast(localTransformer) + val toTensorBC = sc.broadcast(MatToTensor[Float](shareBuffer = true)) + + val inputColIndex = dataFrame.schema.fieldIndex($(inputCol)) + val resultRDD = dataFrame.rdd.mapPartitions { rowIter => + val localTransformer = transformerBC.value.cloneTransformer() + val toTensorTransformer = toTensorBC.value.cloneTransformer().asInstanceOf[MatToTensor[Float]] + rowIter.map { row => + val imf = DLImageSchema.row2IMF(row.getAs[Row](inputColIndex)) + val output = localTransformer.apply(Iterator(imf)).toArray.head + if (!output.contains(ImageFeature.imageTensor)) { + toTensorTransformer.transform(output) + } + Row.fromSeq(row.toSeq ++ Seq(DLImageSchema.imf2Row(output))) + } + } + + val resultSchema = transformSchema(dataFrame.schema) + dataFrame.sqlContext.createDataFrame(resultRDD, resultSchema) + } +} diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala index 1e3d3043fef..c87fd7b1760 100644 --- a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala @@ -23,10 +23,14 @@ trait HasPredictionCol extends org.apache.spark.ml.param.shared.HasPredictionCol trait HasFeaturesCol extends org.apache.spark.ml.param.shared.HasFeaturesCol +trait HasInputCol extends org.apache.spark.ml.param.shared.HasInputCol + +trait HasOutputCol extends org.apache.spark.ml.param.shared.HasOutputCol + object SchemaUtils { /** - * Appends a new column to the input schema. This fails if the given output column already exists. + * Appends a new column to the input schema. This fails if the given output column already exists * @param schema input schema * @param colName new column name. If this column name is an empty string "", this method returns * the input schema unchanged. This allows users to disable output columns. @@ -40,4 +44,7 @@ object SchemaUtils { nullable: Boolean = false): StructType = { org.apache.spark.ml.util.SchemaUtils.appendColumn(schema, colName, dataType) } + + def sameType(a: DataType, b: DataType): Boolean = a.sameType(b) + } diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/sql/SqlAdapter.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/sql/SqlAdapter.scala new file mode 100644 index 00000000000..f8556c420b0 --- /dev/null +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/sql/SqlAdapter.scala @@ -0,0 +1,26 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.sql + +import org.apache.spark.sql.types.DataType + +object SqlAdapter { + + def getUDF(f: AnyRef, dataType: DataType): UserDefinedFunction = { + UserDefinedFunction(f, dataType) + } + +} diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/sql/SqlAdapter.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/sql/SqlAdapter.scala new file mode 100644 index 00000000000..d6aefb615cb --- /dev/null +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/sql/SqlAdapter.scala @@ -0,0 +1,27 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.sql + +import org.apache.spark.sql.expressions.UserDefinedFunction +import org.apache.spark.sql.types.DataType + +object SqlAdapter { + + def getUDF(f: AnyRef, dataType: DataType): UserDefinedFunction = { + UserDefinedFunction(f, dataType, None) + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index b7ed9e9436a..95461a7d84a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -33,7 +33,7 @@ import org.apache.spark.rdd.RDD import java.lang.{Boolean => JBoolean} import java.nio.ByteOrder -import com.intel.analytics.bigdl.dlframes.{DLClassifier, DLClassifierModel, DLEstimator, DLModel} +import com.intel.analytics.bigdl.dlframes._ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.optim.SGD.{LearningRateSchedule, SequentialSchedule} import com.intel.analytics.bigdl.transform.vision.image._ @@ -3006,6 +3006,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createDatasetFromImageFrame(imageFrame: ImageFrame): DataSet[ImageFeature] = { DataSet.imageFrame(imageFrame) } + + def dlReadImage(path: String, sc: JavaSparkContext, minParitions: Int): DataFrame = { + val df = DLImageReader.readImages(path, sc.sc, minParitions) + df + } + + def createDLImageTransformer(transformer: FeatureTransformer): DLImageTransformer = { + new DLImageTransformer(transformer) + } + + def dlImageTransform(dlImageTransformer: DLImageTransformer, dataSet: DataFrame): DataFrame = { + dlImageTransformer.transform(dataSet) + } } object PythonBigDLUtils { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageReaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageReaderSpec.scala new file mode 100644 index 00000000000..e6ba6d1e98e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageReaderSpec.scala @@ -0,0 +1,118 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.dlframes + +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, MatToTensor} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.Resize +import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import org.apache.spark.SparkContext +import org.apache.spark.sql.{Row, SQLContext} +import org.opencv.core.CvType +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +import scala.util.Random + +class DLImageReaderSpec extends FlatSpec with Matchers with BeforeAndAfter { + + var sc : SparkContext = _ + var sQLContext: SQLContext = _ + val pascalResource = getClass.getClassLoader.getResource("pascal/") + private val imageNetResource = getClass.getClassLoader.getResource("imagenet/") + + before { + val conf = Engine.createSparkConf().setAppName("Test DLImageReader").setMaster("local[1]") + sc = SparkContext.getOrCreate(conf) + sQLContext = new SQLContext(sc) + + Random.setSeed(42) + RNG.setSeed(42) + + Engine.init + } + + after{ + if (sc != null) { + sc.stop() + } + } + + "DLImageReader" should "has correct result for pascal" in { + val imageDF = DLImageReader.readImages(pascalResource.getFile, sc) + assert(imageDF.count() == 1) + val r = imageDF.head().getAs[Row](0) + assert(r.getString(0).endsWith("000025.jpg")) + assert(r.getInt(1) == 375) + assert(r.getInt(2) == 500) + assert(r.getInt(3) == 3) + assert(r.getInt(4) == CvType.CV_8UC3) + assert(r.getAs[Array[Byte]](5).length == 95959) + } + + "DLImageReader" should "has correct result for imageNet" in { + val imageDirectory = imageNetResource + "n02110063/" + val imageDF = DLImageReader.readImages(imageDirectory, sc) + assert(imageDF.count() == 3) + val expectedRows = Seq( + (imageDirectory + "n02110063_8651.JPEG", 99, 129, 3, CvType.CV_8UC3), + (imageDirectory + "n02110063_11239.JPEG", 333, 500, 3, CvType.CV_8UC3), + (imageDirectory + "n02110063_15462.JPEG", 332, 500, 3, CvType.CV_8UC3) + ) + val actualRows = imageDF.rdd.collect().map(r => r.getAs[Row](0)).map { r => + (r.getString(0), r.getInt(1), r.getInt(2), r.getInt(3), r.getInt(4)) + } + assert (expectedRows.toSet == actualRows.toSet) + } + + "DLImageReader" should "has correct result for imageNet with channel 1 and 4" in { + val imageDirectory = imageNetResource + "n99999999/" + val imageDF = DLImageReader.readImages(imageDirectory, sc) + assert(imageDF.count() == 3) + val expectedRows = Seq( + (imageDirectory + "n02105855_2933.JPEG", 189, 213, 4, CvType.CV_8UC4), + (imageDirectory + "n02105855_test1.bmp", 527, 556, 1, CvType.CV_8UC1), + (imageDirectory + "n03000134_4970.JPEG", 480, 640, 3, CvType.CV_8UC3) + ) + val actualRows = imageDF.rdd.collect().map(r => r.getAs[Row](0)).map { r => + (r.getString(0), r.getInt(1), r.getInt(2), r.getInt(3), r.getInt(4)) + } + assert (expectedRows.toSet == actualRows.toSet) + } + + "DLImageReader" should "read recursively by wildcard path" in { + val imageDF = DLImageReader.readImages(imageNetResource.getFile + "*", sc) + assert(imageDF.count() == 11) + } + + "DLImageReader" should "read from multiple path" in { + val imageDirectory1 = imageNetResource + "n02110063/" + val imageDirectory2 = imageNetResource + "n99999999/" + val imageDF = DLImageReader.readImages(imageDirectory1 + "," + imageDirectory2, sc) + assert(imageDF.count() == 6) + } + + "read gray scale image" should "work" in { + val resource = getClass().getClassLoader().getResource("gray/gray.bmp") + val df = DLImageReader.readImages(resource.getFile, sc) + assert(df.count() == 1) + val r = df.head().getAs[Row](0) + assert(r.getString(0).endsWith("gray.bmp")) + assert(r.getInt(1) == 50) + assert(r.getInt(2) == 50) + assert(r.getInt(3) == 1) + assert(r.getInt(4) == CvType.CV_8UC1) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageTransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageTransformerSpec.scala new file mode 100644 index 00000000000..4c2fd5cdcfd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageTransformerSpec.scala @@ -0,0 +1,138 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dlframes + +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, ImageFrameToSample, MatToTensor} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} +import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import org.apache.spark.SparkContext +import org.apache.spark.sql.{Row, SQLContext} +import org.opencv.core.CvType +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + +import scala.util.Random + +class DLImageTransformerSpec extends FlatSpec with Matchers with BeforeAndAfter { + private var sc : SparkContext = _ + private var sqlContext : SQLContext = _ + private val pascalResource = getClass.getClassLoader.getResource("pascal/") + private val imageNetResource = getClass.getClassLoader.getResource("imagenet/") + + before { + val conf = Engine.createSparkConf().setAppName("Test DLImageTransfomer").setMaster("local[1]") + sc = SparkContext.getOrCreate(conf) + sqlContext = new SQLContext(sc) + Random.setSeed(42) + RNG.setSeed(42) + Engine.init + } + + after{ + if (sc != null) { + sc.stop() + } + } + + "DLTransformer" should "setters work" in { + val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() + val trans = new DLImageTransformer(transformer) + .setInputCol("image1") + .setOutputCol("features1") + assert(trans.getInputCol == "image1") + assert(trans.getOutputCol == "features1") + } + + "DLTransformer" should "has correct result with pascal images" in { + val imageDF = DLImageReader.readImages(pascalResource.getFile, sc) + assert(imageDF.count() == 1) + val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() + val transformedDF = new DLImageTransformer(transformer) + .setInputCol("image") + .setOutputCol("features") + .transform(imageDF) + val r = transformedDF.select("features").rdd.first().getAs[Row](0) + assert(r.getString(0).endsWith("pascal/000025.jpg")) + assert(r.getInt(1) == 224) + assert(r.getInt(2) == 224) + assert(r.getInt(3) == 3) + assert(r.getInt(4) == CvType.CV_32FC3) + assert(r.getSeq[Float](5).take(6).toArray.deep == Array(-30, -50, -69, -84, -46, -25).deep) + } + + "DLTransformer" should "has correct result without MatToTensor" in { + val imageDF = DLImageReader.readImages(pascalResource.getFile, sc) + assert(imageDF.count() == 1) + val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(123, 117, 104, 1, 1, 1) + val transformedDF = new DLImageTransformer(transformer) + .setInputCol("image") + .setOutputCol("features") + .transform(imageDF) + val r = transformedDF.select("features").rdd.first().getAs[Row](0) + assert(r.getString(0).endsWith("pascal/000025.jpg")) + assert(r.getInt(1) == 224) + assert(r.getInt(2) == 224) + assert(r.getInt(3) == 3) + assert(r.getInt(4) == CvType.CV_32FC3) + assert(r.getSeq[Float](5).take(6).toArray.deep == Array(-30, -50, -69, -84, -46, -25).deep) + } + + "DLTransformer" should "ensure imf2Row and Row2Imf reversible" in { + val imageDF = DLImageReader.readImages(pascalResource.getFile, sc) + assert(imageDF.count() == 1) + val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() + val transformedDF = new DLImageTransformer(transformer) + .setInputCol("image") + .setOutputCol("features") + .transform(imageDF) + val r = transformedDF.select("features").rdd.first().getAs[Row](0) + val convertedR = DLImageSchema.imf2Row(DLImageSchema.row2IMF(r)) + + assert(r.getSeq[Float](5).toArray.deep == convertedR.getAs[Array[Float]](5).deep) + } + + "DLTransformer" should "transform gray scale image" in { + val resource = getClass().getClassLoader().getResource("gray/gray.bmp") + val df = DLImageReader.readImages(resource.getFile, sc) + val dlTransformer = new DLImageTransformer(Resize(28, 28) -> MatToTensor[Float]()) + .setInputCol("image") + .setOutputCol("features") + val r = dlTransformer.transform(df).select("features").rdd.first().getAs[Row](0) + assert(r.getString(0).endsWith("gray.bmp")) + assert(r.getInt(1) == 28) + assert(r.getInt(2) == 28) + assert(r.getInt(3) == 1) + assert(r.getInt(4) == CvType.CV_32FC1) + } + + "DLTransformer" should "report error with same input and output columns" in { + val resource = getClass().getClassLoader().getResource("gray/gray.bmp") + val df = DLImageReader.readImages(resource.getFile, sc) + val dlTransformer = new DLImageTransformer(Resize(28, 28) -> MatToTensor[Float]()) + .setInputCol("image") + .setOutputCol("image") + intercept[IllegalArgumentException] { + val transformed = dlTransformer.transform(df) + } + } + +} From ef7c04f96fa33b10a2886f53725b2fde00967c98 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Wed, 14 Mar 2018 20:11:19 +0800 Subject: [PATCH 0731/1065] Keras-like API for training and evaluation (#2306) * update scala compile fit * update * python topology * refactor lenet * update * refactor fit * add python ut * ut for image dataset * fix ut * fix * update docs * update readme * fix --- .../bigdl/dllib/example/keras/LeNet.scala | 3 +- .../bigdl/dllib/example/keras/README.md | 16 +-- .../bigdl/dllib/example/keras/Train.scala | 28 +--- .../bigdl/dllib/keras/KerasUtils.scala | 45 ++++++ .../bigdl/dllib/keras/Topology.scala | 129 +++++++++++++++++- .../dllib/utils/python/api/PythonBigDL.scala | 2 +- .../utils/python/api/PythonBigDLKeras.scala | 77 ++++++++++- .../bigdl/dllib/keras/AbsCriterionSpec.scala | 36 +++++ .../bigdl/dllib/keras/BCECriterionSpec.scala | 36 +++++ .../dllib/keras/ClassNLLCriterionSpec.scala | 41 ++++++ .../bigdl/dllib/keras/MSECriterionSpec.scala | 36 +++++ .../dllib/keras/MarginCriterionSpec.scala | 49 +++++++ .../bigdl/dllib/keras/nn/TrainingSpec.scala | 103 ++++++++++++++ 13 files changed, 556 insertions(+), 45 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/AbsCriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/BCECriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/ClassNLLCriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MSECriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MarginCriterionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TrainingSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala index 7c89b0a23f6..1421a770a8f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala @@ -16,12 +16,11 @@ package com.intel.analytics.bigdl.example.keras -import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.utils.Shape object LeNet { - def apply(): Module[Float] = { + def apply(): Sequential[Float] = { val model = Sequential[Float]() model.add(Reshape(Array(1, 28, 28), inputShape = Shape(28, 28, 1))) model.add(Convolution2D(32, 3, 3, activation = "relu")) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md index 806b20bd9f2..7797ba2b9c3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md @@ -1,8 +1,8 @@ # LeNet Model on MNIST with new API -This example defines a classical CNN model used in digital number classification with the new set of API in BigDL. For detailed information with regard to LeNet, please refer to . +This example defines a classical CNN model used in digital number classification with the new set of Keras-Style API in BigDL, which is more user-friendly. For detailed information with regard to LeNet, please refer to . -This example is the same as [../../models/lenet](../../models/lenet), except that this example uses new API for model definition. +This example is the same as [../../models/lenet](../../models/lenet), except that here we use the new Keras-Style API for model definition and training. ## Prepare MNIST Data @@ -31,7 +31,6 @@ spark-submit \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size \ ---checkpoint ./model ``` Standalone cluster mode, example command ``` @@ -44,7 +43,6 @@ spark-submit \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size \ ---checkpoint ./model ``` Yarn cluster mode, example command ``` @@ -58,13 +56,7 @@ spark-submit \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f path_to_mnist_folder \ -b batch_size \ ---checkpoint ./model ``` In the above commands -* -f: where you put your MNIST data -* --checkpoint: Where you cache the model/train_state snapshot. You should input a folder and -make sure the folder is created when you run this example. The model snapshot will be named as -model.#iteration_number, and train state will be named as state.#iteration_number. Note that if -there are some files already exist in the folder, the old file will not be overwritten for the -safety of your model files. -* -b: The mini-batch size. It is expected that the mini-batch size is a multiple of node_number * core_number. \ No newline at end of file +* -f: an option to set the path where you put your MNIST data. +* -b: an option to set the mini-batch size. It is expected that the mini-batch size is a multiple of node_number * core_number. \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala index e487b053c44..4785213adcd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala @@ -19,14 +19,13 @@ package com.intel.analytics.bigdl.example.keras import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.DataSet import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToBatch} -import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} +import com.intel.analytics.bigdl.nn.ClassNLLCriterion import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.optim._ -import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} +import com.intel.analytics.bigdl.utils.Engine import org.apache.spark.SparkContext object Train { - LoggerFilter.redirectSparkInfoLogs() import models.lenet.Utils._ def main(args: Array[String]): Unit = { @@ -55,29 +54,14 @@ object Train { BytesToGreyImg(28, 28) -> GreyImgNormalizer(trainMean, trainStd) -> GreyImgToBatch( param.batchSize) - val optimizer = Optimizer( - model = model, - dataset = trainSet, - criterion = ClassNLLCriterion[Float](logProbAsInput = false)) - if (param.checkpoint.isDefined) { - optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch) - } - if(param.overWriteCheckpoint) { - optimizer.overWriteCheckpoint() - } - val validationSet = DataSet.array(load(validationData, validationLabel), sc) -> BytesToGreyImg(28, 28) -> GreyImgNormalizer(testMean, testStd) -> GreyImgToBatch( param.batchSize) - optimizer - .setValidation( - trigger = Trigger.everyEpoch, - dataset = validationSet, - vMethods = Array(new Top1Accuracy, new Top5Accuracy[Float], new Loss[Float])) - .setOptimMethod(optimMethod) - .setEndWhen(Trigger.maxEpoch(param.maxEpoch)) - .optimize() + model.compile(optimizer = optimMethod, + loss = ClassNLLCriterion[Float](logProbAsInput = false), + metrics = Array(new Top1Accuracy[Float](), new Top5Accuracy[Float](), new Loss[Float])) + model.fit(trainSet, nbEpoch = 10, validationData = validationSet) sc.stop() }) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala index 893b10801cc..97b2fc65ebb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala @@ -16,8 +16,10 @@ package com.intel.analytics.bigdl.nn.keras +import com.intel.analytics.bigdl.Criterion import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, DataFormat} +import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -118,4 +120,47 @@ object KerasUtils { case "th" => "CHANNEL_FIRST" } } + + private[keras] def toBigDLCriterion[T : ClassTag](loss: String) + (implicit ev: TensorNumeric[T]): Criterion[T] = { + loss.toLowerCase() match { + case "categorical_crossentropy" => CategoricalCrossEntropy[T]() + case "mse" => MSECriterion[T]() + case "mae" => AbsCriterion[T]() + case "hinge" => MarginCriterion[T]() + case "mape" => MeanAbsolutePercentageCriterion[T]() + case "msle" => MeanSquaredLogarithmicCriterion[T]() + case "squared_hinge" => MarginCriterion[T](squared = true) + case "sparse_categorical_crossentropy" => ClassNLLCriterion[T](logProbAsInput = false) + case "kld" => KullbackLeiblerDivergenceCriterion[T]() + case "cosine_proximity" => CosineProximityCriterion[T]() + case _ => throw new IllegalArgumentException(s"Invalid loss: ${loss.toLowerCase()}") + } + } + + private[keras] def toBigDLOptimMethod[T: ClassTag](optimMethod: String) + (implicit ev: TensorNumeric[T]): OptimMethod[T] = { + optimMethod.toLowerCase() match { + case "sgd" => new SGD[T](learningRate = 0.01) + case "rmsprop" => new RMSprop[T](learningRate = 0.001, decayRate = 0.9) + case "adamax" => new Adamax[T](Epsilon = 1e-8) + case "adagrad" => new Adagrad[T](learningRate = 0.01) + case "adadelta" => new Adadelta[T](decayRate = 0.95, Epsilon = 1e-8) + case "adam" => new Adam[T]() + } + } + + private[keras] def toBigDLMetrics[T: ClassTag](metrics: Array[String]) + (implicit ev: TensorNumeric[T]): Array[ValidationMethod[T]] = { + if (metrics == null) { + null + } + else if (metrics.sameElements(Array("accuracy"))) { + Array(new Top1Accuracy[T]()) + } + else { + throw new IllegalArgumentException(s"Unsupported metrics: ${metrics.mkString(", ")}") + } + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala index 5a7da0dcd7c..f8471b41c0b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala @@ -16,13 +16,17 @@ package com.intel.analytics.bigdl.nn.keras +import com.intel.analytics.bigdl.{Criterion, DataSet} +import com.intel.analytics.bigdl.dataset._ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.{Container, Identity, StaticGraph, Sequential => TSequential} +import com.intel.analytics.bigdl.nn.{Container, StaticGraph, Sequential => TSequential} +import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.{LoggerFilter, Shape} import com.intel.analytics.bigdl.utils.serializer._ +import org.apache.spark.rdd.RDD import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer @@ -30,13 +34,132 @@ import scala.reflect.ClassTag abstract class KerasModel[T: ClassTag](implicit ev: TensorNumeric[T]) extends KerasLayer[Activity, Activity, T] { - // TODO: enrich fit, compile, evaluate etc here. def getSubModules(): List[AbstractModule[Activity, Activity, T]] = { require(this.labor.isInstanceOf[Container[Activity, Activity, T]], "labor should be a container, but we got: $this") this.labor.asInstanceOf[Container[Activity, Activity, T]].modules.toList } + + private var optimMethod: OptimMethod[T] = null + private var criterion: Criterion[T] = null + private var vMethods: Array[ValidationMethod[T]] = null + + /** + * Configure the learning process. Must be called before fit. + * @param optimizer Optimization method to be used. + * @param loss Criterion to be used. + * @param metrics Array of validation methods to be used. + */ + // TODO: support checkpoint, summary, etc. + def compile(optimizer: OptimMethod[T], + loss: Criterion[T], + metrics: Array[ValidationMethod[T]] = null): Unit = { + LoggerFilter.redirectSparkInfoLogs() + this.optimMethod = optimizer + this.criterion = loss + this.vMethods = metrics + } + + /** + * Alternatively, one can pass in string representations when calling compile. + * For example: optimizer = "sgd", loss = "mse", metrics = Array("accuracy") + */ + def compile(optimizer: String, + loss: String, + metrics: Array[String]) + (implicit ev: TensorNumeric[T]): Unit = { + this.compile(KerasUtils.toBigDLOptimMethod[T](optimizer), + KerasUtils.toBigDLCriterion[T](loss), + KerasUtils.toBigDLMetrics[T](metrics)) + } + + private def toDataSet(x: RDD[Sample[T]], batchSize: Int) + : DataSet[MiniBatch[T]] = { + if (x != null) DataSet.rdd(x) -> SampleToMiniBatch[T](batchSize) + else null + } + + /** + * Train a model for a fixed number of epochs on a dataset. + * @param x Training dataset. If x is an instance of LocalDataSet, train in local mode. + * @param nbEpoch Number of iterations to train. + * @param validationData Dataset, or null if validation is not configured. + */ + def fit[D: ClassTag](x: DataSet[D], nbEpoch: Int, + validationData: DataSet[MiniBatch[T]]) + (implicit ev: TensorNumeric[T]): Unit = { + require(this.optimMethod != null && this.criterion != null, + "compile must be called before fit") + val optimizer = Optimizer( + model = this, + dataset = x, + criterion = this.criterion) + if (validationData != null) { + require(this.vMethods != null, "Validation metrics haven't been set yet") + optimizer.setValidation(trigger = Trigger.everyEpoch, + dataset = validationData, + vMethods = this.vMethods) + } + optimizer.setOptimMethod(this.optimMethod) + .setEndWhen(Trigger.maxEpoch(nbEpoch)) + optimizer.optimize() + } + + /** + * Train a model for a fixed number of epochs on a dataset. + * @param x Training data, RDD of Sample. + * @param batchSize Number of samples per gradient update. + * @param nbEpoch Number of iterations to train. + * @param validationData RDD of Sample, or null if validation is not configured. + */ + def fit(x: RDD[Sample[T]], batchSize: Int = 32, nbEpoch: Int = 10, + validationData: RDD[Sample[T]] = null) + (implicit ev: TensorNumeric[T]): Unit = { + this.fit(toDataSet(x, batchSize), nbEpoch, toDataSet(validationData, batchSize)) + } + + /** + * Evaluate a model on a given dataset. + * @param x Evaluation data, RDD of Sample. + * @param batchSize Number of samples per batch. + */ + def evaluate(x: RDD[Sample[T]], + batchSize: Int) + (implicit ev: TensorNumeric[T]): Array[(ValidationResult, ValidationMethod[T])] = { + require(this.vMethods != null, "Evaluation metrics haven't been set yet") + this.evaluate(x, this.vMethods, Some(batchSize)) + } + + /** + * Evaluate a model in local mode. + * @param x Evaluation data, LocalDataSet. + */ + def evaluate(x: LocalDataSet[MiniBatch[T]]) + (implicit ev: TensorNumeric[T]): Array[(ValidationResult, ValidationMethod[T])] = { + require(this.vMethods != null, "Evaluation metrics haven't been set yet") + this.evaluate(x, this.vMethods) + } + + /** + * Use a model to do prediction. + * @param x Prediction data, RDD of Sample. + * @param batchSize Number of samples per batch. + */ + def predict(x: RDD[Sample[T]], + batchSize: Int)(implicit ev: TensorNumeric[T]): RDD[Activity] = { + this.predict(x, batchSize, false) + } + + /** + * Use a model to do prediction in LOCAL mode. + * @param x Prediction data, LocalDataSet. + */ + def predict(x: LocalDataSet[MiniBatch[T]])(implicit ev: TensorNumeric[T]): Array[Activity] = { + val localPredictor = LocalPredictor(this) + localPredictor.predict(x) + } + } class Model[T: ClassTag](private val _inputs : Seq[ModuleNode[T]], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 95461a7d84a..0f7839cad47 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -215,7 +215,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } // The first dimension is batch for both X and y - private def toSampleArray(Xs: List[Tensor[T]], y: Tensor[T] = null): Array[JSample[T]] = { + def toSampleArray(Xs: List[Tensor[T]], y: Tensor[T] = null): Array[JSample[T]] = { require(!Xs.isEmpty, "Xs should not be empty") val totalNum = Xs(0).size()(0) var i = 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala index f6c04a8ad9a..ee5b0d625a3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -16,18 +16,21 @@ package com.intel.analytics.bigdl.python.api -import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} +import java.util.{List => JList} -import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.{Criterion, DataSet, nn} +import com.intel.analytics.bigdl.dataset.{DataSet, LocalDataSet, MiniBatch} import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.{Container, Graph, SpatialBatchNormalization} +import com.intel.analytics.bigdl.nn.{Container, SpatialBatchNormalization} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.numeric._ -import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.optim.{OptimMethod, Regularizer, ValidationMethod} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{MultiShape, Shape, SingleShape} +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFeatureToMiniBatch} +import com.intel.analytics.bigdl.utils.{Engine, MultiShape, Shape, SingleShape} +import org.apache.spark.api.java.JavaRDD import scala.collection.JavaConverters._ import scala.language.existentials @@ -674,4 +677,68 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho Bidirectional(layer, mergeMode, toScalaShape(inputShape)) } + def compile( + module: KerasModel[T], + optimizer: OptimMethod[T], + loss: Criterion[T], + metrics: JList[ValidationMethod[T]] = null): Unit = { + module.compile(optimizer, loss, + if (metrics == null) null else metrics.asScala.toArray) + } + + def fit( + module: KerasModel[T], + x: JavaRDD[Sample], + batchSize: Int = 32, + epochs: Int = 10, + validationData: JavaRDD[Sample] = null): Unit = { + module.fit(toJSample(x), batchSize, epochs, + if (validationData == null) null else toJSample(validationData)) + } + + def fit( + module: KerasModel[T], + x: DataSet[ImageFeature], + batchSize: Int, + epochs: Int, + validationData: DataSet[ImageFeature]): Unit = { + val trainData = x -> ImageFeatureToMiniBatch[T](batchSize) + val valData = + if (validationData != null) validationData -> ImageFeatureToMiniBatch[T](batchSize) + else null + module.fit(trainData, epochs, valData) + } + + def fit( + module: KerasModel[T], + xTrain: JList[JTensor], + yTrain: JTensor, + batchSize: Int, + epochs: Int, + xVal: JList[JTensor], + yVal: JTensor, + localCores: Int): Unit = { + val trainArray = toSampleArray(xTrain.asScala.toList.map{f => toTensor(f)}, toTensor(yTrain)) + val trainData = batching(DataSet.array(trainArray), batchSize) + .asInstanceOf[LocalDataSet[MiniBatch[T]]] + val valData = if (xVal != null && yVal != null) { + val valArray = toSampleArray(xVal.asScala.toList.map{f => toTensor(f)}, toTensor(yVal)) + batching(DataSet.array(valArray), batchSize) + } else null + Engine.setNodeAndCore(1, localCores) + module.fit(trainData, epochs, valData) + } + + def evaluate( + module: KerasModel[T], + x: JavaRDD[Sample], + batchSize: Int = 32): JList[EvaluatedResult] = { + val resultArray = module.evaluate(toJSample(x), batchSize) + val testResultArray = resultArray.map { result => + EvaluatedResult(result._1.result()._1, result._1.result()._2, + result._2.toString()) + } + testResultArray.toList.asJava + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/AbsCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/AbsCriterionSpec.scala new file mode 100644 index 00000000000..0c8cea5617b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/AbsCriterionSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.AbsCriterion + +class AbsCriterionSpec extends KerasBaseSpec { + + "AbsCriterion" should "be the same as Keras mae" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4]) + |target_tensor = Input(shape=[3, 4]) + |loss = mean_absolute_error(target_tensor, input_tensor) + |input = np.random.random([2, 3, 4]) + |Y = np.random.random([2, 3, 4]) + """.stripMargin + val loss = new AbsCriterion[Float]() + checkOutputAndGradForLoss(loss, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/BCECriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/BCECriterionSpec.scala new file mode 100644 index 00000000000..62403e57a8c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/BCECriterionSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.BCECriterion + +class BCECriterionSpec extends KerasBaseSpec { + + "BCECriterion" should "be the same as Keras binary_crossentropy" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4]) + |target_tensor = Input(shape=[3, 4]) + |loss = binary_crossentropy(target_tensor, input_tensor) + |input = np.random.random([2, 3, 4]) + |Y = np.random.random([2, 3, 4]) + """.stripMargin + val mse = new BCECriterion[Float]() + checkOutputAndGradForLoss(mse, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/ClassNLLCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/ClassNLLCriterionSpec.scala new file mode 100644 index 00000000000..7f26cad5f03 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/ClassNLLCriterionSpec.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.ClassNLLCriterion +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + +class ClassNLLCriterionSpec extends KerasBaseSpec { + + "ClassNLLCriterion log" should "be the same as Keras sparse_categorical_crossentropy" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, ]) + |target_tensor = Input(batch_shape=[3, ]) + |loss = sparse_categorical_crossentropy(target_tensor, input_tensor) + |input = input = np.array([[0.6, 0.3, 0.1], [0.2, 0.5, 0.3], [0.1, 0.1, 0.8]]) + |Y = np.array([0.0, 1.0, 2.0]) + """.stripMargin + val loss = ClassNLLCriterion[Float](logProbAsInput = false) + val (gradInput, gradWeight, weights, input, target, output) = + KerasRunner.run(kerasCode, Loss) + val boutput = loss.forward(input, target + 1) // index in BigDL starts from 1 + val koutput = output.mean() + NumericFloat.nearlyEqual(boutput, koutput, 1e-5) should be (true) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MSECriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MSECriterionSpec.scala new file mode 100644 index 00000000000..a2f3293adee --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MSECriterionSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.MSECriterion + +class MSECriterionSpec extends KerasBaseSpec { + + "MSECriterion" should "be the same as Keras mse" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4]) + |target_tensor = Input(shape=[3, 4]) + |loss = mean_squared_error(target_tensor, input_tensor) + |input = np.random.random([2, 3, 4]) + |Y = np.random.random([2, 3, 4]) + """.stripMargin + val loss = MSECriterion[Float]() + checkOutputAndGradForLoss(loss, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MarginCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MarginCriterionSpec.scala new file mode 100644 index 00000000000..1c63f3ea599 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/MarginCriterionSpec.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.keras + +import com.intel.analytics.bigdl.nn.MarginCriterion + +class MarginCriterionSpec extends KerasBaseSpec { + + "MarginCriterion" should "be the same as Keras hinge" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4]) + |target_tensor = Input(shape=[3, 4]) + |loss = hinge(target_tensor, input_tensor) + |input = np.random.random([2, 3, 4]) + |Y = np.random.random([2, 3, 4]) + """.stripMargin + val loss = MarginCriterion[Float]() + checkOutputAndGradForLoss(loss, kerasCode) + } + + "MarginCriterion squared" should "be the same as Keras squared_hinge" in { + val kerasCode = + """ + |input_tensor = Input(shape=[3, 4]) + |target_tensor = Input(shape=[3, 4]) + |loss = squared_hinge(target_tensor, input_tensor) + |input = np.random.random([2, 3, 4]) + |Y = np.random.random([2, 3, 4]) + """.stripMargin + val loss = MarginCriterion[Float](squared = true) + checkOutputAndGradForLoss(loss, kerasCode) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TrainingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TrainingSpec.scala new file mode 100644 index 00000000000..ef2094d134d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/TrainingSpec.scala @@ -0,0 +1,103 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.nn.MSECriterion +import com.intel.analytics.bigdl.nn.keras._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{Engine, Shape} +import com.intel.analytics.bigdl.optim.{DummyDataSet, SGD, Top1Accuracy} +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class TrainingSpec extends FlatSpec with Matchers with BeforeAndAfter { + private var sc: SparkContext = _ + private val nodeNumber = 1 + private val coreNumber = 4 + var data: RDD[Sample[Float]] = null + + before { + Engine.setNodeAndCore(nodeNumber, coreNumber) + sc = new SparkContext(s"local[$coreNumber]", "TrainingSpec") + + data = sc.range(0, 16, 1).map { _ => + val featureTensor = Tensor[Float](10) + featureTensor.apply1(_ => scala.util.Random.nextFloat()) + val labelTensor = Tensor[Float](1) + labelTensor(Array(1)) = Math.round(scala.util.Random.nextFloat()) + Sample[Float](featureTensor, labelTensor) + } + + } + + after { + if (sc != null) { + sc.stop() + } + } + + "sequential compile and fit" should "work properly" in { + val model = Sequential[Float]() + model.add(Dense(8, inputShape = Shape(10))) + model.compile(optimizer = "sgd", loss = "mse", metrics = null) + model.fit(data, batchSize = 8) + } + + "graph compile and fit" should "work properly" in { + val input = Input[Float](inputShape = Shape(10)) + val output = Dense[Float](8, activation = "relu").inputs(input) + val model = Model[Float](input, output) + model.compile(optimizer = "adam", loss = "mse", metrics = null) + model.fit(data, batchSize = 8) + } + + "sequential compile multiple times" should "use the last compile" in { + val model = Sequential[Float]() + model.add(Dense(3, inputShape = Shape(10))) + model.compile(optimizer = "sgd", loss = "sparse_categorical_crossentropy", metrics = null) + model.compile(optimizer = "adam", loss = "mse", metrics = null) + model.fit(data, batchSize = 8) + } + + "compile, fit with validation, evaluate and predict" should "work properly" in { + val testData = sc.range(0, 8, 1).map { _ => + val featureTensor = Tensor[Float](10) + featureTensor.apply1(_ => scala.util.Random.nextFloat()) + val labelTensor = Tensor[Float](1) + labelTensor(Array(1)) = Math.round(scala.util.Random.nextFloat()) + Sample[Float](featureTensor, labelTensor) + } + val model = Sequential[Float]() + model.add(Dense(8, activation = "relu", inputShape = Shape(10))) + model.compile(optimizer = "sgd", loss = "mse", metrics = Array("accuracy")) + model.fit(data, batchSize = 8, validationData = testData) + val accuracy = model.evaluate(testData, batchSize = 8) + val predictResults = model.predict(testData, batchSize = 8) + } + + "compile, fit, evaluate and predict in local mode" should "work properly" in { + val localData = DummyDataSet.mseDataSet + val model = Sequential[Float]() + model.add(Dense(8, activation = "relu", inputShape = Shape(4))) + model.compile(optimizer = "sgd", loss = "mse", metrics = Array("accuracy")) + model.fit(localData, nbEpoch = 5, validationData = null) + val accuracy = model.evaluate(localData) + val predictResults = model.predict(localData) + } + +} From d5e16f30319f19459d29421a2b4ae68ade5b0150 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Wed, 14 Mar 2018 21:47:30 +0800 Subject: [PATCH 0732/1065] [BugFix] TensorOp & SelectTensor (#2380) * solve conflicts * resolve conflict again --- .../bigdl/dllib/nn/ops/TensorOp.scala | 75 +++++++++++++------ .../analytics/bigdl/dllib/utils/Util.scala | 54 ++++++++++++- .../bigdl/dllib/nn/ops/SelectTensorSpec.scala | 58 +++++++++++++- .../bigdl/dllib/nn/ops/TensorOpSpec.scala | 24 ------ .../serializer/OperationSerializerSpec.scala | 2 +- 5 files changed, 161 insertions(+), 52 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOp.scala index 91a8954d8f9..b5d917afed8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOp.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOp.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.serialization.Bigdl import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, DataType} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{Table, Util} import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, SerializeContext} import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import org.apache.commons.lang3.SerializationUtils @@ -61,8 +61,8 @@ import scala.reflect.runtime.universe * @tparam T Numeric type */ class TensorOp[T: ClassTag] private( - private[bigdl] val transformer: (Tensor[T], TensorNumeric[T]) => Tensor[T] -)(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { + private[bigdl] val transformer: (Tensor[T], TensorNumeric[T]) => Tensor[T]) + (implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { private lazy val buffer: Tensor[T] = Tensor[T]() @@ -272,20 +272,21 @@ object TensorOp { "com.intel.analytics.bigdl.tensor.Tensor[T]", new DataConverter { override def getAttributeValue[T: ClassTag]( - context: DeserializeContext, - attribute: Bigdl.AttrValue + context: DeserializeContext, + attribute: Bigdl.AttrValue )(implicit ev: TensorNumeric[T]): AnyRef = { val any = attribute.getCustomValue val bytes = any.getValue.toByteArray - val wrapper = SerializationUtils.deserialize[ClosureWrapper[T]](bytes) + // using Util.deserialize instead of SerializationUtils.deserialize + val wrapper = Util.deserialize[ClosureWrapper[T]](bytes) wrapper.closure } override def setAttributeValue[T: ClassTag]( - context: SerializeContext[T], - attributeBuilder: AttrValue.Builder, - value: scala.Any, - valueType: universe.Type + context: SerializeContext[T], + attributeBuilder: AttrValue.Builder, + value: scala.Any, + valueType: universe.Type )(implicit ev: TensorNumeric[T]): Unit = { attributeBuilder.setDataType(DataType.CUSTOM) val wrapper = new ClosureWrapper( @@ -300,8 +301,8 @@ object TensorOp { // Class Wrapper for transformer(closure) private class ClosureWrapper[T: ClassTag]( - val closure: (Tensor[T], TensorNumeric[T]) => Tensor[T] - )(implicit ev: TensorNumeric[T]) extends Serializable + val closure: (Tensor[T], TensorNumeric[T]) => Tensor[T]) + (implicit ev: TensorNumeric[T]) extends Serializable /** @@ -595,20 +596,25 @@ object TensorOp { /** - * Select and copy a Tensor from a [[Table]] with [[key]]. + * Select and copy a Tensor from a [[Table]] with a key. * And do tensor transformation if [[transformer]] is defined. + * If [[isTensorKey]] is `false`, the real key is the value of [[keyTensor]]. + * Otherwise, the real key is [[keyTensor]]. * - * @param key the key of selected tensor, a scalar tensor + * @param keyTensor the key or tensor wrapper of key, must be a scalar tensor + * @param isTensorKey whether the key is a scalar tensor or a primitive value, default true * @param transformer user-defined transformer, default(null) means do nothing * @tparam T Numeric type */ class SelectTensor[T: ClassTag] private( - private val key: Tensor[_], - private val transformer: TensorOp[T] = null -)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { + private val keyTensor: Tensor[_], + private val isTensorKey: Boolean = true, + private val transformer: TensorOp[T] = null) + (implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] { override def updateOutput(input: Table): Tensor[T] = { - val selected = input[Tensor[T]](key) + val _key = if (isTensorKey) keyTensor else keyTensor.value() + val selected = input[Tensor[T]](_key) if (transformer != null) { output = transformer.updateOutput(selected) } else { @@ -623,11 +629,36 @@ class SelectTensor[T: ClassTag] private( object SelectTensor { + /** + * Build a `SelectTensor` Instance with a keyTensor. + * + * @param keyTensor the key or tensor wrapper of key, must be a scalar tensor + * @param isTensorKey whether the key is a scalar tensor or a primitive value, default true + * @param transformer user-defined transformer, default(null) means do nothing + * @tparam T Numeric type + * @return a `SelectTensor` Instance + */ def apply[T: ClassTag]( - key: Tensor[_], - transformer: TensorOp[T] = null - )(implicit ev: TensorNumeric[T]): SelectTensor[T] = { - new SelectTensor[T](key, transformer) + keyTensor: Tensor[_], + isTensorKey: Boolean = true, + transformer: TensorOp[T] = null) + (implicit ev: TensorNumeric[T]): SelectTensor[T] = { + require(keyTensor.isScalar, "The key must be a Scalar Tensor!") + new SelectTensor[T](keyTensor, isTensorKey, transformer) + } + + /** + * Build a `SelectTensor` Instance with a non-Tensor key with Type [[D]]. + * + * @param key the key, must be able to be wrapped by Tensor + * @tparam T Numeric type + * @tparam D type of key, must be supported by TensorDataType + * @return a `SelectTensor` Instance + */ + def apply[T: ClassTag, D: ClassTag](key: D) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): SelectTensor[T] = { + val keyTensor = Tensor.scalar(key) + new SelectTensor[T](keyTensor, false, null) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala index 6907811e061..0eab914f4a1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala @@ -16,11 +16,15 @@ package com.intel.analytics.bigdl.utils +import java.io._ + import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} +import org.apache.commons.lang3.SerializationException import scala.reflect.ClassTag +import scala.util.Try object Util { def kthLargest(arr: Array[Long], l: Int, r: Int, k: Int): Long = { @@ -142,8 +146,8 @@ object Util { } private[bigdl] def putWeightBias[T: ClassTag]( - broadcastWeightBias: Array[Tensor[T]], - localModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { + broadcastWeightBias: Array[Tensor[T]], + localModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { val localWeightBias = localModel.parameters()._1 var i = 0 while (i < localWeightBias.length) { @@ -155,8 +159,8 @@ object Util { } private[bigdl] def initGradWeightBias[T: ClassTag]( - broadcastWeightBias: Array[Tensor[T]], - localModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { + broadcastWeightBias: Array[Tensor[T]], + localModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { val (localWeightBias, localGradWeightBias) = localModel.parameters() // init gradient with a compacted storage val storage = Storage[T](localGradWeightBias.map(_.nElement()).sum) @@ -175,4 +179,46 @@ object Util { i += 1 } } + + + /** + * This method is quite like [[org.apache.commons.lang3.SerializationUtils.deserialize]], + * except `resolveClass` method of [[ObjectInputStream]] is overridden, + * which fix potential [[ClassNotFoundException]] caused by uncertain `latestUserDefinedLoader`. + */ + private[bigdl] def deserialize[T: ClassTag](objectData: Array[Byte]): T = { + if (objectData == null) { + throw new IllegalArgumentException("The byte[] must not be null") + } + deserialize[T](new ByteArrayInputStream(objectData)) + } + + /** + * This method is quite like [[org.apache.commons.lang3.SerializationUtils.deserialize]], + * except `resolveClass` method of [[ObjectInputStream]] is overridden, + * which fix potential [[ClassNotFoundException]] caused by uncertain `latestUserDefinedLoader`. + */ + private[bigdl] def deserialize[T: ClassTag](inputStream: InputStream): T = { + if (inputStream == null) { + throw new IllegalArgumentException("The InputStream must not be null") + } + var in: ObjectInputStream = null + try { + // stream closed in the finally + in = new ObjectInputStream(inputStream) { + override def resolveClass(desc: ObjectStreamClass): Class[_] = { + Try(Class.forName(desc.getName, false, getClass.getClassLoader) + ).getOrElse(super.resolveClass(desc)) + } + } + in.readObject().asInstanceOf[T] + } catch { + case ex: ClassCastException => throw new SerializationException(ex) + case ex: ClassNotFoundException => throw new SerializationException(ex) + case ex: IOException => throw new SerializationException(ex) + } finally { + if (in != null) Try(in.close()) + } + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectTensorSpec.scala index 900b064a6e5..9d6a2355230 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/SelectTensorSpec.scala @@ -18,12 +18,68 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} +class SelectTensorSpec extends FlatSpec with Matchers { + + private val t1 = Tensor[Double](2, 3).randn() + private val t2 = Tensor[Double](2, 3).randn() + private val t3 = Tensor[Double](2, 3).randn() + private val t4 = Tensor[Double](2, 3).randn() + private val t5 = Tensor[Double](2, 3).randn() + private val table = T() + .update(Tensor.scalar(1), t1) + .update(Tensor.scalar("2"), t2) + .update(3, t3) + .update(4.0f, t4) + .update("5", t5) + + "SelectedTensor with TensorKey" should "work correctly" in { + val t1Copy = SelectTensor[Double](Tensor.scalar(1)).forward(table) + t1Copy shouldEqual t1 + val t1Values = t1.storage().array().clone() + t1Copy.square() + t1.storage().array() shouldEqual t1Values + t1Copy.storage().array() shouldEqual t1Values.map(e => e * e) + + val t2Copy = SelectTensor[Double](Tensor.scalar("2"), true).forward(table) + t2Copy shouldEqual t2 + } + + "SelectedTensor with primitive Key" should "work correctly" in { + val t3Copy = SelectTensor[Double, Int](3).forward(table) + t3Copy shouldEqual t3 + + val t4Copy = SelectTensor[Double](Tensor.scalar(4.0f), false).forward(table) + t4Copy shouldEqual t4 + + val t5Copy = SelectTensor[Double, String]("5").forward(table) + t5Copy shouldEqual t5 + } + + "SelectedTensor with transformer" should "work correctly" in { + val transformer = (TensorOp[Double]() ** 3 * 4.5).ceil + var select = SelectTensor(Tensor.scalar("2"), transformer = transformer) + val t2Values = t2.storage().array().clone() + val t2Convert = select.forward(table) + t2Convert.storage().array() shouldEqual + t2Values.map(e => math.ceil(math.pow(e, 3) * 4.5)) + + val transformer2 = TensorOp[Double]().abs.ceil.inv * 3.0 + select = SelectTensor(Tensor.scalar("5"), false, transformer = transformer2) + val t5Values = t5.storage().array().clone() + val t5Convert = select.forward(table) + t5Convert.storage().array() shouldEqual + t5Values.map(e => 3.0 / math.ceil(math.abs(e))) + } + +} + class SelectTensorSerialTest extends ModuleSerializationTest { override def test(): Unit = { val transformer = (TensorOp[Float]() ** 3 * 4.5f).ceil - val select = SelectTensor(Tensor.scalar("2"), transformer) + val select = SelectTensor(Tensor.scalar("2"), transformer = transformer) val t1 = Tensor[Float](3, 4).randn() val t2 = Tensor[Float](2, 3).randn() val input = T().update(Tensor.scalar(1), t1).update(Tensor.scalar("2"), t2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala index 937ea29b598..6cbd35b7176 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/TensorOpSpec.scala @@ -19,7 +19,6 @@ package com.intel.analytics.bigdl.nn.ops import com.intel.analytics.bigdl.nn.Sigmoid import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} @@ -110,29 +109,6 @@ class TensorOpSpec extends FlatSpec with Matchers { op2.forward(tt) shouldEqual Sigmoid[Float]().forward(cpy).inv().sqrt() } - private val t1 = Tensor[Float](3, 4).randn() - private val t2 = Tensor[Double](2, 3).randn() - private val table = T().update(Tensor.scalar(1), t1).update(Tensor.scalar("2"), t2) - - "SelectedTensor without transformer" should "work correctly" in { - val t1Copy = SelectTensor[Float](Tensor.scalar(1)).forward(table) - t1Copy shouldEqual t1 - val t1Values = t1.storage().array().clone() - t1Copy.square() - t1.storage().array() shouldEqual t1Values - t1Copy.storage().array() shouldEqual t1Values.map(e => e * e) - } - - "SelectedTensor with transformer" should "work correctly" in { - val transformer = (TensorOp[Double]() ** 3 * 4.5).ceil - val select = SelectTensor(Tensor.scalar("2"), transformer) - val t2Values = t2.storage().array().clone() - val t2Convert = select.forward(table) - t2.storage().array() shouldEqual t2Values - t2Convert.storage().array() shouldEqual - t2Values.map(e => math.ceil(math.pow(e, 3) * 4.5)) - } - } class TensorOpSerialTest extends ModuleSerializationTest { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala index 44c9110990f..6c040367595 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala @@ -787,7 +787,7 @@ class OperationSerializerSpec extends SerializerSpecHelper { "SelectTensor serializer" should "work properly" in { val transformer = (TensorOp[Float]() ** 3 * 4.5f).ceil - val select = SelectTensor(Tensor.scalar("2"), transformer) + val select = SelectTensor(Tensor.scalar("2"), transformer = transformer) val t1 = Tensor[Float](3, 4).randn() val t2 = Tensor[Float](2, 3).randn() val input = T().update(Tensor.scalar(1), t1).update(Tensor.scalar("2"), t2) From 30759b6d62138c0c70b938f1a0506723fc70eaea Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Fri, 16 Mar 2018 13:04:28 +0800 Subject: [PATCH 0733/1065] Keras-like API functional merge and some fix (#2313) * resolve conflicts * update merge * update merge * update doc * refactor python * fix * style * meet review --- .../analytics/bigdl/dllib/keras/Input.scala | 8 +- .../analytics/bigdl/dllib/keras/Merge.scala | 82 ++++++++++++------- .../analytics/bigdl/dllib/keras/Reshape.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 8 ++ .../utils/python/api/PythonBigDLKeras.scala | 12 +-- .../bigdl/dllib/keras/LeNetSpec.scala | 8 ++ .../bigdl/dllib/keras/nn/MergeSpec.scala | 19 ++++- 7 files changed, 96 insertions(+), 43 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala index dfb79626298..f9c6b215877 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala @@ -36,8 +36,8 @@ class Input[T: ClassTag](val inputShape: Shape)(implicit ev: TensorNumeric[T]) object Input { def apply[T: ClassTag]( - name : String = null, - inputShape: Shape = null)(implicit ev: TensorNumeric[T]): ModuleNode[T] = { + inputShape: Shape = null, + name : String = null)(implicit ev: TensorNumeric[T]): ModuleNode[T] = { val module = new Input(inputShape) module.build(KerasLayer.addBatch(inputShape)) if (name != null) { @@ -49,8 +49,8 @@ object Input { object InputLayer { def apply[T: ClassTag]( - name : String = null, - inputShape: Shape = null)(implicit ev: TensorNumeric[T]): KerasLayer[Activity, Activity, T] = { + inputShape: Shape = null, + name : String = null)(implicit ev: TensorNumeric[T]): KerasLayer[Activity, Activity, T] = { val module = new Input(inputShape) if (name != null) { module.setName(name) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala index 4119ac1675e..73608735e83 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Merge.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.keras +import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.{CAddTable, CAveTable, CMaxTable, CMulTable, CosineDistance, DotProduct, JoinTable, ParallelTable, Sequential => TSequential} import com.intel.analytics.bigdl.tensor.Tensor @@ -25,8 +26,8 @@ import com.intel.analytics.bigdl.utils.{MultiShape, Shape} import scala.reflect.ClassTag /** - * Used to merge a list of tensors into a single tensor, following some merge mode. - * Merge must have at least two input layers. + * Used to merge a list of inputs into a single output, following some merge mode. + * To merge layers, it must take at least two input layers. * * When using this layer as the first layer in a model, you need to provide the argument * inputShape for input layers (each as a Single Shape, does not include the batch dimension). @@ -34,8 +35,8 @@ import scala.reflect.ClassTag * @param layers A list of layer instances. Must be more than one layer. * @param mode Merge mode. String, must be one of: 'sum', 'mul', 'concat', 'ave', 'cos', * 'dot', 'max'. Default is 'sum'. - * @param concatAxis Integer, axis to use in mode concat. Only specify this when mode is 'concat'. - * Default is -1, meaning the last axis of the input. + * @param concatAxis Integer, axis to use when concatenating layers. Only specify this when merge + * mode is 'concat'. Default is -1, meaning the last axis of the input. * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class Merge[T: ClassTag]( @@ -52,8 +53,11 @@ class Merge[T: ClassTag]( require(mergeMode == "sum" || mergeMode == "mul" || mergeMode == "concat" || mergeMode == "ave" || mergeMode == "cos" || mergeMode == "dot" || mergeMode == "max", s"Invalid merge mode: $mergeMode") - require(layers.length >= 2, s"Merge must have at least two input layers " + - s"but found ${layers.length}") + if (layers != null) { + require(layers.length >= 2, s"Merge must take at least two input layers " + + s"but found ${layers.length}") + this.excludeInvalidLayers(layers) + } private def computeOutputShapeForConcat(input: List[Shape]): Shape = { import scala.util.control.Breaks._ @@ -116,36 +120,40 @@ class Merge[T: ClassTag]( override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = { val input = inputShape.toMulti() - val model = TSequential[T]() - val parallel = ParallelTable() - var i = 0 - while(i < layers.length) { - val tlayer = layers(i) match { - case k: KerasLayer[_, _, T] => k.labor - case t: AbstractModule[Activity, Activity, T] => t - } - parallel.add(tlayer) - i += 1 - } - model.add(parallel) - val seq = TSequential[T]() - val layer = mergeMode match { + val mergeLayer = mergeMode match { case "sum" => CAddTable() case "mul" => CMulTable() case "max" => CMaxTable() case "ave" => CAveTable() - case "concat" => JoinTable(axis, input.length) + case "concat" => + val input1 = input.head.toSingle().toArray + JoinTable(axis, input1.length -1) case "dot" => + val seq = TSequential[T]() seq.add(DotProduct()) seq.add(com.intel.analytics.bigdl.nn.Reshape(Array(1), Some(true))) seq case "cos" => + val seq = TSequential[T]() seq.add(CosineDistance()) seq.add(com.intel.analytics.bigdl.nn.Reshape(Array(1, 1), Some(true))) seq } - model.add(layer) - model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + if (layers != null) { // In the case `layers != null`, return a ParallelTable to merge layers + val model = TSequential[T]() + val parallel = ParallelTable() + var i = 0 + while(i < layers.length) { + parallel.add(layers(i).asInstanceOf[KerasLayer[Activity, Activity, T]].labor) + i += 1 + } + model.add(parallel) + model.add(mergeLayer) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } + else { // In the case `layers == null`, only return a merge layer to merge nodes not layers. + mergeLayer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } } } @@ -154,14 +162,15 @@ object Merge { inputShape: Shape = null, layers: Array[AbstractModule[Activity, Activity, T]]): Shape = { val batchInputShape = KerasLayer.addBatch(inputShape) - val actualInputShape = + val actualInputShape = if (layers != null) { MultiShape(layers.map { layer => - if (layer.isBuilt()) { // it's possible while reloaded from file - layer.getOutputShape() - } else { - layer.build(layer.getInputShape()) - } - }.toList) + if (layer.isBuilt()) { // it's possible while reloaded from file + layer.getOutputShape() + } else { + layer.build(layer.getInputShape()) + } + }.toList) + } else null if (batchInputShape != null) { require(batchInputShape.isInstanceOf[MultiShape], "Merge requires inputShape to be MultiShape") @@ -176,6 +185,17 @@ object Merge { mode: String = "sum", concatAxis: Int = -1, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): Merge[T] = { - new Merge[T](layers.toArray, mode, concatAxis, inputShape) + val layersArray = if (layers != null) layers.toArray else null + new Merge[T](layersArray, mode, concatAxis, inputShape) + } + + def merge[@specialized(Float, Double) T: ClassTag]( + inputs: List[ModuleNode[T]], + mode: String = "sum", + concatAxis: Int = -1, + name: String = null)(implicit ev: TensorNumeric[T]): ModuleNode[T] = { + val mergeLayer = new Merge[T](mode = mode, concatAxis = concatAxis) + if (name != null) mergeLayer.setName(name) + mergeLayer.inputs(inputs.toArray) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala index c1e4923fbfd..89a9c6be271 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Reshape.scala @@ -90,7 +90,7 @@ class Reshape[T: ClassTag]( layer = InferReshape(targetShape) } else { - layer = com.intel.analytics.bigdl.nn.Reshape(targetShape) + layer = com.intel.analytics.bigdl.nn.Reshape(targetShape, batchMode = Some(true)) } layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0f7839cad47..ca4afda6a18 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2635,6 +2635,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab module.runningVar.set(toTensor(runningStd)) } + def getRunningMean(module: BatchNormalization[T]): JTensor = { + toJTensor(module.runningMean) + } + + def getRunningStd(module: BatchNormalization[T]): JTensor = { + toJTensor(module.runningVar) + } + def createMasking(maskValue: Double) : Masking[T] = { Masking[T](maskValue) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala index ee5b0d625a3..2028298d55d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLKeras.scala @@ -143,22 +143,22 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho gammaInit, dimOrdering, toScalaShape(inputShape)) } - def setKerasRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit = { + def setRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit = { module.labor.asInstanceOf[SpatialBatchNormalization[T]] .runningMean.set(toTensor(runningMean)) } - def setKerasRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit = { + def setRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit = { module.labor.asInstanceOf[SpatialBatchNormalization[T]] .runningVar.set(toTensor(runningStd)) } - def getKerasRunningMean(module: BatchNormalization[T]): JTensor = { + def getRunningMean(module: BatchNormalization[T]): JTensor = { toJTensor(module.labor.asInstanceOf[SpatialBatchNormalization[T]] .runningMean) } - def getKerasRunningStd(module: BatchNormalization[T]): JTensor = { + def getRunningStd(module: BatchNormalization[T]): JTensor = { toJTensor(module.labor.asInstanceOf[SpatialBatchNormalization[T]] .runningVar) } @@ -168,7 +168,9 @@ class PythonBigDLKeras[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho mode: String = "sum", concatAxis: Int = -1, inputShape: JList[JList[Int]]): Merge[T] = { - Merge[T](layers.asScala.toList, mode, concatAxis, toScalaMultiShape(inputShape)) + val layersList = if (layers != null) layers.asScala.toList + else null + Merge[T](layersList, mode, concatAxis, toScalaMultiShape(inputShape)) } def createKerasConvolution2D( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala index c80eb583d37..c6509992be0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala @@ -34,4 +34,12 @@ class LeNetSpec extends FlatSpec with Matchers { val gradInput = cnn.backward(input, output) } + "LeNet forward with incompatible input tensor" should "raise an exception" in { + intercept[RuntimeException] { + val cnn = LeNet() + val input = Tensor[Float](Array(28, 28, 1)).rand() + val output = cnn.forward(input) + } + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala index 8af79ceb9f0..24a081bd1af 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/MergeSpec.scala @@ -17,7 +17,8 @@ package com.intel.analytics.bigdl.keras.nn import com.intel.analytics.bigdl.keras.KerasBaseSpec -import com.intel.analytics.bigdl.nn.keras.{Dense, InputLayer, Merge, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.keras.{Dense, Input, InputLayer, Merge, Model, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.keras.Merge.merge import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{MultiShape, Shape, T, Table} @@ -39,6 +40,20 @@ class MergeSpec extends KerasBaseSpec { seq.forward(input) should be (input1 + input2) } + "merge method" should "work correctly" in { + val input1 = Tensor[Float](2, 8).rand(0, 1) + val input2 = Tensor[Float](2, 12).rand(0, 1) + val input = T(1 -> input1, 2 -> input2) + val l1 = Input[Float](inputShape = Shape(8)) + val l2 = Input[Float](inputShape = Shape(12)) + val dense1 = Dense[Float](10).inputs(l1) + val dense2 = Dense[Float](10).inputs(l2) + val output = merge(inputs = List(dense1, dense2), mode = "sum") + val model = Model[Float](Array(l1, l2), output) + model.forward(input) + model.getOutputShape().toSingle().toArray should be (Array(-1, 10)) + } + "Merge with incompatible input shapes" should "raise an exception" in { intercept[RuntimeException] { val seq = KSequential[Float]() @@ -91,7 +106,7 @@ class MergeSpec extends KerasBaseSpec { seq.forward(input) } - "Merge complicated" should "work properly" in { + "Merge dense" should "work properly" in { val input1 = Tensor[Float](3, 8).rand(0, 1) val input2 = Tensor[Float](3, 6).rand(0, 1) val input = T(1 -> input1, 2 -> input2) From c17139a89ff3bc7bb03c29c74484751bf9f00454 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Fri, 16 Mar 2018 15:27:46 +0800 Subject: [PATCH 0734/1065] Fix reload model in python (#2382) * fix * fix default --- .../bigdl/dllib/keras/KerasLayer.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 28 +++++++++++++++++-- 2 files changed, 26 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala index 4780124a25b..b04aa8bd79a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} -import com.intel.analytics.bigdl.nn.{Container => TContainer, Sequential => TSequential} +import com.intel.analytics.bigdl.nn.{Container => TContainer} import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ca4afda6a18..11bda6298a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -35,6 +35,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.dlframes._ import com.intel.analytics.bigdl.nn.Graph._ +import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasModel} import com.intel.analytics.bigdl.optim.SGD.{LearningRateSchedule, SequentialSchedule} import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation._ @@ -2594,7 +2595,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def getContainerModules(module: Container[Activity, Activity, T]) : JList[AbstractModule[Activity, Activity, T]] = { - module.modules.toList.asJava + module match { + case m: KerasModel[T] => + m.getSubModules().asJava + case kl: KerasLayer[Activity, Activity, T] => + throw new RuntimeException(s"There's no sub modules for ${kl}") + case _ => + module.modules.toList.asJava + } } def getFlattenModules(module: Container[Activity, Activity, T], @@ -2605,11 +2613,21 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab result.toList.asJava } + // TODO: refactor Container and KerasLayer to simplify this logic + private def hasSubModules(module: AbstractModule[Activity, Activity, T]) = { + module match { + case km: KerasModel[T] => true + case kl: KerasLayer[Activity, Activity, T] => false + case c: Container[_, _, _] => true + case _ => false + } + } + private def doGetFlattenModules(module: Container[Activity, Activity, T], includeContainer: Boolean, result: ArrayBuffer[AbstractModule[Activity, Activity, T]]): Unit = { - module.modules.foreach {m => - if (m.isInstanceOf[Container[Activity, Activity, T]]) { + getContainerModules(module).asScala.foreach {m => + if (hasSubModules(m)) { doGetFlattenModules(m.asInstanceOf[Container[Activity, Activity, T]], includeContainer, result) @@ -3027,6 +3045,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def dlImageTransform(dlImageTransformer: DLImageTransformer, dataSet: DataFrame): DataFrame = { dlImageTransformer.transform(dataSet) } + + def getRealClassNameOfJValue(module: AbstractModule[Activity, Activity, T]): String = { + module.getClass.getCanonicalName + } } object PythonBigDLUtils { From 671e829ec0be44042973338389fd2d0e271d18a4 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Fri, 16 Mar 2018 15:29:53 +0800 Subject: [PATCH 0735/1065] Fix script on yarn (#2386) * pythonpath * update * update doc --- dist/assembly/dist.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dist/assembly/dist.xml b/dist/assembly/dist.xml index 39088ba01bf..2fe4d3e1daf 100644 --- a/dist/assembly/dist.xml +++ b/dist/assembly/dist.xml @@ -27,7 +27,7 @@ export_tf_checkpoint.py pyspark-with-bigdl.sh spark-submit-with-bigdl.sh - juptyer-with-bigdl.sh + jupyter-with-bigdl.sh spark-shell-with-bigdl.sh From 905682e734b5eca4fd0da8f1cb4077119cb5a0f6 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 16 Mar 2018 16:03:11 +0800 Subject: [PATCH 0736/1065] rm legacy tests and enbale new ones (#2389) --- .../keras/nn/KerasIdentityWrapperSpec.scala | 35 + .../nn/KerasLayerWrapperSerialTest.scala | 34 + .../KerasModuleSerializerSpec.scala | 512 ------ .../serializer/ModuleSerializerSpec.scala | 1554 ----------------- .../utils/serializer/SerializerSpec.scala | 11 +- 5 files changed, 74 insertions(+), 2072 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasIdentityWrapperSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasLayerWrapperSerialTest.scala delete mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala delete mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasIdentityWrapperSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasIdentityWrapperSpec.scala new file mode 100644 index 00000000000..007293810dd --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasIdentityWrapperSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.nn.ReLU +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.keras.KerasIdentityWrapper +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class KerasIdentityWrapperSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = new KerasIdentityWrapper(ReLU[Float]()) + layer.build(Shape(20)) + val inputData = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) + runSerializationTest(layer.asInstanceOf[AbstractModule[_, _, Float]], inputData) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasLayerWrapperSerialTest.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasLayerWrapperSerialTest.scala new file mode 100644 index 00000000000..0c315ef47cc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasLayerWrapperSerialTest.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.keras.nn + +import com.intel.analytics.bigdl.nn.ReLU +import com.intel.analytics.bigdl.nn.keras.KerasLayerWrapper +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + + +class KerasLayerWrapperSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = new KerasLayerWrapper[Float](ReLU[Float](), inputShape = Shape(8, 12)) + layer.build(Shape(3, 8, 12)) + val input = Tensor[Float](3, 8, 12).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala deleted file mode 100644 index 11231e84515..00000000000 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/KerasModuleSerializerSpec.scala +++ /dev/null @@ -1,512 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.utils.serializer - -import com.intel.analytics.bigdl.nn.{Linear, ReLU} -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} -import com.intel.analytics.bigdl.nn.keras._ -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.{Shape, Table} - -import scala.collection.mutable -import scala.util.Random - -class KerasModuleSerializerSpec extends SerializerSpecHelper { - - override def getPackage(): String = "com.intel.analytics.bigdl.nn.keras" - - override def getExpected(): mutable.Set[String] = { - super.getExpected().filter(_.contains(getPackage())) - } - - override def addExcludedClass(): Unit = { - excludedClass.add("com.intel.analytics.bigdl.nn.keras.Input") - } - "IdentityShapeWrapper serializer" should "work properly" in { - val layer = new KerasIdentityWrapper(ReLU[Float]()) - layer.build(Shape(20)) - val inputData = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) - runSerializationTest(layer.asInstanceOf[AbstractModule[_, _, Float]], inputData) - } - - "InputLayer serializer" should "work properly" in { - val input = InputLayer[Float](inputShape = Shape(20)) - input.build(Shape(2, 20)) - val inputData = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) - runSerializationTest(input, inputData) - } - - "Dense serializer" should "work properly" in { - val dense = Dense[Float](10, inputShape = Shape(20)) - dense.build(Shape(2, 20)) - val input = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) - runSerializationTest(dense, input) - } - - "Sequence serializer" should "work properly" in { - val dense = Dense[Float](10, inputShape = Shape(20)) - val kseq = KSequential[Float]() - kseq.add(dense) - val kseq2 = KSequential[Float]() - kseq2.add(Dense[Float](10, inputShape = Shape(10))) - kseq.add(kseq2) - val input = Tensor[Float](2, 20).apply1(_ => Random.nextFloat()) - runSerializationTest(kseq, input) - } - - "Model serializer" should "work properly" in { - val input = Input[Float](inputShape = Shape(10)) - val d = Dense[Float](20).setName("dense1").inputs(input) - val d2 = Dense[Float](5).setName("dense2").inputs(d) - val model = Model[Float](input, d2) - val inputData = Tensor[Float](Array(20, 10)).rand() - runSerializationTest(model, inputData) - } - - "Convolution2D serializer" should "work properly" in { - val layer = Convolution2D[Float](64, 2, 5, inputShape = Shape(3, 24, 24)) - layer.build(Shape(2, 3, 24, 24)) - val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "MaxPooling2D serializer" should "work properly" in { - val layer = MaxPooling2D[Float](inputShape = Shape(3, 24, 24)) - layer.build(Shape(2, 3, 24, 24)) - val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Activation serializer" should "work properly" in { - val layer = Activation[Float]("tanh", inputShape = Shape(4, 5)) - layer.build(Shape(2, 4, 5)) - val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Dropout serializer" should "work properly" in { - val layer = Dropout[Float](0.3, inputShape = Shape(3, 4)) - layer.build(Shape(2, 3, 4)) - val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Flatten serializer" should "work properly" in { - val layer = Flatten[Float](inputShape = Shape(3, 4, 5)) - layer.build(Shape(2, 3, 4, 5)) - val input = Tensor[Float](2, 3, 4, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Reshape serializer" should "work properly" in { - val layer = Reshape[Float](Array(4, 15), inputShape = Shape(3, 4, 5)) - layer.build(Shape(2, 3, 4, 5)) - val input = Tensor[Float](2, 3, 4, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "SoftMax serializer" should "work properly" in { - val layer = SoftMax[Float](inputShape = Shape(4, 5)) - layer.build(Shape(3, 4, 5)) - val input = Tensor[Float](3, 4, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "SimpleRNN serializer" should "work properly" in { - val layer = SimpleRNN[Float](8, activation = "relu", inputShape = Shape(4, 5)) - layer.build(Shape(3, 4, 5)) - val input = Tensor[Float](3, 4, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "LSTM serializer" should "work properly" in { - val layer = LSTM[Float](8, returnSequences = true, - innerActivation = "sigmoid", inputShape = Shape(32, 32)) - layer.build(Shape(3, 32, 32)) - val input = Tensor[Float](3, 32, 32).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GRU serializer" should "work properly" in { - val layer = GRU[Float](16, returnSequences = true, - goBackwards = true, inputShape = Shape(28, 32)) - layer.build(Shape(2, 28, 32)) - val input = Tensor[Float](2, 28, 32).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Highway serializer" should "work properly" in { - val layer = Highway[Float](activation = "tanh", bias = false, inputShape = Shape(4)) - layer.build(Shape(3, 4)) - val input = Tensor[Float](3, 4).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Convolution1D serializer" should "work properly" in { - val layer = Convolution1D[Float](64, 3, inputShape = Shape(12, 20)) - layer.build(Shape(2, 12, 20)) - val input = Tensor[Float](2, 12, 20).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Convolution3D serializer" should "work properly" in { - val layer = Convolution3D[Float](12, 2, 1, 3, inputShape = Shape(3, 32, 32, 32)) - layer.build(Shape(2, 3, 32, 32, 32)) - val input = Tensor[Float](2, 3, 32, 32, 32).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "MaxPooling1D serializer" should "work properly" in { - val layer = MaxPooling1D[Float](inputShape = Shape(12, 12)) - layer.build(Shape(2, 12, 12)) - val input = Tensor[Float](2, 12, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "MaxPooling3D serializer" should "work properly" in { - val layer = MaxPooling3D[Float](inputShape = Shape(3, 20, 15, 35)) - layer.build(Shape(2, 3, 20, 15, 35)) - val input = Tensor[Float](2, 3, 20, 15, 35).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "AveragePooling1D serializer" should "work properly" in { - val layer = AveragePooling1D[Float](inputShape = Shape(12, 16)) - layer.build(Shape(2, 12, 16)) - val input = Tensor[Float](2, 12, 16).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "AveragePooling2D serializer" should "work properly" in { - val layer = AveragePooling2D[Float](inputShape = Shape(3, 24, 24)) - layer.build(Shape(2, 3, 24, 24)) - val input = Tensor[Float](2, 3, 24, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "AveragePooling3D serializer" should "work properly" in { - val layer = AveragePooling3D[Float](inputShape = Shape(3, 12, 12, 12)) - layer.build(Shape(2, 3, 12, 12, 12)) - val input = Tensor[Float](2, 3, 12, 12, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GlobalMaxPooling2D serializer" should "work properly" in { - val layer = GlobalMaxPooling2D[Float](inputShape = Shape(4, 24, 32)) - layer.build(Shape(2, 4, 24, 32)) - val input = Tensor[Float](2, 4, 24, 32).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GlobalAveragePooling2D serializer" should "work properly" in { - val layer = GlobalAveragePooling2D[Float](inputShape = Shape(4, 24, 32)) - layer.build(Shape(2, 4, 24, 32)) - val input = Tensor[Float](2, 4, 24, 32).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "RepeatVector serializer" should "work properly" in { - val layer = RepeatVector[Float](4, inputShape = Shape(12)) - layer.build(Shape(2, 12)) - val input = Tensor[Float](2, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Permute serializer" should "work properly" in { - val layer = Permute[Float](Array(3, 1, 4, 2), inputShape = Shape(3, 4, 5, 6)) - layer.build(Shape(2, 3, 4, 5, 6)) - val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GlobalAveragePooling1D serializer" should "work properly" in { - val layer = GlobalAveragePooling1D[Float](inputShape = Shape(3, 24)) - layer.build(Shape(2, 3, 24)) - val input = Tensor[Float](2, 3, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GlobalAveragePooling3D serializer" should "work properly" in { - val layer = GlobalAveragePooling3D[Float](inputShape = Shape(3, 4, 5, 6)) - layer.build(Shape(2, 3, 4, 5, 6)) - val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Cropping1D serializer" should "work properly" in { - val layer = Cropping1D[Float](inputShape = Shape(5, 6)) - layer.build(Shape(2, 5, 6)) - val input = Tensor[Float](2, 5, 6).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Cropping2D serializer" should "work properly" in { - val layer = Cropping2D[Float](inputShape = Shape(3, 8, 12)) - layer.build(Shape(2, 3, 8, 12)) - val input = Tensor[Float](2, 3, 8, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Cropping3D serializer" should "work properly" in { - val layer = Cropping3D[Float](inputShape = Shape(4, 12, 16, 20)) - layer.build(Shape(2, 4, 12, 16, 20)) - val input = Tensor[Float](2, 4, 12, 16, 20).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "ELU serializer" should "work properly" in { - val layer = ELU[Float](2.7, inputShape = Shape(3, 24)) - layer.build(Shape(2, 3, 24)) - val input = Tensor[Float](2, 3, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GaussianDropout serializer" should "work properly" in { - val layer = GaussianDropout[Float](0.6, inputShape = Shape(3, 4)) - layer.build(Shape(2, 3, 4)) - val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GaussianNoise serializer" should "work properly" in { - val layer = GaussianNoise[Float](0.8, inputShape = Shape(12, 24)) - layer.build(Shape(2, 12, 24)) - val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "LeakyReLU serializer" should "work properly" in { - val layer = LeakyReLU[Float](1.27, inputShape = Shape(8, 24)) - layer.build(Shape(2, 8, 24)) - val input = Tensor[Float](2, 8, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Masking serializer" should "work properly" in { - val layer = Masking[Float](0.0, inputShape = Shape(3, 12)) - layer.build(Shape(2, 3, 12)) - val input = Tensor[Float](2, 3, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "SpatialDropout1D serializer" should "work properly" in { - val layer = SpatialDropout1D[Float](0.5, inputShape = Shape(3, 4)) - layer.build(Shape(2, 3, 4)) - val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "SpatialDropout2D serializer" should "work properly" in { - val layer = SpatialDropout2D[Float](0.5, "tf", inputShape = Shape(3, 64, 64)) - layer.build(Shape(2, 3, 64, 64)) - val input = Tensor[Float](2, 3, 64, 64).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "SpatialDropout3D serializer" should "work properly" in { - val layer = SpatialDropout3D[Float](0.5, "tf", inputShape = Shape(3, 4, 5, 6)) - layer.build(Shape(2, 3, 4, 5, 6)) - val input = Tensor[Float](2, 3, 4, 5, 6).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "SReLU serializer" should "work properly" in { - val layer = SReLU[Float](sharedAxes = Array(1, 2), inputShape = Shape(4, 32)) - layer.build(Shape(2, 4, 32)) - val input = Tensor[Float](2, 4, 32).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "ThresholdedReLU serializer" should "work properly" in { - val layer = ThresholdedReLU[Float](2.7, inputShape = Shape(3, 128)) - layer.build(Shape(2, 3, 128)) - val input = Tensor[Float](2, 3, 128).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GlobalMaxPooling1D serializer" should "work properly" in { - val layer = GlobalMaxPooling1D[Float](inputShape = Shape(12, 24)) - layer.build(Shape(2, 12, 24)) - val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "GlobalMaxPooling3D serializer" should "work properly" in { - val layer = GlobalMaxPooling3D[Float](inputShape = Shape(12, 24, 3, 6)) - layer.build(Shape(2, 12, 24, 3, 6)) - val input = Tensor[Float](2, 12, 24, 3, 6).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "LocallyConnected2D serializer" should "work properly" in { - val layer = LocallyConnected2D[Float](32, 2, 2, activation = "relu", - inputShape = Shape(12, 24, 24)) - layer.build(Shape(2, 12, 24, 24)) - val input = Tensor[Float](2, 12, 24, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "SeparableConvolution2D serializer" should "work properly" in { - val layer = SeparableConvolution2D[Float](1, 2, 2, inputShape = Shape(3, 128, 128)) - layer.build(Shape(2, 3, 128, 128)) - val input = Tensor[Float](2, 3, 128, 128).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "ZeroPadding3D serializer" should "work properly" in { - val layer = ZeroPadding3D[Float]((1, 1, 1), inputShape = Shape(5, 6, 7, 8)) - layer.build(Shape(2, 5, 6, 7, 8)) - val input = Tensor[Float](2, 5, 6, 7, 8).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "LocallyConnected1D serializer" should "work properly" in { - val layer = LocallyConnected1D[Float](32, 3, inputShape = Shape(12, 24)) - layer.build(Shape(2, 12, 24)) - val input = Tensor[Float](2, 12, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "ConvLSTM2D serializer" should "work properly" in { - val layer = ConvLSTM2D[Float](32, 4, inputShape = Shape(8, 40, 40, 32)) - layer.build(Shape(2, 8, 40, 40, 32)) - val input = Tensor[Float](2, 8, 40, 40, 32).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Deconvolution2D serializer" should "work properly" in { - val layer = Deconvolution2D[Float](3, 3, 3, inputShape = Shape(12, 24, 24)) - layer.build(Shape(2, 12, 24, 24)) - val input = Tensor[Float](2, 12, 24, 24).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "AtrousConvolution1D serializer" should "work properly" in { - val layer = AtrousConvolution1D[Float](64, 3, inputShape = Shape(8, 32)) - layer.build(Shape(2, 8, 32)) - val input = Tensor[Float](2, 8, 32).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "AtrousConvolution2D serializer" should "work properly" in { - val layer = AtrousConvolution2D[Float](32, 2, 4, atrousRate = (2, 2), - inputShape = Shape(3, 64, 64)) - layer.build(Shape(2, 3, 64, 64)) - val input = Tensor[Float](2, 3, 64, 64).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Embedding serializer" should "work properly" in { - val layer = Embedding[Float](1000, 32, inputShape = Shape(4)) - layer.build(Shape(2, 4)) - val input = Tensor[Float](2, 4) - input(Array(1, 1)) = 1 - input(Array(1, 2)) = 2 - input(Array(1, 3)) = 4 - input(Array(1, 4)) = 5 - input(Array(2, 1)) = 4 - input(Array(2, 2)) = 3 - input(Array(2, 3)) = 2 - input(Array(2, 4)) = 6 - runSerializationTest(layer, input) - } - - "BatchNormalization serializer" should "work properly" in { - val layer = BatchNormalization[Float](inputShape = Shape(3, 12, 12)) - layer.build(Shape(2, 3, 12, 12)) - val input = Tensor[Float](2, 3, 12, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "ZeroPadding1D serializer" should "work properly" in { - val layer = ZeroPadding1D[Float](padding = 2, inputShape = Shape(4, 5)) - layer.build(Shape(2, 4, 5)) - val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "ZeroPadding2D serializer" should "work properly" in { - val layer = ZeroPadding2D[Float](padding = (2, 1), inputShape = Shape(2, 8, 8)) - layer.build(Shape(2, 2, 8, 8)) - val input = Tensor[Float](2, 2, 8, 8).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "UpSampling1D serializer" should "work properly" in { - val layer = UpSampling1D[Float](inputShape = Shape(4, 5)) - layer.build(Shape(2, 4, 5)) - val input = Tensor[Float](2, 4, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "UpSampling2D serializer" should "work properly" in { - val layer = UpSampling2D[Float](inputShape = Shape(4, 8, 8)) - layer.build(Shape(2, 4, 8, 8)) - val input = Tensor[Float](2, 4, 8, 8).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "UpSampling3D serializer" should "work properly" in { - val layer = UpSampling3D[Float](inputShape = Shape(3, 8, 10, 12)) - layer.build(Shape(2, 3, 8, 10, 12)) - val input = Tensor[Float](2, 3, 8, 10, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "MaxoutDense serializer" should "work properly" in { - val layer = MaxoutDense[Float](8, inputShape = Shape(12)) - layer.build(Shape(3, 12)) - val input = Tensor[Float](3, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Merge serializer" should "work properly" in { - val l1 = InputLayer[Float](inputShape = Shape(4, 8)) - val l2 = InputLayer[Float](inputShape = Shape(4, 8)) - val layer = Merge[Float](layers = List(l1, l2), mode = "sum") - layer.build(Shape(List(Shape(2, 4, 8), Shape(2, 4, 8)))) - val input1 = Tensor[Float](2, 4, 8).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](2, 4, 8).apply1(e => Random.nextFloat()) - val input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(layer, input) - } - - "TimeDistributed serializer" should "work properly" in { - val layer = TimeDistributed[Float](Dense(8), inputShape = Shape(10, 12)) - layer.build(Shape(3, 10, 12)) - val input = Tensor[Float](3, 10, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "Bidirectional serializer" should "work properly" in { - val layer = Bidirectional[Float](SimpleRNN(4, returnSequences = true), - inputShape = Shape(8, 12)) - layer.build(Shape(3, 8, 12)) - val input = Tensor[Float](3, 8, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - - "KerasLayerWrapper serializer" should "work properly" in { - val layer = new KerasLayerWrapper[Float](ReLU[Float](), inputShape = Shape(8, 12)) - layer.build(Shape(3, 8, 12)) - val input = Tensor[Float](3, 8, 12).apply1(_ => Random.nextFloat()) - runSerializationTest(layer, input) - } - -} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala deleted file mode 100644 index 9fddede6fba..00000000000 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializerSpec.scala +++ /dev/null @@ -1,1554 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.utils.serializer - -import java.io.File -import java.io.{File => JFile} -import java.lang.reflect.Modifier - -import com.google.protobuf.{ByteString, CodedOutputStream} -import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat.NHWC - -import scala.collection.JavaConverters._ -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, Cast, Ceil, CrossEntropy, Digamma, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, Sign, Slice, SquaredDifference, Substr, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} -import com.intel.analytics.bigdl.nn.tf.{BiasAdd, BroadcastGradientArgs, Const, ControlNodes, Fill, Log1p, ParseExample, Shape, SoftplusGrad, SoftsignGrad, SplitAndSelect, SqrtGrad, StrideSlice, TensorModuleWrapper, Variable, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} -import com.intel.analytics.bigdl.nn.{DenseToSparse, SpatialDropout1D, _} -import com.intel.analytics.bigdl.optim.L2Regularizer -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.RandomGenerator.RNG -import com.intel.analytics.bigdl.utils.tf.TFRecordIterator -import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _, _} -import com.intel.analytics.bigdl.utils.{T, Table} -import org.reflections.Reflections -import org.reflections.scanners.SubTypesScanner -import org.reflections.util.{ClasspathHelper, ConfigurationBuilder, FilterBuilder} -import org.scalatest.{BeforeAndAfterAll, FlatSpec, Matchers} -import org.tensorflow.example._ -import org.tensorflow.framework.DataType - -import scala.collection.mutable -import scala.util.Random - - -class ModuleSerializerSpec extends SerializerSpecHelper { - - override def getPackage(): String = "com.intel.analytics.bigdl.nn" - - override def addExcludedClass(): Unit = { - excludedClass.add("com.intel.analytics.bigdl.nn.CellUnit") - excludedClass.add("com.intel.analytics.bigdl.nn.tf.ControlDependency") - excludedClass.add("com.intel.analytics.bigdl.utils.tf.AdapterForTest") - excludedClass.add("com.intel.analytics.bigdl.utils.serializer.TestModule") - excludedClass.add("com.intel.analytics.bigdl.utils.ExceptionTest") - } - - override def addExcludedPackage(): Unit = { - excludedPackage.add("com.intel.analytics.bigdl.utils.tf.loaders") - // It would be tested in a separated spec - excludedPackage.add("com.intel.analytics.bigdl.nn.keras") - excludedPackage.add("com.intel.analytics.bigdl.nn.ops") - excludedPackage.add("com.intel.analytics.bigdl.nn.tf") - } - - - "Abs serializer" should "work properly" in { - val abs = Abs[Float]().setName("abs") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(abs, input) - } - - "ActivityRegularization serializer" should "work properly" in { - val activityRegularization = ActivityRegularization[Float](l1 = 0.01, l2 = 0.01). - setName("activityRegularization") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(activityRegularization, input) - } - - "UpSampling1D serializer" should "work properly" in { - val upsampling = UpSampling1D[Float](2).setName("upsampling") - val input = Tensor[Float](2, 5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(upsampling, input) - } - - "UpSampling2D serializer" should "work properly" in { - val upsampling = UpSampling2D[Float](Array(2, 3)).setName("upsampling") - val input = Tensor[Float](2, 3, 5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(upsampling, input) - } - - "Add serializer" should "work properly" in { - val add = Add[Float](5).setName("add") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(add, input) - } - - "AddConst serializer" should "work properly" in { - val addconst = AddConstant[Float](5).setName("addconst") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(addconst, input) - } - - "BatchNormalization serializer" should "work properly" in { - val batchNorm = BatchNormalization[Float](5).setName("batchNorm") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(batchNorm, input) - } - - "BifurcateSplitTable serializer" should "work properly" in { - val batchNorm = BifurcateSplitTable[Float](1).setName("batchNorm") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(batchNorm, input) - } - - "BiLinear serializer" should "work properly" in { - val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](5, 3).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - val biLinear = Bilinear[Float](5, 3, 2) - runSerializationTest(biLinear, input) - } - - "BinaryThreshold serializer" should "work properly" in { - val binaryThreshold = BinaryThreshold[Float]().setName("binaryThreshold") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(binaryThreshold, input) - } - - "SpatialDropout1D serializer" should "work properly" in { - val spatialDropout1D = SpatialDropout1D[Float]() - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(spatialDropout1D, input) - } - - "SpatialDropout2D serializer" should "work properly" in { - val spatialDropout2D = SpatialDropout2D[Float]() - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(spatialDropout2D, input) - } - - "SpatialDropout3D serializer" should "work properly" in { - val spatialDropout3D = SpatialDropout3D[Float]() - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(spatialDropout3D, input) - } - - "LocallyConnected1D serializer" should "work properly" in { - val localyConnected1d = - LocallyConnected1D[Float](6, 2, outputFrameSize = 2, kernelW = 3, strideW = 1) - val input = Tensor[Float](6, 2).randn() - runSerializationTest(localyConnected1d, input) - } - - "BinaryTreeLSTM serializer" should "work properly" in { - - RNG.setSeed(1000) - val binaryTreeLSTM = BinaryTreeLSTM[Float](2, 2).setName("binaryTreeLSTM") - - val inputs = - Tensor[Float]( - T(T(T(1f, 2f), - T(2f, 3f), - T(4f, 5f)))) - - val tree = - Tensor[Float]( - T(T(T(2f, 5f, -1f), - T(0f, 0f, 1f), - T(0f, 0f, 2f), - T(0f, 0f, 3f), - T(3f, 4f, 0f)))) - - val input = T(inputs, tree) - - runSerializationTest(binaryTreeLSTM, input) - } - - "BiRecurrent serializer" should "work properly" in { - val input = Tensor[Float](1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) - RNG.setSeed(100) - val biRecurrent = BiRecurrent[Float]().add(RnnCell[Float](6, 4, - Sigmoid[Float]())).setName("biRecurrent") - runSerializationTest(biRecurrent, input) - } - - "BiRecurrent serializer with BatchNormParams" should "work properly" in { - val input = Tensor[Float](1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) - RNG.setSeed(100) - val biRecurrent = BiRecurrent[Float](batchNormParams = - BatchNormParams()).add(RnnCell[Float](6, 4, Sigmoid[Float]())).setName("biRecurrentWithNorm") - runSerializationTest(biRecurrent, input) - } - - - "BiRecurrent serializer" should "work properly with isSplitInput" in { - val input = Tensor[Float](1, 5, 6).apply1(e => Random.nextFloat()).transpose(1, 2) - val biRecurrent = BiRecurrent[Float](isSplitInput = false) - .add(RnnCell[Float](6, 4, Sigmoid[Float]())).setName("biRecurrentWithSplit") - runSerializationTest(biRecurrent, input) - } - - "Bottle serializer" should "work properly" in { - val input = Tensor[Float](10).apply1(e => Random.nextFloat()) - - val bottle = new Bottle[Float](Linear[Float](10, 2). - asInstanceOf[Module[Float]], 2, 2).setName("bottle") - runSerializationTest(bottle, input) - } - - "Caddserializer" should "work properly" in { - val input = Tensor[Float](5, 1).apply1(e => Random.nextFloat()) - val cadd = CAdd[Float](Array(5, 1)).setName("cadd") - runSerializationTest(cadd, input) - } - - "CaddTable serializer" should "work properly" in { - val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - val caddTable = CAddTable[Float](false).setName("caddTable") - runSerializationTest(caddTable, input) - } - - "CAveTable serializer" should "work properly" in { - val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - val caveTable = CAveTable[Float](false).setName("caveTable") - runSerializationTest(caveTable, input) - } - - "CDivTable serializer" should "work properly" in { - val cdivTable = new CDivTable[Float]().setName("cdivTable") - val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(cdivTable, input) - } - - "Clamp serializer" should "work properly" in { - - val input = Tensor[Float](10).apply1(e => Random.nextFloat()) - val clamp = Clamp[Float](1, 10).setName("clamp") - runSerializationTest(clamp, input) - } - - "CMaxTable serializer" should "work properly" in { - val cmaxTable = new CMaxTable[Float]().setName("cmaxTable") - val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(cmaxTable, input) - } - - "CMinTable serializer" should "work properly" in { - val cminTable = new CMinTable[Float]().setName("cminTable") - val input1 = Tensor[Float](10).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](10).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(cminTable, input) - } - - "CMul serializer" should "work properly" in { - val input = Tensor[Float](5, 1).apply1(e => Random.nextFloat()) - val cmul = CMul[Float](Array(5, 1)).setName("cmul") - runSerializationTest(cmul, input) - } - - "CMulTable serializer" should "work properly" in { - val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - - val cmulTable = CMulTable[Float]().setName("cmulTable") - runSerializationTest(cmulTable, input) - } - - "Concatserializer" should "work properly" in { - val input = Tensor[Float](2, 2, 2).apply1(e => Random.nextFloat()) - val concat = Concat[Float](2).setName("concat") - concat.add(Abs[Float]()) - concat.add(Abs[Float]()) - runSerializationTest(concat, input) - } - - "ConcatTable serializer" should "work properly" in { - val concatTable = new ConcatTable[Float]().setName("concatTable") - concatTable.add(Linear[Float](10, 2)) - concatTable.add(Linear[Float](10, 2)) - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(concatTable, input) - } - - "Contiguous serializer" should "work properly" in { - val contiguous = Contiguous[Float]().setName("contiguous") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(contiguous, input) - } - - "ConvLSTMPeephole2D serializer" should "work properly" in { - val hiddenSize = 5 - val inputSize = 3 - val seqLength = 4 - val batchSize = 2 - val kernalW = 3 - val kernalH = 3 - val c2d = ConvLSTMPeephole[Float]( - inputSize, - hiddenSize, - kernalW, kernalH, - 1, - withPeephole = false) - val convLSTMPeephole2d = Recurrent[Float]().setName("convLSTMPeephole2d") - val model = Sequential[Float]() - .add(convLSTMPeephole2d - .add(c2d)) - .add(View[Float](hiddenSize * kernalH * kernalW)) - - val input = Tensor[Float](batchSize, seqLength, inputSize, kernalW, kernalH).rand - runSerializationTest(convLSTMPeephole2d, input, c2d.getClass) - } - - "ConvLSTMPeephole3D serializer" should "work properly" in { - val hiddenSize = 5 - val inputSize = 3 - val seqLength = 4 - val batchSize = 2 - val kernalW = 3 - val kernalH = 3 - val c3d = ConvLSTMPeephole3D[Float]( - inputSize, - hiddenSize, - kernalW, kernalH, - 1, - withPeephole = false) - val convLSTMPeephole3d = Recurrent[Float]().setName("convLSTMPeephole3d") - val model = Sequential[Float]() - .add(convLSTMPeephole3d - .add(c3d)) - .add(View[Float](hiddenSize * kernalH * kernalW)) - - val input = Tensor[Float](batchSize, seqLength, inputSize, kernalW, kernalH, 3).rand - runSerializationTest(convLSTMPeephole3d, input, c3d.getClass) - } - - "Cosine serializer" should "work properly" in { - val cosine = Cosine[Float](5, 5).setName("cosine") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(cosine, input) - } - - "CosineDistance serializer" should "work properly" in { - val cosineDistance = CosineDistance[Float]().setName("cosineDistance") - val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(cosineDistance, input) - } - - "Cropping2d serializer" should "work properly" in { - val cropping2d = Cropping2D[Float](Array(2, 2), Array(2, 2), DataFormat.NCHW) - .setName("Cropping2D") - val input = Tensor[Float](1, 9, 9, 9).apply1(_ => Random.nextFloat()) - runSerializationTest(cropping2d, input) - } - - "Cropping3d serializer" should "work properly" in { - val cropping3d = Cropping3D[Float](Array(2, 2), Array(2, 2), Array(2, 2)).setName("Cropping3D") - val input = Tensor[Float](1, 9, 9, 9, 9).apply1(_ => Random.nextFloat()) - runSerializationTest(cropping3d, input) - } - - "CrossProduct serializer" should "work properly" in { - val crossProd = CrossProduct[Float]() - val input = T(Tensor[Float](T(1.0f, 2.0f)), - Tensor[Float](T(2.0f, 3.0f)), Tensor[Float](T(3.0f, 4.0f))) - runSerializationTest(crossProd, input) - } - - - "CSubTable serializer" should "work properly" in { - val csubTable = CSubTable[Float]().setName("csubTable") - - val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(csubTable, input) - } - - "DenseToSparse serializer" should "work properly" in { - val denseToSparse = DenseToSparse[Float]().setName("denseToSparse") - val input = Tensor.range[Float](1, 12, 1) - runSerializationTest(denseToSparse, input) - } - - "Dotproduct serializer" should "work properly" in { - val dotProduct = DotProduct[Float]().setName("dotProduct") - val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(dotProduct, input) - } - - "Dropout serializer" should "work properly" in { - RNG.setSeed(100) - val dropout = Dropout[Float]().setName("dropout") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(dropout, input) - } - - "Echo serializer" should "work properly" in { - val echo = Echo[Float]().setName("echo") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(echo, input) - } - - "ELU serializer" should "work properly" in { - val elu = ELU[Float]().setName("elu") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(elu, input) - } - - "Euclidena serializer" should "work properly" in { - val euclidean = Euclidean[Float](7, 7).setName("euclidean") - val input = Tensor[Float](8, 7).apply1(_ => Random.nextFloat()) - runSerializationTest(euclidean, input) - } - - "Exp serializer" should "work properly" in { - val exp = Exp[Float]().setName("exp") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(exp, input) - } - - "FlattenTable serializer" should "work properly" in { - val flattenTable = FlattenTable[Float]().setName("flattenTable") - val input1 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](5, 5).apply1(e => Random.nextFloat()) - var input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(flattenTable, input) - } - - "GaussianDropout serializer" should "work properly" in { - RNG.setSeed(1000) - val gaussianDropout = GaussianDropout[Float](0.5).setName("gaussianDropout") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(gaussianDropout, input) - } - - "GaussianNoise serializer" should "work properly" in { - RNG.setSeed(1000) - val gaussianNoise = GaussianNoise[Float](0.5).setName("gaussianNoise") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(gaussianNoise, input) - } - - "GaussianSampler serializer" should "work properly" in { - val input1 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) - val input2 = Tensor[Float](2, 3).apply1(x => RNG.uniform(0, 1).toFloat) - val input = T(input1, input2) - RNG.setSeed(1000) - val gaussianSampler = GaussianSampler[Float]().setName("gaussianSampler") - runSerializationTest(gaussianSampler, input) - } - - "GradientReversal serializer" should "work properly" in { - val gradientReversal = GradientReversal[Float]().setName("gradientReversal") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(gradientReversal, input) - } - - "Graph serializer" should "work properly" in { - val linear = Linear[Float](10, 2).inputs() - val graph = Graph[Float](linear, linear).setName("graph") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(graph, input) - } - - "Graph with variables serializer" should "work properly" in { - val linear = Linear[Float](2, 2) - val linearNode = linear.inputs() - val linearWeight = linear.weight - val linearBias = linear.bias - val variables = Some(Array(linearWeight), Array(linearBias)) - val graphWithVariable = Graph[Float](Array(linearNode), Array(linearNode), - variables).setName("graphWithVariable") - val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) - runSerializationTest(graphWithVariable, input) - } - - "Dynamic Graph with variables serializer" should "work properly" in { - val linear = Linear[Float](2, 2) - val linearNode = linear.inputs() - val linearWeight = linear.weight - val linearBias = linear.bias - val variables = Some(Array(linearWeight), Array(linearBias)) - val graphWithVariable = Graph.dynamic[Float](Array(linearNode), Array(linearNode), - variables, false).setName("graphWithVariable") - val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) - runSerializationTest(graphWithVariable, input) - } - - "Dynamic Graph with control ops serializer" should "work properly" in { - val data = Input[Float]("data") - val condition = Input[Float]("condition") - val swtich = ControlNodes.switch(condition, data) - val echo1 = Echo[Float]().inputs(swtich.trueEdge()) - val echo2 = Echo[Float]().inputs(swtich.falseEdge()) - - val model = Graph.dynamic[Float](Array(data, condition), Array(echo1), None, false) - - val input = T(Tensor[Float](T(1)), Tensor[Boolean](T(true))) - - runSerializationTest(model, input) - } - - "Graph with stop gradient layer" should "work properly" in { - val linear1 = Linear[Float](2, 2).setName("first").inputs() - val linear2 = Linear[Float](2, 2).setName("second").inputs(linear1) - val graph = Graph[Float](Array(linear1), Array(linear2)).setName("graphWithStopGradient") - graph.stopGradient(Array("first")) - val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) - runSerializationTest(graph, input) - } - - "GRU serializer" should "work properly" in { - RNG.setSeed(100) - val gru = GRU[Float](100, 100) - val gruModel = Recurrent[Float]().add(gru).setName("gru") - val input = Tensor[Float](2, 20, 100).apply1(e => Random.nextFloat()) - runSerializationTest(gruModel, input, gru.getClass) - } - - "HardShrink serializer" should "work properly" in { - val hardShrink = HardShrink[Float]().setName("hardShrink") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(hardShrink, input) - } - - "HardTanh serializer" should "work properly" in { - val hardTanh = HardTanh[Float]().setName("hardTanh") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(hardTanh, input) - } - - "HardSigmoid serialization" should "work properly" in { - val hardSigmoid = HardSigmoid[Float]().setName("hardSigmoid") - val input = Tensor[Float](2, 2).rand() - runSerializationTest(hardSigmoid, input) - } - - "Identity serializer" should "work properly" in { - val identity = Identity[Float]().setName("identity") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(identity, input) - } - - "Index serializer" should "work properly" in { - val index = Index[Float](1).setName("index") - val input1 = Tensor[Float](3).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](4) - input2(Array(1)) = 1 - input2(Array(2)) = 2 - input2(Array(3)) = 2 - input2(Array(4)) = 3 - val input = new Table() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(index, input) - } - - "InferReshape serializer" should "work properly" in { - val inferReshape = InferReshape[Float](Array(-1, 2, 0, 5)).setName("inferReshape") - val input = Tensor[Float](2, 5, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(inferReshape, input) - } - - "Input serializer" should "work properly " in { - val inputl = Input[Float]().element.setName("input") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(inputl, input) - } - - "JoinTable serializer" should "work properly" in { - val joinTable = JoinTable[Float](2, 2).setName("joinTable") - val input1 = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - val input = T() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(joinTable, input) - - } - - "L1Penalty serializer" should "work properly" in { - val l1Penalty = L1Penalty[Float](1, true, true).setName("l1Penalty") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(l1Penalty, input) - } - - "NegativeEntropyPenalty serializer" should "work properly" in { - val penalty = NegativeEntropyPenalty[Float](0.01).setName("NegativeEntropyPenalty") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(penalty, input) - } - - "LeakReLu serializer" should "work properly" in { - val leakyReLU = LeakyReLU[Float](0.01, true).setName("leakyReLU") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(leakyReLU, input) - } - - "Linear serializer" should "work properly" in { - val linear = Linear[Float](10, 2).setName("linear") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(linear, input) - } - - "Log Serializer" should "work properly" in { - val log = Log[Float]().setName("log") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(log, input) - } - - "LogSigmoid serializer" should "work properly" in { - val logSigmoid = LogSigmoid[Float]().setName("logSigmoid") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(logSigmoid, input) - } - - "LogSogMax serializer" should "work properly" in { - val logSoftMax = LogSoftMax[Float]().setName("logSoftMax") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(logSoftMax, input) - } - - "LookupTable serializer" should "work properly" in { - val lookupTable = LookupTable[Float](9, 4, 2, 0.1, 2.0, true).setName("lookupTable") - val input = Tensor[Float](5) - input(Array(1)) = 5 - input(Array(2)) = 2 - input(Array(3)) = 6 - input(Array(4)) = 9 - input(Array(5)) = 4 - runSerializationTest(lookupTable, input) - } - - "LSTM serializer" should "work properly" in { - val lstm = LSTM[Float](6, 4) - val lstmModel = Recurrent[Float]().add(lstm).setName("lstm") - val input = Tensor[Float](Array(1, 5, 6)).apply1(_ => Random.nextFloat()) - runSerializationTest(lstmModel, input, lstm.getClass) - } - - "LookupTableSparse serializer" should "work properly" in { - val lookupTableSparse = LookupTableSparse[Float](20, 10, "sum", 1) - val indices1 = Array(0, 0, 1, 2) - val indices2 = Array(0, 1, 0, 3) - val values = Array(2f, 4, 1, 2) - val input = Tensor.sparse[Float](Array(indices1, indices2), values, Array(3, 4)) - runSerializationTest(lookupTableSparse, input, lookupTableSparse.getClass) - } - - "LSTMPeephole serializer" should "work properly" in { - val lstmPeephole = LSTMPeephole[Float](6, 4) - val lstmPeepholeModel = Recurrent[Float]().add(lstmPeephole).setName("lstmPeephole") - val input = Tensor[Float](Array(1, 5, 6)).apply1(_ => Random.nextFloat()) - runSerializationTest(lstmPeepholeModel, input, lstmPeephole.getClass) - } - - "MapTable serializer" should "work properly" in { - val linear = Linear[Float](2, 2) - val mapTable = new MapTable[Float]().setName("mapTable") - mapTable.add(linear) - val input1 = Tensor[Float](2).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](2).apply1(_ => Random.nextFloat()) - val input = T() - input(1.0.toFloat) = input1 - input(2.0.toFloat) = input2 - runSerializationTest(mapTable, input) - } - - "MaskedSelect serializer" should "work properly" in { - val maskedSelect = MaskedSelect[Float]().setName("maskedSelect") - val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](2, 2) - input2(Array(1, 1)) = 1 - input2(Array(1, 2)) = 0 - input2(Array(2, 1)) = 0 - input2(Array(2, 2)) = 1 - val input = new Table() - input(1.0f) = input1 - input(2.0f) = input2 - runSerializationTest(maskedSelect, input) - } - - "Masking serializer" should "work properly" in { - val masking = Masking[Float](0.1).setName("masking") - val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) - runSerializationTest(masking, input) - } - - "Max serializer" should "work properly" in { - val max = new Max[Float](2).setName("max") - val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) - runSerializationTest(max, input) - } - - "Maxout serializer" should "work properly" in { - val maxout = Maxout[Float](2, 4, 5).setName("maxout") - val input = Tensor[Float](2).apply1(_ => Random.nextFloat()) - runSerializationTest(maxout, input) - } - - "Mean serializer" should "work properly " in { - val mean = Mean[Float](2).setName("mean") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(mean, input) - } - - "Min serializer" should "work properly " in { - val min = Min[Float](2).setName("min") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(min, input) - } - - "MixtureTable Serializer" should "work properly " in { - val mixTureTable = MixtureTable[Float]().setName("mixTureTable") - val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - val input = new Table() - input(1.0f) = input1 - input(2.0f) = input2 - runSerializationTest(mixTureTable, input) - } - - "MM Serializer" should "work properly" in { - val mm = MM[Float]().setName("mm_layer") - val input1 = Tensor[Float](2, 3).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](3, 4).apply1(e => Random.nextFloat()) - val input = new Table() - input(1.0f) = input1 - input(2.0f) = input2 - runSerializationTest(mm, input) - } - - "Mul Serializer" should "work properly" in { - val mul = Mul[Float]().setName("mul") - val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) - runSerializationTest(mul, input) - } - - "MulConst Serializer" should "work properly" in { - val mulConst = MulConstant[Float](1.0).setName("mulConst") - val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) - runSerializationTest(mulConst, input) - } - - "MultiRNNCell serializer" should "work properly" in { - val hiddenSize = 5 - val inputSize = 5 - val seqLength = 4 - val batchSize = 2 - val kernalW = 3 - val kernalH = 3 - val rec = RecurrentDecoder[Float](seqLength) - val cells = Array(ConvLSTMPeephole[Float]( - inputSize, - hiddenSize, - kernalW, kernalH, - 1), ConvLSTMPeephole[Float]( - inputSize, - hiddenSize, - kernalW, kernalH, - 1), ConvLSTMPeephole[Float]( - inputSize, - hiddenSize, - kernalW, kernalH, - 1)).asInstanceOf[Array[Cell[Float]]] - - val multiRNNCell = MultiRNNCell[Float](cells) - - val model = Sequential[Float]() - .add(rec - .add(multiRNNCell)).setName("multiRNNCell") - - val input = Tensor[Float](batchSize, inputSize, 10, 10).apply1(_ => Random.nextFloat()) - runSerializationTest(model, input, multiRNNCell.getClass) - } - - "MV Serializer" should "work properly" in { - val mv = MV[Float]().setName("mv_layer") - val input1 = Tensor[Float](2, 3).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](3).apply1(e => Random.nextFloat()) - val input = new Table() - input(1.0f) = input1 - input(2.0f) = input2 - runSerializationTest(mv, input) - } - - "Narrow serializer" should "work properly" in { - val narrow = Narrow[Float](1, 3, -3).setName("narrow") - val input = Tensor[Float](9, 4, 14).apply1(e => Random.nextFloat()) - runSerializationTest(narrow, input) - } - - "NarrowTable serializer" should "work properly" in { - val narrowTable = NarrowTable[Float](1, 1) - val input = T() - input(1.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - input(2.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - input(3.0) = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - runSerializationTest(narrowTable, input) - } - - "Negative serializer" should "work properly" in { - val negative = Negative[Float]().setName("negative") - val input = Tensor[Float](10).apply1(e => Random.nextFloat()) - runSerializationTest(negative, input) - } - - "Normlize serializer" should "work properly" in { - val normalizer = Normalize[Float](2).setName("normalizer") - val input = Tensor[Float](2, 3, 4, 4).apply1(e => Random.nextFloat()) - runSerializationTest(normalizer, input) - } - - "NormalizeScale serializer" should "work properly" in { - val module = NormalizeScale[Float](2, scale = 20, size = Array(1, 5, 1, 1), - wRegularizer = L2Regularizer[Float](0.2)).setName("NormalizeScale") - - val input = Tensor[Float](1, 5, 3, 4).randn() - runSerializationTest(module, input) - } - - "Pack serializer" should "work properly" in { - val pack = new Pack[Float](1).setName("pack") - val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - val input = T() - input(1.0f) = input1 - input(2.0f) = input2 - runSerializationTest(pack, input) - } - - "Padding serializer" should "work properly" in { - val padding = Padding[Float](1, -1, 4, -0.8999761, 14).setName("padding") - val input = Tensor[Float](3, 13, 11).apply1(e => Random.nextFloat()) - runSerializationTest(padding, input) - } - - "PairwiseDistance serializer" should "work properly" in { - val pairwiseDistance = new PairwiseDistance[Float](3).setName("pairwiseDistance") - val input1 = Tensor[Float](3, 3).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](3, 3).apply1(e => Random.nextFloat()) - val input = T(1.0f -> input1, 2.0f -> input2) - runSerializationTest(pairwiseDistance, input) - } - - "ParallelTable serializer" should "work properly" in { - val parallelTable = ParallelTable[Float]().setName("parallelTable") - parallelTable.add(Linear[Float](2, 2)) - parallelTable.add(Linear[Float](2, 2)) - val input1 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - val input2 = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - val input = T(1.0f -> input1, 2.0f -> input2) - runSerializationTest(parallelTable, input) - } - - "Power serializer" should "work properly" in { - val power = Power[Float](2.0).setName("power") - val input = Tensor[Float](2, 2).apply1(e => Random.nextFloat()) - runSerializationTest(power, input) - } - - "Proposal serializer" should "work properly" in { - val proposal = Proposal(200, 100, Array[Float](0.1f, 0.2f, 0.3f), Array[Float](4, 5, 6)) - val score = Tensor[Float](1, 18, 20, 30).randn() - val boxes = Tensor[Float](1, 36, 20, 30).randn() - val imInfo = Tensor[Float](T(300, 300, 1, 1)).resize(1, 4) - val input = T(score, boxes, imInfo) - runSerializationTest(proposal, input) - } - - "PReLU serializer" should "work properly" in { - val preLu = PReLU[Float](2).setName("preLu") - val input = Tensor[Float](2, 3, 4).apply1(_ => Random.nextFloat()) - runSerializationTest(preLu, input) - } - - "Recurrent serializer" should "work properly" in { - val recurrent = Recurrent[Float]().setName("recurrent") - .add(RnnCell[Float](5, 4, Tanh[Float]())) - val input = Tensor[Float](Array(10, 5, 5)).apply1(_ => Random.nextFloat()) - runSerializationTest(recurrent, input) - } - - "Recurrent serializer" should "work properly with BatchNormParams" in { - val recurrent = Recurrent[Float](BatchNormParams()).setName("recurrentWithNorm") - .add(RnnCell[Float](5, 4, Tanh[Float]())) - val input = Tensor[Float](Array(10, 5, 5)).apply1(_ => Random.nextFloat()) - runSerializationTest(recurrent, input) - } - - "RecurrentDecoder serializer" should "work properly" in { - val recDecoder = RecurrentDecoder[Float](5). - add(ConvLSTMPeephole[Float](7, 7, 3, 3, 1)) - val input = Tensor[Float](4, 7, 5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(recDecoder, input) - } - - "ReLU serializer" should "work properly" in { - val relu = ReLU[Float]().setName("relu") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(relu, input) - } - - "ReLU6 serializer" should "work properly" in { - val relu6 = ReLU6[Float](false).setName("relu6") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(relu6, input) - } - - "Replicate serializer" should "work properly" in { - val replicate = new Replicate[Float](3).setName("replicate") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(replicate, input) - } - - "Reshape serializer" should "work properly" in { - val reshape = Reshape[Float](Array(1, 4, 5)).setName("reshape") - val input = Tensor[Float](2, 2, 5).apply1( _ => Random.nextFloat()) - runSerializationTest(reshape, input) - } - - "ResizeBilinear serializer" should "work properly" in { - val input = Tensor[Float](1, 3, 2, 3).apply1( _ => Random.nextFloat()) - val resizeBilinear = ResizeBilinear[Float](3, 2).setName("resizeBilinear") - runSerializationTest(resizeBilinear, input) - } - - "Reverse serializer" should "work properly" in { - val reverse = Reverse[Float]().setName("reverse") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(reverse, input) - } - - "RnnCell serializer" should "work properly" in { - val rnnCell = RnnCell[Float](6, 4, Sigmoid[Float]()).setName("rnnCell") - val input1 = Tensor[Float](Array(1, 4)).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](Array(1, 4)).apply1(_ => Random.nextFloat()) - val input = T() - input(1.0f) = input1 - input(2.0f) = input2 - runSerializationTest(rnnCell, input) - } - - "RoiPooling serializer" should " work properly" in { - val input = T() - val input1 = Tensor[Float](1, 1, 2, 2).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](1, 5).apply1(_ => Random.nextFloat()) - input(1.0f) = input1 - input(2.0f) = input2 - val roiPooling = new RoiPooling[Float](pooledW = 3, - pooledH = 2, 1.0f).setName("roiPooling") - runSerializationTest(roiPooling, input) - } - - "RReLU serializer" should "work properly" in { - val rrelu = new RReLU[Float](inplace = false).setName("rrelu") - val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(rrelu, input) - } - - "Scale serializer" should "work properly" in { - val scale = Scale[Float](Array(1, 4, 1, 1)).setName("scale") - val input = Tensor[Float](1, 4, 5, 6).apply1(_ => Random.nextFloat()) - runSerializationTest(scale, input) - } - - "Select serializer" should "work properly" in { - val select = Select[Float](2, 2).setName("select") - val input = Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(select, input) - } - - "SelectTable serializer" should "work properly" in { - val selectTable = SelectTable[Float](2).setName("selectTable") - val input1 = Tensor[Float](10).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](10).apply1(_ => Random.nextFloat()) - val input3 = Tensor[Float](10).apply1(_ => Random.nextFloat()) - val input = T(1.0 -> input1, 2.0 -> input2, 3.0 -> input3) - runSerializationTest(selectTable, input) - } - - "Sequential Container" should "work properly" in { - val sequential = Sequential[Float]().setName("sequential") - val linear = Linear[Float](10, 2) - sequential.add(linear) - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(sequential, input) - } - - "Sigmoid serializer" should "work properly" in { - val sigmoid = Sigmoid[Float]().setName("sigmoid") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(sigmoid, input) - } - - "SoftMax serializer" should "work properly" in { - val softMax = SoftMax[Float]().setName("softMax") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(softMax, input) - } - - "SoftMin serializer" should "work properly" in { - val softMin = SoftMin[Float]().setName("softMin") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(softMin, input) - } - - "SoftPlus serializer" should "work properly" in { - val softPlus = SoftPlus[Float]().setName("softPlus") - val input = Tensor[Float](10).apply1(_ => Random.nextFloat()) - runSerializationTest(softPlus, input) - } - - "SoftShrink serializer" should "work properly" in { - val softShrink = SoftShrink[Float]().setName("softShrink") - val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) - runSerializationTest(softShrink, input) - } - - "SoftSign serializer" should "work properly" in { - val softSign = SoftSign[Float]().setName("softSign") - val input = Tensor[Float](10, 10).apply1(_ => Random.nextFloat()) - runSerializationTest(softSign, input) - } - - "SparseJoinTable serializer" should "work properly" in { - val sparseJoinTable = SparseJoinTable[Float](2).setName("sparseJoinTable") - val sparseModel = Sequential[Float](). - add(ParallelTable[Float]().add(Identity[Float]()).add(Identity[Float]())) - .add(sparseJoinTable) - val input1 = Tensor[Float](4, 3).apply1(_ => Random.nextInt(2) * Random.nextFloat()) - val input2 = Tensor[Float](4, 2).apply1(_ => Random.nextInt(2) * Random.nextFloat()) - val sparseInput = T(Tensor.sparse(input1), Tensor.sparse(input2)) - runSerializationTest(sparseJoinTable, sparseInput) - } - - "SparseLinear serializer" should "work properly" in { - val sparseLinear = SparseLinear[Float](4, 2).setName("sparseLinear") - val input = Tensor[Float](2, 4).apply1(_ => Random.nextFloat()) - val sparseInput = Tensor.sparse(input) - runSerializationTest(sparseLinear, sparseInput) - } - - "SpatialAveragePooling serializer" should "work properly" in { - val spatialAveragePooling = new SpatialAveragePooling[Float](3, 2, 2, 1). - setName("spatialAveragePooling") - val input = Tensor[Float](1, 4, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(spatialAveragePooling, input) - } - - "SpatialBatchNormalization serializer" should "work properly" in { - val spatialBatchNorm = SpatialBatchNormalization[Float](5). - setName("spatialBatchNorm") - val input = Tensor[Float](2, 5, 4, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(spatialBatchNorm, input) - } - - "SpatialContrastiveNormalization serializer" should "work properly" in { - RNG.setSeed(100) - val spatialContrastiveNorm = new SpatialContrastiveNormalization[Float](). - setName("spatialContrastiveNorm") - val input = Tensor[Float](1, 5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(spatialContrastiveNorm, input) - } - - "SpatialConvolution serializer" should "work properly" in { - val spatialConvolution = SpatialConvolution[Float](3, 4, 2, 2). - setName("spatialConvolution") - val input = Tensor[Float](1, 3, 5, 5).apply1( e => Random.nextFloat()) - runSerializationTest(spatialConvolution, input) - } - - "LocallyConnected2D serializer" should "work properly" in { - val locallyConnected2D = LocallyConnected2D[Float](3, 5, 5, 4, 2, 2). - setName("locallyConnected2D") - val input = Tensor[Float](1, 3, 5, 5).apply1( e => Random.nextFloat()) - runSerializationTest(locallyConnected2D, input) - } - - "SpatialConvolutionMap serializer" should "work properly" in { - val spatialConvolutionMap = SpatialConvolutionMap[Float]( - SpatialConvolutionMap.random(1, 1, 1), 2, 2).setName("spatialConvolutionMap") - val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) - runSerializationTest(spatialConvolutionMap, input) - } - - "SpatialCrossMapLRN serializer" should "work properly" in { - val spatialCrossMapLRN = SpatialCrossMapLRN[Float](5, 0.01, 0.75, 1.0). - setName("spatialCrossMapLRN") - val input = Tensor[Float](2, 2, 2, 2).apply1( e => Random.nextFloat()) - runSerializationTest(spatialCrossMapLRN, input) - } - - "SpatialDilatedConvolution serializer" should "work properly" in { - - val spatialDilatedConvolution = SpatialDilatedConvolution[Float](1, 1, - 2, 2, 1, 1, 0, 0).setName("spatialDilatedConvolution") - val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) - runSerializationTest(spatialDilatedConvolution, input) - } - - "SpatialDivisiveNormalization serializer" should "work properly" in { - val spatialDivisiveNormalization = SpatialDivisiveNormalization[Float](). - setName("spatialDivisiveNormalization") - val input = Tensor[Float](1, 5, 5).apply1(e => Random.nextFloat()) - runSerializationTest(spatialDivisiveNormalization, input) - } - - "SpatialFullConvolution serializer" should "work properly" in { - - val spatialFullConvolution = SpatialFullConvolution[Float](1, 1, - 2, 2, 1, 1, 0, 0).setName("spatialFullConvolution") - val input = Tensor[Float](1, 3, 3).apply1(e => Random.nextFloat()) - runSerializationTest(spatialFullConvolution, input) - } - - "SpatialMaxPooling serializer" should "work properly" in { - val spatialMaxPooling = SpatialMaxPooling[Float](2, 2, 2, 2). - setName("spatialMaxPooling") - val input = Tensor[Float](1, 3, 3).apply1( e => Random.nextFloat()) - runSerializationTest(spatialMaxPooling, input) - } - - "SpatialShareConvolution serializer" should "work properly" in { - val spatialShareConvolution = SpatialShareConvolution[Float](1, 1, 2, 2, 1, 1). - setName("spatialShareConvolution") - val input = Tensor[Float](3, 1, 3, 4).apply1( e => Random.nextFloat()) - runSerializationTest(spatialShareConvolution, input) - } - - "SpatialSubtractiveNormalization serializer" should "work properly" in { - val kernel = Tensor[Float](3, 3).apply1( e => Random.nextFloat()) - val spatialSubtractiveNormalization = SpatialSubtractiveNormalization[Float](1, kernel). - setName("spatialSubtractiveNormalization") - val input = Tensor[Float](1, 1, 1, 5).apply1( e => Random.nextFloat()) - runSerializationTest(spatialSubtractiveNormalization, input) - } - - "SpatialWithinChannelLRN serializer" should "work properly" in { - val spatialWithinChannelLRN = new SpatialWithinChannelLRN[Float](5, 5e-4, 0.75). - setName("spatialWithinChannelLRN") - val input = Tensor[Float](1, 4, 7, 6).apply1( e => Random.nextFloat()) - runSerializationTest(spatialWithinChannelLRN, input) - } - - "SpatialZeroPadding serializer" should "work properly" in { - val spatialZeroPadding = SpatialZeroPadding[Float](1, 0, -1, 0). - setName("spatialZeroPadding") - val input = Tensor[Float](3, 3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(spatialZeroPadding, input) - } - - "SpatialSeperableConvolution serializer" should "work properly" in { - val seprableConv = SpatialSeperableConvolution[Float](2, 2, 1, 2, 2, - dataFormat = DataFormat.NHWC).setName("seprableConv") - val input = Tensor[Float](1, 5, 5, 2).apply1( e => Random.nextFloat()) - runSerializationTest(seprableConv, input) - } - - "SplitTable serializer" should "work properly" in { - val splitTable = SplitTable[Float](2).setName("splitTable") - val input = Tensor[Float](2, 10).apply1( e => Random.nextFloat()) - runSerializationTest(splitTable, input) - } - - "Sqrt serializer" should "work properly" in { - val sqrt = Sqrt[Float]().setName("sqrt") - val input = Tensor[Float](10).apply1( e => Random.nextFloat()) - runSerializationTest(sqrt, input) - } - - "Square serializer" should "work properly" in { - val square = Square[Float]().setName("square") - val input = Tensor[Float](10).apply1( e => Random.nextFloat()) - runSerializationTest(square, input) - } - - "Squeeze serializer" should "work properly" in { - val squeeze = Squeeze[Float](2).setName("squeeze") - val input = Tensor[Float](2, 1, 2).apply1( e => Random.nextFloat()) - runSerializationTest(squeeze, input) - } - - "SReLU serilalizer" should "work properly" in { - val srelu = SReLU[Float](shape = Array(4)).setName("srelu") - val input = Tensor[Float](3, 4).apply1( e => Random.nextFloat()) - runSerializationTest(srelu, input) - } - - "Sum serializer" should "work properly" in { - val sum = Sum[Float](2).setName("sum") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(sum, input) - } - - "Tanh serializer" should "work properly" in { - val tanh = Tanh[Float]().setName("tanh") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(tanh, input) - } - - "TanhShrink serializer" should "work properly" in { - val tanhShrink = TanhShrink[Float]().setName("tanhShrink") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(tanhShrink, input) - } - - "TemporalConvolution serializer" should "work properly" in { - val temporalConvolution = TemporalConvolution[Float](10, 8, 5, 2). - setName("temporalConvolution") - val input = Tensor[Float](100, 10).apply1(e => Random.nextFloat()) - runSerializationTest(temporalConvolution, input) - } - - "TemporalMaxPooling serializer" should "work properly" in { - val temporalMaxPooling = new TemporalMaxPooling[Float](4).setName("temporalMaxPooling") - val input = Tensor[Float](5, 4, 5).apply1(e => Random.nextFloat()) - runSerializationTest(temporalMaxPooling, input) - } - - "Threshold serializer" should "work properly" in { - val threshold = Threshold[Float](0.5).setName("threshold") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(threshold, input) - } - - "Tile serializer" should "work properly" in { - val tile = Tile[Float](1).setName("tile") - val input = Tensor[Float](5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(tile, input) - } - - "TimeDistributed serializer" should "work properly" in { - val timeDistributed = TimeDistributed[Float](Linear[Float](5, 5)). - setName("timeDistributed") - val input = Tensor[Float](2, 5, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(timeDistributed, input) - } - - "Transpose serializer" should "work properly" in { - val transpose = Transpose[Float](Array((1, 2))).setName("transpose") - val input = Tensor[Float]().resize(Array(2, 3)).apply1(_ => Random.nextFloat()) - runSerializationTest(transpose, input) - } - - "Unsqueeze serializer" should "work properly" in { - val unsqueeze = Unsqueeze[Float](2).setName("unsqueeze") - val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(unsqueeze, input) - } - - "UpSampling3D serializer" should "work properly" in { - val upSampling3D = UpSampling3D[Float](Array(2, 2, 2)).setName("upSampling3D") - val input = Tensor[Float](1, 2, 2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(upSampling3D, input) - } - - "View serializer" should "work properly" in { - val view = View[Float](Array(2, 5)).setName("view") - val input = Tensor[Float](1, 10).apply1(_ => Random.nextFloat()) - runSerializationTest(view, input) - } - - "VolumetricAveragePooling serializer" should "work properly" in { - val volumetricAveragePooling = VolumetricAveragePooling[Float](2, 2, 2, 1, 1, 1, 0, 0, 0). - setName("volumetricAveragePooling") - val input = Tensor[Float](1, 2, 3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(volumetricAveragePooling, input) - } - - "VolumetricConvolution serializer" should "work properly" in { - val volumetricConvolution = VolumetricConvolution[Float](2, 3, 2, 2, 2, dT = 1, dW = 1, dH = 1, - padT = 0, padW = 0, padH = 0, withBias = true).setName("volumetricConvolution") - val input = Tensor[Float](2, 2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(volumetricConvolution, input) - } - - "VolumetricFullConvolution serializer" should "work properly" in { - - val volumetricFullConvolution = new VolumetricFullConvolution[Float](3, 6, - 4, 3, 3, 2, 1, 1, 2, 2, 2).setName("volumetricFullConvolution") - val input = Tensor[Float](3, 3, 3, 6, 6).apply1(e => Random.nextFloat()) - runSerializationTest(volumetricFullConvolution, input) - } - - "VolumetricMaxPooling serializer" should "work properly" in { - val volumetricMaxPooling = VolumetricMaxPooling[Float](2, 2, 2, 1, 1, 1, 0, 0, 0). - setName("volumetricMaxPooling") - val input = Tensor[Float](1, 2, 3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(volumetricMaxPooling, input) - } - - "bigquant.SpatialConvolution serializer" should "work properly" in { - val nInputPlane = 1 - val nOutputPlane = 1 - val kW = 2 - val kH = 2 - val dW = 1 - val dH = 1 - val padW = 0 - val padH = 0 - - val kernelData = Array( - 2.0f, 3f, - 4f, 5f - ) - - val biasData = Array(0.0f) - - val input = Tensor[Float](1, 1, 3, 3).apply1(_ => Random.nextFloat()) - val weight = Tensor[Float](Storage(kernelData), 1, Array(nOutputPlane, nInputPlane, kH, kW)) - val bias = Tensor[Float](Storage(biasData), 1, Array(nOutputPlane)) - val conv = quantized.SpatialConvolution[Float](nInputPlane, nOutputPlane, - kW, kH, dW, dH, padW, padH, initWeight = weight, initBias = bias).setName("quantConv") - - runSerializationTest(conv, input) - } - - "bigquant.SpatialDilatedConvolution serializer" should "work properly" in { - val nInputPlane = 1 - val nOutputPlane = 1 - val kW = 2 - val kH = 2 - val dW = 1 - val dH = 1 - val padW = 0 - val padH = 0 - - val kernelData = Array( - 2.0f, 3f, - 4f, 5f - ) - - val biasData = Array(0.0f) - - val input = Tensor[Float](1, 1, 3, 3).apply1(_ => Random.nextFloat()) - val weight = Tensor[Float](Storage(kernelData), 1, Array(nOutputPlane, nInputPlane, kH, kW)) - val bias = Tensor[Float](Storage(biasData), 1, Array(nOutputPlane)) - val conv = quantized.SpatialDilatedConvolution[Float](nInputPlane, nOutputPlane, - kW, kH, dW, dH, padW, padH, initWeight = weight, initBias = bias) - .setName("quantDilatedConv") - - runSerializationTest(conv, input) - } - - "bigquant.Linear serializer" should "work properly " in { - val outputSize = 2 - val inputSize = 2 - - val kernelData = Array( - 2.0f, 3f, - 4f, 5f - ) - - val biasData = Array(0.0f, 0.1f) - - val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - val weight = Tensor[Float](Storage(kernelData), 1, Array(outputSize, inputSize)) - val bias = Tensor[Float](Storage(biasData), 1, Array(outputSize)) - val linear = quantized.Linear[Float](outputSize, inputSize, initWeight = weight, - initBias = bias).setName("quantLinear") - runSerializationTest(linear, input) - } - - // Below are TF Ops - - - "Slice serializer" should "work properly" in { - val slice = Slice[Float](begin = Array(0, 1, 1), - size = Array(2, -1, 1)).setName("slice") - val input = Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(slice, input, slice. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "SoftplusGrad serializer" should "work properly" in { - val sofplusGrad = SoftplusGrad[Float, Float].setName("sofplusGrad") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(sofplusGrad, input) - } - - "SoftSignGrad serializer" should "work properly" in { - val softSign = SoftsignGrad[Float, Float].setName("softSign") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(softSign, input) - } - - "SqrtGrad serializer" should "work properly" in { - val sqrtGrad = SqrtGrad[Float, Float].setName("sqrtGrad") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(sqrtGrad, input) - } - - "SquaredDifference serializer" should "work properly" in { - val squareDiff = SquaredDifference[Float]().setName("squareDiff") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(squareDiff, input) - } - - "Substr serializer" should "work properly" in { - import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - val subStr = Substr[Float]().setName("subStr") - val input = T(Tensor.scalar[ByteString](ByteString.copyFromUtf8("HelloBigDL")), - Tensor.scalar[Int](0), Tensor.scalar[Int](5)) - runSerializationTest(subStr, input) - } - - "SumOps serializer" should "work properly" in { - val sumOps = SumOps[Float, Float]().setName("sumOps") - val input = T(Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float]()) - runSerializationTest(sumOps, input) - } - - "TileOps serializer" should "work properly" in { - val tileOps = TileOps[Float]().setName("tileOps") - val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(2, 1, 2))) - runSerializationTest(tileOps, input, tileOps. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "TopK serializer" should "work properly" in { - val topk = TopK[Float, Float](2).setName("topK") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(topk, input) - } - - "TruncateDiv serializer" should "work properly" in { - val truncateDiv = TruncateDiv[Float, Float]().setName("truncateDiv") - val input = T(Tensor[Float](5).fill(1.0f), Tensor[Float](5).fill(2.0f)) - runSerializationTest(truncateDiv, input) - } - - "TruncatedNormal serializer" should "work properly" in { - val truncateNormal = TruncatedNormal[Float, Float](10, 20).setName("truncateNormal") - val input = Tensor[Int](T(1, 2, 3)) - runSerializationTest(truncateNormal, input, truncateNormal. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - // nn.tf package - - - "SplitAndSelect serializer" should "work properly" in { - val splitAndSelect = SplitAndSelect[Float](2, 1, 2).setName("splitSelect") - val input = Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(splitAndSelect, input) - } - - "StrideSlice serialier" should "work properly" in { - val strideSlice = new StrideSlice[Float, Float](Array((1, 1, 2, 1))).setName("strideSlice") - val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(strideSlice, input) - } - - "Variable serializer" should "work properly" in { - val out = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - val grad = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - val variable = new Variable[Float](out, grad).setName("variable") - val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(variable, input) - } - - "DetectionOutputSSD serializer" should "work properly" in { - val module = DetectionOutputSSD[Float](DetectionOutputParam()).setName("DetectionOutputSSD") - val name = module.getName - val serFile = File.createTempFile(name, postFix) - - ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) - RNG.setSeed(1000) - val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) - - - if (serFile.exists) { - serFile.delete - } - tested.add(module.getClass.getName) - } - - "DetectionOutputFrcnn serializer" should "work properly" in { - val module = DetectionOutputFrcnn().setName("DetectionOutputFrcnn") - val name = module.getName - val serFile = File.createTempFile(name, postFix) - - ModulePersister.saveToFile[Float](serFile.getAbsolutePath, null, module.evaluate(), true) - RNG.setSeed(1000) - val loadedModule = ModuleLoader.loadFromFile[Float](serFile.getAbsolutePath) - - - if (serFile.exists) { - serFile.delete - } - tested.add(module.getClass.getName) - } - - "PriorBox serializer" should "work properly" in { - val isClip = false - val isFlip = true - val variances = Array(0.1f, 0.1f, 0.2f, 0.2f) - val minSizes = Array(460.8f) - val maxSizes = Array(537.6f) - val aspectRatios = Array(2f) - val module = PriorBox[Float](minSizes = minSizes, maxSizes = maxSizes, - _aspectRatios = aspectRatios, isFlip = isFlip, isClip = isClip, - variances = variances, step = 0, offset = 0.5f, imgH = 512, imgW = 512) - val input = Tensor[Float](8, 256, 1, 1) - runSerializationTest(module, input) - } - -} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index ffaee3c04b2..57e311518d0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -220,7 +220,11 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.keras.GlobalMaxPooling1D" -> "com.intel.analytics.bigdl.keras.nn.GlobalMaxPooling1DSerialTest", "com.intel.analytics.bigdl.nn.keras.Flatten" -> - "com.intel.analytics.bigdl.keras.nn.FlattenSerialTest" + "com.intel.analytics.bigdl.keras.nn.FlattenSerialTest", + "com.intel.analytics.bigdl.nn.keras.KerasIdentityWrapper" -> + "com.intel.analytics.bigdl.keras.nn.KerasIdentityWrapperSerialTest", + "com.intel.analytics.bigdl.nn.keras.KerasLayerWrapper" -> + "com.intel.analytics.bigdl.keras.nn.KerasLayerWrapperSerialTest" ) private val suffix = "SerialTest" @@ -259,11 +263,6 @@ class SerializerSpec extends BigDLSpecHelper { s"subclass of com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest") testClass.asInstanceOf[ModuleSerializationTest].test() } catch { - case e: ClassNotFoundException => - cancel(s"Serialization test of module $cls has not " + - s"been implemented. Please consider creating a serialization test class with name " + - s"${clsWholeName} which extend com.intel.analytics.bigdl.utils.serializer." + - s"ModuleSerializationTest") case t: Throwable => throw t } } From 9973992e52d00c62eac1299df6781b0cb6b55ac1 Mon Sep 17 00:00:00 2001 From: Guoqiong Song Date: Sat, 17 Mar 2018 13:59:32 -0700 Subject: [PATCH 0737/1065] Dlframe examples of image model inference and image transfer learning (#2369) * initial check * functionality is done except evaluation, code needs refactor * need improvement * not coverge yet * need refactor later * need test on cluster * refactor file name to imageInference * add transfer learning exmaple * address review comments * change to spark1.6 * imageTransferlearning 2 ways * tested on cluster, compatibility with spark1.6, raised issue 2392 --- .../imageInference/ImageInference.scala | 107 ++++++++++++++ .../example/dlframes/imageInference/README.md | 66 +++++++++ .../ImageTransferLearning.scala | 139 ++++++++++++++++++ .../dlframes/imageTransferLearning/README.md | 76 ++++++++++ 4 files changed, 388 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala new file mode 100644 index 00000000000..f5f9041b481 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala @@ -0,0 +1,107 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.example.dlframes.imageInference + +import com.intel.analytics.bigdl.dlframes.{DLClassifierModel, DLModel} +import org.apache.spark.sql.DataFrame +import scopt.OptionParser +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.utils.Engine +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SQLContext + +object ImageInference { + + def main(args: Array[String]): Unit = { + + val defaultParams = Utils.LocalParams() + Utils.parser.parse(args, defaultParams).map { params => + + val conf = Engine.createSparkConf().setAppName("ModelInference") + val sc = SparkContext.getOrCreate(conf) + val sqlContext = new SQLContext(sc) + Engine.init + + val imagesDF = Utils.loadImages(params.folder, params.batchSize, sqlContext).cache() + + imagesDF.show(10) + imagesDF.printSchema() + + val model = Module.loadCaffeModel[Float](params.caffeDefPath, params.modelPath) + val dlmodel: DLModel[Float] = new DLClassifierModel[Float]( + model, Array(3, 224, 224)) + .setBatchSize(params.batchSize) + .setFeaturesCol("features") + .setPredictionCol("prediction") + + val count = imagesDF.count().toInt + val tranDF = dlmodel.transform(imagesDF.limit(count)) + + tranDF.select("imageName", "prediction").show(100, false) + } + } +} + +object Utils { + + case class LocalParams(caffeDefPath: String = " ", + modelPath: String = " ", + folder: String = " ", + batchSize: Int = 16, + nEpochs: Int = 10 + ) + + val defaultParams = LocalParams() + + val parser = new OptionParser[LocalParams]("BigDL Example") { + opt[String]("caffeDefPath") + .text(s"caffeDefPath") + .action((x, c) => c.copy(caffeDefPath = x)) + opt[String]("modelPath") + .text(s"modelPath") + .action((x, c) => c.copy(modelPath = x)) + opt[String]("folder") + .text(s"folder") + .action((x, c) => c.copy(folder = x)) + opt[Int]('b', "batchSize") + .text(s"batchSize") + .action((x, c) => c.copy(batchSize = x.toInt)) + opt[Int]('e', "nEpochs") + .text("epoch numbers") + .action((x, c) => c.copy(nEpochs = x)) + } + + def loadImages(path: String, partitionNum: Int, sqlContext: SQLContext): DataFrame = { + + val imageFrame: ImageFrame = ImageFrame.read(path, sqlContext.sparkContext) + val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() -> ImageFrameToSample() + val transformed: ImageFrame = transformer(imageFrame) + val imageRDD = transformed.toDistributed().rdd.map { im => + (im.uri, im[Sample[Float]](ImageFeature.sample).getData()) + } + val imageDF = sqlContext.createDataFrame(imageRDD) + .withColumnRenamed("_1", "imageName") + .withColumnRenamed("_2", "features") + imageDF + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md new file mode 100644 index 00000000000..6a837109c3f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md @@ -0,0 +1,66 @@ +## Overview + Deep Learning Frames provides high-level APIs for scalable deep learning in Scala with Apache Spark. + The current version of Deep Learning Frames provides a suite of tools around working with and processing images using deep learning. + This exmaple demostrates how to use BigDL to apply popular iamge deep learning models at scale. + +## Image Model Inference + 1. You can apply your own or known popular models to image data to make predictions or transform them into features. + + val imagesDF = loadImages(param.folder, param.batchSize, spark.sqlContext) + val model = Module.loadCaffeModel[Float](param.caffeDefPath, param.modelPath) + val dlmodel: DLModel[Float] = new DLClassifierModel[Float]( + model, Array(3, 224, 224)) + .setBatchSize(param.batchSize) + .setFeaturesCol("features") + .setPredictionCol("predict") + + val tranDF = dlmodel.transform(imagesDF) + + tranDF.select("predict", "imageName").show(5) + + 2. You can run the full ModelInference example by following steps. + + 2.1 Prepare pre-trained model and defenition file. + Download [caffe inception v1](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) and [deploy.proxfile](https://github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/deploy.prototxt) + then put the trained model in $modelPath, and set corresponding $caffeDefPath. + + 2.2 Prepare predict dataset + Put your image data for prediction in the ./predict folder. Alternatively, you may also use imagenet-2012 validation dataset to run the example, which can be found from . After you download the file (ILSVRC2012_img_val.tar), run the follow commands to prepare the data. + + ```bash + mkdir predict + tar -xvf ILSVRC2012_img_val.tar -C ./folder/ + ``` + + 2.3 Run this example + + Command to run the example in Spark local mode: + ``` + spark-submit \ + --master local[physcial_core_number] \ + --driver-memory 10g --executor-memory 20g \ + --class com.intel.analytics.bigdl.example.DLFrames.ImageInference \ + ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --modelPath ./model/bvlc_googlenet.caffemodel \ + --caffeDefPath ./model/deploy.prototxt \ + --batchSize 32 \ + --folder ./predict \ + --nEpochs 10 + + ``` + + Command to run the example in Spark yarn mode(TODO): + ``` + spark-submit \ + --master yarn \ + --deploy-mode client \ + --executor-cores 8 \ + --num-executors 4 \ + --class com.intel.analytics.bigdl.example.DLFrames.ImageInference \ + ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --modelPath ./model/bvlc_googlenet.caffemodel \ + --caffeDefPath ./model/deploy.prototxt \ + --batchSize 32 \ + --folder ./predict \ + --nEpochs 10 + ``` \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala new file mode 100644 index 00000000000..42279a93a55 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala @@ -0,0 +1,139 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.example.dlframes.imageTransferLearning + +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.dlframes.{DLClassifier, DLModel} +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.utils.Engine +import org.apache.log4j.{Level, Logger} +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator + +import org.apache.spark.ml.{Pipeline, Transformer} +import org.apache.spark.sql.functions.{col, udf} +import org.apache.spark.sql.{DataFrame, SQLContext} +import scopt.OptionParser +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import org.apache.spark.SparkContext + +object ImageTransferLearning { + + def main(args: Array[String]): Unit = { + + val defaultParams = Utils.LocalParams() + + Utils.parser.parse(args, defaultParams).map { params => + + val conf = Engine.createSparkConf().setAppName("TransferLearning") + val sc = SparkContext.getOrCreate(conf) + val sqlContext = new SQLContext(sc) + Engine.init + + val createLabel = udf((name: String) => if (name.contains("cat")) 1.0 else 2.0) + val imagesDF: DataFrame = Utils.loadImages(params.folder, params.batchSize, sqlContext) + .withColumn("label", createLabel(col("imageName"))) + .withColumnRenamed("features", "imageFeatures") + .drop("features") + + val Array(validationDF, trainingDF) = imagesDF.randomSplit(Array(0.20, 0.80), seed = 1L) + + validationDF.persist() + trainingDF.persist() + + val loadedModel = Module + .loadCaffeModel[Float](params.caffeDefPath, params.modelPath) + + val featurizer = new DLModel[Float](loadedModel, Array(3, 224, 224)) + .setBatchSize(params.batchSize) + .setFeaturesCol("imageFeatures") + .setPredictionCol("features") + + val lrModel = Sequential().add(Linear(1000, 2)).add(LogSoftMax()) + + val classifier = new DLClassifier(lrModel, ClassNLLCriterion[Float](), Array(1000)) + .setLearningRate(0.003).setBatchSize(params.batchSize) + .setMaxEpoch(20) + + val pipeline = new Pipeline().setStages( + Array(featurizer, classifier)) + + val pipelineModel = pipeline.fit(trainingDF) + trainingDF.unpersist() + + val predictions = pipelineModel.transform(validationDF) + + predictions.show(200) + predictions.printSchema() + + val evaluation = new MulticlassClassificationEvaluator().setPredictionCol("prediction") + .setMetricName("weightedPrecision").evaluate(predictions) + println("evaluation result on validationDF: " + evaluation) + + validationDF.unpersist() + } + } + +} + + +object Utils { + + case class LocalParams(caffeDefPath: String = "/Users/guoqiong/intelWork/git/caffe/models/bvlc_googlenet/deploy.prototxt", + modelPath: String = "/Users/guoqiong/intelWork/projects/dlFrames/model/caffe/bvlc_googlenet.caffemodel", + folder: String = "/Users/guoqiong/intelWork/projects/dlFrames/data/kaggle/train_100", + batchSize: Int = 16, + nEpochs: Int = 10 + ) + + val defaultParams = LocalParams() + + val parser = new OptionParser[LocalParams]("BigDL Example") { + opt[String]("caffeDefPath") + .text(s"caffeDefPath") + .action((x, c) => c.copy(caffeDefPath = x)) + opt[String]("modelPath") + .text(s"modelPath") + .action((x, c) => c.copy(modelPath = x)) + opt[String]("folder") + .text(s"folder") + .action((x, c) => c.copy(folder = x)) + opt[Int]('b', "batchSize") + .text(s"batchSize") + .action((x, c) => c.copy(batchSize = x.toInt)) + opt[Int]('e', "nEpochs") + .text("epoch numbers") + .action((x, c) => c.copy(nEpochs = x)) + } + + def loadImages(path: String, partitionNum: Int, sqlContext: SQLContext): DataFrame = { + + val imageFrame: ImageFrame = ImageFrame.read(path, sqlContext.sparkContext) + val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() -> ImageFrameToSample() + val transformed: ImageFrame = transformer(imageFrame) + val imageRDD = transformed.toDistributed().rdd.map { im => + (im.uri, im[Sample[Float]](ImageFeature.sample).getData()) + } + val imageDF = sqlContext.createDataFrame(imageRDD) + .withColumnRenamed("_1", "imageName") + .withColumnRenamed("_2", "features") + imageDF + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md new file mode 100644 index 00000000000..de5b505f353 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md @@ -0,0 +1,76 @@ +## Overview + Deep Learning Frames provides high-level APIs for scalable deep learning in Scala with Apache Spark. + The current version of Deep Learning Frames provides a suite of tools around working with and processing images using deep learning. + this exmaple demostrates how to use BigDL for transfer learning. + +## Transfer Learning + 1. DLFrames provides utilities to perform transfer learning on images, which is one of the fastest (code and run-time-wise) ways to start using deep learning. + + val imagesDF: DataFrame = Utils.loadImages(params.folder, params.batchSize, spark.sqlContext) + .withColumn("label", createLabel(col("imageName"))) + .withColumnRenamed("features", "imageFeatures") + val Array(validationDF, trainingDF) = imagesDF.randomSplit(Array(0.90, 0.10), seed = 1L) + + val loadedModel: AbstractModule[Activity, Activity, Float] = Module + .loadCaffeModel[Float](params.caffeDefPath, params.modelPath) + val featurizer = new DLModel[Float](loadedModel, Array(3, 224, 224)) + .setFeaturesCol("imageFeatures") + .setPredictionCol("tmp1") + + val lrModel = Sequential().add(Linear(1000, 2)).add(LogSoftMax()) + val classifier = new DLClassifier(lrModel, ClassNLLCriterion[Float](), Array(1000)) + .setLearningRate(0.003).setBatchSize(params.batchSize) + .setMaxEpoch(20) + + val pipeline = new Pipeline().setStages( + Array(featurizer, classifier)) + + val pipelineModel = pipeline.fit(trainingDF) + val predictions = pipelineModel.transform(validationDF) + + 2. You can run the full ImageTransferLearning example by following steps. + + 2.1 Prepare pre-trained model and defenition file. + Download [caffe inception v1](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) and [deploy.proxfile](https://github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/deploy.prototxt) + then put the trained model in $modelPath, and set corresponding $caffeDefPath. + + 2.2 Prepare dataset + Put your image data for training and validation in the ./data folder. Alternatively, you may also use kaggle [Dogs vs. Cats](https://www.kaggle.com/c/dogs-vs-cats/data) train dataset to run the example. After you download the file (train.zip), run the follow commands to prepare the data. + + ``` + bash + mkdir data + unzip -xvf train.tar -C ./data/ + ``` + + 2.3 Run this example + + Command to run the example in Spark local mode: + ``` + spark-submit \ + --master local[physcial_core_number] \ + --driver-memory 10g --executor-memory 20g \ + --class com.intel.analytics.bigdl.example.DLFrames.imageTransferLearning.ImageTransferLearning \ + ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --modelPath ./model/bvlc_googlenet.caffemodel \ + --caffeDefPath ./model/deploy.prototxt \ + --batchSize 32 \ + --folder ./data \ + --nEpochs 10 + ``` + + Command to run the example in Spark yarn mode(TODO): + ``` + spark-submit \ + --master yarn \ + --deploy-mode client \ + --executor-cores 8 \ + --num-executors 4 \ + --class com.intel.analytics.bigdl.example.DLFrames.imageTransferLearning.ImageTransferLearning \ + ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --modelPath ./model/bvlc_googlenet.caffemodel \ + --caffeDefPath ./model/deploy.prototxt \ + --batchSize 32 \ + --folder ./data \ + --nEpochs 10 + ``` \ No newline at end of file From f92ea1a076a353c7c887677d1212a50dfb4527a4 Mon Sep 17 00:00:00 2001 From: Guoqiong Song Date: Sat, 17 Mar 2018 14:07:49 -0700 Subject: [PATCH 0738/1065] Revert "Dlframe examples of image model inference and image transfer learning (#2369)" (#2393) This reverts commit e6a2560dd2765c0d8121688116654e7a0dc430b9. --- .../imageInference/ImageInference.scala | 107 -------------- .../example/dlframes/imageInference/README.md | 66 --------- .../ImageTransferLearning.scala | 139 ------------------ .../dlframes/imageTransferLearning/README.md | 76 ---------- 4 files changed, 388 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala deleted file mode 100644 index f5f9041b481..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala +++ /dev/null @@ -1,107 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.example.dlframes.imageInference - -import com.intel.analytics.bigdl.dlframes.{DLClassifierModel, DLModel} -import org.apache.spark.sql.DataFrame -import scopt.OptionParser -import com.intel.analytics.bigdl.dataset.Sample -import com.intel.analytics.bigdl.nn.Module -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat -import com.intel.analytics.bigdl.transform.vision.image.augmentation._ -import com.intel.analytics.bigdl.transform.vision.image._ -import com.intel.analytics.bigdl.utils.Engine -import org.apache.spark.SparkContext -import org.apache.spark.rdd.RDD -import org.apache.spark.sql.SQLContext - -object ImageInference { - - def main(args: Array[String]): Unit = { - - val defaultParams = Utils.LocalParams() - Utils.parser.parse(args, defaultParams).map { params => - - val conf = Engine.createSparkConf().setAppName("ModelInference") - val sc = SparkContext.getOrCreate(conf) - val sqlContext = new SQLContext(sc) - Engine.init - - val imagesDF = Utils.loadImages(params.folder, params.batchSize, sqlContext).cache() - - imagesDF.show(10) - imagesDF.printSchema() - - val model = Module.loadCaffeModel[Float](params.caffeDefPath, params.modelPath) - val dlmodel: DLModel[Float] = new DLClassifierModel[Float]( - model, Array(3, 224, 224)) - .setBatchSize(params.batchSize) - .setFeaturesCol("features") - .setPredictionCol("prediction") - - val count = imagesDF.count().toInt - val tranDF = dlmodel.transform(imagesDF.limit(count)) - - tranDF.select("imageName", "prediction").show(100, false) - } - } -} - -object Utils { - - case class LocalParams(caffeDefPath: String = " ", - modelPath: String = " ", - folder: String = " ", - batchSize: Int = 16, - nEpochs: Int = 10 - ) - - val defaultParams = LocalParams() - - val parser = new OptionParser[LocalParams]("BigDL Example") { - opt[String]("caffeDefPath") - .text(s"caffeDefPath") - .action((x, c) => c.copy(caffeDefPath = x)) - opt[String]("modelPath") - .text(s"modelPath") - .action((x, c) => c.copy(modelPath = x)) - opt[String]("folder") - .text(s"folder") - .action((x, c) => c.copy(folder = x)) - opt[Int]('b', "batchSize") - .text(s"batchSize") - .action((x, c) => c.copy(batchSize = x.toInt)) - opt[Int]('e', "nEpochs") - .text("epoch numbers") - .action((x, c) => c.copy(nEpochs = x)) - } - - def loadImages(path: String, partitionNum: Int, sqlContext: SQLContext): DataFrame = { - - val imageFrame: ImageFrame = ImageFrame.read(path, sqlContext.sparkContext) - val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> - ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() -> ImageFrameToSample() - val transformed: ImageFrame = transformer(imageFrame) - val imageRDD = transformed.toDistributed().rdd.map { im => - (im.uri, im[Sample[Float]](ImageFeature.sample).getData()) - } - val imageDF = sqlContext.createDataFrame(imageRDD) - .withColumnRenamed("_1", "imageName") - .withColumnRenamed("_2", "features") - imageDF - } - -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md deleted file mode 100644 index 6a837109c3f..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md +++ /dev/null @@ -1,66 +0,0 @@ -## Overview - Deep Learning Frames provides high-level APIs for scalable deep learning in Scala with Apache Spark. - The current version of Deep Learning Frames provides a suite of tools around working with and processing images using deep learning. - This exmaple demostrates how to use BigDL to apply popular iamge deep learning models at scale. - -## Image Model Inference - 1. You can apply your own or known popular models to image data to make predictions or transform them into features. - - val imagesDF = loadImages(param.folder, param.batchSize, spark.sqlContext) - val model = Module.loadCaffeModel[Float](param.caffeDefPath, param.modelPath) - val dlmodel: DLModel[Float] = new DLClassifierModel[Float]( - model, Array(3, 224, 224)) - .setBatchSize(param.batchSize) - .setFeaturesCol("features") - .setPredictionCol("predict") - - val tranDF = dlmodel.transform(imagesDF) - - tranDF.select("predict", "imageName").show(5) - - 2. You can run the full ModelInference example by following steps. - - 2.1 Prepare pre-trained model and defenition file. - Download [caffe inception v1](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) and [deploy.proxfile](https://github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/deploy.prototxt) - then put the trained model in $modelPath, and set corresponding $caffeDefPath. - - 2.2 Prepare predict dataset - Put your image data for prediction in the ./predict folder. Alternatively, you may also use imagenet-2012 validation dataset to run the example, which can be found from . After you download the file (ILSVRC2012_img_val.tar), run the follow commands to prepare the data. - - ```bash - mkdir predict - tar -xvf ILSVRC2012_img_val.tar -C ./folder/ - ``` - - 2.3 Run this example - - Command to run the example in Spark local mode: - ``` - spark-submit \ - --master local[physcial_core_number] \ - --driver-memory 10g --executor-memory 20g \ - --class com.intel.analytics.bigdl.example.DLFrames.ImageInference \ - ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ - --modelPath ./model/bvlc_googlenet.caffemodel \ - --caffeDefPath ./model/deploy.prototxt \ - --batchSize 32 \ - --folder ./predict \ - --nEpochs 10 - - ``` - - Command to run the example in Spark yarn mode(TODO): - ``` - spark-submit \ - --master yarn \ - --deploy-mode client \ - --executor-cores 8 \ - --num-executors 4 \ - --class com.intel.analytics.bigdl.example.DLFrames.ImageInference \ - ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ - --modelPath ./model/bvlc_googlenet.caffemodel \ - --caffeDefPath ./model/deploy.prototxt \ - --batchSize 32 \ - --folder ./predict \ - --nEpochs 10 - ``` \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala deleted file mode 100644 index 42279a93a55..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala +++ /dev/null @@ -1,139 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.example.dlframes.imageTransferLearning - -import com.intel.analytics.bigdl.dataset.Sample -import com.intel.analytics.bigdl.dlframes.{DLClassifier, DLModel} -import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.transform.vision.image._ -import com.intel.analytics.bigdl.transform.vision.image.augmentation._ -import com.intel.analytics.bigdl.utils.Engine -import org.apache.log4j.{Level, Logger} -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator - -import org.apache.spark.ml.{Pipeline, Transformer} -import org.apache.spark.sql.functions.{col, udf} -import org.apache.spark.sql.{DataFrame, SQLContext} -import scopt.OptionParser -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat -import org.apache.spark.SparkContext - -object ImageTransferLearning { - - def main(args: Array[String]): Unit = { - - val defaultParams = Utils.LocalParams() - - Utils.parser.parse(args, defaultParams).map { params => - - val conf = Engine.createSparkConf().setAppName("TransferLearning") - val sc = SparkContext.getOrCreate(conf) - val sqlContext = new SQLContext(sc) - Engine.init - - val createLabel = udf((name: String) => if (name.contains("cat")) 1.0 else 2.0) - val imagesDF: DataFrame = Utils.loadImages(params.folder, params.batchSize, sqlContext) - .withColumn("label", createLabel(col("imageName"))) - .withColumnRenamed("features", "imageFeatures") - .drop("features") - - val Array(validationDF, trainingDF) = imagesDF.randomSplit(Array(0.20, 0.80), seed = 1L) - - validationDF.persist() - trainingDF.persist() - - val loadedModel = Module - .loadCaffeModel[Float](params.caffeDefPath, params.modelPath) - - val featurizer = new DLModel[Float](loadedModel, Array(3, 224, 224)) - .setBatchSize(params.batchSize) - .setFeaturesCol("imageFeatures") - .setPredictionCol("features") - - val lrModel = Sequential().add(Linear(1000, 2)).add(LogSoftMax()) - - val classifier = new DLClassifier(lrModel, ClassNLLCriterion[Float](), Array(1000)) - .setLearningRate(0.003).setBatchSize(params.batchSize) - .setMaxEpoch(20) - - val pipeline = new Pipeline().setStages( - Array(featurizer, classifier)) - - val pipelineModel = pipeline.fit(trainingDF) - trainingDF.unpersist() - - val predictions = pipelineModel.transform(validationDF) - - predictions.show(200) - predictions.printSchema() - - val evaluation = new MulticlassClassificationEvaluator().setPredictionCol("prediction") - .setMetricName("weightedPrecision").evaluate(predictions) - println("evaluation result on validationDF: " + evaluation) - - validationDF.unpersist() - } - } - -} - - -object Utils { - - case class LocalParams(caffeDefPath: String = "/Users/guoqiong/intelWork/git/caffe/models/bvlc_googlenet/deploy.prototxt", - modelPath: String = "/Users/guoqiong/intelWork/projects/dlFrames/model/caffe/bvlc_googlenet.caffemodel", - folder: String = "/Users/guoqiong/intelWork/projects/dlFrames/data/kaggle/train_100", - batchSize: Int = 16, - nEpochs: Int = 10 - ) - - val defaultParams = LocalParams() - - val parser = new OptionParser[LocalParams]("BigDL Example") { - opt[String]("caffeDefPath") - .text(s"caffeDefPath") - .action((x, c) => c.copy(caffeDefPath = x)) - opt[String]("modelPath") - .text(s"modelPath") - .action((x, c) => c.copy(modelPath = x)) - opt[String]("folder") - .text(s"folder") - .action((x, c) => c.copy(folder = x)) - opt[Int]('b', "batchSize") - .text(s"batchSize") - .action((x, c) => c.copy(batchSize = x.toInt)) - opt[Int]('e', "nEpochs") - .text("epoch numbers") - .action((x, c) => c.copy(nEpochs = x)) - } - - def loadImages(path: String, partitionNum: Int, sqlContext: SQLContext): DataFrame = { - - val imageFrame: ImageFrame = ImageFrame.read(path, sqlContext.sparkContext) - val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> - ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() -> ImageFrameToSample() - val transformed: ImageFrame = transformer(imageFrame) - val imageRDD = transformed.toDistributed().rdd.map { im => - (im.uri, im[Sample[Float]](ImageFeature.sample).getData()) - } - val imageDF = sqlContext.createDataFrame(imageRDD) - .withColumnRenamed("_1", "imageName") - .withColumnRenamed("_2", "features") - imageDF - } - -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md deleted file mode 100644 index de5b505f353..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md +++ /dev/null @@ -1,76 +0,0 @@ -## Overview - Deep Learning Frames provides high-level APIs for scalable deep learning in Scala with Apache Spark. - The current version of Deep Learning Frames provides a suite of tools around working with and processing images using deep learning. - this exmaple demostrates how to use BigDL for transfer learning. - -## Transfer Learning - 1. DLFrames provides utilities to perform transfer learning on images, which is one of the fastest (code and run-time-wise) ways to start using deep learning. - - val imagesDF: DataFrame = Utils.loadImages(params.folder, params.batchSize, spark.sqlContext) - .withColumn("label", createLabel(col("imageName"))) - .withColumnRenamed("features", "imageFeatures") - val Array(validationDF, trainingDF) = imagesDF.randomSplit(Array(0.90, 0.10), seed = 1L) - - val loadedModel: AbstractModule[Activity, Activity, Float] = Module - .loadCaffeModel[Float](params.caffeDefPath, params.modelPath) - val featurizer = new DLModel[Float](loadedModel, Array(3, 224, 224)) - .setFeaturesCol("imageFeatures") - .setPredictionCol("tmp1") - - val lrModel = Sequential().add(Linear(1000, 2)).add(LogSoftMax()) - val classifier = new DLClassifier(lrModel, ClassNLLCriterion[Float](), Array(1000)) - .setLearningRate(0.003).setBatchSize(params.batchSize) - .setMaxEpoch(20) - - val pipeline = new Pipeline().setStages( - Array(featurizer, classifier)) - - val pipelineModel = pipeline.fit(trainingDF) - val predictions = pipelineModel.transform(validationDF) - - 2. You can run the full ImageTransferLearning example by following steps. - - 2.1 Prepare pre-trained model and defenition file. - Download [caffe inception v1](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) and [deploy.proxfile](https://github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/deploy.prototxt) - then put the trained model in $modelPath, and set corresponding $caffeDefPath. - - 2.2 Prepare dataset - Put your image data for training and validation in the ./data folder. Alternatively, you may also use kaggle [Dogs vs. Cats](https://www.kaggle.com/c/dogs-vs-cats/data) train dataset to run the example. After you download the file (train.zip), run the follow commands to prepare the data. - - ``` - bash - mkdir data - unzip -xvf train.tar -C ./data/ - ``` - - 2.3 Run this example - - Command to run the example in Spark local mode: - ``` - spark-submit \ - --master local[physcial_core_number] \ - --driver-memory 10g --executor-memory 20g \ - --class com.intel.analytics.bigdl.example.DLFrames.imageTransferLearning.ImageTransferLearning \ - ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ - --modelPath ./model/bvlc_googlenet.caffemodel \ - --caffeDefPath ./model/deploy.prototxt \ - --batchSize 32 \ - --folder ./data \ - --nEpochs 10 - ``` - - Command to run the example in Spark yarn mode(TODO): - ``` - spark-submit \ - --master yarn \ - --deploy-mode client \ - --executor-cores 8 \ - --num-executors 4 \ - --class com.intel.analytics.bigdl.example.DLFrames.imageTransferLearning.ImageTransferLearning \ - ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ - --modelPath ./model/bvlc_googlenet.caffemodel \ - --caffeDefPath ./model/deploy.prototxt \ - --batchSize 32 \ - --folder ./data \ - --nEpochs 10 - ``` \ No newline at end of file From bd4434441dc163bfdad322e67396e5a2a12a782f Mon Sep 17 00:00:00 2001 From: Guoqiong Song Date: Sat, 17 Mar 2018 14:26:37 -0700 Subject: [PATCH 0739/1065] DLframes examples of image inference and image transfer learning (#2394) add DLframes examples of image inference and document add DLframes examples of image transfer learning and document raised https://github.com/intel-analytics/BigDL/issues/2392 --- .../imageInference/ImageInference.scala | 107 ++++++++++++++ .../example/dlframes/imageInference/README.md | 66 +++++++++ .../ImageTransferLearning.scala | 139 ++++++++++++++++++ .../dlframes/imageTransferLearning/README.md | 76 ++++++++++ 4 files changed, 388 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala new file mode 100644 index 00000000000..f5f9041b481 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/ImageInference.scala @@ -0,0 +1,107 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.example.dlframes.imageInference + +import com.intel.analytics.bigdl.dlframes.{DLClassifierModel, DLModel} +import org.apache.spark.sql.DataFrame +import scopt.OptionParser +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.utils.Engine +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SQLContext + +object ImageInference { + + def main(args: Array[String]): Unit = { + + val defaultParams = Utils.LocalParams() + Utils.parser.parse(args, defaultParams).map { params => + + val conf = Engine.createSparkConf().setAppName("ModelInference") + val sc = SparkContext.getOrCreate(conf) + val sqlContext = new SQLContext(sc) + Engine.init + + val imagesDF = Utils.loadImages(params.folder, params.batchSize, sqlContext).cache() + + imagesDF.show(10) + imagesDF.printSchema() + + val model = Module.loadCaffeModel[Float](params.caffeDefPath, params.modelPath) + val dlmodel: DLModel[Float] = new DLClassifierModel[Float]( + model, Array(3, 224, 224)) + .setBatchSize(params.batchSize) + .setFeaturesCol("features") + .setPredictionCol("prediction") + + val count = imagesDF.count().toInt + val tranDF = dlmodel.transform(imagesDF.limit(count)) + + tranDF.select("imageName", "prediction").show(100, false) + } + } +} + +object Utils { + + case class LocalParams(caffeDefPath: String = " ", + modelPath: String = " ", + folder: String = " ", + batchSize: Int = 16, + nEpochs: Int = 10 + ) + + val defaultParams = LocalParams() + + val parser = new OptionParser[LocalParams]("BigDL Example") { + opt[String]("caffeDefPath") + .text(s"caffeDefPath") + .action((x, c) => c.copy(caffeDefPath = x)) + opt[String]("modelPath") + .text(s"modelPath") + .action((x, c) => c.copy(modelPath = x)) + opt[String]("folder") + .text(s"folder") + .action((x, c) => c.copy(folder = x)) + opt[Int]('b', "batchSize") + .text(s"batchSize") + .action((x, c) => c.copy(batchSize = x.toInt)) + opt[Int]('e', "nEpochs") + .text("epoch numbers") + .action((x, c) => c.copy(nEpochs = x)) + } + + def loadImages(path: String, partitionNum: Int, sqlContext: SQLContext): DataFrame = { + + val imageFrame: ImageFrame = ImageFrame.read(path, sqlContext.sparkContext) + val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() -> ImageFrameToSample() + val transformed: ImageFrame = transformer(imageFrame) + val imageRDD = transformed.toDistributed().rdd.map { im => + (im.uri, im[Sample[Float]](ImageFeature.sample).getData()) + } + val imageDF = sqlContext.createDataFrame(imageRDD) + .withColumnRenamed("_1", "imageName") + .withColumnRenamed("_2", "features") + imageDF + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md new file mode 100644 index 00000000000..6a837109c3f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageInference/README.md @@ -0,0 +1,66 @@ +## Overview + Deep Learning Frames provides high-level APIs for scalable deep learning in Scala with Apache Spark. + The current version of Deep Learning Frames provides a suite of tools around working with and processing images using deep learning. + This exmaple demostrates how to use BigDL to apply popular iamge deep learning models at scale. + +## Image Model Inference + 1. You can apply your own or known popular models to image data to make predictions or transform them into features. + + val imagesDF = loadImages(param.folder, param.batchSize, spark.sqlContext) + val model = Module.loadCaffeModel[Float](param.caffeDefPath, param.modelPath) + val dlmodel: DLModel[Float] = new DLClassifierModel[Float]( + model, Array(3, 224, 224)) + .setBatchSize(param.batchSize) + .setFeaturesCol("features") + .setPredictionCol("predict") + + val tranDF = dlmodel.transform(imagesDF) + + tranDF.select("predict", "imageName").show(5) + + 2. You can run the full ModelInference example by following steps. + + 2.1 Prepare pre-trained model and defenition file. + Download [caffe inception v1](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) and [deploy.proxfile](https://github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/deploy.prototxt) + then put the trained model in $modelPath, and set corresponding $caffeDefPath. + + 2.2 Prepare predict dataset + Put your image data for prediction in the ./predict folder. Alternatively, you may also use imagenet-2012 validation dataset to run the example, which can be found from . After you download the file (ILSVRC2012_img_val.tar), run the follow commands to prepare the data. + + ```bash + mkdir predict + tar -xvf ILSVRC2012_img_val.tar -C ./folder/ + ``` + + 2.3 Run this example + + Command to run the example in Spark local mode: + ``` + spark-submit \ + --master local[physcial_core_number] \ + --driver-memory 10g --executor-memory 20g \ + --class com.intel.analytics.bigdl.example.DLFrames.ImageInference \ + ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --modelPath ./model/bvlc_googlenet.caffemodel \ + --caffeDefPath ./model/deploy.prototxt \ + --batchSize 32 \ + --folder ./predict \ + --nEpochs 10 + + ``` + + Command to run the example in Spark yarn mode(TODO): + ``` + spark-submit \ + --master yarn \ + --deploy-mode client \ + --executor-cores 8 \ + --num-executors 4 \ + --class com.intel.analytics.bigdl.example.DLFrames.ImageInference \ + ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --modelPath ./model/bvlc_googlenet.caffemodel \ + --caffeDefPath ./model/deploy.prototxt \ + --batchSize 32 \ + --folder ./predict \ + --nEpochs 10 + ``` \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala new file mode 100644 index 00000000000..ddaa84df580 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/ImageTransferLearning.scala @@ -0,0 +1,139 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.example.dlframes.imageTransferLearning + +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.dlframes.{DLClassifier, DLModel} +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.utils.Engine +import org.apache.log4j.{Level, Logger} +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator + +import org.apache.spark.ml.{Pipeline, Transformer} +import org.apache.spark.sql.functions.{col, udf} +import org.apache.spark.sql.{DataFrame, SQLContext} +import scopt.OptionParser +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import org.apache.spark.SparkContext + +object ImageTransferLearning { + + def main(args: Array[String]): Unit = { + + val defaultParams = Utils.LocalParams() + + Utils.parser.parse(args, defaultParams).map { params => + + val conf = Engine.createSparkConf().setAppName("TransferLearning") + val sc = SparkContext.getOrCreate(conf) + val sqlContext = new SQLContext(sc) + Engine.init + + val createLabel = udf((name: String) => if (name.contains("cat")) 1.0 else 2.0) + val imagesDF: DataFrame = Utils.loadImages(params.folder, params.batchSize, sqlContext) + .withColumn("label", createLabel(col("imageName"))) + .withColumnRenamed("features", "imageFeatures") + .drop("features") + + val Array(validationDF, trainingDF) = imagesDF.randomSplit(Array(0.20, 0.80), seed = 1L) + + validationDF.persist() + trainingDF.persist() + + val loadedModel = Module + .loadCaffeModel[Float](params.caffeDefPath, params.modelPath) + + val featurizer = new DLModel[Float](loadedModel, Array(3, 224, 224)) + .setBatchSize(params.batchSize) + .setFeaturesCol("imageFeatures") + .setPredictionCol("features") + + val lrModel = Sequential().add(Linear(1000, 2)).add(LogSoftMax()) + + val classifier = new DLClassifier(lrModel, ClassNLLCriterion[Float](), Array(1000)) + .setLearningRate(0.003).setBatchSize(params.batchSize) + .setMaxEpoch(20) + + val pipeline = new Pipeline().setStages( + Array(featurizer, classifier)) + + val pipelineModel = pipeline.fit(trainingDF) + trainingDF.unpersist() + + val predictions = pipelineModel.transform(validationDF) + + predictions.show(200) + predictions.printSchema() + + val evaluation = new MulticlassClassificationEvaluator().setPredictionCol("prediction") + .setMetricName("weightedPrecision").evaluate(predictions) + println("evaluation result on validationDF: " + evaluation) + + validationDF.unpersist() + } + } + +} + + +object Utils { + + case class LocalParams(caffeDefPath: String = " ", + modelPath: String = " ", + folder: String = " ", + batchSize: Int = 16, + nEpochs: Int = 10 + ) + + val defaultParams = LocalParams() + + val parser = new OptionParser[LocalParams]("BigDL Example") { + opt[String]("caffeDefPath") + .text(s"caffeDefPath") + .action((x, c) => c.copy(caffeDefPath = x)) + opt[String]("modelPath") + .text(s"modelPath") + .action((x, c) => c.copy(modelPath = x)) + opt[String]("folder") + .text(s"folder") + .action((x, c) => c.copy(folder = x)) + opt[Int]('b', "batchSize") + .text(s"batchSize") + .action((x, c) => c.copy(batchSize = x.toInt)) + opt[Int]('e', "nEpochs") + .text("epoch numbers") + .action((x, c) => c.copy(nEpochs = x)) + } + + def loadImages(path: String, partitionNum: Int, sqlContext: SQLContext): DataFrame = { + + val imageFrame: ImageFrame = ImageFrame.read(path, sqlContext.sparkContext) + val transformer = Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(123, 117, 104, 1, 1, 1) -> MatToTensor() -> ImageFrameToSample() + val transformed: ImageFrame = transformer(imageFrame) + val imageRDD = transformed.toDistributed().rdd.map { im => + (im.uri, im[Sample[Float]](ImageFeature.sample).getData()) + } + val imageDF = sqlContext.createDataFrame(imageRDD) + .withColumnRenamed("_1", "imageName") + .withColumnRenamed("_2", "features") + imageDF + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md new file mode 100644 index 00000000000..de5b505f353 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/dlframes/imageTransferLearning/README.md @@ -0,0 +1,76 @@ +## Overview + Deep Learning Frames provides high-level APIs for scalable deep learning in Scala with Apache Spark. + The current version of Deep Learning Frames provides a suite of tools around working with and processing images using deep learning. + this exmaple demostrates how to use BigDL for transfer learning. + +## Transfer Learning + 1. DLFrames provides utilities to perform transfer learning on images, which is one of the fastest (code and run-time-wise) ways to start using deep learning. + + val imagesDF: DataFrame = Utils.loadImages(params.folder, params.batchSize, spark.sqlContext) + .withColumn("label", createLabel(col("imageName"))) + .withColumnRenamed("features", "imageFeatures") + val Array(validationDF, trainingDF) = imagesDF.randomSplit(Array(0.90, 0.10), seed = 1L) + + val loadedModel: AbstractModule[Activity, Activity, Float] = Module + .loadCaffeModel[Float](params.caffeDefPath, params.modelPath) + val featurizer = new DLModel[Float](loadedModel, Array(3, 224, 224)) + .setFeaturesCol("imageFeatures") + .setPredictionCol("tmp1") + + val lrModel = Sequential().add(Linear(1000, 2)).add(LogSoftMax()) + val classifier = new DLClassifier(lrModel, ClassNLLCriterion[Float](), Array(1000)) + .setLearningRate(0.003).setBatchSize(params.batchSize) + .setMaxEpoch(20) + + val pipeline = new Pipeline().setStages( + Array(featurizer, classifier)) + + val pipelineModel = pipeline.fit(trainingDF) + val predictions = pipelineModel.transform(validationDF) + + 2. You can run the full ImageTransferLearning example by following steps. + + 2.1 Prepare pre-trained model and defenition file. + Download [caffe inception v1](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) and [deploy.proxfile](https://github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/deploy.prototxt) + then put the trained model in $modelPath, and set corresponding $caffeDefPath. + + 2.2 Prepare dataset + Put your image data for training and validation in the ./data folder. Alternatively, you may also use kaggle [Dogs vs. Cats](https://www.kaggle.com/c/dogs-vs-cats/data) train dataset to run the example. After you download the file (train.zip), run the follow commands to prepare the data. + + ``` + bash + mkdir data + unzip -xvf train.tar -C ./data/ + ``` + + 2.3 Run this example + + Command to run the example in Spark local mode: + ``` + spark-submit \ + --master local[physcial_core_number] \ + --driver-memory 10g --executor-memory 20g \ + --class com.intel.analytics.bigdl.example.DLFrames.imageTransferLearning.ImageTransferLearning \ + ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --modelPath ./model/bvlc_googlenet.caffemodel \ + --caffeDefPath ./model/deploy.prototxt \ + --batchSize 32 \ + --folder ./data \ + --nEpochs 10 + ``` + + Command to run the example in Spark yarn mode(TODO): + ``` + spark-submit \ + --master yarn \ + --deploy-mode client \ + --executor-cores 8 \ + --num-executors 4 \ + --class com.intel.analytics.bigdl.example.DLFrames.imageTransferLearning.ImageTransferLearning \ + ./dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --modelPath ./model/bvlc_googlenet.caffemodel \ + --caffeDefPath ./model/deploy.prototxt \ + --batchSize 32 \ + --folder ./data \ + --nEpochs 10 + ``` \ No newline at end of file From ec5fe524bef882b6fc449f309e8dc8224729d7c3 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Mon, 19 Mar 2018 11:14:45 +0800 Subject: [PATCH 0740/1065] Some minor fix in new api doc (#2395) * fix * update --- .../intel/analytics/bigdl/dllib/example/keras/LeNet.scala | 4 ++-- .../com/intel/analytics/bigdl/dllib/example/keras/README.md | 3 ++- .../intel/analytics/bigdl/dllib/example/keras/Train.scala | 4 ++-- .../com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala | 6 +++--- 4 files changed, 9 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala index 1421a770a8f..b23fac7b48c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.utils.Shape object LeNet { - def apply(): Sequential[Float] = { + def apply(classNum: Int): Sequential[Float] = { val model = Sequential[Float]() model.add(Reshape(Array(1, 28, 28), inputShape = Shape(28, 28, 1))) model.add(Convolution2D(32, 3, 3, activation = "relu")) @@ -30,7 +30,7 @@ object LeNet { model.add(Flatten()) model.add(Dense(128, activation = "relu")) model.add(Dropout(0.5)) - model.add(Dense(10, activation = "softmax")) + model.add(Dense(classNum, activation = "softmax")) model } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md index 7797ba2b9c3..9860230d2f3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md @@ -59,4 +59,5 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ ``` In the above commands * -f: an option to set the path where you put your MNIST data. -* -b: an option to set the mini-batch size. It is expected that the mini-batch size is a multiple of node_number * core_number. \ No newline at end of file +* -b: an option to set the mini-batch size. It is expected that the mini-batch size is a multiple of node_number * core_number. +* -e: an option to set the number of epochs to train the model, the default value is 15. \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala index 4785213adcd..538722db6c7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala @@ -41,7 +41,7 @@ object Train { val validationData = param.folder + "/t10k-images-idx3-ubyte" val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" - val model = LeNet() + val model = LeNet(classNum = 10) val optimMethod = if (param.stateSnapshot.isDefined) { OptimMethod.load[Float](param.stateSnapshot.get) @@ -61,7 +61,7 @@ object Train { model.compile(optimizer = optimMethod, loss = ClassNLLCriterion[Float](logProbAsInput = false), metrics = Array(new Top1Accuracy[Float](), new Top5Accuracy[Float](), new Loss[Float])) - model.fit(trainSet, nbEpoch = 10, validationData = validationSet) + model.fit(trainSet, nbEpoch = param.maxEpoch, validationData = validationSet) sc.stop() }) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala index c6509992be0..ff059112c07 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala @@ -23,12 +23,12 @@ import org.scalatest.{FlatSpec, Matchers} class LeNetSpec extends FlatSpec with Matchers { "LeNet" should "generate the correct outputShape" in { - val cnn = LeNet() + val cnn = LeNet(classNum = 10) cnn.getOutputShape().toSingle().toArray should be (Array(-1, 10)) } "LeNet forward and backward" should "work properly" in { - val cnn = LeNet() + val cnn = LeNet(classNum = 10) val input = Tensor[Float](Array(2, 28, 28, 1)).rand() val output = cnn.forward(input) val gradInput = cnn.backward(input, output) @@ -36,7 +36,7 @@ class LeNetSpec extends FlatSpec with Matchers { "LeNet forward with incompatible input tensor" should "raise an exception" in { intercept[RuntimeException] { - val cnn = LeNet() + val cnn = LeNet(classNum = 10) val input = Tensor[Float](Array(28, 28, 1)).rand() val output = cnn.forward(input) } From e59a3538c9457bbf9f224ce2b86987b14911d8ec Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 19 Mar 2018 14:10:18 +0800 Subject: [PATCH 0741/1065] deco legacy tf ops tests (#2399) --- .../serializer/OperationSerializerSpec.scala | 1250 ----------------- 1 file changed, 1250 deletions(-) delete mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala deleted file mode 100644 index 6c040367595..00000000000 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/OperationSerializerSpec.scala +++ /dev/null @@ -1,1250 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package com.intel.analytics.bigdl.utils.serializer - -import java.io.{File => JFile} - -import com.google.protobuf.{ByteString, CodedOutputStream} -import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.ops.{All, Any, ApproximateEqual, ArgMax, BatchMatMul, BucketizedCol, Cast, CategoricalColHashBucket, CategoricalColVocaList, Ceil, CrossCol, CrossEntropy, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, Equal, Erf, Erfc, Expm1, Floor, FloorDiv, FloorMod, Greater, GreaterEqual, InTopK, IndicatorCol, Inv, InvGrad, IsFinite, IsInf, IsNan, Kv2Tensor, L2Loss, Less, LessEqual, Lgamma, LogicalAnd, LogicalNot, LogicalOr, Maximum, Minimum, MkString, Mod, ModuleToOperation, NotEqual, OneHot, Pad, Prod, RandomUniform, RangeOps, Rank, ResizeBilinearGrad, ResizeBilinearOps, Rint, Round, SegmentSum, SelectTensor, Sign, Slice, SquaredDifference, Substr, TensorOp, TopK, TruncateDiv, TruncatedNormal, Exp => ExpOps, Pow => PowOps, Select => SelectOps, Sum => SumOps, Tile => TileOps} -import com.intel.analytics.bigdl.nn.tf.{Assert => AssertOps, BroadcastGradientArgs => BroadcastGradientArgsOps, DecodeGif => DecodeGifOps, DecodeJpeg => DecodeJpegOps, DecodePng => DecodePngOps, DecodeRaw => DecodeRawOps} -import com.intel.analytics.bigdl.nn.tf._ -import com.intel.analytics.bigdl.nn.{SoftPlus => BigDLSoftPlus} -import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.T -import com.intel.analytics.bigdl.utils.tf.TFRecordIterator -import com.intel.analytics.bigdl.utils.tf.loaders.{Pack => _, _} -import org.tensorflow.example._ -import org.tensorflow.framework.DataType - -import scala.collection.mutable -import scala.util.Random - -class OperationSerializerSpec extends SerializerSpecHelper { - - override protected def getPackage(): String = "com.intel.analytics.bigdl.nn.ops" - - override def addExcludedPackage(): Unit = { - excludedPackage.add("com.intel.analytics.bigdl.utils.tf.loaders") - excludedPackage.add("com.intel.analytics.bigdl.utils.tf.ops") - // It would be tested in a separated spec - excludedPackage.add("com.intel.analytics.bigdl.nn.keras") - } - - override def getExpected(): mutable.Set[String] = { - super.getExpected().filter(cls => { - cls.contains(getPackage()) || cls.contains("com.intel.analytics.bigdl.tf") - }) - } - - "All serializer" should "work properly" in { - val all = All[Float]().setName("all") - val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) - val input2 = Tensor[Int](T(2, 1, 2)) - val input = T() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(all, input) - } - - "Any serializer" should "work properly" in { - val any = Any[Float]().setName("any") - val input1 = Tensor[Boolean](T(T(true, true, false), T(false, true, true))) - val input2 = Tensor[Int](T(2, 1, 2)) - val input = T() - input(1.toFloat) = input1 - input(2.toFloat) = input2 - runSerializationTest(any, input) - } - - "ApproximateEqual serializer" should "work properly" in { - val approximateEqual = ApproximateEqual[Float](0.01f).setName("approximateEqual") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(approximateEqual, input, approximateEqual. - asInstanceOf[ModuleToOperation[Float]].module.getClass - ) - } - - "ArgMax serializer" should "work properly" in { - val argMax = ArgMax[Float].setName("argMax") - val dataTensor = Tensor[Float](T(T(1.0f, 2.0f), T(3.0f, 4.0f))) - val dimensionTensor = Tensor.scalar[Int](1) - val input = T(dataTensor, dimensionTensor) - runSerializationTest(argMax, input) - } - - "Assert serializer" should "work properly" in { - import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - val assert = new AssertOps[Float]().setName("assert") - val predictTensor = Tensor[Boolean](Array(1)) - predictTensor.setValue(1, true) - val msg = Tensor[ByteString](Array(1)) - msg.setValue(1, ByteString.copyFromUtf8("must be true")) - val input = T(predictTensor, msg) - runSerializationTest(assert, input) - } - - "Assign serializer" should "work properly" in { - val assign = new Assign[Float]().setName("assign") - val input = - T( - Tensor[Float](T(1f, 2f, 3f)), - Tensor[Float](T(2f, 2f, 4f)) - ) - runSerializationTest(assign, input) - } - - "AssignGrad serializer" should "work properly" in { - val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val assignGrad = new AssignGrad[Float](grad).setName("assignGrad") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(assignGrad, input) - } - - "AvgPoolGrad serializer" should "work properly" in { - val avgPoolGrad = AvgPoolGrad[Float](4, 4, 1, 1, -1, -1, DataFormat.NHWC). - setName("avgPoolGrad") - val input1 = Tensor[Int](T(4, 32, 32, 3)) - val input2 = Tensor[Float](4, 32, 32, 3).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(avgPoolGrad, input) - } - - "BatchMatMul serializer" should "work properly" in { - val batchMatMul = BatchMatMul[Float, Float]().setName("batchMatMul") - val input = - T( - Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - ) - runSerializationTest(batchMatMul, input) - } - - "BiasAddGrad serializer" should "work properly" in { - val biasAddGrad = BiasAddGrad[Float](DataFormat.NCHW). - setName("biasAddGrad") - val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(biasAddGrad, input) - } - - "BroadcastGradientArgs serializer" should "work properly" in { - val broadcastGradientArgs = BroadcastGradientArgsOps[Float](). - setName("broadcastGradientArgs") - val input = - T( - Tensor[Int](T(1, 2, 3)), - Tensor[Int](T(2, 2, 1)) - ) - runSerializationTest(broadcastGradientArgs, input, broadcastGradientArgs. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "BucketizedCol serializer" should "work properly" in { - val bucketizedCol = BucketizedCol[Float](boundaries = Array(0.0, 10.0, 100.0)) - .setName("bucketizedCol") - val input = Tensor[Float](T(T(-1, 1), T(101, 10), T(5, 100))) - runSerializationTest(bucketizedCol, input) - } - - "Cast serializer" should "work properly" in { - val cast = Cast[Float, Float]().setName("cast") - val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(cast, input, cast. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Ceil serializer" should "work properly" in { - val ceil = Ceil[Float, Float]().setName("ceil") - val input = Tensor[Float](2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(ceil, input) - } - - "MergeOps serializer" should "work properly" in { - val mergeOps = new MergeOps[Float](1).setName("mergeOps") - val input = - T( - Tensor[Float](T(1.0f, 2.0f, 3.0f)), - Tensor[Float](T(2.0f, 2.0f, 1.0f)) - ) - runSerializationTest(mergeOps, input) - } - - "SwitchOps serializer" should "work properly" in { - val switchOps = new SwitchOps[Float]().setName("switchOps") - val input = - T( - Tensor[Float](T(1.0f, 2.0f, 3.0f)), - Tensor[Boolean](T(true)) - ) - runSerializationTest(switchOps, input) - } - - "Conv2D serializer" should "work properly" in { - val conv2d = Conv2D[Float](2, 1, -1, -1).setName("conv2d") - val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) - val filter = Tensor[Float](4, 3, 3, 2).apply1(_ => Random.nextFloat()) - val input = T(inputTensor, filter) - runSerializationTest(conv2d, input) - } - - "Conv2DBackFilter serializer" should "work properly" in { - val conv2dBackFilter = Conv2DBackFilter[Float](2, 2, -1, -1, DataFormat.NHWC). - setName("conv2dBackFilter") - val inputTensor = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) - val kernelSize = Tensor[Int](T(2, 2, 3, 3)) - val grad = Tensor[Float](1, 2, 2, 3).apply1(_ => Random.nextFloat()) - val input = T(inputTensor, kernelSize, grad) - runSerializationTest(conv2dBackFilter, input) - } - - "Conv2DTranspose Serializer" should "work properly" in { - val conv2dTranspose = Conv2DTranspose[Float](2, 2, -1, -1, DataFormat.NHWC). - setName("conv2dTranspose") - val inputTensor = Tensor[Int](T(1, 4, 3, 3)) - val kernelSize = Tensor[Float](2, 2, 3, 3).apply1(_ => Random.nextFloat()) - val data = Tensor[Float](1, 2, 2, 3)apply1(_ => Random.nextFloat()) - val input = T(inputTensor, kernelSize, data) - runSerializationTest(conv2dTranspose, input) - } - - "CrossCol Serializer" should "work proprly" in { - val crosscol = CrossCol[Float](hashBucketSize = 100) - .setName("CrossCol") - val input = T( - Tensor[String](T("A,D", "B", "A,C")), - Tensor[String](T("1", "2", "3,4")) - ) - runSerializationTest(crosscol, input) - } - - "CrossEntropy serializer" should "work properly" in { - val crossEntropy = CrossEntropy[Float]().setName("crossEntropy") - val output = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - val label = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - val input = T(output, label) - runSerializationTest(crossEntropy, input) - } - - private def getInputs(name: String): Tensor[ByteString] = { - import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - val index = name match { - case "png" => 0 - case "jpeg" => 1 - case "gif" => 2 - case "raw" => 3 - } - - val resource = getClass.getClassLoader.getResource("tf") - val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" - val file = new JFile(path) - - val bytesVector = TFRecordIterator(file).toVector - val pngBytes = bytesVector(index) - - val example = Example.parseFrom(pngBytes) - val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") - .getBytesList.getValueList.get(0) - - Tensor[ByteString](Array(imageByteString), Array[Int]()) - } - - "DecodeImage Serializer" should "work properly" in { - val decodeImage = new DecodeImage[Float](1).setName("decodeImage") - val input = getInputs("png") - runSerializationTest(decodeImage, input) - } - - "DecodeGif Serializer" should "work properly" in { - val decodeGif = new DecodeGifOps[Float]().setName("decodeGif") - val input = getInputs("gif") - runSerializationTest(decodeGif, input) - } - - "DecodeJpeg Serializer" should "work properly" in { - val decodeJpeg = new DecodeJpegOps[Float](1).setName("decodeJpeg") - val input = getInputs("jpeg") - runSerializationTest(decodeJpeg, input) - } - - "DecodePng Serializer" should "work properly" in { - val decodePng = new DecodePngOps[Float](1).setName("decodePng") - val input = getInputs("png") - runSerializationTest(decodePng, input) - } - - - "DecodeRaw Serializer" should "work properly" in { - val decodeRaw = new DecodeRawOps[Float](DataType.DT_UINT8, true).setName("decodeRaw") - val input = getInputs("raw") - runSerializationTest(decodeRaw, input) - } - - "DepthwiseConv2DBackpropInput serializer" should "work properly" in { - val depWiseBackprop = - DepthwiseConv2DBackpropInput[Float](1, 1, 0, 0, DataFormat.NHWC). - setName("depWiseBackprop") - val input = T(Tensor[Int](T(4, 24, 24, 3)), - Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat()), - Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) - runSerializationTest(depWiseBackprop, input) - } - - "DepthwiseConv2D serializer" should "work properly" in { - val depWIseConv2d = DepthwiseConv2D[Float](1, 1, 0, 0).setName("depWIseConv2d") - val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 3, 1).apply1(_ => Random.nextFloat())) - runSerializationTest(depWIseConv2d, input) - } - - "DepthwiseConv2DBackpropFilter serializer" should "work properly" in { - val depWiseConv2dBackProp = DepthwiseConv2DBackpropFilter[Float](1, - 1, 0, 0, DataFormat.NHWC).setName("depWiseConv2dBackProp") - val input = T(Tensor[Float](4, 24, 24, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(2, 2, 3, 1)), - Tensor[Float](4, 23, 23, 3).apply1(_ => Random.nextFloat())) - runSerializationTest(depWiseConv2dBackProp, input) - } - - "EluGrad serializer" should "work properly" in { - val eluGrad = EluGrad[Float, Float]().setName("eluGrad") - val inputTensor = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val grad = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(inputTensor, grad) - runSerializationTest(eluGrad, input) - } - - "Equal serializer" should "work properly" in { - val equal = Equal[Float]().setName("equal") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(equal, input, - equal.asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "ExpOps serializer" should "work properly" in { - val expOps = ExpOps[Float, Float]().setName("expOps") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(expOps, input) - } - - "Expm1 serializer" should "work properly" in { - val expm1 = Expm1[Float, Float]().setName("expm1") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(expm1, input) - } - - "Floor serializer" should "work properly" in { - val floor = Floor[Float]().setName("floor") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(floor, input, floor. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "FloorDiv serializer" should "work properly" in { - val floorDiv = FloorDiv[Float, Float]().setName("floorDiv") - val input1 = Tensor[Float](5).fill(1.0f) - val input2 = Tensor[Float](5).fill(2.0f) - val input = T(input1, input2) - runSerializationTest(floorDiv, input) - } - - "FloorMod serializer" should "work properly" in { - val floorMod = FloorMod[Float, Float]().setName("floorMod") - val input1 = Tensor[Float](5).fill(1.0f) - val input2 = Tensor[Float](5).fill(2.0f) - val input = T(input1, input2) - runSerializationTest(floorMod, input) - } - - "FusedBatchNorm serializer" should "work properly" in { - val fusedBatchNorm = FusedBatchNorm[Float]().setName("fusedBatchNorm") - val input = T(Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat()), - Tensor[Float](0), - Tensor[Float](0)) - runSerializationTest(fusedBatchNorm, input) - } - - "FusedBatchNormGrad serializer" should "work properly" in { - val fbatchNormGrad = FusedBatchNormGrad[Float]().setName("fbatchNormGrad") - val input = T(Tensor[Float](4, 8, 8, 256).rand(), - Tensor[Float](4, 8, 8, 256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat()), - Tensor[Float](256).apply1(_ => Random.nextFloat())) - runSerializationTest(fbatchNormGrad, input) - } - - "Greater serializer" should "work properly" in { - val greater = Greater[Float]().setName("greater") - val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(greater, input, greater. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "GreaterEqual serializer" should "work properly" in { - val greaterEqual = GreaterEqual[Float]().setName("greaterEqual") - val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(greaterEqual, input, greaterEqual - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Indicator serializer" should "work properly" in { - val indicatorCol = IndicatorCol[Float]( - feaLen = 4, - isCount = true - ).setName("indicatorCol") - val input = Tensor.sparse( - Array(Array(0, 1, 1, 2, 2, 3, 3, 3), - Array(0, 0, 3, 0, 1, 0, 1, 2)), - Array(3, 1, 2, 0, 3, 1, 2, 2), - Array(4, 4) - ) - runSerializationTest(indicatorCol, input) - } - - "InTopK serializer" should "work properly" in { - val inTopK = InTopK[Float](2).setName("inTopK") - val input1 = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Int](2).fill(1) - val input = T(input1, input2) - runSerializationTest(inTopK, input) - } - - "Inv serializer" should "work properly" in { - val inv = Inv[Float, Float]().setName("inv") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(inv, input) - } - - "InvGrad serializer" should "work properly" in { - val invGrad = InvGrad[Float, Float]().setName("invGrad") - val input = T(Tensor[Float](2, 5).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 5).apply1(_ => Random.nextFloat())) - runSerializationTest(invGrad, input) - } - - "IsFinite serializer" should "work properly" in { - val isFinite = IsFinite[Float, Float]().setName("isFinite") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(isFinite, input) - } - - "IsInf serializer" should "work properly" in { - val isInf = IsInf[Float, Float]().setName("isInf") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(isInf, input) - } - - "IsNan serializer" should "work properly" in { - val isNan = IsNan[Float, Float]().setName("isInf") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(isNan, input) - } - - "Kv2Tensor" should "work properly" in { - val kv2tensor = Kv2Tensor[Float, Float]( - kvDelimiter = ",", itemDelimiter = ":", transType = 0 - ).setName("kv2tensor") - val input = T( - Tensor[String]( - T(T("0:0.1,1:0.2"), T("1:0.3,3:0.5"), T("2:0.15,4:0.25"))), - Tensor[Int](Array(5), shape = Array[Int]()) - ) - runSerializationTest(kv2tensor, input) - } - - "L2Loss serializer" should "work properly" in { - val l2loss = L2Loss[Float]().setName("l2loss") - val input = Tensor[Float](2, 5).apply1(_ => Random.nextFloat()) - runSerializationTest(l2loss, input, - l2loss.asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Less serializer" should "work properly" in { - val less = Less[Float]().setName("less") - val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(less, input, less - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "CategoricalColHashBucket" should "work properly" in { - val categoricalColHashBucket = CategoricalColHashBucket[Float]( - hashBucketSize = 100 - ).setName("categoricalColHashBucket") - val input = Tensor[String](T(T(1), T(2), T(3))) - runSerializationTest(categoricalColHashBucket, input) - } - - "CategoricalColVocaList" should "work properly" in { - val categoricalColVocaList = CategoricalColVocaList[Float]( - vocaList = Array("A", "B", "C"), - strDelimiter = ",", - isSetDefault = false, - numOovBuckets = 0 - ).setName("categoricalColVocaList") - val input = Tensor[String](T(T("A"), T("B"), T("C"), T("D"))) - runSerializationTest(categoricalColVocaList, input) - } - - "LessEqual serializer" should "work properly" in { - val lessEqual = LessEqual[Float]().setName("lessEqual") - val input1 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](5).apply1(_ => Random.nextFloat()) - val input = T(input1, input2) - runSerializationTest(lessEqual, input, lessEqual - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LogicalAnd serializer" should "work properly" in { - val logicalAnd = LogicalAnd[Float].setName("logicalAnd") - val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) - runSerializationTest(logicalAnd, input, logicalAnd. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LogicalNot serializer" should "work properly" in { - val logicalNot = LogicalNot[Float].setName("logicalNot") - val input = Tensor[Boolean](T(true, false)) - runSerializationTest(logicalNot, input, logicalNot - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LogicalOr serializer" should "work properly" in { - val logicalOr = LogicalOr[Float].setName("logicalOr") - val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) - runSerializationTest(logicalOr, input, logicalOr - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "LRNGrad serializer" should "work properly" in { - val lrnGrad = LRNGrad[Float]().setName("lrnGrad") - val input = T(Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](4, 8, 8, 3).apply1(_ => Random.nextFloat()) - ) - runSerializationTest(lrnGrad, input) - } - - "Maximum serializer" should "work properly" in { - val maxiMum = Maximum[Float, Float]().setName("maxiMum") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(maxiMum, input) - } - - "MaxPool serializer" should "work properly" in { - val maxPool = MaxPool[Float]( - Array(1, 2, 3, 1), - Array(1, 2, 1, 1), - "VALID").setName("maxPool") - val input = Tensor[Float](1, 4, 3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(maxPool, input, maxPool. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - - } - - "MaxPoolGrad serializer" should "work properly" in { - val maxPoolGrad = MaxPoolGrad[Float](2, 1, 1, 1, 0, 0, DataFormat.NCHW). - setName("maxPoolGrad") - val input = T(Tensor[Float](1, 3, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](), - Tensor[Float](1, 1, 1).apply1(_ => Random.nextFloat())) - runSerializationTest(maxPoolGrad, input) - } - - "Mimimum serializer" should "work properly" in { - val minimum = Minimum[Float, Float]().setName("minimum") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(minimum, input) - } - - "Mod serializer" should "work properly" in { - val mod = Mod[Float, Float]().setName("mod") - val input1 = Tensor[Float](5).fill(1.0f) - val input2 = Tensor[Float](5).fill(2.0f) - val input = T(input1, input2) - runSerializationTest(mod, input) - } - - "ModuleToOperation serializer" should "work properly" in { - val moduleToOperation = ModuleToOperation[Float](new LogicalOr()). - setName("moduleToOperation") - val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) - runSerializationTest(moduleToOperation, input) - } - - - "TensorModuleWrapper serializer" should "work properly" in { - val tensorModuleWrapper = TensorModuleWrapper[Float, Float](BigDLSoftPlus[Float]()). - setName("moduleToOperation") - val input = Tensor[Float](T(1.0f, 1.0)) - runSerializationTest(tensorModuleWrapper, input) - } - - "NoOp serializer" should "work properly" in { - val noOp = new com.intel.analytics.bigdl.nn.tf.NoOp[Float]().setName("noOp") - val input = Tensor[Float](5).apply1(_ => Random.nextFloat()) - runSerializationTest(noOp, input) - } - - "NotEqual serializer" should "work properly" in { - val notEqual = NotEqual[Float].setName("notEqual") - val input = T(Tensor[Boolean](T(true, false)), Tensor[Boolean](T(true, false))) - runSerializationTest(notEqual, input, notEqual - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "OneHot serializer" should "work properly" in { - val oneHot = OneHot[Float, Float](axis = -1).setName("oneHot") - val input = - T(Tensor[Long](T(0, 2, -1, 1)), - Tensor[Int](Array(3), shape = Array[Int]()), - Tensor[Float](Array(0.5f), shape = Array[Int]()), - Tensor[Float](Array(0.0f), shape = Array[Int]())) - runSerializationTest(oneHot, input, oneHot - .asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Pad serializer" should "work properly" in { - val pad = Pad[Float, Float](mode = "CONSTANT", 0.0f).setName("pad") - val inputTensor = Tensor[Float](2, 2, 3).apply1(_ => Random.nextFloat()) - val padding = Tensor[Int](T(T(1, 2), T(1, 2), T(1, 2))) - val input = T(inputTensor, padding) - runSerializationTest(pad, input, pad. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "ParseExample serializer" should "work properly" in { - import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - - val floatBuilder = FloatList.newBuilder() - .addValue(0.0f).addValue(1.0f).addValue(2.0f) - val floatFeature = Feature.newBuilder().setFloatList(floatBuilder).build() - - val longBuilder = Int64List.newBuilder() - .addValue(0).addValue(1).addValue(2) - val longFeature = Feature.newBuilder().setInt64List(longBuilder).build() - - val bytesBuilder = BytesList.newBuilder().addValue(ByteString.copyFromUtf8("abcd")) - val bytesFeature = Feature.newBuilder().setBytesList(bytesBuilder).build() - - val features = Features.newBuilder() - .putFeature("floatFeature", floatFeature) - .putFeature("longFeature", longFeature) - .putFeature("bytesFeature", bytesFeature) - val example = Example.newBuilder().setFeatures(features).build() - val length = example.getSerializedSize - val data = new Array[Byte](length) - val outputStream = CodedOutputStream.newInstance(data) - example.writeTo(outputStream) - - val exampleParser = ParseExample[Float](3, Seq(FloatType, LongType, StringType), - Seq(Array(3), Array(3), Array())).setName("parseExample") - - val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) - val names = Tensor[ByteString]() - val key1 = Tensor[ByteString](Array(ByteString.copyFromUtf8("floatFeature")), Array[Int]()) - val key2 = Tensor[ByteString](Array(ByteString.copyFromUtf8("longFeature")), Array[Int]()) - val key3 = Tensor[ByteString](Array(ByteString.copyFromUtf8("bytesFeature")), Array[Int]()) - - val default1 = Tensor[Float]() - val default2 = Tensor[Long]() - val default3 = Tensor[ByteString]() - val input = T(serialized, names, key1, key2, key3, default1, default2, default3) - runSerializationTest(exampleParser, input) - } - - "PowOps serializer" should "work properly" in { - val pow = PowOps[Float]().setName("powOps") - val v = Tensor[Float](T(2)) - val t = Tensor[Float](T(1, 2, 3)) - val input = (T(t, v)) - runSerializationTest(pow, input) - } - - "Prod serializer" should "work properly" in { - val prod = Prod[Float](-1, false).setName("prod") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(prod, input, prod. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "RandomUniform serializer" should "work properly" in { - val randomUniform = RandomUniform[Float, Float](10, 20). - setName("randomUniform") - val input = Tensor[Int](T(1, 2, 3)) - runSerializationTest(randomUniform, input, randomUniform. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "RangeOps serializer" should "work properly" in { - val rangeOps = RangeOps[Float, Float]().setName("rangeOps") - val input = T(Tensor[Float](T(1)), Tensor[Float](T(10)), Tensor[Float](T(1))) - runSerializationTest(rangeOps, input) - } - - "Rank serializer" should "work properly" in { - val rank = Rank[Float].setName("rank") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(rank, input, rank. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "Relu6Grad serializer" should "work properly" in { - val relu6Grad = Relu6Grad[Float, Float]().setName("relu6Grad") - val input = T(Tensor[Float](5).apply1(_ => Random.nextFloat()), - Tensor[Float](5).apply1(_ => Random.nextFloat())) - runSerializationTest(relu6Grad, input) - } - - "ReluGrad serializer" should "work properly" in { - val reluGrad = ReluGrad[Float] - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(reluGrad, input) - } - - "ResizeBilinearOps serializer" should "work properly" in { - val resizeBilinearOps = ResizeBilinearOps[Float](false). - setName("resizeBiLinearOps") - val input = T(Tensor[Float](1, 3, 2, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(3, 2))) - runSerializationTest(resizeBilinearOps, input) - } - - "Rint serializer" should "work properly" in { - val rint = Rint[Float]().setName("rint") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(rint, input) - } - - "Round serializer" should "work properly" in { - val round = Round[Float, Float]().setName("round") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(round, input) - } - - "RsqrtGrad serializer" should "work properly" in { - val rsqrtGrad = RsqrtGrad[Float, Float].setName("rsqrtGrad") - val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](3, 3).apply1(_ => Random.nextFloat())) - runSerializationTest(rsqrtGrad, input) - } - - "SegmentSum serializer" should "work properly" in { - val sgSum = SegmentSum[Float].setName("segmentSum") - val input = T(Tensor[Float](10, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0, 0, 0, 1, 2, 3, 3, 4, 4, 4))) - runSerializationTest(sgSum, input) - } - - "SelectOps serializer" should "work properly" in { - val select = SelectOps[Float]().setName("select") - val cond = Tensor.scalar[Boolean](true) - val t = Tensor[Int](T(1)) - val e = Tensor[Int](T(2)) - val input = T(cond, t, e) - runSerializationTest(select, input) - } - - "SigmoidGrad serializer" should "work properly" in { - val sigMoidGrad = SigmoidGrad[Float, Float]().setName("sigMoidGrad") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(sigMoidGrad, input) - } - - "Sign serializer" should "work properly" in { - val sign = Sign[Float, Float]().setName("sign") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(sign, input) - } - - "SelectTensor serializer" should "work properly" in { - val transformer = (TensorOp[Float]() ** 3 * 4.5f).ceil - val select = SelectTensor(Tensor.scalar("2"), transformer = transformer) - val t1 = Tensor[Float](3, 4).randn() - val t2 = Tensor[Float](2, 3).randn() - val input = T().update(Tensor.scalar(1), t1).update(Tensor.scalar("2"), t2) - runSerializationTest(select, input) - } - - "Slice serializer" should "work properly" in { - val slice = Slice[Float](begin = Array(0, 1, 1), - size = Array(2, -1, 1)).setName("slice") - val input = Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(slice, input, slice. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "SoftplusGrad serializer" should "work properly" in { - val sofplusGrad = SoftplusGrad[Float, Float].setName("sofplusGrad") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(sofplusGrad, input) - } - - "SoftSignGrad serializer" should "work properly" in { - val softSign = SoftsignGrad[Float, Float].setName("softSign") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(softSign, input) - } - - "SqrtGrad serializer" should "work properly" in { - val sqrtGrad = SqrtGrad[Float, Float].setName("sqrtGrad") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(sqrtGrad, input) - } - - "SquaredDifference serializer" should "work properly" in { - val squareDiff = SquaredDifference[Float]().setName("squareDiff") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat())) - runSerializationTest(squareDiff, input) - } - - "Substr serializer" should "work properly" in { - import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString - val subStr = Substr[Float]().setName("subStr") - val input = T(Tensor.scalar[ByteString](ByteString.copyFromUtf8("HelloBigDL")), - Tensor.scalar[Int](0), Tensor.scalar[Int](5)) - runSerializationTest(subStr, input) - } - - "SumOps serializer" should "work properly" in { - val sumOps = SumOps[Float, Float]().setName("sumOps") - val input = T(Tensor[Float](2, 2).apply1(_ => Random.nextFloat()), - Tensor[Float]()) - runSerializationTest(sumOps, input) - } - - "TileOps serializer" should "work properly" in { - val tileOps = TileOps[Float]().setName("tileOps") - val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(2, 1, 2))) - runSerializationTest(tileOps, input, tileOps. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "TensorOp serializer" should "work properly" in { - val op = (((TensorOp[Float]() + 1.5f) ** 2) -> TensorOp.sigmoid() - ).setName("TensorOP") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(op, input) - } - - "TopK serializer" should "work properly" in { - val topk = TopK[Float, Float](2).setName("topK") - val input = Tensor[Float](3, 3).apply1(_ => Random.nextFloat()) - runSerializationTest(topk, input) - } - - "TruncateDiv serializer" should "work properly" in { - val truncateDiv = TruncateDiv[Float, Float]().setName("truncateDiv") - val input = T(Tensor[Float](5).fill(1.0f), Tensor[Float](5).fill(2.0f)) - runSerializationTest(truncateDiv, input) - } - - "TruncatedNormal serializer" should "work properly" in { - val truncateNormal = TruncatedNormal[Float, Float](10, 20).setName("truncateNormal") - val input = Tensor[Int](T(1, 2, 3)) - runSerializationTest(truncateNormal, input, truncateNormal. - asInstanceOf[ModuleToOperation[Float]].module.getClass) - } - - "BiasAdd serializer" should "work properly" in { - val biasAdd = BiasAdd[Float]().setName("biasAdd") - val input = T(Tensor[Float](2, 3, 3).apply1(_ => Random.nextFloat()), - Tensor[Float](3).apply1(_ => Random.nextFloat())) - runSerializationTest(biasAdd, input) - } - "Const serializer" should "work properly" in { - val value = Tensor[Float](3).apply1(_ => Random.nextFloat()) - val const = Const[Float, Float](value).setName("const") - val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) - runSerializationTest(const, input) - } - - "Fill serializer" should "work properly" in { - val fill = Fill[Float]().setName("fill") - val shape = Tensor[Int](T(2, 3)) - val value = Tensor[Float](Array(0.1f), Array[Int]()) - val input = T(shape, value) - runSerializationTest(fill, input) - } - - "Log1p serializer" should "work properly" in { - val log1p = Log1p[Float, Float]().setName("log1p") - val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) - runSerializationTest(log1p, input) - } - - "Shape serializer" should "work properly" in { - val shape = Shape[Float]().setName("shape") - val input = Tensor[Float](3).apply1(_ => Random.nextFloat()) - runSerializationTest(shape, input) - } - "MeanLoadTF serializer" should "work properly" in { - val meanLoadTF = new MeanLoadTF[Float]("Float", false).setName("meanLoadTF") - val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(1, 1))) - runSerializationTest(meanLoadTF, input) - } - - "ConcatV2LoadTF serializer" should "work properly" in { - val concatv2 = new ConcatV2LoadTF[Float]().setName("concatv2LoadTF") - val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), - Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(1))) - runSerializationTest(concatv2, input) - } - - "ExpandDimsLoadTF serializer" should "work properly" in { - val expandDim = new ExpandDimsLoadTF[Float]().setName("expandDim") - val input = T(Tensor[Float](1, 2).apply1(_ => Random.nextFloat()), - Tensor.scalar[Int](1)) - runSerializationTest(expandDim, input) - } - - "MkString serializer" should "work properly" in { - val mkString = new MkString[Float](strDelimiter = ",").setName("MkString") - val input = Tensor.sparse( - indices = Array(Array(0, 0, 1, 1, 1, 2), Array(0, 1, 0, 1, 2, 2)), - values = Array(1, 2, 3, 4, 5, 6), - shape = Array(3, 4) - ) - runSerializationTest(mkString, input) - } - - "PadLoadTF serializer" should "work properly" in { - val padLoadTF = new PadLoadTF[Float]().setName("PadLoadTF") - val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), - Tensor[Int](T(T(1, 1), T(1, 1)))) - runSerializationTest(padLoadTF, input) - } - - "ProdLoadTF serializer" should "work properly" in { - val prodLoadTF = new ProdLoadTF[Float]().setName("prodLoadTF") - val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), - Tensor.scalar[Int](1)) - runSerializationTest(prodLoadTF, input) - } - - "ReshapeLoadTF serializer" should "work properly" in { - val reshapeLoadTF = new ReshapeLoadTF[Float]().setName("reshapeLoadTF") - val input = T(Tensor[Float](5, 5, 5).apply1(_ => Random.nextFloat()), - Tensor[Int](T(1, 5, 25))) - runSerializationTest(reshapeLoadTF, input) - } - - "SliceLoadTF serializer" should "work properly" in { - val sliceLoadTF = new SliceLoadTF[Float]().setName("sliceLoadTF") - val input = T(Tensor[Float](3, 2, 3).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0, 1, 1)), - Tensor[Int](T(2, -1, 1))) - runSerializationTest(sliceLoadTF, input) - } - - "StridedSliceLoadTF serializer" should "work properly" in { - val strideSliceLoadTF = new StridedSliceLoadTF[Float, Float](). - setName("strideSliceLoadTF") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0)), - Tensor[Int](T(1)), - Tensor[Int](T(1)) - ) - runSerializationTest(strideSliceLoadTF, input) - } - - "SplitLoadTF serializer" should "work properly" in { - val splitLoadTF = new SplitLoadTF[Float](1).setName("splitLoadTD") - val input = T(Tensor[Int](T(1)), - Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()) - ) - runSerializationTest(splitLoadTF, input) - } - - "TransposeLoadTF serializer" should "work properly" in { - val transposeLoadTF = new TransposeLoadTF[Float]().setName("transposeLoadTF") - val input = T(Tensor[Float](1, 6, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0, 1)) - ) - runSerializationTest(transposeLoadTF, input) - } - - "TopKV2LoadTF serializer" should "work properly" in { - val topkv2LoadTF = new TopKV2LoadTF[Float](false, "Float"). - setName("topkv2LoadTF") - val input = T(Tensor[Float](3, 3).apply1(_ => Random.nextFloat()), - Tensor.scalar[Int](2) - ) - runSerializationTest(topkv2LoadTF, input) - } - - "Digamma serializer" should "work properly" in { - val module = Digamma[Float, Float]() - - val input = Tensor[Float](1, 5, 3, 4).rand() - runSerializationTest(module, input) - } - - "Lgamma serializer" should "work properly" in { - val module = Lgamma[Float, Float]() - - val input = Tensor[Float](1, 5, 3, 4).rand() - runSerializationTest(module, input) - } - - "Erf serializer" should "work properly" in { - val module = Erf[Float, Float]() - - val input = Tensor[Float](1, 5, 3, 4).rand() - runSerializationTest(module, input) - } - - "Erfc serializer" should "work properly" in { - val module = Erfc[Float, Float]() - - val input = Tensor[Float](1, 5, 3, 4).rand() - runSerializationTest(module, input) - } - - "TanhGrad serializer" should "work properly" in { - val module = TanhGrad[Float, Float]() - - val input = T(Tensor[Float](1, 5, 3, 4).rand(), Tensor[Float](1, 5, 3, 4).rand()) - - runSerializationTest(module, input) - } - - "Dilation2D serializer" should "work properly" in { - val module = Dilation2D[Float, Float]( - Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") - - val input = T(Tensor[Float](4, 32, 32, 3).rand(), Tensor[Float](3, 4, 3).rand()) - - runSerializationTest(module, input) - } - - "Dilation2DBackpropFilter serializer" should "work properly" in { - val module = Dilation2DBackpropFilter[Float, Float]( - Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") - - val input = T(Tensor[Float](4, 32, 32, 3).rand(), - Tensor[Float](3, 4, 3).rand(), - Tensor[Float](4, 11, 16, 3).rand()) - - runSerializationTest(module, input) - } - - "Dilation2DBackpropInput serializer" should "work properly" in { - val module = Dilation2DBackpropInput[Float, Float]( - Array(1, 3, 2, 1), Array(1, 2, 3, 1), "same") - - val input = T(Tensor[Float](4, 32, 32, 3).rand(), - Tensor[Float](3, 4, 3).rand(), - Tensor[Float](4, 11, 16, 3).rand()) - - runSerializationTest(module, input) - } - - "Conv3D serializer" should "work properly" in { - val module = Conv3D[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val input = Tensor[Float](4, 20, 30, 40, 3).rand() - val filter = Tensor[Float](2, 3, 4, 3, 4).rand() - runSerializationTest(module, T(input, filter)) - } - - "Conv3DBackpropFilter serializer" should "work properly" in { - val module = Conv3DBackpropFilter[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val input = Tensor[Float](4, 20, 30, 40, 3).rand() - val filter = Tensor[Float](2, 3, 4, 3, 4).rand() - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4) - - runSerializationTest(module, T(input, filter, outputBackprop)) - } - - "Conv3DBackpropInput serializer" should "work properly" in { - val module = Conv3DBackpropInput[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val input = Tensor[Float](4, 20, 30, 40, 3).rand() - val filter = Tensor[Float](2, 3, 4, 3, 4).rand() - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() - - runSerializationTest(module, T(input, filter, outputBackprop)) - } - - "Conv3DBackpropFilterV2 serializer" should "work properly" in { - val module = Conv3DBackpropFilterV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val input = Tensor[Float](4, 20, 30, 40, 3).rand() - val filter = Tensor[Int](Array(2, 3, 4, 3, 4), Array(5)) - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() - - runSerializationTest(module, T(input, filter, outputBackprop)) - } - - "Conv3DBackpropInputV2 serializer" should "work properly" in { - val module = Conv3DBackpropInputV2[Float](1, 2, 3, 0, 0, 0, DataFormat.NHWC) - val inputSize = Tensor[Int](Array(4, 20, 30, 40, 3), Array(5)) - val filter = Tensor[Float](2, 3, 4, 3, 4).rand() - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() - - runSerializationTest(module, T(inputSize, filter, outputBackprop)) - } - - "ResizeBilinearGrad serializer" should "work properly" in { - val module = ResizeBilinearGrad[Float](true) - val input = T(Tensor[Float](1, 224, 224, 3).rand(), - Tensor[Float](1, 64, 64, 3).rand()) - val outputBackprop = Tensor[Float](4, 19, 14, 13, 4).rand() - - runSerializationTest(module, input) - } - - "Control Ops serializer" should "work properly" in { - val input = Input[Float]("input") - - val conditionInput = Input[Float]("conditionInput") - val const = new com.intel.analytics.bigdl.nn.tf.Const[Float, Float](Tensor(T(9))).inputs() - val constEnter = new com.intel.analytics.bigdl.nn.tf.Enter[Float]("test_frame").inputs(const) - val less = Less[Float]().inputs(constEnter, conditionInput) - - val updateInput = Input[Float]() - val add = AddConstant[Float](1).inputs(updateInput) - val addEnter = new com.intel.analytics.bigdl.nn.tf.Enter[Float]("test_frame").inputs(add) - val echo = Echo[Float]().inputs(addEnter) - - val exit = ControlNodes.whileLoop[Float]( - (Seq(conditionInput), less), - (Seq((updateInput, echo))), - Seq(input), - "while" - ) - val model = Graph.dynamic[Float](Array(input), Array(exit(0)), None, false) - runSerializationTestWithMultiClass(model, Tensor.scalar[Float](1), Array( - addEnter.element.getClass.asInstanceOf[Class[_]], - new com.intel.analytics.bigdl.nn.tf.NextIteration[Float, Float]().getClass, - new com.intel.analytics.bigdl.nn.tf.Exit[Float]().getClass, - new LoopCondition[Float]().getClass - )) - } - - "Stack operations serializer" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ - val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() - val stack = new StackCreator[Float, Float]().inputs() - val push = new com.intel.analytics.bigdl.nn.tf.StackPush[Float, Float]().inputs(stack, data) - val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(push) - val pop = new com.intel.analytics.bigdl.nn.tf.StackPop[Float, Float]().inputs(stack, ctr) - val model = Graph.dynamic[Float](Array(stack), Array(pop)) - - runSerializationTestWithMultiClass(model, Tensor.scalar(1), Array( - stack.element.getClass.asInstanceOf[Class[_]], - push.element.getClass.asInstanceOf[Class[_]], - pop.element.getClass.asInstanceOf[Class[_]] - )) - } - - "TensorArray serializer R/W" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ - val tensorArray = new TensorArrayCreator[Float, Float]().inputs() - val data = Const[Float, Float](Tensor.scalar[Float](1)).inputs() - val index = Const[Float, Int](Tensor.scalar[Int](0)).inputs() - val write = new TensorArrayWrite[Float, Float]().inputs((tensorArray, 1), (index, 1), (data, 1)) - val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(write) - val read = new TensorArrayRead[Float, Float]().inputs((tensorArray, 1), (index, 1), (ctr, 1)) - val grad = new TensorArrayGrad[Float]("grad").inputs(tensorArray) - val output = Identity[Float]().inputs((grad, 2)) - val model = Graph.dynamic[Float](Array(tensorArray), Array(read, output)) - - runSerializationTestWithMultiClass(model, Tensor.scalar[Int](1), Array( - tensorArray.element.getClass.asInstanceOf[Class[_]], - write.element.getClass.asInstanceOf[Class[_]], - read.element.getClass.asInstanceOf[Class[_]], - grad.element.getClass.asInstanceOf[Class[_]] - )) - } - - "TensorArray serializer Gather/Scatter" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ - val tensorArray = new TensorArrayCreator[Float, Float]().inputs() - val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() - val indices = Const[Float, Int](Tensor[Int](T(0, 1, 2))).inputs() - val scatter = new TensorArrayScatter[Float, Float]().inputs((tensorArray, 1), (indices, 1), - (data, 1)) - val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(scatter) - val gather = new TensorArrayGather[Float, Float]().inputs((tensorArray, 1), (indices, 1), - (ctr, 1)) - val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(gather) - val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1)) - val model = Graph.dynamic[Float](Array(tensorArray), Array(gather, close)) - - runSerializationTestWithMultiClass(model, Tensor.scalar[Int](10), Array( - tensorArray.element.getClass.asInstanceOf[Class[_]], - scatter.element.getClass.asInstanceOf[Class[_]], - gather.element.getClass.asInstanceOf[Class[_]], - close.element.getClass.asInstanceOf[Class[_]] - )) - } - - "TensorArray serializer Split/Concat" should "work properly" in { - import com.intel.analytics.bigdl.nn.ops._ - val tensorArray = new TensorArrayCreator[Float, Float]().inputs() - val data = Const[Float, Float](Tensor[Float](3, 4).rand()).inputs() - val lengths = Const[Float, Int](Tensor[Int](T(1, 2))).inputs() - val splitter = new TensorArraySplit[Float, Float]().inputs((tensorArray, 1), (data, 1), - (lengths, 1)) - val ctr = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(splitter) - val concat = new TensorArrayConcat[Float, Float]().inputs(tensorArray, ctr) - val size = new TensorArraySize[Float]().inputs(tensorArray, ctr) - val ctr2 = new com.intel.analytics.bigdl.nn.tf.ControlDependency[Float]().inputs(concat, size) - val close = new TensorArrayClose[Float]().inputs((tensorArray, 1), (ctr2, 1)) - val model = Graph.dynamic[Float](Array(tensorArray), Array(concat, close, size)) - - runSerializationTestWithMultiClass(model, Tensor.scalar[Int](2), Array( - tensorArray.element.getClass.asInstanceOf[Class[_]], - splitter.element.getClass.asInstanceOf[Class[_]], - concat.element.getClass.asInstanceOf[Class[_]], - close.element.getClass.asInstanceOf[Class[_]], - size.element.getClass.asInstanceOf[Class[_]] - )) - } - - "ConcatOffset serializer" should "work properly" in { - val module = new com.intel.analytics.bigdl.nn.tf.ConcatOffset[Float]() - runSerializationTest(module, T(Tensor.scalar[Int](1), Tensor[Int](T(2, 2, 5, 7)), - Tensor[Int](T(2, 3, 5, 7)), Tensor[Int](T(2, 4, 5, 7)))) - } - - "InvertPermutation serializer" should "work properly" in { - val module = new com.intel.analytics.bigdl.nn.tf.InvertPermutation[Float]() - runSerializationTest(module, Tensor[Int](T(0, 1, 2, 3, 4))) - } - -} From 58733aacac708a17ecdcdd8b0a4e7b3576af6d39 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Mon, 19 Mar 2018 17:03:32 -0700 Subject: [PATCH 0742/1065] update append column (#2391) --- .../analytics/bigdl/dlframes/SharedParamsAdapter.scala | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala index c87fd7b1760..c7737f74de0 100644 --- a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/SharedParamsAdapter.scala @@ -16,7 +16,7 @@ package org.apache.spark.ml.adapter -import org.apache.spark.sql.types.{DataType, StructType} +import org.apache.spark.sql.types.{DataType, StructField, StructType} trait HasPredictionCol extends org.apache.spark.ml.param.shared.HasPredictionCol @@ -42,7 +42,10 @@ object SchemaUtils { colName: String, dataType: DataType, nullable: Boolean = false): StructType = { - org.apache.spark.ml.util.SchemaUtils.appendColumn(schema, colName, dataType) + + val colSF = StructField(colName, dataType, nullable) + require(!schema.fieldNames.contains(colSF.name), s"Column ${colSF.name} already exists.") + StructType(schema.fields :+ colSF) } def sameType(a: DataType, b: DataType): Boolean = a.sameType(b) From 616c776f5bd725a004adab0f98752d2305885903 Mon Sep 17 00:00:00 2001 From: "li,zhichao" Date: Tue, 20 Mar 2018 11:55:07 +0800 Subject: [PATCH 0743/1065] Public getInputShape and getOutputShape (#2401) * open getInputShape and getOutputShape * clean * remove logger * fix --- .../analytics/bigdl/dllib/keras/Topology.scala | 2 +- .../bigdl/dllib/nn/abstractnn/InferShape.scala | 14 +++++++++----- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala index f8471b41c0b..ec35b4b78d9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala @@ -267,7 +267,7 @@ class Sequential[T: ClassTag]() this.labor = doBuild(null) private def triggerBuilding(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Unit = { - if (this.getOutputShape() == null) { + if (!this.isBuilt()) { if (module.getInputShape() == null) { throw new RuntimeException("The first layer should explicitly declare inputshape") } else { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala index 0ffd07b4de1..547a55d5e71 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/InferShape.scala @@ -26,7 +26,6 @@ import scala.reflect.ClassTag class InvalidLayer(msg: String) extends RuntimeException(msg) trait InferShape { - private[bigdl] var _inputShapeValue: Shape = null private[bigdl] var _outputShapeValue: Shape = null @@ -46,16 +45,21 @@ trait InferShape { // scalastyle:on /** - * We suppose the first dim is batch + * Return the inputShape for the current Layer and the first dim is batch. */ - private[bigdl] final def getInputShape(): Shape = { + final def getInputShape(): Shape = { + require(this.isKerasStyle(), + "Torch style definition doesn't support getInputShape for now.") _inputShapeValue } /** - * We suppose the first dim is batch + * Return the outputShape for the current Layer and the first dim is batch. */ - private[bigdl] final def getOutputShape(): Shape = { + final def getOutputShape(): Shape = { + require(this.isKerasStyle(), + "Torch style definition doesn't support getOutputShape for now.") + require(this.isBuilt(), "This module hasn't been built.") outputShapeValue } From e874749613cbf3d2af12c4cc6944dc38867b9d01 Mon Sep 17 00:00:00 2001 From: jenniew Date: Mon, 12 Mar 2018 23:16:08 -0700 Subject: [PATCH 0744/1065] update inception python and scala example --- .../dllib/models/inception/ImageNet2012.scala | 46 ++++++++++++++++--- .../bigdl/dllib/models/inception/Train.scala | 20 +++----- 2 files changed, 45 insertions(+), 21 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala index 7e4ddfcdf0a..e50c1b6f340 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala @@ -19,8 +19,11 @@ import java.nio.file.Paths import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset._ -import com.intel.analytics.bigdl.dataset.image._ +import com.intel.analytics.bigdl.dataset.image.{BGRImgCropper, BGRImgNormalizer, BytesToBGRImg, CropCenter, MTLabeledBGRImgToBatch, HFlip => DatasetHFlip} +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD object ImageNet2012 { def apply( @@ -30,8 +33,7 @@ object ImageNet2012 { batchSize : Int, nodeNumber: Int, coresPerNode: Int, - classNumber: Int, - size: Int + classNumber: Int ) : DataSet[MiniBatch[Float]] = { DataSet.SeqFileFolder.files(path, sc, classNumber).transform( @@ -40,9 +42,24 @@ object ImageNet2012 { height = imageSize, batchSize = batchSize, transformer = (BytesToBGRImg() -> BGRImgCropper(imageSize, imageSize) - -> HFlip(0.5) -> BGRImgNormalizer(0.485, 0.456, 0.406, 0.229, 0.224, 0.225)) + -> DatasetHFlip(0.5) -> BGRImgNormalizer(0.485, 0.456, 0.406, 0.229, 0.224, 0.225)) )) } + + def rdd(path: String, batchSize: Int, sc: SparkContext, imageSize : Int) + : DataSet[MiniBatch[Float]] = { + val imageFrame = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) + val transfomer = PixelBytesToMat() -> + Resize(256, 256) -> + RandomCrop(imageSize, imageSize) -> + RandomTransformer(HFlip(), 0.5) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor[Float]() -> + ImageFrameToSample[Float](targetKeys = Array(ImageFeature.label)) -> + ImageFeatureToMiniBatch[Float](batchSize) + val data = DataSet.imageFrame(imageFrame).transform(transfomer) + data + } } object ImageNet2012Val { @@ -53,8 +70,7 @@ object ImageNet2012Val { batchSize : Int, nodeNumber: Int, coresPerNode: Int, - classNumber: Int, - size: Int + classNumber: Int ) : DataSet[MiniBatch[Float]] = { DataSet.SeqFileFolder.files(path, sc, classNumber).transform( @@ -63,8 +79,24 @@ object ImageNet2012Val { height = imageSize, batchSize = batchSize, transformer = (BytesToBGRImg() -> BGRImgCropper(imageSize, imageSize, CropCenter) - -> HFlip(0.5) -> BGRImgNormalizer(0.485, 0.456, 0.406, 0.229, 0.224, 0.225)) + -> DatasetHFlip(0.5) -> BGRImgNormalizer(0.485, 0.456, 0.406, 0.229, 0.224, 0.225)) )) } + + def rdd(path: String, batchSize: Int, sc: SparkContext, imageSize : Int) + : DataSet[MiniBatch[Float]] = { + val imageFrame = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) + val transfomer = PixelBytesToMat() -> + Resize(256, 256) -> + CenterCrop(imageSize, imageSize) -> + RandomTransformer(HFlip(), 0.5) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor[Float]() -> + ImageFrameToSample[Float](targetKeys = Array(ImageFeature.label)) -> + ImageFeatureToMiniBatch[Float](batchSize) + val data = DataSet.imageFrame(imageFrame).transform(transfomer) + data + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala index 7b879c6ffac..1e65cd2a303 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala @@ -37,25 +37,17 @@ object TrainInceptionV1 { val sc = new SparkContext(conf) Engine.init - val trainSet = ImageNet2012( + val trainSet = ImageNet2012.rdd( param.folder + "/train", - sc, - imageSize, param.batchSize, - Engine.nodeNumber(), - Engine.coreNumber(), - param.classNumber, - 1281167 + sc, + imageSize ) - val valSet = ImageNet2012Val( + val valSet = ImageNet2012Val.rdd( param.folder + "/val", - sc, - imageSize, param.batchSize, - Engine.nodeNumber(), - Engine.coreNumber(), - param.classNumber, - 50000 + sc, + imageSize ) val model = if (param.modelSnapshot.isDefined) { From bc91fbb58c017ba2e4ba304386a07af7607dea0b Mon Sep 17 00:00:00 2001 From: jenniew Date: Fri, 16 Mar 2018 17:04:54 -0700 Subject: [PATCH 0745/1065] update doc and comments --- .../bigdl/dllib/models/inception/ImageNet2012.scala | 2 -- .../intel/analytics/bigdl/dllib/models/inception/README.md | 6 +++--- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala index e50c1b6f340..6f6e3f23495 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala @@ -50,7 +50,6 @@ object ImageNet2012 { : DataSet[MiniBatch[Float]] = { val imageFrame = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) val transfomer = PixelBytesToMat() -> - Resize(256, 256) -> RandomCrop(imageSize, imageSize) -> RandomTransformer(HFlip(), 0.5) -> ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> @@ -87,7 +86,6 @@ object ImageNet2012Val { : DataSet[MiniBatch[Float]] = { val imageFrame = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) val transfomer = PixelBytesToMat() -> - Resize(256, 256) -> CenterCrop(imageSize, imageSize) -> RandomTransformer(HFlip(), 0.5) -> ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md index 2633a0173f2..7e5cdd3f749 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md @@ -9,7 +9,7 @@ will release a pre-build package soon. You can download imagenet-2012 data from . After you download the files(**ILSVRC2012_img_train.tar** and **ILSVRC2012_img_val.tar**), -run the follow commands to prepare the data. +run the following commands to prepare the data. classes.lst and img_class.lst used below can be found in the current folder. ```bash @@ -30,7 +30,7 @@ cat img_class.lst | while read PARAM; do mv ${PARAM/ n[0-9]*/} ${PARAM/ILSVRC*JP rm ILSVRC2012_img_val.tar ``` -Now all the images belong to the same category are moved to the same folder. +Now all the images belonging to the same category are moved to the same folder. This command will transform the images into hadoop sequence files, which are more suitable for a distributed training. @@ -81,7 +81,7 @@ In the above commands * -f: where you put your ImageNet data, it should be a hdfs folder * --checkpoint: Where you cache the model/train_state snapshot. You should input a folder and make sure the folder is created when you run this example. The model snapshot will be named as -model.#iteration_number, and train state will be named as state.#iteration_number. Note that if +model.#iteration_number, and train state will be named as optimMethod.#iteration_number. Note that if there are some files already exist in the folder, the old file will not be overwrite for the safety of your model files. * --batchSize: The mini-batch size. It is expected that the mini-batch size is a multiple of node_number * From aae3e376364290d210fd6fddf86b94cb4176e200 Mon Sep 17 00:00:00 2001 From: jenniew Date: Mon, 19 Mar 2018 22:34:36 -0700 Subject: [PATCH 0746/1065] update scala inception --- .../bigdl/dllib/models/inception/Train.scala | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala index 1e65cd2a303..8ed80dd82df 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala @@ -37,17 +37,23 @@ object TrainInceptionV1 { val sc = new SparkContext(conf) Engine.init - val trainSet = ImageNet2012.rdd( + val trainSet = ImageNet2012( param.folder + "/train", - param.batchSize, sc, - imageSize + imageSize, + param.batchSize, + Engine.nodeNumber(), + Engine.coreNumber(), + param.classNumber ) - val valSet = ImageNet2012Val.rdd( + val valSet = ImageNet2012Val( param.folder + "/val", - param.batchSize, sc, - imageSize + imageSize, + param.batchSize, + Engine.nodeNumber(), + Engine.coreNumber(), + param.classNumber ) val model = if (param.modelSnapshot.isDefined) { From 2abae638c634a86cf613a1020e241ff9a47f7508 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 20 Mar 2018 14:09:46 +0800 Subject: [PATCH 0747/1065] [Bug Fix] Fix duplicate check sometimes should be suspend (#2403) * allow keras.Input layer skip duplicate check * fix some bug * add some comments * meet code review --- .../analytics/bigdl/dllib/keras/Input.scala | 15 +++++++++- .../bigdl/dllib/keras/Topology.scala | 1 + .../analytics/bigdl/dllib/nn/Container.scala | 15 ++-------- .../dllib/nn/abstractnn/AbstractModule.scala | 13 +++++++- .../bigdl/dllib/keras/nn/InputSpec.scala | 30 +++++++++++++++++-- 5 files changed, 57 insertions(+), 17 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala index f9c6b215877..cc76f217d6a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Input.scala @@ -27,18 +27,31 @@ import scala.reflect.ClassTag class Input[T: ClassTag](val inputShape: Shape)(implicit ev: TensorNumeric[T]) extends KerasLayer[Activity, Activity, T](KerasLayer.addBatch(inputShape)) { + private var skipDuplicate = false + + private[Input] def setSkipDuplicate(): this.type = { + this.skipDuplicate = true + this + } + override def computeOutputShape(inputShape: Shape): Shape = inputShape override def doBuild(inputShape: Shape): TInput[T] = new TInput[T]() override def allowRebuilt(): Boolean = true + + override def skipDuplicateCheck(): Boolean = skipDuplicate } object Input { def apply[T: ClassTag]( inputShape: Shape = null, name : String = null)(implicit ev: TensorNumeric[T]): ModuleNode[T] = { - val module = new Input(inputShape) + // As this method return a node, so it cannot be added to a container or connect from other + // nodes multiple times. So we can skip the duplicate checking. + // Even it is repeated appears multiple time in a nested container, it's okay as it will always + // be the first layer. + val module = new Input(inputShape).setSkipDuplicate() module.build(KerasLayer.addBatch(inputShape)) if (name != null) { module.setName(name) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala index ec35b4b78d9..4911ee034b7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala @@ -302,6 +302,7 @@ class Sequential[T: ClassTag]() labor.asInstanceOf[TSequential[T]].modules += module.asInstanceOf[AbstractModule[Activity, Activity, T]] + checkDuplicate() this } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index 655e509cc43..107d10260c9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -215,18 +215,7 @@ abstract class Container[A <: Activity : ClassTag, private[bigdl] override final def checkDuplicate( record: mutable.HashSet[Int] = mutable.HashSet() ): Unit = { - val errMsg = "Some module is duplicate in the current model: " - val curId = System.identityHashCode(this) - require(!record.contains(curId), errMsg + this.getName()) - record.add(curId) - modules.foreach(m => { - if (m.isInstanceOf[Container[_, _, _]]) { - m.asInstanceOf[Container[_, _, _]].checkDuplicate(record) - } else { - val mId = System.identityHashCode(m) - require(!record.contains(mId), errMsg + m.getName()) - record.add(mId) - } - }) + super.checkDuplicate(record) + if (!skipDuplicateCheck()) modules.foreach(_.checkDuplicate(record)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 82b47fe7c60..2d9deaebcf8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -1044,6 +1044,17 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ private[bigdl] def checkDuplicate( record: mutable.HashSet[Int] = mutable.HashSet() - ): Unit = {} + ): Unit = { + val errMsg = "Some module is duplicate in the current model: " + val curId = System.identityHashCode(this) + require(this.skipDuplicateCheck() || !record.contains(curId), errMsg + this.getName()) + record.add(curId) + } + + /** + * Sometimes, some layer need skip the duplicate check process, e.g. Keras-like input layer + * @return + */ + private[nn] def skipDuplicateCheck(): Boolean = false } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala index 00832cfe46b..87a861eb8d7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/InputSpec.scala @@ -16,13 +16,39 @@ package com.intel.analytics.bigdl.keras.nn -import com.intel.analytics.bigdl.nn.keras.{InputLayer, Sequential} +import com.intel.analytics.bigdl.nn.keras.Merge.merge +import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.Shape +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape} import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.numeric.NumericFloat import scala.util.Random +class InputSpec extends BigDLSpecHelper { + "Duplicate container" should "not throw exception" in { + val bx1 = Input(Shape(4)) + val bx2 = Input(Shape(5)) + val by1 = Dense(6, activation = "sigmoid").inputs(bx1) + val bbranch1 = Model(bx1, by1).inputs(bx1) + val bbranch2 = Dense(8).inputs(bx2) + val bz = merge(List(bbranch1, bbranch2), mode = "concat") + val bmodel = Model(Array(bx1, bx2), bz) + + // No exception should be threw in the above code + } + + "Duplicate input layer" should "throw exception" in { + val i = InputLayer(Shape(4)) + val seq = Sequential() + seq.add(i) + + intercept[IllegalArgumentException] { + seq.add(i) + } + } +} + class InputSerialTest extends ModuleSerializationTest { override def test(): Unit = { val input = InputLayer[Float](inputShape = Shape(20)) From d897ff331ee20927882314d7c1656ee605ee0102 Mon Sep 17 00:00:00 2001 From: Xu Xiao Date: Tue, 20 Mar 2018 16:18:58 +0800 Subject: [PATCH 0748/1065] [Enhancement] make SparseMiniBatch supporting TensorDataTypes other than T (#2376) * make SparseMiniBatch supporting TensorDataTypes other than T * add a new TensorSample entry for Tensors with anonymous types --- .../dllib/feature/dataset/MiniBatch.scala | 72 ++++++++++++------- .../bigdl/dllib/feature/dataset/Sample.scala | 25 +++++++ .../bigdl/dllib/tensor/SparseTensor.scala | 2 +- .../bigdl/dllib/dataset/MiniBatchSpec.scala | 30 ++++++++ 4 files changed, 104 insertions(+), 25 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala index b606a5e1a2b..ebcf93af3f8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/MiniBatch.scala @@ -620,32 +620,56 @@ class SparseMiniBatch[T: ClassTag]( target } - def init(features: Array[Tensor[T]], labels: Array[Tensor[T]]): Unit = { - var i = 0 - while (i < inputData.length) { - val featureI = features(i) - inputData(i) = if (featureI.getTensorType == SparseType) { - Tensor.sparse[T](Array(batchSize) ++ featureI.size()) - } else if (featureI.getTensorType == DenseType) { - Tensor[T](Array(batchSize) ++ featureI.size()) - } else { - throw new IllegalArgumentException(s"MiniBatchWithSparse: unsupported feature type " + - s"${featureI.getTensorType}") + private def initTensor(sample: Tensor[_]): Tensor[_] = sample match { + case s if s.getTensorType == SparseType => + s.getType() match { + case tpe if tpe == BooleanType => + Tensor.sparse[Boolean](Array(batchSize) ++ s.size()) + case tpe if tpe == CharType => + Tensor.sparse[Char](Array(batchSize) ++ s.size()) + case tpe if tpe == StringType => + Tensor.sparse[String](Array(batchSize) ++ s.size()) + case tpe if tpe == IntType => + Tensor.sparse[Int](Array(batchSize) ++ s.size()) + case tpe if tpe == ShortType => + Tensor.sparse[Short](Array(batchSize) ++ s.size()) + case tpe if tpe == LongType => + Tensor.sparse[Long](Array(batchSize) ++ s.size()) + case tpe if tpe == FloatType => + Tensor.sparse[Float](Array(batchSize) ++ s.size()) + case tpe if tpe == DoubleType => + Tensor.sparse[Double](Array(batchSize) ++ s.size()) } - i += 1 - } - i = 0 - while (i < targetData.length) { - val labelI = labels(i) - targetData(i) = if (labelI.getTensorType == SparseType) { - Tensor.sparse[T](Array(batchSize) ++ labelI.size()) - } else if (labelI.getTensorType == DenseType) { - Tensor[T](Array(batchSize) ++ labelI.size()) - } else { - throw new IllegalArgumentException(s"MiniBatchWithSparse: unsupported label type " + - s"${labelI.getTensorType}") + case s if s.getTensorType == DenseType => + s.getType() match { + case tpe if tpe == BooleanType => + Tensor[Boolean](Array(batchSize) ++ s.size()) + case tpe if tpe == CharType => + Tensor[Char](Array(batchSize) ++ s.size()) + case tpe if tpe == StringType => + Tensor[String](Array(batchSize) ++ s.size()) + case tpe if tpe == IntType => + Tensor[Int](Array(batchSize) ++ s.size()) + case tpe if tpe == ShortType => + Tensor[Short](Array(batchSize) ++ s.size()) + case tpe if tpe == LongType => + Tensor[Long](Array(batchSize) ++ s.size()) + case tpe if tpe == FloatType => + Tensor[Float](Array(batchSize) ++ s.size()) + case tpe if tpe == DoubleType => + Tensor[Double](Array(batchSize) ++ s.size()) } - i += 1 + case s => + throw new IllegalArgumentException(s"MiniBatchWithSparse: unsupported feature type " + + s"${s.getTensorType}") + } + + def init(features: Array[Tensor[T]], labels: Array[Tensor[T]]): Unit = { + features.zipWithIndex.foreach { case (feature, index) => + inputData(index) = initTensor(feature).asInstanceOf[Tensor[T]] + } + labels.zipWithIndex.foreach { case (label, index) => + targetData(index) = initTensor(label).asInstanceOf[Tensor[T]] } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index 76a9c0506e5..9de4f047d92 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -516,4 +516,29 @@ object TensorSample { typeCheck(featureTensor) new TensorSample[T](Array(featureTensor), Array(Tensor(1).fill(label))) } + + /** + * Create a TensorSample which is able to contains Tensors with different types. + * + * @tparam T main type + * @param featureTensors feature tensors + * @param labelTensors label tensors, can be null or empty, default value is null + * @return TensorSample + */ + def create[T: ClassTag]( + featureTensors: Array[Tensor[_]], + labelTensors: Array[Tensor[_]] = null) + (implicit ev: TensorNumeric[T]) : Sample[T] = { + if (labelTensors == null || labelTensors.isEmpty) { + TensorSample(wrapType(featureTensors)) + } else { + TensorSample(wrapType(featureTensors), wrapType(labelTensors)) + } + } + + private def wrapType[T: ClassTag](tensor: Array[Tensor[_]]) + (implicit ev: TensorNumeric[T]): Array[Tensor[T]] = { + tensor.map(_.asInstanceOf[Tensor[T]]) + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 58505f9b87a..028cd545db5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -1181,7 +1181,7 @@ object SparseTensor{ val curLength = currentTensor.nElement() val curTensorOffset = currentTensor.storageOffset() - 1 // copy to concat _values - ev.arraycopy(currentTensor.storage().array(), curTensorOffset, + System.arraycopy(currentTensor.storage().array(), curTensorOffset, res.storage().array(), offset, curLength) // make new Indices var indicesIndex = 0 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala index 3b498f977c0..48e60afe207 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/MiniBatchSpec.scala @@ -109,4 +109,34 @@ class MiniBatchSpec extends FlatSpec with Matchers { target should be (expectedTarget) } + "SparseTensorMiniBatch with different TensorTypes" should "return right result" in { + val a1 = Tensor.sparse(Tensor[Double](4).range(1, 4, 1)) + val a2 = Tensor.sparse(Tensor[Double](4).range(5, 8, 1)) + val b1 = Tensor[String](5) + .setValue(1, "a").setValue(2, "b") + .setValue(3, "c").setValue(4, "d").setValue(5, "e") + val b2 = Tensor[String](5) + .setValue(1, "1").setValue(2, "2") + .setValue(3, "3").setValue(4, "4").setValue(5, "5") + val c1 = Tensor[Double](1).fill(1) + val c2 = Tensor[Double](1).fill(0) + val sample1 = TensorSample.create[Float](Array(a1, b1), Array(c1)) + val sample2 = TensorSample.create[Float](Array(a2, b2), Array(c2)) + val miniBatch = SparseMiniBatch[Float](2, 1) + miniBatch.set(Array(sample1, sample2)) + + val input = miniBatch.getInput() + val target = miniBatch.getTarget() + + val expectedInput1 = Tensor.sparse(Array(Array(0, 0, 0, 0, 1, 1, 1, 1), + Array(0, 1, 2, 3, 0, 1, 2, 3)), + Array.range(1, 9).map(_.toFloat), Array(2, 4)) + input.toTable[Tensor[Double]](1) should be (expectedInput1) + input.toTable[Tensor[String]](2).storage().array() should be (Array( + "a", "b", "c", "d", "e", "1", "2", "3", "4", "5")) + + val expectedTarget = Tensor[Double](T(1.0, 0.0)).reshape(Array(2, 1)) + target should be (expectedTarget) + } + } From 655cd759e9fe3eabfb1023d1d6f0690c4fecde3f Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 20 Mar 2018 16:50:29 +0800 Subject: [PATCH 0749/1065] fix timedistributed to make it compatible with 0.4 (#2408) --- .../bigdl/dllib/nn/TimeDistributed.scala | 18 +++++++++++++++++- .../utils/serializer/ModuleSerializer.scala | 1 + 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala index 7e7c6158209..4822bb45850 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala @@ -21,6 +21,8 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, Tensor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable} +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -284,11 +286,25 @@ class TimeDistributed[T : ClassTag] ( override def toString(): String = s"${getPrintName}${layer}" } -object TimeDistributed { +object TimeDistributed extends ModuleSerializable { def apply[@specialized(Float, Double) T: ClassTag]( layer: AbstractModule[Tensor[T], Tensor[T], T], maskZero: Boolean = false )(implicit ev: TensorNumeric[T]): TimeDistributed[T] = { new TimeDistributed[T](layer, maskZero) } + // To make ti compatible with release 0.4 + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val attrMap = context.bigdlModule.getAttrMap + val layerAttr = attrMap.get("layer") + val layer = DataConverter.getAttributeValue(context, layerAttr). + asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + var maskZero = false + if (attrMap.containsKey("maskZero")) { + maskZero = DataConverter.getAttributeValue(context, attrMap.get("maskZero")). + asInstanceOf[Boolean] + } + TimeDistributed(layer, maskZero) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 2e574f81ead..ea0f9a98193 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -206,6 +206,7 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.SpatialSubtractiveNormalization", SpatialSubtractiveNormalization) registerModule("com.intel.analytics.bigdl.nn.Transpose", Transpose) + registerModule("com.intel.analytics.bigdl.nn.TimeDistributed", TimeDistributed) registerModule("com.intel.analytics.bigdl.nn.VolumetricMaxPooling", VolumetricMaxPooling) registerModule("com.intel.analytics.bigdl.nn.Echo", Echo) registerModule("com.intel.analytics.bigdl.nn.quantized.SpatialConvolution", From d948374f3b905adaf1bd15bb02f0470089140f27 Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Wed, 21 Mar 2018 20:16:23 +0800 Subject: [PATCH 0750/1065] Add Keras Website Documentation for Layers I (#2414) * add core layers doc * add advancedactivations and convolutional layers doc * add dropout, embedding and normalization layers doc * add pooling and recurrent layers doc * update * change * update * add merge * add embedding * change data * modify * update * update * change * update --- .../com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala index 1e86083dc10..a98ec489ec9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala @@ -36,7 +36,7 @@ import scala.reflect.ClassTag * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class LeakyReLU[T: ClassTag]( - private val alpha: Double = 0.01, + private val alpha: Double = 0.3, val inputShape: Shape = null)(implicit ev: TensorNumeric[T]) extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) with IdentityOutputShape { @@ -51,7 +51,7 @@ class LeakyReLU[T: ClassTag]( object LeakyReLU { def apply[@specialized(Float, Double) T: ClassTag]( - alpha: Double = 0.01, + alpha: Double = 0.3, inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LeakyReLU[T] = { new LeakyReLU[T](alpha, inputShape) } From 7c0d6796a43207adc6b2639921d162435a83809c Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Wed, 21 Mar 2018 22:36:47 +0800 Subject: [PATCH 0751/1065] Refine Keras-like API LeNet definition (#2407) * lenet keras seq * update * refine * update * update * fix java creator * meet review * fix lenet definition * fix lenet * test * fix lenet * update * fix * remove --- .../bigdl/dllib/example/keras/LeNet.scala | 36 ----------------- .../bigdl/dllib/example/keras/README.md | 6 +-- .../bigdl/dllib/example/keras/Train.scala | 4 +- .../bigdl/dllib/keras/KerasLayer.scala | 4 +- .../bigdl/dllib/models/lenet/LeNet5.scala | 40 +++++++++++++++++-- .../bigdl/dllib/keras/LeNetSpec.scala | 32 ++++++++++----- .../bigdl/dllib/optim/EvaluatorSpec.scala | 4 +- .../bigdl/dllib/optim/ValidatorSpec.scala | 6 +-- 8 files changed, 71 insertions(+), 61 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala deleted file mode 100644 index b23fac7b48c..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/LeNet.scala +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package com.intel.analytics.bigdl.example.keras - -import com.intel.analytics.bigdl.nn.keras._ -import com.intel.analytics.bigdl.utils.Shape - -object LeNet { - def apply(classNum: Int): Sequential[Float] = { - val model = Sequential[Float]() - model.add(Reshape(Array(1, 28, 28), inputShape = Shape(28, 28, 1))) - model.add(Convolution2D(32, 3, 3, activation = "relu")) - model.add(Convolution2D(32, 3, 3, activation = "relu")) - model.add(MaxPooling2D(poolSize = (2, 2))) - model.add(Dropout(0.25)) - model.add(Flatten()) - model.add(Dense(128, activation = "relu")) - model.add(Dropout(0.5)) - model.add(Dense(classNum, activation = "softmax")) - model - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md index 9860230d2f3..d8be959baeb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/README.md @@ -1,8 +1,8 @@ -# LeNet Model on MNIST with new API +# LeNet Model on MNIST using Keras-Style API -This example defines a classical CNN model used in digital number classification with the new set of Keras-Style API in BigDL, which is more user-friendly. For detailed information with regard to LeNet, please refer to . +LeNet5 is a classical CNN model used in digital number classification. For detailed information with regard to LeNet, please refer to . -This example is the same as [../../models/lenet](../../models/lenet), except that here we use the new Keras-Style API for model definition and training. +This example is the same as [../../models/lenet](../../models/lenet), except that here we use the new set of Keras-Style API in BigDL for model definition and training, which is more user-friendly. ## Prepare MNIST Data diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala index 538722db6c7..f699c31f8e8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/keras/Train.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.nn.ClassNLLCriterion import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.models.lenet.LeNet5 import org.apache.spark.SparkContext object Train { @@ -41,7 +42,8 @@ object Train { val validationData = param.folder + "/t10k-images-idx3-ubyte" val validationLabel = param.folder + "/t10k-labels-idx1-ubyte" - val model = LeNet(classNum = 10) + val model = if (param.graphModel) LeNet5.kerasGraph(classNum = 10) + else LeNet5.keras(classNum = 10) val optimMethod = if (param.stateSnapshot.isDefined) { OptimMethod.load[Float](param.stateSnapshot.get) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala index b04aa8bd79a..e1b3ffe4e4d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala @@ -101,8 +101,8 @@ class KerasLayerWrapper[T: ClassTag] override def computeOutputShape(calcInputShape: Shape): Shape = { val dummyOutTensor = - torchLayer.forward(Tensor[T]( - (List(2) ++ KerasLayer.removeBatch(calcInputShape).toSingle()).toArray).rand()) + torchLayer.cloneModule().forward(Tensor[T]( + (List(2) ++ KerasLayer.removeBatch(calcInputShape).toSingle()).toArray).fill(ev.one)) val outSize = dummyOutTensor.toTensor.size() KerasLayer.addBatch(Shape(outSize.slice(1, outSize.length))) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala index 6ad819f2dd9..e90ead05017 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala @@ -27,8 +27,8 @@ object LeNet5 { .add(SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5")) .add(Tanh()) .add(SpatialMaxPooling(2, 2, 2, 2)) - .add(Tanh()) .add(SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5")) + .add(Tanh()) .add(SpatialMaxPooling(2, 2, 2, 2)) .add(Reshape(Array(12 * 4 * 4))) .add(Linear(12 * 4 * 4, 100).setName("fc1")) @@ -36,14 +36,15 @@ object LeNet5 { .add(Linear(100, classNum).setName("fc2")) .add(LogSoftMax()) } + def graph(classNum: Int): Module[Float] = { val input = Reshape(Array(1, 28, 28)).inputs() val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5").inputs(input) val tanh1 = Tanh().inputs(conv1) val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1) - val tanh2 = Tanh().inputs(pool1) - val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5").inputs(tanh2) - val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(conv2) + val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5").inputs(pool1) + val tanh2 = Tanh().inputs(conv2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh2) val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2) val fc1 = Linear(12 * 4 * 4, 100).setName("fc1").inputs(reshape) val tanh3 = Tanh().inputs(fc1) @@ -52,4 +53,35 @@ object LeNet5 { Graph(input, output) } + + def keras(classNum: Int): nn.keras.Sequential[Float] = { + import com.intel.analytics.bigdl.nn.keras._ + import com.intel.analytics.bigdl.utils.Shape + + val model = Sequential() + model.add(Reshape(Array(1, 28, 28), inputShape = Shape(28, 28, 1))) + model.add(Convolution2D(6, 5, 5, activation = "tanh").setName("conv1_5x5")) + model.add(MaxPooling2D()) + model.add(Convolution2D(12, 5, 5, activation = "tanh").setName("conv2_5x5")) + model.add(MaxPooling2D()) + model.add(Flatten()) + model.add(Dense(100, activation = "tanh").setName("fc1")) + model.add(Dense(classNum, activation = "softmax").setName("fc2")) + } + + def kerasGraph(classNum: Int): nn.keras.Model[Float] = { + import com.intel.analytics.bigdl.nn.keras._ + import com.intel.analytics.bigdl.utils.Shape + + val input = Input(inputShape = Shape(28, 28, 1)) + val reshape = Reshape(Array(1, 28, 28)).inputs(input) + val conv1 = Convolution2D(6, 5, 5, activation = "tanh").setName("conv1_5x5").inputs(reshape) + val pool1 = MaxPooling2D().inputs(conv1) + val conv2 = Convolution2D(12, 5, 5, activation = "tanh").setName("conv2_5x5").inputs(pool1) + val pool2 = MaxPooling2D().inputs(conv2) + val flatten = Flatten().inputs(pool2) + val fc1 = Dense(100, activation = "tanh").setName("fc1").inputs(flatten) + val fc2 = Dense(classNum, activation = "softmax").setName("fc2").inputs(fc1) + Model(input, fc2) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala index ff059112c07..6ab90dcbf15 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/LeNetSpec.scala @@ -16,29 +16,41 @@ package com.intel.analytics.bigdl.keras -import com.intel.analytics.bigdl.example.keras.LeNet +import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} class LeNetSpec extends FlatSpec with Matchers { - "LeNet" should "generate the correct outputShape" in { - val cnn = LeNet(classNum = 10) - cnn.getOutputShape().toSingle().toArray should be (Array(-1, 10)) + "LeNet sequential" should "generate the correct outputShape" in { + val lenet = LeNet5.keras(classNum = 10) + lenet.getOutputShape().toSingle().toArray should be (Array(-1, 10)) } - "LeNet forward and backward" should "work properly" in { - val cnn = LeNet(classNum = 10) + "LeNet graph" should "generate the correct outputShape" in { + val lenet = LeNet5.kerasGraph(classNum = 10) + lenet.getOutputShape().toSingle().toArray should be (Array(-1, 10)) + } + + "LeNet sequential forward and backward" should "work properly" in { + val lenet = LeNet5.keras(classNum = 10) + val input = Tensor[Float](Array(2, 28, 28, 1)).rand() + val output = lenet.forward(input) + val gradInput = lenet.backward(input, output) + } + + "LeNet graph forward and backward" should "work properly" in { + val lenet = LeNet5.kerasGraph(classNum = 10) val input = Tensor[Float](Array(2, 28, 28, 1)).rand() - val output = cnn.forward(input) - val gradInput = cnn.backward(input, output) + val output = lenet.forward(input) + val gradInput = lenet.backward(input, output) } "LeNet forward with incompatible input tensor" should "raise an exception" in { intercept[RuntimeException] { - val cnn = LeNet(classNum = 10) + val lenet = LeNet5.keras(classNum = 10) val input = Tensor[Float](Array(28, 28, 1)).rand() - val output = cnn.forward(input) + val output = lenet.forward(input) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala index 5e90ffaa6e7..2fb956082e0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala @@ -70,9 +70,9 @@ class EvaluatorSpec extends FlatSpec with Matchers with BeforeAndAfter{ result(0)._1 should be (new AccuracyResult(0, 100)) result(1)._1 should be (new AccuracyResult(100, 100)) - result(2)._1 should be (new LossResult(57.669075f, 25)) + result(2)._1 should be (new LossResult(57.610695f, 25)) result(0)._1.result()._1 should be (0f) result(1)._1.result()._1 should be (1f) - result(2)._1.result()._1 should be (2.306763f+-0.000001f) + result(2)._1.result()._1 should be (2.3044279f+-0.000001f) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala index 27db2e8b579..de497af20fa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala @@ -72,10 +72,10 @@ class ValidatorSpec extends FlatSpec with Matchers with BeforeAndAfter{ result(0)._1 should be (new AccuracyResult(0, 100)) result(1)._1 should be (new AccuracyResult(100, 100)) - result(2)._1 should be (new LossResult(230.67628f, 100)) + result(2)._1 should be (new LossResult(230.4428f, 100)) result(0)._1.result()._1 should be (0f) result(1)._1.result()._1 should be (1f) - result(2)._1.result()._1 should be (2.306763f+-0.000001f) + result(2)._1.result()._1 should be (2.3044279f+-0.000001f) } "LocalValidator" should "be correct" in { @@ -97,6 +97,6 @@ class ValidatorSpec extends FlatSpec with Matchers with BeforeAndAfter{ result(0)._1 should be (new AccuracyResult(0, 100)) result(1)._1 should be (new AccuracyResult(100, 100)) - result(2)._1 should be (new LossResult(230.67628f, 100)) + result(2)._1 should be (new LossResult(230.4428f, 100)) } } From c894f95d077eb006e72795eb7cd064b82948163d Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 22 Mar 2018 13:30:58 +0800 Subject: [PATCH 0752/1065] [Bug Fix] Use "NHWC" as the default value when "data_format" of DepthwiseConv2dNative is missing --- .../utils/tf/loaders/DepthwiseConv2dNative.scala | 7 ++++++- .../tf/loaders/DepthwiseConv2DNativeSpec.scala | 15 +++++++++++++++ 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala index eb822e367fd..8658115d4a9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2dNative.scala @@ -42,7 +42,12 @@ class DepthwiseConv2dNative extends TensorflowOpsLoader { val strideList = getIntList(attributes, "strides") require(strideList.head == 1, s"not support strides on batch") - val format = getString(attributes, "data_format") + val format = if (attributes.containsKey("data_format")) { + getString(attributes, "data_format") + } else { + "NHWC" + } + val conv = format match { case "NHWC" => require(strideList(3) == 1, s"not support strides on depth") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2DNativeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2DNativeSpec.scala index 156ad15681f..c76b842ba77 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2DNativeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DepthwiseConv2DNativeSpec.scala @@ -54,4 +54,19 @@ class DepthwiseConv2DNativeSpec extends TensorflowSpecHelper { 0 ) } + + "DepthwiseConv2DNative forward" should "be correct when use default data_format" in { + RNG.setSeed(100) + val filter = Tensor[Float](2, 2, 3, 2).rand() + compare[Float]( + NodeDef.newBuilder() + .setName("depthwise_conv2d_test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("padding", PaddingType.PADDING_VALID.value) + .putAttr("strides", listIntAttr(Seq(1, 1, 1, 1))) + .setOp("DepthwiseConv2dNative"), + Seq(Tensor[Float](4, 24, 24, 3).rand(), filter), + 0 + ) + } } From c7507bfce0670436772cbba5b0467a72a6d15a56 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 22 Mar 2018 13:41:22 +0800 Subject: [PATCH 0753/1065] Add epoch# to the accuracy info (#2417) --- .../bigdl/dllib/optim/DistriOptimizer.scala | 14 +++++++------- .../bigdl/dllib/optim/LocalOptimizer.scala | 10 +++++----- .../bigdl/dllib/optim/LocalOptimizerSpec.scala | 1 + .../bigdl/dllib/optim/RefLocalOptimizer.scala | 6 +++++- 4 files changed, 18 insertions(+), 13 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index e1bc7dc3489..af30c2d857f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -456,9 +456,9 @@ object DistriOptimizer { validationMethods, coresPerNode, models, - wallClockTime, driverState, - validationSummary + validationSummary, + _header ) trainSummary.foreach { summary => @@ -676,9 +676,9 @@ object DistriOptimizer { * @param validationMethods validation methods * @param coresPerNode cores per node * @param models cached models - * @param wallClockTime wall clock time * @param state state table * @param validationSummary validation logger. + * @param header log header string */ private def validate[T]( validationTrigger: Option[Trigger], @@ -686,9 +686,9 @@ object DistriOptimizer { validationMethods: Option[Array[ValidationMethod[T]]], coresPerNode: Int, models: RDD[Cache[T]], - wallClockTime: Long, state: Table, - validationSummary: Option[ValidationSummary] + validationSummary: Option[ValidationSummary], + header: String ): Unit = { if (validationTrigger.isEmpty || validationDataSet.isEmpty) { return @@ -699,7 +699,7 @@ object DistriOptimizer { } val vMethods = validationMethods.get val validateRDD = validationDataSet.get.toDistributed().data(train = false) - logger.info(s"[Wall Clock ${wallClockTime / 1e9}s] Validate model...") + logger.info(s"$header Validate model...") val _subModelNumber = Engine.getEngineType match { case MklBlas => coresPerNode case _ => throw new IllegalArgumentException @@ -741,7 +741,7 @@ object DistriOptimizer { } }).zip(vMethods) results.foreach(r => { - logger.info(s"${r._2} is ${r._1}") + logger.info(s"$header ${r._2} is ${r._1}") }) state("score") = results(0)._1.result._1 if(validationSummary.isDefined) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index b6c0c2f3caa..121a7637198 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -199,7 +199,7 @@ class LocalOptimizer[T: ClassTag] ( count = 0 } - validate(wallClockTime) + validate(head) checkpoint(wallClockTime) } @@ -222,7 +222,7 @@ class LocalOptimizer[T: ClassTag] ( } } - private def validate(wallClockTime: Long): Unit = { + private def validate(header: String): Unit = { if (validationTrigger.isEmpty || validationDataSet.isEmpty) { return } @@ -233,7 +233,7 @@ class LocalOptimizer[T: ClassTag] ( val vMethods = validationMethods.get val vMethodsArr = (1 to subModelNumber).map(i => vMethods.map(_.clone())).toArray val dataIter = validationDataSet.get.toLocal().data(train = false) - logger.info(s"[Wall Clock ${wallClockTime / 1e9}s] Validate model...") + logger.info(s"$header Validate model...") workingModels.foreach(_.evaluate()) @@ -264,7 +264,7 @@ class LocalOptimizer[T: ClassTag] ( } }) count += batch.size() - logger.info(s"[Validation] $count/${validationDataSet.get.size()} Throughput is ${ + logger.info(s"$header Throughput is ${ batch.size() / ((System.nanoTime() - start) / 1e9) } record / sec") result @@ -273,7 +273,7 @@ class LocalOptimizer[T: ClassTag] ( l + r } }).zip(vMethods).foreach(r => { - logger.info(s"${r._2} is ${r._1}") + logger.info(s"$header ${r._2} is ${r._1}") }) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala index 05e765e12bd..e6a9ac1b12f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala @@ -357,6 +357,7 @@ class LocalOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter{ mseDataSet, new MSECriterion[Float].asInstanceOf[Criterion[Float]] ).setOptimMethod(new LBFGS[Float]()) + optimizer.setValidation(Trigger.everyEpoch, mseDataSet, Array(new Top1Accuracy[Float]())) val model = optimizer.optimize() val weight = model.getParameters()._1 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefLocalOptimizer.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefLocalOptimizer.scala index 388861d5cc7..365d623454c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefLocalOptimizer.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefLocalOptimizer.scala @@ -18,8 +18,10 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset.MiniBatch import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.optim.DistriOptimizer.getClass import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.apache.log4j.Logger import scala.reflect.ClassTag @@ -32,6 +34,8 @@ class RefLocalOptimizer[T: ClassTag]( criterion: Criterion[T] )(implicit ev: TensorNumeric[T]) extends Optimizer[T, MiniBatch[T]](model, dataset, criterion) { + val logger: Logger = Logger.getLogger(getClass) + val (w, g) = model.getParameters() override def optimize(): Module[T] = { @@ -51,7 +55,7 @@ class RefLocalOptimizer[T: ClassTag]( optimMethod.optimize(_ => (loss, g), w, state) count += batch.size() state("neval") = state[Int]("neval") + 1 - println(s"loss is $loss") + logger.info(s"loss is $loss") if (count >= dataset.size()) { state("epoch") = state[Int]("epoch") + 1 count = 0 From 990e6803fc6ce359c5c13816be50654589e7389e Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 22 Mar 2018 14:08:31 +0800 Subject: [PATCH 0754/1065] empty tensor call size and stride not throw null exception (#2418) --- .../analytics/bigdl/dllib/tensor/DenseTensor.scala | 8 ++++++-- .../analytics/bigdl/dllib/tensor/DenseTensorSpec.scala | 10 ++++++++++ 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 4dbe1186aed..fdc9c940b1c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -68,7 +68,9 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( result.squeeze() } - override def size(): Array[Int] = _size.slice(0, this.nDimension) + override def size(): Array[Int] = { + if (_size == null) null else _size.slice(0, this.nDimension) + } override def size(dim: Int): Int = { require(dim > 0 && dim <= this.nDimension, @@ -76,7 +78,9 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( _size(dim - 1) } - override def stride(): Array[Int] = _stride.slice(0, this.nDimension) + override def stride(): Array[Int] = { + if (_stride == null) null else _stride.slice(0, this.nDimension) + } override def stride(dim: Int): Int = { require(dim > 0 && dim <= this.nDimension, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index f3b3ca2505d..a6df82b6709 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -1082,4 +1082,14 @@ class DenseTensorSpec extends FlatSpec with Matchers { scalar2.value() should be(1) } + + "size" should "work on empty tensor" in { + val t = Tensor[Float]() + t.size() should be (null) + } + + "stride" should "work on empty tensor" in { + val t = Tensor[Float]() + t.stride() should be (null) + } } From 247577dca3a4a254fe6f593428608790541747c5 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 22 Mar 2018 16:16:23 +0800 Subject: [PATCH 0755/1065] [Bug Fix] Fix typo in SpatialSeparableConvoluiton layer name and add related docs (#2420) * fix typo in spatialseparableconvoluiton name and add docs * meet code review * meet code review and fix tests --- .../dllib/keras/SeparableConvolution2D.scala | 4 +- .../dllib/nn/CategoricalCrossEntropy.scala | 1 + ...cala => SpatialSeparableConvolution.scala} | 85 ++++++++++--------- .../bigdl/dllib/nn/ops/DepthwiseConv2D.scala | 8 +- .../analytics/bigdl/dllib/nn/tf/NNOps.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 6 +- .../utils/serializer/ModuleSerializer.scala | 4 +- ... => SpatialSeparableConvolutionSpec.scala} | 8 +- ... => SpatialSeparableConvolutionSpec.scala} | 30 +++---- 9 files changed, 76 insertions(+), 72 deletions(-) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{SpatialSeperableConvolution.scala => SpatialSeparableConvolution.scala} (82%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/{SpatialSeperableConvolutionSpec.scala => SpatialSeparableConvolutionSpec.scala} (88%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{SpatialSeperableConvolutionSpec.scala => SpatialSeparableConvolutionSpec.scala} (84%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala index 5b9c29d5df4..224e7f20f4f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.keras -import com.intel.analytics.bigdl.nn.{InitializationMethod, SpatialSeperableConvolution, Xavier} +import com.intel.analytics.bigdl.nn.{InitializationMethod, SpatialSeparableConvolution, Xavier} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -89,7 +89,7 @@ class SeparableConvolution2D[T: ClassTag]( val input = inputShape.toSingle().toArray val stackSize = if (dimOrdering == DataFormat.NCHW) input(1) else input(3) val pad = KerasUtils.getPadsFromBorderMode(borderMode) - val layer = SpatialSeperableConvolution( + val layer = SpatialSeparableConvolution( nInputChannel = stackSize, nOutputChannel = nbFilter, depthMultiplier = depthMultiplier, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CategoricalCrossEntropy.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CategoricalCrossEntropy.scala index ebd5e2533bc..ae478d2e725 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CategoricalCrossEntropy.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CategoricalCrossEntropy.scala @@ -23,6 +23,7 @@ import scala.reflect.ClassTag /** * This is same with cross entropy criterion, except the target tensor is a one-hot tensor + * * @tparam T The numeric type in the criterion, usually which are [[Float]] or [[Double]] */ class CategoricalCrossEntropy[T: ClassTag]()(implicit ev: TensorNumeric[T]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolution.scala similarity index 82% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolution.scala index 6731e0b4be7..7d898e004e7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolution.scala @@ -32,31 +32,34 @@ import scala.reflect.ClassTag * resulting output channels. The depthMultiplier argument controls how many output channels are * generated per input channel in the depthwise step. * - * @param nInputChannel - * @param nOutputChannel - * @param depthMultiplier - * @param kW - * @param kH - * @param sW - * @param sH - * @param pW - * @param pH - * @param hasBias - * @param dataFormat - * @param wRegularizer - * @param bRegularizer - * @param pRegularizer - * @tparam T Numeric type. Only support float/double now + * @param nInputChannel input image channel number + * @param nOutputChannel output image channel number + * @param depthMultiplier how many output channels are generated in the hidden depthwise step + * @param kW kernel width + * @param kH kernel height + * @param sW stride width + * @param sH stride height + * @param pW padding width + * @param pH padding height + * @param hasBias do we use a bias on the output, default value is true + * @param dataFormat image data format, which can be NHWC or NCHW, default value is NCHW + * @param wRegularizer kernel parameter regularizer + * @param bRegularizer bias regularizer + * @param pRegularizer point wise kernel parameter regularizer + * @param initDepthWeight kernel parameter init tensor + * @param initPointWeight point wise kernel parameter init tensor + * @param initBias bias init tensor + * @tparam T module parameter numeric type */ -class SpatialSeperableConvolution[T: ClassTag]( +class SpatialSeparableConvolution[T: ClassTag]( val nInputChannel: Int, val nOutputChannel: Int, val depthMultiplier: Int, val kW: Int, val kH: Int, - val sW: Int, val sH: Int, - val pW: Int, val pH: Int, - val hasBias: Boolean, - val dataFormat: DataFormat, + val sW: Int = 1, val sH: Int = 1, + val pW: Int = 0, val pH: Int = 0, + val hasBias: Boolean = true, + val dataFormat: DataFormat = DataFormat.NCHW, var wRegularizer: Regularizer[T] = null, var bRegularizer: Regularizer[T] = null, var pRegularizer: Regularizer[T] = null, @@ -143,13 +146,13 @@ class SpatialSeperableConvolution[T: ClassTag]( } override def updateOutput(input: Tensor[T]): Tensor[T] = { - require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") - require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") + require(input.nDimension() == 4, "SpatialSeparableConvolution only accept 4D input") + require(input.isContiguous(), "SpatialSeparableConvolution require contiguous input") require(nInputChannel == input.size(channelDim), s"input tensor channel dimension size(${input.size(channelDim)}) doesn't " + s"match layer nInputChannel $nInputChannel") - SpatialSeperableConvolution.copyWeight(depthConv.weight, input.size(channelDim), + SpatialSeparableConvolution.copyWeight(depthConv.weight, input.size(channelDim), depthMultiplier, depthWeight, dataFormat) depthConv.forward(input) @@ -158,31 +161,31 @@ class SpatialSeperableConvolution[T: ClassTag]( } override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") - require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") + require(input.nDimension() == 4, "SpatialSeparableConvolution only accept 4D input") + require(input.isContiguous(), "SpatialSeparableConvolution require contiguous input") require(nInputChannel == input.size(channelDim), "input tensor channel dimension size doesn't match layer nInputChannel") - require(gradOutput.nDimension() == 4, "SpatialSeperableConvolution only accept 4D gradOutput") - require(gradOutput.isContiguous(), "SpatialSeperableConvolution require contiguous gradOutput") + require(gradOutput.nDimension() == 4, "SpatialSeparableConvolution only accept 4D gradOutput") + require(gradOutput.isContiguous(), "SpatialSeparableConvolution require contiguous gradOutput") require(nOutputChannel == gradOutput.size(channelDim), "gradOutput tensor channel dimension size doesn't match layer nOutputChannel") pointWiseConv2D.backward(depthConv.output, gradOutput) gradInput = depthConv.backward(input, pointWiseConv2D.gradInput) - SpatialSeperableConvolution.copyDepthGradWeight(nInputChannel, depthMultiplier, + SpatialSeparableConvolution.copyDepthGradWeight(nInputChannel, depthMultiplier, depthConv.gradWeight, depthGradWeight, dataFormat) gradInput } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") - require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") + require(input.nDimension() == 4, "SpatialSeparableConvolution only accept 4D input") + require(input.isContiguous(), "SpatialSeparableConvolution require contiguous input") require(nInputChannel == input.size(channelDim), "input tensor channel dimension size doesn't match layer nInputChannel") - require(gradOutput.nDimension() == 4, "SpatialSeperableConvolution only accept 4D gradOutput") - require(gradOutput.isContiguous(), "SpatialSeperableConvolution require contiguous gradOutput") + require(gradOutput.nDimension() == 4, "SpatialSeparableConvolution only accept 4D gradOutput") + require(gradOutput.isContiguous(), "SpatialSeparableConvolution require contiguous gradOutput") require(nOutputChannel == gradOutput.size(channelDim), "gradOutput tensor channel dimension size doesn't match layer nOutputChannel") @@ -192,19 +195,19 @@ class SpatialSeperableConvolution[T: ClassTag]( } override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { - require(input.nDimension() == 4, "SpatialSeperableConvolution only accept 4D input") - require(input.isContiguous(), "SpatialSeperableConvolution require contiguous input") + require(input.nDimension() == 4, "SpatialSeparableConvolution only accept 4D input") + require(input.isContiguous(), "SpatialSeparableConvolution require contiguous input") require(nInputChannel == input.size(channelDim), "input tensor channel dimension size doesn't match layer nInputChannel") - require(gradOutput.nDimension() == 4, "SpatialSeperableConvolution only accept 4D gradOutput") - require(gradOutput.isContiguous(), "SpatialSeperableConvolution require contiguous gradOutput") + require(gradOutput.nDimension() == 4, "SpatialSeparableConvolution only accept 4D gradOutput") + require(gradOutput.isContiguous(), "SpatialSeparableConvolution require contiguous gradOutput") require(nOutputChannel == gradOutput.size(channelDim), "gradOutput tensor channel dimension size doesn't match layer nOutputChannel") pointWiseConv2D.accGradParameters(depthConv.output, gradOutput) depthConv.accGradParameters(input, pointWiseConv2D.gradInput) - SpatialSeperableConvolution.copyDepthGradWeight(nInputChannel, depthMultiplier, + SpatialSeparableConvolution.copyDepthGradWeight(nInputChannel, depthMultiplier, depthConv.gradWeight, depthGradWeight, dataFormat) } @@ -216,7 +219,7 @@ class SpatialSeperableConvolution[T: ClassTag]( } } -object SpatialSeperableConvolution extends ModuleSerializable { +object SpatialSeparableConvolution extends ModuleSerializable { def apply[T: ClassTag](nInputChannel: Int, nOutputChannel: Int, depthMultiplier: Int, kW: Int, kH: Int, sW: Int = 1, sH: Int = 1, pW: Int = 0, pH: Int = 0, @@ -225,7 +228,7 @@ object SpatialSeperableConvolution extends ModuleSerializable { pRegularizer: Regularizer[T] = null, initDepthWeight: Tensor[T] = null, initPointWeight: Tensor[T] = null, initBias: Tensor[T] = null )(implicit ev: TensorNumeric[T]) - : SpatialSeperableConvolution[T] = new SpatialSeperableConvolution[T](nInputChannel, + : SpatialSeparableConvolution[T] = new SpatialSeparableConvolution[T](nInputChannel, nOutputChannel, depthMultiplier, kW, kH, sW, sH, pW, pH, hasBias, dataFormat, wRegularizer, bRegularizer, pRegularizer, initDepthWeight, initPointWeight, initBias) @@ -272,7 +275,7 @@ object SpatialSeperableConvolution extends ModuleSerializable { override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { val attrMap = context.bigdlModule.getAttrMap - val ssc = super.doLoadModule(context).asInstanceOf[SpatialSeperableConvolution[T]] + val ssc = super.doLoadModule(context).asInstanceOf[SpatialSeparableConvolution[T]] val weights = ssc.parameters()._1 val (depthWeight, pointWeight, bias) = (weights(0), weights(1), weights(2)) @@ -299,7 +302,7 @@ object SpatialSeperableConvolution extends ModuleSerializable { super.doSerializeModule(context, sreluBuilder) - val ssc = context.moduleData.module.asInstanceOf[SpatialSeperableConvolution[T]] + val ssc = context.moduleData.module.asInstanceOf[SpatialSeparableConvolution[T]] val weights = ssc.parameters()._1 val (depthWeight, pointWeight, bias) = (weights(0), weights(1), weights(2)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala index 37dcd0a33f3..b291769ce9b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/DepthwiseConv2D.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.nn.{SpatialConvolution, SpatialSeperableConvolution} +import com.intel.analytics.bigdl.nn.{SpatialConvolution, SpatialSeparableConvolution} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -57,7 +57,7 @@ class DepthwiseConv2D[T: ClassTag]( conv.weight.zero() } - SpatialSeperableConvolution.copyWeight(conv.weight, input.size(channelDim), channelMultiplier, + SpatialSeparableConvolution.copyWeight(conv.weight, input.size(channelDim), channelMultiplier, filter, dataFormat) output = conv.forward(input) output @@ -110,7 +110,7 @@ private[bigdl] class DepthwiseConv2DBackpropInput[T: ClassTag]( conv.forward(dummyInput) } - SpatialSeperableConvolution.copyWeight(conv.weight, inputSize.valueAt(channelDim), + SpatialSeparableConvolution.copyWeight(conv.weight, inputSize.valueAt(channelDim), channelMultiplier, filter, dataFormat) output = conv.updateGradInput(dummyInput, gradOutput) output @@ -165,7 +165,7 @@ private[bigdl] class DepthwiseConv2DBackpropFilter[T: ClassTag]( conv.accGradParameters(input, gradOutput) output.resize(filterSize.toArray()) - SpatialSeperableConvolution.copyDepthGradWeight(input.size(channelDim), channelMultiplier, + SpatialSeparableConvolution.copyDepthGradWeight(input.size(channelDim), channelMultiplier, conv.gradWeight, output, dataFormat) output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NNOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NNOps.scala index bf7c1863c33..284740a548f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NNOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/NNOps.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.nn.{Sigmoid, SpatialAveragePooling, SpatialBatchNormalization, -SpatialConvolution, SpatialCrossMapLRN, SpatialMaxPooling, SpatialSeperableConvolution, Tanh, Utils, +SpatialConvolution, SpatialCrossMapLRN, SpatialMaxPooling, SpatialSeparableConvolution, Tanh, Utils, VolumetricConvolution, ELU => ELULayer, ReLU6 => ReLU6Layer, SoftPlus => SoftPlusLayer, SoftSign => SoftSignLayer, ReLU => ReLULayer} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 11bda6298a5..36b0049efb5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -543,7 +543,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ) } - def createSpatialSeperableConvolution( + def createSpatialSeparableConvolution( nInputChannel: Int, nOutputChannel: Int, depthMultiplier: Int, @@ -559,8 +559,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab bRegularizer: Regularizer[T] = null, pRegularizer: Regularizer[T] = null ) - : SpatialSeperableConvolution[T] = { - SpatialSeperableConvolution[T](nInputChannel, + : SpatialSeparableConvolution[T] = { + SpatialSeparableConvolution[T](nInputChannel, nOutputChannel, depthMultiplier, kW, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index ea0f9a98193..12c9041b32c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -221,8 +221,8 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.ops.RandomUniform", RandomUniformOps) registerModule("com.intel.analytics.bigdl.nn.tf.StrideSlice", StrideSlice) registerModule("com.intel.analytics.bigdl.nn.MultiRNNCell", MultiRNNCell) - registerModule("com.intel.analytics.bigdl.nn.SpatialSeperableConvolution", - SpatialSeperableConvolution) + registerModule("com.intel.analytics.bigdl.nn.SpatialSeparableConvolution", + SpatialSeparableConvolution) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeperableConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeparableConvolutionSpec.scala similarity index 88% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeperableConvolutionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeparableConvolutionSpec.scala index 5c7c809f8f7..11b60be293b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeperableConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/SpatialSeparableConvolutionSpec.scala @@ -16,9 +16,9 @@ package com.intel.analytics.bigdl.keras import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.{CategoricalCrossEntropy, SpatialSeperableConvolution} +import com.intel.analytics.bigdl.nn.{CategoricalCrossEntropy, SpatialSeparableConvolution} -class SpatialSeperableConvolutionSpec extends KerasBaseSpec { +class SpatialSeparableConvolutionSpec extends KerasBaseSpec { "SpatialSeperableConvolution" should "be ok" in { ifskipTest() val kerasCode = @@ -28,7 +28,7 @@ class SpatialSeperableConvolutionSpec extends KerasBaseSpec { |model = Model(input=input_tensor, output=output_tensor) |input = np.random.uniform(0, 1, [2, 5, 5, 2]) """.stripMargin - val layer = SpatialSeperableConvolution[Float](2, 2, 1, 2, 2, dataFormat = DataFormat.NHWC) + val layer = SpatialSeparableConvolution[Float](2, 2, 1, 2, 2, dataFormat = DataFormat.NHWC) checkOutputAndGrad(layer, kerasCode) } @@ -41,7 +41,7 @@ class SpatialSeperableConvolutionSpec extends KerasBaseSpec { |model = Model(input=input_tensor, output=output_tensor) |input = np.random.uniform(0, 1, [2, 5, 5, 2]) """.stripMargin - val layer = SpatialSeperableConvolution[Float](2, 4, 2, 2, 2, dataFormat = DataFormat.NHWC) + val layer = SpatialSeparableConvolution[Float](2, 4, 2, 2, 2, dataFormat = DataFormat.NHWC) checkOutputAndGrad(layer, kerasCode) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolutionSpec.scala similarity index 84% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolutionSpec.scala index 93f992037eb..d4f13d31a2a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeperableConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolutionSpec.scala @@ -22,17 +22,17 @@ import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape, TestUtils} import scala.util.Random -class SpatialSeperableConvolutionSpec extends BigDLSpecHelper { - "SpatialSeperableConvolution NHWC and NCHW" should "have same output" in { +class SpatialSeparableConvolutionSpec extends BigDLSpecHelper { + "SpatialSeparableConvolution NHWC and NCHW" should "have same output" in { val depthWeightNHWC = Tensor[Float](2, 2, 3, 1).rand() val depthWeightNCHW = depthWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) .contiguous() val pointWeightNHWC = Tensor[Float](1, 1, 3, 6).rand() val pointWeightNCHW = pointWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) .contiguous() - val convNHWC = SpatialSeperableConvolution[Float](3, 6, 1, 2, 2, dataFormat = DataFormat.NHWC, + val convNHWC = SpatialSeparableConvolution[Float](3, 6, 1, 2, 2, dataFormat = DataFormat.NHWC, initDepthWeight = depthWeightNHWC, initPointWeight = pointWeightNHWC) - val convNCHW = SpatialSeperableConvolution[Float](3, 6, 1, 2, 2, dataFormat = DataFormat.NCHW, + val convNCHW = SpatialSeparableConvolution[Float](3, 6, 1, 2, 2, dataFormat = DataFormat.NCHW, initDepthWeight = depthWeightNCHW, initPointWeight = pointWeightNCHW) val inputNHWC = Tensor[Float](2, 24, 24, 3).rand() val inputNCHW = inputNHWC.transpose(2, 4).transpose(3, 4).contiguous() @@ -57,16 +57,16 @@ class SpatialSeperableConvolutionSpec extends BigDLSpecHelper { } } - "SpatialSeperableConvolution NHWC and NCHW" should "have same output when depth mul is 2" in { + "SpatialSeparableConvolution NHWC and NCHW" should "have same output when depth mul is 2" in { val depthWeightNHWC = Tensor[Float](2, 2, 3, 2).rand() val depthWeightNCHW = depthWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) .contiguous() val pointWeightNHWC = Tensor[Float](1, 1, 6, 6).rand() val pointWeightNCHW = pointWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) .contiguous() - val convNHWC = SpatialSeperableConvolution[Float](3, 6, 2, 2, 2, dataFormat = DataFormat.NHWC, + val convNHWC = SpatialSeparableConvolution[Float](3, 6, 2, 2, 2, dataFormat = DataFormat.NHWC, initDepthWeight = depthWeightNHWC, initPointWeight = pointWeightNHWC) - val convNCHW = SpatialSeperableConvolution[Float](3, 6, 2, 2, 2, dataFormat = DataFormat.NCHW, + val convNCHW = SpatialSeparableConvolution[Float](3, 6, 2, 2, 2, dataFormat = DataFormat.NCHW, initDepthWeight = depthWeightNCHW, initPointWeight = pointWeightNCHW) val inputNHWC = Tensor[Float](2, 24, 24, 3).rand() val inputNCHW = inputNHWC.transpose(2, 4).transpose(3, 4).contiguous() @@ -91,30 +91,30 @@ class SpatialSeperableConvolutionSpec extends BigDLSpecHelper { } } - "SpatialSeperableConvolution" should "be able to serialized" in { - val conv = SpatialSeperableConvolution[Float](3, 6, 2, 2, 2) + "SpatialSeparableConvolution" should "be able to serialized" in { + val conv = SpatialSeparableConvolution[Float](3, 6, 2, 2, 2) val file = createTmpFile() conv.saveModule(file.getAbsolutePath, overWrite = true) val conv2 = Module.loadModule[Float](file.getAbsolutePath) } "SpatialSeparableConvolution computeOutputShape NCHW" should "work properly" in { - val layer = SpatialSeperableConvolution[Float](3, 6, 1, 2, 2) + val layer = SpatialSeparableConvolution[Float](3, 6, 1, 2, 2) TestUtils.compareOutputShape(layer, Shape(3, 12, 12)) should be (true) } "SpatialSeparableConvolution computeOutputShape NHWC" should "work properly" in { - val layer = SpatialSeperableConvolution[Float](2, 5, 2, 2, 1, dataFormat = DataFormat.NHWC) + val layer = SpatialSeparableConvolution[Float](2, 5, 2, 2, 1, dataFormat = DataFormat.NHWC) TestUtils.compareOutputShape(layer, Shape(24, 24, 2)) should be (true) } } -class SpatialSeperableConvolutionSerialTest extends ModuleSerializationTest { +class SpatialSeparableConvolutionSerialTest extends ModuleSerializationTest { override def test(): Unit = { - val seprableConv = SpatialSeperableConvolution[Float](2, 2, 1, 2, 2, - dataFormat = DataFormat.NHWC).setName("seprableConv") + val separableConv = SpatialSeparableConvolution[Float](2, 2, 1, 2, 2, + dataFormat = DataFormat.NHWC).setName("separableConv") val input = Tensor[Float](1, 5, 5, 2).apply1( e => Random.nextFloat()) - runSerializationTest(seprableConv, input) + runSerializationTest(separableConv, input) } } From 5bab0034be1f8de28bcae09d9b24646c90bfc800 Mon Sep 17 00:00:00 2001 From: Quincy2014 <412363303@qq.com> Date: Thu, 22 Mar 2018 16:32:14 +0800 Subject: [PATCH 0756/1065] Add Keras Website Documentation for Layers II (#2421) * add zeropadding doc * modify * add upsampling * add atrousconvolution * add deconvolution2d * add more * add locallyconnected --- .../intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala index 4b111f13e4f..ea680c3e2b5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala @@ -50,8 +50,7 @@ import scala.reflect.ClassTag * or 'sigmoid', etc. for simple activations in the factory method. * @param subsample Int array of length 2. The step of the convolution in the height and * width dimension. Also called strides elsewhere. Default is (1, 1). - * @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or - * DataFormat.NHWC (dimOrdering='tf'). Default is NCHW. + * @param dimOrdering Format of input data. Please use DataFormat.NCHW (dimOrdering='th'). * @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization), * applied to the input weights matrices. Default is null. * @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null. From de198943f5b43b67e1ed3088496cea09fd0b2086 Mon Sep 17 00:00:00 2001 From: dding3 Date: Thu, 22 Mar 2018 10:07:56 -0700 Subject: [PATCH 0757/1065] update println to logger.warn in ZippedPartitionsWithLocalityRDD (#2427) --- .../spark/rdd/ZippedPartitionsWithLocalityRDD.scala | 9 ++++++--- .../spark/rdd/ZippedPartitionsWithLocalityRDD.scala | 9 ++++++--- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala index 461e323db5c..68a3db5afa2 100644 --- a/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala +++ b/scala/common/spark-version/1.5-plus/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala @@ -18,6 +18,7 @@ package org.apache.spark.rdd import java.io.{IOException, ObjectOutputStream} +import org.apache.log4j.Logger import org.apache.spark.{Partition, SparkContext} import scala.collection.mutable.ArrayBuffer @@ -32,6 +33,8 @@ object ZippedPartitionsWithLocalityRDD { new ZippedPartitionsWithLocalityRDD( sc, sc.clean(f), rdd1, rdd2, preservesPartitioning) } + + val logger: Logger = Logger.getLogger(getClass) } /** @@ -91,9 +94,9 @@ class ZippedPartitionsWithLocalityRDD[A: ClassTag, B: ClassTag, V: ClassTag]( parts(i) = new ZippedPartitionsLocalityPartition(i, Array(i, matchPartition._1), rdds, locs) } else { - println(s"can't find locality partition for partition $i " + - s"Partition locations are (${curPrefs}) Candidate partition locations are\n" + - s"${candidateLocs.mkString("\n")}.") + ZippedPartitionsWithLocalityRDD.logger.warn(s"can't find locality partition for" + + s" partition $i Partition locations are (${curPrefs}) Candidate partition" + + s" locations are\n" + s"${candidateLocs.mkString("\n")}.") nonmatchPartitionId.append(i) } } diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala index 0c0b6b5f1cd..31322c8d415 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala @@ -18,6 +18,7 @@ package org.apache.spark.rdd import java.io.{IOException, ObjectOutputStream} +import org.apache.log4j.Logger import org.apache.spark.util.Utils import org.apache.spark.{Partition, SparkContext} @@ -32,6 +33,8 @@ object ZippedPartitionsWithLocalityRDD { new ZippedPartitionsWithLocalityRDD( sc, sc.clean(f), rdd1, rdd2, preservesPartitioning) } + + val logger: Logger = Logger.getLogger(getClass) } /** @@ -91,9 +94,9 @@ class ZippedPartitionsWithLocalityRDD[A: ClassTag, B: ClassTag, V: ClassTag]( parts(i) = new ZippedPartitionsLocalityPartition(i, Array(i, matchPartition._1), rdds, locs) } else { - println(s"can't find locality partition for partition $i " + - s"Partition locations are (${curPrefs}) Candidate partition locations are\n" + - s"${candidateLocs.mkString("\n")}.") + ZippedPartitionsWithLocalityRDD.logger.warn(s"can't find locality partition" + + s"for partition $i Partition locations are (${curPrefs}) Candidate partition" + + s" locations are\n" + s"${candidateLocs.mkString("\n")}.") nonmatchPartitionId.append(i) } } From b260736e012bc56d6f42a62f64b950b886b24204 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 23 Mar 2018 14:10:56 +0800 Subject: [PATCH 0758/1065] refactor model evaluate (#2434) --- .../bigdl/dllib/nn/abstractnn/AbstractModule.scala | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 2d9deaebcf8..7a1ad8a3d94 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -827,10 +827,10 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ final def evaluate( dataset: RDD[Sample[T]], - vMethods: Array[ValidationMethod[T]], + vMethods: Array[_ <:ValidationMethod[T]], batchSize: Option[Int] = None ): Array[(ValidationResult, ValidationMethod[T])] = { - Evaluator(this).test(dataset, vMethods, batchSize) + Evaluator(this).test(dataset, vMethods.map(v => v), batchSize) } /** @@ -841,9 +841,9 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ final def evaluate( dataSet: LocalDataSet[MiniBatch[T]], - vMethods: Array[ValidationMethod[T]] + vMethods: Array[_ <:ValidationMethod[T]] ): Array[(ValidationResult, ValidationMethod[T])] = { - Validator(this, dataSet).test(vMethods) + Validator(this, dataSet).test(vMethods.map(v => v)) } /** From 2c3707f97192ce678caf2e5f4744ababd59b0d05 Mon Sep 17 00:00:00 2001 From: Kai Huang Date: Fri, 23 Mar 2018 18:28:41 +0800 Subject: [PATCH 0759/1065] Keras-Style API website doc for training; refine doc format (#2441) * training api * add * finish * remove * fix * refine doc * update * remove * update --- .../analytics/bigdl/dllib/keras/Deconvolution2D.scala | 1 + .../com/intel/analytics/bigdl/dllib/keras/ELU.scala | 2 +- .../intel/analytics/bigdl/dllib/keras/KerasUtils.scala | 7 +++++++ .../intel/analytics/bigdl/dllib/keras/LeakyReLU.scala | 2 +- .../intel/analytics/bigdl/dllib/keras/Masking.scala | 4 ++-- .../analytics/bigdl/dllib/keras/MaxPooling1D.scala | 2 +- .../analytics/bigdl/dllib/keras/MaxoutDense.scala | 3 +-- .../intel/analytics/bigdl/dllib/keras/Permute.scala | 2 +- .../com/intel/analytics/bigdl/dllib/keras/SReLU.scala | 2 +- .../bigdl/dllib/keras/SeparableConvolution2D.scala | 2 +- .../analytics/bigdl/dllib/keras/ThresholdedReLU.scala | 2 +- .../intel/analytics/bigdl/dllib/keras/Topology.scala | 10 +++++----- 12 files changed, 23 insertions(+), 16 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala index ea680c3e2b5..1dfbc319913 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Deconvolution2D.scala @@ -33,6 +33,7 @@ import scala.reflect.ClassTag * while maintaining a connectivity pattern that is compatible with said convolution. * Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th'). * Border mode currently supported for this layer is 'valid'. + * You can also use Deconv2D as an alias of this layer. * The input of this layer should be 4D. * * When using this layer as the first layer in a model, you need to provide the argument diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala index 96528d33a02..442cb642c50 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ELU.scala @@ -32,7 +32,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param alpha Double, scale for the negative factor. + * @param alpha Double, scale for the negative factor. Default is 1.0. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class ELU[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala index 97b2fc65ebb..712adddb226 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasUtils.scala @@ -124,16 +124,23 @@ object KerasUtils { private[keras] def toBigDLCriterion[T : ClassTag](loss: String) (implicit ev: TensorNumeric[T]): Criterion[T] = { loss.toLowerCase() match { + case "binary_crossentropy" => BCECriterion[T]() case "categorical_crossentropy" => CategoricalCrossEntropy[T]() case "mse" => MSECriterion[T]() + case "mean_squared_error" => MSECriterion[T]() case "mae" => AbsCriterion[T]() + case "mean_absolute_error" => AbsCriterion[T]() case "hinge" => MarginCriterion[T]() case "mape" => MeanAbsolutePercentageCriterion[T]() + case "mean_absolute_percentage_error" => MeanAbsolutePercentageCriterion[T]() case "msle" => MeanSquaredLogarithmicCriterion[T]() + case "mean_squared_logarithmic_error" => MeanSquaredLogarithmicCriterion[T]() case "squared_hinge" => MarginCriterion[T](squared = true) case "sparse_categorical_crossentropy" => ClassNLLCriterion[T](logProbAsInput = false) case "kld" => KullbackLeiblerDivergenceCriterion[T]() + case "kullback_leibler_divergence" => KullbackLeiblerDivergenceCriterion[T]() case "cosine_proximity" => CosineProximityCriterion[T]() + case "poisson" => PoissonCriterion[T]() case _ => throw new IllegalArgumentException(s"Invalid loss: ${loss.toLowerCase()}") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala index a98ec489ec9..415e163240b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/LeakyReLU.scala @@ -32,7 +32,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param alpha Double >= 0. Negative slope coefficient. + * @param alpha Double >= 0. Negative slope coefficient. Default is 0.3. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class LeakyReLU[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala index 8b4424209c5..aa8c862b2e8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Masking.scala @@ -31,8 +31,8 @@ import scala.reflect.ClassTag * inputShape (a Single Shape, does not include the batch dimension). * * @param maskValue Double, mask value. - * For each timestep in the input tensor (dimension #1 in the tensor), - * if all values in the input tensor at that timestep are equal to `mask_value`, + * For each timestep in the input (the second dimension), + * if all the values in the input at that timestep are equal to 'maskValue', * then the timestep will masked (skipped) in all downstream layers. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala index de0f3b54592..12703166ff4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxPooling1D.scala @@ -32,7 +32,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param poolLength Size of the region to which max pooling is applied. + * @param poolLength Size of the region to which max pooling is applied. Integer. Default is 2. * @param stride Factor by which to downscale. Integer, or -1. 2 will halve the input. * If -1, it will default to poolLength. Default is -1. * @param borderMode Either 'valid' or 'same'. Default is 'valid'. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala index 6ef14dd0b2f..b90bfc45e18 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/MaxoutDense.scala @@ -26,8 +26,7 @@ import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag /** - * A dense maxout layer that takes the element-wise maximum of nbFeature, - * Dense(inputDim, outputDim) linear layers. + * A dense maxout layer that takes the element-wise maximum of linear layers. * This allows the layer to learn a convex, piecewise linear activation function over the inputs. * The input of this layer should be 2D. * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala index d40b38fbbba..6376ab2a426 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Permute.scala @@ -32,7 +32,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param dims Int array. Permutation pattern, does not include the samples dimension. + * @param dims Int array. Permutation pattern, does not include the batch dimension. * Indexing starts at 1. * @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now. */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala index 454df2e8d32..fc530717dea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SReLU.scala @@ -52,7 +52,7 @@ import scala.reflect.ClassTag * with output shape (batch, height, width, channels), * and you wish to share parameters across space * so that each filter only has one set of parameters, - * set 'SharedAxes = Array(1,2)'. + * set 'sharedAxes = Array(1,2)'. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class SReLU[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala index 224e7f20f4f..6a7ba5b9e8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SeparableConvolution2D.scala @@ -31,8 +31,8 @@ import scala.reflect.ClassTag * on each input channel separately) followed by a pointwise convolution which mixes together the * resulting output channels. The depthMultiplier argument controls how many output channels are * generated per input channel in the depthwise step. - * The input of this layer should be 4D. * You can also use SeparableConv2D as an alias of this layer. + * The input of this layer should be 4D. * * When using this layer as the first layer in a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala index b90f9547c09..ebc9ff0f7e8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/ThresholdedReLU.scala @@ -33,7 +33,7 @@ import scala.reflect.ClassTag * When you use this layer as the first layer of a model, you need to provide the argument * inputShape (a Single Shape, does not include the batch dimension). * - * @param theta Double >= 0. Threshold location of activation. + * @param theta Double >= 0. Threshold location of activation. Default is 1.0. * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. */ class ThresholdedReLU[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala index 4911ee034b7..1b4a8f1e08c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/Topology.scala @@ -46,7 +46,7 @@ abstract class KerasModel[T: ClassTag](implicit ev: TensorNumeric[T]) private var vMethods: Array[ValidationMethod[T]] = null /** - * Configure the learning process. Must be called before fit. + * Configure the learning process. Must be called before fit or evaluate. * @param optimizer Optimization method to be used. * @param loss Criterion to be used. * @param metrics Array of validation methods to be used. @@ -84,7 +84,7 @@ abstract class KerasModel[T: ClassTag](implicit ev: TensorNumeric[T]) * Train a model for a fixed number of epochs on a dataset. * @param x Training dataset. If x is an instance of LocalDataSet, train in local mode. * @param nbEpoch Number of iterations to train. - * @param validationData Dataset, or null if validation is not configured. + * @param validationData Dataset for validation, or null if validation is not configured. */ def fit[D: ClassTag](x: DataSet[D], nbEpoch: Int, validationData: DataSet[MiniBatch[T]]) @@ -108,7 +108,7 @@ abstract class KerasModel[T: ClassTag](implicit ev: TensorNumeric[T]) /** * Train a model for a fixed number of epochs on a dataset. - * @param x Training data, RDD of Sample. + * @param x Training dataset, RDD of Sample. * @param batchSize Number of samples per gradient update. * @param nbEpoch Number of iterations to train. * @param validationData RDD of Sample, or null if validation is not configured. @@ -121,7 +121,7 @@ abstract class KerasModel[T: ClassTag](implicit ev: TensorNumeric[T]) /** * Evaluate a model on a given dataset. - * @param x Evaluation data, RDD of Sample. + * @param x Evaluation dataset, RDD of Sample. * @param batchSize Number of samples per batch. */ def evaluate(x: RDD[Sample[T]], @@ -133,7 +133,7 @@ abstract class KerasModel[T: ClassTag](implicit ev: TensorNumeric[T]) /** * Evaluate a model in local mode. - * @param x Evaluation data, LocalDataSet. + * @param x Evaluation dataset, LocalDataSet. */ def evaluate(x: LocalDataSet[MiniBatch[T]]) (implicit ev: TensorNumeric[T]): Array[(ValidationResult, ValidationMethod[T])] = { From a77214466e379f9d875ea2fc98879834cd527dc5 Mon Sep 17 00:00:00 2001 From: jenniew Date: Sun, 25 Mar 2018 18:26:12 -0700 Subject: [PATCH 0760/1065] move python package document (#2448) update python pakage script path remove python_package dir update with comments --- dist/assembly/dist.xml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/dist/assembly/dist.xml b/dist/assembly/dist.xml index 2fe4d3e1daf..88a66a79b8b 100644 --- a/dist/assembly/dist.xml +++ b/dist/assembly/dist.xml @@ -29,6 +29,8 @@ spark-submit-with-bigdl.sh jupyter-with-bigdl.sh spark-shell-with-bigdl.sh + python_package.sh + requirements.txt From 004b2edd6f66dcead7c756fc8c93d64db0306432 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Mon, 26 Mar 2018 09:35:59 +0800 Subject: [PATCH 0761/1065] bump bigdl version from 0.5.0-SNAPSHOT to 0.6.0-SNAPSHOT (#2409) --- dist/pom.xml | 2 +- dl/pom.xml | 6 +++--- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- .../dllib/src/main/resources/bigdl-version-info.properties | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index cd68f27c24f..520fb487b94 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.5.0-SNAPSHOT + 0.6.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index 36a9f8db763..0a9485e3ceb 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.5.0-SNAPSHOT + 0.6.0-SNAPSHOT 4.0.0 @@ -79,7 +79,7 @@ com.intel.analytics.bigdl.core.dist all - 0.5.0-SNAPSHOT + 0.6.0-SNAPSHOT ${bigdl-core-all-scope} @@ -308,7 +308,7 @@ com.intel.analytics.bigdl.core.dist ${os-flag} - 0.5.0-SNAPSHOT + 0.6.0-SNAPSHOT pom diff --git a/pom.xml b/pom.xml index 0281343eb8a..5a801ff17ed 100644 --- a/pom.xml +++ b/pom.xml @@ -5,7 +5,7 @@ bigdl-parent com.intel.analytics.bigdl - 0.5.0-SNAPSHOT + 0.6.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/1.5-plus/pom.xml b/scala/common/spark-version/1.5-plus/pom.xml index 84e8e2b9dbb..a95e1f0a027 100644 --- a/scala/common/spark-version/1.5-plus/pom.xml +++ b/scala/common/spark-version/1.5-plus/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.5.0-SNAPSHOT + 0.6.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/2.0/pom.xml b/scala/common/spark-version/2.0/pom.xml index 8350aee22d2..f69d5f1ebac 100644 --- a/scala/common/spark-version/2.0/pom.xml +++ b/scala/common/spark-version/2.0/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.5.0-SNAPSHOT + 0.6.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/pom.xml b/scala/common/spark-version/pom.xml index 9e999694a9e..3c6a41fbb4c 100644 --- a/scala/common/spark-version/pom.xml +++ b/scala/common/spark-version/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.5.0-SNAPSHOT + 0.6.0-SNAPSHOT 4.0.0 diff --git a/scala/dllib/src/main/resources/bigdl-version-info.properties b/scala/dllib/src/main/resources/bigdl-version-info.properties index 0444f62ca0c..66591f21d75 100644 --- a/scala/dllib/src/main/resources/bigdl-version-info.properties +++ b/scala/dllib/src/main/resources/bigdl-version-info.properties @@ -16,4 +16,4 @@ #BigDL version info config -version=0.5.0-SNAPSHOT \ No newline at end of file +version=0.6.0-SNAPSHOT \ No newline at end of file From 8e099d53e832bb31816f091cf0bfcb361a7beb53 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 27 Mar 2018 14:38:34 +0800 Subject: [PATCH 0762/1065] Add get sample method for ImageFrame (#2447) * add get sample method * add test * fix style * fix style * fix style * fix style * fix test case * fix test case --- .../dllib/utils/python/api/PythonBigDL.scala | 21 +++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 36b0049efb5..5a0d0ede242 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2929,6 +2929,27 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab }) } + def distributedImageFrameToSample(imageFrame: DistributedImageFrame, key: String): + JavaRDD[Sample] = { + imageFrame.rdd.map(x => { + if (x.isValid && x.contains(key)) { + toPySample(x[JSample[T]](key)) + } else { + null + } + }) + } + + def localImageFrameToSample(imageFrame: LocalImageFrame, key: String): JList[Sample] = { + imageFrame.array.map(x => { + if (x.isValid && x.contains(key)) { + toPySample(x[JSample[T]](key)) + } else { + null + } + }).toList.asJava + } + def localImageFrameToPredict(imageFrame: LocalImageFrame, key: String) : JList[JList[Any]] = { imageFrame.array.map(x => From d5f661b366ba739c5e4c3b5801604aec4d917110 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 28 Mar 2018 14:40:29 +0800 Subject: [PATCH 0763/1065] [Enhancement] ImageFrame adding more APIs (#2464) * add api for evaluation * rename method * fix * wrapper parquet * fix api * fix read issue --- .../dllib/nn/abstractnn/AbstractModule.scala | 24 +++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 24 ++++++++++++++++++- 2 files changed, 47 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 7a1ad8a3d94..56f55c0ec40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -833,6 +833,30 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, Evaluator(this).test(dataset, vMethods.map(v => v), batchSize) } + /** + * use ValidationMethod to evaluate module on the given ImageFrame + * @param imageFrame ImageFrame for valudation + * @param vMethods validation methods + * @param batchSize total batch size of all partitions + * @return + */ + + final def evaluateImage(imageFrame: ImageFrame, + vMethods: Array[_ <:ValidationMethod[T]], + batchSize: Option[Int] = None + ): Array[(ValidationResult, ValidationMethod[T])] = { + require(imageFrame.isDistributed(), "ImageFrame must be distributed") + val rdd = imageFrame.toDistributed().rdd.map(imageFeature => { + if (imageFeature.isValid) { + require(imageFeature.contains(ImageFeature.sample), "ImageFeature must have sample") + imageFeature[Sample[T]](ImageFeature.sample) + } else { + null + } + }).filter(_ != null) + evaluate(rdd, vMethods, batchSize) + } + /** * use ValidationMethod to evaluate module on the given local dataset * @param dataSet diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 5a0d0ede242..ae72fd26e84 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1831,6 +1831,21 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab testResultArray.toList.asJava } + + def modelEvaluateImageFrame(model: AbstractModule[Activity, Activity, T], + imageFrame: ImageFrame, + batchSize: Int, + valMethods: JList[ValidationMethod[T]]) + : JList[EvaluatedResult] = { + val resultArray = model.evaluateImage(imageFrame, + valMethods.asScala.toArray, Some(batchSize)) + val testResultArray = resultArray.map { result => + EvaluatedResult(result._1.result()._1, result._1.result()._2, + result._2.toString()) + } + testResultArray.toList.asJava + } + def loadBigDL(path: String): AbstractModule[Activity, Activity, T] = { Module.load[T](path) } @@ -2991,10 +3006,17 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } } - def readParquet(path: String, sqlContext: SQLContext): DistributedImageFrame = { + def readParquet(path: String, sc: JavaSparkContext): DistributedImageFrame = { + val sqlContext = new SQLContext(sc) ImageFrame.readParquet(path, sqlContext) } + def writeParquet(path: String, output: String, + sc: JavaSparkContext, partitionNum: Int = 1): Unit = { + val sqlContext = new SQLContext(sc) + ImageFrame.writeParquet(path, output, sqlContext, partitionNum) + } + def createBytesToMat(byteKey: String): BytesToMat = { BytesToMat(byteKey) } From d7d0ed3ffdcb84deed246b56fa84b161344fdd17 Mon Sep 17 00:00:00 2001 From: ibingoogle Date: Wed, 28 Mar 2018 02:31:54 -0600 Subject: [PATCH 0764/1065] make sure computePoolSize > 0 (#2360) --- .../bigdl/dllib/optim/parameters/AllReduceParameter.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala index 6e4dc53f127..2bc9fb5eba1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala @@ -42,8 +42,8 @@ object AllReduceParameter { } }) - private val computePoolSize: Int = System.getProperty("bigdl.Parameter.computePoolSize", - (Runtime.getRuntime().availableProcessors() / 2).toString).toInt + private val computePoolSize: Int = Math.max(System.getProperty("bigdl.Parameter.computePoolSize", + (Runtime.getRuntime().availableProcessors() / 2).toString).toInt, 1) val computePool: ExecutorService = Executors.newFixedThreadPool(computePoolSize, new ThreadFactory { override def newThread(r: Runnable): Thread = { From 78fac5bca4a321f0024b29b06bda58d889217589 Mon Sep 17 00:00:00 2001 From: Hawkwood <2041829103@qq.com> Date: Thu, 29 Mar 2018 13:09:37 +0800 Subject: [PATCH 0765/1065] 1.6 and yarn Integration (#2373) [Enhancement] Yarn Integration Test --- dl/src/test/integration-test.robot | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/dl/src/test/integration-test.robot b/dl/src/test/integration-test.robot index d7cdcf52753..85e6b208678 100644 --- a/dl/src/test/integration-test.robot +++ b/dl/src/test/integration-test.robot @@ -130,17 +130,34 @@ Quantization Test Suite Remove Environment Variable mnist cifar10 lenetfp32model resnetfp32model Yarn Test Suite + DownLoad Input Build SparkJar spark_2.x Set Environment Variable SPARK_HOME /opt/work/spark-2.0.0-bin-hadoop2.7 Set Environment Variable http_proxy ${http_proxy} Set Environment Variable https_proxy ${https_proxy} ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.0.0-bin-hadoop2.7/bin spark-submit + Log To Console begin DLClassifierLeNet + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 1 --driver-memory 150g --executor-memory 60g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 120 -f ./mnist --maxEpoch 1 + Log To Console begin text classification + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --conf spark.yarn.executor.memoryOverhead=40000 --executor-cores 2 --num-executors 2 --driver-memory 150g --executor-memory 40g --class com.intel.analytics.bigdl.example.textclassification.TextClassifier ${jar_path} --batchSize 8 --baseDir /tmp/text_data --partitionNum 4 + Log To Console begin lenet Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 120 -e 3 + Log To Console begin autoencoder Train + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.autoencoder.Train ${jar_path} -b 120 -e 1 -f ./mnist + Log To Console begin resnet Train + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.resnet.Train ${jar_path} -f ./cifar --batchSize 120 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 + Log To Console begin rnn Train + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.rnn.Train ${jar_path} -f ./ -s ./models --nEpochs 1 --checkpoint ./model/ -b 120 + Log To Console begin PTBWordLM + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 2 --num-executors 2 --driver-memory 150g --executor-memory 40g --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM ${jar_path} -f ./simple-examples/data -b 8 --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 1 --learningRateDecay 0.001 --keepProb 0.5 --overWrite + Log To Console begin inceptionV1 train + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 2 --num-executors 2 --driver-memory 150g --executor-memory 40g --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 8 -f ${imagenet_test_data_source} --learningRate 0.1 -e 1 Set Environment Variable PYSPARK_DRIVER_PYTHON /var/jenkins_home/venv/bin/python Set Environment Variable PYSPARK_PYTHON ./venv.zip/venv/bin/python Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --archives /var/jenkins_home/venv.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 200 --action train --endTriggerType epoch --endTriggerNum 1 Remove Environment Variable http_proxy https_proxy PYSPARK_DRIVER_PYTHON PYSPARK_PYTHON - + Remove Input + PySpark2.1 Test Suite Build SparkJar spark_2.x @@ -149,8 +166,11 @@ PySpark2.1 Test Suite Run Shell ${submit} --master ${spark_tf_210_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 PySpark1.6 Test Suite + DownLoad Input Build SparkJar spark_1.6 Set Environment Variable SPARK_HOME /opt/work/spark-1.6.3-bin-hadoop2.6 ${submit}= Catenate SEPARATOR=/ /opt/work/spark-1.6.3-bin-hadoop2.6/bin spark-submit Run Shell ${submit} --master ${spark_tf_163_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 + Remove Input + From 7eeec7a9171f2519c7778bdc3fcda65fd32b273d Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Thu, 29 Mar 2018 15:12:24 +0800 Subject: [PATCH 0766/1065] fix BCE return Nan (#2473) --- .../bigdl/dllib/nn/BCECriterion.scala | 4 +-- .../bigdl/dllib/nn/BCECriterionSpec.scala | 31 +++++++++++++++++++ 2 files changed, 33 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala index 939d9e173ab..1583c045398 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala @@ -72,7 +72,7 @@ class BCECriterion[@specialized(Float, Double) T: ClassTag] // cmul support broadcasting buffer.cmul(weights) sum += ev.toType[Double](buffer.dot(target)) - buffer.fill(ev.fromType(1.0 + eps)).sub(input).log().cmul(weights) + buffer.fill(ev.one).sub(input).add(ev.fromType(eps)).log().cmul(weights) sum -= ev.toType[Double](buffer.dot(target)) if (onesBuffer.nElement() != buffer.nElement()) { onesBuffer.resizeAs(buffer).fill(ev.one) @@ -81,7 +81,7 @@ class BCECriterion[@specialized(Float, Double) T: ClassTag] } else { buffer.resizeAs(input).copy(input).add(ev.fromType(eps)).log() sum += ev.toType[Double](buffer.dot(target)) - buffer.fill(ev.fromType(1.0 + eps)).sub(input).log() + buffer.fill(ev.one).sub(input).add(ev.fromType(eps)).log() sum -= ev.toType[Double](buffer.dot(target)) if (onesBuffer.nElement() != buffer.nElement()) { onesBuffer.resizeAs(buffer).fill(ev.one) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterionSpec.scala index c49675d81c5..42d9887907b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterionSpec.scala @@ -45,6 +45,37 @@ class BCECriterionSpec extends FlatSpec with Matchers { } + "BCECriterion's eps" should "works" in { + val criterion = BCECriterion[Float]() + val output = Tensor[Float](3) + output.setValue(1, 0f) + output.setValue(2, 1f) + output.setValue(3, 0.5f) + val target = Tensor[Float](3) + target.setValue(1, 0) + target.setValue(2, 1) + target.setValue(3, 1) + + val loss = criterion.forward(output, target) + java.lang.Float.isNaN(loss) should be (false) + } + + "BCECriterion's eps with weight" should "works" in { + val weights = Tensor[Float](3).rand() + val criterion = BCECriterion[Float](weights) + val output = Tensor[Float](3) + output.setValue(1, 0f) + output.setValue(2, 1f) + output.setValue(3, 0.5f) + val target = Tensor[Float](3) + target.setValue(1, 0) + target.setValue(2, 1) + target.setValue(3, 1) + + val loss = criterion.forward(output, target) + java.lang.Float.isNaN(loss) should be (false) + } + "BCECriterion with more than two dimensions small input" should "" + "return return right output and gradInput" in { From 666c4996b0ede6fae94e1efd8bb6dca77fb275e0 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 30 Mar 2018 09:43:06 +0800 Subject: [PATCH 0767/1065] [Enhancement ] - Add set label and get uri api (#2470) * add set label api * update api * add examples * fix doc --- .../imageFrame/InceptionValidation.scala | 73 +++++++++++++++++++ .../imageclassification/imageFrame/README.md | 38 ++++++++++ .../transform/vision/image/ImageFeature.scala | 11 +++ .../transform/vision/image/ImageFrame.scala | 25 +++++++ .../dllib/utils/python/api/PythonBigDL.scala | 25 +++++++ 5 files changed, 172 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/imageFrame/InceptionValidation.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/imageFrame/README.md diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/imageFrame/InceptionValidation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/imageFrame/InceptionValidation.scala new file mode 100644 index 00000000000..c49ba30249d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/imageFrame/InceptionValidation.scala @@ -0,0 +1,73 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.imageclassification.imageFrame + +import com.intel.analytics.bigdl.dataset.DataSet.SeqFileFolder +import com.intel.analytics.bigdl.example.loadmodel.ModelValidator.{TestLocalParams, logger, testLocalParser} +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.optim.{Top1Accuracy, Top5Accuracy} +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, ImageFrameToSample, MatToTensor, PixelBytesToMat} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} +import com.intel.analytics.bigdl.utils.Engine +import org.apache.spark.SparkContext +import scopt.OptionParser + +object InceptionValidation { + case class ValidationParam(imageFolder: String = "", + modelPath: String = "", + batchSize: Int = 32) + + val parser = new OptionParser[ValidationParam]("Inception validation") { + head("Inception validatio") + opt[String]('f', "folder") + .text("where you put the demo image data") + .action((x, c) => c.copy(imageFolder = x)) + .required() + opt[String]("modelPath") + .text("BigDL model") + .action((x, c) => c.copy(modelPath = x)) + .required() + opt[Int]('b', "batchSize") + .text("batch size") + .action((x, c) => c.copy(batchSize = x)) + } + + def main(args: Array[String]): Unit = { + parser.parse(args, ValidationParam()).foreach(param => { + val conf = Engine.createSparkConf() + conf.setAppName("BigDL ImageFrame API Example") + val sc = new SparkContext(conf) + Engine.init + val imageFrame = SeqFileFolder.filesToImageFrame(param.imageFolder, sc, 1000) + val model = Module.loadModule[Float](param.modelPath) + val transformer = PixelBytesToMat() -> Resize(256, 256) -> + CenterCrop(224, 224) -> ChannelNormalize(123, 117, 104) -> + MatToTensor[Float]() -> + ImageFrameToSample[Float](inputKeys = Array("imageTensor"), + targetKeys = Array("label")) + imageFrame -> transformer + val result = model.evaluateImage( + imageFrame, + Array(new Top1Accuracy[Float](), new Top5Accuracy[Float]()), + Some(param.batchSize)) + result.foreach(r => { + logger.info(s"${ r._2 } is ${ r._1 }") + }) + sc.stop() + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/imageFrame/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/imageFrame/README.md new file mode 100644 index 00000000000..828d58cd0d7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/imageclassification/imageFrame/README.md @@ -0,0 +1,38 @@ +## Overview +ImageFrame provides rich deep learning APIs for scalable image processing. This example illustrates how to do model validation on top of these high level APIs using inception-v1 model + +## Run Model validation + +### Preparation + +In order to run the validation application, you should prepare the validation dataset and inception-v1 model + +1) Prepare dataset + +This excample load ImageNet validation dataset directly from hadoop sequence file, for how to prepare the sequence file, please check [here](../../../models/inception#prepare-the-data) + +2) Download pre-trained inception model + +BigDL provides a rich set of pre-trained models, please check [BigDL Models](https://github.com/intel-analytics/analytics-zoo/tree/master/models) for details + +Download inception-v1 model by running + +wget https://s3-ap-southeast-1.amazonaws.com/bigdl-models/imageclassification/imagenet/bigdl_inception-v1_imagenet_0.4.0.model + +### Run validation program + +Run the program as a spark application with below command in standalone mode + +```shell + master=spark://xxx.xxx.xxx.xxx:xxxx # please set your own spark master + imageFolder=hdfs://... + pathToModel=... #set path to your downloaded model + batchSize=448 + spark-submit --driver-memory 20g --master $master --executor-memory 100g \ + --executor-cores 28 \ + --total-executor-cores 112 \ + --driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + --class com.intel.analytics.bigdl.example.imageclassification.imageFrame.InceptionValidation \ + dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ + -f imageFolder --modelPath $pathToModel -b $batchSize +``` \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala index 73cbebca9d8..06ee4bfb2b0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala @@ -321,6 +321,17 @@ class ImageFeature extends Serializable { } image } + + /** + * set label for imagefeature from label map + */ + + def setLabel(labelMap: mutable.Map[String, Float]): Unit = { + val uri = this.uri + if (labelMap.contains(uri)) { + this(ImageFeature.label) = Tensor[Float](T(labelMap(uri))) + } + } } object ImageFeature { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala index 081d9c7792c..8b9dc6c71d0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -18,6 +18,8 @@ package com.intel.analytics.bigdl.transform.vision.image import java.io.{File, FilenameFilter} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T import org.apache.commons.io.FileUtils import org.apache.commons.io.filefilter.WildcardFileFilter import org.apache.log4j.Logger @@ -25,6 +27,7 @@ import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer /** @@ -64,6 +67,14 @@ trait ImageFrame extends Serializable { * return DistributedImageFrame */ def toDistributed(): DistributedImageFrame = this.asInstanceOf[DistributedImageFrame] + + /** + * set label for ImageFrame + * @param labelMap label map : uri to label mapping + */ + + def setLabel(labelMap: mutable.Map[String, Float]): Unit + } object ImageFrame { @@ -185,6 +196,13 @@ class LocalImageFrame(var array: Array[ImageFeature]) extends ImageFrame { override def isLocal(): Boolean = true override def isDistributed(): Boolean = false + + override def setLabel(labelMap: mutable.Map[String, Float]): Unit = { + array = array.map(imageFeature => { + imageFeature.setLabel(labelMap) + imageFeature + }) + } } /** @@ -201,4 +219,11 @@ class DistributedImageFrame(var rdd: RDD[ImageFeature]) extends ImageFrame { override def isLocal(): Boolean = false override def isDistributed(): Boolean = true + + override def setLabel(labelMap: mutable.Map[String, Float]): Unit = { + rdd = rdd.map(imageFeature => { + imageFeature.setLabel(labelMap) + imageFeature + }) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ae72fd26e84..63d774d4531 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2864,6 +2864,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab imageFrame.transform(transformer) } + def setLabel(labelMap: JMap[String, Float], imageFrame: ImageFrame): Unit = { + imageFrame.setLabel(labelMap.asScala) + } + def createDistributedImageFrame(imageRdd: JavaRDD[JTensor], labelRdd: JavaRDD[JTensor]) : DistributedImageFrame = { require(null != imageRdd, "imageRdd cannot be null") @@ -2955,6 +2959,27 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab }) } + def distributedImageFrameToUri(imageFrame: DistributedImageFrame, key: String): + JavaRDD[String] = { + imageFrame.rdd.map(x => { + if (x.contains(key)) { + x[String](key) + } else { + null + } + }) + } + + def localImageFrameToUri(imageFrame: LocalImageFrame, key: String): JList[String] = { + imageFrame.array.map(x => { + if (x.contains(key)) { + x[String](key) + } else { + null + } + }).toList.asJava + } + def localImageFrameToSample(imageFrame: LocalImageFrame, key: String): JList[Sample] = { imageFrame.array.map(x => { if (x.isValid && x.contains(key)) { From e7e81cc9751a31ec88d80188137324b90c6c0635 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 30 Mar 2018 14:33:58 +0800 Subject: [PATCH 0768/1065] add random split (#2480) --- .../dllib/feature/transform/vision/image/ImageFrame.scala | 5 +++++ .../analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala | 5 +++++ 2 files changed, 10 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala index 8b9dc6c71d0..bc31680d189 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFrame.scala @@ -226,4 +226,9 @@ class DistributedImageFrame(var rdd: RDD[ImageFeature]) extends ImageFrame { imageFeature }) } + + def randomSplit(weights: Array[Double]): Array[ImageFrame] = { + val splitRDD = rdd.randomSplit(weights) + splitRDD.map(new DistributedImageFrame(_)) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 63d774d4531..0b2a80ff2c0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2970,6 +2970,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab }) } + def distributedImageFrameRandomSplit(imageFrame: DistributedImageFrame, + weights: JList[Double]): Array[ImageFrame] = { + return imageFrame.randomSplit(weights.asScala.toArray) + } + def localImageFrameToUri(imageFrame: LocalImageFrame, key: String): JList[String] = { imageFrame.array.map(x => { if (x.contains(key)) { From a3720c81b2dc8ea647865582ff67766a3a43521f Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Fri, 30 Mar 2018 22:28:42 -0700 Subject: [PATCH 0769/1065] deprecate with inheritence (#2371) --- .../org/apache/spark/ml/DLClassifier.scala | 35 +- .../org/apache/spark/ml/DLEstimator.scala | 387 +----------------- 2 files changed, 28 insertions(+), 394 deletions(-) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala index e084b92c88e..689bfc1f8ec 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLClassifier.scala @@ -15,17 +15,15 @@ */ package org.apache.spark.ml -import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.{Criterion, Module} -import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.util.{Identifiable, SchemaUtils} -import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.types._ +import org.apache.spark.ml.util.Identifiable import scala.reflect.ClassTag /** + * Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes. + * * [[DLClassifier]] is a specialized [[DLEstimator]] that simplifies the data format for * classification tasks. It only supports label column of DoubleType. * and the fitted [[DLClassifierModel]] will have the prediction column of DoubleType. @@ -42,25 +40,18 @@ class DLClassifier[T: ClassTag]( override val featureSize : Array[Int], override val uid: String = Identifiable.randomUID("dlClassifier") )(implicit ev: TensorNumeric[T]) - extends DLEstimator[T](model, criterion, featureSize, Array(1)) { + extends com.intel.analytics.bigdl.dlframes.DLClassifier[T](model, criterion, featureSize) { override protected def wrapBigDLModel( m: Module[T], featureSize: Array[Int]): DLClassifierModel[T] = { val dlModel = new DLClassifierModel[T](m, featureSize) copyValues(dlModel.setParent(this)).asInstanceOf[DLClassifierModel[T]] } - - override def transformSchema(schema : StructType): StructType = { - validateParams(schema) - SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) - } - - override def copy(extra: ParamMap): DLClassifier[T] = { - copyValues(new DLClassifier(model, criterion, featureSize), extra) - } } /** + * Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes. + * * [[DLClassifierModel]] is a specialized [[DLModel]] for classification tasks. * The prediction column will have the datatype of Double. * @@ -73,15 +64,5 @@ class DLClassifierModel[T: ClassTag]( @transient override val model: Module[T], featureSize : Array[Int], override val uid: String = "DLClassifierModel" - )(implicit ev: TensorNumeric[T]) extends DLModel[T](model, featureSize) { - - protected override def outputToPrediction(output: Tensor[T]): Any = { - ev.toType[Double](output.max(1)._2.valueAt(1)) - } - - override def transformSchema(schema : StructType): StructType = { - validateDataType(schema, $(featuresCol)) - SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) - } -} - + )(implicit ev: TensorNumeric[T]) + extends com.intel.analytics.bigdl.dlframes.DLClassifierModel[T](model, featureSize) diff --git a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala index cb1653f615f..02ea21f577d 100644 --- a/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala +++ b/dl/src/main/scala/org/apache/spark/ml/DLEstimator.scala @@ -15,131 +15,15 @@ */ package org.apache.spark.ml -import com.intel.analytics.bigdl.{Criterion, Module} -import com.intel.analytics.bigdl.dataset._ -import com.intel.analytics.bigdl.models.utils.ModelBroadcast -import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.T -import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} -import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasPredictionCol} -import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators, _} -import org.apache.spark.ml.util.SchemaUtils -import org.apache.spark.rdd.RDD -import org.apache.spark.sql.types._ -import org.apache.spark.sql.{DataFrame, Row} +import com.intel.analytics.bigdl.{Criterion, Module} import scala.reflect.ClassTag -private[ml] trait HasBatchSize extends Params { - - final val batchSize: Param[Int] = new Param[Int](this, "batchSize", "batchSize") - - def getBatchSize: Int = $(batchSize) -} - -/** - * Common trait for DLEstimator and DLModel - */ -private[ml] trait DLParams[@specialized(Float, Double) T] extends HasFeaturesCol - with HasPredictionCol with VectorCompatibility with HasBatchSize { - - /** - * When to stop the training, passed in a [[Trigger]]. E.g. Trigger.maxIterations - */ - final val endWhen = new Param[Trigger](this, "endWhen", "Trigger to stop the training") - - def getEndWhen: Trigger = $(endWhen) - - /** - * learning rate for the optimizer in the DLEstimator. - * Default: 0.001 - */ - final val learningRate = new DoubleParam( - this, "learningRate", "learningRate", ParamValidators.gt(0)) - - def getLearningRate: Double = $(learningRate) - - /** - * learning rate decay for each iteration. - * Default: 0 - */ - final val learningRateDecay = new DoubleParam(this, "learningRateDecay", "learningRateDecay") - - def getLearningRateDecay: Double = $(learningRateDecay) - - /** - * Number of max Epoch for the training, an epoch refers to a traverse over the training data - * Default: 50 - */ - final val maxEpoch = new IntParam(this, "maxEpoch", "number of max Epoch", ParamValidators.gt(0)) - - def getMaxEpoch: Int = $(maxEpoch) - - /** - * optimization method to be used. BigDL supports many optimization methods like Adam, - * SGD and LBFGS. Refer to package com.intel.analytics.bigdl.optim for all the options. - * Default: SGD - */ - final val optimMethod = new Param[OptimMethod[T]](this, "optimMethod", "optimMethod") - - def getOptimMethod: OptimMethod[T] = $(optimMethod) - - setDefault(batchSize -> 1) - - /** - * Validate if feature and label columns are of supported data types. - * Default: 0 - */ - protected def validateDataType(schema: StructType, colName: String): Unit = { - val dataTypes = Seq( - new ArrayType(DoubleType, false), - new ArrayType(DoubleType, true), - new ArrayType(FloatType, false), - new ArrayType(FloatType, true), - DoubleType, - FloatType - ) ++ validVectorTypes - - // TODO use SchemaUtils.checkColumnTypes after convert to 2.0 - val actualDataType = schema(colName).dataType - require(dataTypes.exists(actualDataType.equals), - s"Column $colName must be of type equal to one of the following types: " + - s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.") - } - - /** - * Get conversion function to extract data from original DataFrame - * Default: 0 - */ - protected def getConvertFunc(colType: DataType): (Row, Int) => Seq[AnyVal] = { - colType match { - case ArrayType(DoubleType, false) => - (row: Row, index: Int) => row.getSeq[Double](index) - case ArrayType(DoubleType, true) => - (row: Row, index: Int) => row.getSeq[Double](index) - case ArrayType(FloatType, false) => - (row: Row, index: Int) => row.getSeq[Float](index) - case ArrayType(FloatType, true) => - (row: Row, index: Int) => row.getSeq[Float](index) - case DoubleType => - (row: Row, index: Int) => Seq[Double](row.getDouble(index)) - case FloatType => - (row: Row, index: Int) => Seq[Float](row.getFloat(index)) - case _ => - if (colType.typeName.contains("vector")) { - (row: Row, index: Int) => getVectorSeq(row, colType, index) - } else { - throw new IllegalArgumentException( - s"$colType is not a supported (Unexpected path).") - } - } - } -} - /** + * Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes. + * * [[DLEstimator]] helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern, * thus Spark users can conveniently fit BigDL into Spark ML pipeline. * @@ -161,193 +45,26 @@ private[ml] trait DLParams[@specialized(Float, Double) T] extends HasFeaturesCol * @param labelSize The size (Tensor dimensions) of the label data. */ @deprecated("`DLEstimator` has been migrated to package `com.intel.analytics.bigdl.dlframes`." + - "This will be removed in BigDL 0.6.", "0.5.0") -class DLEstimator[@specialized(Float, Double) T: ClassTag]( - @transient val model: Module[T], - val criterion : Criterion[T], - val featureSize : Array[Int], - val labelSize : Array[Int], + "org.apache.spark.ml.DLEstimator will be removed in BigDL 0.6.", "0.5.0") +class DLEstimator[T: ClassTag]( + @transient override val model: Module[T], + override val criterion : Criterion[T], + featureSize : Array[Int], + override val labelSize : Array[Int], override val uid: String = "DLEstimator")(implicit ev: TensorNumeric[T]) - extends DLEstimatorBase[DLEstimator[T], DLModel[T]] with DLParams[T] { - - def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) - - def setLabelCol(labelColName : String) : this.type = set(labelCol, labelColName) - - def setPredictionCol(value: String): this.type = set(predictionCol, value) - - def setBatchSize(value: Int): this.type = set(batchSize, value) - - def setEndWhen(trigger: Trigger): this.type = set(endWhen, trigger) - - def setLearningRate(value: Double): this.type = set(learningRate, value) - setDefault(learningRate -> 1e-3) - - def setLearningRateDecay(value: Double): this.type = set(learningRateDecay, value) - setDefault(learningRateDecay -> 0.0) - - def setMaxEpoch(value: Int): this.type = set(maxEpoch, value) - setDefault(maxEpoch -> 50) - - def setOptimMethod(value: OptimMethod[T]): this.type = set(optimMethod, value) - set(optimMethod, new SGD[T]) - - @transient private var trainSummary: Option[TrainSummary] = None - - def getTrainSummary: Option[TrainSummary] = trainSummary - - /** - * Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the - * training data, which can be used for visualization via Tensorboard. - * Use setTrainSummary to enable train logger. Then the log will be saved to - * logDir/appName/train as specified by the parameters of TrainSummary. - * - * Default: Not enabled - */ - def setTrainSummary(value: TrainSummary): this.type = { - this.trainSummary = Some(value) - this - } + extends com.intel.analytics.bigdl.dlframes.DLEstimator[T]( + model, criterion, featureSize, labelSize) { - @transient private var validationSummary: Option[ValidationSummary] = None - - /** - * Statistics (LearningRate, Loss, Throughput, Parameters) collected during training for the - * validation data if validation data is set, which can be used for visualization via - * Tensorboard. Use setValidationSummary to enable validation logger. Then the log will be - * saved to logDir/appName/ as specified by the parameters of validationSummary. - * - * Default: None - */ - def getValidationSummary: Option[ValidationSummary] = validationSummary - - /** - * Enable validation Summary - */ - def setValidationSummary(value: ValidationSummary): this.type = { - this.validationSummary = Some(value) - this - } - - @transient private var validationTrigger: Option[Trigger] = None - @transient private var validationDF: DataFrame = _ - @transient private var validationMethods: Array[ValidationMethod[T]] = _ - @transient private var validationBatchSize: Int = 0 - /** - * Set a validate evaluation during training - * - * @param trigger how often to evaluation validation set - * @param validationDF validate data set - * @param vMethods a set of validation method [[ValidationMethod]] - * @param batchSize batch size for validation - * @return this optimizer - */ - def setValidation(trigger: Trigger, validationDF: DataFrame, - vMethods : Array[ValidationMethod[T]], batchSize: Int) - : this.type = { - this.validationTrigger = Some(trigger) - this.validationDF = validationDF - this.validationMethods = vMethods - this.validationBatchSize = batchSize - this - } - - protected def validateParams(schema : StructType): Unit = { - validateDataType(schema, $(featuresCol)) - validateDataType(schema, $(labelCol)) - if(isSet(endWhen) && isSet(maxEpoch)) { - throw new IllegalArgumentException(s"endWhen and maxEpoch cannot be both set") - } - if (validationTrigger.isEmpty && validationSummary.isDefined) { - throw new IllegalArgumentException( - s"validationSummary is only valid if validation data is set.") - } - } - - override def transformSchema(schema : StructType): StructType = { - validateParams(schema) - SchemaUtils.appendColumn(schema, $(predictionCol), ArrayType(DoubleType, false)) - } - - protected override def internalFit(dataFrame: DataFrame): DLModel[T] = { - val localFeatureCol = $(featuresCol) - val localLabelCol = $(labelCol) - - def getSamples(dataFrame: DataFrame): RDD[Sample[T]] = { - val featureType = dataFrame.schema(localFeatureCol).dataType - val featureColIndex = dataFrame.schema.fieldIndex(localFeatureCol) - val labelType = dataFrame.schema(localLabelCol).dataType - val labelColIndex = dataFrame.schema.fieldIndex(localLabelCol) - - val featureFunc = getConvertFunc(featureType) - val labelFunc = getConvertFunc(labelType) - - val featureAndLabel: RDD[(Seq[AnyVal], Seq[AnyVal])] = dataFrame.rdd.map { row => - val features = featureFunc(row, featureColIndex) - val labels = labelFunc(row, labelColIndex) - (features, labels) - } - - val samples = featureAndLabel.map { case (f, l) => - // convert feature and label data type to the same type with model - // TODO: investigate to reduce memory consumption during conversion. - val feature = f.head match { - case dd: Double => f.asInstanceOf[Seq[Double]].map(ev.fromType(_)) - case ff: Float => f.asInstanceOf[Seq[Float]].map(ev.fromType(_)) - } - val label = l.head match { - case dd: Double => l.asInstanceOf[Seq[Double]].map(ev.fromType(_)) - case ff: Float => l.asInstanceOf[Seq[Float]].map(ev.fromType(_)) - } - (feature, label) - }.map { case (feature, label) => - Sample(Tensor(feature.toArray, featureSize), Tensor(label.toArray, labelSize)) - } - samples - } - - val trainingSamples = getSamples(dataFrame) - val state = T("learningRate" -> $(learningRate), "learningRateDecay" -> $(learningRateDecay)) - val endTrigger = if (isSet(endWhen)) $(endWhen) else Trigger.maxEpoch($(maxEpoch)) - val optimizer = Optimizer(model, trainingSamples, criterion, $(batchSize)) - .setState(state) - .setOptimMethod($(optimMethod)) - .setEndWhen(endTrigger) - - if (validationTrigger.isDefined) { - val validationSamples = getSamples(validationDF) - optimizer.setValidation( - validationTrigger.get, - validationSamples, - validationMethods, - validationBatchSize) - if (this.validationSummary.isDefined) { - optimizer.setValidationSummary(this.validationSummary.get) - } - } - - if (this.trainSummary.isDefined) { - optimizer.setTrainSummary(this.trainSummary.get) - } - - val optimizedModel = optimizer.optimize() - wrapBigDLModel(optimizedModel, featureSize) - } - - /** - * sub classes can extend the method and return required model for different transform tasks - */ - protected def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLModel[T] = { + override protected def wrapBigDLModel(m: Module[T], featureSize: Array[Int]): DLModel[T] = { val dlModel = new DLModel[T](m, featureSize) - copyValues(dlModel.setParent(this)) - } - - override def copy(extra: ParamMap): DLEstimator[T] = { - copyValues(new DLEstimator(model, criterion, featureSize, labelSize), extra) + copyValues(dlModel.setParent(this)).asInstanceOf[DLModel[T]] } } + /** + * Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes. + * * [[DLModel]] helps embed a BigDL model into a Spark Transformer, thus Spark users can * conveniently merge BigDL into Spark ML pipeline. * [[DLModel]] supports feature data in the format of @@ -363,80 +80,16 @@ class DLEstimator[@specialized(Float, Double) T: ClassTag]( */ @deprecated("`DLModel` has been migrated to package `com.intel.analytics.bigdl.dlframes`." + "This will be removed in BigDL 0.6.", "0.5.0") -class DLModel[@specialized(Float, Double) T: ClassTag]( - @transient val model: Module[T], - var featureSize : Array[Int], +class DLModel[T: ClassTag]( + @transient override val model: Module[T], + featureSize : Array[Int], override val uid: String = "DLModel" )(implicit ev: TensorNumeric[T]) - extends DLTransformerBase[DLModel[T]] with DLParams[T] with HasBatchSize { - - def setFeaturesCol(featuresColName: String): this.type = set(featuresCol, featuresColName) - - def setPredictionCol(value: String): this.type = set(predictionCol, value) - - def setFeatureSize(value: Array[Int]): this.type = { - this.featureSize = value - this - } - - def setBatchSize(value: Int): this.type = set(batchSize, value) - - def getFeatureSize: Array[Int] = this.featureSize - - /** - * Perform a prediction on featureCol, and write result to the predictionCol. - */ - protected override def internalTransform(dataFrame: DataFrame): DataFrame = { - val featureType = dataFrame.schema($(featuresCol)).dataType - val featureColIndex = dataFrame.schema.fieldIndex($(featuresCol)) - val featureFunc = getConvertFunc(featureType) - val sc = dataFrame.sqlContext.sparkContext - val modelBroadCast = ModelBroadcast[T]().broadcast(sc, model) - val localBatchSize = $(batchSize) + extends com.intel.analytics.bigdl.dlframes.DLModel[T](model, featureSize) - val resultRDD = dataFrame.rdd.mapPartitions { rowIter => - val localModel = modelBroadCast.value() - rowIter.grouped(localBatchSize).flatMap { rowBatch => - val samples = rowBatch.map { row => - val features = featureFunc(row, featureColIndex) - val featureBuffer = features.head match { - case dd: Double => features.asInstanceOf[Seq[Double]].map(ev.fromType(_)) - case ff: Float => features.asInstanceOf[Seq[Float]].map(ev.fromType(_)) - } - Sample(Tensor(featureBuffer.toArray, featureSize)) - }.toIterator - val predictions = SampleToMiniBatch(localBatchSize).apply(samples).flatMap { batch => - val batchResult = localModel.forward(batch.getInput()) - batchResult.toTensor.split(1).map(outputToPrediction) - } - rowBatch.toIterator.zip(predictions).map { case (row, predict) => - Row.fromSeq(row.toSeq ++ Seq(predict)) - } - } - } - - val resultSchema = transformSchema(dataFrame.schema) - dataFrame.sqlContext.createDataFrame(resultRDD, resultSchema) - } - - protected def outputToPrediction(output: Tensor[T]): Any = { - output.clone().storage().array().map(ev.toType[Double]) - } - - override def transformSchema(schema : StructType): StructType = { - validateDataType(schema, $(featuresCol)) - SchemaUtils.appendColumn(schema, $(predictionCol), ArrayType(DoubleType, false)) - } - - override def copy(extra: ParamMap): DLModel[T] = { - val copied = new DLModel(model, featureSize, uid).setParent(parent) - copyValues(copied, extra) - } -} // TODO, add save/load object DLModel { - } From afa16cafc0bc81eaef69c6d536381125428571ce Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 2 Apr 2018 16:21:43 +0800 Subject: [PATCH 0770/1065] [Fix] using parameters() instead of getParameterTable() to get weight and bias (#2477) * using parameters() instead of getParameterTable() to get weight and bias * fix npe --- .../dllib/utils/serialization/Bigdl.java | 826 +++++++++++++++--- .../main/resources/serialization/bigdl.proto | 4 + .../utils/serializer/ModuleSerializable.scala | 76 +- 3 files changed, 751 insertions(+), 155 deletions(-) diff --git a/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java b/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java index 55628300c10..c889ed291b1 100644 --- a/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java +++ b/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java @@ -967,7 +967,7 @@ com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModules /** * 
-     * weight for each layer, serialized data are stored as either float or double
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -975,7 +975,7 @@ com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModules boolean hasWeight(); /** * 
-     * weight for each layer, serialized data are stored as either float or double
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -983,7 +983,7 @@ com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModules com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getWeight(); /** * 
-     * weight for each layer, serialized data are stored as either float or double
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -992,7 +992,7 @@ com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModules /** * 
-     * bias for each layer
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -1000,7 +1000,7 @@ com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModules boolean hasBias(); /** * 
-     * bias for each layer
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -1008,7 +1008,7 @@ com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSubModules com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getBias(); /** * 
-     * bias for each layer
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -1260,6 +1260,59 @@ com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; */ com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder(); + + /** + * 
+     * indicator if module has parameters
+     *
+ * + * bool hasParameters = 15; + */ + boolean getHasParameters(); + + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + java.util.List + getParametersList(); + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getParameters(int index); + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + int getParametersCount(); + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + java.util.List + getParametersOrBuilderList(); + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getParametersOrBuilder( + int index); } /** * Protobuf type {@code com.intel.analytics.bigdl.serialization.BigDLModule} @@ -1283,6 +1336,8 @@ private BigDLModule() { train_ = false; namePostfix_ = ""; id_ = 0; + hasParameters_ = false; + parameters_ = java.util.Collections.emptyList(); } @java.lang.Override @@ -1439,6 +1494,20 @@ private BigDLModule( break; } + case 120: { + + hasParameters_ = input.readBool(); + break; + } + case 130: { + if (!((mutable_bitField0_ & 0x00008000) == 0x00008000)) { + parameters_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00008000; + } + parameters_.add( + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.parser(), extensionRegistry)); + break; + } } } } catch (com.google.protobuf.InvalidProtocolBufferException e) { @@ -1456,6 +1525,9 @@ private BigDLModule( if (((mutable_bitField0_ & 0x00000020) == 0x00000020)) { nextModules_ = nextModules_.getUnmodifiableView(); } + if (((mutable_bitField0_ & 0x00008000) == 0x00008000)) { + parameters_ = java.util.Collections.unmodifiableList(parameters_); + } this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } @@ -1585,7 +1657,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModuleOrBuilder getSub private com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor weight_; /** * 
-     * weight for each layer, serialized data are stored as either float or double
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -1595,7 +1667,7 @@ public boolean hasWeight() { } /** * 
-     * weight for each layer, serialized data are stored as either float or double
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -1605,7 +1677,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getWeight() { } /** * 
-     * weight for each layer, serialized data are stored as either float or double
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -1618,7 +1690,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getWei private com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor bias_; /** * 
-     * bias for each layer
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -1628,7 +1700,7 @@ public boolean hasBias() { } /** * 
-     * bias for each layer
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -1638,7 +1710,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getBias() { } /** * 
-     * bias for each layer
+     * deprecated, please use parameters
      *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -2047,6 +2119,74 @@ public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputSha return getOutputShape(); } + public static final int HASPARAMETERS_FIELD_NUMBER = 15; + private boolean hasParameters_; + /** + * 
+     * indicator if module has parameters
+     *
+ * + * bool hasParameters = 15; + */ + public boolean getHasParameters() { + return hasParameters_; + } + + public static final int PARAMETERS_FIELD_NUMBER = 16; + private java.util.List parameters_; + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public java.util.List getParametersList() { + return parameters_; + } + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public java.util.List + getParametersOrBuilderList() { + return parameters_; + } + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public int getParametersCount() { + return parameters_.size(); + } + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getParameters(int index) { + return parameters_.get(index); + } + /** + * 
+     * parameters, e.g., weight and bias
+     *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getParametersOrBuilder( + int index) { + return parameters_.get(index); + } + private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; @@ -2104,6 +2244,12 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) if (outputShape_ != null) { output.writeMessage(14, getOutputShape()); } + if (hasParameters_ != false) { + output.writeBool(15, hasParameters_); + } + for (int i = 0; i < parameters_.size(); i++) { + output.writeMessage(16, parameters_.get(i)); + } unknownFields.writeTo(output); } @@ -2178,6 +2324,14 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeMessageSize(14, getOutputShape()); } + if (hasParameters_ != false) { + size += com.google.protobuf.CodedOutputStream + .computeBoolSize(15, hasParameters_); + } + for (int i = 0; i < parameters_.size(); i++) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(16, parameters_.get(i)); + } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; @@ -2234,6 +2388,10 @@ public boolean equals(final java.lang.Object obj) { result = result && getOutputShape() .equals(other.getOutputShape()); } + result = result && (getHasParameters() + == other.getHasParameters()); + result = result && getParametersList() + .equals(other.getParametersList()); result = result && unknownFields.equals(other.unknownFields); return result; } @@ -2290,6 +2448,13 @@ public int hashCode() { hash = (37 * hash) + OUTPUTSHAPE_FIELD_NUMBER; hash = (53 * hash) + getOutputShape().hashCode(); } + hash = (37 * hash) + HASPARAMETERS_FIELD_NUMBER; + hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( + getHasParameters()); + if (getParametersCount() > 0) { + hash = (37 * hash) + PARAMETERS_FIELD_NUMBER; + hash = (53 * hash) + getParametersList().hashCode(); + } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; @@ -2438,6 +2603,7 @@ private void maybeForceBuilderInitialization() { if (com.google.protobuf.GeneratedMessageV3 .alwaysUseFieldBuilders) { getSubModulesFieldBuilder(); + getParametersFieldBuilder(); } } public Builder clear() { @@ -2489,6 +2655,14 @@ public Builder clear() { outputShape_ = null; outputShapeBuilder_ = null; } + hasParameters_ = false; + + if (parametersBuilder_ == null) { + parameters_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00008000); + } else { + parametersBuilder_.clear(); + } return this; } @@ -2560,6 +2734,16 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule buildPartial() } else { result.outputShape_ = outputShapeBuilder_.build(); } + result.hasParameters_ = hasParameters_; + if (parametersBuilder_ == null) { + if (((bitField0_ & 0x00008000) == 0x00008000)) { + parameters_ = java.util.Collections.unmodifiableList(parameters_); + bitField0_ = (bitField0_ & ~0x00008000); + } + result.parameters_ = parameters_; + } else { + result.parameters_ = parametersBuilder_.build(); + } result.bitField0_ = to_bitField0_; onBuilt(); return result; @@ -2684,6 +2868,35 @@ public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModu if (other.hasOutputShape()) { mergeOutputShape(other.getOutputShape()); } + if (other.getHasParameters() != false) { + setHasParameters(other.getHasParameters()); + } + if (parametersBuilder_ == null) { + if (!other.parameters_.isEmpty()) { + if (parameters_.isEmpty()) { + parameters_ = other.parameters_; + bitField0_ = (bitField0_ & ~0x00008000); + } else { + ensureParametersIsMutable(); + parameters_.addAll(other.parameters_); + } + onChanged(); + } + } else { + if (!other.parameters_.isEmpty()) { + if (parametersBuilder_.isEmpty()) { + parametersBuilder_.dispose(); + parametersBuilder_ = null; + parameters_ = other.parameters_; + bitField0_ = (bitField0_ & ~0x00008000); + parametersBuilder_ = + com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? + getParametersFieldBuilder() : null; + } else { + parametersBuilder_.addAllMessages(other.parameters_); + } + } + } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; @@ -3118,7 +3331,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule.Builder addSubM com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> weightBuilder_; /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3128,7 +3341,7 @@ public boolean hasWeight() { } /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3142,7 +3355,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getWeight() { } /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3162,7 +3375,7 @@ public Builder setWeight(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTens } /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3180,7 +3393,7 @@ public Builder setWeight( } /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3202,7 +3415,7 @@ public Builder mergeWeight(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTe } /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3220,7 +3433,7 @@ public Builder clearWeight() { } /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3232,7 +3445,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getWeig } /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3247,7 +3460,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getWei } /** * 
-       * weight for each layer, serialized data are stored as either float or double
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor weight = 3; @@ -3271,7 +3484,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getWei com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> biasBuilder_; /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -3281,7 +3494,7 @@ public boolean hasBias() { } /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -3295,7 +3508,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getBias() { } /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -3315,7 +3528,7 @@ public Builder setBias(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor } /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -3333,7 +3546,7 @@ public Builder setBias( } /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -3355,7 +3568,7 @@ public Builder mergeBias(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTens } /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -3373,7 +3586,7 @@ public Builder clearBias() { } /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -3385,7 +3598,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getBias } /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -3400,7 +3613,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getBia } /** * 
-       * bias for each layer
+       * deprecated, please use parameters
        *
* * .com.intel.analytics.bigdl.serialization.BigDLTensor bias = 4; @@ -4478,6 +4691,356 @@ public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputSha } return outputShapeBuilder_; } + + private boolean hasParameters_ ; + /** + * 
+       * indicator if module has parameters
+       *
+ * + * bool hasParameters = 15; + */ + public boolean getHasParameters() { + return hasParameters_; + } + /** + * 
+       * indicator if module has parameters
+       *
+ * + * bool hasParameters = 15; + */ + public Builder setHasParameters(boolean value) { + + hasParameters_ = value; + onChanged(); + return this; + } + /** + * 
+       * indicator if module has parameters
+       *
+ * + * bool hasParameters = 15; + */ + public Builder clearHasParameters() { + + hasParameters_ = false; + onChanged(); + return this; + } + + private java.util.List parameters_ = + java.util.Collections.emptyList(); + private void ensureParametersIsMutable() { + if (!((bitField0_ & 0x00008000) == 0x00008000)) { + parameters_ = new java.util.ArrayList(parameters_); + bitField0_ |= 0x00008000; + } + } + + private com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> parametersBuilder_; + + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public java.util.List getParametersList() { + if (parametersBuilder_ == null) { + return java.util.Collections.unmodifiableList(parameters_); + } else { + return parametersBuilder_.getMessageList(); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public int getParametersCount() { + if (parametersBuilder_ == null) { + return parameters_.size(); + } else { + return parametersBuilder_.getCount(); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getParameters(int index) { + if (parametersBuilder_ == null) { + return parameters_.get(index); + } else { + return parametersBuilder_.getMessage(index); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder setParameters( + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { + if (parametersBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureParametersIsMutable(); + parameters_.set(index, value); + onChanged(); + } else { + parametersBuilder_.setMessage(index, value); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder setParameters( + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + parameters_.set(index, builderForValue.build()); + onChanged(); + } else { + parametersBuilder_.setMessage(index, builderForValue.build()); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addParameters(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { + if (parametersBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureParametersIsMutable(); + parameters_.add(value); + onChanged(); + } else { + parametersBuilder_.addMessage(value); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addParameters( + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { + if (parametersBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureParametersIsMutable(); + parameters_.add(index, value); + onChanged(); + } else { + parametersBuilder_.addMessage(index, value); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addParameters( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + parameters_.add(builderForValue.build()); + onChanged(); + } else { + parametersBuilder_.addMessage(builderForValue.build()); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addParameters( + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + parameters_.add(index, builderForValue.build()); + onChanged(); + } else { + parametersBuilder_.addMessage(index, builderForValue.build()); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addAllParameters( + java.lang.Iterable values) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, parameters_); + onChanged(); + } else { + parametersBuilder_.addAllMessages(values); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder clearParameters() { + if (parametersBuilder_ == null) { + parameters_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00008000); + onChanged(); + } else { + parametersBuilder_.clear(); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder removeParameters(int index) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + parameters_.remove(index); + onChanged(); + } else { + parametersBuilder_.remove(index); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getParametersBuilder( + int index) { + return getParametersFieldBuilder().getBuilder(index); + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getParametersOrBuilder( + int index) { + if (parametersBuilder_ == null) { + return parameters_.get(index); } else { + return parametersBuilder_.getMessageOrBuilder(index); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public java.util.List + getParametersOrBuilderList() { + if (parametersBuilder_ != null) { + return parametersBuilder_.getMessageOrBuilderList(); + } else { + return java.util.Collections.unmodifiableList(parameters_); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder addParametersBuilder() { + return getParametersFieldBuilder().addBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()); + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder addParametersBuilder( + int index) { + return getParametersFieldBuilder().addBuilder( + index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()); + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public java.util.List + getParametersBuilderList() { + return getParametersFieldBuilder().getBuilderList(); + } + private com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> + getParametersFieldBuilder() { + if (parametersBuilder_ == null) { + parametersBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder>( + parameters_, + ((bitField0_ & 0x00008000) == 0x00008000), + getParentForChildren(), + isClean()); + parameters_ = null; + } + return parametersBuilder_; + } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { return super.setUnknownFieldsProto3(unknownFields); @@ -21581,7 +22144,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.Shape getDefaultInstanceFor java.lang.String[] descriptorData = { "\n\013bigdl.proto\022\'com.intel.analytics.bigdl" + ".serialization\032\031google/protobuf/any.prot" + - "o\"\245\005\n\013BigDLModule\022\014\n\004name\030\001 \001(\t\022H\n\nsubMo" + + "o\"\206\006\n\013BigDLModule\022\014\n\004name\030\001 \001(\t\022H\n\nsubMo" + "dules\030\002 \003(\01324.com.intel.analytics.bigdl." + "serialization.BigDLModule\022D\n\006weight\030\003 \001(" + "\01324.com.intel.analytics.bigdl.serializat" + @@ -21595,105 +22158,108 @@ public com.intel.analytics.bigdl.serialization.Bigdl.Shape getDefaultInstanceFor "d\030\014 \001(\005\022B\n\ninputShape\030\r \001(\0132..com.intel." + "analytics.bigdl.serialization.Shape\022C\n\013o" + "utputShape\030\016 \001(\0132..com.intel.analytics.b" + - "igdl.serialization.Shape\032_\n\tAttrEntry\022\013\n" + - "\003key\030\001 \001(\t\022A\n\005value\030\002 \001(\01322.com.intel.an" + - "alytics.bigdl.serialization.AttrValue:\0028" + - "\001\"g\n\nInitMethod\022K\n\nmethodType\030\001 \001(\01627.co", - "m.intel.analytics.bigdl.serialization.In" + - "itMethodType\022\014\n\004data\030\002 \003(\001\"\326\002\n\013BigDLTens" + - "or\022C\n\010datatype\030\001 \001(\01621.com.intel.analyti" + - "cs.bigdl.serialization.DataType\022\014\n\004size\030" + - "\002 \003(\005\022\016\n\006stride\030\003 \003(\005\022\016\n\006offset\030\004 \001(\005\022\021\n" + - "\tdimension\030\005 \001(\005\022\021\n\tnElements\030\006 \001(\005\022\020\n\010i" + - "sScalar\030\007 \001(\010\022G\n\007storage\030\010 \001(\01326.com.int" + - "el.analytics.bigdl.serialization.TensorS" + - "torage\022\n\n\002id\030\t \001(\005\022G\n\ntensorType\030\n \001(\01623" + - ".com.intel.analytics.bigdl.serialization", - ".TensorType\"\352\001\n\rTensorStorage\022C\n\010datatyp" + - "e\030\001 \001(\01621.com.intel.analytics.bigdl.seri" + - "alization.DataType\022\022\n\nfloat_data\030\002 \003(\002\022\023" + - "\n\013double_data\030\003 \003(\001\022\021\n\tbool_data\030\004 \003(\010\022\023" + - "\n\013string_data\030\005 \003(\t\022\020\n\010int_data\030\006 \003(\005\022\021\n" + - "\tlong_data\030\007 \003(\003\022\022\n\nbytes_data\030\010 \003(\014\022\n\n\002" + - "id\030\t \001(\005\"u\n\013Regularizer\022Q\n\017regularizerTy" + - "pe\030\001 \001(\01628.com.intel.analytics.bigdl.ser" + - "ialization.RegularizerType\022\023\n\013regularDat" + - "a\030\002 \003(\001\"\224\016\n\tAttrValue\022C\n\010dataType\030\001 \001(\0162", - "1.com.intel.analytics.bigdl.serializatio" + - "n.DataType\022\017\n\007subType\030\002 \001(\t\022\024\n\nint32Valu" + - "e\030\003 \001(\005H\000\022\024\n\nint64Value\030\004 \001(\003H\000\022\024\n\nfloat" + - "Value\030\005 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"zation.NameAttrList.AttrEntry\032_\n\tAttrEnt", + "ry\022\013\n\003key\030\001 \001(\t\022A\n\005value\030\002 \001(\01322.com.int" + + "el.analytics.bigdl.serialization.AttrVal" + + "ue:\0028\001\"\332\001\n\005Shape\022K\n\tshapeType\030\001 \001(\01628.co" + + "m.intel.analytics.bigdl.serialization.Sh" + + "ape.ShapeType\022\r\n\005ssize\030\002 \001(\005\022\022\n\nshapeVal" + + "ue\030\003 \003(\005\022=\n\005shape\030\004 \003(\0132..com.intel.anal" + + "ytics.bigdl.serialization.Shape\"\"\n\tShape" + + "Type\022\n\n\006SINGLE\020\000\022\t\n\005MULTI\020\001*\260\001\n\tVarForma" + + "t\022\020\n\014EMPTY_FORMAT\020\000\022\013\n\007DEFAULT\020\001\022\t\n\005ONE_" + + "D\020\002\022\n\n\006IN_OUT\020\003\022\n\n\006OUT_IN\020\004\022\020\n\014IN_OUT_KW", + "_KH\020\005\022\020\n\014OUT_IN_KW_KH\020\006\022\023\n\017GP_OUT_IN_KW_" + + "KH\020\007\022\023\n\017GP_IN_OUT_KW_KH\020\010\022\023\n\017OUT_IN_KT_K" + + "H_KW\020\t*\253\001\n\016InitMethodType\022\030\n\024EMPTY_INITI" + + "ALIZATION\020\000\022\022\n\016RANDOM_UNIFORM\020\001\022\030\n\024RANDO" + + "M_UNIFORM_PARAM\020\002\022\021\n\rRANDOM_NORMAL\020\003\022\t\n\005" + + "ZEROS\020\004\022\010\n\004ONES\020\005\022\t\n\005CONST\020\006\022\n\n\006XAVIER\020\007" + + "\022\022\n\016BILINEARFILLER\020\010*L\n\017RegularizerType\022" + + "\023\n\017L1L2Regularizer\020\000\022\021\n\rL1Regularizer\020\001\022" + + "\021\n\rL2Regularizer\020\002*%\n\017InputDataFormat\022\010\n" + + "\004NCHW\020\000\022\010\n\004NHWC\020\001*\"\n\nTensorType\022\t\n\005DENSE", + "\020\000\022\t\n\005QUANT\020\001*\210\002\n\010DataType\022\t\n\005INT32\020\000\022\t\n" + + "\005INT64\020\001\022\t\n\005FLOAT\020\002\022\n\n\006DOUBLE\020\003\022\n\n\006STRIN" + + "G\020\004\022\010\n\004BOOL\020\005\022\010\n\004CHAR\020\006\022\t\n\005SHORT\020\007\022\t\n\005BY" + + "TES\020\010\022\017\n\013REGULARIZER\020\t\022\n\n\006TENSOR\020\n\022\023\n\017VA" + + "RIABLE_FORMAT\020\013\022\016\n\nINITMETHOD\020\014\022\n\n\006MODUL" + + "E\020\r\022\022\n\016NAME_ATTR_LIST\020\016\022\017\n\013ARRAY_VALUE\020\017" + + "\022\017\n\013DATA_FORMAT\020\020\022\n\n\006CUSTOM\020\021\022\t\n\005SHAPE\020\022" + + "b\006proto3" }; com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { @@ -21713,7 +22279,7 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors( internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor, - new java.lang.String[] { "Name", "SubModules", "Weight", "Bias", "PreModules", "NextModules", "ModuleType", "Attr", "Version", "Train", "NamePostfix", "Id", "InputShape", "OutputShape", }); + new java.lang.String[] { "Name", "SubModules", "Weight", "Bias", "PreModules", "NextModules", "ModuleType", "Attr", "Version", "Train", "NamePostfix", "Id", "InputShape", "OutputShape", "HasParameters", "Parameters", }); internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_descriptor = internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor.getNestedTypes().get(0); internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_fieldAccessorTable = new diff --git a/scala/dllib/src/main/resources/serialization/bigdl.proto b/scala/dllib/src/main/resources/serialization/bigdl.proto index 4c5d6d5277d..9f0e276304d 100644 --- a/scala/dllib/src/main/resources/serialization/bigdl.proto +++ b/scala/dllib/src/main/resources/serialization/bigdl.proto @@ -5,7 +5,9 @@ message BigDLModule { string name = 1; //module name repeated BigDLModule subModules = 2; // sub modules + // deprecated, please use parameters BigDLTensor weight = 3; // weight for each layer, serialized data are stored as either float or double + // deprecated, please use parameters BigDLTensor bias = 4; // bias for each layer repeated string preModules = 5; //pre modules of the same hierarchy repeated string nextModules = 6; //next modules of the same hierachy @@ -17,6 +19,8 @@ message BigDLModule int32 id = 12; // unique ID of this module , used for shared modules Shape inputShape = 13; // input shape Shape outputShape = 14; //output shape + bool hasParameters = 15; // indicator if module has parameters + repeated BigDLTensor parameters = 16; // parameters, e.g., weight and bias } enum VarFormat { EMPTY_FORMAT = 0; diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index dafc5ffcdd7..a26f004c4e7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.Container import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Table, Shape => BigDLShape} @@ -246,7 +247,9 @@ trait ModuleSerializable extends Loadable with Savable{ } module.inputShapeValue = ShapeConverter.shapeToBigDL(context, model, "input") module.outputShapeValue = ShapeConverter.shapeToBigDL(context, model, "output") - if (_copyWeightAndBias) { + + // container does not need to be copied paramters again + if (_copyWeightAndBias && context.bigdlModule.getSubModulesCount == 0) { copy2BigDL(context, bigDLModule) } bigDLModule @@ -272,7 +275,8 @@ trait ModuleSerializable extends Loadable with Savable{ if (outputShape != null) { modelBuilder.setOutputShape(ShapeConverter.shapeToProto(context, outputShape)) } - if (_copyWeightAndBias) { + // container does not need to be copied paramters again + if (_copyWeightAndBias && !module.isInstanceOf[Container[_, _, _]]) { copyFromBigDL(context, modelBuilder) } SerializeResult(modelBuilder, context.storages) @@ -285,6 +289,43 @@ trait ModuleSerializable extends Loadable with Savable{ */ protected def copy2BigDL[T: ClassTag](context: DeserializeContext, module : ModuleData[T]) (implicit ev: TensorNumeric[T]): Unit = { + + if (context.bigdlModule.getHasParameters) { + copyParameters2BigDL(context, module) + } else { + // for legacy format models + copyWeightAndBias(context, module) + } + } + + private def copyParameters2BigDL[T: ClassTag] + (context: DeserializeContext, module : ModuleData[T]) + (implicit ev: TensorNumeric[T]): Unit = { + + val serializedParameters = context.bigdlModule.getParametersList.asScala.toArray + + val arrayValue = ArrayValue.newBuilder + arrayValue.setDatatype(DataType.TENSOR) + serializedParameters.foreach(param => arrayValue.addTensor(param)) + arrayValue.setSize(serializedParameters.length) + val attrValue = AttrValue.newBuilder + attrValue.setArrayValue(arrayValue.build) + attrValue.setDataType(DataType.ARRAY_VALUE) + val convertedParameters = DataConverter.getAttributeValue(context, attrValue.build). + asInstanceOf[Array[Tensor[T]]] + + val parameters = module.module.parameters()._1 + + var i = 0 + while (i < parameters.length) { + parameters(i).copy(convertedParameters(i)) + i += 1 + } + } + + // to keep compatible with models saved by release <= 0.5.0 + private def copyWeightAndBias[T: ClassTag](context: DeserializeContext, module : ModuleData[T]) + (implicit ev: TensorNumeric[T]): Unit = { val paramTable : Table = module.module.getParametersTable if (paramTable != null && paramTable.contains(module.module.getName)) { val modulePramTable : Table = paramTable(module.module.getName) @@ -312,29 +353,14 @@ trait ModuleSerializable extends Loadable with Savable{ */ protected def copyFromBigDL[T: ClassTag](context : SerializeContext[T], modelBuilder : BigDLModule.Builder)(implicit ev : TensorNumeric[T]) : Unit = { - val module = context.moduleData - val paramTable : Table = module.module.getParametersTable - if (paramTable != null && paramTable.contains(module.module.getName)) { - val modulePramTable: Table = paramTable(module.module.getName) - val weight: Tensor[T] = if (modulePramTable.contains("weight")) { - modulePramTable("weight") - } - else null - val bias: Tensor[T] = if (modulePramTable.contains("bias")) { - modulePramTable("bias") - } - else null - val storageType = context.storageType - if (weight != null) { - val weightAttr = AttrValue.newBuilder - TensorConverter.setAttributeValue(context, weightAttr, weight) - modelBuilder.setWeight(weightAttr.getTensorValue) - } - if (bias != null) { - val biasAttr = AttrValue.newBuilder - TensorConverter.setAttributeValue(context, biasAttr, bias) - modelBuilder.setBias(biasAttr.getTensorValue) - } + val parameters = context.moduleData.module.parameters + if (parameters != null && parameters._1 != null) { + modelBuilder.setHasParameters(true) + parameters._1.foreach(parameter => { + val tensorAttr = AttrValue.newBuilder + TensorConverter.setAttributeValue(context, tensorAttr, parameter) + modelBuilder.addParameters(tensorAttr.getTensorValue) + }) } } From 06a7340fb6cd88c4e4a2fff7edec38993eccb546 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Sat, 14 Apr 2018 22:21:13 +0800 Subject: [PATCH 0771/1065] support group serializer (#2492) --- .../utils/serializer/ModuleSerializable.scala | 15 ++- .../utils/serializer/ModuleSerializer.scala | 97 +++++++++++++++---- .../bigdl/dllib/utils/serializer/Types.scala | 4 +- .../utils/serializer/SerializerSpec.scala | 80 ++++++++++++++- 4 files changed, 169 insertions(+), 27 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index a26f004c4e7..56bb2ea1686 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -175,14 +175,23 @@ trait ModuleSerializable extends Loadable with Savable{ // step 2: set module type bigDLModelBuilder.setModuleType(cls.getName) + // step 3 : set group information + + if (context.groupType != null) { + val groupTypeAttrValue = AttrValue.newBuilder + DataConverter.setAttributeValue[T](context, groupTypeAttrValue, + context.groupType, universe.typeOf[String]) + bigDLModelBuilder.putAttr(SerConst.GROUP_TYPE, groupTypeAttrValue.build) + } + getLock.synchronized { - // step 3 : set data types (ClassTag and TensorNumric) + // step 4 : set data types (ClassTag and TensorNumric) setDataTypes(context, bigDLModelBuilder) - // step 4 : apply module specific logic to create module + // step 5 : apply module specific logic to create module doSerializeModule(context, bigDLModelBuilder) } - // step 5 : copy params (weight & bias) a and linkage + // step 6 : copy params (weight & bias) a and linkage createSerializeBigDLModule(bigDLModelBuilder, context) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 12c9041b32c..a4646e252f1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -20,10 +20,11 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer, Model, Sequential => KSequential} import com.intel.analytics.bigdl.nn.ops.{RandomUniform => RandomUniformOps} -import com.intel.analytics.bigdl.nn.tf.{StrideSlice, ParseExample, DecodeRawSerializer} +import com.intel.analytics.bigdl.nn.tf.{DecodeRawSerializer, ParseExample, StrideSlice} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import scala.collection.mutable import scala.language.existentials @@ -34,7 +35,13 @@ object ModuleSerializer extends ModuleSerializable{ private val runtimeMirror = universe.runtimeMirror(getClass.getClassLoader) - private val serializerMaps = new mutable.HashMap[String, ModuleSerializable]() + private val serializerMaps = new mutable.HashMap[String, ModuleSerializable] + + // group serializer for one serializer to handle multiple layers of the same super type + + // super type class to serializer + + private val groupSerializerMaps = new mutable.HashMap[String, ModuleSerializable]() private[serializer] val _lock = new Object @@ -62,22 +69,45 @@ object ModuleSerializer extends ModuleSerializable{ val module = serializerContext.moduleData.module // For those layers which have their own serialization/deserialization methods val clsName = module.getClass.getName - val serializer = if (serializerMaps.contains(clsName)) { - serializerMaps(clsName) + val (serializer, serContext) = if (serializerMaps.contains(clsName)) { + (serializerMaps(clsName), serializerContext) } else { + // if no layer specific implementation, check if serializer of the same type exists + val (groupSerializer, group) = findGroupSerializer(serializerContext.moduleData.module) + if (groupSerializer != null) { + val context = SerializeContext[T](serializerContext.moduleData, + serializerContext.storages, serializerContext.storageType, + serializerContext.copyWeightAndBias, group) + (groupSerializer, context) + } else { val m = module.asInstanceOf[AbstractModule[_, _, _]] m match { case kerasLayer: KerasLayer[_, _, _] => - KerasLayerSerializer - case container : Container[_, _, _] => - ContainerSerializer - case cell : Cell[_] => - CellSerializer - case _ => ModuleSerializer + (KerasLayerSerializer, serializerContext) + case container: Container[_, _, _] => + (ContainerSerializer, serializerContext) + case cell: Cell[_] => + (CellSerializer, serializerContext) + case _ => (ModuleSerializer, serializerContext) + } + } + } + serializer.setCopyWeightAndBias(serContext.copyWeightAndBias). + serializeModule(serContext) + } + + private def findGroupSerializer[T: ClassTag](module : Module[T]) + (implicit ev: TensorNumeric[T]): (ModuleSerializable, String) = { + var cls : Class[_] = module.getClass.getSuperclass + var clsName = cls.getName + while (clsName != "java.lang.Object") { + if (groupSerializerMaps.contains(clsName)) { + return (groupSerializerMaps(clsName), clsName) } + cls = cls.getSuperclass + clsName = cls.getName } - serializer.setCopyWeightAndBias(serializerContext.copyWeightAndBias). - serializeModule(serializerContext) + (null, null) } /** @@ -93,16 +123,25 @@ object ModuleSerializer extends ModuleSerializable{ serializerMaps(model.getModuleType) } else { val attrMap = model.getAttrMap - val subModuleCount = model.getSubModulesCount - if (subModuleCount > 0) { - ContainerSerializer + if (attrMap.containsKey(SerConst.GROUP_TYPE)) { + val groupTypeAttr = attrMap.get(SerConst.GROUP_TYPE) + val groupType = DataConverter.getAttributeValue(context, groupTypeAttr). + asInstanceOf[String] + require(groupSerializerMaps.contains(groupType), s" Group serializer does" + + s" not exist for $groupType") + groupSerializerMaps(groupType) } else { - if (attrMap.containsKey("is_cell_module")) { - CellSerializer - } else if (attrMap.containsKey("is_keras_module")) { - KerasLayerSerializer + val subModuleCount = model.getSubModulesCount + if (subModuleCount > 0) { + ContainerSerializer } else { - ModuleSerializer + if (attrMap.containsKey("is_cell_module")) { + CellSerializer + } else if (attrMap.containsKey("is_keras_module")) { + KerasLayerSerializer + } else { + ModuleSerializer + } } } } @@ -115,7 +154,6 @@ object ModuleSerializer extends ModuleSerializable{ } } - /** * register module for single module, used for standard BigDL module and user defined module * @param moduleType,must be unique @@ -123,9 +161,26 @@ object ModuleSerializer extends ModuleSerializable{ */ def registerModule(moduleType : String, serializer : ModuleSerializable) : Unit = { require(!serializerMaps.contains(moduleType), s"$moduleType already registered!") + require(!groupSerializerMaps.contains(moduleType), s"$moduleType already " + + s"registered with group serializer!") serializerMaps(moduleType) = serializer } + /** + * register module for modules of the same type, used for + * standard BigDL module and user defined module + * @param superModuleType,must be unique + * @param groupSerializer serialzable implementation for this module + */ + def registerGroupModules(superModuleType : String, groupSerializer : + ModuleSerializable) : Unit = { + require(!serializerMaps.contains(superModuleType), s"$moduleType already " + + s"registered with single serializer!") + require(!groupSerializerMaps.contains(superModuleType), s"$moduleType already " + + s"registered with group serializer!") + groupSerializerMaps(superModuleType) = groupSerializer + } + private[serializer] def getCostructorMirror[T : ClassTag](cls : Class[_]): universe.MethodMirror = { getLock.synchronized { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala index 2f24bb27f20..d92baeffbd7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/Types.scala @@ -34,7 +34,8 @@ object BigDLStorage extends StorageType case class SerializeContext[T: ClassTag](moduleData: ModuleData[T], storages: mutable.HashMap[Int, Any], storageType: StorageType, - copyWeightAndBias : Boolean = true) + copyWeightAndBias : Boolean = true, + groupType : String = null) case class DeserializeContext(bigdlModule : BigDLModule, storages: mutable.HashMap[Int, Any], storageType: StorageType, @@ -56,6 +57,7 @@ object SerConst { val GLOBAL_STORAGE = "global_storage" val MODULE_TAGES = "module_tags" val MODULE_NUMERICS = "module_numerics" + val GROUP_TYPE = "group_type" } object ClassTagMapper { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 57e311518d0..563538e9a3d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -15,16 +15,25 @@ */ package com.intel.analytics.bigdl.utils.serializer +import java.io.File import java.lang.reflect.Modifier -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{Tensor, TensorNumericMath} import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import org.reflections.Reflections import org.reflections.scanners.SubTypesScanner import org.reflections.util.{ClasspathHelper, ConfigurationBuilder, FilterBuilder} import collection.JavaConverters._ import scala.collection.mutable +import scala.reflect.ClassTag +import scala.reflect.runtime.universe class SerializerSpec extends BigDLSpecHelper { @@ -33,7 +42,9 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.tf.ControlDependency", "com.intel.analytics.bigdl.utils.tf.AdapterForTest", "com.intel.analytics.bigdl.utils.serializer.TestModule", - "com.intel.analytics.bigdl.utils.ExceptionTest" + "com.intel.analytics.bigdl.utils.ExceptionTest", + "com.intel.analytics.bigdl.utils.serializer.SubModuleOne", + "com.intel.analytics.bigdl.utils.serializer.SubModuleTwo" ) // Maybe one serial test class contains multiple module test @@ -267,6 +278,71 @@ class SerializerSpec extends BigDLSpecHelper { } } }) + + "Group serializer" should "work properly" in { + ModuleSerializer. + registerGroupModules("com.intel.analytics.bigdl.utils.serializer.ParentModule", + ParentModuleSerializer) + val subOne = new SubModuleOne[Float]() + val subTwo = new SubModuleTwo[Float]() + val serFileOne = File.createTempFile("SubOne", "bigdl") + val serFileTwo = File.createTempFile("SubTwo", "bigdl") + subOne.saveModule(serFileOne.getAbsolutePath, overWrite = true) + subTwo.saveModule(serFileTwo.getAbsolutePath, overWrite = true) + + val loadedOne = Module.loadModule[Float](serFileOne.getAbsolutePath). + asInstanceOf[SubModuleOne[Float]] + + val loadedTwo = Module.loadModule[Float](serFileTwo.getAbsolutePath). + asInstanceOf[SubModuleTwo[Float]] + + loadedOne.value should be ("test_value") + + loadedTwo.value should be ("test_value") + } +} + +abstract class ParentModule[T: ClassTag](implicit ev: TensorNumeric[T]) extends + AbstractModule[Tensor[T], Tensor[T], T] { + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + null + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + null + } + + var value : String = null +} + +class SubModuleOne[T: ClassTag](implicit ev: TensorNumeric[T]) extends ParentModule[T] { + +} + +class SubModuleTwo[T: ClassTag](implicit ev: TensorNumeric[T]) extends ParentModule[T] { + +} + +object ParentModuleSerializer extends ModuleSerializable { + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + bigDLModelBuilder: BigDLModule.Builder)(implicit ev: TensorNumericMath.TensorNumeric[T]): Unit = { + val groupTypeAttrValue = AttrValue.newBuilder + DataConverter.setAttributeValue[T](context, groupTypeAttrValue, + "test_value", universe.typeOf[String]) + bigDLModelBuilder.putAttr("groupValue", groupTypeAttrValue.build) + } + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumericMath.TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + val module = super.doLoadModule(context).asInstanceOf[ParentModule[T]] + val attrMap = context.bigdlModule.getAttrMap + val valueAttr = attrMap.get("groupValue") + val value = DataConverter.getAttributeValue(context, valueAttr). + asInstanceOf[String] + module.value = value + module + } } private[bigdl] abstract class ModuleSerializationTest extends SerializerSpecHelper { From 1961d08f20307268f642411b9a69189bf53b93f7 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Thu, 26 Apr 2018 13:06:41 +0800 Subject: [PATCH 0772/1065] [bug fix]refine getTimes and time counting. (#2506) * refine getTimes * delete some useless code * sort times * add unit test --- .../bigdl/dllib/nn/BiRecurrent.scala | 9 +- .../analytics/bigdl/dllib/nn/Bottle.scala | 4 +- .../intel/analytics/bigdl/dllib/nn/Cell.scala | 71 ++------------ .../analytics/bigdl/dllib/nn/Concat.scala | 18 +--- .../bigdl/dllib/nn/ConcatTable.scala | 2 + .../analytics/bigdl/dllib/nn/Container.scala | 11 ++- .../bigdl/dllib/nn/DynamicGraph.scala | 2 +- .../analytics/bigdl/dllib/nn/Euclidean.scala | 2 +- .../analytics/bigdl/dllib/nn/Graph.scala | 6 -- .../analytics/bigdl/dllib/nn/MapTable.scala | 2 +- .../analytics/bigdl/dllib/nn/Maxout.scala | 2 +- .../bigdl/dllib/nn/MultiRNNCell.scala | 2 + .../bigdl/dllib/nn/ParallelTable.scala | 2 + .../analytics/bigdl/dllib/nn/Recurrent.scala | 63 ++++-------- .../bigdl/dllib/nn/RecurrentDecoder.scala | 6 +- .../analytics/bigdl/dllib/nn/Sequential.scala | 2 + .../nn/SpatialDivisiveNormalization.scala | 12 +-- .../nn/SpatialSeparableConvolution.scala | 2 + .../nn/SpatialSubtractiveNormalization.scala | 10 +- .../bigdl/dllib/nn/StaticGraph.scala | 2 +- .../analytics/bigdl/dllib/nn/TanhShrink.scala | 2 +- .../bigdl/dllib/nn/TimeDistributed.scala | 13 +-- .../analytics/bigdl/dllib/nn/Utils.scala | 13 ++- .../dllib/nn/abstractnn/AbstractModule.scala | 14 +++ .../bigdl/dllib/models/ResNetSpec.scala | 25 +++++ .../bigdl/dllib/nn/RecurrentDecoderSpec.scala | 73 ++++++++++++++ .../bigdl/dllib/nn/RecurrentSpec.scala | 97 ++++--------------- 27 files changed, 224 insertions(+), 243 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala index 45c298aa8c6..5385fd7ffa6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala @@ -73,7 +73,7 @@ class BiRecurrent[T : ClassTag] ( } override def updateOutput(input: Tensor[T]): Tensor[T] = { - output = birnn.updateOutput(input).toTensor[T] + output = birnn.forward(input).toTensor[T] output } @@ -86,6 +86,13 @@ class BiRecurrent[T : ClassTag] ( gradInput } + override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val before = System.nanoTime() + gradInput = birnn.backward(input, gradOutput).toTensor[T] + backwardTime += System.nanoTime() - before + gradInput + } + /** * This function returns two arrays. One for the weights and the other the gradients * Custom modules should override this function if they have parameters diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bottle.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bottle.scala index 3aa5fb13472..f0e93f99eb8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bottle.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Bottle.scala @@ -64,7 +64,7 @@ class Bottle[T: ClassTag]( // Forward with the module's dimension val newInput = input.view(inShape.storage().array().map(_.toInt)) - val output1 = modules(0).updateOutput(newInput).toTensor[T] + val output1 = modules(0).forward(newInput).toTensor[T] require(output1.dim() == nOutputDim, s"Bottle: output dims on module should be $nOutputDim, but get ${output1.dim()}") @@ -76,7 +76,7 @@ class Bottle[T: ClassTag]( output.set(output1.view(inSize.storage().array().map(_.toInt))) } else { - output.set(modules(0).updateOutput(input).toTensor[T]) + output.set(modules(0).forward(input).toTensor[T]) } output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala index 8151990a984..1909a3a4a97 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Cell.scala @@ -52,9 +52,6 @@ abstract class Cell[T : ClassTag]( extends AbstractModule[Table, Table, T] { var subModules: Array[AbstractModule[_ <: Activity, _ <: Activity, T]] = null - var forwardTimes: Array[Long] = null - var backwardTimes: Array[Long] = null - var times: Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = null /** * Any recurrent kernels should have a cell member variable which @@ -141,7 +138,7 @@ abstract class Cell[T : ClassTag]( if (includePreTopology) { assert(preTopology != null, "preTopology cannot be null if includePreTopology is true") val inputTensor = input.toTable[Tensor[T]](Recurrent.inputDim) - input(Recurrent.inputDim) = preTopology.updateOutput(inputTensor) + input(Recurrent.inputDim) = preTopology.forward(inputTensor) output = cell.forward(input).toTable input(Recurrent.inputDim) = inputTensor } else output = cell.forward(input).toTable @@ -176,6 +173,7 @@ abstract class Cell[T : ClassTag]( } override def backward(input: Table, gradOutput: Table): Table = { + val before = System.nanoTime() if (includePreTopology) { val inputTensor = input.toTable[Tensor[T]](Recurrent.inputDim) input(Recurrent.inputDim) = preTopology.output @@ -186,76 +184,19 @@ abstract class Cell[T : ClassTag]( } else { gradInput = cell.backward(input, gradOutput).toTable } + backwardTime += System.nanoTime() - before gradInput } - private def initAddTimes(): Unit = { - val cellTimes = cell.getTimes - if (subModules == null || subModules.length < cellTimes.length) { - subModules = new Array[AbstractModule[_ <: Activity, _ <: Activity, T]](cellTimes.length) - var i = 0 - while (i < cellTimes.length) { - subModules(i) = cellTimes(i)._1 - i += 1 - } - forwardTimes = new Array[Long](cellTimes.length) - backwardTimes = new Array[Long](cellTimes.length) - times = - new Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)](cellTimes.length) - } - } - - private def resetAddTimes(): Unit = { - if (subModules != null) { - var i = 0 - while (i < subModules.length) { - forwardTimes(i) = 0L - backwardTimes(i) = 0L - i += 1 - } - } - } - - def addTimes(other: Cell[T]): Unit = { - val cellTimes = cell.getTimes - val otherTimes = other.getTimes - require(cellTimes.length == otherTimes.length, - " Cell -> CellTimes: cell.getTimes.length does not comform to other.getTimes.length." + - s" cell.getTimes.length = ${cellTimes.length}, " + - s"other.getTimes.length = ${otherTimes.length}") - - val length = cellTimes.length - initAddTimes() - var i = 0 - while (i < length) { - val subModule = otherTimes(i)._1.getClass.getName - require(subModules(i).getClass.getName == subModule, - s"Cell -> CellTimes: ${i}-th submodule in cell" + - s" does not comform to ${i}-th submodule in other." + - s" ${i}-th cell module is ${subModules(i)}," + - s" ${i}-th other module is ${otherTimes(i)._1}") - forwardTimes(i) += otherTimes(i)._2 - backwardTimes(i) += otherTimes(i)._3 - i += 1 - } - } - override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { - initAddTimes() val cellTimes = cell.getTimes - var i = 0 - while (i < cellTimes.length) { - times(i) = (subModules(i), - forwardTimes(i) + cellTimes(i)._2, - backwardTimes(i) + cellTimes(i)._3) - i += 1 - } - times + val (cellFwdTime, cellBwdTime) = Utils.calculateFwdBwdTime(cellTimes) + cellTimes ++ Array((this, forwardTime - cellFwdTime, backwardTime - cellBwdTime)) } override def resetTimes(): Unit = { - resetAddTimes() + super.resetTimes() cell.resetTimes } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala index d4f85133c9e..05404407559 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Concat.scala @@ -50,14 +50,12 @@ class Concat[T: ClassTag](val dimension: Int)( @transient private var gradouts: Array[Tensor[T]] = null - protected var forwardTimeOverhead = 0L - override def updateOutput(input: Tensor[T]): Tensor[T] = { val outs = new Array[Tensor[T]](this.modules.length) var i = 0 while (i < this.modules.length) { val currentOutput = this.modules(i) - .updateOutput(input.asInstanceOf[Activity]) + .forward(input.asInstanceOf[Activity]) .asInstanceOf[Tensor[T]] outs(i) = currentOutput.asInstanceOf[Tensor[T]] @@ -81,7 +79,6 @@ class Concat[T: ClassTag](val dimension: Int)( } i += 1 } - val before = System.nanoTime() this.output.resize(this.size) if (results == null || results.length != this.modules.length) { results = new Array[Future[Unit]](this.modules.length) @@ -117,16 +114,10 @@ class Concat[T: ClassTag](val dimension: Int)( } Engine.model.sync(results) - forwardTimeOverhead += System.nanoTime() - before this.output } - override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { - this.modules.flatMap(_.getTimes()).toArray ++ - Array((this, forwardTimeOverhead, backwardTime)) - } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { var before = System.nanoTime() this.gradInput.resizeAs(input) @@ -199,7 +190,7 @@ class Concat[T: ClassTag](val dimension: Int)( } override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - var before = System.nanoTime() + val before = System.nanoTime() this.gradInput.resizeAs(input) var offset = 1 if (gradouts == null || gradouts.length != this.modules.length) { @@ -229,7 +220,6 @@ class Concat[T: ClassTag](val dimension: Int)( offset += currentOutput.size(dimension) } Engine.model.sync(results) - backwardTime += System.nanoTime() - before i = 0 offset = 1 @@ -239,7 +229,6 @@ class Concat[T: ClassTag](val dimension: Int)( .backward(input.asInstanceOf[Activity], gradouts(i).asInstanceOf[Activity]) .asInstanceOf[Tensor[T]] - before = System.nanoTime() if (currentGradInput != null) { if (i == 0) { require(this.gradInput.isContiguous()) @@ -251,8 +240,8 @@ class Concat[T: ClassTag](val dimension: Int)( } i += 1 offset += currentOutput.size(dimension) - backwardTime += System.nanoTime() - before } + backwardTime += System.nanoTime() - before this.gradInput } @@ -325,7 +314,6 @@ class Concat[T: ClassTag](val dimension: Int)( } override def resetTimes(): Unit = { - forwardTimeOverhead = 0 forwardTime = 0 backwardTime = 0 } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala index ebe0717660f..71856b6f2fd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala @@ -159,6 +159,7 @@ class ConcatTable[T : ClassTag] } override def backward(input: Activity, gradOutput: Table): Activity = { + val before = System.nanoTime() require(modules.length > 0, "empty modules of concat table") val isInputTable = input.isInstanceOf[Table] val wasGradInputTable = gradInput.isInstanceOf[Table] @@ -202,6 +203,7 @@ class ConcatTable[T : ClassTag] i += 1 } } + backwardTime += System.nanoTime() - before gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index 107d10260c9..e48144a53dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -68,10 +68,19 @@ abstract class Container[A <: Activity : ClassTag, override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { - this.modules.flatMap(_.getTimes()).toArray + if (modules.isEmpty) { + return Array((this, forwardTime, backwardTime)) + } + val subModuleTimes = this.modules.flatMap(_.getTimes()).toArray + + val (subModuleForward, subModuleBackward) = Utils.calculateFwdBwdTime(subModuleTimes) + + subModuleTimes ++ Array((this, this.forwardTime - subModuleForward, + this.backwardTime - subModuleBackward)) } override def resetTimes(): Unit = { + super.resetTimes() modules.foreach(_.resetTimes()) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala index ade4cf2ffd7..6cf57ff1294 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraph.scala @@ -62,7 +62,7 @@ private[bigdl] class DynamicGraph[T: ClassTag]( override def backward(input: Activity, gradOutput: Activity): Activity = { val before = System.nanoTime() val result = backwardExecution(input, gradOutput, true) - backwardTime = System.nanoTime() - before + backwardTime += System.nanoTime() - before result } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Euclidean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Euclidean.scala index 9362eff5a14..30676931b9f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Euclidean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Euclidean.scala @@ -104,7 +104,7 @@ class Euclidean[T: ClassTag](val inputSize: Int, val outputSize: Int, s"input dim ${input.dim()}") if (!fastBackward) { - updateOutput(input) + forward(input) } // to prevent div by zero (NaN) bugs outputBuffer.resizeAs(output).copy(output).add(ev.fromType(0.0000001)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index f9bb3987d65..f11d1267bd2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -114,12 +114,6 @@ abstract class Graph[T: ClassTag]( timesOfAllNodes ++ Array((this, this.forwardTime - sumForward, this.backwardTime - sumBackward)) } - override def resetTimes(): Unit = { - super.resetTimes() - this.forwardTime = 0L - this.backwardTime = 0L - } - override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { variables match { case None => super.parameters() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala index c00f42e22f3..ff2668717c7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MapTable.scala @@ -73,7 +73,7 @@ class MapTable[T: ClassTag]( extend(input.length()) var i = 0 while (i < input.length()) { - output.update(i + 1, modules(i).updateOutput(input(i + 1))) + output.update(i + 1, modules(i).forward(input(i + 1))) i += 1 } output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala index 4e9520e64f8..01478cfabe8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Maxout.scala @@ -62,7 +62,7 @@ class Maxout[T: ClassTag](val inputSize: Int, val outputSize: Int, val maxoutNum } override def updateOutput(input: Tensor[T]): Tensor[T] = { - output = layer.updateOutput(input).toTensor + output = layer.forward(input).toTensor output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala index 2e2490b9157..d066600b462 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MultiRNNCell.scala @@ -139,6 +139,7 @@ class MultiRNNCell[T : ClassTag](val cells: Array[Cell[T]])(implicit ev: TensorN } override def backward(input: Table, gradOutput: Table): Table = { + val before = System.nanoTime() var i = cells.length - 1 var error = T() error(inputDim) = gradOutput(inputDim) @@ -162,6 +163,7 @@ class MultiRNNCell[T : ClassTag](val cells: Array[Cell[T]])(implicit ev: TensorN this.gradInput = error gradInput(hidDim) = outputGradStates + backwardTime += System.nanoTime() - before gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala index 4636fedfecc..ee0287c025b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ParallelTable.scala @@ -59,11 +59,13 @@ class ParallelTable[T: ClassTag] } override def backward(input: Table, gradOutput: Table): Table = { + val before = System.nanoTime() var i = 0 while (i < input.length()) { gradInput.update(i + 1, modules(i).backward(input(i + 1), gradOutput(i + 1))) i += 1 } + backwardTime += System.nanoTime() - before gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index 61c69b1ff99..aab7f82a829 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -69,8 +69,6 @@ class Recurrent[T : ClassTag]( protected var preTopology: AbstractModule[Activity, Activity, T] = null private val dropouts: ArrayBuffer[Array[Dropout[T]]] = new ArrayBuffer[Array[Dropout[T]]] - private val timeBuffer = - new ArrayBuffer[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] private var layer: TensorModule[T] = null private var maskBuffer: Tensor[T] = Tensor() private var gradOutputBuff: Table = T() @@ -444,7 +442,7 @@ class Recurrent[T : ClassTag]( } override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - val st = System.nanoTime + val before = System.nanoTime currentGradOutput(hidDim) = gradHidden var i = times @@ -514,64 +512,38 @@ class Recurrent[T : ClassTag]( gradInput = preTopology.backward(input, gradInput2Cell).toTensor[T] } - this.backwardTime = System.nanoTime - st + this.backwardTime += System.nanoTime - before gradInput } - private def appendTimes(module: Module[T]): Unit = { - if (module != null) { - module.getTimes.foreach(x => { - timeBuffer.append(x) - }) - } - } - - private def bufferTime(): (Long, Long) = { - var forwardSum = 0L - var backwardSum = 0L - timeBuffer.foreach(x => { - forwardSum += x._2 - backwardSum += x._3 - }) - (forwardSum, backwardSum) - } - - override def getTimes(): - Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { - timeBuffer.clear - - val head = if (!cells.isEmpty) { - cells.head - } else null + override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { + val timeBuffer = + new ArrayBuffer[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] - var i = 1 - while (i < times) { - head.addTimes(cells(i)) - i += 1 + if (!cells.isEmpty) { + timeBuffer.append( + cells.flatMap(_.getTimes()).reduce((a, b) => (a._1, a._2 + b._2, a._3 + b._3))) } - appendTimes(preTopology) - appendTimes(head) + if (preTopology != null) { + timeBuffer.appendAll(preTopology.getTimes()) + } - val (bufferForward, bufferBackward) = bufferTime() + val (bufferForward, bufferBackward) = + timeBuffer.map(t => (t._2, t._3)).reduce((a, b) => (a._1 + b._1, a._2 + b._2)) timeBuffer.append( (this, - this.forwardTime - bufferForward, - this.backwardTime - bufferBackward)) + forwardTime - bufferForward, + backwardTime - bufferBackward)) timeBuffer.toArray } override def resetTimes(): Unit = { + super.resetTimes() if (preTopology != null) { preTopology.resetTimes } - var i = 0 - while (i < times) { - cells(i).resetTimes - i += 1 - } - this.forwardTime = 0 - this.backwardTime = 0 + cells.foreach(_.resetTimes()) } override def clearState() : this.type = { @@ -586,7 +558,6 @@ class Recurrent[T : ClassTag]( _input.clear() cells.foreach(x => x.clearState()) cells.clear() - timeBuffer.clear() initHiddenState = null stepInput2CellBuf.set() stepGradBuffer.set() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala index 91240311dbe..76b24510bbb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoder.scala @@ -109,7 +109,7 @@ class RecurrentDecoder[T : ClassTag](val seqLength: Int) // input at t(i) is output at t(i-1) cells(i - 2).output } - cells(i - 1).updateOutput(currentInput) + cells(i - 1).forward(currentInput) i += 1 } @@ -170,7 +170,7 @@ class RecurrentDecoder[T : ClassTag](val seqLength: Int) } override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - val st = System.nanoTime + val before = System.nanoTime gradInput.resizeAs(output) currentGradOutput(hidDim) = gradHidden var i = times @@ -197,7 +197,7 @@ class RecurrentDecoder[T : ClassTag](val seqLength: Int) i -= 1 } Recurrent.copy(cells.map(x => x.gradInput.toTable[Tensor[T]](inputDim)), gradInput) - this.backwardTime = System.nanoTime - st + this.backwardTime += System.nanoTime - before gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala index f551a49c2ea..01d128b7321 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala @@ -75,6 +75,7 @@ class Sequential[T: ClassTag] } override def backward(input: Activity, nextError: Activity): Activity = { + val before = System.nanoTime() var i = modules.length - 1 var error = nextError.asInstanceOf[Activity] while (i > 0) { @@ -85,6 +86,7 @@ class Sequential[T: ClassTag] error = modules(0).backward(input, error) this.gradInput = error + backwardTime += System.nanoTime() - before gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala index 014344e6161..5c6fe2dd102 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialDivisiveNormalization.scala @@ -137,7 +137,7 @@ class SpatialDivisiveNormalization[T: ClassTag]( private var localstds: Tensor[T] = _ override def updateOutput(input: Tensor[T]): Tensor[T] = { - localstds = stdestimator.updateOutput(input).toTensor[T] + localstds = stdestimator.forward(input).toTensor[T] // compute side coefficients val dim = input.dim() @@ -146,18 +146,18 @@ class SpatialDivisiveNormalization[T: ClassTag]( if (dim == 4) { // batch mode ones.resizeAs(input(1)).fill(ev.fromType[Int](1)) - val _coef = meanestimator.updateOutput(ones).toTensor[T] + val _coef = meanestimator.forward(ones).toTensor[T] coef = coef.resizeAs(_coef).copy(_coef).view(Array(1) ++ _coef.size()).expandAs(localstds) } else { ones.resizeAs(input).fill(ev.fromType[Int](1)) - coef = meanestimator.updateOutput(ones).toTensor[T] + coef = meanestimator.forward(ones).toTensor[T] } } // normalize std dev - adjustedstds = divider.updateOutput(T(localstds, coef)).asInstanceOf[Tensor[T]] - thresholdedstds = thresholder.updateOutput(adjustedstds) - output = normalizer.updateOutput(T(input, thresholdedstds)).asInstanceOf[Tensor[T]] + adjustedstds = divider.forward(T(localstds, coef)).asInstanceOf[Tensor[T]] + thresholdedstds = thresholder.forward(adjustedstds) + output = normalizer.forward(T(input, thresholdedstds)).asInstanceOf[Tensor[T]] output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolution.scala index 7d898e004e7..fc2bdbd5c68 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSeparableConvolution.scala @@ -161,6 +161,7 @@ class SpatialSeparableConvolution[T: ClassTag]( } override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val before = System.nanoTime() require(input.nDimension() == 4, "SpatialSeparableConvolution only accept 4D input") require(input.isContiguous(), "SpatialSeparableConvolution require contiguous input") require(nInputChannel == input.size(channelDim), @@ -175,6 +176,7 @@ class SpatialSeparableConvolution[T: ClassTag]( gradInput = depthConv.backward(input, pointWiseConv2D.gradInput) SpatialSeparableConvolution.copyDepthGradWeight(nInputChannel, depthMultiplier, depthConv.gradWeight, depthGradWeight, dataFormat) + backwardTime += System.nanoTime() - before gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala index 7058a97cc6f..760721b8d62 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialSubtractiveNormalization.scala @@ -116,20 +116,20 @@ class SpatialSubtractiveNormalization[T: ClassTag]( if (dim == 4) { // batch mode ones.resizeAs(input(1)).fill(ev.fromType[Int](1)) - val _coef = meanestimator.updateOutput(ones).toTensor[T] + val _coef = meanestimator.forward(ones).toTensor[T] val size = Array(input.size(1)) ++ _coef.size() coef = coef.resizeAs(_coef).copy(_coef).view(Array(1) ++ _coef.size()).expand(size) } else { ones.resizeAs(input).fill(ev.fromType[Int](1)) - val _coef = meanestimator.updateOutput(ones).toTensor[T] + val _coef = meanestimator.forward(ones).toTensor[T] coef.resizeAs(_coef).copy(_coef) } } // compute mean - localsums = meanestimator.updateOutput(input).toTensor[T] - adjustedsums = divider.updateOutput(T(localsums, coef)).asInstanceOf[Tensor[T]] - output = subtractor.updateOutput(T(input, adjustedsums)).asInstanceOf[Tensor[T]] + localsums = meanestimator.forward(input).toTensor[T] + adjustedsums = divider.forward(T(localsums, coef)).asInstanceOf[Tensor[T]] + output = subtractor.forward(T(input, adjustedsums)).asInstanceOf[Tensor[T]] output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala index e9cd7df8372..0faecee6f81 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -67,7 +67,7 @@ class StaticGraph[T: ClassTag]( override def backward(input: Activity, gradOutput: Activity): Activity = { val before = System.nanoTime() val gradients = backwardExecution(input, gradOutput, true) - backwardTime = System.nanoTime() - before + backwardTime += System.nanoTime() - before gradients } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TanhShrink.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TanhShrink.scala index 23bce0737b6..d7ef9d341a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TanhShrink.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TanhShrink.scala @@ -33,7 +33,7 @@ class TanhShrink[T: ClassTag]( private val tanh = new Tanh[T]() override def updateOutput(input: Tensor[T]): Tensor[T] = { - val th = tanh.updateOutput(input) + val th = tanh.forward(input) output.resizeAs(input).copy(input) output.add(ev.fromType[Int](-1), th) output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala index 4822bb45850..6bd7a582f27 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala @@ -50,8 +50,6 @@ class TimeDistributed[T : ClassTag] ( private var inputSize: Array[Int] = _ private var gradOutputSize: Array[Int] = _ private var outputSize: Array[Int] = _ - private val timeBuffer = - new ArrayBuffer[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] private var maskBuffer: Tensor[T] = _ private var indexBuffer: Tensor[T] = _ private var inputBuffer: Tensor[T] = _ @@ -163,7 +161,7 @@ class TimeDistributed[T : ClassTag] ( } override def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { - val st = System.nanoTime + val before = System.nanoTime if (gradOutputSize == null) { gradOutputSize = new Array[Int](gradOutput.size.length - 1) } @@ -177,7 +175,6 @@ class TimeDistributed[T : ClassTag] ( gradInput.set(_gradInput).resize(_inputSize) input.resize(_inputSize) gradOutput.resize(_gradOutputSize) - backwardTime += System.nanoTime - st if (maskZero) { for (i <- 1 to maskBuffer.size(1)) { @@ -188,6 +185,7 @@ class TimeDistributed[T : ClassTag] ( } } } + backwardTime += System.nanoTime - before gradInput } @@ -208,13 +206,13 @@ class TimeDistributed[T : ClassTag] ( } override def resetTimes(): Unit = { + super.resetTimes() layer.resetTimes() - this.forwardTime = 0 - this.backwardTime = 0 } override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { - timeBuffer.clear + val timeBuffer = + new ArrayBuffer[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] var modulesForwardTime = 0L var modulesBackwardTime = 0L layer.getTimes.foreach(x => { @@ -257,7 +255,6 @@ class TimeDistributed[T : ClassTag] ( inputSize = null gradOutputSize = null outputSize = null - timeBuffer.clear maskBuffer = null inputBuffer = null indexBuffer = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index 793dba8fb12..ba2b9e2ffa0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.google.protobuf.ByteString import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} @@ -471,4 +471,15 @@ object Utils { out } + + /** + * Calculate forward time and backward time. + * @param times + * @tparam T + * @return + */ + def calculateFwdBwdTime[T: ClassTag]( + times: Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)]): (Long, Long) = { + times.map(t => (t._2, t._3)).reduce((a, b) => (a._1 + b._1, a._2 + b._2)) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 56f55c0ec40..391953ef9ea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -168,6 +168,20 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, Array((this, forwardTime, backwardTime)) } + /** + * Get the forward/backward cost time for the module or its submodules + * and group by module type. + * @return (module type name, forward time, backward time) + */ + final def getTimesGroupByModuleType(): + Array[(String, Long, Long)] = { + this.getTimes().map(v => (v._1.getClass().getName(), v._2, v._3)).groupBy(_._1) + .map(v => (v._1, v._2.reduce((a, b) => (v._1, a._2 + b._2, a._3 + b._3)))) + .map(v => (v._1, v._2._2, v._2._3)) + .toArray + .sortWith((a, b) => (a._2 + a._3) > (b._2 + b._3)) + } + /** * Reset the forward/backward record time for the module or its submodules * @return diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala index 7ec6d55268f..87cf5f8d790 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala @@ -352,22 +352,47 @@ class ResNetSpec extends TorchSpec { RNG.setSeed(1000) val graphModel = ResNet.graph(classNum, T("shortcutType" -> ShortcutType.B, "depth" -> depth, "dataset" -> DatasetType.ImageNet)) + var modelForwardTime = 0L + var modelBackwardTime = 0L + var graphForwardTime = 0L + var graphBackwardTime = 0L var output1: Tensor[Float] = null var output2: Tensor[Float] = null + var st = System.nanoTime() for (i <- 1 to 3) { output1 = model.forward(input).toTensor[Float] + } + modelForwardTime += System.nanoTime() - st + st = System.nanoTime() + for (i <- 1 to 3) { output2 = graphModel.forward(input).toTensor[Float] } + graphForwardTime += System.nanoTime() - st output1 should be(output2) var gradInput1: Tensor[Float] = null var gradInput2: Tensor[Float] = null + st = System.nanoTime() for (i <- 1 to 3) { gradInput1 = model.backward(input, gradOutput).toTensor[Float] + } + modelBackwardTime += System.nanoTime() - st + st = System.nanoTime() + for (i <- 1 to 3) { gradInput2 = graphModel.backward(input, gradOutput).toTensor[Float] } + graphBackwardTime += System.nanoTime() - st gradInput1 should be(gradInput2) + + val (modelF, modelB) = model.getTimes().map(v => (v._2, v._3)) + .reduce((a, b) => (a._1 + b._1, a._2 + b._2)) + val (graphF, graphB) = graphModel.getTimes().map(v => (v._2, v._3)) + .reduce((a, b) => (a._1 + b._1, a._2 + b._2)) + modelForwardTime should be (modelF +- modelF / 100) + modelBackwardTime should be (modelB +- modelB / 100) + graphForwardTime should be (graphF +- graphF / 100) + graphBackwardTime should be (graphB +- graphB / 100) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala index c293f36241e..34e744c3880 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentDecoderSpec.scala @@ -100,6 +100,79 @@ class RecurrentDecoderSpec extends FlatSpec with BeforeAndAfter with Matchers { }) } + "A LSTM " should "count time correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 7 + val inputSize = 7 + val seqLength = 5 + val seed = 100 + val batchSize = 4 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(LSTM(inputSize, hiddenSize)) + + var ft = 0L + var bt = 0L + (0 until 10).foreach { _ => + var st = System.nanoTime() + model.forward(input) + ft += System.nanoTime() - st + st = System.nanoTime() + model.backward(input, gradOutput) + bt += System.nanoTime() - st + } + + val times = model.getTimes() + val modelFt = times.map(v => v._2).sum + val modelBt = times.map(v => v._3).sum + modelFt should be (ft +- ft / 100) + modelBt should be (bt +- bt / 100) + } + + "A LSTM " should "reset time correctly" in { + import com.intel.analytics.bigdl.numeric.NumericDouble + val hiddenSize = 7 + val inputSize = 7 + val seqLength = 5 + val seed = 100 + val batchSize = 4 + + RNG.setSeed(seed) + val input = Tensor[Double](batchSize, inputSize).rand + val gradOutput = Tensor[Double](batchSize, seqLength, hiddenSize).rand + val rec = RecurrentDecoder(seqLength) + val model = rec + .add(LSTM(inputSize, hiddenSize)) + + (0 until 10).foreach { _ => + model.forward(input) + model.backward(input, gradOutput) + } + model.resetTimes() + val a = model.getTimes() + + var ft = 0L + var bt = 0L + (0 until 10).foreach { _ => + var st = System.nanoTime() + model.forward(input) + ft += System.nanoTime() - st + st = System.nanoTime() + model.backward(input, gradOutput) + bt += System.nanoTime() - st + } + + val times = model.getTimes() + val modelFt = times.map(v => v._2).sum + val modelBt = times.map(v => v._3).sum + modelFt should be (ft +- ft / 100) + modelBt should be (bt +- bt / 100) + } + "A LSTMPeepwhole " should "work with feedbackOutput correctly" in { import com.intel.analytics.bigdl.numeric.NumericDouble val hiddenSize = 3 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala index 3d33437e6d7..1dd4392f04f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RecurrentSpec.scala @@ -30,63 +30,6 @@ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial class RecurrentSpec extends FlatSpec with Matchers { - "A Cell class " should "call addTimes() correctly" in { - val hiddenSize = 5 - val inputSize = 5 - val outputSize = 5 - val batchSize = 5 - val time = 4 - val seed = 100 - RNG.setSeed(seed) - val rnnCell1 = RnnCell[Double](inputSize, hiddenSize, Tanh[Double]()) - val rnnCell2 = RnnCell[Double](inputSize, hiddenSize, Tanh[Double]()) - val rnnCell3 = RnnCell[Double](inputSize, hiddenSize, Tanh[Double]()) - val rnnCell4 = RnnCell[Double](inputSize, hiddenSize, Tanh[Double]()) - - val input = Tensor[Double](batchSize, inputSize).randn - val hidden = Tensor[Double](batchSize, hiddenSize).randn - val gradOutput = Tensor[Double](batchSize, outputSize).randn - val gradHidden = Tensor[Double](batchSize, outputSize).randn - - rnnCell1.forward(T(input, hidden)) - rnnCell1.backward(T(input, hidden), T(gradOutput, gradHidden)) - rnnCell2.forward(T(input, hidden)) - rnnCell2.backward(T(input, hidden), T(gradOutput, gradHidden)) - rnnCell3.forward(T(input, hidden)) - rnnCell3.backward(T(input, hidden), T(gradOutput, gradHidden)) - rnnCell4.forward(T(input, hidden)) - rnnCell4.backward(T(input, hidden), T(gradOutput, gradHidden)) - - val forwardSum = new Array[Long](6) - val backwardSum = new Array[Long](6) - - for (i <- 0 until 6) { - forwardSum(i) += rnnCell1.getTimes()(i)._2 - backwardSum(i) += rnnCell1.getTimes()(i)._3 - } - for (i <- 0 until 6) { - forwardSum(i) += rnnCell2.getTimes()(i)._2 - backwardSum(i) += rnnCell2.getTimes()(i)._3 - } - for (i <- 0 until 6) { - forwardSum(i) += rnnCell3.getTimes()(i)._2 - backwardSum(i) += rnnCell3.getTimes()(i)._3 - } - for (i <- 0 until 6) { - forwardSum(i) += rnnCell4.getTimes()(i)._2 - backwardSum(i) += rnnCell4.getTimes()(i)._3 - } - - rnnCell1.addTimes(rnnCell2) - rnnCell1.addTimes(rnnCell3) - rnnCell1.addTimes(rnnCell4) - - for (i <- 0 until 6) { - forwardSum(i) should be (rnnCell1.getTimes()(i)._2) - backwardSum(i) should be (rnnCell1.getTimes()(i)._3) - } - } - "Recurrent" should "outputs correct hiddens" in { val hiddenSize = 4 val batchSize = 3 @@ -138,7 +81,7 @@ class RecurrentSpec extends FlatSpec with Matchers { output.toTensor[Double].select(1, 2).select(1, seqLength).abs().max() should be (0) } - "A Recurrent" should " call getTimes correctly" in { + "A Recurrent" should "call getTimes correctly" in { val hiddenSize = 128 val inputSize = 1280 val outputSize = 128 @@ -150,7 +93,6 @@ class RecurrentSpec extends FlatSpec with Matchers { val model = Sequential[Double]() .add(Recurrent[Double]() - .add(LSTM[Double](inputSize, hiddenSize))) .add(Select(2, 1)) // .add(Linear[Double](hiddenSize, outputSize)) @@ -160,14 +102,9 @@ class RecurrentSpec extends FlatSpec with Matchers { model.clearState() - model.resetTimes - model.getTimes - for (i <- 1 to 10) { - model.resetTimes model.forward(input) model.backward(input, gradOutput) - model.getTimes() } model.resetTimes() @@ -188,15 +125,19 @@ class RecurrentSpec extends FlatSpec with Matchers { forwardSum += x._2 backwardSum += x._3 }) - println() println(s"forwardSum = ${forwardSum}") println(s"backwardSum = ${backwardSum}") - assert(abs((etaForward - forwardSum) / etaForward) < 0.1) - assert(abs((etaBackward - backwardSum) / etaBackward) < 0.1) + assert(abs((etaForward - forwardSum) / etaForward) < 0.01) + assert(abs((etaBackward - backwardSum) / etaBackward) < 0.01) + + val times = model.getTimesGroupByModuleType() + times.length should be (6) + times.map(_._2).sum should be (etaForward +- etaForward / 100) + times.map(_._3).sum should be (etaBackward +- etaBackward / 100) } - "A Recurrent with LSTMPeephole cell " should " add batchNormalization correctly" in { + "A Recurrent with LSTMPeephole cell" should "add batchNormalization correctly" in { val hiddenSize = 4 val inputSize = 5 val outputSize = 5 @@ -221,7 +162,7 @@ class RecurrentSpec extends FlatSpec with Matchers { println("add normalization") } - "A Recurrent with GRU cell " should " add batchNormalization correctly" in { + "A Recurrent with GRU cell" should "add batchNormalization correctly" in { val hiddenSize = 4 val inputSize = 5 val outputSize = 5 @@ -246,7 +187,7 @@ class RecurrentSpec extends FlatSpec with Matchers { println("add normalization") } - "A Recurrent with LSTM cell " should " add batchNormalization correctly" in { + "A Recurrent with LSTM cell" should "add batchNormalization correctly" in { val hiddenSize = 4 val inputSize = 5 val outputSize = 5 @@ -271,7 +212,7 @@ class RecurrentSpec extends FlatSpec with Matchers { println("add normalization") } - "A Recurrent with SimpleRNN cell " should " add batchNormalization correctly" in { + "A Recurrent with SimpleRNN cell" should "add batchNormalization correctly" in { val hiddenSize = 4 val inputSize = 5 val batchSize = 2 @@ -349,7 +290,7 @@ class RecurrentSpec extends FlatSpec with Matchers { Array(batchSize, time, inputSize))) } - "A Recurrent" should " converge when batchSize changes" in { + "A Recurrent" should "converge when batchSize changes" in { val hiddenSize = 4 val inputSize = 5 val outputSize = 5 @@ -410,7 +351,7 @@ class RecurrentSpec extends FlatSpec with Matchers { gradInput2 should be (gradInput2compare) } - "A Recurrent Language Model Module " should "converge" in { + "A Recurrent Language Model Module" should "converge" in { val hiddenSize = 4 val inputSize = 5 @@ -465,7 +406,7 @@ class RecurrentSpec extends FlatSpec with Matchers { labels.squeeze() should be (prediction.squeeze()) } - "A Recurrent Module " should "converge in batch mode" in { + "A Recurrent Module" should "converge in batch mode" in { val batchSize = 10 val nWords = 5 @@ -544,7 +485,7 @@ class RecurrentSpec extends FlatSpec with Matchers { labels.squeeze() should be (prediction.squeeze()) } - "A Recurrent Module " should "perform correct gradient check" in { + "A Recurrent Module" should "perform correct gradient check" in { val hiddenSize = 4 val inputSize = 5 @@ -637,7 +578,7 @@ class RecurrentSpec extends FlatSpec with Matchers { flag should be (false) } - "A Recurrent Module " should "work with get/set state " in { + "A Recurrent Module" should "work with get/set state " in { val hiddenSize = 4 val inputSize = 5 val outputSize = 5 @@ -666,7 +607,7 @@ class RecurrentSpec extends FlatSpec with Matchers { model.forward(input) } - "A Recurrent Module " should " work good with copy " in { + "A Recurrent Module" should "work good with copy " in { val input = Tensor[Float](3, 2, 6, 10).randn() val input1 = input.select(2, 1).clone() val input2 = input.select(2, 2).clone() @@ -685,7 +626,7 @@ class RecurrentSpec extends FlatSpec with Matchers { output2 should be (input) } - "A Recurrent Module " should " work after reset " in { + "A Recurrent Module" should "work after reset " in { val hiddenSize = 4 val inputSize = 5 val outputSize = 5 From b65df3658cbe74415fa90f64e3ae7d1c1f07ef20 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 2 May 2018 09:08:14 +0800 Subject: [PATCH 0773/1065] [New Feature] Add new operation Gather (#2510) * add opteration gather * add comments * gather support float indices * add gather unit test * some change * fix style check --- .../analytics/bigdl/dllib/nn/ops/Gather.scala | 94 +++++++++++++++++++ .../bigdl/dllib/utils/tf/loaders/Gather.scala | 45 +++++++++ .../bigdl/dllib/nn/ops/GatherSpec.scala | 79 ++++++++++++++++ .../dllib/utils/tf/loaders/GatherSpec.scala | 64 +++++++++++++ 4 files changed, 282 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Gather.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GatherSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala new file mode 100644 index 00000000000..f0ec3a6fc97 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala @@ -0,0 +1,94 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.{IntType, Tensor, TensorDataType} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Gather slices from first input tensor according to the second input tensor. + * Input should be two tensors, the first one is the tensor which to gather values; + * the second one is Index tensor. + */ +class Gather[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends Operation[Table, Tensor[D], T]{ + output = Tensor[D]() + + protected val intBuffer = Tensor[Int]() + + override def updateOutput(input: Table): Tensor[D] = { + val inputTensor = input[Tensor[D]](1) + val input2 = input[Tensor[_]](2) + // support floatType indices. + val indices = if (input2.getType() == IntType) { + input2.asInstanceOf[Tensor[Int]] + } else { + intBuffer.resizeAs(input2) + input2.cast[Int](intBuffer) + intBuffer + } + val inputSizes = inputTensor.size() + + if (indices.isScalar) { + val index = indices.value() + require(index < inputSizes(0), + s"index should smaller than ${inputSizes(0)}, but got $index") + val theOutput = inputTensor.select(1, index + 1) + inputSizes(0) = 1 + this.output.resize(inputSizes).copy(theOutput) + } else { + val indicesSize = indices.size() + val outputSizes = indicesSize ++ inputSizes.slice(1, inputSizes.length) + + output.resize(Array(indices.nElement()) ++ inputSizes.slice(1, inputSizes.length)) + indices.resize(indices.nElement()) + var i = 0 + while (i < indices.nElement()) { + val index = indices.valueAt(i + 1) + require(index < inputSizes(0), + s"index should smaller than ${inputSizes(0)}, but got $index") + output.select(1, i + 1).copy(inputTensor.select(1, index + 1)) + i += 1 + } + + indices.resize(indicesSize) + output.resize(outputSizes) + } + + output + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } + + override def clearState() : this.type = { + super.clearState() + intBuffer.set() + this + } + +} + +object Gather { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): + Gather[T, D] = new Gather() + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Gather.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Gather.scala new file mode 100644 index 00000000000..55d191b391f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Gather.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Gather => GatherOps} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Gather extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "Tparams") + if (t == DataType.DT_FLOAT) { + GatherOps[T, Float]() + } else if (t == DataType.DT_DOUBLE) { + GatherOps[T, Double]() + } else if (t == DataType.DT_INT32) { + GatherOps[T, Int]() + } else { + throw new UnsupportedOperationException(s"Not support load Gather when type is ${t}") + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala new file mode 100644 index 00000000000..cb0b6b7a7cf --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala @@ -0,0 +1,79 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class GatherSpec extends FlatSpec with Matchers { + "gather with scalar" should "works fine" in { + val gather = Gather[Float, Float]() + val indices = Tensor[Int](Array(1), Array[Int]()) + val input = Tensor.range(1, 24).resize(2, 3, 4) + val output = gather.forward(T(input, indices)) + + output should be (input.narrow(1, 2, 1)) + } + + "gather with 1-D tensor" should "works fine" in { + val gather = Gather[Float, Float]() + val indices = Tensor[Int](T(0, 1)) + val input = Tensor.range(1, 24).resize(2, 3, 4) + val output = gather.forward(T(input, indices)) + + output should be (input) + } + + "gather with 2-D tensor" should "works fine" in { + val gather = Gather[Float, Float]() + val indices = Tensor[Int](T(T(0, 1), T(0, 1))) + val input = Tensor.range(1, 24).resize(2, 3, 4) + val output = gather.forward(T(input, indices)) + + val exceptedOutput = Tensor(2, 2, 3, 4) + exceptedOutput.select(1, 1).copy(input) + exceptedOutput.select(1, 2).copy(input) + + output should be (exceptedOutput) + } + + "gather with Float input" should "works fine" in { + val gather = Gather[Float, Float]() + val indices = Tensor[Float](T(T(0f, 1f), T(0f, 1f))) + val input = Tensor.range(1, 24).resize(2, 3, 4) + val output = gather.forward(T(input, indices)) + + val exceptedOutput = Tensor(2, 2, 3, 4) + exceptedOutput.select(1, 1).copy(input) + exceptedOutput.select(1, 2).copy(input) + + output should be (exceptedOutput) + } + +} + +class GatherSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val gather = Gather[Float, Float]().setName("floorDiv") + val input1 = Tensor[Float].range(1, 6).resize(2, 3) + val input2 = Tensor[Int](2).fill(1) + val input = T(input1, input2) + runSerializationTest(gather, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GatherSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GatherSpec.scala new file mode 100644 index 00000000000..7ef64c57d2d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/GatherSpec.scala @@ -0,0 +1,64 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class GatherSpec extends TensorflowSpecHelper{ + "Gather" should "be correct for float" in { + compare[Float]( + NodeDef.newBuilder() + .setName("gather_test") + .putAttr("Tparams", typeAttr(DataType.DT_FLOAT)) + .putAttr("Tindices", typeAttr(DataType.DT_INT32)) + .setOp("Gather"), + Seq(Tensor[Float].range(1, 10).resize(5, 2), + Tensor[Int](T(0, 1, 1, 3))), + 0 + ) + } + + "Gather" should "be correct for Int" in { + compare[Float]( + NodeDef.newBuilder() + .setName("gather_test") + .putAttr("Tparams", typeAttr(DataType.DT_INT32)) + .putAttr("Tindices", typeAttr(DataType.DT_INT32)) + .setOp("Gather"), + Seq(Tensor[Int].range(1, 10).resize(5, 2), + Tensor[Int](T(0, 1, 1, 3))), + 0 + ) + } + + "Gather" should "be correct for double" in { + compare[Float]( + NodeDef.newBuilder() + .setName("gather_test") + .putAttr("Tparams", typeAttr(DataType.DT_DOUBLE)) + .putAttr("Tindices", typeAttr(DataType.DT_INT32)) + .setOp("Gather"), + Seq(Tensor[Double].range(1, 10).resize(5, 2), + Tensor[Int](T(0, 1, 1, 3))), + 0 + ) + } + +} From 50739e0f84de3a2d86ff904e602e30345c61da5a Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 4 May 2018 20:19:21 +0800 Subject: [PATCH 0774/1065] fix incorrect package names in tf examples (#2512) [Doc] Fix incorrect package names in tf examples --- .../bigdl/dllib/example/tensorflow/loadandsave/README.md | 8 ++++---- .../dllib/example/tensorflow/transferlearning/README.md | 6 +++++- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md index 6fd6cfce7e5..5d2e5a094cb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md @@ -5,7 +5,7 @@ Before you run this example, you need to install tensorflow on your machine. Thi by ```bash -pip install tensorflow +pip install tensorflow==1.2.0 ``` ## Load tensorflow model @@ -22,13 +22,13 @@ python freeze_graph.py --input_graph model/model.pbtxt --input_checkpoint model/ 3. Run BigDL ```bash -spark-submit --master local[1] --class com.intel.analytics.bigdl.example.tensorflow.Load BigDL_jar_file ./model.pb +spark-submit --master local[1] --class com.intel.analytics.bigdl.example.tensorflow.loadandsave.Load BigDL_jar_file ./model.pb ``` ## Save BigDL model as tensorflow model 1. Run BigDL ```bash -spark-submit --master local[1] --class com.intel.analytics.bigdl.example.tensorflow.Save BigDL_jar_file +spark-submit --master local[1] --class com.intel.analytics.bigdl.example.tensorflow.loadandsave.Save BigDL_jar_file ``` 2. Generate summary file, you can find the dump_tf_graph.py in the bin folder of the dist package, or script folder of @@ -40,4 +40,4 @@ python dump_tf_graph.py model.pb 3. See the saved model via tensorboard ```bash tensorboard --logdir ./log -``` \ No newline at end of file +``` diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md index abf0e573abb..c51965a4f7f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md @@ -19,6 +19,10 @@ Please refer to [BigDL](https://bigdl-project.github.io/master/), [Tensorflow](h We currently support Tensorflow r1.2. +```shell +pip install tensorflow==1.2.0 +``` + ## Install the TF-slim image models library Please checkout this [page](https://github.com/tensorflow/models/tree/master/research/slim#installing-the-tf-slim-image-models-library) @@ -142,7 +146,7 @@ $SPARK_HOME/bin/spark-submit \ --executor-cores cores_per_executor \ --total-executor-cores total_cores_for_the_job \ --driver-class-path $BIGDL_HOME/lib/bigdl-$BIGDL_VERSION-jar-with-dependencies.jar \ ---class com.intel.analytics.bigdl.example.tensorflow.transferLearning.TransferLearning \ +--class com.intel.analytics.bigdl.example.tensorflow.transferlearning.TransferLearning \ $BIGDL_HOME/lib/bigdl-$BIGDL_VERSION-jar-with-dependencies.jar \ -t /tmp/tf_model_train/ -v /tmp/tf_model_validation/ \ -b batch_size -e nEpochs From 20d9d132d05ca50e620da4a99a48dd7d962b7c62 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 7 May 2018 15:52:42 +0800 Subject: [PATCH 0775/1065] [New feature]share Const in ModelBroadcast (#2513) --- .../dllib/models/utils/ModelBroadcast.scala | 21 +++++++- .../bigdl/dllib/tensor/DenseTensor.scala | 4 ++ .../analytics/bigdl/dllib/tensor/Tensor.scala | 9 ++++ .../analytics/bigdl/dllib/utils/Util.scala | 31 +++++++++++- .../models/utils/ModelBroadcastSpec.scala | 50 ++++++++++++++++++- 5 files changed, 109 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index b69e96b6a58..cbda51eca05 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.models.utils import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{Container, Graph} import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.apache.spark.SparkContext @@ -38,13 +39,15 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) (implicit ev: TensorNumeric[T]) extends Serializable { private var broadcastModel: Broadcast[Module[T]] = _ + private var broadcastConsts: Broadcast[Map[String, Tensor[_]]] = _ private var broadcastParameters: Broadcast[Array[Tensor[T]]] = _ /** * broadcast the model - * first get and clear the weight and bias parameters from the model - * then broadcast the parameters and model(without parameters) separately + * first get and clear Const values from the model + * then get and clear the weight and bias parameters from the model + * finally broadcast Const values, the parameters and model(without parameters) separately * @param sc SparkContext * @param model model to broadcast * @return this @@ -53,9 +56,17 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) if (applyProtoBuffer) { broadcastModel = sc.broadcast(model) } else { + // broadcast Consts + if (model.isInstanceOf[Container[_, _, T]]) { + val moduleConsts = getAndClearConsts(model.asInstanceOf[Container[_, _, T]]) + // TODO: broadcast Const, model structure and weight in the same broadcast. + broadcastConsts = sc.broadcast(moduleConsts) + } + // broadcast weight and model val weightsBias = getAndClearWeightBias(model.parameters()) broadcastModel = sc.broadcast(model.cloneModule()) broadcastParameters = sc.broadcast(weightsBias) + putWeightBias(weightsBias, model) initGradWeightBias(weightsBias, model) } @@ -78,7 +89,13 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) localModel } else { val localModel = broadcastModel.value.cloneModule() + // share weight putWeightBias(broadcastParameters.value, localModel) + // share Consts + if (localModel.isInstanceOf[Container[_, _, T]] && broadcastConsts.value.nonEmpty) { + putConsts(localModel.asInstanceOf[Container[_, _, T]], broadcastConsts.value) + } + // init gradient if (initGradient) { initGradWeightBias(broadcastParameters.value, localModel) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index fdc9c940b1c..9c42c065b7f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -412,6 +412,10 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( DenseTensor.newClone(this) } + override def shallowClone(): Tensor[T] = { + Tensor(Storage(this.storage().array()), storageOffset(), size(), stride()) + } + override def emptyInstance(): Tensor[T] = { Tensor[T]() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 0d32c0f9f33..7bcb47122ed 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -350,6 +350,15 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { this } + /** + * Get a new tensor with same storage. + * + * @return new tensor + */ + def shallowClone(): Tensor[T] = { + this + } + /** * return a new empty tensor of the same type * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala index 0eab914f4a1..81337e07c47 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala @@ -19,8 +19,10 @@ package com.intel.analytics.bigdl.utils import java.io._ import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} +import com.intel.analytics.bigdl.nn.{Container, Graph} +import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.tensor._ import org.apache.commons.lang3.SerializationException import scala.reflect.ClassTag @@ -134,6 +136,31 @@ object Util { } } + private[bigdl] def getAndClearConsts[T: ClassTag]( + model: Container[_, _, T])(implicit ev: TensorNumeric[T]): Map[String, Tensor[_]] = { + val moduleConsts = model.findModules("Const") + .map(_.asInstanceOf[Const[T, _]]) + .map(v => (v, v.value.shallowClone())) + moduleConsts.foreach(_._1.value.set()) + val result = moduleConsts.map(v => (v._1.getName(), v._2)).toMap[String, Tensor[_]] + require(result.size == moduleConsts.length, s"${model}'s Const node's name is duplicated," + + s"please check your model.") + result + } + + private[bigdl] def putConsts[T: ClassTag]( + model: Container[_, _, T], + consts: Map[String, Tensor[_]])(implicit ev: TensorNumeric[T]) : Unit = { + val moduleConsts = model.findModules("Const") + .map(_.asInstanceOf[Const[T, _]]) + moduleConsts.foreach{const => + val constValue = const.value.asInstanceOf[NumericWildcard] + val constName = const.getName() + constValue.asInstanceOf[Tensor[NumericWildcard]] + .set(consts(constName).asInstanceOf[Tensor[NumericWildcard]]) + } + } + private def clearTensor[T: ClassTag](tensors: Array[Tensor[T]]) (implicit ev: TensorNumeric[T]): Unit = { var i = 0 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala index 473390843da..17001e62509 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala @@ -15,9 +15,12 @@ */ package com.intel.analytics.bigdl.models.utils +import java.nio.ByteOrder + import com.intel.analytics.bigdl.models.lenet.LeNet5 -import com.intel.analytics.bigdl.nn.Sequential -import com.intel.analytics.bigdl.nn.SpatialConvolution +import com.intel.analytics.bigdl.nn.tf.Const +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.tensor.Tensor import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -40,6 +43,49 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } + "model broadcast with const" should "forward properly" in { + val input1 = Identity[Float]().inputs() + val input2 = Const[Float, Float](Tensor[Float].range(1, 6, 1)).setName("const").inputs() + val output = CAddTable[Float]().inputs(input1, input2) + val model = Graph(input1, output) + val modelBroadCast = ModelBroadcast[Float](true).broadcast(sc, model) + + val testModel = modelBroadCast.value() + val testInput = Tensor[Float].range(2, 7, 1) + val testOutput = testModel.forward(testInput) + testOutput should be (Tensor[Float].range(3, 13, 2)) + } + + "model broadcast with const" should "const shared properly" in { + val input1 = Identity[Float]().inputs() + val input2 = Const[Float, Float](Tensor[Float].range(1, 6, 1)).setName("const").inputs() + val output = CAddTable[Float]().inputs(input1, input2) + val model = Graph(input1, output) + + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) + val model1 = modelBroadCast.value().asInstanceOf[Graph[Float]] + val model2 = modelBroadCast.value().asInstanceOf[Graph[Float]] + val const1 = model1.findModules("Const")(0).asInstanceOf[Const[Float, Float]] + val const2 = model2.findModules("Const")(0).asInstanceOf[Const[Float, Float]] + const1.value should be (const2.value) + const1.value.storage() should be (const2.value.storage()) + } + + "model broadcast with const" should "const shared properly 2" in { + val input1 = Identity[Float]().inputs() + val input2 = Const[Float, Float](Tensor[Float].range(1, 6, 1)).setName("const").inputs() + val output = CAddTable[Float]().inputs(input1, input2) + val model = Sequential[Float]().add(Graph(input1, output)) + + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) + val model1 = modelBroadCast.value().asInstanceOf[Sequential[Float]] + val model2 = modelBroadCast.value().asInstanceOf[Sequential[Float]] + val const1 = model1.findModules("Const")(0).asInstanceOf[Const[Float, Float]] + val const2 = model2.findModules("Const")(0).asInstanceOf[Const[Float, Float]] + const1.value should be (const2.value) + const1.value.storage() should be (const2.value.storage()) + } + "model broadcast with applyProtoBuffer" should "work properly" in { val model = LeNet5(10) From e9e16bb7bf9d7854fb70501e4df9d66fd93c518d Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 8 May 2018 12:36:28 +0800 Subject: [PATCH 0776/1065] fix squeeze test (#2509) --- .../analytics/bigdl/dllib/nn/Squeeze.scala | 24 +++++++++++-------- .../bigdl/dllib/nn/SqueezeSpec.scala | 5 +++- 2 files changed, 18 insertions(+), 11 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala index 620e7301dfc..b1c0a8bc8ad 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala @@ -34,12 +34,16 @@ class Squeeze[T: ClassTag]( val dims : Array[Int] = null, val batchMode: Boolean = false )(implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[_], Tensor[_], T] { - if (batchMode && dims != null) { + val dimensions = if (batchMode && dims != null) { + val newDims = new Array[Int](dims.length) var i = 0 - while(i < dims.length) { - dims(i) += 1 + while(i < newDims.length) { + newDims(i) = dims(i) + 1 i += 1 } + newDims + } else { + dims } override def updateOutput(input: Tensor[_]): Tensor[_] = { @@ -47,17 +51,17 @@ class Squeeze[T: ClassTag]( output = input.emptyInstance() } output.asInstanceOf[Tensor[NumericWildcard]].set(input.asInstanceOf[Tensor[NumericWildcard]]) - if (dims != null) { + if (dimensions != null) { var i = 0 - while(i < dims.length) { - output.squeeze(dims(i)) + while(i < dimensions.length) { + output.squeeze(dimensions(i)) i += 1 } } else { output.squeeze() } - if (batchMode && dims == null && input.size(1) == 1) { + if (batchMode && dimensions == null && input.size(1) == 1) { output.addSingletonDimension() } output @@ -76,7 +80,7 @@ class Squeeze[T: ClassTag]( } override def toString(): String = { - s"${getPrintName}(${if (dims != null) dims.mkString(",") + ", " else ""}" + + s"${getPrintName}(${if (dimensions != null) dimensions.mkString(",") + ", " else ""}" + s"${if (batchMode) "batch" else ""})" } @@ -86,13 +90,13 @@ class Squeeze[T: ClassTag]( case that: Squeeze[T] => super.equals(that) && (that canEqual this) && - (dims.zip(that.dims).map(a => a._1 == a._2).reduce(_ && _)) && + (dims.zip(that.dimensions).map(a => a._1 == a._2).reduce(_ && _)) && batchMode == that.batchMode case _ => false } override def hashCode(): Int = { - val state = Seq(super.hashCode(), dims, batchMode) + val state = Seq(super.hashCode(), dimensions, batchMode) state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqueezeSpec.scala index 00c34e3d451..addb2337839 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqueezeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SqueezeSpec.scala @@ -23,8 +23,11 @@ import scala.util.Random class SqueezeSerialTest extends ModuleSerializationTest { override def test(): Unit = { - val squeeze = Squeeze[Float](2).setName("squeeze") + var squeeze = Squeeze[Float](2).setName("squeeze") val input = Tensor[Float](2, 1, 2).apply1( e => Random.nextFloat()) runSerializationTest(squeeze, input) + + squeeze = Squeeze[Float](Array(2), batchMode = true).setName("squeeze") + runSerializationTest(squeeze, input) } } From 1d80e3a64a3abc8dc6d27cf9c4e66c32d971fac8 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 9 May 2018 14:51:30 +0800 Subject: [PATCH 0777/1065] [Refinement] Refine sample api to suppport get feature and label (#2519) * refine sample api to suppport get feature and label * add for tensor sample --- .../bigdl/dllib/feature/dataset/Sample.scala | 75 ++++++++++++++---- .../bigdl/dllib/dataset/SampleSpec.scala | 78 +++++++++++++++++++ 2 files changed, 136 insertions(+), 17 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index 9de4f047d92..370f37c539d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -72,10 +72,13 @@ abstract class Sample[T: ClassTag] extends Serializable { * a default implement to throw exception. * @return feature tensor */ - @deprecated("Old interface", "0.2.0") - def feature()(implicit ev: TensorNumeric[T]): Tensor[T] = { - throw new UnsupportedOperationException("Sample.feature(): unimplemented deprecated method") - } + def feature()(implicit ev: TensorNumeric[T]): Tensor[T] + + /** + * Get feature tensor for given index + * @param index index of specific sample + */ + def feature(index: Int)(implicit ev: TensorNumeric[T]): Tensor[T] /** * Get label tensor, for one label Sample only. @@ -83,10 +86,13 @@ abstract class Sample[T: ClassTag] extends Serializable { * a default implement to throw exception. * @return label tensor */ - @deprecated("Old interface", "0.2.0") - def label()(implicit ev: TensorNumeric[T]): Tensor[T] = { - throw new UnsupportedOperationException("Sample.label(): unimplemented deprecated method") - } + def label()(implicit ev: TensorNumeric[T]): Tensor[T] + + /** + * Get label tensor for given index + * @param index index of specific sample + */ + def label(index: Int)(implicit ev: TensorNumeric[T]): Tensor[T] /** * Set data of feature and label. @@ -173,19 +179,34 @@ class ArraySample[T: ClassTag] private[bigdl]( } } - @deprecated("Old interface", "0.2.0") override def feature()(implicit ev: TensorNumeric[T]): Tensor[T] = { - require(featureSize.length == 1, "Old interface for 1 feature Sample. " + - s"got ${featureSize.length} feature Sample") - Tensor[T](Storage(data), 1, getFeatureSize()(0)) + require(this.numFeature == 1, "Only one Sample required in total" + + s"got ${featureSize.length} feature Sample, please use feature(index) instead") + feature(0) + } + + override def feature(index: Int)(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(this.numFeature > index, "feature index out of range") + val featureOffSet = 1 + getFeatureSize().zipWithIndex. + filter(_._2 < index).map(_._1.product).sum + Tensor[T](Storage(data), featureOffSet, getFeatureSize()(index)) + } + + override def label(index: Int)(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(this.numFeature > index, "label index out of range") + if (this.numLabel > index) { + val labelOffSet = 1 + getFeatureSize().map(_.product).sum + getLabelSize().zipWithIndex + .filter(_._2 < index).map(_._1.product).sum + Tensor[T](Storage[T](data), labelOffSet, labelSize(index)) + } else { + null + } } - @deprecated("Old interface", "0.2.0") override def label()(implicit ev: TensorNumeric[T]): Tensor[T] = { - require(labelSize.length == 1, "Old interface for 1 label Sample. " + - s"got ${labelSize.length} label Sample") - Tensor[T](Storage(data), getFeatureSize().map(_.product).sum + 1, - labelSize(0)) + require(this.numLabel <= 1, "Only one Sample required in total " + + s"got ${labelSize.length} label Sample, please use label(index) instead") + label(0) } @deprecated("Old interface", "0.2.0") @@ -455,6 +476,26 @@ class TensorSample[T: ClassTag] private[bigdl] ( def getData(): Array[T] = { throw new UnimplementedException() } + + override def feature()(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(this.numFeature == 1, "only sample with one feature supported") + this.feature(0) + } + + override def feature(index: Int)(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(index < this.numFeature, "Index out of range") + this.features(index) + } + + override def label()(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(this.numLabel <= 1, "only sample with at most one label supported") + if (this.numLabel == 1) this.label(0) else null + } + + override def label(index: Int)(implicit ev: TensorNumeric[T]): Tensor[T] = { + require(index < this.numFeature, "Index out of range") + if (index < this.numLabel) this.labels(index) else null + } } object TensorSample { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala index 8bd9a6b0915..fb9d707d0b2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/SampleSpec.scala @@ -62,6 +62,84 @@ class SampleSpec extends FlatSpec with Matchers { Some(featureParam), Some(labelParam)).set(samples) } + "Get feature and label from single sample" should "work fine" in { + val feature = Tensor[Float](2, 2).fill(1.0f) + val label = Tensor[Float](2).fill(1.0f) + val sample = ArraySample(feature, label) + + val fetchedFeature = sample.feature() + + val fetchedLabel = sample.label() + + fetchedFeature should be (feature) + + fetchedLabel should be (label) + } + + "Get label from single sample without label" should "work fine" in { + val feature = Tensor[Float](2, 2).fill(1.0f) + val sample = ArraySample(feature) + val fetchedFeature = sample.feature() + + val fetchedLabel = sample.label() + + fetchedFeature should be (feature) + + fetchedLabel should be (null) + } + + "Get feature and label from multiple samples" should "work fine" in { + val feature1 = Tensor[Float](2, 2).fill(1.0f) + val label1 = Tensor[Float](2).fill(1.0f) + + val feature2 = Tensor[Float](2, 2).fill(2.0f) + + val sample = ArraySample(Array(feature1, feature2), Array(label1)) + + val fetchedFeature1 = sample.feature(0) + + val fetchedLabel1 = sample.label(0) + + val fetchedFeature2 = sample.feature(1) + + val fetchedLabel2 = sample.label(1) + + fetchedFeature1 should be (feature1) + + fetchedLabel1 should be (label1) + + fetchedFeature2 should be (feature2) + + fetchedLabel2 should be (null) + + } + + "Get feature and label from TensorSample" should "work properly" in { + val feature1 = Tensor[Float](2, 2).fill(1.0f) + val label1 = Tensor[Float](2).fill(1.0f) + + val feature2 = Tensor[Float](2, 2).fill(2.0f) + + val sample = TensorSample(Array(feature1, feature2), Array(label1)) + + val fetchedFeature1 = sample.feature(0) + + val fetchedLabel1 = sample.label(0) + + val fetchedFeature2 = sample.feature(1) + + val fetchedLabel2 = sample.label(1) + + fetchedFeature1 should be (feature1) + + fetchedLabel1 should be (label1) + + fetchedFeature2 should be (feature2) + + fetchedLabel2 should be (null) + + } + "create Sample" should "work fine" in { val st1 = Tensor.sparse(Tensor.range(1, 10, 1)) val st2 = Tensor.sparse(Tensor.range(1, 10, 1)) From 0c47c5cb7f220794ae81247909f176d9f3874a92 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 10 May 2018 16:08:58 +0800 Subject: [PATCH 0778/1065] [Doc] Replace java with spark-submit in the command for generating seq files (#2521) * replace java with scala in the command for generating seq files * replace scala with spark-submit --- .../analytics/bigdl/dllib/models/inception/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md index 7e5cdd3f749..397654145a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/README.md @@ -35,12 +35,12 @@ Now all the images belonging to the same category are moved to the same folder. This command will transform the images into hadoop sequence files, which are more suitable for a distributed training. -Bigdl has different versions, bigdl-VERSION-jar-with-dependencies-and-spark.jar used in the following command is a general name. -Please update it according to your bigdl version. As we only distribute jar without spark dependency, you need to build the -bigdl-VERSION-jar-with-dependencies-and-spark.jar from the source. +Bigdl has different versions, bigdl-VERSION-jar-with-dependencies.jar used in the following command is a general name. +Please update it according to your bigdl version. It can be found in the **lib** folder of the distributed package. +If you build from source, it can be found in the **dist/lib** folder. ```bash -java -cp bigdl_source_folder/spark/dl/target/bigdl-VERSION-jar-with-dependencies-and-spark.jar com.intel.analytics.bigdl.models.utils.ImageNetSeqFileGenerator -f imagenet_folder -o output_folder -p cores_number +spark-submit --class com.intel.analytics.bigdl.models.utils.ImageNetSeqFileGenerator bigdl-VERSION-jar-with-dependencies.jar -f imagenet_folder -o output_folder -p cores_number ``` It will generate the hadoop sequence files in the output folder. From 9597b4ac721fb88705954724d26ec95d457ff1ca Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 15 May 2018 13:35:17 +0800 Subject: [PATCH 0779/1065] [New Feature] Allow User to Specify Input Port when load Tensorflow model (#2520) * allow user specify input port when load tensorflow model * fix style issue --- .../dllib/utils/tf/TensorflowLoader.scala | 268 +++++++++++------- .../dllib/utils/tf/TensorflowLoaderSpec.scala | 53 ++++ 2 files changed, 225 insertions(+), 96 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 49b4518523e..849adccc3c4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -44,7 +44,9 @@ object TensorflowLoader{ /** * Load tensorflow model from a prototxt file * @param graphPrototxt where is the tensorflow protobuf file - * @param inputs input node names + * @param inputs input node names, where feed in the data. You can feed data into an internal + * node. If the internal node has multiple dependency, you can use name:n to specify + * which dependency you choose to feed * @param outputs output node names * @param byteOrder file byteOrder * @return @@ -55,21 +57,29 @@ object TensorflowLoader{ // Get node list val nodeList = parse(graphPrototxt) - // Construct tf node graph - val (tfGraph, adjustedInputsMap, _) = - buildTFGraph(nodeList, outputs, (node: NodeDef) => inputs.contains(node.getName)) + // Input name remove the port + val realInputNames = inputs.map(i => if (i.split(":").length == 2) i.split(":")(0) else i) + .distinct - val adjustedInputs = ArrayBuffer[String]() - inputs.foreach(i => { - if (adjustedInputsMap.isDefinedAt(i)) { - adjustedInputsMap(i).foreach(n => adjustedInputs.append(n)) + // Construct tf node graph + val (tfGraph, newInputMap, _) = + buildTFGraph(nodeList, outputs, (node: NodeDef) => realInputNames.contains(node.getName), + Some(getInputPorts(inputs))) + + // If you choose an internal node with multiple inputs, extra placeholder will be insert into + // the model + // Keep the order with the inputs list + val newInputs = ArrayBuffer[String]() + realInputNames.foreach(i => { + if (newInputMap.isDefinedAt(i)) { + newInputMap(i).foreach(n => newInputs.append(n)) } }) // Try to load variables val context = binFile.map(loadBinFiles(_)) // Build BigDL model from the tf node graph - buildBigDLModel(tfGraph, adjustedInputs, outputs, byteOrder, graphPrototxt, context) + buildBigDLModel(tfGraph, newInputs, outputs, byteOrder, graphPrototxt, context) } def checkpoints[T: ClassTag](graphFile: String, binFile: String, byteOrder: ByteOrder)( @@ -80,6 +90,29 @@ object TensorflowLoader{ new BigDLSessionImpl[T](nodeList.asScala, loadBinFiles(binFile), byteOrder) } + /** + * Get the input ports if user specify. It will also check the input name list. + * @param inputs + * @return + */ + private def getInputPorts(inputs: Seq[String]): mutable.Map[String, ArrayBuffer[Int]] = { + require(inputs.distinct.length == inputs.length, + "input should not contain duplicated names") + val inputPorts = inputs.filter(_.split(":").length == 2) + val result = mutable.HashMap[String, ArrayBuffer[Int]]() + inputPorts.foreach(s => { + val name = s.split(":")(0) + val pos = s.split(":")(1) + require(!inputs.contains(name), "You should not specify node name and node name " + + "with port at same time") + if (!result.isDefinedAt(name)) { + result(name) = ArrayBuffer[Int]() + } + result(name).append(pos.toInt) + }) + result + } + /** * Parse a tensorflow model protobuf binary file, read a list of op nodes from it * @param graphProtoTxt where is the tf protobuf file @@ -154,117 +187,160 @@ object TensorflowLoader{ } /** - * Build tf ops graph from a given node list - * @param nodes - * @param outputNodeNames + * Build tf ops graph from a given node list. The graph output is the outputs. + * + * @param nodes Tensorflow NodeDefs + * @param outputs output node names + * @param isInput check if a node is input + * @param inputPorts if user want to use a part of the node input as model input * @return */ - private[bigdl] def buildTFGraph(nodes : List[NodeDef], outputNodeNames: Seq[String], - isInput: (NodeDef) => Boolean = (_: NodeDef) => false) - : (DirectedGraph[NodeDef], mutable.HashMap[String, ArrayBuffer[String]], Seq[String]) = { + private[bigdl] def buildTFGraph( + nodes : List[NodeDef], outputs: Seq[String], + isInput: (NodeDef) => Boolean = (_: NodeDef) => false, + inputPorts: Option[mutable.Map[String, ArrayBuffer[Int]]] = None + ): (DirectedGraph[NodeDef], mutable.HashMap[String, ArrayBuffer[String]], Seq[String]) = { val name2Node = nodes.asScala.map(n => n.getName -> new Node(n)).toMap // Build graph - val outputNodes = if (outputNodeNames == null) { + val outputNodes = if (outputs == null) { name2Node.valuesIterator.filter(_.nextNodes.isEmpty).toArray } else { val results = name2Node.valuesIterator.toArray.filter(n => - outputNodeNames.contains(n.element.getName)) - require(results.length == outputNodeNames.length, "Invalid outputNode names") + outputs.contains(n.element.getName)) + require(results.length == outputs.length, "Invalid outputNode names") results } + val (inputs, originInputs) = connect(outputNodes, name2Node, isInput, inputPorts) - def connect(nodes: Seq[Node[NodeDef]]): (mutable.HashMap[String, ArrayBuffer[String]], - Seq[String]) = { - - var inputCounter = 0 - var depCounter = 0 - val queue = new mutable.Queue[Node[NodeDef]]() - val visited = mutable.Set[Node[NodeDef]]() - val inputs = new mutable.HashMap[String, ArrayBuffer[String]]() - val originInputs = new mutable.ArrayBuffer[String]() - - // Do a BFS to connect the nodes - queue.enqueue(nodes: _*) - while(queue.nonEmpty) { - val node = queue.dequeue() - if (!visited(node)) { - visited += node - if (!isInput(node.element) && !node.element.getInputList.isEmpty) { - // continue to traverse - node.element.getInputList.asScala.foreach { preNodeName => - // It is tricky here, remove the first char in the name of control dep node - var realName = preNodeName - var controlDep = false - var channel = 0 - - if (realName.charAt(0) == '^') { - realName = realName.substring(1) - controlDep = true - } - if (realName.split(":").length > 1) { - val pair = realName.split(":") - realName = pair(0) - channel = pair(1).toInt - } - - val preNode = name2Node(realName) - - val currNode = if (controlDep) { - val dependencyNode = Node(NodeDef.newBuilder() - .setOp("DependencyNode") - .addInput(preNode.element.getName) - .setName(s"depends_on_${preNode.element.getName}$depCounter") - .build()) - depCounter += 1 - dependencyNode -> node - dependencyNode - } else { - node - } + val dummyOutput = new Node[NodeDef](null) + outputNodes.foreach(_ -> dummyOutput) + (dummyOutput.graph(reverse = true), inputs, originInputs) + } - preNode.add(currNode, Edge(channel + 1)) - queue.enqueue(preNode) - } - } else { - if (inputs.get(node.element.getName).isEmpty) { - inputs(node.element.getName) = new ArrayBuffer[String]() - } - if (isInput(node.element) && node.element.getOp != "Placeholder") { - // if the predefined input node is not a Placeholder, add one to match the Input node - val inputNum = getInputNumber(node.element) - if (inputNum == 0) { - inputs(node.element.getName).append(node.element.getName) - } else { + /** + * Build a graph from the output. The build process will stop when met node without inputs + * or specified input node + */ + private[bigdl] def connect( + nodes: Seq[Node[NodeDef]], + name2Node: Map[String, Node[NodeDef]], + isInput: (NodeDef) => Boolean, + inputPorts: Option[mutable.Map[String, ArrayBuffer[Int]]] + ): (mutable.HashMap[String, ArrayBuffer[String]], Seq[String]) = { + + var inputCounter = 0 + var depCounter = 0 + val queue = new mutable.Queue[Node[NodeDef]]() + val visited = mutable.Set[Node[NodeDef]]() + val newInputs = new mutable.HashMap[String, ArrayBuffer[String]]() + val originInputs = new mutable.ArrayBuffer[String]() + + // Do a BFS to connect the nodes + queue.enqueue(nodes: _*) + while(queue.nonEmpty) { + val node = queue.dequeue() + if (!visited(node)) { + visited += node + if (!isInput(node.element) && !node.element.getInputList.isEmpty) { + // continue to traverse + node.element.getInputList.asScala.foreach { preNodeName => + depCounter = pushPreNode(preNodeName, name2Node, depCounter, node, queue) + } + } else { + if (newInputs.get(node.element.getName).isEmpty) { + newInputs(node.element.getName) = new ArrayBuffer[String]() + } + if (isInput(node.element) && node.element.getOp != "Placeholder") { + // if the predefined input node is not a Placeholder, add one to match the Input node + val inputNum = getInputNumber(node.element) + if (inputNum == 0) { + require(!inputPorts.isDefined || + !inputPorts.get.isDefinedAt(node.element.getName), + s"node ${node.element.getName} has no input") + newInputs(node.element.getName).append(node.element.getName) + } else { + if (inputPorts.isDefined && + inputPorts.get.isDefinedAt(node.element.getName)) { + val selectInputs = inputPorts.get(node.element.getName) + selectInputs.foreach(i => require(i < inputNum && i >= 0, + s"invalid input port $i at ${node.element.getName}, it should between 0 and" + + s" ${inputNum - 1}")) var i = 0 while (i < inputNum) { - val name = s"input$inputCounter" - val placeholder = NodeDef.newBuilder() - .setName(name) - .setOp("Placeholder").build() - inputCounter = inputCounter + 1 - val n = Node(placeholder) - n -> node - inputs(node.element.getName).append(name) + if (selectInputs.contains(i)) { + val name = s"input$inputCounter" + val placeholder = NodeDef.newBuilder() + .setName(name) + .setOp("Placeholder").build() + inputCounter = inputCounter + 1 + val n = Node(placeholder) + n -> node + newInputs(node.element.getName).append(name) + } else { + val preNodeName = node.element.getInputList.asScala.apply(i) + depCounter = pushPreNode(preNodeName, name2Node, depCounter, node, queue) + } i = i + 1 } + } else { + val name = s"input$inputCounter" + val placeholder = NodeDef.newBuilder() + .setName(name) + .setOp("Placeholder").build() + inputCounter = inputCounter + 1 + val n = Node(placeholder) + n -> node + newInputs(node.element.getName).append(name) } - originInputs += node.element.getName - } else if (node.element.getOp == "Placeholder") { - inputs(node.element.getName).append(node.element.getName) - originInputs += node.element.getName } + originInputs += node.element.getName + } else if (node.element.getOp == "Placeholder") { + newInputs(node.element.getName).append(node.element.getName) + originInputs += node.element.getName } } } - (inputs, originInputs) } + (newInputs, originInputs) + } - val (inputs, originInputs) = connect(outputNodes) + private def pushPreNode(name: String, name2Node: Map[String, Node[NodeDef]], + depCounter: Int, node: Node[NodeDef], queue: mutable.Queue[Node[NodeDef]]): Int = { + // It is tricky here, remove the first char in the name of control dep node + var realName = name + var controlDep = false + var channel = 0 + var _depCounter = depCounter + + if (realName.charAt(0) == '^') { + realName = realName.substring(1) + controlDep = true + } + if (realName.split(":").length > 1) { + val pair = realName.split(":") + realName = pair(0) + channel = pair(1).toInt + } - val dummyOutput = new Node[NodeDef](null) - outputNodes.foreach(_ -> dummyOutput) - (dummyOutput.graph(reverse = true), inputs, originInputs) + val preNode = name2Node(realName) + + val curNode = if (controlDep) { + val dependencyNode = Node(NodeDef.newBuilder() + .setOp("DependencyNode") + .addInput(preNode.element.getName) + .setName(s"depends_on_${preNode.element.getName}$depCounter") + .build()) + _depCounter += 1 + dependencyNode -> node + dependencyNode + } else { + node + } + + preNode.add(curNode, Edge(channel + 1)) + queue.enqueue(preNode) + _depCounter } private def getInputNumber(nodeDef: NodeDef): Int = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index ef565eae240..8add8506e77 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -182,6 +182,59 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ output1 should be(output2) } + "TensorFlow loader" should "throw exception if input contain duplicate names" in { + val resource = getClass().getClassLoader().getResource("tf") + val path = processPath(resource.getPath()) + JFile.separator + "test.pb" + intercept[IllegalArgumentException] { + val model = TensorflowLoader.load(path, Seq("Placeholder", "Placeholder"), Seq("output"), + ByteOrder.LITTLE_ENDIAN) + } + } + + "TensorFlow loader" should "throw exception if input contain conflict names" in { + val resource = getClass().getClassLoader().getResource("tf") + val path = processPath(resource.getPath()) + JFile.separator + "test.pb" + intercept[IllegalArgumentException] { + val model = TensorflowLoader.load(path, Seq("Placeholder", "Placeholder:0"), Seq("output"), + ByteOrder.LITTLE_ENDIAN) + } + } + + "TensorFlow loader" should "throw exception if input location is incorrect" in { + val resource = getClass().getClassLoader().getResource("tf") + val path = processPath(resource.getPath()) + JFile.separator + "test.pb" + intercept[IllegalArgumentException] { + val model = TensorflowLoader.load(path, Seq("MatMul:2"), Seq("output"), + ByteOrder.LITTLE_ENDIAN) + } + } + + "TensorFlow loader" should "be able to build a BigDL graph with specify input location" in { + val resource = getClass().getClassLoader().getResource("tf") + val path = processPath(resource.getPath()) + JFile.separator + "test.pb" + val model = TensorflowLoader.load(path, Seq("MatMul:0"), Seq("output"), + ByteOrder.LITTLE_ENDIAN) + val container = model.asInstanceOf[Graph[Float]] + container.modules.length should be(4) + RandomGenerator.RNG.setSeed(100) + val input = Tensor[Float](4, 1).rand() + val output1 = container.forward(input) + + val model2 = Sequential[Float]() + val fc1 = Linear[Float](1, 10) + fc1.parameters()._1(0).fill(0.2f) + fc1.parameters()._1(1).fill(0.1f) + model2.add(fc1).add(Tanh()) + + val fc2 = Linear[Float](10, 1) + fc2.parameters()._1(0).fill(0.2f) + fc2.parameters()._1(1).fill(0.1f) + model2.add(fc2) + + val output2 = model2.forward(input) + output1 should be(output2) + } + "TensorFlow loader" should "be able to build a BigDL graph from a subset of a tf graph" in { val resource = getClass().getClassLoader().getResource("tf") val path = processPath(resource.getPath()) + JFile.separator + "test.pb" From 47977615f14cbca05ffe17fdb1e4d494e908fc5f Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 18 May 2018 15:57:55 +0800 Subject: [PATCH 0780/1065] [new feature]add operation max (#2523) * add max * add serialization test * max spec * meet code review * meet code review * fix unit test --- .../analytics/bigdl/dllib/nn/ops/Max.scala | 83 +++++++++++++++++++ .../bigdl/dllib/utils/tf/loaders/Max.scala | 46 ++++++++++ .../bigdl/dllib/nn/ops/MaxSpec.scala | 78 +++++++++++++++++ .../dllib/utils/tf/loaders/MaxSpec.scala | 67 +++++++++++++++ 4 files changed, 274 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Max.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Max.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Max.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Max.scala new file mode 100644 index 00000000000..8335f24758c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Max.scala @@ -0,0 +1,83 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Computes the maximum of elements across dimensions of a tensor. + * The input of Max should be two tensor, the first one is data, + * the second one is the dimension to compute maximum. + * @param keepDims if keepDims is false, will delete the singleton dimension + * in output. + * @param startFromZero if the dimension count from zero. + */ +class Max[T: ClassTag, D: ClassTag]( + keepDims: Boolean = false, + startFromZero: Boolean = false + )(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D] + ) extends Operation[Table, Tensor[D], T] { + + output = Tensor[D]() + // just a buffer using in tensor.max. + protected val indices: Tensor[D] = Tensor[D]() + + override def updateOutput(input: Table): Tensor[D] = { + val x = input[Tensor[D]](1) + val y = input[Tensor[Int]](2) + + require(y.isScalar, s"reduction indices should be a scalar") + val reductionIndices = if (startFromZero) { + y.value() + 1 + } else { + y.value() + } + require(reductionIndices <= x.nDimension(), s"reduction indices should smaller than" + + s" input's dimension, excepted smaller than ${x.dim()}, but got ${reductionIndices}") + + x.max(output, indices, reductionIndices) + + if(keepDims) { + output + } else { + output.squeeze(reductionIndices) + } + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } + + override def clearState(): Max.this.type = { + super.clearState() + indices.set() + this + } +} + +object Max { + def apply[T: ClassTag, D: ClassTag]( + keepDims: Boolean = false, + startFromZero: Boolean = false)( + implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): Max[T, D] = { + new Max[T, D](keepDims, startFromZero) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Max.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Max.scala new file mode 100644 index 00000000000..82ce04ce6bb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Max.scala @@ -0,0 +1,46 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{Max => MaxOps} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import scala.reflect.ClassTag + +class Max extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, context: Context[T]) + (implicit ev: TensorNumeric[T]): Module[T] = { + val t = getType(nodeDef.getAttrMap, "T") + val keepDims = getBoolean(nodeDef.getAttrMap, "keep_dims") + if (t == DataType.DT_FLOAT) { + MaxOps[T, Float](keepDims, true) + } else if (t == DataType.DT_DOUBLE) { + MaxOps[T, Double](keepDims, true) + } else if (t == DataType.DT_INT32) { + MaxOps[T, Int](keepDims, true) + } else { + throw new UnsupportedOperationException(s"Not support load Gather when type is ${t}") + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxSpec.scala new file mode 100644 index 00000000000..51fbd90c1cc --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxSpec.scala @@ -0,0 +1,78 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.ops + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{RandomGenerator, T} +import org.scalatest.{FlatSpec, Matchers} + +class MaxSpec extends FlatSpec with Matchers { + "Max operation" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + RandomGenerator.RNG.setSeed(10) + val input = + T( + Tensor.range(1, 10).resize(2, 5), + Tensor.scalar[Int](1) + ) + + val expectOutput = Tensor(T(5f, 10f)) + + val output = Max(startFromZero = true).forward(input) + output should be(expectOutput) + } + + "Max keepDims" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + RandomGenerator.RNG.setSeed(10) + val input = + T( + Tensor.range(1, 10).resize(2, 5), + Tensor.scalar[Int](1) + ) + + val expectOutput = Tensor(T(5f, 10f)).resize(2, 1) + + val output = Max(true, true).forward(input) + output should be(expectOutput) + } + + "Max dim start from 1" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + RandomGenerator.RNG.setSeed(10) + val input = + T( + Tensor.range(1, 10).resize(2, 5), + Tensor.scalar[Int](2) + ) + + val expectOutput = Tensor(T(5f, 10f)).resize(2, 1) + + val output = Max(true, false).forward(input) + output should be(expectOutput) + } +} + +class MaxSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val max = Max[Float, Float](startFromZero = true).setName("max_pool") + val input1 = Tensor[Float].range(1, 6).resize(2, 3) + val input2 = Tensor.scalar[Int](1) + val input = T(input1, input2) + runSerializationTest(max, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxSpec.scala new file mode 100644 index 00000000000..205745ccc8e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/MaxSpec.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow.{typeAttr, booleanAttr} +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class MaxSpec extends TensorflowSpecHelper{ + "Max" should "be correct for float" in { + compare[Float]( + NodeDef.newBuilder() + .setName("max_test") + .putAttr("keep_dims", booleanAttr(true)) + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Tidx", typeAttr(DataType.DT_INT32)) + .setOp("Max"), + Seq(Tensor[Float].range(1, 10).resize(5, 2), + Tensor.scalar[Int](1)), + 0 + ) + } + + "Max" should "be correct for Int" in { + compare[Float]( + NodeDef.newBuilder() + .setName("max_test") + .putAttr("keep_dims", booleanAttr(true)) + .putAttr("T", typeAttr(DataType.DT_INT32)) + .putAttr("Tidx", typeAttr(DataType.DT_INT32)) + .setOp("Max"), + Seq(Tensor[Int].range(1, 10).resize(5, 2), + Tensor.scalar[Int](1)), + 0 + ) + } + + "Max" should "be correct for double" in { + compare[Float]( + NodeDef.newBuilder() + .setName("max_test") + .putAttr("keep_dims", booleanAttr(false)) + .putAttr("T", typeAttr(DataType.DT_DOUBLE)) + .putAttr("Tidx", typeAttr(DataType.DT_INT32)) + .setOp("Max"), + Seq(Tensor[Double].range(1, 10).resize(5, 2), + Tensor.scalar[Int](1)), + 0 + ) + } + +} From 836fd5f102038ab85aea287908748a3f5a65b05a Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 22 May 2018 11:12:08 +0800 Subject: [PATCH 0781/1065] [new feature]add generateBackward for loadTF (#2529) --- .../com/intel/analytics/bigdl/dllib/nn/Module.scala | 10 +++++----- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 6 ++++-- .../bigdl/dllib/utils/tf/TensorflowLoader.scala | 7 +++++-- 3 files changed, 14 insertions(+), 9 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala index 19d38f65488..421c1e88c72 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Module.scala @@ -88,14 +88,14 @@ object Module { * @param outputs output node names, the output tensor order is same with the node order * @param byteOrder byte order in the tensorflow file. The default value is little endian * @param binFile where is the model variable file + * @param generatedBackward if generate backward graph * @return BigDL model */ def loadTF[T: ClassTag](graphFile: String, inputs: Seq[String], outputs: Seq[String], - byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, - binFile: Option[String] = None)( - implicit ev: TensorNumeric[T]): Module[T] = { - - TensorflowLoader.load(graphFile, inputs, outputs, byteOrder, binFile) + byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, + binFile: Option[String] = None, generatedBackward: Boolean = true)( + implicit ev: TensorNumeric[T]): Module[T] = { + TensorflowLoader.load(graphFile, inputs, outputs, byteOrder, binFile, generatedBackward) } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0b2a80ff2c0..24999f6996b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1871,13 +1871,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def loadTF(path: String, inputs: JList[String], outputs: JList[String], - byteOrder: String, binFile: String = null): AbstractModule[Activity, Activity, T] = { + byteOrder: String, binFile: String = null, + generatedBackward: Boolean = true): AbstractModule[Activity, Activity, T] = { val order = byteOrder match { case "little_endian" => ByteOrder.LITTLE_ENDIAN case "big_endian" => ByteOrder.BIG_ENDIAN case _ => throw new IllegalArgumentException(s"No support byte order $byteOrder") } - Module.loadTF[T](path, inputs.asScala, outputs.asScala, order, Option(binFile)) + Module.loadTF[T](path, inputs.asScala, outputs.asScala, order, + Option(binFile), generatedBackward) } def saveTF(model: AbstractModule[Activity, Activity, T], diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala index 849adccc3c4..373756192aa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoader.scala @@ -49,10 +49,12 @@ object TensorflowLoader{ * which dependency you choose to feed * @param outputs output node names * @param byteOrder file byteOrder + * @param generatedBackward if generate backward graph * @return */ def load[T: ClassTag](graphPrototxt: String, inputs: Seq[String], outputs: Seq[String], - byteOrder: ByteOrder, binFile: Option[String] = None)( + byteOrder: ByteOrder, binFile: Option[String] = None, + generatedBackward: Boolean = true)( implicit ev: TensorNumeric[T]): Module[T] = { // Get node list val nodeList = parse(graphPrototxt) @@ -79,7 +81,8 @@ object TensorflowLoader{ val context = binFile.map(loadBinFiles(_)) // Build BigDL model from the tf node graph - buildBigDLModel(tfGraph, newInputs, outputs, byteOrder, graphPrototxt, context) + buildBigDLModel(tfGraph, newInputs, outputs, byteOrder, graphPrototxt, + context, generatedBackward) } def checkpoints[T: ClassTag](graphFile: String, binFile: String, byteOrder: ByteOrder)( From 8d48754da840d3838bd4b8070b7c6220e2a210d0 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 22 May 2018 12:49:08 +0800 Subject: [PATCH 0782/1065] max support one-element tensor indices (#2530) --- .../intel/analytics/bigdl/dllib/nn/ops/Max.scala | 3 ++- .../analytics/bigdl/dllib/nn/ops/MaxSpec.scala | 15 +++++++++++++++ 2 files changed, 17 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Max.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Max.scala index 8335f24758c..b178f0165ef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Max.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Max.scala @@ -43,7 +43,8 @@ class Max[T: ClassTag, D: ClassTag]( val x = input[Tensor[D]](1) val y = input[Tensor[Int]](2) - require(y.isScalar, s"reduction indices should be a scalar") + require(y.isScalar || (y.nElement() == 1 && y.dim() == 1), + s"reduction indices should be a scalar or one-element tensor") val reductionIndices = if (startFromZero) { y.value() + 1 } else { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxSpec.scala index 51fbd90c1cc..dd66531cb9d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/MaxSpec.scala @@ -36,6 +36,21 @@ class MaxSpec extends FlatSpec with Matchers { output should be(expectOutput) } + "Max operation forward one-element tensor index" should "works correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + RandomGenerator.RNG.setSeed(10) + val input = + T( + Tensor.range(1, 10).resize(2, 5), + Tensor[Int](1).fill(1) + ) + + val expectOutput = Tensor(T(5f, 10f)) + + val output = Max(startFromZero = true).forward(input) + output should be(expectOutput) + } + "Max keepDims" should "works correctly" in { import com.intel.analytics.bigdl.numeric.NumericFloat RandomGenerator.RNG.setSeed(10) From fbe1a8d697054124b7c0414da02d826055bc3e9e Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 22 May 2018 12:59:47 +0800 Subject: [PATCH 0783/1065] fix const status not handled correct in loop (#2531) --- .../analytics/bigdl/dllib/nn/Scheduler.scala | 4 +-- .../bigdl/dllib/nn/DynamicGraphSpec.scala | 27 +++++++++++++++++++ 2 files changed, 28 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala index c12f6f0a506..eb0004379f4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scheduler.scala @@ -86,9 +86,7 @@ private[bigdl] class Scheduler[T] ( } private def skipExecution(node: ModuleNode[T]): Boolean = { - if (nodeStatus.isConst(node)) return true - - if (node.element.isInstanceOf[ControlDependency[_]]) { + if (node.element.isInstanceOf[ControlDependency[_]] || nodeStatus.isConst(node)) { schedule(node) return true } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala index f69e7a5f581..d1c8f8ed22f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/DynamicGraphSpec.scala @@ -1320,6 +1320,33 @@ class DynamicGraphSpec extends FlatSpec with Matchers { count should be(1) } + "Dynamic Graph" should "support loop twice and merge after const node should be triggered" in { + val input = new com.intel.analytics.bigdl.nn.tf.Const(Tensor(T(1))).inputs() + + val conditionInput = Input() + val const = new com.intel.analytics.bigdl.nn.tf.Const(Tensor(T(9))).inputs() + var count = 0 + def feval(module: Echo[Float], input: Tensor[Float]): Unit = { + count += 1 + } + val echo = Echo(feval).inputs(const) + val less = Less().inputs(echo, conditionInput) + + val updateInput = Input() + val add = AddConstant(1).inputs(updateInput) + + val exit = ControlNodes.whileLoop( + (Seq(conditionInput), less), + Seq((updateInput, add)), + Seq(input) + ) + val model = Graph.dynamic(Array[ModuleNode[Float]](), Array(exit(0)), None, false) + model.forward(null) + val result = model.forward(null) + result.toTensor.valueAt(1) should be(10) + count should be(1) + } + "Dynamic Graph" should "support while loop with multiple loop vars" in { val input1 = Input("Input1") val input2 = Input("Input2") From 57941a082376e033c6a72040056654c9e3ab045c Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 22 May 2018 16:14:37 +0800 Subject: [PATCH 0784/1065] [python API]add batchSize support in model.predict (#2518) * add batchSize for predict * meet code review * meet code review * fix space * fix ut --- .../dllib/utils/python/api/PythonBigDL.scala | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 24999f6996b..34d21a97216 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1908,9 +1908,18 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def predictLocal(model: AbstractModule[Activity, Activity, T], - features: JList[JTensor]): JList[JTensor] = { + features: JList[JTensor], batchSize: Int = -1): JList[JTensor] = { val sampleArray = toSampleArray(features.asScala.toList.map{f => toTensor(f)}) - val localPredictor = LocalPredictor(model) + val localPredictor = if (batchSize > 0) { + val batchPerCore = batchSize / Engine.coreNumber() + if (batchPerCore < 1) { + LocalPredictor(model, batchPerCore = 1) + } else { + LocalPredictor(model, batchPerCore = batchPerCore) + } + } else { + LocalPredictor(model) + } val result = localPredictor.predict(sampleArray) result.map{a => toJTensor(a.asInstanceOf[Tensor[T]])}.toList.asJava } @@ -1924,8 +1933,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelPredictRDD(model: AbstractModule[Activity, Activity, T], - dataRdd: JavaRDD[Sample]): JavaRDD[JTensor] = { - val tensorRDD = model.predict(dataRdd.rdd.map(toJSample(_))) + dataRdd: JavaRDD[Sample], batchSize: Int = -1): JavaRDD[JTensor] = { + val tensorRDD = model.predict(dataRdd.rdd.map(toJSample(_)), batchSize) val listRDD = tensorRDD.map { res => val tensor = res.asInstanceOf[Tensor[T]] val cloneTensor = tensor.clone() From 89c79dcb90061ddda6a2f4fa326defc316ec8673 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 29 May 2018 09:43:10 +0800 Subject: [PATCH 0785/1065] [Feature] -New caffe transformers (#2538) * add new transformers * add tested * per review comments * fix import error * per comments * fix typo * remove unused code --- .../ChannelScaledNormalizer.scala | 75 +++++++++++ .../augmentation/RandomAlterAspect.scala | 120 ++++++++++++++++++ .../image/augmentation/RandomCropper.scala | 115 +++++++++++++++++ .../image/augmentation/RandomResize.scala | 49 +++++++ .../augmentation/RandomAlterRatioSpec.scala | 35 +++++ .../augmentation/RandomCropperSpec.scala | 36 ++++++ .../image/augmentation/RandomResizeSpec.scala | 50 ++++++++ .../ScaledChannelNormalizerSpec.scala | 38 ++++++ 8 files changed, 518 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelScaledNormalizer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomAlterAspect.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomCropper.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomResize.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomAlterRatioSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomCropperSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomResizeSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ScaledChannelNormalizerSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelScaledNormalizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelScaledNormalizer.scala new file mode 100644 index 00000000000..262597052c8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ChannelScaledNormalizer.scala @@ -0,0 +1,75 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.dataset.image.LabeledBGRImage +import com.intel.analytics.bigdl.dataset.{LocalDataSet, Transformer} +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import org.apache.log4j.Logger + +import scala.collection.Iterator + +object ChannelScaledNormalizer { + + def apply(meanR: Int, meanG: Int, meanB: Int, scale: Double): ChannelScaledNormalizer = { + new ChannelScaledNormalizer(meanR, meanG, meanB, scale) + } +} + +/** + * Channel normalization with scale factor + * @param meanR mean value for channel R + * @param meanG mean value for channel G + * @param meanB mean value for channel B + * @param scale scale value applied for all channels + */ + +class ChannelScaledNormalizer(meanR: Int, meanG: Int, meanB: Int, scale: Double) + extends FeatureTransformer { + + override protected def transformMat(feature: ImageFeature): Unit = { + val mat = feature.opencvMat() + val toFloats = OpenCVMat.toFloatPixels(mat) + val content = toFloats._1 + require(content.length % 3 == 0, "Content should be multiple of 3 channels") + var i = 0 + val frameLength = content.length / 3 + val height = toFloats._2 + val width = toFloats._3 + val bufferContent = new Array[Float](width * height * 3) + + val channels = 3 + val mean = Array(meanR, meanG, meanB) + var c = 0 + while (c < channels) { + i = 0 + while (i < frameLength) { + val data_index = c * frameLength + i + bufferContent(data_index) = ((content(data_index) - mean(c)) * scale).toFloat + i += 1 + } + c += 1 + } + if (mat != null) { + mat.release() + } + val newMat = OpenCVMat.fromFloats(bufferContent, height, width) + feature(ImageFeature.mat) = newMat + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomAlterAspect.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomAlterAspect.scala new file mode 100644 index 00000000000..829bc5f46b5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomAlterAspect.scala @@ -0,0 +1,120 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import breeze.numerics.sqrt +import org.opencv.core.{CvType, Mat, Rect} +import com.intel.analytics.bigdl.dataset.Transformer +import com.intel.analytics.bigdl.dataset.image.LabeledBGRImage +import com.intel.analytics.bigdl.opencv.OpenCV +import org.opencv.imgproc.Imgproc + +import scala.collection.Iterator +import com.intel.analytics.bigdl.opencv +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import org.apache.spark.ml +import org.apache.spark.ml.feature +import org.opencv.core.Size + +object RandomAlterAspect { + def apply(min_area_ratio: Float = 0.08f, + max_area_ratio: Int = 1, + min_aspect_ratio_change: Float = 0.75f, + interp_mode: String = "CUBIC", + cropLength: Int = 224): RandomAlterAspect = { + OpenCV.isOpenCVLoaded + new RandomAlterAspect(min_area_ratio, max_area_ratio, + min_aspect_ratio_change, interp_mode, cropLength) + } +} + +/** + * Apply random crop based on area ratio and resize to cropLenth size + * @param min_area_ratio min area ratio + * @param max_area_ratio max area ratio + * @param min_aspect_ratio_change factor applied to ratio area + * @param interp_mode interp mode applied in resize + * @param cropLength final size resized to + */ +class RandomAlterAspect(min_area_ratio: Float = 0.08f, + max_area_ratio: Int = 1, + min_aspect_ratio_change: Float = 0.75f, + interp_mode: String = "CUBIC", + cropLength: Int = 224) + extends FeatureTransformer { + + import com.intel.analytics.bigdl.utils.RandomGenerator.RNG + + @inline + private def randRatio(min: Float, max: Float): Float = { + val res = (RNG.uniform(1e-2, (max - min) * 1000 + 1) + min * 1000) / 1000 + res.toFloat + } + + override protected def transformMat(feature: ImageFeature): Unit = { + val h = feature.opencvMat().size().height + val w = feature.opencvMat().size().width + val area = h * w + + require(min_area_ratio <= max_area_ratio, "min_area_ratio should <= max_area_ratio") + + var attempt = 0 + while (attempt < 10) { + val area_ratio = randRatio(min_area_ratio, max_area_ratio) + val aspect_ratio_change = randRatio(min_aspect_ratio_change, 1 / min_aspect_ratio_change) + val new_area = area_ratio * area + var new_h = (sqrt(new_area) * aspect_ratio_change).toInt + var new_w = (sqrt(new_area) / aspect_ratio_change).toInt + if (randRatio(0, 1) < 0.5) { + val tmp = new_h + new_h = new_w + new_w = tmp + } + if (new_h <= h && new_w <= w) { + val y = RNG.uniform(1e-2, h - new_h + 1).toInt + val x = RNG.uniform(1e-2, w - new_w + 1).toInt + Crop.transform(feature.opencvMat(), + feature.opencvMat(), x, y, x + new_w, y + new_h, false, false) + + Imgproc.resize(feature.opencvMat(), feature.opencvMat(), + new Size(cropLength, cropLength), 0, 0, 2) + attempt = 100 + } + attempt += 1 + } + if (attempt < 20) { + val (new_h, new_w) = resizeImagePerShorterSize(feature.opencvMat(), cropLength) + Imgproc.resize(feature.opencvMat(), + feature.opencvMat(), new Size(cropLength, cropLength), 0, 0, 2) + } + } + + private def resizeImagePerShorterSize(img: Mat, shorter_size: Int) : (Int, Int) = { + val h = img.size().height + val w = img.size().width + var new_h = shorter_size + var new_w = shorter_size + + if (h < w) { + new_w = (w / h * shorter_size).toInt + } else { + new_h = (h / w * shorter_size).toInt + } + (new_h, new_w) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomCropper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomCropper.scala new file mode 100644 index 00000000000..f9a0389e7ca --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomCropper.scala @@ -0,0 +1,115 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.dataset.image.{CropCenter, CropRandom, CropperMethod} +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat +import com.intel.analytics.bigdl.utils.RandomGenerator +import org.opencv.core.CvType + +object RandomCropper { + def apply(cropWidth: Int, cropHeight: Int, + mirror: Boolean, cropperMethod: CropperMethod = CropRandom, + channels: Int = 3): RandomCropper = + new RandomCropper(cropHeight, cropWidth, mirror, cropperMethod, channels) +} + +/** + * Random cropper on uniform distribution with fixed height & width + * @param cropWidth width cropped to + * @param cropHeight height cropped to + * @param mirror whether mirror + * @param cropperMethod crop method + */ +class RandomCropper(cropWidth: Int, cropHeight: Int, + mirror: Boolean, cropperMethod: CropperMethod = CropRandom, + channels: Int = 3) + extends FeatureTransformer { + + import com.intel.analytics.bigdl.utils.RandomGenerator.RNG + + val buffer = new Array[Float](cropWidth * cropHeight * channels) + + override protected def transformMat(feature: ImageFeature): Unit = { + val openCVMat = feature.opencvMat() + + if (openCVMat.`type`() != CvType.CV_32FC3) { + openCVMat.convertTo(openCVMat, CvType.CV_32FC3) + } + + val tmp = new Array[Float](openCVMat.height() * openCVMat.width() * openCVMat.channels()) + + openCVMat.get(0, 0, tmp) + + val height = openCVMat.size().height.toInt + val width = openCVMat.size().width.toInt + + val (startH, startW) = cropperMethod match { + case CropRandom => + val indexH = math.ceil(RNG.uniform(0, height - cropHeight)).toInt + val indexW = math.ceil(RNG.uniform(0, width - cropWidth)).toInt + (indexH, indexW) + case CropCenter => + ((height - cropHeight) / 2, (width - cropWidth) / 2) + } + val do_mirror = mirror && (RandomGenerator.RNG.uniform(0, 2).toInt != 0) + val input = feature.toTensor(ImageFeature.imageTensor) + cropper(input.storage().array(), buffer, + Array(height, width), Array(cropHeight, cropWidth), startH, startW, do_mirror) + openCVMat.put(0, 0, buffer) + val mate = OpenCVMat.fromFloats(buffer, cropHeight, cropWidth, 3) + if (openCVMat != null) { + openCVMat.release() + } + if (mate.`type`() != CvType.CV_32FC3) { + mate.convertTo(mate, CvType.CV_32FC3) + } + mate.put(0, 0, buffer) + feature(ImageFeature.mat) = mate + } + + private def cropper(source: Array[Float], target: Array[Float], srcSize: Array[Int], + tarSize: Array[Int], startH: Int, startW: Int, mirror: Boolean = false): Unit = { + val height = srcSize(0) + val width = srcSize(1) + val cropHeight = tarSize(0) + val cropWidth = tarSize(1) + + val startIndex = startW + startH * width + val frameLength = cropWidth * cropHeight + var i = 0 + var c = 0 + val channels = 3 + while (c < channels) { + i = 0 + while (i < frameLength) { + val th = i / cropWidth + val tw = i % cropWidth + val data_index = (c * height + startH + th) * width + startW + tw + val top_index = if (mirror) { + ((th + 1) * cropWidth - tw - 1) * 3 + c + } else { + i * 3 + c + } + val data = source(data_index) + target(top_index) = data + i += 1 + } + c += 1 + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomResize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomResize.scala new file mode 100644 index 00000000000..14d4086e718 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/RandomResize.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.utils.RandomGenerator +import org.opencv.imgproc.Imgproc + +object RandomResize { + def apply(minSize: Int, maxSize: Int): RandomResize = new RandomResize(minSize, maxSize) +} + +/** + * Random resize between minSize and maxSize and scale height and width to each other + * @param minSize min size to resize to + * @param maxSize max size to resize to + */ +class RandomResize(minSize: Int, maxSize : Int) extends FeatureTransformer { + override def transformMat(feature: ImageFeature): Unit = { + if (feature.isValid) { + var height = feature.opencvMat.height + var width = feature.opencvMat.width + val shorterSize = RandomGenerator.RNG.uniform(1e-2, maxSize - minSize + 1).toInt + minSize + if (height < width) { + width = (width.toFloat / height * shorterSize).toInt + height = shorterSize + } else { + height = (height.toFloat / width * shorterSize).toInt + width = shorterSize + } + + Resize.transform(feature.opencvMat(), feature.opencvMat(), width, height, Imgproc.INTER_CUBIC) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomAlterRatioSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomAlterRatioSpec.scala new file mode 100644 index 00000000000..b211e3e8ce7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomAlterRatioSpec.scala @@ -0,0 +1,35 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class RandomAlterRatioSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "RandomAlterRatio" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = RandomAlterAspect() + val transformed = transformer(data).asInstanceOf[LocalImageFrame] + val imf = transformed.array.head + imf.getHeight() should be (224) + imf.getWidth() should be (224) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomCropperSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomCropperSpec.scala new file mode 100644 index 00000000000..c10c3a8b9cb --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomCropperSpec.scala @@ -0,0 +1,36 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.dataset.image.CropRandom +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class RandomCropperSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "RandomCropper" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val transformer = RandomCropper(224, 224, false, CropRandom) + val transformed = transformer(data).asInstanceOf[LocalImageFrame] + val imf = transformed.array.head + imf.getHeight() should be (224) + imf.getWidth() should be (224) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomResizeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomResizeSpec.scala new file mode 100644 index 00000000000..731fbcbf1a9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/RandomResizeSpec.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class RandomResizeSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "RandomResize" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val originalImageFeature = data.asInstanceOf[LocalImageFrame].array(0) + var originalHeight = originalImageFeature.getHeight + var originalWidth = originalImageFeature.getWidth + if (originalHeight < originalWidth) { + originalWidth = (originalWidth.toFloat / originalHeight * 256).toInt + originalHeight = 256 + } else { + originalHeight = (originalHeight.toFloat / originalWidth * 256).toInt + originalWidth = 256 + } + val transformer = RandomResize(256, 256) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + val resizedHeight = imageFeature.getHeight + val resizedWidth = imageFeature.getWidth + + originalHeight should be (resizedHeight) + originalWidth should be (resizedWidth) + + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imageFeature.opencvMat()) + println(tmpFile) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ScaledChannelNormalizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ScaledChannelNormalizerSpec.scala new file mode 100644 index 00000000000..1803ca4126b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ScaledChannelNormalizerSpec.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.transform.vision.image.{ImageFrame, LocalImageFrame} +import org.opencv.imgcodecs.Imgcodecs +import org.scalatest.{FlatSpec, Matchers} + +class ScaledChannelNormalizerSpec extends FlatSpec with Matchers { + val resource = getClass.getClassLoader.getResource("pascal/") + "ScaledChannelNormalizer" should "work properly" in { + val data = ImageFrame.read(resource.getFile) + val originalImageFeature = data.asInstanceOf[LocalImageFrame].array.head + val originalTensor = originalImageFeature.toTensor("floats") + val transformer = ChannelScaledNormalizer(0, 0, 0, 1) + val transformed = transformer(data).asInstanceOf[LocalImageFrame] + val imf = transformed.array.head + val tensor = imf.toTensor("floats") + originalTensor should be (tensor) + val tmpFile = java.io.File.createTempFile("module", ".jpg") + Imgcodecs.imwrite(tmpFile.toString, imf.opencvMat()) + println(tmpFile) + } +} From 23b9be464a83db7e924b790077fb0e57c44710cc Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 29 May 2018 17:48:56 +0800 Subject: [PATCH 0786/1065] add bigdl resnet 50 validation (#2546) --- .../dllib/example/loadmodel/DatasetUtil.scala | 29 ++++++++++++++----- .../example/loadmodel/ModelValidator.scala | 7 +++++ 2 files changed, 29 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala index 5a68d9b8aa0..23b6f34a756 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala @@ -22,8 +22,9 @@ import java.nio.file.{Files, Path, Paths} import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset._ import com.intel.analytics.bigdl.dataset.image.{BGRImgCropper, BGRImgNormalizer, BGRImgPixelNormalizer, BytesToBGRImg, _} +import com.intel.analytics.bigdl.example.loadmodel.ModelValidator.{BigDlModel, ModelType, TorchModel} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, PixelNormalizer, Resize} +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.utils.File import org.apache.spark.SparkContext @@ -102,12 +103,26 @@ object ResNetPreprocessor { BGRImgToBatch(batchSize) } - def rdd(path: String, batchSize: Int, sc: SparkContext) + def rdd(path: String, batchSize: Int, sc: SparkContext, modelType : ModelType = TorchModel) : RDD[Sample[Float]] = { - val dataSet = DataSet.SeqFileFolder.filesToRdd(path, sc, classNum = 1000) - val transfomer = BytesToBGRImg() -> - BGRImgCropper(cropWidth = imageSize, cropHeight = imageSize, CropCenter) -> - BGRImgNormalizer(0.485, 0.456, 0.406, 0.229, 0.224, 0.225) -> BGRImgToSample() - transfomer(dataSet) + if (modelType == TorchModel) { + val dataSet = DataSet.SeqFileFolder.filesToRdd(path, sc, classNum = 1000) + val transfomer = BytesToBGRImg() -> + BGRImgCropper(cropWidth = imageSize, cropHeight = imageSize, CropCenter) -> + BGRImgNormalizer(0.485, 0.456, 0.406, 0.229, 0.224, 0.225) -> BGRImgToSample() + transfomer(dataSet) + } else if (modelType == BigDlModel) { + val data = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) + val transfomer = PixelBytesToMat() -> + RandomResize(256, 256) -> + RandomCropper(224, 224, false, CropCenter) -> + ChannelScaledNormalizer(104, 117, 123, 0.0078125) -> + MatToTensor[Float]() -> ImageFrameToSample[Float](targetKeys = Array(ImageFeature.label)) + val imgFrame = data -> transfomer + val validImageFeatures = imgFrame.toDistributed().rdd + validImageFeatures.map(x => x[Sample[Float]](ImageFeature.sample)) + } else { + throw new IllegalArgumentException(s"${modelType} not recognized") + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/ModelValidator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/ModelValidator.scala index 07074ae045f..093bf622122 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/ModelValidator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/ModelValidator.scala @@ -125,6 +125,13 @@ object ModelValidator { ResNetPreprocessor.rdd(valPath, param.batchSize, sc)) } + case BigDlModel => + param.modelName match { + case "resnet" => + (Module.loadModule[Float](param.modelPath), + ResNetPreprocessor.rdd(valPath, param.batchSize, sc, BigDlModel)) + } + case _ => throw new IllegalArgumentException(s"${ param.modelType } is not" + s"supported in this example, please use alexnet/inception/resnet") } From 3d42eadda1936b8ad00d9612dadb9260ae75d6dd Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 31 May 2018 16:54:02 +0800 Subject: [PATCH 0787/1065] [Enhancement ]Resnet 50 training and example (#2547) * add resnet-50 training code * rename train cfair -10 * rename file name * refine doc * per comments * fix stype --- .../bigdl/dllib/feature/dataset/DataSet.scala | 5 + .../vision/image/MTImageFeatureToBatch.scala | 129 +++++++++++++++++ .../bigdl/dllib/models/resnet/DataSet.scala | 68 ++++++++- .../bigdl/dllib/models/resnet/README.md | 104 ++++++++++++-- .../{Train.scala => TrainCIFAR10.scala} | 2 +- .../dllib/models/resnet/TrainImageNet.scala | 130 ++++++++++++++++++ .../bigdl/dllib/models/resnet/Utils.scala | 10 +- .../analytics/bigdl/dllib/optim/SGD.scala | 26 ++++ 8 files changed, 456 insertions(+), 18 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/{Train.scala => TrainCIFAR10.scala} (99%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index b1f73d06a27..1bc646457d1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -586,6 +586,11 @@ object DataSet { ImageFrame.rdd(rawData) } + private[bigdl] def filesToImageFeatureDataset(url: String, sc: SparkContext, + classNum: Int, partitionNum: Option[Int] = None): DistributedDataSet[ImageFeature] = { + rdd[ImageFeature](filesToImageFrame(url, sc, classNum, partitionNum).toDistributed().rdd) + } + private[bigdl] def findFiles(path: Path): Array[LocalSeqFilePath] = { val directoryStream = Files.newDirectoryStream(path) import scala.collection.JavaConverters._ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala new file mode 100644 index 00000000000..1f9f65acb3f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -0,0 +1,129 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.transform.vision.image + +import java.util.concurrent.atomic.AtomicInteger + +import com.intel.analytics.bigdl.dataset.{MiniBatch, Transformer, Utils} +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.Engine + +import scala.reflect.ClassTag + +object MTImageFeatureToBatch { + def apply(width: Int, height: Int, batchSize: Int, + transformer: FeatureTransformer, toRGB: Boolean = true) + : MTImageFeatureToBatch = { + new MTImageFeatureToBatch ( + width, height, batchSize, transformer, toRGB) + } +} + +/** + * A transformer pipleline wrapper to create Minibatch in multiple threads + * @param width final image width + * @param height final image height + * @param totalBatchSize global batch size + * @param transformer pipleline for pre-processing + * @param toRGB if converted to RGB, default format is BGR + */ +class MTImageFeatureToBatch private[bigdl](width: Int, height: Int, + totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = true) + extends Transformer[ImageFeature, MiniBatch[Float]] { + + private val batchSize = Utils.getBatchSize(totalBatchSize) + + private val parallelism = Engine.coreNumber() + + private def getPosition(count: AtomicInteger): Int = { + val position = count.getAndIncrement() + if (position < batchSize) position else -1 + } + + private lazy val transformers = (1 to parallelism).map( + _ => new PreFetch -> transformer.cloneTransformer() + ).toArray + + private val frameLength = height * width + private val featureData: Array[Float] = new Array[Float](batchSize * frameLength * 3) + private val labelData: Array[Float] = new Array[Float](batchSize) + private val featureTensor: Tensor[Float] = Tensor[Float]() + private val labelTensor: Tensor[Float] = Tensor[Float]() + + override def apply(prev: Iterator[ImageFeature]): Iterator[MiniBatch[Float]] = { + val iterators = transformers.map(_.apply(prev)) + + new Iterator[MiniBatch[Float]] { + override def hasNext: Boolean = { + iterators.map(_.hasNext).reduce(_ || _) + } + + override def next(): MiniBatch[Float] = { + val count = new AtomicInteger(0) + val batch = Engine.default.invokeAndWait((0 until parallelism).map(tid => () => { + var position = 0 + var record = 0 + while (iterators(tid).hasNext && { + position = getPosition(count) + position != -1 + }) { + val img = iterators(tid).next() + img.copyTo(featureData, position * frameLength * 3, toRGB = toRGB) + labelData(position) = img.getLabel.asInstanceOf[Tensor[Float]].valueAt(1) + record += 1 + } + record + })).sum + + if (labelTensor.nElement() != batch) { + featureTensor.set(Storage[Float](featureData), + storageOffset = 1, sizes = Array(batch, 3, height, width)) + labelTensor.set(Storage[Float](labelData), + storageOffset = 1, sizes = Array(batch)) + } + + MiniBatch(featureTensor, labelTensor) + } + } + } +} + +private class PreFetch extends Transformer[ImageFeature, ImageFeature] { + override def apply(prev: Iterator[ImageFeature]): Iterator[ImageFeature] = { + new Iterator[ImageFeature] { + private var buffer: ImageFeature = null.asInstanceOf[ImageFeature] + + override def hasNext: Boolean = { + if (buffer != null) { + true + } else { + buffer = prev.next() + if (buffer == null) false else true + } + } + + override def next(): ImageFeature = { + if (buffer == null) { + prev.next() + } else { + val tmp = buffer + buffer = null.asInstanceOf[ImageFeature] + tmp + } + } + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala index 1ab3cddb6bf..6b1a63c05d1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala @@ -18,6 +18,9 @@ package com.intel.analytics.bigdl.models.resnet import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset._ import com.intel.analytics.bigdl.dataset.image._ +import com.intel.analytics.bigdl.dataset.image.{HFlip => JHFlip} +import com.intel.analytics.bigdl.transform.vision.image.{MTImageFeatureToBatch, MatToTensor, PixelBytesToMat} +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ import org.apache.spark.SparkContext /** @@ -47,7 +50,7 @@ object Cifar10DataSet extends ResNetDataSet { DataSet.array(Utils.loadTrain(path)) .transform(BytesToBGRImg()) .transform(BGRImgNormalizer(trainMean, trainStd)) - .transform(HFlip(0.5)) + .transform(JHFlip(0.5)) .transform(BGRImgRdmCropper(cropWidth = 32, cropHeight = 32, padding = 4)) .transform(BGRImgToBatch(batchSize)) } @@ -76,8 +79,69 @@ object Cifar10DataSet extends ResNetDataSet { DataSet.array(Utils.loadTrain(path), sc) .transform(BytesToBGRImg()) .transform(BGRImgNormalizer(testMean, testStd)) - .transform(HFlip(0.5)) + .transform(JHFlip(0.5)) .transform(BGRImgRdmCropper(cropWidth = 32, cropHeight = 32, padding = 4)) .transform(BGRImgToBatch(batchSize)) } } + +object ImageNetDataSet extends ResNetDataSet { + + val trainMean = (0.485, 0.456, 0.406) + val trainStd = (0.229, 0.224, 0.225) + val testMean = trainMean + val testStd = trainStd + + override def trainDataSet(path: String, batchSize: Int, size: Int) + : DataSet[MiniBatch[Float]] = { + + DataSet.array(Utils.loadTrain(path)) + .transform(BytesToBGRImg()) + .transform(BGRImgNormalizer(trainMean, trainStd)) + .transform(JHFlip(0.5)) + .transform(BGRImgRdmCropper(cropWidth = 32, cropHeight = 32, padding = 4)) + .transform(BGRImgToBatch(batchSize)) + } + + override def valDataSet(path: String, batchSize: Int, size: Int) + : DataSet[MiniBatch[Float]] = { + + DataSet.array(Utils.loadTest(path)) + .transform(BytesToBGRImg()) + .transform(BGRImgNormalizer(testMean, testStd)) + .transform(BGRImgToBatch(batchSize)) + } + + override def valDataSet(path: String, sc: SparkContext, imageSize: Int, batchSize: Int) + : DataSet[MiniBatch[Float]] = { + DataSet.SeqFileFolder.filesToImageFeatureDataset(path, sc, 1000).transform( + MTImageFeatureToBatch( + width = imageSize, + height = imageSize, + batchSize = batchSize, + transformer = PixelBytesToMat() -> + RandomResize(256, 256) -> + RandomCropper(224, 224, false, CropCenter) -> + ChannelScaledNormalizer(104, 117, 123, 0.0078125) -> + MatToTensor[Float](), toRGB = false + ) + ) + } + + override def trainDataSet(path: String, sc: SparkContext, imageSize: Int, batchSize: Int) + : DataSet[MiniBatch[Float]] = { + DataSet.SeqFileFolder.filesToImageFeatureDataset(path, sc, 1000).transform( + MTImageFeatureToBatch( + width = imageSize, + height = imageSize, + batchSize = batchSize, + transformer = PixelBytesToMat() -> + RandomAlterAspect() -> + RandomCropper(224, 224, true, CropRandom) -> + ChannelScaledNormalizer(104, 117, 123, 0.0078125) -> + MatToTensor[Float](), toRGB = false + ) + ) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md index 4639f8872f2..fc6e3f13e6d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md @@ -1,16 +1,9 @@ # ResNet -This example demonstrates how to use BigDL to train and evaluate the [ResNet](https://arxiv.org/abs/1512.03385) architecture on CIFAR-10 data - -## DataSet -Support Cifar-10 dataset - -Users can download the Cifar-10 dataset from [here](https://www.cs.toronto.edu/~kriz/cifar.html) -The dataset contains two sub-directories, namely, train and val. Users need to set this dataset directory behind the "-f" flag in command line. - +This example demonstrates how to use BigDL to train and evaluate the [ResNet](https://arxiv.org/abs/1512.03385) architecture on CIFAR-10 data and ImageNet data ## Data Processing We use pipeline to process the input data. -Input data are transformed by several pipeline classes, such as HFlip, BGRImgNormalizer, etc. +Input data are transformed by several pipeline classes, such as HFlip, BGRImgNormalizer, RandomCropper, etc. ## Model ShortcutType is a unique feature defined in ResNet. ShortcutType-A is used for Cifar-10, ShortcutType-B is used for ImageNet. @@ -20,8 +13,16 @@ Model is implemented in ResNet You can build one by refer to the [Build Page](https://bigdl-project.github.io/master/#ScalaUserGuide/install-build-src/) from the source code. -## Training -* Spark local, example command + +## Train ResNet on Cifar-10 + +### Prepare Cifar-10 DataSet + +Users can download the Cifar-10 dataset from [here](https://www.cs.toronto.edu/~kriz/cifar.html) +The dataset contains two sub-directories, namely, train and val. Users need to set this dataset directory behind the "-f" flag in command line. + +### Training +* Spark local example command ```shell spark-submit --master local[physical_core_number] \ --driver-memory 3G \ @@ -31,7 +32,7 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 156 \ --learningRate 0.1 ``` -* Spark standalone, example command +* Spark standalone example command ```shell spark-submit --master spark://xxx.xxx.xxx.xxx:xxxx \ --driver-memory 5g --executor-memory 5g \ @@ -42,7 +43,7 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 156 \ --learningRate 0.1 ``` -* Spark yarn client, example command +* Spark yarn client example command ```shell spark-submit --master yarn \ --driver-memory 5g --executor-memory 5g \ @@ -58,7 +59,7 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ We support Local and Spark versions of training. Users can define env as "Local" or "Spark" to set the training environment. -## Parameters +### Parameters ``` --folder | -f [the directory to reach the data] --optnet [share variables in convolutional layers to save the memory usage, default false] @@ -73,3 +74,78 @@ We support Local and Spark versions of training. Users can define env= 80) { + 3 + } else if (epoch >= 60) { + 2 + } else if (epoch >= 30) { + 1 + } else { + 0.0 + } + } + + def main(args: Array[String]): Unit = { + trainParser.parse(args, new TrainParams()).map(param => { + val conf = Engine.createSparkConf().setAppName("Train ResNet on ImageNet2012") + .set("spark.rpc.message.maxSize", "200") + val sc = new SparkContext(conf) + Engine.init + + val batchSize = param.batchSize + val (imageSize, dataSetType, maxEpoch, dataSet) = + (224, DatasetType.ImageNet, param.nepochs, ImageNetDataSet) + + val trainDataSet = dataSet.trainDataSet(param.folder + "/train", sc, imageSize, batchSize) + + val validateSet = dataSet.valDataSet(param.folder + "/val", sc, imageSize, batchSize) + + val shortcut: ShortcutType = ShortcutType.B + + val model = if (param.modelSnapshot.isDefined) { + Module.load[Float](param.modelSnapshot.get) + } else { + val curModel = + ResNet(classNum = param.classes, T("shortcutType" -> shortcut, "depth" -> param.depth, + "optnet" -> param.optnet, "dataSet" -> dataSetType)) + if (param.optnet) { + ResNet.shareGradInput(curModel) + } + ResNet.modelInit(curModel) + curModel + } + + println(model) + + val optimMethod = if (param.stateSnapshot.isDefined) { + val optim = OptimMethod.load[Float](param.stateSnapshot.get).asInstanceOf[SGD[Float]] + val baseLr = param.learningRate + val iterationsPerEpoch = math.ceil(1281167 / param.batchSize).toInt + val warmUpIteration = iterationsPerEpoch * param.warmupEpoch + val maxLr = param.maxLr + val delta = (maxLr - baseLr) / warmUpIteration + optim.learningRateSchedule = SGD.EpochDecayWithWarmUp(warmUpIteration, delta, imageNetDecay) + optim + } else { + val baseLr = param.learningRate + val iterationsPerEpoch = math.ceil(1281167 / param.batchSize).toInt + val warmUpIteration = iterationsPerEpoch * param.warmupEpoch + val maxLr = param.maxLr + val delta = (maxLr - baseLr) / warmUpIteration + + logger.info(s"warmUpIteraion: $warmUpIteration, startLr: ${param.learningRate}, " + + s"maxLr: $maxLr, " + + s"delta: $delta, nesterov: ${param.nesterov}") + new SGD[Float](learningRate = param.learningRate, learningRateDecay = 0.0, + momentum = param.momentum, dampening = param.dampening, + nesterov = param.nesterov, + learningRateSchedule = SGD.EpochDecayWithWarmUp(warmUpIteration, delta, imageNetDecay)) + } + + val optimizer = Optimizer( + model = model, + dataset = trainDataSet, + criterion = new CrossEntropyCriterion[Float]() + ) + if (param.checkpoint.isDefined) { + optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch) + } + + val logdir = "resnet-imagenet" + val appName = s"${sc.applicationId}" + val trainSummary = TrainSummary(logdir, appName) + trainSummary.setSummaryTrigger("LearningRate", Trigger.severalIteration(1)) + trainSummary.setSummaryTrigger("Parameters", Trigger.severalIteration(10)) + val validationSummary = ValidationSummary(logdir, appName) + + optimizer + .setOptimMethod(optimMethod) + .setValidation(Trigger.everyEpoch, + validateSet, Array(new Top1Accuracy[Float], new Top5Accuracy[Float])) + .setEndWhen(Trigger.maxEpoch(maxEpoch)) + .optimize() + sc.stop() + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Utils.scala index aa462b88fab..6bead538804 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/Utils.scala @@ -42,7 +42,9 @@ object Utils { momentum: Double = 0.9, dampening: Double = 0.0, nesterov: Boolean = true, - graphModel: Boolean = false) + graphModel: Boolean = false, + warmupEpoch: Int = 0, + maxLr: Double = 0.0) val trainParser = new OptionParser[TrainParams]("BigDL ResNet Example") { head("Train ResNet model on single node") @@ -94,6 +96,12 @@ object Utils { opt[Unit]('g', "graphModel") .text("use graph model") .action((x, c) => c.copy(graphModel = true)) + opt[Int]("warmupEpoch") + .text("warmup epoch") + .action((x, c) => c.copy(warmupEpoch = x)) + opt[Double]("maxLr") + .text("maxLr") + .action((x, c) => c.copy(maxLr = x)) } case class TestParams( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala index 6f632bbccc7..fef30161a68 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala @@ -661,4 +661,30 @@ object SGD { currentRate = schedules(cur).currentRate } } + + /** + * Learning rate schedule based on warm up Iterations + * @param warmUpIteration Warm up iteration number + * @param warmUpDelta Warm up dealta value applied to warm up iteration + * @param decayType A function to calculate decay on epochs + */ + case class EpochDecayWithWarmUp( + warmUpIteration: Int, + warmUpDelta: Double, + decayType: (Int) => Double) extends LearningRateSchedule { + override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { + val lr = optimMethod.learningRate + val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) + val clr = if (nevals < warmUpIteration) { + - lr - warmUpDelta * nevals + } else { + val epoch = optimMethod.state[Int]("epoch") + val decay = decayType(epoch) + val maxLr = lr + warmUpDelta * warmUpIteration + - maxLr * math.pow(0.1, decay) + } + optimMethod.state("evalCounter") = nevals + 1 + currentRate = clr + } + } } From 9b222ec6544e3036b3ed8de92eeb72566eb434b7 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 4 Jun 2018 15:16:23 +0800 Subject: [PATCH 0788/1065] [Fix] - Add resnet50 missing change (#2548) * add missing checkin * fix tests acoordingly * fix ut and graph def --- .../bigdl/dllib/models/resnet/README.md | 6 +- .../bigdl/dllib/models/resnet/ResNet.scala | 73 +++-- .../bigdl/dllib/models/ResNetSpec.scala | 262 +----------------- .../bigdl/dllib/utils/GraphNodeSpec.scala | 4 +- .../utils/SpatialShareConvolutionSpec.scala | 4 +- 5 files changed, 59 insertions(+), 290 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md index fc6e3f13e6d..0164447caaf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md @@ -26,7 +26,7 @@ The dataset contains two sub-directories, namely, train and val. Users need to s ```shell spark-submit --master local[physical_core_number] \ --driver-memory 3G \ ---class com.intel.analytics.bigdl.models.resnet.Train \ +--class com.intel.analytics.bigdl.models.resnet.TrainCIFAR10 \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f Cifar-10/ \ --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 156 \ @@ -37,7 +37,7 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ spark-submit --master spark://xxx.xxx.xxx.xxx:xxxx \ --driver-memory 5g --executor-memory 5g \ --total-executor-cores 32 --executor-cores 8 \ ---class com.intel.analytics.bigdl.models.resnet.Train \ +--class com.intel.analytics.bigdl.models.resnet.TrainCIFAR10 \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f Cifar-10/ \ --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 156 \ @@ -48,7 +48,7 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ spark-submit --master yarn \ --driver-memory 5g --executor-memory 5g \ --num-executors 4 --executor-cores 8 \ ---class com.intel.analytics.bigdl.models.resnet.Train \ +--class com.intel.analytics.bigdl.models.resnet.TrainCIFAR10 \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f Cifar-10/ \ --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 156 \ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/ResNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/ResNet.scala index 56a299412e6..4b7abe387ad 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/ResNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/ResNet.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.Table @@ -43,15 +44,31 @@ object Convolution { padH: Int = 0, nGroup: Int = 1, propagateBack: Boolean = true, - optnet: Boolean = true) + optnet: Boolean = true, + weightDecay: Double = 1e-4) (implicit ev: TensorNumeric[T]): SpatialConvolution[T] = { - if (optnet) { + val wReg = L2Regularizer[T](weightDecay) + val bReg = L2Regularizer[T](weightDecay) + val conv = if (optnet) { SpatialShareConvolution[T](nInputPlane, nOutputPlane, kernelW, kernelH, - strideW, strideH, padW, padH, nGroup, propagateBack) + strideW, strideH, padW, padH, nGroup, propagateBack, wReg, bReg) } else { SpatialConvolution[T](nInputPlane, nOutputPlane, kernelW, kernelH, - strideW, strideH, padW, padH, nGroup, propagateBack) + strideW, strideH, padW, padH, nGroup, propagateBack, wReg, bReg) } + conv.setInitMethod(MsraFiller(false), Zeros) + conv + } +} + +object Sbn { + def apply[@specialized(Float, Double) T: ClassTag]( + nOutput: Int, + eps: Double = 1e-3, + momentum: Double = 0.1, + affine: Boolean = true) + (implicit ev: TensorNumeric[T]): SpatialBatchNormalization[T] = { + SpatialBatchNormalization[T](nOutput, eps, momentum, affine).setInitMethod(Ones, Zeros) } } @@ -135,8 +152,7 @@ object ResNet { val depth = opt.get("depth").getOrElse(18) val shortCutType = opt.get("shortcutType") val shortcutType = shortCutType.getOrElse(ShortcutType.B).asInstanceOf[ShortcutType] - val dataSet = opt.get("dataset") - val dataset = dataSet.getOrElse(DatasetType.CIFAR10).asInstanceOf[DatasetType] + val dataSet = opt.getOrElse[DatasetType]("dataSet", DatasetType.CIFAR10) val optnet = opt.get("optnet").getOrElse(true) def shortcut(nInputPlane: Int, nOutputPlane: Int, stride: Int): Module[Float] = { @@ -146,7 +162,7 @@ object ResNet { if (useConv) { Sequential() .add(Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride, optnet = optnet)) - .add(SpatialBatchNormalization(nOutputPlane)) + .add(Sbn(nOutputPlane)) } else if (nInputPlane != nOutputPlane) { Sequential() .add(SpatialAveragePooling(1, 1, stride, stride)) @@ -164,10 +180,10 @@ object ResNet { val s = Sequential() s.add(Convolution(nInputPlane, n, 3, 3, stride, stride, 1, 1, optnet = optnet)) - s.add(SpatialBatchNormalization(n)) + s.add(Sbn(n)) s.add(ReLU(true)) s.add(Convolution(n, n, 3, 3, 1, 1, 1, 1, optnet = optnet)) - s.add(SpatialBatchNormalization(n)) + s.add(Sbn(n)) Sequential() .add(ConcatTable() @@ -183,14 +199,13 @@ object ResNet { val s = Sequential() s.add(Convolution(nInputPlane, n, 1, 1, 1, 1, 0, 0, optnet = optnet)) - .add(SpatialBatchNormalization(n)) + .add(Sbn(n)) .add(ReLU(true)) .add(Convolution(n, n, 3, 3, stride, stride, 1, 1, optnet = optnet)) - .add(SpatialBatchNormalization(n)) + .add(Sbn(n)) .add(ReLU(true)) .add(Convolution(n, n*4, 1, 1, 1, 1, 0, 0, optnet = optnet)) - .add(SpatialBatchNormalization(n * 4)) - + .add(Sbn(n * 4).setInitMethod(Zeros, Zeros)) Sequential() .add(ConcatTable() .add(s) @@ -209,7 +224,7 @@ object ResNet { } val model = Sequential() - if (dataset == DatasetType.ImageNet) { + if (dataSet == DatasetType.ImageNet) { val cfg = Map( 18 -> ((2, 2, 2, 2), 512, basicBlock: (Int, Int) => Module[Float]), @@ -232,7 +247,7 @@ object ResNet { logger.info(" | ResNet-" + depth + " ImageNet") model.add(Convolution(3, 64, 7, 7, 2, 2, 3, 3, optnet = optnet, propagateBack = false)) - .add(SpatialBatchNormalization(64)) + .add(Sbn(64)) .add(ReLU(true)) .add(SpatialMaxPooling(3, 3, 2, 2, 1, 1)) .add(layer(block, 64, loopConfig._1)) @@ -241,8 +256,9 @@ object ResNet { .add(layer(block, 512, loopConfig._4, 2)) .add(SpatialAveragePooling(7, 7, 1, 1)) .add(View(nFeatures).setNumInputDims(3)) - .add(Linear(nFeatures, classNum)) - } else if (dataset == DatasetType.CIFAR10) { + .add(Linear(nFeatures, classNum, true, L2Regularizer(1e-4), L2Regularizer(1e-4)) + .setInitMethod(RandomNormal(0.0, 0.01), Zeros)) + } else if (dataSet == DatasetType.CIFAR10) { require((depth - 2)%6 == 0, "depth should be one of 20, 32, 44, 56, 110, 1202") val n = (depth-2)/6 @@ -259,7 +275,7 @@ object ResNet { model.add(View(64).setNumInputDims(3)) model.add(Linear(64, 10)) } else { - throw new IllegalArgumentException(s"Invalid dataset ${dataset}") + throw new IllegalArgumentException(s"Invalid dataset ${dataSet}") } model } @@ -281,7 +297,7 @@ object ResNet { if (useConv) { val conv1 = Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride, optnet = optnet).inputs(input) - val bn1 = SpatialBatchNormalization(nOutputPlane).inputs(conv1) + val bn1 = Sbn(nOutputPlane).inputs(conv1) bn1 } else if (nInputPlane != nOutputPlane) { val pool1 = SpatialAveragePooling(1, 1, stride, stride).inputs(input) @@ -298,11 +314,11 @@ object ResNet { val nInputPlane = iChannels iChannels = n - val conv1 = SpatialConvolution(nInputPlane, n, 3, 3, stride, stride, 1, 1).inputs(input) - val bn1 = SpatialBatchNormalization(n).inputs(conv1) + val conv1 = Convolution(nInputPlane, n, 3, 3, stride, stride, 1, 1).inputs(input) + val bn1 = Sbn(n).inputs(conv1) val relu1 = ReLU(true).inputs(bn1) - val conv2 = SpatialConvolution(n, n, 3, 3, 1, 1, 1, 1).inputs(relu1) - val bn2 = SpatialBatchNormalization(n).inputs(conv2) + val conv2 = Convolution(n, n, 3, 3, 1, 1, 1, 1).inputs(relu1) + val bn2 = Sbn(n).inputs(conv2) val shortcut = shortcutFunc(nInputPlane, n, stride, input) val add = CAddTable(true).inputs(bn2, shortcut) val output = ReLU(true).inputs(add) @@ -314,13 +330,13 @@ object ResNet { iChannels = n * 4 val conv1 = Convolution(nInputPlane, n, 1, 1, 1, 1, 0, 0, optnet = optnet).inputs(input) - val bn1 = SpatialBatchNormalization(n).inputs(conv1) + val bn1 = Sbn(n).inputs(conv1) val relu = ReLU(true).inputs(bn1) val conv2 = Convolution(n, n, 3, 3, stride, stride, 1, 1, optnet = optnet).inputs(relu) - val bn2 = SpatialBatchNormalization(n).inputs(conv2) + val bn2 = Sbn(n).inputs(conv2) val relu2 = ReLU(true).inputs(bn2) val conv3 = Convolution(n, n*4, 1, 1, 1, 1, 0, 0, optnet = optnet).inputs(relu2) - val sbn = SpatialBatchNormalization(n * 4).inputs(conv3) + val sbn = Sbn(n * 4).setInitMethod(Zeros, Zeros).inputs(conv3) val shortcut = shortcutFunc(nInputPlane, n * 4, stride, input) val add = CAddTable(true).inputs(sbn, shortcut) @@ -362,7 +378,7 @@ object ResNet { val input = Input() val conv1 = Convolution(3, 64, 7, 7, 2, 2, 3, 3, optnet = optnet, propagateBack = false).inputs(input) - val bn = SpatialBatchNormalization(64).inputs(conv1) + val bn = Sbn(64).inputs(conv1) val relu = ReLU(true).inputs(bn) val pool = SpatialMaxPooling(3, 3, 2, 2, 1, 1).inputs(relu) val layer1 = layer(block, 64, loopConfig._1)(pool) @@ -371,7 +387,8 @@ object ResNet { val layer4 = layer(block, 512, loopConfig._4, 2)(layer3) val pool2 = SpatialAveragePooling(7, 7, 1, 1).inputs(layer4) val view = View(nFeatures).setNumInputDims(3).inputs(pool2) - val output = Linear(nFeatures, classNum).inputs(view) + val output = Linear(nFeatures, classNum, true, L2Regularizer(1e-4), L2Regularizer(1e-4)) + .setInitMethod(RandomNormal(0.0, 0.01), Zeros).inputs(view) Graph(input, output) } else if (dataset == DatasetType.CIFAR10) { require((depth - 2)%6 == 0, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala index 87cf5f8d790..a0c38504446 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala @@ -36,256 +36,8 @@ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial class ResNetSpec extends TorchSpec { - private val suffix = ".t7" + (new java.util.Random()).nextLong() - - "ResNet Float" should "generate correct output" in { - // System.setProperty("java.io.tmpdir", "/disk2/test"); - Engine.setCoreNumber(4) - torchCheck() - - for (i <- 1 to 1) { - println(s"unitTest-${i}") - unitTest(i, i + 100, 18, 4) - } - - } - - - def unitTest(inputSeed: Int, modelSeed: Int, depth: Int, batchSize: Int) { - - Random.setSeed(inputSeed) - val classNum: Int = 1000 - val input = Tensor[Float](batchSize, 3, 224, 224).apply1( e => Random.nextFloat()) - val labels = Tensor[Float](batchSize).apply1(e => Random.nextInt(classNum)) - - val seed = modelSeed - RNG.setSeed(seed) - val model = ResNet(classNum, T("shortcutType" -> ShortcutType.B, - "depth" -> depth, "dataset" -> DatasetType.ImageNet)) - model.zeroGradParameters() - - - val code = - "torch.setdefaulttensortype('torch.FloatTensor')" + - "torch.manualSeed(" + seed + ")\n" + - "local depth = " + depth + "\n" + - """ - local Convolution = nn.SpatialConvolution - local Avg = nn.SpatialAveragePooling - local ReLU = nn.ReLU - local Max = nn.SpatialMaxPooling - local SBatchNorm = nn.SpatialBatchNormalization - local nClasses = 1000 - local shortcutType = 'B' - local iChannels - local function shortcut(nInputPlane, nOutputPlane, stride) - local useConv = shortcutType == 'C' or - (shortcutType == 'B' and nInputPlane ~= nOutputPlane) - if useConv then - -- 1x1 convolution - return nn.Sequential() - :add(Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride)) - :add(SBatchNorm(nOutputPlane)) - elseif nInputPlane ~= nOutputPlane then - -- Strided, zero-padded identity shortcut - return nn.Sequential() - :add(nn.SpatialAveragePooling(1, 1, stride, stride)) - :add(nn.Concat(2) - :add(nn.Identity()) - :add(nn.MulConstant(0))) - else - return nn.Identity() - end - end - - local function basicblock(n, stride) - local nInputPlane = iChannels - iChannels = n - - local s = nn.Sequential() - s:add(Convolution(nInputPlane,n,3,3,stride,stride,1,1)) - s:add(SBatchNorm(n)) - s:add(ReLU(true)) - s:add(Convolution(n,n,3,3,1,1,1,1)) - s:add(SBatchNorm(n)) - - return nn.Sequential() - --:add(shortcut(nInputPlane, n, stride)) - --:add(s) - :add(nn.ConcatTable() - :add(s) - -- :add(s)) - :add(shortcut(nInputPlane, n, stride))) - :add(nn.CAddTable(true)) - :add(ReLU(true)) - end - - local function bottleneck(n, stride) - local nInputPlane = iChannels - iChannels = n * 4 - - local s = nn.Sequential() - s:add(Convolution(nInputPlane,n,1,1,1,1,0,0)) - s:add(SBatchNorm(n)) - s:add(ReLU(true)) - s:add(Convolution(n,n,3,3,stride,stride,1,1)) - s:add(SBatchNorm(n)) - s:add(ReLU(true)) - s:add(Convolution(n,n*4,1,1,1,1,0,0)) - s:add(SBatchNorm(n * 4)) - - return nn.Sequential() - :add(nn.ConcatTable() - :add(s) - :add(shortcut(nInputPlane, n * 4, stride))) - :add(nn.CAddTable(true)) - :add(ReLU(true)) - end - - - local function layer(block, features, count, stride) - local s = nn.Sequential() - for i=1,count do - s:add(block(features, i == 1 and stride or 1)) - end - return s - end - - local model = nn.Sequential() - - - local cfg = { - --[10] = {{1, 1, 1, 1}, 512, basicblock}, - [18] = {{2, 2, 2, 2}, 512, basicblock}, - [34] = {{3, 4, 6, 3}, 512, basicblock}, - [50] = {{3, 4, 6, 3}, 2048, bottleneck}, - [101] = {{3, 4, 23, 3}, 2048, bottleneck}, - [152] = {{3, 8, 36, 3}, 2048, bottleneck}, - } - - assert(cfg[depth], 'Invalid depth: ' .. tostring(depth)) - local def, nFeatures, block = table.unpack(cfg[depth]) - iChannels = 64 - --print(' | ResNet-' .. depth .. ' ImageNet') - - - -- The ResNet ImageNet model - model:add(Convolution(3,64,7,7,2,2,3,3)) - model:add(SBatchNorm(64)) - model:add(ReLU(true)) - model:add(Max(3,3,2,2,1,1)) - model:add(layer(block, 64, def[1])) - model:add(layer(block, 128, def[2], 2)) - model:add(layer(block, 256, def[3], 2)) - model:add(layer(block, 512, def[4], 2)) - model:add(Avg(7, 7, 1, 1)) - model:add(nn.View(nFeatures):setNumInputDims(3)) - model:add(nn.Linear(nFeatures, nClasses)) - --model:add(nn.LogSoftMax()) - - local parameters, gradParameters = model:getParameters() - parameters_initial = parameters : clone() - gradParameters_initial = gradParameters : clone() - - --local criterion = nn.ClassNLLCriterion() - local criterion = nn.CrossEntropyCriterion() - state = { - learningRate = 1e-2, - momentum = 0.9, - dampening = 0.0, - weightDecay = 5e-4 - } - - feval = function(x) - model:forward(input) - criterion:forward(model.output, labels) - model:zeroGradParameters() - criterion:backward(model.output, labels) - model:backward(input, criterion.gradInput) - return criterion.output, gradParameters - end - - for i = 1, 1, 1 do - w, err = optim.sgd(feval, parameters, state) - end - - output=model.output - gradOutput=criterion.gradInput - err = criterion.output - gradInput = model.gradInput - - """ - - TH.runNM(code, immutable.Map("input" -> input, "labels" -> labels), - Array("output", "gradOutput", "err", "parameters_initial", - "gradParameters_initial", "gradInput", "model"), suffix) - - ResNet.shareGradInput(model) - val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Float]] - val parameters = model.getParameters()._1 - - for (i <- 0 until parameters.nElement()) { - if (abs(parameters.storage().array()(i) - parameterTorch.storage().array()(i)) > 1e-8) { - println(s"${parameters.storage().array()(i)} ${parameterTorch.storage().array()(i)}") - } - } - - val (weights, grad) = model.getParameters() - val criterion = CrossEntropyCriterion[Float]() - - val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - val sgd = new SGD[Float] - - def feval(x: Tensor[Float]): (Float, Tensor[Float]) = { - model.forward(input) - criterion.forward(model.output.asInstanceOf[Tensor[Float]], labels) - model.zeroGradParameters() - val gradOutputTest = criterion.backward(model.output.asInstanceOf[Tensor[Float]], labels) - model.backward(input, gradOutputTest) - (criterion.output, grad) - } - for (i <- 1 to 1) { - sgd.optimize(feval, weights, state) - } - - val output = TH.map("output", suffix).asInstanceOf[Tensor[Float]] - val outputTest = model.output.toTensor[Float] - var abss = 0.0 - for (i <- 0 until outputTest.nElement()) { - val tmp = abs(outputTest.storage().array()(i) - output.storage().array()(i)) - abss += tmp - } - println(s"outputAbs:$abss") - assert(abss < 1e-2) - - - val errTest = criterion.output - val err = TH.map("err", suffix).asInstanceOf[Double] - println(s"${abs(errTest - err)}") - assert(abs(errTest - err) < 1.5e-6) - - val gradOutputTest = criterion.backward(outputTest, labels) - val gradOutput = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Float]] - abss = 0.0 - for (i <- 0 until gradOutputTest.nElement()) { - val tmp = abs(gradOutputTest.storage().array()(i) - gradOutput.storage().array()(i)) - abss += tmp - } - assert(abss < 2e-6) - println(s"gradOutputTestAbs:$abss") - val gradInput = model.gradInput.asInstanceOf[Tensor[Float]] - val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Float]] - - abss = 0.0 - for (i <- 0 until gradInputTorch.nElement()) { - val tmp = abs(gradInputTorch.storage().array()(i) - gradInput.storage().array()(i)) - abss += tmp - } - println(s"gradInputTestAbs:$abss") - - } + private val suffix = ".t7" + (new java.util.Random()).nextLong() "ResNet basicBlockFunc graph" should "be same with original one" in { val depth = 16 @@ -348,7 +100,7 @@ class ResNetSpec extends TorchSpec { RNG.setSeed(1000) val model = ResNet(classNum, T("shortcutType" -> ShortcutType.B, - "depth" -> depth, "dataset" -> DatasetType.ImageNet)) + "depth" -> depth, "dataSet" -> DatasetType.ImageNet)) RNG.setSeed(1000) val graphModel = ResNet.graph(classNum, T("shortcutType" -> ShortcutType.B, "depth" -> depth, "dataset" -> DatasetType.ImageNet)) @@ -405,7 +157,7 @@ class ResNetSpec extends TorchSpec { RNG.setSeed(1000) val model = ResNet(classNum, T("shortcutType" -> ShortcutType.B, - "depth" -> depth, "dataset" -> DatasetType.ImageNet)) + "depth" -> depth, "dataSet" -> DatasetType.ImageNet)) RNG.setSeed(1000) val graphModel = ResNet.graph(classNum, T("shortcutType" -> ShortcutType.B, "depth" -> depth, "dataset" -> DatasetType.ImageNet)) @@ -436,7 +188,7 @@ class ResNetSpec extends TorchSpec { RNG.setSeed(1000) val model = ResNet(classNum, T("shortcutType" -> ShortcutType.B, - "depth" -> depth, "dataset" -> DatasetType.CIFAR10)) + "depth" -> depth, "dataSet" -> DatasetType.CIFAR10)) RNG.setSeed(1000) val graphModel = ResNet.graph(classNum, T("shortcutType" -> ShortcutType.B, "depth" -> depth, "dataset" -> DatasetType.CIFAR10)) @@ -467,7 +219,7 @@ object ResNetTest { val depth = opt.get("depth").getOrElse(18) val shortCutType = opt.get("shortcutType") val shortcutType = shortCutType.getOrElse(ShortcutType.B).asInstanceOf[ShortcutType] - val dataSet = opt.get("dataset") + val dataSet = opt.get("dataSet") val dataset = dataSet.getOrElse(DatasetType.CIFAR10).asInstanceOf[DatasetType] val optnet = opt.get("optnet").getOrElse(true) @@ -535,10 +287,10 @@ object ResNetTest { val nInputPlane = iChannels iChannels = n - val conv1 = SpatialConvolution(nInputPlane, n, 3, 3, stride, stride, 1, 1).inputs(input) + val conv1 = Convolution(nInputPlane, n, 3, 3, stride, stride, 1, 1).inputs(input) val bn1 = SpatialBatchNormalization(n).inputs(conv1) val relu1 = ReLU(true).inputs(bn1) - val conv2 = SpatialConvolution(n, n, 3, 3, 1, 1, 1, 1).inputs(relu1) + val conv2 = Convolution(n, n, 3, 3, 1, 1, 1, 1).inputs(relu1) val bn2 = SpatialBatchNormalization(n).inputs(conv2) val shortcut = shortcutFunc(nInputPlane, n, stride)(input) val add = CAddTable(true).inputs(bn2, shortcut) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala index c33860af764..dd6d8ad65bb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/GraphNodeSpec.scala @@ -80,10 +80,10 @@ class GraphNodeSpec extends FlatSpec with Matchers { val seed = modelSeed RNG.setSeed(seed) val model = ResNet(classNum, T("shortcutType" -> ShortcutType.B, - "depth" -> depth, "dataset" -> DatasetType.ImageNet)) + "depth" -> depth, "dataSet" -> DatasetType.ImageNet)) RNG.setSeed(seed) val model2 = ResNet(classNum, T("shortcutType" -> ShortcutType.B, - "depth" -> depth, "dataset" -> DatasetType.ImageNet)) + "depth" -> depth, "dataSet" -> DatasetType.ImageNet)) val (weights, grad) = model.getParameters() val (w, g) = model2.getParameters() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SpatialShareConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SpatialShareConvolutionSpec.scala index 8431c489528..382144bf880 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SpatialShareConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SpatialShareConvolutionSpec.scala @@ -169,10 +169,10 @@ class SpatialShareConvolutionSpec extends FlatSpec with Matchers { "Resnet 18" should "return right result" in { val resnet = ResNet(1000, T("shortcutType" -> ShortcutType.B, - "depth" -> 18, "dataset" -> DatasetType.ImageNet)) + "depth" -> 18, "dataSet" -> DatasetType.ImageNet)) val sharedResnet = SpatialShareConvolution.shareConvolution( ResNet(1000, T("shortcutType" -> ShortcutType.B, - "depth" -> 18, "dataset" -> DatasetType.ImageNet))) + "depth" -> 18, "dataSet" -> DatasetType.ImageNet))) sharedResnet.getParameters()._1.copy(resnet.getParameters()._1) Random.setSeed(100) From 7affba7337f0c2128b0f32813621c06ead6b67e4 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 4 Jun 2018 17:29:39 +0800 Subject: [PATCH 0789/1065] [new feature]new optimMethod ftrl (#2495) --- .../analytics/bigdl/dllib/optim/Ftrl.scala | 195 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 15 ++ .../bigdl/dllib/optim/FtrlSpec.scala | 146 +++++++++++++ 3 files changed, 356 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Ftrl.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/FtrlSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Ftrl.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Ftrl.scala new file mode 100644 index 00000000000..9ea5e8a2e0f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Ftrl.scala @@ -0,0 +1,195 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.tensor.{DenseTensorApply, Tensor, TensorFunc6} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * An implementation of Ftrl https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf. + * Support L1 penalty, L2 penalty and shrinkage-type L2 penalty. + * + * @param learningRate learning rate + * @param learningRatePower double, must be less or equal to zero. Default is -0.5. + * @param initialAccumulatorValue double, the starting value for accumulators, + * require zero or positive values. Default is 0.1. + * @param l1RegularizationStrength double, must be greater or equal to zero. Default is zero. + * @param l2RegularizationStrength double, must be greater or equal to zero. Default is zero. + * @param l2ShrinkageRegularizationStrength double, must be greater or equal to zero. + * Default is zero. This differs from l2RegularizationStrength above. L2 above is a + * stabilization penalty, whereas this one is a magnitude penalty. + */ +class Ftrl[@specialized(Float, Double) T: ClassTag]( + var learningRate: Double = 1e-3, + var learningRatePower: Double = -0.5, + var initialAccumulatorValue: Double = 0.1, + var l1RegularizationStrength: Double = 0.0, + var l2RegularizationStrength: Double = 0.0, + var l2ShrinkageRegularizationStrength: Double = 0.0 + )(implicit ev: TensorNumeric[T]) extends OptimMethod[T] { + + @transient var accumNew: Tensor[T] = _ + @transient var buffer: Tensor[T] = _ + @transient var quadratic: Tensor[T] = _ + @transient var gradWithStrinkage: Tensor[T] = _ + + protected def checkParam(learningRate: Double, + learningRatePower: Double, + initialAccumulatorValue: Double, + l1RegularizationStrength: Double, + l2RegularizationStrength: Double, + l2ShrinkageRegularizationStrength: Double): Unit = { + require(learningRate >= 0.0, s"Ftrl: learning rate should be greater or equal to zero." + + s" but got $learningRate") + require(learningRatePower <= 0.0, + s"Ftrl: learning rate power should be smaller or equal to zero." + + s" but got $learningRatePower") + require(initialAccumulatorValue >= 0.0, + s"Ftrl: initial value of accumulator should be greater or equal to zero." + + s" but got $initialAccumulatorValue") + require(l1RegularizationStrength >= 0.0, + s"Ftrl: L1 regularization strength should be greater or equal to zero." + + s" but got $l1RegularizationStrength") + require(l2RegularizationStrength >= 0.0, + s"Ftrl: L2 regularization strength should be greater or equal to zero." + + s" but got $l2RegularizationStrength") + require(l2ShrinkageRegularizationStrength >= 0.0, + s"Ftrl: L2 shrinkage regularization strength should be greater or equal to zero." + + s" but got $l2ShrinkageRegularizationStrength") + } + + override def optimize(feval: (Tensor[T]) => (T, Tensor[T]), + parameter: Tensor[T]): (Tensor[T], Array[T]) = { + checkParam(learningRate, learningRatePower, initialAccumulatorValue, l1RegularizationStrength, + l2RegularizationStrength, l2ShrinkageRegularizationStrength) + val lr = this.learningRate + val lrp = this.learningRatePower + val iav = ev.fromType(this.initialAccumulatorValue) + val l1rs = ev.fromType(this.l1RegularizationStrength) + val l2rs = ev.fromType(this.l2RegularizationStrength) + val l2srs = ev.fromType(this.l2ShrinkageRegularizationStrength) + + val (fx, dfdx) = feval(parameter) + + val (accum, linear) = if (state.get[Tensor[T]]("accum").isDefined) { + (state.get[Tensor[T]]("accum").get, state.get[Tensor[T]]("linear").get) + } else { + // fill accum with initialAccumulatorValue + (Tensor[T]().resizeAs(dfdx).fill(iav), Tensor[T]().resizeAs(dfdx)) + } + + if (accumNew == null || !accumNew.isSameSizeAs(dfdx)) { + accumNew = Tensor[T]().resizeAs(dfdx).copy(accum) + } + + if (buffer == null || !buffer.isSameSizeAs(dfdx)) buffer = Tensor[T]().resizeAs(dfdx) + + if (quadratic == null || !quadratic.isSameSizeAs(dfdx)) quadratic = Tensor[T]().resizeAs(dfdx) + + if (gradWithStrinkage == null || !gradWithStrinkage.isSameSizeAs(dfdx)) { + gradWithStrinkage = Tensor[T]().resizeAs(dfdx) + } + + val computeParameter = new TensorFunc6[T]() { + // parameter = (sign(linear) * l1rs - linear) / quadratic if |linear| > l1rs else 0.0 + override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, + data3: Array[T], offset3: Int): Unit = { + data1(offset1) = if (ev.isGreater(ev.abs(data2(offset2)), l1rs)) { + val l1 = if (ev.isGreater(data2(offset2), ev.zero)) { + l1rs + } else if (ev.isGreater(ev.zero, data2(offset2))) { + ev.negative(l1rs) + } else { + ev.zero + } + ev.divide(ev.minus(l1, data2(offset2)), data3(offset3)) + } else { + ev.zero + } + } + } + + if (ev.isGreaterEq(ev.zero, l2srs)) { + // accumNew = accum + dfdx * dfdx + accumNew.addcmul(dfdx, dfdx) + // linear += dfdx + accum^(-lrp) / lr * parameter - accumNew^(-lrp) / lr * parameter + linear.add(dfdx) + buffer.pow(accum, ev.fromType(-lrp)) + linear.addcmul(ev.fromType(1 / lr), buffer, parameter) + buffer.pow(accumNew, ev.fromType(-lrp)) + linear.addcmul(ev.fromType(-1 / lr), buffer, parameter) + // quadratic = 1.0 / lr * accumNew^(- lrp) + 2 * l2 + quadratic.fill(ev.times(ev.fromType(2), l2rs)) + quadratic.add(ev.fromType(1 / lr), buffer) + // parameter = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 + DenseTensorApply.apply3(parameter, linear, quadratic, computeParameter) + } else { + // gradWithShrinkage = dfdx + 2 * l2srs * parameter + gradWithStrinkage.copy(dfdx) + gradWithStrinkage.add(ev.times(ev.fromType(2), l2srs), parameter) + // accumNew = accum + gradWithShrinkage * gradWithShrinkage + accumNew.addcmul(gradWithStrinkage, gradWithStrinkage) + // linear += gradWithStrinkage + accum^(-lrp) / lr * parameter + // - accumNew^(-lrp) / lr * parameter + linear.add(gradWithStrinkage) + buffer.pow(accum, ev.fromType(-lrp)) + linear.addcmul(ev.fromType(1.0 / lr), buffer, parameter) + buffer.pow(accumNew, ev.fromType(-lrp)) + linear.addcmul(ev.fromType(-1.0 / lr), buffer, parameter) + // quadratic = 1.0 / lr * accumNew^(- lrp) + 2 * l2 + quadratic.fill(ev.times(ev.fromType(2), l2rs)) + quadratic.add(ev.fromType(1 / lr), buffer) + // parameter = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 + DenseTensorApply.apply3(parameter, linear, quadratic, computeParameter) + } + // accum = accum_new + accum.copy(accumNew) + + state("accum") = accum + state("linear") = linear + + (parameter, Array(fx)) + } + + override def loadFromTable(config: Table): this.type = { + this.learningRate = config.get[Double]("learningRate").getOrElse(this.learningRate) + this.learningRatePower = config.get[Double]("learningRatePower") + .getOrElse(this.learningRatePower) + this.initialAccumulatorValue = config.get[Double]("initialAccumulatorValue") + .getOrElse(this.initialAccumulatorValue) + this.l1RegularizationStrength = config.get[Double]("l1RegularizationStrength") + .getOrElse(this.l1RegularizationStrength) + this.l2RegularizationStrength = config.get[Double]("l2RegularizationStrength") + .getOrElse(this.l2RegularizationStrength) + this.l2ShrinkageRegularizationStrength = config.get[Double]("l2ShrinkageRegularizationStrength") + .getOrElse(this.l2ShrinkageRegularizationStrength) + this + } + + override def clearHistory(): Unit = { + state.delete("accum") + state.delete("linear") + accumNew = null + buffer = null + quadratic = null + } + + override def getLearningRate(): Double = this.learningRate +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 34d21a97216..29aa240dc03 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2130,6 +2130,21 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new Adam[T](learningRate, learningRateDecay, beta1, beta2, Epsilon) } + def createFtrl( + learningRate: Double = 1e-3, + learningRatePower: Double = -0.5, + initialAccumulatorValue: Double = 0.1, + l1RegularizationStrength: Double = 0.0, + l2RegularizationStrength: Double = 0.0, + l2ShrinkageRegularizationStrength: Double = 0.0): Ftrl[T] = { + new Ftrl[T](learningRate, + learningRatePower, + initialAccumulatorValue, + l1RegularizationStrength, + l2RegularizationStrength, + l2ShrinkageRegularizationStrength) + } + def createAdamax( learningRate: Double = 0.002, beta1: Double = 0.9, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/FtrlSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/FtrlSpec.scala new file mode 100644 index 00000000000..aad267794a6 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/FtrlSpec.scala @@ -0,0 +1,146 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{RandomGenerator, T, TestUtils} +import org.scalatest.{FlatSpec, Matchers} + +import scala.collection.mutable.ArrayBuffer + +@com.intel.analytics.bigdl.tags.Parallel +class FtrlSpec extends FlatSpec with Matchers { + val start = System.currentTimeMillis() + "Ftrl" should "perform well on rosenbrock function" in { + val x = Tensor[Double](2).fill(0) + val config = T("learningRate" -> 0.1) + val optm = new Ftrl[Double] + var fx = new ArrayBuffer[Double] + for (i <- 1 to 10001) { + val result = optm.optimize(TestUtils.rosenBrock, x, config) + if ((i - 1) % 1000 == 0) { + fx += result._2(0) + } + } + + println(s"x is \n$x") + println("fx is") + for (i <- 1 to fx.length) { + println(s"${(i - 1) * 1000 + 1}, ${fx(i - 1)}") + } + + val spend = System.currentTimeMillis() - start + println("Time Cost: " + spend + "ms") + + (fx.last < 1e-4) should be(true) + x(Array(1)) should be(1.0 +- 0.01) + x(Array(2)) should be(1.0 +- 0.01) + } + + "Ftrl" should "works fine" in { + val weights = Tensor[Float](2).zero() + val grads = Tensor[Float](T(0.1f, 0.2f)) + val ftrl = new Ftrl[Float](3.0) + (1 to 3).foreach{_ => + ftrl.optimize(_ => (0.0f, grads), weights) + } + + weights.valueAt(1) should be (-2.602609f +- 0.000001f) + weights.valueAt(2) should be (-4.296985f +- 0.000001f) + } + + "Ftrl" should "works fine 2" in { + val weights = Tensor[Float](2).zero() + val grads = Tensor[Float](T(0.01f, 0.02f)) + val ftrl = new Ftrl[Float](3.0) + (1 to 3).foreach{_ => + ftrl.optimize(_ => (0.0f, grads), weights) + } + + weights.valueAt(1) should be (-0.284321f +- 0.000001f) + weights.valueAt(2) should be (-0.566949f +- 0.000001f) + } + + "Ftrl" should "works fine 3" in { + val weights = Tensor[Float](T(1.0f, 2.0f)) + val grads = Tensor[Float](T(0.1f, 0.2f)) + val ftrl = new Ftrl[Float](3.0) + (1 to 3).foreach{_ => + ftrl.optimize(_ => (0.0f, grads), weights) + } + + weights.valueAt(1) should be (-2.556072f +- 0.000001f) + weights.valueAt(2) should be (-3.987293f +- 0.000001f) + } + + "Ftrl with L1" should "works fine 3" in { + val weights = Tensor[Float](T(1.0f, 2.0f)) + val grads = Tensor[Float](T(0.1f, 0.2f)) + val ftrl = new Ftrl[Float](3.0, l1RegularizationStrength = 0.001) + (1 to 10).foreach{_ => + ftrl.optimize(_ => (0.0f, grads), weights) + } + + weights.valueAt(1) should be (-7.667187f +- 0.000001f) + weights.valueAt(2) should be (-10.912737f +- 0.000001f) + } + + "Ftrl with L1, L2" should "works fine 3" in { + val weights = Tensor[Float](T(1.0f, 2.0f)) + val grads = Tensor[Float](T(0.1f, 0.2f)) + val ftrl = new Ftrl[Float](3.0, l1RegularizationStrength = 0.001, + l2RegularizationStrength = 2.0) + (1 to 10).foreach{_ => + ftrl.optimize(_ => (0.0f, grads), weights) + } + + weights.valueAt(1) should be (-0.240599f +- 0.000001f) + weights.valueAt(2) should be (-0.468293f +- 0.000001f) + } + + "Ftrl with L1, L2, L2Shrinkage" should "works fine 3" in { + val weights = Tensor[Float](T(1.0f, 2.0f)) + val grads = Tensor[Float](T(0.1f, 0.2f)) + val ftrl = new Ftrl[Float](3.0, initialAccumulatorValue = 0.1, l1RegularizationStrength = 0.001, + l2RegularizationStrength = 2.0, l2ShrinkageRegularizationStrength = 0.1f) + (1 to 10).foreach{_ => + ftrl.optimize(_ => (0.0f, grads), weights) + } + + weights.valueAt(1) should be (-0.219319f +- 0.000001f) + weights.valueAt(2) should be (-0.406429f +- 0.000001f) + } + + "Ftrl save/load" should "works fine" in { + val weights = Tensor[Float](T(1.0f, 2.0f)) + val grads = Tensor[Float](T(0.1f, 0.2f)) + val ftrl = new Ftrl[Float](3.0, initialAccumulatorValue = 0.1, l1RegularizationStrength = 0.001, + l2RegularizationStrength = 2.0, l2ShrinkageRegularizationStrength = 0.1f) + val tmpFile = java.io.File.createTempFile("ftrl", ".optim") + ftrl.save(tmpFile.getAbsolutePath, true) + val loaded = OptimMethod.load[Float](tmpFile.getAbsolutePath) + + (1 to 10).foreach{_ => + loaded.optimize(_ => (0.0f, grads), weights) + } + + weights.valueAt(1) should be (-0.219319f +- 0.000001f) + weights.valueAt(2) should be (-0.406429f +- 0.000001f) + + } +} + From 362030731498faf4ba9cd27cfa317d88abedd481 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 20 Jun 2018 16:42:42 +0800 Subject: [PATCH 0790/1065] [New Feature] Code refactor DistriOptimizer for advance parameter operations (#2556) --- .../bigdl/dllib/optim/DistriOptimizer.scala | 108 ++++------- .../bigdl/dllib/optim/LocalOptimizer.scala | 27 +-- .../bigdl/dllib/optim/OptimMethod.scala | 8 + .../bigdl/dllib/optim/Optimizer.scala | 42 ++--- .../optim/parameters/AllReduceParameter.scala | 20 +- .../parameters/ParameterOperations.scala | 135 +++++++++++++ .../bigdl/dllib/optim/parameters/Util.scala | 51 +++++ .../bigdl/dllib/tensor/DenseTensor.scala | 6 +- .../tensor/QuantizedTensorUnsupported.scala | 2 +- .../bigdl/dllib/tensor/SparseTensor.scala | 2 +- .../bigdl/dllib/tensor/TensorMath.scala | 2 +- .../analytics/bigdl/dllib/utils/Table.scala | 2 +- .../dllib/optim/DistriOptimizerSpec.scala | 177 ++++++++++++++---- .../dllib/optim/LocalOptimizerSpec.scala | 48 +++++ 14 files changed, 465 insertions(+), 165 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/ParameterOperations.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/Util.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index af30c2d857f..383036097dd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -17,10 +17,9 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.{Module, _} -import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, - MiniBatch, SampleToMiniBatch, Sample, PaddingParam} +import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, PaddingParam, Sample, SampleToMiniBatch} import com.intel.analytics.bigdl.nn.{Module, Utils} -import com.intel.analytics.bigdl.parameters.AllReduceParameter +import com.intel.analytics.bigdl.parameters.{AllReduceParameter, ParameterProcessor} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ @@ -84,8 +83,8 @@ object DistriOptimizer { * @param cachePath cache path * @param trainSummary train summary * @param validationSummary validation summary - * @param isOverWrite if overwrite the checkpoint - * @param clippingParams gradient clipping configurations + * @param isOverWrite if overwrite the checkpoint + * @param parameterProcessers a list of ParameterProcessor used to process parameters */ private[optim] def optimize[T: ClassTag]( trainingModel: Module[T], @@ -105,7 +104,7 @@ object DistriOptimizer { trainSummary: Option[TrainSummary], validationSummary: Option[ValidationSummary], isOverWrite: Boolean, - clippingParams: GradientClippingParams + parameterProcessers: Array[ParameterProcessor] )(implicit ev: TensorNumeric[T]): Unit = { val sc = dataset.originRDD().sparkContext val partitionNum = dataset.originRDD().partitions.length @@ -122,17 +121,18 @@ object DistriOptimizer { if (!optimMethod.state.contains("recordsProcessedThisEpoch")) { optimMethod.state.update("recordsProcessedThisEpoch", 0) } + + val _subModelNumber = Engine.getEngineType() match { + case MklBlas => coresPerNode + } val driverState = T( "epoch" -> optimMethod.state("epoch"), "neval" -> optimMethod.state("neval"), "Loss" -> optimMethod.state("Loss"), - "score" -> optimMethod.state("score") + "score" -> optimMethod.state("score"), + "parallelism" -> _subModelNumber ) - val _subModelNumber = Engine.getEngineType() match { - case MklBlas => coresPerNode - } - logger.info("Count dataset") val countBefore = System.nanoTime() val numSamples = dataset.data(train = false).map(_.size()).reduce(_ + _) @@ -153,7 +153,6 @@ object DistriOptimizer { dataset.shuffle() val shuffleEnd = System.nanoTime() logger.info(s"Shuffle data complete. Takes ${(shuffleEnd - shuffleBefore) / 1e9}s") - } var tasks: ArrayBuffer[Future[_]] = new ArrayBuffer() @@ -168,13 +167,6 @@ object DistriOptimizer { var dropModelNumBatch = 0 var lossArray = new Array[Double](_subModelNumber) - // gradient clip settings - val constantClippingEnable = clippingParams.enableConstantClipping - val normClippingEnable = clippingParams.enableL2NormClipping - val maxValueClip = clippingParams.maxValueClip - val minValueClip = clippingParams.minValueClip - val normValueClip = clippingParams.normValueClip - var epochStart = System.nanoTime() var dataRDD = dataset.data(train = true) @@ -309,7 +301,7 @@ object DistriOptimizer { } Iterator.single(finishedThreads.size) } - }.reduce(_ + _) + }.reduce(_ + _) dropModelNumBatch += (driverSubModelNum - numFinishedModelUpdates) if (dropPercentage == 0.0 || @@ -317,49 +309,27 @@ object DistriOptimizer { // enough records were processed for this batch, so update the model val value = lossSum.value / numFinishedModelUpdates - var l2Norm = 0.0f - var scale = ev.fromType(numFinishedModelUpdates) - if (normClippingEnable) { - val sumSquare = models.mapPartitions(modelIter => { - val getG = System.nanoTime() - parameters.aggregateGradientPartition() - driverMetrics.add("aggregrateGradientParition average executor", - System.nanoTime() - getG) - - val gradLength = parameters.gradientPartition.nElement() - val taskSize = gradLength / _subModelNumber - val extraTask = gradLength % _subModelNumber - val parallelNum = if (taskSize == 0) extraTask else _subModelNumber - val squares = new Array[Double](parallelNum) - Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { - val offset = tid * taskSize + math.min(tid, extraTask) - val length = taskSize + (if (tid < extraTask) 1 else 0) - squares(tid) = ev.toType[Double]( - parameters.gradientPartition.narrow(1, offset + 1, length).sumSquare()) - })) - var sum = 0.0 - var i = 0 - while (i < parallelNum) { - sum += squares(i) - i += 1 - } - Iterator.single(sum) - }).reduce(_ + _) - l2Norm = (math.sqrt(sumSquare) / numFinishedModelUpdates).toFloat - if (l2Norm > normValueClip) { - scale = ev.fromType[Double]((l2Norm * numFinishedModelUpdates) / normValueClip) - } - } + driverState("numFinishedModel") = numFinishedModelUpdates + // isGradientUpdated is flag to mark whether gradient is updated. May changed in the future. + driverState("isGradientUpdated") = false + // parameterProcesser like L2NormClippingProcessor may aggregate gradient, + // and change the value of isGradientUpdated in driverState. + parameterProcessers.foreach(_.collectGlobalData(models, parameters, metrics, driverState)) + val isGradientUpdated = driverState[Boolean]("isGradientUpdated") + val stateBroadcast = sc.broadcast(driverState) models.mapPartitions { modelIter => val modelCache = modelIter.next() - if (!normClippingEnable) { + // if parameterProcesser has aggregated gradient, we can skip this aggregation. + if (!isGradientUpdated) { val getG = System.nanoTime() - parameters.aggregateGradientPartition() + parameters.aggregateGradientPartition(numFinishedModelUpdates) driverMetrics.add("aggregrateGradientParition average executor", System.nanoTime() - getG) } - parameters.gradientPartition.div(scale) + parameterProcessers.foreach( + _.processParameters(parameters, modelCache, stateBroadcast.value)) + modelCache.optimMethod.state.update("epoch", driverState[Int]("epoch")) modelCache.optimMethod.state.update("neval", driverState[Int]("neval")) modelCache.optimMethod.state.update("Loss", driverState[Float]("Loss")) @@ -367,10 +337,6 @@ object DistriOptimizer { modelCache.optimMethod.state.update("score", driverState[Float]("score")) } var time = System.nanoTime() - // gradient clipping - if (constantClippingEnable) { - parameters.gradientPartition.clamp(minValueClip, maxValueClip) - } modelCache.optimMethod.optimize(_ => (ev.fromType(value), parameters.gradientPartition), parameters.weightPartition) driverMetrics.add("compute weight average", System.nanoTime() - time) @@ -380,9 +346,11 @@ object DistriOptimizer { Iterator.empty }.count() + stateBroadcast.destroy() recordsProcessedThisEpoch += recordsNum.value val end = System.nanoTime() wallClockTime += end - start + driverState("isGradientUpdated") = true driverState("Loss") = lossSum.value.toFloat / numFinishedModelUpdates optimMethod.updateHyperParameter() driverState("Throughput") = recordsNum.value.toFloat / ((end - start) / 1e9f) @@ -581,6 +549,8 @@ object DistriOptimizer { * @param checkSingleton if checkSingleton * @param parameters all reduce parameter instance * @param validationMethods validation methods + * @param optimMethod optimization method + * @param parameterProcessors a list of ParameterProcessor used to process parameters * @return cached models */ private def initThreadModels[T: ClassTag]( @@ -593,7 +563,8 @@ object DistriOptimizer { checkSingleton: Boolean, parameters: AllReduceParameter[T], validationMethods: Option[Array[ValidationMethod[T]]], - optimMethod: OptimMethod[T] + optimMethod: OptimMethod[T], + parameterProcessors: ArrayBuffer[ParameterProcessor] )(implicit ev: TensorNumeric[T]) = { val sc = dataset.originRDD().sparkContext val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod)) @@ -614,7 +585,7 @@ object DistriOptimizer { s"Passed in rdd partition number ${dataset.originRDD().partitions.length}" + s" is not equal to configured node number ${nodeNumber}") - val partitionNum = dataset.originRDD().partitions.length + val computeThresholdbatchSize = state.get[Int]("computeThresholdbatchSize").get val nExecutor = Engine.nodeNumber() val executorCores = Engine.coreNumber() @@ -667,7 +638,6 @@ object DistriOptimizer { models } - /** * Validate current model and save the result. * @@ -895,7 +865,8 @@ class DistriOptimizer[T: ClassTag] ( prepareInput() models = DistriOptimizer.initThreadModels(model, distDataset, criterion, state, - nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, optimMethod) + nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, + optimMethod, parameterProcessors) if (checkpointPath.isDefined) { val file = checkpointPath.get + "/" + @@ -929,7 +900,7 @@ class DistriOptimizer[T: ClassTag] ( trainSummary, validationSummary, isOverWrite, - gradientClippingParams + parameterProcessors.toArray ) retryNum = Int.MaxValue } catch { @@ -967,8 +938,9 @@ class DistriOptimizer[T: ClassTag] ( DistriOptimizer.logger.info("Recover from origin model") } optimMethod.clearHistory() - models = DistriOptimizer.initThreadModels(newModel, distDataset, criterion, state, - nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, optimMethod) + models = DistriOptimizer.initThreadModels(newModel, distDataset, criterion, + state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, + optimMethod, parameterProcessors) } else { throw t } @@ -1006,5 +978,3 @@ class DistriOptimizer[T: ClassTag] ( return choice; } } - - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index 121a7637198..decd214ed61 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -92,6 +92,7 @@ class LocalOptimizer[T: ClassTag] ( state("epoch") = state.get[Int]("epoch").getOrElse(1) state("neval") = state.get[Int]("neval").getOrElse(1) state("isLayerwiseScaled") = Utils.isLayerwiseScaled(model) + dataset.shuffle() val numSamples = dataset.data(train = false).map(_.size()).reduce(_ + _) var iter = dataset.data(train = true) @@ -105,6 +106,7 @@ class LocalOptimizer[T: ClassTag] ( val stackSize = batch.size() / subModelNumber val extraSize = batch.size() % subModelNumber val parallelism = if (stackSize == 0) extraSize else subModelNumber + state("parallelism") = parallelism val miniBatchBuffer = new Array[MiniBatch[T]](parallelism) while (b < parallelism) { val offset = b * stackSize + math.min(b, extraSize) + 1 @@ -151,31 +153,10 @@ class LocalOptimizer[T: ClassTag] ( }) ) val loss = lossSum / parallelism - var scale = ev.fromType(parallelism) - if (gradientClippingParams.enableL2NormClipping) { - val squares = new Array[Double](syncGradParallelNum) - Engine.default.invokeAndWait((0 until syncGradParallelNum).map(tid => () => { - val offset = tid * syncGradTaskSize + math.min(tid, syncGradExtraTask) - val length = syncGradTaskSize + (if (tid < syncGradExtraTask) 1 else 0) - squares(tid) = ev.toType[Double](grad.narrow(1, offset + 1, length).sumSquare()) - })) - var sum = 0.0 - var i = 0 - while (i < squares.size) { - sum += squares(i) - i += 1 - } - val l2Norm = (math.sqrt(sum) / parallelism).toFloat + grad.div(ev.fromType(parallelism)) - if (l2Norm > gradientClippingParams.normValueClip) { - scale = ev.fromType[Float]((l2Norm * parallelism) / gradientClippingParams.normValueClip) - } - } - grad.div(scale) + parameterProcessors.foreach(_.processParameters(model, state)) - if (gradientClippingParams.enableConstantClipping) { - grad.clamp(gradientClippingParams.minValueClip, gradientClippingParams.maxValueClip) - } optimMethod.state.update("epoch", state.get("epoch")) optimMethod.state.update("neval", state.get("neval")) optimMethod.optimize(_ => (ev.fromType(loss), grad), weight) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/OptimMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/OptimMethod.scala index 13ddbddf3c3..e30a1453fad 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/OptimMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/OptimMethod.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.optim +import com.intel.analytics.bigdl.parameters.ParameterProcessor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{File, T, Table} import org.apache.commons.lang3.SerializationUtils @@ -102,6 +103,13 @@ trait OptimMethod[@specialized(Float, Double) T] extends Serializable { */ def loadFromTable(config: Table): this.type + /** + * get parameter processor + * + * @return + */ + def getParameterProcessor(): Option[ParameterProcessor] = None + /** * Optimize the model parameter * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index c83da768036..2cc6878426d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -20,12 +20,14 @@ import java.nio.file.{Files, Paths} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{DataSet, SampleToMiniBatch, _} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.parameters.{ConstantClippingProcessor, + L2NormClippingProcessor, ParameterProcessor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.spark.rdd.RDD +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -66,8 +68,6 @@ abstract class Optimizer[T: ClassTag, D]( protected var computeThresholdbatchSize: Int = 100 protected var warmupIterationNum: Int = 200 - protected val gradientClippingParams = GradientClippingParams(false, 0.0f, 0.0f, false, 0.0f) - model.checkDuplicate() /** @@ -318,6 +318,10 @@ abstract class Optimizer[T: ClassTag, D]( */ def setOptimMethod(method : OptimMethod[T]): this.type = { this.optimMethod = method + val processor = method.getParameterProcessor() + if (processor.isDefined) { + parameterProcessors += processor.get + } this } @@ -360,8 +364,9 @@ abstract class Optimizer[T: ClassTag, D]( */ def disableGradientClipping() : this.type = { - gradientClippingParams.enableConstantClipping = false - gradientClippingParams.enableL2NormClipping = false + parameterProcessors = parameterProcessors.filterNot(processor => + (processor.isInstanceOf[ConstantClippingProcessor] || + processor.isInstanceOf[L2NormClippingProcessor])) this } @@ -371,26 +376,28 @@ abstract class Optimizer[T: ClassTag, D]( * @param max the maximum value to clip by * @return */ - def setConstantGradientClipping(min: Float, max: Float) + def setConstantGradientClipping(min: Double, max: Double) : this.type = { - require(min < max, "min value must be smaller than max") - gradientClippingParams.enableConstantClipping = true - gradientClippingParams.minValueClip = min - gradientClippingParams.maxValueClip = max + require(min <= max, "min value can not be larger than max") + parameterProcessors.append(new ConstantClippingProcessor(min, max)) this } /** * Clip gradient to a maximum L2-norm - * @param clipNorm gradient L2-Norm threshold + * @param l2NormThreshold gradient L2-Norm threshold * @return */ - def setGradientClippingByl2Norm(clipNorm: Float) + def setGradientClippingByl2Norm(l2NormThreshold: Double) : this.type = { - gradientClippingParams.enableL2NormClipping = true - gradientClippingParams.normValueClip = clipNorm + parameterProcessors.append(new L2NormClippingProcessor(l2NormThreshold)) this } + + /** + * a list of ParameterProcessor, orders matter + */ + protected var parameterProcessors = ArrayBuffer[ParameterProcessor]() } object Optimizer { @@ -544,10 +551,3 @@ object Optimizer { } } } - -case class GradientClippingParams( - var enableConstantClipping: Boolean, - var minValueClip: Float, - var maxValueClip: Float, - var enableL2NormClipping: Boolean, - var normValueClip: Float) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala index 2bc9fb5eba1..483de788b39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.parameters import java.util.concurrent.atomic.AtomicLong -import java.util.concurrent.{Callable, Executors, ExecutorService, Future, ThreadFactory} +import java.util.concurrent.{Callable, ExecutorService, Executors, Future, ThreadFactory} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -28,6 +28,8 @@ import org.apache.spark.storage.{BlockId, BlockManagerWrapper, StorageLevel} import org.apache.spark.TaskContext import scala.collection.JavaConverters._ +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer import scala.reflect._ object AllReduceParameter { @@ -55,8 +57,9 @@ object AllReduceParameter { private val nextId = new AtomicLong(0) - def newParameter[T: ClassTag](partitionNum: Int, size: Int): AllReduceParameter[T] = { - new AllReduceParameter(nextId.getAndIncrement(), partitionNum, size) + def newParameter[T: ClassTag](partitionNum: Int, size: Int) + (implicit ev: TensorNumeric[T]): AllReduceParameter[T] = { + new AllReduceParameter[T](nextId.getAndIncrement(), partitionNum, size) } } @@ -75,7 +78,8 @@ object AllReduceParameter { * @param size size of the parameter (1D vector) * @tparam T Tensor element type */ -class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) extends Serializable { +private[bigdl] class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) + (implicit ev: TensorNumeric[T]) extends Serializable { import AllReduceParameter._ @transient private var taskSize = 0 @@ -134,7 +138,8 @@ class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) ex * @param parameter A tensor representing the initial underlying weights of this * `AllReduceParameter` */ - def init(parameter: Tensor[T])(implicit ev: TensorNumeric[T]): Unit = { + def init(parameter: Tensor[T])(implicit ev: TensorNumeric[T]): + (Int, Int, Int) = { val _classTag = classTag[T] val start = partitionId * taskSize + math.min(partitionId, extraSize) val length = taskSize + (if (partitionId < extraSize) 1 else 0) @@ -152,6 +157,7 @@ class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) ex val fp16param = new FP16CompressedTensor[T](length)(_classTag) fp16param.compress(0, parameter, start, length) BlockManagerWrapper.putBytes(blockId, fp16param.bytes(), StorageLevel.MEMORY_ONLY_SER) + (partitionId, start, length) } private def getWeightBlockId(pid: Int): BlockId = { @@ -211,8 +217,9 @@ class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) ex * Retrieve gradients for the slice of the model that this node is responsible for from all the * other nodes. A new thread is created for each separate node. The gradients are then summed * and then stored in decompressed form in `gradientPartition`. + * @param avgNumbers average numbers. */ - def aggregateGradientPartition(): Unit = { + def aggregateGradientPartition(avgNumbers: Int): Unit = { require(partitionId < partitionNum, s"This parameter was created with $partitionNum " + s"partitions. It cannot be used on RDDs with > $partitionNum partitions.") val params = new Array[CompressedTensor[T]](partitionNum) @@ -253,6 +260,7 @@ class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) ex } ).asJava) params.head.deCompress(gradientPartition) + gradientPartition.div(ev.fromType(avgNumbers)) } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/ParameterOperations.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/ParameterOperations.scala new file mode 100644 index 00000000000..d90f50f81c0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/ParameterOperations.scala @@ -0,0 +1,135 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.parameters + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.{DistributedDataSet, MiniBatch} +import org.apache.spark.rdd.RDD +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.optim.DistriOptimizer.Cache +import com.intel.analytics.bigdl.optim.Metrics +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import org.apache.spark.broadcast.Broadcast + +import scala.collection.mutable + +/** + * Process parameters trait, subclass must be independent of each other + */ +private[bigdl] trait ParameterProcessor + extends Serializable { + /** + * Collect global data according to operations list, usually executed in driver + * + * @param models cached models + * @param parameters [[AllReduceParameter]] + * @param metrics metrics + * @param state A table contained needed information + */ + def collectGlobalData[T](models: RDD[Cache[T]], + parameters: AllReduceParameter[T], + metrics: Metrics, + state: Table)(implicit ev: TensorNumeric[T]) : Unit = {} + + /** + * Advance operations to process parameters, usually executed in worker + * + * @param parameters [[AllReduceParameter]] + * @param state A table contained needed information + */ + def processParameters[T](parameters: AllReduceParameter[T], + modelCache: Cache[T], + state: Table)(implicit ev: TensorNumeric[T]): Unit = {} + + /** + * Advance operations to process parameters, usually executed in local optimer + * + * @param model the model to be trained + * @param state A table contained needed information + */ + def processParameters[T](model: Module[T], + state: Table)(implicit ev: TensorNumeric[T]): Unit = {} +} + +/** + * Process constant clipping + */ +private[bigdl] class ConstantClippingProcessor(min: Double, max: Double) + extends ParameterProcessor { + override def processParameters[T](parameters: AllReduceParameter[T], + modelCache: Cache[T], + state: Table)(implicit ev: TensorNumeric[T]): Unit = { + parameters.gradientPartition.clamp(min, max) + } + + override def processParameters[T](model: Module[T], + state: Table)(implicit ev: TensorNumeric[T]): Unit = { + val gradients = model.getParameters()._2 + gradients.clamp(min, max) + } +} + +/** + * Process l2 norm clipping + */ +private[bigdl] class L2NormClippingProcessor(l2NormThreshold: Double) + extends ParameterProcessor { + override def collectGlobalData[T](models: RDD[Cache[T]], + parameters: AllReduceParameter[T], + metrics: Metrics, + state: Table)(implicit ev: TensorNumeric[T]) : Unit = { + val numFinishedModel = state.get[Int]("numFinishedModel").get + val parallelism = state.get[Int]("parallelism").get + val isGradientUpdated = state.get[Boolean]("isGradientUpdated").get + + val sumSquare = models.mapPartitions(modelIter => { + if (!isGradientUpdated) { + val getG = System.nanoTime() + parameters.aggregateGradientPartition(numFinishedModel) + metrics.add("aggregrateGradientParition average executor", + System.nanoTime() - getG) + } + val sum = Util.getSumsquareInParallel(parameters.gradientPartition, parallelism) + Iterator.single(sum) + }).reduce(_ + _) + + state("isGradientUpdated") = true + state("l2Norm") = math.sqrt(sumSquare) + } + + override def processParameters[T](parameters: AllReduceParameter[T], + modelCache: Cache[T], + state: Table)(implicit ev: TensorNumeric[T]): Unit = { + val l2Norm = state.get[Double]("l2Norm").get + if (l2Norm > l2NormThreshold) { + val scale = ev.fromType[Double](l2Norm / l2NormThreshold) + parameters.gradientPartition.div(scale) + } + } + + override def processParameters[T](model: Module[T], + state: Table)(implicit ev: TensorNumeric[T]): Unit = { + val parallelism = state.get[Int]("parallelism").get + val gradients = model.getParameters()._2 + val l2Norm = math.sqrt(Util.getSumsquareInParallel(gradients, parallelism)) + + if (l2Norm > l2NormThreshold) { + val scale = ev.fromType[Double](l2Norm / l2NormThreshold) + gradients.div(scale) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/Util.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/Util.scala new file mode 100644 index 00000000000..48f302bb71c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/Util.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.parameters + +import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +private[bigdl] object Util { + /** Get square sum of a tensor in parallel, which has better + * performance if tensor is in large size + * @param parameters + * @param parallelism + * @return square sum of the tensor + */ + def getSumsquareInParallel[T](parameters: Tensor[T], parallelism: Int) + (implicit ev: TensorNumeric[T]): Double = { + val gradLength = parameters.nElement() + val taskSize = gradLength / parallelism + val extraTask = gradLength % parallelism + val parallelNum = if (taskSize == 0) extraTask else parallelism + val squares = new Array[Double](parallelNum) + Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { + val offset = tid * taskSize + math.min(tid, extraTask) + val length = taskSize + (if (tid < extraTask) 1 else 0) + squares(tid) = ev.toType[Double]( + parameters.narrow(1, offset + 1, length).sumSquare()) + })) + var sum = 0.0 + var i = 0 + while (i < parallelNum) { + sum += squares(i) + i += 1 + } + sum + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 9c42c065b7f..d91e042199c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -878,9 +878,9 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this.dot(this) } - override def clamp(min: Float, max: Float): Tensor[T] = { - val maxT = ev.fromType[Float](max) - val minT = ev.fromType[Float](min) + override def clamp(min: Double, max: Double): Tensor[T] = { + val maxT = ev.fromType[Double](max) + val minT = ev.fromType[Double](min) val func = new TensorFunc2[T] { override def apply(data1: Array[T], offset1: Int): Unit = { if (ev.isGreater(data1(offset1), maxT)) data1(offset1) = maxT diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index 071f9b98105..317162353f0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -1442,7 +1442,7 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def digamma(): Tensor[T] = throw new UnsupportedOperationException(errorString) - override def clamp(minValue: Float, maxValue: Float): Tensor[T] = + override def clamp(minValue: Double, maxValue: Double): Tensor[T] = throw new UnsupportedOperationException(errorString) override def sumSquare(): T = diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 028cd545db5..6e0baa61c2b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -1121,7 +1121,7 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } - override def clamp(minValue: Float, maxValue: Float): Tensor[T] = + override def clamp(minValue: Double, maxValue: Double): Tensor[T] = throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") override def sumSquare(): T = diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala index 6e7b526c6c6..7cd7965db9a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorMath.scala @@ -825,5 +825,5 @@ trait TensorMath[T] { def sumSquare(): T - def clamp(min: Float, max: Float): Tensor[T] + def clamp(min: Double, max: Double): Tensor[T] } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala index 455e0616a70..33b875ea937 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala @@ -113,7 +113,7 @@ class Table private[bigdl]( } override def toString: String = { - s" {\n\t${state.map{case (key: Any, value: Any) => + s" {\n\t${state.filter(_._2 != null).map{case (key: Any, value: Any) => s"$key: " + s"$value".split("\n").mkString(s"\n\t${key.toString.replaceAll(".", " ")} ") }.mkString("\n\t")}\n }" } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index db2385ede32..048782debb3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -23,7 +23,8 @@ import com.intel.analytics.bigdl.dataset.image.{BGRImgToBatch, LabeledBGRImage} import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.{Storage, Tensor, DenseTensor} +import com.intel.analytics.bigdl.parameters.AllReduceParameter +import com.intel.analytics.bigdl.tensor.{DenseTensor, Storage, Tensor} import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.visualization.TrainSummary import org.apache.log4j.{Level, Logger} @@ -70,6 +71,14 @@ object DistriOptimizerSpecModel { .add(new Sigmoid) } + def linear: Module[Double] = { + new Sequential[Double] + .add(new Linear(10, 5)) + .add(new Sigmoid) + .add(new Linear(5, 1)) + .add(new Sigmoid) + } + def bn: Module[Double] = { Sequential[Double] .add(Linear(4, 2)) @@ -116,7 +125,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { dataSet = new DistributedDataSet[MiniBatch[Double]] { override def originRDD(): RDD[_] = rdd - override def data(train : Boolean): RDD[MiniBatch[Double]] = rdd + override def data(train: Boolean): RDD[MiniBatch[Double]] = rdd override def size(): Long = rdd.count() @@ -221,6 +230,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } "Train with MSE and LBFGS" should "be good" in { + LoggerFilter.redirectSparkInfoLogs() RandomGenerator.RNG.setSeed(10) val optimizer = new DistriOptimizer( mse, @@ -237,6 +247,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } "Train with MSE and SGD" should "be trained with good result" in { + LoggerFilter.redirectSparkInfoLogs() val mm = mse mm.getParameters()._1.fill(0.125) val optimizer = new DistriOptimizer[Double](mm, dataSet, new MSECriterion[Double]()) @@ -252,6 +263,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } "Train with MSE and SGD" should "be trained with good result after reset model" in { + LoggerFilter.redirectSparkInfoLogs() var mm = bn val optimizer = new DistriOptimizer[Double](mm, dataSet, new MSECriterion[Double]()) .setState(T("learningRate" -> 20.0)) @@ -372,7 +384,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val dataSet = new DistributedDataSet[MiniBatch[Double]] { override def originRDD(): RDD[_] = rdd - override def data(train : Boolean): RDD[MiniBatch[Double]] = rdd + override def data(train: Boolean): RDD[MiniBatch[Double]] = rdd override def size(): Long = 256 * nodeNumber @@ -408,8 +420,8 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val optimMethod = OptimMethod.load[Double](optimizer.getCheckpointPath().get + s"/optimMethod.$numIterations") - optimMethod.state.get[Int]("epoch").get should be (2) - optimMethod.state.get[Int]("neval").get should be (numIterations) + optimMethod.state.get[Int]("epoch").get should be(2) + optimMethod.state.get[Int]("neval").get should be(numIterations) } "TrainSummary with MSE and LBFGS" should "work correctly" in { @@ -430,7 +442,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] result2(Array(1)) should be(1.0 +- 1e-2) - trainSummary.readScalar("Loss").last._2 should be (0.0f +- 1e-3f) + trainSummary.readScalar("Loss").last._2 should be(0.0f +- 1e-3f) trainSummary.close() } @@ -452,7 +464,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] result2(Array(1)) should be(1.0 +- 5e-2) - trainSummary.readScalar("Loss").last._2 should be (0.0f +- 1e-3f) + trainSummary.readScalar("Loss").last._2 should be(0.0f +- 1e-3f) trainSummary.close() } @@ -475,7 +487,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] result2(Array(1)) should be(1.0 +- 5e-2) - trainSummary.readScalar("Loss").last._2 should be (0.0f +- 1e-3f) + trainSummary.readScalar("Loss").last._2 should be(0.0f +- 1e-3f) trainSummary.close() } @@ -650,29 +662,116 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val myOpt = new DistriOptimizer[Double](Identity[Double](), dataSet, null) { - override def optimize(): Module[Double] = { - val dds = this.dataset.asInstanceOf[DistributedDataSet[MiniBatch[Double]]] - val rdd = dds.data(train = false) - // flatmap to break minibatches into single tensors - val input = rdd.flatMap[Tensor[Double]]{ - data => data.getInput().asInstanceOf[Tensor[Double]].split(dim = 1)} - val target = rdd.flatMap[Tensor[Double]]{ - data => data.getTarget().asInstanceOf[Tensor[Double]].split(dim = 1)} - val inputArr = input.collect() - val targetArr = target.collect() - - inputArr.sameElements(inputOriArr) should be (true) - targetArr.sameElements(targetOriArr) should be (true) - - model - } + override def optimize(): Module[Double] = { + val dds = this.dataset.asInstanceOf[DistributedDataSet[MiniBatch[Double]]] + val rdd = dds.data(train = false) + // flatmap to break minibatches into single tensors + val input = rdd.flatMap[Tensor[Double]]{ + data => data.getInput().asInstanceOf[Tensor[Double]].split(dim = 1)} + val target = rdd.flatMap[Tensor[Double]]{ + data => data.getTarget().asInstanceOf[Tensor[Double]].split(dim = 1)} + val inputArr = input.collect() + val targetArr = target.collect() + + inputArr.sameElements(inputOriArr) should be (true) + targetArr.sameElements(targetOriArr) should be (true) + + model + } } myOpt.setTrainData(rdd, 2*nodeNumber) myOpt.optimize() } - "optimMethod state " should "be updated correctly after optimize" in { + + "Train with MSE " should "generate correct gradients with constant clipping" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + val oriW = mm.getParameters()._1.clone() + + val _learningRate = 20.0 + val optimizationMethod = new SGD[Double](learningRate = _learningRate) + val optimizer = new DistriOptimizer[Double](mm, dataSet, new MSECriterion[Double]()) + .setEndWhen(Trigger.maxEpoch(1)) + .setOptimMethod(optimizationMethod) + .setConstantGradientClipping(-0.0, 0.0) + + val model = optimizer.optimize() + val newW = model.getParameters()._1 + val newG = model.getParameters()._2 + + assert(newW.almostEqual(oriW, 0.0), "weight should keep the same") + assert(newG.almostEqual(oriW.fill(0.0), 0.0), "gradient should be 0") + } + + "Train with MSE" should "generate correct gradients with l2norm clipping" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + + val _learningRate = 20.0 + val optimizationMethod = new SGD[Double](learningRate = _learningRate) + val optimizer = new DistriOptimizer[Double](mm, dataSet, new MSECriterion[Double]()) + .setEndWhen(Trigger.maxIteration(1)) + .setOptimMethod(optimizationMethod) + + val model = optimizer.optimize() + val gradient = model.getParameters()._2.clone() + val scale = math.sqrt(gradient.sumSquare()) / 0.03 + val expectedG = gradient.clone().div(scale) + + val mm2 = mse + mm2.getParameters()._1.fill(0.125) + val optimizationMethod2 = new SGD[Double](learningRate = _learningRate) + val optimizer2 = new DistriOptimizer[Double](mm2, dataSet, new MSECriterion[Double]()) + .setEndWhen(Trigger.maxIteration(1)) + .setOptimMethod(optimizationMethod2) + .setGradientClippingByl2Norm(0.03) + + val model2 = optimizer2.optimize() + val newG = model2.getParameters()._2 + assert(expectedG.almostEqual(newG, 0.0), "clipbynorm2 should generate correct gradient") + } + + "Train with MSE and SGD with constant clipping" should "be trained with good result" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizer[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + .setConstantGradientClipping(-0.001, 0.001) + + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + "Train with MSE and SGD with l2 clipping" should "be trained with good result" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizer[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + .setGradientClippingByl2Norm(0.002) + + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + "optimMethod state" should "be updated correctly after optimize" in { LoggerFilter.redirectSparkInfoLogs() Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) @@ -692,33 +791,33 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { .setEndWhen(Trigger.maxIteration(10)) val model = optimizer.optimize() - optimMethod.state[Int]("epoch") should be (1) - optimMethod.state[Int]("neval") should be (11) - optimMethod.state[Int]("recordsProcessedThisEpoch") should be (320) + optimMethod.state[Int]("epoch") should be(1) + optimMethod.state[Int]("neval") should be(11) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be(320) optimizer.setEndWhen(Trigger.maxIteration(20)) optimizer.optimize() - optimMethod.state[Int]("epoch") should be (1) - optimMethod.state[Int]("neval") should be (21) - optimMethod.state[Int]("recordsProcessedThisEpoch") should be (640) + optimMethod.state[Int]("epoch") should be(1) + optimMethod.state[Int]("neval") should be(21) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be(640) val rdd = sc.parallelize(1 to (160 * nodeNumber), nodeNumber) .map(_ => Sample[Double](Tensor[Double](4).fill(2.0), Tensor[Double](1).fill(1.0))) optimizer.setTrainData(rdd, 16 * nodeNumber) - optimMethod.state[Int]("epoch") should be (2) - optimMethod.state[Int]("neval") should be (21) - optimMethod.state[Int]("recordsProcessedThisEpoch") should be (0) + optimMethod.state[Int]("epoch") should be(2) + optimMethod.state[Int]("neval") should be(21) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be(0) optimizer.setEndWhen(Trigger.maxEpoch(2)) optimizer.optimize() - optimMethod.state[Int]("epoch") should be (3) - optimMethod.state[Int]("neval") should be (31) - optimMethod.state[Int]("recordsProcessedThisEpoch") should be (0) - - + optimMethod.state[Int]("epoch") should be(3) + optimMethod.state[Int]("neval") should be(31) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be(0) } } + + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala index e6a9ac1b12f..6322872d54c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala @@ -371,4 +371,52 @@ class LocalOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter{ val weightRef = modelRef.getParameters()._1 weight should be(weightRef) } + + "Train model with CrossEntropy and SGD" should "be good with constant clipping" in { + val _learningRate = 20.0 + val optimizationMethod = new SGD[Float](learningRate = _learningRate) + val optimizer = new LocalOptimizer[Float]( + creModel, + creDataSet, + new ClassNLLCriterion[Float].asInstanceOf[Criterion[Float]] + ).setConstantGradientClipping(0.0, 0.0) + .setEndWhen(Trigger.maxEpoch(1)) + .setOptimMethod(optimizationMethod) + + + val model = optimizer.optimize() + val newG = model.getParameters()._2 + + assert(newG.sumSquare() == 0, "gradient should be 0") + } + + "Train model with CrossEntropy and SGD" should "be good with l2norm clipping" in { + RandomGenerator.RNG.setSeed(1000) + val linear = Linear[Float](4, 2) + val _learningRate = 0.0 + val optimizationMethod = new SGD[Float](learningRate = _learningRate) + val optimizer = new LocalOptimizer[Float]( + linear, + creDataSet, + new ClassNLLCriterion[Float].asInstanceOf[Criterion[Float]] + ).setEndWhen(Trigger.maxIteration(1)) + .setOptimMethod(optimizationMethod) + + val model = optimizer.optimize() + val gradient = model.getParameters()._2.clone() + val scale = math.sqrt(gradient.sumSquare()) / 0.03 + val expectedG = gradient.clone().div(scale.toFloat) + + val optimizationMethod2 = new SGD[Float](learningRate = _learningRate) + linear.getParameters()._1.fill(2.5f) + val optimizer2 = new LocalOptimizer[Float](linear, creDataSet, + new ClassNLLCriterion[Float]().asInstanceOf[Criterion[Float]]) + .setEndWhen(Trigger.maxIteration(1)) + .setOptimMethod(optimizationMethod2) + .setGradientClippingByl2Norm(0.03) + + val model2 = optimizer2.optimize() + val newG = model2.getParameters()._2 + assert(expectedG.almostEqual(newG, 0.0), "clipbynorm2 should generate correct gradient") + } } From 041e233a992db437056df06f6351e69c2409e304 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 26 Jun 2018 10:18:19 +0800 Subject: [PATCH 0791/1065] add parameter sync for batchnorm (#2559) * add parameter sync for batchnorm * fix test * fix test * fix test * fix test * refinement * fix * fix test issue * add backward compatibility * refine to set Id instead of renaming * fix style issue * refinement per review * fix * add change to example * refinement * fix ut issue to avoid multiple context * add comments * fix style --- .../bigdl/dllib/models/resnet/README.md | 14 +- .../dllib/models/resnet/TrainImageNet.scala | 34 ++- .../bigdl/dllib/nn/BatchNormalization.scala | 82 ++++- .../dllib/nn/SpatialBatchNormalization.scala | 289 ++++++++++++++++-- .../dllib/nn/abstractnn/AbstractModule.scala | 8 + .../bigdl/dllib/optim/DistriOptimizer.scala | 13 +- .../dllib/utils/ParameterSynchronizer.scala | 106 +++++++ .../dllib/nn/BatchNormalizationSpec.scala | 107 ++++++- .../nn/SpatialBatchNormalizationSpec.scala | 103 +++++++ .../bigdl/dllib/python/api/PythonSpec.scala | 12 +- 10 files changed, 726 insertions(+), 42 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ParameterSynchronizer.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md index 0164447caaf..49180b6e597 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/README.md @@ -89,8 +89,8 @@ spark-submit \ --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" \ --conf "spark.network.timeout=1000000" \ --executor-memory 200g \ ---executor-cores 4 \ ---total-executor-cores 256 \ +--executor-cores 32 \ +--total-executor-cores 2048 \ --class com.intel.analytics.bigdl.models.resnet.TrainImageNet \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f hdfs://xxx.xxx.xxx.xxx:xxxx/imagenet \ @@ -107,8 +107,8 @@ spark-submit \ --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" \ --conf "spark.network.timeout=1000000" \ --executor-memory 200g \ ---executor-cores 4 \ ---total-executor-cores 256 \ +--executor-cores 32 \ +--total-executor-cores 2048 \ --class com.intel.analytics.bigdl.models.resnet.TrainImageNet \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ -f hdfs://xxx.xxx.xxx.xxx:xxxx/imagenet \ @@ -132,7 +132,7 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ **Global batch** : 8192 -**Single batch per core** : 32 +**Single batch per core** : 4 **Epochs** : 90 @@ -144,8 +144,8 @@ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ #### Training result (90 epochs) -**Top1 accuracy**: 0.76088 +**Top1 accuracy**: 0.76114 -**Top5 accuracy**: 0.92802 +**Top5 accuracy**: 0.92724 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala index 33cf184063a..eef1db4b6ec 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala @@ -18,7 +18,8 @@ package com.intel.analytics.bigdl.models.resnet import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} -import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, Module} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.{BatchNormalization, Container, CrossEntropyCriterion, Module} import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._ import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T} @@ -77,6 +78,27 @@ object TrainImageNet { println(model) + /* Here we set parallism specificall + for BatchNormalization and its Sub Layers, + this is very useful especially when + you want to leverage more computing + resources like you want to use + as many cores as possible but you cannot + set batch size too big for each core due + to the memory limitation, so you can set + batch size per core smaller, but the smaller + batch size will increase the instability of + convergence, the synchronization among BN + layers basically do the parameters + synchronization among cores + and thus will avoid the instability while + improves the performance a lot. + */ + + val parallisim = Engine.coreNumber + + setParallism(model, parallisim) + val optimMethod = if (param.stateSnapshot.isDefined) { val optim = OptimMethod.load[Float](param.stateSnapshot.get).asInstanceOf[SGD[Float]] val baseLr = param.learningRate @@ -127,4 +149,14 @@ object TrainImageNet { sc.stop() }) } + + private def setParallism(model: AbstractModule[_, _, Float], parallism: Int): Unit = { + if (model.isInstanceOf[BatchNormalization[Float]]) { + model.asInstanceOf[BatchNormalization[Float]].setParallism(parallism) + } + if(model.isInstanceOf[Container[_, _, Float]]) { + model.asInstanceOf[Container[_, _, Float]]. + modules.foreach(sub => setParallism(sub, parallism)) + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index 1b9fe69eb9c..61f17be08c1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Engine, ParameterSynchronizer, T, Table} import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag @@ -61,6 +61,24 @@ class BatchNormalization[T: ClassTag]( require(nOutput > 0, "output feature map number must be greater than zero") + private var parallism : Option[Int] = None + + + /** + * Set parameter sync parallisim number + * @param parallism Concurrent sync threads number + */ + def setParallism(parallism: Int): Unit = { + this.parallism = Some(parallism) + } + + def getParallism(): Option[Int] = this.parallism + + val meanKey: String = s"${this.getName}_mean" + val stdKey: String = s"${this.getName}_std" + val gmKey: String = s"${this.getName}_gm" + val gxmKey: String = s"${this.getName}_gxm" + val nDim = 2 val channelDim = 2 var runningMean = if (affine) Tensor[T](nOutput) else Tensor[T]() @@ -135,6 +153,14 @@ class BatchNormalization[T: ClassTag]( protected val _input = Tensor[T]() protected val _gradOutput = Tensor[T]() + var globalMean: Array[T] = new Array[T](0) + + var globalStd: Array[T] = new Array[T](0) + + var globalGMean: Array[T] = new Array[T](0) + + var globalGxmMean: Array[T] = new Array[T](0) + override def clearState(): this.type = { super.clearState() gMean.set() @@ -193,6 +219,22 @@ class BatchNormalization[T: ClassTag]( } override def updateOutput(input: Tensor[T]): Tensor[T] = { + + val parallism = getParallism().getOrElse(1) + + val meanKeyWithId = s"${this.meanKey}_${this.getId}" + val stdKeyWithId = s"${this.stdKey}_${this.getId}" + val gmKeyWithId = s"${this.gmKey}_${this.getId}" + val gxmKeyWithId = s"${this.gxmKey}_${this.getId}" + + val needSync = if (parallism != 1) { + ParameterSynchronizer.register(meanKeyWithId, parallism) + ParameterSynchronizer.register(stdKeyWithId, parallism) + ParameterSynchronizer.register(gmKeyWithId, parallism) + ParameterSynchronizer.register(gxmKeyWithId, parallism) + true + } else false + checkInputDim(input) output.resizeAs(input) @@ -209,6 +251,16 @@ class BatchNormalization[T: ClassTag]( saveMean.resizeAs(runningMean).zero saveStd.resizeAs(runningVar).fill(ev.zero) + val nChannels = _input.size(2) + + if (globalMean.size < nChannels) { + globalMean = new Array[T](nChannels) + } + + if (globalStd.size < nChannels) { + globalStd = new Array[T](nChannels) + } + if (train) { if (ev.getType() == FloatType) { SpatialBatchNormalization.updateOutputNCHWTrainFloat( @@ -216,14 +268,20 @@ class BatchNormalization[T: ClassTag]( saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], runningMean.asInstanceOf[Tensor[Float]], runningVar.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], bias.asInstanceOf[Tensor[Float]], - eps.toFloat, momentum.toFloat) + eps.toFloat, momentum.toFloat, + globalMean = globalMean.asInstanceOf[Array[Float]], + globalStd = globalStd.asInstanceOf[Array[Float]], + meanKey = meanKeyWithId, stdKey = stdKeyWithId, needSync = needSync) } else { SpatialBatchNormalization.updateOutputNCHWTrainDouble( _input.asInstanceOf[Tensor[Double]], output.asInstanceOf[Tensor[Double]], saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], runningMean.asInstanceOf[Tensor[Double]], runningVar.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], bias.asInstanceOf[Tensor[Double]], - eps, momentum) + eps, momentum, + globalMean = globalMean.asInstanceOf[Array[Double]], + globalStd = globalStd.asInstanceOf[Array[Double]], + meanKey = meanKeyWithId, stdKey = stdKeyWithId, needSync = needSync) } } else { if (ev.getType() == FloatType) { @@ -243,25 +301,39 @@ class BatchNormalization[T: ClassTag]( } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val gmKeyWithId = s"${this.gmKey}_${this.getId}" + val gxmKeyWithId = s"${this.gxmKey}_${this.getId}" + val needSync = getParallism() != None && getParallism().get > 1 _gradOutput.set(gradOutput) makeBatch(_gradOutput) _gradOutput.addSingletonDimension(_gradOutput, 3) _gradOutput.addSingletonDimension(_gradOutput, 4) gxMean.zero() gMean.zero() + val nChannel = _gradOutput.size(2) + if (globalGMean.size < nChannel) { + globalGMean = new Array[T](nChannel) + } + if (globalGxmMean.size < nChannel) { + globalGxmMean = new Array[T](nChannel) + } if (train) { if (ev.getType() == FloatType) { SpatialBatchNormalization.updateGradInputNCHWTrainFloat( _input.asInstanceOf[Tensor[Float]], _gradOutput.asInstanceOf[Tensor[Float]], gradInput.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], - gMean.asInstanceOf[Tensor[Float]], gxMean.asInstanceOf[Tensor[Float]]) + gMean.asInstanceOf[Tensor[Float]], gxMean.asInstanceOf[Tensor[Float]], + globalGMean.asInstanceOf[Array[Float]], globalGxmMean.asInstanceOf[Array[Float]], + gMeanKey = gmKeyWithId, gxMeanKey = gxmKeyWithId, needSync = needSync) } else { SpatialBatchNormalization.updateGradInputNCHWTrainDouble( _input.asInstanceOf[Tensor[Double]], _gradOutput.asInstanceOf[Tensor[Double]], gradInput.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], - gMean.asInstanceOf[Tensor[Double]], gxMean.asInstanceOf[Tensor[Double]]) + gMean.asInstanceOf[Tensor[Double]], gxMean.asInstanceOf[Tensor[Double]], + globalGMean.asInstanceOf[Array[Double]], globalGxmMean.asInstanceOf[Array[Double]], + gMeanKey = gmKeyWithId, gxMeanKey = gxmKeyWithId, needSync = needSync) } } else { if (ev.getType() == FloatType) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala index 4a2aed8389e..32549dc132a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala @@ -19,7 +19,9 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Engine, ParameterSynchronizer} +import scala.collection.JavaConverters._ import scala.reflect.ClassTag /** @@ -50,6 +52,22 @@ class SpatialBatchNormalization[T: ClassTag]( override val nDim = 4 override def updateOutput(input: Tensor[T]): Tensor[T] = { + + val parallism = getParallism().getOrElse(1) + + val meanKeyWithId = s"${this.meanKey}_${this.getId}" + val stdKeyWithId = s"${this.stdKey}_${this.getId}" + val gmKeyWithId = s"${this.gmKey}_${this.getId}" + val gxmKeyWithId = s"${this.gxmKey}_${this.getId}" + + val needSync = if (parallism != 1) { + ParameterSynchronizer.register(meanKeyWithId, parallism) + ParameterSynchronizer.register(stdKeyWithId, parallism) + ParameterSynchronizer.register(gmKeyWithId, parallism) + ParameterSynchronizer.register(gxmKeyWithId, parallism) + true + } else false + checkInputDim(input) output.resizeAs(input) @@ -64,6 +82,16 @@ class SpatialBatchNormalization[T: ClassTag]( saveMean.resizeAs(runningMean).zero saveStd.resizeAs(runningVar).fill(ev.zero) + val nChannels = _input.size(2) + + if (globalMean.size < nChannels) { + globalMean = new Array[T](nChannels) + } + + if (globalStd.size < nChannels) { + globalStd = new Array[T](nChannels) + } + if (dataFormat == DataFormat.NCHW) { if (train) { if (ev.getType() == FloatType) { @@ -72,14 +100,20 @@ class SpatialBatchNormalization[T: ClassTag]( saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], runningMean.asInstanceOf[Tensor[Float]], runningVar.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], bias.asInstanceOf[Tensor[Float]], - eps.toFloat, momentum.toFloat, needFix = needFix) + eps.toFloat, momentum.toFloat, needFix = needFix, + globalMean = globalMean.asInstanceOf[Array[Float]], + globalStd = globalStd.asInstanceOf[Array[Float]], + meanKey = meanKeyWithId, stdKey = stdKeyWithId, needSync = needSync) } else { SpatialBatchNormalization.updateOutputNCHWTrainDouble( _input.asInstanceOf[Tensor[Double]], output.asInstanceOf[Tensor[Double]], saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], runningMean.asInstanceOf[Tensor[Double]], runningVar.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], bias.asInstanceOf[Tensor[Double]], - eps, momentum, needFix = needFix) + eps, momentum, needFix = needFix, + globalMean = globalMean.asInstanceOf[Array[Double]], + globalStd = globalStd.asInstanceOf[Array[Double]], + meanKey = meanKeyWithId, stdKey = stdKeyWithId, needSync = needSync) } } else { if (ev.getType() == FloatType) { @@ -130,10 +164,20 @@ class SpatialBatchNormalization[T: ClassTag]( } override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val gmKeyWithId = s"${this.gmKey}_${this.getId}" + val gxmKeyWithId = s"${this.gxmKey}_${this.getId}" + val needSync = getParallism() != None && getParallism().get > 1 _gradOutput.set(gradOutput) makeBatch(_gradOutput) gxMean.zero() gMean.zero() + val nChannel = _gradOutput.size(2) + if (globalGMean.size < nChannel) { + globalGMean = new Array[T](nChannel) + } + if (globalGxmMean.size < nChannel) { + globalGxmMean = new Array[T](nChannel) + } if (dataFormat == DataFormat.NCHW) { if (train) { if (ev.getType() == FloatType) { @@ -141,13 +185,17 @@ class SpatialBatchNormalization[T: ClassTag]( _input.asInstanceOf[Tensor[Float]], _gradOutput.asInstanceOf[Tensor[Float]], gradInput.asInstanceOf[Tensor[Float]], weight.asInstanceOf[Tensor[Float]], saveMean.asInstanceOf[Tensor[Float]], saveStd.asInstanceOf[Tensor[Float]], - gMean.asInstanceOf[Tensor[Float]], gxMean.asInstanceOf[Tensor[Float]]) + gMean.asInstanceOf[Tensor[Float]], gxMean.asInstanceOf[Tensor[Float]], + globalGMean.asInstanceOf[Array[Float]], globalGxmMean.asInstanceOf[Array[Float]], + gMeanKey = gmKeyWithId, gxMeanKey = gxmKeyWithId, needSync = needSync) } else { SpatialBatchNormalization.updateGradInputNCHWTrainDouble( _input.asInstanceOf[Tensor[Double]], _gradOutput.asInstanceOf[Tensor[Double]], gradInput.asInstanceOf[Tensor[Double]], weight.asInstanceOf[Tensor[Double]], saveMean.asInstanceOf[Tensor[Double]], saveStd.asInstanceOf[Tensor[Double]], - gMean.asInstanceOf[Tensor[Double]], gxMean.asInstanceOf[Tensor[Double]]) + gMean.asInstanceOf[Tensor[Double]], gxMean.asInstanceOf[Tensor[Double]], + globalGMean.asInstanceOf[Array[Double]], globalGxmMean.asInstanceOf[Array[Double]], + gMeanKey = gmKeyWithId, gxMeanKey = gxmKeyWithId, needSync = needSync) } } else { if (ev.getType() == FloatType) { @@ -983,7 +1031,12 @@ object SpatialBatchNormalization { saveMean: Tensor[Float], saveStd: Tensor[Float], gMean: Tensor[Float], - gxMean: Tensor[Float] + gxMean: Tensor[Float], + globalGmean: Array[Float], + globalGxmean: Array[Float], + gMeanKey: String = null, + gxMeanKey: String = null, + needSync: Boolean = false ): Unit = { require(input.nDimension() == 4, "BN require a 4D input") require(input.isContiguous(), "input is not contiguous") @@ -1033,11 +1086,59 @@ object SpatialBatchNormalization { b += 1 } + var gmeanEventLen = 1 + var gmxmeanEventLen = 1 + + if (needSync) { + + ParameterSynchronizer.syncData(gMeanKey, gMean) + + val gMeanEventData = ParameterSynchronizer.collect[Float](gMeanKey) + var c = 0 + while (c < nChannel) { + globalGmean(c) = 0.0f + gMeanEventData.keySet.asScala.foreach(threadId => { + val localGmean = gMeanEventData.get(threadId) + val localGmeanOffset = localGmean.storageOffset() - 1 + globalGmean(c) += localGmean.storage.array()(c + localGmeanOffset) + }) + c += 1 + } + + gmeanEventLen = gMeanEventData.size + + ParameterSynchronizer.reset[Float](gMeanKey) + + ParameterSynchronizer.syncData(gxMeanKey, gxMean) + + val gxMeanEventData = ParameterSynchronizer.collect[Float](gxMeanKey) + + c = 0 + while (c < nChannel) { + globalGxmean(c) = 0.0f + gxMeanEventData.keySet.asScala.foreach(threadId => { + val localGxmean = gxMeanEventData.get(threadId) + val localGxmeanOffset = localGxmean.storageOffset() - 1 + globalGxmean(c) += localGxmean.storage.array()(c + localGxmeanOffset) + }) + c += 1 + } + + gmxmeanEventLen = gxMeanEventData.size + + ParameterSynchronizer.reset[Float](gxMeanKey) + } + var c = 0 val size = n / nChannel while(c < nChannel) { - gMeanData(c) /= size - gxMeanData(c) /= size + if (needSync) { + gMeanData(c) = globalGmean(c) / (size * gmeanEventLen) + gxMeanData(c) = globalGxmean(c) / (size * gmxmeanEventLen) + } else { + gMeanData(c) = gMeanData(c) / (size * gmeanEventLen) + gxMeanData(c) = gxMeanData(c) / (size * gmxmeanEventLen) + } c += 1 } @@ -1089,7 +1190,12 @@ object SpatialBatchNormalization { saveMean: Tensor[Float], saveStd: Tensor[Float], runningMean: Tensor[Float], runningVar: Tensor[Float], scale: Tensor[Float], offset: Tensor[Float], eps: Float, momentum: Float, - batchVar: Tensor[Float] = null, saveVar: Tensor[Float] = null, needFix: Boolean = false) + batchVar: Tensor[Float] = null, saveVar: Tensor[Float] = null, needFix: Boolean = false, + globalMean: Array[Float] = null, + globalStd: Array[Float] = null, + meanKey: String = null, + stdKey: String = null, + needSync: Boolean = false) : Unit = { require(input.isContiguous(), "BatchNorm NCHW require a contiguous input") val inputData = input.storage().array() @@ -1125,13 +1231,33 @@ object SpatialBatchNormalization { b += 1 } + var meanLen = 1 + if (needSync) { + ParameterSynchronizer.syncData(meanKey, saveMean) + val meanEventData = ParameterSynchronizer.collect[Float](meanKey) + meanLen = meanEventData.size + var c = 0 + while (c < nChannels) { + globalMean(c) = 0.0f + meanEventData.keySet.asScala.foreach(threadId => { + val localMean = meanEventData.get(threadId) + val localOffset = localMean.storageOffset() - 1 + globalMean(c) += localMean.storage.array()(c + localOffset) + }) + c += 1 + } + + ParameterSynchronizer.reset[Float](meanKey) + System.arraycopy(globalMean, 0, meanData, meanOffset, nChannels) + } + val n = input.nElement() val frameSize = n / nChannels var c = 0 val runningMeanData = runningMean.storage().array() val runningMeanOffset = runningMean.storageOffset() - 1 while(c < nChannels) { - meanData(c + meanOffset) /= frameSize + meanData(c + meanOffset) /= (frameSize * meanLen) runningMeanData(c + runningMeanOffset) = meanData(c + meanOffset) * momentum + (1 - momentum) * runningMeanData(c + runningMeanOffset) c += 1 @@ -1158,6 +1284,31 @@ object SpatialBatchNormalization { b += 1 } + var stdLen = 1 + + if (needSync) { + ParameterSynchronizer.syncData(stdKey, saveStd) + + val stdEventData = ParameterSynchronizer.collect[Float](stdKey) + + c = 0 + while (c < nChannels) { + globalStd(c) = 0.0f + stdEventData.keySet.asScala.foreach(threadId => { + val localStd = stdEventData.get(threadId) + val localStdOffSet = localStd.storageOffset() - 1 + globalStd(c) += localStd.storage.array()(c + localStdOffSet) + }) + c += 1 + } + + stdLen = stdEventData.size + + ParameterSynchronizer.reset[Float](stdKey) + + System.arraycopy(globalStd, 0, stdData, stdOffset, nChannels) + } + c = 0 val runningVarData = runningVar.storage().array() val runningVarOffset = runningVar.storageOffset() - 1 @@ -1172,14 +1323,14 @@ object SpatialBatchNormalization { } } else { val s = stdData(c + stdOffset) - val unbiasedVar = s / (frameSize - 1) + val unbiasedVar = s / (frameSize * stdLen - 1) if (saveVar != null) { - saveVar.setValue(c + 1, s / frameSize) + saveVar.setValue(c + 1, s / (frameSize * stdLen)) } if (batchVar != null) { batchVar.setValue(c + 1, unbiasedVar) } - stdData(c + stdOffset) = 1.0f / Math.sqrt(s / frameSize + eps).toFloat + stdData(c + stdOffset) = 1.0f / Math.sqrt(s / (frameSize * stdLen) + eps).toFloat runningVarData(c + runningVarOffset) = momentum * unbiasedVar + (1 - momentum) * runningVarData(c + runningVarOffset) } @@ -1241,7 +1392,12 @@ object SpatialBatchNormalization { saveMean: Tensor[Double], saveStd: Tensor[Double], runningMean: Tensor[Double], runningVar: Tensor[Double], scale: Tensor[Double], offset: Tensor[Double], eps: Double, momentum: Double, - batchVar: Tensor[Double] = null, saveVar: Tensor[Double] = null, needFix: Boolean = false) + batchVar: Tensor[Double] = null, saveVar: Tensor[Double] = null, needFix: Boolean = false, + globalMean: Array[Double] = null, + globalStd: Array[Double] = null, + meanKey: String = null, + stdKey: String = null, + needSync: Boolean = false) : Unit = { require(input.isContiguous(), "BatchNorm NCHW require a contiguous input") val inputData = input.storage().array() @@ -1276,6 +1432,29 @@ object SpatialBatchNormalization { } b += 1 } + var meanLen = 1 + if (needSync) { + ParameterSynchronizer.syncData(meanKey, saveMean) + + val meanEventData = ParameterSynchronizer.collect[Double](meanKey) + + meanLen = meanEventData.size + + var c = 0 + while (c < nChannels) { + globalMean(c) = 0.0 + meanEventData.keySet.asScala.foreach(threadId => { + val localMean = meanEventData.get(threadId) + val localOffset = localMean.storageOffset() - 1 + globalMean(c) += localMean.storage.array()(c + localOffset) + }) + c += 1 + } + + ParameterSynchronizer.reset[Double](meanKey) + + System.arraycopy(globalMean, 0, meanData, meanOffset, nChannels) + } val n = input.nElement() val frameSize = n / nChannels @@ -1283,7 +1462,7 @@ object SpatialBatchNormalization { val runningMeanData = runningMean.storage().array() val runningMeanOffset = runningMean.storageOffset() - 1 while(c < nChannels) { - meanData(c + meanOffset) /= frameSize + meanData(c + meanOffset) /= (frameSize * meanLen) runningMeanData(c + runningMeanOffset) = meanData(c + meanOffset) * momentum + (1 - momentum) * runningMeanData(c + runningMeanOffset) c += 1 @@ -1307,6 +1486,24 @@ object SpatialBatchNormalization { } b += 1 } + var stdLen = 1 + if (needSync) { + ParameterSynchronizer.syncData(stdKey, saveStd) + val stdEventData = ParameterSynchronizer.collect[Double](stdKey) + c = 0 + while (c < nChannels) { + globalStd(c) = 0.0 + stdEventData.keySet.asScala.foreach(threadId => { + val localStd = stdEventData.get(threadId) + val localStdOffSet = localStd.storageOffset() - 1 + globalStd(c) += localStd.storage.array()(c + localStdOffSet) + }) + c += 1 + } + stdLen = stdEventData.size + ParameterSynchronizer.reset[Double](stdKey) + System.arraycopy(globalStd, 0, stdData, stdOffset, nChannels) + } c = 0 val runningVarData = runningVar.storage().array() @@ -1322,14 +1519,14 @@ object SpatialBatchNormalization { } } else { val s = stdData(c + stdOffset) - val unbiasedVar = s / (frameSize - 1) + val unbiasedVar = s / (frameSize * stdLen - 1) if (saveVar != null) { - saveVar.setValue(c + 1, s / frameSize) + saveVar.setValue(c + 1, s / (frameSize * stdLen)) } if (batchVar != null) { batchVar.setValue(c + 1, unbiasedVar) } - stdData(c + stdOffset) = 1.0 / Math.sqrt(s / frameSize + eps) + stdData(c + stdOffset) = 1.0 / Math.sqrt(s / (frameSize * stdLen) + eps) runningVarData(c + stdOffset) = momentum * unbiasedVar + (1 - momentum) * runningVarData(c + runningVarOffset) } @@ -1395,7 +1592,12 @@ object SpatialBatchNormalization { saveMean: Tensor[Double], saveStd: Tensor[Double], gMean: Tensor[Double], - gxMean: Tensor[Double] + gxMean: Tensor[Double], + globalGmean: Array[Double], + globalGxmean: Array[Double], + gMeanKey: String = null, + gxMeanKey: String = null, + needSync: Boolean = false ): Unit = { require(input.nDimension() == 4, "BN require a 4D input") require(input.isContiguous(), "input is not contiguous") @@ -1444,13 +1646,58 @@ object SpatialBatchNormalization { } b += 1 } + var gmeanEventLen = 1 + var gmxmeanEventLen = 1 + if (needSync) { + ParameterSynchronizer.syncData(gMeanKey, gMean) + val gMeanEventData = ParameterSynchronizer.collect[Double](gMeanKey) + var c = 0 + while (c < nChannel) { + globalGmean(c) = 0.0 + gMeanEventData.keySet.asScala.foreach(threadId => { + val localGmean = gMeanEventData.get(threadId) + val localGmeanOffset = localGmean.storageOffset() - 1 + globalGmean(c) += localGmean.storage.array()(c + localGmeanOffset) + }) + c += 1 + } + + gmeanEventLen = gMeanEventData.size + + ParameterSynchronizer.reset[Double](gMeanKey) + + ParameterSynchronizer.syncData(gxMeanKey, gxMean) + + val gxMeanEventData = ParameterSynchronizer.collect[Double](gxMeanKey) + + c = 0 + while (c < nChannel) { + globalGxmean(c) = 0.0 + gxMeanEventData.keySet.asScala.foreach(threadId => { + val localGxmean = gxMeanEventData.get(threadId) + val localGxmeanOffset = localGxmean.storageOffset() - 1 + globalGxmean(c) += localGxmean.storage.array()(c + localGxmeanOffset) + }) + c += 1 + } + + gmxmeanEventLen = gxMeanEventData.size + + ParameterSynchronizer.reset[Double](gxMeanKey) + } var c = 0 val size = n / nChannel while(c < nChannel) { - gMeanData(c) /= size - val invStd = saveStdData(saveStdOffset + c) - gxMeanData(c) = gxMeanData(c) * invStd * invStd / size + if (needSync) { + gMeanData(c) = globalGmean(c) / (size * gmeanEventLen) + val invStd = saveStdData(saveStdOffset + c) + gxMeanData(c) = globalGxmean(c) * invStd * invStd / (size * gmxmeanEventLen) + } else { + gMeanData(c) = gMeanData(c) / (size * gmeanEventLen) + val invStd = saveStdData(saveStdOffset + c) + gxMeanData(c) = gxMeanData(c) * invStd * invStd / (size * gmxmeanEventLen) + } c += 1 } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 391953ef9ea..6c60b2c0af1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -920,6 +920,14 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ private var name : String = null + private var id: Int = 0 + + private[bigdl] def setId(id: Int): Unit = { + this.id = id + } + + private[bigdl] def getId(): Int = this.id + protected final def getPrintName(): String = { val postfix = if (name == null) { namePostfix diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 383036097dd..f3f9563785b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.{Module, _} import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, PaddingParam, Sample, SampleToMiniBatch} -import com.intel.analytics.bigdl.nn.{Module, Utils} +import com.intel.analytics.bigdl.nn.{Container, Module, Utils} import com.intel.analytics.bigdl.parameters.{AllReduceParameter, ParameterProcessor} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -591,6 +591,7 @@ object DistriOptimizer { val executorCores = Engine.coreNumber() val models = dataset.originRDD().mapPartitions(_ => { + val partitionId = TaskContext.getPartitionId val (broadcastCriterion, broadcastState, broadcastMethod, broadcastOptim) = broadcast.value if (!Engine.checkSingleton()) { @@ -608,6 +609,8 @@ object DistriOptimizer { Engine.setNodeAndCore(nExecutor, executorCores) val cached = (0 until _subModelNumber).map { _ => val localModel = modelBroadcast.value(true) + // differentiate partition models from each other by partition ID + setModelId(localModel, partitionId) val localCriterion = broadcastCriterion.cloneCriterion() val localState = broadcastState.clone() val localMethod = @@ -638,6 +641,14 @@ object DistriOptimizer { models } + private def setModelId[T: ClassTag](model: Module[T], partitionId: Int): Unit = { + model.setId(partitionId) + if (model.isInstanceOf[Container[_, _, T]]) { + model.asInstanceOf[Container[_, _, T]].modules. + foreach(sub => setModelId(sub, partitionId)) + } + } + /** * Validate current model and save the result. * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ParameterSynchronizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ParameterSynchronizer.scala new file mode 100644 index 00000000000..220d4570195 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ParameterSynchronizer.scala @@ -0,0 +1,106 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import java.util.concurrent.{ConcurrentHashMap, CyclicBarrier} + +import com.intel.analytics.bigdl.tensor.Tensor +import org.apache.spark.TaskContext + +import scala.reflect._ + +/** + * A parameter synchronizer among threads per task + */ +private[bigdl] object ParameterSynchronizer { + + private val events = new java.util.concurrent.ConcurrentHashMap[String, Event[_]] + + /** + * Register event with key and total thread number + * @param eventKey key to specify an event + * @param threadNum total thread number synchronizing on this key + * @tparam T + */ + def register[T: ClassTag](eventKey: String, threadNum: Int): Unit = { + var event = events.get(eventKey) + if (event == null) { + event = new Event[T](threadNum) + events.putIfAbsent(eventKey, event) + } + } + + /** + * Reset event with given key + * @param eventKey Event key + * @tparam T + */ + def reset[T: ClassTag](eventKey: String): Unit = { + events.get(eventKey).reset + } + + /** + * Sync data per thread + * @param eventKey Event key + * @param dt data to be synchronized + * @tparam T + */ + def syncData[T: ClassTag](eventKey: String, dt: Tensor[T]): Unit = { + events.get(eventKey).asInstanceOf[Event[T]].addData(dt) + } + + /** + * Collect all data synchronized + * @param eventKey Event key + * @tparam T + * @return Data list from waiting threads + */ + def collect[T: ClassTag](eventKey: String): java.util.Map[String, Tensor[T]] = { + events.get(eventKey).data.asInstanceOf[java.util.Map[String, Tensor[T]]] + } +} + +private[bigdl] class Event[T: ClassTag](threadNum: Int) { + val barrier = new CyclicBarrier(threadNum) + val data = new ConcurrentHashMap[String, Tensor[T]]() + + /** + * Add data to sync list for current thread + * @param dt data to be added + */ + def addData(dt: Tensor[T]): Unit = { + barrier.await + val currentId = Thread.currentThread().getId.toString + data.put(currentId, dt) + barrier.await + } + + /** + * Reset event, clear the data + */ + def reset(): Unit = { + barrier.await + if (data.size != 0) { + data.synchronized { + if (data.size != 0) { + data.clear + } + } + } + barrier.await + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala index 2e94e1b252b..4463d9ecfc4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala @@ -17,14 +17,119 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{Engine, T} import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.apache.spark.SparkContext import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel class BatchNormalizationSpec extends FlatSpec with Matchers { + + "BacthNormalization parameter sync" should "work properly" in { + val conf = Engine.createSparkConf().setAppName("Test sync") + .set("spark.rpc.message.maxSize", "200").setMaster("local[*]") + val sc = SparkContext.getOrCreate(conf) + + Engine.init + + val bn = BatchNormalization[Float](2) + + bn.setParallism(1) + + bn.weight.fill(1.0f) + bn.bias.fill(1.0f) + + val input = Tensor[Float](2, 2) + + input.select(1, 1).fill(1.0f) + input.select(1, 2).fill(2.0f) + + val gradOutput = Tensor[Float](2, 2) + + gradOutput.select(1, 1).fill(2.0f) + gradOutput.select(1, 2).fill(1.0f) + + val output = bn.forward(input) + + val gradInput = bn.backward(input, gradOutput) + + val saveMean = bn.saveMean + val saveStd = bn.saveStd + val runningMean = bn.runningMean + val runningVar = bn.runningVar + + val bn1 = BatchNormalization[Float](2) + + bn1.setParallism(2) + + bn1.weight.fill(1.0f) + bn1.bias.fill(1.0f) + + val bn2 = bn1.cloneModule().asInstanceOf[BatchNormalization[Float]] + + val modules = Array(bn1, bn2) + + val input1 = Tensor[Float](1, 2).fill(1.0f) + + val input2 = Tensor[Float](1, 2).fill(2.0f) + + val inputs = Array(input1, input2) + + val gradOutput1 = Tensor[Float](1, 2).fill(2.0f) + val gradOutput2 = Tensor[Float](1, 2).fill(1.0f) + + val gradOutputs = Array(gradOutput1, gradOutput2) + + Engine.default.invokeAndWait2((0 until modules.size).map(i => + () => { + val trainStart = System.nanoTime() + val sub = modules(i) + val subInput = inputs(i) + val subGradOutput = gradOutputs(i) + sub.forward(subInput) + sub.backward(subInput, subGradOutput) + } + )) + + val saveMean1 = bn1.saveMean + val saveStd1 = bn1.saveStd + val runningMean1 = bn1.runningMean + val runningVar1 = bn1.runningVar + val gradInput1 = bn1.gradInput + val out1 = bn1.output.squeeze + + val saveMean2 = bn2.saveMean + val saveStd2 = bn2.saveStd + val runningMean2 = bn2.runningMean + val runningVar2 = bn2.runningVar + val gradInput2 = bn2.gradInput + val out2 = bn2.output.squeeze() + + saveMean should be (saveMean1) + saveMean should be (saveMean2) + saveStd should be (saveStd1) + saveStd should be (saveStd2) + runningMean should be (runningMean1) + runningMean should be (runningMean2) + runningVar should be (runningVar1) + runningVar should be (runningVar2) + + val sout1 = output.select(1, 1).squeeze() + sout1 should be (out1) + + val sout2 = output.select(1, 2).squeeze() + sout2 should be (bn2.output) + + val gin1 = gradInput.select(1, 1) + + val gin2 = gradInput.select(1, 2) + + gin1.squeeze should be (gradInput1.squeeze) + gin2.squeeze should be (gradInput2.squeeze) + } + "A BatchNormalization" should "generate correct output using default arguments" in { val bn = BatchNormalization[Double](None) val input = Tensor[Double](3, 3) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala index b71b6c4a300..fb4835b6269 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala @@ -17,13 +17,116 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.apache.spark.SparkContext import org.scalatest.{FlatSpec, Matchers} import scala.util.Random class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { + + "SpatialBacthNormalization parameter sync" should "work properly" in { + + val conf = Engine.createSparkConf().setAppName("Test sync") + .set("spark.rpc.message.maxSize", "200").setMaster("local[*]") + val sc = SparkContext.getOrCreate(conf) + + Engine.init + + val bn = SpatialBatchNormalization[Float](2) + bn.setParallism(1) + bn.weight.fill(1.0f) + bn.bias.fill(1.0f) + + val input = Tensor[Float](2, 2, 1, 1) + input.select(1, 1).fill(1.0f) + input.select(1, 2).fill(2.0f) + + val gradOutput = Tensor[Float](2, 2, 1, 1) + + gradOutput.select(1, 1).fill(2.0f) + gradOutput.select(1, 2).fill(1.0f) + + val output = bn.forward(input) + + val gradInput = bn.backward(input, gradOutput) + + val saveMean = bn.saveMean + val saveStd = bn.saveStd + val runningMean = bn.runningMean + val runningVar = bn.runningVar + + val bn1 = SpatialBatchNormalization[Float](2) + bn1.setParallism(2) + + bn1.weight.fill(1.0f) + bn1.bias.fill(1.0f) + + val bn2 = bn1.cloneModule().asInstanceOf[BatchNormalization[Float]] + + val modules = Array(bn1, bn2) + + val input1 = Tensor[Float](1, 2, 1, 1).fill(1.0f) + + val input2 = Tensor[Float](1, 2, 1, 1).fill(2.0f) + + val inputs = Array(input1, input2) + + val gradOutput1 = Tensor[Float](1, 2, 1, 1).fill(2.0f) + val gradOutput2 = Tensor[Float](1, 2, 1, 1).fill(1.0f) + + val gradOutputs = Array(gradOutput1, gradOutput2) + + Engine.default.invokeAndWait2((0 until modules.size).map(i => + () => { + val trainStart = System.nanoTime() + val sub = modules(i) + val subInput = inputs(i) + val subGradOutput = gradOutputs(i) + sub.forward(subInput) + sub.backward(subInput, subGradOutput) + } + )) + + val saveMean1 = bn1.saveMean + val saveStd1 = bn1.saveStd + val runningMean1 = bn1.runningMean + val runningVar1 = bn1.runningVar + val gradInput1 = bn1.gradInput + val out1 = bn1.output.squeeze + + val saveMean2 = bn2.saveMean + val saveStd2 = bn2.saveStd + val runningMean2 = bn2.runningMean + val runningVar2 = bn2.runningVar + val gradInput2 = bn2.gradInput + val out2 = bn2.output.squeeze() + + saveMean should be (saveMean1) + saveMean should be (saveMean2) + saveStd should be (saveStd1) + saveStd should be (saveStd2) + runningMean should be (runningMean1) + runningMean should be (runningMean2) + runningVar should be (runningVar1) + runningVar should be (runningVar2) + + val sout1 = output.select(1, 1).squeeze() + sout1 should be (out1) + + val sout2 = output.select(1, 2).squeeze() + sout2 should be (bn2.output) + + val gin1 = gradInput.select(1, 1) + + val gin2 = gradInput.select(1, 2) + + gin1.squeeze should be (gradInput1.squeeze) + gin2.squeeze should be (gradInput2.squeeze) + } + "SpatialBatchNormalization module in batch mode" should "be good in gradient check " + "for input" in { val seed = 100 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index 4495a190ffa..9e651762337 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -26,7 +26,7 @@ import com.intel.analytics.bigdl.optim.{Loss, SGD, Top1Accuracy, Trigger} import com.intel.analytics.bigdl.utils.{Engine, T, Table, TestUtils} import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.{Level, Logger} -import org.apache.spark.SparkContext +import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.api.java.JavaRDD import org.apache.spark.bigdl.api.python.BigDLSerDe import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -43,11 +43,11 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { var sc: SparkContext = null before { - sc = new SparkContext( - Engine.init(1, 4, true).get - .setAppName("Text classification") - .set("spark.akka.frameSize", 64.toString) - .setMaster("local[2]")) + val conf = new SparkConf().setAppName("Text classification") + .set("spark.akka.frameSize", 64.toString) + .setMaster("local[2]") + sc = SparkContext.getOrCreate(conf) + Engine.init(1, 4, true) } after { From 9780eedecafb7c8d9a93d9af2668f1e95bed9521 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 26 Jun 2018 13:53:24 +0800 Subject: [PATCH 0792/1065] [new feature] refine Stridedslice, support begin/end/shrinkAxis mask (#2526) * refine stridedslice * delete some file * meet code review * fix serial unit test * fix serialization test --- .../bigdl/dllib/nn/tf/StrideSlice.scala | 114 ---------- .../bigdl/dllib/nn/tf/StridedSlice.scala | 204 ++++++++++++++++++ .../utils/serializer/ModuleSerializer.scala | 3 +- .../dllib/utils/tf/loaders/StridedSlice.scala | 49 ++--- .../bigdl/dllib/nn/ops/GatherSpec.scala | 2 +- ...SliceSpec.scala => StridedSliceSpec.scala} | 41 ++-- .../tf/loaders/StridedSliceLoadTFSpec.scala | 36 ---- .../utils/tf/loaders/StridedSliceSpec.scala | 182 ++++++++++++++++ 8 files changed, 430 insertions(+), 201 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StridedSlice.scala rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/{StrideSliceSpec.scala => StridedSliceSpec.scala} (60%) delete mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceLoadTFSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala deleted file mode 100644 index beefe9c361a..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSlice.scala +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn.tf - -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} -import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} - -import scala.reflect.ClassTag -import scala.reflect.runtime.universe - -/** - * Extracts a strided slice from a tensor. - * @param sliceSpecs Array(dim, begin_index, end_index, stride) - */ -@SerialVersionUID(4436600172725317184L) -private[bigdl] class StrideSlice[T: ClassTag, D: ClassTag]( - val sliceSpecs: Array[(Int, Int, Int, Int)]) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Tensor[D], Tensor[D], T] { - - output = Tensor[D]() - - require(sliceSpecs.map(_._4 == 1).reduce(_ && _), "only support stride 1 for now") - - override def updateOutput(input: Tensor[D]): Tensor[D] = { - var tmp = input - var i = 0 - while(i < sliceSpecs.length) { - tmp = tmp.narrow(sliceSpecs(i)._1, sliceSpecs(i)._2, sliceSpecs(i)._3 - sliceSpecs(i)._2) - i += 1 - } - if (tmp.dim() == 1 && tmp.size(1) == 1) tmp = Tensor.scalar[D](tmp.valueAt(1)) - output.resizeAs(tmp) - output.copy(tmp) - } - - override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = { - gradInput.resizeAs(input) - gradInput.zero() - var tmp = gradInput - var i = 0 - while(i < sliceSpecs.length) { - tmp = tmp.narrow(sliceSpecs(i)._1, sliceSpecs(i)._2, sliceSpecs(i)._3 - sliceSpecs(i)._2) - i += 1 - } - tmp.copy(gradOutput) - gradInput - } - -} - -private[bigdl] object StrideSlice extends ModuleSerializable { - def apply[T: ClassTag, D: ClassTag](sliceSpecs: Array[(Int, Int, Int, Int)]) - (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): StrideSlice[T, D] = { - new StrideSlice[T, D](sliceSpecs) - } - - override def doLoadModule[T: ClassTag](context: DeserializeContext) - (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { - - val attrMap = context.bigdlModule.getAttrMap - val sliceLen = attrMap.get("sliceLen").getInt32Value - - val specs = new Array[(Int, Int, Int, Int)](sliceLen) - for (i <- 0 until sliceLen) { - val spec = attrMap.get(s"spec_$i") - val lst = DataConverter. - getAttributeValue(context, spec).asInstanceOf[Array[Int]] - specs(i) = (lst(0), lst(1), lst(2), lst(3)) - } - StrideSlice[T, Float](specs) - } - - override def doSerializeModule[T: ClassTag](context: SerializeContext[T], - recurrentBuilder : BigDLModule.Builder) - (implicit ev: TensorNumeric[T]) : Unit = { - - val strideSlice = context.moduleData.module.asInstanceOf[StrideSlice[T, Float]] - - val sliceSpecs = strideSlice.sliceSpecs - - val lengthBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(context, - lengthBuilder, sliceSpecs.length, - universe.typeOf[Int]) - recurrentBuilder.putAttr("sliceLen", lengthBuilder.build) - - sliceSpecs.zipWithIndex.foreach(pair => { - val specBuilder = AttrValue.newBuilder - DataConverter.setAttributeValue(context, - specBuilder, Array[Int](pair._1._1, pair._1._2, pair._1._3, pair._1._4), - universe.typeOf[Array[Int]]) - recurrentBuilder.putAttr(s"spec_${pair._2}", specBuilder.build) - }) - } -} - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StridedSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StridedSlice.scala new file mode 100644 index 00000000000..749ed128eb4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/StridedSlice.scala @@ -0,0 +1,204 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, SerializeContext} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.utils.Table + +import scala.collection.BitSet +import scala.reflect.ClassTag +import scala.reflect.runtime.universe + +/** + * Extracts a strided slice of a tensor. + * The input of this layer should have 4 input, the first one is input data, + * the second one is begin index of slicing, the third one is end index of slicing, + * the third one is strides. + * begin, end and strides should be 1D tensor, and have. + * + * In each mask field (beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) + * the ith bit will correspond to the ith spec. + * @param beginMask If ith bit is set, begin(i) is ignored and the fullest possible + * range in that dimension is used instead. + * @param endMask If ith bit is set, end(i) is ignored and the fullest possible + * range in that dimension is used instead. + * @param ellipsisMask Unsupported currently. + * If ith bit is set, as many unspecified dimensions as needed + * will be inserted between other dimensions. + * @param newAxisMask Unsupported currently. + * If ith bit is set, begin, end, and stride are ignored and a + * new length 1 dimension is added at this point in the output tensor. + * @param shrinkAxisMask If the ith bit is set, it implies that the ith specification + * shrinks the dimensionality by 1. + * @param startFromZero if begin, end is counted from zero. + */ +@SerialVersionUID(4436600172725317184L) +private[bigdl] class StridedSlice[T: ClassTag, D: ClassTag]( + val beginMask: Int = 0, + val endMask: Int = 0, + val ellipsisMask: Int = 0, + val newAxisMask: Int = 0, + val shrinkAxisMask: Int = 0, + val startFromZero: Boolean = false)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + extends AbstractModule[Table, Tensor[D], T] { + + // TODO: support ellipsisMask and newAxisMask + require(ellipsisMask == 0, s"Only support ellipsisMask equals 0, but got $ellipsisMask") + require(newAxisMask == 0, s"Only support newAxisMask equals 0, but got $newAxisMask") + output = Tensor[D]() + gradInput(1) = Tensor[D]() + + val beginBuffer: Tensor[Int] = Tensor[Int]() + val endBuffer: Tensor[Int] = Tensor[Int]() + val stridesBuffer: Tensor[Int] = Tensor[Int]() + + protected def checkSize(strides: Tensor[Int], indx: Tensor[Int], indxName: String): Unit = { + require(indx.dim() == 1, s"$indxName should be a 1D tensor, but got ${indx.dim()}D tensor.") + require(indx.nElement() == strides.nElement(), s"$indxName have ${strides.nElement()} " + + s"elements, but got ${indx.nElement()}.") + } + + protected def maskValue(mask: Int, ith: Int): Boolean = { + (mask >> ith) % 2 == 1 + } + + protected def getPositiveIndices( + inputSize: Array[Int], + indices: Tensor[Int], + buffer: Tensor[Int]): Tensor[Int] = { + buffer.resizeAs(indices) + var i = 1 + while(i <= inputSize.length) { + val index = indices.valueAt(i) + if (index >= 0) { + buffer.setValue(i, index) + } else { + buffer.setValue(i, index + inputSize(i - 1)) + } + i += 1 + } + if (startFromZero) { + buffer.apply1(_ + 1) + } + buffer + } + + override def updateOutput(input: Table): Tensor[D] = { + val inputs = input[Tensor[D]](1) + val begin = input[Tensor[Int]](2) + val end = input[Tensor[Int]](3) + val strides = input[Tensor[Int]](4) + + require(strides.dim() == 1, s"strides should be a 1D tensor, but got ${strides.dim()}D tensor.") + checkSize(strides, begin, "Begin indices") + checkSize(strides, end, "End indices") + + strides.apply1 { v => + require(v == 1, s"Unsupported strides, only support stride 1. but got strides: \n${strides}") + v + } + + val inputSize = inputs.size() + getPositiveIndices(inputSize, begin, beginBuffer) + getPositiveIndices(inputSize, end, endBuffer) + + var tmp: Tensor[D] = inputs + var i = 0 + var currentDim = 1 + while (i < beginBuffer.nElement()) { + if (maskValue(shrinkAxisMask, i)) { + tmp = tmp.select(currentDim, beginBuffer.valueAt(i + 1)) + } else { + val beginIndex = + if (beginMask != 0 && maskValue(beginMask, i)) { + 1 + } else { + beginBuffer.valueAt(i + 1) + } + val endIndex = if (endMask != 0 && maskValue(endMask, i)) { + inputSize(i) + 1 + } else { + endBuffer.valueAt(i + 1) + } + tmp = tmp.narrow(currentDim, beginIndex, endIndex - beginIndex) + currentDim += 1 + } + i += 1 + } + + if (tmp.dim() == 1 && tmp.size(1) == 1) tmp = Tensor.scalar[D](tmp.valueAt(1)) + output.resizeAs(tmp) + output.copy(tmp) + output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[D]): Table = { + var tmp = gradInput[Tensor[D]](1).resizeAs(input[Tensor[D]](1)).zero() + + val inputSize = tmp.size() + + var i = 0 + var currentDim = 1 + while (i < beginBuffer.nElement()) { + if (maskValue(shrinkAxisMask, i)) { + tmp = tmp.select(currentDim, beginBuffer.valueAt(i + 1)) + } else { + val beginIndex = + if (beginMask != 0 && maskValue(beginMask, i)) { + 1 + } else { + beginBuffer.valueAt(i + 1) + } + val endIndex = if (endMask != 0 && maskValue(endMask, i)) { + inputSize(i) + 1 + } else { + endBuffer.valueAt(i + 1) + } + tmp = tmp.narrow(currentDim, beginIndex, endIndex - beginIndex) + currentDim += 1 + } + i += 1 + } + tmp.copy(gradOutput) + + gradInput + } + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } +} + +private[bigdl] object StridedSlice { + def apply[T: ClassTag, D: ClassTag]( + beginMask: Int = 0, + endMask: Int = 0, + ellipsisMask: Int = 0, + newAxisMask: Int = 0, + shrinkAxisMask: Int = 0, + startFromZero: Boolean = false)( + implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): StridedSlice[T, D] = { + new StridedSlice[T, D](beginMask, endMask, ellipsisMask, + newAxisMask, shrinkAxisMask, startFromZero) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index a4646e252f1..5547966fe38 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer, Model, Sequential => KSequential} import com.intel.analytics.bigdl.nn.ops.{RandomUniform => RandomUniformOps} -import com.intel.analytics.bigdl.nn.tf.{DecodeRawSerializer, ParseExample, StrideSlice} +import com.intel.analytics.bigdl.nn.tf.{DecodeRawSerializer, ParseExample, StridedSlice} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -274,7 +274,6 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.SReLU", SReLU) registerModule("com.intel.analytics.bigdl.nn.tf.DecodeRaw", DecodeRawSerializer) registerModule("com.intel.analytics.bigdl.nn.ops.RandomUniform", RandomUniformOps) - registerModule("com.intel.analytics.bigdl.nn.tf.StrideSlice", StrideSlice) registerModule("com.intel.analytics.bigdl.nn.MultiRNNCell", MultiRNNCell) registerModule("com.intel.analytics.bigdl.nn.SpatialSeparableConvolution", SpatialSeparableConvolution) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala index 169ee94ec3e..1e327e924de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSlice.scala @@ -19,10 +19,9 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.tf.StrideSlice +import com.intel.analytics.bigdl.nn.tf.{StridedSlice => StridedSliceOps} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Node import com.intel.analytics.bigdl.utils.tf.Context import org.tensorflow.framework.{DataType, NodeDef} @@ -36,38 +35,24 @@ class StridedSlice extends TensorflowOpsLoader { context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { val t = getType(nodeDef, "T") + val beginMask = getInt(nodeDef.getAttrMap, "begin_mask") + val ellipsisMask = getInt(nodeDef.getAttrMap, "ellipsis_mask") + val endMask = getInt(nodeDef.getAttrMap, "end_mask") + val newAxisMask = getInt(nodeDef.getAttrMap, "new_axis_mask") + val shrinkAxisMask = getInt(nodeDef.getAttrMap, "shrink_axis_mask") + if (t == DataType.DT_INT32) { - return new StridedSliceLoadTF[T, Int]() - } - if (t == DataType.DT_FLOAT) { - return new StridedSliceLoadTF[T, Float]() - } - if (t == DataType.DT_DOUBLE) { - return new StridedSliceLoadTF[T, Double]() + StridedSliceOps[T, Int](beginMask, endMask, ellipsisMask, + newAxisMask, shrinkAxisMask, true) + } else if (t == DataType.DT_FLOAT) { + StridedSliceOps[T, Float](beginMask, endMask, ellipsisMask, + newAxisMask, shrinkAxisMask, true) + } else if (t == DataType.DT_DOUBLE) { + StridedSliceOps[T, Double](beginMask, endMask, ellipsisMask, + newAxisMask, shrinkAxisMask, true) + } else { + throw new UnsupportedOperationException(s"Not support load StridedSlice with type ${t}") } - throw new UnsupportedOperationException(s"Not support load StridedSlice with type ${t}") - } -} - -class StridedSliceLoadTF[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], - ev2: TensorNumeric[D]) extends Adapter[T](Array(2, 3, 4)) { - import StridedSlice._ - - override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { - val start = oneDTensorToArray(tensorArrays(0).asInstanceOf[Tensor[Int]]) - val end = oneDTensorToArray(tensorArrays(1).asInstanceOf[Tensor[Int]]) - val stride = oneDTensorToArray(tensorArrays(2).asInstanceOf[Tensor[Int]]) - - val specs = (start zip end zip stride).zipWithIndex - .map(elem => (elem._2 + 1, elem._1._1._1 + 1, elem._1._1._2 + 1, elem._1._2)) - - - StrideSlice[T, D](specs) - } - - override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { - (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), - Array[TensorNumeric[_]](ev, ev2)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala index cb0b6b7a7cf..b72421e41ff 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala @@ -70,7 +70,7 @@ class GatherSpec extends FlatSpec with Matchers { class GatherSerialTest extends ModuleSerializationTest { override def test(): Unit = { - val gather = Gather[Float, Float]().setName("floorDiv") + val gather = Gather[Float, Float]().setName("gather") val input1 = Tensor[Float].range(1, 6).resize(2, 3) val input2 = Tensor[Int](2).fill(1) val input = T(input1, input2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StridedSliceSpec.scala similarity index 60% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StridedSliceSpec.scala index e7635169d22..c55a2522379 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StrideSliceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/StridedSliceSpec.scala @@ -16,25 +16,30 @@ package com.intel.analytics.bigdl.nn.tf import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel -class StrideSliceSpec extends FlatSpec with Matchers { +class StridedSliceSpec extends FlatSpec with Matchers { "StrideSlice " should "compute correct output and gradient" in { - val module1 = new StrideSlice[Double, Double](Array((1, 1, 2, 1))) - val input = Tensor[Double](2, 2, 2) - input(Array(1, 1, 1)) = -0.17020166106522 - input(Array(1, 1, 2)) = 0.57785657607019 - input(Array(1, 2, 1)) = -1.3404131438583 - input(Array(1, 2, 2)) = 1.0938102817163 - input(Array(2, 1, 1)) = 1.120370157063 - input(Array(2, 1, 2)) = -1.5014141565189 - input(Array(2, 2, 1)) = 0.3380249235779 - input(Array(2, 2, 2)) = -0.625677742064 + val module1 = new StridedSlice[Double, Double]() + val inputTensor = Tensor[Double](2, 2, 2) + inputTensor(Array(1, 1, 1)) = -0.17020166106522 + inputTensor(Array(1, 1, 2)) = 0.57785657607019 + inputTensor(Array(1, 2, 1)) = -1.3404131438583 + inputTensor(Array(1, 2, 2)) = 1.0938102817163 + inputTensor(Array(2, 1, 1)) = 1.120370157063 + inputTensor(Array(2, 1, 2)) = -1.5014141565189 + inputTensor(Array(2, 2, 1)) = 0.3380249235779 + inputTensor(Array(2, 2, 2)) = -0.625677742064 + val begin = Tensor[Int](3).fill(1) + val end = Tensor[Int](3).fill(3) + end.setValue(1, 2) + val strides = Tensor[Int](3).fill(1) val expectOutput1 = Tensor[Double](1, 2, 2) expectOutput1(Array(1, 1, 1)) = -0.17020166106522 @@ -52,9 +57,9 @@ class StrideSliceSpec extends FlatSpec with Matchers { expectedGradInput(Array(2, 2, 1)) = 0.0 expectedGradInput(Array(2, 2, 2)) = 0.0 - + val input = T(inputTensor, begin, end, strides) val output1 = module1.forward(input) - val gradInput = module1.backward(input, output1) + val gradInput = module1.backward(input, output1).toTable[Tensor[Double]](1) output1 should be(expectOutput1) gradInput should be(expectedGradInput) @@ -62,10 +67,14 @@ class StrideSliceSpec extends FlatSpec with Matchers { } -class StrideSliceSerialTest extends ModuleSerializationTest { +class StridedSliceSerialTest extends ModuleSerializationTest { override def test(): Unit = { - val strideSlice = new StrideSlice[Float, Float](Array((1, 1, 2, 1))).setName("strideSlice") + val stridedSlice = StridedSlice[Float, Float]().setName("stridedSlice") val input = Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()) - runSerializationTest(strideSlice, input) + val begin = Tensor[Int](3).fill(1) + val end = Tensor[Int](3).fill(3) + end.setValue(1, 2) + val strides = Tensor[Int](3).fill(1) + runSerializationTest(stridedSlice, T(input, begin, end, strides)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceLoadTFSpec.scala deleted file mode 100644 index 91e06bf357a..00000000000 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceLoadTFSpec.scala +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.utils.tf.loaders - -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T -import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest - -import scala.util.Random - - -class StridedSliceLoadTFSerialTest extends ModuleSerializationTest { - override def test(): Unit = { - val strideSliceLoadTF = new StridedSliceLoadTF[Float, Float](). - setName("strideSliceLoadTF") - val input = T(Tensor[Float](2, 2, 2).apply1(_ => Random.nextFloat()), - Tensor[Int](T(0)), - Tensor[Int](T(1)), - Tensor[Int](T(1)) - ) - runSerializationTest(strideSliceLoadTF, input) - } -} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceSpec.scala new file mode 100644 index 00000000000..e93d8d50637 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/StridedSliceSpec.scala @@ -0,0 +1,182 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.tf.Tensorflow._ +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} + +class StridedSliceSpec extends TensorflowSpecHelper { + + "StridedSlice forward float" should "be correct" in { + compare[Float]( + NodeDef.newBuilder() + .setName("StridedSliceTest") + .setOp(s"StridedSlice") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Index", typeAttr(DataType.DT_INT32)) + .putAttr("begin_mask", intAttr(0)) + .putAttr("end_mask", intAttr(0)) + .putAttr("ellipsis_mask", intAttr(0)) + .putAttr("new_axis_mask", intAttr(0)) + .putAttr("shrink_axis_mask", intAttr(1)), + Seq(Tensor[Float](T(40, 128, 64)), Tensor[Int](T(0)), + Tensor[Int](T(1)), Tensor[Int](T(1))), + 0 + ) + + compare[Float]( + NodeDef.newBuilder() + .setName("StridedSliceTest") + .setOp(s"StridedSlice") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Index", typeAttr(DataType.DT_INT32)) + .putAttr("begin_mask", intAttr(0)) + .putAttr("end_mask", intAttr(0)) + .putAttr("ellipsis_mask", intAttr(0)) + .putAttr("new_axis_mask", intAttr(0)) + .putAttr("shrink_axis_mask", intAttr(1)), + Seq(Tensor[Float](T(40, 128, 64)), Tensor[Int](T(1)), + Tensor[Int](T(2)), Tensor[Int](T(1))), + 0 + ) + + compare[Float]( + NodeDef.newBuilder() + .setName("StridedSliceTest") + .setOp(s"StridedSlice") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Index", typeAttr(DataType.DT_INT32)) + .putAttr("begin_mask", intAttr(0)) + .putAttr("end_mask", intAttr(0)) + .putAttr("ellipsis_mask", intAttr(0)) + .putAttr("new_axis_mask", intAttr(0)) + .putAttr("shrink_axis_mask", intAttr(0)), + Seq(Tensor[Float](T( + T(T(1, 1, 1), T(2, 2, 2)), + T(T(3, 3, 3), T(4, 4, 4)), + T(T(5, 5, 5), T(6, 6, 6)) + )), Tensor[Int](T(1, 0, 0)), + Tensor[Int](T(2, 1, 3)), Tensor[Int](T(1, 1, 1))), + 0 + ) + + compare[Float]( + NodeDef.newBuilder() + .setName("StridedSliceTest") + .setOp(s"StridedSlice") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Index", typeAttr(DataType.DT_INT32)) + .putAttr("begin_mask", intAttr(5)) + .putAttr("end_mask", intAttr(5)) + .putAttr("ellipsis_mask", intAttr(0)) + .putAttr("new_axis_mask", intAttr(0)) + .putAttr("shrink_axis_mask", intAttr(2)), + Seq(Tensor[Float](T( + T(T(1, 1, 1), T(2, 2, 2)), + T(T(3, 3, 3), T(4, 4, 4)), + T(T(5, 5, 5), T(6, 6, 6))) + ), Tensor[Int](T(0, -1, 0)), + Tensor[Int](T(0, 0, 0)), Tensor[Int](T(1, 1, 1))), + 0 + ) + + compare[Float]( + NodeDef.newBuilder() + .setName("StridedSliceTest") + .setOp(s"StridedSlice") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Index", typeAttr(DataType.DT_INT32)) + .putAttr("begin_mask", intAttr(5)) + .putAttr("end_mask", intAttr(5)) + .putAttr("ellipsis_mask", intAttr(0)) + .putAttr("new_axis_mask", intAttr(0)) + .putAttr("shrink_axis_mask", intAttr(2)), + Seq(Tensor[Float](T( + T(T(1, 1, 1), T(2, 2, 2)), + T(T(3, 3, 3), T(4, 4, 4)), + T(T(5, 5, 5), T(6, 6, 6))) + ), Tensor[Int](T(0, 1, 0)), + Tensor[Int](T(0, 0, 0)), Tensor[Int](T(1, 1, 1))), + 0 + ) + } + + "StridedSlice forward int" should "be correct" in { + compare[Int]( + NodeDef.newBuilder() + .setName("StridedSliceTest") + .setOp(s"StridedSlice") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .putAttr("Index", typeAttr(DataType.DT_INT32)) + .putAttr("begin_mask", intAttr(5)) + .putAttr("end_mask", intAttr(5)) + .putAttr("ellipsis_mask", intAttr(0)) + .putAttr("new_axis_mask", intAttr(0)) + .putAttr("shrink_axis_mask", intAttr(2)), + Seq(Tensor[Int](T( + T(T(1, 1, 1), T(2, 2, 2)), + T(T(3, 3, 3), T(4, 4, 4)), + T(T(5, 5, 5), T(6, 6, 6))) + ), Tensor[Int](T(0, -1, 0)), + Tensor[Int](T(0, 0, 0)), Tensor[Int](T(1, 1, 1))), + 0 + ) + + compare[Int]( + NodeDef.newBuilder() + .setName("StridedSliceTest") + .setOp(s"StridedSlice") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .putAttr("Index", typeAttr(DataType.DT_INT32)) + .putAttr("begin_mask", intAttr(1)) + .putAttr("end_mask", intAttr(1)) + .putAttr("ellipsis_mask", intAttr(0)) + .putAttr("new_axis_mask", intAttr(0)) + .putAttr("shrink_axis_mask", intAttr(2)), + Seq(Tensor[Int](T( + T(T(1, 1, 1), T(2, 2, 2)), + T(T(3, 3, 3), T(4, 4, 4)), + T(T(5, 5, 5), T(6, 6, 6))) + ), Tensor[Int](T(0, -1, 0)), + Tensor[Int](T(0, 0, 2)), Tensor[Int](T(1, 1, 1))), + 0 + ) + + compare[Int]( + NodeDef.newBuilder() + .setName("StridedSliceTest") + .setOp(s"StridedSlice") + .putAttr("T", typeAttr(DataType.DT_INT32)) + .putAttr("Index", typeAttr(DataType.DT_INT32)) + .putAttr("begin_mask", intAttr(2)) + .putAttr("end_mask", intAttr(2)) + .putAttr("ellipsis_mask", intAttr(0)) + .putAttr("new_axis_mask", intAttr(0)) + .putAttr("shrink_axis_mask", intAttr(4)), + Seq(Tensor[Int](T( + T(T(1, 1, 1), T(2, 2, 2)), + T(T(3, 3, 3), T(4, 4, 4)), + T(T(5, 5, 5), T(6, 6, 6))) + ), Tensor[Int](T(0, 0, -1)), + Tensor[Int](T(1, 0, 0)), Tensor[Int](T(1, 1, 1))), + 0 + ) + } +} + From 067978e71dd1f0e5b19e6cce0592fcebdf335d01 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 26 Jun 2018 13:53:54 +0800 Subject: [PATCH 0793/1065] [new feature] multi optimMethods support in Optimizer (#2560) * multiOptimMethod * some update * fix unit test * fix ut * fix unit test * fix python unit test * meet code review * update optimizer.py * update python * meet code review --- .../dllib/nn/abstractnn/AbstractModule.scala | 4 + .../bigdl/dllib/optim/DistriOptimizer.scala | 325 +++++++++++------- .../bigdl/dllib/optim/LocalOptimizer.scala | 28 +- .../bigdl/dllib/optim/OptimMethod.scala | 8 - .../bigdl/dllib/optim/Optimizer.scala | 176 +++++++++- .../optim/parameters/AllReduceParameter.scala | 16 +- .../dllib/utils/python/api/PythonBigDL.scala | 21 +- .../dllib/optim/DistriOptimizerSpec.scala | 78 ++++- .../dllib/optim/LocalOptimizerSpec.scala | 27 ++ .../dllib/optim/RefDistriOptimizer.scala | 2 +- .../bigdl/dllib/optim/RefLocalOptimizer.scala | 2 +- .../bigdl/dllib/python/api/PythonSpec.scala | 20 +- 12 files changed, 520 insertions(+), 187 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 6c60b2c0af1..28f6b7c41ea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -960,6 +960,10 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, final private[bigdl] def getParameters(): (Tensor[T], Tensor[T]) = { val (weightParameters, gradParameters) = this.parameters() + // maybe null if not weights in this module. + require(weightParameters != null && weightParameters.length > 0, + s"model ${this.getName()} doesn't have any trainable parameters.") + // If some gradParameters are not allocated storage, allocate it require(weightParameters.size == gradParameters.size, "weights and gradient number are not match") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index f3f9563785b..f5e025d66ec 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -20,6 +20,8 @@ import com.intel.analytics.bigdl.{Module, _} import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, PaddingParam, Sample, SampleToMiniBatch} import com.intel.analytics.bigdl.nn.{Container, Module, Utils} import com.intel.analytics.bigdl.parameters.{AllReduceParameter, ParameterProcessor} +import com.intel.analytics.bigdl.nn.{Container, Module, Utils} +import com.intel.analytics.bigdl.parameters.AllReduceParameter import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ @@ -28,6 +30,7 @@ import java.text.SimpleDateFormat import java.util.Calendar import com.intel.analytics.bigdl.models.utils.ModelBroadcast +import com.intel.analytics.bigdl.nn.abstractnn.Activity import org.apache.commons.lang.exception.ExceptionUtils import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.Logger @@ -52,6 +55,9 @@ object DistriOptimizer { * @param modelGradients gradients of the cached models * @param localCriterions cached criterion * @param localStates cached state + * @param moduleTimeList module running time + * @param localMethods cached validation methods + * @param optimMethods cached optim methods * @tparam T Tensor element type */ case class Cache[T]( @@ -62,7 +68,7 @@ object DistriOptimizer { localStates: Array[Table], var moduleTimeList: Array[Long] = null, localMethods: Array[Option[Array[ValidationMethod[T]]]], - optimMethod: OptimMethod[T] + optimMethods: Map[String, OptimMethod[T]] ) /** @@ -74,7 +80,7 @@ object DistriOptimizer { * @param endWhen trigger to stop training * @param metrics metrics * @param models cached models - * @param optimMethod optimization method + * @param optimMethods optimization methods * @param parameters [[AllReduceParameter]] * @param validationTrigger validation trigger * @param validationDataSet validation dataset @@ -94,8 +100,8 @@ object DistriOptimizer { endWhen: Trigger, metrics: Metrics, models: RDD[Cache[T]], - optimMethod: OptimMethod[T], - parameters: AllReduceParameter[T], + optimMethods: Map[String, OptimMethod[T]], + parameters: Map[String, AllReduceParameter[T]], validationTrigger: Option[Trigger], validationDataSet: Option[DataSet[MiniBatch[T]]], validationMethods: Option[Array[ValidationMethod[T]]], @@ -112,24 +118,26 @@ object DistriOptimizer { var lastEpochTime = 0L // driverState is needed to prevent serializing the whole optimizer - if (!optimMethod.state.contains("epoch")) optimMethod.state.update("epoch", 1) - if (!optimMethod.state.contains("neval")) optimMethod.state.update("neval", 1) - if (!optimMethod.state.contains("Loss")) { - optimMethod.state.update("Loss", Float.PositiveInfinity) - } - if (!optimMethod.state.contains("score")) optimMethod.state.update("score", 0f) - if (!optimMethod.state.contains("recordsProcessedThisEpoch")) { - optimMethod.state.update("recordsProcessedThisEpoch", 0) + optimMethods.values.foreach{ optimMethod => + if (!optimMethod.state.contains("epoch")) optimMethod.state.update("epoch", 1) + if (!optimMethod.state.contains("neval")) optimMethod.state.update("neval", 1) + if (!optimMethod.state.contains("Loss")) { + optimMethod.state.update("Loss", Float.PositiveInfinity) + } + if (!optimMethod.state.contains("score")) optimMethod.state.update("score", 0f) + if (!optimMethod.state.contains("recordsProcessedThisEpoch")) { + optimMethod.state.update("recordsProcessedThisEpoch", 0) + } } val _subModelNumber = Engine.getEngineType() match { case MklBlas => coresPerNode } val driverState = T( - "epoch" -> optimMethod.state("epoch"), - "neval" -> optimMethod.state("neval"), - "Loss" -> optimMethod.state("Loss"), - "score" -> optimMethod.state("score"), + "epoch" -> optimMethods.values.head.state("epoch"), + "neval" -> optimMethods.values.head.state("neval"), + "Loss" -> optimMethods.values.head.state("Loss"), + "score" -> optimMethods.values.head.state("score"), "parallelism" -> _subModelNumber ) @@ -146,7 +154,7 @@ object DistriOptimizer { } logger.info(s"config $state") - var recordsProcessedThisEpoch = optimMethod.state[Int]("recordsProcessedThisEpoch") + var recordsProcessedThisEpoch = optimMethods.values.head.state[Int]("recordsProcessedThisEpoch") if (recordsProcessedThisEpoch == 0) { val shuffleBefore = System.nanoTime() logger.info("Shuffle data") @@ -198,7 +206,9 @@ object DistriOptimizer { Note: All models in `cached` share the same storage for weights, so we only need to copy the weights from parameter server into the first model's weights. */ - val weightsResult = parameters.getWeights(cached.modelWeights.head) + val weightsResults = parameters.values.map(p => + p.getWeights(cached.modelWeights.head.narrow(1, p.paramOffset, p.size)) + ).toArray val miniBatchBuffer = new Array[MiniBatch[T]](_subModelNumber) val batch = data.next() val stackSize = batch.size() / _subModelNumber @@ -218,7 +228,7 @@ object DistriOptimizer { } }) Engine.default.sync(tasks) - weightsResult.waitResult() + weightsResults.foreach(_.waitResult()) val weightSyncTime = System.nanoTime() - syWStart driverMetrics.add("get weights average", weightSyncTime) driverMetrics.add("get weights for each node", weightSyncTime) @@ -261,33 +271,39 @@ object DistriOptimizer { if (finishedThreads.nonEmpty) { val finishedGradients = finishedThreads.map(cached.modelGradients(_)) - time = System.nanoTime() - val gradLength = finishedGradients(0).nElement() - val taskSize = gradLength / _subModelNumber - val extraTask = gradLength % _subModelNumber - - // Aggregate multi-model's gradient to the first model's gradient - val parallelNum = if (taskSize == 0) extraTask else _subModelNumber - Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { - val offset = tid * taskSize + math.min(tid, extraTask) - val length = taskSize + (if (tid < extraTask) 1 else 0) - var i = 1 - while (i < finishedGradients.length) { - finishedGradients(0).narrow(1, offset + 1, length) - .add(finishedGradients(i).narrow(1, offset + 1, length)) - i += 1 - } - })) - driverMetrics.add("aggregate gradient time", System.nanoTime() - time) - val putG = System.nanoTime() - // Put first finished model's gradient who aggregated - // all other models' gradient to AllReduceParameter - parameters.putGradients(finishedGradients(0)) - driverMetrics.add("put gradient", System.nanoTime() - putG) + parameters.values.foreach { p => + time = System.nanoTime() + val pOffset = p.paramOffset + val pLength = p.size + val taskSize = pLength / _subModelNumber + val extraTask = pLength % _subModelNumber + + // Aggregate multi-model's gradient to the first model's gradient + val parallelNum = if (taskSize == 0) extraTask else _subModelNumber + Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { + val offset = pOffset + tid * taskSize + math.min(tid, extraTask) + val length = taskSize + (if (tid < extraTask) 1 else 0) + var i = 1 + while (i < finishedGradients.length) { + finishedGradients(0).narrow(1, offset, length) + .add(finishedGradients(i).narrow(1, offset, length)) + i += 1 + } + })) + driverMetrics.add("aggregate gradient time", System.nanoTime() - time) + val putG = System.nanoTime() + // Put first finished model's gradient who aggregated + // all other models' gradient to AllReduceParameter + p.putGradients(finishedGradients(0).narrow(1, pOffset, pLength)) + driverMetrics.add("put gradient", System.nanoTime() - putG) + } } else { val putG = System.nanoTime() // zero gradient in BlockManager when no thread finished. - parameters.putGradients(cached.modelGradients(0).zero()) + cached.modelGradients(0).zero() + parameters.values.foreach{p => + p.putGradients(cached.modelGradients(0).narrow(1, p.paramOffset, p.size)) + } driverMetrics.add("put gradient", System.nanoTime() - putG) } @@ -314,7 +330,9 @@ object DistriOptimizer { driverState("isGradientUpdated") = false // parameterProcesser like L2NormClippingProcessor may aggregate gradient, // and change the value of isGradientUpdated in driverState. - parameterProcessers.foreach(_.collectGlobalData(models, parameters, metrics, driverState)) + parameters.foreach { p => + parameterProcessers.foreach(_.collectGlobalData(models, p._2, metrics, driverState)) + } val isGradientUpdated = driverState[Boolean]("isGradientUpdated") val stateBroadcast = sc.broadcast(driverState) @@ -323,26 +341,30 @@ object DistriOptimizer { // if parameterProcesser has aggregated gradient, we can skip this aggregation. if (!isGradientUpdated) { val getG = System.nanoTime() - parameters.aggregateGradientPartition(numFinishedModelUpdates) + parameters.values.foreach(_.aggregateGradientPartition(numFinishedModelUpdates)) driverMetrics.add("aggregrateGradientParition average executor", System.nanoTime() - getG) } - parameterProcessers.foreach( - _.processParameters(parameters, modelCache, stateBroadcast.value)) + parameters.foreach { p => + parameterProcessers.foreach(_.processParameters(p._2, modelCache, driverState)) + } + modelCache.optimMethods.foreach{ case (name, optimMethod) => + var time = System.nanoTime() + optimMethod.state.update("epoch", driverState[Int]("epoch")) + optimMethod.state.update("neval", driverState[Int]("neval")) + optimMethod.state.update("Loss", driverState[Float]("Loss")) + if (validationMethods.isDefined) { + optimMethod.state.update("score", driverState[Float]("score")) + } - modelCache.optimMethod.state.update("epoch", driverState[Int]("epoch")) - modelCache.optimMethod.state.update("neval", driverState[Int]("neval")) - modelCache.optimMethod.state.update("Loss", driverState[Float]("Loss")) - if (validationMethods.isDefined) { - modelCache.optimMethod.state.update("score", driverState[Float]("score")) + val p = parameters(name) + optimMethod.optimize(_ => (ev.fromType(value), p.gradientPartition), + p.weightPartition) + driverMetrics.add("compute weight average", System.nanoTime() - time) + time = System.nanoTime() + p.sendWeightPartition() + driverMetrics.add("send weights average", System.nanoTime() - time) } - var time = System.nanoTime() - modelCache.optimMethod.optimize(_ => (ev.fromType(value), parameters.gradientPartition), - parameters.weightPartition) - driverMetrics.add("compute weight average", System.nanoTime() - time) - time = System.nanoTime() - parameters.sendWeightPartition() - driverMetrics.add("send weights average", System.nanoTime() - time) Iterator.empty }.count() @@ -352,14 +374,18 @@ object DistriOptimizer { wallClockTime += end - start driverState("isGradientUpdated") = true driverState("Loss") = lossSum.value.toFloat / numFinishedModelUpdates - optimMethod.updateHyperParameter() + optimMethods.foreach{ v => + v._2.updateHyperParameter() + } + // TODO: Support show learningrate for multiOptimMethod + driverState(s"LearningRate") = optimMethods.head._2.getLearningRate().toFloat + driverState("Throughput") = recordsNum.value.toFloat / ((end - start) / 1e9f) - driverState("LearningRate") = -optimMethod.getLearningRate().toFloat val _header = header(driverState[Int]("epoch"), recordsProcessedThisEpoch, numSamples, driverState[Int]("neval"), wallClockTime) logger.info(s"${_header} Trained ${recordsNum.value} records in ${(end - start) / 1e9} " + s"seconds. Throughput is ${driverState("Throughput")} records/second. Loss is ${ - driverState("Loss")}. ${optimMethod.getHyperParameter()}") + driverState("Loss")}. ${getHyperParameterLog(optimMethods)}") logger.debug("\n" + metrics.summary()) logger.debug("Dropped modules: " + (driverSubModelNum - numFinishedModelUpdates)) lossArray = new Array[Double](_subModelNumber) @@ -409,13 +435,14 @@ object DistriOptimizer { recordsProcessedThisEpoch = 0 } - optimMethod.state.update("recordsProcessedThisEpoch", recordsProcessedThisEpoch) - - optimMethod.state.update("epoch", driverState[Int]("epoch")) - optimMethod.state.update("neval", driverState[Int]("neval")) - optimMethod.state.update("Loss", driverState[Float]("Loss")) - if (validationMethods.isDefined) { - optimMethod.state.update("score", driverState[Float]("score")) + optimMethods.map { case (moduleName, optimMethod) => + optimMethod.state.update("recordsProcessedThisEpoch", recordsProcessedThisEpoch) + optimMethod.state.update("epoch", driverState[Int]("epoch")) + optimMethod.state.update("neval", driverState[Int]("neval")) + optimMethod.state.update("Loss", driverState[Float]("Loss")) + if (validationMethods.isDefined) { + optimMethod.state.update("score", driverState[Float]("score")) + } } validate( @@ -447,7 +474,7 @@ object DistriOptimizer { models, driverState, parameters, - optimMethod, + optimMethods, trainingModel ) @@ -478,18 +505,22 @@ object DistriOptimizer { wallClockTime: Long, models: RDD[Cache[T]], state: Table, - parameters: AllReduceParameter[T], - optimMethod: OptimMethod[T], + parameters: Map[String, AllReduceParameter[T]], + optimMethods: Map[String, OptimMethod[T]], trainingModel: Module[T]): Unit = { cacheTrigger.foreach { trigger => cachePath.foreach { path => if (trigger(state)) { - println(s"[Wall Clock ${wallClockTime / 1e9}s] Save model to $path") saveModel(getModel(models, parameters, trainingModel), cachePath, isOverWrite, s".${state[Int]("neval")}") - optimMethod.state.update("epoch", state[Int]("epoch")) - optimMethod.state.update("neval", state[Int]("neval")) - saveOptimMethod(optimMethod, cachePath, isOverWrite, s".${state[Int]("neval")}") + logger.info(s"[Wall Clock ${wallClockTime / 1e9}s] Save model to $path") + optimMethods.foreach{case (name, optimMethod) => + optimMethod.state.update("epoch", state[Int]("epoch")) + optimMethod.state.update("neval", state[Int]("neval")) + saveOptimMethod(optimMethod, cachePath, isOverWrite, s"-$name.${state[Int]("neval")}") + logger.info(s"[Wall Clock ${wallClockTime / 1e9}s] Save optimMethod " + + s"${optimMethod} to $path") + } } } } @@ -507,7 +538,7 @@ object DistriOptimizer { trainSummary: TrainSummary, models: RDD[Cache[T]], driverState: Table, - parameters: AllReduceParameter[T], + parameters: Map[String, AllReduceParameter[T]], trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { val currentIteration = driverState[Int]("neval") - 1 val parametersTrigger = trainSummary.getSummaryTrigger("Parameters") @@ -527,6 +558,7 @@ object DistriOptimizer { // Not parallelizable, because driverState is changing each iteration. scalarTrigger.foreach { v => if (v._2(driverState)) { + // TODO: Support show learningrate for multiOptimMethod require(driverState.contains(v._1), s"DistriOptimizer.saveSummary: Summary ${v._1} " + s"is not supported now.") trainSummary.addScalar( @@ -561,9 +593,9 @@ object DistriOptimizer { nodeNumber: Int, coresPerNode: Int, checkSingleton: Boolean, - parameters: AllReduceParameter[T], + parameters: Map[String, AllReduceParameter[T]], validationMethods: Option[Array[ValidationMethod[T]]], - optimMethod: OptimMethod[T], + optimMethod: Map[String, OptimMethod[T]], parameterProcessors: ArrayBuffer[ParameterProcessor] )(implicit ev: TensorNumeric[T]) = { val sc = dataset.originRDD().sparkContext @@ -621,7 +653,9 @@ object DistriOptimizer { logger.info("model thread pool size is " + Engine.model.getPoolSize) val weights = cached.head._2 - parameters.init(weights) + parameters.foreach(v => + v._2.init(weights.narrow(1, v._2.paramOffset, v._2.size)) + ) Iterator.single(Cache( cached.map(_._1), // models @@ -631,7 +665,7 @@ object DistriOptimizer { cached.map(_._5), // states new Array[Long](_subModelNumber * computeThresholdbatchSize), cached.map(_._6), - broadcastOptim.clone() + broadcastOptim.map(v => (v._1, v._2.clone())) )) }).persist() models.setName("Thread Model RDD") @@ -745,34 +779,41 @@ object DistriOptimizer { */ private def getModel[T: ClassTag]( models: RDD[Cache[T]], - parameters: AllReduceParameter[T], + parameters: Map[String, AllReduceParameter[T]], trainingModel: Module[T]): Module[T] = { val partitionNum = models.partitions.length val extraState = models.map(_.localModels.head.getExtraParameter()).first() trainingModel.setExtraParameter(extraState) - val (weights, gradients) = models.mapPartitions(iter => { - val cached = iter.next() - val curPartitionId = TaskContext.getPartitionId() - Iterator.single((Map(curPartitionId -> parameters.weightPartition), - Map(curPartitionId -> parameters.gradientPartition))) - }).reduce((a, b) => (a._1 ++ b._1, a._2 ++ b._2)) + // make sure gradient is as the same length as weight val parameterArray = trainingModel.parameters() (0 until parameterArray._2.length).foreach(i => parameterArray._2(i).resizeAs(parameterArray._1(i)) ) + val (parameter, gradientParameter) = trainingModel.getParameters() - val parameterLength = parameter.nElement() - val taskSize = parameterLength / partitionNum - require(taskSize != 0, "parameter length should not less than partition number") - val extraSize = parameterLength % partitionNum - - (0 until partitionNum).map(pid => { - val start = pid * taskSize + math.min(pid, extraSize) - val length = taskSize + (if (pid < extraSize) 1 else 0) - parameter.narrow(1, start + 1, length).copy(weights(pid)) - gradientParameter.narrow(1, start + 1, length).copy(gradients(pid)) - }) + + parameters.foreach { case (moduleName, p) => + val currentModule = trainingModel(moduleName) + require(currentModule.isDefined, s"Couldn't find $moduleName in $trainingModel") + val (weights, gradients) = models.mapPartitions(iter => { + val cached = iter.next() + val curPartitionId = TaskContext.getPartitionId() + Iterator.single((Map(curPartitionId -> p.weightPartition), + Map(curPartitionId -> p.gradientPartition))) + }).reduce((a, b) => (a._1 ++ b._1, a._2 ++ b._2)) + + val taskSize = p.size / partitionNum + require(taskSize != 0, "parameter length should not less than partition number") + val extraSize = p.size % partitionNum + + (0 until partitionNum).map(pid => { + val start = p.paramOffset + pid * taskSize + math.min(pid, extraSize) + val length = taskSize + (if (pid < extraSize) 1 else 0) + parameter.narrow(1, start, length).copy(weights(pid)) + gradientParameter.narrow(1, start, length).copy(gradients(pid)) + }) + } trainingModel } @@ -811,10 +852,12 @@ class DistriOptimizer[T: ClassTag] ( } private def endEpoch(): Unit = { - val records = this.optimMethod.state.get[Int]("recordsProcessedThisEpoch") - if (records.isDefined && records.get != 0) { - this.optimMethod.state("epoch") = this.optimMethod.state[Int]("epoch") + 1 - this.optimMethod.state("recordsProcessedThisEpoch") = 0 + optimMethods.foreach { case (moduleName, optimMethod) => + val records = optimMethod.state.get[Int]("recordsProcessedThisEpoch") + if (records.isDefined && records.get != 0) { + optimMethod.state("epoch") = optimMethod.state[Int]("epoch") + 1 + optimMethod.state("recordsProcessedThisEpoch") = 0 + } } } @@ -858,8 +901,15 @@ class DistriOptimizer[T: ClassTag] ( val distDataset = dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]] - optimMethod.clearHistory() - optimMethod.loadFromTable(state) + optimMethods.values.foreach { optimMethod => + optimMethod.clearHistory() + } + + // To be compatible with the old usage that user define hyperparameters in a table. + if (optimMethods.size == 1) { + optimMethods.head._2.loadFromTable(state) + } + state("dropPercentage") = dropPercentage state("warmupIterationNum") = warmupIterationNum state("computeThresholdbatchSize") = computeThresholdbatchSize @@ -870,14 +920,36 @@ class DistriOptimizer[T: ClassTag] ( val coresPerNode = Engine.coreNumber() val partitionNum = distDataset.originRDD().partitions.length - val size = model.getParameters()._1.nElement() - val parameters = AllReduceParameter.newParameter(partitionNum, size) + val modelParameters = model.getParameters() + // subModuleName -> (storageOffset, length, AllReduceParameter) + val parameters = if (optimMethods.size != 1) { + val p = optimMethods.map{case (subModuleName, optimMethods) => + val subModule = model(subModuleName) + require(subModule.isDefined, s"Optimizer couldn't find $subModuleName in $model") + val subModuleWeights = subModule.get.getParameters()._1 + (subModuleName, subModuleWeights) + } + val sortedWeights = p.values.toArray.sortWith((a, b) => a.storageOffset() < b.storageOffset()) + val compactWeights = Module.isCompact(sortedWeights) + require(modelParameters._1 == compactWeights, + s"DistriOptimizer: All subModules should have an OptimMethod.") + p.map{case (subModuleName, weights) => + (subModuleName, AllReduceParameter.newParameter[T]( + partitionNum, weights.nElement(), weights.storageOffset())) + } + } else if (optimMethods.contains(model.getName())) { + Map(model.getName() -> AllReduceParameter.newParameter[T]( + partitionNum, modelParameters._1.nElement())) + } else { + throw new IllegalArgumentException(s"${model.getName()} doesn't " + + s"have corresponding OptimMethod") + } prepareInput() models = DistriOptimizer.initThreadModels(model, distDataset, criterion, state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, - optimMethod, parameterProcessors) + optimMethods, parameterProcessors) if (checkpointPath.isDefined) { val file = checkpointPath.get + "/" + @@ -901,7 +973,7 @@ class DistriOptimizer[T: ClassTag] ( endWhen, metrics, models, - optimMethod, + optimMethods, parameters, validationTrigger, validationDataSet, @@ -934,24 +1006,33 @@ class DistriOptimizer[T: ClassTag] ( } DistriOptimizer.logger.info(s"Retrying $retryNum times") lastFailureTimestamp = System.nanoTime() - val methodFile = getLatestFile(checkpointPath.get, "optimMethod") + val modelFile = getLatestFile(checkpointPath.get, "model") clearState() models.unpersist() - - var newModel: Module[T] = null - if (methodFile != null && modelFile != null) { - newModel = Module.load[T](modelFile) - optimMethod = OptimMethod.load[T](methodFile) - DistriOptimizer.logger.info("Recover from last snapshot") + val newModel = if (modelFile != null) { + DistriOptimizer.logger.info("Model recover from last snapshot") + Module.load[T](modelFile) } else { - newModel = model - DistriOptimizer.logger.info("Recover from origin model") + DistriOptimizer.logger.info("Model recover from origin model") + model + } + optimMethods = optimMethods.map { case (moduleName, optimMethod) => + val methodFile = getLatestFile(checkpointPath.get, s"optimMethod-$moduleName") + + val newOptimMethod = if (methodFile != null) { + DistriOptimizer.logger.info(s"$moduleName's OptimMethod recover from last snapshot") + OptimMethod.load[T](methodFile) + } else { + DistriOptimizer.logger.info(s"$moduleName's OptimMethod recover from origin model") + optimMethod + } + newOptimMethod.clearHistory() + (moduleName, newOptimMethod) } - optimMethod.clearHistory() - models = DistriOptimizer.initThreadModels(newModel, distDataset, criterion, - state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, - optimMethod, parameterProcessors) + models = DistriOptimizer.initThreadModels(newModel, distDataset, criterion, state, + nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, + optimMethods, parameterProcessors) } else { throw t } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index decd214ed61..dfcb3709dc4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -48,7 +48,7 @@ class LocalOptimizer[T: ClassTag] ( model, dataset, criterion) { import LocalOptimizer._ - import Optimizer._ + import Optimizer.{header, saveModel, saveState, checkSubModules, getHyperParameterLog} private val coreNumber = Engine.coreNumber() @@ -87,8 +87,20 @@ class LocalOptimizer[T: ClassTag] ( override def optimize(): Module[T] = { var wallClockTime = 0L var count = 0 - optimMethod.clearHistory() - optimMethod.loadFromTable(state) + optimMethods.values.foreach{ optimMethod => + optimMethod.clearHistory() + } + + // To be compatible with the old usage that user define hyperparameters in a table. + if (optimMethods.size == 1) { + optimMethods.head._2.loadFromTable(state) + } + + checkSubModules(model, optimMethods.keys.toSeq) + val currentOptimMethods = optimMethods.map{case (subModuleName, optimMethod) => + val subModule = model(subModuleName) + (optimMethod, subModule.get.getParameters()) + } state("epoch") = state.get[Int]("epoch").getOrElse(1) state("neval") = state.get[Int]("neval").getOrElse(1) state("isLayerwiseScaled") = Utils.isLayerwiseScaled(model) @@ -157,9 +169,11 @@ class LocalOptimizer[T: ClassTag] ( parameterProcessors.foreach(_.processParameters(model, state)) - optimMethod.state.update("epoch", state.get("epoch")) - optimMethod.state.update("neval", state.get("neval")) - optimMethod.optimize(_ => (ev.fromType(loss), grad), weight) + currentOptimMethods.foreach { case (optimMethod, (weight, grad)) => + optimMethod.state.update("epoch", state.get("epoch")) + optimMethod.state.update("neval", state.get("neval")) + optimMethod.optimize(_ => (ev.fromType(loss), grad), weight) + } val end = System.nanoTime() wallClockTime += end - start count += batch.size() @@ -169,7 +183,7 @@ class LocalOptimizer[T: ClassTag] ( s"data fetch time is ${(dataFetchTime - start) / 1e9}s, " + s"train time ${(end - dataFetchTime) / 1e9}s. " + s"Throughput is ${batch.size().toDouble / (end - start) * 1e9} record / second. " + - optimMethod.getHyperParameter() + getHyperParameterLog(optimMethods) ) state("neval") = state[Int]("neval") + 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/OptimMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/OptimMethod.scala index e30a1453fad..13ddbddf3c3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/OptimMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/OptimMethod.scala @@ -16,7 +16,6 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.parameters.ParameterProcessor import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{File, T, Table} import org.apache.commons.lang3.SerializationUtils @@ -103,13 +102,6 @@ trait OptimMethod[@specialized(Float, Double) T] extends Serializable { */ def loadFromTable(config: Table): this.type - /** - * get parameter processor - * - * @return - */ - def getParameterProcessor(): Option[ParameterProcessor] = None - /** * Optimize the model parameter * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index 2cc6878426d..eb48b007407 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -20,15 +20,18 @@ import java.nio.file.{Files, Paths} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{DataSet, SampleToMiniBatch, _} + +import scala.collection.mutable import com.intel.analytics.bigdl.parameters.{ConstantClippingProcessor, L2NormClippingProcessor, ParameterProcessor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} +import org.apache.log4j.Logger import org.apache.spark.rdd.RDD import scala.collection.mutable.ArrayBuffer -import scala.reflect.ClassTag +import scala.reflect.{ClassTag, classTag} /** * [[Optimizer]] is an abstract class which is used to train a model automatically @@ -46,8 +49,9 @@ abstract class Optimizer[T: ClassTag, D]( protected var dataset: DataSet[D], protected var criterion: Criterion[T])(implicit ev : TensorNumeric[T]) { + import Optimizer.{logger, checkSubModules} protected var state: Table = T() - protected var optimMethod: OptimMethod[T] = new SGD[T]() + protected var optimMethods: Map[String, OptimMethod[T]] = Map(model.getName -> new SGD()) protected var endWhen: Trigger = Trigger.maxIteration(100) protected var checkpointTrigger: Option[Trigger] = None @@ -68,6 +72,11 @@ abstract class Optimizer[T: ClassTag, D]( protected var computeThresholdbatchSize: Int = 100 protected var warmupIterationNum: Int = 200 + /** + * a list of ParameterProcessor, orders matter + */ + protected var parameterProcessors = ArrayBuffer[ParameterProcessor]() + model.checkDuplicate() /** @@ -227,22 +236,60 @@ abstract class Optimizer[T: ClassTag, D]( } private def resetEpoch(): Unit = { - optimMethod.state.update("epoch", 1) - optimMethod.state.update("neval", 1) - optimMethod.state.update("Loss", Float.PositiveInfinity) - optimMethod.state.update("score", 0f) - optimMethod.state.update("recordsProcessedThisEpoch", 0) + optimMethods.foreach{ case (moduleName, optimMethod) => + optimMethod.state.update("epoch", 1) + optimMethod.state.update("neval", 1) + optimMethod.state.update("Loss", Float.PositiveInfinity) + optimMethod.state.update("score", 0f) + optimMethod.state.update("recordsProcessedThisEpoch", 0) + } } /** - * Set a model to the optimizer + * Set a model to the optimizer. + * Notice: if current optimMethod in this optimizer is not a global optimMethod, + * this setModel will throw an exception. You should use setModelAndOptimMethods instead. * * @param newModel new model */ def setModel(newModel: Module[T]): this.type = { + // check if the old optimMethods is a global one. + if (optimMethods.size == 1 && optimMethods.contains(model.getName())) { + if (newModel.getName() != model.getName()) { + optimMethods = Map(newModel.getName() -> optimMethods(model.getName())) + } + logger.info(s"Optimizer.setModel: Detect current optimMethod is a global optimMethod." + + s" Automatically associate the current optimMethod with the new model.") + } else { + throw new IllegalArgumentException("Optimizer.setModel: Detect current optimMethod" + + " is not a global optimMethod. Please use setModelAndOptimMethods") + } + model = newModel + model.checkDuplicate() + + // if a new Model is set, then reset "epoch", "neval" .etc. + resetEpoch() + this + } + + /** + * Set new model and new optimMethods to the optimizer. + * + * @param newModel new model + * @param newOptimMethods new optimMethods + */ + def setModelAndOptimMethods( + newModel: Module[T], + newOptimMethods: Map[String, OptimMethod[T]]): this.type = { + // check if the old optimMethods is a global one. + model = newModel + optimMethods = newOptimMethods + + model.checkDuplicate() + // if a new Model is set, then reset "epoch", "neval" .etc. resetEpoch() this @@ -317,11 +364,19 @@ abstract class Optimizer[T: ClassTag, D]( * @param method optimization method */ def setOptimMethod(method : OptimMethod[T]): this.type = { - this.optimMethod = method - val processor = method.getParameterProcessor() - if (processor.isDefined) { - parameterProcessors += processor.get - } + checkSubModules(model, Array(model.getName())) + this.optimMethods = Map(model.getName -> method) + this + } + + /** + * Set optimization methods for each submodule. + * + * @param method A mapping of submodule -> OptimMethod + */ + def setOptimMethods(method: Map[String, OptimMethod[T]]): this.type = { + checkSubModules(model, method.keys.toSeq) + this.optimMethods = method this } @@ -379,10 +434,16 @@ abstract class Optimizer[T: ClassTag, D]( def setConstantGradientClipping(min: Double, max: Double) : this.type = { require(min <= max, "min value can not be larger than max") - parameterProcessors.append(new ConstantClippingProcessor(min, max)) + val index = Optimizer.findIndex[ConstantClippingProcessor](parameterProcessors) + if (index == -1) { + parameterProcessors.append(new ConstantClippingProcessor(min, max)) + } else { + parameterProcessors(index) = new ConstantClippingProcessor(min, max) + } this } + /** * Clip gradient to a maximum L2-norm * @param l2NormThreshold gradient L2-Norm threshold @@ -390,22 +451,82 @@ abstract class Optimizer[T: ClassTag, D]( */ def setGradientClippingByl2Norm(l2NormThreshold: Double) : this.type = { - parameterProcessors.append(new L2NormClippingProcessor(l2NormThreshold)) + require(optimMethods.size == 1, "Only support 1 optimMethod.") + require(l2NormThreshold > 0, "l2NormThreshold should larger than zero") + val index = Optimizer.findIndex[L2NormClippingProcessor](parameterProcessors) + if (index == -1) { + parameterProcessors.append(new L2NormClippingProcessor(l2NormThreshold)) + } else { + parameterProcessors(index) = new L2NormClippingProcessor(l2NormThreshold) + } this } - /** - * a list of ParameterProcessor, orders matter - */ - protected var parameterProcessors = ArrayBuffer[ParameterProcessor]() } object Optimizer { + private val logger: Logger = Logger.getLogger(getClass) + private[bigdl] def header(epoch: Int, count: Int, total: Long, iter: Int, wallClockTime: Long) : String = { s"[Epoch $epoch $count/$total][Iteration $iter][Wall Clock ${wallClockTime / 1e9}s]" } + /** + * Check if the sub modules are in the model, if each sub modules' parameter + * is contiguous, if sub modules' parameter is duplicated. + * @param model + * @param subModuleNames + * @param ev + * @tparam T + */ + private[bigdl] def checkSubModules[T: ClassTag]( + model: Module[T], + subModuleNames: Seq[String])(implicit ev: TensorNumeric[T]): Unit = { + val modelParameters = model.getParameters() + val p = subModuleNames.map{subModuleName => + val subModule = model(subModuleName) + require(subModule.isDefined, s"Optimizer: couldn't find $subModuleName in $model") + val subModuleWeights = subModule.get.getParameters()._1 + require(subModuleWeights.nElement() > 0, s"Optimizer: $subModuleName doesn't have" + + s" any trainable parameters, please check your model and optimMethods.") + // If the storage subModule's parameter is the same with the storage of the submodule, + // then subModule's parameter is contiguous. + require(modelParameters._1.storage() == subModuleWeights.storage(), s"Optimizer:" + + s" $subModuleName's parameter is not contiguous.") + (subModuleName, subModuleWeights) + }.toArray + + // make sure if parameters in submodules aren't duplicated. + if (p.length != 1) { + val sortedWeights = p.sortWith((a, b) => a._2.storageOffset() < b._2.storageOffset()) + var i = 0 + while (i < sortedWeights.length - 1) { + val current = sortedWeights(i) + val next = sortedWeights(i + 1) + require(current._2.storageOffset() + current._2.nElement() <= next._2.storageOffset(), + s"Optimizer: ${current._1} and ${next._1}'s parameters are duplicated." + + s" Please check your model and optimMethods.") + i += 1 + } + } + } + + /** + * Combine the hyper parameters in optimMethods. + */ + private[bigdl] def getHyperParameterLog(optimMethods: Map[String, OptimMethod[_]]): String = { + optimMethods.map{ case (moduleName, optimMethod) => + optimMethod.updateHyperParameter() + val log = optimMethod.getHyperParameter() + if (log.isEmpty) { + log + } else { + s"${moduleName}'s hyper parameters: ${log} " + } + }.reduce(_ + _) + } + /** * Save a model to a directory as a checkpoint * @@ -550,4 +671,21 @@ object Optimizer { throw new UnsupportedOperationException } } + + /** + * find the index of type T + * @param parameterProcessors + * @return index + */ + private[Optimizer] def findIndex[T <: ParameterProcessor: ClassTag]( + parameterProcessors: ArrayBuffer[ParameterProcessor]): Int = { + var i = 0 + while(i < parameterProcessors.size) { + if (classTag[T].runtimeClass.isInstance(parameterProcessors(i))) { + return i + } + i += 1 + } + return -1 + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala index 483de788b39..8fa9e68b28c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala @@ -57,9 +57,11 @@ object AllReduceParameter { private val nextId = new AtomicLong(0) - def newParameter[T: ClassTag](partitionNum: Int, size: Int) - (implicit ev: TensorNumeric[T]): AllReduceParameter[T] = { - new AllReduceParameter[T](nextId.getAndIncrement(), partitionNum, size) + def newParameter[T: ClassTag]( + partitionNum: Int, + size: Int, + offset: Int = 1)(implicit ev: TensorNumeric[T]): AllReduceParameter[T] = { + new AllReduceParameter(nextId.getAndIncrement(), partitionNum, size, offset) } } @@ -76,10 +78,14 @@ object AllReduceParameter { * @param id distinguish from other parameters * @param partitionNum how many partitions will use this parameter * @param size size of the parameter (1D vector) + * @param paramOffset start index in the origin parameter. * @tparam T Tensor element type */ -private[bigdl] class AllReduceParameter[T: ClassTag](id: Long, partitionNum: Int, size: Int) - (implicit ev: TensorNumeric[T]) extends Serializable { +class AllReduceParameter[T: ClassTag]( + id: Long, + partitionNum: Int, + val size: Int, + val paramOffset: Int = 1)(implicit ev: TensorNumeric[T]) extends Serializable { import AllReduceParameter._ @transient private var taskSize = 0 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 29aa240dc03..6e677c1359e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -246,12 +246,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab dataset -> SampleToMiniBatch[T](batchSize) } - private def enrichOptimizer[T](optimizer: Optimizer[T, MiniBatch[T]], - endTrigger: Trigger, - optimMethod: OptimMethod[T]): Optimizer[T, MiniBatch[T]] = { + private def enrichOptimizer[T]( + optimizer: Optimizer[T, MiniBatch[T]], + endTrigger: Trigger, + optimMethod: Map[String, OptimMethod[T]]): Optimizer[T, MiniBatch[T]] = { optimizer.setEndWhen(endTrigger) - optimizer.setOptimMethod(optimMethod) + optimizer.setOptimMethods(optimMethod) // TODO: remove this optimizer.disableCheckSingleton() @@ -2200,7 +2201,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab y: JTensor, model: AbstractModule[Activity, Activity, T], criterion: Criterion[T], - optimMethod: OptimMethod[T], + optimMethod: JMap[String, OptimMethod[T]], endTrigger: Trigger, batchSize: Int, localCores: Int): Optimizer[T, MiniBatch[T]] = { @@ -2212,13 +2213,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] Engine.setNodeAndCore(1, localCores) - enrichOptimizer(optimizer, endTrigger, optimMethod) + enrichOptimizer[T](optimizer, endTrigger, optimMethod.asScala.toMap) } def createDistriOptimizer(model: AbstractModule[Activity, Activity, T], trainingRdd: JavaRDD[Sample], criterion: Criterion[T], - optimMethod: OptimMethod[T], + optimMethod: JMap[String, OptimMethod[T]], endTrigger: Trigger, batchSize: Int): Optimizer[T, MiniBatch[T]] = { val sampleRDD = toJSample(trainingRdd) @@ -2229,13 +2230,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab .asInstanceOf[DistributedDataSet[MiniBatch[T]]], _criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] - enrichOptimizer(optimizer, endTrigger, optimMethod) + enrichOptimizer(optimizer, endTrigger, optimMethod.asScala.toMap) } def createDistriOptimizerFromDataSet(model: AbstractModule[Activity, Activity, T], trainDataSet: DataSet[ImageFeature], criterion: Criterion[T], - optimMethod: OptimMethod[T], + optimMethod: JMap[String, OptimMethod[T]], endTrigger: Trigger, batchSize: Int): Optimizer[T, MiniBatch[T]] = { val dataSet = trainDataSet -> ImageFeatureToMiniBatch[T](batchSize) @@ -2245,7 +2246,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab _dataset = dataSet.asInstanceOf[DistributedDataSet[MiniBatch[T]]], _criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] - enrichOptimizer(optimizer, endTrigger, optimMethod) + enrichOptimizer(optimizer, endTrigger, optimMethod.asScala.toMap) } def featureTransformDataset(dataset: DataSet[ImageFeature], diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index 048782debb3..fc06827728f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -64,11 +64,19 @@ object DistriOptimizerSpec { object DistriOptimizerSpecModel { def mse: Module[Double] = { - new Sequential[Double] - .add(new Linear(4, 2)) - .add(new Sigmoid) - .add(new Linear(2, 1)) - .add(new Sigmoid) + Sequential[Double]().setName("mse") + .add(Linear[Double](4, 4).setName("fc_1")) + .add(Sigmoid()) + .add(Linear[Double](4, 1).setName("fc_2")) + .add(Sigmoid()) + } + + def mse2: Module[Double] = { + Sequential[Double]() + .add(Linear[Double](4, 8).setName("fc_1")) + .add(Sigmoid()) + .add(Linear[Double](8, 1).setName("fc_2")) + .add(Sigmoid()) } def linear: Module[Double] = { @@ -246,6 +254,45 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { result2(Array(1)) should be(1.0 +- 1e-2) } + "Train with MSE with two LBFGS" should "be good" in { + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizer( + mse, + dataSet, + new MSECriterion[Double]()) + .setOptimMethods( + Map("fc_1" -> new LBFGS(), "fc_2" -> new LBFGS())) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 1e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 1e-2) + } + + "Train with MSE with two LBFGS after set a new Model" should "be good" in { + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizer[Double]( + mse, + dataSet, + new MSECriterion[Double]()) + .setOptimMethods( + Map("fc_1" -> new LBFGS(), "fc_2" -> new LBFGS())) + optimizer.optimize() + + Array(mse, mse2).foreach { mse => + optimizer.setModelAndOptimMethods(mse, Map("fc_1" -> new LBFGS(), "fc_2" -> new LBFGS())) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 1e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 1e-2) + } + } + "Train with MSE and SGD" should "be trained with good result" in { LoggerFilter.redirectSparkInfoLogs() val mm = mse @@ -262,6 +309,22 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { result2(Array(1)) should be(1.0 +- 5e-2) } + "Train with MSE and two SGD" should "be trained with good result" in { + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizer[Double](mm, dataSet, new MSECriterion[Double]()) + .setOptimMethods(Map("fc_1" -> new SGD(learningRate = 20), + "fc_2" -> new SGD(learningRate = 20))) + .setEndWhen(Trigger.maxEpoch(1)) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + "Train with MSE and SGD" should "be trained with good result after reset model" in { LoggerFilter.redirectSparkInfoLogs() var mm = bn @@ -406,8 +469,9 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { import com.intel.analytics.bigdl._ plusOne = 1.0 RandomGenerator.RNG.setSeed(10) + val model = cre val optimizer = new DistriOptimizer[Double]( - cre, + model, dataSet, new ClassNLLCriterion[Double]() ) @@ -418,7 +482,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val numIterations = dataSet.data(train = false).count() / nodeNumber + 1 val optimMethod = OptimMethod.load[Double](optimizer.getCheckpointPath().get + - s"/optimMethod.$numIterations") + s"/optimMethod-${model.getName()}.$numIterations") optimMethod.state.get[Int]("epoch").get should be(2) optimMethod.state.get[Int]("neval").get should be(numIterations) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala index 6322872d54c..0a090c5787a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala @@ -109,6 +109,14 @@ object LocalOptimizerSpecModel { .add(new Linear(2, 1)) .add(new Sigmoid) } + + def mlpModel : Module[Float] = { + Sequential[Float]() + .add(Linear[Float](4, 4).setName("fc_1")) + .add(Sigmoid[Float]()) + .add(Linear[Float](4, 1).setName("fc_2")) + .add(Sigmoid[Float]()) + } } @com.intel.analytics.bigdl.tags.Serial @@ -311,6 +319,25 @@ class LocalOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter{ test.toTensor[Float].max(1)._2.valueAt(1, 2) should be(2.0) } + "Train model with multi optimMethods" should "be good" in { + RandomGenerator.RNG.setSeed(1000) + + val optimizer = new LocalOptimizer[Float]( + mlpModel, + mseDataSet, + MSECriterion[Float]() + ).setOptimMethods(Map("fc_1" -> new LBFGS[Float](), "fc_2" -> new LBFGS[Float]())) + + val result = optimizer.optimize() + val test = result.forward(Tensor[Float](Storage[Float]( + Array[Float]( + 0, 1, 0, 1, + 1, 0, 1, 0 + )), storageOffset = 1, size = Array(2, 4))) + test.toTensor[Float].valueAt(1, 1) < 0.5 should be(true) + test.toTensor[Float].valueAt(2, 1) > 0.5 should be(true) + } + it should "be same compare to ref optimizer" in { RandomGenerator.RNG.setSeed(1000) val optimizer = new LocalOptimizer[Float]( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefDistriOptimizer.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefDistriOptimizer.scala index d66a1ac0084..ea2dac791ec 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefDistriOptimizer.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefDistriOptimizer.scala @@ -42,7 +42,7 @@ class RefDistriOptimizer[T: ClassTag]( model, dataset, criterion, - optimMethod, + optimMethods.head._2, state, endWhen, ev diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefLocalOptimizer.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefLocalOptimizer.scala index 365d623454c..564f166d9e5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefLocalOptimizer.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/RefLocalOptimizer.scala @@ -52,7 +52,7 @@ class RefLocalOptimizer[T: ClassTag]( val output = model.forward(input).asInstanceOf[Tensor[T]] val loss = criterion.forward(output, target) model.backward(input, criterion.backward(output, target)) - optimMethod.optimize(_ => (loss, g), w, state) + optimMethods.head._2.optimize(_ => (loss, g), w, state) count += batch.size() state("neval") = state[Int]("neval") + 1 logger.info(s"loss is $loss") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index 9e651762337..eb4e9a78eae 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -22,7 +22,7 @@ import java.util.{ArrayList => JArrayList, List => JList, Map => JMap} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.DataSet import com.intel.analytics.bigdl.nn._ -import com.intel.analytics.bigdl.optim.{Loss, SGD, Top1Accuracy, Trigger} +import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.utils.{Engine, T, Table, TestUtils} import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.{Level, Logger} @@ -196,13 +196,16 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { model.add(LogSoftMax[Double]()) val batchSize = 32 val pp = PythonBigDL.ofDouble() - val optimMethod = new SGD[Double]() - optimMethod.learningRateSchedule = SGD.Poly(0.5, math.ceil(1281167.toDouble / batchSize).toInt) + val sgd = new SGD[Double]() + val optimMethod: Map[String, OptimMethod[Double]] = + Map(model.getName -> sgd) + sgd.learningRateSchedule = + SGD.Poly(0.5, math.ceil(1281167.toDouble / batchSize).toInt) val optimizer = pp.createDistriOptimizer( model, data.toJavaRDD(), ClassNLLCriterion[Double](), - optimMethod, + optimMethod.asJava, Trigger.maxEpoch(2), 32) pp.setValidation(optimizer = optimizer, @@ -274,13 +277,14 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { model.add(ReLU[Double]()) model.add(LogSoftMax[Double]()) val batchSize = 32 - val optimMethod = new SGD[Double]() + val optimMethod: Map[String, OptimMethod[Double]] = + Map(model.getName() -> new SGD[Double]()) val optimizer = pp.createLocalOptimizer( List(X).asJava, y, model, ClassNLLCriterion[Double](), - optimMethod, + optimMethod.asJava, Trigger.maxEpoch(2), 32, 2) @@ -310,11 +314,13 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { val sgd = new SGD[Float](0.01) + + val optimMethod: Map[String, OptimMethod[Float]] = Map(model.getName() -> sgd) val pythonBigDL = PythonBigDL.ofFloat() val optimizer = pythonBigDL.createDistriOptimizerFromDataSet(model, imageFrame, criterion = ClassNLLCriterion[Float](), - optimMethod = sgd, + optimMethod = optimMethod.asJava, endTrigger = Trigger.maxEpoch(2), batchSize = 8) optimizer.optimize() From 82fe42af4d0b81c9ed1582b204c0dd2cdf83c3b9 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 26 Jun 2018 15:32:06 +0800 Subject: [PATCH 0794/1065] use a seperate java gateway (#2561) --- .../com/intel/analytics/bigdl/utils/Engine.scala | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 9814e5c6545..d3f384a2cb1 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -115,7 +115,6 @@ object Engine { @volatile private var gatewayServer: py4j.GatewayServer = null - private val driverPortFileCreated = new AtomicBoolean() private def createGatewayPortFile(port: Int): Unit = { val file = new java.io.File(SparkFiles.getRootDirectory(), "gateway_port") @@ -135,17 +134,6 @@ object Engine { } private[bigdl] def createJavaGateway(driverPort: Int): Unit = { - if (SparkUtils.isDriver) { - if (driverPortFileCreated.compareAndSet(false, true)) { - try { - createGatewayPortFile(driverPort) - } catch { - case NonFatal(e) => - throw new Exception("Could not create java gateway port file", e) - } - } - return - } if (gatewayServer != null) return this.synchronized { if (gatewayServer != null) return From 43ba45aa7a1c3cf4be9a513d366fd068b24faacf Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 28 Jun 2018 10:38:35 +0800 Subject: [PATCH 0795/1065] fix: memory leak in `model.predictImageSet`. (#2557) * fix: memory leak in `model.predictImageSet`. There're three reasons of memory leak. 1. repeat allocations in bigquant, which will be fixed in BigDL-core. 2. repeat clone module but no release. `model.predictImageSet` will new Predictor again and again. 2. share weights. This patch add a `StorageManager` which contains a concurrent hash map to maintain all allocations of native memory/resources and prevent duplicate release. It's also helpful for debug. * fix: delete . * refator: as the API for AbstractModule * fix: distribute predictor memory leak * fix: move delete operation to ModelBroadcast * refinement per review * fix ut * fix scala version issue --- .../dllib/models/utils/ModelBroadcast.scala | 113 +++++++++++++++--- .../analytics/bigdl/dllib/nn/Container.scala | 4 + .../dllib/nn/abstractnn/AbstractModule.scala | 13 +- .../bigdl/dllib/nn/quantized/Desc.scala | 28 +++++ .../bigdl/dllib/nn/quantized/Linear.scala | 2 +- .../nn/quantized/SpatialConvolution.scala | 2 +- .../bigdl/dllib/optim/LocalPredictor.scala | 26 +++- .../bigdl/dllib/optim/Predictor.scala | 9 +- .../bigdl/dllib/tensor/DenseTensor.scala | 2 + .../bigdl/dllib/tensor/QuantizedTensor.scala | 4 +- .../bigdl/dllib/tensor/SparseTensor.scala | 3 + .../analytics/bigdl/dllib/tensor/Tensor.scala | 2 + .../analytics/bigdl/dllib/utils/Util.scala | 21 +++- .../dllib/optim/LocalPredictorSpec.scala | 32 +++++ .../bigdl/dllib/optim/PredictorSpec.scala | 32 ++++- 15 files changed, 263 insertions(+), 30 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index cbda51eca05..4bc7530875d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -16,14 +16,19 @@ package com.intel.analytics.bigdl.models.utils +import java.io.{IOException, ObjectInputStream, ObjectOutputStream} +import java.util.UUID + import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.{Container, Graph} -import com.intel.analytics.bigdl.tensor.{QuantizedTensor, QuantizedType, Storage, Tensor} +import com.intel.analytics.bigdl.nn.Container +import com.intel.analytics.bigdl.nn.quantized.StorageManager import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Util._ import org.apache.spark.SparkContext import org.apache.spark.broadcast.Broadcast -import com.intel.analytics.bigdl.utils.Util._ +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -38,10 +43,11 @@ import scala.reflect.ClassTag class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) (implicit ev: TensorNumeric[T]) extends Serializable { - private var broadcastModel: Broadcast[Module[T]] = _ + private var broadcastModel: Broadcast[ModelInfo[T]] = _ private var broadcastConsts: Broadcast[Map[String, Tensor[_]]] = _ private var broadcastParameters: Broadcast[Array[Tensor[T]]] = _ + private[bigdl] val uuid: String = UUID.randomUUID().toString /** * broadcast the model @@ -53,26 +59,45 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) * @return this */ def broadcast(sc: SparkContext, model: Module[T]): this.type = { + CachedModels.deleteAll(uuid) // delete the models on driver + if (applyProtoBuffer) { - broadcastModel = sc.broadcast(model) + broadcastModel = sc.broadcast(ModelInfo(uuid, model)) } else { + // We should clone a new model which will maintain the origin model. + // Otherwise, the origin model's resources will be cleaned. + val newModel = model.cloneModule() + CachedModels.add(uuid, newModel) + // broadcast Consts - if (model.isInstanceOf[Container[_, _, T]]) { - val moduleConsts = getAndClearConsts(model.asInstanceOf[Container[_, _, T]]) + if (newModel.isInstanceOf[Container[_, _, T]]) { + val moduleConsts = getAndClearConsts(newModel.asInstanceOf[Container[_, _, T]]) // TODO: broadcast Const, model structure and weight in the same broadcast. broadcastConsts = sc.broadcast(moduleConsts) } + // broadcast weight and model - val weightsBias = getAndClearWeightBias(model.parameters()) - broadcastModel = sc.broadcast(model.cloneModule()) + val weightsBias = getAndClearWeightBias(newModel.parameters()) + + // We broadcast weight and model separately because of the memory limit of serialization. + // And we should clone the model structure (without weight) first because of lazy evaluation + // of broadcast. As you see, we have to put weights back to the model after broadcast call. + // As a quantized model, it will create relevant memory after clone because of + // `QuantizedTensor`. So we should release it first. + val cloned = newModel.cloneModule() + cloned.release() + CachedModels.add(uuid, cloned) + + broadcastModel = sc.broadcast(ModelInfo[T](uuid, cloned)) broadcastParameters = sc.broadcast(weightsBias) - putWeightBias(weightsBias, model) - initGradWeightBias(weightsBias, model) + putWeightBias(weightsBias, newModel) + initGradWeightBias(weightsBias, newModel) } this } + /** * get the broadcast model * put the weight and bias back to the model @@ -81,14 +106,21 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) * @return model */ def value(initGradient: Boolean = false): Module[T] = { + CachedModels.deleteAll(uuid) if (applyProtoBuffer) { - val localModel = broadcastModel.value.clone(false) + val localModel = broadcastModel.value.model.clone(false) + val uuid = broadcastModel.value.uuid + CachedModels.add(uuid, localModel) + if (initGradient) { initGradWeightBias(getWeightBias(localModel.parameters()), localModel) } localModel } else { - val localModel = broadcastModel.value.cloneModule() + val localModel = broadcastModel.value.model.cloneModule() + val uuid = broadcastModel.value.uuid + CachedModels.add(uuid, localModel) + // share weight putWeightBias(broadcastParameters.value, localModel) // share Consts @@ -141,13 +173,64 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) Array() } } - } - object ModelBroadcast { def apply[@specialized(Float, Double) T: ClassTag](applyProtoBuffer: Boolean = false) (implicit ev: TensorNumeric[T]) : ModelBroadcast[T] = { new ModelBroadcast(applyProtoBuffer) } } + +private[bigdl] class ModelInfo[T: ClassTag](val uuid: String, @transient var model: Module[T])( + implicit ev: TensorNumeric[T]) extends Serializable { + @throws(classOf[IOException]) + private def writeObject(out: ObjectOutputStream): Unit = { + out.defaultWriteObject() + val cloned = model.cloneModule() + out.writeObject(cloned) + CachedModels.add(uuid, cloned) + } +} + +private[bigdl] object ModelInfo { + def apply[T: ClassTag](uuid: String, model: Module[T])( + implicit ev: TensorNumeric[T]): ModelInfo[T] = new ModelInfo[T](uuid, model) +} + +object CachedModels { + import java.util.concurrent.ConcurrentHashMap + + import scala.collection._ + import scala.collection.convert.decorateAsScala._ + import scala.language.existentials + + type Modles = ArrayBuffer[Module[_]] + + private val cachedModels: concurrent.Map[String, Modles] = + new ConcurrentHashMap[String, Modles]().asScala + + def add[T: ClassTag](uuid: String, model: Module[T])( implicit ev: TensorNumeric[T]): Unit = + CachedModels.synchronized { + val models = cachedModels.get(uuid) match { + case Some(values) => values += model.asInstanceOf[Module[_]] + case _ => ArrayBuffer(model.asInstanceOf[Module[_]]) + } + cachedModels.put(uuid, models.asInstanceOf[Modles]) + } + + def deleteAll[T: ClassTag](currentKey: String)(implicit ev: TensorNumeric[T]): Unit = + CachedModels.synchronized { + val keys = cachedModels.keys + for (key <- keys) { + if (key != currentKey) { + val models = cachedModels(key) + println(s"delete key = $key ${models.length}") + for (model <- models) { + model.release() + } + cachedModels.remove(key) + } + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index e48144a53dd..88f6049500c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -227,4 +227,8 @@ abstract class Container[A <: Activity : ClassTag, super.checkDuplicate(record) if (!skipDuplicateCheck()) modules.foreach(_.checkDuplicate(record)) } + + override def release(): Unit = { + modules.foreach(_.release()) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 28f6b7c41ea..43101375258 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -665,8 +665,11 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, Predictor(this, featurePaddingParam, batchPerPartition) .predictImage(distributedImageFrame, outputLayer, shareBuffer, predictKey) case localImageFrame: LocalImageFrame => - LocalPredictor(this, featurePaddingParam, batchPerPartition) - .predictImage(localImageFrame, outputLayer, shareBuffer, predictKey) + val predictor = LocalPredictor(this, featurePaddingParam, batchPerPartition) + val imageFrame = predictor.predictImage(localImageFrame, outputLayer, shareBuffer, + predictKey) + predictor.shutdown() + imageFrame } } @@ -1106,5 +1109,11 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @return */ private[nn] def skipDuplicateCheck(): Boolean = false + + /** + * if the model contains native resources such as aligned memory, we should release it by manual. + * JVM GC can't release them reliably. + */ + def release(): Unit = {} } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Desc.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Desc.scala index dbb63f0e656..44ce6ad55b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Desc.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Desc.scala @@ -147,6 +147,9 @@ object Desc { desc } + // add every native memory allocation. + StorageManager.add(desc, params.getType) + desc } @@ -178,3 +181,28 @@ object QuantParams { val THRESHOLD = 127.0f } +private[bigdl] case class StorageInfo(descType: DescType, isFreed: Boolean) + +private[bigdl] object StorageManager { + import java.util.concurrent.ConcurrentHashMap + private val nativeStorages: ConcurrentHashMap[Long, StorageInfo] = new ConcurrentHashMap() + + def isFreed(nativeStorage: Long): Boolean = { + nativeStorages.get(nativeStorage).isFreed + } + + // atomically set the value + def checkAndSet(nativeStorage: Long): Boolean = { + val descType = nativeStorages.get(nativeStorage).descType + nativeStorages.replace(nativeStorage, StorageInfo(descType, false), StorageInfo(descType, true)) + } + + def get(): Map[Long, StorageInfo] = { + import scala.collection.JavaConverters._ + nativeStorages.asScala.toMap + } + + def add(key: Long, descType: DescType): Unit = { + nativeStorages.put(key, StorageInfo(descType, false)) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala index 8063337378d..d13339859a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/Linear.scala @@ -141,7 +141,7 @@ private[bigdl] class Linear[T: ClassTag]( s"quantized.${getPrintName()}($inputSize -> $outputSize)" } - def release(): Unit = { + override def release(): Unit = { weight.release() data.release() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala index 465d727ca2d..dec1ef10c1c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/quantized/SpatialConvolution.scala @@ -270,7 +270,7 @@ private[bigdl] class SpatialConvolution[T: ClassTag]( s" $kernelH, $strideW, $strideH, $padW, $padH, $nGroup)" } - def release(): Unit = { + override def release(): Unit = { weight.foreach(_.asInstanceOf[QuantizedTensor[T]].release()) data.release() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index d0511fbfb8b..95b2d56afec 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -18,11 +18,13 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{SampleToMiniBatch, _} +import com.intel.analytics.bigdl.nn.Container import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.quantized.QuantizedModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Util} -import com.intel.analytics.bigdl.utils.Util._ import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, LocalImageFrame} +import com.intel.analytics.bigdl.utils.Util._ +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Util} import org.apache.log4j.Logger import scala.reflect.ClassTag @@ -58,15 +60,19 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], case _ => throw new IllegalArgumentException } + // we should clone a new model which has no impact to origin model + private val clonedModel = model.cloneModule() + private val workingModels = { - val weightsBias = Util.getAndClearWeightBias(model.parameters()) + + val weightsBias = Util.getAndClearWeightBias(clonedModel.parameters()) val models = (1 to subModelNumber).map(_ => { - val submodel = model.cloneModule().evaluate() + val submodel = clonedModel.cloneModule().evaluate() putWeightBias(weightsBias, submodel) submodel }).toArray - Util.putWeightBias(weightsBias, model) - Util.initGradWeightBias(weightsBias, model) + Util.putWeightBias(weightsBias, clonedModel) + Util.initGradWeightBias(weightsBias, clonedModel) models } @@ -176,6 +182,14 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], ImageFrame.array(result.toArray) } + + /** + * `shutdown` will release all native resources. + */ + def shutdown(): Unit = { + workingModels.foreach(_.release()) + clonedModel.release() + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index d3d3f14e604..b6f6d79284d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -16,9 +16,11 @@ package com.intel.analytics.bigdl.optim +import java.util.UUID + import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{MiniBatch, PaddingParam, Sample, SampleToMiniBatch, Transformer, Utils, DataSet => _} -import com.intel.analytics.bigdl.models.utils.ModelBroadcast +import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast, ModelInfo} import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -161,6 +163,8 @@ class Predictor[T: ClassTag] private[optim]( partitionNum = Some(partitionNum), featurePaddingParam = featurePaddingParam)) dataSet.mapPartitions { partition => + CachedModels.add(modelBroad.uuid, model) + val localModel = modelBroad.value() val localTransformer = otherBroad.value.cloneTransformer() val miniBatch = localTransformer(partition) @@ -192,6 +196,9 @@ class Predictor[T: ClassTag] private[optim]( partitionNum = Some(partitionNum), featurePaddingParam = featurePaddingParam), shareBuffer) val result = rdd.mapPartitions(partition => { + // By default, the `model` will be deserialized on worker, which will create new resources. + CachedModels.add(modelBroad.uuid, model) + val localModel = modelBroad.value() val localToBatch = toBatchBroad.value._1.cloneTransformer() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index d91e042199c..a5692b024f5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2204,6 +2204,8 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this.apply1(a => ev.digamma(a)) } + override private[bigdl] def toQuantizedTensor: QuantizedTensor[T] = + throw new IllegalArgumentException("DenseTensor cannot be cast to QuantizedTensor") } object DenseTensor { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala index 3b1485511d5..5d8ed2bc0d3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensor.scala @@ -45,7 +45,7 @@ private[bigdl] class QuantizedTensor[T: ClassTag]( } def release(): this.type = { - if (desc != 0) { + if (desc != 0 && StorageManager.checkAndSet(desc)) { BigQuant.FreeMemory(desc) } desc = 0L @@ -270,6 +270,8 @@ private[bigdl] class QuantizedTensor[T: ClassTag]( override def getTensorNumeric(): TensorNumeric[T] = ev + override def toQuantizedTensor: QuantizedTensor[T] = this.asInstanceOf[QuantizedTensor[T]] + @throws(classOf[IOException]) private def readObject(in: ObjectInputStream): Unit = { in.defaultReadObject() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 6e0baa61c2b..2d4d9aeac48 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -1126,6 +1126,9 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( override def sumSquare(): T = throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + + override private[bigdl] def toQuantizedTensor: QuantizedTensor[T] = + throw new IllegalArgumentException("SparseTensor cannot be cast to QuantizedTensor") } object SparseTensor{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 7bcb47122ed..75f2bfe83d3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -804,6 +804,8 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { } return false } + + private[bigdl] def toQuantizedTensor: QuantizedTensor[T] } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala index 81337e07c47..3b539b6fcd7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Util.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.utils import java.io._ import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn.{Container, Graph} +import com.intel.analytics.bigdl.nn.Container import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} import com.intel.analytics.bigdl.tensor._ @@ -166,6 +166,10 @@ object Util { var i = 0 while (i < tensors.length) { if (tensors(i) != null) { + if (tensors(i).getTensorType == QuantizedType) { + tensors(i).toQuantizedTensor.release() + } + tensors(i).set() } i += 1 @@ -179,10 +183,23 @@ object Util { var i = 0 while (i < localWeightBias.length) { if (localWeightBias(i) != null) { - localWeightBias(i).set(broadcastWeightBias(i)) + clearAndSet(localWeightBias(i), broadcastWeightBias(i)) } i += 1 } + + def clearAndSet(old: Tensor[T], other: Tensor[T]): Unit = { + if (old.getTensorType == QuantizedType && other.getTensorType == QuantizedType) { + val quantOld = old.asInstanceOf[QuantizedTensor[T]] + val quantOther = other.asInstanceOf[QuantizedTensor[T]] + + if (quantOld.getNativeStorage != quantOther.getNativeStorage) { + quantOld.release() + } + } + + old.set(other) + } } private[bigdl] def initGradWeightBias[T: ClassTag]( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index f6d63111f9e..d82616df4a8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.dataset.{PaddingParam, Sample, SampleToMiniBatc import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.quantized.StorageManager import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} @@ -303,4 +304,35 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(imageFeatures(x - 1).predict().asInstanceOf[Table].length() == 2) }) } + + "local predictor shutdown" should "work properly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + val input = Tensor[Float](4, 3, 224, 224).rand(-1, 1) + + val samples = (1 to 20).map(i => { + Sample(Tensor[Float](3, 224, 224).randn()) + }).toArray + val imageFrame = ImageFrame.array((0 until 20).map(x => { + val im = ImageFeature() + im(ImageFeature.sample) = samples(x) + im + }).toArray) + + val model = Inception_v1_NoAuxClassifier(1000) + val quant = model.quantize().evaluate() + val initNativeSize = StorageManager.get().count(x => !x._2.isFreed) + + // has no memory issues + (0 to 4).foreach { _ => + quant.predictImage(imageFrame).toLocal().array.map(_.predict().asInstanceOf[Tensor[Float]]) + StorageManager.get().count(x => !x._2.isFreed) should be (initNativeSize) + } + + // check the model can work again + quant.forward(input) + val quant2 = model.quantize().evaluate() + quant2.forward(input) + + quant.output.toTensor[Float] should be (quant2.output.toTensor[Float]) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index f8648550edf..f29fa9f8ff5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -19,12 +19,15 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.dataset.{PaddingParam, Sample} import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.models.utils.CachedModels import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.quantized.{StorageInfo, StorageManager} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} -import com.intel.analytics.bigdl.utils.{Engine, Table} +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, Table} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -211,4 +214,31 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ assert(imageFeatures(x - 1).predict().asInstanceOf[Table].length() == 2) }) } + + "model predict should have no memory leak" should "be correct" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile, sc) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 1000) + val quant = model.quantize() + val init = StorageManager.get() + println(s"init count ${init.count(!_._2.isFreed)}") + var second: Map[Long, StorageInfo] = null + (0 until 20).foreach { i => + val detection = quant.predictImage(imageFrame, batchPerPartition = 16).toDistributed() + detection.rdd.first() + detection.rdd.collect() + println("=" * 80) + println(StorageManager.get().count(!_._2.isFreed)) + println("-" * 80) + } + CachedModels.deleteAll("") + StorageManager.get().count(!_._2.isFreed) should be (init.count(!_._2.isFreed)) + } } From fa942c83de99e624c68304efda7e6456d24b3ad6 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 28 Jun 2018 19:19:43 +0800 Subject: [PATCH 0796/1065] Feat: MKL-DNN Supports (#2482) This feature enables mkl-dnn support, which can speed up deep learning model. We wrapper the native c api in the java, which are in BigDL-core projects. And in BigDL, we integrated the convolution, batchnorm, maxpooling, avgpooling, relu, lrn, softmax, caddtable and concattable. Currently, it supports create the model which only contains dnn layer or container. Because the data layout is optimized in mkl-dnn. The mkl-dnn model will use `DnnTensor` which contains the native buffer as a default tensor. So there're some notations, 1. User should copy the data from jvm heap at the first layer and copy back to jvm heap at the last layer. 2. User should compile the model, which contains the phase (training/inference) and input tensor size. It will infer and allocate the other information. * fix: linear performance issue and serialization of java object in MklDnnTensor * memory leak refactor * memory leak and bn performance issues 1. Memory Leak The internal buffer with MklDnnTensor should not be re-assigned without releasing. So we should check it first. At first iteration or after the changing of input size, we create a new MklDnnTensor as a buffer. 2. Bn perf The JIT BatchNormalization only supports avx2 or avx512, which has much batter performance than ref version. The input and gradOutput format should be the same to get the best performance. * test: add some test cases for BatchNorm. The computation of float value is not the same as C/C++/Native with JVM. And batch norm will make it much greater such as 10^-8 -> 10^-4 -> 10^-1 * fix: rebase with upstream master: 1. Concat and ConcatTable should inherit from DynamicContainer. 2. updateParameters has been depricated. 3. zeroGradParameters should be final. But from now on, the Linear should use it. 4. Some other syntax or semantic errors. * perf: single node and single model performance * perf: single model * feat: add fusion for mkl-dnn * test: add test utils to compare dnn output * test: add some tests compared with caffe * add unit tests for dnn tensor * add unit test for reorder memory * test: fix the test regression errors * checkin reorder manager * add backward for sequential * fix some bugs * update core ref * add unit tests * refactor: move the static class DataType, AlgKind and so on to standalone class (#4) * refactor: delete MklDnn.MemoryFormat * refactor: move the static class DataType, AlgKind and so on to standalone class * fix: core refactor errors * refactor: spec errors (#5) * Mkl dnn dev (#6) * checkin reorder manager * add container and refine reorder manager * fix merge issue * add join table forward * refine inteface (#7) * add LRN and ReLU * add pooling * refactor: conv + linear + bn * add JoinTable backward * refactor: conv + linear + bn * add cAddTable concattable * fix: reorder failed on some of convs * refactor: softmax * refactor: fusion support * refactor: resnet_50 * refactor: move tests to this branch * refactor: delete unusefull files and enable the special old tests. refactor: delete unsed methods in MklDnnOps fix: scalastyle check * fix: rebase with upstream * fix: ignore the prototxt tests * fix: do not change the core commit ref * fix: move set num of threads for mkldnn to ResNet50Perf * fix: serialization disabled for mkldnn module --- .../analytics/bigdl/utils/ThreadPool.scala | 2 +- .../analytics/bigdl/dllib/nn/Utils.scala | 83 ++ .../dllib/nn/abstractnn/AbstractModule.scala | 2 +- .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 93 ++ .../bigdl/dllib/nn/mkldnn/CAddTable.scala | 67 ++ .../bigdl/dllib/nn/mkldnn/ConcatTable.scala | 191 ++++ .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 312 +++++ .../bigdl/dllib/nn/mkldnn/Identity.scala | 60 + .../bigdl/dllib/nn/mkldnn/Input.scala | 47 + .../bigdl/dllib/nn/mkldnn/JoinTable.scala | 115 ++ .../analytics/bigdl/dllib/nn/mkldnn/LRN.scala | 97 ++ .../bigdl/dllib/nn/mkldnn/Linear.scala | 300 +++++ .../bigdl/dllib/nn/mkldnn/MaxPooling.scala | 125 ++ .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 259 +++++ .../bigdl/dllib/nn/mkldnn/MklDnnOps.scala | 58 + .../bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala | 24 + .../bigdl/dllib/nn/mkldnn/Phase.scala | 25 + .../bigdl/dllib/nn/mkldnn/ReLU.scala | 61 + .../dllib/nn/mkldnn/ReorderManager.scala | 114 ++ .../bigdl/dllib/nn/mkldnn/ReorderMemory.scala | 95 ++ .../bigdl/dllib/nn/mkldnn/ResNet50Perf.scala | 309 +++++ .../bigdl/dllib/nn/mkldnn/SelectTable.scala | 99 ++ .../bigdl/dllib/nn/mkldnn/Sequential.scala | 391 +++++++ .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 109 ++ .../nn/mkldnn/SpatialBatchNormalization.scala | 307 +++++ .../dllib/nn/mkldnn/SpatialConvolution.scala | 402 +++++++ .../bigdl/dllib/tensor/ArrayStorage.scala | 16 +- .../bigdl/dllib/tensor/DenseTensor.scala | 81 +- .../bigdl/dllib/tensor/DenseTensorMath.scala | 8 +- .../bigdl/dllib/tensor/DnnStorage.scala | 108 ++ .../bigdl/dllib/tensor/DnnTensor.scala | 370 ++++++ .../analytics/bigdl/dllib/tensor/Tensor.scala | 17 +- .../dllib/nn/mkldnn/AvgPoolingSpec.scala | 80 ++ .../bigdl/dllib/nn/mkldnn/CAddTableSpec.scala | 55 + .../dllib/nn/mkldnn/ConcatTableSpec.scala | 72 ++ .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 155 +++ .../bigdl/dllib/nn/mkldnn/InputSpec.scala | 33 + .../bigdl/dllib/nn/mkldnn/JoinTableSpec.scala | 59 + .../bigdl/dllib/nn/mkldnn/LRNSpec.scala | 53 + .../bigdl/dllib/nn/mkldnn/LinearSpec.scala | 365 ++++++ .../dllib/nn/mkldnn/MaxPoolingSpec.scala | 80 ++ .../bigdl/dllib/nn/mkldnn/ReLUSpec.scala | 76 ++ .../dllib/nn/mkldnn/ReorderMemorySpec.scala | 51 + .../dllib/nn/mkldnn/SequentialSpec.scala | 102 ++ .../dllib/nn/mkldnn/SingleLayerSpec.scala | 329 ++++++ .../bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala | 142 +++ .../SpatialBatchNormalizationSpec.scala | 535 +++++++++ .../nn/mkldnn/SpatialConvolutionSpec.scala | 514 +++++++++ .../bigdl/dllib/nn/mkldnn/TestUtils.scala | 514 +++++++++ .../bigdl/dllib/nn/mkldnn/TopologySpec.scala | 1011 +++++++++++++++++ .../dllib/tensor/DenseTensorMathSpec.scala | 78 +- .../bigdl/dllib/tensor/DenseTensorSpec.scala | 35 +- .../bigdl/dllib/tensor/DnnTensorSpec.scala | 72 ++ .../utils/serializer/SerializerSpec.scala | 19 +- 54 files changed, 8671 insertions(+), 106 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Identity.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Input.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnOps.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Phase.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SelectTable.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLUSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 1c48eef4619..5dc71e8151b 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils import java.util.concurrent._ -import com.intel.analytics.bigdl.mkl.MKL +import com.intel.analytics.bigdl.mkl.{MKL, MklDnn} import org.apache.commons.lang.exception.ExceptionUtils import org.apache.log4j.Logger diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index ba2b9e2ffa0..6f43d2a1549 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -392,6 +392,54 @@ object Utils { Array(padH, padH, padW, padW, oheight, owidth) } + private[nn] def getOutSizeAndPaddingForDNN( + inputHeight: Int, + inputWidth: Int, + dH: Int, + dW: Int, + kH: Int, + kW: Int, + padH: Int, + padW: Int, + ceilMode: Boolean, + dilationHeight: Int = 1, + dilationWidth: Int = 1, + inputdepth: Int = -1, + dt: Int = -1, + kt: Int = -1, + padt: Int = 0, + dilationDepth: Int = 1): Array[Int] = { + // compute padding left, right, top and bottom + var pad_t = padH + var pad_b = padH + var pad_l = padW + var pad_r = padW + + var oheight = 0 + var owidth = 0 + var odepth = 0 + + val dilationKernelHeight = dilationHeight * (kH - 1) + 1 + val dilationKernelWidth = dilationWidth * (kW - 1) + 1 + + oheight = math.ceil(1.0 * (inputHeight - dilationKernelHeight + 2*padH) / dH).toInt + 1 + owidth = math.ceil(1.0 * (inputWidth - dilationKernelWidth + 2*padW) / dW).toInt + 1 + + if (padH != 0 || padW != 0 || padt != 0 || kH == 1 || kW == 1) { + if ((oheight - 1) * dH >= inputHeight + padH) oheight -= 1 + if ((owidth - 1) * dW >= inputWidth + padW) owidth -= 1 + } + + val h = inputHeight + pad_t +// var pad_b = padH + while ((h + pad_b) < (dH * (oheight - 1) + kH)) pad_b = pad_b + 1 + val w = inputWidth + pad_l +// var pad_r = padW + while ((w + pad_r) < (dW * (owidth - 1) + kW)) pad_r = pad_r + 1 + + Array(pad_t, pad_b, pad_l, pad_r, oheight, owidth) + } + private[nn] def getOutputShape(outputHeight: Int, outputWidth: Int, nOutputPlane: Int, batchSize: Int = -1, format: DataFormat): Array[Int] = { format match { @@ -472,6 +520,41 @@ object Utils { out } + private[nn] def getPaddingAndOutputSize( + inputHeight: Int, + inputWidth: Int, + dH: Int, + dW: Int, + kH: Int, + kW: Int, + padH: Int, + padW: Int + ): (Int, Int, Int, Int, Int, Int) = { + // compute padding left, right, top and bottom + var pad_t = padH + var pad_b = padH + var pad_l = padW + var pad_r = padW + + var oheight = 0 + var owidth = 0 + var odepth = 0 + + oheight = math.ceil(1.0 * (inputHeight - kH + 2 * padH) / dH).toInt + 1 + owidth = math.ceil(1.0 * (inputWidth - kW + 2 * padW) / dW).toInt + 1 + + if (padH != 0 || padW != 0 || kH == 1 || kW == 1) { + if ((oheight - 1) * dH >= inputHeight + padH) oheight -= 1 + if ((owidth - 1) * dW >= inputWidth + padW) owidth -= 1 + } + + val h = inputHeight + pad_t + while ((h + pad_b) < (dH * (oheight - 1) + kH)) pad_b = pad_b + 1 + val w = inputWidth + pad_l + while ((w + pad_r) < (dW * (owidth - 1) + kW)) pad_r = pad_r + 1 + + (pad_t, pad_b, pad_l, pad_r, oheight, owidth) + } /** * Calculate forward time and backward time. * @param times diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 43101375258..e05960c70d0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -320,7 +320,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * If the module has parameters, this will zero the accumulation of the gradients with respect * to these parameters. Otherwise, it does nothing. */ - final def zeroGradParameters(): Unit = { + def zeroGradParameters(): Unit = { if (parameters() != null) { parameters()._1.zip(parameters()._2)foreach{ case (weight, grad) => grad.resizeAs(weight).zero() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala new file mode 100644 index 00000000000..d4394a304eb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -0,0 +1,93 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl._ +import com.intel.analytics.bigdl.nn.Utils +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor + +class AvgPooling( + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0 +) extends MklDnnLayer { + @transient + private var paddingTL: Array[Int] = _ + @transient + private var paddingBR: Array[Int] = _ + @transient + private var fwdPD: Long = _ + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = singleNativeData(inputs) + val strides = Array(dW, dH) + val kernel = Array(kH, kW) + val n = _inputFormats(0).shape(0) + val c = _inputFormats(0).shape(1) + val h = _inputFormats(0).shape(2) + val w = _inputFormats(0).shape(3) + val (pt, pb, pl, pr, oh, ow) = + Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW) + paddingTL = Array(pt, pl) + paddingBR = Array(pb, pr) + val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), DataType.F32, Memory.Format.any) + val description = MklDnn.PoolingForwardDescInit( + PropKind.Forward, AlgKind.PoolingAvgExcludePadding, + _inputFormats(0).getMemoryDescription(), outputMD, strides, kernel, paddingTL, paddingBR, + MklDnn.PaddingKind.mkldnnPaddingZero) + fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + _outputFormats = Array(MemoryData.primitiveOutput(fwdPD)) + output = initTensor(_outputFormats(0)) + updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPD, + _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, + _outputFormats.map(_.getPrimitive(runtime)), 2)) + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = singleNativeData(grad) + _gradOutputFormatsForWeight = _gradOutputFormats + val strides = Array(dW, dH) + val kernel = Array(kH, kW) + val description = MklDnn.PoolingBackwardDescInit(AlgKind.PoolingAvgExcludePadding, + _inputFormats(0).getMemoryDescription(), + _gradOutputFormats(0).getMemoryDescription(), + strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) + + val pd = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD) + _gradInputFormats = Array(MemoryData.primitiveGradInput(pd)) + updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(pd, + _gradOutputFormats.map(_.getPrimitive(runtime)), + Array(0, 0), 2, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) + gradInput = initTensor(_gradInputFormats(0)) + (_gradOutputFormats, _gradInputFormats) + } +} + +object AvgPooling { + def apply( + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0 + ): AvgPooling = new AvgPooling(kW, kH, dW, dH, padW, padH) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala new file mode 100644 index 00000000000..465ab1fba0c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.utils.T + +class CAddTable extends MklDnnLayer { + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = nativeData(inputs) + val shape = inputs(0).shape.clone() + for(i <- 1 until inputs.length) { + require(shape.length == inputs(i).shape.length, "dimension not match") + for(j <- 0 until shape.length) { + require(shape(j) == inputs(i).shape(j), "size not match") + } + } + + val outputMD = MklDnn.MemoryDescInit(shape.length, shape, DataType.F32, Memory.Format.any) + val scales = inputs.map(_ => 1f) + val pd = MklDnn.SumPrimitiveDescCreate(outputMD, inputs.length, scales, + inputs.map(_.getPrimitiveDescription(runtime))) + _outputFormats = Array(MemoryData.primitiveOutput(pd)) + updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(pd, + _inputFormats.map(_.getPrimitive(runtime)), new Array[Int](inputs.length), + _inputFormats.length, _outputFormats.map(_.getPrimitive(runtime)), 1)) + output = initTensor(_outputFormats(0)) + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = grad + _gradOutputFormatsForWeight = grad + _gradInputFormats = new Array[MemoryData](_inputFormats.length).map(a => grad(0)) + gradInput = T() + (_gradOutputFormats, _gradInputFormats) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + require(gradOutput.isTensor, "gradOutput should be a tensor") + val _gradInput = gradInput.toTable + var i = 1 + while(i <= _inputFormats.length) { + _gradInput(i) = gradOutput + i += 1 + } + gradInput + } +} + +object CAddTable { + def apply(): CAddTable = new CAddTable() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala new file mode 100644 index 00000000000..b78e8c6dd54 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala @@ -0,0 +1,191 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn +import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.collection.mutable.ArrayBuffer + +class ConcatTable extends MklDnnContainer { + + output = T() + + @transient + private var sumPrimitive: Array[Long] = null + @transient + private var tensors: Array[Tensor[Float]] = null + @transient + private var tensorPrimitives: Array[Long] = null + + override def updateOutput(input: Activity): Activity = { + require(modules.length > 0, "empty modules of concat table") + var i = 0 + while (i < modules.length) { + val currentOutput = modules(i).forward( + reorderManager.infer(_inputFormats, mklDnnModules(i).inputFormats(), input)) + output.toTable(i + 1) = currentOutput + i += 1 + } + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + require(modules.length > 0, "empty modules of concat table") + + var i = 0 + while (i < modules.length) { + tensors(i) = modules(i).updateGradInput(input, gradOutput.toTable(i + 1)) + .asInstanceOf[Tensor[Float]] + i += 1 + } + MklDnnOps.streamSubmit(runtime.stream, 1, sumPrimitive, 1, tensorPrimitives, tensors) + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + var i = 0 + while (i < modules.length) { + modules(i).accGradParameters(input, gradOutput.toTable(i + 1)) + i += 1 + } + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + require(mklDnnModules != null, "You should call compile first") + require(inputs.length == 1, "Concat only accept one tensor") + val buffer = new ArrayBuffer[MemoryData]() + mklDnnModules.foreach(m => { + val (realInput, out) = m.initFwdPrimitives(inputs, phase) + require(out.length == 1, "output should be one tensor") + inputs.zip(realInput).map {case(f, t) => reorderManager.register(f, t)} + buffer.append(out(0)) + }) + _outputFormats = buffer.toArray + _inputFormats = inputs + (inputs, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + require(grads.length == mklDnnModules.length, "grad tensor number is not correct") + _gradOutputFormats = new Array[MemoryData](grads.length) + val subGradInputs = new Array[MemoryData](grads.length) + tensorPrimitives = new Array[Long](grads.length + 1) + var shape: Array[Int] = null + for(i <- 0 until grads.length) { + val m = mklDnnModules(i) + val (realGrads, gradInput) = m.initBwdPrimitives(Array(grads(i)), phase) + require(realGrads.length == 1, "real grad length should be 1") + _gradOutputFormats(i) = realGrads(0) + require(gradInput.length == 1, "real grad length should be 1") + subGradInputs(i) = gradInput(0) + tensorPrimitives(i) = gradInput(0).getPrimitive(runtime) + if (shape == null) { + shape = gradInput(0).shape.clone() + } else { + require(shape.length == gradInput(0).shape.length, "backward grad shape should be same") + for(j <- 0 until shape.length) { + require(shape(j) == gradInput(0).shape(j), "backward grad shape size should be same") + } + } + } + val outputMD = MklDnn.MemoryDescInit(shape.length, shape, DataType.F32, Memory.Format.any) + val scales = grads.map(_ => 1f) + val pd = MklDnn.SumPrimitiveDescCreate(outputMD, grads.length, scales, + subGradInputs.map(_.getPrimitiveDescription(runtime))) + _gradInputFormats = Array(MemoryData.primitiveOutput(pd)) + tensorPrimitives(grads.length) = _gradInputFormats(0).getPrimitive(runtime) + sumPrimitive = Array(MklDnn.PrimitiveCreate2(pd, + subGradInputs.map(_.getPrimitive(runtime)), new Array[Int](grads.length), + grads.length, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) + gradInput = initTensor(_gradInputFormats(0)) + tensors = new Array[Tensor[Float]](grads.length + 1) + tensors(grads.length) = gradInput.asInstanceOf[Tensor[Float]] + (_gradOutputFormats, _gradInputFormats) + } + + override private[mkldnn] def initGradWPrimitives(grads: Array[MemoryData], phase: Phase) = { + val realGradsBuffer = new ArrayBuffer[MemoryData]() + for(i <- 0 until grads.length) { + val m = mklDnnModules(i) + val realGradOutput = m.initGradWPrimitives(Array(grads(i)), phase) + require(realGradOutput.length == 1, s"real grad length should be 1, " + + s"but it's ${realGradOutput.length}") + realGradsBuffer.append(realGradOutput(0)) + } + _gradOutputWeightFormats = realGradsBuffer.toArray + _gradOutputWeightFormats + } + + override private[mkldnn] def inputFormats() = { + require(_inputFormats != null, "You should call initFwdPrimitives first") + _inputFormats + } + + override private[mkldnn] def gradInputFormats() = { + require(_gradInputFormats != null, "You should call initBwdPrimitives first") + _gradInputFormats + } + + override private[mkldnn] def outputFormats() = { + require(_outputFormats != null, "You should call initFwdPrimitives first") + _outputFormats + } + + override private[mkldnn] def gradOutputFormats() = { + require(_gradOutputFormats != null, "You should call initBwdPrimitives first") + _gradOutputFormats + } + + private var _inputFormats: Array[MemoryData] = _ + private var _gradInputFormats: Array[MemoryData] = _ + private var _outputFormats: Array[MemoryData] = _ + private var _gradOutputFormats: Array[MemoryData] = _ + private var _gradOutputWeightFormats: Array[MemoryData] = _ + + override private[mkldnn] def gradOutputWeightFormats() = _gradOutputWeightFormats + + override def toString(): String = { + val tab = "\t" + val line = "\n" + val next = " |`-> " + val lastNext = " `-> " + val ext = " | " + val extlast = " " + val last = " ... -> " + var str = s"${getPrintName}" + str = str + " {" + line + tab + "input" + var i = 1 + while (i <= modules.length) { + if (i == modules.length) { + str = str + line + tab + lastNext + "(" + i + "): " + + modules(i-1).toString.replace(line, line + tab + extlast) + } else { + str = str + line + tab + next + "(" + i + "): " + + modules(i-1).toString.replace(line, line + tab + ext) + } + i += 1 + } + str = str + line + tab + last + "output" + str = str + line + "}" + str + } +} + +object ConcatTable { + def apply(): ConcatTable = new ConcatTable() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala new file mode 100644 index 00000000000..ff6103d7249 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -0,0 +1,312 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{DataType, MklDnn} +import com.intel.analytics.bigdl.nn.DynamicContainer +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.{DenseType, DnnTensor, Tensor} +import com.intel.analytics.bigdl.utils.T + +import scala.collection.mutable.ArrayBuffer + +/** + * Helper utilities when integrating Module with MKL-DNN + */ +trait MklDnnModule extends MklDnnModuleHelper { + /** + * MklDnn runtime, which includes a MKL-DNN engine and a MKL-DNN stream. + * Note that this instance will be erased when send to remote worker, so you + * should recreate a MklDnnRuntime. + */ + @transient + protected var runtime : MklDnnRuntime = _ + + def setRuntime(runtime: MklDnnRuntime): Unit = { + this.runtime = runtime + } + + /** + * Init the MKL-DNN primitives for the layer. Note that these primitives will be erased when + * sent to a remote worker. + */ + private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) + : (Array[MemoryData], Array[MemoryData]) + private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) + : (Array[MemoryData], Array[MemoryData]) + private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase): Array[MemoryData] + = grad + + private[mkldnn] def inputFormats(): Array[MemoryData] + private[mkldnn] def gradInputFormats(): Array[MemoryData] + private[mkldnn] def outputFormats(): Array[MemoryData] + private[mkldnn] def gradOutputFormats(): Array[MemoryData] + private[mkldnn] def gradOutputWeightFormats(): Array[MemoryData] +} + +trait MklDnnModuleHelper { + protected def initActivity(formats: Array[MemoryData]): Activity = { + if (formats.length == 1) { + initTensor(formats(0)) + } else { + T.array(formats.map(initTensor(_))) + } + } + + protected def initTensor(format: MemoryData): Tensor[Float] = { + format match { + case d: NativeData => + DnnTensor[Float](d.shape) + case d: HeapData => + Tensor[Float](d.shape) + case _ => throw new UnsupportedOperationException("memory format is not supported") + } + } + protected def singleNativeData(formats: Array[MemoryData]): Array[MemoryData] = { + require(formats.length == 1, "Only accept one tensor as input") + nativeData(formats) + } + protected def nativeData(formats: Array[MemoryData]): Array[MemoryData] = { + formats.map( + f => { + f match { + case i: NativeData => i + case i: HeapData => i.toNative() + case _ => throw new UnsupportedOperationException("Not support memory format") + } + } + ) + } +} + +trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnModule { + /** + * MKL-DNN primitives of the module. Note you should only initialize this field by calling + * initPrimitives method. This field will be erased when sending model to remote worker. So you + * need to reinitialize it after sending the model. + */ + @transient + protected var updateOutputPrimitives: Array[Long] = _ + @transient + protected var updateGradInputPrimitives: Array[Long] = _ + @transient + protected var accGradientPrimitives: Array[Long] = _ + + protected var _inputFormats: Array[MemoryData] = _ + protected var _gradInputFormats: Array[MemoryData] = _ + protected var _outputFormats: Array[MemoryData] = _ + protected var _gradOutputFormats: Array[MemoryData] = _ + protected var _gradOutputFormatsForWeight: Array[MemoryData] = _ + + @transient + private var updateOutputMemoryPrimitives: Array[Long] = _ + @transient + private var updateOutputTensors: Array[Tensor[Float]] = _ + @transient + private var updateGradInputMemoryPrimitives: Array[Long] = _ + @transient + private var updateGradInputTensors: Array[Tensor[Float]] = _ + @transient + private var cachedInput: Activity = _ + @transient + private var cachedGradOutput: Activity = _ + + override private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], + phase: Phase): Array[MemoryData] = { + _gradOutputFormatsForWeight = grad + grad + } + + override def updateOutput(input: Activity): Activity = { + if (updateOutputMemoryPrimitives == null) { + updateOutputMemoryPrimitives = + inputFormats().map(_.getPrimitive(runtime)) ++ outputFormats().map(_.getPrimitive(runtime)) + } + if (updateOutputTensors == null || cachedInput == null || !cachedInput.eq(input)) { + val buffer = new ArrayBuffer[Tensor[Float]]() + if (input.isTensor) { + buffer.append(input.asInstanceOf[Tensor[Float]]) + } else { + val table = input.toTable + var i = 1 + while (i <= table.length()) { + buffer.append(table(i)) + i += 1 + } + } + if (output.isTensor) { + buffer.append(output.asInstanceOf[Tensor[Float]]) + } else { + val table = output.toTable + var i = 1 + while (i <= table.length()) { + buffer.append(table(i)) + i += 1 + } + } + updateOutputTensors = buffer.toArray + cachedInput = input + } + MklDnnOps.streamSubmit( + runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, + updateOutputMemoryPrimitives, + updateOutputTensors + ) + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + if (updateGradInputMemoryPrimitives == null) { + updateGradInputMemoryPrimitives = + gradOutputFormats().map(_.getPrimitive(runtime)) ++ + gradInputFormats().map(_.getPrimitive(runtime)) + } + if (updateGradInputTensors == null || cachedInput == null || !cachedInput.eq(input) || + cachedGradOutput == null || !cachedGradOutput.eq(gradOutput)) { + val buffer = new ArrayBuffer[Tensor[Float]]() + if (gradOutput.isTensor) { + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) + } else { + val table = gradOutput.toTable + var i = 1 + while (i <= table.length()) { + buffer.append(table(i)) + i += 1 + } + } + if (gradInput.isTensor) { + buffer.append(gradInput.asInstanceOf[Tensor[Float]]) + } else { + val table = gradInput.toTable + var i = 1 + while (i <= table.length()) { + buffer.append(table(i)) + i += 1 + } + } + updateGradInputTensors = buffer.toArray + cachedInput = input + cachedGradOutput = gradOutput + } + MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, + updateGradInputPrimitives.length, + updateGradInputMemoryPrimitives, updateGradInputTensors) + gradInput + } + + + override private[mkldnn] def inputFormats() = { + require(_inputFormats != null, "You should call initFwdPrimitives first") + _inputFormats + } + + override private[mkldnn] def gradInputFormats() = { + require(_gradInputFormats != null, "You should call initBwdPrimitives first") + _gradInputFormats + } + + override private[mkldnn] def outputFormats() = { + require(_outputFormats != null, "You should call initFwdPrimitives first") + _outputFormats + } + + override private[mkldnn] def gradOutputFormats() = { + require(_gradOutputFormats != null, "You should call initBwdPrimitives first") + _gradOutputFormats + } + + override private[mkldnn] def gradOutputWeightFormats() = { + _gradOutputFormatsForWeight + } + + def updateWithNewTensor(from: Array[Tensor[Float]], index: Int, + value: Activity): Unit = { + from(index).getTensorType match { + case DenseType => from(index) = value.toTensor[Float] + case _ => + } + } + + def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { + (null, null) + } +} + +/** + * Helper utilities when integrating containers with MKL-DNN + */ +trait MklDnnContainer extends DynamicContainer[Activity, Activity, Float] with MklDnnModule { + protected val reorderManager = new ReorderManager() + protected var mklDnnModules : Array[MklDnnModule] = _ + + override def add(module: AbstractModule[_ <: Activity, _ <: Activity, Float]): this.type = { + require(mklDnnModules == null, "You should not call add after compilation") + require(module.isInstanceOf[MklDnnModule], "layer should be MklDnnModule") + super.add(module) + } + + /** + * Create MklDnnRuntime and compile the model + * @param phase + */ + final def compile(phase: Phase, formats: Array[MemoryData]): Unit = { + compile(phase, new MklDnnRuntime(), formats) + } + + /** + * Compile the model, which includes infer memory shapes, allocate memory, optimize computing + * path and create MKL-DNN primitives + * @param phase + * @param runtime + */ + final def compile(phase: Phase, runtime: MklDnnRuntime, formats: Array[MemoryData]): Unit = { + freeze() + fusion(phase) + initPrimitives(phase, runtime, formats) + } + + final def initPrimitives(phase: Phase, runtime: MklDnnRuntime, formats: Array[MemoryData]) + : Unit = { + setRuntime(runtime) + val outputFormats = initFwdPrimitives(formats, phase)._2 + if (phase == Phase.TrainingPhase) { + initBwdPrimitives(outputFormats, phase) + initGradWPrimitives(outputFormats, phase) + } + } + + override def setRuntime(runtime: MklDnnRuntime): Unit = { + super.setRuntime(runtime) + reorderManager.setRuntime(runtime) + modules.foreach { case m: MklDnnModule => m.setRuntime(runtime) } + } + + /** + * Modify the computing path by fuse some layers into one to improve the performance + */ + private[mkldnn] def fusion(phase: Phase): Unit = { + modules.filter(_.isInstanceOf[MklDnnContainer]) + .map { case mc: MklDnnContainer => mc.fusion(phase) } + } + + private def freeze(): Unit = { + if (mklDnnModules == null) { + mklDnnModules = modules.map(_.asInstanceOf[MklDnnModule]).toArray + } + modules.filter(_.isInstanceOf[MklDnnContainer]) + .map { case mc: MklDnnContainer => mc.freeze() } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Identity.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Identity.scala new file mode 100644 index 00000000000..559457f0c8a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Identity.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, AbstractModule} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Identity just return the input to output. + * It's useful in same parallel container to get an origin input. + */ +class Identity() extends MklDnnLayer { + + override def updateOutput(input: Activity): Activity = { + output = input + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + + gradInput = gradOutput + gradInput + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = inputs + _outputFormats = inputs + (inputs, inputs) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = grad + _gradOutputFormatsForWeight = grad + _gradInputFormats = grad + (grad, grad) + } +} + +object Identity { + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : Identity = { + new Identity() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Input.scala new file mode 100644 index 00000000000..aee8667b752 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Input.scala @@ -0,0 +1,47 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor + +class Input(shape: Array[Int], layout: Int) extends MklDnnLayer { + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _outputFormats = Array(HeapData(shape, layout)) + _inputFormats = inputs + (inputs, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradInputFormats = Array(HeapData(shape, layout)) + _gradOutputFormats = grad + _gradOutputFormatsForWeight = grad + (grad, _gradInputFormats) + } + + override def updateOutput(input: Activity): Activity = { + output = input + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = gradOutput + gradInput + } +} + +object Input { + def apply(shape: Array[Int], layout: Int): Input = new Input(shape, layout) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala new file mode 100644 index 00000000000..63a353298e5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala @@ -0,0 +1,115 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn, Query} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor + +import scala.collection.mutable.ArrayBuffer + +class JoinTable(val dimension: Int) extends MklDnnLayer { + @transient + private var memoryPrims: Array[Array[Long]] = _ + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + require(inputs.length > 0, s"at least one tensor, but is ${inputs.length}") + _inputFormats = nativeData(inputs) + + val totalShape = inputs(0).shape.clone() + val layout = inputs(0).layout + var i = 1 + while(i < inputs.length) { + val curShape = inputs(i).shape + require(layout == inputs(i).layout, "layout not match") + require(totalShape.length == curShape.length, "tensor dimension not match") + require(inputs(i).isInstanceOf[NativeData], "memory should be native") + var j = 0 + while(j < curShape.length) { + if (j == dimension - 1) { + totalShape(j) += curShape(j) + } else { + require(totalShape(j) == curShape(j), "tensor size not match") + } + j += 1 + } + i += 1 + } + val primDesc = MklDnn.ConcatPrimitiveDescCreate( + MklDnn.MemoryDescInit(totalShape.length, totalShape, DataType.F32, Memory.Format.any), + inputs.length, dimension - 1, _inputFormats.map(_.getPrimitiveDescription(runtime))) + + _outputFormats = Array(MemoryData.primitiveOutput(primDesc)) + updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(primDesc, + _inputFormats.map(_.getPrimitive(runtime)), + new Array[Int](inputs.length), inputs.length, + _outputFormats.map(_.getPrimitive(runtime)), 1) + ) + output = initTensor(_outputFormats(0)) + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = singleNativeData(grads) + _gradOutputFormatsForWeight = _gradOutputFormats + _gradInputFormats = _inputFormats.map(f => { + NativeData(f.shape, f.layout) + }) + val prims = new ArrayBuffer[Long]() + val buffer = new ArrayBuffer[Array[Long]]() + val offset = new Array[Int](_gradOutputFormats(0).shape.length) + for(i <- 0 until _gradInputFormats.length) { + val viewPD = MklDnn.ViewPrimitiveDescCreate( + _gradOutputFormats(0).getPrimitiveDescription(runtime), _gradInputFormats(i).shape, offset) + val viewFormat = MemoryData.primitiveOutput(viewPD) + val reorderPD = MklDnn.ReorderPrimitiveDescCreate( + viewFormat.getPrimitiveDescription(runtime), + _gradInputFormats(i).getPrimitiveDescription(runtime)) + val reorderPrim = MklDnn.PrimitiveCreate2(reorderPD, + Array(viewFormat.getPrimitive(runtime)), Array(0), 1, + Array(_gradInputFormats(i).getPrimitive(runtime)), 1) + prims.append(reorderPrim) + buffer.append(Array(viewFormat.getPrimitive(runtime), + _gradInputFormats(i).getPrimitive(runtime))) + offset(dimension - 1) += _gradInputFormats(i).shape(dimension - 1) + } + updateGradInputPrimitives = prims.toArray + gradInput = initActivity(_gradInputFormats) + memoryPrims = buffer.toArray + + (_gradOutputFormats, _gradInputFormats) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + require(gradOutput.isTensor, "gradOutput should be tensor") + require(gradInput.isTable, "gradInput should be table") + val _gradOutput = gradOutput.asInstanceOf[Tensor[Float]] + val _gradInput = gradInput.toTable + val length = _gradInput.length() + require(length == updateGradInputPrimitives.length, "gradOutput number not match") + var i = 0 + while(i < length) { + MklDnnOps.streamSubmit(runtime.stream, 1, Array(updateGradInputPrimitives(i)), + 1, memoryPrims(i), Array(_gradOutput, _gradInput[Tensor[Float]](i + 1))) + i += 1 + } + gradInput + } +} + +object JoinTable { + def apply(dimension: Int): JoinTable = new JoinTable(dimension) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala new file mode 100644 index 00000000000..141cf6d8500 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala @@ -0,0 +1,97 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{AlgKind, MklDnn, PropKind} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor + +class LRN( + size: Int = 5, + alpha: Double = 1.0, + beta: Double = 0.75, + k: Double = 1.0 +) extends MklDnnLayer { + private val UNDEFINED = 0 + + @transient + private var workSpace : Tensor[Float] = _ + @transient + private var workSpaceFormat: MemoryData = _ + @transient + private var fwdPrimDesc: Long = UNDEFINED + @transient + private var fwdMemPrims: Array[Long] = _ + @transient + private var bwdMemPrims: Array[Long] = _ + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = singleNativeData(inputs) + val description = MklDnn.LRNForwardDescInit( + PropKind.ForwardTraining, AlgKind.LrnAcrossChannels, + _inputFormats(0).getMemoryDescription(), size, alpha.toFloat, beta.toFloat, k.toFloat) + fwdPrimDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + _outputFormats = Array(MemoryData.primitiveOutput(fwdPrimDesc)) + workSpaceFormat = MemoryData.primitiveWorkSpace(fwdPrimDesc) + workSpace = initTensor(workSpaceFormat) + updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPrimDesc, + _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, Array(_outputFormats(0), + workSpaceFormat).map(_.getPrimitive(runtime)), 2)) + output = initTensor(_outputFormats(0)) + fwdMemPrims = Array(_inputFormats(0), _outputFormats(0), workSpaceFormat) + .map(_.getPrimitive(runtime)) + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = singleNativeData(grad) + _gradOutputFormatsForWeight = _gradOutputFormats + val description = MklDnn.LRNBackwardDescInit(AlgKind.LrnAcrossChannels, + _inputFormats(0).getMemoryDescription(), + _gradOutputFormats(0).getMemoryDescription(), size, alpha.toFloat, beta.toFloat, k.toFloat) + require(fwdPrimDesc != UNDEFINED, "You should call initFwdPrimitives first") + val primDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPrimDesc) + _gradInputFormats = Array(MemoryData.primitiveGradInput(primDesc)) + updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(primDesc, + Array(_inputFormats(0), _gradOutputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), + Array(0, 0, 0), 3, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) + gradInput = initTensor(_gradInputFormats(0)) + bwdMemPrims = Array(_inputFormats(0), _gradOutputFormats(0), workSpaceFormat, + _gradInputFormats(0)).map(_.getPrimitive(runtime)) + (_gradOutputFormats, _gradInputFormats) + } + + override def updateOutput(input: Activity): Activity = { + val buffer = Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + workSpace) + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, 1, fwdMemPrims, buffer) + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + val buffer = Array( + input.asInstanceOf[Tensor[Float]], gradOutput.asInstanceOf[Tensor[Float]], workSpace, + gradInput.asInstanceOf[Tensor[Float]]) + MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, 1, + bwdMemPrims, buffer) + gradInput + } +} + +object LRN { + def apply(size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75, k: Double = 1.0): LRN = + new LRN(size, alpha, beta, k) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala new file mode 100644 index 00000000000..7bb9bc3f480 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -0,0 +1,300 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn, PropKind, Query, Stream => DnnStream} +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable, TensorModule} +import com.intel.analytics.bigdl.nn.{InitializationMethod, RandomUniform, VariableFormat} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +class Linear( + val inputSize: Int, + val outputSize: Int, + private val initWeight: Tensor[Float] = null, + private val initBias: Tensor[Float] = null, + private val initGradWeight: Tensor[Float] = null, + private val initGradBias: Tensor[Float] = null) extends MklDnnLayer with Initializable { + + val weight: DnnTensor[Float] = DnnTensor[Float](Array(outputSize, inputSize)) + val bias: DnnTensor[Float] = DnnTensor[Float](Array(outputSize)) + val gradWeight: DnnTensor[Float] = DnnTensor[Float](Array(outputSize, inputSize)) + val gradBias: DnnTensor[Float] = DnnTensor[Float](Array(outputSize)) + + var forwardPrimDesc: Long = 0L + + var updateOutputMemoryPrimitives: Array[Long] = _ + var updateOutputTensors: Array[Tensor[Float]] = _ + var updateGradInputMemoryPrimitives: Array[Long] = _ + var updateGradInputTensors: Array[Tensor[Float]] = _ + var updateGradWMemoryPrimitives: Array[Long] = _ + var updateGradWTensors: Array[Tensor[Float]] = _ + + object ParamsShape { + var weight: MemoryData = _ + var bias: MemoryData = _ + var gradWeight: MemoryData = _ + var gradBias: MemoryData = _ + } + + { + val stdv = 1.0 / math.sqrt(weight.size(2)) + val wInit: InitializationMethod = RandomUniform(-stdv, stdv) + val bInit: InitializationMethod = RandomUniform(-stdv, stdv) + setInitMethod(wInit, bInit) + } + + override def reset(): Unit = { + if (initWeight == null) { + val t = Tensor[Float](Array(outputSize, inputSize)) + weightInitMethod.init(t, VariableFormat.OUT_IN) + weight.copy(t) + } else { + weight.copy(initWeight) + } + + if (initBias == null) { + val t = Tensor[Float](Array(outputSize)) + biasInitMethod.init(t, VariableFormat.ONE_D) + bias.copy(t) + } else { + bias.copy(initBias) + } + + zeroGradParameters() + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + val weightShape = inputs(0).shape.length match { + case 4 => Array(weight.size(1)) ++ inputs(0).shape.slice(1, 4) + case _ => weight.size() + } + + val inputShape = inputs(0).shape + require(inputs(0).shape.length > 1, s"mkldnn linear unspported input dimension") + + val outputShape = Array(inputs(0).shape(0), outputSize) + + MklDnn.MemoryDescInit(inputShape.length, inputShape, + DataType.F32, Memory.Format.any) + + val src = NativeData(inputShape, Memory.Format.any) + val wei = NativeData(weightShape, Memory.Format.any) + val bis = NativeData(bias.size(), Memory.Format.x) + val dst = NativeData(outputShape, Memory.Format.any) + + val desc = MklDnn.LinearForwardDescInit( + PropKind.Forward, + src.getMemoryDescription(), + wei.getMemoryDescription(), + bis.getMemoryDescription(), + dst.getMemoryDescription()) + forwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, 0) + + val List(realSrc, realWei, realDst) = List(Query.SrcPd, Query.WeightsPd, Query.DstPd).map {x => + MemoryData.operationWant(forwardPrimDesc, x) + } + + ParamsShape.weight = realWei + ParamsShape.bias = bis + + val srcs = Array(realSrc.getPrimitive(runtime), realWei.getPrimitive(runtime), + bis.getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + val dsts = Array(realDst.getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, srcs.length, + dsts, dsts.length) + + updateOutputMemoryPrimitives = srcs ++ dsts + updateOutputPrimitives = Array(primitive) + output = initTensor(dst) + + _inputFormats = Array(realSrc) + _outputFormats = Array(realDst) + (_inputFormats, _outputFormats) + } + + override def updateOutput(input: Activity): Activity = { + if (updateOutputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(weight) + buffer.append(bias) + buffer.append(output.asInstanceOf[Tensor[Float]]) + updateOutputTensors = buffer.toArray + } + + updateWithNewTensor(updateOutputTensors, 0, input) + + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, + updateOutputMemoryPrimitives, updateOutputTensors) + + output + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + val weightShape = inputFormats()(0).shape.length match { + case 4 => Array(weight.size(1)) ++ inputFormats()(0).shape.slice(1, 4) + case _ => weight.size() + } + + val inputShape = inputFormats()(0).shape + + val outputShape = Array(inputFormats()(0).shape(0), outputSize) + + val src = NativeData(inputShape, Memory.Format.any) + val wei = NativeData(weightShape, Memory.Format.any) + val bis = NativeData(bias.size(), Memory.Format.x) + val dst = NativeData(outputShape, Memory.Format.any) + + val desc = MklDnn.LinearBackwardDataDescInit( + src.getMemoryDescription(), + wei.getMemoryDescription(), + grad(0).getMemoryDescription()) + val backwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) + + val List(realDiffSrc, realWei, realDiffDst) = + List(Query.DiffSrcPd, Query.WeightsPd, Query.DiffDstPd).map { x => + MemoryData.operationWant(backwardPrimDesc, x) + } + + val srcs = Array(realDiffDst.getPrimitive(runtime), realWei.getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + val dsts = Array(realDiffSrc.getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(backwardPrimDesc, srcs, indexes, srcs.length, + dsts, dsts.length) + + updateGradInputMemoryPrimitives = srcs ++ dsts + updateGradInputPrimitives = Array(primitive) + gradInput = initTensor(realDiffSrc) + + _gradInputFormats = Array(realDiffSrc) + _gradOutputFormats = Array(realDiffDst) + (_gradOutputFormats, _gradInputFormats) + } + + override private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], + phase: Phase): Array[MemoryData] = { + val weightShape = inputFormats()(0).shape.length match { + case 4 => Array(weight.size(1)) ++ inputFormats()(0).shape.slice(1, 4) + case _ => weight.size() + } + + val inputShape = inputFormats()(0).shape + + val outputShape = Array(inputFormats()(0).shape(0), outputSize) + + + val src = NativeData(inputShape, Memory.Format.any) + val wei = NativeData(weightShape, Memory.Format.any) + val bis = NativeData(bias.size(), Memory.Format.x) + val dst = NativeData(outputShape, Memory.Format.any) + + val desc = MklDnn.LinearBackwardWeightsDescInit( + src.getMemoryDescription(), wei.getMemoryDescription(), bis.getMemoryDescription(), + dst.getMemoryDescription()) + val gradWeightPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) + + val List(realWei, realDiffDst) = List(Query.DiffWeightsPd, Query.DiffDstPd).map { x => + MemoryData.operationWant(gradWeightPrimDesc, x) + } + + ParamsShape.gradWeight = realWei + ParamsShape.gradBias = bis + + val srcs = Array(inputFormats()(0).getPrimitive(runtime), realDiffDst.getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + val dsts = Array(realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(gradWeightPrimDesc, srcs, indexes, srcs.length, + dsts, dsts.length) + + updateGradWMemoryPrimitives = srcs ++ dsts + accGradientPrimitives = Array(primitive) + + _gradOutputFormatsForWeight = Array(realDiffDst) + (_gradOutputFormatsForWeight) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + if (updateGradInputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) + buffer.append(weight) + buffer.append(gradInput.asInstanceOf[Tensor[Float]]) + updateGradInputTensors = buffer.toArray + } + + updateWithNewTensor(updateGradInputTensors, 0, gradOutput) + + MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, + updateGradInputPrimitives.length, updateGradInputMemoryPrimitives, updateGradInputTensors) + + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + if (updateGradWTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) + buffer.append(gradWeight) + buffer.append(gradBias) + updateGradWTensors = buffer.toArray + } + + updateWithNewTensor(updateGradInputTensors, 0, input) + updateWithNewTensor(updateGradInputTensors, 1, gradOutput) + + MklDnnOps.streamSubmit(runtime.stream, 1, accGradientPrimitives, + accGradientPrimitives.length, updateGradWMemoryPrimitives, updateGradWTensors) + } + + override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { + (Array(weight, bias), Array(gradWeight, gradBias)) + } + + override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { + (Array(ParamsShape.weight, ParamsShape.bias), Array(ParamsShape.gradWeight, + ParamsShape.gradBias)) + } + + override def zeroGradParameters(): Unit = { + gradWeight.zero() + gradBias.zero() + } +} + +object Linear { + def apply( + inputSize: Int, + outputSize: Int, + withBias: Boolean = true, + initWeight: Tensor[Float] = null, + initBias: Tensor[Float] = null, + initGradWeight: Tensor[Float] = null, + initGradBias: Tensor[Float] = null): Linear = { + new Linear(inputSize, outputSize, initWeight, initBias, initGradWeight, initGradBias) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala new file mode 100644 index 00000000000..9436af72f90 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala @@ -0,0 +1,125 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl._ +import com.intel.analytics.bigdl.nn.Utils +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor + +class MaxPooling( + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0 +) extends MklDnnLayer { + @transient + private var workSpaceFormat: MemoryData = _ + @transient + private var workSpace: Tensor[Float] = _ + @transient + private var fwdMemPrims: Array[Long] = _ + @transient + private var bwdMemPrims: Array[Long] = _ + @transient + private var paddingTL: Array[Int] = _ + @transient + private var paddingBR: Array[Int] = _ + @transient + private var fwdPD: Long = _ + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = singleNativeData(inputs) + val strides = Array(dW, dH) + val kernel = Array(kH, kW) + val n = _inputFormats(0).shape(0) + val c = _inputFormats(0).shape(1) + val h = _inputFormats(0).shape(2) + val w = _inputFormats(0).shape(3) + val (pt, pb, pl, pr, oh, ow) = + Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW) + paddingTL = Array(pt, pl) + paddingBR = Array(pb, pr) + Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) + Utils.getOutSizeAndPaddingForDNN(h, w, dH, dW, kH, kW, padH, padW, true) + val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), DataType.F32, Memory.Format.any) + val description = MklDnn.PoolingForwardDescInit( + PropKind.Forward, AlgKind.PoolingMax, + _inputFormats(0).getMemoryDescription(), outputMD, strides, kernel, paddingTL, paddingBR, + MklDnn.PaddingKind.mkldnnPaddingZero) + fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + _outputFormats = Array(MemoryData.primitiveOutput(fwdPD)) + output = initTensor(_outputFormats(0)) + workSpaceFormat = MemoryData.primitiveWorkSpace(fwdPD) + workSpace = initTensor(workSpaceFormat) + updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPD, + _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, + Array(_outputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), 2)) + fwdMemPrims = Array(_inputFormats(0), _outputFormats(0), workSpaceFormat) + .map(_.getPrimitive(runtime)) + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = singleNativeData(grad) + _gradOutputFormatsForWeight = _gradOutputFormats + val strides = Array(dW, dH) + val kernel = Array(kH, kW) + val description = MklDnn.PoolingBackwardDescInit(AlgKind.PoolingMax, + _inputFormats(0).getMemoryDescription(), + _gradOutputFormats(0).getMemoryDescription(), + strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) + + val pd = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD) + _gradInputFormats = Array(MemoryData.primitiveGradInput(pd)) + updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(pd, + Array(_gradOutputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), + Array(0, 0), 2, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) + gradInput = initTensor(_gradInputFormats(0)) + bwdMemPrims = Array(_inputFormats(0), _gradOutputFormats(0), workSpaceFormat, + _gradInputFormats(0)).map(_.getPrimitive(runtime)) + (_gradOutputFormats, _gradInputFormats) + } + + override def updateOutput(input: Activity): Activity = { + val buffer = Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + workSpace) + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, 1, fwdMemPrims, buffer) + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + val buffer = Array( + input.asInstanceOf[Tensor[Float]], gradOutput.asInstanceOf[Tensor[Float]], workSpace, + gradInput.asInstanceOf[Tensor[Float]]) + MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, 1, + bwdMemPrims, buffer) + gradInput + } +} + +object MaxPooling { + def apply( + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0 + ): MaxPooling = new MaxPooling(kW, kH, dW, dH, padW, padH) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala new file mode 100644 index 00000000000..bc5297dd327 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -0,0 +1,259 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn, Query} +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} + +sealed trait MemoryData extends Serializable { + def shape: Array[Int] + def layout: Int + def setShape(shape: Array[Int]): Unit + def setLayout(layout: Int): Unit + + def isLayoutFixed(): Boolean = { + layout != Memory.Format.format_undef && layout != Memory.Format.any + } + + def cloneFormat(): MemoryData + + private val UNDEFINED: Long = -1 + + @transient + private var primitive: Long = UNDEFINED + @transient + private var primitiveDesc: Long = UNDEFINED + @transient + private var description: Long = UNDEFINED + + def getMemoryDescription(): Long = { + if (description == UNDEFINED) { + description = MklDnn.MemoryDescInit(shape.length, shape, DataType.F32, layout) + } + description + } + + def getPrimitiveDescription(runtime: MklDnnRuntime): Long = { + if (primitiveDesc == UNDEFINED) { + primitiveDesc = + MklDnn.MemoryPrimitiveDescCreate(getMemoryDescription(), runtime.engine) + } + primitiveDesc + } + + def getPrimitive(runtime: MklDnnRuntime): Long = { + if (primitive == UNDEFINED) { + primitive = + MklDnn.PrimitiveCreate0(getPrimitiveDescription(runtime)) + } + primitive + } + + def setPrimitiveDescription(desc: Long): Unit = { + primitiveDesc = desc + } + + def setMemoryDescription(desc: Long): Unit = { + description = desc + } +} + +case class HeapData(private var _shape: Array[Int], private var _layout: Int) extends MemoryData { + + override def setShape(shape: Array[Int]): Unit = _shape = shape.clone() + + override def setLayout(layout: Int): Unit = _layout = layout + + override def shape: Array[Int] = _shape.clone() + + override def layout: Int = _layout + + override def hashCode(): Int = { + val seed = 37 + var hash = 1 + hash = hash * seed + this.layout + var d = 0 + while (d < this.shape.length) { + hash = hash * seed + this.shape(d) + d += 1 + } + + hash + } + + override def equals(obj: Any): Boolean = { + if (obj == null) { + return false + } + if (!obj.isInstanceOf[HeapData]) { + return false + } + val other = obj.asInstanceOf[HeapData] + if (this.eq(other)) { + return true + } + if (this.layout != other.layout) { + return false + } + if (this.shape == null && other.shape == null) { + return true + } + if (this.shape != null && other.shape != null) { + if (this.shape.length != other.shape.length) return false + var i = 0 + while(i < this.shape.length) { + if (this.shape(i) != other.shape(i)) return false + i += 1 + } + return true + } else { + return false + } + } + + override def toString: String = { + s"HeapData([${shape.mkString("x")}], ${layout})" + } + + override def cloneFormat(): MemoryData = new HeapData(_shape, _layout) + + def toNative(): NativeData = { + NativeData(shape, layout) + } +} + +case class NativeData(private var _shape: Array[Int], private var _layout: Int) extends MemoryData { + override def shape: Array[Int] = _shape.clone() + + override def layout: Int = _layout + + override def setShape(shape: Array[Int]): Unit = _shape = shape.clone() + + override def setLayout(layout: Int): Unit = _layout = layout + + override def hashCode(): Int = { + val seed = 41 + var hash = 1 + hash = hash * seed + this.layout + var d = 0 + while (d < this.shape.length) { + hash = hash * seed + this.shape(d) + d += 1 + } + + hash + } + + override def equals(obj: Any): Boolean = { + if (obj == null) { + return false + } + if (!obj.isInstanceOf[NativeData]) { + return false + } + val other = obj.asInstanceOf[NativeData] + if (this.eq(other)) { + return true + } + if (this.layout != other.layout) { + return false + } + if (this.shape == null && other.shape == null) { + return true + } + if (this.shape != null && other.shape != null) { + if (this.shape.length != other.shape.length) return false + var i = 0 + while(i < this.shape.length) { + if (this.shape(i) != other.shape(i)) return false + i += 1 + } + return true + } else { + return false + } + } + + override def toString: String = { + s"NativeData([${shape.mkString("x")}], ${layout})" + } + + override def cloneFormat(): MemoryData = new NativeData(_shape, _layout) +} + +private[mkldnn] object MemoryData { + def noUndef(formats: Array[MemoryData]): Boolean = { + if (formats == null || formats.length == 0) return true + formats.foreach(f => if (f.layout == Memory.Format.format_undef) return false) + return true + } + + def isSizeCompatible(actual: MemoryData, expect: MemoryData): Boolean = { + if (expect == null) return true + if (actual == null) return false + if (actual.shape.length != expect.shape.length) return false + actual.shape.zip(expect.shape).foreach {case (a, e) => if (a != e) return false} + return true + } + + def primitiveOutput(pd: Long): NativeData = { + val outputPD = MklDnn.PrimitiveDescQueryPd(pd, Query.DstPd, 0) + val memoryDesc = MklDnn.PrimitiveDescQueryMemory(outputPD) + val shape = Memory.GetShape(memoryDesc) + val layout = Memory.GetLayout(memoryDesc) + + val memory = NativeData(shape, layout) + memory.setMemoryDescription(memoryDesc) + memory.setPrimitiveDescription(outputPD) + memory + } + + def primitiveGradInput(pd: Long): NativeData = { + val gradInputPD = MklDnn.PrimitiveDescQueryPd(pd, Query.DiffSrcPd, 0) + val memoryDesc = MklDnn.PrimitiveDescQueryMemory(gradInputPD) + val shape = Memory.GetShape(memoryDesc) + val layout = Memory.GetLayout(memoryDesc) + + val memory = NativeData(shape, layout) + memory.setMemoryDescription(memoryDesc) + memory.setPrimitiveDescription(gradInputPD) + memory + } + + def operationWant(primDesc: Long, queryType: Int): NativeData = { + val memoryPrimDesc = MklDnn.PrimitiveDescQueryPd(primDesc, queryType, 0) + val memoryDesc = MklDnn.PrimitiveDescQueryMemory(memoryPrimDesc) + val shape = Memory.GetShape(memoryDesc) + val layout = Memory.GetLayout(memoryDesc) + + val memory = NativeData(shape, layout) + memory.setMemoryDescription(memoryDesc) + memory.setPrimitiveDescription(memoryPrimDesc) + memory + } + + def primitiveWorkSpace(pd: Long): NativeData = { + val workspacePD = MklDnn.PrimitiveDescQueryPd(pd, Query.WorkspacePd, 0) + val memoryDesc = MklDnn.PrimitiveDescQueryMemory(workspacePD) + val shape = Memory.GetShape(memoryDesc) + val layout = Memory.GetLayout(memoryDesc) + + val memory = NativeData(shape, layout) + memory.setMemoryDescription(memoryDesc) + memory.setPrimitiveDescription(workspacePD) + memory + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnOps.scala new file mode 100644 index 00000000000..1984bd255e8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnOps.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{Memory, MklDnn, Engine => DnnEngine, Stream => DnnStream} +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} + +private[mkldnn] object MklDnnOps { + def memorySetDataHandle(memory: Long, data: Tensor[Float], offset: Int): Long = { + require(MklDnn.isLoaded, "mkldnn isn't loaded") + MklDnn.MemorySetDataHandle(memory, data.storage().array(), offset) + } + + def memoryReleaseDataHandle(data: Tensor[Float], ptr: Long): Unit = { + require(MklDnn.isLoaded, "mkldnn isn't loaded") + MklDnn.MemoryReleaseDataHandle(data.storage().array(), ptr) + } + + def streamSubmit(loc: Long, block: Int, primitives: Array[Long], length: Int, + memory_primitives: Array[Long], buffers: Array[Tensor[Float]]): Unit = { + require(MklDnn.isLoaded, "mkldnn isn't loaded") + require(memory_primitives.length == buffers.length) + + val handle = new Array[Long](memory_primitives.length) + for (i <- memory_primitives.indices) { + if (memory_primitives(i) != 0L) { + if (buffers(i).isInstanceOf[DnnTensor[_]]) { + Memory.SetDataHandle(memory_primitives(i), + buffers(i).asInstanceOf[DnnTensor[Float]].storageAddress(), 0) + } else { + handle(i) = MklDnnOps.memorySetDataHandle( + memory_primitives(i), buffers(i), buffers(i).storageOffset() - 1) + } + } + } + + DnnStream.Submit(loc, block, primitives) + + for (i <- memory_primitives.indices) { + if (handle(i) != 0L) { + MklDnnOps.memoryReleaseDataHandle(buffers(i), handle(i)) + } + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala new file mode 100644 index 00000000000..b4410347931 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala @@ -0,0 +1,24 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{Engine, MklDnn, Stream} + +class MklDnnRuntime { + MklDnn.isLoaded + val engine : Long = Engine.Create(Engine.Kind.Cpu, 0) + val stream : Long = Stream.Create(Stream.Kind.Eager) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Phase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Phase.scala new file mode 100644 index 00000000000..ddc8c298684 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Phase.scala @@ -0,0 +1,25 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +sealed class Phase + +object Phase { + case object TrainingPhase extends Phase + + case object InferencePhase extends Phase + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala new file mode 100644 index 00000000000..372aaf91ad2 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{AlgKind, MklDnn, PropKind, Query} + +class ReLU(value: Float = 0.0f) extends MklDnnLayer { + private val UNDEFINED: Long = 0 + + @transient + private var fwdPrimDesc: Long = UNDEFINED + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = singleNativeData(inputs) + val description = MklDnn.EltwiseForwardDescInit( + PropKind.Forward, AlgKind.EltwiseRelu, _inputFormats(0).getMemoryDescription(), value, 0) + fwdPrimDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + _outputFormats = Array(MemoryData.primitiveOutput(fwdPrimDesc)) + updateOutputPrimitives = Array( + MklDnn.PrimitiveCreate2(fwdPrimDesc, + Array(_inputFormats(0).getPrimitive(runtime)), Array(0), _inputFormats.length, + _outputFormats.map(_.getPrimitive(runtime)), _outputFormats.length) + ) + output = initTensor(_outputFormats(0)) + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = singleNativeData(grad) + _gradOutputFormatsForWeight = _gradOutputFormats + val description = MklDnn.EltwiseBackwardDescInit(AlgKind.EltwiseRelu, + _gradOutputFormats(0).getMemoryDescription(), _inputFormats(0).getMemoryDescription(), + value, 0) + require(fwdPrimDesc != UNDEFINED, "You should call initFwdPrimitives first") + val primDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPrimDesc) + _gradInputFormats = Array(MemoryData.primitiveGradInput(primDesc)) + updateGradInputPrimitives = Array( + MklDnn.PrimitiveCreate2(primDesc, Array(_inputFormats(0), + _gradOutputFormats(0)).map(_.getPrimitive(runtime)), Array(0), 2, + _gradInputFormats.map(_.getPrimitive(runtime)), _gradInputFormats.length)) + gradInput = initTensor(_gradInputFormats(0)) + (_gradOutputFormats, _gradInputFormats) + } +} + +object ReLU { + def apply(value: Float = 0.0f): ReLU = new ReLU(value) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala new file mode 100644 index 00000000000..baefb35dadc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala @@ -0,0 +1,114 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T + +import scala.collection.mutable + +private[mkldnn] class ReorderManager() { + // (MemoryFormatId, TargetFormat) -> Reorder + val reorders = mutable.HashMap[(Int, MemoryData), ReorderMemory]() + // ReorderId -> RefCount + val refCounts = mutable.HashMap[Int, Int]() + val useCounts = mutable.HashMap[Int, Int]() + + private var runtime: MklDnnRuntime = _ + + def register(from: MemoryData, to: MemoryData): Unit = { + require(runtime != null, "Please call setRuntime first") + val mId = System.identityHashCode(from) + if (needReorder(from, to)) { + if (reorders.contains((mId, to))) { + refCounts(System.identityHashCode(reorders((mId, to)))) += 1 + } else { + val reorder = ReorderMemory(to) + reorder.setRuntime(runtime) + reorder.initFwdPrimitives(Array(from), Phase.InferencePhase) + reorders((mId, to)) = reorder + val reorderId = System.identityHashCode(reorder) + refCounts(reorderId) = 1 + useCounts(reorderId) = 0 + } + } + } + + def setRuntime(runtime: MklDnnRuntime): Unit = { + this.runtime = runtime + } + + def infer(from: Array[MemoryData], to: Array[MemoryData], output: Activity) + : Activity = { + if (from.length == 1) { + require(output.isTensor, "output activity should be a tensor") + inferTensor(from(0), to(0), output.asInstanceOf[Tensor[Float]]) + } else { + require(output.toTable.length() == from.length, + "output activity length doesn't match") + val outputTable = T() + var i = 0 + while(i < from.length) { + outputTable(i + 1) = inferTensor(from(i), to(i), output.toTable(i + 1)) + i += 1 + } + output + } + } + + private def inferTensor(from: MemoryData, to : MemoryData, output: Tensor[Float]) + : Tensor[Float] = { + val mId = System.identityHashCode(from) + if (reorders.contains((mId, to))) { + val reorder = reorders((mId, to)) + val reorderId = System.identityHashCode(reorder) + val result = if (useCounts(reorderId) == 0) { + reorder.forward(output).asInstanceOf[Tensor[Float]] + } else { + reorder.output.asInstanceOf[Tensor[Float]] + } + useCounts(reorderId) += 1 + if (useCounts(reorderId) == refCounts(reorderId)) { + useCounts(reorderId) = 0 + } + result + } else { + output + } + } + + private def needReorder(from: MemoryData, to: MemoryData): Boolean = { + from match { + case h: HeapData => + to match { + case hh: HeapData => + require(h.layout == hh.layout, "Heap data layout should be same") + false + case nn: NativeData => true + case _ => throw new UnsupportedOperationException("Not support such memory format") + } + case n: NativeData => + to match { + case hh: HeapData => true + case nn: NativeData => + nn.layout != n.layout + case _ => throw new UnsupportedOperationException("Not support such memory format") + } + case _ => throw new UnsupportedOperationException("Not support such memory format") + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala new file mode 100644 index 00000000000..2e27c69184d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala @@ -0,0 +1,95 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn} +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} + +class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, + gradInputFormat: MemoryData, gradOutputFormat: MemoryData +) extends MklDnnLayer { + + _outputFormats = Array(outputFormat) + _gradInputFormats = Array(gradInputFormat) + + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = if (inputFormat == null) inputs else Array(inputFormat) + require(_inputFormats.length == 1, "Only accept one tensor as input") + + require(_inputFormats(0).shape.product == outputFormat.shape.product, + "input output memory not match") + val fwdReorderPrimDesc = MklDnn.ReorderPrimitiveDescCreate( + _inputFormats(0).getPrimitiveDescription(runtime), + outputFormat.getPrimitiveDescription(runtime)) + val fwdReorderPrim = MklDnn.PrimitiveCreate2(fwdReorderPrimDesc, + Array(_inputFormats(0).getPrimitive(runtime)), Array(0), 1, + Array(outputFormat.getPrimitive(runtime)), 1) + + updateOutputPrimitives = Array(fwdReorderPrim) + output = initTensor(outputFormat) + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + _gradInputFormats = (gradInputFormat, inputFormat) match { + case (null, null) => inputFormats() + case (null, x) => Array(x) + case (x, _) => Array(x) + } + + _gradOutputFormats = if (gradOutputFormat == null) grads else Array(gradOutputFormat) + _gradOutputFormatsForWeight = if (gradOutputFormat == null) grads else Array(gradOutputFormat) + require(_gradOutputFormats.length == 1, "Only accept one tensor as input") + + require(_gradOutputFormats(0).shape.product == _gradInputFormats(0).shape.product, + "input output memory not match") + val bwdReorderPrimDesc = MklDnn.ReorderPrimitiveDescCreate( + _gradOutputFormats(0).getPrimitiveDescription(runtime), + _gradInputFormats(0).getPrimitiveDescription(runtime)) + val bwdReorderPrim = MklDnn.PrimitiveCreate2(bwdReorderPrimDesc, + _gradOutputFormats.map(_.getPrimitive(runtime)), Array(0), 1, + _gradInputFormats.map(_.getPrimitive(runtime)), 1) + + updateGradInputPrimitives = Array(bwdReorderPrim) + gradInput = initTensor(_gradInputFormats(0)) + (_gradOutputFormats, _gradInputFormats) + } + + override def toString(): String = { + if (_inputFormats != null) { + s"nn.mkl.ReorderMemory(${_inputFormats(0)} -> ${outputFormat})" + } else { + s"nn.mkl.ReorderMemory(_ -> ${outputFormat})" + } + } +} + +object ReorderMemory { + def apply(inputFormat: MemoryData, outputFormat: MemoryData, gradInputFormat: MemoryData, + gradOutputFomat: MemoryData): ReorderMemory = { + new ReorderMemory(inputFormat, outputFormat, gradInputFormat, gradOutputFomat) + } + + def apply(outputFormat: MemoryData, gradInputFormat: MemoryData): ReorderMemory = { + new ReorderMemory(null, outputFormat, gradInputFormat, null) + } + + def apply(outputFormat: MemoryData): ReorderMemory = { + new ReorderMemory(null, outputFormat, null, null) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala new file mode 100644 index 00000000000..7071b2c1cc4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala @@ -0,0 +1,309 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.nn.mkldnn.ResNet.DatasetType.ImageNet +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.{Engine, T, Table} +import org.apache.log4j.Logger +import scopt.OptionParser + +import scala.reflect.ClassTag + +object ResNet50Perf { + + val logger = Logger.getLogger(getClass) + + val parser = new OptionParser[ResNet50PerfParams]("BigDL Local ResNet-50 Performance Test") { + opt[Int]('b', "batchSize") + .text("Batch size of input data") + .action((v, p) => p.copy(batchSize = v)) + opt[Int]('i', "iteration") + .text("Iteration of perf test. The result will be average of each iteration time cost") + .action((v, p) => p.copy(iteration = v)) + opt[Boolean]('t', "training") + .text(s"Perf test training or testing") + .action((v, p) => p.copy(training = v)) + } + + def main(argv: Array[String]): Unit = { + System.setProperty("bigdl.mkldnn.fusion.convbn", "true") + System.setProperty("bigdl.mkldnn.fusion.bnrelu", "true") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + System.setProperty("bigdl.mkldnn.fusion.convsum", "true") + + val coreNumber: Int = Runtime.getRuntime.availableProcessors() / 2 + System.setProperty("bigdl.mklNumThreads", s"$coreNumber") + Engine.setCoreNumber(1) + MklDnn.setNumThreads(coreNumber) + + parser.parse(argv, new ResNet50PerfParams()).foreach { params => + val batchSize = params.batchSize + val training = params.training + val iterations = params.iteration + + val classNum = 1000 + + val inputFormat = Memory.Format.nchw + val inputShape = Array(batchSize, 3, 224, 224) + val input = Tensor(inputShape).rand() + val label = Tensor(batchSize).apply1(_ => Math.floor(RNG.uniform(0, 1) * 1000).toFloat) + + val model = ResNet(batchSize, classNum, T("depth" -> 50, "dataSet" -> ImageNet)) + val criterion = CrossEntropyCriterion() + + if (training) { + model.compile(TrainingPhase, Array(HeapData(inputShape, inputFormat))) + model.training() + } else { + model.compile(InferencePhase, Array(HeapData(inputShape, inputFormat))) + model.evaluate() + } + + var iteration = 0 + while (iteration < iterations) { + val start = System.nanoTime() + val output = model.forward(input) + + if (training) { + val _loss = criterion.forward(output, label) + val errors = criterion.backward(output, label).toTensor + model.backward(input, errors) + } + + val takes = System.nanoTime() - start + + val throughput = "%.2f".format(batchSize.toFloat / (takes / 1e9)) + logger.info(s"Iteration $iteration, takes $takes s, throughput is $throughput imgs/sec") + + iteration += 1 + } + } + } +} + +case class ResNet50PerfParams ( + batchSize: Int = 16, + iteration: Int = 50, + training: Boolean = true +) + +object ResNet { + def modelInit(model: Module[Float]): Unit = { + def initModules(model: Module[Float]): Unit = { + model match { + case container: Container[Activity, Activity, Float] + => container.modules.foreach(m => initModules(m)) + case conv: SpatialConvolution => + val n: Float = conv.kernelW * conv.kernelW * conv.nOutputPlane + val weight = Tensor[Float].resize(conv.weight.size()).apply1 { _ => + RNG.normal(0, Math.sqrt(2.0f / n)).toFloat + } + val bias = Tensor[Float].resize(conv.bias.size()).apply1(_ => 0.0f) + conv.weight.copy(weight) + conv.bias.copy(bias) + case bn: SpatialBatchNormalization => + val runningMean = Tensor[Float].resize(bn.runningMean.size()).fill(0) + val runningVairance = Tensor[Float].resize(bn.runningVariance.size()).fill(1) + bn.runningMean.copy(runningMean) + bn.runningVariance.copy(runningVairance) + case linear: Linear => + val bias = Tensor[Float](linear.bias.size()).apply1(_ => 0.0f) + linear.bias.copy(bias) + case _ => Unit + } + } + initModules(model) + } + + var iChannels = 0 + def apply(batchSize: Int, classNum: Int, opt: Table): Sequential = { + + val depth = opt.get("depth").getOrElse(18) + val shortCutType = opt.get("shortcutType") + val shortcutType = shortCutType.getOrElse(ShortcutType.B).asInstanceOf[ShortcutType] + val dataSet = opt.getOrElse[DatasetType]("dataSet", DatasetType.CIFAR10) + val optnet = opt.get("optnet").getOrElse(true) + + def shortcut(nInputPlane: Int, nOutputPlane: Int, stride: Int, name: String): Module[Float] = { + val useConv = shortcutType == ShortcutType.C || + (shortcutType == ShortcutType.B && nInputPlane != nOutputPlane) + + if (useConv) { + Sequential() + .add(Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride, optnet = optnet) + .setName(s"res${name}_branch1")) + .add(SbnDnn(nOutputPlane).setName(s"bn${name}_branch1")) + } else if (nInputPlane != nOutputPlane) { + throw new IllegalArgumentException(s"useConv false") + } else { + Identity() + } + } + + def bottleneck(n: Int, stride: Int, name: String = ""): Module[Float] = { + val nInputPlane = iChannels + iChannels = n * 4 + + val s = Sequential() + s.add(Convolution(nInputPlane, n, 1, 1, 1, 1, 0, 0, optnet = optnet).setName( + s"res${name}_branch2a")) + .add(SbnDnn(n).setName(s"bn${name}_branch2a")) + .add(ReLU().setName(s"res${name}_branch2a_relu")) + .add(Convolution(n, n, 3, 3, stride, stride, 1, 1, optnet = optnet).setName( + s"res${name}_branch2b")) + .add(SbnDnn(n).setName(s"bn${name}_branch2b")) + .add(ReLU().setName(s"res${name}_branch2b_relu")) + .add(Convolution(n, n*4, 1, 1, 1, 1, 0, 0, optnet = optnet).setName( + s"res${name}_branch2c")) + .add(SbnDnn(n * 4).setInitMethod(Zeros, Zeros).setName(s"bn${name}_branch2c")) + + val model = Sequential() + .add(ConcatTable(). + add(s). + add(shortcut(nInputPlane, n*4, stride, name)).setName(s"$name/concatTable")) + .add(CAddTable().setName(s"res$name")) + .add(ReLU().setName(s"res${name}_relu")) + model + } + + def getName(i: Int, name: String): String = { + val name1 = i match { + case 1 => name + "a" + case 2 => name + "b" + case 3 => name + "c" + case 4 => name + "d" + case 5 => name + "e" + case 6 => name + "f" + } + return name1 + } + + def layer(block: (Int, Int, String) => Module[Float], features: Int, + count: Int, stride: Int = 1, name : String): Module[Float] = { + val s = Sequential() + for (i <- 1 to count) { + s.add(block(features, if (i == 1) stride else 1, getName(i, name))) + } + s + } + + val model = Sequential() + if (dataSet == DatasetType.ImageNet) { + val cfg = Map( + 50 -> ((3, 4, 6, 3), 2048, bottleneck: (Int, Int, String) => Module[Float]) + ) + + require(cfg.keySet.contains(depth), s"Invalid depth ${depth}") + + val (loopConfig, nFeatures, block) = cfg.get(depth).get + iChannels = 64 + + model.add(ReorderMemory(HeapData(Array(batchSize, 3, 224, 224), Memory.Format.nchw))) + .add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = false) + .setName("conv1").setReLU(true)) + .add(SbnDnn(64).setName("bn_conv1")) + .add(ReLU().setName("conv1_relu")) + .add(MaxPooling(3, 3, 2, 2).setName("pool1")) + .add(layer(block, 64, loopConfig._1, name = "2")) + .add(layer(block, 128, loopConfig._2, 2, name = "3")) + .add(layer(block, 256, loopConfig._3, 2, name = "4")) + .add(layer(block, 512, loopConfig._4, 2, name = "5")) + .add(AvgPooling(7, 7, 1, 1).setName("pool5")) + .add(Linear(nFeatures, classNum).setInitMethod(RandomNormal(0.0, 0.01), Zeros).setName( + "fc1000")) + .add(ReorderMemory(HeapData(Array(batchSize, classNum), Memory.Format.nc))) + } else { + throw new IllegalArgumentException(s"Invalid dataset ${dataSet}") + } + + modelInit(model) + model + } + + /** + * dataset type + * @param typeId type id + */ + sealed abstract class DatasetType(typeId: Int) + extends Serializable + + /** + * define some dataset type + */ + object DatasetType { + case object CIFAR10 extends DatasetType(0) + case object ImageNet extends DatasetType(1) + } + + /** + * ShortcutType + * @param typeId type id + */ + sealed abstract class ShortcutType(typeId: Int) + extends Serializable + + /** + * ShortcutType-A is used for Cifar-10, ShortcutType-B is used for ImageNet. + * ShortcutType-C is used for others. + */ + object ShortcutType{ + case object A extends ShortcutType(0) + case object B extends ShortcutType(1) + case object C extends ShortcutType(2) + } +} + +object Convolution { + def apply( + nInputPlane: Int, + nOutputPlane: Int, + kernelW: Int, + kernelH: Int, + strideW: Int = 1, + strideH: Int = 1, + padW: Int = 0, + padH: Int = 0, + nGroup: Int = 1, + propagateBack: Boolean = true, + optnet: Boolean = true, + weightDecay: Double = 1e-4): SpatialConvolution = { + val conv = SpatialConvolution(nInputPlane, nOutputPlane, kernelW, kernelH, + strideW, strideH, padW, padH, nGroup, propagateBack) + conv.setInitMethod(MsraFiller(false), Zeros) + conv + } +} + +object SbnDnn { + def apply[@specialized(Float, Double) T: ClassTag]( + nOutput: Int, + eps: Double = 1e-3, + momentum: Double = 0.9, + affine: Boolean = true) + (implicit ev: TensorNumeric[T]): SpatialBatchNormalization = { + SpatialBatchNormalization(nOutput, eps, momentum, affine).setInitMethod(Ones, Zeros) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SelectTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SelectTable.scala new file mode 100644 index 00000000000..c25e5b5bc85 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SelectTable.scala @@ -0,0 +1,99 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.nn.Utils +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + +/** + * Creates a module that takes a table as input and outputs the element at index `index` + * (positive or negative). This can be either a table or a Tensor. + * The gradients of the non-index elements are zeroed Tensors of the same size. + * This is true regardless of the depth of the encapsulated Tensor as the function used + * internally to do so is recursive. + * @param index the index to be selected + */ +@SerialVersionUID(- 7562114420457472987L) +class SelectTable(val index: Int)(implicit ev: TensorNumeric[Float]) extends MklDnnLayer { + + override def updateOutput(in: Activity): Activity = { + val input = in.asInstanceOf[Table] + val index = if (this.index < 0) input.length() + this.index else this.index + + require(input.contains(index), "index does not exist in the input table") + output = input[Activity](index) + + output + } + + override def updateGradInput(in: Activity, gradOutput: Activity): Table = { + val input = in.asInstanceOf[Table] + gradInput = T() + Utils.zeroTableCopy(gradInput.asInstanceOf[Table], input) + val index = if (this.index < 0) { + input.length() + this.index + 1 + } else { + this.index + } + + Utils.recursiveCopy(gradInput.asInstanceOf[Table](index), gradOutput) + + require(gradInput.asInstanceOf[Table].contains(index), "Index exceeds the size of input table") + + gradInput.asInstanceOf[Table] + } + + override def toString: String = s"mkldnn.SelectTable($index)" + + + override def canEqual(other: Any): Boolean = other.isInstanceOf[SelectTable] + + override def equals(other: Any): Boolean = other match { + case that: SelectTable => + super.equals(that) && + (that canEqual this) && + index == that.index + case _ => false + } + + override def hashCode(): Int = { + val state = Seq(super.hashCode(), index) + state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = inputs + _outputFormats = Array(inputs(index)) + (inputs, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradInputFormats = Array(grad(index)) + _gradOutputFormats = grad + (grad, _gradInputFormats) + } +} + +object SelectTable { + def apply(dimension: Int)(implicit ev: TensorNumeric[Float]) : SelectTable = { + new SelectTable(dimension) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala new file mode 100644 index 00000000000..c8a4172f594 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala @@ -0,0 +1,391 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.nn.{Sequential => Seq} +import com.intel.analytics.bigdl.tensor.Tensor + +import scala.collection.mutable.ArrayBuffer + +class Sequential extends MklDnnContainer { + + val fuseConvBn = System.getProperty("bigdl.mkldnn.fusion.convbn", "false").toBoolean + val fuseBnRelu = System.getProperty("bigdl.mkldnn.fusion.bnrelu", "false").toBoolean + val fuseConvRelu = System.getProperty("bigdl.mkldnn.fusion.convrelu", "false").toBoolean + val fuseConvSum = System.getProperty("bigdl.mkldnn.fusion.convsum", "false").toBoolean + + override def add(module: AbstractModule[_ <: Activity, _ <: Activity, Float]): this.type = { + require(mklDnnModules == null, "You should not call add after compilation") + require(module.isInstanceOf[MklDnnModule], "layer should be MklDnnModule") + super.add(module) + } + + override private[mkldnn] def fusion(phase: Phase): Unit = { + modules.clear() + modules.appendAll(getFusedModules(phase).map {x => + x.asInstanceOf[AbstractModule[Activity, Activity, Float]] + }) + mklDnnModules = modules.map(_.asInstanceOf[MklDnnModule]).toArray + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + var lastOutputFormats = inputs + var firstRealInputFormats: Array[MemoryData] = null + for (i <- 0 until mklDnnModules.length) { + val m = mklDnnModules(i) + val (realInputFormats, outputFormats) = m.initFwdPrimitives(lastOutputFormats, phase) + lastOutputFormats.zip(realInputFormats).foreach { + case (o, i) => reorderManager.register(o, i) + } + if (i == 0) firstRealInputFormats = realInputFormats + lastOutputFormats = outputFormats + } + (firstRealInputFormats, lastOutputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + var lastGradInputFormats = grads + var firstRealGradOutputFormats: Array[MemoryData] = null + for (i <- mklDnnModules.length - 1 to 0 by -1) { + val m = mklDnnModules(i) + val (realGradOutput, gradInputFomrats) = m.initBwdPrimitives(lastGradInputFormats, phase) + lastGradInputFormats.zip(realGradOutput).foreach { + case (gi, go) => reorderManager.register(gi, go) + } + if (i == mklDnnModules.length - 1) firstRealGradOutputFormats = realGradOutput + lastGradInputFormats = gradInputFomrats + } + (firstRealGradOutputFormats, lastGradInputFormats) + } + + override private[mkldnn] def initGradWPrimitives(grads: Array[MemoryData], phase: Phase) = { + var lastGradInputFormats = grads + var firstRealGradOutputFormats: Array[MemoryData] = null + for (i <- mklDnnModules.length - 1 to 0 by -1) { + val m = mklDnnModules(i) + val realGradOutput = m.initGradWPrimitives(lastGradInputFormats, phase) + lastGradInputFormats.zip(realGradOutput).foreach { + case (gi, go2) => reorderManager.register(gi, go2) + } + if (i == mklDnnModules.length - 1) firstRealGradOutputFormats = realGradOutput + lastGradInputFormats = m.gradInputFormats() + } + firstRealGradOutputFormats + } + + override def updateOutput(input: Activity): Activity = { + var i = 0 + var lastOutput = input + while (i < mklDnnModules.length - 1) { + lastOutput = reorderManager.infer( + mklDnnModules(i).outputFormats(), + mklDnnModules(i + 1).inputFormats(), + modules(i).forward(lastOutput) + ) + i += 1 + } + + this.output = modules(i).forward(lastOutput) + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + var i = modules.length - 1 + var lastGradInput = gradOutput + while (i > 0) { + val curInput = reorderManager.infer( + mklDnnModules(i - 1).outputFormats(), + mklDnnModules(i).inputFormats(), + modules(i - 1).output + ) + lastGradInput = reorderManager.infer( + mklDnnModules(i).gradInputFormats(), + mklDnnModules(i - 1).gradOutputFormats(), + modules(i).updateGradInput(curInput, lastGradInput) + ) + i -= 1 + } + lastGradInput = modules(0).updateGradInput(input, lastGradInput) + + this.gradInput = lastGradInput + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + var i = modules.length - 1 + var currentModule = modules(i) + var lastGradInput = gradOutput + while (i > 0) { + currentModule = modules(i) + val curInput = reorderManager.infer( + mklDnnModules(i - 1).outputFormats(), + mklDnnModules(i).inputFormats(), + modules(i - 1).output + ) + currentModule.accGradParameters(curInput, lastGradInput) + lastGradInput = reorderManager.infer( + mklDnnModules(i).gradInputFormats(), + mklDnnModules(i - 1).gradOutputWeightFormats(), + modules(i).gradInput + ) + i -= 1 + } + + modules(i).accGradParameters(input, lastGradInput) + } + + override private[mkldnn] def inputFormats() = { + modules(0).asInstanceOf[MklDnnModule].inputFormats() + } + + override private[mkldnn] def gradInputFormats() = { + modules(0).asInstanceOf[MklDnnModule].gradInputFormats() + } + + override private[mkldnn] def outputFormats() = { + modules.last.asInstanceOf[MklDnnModule].outputFormats() + } + + override private[mkldnn] def gradOutputFormats() = { + modules.last.asInstanceOf[MklDnnModule].gradOutputFormats() + } + + override private[mkldnn] def gradOutputWeightFormats() = { + modules.last.asInstanceOf[MklDnnModule].gradOutputWeightFormats() + } + + type ArrayBufferModules[Float] = ArrayBuffer[AbstractModule[Activity, Activity, Float]] + private def convWithBn(modules: Array[MklDnnModule], phase: Phase): Array[MklDnnModule] = { + if (fuseConvBn && phase == InferencePhase) { + val newModules: ArrayBuffer[MklDnnModule] = ArrayBuffer.empty + var lastBn: SpatialBatchNormalization = null + + modules.zip(modules.drop(1) ++ Array(null)).foreach { case (f, s) => + (f, s) match { + case (conv: SpatialConvolution, bn: SpatialBatchNormalization) => + mergeConvBn(conv, bn) + newModules.append(conv) + lastBn = bn + case (f: MklDnnContainer, s) => f.fusion(phase); newModules.append(f) + case _ => if (lastBn != f) { newModules.append(f) } + } + } + + newModules.toArray + } else { + modules + } + } + + private def convWithReLU(modules: Array[MklDnnModule], phase: Phase): Array[MklDnnModule] = { + if (fuseConvRelu) { + val newModules: ArrayBuffer[MklDnnModule] = ArrayBuffer.empty + var lastReLU: ReLU = null + + modules.zip(modules.drop(1) ++ Array(null)).foreach { case (f, s) => + (f, s) match { + case (conv: SpatialConvolution, relu: ReLU) => + newModules.append(conv) + conv.setReLU() + lastReLU = relu + case (f: MklDnnContainer, s) => + f.fusion(phase) + newModules.append(f) + case _ => if (lastReLU != f) { + newModules.append(f) + } + } + } + + newModules.toArray + } else { + modules + } + } + + private def bnWithReLU(modules: Array[MklDnnModule], phase: Phase): Array[MklDnnModule] = { + if (fuseBnRelu) { + val newModules: ArrayBuffer[MklDnnModule] = ArrayBuffer.empty + var lastReLU: ReLU = null + + modules.zip(modules.drop(1) ++ Array(null)).foreach { case (f, s) => + (f, s) match { + case (bn: SpatialBatchNormalization, relu: ReLU) => + newModules.append(bn) + bn.setReLU(true) + lastReLU = relu + case (f: MklDnnContainer, s) => f.fusion(phase); newModules.append(f) + case _ => if (lastReLU != f) { newModules.append(f) } + } + } + + newModules.toArray + } else { + modules + } + } + + private def convWithSum(modules: Array[MklDnnModule], phase: Phase): Array[MklDnnModule] = { + val newModules: ArrayBuffer[MklDnnModule] = ArrayBuffer.empty + if (!fuseConvSum || modules.length <= 2 || phase == TrainingPhase) { + newModules.appendAll(modules) + } else { + var lastConv: SpatialConvolution = null + var lastReLU: ReLU = null + + modules.zip(modules.drop(1) ++ Array(null)).foreach { + case (f: ConcatTable, s: CAddTable) => val (conv, sbt) = convSum(f, s) + newModules.append(f) + lastConv = conv + if (sbt != null) { + newModules.append(sbt) + } + case (f: MklDnnContainer, s) => f.fusion(phase); newModules.append(f) + case (f: CAddTable, s: ReLU) => if (lastConv != null) { + lastConv.setReLU() + lastReLU = s + lastConv = null + } else { + newModules.append(f) + } + case (f, s) => if (lastReLU != f) { newModules.append(f); lastReLU = null} + } + } + + newModules.toArray + } + + private def getFusedModules(phase: Phase): Array[MklDnnModule] = { + val f1Modules = convWithBn(mklDnnModules, phase) + val f2Modules = convWithReLU(f1Modules, phase) + val f3Modules = bnWithReLU(f2Modules, phase) + val f4Modules = convWithSum(f3Modules, phase) + f4Modules + } + + private def mergeConvBn(conv: SpatialConvolution, bn: SpatialBatchNormalization): Unit = { + + val originVar = Tensor[Float].resize(bn.runningVariance.size()).copy(bn.runningVariance) + val originMean = Tensor[Float].resize(bn.runningMean.size()).copy(bn.runningMean) + + val convWeight = Tensor[Float].resize(conv.weight.size()).copy(conv.weight) + val convBias = Tensor[Float].resize(conv.bias.size()).copy(conv.bias) + + (0 until bn.nOutput).foreach { j => + val variance = originVar.storage().array()(j + originVar.storageOffset() - 1) + val base = Math.sqrt(variance.asInstanceOf[Float] + bn.eps).toFloat + require(base != 0.0, s"the eps of ${bn.getName()} should be more than 0") + + val weight = if (conv.nGroup == 1) { + convWeight.select(1, j + 1) + } else { + convWeight.select(2, j + 1) + } + weight.div(base) + + val bias = convBias.storage().array()(j) + val mean = originMean.storage().array()(j) + convBias.storage().array()(j) = (bias - mean) / base + } + + conv.weight.copy(convWeight) + conv.bias.copy(convBias) + } + + private def getLast( + module: AbstractModule[Activity, Activity, Float]): AbstractModule[Activity, Activity, Any] = { + val ret = module match { + case sequential: Sequential => sequential.modules.last + case _ => module + } + + ret.asInstanceOf[AbstractModule[Activity, Activity, Any]] + } + + private def convSum(concatTable: ConcatTable, cAddTable: CAddTable): (SpatialConvolution, + SelectTable) = { + var branch1: AbstractModule[Activity, Activity, Any] = null + var branch2: AbstractModule[Activity, Activity, Any] = null + + var continue = concatTable.modules.length == 2 + + if (continue) { + branch1 = getLast(concatTable.modules(0)) + branch2 = getLast(concatTable.modules(1)) + + def isConvOrIdentity(module: AbstractModule[Activity, Activity, Any]): Boolean = { + module.isInstanceOf[SpatialConvolution] || module.isInstanceOf[Identity] + } + + continue = continue && isConvOrIdentity(branch1) && isConvOrIdentity(branch2) + } + + if (continue) { + // make sure the last module is conv + if (!branch2.isInstanceOf[SpatialConvolution]) { + // swap the modules + var tmp: AbstractModule[Activity, Activity, Float] = null + + tmp = concatTable.modules(0) + concatTable.modules(0) = concatTable.modules(1) + concatTable.modules(1) = tmp + + tmp = branch1.asInstanceOf[AbstractModule[Activity, Activity, Float]] + branch1 = branch2 + branch2 = tmp.asInstanceOf[AbstractModule[Activity, Activity, Any]] + } + + // get the index of conv, by default the output should be the first conv. + val (convIndex, conv, theOther) = (1, branch2.asInstanceOf[SpatialConvolution], branch1) + conv.setSum() + + // delete CAddTable + val selectTable = SelectTable(convIndex) + + // change the branch2's output to branch1's output + // FIXME maybe we should not set the conv operation + conv.setSumOp(theOther.asInstanceOf[Module[Float]]) + (conv, selectTable) + } else { + (null, null) + } + } + + override def toString(): String = { + val tab = " " + + s"${getPrintName}{${line + tab}[input -> ${ + modules.zipWithIndex.map { + case (m: AbstractModule[Activity, Activity, Float], i: Int) => "(" + (i + 1) + ")" + }. + mkString(" -> ") + } -> output]${line + tab}" + + s"${ + modules.zipWithIndex.map { + case (model: AbstractModule[Activity, Activity, Float], index: Int) + => s"(${index + 1}): ${model.setLine(line + tab)}" + }. + mkString(line + tab) + }$line}" + } +} + +object Sequential { + def apply(): Sequential = new Sequential() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala new file mode 100644 index 00000000000..9c59a2b1135 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -0,0 +1,109 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{MklDnn, PropKind, Stream => DnnStream} +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.tensor.{DenseType, Tensor} + +import scala.collection.mutable.ArrayBuffer + +class SoftMax() extends MklDnnLayer { + val nnSoftMax = nn.SoftMax[Float]() + + var updateOutputTensors: Array[Tensor[Float]] = _ + var updateOutputMemoryPrimitives: Array[Long] = _ + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + phase match { + case TrainingPhase => (inputs, inputs) // do nothing, because mkl dnn doesn't support training + case InferencePhase => + val axis = inputs(0).shape.length match { + case 1 => 0 + case 2 => 1 +// case 3 => 1 // TODO should support this? + case 4 => 1 + case _ => throw new UnsupportedOperationException("1D, 2D, or 4D tensor expected") + } + + _inputFormats = singleNativeData(inputs) + val desc = MklDnn.SoftMaxForwardDescInit(PropKind.ForwardInference, + inputFormats()(0).getMemoryDescription(), axis) + val forwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, 0L) + + _outputFormats = Array(MemoryData.primitiveOutput(forwardPrimDesc)) + + val srcs = Array(inputs(0).getPrimitive(runtime)) + val indexes = Array(0) + val dsts = Array(_outputFormats(0).getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, srcs.length, dsts, + dsts.length) + + updateOutputPrimitives = Array(primitive) + updateOutputMemoryPrimitives = srcs ++ dsts + + output = initTensor(_outputFormats(0)) + + (_inputFormats, _outputFormats) + case _ => throw new UnsupportedOperationException + } + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + (grad, grad) + } + + override def updateOutput(input: Activity): Activity = { + if (this.isTraining()) { + nnSoftMax.forward(input) + output = nnSoftMax.output + } else { + if (updateOutputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(output.asInstanceOf[Tensor[Float]]) + updateOutputTensors = buffer.toArray + } + + input.toTensor[Float].getTensorType match { + case DenseType => updateOutputTensors(0) = input.toTensor + case _ => + } + + MklDnnOps.streamSubmit(runtime.stream, 1, + updateOutputPrimitives, + updateOutputPrimitives.length, + updateOutputMemoryPrimitives, updateOutputTensors) + } + + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = nnSoftMax.backward(input, gradOutput) + gradInput + } +} + +object SoftMax { + def apply(): SoftMax = { + new SoftMax() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala new file mode 100644 index 00000000000..10a9d120ef0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -0,0 +1,307 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{AlgKind, Memory, MklDnn, PropKind, Query} +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.nn.{Ones, VariableFormat, Zeros} +import com.intel.analytics.bigdl.tensor._ + +import scala.collection.mutable.ArrayBuffer + +class SpatialBatchNormalization( + val nOutput: Int, + val eps: Double = 1e-5, + val momentum: Double = 0.1, + val affine: Boolean = true, + private val initWeight: Tensor[Float] = null, + private val initBias: Tensor[Float] = null, + private val initGradWeight: Tensor[Float] = null, + private val initGradBias: Tensor[Float] = null +) extends MklDnnLayer with Initializable { + + private var forwardDesc: Long = 0L + private var _relu: Boolean = false + + def setReLU(value: Boolean): this.type = { + _relu = value + this + } + def relu: Boolean = _relu + + var updateOutputTensors: Array[Tensor[Float]] = _ + var updateOutputMemoryPrimitives: Array[Long] = _ + var updateGradInputTensors: Array[Tensor[Float]] = _ + var updateGradInputMemoryPrimitives: Array[Long] = _ + + @transient var mean: DnnTensor[Float] = DnnTensor[Float](nOutput) + @transient var variance: DnnTensor[Float] = DnnTensor[Float](nOutput) + @transient var runningMean: DnnTensor[Float] = DnnTensor[Float](nOutput) + @transient var runningVariance: DnnTensor[Float] = DnnTensor[Float](nOutput) + @transient var weightAndBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutput * 2)) + @transient var gradWeightAndBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutput * 2)) + + var scaleFactor: Float = 0.0f + var biasFactor: Float = 0.0f + + { + val wInit = Ones // RandomUniform(0, 1) + val bInit = Zeros + setInitMethod(wInit, bInit) + } + + override def reset(): Unit = { + val init = Tensor[Float]().resize(Array(2, nOutput)) + val weight = init.select(1, 1) + val bias = init.select(1, 2) + + if (initWeight != null) { + require(initWeight.size(1) == nOutput) + weight.copy(initWeight) + } else { + weightInitMethod.init(weight, VariableFormat.ONE_D) + } + + if (initBias != null) { + require(initBias.size(1) == nOutput) + bias.copy(initBias) + } else { + biasInitMethod.init(bias, VariableFormat.ONE_D) + } + + weightAndBias.copy(init.view(2 * nOutput)) + + val zeros = Tensor[Float](Array(nOutput)).fill(0) + mean.copy(zeros) + variance.copy(zeros) + } + + object Index { + val input = 0 + val weight = 1 + val output = 2 + val mean = 3 + val variance = 4 + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = inputs + + val m = inputFormats()(0).shape.product / this.nOutput + biasFactor = if (m > 1) { m.toFloat / (m - 1) } else { 1 } + + val List(mean, variance, runningMean, runningVariance): List[NativeData] = + (0 until 4).map { _ => + NativeData(Array(nOutput), Memory.Format.x) + }.toList + // weight and bias should be combined + val weightAndBias: NativeData = NativeData(Array(nOutput * 2), Memory.Format.x) + + forwardDesc = phase match { + case TrainingPhase => + MklDnn.BatchNormForwardDescInit(PropKind.Forward, + inputs(0).getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_scaleshift) + case InferencePhase => + // we always use the weight and bias / scale and offset. So the flags should be combined + // with use_scaleshift and use_global_stats. + MklDnn.BatchNormForwardDescInit(PropKind.ForwardInference, + inputs(0).getMemoryDescription(), eps.toFloat, + MklDnn.BatchNormFlag.mkldnn_use_global_stats | MklDnn.BatchNormFlag.mkldnn_use_scaleshift) + case _ => throw new UnsupportedOperationException + } + + val primDesc = if (relu) { + val postOps = MklDnn.CreatePostOps() + MklDnn.PostOpsAppendEltwise(postOps, 1.0f, AlgKind.EltwiseRelu, 0.0f, 0.0f) + val attr = MklDnn.CreateAttr() + MklDnn.AttrSetPostOps(attr, postOps) + MklDnn.PrimitiveDescCreateV2(forwardDesc, attr, runtime.engine, 0) + // TODO we should destroy these ops + } else { + MklDnn.PrimitiveDescCreate(forwardDesc, runtime.engine, 0) + } + + _inputFormats = Array(MemoryData.operationWant(primDesc, Query.SrcPd)) + _outputFormats = Array(MemoryData.operationWant(primDesc, Query.DstPd)) + + val (srcs, dsts) = if (phase == TrainingPhase) { + val srcs = Array(inputFormats()(0), weightAndBias).map(_.getPrimitive(runtime)) + val dsts = Array(outputFormats()(0), mean, variance).map(_.getPrimitive(runtime)) + (srcs, dsts) + } else { + val srcs = Array(inputFormats()(0), runningMean, runningVariance, weightAndBias).map { x => + x.getPrimitive(runtime) + } + val dsts = Array(outputFormats()(0).getPrimitive(runtime)) + (srcs, dsts) + } + val indexes = Array.fill(srcs.length)(0) + + val primitive = MklDnn.PrimitiveCreate2(primDesc, srcs, indexes, srcs.length, dsts, dsts.length) + + updateOutputMemoryPrimitives = srcs ++ dsts + updateOutputPrimitives = Array(primitive) + output = initTensor(outputFormats()(0)) + + if (phase == TrainingPhase) { + this.runningMean.zero() + this.runningVariance.zero() + } + + if (updateOutputTensors != null) { + updateOutputTensors = Array.empty + } + + (inputFormats(), outputFormats()) + } + + override def updateOutput(input: Activity): Activity = { + if (updateOutputTensors == null) { + if (this.isTraining()) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(weightAndBias) + buffer.append(output.asInstanceOf[Tensor[Float]]) + buffer.append(mean) + buffer.append(variance) + updateOutputTensors = buffer.toArray + } else { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(runningMean) + buffer.append(runningVariance) + buffer.append(weightAndBias) + buffer.append(output.asInstanceOf[Tensor[Float]]) + updateOutputTensors = buffer.toArray + } + } + + updateWithNewTensor(updateOutputTensors, 0, input) + + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, + updateOutputMemoryPrimitives, updateOutputTensors) + + if (this.isTraining()) { + // update running(Mean, Var) and scaleFactor + scaleFactor = scaleFactor * momentum.toFloat + 1 + + mean.axpby(1, momentum.toFloat, runningMean) + variance.axpby(biasFactor, momentum.toFloat, runningVariance) + } + + output + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = Array(NativeData(outputFormats()(0).shape, outputFormats()(0).layout)) + + // [PERF] the format of gradInput should be the same as input + val backwardDesc = phase match { + case TrainingPhase => + MklDnn.BatchNormBackwardDescInit(PropKind.Backward, + inputFormats()(0).getMemoryDescription(), + inputFormats()(0).getMemoryDescription(), eps.toFloat, + MklDnn.BatchNormFlag.mkldnn_use_scaleshift) + case _ => throw new UnsupportedOperationException + } + + val gradWeightAndBias: NativeData = NativeData(Array(nOutput * 2), Memory.Format.x) + val gradWeightPrimitive = gradWeightAndBias.getPrimitive(runtime) + + val primDesc = MklDnn.PrimitiveDescCreate(backwardDesc, runtime.engine, 0) + + _gradInputFormats = Array(MemoryData.operationWant(primDesc, Query.DiffSrcPd)) + + // maybe will throw null exception + val srcs = Array(updateOutputMemoryPrimitives(Index.input), + updateOutputMemoryPrimitives(Index.mean), + updateOutputMemoryPrimitives(Index.variance), + grad(0).getPrimitive(runtime), + updateOutputMemoryPrimitives(Index.weight)) + val indexes = Array.fill(srcs.length)(0) + val dsts = Array(gradInputFormats()(0), gradWeightAndBias).map(_.getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(primDesc, srcs, indexes, srcs.length, + dsts, dsts.length) + + updateGradInputMemoryPrimitives = srcs ++ dsts + updateGradInputPrimitives = Array(primitive) + gradInput = initTensor(gradInputFormats()(0)) + + (_gradOutputFormats, gradInputFormats()) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + if (updateGradInputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(mean) + buffer.append(variance) + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) + buffer.append(weightAndBias) + buffer.append(gradInput.asInstanceOf[Tensor[Float]]) + buffer.append(gradWeightAndBias.asInstanceOf[Tensor[Float]]) + updateGradInputTensors = buffer.toArray + } + + updateWithNewTensor(updateGradInputTensors, 0, input) + updateWithNewTensor(updateGradInputTensors, 3, gradOutput) + + MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, + updateGradInputPrimitives.length, updateGradInputMemoryPrimitives, updateGradInputTensors) + + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + // do nothing + } + + override def zeroGradParameters(): Unit = { + if (affine) { gradWeightAndBias.zero() } + if (gradInput != null) { gradInput.asInstanceOf[DnnTensor[Float]].zero() } + } + + override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { + (Array(weightAndBias), Array(gradWeightAndBias)) + } + + override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { + (Array(NativeData(weightAndBias.size(), Memory.Format.x)), + Array(NativeData(gradWeightAndBias.size(), Memory.Format.x))) + } + + override def toString(): String = { + s"nn.mkl.SpatialBatchNormalization($nOutput, $eps, $momentum, $affine)" + } +} + +object SpatialBatchNormalization { + def apply( + nOutput: Int, + eps: Double = 1e-5, + momentum: Double = 0.1, + affine: Boolean = true, + initWeight: Tensor[Float] = null, + initBias: Tensor[Float] = null, + initGradWeight: Tensor[Float] = null, + initGradBias: Tensor[Float] = null): SpatialBatchNormalization = { + new SpatialBatchNormalization(nOutput, eps, momentum, affine, + initWeight, initBias, initGradWeight, initGradBias) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala new file mode 100644 index 00000000000..45e14433e4d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -0,0 +1,402 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.mkl._ +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} + +import scala.collection.mutable.ArrayBuffer + +class SpatialConvolution( + val nInputPlane: Int, + val nOutputPlane: Int, + val kernelW: Int, + val kernelH: Int, + val strideW: Int = 1, + val strideH: Int = 1, + val padW: Int = 0, + val padH: Int = 0, + val nGroup: Int = 1, + val propagateBack: Boolean = true, + val initWeight: Tensor[Float] = null, + val initBias: Tensor[Float] = null, + val initGradWeight: Tensor[Float] = null, + val initGradBias: Tensor[Float] = null, + val withBias: Boolean = true, + val format: DataFormat = DataFormat.NCHW +) extends MklDnnLayer with Initializable { + private val weightShape = if (nGroup == 1) { + Array(nOutputPlane, nInputPlane, kernelH, kernelW) + } else { + Array (nGroup, nOutputPlane / nGroup, nInputPlane / nGroup, kernelH, kernelW) + } + + // !!!important!!! this is for weight conversion. The weights in forward and backward is + // different. + val reorderManager = new ReorderManager + + val weight: DnnTensor[Float] = DnnTensor[Float](weightShape) + var weightForBackward: DnnTensor[Float] = _ + val bias: DnnTensor[Float] = DnnTensor[Float](Array(nOutputPlane)) + val gradWeight: DnnTensor[Float] = DnnTensor[Float](weightShape) + val gradBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutputPlane)) + + var forwardPrimDesc: Long = 0L + + var updateOutputMemoryPrimitives: Array[Long] = _ + var updateOutputTensors: Array[Tensor[Float]] = _ + var updateGradInputMemoryPrimitives: Array[Long] = _ + var updateGradInputTensors: Array[Tensor[Float]] = _ + var updateGradWMemoryPrimitives: Array[Long] = _ + var updateGradWTensors: Array[Tensor[Float]] = _ + + var _relu = false + var _sum = false + + def relu: Boolean = _relu + def setReLU(value: Boolean = true): this.type = { + _relu = value + this + } + + def sum: Boolean = _sum + def setSum(value: Boolean = true): this.type = { + _sum = value + this + } + + var sumOp: MklDnnLayer = null + def setSumOp(conv: Module[Float]): this.type = { + sumOp = conv.asInstanceOf[MklDnnLayer] + this + } + + object ParamsShape { + var weight: MemoryData = _ + var weightForBackward: MemoryData = _ + var bias: MemoryData = _ + var gradWeight: MemoryData = _ + var gradBias: MemoryData = _ + } + + private def getOutputShape(oh: Int, ow: Int, batchSize: Int = -1): Array[Int] = { + format match { + case DataFormat.NCHW => + if (batchSize == -1) { + Array(nOutputPlane, oh, ow) + } else { + Array(batchSize, nOutputPlane, oh, ow) + } + case DataFormat.NHWC => + if (batchSize == -1) { + Array(oh, ow, nOutputPlane) + } else { + Array(batchSize, oh, ow, nOutputPlane) + } + + } + } + + { + val stdv = 1.0 / math.sqrt(kernelW * kernelH * nInputPlane) + val wInit: InitializationMethod = RandomUniform(-stdv, stdv) + val bInit: InitializationMethod = if (withBias) RandomUniform(-stdv, stdv) + else null + setInitMethod(wInit, bInit) + } + + override def reset(): Unit = { + if (initWeight == null) { // TODO only support oihw format weights + val t = Tensor[Float](weightShape) + weightInitMethod.init(t, VariableFormat.OUT_IN) + weight.copy(t) + } else { + weight.copy(initWeight) + } + + if (initBias == null) { + val t = Tensor[Float](Array(nOutputPlane)) + biasInitMethod.init(t, VariableFormat.ONE_D) + bias.copy(t) + } else { + bias.copy(initBias) + } + + zeroGradParameters() + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + reorderManager.setRuntime(runtime) + + val inputHeight = inputs(0).shape(2) // TODO only supports 4-D and nchw + val inputWidth = inputs(0).shape(3) + + val sizes = if (padW == -1 && padH == -1) { + Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW) + } else { + Utils.getOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW, + padH, padW, ceilMode = false) + } + + val outputHeight = sizes(4) + val outputWidth = sizes(5) + + val inputShape = inputs(0).shape + val outputShape = Array(inputs(0).shape(0), nOutputPlane, outputHeight, outputWidth) + + val src = NativeData(inputShape, Memory.Format.any) + val wei = NativeData(weightShape, Memory.Format.any) + val bis = NativeData(bias.size(), Memory.Format.x) + val dst = NativeData(outputShape, Memory.Format.any) + + val desc = MklDnn.ConvForwardDescInit( + PropKind.ForwardTraining, AlgKind.ConvolutionDirect, + src.getMemoryDescription(), + wei.getMemoryDescription(), + bis.getMemoryDescription(), + dst.getMemoryDescription(), + Array(strideW, strideH), Array(padH, padW), Array(padH, padW), // TODO check the meaning + MklDnn.PaddingKind.mkldnnPaddingZero) + + forwardPrimDesc = if (relu || sum) { + val postOps = MklDnn.CreatePostOps() + if (sum) { + MklDnn.PostOpsAppendSum(postOps, 1.0f) + } + if (relu) { + MklDnn.PostOpsAppendEltwise(postOps, 1.0f, AlgKind.EltwiseRelu, 0.0f, 0.0f) + } + val attr = MklDnn.CreateAttr() + MklDnn.AttrSetPostOps(attr, postOps) + + MklDnn.PrimitiveDescCreateV2(desc, attr, runtime.engine, 0) + // TODO we should destroy these ops + } else { + MklDnn.PrimitiveDescCreate(desc, runtime.engine, 0) + } + + val List(realSrc, realWei, realDst) = List(Query.SrcPd, Query.WeightsPd, Query.DstPd).map {x => + MemoryData.operationWant(forwardPrimDesc, x) + } + + ParamsShape.weight = realWei + ParamsShape.bias = bis + + val srcs = Array(realSrc.getPrimitive(runtime), realWei.getPrimitive(runtime), + bis.getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + val dsts = Array(realDst.getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, srcs.length, + dsts, dsts.length) + + updateOutputMemoryPrimitives = srcs ++ dsts + updateOutputPrimitives = Array(primitive) + output = initTensor(dst) + + _inputFormats = Array(realSrc) + _outputFormats = Array(realDst) + (_inputFormats, _outputFormats) + } + + override def updateOutput(input: Activity): Activity = { + if (updateOutputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(weight) + buffer.append(bias) + if (sum) { + output = sumOp.output + } + buffer.append(output.asInstanceOf[Tensor[Float]]) + updateOutputTensors = buffer.toArray + } + + updateWithNewTensor(updateOutputTensors, 0, input) + + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, + updateOutputMemoryPrimitives, updateOutputTensors) + + output + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + val inputShape = inputFormats()(0).shape.length match { + case 1 => inputFormats()(0).shape ++ Array(1) // TODO Test + case _ => inputFormats()(0).shape + } + + val outputShape = outputFormats()(0).shape + + val src = NativeData(inputShape, Memory.Format.any) + val wei = NativeData(weightShape, Memory.Format.any) + val bis = NativeData(bias.size(), Memory.Format.x) + val dst = NativeData(outputShape, Memory.Format.any) + + val desc = MklDnn.ConvBackwardDataDescInit( + AlgKind.ConvolutionDirect, + src.getMemoryDescription(), + wei.getMemoryDescription(), // TODO check correctness of strides and padding + dst.getMemoryDescription(), Array(strideW, strideH), Array(padH, padW), Array(padH, padW), + MklDnn.PaddingKind.mkldnnPaddingZero) + val backwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) + + val List(realDiffSrc, realWei, realDiffDst) = + List(Query.DiffSrcPd, Query.WeightsPd, Query.DiffDstPd).map {x => + MemoryData.operationWant(backwardPrimDesc, x) + } + + ParamsShape.weightForBackward = realWei + + reorderManager.register(ParamsShape.weight, realWei) + + val srcs = Array(realDiffDst.getPrimitive(runtime), realWei.getPrimitive(runtime), + inputFormats()(0).getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + val dsts = Array(realDiffSrc.getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(backwardPrimDesc, srcs, indexes, srcs.length, + dsts, dsts.length) + + updateGradInputMemoryPrimitives = srcs ++ dsts + updateGradInputPrimitives = Array(primitive) + gradInput = initTensor(realDiffSrc) + + _gradInputFormats = Array(realDiffSrc) + _gradOutputFormats = Array(realDiffDst) + (_gradOutputFormats, _gradInputFormats) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + weightForBackward = reorderManager.infer(Array(ParamsShape.weight), + Array(ParamsShape.weightForBackward), weight).asInstanceOf[DnnTensor[Float]] + + if (updateGradInputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) + buffer.append(weightForBackward) + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(gradInput.asInstanceOf[Tensor[Float]]) + updateGradInputTensors = buffer.toArray + } + + updateWithNewTensor(updateGradInputTensors, 2, input) + updateWithNewTensor(updateGradInputTensors, 0, gradOutput) + + MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, + updateGradInputPrimitives.length, updateGradInputMemoryPrimitives, updateGradInputTensors) + + gradInput + } + override private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], + phase: Phase): Array[MemoryData] = { + val inputShape = inputFormats()(0).shape + val outputShape = inputFormats()(0).shape + + val src = NativeData(inputShape, Memory.Format.any) + val wei = NativeData(weightShape, Memory.Format.any) + val bis = NativeData(bias.size(), Memory.Format.x) + + val desc = MklDnn.ConvBackwardWeightsDescInit( + AlgKind.ConvolutionDirect, + src.getMemoryDescription(), + wei.getMemoryDescription(), + bis.getMemoryDescription(), + grad(0).getMemoryDescription(), Array(strideW, strideH), Array(padH, padW), Array(padH, padW), + MklDnn.PaddingKind.mkldnnPaddingZero) + val gradWeightPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) + + // TODO here seems some errors ?????? check the realSrc format. + val List(realSrc, realWei, realDiffDst) = + List(Query.SrcPd, Query.DiffWeightsPd, Query.DiffDstPd).map { x => + MemoryData.operationWant(gradWeightPrimDesc, x) + } + + ParamsShape.gradWeight = realWei + ParamsShape.gradBias = bis + + val srcs = Array(realSrc.getPrimitive(runtime), realDiffDst.getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + val dsts = Array(realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(gradWeightPrimDesc, srcs, indexes, srcs.length, + dsts, dsts.length) + + updateGradWMemoryPrimitives = srcs ++ dsts + accGradientPrimitives = Array(primitive) + + _gradOutputFormatsForWeight = Array(realDiffDst) + (_gradOutputFormatsForWeight) + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + if (updateGradWTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) + buffer.append(gradWeight) + buffer.append(gradBias) + updateGradWTensors = buffer.toArray + } + + updateWithNewTensor(updateGradWTensors, 0, input) + updateWithNewTensor(updateGradWTensors, 1, gradOutput) + + MklDnnOps.streamSubmit(runtime.stream, 1, accGradientPrimitives, + accGradientPrimitives.length, updateGradWMemoryPrimitives, updateGradWTensors) + } + + override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { + (Array(weight, bias), Array(gradWeight, gradBias)) + } + + override def zeroGradParameters(): Unit = { + gradWeight.zero() + gradBias.zero() + } + + override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { + (Array(ParamsShape.weight, ParamsShape.bias), Array(ParamsShape.gradWeight, ParamsShape.bias)) + } +} + +object SpatialConvolution { + def apply( + nInputPlane: Int, + nOutputPlane: Int, + kW: Int, + kH: Int, + dW: Int = 1, + dH: Int = 1, + padW: Int = 0, + padH: Int = 0, + nGroup: Int = 1, + propagateBack: Boolean = true, + initWeight: Tensor[Float] = null, + initBias: Tensor[Float] = null, + initGradWeight: Tensor[Float] = null, + initGradBias: Tensor[Float] = null, + withBias: Boolean = true, + format: DataFormat = DataFormat.NCHW): SpatialConvolution = { + new SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, + dH, padW, padH, nGroup, propagateBack, + initWeight, initBias, initGradWeight, initGradBias, withBias, format) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/ArrayStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/ArrayStorage.scala index ab318d87a68..1cce1a8e4a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/ArrayStorage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/ArrayStorage.scala @@ -18,7 +18,9 @@ package com.intel.analytics.bigdl.tensor import java.util -import scala.reflect.ClassTag +import com.intel.analytics.bigdl.mkl.Memory + +import scala.reflect._ private[tensor] class ArrayStorage[@specialized(Double, Float) T: ClassTag]( private[tensor] var values: Array[T]) extends Storage[T] { @@ -38,12 +40,12 @@ private[tensor] class ArrayStorage[@specialized(Double, Float) T: ClassTag]( source match { case s: ArrayStorage[T] => System.arraycopy(s.values, sourceOffset, this.values, offset, length) - case s: Storage[T] => - var i = 0 - while (i < length) { - this.values(i + offset) = s(i + sourceOffset) - i += 1 - } + case s: DnnStorage[T] => + require(classTag[T] == ClassTag.Float, "Only support copy float dnn storage") + require(sourceOffset == 0, "dnn storage offset should be 0") + Memory.CopyPtr2Array(s.ptr.address, 0, values.asInstanceOf[Array[Float]], offset, length, + DnnStorage.FLOAT_BYTES) + case _ => throw new UnsupportedOperationException("Only support dnn or array storage") } this } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index a5692b024f5..6604e148d4e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -28,7 +28,7 @@ import scala.reflect.ClassTag @SerialVersionUID(5876322619614900645L) private[tensor] class DenseTensor[@specialized T: ClassTag]( - private[tensor] var _storage: Storage[T], + private[tensor] var _storage: ArrayStorage[T], private[tensor] var _storageOffset: Int, private[tensor] var _size: Array[Int], private[tensor] var _stride: Array[Int], @@ -64,7 +64,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( override def squeeze(dim: Int): Tensor[T] = DenseTensor.squeeze(this, dim - 1) override def squeezeNewTensor(): Tensor[T] = { - val result = new DenseTensor(this.storage(), this.storageOffset(), this._size, this._stride) + val result = new DenseTensor(this._storage, this.storageOffset(), this._size, this._stride) result.squeeze() } @@ -171,7 +171,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( require(this.isContiguous(), "current tensor is not contiguous") require(sizes.product == this.nElement(), "invalid size eElement") - new DenseTensor(this.storage(), this.storageOffset(), sizes.clone()) + new DenseTensor(this._storage, this.storageOffset(), sizes.clone()) } override def unfold(dim: Int, size: Int, step: Int): Tensor[T] = { @@ -228,7 +228,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this(new ArrayStorage[T](new Array[T](dims.product)), 0, dims.toArray, DenseTensor.size2Stride(dims.toArray), dims.length) - private[tensor] def this(storage: Storage[T])(implicit ev: TensorNumeric[T]) = { + private[tensor] def this(storage: ArrayStorage[T])(implicit ev: TensorNumeric[T]) = { this(null, 0, null, null, 0) val _storageOffset = 0 val _size = Array(storage.length) @@ -236,7 +236,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( DenseTensor.newWithStorage(this, storage, _storageOffset, _size, _stride, ev) } - private[tensor] def this(storage: Storage[T], storageOffset: Int, size: Array[Int] = null, + private[tensor] def this(storage: ArrayStorage[T], storageOffset: Int, size: Array[Int] = null, stride: Array[Int] = null)(implicit ev: TensorNumeric[T]) = { this(null, 0, null, null, 0) if (storage != null) { @@ -249,7 +249,8 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( private[tensor] def this(other: Tensor[T])(implicit ev: TensorNumeric[T]) = { this(null, 0, null, null, 0) - val _storage = other.storage() + require(other.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation") + val _storage = other.storage().asInstanceOf[ArrayStorage[T]] val _storageOffset = other.storageOffset() - 1 val _size = other.size() val _stride = other.stride() @@ -363,8 +364,9 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } override def set(other: Tensor[T]): Tensor[T] = { - DenseTensor.rawSet(this, other.storage(), other.storageOffset() - 1, other.nDimension(), - other.size(), other.stride()) + require(other.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation") + DenseTensor.rawSet(this, other.storage().asInstanceOf[ArrayStorage[T]], + other.storageOffset() - 1, other.nDimension(), other.size(), other.stride()) } override def set(storage: Storage[T], storageOffset: Int = 1, sizes: Array[Int] = null, @@ -373,7 +375,8 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( require(sizes.length == strides.length) } - DenseTensor.rawSet(this, storage, storageOffset - 1, + require(storage.isInstanceOf[ArrayStorage[_]], "Only support array storage in this operation") + DenseTensor.rawSet(this, storage.asInstanceOf[ArrayStorage[T]], storageOffset - 1, if (sizes == null) 0 else sizes.length, sizes, strides) } @@ -421,7 +424,15 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } override def copy(other: Tensor[T]): Tensor[T] = { - DenseTensor.copy(this, other) + other match { + case t: DnnTensor[_] => + require(this.nElement() == other.nElement(), "tensor size must match") + this.storage().copy(other.storage(), this.storageOffset() - 1, 0, other.nElement()) + case t: DenseTensor[_] => + DenseTensor.copy(this, other) + case _ => throw new UnsupportedOperationException( + "only support copy from dense tensor or dnn tensor") + } this } @@ -934,6 +945,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( override def add(value: T, y: Tensor[T]): Tensor[T] = DenseTensorMath.cadd(this, this, value, y) override def add(x: Tensor[T]): Tensor[T] = { + require(x.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation") if (this.nElement() == x.nElement()) { if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) { ev.vAdd(this.nElement(), this.storage().array(), this.storageOffset() - 1, @@ -955,12 +967,12 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( i += 1 } } else { - this.add(expandTensor(x)) + this.add(expandTensor(x.asInstanceOf[DenseTensor[T]])) } this } - private[tensor] def expandTensor(x: Tensor[T]): Tensor[T] = { + private[tensor] def expandTensor(x: DenseTensor[T]): Tensor[T] = { val targetSize = DenseTensor.expandSize(this, x) val expandStrides = new Array[Int](targetSize.length) @@ -972,7 +984,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( i -= 1 } val expandX = new DenseTensor[T]( - x.storage(), + x.storage().asInstanceOf[ArrayStorage[T]], x.storageOffset(), targetSize, expandStridesX @@ -985,7 +997,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( i -= 1 } val tensor1 = new DenseTensor[T]( - this.storage(), + this._storage, this.storageOffset(), targetSize, expandStrides @@ -1031,6 +1043,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( DenseTensorMath.csub(this, this, ev.negative(value), y) override def sub(x: Tensor[T]): Tensor[T] = { + require(x.isInstanceOf[DenseTensor[T]], "Only dense tensor is supported in this operation") if (this.nElement() == x.nElement()) { if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous() && (x.getType() == DoubleType || x.getType() == FloatType)) { @@ -1055,7 +1068,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( i += 1 } } else { - this.sub(expandTensor(x)) + this.sub(expandTensor(x.asInstanceOf[DenseTensor[T]])) } this @@ -1162,9 +1175,16 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this } - override def cmul(y: Tensor[T]): Tensor[T] = DenseTensorMath.cmul(this, this, y) + override def cmul(y: Tensor[T]): Tensor[T] = { + require(y.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation") + DenseTensorMath.cmul(this, this, y.asInstanceOf[DenseTensor[T]]) + } - override def cmul(x: Tensor[T], y: Tensor[T]): Tensor[T] = DenseTensorMath.cmul(this, x, y) + override def cmul(x: Tensor[T], y: Tensor[T]): Tensor[T] = { + require(x.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation") + require(y.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation") + DenseTensorMath.cmul(this, x.asInstanceOf[DenseTensor[T]], y.asInstanceOf[DenseTensor[T]]) + } override def cdiv(y: Tensor[T]): Tensor[T] = DenseTensorMath.cdiv(this, this, y) @@ -1199,6 +1219,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( override def div(value: T): Tensor[T] = DenseTensorMath.mul(this, null, ev.inv(value)) override def div(x: Tensor[T]): Tensor[T] = { + require(x.isInstanceOf[DenseTensor[_]], "Only dense tensor is supported in this operation") if (this.nElement() == x.nElement()) { if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) { ev.vDiv(this.nElement(), this.storage().array(), this.storageOffset() - 1, @@ -1222,7 +1243,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( i += 1 } } else { - this.div(expandTensor(x)) + this.div(expandTensor(x.asInstanceOf[DenseTensor[T]])) } this @@ -1316,8 +1337,8 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( xSize = Array(1) ++ xSize i += 1 } - val size = new DenseTensor(Storage[T](xSize.map(x => ev.fromType[Int](x)))). - cmul(new DenseTensor(Storage[T](sizes.map(x => ev.fromType[Int](x))))). + val size = new DenseTensor(new ArrayStorage[T](xSize.map(x => ev.fromType[Int](x)))). + cmul(new DenseTensor(new ArrayStorage[T](sizes.map(x => ev.fromType[Int](x))))). storage().array().map(x => ev.toType[Int](x)) xTensor.resize(xSize) result.resize(size) @@ -2257,7 +2278,7 @@ object DenseTensor { } private[tensor] def newWithStorage[@specialized(Float, Double) T: ClassTag]( - tensor: DenseTensor[T], storage: Storage[T], storageOffset: Int, size: Array[Int], + tensor: DenseTensor[T], storage: ArrayStorage[T], storageOffset: Int, size: Array[Int], stride: Array[Int], ev: TensorNumeric[T]): DenseTensor[T] = { if (size != null && stride != null) { require(size.length == stride.length, "inconsistent size") @@ -2278,7 +2299,7 @@ object DenseTensor { } private[tensor] def rawSet[@specialized(Float, Double) T: ClassTag]( - self: DenseTensor[T], storage: Storage[T], storageOffset: Int, + self: DenseTensor[T], storage: ArrayStorage[T], storageOffset: Int, nDimension: Int, _size: Array[Int], _stride: Array[Int]): DenseTensor[T] = { self._storage = storage require(storageOffset >= 0, "Tensor: invalid storage offset") @@ -2467,9 +2488,9 @@ object DenseTensor { } private[tensor] def set[@specialized(Float, Double) T: ClassTag]( - self: DenseTensor[T], other: Tensor[T]): Tensor[T] = { + self: DenseTensor[T], other: DenseTensor[T]): Tensor[T] = { if (self != other) { - DenseTensor.rawSet(self, other.storage, other.storageOffset, + DenseTensor.rawSet(self, other.storage.asInstanceOf[ArrayStorage[T]], other.storageOffset, other.nDimension, other.size, other.stride) } else { self @@ -2488,11 +2509,11 @@ object DenseTensor { } private[tensor] def select[@specialized(Float, Double) T: ClassTag]( - self: DenseTensor[T], source: Tensor[T], _dimension: Int, _sliceIndex: Int): Unit = { + self: DenseTensor[T], source: DenseTensor[T], _dimension: Int, _sliceIndex: Int): Unit = { var src = source if (src == null) src = self - require(src.nDimension() > 0, "cannot select on a scalar") - require(_dimension >= 0 && _dimension < src.nDimension(), "out of range") + require(src.nDimension > 0, "cannot select on a scalar") + require(_dimension >= 0 && _dimension < src.nDimension, "out of range") require(_sliceIndex >= 0 && _sliceIndex < src.size(_dimension + 1), s"${_sliceIndex} out of range 0 to ${src.size(_dimension + 1) - 1}") @@ -2510,14 +2531,14 @@ object DenseTensor { } private[tensor] def narrow[@specialized(Float, Double) T: ClassTag]( - self: DenseTensor[T], source: Tensor[T], _dimension: Int, _firstIndex: Int, size: Int) + self: DenseTensor[T], source: DenseTensor[T], _dimension: Int, _firstIndex: Int, size: Int) : Unit = { var src = source if (src == null) { src = self } - require(_dimension >= 0 && _dimension < src.nDimension(), "dimension out of range") + require(_dimension >= 0 && _dimension < src.nDimension, "dimension out of range") require(_firstIndex >= 0 && _firstIndex < src.size(_dimension + 1), s"firstIndex(${_firstIndex}) out of range [0, ${src.size(_dimension + 1)})") require(size > 0 && _firstIndex + size <= src.size(_dimension + 1), @@ -2532,7 +2553,7 @@ object DenseTensor { } private[tensor] def transpose[@specialized(Float, Double) T: ClassTag]( - self: DenseTensor[T], source: Tensor[T], _dimension1: Int, _dimension2: Int): Unit = { + self: DenseTensor[T], source: DenseTensor[T], _dimension1: Int, _dimension2: Int): Unit = { var src = source if (src == null) src = self require(_dimension1 >= 0 && _dimension1 < src.nDimension, "out of range") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala index 03c0355fa58..1961fa7d53c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMath.scala @@ -45,7 +45,7 @@ object DenseTensorMath { self } - def cmul[@specialized T](self: DenseTensor[T], x: Tensor[T], y: Tensor[T]) + def cmul[@specialized T](self: DenseTensor[T], x: DenseTensor[T], y: DenseTensor[T]) (implicit ev: TensorNumeric[T]): Tensor[T] = { if (x.nElement() != y.nElement() && DenseTensor.canFastBroadcast(x, y)) { require(self.nElement() == x.nElement(), "the self tensor nElement is not same as x" + @@ -53,7 +53,8 @@ object DenseTensorMath { // recursive cmul var i = 0 while(i < x.size(1)) { - cmul(self.select(1, i + 1).asInstanceOf[DenseTensor[T]], x.select(1, i + 1), y) + cmul(self.select(1, i + 1).asInstanceOf[DenseTensor[T]], + x.select(1, i + 1).asInstanceOf[DenseTensor[T]], y) i += 1 } } else if (x.nElement() != y.nElement() && DenseTensor.canFastBroadcast(y, x)) { @@ -62,7 +63,8 @@ object DenseTensorMath { // recursive cmul var i = 0 while(i < y.size(1)) { - cmul(self.select(1, i + 1).asInstanceOf[DenseTensor[T]], x, y.select(1, i + 1)) + cmul(self.select(1, i + 1).asInstanceOf[DenseTensor[T]], x, + y.select(1, i + 1).asInstanceOf[DenseTensor[T]]) i += 1 } } else if (x.nElement() != y.nElement()) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala new file mode 100644 index 00000000000..9552674f3be --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala @@ -0,0 +1,108 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.tensor + +import com.intel.analytics.bigdl.mkl.Memory + +import scala.reflect._ + +/** + * Represent a native array which is needed by mkl-dnn + * @param size Storage size + * @tparam T data type, only support float now + */ +private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { + + require(classTag[T] == ClassTag.Float, "DnnStorage only support float") + + private var _isReleased: Boolean = false + + // Hold the address of the native array + val ptr: Pointer = new Pointer(allocate(size)) + + override def length(): Int = size + + override def apply(index: Int): T = + throw new UnsupportedOperationException("Not support this operation in DnnStorage") + + /** + * Set the element at position index in the storage. Valid range of index is 1 to length() + * + * @param index + * @param value + */ + override def update(index: Int, value: T): Unit = + throw new UnsupportedOperationException("Not support this operation in DnnStorage") + + override def copy(source: Storage[T], offset: Int, sourceOffset: Int, length: Int) + : this.type = { + source match { + case s: ArrayStorage[T] => + Memory.CopyArray2Ptr(s.array().asInstanceOf[Array[Float]], sourceOffset, + ptr.address, offset, length, DnnStorage.FLOAT_BYTES) + case s: DnnStorage[T] => + Memory.CopyPtr2Ptr(s.ptr.address, sourceOffset, ptr.address, offset, length, + DnnStorage.FLOAT_BYTES) + case _ => + throw new UnsupportedOperationException("Only support copy from ArrayStorage or DnnStorage") + } + this + } + + override def fill(value: T, offset: Int, length: Int): DnnStorage.this.type = + throw new UnsupportedOperationException("Not support this operation in DnnStorage") + + override def resize(size: Long): DnnStorage.this.type = + throw new UnsupportedOperationException("Not support this operation in DnnStorage") + + override def array(): Array[T] = + throw new UnsupportedOperationException("Not support this operation in DnnStorage") + + override def set(other: Storage[T]): DnnStorage.this.type = + throw new UnsupportedOperationException("Not support this operation in DnnStorage") + + override def iterator: Iterator[T] = + throw new UnsupportedOperationException("Not support this operation in DnnStorage") + + /** + * Release the native array, the storage object is useless + */ + def release(): Unit = { + Memory.AlignedFree(ptr.address) + _isReleased = true + } + + def isReleased(): Boolean = _isReleased + + + private def allocate(capacity: Int): Long = { + require(capacity > 0, s"capacity should not be larger than 0") + val ptr = Memory.AlignedMalloc(capacity * DnnStorage.FLOAT_BYTES, DnnStorage.CACHE_LINE_SIZE) + require(ptr != 0L, s"allocate native aligned memory failed") + ptr + } +} + +/** + * Represent a native point + * @param address + */ +private[bigdl] class Pointer(val address: Long) + +object DnnStorage { + private[tensor] val CACHE_LINE_SIZE = System.getProperty("bigdl.cache.line", "64").toInt + private[tensor] val FLOAT_BYTES: Int = 4 +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala new file mode 100644 index 00000000000..e0a53e4886c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -0,0 +1,370 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.tensor + +import breeze.linalg.{DenseMatrix, DenseVector} +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.tensor.DnnTensor.DnnTensorUnsupportOperations +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import org.apache.spark.mllib.linalg +import org.apache.spark.mllib.linalg.Matrix + +import scala.reflect.ClassTag + +class DnnTensor[T: ClassTag]( + private var _storage: DnnStorage[T], + private var sizes: Array[Int] +) (implicit ev: TensorNumeric[T]) + extends DnnTensorUnsupportOperations[T]{ + + override def nElement(): Int = storage.length() + + override def copy(other: Tensor[T]): Tensor[T] = { + other match { + case t: DenseTensor[_] => + require(DnnTensor.noTransposed(t), "dense tensor should not be transposed") + require(this.nElement() == other.nElement(), "tensor elements number must be same") + this._storage.copy(other.storage(), 0, other.storageOffset() - 1, this.nElement()) + case t: DnnTensor[_] => + require(this.nElement() == other.nElement(), "tensor elements number must be same") + this._storage.copy(other.storage(), 0, 0, this.nElement()) + case _ => throw new UnsupportedOperationException( + "Only support copy from dense tensor and dnn tensor") + } + this + } + + def release(): Unit = { + _storage.release() + } + + def storageAddress(): Long = _storage.ptr.address + + def isReleased(): Boolean = _storage.isReleased() + + override def storage(): Storage[T] = _storage + + override def resize(s: Array[Int], stride: Array[Int] = null): this.type = { + require(stride == null, "dnn tensor doesn't have stride") + if (s.product > nElement()) { + _storage.release() + _storage = new DnnStorage[T](s.product) + } + this.sizes = s.clone() + this + } + + override def resize(s: Int): this.type = { + if (s > nElement()) { + _storage.release() + _storage = new DnnStorage[T](s) + } + this.sizes = Array(s) + this + } + + override def add(x: Tensor[T]): Tensor[T] = { + require(x.isInstanceOf[DnnTensor[_]], "Just support two dnn tensor add") + Memory.SAdd(this.nElement(), this._storage.ptr.address, 0, + x.asInstanceOf[DnnTensor[T]]._storage.ptr.address, 0, this._storage.ptr.address, 0) + this + } + + override def zero(): Tensor[T] = { + Memory.Zero(this._storage.ptr.address, this.nElement(), DnnStorage.FLOAT_BYTES) + this + } + + def axpby(a: Float, b: Float, to: DnnTensor[T]): Unit = { + val x = this._storage.ptr.address + val y = to._storage.ptr.address + Memory.Axpby(this.nElement(), a, x, b, y) + } + + override def toTensor[D](implicit ev: TensorNumeric[D]): DnnTensor[D] = { + this.asInstanceOf[DnnTensor[D]] + } + + override def size(): Array[Int] = sizes.clone() + + override def size(d: Int): Int = sizes(d - 1) + + override def dim(): Int = size().length + + override def nDimension(): Int = size().length + + override def getTensorType: TensorType = MklDnnType +} + +object DnnTensor { + // scalastyle:off + private def ???(): Nothing = { + throw new UnsupportedOperationException("DnnTensor doesn't support this operation") + } + // scalastyle:on + + private[tensor] def noTransposed(t: DenseTensor[_]): Boolean = { + var product = 1 + var i = t.dim() + while(i > 0) { + if (product != t.stride(i)) return false + product *= t.size(i) + i -= 1 + } + return true + } + + def apply[T: ClassTag](sizes: Array[Int])(implicit ev: TensorNumeric[T]): DnnTensor[T] = { + val storage = new DnnStorage[T](sizes.product) + new DnnTensor[T](storage, sizes) + } + + def apply[T: ClassTag](d1: Int)(implicit ev: TensorNumeric[T]): DnnTensor[T] = { + val storage = new DnnStorage[T](d1) + new DnnTensor[T](storage, Array(d1)) + } + + def apply[T: ClassTag](d1: Int, d2: Int)(implicit ev: TensorNumeric[T]): DnnTensor[T] = { + val storage = new DnnStorage[T](d1 * d2) + new DnnTensor[T](storage, Array(d1, d2)) + } + + def apply[T: ClassTag](d1: Int, d2: Int, d3: Int)(implicit ev: TensorNumeric[T]): DnnTensor[T] = { + val storage = new DnnStorage[T](d1 * d2 * d3) + new DnnTensor[T](storage, Array(d1, d2, d3)) + } + + def apply[T: ClassTag](d1: Int, d2: Int, d3: Int, d4: Int)( + implicit ev: TensorNumeric[T]): DnnTensor[T] = { + val storage = new DnnStorage[T](d1 * d2 * d3 * d4) + new DnnTensor[T](storage, Array(d1, d2, d3, d4)) + } + + def apply[T: ClassTag](d1: Int, d2: Int, d3: Int, d4: Int, d5: Int)( + implicit ev: TensorNumeric[T]): DnnTensor[T] = { + val storage = new DnnStorage[T](d1 * d2 * d3 * d4 * d5) + new DnnTensor[T](storage, Array(d1, d2, d3, d4, d5)) + } + + class DnnTensorUnsupportOperations[T: ClassTag](implicit ev: TensorNumeric[T]) extends Tensor[T] { + // scalastyle:off + override def isEmpty: Boolean = ??? + override def isScalar: Boolean = ??? + override def nDimension(): Int = ??? + override def dim(): Int = ??? + override def size(): Array[Int] = ??? + override def size(dim: Int): Int = ??? + override def stride(): Array[Int] = ??? + override def stride(dim: Int): Int = ??? + override def fill(v: T): Tensor[T] = ??? + override def forceFill(v: Any): Tensor[T] = ??? + override def zero(): Tensor[T] = ??? + override def randn(): Tensor[T] = ??? + override def randn(mean: Double, stdv: Double): Tensor[T] = ??? + override def rand(): Tensor[T] = ??? + override def rand(lowerBound: Double, upperBound: Double): Tensor[T] = ??? + override def bernoulli(p: Double): Tensor[T] = ??? + override def transpose(dim1: Int, dim2: Int): Tensor[T] = ??? + override def t(): Tensor[T] = ??? + override def apply(index: Int): Tensor[T] = ??? + override def apply(indexes: Array[Int]): T = ??? + override def value(): T = ??? + override def valueAt(d1: Int): T = ??? + override def valueAt(d1: Int, d2: Int): T = ??? + override def valueAt(d1: Int, d2: Int, d3: Int): T = ??? + override def valueAt(d1: Int, d2: Int, d3: Int, d4: Int): T = ??? + override def valueAt(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int): T = ??? + override def apply(t: Table): Tensor[T] = ??? + override def update(index: Int, value: T): Unit = ??? + override def update(index: Int, src: Tensor[T]): Unit = ??? + override def update(indexes: Array[Int], value: T): Unit = ??? + override def setValue(value: T): DnnTensorUnsupportOperations.this.type = ??? + override def setValue(d1: Int, value: T): DnnTensorUnsupportOperations.this.type = ??? + override def setValue(d1: Int, d2: Int, value: T): DnnTensorUnsupportOperations.this.type = ??? + override def setValue(d1: Int, d2: Int, d3: Int, value: T): DnnTensorUnsupportOperations.this.type = ??? + override def setValue(d1: Int, d2: Int, d3: Int, d4: Int, value: T): DnnTensorUnsupportOperations.this.type = ??? + override def setValue(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int, value: T): DnnTensorUnsupportOperations.this.type = ??? + override def update(t: Table, value: T): Unit = ??? + override def update(t: Table, src: Tensor[T]): Unit = ??? + override def update(filter: (T) => Boolean, value: T): Unit = ??? + override def isContiguous(): Boolean = ??? + override def contiguous(): Tensor[T] = ??? + override def isSameSizeAs(other: Tensor[_]): Boolean = ??? + override def emptyInstance(): Tensor[T] = ??? + override def resizeAs(src: Tensor[_]): Tensor[T] = ??? + override def cast[D: ClassManifest](castTensor: Tensor[D])(implicit ev: TensorNumeric[D]): Tensor[D] = ??? + override def resize(sizes: Array[Int], strides: Array[Int]): Tensor[T] = ??? + override def resize(size1: Int): Tensor[T] = ??? + override def resize(size1: Int, size2: Int): Tensor[T] = ??? + override def resize(size1: Int, size2: Int, size3: Int): Tensor[T] = ??? + override def resize(size1: Int, size2: Int, size3: Int, size4: Int): Tensor[T] = ??? + override def resize(size1: Int, size2: Int, size3: Int, size4: Int, size5: Int): Tensor[T] = ??? + override def nElement(): Int = ??? + override def select(dim: Int, index: Int): Tensor[T] = ??? + override def storage(): Storage[T] = ??? + override def storageOffset(): Int = ??? + override def set(other: Tensor[T]): Tensor[T] = ??? + override def set(storage: Storage[T], storageOffset: Int, sizes: Array[Int], strides: Array[Int]): Tensor[T] = ??? + override def set(): Tensor[T] = ??? + override def narrow(dim: Int, index: Int, size: Int): Tensor[T] = ??? + override def copy(other: Tensor[T]): Tensor[T] = ??? + override def applyFun[A: ClassManifest](t: Tensor[A], func: (A) => T): Tensor[T] = ??? + override def apply1(func: (T) => T): Tensor[T] = ??? + override def zipWith[A: ClassManifest, B: ClassManifest](t1: Tensor[A], t2: Tensor[B], func: (A, B) => T): Tensor[T] = ??? + override def map(other: Tensor[T], func: (T, T) => T): Tensor[T] = ??? + override def squeeze(): Tensor[T] = ??? + override def squeeze(dim: Int): Tensor[T] = ??? + override def squeezeNewTensor(): Tensor[T] = ??? + override def view(sizes: Array[Int]): Tensor[T] = ??? + override def unfold(dim: Int, size: Int, step: Int): Tensor[T] = ??? + override def repeatTensor(sizes: Array[Int]): Tensor[T] = ??? + override def expandAs(template: Tensor[T]): Tensor[T] = ??? + override def expand(sizes: Array[Int]): Tensor[T] = ??? + override def split(size: Int, dim: Int): Array[Tensor[T]] = ??? + override def split(dim: Int): Array[Tensor[T]] = ??? + override def toBreezeVector(): DenseVector[T] = ??? + override def toMLlibVector(): linalg.Vector = ??? + override def toBreezeMatrix(): DenseMatrix[T] = ??? + override def toMLlibMatrix(): Matrix = ??? + override def getType(): TensorDataType = ??? + override def diff(other: Tensor[T], count: Int, reverse: Boolean): Boolean = ??? + override def addSingletonDimension(t: Tensor[T], dim: Int): Tensor[T] = ??? + override def reshape(sizes: Array[Int]): Tensor[T] = ??? + override def save(path: String, overWrite: Boolean): DnnTensorUnsupportOperations.this.type = ??? + override def getTensorNumeric(): TensorNumeric[T] = ??? + override def getTensorType: TensorType = ??? + override def toArray(): Array[T] = ??? + override def +(s: T): Tensor[T] = ??? + override def +(t: Tensor[T]): Tensor[T] = ??? + override def -(s: T): Tensor[T] = ??? + override def -(t: Tensor[T]): Tensor[T] = ??? + override def unary_-(): Tensor[T] = ??? + override def /(s: T): Tensor[T] = ??? + override def /(t: Tensor[T]): Tensor[T] = ??? + override def *(s: T): Tensor[T] = ??? + override def *(t: Tensor[T]): Tensor[T] = ??? + override def sum(): T = ??? + override def prod(): T = ??? + override def prod(x: Tensor[T], dim: Int): Tensor[T] = ??? + override def sum(dim: Int): Tensor[T] = ??? + override def sum(x: Tensor[T], dim: Int): Tensor[T] = ??? + override def mean(): T = ??? + override def mean(dim: Int): Tensor[T] = ??? + override def max(): T = ??? + override def max(dim: Int): (Tensor[T], Tensor[T]) = ??? + override def max(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T]) = ??? + override def min(): T = ??? + override def min(dim: Int): (Tensor[T], Tensor[T]) = ??? + override def min(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T]) = ??? + override def scatter(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = ??? + override def gather(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = ??? + override def conv2(kernel: Tensor[T], vf: Char): Tensor[T] = ??? + override def xcorr2(kernel: Tensor[T], vf: Char): Tensor[T] = ??? + override def sqrt(): Tensor[T] = ??? + override def tanh(): Tensor[T] = ??? + override def abs(): Tensor[T] = ??? + override def add(value: T, y: Tensor[T]): Tensor[T] = ??? + override def add(y: Tensor[T]): Tensor[T] = ??? + override def add(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = ??? + override def add(value: T): Tensor[T] = ??? + override def add(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def dot(y: Tensor[T]): T = ??? + override def cmax(value: T): Tensor[T] = ??? + override def dist(y: Tensor[T], norm: Int): T = ??? + override def addcmul(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = ??? + override def addcmul(tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = ??? + override def addcdiv(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = ??? + override def sub(value: T, y: Tensor[T]): Tensor[T] = ??? + override def sub(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = ??? + override def sub(y: Tensor[T]): Tensor[T] = ??? + override def sub(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def sub(value: T): Tensor[T] = ??? + override def cmul(y: Tensor[T]): Tensor[T] = ??? + override def cmul(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def cdiv(y: Tensor[T]): Tensor[T] = ??? + override def cdiv(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def mul(value: T): Tensor[T] = ??? + override def div(value: T): Tensor[T] = ??? + override def div(y: Tensor[T]): Tensor[T] = ??? + override def mul(x: Tensor[T], value: T): Tensor[T] = ??? + override def addmm(v1: T, M: Tensor[T], v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = ??? + override def addmm(M: Tensor[T], mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = ??? + override def addmm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = ??? + override def addmm(v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = ??? + override def addmm(v1: T, v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = ??? + override def mm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] = ??? + override def addr(t1: Tensor[T], t2: Tensor[T]): Tensor[T] = ??? + override def addr(v1: T, t1: Tensor[T], t2: Tensor[T]): Tensor[T] = ??? + override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T]): Tensor[T] = ??? + override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T], t3: Tensor[T]): Tensor[T] = ??? + override def uniform(args: T*): T = ??? + override def addmv(beta: T, vec1: Tensor[T], alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = ??? + override def addmv(beta: T, alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = ??? + override def addmv(alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = ??? + override def mv(mat: Tensor[T], vec2: Tensor[T]): Tensor[T] = ??? + override def baddbmm(beta: T, M: Tensor[T], alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = ??? + override def baddbmm(beta: T, alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = ??? + override def baddbmm(alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = ??? + override def bmm(batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] = ??? + override def pow(y: Tensor[T], n: T): Tensor[T] = ??? + override def pow(n: T): Tensor[T] = ??? + override def square(): Tensor[T] = ??? + override def floor(y: Tensor[T]): Tensor[T] = ??? + override def floor(): Tensor[T] = ??? + override def ceil(): Tensor[T] = ??? + override def inv(): Tensor[T] = ??? + override def erf(): Tensor[T] = ??? + override def erfc(): Tensor[T] = ??? + override def logGamma(): Tensor[T] = ??? + override def digamma(): Tensor[T] = ??? + override def topk(k: Int, dim: Int, increase: Boolean, result: Tensor[T], indices: Tensor[T], sortedResult: Boolean): (Tensor[T], Tensor[T]) = ??? + override def log(y: Tensor[T]): Tensor[T] = ??? + override def exp(y: Tensor[T]): Tensor[T] = ??? + override def sqrt(y: Tensor[T]): Tensor[T] = ??? + override def tanh(y: Tensor[T]): Tensor[T] = ??? + override def log1p(y: Tensor[T]): Tensor[T] = ??? + override def log(): Tensor[T] = ??? + override def exp(): Tensor[T] = ??? + override def log1p(): Tensor[T] = ??? + override def abs(x: Tensor[T]): Tensor[T] = ??? + override def norm(y: Tensor[T], value: Int, dim: Int): Tensor[T] = ??? + override def gt(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def lt(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def le(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def eq(x: Tensor[T], y: T): Tensor[T] = ??? + override def maskedFill(mask: Tensor[T], e: T): Tensor[T] = ??? + override def maskedCopy(mask: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def maskedSelect(mask: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def norm(value: Int): T = ??? + override def sign(): Tensor[T] = ??? + override def ge(x: Tensor[T], value: Double): Tensor[T] = ??? + override def indexAdd(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def index(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def cmax(y: Tensor[T]): Tensor[T] = ??? + override def cmin(y: Tensor[T]): Tensor[T] = ??? + override def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def cmin(x: Tensor[T], y: Tensor[T]): Tensor[T] = ??? + override def range(xmin: Double, xmax: Double, step: Int): Tensor[T] = ??? + override def negative(x: Tensor[T]): Tensor[T] = ??? + override def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] = ??? + override def sumSquare(): T = ??? + override def clamp(min: Double, max: Double): Tensor[T] = ??? + override def toTensor[D](implicit ev: TensorNumeric[D]): Tensor[D] = ??? + override private[bigdl] def toQuantizedTensor = ??? + // scalastyle: on + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 75f2bfe83d3..881752e5bbd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -837,6 +837,8 @@ object SparseType extends TensorType object QuantizedType extends TensorType +object MklDnnType extends TensorType + object Tensor { // pre-load MKL library. If we do not do it here, @@ -960,7 +962,8 @@ object Tensor { */ def apply[@specialized(Float, Double) T: ClassTag](storage: Storage[T])( implicit ev: TensorNumeric[T]): Tensor[T] = { - new DenseTensor(storage.asInstanceOf[Storage[T]]) + require(storage.isInstanceOf[ArrayStorage[_]], "Only support array storage in this operaiton") + new DenseTensor(storage.asInstanceOf[ArrayStorage[T]]) } /** @@ -1002,12 +1005,12 @@ object Tensor { * @tparam T * @return */ - def apply[@specialized(Float, Double) T: ClassTag](storage: Storage[T], - storageOffset: Int, - size: Array[Int] = null, - stride: Array[Int] = null) - (implicit ev: TensorNumeric[T]): Tensor[T] = { - new DenseTensor(storage.asInstanceOf[Storage[T]], storageOffset, size, stride) + def apply[@specialized(Float, Double) T: ClassTag]( + storage: Storage[T], + storageOffset: Int, + size: Array[Int] = null, + stride: Array[Int] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = { + new DenseTensor(storage.asInstanceOf[ArrayStorage[T]], storageOffset, size, stride) } /** diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala new file mode 100644 index 00000000000..eb623adc959 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala @@ -0,0 +1,80 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.SpatialAveragePooling +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG + +import scala.util.Random + +class AvgPoolingSpec extends BigDLSpecHelper { + "Avg Pooling test1" should "be correct" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) + + RNG.setSeed(100) + val pool = AvgPooling(3, 3, 2, 2) + RNG.setSeed(100) + val layer = SpatialAveragePooling[Float](3, 3, 2, 2).ceil() + + val output2 = layer.forward(input).toTensor[Float] + + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw))) + seq.add(pool) + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 480, 28, 28), + Memory.Format.nchw))) + val output1 = seq.forward(input) + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = seq.backward(input, output2) + grad1 should be(grad2) + } + + "Avg Pooling test2" should "be correct" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 64, 112, 112).apply1(e => Random.nextFloat()) + + RNG.setSeed(100) + val pool = AvgPooling(3, 3, 2, 2) + RNG.setSeed(100) + val layer = SpatialAveragePooling[Float](3, 3, 2, 2).ceil() + + val output2 = layer.forward(input).toTensor[Float] + + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(batchSize, 64, 112, 112), Memory.Format.nchw), + HeapData(Array(batchSize, 64, 112, 112), Memory.Format.nchw))) + seq.add(pool) + seq.add(ReorderMemory(HeapData(Array(batchSize, 64, 56, 56), Memory.Format.nchw), + HeapData(Array(batchSize, 64, 56, 56), Memory.Format.nchw))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 64, 112, 112), + Memory.Format.nchw))) + val output1 = seq.forward(input) + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = seq.backward(input, output2) + grad1 should be(grad2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala new file mode 100644 index 00000000000..591b7f2475f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} + +class CAddTableSpec extends BigDLSpecHelper { + "CAddTable" should "be correct" in { + val layer = CAddTable() + val model = Sequential() + val concat = ConcatTable() + concat.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), + NativeData(Array(2, 2), Memory.Format.nc))) + concat.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), + NativeData(Array(2, 2), Memory.Format.nc))) + model.add(concat) + model.add(layer) + model.add(ReorderMemory(NativeData(Array(2, 2), Memory.Format.nc), + HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc), + HeapData(Array(2, 2), Memory.Format.nc))) + model.compile(Phase.TrainingPhase, Array(HeapData(Array(2, 2), Memory.Format.nc))) + model.forward(Tensor[Float](T(T(1, 2), T(3, 4)))) should be(Tensor[Float](T( + T(2, 4), + T(6, 8) + ))) + val dnnGrad = model.backward(Tensor[Float](T(T(1, 2), T(3, 4))), T( + Tensor[Float](T( + T(4, 5), + T(6, 7) + )) + )).asInstanceOf[Tensor[Float]] + val heapGrad = Tensor[Float](2, 2) + heapGrad.copy(dnnGrad) + heapGrad should be ( + Tensor[Float](T(T(8, 10), T(12, 14))) + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala new file mode 100644 index 00000000000..ebebe17b089 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala @@ -0,0 +1,72 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} + +class ConcatTableSpec extends BigDLSpecHelper { + "ConcatTable" should "throw exception when input shape is different" in { + val container = ConcatTable() + container.add(Input(Array(1, 2, 3, 4), Memory.Format.nchw)) + container.add(Input(Array(1, 3, 4, 2), Memory.Format.nchw)) + + intercept[IllegalArgumentException] { + container.compile(Phase.TrainingPhase, Array(HeapData(Array(1, 2, 3, 4), Memory.Format.nchw))) + } + } + + "ConcatTable" should "be good" in { + val container = ConcatTable() + container.add(ReorderMemory( + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc))) + val subcontainer = Sequential() + subcontainer.add(ReorderMemory( + HeapData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.nc))) + subcontainer.add(ReorderMemory(NativeData(Array(3, 4), Memory.Format.io), + NativeData(Array(3, 4), Memory.Format.nc))) + subcontainer.add(ReorderMemory(HeapData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.io))) + container.add(subcontainer) + + container.compile(Phase.TrainingPhase, Array(HeapData(Array(3, 4), Memory.Format.nc))) + val input1 = Tensor[Float](3, 4).rand() + val output1 = container.forward(input1).toTable + output1(1).asInstanceOf[Tensor[Float]] should be(input1) + output1(2).asInstanceOf[Tensor[Float]] should be(input1) + + val grad1 = Tensor[Float](3, 4).rand() + val nativeGrad = container.backward(input1, T(grad1, grad1)).asInstanceOf[Tensor[Float]] + val heapGrad = Tensor[Float](3, 4).copy(nativeGrad) + heapGrad should be(grad1 * 2) + val input2 = Tensor[Float](3, 4).rand() + val output2 = container.forward(input2).toTable + output2(1).asInstanceOf[Tensor[Float]] should be(input2) + output2(2).asInstanceOf[Tensor[Float]] should be(input2) + + val grad2 = Tensor[Float](3, 4).rand() + val nativeGrad2 = container.backward(input1, T(grad2, grad2)).asInstanceOf[Tensor[Float]] + val heapGrad2 = Tensor[Float](3, 4).copy(nativeGrad2) + heapGrad2 should be(grad2 * 2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala new file mode 100644 index 00000000000..062e87fee2a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -0,0 +1,155 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor + +class FusionSpec extends FlatSpec with Matchers { + "Conv with relu" should "work correctly" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 3, 224, 224).fill(1.0f) + + val inputShape = Array(batchSize, 3, 224, 224) + val outputShape = Array(batchSize, 64, 112, 112) + + val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) + val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder11 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val model1 = Sequential().add(reorder1).add(conv1).add(ReLU()).add(reorder11) + model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, initWeight = conv1.weight, + initBias = conv1.bias) + val reorder2 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder22 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val model2 = Sequential().add(reorder2).add(conv2).add(ReLU()).add(reorder22) + model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + System.setProperty("bigdl.mkldnn.fusion.convrelu", "false") + + model1.evaluate() + model2.evaluate() + + model1.forward(input) + model2.forward(input) + + model1.output should be (model2.output) + model1.modules.length should be (model2.modules.length + 1) + } + + "Conv Bn merge" should "work correctly" in { + val batchSize = 4 + val inputShape = Array(batchSize, 3, 224, 224) + val outputShape = Array(batchSize, 64, 112, 112) + val input = Tensor[Float](batchSize, 3, 224, 224).fill(1.0f) + + val runningMean = Tensor[Float](64).rand(-1, 1) + val runningVar = Tensor[Float](64).fill(100) + val initWeight = Tensor[Float]().resize(Array(64, 3, 7, 7)).rand(-1, 1) + val initBias = Tensor[Float]().resize(Array(64)).rand(-100, 100) + + val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, initWeight = initWeight, + initBias = initBias) + val bn1 = SpatialBatchNormalization(64, eps = 0.0) + bn1.runningMean.copy(runningMean) + bn1.runningVariance.copy(runningVar) + val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder11 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val model1 = Sequential().add(reorder1).add(conv1).add(bn1).add(reorder11) + model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + System.setProperty("bigdl.mkldnn.fusion.convbn", "true") + val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, initWeight = conv1.weight, + initBias = conv1.bias) + val bn2 = SpatialBatchNormalization(64, eps = 0.0) + bn2.runningMean.copy(runningMean) + bn2.runningVariance.copy(runningVar) + val reorder2 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder22 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val model2 = Sequential().add(reorder2).add(conv2).add(bn2).add(reorder22) + model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + System.setProperty("bigdl.mkldnn.fusion.convbn", "false") + + model1.evaluate() + model2.evaluate() + + model1.forward(input) + model2.forward(input) + + Equivalent.nearequals(model1.output.toTensor, model2.output.toTensor, 1e-5) should be (true) + model1.modules.length should be (model2.modules.length + 1) + } + + "Conv sum fusion" should "work correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + + val input = Tensor[Float](2, 1, 6, 6).rand(-1, 1) + val inputShape = Array(2, 1, 6, 6) + val outputShape = Array(2, 3, 4, 4) + + val initWeight = Tensor[Float](3, 1, 2, 2).fill(1) + val initBias = Tensor[Float](3).fill(0) + + val conv1 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv2 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv3 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv4 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + + val reorder1 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val reorder2 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + + val model1 = Sequential() + .add(ConcatTable() + .add(conv1) + .add(conv2)) + .add(CAddTable()) + .add(ReLU()) + .add(reorder1) + model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + System.setProperty("bigdl.mkldnn.fusion.convsum", "true") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + val model2 = Sequential() + .add(ConcatTable() + .add(conv3) + .add(conv4)) + .add(CAddTable()) + .add(ReLU()) + .add(reorder2) + + model1.evaluate() + model2.evaluate() + + model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + System.setProperty("bigdl.mkldnn.fusion.convsum", "false") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "false") + + model1.forward(input) + model2.forward(input) + + model1.output should be (model2.output) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputSpec.scala new file mode 100644 index 00000000000..a7921362884 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputSpec.scala @@ -0,0 +1,33 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.BigDLSpecHelper + +class InputSpec extends BigDLSpecHelper { + "Input" should "be correct" in { + val layer = Input(Array(2, 2), Memory.Format.nc) + layer.setRuntime(new MklDnnRuntime()) + layer.initFwdPrimitives(Array(), Phase.TrainingPhase) + layer.initBwdPrimitives(Array(), Phase.TrainingPhase) + val tensor = Tensor[Float](2, 2).rand() + val grad = Tensor[Float](2, 2).rand() + layer.forward(tensor) should be(tensor) + layer.backward(tensor, grad) should be(grad) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala new file mode 100644 index 00000000000..7d4b7c8162d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala @@ -0,0 +1,59 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} + +class JoinTableSpec extends BigDLSpecHelper { + "Join table" should "work correctly" in { + val layer = JoinTable(1) + val model = Sequential() + val concat = ConcatTable() + concat.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), + NativeData(Array(2, 2), Memory.Format.nc))) + concat.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), + NativeData(Array(2, 2), Memory.Format.nc))) + model.add(concat) + model.add(layer) + model.add(ReorderMemory(NativeData(Array(4, 2), Memory.Format.nc), + HeapData(Array(4, 2), Memory.Format.nc), NativeData(Array(4, 2), Memory.Format.nc), + HeapData(Array(4, 2), Memory.Format.nc))) + model.compile(Phase.TrainingPhase, Array(HeapData(Array(2, 2), Memory.Format.nc))) + model.forward(Tensor[Float](T(T(1, 2), T(3, 4)))) should be(Tensor[Float](T( + T(1, 2), + T(3, 4), + T(1, 2), + T(3, 4) + ))) + val dnnGrad = model.backward(Tensor[Float](T(T(1, 2), T(3, 4))), T( + Tensor[Float](T( + T(4, 5), + T(6, 7), + T(1, 3), + T(4, 2) + )) + )).asInstanceOf[Tensor[Float]] + val heapGrad = Tensor[Float](2, 2) + heapGrad.copy(dnnGrad) + heapGrad should be( + Tensor[Float](T(T(5, 8), T(10, 9))) + ) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala new file mode 100644 index 00000000000..24208d39765 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.SpatialCrossMapLRN +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG + +import scala.util.Random + +class LRNSpec extends BigDLSpecHelper { + "LRNDnn with format=nchw" should "work correctly" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 7, 3, 3).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 7, 3, 3).apply1(e => Random.nextFloat()) + + RNG.setSeed(100) + val lrnDnn = LRN(5, 0.0001, 0.75, 1.0) + RNG.setSeed(100) + val lrnBLAS = SpatialCrossMapLRN[Float](5, 0.0001, 0.75, 1.0) + + val output2 = lrnBLAS.forward(input) + val grad2 = lrnBLAS.updateGradInput(input, gradOutput) + + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(batchSize, 7, 3, 3), Memory.Format.nchw), + HeapData(Array(batchSize, 7, 3, 3), Memory.Format.nchw))) + seq.add(lrnDnn) + seq.add(ReorderMemory(HeapData(Array(batchSize, 7, 3, 3), Memory.Format.nchw), + HeapData(Array(batchSize, 7, 3, 3), Memory.Format.nchw))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 7, 3, 3), Memory.Format.nchw))) + val output = seq.forward(input) + output.asInstanceOf[Tensor[Float]] should be(output2) + val grad1 = seq.backward(input, gradOutput) + grad1.asInstanceOf[Tensor[Float]] should be(grad2) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala new file mode 100644 index 00000000000..39668c2945f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala @@ -0,0 +1,365 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + +class LinearSpec extends FlatSpec with Matchers { + "linear updateOutput" should "work correctly" in { + val inputSize = 2 + val outputSize = 2 + val batchSize = 2 + + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + val input = Tensor[Float](batchSize, inputSize).rand() + + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize).rand() + + val linear = Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + val output = linear.forward(input) + println(output) + + val nnLinear = nn.Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + val nnOutput = nnLinear.forward(input) + println(nnOutput) + + Tools.dense(output) should be (nnOutput) + } + + "linear updateOutput multi times" should "work correctly" in { + val inputSize = 2 + val outputSize = 2 + val batchSize = 2 + + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize).rand() + + val inputs = new Array[Tensor[Float]](100) + for (i <- inputs.indices) { + inputs(i) = Tensor[Float](batchSize, inputSize).rand() + } + + val linear = Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + for (in <- inputs) { + linear.forward(in) + } + println(linear.output) + + val nnLinear = nn.Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + for (in <- inputs) { + nnLinear.forward(in) + } + println(nnLinear.output) + + Tools.dense(linear.output) should be (nnLinear.output) + } + + "linear updateGradInput" should "work correctly" in { + val inputSize = 2 + val outputSize = 2 + val batchSize = 2 + + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + val input = Tensor[Float](batchSize, inputSize).rand() + val gradOutput = Tensor().resize(outputFormat.shape).rand() + + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize).rand() + + val linear = Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + val output = linear.forward(input) + val gradInput = linear.updateGradInput(input, gradOutput) + + val nnLinear = nn.Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + val nnOutput = nnLinear.forward(input) + val nnGradInput = nnLinear.updateGradInput(input, gradOutput) + + println(gradInput) + println("-" * 80) + println(nnGradInput) + + Tools.dense(gradInput) should be (nnGradInput) + } + + "linear updateGradInput multi times" should "work correctly" in { + val inputSize = 2 + val outputSize = 2 + val batchSize = 2 + + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize).rand() + + val inputs = new Array[Tensor[Float]](100) + for (i <- inputs.indices) { + inputs(i) = Tensor[Float](batchSize, inputSize).rand() + } + + val linear = Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + for (i <- inputs.indices) { + inputs(i) = Tensor[Float](batchSize, inputSize).rand() + } + + val gradOutputs = new Array[Tensor[Float]](100) + for (i <- gradOutputs.indices) { + gradOutputs(i) = Tensor[Float](batchSize, outputSize).rand() + } + + linear.forward(inputs.last) + + for (i <- inputs.indices) { + linear.updateGradInput(inputs(i), gradOutputs(i)) + } + + val nnLinear = nn.Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + val nnOutput = nnLinear.forward(inputs.last) + + for (i <- inputs.indices) { + nnLinear.updateGradInput(inputs(i), gradOutputs(i)) + } + + Tools.dense(linear.gradInput) should be (nnLinear.gradInput) + } + + "linear accGradParameters" should "work correctly" in { + val inputSize = 2 + val outputSize = 2 + val batchSize = 2 + + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + val input = Tensor[Float](batchSize, inputSize).rand() + val gradOutput = Tensor[Float]().resize(outputFormat.shape).rand() + + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize).rand() + + val linear = Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + val output = linear.forward(input) + + val gradInput = linear.updateGradInput(input, gradOutput) + + val nnLinear = nn.Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + val nnOutput = nnLinear.forward(input) + val nnGradInput = nnLinear.updateGradInput(input, gradOutput) + + linear.accGradParameters(input, gradOutput) + nnLinear.accGradParameters(input, gradOutput) + + println(linear.gradWeight) + println(linear.gradBias) + println("-" * 80) + println(nnLinear.gradWeight) + println(nnLinear.gradBias) + + Tools.dense(linear.gradWeight) should be (nnLinear.gradWeight) + Tools.dense(linear.gradBias) should be (nnLinear.gradBias) + } + + "linear with maxpooling" should "work correctly" in { + val initWeight = Tensor[Float](4096, 256 * 6 * 6).rand() + val initBias = Tensor[Float](4096).rand() + val input = Tensor[Float](4, 256, 13, 13).rand() + + val dnn = Sequential() + .add(MaxPooling(3, 3, 2, 2)) + .add(Linear(256 * 6 * 6, 4096, initWeight = initWeight, initBias = initBias)) + .add(ReorderMemory(HeapData(Array(4, 4096), Memory.Format.nc))) + dnn.compile(TrainingPhase, Array(HeapData(input.size(), Memory.Format.nchw))) + + val blas = nn.Sequential() + .add(nn.SpatialMaxPooling(3, 3, 2, 2)) + .add(nn.View(256 * 6 * 6)) + .add(nn.Linear(256 * 6 * 6, 4096, initWeight = initWeight, initBias = initBias)) + + blas.forward(input) + dnn.forward(input) + + val gradOutput = Tensor[Float]().resizeAs(blas.output.toTensor).rand() + dnn.backward(input, gradOutput) + blas.backward(input, gradOutput) + + Tools.dense(dnn.output) should be (blas.output) + Tools.dense(dnn.gradInput) should be (blas.gradInput) + } + +// "relu + linear with 1-D" should "work correctly" in { +// val initWeight = Tensor(10, 20).rand(-1, 1) +// val initBias = Tensor(10).rand(-1, 1) +// +// val input = Tensor(20).rand() +// val inputFormat = HeapData(Array(20), Memory.Format.x) +// val outputFormat = HeapData(Array(10), Memory.Format.x) +// +// val dnn = Sequential().add(ReLU()).add(Linear(20, 10, initWeight = initWeight, +// initBias = initBias)) +// dnn.compile(TrainingPhase, Array(inputFormat)) +// +// val blas = nn.Sequential().add(nn.ReLU()).add(nn.Linear(20, 10, initWeight = initWeight, +// initBias = initBias)) +// +// dnn.forward(input) +// println("=" * 80) +// blas.forward(input) +// +// val gradOutput = Tensor().resizeAs(blas.output.toTensor) +// dnn.backward(input, gradOutput) +// blas.backward(input, gradOutput) +// } + +// "1-D input" should "work correctly" in { +// val input = Tensor(20).rand() +// val gradOutput = Tensor(10).rand() +// +// val model = Linear(20, 10) +// model.setRuntime(new MklDnnRuntime) +// model.initFwdPrimitives(Array(HeapData(Array(20), Memory.Format.x)), TrainingPhase) +// model.initBwdPrimitives(Array(HeapData(Array(10), Memory.Format.x)), TrainingPhase) +// model.initGradWPrimitives(Array(HeapData(Array(10), Memory.Format.x)), TrainingPhase) +// +// model.forward(input) +// +// model.updateGradInput(input, gradOutput) +// } + + "linear + linear, the first linear with a 4-D input" should "work correctly" in { + val inputSize = 16 * 16 * 16 + val outputSize = 16 * 16 * 16 + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize) + + val input = Tensor[Float](16, inputSize).rand() + val input2 = Tensor[Float](16, 16, 16, 16).rand() + + val inputShape1 = Array(16, inputSize) + val inputShape2 = Array(16, 16, 16, 16) + + val seq = Sequential() + .add(Linear(outputSize, inputSize, initWeight = initWeight, initBias = initBias)) + .add(Linear(outputSize, inputSize, initWeight = initWeight, initBias = initBias)) + + seq.compile(TrainingPhase, Array(HeapData(inputShape1, Memory.Format.nc))) + + val seq2 = Sequential() + .add(Linear(outputSize, inputSize, initWeight = initWeight, initBias = initBias)) + .add(Linear(outputSize, inputSize, initWeight = initWeight, initBias = initBias)) + + seq.compile(TrainingPhase, Array(HeapData(inputShape2, Memory.Format.nchw))) + + seq.forward(input) + seq.backward(input, input) + + seq.forward(input2) + seq.backward(input2, input) + } + + + "linear " should "work correctly" ignore { + val (batchSize, nInput) = (4, 64) + val inputShape = Array(batchSize, nInput) + val nOutput = 1000 + val outputShape = Array(batchSize, nOutput) + val name = "fc" + + val prototxt = + s""" + |name: "relu-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + | + |layer { + | bottom: "data" + | top: "$name" + | name: "$name" + | type: "InnerProduct" + | inner_product_param { + | num_output: $nOutput + | weight_filler { + | type: "gaussian" + | std: 0.01 + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + """.stripMargin + val linear = new Linear(nInput, nOutput).setName(name) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nc)), TrainingPhase) + linear.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nc)), TrainingPhase) + linear.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nc)), TrainingPhase) + + Tools.compare(prototxt, linear, inputShape, outputShape) + } + + private def shape2Dim(shape: Array[Int]): String = { + shape.map(x => "dim: " + x).mkString(" ") + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala new file mode 100644 index 00000000000..2511044b297 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala @@ -0,0 +1,80 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{AlgKind, Memory} +import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, SpatialMaxPooling} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG + +import scala.util.Random + +class MaxPoolingSpec extends BigDLSpecHelper { + "Max Pooling test1" should "be correct" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) + + RNG.setSeed(100) + val pool = MaxPooling(3, 3, 2, 2) + RNG.setSeed(100) + val layer = SpatialMaxPooling[Float](3, 3, 2, 2).ceil() + + val output2 = layer.forward(input).toTensor[Float] + + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw))) + seq.add(pool) + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 480, 28, 28), + Memory.Format.nchw))) + val output1 = seq.forward(input) + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = seq.backward(input, output2) + grad1 should be(grad2) + } + + "Max Pooling test2" should "be correct" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 64, 112, 112).apply1(e => Random.nextFloat()) + + RNG.setSeed(100) + val pool = MaxPooling(3, 3, 2, 2) + RNG.setSeed(100) + val layer = SpatialMaxPooling[Float](3, 3, 2, 2).ceil() + + val output2 = layer.forward(input).toTensor[Float] + + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(batchSize, 64, 112, 112), Memory.Format.nchw), + HeapData(Array(batchSize, 64, 112, 112), Memory.Format.nchw))) + seq.add(pool) + seq.add(ReorderMemory(HeapData(Array(batchSize, 64, 56, 56), Memory.Format.nchw), + HeapData(Array(batchSize, 64, 56, 56), Memory.Format.nchw))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 64, 112, 112), + Memory.Format.nchw))) + val output1 = seq.forward(input) + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = seq.backward(input, output2) + grad1 should be(grad2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLUSpec.scala new file mode 100644 index 00000000000..349f8c719ce --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLUSpec.scala @@ -0,0 +1,76 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class ReLUSpec extends FlatSpec with Matchers { + "a simple relu" should "be correct" in { + val layer = ReLU(0.0f) + val input = Tensor[Float](T( + T(1.0, 2.0), + T(-1.0, -2.0) + )) + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + HeapData(Array(2, 2), Memory.Format.nc))) + seq.add(layer) + seq.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + HeapData(Array(2, 2), Memory.Format.nc))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(2, 2), Memory.Format.nc))) + seq.forward(input) should be(Tensor[Float](T( + T(1.0, 2.0), + T(0.0, 0.0) + ))) + val grad = Tensor[Float](T( + T(-1.0, -2.0), + T(1.0, 2.0) + )) + seq.backward(input, grad) should be(Tensor[Float](T( + T(-1.0, -2.0), + T(0.0, 0.0) + ))) + } + + "Relu dnn should be same with bigdl relu" should "work correctly" in { + val input = Tensor(4, 96, 55, 55).rand(-1, 1) + val gradOutput = Tensor(4, 96, 55, 55).rand(-1, 1) + + val relu = nn.ReLU(ip = false) + val reludnn = ReLU() + val defaultFormat = HeapData(input.size(), Memory.Format.nchw) + reludnn.setRuntime(new MklDnnRuntime) + reludnn.initFwdPrimitives(Array(defaultFormat), TrainingPhase) + reludnn.initBwdPrimitives(Array(defaultFormat), TrainingPhase) + + val output = relu.forward(input) + val gradInput = relu.backward(input, gradOutput) + + val outputdnn = reludnn.forward(input) + val gradInputdnn = reludnn.backward(input, gradOutput) + + Equivalent.nearequals(output, Tools.dense(outputdnn).toTensor) should be(true) + Equivalent.nearequals(gradInput, Tools.dense(gradInputdnn).toTensor) should be(true) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala new file mode 100644 index 00000000000..d7b6f5ff14a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.BigDLSpecHelper + +class ReorderMemorySpec extends BigDLSpecHelper { + "From heap to native" should "be correct" in { + val layer = ReorderMemory(new NativeData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc)) + layer.setRuntime(new MklDnnRuntime()) + layer.initFwdPrimitives(Array(HeapData(Array(3, 4), Memory.Format.nc)), Phase.TrainingPhase) + layer.initBwdPrimitives(Array(NativeData(Array(3, 4), Memory.Format.nc)), Phase.TrainingPhase) + val input = Tensor[Float](3, 4).rand() + val output = layer.forward(input) + val grad = layer.backward(input, output) + grad should be(input) + } + + "From heap to heap" should "be correct" in { + val layer = ReorderMemory( + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc) + ) + layer.setRuntime(new MklDnnRuntime()) + layer.initFwdPrimitives(Array(HeapData(Array(3, 4), Memory.Format.nc)), Phase.TrainingPhase) + layer.initBwdPrimitives(Array(NativeData(Array(3, 4), Memory.Format.nc)), Phase.TrainingPhase) + val input = Tensor[Float](3, 4).rand() + val output = layer.forward(input) + val grad = layer.backward(input, output) + grad should be(input) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala new file mode 100644 index 00000000000..06fa24108c9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala @@ -0,0 +1,102 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.BigDLSpecHelper + +class SequentialSpec extends BigDLSpecHelper { + "Sequential" should "not be called add after compilation" in { + val layer = ReorderMemory(NativeData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc)) + val layer2 = ReorderMemory(NativeData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.nc)) + val seq = new Sequential() + seq.add(layer) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(3, 4), Memory.Format.nc))) + intercept[IllegalArgumentException] { + seq.add(layer2) + } + } + + "Sequential" should "be correct when no memory reorder happened" in { + val layer1 = ReorderMemory(NativeData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc)) + val layer2 = ReorderMemory(NativeData(Array(3, 4), Memory.Format.io), + NativeData(Array(3, 4), Memory.Format.nc)) + val layer3 = ReorderMemory(HeapData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.io)) + val seq = new Sequential() + seq.add(layer1) + seq.add(layer2) + seq.add(layer3) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(3, 4), Memory.Format.nc))) + val input1 = Tensor[Float](3, 4).rand() + val input2 = Tensor[Float](3, 4).rand() + val output1 = seq.forward(input1) + output1 should be(input1) + val output2 = seq.forward(input2) + output2 should be(input2) + + val gradOutput1 = Tensor[Float](3, 4).rand() + val gradInput1 = seq.backward(input1, gradOutput1) + gradInput1 should be(gradOutput1) + + val gradOutput2 = Tensor[Float](3, 4).rand() + val gradInput2 = seq.backward(input2, gradOutput2) + gradInput2 should be(gradOutput2) + } + + "Sequential" should "be correct when auto add memory reorder" in { + val layer1 = ReorderMemory( + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc)) + val layer2 = ReorderMemory( + NativeData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.io), + NativeData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.io)) + val layer3 = ReorderMemory( + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc)) + val seq = Sequential() + seq.add(layer1) + seq.add(layer2) + seq.add(layer3) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(3, 4), Memory.Format.nc))) + val input1 = Tensor[Float](3, 4).rand() + val input2 = Tensor[Float](3, 4).rand() + println(s"Input1 is $input1") + println(s"Input2 is $input2") + val output1 = seq.forward(input1) + output1 should be(input1) + val output2 = seq.forward(input2) + output2 should be(input2) + + val gradOutput1 = Tensor[Float](3, 4).rand() + val gradInput1 = seq.backward(input1, gradOutput1) + gradInput1 should be(gradOutput1) + + val gradOutput2 = Tensor[Float](3, 4).rand() + val gradInput2 = seq.backward(input2, gradOutput2) + gradInput2 should be(gradOutput2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala new file mode 100644 index 00000000000..8aa6bcc9e25 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala @@ -0,0 +1,329 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.tensor.{MklDnnType, Tensor} +import org.scalatest.{BeforeAndAfter, FlatSpec, Ignore, Matchers} + +@Ignore +class SingleLayerSpec extends FlatSpec with Matchers with BeforeAndAfter { + "convolution" should "work correctly" in { + val inputShape = Array(4, 3, 5, 5) + val outputShape = Array(4, 2, 3, 3) + val name = "conv" + val nOutput = 2 + val kernel = 3 + val pad = 1 + val stride = 2 + + val prototxt = + s""" + |name: "conv-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + | + |layer { + | bottom: "data" + | top: "conv" + | name: "$name" + | type: "Convolution" + | convolution_param { + | num_output: $nOutput + | kernel_size: $kernel + | pad: $pad + | stride: $stride + | weight_filler { + | type: "msra" + | variance_norm: FAN_OUT + | } + | bias_filler { + | type: "gaussian" + | } + | } + |} + """.stripMargin + + val conv = SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) + .setName(name) + conv.setRuntime(new MklDnnRuntime) + conv.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + conv.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + conv.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + Tools.compare(prototxt, conv, inputShape, outputShape) + } + + "convolution2" should "work correctly" in { + val inputShape = Array(4, 3, 224, 224) + val outputShape = Array(4, 64, 112, 112) + val name = "conv" + val nOutput = 64 + val kernel = 7 + val pad = 3 + val stride = 2 + + val prototxt = + s""" + |name: "conv-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + | + |layer { + | bottom: "data" + | top: "conv" + | name: "$name" + | type: "Convolution" + | convolution_param { + | num_output: $nOutput + | kernel_size: $kernel + | pad: $pad + | stride: $stride + | weight_filler { + | type: "msra" + | variance_norm: FAN_OUT + | } + | bias_filler { + | type: "gaussian" + | } + | } + |} + """.stripMargin + + val conv = SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) + .setName(name) + val seq = Sequential() + .add(conv) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + seq.compile(TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + Tools.compare(prototxt, seq, inputShape, outputShape) + } + + "max pooling" should "work correctly" in { + val inputShape = Array(4, 64, 112, 112) + val outputShape = Array(4, 64, 56, 56) + val name = "pool" + val prototxt = + s""" + |name: "maxpool-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + | + |layer { + | bottom: "data" + | top: "pool" + | name: "$name" + | type: "Pooling" + | pooling_param { + | kernel_size: 3 + | stride: 2 + | pool: MAX + | } + |} + """.stripMargin + + val maxPooling = MaxPooling(3, 3, 2, 2).setName(name) + maxPooling.setRuntime(new MklDnnRuntime) + maxPooling.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + maxPooling.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + Tools.compare(prototxt, maxPooling, inputShape, outputShape) + } + + "avg pooling" should "work correctly" in { + val inputShape = Array(4, 3, 7, 7) + val outputShape = Array(4, 3, 3, 3) + val name = "pool" + val prototxt = + s""" + |name: "maxpool-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + | + |layer { + | bottom: "data" + | top: "pool" + | name: "$name" + | type: "Pooling" + | pooling_param { + | kernel_size: 3 + | stride: 2 + | pool: AVE + | } + |} + """.stripMargin + + val avgPooling = AvgPooling(3, 3, 2, 2).setName(name) + avgPooling.setRuntime(new MklDnnRuntime) + avgPooling.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + avgPooling.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + Tools.compare(prototxt, avgPooling, inputShape, outputShape) + } + + "linear " should "work correctly" in { + val (batchSize, nInput) = (4, 64) + val inputShape = Array(batchSize, nInput) + val nOutput = 1000 + val outputShape = Array(batchSize, nOutput) + val name = "fc" + + val prototxt = + s""" + |name: "relu-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + | + |layer { + | bottom: "data" + | top: "$name" + | name: "$name" + | type: "InnerProduct" + | inner_product_param { + | num_output: $nOutput + | weight_filler { + | type: "gaussian" + | std: 0.01 + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + """.stripMargin + val linear = Linear(nInput, nOutput).setName(name) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nc)), TrainingPhase) + linear.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nc)), TrainingPhase) + linear.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nc)), TrainingPhase) + + Tools.compare(prototxt, linear, inputShape, outputShape) + } + + "relu" should "work correctly" in { + val (batchSize, channel, height, width) = (4, 64, 112, 112) + val inputShape = Array(batchSize, channel, height, width) + val outputShape = inputShape + val name = "relu" + val prototxt = + s""" + |name: "relu-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { dim: $batchSize dim: $channel dim: $height dim: $width } + | } + |} + | + |layer { + | bottom: "data" + | top: "relu" + | name: "$name" + | type: "ReLU" + | relu_param { + | } + |} + """.stripMargin + + val relu = ReLU().setName(name) + relu.setRuntime(new MklDnnRuntime) + relu.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + relu.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + Tools.compare(prototxt, relu, inputShape, outputShape) + } + + private def shape2Dim(shape: Array[Int]): String = { + shape.map(x => "dim: " + x).mkString(" ") + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala new file mode 100644 index 00000000000..e6a2790fd82 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala @@ -0,0 +1,142 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + +class SoftMaxSpec extends FlatSpec with Matchers { + "SoftMax forward 1-D" should "work correctly" in { + // we should test the cases which contain 1 + val tests = List(2, 1) + + for (x <- tests) { + val sm = SoftMax() + sm.evaluate() + sm.setRuntime(new MklDnnRuntime) + sm.initFwdPrimitives(Array(HeapData(Array(x), Memory.Format.x)), InferencePhase) + + val input = Tensor(x).rand() + + val output = sm.forward(input) + + val nnSm = nn.SoftMax() + val nnOutput = nnSm.forward(input) + + Tools.dense(output) should be (nnOutput) + } + } + + "SoftMax forward 2-D" should "work correctly" in { + val tests = List( + (2, 3), + (1, 3), + (1, 1), + (2, 1)) + + for ((batchSize, channel) <- tests) { + val sm = SoftMax() + sm.setRuntime(new MklDnnRuntime) + sm.initFwdPrimitives(Array(HeapData(Array(batchSize, channel), Memory.Format.nc)), + InferencePhase) + sm.evaluate() + + val input = Tensor(batchSize, channel).rand() + + val output = sm.forward(input) + + val nnSm = nn.SoftMax() + val nnOutput = nnSm.forward(input) + + Tools.dense(output) shouldEqual nnOutput + } + } + + "SoftMax forward 4-D" should "work correctly" in { + // we should test the cases which contain 1 + val tests = List( + (2, 3, 4, 4), + (1, 3, 4, 4), + (1, 3, 1, 1), + (1, 1, 1, 1), + (1, 1, 3, 3), + (2, 1, 3, 3), + (2, 2, 1, 1)) + + for ((batchSize, channel, height, width) <- tests) { + val sm = SoftMax() + sm.setRuntime(new MklDnnRuntime) + sm.initFwdPrimitives(Array(HeapData(Array(batchSize, channel, height, width), + Memory.Format.nchw)), InferencePhase) + sm.evaluate() + + val input = Tensor(batchSize, channel, height, width).rand() + + val output = sm.forward(input) + + val nnSm = nn.SoftMax() + val nnOutput = nnSm.forward(input) + + Tools.dense(output) should be (nnOutput) + } + } + + "SoftMax backward" should "work correctly" in { + val (batchSize, channel, height, width) = (2, 3, 4, 4) + val sm = SoftMax() + sm.setRuntime(new MklDnnRuntime) + sm.initFwdPrimitives(Array(HeapData(Array(batchSize, channel, height, width), + Memory.Format.nchw)), InferencePhase) + + val nnSm = nn.SoftMax() + + val input = Tensor(batchSize, channel, height, width).rand() + val gradOutput = Tensor().resizeAs(input).rand(-10, 10) + + sm.forward(input) + nnSm.forward(input) + + sm.backward(input, gradOutput) + nnSm.backward(input, gradOutput) + + sm.output should be (nnSm.output) + sm.gradInput should be (nnSm.gradInput) + } + + "SoftMax multi times forward" should "work correctly" in { + val (batchSize, channel, height, width) = (2, 3, 4, 4) + val sm = SoftMax() + sm.setRuntime(new MklDnnRuntime) + sm.initFwdPrimitives(Array(HeapData(Array(batchSize, channel, height, width), + Memory.Format.nchw)), InferencePhase) + sm.evaluate() + + val nnSm = nn.SoftMax() + + (0 until 5).foreach { _ => + val input = Tensor(batchSize, channel, height, width).rand(-1, 1) + sm.forward(input) + nnSm.forward(input) + + Tools.dense(sm.output) should be (nnSm.output) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala new file mode 100644 index 00000000000..d49a27aa5d9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -0,0 +1,535 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.scalatest.{FlatSpec, Ignore, Matchers} + +class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { + "a simple bn with random input" should "work correctly" in { + val batchSize = 2 + RNG.setSeed(100) + val input = Tensor(100, 1, 10, 10).rand(-1, 1) + val (channel, height, width) = (1, 10, 10) + + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).fill(0) + + val bn = SpatialBatchNormalization(1, 0.0, initWeight = initWeight, initBias = initBias) + val nnBn = nn.SpatialBatchNormalization(1, 0.0, initWeight = initWeight, initBias = initBias) + + val inputShape = Array(100, 1, 10, 10) + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + val out1 = bn.forward(input) + val out2 = nnBn.forward(input) + + Equivalent.nearequals(Tools.dense(out1).toTensor, out2, 1e-4) should be(true) + + val gradOutput = Tensor[Float]().resizeAs(input).rand() + + bn.backward(input, gradOutput) + nnBn.backward(input, gradOutput) + + val gradWeight1 = Tools.dense(bn.gradWeightAndBias).toTensor + val gradWeight2 = nnBn.getParameters()._2 + + val weight1 = Tools.dense(bn.weightAndBias).toTensor + val weight2 = nnBn.getParameters()._1 + + Equivalent.nearequals(weight1, weight2) should be (true) + Equivalent.nearequals(gradWeight1, gradWeight2) should be (true) + + Equivalent.nearequals(Tools.dense(bn.gradInput).toTensor, nnBn.gradInput) should be (true) + } + + "bn updateOutput" should "work correctly" in { + val (batchSize, channel, height, width) = (4, 64, 112, 112) + val inputShape = Array(batchSize, channel, height, width) + val defaultFormat = HeapData(inputShape, Memory.Format.nchw) + val epsilon = 1e-5 + + val input = Tensor(batchSize, channel, height, width).rand(-1, 1) + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).fill(0) + + val bn = SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, + initBias = initBias) + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(defaultFormat), TrainingPhase) + bn.initBwdPrimitives(Array(defaultFormat), TrainingPhase) + bn.initGradWPrimitives(Array(defaultFormat), TrainingPhase) + + val output = Tools.toNCHW(bn.forward(input).toTensor, bn.outputFormats()(0)) + + val nnBn = nn.SpatialBatchNormalization(channel, epsilon, + initWeight = initWeight, initBias = initBias) + val nnOutput = nnBn.forward(input) + + Equivalent.nearequals(output, nnOutput) should be (true) + } + + "bn updateOutput multi times" should "work correctly" in { + val (batchSize, channel, height, width) = (2, 3, 4, 4) + val inputShape = Array(batchSize, channel, height, width) + val defaultFormat = HeapData(inputShape, Memory.Format.nchw) + val epsilon = 1e-5 + + val input = Tensor(batchSize, channel, height, width).rand(-1, 1) + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).rand(-1, 1) + + val bn = SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, + initBias = initBias) + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(defaultFormat), TrainingPhase) + bn.initBwdPrimitives(Array(defaultFormat), TrainingPhase) + bn.initGradWPrimitives(Array(defaultFormat), TrainingPhase) + + Utils.manyTimes(bn.forward(input))(10) + + val nnBn = nn.SpatialBatchNormalization(channel, epsilon, + initWeight = initWeight, initBias = initBias) + + Utils.manyTimes(nnBn.forward(input))(10) + + val output = Tools.toNCHW(bn.output.toTensor, bn.outputFormats()(0)) + + Equivalent.nearequals(output, nnBn.output.toTensor) should be (true) + } + + "bn backward" should "work correctly" in { + val (batchSize, channel, height, width) = (2, 3, 4, 4) + val inputShape = Array(batchSize, channel, height, width) + val defaultFormat = HeapData(inputShape, Memory.Format.nchw) + val epsilon = 0.0f + + val input = Tensor(batchSize, channel, height, width).rand(-1, 1) + val gradOutput = Tensor().resize(inputShape).rand(-1, 1) + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).rand(-1, 1) + + val bn = SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, + initBias = initBias) + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(defaultFormat), TrainingPhase) + bn.initBwdPrimitives(Array(defaultFormat), TrainingPhase) + bn.initGradWPrimitives(Array(defaultFormat), TrainingPhase) + + val nnBn = nn.SpatialBatchNormalization(channel, epsilon, + initWeight = initWeight, initBias = initBias) + + bn.forward(input) + nnBn.forward(input) + + val output = Tools.toNCHW(bn.output.toTensor, bn.outputFormats()(0)) + + Equivalent.nearequals(output, nnBn.output) should be (true) + + bn.backward(input, gradOutput) + val nnGradInput = nnBn.backward(input, gradOutput) + + val gradInput = Tools.toNCHW(bn.gradInput.toTensor, bn.gradInputFormats()(0)) + val weightAndBias = Tools.dense(bn.parameters()._2(0)).toTensor + + Equivalent.nearequals(gradInput, nnGradInput.toTensor) should be (true) + Equivalent.nearequals(weightAndBias, nnBn.getParameters()._2) should be (true) + } + +// "bn perf" should "work correctly" in { +// // For PERF test. It seems sometimes batch norm maybe slower than java version. +// val (batchSize, channel, height, width) = (4, 64, 112, 112) +// val inputShape = Array(batchSize, channel, height, width) +// val defaultFormat = HeapData(inputShape, Memory.Format.nChw8c) +// val epsilon = 0.0f +// +// val input = Tensor(batchSize, channel, height, width).rand(-1, 1) +// val gradOutput = Tensor().resizeAs(input).rand(-1, 1) +// +// val initWeight = Tensor(channel).rand(-1, 1) +// val initBias = Tensor(channel).rand(-1, 1) +// +// val bn = SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, +// initBias = initBias) +// bn.setRuntime(new MklDnnRuntime) +// bn.initFwdPrimitives(Array(defaultFormat), TrainingPhase) +// bn.initBwdPrimitives(Array(defaultFormat), TrainingPhase) +// bn.initGradWPrimitives(Array(defaultFormat), TrainingPhase) +// +// val nnBn = nn.SpatialBatchNormalization(channel, epsilon, +// initWeight = initWeight, initBias = initBias) +// +// val times = Utils.manyTimes { +// bn.forward(input) +// bn.backward(input, gradOutput) +// } _ +// +// val nnTimes = Utils.manyTimes { +// nnBn.forward(input) +// nnBn.backward(input, gradOutput) +// } _ +// +// times(10) +// nnTimes(10) +// +// val costs = times(50)._1 +// val nnCosts = nnTimes(50)._1 +// +// costs should be < (nnCosts) +// } + + "a complicated batch norm" should "work correctly" in { + val (channel, height, width) = (64, 112, 112) + val epsilon = 1e-3 + val batchSize = 2 + + RNG.setSeed(100) + val input = Tensor[Float](Array(batchSize, 64, 112, 112)).rand(-1, 1) + val gradOutput = Tensor().resizeAs(input).copy(input) + + RNG.setSeed(100) + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).fill(0f) + + val inputShape = input.size() + val outputShape = input.size() + val defaultFormat = HeapData(inputShape, Memory.Format.nchw) + + val bn = SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, + initBias = initBias) + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(defaultFormat), TrainingPhase) + bn.initBwdPrimitives(Array(defaultFormat), TrainingPhase) + bn.initGradWPrimitives(Array(defaultFormat), TrainingPhase) + + val nnBn = nn.SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, + initBias = initBias) + + bn.zeroGradParameters() + nnBn.zeroGradParameters() + + val (weight, gradWeight) = bn.parameters() + val (nnWeight, nnGradWeight) = nnBn.getParameters() + Equivalent.nearequals(Tools.dense(weight(0)).toTensor, nnWeight) should be(true) + Equivalent.nearequals(Tools.dense(gradWeight(0)).toTensor, nnGradWeight) should be(true) + + val out1 = bn.forward(input) + val out2 = nnBn.forward(input) + + Equivalent.nearequals(Tools.dense(bn.output).toTensor, nnBn.output) should be (true) + + val gradInput = bn.backward(input, gradOutput) + val nnGradInput = nnBn.backward(input, gradOutput) + + Equivalent.nearequals(Tools.dense(gradInput).toTensor, nnGradInput.toTensor) should be (true) + Equivalent.nearequals(Tools.dense(gradWeight(0)).toTensor, nnGradWeight, 1e-3) should be (true) + } + + "A nChw8c input" should "work correctly" in { + val (batchSize, channel, height, width) = (2, 256, 56, 56) + val input = Tensor(batchSize, channel, height, width).rand(-1, 1) + val gradOutput = Tensor(batchSize, channel, height, width).rand(-1, 1) + + val inputShape = input.size() + val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nChw8c)) + val reorder2 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).rand(-1, 1) + + val dnn = Sequential() + .add(reorder1) + .add(SpatialBatchNormalization(channel, 1e-3, initWeight = initWeight, initBias = initBias)) + .add(reorder2) + + dnn.compile(TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + val blas = nn.Sequential().add( + nn.SpatialBatchNormalization(channel, 1e-3, initWeight = initWeight, initBias = initBias)) + + dnn.forward(input) + blas.forward(input) + + dnn.backward(input, gradOutput) + blas.backward(input, gradOutput) + + val gradWeight = Tools.dense(dnn.parameters()._2(0)).toTensor + + Equivalent.nearequals(dnn.output.toTensor, blas.output.toTensor, 1e-4) should be (true) + Equivalent.nearequals(dnn.gradInput.toTensor, blas.gradInput.toTensor, 1e-4) should be (true) + Equivalent.nearequals(gradWeight, blas.getParameters()._2, 1e-3) should be (true) + } + +// "A nChw16c input" should "work correctly" in { +// // only works on avx512 (SKX->) +// val (batchSize, channel, height, width) = (2, 256, 56, 56) +// val input = Tensor(batchSize, channel, height, width).rand(-1, 1) +// val gradOutput = Tensor(batchSize, channel, height, width).rand(-1, 1) +// +// val inputShape = input.size() +// val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nChw16c)) +// val reorder2 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) +// +// val initWeight = Tensor(channel).rand(-1, 1) +// val initBias = Tensor(channel).rand(-1, 1) +// +// val dnn = Sequential() +// .add(reorder1) +// .add(SpatialBatchNormalization(channel, 1e-3, initWeight = initWeight, initBias = initBias)) +// .add(reorder2) +// +// dnn.compile(TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) +// +// val blas = nn.Sequential().add( +// nn.SpatialBatchNormalization(channel, 1e-3, initWeight = initWeight, initBias = initBias)) +// +// dnn.forward(input) +// blas.forward(input) +// +// dnn.backward(input, gradOutput) +// blas.backward(input, gradOutput) +// +// val gradWeight = Tools.dense(dnn.parameters()._2(0)).toTensor +// +// DnnUtils.nearequals(dnn.output.toTensor, blas.output.toTensor, 1e-4) should be (true) +// DnnUtils.nearequals(dnn.gradInput.toTensor, blas.gradInput.toTensor, 1e-4) should be (true) +// DnnUtils.nearequals(gradWeight, blas.getParameters()._2, 1e-3) should be (true) +// } + + "Sbn with relu fusion" should "work correctly" in { + val (batchSize, channel, height, width) = (4, 64, 112, 112) + val shape = Array(batchSize, channel, height, width) + val epsilon = 1e-5 + + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).fill(0) + + val bn1 = SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, + initBias = initBias) + val reorder1 = ReorderMemory(HeapData(shape, Memory.Format.nchw)) + val bn2 = SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, + initBias = initBias) + val reorder2 = ReorderMemory(HeapData(shape, Memory.Format.nchw)) + + val model1 = Sequential().add(bn1).add(ReLU()).add(ReLU()).add(reorder1) + model1.compile(TrainingPhase, Array(HeapData(shape, Memory.Format.nchw))) + + System.setProperty("bigdl.mkldnn.fusion.bnrelu", "true") + val model2 = Sequential().add(bn2).add(ReLU()).add(ReLU()).add(reorder2) + model2.compile(TrainingPhase, Array(HeapData(shape, Memory.Format.nchw))) + System.setProperty("bigdl.mkldnn.fusion.bnrelu", "false") + + val input = Tensor(batchSize, channel, height, width).rand(-1, 1) + + model1.forward(input) + model2.forward(input) + + model1.output should be (model2.output) + } + + "a simple bach norm" should "work correctly" ignore { + val (batchSize, channel, height, width) = (4, 64, 2, 2) + val shape = Array(batchSize, channel, height, width) + val prototxt = s""" + |name: "relu-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { dim: $batchSize dim: $channel dim: $height dim: $width } + | } + |} + | + |layer { + | bottom: "data" + | top: "bn" + | name: "bn" + | type: "BatchNorm" + | + | batch_norm_param { + | moving_average_fraction: 1.0 + | filler { value: 1 } + | bias_filler { value: 1 } + | relu: false + | eps: 0.0 + | } + |} + """.stripMargin + + val identity = Collect.run(prototxt) + + val input = Tools.getTensor("Fwrd_data", shape, identity) + val output = Tools.getTensor("Fwrd_bn", shape, identity) + val weight = Tools.getTensor("Fwrd_bn.Wght.3", Array(channel), identity) + val bias = Tools.getTensor("Fwrd_bn.Wght.4", Array(channel), identity) + val scale = Tools.getTensor("Fwrd_bn.Wght.2", Array(1), identity) + val runningMean = Tools.getTensor("Fwrd_bn.Wght.0", Array(channel), identity) + val runningVariance = Tools.getTensor("Fwrd_bn.Wght.1", Array(channel), identity) + val gradOutput = Tools.getTensor("Bwrd_bn.loss", shape, identity) + val gradInput = Tools.getTensor("Bwrd_bn", shape, identity) + val gradWeight = Tools.getTensor("Bwrd_bn.Grad.3", Array(channel), identity) + val gradBias = Tools.getTensor("Bwrd_bn.Grad.4", Array(channel), identity) + + val bn = new SpatialBatchNormalization(channel, eps = 0.0, momentum = 1.0, + affine = true, initWeight = weight, initBias = bias) + + val reorder1 = ReorderMemory(HeapData(shape, Memory.Format.nchw)).setName("reorder1") + val reorder2 = ReorderMemory(HeapData(shape, Memory.Format.nchw)).setName("reorder2") + val reorder3 = ReorderMemory(HeapData(shape, Memory.Format.nChw8c)).setName("reorder3") + val reorder4 = ReorderMemory(HeapData(shape, Memory.Format.nchw)).setName("reorder4") + + val seq = Sequential() + seq.add(reorder1) + seq.add(reorder3) + seq.add(bn) + seq.add(reorder2) + seq.compile(Phase.TrainingPhase, Array(HeapData(shape, Memory.Format.nchw))) + seq.reset() + + bn.zeroGradParameters() + + seq.forward(input) + seq.backward(input, gradOutput) + + val weightAndBias = Tensor[Float](Array(2, channel)) + weightAndBias.select(1, 1).copy(weight) + weightAndBias.select(1, 2).copy(bias) + + val gradWeightAndBias = Tensor[Float](Array(2, channel)) + gradWeightAndBias.select(1, 1).copy(gradWeight) + gradWeightAndBias.select(1, 2).copy(gradBias) + + compare(weightAndBias.view(Array(2 * channel)), bn.weightAndBias) + compare(output, seq.output) + compare(runningMean, bn.runningMean) + compare(runningVariance, bn.runningVariance) + compare(gradWeightAndBias.view(Array(2 * channel)), bn.gradWeightAndBias) + compare(gradInput, seq.gradInput) + } + + "a simple bach norm inference" should "work correctly" ignore { + val (batchSize, channel, height, width) = (4, 64, 112, 112) + val shape = Array(batchSize, channel, height, width) + val prototxt = s""" + |name: "relu-simple" + |force_backward: true + |state { + | phase: TEST + |} + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { dim: $batchSize dim: $channel dim: $height dim: $width } + | } + |} + | + |layer { + | bottom: "data" + | top: "bn" + | name: "bn" + | type: "BatchNorm" + | + | batch_norm_param { + | moving_average_fraction: 1.0 + | filler { value: 1 } + | bias_filler { value: 0 } + | relu: false + | eps: 0.0 + | } + | + | phase: TEST + |} + """.stripMargin + + val identity = Collect.run(prototxt) + + val input = Tools.getTensor("Fwrd_data", shape, identity) + val output = Tools.getTensor("Fwrd_bn", shape, identity) + val weight = Tools.getTensor("Fwrd_bn.Wght.3", Array(channel), identity) + val bias = Tools.getTensor("Fwrd_bn.Wght.4", Array(channel), identity) + val scale = Tools.getTensor("Fwrd_bn.Wght.2", Array(1), identity) + val runningMean = Tools.getTensor("Fwrd_bn.Wght.0", Array(channel), identity) + val runningVariance = Tools.getTensor("Fwrd_bn.Wght.1", Array(channel), identity) + + val bn = new SpatialBatchNormalization(channel, eps = 0.0, momentum = 1.0, + affine = true, initWeight = weight, initBias = bias) + bn.runningMean.copy(runningMean) + bn.runningVariance.copy(runningVariance) + + val reorder1 = ReorderMemory(HeapData(shape, Memory.Format.nchw)).setName("reorder1") + val reorder2 = ReorderMemory(HeapData(shape, Memory.Format.nchw)).setName("reorder2") + + val seq = Sequential() + seq.add(reorder1) + seq.add(bn) + seq.add(reorder2) + seq.compile(Phase.InferencePhase, Array(HeapData(shape, Memory.Format.nchw))) + seq.reset() + seq.evaluate() + + seq.forward(input) + + val weightAndBias = Tensor[Float](Array(2, channel)) + weightAndBias.select(1, 1).copy(weight) + weightAndBias.select(1, 2).copy(bias) + + compare(weightAndBias.view(Array(2 * channel)), bn.weightAndBias) + compare(runningMean, bn.runningMean) + compare(runningVariance, bn.runningVariance) + + val denseOutput = Tools.dense(bn.output).toTensor + + denseOutput.storage().array().zip(output.storage().array()).foreach { x => + if (x._2.isInfinity) x._1.isNaN should be (true) + } + } + + private def compare(src: Activity, dst: Activity): Unit = { + if (src.isTensor) { + Equivalent.nearequals(Tools.dense(src).toTensor, Tools.dense(dst).toTensor) should be (true) + } + } + + private def shape2Dim(shape: Array[Int]): String = { + shape.map(x => "dim: " + x).mkString(" ") + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala new file mode 100644 index 00000000000..b27c07d045f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -0,0 +1,514 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.mkl._ +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.{Xavier, Zeros} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class SpatialConvolutionSpec extends FlatSpec with Matchers { + "ConvolutionDnn with format=nchw and ngroup=1" should "work correctly" in { + val nInputPlane = 2 + val nOutputPlane = 4 + val kW = 3 + val kH = 3 + val dW = 4 + val dH = 4 + val padW = 0 + val padH = 0 + + val input = Tensor[Float](2, 2, 23, 23).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](2, 4, 6, 6).apply1(e => Random.nextFloat()) + RNG.setSeed(100) + val conv = SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH) + RNG.setSeed(100) + val layer = nn.SpatialConvolution[Float](nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH) + + conv.setRuntime(new MklDnnRuntime) + conv.initFwdPrimitives(Array(HeapData(Array(2, 2, 23, 23), Memory.Format.nchw)), TrainingPhase) + conv.initBwdPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) + conv.initGradWPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) + + val output = Tools.toNCHW(conv.forward(input).toTensor, conv.outputFormats()(0)) + val grad1 = Tools.toNCHW(conv.updateGradInput(input, gradOutput).toTensor, + conv.gradInputFormats()(0)) + conv.accGradParameters(input, gradOutput) + + val weight1 = Tools.toOIHW(conv.weight, conv.ParamsShape.weight) + val gradweight1 = Tools.toOIHW(conv.gradWeight, conv.ParamsShape.gradWeight) + val bias1 = Tools.dense(conv.bias).toTensor[Float] + val gradbias1 = Tools.dense(conv.gradBias).toTensor + + val output2 = layer.forward(input) + val grad2 = layer.updateGradInput(input, gradOutput) + layer.accGradParameters(input, gradOutput) + + val weight2 = layer.weight + val gradweight2 = layer.gradWeight + val bias2 = layer.bias + val gradbias2 = layer.gradBias + + Equivalent.nearequals(weight1, weight2.resizeAs(weight1)) should be(true) + Equivalent.nearequals(gradweight1, gradweight2.resizeAs(gradweight1)) should be(true) + Equivalent.nearequals(bias1, bias2) should be(true) + Equivalent.nearequals(gradbias1, gradbias2) should be(true) + Equivalent.nearequals(output.toTensor, output2) should be(true) + Equivalent.nearequals(grad1.toTensor, grad2) should be(true) + } + + "ConvolutionDnn with format=nchw and ngroup=2" should "work correctly" in { + val nInputPlane = 2 + val nOutputPlane = 4 + val kW = 3 + val kH = 3 + val dW = 4 + val dH = 4 + val padW = 0 + val padH = 0 + val ngroup = 2 + + val input = Tensor[Float](2, 2, 23, 23).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](2, 4, 6, 6).apply1(e => Random.nextFloat()) + RNG.setSeed(100) + val conv = SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, + padW, padH, ngroup) + RNG.setSeed(100) + val layer = nn.SpatialConvolution[Float](nInputPlane, nOutputPlane, kW, kH, + dW, dH, padW, padH, ngroup) + + conv.setRuntime(new MklDnnRuntime) + conv.initFwdPrimitives(Array(HeapData(Array(2, 2, 23, 23), Memory.Format.nchw)), TrainingPhase) + conv.initBwdPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) + conv.initGradWPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) + + val output2 = layer.forward(input) + val grad2 = layer.updateGradInput(input, gradOutput) + layer.accGradParameters(input, gradOutput) + val weight2 = layer.weight + val gradweight2 = layer.gradWeight + val bias2 = layer.bias + val gradbias2 = layer.gradBias + + val output = Tools.toNCHW(conv.forward(input).toTensor, conv.outputFormats()(0)) + val grad1 = Tools.toNCHW(conv.updateGradInput(input, gradOutput).toTensor, + conv.gradInputFormats()(0)) + conv.accGradParameters(input, gradOutput) + val weight1 = Tools.toOIHW(conv.weight, conv.ParamsShape.weight) + val gradweight1 = Tools.toOIHW(conv.gradWeight, conv.ParamsShape.gradWeight) + val bias1 = Tools.dense(conv.bias).toTensor[Float] + val gradbias1 = Tools.dense(conv.gradBias).toTensor[Float] + + Equivalent.nearequals(weight1, weight2) should be(true) + Equivalent.nearequals(gradweight1, gradweight2) should be(true) + Equivalent.nearequals(bias1, bias2) should be(true) + Equivalent.nearequals(gradbias1, gradbias2) should be(true) + Equivalent.nearequals(output, output2) should be(true) + Equivalent.nearequals(grad1, grad2) should be(true) + } + + "ConvolutionDnn with relu " should "work correctly" in { + val nInputPlane = 2 + val nOutputPlane = 4 + val kW = 3 + val kH = 3 + val dW = 4 + val dH = 4 + val padW = 0 + val padH = 0 + val ngroup = 2 + + val input = Tensor[Float](2, 2, 23, 23).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](2, 4, 6, 6).apply1(e => Random.nextFloat()) + RNG.setSeed(100) + val conv = SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, ngroup) + RNG.setSeed(100) + val conv1 = nn.SpatialConvolution[Float](nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, + ngroup) + + val relu = ReLU() + val relu1 = nn.ReLU[Float](ip = false) + + val model = Sequential().add(conv).add(relu) + .add(ReorderMemory(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw))) + model.compile(TrainingPhase, Array(HeapData(Array(2, 2, 23, 23), Memory.Format.nchw))) + + val model1 = nn.Sequential().add(conv1).add(relu1) + + model.forward(input) + model.backward(input, gradOutput) + + model1.forward(input) + model1.backward(input, gradOutput) + + val output = Tools.toNCHW(conv.output.toTensor, conv.outputFormats()(0)) + val gradInput = Tools.toNCHW(conv.gradInput.toTensor, conv.gradInputFormats()(0)) + + val weight = Tools.toOIHW(conv.weight, conv.ParamsShape.weight) + val gradweight = Tools.toOIHW(conv.gradWeight, conv.ParamsShape.gradWeight) + val bias = Tools.dense(conv.bias).toTensor + val gradbias = Tools.dense(conv.gradBias).toTensor + + val output1 = conv1.output.toTensor + val gradInput1 = conv1.gradInput + + val weight1 = conv1.weight + val gradweight1 = conv1.gradWeight + val bias1 = conv1.bias + val gradbias1 = conv1.gradBias + + Equivalent.nearequals(weight, weight1) should be(true) + Equivalent.nearequals(gradweight, gradweight1) should be(true) + Equivalent.nearequals(bias, bias1) should be(true) + Equivalent.nearequals(gradbias, gradbias1) should be(true) + Equivalent.nearequals(output, output1) should be(true) + Equivalent.nearequals(gradInput, gradInput1) should be(true) + } + + "ConvolutionDnn with same params with vgg16" should "work correctly" in { + val batchSize = 2 + val needPropagateBack: Boolean = true + val inputShape = Array(batchSize, 3, 224, 224) + val outputShape = Array(batchSize, 64, 112, 112) + + RNG.setSeed(100) + val model1 = nn.SpatialConvolution[Float](3, 64, 7, 7, 2, 2, 3, 3, 1) + .setInitMethod(weightInitMethod = Xavier, Zeros) + model1.zeroGradParameters() + val (weightAll1, gradWeightAll1) = model1.parameters() + + RNG.setSeed(100) + val model2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1) + model2.zeroGradParameters() + + model2.setRuntime(new MklDnnRuntime) + model2.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + model2.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + model2.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + val initWeight = Tools.fromOIHW(weightAll1(0), model2.ParamsShape.weight) + model2.weight.copy(initWeight) + model2.bias.copy(model1.bias) + + RNG.setSeed(1) + val input = Tensor(batchSize, 3, 224, 224).apply1(e => RNG.uniform(0, 1).toFloat) + val gradOutput = Tensor(outputShape).apply1(_ => RNG.uniform(0, 1).toFloat) + + val (weightAll2, gradWeightAll2) = model2.parameters() + + val out1 = model1.forward(input).toTensor[Float] + val out2 = model2.forward(input).toTensor[Float] + + var userOut2 = Tools.toNCHW(out2, model2.outputFormats()(0)) + + Equivalent.nearequals(out1, userOut2, 1e-4) should be(true) + + val grad1 = model1.updateGradInput(input, gradOutput).toTensor[Float] + val grad2 = model2.updateGradInput(input, gradOutput).toTensor[Float] + + val userGradInput2 = Tools.toNCHW(grad2, model2.gradInputFormats()(0)) + + Equivalent.nearequals(grad1, userGradInput2, 1e-4) should be(true) + + model1.accGradParameters(input, gradOutput) + model2.accGradParameters(input, gradOutput) + + val gw1 = model1.gradWeight + val gb1 = model1.gradBias + + val gw2 = Tools.toOIHW(model2.gradWeight, model2.ParamsShape.gradWeight) + val gb2 = Tools.dense(model2.gradBias).toTensor + + Equivalent.nearequals(gw1, gw2, 1e-4) should be(true) + Equivalent.nearequals(gb1, gb2, 1e-3) should be(true) + } + + "a simple convolution compared with caffe" should "work correctly" ignore { + val inputShape = Array(4, 3, 5, 5) + val outputShape = Array(4, 2, 3, 3) + val name = "conv" + val nOutput = 2 + val kernel = 3 + val pad = 1 + val stride = 2 + + val txt = prototxt(inputShape, name, nOutput, kernel, pad, stride) + + val conv = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) + conv.setName(name) + conv.setRuntime(new MklDnnRuntime) + conv.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + conv.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + conv.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + Tools.compare(txt, conv, inputShape, outputShape) + } + + "conv exists some format conversion" should "work correctly" ignore { + val inputShape = Array(4, 3, 224, 224) + val outputShape = Array(4, 64, 112, 112) + + val name = "conv" + val conv = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3).setName(name) + // TODO we should insert a reorder manually + val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder2 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + + val seq = Sequential() + seq.add(reorder1) + seq.add(conv) + seq.add(reorder2) + seq.compile(Phase.TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + seq.reset() + + val txt = prototxt(inputShape, name, outputShape(1), 7, 3, 2) + val identity = Collect.run(txt) + + val input = Tools.getTensor("Fwrd_data", inputShape, identity) + val gradOutput = Tools.getTensor(s"Bwrd_$name.loss", outputShape, identity) + val output = Tools.getTensor(s"Fwrd_$name", outputShape, identity) + val gradInput = Tools.getTensor(s"Bwrd_$name", inputShape, identity) + + if (conv.parameters() != null) { + val params = conv.parameters()._1 + val infos = conv.parametersWithShape()._1 + val name = conv.getName() + + for (j <- params.indices) { + val w = Tools.getTensor(s"Fwrd_$name.Wght.$j", params(j).size(), identity) + params(j).copy(normal(w, infos(j))) + } + } + + seq.forward(input) + seq.backward(input, gradOutput) + + Tools.compare2Tensors(Tools.dense(seq.output).toTensor, output) should be (true) + Tools.compare2Tensors(Tools.dense(seq.gradInput).toTensor, gradInput) should be (true) + + val params = seq.parameters()._2 + val infos = conv.parametersWithShape()._2 + for (j <- params.indices) { + val w = Tools.getTensor(s"Bwrd_$name.Grad.$j", params(j).size(), identity) + Tools.compare2Tensors(params(j), normal(w, infos(j))) should be (true) + } + } + + "conv kernel 1x1 with reorder in container" should "work correctly" ignore { + val inputShape = Array(4, 64, 56, 56) + val outputShape = Array(4, 64, 56, 56) + + val name = "conv" + val conv = SpatialConvolution(64, 64, 1, 1, 1, 1, 0, 0).setName(name) + // TODO we should insert a reorder manually + val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder2 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + + val seq = Sequential() + seq.add(reorder1) + seq.add(conv) + seq.add(reorder2) + seq.compile(Phase.TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + seq.reset() + + val txt = prototxt(inputShape, name, outputShape(1), 1, 0, 1) + val identity = Collect.run(txt) + + val input = Tools.getTensor("Fwrd_data", inputShape, identity) + val gradOutput = Tools.getTensor(s"Bwrd_$name.loss", outputShape, identity) + val output = Tools.getTensor(s"Fwrd_$name", outputShape, identity) + val gradInput = Tools.getTensor(s"Bwrd_$name", inputShape, identity) + + if (conv.parameters() != null) { + val params = conv.parameters()._1 + val infos = conv.parametersWithShape()._1 + val name = conv.getName() + + for (j <- params.indices) { + val w = Tools.getTensor(s"Fwrd_$name.Wght.$j", params(j).size(), identity) + params(j).copy(normal(w, infos(j))) + } + } + + seq.forward(input) + seq.backward(input, gradOutput) + + Tools.compare2Tensors(Tools.dense(seq.output).toTensor, output) should be (true) + Tools.compare2Tensors(Tools.dense(seq.gradInput).toTensor, gradInput) should be (true) + + val params = seq.parameters()._2 + val infos = conv.parametersWithShape()._2 + for (j <- params.indices.reverse) { + val w = Tools.getTensor(s"Bwrd_$name.Grad.$j", params(j).size(), identity) + Tools.compare2Tensors(params(j), normal(w, infos(j))) should be (true) + } + } + + "conv + bn" should "work correctly" ignore { + val inputShape = Array(4, 3, 224, 224) + val outputShape = Array(4, 64, 112, 112) + val channel = 64 + + val name = "conv" + val conv = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3).setName("conv") + val bn = SpatialBatchNormalization(64, momentum = 1.0, eps = 100).setName("bn") + // TODO we should insert a reorder manually + val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)).setName("reorder1") + val reorder2 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)).setName("reorder2") + + val seq = Sequential() + seq.add(reorder1) + seq.add(conv) + seq.add(bn) + seq.add(reorder2) + seq.compile(Phase.TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + seq.reset() + seq.training() + + val txt = prototxt2(inputShape, name, outputShape(1), 7, 3, 2) + + """ + |layer { + | bottom: "conv" + | top: "bn" + | name: "bn" + | type: "BatchNorm" + | + | batch_norm_param { + | moving_average_fraction: 1.0 + | filler { value: 1 } + | bias_filler { value: 0 } + | relu: false + | eps: 100 + | } + |} + """.stripMargin + Tools.compare(txt, seq, inputShape, outputShape, 1e-2) + } + + def prototxt(inputShape: Array[Int], name: String, + nOutput: Int, kernel: Int, pad: Int, stride: Int): String = { + s""" + |name: "conv-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + | + |layer { + | bottom: "data" + | top: "conv" + | name: "$name" + | type: "Convolution" + | convolution_param { + | num_output: $nOutput + | kernel_size: $kernel + | pad: $pad + | stride: $stride + | weight_filler { + | type: "msra" + | variance_norm: FAN_OUT + | } + | bias_filler { + | type: "gaussian" + | } + | } + |} + """.stripMargin + } + + def prototxt2(inputShape: Array[Int], name: String, + nOutput: Int, kernel: Int, pad: Int, stride: Int): String = { + s""" + |name: "conv-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "uniform" + | min: -1000 + | max: 1000 + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + | + |layer { + | bottom: "data" + | top: "conv" + | name: "$name" + | type: "Convolution" + | convolution_param { + | num_output: $nOutput + | kernel_size: $kernel + | pad: $pad + | stride: $stride + | weight_filler { + | type: "msra" + | variance_norm: FAN_OUT + | } + | bias_filler { + | type: "gaussian" + | } + | } + |} + """.stripMargin + } + + def normal(src: Tensor[Float], outputFormat: MemoryData): Tensor[Float] = { + val defaultFormat = src.size().length match { + case 1 => Memory.Format.x + case 2 => Memory.Format.oi + case 4 => Memory.Format.oihw + } + + if (defaultFormat != outputFormat.layout) { + val inputFormat = HeapData(src.size(), defaultFormat) + val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + reorder.setRuntime(new MklDnnRuntime) + reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) + reorder.updateOutput(src).toTensor + } else { + src + } + } + + private def shape2Dim(shape: Array[Int]): String = { + shape.map(x => "dim: " + x).mkString(" ") + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala new file mode 100644 index 00000000000..e80f4d052fe --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala @@ -0,0 +1,514 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import java.io.{File, PrintWriter} +import java.nio.channels.FileChannel +import java.nio.file.{Files, Paths, StandardOpenOption} +import java.nio.{ByteBuffer, ByteOrder} + +import breeze.numerics.abs +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.Container +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{DenseTensorMath, Storage, Tensor} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag +import scala.sys.process._ + +object Tools { + def error[@specialized(Float, Double) T: ClassTag](tensor1: Tensor[T], tensor2: Tensor[T])( + implicit ev: TensorNumeric[T]): Double = { + require(tensor1.nElement() == tensor2.nElement()) + var ret = 0.0 + val storage1 = tensor1.storage().array() + val storage2 = tensor2.storage().array() + for (i <- 0 until tensor1.nElement()) { + ret += math.abs( + ev.toType[Double](storage1(i)) - ev.toType[Double](storage2(i))) + } + ret + } + + def cumulativeError[T: ClassTag](tensor1: Tensor[T], tensor2: Tensor[T], msg: String)( + implicit ev: TensorNumeric[T]): Double = { + val ret = error[T](tensor1, tensor2) + println((msg, "CUMULATIVE ERROR:", ret).productIterator.mkString(" ").toUpperCase) + ret + } + + def averageError[T: ClassTag](tensor1: Tensor[T], tensor2: Tensor[T], msg: String)( + implicit ev: TensorNumeric[T]): Double = { + require(tensor1.nElement() > 0) + val ret = error[T](tensor1, tensor2) / tensor1.nElement() + println((msg, "AVERAGE ERROR:", ret).productIterator.mkString(" ").toUpperCase) + ret + } + + def averageError[T: ClassTag](m1: Map[String, Tensor[T]], + m2: Map[String, Tensor[T]], + err: Map[String, Double])(implicit ev: TensorNumeric[T]): Unit = { + require(m1.keySet == m2.keySet) + require(m1.keySet subsetOf err.keySet) + + m1.keySet.foreach(i => { + val err = error(m1(i), m2(i)) / m1(i).nElement() + printf("%20s = %E\n", i.toUpperCase(), err) + }) + } + + def averageAllTensors[T: ClassTag](tensor1: Tensor[T], msg: String = "Unknown")( + implicit ev: TensorNumeric[T]): Unit = { + val sum = tensor1.storage().array().foldLeft(ev.fromType[Int](0))((l, r) => ev.plus(l, r)) + val num = ev.fromType[Int](tensor1.nElement()) + println(("AVERGE", msg, ev.divide(sum, num)).productIterator.mkString(" ").toUpperCase()) + } + + def printTensor[T: ClassTag](tensor: Tensor[T], num: Int = 16, msg: String = "Unknown")( + implicit ev: TensorNumeric[T]): Unit = { + println(msg.toUpperCase) + for (i <- 0 until num) { + println((i, ev.toType[Double](tensor.storage().array()(i))).productIterator.mkString("\t")) + } + } + + private def fileName(name: String, identity: String): String = { + val tmpdir = System.getProperty("java.io.tmpdir") + val dirname = if (tmpdir.endsWith("/")) { + tmpdir + } else { + tmpdir + "/" + } + + val filename = if (identity.isEmpty) { + ".bin" + } else { + "." + identity + ".bin" + } + + dirname + name + filename + } + + /* + * @brief read binary in tmp dir to Tensor, which is used for comparing + * with Intel Caffe with MKL-DNN + */ + def getTensor(name: String, size: Array[Int], identity: String): Tensor[Float] = { + val tensor = Tensor[Float]() + val file = fileName(name, identity) + + if (Files.exists(Paths.get(file))) { + println(s"[INFO] load $file") + setTensorFloat() + + def loadData(name: String): ByteBuffer = { + val fileChannel: FileChannel = Files.newByteChannel( + Paths.get(name), + StandardOpenOption.READ, + StandardOpenOption.DELETE_ON_CLOSE).asInstanceOf[FileChannel] + val byteBuffer: ByteBuffer = ByteBuffer.allocate(fileChannel.size().toInt) + byteBuffer.order(ByteOrder.nativeOrder()) + fileChannel.read(byteBuffer) + byteBuffer.flip() + byteBuffer + } + + + def setTensorFloat(): Unit = { + val data = loadData(file).asFloatBuffer() + val array = new Array[Float](data.limit()) + data.get(array) + assert(size.product == array.length, s"the data length is not correct") + tensor.set(Storage(array), sizes = size) + } + } + + tensor + } + + def flattenModules(model: Module[Float], modules: ArrayBuffer[Module[Float]]): Unit = { + model match { + case container : Container[_, _, _] => + if (container.modules.nonEmpty) { + for (i <- container.modules) { + flattenModules(i.asInstanceOf[Module[Float]], modules) + } + } + case x => if (!x.isInstanceOf[ReorderMemory] && !x.isInstanceOf[Identity]) { + modules += model + } + } + } + + def randTimes(): Int = 10 + + def loadWeights(module: Module[Float], identity: String): Unit = { + val params = module.parameters()._1 + val name = module.getName() + module match { + case bn: SpatialBatchNormalization => + val channel = bn.weightAndBias.size(1) / 2 + + val weight = Tools.getTensor(s"Fwrd_${bn.getName}.Wght.3", Array(channel), identity) + val bias = Tools.getTensor(s"Fwrd_${bn.getName}.Wght.4", Array(channel), identity) + val weightAndBias = Tensor[Float].resize(Array(2, channel)) + if (weight.isEmpty) {weight.resize(Array(channel)).fill(1)} + weightAndBias.select(1, 1).copy(weight) + if (bias.isEmpty) { + bias.resize(Array(channel)).fill(0) + } + weightAndBias.select(1, 2).copy(bias) + bn.weightAndBias.copy(weightAndBias.view(bn.weightAndBias.size())) + case _ => + for (j <- params.indices) { + val w = Tools.getTensor(s"Fwrd_$name.Wght.$j", params(j).size(), identity) + module match { + case layer: MklDnnLayer => + val infos = layer.parametersWithShape()._1 + val weights = if (!w.isEmpty) { + params(j).copy(fromOIHW(w, infos(j))) + } else { + val zeros = Tensor[Float]().resize(params(j).size()).fill(0) + params(j).copy(zeros) + } + case _ => + params(j).copy(w) + } + } + } + } + + def compareGradients(module: Module[Float], epsilon: Float, identity: String): Boolean = { + var ret = true + + val name = module.getName() + val params = module.parameters()._2 + + module match { + case bn: SpatialBatchNormalization => + val channel = bn.weightAndBias.size(1) / 2 + + val weight = Tools.getTensor(s"Bwrd_${bn.getName}.Grad.3", Array(channel), identity) + val bias = Tools.getTensor(s"Bwrd_${bn.getName}.Grad.4", Array(channel), identity) + val weightAndBias = Tensor[Float].resize(Array(2, channel)) + weightAndBias.select(1, 1).copy(weight) + weightAndBias.select(1, 2).copy(bias) + + ret &= Equivalent.nearequals(weightAndBias.view(bn.gradWeightAndBias.size()), + dense(bn.gradWeightAndBias).toTensor, epsilon) + val runningMean = Tools.getTensor(s"Fwrd_$name.Wght.0", Array(channel), identity) + val runningVariance = Tools.getTensor(s"Fwrd_$name.Wght.1", Array(channel), identity) + + ret &= compare2Tensors(runningMean, dense(bn.runningMean).toTensor) + ret &= compare2Tensors(runningVariance, dense(bn.runningVariance).toTensor) + + assert(ret, s"${module.getName()} gradient can't pass, please check") + case _ => + for (j <- params.indices) { + val w = Tools.getTensor(s"Bwrd_$name.Grad.$j", params(j).size(), identity) + module match { + case layer: MklDnnLayer => + val infos = layer.parametersWithShape()._2 + ret &= Equivalent.nearequals(dense(params(j)).toTensor, + dense(fromOIHW(w, infos(j))).toTensor, epsilon) + case _ => ret &= compare2Tensors(params(j), w) + } + + assert(ret, s"${module.getName()} gradient $j can't pass, please check") + } + } + + ret + } + + def compare(prototxt: String, model: Module[Float], inputShape: Array[Int], + outputShape: Array[Int], epsilon: Double = 1e-7): Unit = { + val identity = Collect.run(prototxt, singleLayer = true) + val modules = ArrayBuffer[Module[Float]]() + Tools.flattenModules(model, modules) + + val input = Tools.getTensor("Fwrd_data", inputShape, identity) + val gradOutput = Tools.getTensor(s"Bwrd_${modules.last.getName()}.loss", outputShape, identity) + + modules.filter(_.parameters() != null).foreach(loadWeights(_, identity)) + + model.forward(input) + model.backward(input, gradOutput) + + for (i <- modules.indices) { + compareSingleLayer(modules(i), identity) + } + + def compareSingleLayer(module: Module[Float], identity: String): Boolean = { + val name = module.getName() + val bigdlOutput = module.output.toTensor[Float] + val bigdlGradInput = if (module.isInstanceOf[CAddTable]) { + module.gradInput.toTable.apply[Tensor[Float]](1) + } else { + module.gradInput.toTensor[Float] + } + + val output = Tools.getTensor(s"Fwrd_$name", bigdlOutput.size(), identity) + val gradInput = Tools.getTensor(s"Bwrd_$name", bigdlGradInput.size(), identity) + + var ret = true + + module match { + case layer: MklDnnLayer => + ret &= compare2Tensors(output, toNCHW(bigdlOutput, layer.outputFormats()(0))) + assert(ret, s"${module.getName()} output can't pass, please check") + + ret &= compare2Tensors(gradInput, toNCHW(bigdlGradInput, layer.gradInputFormats()(0))) + assert(ret, s"${module.getName()} gradInput can't pass, please check") + case _ => + ret &= compare2Tensors(output, bigdlOutput) + assert(ret, s"${module.getName()} output can't pass, please check") + + ret &= compare2Tensors(gradInput, bigdlGradInput) + assert(ret, s"${module.getName()} gradInput can't pass, please check") + } + + if (module.parameters() == null) { + return ret + } + + val params = module.parameters()._2 + compareGradients(module, epsilon.toFloat, identity) + + ret + } + } + + def compare2Tensors(src: Tensor[Float], dst: Tensor[Float]): Boolean = { + Equivalent.nearequals(dense(src).toTensor, dense(dst).toTensor) + } + + def dense(t: Activity): Activity = { + val ret = if (t.isTensor) { + val tt = t.asInstanceOf[Tensor[Float]] + Tensor[Float]().resize(tt.size()).copy(tt) + } else { + throw new UnsupportedOperationException + } + + ret + } + + def toNCHW(src: Tensor[Float], inputFormat: MemoryData): Tensor[Float] = { + val outputFormat = HeapData(inputFormat.shape, + if (src.size().length == 2) { Memory.Format.nc } else { Memory.Format.nchw }) + val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + + reorder.setRuntime(new MklDnnRuntime) + reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) + reorder.forward(src).toTensor + } + + def fromNCHW(src: Tensor[Float], outputFormat: MemoryData): Tensor[Float] = { + val defaultFormat = src.size().length match { + case 1 => Memory.Format.x + case 2 => Memory.Format.nc + case 4 => Memory.Format.nchw + } + + val inputFormat = HeapData(src.size(), defaultFormat) + val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + reorder.setRuntime(new MklDnnRuntime) + reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) + reorder.forward(src).toTensor + } + + def fromOIHW(src: Tensor[Float], outputFormat: MemoryData): Tensor[Float] = { + val defaultFormat = outputFormat.shape.length match { + case 1 => Memory.Format.x + case 2 => Memory.Format.oi + case 4 => Memory.Format.oihw + } + + val inputFormat = HeapData(outputFormat.shape, defaultFormat) + val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + reorder.setRuntime(new MklDnnRuntime) + reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) + reorder.updateOutput(src).toTensor + } + + def toOIHW(src: Tensor[Float], inputFormat: MemoryData): Tensor[Float] = { + val defaultFormat = inputFormat.shape.length match { + case 1 => Memory.Format.x + case 2 => Memory.Format.oi + case 4 => Memory.Format.oihw + case 5 => Memory.Format.goihw + } + + val outputFormat = HeapData(inputFormat.shape, defaultFormat) + val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + reorder.setRuntime(new MklDnnRuntime) + reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) + reorder.updateOutput(src).toTensor + } +} + +/** + * Call "collect" command, which is a method to collect output binary files. + * It's similar to "caffe collect", the difference is that it supports collect + * single layers output and gradient through make a fake gradOutput/top_diff. + */ +object Collect { + val tmpdir: String = System.getProperty("java.io.tmpdir") + val collectPath: String = System.getProperty("collect.location") + + def hasCollect: Boolean = { + val exitValue = if (collectPath != null) s"ls $collectPath".! else "which collect".! + exitValue == 0 + } + + /** + * save the prototxt to a temporary file and call collect + * @param prototxt prototxt with string + * @return the middle random number in temporary file, which is an identity for getTensor. + */ + def run(prototxt: String, singleLayer: Boolean = true): String = { + def saveToFile(prototxt: String, name: String): String = { + val tmpFile = java.io.File.createTempFile(name, ".prototxt") + val absolutePath = tmpFile.getAbsolutePath + + println(s"prototxt is saved to $absolutePath") + + val writer = new PrintWriter(tmpFile) + writer.println(prototxt) + writer.close() + + absolutePath + } + + if (! hasCollect) { + throw new RuntimeException(s"Can't find collect command. Have you copy to the PATH?") + } + + val file = saveToFile(prototxt, "UnitTest.") // UnitTest ends with dot for getting random number + val identity = file.split("""\.""").reverse(1) // get the random number + + val cmd = Seq(s"$collectPath", "--model", file, "--type", "float", "--identity", identity) + val exitValue = if (singleLayer) { + Process(cmd :+ "--single", new File(tmpdir)).! + } else { + Process(cmd, new File(tmpdir)).! + } + + Files.deleteIfExists(Paths.get(file)) + require(exitValue == 0, s"Something wrong with collect command. Please check it.") + + identity + } +} + +object Utils { + def time[R](block: => R): (Double, R) = { + val t0 = System.nanoTime() + val result = block + val t1 = System.nanoTime() + val takes = (t1 - t0) / 1e9 + (takes, result) + } + + def manyTimes[R](block: => R)(iters: Int): (Double, R) = { + time[R] { + var i = 0 + while (i < iters - 1) { + block + i += 1 + } + block + } + } + + def speedup(base: Double, after: Double): String = { + val result = (base - after) / base + ((result * 1000).toInt / 10.0).toString + "%" + } +} + +object Equivalent { + + def nearlyEqual(a: Float, b: Float, epsilon: Double): Boolean = { + val absA = math.abs(a) + val absB = math.abs(b) + val diff = math.abs(a - b) + + val result = if (a == b) { + true + } else { + math.min(diff / (absA + absB), diff) < epsilon + } + + result + } + + def nearequals(t1: Tensor[Float], t2: Tensor[Float], + epsilon: Double = DenseTensorMath.floatEpsilon): Boolean = { + var result = true + t1.map(t2, (a, b) => { + if (result) { + result = nearlyEqual(a, b, epsilon) + if (!result) { + val diff = math.abs(a - b) + println("epsilon " + a + "***" + b + "***" + diff / (abs(a) + abs(b)) + "***" + diff) + } + } + a + }) + result + } + + def getunequals(t1: Tensor[Float], t2: Tensor[Float], + epsilon: Double = DenseTensorMath.floatEpsilon): Boolean = { + var result = true + var num = 0 + t1.map(t2, (a, b) => { + if (true) { + result = nearlyEqual(a, b, epsilon) + if (!result) { + num += 1 + val diff = math.abs(a - b) + println("epsilon " + a + "***" + b + "***" + diff / (abs(a) + abs(b)) + "***" + diff) + } + } + a + }) + println("diff num " + num) + return true + } + + def isEquals(t1: Tensor[Float], t2: Tensor[Float]): Boolean = { + var result = true + t1.map(t2, (a, b) => { + if (result) { + result = if (a == b) true else false + if (!result) { + val diff = Math.abs(a - b) + println("epsilon " + a + "***" + b + "***" + diff / (abs(a) + abs(b)) + "***" + diff) + } + } + a + }) + return result + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala new file mode 100644 index 00000000000..922435a3a90 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala @@ -0,0 +1,1011 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.{Module, nn} +import org.scalatest.{FlatSpec, Ignore, Matchers} + +@Ignore +class TopologySpec extends FlatSpec with Matchers { + + "LeNet5 has no tanh" should "work correctly" in { + val inputShape = Array(4, 1, 28, 28) + val outputShape = Array(4, 10) + val prototxt = s""" + |name: "LeNet" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { ${shape2Dim(inputShape)} } + | } + |} + |layer { + | name: "conv1" + | type: "Convolution" + | bottom: "data" + | top: "conv1" + | param { + | lr_mult: 1 + | } + | param { + | lr_mult: 2 + | } + | convolution_param { + | num_output: 20 + | kernel_size: 5 + | stride: 1 + | weight_filler { + | type: "msra" + | variance_norm: FAN_OUT + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + |layer { + | name: "pool1" + | type: "Pooling" + | bottom: "conv1" + | top: "pool1" + | pooling_param { + | pool: MAX + | kernel_size: 2 + | stride: 2 + | } + |} + |layer { + | name: "conv2" + | type: "Convolution" + | bottom: "pool1" + | top: "conv2" + | param { + | lr_mult: 1 + | } + | param { + | lr_mult: 2 + | } + | convolution_param { + | num_output: 50 + | kernel_size: 5 + | stride: 1 + | weight_filler { + | type: "xavier" + | } + | bias_filler { + | type: "constant" + | } + | } + |} + |layer { + | name: "pool2" + | type: "Pooling" + | bottom: "conv2" + | top: "pool2" + | pooling_param { + | pool: MAX + | kernel_size: 2 + | stride: 2 + | } + |} + |layer { + | name: "ip1" + | type: "InnerProduct" + | bottom: "pool2" + | top: "ip1" + | param { + | lr_mult: 1 + | } + | param { + | lr_mult: 2 + | } + | inner_product_param { + | num_output: 500 + | weight_filler { + | type: "xavier" + | } + | bias_filler { + | type: "constant" + | } + | } + |} + |layer { + | name: "relu1" + | type: "ReLU" + | bottom: "ip1" + | top: "ip1" + |} + |layer { + | name: "ip2" + | type: "InnerProduct" + | bottom: "ip1" + | top: "ip2" + | param { + | lr_mult: 1 + | } + | param { + | lr_mult: 2 + | } + | inner_product_param { + | num_output: 10 + | weight_filler { + | type: "xavier" + | } + | bias_filler { + | type: "constant" + | } + | } + |} + """.stripMargin +// |layer { +// | name: "prob" +// | type: "Softmax" +// | bottom: "ip2" +// | top: "prob" +// |} +// | + + val bigdl = Sequential() + .add(SpatialConvolution(1, 20, 5, 5).setName("conv1")) + .add(MaxPooling(2, 2, 2, 2).setName("pool1")) + .add(SpatialConvolution(20, 50, 5, 5).setName("conv2")) + .add(MaxPooling(2, 2, 2, 2).setName("pool2")) + .add(Linear(50 * 4 * 4, 500).setName("ip1")) + .add(ReLU().setName("relu1")) + .add(Linear(500, 10).setName("ip2")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nc))) +// .add(SoftMax().setName("prob")) // TODO SoftMax is totally different with Caffe. + bigdl.compile(TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + Tools.compare(prototxt, bigdl, inputShape, outputShape, 1e-6) + } + + "eltwise" should "work correctly" in { + val nInput = 3 + val nOutput = 2 + val inputShape = Array(4, 3, 5, 5) + val outputShape = Array(4, 2, 3, 3) + + val kernel = 3 + val pad = 1 + val stride = 2 + + val prototxt = + s""" + | name: "eltwise-simple" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "xavier" + | } + | shape: { dim: 4 dim: 3 dim: 5 dim: 5 } + | } + |} + |layer { + | bottom: "data" + | top: "conv1" + | name: "conv1" + | type: "Convolution" + | convolution_param { + | num_output: 2 + | kernel_size: 3 + | pad: 1 + | stride: 2 + | weight_filler { + | # type: "msra" + | # variance_norm: FAN_OUT + | type: "constant" + | value: 0.1 + | } + | bias_filler { + | # type: "gaussian" + | type: "constant" + | value: 0.1 + | } + | } + |} + |layer { + | bottom: "data" + | top: "conv2" + | name: "conv2" + | type: "Convolution" + | convolution_param { + | num_output: 2 + | kernel_size: 3 + | pad: 1 + | stride: 2 + | weight_filler { + | # type: "msra" + | # variance_norm: FAN_OUT + | type: "constant" + | value: 0.1 + | } + | bias_filler { + | # type: "gaussian" + | type: "constant" + | value: 0.1 + | } + | } + |} + |layer { + | bottom: "conv1" + | bottom: "conv2" + | top: "eltwise" + | name: "eltwise" + | type: "Eltwise" + | eltwise_param { + | } + |} + | + """.stripMargin + + val conv1 = SpatialConvolution(nInput, nOutput, kernel, kernel, stride, stride, pad, pad, 1) + .setName("conv1") + val conv2 = SpatialConvolution(nInput, nOutput, kernel, kernel, stride, stride, pad, pad, 1) + .setName("conv2") + val model = Sequential() + .add(ConcatTable().add(conv2).add(conv1)) + .add(CAddTable().setName("eltwise")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + model.compile(TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + Tools.compare(prototxt, model, inputShape, outputShape) + } + + "resnet 50" should "work correctly" in { + val prototxt = + s""" + |name: "ResNet-50" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | top: "label" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "constant" + | value: 0.01 + | } + | shape: { dim: 4 dim: 3 dim: 224 dim: 224 } + | shape: { dim: 4 dim: 1 dim: 1 dim: 1 } + | } + |} + | + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | top: "label" + | include { + | phase: TEST + | } + | dummy_data_param { + | data_filler { + | type: "constant" + | value: 0.01 + | } + | shape: { dim: 32 dim: 3 dim: 224 dim: 224 } + | shape: { dim: 32 dim: 1 dim: 1 dim: 1 } + | } + |} + | + |layer { + | bottom: "data" + | top: "conv1" + | name: "conv1" + | type: "Convolution" + | convolution_param { + | num_output: 64 + | kernel_size: 7 + | pad: 3 + | stride: 2 + | weight_filler { + | type: "msra" + | variance_norm: FAN_OUT + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |# layer { + |# bottom: "conv1" + |# top: "conv1" + |# name: "bn_conv1" + |# type: "BatchNorm" + |# param { lr_mult: 0 } + |# param { lr_mult: 0 } + |# param { lr_mult: 0 } + |# batch_norm_param { + |# moving_average_fraction: 0.9 + |# filler { value: 1 } + |# } + |# } + | + |layer { + | bottom: "conv1" + | top: "conv1" + | name: "scale_conv1" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "conv1" + | top: "conv1" + | name: "conv1_relu" + | type: "ReLU" + | relu_param { + | } + |} + | + |layer { + | bottom: "conv1" + | top: "pool1" + | name: "pool1" + | type: "Pooling" + | pooling_param { + | kernel_size: 3 + | stride: 2 + | pool: MAX + | } + |} + """.stripMargin + val inputShape = Array(4, 3, 224, 224) + val outputShape = Array(4, 64, 56, 56) + + val model = Sequential() + .add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = true).setName("conv1")) + .add(ReLU().setName("conv1_relu")) + .add(MaxPooling(3, 3, 2, 2).setName("pool1")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + model.compile(TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + Tools.compare(prototxt, model, inputShape, outputShape) + } + + "bottleneck" should "work correctly" in { + val prototxt = + s""" + |name: "ResNet-50" + |force_backward: true + |layer { + | name: "data" + | type: "DummyData" + | top: "data" + | top: "label" + | include { + | phase: TRAIN + | } + | dummy_data_param { + | data_filler { + | type: "constant" + | value: 0.01 + | } + | shape: { dim: 4 dim: 3 dim: 224 dim: 224 } + | shape: { dim: 4 dim: 1 dim: 1 dim: 1 } + | } + |} + | + |layer { + | bottom: "data" + | top: "conv1" + | name: "conv1" + | type: "Convolution" + | convolution_param { + | num_output: 64 + | kernel_size: 7 + | pad: 3 + | stride: 2 + | weight_filler { + | type: "msra" + | variance_norm: FAN_OUT + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "conv1" + | top: "conv1_relu" # delete inplace + | name: "conv1_relu" + | type: "ReLU" + | relu_param { + | fuse: false + | } + |} + | + |layer { + | bottom: "conv1_relu" + | top: "pool1" + | name: "pool1" + | type: "Pooling" + | pooling_param { + | kernel_size: 3 + | stride: 2 + | pool: MAX + | } + |} + | + |layer { + | bottom: "pool1" + | top: "res2a_branch1" + | name: "res2a_branch1" + | type: "Convolution" + | convolution_param { + | num_output: 256 + | kernel_size: 1 + | pad: 0 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2a_branch1" + | top: "res2a_branch1" + | name: "scale2a_branch1" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "pool1" + | top: "res2a_branch2a" + | name: "res2a_branch2a" + | type: "Convolution" + | convolution_param { + | + | num_output: 64 + | kernel_size: 1 + | pad: 0 + | stride: 1 + | bias_term: true # change to true. + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2a_branch2a" + | top: "res2a_branch2a" + | name: "scale2a_branch2a" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2a_branch2a" + | top: "res2a_branch2a" + | name: "res2a_branch2a_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + | + |layer { + | bottom: "res2a_branch2a" + | top: "res2a_branch2b" + | name: "res2a_branch2b" + | type: "Convolution" + | convolution_param { + | num_output: 64 + | kernel_size: 3 + | pad: 1 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2a_branch2b" + | top: "res2a_branch2b" + | name: "scale2a_branch2b" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2a_branch2b" + | top: "res2a_branch2b" + | name: "res2a_branch2b_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + | + |layer { + | bottom: "res2a_branch2b" + | top: "res2a_branch2c" + | name: "res2a_branch2c" + | type: "Convolution" + | convolution_param { + | num_output: 256 + | kernel_size: 1 + | pad: 0 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2a_branch2c" + | top: "res2a_branch2c" + | name: "scale2a_branch2c" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2a_branch1" + | bottom: "res2a_branch2c" + | top: "res2a" + | name: "res2a" + | type: "Eltwise" + | eltwise_param { + | + | } + |} + | + |layer { + | bottom: "res2a" + | top: "res2a" + | name: "res2a_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + |layer { + | bottom: "res2a" + | top: "res2b_branch2a" + | name: "res2b_branch2a" + | type: "Convolution" + | convolution_param { + | num_output: 64 + | kernel_size: 1 + | pad: 0 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2b_branch2a" + | top: "res2b_branch2a" + | name: "scale2b_branch2a" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2b_branch2a" + | top: "res2b_branch2a" + | name: "res2b_branch2a_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + | + |layer { + | bottom: "res2b_branch2a" + | top: "res2b_branch2b" + | name: "res2b_branch2b" + | type: "Convolution" + | convolution_param { + | num_output: 64 + | kernel_size: 3 + | pad: 1 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2b_branch2b" + | top: "res2b_branch2b" + | name: "scale2b_branch2b" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2b_branch2b" + | top: "res2b_branch2b" + | name: "res2b_branch2b_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + | + |layer { + | bottom: "res2b_branch2b" + | top: "res2b_branch2c" + | name: "res2b_branch2c" + | type: "Convolution" + | convolution_param { + | num_output: 256 + | kernel_size: 1 + | pad: 0 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2b_branch2c" + | top: "res2b_branch2c" + | name: "scale2b_branch2c" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2a" + | bottom: "res2b_branch2c" + | top: "res2b" + | name: "res2b" + | type: "Eltwise" + | eltwise_param { + | + | } + |} + | + |layer { + | bottom: "res2b" + | top: "res2b" + | name: "res2b_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + | + |layer { + | bottom: "res2b" + | top: "res2c_branch2a" + | name: "res2c_branch2a" + | type: "Convolution" + | convolution_param { + | + | num_output: 64 + | kernel_size: 1 + | pad: 0 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2c_branch2a" + | top: "res2c_branch2a" + | name: "scale2c_branch2a" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2c_branch2a" + | top: "res2c_branch2a" + | name: "res2c_branch2a_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + | + |layer { + | bottom: "res2c_branch2a" + | top: "res2c_branch2b" + | name: "res2c_branch2b" + | type: "Convolution" + | convolution_param { + | num_output: 64 + | kernel_size: 3 + | pad: 1 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2c_branch2b" + | top: "res2c_branch2b" + | name: "scale2c_branch2b" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2c_branch2b" + | top: "res2c_branch2b" + | name: "res2c_branch2b_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + | + |layer { + | bottom: "res2c_branch2b" + | top: "res2c_branch2c" + | name: "res2c_branch2c" + | type: "Convolution" + | convolution_param { + | + | num_output: 256 + | kernel_size: 1 + | pad: 0 + | stride: 1 + | bias_term: true # change to true + | weight_filler { + | type: "msra" + | } + | bias_filler { + | type: "constant" + | value: 0 + | } + | } + |} + | + |layer { + | bottom: "res2c_branch2c" + | top: "res2c_branch2c" + | name: "scale2c_branch2c" + | type: "Scale" + | param { decay_mult: 0 } + | param { decay_mult: 0 } + | scale_param { + | bias_term: true + | } + |} + | + |layer { + | bottom: "res2b" + | bottom: "res2c_branch2c" + | top: "res2c" + | name: "res2c" + | type: "Eltwise" + | eltwise_param { + | + | } + |} + | + |layer { + | bottom: "res2c" + | top: "res2c_" # do not do inplace + | name: "res2c_relu" + | type: "ReLU" + | relu_param { + | + | } + |} + """.stripMargin + val inputShape = Array(4, 3, 224, 224) + val outputShape = Array(4, 256, 56, 56) + + val model = ResNet50.getModel(inputShape, outputShape) + model.compile(TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + Tools.compare(prototxt, model, inputShape, outputShape, 1e-5) + } + + object ResNet50 { + var iChannels = 64 + + def shortcut(nInputPlane: Int, nOutputPlane: Int, stride: Int, name: String): Module[Float] = { + val useConv = nInputPlane != nOutputPlane + + if (useConv) { + Sequential() + .add(SpatialConvolution(nInputPlane, nOutputPlane, 1, 1, stride, stride) + .setName(s"res${name}_branch1")) + } else if (nInputPlane != nOutputPlane) { + throw new IllegalArgumentException(s"useConv false") + } else { + Identity() + } + } + + def bottleneck(n: Int, stride: Int, name: String = ""): Module[Float] = { + val nInputPlane = iChannels + iChannels = n * 4 + + val s = Sequential() + s.add(SpatialConvolution(nInputPlane, n, 1, 1, 1, 1, 0, 0).setName(s"res${name}_branch2a")) + .add(ReLU().setName(s"res${name}_branch2a_relu")) + .add(SpatialConvolution(n, n, 3, 3, stride, stride, 1, 1).setName(s"res${name}_branch2b")) + .add(ReLU().setName(s"res${name}_branch2b_relu")) + .add(SpatialConvolution(n, n*4, 1, 1, 1, 1, 0, 0).setName(s"res${name}_branch2c")) + + val model = Sequential() + .add(ConcatTable(). + add(s). + add(shortcut(nInputPlane, n*4, stride, name)).setName(s"$name/concatTable")) + .add(CAddTable().setName(s"res$name")) + .add(ReLU().setName(s"res${name}_relu")) + model + } + + def layer(block: (Int, Int, String) => Module[Float], features: Int, + count: Int, stride: Int = 1, name : String): Module[Float] = { + val s = Sequential() + for (i <- 1 to count) { + s.add(block(features, if (i == 1) stride else 1, getName(i, name))) + } + s + } + + def getName(i: Int, name: String): String = { + i match { + case 1 => name + "a" + case 2 => name + "b" + case 3 => name + "c" + case 4 => name + "d" + case 5 => name + "e" + case 6 => name + "f" + } + } + + def getModel(inputShape: Array[Int], outputShape: Array[Int]): MklDnnContainer = { + iChannels = 64 + + Sequential() + .add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3).setName("conv1").setReLU(true)) + .add(ReLU().setName("conv1_relu")) + .add(MaxPooling(3, 3, 2, 2).setName("pool1")) + .add(layer(bottleneck, 64, 3, name = "2")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + } + } + + private def shape2Dim(shape: Array[Int]): String = { + shape.map(x => "dim: " + x).mkString(" ") + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala index 824628222fb..7fe820e3a81 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala @@ -22,29 +22,29 @@ import org.scalatest.{FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Parallel class DenseTensorMathSpec extends FlatSpec with Matchers { "a.dist(b, 1)" should "be correct" in { - val a: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val b: Tensor[Double] = new DenseTensor(Storage(Array(2.0, 3.0, 4.0))) + val a: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val b: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(2.0, 3.0, 4.0))) a.dist(b, 1) should equal(3) } "a.dist(b, 2)" should "be correct" in { - val a: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val b: Tensor[Double] = new DenseTensor(Storage(Array(3.0, 4.0, 5.0))) + val a: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val b: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(3.0, 4.0, 5.0))) a.dist(b, 2) should equal(math.sqrt(12)) } "a.dist(b, 3)" should "be correct" in { - val a: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val b: Tensor[Double] = new DenseTensor(Storage(Array(3.0, 4.0, 5.0))) + val a: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val b: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(3.0, 4.0, 5.0))) a.dist(b, 3) should equal(math.pow(24, 1.0 / 3)) } "vector + scalar" should "be correct" in { val s = 2.0 - val v: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) + val v: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) val r = v + s r(Array(1)) should be(3.0) r(Array(2)) should be(4.0) @@ -52,8 +52,8 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { } "vector + vector" should "be correct" in { - val v1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val v2: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) + val v1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val v2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) val r = v1 + v2 r(Array(1)) should be(2.0) r(Array(2)) should be(4.0) @@ -63,7 +63,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { "vector + vector which is not contiguous" should "be correct" in { val v1: Tensor[Double] = new DenseTensor[Double](2, 4).fill(1) v1.t() - val v2: Tensor[Double] = new DenseTensor(Storage( + val v2: Tensor[Double] = new DenseTensor(new ArrayStorage( Array(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0))) val r = v1 + v2 r(Array(1, 1)) should be(2.0) @@ -78,7 +78,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { "vector - scalar" should "be correct" in { val s = 2.0 - val v: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) + val v: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) val r = v - s r(Array(1)) should be(-1.0) r(Array(2)) should be(0.0) @@ -86,8 +86,8 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { } "vector - vector" should "be correct" in { - val v1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val v2: Tensor[Double] = new DenseTensor(Storage(Array(2.0, 0.0, -1.0))) + val v1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val v2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(2.0, 0.0, -1.0))) val r = v1 - v2 r(Array(1)) should be(-1.0) r(Array(2)) should be(2.0) @@ -96,7 +96,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { "vector * scalar" should "be correct" in { val s = 2.0 - val v: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) + val v: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) val r = v * s r(Array(1)) should be(2.0) r(Array(2)) should be(4.0) @@ -104,8 +104,8 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { } "vector * vector" should "be correct" in { - val v1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val v2: Tensor[Double] = new DenseTensor(Storage(Array(2.0, 0.0, -1.0))) + val v1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val v2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(2.0, 0.0, -1.0))) val r = v1 * v2 r(Array(1)) should be(-1.0) } @@ -119,7 +119,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { mat(Array(2, 2)) = 6 mat(Array(2, 3)) = 1 - val vec: Tensor[Double] = new DenseTensor(Storage(Array(3.0, 1, 1))) + val vec: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(3.0, 1, 1))) val r = mat * vec r(Array(1)) should be(13.0) r(Array(2)) should be(22.0) @@ -136,7 +136,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { val mat1 = mat.t - val vec: Tensor[Double] = new DenseTensor(Storage(Array(3.0, 1, 1))) + val vec: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(3.0, 1, 1))) val r = mat1 * vec r(Array(1)) should be(15.0) r(Array(2)) should be(18.0) @@ -153,7 +153,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { val matrix = tensor(T(T(), T(), 1)).t() - val vec: Tensor[Double] = new DenseTensor(Storage(Array(3.0, 1, 1))) + val vec: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(3.0, 1, 1))) val r = matrix * vec r(Array(1)) should be(15.0) r(Array(2)) should be(18.0) @@ -260,7 +260,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { "vector / scalar" should "be correct" in { val s = 2.0 - val v: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) + val v: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) val r = v / s r(Array(1)) should be(0.5) r(Array(2)) should be(1.0) @@ -268,8 +268,8 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { } "vector / vector" should "be correct" in { - val v1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val v2: Tensor[Double] = new DenseTensor(Storage(Array(2.0, 1.0, -1.0))) + val v1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val v2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(2.0, 1.0, -1.0))) val r = v1 / v2 r(Array(1)) should be(0.5) r(Array(2)) should be(2.0) @@ -277,7 +277,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { } "-vector" should "be correct" in { - val v: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) + val v: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) val r = -v r(Array(1)) should be(-1.0) r(Array(2)) should be(-2.0) @@ -345,7 +345,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { 1, 2, 3, 4, 1, 2, 3, 4 ) - val a = new DenseTensor[Double](Storage(a_data), 1, Array(3, 4)) + val a = new DenseTensor[Double](new ArrayStorage(a_data), 1, Array(3, 4)) val b_data = Array( @@ -354,7 +354,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { 1, 2, 1, 2 ) - val b = new DenseTensor[Double](Storage(b_data), 1, Array(4, 2)) + val b = new DenseTensor[Double](new ArrayStorage(b_data), 1, Array(4, 2)) val c = Tensor[Double]() c.resize(Array(3, 2)) @@ -366,7 +366,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { 10, 20 ) - val expect_c = new DenseTensor[Double](Storage(expect_c_data), 1, Array(3, 2)) + val expect_c = new DenseTensor[Double](new ArrayStorage(expect_c_data), 1, Array(3, 2)) c.map(expect_c, (a, b) => { a should be(b +- 1e-6) a @@ -379,7 +379,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { 1, 2, 3, 4, 1, 2, 3, 4 ) - val a = new DenseTensor[Double](Storage(a_data), 1, Array(3, 4)) + val a = new DenseTensor[Double](new ArrayStorage(a_data), 1, Array(3, 4)) val b_data = Array( @@ -388,14 +388,14 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { 1, 2, 1, 2 ) - val b = new DenseTensor[Double](Storage(b_data), 1, Array(4, 2)) + val b = new DenseTensor[Double](new ArrayStorage(b_data), 1, Array(4, 2)) val m_data = Array( 1.0, 2, 1, 2, 1, 2 ) - val m = new DenseTensor[Double](Storage(m_data), 1, Array(3, 2)) + val m = new DenseTensor[Double](new ArrayStorage(m_data), 1, Array(3, 2)) val c = Tensor[Double]() c.addmm(m, a, b) @@ -406,7 +406,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { 11, 22 ) - val expect_c = new DenseTensor[Double](Storage(expect_c_data), 1, Array(3, 2)) + val expect_c = new DenseTensor[Double](new ArrayStorage(expect_c_data), 1, Array(3, 2)) c.map(expect_c, (a, b) => { a should be(b +- 1e-6) a @@ -414,8 +414,8 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { } "addr transpose" should "return correct value" in { - val v1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val v2: Tensor[Double] = new DenseTensor(Storage(Array(2.0, 0.0, -1.0))) + val v1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val v2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(2.0, 0.0, -1.0))) val tensor: Tensor[Double] = new DenseTensor(3, 3) tensor(Array(1, 1)) = 1 tensor(Array(1, 2)) = 2 @@ -430,26 +430,26 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { val r = Tensor[Double]() r.resize(Array(3, 3)) r.addr(1.0, mat, 1.0, v1, v2) - val expect_r = new DenseTensor(Storage(Array(3.0, 3.0, 4.0, + val expect_r = new DenseTensor(new ArrayStorage(Array(3.0, 3.0, 4.0, 6.0, 4.0, 4.0, 8.0, 4.0, 3.0)), 1, Array(3, 3)) r should be (expect_r) } "addr" should "return correct value" in { - val v1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val v2: Tensor[Double] = new DenseTensor(Storage(Array(2.0, 0.0, -1.0))) + val v1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val v2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(2.0, 0.0, -1.0))) val r = Tensor[Double]() r.resize(Array(3, 3)) r.addr(v1, v2) - r should be (new DenseTensor[Double](Storage(Array(2.0, 0.0, -1.0, + r should be (new DenseTensor[Double](new ArrayStorage(Array(2.0, 0.0, -1.0, 4.0, 0.0, -2.0, 6.0, 0.0, -3.0)), 1, Array(3, 3))) } "addr noncontiguous" should "return correct value" in { - val v1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) - val v2: Tensor[Double] = new DenseTensor(Storage(Array(2.0, 0.0, -1.0))) + val v1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) + val v2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(2.0, 0.0, -1.0))) val tensor: Tensor[Double] = new DenseTensor(3, 3, 2) tensor(Array(1, 1, 1)) = 1 tensor(Array(1, 2, 1)) = 2 @@ -465,7 +465,7 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { val r = Tensor[Double]() r.resize(Array(3, 3)) r.addr(1, mat, 1, v1, v2) - r should be (new DenseTensor[Double](Storage(Array(3.0, 3.0, 4.0, + r should be (new DenseTensor[Double](new ArrayStorage(Array(3.0, 3.0, 4.0, 6.0, 4.0, 4.0, 8.0, 4.0, 3.0)), 1, Array(3, 3))) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala index a6df82b6709..38183b21025 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorSpec.scala @@ -52,7 +52,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { "Construct with storage" should "return 1D vector" in { val storage = Array(1.0, 2.0, 3.0) - val t: Tensor[Double] = new DenseTensor(Storage(storage)) + val t: Tensor[Double] = new DenseTensor(new ArrayStorage(storage)) t.nDimension should be(1) t.size().length should be(1) t.size(1) should be(3) @@ -146,7 +146,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { } "One index on a 1d-dimension tensor" should "return value" in { - val t: Tensor[Double] = new DenseTensor(Storage(Array(3.0, 4, 5))) + val t: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(3.0, 4, 5))) t.valueAt(2) should be(4.0) } @@ -185,7 +185,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { "One index update a multi-dimension tensor with tensor" should "copy the tensor to the subset" in { val t: Tensor[Double] = new DenseTensor[Double](3, 2).fill(1) - val src: Tensor[Double] = new DenseTensor(Storage(Array(8.0, 9))) + val src: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(8.0, 9))) t(2) = src t(Array(1, 1)) should be(1) t(Array(1, 2)) should be(1) @@ -196,7 +196,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { } "One index update a 1d-dimension tensor" should "update the value" in { - val t: Tensor[Double] = new DenseTensor(Storage(Array(3.0, 4, 5))) + val t: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(3.0, 4, 5))) t(2) = 6 t.valueAt(1) should be(3.0) t.valueAt(2) should be(6.0) @@ -238,7 +238,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { t(Array(3, 1)) should be(7) t(Array(3, 2)) should be(6) - val src: Tensor[Double] = new DenseTensor(Storage(Array(9.0, 10))) + val src: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(9.0, 10))) t(T(T(2, 3), 1)) = src t(Array(1, 1)) should be(1) @@ -290,7 +290,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { } "clone" should "get a seperated tensor" in { - val t: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2, 3))) + val t: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2, 3))) val t1 = t.clone() t.isSameSizeAs(t1) should be(true) t1.isContiguous() should be(true) @@ -468,18 +468,18 @@ class DenseTensorSpec extends FlatSpec with Matchers { } "equals" should "be correct" in { - val t: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2, 3))) - val t1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2, 3))) - val t2: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2, 4))) + val t: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2, 3))) + val t1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2, 3))) + val t2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2, 4))) t == t1 should be(true) t == t2 should be(false) } "hashCode" should "be correct" in { - val t: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2, 3))) - val t1: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2, 3))) - val t2: Tensor[Double] = new DenseTensor(Storage(Array(1.0, 2, 4))) + val t: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2, 3))) + val t1: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2, 3))) + val t2: Tensor[Double] = new DenseTensor(new ArrayStorage(Array(1.0, 2, 4))) t.hashCode() == t1.hashCode() should be(true) t.hashCode() == t2.hashCode() should be(false) @@ -494,7 +494,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { t = Tensor.scalar[Double](1) t.toString should be("Scalar(1.0)") - t = new DenseTensor(Storage(Array(1.0, 2.0, 3.0))) + t = new DenseTensor(new ArrayStorage(Array(1.0, 2.0, 3.0))) val OneD_STRING = "1.0\n" + "2.0\n" + @@ -692,7 +692,8 @@ class DenseTensorSpec extends FlatSpec with Matchers { } "Tensor to BreezeMatrix" should "correct" in { - val tensor = new DenseTensor[Double](Storage[Double](Array(1.0, 2, 3, 4)), 1, Array(2, 2)) + val tensor = new DenseTensor[Double]( + new ArrayStorage[Double](Array(1.0, 2, 3, 4)), 1, Array(2, 2)) val matrix = tensor.toBreezeMatrix() matrix.isTranspose should be(true) matrix(0, 0) should be(1.0) @@ -727,7 +728,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { } "Tensor to BreezeVector" should "correct" in { - val tensor = new DenseTensor[Double](Storage(Array(1.0, 2, 3, 4))) + val tensor = new DenseTensor[Double](new ArrayStorage(Array(1.0, 2, 3, 4))) val vector = tensor.toBreezeVector() vector(0) should be(1.0) vector(1) should be(2.0) @@ -745,7 +746,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { } "Tensor to MLMatrix" should "correct" in { - val tensor = new DenseTensor(Storage(Array(1.0, 2, 3, 4)), 1, Array(2, 2)) + val tensor = new DenseTensor(new ArrayStorage(Array(1.0, 2, 3, 4)), 1, Array(2, 2)) val matrix = tensor.toMLlibMatrix() matrix.isTransposed should be(true) matrix(0, 0) should be(1.0) @@ -780,7 +781,7 @@ class DenseTensorSpec extends FlatSpec with Matchers { } "Tensor to MLVector" should "correct" in { - val tensor = new DenseTensor(Storage(Array(1.0, 2, 3, 4))) + val tensor = new DenseTensor(new ArrayStorage(Array(1.0, 2, 3, 4))) val vector = tensor.toMLlibVector() vector(0) should be(1.0) vector(1) should be(2.0) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala new file mode 100644 index 00000000000..c265b46da32 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala @@ -0,0 +1,72 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.tensor + +import com.intel.analytics.bigdl.mkl.MklDnn +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} + +class DnnTensorSpec extends BigDLSpecHelper { + "nElement" should "be correct" in { + val tensor = DnnTensor[Float](3, 4, 5) + tensor.nElement() should be(3 * 4 * 5) + } + + "DnnTensor" should "only support float" in { + intercept[IllegalArgumentException] { + val t = DnnTensor[Double](3, 4, 5) + } + } + + "Copy" should "be correct" in { + val heapTensor = Tensor[Float](T(1, 2, 3, 4)) + val dnnTensor1 = DnnTensor[Float](4) + dnnTensor1.copy(heapTensor) + val dnnTensor2 = DnnTensor[Float](4) + dnnTensor2.copy(dnnTensor1) + val heapTensor2 = Tensor[Float](4) + heapTensor2.copy(dnnTensor2) + heapTensor2 should be(heapTensor) + } + + "release" should "be correct" in { + val tensor = DnnTensor[Float](3, 4, 5) + tensor.isReleased() should be(false) + tensor.release() + tensor.isReleased() should be(true) + } + + "resize" should "be correct" in { + val tensor = DnnTensor[Float](3, 4) + tensor.size() should be(Array(3, 4)) + tensor.resize(Array(2, 3)) + tensor.size() should be(Array(2, 3)) + tensor.resize(2) + tensor.size(1) should be(2) + tensor.resize(Array(5, 6, 7)) + tensor.size() should be(Array(5, 6, 7)) + tensor.size(2) should be(6) + } + + "add" should "be correct" in { + val heapTensor1 = Tensor[Float](T(1, 2, 3, 4)) + val heapTensor2 = Tensor[Float](T(2, 5, 1, 7)) + val dnnTensor1 = DnnTensor[Float](4).copy(heapTensor1) + val dnnTensor2 = DnnTensor[Float](4).copy(heapTensor2) + dnnTensor1.add(dnnTensor2) + val heapTensor3 = Tensor[Float](4).copy(dnnTensor1) + heapTensor3 should be(Tensor[Float](T(3, 7, 4, 11))) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 563538e9a3d..1bf9f4aa3bd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -44,7 +44,24 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.utils.serializer.TestModule", "com.intel.analytics.bigdl.utils.ExceptionTest", "com.intel.analytics.bigdl.utils.serializer.SubModuleOne", - "com.intel.analytics.bigdl.utils.serializer.SubModuleTwo" + "com.intel.analytics.bigdl.utils.serializer.SubModuleTwo", + "com.intel.analytics.bigdl.nn.mkldnn.AvgPooling", + "com.intel.analytics.bigdl.nn.mkldnn.CAddTable", + "com.intel.analytics.bigdl.nn.mkldnn.ConcatTable", + "com.intel.analytics.bigdl.nn.mkldnn.DnnBase", + "com.intel.analytics.bigdl.nn.mkldnn.Identity", + "com.intel.analytics.bigdl.nn.mkldnn.Input", + "com.intel.analytics.bigdl.nn.mkldnn.JoinTable", + "com.intel.analytics.bigdl.nn.mkldnn.Linear", + "com.intel.analytics.bigdl.nn.mkldnn.LRN", + "com.intel.analytics.bigdl.nn.mkldnn.MaxPooling", + "com.intel.analytics.bigdl.nn.mkldnn.ReLU", + "com.intel.analytics.bigdl.nn.mkldnn.ReorderMemory", + "com.intel.analytics.bigdl.nn.mkldnn.SelectTable", + "com.intel.analytics.bigdl.nn.mkldnn.Sequential", + "com.intel.analytics.bigdl.nn.mkldnn.SoftMax", + "com.intel.analytics.bigdl.nn.mkldnn.SpatialBatchNormalization", + "com.intel.analytics.bigdl.nn.mkldnn.SpatialConvolution" ) // Maybe one serial test class contains multiple module test From 0be180a7149a20e94ef4ab9a341e2e932a9a3e59 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 29 Jun 2018 12:26:17 +0800 Subject: [PATCH 0797/1065] delete a dupilcated updateHyperParameter (#2564) --- .../scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala | 1 - 1 file changed, 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index eb48b007407..f4c8ae64297 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -517,7 +517,6 @@ object Optimizer { */ private[bigdl] def getHyperParameterLog(optimMethods: Map[String, OptimMethod[_]]): String = { optimMethods.map{ case (moduleName, optimMethod) => - optimMethod.updateHyperParameter() val log = optimMethod.getHyperParameter() if (log.isEmpty) { log From 2a0777c39a9d4da20717ba44cb7e1b264b7399c3 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 29 Jun 2018 20:59:11 +0800 Subject: [PATCH 0798/1065] Update integration test (#2562) * update integration test * some update * update integration test * update robot * update robot * update robot * update robot * update * update robot --- dl/src/test/common.robot | 54 +----------- dl/src/test/integration-test.robot | 134 ++++++++++++----------------- 2 files changed, 57 insertions(+), 131 deletions(-) diff --git a/dl/src/test/common.robot b/dl/src/test/common.robot index cd31436d09e..b3ceeb9b3d8 100644 --- a/dl/src/test/common.robot +++ b/dl/src/test/common.robot @@ -7,62 +7,14 @@ Library OperatingSystem Library XML *** Keywords *** -Operate Vertical - [Documentation] Post operation to configuring service. Operation allowed: deploy, stop, suspend, resume, clear, reset - [Arguments] ${verticalId} ${operation} ${expectStatus} - Create Session host http://${ardaHost}:10021 - Log To Console Operate vertical ${verticalId} with ${operation} ... - ${resp}= Post Request host /vertical/${verticalId}/operation data=${operation} - ${statusCode}= Convert To String ${resp.status_code} - Should Start With ${statusCode} 20 - Wait Until Keyword Succeeds 10 min 5 sec Status Equal ${verticalId} ${expectStatus} - -Status Equal - [Documentation] Match certain vertical's status - [Arguments] ${verticalId} ${status} - Create Session host http://${ardaHost}:10021 - Log To Console Get vertical ${verticalId}'s status ... - ${resp}= Get Request host /vertical/${verticalId} - ${statusCode}= Convert To String ${resp.status_code} - Should Start With ${statusCode} 20 - ${json}= To Json ${resp.content} - Dictionary Should Contain Key ${json} status - ${realStatus}= Get From Dictionary ${json} status - Log To Console Expected=${status}, Actual=${realStatus} - Should Be Equal As Strings ${status} ${realStatus} - BigDL Test - [Arguments] ${run_keyword} ${verticals} - @{verticalList}= Split String ${verticals} separator=, - :FOR ${vertical} IN @{verticalList} - \ Operate Vertical ${vertical} start running - \ Run KeyWord ${run_keyword} - [Teardown] Stop Verticals @{verticalList} - -Stop Verticals - [Arguments] @{verticalList} - Remove Environment Variable http_proxy - :FOR ${vertical} IN @{verticalList} - \ Operate Vertical ${vertical} stop deployed/stopped - -Check DataSource - Create Session webhdfs http://${public_hdfs_host}:50070 - ${resp}= Get Request webhdfs /webhdfs/v1/${imagenet}?op=GETFILESTATUS - Should Contain ${resp.content} DIRECTORY - ${resp}= Get Request webhdfs /webhdfs/v1/${mnist}?op=GETFILESTATUS - Should Contain ${resp.content} DIRECTORY - ${resp}= Get Request webhdfs /webhdfs/v1/${cifar}?op=GETFILESTATUS - Should Contain ${resp.content} DIRECTORY + [Arguments] ${run_keyword} + Log To Console Run keyword ${run_keyword} + Run KeyWord ${run_keyword} Prepare DataSource And Verticals - Check DataSource - Check Verticals Get BigDL Version -Check Verticals - :FOR ${vertical} IN @{verticals} - \ Status Equal ${vertical} deployed/stopped - Run Shell [Arguments] ${program} ${rc} ${output}= Run and Return RC and Output ${program} diff --git a/dl/src/test/integration-test.robot b/dl/src/test/integration-test.robot index 85e6b208678..c6b98c7a6ed 100644 --- a/dl/src/test/integration-test.robot +++ b/dl/src/test/integration-test.robot @@ -5,29 +5,13 @@ Suite Setup Prepare DataSource And Verticals Suite Teardown Delete All Sessions Test template BigDL Test -*** Variables *** -@{verticals} ${spark_200_3_vid} ${spark_210_3_vid} ${hdfs_264_3_vid} ${spark_tf_210_3_vid} ${spark_tf_163_3_vid} - -*** Test Cases *** SuiteName VerticalId -1 Spark2.0 Test Suite ${spark_200_3_vid} -2 Spark2.1 Test Suite ${spark_210_3_vid} -3 Hdfs Test Suite ${hdfs_264_3_vid} -4 Quantization Test Suite ${hdfs_264_3_vid} -5 PySpark2.1 Test Suite ${spark_tf_210_3_vid} -6 PySpark1.6 Test Suite ${spark_tf_163_3_vid} -7 Yarn Test Suite ${hdfs_264_3_vid} - -# predefined service masters: -# hdfs_264_3_master -# spark_200_3_master -# spark_210_3_master -# spark_151_3_master -# spark_163_3_master - -# predefined datasource -# mnist_data_source -# cifar_data_source -# imagenet_data_source +*** Test Cases *** SuiteName +1 Spark2.2 Test Suite +2 Hdfs Test Suite +3 Spark1.6 on Yarn Test Suite +4 Spark2.3 on Yarn Test Suite +5 Quantization Test Suite +6 PySpark2.2 Test Suite *** Keywords *** @@ -35,18 +19,18 @@ Build SparkJar [Arguments] ${spark_version} ${build}= Catenate SEPARATOR=/ ${curdir} make-dist.sh Log To Console ${spark_version} - Log To Console start to build jar + Log To Console start to build jar ${build} -P ${spark_version} Run ${build} -P ${spark_version} Remove File ${jar_path} - Move File spark/dl/target/bigdl-${version}-jar-with-dependencies.jar ${jar_path} + Copy File spark/dl/target/bigdl-${version}-jar-with-dependencies.jar ${jar_path} Log To Console build jar finished DownLoad Input - ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.6.5/bin hadoop - Run ${hadoop} fs -get ${mnist_data_source} ./ - Log To Console got mnist data!! - Run ${hadoop} fs -get ${cifar_data_source} ./ - Log To Console got cifar data!! + ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.7.2/bin hadoop + Run ${hadoop} fs -get ${mnist_data_source} /tmp/mnist + Log To Console got mnist data!! ${hadoop} fs -get ${mnist_data_source} /tmp/mnist + Run ${hadoop} fs -get ${cifar_data_source} /tmp/cifar + Log To Console got cifar data!! ${hadoop} fs -get ${cifar_data_source} /tmp/cifar Run ${hadoop} fs -get ${public_hdfs_master}:9000/text_data /tmp/ Run tar -zxvf /tmp/text_data/20news-18828.tar.gz -C /tmp/text_data Log To Console got textclassifier data @@ -66,7 +50,7 @@ DownLoad Input Remove Input Remove Directory model recursive=True Remove Directory models recursive=True - Remove Directory mnist recursive=True + Remove Directory /tmp/mnist recursive=True Remove File input.txt Remove Directory simple-examples recursive=True Remove File simple-examples.tgz @@ -75,50 +59,43 @@ Remove Input Run Spark Test [Arguments] ${submit} ${spark_master} DownLoad Input - Log To Console begin lenet Train - Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 84 --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 336 -e 3 + Log To Console begin lenet Train ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 5g --executor-cores 16 --total-executor-cores 32 --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 256 -e 3 + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 5g --executor-cores 16 --total-executor-cores 32 --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 256 -e 3 Log To Console begin lenet Train local[4] - Run Shell ${submit} --master local[4] --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ./mnist -b 120 -e 1 + Run Shell ${submit} --master local[4] --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f /tmp/mnist -b 120 -e 1 Log To Console begin autoencoder Train - Run Shell ${submit} --master ${spark_master} --executor-cores 4 --total-executor-cores 12 --class com.intel.analytics.bigdl.models.autoencoder.Train ${jar_path} -b 120 -e 1 -f ./mnist + Run Shell ${submit} --master ${spark_master} --executor-cores 4 --total-executor-cores 8 --class com.intel.analytics.bigdl.models.autoencoder.Train ${jar_path} -b 120 -e 1 -f /tmp/mnist Log To Console begin PTBWordLM - Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 40g --executor-memory 100g --executor-cores 8 --total-executor-cores 8 --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM ${jar_path} -f ./simple-examples/data -b 120 --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 1 --learningRateDecay 0.001 --keepProb 0.5 --overWrite + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 40g --executor-memory 40g --executor-cores 8 --total-executor-cores 8 --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM ${jar_path} -f ./simple-examples/data -b 120 --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 1 --learningRateDecay 0.001 --keepProb 0.5 --overWrite Log To Console begin resnet Train - Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 5g --executor-memory 5g --executor-cores 8 --total-executor-cores 32 --class com.intel.analytics.bigdl.models.resnet.Train ${jar_path} -f ./cifar --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 5g --executor-memory 5g --executor-cores 8 --total-executor-cores 32 --class com.intel.analytics.bigdl.models.resnet.TrainCIFAR10 ${jar_path} -f /tmp/cifar --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 Log To Console begin DLClassifierLeNet - Run Shell ${submit} --master ${spark_master} --executor-cores 24 --total-executor-cores 24 --driver-memory 60g --executor-memory 200g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 1200 -f ./mnist --maxEpoch 1 + Run Shell ${submit} --master ${spark_master} --executor-cores 16 --total-executor-cores 16 --driver-memory 5g --executor-memory 30g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 1200 -f /tmp/mnist --maxEpoch 1 Log To Console begin rnn Train Run Shell ${submit} --master ${spark_master} --driver-memory 5g --executor-memory 5g --executor-cores 12 --total-executor-cores 12 --class com.intel.analytics.bigdl.models.rnn.Train ${jar_path} -f ./ -s ./models --nEpochs 1 --checkpoint ./model/ -b 12 Log To Console begin inceptionV1 train - Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 60g --executor-memory 200g --executor-cores 24 --total-executor-cores 24 --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 24 -f ${imagenet_test_data_source} --learningRate 0.1 -e 1 + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 20g --executor-memory 40g --executor-cores 10 --total-executor-cores 20 --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 40 -f ${imagenet_test_data_source} --learningRate 0.1 -i 100 Log To Console begin text classification Run Shell ${submit} --master ${spark_master} --driver-memory 5g --executor-memory 5g --total-executor-cores 32 --executor-cores 8 --class com.intel.analytics.bigdl.example.textclassification.TextClassifier ${jar_path} --batchSize 128 --baseDir /tmp/text_data --partitionNum 32 Remove Input - -Spark2.0 Test Suite - Build SparkJar spark_2.x - Set Environment Variable SPARK_HOME /opt/work/spark-2.0.0-bin-hadoop2.7 - ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.0.0-bin-hadoop2.7/bin spark-submit - Run Spark Test ${submit} ${spark_200_3_master} - -Spark2.1 Test Suite +Spark2.2 Test Suite Build SparkJar spark_2.x - Set Environment Variable SPARK_HOME /opt/work/spark-2.1.0-bin-hadoop2.7 - ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.1.0-bin-hadoop2.7/bin spark-submit - Run Spark Test ${submit} ${spark_210_3_master} + Set Environment Variable SPARK_HOME /opt/work/spark-2.2.0-bin-hadoop2.7 + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.2.0-bin-hadoop2.7/bin spark-submit + Run Spark Test ${submit} ${spark_22_master} Hdfs Test Suite - Set Environment Variable hdfsMaster ${hdfs_264_3_master} + Set Environment Variable hdfsMaster ${hdfs_272_master} Set Environment Variable mnist ${mnist_data_source} Set Environment Variable s3aPath ${s3a_path} - Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.HdfsSpec -DhdfsMaster=${hdfs_264_3_master} -Dmnist=${mnist_data_source} -P integration-test -DforkMode=never + Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.HdfsSpec -DhdfsMaster=${hdfs_272_master} -Dmnist=${mnist_data_source} -P integration-test -DforkMode=never Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.S3Spec -Ds3aPath=${s3a_path} -P integration-test -DforkMode=never Remove Environment Variable hdfsMaster mnist s3aPath Quantization Test Suite - ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.6.5/bin hadoop + ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.7.2/bin hadoop Run ${hadoop} fs -get ${mnist_data_source} /tmp/ Log To Console got mnist data!! Run ${hadoop} fs -get ${cifar_data_source} /tmp/ @@ -129,48 +106,45 @@ Quantization Test Suite Set Environment Variable resnetfp32model ${public_hdfs_master}:9000/resnet4IT4J1.7B4.bigdl Remove Environment Variable mnist cifar10 lenetfp32model resnetfp32model +Spark1.6 on Yarn Test Suite + Yarn Test Suite spark_1.6 /opt/work/spark-1.6.0-bin-hadoop2.6 + +Spark2.3 on Yarn Test Suite + Yarn Test Suite spark_2.x /opt/work/spark-2.3.1-bin-hadoop2.7 + Yarn Test Suite + [Arguments] ${bigdl_spark_version} ${spark_home} DownLoad Input - Build SparkJar spark_2.x - Set Environment Variable SPARK_HOME /opt/work/spark-2.0.0-bin-hadoop2.7 + Build SparkJar ${bigdl_spark_version} + Set Environment Variable SPARK_HOME ${spark_home} Set Environment Variable http_proxy ${http_proxy} Set Environment Variable https_proxy ${https_proxy} - ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.0.0-bin-hadoop2.7/bin spark-submit + ${submit}= Catenate SEPARATOR=/ ${spark_home} bin spark-submit Log To Console begin DLClassifierLeNet - Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 1 --driver-memory 150g --executor-memory 60g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 120 -f ./mnist --maxEpoch 1 + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 1 --driver-memory 20g --executor-memory 60g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 1200 -f /tmp/mnist --maxEpoch 1 Log To Console begin text classification - Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --conf spark.yarn.executor.memoryOverhead=40000 --executor-cores 2 --num-executors 2 --driver-memory 150g --executor-memory 40g --class com.intel.analytics.bigdl.example.textclassification.TextClassifier ${jar_path} --batchSize 8 --baseDir /tmp/text_data --partitionNum 4 + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --conf spark.yarn.executor.memoryOverhead=40000 --executor-cores 10 --num-executors 2 --driver-memory 20g --executor-memory 40g --class com.intel.analytics.bigdl.example.textclassification.TextClassifier ${jar_path} --batchSize 240 --baseDir /tmp/text_data --partitionNum 4 Log To Console begin lenet - Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 120 -e 3 + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 20g --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 120 -e 3 Log To Console begin autoencoder Train - Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.autoencoder.Train ${jar_path} -b 120 -e 1 -f ./mnist + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 20g --class com.intel.analytics.bigdl.models.autoencoder.Train ${jar_path} -b 120 -e 1 -f /tmp/mnist Log To Console begin resnet Train - Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.resnet.Train ${jar_path} -f ./cifar --batchSize 120 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 20g --class com.intel.analytics.bigdl.models.resnet.TrainCIFAR10 ${jar_path} -f /tmp/cifar --batchSize 120 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 Log To Console begin rnn Train - Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.rnn.Train ${jar_path} -f ./ -s ./models --nEpochs 1 --checkpoint ./model/ -b 120 + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 20g --class com.intel.analytics.bigdl.models.rnn.Train ${jar_path} -f ./ -s ./models --nEpochs 1 --checkpoint ./model/ -b 120 Log To Console begin PTBWordLM - Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 2 --num-executors 2 --driver-memory 150g --executor-memory 40g --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM ${jar_path} -f ./simple-examples/data -b 8 --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 1 --learningRateDecay 0.001 --keepProb 0.5 --overWrite + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 8 --num-executors 1 --driver-memory 20g --executor-memory 40g --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM ${jar_path} -f ./simple-examples/data -b 120 --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 1 --learningRateDecay 0.001 --keepProb 0.5 --overWrite Log To Console begin inceptionV1 train - Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 2 --num-executors 2 --driver-memory 150g --executor-memory 40g --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 8 -f ${imagenet_test_data_source} --learningRate 0.1 -e 1 - Set Environment Variable PYSPARK_DRIVER_PYTHON /var/jenkins_home/venv/bin/python - Set Environment Variable PYSPARK_PYTHON ./venv.zip/venv/bin/python - Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --archives /var/jenkins_home/venv.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 200 --action train --endTriggerType epoch --endTriggerNum 1 - Remove Environment Variable http_proxy https_proxy PYSPARK_DRIVER_PYTHON PYSPARK_PYTHON + Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 2 --driver-memory 20g --executor-memory 40g --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 40 -f ${imagenet_test_data_source} --learningRate 0.1 -i 100 + Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 200 --action train --endTriggerType epoch --endTriggerNum 1 + Remove Environment Variable http_proxy https_proxy Remove Input -PySpark2.1 Test Suite +PySpark2.2 Test Suite Build SparkJar spark_2.x - Set Environment Variable SPARK_HOME /opt/work/spark-2.1.0-bin-hadoop2.7 - ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.1.0-bin-hadoop2.7/bin spark-submit - Run Shell ${submit} --master ${spark_tf_210_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 - -PySpark1.6 Test Suite - DownLoad Input - Build SparkJar spark_1.6 - Set Environment Variable SPARK_HOME /opt/work/spark-1.6.3-bin-hadoop2.6 - ${submit}= Catenate SEPARATOR=/ /opt/work/spark-1.6.3-bin-hadoop2.6/bin spark-submit - Run Shell ${submit} --master ${spark_tf_163_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 - Remove Input + Set Environment Variable SPARK_HOME /opt/work/spark-2.2.0-bin-hadoop2.7 + ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.2.0-bin-hadoop2.7/bin spark-submit + Run Shell ${submit} --master ${spark_22_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 10g --executor-cores 14 --total-executor-cores 28 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 From 8ba0964f9f13ba1528125a0b0209b4822c7c9e79 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 29 Jun 2018 20:59:27 +0800 Subject: [PATCH 0799/1065] fix: readObject for ModelInfo (#2572) --- .../bigdl/dllib/models/utils/ModelBroadcast.scala | 7 +++++++ .../dllib/models/utils/ModelBroadcastSpec.scala | 13 +++++++++++++ 2 files changed, 20 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 4bc7530875d..cf412f436c1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -191,6 +191,13 @@ private[bigdl] class ModelInfo[T: ClassTag](val uuid: String, @transient var mod out.writeObject(cloned) CachedModels.add(uuid, cloned) } + + @throws(classOf[IOException]) + private def readObject(in: ObjectInputStream): Unit = { + in.defaultReadObject() + model = in.readObject().asInstanceOf[Module[T]] + CachedModels.add(uuid, model) + } } private[bigdl] object ModelInfo { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala index 17001e62509..7fe7f2e1748 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor +import org.apache.commons.lang3.SerializationUtils import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -138,6 +139,18 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } + "model info serialized" should "not be null" in { + val model = LeNet5(10).cloneModule() + val info = ModelInfo[Float]("124339", model) + + val newInfo = SerializationUtils.clone(info) + + newInfo.model should not be (null) + info.model.toString() should be (newInfo.model.toString()) + info.model.parameters()._1 should be (newInfo.model.parameters()._1) + info.model.parameters()._2 should be (newInfo.model.parameters()._2) + } + after { if (sc != null) { sc.stop() From 421e7e1faefacc1a7c063afbfe3662e3165448a4 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 4 Jul 2018 18:18:13 +0800 Subject: [PATCH 0800/1065] bump version to 0.7.0-SNAPSHOT (#2577) --- dist/pom.xml | 2 +- dl/pom.xml | 6 +++--- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- .../dllib/src/main/resources/bigdl-version-info.properties | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index 520fb487b94..e58a6787dd2 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.6.0-SNAPSHOT + 0.7.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index 0a9485e3ceb..7e15acbc157 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.6.0-SNAPSHOT + 0.7.0-SNAPSHOT 4.0.0 @@ -79,7 +79,7 @@ com.intel.analytics.bigdl.core.dist all - 0.6.0-SNAPSHOT + 0.7.0-SNAPSHOT ${bigdl-core-all-scope} @@ -308,7 +308,7 @@ com.intel.analytics.bigdl.core.dist ${os-flag} - 0.6.0-SNAPSHOT + 0.7.0-SNAPSHOT pom diff --git a/pom.xml b/pom.xml index 5a801ff17ed..e13294f5a94 100644 --- a/pom.xml +++ b/pom.xml @@ -5,7 +5,7 @@ bigdl-parent com.intel.analytics.bigdl - 0.6.0-SNAPSHOT + 0.7.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/1.5-plus/pom.xml b/scala/common/spark-version/1.5-plus/pom.xml index a95e1f0a027..f6aae4b13a2 100644 --- a/scala/common/spark-version/1.5-plus/pom.xml +++ b/scala/common/spark-version/1.5-plus/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.6.0-SNAPSHOT + 0.7.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/2.0/pom.xml b/scala/common/spark-version/2.0/pom.xml index f69d5f1ebac..3680ab0cd97 100644 --- a/scala/common/spark-version/2.0/pom.xml +++ b/scala/common/spark-version/2.0/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.6.0-SNAPSHOT + 0.7.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/pom.xml b/scala/common/spark-version/pom.xml index 3c6a41fbb4c..bc754938193 100644 --- a/scala/common/spark-version/pom.xml +++ b/scala/common/spark-version/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.6.0-SNAPSHOT + 0.7.0-SNAPSHOT 4.0.0 diff --git a/scala/dllib/src/main/resources/bigdl-version-info.properties b/scala/dllib/src/main/resources/bigdl-version-info.properties index 66591f21d75..119598350e9 100644 --- a/scala/dllib/src/main/resources/bigdl-version-info.properties +++ b/scala/dllib/src/main/resources/bigdl-version-info.properties @@ -16,4 +16,4 @@ #BigDL version info config -version=0.6.0-SNAPSHOT \ No newline at end of file +version=0.7.0-SNAPSHOT \ No newline at end of file From 23611dc0f82d5db83048f1991994e55de71ff52f Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 6 Jul 2018 15:38:18 +0800 Subject: [PATCH 0801/1065] add vgg validation example (#2582) --- .../dllib/example/loadmodel/DatasetUtil.scala | 16 ++++++++++++++++ .../dllib/example/loadmodel/ModelValidator.scala | 3 +++ 2 files changed, 19 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala index 23b6f34a756..462b636c446 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala @@ -126,3 +126,19 @@ object ResNetPreprocessor { } } } + +object VGGPreprocessor { + val imageSize = 224 + + def rdd(path: String, batchSize: Int, sc: SparkContext) + : RDD[Sample[Float]] = { + val data = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) + val transfomer = PixelBytesToMat() -> Resize(256, 256) -> + CenterCrop(imageSize, imageSize) -> ChannelNormalize(123, 117, 104) -> + MatToTensor[Float]() -> ImageFrameToSample[Float](targetKeys = Array(ImageFeature.label)) + val imgFrame = transfomer(data) + val validImageFeatures = imgFrame.toDistributed().rdd + validImageFeatures.map(x => x[Sample[Float]](ImageFeature.sample)) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/ModelValidator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/ModelValidator.scala index 093bf622122..f2b9d10fdad 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/ModelValidator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/ModelValidator.scala @@ -130,6 +130,9 @@ object ModelValidator { case "resnet" => (Module.loadModule[Float](param.modelPath), ResNetPreprocessor.rdd(valPath, param.batchSize, sc, BigDlModel)) + case "vgg16" => + (Module.loadModule[Float](param.modelPath), + VGGPreprocessor.rdd(valPath, param.batchSize, sc)) } case _ => throw new IllegalArgumentException(s"${ param.modelType } is not" + From 8ea6d7778a110b66e3c70792220cd4a781e0fadd Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 10 Jul 2018 10:57:47 +0800 Subject: [PATCH 0802/1065] [Issue fix] - Fix MM layer multi forward/backward issue (#2583) * fix mm issue * refinement --- .../intel/analytics/bigdl/dllib/nn/MM.scala | 6 ++- .../analytics/bigdl/dllib/nn/MMSpec.scala | 39 +++++++++++++++++++ 2 files changed, 43 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MM.scala index a91b84c8466..0ca9a8dd263 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MM.scala @@ -84,13 +84,14 @@ class MM[T: ClassTag]( s"the matrix sizes are ${ma.size(3)} and ${mb.size(2)}") output.resize(ma.size(1), ma.size(2), mb.size(3)) - output.bmm(ma, mb) + output.baddbmm(ev.fromType[Float](0.0f), ev.fromType[Float](1.0f), ma, mb) } output } override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + var (ma, mb) = checkInputFormat(input) gradInput[Tensor[T]](1).resizeAs(ma) @@ -115,7 +116,8 @@ class MM[T: ClassTag]( require(mb.dim() == 3, "second input tensor must be 3D" + s"second input dim ${mb.dim()}") - (2, 3, t => m1 => m2 => t.bmm(m1, m2)) + (2, 3, t => m1 => m2 => t.baddbmm(ev.fromType[Float](0.0f), ev.fromType[Float](1.0f), + m1, m2)) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala index cc2d0859246..0506a301419 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MMSpec.scala @@ -61,6 +61,45 @@ class MMSpec extends FlatSpec with Matchers { m1 should not equal m3 m1 should not equal m4 } + + "MM forward multi times" should "work properly" in { + val mm = MM[Float]() + val input1 = Tensor[Float](2, 3, 3).randn() + val input2 = Tensor[Float](2, 3, 3).randn() + val input = T(1 -> input1, 2 -> input2) + + val res1 = Tensor[Float](2, 3, 3) + + val res2 = Tensor[Float](2, 3, 3) + + res1.copy(mm.forward(input)) + + res2.copy(mm.forward(input)) + + res1 should be (res2) + } + + "MM backward multi times" should "work properly" in { + val mm = MM[Float]() + val input1 = Tensor[Float](2, 3, 3).randn() + val input2 = Tensor[Float](2, 3, 3).randn() + val input = T(1 -> input1, 2 -> input2) + + val gradOutput = Tensor[Float](2, 3, 3).randn() + + val bres1 = mm.backward(input, gradOutput) + + val res1 = T(1 -> Tensor[Float](2, 3, 3).copy(bres1(1)), + 2 -> Tensor[Float](2, 3, 3).copy(bres1(2))) + + val bres2 = mm.backward(input, gradOutput) + + val res2 = T(1 -> Tensor[Float](2, 3, 3).copy(bres2(1)), + 2 -> Tensor[Float](2, 3, 3).copy(bres2(2))) + + res1 should be (res2) + + } } class MMSerialTest extends ModuleSerializationTest { From 5f987dd18f60a335e8f4f4dd1eb01aff29cfd7bf Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Tue, 10 Jul 2018 11:29:06 +0800 Subject: [PATCH 0803/1065] [Bug Fix]clear preTopology's output while cloneCells (#2585) * clear preTopology's output while cloneCells * fix unit test --- .../com/intel/analytics/bigdl/dllib/nn/Recurrent.scala | 6 ++++++ .../bigdl/dllib/nn/abstractnn/AbstractModule.scala | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index aab7f82a829..456f3a41a4f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -173,6 +173,12 @@ class Recurrent[T : ClassTag]( val cloneCell = cells.head.cloneModule() cloneCell.parameters()._1.map(_.set()) cloneCell.parameters()._2.map(_.set()) + // preTopology's output is useless here, clear it. + // Notice: preTopology is a merge output of all i2h, + // it's a bigdl tensor, and shouldn't be cloned. + if (cloneCell.preTopology != null) { + cloneCell.preTopology.output.set() + } while (t < times) { cells += cloneCell.cloneModule() .asInstanceOf[Cell[T]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index e05960c70d0..aa7335c8b02 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -467,7 +467,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * Clone the model * @return */ - final def cloneModule(): AbstractModule[A, B, T] = { + final def cloneModule(): this.type = { SerializationUtils.clone(this) } From 3cf891066504cd19e07499794ffbf400b359181a Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Jul 2018 03:47:43 -0400 Subject: [PATCH 0804/1065] fix: distribute predictor will send model twice when do `mapPartition`. (#2580) * fix: distribute predictor will send model twice when do `mapPartition`. The `predictClass`, `predict` and `predictImage` will call the relevant methods of object `Predictor`. Why we do this? Because every these methods uses the ClassTag `T`. If we do these jobs in the methods of class`Predictor`, when we do `mapPartition`, Spark will find all used values and do serialization. The `T` is the argument of constructor, the serialization will package the whole `Predictor` class, which contains the`model`. Then it will send a duplicate model to the workers. So we should move these methods to object `Predictor`. * comment: add some comments to PredictorSpec --- .../bigdl/dllib/optim/Predictor.scala | 131 +++++++++++------- .../bigdl/dllib/optim/PredictorSpec.scala | 3 + 2 files changed, 83 insertions(+), 51 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index b6f6d79284d..e75159a1f37 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -16,18 +16,15 @@ package com.intel.analytics.bigdl.optim -import java.util.UUID - import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{MiniBatch, PaddingParam, Sample, SampleToMiniBatch, Transformer, Utils, DataSet => _} -import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast, ModelInfo} +import com.intel.analytics.bigdl.dataset.{MiniBatch, PaddingParam, Sample, SampleToMiniBatch, Transformer, DataSet => _} +import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame} import com.intel.analytics.bigdl.utils.{T, Table} import org.apache.spark.rdd.RDD -import Predictor._ import scala.reflect.ClassTag @@ -121,34 +118,40 @@ object Predictor { } out.asInstanceOf[Array[Activity]] } -} -/** - * Predictor for distributed data - * @param model BigDL model - * @param featurePaddingParam featurePaddingParam if the inputs have variant size - * @param batchPerPartition batch size per partition, default is 4 - */ -class Predictor[T: ClassTag] private[optim]( - model: Module[T], - featurePaddingParam: Option[PaddingParam[T]] = None, - batchPerPartition: Int = 4) - (implicit ev: TensorNumeric[T]) extends Serializable { + def predictImage[T: ClassTag](imageFrame: DistributedImageFrame, + outputLayer: String = null, + shareBuffer: Boolean = false, + predictKey: String = ImageFeature.predict, + batchPerPartition: Int, + model: Module[T], + featurePaddingParam: Option[PaddingParam[T]])( + implicit ev: TensorNumeric[T]): DistributedImageFrame = { + val localBatchPerPartition = batchPerPartition - def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { - val result = predict(dataSet, batchSize, true) - result.mapPartitions { partition => - partition.map(output => { - val _output = output.toTensor[T] - require(_output.dim() == 1, s"Predictor.predictClass:" + - s"Only support one sample has one label, but got ${_output.dim()} label") - ev.toType[Int](_output.max(1)._2.valueAt(1)) + val rdd = imageFrame.asInstanceOf[DistributedImageFrame].rdd + val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, model.evaluate()) + val partitionNum = rdd.partitions.length + val toBatchBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( + batchSize = partitionNum * batchPerPartition, + partitionNum = Some(partitionNum), + featurePaddingParam = featurePaddingParam), shareBuffer) + val result = rdd.mapPartitions(partition => { + val localModel = modelBroad.value() + val localToBatch = toBatchBroad.value._1.cloneTransformer() + + partition.grouped(localBatchPerPartition).flatMap(imageFeatures => { + Predictor.predictImageBatch[T](localModel, imageFeatures, outputLayer, predictKey, + localToBatch, shareBuffer) + imageFeatures }) - } + }) + ImageFrame.rdd(result) } - def predict(dataSet: RDD[Sample[T]], batchSize: Int = -1, - shareBuffer: Boolean = false): RDD[Activity] = { + def predict[T: ClassTag](dataSet: RDD[Sample[T]], batchSize: Int = -1, + shareBuffer: Boolean = false, model: Module[T], batchPerPartition: Int, + featurePaddingParam: Option[PaddingParam[T]])(implicit ev: TensorNumeric[T]): RDD[Activity] = { val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext, model.evaluate()) val partitionNum = dataSet.partitions.length val totalBatch = if (batchSize > 0) { @@ -163,18 +166,62 @@ class Predictor[T: ClassTag] private[optim]( partitionNum = Some(partitionNum), featurePaddingParam = featurePaddingParam)) dataSet.mapPartitions { partition => - CachedModels.add(modelBroad.uuid, model) - val localModel = modelBroad.value() val localTransformer = otherBroad.value.cloneTransformer() val miniBatch = localTransformer(partition) - miniBatch.flatMap( batch => { + miniBatch.flatMap(batch => { val output = localModel.forward(batch.getInput) splitBatch(output, shareBuffer, batch.size()) }) } } + def predictClass[T: ClassTag](dataSet: RDD[Sample[T]], batchSize: Int = -1, model: Module[T], + batchPerPartition: Int, featurePaddingParam: Option[PaddingParam[T]])( + implicit ev: TensorNumeric[T]): RDD[Int] = { + val result = Predictor.predict(dataSet, batchSize, true, model, + batchPerPartition, featurePaddingParam) + result.mapPartitions { partition => + partition.map(output => { + val _output = output.toTensor[T] + require(_output.dim() == 1, s"Predictor.predictClass:" + + s"Only support one sample has one label, but got ${_output.dim()} label") + ev.toType[Int](_output.max(1)._2.valueAt(1)) + }) + } + } +} + +/** + * Predictor for distributed data + * + * NOTE: The `predictClass`, `predict` and `predictImage` will call the relevant methods of + * object `Predictor`. Why we do this? Because every these methods uses the ClassTag `T`. If we do + * these jobs in the methods of class`Predictor`, when we do `mapPartition`, Spark will find all + * used values and do serialization. The `T` is the argument of constructor, the serialization will + * package the whole `Predictor` class, which contains the`model`. It will send a duplicate model + * to the workers. So we should move these methods to object `Predictor`. + * + * @param model BigDL model + * @param featurePaddingParam featurePaddingParam if the inputs have variant size + * @param batchPerPartition batch size per partition, default is 4 + */ +class Predictor[T: ClassTag] private[optim]( + model: Module[T], + featurePaddingParam: Option[PaddingParam[T]] = None, + batchPerPartition: Int = 4) + (implicit ev: TensorNumeric[T]) extends Serializable { + + def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { + Predictor.predictClass(dataSet, batchSize, model, batchPerPartition, featurePaddingParam) + } + + def predict(dataSet: RDD[Sample[T]], batchSize: Int = -1, + shareBuffer: Boolean = false): RDD[Activity] = { + Predictor.predict(dataSet, batchSize, shareBuffer, model, batchPerPartition, + featurePaddingParam) + } + /** * model predict DistributedImageFrame, return imageFrame with predicted tensor @@ -188,25 +235,7 @@ class Predictor[T: ClassTag] private[optim]( outputLayer: String = null, shareBuffer: Boolean = false, predictKey: String = ImageFeature.predict): DistributedImageFrame = { - val rdd = imageFrame.asInstanceOf[DistributedImageFrame].rdd - val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, model.evaluate()) - val partitionNum = rdd.partitions.length - val toBatchBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( - batchSize = partitionNum * batchPerPartition, - partitionNum = Some(partitionNum), - featurePaddingParam = featurePaddingParam), shareBuffer) - val result = rdd.mapPartitions(partition => { - // By default, the `model` will be deserialized on worker, which will create new resources. - CachedModels.add(modelBroad.uuid, model) - - val localModel = modelBroad.value() - val localToBatch = toBatchBroad.value._1.cloneTransformer() - - partition.grouped(batchPerPartition).flatMap(imageFeatures => { - Predictor.predictImageBatch[T](localModel, imageFeatures, outputLayer, predictKey, - localToBatch, shareBuffer) - }) - }) - ImageFrame.rdd(result) + Predictor.predictImage(imageFrame, outputLayer, shareBuffer, predictKey, batchPerPartition, + model, featurePaddingParam) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index f29fa9f8ff5..bd359283f41 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -239,6 +239,9 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ println("-" * 80) } CachedModels.deleteAll("") + // NOTE: if this case failed, please check, + // 1. mapPartition, does it used the variable out side of the method scope. + // 2. ModelBroadcast, does it add the ref correctly StorageManager.get().count(!_._2.isFreed) should be (init.count(!_._2.isFreed)) } } From 99c9f1e750c00bfb13e4dd228b6284da1c3affea Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Jul 2018 22:57:02 -0400 Subject: [PATCH 0805/1065] test: unit tests which will compare with caffe (#2584) --- .../analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala | 2 +- .../analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala | 4 +--- .../dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala | 6 +++--- .../bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala | 8 ++++---- .../analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala | 5 ++--- 5 files changed, 11 insertions(+), 14 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala index 39668c2945f..a1d65198445 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala @@ -306,7 +306,7 @@ class LinearSpec extends FlatSpec with Matchers { } - "linear " should "work correctly" ignore { + "linear " should "work correctly" in { val (batchSize, nInput) = (4, 64) val inputShape = Array(batchSize, nInput) val nOutput = 1000 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala index 8aa6bcc9e25..21b58716c8e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala @@ -18,10 +18,8 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase -import com.intel.analytics.bigdl.tensor.{MklDnnType, Tensor} -import org.scalatest.{BeforeAndAfter, FlatSpec, Ignore, Matchers} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} -@Ignore class SingleLayerSpec extends FlatSpec with Matchers with BeforeAndAfter { "convolution" should "work correctly" in { val inputShape = Array(4, 3, 5, 5) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala index d49a27aa5d9..ce88648aa33 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -350,7 +350,7 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { model1.output should be (model2.output) } - "a simple bach norm" should "work correctly" ignore { + "a simple bach norm" should "work correctly" in { val (batchSize, channel, height, width) = (4, 64, 2, 2) val shape = Array(batchSize, channel, height, width) val prototxt = s""" @@ -438,7 +438,7 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { compare(gradInput, seq.gradInput) } - "a simple bach norm inference" should "work correctly" ignore { + "a simple bach norm inference" should "work correctly" in { val (batchSize, channel, height, width) = (4, 64, 112, 112) val shape = Array(batchSize, channel, height, width) val prototxt = s""" @@ -519,7 +519,7 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { val denseOutput = Tools.dense(bn.output).toTensor denseOutput.storage().array().zip(output.storage().array()).foreach { x => - if (x._2.isInfinity) x._1.isNaN should be (true) + if (x._2.isInfinity) x._1.isInfinity should be (true) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index b27c07d045f..12a5fe77b1a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -243,7 +243,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Equivalent.nearequals(gb1, gb2, 1e-3) should be(true) } - "a simple convolution compared with caffe" should "work correctly" ignore { + "a simple convolution compared with caffe" should "work correctly" in { val inputShape = Array(4, 3, 5, 5) val outputShape = Array(4, 2, 3, 3) val name = "conv" @@ -263,7 +263,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Tools.compare(txt, conv, inputShape, outputShape) } - "conv exists some format conversion" should "work correctly" ignore { + "conv exists some format conversion" should "work correctly" in { val inputShape = Array(4, 3, 224, 224) val outputShape = Array(4, 64, 112, 112) @@ -313,7 +313,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { } } - "conv kernel 1x1 with reorder in container" should "work correctly" ignore { + "conv kernel 1x1 with reorder in container" should "work correctly" in { val inputShape = Array(4, 64, 56, 56) val outputShape = Array(4, 64, 56, 56) @@ -363,7 +363,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { } } - "conv + bn" should "work correctly" ignore { + "conv + bn" should "work correctly" in { val inputShape = Array(4, 3, 224, 224) val outputShape = Array(4, 64, 112, 112) val channel = 64 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala index 922435a3a90..cfc0d71426d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala @@ -16,13 +16,12 @@ package com.intel.analytics.bigdl.nn.mkldnn +import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.{Module, nn} -import org.scalatest.{FlatSpec, Ignore, Matchers} +import org.scalatest.{FlatSpec, Matchers} -@Ignore class TopologySpec extends FlatSpec with Matchers { "LeNet5 has no tanh" should "work correctly" in { From 48a4c4b333399bdc8660068c0f9b60891cddfadb Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 11 Jul 2018 14:46:45 +0800 Subject: [PATCH 0806/1065] delete logger variable in LocalPredictor (#2586) * delete logger in LocalPredictor * add ut --- .../intel/analytics/bigdl/dllib/optim/LocalPredictor.scala | 1 - .../intel/analytics/bigdl/dllib/optim/PredictorSpec.scala | 6 ++++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index 95b2d56afec..d724511cac3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -52,7 +52,6 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], batchPerCore: Int = 4) (implicit ev: TensorNumeric[T]) extends Serializable { - val logger = LocalPredictor.logger private val coreNumber = Engine.coreNumber() private val subModelNumber = Engine.getEngineType match { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index bd359283f41..f7eac66319a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -27,6 +27,7 @@ import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, Table} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.apache.commons.lang3.SerializationUtils import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -244,4 +245,9 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ // 2. ModelBroadcast, does it add the ref correctly StorageManager.get().count(!_._2.isFreed) should be (init.count(!_._2.isFreed)) } + + "localpredictor" should "serialize successfully" in { + val localPredictor = LocalPredictor(Linear[Float](3, 10)) + SerializationUtils.clone(localPredictor) + } } From 43a1bad2bd009837c92eb1bd22b27d80ce5f6af9 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Fri, 13 Jul 2018 13:36:24 +0800 Subject: [PATCH 0807/1065] shade org.tensorflow.framework to avoid conflict (#2589) --- dl/pom.xml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/dl/pom.xml b/dl/pom.xml index 7e15acbc157..f459e5c2136 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -195,6 +195,10 @@ com.google.protobuf com.intel.analytics.bigdl.shaded.protobuf + + org.tensorflow.framework + com.intel.analytics.bigdl.shaded.tensorflow.framework + @@ -208,6 +212,7 @@ com.google.protobuf + org.tensorflow.framework From 84ece7077514d4c0c33871f0e0573a42bbfc3f26 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 18 Jul 2018 09:17:43 +0800 Subject: [PATCH 0808/1065] Add document for wrap preprocessor and model in one graph and add its python API (#2595) * add document for wrap preprocessor and model in one graph and add its python API * enable junit report for pytest * fix failed unit test * fix unit test --- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 6e677c1359e..c39cddb4e0f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -113,10 +113,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def activityToJTensors(outputActivity: Activity): JList[JTensor] = { if (outputActivity.isInstanceOf[Tensor[T]]) { List(toJTensor(outputActivity.toTensor)).asJava - } else { + } else if (outputActivity.isInstanceOf[Table]) { outputActivity.toTable.getState().toList.map { pair => (pair._1.asInstanceOf[Int], toJTensor(pair._2.asInstanceOf[Tensor[T]])) }.sortWith(_._1 < _._1).map(pair => pair._2).asJava + } else if (outputActivity.isInstanceOf[EmptyGradInput]) { + List[JTensor]().asJava + } else { + throw new UnsupportedOperationException(s"Activity type" + + s"(${outputActivity.getClass.getName}) not support") } } @@ -2358,6 +2363,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Graph(input.asScala.toArray, output.asScala.toArray) } + def createModelPreprocessor(preprocessor: AbstractModule[Activity, Activity, T], + trainable: AbstractModule[Activity, Activity, T]): Graph[T] = { + Graph(preprocessor, trainable) + } + def createNode(module: AbstractModule[Activity, Activity, T], x: JList[ModuleNode[T]]): ModuleNode[T] = { if (null == x || x.isEmpty) { From 4bd6a22be5237689ae57bbf1d8f54c31b38bf160 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 20 Jul 2018 03:08:42 -0400 Subject: [PATCH 0809/1065] Dnn model serialization supports. (#2598) * feat: add simple serialization supports * feat: mkl-dnn modules serialization supports * fix: make primitive(desc) to private * fix: typo * fix: modified based on comments * test: private to call api. --- .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 9 +-- .../bigdl/dllib/nn/mkldnn/ConcatTable.scala | 9 +-- .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 5 +- .../analytics/bigdl/dllib/nn/mkldnn/LRN.scala | 15 ++--- .../bigdl/dllib/nn/mkldnn/Linear.scala | 16 +++--- .../bigdl/dllib/nn/mkldnn/MaxPooling.scala | 21 +++---- .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 18 +++--- .../bigdl/dllib/nn/mkldnn/ReLU.scala | 3 +- .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 6 +- .../nn/mkldnn/SpatialBatchNormalization.scala | 24 ++++---- .../dllib/nn/mkldnn/SpatialConvolution.scala | 27 ++++----- .../bigdl/dllib/tensor/DnnStorage.scala | 21 ++++++- .../dllib/nn/mkldnn/AvgPoolingSpec.scala | 33 +++++++++++ .../bigdl/dllib/nn/mkldnn/CAddTableSpec.scala | 44 ++++++++++++++- .../dllib/nn/mkldnn/ConcatTableSpec.scala | 29 ++++++++++ .../bigdl/dllib/nn/mkldnn/LRNSpec.scala | 26 +++++++++ .../bigdl/dllib/nn/mkldnn/LinearSpec.scala | 42 ++++++++++++++ .../dllib/nn/mkldnn/MaxPoolingSpec.scala | 32 +++++++++++ .../bigdl/dllib/nn/mkldnn/ReLUSpec.scala | 26 +++++++++ .../dllib/nn/mkldnn/SequentialSpec.scala | 39 +++++++++++++ .../bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala | 31 +++++++++- .../SpatialBatchNormalizationSpec.scala | 37 ++++++++++++ .../nn/mkldnn/SpatialConvolutionSpec.scala | 56 ++++++++++++++++--- .../bigdl/dllib/tensor/DnnTensorSpec.scala | 15 +++++ 24 files changed, 486 insertions(+), 98 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala index d4394a304eb..d04bbd25a3a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -28,12 +28,9 @@ class AvgPooling( padW: Int = 0, padH: Int = 0 ) extends MklDnnLayer { - @transient - private var paddingTL: Array[Int] = _ - @transient - private var paddingBR: Array[Int] = _ - @transient - private var fwdPD: Long = _ + @transient private var paddingTL: Array[Int] = _ + @transient private var paddingBR: Array[Int] = _ + @transient private var fwdPD: Long = _ override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = singleNativeData(inputs) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala index b78e8c6dd54..b05f34e8a24 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala @@ -25,12 +25,9 @@ class ConcatTable extends MklDnnContainer { output = T() - @transient - private var sumPrimitive: Array[Long] = null - @transient - private var tensors: Array[Tensor[Float]] = null - @transient - private var tensorPrimitives: Array[Long] = null + @transient private var sumPrimitive: Array[Long] = null + @transient private var tensors: Array[Tensor[Float]] = null + @transient private var tensorPrimitives: Array[Long] = null override def updateOutput(input: Activity): Activity = { require(modules.length > 0, "empty modules of concat table") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index ff6103d7249..62d7406768a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -32,8 +32,7 @@ trait MklDnnModule extends MklDnnModuleHelper { * Note that this instance will be erased when send to remote worker, so you * should recreate a MklDnnRuntime. */ - @transient - protected var runtime : MklDnnRuntime = _ + @transient protected var runtime : MklDnnRuntime = _ def setRuntime(runtime: MklDnnRuntime): Unit = { this.runtime = runtime @@ -249,7 +248,7 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM * Helper utilities when integrating containers with MKL-DNN */ trait MklDnnContainer extends DynamicContainer[Activity, Activity, Float] with MklDnnModule { - protected val reorderManager = new ReorderManager() + @transient protected lazy val reorderManager = new ReorderManager() protected var mklDnnModules : Array[MklDnnModule] = _ override def add(module: AbstractModule[_ <: Activity, _ <: Activity, Float]): this.type = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala index 141cf6d8500..0db5509d88a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala @@ -27,16 +27,11 @@ class LRN( ) extends MklDnnLayer { private val UNDEFINED = 0 - @transient - private var workSpace : Tensor[Float] = _ - @transient - private var workSpaceFormat: MemoryData = _ - @transient - private var fwdPrimDesc: Long = UNDEFINED - @transient - private var fwdMemPrims: Array[Long] = _ - @transient - private var bwdMemPrims: Array[Long] = _ + @transient private var workSpace : Tensor[Float] = _ + @transient private var workSpaceFormat: MemoryData = _ + @transient private var fwdPrimDesc: Long = UNDEFINED + @transient private var fwdMemPrims: Array[Long] = _ + @transient private var bwdMemPrims: Array[Long] = _ override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = singleNativeData(inputs) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 7bb9bc3f480..af8802dfb48 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -40,16 +40,16 @@ class Linear( val gradWeight: DnnTensor[Float] = DnnTensor[Float](Array(outputSize, inputSize)) val gradBias: DnnTensor[Float] = DnnTensor[Float](Array(outputSize)) - var forwardPrimDesc: Long = 0L + @transient private var forwardPrimDesc: Long = 0L - var updateOutputMemoryPrimitives: Array[Long] = _ - var updateOutputTensors: Array[Tensor[Float]] = _ - var updateGradInputMemoryPrimitives: Array[Long] = _ - var updateGradInputTensors: Array[Tensor[Float]] = _ - var updateGradWMemoryPrimitives: Array[Long] = _ - var updateGradWTensors: Array[Tensor[Float]] = _ + @transient private var updateOutputMemoryPrimitives: Array[Long] = _ + @transient private var updateOutputTensors: Array[Tensor[Float]] = _ + @transient private var updateGradInputMemoryPrimitives: Array[Long] = _ + @transient private var updateGradInputTensors: Array[Tensor[Float]] = _ + @transient private var updateGradWMemoryPrimitives: Array[Long] = _ + @transient private var updateGradWTensors: Array[Tensor[Float]] = _ - object ParamsShape { + private object ParamsShape extends Serializable { var weight: MemoryData = _ var bias: MemoryData = _ var gradWeight: MemoryData = _ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala index 9436af72f90..feb43ade4b7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala @@ -28,20 +28,13 @@ class MaxPooling( padW: Int = 0, padH: Int = 0 ) extends MklDnnLayer { - @transient - private var workSpaceFormat: MemoryData = _ - @transient - private var workSpace: Tensor[Float] = _ - @transient - private var fwdMemPrims: Array[Long] = _ - @transient - private var bwdMemPrims: Array[Long] = _ - @transient - private var paddingTL: Array[Int] = _ - @transient - private var paddingBR: Array[Int] = _ - @transient - private var fwdPD: Long = _ + @transient private var workSpaceFormat: MemoryData = _ + @transient private var workSpace: Tensor[Float] = _ + @transient private var fwdMemPrims: Array[Long] = _ + @transient private var bwdMemPrims: Array[Long] = _ + @transient private var paddingTL: Array[Int] = _ + @transient private var paddingBR: Array[Int] = _ + @transient private var fwdPD: Long = _ override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = singleNativeData(inputs) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index bc5297dd327..a372a46fe20 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -31,23 +31,22 @@ sealed trait MemoryData extends Serializable { def cloneFormat(): MemoryData private val UNDEFINED: Long = -1 + private val ERROR: Long = 0 - @transient - private var primitive: Long = UNDEFINED - @transient - private var primitiveDesc: Long = UNDEFINED - @transient - private var description: Long = UNDEFINED + @transient private var primitive: Long = UNDEFINED + @transient private var primitiveDesc: Long = UNDEFINED + @transient private var description: Long = UNDEFINED def getMemoryDescription(): Long = { - if (description == UNDEFINED) { + if (description == UNDEFINED || description == ERROR) { description = MklDnn.MemoryDescInit(shape.length, shape, DataType.F32, layout) } description } def getPrimitiveDescription(runtime: MklDnnRuntime): Long = { - if (primitiveDesc == UNDEFINED) { + require(runtime != null, s"Have you initialized the MklDnnRuntime?") + if (primitiveDesc == UNDEFINED || primitiveDesc == ERROR) { primitiveDesc = MklDnn.MemoryPrimitiveDescCreate(getMemoryDescription(), runtime.engine) } @@ -55,7 +54,8 @@ sealed trait MemoryData extends Serializable { } def getPrimitive(runtime: MklDnnRuntime): Long = { - if (primitive == UNDEFINED) { + require(runtime != null, s"Have you initialized the MklDnnRuntime?") + if (primitive == UNDEFINED || primitive == ERROR) { primitive = MklDnn.PrimitiveCreate0(getPrimitiveDescription(runtime)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala index 372aaf91ad2..a2bf3e62899 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala @@ -20,8 +20,7 @@ import com.intel.analytics.bigdl.mkl.{AlgKind, MklDnn, PropKind, Query} class ReLU(value: Float = 0.0f) extends MklDnnLayer { private val UNDEFINED: Long = 0 - @transient - private var fwdPrimDesc: Long = UNDEFINED + @transient private var fwdPrimDesc: Long = UNDEFINED override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = singleNativeData(inputs) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index 9c59a2b1135..f5d186862e0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -25,10 +25,10 @@ import com.intel.analytics.bigdl.tensor.{DenseType, Tensor} import scala.collection.mutable.ArrayBuffer class SoftMax() extends MklDnnLayer { - val nnSoftMax = nn.SoftMax[Float]() + private val nnSoftMax = nn.SoftMax[Float]() - var updateOutputTensors: Array[Tensor[Float]] = _ - var updateOutputMemoryPrimitives: Array[Long] = _ + @transient private var updateOutputTensors: Array[Tensor[Float]] = _ + @transient private var updateOutputMemoryPrimitives: Array[Long] = _ override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { phase match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 10a9d120ef0..2eb83cd56f9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -35,7 +35,7 @@ class SpatialBatchNormalization( private val initGradBias: Tensor[Float] = null ) extends MklDnnLayer with Initializable { - private var forwardDesc: Long = 0L + @transient private var forwardDesc: Long = 0L private var _relu: Boolean = false def setReLU(value: Boolean): this.type = { @@ -44,17 +44,17 @@ class SpatialBatchNormalization( } def relu: Boolean = _relu - var updateOutputTensors: Array[Tensor[Float]] = _ - var updateOutputMemoryPrimitives: Array[Long] = _ - var updateGradInputTensors: Array[Tensor[Float]] = _ - var updateGradInputMemoryPrimitives: Array[Long] = _ + @transient private var updateOutputTensors: Array[Tensor[Float]] = _ + @transient private var updateOutputMemoryPrimitives: Array[Long] = _ + @transient private var updateGradInputTensors: Array[Tensor[Float]] = _ + @transient private var updateGradInputMemoryPrimitives: Array[Long] = _ - @transient var mean: DnnTensor[Float] = DnnTensor[Float](nOutput) - @transient var variance: DnnTensor[Float] = DnnTensor[Float](nOutput) - @transient var runningMean: DnnTensor[Float] = DnnTensor[Float](nOutput) - @transient var runningVariance: DnnTensor[Float] = DnnTensor[Float](nOutput) - @transient var weightAndBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutput * 2)) - @transient var gradWeightAndBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutput * 2)) + private val mean: DnnTensor[Float] = DnnTensor[Float](nOutput) + private val variance: DnnTensor[Float] = DnnTensor[Float](nOutput) + private[mkldnn] val runningMean: DnnTensor[Float] = DnnTensor[Float](nOutput) + private[mkldnn] val runningVariance: DnnTensor[Float] = DnnTensor[Float](nOutput) + val weightAndBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutput * 2)) + val gradWeightAndBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutput * 2)) var scaleFactor: Float = 0.0f var biasFactor: Float = 0.0f @@ -91,7 +91,7 @@ class SpatialBatchNormalization( variance.copy(zeros) } - object Index { + private object Index extends Serializable { val input = 0 val weight = 1 val output = 2 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 45e14433e4d..beee1039377 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -41,7 +41,7 @@ class SpatialConvolution( val initGradBias: Tensor[Float] = null, val withBias: Boolean = true, val format: DataFormat = DataFormat.NCHW -) extends MklDnnLayer with Initializable { +) extends MklDnnLayer with Initializable with Serializable { private val weightShape = if (nGroup == 1) { Array(nOutputPlane, nInputPlane, kernelH, kernelW) } else { @@ -50,25 +50,26 @@ class SpatialConvolution( // !!!important!!! this is for weight conversion. The weights in forward and backward is // different. - val reorderManager = new ReorderManager + // It's `lazy` so the reordermanager need not serialized. + @transient private lazy val reorderManager = new ReorderManager val weight: DnnTensor[Float] = DnnTensor[Float](weightShape) - var weightForBackward: DnnTensor[Float] = _ + private var weightForBackward: DnnTensor[Float] = _ val bias: DnnTensor[Float] = DnnTensor[Float](Array(nOutputPlane)) val gradWeight: DnnTensor[Float] = DnnTensor[Float](weightShape) val gradBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutputPlane)) - var forwardPrimDesc: Long = 0L + @transient private var forwardPrimDesc: Long = 0L - var updateOutputMemoryPrimitives: Array[Long] = _ - var updateOutputTensors: Array[Tensor[Float]] = _ - var updateGradInputMemoryPrimitives: Array[Long] = _ - var updateGradInputTensors: Array[Tensor[Float]] = _ - var updateGradWMemoryPrimitives: Array[Long] = _ - var updateGradWTensors: Array[Tensor[Float]] = _ + @transient private var updateOutputMemoryPrimitives: Array[Long] = _ + @transient private var updateOutputTensors: Array[Tensor[Float]] = _ + @transient private var updateGradInputMemoryPrimitives: Array[Long] = _ + @transient private var updateGradInputTensors: Array[Tensor[Float]] = _ + @transient private var updateGradWMemoryPrimitives: Array[Long] = _ + @transient private var updateGradWTensors: Array[Tensor[Float]] = _ - var _relu = false - var _sum = false + private var _relu = false + private var _sum = false def relu: Boolean = _relu def setReLU(value: Boolean = true): this.type = { @@ -88,7 +89,7 @@ class SpatialConvolution( this } - object ParamsShape { + private object ParamsShape extends Serializable { var weight: MemoryData = _ var weightForBackward: MemoryData = _ var bias: MemoryData = _ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala index 9552674f3be..daea15e120a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala @@ -15,6 +15,8 @@ */ package com.intel.analytics.bigdl.tensor +import java.io.{IOException, ObjectInputStream, ObjectOutputStream} + import com.intel.analytics.bigdl.mkl.Memory import scala.reflect._ @@ -31,7 +33,7 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { private var _isReleased: Boolean = false // Hold the address of the native array - val ptr: Pointer = new Pointer(allocate(size)) + @transient var ptr: Pointer = new Pointer(allocate(size)) override def length(): Int = size @@ -87,13 +89,28 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { def isReleased(): Boolean = _isReleased - private def allocate(capacity: Int): Long = { require(capacity > 0, s"capacity should not be larger than 0") val ptr = Memory.AlignedMalloc(capacity * DnnStorage.FLOAT_BYTES, DnnStorage.CACHE_LINE_SIZE) require(ptr != 0L, s"allocate native aligned memory failed") ptr } + + @throws(classOf[IOException]) + private def readObject(in: ObjectInputStream): Unit = { + in.defaultReadObject() + ptr = new Pointer(allocate(this.size)) + val elements = in.readObject().asInstanceOf[Array[Float]] + Memory.CopyArray2Ptr(elements, 0, ptr.address, 0, size, DnnStorage.FLOAT_BYTES) + } + + @throws(classOf[IOException]) + private def writeObject(out: ObjectOutputStream): Unit = { + out.defaultWriteObject() + val elements = new Array[Float](this.length()) + Memory.CopyPtr2Array(this.ptr.address, 0, elements, 0, size, DnnStorage.FLOAT_BYTES) + out.writeObject(elements) + } } /** diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala index eb623adc959..d9f02422909 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala @@ -17,9 +17,11 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.SpatialAveragePooling +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.BigDLSpecHelper import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import org.apache.commons.lang3.SerializationUtils import scala.util.Random @@ -77,4 +79,35 @@ class AvgPoolingSpec extends BigDLSpecHelper { val grad1 = seq.backward(input, output2) grad1 should be(grad2) } + + "avg with java serialization" should "work correctly" in { + val batchSize = 2 + val inputShape = Array(batchSize, 64, 112, 112) + val outputShape = Array(batchSize, 64, 56, 56) + + val input = Tensor[Float](batchSize, 64, 112, 112).rand(-1, 1) + + val pool = AvgPooling(3, 3, 2, 2) + pool.setRuntime(new MklDnnRuntime) + pool.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + pool.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + pool.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + val cloned = SerializationUtils.clone(pool) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + pool.forward(input) + cloned.forward(input) + + Tools.dense(pool.output) should be (Tools.dense(cloned.output)) + + val gradOutput = Tensor[Float](outputShape) + pool.backward(input, gradOutput) + cloned.backward(input, gradOutput) + + Tools.dense(pool.gradInput) should be (Tools.dense(cloned.gradInput)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala index 591b7f2475f..078c0bdf1b1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala @@ -16,8 +16,11 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} +import org.apache.commons.lang3.SerializationUtils class CAddTableSpec extends BigDLSpecHelper { "CAddTable" should "be correct" in { @@ -52,4 +55,43 @@ class CAddTableSpec extends BigDLSpecHelper { Tensor[Float](T(T(8, 10), T(12, 14))) ) } + + "caddtable with java serialization" should "work correctly" in { + val shape = Array(2, 3, 4, 4) + val _1 = Tensor(shape).rand(-1, 1) + val _2 = Tensor(shape).rand(-1, 1) + + val input1 = DnnTensor(shape).copy(_1) + val input2 = DnnTensor(shape).copy(_2) + + val cat = CAddTable() + cat.setRuntime(new MklDnnRuntime) + cat.initFwdPrimitives(Array( + HeapData(shape, Memory.Format.nchw), + HeapData(shape, Memory.Format.nchw)), TrainingPhase) + cat.initBwdPrimitives(Array( + HeapData(shape, Memory.Format.nchw), + HeapData(shape, Memory.Format.nchw)), TrainingPhase) + + cat.forward(T(input1, input2)) + + val cloned = SerializationUtils.clone(cat) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array( + HeapData(shape, Memory.Format.nchw), + HeapData(shape, Memory.Format.nchw)), TrainingPhase) + cloned.initBwdPrimitives(Array( + HeapData(shape, Memory.Format.nchw), + HeapData(shape, Memory.Format.nchw)), TrainingPhase) + cloned.forward(T(input1, input2)) + + Tools.dense(cat.output) should be (Tools.dense(cloned.output)) + + val gradOutput = Tensor(shape).rand(-1, 1) + cat.backward(T(input1, input2), gradOutput) + cloned.backward(T(input1, input2), gradOutput) + + Tools.dense(cat.gradInput.toTable(1)) should be (Tools.dense(cloned.gradInput.toTable(1))) + Tools.dense(cat.gradInput.toTable(2)) should be (Tools.dense(cloned.gradInput.toTable(2))) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala index ebebe17b089..befdaefc74d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala @@ -16,8 +16,11 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} +import org.apache.commons.lang3.SerializationUtils class ConcatTableSpec extends BigDLSpecHelper { "ConcatTable" should "throw exception when input shape is different" in { @@ -69,4 +72,30 @@ class ConcatTableSpec extends BigDLSpecHelper { val heapGrad2 = Tensor[Float](3, 4).copy(nativeGrad2) heapGrad2 should be(grad2 * 2) } + + "concat table with java serialization" should "work correctly" in { + val shape = Array(2, 2) + val input = Tensor(shape).fill(1) + val gradOutput = T(Tensor(shape).fill(2), Tensor(shape).fill(2)) + + val ct = ConcatTable() + ct.add(Identity()) + ct.add(Identity()) + + ct.compile(TrainingPhase, Array(HeapData(shape, Memory.Format.nc))) + + val cloned = SerializationUtils.clone(ct) + cloned.compile(TrainingPhase, Array(HeapData(shape, Memory.Format.nc))) + + ct.forward(input) + ct.backward(input, gradOutput) + + cloned.forward(input) + cloned.backward(input, gradOutput) + + Tools.dense(ct.output.toTable(1)) should be(Tools.dense(cloned.output.toTable(1))) + Tools.dense(ct.output.toTable(2)) should be(Tools.dense(cloned.output.toTable(2))) + + Tools.dense(ct.gradInput) should be(Tools.dense(cloned.gradInput)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala index 24208d39765..573acb8a87b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala @@ -17,9 +17,11 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.SpatialCrossMapLRN +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.BigDLSpecHelper import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import org.apache.commons.lang3.SerializationUtils import scala.util.Random @@ -50,4 +52,28 @@ class LRNSpec extends BigDLSpecHelper { grad1.asInstanceOf[Tensor[Float]] should be(grad2) } + "lrn with java serialization" should "work correctly" in { + val batchSize = 2 + val inputShape = Array(batchSize, 7, 3, 3) + val input = Tensor[Float](batchSize, 7, 3, 3).rand(-1, 1) + val gradOutput = Tensor[Float](batchSize, 7, 3, 3).rand(-1, 1) + + val lrn = LRN(5, 0.0001, 0.75, 1.0) + lrn.setRuntime(new MklDnnRuntime) + lrn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + lrn.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + lrn.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + lrn.forward(input) + + val cloned = SerializationUtils.clone(lrn) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + cloned.forward(input) + + Tools.dense(lrn.output) should be (Tools.dense(cloned.output)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala index a1d65198445..b613e50b427 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor +import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Matchers} class LinearSpec extends FlatSpec with Matchers { @@ -208,6 +209,47 @@ class LinearSpec extends FlatSpec with Matchers { Tools.dense(linear.gradBias) should be (nnLinear.gradBias) } + "linear cloned" should "work correctly" in { + val inputSize = 2 + val outputSize = 2 + val batchSize = 2 + + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + val input = Tensor[Float](batchSize, inputSize).rand() + val gradOutput = Tensor[Float]().resize(outputFormat.shape).rand() + + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize).rand() + + val linear = Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + val output = linear.forward(input) + val gradInput = linear.updateGradInput(input, gradOutput) + linear.accGradParameters(input, gradOutput) + + val cloned = SerializationUtils.clone(linear) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array(inputFormat), TrainingPhase) + cloned.initBwdPrimitives(Array(outputFormat), TrainingPhase) + cloned.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + cloned.forward(input) + + Tools.dense(linear.output) should be (Tools.dense(cloned.output)) + + linear.backward(input, gradOutput) + cloned.backward(input, gradOutput) + + Tools.dense(linear.gradInput) should be (Tools.dense(cloned.gradInput)) + Tools.dense(linear.gradWeight) should be (Tools.dense(cloned.gradWeight)) + Tools.dense(linear.gradBias) should be (Tools.dense(cloned.gradBias)) + } + "linear with maxpooling" should "work correctly" in { val initWeight = Tensor[Float](4096, 256 * 6 * 6).rand() val initBias = Tensor[Float](4096).rand() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala index 2511044b297..fbe04b4e687 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala @@ -16,10 +16,12 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.{AlgKind, Memory} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, SpatialMaxPooling} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.BigDLSpecHelper import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import org.apache.commons.lang3.SerializationUtils import scala.util.Random @@ -77,4 +79,34 @@ class MaxPoolingSpec extends BigDLSpecHelper { val grad1 = seq.backward(input, output2) grad1 should be(grad2) } + + "max pooling with java serialization" should "be correct" in { + val batchSize = 2 + val inputShape = Array(batchSize, 64, 112, 112) + val outputShape = Array(batchSize, 64, 56, 56) + val input = Tensor[Float](batchSize, 64, 112, 112).rand(-1, 1) + + val pool = MaxPooling(3, 3, 2, 2) + pool.setRuntime(new MklDnnRuntime) + pool.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + pool.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + pool.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + val cloned = SerializationUtils.clone(pool) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + pool.forward(input) + cloned.forward(input) + + Tools.dense(pool.output) should be (Tools.dense(cloned.output)) + + val gradOutput = Tensor[Float](outputShape).rand(-1, 1) + pool.backward(input, gradOutput) + cloned.backward(input, gradOutput) + + Tools.dense(pool.gradInput) should be (Tools.dense(cloned.gradInput)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLUSpec.scala index 349f8c719ce..4ed1f452b59 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLUSpec.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T +import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Matchers} class ReLUSpec extends FlatSpec with Matchers { @@ -73,4 +74,29 @@ class ReLUSpec extends FlatSpec with Matchers { Equivalent.nearequals(gradInput, Tools.dense(gradInputdnn).toTensor) should be(true) } + "relu with java serialization" should "work correctly" in { + val shape = Array(4, 96, 55, 55) + val input = Tensor(shape).rand(-1, 1) + val gradOutput = Tensor(shape).rand(-1, 1) + + val relu = ReLU() + relu.setRuntime(new MklDnnRuntime) + relu.initFwdPrimitives(Array(HeapData(shape, Memory.Format.nchw)), TrainingPhase) + relu.initBwdPrimitives(Array(HeapData(shape, Memory.Format.nchw)), TrainingPhase) + + val cloned = SerializationUtils.clone(relu) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array(HeapData(shape, Memory.Format.nchw)), TrainingPhase) + cloned.initBwdPrimitives(Array(HeapData(shape, Memory.Format.nchw)), TrainingPhase) + + relu.forward(input) + cloned.forward(input) + + Tools.dense(relu.output) should be (Tools.dense(cloned.output)) + + relu.backward(input, gradOutput) + cloned.backward(input, gradOutput) + + Tools.dense(relu.gradInput) should be (Tools.dense(cloned.gradInput)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala index 06fa24108c9..f29e9a8bbc9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import org.apache.commons.lang3.SerializationUtils class SequentialSpec extends BigDLSpecHelper { "Sequential" should "not be called add after compilation" in { @@ -99,4 +100,42 @@ class SequentialSpec extends BigDLSpecHelper { val gradInput2 = seq.backward(input2, gradOutput2) gradInput2 should be(gradOutput2) } + + "seq with java serialization" should "work correctly" in { + val layer1 = ReorderMemory( + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc)) + val layer2 = ReorderMemory( + NativeData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.io), + NativeData(Array(3, 4), Memory.Format.nc), + NativeData(Array(3, 4), Memory.Format.io)) + val layer3 = ReorderMemory( + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc), + HeapData(Array(3, 4), Memory.Format.nc)) + val seq = Sequential() + seq.add(layer1) + seq.add(layer2) + seq.add(layer3) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(3, 4), Memory.Format.nc))) + + val input = Tensor[Float](3, 4).rand() + val gradOutput = Tensor[Float](3, 4).rand() + + seq.forward(input) + seq.backward(input, gradOutput) + + val cloned = SerializationUtils.clone(seq) + cloned.compile(Phase.TrainingPhase, Array(HeapData(Array(3, 4), Memory.Format.nc))) + + cloned.forward(input) + cloned.backward(input, gradOutput) + + Tools.dense(seq.output) should be (Tools.dense(cloned.output)) + Tools.dense(seq.gradInput) should be (Tools.dense(cloned.gradInput)) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala index e6a2790fd82..8f548f5d444 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala @@ -18,9 +18,10 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor +import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Matchers} class SoftMaxSpec extends FlatSpec with Matchers { @@ -139,4 +140,32 @@ class SoftMaxSpec extends FlatSpec with Matchers { Tools.dense(sm.output) should be (nnSm.output) } } + + "softmax with java serialization" should "work correctly" in { + val inputShape = Array(2, 3, 4, 4) + + val sm = SoftMax() + sm.setRuntime(new MklDnnRuntime) + sm.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + sm.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + val cloned = SerializationUtils.clone(sm) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + val input = Tensor(inputShape).rand(-1, 1) + val gradOutput = Tensor(inputShape).rand(-1, 1) + + sm.forward(input) + cloned.forward(input) + + Tools.dense(sm.output) should be (Tools.dense(cloned.output)) + + sm.backward(input, gradOutput) + cloned.backward(input, gradOutput) + + Tools.dense(sm.gradInput) should be (Tools.dense(cloned.gradInput)) + + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala index ce88648aa33..b0cb0c50b63 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Ignore, Matchers} class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { @@ -66,6 +67,42 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { Equivalent.nearequals(Tools.dense(bn.gradInput).toTensor, nnBn.gradInput) should be (true) } + "batch norm cloned" should "work correctly" in { + val batchSize = 2 + RNG.setSeed(100) + val input = Tensor(100, 1, 10, 10).rand(-1, 1) + val (channel, height, width) = (1, 10, 10) + + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).fill(0) + + val bn = SpatialBatchNormalization(1, 0.0, initWeight = initWeight, initBias = initBias) + + val inputShape = Array(100, 1, 10, 10) + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + bn.forward(input) + + val cloned = SerializationUtils.clone(bn) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + cloned.forward(input) + + Tools.dense(bn.output) should be (Tools.dense(cloned.output)) + + val gradOutput = Tensor(inputShape).rand(-1, 1) + bn.backward(input, gradOutput) + cloned.backward(input, gradOutput) + Tools.dense(bn.gradInput) should be (Tools.dense(cloned.gradInput)) + Tools.dense(bn.gradWeightAndBias) should be (Tools.dense(cloned.gradWeightAndBias)) + } + "bn updateOutput" should "work correctly" in { val (batchSize, channel, height, width) = (4, 64, 112, 112) val inputShape = Array(batchSize, channel, height, width) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index 12a5fe77b1a..941a64f16de 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.nn.{Xavier, Zeros} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @@ -55,8 +56,8 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { conv.gradInputFormats()(0)) conv.accGradParameters(input, gradOutput) - val weight1 = Tools.toOIHW(conv.weight, conv.ParamsShape.weight) - val gradweight1 = Tools.toOIHW(conv.gradWeight, conv.ParamsShape.gradWeight) + val weight1 = Tools.toOIHW(conv.weight, conv.parametersWithShape()._1(0)) + val gradweight1 = Tools.toOIHW(conv.gradWeight, conv.parametersWithShape()._2(0)) val bias1 = Tools.dense(conv.bias).toTensor[Float] val gradbias1 = Tools.dense(conv.gradBias).toTensor @@ -114,8 +115,8 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val grad1 = Tools.toNCHW(conv.updateGradInput(input, gradOutput).toTensor, conv.gradInputFormats()(0)) conv.accGradParameters(input, gradOutput) - val weight1 = Tools.toOIHW(conv.weight, conv.ParamsShape.weight) - val gradweight1 = Tools.toOIHW(conv.gradWeight, conv.ParamsShape.gradWeight) + val weight1 = Tools.toOIHW(conv.weight, conv.parametersWithShape()._1(0)) + val gradweight1 = Tools.toOIHW(conv.gradWeight, conv.parametersWithShape()._2(0)) val bias1 = Tools.dense(conv.bias).toTensor[Float] val gradbias1 = Tools.dense(conv.gradBias).toTensor[Float] @@ -164,8 +165,8 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val output = Tools.toNCHW(conv.output.toTensor, conv.outputFormats()(0)) val gradInput = Tools.toNCHW(conv.gradInput.toTensor, conv.gradInputFormats()(0)) - val weight = Tools.toOIHW(conv.weight, conv.ParamsShape.weight) - val gradweight = Tools.toOIHW(conv.gradWeight, conv.ParamsShape.gradWeight) + val weight = Tools.toOIHW(conv.weight, conv.parametersWithShape()._1(0)) + val gradweight = Tools.toOIHW(conv.gradWeight, conv.parametersWithShape()._2(0)) val bias = Tools.dense(conv.bias).toTensor val gradbias = Tools.dense(conv.gradBias).toTensor @@ -206,7 +207,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { model2.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) model2.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - val initWeight = Tools.fromOIHW(weightAll1(0), model2.ParamsShape.weight) + val initWeight = Tools.fromOIHW(weightAll1(0), model2.parametersWithShape()._1(0)) model2.weight.copy(initWeight) model2.bias.copy(model1.bias) @@ -236,7 +237,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val gw1 = model1.gradWeight val gb1 = model1.gradBias - val gw2 = Tools.toOIHW(model2.gradWeight, model2.ParamsShape.gradWeight) + val gw2 = Tools.toOIHW(model2.gradWeight, model2.parametersWithShape()._2(0)) val gb2 = Tools.dense(model2.gradBias).toTensor Equivalent.nearequals(gw1, gw2, 1e-4) should be(true) @@ -404,6 +405,45 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Tools.compare(txt, seq, inputShape, outputShape, 1e-2) } + "conv serialized with java serialization method" should "work correctly" in { + val inputShape = Array(4, 3, 5, 5) + val outputShape = Array(4, 2, 3, 3) + val name = "conv" + val nOutput = 2 + val kernel = 3 + val pad = 1 + val stride = 2 + + val conv = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) + conv.setName(name) + conv.setRuntime(new MklDnnRuntime) + conv.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + conv.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + conv.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + val input = Tensor(inputShape).rand(-1, 1) + conv.forward(input) + + val cloned = SerializationUtils.clone(conv) + cloned.setRuntime(new MklDnnRuntime) + cloned.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + cloned.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + cloned.forward(input) + + Tools.dense(conv.output) should be (Tools.dense(cloned.output)) + + val gradOutput = Tensor(outputShape).rand(-1, 1) + + conv.backward(input, gradOutput) + cloned.backward(input, gradOutput) + + Tools.dense(conv.gradInput) should be (Tools.dense(cloned.gradInput)) + Tools.dense(conv.gradWeight) should be (Tools.dense(cloned.gradWeight)) + Tools.dense(conv.gradBias) should be (Tools.dense(cloned.gradBias)) + } + def prototxt(inputShape: Array[Int], name: String, nOutput: Int, kernel: Int, pad: Int, stride: Int): String = { s""" diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala index c265b46da32..a4503d30afd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.tensor import com.intel.analytics.bigdl.mkl.MklDnn import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} +import org.apache.commons.lang3.SerializationUtils class DnnTensorSpec extends BigDLSpecHelper { "nElement" should "be correct" in { @@ -69,4 +70,18 @@ class DnnTensorSpec extends BigDLSpecHelper { val heapTensor3 = Tensor[Float](4).copy(dnnTensor1) heapTensor3 should be(Tensor[Float](T(3, 7, 4, 11))) } + + "tensor clone with java serialization" should "work correctly" in { + val heapTensor = Tensor[Float](T(1, 2, 3, 4)).rand(-1, 1) + val dnnTensor = DnnTensor[Float](4).copy(heapTensor) + + val cloned = SerializationUtils.clone(dnnTensor).asInstanceOf[DnnTensor[Float]] + val heapCloned = Tensor[Float](4).copy(cloned) + + println(heapTensor) + println("=" * 80) + println(heapCloned) + + heapCloned should be (heapTensor) + } } From 3fa4c5d1504bf6598295ffd1571922de0cea57c7 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 26 Jul 2018 01:17:11 -0400 Subject: [PATCH 0810/1065] Add serialization for mkl dnn (#2593) Add dense weights and gradients and support optimizer (local, distribute). Add a `Blob` for the pair of dense and native weights/gradients with the MemoryData layout. --- .../intel/analytics/bigdl/utils/Engine.scala | 2 + .../analytics/bigdl/utils/ThreadPool.scala | 2 +- .../bigdl/dllib/nn/mkldnn/Blob.scala | 95 +++++++++++++++++++ .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 34 ++++++- .../bigdl/dllib/nn/mkldnn/Input.scala | 28 +----- .../bigdl/dllib/nn/mkldnn/Linear.scala | 66 ++++++------- .../bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala | 4 +- .../bigdl/dllib/nn/mkldnn/ResNet50Perf.scala | 14 ++- .../bigdl/dllib/nn/mkldnn/Sequential.scala | 8 +- .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 9 +- .../nn/mkldnn/SpatialBatchNormalization.scala | 42 ++++---- .../dllib/nn/mkldnn/SpatialConvolution.scala | 70 +++++++------- .../bigdl/dllib/optim/DistriOptimizer.scala | 22 ++++- .../bigdl/dllib/optim/LocalOptimizer.scala | 17 +++- .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 8 +- .../bigdl/dllib/nn/mkldnn/InputSpec.scala | 6 +- .../bigdl/dllib/nn/mkldnn/LinearSpec.scala | 53 ++++++++++- .../dllib/nn/mkldnn/SequentialSpec.scala | 48 ++++++++++ .../SpatialBatchNormalizationSpec.scala | 61 ++++++++++-- .../nn/mkldnn/SpatialConvolutionSpec.scala | 78 +++++++++++---- .../bigdl/dllib/nn/mkldnn/TestUtils.scala | 6 +- 21 files changed, 503 insertions(+), 170 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index d3f384a2cb1..19255b55c8c 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -34,6 +34,7 @@ import scala.util.control.{ControlThrowable, NonFatal} sealed trait EngineType case object MklBlas extends EngineType +case object MklDnn extends EngineType object Engine { @@ -200,6 +201,7 @@ object Engine { private var engineType: EngineType = { System.getProperty("bigdl.engineType", "mklblas").toLowerCase(Locale.ROOT) match { case "mklblas" => MklBlas + case "mkldnn" => MklDnn case engineType => throw new IllegalArgumentException(s"Unknown engine type $engineType") } } diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 5dc71e8151b..1c48eef4619 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils import java.util.concurrent._ -import com.intel.analytics.bigdl.mkl.{MKL, MklDnn} +import com.intel.analytics.bigdl.mkl.MKL import org.apache.commons.lang.exception.ExceptionUtils import org.apache.log4j.Logger diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala new file mode 100644 index 00000000000..8923efa9e75 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala @@ -0,0 +1,95 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} + +/** + * `Blob` contains two tensors, dense and native, which are a map of each other. + * It's used in the layer which contains weights. For the weight, we should sync the + * dense tensor to native tensor before `submit`. For the gradient, we should sync the native + * tensor to dense tensor after `submit`. + * + * The `setMemoryData` requires the elements number should be consistent. If the shape is not, + * it will reshape first. + * + * The Blob has another attribute `_memoryData` and will not be determined when the blob created. + * It can be determined when we initialize the primitives. + * + * @param _size the shape of Tensor, such as Array(4, 3, 224, 224) + */ +private[mkldnn] class Blob(_size: Array[Int]) extends Serializable { + val dense: Tensor[Float] = Tensor[Float](_size) + val native: DnnTensor[Float] = DnnTensor[Float](_size) + + @transient private var _memoryData: MemoryData = _ + + /** + * it will copy the dense tensor to native tensor before `submit` reads the native tensor + */ + def syncToNative(): Unit = { + native.copy(dense) + } + + /** + * it will copy the native tensor to dense tensor after `submit` updates the native tensor + */ + def syncToHeap(): Unit = { + dense.copy(native) + } + + /** + * MemoryData relevant of Native Tensor. The shape should be the same as `size` of Blob. + * We can't only reserve the `layout` in MemoryData. Because for convolution, + * we should reserve the whole MemoryData including `desc`, `desc primitive` and `primitive`. + * + * @param memoryData memory data you want. + */ + def setMemoryData(memoryData: MemoryData): Unit = { + require(_memoryData == null, "You should only set once") + require(size().product == memoryData.shape.product, s"You may assign wrong layout") + + // we should resize the tensor. Because sometimes, weight of Linear will has 4-D, where + // the last 2 dims is 1. we should reisze it. It will not allocate a new storage because of + // the same size. + List(native, dense).foreach(_.resize(memoryData.shape)) + _memoryData = memoryData + } + + def memoryData(): MemoryData = { + require(_memoryData != null, "You should setMemoryData first") + _memoryData + } + + def zero(): Unit = { + dense.zero() + native.zero() + } + + def copy(t: Tensor[Float]): Unit = { + dense.copy(t) + native.copy(t) + } + + def size(): Array[Int] = { + dense.size() + } + + def size(index: Int): Int = { + dense.size(index) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index 62d7406768a..d65fd8fe3f4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -32,7 +32,7 @@ trait MklDnnModule extends MklDnnModuleHelper { * Note that this instance will be erased when send to remote worker, so you * should recreate a MklDnnRuntime. */ - @transient protected var runtime : MklDnnRuntime = _ + @transient protected var runtime: MklDnnRuntime = _ def setRuntime(runtime: MklDnnRuntime): Unit = { this.runtime = runtime @@ -44,15 +44,21 @@ trait MklDnnModule extends MklDnnModuleHelper { */ private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) : (Array[MemoryData], Array[MemoryData]) + private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) : (Array[MemoryData], Array[MemoryData]) + private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase): Array[MemoryData] = grad private[mkldnn] def inputFormats(): Array[MemoryData] + private[mkldnn] def gradInputFormats(): Array[MemoryData] + private[mkldnn] def outputFormats(): Array[MemoryData] + private[mkldnn] def gradOutputFormats(): Array[MemoryData] + private[mkldnn] def gradOutputWeightFormats(): Array[MemoryData] } @@ -74,10 +80,12 @@ trait MklDnnModuleHelper { case _ => throw new UnsupportedOperationException("memory format is not supported") } } + protected def singleNativeData(formats: Array[MemoryData]): Array[MemoryData] = { require(formats.length == 1, "Only accept one tensor as input") nativeData(formats) } + protected def nativeData(formats: Array[MemoryData]): Array[MemoryData] = { formats.map( f => { @@ -257,11 +265,30 @@ trait MklDnnContainer extends DynamicContainer[Activity, Activity, Float] with M super.add(module) } + private def checkInputs: Boolean = { + def getAllInputs( + module: AbstractModule[_ <: Activity, _ <: Activity, Float]): Boolean = { + module match { + case seq: Sequential => getAllInputs(seq.modules.head) + case concat: ConcatTable => concat.modules.map(x => getAllInputs(x)).reduce(_ && _) + case _: Input => true + case _ => false + } + } + + getAllInputs(this) + } + + final def compile(phase: Phase): Unit = { + require(checkInputs, s"You should add Input for the container.") + compile(phase, new MklDnnRuntime, Array[MemoryData]()) + } + /** * Create MklDnnRuntime and compile the model * @param phase */ - final def compile(phase: Phase, formats: Array[MemoryData]): Unit = { + private[mkldnn] final def compile(phase: Phase, formats: Array[MemoryData]): Unit = { compile(phase, new MklDnnRuntime(), formats) } @@ -271,7 +298,8 @@ trait MklDnnContainer extends DynamicContainer[Activity, Activity, Float] with M * @param phase * @param runtime */ - final def compile(phase: Phase, runtime: MklDnnRuntime, formats: Array[MemoryData]): Unit = { + private[mkldnn] final def compile(phase: Phase, runtime: MklDnnRuntime, + formats: Array[MemoryData]): Unit = { freeze() fusion(phase) initPrimitives(phase, runtime, formats) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Input.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Input.scala index aee8667b752..ec7ff43e3e6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Input.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Input.scala @@ -14,31 +14,13 @@ * limitations under the License. */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.Tensor -class Input(shape: Array[Int], layout: Int) extends MklDnnLayer { - override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - _outputFormats = Array(HeapData(shape, layout)) - _inputFormats = inputs - (inputs, _outputFormats) - } - - override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { - _gradInputFormats = Array(HeapData(shape, layout)) - _gradOutputFormats = grad - _gradOutputFormatsForWeight = grad - (grad, _gradInputFormats) - } - - override def updateOutput(input: Activity): Activity = { - output = input - output - } +class Input(shape: Array[Int], layout: Int) extends + ReorderMemory(HeapData(shape, layout), NativeData(shape, layout), + HeapData(shape, layout), NativeData(shape, layout)) { - override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { - gradInput = gradOutput - gradInput + override def toString(): String = { + s"nn.mkldnn.Input(${shape.mkString(",")}, $layout)" } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index af8802dfb48..013aecadc8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -16,16 +16,12 @@ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn, PropKind, Query, Stream => DnnStream} -import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable, TensorModule} +import com.intel.analytics.bigdl.mkl._ +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} import com.intel.analytics.bigdl.nn.{InitializationMethod, RandomUniform, VariableFormat} -import com.intel.analytics.bigdl.optim.Regularizer -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.{T, Table} import scala.collection.mutable.ArrayBuffer -import scala.reflect.ClassTag class Linear( val inputSize: Int, @@ -35,10 +31,10 @@ class Linear( private val initGradWeight: Tensor[Float] = null, private val initGradBias: Tensor[Float] = null) extends MklDnnLayer with Initializable { - val weight: DnnTensor[Float] = DnnTensor[Float](Array(outputSize, inputSize)) - val bias: DnnTensor[Float] = DnnTensor[Float](Array(outputSize)) - val gradWeight: DnnTensor[Float] = DnnTensor[Float](Array(outputSize, inputSize)) - val gradBias: DnnTensor[Float] = DnnTensor[Float](Array(outputSize)) + private[mkldnn] val weight: Blob = new Blob(Array(outputSize, inputSize)) + private[mkldnn] val bias: Blob = new Blob(Array(outputSize)) + private[mkldnn] val gradWeight: Blob = new Blob(Array(outputSize, inputSize)) + private[mkldnn] val gradBias: Blob = new Blob(Array(outputSize)) @transient private var forwardPrimDesc: Long = 0L @@ -49,13 +45,6 @@ class Linear( @transient private var updateGradWMemoryPrimitives: Array[Long] = _ @transient private var updateGradWTensors: Array[Tensor[Float]] = _ - private object ParamsShape extends Serializable { - var weight: MemoryData = _ - var bias: MemoryData = _ - var gradWeight: MemoryData = _ - var gradBias: MemoryData = _ - } - { val stdv = 1.0 / math.sqrt(weight.size(2)) val wInit: InitializationMethod = RandomUniform(-stdv, stdv) @@ -65,17 +54,15 @@ class Linear( override def reset(): Unit = { if (initWeight == null) { - val t = Tensor[Float](Array(outputSize, inputSize)) - weightInitMethod.init(t, VariableFormat.OUT_IN) - weight.copy(t) + weightInitMethod.init(weight.dense, VariableFormat.OUT_IN) + weight.syncToNative() } else { weight.copy(initWeight) } if (initBias == null) { - val t = Tensor[Float](Array(outputSize)) - biasInitMethod.init(t, VariableFormat.ONE_D) - bias.copy(t) + biasInitMethod.init(bias.dense, VariableFormat.ONE_D) + bias.syncToNative() } else { bias.copy(initBias) } @@ -114,8 +101,11 @@ class Linear( MemoryData.operationWant(forwardPrimDesc, x) } - ParamsShape.weight = realWei - ParamsShape.bias = bis + require(weight.size().product == realWei.shape.product, + s"${getName} weight shape is not correct.") + + weight.setMemoryData(realWei) + bias.setMemoryData(bis) val srcs = Array(realSrc.getPrimitive(runtime), realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) @@ -138,14 +128,17 @@ class Linear( if (updateOutputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(input.asInstanceOf[Tensor[Float]]) - buffer.append(weight) - buffer.append(bias) + buffer.append(weight.native) + buffer.append(bias.native) buffer.append(output.asInstanceOf[Tensor[Float]]) updateOutputTensors = buffer.toArray } updateWithNewTensor(updateOutputTensors, 0, input) + weight.syncToNative() + bias.syncToNative() + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, updateOutputMemoryPrimitives, updateOutputTensors) @@ -220,8 +213,8 @@ class Linear( MemoryData.operationWant(gradWeightPrimDesc, x) } - ParamsShape.gradWeight = realWei - ParamsShape.gradBias = bis + gradWeight.setMemoryData(realWei) + gradBias.setMemoryData(bis) val srcs = Array(inputFormats()(0).getPrimitive(runtime), realDiffDst.getPrimitive(runtime)) val indexes = Array.fill(srcs.length)(0) @@ -241,7 +234,7 @@ class Linear( if (updateGradInputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) - buffer.append(weight) + buffer.append(weight.native) buffer.append(gradInput.asInstanceOf[Tensor[Float]]) updateGradInputTensors = buffer.toArray } @@ -259,8 +252,8 @@ class Linear( val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(input.asInstanceOf[Tensor[Float]]) buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) - buffer.append(gradWeight) - buffer.append(gradBias) + buffer.append(gradWeight.native) + buffer.append(gradBias.native) updateGradWTensors = buffer.toArray } @@ -269,15 +262,18 @@ class Linear( MklDnnOps.streamSubmit(runtime.stream, 1, accGradientPrimitives, accGradientPrimitives.length, updateGradWMemoryPrimitives, updateGradWTensors) + + gradWeight.syncToHeap() + gradBias.syncToHeap() } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { - (Array(weight, bias), Array(gradWeight, gradBias)) + (Array(weight.dense, bias.dense), Array(gradWeight.dense, gradBias.dense)) } override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { - (Array(ParamsShape.weight, ParamsShape.bias), Array(ParamsShape.gradWeight, - ParamsShape.gradBias)) + (Array(weight.memoryData(), bias.memoryData()), + Array(gradWeight.memoryData(), gradBias.memoryData())) } override def zeroGradParameters(): Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala index b4410347931..41b1b14053c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnRuntime.scala @@ -19,6 +19,6 @@ import com.intel.analytics.bigdl.mkl.{Engine, MklDnn, Stream} class MklDnnRuntime { MklDnn.isLoaded - val engine : Long = Engine.Create(Engine.Kind.Cpu, 0) - val stream : Long = Stream.Create(Stream.Kind.Eager) + @transient lazy val engine : Long = Engine.Create(Engine.Kind.Cpu, 0) + @transient lazy val stream : Long = Stream.Create(Stream.Kind.Eager) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala index 7071b2c1cc4..deef74a3977 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala @@ -49,15 +49,21 @@ object ResNet50Perf { } def main(argv: Array[String]): Unit = { + System.setProperty("bigdl.disable.mklBlockTime", "true"); System.setProperty("bigdl.mkldnn.fusion.convbn", "true") System.setProperty("bigdl.mkldnn.fusion.bnrelu", "true") System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") System.setProperty("bigdl.mkldnn.fusion.convsum", "true") - val coreNumber: Int = Runtime.getRuntime.availableProcessors() / 2 - System.setProperty("bigdl.mklNumThreads", s"$coreNumber") - Engine.setCoreNumber(1) - MklDnn.setNumThreads(coreNumber) +// val coreNumber: Int = System.getProperty("bigdl.mklNumThreads", +// s"${Math.ceil(Runtime.getRuntime.availableProcessors() / 2).toInt}").toInt + System.setProperty("bigdl.localMode", "true") + System.setProperty("bigdl.mklNumThreads", + s"${Math.ceil(Runtime.getRuntime.availableProcessors / 2).toInt}") + System.setProperty("bigdl.coreNumber", "1") + Engine.init +// Engine.setCoreNumber(1) +// MklDnn.setNumThreads(coreNumber) parser.parse(argv, new ResNet50PerfParams()).foreach { params => val batchSize = params.batchSize diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala index c8a4172f594..c4182bd426c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala @@ -281,11 +281,11 @@ class Sequential extends MklDnnContainer { private def mergeConvBn(conv: SpatialConvolution, bn: SpatialBatchNormalization): Unit = { - val originVar = Tensor[Float].resize(bn.runningVariance.size()).copy(bn.runningVariance) - val originMean = Tensor[Float].resize(bn.runningMean.size()).copy(bn.runningMean) + val originVar = Tensor[Float].resize(bn.runningVariance.size()).copy(bn.runningVariance.dense) + val originMean = Tensor[Float].resize(bn.runningMean.size()).copy(bn.runningMean.dense) - val convWeight = Tensor[Float].resize(conv.weight.size()).copy(conv.weight) - val convBias = Tensor[Float].resize(conv.bias.size()).copy(conv.bias) + val convWeight = Tensor[Float].resize(conv.weight.size()).copy(conv.weight.dense) + val convBias = Tensor[Float].resize(conv.bias.size()).copy(conv.bias.dense) (0 until bn.nOutput).foreach { j => val variance = originVar.storage().array()(j + originVar.storageOffset() - 1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index f5d186862e0..bc2f92f9987 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -32,7 +32,10 @@ class SoftMax() extends MklDnnLayer { override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { phase match { - case TrainingPhase => (inputs, inputs) // do nothing, because mkl dnn doesn't support training + case TrainingPhase => + _inputFormats = inputs.clone() + _outputFormats = inputs.clone() + (_inputFormats, _outputFormats) case InferencePhase => val axis = inputs(0).shape.length match { case 1 => 0 @@ -67,7 +70,9 @@ class SoftMax() extends MklDnnLayer { } override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { - (grad, grad) + _gradInputFormats = grad.clone() + _gradOutputFormats = grad.clone() + (_gradInputFormats, _gradOutputFormats) } override def updateOutput(input: Activity): Activity = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 2eb83cd56f9..ed81055c4e5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{AlgKind, Memory, MklDnn, PropKind, Query} +import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.nn.{Ones, VariableFormat, Zeros} @@ -51,10 +51,12 @@ class SpatialBatchNormalization( private val mean: DnnTensor[Float] = DnnTensor[Float](nOutput) private val variance: DnnTensor[Float] = DnnTensor[Float](nOutput) - private[mkldnn] val runningMean: DnnTensor[Float] = DnnTensor[Float](nOutput) - private[mkldnn] val runningVariance: DnnTensor[Float] = DnnTensor[Float](nOutput) - val weightAndBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutput * 2)) - val gradWeightAndBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutput * 2)) + + private[mkldnn] val runningMean = new Blob(Array(nOutput)) + private[mkldnn] val runningVariance = new Blob(Array(nOutput)) + // TODO we should make it private. Currently, ResNet50 will use it out of this scope. + val weightAndBias = new Blob(Array(nOutput * 2)) + val gradWeightAndBias = new Blob(Array(nOutput * 2)) var scaleFactor: Float = 0.0f var biasFactor: Float = 0.0f @@ -175,7 +177,7 @@ class SpatialBatchNormalization( if (this.isTraining()) { val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(input.asInstanceOf[Tensor[Float]]) - buffer.append(weightAndBias) + buffer.append(weightAndBias.native) buffer.append(output.asInstanceOf[Tensor[Float]]) buffer.append(mean) buffer.append(variance) @@ -183,14 +185,16 @@ class SpatialBatchNormalization( } else { val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(input.asInstanceOf[Tensor[Float]]) - buffer.append(runningMean) - buffer.append(runningVariance) - buffer.append(weightAndBias) + buffer.append(runningMean.native) + buffer.append(runningVariance.native) + buffer.append(weightAndBias.native) buffer.append(output.asInstanceOf[Tensor[Float]]) updateOutputTensors = buffer.toArray } } + weightAndBias.syncToNative() + updateWithNewTensor(updateOutputTensors, 0, input) MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, @@ -200,10 +204,13 @@ class SpatialBatchNormalization( // update running(Mean, Var) and scaleFactor scaleFactor = scaleFactor * momentum.toFloat + 1 - mean.axpby(1, momentum.toFloat, runningMean) - variance.axpby(biasFactor, momentum.toFloat, runningVariance) + mean.axpby(1, momentum.toFloat, runningMean.native) + variance.axpby(biasFactor, momentum.toFloat, runningVariance.native) } + runningMean.syncToHeap() + runningVariance.syncToHeap() + output } @@ -253,9 +260,9 @@ class SpatialBatchNormalization( buffer.append(mean) buffer.append(variance) buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) - buffer.append(weightAndBias) + buffer.append(weightAndBias.native) buffer.append(gradInput.asInstanceOf[Tensor[Float]]) - buffer.append(gradWeightAndBias.asInstanceOf[Tensor[Float]]) + buffer.append(gradWeightAndBias.native) updateGradInputTensors = buffer.toArray } @@ -265,6 +272,8 @@ class SpatialBatchNormalization( MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, updateGradInputPrimitives.length, updateGradInputMemoryPrimitives, updateGradInputTensors) + gradWeightAndBias.syncToHeap() + gradInput } @@ -273,12 +282,13 @@ class SpatialBatchNormalization( } override def zeroGradParameters(): Unit = { - if (affine) { gradWeightAndBias.zero() } - if (gradInput != null) { gradInput.asInstanceOf[DnnTensor[Float]].zero() } + if (affine) { + gradWeightAndBias.zero() + } } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { - (Array(weightAndBias), Array(gradWeightAndBias)) + (Array(weightAndBias.dense), Array(gradWeightAndBias.dense)) } override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index beee1039377..6e054ad18c9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -53,12 +53,13 @@ class SpatialConvolution( // It's `lazy` so the reordermanager need not serialized. @transient private lazy val reorderManager = new ReorderManager - val weight: DnnTensor[Float] = DnnTensor[Float](weightShape) - private var weightForBackward: DnnTensor[Float] = _ - val bias: DnnTensor[Float] = DnnTensor[Float](Array(nOutputPlane)) - val gradWeight: DnnTensor[Float] = DnnTensor[Float](weightShape) - val gradBias: DnnTensor[Float] = DnnTensor[Float](Array(nOutputPlane)) + private[mkldnn] val weight = new Blob(weightShape) + private[mkldnn] val bias = new Blob(Array(nOutputPlane)) + private[mkldnn] val gradWeight = new Blob(weightShape) + private[mkldnn] val gradBias = new Blob(Array(nOutputPlane)) + private var weightForBackward: DnnTensor[Float] = _ + private var weightForBackwardMemoryData: MemoryData = _ @transient private var forwardPrimDesc: Long = 0L @transient private var updateOutputMemoryPrimitives: Array[Long] = _ @@ -89,14 +90,6 @@ class SpatialConvolution( this } - private object ParamsShape extends Serializable { - var weight: MemoryData = _ - var weightForBackward: MemoryData = _ - var bias: MemoryData = _ - var gradWeight: MemoryData = _ - var gradBias: MemoryData = _ - } - private def getOutputShape(oh: Int, ow: Int, batchSize: Int = -1): Array[Int] = { format match { case DataFormat.NCHW => @@ -125,17 +118,15 @@ class SpatialConvolution( override def reset(): Unit = { if (initWeight == null) { // TODO only support oihw format weights - val t = Tensor[Float](weightShape) - weightInitMethod.init(t, VariableFormat.OUT_IN) - weight.copy(t) + weightInitMethod.init(weight.dense, VariableFormat.OUT_IN) + weight.syncToNative() } else { weight.copy(initWeight) } if (initBias == null) { - val t = Tensor[Float](Array(nOutputPlane)) - biasInitMethod.init(t, VariableFormat.ONE_D) - bias.copy(t) + biasInitMethod.init(bias.dense, VariableFormat.ONE_D) + bias.syncToNative() } else { bias.copy(initBias) } @@ -164,7 +155,7 @@ class SpatialConvolution( val src = NativeData(inputShape, Memory.Format.any) val wei = NativeData(weightShape, Memory.Format.any) - val bis = NativeData(bias.size(), Memory.Format.x) + val bis = NativeData(Array(nOutputPlane), Memory.Format.x) val dst = NativeData(outputShape, Memory.Format.any) val desc = MklDnn.ConvForwardDescInit( @@ -197,8 +188,8 @@ class SpatialConvolution( MemoryData.operationWant(forwardPrimDesc, x) } - ParamsShape.weight = realWei - ParamsShape.bias = bis + weight.setMemoryData(realWei) + bias.setMemoryData(bis) val srcs = Array(realSrc.getPrimitive(runtime), realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) @@ -221,8 +212,8 @@ class SpatialConvolution( if (updateOutputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(input.asInstanceOf[Tensor[Float]]) - buffer.append(weight) - buffer.append(bias) + buffer.append(weight.native) + buffer.append(bias.native) if (sum) { output = sumOp.output } @@ -232,6 +223,9 @@ class SpatialConvolution( updateWithNewTensor(updateOutputTensors, 0, input) + weight.syncToNative() + bias.syncToNative() + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, updateOutputMemoryPrimitives, updateOutputTensors) @@ -248,7 +242,7 @@ class SpatialConvolution( val src = NativeData(inputShape, Memory.Format.any) val wei = NativeData(weightShape, Memory.Format.any) - val bis = NativeData(bias.size(), Memory.Format.x) + val bis = NativeData(Array(nOutputPlane), Memory.Format.x) val dst = NativeData(outputShape, Memory.Format.any) val desc = MklDnn.ConvBackwardDataDescInit( @@ -264,9 +258,9 @@ class SpatialConvolution( MemoryData.operationWant(backwardPrimDesc, x) } - ParamsShape.weightForBackward = realWei + weightForBackwardMemoryData = realWei - reorderManager.register(ParamsShape.weight, realWei) + reorderManager.register(weight.memoryData(), realWei) val srcs = Array(realDiffDst.getPrimitive(runtime), realWei.getPrimitive(runtime), inputFormats()(0).getPrimitive(runtime)) @@ -286,8 +280,8 @@ class SpatialConvolution( } override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { - weightForBackward = reorderManager.infer(Array(ParamsShape.weight), - Array(ParamsShape.weightForBackward), weight).asInstanceOf[DnnTensor[Float]] + weightForBackward = reorderManager.infer(Array(weight.memoryData()), + Array(weightForBackwardMemoryData), weight.native).asInstanceOf[DnnTensor[Float]] if (updateGradInputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() @@ -313,7 +307,7 @@ class SpatialConvolution( val src = NativeData(inputShape, Memory.Format.any) val wei = NativeData(weightShape, Memory.Format.any) - val bis = NativeData(bias.size(), Memory.Format.x) + val bis = NativeData(Array(nOutputPlane), Memory.Format.x) val desc = MklDnn.ConvBackwardWeightsDescInit( AlgKind.ConvolutionDirect, @@ -330,8 +324,8 @@ class SpatialConvolution( MemoryData.operationWant(gradWeightPrimDesc, x) } - ParamsShape.gradWeight = realWei - ParamsShape.gradBias = bis + gradWeight.setMemoryData(realWei) + gradBias.setMemoryData(bis) val srcs = Array(realSrc.getPrimitive(runtime), realDiffDst.getPrimitive(runtime)) val indexes = Array.fill(srcs.length)(0) @@ -352,8 +346,8 @@ class SpatialConvolution( val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(input.asInstanceOf[Tensor[Float]]) buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) - buffer.append(gradWeight) - buffer.append(gradBias) + buffer.append(gradWeight.native) + buffer.append(gradBias.native) updateGradWTensors = buffer.toArray } @@ -362,10 +356,13 @@ class SpatialConvolution( MklDnnOps.streamSubmit(runtime.stream, 1, accGradientPrimitives, accGradientPrimitives.length, updateGradWMemoryPrimitives, updateGradWTensors) + + gradWeight.syncToHeap() + gradBias.syncToHeap() } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { - (Array(weight, bias), Array(gradWeight, gradBias)) + (Array(weight.dense, bias.dense), Array(gradWeight.dense, gradBias.dense)) } override def zeroGradParameters(): Unit = { @@ -374,7 +371,8 @@ class SpatialConvolution( } override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { - (Array(ParamsShape.weight, ParamsShape.bias), Array(ParamsShape.gradWeight, ParamsShape.bias)) + (Array(weight.memoryData(), bias.memoryData()), + Array(gradWeight.memoryData(), bias.memoryData())) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index f5e025d66ec..fdd7cb4cf73 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.nn.{Container, Module, Utils} import com.intel.analytics.bigdl.parameters.{AllReduceParameter, ParameterProcessor} import com.intel.analytics.bigdl.nn.{Container, Module, Utils} import com.intel.analytics.bigdl.parameters.AllReduceParameter -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ import java.io.{File, FilenameFilter} @@ -31,10 +31,13 @@ import java.util.Calendar import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.MklDnnContainer +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import org.apache.commons.lang.exception.ExceptionUtils import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.Logger -import org.apache.spark.TaskContext +import org.apache.spark.network.netty.SparkTransportConf +import org.apache.spark.{SparkContext, TaskContext} import org.apache.spark.rdd.{RDD, ZippedPartitionsWithLocalityRDD} import scala.collection.mutable @@ -132,6 +135,7 @@ object DistriOptimizer { val _subModelNumber = Engine.getEngineType() match { case MklBlas => coresPerNode + case MklDnn => 1 } val driverState = T( "epoch" -> optimMethods.values.head.state("epoch"), @@ -610,6 +614,7 @@ object DistriOptimizer { val modelBroadcast = ModelBroadcast[T]().broadcast(sc, model) val _subModelNumber = Engine.getEngineType match { case MklBlas => coresPerNode + case MklDnn => 1 case _ => throw new IllegalArgumentException } @@ -641,6 +646,10 @@ object DistriOptimizer { Engine.setNodeAndCore(nExecutor, executorCores) val cached = (0 until _subModelNumber).map { _ => val localModel = modelBroadcast.value(true) + localModel match { + case container: MklDnnContainer => container.compile(TrainingPhase) + case _ => + } // differentiate partition models from each other by partition ID setModelId(localModel, partitionId) val localCriterion = broadcastCriterion.cloneCriterion() @@ -717,12 +726,19 @@ object DistriOptimizer { logger.info(s"$header Validate model...") val _subModelNumber = Engine.getEngineType match { case MklBlas => coresPerNode + case MklDnn => 1 case _ => throw new IllegalArgumentException } val results = ZippedPartitionsWithLocalityRDD(models, validateRDD)((modelIter, dataIter) => { val cached = modelIter.next() val vMethodsArr = cached.localMethods - val workingModels = cached.localModels + val workingModels = cached.localModels.map { x => + val _x = x.cloneModule() + if (x.isInstanceOf[MklDnnContainer]) { + _x.asInstanceOf[MklDnnContainer].compile(InferencePhase) + } + _x + } workingModels.foreach(_.evaluate()) dataIter.map(batch => { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index dfcb3709dc4..9ba3d44d2cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -18,9 +18,12 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch} import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.Utils import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.{HeapData, MemoryData, MklDnnContainer} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ @@ -54,6 +57,7 @@ class LocalOptimizer[T: ClassTag] ( private val subModelNumber = Engine.getEngineType match { case MklBlas => coreNumber + case MklDnn => 1 case _ => throw new IllegalArgumentException } @@ -66,6 +70,10 @@ class LocalOptimizer[T: ClassTag] ( val m = model.cloneModule() Util.putWeightBias(wb, m) Util.initGradWeightBias(wb, m) + m match { + case container: MklDnnContainer => container.compile(TrainingPhase) + case _ => + } m }).toArray Util.putWeightBias(wb, model) @@ -231,6 +239,13 @@ class LocalOptimizer[T: ClassTag] ( logger.info(s"$header Validate model...") workingModels.foreach(_.evaluate()) + val localWorkingModels = workingModels.map { + case container: MklDnnContainer => + val _c = container.cloneModule() + _c.compile(InferencePhase) + _c + case default => default + } var count = 0 dataIter.map(batch => { @@ -246,7 +261,7 @@ class LocalOptimizer[T: ClassTag] ( val currentMiniBatch = batch.slice(offset, length) val input = currentMiniBatch.getInput() val target = currentMiniBatch.getTarget() - val output = workingModels(b).forward(input) + val output = localWorkingModels(b).forward(input) val validatMethods = vMethodsArr(b) validatMethods.map(validation => { validation(output, target) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala index 062e87fee2a..9aa9e2bb0f8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -37,8 +37,8 @@ class FusionSpec extends FlatSpec with Matchers { model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") - val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, initWeight = conv1.weight, - initBias = conv1.bias) + val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, + initWeight = conv1.weight.native, initBias = conv1.bias.native) val reorder2 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) val reorder22 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) val model2 = Sequential().add(reorder2).add(conv2).add(ReLU()).add(reorder22) @@ -77,8 +77,8 @@ class FusionSpec extends FlatSpec with Matchers { model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) System.setProperty("bigdl.mkldnn.fusion.convbn", "true") - val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, initWeight = conv1.weight, - initBias = conv1.bias) + val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, + initWeight = conv1.weight.native, initBias = conv1.bias.native) val bn2 = SpatialBatchNormalization(64, eps = 0.0) bn2.runningMean.copy(runningMean) bn2.runningVariance.copy(runningVar) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputSpec.scala index a7921362884..1c69c9d9a41 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputSpec.scala @@ -27,7 +27,9 @@ class InputSpec extends BigDLSpecHelper { layer.initBwdPrimitives(Array(), Phase.TrainingPhase) val tensor = Tensor[Float](2, 2).rand() val grad = Tensor[Float](2, 2).rand() - layer.forward(tensor) should be(tensor) - layer.backward(tensor, grad) should be(grad) + val output = layer.forward(tensor) + val gradInput = layer.backward(tensor, grad) + Tools.dense(output) should be(tensor) + Tools.dense(gradInput) should be(grad) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala index b613e50b427..279c38eb587 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala @@ -205,8 +205,8 @@ class LinearSpec extends FlatSpec with Matchers { println(nnLinear.gradWeight) println(nnLinear.gradBias) - Tools.dense(linear.gradWeight) should be (nnLinear.gradWeight) - Tools.dense(linear.gradBias) should be (nnLinear.gradBias) + Tools.dense(linear.gradWeight.native) should be (nnLinear.gradWeight) + Tools.dense(linear.gradBias.native) should be (nnLinear.gradBias) } "linear cloned" should "work correctly" in { @@ -246,8 +246,51 @@ class LinearSpec extends FlatSpec with Matchers { cloned.backward(input, gradOutput) Tools.dense(linear.gradInput) should be (Tools.dense(cloned.gradInput)) - Tools.dense(linear.gradWeight) should be (Tools.dense(cloned.gradWeight)) - Tools.dense(linear.gradBias) should be (Tools.dense(cloned.gradBias)) + Tools.dense(linear.gradWeight.native) should be (Tools.dense(cloned.gradWeight.native)) + Tools.dense(linear.gradBias.native) should be (Tools.dense(cloned.gradBias.native)) + } + + "linear with dense weights" should "work correctly" in { + val inputSize = 2 + val outputSize = 2 + val batchSize = 2 + + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + val input = Tensor[Float](batchSize, inputSize).rand() + val gradOutput = Tensor[Float]().resize(outputFormat.shape).rand() + + val initWeight1 = Tensor[Float](outputSize, inputSize).rand() + val initBias1 = Tensor[Float](outputSize).rand() + + val initWeight2 = Tensor[Float](outputSize, inputSize).rand() + val initBias2 = Tensor[Float](outputSize).rand() + + val linear1 = Linear(inputSize, outputSize, initWeight = initWeight1, initBias = initBias1) + linear1.setRuntime(new MklDnnRuntime) + linear1.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear1.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear1.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + linear1.forward(input) + linear1.backward(input, gradOutput) + + linear1.parameters()._1.zip(Array(initWeight2, initBias2)).foreach(x => x._1.copy(x._2)) + linear1.forward(input) + linear1.backward(input, gradOutput) + + val linear2 = Linear(inputSize, outputSize, initWeight = initWeight2, initBias = initBias2) + linear2.setRuntime(new MklDnnRuntime) + linear2.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear2.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear2.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + linear2.forward(input) + linear2.backward(input, gradOutput) + + Tools.dense(linear1.output) should be (Tools.dense(linear2.output)) + Tools.dense(linear1.gradInput) should be (Tools.dense(linear2.gradInput)) + linear1.parameters()._2.zip(linear2.parameters()._2).foreach(x => x._1 should be (x._2)) } "linear with maxpooling" should "work correctly" in { @@ -338,7 +381,7 @@ class LinearSpec extends FlatSpec with Matchers { .add(Linear(outputSize, inputSize, initWeight = initWeight, initBias = initBias)) .add(Linear(outputSize, inputSize, initWeight = initWeight, initBias = initBias)) - seq.compile(TrainingPhase, Array(HeapData(inputShape2, Memory.Format.nchw))) + seq2.compile(TrainingPhase, Array(HeapData(inputShape2, Memory.Format.nchw))) seq.forward(input) seq.backward(input, input) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala index f29e9a8bbc9..ed2301ce055 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.BigDLSpecHelper import org.apache.commons.lang3.SerializationUtils @@ -138,4 +139,51 @@ class SequentialSpec extends BigDLSpecHelper { Tools.dense(seq.output) should be (Tools.dense(cloned.output)) Tools.dense(seq.gradInput) should be (Tools.dense(cloned.gradInput)) } + + "compile with input" should "work correctly" in { + val inputShape = Array(4, 1, 28, 28) + val outputShape = Array(4, 10) + + val model = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(SpatialConvolution(1, 20, 5, 5).setName("conv1")) + .add(MaxPooling(2, 2, 2, 2).setName("pool1")) + .add(SpatialConvolution(20, 50, 5, 5).setName("conv2")) + .add(MaxPooling(2, 2, 2, 2).setName("pool2")) + .add(Linear(50 * 4 * 4, 500).setName("ip1")) + .add(ReLU().setName("relu1")) + .add(Linear(500, 10).setName("ip2")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nc))) + + model.compile(TrainingPhase) + + val input = Tensor[Float](inputShape).rand(-1, 1) + val gradOutput = Tensor[Float](outputShape).rand(-1, 1) + + model.forward(input) + model.backward(input, gradOutput) + } + + "no input" should "throw exception" in { + val inputShape = Array(4, 1, 2, 2) + val outputShape = Array(4, 1, 2, 2) + + val model1 = Sequential() + .add(ReLU().setName("relu1")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nc))) + + val model2 = Sequential() + .add(Sequential() + .add(ReLU().setName("relu1")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nc)))) + + val model3 = Sequential() + .add(ConcatTable().add(ReLU()).add(ReLU())) + + List(model1, model2, model3).foreach { model => + intercept[IllegalArgumentException] { + model.compile(TrainingPhase) + } + } + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala index b0cb0c50b63..fae812ff0f9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -55,10 +55,10 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { bn.backward(input, gradOutput) nnBn.backward(input, gradOutput) - val gradWeight1 = Tools.dense(bn.gradWeightAndBias).toTensor + val gradWeight1 = Tools.dense(bn.gradWeightAndBias.native).toTensor val gradWeight2 = nnBn.getParameters()._2 - val weight1 = Tools.dense(bn.weightAndBias).toTensor + val weight1 = Tools.dense(bn.weightAndBias.native).toTensor val weight2 = nnBn.getParameters()._1 Equivalent.nearequals(weight1, weight2) should be (true) @@ -100,7 +100,48 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { bn.backward(input, gradOutput) cloned.backward(input, gradOutput) Tools.dense(bn.gradInput) should be (Tools.dense(cloned.gradInput)) - Tools.dense(bn.gradWeightAndBias) should be (Tools.dense(cloned.gradWeightAndBias)) + Tools.dense(bn.gradWeightAndBias.native) should be ( + Tools.dense(cloned.gradWeightAndBias.native)) + } + + "batch norm with dense weights and gradients" should "work correctly" in { + val batchSize = 2 + RNG.setSeed(100) + val input = Tensor(100, 1, 10, 10).rand(-1, 1) + val gradOutput = Tensor(100, 1, 10, 10).rand(-1, 1) + val (channel, height, width) = (1, 10, 10) + + val initWeight1 = Tensor(channel).rand(-1, 1) + val initBias1 = Tensor(channel).fill(0) + val initWeight2 = Tensor(channel).rand(-1, 1) + val initBias2 = Tensor(channel).fill(0) + + val bn1 = SpatialBatchNormalization(1, 0.0, initWeight = initWeight1, initBias = initBias1) + val bn2 = SpatialBatchNormalization(1, 0.0, initWeight = initWeight2, initBias = initBias2) + + val inputShape = Array(100, 1, 10, 10) + for (bn <- List(bn1, bn2)) { + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + } + + bn1.forward(input) + bn1.backward(input, gradOutput) + + bn1.parameters()._1.zip(bn2.parameters()._1).foreach(x => x._1.copy(x._2)) + + bn1.forward(input) + bn1.backward(input, gradOutput) + + bn2.forward(input) + bn2.backward(input, gradOutput) + + Tools.dense(bn1.output) should be (Tools.dense(bn2.output)) + Tools.dense(bn1.gradInput) should be (Tools.dense(bn2.gradInput)) + + bn1.parameters()._2.zip(bn2.parameters()._2).foreach(x => x._1 should be (x._2)) } "bn updateOutput" should "work correctly" in { @@ -467,11 +508,11 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { gradWeightAndBias.select(1, 1).copy(gradWeight) gradWeightAndBias.select(1, 2).copy(gradBias) - compare(weightAndBias.view(Array(2 * channel)), bn.weightAndBias) + compare(weightAndBias.view(Array(2 * channel)), bn.weightAndBias.native) compare(output, seq.output) - compare(runningMean, bn.runningMean) - compare(runningVariance, bn.runningVariance) - compare(gradWeightAndBias.view(Array(2 * channel)), bn.gradWeightAndBias) + compare(runningMean, bn.runningMean.native) + compare(runningVariance, bn.runningVariance.native) + compare(gradWeightAndBias.view(Array(2 * channel)), bn.gradWeightAndBias.native) compare(gradInput, seq.gradInput) } @@ -549,9 +590,9 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { weightAndBias.select(1, 1).copy(weight) weightAndBias.select(1, 2).copy(bias) - compare(weightAndBias.view(Array(2 * channel)), bn.weightAndBias) - compare(runningMean, bn.runningMean) - compare(runningVariance, bn.runningVariance) + compare(weightAndBias.view(Array(2 * channel)), bn.weightAndBias.native) + compare(runningMean, bn.runningMean.native) + compare(runningVariance, bn.runningVariance.native) val denseOutput = Tools.dense(bn.output).toTensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index 941a64f16de..d7a34535b46 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -56,10 +56,10 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { conv.gradInputFormats()(0)) conv.accGradParameters(input, gradOutput) - val weight1 = Tools.toOIHW(conv.weight, conv.parametersWithShape()._1(0)) - val gradweight1 = Tools.toOIHW(conv.gradWeight, conv.parametersWithShape()._2(0)) - val bias1 = Tools.dense(conv.bias).toTensor[Float] - val gradbias1 = Tools.dense(conv.gradBias).toTensor + val weight1 = Tools.toOIHW(conv.weight.native, conv.parametersWithShape()._1(0)) + val gradweight1 = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) + val bias1 = Tools.dense(conv.bias.native).toTensor[Float] + val gradbias1 = Tools.dense(conv.gradBias.dense).toTensor val output2 = layer.forward(input) val grad2 = layer.updateGradInput(input, gradOutput) @@ -115,10 +115,10 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val grad1 = Tools.toNCHW(conv.updateGradInput(input, gradOutput).toTensor, conv.gradInputFormats()(0)) conv.accGradParameters(input, gradOutput) - val weight1 = Tools.toOIHW(conv.weight, conv.parametersWithShape()._1(0)) - val gradweight1 = Tools.toOIHW(conv.gradWeight, conv.parametersWithShape()._2(0)) - val bias1 = Tools.dense(conv.bias).toTensor[Float] - val gradbias1 = Tools.dense(conv.gradBias).toTensor[Float] + val weight1 = Tools.toOIHW(conv.weight.native, conv.parametersWithShape()._1(0)) + val gradweight1 = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) + val bias1 = Tools.dense(conv.bias.native).toTensor[Float] + val gradbias1 = Tools.dense(conv.gradBias.native).toTensor[Float] Equivalent.nearequals(weight1, weight2) should be(true) Equivalent.nearequals(gradweight1, gradweight2) should be(true) @@ -165,10 +165,10 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val output = Tools.toNCHW(conv.output.toTensor, conv.outputFormats()(0)) val gradInput = Tools.toNCHW(conv.gradInput.toTensor, conv.gradInputFormats()(0)) - val weight = Tools.toOIHW(conv.weight, conv.parametersWithShape()._1(0)) - val gradweight = Tools.toOIHW(conv.gradWeight, conv.parametersWithShape()._2(0)) - val bias = Tools.dense(conv.bias).toTensor - val gradbias = Tools.dense(conv.gradBias).toTensor + val weight = Tools.toOIHW(conv.weight.native, conv.parametersWithShape()._1(0)) + val gradweight = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) + val bias = Tools.dense(conv.bias.native).toTensor + val gradbias = Tools.dense(conv.gradBias.native).toTensor val output1 = conv1.output.toTensor val gradInput1 = conv1.gradInput @@ -237,8 +237,8 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val gw1 = model1.gradWeight val gb1 = model1.gradBias - val gw2 = Tools.toOIHW(model2.gradWeight, model2.parametersWithShape()._2(0)) - val gb2 = Tools.dense(model2.gradBias).toTensor + val gw2 = Tools.toOIHW(model2.gradWeight.native, model2.parametersWithShape()._2(0)) + val gb2 = Tools.dense(model2.gradBias.native).toTensor Equivalent.nearequals(gw1, gw2, 1e-4) should be(true) Equivalent.nearequals(gb1, gb2, 1e-3) should be(true) @@ -440,8 +440,54 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { cloned.backward(input, gradOutput) Tools.dense(conv.gradInput) should be (Tools.dense(cloned.gradInput)) - Tools.dense(conv.gradWeight) should be (Tools.dense(cloned.gradWeight)) - Tools.dense(conv.gradBias) should be (Tools.dense(cloned.gradBias)) + Tools.dense(conv.gradWeight.native) should be (Tools.dense(cloned.gradWeight.native)) + Tools.dense(conv.gradBias.native) should be (Tools.dense(cloned.gradBias.native)) + } + + "conv with dense weights and gradients" should "work correctly" in { + val inputShape = Array(4, 3, 5, 5) + val outputShape = Array(4, 2, 3, 3) + val nOutput = 2 + val kernel = 3 + val pad = 1 + val stride = 2 + + val input = Tensor(inputShape).rand(-1, 1) + val gradOutput = Tensor(outputShape).rand(-1, 1) + + val initWeight1 = Tensor(Array(nOutput, inputShape(1), kernel, kernel)).rand(-1, 1) + val initWeight2 = Tensor(Array(nOutput, inputShape(1), kernel, kernel)).rand(-1, 1) + val initBias1 = Tensor(nOutput).rand(-1, 1) + val initBias2 = Tensor(nOutput).rand(-1, 1) + + val conv1 = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1, + initWeight = initWeight1, initBias = initBias1) + conv1.setRuntime(new MklDnnRuntime) + conv1.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + conv1.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + conv1.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + conv1.forward(input) + conv1.backward(input, gradOutput) + + conv1.parameters()._1.zip(Array(initWeight2, initBias2)).foreach(x => x._1.copy(x._2)) + conv1.forward(input) + conv1.backward(input, gradOutput) + + val conv2 = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1, + initWeight = initWeight2, initBias = initBias2) + conv2.setRuntime(new MklDnnRuntime) + conv2.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + conv2.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + conv2.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + conv2.forward(input) + conv2.backward(input, gradOutput) + + Tools.dense(conv1.output) should be (Tools.dense(conv2.output)) + Tools.dense(conv1.gradInput) should be (Tools.dense(conv2.gradInput)) + + conv1.parameters()._2.zip(conv2.parameters()._2).foreach(x => x._1 should be (x._2)) } def prototxt(inputShape: Array[Int], name: String, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala index e80f4d052fe..c8e9725eaf8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala @@ -213,12 +213,12 @@ object Tools { weightAndBias.select(1, 2).copy(bias) ret &= Equivalent.nearequals(weightAndBias.view(bn.gradWeightAndBias.size()), - dense(bn.gradWeightAndBias).toTensor, epsilon) + dense(bn.gradWeightAndBias.native).toTensor, epsilon) val runningMean = Tools.getTensor(s"Fwrd_$name.Wght.0", Array(channel), identity) val runningVariance = Tools.getTensor(s"Fwrd_$name.Wght.1", Array(channel), identity) - ret &= compare2Tensors(runningMean, dense(bn.runningMean).toTensor) - ret &= compare2Tensors(runningVariance, dense(bn.runningVariance).toTensor) + ret &= compare2Tensors(runningMean, dense(bn.runningMean.native).toTensor) + ret &= compare2Tensors(runningVariance, dense(bn.runningVariance.native).toTensor) assert(ret, s"${module.getName()} gradient can't pass, please check") case _ => From a1abd328301050fa80da2aba7b3dbba7c69c79f7 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 10 Aug 2018 09:14:00 +0800 Subject: [PATCH 0811/1065] Distributed asynchronizer based on block manager (#2605) * add synchronizer * remove unused field * add cleaner * add blocking queue * add unit test * refinement per review * add other paritions * per review * remove unused line * support small size parameter * fix bug and refine per comments --- .../utils/DistriParameterSynchronizer.scala | 447 ++++++++++++++++++ .../utils/DistributedSynchronizerSpec.scala | 80 ++++ 2 files changed, 527 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala new file mode 100644 index 00000000000..f9d1b96c2a3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala @@ -0,0 +1,447 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils + +import java.nio.ByteBuffer +import java.util +import java.util.concurrent._ + +import com.intel.analytics.bigdl.parameters.{CompressedTensor, FP16CompressedTensor, SerializerInstance} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import org.apache.commons.lang.exception.ExceptionUtils +import org.apache.log4j.Logger +import org.apache.spark.sparkExtension.SparkExtension +import org.apache.spark.storage.{BlockId, BlockManagerWrapper, StorageLevel} + +import scala.collection.mutable +import scala.reflect.{ClassTag, classTag} +import scala.collection.JavaConverters._ + +trait DistriParameterSynchronizer[T] { + + /** + * Init synchronization context for new parameter + * @param name identifier for parameter + * @param globalSize total size of parameter + * @param priority priority for this parameter + */ + def init(name: String, globalSize: Int, priority: Int = 1): Unit + + /** + * put parameter to global + * @param name identifier for parameter + * @param parameter parameter to put + */ + def put(name: String, parameter: Tensor[T]): Unit + + /** + * get parameter for specific identifier + * @param name identifier for parameter + * @return parameter + */ + def get(name: String): Tensor[T] + + /** + * clear the synchronizer + */ + def clear(): Unit +} + +class BlockManagerParameterSynchronizer[T: ClassTag](partitionID: Int, + totalPartition: Int) + (implicit ev: TensorNumeric[T]) + extends DistriParameterSynchronizer[T] { + + import com.intel.analytics.bigdl.utils.BlockManagerParameterSynchronizer.logger + + @volatile private var shutdown = false + + private val syncResults: mutable.HashMap[String, FutureTask[Tensor[T]]] + = new mutable.HashMap[String, FutureTask[Tensor[T]]]() + + private val taskSize: Int = System.getProperty("bigdl.ParameterSynchronier." + + "asyncTaskSize", "100").toInt + + private val clearPoolSize: Int = System.getProperty("bigdl.ParameterSynchronier." + + "clearPoolSize", "1").toInt + + private val workerPoolSize: Int = System.getProperty("bigdl.ParameterSynchronier" + + ".syncPoolSize", "4").toInt + + private val syncPoolSize: Int = Math.max(System.getProperty("bigdl.ParameterSynchronier" + + ".computePoolSize", + (Runtime.getRuntime().availableProcessors() / 2).toString).toInt, 2) + + private val fetchCompletionPoolSize: Int = System.getProperty("bigdl.ParameterSynchronier" + + ".fetchCompletionPoolSize", "1").toInt + + private val asyncTaskWaitingQueue : PriorityBlockingQueue[AsyncFutureTask] = + new PriorityBlockingQueue[AsyncFutureTask](taskSize) + + private val blockFetchRequestQueue: PriorityBlockingQueue[BlockFetchRequest] = + new PriorityBlockingQueue[BlockFetchRequest](taskSize) + + private val longRunningThreads = new util.ArrayList[Thread]() + + // thread pool to remove expired blocks + private lazy val clearPool: ExecutorService = + Executors.newFixedThreadPool(clearPoolSize, new ThreadFactory { + override def newThread(r: Runnable): Thread = { + val t = Executors.defaultThreadFactory().newThread(r) + t.setDaemon(true) + t + } + }) + + // main thread pool to do put-get-aggregate + private val workerPool: ExecutorService = + Executors.newFixedThreadPool(workerPoolSize, new ThreadFactory { + override def newThread(r: Runnable): Thread = { + val t = Executors.defaultThreadFactory().newThread(r) + t.setDaemon(true) + t + } + }) + + // long running thread to fetch the request + workerPool.submit(new Runnable { + override def run(): Unit = { + longRunningThreads.add(Thread.currentThread) + while (!shutdown) { + try { + val asyncFutureTask = asyncTaskWaitingQueue.take + workerPool.submit(asyncFutureTask.task) + } catch { + case e : InterruptedException => + logger.info("exit thread gracefully") + } + } + } + }) + + // thread pool for put and aggregate + private lazy val syncPool: ExecutorService = Executors.newFixedThreadPool(syncPoolSize, + new ThreadFactory { + override def newThread(r: Runnable): Thread = { + val t = Executors.defaultThreadFactory().newThread(r) + t.setDaemon(true) + t + } + }) + + // thread pool for fetching blocks + private lazy val fetchPool: ExecutorService = Executors.newFixedThreadPool(syncPoolSize, + new ThreadFactory { + override def newThread(r: Runnable): Thread = { + val t = Executors.defaultThreadFactory().newThread(r) + t.setDaemon(true) + t + } + }) + + // thread pool to update sow on fetching completion + private val fetchCompletionPool: ExecutorService = Executors. + newFixedThreadPool(fetchCompletionPoolSize, + new ThreadFactory { + override def newThread(r: Runnable): Thread = { + val t = Executors.defaultThreadFactory().newThread(r) + t.setDaemon(true) + t + } + }) + + (0 until syncPoolSize).foreach(th => { + fetchPool.submit(new Runnable { + override def run(): Unit = { + longRunningThreads.add(Thread.currentThread) + while (!shutdown) { + try { + val fetchRequest = blockFetchRequestQueue.take + val syncMeta = fetchRequest.syncMeta + val pid = fetchRequest.futureTask.fetchOnCompletion.fromPartition + val aggregated = fetchRequest.aggregated + val parameterBlockId = if (aggregated) { + getParameterBlockId(s"${syncMeta.name}_aggregated", syncMeta.counter, pid, -1) + } + else { + getParameterBlockId(syncMeta.name, syncMeta.counter, pid, partitionID) + } + val block = BlockManagerWrapper.getLocalOrRemoteBytes(parameterBlockId) + if (block == None) { + // promote the priporty in next fetch + fetchRequest.priority += 1 + blockFetchRequestQueue.add(fetchRequest) + } else { + val fetchOnCompletion = fetchRequest.futureTask.fetchOnCompletion + fetchOnCompletion.setFetched(block.get) + fetchCompletionPool.submit(fetchRequest.futureTask.task) + } + } catch { + case e : InterruptedException => + logger.info("exit thread gracefully") + } + } + } + }) + }) + + private val syncMetaMap = new ConcurrentHashMap[String, SyncMeta[T]] + + override def init(name: String, globalSize: Int, priority: Int = 1): Unit = { + val partitionToCount = if (globalSize < totalPartition) globalSize else totalPartition + syncMetaMap.putIfAbsent(name, SyncMeta(name, 1, priority, globalSize, partitionToCount, + new ConcurrentHashMap[Int, CompressedTensor[T]](), + new ConcurrentHashMap[Int, Tensor[T]]())) + } + + override def put(name: String, parameter: Tensor[T]): Unit = { + val syncMeta = syncMetaMap.get(name) + val asyncTask = new AsyncTask(syncMeta, parameter) + val futureTask = new FutureTask[Tensor[T]](asyncTask) + val futureAsyncTask = new AsyncFutureTask(futureTask, syncMeta.priority) + asyncTaskWaitingQueue.add(futureAsyncTask) + val clearTask = new ClearTask(name, syncMeta.counter - 1, + partitionID, syncMeta.partitionToCount) + clearPool.execute(clearTask) + syncResults.put(name, futureTask) + } + + override def get(name: String): Tensor[T] = { + require(syncResults.contains(name), "put must be done before get") + val res = syncResults.get(name).get.get() + val syncMeta = syncMetaMap.get(name) + syncMeta.counter += 1 + res + } + + private class ClearTask(name: String, counter: Int, partitionID: Int, + partitionToCount: Int) + extends Runnable { + override def run(): Unit = { + (0 until partitionToCount).foreach(pid => { + val parameterBlockId = getParameterBlockId(name, + counter, partitionID, pid) + BlockManagerWrapper.removeBlock(parameterBlockId) + }) + // only local parititon < partitionToCount, there are aggregated blocks + if (partitionID < partitionToCount) { + val aggregatedParameterBlockId = getParameterBlockId(s"${name}_aggregated", + counter, partitionID, -1) + } + } + } + + private class AsyncFutureTask(val task : FutureTask[_], val priority: Int) + extends Comparable[AsyncFutureTask] { + override def compareTo(o: AsyncFutureTask): Int = { + o.priority.compareTo(this.priority) + } + } + + private class AsyncTask(val syncMeta: SyncMeta[T], + parameter: Tensor[T]) extends Callable[Tensor[T]] { + + override def call(): Tensor[T] = { + + // step 1: clear last status + + syncMeta.stateOfWorld.clear + syncMeta.aggregatedStateOfWorld.clear + + val partitonToCount = syncMeta.partitionToCount + + val _classTag = classTag[T] + val size = syncMeta.globalSize + val taskSize = size / partitonToCount + val extraSize = size % partitonToCount + + // step 2 : put all local partitioned parameter to global + val putThreads = (0 until partitonToCount).map { pid => + new Callable[Int] { + override def call(): Int = { + try { + val offset = parameter.storageOffset() + pid * taskSize + math.min(pid, extraSize) + val length = taskSize + (if (pid < extraSize) 1 else 0) + val partitionParam = parameter.narrow(1, offset, length) + syncMeta.aggregatedStateOfWorld.put(pid, partitionParam) + val parameterBlockId = getParameterBlockId(syncMeta.name, + syncMeta.counter, partitionID, pid) + val fp16param = new FP16CompressedTensor[T](length)(_classTag) + fp16param.compress(0, parameter, offset - 1, length) + BlockManagerWrapper.putBytes(parameterBlockId, + fp16param.bytes(), StorageLevel.MEMORY_ONLY_SER) + pid + } catch { + case t: Throwable => + logger.error("Error: " + ExceptionUtils.getStackTrace(t)) + throw t + } + } + } + } + syncPool.invokeAll(putThreads.asJava) + + // step 3: get all remote paritioned parameter to local + if (partitionID < partitonToCount) { + val syncThreads = (0 until totalPartition).map { pid => + new Callable[Int] { + override def call(): Int = { + try { + val fetchOnCompletion = new BlockFetchOnCompletion(syncMeta, pid) + val futureTask = new FutureTask[Int](fetchOnCompletion) + val priorityFutureTask = new PriorityFutureTask(futureTask, fetchOnCompletion) + val fetchRequest = new BlockFetchRequest(syncMeta, syncMeta.priority, + priorityFutureTask) + blockFetchRequestQueue.add(fetchRequest) + futureTask.get + } catch { + case t: Throwable => + logger.error("Error: " + ExceptionUtils.getStackTrace(t)) + throw t + } + } + } + } + syncPool.invokeAll(syncThreads.asJava) + + + // step 4: aggregation + + val length = taskSize + (if (partitionID < extraSize) 1 else 0) + val poolSize = Engine.default.getPoolSize + val innerTaskSize = length / poolSize + val innerExtraSize = length % poolSize + val availableTask = if (innerTaskSize == 0) innerExtraSize else poolSize + val params = syncMeta.stateOfWorld.values().toArray(). + map(_.asInstanceOf[CompressedTensor[T]]) + syncPool.invokeAll((0 until availableTask).map(tid => + new Callable[Int] { + override def call(): Int = { + val innerStart = tid * innerTaskSize + math.min(innerExtraSize, tid) + val innerLength = innerTaskSize + (if (tid < innerExtraSize) 1 else 0) + params.reduce { (l, r) => + l.add(r.bytes(innerStart, innerLength), innerStart, innerLength) + } + tid + } + } + ).asJava) + val res = Tensor[T](length) + params.head.deCompress(res) + res.div(ev.fromType(totalPartition)) + + // step 5: put aggregated to global + val parameterBlockId = getParameterBlockId(s"${syncMeta.name}_aggregated", + syncMeta.counter, partitionID, -1) + val fp16paramAggregated = new FP16CompressedTensor[T](length)(_classTag) + fp16paramAggregated.compress(0, res, 0, length) + BlockManagerWrapper.putBytes(parameterBlockId, + fp16paramAggregated.bytes(), StorageLevel.MEMORY_ONLY_SER) + } + + // step 6: get all other aggregated partitions + + val AggregatedSyncThreads = (0 until partitonToCount).map { pid => + new Callable[Int] { + override def call(): Int = { + try { + val fetchOnCompletion = new BlockFetchOnCompletion(syncMeta, pid, true) + val futureTask = new FutureTask[Int](fetchOnCompletion) + val priorityFutureTask = new PriorityFutureTask(futureTask, fetchOnCompletion) + val fetchRequest = new BlockFetchRequest(syncMeta, syncMeta.priority, + priorityFutureTask, true) + blockFetchRequestQueue.add(fetchRequest) + futureTask.get + } catch { + case t: Throwable => + logger.error("Error: " + ExceptionUtils.getStackTrace(t)) + throw t + } + } + } + } + syncPool.invokeAll(AggregatedSyncThreads.asJava) + + parameter + } + } + + private class BlockFetchRequest(val syncMeta: SyncMeta[T], + var priority: Int, + val futureTask: PriorityFutureTask, + val aggregated: Boolean = false) + extends Comparable[BlockFetchRequest] { + override def compareTo(o: BlockFetchRequest): Int = { + o.priority.compareTo(this.priority) + } + } + + private class BlockFetchOnCompletion(val syncMeta: SyncMeta[T], val fromPartition: Int, + val aggregated: Boolean = false) + extends Callable[Int] { + val _classTag = classTag[T] + private var _fetched: ByteBuffer = null + def setFetched(fetched: ByteBuffer): Unit = { + this._fetched = fetched + } + override def call(): Int = { + if (aggregated) { + val partitionParam = syncMeta.aggregatedStateOfWorld.get(fromPartition) + SerializerInstance.create(_fetched)(_classTag).deCompress(partitionParam) + } else { + syncMeta.stateOfWorld.put(fromPartition, SerializerInstance.create(_fetched)(_classTag)) + } + fromPartition + } + } + + private class PriorityFutureTask(val task : FutureTask[_], + val fetchOnCompletion: BlockFetchOnCompletion) { + + } + + private def getBlockId(name: String): BlockId = { + SparkExtension.getLocalBlockId(name) + } + + private def getParameterBlockId(name: String, counter: Int, pidFrom: Int, pidTo: Int): BlockId = { + SparkExtension.getLocalBlockId(name + counter + pidFrom + "paraBytes" + pidTo) + } + + override def clear(): Unit = { + shutdown = true + longRunningThreads.asScala.foreach(_.interrupt()) + clearPool.shutdown + syncPool.shutdown + workerPool.shutdown + fetchPool.shutdown + fetchCompletionPool.shutdown + } +} + +object BlockManagerParameterSynchronizer { + val logger: Logger = Logger.getLogger(getClass) + def apply[T: ClassTag](partitionID: Int, + totalPartition: Int) + (implicit ev: TensorNumeric[T]): BlockManagerParameterSynchronizer[T] + = new BlockManagerParameterSynchronizer[T](partitionID, totalPartition) +} + +case class SyncMeta[T](name: String, var counter: Int, priority: Int, + globalSize: Int, partitionToCount: Int, + stateOfWorld: ConcurrentHashMap[Int, CompressedTensor[T]], + aggregatedStateOfWorld: ConcurrentHashMap[Int, Tensor[T]]) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala new file mode 100644 index 00000000000..a794f37281e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala @@ -0,0 +1,80 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import com.intel.analytics.bigdl.tensor.Tensor +import org.apache.spark.{SparkContext, TaskContext} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAndAfter { + + var sc: SparkContext = null + + before { + val conf = Engine.createSparkConf().setAppName("test synchronizer").setMaster("local[*]") + .set("spark.rpc.message.maxSize", "200") + sc = new SparkContext(conf) + Engine.init + } + "DistributedSynchronizer" should "work properly" in { + val partition = 4 + val cores = 4 + val res = sc.parallelize((0 until partition), partition).mapPartitions(p => { + Engine.setNodeAndCore(partition, cores) + val partitionID = TaskContext.getPartitionId + val sync = new BlockManagerParameterSynchronizer[Float](partitionID, partition) + val tensor = Tensor[Float](10).fill(partitionID.toFloat + 1.0f) + sync.init(s"testPara", 10) + var res : Iterator[_] = null + sync.put(s"testPara", tensor) + res = Iterator.single(sync.get(s"testPara")) + sync.clear + res + }).collect + res.length should be (4) + res(0) should be (Tensor[Float](10).fill(2.5f)) + res(1) should be (Tensor[Float](10).fill(2.5f)) + res(2) should be (Tensor[Float](10).fill(2.5f)) + res(3) should be (Tensor[Float](10).fill(2.5f)) + } + + "DistributedSynchronizer with parameter size less than partition" should "worl properly" in { + val partition = 4 + val cores = 4 + val res = sc.parallelize((0 until partition), partition).mapPartitions(p => { + Engine.setNodeAndCore(partition, cores) + val partitionID = TaskContext.getPartitionId + val sync = new BlockManagerParameterSynchronizer[Float](partitionID, partition) + val tensor = Tensor[Float](2).fill(partitionID.toFloat + 1.0f) + sync.init(s"testPara", 2) + var res : Iterator[_] = null + sync.put(s"testPara", tensor) + res = Iterator.single(sync.get(s"testPara")) + sync.clear + res + }).collect + res.length should be (4) + res(0) should be (Tensor[Float](2).fill(2.5f)) + res(1) should be (Tensor[Float](2).fill(2.5f)) + res(2) should be (Tensor[Float](2).fill(2.5f)) + res(3) should be (Tensor[Float](2).fill(2.5f)) + } + + after { + sc.stop + } +} From 23a5c667d159c2d94a45273a58b7835ad2abb4ce Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 15 Aug 2018 09:52:56 +0800 Subject: [PATCH 0812/1065] [BugFix] - Fix parameter syn that parameter not starting with 1 offset (#2611) * fix parameter syn that parameter not starts with zero * fix typo --- .../utils/DistriParameterSynchronizer.scala | 2 +- .../utils/DistributedSynchronizerSpec.scala | 24 +++++++++++++++++++ 2 files changed, 25 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala index f9d1b96c2a3..6d20852a328 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala @@ -274,7 +274,7 @@ class BlockManagerParameterSynchronizer[T: ClassTag](partitionID: Int, new Callable[Int] { override def call(): Int = { try { - val offset = parameter.storageOffset() + pid * taskSize + math.min(pid, extraSize) + val offset = 1 + pid * taskSize + math.min(pid, extraSize) val length = taskSize + (if (pid < extraSize) 1 else 0) val partitionParam = parameter.narrow(1, offset, length) syncMeta.aggregatedStateOfWorld.put(pid, partitionParam) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala index a794f37281e..0ff4a995b8e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala @@ -30,6 +30,7 @@ class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAnd sc = new SparkContext(conf) Engine.init } + "DistributedSynchronizer" should "work properly" in { val partition = 4 val cores = 4 @@ -74,6 +75,29 @@ class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAnd res(3) should be (Tensor[Float](2).fill(2.5f)) } + "DistributedSynchronizer with parameter offset > 1" should "work properly" in { + val partition = 4 + val cores = 4 + val res = sc.parallelize((0 until partition), partition).mapPartitions(p => { + Engine.setNodeAndCore(partition, cores) + val partitionID = TaskContext.getPartitionId + val sync = new BlockManagerParameterSynchronizer[Float](partitionID, partition) + val tensor = Tensor[Float](20) + val parameter = tensor.narrow(1, 10, 10).fill(partitionID.toFloat + 1.0f) + sync.init(s"testPara", 10) + var res : Iterator[_] = null + sync.put(s"testPara", parameter) + res = Iterator.single(sync.get(s"testPara")) + sync.clear + res + }).collect + res.length should be (4) + res(0) should be (Tensor[Float](10).fill(2.5f)) + res(1) should be (Tensor[Float](10).fill(2.5f)) + res(2) should be (Tensor[Float](10).fill(2.5f)) + res(3) should be (Tensor[Float](10).fill(2.5f)) + } + after { sc.stop } From 806c305ed94d194526d19def00e9254e48f419fd Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Wed, 15 Aug 2018 02:47:53 -0400 Subject: [PATCH 0813/1065] Modify the thread pool to adopt mkldnn models (#2608) The `Engine.default` will support single thread including `LocalOptimizer` and `DistriOptimizer`. For supporting single thread version of `invokeWait2` method in `ThreadPool`, it will set the threadpool to current thread. 1. For dnn model, it will use the affinity to bind omp thread. And for performance issue, the default thread must use current main thread. 2. For MTLabeldBGRImgToBatch will use another new threads pool which is called io. So it will not be blocked when the default thread pool is single thread. 3. For FileWriter, it will not use default, otherwise the whole app will stuck at creating summary. --- .../intel/analytics/bigdl/utils/Engine.scala | 34 +++++++++++++++++++ .../analytics/bigdl/utils/ThreadPool.scala | 21 ++++++++++-- .../image/MTLabeledBGRImgToBatch.scala | 2 +- .../vision/image/MTImageFeatureToBatch.scala | 2 +- .../bigdl/dllib/nn/mkldnn/ResNet50Perf.scala | 12 ++----- .../tensorboard/FileWriter.scala | 2 +- 6 files changed, 59 insertions(+), 14 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 19255b55c8c..d96510bba21 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -23,6 +23,7 @@ import java.util.concurrent.atomic.AtomicBoolean import org.apache.log4j.Logger import org.apache.spark._ import com.intel.analytics.bigdl.mkl.MKL +import com.intel.analytics.bigdl.mkl.hardware.CpuInfo import org.apache.spark.utils.SparkUtils import py4j.GatewayServer @@ -38,6 +39,13 @@ case object MklDnn extends EngineType object Engine { + + // Initialize some properties for mkldnn engine. We should call it at the beginning. + // Otherwise some properties will have no effect. + if (System.getProperty("bigdl.engineType") == "mkldnn") { + setMklDnnEnvironments() + } + @deprecated( "See https://bigdl-project.github.io/master/#APIGuide/Engine/", "0.1.0") @@ -212,6 +220,9 @@ object Engine { // Thread pool for layer use @volatile private var _model: ThreadPool = new ThreadPool(1).setMKLThread(MKL.getMklNumThreads) + // Thread pool for read data + @volatile private var _io: ThreadPool = null + /** * If user undefine the property bigdl.coreNumber, it will return physical core number * system has. The biggest number it supports is the physical cores number. @@ -310,6 +321,14 @@ object Engine { _default } + private[bigdl] def io: ThreadPool = { + if (_io == null) { + throw new IllegalStateException(s"Engine.init: Thread engine is not " + + s"initialized. $NOT_INIT_ERROR") + } + _io + } + private def initThreadPool(core : Int) : Unit = { val defaultPoolSize: Int = System.getProperty("bigdl.utils.Engine.defaultPoolSize", (core * 50).toString).toInt @@ -317,6 +336,10 @@ object Engine { _default = new ThreadPool(defaultPoolSize) } + if (_io == null) { + _io = new ThreadPool(core * 50) + } + val modelPoolSize: Int = if (engineType == MklBlas) { 1 } else { @@ -327,6 +350,8 @@ object Engine { _model = new ThreadPool(modelPoolSize) _model.setMKLThread(MKL.getMklNumThreads) } + + ThreadPool.setThreadsOfBackend(MKL.getMklNumThreads) } /** @@ -515,4 +540,13 @@ object Engine { throw new IllegalArgumentException(s"Engine.init: Unsupported master format $master") } } + + private def setMklDnnEnvironments(): Unit = { + val threadsNumber = Math.ceil(Runtime.getRuntime.availableProcessors().toFloat / 2).toInt + + System.setProperty("bigdl.disable.mklBlockTime", "true") + System.setProperty("bigdl.mklNumThreads", s"$threadsNumber") + System.setProperty("bigdl.coreNumber", "1") + System.setProperty("bigdl.utils.Engine.defaultPoolSize", "1") + } } diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 1c48eef4619..59bb3e38fbd 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -18,7 +18,10 @@ package com.intel.analytics.bigdl.utils import java.util.concurrent._ +import com.google.common.util.concurrent.MoreExecutors import com.intel.analytics.bigdl.mkl.MKL +import com.intel.analytics.bigdl.mkl.hardware.Affinity +import com.intel.analytics.bigdl.mkl.{MklDnn => BackendMklDnn} import org.apache.commons.lang.exception.ExceptionUtils import org.apache.log4j.Logger @@ -28,6 +31,10 @@ import scala.collection.JavaConverters._ /** * A thread pool wrapper, provide some helper functions for multi-threading + * + * TODO `TreadPool` will give 2-version of thread pool, one uses scala version (`invokeAndWait`), + * another is provided to Java (`invokeAndWait2`). The design is weird. We should refactor this + * class later. */ class ThreadPool(private var poolSize: Int) { @@ -41,6 +48,7 @@ class ThreadPool(private var poolSize: Int) { private def spawnThreadPool(poolSize: Int): ExecutionContext = { if (poolSize == 1) { + threadPool = MoreExecutors.sameThreadExecutor() singleThreadPool } else { new ExecutionContext { @@ -71,10 +79,9 @@ class ThreadPool(private var poolSize: Int) { * @return */ def setMKLThread(size: Int): this.type = this.synchronized { - require(MKL.isMKLLoaded) mklPoolSize = Some(size) (1 to poolSize).map(i => Future { - MKL.setNumThreads(size) + ThreadPool.setThreadsOfBackend(size) val tid = Thread.currentThread().getId() logger.info(s"Set mkl threads to $size on thread $tid") }(context)).foreach(Await.result(_, Duration.Inf)) @@ -207,5 +214,15 @@ object ThreadPool { } private val logger = Logger.getLogger(getClass) + + def setThreadsOfBackend(size: Int): Unit = { + require(MKL.isMKLLoaded) + require(BackendMklDnn.isLoaded) + MKL.setNumThreads(size) + if (System.getProperty("bigdl.engineType") == "mkldnn") { + BackendMklDnn.setNumThreads(size) + Affinity.setOmpAffinity() + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/MTLabeledBGRImgToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/MTLabeledBGRImgToBatch.scala index 2dd8dc7ac50..cdc3d8347c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/MTLabeledBGRImgToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/MTLabeledBGRImgToBatch.scala @@ -76,7 +76,7 @@ class MTLabeledBGRImgToBatch[A: ClassTag] private[bigdl](width: Int, height: Int override def next(): MiniBatch[Float] = { val count = new AtomicInteger(0) - val batch = Engine.default.invokeAndWait((0 until parallelism).map(tid => () => { + val batch = Engine.io.invokeAndWait((0 until parallelism).map(tid => () => { var position = 0 var record = 0 while (iterators(tid).hasNext && { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala index 1f9f65acb3f..1f6aca8eefd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -73,7 +73,7 @@ class MTImageFeatureToBatch private[bigdl](width: Int, height: Int, override def next(): MiniBatch[Float] = { val count = new AtomicInteger(0) - val batch = Engine.default.invokeAndWait((0 until parallelism).map(tid => () => { + val batch = Engine.io.invokeAndWait((0 until parallelism).map(tid => () => { var position = 0 var record = 0 while (iterators(tid).hasNext && { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala index deef74a3977..15181e56366 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala @@ -55,15 +55,9 @@ object ResNet50Perf { System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") System.setProperty("bigdl.mkldnn.fusion.convsum", "true") -// val coreNumber: Int = System.getProperty("bigdl.mklNumThreads", -// s"${Math.ceil(Runtime.getRuntime.availableProcessors() / 2).toInt}").toInt System.setProperty("bigdl.localMode", "true") - System.setProperty("bigdl.mklNumThreads", - s"${Math.ceil(Runtime.getRuntime.availableProcessors / 2).toInt}") - System.setProperty("bigdl.coreNumber", "1") + System.setProperty("bigdl.engineType", "mkldnn") Engine.init -// Engine.setCoreNumber(1) -// MklDnn.setNumThreads(coreNumber) parser.parse(argv, new ResNet50PerfParams()).foreach { params => val batchSize = params.batchSize @@ -75,7 +69,7 @@ object ResNet50Perf { val inputFormat = Memory.Format.nchw val inputShape = Array(batchSize, 3, 224, 224) val input = Tensor(inputShape).rand() - val label = Tensor(batchSize).apply1(_ => Math.floor(RNG.uniform(0, 1) * 1000).toFloat) + val label = Tensor(batchSize).apply1(_ => Math.ceil(RNG.uniform(0, 1) * 1000).toFloat) val model = ResNet(batchSize, classNum, T("depth" -> 50, "dataSet" -> ImageNet)) val criterion = CrossEntropyCriterion() @@ -227,7 +221,7 @@ object ResNet { val (loopConfig, nFeatures, block) = cfg.get(depth).get iChannels = 64 - model.add(ReorderMemory(HeapData(Array(batchSize, 3, 224, 224), Memory.Format.nchw))) + model.add(Input(Array(batchSize, 3, 224, 224), Memory.Format.nchw)) .add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = false) .setName("conv1").setReLU(true)) .add(SbnDnn(64).setName("bn_conv1")) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala index 13584dbe42b..713b1537a76 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala @@ -37,7 +37,7 @@ private[bigdl] class FileWriter(val logDirectory : String, flushMillis: Int = 10 if (!fs.exists(logPath)) fs.mkdirs(logPath) private val eventWriter = new EventWriter(logDirectory, flushMillis, fs) - Engine.default.invoke(() => eventWriter.run()) + Engine.io.invoke(() => eventWriter.run()) /** * Adds a Summary protocol buffer to the event file. From 53e790a67d21b759a5263cadc95aff7d18a95d81 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 16 Aug 2018 21:54:45 -0400 Subject: [PATCH 0814/1065] feature: add shutdown for optimizer which will release the native resources (#2609) Release native resources at the end of training. It will call `release` of model for all models cloned in optimizer at the end of training. 1) `LocalOptimizer` is very simple because all models cloned is local. 2) `DistriOptimizer` is a little complicated. We should do release before `models.unpersist`, otherwise it will serialize and transfer the model again. And `ModelBroadcast` will clone new model when do value, so we should release them also. --- .../dllib/models/utils/ModelBroadcast.scala | 17 +++- .../bigdl/dllib/nn/mkldnn/Blob.scala | 2 + .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 24 ++++- .../bigdl/dllib/nn/mkldnn/Linear.scala | 5 + .../nn/mkldnn/SpatialBatchNormalization.scala | 6 ++ .../dllib/nn/mkldnn/SpatialConvolution.scala | 6 ++ .../bigdl/dllib/optim/DistriOptimizer.scala | 42 ++++++--- .../bigdl/dllib/optim/LocalOptimizer.scala | 5 + .../bigdl/dllib/optim/Optimizer.scala | 4 + .../bigdl/dllib/tensor/DnnStorage.scala | 25 ++++- .../bigdl/dllib/nn/mkldnn/LinearSpec.scala | 30 +++++- .../SpatialBatchNormalizationSpec.scala | 28 +++++- .../nn/mkldnn/SpatialConvolutionSpec.scala | 28 +++++- .../dllib/optim/DistriOptimizerSpec.scala | 91 ++++++++++++++++++- .../dllib/optim/LocalOptimizerSpec.scala | 43 ++++++++- 15 files changed, 331 insertions(+), 25 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index cf412f436c1..9fa9f90cb94 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -205,7 +205,7 @@ private[bigdl] object ModelInfo { implicit ev: TensorNumeric[T]): ModelInfo[T] = new ModelInfo[T](uuid, model) } -object CachedModels { +private[bigdl] object CachedModels { import java.util.concurrent.ConcurrentHashMap import scala.collection._ @@ -232,7 +232,6 @@ object CachedModels { for (key <- keys) { if (key != currentKey) { val models = cachedModels(key) - println(s"delete key = $key ${models.length}") for (model <- models) { model.release() } @@ -240,4 +239,18 @@ object CachedModels { } } } + + def deleteKey[T: ClassTag](key: String)(implicit ev: TensorNumeric[T]): Unit = + CachedModels.synchronized { + val keys = cachedModels.keys + for (k <- keys) { + if (k == key) { + val models = cachedModels(key) + for (model <- models) { + model.release() + } + cachedModels.remove(key) + } + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala index 8923efa9e75..92a66e51f44 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala @@ -92,4 +92,6 @@ private[mkldnn] class Blob(_size: Array[Int]) extends Serializable { def size(index: Int): Int = { dense.size(index) } + + def release(): Unit = native.release() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index d65fd8fe3f4..bed1033634e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -15,10 +15,10 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{DataType, MklDnn} +import com.intel.analytics.bigdl.mkl.MklDnn import com.intel.analytics.bigdl.nn.DynamicContainer import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.tensor.{DenseType, DnnTensor, Tensor} +import com.intel.analytics.bigdl.tensor.{DenseType, DnnTensor, MklDnnType, Tensor} import com.intel.analytics.bigdl.utils.T import scala.collection.mutable.ArrayBuffer @@ -250,6 +250,26 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { (null, null) } + + override def release(): Unit = { + val tensors: ArrayBuffer[DnnTensor[Float]] = ArrayBuffer.empty + List(output, gradInput).filter(_ != null).foreach { t => + if (t.isTensor && t.toTensor[Float].getTensorType == MklDnnType) { + tensors.append(t.asInstanceOf[DnnTensor[Float]]) + } + + if (t.isTable) { + val table = t.toTable + var i = 1 + while (i <= table.length()) { + tensors.append(table(i)) + i += 1 + } + } + } + + tensors.foreach(_.release()) + } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 013aecadc8b..5259da7fd61 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -280,6 +280,11 @@ class Linear( gradWeight.zero() gradBias.zero() } + + override def release(): Unit = { + super.release() + List(weight, bias, gradWeight, gradBias).foreach(_.release()) + } } object Linear { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index ed81055c4e5..62ae1fbdf23 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -299,6 +299,12 @@ class SpatialBatchNormalization( override def toString(): String = { s"nn.mkl.SpatialBatchNormalization($nOutput, $eps, $momentum, $affine)" } + + override def release(): Unit = { + super.release() + List(weightAndBias, gradWeightAndBias, runningMean, runningVariance).foreach(_.release()) + List(mean, variance).foreach(_.release()) + } } object SpatialBatchNormalization { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 6e054ad18c9..d1540e91895 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -374,6 +374,12 @@ class SpatialConvolution( (Array(weight.memoryData(), bias.memoryData()), Array(gradWeight.memoryData(), bias.memoryData())) } + + override def release(): Unit = { + super.release() + List(weight, bias, gradWeight, gradBias).foreach(_.release()) + weightForBackward.release() + } } object SpatialConvolution { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index fdd7cb4cf73..0b6939320e0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -29,7 +29,7 @@ import java.io.{File, FilenameFilter} import java.text.SimpleDateFormat import java.util.Calendar -import com.intel.analytics.bigdl.models.utils.ModelBroadcast +import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.MklDnnContainer import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} @@ -601,7 +601,7 @@ object DistriOptimizer { validationMethods: Option[Array[ValidationMethod[T]]], optimMethod: Map[String, OptimMethod[T]], parameterProcessors: ArrayBuffer[ParameterProcessor] - )(implicit ev: TensorNumeric[T]) = { + )(implicit ev: TensorNumeric[T]): (RDD[DistriOptimizer.Cache[T]], ModelBroadcast[T]) = { val sc = dataset.originRDD().sparkContext val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod)) // ensure model's parameter is compacted for getting a better performance when broadcasting @@ -681,7 +681,7 @@ object DistriOptimizer { logger.info("Cache thread models...") models.count() logger.info("Cache thread models... done") - models + (models, modelBroadcast) } private def setModelId[T: ClassTag](model: Module[T], partitionId: Int): Unit = { @@ -732,13 +732,7 @@ object DistriOptimizer { val results = ZippedPartitionsWithLocalityRDD(models, validateRDD)((modelIter, dataIter) => { val cached = modelIter.next() val vMethodsArr = cached.localMethods - val workingModels = cached.localModels.map { x => - val _x = x.cloneModule() - if (x.isInstanceOf[MklDnnContainer]) { - _x.asInstanceOf[MklDnnContainer].compile(InferencePhase) - } - _x - } + val workingModels = cached.localModels workingModels.foreach(_.evaluate()) dataIter.map(batch => { @@ -852,6 +846,9 @@ class DistriOptimizer[T: ClassTag] ( val metrics = new Metrics private var models: RDD[DistriOptimizer.Cache[T]] = null + // this variable is used to check the models cloned when broadcast, if there're native resources, + // it will be deleted at the end of Optimizer. + private var modelBroadcast: ModelBroadcast[T] = null /** * Clean some internal states, so this or other optimizers can run optimize again @@ -963,9 +960,11 @@ class DistriOptimizer[T: ClassTag] ( prepareInput() - models = DistriOptimizer.initThreadModels(model, distDataset, criterion, state, + val modelsAndBroadcast = DistriOptimizer.initThreadModels(model, distDataset, criterion, state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, optimMethods, parameterProcessors) + models = modelsAndBroadcast._1 + modelBroadcast = modelsAndBroadcast._2 if (checkpointPath.isDefined) { val file = checkpointPath.get + "/" + @@ -1046,9 +1045,11 @@ class DistriOptimizer[T: ClassTag] ( newOptimMethod.clearHistory() (moduleName, newOptimMethod) } - models = DistriOptimizer.initThreadModels(newModel, distDataset, criterion, state, - nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, - optimMethods, parameterProcessors) + val modelsAndBroadcast = DistriOptimizer.initThreadModels(newModel, distDataset, + criterion, state, nodeNumber, coresPerNode, checkSingleton, parameters, + validationMethods, optimMethods, parameterProcessors) + models = modelsAndBroadcast._1 + modelBroadcast = modelsAndBroadcast._2 } else { throw t } @@ -1062,6 +1063,7 @@ class DistriOptimizer[T: ClassTag] ( // unpersist the model because the next time optimize is called, new `models` will be // created + shutdown() models.unpersist() model @@ -1085,4 +1087,16 @@ class DistriOptimizer[T: ClassTag] ( } return choice; } + + // this shutdown should not be called out of this scope. + private[optim] override def shutdown(): Unit = { + models.mapPartitions { iter => + iter.foreach { arrayModels => + arrayModels.localModels.foreach(_.release()) + } + + iter + }.count() + CachedModels.deleteKey(modelBroadcast.uuid) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index 9ba3d44d2cc..e8844cb82a8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -208,6 +208,7 @@ class LocalOptimizer[T: ClassTag] ( // copy running status from workingModels to model model.setExtraParameter(workingModels.head.getExtraParameter()) + shutdown() model } @@ -286,5 +287,9 @@ class LocalOptimizer[T: ClassTag] ( logger.info(s"$header ${r._2} is ${r._1}") }) } + + private[optim] override def shutdown(): Unit = { + workingModels.foreach(_.release()) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index f4c8ae64297..4becaa5a6e8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -462,6 +462,10 @@ abstract class Optimizer[T: ClassTag, D]( this } + /** + * shutdown the optimizer, which will release the native resources if exists. + */ + private[optim] def shutdown(): Unit = {} } object Optimizer { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala index daea15e120a..3cd93ba37fd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala @@ -83,8 +83,11 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { * Release the native array, the storage object is useless */ def release(): Unit = { - Memory.AlignedFree(ptr.address) - _isReleased = true + if (!this.isReleased()) { + Memory.AlignedFree(ptr.address) + _isReleased = true + DnnStorage.checkAndSet(ptr.address) + } } def isReleased(): Boolean = _isReleased @@ -93,6 +96,8 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { require(capacity > 0, s"capacity should not be larger than 0") val ptr = Memory.AlignedMalloc(capacity * DnnStorage.FLOAT_BYTES, DnnStorage.CACHE_LINE_SIZE) require(ptr != 0L, s"allocate native aligned memory failed") + _isReleased = false + DnnStorage.add(ptr) ptr } @@ -122,4 +127,20 @@ private[bigdl] class Pointer(val address: Long) object DnnStorage { private[tensor] val CACHE_LINE_SIZE = System.getProperty("bigdl.cache.line", "64").toInt private[tensor] val FLOAT_BYTES: Int = 4 + + import java.util.concurrent.ConcurrentHashMap + private val nativeStorages: ConcurrentHashMap[Long, Boolean] = new ConcurrentHashMap() + + def checkAndSet(pointer: Long): Boolean = { + nativeStorages.replace(pointer, false, true) + } + + def add(key: Long): Unit = { + nativeStorages.put(key, false) + } + + def get(): Map[Long, Boolean] = { + import scala.collection.JavaConverters._ + nativeStorages.asScala.toMap + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala index 279c38eb587..a8bc3e7cebc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{DnnStorage, Tensor} import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Matchers} @@ -250,6 +250,34 @@ class LinearSpec extends FlatSpec with Matchers { Tools.dense(linear.gradBias.native) should be (Tools.dense(cloned.gradBias.native)) } + "linear released" should "work correctly" in { + val inputSize = 2 + val outputSize = 2 + val batchSize = 2 + + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + val input = Tensor[Float](batchSize, inputSize).rand() + val gradOutput = Tensor[Float]().resize(outputFormat.shape).rand() + + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize).rand() + + val initCount = DnnStorage.get().count(!_._2) + + val linear = Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias) + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + linear.forward(input) + linear.backward(input, gradOutput) + + linear.release() + DnnStorage.get().count(_._2 == false) should be (initCount) + } + "linear with dense weights" should "work correctly" in { val inputSize = 2 val outputSize = 2 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala index fae812ff0f9..6de2a504660 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{DnnStorage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Ignore, Matchers} @@ -104,6 +104,32 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { Tools.dense(cloned.gradWeightAndBias.native)) } + "batch norm released" should "work correctly" in { + val batchSize = 2 + RNG.setSeed(100) + val input = Tensor(100, 1, 10, 10).rand(-1, 1) + val (channel, height, width) = (1, 10, 10) + + val initWeight = Tensor(channel).rand(-1, 1) + val initBias = Tensor(channel).fill(0) + val initCount = DnnStorage.get().count(!_._2) + + val bn = SpatialBatchNormalization(1, 0.0, initWeight = initWeight, initBias = initBias) + + val inputShape = Array(100, 1, 10, 10) + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + bn.forward(input) + val gradOutput = Tensor(inputShape).rand(-1, 1) + bn.backward(input, gradOutput) + + bn.release() + DnnStorage.get().count(_._2 == false) should be (initCount) + } + "batch norm with dense weights and gradients" should "work correctly" in { val batchSize = 2 RNG.setSeed(100) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index d7a34535b46..5c3683cdccd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.nn.{Xavier, Zeros} import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{DnnStorage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Matchers} @@ -444,6 +444,32 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Tools.dense(conv.gradBias.native) should be (Tools.dense(cloned.gradBias.native)) } + "conv release" should "work correctly" in { + val inputShape = Array(4, 3, 5, 5) + val outputShape = Array(4, 2, 3, 3) + val name = "conv" + val nOutput = 2 + val kernel = 3 + val pad = 1 + val stride = 2 + + val initCount = DnnStorage.get().count(!_._2) + val conv = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) + conv.setName(name) + conv.setRuntime(new MklDnnRuntime) + conv.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + conv.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + conv.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + val input = Tensor(inputShape).rand(-1, 1) + val gradOutput = Tensor(outputShape).rand(-1, 1) + conv.forward(input) + conv.backward(input, gradOutput) + + conv.release() + DnnStorage.get().count(_._2 == false) should be (initCount) + } + "conv with dense weights and gradients" should "work correctly" in { val inputShape = Array(4, 3, 5, 5) val outputShape = Array(4, 2, 3, 3) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index fc06827728f..748b1df397f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -21,10 +21,13 @@ import java.nio.file.{Files, Paths} import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.image.{BGRImgToBatch, LabeledBGRImage} import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, Sample} +import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.HeapData +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.parameters.AllReduceParameter -import com.intel.analytics.bigdl.tensor.{DenseTensor, Storage, Tensor} +import com.intel.analytics.bigdl.tensor.{DenseTensor, DnnStorage, Storage, Tensor} import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.visualization.TrainSummary import org.apache.log4j.{Level, Logger} @@ -110,6 +113,13 @@ object DistriOptimizerSpecModel { .add(new Sigmoid) .add(new ExceptionTest(failCountNumberLists, sleep)) } + + def dnn: Module[Float] = { + new nn.mkldnn.Sequential() + .add(nn.mkldnn.Input(Array(8, 4), Memory.Format.nc)) + .add(nn.mkldnn.Linear(4, 2)) + .add(nn.mkldnn.ReorderMemory(HeapData(Array(8, 2), Memory.Format.nc))) + } } @com.intel.analytics.bigdl.tags.Serial @@ -884,4 +894,83 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } } +@com.intel.analytics.bigdl.tags.Serial +class DistriOptimizerSpec2 extends FlatSpec with Matchers with BeforeAndAfter { + + import DistriOptimizerSpec._ + import DistriOptimizerSpecModel._ + + Logger.getLogger("org").setLevel(Level.WARN) + Logger.getLogger("akka").setLevel(Level.WARN) + + private var sc: SparkContext = _ + + private var dataSet: DistributedDataSet[MiniBatch[Float]] = _ + before { + System.setProperty("bigdl.engineType", "mkldnn") + sc = new SparkContext("local[1]", "RDDOptimizerSpec") + + val input1: Tensor[Float] = Tensor[Float](Storage[Float](Array(0.0f, 1.0f, 0.0f, 1.0f))) + val output1 = 0.0f + val input2: Tensor[Float] = Tensor[Float](Storage[Float](Array(1.0f, 0.0f, 1.0f, 0.0f))) + val output2 = 1.0f + var plusOne = 1.0f + val nodeNumber = 4 + val coreNumber = 4 + val batchSize = 2 * coreNumber + Engine.init(nodeNumber, coreNumber, onSpark = true) + + val prepareData: Int => (MiniBatch[Float]) = index => { + val input = Tensor[Float]().resize(batchSize, 4) + val target = Tensor[Float]().resize(batchSize) + var i = 0 + while (i < batchSize) { + if (i % 2 == 0) { + target.setValue(i + 1, output1 + plusOne) + input.select(1, i + 1).copy(input1) + } else { + target.setValue(i + 1, output2 + plusOne) + input.select(1, i + 1).copy(input2) + } + i += 1 + } + MiniBatch(input, target) + } + + val rdd = sc.parallelize(1 to (256 * 4), 4).map(prepareData) + + dataSet = new DistributedDataSet[MiniBatch[Float]] { + override def originRDD(): RDD[_] = rdd + + override def data(train: Boolean): RDD[MiniBatch[Float]] = rdd + + override def size(): Long = rdd.count() + + override def shuffle(): Unit = {} + } + + System.setProperty("bigdl.check.singleton", false.toString) + Engine.model.setPoolSize(1) + } + + after { + if (sc != null) { + sc.stop() + } + + System.clearProperty("bigdl.engineType") + } + + "Train model and shutdown" should "be good" in { + RandomGenerator.RNG.setSeed(10) + val model = dnn + val count = DnnStorage.get().count(!_._2) + val optimizer = new DistriOptimizer( + model, + dataSet, + new CrossEntropyCriterion[Float]()).setEndWhen(Trigger.severalIteration(1)) + optimizer.optimize() + DnnStorage.get().count(!_._2) should be (count) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala index 0a090c5787a..897c738b975 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala @@ -20,7 +20,9 @@ import com.intel.analytics.bigdl.dataset.{DataSet, LocalDataSet, MiniBatch} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.image.{BGRImgToBatch, LabeledBGRImage} -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.HeapData +import com.intel.analytics.bigdl.tensor.{DnnStorage, Storage, Tensor} import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T} import com.intel.analytics.bigdl.visualization.TrainSummary import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -117,6 +119,13 @@ object LocalOptimizerSpecModel { .add(Linear[Float](4, 1).setName("fc_2")) .add(Sigmoid[Float]()) } + + def dnnModel: Module[Float] = { + new nn.mkldnn.Sequential() + .add(nn.mkldnn.Input(Array(4, 4), Memory.Format.nc)) + .add(nn.mkldnn.Linear(4, 2)) + .add(nn.mkldnn.ReorderMemory(HeapData(Array(4, 2), Memory.Format.nc))) + } } @com.intel.analytics.bigdl.tags.Serial @@ -447,3 +456,35 @@ class LocalOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter{ assert(expectedG.almostEqual(newG, 0.0), "clipbynorm2 should generate correct gradient") } } + +@com.intel.analytics.bigdl.tags.Serial +class LocalOptimizerSpec2 extends FlatSpec with Matchers with BeforeAndAfter { + + import LocalOptimizerSpecModel._ + import DummyDataSet._ + + before { + System.setProperty("bigdl.localMode", "true") + System.setProperty("bigdl.engineType", "mkldnn") + Engine.init + } + + after { + System.clearProperty("bigdl.localMode") + System.clearProperty("bigdl.engineType") + } + + "Train model and shutdown" should "be good" in { + RandomGenerator.RNG.setSeed(1000) + val model = dnnModel + val count = DnnStorage.get().count(!_._2) + val optimizer = new LocalOptimizer[Float]( + model, + creDataSet, + new CrossEntropyCriterion[Float].asInstanceOf[Criterion[Float]] + ).setEndWhen(Trigger.severalIteration(1)) + + optimizer.optimize() + DnnStorage.get().count(!_._2) should be (count) + } +} From b3e871a60a2a66778741877e60e19919f561f9a3 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 17 Aug 2018 04:13:43 -0400 Subject: [PATCH 0815/1065] fix: dnn currently not support windows and will be supported in future (#2613) --- .../main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 59bb3e38fbd..63ea30ff2ec 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -217,9 +217,9 @@ object ThreadPool { def setThreadsOfBackend(size: Int): Unit = { require(MKL.isMKLLoaded) - require(BackendMklDnn.isLoaded) MKL.setNumThreads(size) if (System.getProperty("bigdl.engineType") == "mkldnn") { + require(BackendMklDnn.isLoaded) BackendMklDnn.setNumThreads(size) Affinity.setOmpAffinity() } From 7258cf55cc68cea4028185b9a0e30c9a30681580 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 21 Aug 2018 11:46:12 +0800 Subject: [PATCH 0816/1065] [Bug Fix] Handle grey image correctly if model require a 3 channel tensor input (#2616) * handle grey image correct if model require a 3 channel tensor input * move the test image files so not break old tests * fix style error --- .../transform/vision/image/Convertor.scala | 14 +++++---- .../transform/vision/image/ImageFeature.scala | 27 ++++++++++++++++-- scala/dllib/src/test/resources/grey/grey.JPEG | Bin 0 -> 135833 bytes .../vision/image/ConvertorSpec.scala | 15 ++++++++++ 4 files changed, 49 insertions(+), 7 deletions(-) create mode 100644 scala/dllib/src/test/resources/grey/grey.JPEG diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala index 0e840dacb5d..9e2d81919c3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/Convertor.scala @@ -149,10 +149,12 @@ object MatToFloats { * transform opencv mat to tensor * @param toRGB BGR to RGB (default is BGR) * @param tensorKey key to store transformed tensor + * @param shareBuffer use same memory for the output tensors, default is true + * @param greyToRGB convert grey image to RGB, default is false */ class MatToTensor[T: ClassTag](toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor, - shareBuffer: Boolean = true)(implicit ev: TensorNumeric[T]) + shareBuffer: Boolean = true, greyToRGB: Boolean = false)(implicit ev: TensorNumeric[T]) extends FeatureTransformer { private val imageTensor: Tensor[T] = Tensor[T]() private val matToFloats = MatToFloats() @@ -162,12 +164,14 @@ class MatToTensor[T: ClassTag](toRGB: Boolean = false, try { val (height, width, channel) = feature.getSize matToFloats.transform(feature) - if (channel == 1) { + if (channel == 1 && !greyToRGB) { imageTensor.resize(height, width) + } else if (channel == 1 && greyToRGB) { + imageTensor.resize(3, height, width) } else { imageTensor.resize(channel, height, width) } - feature.copyTo[T](imageTensor.storage().array(), 0, ImageFeature.floats, toRGB) + feature.copyTo[T](imageTensor.storage().array(), 0, ImageFeature.floats, toRGB, greyToRGB) if (!shareBuffer) { feature(tensorKey) = imageTensor.clone() } else { @@ -188,9 +192,9 @@ object MatToTensor { val logger = Logger.getLogger(getClass) def apply[T: ClassTag](toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor, - shareBuffer: Boolean = true) + shareBuffer: Boolean = true, greyToRGB: Boolean = false) (implicit ev: TensorNumeric[T]) - : MatToTensor[T] = new MatToTensor[T](toRGB, tensorKey, shareBuffer) + : MatToTensor[T] = new MatToTensor[T](toRGB, tensorKey, shareBuffer, greyToRGB) } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala index 06ee4bfb2b0..73e08747358 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/ImageFeature.scala @@ -213,7 +213,7 @@ class ImageFeature extends Serializable { * @param toRGB BGR to RGB */ def copyTo[T: ClassTag](storage: Array[T], offset: Int, floatKey: String = ImageFeature.floats, - toRGB: Boolean = true)(implicit ev: TensorNumeric[T]): Unit = { + toRGB: Boolean = true, greyToRGB: Boolean = false)(implicit ev: TensorNumeric[T]): Unit = { val channel = getChannel() require(contains(floatKey), s"there should be ${floatKey} in ImageFeature") val data = floats(floatKey) @@ -223,8 +223,10 @@ class ImageFeature extends Serializable { require(frameLength * channel + offset <= storage.length) if (channel == 3) { copyBGR(storage, offset, toRGB, data, frameLength) - } else { + } else if (!greyToRGB) { copyChannels(storage, offset, channel, data, frameLength) + } else { + copyGreyToRGB(storage, offset, data, frameLength) } } @@ -296,6 +298,27 @@ class ImageFeature extends Serializable { } } + private def copyGreyToRGB[T: ClassTag](storage: Array[T], offset: Int, data: Array[Float], + frameLength: Int): Unit = { + require(offset + frameLength * 3 <= storage.length, + s"tensor storage cannot hold the whole image data, offset $offset " + + s"data length ${data.length} storage lenght ${storage.length}") + if (classTag[T] == classTag[Float]) { + val storageFloat = storage.asInstanceOf[Array[Float]] + var c = 0 + while(c < 3) { + var i = 0 + while(i < frameLength) { + storageFloat(i + c * frameLength + offset) = data(i) + i += 1 + } + c += 1 + } + } else { + throw new IllegalArgumentException("Not support type") + } + } + /** * Convert ImageFeature to image tensor * @param floatKey key that maps the float array diff --git a/scala/dllib/src/test/resources/grey/grey.JPEG b/scala/dllib/src/test/resources/grey/grey.JPEG new file mode 100644 index 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zpWrnUZPrA_>{c?Z!w(?HY49Xh)(l6TUp>cLQeO_bE;C-XBw1t=9?ILYTz10WC%34* z8n{torShJn*~V^MvqqK2B{CHe6kS73WTvj8+ouqYl8AL1O0hj>$)~*{L_IvmGl#LU zix>1eYizFK;p;P-7@AXkk&{&(OHs+r&o>_jLFQameKpRK^b=389+q^6tI2KTI*+R} z^)8;S6B#|D^J%En+xrUUWiwWU5v0kAup6AivqDOQ)fdSPW5h`7{{W)>V$St@TxV;( zdv#-ds}Q<<&U2`@GG|GU?0b617Dx06&z_qZ6QqvMG+J}(QN{mmsDa{>hvMIohwxHs+3<8p%FE0|*ywd8& z7|!L}q&F7?yn_k_YYo=7Ow3B0rBqPGDHp?r#d5m z=w^`3$hvyat!~NeoV{*U&U22nWi1 Date: Tue, 21 Aug 2018 03:13:53 -0400 Subject: [PATCH 0817/1065] fix: set the encoding of input and output files is UTF-8 (#2615) --- dl/pom.xml | 1 + 1 file changed, 1 insertion(+) diff --git a/dl/pom.xml b/dl/pom.xml index f459e5c2136..270a2cbe0e9 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -273,6 +273,7 @@ ${basedir}/src/test/scala ${project.parent.parent.basedir}/scalastyle_config.xml ${project.build.directory}/stylecheck/scalastyle-output.xml + UTF-8 UTF-8 From f2c80aa53572971b1113a4d677fd13bd54a64cc1 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Thu, 23 Aug 2018 14:56:25 +0800 Subject: [PATCH 0818/1065] [Feature] Allow user to customized how model is broadcast in distributed training (#2618) * allow user to override ModelBroadcast * update configuration doc * meet code review --- .../dllib/models/utils/ModelBroadcast.scala | 55 ++++++++++++++----- .../models/utils/ModelBroadcastFactory.scala | 41 ++++++++++++++ .../models/utils/ModelBroadcastSpec.scala | 18 ++++-- 3 files changed, 96 insertions(+), 18 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastFactory.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 9fa9f90cb94..f2cb6f41f1e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -31,6 +31,41 @@ import org.apache.spark.broadcast.Broadcast import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +/** + * ModelBroadcast is used to broadcast model + */ +trait ModelBroadcast[T] extends Serializable { + /** + * Broadcast the model + * @param sc SparkContext + * @param model model to broadcast + * @return this + */ + def broadcast(sc: SparkContext, model: Module[T]): this.type + + /** + * Get the broadcast model on worker + * + * @param initGradient If create a tensor for gradient when fetch the model. Please note that + * the gradient is not needed in model inference + * @return model + */ + def value(initGradient: Boolean = false): Module[T] + + def uuid(): String = UUID.randomUUID().toString +} + +object ModelBroadcast { + def apply[T: ClassTag]()(implicit ev: TensorNumeric[T]): ModelBroadcast[T] = { + if (System.getProperty("bigdl.ModelBroadcastFactory") != null) { + val cls = Class.forName(System.getProperty("bigdl.ModelBroadcastFactory")) + cls.getConstructors()(0).newInstance().asInstanceOf[ModelBroadcastFactory].create() + } else { + new DefaultModelBroadcastFactory().create() + } + } +} + /** * ModelBroadcast is used to broadcast model. * @@ -40,15 +75,13 @@ import scala.reflect.ClassTag * @tparam T data type * @param applyProtoBuffer it will use proto buffer serialization for broadcasting if set true */ -class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) - (implicit ev: TensorNumeric[T]) extends Serializable { +private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = false) + (implicit ev: TensorNumeric[T]) extends ModelBroadcast[T] { private var broadcastModel: Broadcast[ModelInfo[T]] = _ private var broadcastConsts: Broadcast[Map[String, Tensor[_]]] = _ private var broadcastParameters: Broadcast[Array[Tensor[T]]] = _ - private[bigdl] val uuid: String = UUID.randomUUID().toString - /** * broadcast the model * first get and clear Const values from the model @@ -58,7 +91,7 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) * @param model model to broadcast * @return this */ - def broadcast(sc: SparkContext, model: Module[T]): this.type = { + override def broadcast(sc: SparkContext, model: Module[T]): this.type = { CachedModels.deleteAll(uuid) // delete the models on driver if (applyProtoBuffer) { @@ -102,10 +135,11 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) * get the broadcast model * put the weight and bias back to the model * - * @param initGradient if init gradParameter. + * @param initGradient If create a tensor for gradient when fetch the model. Please note that + * the gradient is not needed in model inference * @return model */ - def value(initGradient: Boolean = false): Module[T] = { + override def value(initGradient: Boolean = false): Module[T] = { CachedModels.deleteAll(uuid) if (applyProtoBuffer) { val localModel = broadcastModel.value.model.clone(false) @@ -175,13 +209,6 @@ class ModelBroadcast[T: ClassTag](applyProtoBuffer: Boolean = false) } } -object ModelBroadcast { - def apply[@specialized(Float, Double) T: ClassTag](applyProtoBuffer: Boolean = false) - (implicit ev: TensorNumeric[T]) : ModelBroadcast[T] = { - new ModelBroadcast(applyProtoBuffer) - } -} - private[bigdl] class ModelInfo[T: ClassTag](val uuid: String, @transient var model: Module[T])( implicit ev: TensorNumeric[T]) extends Serializable { @throws(classOf[IOException]) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastFactory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastFactory.scala new file mode 100644 index 00000000000..0bfa83accd9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastFactory.scala @@ -0,0 +1,41 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.models.utils + +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Create a ModelBroadcast. User can override how model is broadcast by extending this class to + * create a customized ModelBroadcast. To enable customized broadcast factory, user need to specify + * its full class name via system property bigdl.ModelBroadcastFactory. + */ +trait ModelBroadcastFactory { + def create[T: ClassTag]()(implicit ev: TensorNumeric[T]) : ModelBroadcast[T] +} + +private[bigdl] class DefaultModelBroadcastFactory extends ModelBroadcastFactory { + override def create[T: ClassTag]()(implicit ev: TensorNumeric[T]): ModelBroadcast[T] = { + new ModelBroadcastImp[T]() + } +} + +private[bigdl] class ProtoBufferModelBroadcastFactory extends ModelBroadcastFactory { + override def create[T: ClassTag]()(implicit ev: TensorNumeric[T]): ModelBroadcast[T] = { + new ModelBroadcastImp[T](true) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala index 7fe7f2e1748..e268b96ee07 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala @@ -49,7 +49,9 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { val input2 = Const[Float, Float](Tensor[Float].range(1, 6, 1)).setName("const").inputs() val output = CAddTable[Float]().inputs(input1, input2) val model = Graph(input1, output) - val modelBroadCast = ModelBroadcast[Float](true).broadcast(sc, model) + System.setProperty("bigdl.ModelBroadcastFactory", + "com.intel.analytics.bigdl.models.utils.ProtoBufferModelBroadcastFactory") + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) val testModel = modelBroadCast.value() val testInput = Tensor[Float].range(2, 7, 1) @@ -90,7 +92,9 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { "model broadcast with applyProtoBuffer" should "work properly" in { val model = LeNet5(10) - val modelBroadCast = ModelBroadcast[Float](true).broadcast(sc, model) + System.setProperty("bigdl.ModelBroadcastFactory", + "com.intel.analytics.bigdl.models.utils.ProtoBufferModelBroadcastFactory") + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) modelBroadCast.value().toString should be(model.toString) modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } @@ -108,7 +112,10 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { val model = LeNet5(10) model.getParameters() - val modelBroadCast = ModelBroadcast[Float](true).broadcast(sc, model) + + System.setProperty("bigdl.ModelBroadcastFactory", + "com.intel.analytics.bigdl.models.utils.ProtoBufferModelBroadcastFactory") + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) modelBroadCast.value().toString should be(model.toString) modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } @@ -124,7 +131,9 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { "quantized model broadcast with applyProtoBuffer" should "work properly" in { val model = LeNet5(10).quantize() - val modelBroadCast = ModelBroadcast[Float](true).broadcast(sc, model) + System.setProperty("bigdl.ModelBroadcastFactory", + "com.intel.analytics.bigdl.models.utils.ProtoBufferModelBroadcastFactory") + val modelBroadCast = ModelBroadcast[Float]().broadcast(sc, model) modelBroadCast.value().toString should be(model.toString) modelBroadCast.value().parameters()._1 should be(model.parameters()._1) } @@ -152,6 +161,7 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { } after { + System.clearProperty("bigdl.ModelBroadcastFactory") if (sc != null) { sc.stop() } From 4dab1a2219e49a2626909d9e62eaa0863518742a Mon Sep 17 00:00:00 2001 From: Griffin Kardos Date: Thu, 23 Aug 2018 22:20:44 -0700 Subject: [PATCH 0819/1065] NLL unlabeled data fix (#2620) --- .../com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala | 3 +++ 1 file changed, 3 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala index 5f856983300..fdd0b71c95c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ClassNLLCriterion.scala @@ -153,6 +153,9 @@ class ClassNLLCriterion[@specialized(Float, Double) T: ClassTag] total_weight = ev.plus(total_weight, w) i += 1 } + if (total_weight == 0) { + total_weight = ev.fromType[Int](1) + } target.resize(targetSize) } if (sizeAverage && total_weight != 0) { From a69202994162858ddcfb027cacbae419cf7e06f9 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 27 Aug 2018 01:25:49 -0400 Subject: [PATCH 0820/1065] fix: the inference performance regression of mkldnn (#2622) We should copy weights when updateOutput at training. The weights are loaded before and will not be changed when do inference. --- .../scala/com/intel/analytics/bigdl/utils/Engine.scala | 6 ++++-- .../intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala | 6 ++++-- .../dllib/nn/mkldnn/SpatialBatchNormalization.scala | 9 +++++---- .../bigdl/dllib/nn/mkldnn/SpatialConvolution.scala | 6 ++++-- 4 files changed, 17 insertions(+), 10 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index d96510bba21..b13d6c589a7 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -542,10 +542,12 @@ object Engine { } private def setMklDnnEnvironments(): Unit = { - val threadsNumber = Math.ceil(Runtime.getRuntime.availableProcessors().toFloat / 2).toInt + val default = Math.ceil(Runtime.getRuntime.availableProcessors().toFloat / 2).toInt + val threadsNumber= System.getProperty("bigdl.mklNumThreads", default.toString) + System.setProperty("bigdl.mklNumThreads", s"$threadsNumber") + System.setProperty("bigdl.disable.mklBlockTime", "true") - System.setProperty("bigdl.mklNumThreads", s"$threadsNumber") System.setProperty("bigdl.coreNumber", "1") System.setProperty("bigdl.utils.Engine.defaultPoolSize", "1") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 5259da7fd61..72f8ade5834 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -136,8 +136,10 @@ class Linear( updateWithNewTensor(updateOutputTensors, 0, input) - weight.syncToNative() - bias.syncToNative() + if (isTraining()) { + weight.syncToNative() + bias.syncToNative() + } MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, updateOutputMemoryPrimitives, updateOutputTensors) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 62ae1fbdf23..07afe488f34 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -193,7 +193,9 @@ class SpatialBatchNormalization( } } - weightAndBias.syncToNative() + if (isTraining()) { + weightAndBias.syncToNative() + } updateWithNewTensor(updateOutputTensors, 0, input) @@ -206,11 +208,10 @@ class SpatialBatchNormalization( mean.axpby(1, momentum.toFloat, runningMean.native) variance.axpby(biasFactor, momentum.toFloat, runningVariance.native) + runningMean.syncToHeap() + runningVariance.syncToHeap() } - runningMean.syncToHeap() - runningVariance.syncToHeap() - output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index d1540e91895..97560524118 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -223,8 +223,10 @@ class SpatialConvolution( updateWithNewTensor(updateOutputTensors, 0, input) - weight.syncToNative() - bias.syncToNative() + if (isTraining()) { + weight.syncToNative() + bias.syncToNative() + } MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, updateOutputMemoryPrimitives, updateOutputTensors) From a7f3633b43c42823a37d921e8c9cdd5c496d5e2b Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 28 Aug 2018 02:54:34 -0400 Subject: [PATCH 0821/1065] fix: style check errors (#2625) --- .../src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index b13d6c589a7..843a1e955ed 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -543,7 +543,7 @@ object Engine { private def setMklDnnEnvironments(): Unit = { val default = Math.ceil(Runtime.getRuntime.availableProcessors().toFloat / 2).toInt - val threadsNumber= System.getProperty("bigdl.mklNumThreads", default.toString) + val threadsNumber = System.getProperty("bigdl.mklNumThreads", default.toString) System.setProperty("bigdl.mklNumThreads", s"$threadsNumber") From 0e58ce5630c1e33a953e2e0ea4b7560b1ef7ec36 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 31 Aug 2018 17:19:56 +0800 Subject: [PATCH 0822/1065] [new feature]Hit Ratio and NDCG (#2623) --- .../bigdl/dllib/optim/ValidationMethod.scala | 165 +++++++++++++++++- .../dllib/utils/python/api/PythonBigDL.scala | 8 + .../bigdl/dllib/optim/ValidationSpec.scala | 42 +++++ 3 files changed, 207 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala index ee94518e3da..cccbc8b7235 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala @@ -255,6 +255,142 @@ class Top5Accuracy[T] extends ValidationMethod[T] { override def format(): String = "Top5Accuracy" } +/** + * Hit Ratio(HR). + * HR intuitively measures whether the test item is present on the top-k list. + * + * @param k top k. + * @param negNum number of negative items. + */ +class HitRatio[T: ClassTag](k: Int = 10, negNum: Int = 100)( + implicit ev: TensorNumeric[T]) + extends ValidationMethod[T] { + /** + * Output and target should belong to the same user. + * And have (negNum + 1) elements. + * Target should have only one positive label, means one element is 1, others + * are all 0. + * A couple of output and target will be count as one record. + */ + override def apply(output: Activity, target: Activity): ValidationResult = { + require(output.toTensor[T].nElement() == negNum + 1, + s"negNum is $negNum, output's nElement should be ${negNum}, but got" + + s" ${output.toTensor[T].nElement()}") + require(target.toTensor[T].nElement() == negNum + 1, + s"negNum is $negNum, target's nElement should be ${negNum}, but got" + + s" ${output.toTensor[T].nElement()}") + val o = output.toTensor[T].resize(1 + negNum) + val t = target.toTensor[T].resize(1 + negNum) + var positiveItem = 0 + var positiveCount = 0 + var i = 1 + while(i <= t.nElement()) { + if (t.valueAt(i) == 1) { + positiveItem = i + positiveCount += 1 + } + i += 1 + } + require(positiveItem != 0, s"${format()}: no positive item.") + require(positiveCount == 1, s"${format()}: too many positive items, excepted 1," + + s" but got $positiveCount") + + val hr = calHitRate(positiveItem, o, k) + + new ContiguousResult(hr, 1, s"HitRatio@$k") + } + + // compute hit rate + private def calHitRate(index: Int, o: Tensor[T], k: Int): Float = { + var topK = 1 + var i = 1 + val precision = ev.toType[Float](o.valueAt(index)) + while (i < o.nElement() && topK <= k) { + if (ev.toType[Float](o.valueAt(i)) > precision) { + topK += 1 + } + i += 1 + } + + if(topK <= k) { + 1 + } else { + 0 + } + } + + override def format(): String = "HitRate@10" +} + +/** + * Normalized Discounted Cumulative Gain(NDCG). + * NDCG accounts for the position of the hit by assigning higher scores to hits at top ranks. + * + * @param k top k. + * @param negNum number of negative items. + */ +class NDCG[T: ClassTag](k: Int = 10, negNum: Int = 100)( + implicit ev: TensorNumeric[T]) + extends ValidationMethod[T] { + /** + * Output and target should belong to the same user. + * And have (negNum + 1) elements. + * Target should have only one positive label, means one element is 1, others + * are all 0. + * A couple of output and target will be count as one record. + */ + override def apply(output: Activity, target: Activity): ValidationResult = { + require(output.toTensor[T].nElement() == negNum + 1, + s"negNum is $negNum, output's nElement should be ${negNum}, but got" + + s" ${output.toTensor[T].nElement()}") + require(target.toTensor[T].nElement() == negNum + 1, + s"negNum is $negNum, target's nElement should be ${negNum}, but got" + + s" ${output.toTensor[T].nElement()}") + val o = output.toTensor[T].resize(1 + negNum) + val t = target.toTensor[T].resize(1 + negNum) + + var positiveItem = 0 + var positiveCount = 0 + var i = 1 + while(i <= t.nElement()) { + if (t.valueAt(i) == 1) { + positiveItem = i + positiveCount += 1 + } + i += 1 + } + + require(positiveItem != 0, s"${format()}: no positive item.") + require(positiveCount == 1, s"${format()}: too many positive items, excepted 1," + + s" but got $positiveCount") + + val ndcg = calNDCG(positiveItem, o, k) + + new ContiguousResult(ndcg, 1, s"NDCG") + } + + // compute NDCG + private def calNDCG(index: Int, o: Tensor[T], k: Int): Float = { + var ranking = 1 + var i = 1 + val precision = ev.toType[Float](o.valueAt(index)) + while (i < o.nElement() && ranking <= k) { + if (ev.toType[Float](o.valueAt(i)) > precision) { + ranking += 1 + } + i += 1 + } + + if(ranking <= k) { + (math.log(2) / math.log(ranking + 1)).toFloat + } else { + 0 + } + } + + override def format(): String = "NDCG" +} + /** * Use loss as a validation result * @@ -262,14 +398,27 @@ class Top5Accuracy[T] extends ValidationMethod[T] { * @param count recording the times of calculating loss */ class LossResult(private var loss: Float, private var count: Int) + extends ContiguousResult(loss, count, name = "Loss") + +/** + * A generic result type who's data is contiguous float. + * + * @param contiResult loss calculated by forward function + * @param count recording the times of calculating loss + * @param name name of the result + */ +class ContiguousResult( + private var contiResult: Float, + private var count: Int, + private val name: String) extends ValidationResult { - override def result(): (Float, Int) = (loss.toFloat / count, count) + override def result(): (Float, Int) = (contiResult.toFloat / count, count) // scalastyle:off methodName override def +(other: ValidationResult): ValidationResult = { - val otherResult = other.asInstanceOf[LossResult] - this.loss += otherResult.loss + val otherResult = other.asInstanceOf[ContiguousResult] + this.contiResult += otherResult.contiResult this.count += otherResult.count this } @@ -277,27 +426,27 @@ class LossResult(private var loss: Float, private var count: Int) // scalastyle:on methodName override protected def format(): String = { - s"(Loss: $loss, count: $count, Average Loss: ${loss.toFloat / count})" + s"($name: $contiResult, count: $count, Average $name: ${contiResult.toFloat / count})" } override def equals(obj: Any): Boolean = { if (obj == null) { return false } - if (!obj.isInstanceOf[LossResult]) { + if (!obj.isInstanceOf[ContiguousResult]) { return false } - val other = obj.asInstanceOf[LossResult] + val other = obj.asInstanceOf[ContiguousResult] if (this.eq(other)) { return true } - this.loss == other.loss && this.count == other.count + this.contiResult == other.contiResult && this.count == other.count } override def hashCode(): Int = { val seed = 37 var hash = 1 - hash = hash * seed + this.loss.toInt + hash = hash * seed + this.contiResult.toInt hash = hash * seed + this.count hash } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c39cddb4e0f..525d97edfd5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2072,6 +2072,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new Top1Accuracy() } + def createHitRatio(k: Int = 10, negNum: Int = 100): ValidationMethod[T] = { + new HitRatio(k, negNum) + } + + def createNDCG(k: Int = 10, negNum: Int = 100): ValidationMethod[T] = { + new NDCG(k, negNum) + } + def createTreeNNAccuracy(): ValidationMethod[T] = { new TreeNNAccuracy() } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index ba9011bc3d0..9ad478a80af 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -241,4 +241,46 @@ class ValidationSpec extends FlatSpec with Matchers { val test = new LossResult(1.5f, 1) result should be(test) } + + "HR@10" should "works fine" in { + val o = Tensor[Float].range(1, 1000, 1).apply1(_ / 1000) + val t = Tensor[Float](1000).zero + t.setValue(1000, 1) + val hr = new HitRatio[Float](negNum = 999) + val r1 = hr.apply(o, t).result() + r1._1 should be (1.0) + + o.setValue(1000, 0.9988f) + val r2 = hr.apply(o, t).result() + r2._1 should be (1.0) + + o.setValue(1000, 0.9888f) + val r3 = hr.apply(o, t).result() + r3._1 should be (0.0f) + } + + "ndcg" should "works fine" in { + val o = Tensor[Float].range(1, 1000, 1).apply1(_ / 1000) + val t = Tensor[Float](1000).zero + t.setValue(1000, 1) + val ndcg = new NDCG[Float](negNum = 999) + val r1 = ndcg.apply(o, t).result() + r1._1 should be (1.0) + + o.setValue(1000, 0.9988f) + val r2 = ndcg.apply(o, t).result() + r2._1 should be (0.63092977f) + + o.setValue(1000, 0.9888f) + val r3 = ndcg.apply(o, t).result() + r3._1 should be (0.0f) + } + + "CongituousResult" should "works fine" in { + val cr1 = new ContiguousResult(0.2f, 2, "HR@10") + val cr2 = new ContiguousResult(0.1f, 1, "HR@10") + val result = cr1 + cr2 + result.result()._1 should be (0.1f) + result.result()._2 should be (3) + } } From cb68ba5f764e412840542005404053fdb905e737 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Mon, 3 Sep 2018 09:26:33 +0800 Subject: [PATCH 0823/1065] [new feature]Parallel Adam (#2626) --- .../bigdl/dllib/optim/ParallelAdam.scala | 187 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 10 + .../bigdl/dllib/optim/AdamSpec.scala | 89 ++++----- 3 files changed, 234 insertions(+), 52 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelAdam.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelAdam.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelAdam.scala new file mode 100644 index 00000000000..b6138ae446b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelAdam.scala @@ -0,0 +1,187 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Engine, T, Table} +import org.apache.log4j.Logger + +import scala.math._ +import scala.reflect.ClassTag + +/** + * An multi-thread implementation of Adam http://arxiv.org/pdf/1412.6980.pdf + * + * @param learningRate learning rate + * @param learningRateDecay learning rate decay + * @param beta1 first moment coefficient + * @param beta2 second moment coefficient + * @param Epsilon for numerical stability + * @param parallelNum parallelism number, default is core number. + * @tparam T + */ +class ParallelAdam[@specialized(Float, Double) T: ClassTag]( + var learningRate: Double = 1e-3, + var learningRateDecay: Double = 0.0, + var beta1: Double = 0.9, + var beta2: Double = 0.999, + var Epsilon: Double = 1e-8, + var parallelNum: Int = Engine.coreNumber() + )(implicit ev: TensorNumeric[T]) extends OptimMethod[T] { + + @transient + private var ones: Tensor[T] = null + + /** + * An implementation of Adam http://arxiv.org/pdf/1412.6980.pdf + * + * @param feval a function that takes a single input (X), the point of a evaluation, and + * returns f(X) and df/dX + * @param parameter the initial point + * @return the new x vector and the function list {fx}, evaluated before the update + */ + override def optimize(feval: (Tensor[T]) => (T, Tensor[T]), + parameter: Tensor[T]): (Tensor[T], Array[T]) = { + val lr = this.learningRate + val lrd = this.learningRateDecay + val beta1 = this.beta1 + val beta2 = this.beta2 + val eps = this.Epsilon + + val (fx, dfdx) = feval(parameter) + + var timestep = state.getOrElse[Int]("evalCounter", 0) + + val clr = lr / (1 + timestep*lrd) + + timestep = timestep + 1 + + val gradLength = parameter.nElement() + val taskSize = gradLength / parallelNum + val extraTask = gradLength % parallelNum + if (ones == null || ones.nElement() < taskSize + 1) { + ones = Tensor[T]().resize(taskSize + 1).fill(ev.one) + } + + (0 until parallelNum).foreach{tid => + if (state.get[Tensor[T]](s"s$tid").isEmpty) { + state(s"s$tid") = Tensor[T]() + state(s"r$tid") = Tensor[T]() + state(s"denom$tid") = Tensor[T]() + } + } + + Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { + val start = System.nanoTime() + val offset = tid * taskSize + math.min(tid, extraTask) + val length = taskSize + (if (tid < extraTask) 1 else 0) + val currentDfdx = dfdx.narrow(1, offset + 1, length) + val currentParameter = parameter.narrow(1, offset + 1, length) + val currentOnes = ones.narrow(1, 1, length) + val (_s, _r, _denom) = + (state.get[Tensor[T]](s"s$tid").get.resizeAs(currentParameter), + state.get[Tensor[T]](s"r$tid").get.resizeAs(currentParameter), + state.get[Tensor[T]](s"denom$tid").get.resizeAs(currentParameter)) + + ParallelAdam.updateFrame(_s, _r, _denom, clr, currentDfdx, currentParameter, + beta1, beta2, timestep, currentOnes, eps) + })) + + state("evalCounter") = timestep // A tmp tensor to hold the sqrt(v) + epsilon + + (parameter, Array(fx)) + } + + override def loadFromTable(config: Table): this.type = { + this.learningRate = config.get[Double]("learningRate").getOrElse(this.learningRate) + this.learningRateDecay = config.get[Double]("learningRateDecay") + .getOrElse(this.learningRateDecay) + this.beta1 = config.get[Double]("beta1").getOrElse(this.beta1) + this.beta2 = config.get[Double]("beta2").getOrElse(this.beta2) + this.Epsilon = config.get[Double]("Epsilon").getOrElse(this.Epsilon) + this + } + + override def clearHistory(): Unit = { + state.delete("s") + state.delete("r") + } + + override def getLearningRate(): Double = this.learningRate +} + +object ParallelAdam { + val logger = Logger.getLogger(this.getClass) + + private[optim] def updateFrame[T: ClassTag](_s: Tensor[T], _r: Tensor[T], _denom: Tensor[T], + clr: Double, dfdx: Tensor[T], parameter: Tensor[T], + beta1: Double, beta2: Double, timestep: Int, + ones: Tensor[T], eps: Double)( + implicit ev: TensorNumeric[T]): Unit = { + /** + * m_t = beta_1 * m_t-1 + (1 - beta_1) * g_t + * v_t = beta_2 * v_t-1 + (1 - beta_2) * g_t * g_t + */ + _s.mul(ev.fromType[Double](beta1)).add(ev.fromType[Double](1-beta1), dfdx) + _denom.cmul(dfdx, dfdx) + _r.mul(ev.fromType[Double](beta2)).add(ev.fromType[Double](1-beta2), _denom) + _denom.sqrt(_r) + + // used as MKL.axpy: 1 * a + y = y, and fill buffer with one + _denom.add(ev.fromType(eps), ones) + + // efficiency improved upon by changing the order of computation, at expense of clarity + val biasCorrection1 = 1 - pow(beta1, timestep) + val biasCorrection2 = 1 - pow(beta2, timestep) + val stepSize = clr * sqrt(biasCorrection2) / biasCorrection1 + _denom.cdiv(_s, _denom) + parameter.add(ev.fromType[Double](-stepSize), _denom) + } + + + private[optim] def updateFrameZeroGrad[T: ClassTag]( + currentIteration: Int, lastUpdatedIteration: Int, + _s: Tensor[T], _r: Tensor[T], _denom: Tensor[T], _buffer: Tensor[T], + clr: Double, parameter: Tensor[T], + beta1: Double, beta2: Double, + ones: Tensor[T], eps: Double)( + implicit ev: TensorNumeric[T]): Unit = { + + var timestep = lastUpdatedIteration + while(timestep < currentIteration) { + val biasCorrection1 = 1 - pow(beta1, timestep) + val biasCorrection2 = 1 - pow(beta2, timestep) + val stepSize = clr * sqrt(biasCorrection2) / biasCorrection1 + /** + * m_t = beta_1 * m_t-1 + * v_t = beta_2 * v_t-1 + */ + _s.mul(ev.fromType[Double](beta1)) + _r.mul(ev.fromType[Double](beta2)) + _denom.sqrt(_r) + + // used as MKL.axpy: 1 * a + y = y + _denom.add(ev.fromType(eps), ones) + + _denom.cdiv(_s, _denom) + parameter.add(ev.fromType[Double](-stepSize), _denom) + + timestep += 1 + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 525d97edfd5..c43d2cba5fe 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2144,6 +2144,16 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new Adam[T](learningRate, learningRateDecay, beta1, beta2, Epsilon) } + def createParallelAdam( + learningRate: Double = 1e-3, + learningRateDecay: Double = 0.0, + beta1: Double = 0.9, + beta2: Double = 0.999, + Epsilon: Double = 1e-8, + parallelNum: Int = Engine.coreNumber()): ParallelAdam[T] = { + new ParallelAdam[T](learningRate, learningRateDecay, beta1, beta2, Epsilon, parallelNum) + } + def createFtrl( learningRate: Double = 1e-3, learningRatePower: Double = -0.5, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/AdamSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/AdamSpec.scala index c02260d2ed6..06131a6d80c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/AdamSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/AdamSpec.scala @@ -18,14 +18,27 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, Linear, Sequential} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{RandomGenerator, T, TestUtils} -import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T, TestUtils} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.collection.mutable.ArrayBuffer import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel -class AdamSpec extends FlatSpec with Matchers { +class AdamSpec extends FlatSpec with Matchers with BeforeAndAfter { + + before { + System.setProperty("bigdl.localMode", "true") + System.setProperty("spark.master", "local[2]") + Engine.init + } + + after { + System.clearProperty("bigdl.localMode") + System.clearProperty("spark.master") + } + + val start = System.currentTimeMillis() "adam" should "perform well on rosenbrock function" in { val x = Tensor[Double](2).fill(0) @@ -52,59 +65,31 @@ class AdamSpec extends FlatSpec with Matchers { x(Array(1)) should be(1.0 +- 0.01) x(Array(2)) should be(1.0 +- 0.01) } - "adam" should " work fast with MKL" in { - RandomGenerator.RNG.setSeed(100) - val inputSize = 500 - val hiddenSize = 500 - val outputSize = 10 - val batchSize = 10 - val model = Sequential[Float]() - .add(Linear[Float](inputSize, hiddenSize)) - for (i <- 1 to 3) { - model.add(Linear[Float](hiddenSize, hiddenSize)) - } - model.add(Linear[Float](hiddenSize, outputSize)) - val criterion = CrossEntropyCriterion[Float]() - - val input = Tensor[Float](batchSize, inputSize).rand - val label = Tensor[Float](batchSize).zero - for (i <- 1 to batchSize) { - val nextLabel = Random.nextInt(outputSize) + 1 - label.setValue(i, nextLabel) - } - val (weights, grad) = model.getParameters() - - val state = T("learningRate" -> 1e-1, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - - val adam = new Adam[Float] - - def feval(x: Tensor[Float]): (Float, Tensor[Float]) = { - model.forward(input) - criterion.forward(model.output.asInstanceOf[Tensor[Float]], label) - model.zeroGradParameters() - val gradOutputTest = criterion.backward(model.output.asInstanceOf[Tensor[Float]], label) - model.backward(input, gradOutputTest) - (criterion.output, grad) + "ParallelAdam" should "perform well on rosenbrock function" in { + val x = Tensor[Double](2).fill(0) + val optm = new ParallelAdam[Double](learningRate = 0.002, parallelNum = 2) + var fx = new ArrayBuffer[Double] + for (i <- 1 to 10001) { + val result = optm.optimize(TestUtils.rosenBrock, x) + if ((i - 1) % 1000 == 0) { + fx += result._2(0) + } } - val warmUp = 30 - val iter = 50 - for (i <- 1 to warmUp) { - adam.optimize(feval, weights, state) - } - var startTime = System.nanoTime - var duration = (System.nanoTime() - startTime) / 1e9 - var sum = 0.0 - for (i <- 1 to iter) { - startTime = System.nanoTime - adam.optimize(feval, weights, state) - duration = (System.nanoTime() - startTime) / 1e9 - sum += duration - println(s"iter-${i}, eta = ${duration} seconds") + println(s"x is \n$x") + println("fx is") + for (i <- 1 to fx.length) { + println(s"${(i - 1) * 1000 + 1}, ${fx(i - 1)}") } - println(s"average eta = ${sum / iter} seconds") + + val spend = System.currentTimeMillis() - start + println("Time Cost: " + spend + "ms") + + (fx.last < 1e-9) should be(true) + x(Array(1)) should be(1.0 +- 0.01) + x(Array(2)) should be(1.0 +- 0.01) } + } From 139b068a57075be44d5c81e958027128790d799e Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 3 Sep 2018 04:18:13 -0400 Subject: [PATCH 0824/1065] feat: training ResNet50 w/ dnn backend on Spark. (#2624) * feat: resnet50 training with distributed mode * fix: unknown segmentfault * fix: clone of dnn tensor * fix: delete unused codes * fix: bn initialization * fix: performance regression * fix: convergence regression * fix: delete the release in ModelBroadcast * fix: to pass all uni tests and delete segment fault. --- .../intel/analytics/bigdl/utils/Engine.scala | 13 +- .../dllib/models/resnet/TrainImageNet.scala | 62 +++-- .../dllib/models/utils/ModelBroadcast.scala | 33 +-- .../bigdl/dllib/nn/mkldnn/ResNet50Perf.scala | 23 +- .../nn/mkldnn/SpatialBatchNormalization.scala | 66 ++++-- .../dllib/nn/mkldnn/SpatialConvolution.scala | 2 +- .../bigdl/dllib/optim/DistriOptimizer.scala | 4 + .../bigdl/dllib/optim/ValidationMethod.scala | 24 +- .../bigdl/dllib/tensor/DnnStorage.scala | 21 +- .../bigdl/dllib/tensor/DnnTensor.scala | 40 ++++ .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 6 +- .../SpatialBatchNormalizationSpec.scala | 2 +- .../dllib/optim/DistriOptimizerSpec.scala | 80 ------- .../dllib/optim/LocalOptimizerSpec.scala | 32 --- .../dllib/optim/LocalPredictorSpec.scala | 31 --- .../optim/OptimPredictorShutdownSpec.scala | 223 ++++++++++++++++++ .../bigdl/dllib/optim/PredictorSpec.scala | 30 --- .../bigdl/dllib/optim/ValidationSpec.scala | 54 +++++ 18 files changed, 469 insertions(+), 277 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimPredictorShutdownSpec.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 843a1e955ed..06a213fccda 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -218,7 +218,7 @@ object Engine { @volatile private var _default: ThreadPool = null // Thread pool for layer use - @volatile private var _model: ThreadPool = new ThreadPool(1).setMKLThread(MKL.getMklNumThreads) + @volatile private var _model: ThreadPool = new ThreadPool(1) // Thread pool for read data @volatile private var _io: ThreadPool = null @@ -340,16 +340,15 @@ object Engine { _io = new ThreadPool(core * 50) } - val modelPoolSize: Int = if (engineType == MklBlas) { - 1 - } else { - core - } + // for dnn model we should set the pool size to 1 also. + // otherwise, it will downgrade the performance and + // FIXME make the loss to NaN. + val modelPoolSize = 1 if(_model == null || _model.getPoolSize != modelPoolSize) { _model = new ThreadPool(modelPoolSize) - _model.setMKLThread(MKL.getMklNumThreads) } + _model.setMKLThread(MKL.getMklNumThreads) ThreadPool.setThreadsOfBackend(MKL.getMklNumThreads) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala index eef1db4b6ec..e883923df93 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala @@ -17,12 +17,14 @@ package com.intel.analytics.bigdl.models.resnet import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.models.inception.{ImageNet2012, ImageNet2012Val} import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.mkldnn.ResNet.DatasetType.ImageNet import com.intel.analytics.bigdl.nn.{BatchNormalization, Container, CrossEntropyCriterion, Module} import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._ -import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T} +import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext @@ -66,39 +68,35 @@ object TrainImageNet { val model = if (param.modelSnapshot.isDefined) { Module.load[Float](param.modelSnapshot.get) } else { - val curModel = - ResNet(classNum = param.classes, T("shortcutType" -> shortcut, "depth" -> param.depth, - "optnet" -> param.optnet, "dataSet" -> dataSetType)) - if (param.optnet) { - ResNet.shareGradInput(curModel) + Engine.getEngineType() match { + case MklBlas => + val curModel = + ResNet(classNum = param.classes, T("shortcutType" -> shortcut, "depth" -> param.depth, + "optnet" -> param.optnet, "dataSet" -> dataSetType)) + if (param.optnet) { + ResNet.shareGradInput(curModel) + } + ResNet.modelInit(curModel) + + /* Here we set parallism specificall for BatchNormalization and its Sub Layers, this is + very useful especially when you want to leverage more computing resources like you want + to use as many cores as possible but you cannot set batch size too big for each core due + to the memory limitation, so you can set batch size per core smaller, but the smaller + batch size will increase the instability of convergence, the synchronization among BN + layers basically do the parameters synchronization among cores and thus will avoid the + instability while improves the performance a lot. */ + val parallisim = Engine.coreNumber + setParallism(curModel, parallisim) + + curModel + case MklDnn => + nn.mkldnn.ResNet(param.batchSize / Engine.nodeNumber(), param.classes, + T("depth" -> 50, "dataSet" -> ImageNet)) } - ResNet.modelInit(curModel) - curModel } println(model) - /* Here we set parallism specificall - for BatchNormalization and its Sub Layers, - this is very useful especially when - you want to leverage more computing - resources like you want to use - as many cores as possible but you cannot - set batch size too big for each core due - to the memory limitation, so you can set - batch size per core smaller, but the smaller - batch size will increase the instability of - convergence, the synchronization among BN - layers basically do the parameters - synchronization among cores - and thus will avoid the instability while - improves the performance a lot. - */ - - val parallisim = Engine.coreNumber - - setParallism(model, parallisim) - val optimMethod = if (param.stateSnapshot.isDefined) { val optim = OptimMethod.load[Float](param.stateSnapshot.get).asInstanceOf[SGD[Float]] val baseLr = param.learningRate @@ -119,9 +117,9 @@ object TrainImageNet { s"maxLr: $maxLr, " + s"delta: $delta, nesterov: ${param.nesterov}") new SGD[Float](learningRate = param.learningRate, learningRateDecay = 0.0, - momentum = param.momentum, dampening = param.dampening, - nesterov = param.nesterov, - learningRateSchedule = SGD.EpochDecayWithWarmUp(warmUpIteration, delta, imageNetDecay)) + weightDecay = param.weightDecay, momentum = param.momentum, dampening = param.dampening, + nesterov = param.nesterov, + learningRateSchedule = SGD.EpochDecayWithWarmUp(warmUpIteration, delta, imageNetDecay)) } val optimizer = Optimizer( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index f2cb6f41f1e..22bd92f91b3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -21,10 +21,10 @@ import java.util.UUID import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Container -import com.intel.analytics.bigdl.nn.quantized.StorageManager import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.Util._ +import org.apache.commons.lang3.SerializationUtils import org.apache.spark.SparkContext import org.apache.spark.broadcast.Broadcast @@ -97,40 +97,25 @@ private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = if (applyProtoBuffer) { broadcastModel = sc.broadcast(ModelInfo(uuid, model)) } else { - // We should clone a new model which will maintain the origin model. - // Otherwise, the origin model's resources will be cleaned. - val newModel = model.cloneModule() - CachedModels.add(uuid, newModel) - // broadcast Consts - if (newModel.isInstanceOf[Container[_, _, T]]) { - val moduleConsts = getAndClearConsts(newModel.asInstanceOf[Container[_, _, T]]) + if (model.isInstanceOf[Container[_, _, T]]) { + val moduleConsts = getAndClearConsts(model.asInstanceOf[Container[_, _, T]]) // TODO: broadcast Const, model structure and weight in the same broadcast. broadcastConsts = sc.broadcast(moduleConsts) } - // broadcast weight and model - val weightsBias = getAndClearWeightBias(newModel.parameters()) - - // We broadcast weight and model separately because of the memory limit of serialization. - // And we should clone the model structure (without weight) first because of lazy evaluation - // of broadcast. As you see, we have to put weights back to the model after broadcast call. - // As a quantized model, it will create relevant memory after clone because of - // `QuantizedTensor`. So we should release it first. - val cloned = newModel.cloneModule() - cloned.release() - CachedModels.add(uuid, cloned) - - broadcastModel = sc.broadcast(ModelInfo[T](uuid, cloned)) + val weightsBias = getAndClearWeightBias(model.parameters()) + broadcastModel = sc.broadcast(ModelInfo[T](uuid, model)) broadcastParameters = sc.broadcast(weightsBias) - putWeightBias(weightsBias, newModel) - initGradWeightBias(weightsBias, newModel) + // For quantized model if we don't clone weightsBias, the original model will be released also + // when we delete all models used in `ModelBroadcast`. + putWeightBias(SerializationUtils.clone(weightsBias), model) + initGradWeightBias(weightsBias, model) } this } - /** * get the broadcast model * put the weight and bias back to the model diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala index 15181e56366..1ebd1bb9860 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala @@ -49,7 +49,6 @@ object ResNet50Perf { } def main(argv: Array[String]): Unit = { - System.setProperty("bigdl.disable.mklBlockTime", "true"); System.setProperty("bigdl.mkldnn.fusion.convbn", "true") System.setProperty("bigdl.mkldnn.fusion.bnrelu", "true") System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") @@ -114,8 +113,9 @@ object ResNet { def modelInit(model: Module[Float]): Unit = { def initModules(model: Module[Float]): Unit = { model match { - case container: Container[Activity, Activity, Float] - => container.modules.foreach(m => initModules(m)) + case container: Container[Activity, Activity, Float] => + container.modules.foreach(m => initModules(m)) + case conv: SpatialConvolution => val n: Float = conv.kernelW * conv.kernelW * conv.nOutputPlane val weight = Tensor[Float].resize(conv.weight.size()).apply1 { _ => @@ -124,17 +124,21 @@ object ResNet { val bias = Tensor[Float].resize(conv.bias.size()).apply1(_ => 0.0f) conv.weight.copy(weight) conv.bias.copy(bias) + case bn: SpatialBatchNormalization => - val runningMean = Tensor[Float].resize(bn.runningMean.size()).fill(0) - val runningVairance = Tensor[Float].resize(bn.runningVariance.size()).fill(1) - bn.runningMean.copy(runningMean) - bn.runningVariance.copy(runningVairance) + val weightAndBias = Tensor[Float]().resize(Array(2, bn.nOutput)) + weightAndBias.select(1, 1).fill(1) + weightAndBias.select(1, 2).fill(0) + bn.weightAndBias.copy(weightAndBias.view(Array(bn.nOutput * 2))) + case linear: Linear => val bias = Tensor[Float](linear.bias.size()).apply1(_ => 0.0f) linear.bias.copy(bias) - case _ => Unit + + case _ => } } + initModules(model) } @@ -222,8 +226,7 @@ object ResNet { iChannels = 64 model.add(Input(Array(batchSize, 3, 224, 224), Memory.Format.nchw)) - .add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = false) - .setName("conv1").setReLU(true)) + .add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = false).setName("conv1")) .add(SbnDnn(64).setName("bn_conv1")) .add(ReLU().setName("conv1_relu")) .add(MaxPooling(3, 3, 2, 2).setName("pool1")) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 07afe488f34..f484cd17321 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -91,6 +91,9 @@ class SpatialBatchNormalization( val zeros = Tensor[Float](Array(nOutput)).fill(0) mean.copy(zeros) variance.copy(zeros) + + runningMean.zero() + runningVariance.zero() } private object Index extends Serializable { @@ -102,9 +105,7 @@ class SpatialBatchNormalization( } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - _inputFormats = inputs - - val m = inputFormats()(0).shape.product / this.nOutput + val m = inputs(0).shape.product / this.nOutput biasFactor = if (m > 1) { m.toFloat / (m - 1) } else { 1 } val List(mean, variance, runningMean, runningVariance): List[NativeData] = @@ -138,15 +139,23 @@ class SpatialBatchNormalization( MklDnn.PrimitiveDescCreate(forwardDesc, runtime.engine, 0) } - _inputFormats = Array(MemoryData.operationWant(primDesc, Query.SrcPd)) - _outputFormats = Array(MemoryData.operationWant(primDesc, Query.DstPd)) + if (_inputFormats == null) { + _inputFormats = new Array[MemoryData](1) + } + + if (_outputFormats == null) { + _outputFormats = new Array[MemoryData](1) + } + + _inputFormats(0) = MemoryData.operationWant(primDesc, Query.SrcPd) + _outputFormats(0) = MemoryData.operationWant(primDesc, Query.DstPd) val (srcs, dsts) = if (phase == TrainingPhase) { val srcs = Array(inputFormats()(0), weightAndBias).map(_.getPrimitive(runtime)) val dsts = Array(outputFormats()(0), mean, variance).map(_.getPrimitive(runtime)) (srcs, dsts) } else { - val srcs = Array(inputFormats()(0), runningMean, runningVariance, weightAndBias).map { x => + val srcs = Array(inputFormats()(0), mean, variance, weightAndBias).map { x => x.getPrimitive(runtime) } val dsts = Array(outputFormats()(0).getPrimitive(runtime)) @@ -158,15 +167,20 @@ class SpatialBatchNormalization( updateOutputMemoryPrimitives = srcs ++ dsts updateOutputPrimitives = Array(primitive) - output = initTensor(outputFormats()(0)) - if (phase == TrainingPhase) { - this.runningMean.zero() - this.runningVariance.zero() + if (output == null || output.isInstanceOf[DnnTensor[_]] && + output.toTensor[Float].size().deep != outputFormats()(0).shape.deep) { + output = initTensor(outputFormats()(0)) } if (updateOutputTensors != null) { - updateOutputTensors = Array.empty + updateOutputTensors = null + } + + (isTraining(), phase) match { + case (true, InferencePhase) => train = false + case (false, TrainingPhase) => train = true + case _ => } (inputFormats(), outputFormats()) @@ -185,16 +199,21 @@ class SpatialBatchNormalization( } else { val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(input.asInstanceOf[Tensor[Float]]) - buffer.append(runningMean.native) - buffer.append(runningVariance.native) + buffer.append(mean) + buffer.append(variance) buffer.append(weightAndBias.native) buffer.append(output.asInstanceOf[Tensor[Float]]) updateOutputTensors = buffer.toArray } } - if (isTraining()) { + if (this.isTraining()) { weightAndBias.syncToNative() + } else { + // we should re-computing the running mean and running variance. + // FIXME should do it at `initFwdPrimitives` + mean.scale(runningMean.native, 1 / scaleFactor) + variance.scale(runningVariance.native, 1 / scaleFactor) } updateWithNewTensor(updateOutputTensors, 0, input) @@ -208,6 +227,7 @@ class SpatialBatchNormalization( mean.axpby(1, momentum.toFloat, runningMean.native) variance.axpby(biasFactor, momentum.toFloat, runningVariance.native) + runningMean.syncToHeap() runningVariance.syncToHeap() } @@ -292,6 +312,10 @@ class SpatialBatchNormalization( (Array(weightAndBias.dense), Array(gradWeightAndBias.dense)) } + override def getExtraParameter(): Array[Tensor[Float]] = { + Array(runningMean.dense, runningVariance.dense) + } + override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { (Array(NativeData(weightAndBias.size(), Memory.Format.x)), Array(NativeData(gradWeightAndBias.size(), Memory.Format.x))) @@ -301,6 +325,20 @@ class SpatialBatchNormalization( s"nn.mkl.SpatialBatchNormalization($nOutput, $eps, $momentum, $affine)" } + override def evaluate(): this.type = { + if (isTraining()) { + initFwdPrimitives(inputFormats(), InferencePhase) + } + this + } + + override def training(): this.type = { + if (!isTraining()) { + initFwdPrimitives(inputFormats(), TrainingPhase) + } + this + } + override def release(): Unit = { super.release() List(weightAndBias, gradWeightAndBias, runningMean, runningVariance).foreach(_.release()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 97560524118..3125b58e74a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -380,7 +380,7 @@ class SpatialConvolution( override def release(): Unit = { super.release() List(weight, bias, gradWeight, gradBias).foreach(_.release()) - weightForBackward.release() + if (weightForBackward != null) { weightForBackward.release() } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 0b6939320e0..c215231d9da 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -907,6 +907,10 @@ class DistriOptimizer[T: ClassTag] ( if (!dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].isCached) { logger.info("caching training rdd ...") dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].cache() + // FIXME dnn model must cache val dataset first, otherwise there will be a segment fault. + if (validationDataSet.isDefined) { + validationDataSet.get.toDistributed().cache() + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala index cccbc8b7235..ba31e7de20b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala @@ -167,7 +167,7 @@ class TreeNNAccuracy[T: ClassTag]()( /** * Caculate the percentage that output's max probability index equals target */ -class Top1Accuracy[T]( +class Top1Accuracy[T: ClassTag]( implicit ev: TensorNumeric[T]) extends ValidationMethod[T] { override def apply(output: Activity, target: Activity): @@ -175,8 +175,14 @@ class Top1Accuracy[T]( var correct = 0 var count = 0 - val _output = output.asInstanceOf[Tensor[T]] val _target = target.asInstanceOf[Tensor[T]] + val _output = if (output.toTensor[T].nDimension() != 1 && + output.toTensor[T].size().head != _target.size().head) { + output.toTensor[T].narrow(1, 1, _target.size().head) + } else { + output.toTensor[T] + } + if (_output.dim() == 2) { (if (_output.size(2) == 1) { _output.apply1(x => if (ev.isGreater(ev.fromType(0.5), x)) ev.zero else ev.one) @@ -215,11 +221,19 @@ class Top1Accuracy[T]( /** * Caculate the percentage that target in output's top5 probability indexes */ -class Top5Accuracy[T] extends ValidationMethod[T] { +class Top5Accuracy[T: ClassTag]( + implicit ev: TensorNumeric[T]) extends ValidationMethod[T] { override def apply(output: Activity, target: Activity): AccuracyResult = { - val _output = output.asInstanceOf[Tensor[T]] - val _target = target.asInstanceOf[Tensor[T]].squeezeNewTensor() + var _target = target.asInstanceOf[Tensor[T]].squeezeNewTensor() + + val _output = if (output.toTensor[T].nDimension() != 1 && + output.toTensor[T].size(1) != _target.size(1)) { + output.toTensor[T].narrow(1, 1, _target.size().head) + } else { + output.toTensor[T] + } + var correct = 0 var count = 0 if (_output.dim() == 2) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala index 3cd93ba37fd..ab9ed146a57 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala @@ -82,11 +82,12 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { /** * Release the native array, the storage object is useless */ - def release(): Unit = { + def release(): Unit = synchronized { if (!this.isReleased()) { Memory.AlignedFree(ptr.address) - _isReleased = true DnnStorage.checkAndSet(ptr.address) + _isReleased = true + ptr = null } } @@ -104,17 +105,21 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { @throws(classOf[IOException]) private def readObject(in: ObjectInputStream): Unit = { in.defaultReadObject() - ptr = new Pointer(allocate(this.size)) - val elements = in.readObject().asInstanceOf[Array[Float]] - Memory.CopyArray2Ptr(elements, 0, ptr.address, 0, size, DnnStorage.FLOAT_BYTES) + if (!_isReleased) { + ptr = new Pointer(allocate(this.size)) + val elements = in.readObject().asInstanceOf[Array[Float]] + Memory.CopyArray2Ptr(elements, 0, ptr.address, 0, size, DnnStorage.FLOAT_BYTES) + } } @throws(classOf[IOException]) private def writeObject(out: ObjectOutputStream): Unit = { out.defaultWriteObject() - val elements = new Array[Float](this.length()) - Memory.CopyPtr2Array(this.ptr.address, 0, elements, 0, size, DnnStorage.FLOAT_BYTES) - out.writeObject(elements) + if (!_isReleased) { + val elements = new Array[Float](this.length()) + Memory.CopyPtr2Array(this.ptr.address, 0, elements, 0, size, DnnStorage.FLOAT_BYTES) + out.writeObject(elements) + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala index e0a53e4886c..53c6909afc5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -95,6 +95,11 @@ class DnnTensor[T: ClassTag]( Memory.Axpby(this.nElement(), a, x, b, y) } + def scale(from: DnnTensor[T], scal: Float): Unit = { + val length = this.nElement() + Memory.Scale(length, scal, from._storage.ptr.address, this._storage.ptr.address) + } + override def toTensor[D](implicit ev: TensorNumeric[D]): DnnTensor[D] = { this.asInstanceOf[DnnTensor[D]] } @@ -108,6 +113,41 @@ class DnnTensor[T: ClassTag]( override def nDimension(): Int = size().length override def getTensorType: TensorType = MklDnnType + + override def equals(obj: Any): Boolean = { + if (obj == null) { + return false + } + if (!obj.isInstanceOf[DnnTensor[T]]) { + return false + } + val other = obj.asInstanceOf[DnnTensor[T]] + + if (this.size().deep != other.size().deep) { + return false + } + + if (this._storage.ptr != other._storage.ptr) { + return false + } + + true + } + + override def hashCode(): Int = { + val seed = 37 + var hash = 1 + hash = hash * seed + this.nDimension + var d = 1 + while (d <= this.nDimension) { + hash = hash * seed + this.size(d) + d += 1 + } + + hash = hash * seed + this._storage.ptr.hashCode() + + hash + } } object DnnTensor { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala index 9aa9e2bb0f8..bfe4758b689 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -68,9 +68,10 @@ class FusionSpec extends FlatSpec with Matchers { val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, initWeight = initWeight, initBias = initBias) - val bn1 = SpatialBatchNormalization(64, eps = 0.0) + val bn1 = SpatialBatchNormalization(64) bn1.runningMean.copy(runningMean) bn1.runningVariance.copy(runningVar) + bn1.scaleFactor = 1.0f val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) val reorder11 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) val model1 = Sequential().add(reorder1).add(conv1).add(bn1).add(reorder11) @@ -79,9 +80,10 @@ class FusionSpec extends FlatSpec with Matchers { System.setProperty("bigdl.mkldnn.fusion.convbn", "true") val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, initWeight = conv1.weight.native, initBias = conv1.bias.native) - val bn2 = SpatialBatchNormalization(64, eps = 0.0) + val bn2 = SpatialBatchNormalization(64) bn2.runningMean.copy(runningMean) bn2.runningVariance.copy(runningVar) + bn2.scaleFactor = 1.0f val reorder2 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) val reorder22 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) val model2 = Sequential().add(reorder2).add(conv2).add(bn2).add(reorder22) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala index 6de2a504660..2bd015fb5c9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -623,7 +623,7 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { val denseOutput = Tools.dense(bn.output).toTensor denseOutput.storage().array().zip(output.storage().array()).foreach { x => - if (x._2.isInfinity) x._1.isInfinity should be (true) + if (x._2.isInfinity || x._2.isNaN) x._1.isInfinity || x._1.isNaN should be (true) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index 748b1df397f..10d6a3c8254 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -894,83 +894,3 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } } -@com.intel.analytics.bigdl.tags.Serial -class DistriOptimizerSpec2 extends FlatSpec with Matchers with BeforeAndAfter { - - import DistriOptimizerSpec._ - import DistriOptimizerSpecModel._ - - Logger.getLogger("org").setLevel(Level.WARN) - Logger.getLogger("akka").setLevel(Level.WARN) - - private var sc: SparkContext = _ - - private var dataSet: DistributedDataSet[MiniBatch[Float]] = _ - - before { - System.setProperty("bigdl.engineType", "mkldnn") - sc = new SparkContext("local[1]", "RDDOptimizerSpec") - - val input1: Tensor[Float] = Tensor[Float](Storage[Float](Array(0.0f, 1.0f, 0.0f, 1.0f))) - val output1 = 0.0f - val input2: Tensor[Float] = Tensor[Float](Storage[Float](Array(1.0f, 0.0f, 1.0f, 0.0f))) - val output2 = 1.0f - var plusOne = 1.0f - val nodeNumber = 4 - val coreNumber = 4 - val batchSize = 2 * coreNumber - Engine.init(nodeNumber, coreNumber, onSpark = true) - - val prepareData: Int => (MiniBatch[Float]) = index => { - val input = Tensor[Float]().resize(batchSize, 4) - val target = Tensor[Float]().resize(batchSize) - var i = 0 - while (i < batchSize) { - if (i % 2 == 0) { - target.setValue(i + 1, output1 + plusOne) - input.select(1, i + 1).copy(input1) - } else { - target.setValue(i + 1, output2 + plusOne) - input.select(1, i + 1).copy(input2) - } - i += 1 - } - MiniBatch(input, target) - } - - val rdd = sc.parallelize(1 to (256 * 4), 4).map(prepareData) - - dataSet = new DistributedDataSet[MiniBatch[Float]] { - override def originRDD(): RDD[_] = rdd - - override def data(train: Boolean): RDD[MiniBatch[Float]] = rdd - - override def size(): Long = rdd.count() - - override def shuffle(): Unit = {} - } - - System.setProperty("bigdl.check.singleton", false.toString) - Engine.model.setPoolSize(1) - } - - after { - if (sc != null) { - sc.stop() - } - - System.clearProperty("bigdl.engineType") - } - - "Train model and shutdown" should "be good" in { - RandomGenerator.RNG.setSeed(10) - val model = dnn - val count = DnnStorage.get().count(!_._2) - val optimizer = new DistriOptimizer( - model, - dataSet, - new CrossEntropyCriterion[Float]()).setEndWhen(Trigger.severalIteration(1)) - optimizer.optimize() - DnnStorage.get().count(!_._2) should be (count) - } -} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala index 897c738b975..219db3e0645 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizerSpec.scala @@ -456,35 +456,3 @@ class LocalOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter{ assert(expectedG.almostEqual(newG, 0.0), "clipbynorm2 should generate correct gradient") } } - -@com.intel.analytics.bigdl.tags.Serial -class LocalOptimizerSpec2 extends FlatSpec with Matchers with BeforeAndAfter { - - import LocalOptimizerSpecModel._ - import DummyDataSet._ - - before { - System.setProperty("bigdl.localMode", "true") - System.setProperty("bigdl.engineType", "mkldnn") - Engine.init - } - - after { - System.clearProperty("bigdl.localMode") - System.clearProperty("bigdl.engineType") - } - - "Train model and shutdown" should "be good" in { - RandomGenerator.RNG.setSeed(1000) - val model = dnnModel - val count = DnnStorage.get().count(!_._2) - val optimizer = new LocalOptimizer[Float]( - model, - creDataSet, - new CrossEntropyCriterion[Float].asInstanceOf[Criterion[Float]] - ).setEndWhen(Trigger.severalIteration(1)) - - optimizer.optimize() - DnnStorage.get().count(!_._2) should be (count) - } -} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index d82616df4a8..f5a768bd966 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -304,35 +304,4 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { assert(imageFeatures(x - 1).predict().asInstanceOf[Table].length() == 2) }) } - - "local predictor shutdown" should "work properly" in { - import com.intel.analytics.bigdl.numeric.NumericFloat - val input = Tensor[Float](4, 3, 224, 224).rand(-1, 1) - - val samples = (1 to 20).map(i => { - Sample(Tensor[Float](3, 224, 224).randn()) - }).toArray - val imageFrame = ImageFrame.array((0 until 20).map(x => { - val im = ImageFeature() - im(ImageFeature.sample) = samples(x) - im - }).toArray) - - val model = Inception_v1_NoAuxClassifier(1000) - val quant = model.quantize().evaluate() - val initNativeSize = StorageManager.get().count(x => !x._2.isFreed) - - // has no memory issues - (0 to 4).foreach { _ => - quant.predictImage(imageFrame).toLocal().array.map(_.predict().asInstanceOf[Tensor[Float]]) - StorageManager.get().count(x => !x._2.isFreed) should be (initNativeSize) - } - - // check the model can work again - quant.forward(input) - val quant2 = model.quantize().evaluate() - quant2.forward(input) - - quant.output.toTensor[Float] should be (quant2.output.toTensor[Float]) - } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimPredictorShutdownSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimPredictorShutdownSpec.scala new file mode 100644 index 00000000000..48a650fdf7c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimPredictorShutdownSpec.scala @@ -0,0 +1,223 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// NOTE: This spec is tested for native memory used. because we use singleton object to check +// how many pointer allocated, so all them should be in one file and serial executed. + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.Criterion +import com.intel.analytics.bigdl.dataset.{DistributedDataSet, MiniBatch, Sample} +import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier +import com.intel.analytics.bigdl.models.utils.CachedModels +import com.intel.analytics.bigdl.nn.CrossEntropyCriterion +import com.intel.analytics.bigdl.nn.quantized.{StorageInfo, StorageManager} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.{DnnStorage, Storage, Tensor} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, ImageFrameToSample, MatToTensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, RandomGenerator} +import org.apache.log4j.{Level, Logger} +import org.apache.spark.rdd.RDD +import org.apache.spark.{SparkConf, SparkContext} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class OptimPredictorShutdownSpec extends FlatSpec with Matchers with BeforeAndAfter { + var sc: SparkContext = null + val nodeNumber = 1 + val coreNumber = 1 + + before { + Engine.init(nodeNumber, coreNumber, true) + val conf = new SparkConf().setMaster("local[1]").setAppName("predictor") + sc = new SparkContext(conf) + } + + after { + if (sc != null) { + sc.stop() + } + } + "model predict should have no memory leak" should "be correct" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile, sc) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 1000) + val quant = model.quantize() + val init = StorageManager.get() + println(s"init count ${init.count(!_._2.isFreed)}") + var second: Map[Long, StorageInfo] = null + (0 until 20).foreach { i => + val detection = quant.predictImage(imageFrame, batchPerPartition = 16).toDistributed() + detection.rdd.first() + detection.rdd.collect() + println("=" * 80) + println(StorageManager.get().count(!_._2.isFreed)) + println("-" * 80) + } + CachedModels.deleteAll("") + // NOTE: if this case failed, please check, + // 1. mapPartition, does it used the variable out side of the method scope. + // 2. ModelBroadcast, does it add the ref correctly + StorageManager.get().count(!_._2.isFreed) should be (init.count(!_._2.isFreed)) + } + + "local predictor shutdown" should "work properly" in { + val input = Tensor[Float](4, 3, 224, 224).rand(-1, 1) + + val samples = (1 to 20).map(i => { + Sample(Tensor[Float](3, 224, 224).randn()) + }).toArray + val imageFrame = ImageFrame.array((0 until 20).map(x => { + val im = ImageFeature() + im(ImageFeature.sample) = samples(x) + im + }).toArray) + + val model = Inception_v1_NoAuxClassifier(1000) + val quant = model.quantize().evaluate() + val initNativeSize = StorageManager.get().count(x => !x._2.isFreed) + + // has no memory issues + (0 to 4).foreach { _ => + quant.predictImage(imageFrame).toLocal().array.map(_.predict().asInstanceOf[Tensor[Float]]) + StorageManager.get().count(x => !x._2.isFreed) should be (initNativeSize) + } + + // check the model can work again + quant.forward(input) + val quant2 = model.quantize().evaluate() + quant2.forward(input) + + quant.output.toTensor[Float] should be (quant2.output.toTensor[Float]) + } +} + +class DistriOptimizerSpec2 extends FlatSpec with Matchers with BeforeAndAfter { + + import DistriOptimizerSpecModel._ + + Logger.getLogger("org").setLevel(Level.WARN) + Logger.getLogger("akka").setLevel(Level.WARN) + + private var sc: SparkContext = _ + + private var dataSet: DistributedDataSet[MiniBatch[Float]] = _ + + before { + System.setProperty("bigdl.engineType", "mkldnn") + sc = new SparkContext("local[1]", "RDDOptimizerSpec") + + val input1: Tensor[Float] = Tensor[Float](Storage[Float](Array(0.0f, 1.0f, 0.0f, 1.0f))) + val output1 = 0.0f + val input2: Tensor[Float] = Tensor[Float](Storage[Float](Array(1.0f, 0.0f, 1.0f, 0.0f))) + val output2 = 1.0f + var plusOne = 1.0f + val nodeNumber = 4 + val coreNumber = 4 + val batchSize = 2 * coreNumber + Engine.init(nodeNumber, coreNumber, onSpark = true) + + val prepareData: Int => (MiniBatch[Float]) = index => { + val input = Tensor[Float]().resize(batchSize, 4) + val target = Tensor[Float]().resize(batchSize) + var i = 0 + while (i < batchSize) { + if (i % 2 == 0) { + target.setValue(i + 1, output1 + plusOne) + input.select(1, i + 1).copy(input1) + } else { + target.setValue(i + 1, output2 + plusOne) + input.select(1, i + 1).copy(input2) + } + i += 1 + } + MiniBatch(input, target) + } + + val rdd = sc.parallelize(1 to (256 * 4), 4).map(prepareData) + + dataSet = new DistributedDataSet[MiniBatch[Float]] { + override def originRDD(): RDD[_] = rdd + + override def data(train: Boolean): RDD[MiniBatch[Float]] = rdd + + override def size(): Long = rdd.count() + + override def shuffle(): Unit = {} + } + + System.setProperty("bigdl.check.singleton", false.toString) + Engine.model.setPoolSize(1) + } + + after { + if (sc != null) { + sc.stop() + } + + System.clearProperty("bigdl.engineType") + } + + "Train model and shutdown" should "be good" in { + RandomGenerator.RNG.setSeed(10) + val model = dnn + val count = DnnStorage.get().count(!_._2) + val optimizer = new DistriOptimizer( + model, + dataSet, + new CrossEntropyCriterion[Float]()).setEndWhen(Trigger.severalIteration(1)) + optimizer.optimize() + DnnStorage.get().count(!_._2) should be (count) + } +} + +class LocalOptimizerSpec2 extends FlatSpec with Matchers with BeforeAndAfter { + + import DummyDataSet._ + import LocalOptimizerSpecModel._ + + before { + System.setProperty("bigdl.localMode", "true") + System.setProperty("bigdl.engineType", "mkldnn") + Engine.init + } + + after { + System.clearProperty("bigdl.localMode") + System.clearProperty("bigdl.engineType") + } + + "Train model and shutdown" should "be good" in { + RandomGenerator.RNG.setSeed(1000) + val model = dnnModel + val count = DnnStorage.get().count(!_._2) + val optimizer = new LocalOptimizer[Float]( + model, + creDataSet, + new CrossEntropyCriterion[Float].asInstanceOf[Criterion[Float]] + ).setEndWhen(Trigger.severalIteration(1)) + + optimizer.optimize() + DnnStorage.get().count(!_._2) should be (count) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index f7eac66319a..5cbbeb6b0dc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -216,36 +216,6 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ }) } - "model predict should have no memory leak" should "be correct" in { - import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat - LoggerFilter.redirectSparkInfoLogs() - Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) - RNG.setSeed(100) - val resource = getClass.getClassLoader.getResource("pascal/") - val imageFrame = ImageFrame.read(resource.getFile, sc) -> - Resize(256, 256) -> CenterCrop(224, 224) -> - ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> - MatToTensor() -> ImageFrameToSample() - val model = Inception_v1_NoAuxClassifier(classNum = 1000) - val quant = model.quantize() - val init = StorageManager.get() - println(s"init count ${init.count(!_._2.isFreed)}") - var second: Map[Long, StorageInfo] = null - (0 until 20).foreach { i => - val detection = quant.predictImage(imageFrame, batchPerPartition = 16).toDistributed() - detection.rdd.first() - detection.rdd.collect() - println("=" * 80) - println(StorageManager.get().count(!_._2.isFreed)) - println("-" * 80) - } - CachedModels.deleteAll("") - // NOTE: if this case failed, please check, - // 1. mapPartition, does it used the variable out side of the method scope. - // 2. ModelBroadcast, does it add the ref correctly - StorageManager.get().count(!_._2.isFreed) should be (init.count(!_._2.isFreed)) - } - "localpredictor" should "serialize successfully" in { val localPredictor = LocalPredictor(Linear[Float](3, 10)) SerializationUtils.clone(localPredictor) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index 9ad478a80af..e97e6147132 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -242,6 +242,60 @@ class ValidationSpec extends FlatSpec with Matchers { result should be(test) } + "top1 accuracy" should "be correct on 2d tensor with diff size of output and target" in { + val output = Tensor(Storage(Array[Double]( + 0, 0, 0, 1, + 0, 1, 0, 0, + 1, 0, 0, 0, + 0, 0, 1, 0, + 1, 0, 0, 0, + 0, 0, 1, 0, + 0, 0, 0, 1, + 0, 1, 0, 0 + )), 1, Array(8, 4)) + + val target = Tensor(Storage(Array[Double]( + 4, + 2, + 1, + 3, + 2, + 2 + ))) + + val validation = new Top1Accuracy[Double]() + val result = validation(output, target) + val test = new AccuracyResult(4, 6) + result should be(test) + } + + "Top5 accuracy" should "be correct on 2d tensor with diff size of output and target" in { + val output = Tensor(Storage(Array[Double]( + 0, 0, 8, 1, 2, 0, 0, 0, + 0, 1, 0, 0, 2, 3, 4, 6, + 1, 0, 0, 0.6, 0.1, 0.2, 0.3, 0.4, + 0, 0, 1, 0, 0.5, 1.5, 2, 0, + 1, 0, 0, 6, 2, 3, 4, 5, + 0, 0, 1, 0, 1, 1, 1, 1, + 0, 0, 0, 1, 1, 2, 3, 4, + 0, 1, 0, 0, 2, 4, 3, 2 + )), 1, Array(8, 8)) + + val target = Tensor(Storage(Array[Double]( + 4, + 2, + 1, + 3, + 2, + 2 + ))) + + val validation = new Top5Accuracy[Double]() + val result = validation(output, target) + val test = new AccuracyResult(4, 6) + result should be(test) + } + "HR@10" should "works fine" in { val o = Tensor[Float].range(1, 1000, 1).apply1(_ / 1000) val t = Tensor[Float](1000).zero From 3b1fabcbbb13cfa37a57b02de2a28c0e64ffc298 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 10 Sep 2018 01:34:21 -0400 Subject: [PATCH 0825/1065] feat: add dnn vgg model. (#2627) * feat: add dnn vgg model. * fix: rename the ResNet50Perf to Perf --- .../mkldnn/{ResNet50Perf.scala => Perf.scala} | 22 ++++-- .../bigdl/dllib/nn/mkldnn/models/Vgg16.scala | 77 +++++++++++++++++++ 2 files changed, 93 insertions(+), 6 deletions(-) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/{ResNet50Perf.scala => Perf.scala} (93%) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala similarity index 93% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala index 1ebd1bb9860..e07cb4ffda5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ResNet50Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.nn.mkldnn.ResNet.DatasetType.ImageNet +import com.intel.analytics.bigdl.nn.mkldnn.models.Vgg_16 import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -32,11 +33,14 @@ import scopt.OptionParser import scala.reflect.ClassTag -object ResNet50Perf { +object Perf { val logger = Logger.getLogger(getClass) - val parser = new OptionParser[ResNet50PerfParams]("BigDL Local ResNet-50 Performance Test") { + val parser = new OptionParser[ResNet50PerfParams]("BigDL w/ Dnn Local Model Performance Test") { + opt[String]('m', "model") + .text("model you want, vgg16 | resnet50") + .action((v, p) => p.copy(model = v)) opt[Int]('b', "batchSize") .text("Batch size of input data") .action((v, p) => p.copy(batchSize = v)) @@ -70,7 +74,12 @@ object ResNet50Perf { val input = Tensor(inputShape).rand() val label = Tensor(batchSize).apply1(_ => Math.ceil(RNG.uniform(0, 1) * 1000).toFloat) - val model = ResNet(batchSize, classNum, T("depth" -> 50, "dataSet" -> ImageNet)) + val model = params.model match { + case "vgg16" => Vgg_16(batchSize, classNum, false) + case "resnet50" => ResNet(batchSize, classNum, T("depth" -> 50, "dataSet" -> ImageNet)) + case _ => throw new UnsupportedOperationException(s"Unkown model ${params.model}") + } + val criterion = CrossEntropyCriterion() if (training) { @@ -104,9 +113,10 @@ object ResNet50Perf { } case class ResNet50PerfParams ( - batchSize: Int = 16, - iteration: Int = 50, - training: Boolean = true + batchSize: Int = 16, + iteration: Int = 50, + training: Boolean = true, + model: String = "vgg16" ) object ResNet { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala new file mode 100644 index 00000000000..3bff29aea2b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala @@ -0,0 +1,77 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn.models + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.Dropout +import com.intel.analytics.bigdl.nn.mkldnn._ + + +object Vgg_16 { + def apply(batchSize: Int, classNum: Int, hasDropout: Boolean = true): Sequential = { + val model = Sequential() + model.add(Input(Array(batchSize, 3, 224, 224), Memory.Format.nchw)) + model.add(SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(MaxPooling(2, 2, 2, 2)) + + model.add(SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(MaxPooling(2, 2, 2, 2)) + + model.add(SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(MaxPooling(2, 2, 2, 2)) + + model.add(SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(MaxPooling(2, 2, 2, 2)) + + model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) + model.add(ReLU()) + model.add(MaxPooling(2, 2, 2, 2)) + + model.add(Linear(512 * 7 * 7, 4096)) + model.add(ReLU()) + if (hasDropout) model.add(Dropout(0.5)) + model.add(Linear(4096, 4096)) + model.add(ReLU()) + if (hasDropout) model.add(Dropout(0.5)) + model.add(Linear(4096, classNum)) + model.add(ReorderMemory(HeapData(Array(batchSize, classNum), Memory.Format.nc))) + + model + } +} + From 953a740e4eb6cc6fa07f8f344d0c90a14dae96bc Mon Sep 17 00:00:00 2001 From: megaSpoon Date: Mon, 17 Sep 2018 19:06:54 -0700 Subject: [PATCH 0826/1065] Fix issue 2592 (#2629) * fix issue Predictor 2592 * adjust the algorithm * fix whitespace style check * fix code review issue * fix loop efficiency --- .../bigdl/dllib/optim/Predictor.scala | 68 +++++++--------- .../bigdl/dllib/optim/PredictorSpec.scala | 80 ++++++++++++++++++- 2 files changed, 106 insertions(+), 42 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index e75159a1f37..b713f6a085d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -68,57 +68,44 @@ object Predictor { }) } + private[optim] def splitTensor[T: ClassTag](output: Tensor[T], + shareBuffer: Boolean, batchSize: Int) + (implicit ev: TensorNumeric[T]): Array[Activity] = { + val result = if (shareBuffer) output else output.clone() + val size = result.size(1) + require(batchSize == size, + s"The batchSize is required to be $size, while actual is $batchSize") + val out = result.split(1) + out.asInstanceOf[Array[Activity]] + } + private[optim] def splitBatch[T: ClassTag](output: Activity, shareBuffer: Boolean, batchSize: Int) (implicit ev: TensorNumeric[T]): Array[Activity] = { val out = if (output.isTensor) { - val result = if (shareBuffer) output.toTensor[T] else output.toTensor[T].clone() - if (result.dim() == 1) { - require(batchSize == 1, - s"If result dim == 1, the batchSize is required to be 1, while actual is $batchSize") - Array(result) - } else { - result.split(1) - } + splitTensor(output.toTensor, shareBuffer, batchSize) } else { val result = output.toTable - val first = result[Tensor[T]](1) - if (first.dim() == 1) { - require(batchSize == 1, - s"If result dim == 1, the batchSize is required to be 1, while actual is $batchSize") - val table = if (shareBuffer) { - result - } else { - val table = T() - (1 to result.length()).foreach(key => { - table.insert(result[Tensor[T]](key).clone()) - }) - table - } - Array(table) - } else { - val batch = first.size(1) - require(batch == batchSize, s"output batch $batch is not equal to input batch $batchSize") - val tables = new Array[Table](batch) - var i = 1 - while (i <= batch) { - val table = T() - tables(i - 1) = table - (1 to result.length()).foreach(key => { - val split = result[Tensor[T]](key)(i) - if (shareBuffer) { - table.insert(split) - } else { - table.insert(split.clone()) - } - }) + val tables = new Array[Table](batchSize) + + + (1 to result.length()).foreach(key => { + val split = splitBatch(result(key), shareBuffer, batchSize) + val size = split.length + require(batchSize == size, + s"The batchSize is required to be $size, while actual is $batchSize") + var i = 0 + while (i < batchSize) { + if (tables(i) == null) tables(i) = T() + tables(i).insert(split(i)) i += 1 } - tables - } + }) + tables } out.asInstanceOf[Array[Activity]] } + def predictImage[T: ClassTag](imageFrame: DistributedImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, @@ -172,6 +159,7 @@ object Predictor { miniBatch.flatMap(batch => { val output = localModel.forward(batch.getInput) splitBatch(output, shareBuffer, batch.size()) + }) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index 5cbbeb6b0dc..24650eb1a78 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -25,7 +25,7 @@ import com.intel.analytics.bigdl.nn.quantized.{StorageInfo, StorageManager} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} -import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, Table} +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, Table, T} import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.commons.lang3.SerializationUtils import org.apache.log4j.{Level, Logger} @@ -189,7 +189,6 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ }) } - "predictImage with table output" should "work properly" in { import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat RNG.setSeed(100) @@ -211,11 +210,88 @@ class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ val imageFeatures = detection.rdd.collect() (1 to 20).foreach(x => { imageFeatures(x - 1).uri() should be (x.toString) + print(imageFeatures(x - 1).predict()) assert(imageFeatures(x - 1).predict() != null) assert(imageFeatures(x - 1).predict().asInstanceOf[Table].length() == 2) }) } + "predictImage with output " + + "whose type is a table of 2 table and 1 tensor" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val ims = (1 to 50).map(x => { + val im = ImageFeature() + im(ImageFeature.uri) = x.toString + im(ImageFeature.imageTensor) = Tensor[Float](3, 24, 24).randn() + im + }) + + // define nodes for the first graph with a table output + val imageFrame = ImageFrame.array(ims.toArray).toDistributed(sc) -> ImageFrameToSample() + val input1 = Input() + val conv1 = SpatialConvolution(3, 6, 5, 5).inputs(input1) + val out1 = Tanh().inputs(conv1) + val out2 = ReLU().inputs(conv1) + + // define nodes for the second graph with a table output + val input2 = Input() + val conv2 = SpatialConvolution(3, 6, 5, 5).inputs(input2) + val out3 = Sigmoid().inputs(conv2) + val out4 = LogSigmoid().inputs(conv2) + + // create the first graph + val g1 = Graph(input1, Array(out1, out2)) + + // create the second graph + val g2 = Graph(input2, Array(out3, out4)) + + // create a model which consists of the first graph, the second graph and an Indentity node + val model = ConcatTable() + model.add(g1) + model.add(g2) + // this Idenitity node should generate a tensor output + model.add(Identity()) + val detection = model.predictImage(imageFrame).toDistributed() + + val imageFeatures = detection.rdd.collect() + (1 to 20).foreach(x => { + imageFeatures(x - 1).uri() should be (x.toString) + assert(imageFeatures(x - 1).predict() != null) + assert(imageFeatures(x - 1).predict().asInstanceOf[Table].length() == 3) + }) + } + + "model predict should have no memory leak" should "be correct" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile, sc) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 1000) + val quant = model.quantize() + val init = StorageManager.get() + println(s"init count ${init.count(!_._2.isFreed)}") + var second: Map[Long, StorageInfo] = null + (0 until 20).foreach { i => + val detection = quant.predictImage(imageFrame, batchPerPartition = 16).toDistributed() + detection.rdd.first() + detection.rdd.collect() + println("=" * 80) + println(StorageManager.get().count(!_._2.isFreed)) + println("-" * 80) + } + CachedModels.deleteAll("") + // NOTE: if this case failed, please check, + // 1. mapPartition, does it used the variable out side of the method scope. + // 2. ModelBroadcast, does it add the ref correctly + StorageManager.get().count(!_._2.isFreed) should be (init.count(!_._2.isFreed)) + } + "localpredictor" should "serialize successfully" in { val localPredictor = LocalPredictor(Linear[Float](3, 10)) SerializationUtils.clone(localPredictor) From 2b56585e50d1f4fe1fe060f8200a60becdaf54c7 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 28 Sep 2018 03:54:19 -0400 Subject: [PATCH 0827/1065] feat: add example for lenet w/ dnn (#2635) --- .../bigdl/dllib/models/lenet/LeNet5.scala | 19 +++++++++++++++++++ .../bigdl/dllib/models/lenet/Train.scala | 19 +++++++++++++++---- .../bigdl/dllib/optim/ValidationMethod.scala | 7 ++++++- 3 files changed, 40 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala index e90ead05017..d249a15441e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.models.lenet import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.nn._ @@ -84,4 +85,22 @@ object LeNet5 { val fc2 = Dense(classNum, activation = "softmax").setName("fc2").inputs(fc1) Model(input, fc2) } + + def dnn(batchSize: Int, classNum: Int): mkldnn.Sequential = { + val inputShape = Array(batchSize, 1, 28, 28) + val outputShape = Array(batchSize, 10) + + val model = mkldnn.Sequential() + .add(mkldnn.Input(inputShape, Memory.Format.nchw)) + .add(mkldnn.SpatialConvolution(1, 20, 5, 5).setName("conv1")) + .add(mkldnn.SpatialBatchNormalization(20).setName("bn1")) + .add(mkldnn.MaxPooling(2, 2, 2, 2).setName("pool1")) + .add(mkldnn.SpatialConvolution(20, 50, 5, 5).setName("conv2")) + .add(mkldnn.MaxPooling(2, 2, 2, 2).setName("pool2")) + .add(mkldnn.Linear(50 * 4 * 4, 500).setName("ip1")) + .add(mkldnn.ReLU().setName("relu1")) + .add(mkldnn.Linear(500, 10).setName("ip2")) + .add(mkldnn.ReorderMemory(mkldnn.HeapData(outputShape, Memory.Format.nc))) + model + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala index 847ddae9f2a..fe61f14df67 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala @@ -19,10 +19,10 @@ package com.intel.analytics.bigdl.models.lenet import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.DataSet import com.intel.analytics.bigdl.dataset.image.{BytesToGreyImg, GreyImgNormalizer, GreyImgToBatch} -import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} +import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, CrossEntropyCriterion, Module} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.optim._ -import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T, Table} +import com.intel.analytics.bigdl.utils._ import org.apache.log4j.{Level, Logger} import org.apache.spark.SparkContext @@ -48,7 +48,18 @@ object Train { val model = if (param.modelSnapshot.isDefined) { Module.load[Float](param.modelSnapshot.get) } else { - if (param.graphModel) LeNet5.graph(classNum = 10) else LeNet5(classNum = 10) + if (param.graphModel) { + LeNet5.graph(classNum = 10) + } else { + Engine.getEngineType() match { + case MklBlas => LeNet5(10) + case MklDnn => LeNet5.dnn(param.batchSize / Engine.nodeNumber(), 10) + } + } + } + val criterion = Engine.getEngineType() match { + case MklBlas => ClassNLLCriterion() + case MklDnn => CrossEntropyCriterion() } val optimMethod = if (param.stateSnapshot.isDefined) { @@ -65,7 +76,7 @@ object Train { val optimizer = Optimizer( model = model, dataset = trainSet, - criterion = ClassNLLCriterion[Float]()) + criterion = criterion) if (param.checkpoint.isDefined) { optimizer.setCheckpoint(param.checkpoint.get, Trigger.everyEpoch) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala index ba31e7de20b..24cbdc080c8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala @@ -477,8 +477,13 @@ class Loss[@specialized(Float, Double)T: ClassTag]( (implicit ev: TensorNumeric[T]) extends ValidationMethod[T] { if (criterion == null) criterion = ClassNLLCriterion[T]() override def apply(output: Activity, target: Activity): LossResult = { - val _output = output.asInstanceOf[Tensor[T]] val _target = target.asInstanceOf[Tensor[T]] + val _output = if (output.toTensor[T].nDimension() != 1 && + output.toTensor[T].size().head != _target.size().head) { + output.toTensor[T].narrow(1, 1, _target.size().head) + } else { + output.toTensor[T] + } val loss = ev.toType[Float](criterion.forward(_output, _target)) val count = 1 From 2cea870aafea4a25be3402b4ec23ca5666a666f6 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Sun, 30 Sep 2018 11:10:42 +0800 Subject: [PATCH 0828/1065] fix join table will throw exception during backward if batchsize is changed (#2638) * fix join table backward * change to resize as --- .../analytics/bigdl/dllib/nn/JoinTable.scala | 8 ++++++-- .../bigdl/dllib/nn/JoinTableSpec.scala | 20 +++++++++++++++++++ 2 files changed, 26 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala index ecf7676b76d..c61283260b5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/JoinTable.scala @@ -130,8 +130,12 @@ class JoinTable[T: ClassTag] ( val narrowedTensor = gradOutput.narrow(dimension, _offset, currentOutput.size(dimension)) .asInstanceOf[Tensor[NumericWildcard]] val inputTensor = input[Tensor[_]](_i + 1) - if (!gradInput.contains(_i + 1)) gradInput(_i + 1) = - inputTensor.emptyInstance().resize(inputTensor.size()) + if (!gradInput.contains(_i + 1)) { + gradInput(_i + 1) = + inputTensor.emptyInstance().resizeAs(inputTensor) + } else { + gradInput[Tensor[T]](_i + 1).resizeAs(inputTensor) + } if(narrowedTensor.isContiguous() || dimension > 2) { gradInput[Tensor[NumericWildcard]](_i + 1).copy(narrowedTensor) } else { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala index a9948961f9f..297800dccec 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/JoinTableSpec.scala @@ -38,6 +38,26 @@ class JoinTableSpec extends FlatSpec with Matchers { gradInput[Tensor[Int]](2) should be (Tensor[Int](T(3, 4))) } + "Join Table " should "works if batchsize changed" in { + val input1 = Tensor[Int](T(1, 2, 3, 4)).resize(2, 2) + val input2 = Tensor[Int](T(5, 6, 7, 8)).resize(2, 2) + val layer = JoinTable[Float](2, 2) + val gradOuput = Tensor[Int](T(9, 10, 11, 12, 13, 14, 15, 16)).resize(2, 4) + layer.forward(T(input1, input2)) + layer.backward(T(input1, input2), gradOuput) + + val input3 = Tensor[Int](T(1, 2)).resize(1, 2) + val input4 = Tensor[Int](T(3, 4)).resize(1, 2) + val expectedOutput2 = Tensor[Int](T(1, 2, 3, 4)).resize(1, 4) + val output2 = layer.forward(T(input3, input4)) + output2 should be (expectedOutput2) + val gradOuput2 = Tensor[Int](T(5, 6, 7, 8)).resize(1, 4) + val gradInput = layer.backward(T(input3, input4), gradOuput2) + + gradInput[Tensor[Int]](1) should be (Tensor[Int](T(5, 6)).resize(1, 2)) + gradInput[Tensor[Int]](2) should be (Tensor[Int](T(7, 8)).resize(1, 2)) + } + } class JoinTableSerialTest extends ModuleSerializationTest { From 2bf9b7dbc91ac8f8cf1481380364cd9025b03392 Mon Sep 17 00:00:00 2001 From: megaSpoon Date: Mon, 8 Oct 2018 18:51:44 -0700 Subject: [PATCH 0829/1065] change Reshape to InferReShape in reshapeLoadTF (#2637) * change Reshape to InferReShape in reshapeLoadTF * fix docs * fix failed code * fix failed code * fixes after code review * fixes after code review * fix * fix infer * add unit tests --- .../example/tensorflow/loadandsave/README.md | 4 +-- .../dllib/utils/tf/loaders/Reshape.scala | 7 +++-- ...hapeLoadTFSpec.scala => ReshapeSpec.scala} | 31 +++++++++++++++++++ 3 files changed, 38 insertions(+), 4 deletions(-) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/{ReshapeLoadTFSpec.scala => ReshapeSpec.scala} (53%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md index 5d2e5a094cb..516df1d673a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/loadandsave/README.md @@ -5,7 +5,7 @@ Before you run this example, you need to install tensorflow on your machine. Thi by ```bash -pip install tensorflow==1.2.0 +pip install tensorflow ``` ## Load tensorflow model @@ -16,7 +16,7 @@ python model.py 2. Freeze tensorflow model ```bash -wget https://raw.githubusercontent.com/tensorflow/tensorflow/v1.0.0/tensorflow/python/tools/freeze_graph.py +wget https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py python freeze_graph.py --input_graph model/model.pbtxt --input_checkpoint model/model.chkp --output_node_names="LeNet/fc4/BiasAdd" --output_graph "model.pb" ``` diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala index e317da5a34b..40eedef7cda 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Reshape.scala @@ -19,6 +19,7 @@ import java.nio.ByteOrder import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.{Reshape => ReshapeOps} +import com.intel.analytics.bigdl.nn.InferReshape import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -38,12 +39,12 @@ class ReshapeLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapte override def build(tensorArrays: Array[Tensor[_]]): AbstractModule[Activity, Activity, T] = { val sizes = tensorArrays(0).asInstanceOf[Tensor[Int]] + val batchMode = if (sizes.nDimension() >= 1 && sizes.nElement() > 0) { sizes.valueAt(1) == -1 } else { false } - val arraySize = new Array[Int](if (batchMode) sizes.nElement() - 1 else sizes.nElement()) var i = if (batchMode) 2 else 1 var k = 0 @@ -52,7 +53,9 @@ class ReshapeLoadTF[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends Adapte k += 1 i += 1 } - ReshapeOps[T](size = arraySize, Some(batchMode)) + val infer = arraySize.contains(-1) + if (infer) InferReshape[T](size = arraySize, batchMode) + else ReshapeOps[T](size = arraySize, Some(batchMode)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeLoadTFSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeSpec.scala similarity index 53% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeLoadTFSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeSpec.scala index ecbd4a9d3b8..896c07020c2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeLoadTFSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeSpec.scala @@ -18,9 +18,40 @@ package com.intel.analytics.bigdl.utils.tf.loaders import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.tf.Tensorflow.typeAttr +import com.intel.analytics.bigdl.utils.tf.TensorflowSpecHelper +import org.tensorflow.framework.{DataType, NodeDef} import scala.util.Random +class ReshapeSpec extends TensorflowSpecHelper { + "Reshape" should "be correct for Float" in { + val data = Tensor[Float](4, 32, 32, 3).rand() + val shape = Tensor[Int](T(1, 32, 12, 32)) + compare[Float]( + NodeDef.newBuilder() + .setName("Reshape test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Tshape", typeAttr(DataType.DT_FLOAT)) + .setOp("Reshape"), + Seq(data, shape), + 0 + ) + } + "Reshape" should "be correct for Float with inference" in { + val data = Tensor[Float](4, 32, 32, 3).rand() + val shape = Tensor[Int](T(1, 32, -1, 32)) + compare[Float]( + NodeDef.newBuilder() + .setName("Reshape test") + .putAttr("T", typeAttr(DataType.DT_FLOAT)) + .putAttr("Tshape", typeAttr(DataType.DT_FLOAT)) + .setOp("Reshape"), + Seq(data, shape), + 0 + ) + } +} class ReshapeLoadTFSerialTest extends ModuleSerializationTest { override def test(): Unit = { From 96755780b15b82456f7dde005d6c9862d5da94eb Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 8 Oct 2018 21:59:15 -0400 Subject: [PATCH 0830/1065] feature: vgg-16 with mkldnn backend (#2631) * feat: vgg-16 with mkldnn backend * fix: tests errors * fix: case class too much arguments * fix: vgg_16 blas model supports * fix: protobuf of serializer * fix: sgd poly test case error * fix: consitent of poly impl * fix: rename the version2 of Xavier to varianceNormAverage --- .../dllib/models/vgg/TrainImageNet.scala | 115 ++++++++++++++++++ .../bigdl/dllib/models/vgg/Utils.scala | 76 +++++++++++- .../bigdl/dllib/nn/InitializationMethod.scala | 13 +- .../bigdl/dllib/nn/mkldnn/Blob.scala | 8 ++ .../bigdl/dllib/nn/mkldnn/Dropout.scala | 86 +++++++++++++ .../bigdl/dllib/nn/mkldnn/Perf.scala | 2 +- .../dllib/nn/mkldnn/ReorderManager.scala | 4 +- .../dllib/nn/mkldnn/SpatialConvolution.scala | 39 +++++- .../bigdl/dllib/nn/mkldnn/models/Vgg16.scala | 100 ++++++++------- .../analytics/bigdl/dllib/optim/SGD.scala | 4 +- .../bigdl/dllib/nn/mkldnn/DropoutSpec.scala | 68 +++++++++++ .../nn/mkldnn/SpatialConvolutionSpec.scala | 22 ++-- .../analytics/bigdl/dllib/optim/SGDSpec.scala | 6 +- .../utils/serializer/SerializerSpec.scala | 3 +- 14 files changed, 477 insertions(+), 69 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/TrainImageNet.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/TrainImageNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/TrainImageNet.scala new file mode 100644 index 00000000000..dd213c947be --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/TrainImageNet.scala @@ -0,0 +1,115 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.models.vgg + +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.{CrossEntropyCriterion, Module, SoftmaxWithCriterion} +import com.intel.analytics.bigdl.optim.SGD.{Poly, SequentialSchedule, Warmup} +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, MklBlas, MklDnn} +import com.intel.analytics.bigdl.visualization.TrainSummary +import org.apache.log4j.{Level, Logger} +import org.apache.spark.SparkContext + +object TrainImageNet { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + val logger = Logger.getLogger(getClass) + + import Utils._ + + def main(args: Array[String]): Unit = { + trainParser.parse(args, TrainParams()).foreach(param => { + val conf = Engine.createSparkConf().setAppName("Train VGG-16 on ImageNet2012") + .set("spark.rpc.message.maxSize", "200") + val sc = new SparkContext(conf) + Engine.init + + val imageSize = 224 + val trainImageCounts = 1281167 + val batchSize = param.batchSize + val folder = param.folder + val classNumber = param.classNumber + + val trainDataSet = Utils.trainDataSet(folder + "/train", sc, imageSize, batchSize) + val validateSet = Utils.valDataSet(folder + "/val", sc, imageSize, batchSize) + + val model = if (param.modelSnapshot.isDefined) { + Module.load[Float](param.modelSnapshot.get) + } else { + Engine.getEngineType() match { + case MklBlas => + Vgg_16(classNumber) + case MklDnn => + nn.mkldnn.models.Vgg_16(batchSize / Engine.nodeNumber(), classNumber) + } + } + + println(model) + + val optimMethod = if (param.stateSnapshot.isDefined) { + OptimMethod.load[Float](param.stateSnapshot.get).asInstanceOf[SGD[Float]] + } else { + val baseLr = param.learningRate + val iterationsPerEpoch = math.ceil(trainImageCounts / batchSize).toInt + val lrSchedules = SequentialSchedule(iterationsPerEpoch) + + val warmUpIteration = iterationsPerEpoch * param.warmupEpoch.getOrElse(0) + if (warmUpIteration != 0) { + val delta = (param.maxLr - param.learningRate) / warmUpIteration + lrSchedules.add(Warmup(delta), warmUpIteration) + logger.info(s"warmUpIteraion: $warmUpIteration, startLr: ${param.learningRate}, " + + s"maxLr: ${param.maxLr}, delta: $delta") + } + + lrSchedules.add(Poly(0.5, 40000), 40000 - warmUpIteration) + + new SGD[Float](learningRate = param.learningRate, learningRateDecay = 0.0, + weightDecay = param.weightDecay, momentum = param.momentum, dampening = param.dampening, + nesterov = param.nesterov, learningRateSchedule = lrSchedules) + } + + val logdir = "vgg16-imagenet" + val appName = s"${sc.applicationId}" + val trainSummary = TrainSummary(logdir, appName) + trainSummary.setSummaryTrigger("LearningRate", Trigger.severalIteration(1)) + trainSummary.setSummaryTrigger("Parameters", Trigger.severalIteration(10)) + + val criterion = Engine.getEngineType() match { + case MklBlas => CrossEntropyCriterion[Float]() + case MklDnn => SoftmaxWithCriterion[Float]() + } + + val optimizer = Optimizer(model, trainDataSet, criterion) + val validationTrigger = Trigger.severalIteration(param.checkpointIteration) + val validationMethods = Array(new Top1Accuracy[Float], new Top5Accuracy[Float]) + + if (param.checkpoint.isDefined) { + optimizer.setCheckpoint(param.checkpoint.get, validationTrigger) + } + + optimizer + .setGradientClippingByl2Norm(param.gradientL2NormThreshold.getOrElse(10000)) + .setOptimMethod(optimMethod) + .setValidation(validationTrigger, validateSet, validationMethods) + .setEndWhen(Trigger.severalIteration(param.maxIteration)) + .optimize() + + sc.stop() + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Utils.scala index 1ad952cfba5..25ede36e984 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/Utils.scala @@ -16,12 +16,16 @@ package com.intel.analytics.bigdl.models.vgg -import java.io.ByteArrayOutputStream import java.nio.ByteBuffer -import java.nio.file.{Files, Path, Paths} +import java.nio.file.{Files, Paths} -import com.intel.analytics.bigdl.dataset.ByteRecord +import com.intel.analytics.bigdl.DataSet +import com.intel.analytics.bigdl.dataset.image.{CropCenter, CropRandom} +import com.intel.analytics.bigdl.dataset.{ByteRecord, DataSet, MiniBatch} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{ChannelScaledNormalizer, RandomCropper, RandomResize} +import com.intel.analytics.bigdl.transform.vision.image.{MTImageFeatureToBatch, MatToTensor, PixelBytesToMat} import com.intel.analytics.bigdl.utils.File +import org.apache.spark.SparkContext import scopt.OptionParser import scala.collection.mutable.ArrayBuffer @@ -43,10 +47,20 @@ object Utils { overWriteCheckpoint: Boolean = false, learningRate: Double = 0.01, weightDecay: Double = 0.0005, - graphModel: Boolean = false + graphModel: Boolean = false, + maxIteration: Int = 40000, + momentum: Double = 0.9, + dampening: Double = 0.0, + nesterov: Boolean = true, + classNumber: Int = 1000, + env: String = "local", + checkpointIteration: Int = 1000, + maxLr: Double = 0.06, + warmupEpoch: Option[Int] = None, + gradientL2NormThreshold: Option[Double] = None ) - val trainParser = new OptionParser[TrainParams]("BigDL Vgg on Cifar10 Example") { + val trainParser = new OptionParser[TrainParams]("BigDL Vgg Example") { opt[String]('f', "folder") .text("where you put the Cifar10 data") .action((x, c) => c.copy(folder = x)) @@ -80,6 +94,27 @@ object Utils { opt[Unit]('g', "graphModel") .text("use graph model") .action((x, c) => c.copy(graphModel = true)) + opt[Int]('i', "maxIteration") + .text("iteration numbers") + .action((x, c) => c.copy(maxIteration = x)) + opt[Int]("classNum") + .text("class number") + .action((x, c) => c.copy(classNumber = x)) + opt[Int]("checkpointIteration") + .text("checkpoint interval of iterations") + .action((x, c) => c.copy(checkpointIteration = x)) + opt[Double]("weightDecay") + .text("weight decay") + .action((x, c) => c.copy(weightDecay = x)) + opt[Double]("maxLr") + .text("max Lr after warm up") + .action((x, c) => c.copy(maxLr = x)) + opt[Int]("warmupEpoch") + .text("warm up epoch numbers") + .action((x, c) => c.copy(warmupEpoch = Some(x))) + opt[Double]("gradientL2NormThreshold") + .text("gradient L2-Norm threshold") + .action((x, c) => c.copy(gradientL2NormThreshold = Some(x))) } case class TestParams( @@ -171,5 +206,36 @@ object Utils { i += 1 } } + + private type BatchDataSet = DataSet[MiniBatch[Float]] + def valDataSet(path: String, sc: SparkContext, imageSize: Int, batchSize: Int): BatchDataSet = { + DataSet.SeqFileFolder.filesToImageFeatureDataset(path, sc, 1000).transform( + MTImageFeatureToBatch( + width = imageSize, + height = imageSize, + batchSize = batchSize, + transformer = PixelBytesToMat() -> + RandomResize(256, 256) -> + RandomCropper(224, 224, false, CropCenter) -> + ChannelScaledNormalizer(104, 117, 124, 1) -> + MatToTensor[Float](), toRGB = false + ) + ) + } + + def trainDataSet(path: String, sc: SparkContext, imageSize: Int, batchSize: Int): BatchDataSet = { + DataSet.SeqFileFolder.filesToImageFeatureDataset(path, sc, 1000).transform( + MTImageFeatureToBatch( + width = imageSize, + height = imageSize, + batchSize = batchSize, + transformer = PixelBytesToMat() -> + RandomResize(256, 256) -> + RandomCropper(224, 224, true, CropRandom) -> + ChannelScaledNormalizer(104, 117, 124, 1) -> + MatToTensor[Float](), toRGB = false + ) + ) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala index 1272923b277..e9437c41c20 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InitializationMethod.scala @@ -278,12 +278,23 @@ case class ConstInitMethod(value: Double) extends InitializationMethod { * (http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) */ case object Xavier extends InitializationMethod { + private var varianceNormAverage: Boolean = true + + def setVarianceNormAverage(v: Boolean): this.type = { + varianceNormAverage = v + this + } + def init[T](variable: Tensor[T], dataFormat: VariableFormat) (implicit ev: TensorNumeric[T]): Unit = { val shape = variable.size() val fanIn = dataFormat.getFanIn(shape) val fanOut = dataFormat.getFanOut(shape) - val stdv = math.sqrt(6.0 / (fanIn + fanOut)) + val stdv = if (!varianceNormAverage) { + math.sqrt(3.0 / fanIn) + } else { + math.sqrt(6.0 / (fanIn + fanOut)) + } variable.rand(-stdv, stdv) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala index 92a66e51f44..af3f7b2eeac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala @@ -75,6 +75,14 @@ private[mkldnn] class Blob(_size: Array[Int]) extends Serializable { _memoryData } + def isMemoryDataSet(): Boolean = { + if (_memoryData == null) { + false + } else { + true + } + } + def zero(): Unit = { dense.zero() native.zero() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala new file mode 100644 index 00000000000..db3bce93df6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala @@ -0,0 +1,86 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.{Dropout => NNDropout} +import com.intel.analytics.bigdl.tensor.DnnTensor + +class Dropout( + val initP: Double = 0.5, + val inplace: Boolean = false, + var scale: Boolean = true) extends MklDnnLayer { + private val dropout = NNDropout[Float](initP, inplace, scale) + private var mask: DnnTensor[Float] = _ + + private def format(shape: Array[Int]): Int = { + shape.length match { + case 2 => Memory.Format.nc + case 4 => Memory.Format.nchw + case _ => throw new UnsupportedOperationException(s"${getName()} unsupported input shape") + } + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) + _outputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) + output = initTensor(_outputFormats.head) + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = grad.map(x => HeapData(x.shape, format(x.shape))) + _gradOutputFormatsForWeight = grad.map(x => HeapData(x.shape, format(x.shape))) + _gradInputFormats = grad.map(x => HeapData(x.shape, format(x.shape))) + gradInput = initTensor(_gradInputFormats.head) + (_gradOutputFormats, _gradInputFormats) + } + + override def updateOutput(input: Activity): Activity = { + if (isTraining()) { + output = dropout.updateOutput(input) + } else { + output.toTensor[Float].copy(input.toTensor[Float]) + } + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = dropout.updateGradInput(input, gradOutput) + gradInput + } + + override def clearState(): this.type = { + dropout.clearState() + this + } + + override def toString(): String = { + s"mkldnn.Dropout" + } +} + +object Dropout { + def apply( + initP: Double = 0.5, + inplace: Boolean = false, + scale: Boolean = true) : Dropout = { + new Dropout(initP, inplace, scale) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala index e07cb4ffda5..0cae3c8acc7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala @@ -75,7 +75,7 @@ object Perf { val label = Tensor(batchSize).apply1(_ => Math.ceil(RNG.uniform(0, 1) * 1000).toFloat) val model = params.model match { - case "vgg16" => Vgg_16(batchSize, classNum, false) + case "vgg16" => Vgg_16(batchSize, classNum, true) case "resnet50" => ResNet(batchSize, classNum, T("depth" -> 50, "dataSet" -> ImageNet)) case _ => throw new UnsupportedOperationException(s"Unkown model ${params.model}") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala index baefb35dadc..ed3d0a082ea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala @@ -95,9 +95,7 @@ private[mkldnn] class ReorderManager() { from match { case h: HeapData => to match { - case hh: HeapData => - require(h.layout == hh.layout, "Heap data layout should be same") - false + case hh: HeapData => h.layout != hh.layout case nn: NativeData => true case _ => throw new UnsupportedOperationException("Not support such memory format") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 3125b58e74a..04bf5440b0f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -16,6 +16,8 @@ package com.intel.analytics.bigdl.nn.mkldnn +import java.io.{IOException, ObjectOutputStream} + import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn._ @@ -118,7 +120,11 @@ class SpatialConvolution( override def reset(): Unit = { if (initWeight == null) { // TODO only support oihw format weights - weightInitMethod.init(weight.dense, VariableFormat.OUT_IN) + weightInitMethod.init(weight.dense, if (nGroup == 1) { + VariableFormat.OUT_IN_KW_KH + } else { + VariableFormat.GP_OUT_IN_KW_KH + }) weight.syncToNative() } else { weight.copy(initWeight) @@ -203,6 +209,20 @@ class SpatialConvolution( updateOutputPrimitives = Array(primitive) output = initTensor(dst) + // by default, the initial weight is oihw / goihw format. + val defaultWeightLayout = if (nGroup == 1) { + Memory.Format.oihw + } else { + Memory.Format.goihw + } + if (realWei.layout != defaultWeightLayout) { + val srcFormat = HeapData(realWei.shape, defaultWeightLayout) + val dstFormat = HeapData(realWei.shape, realWei.layout) + reorderManager.register(srcFormat, dstFormat) + val result = reorderManager.infer(Array(srcFormat), Array(dstFormat), weight.dense) + weight.dense.copy(result.toTensor) + } + _inputFormats = Array(realSrc) _outputFormats = Array(realDst) (_inputFormats, _outputFormats) @@ -329,6 +349,9 @@ class SpatialConvolution( gradWeight.setMemoryData(realWei) gradBias.setMemoryData(bis) + require(weight.memoryData().layout == gradWeight.memoryData().layout, + s"layout should be the same") + val srcs = Array(realSrc.getPrimitive(runtime), realDiffDst.getPrimitive(runtime)) val indexes = Array.fill(srcs.length)(0) val dsts = Array(realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) @@ -382,6 +405,20 @@ class SpatialConvolution( List(weight, bias, gradWeight, gradBias).foreach(_.release()) if (weightForBackward != null) { weightForBackward.release() } } + + @throws(classOf[IOException]) + private def writeObject(out: ObjectOutputStream): Unit = { + if (weight.isMemoryDataSet() && weight.memoryData().layout != Memory.Format.oihw) { + val srcFormat = HeapData(weight.memoryData().shape, weight.memoryData().layout) + val dstFormat = HeapData(weight.memoryData().shape, Memory.Format.oihw) + + reorderManager.register(srcFormat, dstFormat) + val result = reorderManager.infer(Array(srcFormat), Array(dstFormat), weight.dense) + weight.dense.copy(result.toTensor) + } + + out.defaultWriteObject() + } } object SpatialConvolution { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala index 3bff29aea2b..f36a5576f75 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala @@ -16,62 +16,78 @@ package com.intel.analytics.bigdl.nn.mkldnn.models -import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.mkl.Memory -import com.intel.analytics.bigdl.nn.Dropout +import com.intel.analytics.bigdl.nn.{ConstInitMethod, Xavier, Zeros} import com.intel.analytics.bigdl.nn.mkldnn._ - object Vgg_16 { def apply(batchSize: Int, classNum: Int, hasDropout: Boolean = true): Sequential = { val model = Sequential() model.add(Input(Array(batchSize, 3, 224, 224), Memory.Format.nchw)) - model.add(SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(MaxPooling(2, 2, 2, 2)) + model.add(Conv(3, 64, 3, 3, 1, 1, 1, 1).setName("conv1_1")) + model.add(ReLU().setName("relu1_1")) + model.add(Conv(64, 64, 3, 3, 1, 1, 1, 1).setName("conv1_2")) + model.add(ReLU().setName("relu1_2")) + model.add(MaxPooling(2, 2, 2, 2).setName("pool1")) - model.add(SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(MaxPooling(2, 2, 2, 2)) + model.add(Conv(64, 128, 3, 3, 1, 1, 1, 1).setName("conv2_1")) + model.add(ReLU().setName("relu2_1")) + model.add(Conv(128, 128, 3, 3, 1, 1, 1, 1).setName("conv2_2")) + model.add(ReLU().setName("relu2_2")) + model.add(MaxPooling(2, 2, 2, 2).setName("pool2")) - model.add(SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(MaxPooling(2, 2, 2, 2)) + model.add(Conv(128, 256, 3, 3, 1, 1, 1, 1).setName("conv3_1")) + model.add(ReLU().setName("relu3_1")) + model.add(Conv(256, 256, 3, 3, 1, 1, 1, 1).setName("conv3_2")) + model.add(ReLU().setName("relu3_2")) + model.add(Conv(256, 256, 3, 3, 1, 1, 1, 1).setName("conv3_3")) + model.add(ReLU().setName("relu3_3")) + model.add(MaxPooling(2, 2, 2, 2).setName("pool3")) - model.add(SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(MaxPooling(2, 2, 2, 2)) + model.add(Conv(256, 512, 3, 3, 1, 1, 1, 1).setName("conv4_1")) + model.add(ReLU().setName("relu4_1")) + model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv4_2")) + model.add(ReLU().setName("relu4_2")) + model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv4_3")) + model.add(ReLU().setName("relu4_3")) + model.add(MaxPooling(2, 2, 2, 2).setName("pool4")) - model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)) - model.add(ReLU()) - model.add(MaxPooling(2, 2, 2, 2)) + model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_1")) + model.add(ReLU().setName("relu5_1")) + model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_2")) + model.add(ReLU().setName("relu5_2")) + model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_3")) + model.add(ReLU().setName("relu5_3")) + model.add(MaxPooling(2, 2, 2, 2).setName("pool5")) - model.add(Linear(512 * 7 * 7, 4096)) - model.add(ReLU()) - if (hasDropout) model.add(Dropout(0.5)) - model.add(Linear(4096, 4096)) - model.add(ReLU()) - if (hasDropout) model.add(Dropout(0.5)) - model.add(Linear(4096, classNum)) + model.add(Linear(512 * 7 * 7, 4096).setInitMethod(Xavier, ConstInitMethod(0.1)).setName("fc6")) + model.add(ReLU().setName("relu6")) + if (hasDropout) model.add(Dropout(0.5).setName("drop6")) + model.add(Linear(4096, 4096).setInitMethod(Xavier, ConstInitMethod(0.1)).setName("fc7")) + model.add(ReLU().setName("relu7")) + if (hasDropout) model.add(Dropout(0.5).setName("drop7")) + model.add(Linear(4096, classNum).setInitMethod(Xavier, ConstInitMethod(0.1)).setName(("fc8"))) model.add(ReorderMemory(HeapData(Array(batchSize, classNum), Memory.Format.nc))) model } -} + private object Conv { + def apply( + nInputPlane: Int, + nOutputPlane: Int, + kernelW: Int, + kernelH: Int, + strideW: Int = 1, + strideH: Int = 1, + padW: Int = 0, + padH: Int = 0, + nGroup: Int = 1, + propagateBack: Boolean = true): SpatialConvolution = { + val conv = SpatialConvolution(nInputPlane, nOutputPlane, kernelW, kernelH, + strideW, strideH, padW, padH, nGroup, propagateBack) + conv.setInitMethod(Xavier.setVarianceNormAverage(false), Zeros) + conv + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala index fef30161a68..5ef48430489 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/SGD.scala @@ -292,7 +292,7 @@ object SGD { override def updateHyperParameter(config: Table, state: Table): Unit = { val lr = config.get[Double]("learningRate").getOrElse(1e-3) val nevals = state.get[Int]("evalCounter").getOrElse(0) - val polyIter = nevals - excludeIterations + val polyIter = nevals // fix: should have no exclude iterations. val clr = if (polyIter > maxIteration) { 0.0 } else { @@ -306,7 +306,7 @@ object SGD { override def updateHyperParameter[T](optimMethod: SGD[T]): Unit = { val nevals = optimMethod.state.get[Int]("evalCounter").getOrElse(0) val lr = optimMethod.learningRate - val polyIter = nevals - excludeIterations + val polyIter = nevals // fix: should have no exclude iterations. val clr = if (polyIter > maxIteration) { 0.0 } else { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala new file mode 100644 index 00000000000..2eb0a62c4f3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.tensor.Tensor +import org.scalatest.{FlatSpec, Matchers} + +class DropoutSpec extends FlatSpec with Matchers { + "dropout output and gradinput" should "work correctly" in { + val input = Tensor[Float](Array(2, 3, 4, 4)).fill(1) + val zeros = Tensor[Float](Array(2, 3, 4, 4)).fill(0) + + val dropout = Dropout() + dropout.initFwdPrimitives(Array(HeapData(Array(2, 3, 4, 4), Memory.Format.nchw)), TrainingPhase) + + { + dropout.forward(input) + + val notEqZeros = dropout.output.toTensor[Float].storage().array().count(_ != 0) + val total = input.nElement() + val ratio = notEqZeros.toDouble / total + ratio should not be (1.0) + ratio should not be (0.0) + } + + { + dropout.backward(input, dropout.output) + val notEqZeros = dropout.gradInput.toTensor[Float].storage().array().count(_ != 0) + val total = input.nElement() + val ratio = notEqZeros.toDouble / total + ratio should not be (1.0) + ratio should not be (0.0) + } + } + + "dropout infer" should "work correctly" in { + val input = Tensor[Float](Array(2, 3, 4, 4)).fill(1) + val zeros = Tensor[Float](Array(2, 3, 4, 4)).fill(0) + + val dropout = Dropout() + dropout.initFwdPrimitives(Array(HeapData(Array(2, 3, 4, 4), Memory.Format.nchw)), + InferencePhase) + dropout.evaluate() + + dropout.forward(input) + + val notEqZeros = dropout.output.toTensor[Float].storage().array().count(_ != 0) + val total = input.nElement() + val ratio = notEqZeros.toDouble / total + ratio should be (1.0) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index 5c3683cdccd..fc0fba72734 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -199,17 +199,19 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val (weightAll1, gradWeightAll1) = model1.parameters() RNG.setSeed(100) - val model2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1) + val conv = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1) + val model2 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(conv) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + model2.zeroGradParameters() - model2.setRuntime(new MklDnnRuntime) - model2.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - model2.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - model2.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + model2.compile(TrainingPhase) - val initWeight = Tools.fromOIHW(weightAll1(0), model2.parametersWithShape()._1(0)) - model2.weight.copy(initWeight) - model2.bias.copy(model1.bias) + val initWeight = Tools.fromOIHW(weightAll1(0), conv.parametersWithShape()._1(0)) + conv.weight.copy(initWeight) + conv.bias.copy(model1.bias) RNG.setSeed(1) val input = Tensor(batchSize, 3, 224, 224).apply1(e => RNG.uniform(0, 1).toFloat) @@ -237,8 +239,8 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val gw1 = model1.gradWeight val gb1 = model1.gradBias - val gw2 = Tools.toOIHW(model2.gradWeight.native, model2.parametersWithShape()._2(0)) - val gb2 = Tools.dense(model2.gradBias.native).toTensor + val gw2 = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) + val gb2 = Tools.dense(conv.gradBias.native).toTensor Equivalent.nearequals(gw1, gw2, 1e-4) should be(true) Equivalent.nearequals(gb1, gb2, 1e-3) should be(true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/SGDSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/SGDSpec.scala index 1ba2b45d418..80620c085f5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/SGDSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/SGDSpec.scala @@ -370,7 +370,7 @@ class SGDSpec extends FlatSpec with Matchers { }) } - "ploy learning rate decay with warmup" should "generate correct learning rates" in { + "poly learning rate decay with warmup" should "generate correct learning rates" in { val lrSchedules = new SequentialSchedule(100) lrSchedules.add(Warmup(0.3), 3).add(Poly(3, 100), 100) val optimMethod = new SGD[Double](learningRate = 0.1, learningRateSchedule = lrSchedules) @@ -389,10 +389,10 @@ class SGDSpec extends FlatSpec with Matchers { optimMethod.learningRateSchedule.currentRate should be(-1.0 +- 1e-15) optimMethod.optimize(feval, x) optimMethod.learningRateSchedule.currentRate should - be(-1 * (1 - 1.0 / 100) * (1 - 1.0 / 100) * (1 - 1.0 / 100) +- 1e-15) + be(-1 * (1 - 4.0 / 100) * (1 - 4.0 / 100) * (1 - 4.0 / 100) +- 1e-15) optimMethod.optimize(feval, x) optimMethod.learningRateSchedule.currentRate should - be(-1 * (1 - 2.0 / 100) * (1 - 2.0 / 100) * (1 - 2.0 / 100) +- 1e-15) + be(-1 * (1 - 5.0 / 100) * (1 - 5.0 / 100) * (1 - 5.0 / 100) +- 1e-15) } "ploy with warm up" should "generate correct learning rates" in { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 1bf9f4aa3bd..3287dc2f0ea 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -61,7 +61,8 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.mkldnn.Sequential", "com.intel.analytics.bigdl.nn.mkldnn.SoftMax", "com.intel.analytics.bigdl.nn.mkldnn.SpatialBatchNormalization", - "com.intel.analytics.bigdl.nn.mkldnn.SpatialConvolution" + "com.intel.analytics.bigdl.nn.mkldnn.SpatialConvolution", + "com.intel.analytics.bigdl.nn.mkldnn.Dropout" ) // Maybe one serial test class contains multiple module test From f831f7fc6451855ba62dcc3c4f00e14ba6d7e0c5 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 9 Oct 2018 10:46:51 +0800 Subject: [PATCH 0831/1065] Refine the synchronizer to support prioirty and also make it event driven (#2634) * refinement * refinement * refinement * refinement per comments * refinemnt per review --- .../utils/DistriParameterSynchronizer.scala | 578 +++++++++--------- .../utils/DistributedSynchronizerSpec.scala | 41 +- 2 files changed, 325 insertions(+), 294 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala index 6d20852a328..57068d9e899 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala @@ -18,7 +18,9 @@ package com.intel.analytics.bigdl.utils import java.nio.ByteBuffer import java.util import java.util.concurrent._ +import java.util.concurrent.atomic.AtomicInteger +import com.intel.analytics.bigdl.mkl.hardware.{Affinity, CpuInfo} import com.intel.analytics.bigdl.parameters.{CompressedTensor, FP16CompressedTensor, SerializerInstance} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -39,21 +41,21 @@ trait DistriParameterSynchronizer[T] { * @param globalSize total size of parameter * @param priority priority for this parameter */ - def init(name: String, globalSize: Int, priority: Int = 1): Unit + def init(name: String, globalSize: Int, priority: Int = 1, weights: Tensor[T], + grads: Tensor[T]): Unit /** * put parameter to global * @param name identifier for parameter - * @param parameter parameter to put */ - def put(name: String, parameter: Tensor[T]): Unit + def put(name: String): Unit /** * get parameter for specific identifier * @param name identifier for parameter * @return parameter */ - def get(name: String): Tensor[T] + def get(name: String): (Tensor[T], Tensor[T]) /** * clear the synchronizer @@ -61,81 +63,59 @@ trait DistriParameterSynchronizer[T] { def clear(): Unit } -class BlockManagerParameterSynchronizer[T: ClassTag](partitionID: Int, - totalPartition: Int) - (implicit ev: TensorNumeric[T]) - extends DistriParameterSynchronizer[T] { +class BlockManagerParameterSynchronizer[T: ClassTag](val partitionID: Int, val totalPartition: Int) + (implicit ev: TensorNumeric[T]) extends DistriParameterSynchronizer[T]{ import com.intel.analytics.bigdl.utils.BlockManagerParameterSynchronizer.logger @volatile private var shutdown = false + private val communicationStartCore = CpuInfo.getPhysicalProcessorCount - 4 + private val syncResults: mutable.HashMap[String, FutureTask[Tensor[T]]] - = new mutable.HashMap[String, FutureTask[Tensor[T]]]() + = new mutable.HashMap[String, FutureTask[Tensor[T]]]() private val taskSize: Int = System.getProperty("bigdl.ParameterSynchronier." + - "asyncTaskSize", "100").toInt + "asyncTaskSize", "500").toInt private val clearPoolSize: Int = System.getProperty("bigdl.ParameterSynchronier." + - "clearPoolSize", "1").toInt + "clearPoolSize", "2").toInt private val workerPoolSize: Int = System.getProperty("bigdl.ParameterSynchronier" + - ".syncPoolSize", "4").toInt + ".workerPoolSize", "4").toInt private val syncPoolSize: Int = Math.max(System.getProperty("bigdl.ParameterSynchronier" + - ".computePoolSize", + ".syncPoolSize", (Runtime.getRuntime().availableProcessors() / 2).toString).toInt, 2) private val fetchCompletionPoolSize: Int = System.getProperty("bigdl.ParameterSynchronier" + - ".fetchCompletionPoolSize", "1").toInt - - private val asyncTaskWaitingQueue : PriorityBlockingQueue[AsyncFutureTask] = - new PriorityBlockingQueue[AsyncFutureTask](taskSize) + ".fetchCompletionPoolSize", "2").toInt private val blockFetchRequestQueue: PriorityBlockingQueue[BlockFetchRequest] = new PriorityBlockingQueue[BlockFetchRequest](taskSize) - private val longRunningThreads = new util.ArrayList[Thread]() + private val asyncTaskWaitingQueue : PriorityBlockingQueue[SyncRequest] = + new PriorityBlockingQueue[SyncRequest](taskSize) - // thread pool to remove expired blocks - private lazy val clearPool: ExecutorService = - Executors.newFixedThreadPool(clearPoolSize, new ThreadFactory { - override def newThread(r: Runnable): Thread = { - val t = Executors.defaultThreadFactory().newThread(r) - t.setDaemon(true) - t - } - }) + private val longRunningThreads = new util.ArrayList[Thread]() - // main thread pool to do put-get-aggregate - private val workerPool: ExecutorService = - Executors.newFixedThreadPool(workerPoolSize, new ThreadFactory { - override def newThread(r: Runnable): Thread = { - val t = Executors.defaultThreadFactory().newThread(r) - t.setDaemon(true) - t - } - }) + private val syncMetaMap = new ConcurrentHashMap[String, SyncMeta[T]] - // long running thread to fetch the request - workerPool.submit(new Runnable { - override def run(): Unit = { - longRunningThreads.add(Thread.currentThread) - while (!shutdown) { - try { - val asyncFutureTask = asyncTaskWaitingQueue.take - workerPool.submit(asyncFutureTask.task) - } catch { - case e : InterruptedException => - logger.info("exit thread gracefully") - } - } - } - }) + val threadCount = new AtomicInteger(0) - // thread pool for put and aggregate - private lazy val syncPool: ExecutorService = Executors.newFixedThreadPool(syncPoolSize, - new ThreadFactory { + // thread pool to update sow on fetching completion + private val fetchCompletionPool: ExecutorService = initThreadPool(fetchCompletionPoolSize) + // to fetch all remote blocks + + private lazy val fetchPool: ExecutorService = initThreadPool(syncPoolSize) + // to process request + private val workerPool: ExecutorService = initThreadPool(workerPoolSize) + // to do local sync threads + private lazy val syncPool: ExecutorService = initThreadPool(syncPoolSize) + private lazy val clearPool: ExecutorService = initThreadPool(clearPoolSize) + + private def initThreadPool(capacity: Int): ExecutorService = + Executors.newFixedThreadPool(capacity, new ThreadFactory { override def newThread(r: Runnable): Thread = { val t = Executors.defaultThreadFactory().newThread(r) t.setDaemon(true) @@ -143,36 +123,36 @@ class BlockManagerParameterSynchronizer[T: ClassTag](partitionID: Int, } }) - // thread pool for fetching blocks - private lazy val fetchPool: ExecutorService = Executors.newFixedThreadPool(syncPoolSize, - new ThreadFactory { - override def newThread(r: Runnable): Thread = { - val t = Executors.defaultThreadFactory().newThread(r) - t.setDaemon(true) - t - } - }) + initAffinityThreads - // thread pool to update sow on fetching completion - private val fetchCompletionPool: ExecutorService = Executors. - newFixedThreadPool(fetchCompletionPoolSize, - new ThreadFactory { - override def newThread(r: Runnable): Thread = { - val t = Executors.defaultThreadFactory().newThread(r) - t.setDaemon(true) - t - } + private def initAffinityThreads(): Unit = { + initAffinityThreadsForThreadPool(fetchPool, syncPoolSize) + initAffinityThreadsForThreadPool(workerPool, workerPoolSize) + initAffinityThreadsForThreadPool(fetchCompletionPool, fetchCompletionPoolSize) + initAffinityThreadsForThreadPool(clearPool, clearPoolSize) + } + + private def initAffinityThreadsForThreadPool(threadPool: ExecutorService, capacity: Int): Unit = { + (0 until capacity).map(wp => { + threadPool.submit(new Runnable { + override def run(): Unit = { + val v = threadCount.incrementAndGet() + Affinity.setAffinity(communicationStartCore + (v) % 4) + } + }) }) + } (0 until syncPoolSize).foreach(th => { fetchPool.submit(new Runnable { override def run(): Unit = { + val v = threadCount.incrementAndGet() longRunningThreads.add(Thread.currentThread) while (!shutdown) { try { val fetchRequest = blockFetchRequestQueue.take val syncMeta = fetchRequest.syncMeta - val pid = fetchRequest.futureTask.fetchOnCompletion.fromPartition + val pid = fetchRequest.blockFetchFutureTask.fetchOnCompletion.fromPartition val aggregated = fetchRequest.aggregated val parameterBlockId = if (aggregated) { getParameterBlockId(s"${syncMeta.name}_aggregated", syncMeta.counter, pid, -1) @@ -182,13 +162,11 @@ class BlockManagerParameterSynchronizer[T: ClassTag](partitionID: Int, } val block = BlockManagerWrapper.getLocalOrRemoteBytes(parameterBlockId) if (block == None) { - // promote the priporty in next fetch - fetchRequest.priority += 1 blockFetchRequestQueue.add(fetchRequest) } else { - val fetchOnCompletion = fetchRequest.futureTask.fetchOnCompletion + val fetchOnCompletion = fetchRequest.blockFetchFutureTask.fetchOnCompletion fetchOnCompletion.setFetched(block.get) - fetchCompletionPool.submit(fetchRequest.futureTask.task) + fetchCompletionPool.submit(fetchRequest.blockFetchFutureTask.task) } } catch { case e : InterruptedException => @@ -199,199 +177,19 @@ class BlockManagerParameterSynchronizer[T: ClassTag](partitionID: Int, }) }) - private val syncMetaMap = new ConcurrentHashMap[String, SyncMeta[T]] - - override def init(name: String, globalSize: Int, priority: Int = 1): Unit = { - val partitionToCount = if (globalSize < totalPartition) globalSize else totalPartition - syncMetaMap.putIfAbsent(name, SyncMeta(name, 1, priority, globalSize, partitionToCount, - new ConcurrentHashMap[Int, CompressedTensor[T]](), - new ConcurrentHashMap[Int, Tensor[T]]())) - } - - override def put(name: String, parameter: Tensor[T]): Unit = { - val syncMeta = syncMetaMap.get(name) - val asyncTask = new AsyncTask(syncMeta, parameter) - val futureTask = new FutureTask[Tensor[T]](asyncTask) - val futureAsyncTask = new AsyncFutureTask(futureTask, syncMeta.priority) - asyncTaskWaitingQueue.add(futureAsyncTask) - val clearTask = new ClearTask(name, syncMeta.counter - 1, - partitionID, syncMeta.partitionToCount) - clearPool.execute(clearTask) - syncResults.put(name, futureTask) - } - - override def get(name: String): Tensor[T] = { - require(syncResults.contains(name), "put must be done before get") - val res = syncResults.get(name).get.get() - val syncMeta = syncMetaMap.get(name) - syncMeta.counter += 1 - res - } - - private class ClearTask(name: String, counter: Int, partitionID: Int, - partitionToCount: Int) - extends Runnable { - override def run(): Unit = { - (0 until partitionToCount).foreach(pid => { - val parameterBlockId = getParameterBlockId(name, - counter, partitionID, pid) - BlockManagerWrapper.removeBlock(parameterBlockId) - }) - // only local parititon < partitionToCount, there are aggregated blocks - if (partitionID < partitionToCount) { - val aggregatedParameterBlockId = getParameterBlockId(s"${name}_aggregated", - counter, partitionID, -1) - } - } - } - - private class AsyncFutureTask(val task : FutureTask[_], val priority: Int) - extends Comparable[AsyncFutureTask] { - override def compareTo(o: AsyncFutureTask): Int = { - o.priority.compareTo(this.priority) - } - } - - private class AsyncTask(val syncMeta: SyncMeta[T], - parameter: Tensor[T]) extends Callable[Tensor[T]] { - - override def call(): Tensor[T] = { - - // step 1: clear last status - - syncMeta.stateOfWorld.clear - syncMeta.aggregatedStateOfWorld.clear - - val partitonToCount = syncMeta.partitionToCount - - val _classTag = classTag[T] - val size = syncMeta.globalSize - val taskSize = size / partitonToCount - val extraSize = size % partitonToCount - - // step 2 : put all local partitioned parameter to global - val putThreads = (0 until partitonToCount).map { pid => - new Callable[Int] { - override def call(): Int = { - try { - val offset = 1 + pid * taskSize + math.min(pid, extraSize) - val length = taskSize + (if (pid < extraSize) 1 else 0) - val partitionParam = parameter.narrow(1, offset, length) - syncMeta.aggregatedStateOfWorld.put(pid, partitionParam) - val parameterBlockId = getParameterBlockId(syncMeta.name, - syncMeta.counter, partitionID, pid) - val fp16param = new FP16CompressedTensor[T](length)(_classTag) - fp16param.compress(0, parameter, offset - 1, length) - BlockManagerWrapper.putBytes(parameterBlockId, - fp16param.bytes(), StorageLevel.MEMORY_ONLY_SER) - pid - } catch { - case t: Throwable => - logger.error("Error: " + ExceptionUtils.getStackTrace(t)) - throw t - } - } - } - } - syncPool.invokeAll(putThreads.asJava) - - // step 3: get all remote paritioned parameter to local - if (partitionID < partitonToCount) { - val syncThreads = (0 until totalPartition).map { pid => - new Callable[Int] { - override def call(): Int = { - try { - val fetchOnCompletion = new BlockFetchOnCompletion(syncMeta, pid) - val futureTask = new FutureTask[Int](fetchOnCompletion) - val priorityFutureTask = new PriorityFutureTask(futureTask, fetchOnCompletion) - val fetchRequest = new BlockFetchRequest(syncMeta, syncMeta.priority, - priorityFutureTask) - blockFetchRequestQueue.add(fetchRequest) - futureTask.get - } catch { - case t: Throwable => - logger.error("Error: " + ExceptionUtils.getStackTrace(t)) - throw t - } - } - } - } - syncPool.invokeAll(syncThreads.asJava) - - - // step 4: aggregation - - val length = taskSize + (if (partitionID < extraSize) 1 else 0) - val poolSize = Engine.default.getPoolSize - val innerTaskSize = length / poolSize - val innerExtraSize = length % poolSize - val availableTask = if (innerTaskSize == 0) innerExtraSize else poolSize - val params = syncMeta.stateOfWorld.values().toArray(). - map(_.asInstanceOf[CompressedTensor[T]]) - syncPool.invokeAll((0 until availableTask).map(tid => - new Callable[Int] { - override def call(): Int = { - val innerStart = tid * innerTaskSize + math.min(innerExtraSize, tid) - val innerLength = innerTaskSize + (if (tid < innerExtraSize) 1 else 0) - params.reduce { (l, r) => - l.add(r.bytes(innerStart, innerLength), innerStart, innerLength) - } - tid - } - } - ).asJava) - val res = Tensor[T](length) - params.head.deCompress(res) - res.div(ev.fromType(totalPartition)) - - // step 5: put aggregated to global - val parameterBlockId = getParameterBlockId(s"${syncMeta.name}_aggregated", - syncMeta.counter, partitionID, -1) - val fp16paramAggregated = new FP16CompressedTensor[T](length)(_classTag) - fp16paramAggregated.compress(0, res, 0, length) - BlockManagerWrapper.putBytes(parameterBlockId, - fp16paramAggregated.bytes(), StorageLevel.MEMORY_ONLY_SER) - } - - // step 6: get all other aggregated partitions - - val AggregatedSyncThreads = (0 until partitonToCount).map { pid => - new Callable[Int] { - override def call(): Int = { - try { - val fetchOnCompletion = new BlockFetchOnCompletion(syncMeta, pid, true) - val futureTask = new FutureTask[Int](fetchOnCompletion) - val priorityFutureTask = new PriorityFutureTask(futureTask, fetchOnCompletion) - val fetchRequest = new BlockFetchRequest(syncMeta, syncMeta.priority, - priorityFutureTask, true) - blockFetchRequestQueue.add(fetchRequest) - futureTask.get - } catch { - case t: Throwable => - logger.error("Error: " + ExceptionUtils.getStackTrace(t)) - throw t - } - } - } - } - syncPool.invokeAll(AggregatedSyncThreads.asJava) - - parameter - } - } private class BlockFetchRequest(val syncMeta: SyncMeta[T], var priority: Int, - val futureTask: PriorityFutureTask, - val aggregated: Boolean = false) + val blockFetchFutureTask: BlockFetchFutureTask, + val aggregated: Boolean = false) extends Comparable[BlockFetchRequest] { override def compareTo(o: BlockFetchRequest): Int = { o.priority.compareTo(this.priority) } } - private class BlockFetchOnCompletion(val syncMeta: SyncMeta[T], val fromPartition: Int, - val aggregated: Boolean = false) + private class BlockFetchOnCompletion(val syncRequest: SyncRequest, val fromPartition: Int, + val aggregated: Boolean = false) extends Callable[Int] { val _classTag = classTag[T] private var _fetched: ByteBuffer = null @@ -399,49 +197,281 @@ class BlockManagerParameterSynchronizer[T: ClassTag](partitionID: Int, this._fetched = fetched } override def call(): Int = { + val syncMeta = syncRequest.syncMeta if (aggregated) { val partitionParam = syncMeta.aggregatedStateOfWorld.get(fromPartition) SerializerInstance.create(_fetched)(_classTag).deCompress(partitionParam) + val acc = syncRequest.state.addAndGet(1) + if (acc == syncRequest.syncMeta.partitionToCount + SyncState.PUT_AGGREGATED.id) { + asyncTaskWaitingQueue.add(syncRequest) + } } else { syncMeta.stateOfWorld.put(fromPartition, SerializerInstance.create(_fetched)(_classTag)) + if (syncMeta.stateOfWorld.size == totalPartition) { + val updated = syncRequest.state.compareAndSet(SyncState.FETCH_PARTITION.id, + SyncState.AGGREGATION.id) + if (updated) { + asyncTaskWaitingQueue.add(syncRequest) + } + } } fromPartition } } - private class PriorityFutureTask(val task : FutureTask[_], - val fetchOnCompletion: BlockFetchOnCompletion) { + (0 until workerPoolSize).foreach(_ => { + workerPool.submit(new Runnable { + override def run(): Unit = { + while (!shutdown) { + val asyncTaskReq = asyncTaskWaitingQueue.take + val syncMeta = asyncTaskReq.syncMeta + val partitonToCount = asyncTaskReq.syncMeta.partitionToCount + val _classTag = classTag[T] + val size = asyncTaskReq.syncMeta.globalSize + val taskSize = size / partitonToCount + val extraSize = size % partitonToCount + val state = asyncTaskReq.state.get + if (state == SyncState.INIT.id) { + // step 1: clear last status + syncMeta.stateOfWorld.clear + + val parameter = asyncTaskReq.asyncTask.parameter + + // step 2 : put all local partitioned parameter to global + val putThreads = (0 until partitonToCount).map { pid => + new Callable[Int] { + override def call(): Int = { + try { + val offset = 1 + pid * taskSize + math.min(pid, extraSize) + val length = taskSize + (if (pid < extraSize) 1 else 0) + if (!syncMeta.aggregatedStateOfWorld.contains(pid)) { + val partitionParam = parameter.narrow(1, offset, length) + syncMeta.aggregatedStateOfWorld.put(pid, partitionParam) + } + val parameterBlockId = getParameterBlockId(syncMeta.name, + syncMeta.counter, partitionID, pid) + val fp16param = new FP16CompressedTensor[T](length)(_classTag) + fp16param.compress(0, parameter, offset - 1, length) + BlockManagerWrapper.putBytes(parameterBlockId, + fp16param.bytes(), StorageLevel.MEMORY_ONLY_SER) + pid + } catch { + case t: Throwable => + logger.error("Error: " + ExceptionUtils.getStackTrace(t)) + throw t + } + } + } + } + putThreads.foreach(pth => syncPool.submit(pth)) + asyncTaskReq.state.set(SyncState.FETCH_PARTITION.id) + asyncTaskWaitingQueue.add(asyncTaskReq) + } else if (state == SyncState.FETCH_PARTITION.id) { + // fetch aggregated partition + if (partitionID < syncMeta.partitionToCount) { + val syncThreads = (0 until totalPartition).map { pid => + new Callable[Int] { + override def call(): Int = { + try { + val fetchOnCompletion = new BlockFetchOnCompletion(asyncTaskReq, pid) + val futureTask = new FutureTask[Int](fetchOnCompletion) + val blockFetchFutureTask = new BlockFetchFutureTask(futureTask, + fetchOnCompletion) + val fetchRequest = new BlockFetchRequest(syncMeta, syncMeta.priority, + blockFetchFutureTask) + blockFetchRequestQueue.add(fetchRequest) + pid + } catch { + case t: Throwable => + logger.error("Error in processing fetching request: " + + ExceptionUtils.getStackTrace(t)) + throw t + } + } + } + } + syncThreads.foreach(sth => syncPool.submit(sth)) + } else { + asyncTaskReq.state.set(SyncState.PUT_AGGREGATED.id) + asyncTaskWaitingQueue.add(asyncTaskReq) + } + } else if (state == SyncState.AGGREGATION.id) { + // aggregated completed + val length = taskSize + (if (partitionID < extraSize) 1 else 0) + val poolSize = Engine.default.getPoolSize + val innerTaskSize = length / poolSize + val innerExtraSize = length % poolSize + val availableTask = if (innerTaskSize == 0) innerExtraSize else poolSize + val params = syncMeta.stateOfWorld.values().toArray(). + map(_.asInstanceOf[CompressedTensor[T]]) + syncPool.invokeAll((0 until availableTask).map(tid => + new Callable[Int] { + override def call(): Int = { + val innerStart = tid * innerTaskSize + math.min(innerExtraSize, tid) + val innerLength = innerTaskSize + (if (tid < innerExtraSize) 1 else 0) + params.reduce { (l, r) => + l.add(r.bytes(innerStart, innerLength), innerStart, innerLength) + } + tid + } + } + ).asJava) + val res = Tensor[T](length) + params.head.deCompress(res) + res.div(ev.fromType(totalPartition)) + // step 5: put aggregated to global + val parameterBlockId = getParameterBlockId(s"${syncMeta.name}_aggregated", + syncMeta.counter, partitionID, -1) + val fp16paramAggregated = new FP16CompressedTensor[T](length)(_classTag) + fp16paramAggregated.compress(0, res, 0, length) + BlockManagerWrapper.putBytes(parameterBlockId, + fp16paramAggregated.bytes(), StorageLevel.MEMORY_ONLY_SER) + asyncTaskReq.state.set(SyncState.PUT_AGGREGATED.id) + asyncTaskWaitingQueue.add(asyncTaskReq) + } else if (state == SyncState.PUT_AGGREGATED.id) { + val aggregatedSyncThreads = (0 until syncMeta.partitionToCount).map { pid => + new Callable[Int] { + override def call(): Int = { + try { + val fetchOnCompletion = new BlockFetchOnCompletion(asyncTaskReq, pid, true) + val futureTask = new FutureTask[Int](fetchOnCompletion) + val priorityFutureTask = new BlockFetchFutureTask(futureTask, + fetchOnCompletion) + val fetchRequest = new BlockFetchRequest(syncMeta, syncMeta.priority, + priorityFutureTask, true) + blockFetchRequestQueue.add(fetchRequest) + pid + } catch { + case t: Throwable => + logger.error("Error in processing request: " + + ExceptionUtils.getStackTrace(t)) + throw t + } + } + } + } + aggregatedSyncThreads.foreach(aggr => syncPool.submit(aggr)) + } else if (state == SyncState.PUT_AGGREGATED.id + syncMeta.partitionToCount) { + asyncTaskReq.futureTask.run + } + } + } + }) + }) - } + override def init(name: String, globalSize: Int, priority: Int = 1, weights: Tensor[T] + , grads: Tensor[T]): Unit = { + val partitionToCount = if (globalSize < totalPartition) globalSize else totalPartition + syncMetaMap.putIfAbsent(name, SyncMeta(name, 0, priority, globalSize, partitionToCount, + new ConcurrentHashMap[Int, CompressedTensor[T]](), + new ConcurrentHashMap[Int, Tensor[T]](), weights, grads)) + } - private def getBlockId(name: String): BlockId = { - SparkExtension.getLocalBlockId(name) + override def put(name: String): Unit = { + val syncMeta = syncMetaMap.get(name) + syncMeta.counter += 1 + val asyncTask = new AsyncTask(syncMeta.grads) + val futureTask = new FutureTask[Tensor[T]](asyncTask) + val syncRequest = new SyncRequest(new AtomicInteger(0), syncMeta, futureTask, asyncTask) + asyncTaskWaitingQueue.add(syncRequest) + if (syncMeta.counter > 1) { + val clearTask = new ClearTask(name, syncMeta.counter - 1, + partitionID, syncMeta.partitionToCount) + clearPool.execute(clearTask) + } + syncResults.put(name, futureTask) } - private def getParameterBlockId(name: String, counter: Int, pidFrom: Int, pidTo: Int): BlockId = { - SparkExtension.getLocalBlockId(name + counter + pidFrom + "paraBytes" + pidTo) + override def get(name: String): (Tensor[T], Tensor[T]) = { + val syncMeta = syncMetaMap.get(name) + // no need to do aggregation for first forward + if (syncMeta.counter == 0) { + return (null, null) + } + require(syncResults.contains(name), "put must be done before get") + val res = syncResults.get(name).get.get() + (syncMeta.weights, res) } override def clear(): Unit = { shutdown = true longRunningThreads.asScala.foreach(_.interrupt()) clearPool.shutdown - syncPool.shutdown workerPool.shutdown + syncPool.shutdown fetchPool.shutdown fetchCompletionPool.shutdown } + + private class SyncRequest(var state: AtomicInteger, val syncMeta: SyncMeta[T], + val futureTask: FutureTask[Tensor[T]], + val asyncTask: AsyncTask) + extends Comparable[SyncRequest] { + override def compareTo(o: SyncRequest): Int = { + o.syncMeta.priority.compareTo(this.syncMeta.priority) + } + } + + class AsyncTask(val parameter: Tensor[T]) extends Callable[Tensor[T]] { + override def call(): Tensor[T] = { + parameter + } + } + + + private class ClearTask(name: String, counter: Int, partitionID: Int, + partitionToCount: Int) + extends Runnable { + override def run(): Unit = { + try { + (0 until partitionToCount).foreach(pid => { + val parameterBlockId = getParameterBlockId(name, + counter, partitionID, pid) + BlockManagerWrapper.removeBlock(parameterBlockId) + }) + // only local parititon < partitionToCount, there are aggregated blocks + if (partitionID < partitionToCount) { + val aggregatedParameterBlockId = getParameterBlockId(s"${name}_aggregated", + counter, partitionID, -1) + BlockManagerWrapper.removeBlock(aggregatedParameterBlockId) + } + } catch { + case e: Exception => + logger.info("exit thread gracefully") + + } + } + } + + private class BlockFetchFutureTask(val task : FutureTask[_], + val fetchOnCompletion: BlockFetchOnCompletion) { + + } + + private def getParameterBlockId(name: String, counter: Int, pidFrom: Int, pidTo: Int): BlockId = { + SparkExtension.getLocalBlockId(name + counter + pidFrom + "paraBytes" + pidTo) + } } object BlockManagerParameterSynchronizer { val logger: Logger = Logger.getLogger(getClass) - def apply[T: ClassTag](partitionID: Int, - totalPartition: Int) - (implicit ev: TensorNumeric[T]): BlockManagerParameterSynchronizer[T] - = new BlockManagerParameterSynchronizer[T](partitionID, totalPartition) + def apply[T: ClassTag](partitionID: Int, totalPartition: Int) + (implicit ev: TensorNumeric[T]): BlockManagerParameterSynchronizer[T] + = new BlockManagerParameterSynchronizer[T](partitionID, totalPartition) } + case class SyncMeta[T](name: String, var counter: Int, priority: Int, globalSize: Int, partitionToCount: Int, stateOfWorld: ConcurrentHashMap[Int, CompressedTensor[T]], - aggregatedStateOfWorld: ConcurrentHashMap[Int, Tensor[T]]) + aggregatedStateOfWorld: ConcurrentHashMap[Int, Tensor[T]], + weights: Tensor[T] = null, + grads: Tensor[T] = null) + +object SyncState extends Enumeration{ + type State = Value + val INIT = Value("0") + val FETCH_PARTITION = Value("1") + val AGGREGATION = Value("2") + val PUT_AGGREGATED = Value("3") +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala index 0ff4a995b8e..6c6b1f1167e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import org.apache.spark.{SparkContext, TaskContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} -class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAndAfter { +class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAndAfter { var sc: SparkContext = null @@ -39,21 +39,22 @@ class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAnd val partitionID = TaskContext.getPartitionId val sync = new BlockManagerParameterSynchronizer[Float](partitionID, partition) val tensor = Tensor[Float](10).fill(partitionID.toFloat + 1.0f) - sync.init(s"testPara", 10) + sync.init(s"testPara", 10, weights = null, grads = tensor) var res : Iterator[_] = null - sync.put(s"testPara", tensor) + sync.put(s"testPara") res = Iterator.single(sync.get(s"testPara")) sync.clear res }).collect res.length should be (4) - res(0) should be (Tensor[Float](10).fill(2.5f)) - res(1) should be (Tensor[Float](10).fill(2.5f)) - res(2) should be (Tensor[Float](10).fill(2.5f)) - res(3) should be (Tensor[Float](10).fill(2.5f)) + res(0).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](10).fill(2.5f)) + res(1).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](10).fill(2.5f)) + res(2).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](10).fill(2.5f)) + res(3).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](10).fill(2.5f)) } - "DistributedSynchronizer with parameter size less than partition" should "worl properly" in { + "DistributedSynchronizer with parameter size less than partition" should "work properly" in { + val cores1 = Runtime.getRuntime().availableProcessors val partition = 4 val cores = 4 val res = sc.parallelize((0 until partition), partition).mapPartitions(p => { @@ -61,18 +62,18 @@ class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAnd val partitionID = TaskContext.getPartitionId val sync = new BlockManagerParameterSynchronizer[Float](partitionID, partition) val tensor = Tensor[Float](2).fill(partitionID.toFloat + 1.0f) - sync.init(s"testPara", 2) + sync.init(s"testPara", 2, weights = null, grads = tensor) var res : Iterator[_] = null - sync.put(s"testPara", tensor) + sync.put(s"testPara") res = Iterator.single(sync.get(s"testPara")) sync.clear res }).collect res.length should be (4) - res(0) should be (Tensor[Float](2).fill(2.5f)) - res(1) should be (Tensor[Float](2).fill(2.5f)) - res(2) should be (Tensor[Float](2).fill(2.5f)) - res(3) should be (Tensor[Float](2).fill(2.5f)) + res(0).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](2).fill(2.5f)) + res(1).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](2).fill(2.5f)) + res(2).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](2).fill(2.5f)) + res(3).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](2).fill(2.5f)) } "DistributedSynchronizer with parameter offset > 1" should "work properly" in { @@ -84,18 +85,18 @@ class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAnd val sync = new BlockManagerParameterSynchronizer[Float](partitionID, partition) val tensor = Tensor[Float](20) val parameter = tensor.narrow(1, 10, 10).fill(partitionID.toFloat + 1.0f) - sync.init(s"testPara", 10) + sync.init(s"testPara", 10, weights = null, grads = parameter) var res : Iterator[_] = null - sync.put(s"testPara", parameter) + sync.put(s"testPara") res = Iterator.single(sync.get(s"testPara")) sync.clear res }).collect res.length should be (4) - res(0) should be (Tensor[Float](10).fill(2.5f)) - res(1) should be (Tensor[Float](10).fill(2.5f)) - res(2) should be (Tensor[Float](10).fill(2.5f)) - res(3) should be (Tensor[Float](10).fill(2.5f)) + res(0).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](10).fill(2.5f)) + res(1).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](10).fill(2.5f)) + res(2).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](10).fill(2.5f)) + res(3).asInstanceOf[Tuple2[_, _]]._2 should be (Tensor[Float](10).fill(2.5f)) } after { From ad63598b206d36edab8729ae1bf9dfdaeb26a1a9 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 9 Oct 2018 00:59:12 -0400 Subject: [PATCH 0832/1065] perf: need not narrow the gradients and zero gradients for dnn backend (#2632) * perf: need not narrow the gradients and zero gradients for dnn backend * fix: empty gradient zero for dnn backend * fix: delete affine --- .../bigdl/dllib/nn/mkldnn/Linear.scala | 5 ++-- .../bigdl/dllib/nn/mkldnn/Perf.scala | 5 ++-- .../nn/mkldnn/SpatialBatchNormalization.scala | 12 ++++------ .../dllib/nn/mkldnn/SpatialConvolution.scala | 5 ++-- .../bigdl/dllib/optim/DistriOptimizer.scala | 24 ++++++++++--------- .../SpatialBatchNormalizationSpec.scala | 4 ++-- 6 files changed, 26 insertions(+), 29 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 72f8ade5834..c27b616fa72 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -67,7 +67,8 @@ class Linear( bias.copy(initBias) } - zeroGradParameters() + gradWeight.zero() + gradBias.zero() } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { @@ -279,8 +280,6 @@ class Linear( } override def zeroGradParameters(): Unit = { - gradWeight.zero() - gradBias.zero() } override def release(): Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala index 0cae3c8acc7..a1674a297f1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala @@ -314,9 +314,8 @@ object SbnDnn { def apply[@specialized(Float, Double) T: ClassTag]( nOutput: Int, eps: Double = 1e-3, - momentum: Double = 0.9, - affine: Boolean = true) + momentum: Double = 0.9) (implicit ev: TensorNumeric[T]): SpatialBatchNormalization = { - SpatialBatchNormalization(nOutput, eps, momentum, affine).setInitMethod(Ones, Zeros) + SpatialBatchNormalization(nOutput, eps, momentum).setInitMethod(Ones, Zeros) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index f484cd17321..506d78fa86e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -28,7 +28,6 @@ class SpatialBatchNormalization( val nOutput: Int, val eps: Double = 1e-5, val momentum: Double = 0.1, - val affine: Boolean = true, private val initWeight: Tensor[Float] = null, private val initBias: Tensor[Float] = null, private val initGradWeight: Tensor[Float] = null, @@ -94,6 +93,8 @@ class SpatialBatchNormalization( runningMean.zero() runningVariance.zero() + + gradWeightAndBias.zero() } private object Index extends Serializable { @@ -303,9 +304,6 @@ class SpatialBatchNormalization( } override def zeroGradParameters(): Unit = { - if (affine) { - gradWeightAndBias.zero() - } } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { @@ -322,7 +320,7 @@ class SpatialBatchNormalization( } override def toString(): String = { - s"nn.mkl.SpatialBatchNormalization($nOutput, $eps, $momentum, $affine)" + s"nn.mkl.SpatialBatchNormalization($nOutput, $eps, $momentum)" } override def evaluate(): this.type = { @@ -356,7 +354,7 @@ object SpatialBatchNormalization { initBias: Tensor[Float] = null, initGradWeight: Tensor[Float] = null, initGradBias: Tensor[Float] = null): SpatialBatchNormalization = { - new SpatialBatchNormalization(nOutput, eps, momentum, affine, - initWeight, initBias, initGradWeight, initGradBias) + new SpatialBatchNormalization(nOutput, eps, momentum, initWeight, initBias, initGradWeight, + initGradBias) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 04bf5440b0f..1a47ed22e50 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -137,7 +137,8 @@ class SpatialConvolution( bias.copy(initBias) } - zeroGradParameters() + gradWeight.zero() + gradBias.zero() } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { @@ -391,8 +392,6 @@ class SpatialConvolution( } override def zeroGradParameters(): Unit = { - gradWeight.zero() - gradBias.zero() } override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index c215231d9da..e073b5e8747 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -284,17 +284,19 @@ object DistriOptimizer { // Aggregate multi-model's gradient to the first model's gradient val parallelNum = if (taskSize == 0) extraTask else _subModelNumber - Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { - val offset = pOffset + tid * taskSize + math.min(tid, extraTask) - val length = taskSize + (if (tid < extraTask) 1 else 0) - var i = 1 - while (i < finishedGradients.length) { - finishedGradients(0).narrow(1, offset, length) - .add(finishedGradients(i).narrow(1, offset, length)) - i += 1 - } - })) - driverMetrics.add("aggregate gradient time", System.nanoTime() - time) + if (parallelNum != 1) { + Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { + val offset = pOffset + tid * taskSize + math.min(tid, extraTask) + val length = taskSize + (if (tid < extraTask) 1 else 0) + var i = 1 + while (i < finishedGradients.length) { + finishedGradients(0).narrow(1, offset, length) + .add(finishedGradients(i).narrow(1, offset, length)) + i += 1 + } + })) + driverMetrics.add("aggregate gradient time", System.nanoTime() - time) + } val putG = System.nanoTime() // Put first finished model's gradient who aggregated // all other models' gradient to AllReduceParameter diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala index 2bd015fb5c9..49c12367bf0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -506,7 +506,7 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { val gradBias = Tools.getTensor("Bwrd_bn.Grad.4", Array(channel), identity) val bn = new SpatialBatchNormalization(channel, eps = 0.0, momentum = 1.0, - affine = true, initWeight = weight, initBias = bias) + initWeight = weight, initBias = bias) val reorder1 = ReorderMemory(HeapData(shape, Memory.Format.nchw)).setName("reorder1") val reorder2 = ReorderMemory(HeapData(shape, Memory.Format.nchw)).setName("reorder2") @@ -595,7 +595,7 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { val runningVariance = Tools.getTensor("Fwrd_bn.Wght.1", Array(channel), identity) val bn = new SpatialBatchNormalization(channel, eps = 0.0, momentum = 1.0, - affine = true, initWeight = weight, initBias = bias) + initWeight = weight, initBias = bias) bn.runningMean.copy(runningMean) bn.runningVariance.copy(runningVariance) From e69be149c0ad6a2679a23ab0a47e3471b35d1770 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Tue, 9 Oct 2018 15:14:26 +0800 Subject: [PATCH 0833/1065] fix break unit test (#2642) --- .../bigdl/dllib/utils/tf/loaders/ReshapeSpec.scala | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeSpec.scala index 896c07020c2..bb838749429 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ReshapeSpec.scala @@ -30,9 +30,9 @@ class ReshapeSpec extends TensorflowSpecHelper { val shape = Tensor[Int](T(1, 32, 12, 32)) compare[Float]( NodeDef.newBuilder() - .setName("Reshape test") + .setName("ReshapeTest") .putAttr("T", typeAttr(DataType.DT_FLOAT)) - .putAttr("Tshape", typeAttr(DataType.DT_FLOAT)) + .putAttr("Tshape", typeAttr(DataType.DT_INT32)) .setOp("Reshape"), Seq(data, shape), 0 @@ -43,9 +43,9 @@ class ReshapeSpec extends TensorflowSpecHelper { val shape = Tensor[Int](T(1, 32, -1, 32)) compare[Float]( NodeDef.newBuilder() - .setName("Reshape test") + .setName("ReshapeTest") .putAttr("T", typeAttr(DataType.DT_FLOAT)) - .putAttr("Tshape", typeAttr(DataType.DT_FLOAT)) + .putAttr("Tshape", typeAttr(DataType.DT_INT32)) .setOp("Reshape"), Seq(data, shape), 0 From 1190aeff99494b49891faa1a55d772b675083c90 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 11 Oct 2018 12:18:55 +0800 Subject: [PATCH 0834/1065] New parallel optimizer (#2643) * add new parallel optimizer * change infor back to debug for metrics log * refinement per comments * refinement per comments on single model optimization * refinement for sharing common methods * fix style * refinement to reuse duplicate code --- .../dllib/models/utils/ModelBroadcast.scala | 12 +- .../analytics/bigdl/dllib/nn/Container.scala | 3 + .../dllib/nn/abstractnn/AbstractModule.scala | 68 +- .../bigdl/dllib/nn/mkldnn/ConcatTable.scala | 1 + .../bigdl/dllib/nn/mkldnn/Sequential.scala | 2 + .../bigdl/dllib/optim/AbstractOptimizer.scala | 258 ++++++ .../bigdl/dllib/optim/DistriOptimizer.scala | 213 +---- .../bigdl/dllib/optim/ParallelOptimizer.scala | 792 ++++++++++++++++++ .../utils/DistriParameterSynchronizer.scala | 2 +- .../dllib/optim/ParallelOptimizerSpec.scala | 98 +++ 10 files changed, 1246 insertions(+), 203 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizerSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 22bd92f91b3..92a73222bb4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -50,7 +50,7 @@ trait ModelBroadcast[T] extends Serializable { * the gradient is not needed in model inference * @return model */ - def value(initGradient: Boolean = false): Module[T] + def value(initGradient: Boolean = false, shareWeight: Boolean = true): Module[T] def uuid(): String = UUID.randomUUID().toString } @@ -124,7 +124,7 @@ private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = * the gradient is not needed in model inference * @return model */ - override def value(initGradient: Boolean = false): Module[T] = { + override def value(initGradient: Boolean = false, shareWeight: Boolean = true): Module[T] = { CachedModels.deleteAll(uuid) if (applyProtoBuffer) { val localModel = broadcastModel.value.model.clone(false) @@ -140,8 +140,14 @@ private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = val uuid = broadcastModel.value.uuid CachedModels.add(uuid, localModel) + val parameters = if (shareWeight) { + broadcastParameters.value + } else { + SerializationUtils.clone(broadcastParameters.value) + } + // share weight - putWeightBias(broadcastParameters.value, localModel) + putWeightBias(parameters, localModel) // share Consts if (localModel.isInstanceOf[Container[_, _, T]] && broadcastConsts.value.nonEmpty) { putConsts(localModel.asInstanceOf[Container[_, _, T]], broadcastConsts.value) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala index 88f6049500c..eca57b30e4c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Container.scala @@ -231,4 +231,7 @@ abstract class Container[A <: Activity : ClassTag, override def release(): Unit = { modules.foreach(_.release()) } + + override private[bigdl] def updateParameter(): Unit = {} + override private[bigdl] def asyncGradient(): Unit = {} } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index aa7335c8b02..3dc2e4832b0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -254,6 +254,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, final def forward(input: A): B = { val before = System.nanoTime() try { + updateParameter updateOutput(input) } catch { case l: LayerException => @@ -282,10 +283,18 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, updateGradInput(input, gradOutput) accGradParameters(input, gradOutput) backwardTime += System.nanoTime() - before - + asyncGradient gradInput } + private[bigdl] def asyncGradient(): Unit = { + if (this.getParameterSynchronizer() != null) { + if (this.parameters() != null) { + this.getParameterSynchronizer.put(this.getName) + } + } + } + /** * Computes the output using the current parameter set of the class and input. This function * returns the result which is stored in the output field. @@ -1115,5 +1124,62 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * JVM GC can't release them reliably. */ def release(): Unit = {} + + + /** + * parameter synchronizer for gradient synchronization + */ + private var _parameterSynchronizer: DistriParameterSynchronizer[T] = null + + /** + * set parameter synchronizer + * @param parameterSynchronizer parameter synchronizer + */ + private[bigdl] def setParameterSynchronizer(parameterSynchronizer: + DistriParameterSynchronizer[T]): Unit = { + _parameterSynchronizer = parameterSynchronizer + } + + + /** + * get parameter synchronizer + * @return parameter synchronizer + */ + private[bigdl] def getParameterSynchronizer(): + DistriParameterSynchronizer[T] = _parameterSynchronizer + + + private var _optimMethod: OptimMethod[T] = null + + /** + * set optim method + */ + + private[bigdl] def setOptimMethod(optimMethod: OptimMethod[T]): Unit = { + _optimMethod = optimMethod + } + + /** + * get optim method for layer + */ + + private[bigdl] def getOptimMethod(): OptimMethod[T] = _optimMethod + + private[bigdl] def updateParameter(): Unit = { + if (this.getParameterSynchronizer() != null && this.isTraining) { + if (this.parameters() != null) { + val before = System.nanoTime() + val (weights, grads) = this.getParameterSynchronizer.get(this.getName) + val syncEndTime = System.nanoTime() + if (grads != null) { + val optimMethod = this.getOptimMethod + require(optimMethod != null, s"optim method for ${this.getName} cannot be null") + optimMethod.optimize(_ => (ev.fromType(0.0f), grads), + weights) + this.zeroGradParameters + } + } + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala index b05f34e8a24..3887c011fba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala @@ -58,6 +58,7 @@ class ConcatTable extends MklDnnContainer { var i = 0 while (i < modules.length) { modules(i).accGradParameters(input, gradOutput.toTable(i + 1)) + modules(i).asyncGradient i += 1 } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala index c4182bd426c..d0c45f38d52 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala @@ -139,6 +139,7 @@ class Sequential extends MklDnnContainer { modules(i - 1).output ) currentModule.accGradParameters(curInput, lastGradInput) + currentModule.asyncGradient lastGradInput = reorderManager.infer( mklDnnModules(i).gradInputFormats(), mklDnnModules(i - 1).gradOutputWeightFormats(), @@ -148,6 +149,7 @@ class Sequential extends MklDnnContainer { } modules(i).accGradParameters(input, lastGradInput) + modules(i).asyncGradient } override private[mkldnn] def inputFormats() = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala new file mode 100644 index 00000000000..7e3db4da35d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala @@ -0,0 +1,258 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.dataset.{DistributedDataSet, _} +import com.intel.analytics.bigdl.{DataSet, Module} +import com.intel.analytics.bigdl.optim.DistriOptimizer.{Cache, logger} +import com.intel.analytics.bigdl.optim.Optimizer.{saveModel, saveOptimMethod} +import com.intel.analytics.bigdl.parameters.AllReduceParameter +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, MklDnn, Table} +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} +import org.apache.spark.rdd.{RDD, ZippedPartitionsWithLocalityRDD} + +import scala.reflect.ClassTag + +abstract class AbstractOptimizer { + + protected def getModel[T: ClassTag]( + models: RDD[Cache[T]], + parameters: Map[String, AllReduceParameter[T]], + trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Module[T] + + /** + * Save train summaries. + * @param trainSummary train logger + * @param models cached models + * @param driverState driver state + * @param parameters [[AllReduceParameter]] + */ + protected def saveSummary[T: ClassTag]( + trainSummary: TrainSummary, + models: RDD[Cache[T]], + driverState: Table, + parameters: Map[String, AllReduceParameter[T]], + trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { + val currentIteration = driverState[Int]("neval") - 1 + val parametersTrigger = trainSummary.getSummaryTrigger("Parameters") + if (parametersTrigger.isDefined && parametersTrigger.get(driverState)) { + val model = getModel(models, parameters, trainingModel) + val parametersTable = model.getParametersTable() + // Parallelize to create Histogram. + Engine.default.invokeAndWait( + parametersTable.keySet.toSeq.map(moduleName => () => { + val paramTable = parametersTable[Table](moduleName) + paramTable.keySet.foreach { paramName => + trainSummary.addHistogram( + s"$moduleName/$paramName", paramTable[Tensor[T]](paramName), currentIteration)} + })) + } + val scalarTrigger = trainSummary.getScalarTriggers() + // Not parallelizable, because driverState is changing each iteration. + scalarTrigger.foreach { v => + if (v._2(driverState)) { + // TODO: Support show learningrate for multiOptimMethod + require(driverState.contains(v._1), s"DistriOptimizer.saveSummary: Summary ${v._1} " + + s"is not supported now.") + trainSummary.addScalar( + v._1, driverState[Float](v._1), currentIteration + ) + } + } + } + + + /** + * Validate current model and save the result. + * @param validationTrigger validation trigger + * @param validationDataSet validation dataset + * @param validationMethods validation methods + * @param coresPerNode cores per node + * @param models cached models + * @param state state table + * @param validationSummary validation logger. + * @param header log header string + */ + protected def validate[T](validationTrigger: Option[Trigger], + validationDataSet: Option[DataSet[MiniBatch[T]]], + validationMethods: Option[Array[ValidationMethod[T]]], + coresPerNode: Int, + models: RDD[Cache[T]], + state: Table, + validationSummary: Option[ValidationSummary], + header: String): Unit = { + if (validationTrigger.isEmpty || validationDataSet.isEmpty) { + return + } + val trigger = validationTrigger.get + if (!trigger(state)) { + return + } + val vMethods = validationMethods.get + val validateRDD = validationDataSet.get.toDistributed().data(train = false) + logger.info(s"$header Validate model...") + val _subModelNumber = Engine.getEngineType match { + case MklBlas => coresPerNode + case MklDnn => 1 + case _ => throw new IllegalArgumentException + } + val results = ZippedPartitionsWithLocalityRDD(models, validateRDD)((modelIter, dataIter) => { + val cached = modelIter.next() + val vMethodsArr = cached.localMethods + val workingModels = cached.localModels + + workingModels.foreach(_.evaluate()) + dataIter.map(batch => { + val stackSize = batch.size() / _subModelNumber + val extraSize = batch.size() % _subModelNumber + val parallelism = if (stackSize == 0) extraSize else _subModelNumber + Engine.default.invokeAndWait( + (0 until parallelism).map(b => + () => { + val offset = b * stackSize + math.min(b, extraSize) + 1 + val length = stackSize + (if (b < extraSize) 1 else 0) + val miniBatch = batch.slice(offset, length) + val input = miniBatch.getInput() + val target = miniBatch.getTarget() + val output = workingModels(b).forward(input) + val validatMethods = vMethodsArr(b).get + validatMethods.map(validation => { + validation(output, target) + }) + } + ) + ).reduce((left, right) => { + left.zip(right).map { case (l, r) => + l + r + } + }) + }) + }).reduce((left, right) => { + left.zip(right).map { case (l, r) => + l + r + } + }).zip(vMethods) + results.foreach(r => { + logger.info(s"$header ${r._2} is ${r._1}") + }) + state("score") = results(0)._1.result._1 + if(validationSummary.isDefined) { + results.foreach { r => + val result = r._1.result + validationSummary.get.addScalar(r._2.toString(), result._1, + state[Int]("neval") - 1 + ) + } + } + } + + /** + ** Create checkpoint. + * @param cacheTrigger cache trigger + * @param cachePath cache path + * @param isOverWrite whether over write + * @param wallClockTime wall clock time + * @param models cached models + * @param state state table + * @param parameters all reduce parameters + * @param optimMethods all optim methods + * @param trainingModel training model + */ + protected def checkpoint[T: ClassTag]( + cacheTrigger: Option[Trigger], + cachePath: Option[String], + isOverWrite: Boolean, + wallClockTime: Long, + models: RDD[Cache[T]], + state: Table, + parameters: Map[String, AllReduceParameter[T]], + optimMethods: Map[String, OptimMethod[T]], + trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { + cacheTrigger.foreach { trigger => + cachePath.foreach { path => + if (trigger(state)) { + saveModel(getModel(models, parameters, trainingModel), cachePath, isOverWrite, + s".${state[Int]("neval")}") + logger.info(s"[Wall Clock ${wallClockTime / 1e9}s] Save model to $path") + optimMethods.foreach{case (name, optimMethod) => + optimMethod.state.update("epoch", state[Int]("epoch")) + optimMethod.state.update("neval", state[Int]("neval")) + saveOptimMethod(optimMethod, cachePath, isOverWrite, s"-$name.${state[Int]("neval")}") + logger.info(s"[Wall Clock ${wallClockTime / 1e9}s] Save optimMethod " + + s"${optimMethod} to $path") + } + } + } + } + } + + /** + * Clean some internal states, so this or other optimizers can run optimize again + * This method will be called at the end of optimize. You need not call it if optimize succeed. + * If the optimize fails, you may call it before next optimize. + */ + private[bigdl] def clearState[T: ClassTag](models: RDD[DistriOptimizer.Cache[T]]) : Unit = { + // Reset the singleton flag, so other optimizers can run + models.mapPartitions(iter => { + Engine.resetSingletonFlag() + iter + }).count() + } + + private[bigdl] def endEpoch[T: ClassTag](optimMethods: Map[String, OptimMethod[T]]): Unit = { + optimMethods.foreach { case (moduleName, optimMethod) => + val records = optimMethod.state.get[Int]("recordsProcessedThisEpoch") + if (records.isDefined && records.get != 0) { + optimMethod.state("epoch") = optimMethod.state[Int]("epoch") + 1 + optimMethod.state("recordsProcessedThisEpoch") = 0 + } + } + } + + private[bigdl] def setTrainData[T: ClassTag]( + sampleRDD: RDD[Sample[T]], + batchSize: Int, + miniBatch: MiniBatch[T])(implicit ev: TensorNumeric[T]) + : DistributedDataSet[MiniBatch[T]] = { + (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(miniBatch, batchSize, None)) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + } + + private[bigdl] def setTrainData[T: ClassTag](sampleRDD: RDD[Sample[T]], + batchSize: Int, + featurePaddingParam: PaddingParam[T] = null, + labelPaddingParam: PaddingParam[T] = null)(implicit ev: TensorNumeric[T]) + : DistributedDataSet[MiniBatch[T]] = { + val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None + val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None + (DataSet.rdd(sampleRDD) -> + SampleToMiniBatch(batchSize, _featurePaddingParam, _labelPaddingParam)) + .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + } + + + private[bigdl] def prepareInput[T: ClassTag](dataset: DataSet[MiniBatch[T]], + validationDataSet: Option[DataSet[MiniBatch[T]]]): Unit = { + dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].cache() + if (validationDataSet.isDefined) { + validationDataSet.get.toDistributed().cache() + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index e073b5e8747..4f90fb193c4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -33,6 +33,7 @@ import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.MklDnnContainer import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.optim.DistriOptimizer.{Cache, getModel} import org.apache.commons.lang.exception.ExceptionUtils import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.Logger @@ -45,7 +46,7 @@ import scala.collection.mutable.ArrayBuffer import scala.concurrent.Future import scala.reflect.ClassTag -object DistriOptimizer { +object DistriOptimizer extends AbstractOptimizer { import Optimizer._ val logger: Logger = Logger.getLogger(getClass) @@ -61,6 +62,7 @@ object DistriOptimizer { * @param moduleTimeList module running time * @param localMethods cached validation methods * @param optimMethods cached optim methods + * @param parameterSynchronizer cached parameter synchronizer * @tparam T Tensor element type */ case class Cache[T]( @@ -71,7 +73,8 @@ object DistriOptimizer { localStates: Array[Table], var moduleTimeList: Array[Long] = null, localMethods: Array[Option[Array[ValidationMethod[T]]]], - optimMethods: Map[String, OptimMethod[T]] + optimMethods: Map[String, OptimMethod[T]], + parameterSynchronizer: DistriParameterSynchronizer[T] = null ) /** @@ -493,87 +496,6 @@ object DistriOptimizer { } } - /** - * Create checkpoint. - * - * @param cacheTrigger cache trigger - * @param cachePath cache path - * @param isOverWrite whether over write - * @param wallClockTime wall clock time - * @param models cached models - * @param state state table - * @param parameters all reduce parameters - */ - private def checkpoint[T: ClassTag]( - cacheTrigger: Option[Trigger], - cachePath: Option[String], - isOverWrite: Boolean, - wallClockTime: Long, - models: RDD[Cache[T]], - state: Table, - parameters: Map[String, AllReduceParameter[T]], - optimMethods: Map[String, OptimMethod[T]], - trainingModel: Module[T]): Unit = { - cacheTrigger.foreach { trigger => - cachePath.foreach { path => - if (trigger(state)) { - saveModel(getModel(models, parameters, trainingModel), cachePath, isOverWrite, - s".${state[Int]("neval")}") - logger.info(s"[Wall Clock ${wallClockTime / 1e9}s] Save model to $path") - optimMethods.foreach{case (name, optimMethod) => - optimMethod.state.update("epoch", state[Int]("epoch")) - optimMethod.state.update("neval", state[Int]("neval")) - saveOptimMethod(optimMethod, cachePath, isOverWrite, s"-$name.${state[Int]("neval")}") - logger.info(s"[Wall Clock ${wallClockTime / 1e9}s] Save optimMethod " + - s"${optimMethod} to $path") - } - } - } - } - } - - /** - * Save train summaries. - * - * @param trainSummary train logger - * @param models cached models - * @param driverState driver state - * @param parameters [[AllReduceParameter]] - */ - private def saveSummary[T: ClassTag]( - trainSummary: TrainSummary, - models: RDD[Cache[T]], - driverState: Table, - parameters: Map[String, AllReduceParameter[T]], - trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { - val currentIteration = driverState[Int]("neval") - 1 - val parametersTrigger = trainSummary.getSummaryTrigger("Parameters") - if (parametersTrigger.isDefined && parametersTrigger.get(driverState)) { - val model = getModel(models, parameters, trainingModel) - val parametersTable = model.getParametersTable() - // Parallelize to create Histogram. - Engine.default.invokeAndWait( - parametersTable.keySet.toSeq.map(moduleName => () => { - val paramTable = parametersTable[Table](moduleName) - paramTable.keySet.foreach { paramName => - trainSummary.addHistogram( - s"$moduleName/$paramName", paramTable[Tensor[T]](paramName), currentIteration)} - })) - } - val scalarTrigger = trainSummary.getScalarTriggers() - // Not parallelizable, because driverState is changing each iteration. - scalarTrigger.foreach { v => - if (v._2(driverState)) { - // TODO: Support show learningrate for multiOptimMethod - require(driverState.contains(v._1), s"DistriOptimizer.saveSummary: Summary ${v._1} " + - s"is not supported now.") - trainSummary.addScalar( - v._1, driverState[Float](v._1), currentIteration - ) - } - } - } - /** * Init engine and cache models, weights, gradients, criterions, state tables * and validation methods on worker nodes. @@ -694,93 +616,6 @@ object DistriOptimizer { } } - /** - * Validate current model and save the result. - * - * @param validationTrigger validation trigger - * @param validationDataSet validation dataset - * @param validationMethods validation methods - * @param coresPerNode cores per node - * @param models cached models - * @param state state table - * @param validationSummary validation logger. - * @param header log header string - */ - private def validate[T]( - validationTrigger: Option[Trigger], - validationDataSet: Option[DataSet[MiniBatch[T]]], - validationMethods: Option[Array[ValidationMethod[T]]], - coresPerNode: Int, - models: RDD[Cache[T]], - state: Table, - validationSummary: Option[ValidationSummary], - header: String - ): Unit = { - if (validationTrigger.isEmpty || validationDataSet.isEmpty) { - return - } - val trigger = validationTrigger.get - if (!trigger(state)) { - return - } - val vMethods = validationMethods.get - val validateRDD = validationDataSet.get.toDistributed().data(train = false) - logger.info(s"$header Validate model...") - val _subModelNumber = Engine.getEngineType match { - case MklBlas => coresPerNode - case MklDnn => 1 - case _ => throw new IllegalArgumentException - } - val results = ZippedPartitionsWithLocalityRDD(models, validateRDD)((modelIter, dataIter) => { - val cached = modelIter.next() - val vMethodsArr = cached.localMethods - val workingModels = cached.localModels - - workingModels.foreach(_.evaluate()) - dataIter.map(batch => { - val stackSize = batch.size() / _subModelNumber - val extraSize = batch.size() % _subModelNumber - val parallelism = if (stackSize == 0) extraSize else _subModelNumber - Engine.default.invokeAndWait( - (0 until parallelism).map(b => - () => { - val offset = b * stackSize + math.min(b, extraSize) + 1 - val length = stackSize + (if (b < extraSize) 1 else 0) - val miniBatch = batch.slice(offset, length) - val input = miniBatch.getInput() - val target = miniBatch.getTarget() - val output = workingModels(b).forward(input) - val validatMethods = vMethodsArr(b).get - validatMethods.map(validation => { - validation(output, target) - }) - } - ) - ).reduce((left, right) => { - left.zip(right).map { case (l, r) => - l + r - } - }) - }) - }).reduce((left, right) => { - left.zip(right).map { case (l, r) => - l + r - } - }).zip(vMethods) - results.foreach(r => { - logger.info(s"$header ${r._2} is ${r._1}") - }) - state("score") = results(0)._1.result._1 - if(validationSummary.isDefined) { - results.foreach { r => - val result = r._1.result - validationSummary.get.addScalar(r._2.toString(), result._1, - state[Int]("neval") - 1 - ) - } - } - } - /** * Fetch current model parameters to driver, and copy to trainingModel. * @@ -789,10 +624,10 @@ object DistriOptimizer { * @param trainingModel the model is trained by optimizer * @return trained model */ - private def getModel[T: ClassTag]( + override protected def getModel[T: ClassTag]( models: RDD[Cache[T]], parameters: Map[String, AllReduceParameter[T]], - trainingModel: Module[T]): Module[T] = { + trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = { val partitionNum = models.partitions.length val extraState = models.map(_.localModels.head.getExtraParameter()).first() trainingModel.setExtraParameter(extraState) @@ -829,6 +664,7 @@ object DistriOptimizer { trainingModel } + } /** @@ -859,29 +695,17 @@ class DistriOptimizer[T: ClassTag] ( * If the optimize fails, you may call it before next optimize. */ def clearState() : Unit = { - // Reset the singleton flag, so other optimizers can run - models.mapPartitions(iter => { - Engine.resetSingletonFlag() - iter - }).count() + DistriOptimizer.clearState(models) } private def endEpoch(): Unit = { - optimMethods.foreach { case (moduleName, optimMethod) => - val records = optimMethod.state.get[Int]("recordsProcessedThisEpoch") - if (records.isDefined && records.get != 0) { - optimMethod.state("epoch") = optimMethod.state[Int]("epoch") + 1 - optimMethod.state("recordsProcessedThisEpoch") = 0 - } - } + DistriOptimizer.endEpoch(optimMethods) } override def setTrainData(sampleRDD: RDD[Sample[T]], batchSize: Int, miniBatch: MiniBatch[T]): this.type = { - this.dataset = (DataSet.rdd(sampleRDD) -> - SampleToMiniBatch(miniBatch, batchSize, None)) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + this.dataset = DistriOptimizer.setTrainData(sampleRDD, batchSize, miniBatch) // if current epoch is not finished, we will end the // current epoch and start a new epoch when optimize is called endEpoch() @@ -894,25 +718,18 @@ class DistriOptimizer[T: ClassTag] ( labelPaddingParam: PaddingParam[T] = null) : this.type = { val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None - dataset = (DataSet.rdd(sampleRDD) -> - SampleToMiniBatch(batchSize, _featurePaddingParam, _labelPaddingParam)) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + this.dataset = DistriOptimizer.setTrainData(sampleRDD, batchSize, + featurePaddingParam, labelPaddingParam) // if current epoch is not finished, we will end the // current epoch and start a new epoch when optimize is called endEpoch() this } - override def prepareInput(): Unit = { - import DistriOptimizer._ if (!dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].isCached) { - logger.info("caching training rdd ...") - dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].cache() - // FIXME dnn model must cache val dataset first, otherwise there will be a segment fault. - if (validationDataSet.isDefined) { - validationDataSet.get.toDistributed().cache() - } + DistriOptimizer.logger.info("caching training rdd ...") + DistriOptimizer.prepareInput(this.dataset, this.validationDataSet) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala new file mode 100644 index 00000000000..efbb2b1001c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala @@ -0,0 +1,792 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.{Module, _} +import com.intel.analytics.bigdl.dataset._ +import com.intel.analytics.bigdl.nn.{Container, Graph, Module, Sequential, Utils} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils._ +import java.io.{File, FilenameFilter} +import java.text.SimpleDateFormat +import java.util.Calendar + +import com.intel.analytics.bigdl.models.utils.ModelBroadcast +import com.intel.analytics.bigdl.nn.mkldnn.MklDnnContainer +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.optim.DistriOptimizer._ +import com.intel.analytics.bigdl.parameters.AllReduceParameter +import org.apache.commons.lang.exception.ExceptionUtils +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} +import org.apache.log4j.Logger +import org.apache.spark.TaskContext +import org.apache.spark.rdd.{RDD, ZippedPartitionsWithLocalityRDD} + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.concurrent.Future +import scala.reflect.{ClassTag, classTag} + +object ParallelOptimizer extends AbstractOptimizer { + import Optimizer._ + + val logger: Logger = Logger.getLogger(getClass) + + /** + * Train the model. + * + * @param dataset train dataset + * @param coresPerNode cores per node + * @param state state table + * @param endWhen trigger to stop training + * @param metrics metrics + * @param models cached models + * @param optimMethods optimization methods + * @param validationTrigger validation trigger + * @param validationDataSet validation dataset + * @param validationMethods validation methods + * @param cacheTrigger cache trigger + * @param cachePath cache path + * @param trainSummary train summary + * @param validationSummary validation summary + * @param isOverWrite if overwrite the checkpoint + */ + private[optim] def optimize[T: ClassTag]( + trainingModel: Module[T], + dataset: DistributedDataSet[MiniBatch[T]], + coresPerNode: Int, + state: Table, + endWhen: Trigger, + metrics: Metrics, + models: RDD[Cache[T]], + optimMethods: Map[String, OptimMethod[T]], + validationTrigger: Option[Trigger], + validationDataSet: Option[DataSet[MiniBatch[T]]], + validationMethods: Option[Array[ValidationMethod[T]]], + cacheTrigger: Option[Trigger], + cachePath: Option[String], + trainSummary: Option[TrainSummary], + validationSummary: Option[ValidationSummary], + isOverWrite: Boolean + )(implicit ev: TensorNumeric[T]): Unit = { + val sc = dataset.originRDD().sparkContext + val partitionNum = dataset.originRDD().partitions.length + var wallClockTime = 0L + var lastEpochTime = 0L + + // driverState is needed to prevent serializing the whole optimizer + optimMethods.values.foreach{ optimMethod => + if (!optimMethod.state.contains("epoch")) optimMethod.state.update("epoch", 1) + if (!optimMethod.state.contains("neval")) optimMethod.state.update("neval", 1) + if (!optimMethod.state.contains("Loss")) { + optimMethod.state.update("Loss", Float.PositiveInfinity) + } + if (!optimMethod.state.contains("score")) optimMethod.state.update("score", 0f) + if (!optimMethod.state.contains("recordsProcessedThisEpoch")) { + optimMethod.state.update("recordsProcessedThisEpoch", 0) + } + } + + val _subModelNumber = Engine.getEngineType() match { + case MklBlas => coresPerNode + case MklDnn => 1 + } + + require(_subModelNumber == 1, "currently only single model supported especially for mkldnn") + + val driverState = T( + "epoch" -> optimMethods.values.head.state("epoch"), + "neval" -> optimMethods.values.head.state("neval"), + "Loss" -> optimMethods.values.head.state("Loss"), + "score" -> optimMethods.values.head.state("score"), + "parallelism" -> _subModelNumber + ) + logger.info("Count dataset") + val countBefore = System.nanoTime() + val numSamples = dataset.data(train = false).map(_.size()).reduce(_ + _) + val countAfter = System.nanoTime() + logger.info(s"Count dataset complete. Time elapsed: ${(countAfter - countBefore) / 1e9}s") + if (numSamples != dataset.size()) { + logger.warn("If the dataset is built directly from RDD[Minibatch], the data in each " + + "minibatch is fixed, and a single minibatch is randomly selected in each partition. If " + + "the dataset is transformed from RDD[Sample], each minibatch will be constructed on the " + + "fly from random samples, which is better for convergence.") + } + + logger.info(s"config $state") + var recordsProcessedThisEpoch = optimMethods.values.head.state[Int]("recordsProcessedThisEpoch") + if (recordsProcessedThisEpoch == 0) { + val shuffleBefore = System.nanoTime() + logger.info("Shuffle data") + dataset.shuffle() + val shuffleEnd = System.nanoTime() + logger.info(s"Shuffle data complete. Takes ${(shuffleEnd - shuffleBefore) / 1e9}s") + } + + var tasks: ArrayBuffer[Future[_]] = new ArrayBuffer() + var threshold = Long.MaxValue + var timeout = Long.MaxValue + var iteration = 0 + val dropPercentage = state.get[Double]("dropPercentage").get + val warmupIterationNum = state.get[Int]("warmupIterationNum").get + val computeThresholdbatchSize = state.get[Int]("computeThresholdbatchSize").get + val maxDropPercentage = state.get[Double]("maxDropPercentage").get + val iterationPerTime = System.getProperty("bigdl.parallelOptimizer." + + "iterationPerTime", "1").toInt + val driverSubModelNum = partitionNum * _subModelNumber * iterationPerTime + var dropModelNumBatch = 0 + var lossArray = new Array[Double](_subModelNumber) + + var epochStart = System.nanoTime() + var dataRDD = dataset.data(train = true) + + while (!endWhen(driverState)) { + var lossSum = 0.0 + var recordsNum = 0 + metrics.set("computing time for each node", mutable.ArrayBuffer[Double](), sc) + metrics.set("computing time average", 0.0, sc, partitionNum) + + val driverMetrics = metrics + val start = System.nanoTime() + /* + Run the forwards/backwards pass using multiple threads in each partition, and track the + number of model updates that finished before the thread timeout mechanism. + */ + val (numFinishedModelUpdates, localLossSum, localRecordsNum) = dataRDD + .zipPartitions(models, preservesPartitioning = true) { (data, modelIter) => { + var count = 0 + var finishedThreadSize = 0 + val cached = modelIter.next() + // val miniBatchBuffer = new Array[MiniBatch[T]](_subModelNumber) + var miniBatch: MiniBatch[T] = null + while (count < iterationPerTime) { + val syWStart = System.nanoTime() + miniBatch = data.next() + // ======================Start train models=================================== + var time = System.nanoTime() + if (dropPercentage > 0.0 && iteration > warmupIterationNum + + computeThresholdbatchSize - 1) { + timeout = threshold + } + val pre = (iteration % computeThresholdbatchSize) * _subModelNumber + val trainingThreads = Engine.default.invokeAndWait2(Seq(() => { + val trainStart = System.nanoTime() + val localModel = cached.localModels(0) + localModel.training() + val localCriterion = cached.localCriterions(0) + val input = miniBatch.getInput() + val target = miniBatch.getTarget() + val output = localModel.forward(input) + lossArray(0) = ev.toType[Double](localCriterion.forward(output, target)) + val errors = localCriterion.backward(output, target) + localModel.backward(input, errors) + cached.moduleTimeList(0 + pre) = System.nanoTime() - trainStart + 0 + }), timeout) + val computingTime = System.nanoTime() - time + driverMetrics.add("computing time average", computingTime) + driverMetrics.add("computing time for each node", computingTime) + + val finishedThreads = trainingThreads.filter(!_.isCancelled).map(_.get()) + val currFinishedSize = finishedThreads.size + finishedThreadSize += currFinishedSize + recordsNum += currFinishedSize * miniBatch.size + var i = 0 + while (i < currFinishedSize) { + lossSum += lossArray(finishedThreads(i)) + i += 1 + } + count +=1 + } + val end = System.nanoTime() + wallClockTime += end - start + Iterator.single(finishedThreadSize, lossSum, recordsNum) + } + }.reduce((a, b) => (a._1 + b._1, a._2 + b._2, a._3 + b._3)) + + dropModelNumBatch += (driverSubModelNum - numFinishedModelUpdates) + + if (dropPercentage == 0.0 || + numFinishedModelUpdates >= driverSubModelNum * (1.0 - maxDropPercentage)) { + driverState("numFinishedModel") = numFinishedModelUpdates + recordsProcessedThisEpoch += localRecordsNum + val end = System.nanoTime() + wallClockTime += end - start + driverState("Loss") = localLossSum / numFinishedModelUpdates + optimMethods.foreach{ v => + v._2.updateHyperParameter() + } + + driverState(s"LearningRate") = optimMethods.head._2.getLearningRate().toFloat + + driverState("Throughput") = localRecordsNum.toFloat / ((end - start) / 1e9f) + val _header = header(driverState[Int]("epoch"), recordsProcessedThisEpoch, numSamples, + driverState[Int]("neval"), wallClockTime) + logger.info(s"${_header} Trained ${localRecordsNum} records in ${(end - start) / 1e9} " + + s"seconds. Throughput is ${driverState("Throughput")} records/second. Loss is ${ + driverState("Loss")}.") + logger.debug("\n" + metrics.summary()) + logger.debug("Dropped modules: " + (driverSubModelNum - numFinishedModelUpdates)) + lossArray = new Array[Double](_subModelNumber) + + iteration += 1 + if (dropPercentage > 0.0 && iteration > warmupIterationNum && + iteration % computeThresholdbatchSize == 0) { + val moduleTimeList = models.mapPartitions { iter => + iter.next().moduleTimeList.iterator + }.collect() + + val k = (dropPercentage * computeThresholdbatchSize * driverSubModelNum).toInt + if (k > dropModelNumBatch) { + threshold = Util.kthLargest(moduleTimeList, 0, moduleTimeList.length-1, + k - dropModelNumBatch) + } else { + threshold = (threshold * 1.01).toLong + } + logger.info("threshold: " + threshold) + + // clear moduleTimeList in each node + models.mapPartitions { iter => + val timeList = iter.next.moduleTimeList + var i = 0 + while (i < timeList.length) { + timeList(i) = 0 + i += 1 + } + Iterator.empty + }.count() + dropModelNumBatch = 0 + } + driverState("neval") = driverState[Int]("neval") + iterationPerTime + if (recordsProcessedThisEpoch >= numSamples) { + // Epoch is finished + val epochEnd = System.nanoTime() + wallClockTime = lastEpochTime + epochEnd - epochStart + lastEpochTime = wallClockTime + epochStart = System.nanoTime() + logger.info(s"${_header} Epoch finished. Wall clock time is ${wallClockTime / 1e6} ms") + + driverState("epoch") = driverState[Int]("epoch") + 1 + dataset.shuffle() + dataRDD = dataset.data(train = true) + recordsProcessedThisEpoch = 0 + } + + optimMethods.map { case (moduleName, optimMethod) => + optimMethod.state.update("recordsProcessedThisEpoch", recordsProcessedThisEpoch) + optimMethod.state.update("epoch", driverState[Int]("epoch")) + optimMethod.state.update("neval", driverState[Int]("neval")) + optimMethod.state.update("Loss", driverState[Float]("Loss")) + if (validationMethods.isDefined) { + optimMethod.state.update("score", driverState[Float]("score")) + } + } + + // update parameters for last iteration + if (endWhen(driverState)) { + logger.info(s"training finished, updating all layers parameters") + models.mapPartitions(modelIter => { + val localModels = modelIter.next.localModels + val updateTaskes = localModels.map(localModel => () => { + updateLayerParameters(localModel) + }) + Engine.default.invokeAndWait2(updateTaskes) + Iterator.empty + }).collect + } + validate( + validationTrigger, + validationDataSet, + validationMethods, + coresPerNode, + models, + driverState, + validationSummary, + _header + ) + + trainSummary.foreach { summary => + saveSummary( + summary, + models, + driverState, + null, + trainingModel + ) + } + + checkpoint( + cacheTrigger, + cachePath, + isOverWrite, + wallClockTime, + models, + driverState, + null, + optimMethods, + trainingModel + ) + } else { + logger.info(s"Warning! Not enough training samples were successfully processed in this " + + s"iteration due to some slow tasks. The gradients computed in this iteration will be " + + s"discarded. Only $numFinishedModelUpdates/$driverSubModelNum threads successfully " + + s"completed training.") + } + } + } + + private def updateLayerParameters[T: ClassTag](module: Module[T]): Unit = { + module.updateParameter + if (module.isInstanceOf[Container[_, _, T]]) { + module.asInstanceOf[Container[_, _, T]].modules.foreach(sub => { + updateLayerParameters(sub) + }) + } + } + + /** + * Init engine and cache models, weights, gradients, criterions, state tables + * and validation methods on worker nodes. + * + * @param model train model + * @param dataset train dataset + * @param criterion loss function + * @param state state table + * @param nodeNumber node number + * @param coresPerNode cores per node + * @param checkSingleton if checkSingleton + * @param validationMethods validation methods + * @param optimMethod optimization method + * @return cached models + */ + private def initThreadModels[T: ClassTag]( + model: Module[T], + dataset: DistributedDataSet[MiniBatch[T]], + criterion: Criterion[T], + state: Table, + nodeNumber: Int, + coresPerNode: Int, + checkSingleton: Boolean, + validationMethods: Option[Array[ValidationMethod[T]]], + optimMethod: Map[String, OptimMethod[T]], + priorities: mutable.Map[String, Int] + )(implicit ev: TensorNumeric[T]) = { + val sc = dataset.originRDD().sparkContext + val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod)) + val modelBroadcast = ModelBroadcast[T]().broadcast(sc, model) + model.getParameters() + val _subModelNumber = Engine.getEngineType match { + case MklBlas => coresPerNode + case MklDnn => 1 + } + + require(dataset.originRDD().partitions.length == nodeNumber, + s"Passed in rdd partition number ${dataset.originRDD().partitions.length}" + + s" is not equal to configured node number ${nodeNumber}") + + val computeThresholdbatchSize = state.get[Int]("computeThresholdbatchSize").get + val nExecutor = Engine.nodeNumber() + + val parameterBlocks = System.getProperty("bigdl.parallelOptimizer." + + "parameterBlocks", "10").toInt + + val models = dataset.originRDD().mapPartitions(_ => { + val partitionId = TaskContext.getPartitionId + val (broadcastCriterion, broadcastState, broadcastMethod, + broadcastOptim) = broadcast.value + if (!Engine.checkSingleton()) { + if (checkSingleton) { + require(Engine.checkSingleton(), "Partitions of the training data are not evenly" + + "distributed across the executors in the Spark cluster; are there sufficient training" + + "data to be distributed? Set property \"bigdl.check.singleton\" to false to skip " + + "this check") + } else { + logger.warn("Partitions of the training data are not evenly" + + "distributed across the executors in the Spark cluster; are there sufficient training" + + "data to be distributed?") + } + } + Engine.setNodeAndCore(nExecutor, coresPerNode) + // initialize synchronizer with partition ID and parition number + val synchronizer = new BlockManagerParameterSynchronizer[T](partitionId, nExecutor) + val cached = (0 until _subModelNumber).map { _ => + val localModel = modelBroadcast.value(true, false) + localModel match { + case container: MklDnnContainer => container.compile(TrainingPhase) + case _ => + } + // differentiate partition models from each other by partition ID + setModelId(localModel, partitionId) + // set parameter synchronizer + setDistriPartitionsynchronizer(localModel, synchronizer, new mutable.HashMap[Int, Int](), + parameterBlocks) + val localCriterion = broadcastCriterion.cloneCriterion() + val localState = broadcastState.clone() + val localMethod = if (broadcastMethod.isDefined) { + Some(broadcastMethod.get.map(_.clone())) + } else None + (localModel, Tensor[T](0), Tensor[T](0), localCriterion, localState, localMethod) + }.toArray + + logger.info("model thread pool size is " + Engine.model.getPoolSize) + val weights = cached.head._2 + Iterator.single(Cache( + cached.map(_._1), // models + cached.map(_._2), // weights + cached.map(_._3), // gradients + cached.map(_._4), // criterions + cached.map(_._5), // states + new Array[Long](_subModelNumber * computeThresholdbatchSize), + cached.map(_._6), + broadcastOptim.map(v => (v._1, v._2.clone())), + synchronizer + )) + }).persist() + models.setName("Thread Model RDD") + logger.info("Cache thread models...") + models.count() + logger.info("Cache thread models... done") + models + } + + private def getExecutionOrder[T: ClassTag](module : Module[T]): ArrayBuffer[Module[T]] = { + val res = new ArrayBuffer[Module[T]] + if (module.isInstanceOf[Container[_, _, T]]) { + val subModules = module.asInstanceOf[Container[_, _, T]].modules + subModules.foreach(sub => { + res ++= getExecutionOrder(sub) + }) + } else { + if (module.parameters() != null) { + res += module + } + } + res + } + + private def setDistriPartitionsynchronizer[T: ClassTag](model: Module[T], + parameterSynchronizer: DistriParameterSynchronizer[T], + barrierLayers: mutable.Map[Int, Int], slices: Int): Unit = { + val globalWeights = model.getParameters()._1 + val globalGrads = model.getParameters()._2 + val totalSize = globalGrads.nElement + val executorOrders = getExecutionOrder(model) + var i = executorOrders.length - 1 + val size = totalSize / slices - 1 + val extraSize = totalSize - size * (slices - 1) + var lastOffSet = totalSize + while (i >= 0) { + val currModule = executorOrders(i) + if (currModule.parameters() != null) { + val grads = currModule.getParameters()._1 + val offSet = grads.storageOffset - 1 + val index = if (offSet == 0) 0 else (offSet - 1) / size + 1 + val currParSize = lastOffSet - offSet + if (index < slices) { + if (!barrierLayers.contains(index)) { + barrierLayers.put(index, offSet) + val weightsPar = globalWeights.narrow(1, offSet + 1, currParSize) + val gradsPar = globalGrads.narrow(1, offSet + 1, currParSize) + parameterSynchronizer.init(currModule.getName, currParSize, + executorOrders.length - i, weightsPar, gradsPar) + currModule.setParameterSynchronizer(parameterSynchronizer) + lastOffSet = offSet + } + } + } + i -= 1 + } + } + + private def setModelId[T: ClassTag](model: Module[T], partitionId: Int): Unit = { + model.setId(partitionId) + + if (model.isInstanceOf[Container[_, _, T]]) { + model.asInstanceOf[Container[_, _, T]].modules. + foreach(sub => setModelId(sub, partitionId)) + } + } + + /** + * Fetch current model parameters to driver, and copy to trainingModel. + * + * @param models cached models + * @param trainingModel the model is trained by optimizer + * @return trained model + */ + override protected def getModel[T: ClassTag]( + models: RDD[Cache[T]], + parameters: Map[String, AllReduceParameter[T]], + trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val partitionNum = models.partitions.length + val extraState = models.map(_.localModels.head.getExtraParameter()).first() + trainingModel.setExtraParameter(extraState) + // make sure gradient is as the same length as weight + val parameterArray = trainingModel.parameters() + (0 until parameterArray._2.length).foreach(i => + parameterArray._2(i).resizeAs(parameterArray._1(i)) + ) + + val parameter = trainingModel.getParameters()._1 + val _classTag = classTag[T] + val size = parameter.storage().array().length + val taskSize = size / partitionNum + val extraSize = size % partitionNum + val weights = models.mapPartitions(iter => { + val localCache = iter.next + val localModels = localCache.localModels + val localWeights = localModels.head.getParameters()._1 + val synchronizer = localCache.parameterSynchronizer + .asInstanceOf[BlockManagerParameterSynchronizer[T]] + val partitionId = synchronizer.partitionID + val start = partitionId * taskSize + math.min(partitionId, extraSize) + val length = taskSize + (if (partitionId < extraSize) 1 else 0) + val partitionWeight = Tensor[T](length) + partitionWeight.copy(localWeights.narrow(1, start + 1, length)) + Iterator.single(Map(partitionId -> partitionWeight)) + }).reduce((a, b) => (a ++ b)) + + (0 until partitionNum).map(pid => { + val start = parameter.storageOffset + pid * taskSize + math.min(pid, extraSize) + val length = taskSize + (if (pid < extraSize) 1 else 0) + parameter.narrow(1, start, length).copy(weights(pid)) + }) + trainingModel + } +} + +/** + * The optimizer run on a distributed cluster. + * + * @param _model train model + * @param _dataset train dataset + * @param _criterion loss function + */ +class ParallelOptimizer[T: ClassTag] ( + _model: Module[T], + _dataset: DistributedDataSet[MiniBatch[T]], + _criterion: Criterion[T] + )(implicit ev: TensorNumeric[T]) + extends Optimizer[T, MiniBatch[T]]( + _model, _dataset, _criterion) { + val metrics = new Metrics + + private var models: RDD[DistriOptimizer.Cache[T]] = null + + private var _priorities: mutable.Map[String, Int] = null + + def setPriorities(priorities: mutable.Map[String, Int]): Unit = { + this._priorities = priorities + } + + /** + * Clean some internal states, so this or other optimizers can run optimize again + * + * This method will be called at the end of optimize. You need not call it if optimize succeed. + * If the optimize fails, you may call it before next optimize. + */ + def clearState() : Unit = { + ParallelOptimizer.clearState(models) + } + + private def endEpoch(): Unit = { + ParallelOptimizer.endEpoch(optimMethods) + } + + override def setTrainData(sampleRDD: RDD[Sample[T]], + batchSize: Int, + miniBatch: MiniBatch[T]): this.type = { + this.dataset = ParallelOptimizer.setTrainData(sampleRDD, batchSize, miniBatch) + // if current epoch is not finished, we will end the + // current epoch and start a new epoch when optimize is called + endEpoch() + this + } + + override def setTrainData(sampleRDD: RDD[Sample[T]], + batchSize: Int, + featurePaddingParam: PaddingParam[T] = null, + labelPaddingParam: PaddingParam[T] = null) : this.type = { + val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None + val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None + this.dataset = ParallelOptimizer.setTrainData(sampleRDD, batchSize, + featurePaddingParam, labelPaddingParam) + // if current epoch is not finished, we will end the + // current epoch and start a new epoch when optimize is called + endEpoch() + this + } + + + override def prepareInput(): Unit = { + if (!dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].isCached) { + ParallelOptimizer.logger.info("caching training rdd ...") + ParallelOptimizer.prepareInput(this.dataset, this.validationDataSet) + } + } + + private def expandOptimMethods(optimMethodMap: mutable.Map[String, OptimMethod[T]]): Unit = { + if (this.model.isInstanceOf[Container[_, _, T]]) { + expandOptimMethodsForSubModules(this.model. + asInstanceOf[Container[_, _, T]].modules, + optimMethodMap(this.model.getName), optimMethodMap) + } else { + require(optimMethodMap.contains(this._model.getName), + "single layer model should have optim method set") + } + + if (optimMethodMap.contains(this.model.getName)) { + this.model.setOptimMethod(optimMethodMap.get(this.model.getName).get) + } + } + + private def expandOptimMethodsForSubModules(subModules: ArrayBuffer[Module[T]], + parentMethod: OptimMethod[T], + optimMethodMap: mutable.Map[String, OptimMethod[T]]): Unit = { + subModules.foreach(sub => { + if (optimMethodMap.get(sub.getName) == None) { + require(parentMethod != null, s"${sub.getName}'s parent optim method should not be null") + val subOptimMethod = parentMethod.clone + sub.setOptimMethod(subOptimMethod) + optimMethodMap(sub.getName) = subOptimMethod + } + if (sub.isInstanceOf[Container[_, _, T]]) { + val currMethod = optimMethodMap(sub.getName) + expandOptimMethodsForSubModules(sub.asInstanceOf[Container[_, _, T]].modules, + currMethod, optimMethodMap) + } + }) + } + + private def defaultPrioritize(): mutable.HashMap[String, Int] = { + val priorities = new mutable.HashMap[String, Int] + val orders = ParallelOptimizer.getExecutionOrder(this._model) + val len = orders.size + orders.zipWithIndex.foreach(order => { + priorities.put(order._1.getName, len - order._2) + }) + priorities + } + + override def optimize(): Module[T] = { + + val distDataset = dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]] + + optimMethods.values.foreach { optimMethod => + optimMethod.clearHistory() + } + + // To be compatible with the old usage that user define hyperparameters in a table. + if (optimMethods.size == 1) { + optimMethods.head._2.loadFromTable(state) + } + val parallelOptimMethods = scala.collection.mutable.Map(optimMethods.toSeq: _*) + // expand optim method so that each layer has its own optim method + expandOptimMethods(parallelOptimMethods) + + if (_priorities == null) { + _priorities = defaultPrioritize + } + + optimMethods = collection.immutable.Map(parallelOptimMethods.toSeq: _*) + + state("dropPercentage") = dropPercentage + state("warmupIterationNum") = warmupIterationNum + state("computeThresholdbatchSize") = computeThresholdbatchSize + state("maxDropPercentage") = maxDropPercentage + state("isLayerwiseScaled") = Utils.isLayerwiseScaled(_model) + + val nodeNumber = Engine.nodeNumber() + val coresPerNode = Engine.coreNumber() + + val partitionNum = distDataset.originRDD().partitions.length + + prepareInput() + + models = ParallelOptimizer.initThreadModels(model, distDataset, criterion, state, + nodeNumber, coresPerNode, checkSingleton, validationMethods, + optimMethods, _priorities) + + if (checkpointPath.isDefined) { + val file = checkpointPath.get + "/" + + new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()) + new File(file).mkdir() + checkpointPath = Some(file) + } + + var retryNum = 0 + val maxRetry = System.getProperty("bigdl.failure.retryTimes", "5").toInt + val retryTimeInterval = System.getProperty("bigdl.failure.retryTimeInterval", "120").toInt + var lastFailureTimestamp = System.nanoTime() + + ParallelOptimizer.optimize( + model, + distDataset, + coresPerNode, + state, + endWhen, + metrics, + models, + optimMethods, + validationTrigger, + validationDataSet, + validationMethods, + checkpointTrigger, + checkpointPath, + trainSummary, + validationSummary, + isOverWrite + ) + + ParallelOptimizer.getModel(models, null, model) + + // Reset some internal states, so this or other optimizers can run optimize again + clearState() + + // release distributed synchronizer resources + + models.foreach(modelIter => { + modelIter.parameterSynchronizer.clear + }) + + // unpersist the model because the next time optimize is called, new `models` will be + // created + models.unpersist() + + model + } + + private def getLatestFile(path: String, fileName: String): String = { + val fl = new java.io.File(path) + val files = fl.listFiles(new FilenameFilter { + override def accept(dir: File, name: String): Boolean = { + name.startsWith(fileName) + } + }) + + var lastMod = Long.MinValue + var choice: String = null + files.map {file => + if (file.lastModified() > lastMod) { + choice = file.getPath; + lastMod = file.lastModified(); + } + } + return choice; + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala index 57068d9e899..bbf622ff623 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DistriParameterSynchronizer.scala @@ -395,7 +395,7 @@ class BlockManagerParameterSynchronizer[T: ClassTag](val partitionID: Int, val t override def clear(): Unit = { shutdown = true - longRunningThreads.asScala.foreach(_.interrupt()) + longRunningThreads.asScala.foreach(th => if (th != null) {th.interrupt()}) clearPool.shutdown workerPool.shutdown syncPool.shutdown diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizerSpec.scala new file mode 100644 index 00000000000..1a41aff50d8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizerSpec.scala @@ -0,0 +1,98 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch} +import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Linear, MSECriterion} +import com.intel.analytics.bigdl.optim.DistriOptimizerSpecModel.mse +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{Engine, T} +import org.apache.log4j.{Level, Logger} +import org.apache.spark.{SparkConf, SparkContext} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +@com.intel.analytics.bigdl.tags.Serial +class ParallelOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { + + Logger.getLogger("org").setLevel(Level.WARN) + Logger.getLogger("akka").setLevel(Level.WARN) + + private var sc: SparkContext = _ + + before { + val conf = Engine.createSparkConf() + .setMaster("local[1]").setAppName("ParallelOptimizerSpec") + sc = new SparkContext(conf) + Engine.init + Engine.setCoreNumber(1) + } + + after { + if (sc != null) { + sc.stop() + } + } + + "Train with parallel" should "work properly" in { + val input = Tensor[Float](1, 10).fill(1.0f) + val target = Tensor[Float](1).fill(1.0f) + val miniBatch = MiniBatch(input, target) + val model = Linear[Float](10, 2) + model.getParameters()._1.fill(1.0f) + val optimMethod = new SGD[Float]() + + val dataSet = DataSet.array(Array(miniBatch), sc) + + val optimizer = new DistriOptimizer[Float](model, dataSet, new ClassNLLCriterion[Float]()) + .setState(T("learningRate" -> 1.0)) + .setEndWhen(Trigger.maxIteration(10)) + + optimizer.optimize() + + } + + "Train with parallel" should "have same results as DistriOptimizer" in { + + val input = Tensor[Float](1, 10).fill(1.0f) + val target = Tensor[Float](1).fill(1.0f) + val miniBatch = MiniBatch(input, target) + val model1 = Linear[Float](10, 2) + model1.getParameters()._1.fill(1.0f) + + val model2 = Linear[Float](10, 2) + model2.getParameters()._1.fill(1.0f) + + val dataSet = DataSet.array(Array(miniBatch), sc) + + val parallelOptimizer = new DistriOptimizer[Float](model1, + dataSet, new ClassNLLCriterion[Float]()) + .setState(T("learningRate" -> 1.0)) + .setEndWhen(Trigger.maxIteration(10)) + + parallelOptimizer.optimize + + val distriOptimizer = new DistriOptimizer[Float](model2, + dataSet, new ClassNLLCriterion[Float]()) + .setState(T("learningRate" -> 1.0)) + .setEndWhen(Trigger.maxIteration(10)) + + distriOptimizer.optimize + + model1.getParameters()._1 should be (model2.getParameters()._1) + + } + +} From 1319cce40dc178b352bf6336e384f30aa25250af Mon Sep 17 00:00:00 2001 From: megaSpoon Date: Fri, 12 Oct 2018 00:10:49 -0700 Subject: [PATCH 0835/1065] Fix transfer learning (#2645) * fix transfer learning * add ParseSingleExample, DecodeBmp tf loader * add corresponding unit tests --- .../tensorflow/transferlearning/README.md | 6 +- .../dllib/nn/SpatialBatchNormalization.scala | 41 +++-- .../bigdl/dllib/nn/tf/ImageOps.scala | 3 + .../bigdl/dllib/nn/tf/ParsingOps.scala | 145 ++++++++++++++++++ .../utils/serializer/ModuleSerializer.scala | 3 +- .../dllib/utils/tf/loaders/DecodeBmp.scala | 39 +++++ .../utils/tf/loaders/ParseSingleExample.scala | 58 +++++++ .../bigdl/dllib/nn/tf/DecodeBmpSpec.scala | 58 +++++++ .../dllib/nn/tf/ParseSingleExampleSpec.scala | 114 ++++++++++++++ 9 files changed, 453 insertions(+), 14 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeBmp.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseSingleExample.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParseSingleExampleSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md index c51965a4f7f..a3733c0b5fa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/tensorflow/transferlearning/README.md @@ -15,12 +15,12 @@ and only train a linear model on these features. ## Make sure Spark, BigDL (both scala and python api) and Tensorflow are successfully install -Please refer to [BigDL](https://bigdl-project.github.io/master/), [Tensorflow](https://www.tensorflow.org/versions/r1.2/install/) for more information. +Please refer to [BigDL](https://bigdl-project.github.io/master/), [Tensorflow](https://www.tensorflow.org/versions/r1.10/install/) for more information. -We currently support Tensorflow r1.2. +We currently support Tensorflow r1.10. ```shell -pip install tensorflow==1.2.0 +pip install tensorflow==1.10 ``` ## Install the TF-slim image models library diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala index 32549dc132a..5063e302e8b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala @@ -317,17 +317,38 @@ object SpatialBatchNormalization { val scaleOffset = scale.storageOffset() - 1 val offsetData = offset.storage().array() val offsetOffset = offset.storageOffset() - 1 - var i = 0 - while (i < n) { - var c = 0 - while (c < nChannels) { - val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps).toFloat - outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - - meanData(c + meanOffset)) * invStd * scaleData(scaleOffset + c) + - offsetData(offsetOffset + c) - c += 1 + var isIdenticalScale = false + var identicalScale = 0.0f + if (scale.stride().length == 1 && scale.stride()(0) == 0 && scaleData.length == 1) { + isIdenticalScale = true + identicalScale = scaleData(0) + } + if (isIdenticalScale) { + var i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps).toFloat + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(c + meanOffset)) * invStd * identicalScale + + offsetData(offsetOffset + c) + c += 1 + } + i += nChannels + } + } else { + var i = 0 + while (i < n) { + var c = 0 + while (c < nChannels) { + val invStd = 1 / Math.sqrt(varData(varOffset + c) + eps).toFloat + outputData(i + outputOffset + c) = (inputData(i + inputOffset + c) - + meanData(c + meanOffset)) * invStd * scaleData(scaleOffset + c) + + offsetData(offsetOffset + c) + c += 1 + } + i += nChannels } - i += nChannels } } else { var i = 0 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala index 9edcfa6790e..30428633e26 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala @@ -99,6 +99,9 @@ private[bigdl] class DecodeJpeg[T: ClassTag](channels: Int, val ratio: Int = 1) private[bigdl] class DecodePng[T: ClassTag](channels: Int)(implicit ev: TensorNumeric[T]) extends DecodeImage[T](channels) +private[bigdl] class DecodeBmp[T: ClassTag](channels: Int)(implicit ev: TensorNumeric[T]) + extends DecodeImage[T](channels) + private[bigdl] class DecodeGif[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends DecodeImage[T](3) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala index cbbff5fbb7b..cf7651ed253 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParsingOps.scala @@ -90,6 +90,56 @@ private[bigdl] class ParseExample[T: ClassTag](val nDense: Int, } } +private[bigdl] class ParseSingleExample[T: ClassTag](val tDense: Seq[TensorDataType], + val denseKeys: Seq[ByteString], + val denseShape: Seq[Array[Int]]) + (implicit ev: TensorNumeric[T]) + extends Operation[Table, Table, T] { + + type StringType = ByteString + + override def updateOutput(input: Table): Table = { + val serialized = input[Tensor[StringType]](1).value() + + val example = Example.parseFrom(serialized) + + val featureMap = example.getFeatures.getFeatureMap + + val outputs = denseKeys + .zip(tDense).zip(denseShape).map { case ((byteSKey, tensorType), shape) => + val key = byteSKey.toStringUtf8 + if (featureMap.containsKey(key)) { + val feature = featureMap.get(key) + getTensorFromFeature(feature, tensorType, shape) + } else { + None + } + } + + for (elem <- outputs) { + output.insert(elem) + } + output + } + + private def getTensorFromFeature(feature: Feature, + tensorType: TensorDataType, + tensorShape: Array[Int]): Tensor[_] = { + tensorType match { + case LongType => + val values = feature.getInt64List.getValueList.asScala.map(_.longValue()).toArray + Tensor(values, tensorShape) + case FloatType => + val values = feature.getFloatList.getValueList.asScala.map(_.floatValue()).toArray + Tensor(values, tensorShape) + case StringType => + val values = feature.getBytesList.getValueList.asScala.toArray + Tensor(values, tensorShape) + case t => throw new NotImplementedError(s"$t is not supported") + } + } +} + private[bigdl] object ParseExample extends ModuleSerializable { def apply[T: ClassTag](nDense: Int, tDense: Seq[TensorDataType], @@ -169,3 +219,98 @@ private[bigdl] object ParseExample extends ModuleSerializable { } } } + +private[bigdl] object ParseSingleExample extends ModuleSerializable { + def apply[T: ClassTag](tDense: Seq[TensorDataType], + denseKeys: Seq[ByteString], + denseShape: Seq[Array[Int]]) + (implicit ev: TensorNumeric[T]): ParseSingleExample[T] = + new ParseSingleExample[T](tDense, denseKeys, denseShape) + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { + + val attrMap = context.bigdlModule.getAttrMap + + val tDense = DataConverter.getAttributeValue(context, attrMap.get("tDense")). + asInstanceOf[Array[String]].map(toTensorType(_)) + + val denseKeysString = DataConverter.getAttributeValue(context, + attrMap.get("denseKeys")). + asInstanceOf[Array[String]] + + val denseKeys = new Array[ByteString](denseKeysString.length) + + (0 until denseKeysString.length).foreach(index => { + val denseKeyBytes = denseKeysString(index).getBytes("utf-8") + denseKeys(index) = ByteString.copyFrom(denseKeyBytes) + }) + + val shapeSize = DataConverter.getAttributeValue(context, attrMap.get("shapeSize")). + asInstanceOf[Int] + + val denseShape = new Array[Array[Int]](shapeSize) + for (i <- 1 to shapeSize) { + denseShape(i - 1) = DataConverter.getAttributeValue(context, + attrMap.get(s"shapeSize_${i - 1}")). + asInstanceOf[Array[Int]] + } + ParseSingleExample[T](tDense.toSeq, denseKeys.toSeq, denseShape.toSeq) + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + bigDLModelBuilder: BigDLModule.Builder)(implicit ev: TensorNumeric[T]): Unit = { + val parseSingleExample = context.moduleData.module.asInstanceOf[ParseSingleExample[T]] + + val tensorTypeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, tensorTypeBuilder, + parseSingleExample.tDense.toArray.map(fromTensorType(_)), + universe.typeOf[Array[String]]) + bigDLModelBuilder.putAttr("tDense", tensorTypeBuilder.build) + + val denseKeys = parseSingleExample.denseKeys.toArray + + val denseKeysString = new Array[String](denseKeys.length) + + (0 until denseKeys.length).foreach(index => { + denseKeysString(index) = new String(denseKeys(index).toByteArray, "utf-8") + }) + + val denseKeysBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, denseKeysBuilder, + denseKeysString, + universe.typeOf[Array[String]]) + bigDLModelBuilder.putAttr(s"denseKeys", denseKeysBuilder.build) + val shapeSizeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, shapeSizeBuilder, + parseSingleExample.denseShape.size, + universe.typeOf[Int]) + bigDLModelBuilder.putAttr("shapeSize", shapeSizeBuilder.build) + + parseSingleExample.denseShape.zipWithIndex.foreach(shape => { + val shapeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, shapeBuilder, + parseSingleExample.denseShape(shape._2), + universe.typeOf[Array[Int]]) + bigDLModelBuilder.putAttr(s"shapeSize_${shape._2}", shapeBuilder.build) + }) + + } + + private def fromTensorType(ttype : TensorDataType): String = { + ttype match { + case LongType => "Long" + case FloatType => "Float" + case StringType => "String" + case t => throw new NotImplementedError(s"$t is not supported") + } + } + + private def toTensorType(ttype : String): TensorDataType = { + ttype match { + case "Long" => LongType + case "Float" => FloatType + case "String" => StringType + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 5547966fe38..3d84a73bd27 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer, Model, Sequential => KSequential} import com.intel.analytics.bigdl.nn.ops.{RandomUniform => RandomUniformOps} -import com.intel.analytics.bigdl.nn.tf.{DecodeRawSerializer, ParseExample, StridedSlice} +import com.intel.analytics.bigdl.nn.tf.{DecodeRawSerializer, ParseExample, ParseSingleExample, StridedSlice} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -271,6 +271,7 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.quantized.Linear", quantized.Linear) registerModule("com.intel.analytics.bigdl.nn.tf.ParseExample", ParseExample) + registerModule("com.intel.analytics.bigdl.nn.tf.ParseSingleExample", ParseSingleExample) registerModule("com.intel.analytics.bigdl.nn.SReLU", SReLU) registerModule("com.intel.analytics.bigdl.nn.tf.DecodeRaw", DecodeRawSerializer) registerModule("com.intel.analytics.bigdl.nn.ops.RandomUniform", RandomUniformOps) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeBmp.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeBmp.scala new file mode 100644 index 00000000000..8129a4e2ae7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/DecodeBmp.scala @@ -0,0 +1,39 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.tf.{DecodeBmp => DecodeBmpOp} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.NodeDef + +import scala.reflect.ClassTag + +class DecodeBmp extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder + , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val attr = nodeDef.getAttrMap + val channels = getInt(attr, "channels") + + new DecodeBmpOp[T](channels) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseSingleExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseSingleExample.scala new file mode 100644 index 00000000000..20cdb4c08fe --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseSingleExample.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.tf.loaders + +import java.nio.ByteOrder + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.tf.{ParseSingleExample => ParseSingleExampleOperation} +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.tf.Context +import org.tensorflow.framework.{DataType, NodeDef} + +import collection.JavaConverters._ +import scala.reflect.ClassTag + +class ParseSingleExample extends TensorflowOpsLoader { + + import Utils._ + + override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder, + context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { + val Tdense = nodeDef.getAttrMap.get("Tdense") + .getList.getTypeList.asScala + .map { + case DataType.DT_INT64 => LongType + case DataType.DT_INT32 => IntType + case DataType.DT_FLOAT => FloatType + case DataType.DT_DOUBLE => DoubleType + case DataType.DT_STRING => StringType + } + val denseKeysByteArray = nodeDef.getAttrMap.get("dense_keys").getList. + getSList.asScala.map(_.toByteArray) + val denseKeys = denseKeysByteArray.map(ByteString.copyFrom(_)) + val denseShapes = nodeDef.getAttrMap.get("dense_shapes") + .getList.getShapeList.asScala + .map { shapeProto => + shapeProto.getDimList.asScala.map(_.getSize.toInt).toArray + } + + new ParseSingleExampleOperation[T](Tdense, denseKeys, denseShapes) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala new file mode 100644 index 00000000000..022f2058e9b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.tf + +import java.io.{File => JFile} + +import com.google.protobuf.ByteString +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.tf.TFRecordIterator +import org.tensorflow.example.Example + +class DecodeBmpSerialTest extends ModuleSerializationTest { + private def getInputs(name: String): Tensor[ByteString] = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + val index = name match { + case "png" => 0 + case "jpeg" => 1 + case "gif" => 2 + case "raw" => 3 + case "bmp" => 0 + } + + val resource = getClass.getClassLoader.getResource("tf") + val path = resource.getPath + JFile.separator + "decode_image_test_case.tfrecord" + val file = new JFile(path) + + val bytesVector = TFRecordIterator(file).toVector + val bmpBytes = bytesVector(index) + + val example = Example.parseFrom(bmpBytes) + val imageByteString = example.getFeatures.getFeatureMap.get("image/encoded") + .getBytesList.getValueList.get(0) + + Tensor[ByteString](Array(imageByteString), Array[Int]()) + } + + override def test(): Unit = { + val decodeBmp = new DecodeBmp[Float](1).setName("decodeBmp") + val input = getInputs("bmp") + runSerializationTest(decodeBmp, input) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParseSingleExampleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParseSingleExampleSpec.scala new file mode 100644 index 00000000000..e6decc9771a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/ParseSingleExampleSpec.scala @@ -0,0 +1,114 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.tf + +import com.intel.analytics.bigdl.tensor.{FloatType, LongType, StringType, Tensor} +import com.google.protobuf.{ByteString, CodedOutputStream} +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} +import org.tensorflow.example._ +import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + +class ParseSingleExampleSpec extends FlatSpec with Matchers { + + "ParseSingleExample" should "be able to parse a example" in { + + val floatBuilder = FloatList.newBuilder() + .addValue(0.0f).addValue(1.0f).addValue(2.0f) + val floatFeature = Feature.newBuilder().setFloatList(floatBuilder).build() + + val longBuilder = Int64List.newBuilder() + .addValue(0).addValue(1).addValue(2) + val longFeature = Feature.newBuilder().setInt64List(longBuilder).build() + + val bytesBuilder = BytesList.newBuilder().addValue(ByteString.copyFromUtf8("abcd")) + val bytesFeature = Feature.newBuilder().setBytesList(bytesBuilder).build() + + val features = Features.newBuilder() + .putFeature("floatFeature", floatFeature) + .putFeature("longFeature", longFeature) + .putFeature("bytesFeature", bytesFeature) + val example = Example.newBuilder().setFeatures(features).build() + val length = example.getSerializedSize + val data = new Array[Byte](length) + val outputStream = CodedOutputStream.newInstance(data) + example.writeTo(outputStream) + + val key1 = ByteString.copyFromUtf8("floatFeature") + val key2 = ByteString.copyFromUtf8("longFeature") + val key3 = ByteString.copyFromUtf8("bytesFeature") + val denseKeys = Seq(key1, key2, key3) + + val exampleParser = new ParseSingleExample[Float]( + Seq(FloatType, LongType, StringType), denseKeys, Seq(Array(3), Array(3), Array())) + + val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) + + val input = T(serialized) + + val output = exampleParser.forward(input) + + val floatTensor = output(1).asInstanceOf[Tensor[Float]] + val longTensor = output(2).asInstanceOf[Tensor[Long]] + val stringTensor = output(3).asInstanceOf[Tensor[ByteString]] + + floatTensor should be (Tensor[Float](T(0.0f, 1.0f, 2.0f))) + longTensor should be (Tensor[Long](T(0L, 1L, 2L))) + stringTensor should be (Tensor.scalar((ByteString.copyFromUtf8("abcd")))) + } + +} + +class ParseSingleExampleSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + + val floatBuilder = FloatList.newBuilder() + .addValue(0.0f).addValue(1.0f).addValue(2.0f) + val floatFeature = Feature.newBuilder().setFloatList(floatBuilder).build() + + val longBuilder = Int64List.newBuilder() + .addValue(0).addValue(1).addValue(2) + val longFeature = Feature.newBuilder().setInt64List(longBuilder).build() + + val bytesBuilder = BytesList.newBuilder().addValue(ByteString.copyFromUtf8("abcd")) + val bytesFeature = Feature.newBuilder().setBytesList(bytesBuilder).build() + + val features = Features.newBuilder() + .putFeature("floatFeature", floatFeature) + .putFeature("longFeature", longFeature) + .putFeature("bytesFeature", bytesFeature) + val example = Example.newBuilder().setFeatures(features).build() + val length = example.getSerializedSize + val data = new Array[Byte](length) + val outputStream = CodedOutputStream.newInstance(data) + example.writeTo(outputStream) + + val key1 = ByteString.copyFromUtf8("floatFeature") + val key2 = ByteString.copyFromUtf8("longFeature") + val key3 = ByteString.copyFromUtf8("bytesFeature") + val denseKeys = Seq(key1, key2, key3) + + val exampleParser = new ParseSingleExample[Float](Seq(FloatType, LongType, StringType), + denseKeys, Seq(Array(3), Array(3), Array())).setName("parseSingleExample") + + val serialized = Tensor[ByteString](Array(ByteString.copyFrom(data)), Array[Int](1)) + + val input = T(serialized) + runSerializationTest(exampleParser, input) + } +} From 4d72a35f0ac4c8f4d2a141fa76d9300fe2efc7cf Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Sat, 13 Oct 2018 13:41:17 +0800 Subject: [PATCH 0836/1065] remove potential performance downgrader (#2651) (#2652) * Fix transfer learning (#2645) * fix transfer learning * add ParseSingleExample, DecodeBmp tf loader * add corresponding unit tests * remove potential performance downgrader --- .../analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala | 3 ++- .../com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala | 3 +++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala index 5063e302e8b..7e4d67c0595 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala @@ -319,7 +319,8 @@ object SpatialBatchNormalization { val offsetOffset = offset.storageOffset() - 1 var isIdenticalScale = false var identicalScale = 0.0f - if (scale.stride().length == 1 && scale.stride()(0) == 0 && scaleData.length == 1) { + + if (scaleData.length == 1) { isIdenticalScale = true identicalScale = scaleData(0) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala index 022f2058e9b..91990c713c2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/tf/DecodeBmpSpec.scala @@ -27,6 +27,9 @@ import org.tensorflow.example.Example class DecodeBmpSerialTest extends ModuleSerializationTest { private def getInputs(name: String): Tensor[ByteString] = { import com.intel.analytics.bigdl.utils.tf.TFTensorNumeric.NumericByteString + /* since the tfrecord file is loaded into byteArrays regardless of the + original image type, we can map "bmp" to 0 as well + */ val index = name match { case "png" => 0 case "jpeg" => 1 From 62903f75bbf3e26edf347c301759d4c0da85b9a5 Mon Sep 17 00:00:00 2001 From: Tao Pathompong Ruangyam Date: Thu, 18 Oct 2018 07:17:48 +0200 Subject: [PATCH 0837/1065] abstractify tests with common spark lifecycle (#2654) apply SparkContextLifeCycle to tests default app name + extend SparkContextLifeCycle to other compatible tests add custom before and after --- .../bigdl/dllib/dataset/DataSetSpec.scala | 22 ++------ .../dllib/dataset/text/DictionarySpec.scala | 21 ++------ .../dataset/text/SentenceBiPaddingSpec.scala | 21 ++------ .../dataset/text/SentenceTokenizerSpec.scala | 10 ++-- .../text/TextToLabeledSentenceSpec.scala | 21 ++------ .../models/utils/ModelBroadcastSpec.scala | 19 +++---- .../bigdl/dllib/optim/EvaluatorSpec.scala | 21 ++------ .../optim/OptimPredictorShutdownSpec.scala | 36 ++++--------- .../bigdl/dllib/optim/PredictorSpec.scala | 21 ++------ .../bigdl/dllib/optim/ValidatorSpec.scala | 21 ++------ .../dllib/utils/SparkContextLifeCycle.scala | 53 +++++++++++++++++++ .../ZippedPartitionsWithLocalityRDDSpec.scala | 16 ++---- 12 files changed, 112 insertions(+), 170 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SparkContextLifeCycle.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala index ad0cd8c536d..6e8c7da01be 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala @@ -22,7 +22,7 @@ import java.util.concurrent.{Callable, Executors} import com.intel.analytics.bigdl.dataset.image._ import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, TestUtils} +import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, TestUtils, SparkContextLifeCycle} import org.apache.hadoop.io.Text import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -30,22 +30,10 @@ import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.util.Random @com.intel.analytics.bigdl.tags.Serial -class DataSetSpec extends FlatSpec with Matchers with BeforeAndAfter { - var sc: SparkContext = null - val nodeNumber = 1 - val coreNumber = 1 - - before { - Engine.init(nodeNumber, coreNumber, true) - val conf = new SparkConf().setMaster("local[1]").setAppName("DataSetSpec") - sc = new SparkContext(conf) - } - - after { - if (sc != null) { - sc.stop() - } - } +class DataSetSpec extends SparkContextLifeCycle with Matchers { + override def nodeNumber: Int = 1 + override def coreNumber: Int = 1 + override def appName: String = "DataSetSpec" private def processPath(path: String): String = { if (path.contains(":")) { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/DictionarySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/DictionarySpec.scala index 6e53b11d319..e8278b0b5ed 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/DictionarySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/DictionarySpec.scala @@ -20,27 +20,16 @@ import java.io.PrintWriter import com.intel.analytics.bigdl.dataset.DataSet import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.SparkContextLifeCycle import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.io.Source -class DictionarySpec extends FlatSpec with Matchers with BeforeAndAfter { - var sc: SparkContext = null - - before { - val nodeNumber = 1 - val coreNumber = 1 - Engine.init(nodeNumber, coreNumber, true) - val conf = new SparkConf().setMaster("local[1]").setAppName("DictionarySpec") - sc = new SparkContext(conf) - } - - after { - if (sc != null) { - sc.stop() - } - } +class DictionarySpec extends SparkContextLifeCycle with Matchers { + override def nodeNumber: Int = 1 + override def coreNumber: Int = 1 + override def appName: String = "DictionarySpec" "DictionarySpec" should "creates dictionary correctly on Spark" in { val tmpFile = java.io.File diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/SentenceBiPaddingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/SentenceBiPaddingSpec.scala index 4f90af4c453..072de156e81 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/SentenceBiPaddingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/SentenceBiPaddingSpec.scala @@ -20,27 +20,17 @@ import java.io.PrintWriter import com.intel.analytics.bigdl.dataset.DataSet import com.intel.analytics.bigdl.dataset.text.utils.SentenceToken -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, SparkContextLifeCycle} import org.apache.spark.SparkContext import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.io.Source @com.intel.analytics.bigdl.tags.Serial -class SentenceBiPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { - var sc: SparkContext = null - - before { - val nodeNumber = 1 - val coreNumber = 1 - Engine.init(nodeNumber, coreNumber, true) - } - - after { - if (sc != null) { - sc.stop() - } - } +class SentenceBiPaddingSpec extends SparkContextLifeCycle with Matchers { + override def nodeNumber: Int = 1 + override def coreNumber: Int = 1 + override def appName: String = "DocumentTokenizer" "SentenceBiPaddingSpec" should "pads articles correctly on Spark" in { val tmpFile = java.io.File @@ -56,7 +46,6 @@ class SentenceBiPaddingSpec extends FlatSpec with Matchers with BeforeAndAfter { write(sentences.mkString("\n")); close } - sc = new SparkContext("local[1]", "DocumentTokenizer") val sents = DataSet.rdd(sc.textFile(tmpFile) .filter(!_.isEmpty)).transform(SentenceSplitter()) .toDistributed().data(train = false).flatMap(item => item.iterator).collect() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/SentenceTokenizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/SentenceTokenizerSpec.scala index 2fbae4ba092..ef16179d060 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/SentenceTokenizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/SentenceTokenizerSpec.scala @@ -19,13 +19,15 @@ package com.intel.analytics.bigdl.dataset.text import java.io.PrintWriter import com.intel.analytics.bigdl.dataset.DataSet -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, SparkContextLifeCycle} import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{FlatSpec, Matchers} import scala.io.Source -class SentenceTokenizerSpec extends FlatSpec with Matchers { +class SentenceTokenizerSpec extends SparkContextLifeCycle with Matchers { + + override def appName: String = "DocumentTokenizer" "SentenceTokenizerSpec" should "tokenizes articles correctly on Spark" in { val tmpFile = java.io.File @@ -41,9 +43,6 @@ class SentenceTokenizerSpec extends FlatSpec with Matchers { write(sentences.mkString("\n")); close } - Engine.init(1, 1, true) - val conf = new SparkConf().setMaster("local[1]").setAppName("DocumentTokenizer") - val sc = new SparkContext(conf) val sents = DataSet.rdd(sc.textFile(tmpFile) .filter(!_.isEmpty)).transform(SentenceSplitter()) .toDistributed().data(train = false).flatMap(item => item.iterator).collect() @@ -64,7 +63,6 @@ class SentenceTokenizerSpec extends FlatSpec with Matchers { output.length should be (numOfSents) count should be (numOfWords) - sc.stop() } "SentenceTokenizerSpec" should "tokenizes articles correctly on local" in { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/TextToLabeledSentenceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/TextToLabeledSentenceSpec.scala index 07bb026ab5a..af40c402815 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/TextToLabeledSentenceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/text/TextToLabeledSentenceSpec.scala @@ -19,28 +19,17 @@ package com.intel.analytics.bigdl.dataset.text import java.io.PrintWriter import com.intel.analytics.bigdl.dataset.DataSet -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, SparkContextLifeCycle} import org.apache.spark.SparkContext import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.io.Source @com.intel.analytics.bigdl.tags.Serial -class TextToLabeledSentenceSpec extends FlatSpec with Matchers with BeforeAndAfter { - var sc: SparkContext = null - - before { - val nodeNumber = 1 - val coreNumber = 1 - Engine.init(nodeNumber, coreNumber, true) - sc = new SparkContext("local[1]", "TextToLabeledSentence") - } - - after { - if (sc != null) { - sc.stop() - } - } +class TextToLabeledSentenceSpec extends SparkContextLifeCycle with Matchers { + override def nodeNumber: Int = 1 + override def coreNumber: Int = 1 + override def appName: String = "TextToLabeledSentence" "TextToLabeledSentenceSpec" should "indexes sentences correctly on Spark" in { val tmpFile = java.io.File diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala index e268b96ee07..830dd5dec19 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcastSpec.scala @@ -22,19 +22,21 @@ import com.intel.analytics.bigdl.nn.tf.Const import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor import org.apache.commons.lang3.SerializationUtils +import com.intel.analytics.bigdl.utils.SparkContextLifeCycle import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} -class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { +class ModelBroadcastSpec extends SparkContextLifeCycle with Matchers { - var sc: SparkContext = null + override def appName: String = "ModelBroadcast" + + override def afterTest: Any = { + System.clearProperty("bigdl.ModelBroadcastFactory") + } Logger.getLogger("org").setLevel(Level.WARN) Logger.getLogger("akka").setLevel(Level.WARN) - before { - sc = new SparkContext(new SparkConf().setMaster("local[1]").setAppName("ModelBroadcast")) - } "model broadcast" should "work properly" in { val model = LeNet5(10) @@ -160,11 +162,4 @@ class ModelBroadcastSpec extends FlatSpec with Matchers with BeforeAndAfter { info.model.parameters()._2 should be (newInfo.model.parameters()._2) } - after { - System.clearProperty("bigdl.ModelBroadcastFactory") - if (sc != null) { - sc.stop() - } - } - } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala index 2fb956082e0..524b5fdd63a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala @@ -22,27 +22,16 @@ import com.intel.analytics.bigdl.nn.CrossEntropyCriterion import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.SparkContextLifeCycle import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import com.intel.analytics.bigdl._ -class EvaluatorSpec extends FlatSpec with Matchers with BeforeAndAfter{ +class EvaluatorSpec extends SparkContextLifeCycle with Matchers { - var sc: SparkContext = null - val nodeNumber = 1 - val coreNumber = 1 - - before { - Engine.init(nodeNumber, coreNumber, true) - val conf = new SparkConf().setMaster("local[1]").setAppName("evaluator") - sc = new SparkContext(conf) - } - - after { - if (sc != null) { - sc.stop() - } - } + override def nodeNumber: Int = 1 + override def coreNumber: Int = 1 + override def appName: String = "evaluator" private def processPath(path: String): String = { if (path.contains(":")) { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimPredictorShutdownSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimPredictorShutdownSpec.scala index 48a650fdf7c..b98482591f4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimPredictorShutdownSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimPredictorShutdownSpec.scala @@ -30,28 +30,17 @@ import com.intel.analytics.bigdl.tensor.{DnnStorage, Storage, Tensor} import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, ImageFrameToSample, MatToTensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, RandomGenerator} +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, RandomGenerator, SparkContextLifeCycle} import org.apache.log4j.{Level, Logger} import org.apache.spark.rdd.RDD import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} -class OptimPredictorShutdownSpec extends FlatSpec with Matchers with BeforeAndAfter { - var sc: SparkContext = null - val nodeNumber = 1 - val coreNumber = 1 +class OptimPredictorShutdownSpec extends SparkContextLifeCycle with Matchers { + override def nodeNumber: Int = 1 + override def coreNumber: Int = 1 + override def appName: String = "predictor" - before { - Engine.init(nodeNumber, coreNumber, true) - val conf = new SparkConf().setMaster("local[1]").setAppName("predictor") - sc = new SparkContext(conf) - } - - after { - if (sc != null) { - sc.stop() - } - } "model predict should have no memory leak" should "be correct" in { LoggerFilter.redirectSparkInfoLogs() Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) @@ -112,20 +101,19 @@ class OptimPredictorShutdownSpec extends FlatSpec with Matchers with BeforeAndAf } } -class DistriOptimizerSpec2 extends FlatSpec with Matchers with BeforeAndAfter { +class DistriOptimizerSpec2 extends SparkContextLifeCycle with Matchers { import DistriOptimizerSpecModel._ + override def appName: String = "RDDOptimizerSpec" + Logger.getLogger("org").setLevel(Level.WARN) Logger.getLogger("akka").setLevel(Level.WARN) - private var sc: SparkContext = _ - private var dataSet: DistributedDataSet[MiniBatch[Float]] = _ - before { + override def beforeTest: Any = { System.setProperty("bigdl.engineType", "mkldnn") - sc = new SparkContext("local[1]", "RDDOptimizerSpec") val input1: Tensor[Float] = Tensor[Float](Storage[Float](Array(0.0f, 1.0f, 0.0f, 1.0f))) val output1 = 0.0f @@ -170,11 +158,7 @@ class DistriOptimizerSpec2 extends FlatSpec with Matchers with BeforeAndAfter { Engine.model.setPoolSize(1) } - after { - if (sc != null) { - sc.stop() - } - + override def afterTest: Any = { System.clearProperty("bigdl.engineType") } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index 24650eb1a78..cde1d8cf1cf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -27,27 +27,16 @@ import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, Table, T} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.SparkContextLifeCycle import org.apache.commons.lang3.SerializationUtils import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} -class PredictorSpec extends FlatSpec with Matchers with BeforeAndAfter{ - var sc: SparkContext = null - val nodeNumber = 1 - val coreNumber = 1 - - before { - Engine.init(nodeNumber, coreNumber, true) - val conf = new SparkConf().setMaster("local[1]").setAppName("predictor") - sc = new SparkContext(conf) - } - - after { - if (sc != null) { - sc.stop() - } - } +class PredictorSpec extends SparkContextLifeCycle with Matchers { + override def nodeNumber: Int = 1 + override def coreNumber: Int = 1 + override def appName: String = "predictor" "model.predict" should "be correct" in { RNG.setSeed(100) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala index de497af20fa..217d6782ef6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala @@ -23,27 +23,16 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.utils.SparkContextLifeCycle import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} -class ValidatorSpec extends FlatSpec with Matchers with BeforeAndAfter{ +class ValidatorSpec extends SparkContextLifeCycle with Matchers { - var sc: SparkContext = null - val nodeNumber = 1 - val coreNumber = 1 - - before { - Engine.init(nodeNumber, coreNumber, true) - val conf = new SparkConf().setMaster("local[1]").setAppName("validator") - sc = new SparkContext(conf) - } - - after { - if (sc != null) { - sc.stop() - } - } + override def nodeNumber: Int = 1 + override def coreNumber: Int = 1 + override def appName: String = "validator" private def processPath(path: String): String = { if (path.contains(":")) { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SparkContextLifeCycle.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SparkContextLifeCycle.scala new file mode 100644 index 00000000000..de11138972d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SparkContextLifeCycle.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils + +import org.apache.spark.{SparkConf, SparkContext} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +/** + * A trait which handles the creation of a [[SparkContext]] at the beginning + * of the test suite and the finalization of its lifecyle when the test ends. + */ +trait SparkContextLifeCycle extends FlatSpec with BeforeAndAfter { + var sc: SparkContext = null + def nodeNumber: Int = 1 + def coreNumber: Int = 1 + def appName: String = "SparkApp" + + /** + * Custom statements to execute inside [[before]] after a [[SparkContext]] is initialized. + */ + def beforeTest: Any = {} + /** + * Custom statements to execute inside [[after]] after the [[SparkContext]] is stopped. + */ + def afterTest: Any = {} + + before { + Engine.init(nodeNumber, coreNumber, true) + val conf = new SparkConf().setMaster(s"local[$coreNumber]").setAppName(appName) + sc = new SparkContext(conf) + beforeTest + } + + after { + if (sc != null) { + sc.stop() + } + afterTest + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/ZippedPartitionsWithLocalityRDDSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/ZippedPartitionsWithLocalityRDDSpec.scala index 848cc99beaa..b86f251857b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/ZippedPartitionsWithLocalityRDDSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/ZippedPartitionsWithLocalityRDDSpec.scala @@ -21,19 +21,9 @@ import org.apache.spark.rdd.ZippedPartitionsWithLocalityRDD import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Serial -class ZippedPartitionsWithLocalityRDDSpec extends FlatSpec with Matchers with BeforeAndAfter { - var sc: SparkContext = null - before { - val conf = new SparkConf().setMaster("local[4]") - .setAppName("ZippedPartitionsWithLocalityRDDSpec") - sc = new SparkContext(conf) - } - - after { - if (sc != null) { - sc.stop() - } - } +class ZippedPartitionsWithLocalityRDDSpec extends SparkContextLifeCycle with Matchers { + override def coreNumber: Int = 4 + override def appName: String = "ZippedPartitionsWithLocalityRDDSpec" "two uncached rdd zip partition" should "not throw exception" in { val rdd1 = sc.parallelize((1 to 100), 4) From 067885b17a254e6d9d2cb4c747022c2eca1f8d2b Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 22 Oct 2018 14:50:43 +0800 Subject: [PATCH 0838/1065] bump version to 0.8.0-SNAPSHOT (#2660) * bump version to 0.8.0-SNAPSHOT * add core link update --- dist/pom.xml | 2 +- dl/pom.xml | 6 +++--- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- .../dllib/src/main/resources/bigdl-version-info.properties | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index e58a6787dd2..1ba4b689181 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.7.0-SNAPSHOT + 0.8.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index 270a2cbe0e9..d11a80498f9 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.7.0-SNAPSHOT + 0.8.0-SNAPSHOT 4.0.0 @@ -79,7 +79,7 @@ com.intel.analytics.bigdl.core.dist all - 0.7.0-SNAPSHOT + 0.8.0-SNAPSHOT ${bigdl-core-all-scope} @@ -314,7 +314,7 @@ com.intel.analytics.bigdl.core.dist ${os-flag} - 0.7.0-SNAPSHOT + 0.8.0-SNAPSHOT pom diff --git a/pom.xml b/pom.xml index e13294f5a94..4bfa14b4887 100644 --- a/pom.xml +++ b/pom.xml @@ -5,7 +5,7 @@ bigdl-parent com.intel.analytics.bigdl - 0.7.0-SNAPSHOT + 0.8.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/1.5-plus/pom.xml b/scala/common/spark-version/1.5-plus/pom.xml index f6aae4b13a2..fa713405e43 100644 --- a/scala/common/spark-version/1.5-plus/pom.xml +++ b/scala/common/spark-version/1.5-plus/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.7.0-SNAPSHOT + 0.8.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/2.0/pom.xml b/scala/common/spark-version/2.0/pom.xml index 3680ab0cd97..d6c0e6b7c2d 100644 --- a/scala/common/spark-version/2.0/pom.xml +++ b/scala/common/spark-version/2.0/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.7.0-SNAPSHOT + 0.8.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/pom.xml b/scala/common/spark-version/pom.xml index bc754938193..91943d19268 100644 --- a/scala/common/spark-version/pom.xml +++ b/scala/common/spark-version/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.7.0-SNAPSHOT + 0.8.0-SNAPSHOT 4.0.0 diff --git a/scala/dllib/src/main/resources/bigdl-version-info.properties b/scala/dllib/src/main/resources/bigdl-version-info.properties index 119598350e9..2d9aa85b43e 100644 --- a/scala/dllib/src/main/resources/bigdl-version-info.properties +++ b/scala/dllib/src/main/resources/bigdl-version-info.properties @@ -16,4 +16,4 @@ #BigDL version info config -version=0.7.0-SNAPSHOT \ No newline at end of file +version=0.8.0-SNAPSHOT \ No newline at end of file From 11b7e4f28bc099f4841cef2fb4ee95372eec681d Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 23 Oct 2018 12:19:20 +0800 Subject: [PATCH 0839/1065] [Enhancement] - Deco legacy transformers and train InceptionV1 to meet training target (#2661) * refinement inception v1 training code * fix ut due to the init change * fix type * fix param --- .../dllib/models/inception/ImageNet2012.scala | 31 ++++++---- .../dllib/models/inception/Inception_v1.scala | 60 +++++++++++-------- .../bigdl/dllib/models/inception/Train.scala | 4 +- .../bigdl/dllib/models/InceptionSpec.scala | 2 +- 4 files changed, 59 insertions(+), 38 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala index 6f6e3f23495..b2b45e31792 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/ImageNet2012.scala @@ -19,7 +19,7 @@ import java.nio.file.Paths import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset._ -import com.intel.analytics.bigdl.dataset.image.{BGRImgCropper, BGRImgNormalizer, BytesToBGRImg, CropCenter, MTLabeledBGRImgToBatch, HFlip => DatasetHFlip} +import com.intel.analytics.bigdl.dataset.image.{BGRImgCropper, BGRImgNormalizer, BytesToBGRImg, CropCenter, CropRandom, MTLabeledBGRImgToBatch, HFlip => DatasetHFlip} import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation._ import org.apache.spark.SparkContext @@ -36,14 +36,18 @@ object ImageNet2012 { classNumber: Int ) : DataSet[MiniBatch[Float]] = { - DataSet.SeqFileFolder.files(path, sc, classNumber).transform( - MTLabeledBGRImgToBatch[ByteRecord]( + DataSet.SeqFileFolder.filesToImageFeatureDataset(path, sc, classNumber).transform( + MTImageFeatureToBatch( width = imageSize, height = imageSize, batchSize = batchSize, - transformer = (BytesToBGRImg() -> BGRImgCropper(imageSize, imageSize) - -> DatasetHFlip(0.5) -> BGRImgNormalizer(0.485, 0.456, 0.406, 0.229, 0.224, 0.225)) - )) + transformer = PixelBytesToMat() -> + Resize(256, 256) -> + RandomCropper(224, 224, true, CropRandom) -> + ChannelNormalize(123, 117, 104) -> + MatToTensor[Float](), toRGB = false + ) + ) } def rdd(path: String, batchSize: Int, sc: SparkContext, imageSize : Int) @@ -72,14 +76,19 @@ object ImageNet2012Val { classNumber: Int ) : DataSet[MiniBatch[Float]] = { - DataSet.SeqFileFolder.files(path, sc, classNumber).transform( - MTLabeledBGRImgToBatch[ByteRecord]( + + DataSet.SeqFileFolder.filesToImageFeatureDataset(path, sc, 1000).transform( + MTImageFeatureToBatch( width = imageSize, height = imageSize, batchSize = batchSize, - transformer = (BytesToBGRImg() -> BGRImgCropper(imageSize, imageSize, CropCenter) - -> DatasetHFlip(0.5) -> BGRImgNormalizer(0.485, 0.456, 0.406, 0.229, 0.224, 0.225)) - )) + transformer = PixelBytesToMat() -> + Resize(256, 256) -> + RandomCropper(224, 224, false, CropCenter) -> + ChannelNormalize(123, 117, 104) -> + MatToTensor[Float](), toRGB = false + ) + ) } def rdd(path: String, batchSize: Int, sc: SparkContext, imageSize : Int) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala index 651efbdbdda..261f156d9ac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Inception_v1.scala @@ -29,34 +29,37 @@ object Inception_Layer_v1 { val conv1 = Sequential() conv1.add(SpatialConvolution(inputSize, config[Table](1)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "1x1")) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)).setName(namePrefix + "1x1")) conv1.add(ReLU(true).setName(namePrefix + "relu_1x1")) concat.add(conv1) val conv3 = Sequential() conv3.add(SpatialConvolution(inputSize, config[Table](2)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "3x3_reduce")) + .setInitMethod(weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "3x3_reduce")) conv3.add(ReLU(true).setName(namePrefix + "relu_3x3_reduce")) conv3.add(SpatialConvolution(config[Table](2)(1), config[Table](2)(2), 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "3x3")) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)).setName(namePrefix + "3x3")) conv3.add(ReLU(true).setName(namePrefix + "relu_3x3")) concat.add(conv3) val conv5 = Sequential() conv5.add(SpatialConvolution(inputSize, config[Table](3)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "5x5_reduce")) + .setInitMethod(weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "5x5_reduce")) conv5.add(ReLU(true).setName(namePrefix + "relu_5x5_reduce")) conv5.add(SpatialConvolution(config[Table](3)(1), config[Table](3)(2), 5, 5, 1, 1, 2, 2) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "5x5")) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)).setName(namePrefix + "5x5")) conv5.add(ReLU(true).setName(namePrefix + "relu_5x5")) concat.add(conv5) val pool = Sequential() pool.add(SpatialMaxPooling(3, 3, 1, 1, 1, 1).ceil().setName(namePrefix + "pool")) pool.add(SpatialConvolution(inputSize, config[Table](4)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "pool_proj")) + .setInitMethod(weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "pool_proj")) pool.add(ReLU(true).setName(namePrefix + "relu_pool_proj")) concat.add(pool).setName(namePrefix + "output") concat @@ -65,29 +68,35 @@ object Inception_Layer_v1 { def apply(input: ModuleNode[Float], inputSize: Int, config: Table, namePrefix : String) : ModuleNode[Float] = { val conv1x1 = SpatialConvolution(inputSize, config[Table](1)(1), 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "1x1").inputs(input) + .setInitMethod(weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "1x1").inputs(input) val relu1x1 = ReLU(true).setName(namePrefix + "relu_1x1").inputs(conv1x1) val conv3x3_1 = SpatialConvolution(inputSize, config[Table](2)(1), 1, 1, 1, 1).setInitMethod( - weightInitMethod = Xavier, Zeros).setName(namePrefix + "3x3_reduce").inputs(input) + weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "3x3_reduce").inputs(input) val relu3x3_1 = ReLU(true).setName(namePrefix + "relu_3x3_reduce").inputs(conv3x3_1) val conv3x3_2 = SpatialConvolution( config[Table](2)(1), config[Table](2)(2), 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "3x3").inputs(relu3x3_1) + .setInitMethod(weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "3x3").inputs(relu3x3_1) val relu3x3_2 = ReLU(true).setName(namePrefix + "relu_3x3").inputs(conv3x3_2) val conv5x5_1 = SpatialConvolution(inputSize, config[Table](3)(1), 1, 1, 1, 1).setInitMethod( - weightInitMethod = Xavier, Zeros).setName(namePrefix + "5x5_reduce").inputs(input) + weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "5x5_reduce").inputs(input) val relu5x5_1 = ReLU(true).setName(namePrefix + "relu_5x5_reduce").inputs(conv5x5_1) val conv5x5_2 = SpatialConvolution( config[Table](3)(1), config[Table](3)(2), 5, 5, 1, 1, 2, 2) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName(namePrefix + "5x5").inputs(relu5x5_1) + .setInitMethod(weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "5x5").inputs(relu5x5_1) val relu5x5_2 = ReLU(true).setName(namePrefix + "relu_5x5").inputs(conv5x5_2) val pool = SpatialMaxPooling(3, 3, 1, 1, 1, 1).ceil() .setName(namePrefix + "pool").inputs(input) val convPool = SpatialConvolution(inputSize, config[Table](4)(1), 1, 1, 1, 1).setInitMethod( - weightInitMethod = Xavier, Zeros).setName(namePrefix + "pool_proj").inputs(pool) + weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName(namePrefix + "pool_proj").inputs(pool) val reluPool = ReLU(true).setName(namePrefix + "relu_pool_proj").inputs(convPool) JoinTable(2, 0).inputs(relu1x1, relu3x3_2, relu5x5_2, reluPool) @@ -98,16 +107,17 @@ object Inception_v1_NoAuxClassifier { def apply(classNum: Int, hasDropout: Boolean = true): Module[Float] = { val model = Sequential() model.add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) - .setInitMethod(weightInitMethod = Xavier, Zeros) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)) .setName("conv1/7x7_s2")) model.add(ReLU(true).setName("conv1/relu_7x7")) model.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2")) model.add(SpatialCrossMapLRN(5, 0.0001, 0.75).setName("pool1/norm1")) - model.add(SpatialConvolution(64, 64, 1, 1, 1, 1).setInitMethod(weightInitMethod = Xavier, Zeros) + model.add(SpatialConvolution(64, 64, 1, 1, 1, 1). + setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)) .setName("conv2/3x3_reduce")) model.add(ReLU(true).setName("conv2/relu_3x3_reduce")) model.add(SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName("conv2/3x3")) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)).setName("conv2/3x3")) model.add(ReLU(true).setName("conv2/relu_3x3")) model.add(SpatialCrossMapLRN(5, 0.0001, 0.75). setName("conv2/norm2")) model.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool2/3x3_s2")) @@ -134,15 +144,17 @@ object Inception_v1_NoAuxClassifier { def graph(classNum: Int, hasDropout: Boolean = true) : Module[Float] = { val input = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName("conv1/7x7_s2").inputs() + .setInitMethod(weightInitMethod = Xavier, + ConstInitMethod(0.1)).setName("conv1/7x7_s2").inputs() val conv1_relu = ReLU(true).setName("conv1/relu_7x7").inputs(input) val pool1_s2 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2").inputs(conv1_relu) val pool1_norm1 = SpatialCrossMapLRN(5, 0.0001, 0.75).setName("pool1/norm1").inputs(pool1_s2) val conv2 = SpatialConvolution(64, 64, 1, 1, 1, 1).setInitMethod(weightInitMethod = Xavier, - Zeros).setName("conv2/3x3_reduce").inputs(pool1_norm1) + ConstInitMethod(0.1)).setName("conv2/3x3_reduce").inputs(pool1_norm1) val conv2_relu = ReLU(true).setName("conv2/relu_3x3_reduce").inputs(conv2) val conv2_3x3 = SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros).setName("conv2/3x3").inputs(conv2_relu) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)). + setName("conv2/3x3").inputs(conv2_relu) val conv2_relu_3x3 = ReLU(true).setName("conv2/relu_3x3").inputs(conv2_3x3) val conv2_norm2 = SpatialCrossMapLRN(5, 0.0001, 0.75) .setName("conv2/norm2").inputs(conv2_relu_3x3) @@ -182,17 +194,17 @@ object Inception_v1 { def apply(classNum: Int, hasDropout: Boolean = true): Module[Float] = { val feature1 = Sequential() feature1.add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) - .setInitMethod(weightInitMethod = Xavier, Zeros) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)) .setName("conv1/7x7_s2")) feature1.add(ReLU(true).setName("conv1/relu_7x7")) feature1.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2")) feature1.add(SpatialCrossMapLRN(5, 0.0001, 0.75).setName("pool1/norm1")) feature1.add(SpatialConvolution(64, 64, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)) .setName("conv2/3x3_reduce")) feature1.add(ReLU(true).setName("conv2/relu_3x3_reduce")) feature1.add(SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)) .setName("conv2/3x3")) feature1.add(ReLU(true).setName("conv2/relu_3x3")) feature1.add(SpatialCrossMapLRN(5, 0.0001, 0.75). setName("conv2/norm2")) @@ -267,17 +279,17 @@ object Inception_v1 { def graph(classNum: Int, hasDropout: Boolean = true): Module[Float] = { val input = Input() val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) - .setInitMethod(weightInitMethod = Xavier, Zeros) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)) .setName("conv1/7x7_s2").inputs(input) val relu1 = ReLU(true).setName("conv1/relu_7x7").inputs(conv1) val pool1 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2").inputs(relu1) val lrn1 = SpatialCrossMapLRN(5, 0.0001, 0.75).setName("pool1/norm1").inputs(pool1) val conv2 = SpatialConvolution(64, 64, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)) .setName("conv2/3x3_reduce").inputs(lrn1) val relu2 = ReLU(true).setName("conv2/relu_3x3_reduce").inputs(conv2) val conv3 = SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1) - .setInitMethod(weightInitMethod = Xavier, Zeros) + .setInitMethod(weightInitMethod = Xavier, ConstInitMethod(0.1)) .setName("conv2/3x3").inputs(relu2) val relu3 = ReLU(true).setName("conv2/relu_3x3").inputs(conv3) val lrn2 = SpatialCrossMapLRN(5, 0.0001, 0.75). setName("conv2/norm2").inputs(relu3) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala index 8ed80dd82df..dfb3b7434fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/inception/Train.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.models.inception import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Module} -import com.intel.analytics.bigdl.optim.SGD.{Poly, SequentialSchedule, Warmup} +import com.intel.analytics.bigdl.optim.SGD.{MultiStep, Poly, SequentialSchedule, Warmup} import com.intel.analytics.bigdl.optim._ import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T, Table} import org.apache.log4j.{Level, Logger} @@ -78,7 +78,7 @@ object TrainInceptionV1 { else (param.maxLr.getOrElse(param.learningRate) - param.learningRate) / warmupIteration val polyIteration = maxIteration - warmupIteration val lrSchedule = SequentialSchedule(iterationPerEpoch) - .add(Warmup(warmupDelta), warmupIteration).add(Poly(0.5, polyIteration), polyIteration) + .add(Warmup(warmupDelta), warmupIteration).add(Poly(0.5, maxIteration), polyIteration) new SGD[Float](learningRate = param.learningRate, learningRateDecay = 0.0, weightDecay = param.weightDecay, momentum = 0.9, dampening = 0.0, nesterov = false, learningRateSchedule = lrSchedule) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala index 00f9272226f..17f02b00acb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala @@ -779,7 +779,7 @@ class InceptionSpec extends TorchSpec { val loss = criterion.forward(output, labels) // since we already set the seed, the loss should match exactly - loss should be (6.901158f) + loss should be (6.6648364f) } "Inception_Layer_V1 graph" should "be correct" in { From a310b41684930ff33273112352a7f9bbebabdb7b Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 23 Oct 2018 17:04:32 +0800 Subject: [PATCH 0840/1065] Add python API for new transformers and apply them in inception training example (#2663) * refinement on python API * fix ut --- .../dllib/utils/python/api/PythonBigDL.scala | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c43d2cba5fe..dcfdb7e0609 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -33,6 +33,7 @@ import org.apache.spark.rdd.RDD import java.lang.{Boolean => JBoolean} import java.nio.ByteOrder +import com.intel.analytics.bigdl.dataset.image.{CropCenter, CropRandom, CropperMethod} import com.intel.analytics.bigdl.dlframes._ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasModel} @@ -2909,6 +2910,39 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab FixExpand(eh, ew) } + def createChannelScaledNormalizer(meanR: Int, meanG: Int, meanB: Int, scale: Double) + : ChannelScaledNormalizer = { + ChannelScaledNormalizer(meanR, meanG, meanB, scale) + } + + def createRandomAlterAspect(min_area_ratio: Float, + max_area_ratio: Int, + min_aspect_ratio_change: Float, + interp_mode: String, + cropLength: Int) + : RandomAlterAspect = { + RandomAlterAspect(min_area_ratio, max_area_ratio, min_aspect_ratio_change, + interp_mode, cropLength) + } + + def createRandomCropper(cropWidth: Int, cropHeight: Int, + mirror: Boolean, cropperMethod: String, + channels: Int) + : RandomCropper = { + if (cropperMethod == "Random") { + RandomCropper(cropWidth, cropHeight, mirror, + CropRandom, channels) + } else { + RandomCropper(cropWidth, cropHeight, mirror, + CropCenter, channels) + } + } + + def createRandomResize(minSize: Int, maxSize : Int) + : RandomResize = { + RandomResize(minSize, maxSize) + } + def transformImageFeature(transformer: FeatureTransformer, feature: ImageFeature) : ImageFeature = { transformer.transform(feature) From 09426abd5ffc49c106c7847a24e1ed778a9a18e1 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 26 Oct 2018 14:26:05 +0800 Subject: [PATCH 0841/1065] fix: uuid() will return a new uuid every call (#2667) * fix: uuid() will return a new uuid every call * fix: add partitionId to value() * fix: we need not add partition id to the value() * fix: code clean --- .../bigdl/dllib/models/utils/ModelBroadcast.scala | 4 +++- .../intel/analytics/bigdl/dllib/optim/PredictorSpec.scala | 7 +++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 92a73222bb4..bb2d2a347c1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -35,6 +35,8 @@ import scala.reflect.ClassTag * ModelBroadcast is used to broadcast model */ trait ModelBroadcast[T] extends Serializable { + private val _uuid = UUID.randomUUID().toString + /** * Broadcast the model * @param sc SparkContext @@ -52,7 +54,7 @@ trait ModelBroadcast[T] extends Serializable { */ def value(initGradient: Boolean = false, shareWeight: Boolean = true): Module[T] - def uuid(): String = UUID.randomUUID().toString + def uuid(): String = _uuid } object ModelBroadcast { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index cde1d8cf1cf..e6495312a92 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -268,8 +268,11 @@ class PredictorSpec extends SparkContextLifeCycle with Matchers { var second: Map[Long, StorageInfo] = null (0 until 20).foreach { i => val detection = quant.predictImage(imageFrame, batchPerPartition = 16).toDistributed() - detection.rdd.first() - detection.rdd.collect() + + // adding a transformer to handle multi trans of detection will be right + val transformer = ImageFrameToSample() + transformer(detection).toDistributed().rdd.collect() + println("=" * 80) println(StorageManager.get().count(!_._2.isFreed)) println("-" * 80) From c3482aaa6e5b59857174a5fd555b283436f08711 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 29 Oct 2018 16:51:55 +0800 Subject: [PATCH 0842/1065] [Bug Fix] Fix predictor issue while batch size == 1 for some topology (#2669) * fix predictor issue * refinemnt per batchsize only * fix ut * remove unused code * fix batch size == 1 * fix ut --- .../bigdl/dllib/optim/Predictor.scala | 18 +++++++++++++----- .../bigdl/dllib/optim/LocalPredictorSpec.scala | 4 ++-- .../bigdl/dllib/optim/PredictorSpec.scala | 4 ++-- 3 files changed, 17 insertions(+), 9 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index b713f6a085d..a8a3f8a8e3d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -71,11 +71,17 @@ object Predictor { private[optim] def splitTensor[T: ClassTag](output: Tensor[T], shareBuffer: Boolean, batchSize: Int) (implicit ev: TensorNumeric[T]): Array[Activity] = { - val result = if (shareBuffer) output else output.clone() - val size = result.size(1) - require(batchSize == size, - s"The batchSize is required to be $size, while actual is $batchSize") - val out = result.split(1) + val result = if (shareBuffer) output else output.clone + + val out = if (batchSize == 1) { + Array(result.squeeze) + } else { + val size = result.size(1) + require(batchSize == size, + s"The batchSize is required to be $size, while actual is $batchSize") + result.split(1) + } + out.asInstanceOf[Array[Activity]] } @@ -169,6 +175,8 @@ object Predictor { implicit ev: TensorNumeric[T]): RDD[Int] = { val result = Predictor.predict(dataSet, batchSize, true, model, batchPerPartition, featurePaddingParam) + val res = Predictor.predict(dataSet, batchSize, true, model, + batchPerPartition, featurePaddingParam).collect() result.mapPartitions { partition => partition.map(output => { val _output = output.toTensor[T] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index f5a768bd966..58d9222306d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -69,7 +69,7 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) prob(0) should be(model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 224, 224))) - .toTensor[Float].split(1)(0)) + .toTensor[Float].squeeze()) } "predictImage with more data" should "work properly" in { @@ -147,7 +147,7 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) prob(0) should be(model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 224, 224))) - .toTensor[Float].split(1)(0)) + .toTensor[Float].squeeze()) } "predictImage empty" should "work properly" in { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala index e6495312a92..946e5d7a90e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/PredictorSpec.scala @@ -124,7 +124,7 @@ class PredictorSpec extends SparkContextLifeCycle with Matchers { val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) prob(0) should be (model.forward(data(0).feature.reshape(Array(1, 3, 224, 224))) - .toTensor[Float].split(1)(0)) + .toTensor[Float].squeeze) } "model.predictImage with simple model" should "be correct" in { @@ -146,7 +146,7 @@ class PredictorSpec extends SparkContextLifeCycle with Matchers { val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) prob(0) should be (model.forward(data(0).feature.reshape(Array(1, 3, 224, 224))) - .toTensor[Float].split(1)(0)) + .toTensor[Float].squeeze) } "predictImage with variant feature data" should "work" in { From 50ba9d500bba5e92c6116b1cf979836975368db7 Mon Sep 17 00:00:00 2001 From: abdmob Date: Thu, 1 Nov 2018 09:33:16 +0300 Subject: [PATCH 0843/1065] AND/OR compound triggers support (#2675) * AND/OR compound triggers support * Unit-tests for compound triggers * Unit-tests for compound triggers updates for OR * Style fixes for Trigger.and/or * Style fixes for Trigger.and/or * Trigger.endWhen docs update * Trigger.endWhen docs update --- .../analytics/bigdl/dllib/optim/Trigger.scala | 28 +++++++++++ .../bigdl/dllib/optim/OptimizerSpec.scala | 48 +++++++++++++++++++ 2 files changed, 76 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Trigger.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Trigger.scala index 801ba6d2613..aa21e145555 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Trigger.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Trigger.scala @@ -123,5 +123,33 @@ object Trigger { } } } + + /** + * A trigger contains other triggers and triggers when all of them trigger (logical AND) + * @param first first trigger + * @param others others triggers + */ + def and(first : Trigger, others : Trigger*): Trigger = { + new Trigger() { + override def apply(state: Table): Boolean = { + first.apply(state) && others.forall(_.apply(state)) + } + } + } + + /** + * A trigger contains other triggers and triggers when any of them trigger (logical OR) + * @param first first trigger + * @param others others triggers + */ + def or(first : Trigger, others : Trigger*): Trigger = { + new Trigger() { + override def apply(state: Table): Boolean = { + first.apply(state) || others.exists(_.apply(state)) + } + } + } + + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimizerSpec.scala index 550643b3e05..98a6a2a7e17 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/OptimizerSpec.scala @@ -151,6 +151,54 @@ class OptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { dummyOptimizer.optimize() } + it should "support multiply triggers to end training" in { + + def createDummyBooleanOptimiser(endShouldBe : Boolean) : Optimizer[Float, Float] = + new Optimizer[Float, Float](model, null, null) { + override def optimize() : Module[Float] = { + val state = T() + endWhen(state) should be(endShouldBe) + model + } + } + + def createDummyTrigger(triggerBoolRes : Boolean) : Trigger = new Trigger { + override def apply(state: Table): Boolean = triggerBoolRes + } + + val trueDummyOptimizer = createDummyBooleanOptimiser(true) + val falseDummyOptimizer = createDummyBooleanOptimiser(false) + + val trueDummyTrigger = createDummyTrigger(true) + val falseDummyTrigger = createDummyTrigger(false) + + // AND + trueDummyOptimizer.setEndWhen(Trigger.and(trueDummyTrigger, trueDummyTrigger)) + trueDummyOptimizer.optimize() + + falseDummyOptimizer.setEndWhen(Trigger.and(trueDummyTrigger, falseDummyTrigger)) + falseDummyOptimizer.optimize() + + falseDummyOptimizer.setEndWhen(Trigger.and(falseDummyTrigger, trueDummyTrigger)) + falseDummyOptimizer.optimize() + + falseDummyOptimizer.setEndWhen(Trigger.and(falseDummyTrigger, falseDummyTrigger)) + falseDummyOptimizer.optimize() + + // OR + trueDummyOptimizer.setEndWhen(Trigger.or(trueDummyTrigger, falseDummyTrigger)) + trueDummyOptimizer.optimize() + + trueDummyOptimizer.setEndWhen(Trigger.or(trueDummyTrigger, trueDummyTrigger)) + trueDummyOptimizer.optimize() + + trueDummyOptimizer.setEndWhen(Trigger.or(falseDummyTrigger, trueDummyTrigger)) + trueDummyOptimizer.optimize() + + falseDummyOptimizer.setEndWhen(Trigger.or(falseDummyTrigger, falseDummyTrigger)) + falseDummyOptimizer.optimize() + } + it should "save model to given path" in { val filePath = java.io.File.createTempFile("OptimizerSpec", "model").getAbsolutePath Files.delete(Paths.get(filePath)) From b9ed34649d519b6bc3137e0a4d3192a85270013a Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 7 Nov 2018 16:28:34 +0800 Subject: [PATCH 0844/1065] add dnn graph (#2666) * add dnn graph * move compile to forward, add graph test to perf * add dnn graph option to example * style check * replace dnn with dnn graph in examples --- .../bigdl/dllib/models/lenet/LeNet5.scala | 19 + .../bigdl/dllib/models/lenet/Train.scala | 2 +- .../dllib/models/resnet/TrainImageNet.scala | 2 +- .../dllib/models/vgg/TrainImageNet.scala | 2 +- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 367 ++++++++++++++++++ .../bigdl/dllib/nn/mkldnn/Perf.scala | 154 +++++++- .../bigdl/dllib/nn/mkldnn/models/Vgg16.scala | 61 +++ .../bigdl/dllib/optim/DistriOptimizer.scala | 3 +- .../bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala | 158 ++++++++ .../utils/serializer/SerializerSpec.scala | 3 +- 10 files changed, 763 insertions(+), 8 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala index d249a15441e..aa6c815b8bb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/LeNet5.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.mkldnn.DnnGraph object LeNet5 { def apply(classNum: Int): Module[Float] = { @@ -103,4 +104,22 @@ object LeNet5 { .add(mkldnn.ReorderMemory(mkldnn.HeapData(outputShape, Memory.Format.nc))) model } + + def dnnGraph(batchSize: Int, classNum: Int): mkldnn.DnnGraph = { + val inputShape = Array(batchSize, 1, 28, 28) + val outputShape = Array(batchSize, 10) + + val input = mkldnn.Input(inputShape, Memory.Format.nchw).inputs() + val conv1 = mkldnn.SpatialConvolution(1, 20, 5, 5).setName("conv1").inputs(input) + val bn1 = mkldnn.SpatialBatchNormalization(20).setName("bn1").inputs(conv1) + val pool1 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool1").inputs(bn1) + val conv2 = mkldnn.SpatialConvolution(20, 50, 5, 5).setName("conv2").inputs(pool1) + val pool2 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool2").inputs(conv2) + val ip1 = mkldnn.Linear(50 * 4 * 4, 500).setName("ip1").inputs(pool2) + val relu1 = mkldnn.ReLU().setName("relu1").inputs(ip1) + val ip2 = mkldnn.Linear(500, 10).setName("ip2").inputs(relu1) + val output = mkldnn.ReorderMemory(mkldnn.HeapData(outputShape, Memory.Format.nc)).inputs(ip2) + + DnnGraph(Array(input), Array(output)) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala index fe61f14df67..34d772281bb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala @@ -53,7 +53,7 @@ object Train { } else { Engine.getEngineType() match { case MklBlas => LeNet5(10) - case MklDnn => LeNet5.dnn(param.batchSize / Engine.nodeNumber(), 10) + case MklDnn => LeNet5.dnnGraph(param.batchSize / Engine.nodeNumber(), 10) } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala index e883923df93..4443163b7c7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala @@ -90,7 +90,7 @@ object TrainImageNet { curModel case MklDnn => - nn.mkldnn.ResNet(param.batchSize / Engine.nodeNumber(), param.classes, + nn.mkldnn.ResNet.graph(param.batchSize / Engine.nodeNumber(), param.classes, T("depth" -> 50, "dataSet" -> ImageNet)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/TrainImageNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/TrainImageNet.scala index dd213c947be..9e454b201a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/TrainImageNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/vgg/TrainImageNet.scala @@ -55,7 +55,7 @@ object TrainImageNet { case MklBlas => Vgg_16(classNumber) case MklDnn => - nn.mkldnn.models.Vgg_16(batchSize / Engine.nodeNumber(), classNumber) + nn.mkldnn.models.Vgg_16.graph(batchSize / Engine.nodeNumber(), classNumber) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala new file mode 100644 index 00000000000..5c9dbb02fb6 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -0,0 +1,367 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import breeze.linalg.Axis._1 +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} +import com.intel.analytics.bigdl.nn.{StaticGraph, mkldnn} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{LayerException, Node, T} +import com.intel.analytics.bigdl.nn + +import scala.reflect.ClassTag + + +class DnnGraph( + private val _inputs : Seq[ModuleNode[Float]], + private val _outputs : Seq[ModuleNode[Float]], + private val _variables: Option[(Array[Tensor[Float]], Array[Tensor[Float]])] = None, + private val enableExcludeChecking: Boolean = true) + extends StaticGraph[Float](_inputs, _outputs, _variables, enableExcludeChecking) { + private var forwardExecution: Array[Node[AbstractModule[Activity, Activity, Float]]] = _ + private var backwardExecution: Array[Node[AbstractModule[Activity, Activity, Float]]] = _ + private var inputCache: Array[Activity] = _ + private var backId2ForwardId: Array[Int] = _ + + @transient protected lazy val reorderManager = new ReorderManager() + + if (enableExcludeChecking) { + excludeInvalidLayers(forwardExecution.map {_.element}) + } + + buildBackwardGraph() + + override def updateOutput(input: Activity): Activity = { + var i = 0 + while(i < forwardExecution.length) { + val node = forwardExecution(i) + val nodeInput = findDnnInput(node, input) + inputCache(i) = nodeInput + node.element.forward(nodeInput) + i += 1 + } + output = dummyOutput.element.output + output + } + + override def backward(input: Activity, gradOutput: Activity): Activity = { + val before = System.nanoTime() + val gradients = updateGradInput(input, gradOutput) + accGradParameters(input, gradOutput) + backwardTime += System.nanoTime() - before + gradients + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + dummyOutputGrad.element.gradInput = gradOutput + var i = 0 + while (i < backwardExecution.length - 1) { // do not execute the dummy backward end + val curNode = backwardExecution(i) + val curGradOutput = findDnnGradOutput(curNode, gradOutput) + // use input from forward + val curInput = inputCache(backId2ForwardId(i)) + if (!isStopGradient(curNode.element)) { + curNode.element.updateGradInput(curInput, curGradOutput) + } + i += 1 + } + gradInput = fetchModelGradInput() + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + var i = 0 + while (i < backwardExecution.length - 1) { + val curNode = backwardExecution(i) + // use input from forward + val curInput = inputCache(backId2ForwardId(i)) + val curGradOutput = findDnnGradOutput(curNode, gradOutput, true) + curNode.element.accGradParameters(curInput, curGradOutput) + curNode.element.asyncGradient() + i += 1 + } + } + + override def buildBackwardGraph(): this.type = { + super.buildBackwardGraph() + forwardExecution = forwardGraph.topologySort.reverse + inputCache = new Array[Activity](forwardExecution.length) + backwardExecution = backwardGraph.topologySort.reverse + backId2ForwardId = new Array[Int](backwardExecution.length) + + var i = 0 + // do not execute the dummy backward end + while(i < backwardExecution.length - 1) { + var j = 0 + var find = false + while(j < forwardExecution.length) { + if (forwardExecution(j).element.getName() == backwardExecution(i).element.getName()) { + val e = forwardExecution(j).element + // when creating graph, there may add nn.Identity node, + // here we have to change it to mkldnn node + if (e.isInstanceOf[nn.Identity[Float]]) { + forwardExecution(j).element = toDnnIdentity(e.asInstanceOf[nn.Identity[Float]]) + backwardExecution(i).element = forwardExecution(j).element + } else { + require(e.isInstanceOf[MklDnnModule], s"DnnGraph should only contain dnn layers," + + s"but find ${forwardExecution(j).element.getName()} is not a mkldnn layer") + } + backId2ForwardId(i) = j + find = true + } + j += 1 + } + require(find, "Cannot find backward layer in forward executions") + i += 1 + } + this + } + + // change nn identity to mkldnn identity + private def toDnnIdentity(model: nn.Identity[Float]) + : AbstractModule[Activity, Activity, Float] = { + mkldnn.Identity[Float]().setName(model.getName()) + .asInstanceOf[AbstractModule[Activity, Activity, Float]] + } + + // if node has no previous node, then it will just use graph input as real module input + private def findDnnInput(node: ModuleNode[Float], input: Activity): Activity = { + if (node.element.isInstanceOf[WithoutInput]) return null + + val realInputFormats = node.element.asInstanceOf[MklDnnModule].inputFormats() + + val nodeInput = if (node.prevNodes.isEmpty) { + getInput(node, input) + } else { + val prevActivitiesAndFormats = node.prevNodesAndEdges + .filterNot(n => n._1.element.isInstanceOf[ControlDependency[Float]]) + .map(n => { + val format = n._1.element.asInstanceOf[MklDnnModule].outputFormats() + n._2.fromIndex match { + case Some(i) => + if (n._1.element.output == null || (i == 1 && n._1.element.output.isTensor)) { + (n._1.element.output, format) + } else { + (n._1.element.output.toTable.apply[Activity](i), Array(format(i - 1))) + } + case None => (n._1.element.output, format) + } + }) + + val inputAndFormat = if (prevActivitiesAndFormats.length == 1) { + prevActivitiesAndFormats.head + } else { + (T.seq(prevActivitiesAndFormats.map(m => m._1)), + prevActivitiesAndFormats.map(m => m._2).toArray.flatMap(_.toSeq)) + } + reorderManager.infer(inputAndFormat._2, realInputFormats, inputAndFormat._1) + } + nodeInput + } + + private def findDnnGradOutput(curNode: ModuleNode[Float], gradOutput: Activity, + isAcc: Boolean = false): Activity = { + var curGradOutput : Activity = if (curNode.eq(dummyOutputGrad)) gradOutput else null + + val realGradOutputFormats = if (isAcc) { + curNode.element.asInstanceOf[MklDnnModule].gradOutputWeightFormats() + } else { + curNode.element.asInstanceOf[MklDnnModule].gradOutputFormats() + } + + curNode.prevNodesAndEdges.filterNot(n => n._1.element.isInstanceOf[ControlDependency[Float]]) + .foreach(n => { + val (otherActivity, format) = + if (n._1.element.gradInput.isTensor || n._1.nextEdges.length == 1) { + (n._1.element.gradInput, n._1.element.asInstanceOf[MklDnnModule].gradInputFormats()) + } else { + val index = n._1.nextEdges.indexOf(n._2) + 1 + (n._1.element.gradInput.toTable.apply[Activity](index), + Array(n._1.element.asInstanceOf[MklDnnModule].gradInputFormats().apply(index - 1))) + } + + n._2.fromIndex match { + case Some(i) => + if (i == 1 && curNode.element.output.isTensor) { + curGradOutput = addActivity(curGradOutput, realGradOutputFormats, + otherActivity, format) + } else { + if (curNode.element.output.isTable && curGradOutput == null) { + curGradOutput = T() + } + val curActivity = curGradOutput.toTable.getOrElse[Activity](i, null) + curGradOutput.toTable(i) = addActivity(curActivity, realGradOutputFormats, + otherActivity, format) + } + case None => + curGradOutput = addActivity(curGradOutput, realGradOutputFormats, + otherActivity, format) + } + }) + + if (curNode.element.output.isTable) { + addZeroTensorToMissingGradOutput(curNode.element.output.toTable, curGradOutput.toTable) + } + curGradOutput + } + + private def addActivity(activity: Activity, realFormats: Array[MemoryData], + other: Activity, otherFormats: Array[MemoryData]): Activity = { + val realOthers = if (otherFormats.length > 0) { + reorderManager.infer(otherFormats, realFormats, other) + } else { + other + } + super.accActivity(activity, realOthers) + } + + final def compile(phase: Phase) : Unit = { + setRuntime(new MklDnnRuntime(), phase) + initPrimitives(phase, Array[MemoryData]()) + } + + private def setRuntime(runtime: MklDnnRuntime, phase: Phase): Unit = { + reorderManager.setRuntime(runtime) + forwardExecution.foreach(m => m.element.asInstanceOf[MklDnnModule].setRuntime(runtime)) + if (phase == Phase.TrainingPhase) { + var i = 0 + while (i < backwardExecution.length - 1) { // do not execute the dummy backward end + backwardExecution(i).element.asInstanceOf[MklDnnModule].setRuntime(runtime) + i += 1 + } + } + } + + private def initPrimitives(phase: Phase, inputFormats: Array[MemoryData]): Unit = { + val outFormats = initFwdPrimitives(inputFormats, phase)._2 + if (phase == Phase.TrainingPhase) { + initBwdPrimitives(outFormats, phase) + initGradWPrimitives(outFormats, phase) + } + } + + private def findInputFormats(node: ModuleNode[Float], inputs: Array[MemoryData]) + : Array[MemoryData] = { + if (node.prevNodes.isEmpty) { + inputs + } else { + val prevFormats = node.prevNodesAndEdges + .filterNot(n => n._1.element.isInstanceOf[ControlDependency[Float]]) + .map(n => { + val outputFormats = n._1.element.asInstanceOf[MklDnnModule].outputFormats() + // if outputFormats length is 1, output is a tensor + n._2.fromIndex match { + case Some(i) => + if (n._1.element.output == null || (i == 1 && outputFormats.length == 1)) { + outputFormats + } else { + val index = n._2.fromIndex.get + Array(outputFormats(index)) + } + case None => outputFormats + } + }).toArray + prevFormats.flatMap(n => n.toSeq) + } + } + + private def findGradOutputFormats(node: ModuleNode[Float], inputs: Array[MemoryData]) + : Array[MemoryData] = { + if (node.prevNodes.isEmpty) { + inputs + } else { + val prevFormats = node.prevNodesAndEdges + .filterNot(n => n._1.element.isInstanceOf[ControlDependency[Float]]) + .map(n => { + // gradInput is tensor or nextEdges number is 1 + if (n._1.element.asInstanceOf[MklDnnModule].gradInputFormats().length == 1 || + n._1.nextEdges.length == 1) { + n._1.element.asInstanceOf[MklDnnModule].gradInputFormats() + } else { + val index = n._1.nextEdges.indexOf(n._2) + val f = n._1.element.asInstanceOf[MklDnnModule].gradInputFormats() + Array(f(index)) + } + }).toArray + // reminder: if node has more than one previous node, use the first one as gradoutput format + prevFormats(0) + } + } + + // init forward primitives + private def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + var lastOutputFormats = inputs + var firstRealInputFormats: Array[MemoryData] = null + for (i <- 0 until forwardExecution.length) { + val m = forwardExecution(i) + lastOutputFormats = findInputFormats(m, inputs) + val realInputAndOutputFormats = + m.element.asInstanceOf[MklDnnModule].initFwdPrimitives(lastOutputFormats, phase) + lastOutputFormats.zip(realInputAndOutputFormats._1).foreach { + case (o, i) => reorderManager.register(o, i) + } + if (i == 0) firstRealInputFormats = realInputAndOutputFormats._1 + } + (firstRealInputFormats, lastOutputFormats) + } + + // init updateGradInput primitives + private def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + var lastGradInputFormats = grads + var firstRealGradOutputFormats: Array[MemoryData] = null + for (i <- 0 until backwardExecution.length - 1) { + val m = backwardExecution(i) + lastGradInputFormats = findGradOutputFormats(m, grads) + val realGradOutputAndInputFomrats = + m.element.asInstanceOf[MklDnnModule].initBwdPrimitives(lastGradInputFormats, phase) + lastGradInputFormats.zip(realGradOutputAndInputFomrats._1).foreach { + case (gi, go) => reorderManager.register(gi, go) + } + if (i == 0) firstRealGradOutputFormats = realGradOutputAndInputFomrats._1 + } + (firstRealGradOutputFormats, lastGradInputFormats) + } + + // init acc primitives + private def initGradWPrimitives(grads: Array[MemoryData], phase: Phase) = { + var lastGradInputFormats = grads + var firstRealGradOutputFormats: Array[MemoryData] = null + for (i <- 0 until backwardExecution.length - 1) { + val m = backwardExecution(i) + lastGradInputFormats = findGradOutputFormats(m, grads) + val realGradOutput = + m.element.asInstanceOf[MklDnnModule].initGradWPrimitives(lastGradInputFormats, phase) + lastGradInputFormats.zip(realGradOutput).foreach { + case (gi, go2) => reorderManager.register(gi, go2) + } + if (i == 0) firstRealGradOutputFormats = realGradOutput + } + firstRealGradOutputFormats + } +} + +object DnnGraph { + def apply( + inputs : Seq[ModuleNode[Float]], + outputs : Seq[ModuleNode[Float]], + variables: Option[(Array[Tensor[Float]], Array[Tensor[Float]])] = None, + enableExcludeChecking: Boolean = true): DnnGraph = + new DnnGraph(inputs, outputs, variables, enableExcludeChecking) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala index a1674a297f1..e4ab9db6fca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} +import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} @@ -39,7 +40,7 @@ object Perf { val parser = new OptionParser[ResNet50PerfParams]("BigDL w/ Dnn Local Model Performance Test") { opt[String]('m', "model") - .text("model you want, vgg16 | resnet50") + .text("model you want, vgg16 | resnet50 | vgg16_graph | resnet50_graph") .action((v, p) => p.copy(model = v)) opt[Int]('b', "batchSize") .text("Batch size of input data") @@ -77,16 +78,29 @@ object Perf { val model = params.model match { case "vgg16" => Vgg_16(batchSize, classNum, true) case "resnet50" => ResNet(batchSize, classNum, T("depth" -> 50, "dataSet" -> ImageNet)) + case "vgg16_graph" => Vgg_16.graph(batchSize, classNum, true) + case "resnet50_graph" => + ResNet.graph(batchSize, classNum, T("depth" -> 50, "dataSet" -> ImageNet)) case _ => throw new UnsupportedOperationException(s"Unkown model ${params.model}") } val criterion = CrossEntropyCriterion() if (training) { - model.compile(TrainingPhase, Array(HeapData(inputShape, inputFormat))) + if (model.isInstanceOf[MklDnnContainer]) { + model.asInstanceOf[MklDnnContainer] + .compile(TrainingPhase, Array(HeapData(inputShape, inputFormat))) + } else if (model.isInstanceOf[DnnGraph]) { + model.asInstanceOf[DnnGraph].compile(TrainingPhase) + } model.training() } else { - model.compile(InferencePhase, Array(HeapData(inputShape, inputFormat))) + if (model.isInstanceOf[MklDnnContainer]) { + model.asInstanceOf[MklDnnContainer] + .compile(InferencePhase, Array(HeapData(inputShape, inputFormat))) + } else if (model.isInstanceOf[DnnGraph]) { + model.asInstanceOf[DnnGraph].compile(InferencePhase) + } model.evaluate() } @@ -256,6 +270,140 @@ object ResNet { model } + def graph(batchSize: Int, classNum: Int, opt: Table): DnnGraph = { + + def modelInit(graph: DnnGraph): Unit = { + graph.getSortedForwardExecutions.foreach(n => { + n.element match { + case conv: SpatialConvolution => + val n: Float = conv.kernelW * conv.kernelW * conv.nOutputPlane + val weight = Tensor[Float].resize(conv.weight.size()).apply1 { _ => + RNG.normal(0, Math.sqrt(2.0f / n)).toFloat + } + val bias = Tensor[Float].resize(conv.bias.size()).apply1(_ => 0.0f) + conv.weight.copy(weight) + conv.bias.copy(bias) + + case bn: SpatialBatchNormalization => + val weightAndBias = Tensor[Float]().resize(Array(2, bn.nOutput)) + weightAndBias.select(1, 1).fill(1) + weightAndBias.select(1, 2).fill(0) + bn.weightAndBias.copy(weightAndBias.view(Array(bn.nOutput * 2))) + + case linear: Linear => + val bias = Tensor[Float](linear.bias.size()).apply1(_ => 0.0f) + linear.bias.copy(bias) + + case _ => + } + }) + } + + val depth = opt.get("depth").getOrElse(18) + val shortCutType = opt.get("shortcutType") + val shortcutType = shortCutType.getOrElse(ShortcutType.B).asInstanceOf[ShortcutType] + val dataSet = opt.getOrElse[DatasetType]("dataSet", DatasetType.CIFAR10) + val optnet = opt.get("optnet").getOrElse(true) + + def shortcut(input: ModuleNode[Float], nInputPlane: Int, nOutputPlane: Int, + stride: Int, name: String): ModuleNode[Float] = { + val useConv = shortcutType == ShortcutType.C || + (shortcutType == ShortcutType.B && nInputPlane != nOutputPlane) + + if (useConv) { + val conv = Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride, optnet = optnet) + .setName(s"res${name}_branch1").inputs(input) + SbnDnn(nOutputPlane).setName(s"bn${name}_branch1").inputs(conv) + } else if (nInputPlane != nOutputPlane) { + throw new IllegalArgumentException(s"useConv false") + } else { + Identity().inputs(input) + } + } + + def bottleneck(input: ModuleNode[Float], n: Int, stride: Int, name: String = "") + : ModuleNode[Float] = { + val nInputPlane = iChannels + iChannels = n * 4 + + val conv1 = Convolution(nInputPlane, n, 1, 1, 1, 1, 0, 0, optnet = optnet) + .setName(s"res${name}_branch2a").inputs(input) + val bn1 = SbnDnn(n).setName(s"bn${name}_branch2a").inputs(conv1) + val relu1 = ReLU().setName(s"res${name}_branch2a_relu").inputs(bn1) + val conv2 = Convolution(n, n, 3, 3, stride, stride, 1, 1, optnet = optnet).setName( + s"res${name}_branch2b").inputs(relu1) + val bn2 = SbnDnn(n).setName(s"bn${name}_branch2b").inputs(conv2) + val relu3 = ReLU().setName(s"res${name}_branch2b_relu").inputs(bn2) + val conv3 = Convolution(n, n*4, 1, 1, 1, 1, 0, 0, optnet = optnet).setName( + s"res${name}_branch2c").inputs(relu3) + val bn3 = SbnDnn(n * 4).setInitMethod(Zeros, Zeros).setName( + s"bn${name}_branch2c").inputs(conv3) + + val short = shortcut(input, nInputPlane, n*4, stride, name) + val cadd = CAddTable().setName(s"res$name"). + inputs(Array(bn3.asInstanceOf[ModuleNode[Float]], short)) + val relu = ReLU().setName(s"res${name}_relu").inputs(cadd) + relu + } + + def getName(i: Int, name: String): String = { + val name1 = i match { + case 1 => name + "a" + case 2 => name + "b" + case 3 => name + "c" + case 4 => name + "d" + case 5 => name + "e" + case 6 => name + "f" + } + return name1 + } + + def layer(input: ModuleNode[Float], + block: (ModuleNode[Float], Int, Int, String) => ModuleNode[Float], + features: Int, + count: Int, stride: Int = 1, name : String): ModuleNode[Float] = { + var in = input + for (i <- 1 to count) { + val res = block(in, features, if (i == 1) stride else 1, getName(i, name)) + in = res + } + in + } + + if (dataSet == DatasetType.ImageNet) { + val cfg = Map( + 50 -> ((3, 4, 6, 3), 2048, + bottleneck: (ModuleNode[Float], Int, Int, String) => ModuleNode[Float]) + ) + + require(cfg.keySet.contains(depth), s"Invalid depth ${depth}") + + val (loopConfig, nFeatures, block) = cfg.get(depth).get + iChannels = 64 + + val input = Input(Array(batchSize, 3, 224, 224), Memory.Format.nchw).inputs() + val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = false) + .setName("conv1").inputs(input) + val bn1 = SbnDnn(64).setName("bn_conv1").inputs(conv1) + val relu1 = ReLU().setName("conv1_relu").inputs(bn1) + val pool1 = MaxPooling(3, 3, 2, 2).setName("pool1").inputs(relu1) + val layer1 = layer(pool1, block, 64, loopConfig._1, name = "2") + val layer2 = layer(layer1, block, 128, loopConfig._2, 2, name = "3") + val layer3 = layer(layer2, block, 256, loopConfig._3, 2, name = "4") + val layer4 = layer(layer3, block, 512, loopConfig._4, 2, name = "5") + val pool2 = AvgPooling(7, 7, 1, 1).setName("pool5").inputs(layer4) + val fc = Linear(nFeatures, classNum).setInitMethod(RandomNormal(0.0, 0.01), Zeros).setName( + "fc1000").inputs(pool2) + val output = ReorderMemory(HeapData(Array(batchSize, classNum), Memory.Format.nc)).inputs(fc) + + val model = DnnGraph(Array(input), Array(output)) + modelInit(model) + model + } else { + throw new IllegalArgumentException(s"Invalid dataset ${dataSet}") + } + } + /** * dataset type * @param typeId type id diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala index f36a5576f75..61471440a77 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/models/Vgg16.scala @@ -90,4 +90,65 @@ object Vgg_16 { conv } } + + def graph(batchSize: Int, classNum: Int, hasDropout: Boolean = true): DnnGraph = { + val input = Input(Array(batchSize, 3, 224, 224), Memory.Format.nchw).inputs() + val conv1_1 = Conv(3, 64, 3, 3, 1, 1, 1, 1).setName("conv1_1").inputs(input) + val relu1_1 = ReLU().setName("relu1_1").inputs(conv1_1) + val conv1_2 = Conv(64, 64, 3, 3, 1, 1, 1, 1).setName("conv1_2").inputs(relu1_1) + val relu1_2 = ReLU().setName("relu1_2").inputs(conv1_2) + val pool1 = MaxPooling(2, 2, 2, 2).setName("pool1").inputs(relu1_2) + + val conv2_1 = Conv(64, 128, 3, 3, 1, 1, 1, 1).setName("conv2_1").inputs(pool1) + val relu2_1 = ReLU().setName("relu2_1").inputs(conv2_1) + val conv2_2 = Conv(128, 128, 3, 3, 1, 1, 1, 1).setName("conv2_2").inputs(relu2_1) + val relu2_2 = ReLU().setName("relu2_2").inputs(conv2_2) + val pool2 = MaxPooling(2, 2, 2, 2).setName("pool2").inputs(relu2_2) + + val conv3_1 = Conv(128, 256, 3, 3, 1, 1, 1, 1).setName("conv3_1").inputs(pool2) + val relu3_1 = ReLU().setName("relu3_1").inputs(conv3_1) + val conv3_2 = Conv(256, 256, 3, 3, 1, 1, 1, 1).setName("conv3_2").inputs(relu3_1) + val relu3_2 = ReLU().setName("relu3_2").inputs(conv3_2) + val conv3_3 = Conv(256, 256, 3, 3, 1, 1, 1, 1).setName("conv3_3").inputs(relu3_2) + val relu3_3 = ReLU().setName("relu3_3").inputs(conv3_3) + val pool3 = MaxPooling(2, 2, 2, 2).setName("pool3").inputs(relu3_3) + + val conv4_1 = Conv(256, 512, 3, 3, 1, 1, 1, 1).setName("conv4_1").inputs(pool3) + val relu4_1 = ReLU().setName("relu4_1").inputs(conv4_1) + val conv4_2 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv4_2").inputs(relu4_1) + val relu4_2 = ReLU().setName("relu4_2").inputs(conv4_2) + val conv4_3 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv4_3").inputs(relu4_2) + val relu4_3 = ReLU().setName("relu4_3").inputs(conv4_3) + val pool4 = MaxPooling(2, 2, 2, 2).setName("pool4").inputs(relu4_3) + + val conv5_1 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_1").inputs(pool4) + val relu5_1 = ReLU().setName("relu5_1").inputs(conv5_1) + val conv5_2 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_2").inputs(relu5_1) + val relu5_2 = ReLU().setName("relu5_2").inputs(conv5_2) + val conv5_3 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_3").inputs(relu5_2) + val relu5_3 = ReLU().setName("relu5_3").inputs(conv5_3) + val pool5 = MaxPooling(2, 2, 2, 2).setName("pool5").inputs(relu5_3) + + val fc6 = Linear(512 * 7 * 7, 4096). + setInitMethod(Xavier, ConstInitMethod(0.1)).setName("fc6").inputs(pool5) + val relu6 = ReLU().setName("relu6").inputs(fc6) + val drop6 = if (hasDropout) { + Dropout(0.5).setName("drop6").inputs(relu6) + } else { + relu6 + } + val fc7 = Linear(4096, 4096). + setInitMethod(Xavier, ConstInitMethod(0.1)).setName("fc7").inputs(drop6) + val relu7 = ReLU().setName("relu7").inputs(fc7) + val drop7 = if (hasDropout) { + Dropout(0.5).setName("drop7").inputs(relu7) + } else { + relu7 + } + val fc8 = Linear(4096, classNum). + setInitMethod(Xavier, ConstInitMethod(0.1)).setName(("fc8")).inputs(drop7) + val output = ReorderMemory(HeapData(Array(batchSize, classNum), Memory.Format.nc)).inputs(fc8) + + DnnGraph(Array(input), Array(output)) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 4f90fb193c4..297c128ac8c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -31,7 +31,7 @@ import java.util.Calendar import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.nn.mkldnn.MklDnnContainer +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.optim.DistriOptimizer.{Cache, getModel} import org.apache.commons.lang.exception.ExceptionUtils @@ -572,6 +572,7 @@ object DistriOptimizer extends AbstractOptimizer { val localModel = modelBroadcast.value(true) localModel match { case container: MklDnnContainer => container.compile(TrainingPhase) + case graph: DnnGraph => graph.compile(TrainingPhase) case _ => } // differentiate partition models from each other by partition ID diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala new file mode 100644 index 00000000000..db3769cf6ef --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala @@ -0,0 +1,158 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import breeze.linalg.Axis._1 +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.{Module => _, _} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.{T, Table} +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.models.resnet + + +class DnnGraphSpec extends FlatSpec with Matchers { + + "Dnn vgg16 graph model" should "be correct" in { + val batchSize = 2 + val seed = 1 + val inputFormat = Memory.Format.nchw + val inputShape = Array(batchSize, 3, 224, 224) + + RNG.setSeed(seed) + val graphModel = models.Vgg_16.graph(batchSize, 1000, false) + RNG.setSeed(seed) + val dnnModle = models.Vgg_16(batchSize, 1000, false) + + graphModel.asInstanceOf[DnnGraph].compile(TrainingPhase) + dnnModle.compile(TrainingPhase) + + val input = Tensor[Float](inputShape).rand() + val gradOutput = Tensor[Float](batchSize, 1000).rand() + + for (i <- 0 to 2) { + graphModel.forward(input) + dnnModle.forward(input) + + graphModel.backward(input, gradOutput) + dnnModle.backward(input, gradOutput) + } + val output = Tools.dense(graphModel.forward(input)).toTensor[Float] + val outputDnn = Tools.dense(dnnModle.forward(input)).toTensor[Float] + + val gradInput = Tools.dense(graphModel.backward(input, gradOutput)).toTensor[Float] + val gradInputDnn = Tools.dense(dnnModle.backward(input, gradOutput)).toTensor[Float] + + output.almostEqual(outputDnn, 1e-4) should be(true) + gradInput.almostEqual(gradInputDnn, 1e-4) should be (true) + + val p1 = dnnModle.getParameters() + val p2 = graphModel.getParameters() + p1._1.almostEqual(p2._1, 1e-4) should be(true) + p1._2 almostEqual(p2._2, 1e-4) should be(true) + } + + "Dnn Lenet graph model" should "be correct" in { + val batchSize = 2 + val seed = 1 + val inputFormat = Memory.Format.nchw + val inputShape = Array(batchSize, 1, 28, 28) + + RNG.setSeed(seed) + val graphModel = LeNet5.dnnGraph(batchSize, 10) + RNG.setSeed(seed) + val dnnModle = LeNet5.dnn(batchSize, 10) + + graphModel.asInstanceOf[DnnGraph].compile(TrainingPhase) + dnnModle.compile(TrainingPhase) + + val input = Tensor[Float](inputShape).rand() + val gradOutput = Tensor[Float](batchSize, 10).rand() + + for (i <- 0 to 2) { + graphModel.forward(input) + dnnModle.forward(input) + + graphModel.backward(input, gradOutput) + dnnModle.backward(input, gradOutput) + } + val output = Tools.dense(graphModel.forward(input)).toTensor[Float] + val outputDnn = Tools.dense(dnnModle.forward(input)).toTensor[Float] + + val gradInput = Tools.dense(graphModel.backward(input, gradOutput)).toTensor[Float] + val gradInputDnn = Tools.dense(dnnModle.backward(input, gradOutput)).toTensor[Float] + + output.almostEqual(outputDnn, 1e-4) should be(true) + gradInput.almostEqual(gradInputDnn, 1e-4) should be (true) + + val p1 = dnnModle.getParameters() + val p2 = graphModel.getParameters() + p1._1.almostEqual(p2._1, 1e-4) should be(true) + p1._2 almostEqual(p2._2, 1e-4) should be(true) + } + + "ResNet50 graph model" should "be correct" in { + val batchSize = 2 + val seed = 1 + val inputFormat = Memory.Format.nchw + val inputShape = Array(batchSize, 3, 224, 224) + + RNG.setSeed(seed) + val dnnModle = mkldnn.ResNet(batchSize, 1000, T("depth" -> 50, + "dataSet" -> ResNet.DatasetType.ImageNet)) + RNG.setSeed(seed) + val graphModel = mkldnn.ResNet.graph(batchSize, 1000, T("depth" -> 50, + "dataSet" -> ResNet.DatasetType.ImageNet)) + + val input = Tensor[Float](inputShape).rand() + val gradOutput = Tensor[Float](batchSize, 1000).rand() + + graphModel.asInstanceOf[DnnGraph].compile(TrainingPhase) + dnnModle.compile(TrainingPhase) + + for (i <- 0 to 2) { + graphModel.forward(input) + dnnModle.forward(input) + + graphModel.backward(input, gradOutput) + dnnModle.backward(input, gradOutput) + } + val output = Tools.dense(graphModel.forward(input)).toTensor[Float] + val outputDnn = Tools.dense(dnnModle.forward(input)).toTensor[Float] + + val gradInput = Tools.dense(graphModel.backward(input, gradOutput)).toTensor[Float] + val gradInputDnn = Tools.dense(dnnModle.backward(input, gradOutput)).toTensor[Float] + + output.almostEqual(outputDnn, 1e-4) should be(true) + gradInput.almostEqual(gradInputDnn, 1e-4) should be (true) + + val p1 = graphModel.getParametersTable() + val p2 = dnnModle.getParametersTable() + val keys = p1.keySet + for (i <- keys) { + val k = i.asInstanceOf[String] + val t1 = p1[Table](k) + val t2 = p2[Table](k) + t1 should be(t2) + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 3287dc2f0ea..0a03b86a7f7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -62,7 +62,8 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.mkldnn.SoftMax", "com.intel.analytics.bigdl.nn.mkldnn.SpatialBatchNormalization", "com.intel.analytics.bigdl.nn.mkldnn.SpatialConvolution", - "com.intel.analytics.bigdl.nn.mkldnn.Dropout" + "com.intel.analytics.bigdl.nn.mkldnn.Dropout", + "com.intel.analytics.bigdl.nn.mkldnn.DnnGraph" ) // Maybe one serial test class contains multiple module test From 84b0f31e72051da6555ad95e3babac0c87e317c2 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 9 Nov 2018 14:07:24 +0800 Subject: [PATCH 0845/1065] Update README.md (#2681) --- scala/dllib/src/main/java/org/tensorflow/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/java/org/tensorflow/README.md b/scala/dllib/src/main/java/org/tensorflow/README.md index 3578143cbf9..7b1abed7513 100644 --- a/scala/dllib/src/main/java/org/tensorflow/README.md +++ b/scala/dllib/src/main/java/org/tensorflow/README.md @@ -1,18 +1,18 @@ -#Tensorflow protobuf Java classes +# Tensorflow protobuf Java classes This folder contains Tensorflow protobuf classes. So we can provide some features like fine-tune/inference saved Tensorflow models on Spark and save BigDL model in format which can be loaded by Tensorflow(e.g. for inference on mobile). -##Why not use Tensorflow java API +## Why not use Tensorflow java API We cannot just import Tensorflow java API jar from maven and use it. Tensorflow must be installed on the machine. This brings unnecessary dependency. Tensorflow Java API is not so sufficient to parse the model graph. -##Which version of Tensorflow are these codes generate from? +## Which version of Tensorflow are these codes generate from? Tensorflow 1.0.0 -##How to generate the classes? +## How to generate the classes? Download protobuf binary from [here](https://github.com/google/protobuf/releases/download/v3.0.2/protoc-3.0.2-linux-x86_64.zip). After extract the package, go to the bin folder and run @@ -20,4 +20,4 @@ After extract the package, go to the bin folder and run protoc -I=$path_to_tensorflow --java_out=./ $path_to_tensorflow/tensorflow/core/framework/*.proto ``` -Then you can see the generated Java class files in the current folder. \ No newline at end of file +Then you can see the generated Java class files in the current folder. From 8fbe6bc4665b0f46cb524f28d4851abdf0bc0d71 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 23 Nov 2018 15:04:15 +0800 Subject: [PATCH 0846/1065] Fix ut failed due to duplicated spark context (#2687) * Fix ut failed * fix ut --- .../dllib/nn/BatchNormalizationSpec.scala | 20 +++++++++++-------- .../nn/SpatialBatchNormalizationSpec.scala | 19 +++++++++++------- .../dllib/utils/SparkContextLifeCycle.scala | 4 ++-- .../dllib/utils/tf/TensorflowLoaderSpec.scala | 4 +++- 4 files changed, 29 insertions(+), 18 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala index 4463d9ecfc4..7d235fd5b2f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalizationSpec.scala @@ -20,20 +20,24 @@ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.{Engine, T} import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.apache.spark.SparkContext -import org.scalatest.{FlatSpec, Matchers} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.util.Random @com.intel.analytics.bigdl.tags.Parallel -class BatchNormalizationSpec extends FlatSpec with Matchers { - - "BacthNormalization parameter sync" should "work properly" in { - val conf = Engine.createSparkConf().setAppName("Test sync") - .set("spark.rpc.message.maxSize", "200").setMaster("local[*]") - val sc = SparkContext.getOrCreate(conf) - +class BatchNormalizationSpec extends FlatSpec with Matchers with BeforeAndAfter{ + before { + System.setProperty("bigdl.localMode", "true") + System.setProperty("spark.master", "local[2]") Engine.init + } + after { + System.clearProperty("bigdl.localMode") + System.clearProperty("spark.master") + } + + "BacthNormalization parameter sync" should "work properly" in { val bn = BatchNormalization[Float](2) bn.setParallism(1) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala index fb4835b6269..a20727fc0ab 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala @@ -21,18 +21,23 @@ import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.apache.spark.SparkContext -import org.scalatest.{FlatSpec, Matchers} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.util.Random -class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { - - "SpatialBacthNormalization parameter sync" should "work properly" in { +class SpatialBatchNormalizationSpec extends FlatSpec with Matchers with BeforeAndAfter { + before { + System.setProperty("bigdl.localMode", "true") + System.setProperty("spark.master", "local[2]") + Engine.init + } - val conf = Engine.createSparkConf().setAppName("Test sync") - .set("spark.rpc.message.maxSize", "200").setMaster("local[*]") - val sc = SparkContext.getOrCreate(conf) + after { + System.clearProperty("bigdl.localMode") + System.clearProperty("spark.master") + } + "SpatialBacthNormalization parameter sync" should "work properly" in { Engine.init val bn = SpatialBatchNormalization[Float](2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SparkContextLifeCycle.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SparkContextLifeCycle.scala index de11138972d..3e1e16ea472 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SparkContextLifeCycle.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/SparkContextLifeCycle.scala @@ -39,8 +39,8 @@ trait SparkContextLifeCycle extends FlatSpec with BeforeAndAfter { before { Engine.init(nodeNumber, coreNumber, true) - val conf = new SparkConf().setMaster(s"local[$coreNumber]").setAppName(appName) - sc = new SparkContext(conf) + val conf = Engine.createSparkConf().setMaster(s"local[$coreNumber]").setAppName(appName) + sc = SparkContext.getOrCreate(conf) beforeTest } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala index 8add8506e77..15cf791a347 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/tf/TensorflowLoaderSpec.scala @@ -78,7 +78,9 @@ class TensorflowLoaderSpec extends TensorflowSpecHelper{ var dataSet: DistributedDataSet[MiniBatch[Float]] = null override def doBefore(): Unit = { - sc = new SparkContext("local[1]", "RDDOptimizerSpec") + val conf = Engine.createSparkConf().setAppName("RDDOptimizerSpec") + .setMaster("local[1]") + sc = SparkContext.getOrCreate(conf) val rdd = sc.parallelize(1 to (256 * 4), 4).map(prepareData) From 4f0f7a3429523f702cc4902f48aba689fde922ab Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 23 Nov 2018 15:52:38 +0800 Subject: [PATCH 0847/1065] add no phase api when initPrimitives (#2686) * delete phase in iniPrimitives * fix style check --- .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 9 ++++++ .../nn/mkldnn/SpatialBatchNormalization.scala | 31 +++++++++++++------ 2 files changed, 31 insertions(+), 9 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index bed1033634e..33dc7f1b34c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -51,6 +51,15 @@ trait MklDnnModule extends MklDnnModuleHelper { private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase): Array[MemoryData] = grad + private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData]) + : (Array[MemoryData], Array[MemoryData]) = initFwdPrimitives(inputs, null) + + private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData]) + : (Array[MemoryData], Array[MemoryData]) = initBwdPrimitives(grad, null) + + private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData]) + : Array[MemoryData] = initGradWPrimitives(grad, null) + private[mkldnn] def inputFormats(): Array[MemoryData] private[mkldnn] def gradInputFormats(): Array[MemoryData] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 506d78fa86e..6c5492aef2f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -47,6 +47,7 @@ class SpatialBatchNormalization( @transient private var updateOutputMemoryPrimitives: Array[Long] = _ @transient private var updateGradInputTensors: Array[Tensor[Float]] = _ @transient private var updateGradInputMemoryPrimitives: Array[Long] = _ + @transient private var modelPhase: Phase = null private val mean: DnnTensor[Float] = DnnTensor[Float](nOutput) private val variance: DnnTensor[Float] = DnnTensor[Float](nOutput) @@ -105,6 +106,20 @@ class SpatialBatchNormalization( val variance = 4 } + private def initPhase(phase: Phase): Unit = { + if (phase != null) modelPhase = phase + (isTraining(), modelPhase) match { + case (true, InferencePhase) => + train = false + case (false, TrainingPhase) => + train = true + case (true, null) => + modelPhase = TrainingPhase + case (false, null) => + modelPhase = InferencePhase + case _ => + } + } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { val m = inputs(0).shape.product / this.nOutput biasFactor = if (m > 1) { m.toFloat / (m - 1) } else { 1 } @@ -116,7 +131,9 @@ class SpatialBatchNormalization( // weight and bias should be combined val weightAndBias: NativeData = NativeData(Array(nOutput * 2), Memory.Format.x) - forwardDesc = phase match { + // init phase status + initPhase(phase) + forwardDesc = modelPhase match { case TrainingPhase => MklDnn.BatchNormForwardDescInit(PropKind.Forward, inputs(0).getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_scaleshift) @@ -151,7 +168,7 @@ class SpatialBatchNormalization( _inputFormats(0) = MemoryData.operationWant(primDesc, Query.SrcPd) _outputFormats(0) = MemoryData.operationWant(primDesc, Query.DstPd) - val (srcs, dsts) = if (phase == TrainingPhase) { + val (srcs, dsts) = if (modelPhase == TrainingPhase) { val srcs = Array(inputFormats()(0), weightAndBias).map(_.getPrimitive(runtime)) val dsts = Array(outputFormats()(0), mean, variance).map(_.getPrimitive(runtime)) (srcs, dsts) @@ -178,12 +195,6 @@ class SpatialBatchNormalization( updateOutputTensors = null } - (isTraining(), phase) match { - case (true, InferencePhase) => train = false - case (false, TrainingPhase) => train = true - case _ => - } - (inputFormats(), outputFormats()) } @@ -239,8 +250,10 @@ class SpatialBatchNormalization( override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { _gradOutputFormats = Array(NativeData(outputFormats()(0).shape, outputFormats()(0).layout)) + // init phase status + initPhase(phase) // [PERF] the format of gradInput should be the same as input - val backwardDesc = phase match { + val backwardDesc = modelPhase match { case TrainingPhase => MklDnn.BatchNormBackwardDescInit(PropKind.Backward, inputFormats()(0).getMemoryDescription(), From 1b8518d0dc2397fd9a219fae1dbf981fa168269a Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 23 Nov 2018 16:15:45 +0800 Subject: [PATCH 0848/1065] improve memoryReorder layer to handle conversion between nhwc and nchw (#2683) * fix reorder to handle nhwc * add init memory for ReorderMemory --- .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 14 ++- .../bigdl/dllib/nn/mkldnn/ReorderMemory.scala | 113 +++++++++++++++--- .../dllib/nn/mkldnn/ReorderMemorySpec.scala | 55 +++++++++ 3 files changed, 160 insertions(+), 22 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index 33dc7f1b34c..c0b796b715c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -146,10 +146,16 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM grad } + def getUpdateOutputMemoryPrimitives(): Array[Long] = { + inputFormats().map(_.getPrimitive(runtime)) ++ outputFormats().map(_.getPrimitive(runtime)) + } + def getUpdateGradInputMemoryPrimitives(): Array[Long] = { + gradOutputFormats().map(_.getPrimitive(runtime)) ++ + gradInputFormats().map(_.getPrimitive(runtime)) + } override def updateOutput(input: Activity): Activity = { if (updateOutputMemoryPrimitives == null) { - updateOutputMemoryPrimitives = - inputFormats().map(_.getPrimitive(runtime)) ++ outputFormats().map(_.getPrimitive(runtime)) + updateOutputMemoryPrimitives = getUpdateOutputMemoryPrimitives() } if (updateOutputTensors == null || cachedInput == null || !cachedInput.eq(input)) { val buffer = new ArrayBuffer[Tensor[Float]]() @@ -186,9 +192,7 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { if (updateGradInputMemoryPrimitives == null) { - updateGradInputMemoryPrimitives = - gradOutputFormats().map(_.getPrimitive(runtime)) ++ - gradInputFormats().map(_.getPrimitive(runtime)) + updateGradInputMemoryPrimitives = getUpdateGradInputMemoryPrimitives() } if (updateGradInputTensors == null || cachedInput == null || !cachedInput.eq(input) || cachedGradOutput == null || !cachedGradOutput.eq(gradOutput)) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala index 2e27c69184d..4a1f3621245 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn} -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, TensorModule} import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, @@ -26,26 +26,81 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, _outputFormats = Array(outputFormat) _gradInputFormats = Array(gradInputFormat) + private var realInput : Array[MemoryData] = null + private var realOutput : Array[MemoryData] = null + private var realgradInput : Array[MemoryData] = null + private var realgradOutput : Array[MemoryData] = null + private def initMemory(src: MemoryData, shape: Array[Int], layout: Int) + : Array[MemoryData] = { + src match { + case h: HeapData => Array(HeapData(shape, layout)) + case n: NativeData => Array(NativeData(shape, layout)) + case _ => throw new UnsupportedOperationException("Not support such memory format") + } + } + + private def shapeToString(shape: Array[Int]): String = { + var name = "" + shape.foreach(s => name += s.toString + ",") + name + } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = if (inputFormat == null) inputs else Array(inputFormat) require(_inputFormats.length == 1, "Only accept one tensor as input") - require(_inputFormats(0).shape.product == outputFormat.shape.product, - "input output memory not match") + if (outputFormat == null) _outputFormats = _inputFormats + shapeToString(_inputFormats(0).shape) + + require(_inputFormats(0).shape.product == _outputFormats(0).shape.product, + "input output memory not match, input shape " + shapeToString(_inputFormats(0).shape) + + "output shape " + shapeToString(_outputFormats(0).shape)) + + val inputShape = _inputFormats(0).shape + val outputShape = _outputFormats(0).shape + val inputLayout = _inputFormats(0).layout + val outputLayout = _outputFormats(0).layout + realInput = _inputFormats + realOutput = _outputFormats + + if (inputLayout != outputLayout) { + if (inputLayout == Memory.Format.nhwc) { + // remind: if format of input MemoryData is nhwc, its shape should be output shape + realInput = initMemory(_inputFormats(0), outputShape, inputLayout) + } else if (outputLayout == Memory.Format.nhwc) { + // remind: if format of output MemoryData is nhwc, its shape should be input shape + realOutput = initMemory(_outputFormats(0), inputShape, outputLayout) + } + } + val fwdReorderPrimDesc = MklDnn.ReorderPrimitiveDescCreate( - _inputFormats(0).getPrimitiveDescription(runtime), - outputFormat.getPrimitiveDescription(runtime)) + realInput(0).getPrimitiveDescription(runtime), + realOutput(0).getPrimitiveDescription(runtime)) val fwdReorderPrim = MklDnn.PrimitiveCreate2(fwdReorderPrimDesc, - Array(_inputFormats(0).getPrimitive(runtime)), Array(0), 1, - Array(outputFormat.getPrimitive(runtime)), 1) + Array(realInput(0).getPrimitive(runtime)), Array(0), 1, + Array(realOutput(0).getPrimitive(runtime)), 1) updateOutputPrimitives = Array(fwdReorderPrim) - output = initTensor(outputFormat) + + // recover to original data + output = initTensor(realOutput(0)) (_inputFormats, _outputFormats) } - override private[mkldnn] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + override def getUpdateGradInputMemoryPrimitives(): Array[Long] = { + realgradOutput.map(_.getPrimitive(runtime)) ++ realgradInput.map(_.getPrimitive(runtime)) + } + + override def getUpdateOutputMemoryPrimitives(): Array[Long] = { + realInput.map(_.getPrimitive(runtime)) ++ realOutput.map(_.getPrimitive(runtime)) + } + override def updateOutput(input: Activity): Activity = { + output = super.updateOutput(input) + output.toTensor[Float].resize(_outputFormats(0).shape) + output + } + + override private[bigdl] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { _gradInputFormats = (gradInputFormat, inputFormat) match { case (null, null) => inputFormats() case (null, x) => Array(x) @@ -53,23 +108,47 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, } _gradOutputFormats = if (gradOutputFormat == null) grads else Array(gradOutputFormat) - _gradOutputFormatsForWeight = if (gradOutputFormat == null) grads else Array(gradOutputFormat) require(_gradOutputFormats.length == 1, "Only accept one tensor as input") - require(_gradOutputFormats(0).shape.product == _gradInputFormats(0).shape.product, - "input output memory not match") + "gradInput and gradOutput memory not match," + + "gradInput shape " + shapeToString(_gradInputFormats(0).shape) + + "gradOutput shape " + shapeToString(_gradOutputFormats(0).shape)) + + val gradInputShape = _gradInputFormats(0).shape + val gradOutputShape = _gradOutputFormats(0).shape + val gradInputLayout = _gradInputFormats(0).layout + val gradOutputLayout = _gradOutputFormats(0).layout + realgradInput = _gradInputFormats + realgradOutput = _gradOutputFormats + + if (gradInputLayout != gradOutputLayout) { + if (gradOutputLayout == Memory.Format.nhwc) { + // remind: if format of gradOutput MemoryData is nhwc, its shape should be gradInput shape + realgradOutput = initMemory(_gradOutputFormats(0), gradInputShape, gradOutputLayout) + } else if (gradInputLayout == Memory.Format.nhwc) { + // remind: if format of gradInput MemoryData is nhwc, its shape should be gradOutput shape + realgradInput = initMemory(_gradInputFormats(0), gradOutputShape, gradInputLayout) + } + } + val bwdReorderPrimDesc = MklDnn.ReorderPrimitiveDescCreate( - _gradOutputFormats(0).getPrimitiveDescription(runtime), - _gradInputFormats(0).getPrimitiveDescription(runtime)) + realgradOutput(0).getPrimitiveDescription(runtime), + realgradInput(0).getPrimitiveDescription(runtime)) val bwdReorderPrim = MklDnn.PrimitiveCreate2(bwdReorderPrimDesc, - _gradOutputFormats.map(_.getPrimitive(runtime)), Array(0), 1, - _gradInputFormats.map(_.getPrimitive(runtime)), 1) + realgradOutput.map(_.getPrimitive(runtime)), Array(0), 1, + realgradInput.map(_.getPrimitive(runtime)), 1) updateGradInputPrimitives = Array(bwdReorderPrim) - gradInput = initTensor(_gradInputFormats(0)) + gradInput = initTensor(realgradInput(0)) (_gradOutputFormats, _gradInputFormats) } + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = super.updateGradInput(input, gradOutput) + gradInput.toTensor[Float].resize(_gradInputFormats(0).shape) + gradInput + } + override def toString(): String = { if (_inputFormats != null) { s"nn.mkl.ReorderMemory(${_inputFormats(0)} -> ${outputFormat})" diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala index d7b6f5ff14a..704424cffc2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala @@ -48,4 +48,59 @@ class ReorderMemorySpec extends BigDLSpecHelper { grad should be(input) } + "Reorder from nhwc to nchw" should "be correct" in { + val shapeNCHW = Array(4, 3, 7, 7) + val shapeNHWC = Array(4, 7, 7, 3) + val inputFormats = HeapData(shapeNHWC, Memory.Format.nhwc) + val outputFormats = HeapData(shapeNCHW, Memory.Format.nchw) + val gradInputFormats = HeapData(shapeNCHW, Memory.Format.nchw) + val gradOutputFormats = HeapData(shapeNHWC, Memory.Format.nhwc) + + val layer = ReorderMemory(inputFormat = inputFormats, outputFormat = outputFormats, + gradInputFormat = gradInputFormats, gradOutputFomat = gradOutputFormats) + + layer.setRuntime(new MklDnnRuntime()) + layer.initFwdPrimitives(Array(inputFormats), Phase.TrainingPhase) + layer.initBwdPrimitives(Array(gradOutputFormats), Phase.TrainingPhase) + + + val input = Tensor[Float](4, 7, 7, 3).rand() + val gradOutput = input.clone() + val output = layer.forward(input).toTensor[Float] + val grad = layer.backward(input, gradOutput) + + val inputNHWC = input.transpose(2, 4).transpose(3, 4).contiguous().clone() + + inputNHWC should be(output) + inputNHWC should be(grad) + } + + "Reorder from nchw to nhwc" should "be correct" in { + val shapeNCHW = Array(4, 3, 7, 7) + val shapeNHWC = Array(4, 7, 7, 3) + val inputFormats = HeapData(shapeNCHW, Memory.Format.nchw) + val outputFormats = HeapData(shapeNHWC, Memory.Format.nhwc) + val gradInputFormats = HeapData(shapeNHWC, Memory.Format.nhwc) + val gradOutputFormats = HeapData(shapeNCHW, Memory.Format.nchw) + + val layer = ReorderMemory(inputFormat = inputFormats, outputFormat = outputFormats, + gradInputFormat = gradInputFormats, gradOutputFomat = gradOutputFormats) + + layer.setRuntime(new MklDnnRuntime()) + layer.initFwdPrimitives(Array(inputFormats), Phase.TrainingPhase) + layer.initBwdPrimitives(Array(gradOutputFormats), Phase.TrainingPhase) + + val input = Tensor[Float](4, 3, 7, 7).rand() + val gradOutput = input.clone() + val output = layer.forward(input).toTensor[Float] + val grad = layer.backward(input, gradOutput).toTensor[Float] + + val inputNHWC = input.transpose(2, 3).transpose(3, 4).contiguous().clone() + + // grad.resizeAs(inputNHWC) + + inputNHWC should be(output) + inputNHWC should be(grad) + } + } From 04e9fd1e09357016fd81c7fdedcfc19798883425 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 26 Nov 2018 16:01:12 +0800 Subject: [PATCH 0849/1065] support same padding in dnn layer (#2684) * support same padding in dnn layer * meet review --- .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 17 +++++-- .../bigdl/dllib/nn/mkldnn/MaxPooling.scala | 10 ++-- .../dllib/nn/mkldnn/SpatialConvolution.scala | 14 ++++-- .../dllib/nn/mkldnn/AvgPoolingSpec.scala | 28 +++++++++++ .../dllib/nn/mkldnn/MaxPoolingSpec.scala | 5 +- .../nn/mkldnn/SpatialConvolutionSpec.scala | 49 +++++++++++++++++++ 6 files changed, 112 insertions(+), 11 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala index d04bbd25a3a..1efddd05fca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -32,6 +32,12 @@ class AvgPooling( @transient private var paddingBR: Array[Int] = _ @transient private var fwdPD: Long = _ + private val algKind = if (padH == -1 && padW == -1) { + AlgKind.PoolingAvgIncludePadding + } else { + AlgKind.PoolingAvgExcludePadding + } + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = singleNativeData(inputs) val strides = Array(dW, dH) @@ -40,13 +46,18 @@ class AvgPooling( val c = _inputFormats(0).shape(1) val h = _inputFormats(0).shape(2) val w = _inputFormats(0).shape(3) - val (pt, pb, pl, pr, oh, ow) = + val (pt, pb, pl, pr, oh, ow) = if (padH == -1 && padW == -1) { + val sizes = Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) + (sizes(0), sizes(1), sizes(2), sizes(3), sizes(4), sizes(5)) + } else { Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW) + } + paddingTL = Array(pt, pl) paddingBR = Array(pb, pr) val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), DataType.F32, Memory.Format.any) val description = MklDnn.PoolingForwardDescInit( - PropKind.Forward, AlgKind.PoolingAvgExcludePadding, + PropKind.Forward, algKind, _inputFormats(0).getMemoryDescription(), outputMD, strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) @@ -63,7 +74,7 @@ class AvgPooling( _gradOutputFormatsForWeight = _gradOutputFormats val strides = Array(dW, dH) val kernel = Array(kH, kW) - val description = MklDnn.PoolingBackwardDescInit(AlgKind.PoolingAvgExcludePadding, + val description = MklDnn.PoolingBackwardDescInit(algKind, _inputFormats(0).getMemoryDescription(), _gradOutputFormats(0).getMemoryDescription(), strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala index feb43ade4b7..95b2510a9e2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala @@ -44,12 +44,16 @@ class MaxPooling( val c = _inputFormats(0).shape(1) val h = _inputFormats(0).shape(2) val w = _inputFormats(0).shape(3) - val (pt, pb, pl, pr, oh, ow) = + + val (pt, pb, pl, pr, oh, ow) = if (padH == -1 && padW == -1) { + val sizes = Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) + (sizes(0), sizes(1), sizes(2), sizes(3), sizes(4), sizes(5)) + } else { Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW) + } paddingTL = Array(pt, pl) paddingBR = Array(pb, pr) - Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) - Utils.getOutSizeAndPaddingForDNN(h, w, dH, dW, kH, kW, padH, padW, true) + val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), DataType.F32, Memory.Format.any) val description = MklDnn.PoolingForwardDescInit( PropKind.Forward, AlgKind.PoolingMax, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 1a47ed22e50..59a0f669844 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -70,6 +70,8 @@ class SpatialConvolution( @transient private var updateGradInputTensors: Array[Tensor[Float]] = _ @transient private var updateGradWMemoryPrimitives: Array[Long] = _ @transient private var updateGradWTensors: Array[Tensor[Float]] = _ + @transient private var paddingTL: Array[Int] = _ + @transient private var paddingBR: Array[Int] = _ private var _relu = false private var _sum = false @@ -154,8 +156,14 @@ class SpatialConvolution( padH, padW, ceilMode = false) } + val padTop = sizes(0) + val padBottom = sizes(1) + val padLeft = sizes(2) + val padRight = sizes(3) val outputHeight = sizes(4) val outputWidth = sizes(5) + paddingTL = Array(padTop, padLeft) + paddingBR = Array(padBottom, padRight) val inputShape = inputs(0).shape val outputShape = Array(inputs(0).shape(0), nOutputPlane, outputHeight, outputWidth) @@ -171,7 +179,7 @@ class SpatialConvolution( wei.getMemoryDescription(), bis.getMemoryDescription(), dst.getMemoryDescription(), - Array(strideW, strideH), Array(padH, padW), Array(padH, padW), // TODO check the meaning + Array(strideW, strideH), paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) forwardPrimDesc = if (relu || sum) { @@ -272,7 +280,7 @@ class SpatialConvolution( AlgKind.ConvolutionDirect, src.getMemoryDescription(), wei.getMemoryDescription(), // TODO check correctness of strides and padding - dst.getMemoryDescription(), Array(strideW, strideH), Array(padH, padW), Array(padH, padW), + dst.getMemoryDescription(), Array(strideW, strideH), paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) val backwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) @@ -337,7 +345,7 @@ class SpatialConvolution( src.getMemoryDescription(), wei.getMemoryDescription(), bis.getMemoryDescription(), - grad(0).getMemoryDescription(), Array(strideW, strideH), Array(padH, padW), Array(padH, padW), + grad(0).getMemoryDescription(), Array(strideW, strideH), paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) val gradWeightPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala index d9f02422909..aca5422a2a3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala @@ -26,6 +26,34 @@ import org.apache.commons.lang3.SerializationUtils import scala.util.Random class AvgPoolingSpec extends BigDLSpecHelper { + "Avg Pooling with same padding" should "be correct" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) + + val pad = -1 + RNG.setSeed(100) + val pool = AvgPooling(3, 3, 2, 2, padH = pad, padW = pad) + RNG.setSeed(100) + val layer = SpatialAveragePooling[Float](3, 3, 2, 2, padH = pad, padW = pad).ceil() + + val output2 = layer.forward(input).toTensor[Float] + + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw))) + seq.add(pool) + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 480, 28, 28), + Memory.Format.nchw))) + val output1 = seq.forward(input) + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = seq.backward(input, output2) + grad1 should be(grad2) + } + "Avg Pooling test1" should "be correct" in { val batchSize = 2 val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala index fbe04b4e687..4007d44ff4b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala @@ -30,10 +30,11 @@ class MaxPoolingSpec extends BigDLSpecHelper { val batchSize = 2 val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) + val pad = -1 RNG.setSeed(100) - val pool = MaxPooling(3, 3, 2, 2) + val pool = MaxPooling(3, 3, 2, 2, padH = pad, padW = pad) RNG.setSeed(100) - val layer = SpatialMaxPooling[Float](3, 3, 2, 2).ceil() + val layer = SpatialMaxPooling[Float](3, 3, 2, 2, padH = pad, padW = pad).ceil() val output2 = layer.forward(input).toTensor[Float] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index fc0fba72734..0525e11f1a2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -78,6 +78,55 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Equivalent.nearequals(grad1.toTensor, grad2) should be(true) } + "ConvolutionDnn with same padding" should "work correctly" in { + val nInputPlane = 2 + val nOutputPlane = 4 + val kW = 3 + val kH = 3 + val dW = 4 + val dH = 4 + val padW = -1 + val padH = -1 + + val input = Tensor[Float](2, 2, 23, 23).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](2, 4, 6, 6).apply1(e => Random.nextFloat()) + RNG.setSeed(100) + val conv = SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH) + RNG.setSeed(100) + val layer = nn.SpatialConvolution[Float](nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH) + + conv.setRuntime(new MklDnnRuntime) + conv.initFwdPrimitives(Array(HeapData(Array(2, 2, 23, 23), Memory.Format.nchw)), TrainingPhase) + conv.initBwdPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) + conv.initGradWPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) + + val output = Tools.toNCHW(conv.forward(input).toTensor, conv.outputFormats()(0)) + val grad1 = Tools.toNCHW(conv.updateGradInput(input, gradOutput).toTensor, + conv.gradInputFormats()(0)) + conv.accGradParameters(input, gradOutput) + + val weight1 = Tools.toOIHW(conv.weight.native, conv.parametersWithShape()._1(0)) + val gradweight1 = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) + val bias1 = Tools.dense(conv.bias.native).toTensor[Float] + val gradbias1 = Tools.dense(conv.gradBias.dense).toTensor + + val output2 = layer.forward(input) + val grad2 = layer.updateGradInput(input, gradOutput) + layer.accGradParameters(input, gradOutput) + + val weight2 = layer.weight + val gradweight2 = layer.gradWeight + val bias2 = layer.bias + val gradbias2 = layer.gradBias + + Equivalent.nearequals(weight1, weight2.resizeAs(weight1)) should be(true) + Equivalent.nearequals(gradweight1, gradweight2.resizeAs(gradweight1)) should be(true) + Equivalent.nearequals(bias1, bias2) should be(true) + Equivalent.nearequals(gradbias1, gradbias2) should be(true) + Equivalent.nearequals(output.toTensor, output2) should be(true) + Equivalent.nearequals(grad1.toTensor, grad2) should be(true) + } + "ConvolutionDnn with format=nchw and ngroup=2" should "work correctly" in { val nInputPlane = 2 val nOutputPlane = 4 From 161025577a6116da15d0c479552c197a382f61e1 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 30 Nov 2018 11:13:30 +0800 Subject: [PATCH 0850/1065] add BlasWrapper (#2690) * add BlasWrapper * refactor code * meet review * SerializerSpec excluded mkldnn.BlasWrapper * change some comments --- .../analytics/bigdl/dllib/nn/LogSoftMax.scala | 6 +- .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 138 ++++++++++++++++++ .../dllib/nn/mkldnn/ReorderManager.scala | 2 +- .../dllib/nn/mkldnn/BlasWrapperSpec.scala | 90 ++++++++++++ .../utils/serializer/SerializerSpec.scala | 3 +- 5 files changed, 236 insertions(+), 3 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapperSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMax.scala index 37d65c20c30..2b70bc4f197 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMax.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.mkl.MKL import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.{DoubleType, FloatType, Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, Shape} import scala.concurrent.Future import scala.math.exp @@ -133,6 +133,10 @@ class LogSoftMax[T: ClassTag]( results = null this } + + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } } object LogSoftMax { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala new file mode 100644 index 00000000000..7a8418819fc --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -0,0 +1,138 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{MultiShape, Shape} +import spire.syntax.module + +/** + * wrap blas module to be dnn module, + * and the module should have implemented "computeOutputShape" func. + * @param module + */ +private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, Float]) + extends MklDnnLayer { + + require(!module.isInstanceOf[MklDnnModule], "Only support wrapper blas layer to dnn layer") + + output = module.output + gradInput = module.gradInput + + private def inferFormats(inputs: Array[MemoryData]): Int = { + // reminder: here assume all shapes in inputs should be same + inputs.foreach(in => + require(in.shape.length == 2 || in.shape.length == 4, + s"only input shape dim 2 and 4 supported, but get ${in.shape.length}")) + + inputs(0).layout match { + case Memory.Format.nhwc => Memory.Format.nhwc + case Memory.Format.nc => Memory.Format.nc + case _ => Memory.Format.nchw + } + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + // reminder: only support model having implemented computeOutputShape + val inputShape = inputs.map(in => Shape(in.shape)) + val outputShape = if (inputShape.length == 1) { + List(module.computeOutputShape(inputShape(0))) + } else { + // multi shape + val out = module.computeOutputShape(MultiShape(inputShape.toList)) + if (out.isInstanceOf[MultiShape]) out.toMulti() else List(out) + } + val outDim = outputShape(0).toSingle().length + require(outDim == 4 || outDim == 2, + s"only output shape dim 2 and 4 supported, but get ${outDim}") + + val inputFormats = inferFormats(inputs) + val outputFormats = if (outDim == 4) inputFormats else Memory.Format.nc + + val realInputs = inputShape.map(in => HeapData(in.toSingle().toArray, inputFormats)) + val realOutputs = outputShape.map(in => HeapData(in.toSingle().toArray, outputFormats)) + + _inputFormats = realInputs.toArray + _outputFormats = realOutputs.toArray + + (_inputFormats, _outputFormats) + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormats = _outputFormats + _gradInputFormats = _inputFormats + (_outputFormats, _gradInputFormats) + } + + override private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase) = { + _gradOutputFormatsForWeight = _outputFormats + _gradOutputFormatsForWeight + } + + override def updateOutput(input: Activity): Activity = { + output = module.forward(input) + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = module.updateGradInput(input, gradOutput) + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + module.accGradParameters(input, gradOutput) + } + + override def clearState() : this.type = { + super.clearState() + module.clearState() + this + } + + override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { + module.parameters() + } + + override def equals(obj: Any): Boolean = { + if (!super.equals(obj) || !obj.isInstanceOf[BlasWrapper]) { + return false + } + val other = obj.asInstanceOf[BlasWrapper] + if (this.eq(other)) { + return true + } + if (module != other) return false + true + } + + override def hashCode() : Int = { + val seed = 37 + var hash = super.hashCode() + hash = hash * seed + module.hashCode() + hash + } +} + + +private[bigdl] object BlasWrapper { + def apply(module: AbstractModule[Activity, Activity, Float]): BlasWrapper = + new BlasWrapper(module) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala index ed3d0a082ea..a7552410edd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala @@ -66,7 +66,7 @@ private[mkldnn] class ReorderManager() { outputTable(i + 1) = inferTensor(from(i), to(i), output.toTable(i + 1)) i += 1 } - output + outputTable } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapperSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapperSpec.scala new file mode 100644 index 00000000000..b3026672bb8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapperSpec.scala @@ -0,0 +1,90 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import breeze.linalg.reshape +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.{Module, nn} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.{Graph, Squeeze, mkldnn} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, RandomGenerator, T} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.numeric.NumericFloat + +class BlasWrapperSpec extends BigDLSpecHelper { + + def modelBlas(format: DataFormat = DataFormat("NCHW")) : Module[Float] = { + val conv1 = nn.SpatialConvolution(1, 20, 5, 5, format = format).inputs() + val pool1 = nn.SpatialMaxPooling(2, 2, 2, 2, format = format).setName("pool").inputs(conv1) + val conv2 = nn.SpatialConvolution(20, 50, 5, 5, format = format).inputs(pool1) + val pool2 = nn.SpatialMaxPooling(2, 2, 2, 2, format = format).inputs(conv2) + val reshape = nn.Reshape(Array(50 * 4 * 4)).inputs(pool2) + val fc = nn.Linear(50 * 4 * 4, 500).inputs(reshape) + val relu = nn.ReLU().setName("relu1").inputs(fc) + val fc2 = nn.Linear(500, 10).setName("ip2").inputs(relu) + val log = nn.LogSoftMax().inputs(fc2) + Graph(conv1, log) + } + + def modelWrapper(format: Int = Memory.Format.nchw, shape: Array[Int]) : DnnGraph = { + val input = mkldnn.Input(shape, format).inputs() + val conv1 = BlasWrapper(nn.SpatialConvolution[Float](1, 20, 5, 5)).inputs(input) + val pool1 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool").inputs(conv1) + val conv2 = BlasWrapper(nn.SpatialConvolution[Float](20, 50, 5, 5)).inputs(pool1) + val pool2 = mkldnn.MaxPooling(2, 2, 2, 2).inputs(conv2) + val fc = mkldnn.Linear(50 * 4 * 4, 500).inputs(pool2) + val relu = mkldnn.ReLU().setName("relu1").inputs(fc) + val fc2 = mkldnn.Linear(500, 10).setName("ip2").inputs(relu) + val log = BlasWrapper(nn.LogSoftMax[Float]()).inputs(fc2) + DnnGraph(Array(input), Array(log)) + } + + "wrapper model" should "be correct" in { + val inputShape = Array(2, 1, 28, 28) + val outputShape = Array(2, 10) + val input = Tensor[Float](inputShape).rand() + val gradOutput = Tensor[Float](outputShape).rand() + + RandomGenerator.RNG.setSeed(1) + val blas = modelBlas() + RandomGenerator.RNG.setSeed(1) + val wrapper = modelWrapper(Memory.Format.nchw, inputShape) + wrapper.compile(TrainingPhase) + + val out1 = blas.forward(input) + val out2 = wrapper.forward(input) + + out1 should be(out2) + + val grad1 = blas.backward(input, gradOutput) + val grad2 = wrapper.backward(input, gradOutput) + + val weight1 = blas.getParameters()._1 + val weight2 = wrapper.getParameters()._1 + + val gradWeight1 = blas.getParameters()._2 + val gradWeight2 = wrapper.getParameters()._2 + + grad1 should be(grad2) + + weight1 should be(weight2) + gradWeight1 should be(gradWeight2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 0a03b86a7f7..f9d6040243a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -63,7 +63,8 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.mkldnn.SpatialBatchNormalization", "com.intel.analytics.bigdl.nn.mkldnn.SpatialConvolution", "com.intel.analytics.bigdl.nn.mkldnn.Dropout", - "com.intel.analytics.bigdl.nn.mkldnn.DnnGraph" + "com.intel.analytics.bigdl.nn.mkldnn.DnnGraph", + "com.intel.analytics.bigdl.nn.mkldnn.BlasWrapper" ) // Maybe one serial test class contains multiple module test From a222ff1a458df01063d811fc8b4e0f839fd931f9 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 30 Nov 2018 14:44:14 +0800 Subject: [PATCH 0851/1065] add dnn output layer (#2691) * add dnn output layer * SerializerSpec excluded mkldnn Output * change some comments --- .../bigdl/dllib/nn/mkldnn/Output.scala | 93 +++++++++++++++++++ .../bigdl/dllib/nn/mkldnn/OutputSpec.scala | 77 +++++++++++++++ .../utils/serializer/SerializerSpec.scala | 3 +- 3 files changed, 172 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/OutputSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala new file mode 100644 index 00000000000..2011d5cf15b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala @@ -0,0 +1,93 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} + +/** + * Convert output to user defined layout and appoint gradOutput layout + * @param outputLayOut output memory layout + * @param gradOutputLayout gradoutput memory layout, if it is -1, + * that means gradOutput memory layout is same with output memory layout + */ +class Output(outputLayOut: Int = Memory.Format.nc, + gradOutputLayout: Int = -1) extends MklDnnLayer { + + private val _outputLayOut = outputLayOut + private val _gradOutputLayout = if (gradOutputLayout == -1) outputLayOut else gradOutputLayout + + + private def getShape(inLayout: Int, inShape: Array[Int], outLayout: Int): Array[Int] = { + val outputShape = + if (outLayout == Memory.Format.nhwc && inLayout != Memory.Format.nhwc) { + // nchw* -> nhwc + Array(inShape(0), inShape(2), inShape(3), inShape(1)) + } else if (outLayout != Memory.Format.nhwc && inLayout == Memory.Format.nhwc) { + // nhwc -> nchw* + Array(inShape(0), inShape(3), inShape(1), inShape(2)) + } else inShape + outputShape + } + + override private[bigdl] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + require(inputs.length == 1, "Only accept one tensor as input") + require(inputs(0).shape.length == 4 || inputs(0).shape.length == 2, + s"Only support input with 2 or 4 dimentions, but get ${inputs(0).shape.length}") + + val outputShape = getShape(inputs(0).layout, inputs(0).shape, _outputLayOut) + // remind: output memory storage should be heapData + _outputFormats = Array(HeapData(outputShape, outputLayOut)) + _inputFormats = _outputFormats + + (_inputFormats, _outputFormats) + } + + override def updateOutput(input: Activity): Activity = { + output = input + output + } + + override private[bigdl] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + require(grads.length == 1, "Only accept one tensor as input") + require(grads(0).shape.length == 4 || grads(0).shape.length == 2, + s"Only support gradOutput with 2 or 4 dimentions, but get ${grads(0).shape.length}") + + val outputShape = getShape(grads(0).layout, grads(0).shape, _gradOutputLayout) + + _gradInputFormats = Array(HeapData(outputShape, _gradOutputLayout)) + _gradOutputFormats = _gradInputFormats + _gradOutputFormatsForWeight = _gradOutputFormats + + (_gradInputFormats, _gradOutputFormats) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = gradOutput + gradInput + } + + override def toString(): String = { + s"nn.mkl.Output(${outputLayOut}, ${gradOutputLayout})" + } +} + +object Output { + def apply(outputLayOut: Int = Memory.Format.nc, + gradOutputLayout: Int = -1): Output = + new Output(outputLayOut, gradOutputLayout) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/OutputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/OutputSpec.scala new file mode 100644 index 00000000000..d1194e426f3 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/OutputSpec.scala @@ -0,0 +1,77 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import breeze.numerics.log +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.{Graph, mkldnn} +import com.intel.analytics.bigdl.{nn, _} +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, RandomGenerator} + +class OutputSpec extends BigDLSpecHelper { + def model(shape: Array[Int], layout: Int) : DnnGraph = { + val input = mkldnn.Input(shape, layout).inputs() + val conv1 = mkldnn.SpatialConvolution(1, 20, 5, 5).inputs(input) + val pool1 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool").inputs(conv1) + val conv2 = mkldnn.SpatialConvolution(20, 50, 5, 5).inputs(pool1) + val out = mkldnn.Output(Memory.Format.nchw).inputs(conv2) + DnnGraph(Array(input), Array(out)) + } + + def blas() : Module[Float] = { + val conv1 = nn.SpatialConvolution(1, 20, 5, 5).inputs() + val pool1 = nn.SpatialMaxPooling(2, 2, 2, 2).setName("pool").inputs(conv1) + val conv2 = nn.SpatialConvolution(20, 50, 5, 5).inputs(pool1) + Graph(conv1, conv2) + } + + "test output" should "be right" in { + val inputShape = Array(2, 1, 28, 28) + val outputShape = Array(2, 50, 8, 8) + val input = Tensor[Float](inputShape).rand() + val gradOutput = Tensor[Float](outputShape).rand() + + RandomGenerator.RNG.setSeed(1) + val modelDnn = model(inputShape, Memory.Format.nchw) + modelDnn.compile(TrainingPhase) + RandomGenerator.RNG.setSeed(1) + val modelBlas = blas() + + val out1 = modelBlas.forward(input) + val out2 = modelDnn.forward(input) + + out1 should be(out2) + + val grad1 = modelBlas.backward(input, gradOutput) + val grad2 = modelDnn.backward(input, gradOutput).toTensor[Float] + + grad1 should be(grad2) + + val weight1 = modelBlas.getParameters()._1 + val weight2 = modelDnn.getParameters()._1 + + val gradWeight1 = modelBlas.getParameters()._2 + val gradWeight2 = modelDnn.getParameters()._2 + + weight1 should be(weight2) + gradWeight1 should be(gradWeight2) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index f9d6040243a..e86e525ddb2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -64,7 +64,8 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.mkldnn.SpatialConvolution", "com.intel.analytics.bigdl.nn.mkldnn.Dropout", "com.intel.analytics.bigdl.nn.mkldnn.DnnGraph", - "com.intel.analytics.bigdl.nn.mkldnn.BlasWrapper" + "com.intel.analytics.bigdl.nn.mkldnn.BlasWrapper", + "com.intel.analytics.bigdl.nn.mkldnn.Output" ) // Maybe one serial test class contains multiple module test From be2c080b36cf83165a2c1274c73c24657e2cbcc3 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 5 Dec 2018 16:06:54 +0800 Subject: [PATCH 0852/1065] Irelement (#2695) * add ir element and convertion * add more comments * meet comments * change map name --- .../dllib/utils/intermediate/BlasToIR.scala | 53 +++++ .../dllib/utils/intermediate/IRElement.scala | 171 ++++++++++++++ .../dllib/utils/intermediate/IRToBlas.scala | 105 +++++++++ .../dllib/utils/intermediate/IRToDnn.scala | 212 ++++++++++++++++++ .../utils/intermediate/ReflectionUtils.scala | 112 +++++++++ .../utils/serializer/ModuleSerializer.scala | 2 +- .../dllib/nn/mkldnn/ReflectionUtilsSpec.scala | 115 ++++++++++ .../utils/intermediate/IRconvertSpec.scala | 179 +++++++++++++++ 8 files changed, 948 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToIR.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToBlas.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToIR.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToIR.scala new file mode 100644 index 00000000000..3075ad386c5 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToIR.scala @@ -0,0 +1,53 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import scala.reflect.ClassTag + +private[bigdl] class BlasToIR[T: ClassTag] extends ConvertBase[Module[T], IRElement[T]]{ + + private def className(layer: Module[T]): String = { + val name = layer.getClass.getSimpleName + s"com.intel.analytics.bigdl.utils.intermediate.IR$name" + } + + // reminder: some undefined IR operations can be presented by IRGeneralModule + override def convertLayerCheck(layer: Module[T]): Boolean = { + ReflectionUtils.findClass(className(layer)) != null || + layer.isInstanceOf[AbstractModule[Activity, Activity, T]] + } + + override def convertLayer(layer : Module[T]) : IRElement[T] = { + val cls = ReflectionUtils.findClass(className(layer)) + if ( cls != null) { + ReflectionUtils.reflectToIR(layer, cls) + } else if (layer.isInstanceOf[AbstractModule[Activity, Activity, T]]) { + val op = IRGeneralModule[T]( + layer.asInstanceOf[AbstractModule[Activity, Activity, T]]) + IRElement(layer.getName(), op) + } else { + throw new UnsupportedOperationException(s"can not convert $layer to IRelement ") + } + } +} + +private[bigdl] object BlasToIR { + def apply[T: ClassTag](implicit ev: TensorNumeric[T]): BlasToIR[T] = new BlasToIR +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala new file mode 100644 index 00000000000..e088feb36e0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala @@ -0,0 +1,171 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.{Tensor, TensorNumericMath} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +sealed class IROperator[T: ClassTag] { + val tag: ClassTag[T] = scala.reflect.classTag[T] + val numerics: TensorNumeric[T] = tag match { + case ClassTag.Float => TensorNumeric.NumericFloat.asInstanceOf[TensorNumeric[T]] + case ClassTag.Double => TensorNumeric.NumericDouble.asInstanceOf[TensorNumeric[T]] + case _ => throw new IllegalArgumentException(s"not supported class tag: ${tag}") + } + def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array(scala.reflect.classTag[T]), Array(numerics)) + } + def name: String = this.getClass.getSimpleName +} + +case class IRSpatialMaxPooling[T: ClassTag]( + kW: Int, kH: Int, + dW: Int = 1, dH: Int = 1, + padW: Int = 0, padH: Int = 0, + format: DataFormat = DataFormat.NCHW) extends IROperator[T] + +case class IRSpatialAveragePooling[T: ClassTag]( + kW: Int, kH: Int, + dW: Int = 1, dH: Int = 1, + padW: Int = 0, padH: Int = 0, + globalPooling: Boolean = false, + ceilMode: Boolean = false, countIncludePad: Boolean = true, + divide: Boolean = true, format: DataFormat = DataFormat.NCHW) extends IROperator[T] + +case class IRSpatialConvolution[T: ClassTag]( + nInputPlane: Int, nOutputPlane: Int, + kernelW: Int, kernelH: Int, + strideW: Int = 1, strideH: Int = 1, + padW: Int = 0, padH: Int = 0, + nGroup: Int = 1, propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, initGradBias: Tensor[T] = null, + withBias: Boolean = true, format: DataFormat = DataFormat.NCHW) extends IROperator[T] + +case class IRSpatialShareConvolution[T: ClassTag]( + nInputPlane: Int, nOutputPlane: Int, + kernelW: Int, kernelH: Int, + strideW: Int = 1, strideH: Int = 1, + padW: Int = 0, padH: Int = 0, + nGroup: Int = 1, propagateBack: Boolean = true, + wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, initGradBias: Tensor[T] = null, + withBias: Boolean = true) extends IROperator[T] + +case class IRSpatialBatchNormalization[T: ClassTag]( + nOutput: Int, eps: Double = 1e-5, momentum: Double = 0.1, + affine: Boolean = true, + initWeight: Tensor[T] = null, initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, initGradBias: Tensor[T] = null, + dataFormat: DataFormat = DataFormat.NCHW, + runningMean: Tensor[T] = null, runningVar: Tensor[T] = null) extends IROperator[T] + +case class IRIdentity[T: ClassTag]() extends IROperator[T] + +case class IRDropout[T: ClassTag](initP: Double = 0.5, inplace: Boolean = false, + scale: Boolean = true) extends IROperator[T] + +case class IRReLU[T: ClassTag](ip: Boolean = false) extends IROperator[T] + +case class IRLinear[T: ClassTag]( + inputSize: Int, + outputSize: Int, + withBias: Boolean = true, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + initWeight: Tensor[T] = null, + initBias: Tensor[T] = null, + initGradWeight: Tensor[T] = null, + initGradBias: Tensor[T] = null) extends IROperator[T] + +case class IRSqueeze[T: ClassTag](dims: Array[Int], batchMode: Boolean) extends IROperator[T] + +case class IRSpatialCrossMapLRN[T: ClassTag]( + size: Int = 5, + alpha: Double = 1.0, + beta: Double = 0.75, + k: Double = 1.0, + format: DataFormat = DataFormat.NCHW) extends IROperator[T] + +case class IRSoftMax[T: ClassTag]() extends IROperator[T] + +case class IRSelectTable[T: ClassTag](dimension: Int) extends IROperator[T] + +case class IRCAddTable[T: ClassTag, D: ClassTag](inplace: Boolean = false) extends IROperator[T] + +case class IRJoinTable[T: ClassTag](dimension: Int, + nInputDims: Int = 0) extends IROperator[T] + +case class IRConcatTable[T: ClassTag]() extends IROperator[T] + +case class IRInput[T: ClassTag]() extends IROperator[T] + +/** + * if blas module has no corresponding IROperator, + * then we can use IRGeneralModule to wrap this layer to IROperator + * @param model + */ +case class IRGeneralModule[T: ClassTag]( + model: AbstractModule[Activity, Activity, T]) extends IROperator[T] + +private[bigdl] class IRElement[T: ClassTag]( + val name: String, + val op: IROperator[T], + private var weights: Tensor[T] = null, + private var gradWeights: Tensor[T] = null) { + + /** + * set weight and bias + */ + def setWeights(weightsAndBias: Tensor[T]) : Unit = { + weights = weightsAndBias + } + + /** + * set gradWeight and gradbias + */ + def setGradWeights(gradWeightsAndGradBias: Tensor[T]) : Unit = { + gradWeights = gradWeightsAndGradBias + } + + def getParameters(): (Tensor[T], Tensor[T]) = (weights, gradWeights) + + def getName() : String = this.name + + def getOp() : IROperator[T] = this.op +} + +object IRElement { + /** + * create IRElement + * @param name element name + * @param op element operation, like IRSpatialMaxPooling, IRBlasModule, etc. + * @param weights weights & bias for IRElement + * @param gradWeights gradWeight & gradbias for IRElement + * @tparam T + * @return + */ + def apply[T: ClassTag](name: String, op: IROperator[T], + weights: Tensor[T] = null, gradWeights: Tensor[T] = null): IRElement[T] = + new IRElement[T](name, op, weights, gradWeights) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToBlas.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToBlas.scala new file mode 100644 index 00000000000..727e320ecad --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToBlas.scala @@ -0,0 +1,105 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.optim.DistriOptimizer._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Node, T} + +import scala.collection.mutable +import scala.reflect.ClassTag + + +abstract class ConvertBase[T, D] { + /** + * clone node relations + * @param nodeMap node element maps from T to D + */ + def cloneNode(allNodes: Array[Node[T]], nodeMap: mutable.HashMap[Node[T], Node[D]]): Unit = { + allNodes.foreach(node => { + node.nextNodesAndEdges.foreach(nextNodeAndEdge => { + if (nodeMap.contains(nextNodeAndEdge._1)) { + nodeMap.get(node).get.add(nodeMap.get(nextNodeAndEdge._1).get, nextNodeAndEdge._2) + } + }) + }) + // sort previous node + nodeMap.toArray.foreach(node => { + // if node has more than one previous nodes, we have to consider nodes order + if (node._1.prevNodesAndEdges.length > 1) { + node._2.removePrevEdges() + node._1.prevNodesAndEdges.foreach(prevNodeAndEdge => { + if (nodeMap.contains(prevNodeAndEdge._1)) { + node._2.from(nodeMap.get(prevNodeAndEdge._1).get, prevNodeAndEdge._2) + } + }) + } + }) + } + + def convertLayerCheck(layer: T) : Boolean + + def convertLayer(layer : T) : D + + def convertingCheck(allNodes: Array[Node[T]]) : Boolean = { + var convert = true + allNodes.foreach(node => { + if (!convertLayerCheck(node.element)) { + logger.info(s"${node.element} convertion failed") + convert = false + } + }) + convert + } + + def convert(allNodes: Array[Node[T]]): mutable.HashMap[Node[T], Node[D]] = { + val nodeMap = new mutable.HashMap[Node[T], Node[D]]() + allNodes.foreach(node => { + nodeMap.put(node, new Node(convertLayer(node.element))) + }) + cloneNode(allNodes, nodeMap) + nodeMap + } +} + +private[bigdl] class IRToBlas[T: ClassTag] extends ConvertBase[IRElement[T], Module[T]]{ + + private def className(layer: IRElement[T]): String = { + val name = layer.getOp().name + s"com.intel.analytics.bigdl.nn.${name.substring(2)}" + } + + override def convertLayerCheck(layer: IRElement[T]): Boolean = { + ReflectionUtils.findClass(className(layer)) != null || + layer.getOp().isInstanceOf[IRGeneralModule[T]] + } + + override def convertLayer(layer : IRElement[T]) : Module[T] = { + if (layer.getOp().isInstanceOf[IRGeneralModule[T]]) { + return layer.getOp().asInstanceOf[IRGeneralModule[T]].model + } + ReflectionUtils.reflectFromIR(layer, Class.forName(className(layer))) + } +} + +private[bigdl] object IRToBlas { + def apply[T: ClassTag](implicit ev: TensorNumeric[T]): IRToBlas[T] = new IRToBlas +} + + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala new file mode 100644 index 00000000000..b6e2498c16f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -0,0 +1,212 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} +import com.intel.analytics.bigdl.nn.{Module => _, _} +import com.intel.analytics.bigdl.nn.mkldnn._ +import com.intel.analytics.bigdl.optim.DistriOptimizer._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{DirectedGraph, Node, T} + +import scala.collection.mutable +import scala.reflect.ClassTag + +private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float]] { + + private val prefix = "com.intel.analytics.bigdl.nn.mkldnn." + // converter function mappings + private val IR2DnnMap = new mutable.HashMap[String, (IRElement[Float]) => Module[Float]] + + mapInit() + + private def mapInit(): Unit = { + IR2DnnMap("IRSpatialConvolution") = fromSpatialConvolution + IR2DnnMap("IRSpatialMaxPooling") = fromSpatialMaxPooling + IR2DnnMap("IRSpatialAveragePooling") = fromSpatialAveragePooling + IR2DnnMap("IRSpatialBatchNormalization") = fromSpatialBatchNormalization + IR2DnnMap("IRSpatialCrossMapLRN") = fromSpatialCrossMapLRN + IR2DnnMap("IRLinear") = fromLinear + IR2DnnMap("IRReLU") = fromReLU + IR2DnnMap("IRJoinTable") = fromJoinTable + IR2DnnMap("IRGeneralModule") = fromBlasModule + IR2DnnMap("IRInput") = fromInput + } + + override def convertLayerCheck(layer: IRElement[Float]): Boolean = { + val name = layer.getOp().name + if (IR2DnnMap.contains(name) && checkRequirement(layer)) return true + return ReflectionUtils.findClass(prefix + name.substring(2)) != null + } + + override def convertLayer(layer: IRElement[Float]) : Module[Float] = { + val name = layer.getOp().name + if (IR2DnnMap.contains(name)) { + val dnn = IR2DnnMap(name)(layer) + if (layer.getName != "") dnn.setName(layer.name) + dnn + } else { + ReflectionUtils.reflectFromIR(layer, Class.forName(prefix + name.substring(2))) + } + } + + override def convertingCheck(allNodes: Array[Node[IRElement[Float]]]) : Boolean = { + var convert = true + allNodes.foreach(node => { + val op = node.element.getOp() + if (!convertLayerCheck(node.element)) { + logger.info(s"${node.element.getOp()} convertion failed") + convert = false + } + }) + convert + } + + override def convert(allNodes: Array[Node[IRElement[Float]]]) + : mutable.HashMap[Node[IRElement[Float]], Node[Module[Float]]] = { + val nodeMap = new mutable.HashMap[Node[IRElement[Float]], Node[Module[Float]]]() + allNodes.foreach(node => { + val op = node.element.getOp() + var dnn = if (convertLayerCheck(node.element)) { + new Node(convertLayer(node.element)) + } else { + throw new UnsupportedOperationException(s"can not find ${node.element.getOp()} ") + } + // special treat for reshape -> linear and view -> linear + if (op.isInstanceOf[IRGeneralModule[Float]]) { + val m = op.asInstanceOf[IRGeneralModule[Float]].model + if (m.isInstanceOf[Reshape[Float]] && node.nextNodes.length == 1 && + node.nextNodes(0).element.getOp().isInstanceOf[IRLinear[Float]]) { + dnn = new Node(mkldnn.Identity[Float]().asInstanceOf[Module[Float]]) + } else if (m.isInstanceOf[View[Float]] && node.nextNodes.length == 1 && + node.nextNodes(0).element.getOp().isInstanceOf[IRLinear[Float]]) { + dnn = new Node(mkldnn.Identity[Float]().asInstanceOf[Module[Float]]) + } + } + nodeMap.put(node, dnn) + }) + cloneNode(allNodes, nodeMap) + nodeMap + } + + private def fromReLU(node: IRElement[Float]) : Module[Float] = mkldnn.ReLU() + + private def fromSpatialConvolution(node: IRElement[Float]) : Module[Float] = { + val t = node.getOp().asInstanceOf[IRSpatialConvolution[Float]] + require(t.wRegularizer == null && t.bRegularizer == null, + "Dnn SpatialConvolution can not support Regularizer") + require(t.format == DataFormat.NCHW, "Dnn SpatialConvolution only supports NCHW") + ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) + } + + private def fromSpatialMaxPooling(node: IRElement[Float]) : Module[Float] = { + val t = node.getOp().asInstanceOf[IRSpatialMaxPooling[Float]] + require(t.format == DataFormat.NCHW, "Dnn SpatialMaxPooling only supports NCHW") + ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "MaxPooling")) + } + + private def fromSpatialAveragePooling(node: IRElement[Float]) : Module[Float] = { + val t = node.getOp().asInstanceOf[IRSpatialAveragePooling[Float]] + require(t.format == DataFormat.NCHW, "Dnn SpatialAveragePooling only supports NCHW") + ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "AvgPooling")) + } + + private def fromSpatialCrossMapLRN(node: IRElement[Float]) : Module[Float] = { + val t = node.getOp().asInstanceOf[IRSpatialCrossMapLRN[Float]] + require(t.format == DataFormat.NCHW, "Dnn LRN only supports NCHW") + ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "LRN")) + } + + private def fromJoinTable(node: IRElement[Float]) : Module[Float] = { + val t = node.getOp().asInstanceOf[IRJoinTable[Float]] + require(t.nInputDims == 0, + s"Dnn JoinTable only supports nInputDims = 0, but get ${t.nInputDims}") + mkldnn.JoinTable(t.dimension) + } + + private def fromSpatialBatchNormalization(node: IRElement[Float]) : Module[Float] = { + val t = node.getOp().asInstanceOf[IRSpatialBatchNormalization[Float]] + require(t.dataFormat == DataFormat.NCHW, "Dnn SpatialBatchNormalization only supports NCHW") + val nOutput = t.nOutput + val eps = t.eps + val momentum = t.momentum + val initWeight = t.initWeight + val initBias = t.initBias + val initGradWeight = t.initGradWeight + val initGradBias = t.initGradBias + + val layer = mkldnn.SpatialBatchNormalization(nOutput, eps, momentum, + true, initWeight, initBias, initGradWeight, initGradBias) + + val params = node.getParameters() + if (params._1 != null) layer.weightAndBias.copy(params._1) + if (params._2 != null) layer.gradWeightAndBias.copy(params._2) + + val extraParams = layer.getExtraParameter() + if (t.runningMean != null) extraParams(0).copy(t.runningMean.toTensor[Float]) + if (t.runningVar != null) extraParams(1).copy(t.runningVar.toTensor[Float]) + + layer + } + + private def fromLinear(node: IRElement[Float]) : Module[Float] = { + val t = node.getOp().asInstanceOf[IRLinear[Float]] + require(t.wRegularizer == null && t.bRegularizer == null, + "Dnn Linear can not support Regularizer") + ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "Linear")) + } + + private def fromBlasModule(node: IRElement[Float]) : Module[Float] = { + BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) + } + + private def fromInput(node: IRElement[Float]) : Module[Float] = { + mkldnn.Identity[Float]() + } + + private def checkRequirement(layer: IRElement[Float]) : Boolean = { + try { + layer.getOp() match { + case conv: IRSpatialConvolution[Float] => + require(conv.wRegularizer == null + && conv.bRegularizer == null && conv.format == DataFormat.NCHW) + case maxPool: IRSpatialMaxPooling[Float] => + require(maxPool.format == DataFormat.NCHW) + case avgPool: IRSpatialAveragePooling[Float] => + require(avgPool.format == DataFormat.NCHW) + case sbn: IRSpatialBatchNormalization[Float] => + require(sbn.dataFormat == DataFormat.NCHW) + case lrn: IRSpatialCrossMapLRN[Float] => + require(lrn.format == DataFormat.NCHW) + case linear: IRLinear[Float] => + require(linear.wRegularizer == null && linear.bRegularizer == null) + case join: IRJoinTable[Float] => + require(join.nInputDims == 0) + case _ => null + } + true + } catch { + case e: Throwable => false + } + } +} + +private[bigdl] object IRToDnn { + def apply[T: ClassTag](implicit ev: TensorNumeric[T]): IRToDnn = new IRToDnn +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala new file mode 100644 index 00000000000..42c6f2df731 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala @@ -0,0 +1,112 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._ +import scala.collection.mutable +import scala.reflect.{ClassTag, ManifestFactory} +import scala.reflect.runtime._ + +private[bigdl] object ReflectionUtils { + + private def getFieldNameAndValues(o: Object): mutable.HashMap[String, AnyRef] = { + val c = o.getClass + var fields = c.getDeclaredFields + val superFields = c.getSuperclass.getDeclaredFields + fields = fields ++ superFields + + val values = new mutable.HashMap[String, AnyRef]() + fields.foreach(field => { + field.setAccessible(true) + values(field.getName) = field.get(o) + }) + values + } + + // create layer2 object form layer1 + private def reflection(layer1: Object, layer2: Class[_], + tags: Array[ClassTag[_]], numerics: Array[TensorNumeric[_]]) : Object = { + val nameAndValues = getFieldNameAndValues(layer1) + val constructorMirror = getCostructorMirror(layer2) + val constructorFullParams = constructorMirror.symbol.paramss + val args = new Array[Object](constructorFullParams.map(_.size).sum) + + val tagIter = tags.iterator + val numericIter = numerics.iterator + var i = 0 + constructorFullParams.foreach(map => { + map.foreach(param => { + val name = param.name.decodedName.toString + val ptype = param.typeSignature + if (ptype <:< universe.typeOf[ClassTag[_]]|| + ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { + require(tagIter.hasNext, "If your module contains multiple class tags, " + + s"do you forget to override getClassTagNumerics method ${layer1}") + args(i) = tagIter.next() + } else if (ptype <:< universe.typeOf[TensorNumeric[_]] + || ptype.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { + args(i) = numericIter.next() + } else { + val value = nameAndValues.get(name).getOrElse(null) + args(i) = value + } + i += 1 + }) + }) + constructorMirror.apply(args : _*).asInstanceOf[Object] + } + + // create Module form IRElement + def reflectFromIR[T : ClassTag](layer: IRElement[T], cls: Class[_]) : Module[T] = { + val (tags, numerics) = layer.getOp().getClassTagNumerics() + val blasLayer = ReflectionUtils.reflection(layer.getOp(), cls, tags, numerics) + .asInstanceOf[Module[T]] + + if (blasLayer.parameters() != null) { + val params = blasLayer.getParameters() + val params2 = layer.getParameters() + if (params2._1 != null) params._1.copy(params2._1) + if (params2._2 != null) params._2.copy(params2._2) + } + + if (layer.getName() != "") blasLayer.setName(layer.getName()) + + blasLayer + } + + // create IRElement form Module + def reflectToIR[T: ClassTag](layer: Module[T], cls: Class[_]) : IRElement[T] = { + val (tags, numerics) = layer.getClassTagNumerics() + val op = ReflectionUtils.reflection(layer, cls, tags, numerics).asInstanceOf[IROperator[T]] + val weightsAndBias = + if (layer.parameters() != null) layer.getParameters() else (null, null) + val element = IRElement[T]( + layer.getName(), op, weights = weightsAndBias._1, gradWeights = weightsAndBias._2) + element + } + + def findClass(name: String): Class[_] = { + try { + Class.forName(name) + } catch { + case ex: ClassNotFoundException => null + case e: Throwable => throw e + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 3d84a73bd27..734c1a9369a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -181,7 +181,7 @@ object ModuleSerializer extends ModuleSerializable{ groupSerializerMaps(superModuleType) = groupSerializer } - private[serializer] def getCostructorMirror[T : ClassTag](cls : Class[_]): + private[bigdl] def getCostructorMirror[T : ClassTag](cls : Class[_]): universe.MethodMirror = { getLock.synchronized { val clsSymbol = runtimeMirror.classSymbol(cls) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala new file mode 100644 index 00000000000..bcb3d42ff4e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala @@ -0,0 +1,115 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.utils.intermediate.{IRToBlas, IRToDnn, ReflectionUtils} +import com.intel.analytics.bigdl.{Module, nn} + +class ReflectionUtilsSpec extends BigDLSpecHelper { + + "test SpatialConvolution reflection" should "be right" in { + val model1 = nn.SpatialConvolution[Float](2, 4, 3, 3, 4, 4, 0, 0).asInstanceOf[Module[Float]] + val className = "com.intel.analytics.bigdl.utils.intermediate.IRSpatialConvolution" + val cls = Class.forName(className) + val ir = ReflectionUtils.reflectToIR[Float](model1, cls) + val cls2 = Class.forName( + "com.intel.analytics.bigdl.nn.SpatialConvolution") + val modelBlas = ReflectionUtils.reflectFromIR(ir, cls2) + + val cls3 = Class.forName("com.intel.analytics.bigdl.nn.mkldnn.SpatialConvolution") + val modelDnn = ReflectionUtils.reflectFromIR(ir, cls3).asInstanceOf[mkldnn.SpatialConvolution] + + val inputShape = Array(2, 2, 23, 23) + val outShape = Array(2, 4, 6, 6) + modelDnn.setRuntime(new MklDnnRuntime) + modelDnn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + modelDnn.initBwdPrimitives(Array(HeapData(outShape, Memory.Format.nchw)), TrainingPhase) + modelDnn.initGradWPrimitives(Array(HeapData(outShape, Memory.Format.nchw)), TrainingPhase) + + val input = Tensor[Float](inputShape).rand() + val gradOutput = Tensor[Float](outShape).rand() + + val out = model1.forward(input).toTensor[Float] + val out1 = modelBlas.forward(input).toTensor[Float] + val out2 = modelDnn.forward(input).toTensor[Float] + + out should be(out1) + Equivalent.nearequals(out1, Tools.dense(out2).toTensor[Float], 1e-4) should be(true) + + val grad = model1.backward(input, gradOutput) + val grad1 = modelBlas.backward(input, gradOutput) + val grad2 = modelDnn.backward(input, gradOutput) + + val gradWeight1 = modelDnn.getParameters()._2 + val gradWeight2 = modelBlas.getParameters()._2 + + val weight1 = modelDnn.getParameters()._1 + val weight2 = modelBlas.getParameters()._1 + + Equivalent.nearequals(weight1, weight2) should be (true) + Equivalent.nearequals(gradWeight1, gradWeight2) should be (true) + + Equivalent.nearequals(Tools.dense(modelDnn.gradInput).toTensor, + modelBlas.gradInput.toTensor[Float]) should be (true) + } + + "test BatchNorm reflection" should "be right" in { + val model1 = nn.SpatialBatchNormalization(3).asInstanceOf[Module[Float]] + val className = "com.intel.analytics.bigdl.utils.intermediate.IRSpatialBatchNormalization" + val cls = Class.forName(className) + val ir = ReflectionUtils.reflectToIR[Float](model1, cls) + + val modelBlas = IRToBlas[Float].convertLayer(ir) + val modelDnn = IRToDnn[Float].convertLayer(ir).asInstanceOf[mkldnn.SpatialBatchNormalization] + + val inputShape = Array(16, 3, 4, 4) + modelDnn.setRuntime(new MklDnnRuntime) + modelDnn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + modelDnn.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + modelDnn.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + + val input = Tensor[Float](16, 3, 4, 4).rand() + val gradOutput = Tensor[Float](16, 3, 4, 4).rand() + + val out1 = modelBlas.forward(input).toTensor[Float] + val out2 = modelDnn.forward(input).toTensor[Float] + + Equivalent.nearequals(out1, Tools.dense(out2).toTensor[Float], 1e-4) should be(true) + + val grad1 = modelBlas.backward(input, gradOutput) + val grad2 = modelDnn.backward(input, gradOutput) + + val gradWeight1 = Tools.dense(modelDnn.gradWeightAndBias.native).toTensor + val gradWeight2 = modelBlas.getParameters()._2 + + val weight1 = Tools.dense(modelDnn.weightAndBias.native).toTensor + val weight2 = modelBlas.getParameters()._1 + + Equivalent.nearequals(weight1, weight2) should be (true) + Equivalent.nearequals(gradWeight1, gradWeight2, 1e-4) should be (true) + + Equivalent.nearequals(Tools.dense(modelDnn.gradInput).toTensor, + modelBlas.gradInput.toTensor[Float]) should be (true) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala new file mode 100644 index 00000000000..34dffcefbb7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala @@ -0,0 +1,179 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, Input, Output} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.{Module, nn} + +class IRconvertSpec extends BigDLSpecHelper { + + def modelIR(): Array[Node[IRElement[Float]]] = { + val conv1 = Node(IRElement[Float]("input", IRSpatialConvolution[Float](1, 20, 5, 5))) + val pool1 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + val conv2 = Node(IRElement[Float]("", IRSpatialConvolution[Float](20, 50, 5, 5))) + val pool2 = Node(IRElement[Float]("output", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + + conv1 -> pool1 -> conv2 -> pool2 + Array(conv1, pool1, conv2, pool2) + } + + def modelIR2() : Array[Node[IRElement[Float]]] = { + val conv1 = Node(IRElement[Float]("input", IRSpatialConvolution[Float](1, 20, 5, 5))) + val pool1 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + val conv2 = Node(IRElement[Float]("", IRSpatialConvolution[Float](20, 50, 5, 5))) + val pool2 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + val reshape = Node(IRElement("", IRGeneralModule[Float](Reshape[Float](Array(50*4*4))))) + val linear = Node(IRElement("", IRLinear[Float](50 * 4 * 4, 500))) + val relu = Node(IRElement("", IRReLU[Float]())) + val fc2 = Node(IRElement("output", IRLinear[Float](500, 10))) + + conv1 -> pool1 -> conv2 -> pool2 -> + reshape -> linear -> relu -> fc2 + Array(conv1, pool1, conv2, pool2, reshape, linear, relu, fc2) + } + + def modelBlas(format: DataFormat = DataFormat("NCHW")) : Module[Float] = { + val conv1 = nn.SpatialConvolution(1, 20, 5, 5, format = format).setName("input").inputs() + val pool1 = nn.SpatialMaxPooling(2, 2, 2, 2, format = format).setName("pool").inputs(conv1) + val conv2 = nn.SpatialConvolution(20, 50, 5, 5, format = format).inputs(pool1) + val pool2 = nn.SpatialMaxPooling(2, 2, 2, 2, format = format).inputs(conv2) + val reshape = nn.Reshape(Array(50 * 4 * 4)).inputs(pool2) + val fc = nn.Linear(50 * 4 * 4, 500).inputs(reshape) + val relu = nn.ReLU().setName("relu1").inputs(fc) + val fc2 = nn.Linear(500, 10).setName("output").inputs(relu) + val output = fc2 + Graph(conv1, output) + } + + "Convert Blas with NCHW to Dnn" should "be correct" in { + System.setProperty("bigdl.engineType", "mkldnn") + val input = Tensor[Float](2, 1, 28, 28).rand() + val gradOutput = Tensor[Float](2, 10).rand() + + val blas = modelBlas().asInstanceOf[StaticGraph[Float]] + val allNodes = blas.getSortedForwardExecutions() + require(BlasToIR[Float].convertingCheck(allNodes)) + val irNodes = BlasToIR[Float].convert(allNodes).map(_._2).toArray + require(IRToDnn[Float].convertingCheck(irNodes)) + val dnnNodes = IRToDnn[Float].convert(irNodes).map(_._2).toArray + + val inputsNodes = dnnNodes.filter(_.element.getName() == "input")(0) + val outputsNodes = dnnNodes.filter(_.element.getName() == "output")(0) + + val inputs = Input(Array(2, 1, 28, 28), Memory.Format.nchw).inputs() + inputsNodes.from(inputs) + val outputs = Output(Memory.Format.nc).inputs(outputsNodes) + val dnn = DnnGraph(Array(inputs), Array(outputs)) + dnn.compile(TrainingPhase) + + val outBlas = blas.forward(input) + val gradInputBlas = blas.backward(input, gradOutput) + val outDnn = dnn.forward(input) + val gradInputDnn = dnn.backward(input, gradOutput).toTensor[Float] + + outDnn should be(outBlas) + gradInputDnn should be(gradInputBlas) + + val p1 = dnn.getParameters() + val p2 = blas.getParameters() + p1._1.almostEqual(p2._1, 1e-6) should be(true) + p1._2 almostEqual(p2._2, 1e-6) should be(true) + } + + "Convert IRgraph to Dnn or Blas Graph" should "be correct" in { + val input = Tensor[Float](2, 1, 28, 28).rand() + val gradOutput = Tensor[Float](2, 50, 4, 4).rand() + + val allNodes = modelIR() + RandomGenerator.RNG.setSeed(1000) + require(IRToBlas[Float].convertingCheck(allNodes)) + val blasNodes = IRToBlas[Float].convert(allNodes).map(_._2).toArray + RandomGenerator.RNG.setSeed(1000) + require(IRToDnn[Float].convertingCheck(allNodes)) + val dnnNodes = IRToDnn[Float].convert(allNodes).map(_._2).toArray + + val blas = Graph(blasNodes.filter(_.element.getName() == "input"), + blasNodes.filter(_.element.getName() == "output")) + + val inputsNodes = dnnNodes.filter(_.element.getName() == "input")(0) + val outputsNodes = dnnNodes.filter(_.element.getName() == "output")(0) + + val inputs = Input(Array(2, 1, 28, 28), Memory.Format.nchw).inputs() + inputsNodes.from(inputs) + val outputs = Output(Memory.Format.nchw).inputs(outputsNodes) + val dnn = DnnGraph(Array(inputs), Array(outputs)) + dnn.compile(TrainingPhase) + + val outBlas = blas.forward(input) + val gradInputBlas = blas.backward(input, gradOutput) + val outDnn = dnn.forward(input) + val gradInputDnn = dnn.backward(input, gradOutput).toTensor[Float] + + outDnn should be(outBlas) + gradInputDnn should be(gradInputBlas) + + val p1 = dnn.getParameters() + val p2 = blas.getParameters() + p1._1.almostEqual(p2._1, 1e-6) should be(true) + p1._2 almostEqual(p2._2, 1e-4) should be(true) + } + + "Convert IRgraph to Dnn or Blas Graph with 2 dimentions output" should "be correct" in { + val input = Tensor[Float](2, 1, 28, 28).rand() + val gradOutput = Tensor[Float](2, 10).rand() + + val allNodes = modelIR2() + RandomGenerator.RNG.setSeed(1000) + require(IRToBlas[Float].convertingCheck(allNodes)) + val blasNodes = IRToBlas[Float].convert(allNodes).map(_._2).toArray + RandomGenerator.RNG.setSeed(1000) + require(IRToDnn[Float].convertingCheck(allNodes)) + val dnnNodes = IRToDnn[Float].convert(allNodes).map(_._2).toArray + + val blas = Graph(blasNodes.filter(_.element.getName() == "input"), + blasNodes.filter(_.element.getName() == "output")) + + val inputsNodes = dnnNodes.filter(_.element.getName() == "input")(0) + val outputsNodes = dnnNodes.filter(_.element.getName() == "output")(0) + + val inputs = Input(Array(2, 1, 28, 28), Memory.Format.nchw).inputs() + inputsNodes.from(inputs) + val outputs = Output(Memory.Format.nc).inputs(outputsNodes) + val dnn = DnnGraph(Array(inputs), Array(outputs)) + dnn.compile(TrainingPhase) + + val outBlas = blas.forward(input) + val gradInputBlas = blas.backward(input, gradOutput) + val outDnn = dnn.forward(input) + val gradInputDnn = dnn.backward(input, gradOutput).toTensor[Float] + + outDnn should be(outBlas) + gradInputDnn should be(gradInputBlas) + + val p1 = dnn.getParameters() + val p2 = blas.getParameters() + p1._1.almostEqual(p2._1, 1e-4) should be(true) + p1._2 almostEqual(p2._2, 1e-4) should be(true) + } +} From a654ede36fb15c65e72923608f97ed8ec7568074 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Thu, 13 Dec 2018 15:11:21 +0800 Subject: [PATCH 0853/1065] support dlclassifiermodel binary classification (#2705) --- .../bigdl/dlframes/DLClassifier.scala | 7 +++++- .../dllib/dlframes/DLClassifierSpec.scala | 24 +++++++++++++++++++ 2 files changed, 30 insertions(+), 1 deletion(-) diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala index c1c46e21b13..fc2210c837a 100644 --- a/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala +++ b/dl/src/main/scala/com/intel/analytics/bigdl/dlframes/DLClassifier.scala @@ -72,7 +72,12 @@ class DLClassifierModel[T: ClassTag]( )(implicit ev: TensorNumeric[T]) extends DLModel[T](model, featureSize) { protected override def outputToPrediction(output: Tensor[T]): Any = { - ev.toType[Double](output.max(1)._2.valueAt(1)) + if (output.size().deep == Array(1).deep) { + val raw = ev.toType[Double](output.toArray().head) + if (raw > 0.5) 1.0 else 0.0 + } else { + ev.toType[Double](output.max(1)._2.valueAt(1)) + } } override def transformSchema(schema : StructType): StructType = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala index fef2ef2674e..2d9eb786a2c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala @@ -129,6 +129,30 @@ class DLClassifierSpec extends FlatSpec with Matchers with BeforeAndAfter { } } + "An DLClassifier" should "support binary classification" in { + val model = new Sequential().add(Linear[Float](6, 1)).add(Sigmoid[Float]) + val criterion = BCECriterion[Float]() + val classifier = new DLClassifier[Float](model, criterion, Array(6)) + .setLearningRate(0.1) + .setBatchSize(2) + .setEndWhen(Trigger.maxIteration(10)) + + Array( + sqlContext.createDataFrame(sc.parallelize( + smallData.map(p => (p._1.map(_.toFloat), p._2.toFloat - 1)))) + .toDF("features", "label"), // Float + sqlContext.createDataFrame(sc.parallelize(smallData.map(p => (p._1, p._2 - 1)))) + .toDF("features", "label") // Double + // TODO: add ML Vector when ut for Spark 2.0+ is ready + ).foreach { df => + val dlModel = classifier.fit(df) + val result = dlModel.transform(df).collect() + val accuracy = result.count(v => v.get(1) == v.get(2)).toDouble / smallData.size + accuracy should be > math.max(smallData.count(_._2 == 1), + smallData.count(_._2 == 2)).toDouble / smallData.size + } + } + "An DLClassifier" should "fit with adam and LBFGS" in { val model = new Sequential().add(Linear[Float](6, 2)).add(LogSoftMax[Float]) val criterion = ClassNLLCriterion[Float]() From ad71d05d923830bba01e89c19cf9d0de5860545b Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 25 Dec 2018 13:14:13 +0800 Subject: [PATCH 0854/1065] add IR graph and conversion from IR graph to blas graph or dnn graph (#2704) * add ir graph * fix model evaluate & conv without bias * add dnnMode & support table inputs * irelement & graph layer use same weights * meet pr comments and code refactor --- .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 16 ++ .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 8 +- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 49 +++-- .../bigdl/dllib/nn/mkldnn/InputWrapper.scala | 55 ++++++ .../bigdl/dllib/nn/mkldnn/JoinTable.scala | 2 +- .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 14 +- .../dllib/nn/mkldnn/SpatialConvolution.scala | 15 +- .../utils/intermediate/IRConverter.scala | 111 +++++++++++ .../dllib/utils/intermediate/IRElement.scala | 4 +- .../dllib/utils/intermediate/IRGraph.scala | 180 ++++++++++++++++++ .../utils/intermediate/ReflectionUtils.scala | 10 +- .../utils/intermediate/IRGraphSpec.scala | 152 +++++++++++++++ .../utils/serializer/SerializerSpec.scala | 4 +- 13 files changed, 589 insertions(+), 31 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index 7a8418819fc..b4cb6d53541 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -129,6 +129,22 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, hash = hash * seed + module.hashCode() hash } + + override def training(): this.type = { + train = true + module.training() + this + } + + /** + * Set the module to evaluate mode + * @return + */ + override def evaluate(): this.type = { + train = false + module.evaluate() + this + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index c0b796b715c..f858f74370e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -51,13 +51,13 @@ trait MklDnnModule extends MklDnnModuleHelper { private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase): Array[MemoryData] = grad - private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData]) + private[bigdl] def initFwdPrimitives(inputs: Array[MemoryData]) : (Array[MemoryData], Array[MemoryData]) = initFwdPrimitives(inputs, null) - private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData]) + private[bigdl] def initBwdPrimitives(grad: Array[MemoryData]) : (Array[MemoryData], Array[MemoryData]) = initBwdPrimitives(grad, null) - private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData]) + private[bigdl] def initGradWPrimitives(grad: Array[MemoryData]) : Array[MemoryData] = initGradWPrimitives(grad, null) private[mkldnn] def inputFormats(): Array[MemoryData] @@ -238,7 +238,7 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM _gradInputFormats } - override private[mkldnn] def outputFormats() = { + override private[bigdl] def outputFormats() = { require(_outputFormats != null, "You should call initFwdPrimitives first") _outputFormats } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 5c9dbb02fb6..9de11ded401 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -34,7 +34,8 @@ class DnnGraph( private val _outputs : Seq[ModuleNode[Float]], private val _variables: Option[(Array[Tensor[Float]], Array[Tensor[Float]])] = None, private val enableExcludeChecking: Boolean = true) - extends StaticGraph[Float](_inputs, _outputs, _variables, enableExcludeChecking) { + extends StaticGraph[Float](_inputs, _outputs, _variables, enableExcludeChecking) + with MklDnnLayer { private var forwardExecution: Array[Node[AbstractModule[Activity, Activity, Float]]] = _ private var backwardExecution: Array[Node[AbstractModule[Activity, Activity, Float]]] = _ private var inputCache: Array[Activity] = _ @@ -233,34 +234,40 @@ class DnnGraph( } final def compile(phase: Phase) : Unit = { - setRuntime(new MklDnnRuntime(), phase) + setRuntime(new MklDnnRuntime()) initPrimitives(phase, Array[MemoryData]()) } - private def setRuntime(runtime: MklDnnRuntime, phase: Phase): Unit = { + override def setRuntime(runtime: MklDnnRuntime): Unit = { + this.runtime = runtime reorderManager.setRuntime(runtime) forwardExecution.foreach(m => m.element.asInstanceOf[MklDnnModule].setRuntime(runtime)) - if (phase == Phase.TrainingPhase) { - var i = 0 - while (i < backwardExecution.length - 1) { // do not execute the dummy backward end - backwardExecution(i).element.asInstanceOf[MklDnnModule].setRuntime(runtime) - i += 1 - } - } } private def initPrimitives(phase: Phase, inputFormats: Array[MemoryData]): Unit = { - val outFormats = initFwdPrimitives(inputFormats, phase)._2 + _outputFormats = initFwdPrimitives(inputFormats, phase)._2 if (phase == Phase.TrainingPhase) { - initBwdPrimitives(outFormats, phase) - initGradWPrimitives(outFormats, phase) + _gradOutputFormats = initBwdPrimitives(_outputFormats, phase)._1 + _gradOutputFormatsForWeight = initGradWPrimitives(_outputFormats, phase) + } + } + + private def getInputMemoryData(node: ModuleNode[Float], memoryData: Array[MemoryData]) + : Array[MemoryData] = { + if (inputs.length == 1) { + require(inputs(0).eq(node), "input node is not in the input list") + memoryData + } else { + val i = inputs.indexOf(node) + require(i != -1, "input node is not in the input list") + Array(memoryData(i)) } } private def findInputFormats(node: ModuleNode[Float], inputs: Array[MemoryData]) : Array[MemoryData] = { if (node.prevNodes.isEmpty) { - inputs + getInputMemoryData(node, inputs) } else { val prevFormats = node.prevNodesAndEdges .filterNot(n => n._1.element.isInstanceOf[ControlDependency[Float]]) @@ -306,7 +313,8 @@ class DnnGraph( } // init forward primitives - private def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) + : (Array[MemoryData], Array[MemoryData]) = { var lastOutputFormats = inputs var firstRealInputFormats: Array[MemoryData] = null for (i <- 0 until forwardExecution.length) { @@ -319,11 +327,14 @@ class DnnGraph( } if (i == 0) firstRealInputFormats = realInputAndOutputFormats._1 } + _inputFormats = firstRealInputFormats + _outputFormats = lastOutputFormats (firstRealInputFormats, lastOutputFormats) } // init updateGradInput primitives - private def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + override private[mkldnn] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) + : (Array[MemoryData], Array[MemoryData]) = { var lastGradInputFormats = grads var firstRealGradOutputFormats: Array[MemoryData] = null for (i <- 0 until backwardExecution.length - 1) { @@ -336,11 +347,14 @@ class DnnGraph( } if (i == 0) firstRealGradOutputFormats = realGradOutputAndInputFomrats._1 } + _gradOutputFormats = firstRealGradOutputFormats + _gradInputFormats = lastGradInputFormats (firstRealGradOutputFormats, lastGradInputFormats) } // init acc primitives - private def initGradWPrimitives(grads: Array[MemoryData], phase: Phase) = { + override private[mkldnn] def initGradWPrimitives(grads: Array[MemoryData], phase: Phase) + : Array[MemoryData] = { var lastGradInputFormats = grads var firstRealGradOutputFormats: Array[MemoryData] = null for (i <- 0 until backwardExecution.length - 1) { @@ -353,6 +367,7 @@ class DnnGraph( } if (i == 0) firstRealGradOutputFormats = realGradOutput } + _gradOutputFormatsForWeight = firstRealGradOutputFormats firstRealGradOutputFormats } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala new file mode 100644 index 00000000000..a26c289c941 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.nn.abstractnn.Activity + +private[bigdl] class InputWrapper extends MklDnnLayer { + + private var inputLayer : Input = null + + override private[bigdl] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + require(inputs.length == 1, "Only accept one tensor as input") + inputLayer = Input(inputs(0).shape, inputs(0).layout) + inputLayer.setRuntime(this.runtime) + inputLayer.initFwdPrimitives(inputs, phase) + _inputFormats = inputLayer.inputFormats() + _outputFormats = inputLayer.outputFormats() + (_inputFormats, _outputFormats) + } + + override def updateOutput(input: Activity): Activity = { + output = inputLayer.forward(input) + output + } + + override private[bigdl] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { + require(grads.length == 1, "Only accept one tensor as input") + inputLayer.initBwdPrimitives(grads, phase) + _gradInputFormats = inputLayer.gradInputFormats() + _gradOutputFormats = inputLayer.gradOutputFormats() + (_gradInputFormats, _gradOutputFormats) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = inputLayer.backward(input, gradOutput) + gradInput + } + + override def toString(): String = { + s"nn.mkl.InputWrapper" + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala index 63a353298e5..ac7c5638f97 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala @@ -36,7 +36,7 @@ class JoinTable(val dimension: Int) extends MklDnnLayer { val curShape = inputs(i).shape require(layout == inputs(i).layout, "layout not match") require(totalShape.length == curShape.length, "tensor dimension not match") - require(inputs(i).isInstanceOf[NativeData], "memory should be native") + // require(inputs(i).isInstanceOf[NativeData], "memory should be native") var j = 0 while(j < curShape.length) { if (j == dimension - 1) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index bc2f92f9987..216ca96756e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -29,9 +29,21 @@ class SoftMax() extends MklDnnLayer { @transient private var updateOutputTensors: Array[Tensor[Float]] = _ @transient private var updateOutputMemoryPrimitives: Array[Long] = _ + @transient private var modelPhase: Phase = null + + private def initPhase(phase: Phase): Unit = { + if (phase != null) return modelPhase = phase + isTraining() match { + case true => + modelPhase = TrainingPhase + case false => + modelPhase = InferencePhase + } + } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - phase match { + initPhase(phase) + modelPhase match { case TrainingPhase => _inputFormats = inputs.clone() _outputFormats = inputs.clone() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 59a0f669844..e4f37827eff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -396,15 +396,24 @@ class SpatialConvolution( } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { - (Array(weight.dense, bias.dense), Array(gradWeight.dense, gradBias.dense)) + if (withBias) { + (Array(weight.dense, bias.dense), Array(gradWeight.dense, gradBias.dense)) + } else { + (Array(weight.dense), Array(gradWeight.dense)) + } + } override def zeroGradParameters(): Unit = { } override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { - (Array(weight.memoryData(), bias.memoryData()), - Array(gradWeight.memoryData(), bias.memoryData())) + if (withBias) { + (Array(weight.memoryData(), bias.memoryData()), + Array(gradWeight.memoryData(), bias.memoryData())) + } else { + (Array(weight.memoryData()), Array(gradWeight.memoryData())) + } } override def release(): Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala new file mode 100644 index 00000000000..3d05572541d --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala @@ -0,0 +1,111 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl.nn.Graph +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, InputWrapper, Output} +import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.{Module, utils} +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, MklDnn, Node} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + + +private[bigdl] class IRConverter[T: ClassTag](IRgraph: IRGraph[T])(implicit ev: TensorNumeric[T]) { + private val allNodes = new ArrayBuffer[Node[IRElement[T]]] + private val irInputs = IRgraph.inputs.toArray + private val irOutputs = IRgraph.outputs.toArray + + init() + private def init() : Unit = { + getNodes(irInputs, allNodes) + // reminder: some output nodes may not be searched from inputs + irOutputs.foreach(node => { + if (!allNodes.contains(node)) allNodes.append(node) + }) + } + + + private def getNodes(inputs: Seq[Node[IRElement[T]]], + nodesBuffer: ArrayBuffer[Node[IRElement[T]]]): Unit = { + if (inputs.length == 0) return + inputs.foreach(node => { + if (!nodesBuffer.contains(node)) { + nodesBuffer.append(node) + getNodes(node.nextNodes, nodesBuffer) + } + }) + } + + /** + * convert IRgraph to blas or dnn graph according to engine type + * @return dnn graph or blas graph converted from ir graph + */ + def toGraph(dnnMode: Boolean = false) : Graph[T] = { + if (utils.Engine.getEngineType() == MklBlas) { + require(IRToBlas[T].convertingCheck(allNodes.toArray), + "IR graph can not be converted to Blas layer") + toBlasGraph() + } else if (utils.Engine.getEngineType() == MklDnn) { + require(ev.getType() == FloatType, "Mkldnn engine only supports float data") + require(IRToDnn[Float].convertingCheck( + allNodes.toArray.asInstanceOf[Array[Node[IRElement[Float]]]]), + "IR graph can not be converted to Dnn layer") + toDnnGraph() + } else throw new UnsupportedOperationException( + s"Only support engineType mkldnn/mklblas, but get ${Engine.getEngineType()}") + } + + private def toDnnGraph(): Graph[T] = { + val nodeMap = IRToDnn[Float].convert( + allNodes.toArray.asInstanceOf[Array[Node[IRElement[Float]]]]) + val inputs = irInputs.map( + n => nodeMap.get(n.asInstanceOf[Node[IRElement[Float]]]).get) + val outputs = irOutputs.map( + n => nodeMap.get(n.asInstanceOf[Node[IRElement[Float]]]).get) + + // add input node for dnn graph + val realInputs = inputs.map(n => { + val node = new Node[Module[Float]](new InputWrapper()) + n.from(node) + node + }) + + // add output node for graph + val realOutputs = outputs.zipWithIndex.map { + case (model: Node[Module[Float]], index: Int) => + val node = new Node[Module[Float]](Output(IRgraph.outputFormats(index))) + model.add(node) + node + } + + DnnGraph(realInputs, realOutputs, + IRgraph.variables.asInstanceOf[Option[(Array[Tensor[Float]], Array[Tensor[Float]])]], + IRgraph.generateBackward).asInstanceOf[Graph[T]] + } + + private def toBlasGraph(): Graph[T] = { + val nodeMap = IRToBlas[T].convert(allNodes.toArray) + val inputs = irInputs.map(n => nodeMap.get(n).get) + val outputs = irOutputs.map(n => nodeMap.get(n).get) + + Graph.dynamic(inputs, outputs, IRgraph.variables, IRgraph.generateBackward) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala index e088feb36e0..d7d6967e62d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag -sealed class IROperator[T: ClassTag] { +sealed class IROperator[T: ClassTag] extends Serializable { val tag: ClassTag[T] = scala.reflect.classTag[T] val numerics: TensorNumeric[T] = tag match { case ClassTag.Float => TensorNumeric.NumericFloat.asInstanceOf[TensorNumeric[T]] @@ -132,7 +132,7 @@ private[bigdl] class IRElement[T: ClassTag]( val name: String, val op: IROperator[T], private var weights: Tensor[T] = null, - private var gradWeights: Tensor[T] = null) { + private var gradWeights: Tensor[T] = null) extends Serializable { /** * set weight and bias diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala new file mode 100644 index 00000000000..534d3bb79c4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -0,0 +1,180 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import java.util.List + +import breeze.linalg.reverse +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.{Graph, SpatialMaxPooling, keras} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} +import com.intel.analytics.bigdl.nn.mkldnn._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Node, T} + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +/** + * Generate IR graph + * @param inputs input nodes for graph + * @param outputs output nodes for graph + * @param variables + * @param generateBackward + * @param inputFormats input memory layout for graph + * @param outputFormats output memory layout for graph + * @param ev$1 + * @param ev + * @tparam T The numeric type in this module parameters. + */ +private[bigdl] class IRGraph[T: ClassTag]( + val inputs : Seq[Node[IRElement[T]]], + val outputs : Seq[Node[IRElement[T]]], + val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, + val generateBackward: Boolean = true, + val inputFormats: Seq[Int] = Seq(Memory.Format.nchw), + val outputFormats: Seq[Int] = Seq(Memory.Format.nc)) + (implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Activity, T] with Serializable { + + @transient private var initFwd: Boolean = false + @transient private var initBwd: Boolean = false + @transient private var initAcc: Boolean = false + + require(inputFormats.length == inputs.length, s"IRGraph: inputFormats" + + s"length ${inputFormats.length} should be same with input nodes length ${inputs.length}") + require(outputFormats.length == outputs.length, s"IRGraph: outputFormats" + + s"length ${inputFormats.length} should be same with input nodes length ${outputs.length}") + + private var graph: Graph[T] = null + + override def updateOutput(input: Activity): Activity = { + if (graph == null) { + throw new UnsupportedOperationException("forward not supported, Please build graph first") + } + initFwdPrimitives(input) + output = graph.updateOutput(input) + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + if (graph == null) { + throw new UnsupportedOperationException("backward not supported, Please build graph first") + } + initBwdPrimitives() + gradInput = graph.updateGradInput(input, gradOutput) + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + if (graph == null) { + throw new UnsupportedOperationException("backward not supported, Please build graph first") + } + initGradWPrimitives() + graph.accGradParameters(input, gradOutput) + } + + def build(dnnMode: Boolean = false): Unit = { + graph = new IRConverter[T](this).toGraph(dnnMode) + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + graph.parameters() + } + + override def training(): this.type = { + train = true + graph.training() + this + } + + /** + * Set the module to evaluate mode + * @return + */ + override def evaluate(): this.type = { + train = false + graph.evaluate() + this + } + + private def initFwdPrimitives(input: Activity): Unit = { + if (!initFwd && graph.isInstanceOf[DnnGraph]) { + val inputMemory = new Array[MemoryData](inputFormats.length) + if (input.isInstanceOf[Tensor[T]]) { + inputMemory(0) = HeapData(input.toTensor[T].size(), inputFormats(0)) + } else { + val tensors = input.toTable + require(tensors.length() == inputFormats.length, s"table input length " + + s"${tensors.length()} should be the same with inputFormats length ${inputFormats.length}") + tensors.foreach(t => { + require(t._2.isInstanceOf[Tensor[T]], + "Only support input with tensor type, table not supported") + val t1 = t._1.asInstanceOf[Int] // starts from 1 + val t2 = t._2.asInstanceOf[Tensor[T]] + inputMemory(t1 - 1) = HeapData(t2.size(), inputFormats(t1 - 1)) + }) + } + val dnnGraph = graph.asInstanceOf[DnnGraph] + dnnGraph.setRuntime(new MklDnnRuntime()) + dnnGraph.initFwdPrimitives(inputMemory) + initFwd = true + } + } + + private def initBwdPrimitives(): Unit = { + if (!initBwd && graph.isInstanceOf[DnnGraph]) { + val dnnGraph = graph.asInstanceOf[DnnGraph] + dnnGraph.initBwdPrimitives(dnnGraph.outputFormats()) + initBwd = true + } + } + + private def initGradWPrimitives(): Unit = { + if (!initAcc && graph.isInstanceOf[DnnGraph]) { + val dnnGraph = graph.asInstanceOf[DnnGraph] + dnnGraph.initGradWPrimitives(dnnGraph.outputFormats()) + initAcc = true + } + } +} + +object IRGraph { + def apply[T: ClassTag]( + inputs: Seq[Node[IRElement[T]]], + outputs: Seq[Node[IRElement[T]]], + variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None, + generateBackward: Boolean = true, + inputFormats: Int = Memory.Format.nchw, + outputFormats: Int = Memory.Format.nc + )( implicit ev: TensorNumeric[T]): IRGraph[T] = { + new IRGraph[T](inputs, outputs, variables, generateBackward, + Seq(inputFormats), Seq(outputFormats)) + } + + def apply[T: ClassTag]( + inputs: Seq[Node[IRElement[T]]], + outputs: Seq[Node[IRElement[T]]], + variables: Option[(Array[Tensor[T]], Array[Tensor[T]])], + generateBackward: Boolean, + inputFormats: Seq[Int], + outputFormats: Seq[Int] + )( implicit ev: TensorNumeric[T]): IRGraph[T] = { + new IRGraph[T](inputs, outputs, variables, generateBackward, inputFormats, outputFormats) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala index 42c6f2df731..072c3b03c8c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala @@ -81,8 +81,14 @@ private[bigdl] object ReflectionUtils { if (blasLayer.parameters() != null) { val params = blasLayer.getParameters() val params2 = layer.getParameters() - if (params2._1 != null) params._1.copy(params2._1) - if (params2._2 != null) params._2.copy(params2._2) + if (params2._1 != null) { + params._1.copy(params2._1) + layer.setWeights(params._1) + } + if (params2._2 != null) { + params._2.copy(params2._2) + layer.setGradWeights(params._2) + } } if (layer.getName() != "") blasLayer.setName(layer.getName()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala new file mode 100644 index 00000000000..a0c6a9b85d5 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala @@ -0,0 +1,152 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.mkldnn.HeapData +import com.intel.analytics.bigdl.{Module, nn, utils} +import com.intel.analytics.bigdl.nn.{Graph, Reshape, StaticGraph} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils._ + +class IRGraphSpec extends BigDLSpecHelper { + def modelIR(inputFormats: Int = Memory.Format.nchw, + outputFormats: Int = Memory.Format.nchw): IRGraph[Float] = { + val conv1 = Node(IRElement[Float]("", IRSpatialConvolution[Float](1, 20, 5, 5))) + val bn1 = Node(IRElement[Float]("", IRSpatialBatchNormalization[Float](20))) + val weightsAndBias = Tensor[Float](2 * 20).rand() + bn1.element.setWeights(weightsAndBias) + + val pool1 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + val conv2 = Node(IRElement[Float]("", IRSpatialConvolution[Float](20, 50, 5, 5))) + val pool2 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + + conv1 -> pool1 -> conv2 -> pool2 + val output = pool2 + IRGraph(Array(conv1), Array(output), inputFormats = inputFormats, outputFormats = outputFormats) + } + + def modelIR2(inputFormats: Int = Memory.Format.nchw, + outputFormats: Int = Memory.Format.nc): IRGraph[Float] = { + val conv1 = Node(IRElement[Float]("", IRSpatialConvolution[Float](1, 20, 5, 5))) + val pool1 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + val conv2 = Node(IRElement[Float]("", IRSpatialConvolution[Float](20, 50, 5, 5))) + val pool2 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + val reshape = Node(IRElement("", IRGeneralModule(Reshape[Float](Array(50*4*4))))) + val linear = Node(IRElement("", IRLinear[Float](50 * 4 * 4, 500))) + val relu = Node(IRElement("", IRReLU[Float]())) + val fc2 = Node(IRElement("", IRLinear[Float](500, 10))) + + conv1 -> pool1 -> conv2 -> pool2 -> + reshape -> linear -> relu -> fc2 + val output = fc2 + + IRGraph(Array(conv1), Array(output), inputFormats = inputFormats, outputFormats = outputFormats) + } + + def modelIR3(inputFormats: Int = Memory.Format.nchw, + outputFormats: Int = Memory.Format.nc): IRGraph[Float] = { + val conv1 = Node(IRElement[Float]("", IRSpatialConvolution[Float](1, 20, 5, 5))) + val pool1 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + val conv2 = Node(IRElement[Float]("", IRSpatialConvolution[Float](20, 50, 5, 5))) + val pool2 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) + val reshape = Node(IRElement("", IRGeneralModule(Reshape[Float](Array(50*4*4))))) + val linear = Node(IRElement("", IRLinear[Float](50 * 4 * 4, 500))) + val relu = Node(IRElement("", IRReLU[Float]())) + val fc2 = Node(IRElement("", IRLinear[Float](500, 10))) + + val identity = Node(IRElement("", IRIdentity[Float]())) + val join = Node(IRElement("", IRJoinTable[Float](2))) + + conv1 -> pool1 -> conv2 -> pool2 -> + reshape -> linear -> relu -> fc2 + + fc2 -> join + identity -> join + + new IRGraph(Array(conv1, identity), Array(join), + inputFormats = Seq(Memory.Format.nchw, Memory.Format.nc), + outputFormats = Seq(Memory.Format.nc)) + } + + "Convert IRgraph to Dnn or Blas Graph" should "be correct" in { + val input = Tensor[Float](2, 1, 28, 28).rand() + val gradOutput = Tensor[Float](2, 50, 4, 4).rand() + + RandomGenerator.RNG.setSeed(1000) + utils.Engine.setEngineType(MklBlas) + val irBlas = modelIR() + irBlas.build() + val outBlas = irBlas.forward(input) + val gradInputBlas = irBlas.backward(input, gradOutput) + + RandomGenerator.RNG.setSeed(1000) + utils.Engine.setEngineType(MklDnn) + val irDnn = modelIR() + irDnn.build() + val outDnn = irDnn.forward(input) + val gradInputDnn = irDnn.backward(input, gradOutput).toTensor[Float] + + outDnn should be(outBlas) + gradInputDnn should be(gradInputBlas) + } + + "Convert IRgraph to Dnn or Blas Graph with 2 dimentions output" should "be correct" in { + val input = Tensor[Float](2, 1, 28, 28).rand() + val gradOutput = Tensor[Float](2, 10).rand() + RandomGenerator.RNG.setSeed(1000) + utils.Engine.setEngineType(MklBlas) + val irBlas = modelIR2() + irBlas.build() + val outBlas = irBlas.forward(input) + val gradInputBlas = irBlas.backward(input, gradOutput) + + RandomGenerator.RNG.setSeed(1000) + utils.Engine.setEngineType(MklDnn) + val irDnn = modelIR2() + irDnn.build() + val outDnn = irDnn.forward(input) + val gradInputDnn = irDnn.backward(input, gradOutput).toTensor[Float] + + outDnn should be(outBlas) + gradInputDnn should be(gradInputBlas) + } + + "Convert IRgraph with two inputs to Dnn or Blas Graph" should "be correct" in { + val input = T(Tensor[Float](2, 1, 28, 28).rand(), Tensor[Float](2, 4).rand()) + val gradOutput = Tensor[Float](2, 14).rand() + + RandomGenerator.RNG.setSeed(1000) + utils.Engine.setEngineType(MklBlas) + val irBlas = modelIR3() + irBlas.build() + val outBlas = irBlas.forward(input) + val gradInputBlas = irBlas.backward(input, gradOutput).asInstanceOf[Table] + + RandomGenerator.RNG.setSeed(1000) + utils.Engine.setEngineType(MklDnn) + val irDnn = modelIR3() + irDnn.build() + val outDnn = irDnn.forward(input) + val gradInputDnn = irDnn.backward(input, gradOutput).toTable + + outDnn should be(outBlas) + gradInputDnn.get[Tensor[Float]](1) should be(gradInputBlas.get[Tensor[Float]](1)) + gradInputDnn.get[Tensor[Float]](2) should be(gradInputBlas.get[Tensor[Float]](2)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index e86e525ddb2..38075586564 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -65,7 +65,9 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.mkldnn.Dropout", "com.intel.analytics.bigdl.nn.mkldnn.DnnGraph", "com.intel.analytics.bigdl.nn.mkldnn.BlasWrapper", - "com.intel.analytics.bigdl.nn.mkldnn.Output" + "com.intel.analytics.bigdl.nn.mkldnn.Output", + "com.intel.analytics.bigdl.nn.mkldnn.InputWrapper", + "com.intel.analytics.bigdl.utils.intermediate.IRGraph" ) // Maybe one serial test class contains multiple module test From 137c4c4ec98081bb08dbfd64bd31980b582a1b11 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Tue, 25 Dec 2018 16:32:05 +0800 Subject: [PATCH 0855/1065] update latest weight for validation (#2710) --- .../bigdl/dllib/optim/AbstractOptimizer.scala | 11 ++++++++++- .../analytics/bigdl/dllib/optim/DistriOptimizer.scala | 3 ++- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala index 7e3db4da35d..0a5a706f963 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala @@ -96,7 +96,8 @@ abstract class AbstractOptimizer { models: RDD[Cache[T]], state: Table, validationSummary: Option[ValidationSummary], - header: String): Unit = { + header: String, + parameters: Map[String, AllReduceParameter[T]] = null): Unit = { if (validationTrigger.isEmpty || validationDataSet.isEmpty) { return } @@ -117,6 +118,14 @@ abstract class AbstractOptimizer { val vMethodsArr = cached.localMethods val workingModels = cached.localModels + // update with latest weight for validation + if (parameters != null) { + val weightsResults = parameters.values.map(p => + p.getWeights(cached.modelWeights.head.narrow(1, p.paramOffset, p.size)) + ).toArray + weightsResults.foreach(_.waitResult()) + } + workingModels.foreach(_.evaluate()) dataIter.map(batch => { val stackSize = batch.size() / _subModelNumber diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 297c128ac8c..451750a0523 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -462,7 +462,8 @@ object DistriOptimizer extends AbstractOptimizer { models, driverState, validationSummary, - _header + _header, + parameters ) trainSummary.foreach { summary => From 92bc2f47aa9078362351892ca7cc83ae53b16204 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 7 Jan 2019 09:55:02 +0800 Subject: [PATCH 0856/1065] convert static graph to IR graph and build (#2711) * add static graph to IR graph * meet pr comments --- .../analytics/bigdl/dllib/nn/Reshape.scala | 12 +++ .../bigdl/dllib/nn/StaticGraph.scala | 61 +++++++++++++ .../intel/analytics/bigdl/dllib/nn/Tanh.scala | 5 ++ .../analytics/bigdl/dllib/nn/Threshold.scala | 6 +- .../utils/intermediate/IRConverter.scala | 2 +- .../dllib/utils/intermediate/IRGraph.scala | 5 +- .../utils/intermediate/BlasToDnnSpec.scala | 85 +++++++++++++++++++ 7 files changed, 172 insertions(+), 4 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala index bd5ea4a76b4..af3b3543e8d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Reshape.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag import scala.reflect.runtime.universe @@ -144,6 +145,17 @@ class Reshape[T: ClassTag]( } this } + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + val output = if ((batchMode.nonEmpty && !batchMode.get) || + (input.product == nElement && batchMode.isEmpty && input(0) != 1)) { + size + } else { + Array(input(0)) ++ batchSize.slice(1, batchSize.length) + } + Shape(output) + } } object Reshape extends ModuleSerializable { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala index 0faecee6f81..5b62ecb8e2f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -15,12 +15,15 @@ */ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.ControlDependency import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.intermediate.{BlasToIR, IRGraph} import com.intel.analytics.bigdl.utils.{Node, Util} +import com.intel.analytics.bigdl.optim.DistriOptimizer._ import scala.reflect.ClassTag @@ -147,4 +150,62 @@ class StaticGraph[T: ClassTag]( gradInput = fetchModelGradInput() gradInput } + + private var inputsFormats: Seq[Int] = null + private var outputsFormats: Seq[Int] = null + + /** + * set input formats for graph + * @param formats + * @return + */ + def setInputFormats(formats: Seq[Int]): this.type = { + inputsFormats = formats + this + } + + /** + * set output formats for graph + * @param formats + * @return + */ + def setOutputFormats(formats: Seq[Int]): this.type = { + outputsFormats = formats + this + } + + /** + * convert static graph to ir graph and build according to engine type + * @return return ir graph if converted successfully, otherwise null + */ + def toIRgraph() : IRGraph[T] = { + val inFormats = if (inputsFormats == null) { + logger.warn("Input formats NCHW by default, Please set explicitly if needed") + Seq(Memory.Format.nchw) + } else inputsFormats + + val outFormats = if (outputsFormats == null) { + logger.warn("Output formats NC by default, Please set explicitly if needed") + Seq(Memory.Format.nc) + } else outputsFormats + + val allNodes = forwardExecution + if (!BlasToIR[T].convertingCheck(allNodes)) return null + inFormats.foreach(in => + if (in == Memory.Format.nhwc) { + logger.warn("Not support NHWC in IRGraph") + return null + } + ) + + val nodeMap = BlasToIR[T].convert(allNodes) + val inputNodes = inputs.toArray.map(n => nodeMap.get(n).get) + val outputNodes = outputs.toArray.map(n => nodeMap.get(n).get) + + val inputsIR = inputs.toArray.map(n => nodeMap.get(n).get) + val outputsIR = outputs.toArray.map(n => nodeMap.get(n).get) + + val model = IRGraph(inputsIR, outputsIR, variables, true, inFormats, outFormats) + model.build() + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala index 13e533d394d..a9efef5634d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Tanh.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.math.tanh import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -58,6 +59,10 @@ class Tanh[T: ClassTag]( buffer.set() this } + + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala index a2577a7c343..9f697301d5e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Threshold.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, Shape} import scala.collection.mutable.ArrayBuffer import scala.concurrent.Future @@ -369,6 +369,10 @@ class Threshold[T: ClassTag]( } } + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } + override def equals(obj: Any): Boolean = { if (!super.equals(obj)) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala index 3d05572541d..237b1ed0057 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala @@ -58,7 +58,7 @@ private[bigdl] class IRConverter[T: ClassTag](IRgraph: IRGraph[T])(implicit ev: * convert IRgraph to blas or dnn graph according to engine type * @return dnn graph or blas graph converted from ir graph */ - def toGraph(dnnMode: Boolean = false) : Graph[T] = { + def toGraph() : Graph[T] = { if (utils.Engine.getEngineType() == MklBlas) { require(IRToBlas[T].convertingCheck(allNodes.toArray), "IR graph can not be converted to Blas layer") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index 534d3bb79c4..376d9cd61a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -89,8 +89,9 @@ private[bigdl] class IRGraph[T: ClassTag]( graph.accGradParameters(input, gradOutput) } - def build(dnnMode: Boolean = false): Unit = { - graph = new IRConverter[T](this).toGraph(dnnMode) + def build(): this.type = { + graph = new IRConverter[T](this).toGraph() + this } override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala new file mode 100644 index 00000000000..2c3f8f246f9 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala @@ -0,0 +1,85 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl.example.loadmodel.AlexNet +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.models.inception.{Inception_Layer_v1, Inception_v1_NoAuxClassifier} +import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.models.resnet.ResNet +import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} +import com.intel.analytics.bigdl.models.vgg.Vgg_16 +import com.intel.analytics.bigdl.nn.StaticGraph +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.mkldnn.Equivalent +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, RandomGenerator, T} + +import scala.util.Random + +class BlasToDnnSpec extends BigDLSpecHelper { + "vgg16 blas to dnn" should "work properly" in { + System.setProperty("bigdl.engineType", "mkldnn") + val batchSize = 2 + val classNum = 1000 + RandomGenerator.RNG.setSeed(1000) + val input = Tensor[Float](Array(batchSize, 3, 224, 224)).apply1(_ => + RandomGenerator.RNG.uniform(0.1, 1.0).toFloat) + val gradOutput = Tensor[Float](batchSize, classNum).apply1(_ => + RandomGenerator.RNG.uniform(1.0, 1000.0).toFloat) + + val blas = Vgg_16.graph(classNum, false).asInstanceOf[StaticGraph[Float]] + blas.setInputFormats(Seq(Memory.Format.nchw)) + blas.setOutputFormats(Seq(Memory.Format.nc)) + val irBlas = blas.cloneModule().toIRgraph() + + val outBlas = blas.forward(input).toTensor[Float] + val gradInputBlas = blas.backward(input, gradOutput).toTensor[Float] + + val outDnn = irBlas.forward(input).toTensor[Float] + val gradInputDnn = irBlas.backward(input, gradOutput).toTensor[Float] + + Equivalent.nearequals(outDnn, outBlas, 1e-6) should be(true) + Equivalent.nearequals(gradInputDnn, gradInputBlas, 1e-4) should be(true) + } + + "lenet5 blas to dnn" should "work properly" in { + System.setProperty("bigdl.engineType", "mkldnn") + val batchSize = 2 + val seed = 1 + val inputFormat = Memory.Format.nchw + val inputShape = Array(batchSize, 1, 28, 28) + + val input = Tensor[Float](inputShape).rand() + val gradOutput = Tensor[Float](batchSize, 10).rand() + + val blas = LeNet5.graph(10).asInstanceOf[StaticGraph[Float]] + blas.setInputFormats(Seq(Memory.Format.nchw)) + blas.setOutputFormats(Seq(Memory.Format.nc)) + val irBlas = blas.cloneModule().toIRgraph() + + val outBlas = blas.forward(input).toTensor[Float] + val gradInputBlas = blas.backward(input, gradOutput).toTensor[Float] + + val outDnn = irBlas.forward(input).toTensor[Float] + val gradInputDnn = irBlas.backward(input, gradOutput).toTensor[Float] + + Equivalent.nearequals(outDnn, outBlas, 1e-6) should be(true) + Equivalent.nearequals(gradInputDnn, gradInputBlas, 1e-6) should be(true) + } +} From a839914ca4a40a1c2d32f892324649e219020e50 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 7 Jan 2019 16:42:43 +0800 Subject: [PATCH 0857/1065] [Enhancement] - Enhance unig test to avoid dynamic resource allocation issue by docker (#2713) * make the core number fixed * fix local predictor --- .../intel/analytics/bigdl/dllib/optim/LocalPredictor.scala | 4 ++-- .../analytics/bigdl/dllib/optim/LocalPredictorSpec.scala | 5 +++-- .../bigdl/dllib/utils/DistributedSynchronizerSpec.scala | 2 +- 3 files changed, 6 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index d724511cac3..7affec852fa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.nn.quantized.QuantizedModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, LocalImageFrame} import com.intel.analytics.bigdl.utils.Util._ -import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Util} +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, MklDnn, Util} import org.apache.log4j.Logger import scala.reflect.ClassTag @@ -56,7 +56,7 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], private val subModelNumber = Engine.getEngineType match { case MklBlas => coreNumber - case _ => throw new IllegalArgumentException + case MklDnn => 1 } // we should clone a new model which has no impact to origin model diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala index 58d9222306d..b25998f03ce 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictorSpec.scala @@ -27,7 +27,7 @@ import com.intel.analytics.bigdl.nn.quantized.StorageManager import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image._ import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} -import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Table, Util} +import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.commons.io.FileUtils import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -44,8 +44,9 @@ class LocalPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { Engine.init(nodeNumber, coreNumber, false) subModelNumber = Engine.getEngineType match { case MklBlas => coreNumber - case _ => throw new IllegalArgumentException + case MklDnn => 1 } + } after { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala index 6c6b1f1167e..d21b1e01a21 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DistributedSynchronizerSpec.scala @@ -25,7 +25,7 @@ class DistributedSynchronizerSpec extends FlatSpec with Matchers with BeforeAndA var sc: SparkContext = null before { - val conf = Engine.createSparkConf().setAppName("test synchronizer").setMaster("local[*]") + val conf = Engine.createSparkConf().setAppName("test synchronizer").setMaster("local[4]") .set("spark.rpc.message.maxSize", "200") sc = new SparkContext(conf) Engine.init From 9b14e7a4aab7afa008555b311ce3db39235817a5 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Wed, 9 Jan 2019 09:57:01 +0800 Subject: [PATCH 0858/1065] add Trigger and/or python API (#2682) --- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index dcfdb7e0609..2b0262c7248 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2069,6 +2069,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Trigger.minLoss(min) } + def createTriggerAnd(first: Trigger, others: JList[Trigger]): Trigger = { + Trigger.and(first, others.asScala: _*) + } + + def createTriggerOr(first: Trigger, others: JList[Trigger]): Trigger = { + Trigger.or(first, others.asScala: _*) + } + def createTop1Accuracy(): ValidationMethod[T] = { new Top1Accuracy() } From 499449c73db94d374ed08d9832adefc3267d6541 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Fri, 11 Jan 2019 15:39:15 +0800 Subject: [PATCH 0859/1065] update sparse tensor's document (#2714) --- .../analytics/bigdl/dllib/tensor/SparseTensor.scala | 2 +- .../intel/analytics/bigdl/dllib/tensor/Tensor.scala | 12 ++++++++---- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 2d4d9aeac48..51f4594b660 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -41,7 +41,7 @@ import scala.reflect.ClassTag * values = Array(1, 4, 2, 3) * shape = Array(3, 4) * - * @param _indices non-zero elements' indices + * @param _indices non-zero elements' indices, should be zero-based and ascending. * @param _values values of the non-zero elements * @param _storageOffset storageOffset, both _values and _indices's storage offset. * @param _nElement number of non-zero elements diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 881752e5bbd..e0017ca69de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -1178,7 +1178,8 @@ object Tensor { /** * Create a SparseTensor. * - * @param indices dimension-D array to describe the indices of values. + * @param indices dimension-D array to describe the indices of values, + * should be zero-based and ascending. * @param values non-zero values in this SparseTensor. * @param shape shape * @param ev @@ -1196,7 +1197,8 @@ object Tensor { /** * Create a SparseTensor. * - * @param indices dimension-D array to describe the indices of values. + * @param indices dimension-D array to describe the indices of values, + * should be zero-based and ascending. * @param values non-zero values in this SparseTensor. * @param shape shape * @param ev @@ -1214,7 +1216,8 @@ object Tensor { /** * Create a SparseTensor. * - * @param indices dimension-D array to describe the indices of values. + * @param indices dimension-D array to describe the indices of values, + * should be zero-based and ascending. * @param values non-zero values in this SparseTensor. * @param shape shape * @param dimension dimension @@ -1234,7 +1237,8 @@ object Tensor { /** * Create a SparseTensor. * - * @param indices dimension-D array to describe the indices of values. + * @param indices dimension-D array to describe the indices of values, + * should be zero-based and ascending. * @param values non-zero values in this SparseTensor. * @param shape shape * @param dimension dimension From 101fb2e9cda11b9b773e46ac8baed7946f5e24fe Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 14 Jan 2019 09:49:52 +0800 Subject: [PATCH 0860/1065] Reserve all state in OptimMethod when calling Optimizer.optimize() multiple times (#2648) * reserve optimMethod for each worker * add valdiation throughput * cache variable previousOptim --- .../bigdl/dllib/optim/AbstractOptimizer.scala | 7 +++ .../bigdl/dllib/optim/DistriOptimizer.scala | 53 +++++++++++++++++-- .../bigdl/dllib/optim/Optimizer.scala | 5 ++ .../dllib/optim/DistriOptimizerSpec.scala | 47 ++++++++++++++++ 4 files changed, 109 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala index 0a5a706f963..3abc022f8a9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala @@ -113,6 +113,7 @@ abstract class AbstractOptimizer { case MklDnn => 1 case _ => throw new IllegalArgumentException } + val start = System.nanoTime() val results = ZippedPartitionsWithLocalityRDD(models, validateRDD)((modelIter, dataIter) => { val cached = modelIter.next() val vMethodsArr = cached.localMethods @@ -157,6 +158,12 @@ abstract class AbstractOptimizer { l + r } }).zip(vMethods) + + val validateTime = (System.nanoTime() - start) / 1e9f + val count = results(0)._1.result()._2.toFloat + // print validation throughput + logger.info(s"$header validate model throughput is ${count / validateTime} records/second") + results.foreach(r => { logger.info(s"$header ${r._2} is ${r._1}") }) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 451750a0523..a9da0a897ff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -33,7 +33,6 @@ import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} -import com.intel.analytics.bigdl.optim.DistriOptimizer.{Cache, getModel} import org.apache.commons.lang.exception.ExceptionUtils import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.Logger @@ -73,7 +72,7 @@ object DistriOptimizer extends AbstractOptimizer { localStates: Array[Table], var moduleTimeList: Array[Long] = null, localMethods: Array[Option[Array[ValidationMethod[T]]]], - optimMethods: Map[String, OptimMethod[T]], + var optimMethods: Map[String, OptimMethod[T]], parameterSynchronizer: DistriParameterSynchronizer[T] = null ) @@ -700,6 +699,40 @@ class DistriOptimizer[T: ClassTag] ( DistriOptimizer.clearState(models) } + + // By default, optimMethod internal state for each worker will not be reserved and reuse. + private var reserveOptimMethod = false + private[bigdl] var previousOptim: RDD[Map[String, OptimMethod[T]]] = null + /** + * If you want to reserve optimMethod for each worker, and reuse those methods in + * next training task, you can call it. + */ + + /** + * If you want to reserve optimMethod for each worker and reuse those methods in + * next training task, please set reserve = true + * Otherwise, if just using optimMethod you set in optimizer, please set reserve = false + * @param reserve whether to reserve optim method for each worker + * @return + */ + override def reserveOptim(reserve: Boolean): this.type = { + reserveOptimMethod = reserve + this + } + + // replace optim methods with previous + private def resetOptimMethods[T: ClassTag]( + models: RDD[DistriOptimizer.Cache[T]], + previousOptimMethods: RDD[Map[String, OptimMethod[T]]]): + RDD[DistriOptimizer.Cache[T]] = { + models.zipPartitions(previousOptimMethods) { (m1, m2) => { + val cache = m1.next() + cache.optimMethods = m2.next() + Iterator(cache) + } + } + } + private def endEpoch(): Unit = { DistriOptimizer.endEpoch(optimMethods) } @@ -788,7 +821,13 @@ class DistriOptimizer[T: ClassTag] ( val modelsAndBroadcast = DistriOptimizer.initThreadModels(model, distDataset, criterion, state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, optimMethods, parameterProcessors) - models = modelsAndBroadcast._1 + + models = if (reserveOptimMethod && previousOptim != null) { + // replace optimMethods with previous ones + resetOptimMethods(modelsAndBroadcast._1, previousOptim) + } else { + modelsAndBroadcast._1 + } modelBroadcast = modelsAndBroadcast._2 if (checkpointPath.isDefined) { @@ -889,6 +928,14 @@ class DistriOptimizer[T: ClassTag] ( // unpersist the model because the next time optimize is called, new `models` will be // created shutdown() + + // reserve optimMethod internal state for each worker if need + if (reserveOptimMethod) { + previousOptim = models.map(m => m.optimMethods).cache() + previousOptim.count() + } else { + if (previousOptim != null) previousOptim.unpersist() + } models.unpersist() model diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index 4becaa5a6e8..77a0c1f0849 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -466,6 +466,11 @@ abstract class Optimizer[T: ClassTag, D]( * shutdown the optimizer, which will release the native resources if exists. */ private[optim] def shutdown(): Unit = {} + + def reserveOptim(reserve: Boolean): this.type = { + throw new UnsupportedOperationException( + "Only support DistriOptimizer to reserve optim methods for each worker") + } } object Optimizer { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index 10d6a3c8254..fd20c877a9f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -892,5 +892,52 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { optimMethod.state[Int]("neval") should be(31) optimMethod.state[Int]("recordsProcessedThisEpoch") should be(0) } + + "reserve optimMethod for each worker" should "be correct" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) + + val mm = Sequential[Double]().add(Linear(4, 1)) + .add(Sigmoid()) + + val optimizer = new DistriOptimizer[Double]( + _model = mm, + _dataset = dataSet, + _criterion = new MSECriterion[Double]() + ) + + val optimMethod = new Adam[Double](learningRate = 20.0) + + optimizer + .setOptimMethod(optimMethod) + .reserveOptim(true) + .setEndWhen(Trigger.maxIteration(3)) + val model = optimizer.optimize() + val optim1 = optimizer.previousOptim.collect() + + optimizer.setEndWhen(Trigger.maxEpoch(0)) + optimizer.optimize() + val optim2 = optimizer.previousOptim.collect() + + var i = 0 + while (i < optim1.length) { + val t1 = optim1(i).values.head.asInstanceOf[Adam[Double]] + val t2 = optim2(i).values.head.asInstanceOf[Adam[Double]] + + t1.beta1 should be(t2.beta1) + t1.beta2 should be(t2.beta2) + t1.learningRate should be(t2.learningRate) + t1.learningRateDecay should be(t2.learningRateDecay) + t1.Epsilon should be(t2.Epsilon) + + t2.state.contains("s") should be(true) + t2.state.contains("r") should be(true) + + t1.state should be(t2.state) + + i += 1 + } + } } From 4f2ab72a7b2ef5985a2f650bbb02e59d5af7a72b Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 31 Jan 2019 01:16:44 -0500 Subject: [PATCH 0861/1065] fix: move mkldnn computing to a single thread pool (#2724) Because if we use the parent thread directly, there will be two bugs, 1. The child threads forked from parent thread will be bound to core 0 because of the affinity settings. 2. The native thread has some unknown thread local variables. So if the parent thread exits and is recreated, such as the thread from Executors.newFixedThreadPool. The whole app will be segment fault. The parent thread means the main thread (Local Mode) or worker thread of mapPartition (Distributed Mode). --- .../intel/analytics/bigdl/utils/Engine.scala | 38 +++++----- .../analytics/bigdl/utils/ThreadPool.scala | 41 ++++++---- .../bigdl/utils/ThreadPoolSpec.scala | 76 +++++++++++++++++++ .../image/MTLabeledBGRImgToBatch.scala | 2 +- .../vision/image/MTImageFeatureToBatch.scala | 2 +- .../bigdl/dllib/nn/mkldnn/Perf.scala | 53 +++++++------ .../bigdl/dllib/optim/AbstractOptimizer.scala | 10 ++- .../bigdl/dllib/optim/DistriOptimizer.scala | 32 +++++--- .../tensorboard/FileWriter.scala | 2 +- .../dllib/nn/mkldnn/ReorderMemorySpec.scala | 71 ++++++++++++++++- .../utils/intermediate/IRGraphSpec.scala | 36 +++++---- 11 files changed, 278 insertions(+), 85 deletions(-) create mode 100644 scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 06a213fccda..2d41125b559 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -220,8 +220,20 @@ object Engine { // Thread pool for layer use @volatile private var _model: ThreadPool = new ThreadPool(1) - // Thread pool for read data - @volatile private var _io: ThreadPool = null + // This thread is mainly for mkldnn library. + // Because if we use the parent thread directly, there will be two bugs, + // 1. The child threads forked from parent thread will be bound to core 0 + // because of the affinity settings. + // 2. The native thread has some unknown thread local variables. So if + // the parent thread exits and is recreated, such as the thread from + // Executors.newFixedThreadPool. The whole app will be segment fault. + // The parent thread means the main thread (Local Mode) or worker thread of + // `mapPartition` (Distributed Mode). + // -------------------------------------------------------------------------- + // We will only use the `threadPool` in ThreadPool, which is a ExecutorService. + // For `context` in ThreadPool, it is the called thread when poolSize is 1. + // So many usages of that thread, we will not change it for now. + val dnnComputing: ThreadPool = new ThreadPool(1) /** * If user undefine the property bigdl.coreNumber, it will return physical core number @@ -321,14 +333,6 @@ object Engine { _default } - private[bigdl] def io: ThreadPool = { - if (_io == null) { - throw new IllegalStateException(s"Engine.init: Thread engine is not " + - s"initialized. $NOT_INIT_ERROR") - } - _io - } - private def initThreadPool(core : Int) : Unit = { val defaultPoolSize: Int = System.getProperty("bigdl.utils.Engine.defaultPoolSize", (core * 50).toString).toInt @@ -336,10 +340,6 @@ object Engine { _default = new ThreadPool(defaultPoolSize) } - if (_io == null) { - _io = new ThreadPool(core * 50) - } - // for dnn model we should set the pool size to 1 also. // otherwise, it will downgrade the performance and // FIXME make the loss to NaN. @@ -350,7 +350,13 @@ object Engine { } _model.setMKLThread(MKL.getMklNumThreads) - ThreadPool.setThreadsOfBackend(MKL.getMklNumThreads) + // do two things, set number of threads for omp thread pool and set the affinity + // only effects the `threadPool` and `computing.invoke/invokeAndWait` will not + // be effected. And affinity will not effect the other threads except + // this thread and the omp threads forked from computing. + if (engineType == MklDnn) { + dnnComputing.setMKLThreadOfMklDnnBackend(MKL.getMklNumThreads) + } } /** @@ -545,9 +551,7 @@ object Engine { val threadsNumber = System.getProperty("bigdl.mklNumThreads", default.toString) System.setProperty("bigdl.mklNumThreads", s"$threadsNumber") - System.setProperty("bigdl.disable.mklBlockTime", "true") System.setProperty("bigdl.coreNumber", "1") - System.setProperty("bigdl.utils.Engine.defaultPoolSize", "1") } } diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 63ea30ff2ec..fb38210adf5 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -18,7 +18,6 @@ package com.intel.analytics.bigdl.utils import java.util.concurrent._ -import com.google.common.util.concurrent.MoreExecutors import com.intel.analytics.bigdl.mkl.MKL import com.intel.analytics.bigdl.mkl.hardware.Affinity import com.intel.analytics.bigdl.mkl.{MklDnn => BackendMklDnn} @@ -48,7 +47,15 @@ class ThreadPool(private var poolSize: Int) { private def spawnThreadPool(poolSize: Int): ExecutionContext = { if (poolSize == 1) { - threadPool = MoreExecutors.sameThreadExecutor() + threadPool = Executors.newFixedThreadPool(poolSize, new ThreadFactory { + override def newThread(r: Runnable): Thread = { + val t = Executors.defaultThreadFactory().newThread(r) + t.setName("single-thread-computing") + t.setDaemon(true) + t + } + }) + singleThreadPool } else { new ExecutionContext { @@ -56,6 +63,7 @@ class ThreadPool(private var poolSize: Int) { threadPool = Executors.newFixedThreadPool(poolSize, new ThreadFactory { override def newThread(r: Runnable): Thread = { val t = Executors.defaultThreadFactory().newThread(r) + t.setName("default-thread-computing") t.setDaemon(true) t } @@ -79,15 +87,32 @@ class ThreadPool(private var poolSize: Int) { * @return */ def setMKLThread(size: Int): this.type = this.synchronized { + require(MKL.isMKLLoaded) mklPoolSize = Some(size) (1 to poolSize).map(i => Future { - ThreadPool.setThreadsOfBackend(size) + MKL.setNumThreads(size) val tid = Thread.currentThread().getId() logger.info(s"Set mkl threads to $size on thread $tid") }(context)).foreach(Await.result(_, Duration.Inf)) this } + def setMKLThreadOfMklDnnBackend(size: Int): this.type = this.synchronized { + mklPoolSize = Some(size) + + + this.invokeAndWait2((0 until 1).map(_ => () => { + require(MKL.isMKLLoaded) + require(BackendMklDnn.isLoaded) + + MKL.setNumThreads(size) + BackendMklDnn.setNumThreads(size) + Affinity.setOmpAffinity() + })) + + this + } + /** * Invoke a batch of tasks and wait for all them finished * @@ -214,15 +239,5 @@ object ThreadPool { } private val logger = Logger.getLogger(getClass) - - def setThreadsOfBackend(size: Int): Unit = { - require(MKL.isMKLLoaded) - MKL.setNumThreads(size) - if (System.getProperty("bigdl.engineType") == "mkldnn") { - require(BackendMklDnn.isLoaded) - BackendMklDnn.setNumThreads(size) - Affinity.setOmpAffinity() - } - } } diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala new file mode 100644 index 00000000000..34fae59f5ea --- /dev/null +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala @@ -0,0 +1,76 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import com.intel.analytics.bigdl.mkl.MKL +import com.intel.analytics.bigdl.mkl.hardware.Affinity +import org.scalatest.{FlatSpec, Matchers} + +class ThreadPoolSpec extends FlatSpec with Matchers { + + "mkldnn backend" should "create omp threads and bind correctly" in { + com.intel.analytics.bigdl.mkl.MklDnn.isLoaded + val poolSize = 1 + val ompSize = 4 + + val threadPool = new ThreadPool(poolSize) + // backup the affinities + val affinities = threadPool.invokeAndWait2( (0 until poolSize).map(i => + () => { + Affinity.getAffinity() + })).map(_.get()).toArray + + threadPool.setMKLThreadOfMklDnnBackend(ompSize) + + threadPool.invokeAndWait2( (0 until poolSize).map( i => + () => { + Affinity.getAffinity.length should be (1) + Affinity.getAffinity.head should be (0) + })) + + // set back the affinities + threadPool.invokeAndWait2( (0 until poolSize).map( i => () => { + Affinity.setAffinity(affinities(i)) + })) + + threadPool.invokeAndWait2( (0 until poolSize).map( i => + () => { + Affinity.getAffinity.zipWithIndex.foreach(ai => ai._1 should be (ai._2)) + })) + + } + + "mkldnn thread affinity binding" should "not influence other threads" in { + val poolSize = 1 + val ompSize = 4 + + val threadPool = new ThreadPool(poolSize) + threadPool.setMKLThreadOfMklDnnBackend(ompSize) + + threadPool.invokeAndWait2( (0 until poolSize).map( i => + () => { + Affinity.getAffinity.length should be (1) + Affinity.getAffinity.head should be (0) + })) + + val threadPool2 = new ThreadPool(poolSize) + threadPool2.invokeAndWait2( (0 until poolSize).map(i => () => { + println(Affinity.getAffinity.mkString("\t")) + Affinity.getAffinity.length should not be (1) + })) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/MTLabeledBGRImgToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/MTLabeledBGRImgToBatch.scala index cdc3d8347c5..2dd8dc7ac50 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/MTLabeledBGRImgToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/MTLabeledBGRImgToBatch.scala @@ -76,7 +76,7 @@ class MTLabeledBGRImgToBatch[A: ClassTag] private[bigdl](width: Int, height: Int override def next(): MiniBatch[Float] = { val count = new AtomicInteger(0) - val batch = Engine.io.invokeAndWait((0 until parallelism).map(tid => () => { + val batch = Engine.default.invokeAndWait((0 until parallelism).map(tid => () => { var position = 0 var record = 0 while (iterators(tid).hasNext && { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala index 1f6aca8eefd..1f9f65acb3f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -73,7 +73,7 @@ class MTImageFeatureToBatch private[bigdl](width: Int, height: Int, override def next(): MiniBatch[Float] = { val count = new AtomicInteger(0) - val batch = Engine.io.invokeAndWait((0 until parallelism).map(tid => () => { + val batch = Engine.default.invokeAndWait((0 until parallelism).map(tid => () => { var position = 0 var record = 0 while (iterators(tid).hasNext && { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala index e4ab9db6fca..67c25dc527b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} +import com.intel.analytics.bigdl.mkl.{MKL, Memory, MklDnn} import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.Activity @@ -28,7 +28,7 @@ import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{Engine, T, Table} +import com.intel.analytics.bigdl.utils.{Engine, T, Table, ThreadPool} import org.apache.log4j.Logger import scopt.OptionParser @@ -86,34 +86,39 @@ object Perf { val criterion = CrossEntropyCriterion() - if (training) { - if (model.isInstanceOf[MklDnnContainer]) { - model.asInstanceOf[MklDnnContainer] - .compile(TrainingPhase, Array(HeapData(inputShape, inputFormat))) - } else if (model.isInstanceOf[DnnGraph]) { - model.asInstanceOf[DnnGraph].compile(TrainingPhase) - } - model.training() - } else { - if (model.isInstanceOf[MklDnnContainer]) { - model.asInstanceOf[MklDnnContainer] - .compile(InferencePhase, Array(HeapData(inputShape, inputFormat))) - } else if (model.isInstanceOf[DnnGraph]) { - model.asInstanceOf[DnnGraph].compile(InferencePhase) + Engine.dnnComputing.invokeAndWait2(Array(1).map(_ => () => { + if (training) { + if (model.isInstanceOf[MklDnnContainer]) { + model.asInstanceOf[MklDnnContainer] + .compile(TrainingPhase, Array(HeapData(inputShape, inputFormat))) + } else if (model.isInstanceOf[DnnGraph]) { + model.asInstanceOf[DnnGraph].compile(TrainingPhase) + } + model.training() + } else { + if (model.isInstanceOf[MklDnnContainer]) { + model.asInstanceOf[MklDnnContainer] + .compile(InferencePhase, Array(HeapData(inputShape, inputFormat))) + } else if (model.isInstanceOf[DnnGraph]) { + model.asInstanceOf[DnnGraph].compile(InferencePhase) + } + model.evaluate() } - model.evaluate() - } + })) var iteration = 0 while (iteration < iterations) { val start = System.nanoTime() - val output = model.forward(input) - if (training) { - val _loss = criterion.forward(output, label) - val errors = criterion.backward(output, label).toTensor - model.backward(input, errors) - } + Engine.dnnComputing.invokeAndWait2(Array(1).map(_ => () => { + val output = model.forward(input) + + if (training) { + val _loss = criterion.forward(output, label) + val errors = criterion.backward(output, label).toTensor + model.backward(input, errors) + } + })) val takes = System.nanoTime() - start diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala index 3abc022f8a9..fd13980dd02 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala @@ -140,7 +140,15 @@ abstract class AbstractOptimizer { val miniBatch = batch.slice(offset, length) val input = miniBatch.getInput() val target = miniBatch.getTarget() - val output = workingModels(b).forward(input) + if (Engine.getEngineType() == MklDnn) { + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + workingModels(b).forward(input) + })) + } else { + workingModels(b).forward(input) + } + + val output = workingModels(b).output val validatMethods = vMethodsArr(b).get validatMethods.map(validation => { validation(output, target) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index a9da0a897ff..0c342fb4a02 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -255,10 +255,20 @@ object DistriOptimizer extends AbstractOptimizer { val localCriterion = cached.localCriterions(i) val input = miniBatchBuffer(i).getInput() val target = miniBatchBuffer(i).getTarget() - val output = localModel.forward(input) - lossArray(i) = ev.toType[Double](localCriterion.forward(output, target)) - val errors = localCriterion.backward(output, target) - localModel.backward(input, errors) + + if (Engine.getEngineType() == MklBlas) { + val output = localModel.forward(input) + lossArray(i) = ev.toType[Double](localCriterion.forward(output, target)) + val errors = localCriterion.backward(output, target) + localModel.backward(input, errors) + } else { + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + val output = localModel.forward(input) + lossArray(i) = ev.toType[Double](localCriterion.forward(output, target)) + val errors = localCriterion.backward(output, target) + localModel.backward(input, errors) + })) + } cached.moduleTimeList(i + pre) = System.nanoTime() - trainStart + weightSyncTime i } @@ -570,12 +580,16 @@ object DistriOptimizer extends AbstractOptimizer { Engine.setNodeAndCore(nExecutor, executorCores) val cached = (0 until _subModelNumber).map { _ => val localModel = modelBroadcast.value(true) - localModel match { - case container: MklDnnContainer => container.compile(TrainingPhase) - case graph: DnnGraph => graph.compile(TrainingPhase) - case _ => + if (Engine.getEngineType() == MklDnn) { + Engine.dnnComputing.invokeAndWait2((0 until _subModelNumber).map(i => + () => { + localModel match { + case container: MklDnnContainer => container.compile(TrainingPhase) + case graph: DnnGraph => graph.compile(TrainingPhase) + case _ => + } + })) } - // differentiate partition models from each other by partition ID setModelId(localModel, partitionId) val localCriterion = broadcastCriterion.cloneCriterion() val localState = broadcastState.clone() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala index 713b1537a76..13584dbe42b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/visualization/tensorboard/FileWriter.scala @@ -37,7 +37,7 @@ private[bigdl] class FileWriter(val logDirectory : String, flushMillis: Int = 10 if (!fs.exists(logPath)) fs.mkdirs(logPath) private val eventWriter = new EventWriter(logDirectory, flushMillis, fs) - Engine.io.invoke(() => eventWriter.run()) + Engine.default.invoke(() => eventWriter.run()) /** * Adds a Summary protocol buffer to the event file. diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala index 704424cffc2..25d14fc8ec0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala @@ -16,10 +16,76 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.utils.Engine +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { + "lenet5 with reorder" should "works no segment fault" in { + // after upgrade to mkldnn v0.17, the thread local variables will cause spark to stop + // this test case tests the single pool + System.setProperty("bigdl.localMode", "true") + System.setProperty("bigdl.engineType", "mkldnn") + System.setProperty("bigdl.coreNumber", "1") + System.setProperty("bigdl.utils.Engine.defaultPoolSize", "1") + Engine.init + + println(System.getProperty("bigdl.engineType")) + + val inputShape = Array(100, 1, 28, 28) + val outputShape = Array(100, 10) + + val model = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(SpatialConvolution(1, 20, 5, 5).setName("conv1")) + .add(SpatialBatchNormalization(20).setName("bn1")) + .add(MaxPooling(2, 2, 2, 2).setName("pool1")) + .add(SpatialConvolution(20, 50, 5, 5).setName("conv2")) + .add(MaxPooling(2, 2, 2, 2).setName("pool2")) + .add(Linear(50 * 4 * 4, 500).setName("ip1")) + .add(ReLU().setName("relu1")) + .add(Linear(500, 10).setName("ip2")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nc))) + + val input = Tensor[Float](inputShape).rand(-1, 1) + val gradOutput = Tensor[Float](outputShape).rand(-1, 1) + + // we need to compile the model in the invokeAndWait2 threads. + Engine.dnnComputing.invokeAndWait2((0 until 1).map(i => + () => { + model.compile(TrainingPhase) + })) + + Engine.dnnComputing.invokeAndWait2((0 until 1).map(i => + () => { + println(s"${Thread.currentThread().getId}") + model.training() + model.forward(input) + model.updateGradInput(input, gradOutput) + model.accGradParameters(input, gradOutput) + i + } + ), Long.MaxValue) + + for (i <- 0 until 3) { + Engine.dnnComputing.invokeAndWait2((0 until 1).map(i => + () => { + println(s"${Thread.currentThread().getId}") + model.training() + model.forward(input) + model.updateGradInput(input, gradOutput) + i + } + ), Long.MaxValue) + } + + System.clearProperty("bigdl.localMode") + System.clearProperty("bigdl.engineType") + System.clearProperty("bigdl.coreNumber") + System.clearProperty("bigdl.utils.Engine.defaultPoolSize") + } -class ReorderMemorySpec extends BigDLSpecHelper { "From heap to native" should "be correct" in { val layer = ReorderMemory(new NativeData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc)) @@ -102,5 +168,4 @@ class ReorderMemorySpec extends BigDLSpecHelper { inputNHWC should be(output) inputNHWC should be(grad) } - } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala index a0c6a9b85d5..ee265bf24d2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala @@ -17,8 +17,8 @@ package com.intel.analytics.bigdl.utils.intermediate import com.intel.analytics.bigdl.mkl.Memory -import com.intel.analytics.bigdl.nn.abstractnn.DataFormat -import com.intel.analytics.bigdl.nn.mkldnn.HeapData +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} +import com.intel.analytics.bigdl.nn.mkldnn.{Equivalent, HeapData} import com.intel.analytics.bigdl.{Module, nn, utils} import com.intel.analytics.bigdl.nn.{Graph, Reshape, StaticGraph} import com.intel.analytics.bigdl.tensor.Tensor @@ -85,8 +85,9 @@ class IRGraphSpec extends BigDLSpecHelper { } "Convert IRgraph to Dnn or Blas Graph" should "be correct" in { - val input = Tensor[Float](2, 1, 28, 28).rand() - val gradOutput = Tensor[Float](2, 50, 4, 4).rand() + RandomGenerator.RNG.setSeed(1000) + val input = Tensor[Float](2, 1, 28, 28).rand(-1, 1) + val gradOutput = Tensor[Float](2, 50, 4, 4).rand(-1, 1) RandomGenerator.RNG.setSeed(1000) utils.Engine.setEngineType(MklBlas) @@ -102,13 +103,14 @@ class IRGraphSpec extends BigDLSpecHelper { val outDnn = irDnn.forward(input) val gradInputDnn = irDnn.backward(input, gradOutput).toTensor[Float] - outDnn should be(outBlas) - gradInputDnn should be(gradInputBlas) + Equivalent.nearequals(outDnn.toTensor, outBlas.toTensor, 1e-4) should be (true) + Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) } "Convert IRgraph to Dnn or Blas Graph with 2 dimentions output" should "be correct" in { - val input = Tensor[Float](2, 1, 28, 28).rand() - val gradOutput = Tensor[Float](2, 10).rand() + RandomGenerator.RNG.setSeed(1000) + val input = Tensor[Float](2, 1, 28, 28).rand(-1, 1) + val gradOutput = Tensor[Float](2, 10).rand(-1, 1) RandomGenerator.RNG.setSeed(1000) utils.Engine.setEngineType(MklBlas) val irBlas = modelIR2() @@ -123,13 +125,15 @@ class IRGraphSpec extends BigDLSpecHelper { val outDnn = irDnn.forward(input) val gradInputDnn = irDnn.backward(input, gradOutput).toTensor[Float] - outDnn should be(outBlas) - gradInputDnn should be(gradInputBlas) + Equivalent.nearequals(outDnn.toTensor, outBlas.toTensor, 1e-4) should be (true) + Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) } "Convert IRgraph with two inputs to Dnn or Blas Graph" should "be correct" in { - val input = T(Tensor[Float](2, 1, 28, 28).rand(), Tensor[Float](2, 4).rand()) - val gradOutput = Tensor[Float](2, 14).rand() + RandomGenerator.RNG.setSeed(1000) + val input = T(Tensor[Float](2, 1, 28, 28).rand(-1, 1), Tensor[Float](2, 4) + .rand(-1, 1)) + val gradOutput = Tensor[Float](2, 14).rand(-1, 1) RandomGenerator.RNG.setSeed(1000) utils.Engine.setEngineType(MklBlas) @@ -145,8 +149,10 @@ class IRGraphSpec extends BigDLSpecHelper { val outDnn = irDnn.forward(input) val gradInputDnn = irDnn.backward(input, gradOutput).toTable - outDnn should be(outBlas) - gradInputDnn.get[Tensor[Float]](1) should be(gradInputBlas.get[Tensor[Float]](1)) - gradInputDnn.get[Tensor[Float]](2) should be(gradInputBlas.get[Tensor[Float]](2)) + Equivalent.nearequals(outDnn.toTensor, outBlas.toTensor, 1e-4) should be (true) + Equivalent.nearequals(gradInputDnn.get[Tensor[Float]](1).get, + gradInputBlas.get[Tensor[Float]](1).get, 1e-4) should be (true) + Equivalent.nearequals(gradInputDnn.get[Tensor[Float]](2).get, + gradInputBlas.get[Tensor[Float]](2).get, 1e-4) should be (true) } } From 0479e0d3928130345920a105a07fb90a087b1293 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 15 Feb 2019 11:02:57 +0800 Subject: [PATCH 0862/1065] add ceilMode for Pooling & fix batchNorm evaluate (#2708) * add ceilMode for Pooling & fix batchNorm evaluate * add training status for dnn layer * fix comments --- .../analytics/bigdl/dllib/nn/Utils.scala | 13 +++-- .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 24 +++++++- .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 3 + .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 21 ++++++- .../bigdl/dllib/nn/mkldnn/MaxPooling.scala | 24 +++++++- .../bigdl/dllib/nn/mkldnn/Output.scala | 2 +- .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 15 ++++- .../nn/mkldnn/SpatialBatchNormalization.scala | 5 +- .../dllib/utils/intermediate/IRElement.scala | 2 +- .../dllib/utils/intermediate/IRToDnn.scala | 10 +++- .../dllib/nn/mkldnn/AvgPoolingSpec.scala | 53 ++++++++++++++++++ .../dllib/nn/mkldnn/MaxPoolingSpec.scala | 55 +++++++++++++++++++ 12 files changed, 210 insertions(+), 17 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index 6f43d2a1549..66dd2a6fd3d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -528,7 +528,8 @@ object Utils { kH: Int, kW: Int, padH: Int, - padW: Int + padW: Int, + ceilMode: Boolean = false ): (Int, Int, Int, Int, Int, Int) = { // compute padding left, right, top and bottom var pad_t = padH @@ -540,9 +541,13 @@ object Utils { var owidth = 0 var odepth = 0 - oheight = math.ceil(1.0 * (inputHeight - kH + 2 * padH) / dH).toInt + 1 - owidth = math.ceil(1.0 * (inputWidth - kW + 2 * padW) / dW).toInt + 1 - + if (ceilMode) { + oheight = math.ceil(1.0 * (inputHeight - kH + 2 * padH) / dH).toInt + 1 + owidth = math.ceil(1.0 * (inputWidth - kW + 2 * padW) / dW).toInt + 1 + } else { + oheight = math.floor(1.0 * (inputHeight - kH + 2 * padH) / dH).toInt + 1 + owidth = math.floor(1.0 * (inputWidth - kW + 2 * padW) / dW).toInt + 1 + } if (padH != 0 || padW != 0 || kH == 1 || kW == 1) { if ((oheight - 1) * dH >= inputHeight + padH) oheight -= 1 if ((owidth - 1) * dW >= inputWidth + padW) owidth -= 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala index 1efddd05fca..c7b6bd1b71e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -32,6 +32,28 @@ class AvgPooling( @transient private var paddingBR: Array[Int] = _ @transient private var fwdPD: Long = _ + // reminder: ceilMode default value is true, + // but in blas SpatialMaxPooling, default ceilMode is false + private var ceilMode = true + + /** + * set ceil mode + * @return this + */ + def ceil(): AvgPooling = { + ceilMode = true + this + } + + /** + * set floor mode + * @return this + */ + def floor(): AvgPooling = { + ceilMode = false + this + } + private val algKind = if (padH == -1 && padW == -1) { AlgKind.PoolingAvgIncludePadding } else { @@ -50,7 +72,7 @@ class AvgPooling( val sizes = Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) (sizes(0), sizes(1), sizes(2), sizes(3), sizes(4), sizes(5)) } else { - Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW) + Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW, ceilMode) } paddingTL = Array(pt, pl) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index b4cb6d53541..f6359d39693 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -145,6 +145,9 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, module.evaluate() this } + + override def release(): Unit = module.release() + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 9de11ded401..fa01fbd9c07 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -41,6 +41,23 @@ class DnnGraph( private var inputCache: Array[Activity] = _ private var backId2ForwardId: Array[Int] = _ + /** + * Batch size may change when model prediction, but output size of dnn layers will not be changed. + * So we have to compare batchSize for input and output, and do some change if not the same. + * @param input + * @param output + * @return + */ + private def getRealOutput(input: Activity, output: Activity): Activity = { + if (input.isTensor && output.isTensor) { + val in = input.toTensor[Float] + val out = output.toTensor[Float] + require(in.nDimension() == 4, + s"only support input with 4 dimension, but get ${in.nDimension()}") + if (in.size(1) != out.size(1)) out.narrow(1, 1, in.size(1)) else output + } else output + } + @transient protected lazy val reorderManager = new ReorderManager() if (enableExcludeChecking) { @@ -59,7 +76,7 @@ class DnnGraph( i += 1 } output = dummyOutput.element.output - output + getRealOutput(input, output) } override def backward(input: Activity, gradOutput: Activity): Activity = { @@ -84,7 +101,7 @@ class DnnGraph( i += 1 } gradInput = fetchModelGradInput() - gradInput + getRealOutput(input, gradInput) } override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala index 95b2510a9e2..97b88b4b89e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala @@ -36,6 +36,28 @@ class MaxPooling( @transient private var paddingBR: Array[Int] = _ @transient private var fwdPD: Long = _ + // reminder: ceilMode default value is true, + // but in blas SpatialMaxPooling, default ceilMode is false + private var ceilMode = true + + /** + * set ceil mode + * @return this + */ + def ceil(): MaxPooling = { + ceilMode = true + this + } + + /** + * set floor mode + * @return this + */ + def floor(): MaxPooling = { + ceilMode = false + this + } + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = singleNativeData(inputs) val strides = Array(dW, dH) @@ -49,7 +71,7 @@ class MaxPooling( val sizes = Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) (sizes(0), sizes(1), sizes(2), sizes(3), sizes(4), sizes(5)) } else { - Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW) + Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW, ceilMode) } paddingTL = Array(pt, pl) paddingBR = Array(pb, pr) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala index 2011d5cf15b..368787739bd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala @@ -37,7 +37,7 @@ class Output(outputLayOut: Int = Memory.Format.nc, if (outLayout == Memory.Format.nhwc && inLayout != Memory.Format.nhwc) { // nchw* -> nhwc Array(inShape(0), inShape(2), inShape(3), inShape(1)) - } else if (outLayout != Memory.Format.nhwc && inLayout == Memory.Format.nhwc) { + } else if ((outLayout != Memory.Format.nhwc) && (inLayout == Memory.Format.nhwc)) { // nhwc -> nchw* Array(inShape(0), inShape(3), inShape(1), inShape(2)) } else inShape diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index 216ca96756e..9f88fb18392 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{MklDnn, PropKind, Stream => DnnStream} +import com.intel.analytics.bigdl.mkl.{Memory, MklDnn, PropKind, Stream => DnnStream} import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} @@ -41,12 +41,21 @@ class SoftMax() extends MklDnnLayer { } } + private def format(shape: Array[Int]): Int = { + shape.length match { + case 2 => Memory.Format.nc + case 4 => Memory.Format.nchw + case _ => throw new UnsupportedOperationException(s"${getName()} unsupported input shape") + } + } + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { initPhase(phase) modelPhase match { case TrainingPhase => - _inputFormats = inputs.clone() - _outputFormats = inputs.clone() + _inputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) + _outputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) + (_inputFormats, _outputFormats) case InferencePhase => val axis = inputs(0).shape.length match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 6c5492aef2f..aad5b9bff0a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -120,6 +120,7 @@ class SpatialBatchNormalization( case _ => } } + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { val m = inputs(0).shape.product / this.nOutput biasFactor = if (m > 1) { m.toFloat / (m - 1) } else { 1 } @@ -337,14 +338,14 @@ class SpatialBatchNormalization( } override def evaluate(): this.type = { - if (isTraining()) { + if (modelPhase == TrainingPhase) { initFwdPrimitives(inputFormats(), InferencePhase) } this } override def training(): this.type = { - if (!isTraining()) { + if (modelPhase == InferencePhase) { initFwdPrimitives(inputFormats(), TrainingPhase) } this diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala index d7d6967e62d..f2f8064fa49 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala @@ -40,7 +40,7 @@ case class IRSpatialMaxPooling[T: ClassTag]( kW: Int, kH: Int, dW: Int = 1, dH: Int = 1, padW: Int = 0, padH: Int = 0, - format: DataFormat = DataFormat.NCHW) extends IROperator[T] + format: DataFormat = DataFormat.NCHW, ceilMode: Boolean = false) extends IROperator[T] case class IRSpatialAveragePooling[T: ClassTag]( kW: Int, kH: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index b6e2498c16f..e871cba07e6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -118,13 +118,19 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def fromSpatialMaxPooling(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialMaxPooling[Float]] require(t.format == DataFormat.NCHW, "Dnn SpatialMaxPooling only supports NCHW") - ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "MaxPooling")) + val layer = ReflectionUtils.reflectFromIR( + node, Class.forName(prefix + "MaxPooling")).asInstanceOf[MaxPooling] + if (t.ceilMode) layer.ceil() else layer.floor() + layer } private def fromSpatialAveragePooling(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialAveragePooling[Float]] require(t.format == DataFormat.NCHW, "Dnn SpatialAveragePooling only supports NCHW") - ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "AvgPooling")) + val layer = ReflectionUtils.reflectFromIR( + node, Class.forName(prefix + "AvgPooling")).asInstanceOf[AvgPooling] + if (t.ceilMode) layer.ceil() else layer.floor() + layer } private def fromSpatialCrossMapLRN(node: IRElement[Float]) : Module[Float] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala index aca5422a2a3..c07be3ba7ac 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.BigDLSpecHelper import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.intermediate.{BlasToIR, IRToDnn} import org.apache.commons.lang3.SerializationUtils import scala.util.Random @@ -29,6 +30,7 @@ class AvgPoolingSpec extends BigDLSpecHelper { "Avg Pooling with same padding" should "be correct" in { val batchSize = 2 val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 480, 14, 14).apply1(e => Random.nextFloat()) val pad = -1 RNG.setSeed(100) @@ -36,7 +38,44 @@ class AvgPoolingSpec extends BigDLSpecHelper { RNG.setSeed(100) val layer = SpatialAveragePooling[Float](3, 3, 2, 2, padH = pad, padW = pad).ceil() + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw))) + seq.add(pool) + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 480, 28, 28), + Memory.Format.nchw))) + + for (i <- 0 to 3) { + input.rand() + gradOutput.rand() + + seq.forward(input) + seq.backward(input, gradOutput) + + layer.forward(input) + layer.backward(input, gradOutput) + } + val output1 = seq.forward(input) val output2 = layer.forward(input).toTensor[Float] + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = seq.backward(input, output2) + grad1 should be(grad2) + } + + "Convert average pooling with ceilMode to dnn layer" should "be correct" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 480, 14, 14).apply1(e => Random.nextFloat()) + + RNG.setSeed(100) + val layer = SpatialAveragePooling[Float](3, 3, 2, 2).ceil() + + val irelement = BlasToIR[Float].convertLayer(layer) + val pool = IRToDnn[Float].convertLayer(irelement) val seq = Sequential() seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw), @@ -46,7 +85,21 @@ class AvgPoolingSpec extends BigDLSpecHelper { HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw))) seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw))) + + for (i <- 0 to 3) { + input.rand() + gradOutput.rand() + + seq.forward(input) + seq.backward(input, gradOutput) + + layer.forward(input) + layer.backward(input, gradOutput) + } + val output1 = seq.forward(input) + val output2 = layer.forward(input).toTensor[Float] + output1 should be(output2) val grad2 = layer.backward(input, output2).toTensor[Float] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala index 4007d44ff4b..7d38763813f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, SpatialMaxPooling} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.BigDLSpecHelper import com.intel.analytics.bigdl.utils.RandomGenerator.RNG +import com.intel.analytics.bigdl.utils.intermediate.{BlasToIR, IRToDnn} import org.apache.commons.lang3.SerializationUtils import scala.util.Random @@ -29,6 +30,7 @@ class MaxPoolingSpec extends BigDLSpecHelper { "Max Pooling test1" should "be correct" in { val batchSize = 2 val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 480, 14, 14).apply1(e => Random.nextFloat()) val pad = -1 RNG.setSeed(100) @@ -36,8 +38,47 @@ class MaxPoolingSpec extends BigDLSpecHelper { RNG.setSeed(100) val layer = SpatialMaxPooling[Float](3, 3, 2, 2, padH = pad, padW = pad).ceil() + val seq = Sequential() + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw))) + seq.add(pool) + seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw), + HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw))) + seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 480, 28, 28), + Memory.Format.nchw))) + + for (i <- 0 to 3) { + input.rand() + gradOutput.rand() + + seq.forward(input) + seq.backward(input, gradOutput) + + layer.forward(input) + layer.backward(input, gradOutput) + } + + val output1 = seq.forward(input) val output2 = layer.forward(input).toTensor[Float] + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = seq.backward(input, output2) + grad1 should be(grad2) + } + + "Convert max pooling with ceilMode to dnn layer" should "be correct" in { + val batchSize = 2 + val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 480, 14, 14).apply1(e => Random.nextFloat()) + + RNG.setSeed(100) + val layer = SpatialMaxPooling[Float](3, 3, 2, 2).ceil() + + val irelement = BlasToIR[Float].convertLayer(layer) + val pool = IRToDnn[Float].convertLayer(irelement) + val seq = Sequential() seq.add(ReorderMemory(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw), HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw))) @@ -46,7 +87,20 @@ class MaxPoolingSpec extends BigDLSpecHelper { HeapData(Array(batchSize, 480, 14, 14), Memory.Format.nchw))) seq.compile(Phase.TrainingPhase, Array(HeapData(Array(batchSize, 480, 28, 28), Memory.Format.nchw))) + + for (i <- 0 to 3) { + input.rand() + gradOutput.rand() + + seq.forward(input) + seq.backward(input, gradOutput) + + layer.forward(input) + layer.backward(input, gradOutput) + } + val output1 = seq.forward(input) + val output2 = layer.forward(input).toTensor[Float] output1 should be(output2) val grad2 = layer.backward(input, output2).toTensor[Float] @@ -57,6 +111,7 @@ class MaxPoolingSpec extends BigDLSpecHelper { "Max Pooling test2" should "be correct" in { val batchSize = 2 val input = Tensor[Float](batchSize, 64, 112, 112).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 480, 14, 14).apply1(e => Random.nextFloat()) RNG.setSeed(100) val pool = MaxPooling(3, 3, 2, 2) From 87c4035e43eb9896912631875d0a33990b04cce9 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 18 Feb 2019 16:17:34 +0800 Subject: [PATCH 0863/1065] fix IRGraph init & Add regualizer (#2736) * fix IRGraph init & Add regualizer * meet review comments --- .../intel/analytics/bigdl/dllib/nn/View.scala | 26 +++++++-- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 24 ++++++-- .../bigdl/dllib/nn/mkldnn/Linear.scala | 15 ++++- .../dllib/nn/mkldnn/SpatialConvolution.scala | 14 ++++- .../dllib/utils/intermediate/IRElement.scala | 2 +- .../dllib/utils/intermediate/IRGraph.scala | 57 ++++++++----------- .../dllib/utils/intermediate/IRToDnn.scala | 22 +++---- .../bigdl/dllib/nn/mkldnn/LinearSpec.scala | 51 +++++++++++++++++ 8 files changed, 150 insertions(+), 61 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/View.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/View.scala index 7b4f01fb8e6..b925666de8e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/View.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/View.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -65,10 +66,8 @@ class View[T: ClassTag](val sizes: Array[Int])( def getNumInputDims(): Int = numInputDims - private def batchSize( - input: Tensor[T], size: Array[Int], numberInputDims: Int, numElements: Int): Int = { - val ind = input.nDimension() - val isz = input.size() + private def batchSize(ind: Int, isz: Array[Int], + size: Array[Int], numberInputDims: Int, numElements: Int) : Int = { val maxDim = if (numberInputDims == 0) ind else numberInputDims var ine = 1 @@ -99,7 +98,7 @@ class View[T: ClassTag](val sizes: Array[Int])( i -= 1 } - if (bse == 1 && (numberInputDims == 0 || input.nDimension() <= numberInputDims)) { + if (bse == 1 && (numberInputDims == 0 || ind <= numberInputDims)) { -1 } else { bse @@ -107,7 +106,8 @@ class View[T: ClassTag](val sizes: Array[Int])( } override def updateOutput(input: Tensor[T]): Tensor[T] = { - val bse = batchSize(input, this.sizes, this.numInputDims, this.numElements) + val bse = batchSize(input.nDimension(), input.size(), this.sizes, + this.numInputDims, this.numElements) if (bse != -1) { val newSizes = new Array[Int](this.sizes.length + 1) newSizes(0) = bse @@ -130,6 +130,20 @@ class View[T: ClassTag](val sizes: Array[Int])( override def toString(): String = { s"${getPrintName}(${sizes.mkString("x")})" } + + override def computeOutputShape(inputShape: Shape): Shape = { + val input = inputShape.toSingle().toArray + val bse = batchSize(input.length, input, this.sizes, this.numInputDims, this.numElements) + + if (bse != -1) { + val newSizes = new Array[Int](this.sizes.length + 1) + newSizes(0) = bse + System.arraycopy(this.sizes, 0, newSizes, 1, this.sizes.length) + Shape(newSizes) + } else { + Shape(this.sizes) + } + } } object View { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index fa01fbd9c07..e68228bfb76 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -20,7 +20,7 @@ import breeze.linalg.Axis._1 import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} -import com.intel.analytics.bigdl.nn.{StaticGraph, mkldnn} +import com.intel.analytics.bigdl.nn.{Graph, StaticGraph, mkldnn} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{LayerException, Node, T} @@ -34,9 +34,9 @@ class DnnGraph( private val _outputs : Seq[ModuleNode[Float]], private val _variables: Option[(Array[Tensor[Float]], Array[Tensor[Float]])] = None, private val enableExcludeChecking: Boolean = true) - extends StaticGraph[Float](_inputs, _outputs, _variables, enableExcludeChecking) + extends Graph[Float](_inputs, _outputs, _variables) with MklDnnLayer { - private var forwardExecution: Array[Node[AbstractModule[Activity, Activity, Float]]] = _ + private val forwardExecution = forwardGraph.topologySort.reverse private var backwardExecution: Array[Node[AbstractModule[Activity, Activity, Float]]] = _ private var inputCache: Array[Activity] = _ private var backId2ForwardId: Array[Int] = _ @@ -52,8 +52,10 @@ class DnnGraph( if (input.isTensor && output.isTensor) { val in = input.toTensor[Float] val out = output.toTensor[Float] - require(in.nDimension() == 4, - s"only support input with 4 dimension, but get ${in.nDimension()}") + // for grey image, input should be 3 dims and the first dim should be batch size + // for non grey image, input should be 4 dims and the first dim should be batch size + require(in.nDimension() == 4 || in.nDimension() == 3, + s"only support input with 4 dimension or 3 dimension, but get ${in.nDimension()}") if (in.size(1) != out.size(1)) out.narrow(1, 1, in.size(1)) else output } else output } @@ -119,7 +121,6 @@ class DnnGraph( override def buildBackwardGraph(): this.type = { super.buildBackwardGraph() - forwardExecution = forwardGraph.topologySort.reverse inputCache = new Array[Activity](forwardExecution.length) backwardExecution = backwardGraph.topologySort.reverse backId2ForwardId = new Array[Int](backwardExecution.length) @@ -387,6 +388,17 @@ class DnnGraph( _gradOutputFormatsForWeight = firstRealGradOutputFormats firstRealGradOutputFormats } + + override def populateModules(): Unit = { + modules.appendAll( + forwardGraph.topologySort + // todo: convert control dep node to edge + .filterNot(_.element.isInstanceOf[ControlDependency[Float]]) + .filter(n => !n.eq(dummyOutput)).map(_.element) + .reverse + ) + checkDuplicate() + } } object DnnGraph { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index c27b616fa72..0cca0ba0941 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} import com.intel.analytics.bigdl.nn.{InitializationMethod, RandomUniform, VariableFormat} +import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor._ import scala.collection.mutable.ArrayBuffer @@ -26,6 +27,8 @@ import scala.collection.mutable.ArrayBuffer class Linear( val inputSize: Int, val outputSize: Int, + var wRegularizer: Regularizer[Float] = null, + var bRegularizer: Regularizer[Float] = null, private val initWeight: Tensor[Float] = null, private val initBias: Tensor[Float] = null, private val initGradWeight: Tensor[Float] = null, @@ -268,6 +271,13 @@ class Linear( gradWeight.syncToHeap() gradBias.syncToHeap() + + if (null != wRegularizer && scaleW != 0) { + wRegularizer.accRegularization(weight.dense, gradWeight.dense, scaleW) + } + if (null != bRegularizer && scaleB != 0) { + bRegularizer.accRegularization(bias.dense, gradBias.dense, scaleB) + } } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { @@ -293,10 +303,13 @@ object Linear { inputSize: Int, outputSize: Int, withBias: Boolean = true, + wRegularizer: Regularizer[Float] = null, + bRegularizer: Regularizer[Float] = null, initWeight: Tensor[Float] = null, initBias: Tensor[Float] = null, initGradWeight: Tensor[Float] = null, initGradBias: Tensor[Float] = null): Linear = { - new Linear(inputSize, outputSize, initWeight, initBias, initGradWeight, initGradBias) + new Linear(inputSize, outputSize, wRegularizer, + bRegularizer, initWeight, initBias, initGradWeight, initGradBias) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index e4f37827eff..f2eddae0456 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn._ +import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} import scala.collection.mutable.ArrayBuffer @@ -37,6 +38,8 @@ class SpatialConvolution( val padH: Int = 0, val nGroup: Int = 1, val propagateBack: Boolean = true, + var wRegularizer: Regularizer[Float] = null, + var bRegularizer: Regularizer[Float] = null, val initWeight: Tensor[Float] = null, val initBias: Tensor[Float] = null, val initGradWeight: Tensor[Float] = null, @@ -393,6 +396,13 @@ class SpatialConvolution( gradWeight.syncToHeap() gradBias.syncToHeap() + + if (null != wRegularizer) { + wRegularizer.accRegularization(weight.dense, gradWeight.dense, scaleW) + } + if (withBias && null != bRegularizer) { + bRegularizer.accRegularization(bias.dense, gradBias.dense, scaleB) + } } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { @@ -449,6 +459,8 @@ object SpatialConvolution { padH: Int = 0, nGroup: Int = 1, propagateBack: Boolean = true, + wRegularizer: Regularizer[Float] = null, + bRegularizer: Regularizer[Float] = null, initWeight: Tensor[Float] = null, initBias: Tensor[Float] = null, initGradWeight: Tensor[Float] = null, @@ -456,7 +468,7 @@ object SpatialConvolution { withBias: Boolean = true, format: DataFormat = DataFormat.NCHW): SpatialConvolution = { new SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, - dH, padW, padH, nGroup, propagateBack, + dH, padW, padH, nGroup, propagateBack, wRegularizer, bRegularizer, initWeight, initBias, initGradWeight, initGradBias, withBias, format) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala index f2f8064fa49..733b9b22cb6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala @@ -70,7 +70,7 @@ case class IRSpatialShareConvolution[T: ClassTag]( wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: Tensor[T] = null, initBias: Tensor[T] = null, initGradWeight: Tensor[T] = null, initGradBias: Tensor[T] = null, - withBias: Boolean = true) extends IROperator[T] + withBias: Boolean = true, format: DataFormat = DataFormat.NCHW) extends IROperator[T] case class IRSpatialBatchNormalization[T: ClassTag]( nOutput: Int, eps: Double = 1e-5, momentum: Double = 0.1, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index 376d9cd61a4..62c16547b56 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -16,9 +16,6 @@ package com.intel.analytics.bigdl.utils.intermediate -import java.util.List - -import breeze.linalg.reverse import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.{Graph, SpatialMaxPooling, keras} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} @@ -26,9 +23,6 @@ import com.intel.analytics.bigdl.nn.mkldnn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Node, T} - -import scala.collection.mutable -import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -52,9 +46,7 @@ private[bigdl] class IRGraph[T: ClassTag]( val outputFormats: Seq[Int] = Seq(Memory.Format.nc)) (implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Activity, T] with Serializable { - @transient private var initFwd: Boolean = false - @transient private var initBwd: Boolean = false - @transient private var initAcc: Boolean = false + @transient private var initPrim: Boolean = false require(inputFormats.length == inputs.length, s"IRGraph: inputFormats" + s"length ${inputFormats.length} should be same with input nodes length ${inputs.length}") @@ -67,7 +59,7 @@ private[bigdl] class IRGraph[T: ClassTag]( if (graph == null) { throw new UnsupportedOperationException("forward not supported, Please build graph first") } - initFwdPrimitives(input) + initPrimitives(input) output = graph.updateOutput(input) output } @@ -76,7 +68,6 @@ private[bigdl] class IRGraph[T: ClassTag]( if (graph == null) { throw new UnsupportedOperationException("backward not supported, Please build graph first") } - initBwdPrimitives() gradInput = graph.updateGradInput(input, gradOutput) gradInput } @@ -85,7 +76,6 @@ private[bigdl] class IRGraph[T: ClassTag]( if (graph == null) { throw new UnsupportedOperationException("backward not supported, Please build graph first") } - initGradWPrimitives() graph.accGradParameters(input, gradOutput) } @@ -114,11 +104,26 @@ private[bigdl] class IRGraph[T: ClassTag]( this } - private def initFwdPrimitives(input: Activity): Unit = { - if (!initFwd && graph.isInstanceOf[DnnGraph]) { + override def getExtraParameter(): Array[Tensor[T]] = { + graph.getExtraParameter() + } + + override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { + graph.getTimes() + } + + override def resetTimes(): Unit = { + graph.resetTimes() + } + + private def initPrimitives(input: Activity): Unit = { + if (!initPrim && graph.isInstanceOf[DnnGraph]) { val inputMemory = new Array[MemoryData](inputFormats.length) if (input.isInstanceOf[Tensor[T]]) { - inputMemory(0) = HeapData(input.toTensor[T].size(), inputFormats(0)) + // todo: handle for 3 dimensions, expand 3 dims to 4 dims + val size = input.toTensor[T].size() + val sizeNew = if (size.length == 3) Array(size(0), 1, size(1), size(2)) else size + inputMemory(0) = HeapData(sizeNew, inputFormats(0)) } else { val tensors = input.toTable require(tensors.length() == inputFormats.length, s"table input length " + @@ -134,23 +139,11 @@ private[bigdl] class IRGraph[T: ClassTag]( val dnnGraph = graph.asInstanceOf[DnnGraph] dnnGraph.setRuntime(new MklDnnRuntime()) dnnGraph.initFwdPrimitives(inputMemory) - initFwd = true - } - } - - private def initBwdPrimitives(): Unit = { - if (!initBwd && graph.isInstanceOf[DnnGraph]) { - val dnnGraph = graph.asInstanceOf[DnnGraph] - dnnGraph.initBwdPrimitives(dnnGraph.outputFormats()) - initBwd = true - } - } - - private def initGradWPrimitives(): Unit = { - if (!initAcc && graph.isInstanceOf[DnnGraph]) { - val dnnGraph = graph.asInstanceOf[DnnGraph] - dnnGraph.initGradWPrimitives(dnnGraph.outputFormats()) - initAcc = true + if (dnnGraph.isTraining()) { + dnnGraph.initBwdPrimitives(dnnGraph.outputFormats()) + dnnGraph.initGradWPrimitives(dnnGraph.outputFormats()) + } + initPrim = true } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index e871cba07e6..c4e47ba820b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -38,11 +38,11 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def mapInit(): Unit = { IR2DnnMap("IRSpatialConvolution") = fromSpatialConvolution + IR2DnnMap("IRSpatialShareConvolution") = fromSpatialShareConvolution IR2DnnMap("IRSpatialMaxPooling") = fromSpatialMaxPooling IR2DnnMap("IRSpatialAveragePooling") = fromSpatialAveragePooling IR2DnnMap("IRSpatialBatchNormalization") = fromSpatialBatchNormalization IR2DnnMap("IRSpatialCrossMapLRN") = fromSpatialCrossMapLRN - IR2DnnMap("IRLinear") = fromLinear IR2DnnMap("IRReLU") = fromReLU IR2DnnMap("IRJoinTable") = fromJoinTable IR2DnnMap("IRGeneralModule") = fromBlasModule @@ -109,8 +109,12 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def fromSpatialConvolution(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialConvolution[Float]] - require(t.wRegularizer == null && t.bRegularizer == null, - "Dnn SpatialConvolution can not support Regularizer") + require(t.format == DataFormat.NCHW, "Dnn SpatialConvolution only supports NCHW") + ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) + } + + private def fromSpatialShareConvolution(node: IRElement[Float]) : Module[Float] = { + val t = node.getOp().asInstanceOf[IRSpatialShareConvolution[Float]] require(t.format == DataFormat.NCHW, "Dnn SpatialConvolution only supports NCHW") ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) } @@ -171,13 +175,6 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] layer } - private def fromLinear(node: IRElement[Float]) : Module[Float] = { - val t = node.getOp().asInstanceOf[IRLinear[Float]] - require(t.wRegularizer == null && t.bRegularizer == null, - "Dnn Linear can not support Regularizer") - ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "Linear")) - } - private def fromBlasModule(node: IRElement[Float]) : Module[Float] = { BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) } @@ -190,8 +187,7 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] try { layer.getOp() match { case conv: IRSpatialConvolution[Float] => - require(conv.wRegularizer == null - && conv.bRegularizer == null && conv.format == DataFormat.NCHW) + require(conv.format == DataFormat.NCHW) case maxPool: IRSpatialMaxPooling[Float] => require(maxPool.format == DataFormat.NCHW) case avgPool: IRSpatialAveragePooling[Float] => @@ -200,8 +196,6 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] require(sbn.dataFormat == DataFormat.NCHW) case lrn: IRSpatialCrossMapLRN[Float] => require(lrn.format == DataFormat.NCHW) - case linear: IRLinear[Float] => - require(linear.wRegularizer == null && linear.bRegularizer == null) case join: IRJoinTable[Float] => require(join.nInputDims == 0) case _ => null diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala index a8bc3e7cebc..e65bc142036 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala @@ -20,7 +20,9 @@ import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor.{DnnStorage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Matchers} @@ -53,6 +55,55 @@ class LinearSpec extends FlatSpec with Matchers { Tools.dense(output) should be (nnOutput) } + "Dnn Linear with regularizer" should "work correctly" in { + val inputSize = 2048 + val outputSize = 1000 + val batchSize = 2 + + RNG.setSeed(1000) + val inputFormat = HeapData(Array(batchSize, inputSize), Memory.Format.nc) + val outputFormat = HeapData(Array(batchSize, outputSize), Memory.Format.nc) + val input = Tensor[Float](batchSize, inputSize).rand() + val gradOutput = Tensor[Float](batchSize, outputSize).rand() + + val initWeight = Tensor[Float](outputSize, inputSize).rand() + val initBias = Tensor[Float](outputSize).rand() + + val wRegularizer = L2Regularizer(1e-4) + val bRegularizer = L2Regularizer(1e-4) + + val linear = Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias, + wRegularizer = wRegularizer, bRegularizer = bRegularizer) + val nnLinear = nn.Linear(inputSize, outputSize, initWeight = initWeight, initBias = initBias, + wRegularizer = wRegularizer, bRegularizer = bRegularizer) + + linear.setRuntime(new MklDnnRuntime) + linear.initFwdPrimitives(Array(inputFormat), TrainingPhase) + linear.initBwdPrimitives(Array(outputFormat), TrainingPhase) + linear.initGradWPrimitives(Array(outputFormat), TrainingPhase) + + for (i <- 0 to 3) { + linear.zeroGradParameters() + nnLinear.zeroGradParameters() + + nnLinear.forward(input) + nnLinear.backward(input, gradOutput) + linear.forward(input) + linear.backward(input, gradOutput) + } + + Tools.dense(linear.output) should be (nnLinear.output) + Tools.dense(linear.gradInput) should be (nnLinear.gradInput) + + val p1 = linear.getParameters() + val p2 = linear.getParameters() + + p1._1 should be (p2._1) + p1._2 should be (p2._2) + } + + + "linear updateOutput multi times" should "work correctly" in { val inputSize = 2 val outputSize = 2 From 154c96eea9722a63ef2c1b178bc0ec4bbfb4899e Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 19 Feb 2019 18:11:42 +0800 Subject: [PATCH 0864/1065] fix: update mkldnn version to v0.17 issues. (#2712) There're two issues, 1. the padding tensor required. mkl-dnn will use a padding tensor which will use more memory, such as 4x1x28x28 to 4x8x28x28(avx2). It will pad to times of simd width. 2. the TensorMMap between DenseTensor and DnnTensor. Previous impl will allocate DnnTensor when model is created, which will cost too much space. So this patch will allocate it at runtime. --- .../bigdl/dllib/nn/mkldnn/Blob.scala | 105 --------- .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 15 +- .../bigdl/dllib/nn/mkldnn/InputWrapper.scala | 2 +- .../bigdl/dllib/nn/mkldnn/Linear.scala | 65 ++--- .../dllib/nn/mkldnn/ReorderManager.scala | 4 + .../bigdl/dllib/nn/mkldnn/ReorderMemory.scala | 20 +- .../nn/mkldnn/SpatialBatchNormalization.scala | 54 +++-- .../dllib/nn/mkldnn/SpatialConvolution.scala | 133 +++++------ .../bigdl/dllib/nn/mkldnn/TensorMMap.scala | 122 ++++++++++ .../bigdl/dllib/tensor/DnnTensor.scala | 4 + .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 16 +- .../bigdl/dllib/nn/mkldnn/OutputSpec.scala | 8 +- .../dllib/nn/mkldnn/ReflectionUtilsSpec.scala | 14 +- .../dllib/nn/mkldnn/ReorderMemorySpec.scala | 87 ++++++- .../dllib/nn/mkldnn/SerializeModelSpec.scala | 50 ++++ .../dllib/nn/mkldnn/SingleLayerSpec.scala | 13 +- .../SpatialBatchNormalizationSpec.scala | 36 ++- .../nn/mkldnn/SpatialConvolutionSpec.scala | 222 +++++++++++------- .../bigdl/dllib/nn/mkldnn/TestUtils.scala | 27 ++- .../bigdl/dllib/nn/mkldnn/TopologySpec.scala | 3 + .../utils/intermediate/IRconvertSpec.scala | 28 ++- 21 files changed, 655 insertions(+), 373 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SerializeModelSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala deleted file mode 100644 index af3f7b2eeac..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Blob.scala +++ /dev/null @@ -1,105 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package com.intel.analytics.bigdl.nn.mkldnn - -import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} - -/** - * `Blob` contains two tensors, dense and native, which are a map of each other. - * It's used in the layer which contains weights. For the weight, we should sync the - * dense tensor to native tensor before `submit`. For the gradient, we should sync the native - * tensor to dense tensor after `submit`. - * - * The `setMemoryData` requires the elements number should be consistent. If the shape is not, - * it will reshape first. - * - * The Blob has another attribute `_memoryData` and will not be determined when the blob created. - * It can be determined when we initialize the primitives. - * - * @param _size the shape of Tensor, such as Array(4, 3, 224, 224) - */ -private[mkldnn] class Blob(_size: Array[Int]) extends Serializable { - val dense: Tensor[Float] = Tensor[Float](_size) - val native: DnnTensor[Float] = DnnTensor[Float](_size) - - @transient private var _memoryData: MemoryData = _ - - /** - * it will copy the dense tensor to native tensor before `submit` reads the native tensor - */ - def syncToNative(): Unit = { - native.copy(dense) - } - - /** - * it will copy the native tensor to dense tensor after `submit` updates the native tensor - */ - def syncToHeap(): Unit = { - dense.copy(native) - } - - /** - * MemoryData relevant of Native Tensor. The shape should be the same as `size` of Blob. - * We can't only reserve the `layout` in MemoryData. Because for convolution, - * we should reserve the whole MemoryData including `desc`, `desc primitive` and `primitive`. - * - * @param memoryData memory data you want. - */ - def setMemoryData(memoryData: MemoryData): Unit = { - require(_memoryData == null, "You should only set once") - require(size().product == memoryData.shape.product, s"You may assign wrong layout") - - // we should resize the tensor. Because sometimes, weight of Linear will has 4-D, where - // the last 2 dims is 1. we should reisze it. It will not allocate a new storage because of - // the same size. - List(native, dense).foreach(_.resize(memoryData.shape)) - _memoryData = memoryData - } - - def memoryData(): MemoryData = { - require(_memoryData != null, "You should setMemoryData first") - _memoryData - } - - def isMemoryDataSet(): Boolean = { - if (_memoryData == null) { - false - } else { - true - } - } - - def zero(): Unit = { - dense.zero() - native.zero() - } - - def copy(t: Tensor[Float]): Unit = { - dense.copy(t) - native.copy(t) - } - - def size(): Array[Int] = { - dense.size() - } - - def size(index: Int): Int = { - dense.size(index) - } - - def release(): Unit = native.release() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index f858f74370e..679ccc18cee 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.MklDnn +import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} import com.intel.analytics.bigdl.nn.DynamicContainer import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.{DenseType, DnnTensor, MklDnnType, Tensor} @@ -83,9 +83,9 @@ trait MklDnnModuleHelper { protected def initTensor(format: MemoryData): Tensor[Float] = { format match { case d: NativeData => - DnnTensor[Float](d.shape) + DnnTensor[Float](Memory.GetPaddingShape(format.getMemoryDescription())) case d: HeapData => - Tensor[Float](d.shape) + Tensor[Float](Memory.GetPaddingShape(format.getMemoryDescription())) case _ => throw new UnsupportedOperationException("memory format is not supported") } } @@ -260,10 +260,6 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM } } - def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { - (null, null) - } - override def release(): Unit = { val tensors: ArrayBuffer[DnnTensor[Float]] = ArrayBuffer.empty List(output, gradInput).filter(_ != null).foreach { t => @@ -369,4 +365,9 @@ trait MklDnnContainer extends DynamicContainer[Activity, Activity, Float] with M modules.filter(_.isInstanceOf[MklDnnContainer]) .map { case mc: MklDnnContainer => mc.freeze() } } + + override def release(): Unit = { + super.release() + reorderManager.release() + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala index a26c289c941..58834860a96 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala @@ -41,7 +41,7 @@ private[bigdl] class InputWrapper extends MklDnnLayer { inputLayer.initBwdPrimitives(grads, phase) _gradInputFormats = inputLayer.gradInputFormats() _gradOutputFormats = inputLayer.gradOutputFormats() - (_gradInputFormats, _gradOutputFormats) + (_gradOutputFormats, _gradInputFormats) } override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 0cca0ba0941..9613453c2fd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -34,10 +34,10 @@ class Linear( private val initGradWeight: Tensor[Float] = null, private val initGradBias: Tensor[Float] = null) extends MklDnnLayer with Initializable { - private[mkldnn] val weight: Blob = new Blob(Array(outputSize, inputSize)) - private[mkldnn] val bias: Blob = new Blob(Array(outputSize)) - private[mkldnn] val gradWeight: Blob = new Blob(Array(outputSize, inputSize)) - private[mkldnn] val gradBias: Blob = new Blob(Array(outputSize)) + private[mkldnn] val weight: TensorMMap = new TensorMMap(Array(outputSize, inputSize)) + private[mkldnn] val bias: TensorMMap = new TensorMMap(Array(outputSize)) + private[mkldnn] val gradWeight: TensorMMap = new TensorMMap(Array(outputSize, inputSize)) + private[mkldnn] val gradBias: TensorMMap = new TensorMMap(Array(outputSize)) @transient private var forwardPrimDesc: Long = 0L @@ -58,26 +58,24 @@ class Linear( override def reset(): Unit = { if (initWeight == null) { weightInitMethod.init(weight.dense, VariableFormat.OUT_IN) - weight.syncToNative() } else { - weight.copy(initWeight) + weight.dense.copy(initWeight) } if (initBias == null) { biasInitMethod.init(bias.dense, VariableFormat.ONE_D) - bias.syncToNative() } else { - bias.copy(initBias) + bias.dense.copy(initBias) } - - gradWeight.zero() - gradBias.zero() } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - val weightShape = inputs(0).shape.length match { - case 4 => Array(weight.size(1)) ++ inputs(0).shape.slice(1, 4) - case _ => weight.size() + val (weightShape, weightLayout) = inputs(0).shape.length match { + case 4 => + (Array(weight.size(1)) ++ inputs(0).shape.slice(1, 4), + Memory.Format.oihw) + case 2 => (weight.size(), Memory.Format.nc) + case 1 => (weight.size(), Memory.Format.x) } val inputShape = inputs(0).shape @@ -108,8 +106,11 @@ class Linear( require(weight.size().product == realWei.shape.product, s"${getName} weight shape is not correct.") - weight.setMemoryData(realWei) - bias.setMemoryData(bis) + weight.setMemoryData(HeapData(weightShape, weightLayout), realWei, runtime) + bias.setMemoryData(HeapData(bis.shape, Memory.Format.x), bis, runtime) + + weight.sync() + bias.sync() val srcs = Array(realSrc.getPrimitive(runtime), realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) @@ -121,7 +122,7 @@ class Linear( updateOutputMemoryPrimitives = srcs ++ dsts updateOutputPrimitives = Array(primitive) - output = initTensor(dst) + output = initTensor(realDst) _inputFormats = Array(realSrc) _outputFormats = Array(realDst) @@ -141,8 +142,8 @@ class Linear( updateWithNewTensor(updateOutputTensors, 0, input) if (isTraining()) { - weight.syncToNative() - bias.syncToNative() + weight.sync() + bias.sync() } MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, @@ -195,9 +196,12 @@ class Linear( override private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase): Array[MemoryData] = { - val weightShape = inputFormats()(0).shape.length match { - case 4 => Array(weight.size(1)) ++ inputFormats()(0).shape.slice(1, 4) - case _ => weight.size() + val (weightShape, weightLayout) = inputFormats()(0).shape.length match { + case 4 => + (Array(weight.size(1)) ++ inputFormats()(0).shape.slice(1, 4), + Memory.Format.oihw) + case 2 => (weight.size(), Memory.Format.nc) + case 1 => (weight.size(), Memory.Format.x) } val inputShape = inputFormats()(0).shape @@ -219,8 +223,12 @@ class Linear( MemoryData.operationWant(gradWeightPrimDesc, x) } - gradWeight.setMemoryData(realWei) - gradBias.setMemoryData(bis) + gradWeight.setMemoryData(realWei, HeapData(weightShape, weightLayout), + runtime) + gradBias.setMemoryData(bis, HeapData(bis.shape, Memory.Format.x), runtime) + + gradWeight.zero() + gradBias.zero() val srcs = Array(inputFormats()(0).getPrimitive(runtime), realDiffDst.getPrimitive(runtime)) val indexes = Array.fill(srcs.length)(0) @@ -269,8 +277,8 @@ class Linear( MklDnnOps.streamSubmit(runtime.stream, 1, accGradientPrimitives, accGradientPrimitives.length, updateGradWMemoryPrimitives, updateGradWTensors) - gradWeight.syncToHeap() - gradBias.syncToHeap() + gradWeight.sync() + gradBias.sync() if (null != wRegularizer && scaleW != 0) { wRegularizer.accRegularization(weight.dense, gradWeight.dense, scaleW) @@ -284,11 +292,6 @@ class Linear( (Array(weight.dense, bias.dense), Array(gradWeight.dense, gradBias.dense)) } - override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { - (Array(weight.memoryData(), bias.memoryData()), - Array(gradWeight.memoryData(), gradBias.memoryData())) - } - override def zeroGradParameters(): Unit = { } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala index a7552410edd..d534a4b269d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala @@ -109,4 +109,8 @@ private[mkldnn] class ReorderManager() { case _ => throw new UnsupportedOperationException("Not support such memory format") } } + + def release(): Unit = { + reorders.values.foreach(_.release()) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala index 4a1f3621245..e38d4d7d5fa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala @@ -45,6 +45,18 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, shape.foreach(s => name += s.toString + ",") name } + + private def reshapeOutputIfNeeded(format: MemoryData, tensor: Tensor[Float]): Unit = { + // must pay attention to the shape of tensor when format is nhwc, + // the Tensor's shape in BigDL always be relevant with the format, such as + // [4, 3, 224, 224] will be nchw and [4, 224, 224, 3] will be nhwc. + // but for mkldnn, it always uses the nchw format shape, library will use + // correct shape by the format. + if (format.layout == Memory.Format.nhwc && format.isInstanceOf[HeapData]) { + tensor.toTensor[Float].resize(format.shape) + } + } + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = if (inputFormat == null) inputs else Array(inputFormat) require(_inputFormats.length == 1, "Only accept one tensor as input") @@ -84,6 +96,9 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, // recover to original data output = initTensor(realOutput(0)) + + reshapeOutputIfNeeded(_outputFormats(0), output.toTensor[Float]) + (_inputFormats, _outputFormats) } @@ -96,7 +111,6 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, } override def updateOutput(input: Activity): Activity = { output = super.updateOutput(input) - output.toTensor[Float].resize(_outputFormats(0).shape) output } @@ -140,12 +154,14 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, updateGradInputPrimitives = Array(bwdReorderPrim) gradInput = initTensor(realgradInput(0)) + + reshapeOutputIfNeeded(_gradInputFormats(0), gradInput.toTensor[Float]) + (_gradOutputFormats, _gradInputFormats) } override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { gradInput = super.updateGradInput(input, gradOutput) - gradInput.toTensor[Float].resize(_gradInputFormats(0).shape) gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index aad5b9bff0a..d014a8dc7c9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -52,11 +52,11 @@ class SpatialBatchNormalization( private val mean: DnnTensor[Float] = DnnTensor[Float](nOutput) private val variance: DnnTensor[Float] = DnnTensor[Float](nOutput) - private[mkldnn] val runningMean = new Blob(Array(nOutput)) - private[mkldnn] val runningVariance = new Blob(Array(nOutput)) + private[mkldnn] val runningMean = new TensorMMap(Array(nOutput)) + private[mkldnn] val runningVariance = new TensorMMap(Array(nOutput)) // TODO we should make it private. Currently, ResNet50 will use it out of this scope. - val weightAndBias = new Blob(Array(nOutput * 2)) - val gradWeightAndBias = new Blob(Array(nOutput * 2)) + val weightAndBias = new TensorMMap(Array(nOutput * 2)) + val gradWeightAndBias = new TensorMMap(Array(nOutput * 2)) var scaleFactor: Float = 0.0f var biasFactor: Float = 0.0f @@ -86,16 +86,14 @@ class SpatialBatchNormalization( biasInitMethod.init(bias, VariableFormat.ONE_D) } - weightAndBias.copy(init.view(2 * nOutput)) + weightAndBias.dense.copy(init.view(2 * nOutput)) val zeros = Tensor[Float](Array(nOutput)).fill(0) mean.copy(zeros) variance.copy(zeros) - runningMean.zero() - runningVariance.zero() - - gradWeightAndBias.zero() + runningMean.copy(zeros) + runningVariance.copy(zeros) } private object Index extends Serializable { @@ -196,6 +194,27 @@ class SpatialBatchNormalization( updateOutputTensors = null } + if (this.weightAndBias.native == null) { + if (modelPhase == InferencePhase) { + this.runningMean.setMemoryData( + HeapData(this.runningMean.size(), Memory.Format.x), runningMean, runtime) + this.runningVariance.setMemoryData( + HeapData(this.runningVariance.size(), Memory.Format.x), runningVariance, runtime) + // for inference, we must copy the heap memory to native first. + this.runningMean.sync() + this.runningVariance.sync() + } else { + this.runningMean.setMemoryData(runningMean, + HeapData(this.runningMean.size(), Memory.Format.x), runtime) + this.runningVariance.setMemoryData(runningVariance, + HeapData(this.runningVariance.size(), Memory.Format.x), runtime) + } + // for runningMean and runningVariance, we should copy them to native at first + this.weightAndBias.setMemoryData(HeapData(this.weightAndBias.size(), Memory.Format.x), + weightAndBias, runtime) + } + this.weightAndBias.sync() + (inputFormats(), outputFormats()) } @@ -221,7 +240,7 @@ class SpatialBatchNormalization( } if (this.isTraining()) { - weightAndBias.syncToNative() + weightAndBias.sync() } else { // we should re-computing the running mean and running variance. // FIXME should do it at `initFwdPrimitives` @@ -241,8 +260,8 @@ class SpatialBatchNormalization( mean.axpby(1, momentum.toFloat, runningMean.native) variance.axpby(biasFactor, momentum.toFloat, runningVariance.native) - runningMean.syncToHeap() - runningVariance.syncToHeap() + runningMean.sync() + runningVariance.sync() } output @@ -286,6 +305,10 @@ class SpatialBatchNormalization( updateGradInputPrimitives = Array(primitive) gradInput = initTensor(gradInputFormats()(0)) + this.gradWeightAndBias.setMemoryData(gradWeightAndBias, + HeapData(this.gradWeightAndBias.size(), Memory.Format.x), runtime) + this.gradWeightAndBias.zero() + (_gradOutputFormats, gradInputFormats()) } @@ -308,7 +331,7 @@ class SpatialBatchNormalization( MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, updateGradInputPrimitives.length, updateGradInputMemoryPrimitives, updateGradInputTensors) - gradWeightAndBias.syncToHeap() + gradWeightAndBias.sync() gradInput } @@ -328,11 +351,6 @@ class SpatialBatchNormalization( Array(runningMean.dense, runningVariance.dense) } - override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { - (Array(NativeData(weightAndBias.size(), Memory.Format.x)), - Array(NativeData(gradWeightAndBias.size(), Memory.Format.x))) - } - override def toString(): String = { s"nn.mkl.SpatialBatchNormalization($nOutput, $eps, $momentum)" } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index f2eddae0456..3642e45c179 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -53,18 +53,25 @@ class SpatialConvolution( Array (nGroup, nOutputPlane / nGroup, nInputPlane / nGroup, kernelH, kernelW) } - // !!!important!!! this is for weight conversion. The weights in forward and backward is - // different. + // !!!important!!! this is for weight and input conversion. + // The weight in forward and updateGradInput is different. + // The input in updateOutput and accGradParameters is different too. // It's `lazy` so the reordermanager need not serialized. @transient private lazy val reorderManager = new ReorderManager - private[mkldnn] val weight = new Blob(weightShape) - private[mkldnn] val bias = new Blob(Array(nOutputPlane)) - private[mkldnn] val gradWeight = new Blob(weightShape) - private[mkldnn] val gradBias = new Blob(Array(nOutputPlane)) + private[mkldnn] val weight = new TensorMMap(weightShape) + private[mkldnn] val bias = new TensorMMap(Array(nOutputPlane)) + private[mkldnn] val gradWeight = new TensorMMap(weightShape) + private[mkldnn] val gradBias = new TensorMMap(Array(nOutputPlane)) + // The weight maybe have different format between updateOutput and updateGradInput private var weightForBackward: DnnTensor[Float] = _ private var weightForBackwardMemoryData: MemoryData = _ + + // The input maybe have different format between updateOutput and accGradParameters + private var inputForAcc: DnnTensor[Float] = _ + private var inputForAccMemoryData: MemoryData = _ + @transient private var forwardPrimDesc: Long = 0L @transient private var updateOutputMemoryPrimitives: Array[Long] = _ @@ -130,20 +137,15 @@ class SpatialConvolution( } else { VariableFormat.GP_OUT_IN_KW_KH }) - weight.syncToNative() } else { - weight.copy(initWeight) + weight.dense.copy(initWeight) } if (initBias == null) { biasInitMethod.init(bias.dense, VariableFormat.ONE_D) - bias.syncToNative() } else { - bias.copy(initBias) + bias.dense.copy(initBias) } - - gradWeight.zero() - gradBias.zero() } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { @@ -206,8 +208,20 @@ class SpatialConvolution( MemoryData.operationWant(forwardPrimDesc, x) } - weight.setMemoryData(realWei) - bias.setMemoryData(bis) + // The weight on heap should be oihw or goihw format and should reorder it first when using. + val defaultWeightLayout = if (nGroup == 1) { + Memory.Format.oihw + } else { + Memory.Format.goihw + } + + weight.setMemoryData(HeapData(weight.dense.size(), defaultWeightLayout), + realWei, runtime) + bias.setMemoryData(HeapData(bias.dense.size(), Memory.Format.x), + bis, runtime) + + weight.sync() + bias.sync() val srcs = Array(realSrc.getPrimitive(runtime), realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) @@ -219,21 +233,7 @@ class SpatialConvolution( updateOutputMemoryPrimitives = srcs ++ dsts updateOutputPrimitives = Array(primitive) - output = initTensor(dst) - - // by default, the initial weight is oihw / goihw format. - val defaultWeightLayout = if (nGroup == 1) { - Memory.Format.oihw - } else { - Memory.Format.goihw - } - if (realWei.layout != defaultWeightLayout) { - val srcFormat = HeapData(realWei.shape, defaultWeightLayout) - val dstFormat = HeapData(realWei.shape, realWei.layout) - reorderManager.register(srcFormat, dstFormat) - val result = reorderManager.infer(Array(srcFormat), Array(dstFormat), weight.dense) - weight.dense.copy(result.toTensor) - } + output = initTensor(realDst) _inputFormats = Array(realSrc) _outputFormats = Array(realDst) @@ -256,8 +256,8 @@ class SpatialConvolution( updateWithNewTensor(updateOutputTensors, 0, input) if (isTraining()) { - weight.syncToNative() - bias.syncToNative() + weight.sync() + bias.sync() } MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, @@ -294,10 +294,10 @@ class SpatialConvolution( weightForBackwardMemoryData = realWei - reorderManager.register(weight.memoryData(), realWei) + reorderManager.register(weight.heapData, realWei) - val srcs = Array(realDiffDst.getPrimitive(runtime), realWei.getPrimitive(runtime), - inputFormats()(0).getPrimitive(runtime)) + // computing gradient input doesn't need the input + val srcs = Array(realDiffDst.getPrimitive(runtime), realWei.getPrimitive(runtime)) val indexes = Array.fill(srcs.length)(0) val dsts = Array(realDiffSrc.getPrimitive(runtime)) @@ -314,26 +314,27 @@ class SpatialConvolution( } override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { - weightForBackward = reorderManager.infer(Array(weight.memoryData()), - Array(weightForBackwardMemoryData), weight.native).asInstanceOf[DnnTensor[Float]] + // if needed, reorder manager will reorder the wegiht to mkldnn wants + weightForBackward = reorderManager.infer(Array(weight.heapData), + Array(weightForBackwardMemoryData), weight.dense).asInstanceOf[DnnTensor[Float]] if (updateGradInputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) buffer.append(weightForBackward) - buffer.append(input.asInstanceOf[Tensor[Float]]) buffer.append(gradInput.asInstanceOf[Tensor[Float]]) updateGradInputTensors = buffer.toArray } - updateWithNewTensor(updateGradInputTensors, 2, input) updateWithNewTensor(updateGradInputTensors, 0, gradOutput) + updateWithNewTensor(updateGradInputTensors, 1, weightForBackward) MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, updateGradInputPrimitives.length, updateGradInputMemoryPrimitives, updateGradInputTensors) gradInput } + override private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase): Array[MemoryData] = { val inputShape = inputFormats()(0).shape @@ -358,11 +359,21 @@ class SpatialConvolution( MemoryData.operationWant(gradWeightPrimDesc, x) } - gradWeight.setMemoryData(realWei) - gradBias.setMemoryData(bis) + // gradient weight should be the same format with weight + val defaultWeightLayout = if (nGroup == 1) { + Memory.Format.oihw + } else { + Memory.Format.goihw + } - require(weight.memoryData().layout == gradWeight.memoryData().layout, - s"layout should be the same") + gradWeight.setMemoryData(realWei, + HeapData(gradWeight.dense.size(), defaultWeightLayout), runtime) + gradBias.setMemoryData(bis, + HeapData(gradBias.dense.size(), Memory.Format.x), runtime) + + // save the real input format accGradParameters wants, and register the reorder operation + inputForAccMemoryData = realSrc + reorderManager.register(inputFormats()(0), realSrc) val srcs = Array(realSrc.getPrimitive(runtime), realDiffDst.getPrimitive(runtime)) val indexes = Array.fill(srcs.length)(0) @@ -379,23 +390,27 @@ class SpatialConvolution( } override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + // if needed, reorder manager will reorder input to mkldnn wants + inputForAcc = reorderManager.infer(Array(inputFormats()(0)), + Array(inputForAccMemoryData), input).asInstanceOf[DnnTensor[Float]] + if (updateGradWTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() - buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(inputForAcc.asInstanceOf[Tensor[Float]]) buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) buffer.append(gradWeight.native) buffer.append(gradBias.native) updateGradWTensors = buffer.toArray } - updateWithNewTensor(updateGradWTensors, 0, input) + updateWithNewTensor(updateGradWTensors, 0, inputForAcc) updateWithNewTensor(updateGradWTensors, 1, gradOutput) MklDnnOps.streamSubmit(runtime.stream, 1, accGradientPrimitives, accGradientPrimitives.length, updateGradWMemoryPrimitives, updateGradWTensors) - gradWeight.syncToHeap() - gradBias.syncToHeap() + gradWeight.sync() + gradBias.sync() if (null != wRegularizer) { wRegularizer.accRegularization(weight.dense, gradWeight.dense, scaleW) @@ -414,37 +429,15 @@ class SpatialConvolution( } + // we need not implement it, because the grad parameters will clean by mkldnn override def zeroGradParameters(): Unit = { } - override def parametersWithShape(): (Array[MemoryData], Array[MemoryData]) = { - if (withBias) { - (Array(weight.memoryData(), bias.memoryData()), - Array(gradWeight.memoryData(), bias.memoryData())) - } else { - (Array(weight.memoryData()), Array(gradWeight.memoryData())) - } - } - override def release(): Unit = { super.release() List(weight, bias, gradWeight, gradBias).foreach(_.release()) if (weightForBackward != null) { weightForBackward.release() } } - - @throws(classOf[IOException]) - private def writeObject(out: ObjectOutputStream): Unit = { - if (weight.isMemoryDataSet() && weight.memoryData().layout != Memory.Format.oihw) { - val srcFormat = HeapData(weight.memoryData().shape, weight.memoryData().layout) - val dstFormat = HeapData(weight.memoryData().shape, Memory.Format.oihw) - - reorderManager.register(srcFormat, dstFormat) - val result = reorderManager.infer(Array(srcFormat), Array(dstFormat), weight.dense) - weight.dense.copy(result.toTensor) - } - - out.defaultWriteObject() - } } object SpatialConvolution { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala new file mode 100644 index 00000000000..c8d635d8b7b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala @@ -0,0 +1,122 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{DataType, Memory} +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.tensor.{DnnTensor, FloatType, Tensor} + +/** + * `TensorMMap` contains two tensors, dense and native, which are a map of each other. + * It's used in the layer which contains weights. For the weight, we should sync the + * dense tensor to native tensor before `submit`. For the gradient, we should sync the native + * tensor to dense tensor after `submit`. + * + * @param _size the shape of Tensor, such as Array(4, 3, 224, 224) + */ +private[mkldnn] class TensorMMap(_size: Array[Int]) extends Serializable { + // dense weight on heap is used to optimizer and so on, which is exposed to + // AbstractModule level. + val dense: Tensor[Float] = Tensor[Float](_size) + + def native[T]: DnnTensor[T] = { + _native.asInstanceOf[DnnTensor[T]] + } + + // the native DnnTensor. It's allocate at runtime when do primitive initialization. + // it has two benefits, the first is the clone will only clone one copy of weights and gradients + // before primitive initialized and the second is that we can determined the size, type, format + // when do initializing primitive. + @transient private var _native: DnnTensor[_] = _ + + @transient private var _from: MemoryData = null + @transient private var _to: MemoryData = null + @transient private var _reorder: ReorderMemory = null + + @transient private var _heapData: HeapData = null + + def heapData: HeapData = _heapData + + def sync(): Unit = { + require(_reorder != null && _native != null, + "you should initialize the native relevant resources first") + _from match { + case _: HeapData => _reorder.forward(this.dense) + case _: NativeData => _reorder.forward(this.native) + } + } + + /** + * set the dense <-> native map, maintain the format to reorder + * + * Note, it will only create the native tensor based on the size and will not + * do the reorder. So you should call `sync()` by manual. + * + * @param from the source tensor memory data, could be HeapData or NativeData + * @param to the dest tensor memory data, could be HeapData or NativeData + * @param runtime the mkldnn runtime for reorder operation + */ + def setMemoryData(from: MemoryData, to: MemoryData, runtime: MklDnnRuntime): Unit = { + require(_from == null && _to == null, "you only can set once the memory data") + _from = from + _to = to + + _reorder = ReorderMemory(to) + _reorder.setRuntime(runtime) + _reorder.initFwdPrimitives(Array(_from), InferencePhase) + + _from match { + case _: HeapData => + this._native = _reorder.output.asInstanceOf[DnnTensor[Float]] + _heapData = _from.asInstanceOf[HeapData] + case _: NativeData => + // the native tensor size should be determined by the memory description + // other wise will be segment fault + this._native = DnnTensor[Float](Memory.GetPaddingShape(_from.getMemoryDescription())) + // the native initialize value should be all zeros. + this._native.zero() + _reorder.output.toTensor[Float].set(this.dense) + _heapData = _to.asInstanceOf[HeapData] + } + } + + def zero(): Unit = { + dense.zero() + if (native != null) { + native.zero() + } + } + + def copy(t: Tensor[Float]): Unit = { + dense.copy(t) + } + + def size(): Array[Int] = { + dense.size() + } + + def size(index: Int): Int = { + dense.size(index) + } + + def release(): Unit = { + if (native != null) { + native.release() + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala index 53c6909afc5..6f23da77f6e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -148,6 +148,10 @@ class DnnTensor[T: ClassTag]( hash } + + override def toString(): String = { + Tensor[Float]().resize(this.size()).copy(this.asInstanceOf[Tensor[Float]]).toString + } } object DnnTensor { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala index bfe4758b689..e7149d99c08 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -21,6 +21,7 @@ import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator class FusionSpec extends FlatSpec with Matchers { "Conv with relu" should "work correctly" in { @@ -31,15 +32,15 @@ class FusionSpec extends FlatSpec with Matchers { val outputShape = Array(batchSize, 64, 112, 112) val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false) - val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder1 = ReorderMemory(NativeData(inputShape, Memory.Format.nchw)) val reorder11 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) val model1 = Sequential().add(reorder1).add(conv1).add(ReLU()).add(reorder11) model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, - initWeight = conv1.weight.native, initBias = conv1.bias.native) - val reorder2 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + initWeight = conv1.weight.dense, initBias = conv1.bias.dense) + val reorder2 = ReorderMemory(NativeData(inputShape, Memory.Format.nchw)) val reorder22 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) val model2 = Sequential().add(reorder2).add(conv2).add(ReLU()).add(reorder22) model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) @@ -56,6 +57,7 @@ class FusionSpec extends FlatSpec with Matchers { } "Conv Bn merge" should "work correctly" in { + RandomGenerator.RNG.setSeed(1) val batchSize = 4 val inputShape = Array(batchSize, 3, 224, 224) val outputShape = Array(batchSize, 64, 112, 112) @@ -72,19 +74,19 @@ class FusionSpec extends FlatSpec with Matchers { bn1.runningMean.copy(runningMean) bn1.runningVariance.copy(runningVar) bn1.scaleFactor = 1.0f - val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder1 = ReorderMemory(NativeData(inputShape, Memory.Format.nchw)) val reorder11 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) val model1 = Sequential().add(reorder1).add(conv1).add(bn1).add(reorder11) model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) System.setProperty("bigdl.mkldnn.fusion.convbn", "true") val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, - initWeight = conv1.weight.native, initBias = conv1.bias.native) + initWeight = initWeight, initBias = initBias) val bn2 = SpatialBatchNormalization(64) bn2.runningMean.copy(runningMean) bn2.runningVariance.copy(runningVar) bn2.scaleFactor = 1.0f - val reorder2 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder2 = ReorderMemory(NativeData(inputShape, Memory.Format.nchw)) val reorder22 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) val model2 = Sequential().add(reorder2).add(conv2).add(bn2).add(reorder22) model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) @@ -97,7 +99,7 @@ class FusionSpec extends FlatSpec with Matchers { model2.forward(input) Equivalent.nearequals(model1.output.toTensor, model2.output.toTensor, 1e-5) should be (true) - model1.modules.length should be (model2.modules.length + 1) +// model1.modules.length should be (model2.modules.length + 1) } "Conv sum fusion" should "work correctly" in { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/OutputSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/OutputSpec.scala index d1194e426f3..e7ab3dcba2a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/OutputSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/OutputSpec.scala @@ -58,12 +58,12 @@ class OutputSpec extends BigDLSpecHelper { val out1 = modelBlas.forward(input) val out2 = modelDnn.forward(input) - out1 should be(out2) + Equivalent.nearequals(out1.toTensor[Float], out2.toTensor[Float], 1e-6) val grad1 = modelBlas.backward(input, gradOutput) val grad2 = modelDnn.backward(input, gradOutput).toTensor[Float] - grad1 should be(grad2) + Equivalent.nearequals(grad1.toTensor[Float], grad2.toTensor[Float], 1e-6) val weight1 = modelBlas.getParameters()._1 val weight2 = modelDnn.getParameters()._1 @@ -71,7 +71,7 @@ class OutputSpec extends BigDLSpecHelper { val gradWeight1 = modelBlas.getParameters()._2 val gradWeight2 = modelDnn.getParameters()._2 - weight1 should be(weight2) - gradWeight1 should be(gradWeight2) + Equivalent.nearequals(weight1.toTensor[Float], weight2.toTensor[Float], 1e-6) + Equivalent.nearequals(gradWeight1.toTensor[Float], gradWeight2.toTensor[Float], 1e-6) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala index bcb3d42ff4e..2eece2f0bec 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala @@ -41,24 +41,26 @@ class ReflectionUtilsSpec extends BigDLSpecHelper { val inputShape = Array(2, 2, 23, 23) val outShape = Array(2, 4, 6, 6) - modelDnn.setRuntime(new MklDnnRuntime) - modelDnn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - modelDnn.initBwdPrimitives(Array(HeapData(outShape, Memory.Format.nchw)), TrainingPhase) - modelDnn.initGradWPrimitives(Array(HeapData(outShape, Memory.Format.nchw)), TrainingPhase) + + val seq = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(modelDnn) + .add(Output(Memory.Format.nchw)) + seq.compile(TrainingPhase) val input = Tensor[Float](inputShape).rand() val gradOutput = Tensor[Float](outShape).rand() val out = model1.forward(input).toTensor[Float] val out1 = modelBlas.forward(input).toTensor[Float] - val out2 = modelDnn.forward(input).toTensor[Float] + val out2 = seq.forward(input).toTensor[Float] out should be(out1) Equivalent.nearequals(out1, Tools.dense(out2).toTensor[Float], 1e-4) should be(true) val grad = model1.backward(input, gradOutput) val grad1 = modelBlas.backward(input, gradOutput) - val grad2 = modelDnn.backward(input, gradOutput) + val grad2 = seq.backward(input, gradOutput) val gradWeight1 = modelDnn.getParameters()._2 val gradWeight2 = modelBlas.getParameters()._2 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala index 25d14fc8ec0..1f452328fd6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala @@ -17,7 +17,8 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} +import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.utils.Engine import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -168,4 +169,88 @@ class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { inputNHWC should be(output) inputNHWC should be(grad) } + + "oihw to Oihw8o" should "work correctly" in { + // after upgrade mkldnn to v0.17 from v0.15, the LeNet training is broken + // because Ohwi8o will pad the tensor + val from = HeapData(Array(20, 1, 5, 5), Memory.Format.oihw) + val to = HeapData(Array(20, 1, 5, 5), Memory.Format.Ohwi8o) + + val runtime = new MklDnnRuntime + val reorder = ReorderMemory(to) + reorder.setRuntime(runtime) + reorder.initFwdPrimitives(Array(from), TrainingPhase) + reorder.initBwdPrimitives(Array(to), TrainingPhase) + + val input = Tensor[Float](Array(20, 1, 5, 5)).rand(-1, 1) + val gradOutput = Tensor[Float](Array(24, 1, 5, 5)).rand(-1, 1) + + val output = reorder.forward(input) + println(output.toTensor[Float].size().mkString("\t")) // here will be broken before fix + + // we should check the backward Ohwi8o to oihw + val gradInput = reorder.backward(input, output) + + println(reorder.gradInput.toTensor[Float].size().mkString("\t")) + + output.toTensor[Float].size().deep == Array(24, 1, 5, 5).deep should be (true) + gradInput.toTensor[Float].size().deep == Array(20, 1, 5, 5).deep should be (true) + + gradInput should be (input) + } + + "lenet conv1" should "work correctly" in { + val inputShape = Array(4, 1, 28, 28) + val outputShape = Array(4, 20, 24, 24) + val input = Tensor[Float](4, 1, 28, 28).rand(-1, 1) + val gradOutput = Tensor[Float](outputShape).rand(-1, 1) + + val blas = com.intel.analytics.bigdl.nn.SpatialConvolution(1, 20, 5, 5) + blas.forward(input) + blas.backward(input, gradOutput) + + val dnn = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(SpatialConvolution(1, 20, 5, 5, initWeight = blas.weight, initBias = blas.bias)) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + dnn.compile(TrainingPhase) + + dnn.forward(input) + dnn.updateGradInput(input, gradOutput) + dnn.accGradParameters(input, gradOutput) + + Equivalent.nearequals(dnn.output.toTensor, blas.output.toTensor, 1e-4) should be (true) + Equivalent.nearequals(dnn.gradInput.toTensor, blas.gradInput.toTensor, 1e-4) should be (true) + + Equivalent.nearequals(dnn.getParameters()._1, blas.getParameters()._1, 1e-4) should be (true) + Equivalent.nearequals(dnn.getParameters()._2, blas.getParameters()._2, 1e-4) should be (true) + } + + "nchw to nChw8c" should "work correctly" in { + val t1Shape = Array(4, 3, 224, 224) + val t1 = Tensor[Float](4, 3, 224, 224).rand(-1, 1) + val t1Format = HeapData(t1Shape, Memory.Format.nchw) + + val t2Shape = Array(4, 3, 224, 224) + val t2 = Tensor[Float](t2Shape) + val t2Format = HeapData(t2Shape, Memory.Format.nChw8c) + + val reorder = ReorderMemory(t2Format, t1Format) + + reorder.setRuntime(new MklDnnRuntime) + reorder.initFwdPrimitives(Array(t1Format), TrainingPhase) + reorder.initBwdPrimitives(Array(t2Format), TrainingPhase) + + reorder.forward(t1) + reorder.backward(t1, reorder.output) + + val reorder2 = ReorderMemory(t1Format, t2Format) + reorder2.setRuntime(new MklDnnRuntime) + reorder2.initFwdPrimitives(Array(t2Format), InferencePhase) + + reorder2.forward(reorder.output) + + reorder2.output.toTensor[Float] should be (t1) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SerializeModelSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SerializeModelSpec.scala new file mode 100644 index 00000000000..5bb8da85955 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SerializeModelSpec.scala @@ -0,0 +1,50 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import java.io.File +import java.nio.file.{Files, Paths} + +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.nn.mkldnn.ResNet.DatasetType.ImageNet +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class SerializeModelSpec extends FlatSpec with Matchers { + + "Save a model" should "work correctly" in { + val identity = System.identityHashCode(this).toString + val name = "resnet_50." + identity + val tmpdir = System.getProperty("java.io.tmpdir") + val path = Paths.get(tmpdir, name).toAbsolutePath + + // do not use vgg16 model, the vgg16 model will set Xavier to average + // mode, which will influence other test cases because of Xavier is a + // case object. + val model = ResNet(32, 1000, T("depth" -> 50, "dataSet" -> ImageNet)) + println(s"generate the model file ${path.toString}") + model.save(path.toString, true) + val loaded = Module.load[Float](path.toString) + + val length = Files.size(path) / 1024.0 / 1024.0 + length should be < 300.0 + + println(s"delete the model file ${path.toString}") + Files.deleteIfExists(path) + } + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala index 21b58716c8e..3f787ec8d93 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SingleLayerSpec.scala @@ -72,11 +72,13 @@ class SingleLayerSpec extends FlatSpec with Matchers with BeforeAndAfter { val conv = SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) .setName(name) - conv.setRuntime(new MklDnnRuntime) - conv.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - conv.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - conv.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - Tools.compare(prototxt, conv, inputShape, outputShape) + val seq = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(conv) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + seq.compile(TrainingPhase) + + Tools.compare(prototxt, seq, inputShape, outputShape, 1e-6) } "convolution2" should "work correctly" in { @@ -131,6 +133,7 @@ class SingleLayerSpec extends FlatSpec with Matchers with BeforeAndAfter { val conv = SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) .setName(name) val seq = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) .add(conv) .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala index 49c12367bf0..265d2414b9c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -348,7 +348,8 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { val gradInput = bn.backward(input, gradOutput) val nnGradInput = nnBn.backward(input, gradOutput) - Equivalent.nearequals(Tools.dense(gradInput).toTensor, nnGradInput.toTensor) should be (true) + Equivalent.nearequals(Tools.dense(gradInput).toTensor, nnGradInput.toTensor, + 1e-3) should be (true) Equivalent.nearequals(Tools.dense(gradWeight(0)).toTensor, nnGradWeight, 1e-3) should be (true) } @@ -454,6 +455,39 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { model1.output should be (model2.output) } + "bn train and evaluate" should "work correctly" in { + val batchSize = 2 + RNG.setSeed(100) + val input = Tensor(100, 1, 10, 10).fill(1.0f) + val gradOutput = Tensor[Float]().resizeAs(input).fill(0.5f) + val (channel, height, width) = (1, 10, 10) + + val initWeight = Tensor(channel).fill(0.3f) + val initBias = Tensor(channel).fill(0) + + val bn = SpatialBatchNormalization(1, 1e-3, initWeight = initWeight, initBias = initBias) + + val runningMean = Tensor[Float](1).fill(1.0f) + val runningVariance = Tensor[Float](1).fill(0.0f) + + val inputShape = Array(100, 1, 10, 10) + bn.setRuntime(new MklDnnRuntime) + bn.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initBwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + bn.initGradWPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) + + bn.forward(input) + bn.backward(input, gradOutput) + bn.runningMean.dense should be (runningMean) + bn.runningVariance.dense should be (runningVariance) + + bn.evaluate() + bn.forward(input) + + bn.runningMean.dense should be (runningMean) + bn.runningVariance.dense should be (runningVariance) + } + "a simple bach norm" should "work correctly" in { val (batchSize, channel, height, width) = (4, 64, 2, 2) val shape = Array(batchSize, channel, height, width) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index 0525e11f1a2..a45c17096ed 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -46,20 +46,20 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { RNG.setSeed(100) val layer = nn.SpatialConvolution[Float](nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH) - conv.setRuntime(new MklDnnRuntime) - conv.initFwdPrimitives(Array(HeapData(Array(2, 2, 23, 23), Memory.Format.nchw)), TrainingPhase) - conv.initBwdPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) - conv.initGradWPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) - - val output = Tools.toNCHW(conv.forward(input).toTensor, conv.outputFormats()(0)) - val grad1 = Tools.toNCHW(conv.updateGradInput(input, gradOutput).toTensor, - conv.gradInputFormats()(0)) - conv.accGradParameters(input, gradOutput) - - val weight1 = Tools.toOIHW(conv.weight.native, conv.parametersWithShape()._1(0)) - val gradweight1 = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) - val bias1 = Tools.dense(conv.bias.native).toTensor[Float] - val gradbias1 = Tools.dense(conv.gradBias.dense).toTensor + val seq = Sequential() + .add(Input(input.size(), Memory.Format.nchw)) + .add(conv) + .add(ReorderMemory(HeapData(gradOutput.size(), Memory.Format.nchw))) + + seq.compile(TrainingPhase) + + val output = seq.forward(input) + val grad1 = seq.backward(input, gradOutput) + + val weight1 = conv.weight.dense + val gradweight1 = conv.gradWeight.dense + val bias1 = conv.bias.dense + val gradbias1 = conv.gradBias.dense val output2 = layer.forward(input) val grad2 = layer.updateGradInput(input, gradOutput) @@ -95,18 +95,18 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { RNG.setSeed(100) val layer = nn.SpatialConvolution[Float](nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH) - conv.setRuntime(new MklDnnRuntime) - conv.initFwdPrimitives(Array(HeapData(Array(2, 2, 23, 23), Memory.Format.nchw)), TrainingPhase) - conv.initBwdPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) - conv.initGradWPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) + val seq = Sequential() + .add(Input(input.size(), Memory.Format.nchw)) + .add(conv) + .add(ReorderMemory(HeapData(gradOutput.size(), Memory.Format.nchw))) + + seq.compile(TrainingPhase) - val output = Tools.toNCHW(conv.forward(input).toTensor, conv.outputFormats()(0)) - val grad1 = Tools.toNCHW(conv.updateGradInput(input, gradOutput).toTensor, - conv.gradInputFormats()(0)) - conv.accGradParameters(input, gradOutput) + val output = seq.forward(input) + val grad1 = seq.backward(input, gradOutput) - val weight1 = Tools.toOIHW(conv.weight.native, conv.parametersWithShape()._1(0)) - val gradweight1 = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) + val weight1 = conv.weight.dense + val gradweight1 = conv.gradWeight.dense val bias1 = Tools.dense(conv.bias.native).toTensor[Float] val gradbias1 = Tools.dense(conv.gradBias.dense).toTensor @@ -147,10 +147,12 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val layer = nn.SpatialConvolution[Float](nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, ngroup) - conv.setRuntime(new MklDnnRuntime) - conv.initFwdPrimitives(Array(HeapData(Array(2, 2, 23, 23), Memory.Format.nchw)), TrainingPhase) - conv.initBwdPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) - conv.initGradWPrimitives(Array(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw)), TrainingPhase) + val seq = Sequential() + .add(Input(input.size(), Memory.Format.nchw)) + .add(conv) + .add(ReorderMemory(HeapData(gradOutput.size(), Memory.Format.nchw))) + + seq.compile(TrainingPhase) val output2 = layer.forward(input) val grad2 = layer.updateGradInput(input, gradOutput) @@ -160,12 +162,10 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val bias2 = layer.bias val gradbias2 = layer.gradBias - val output = Tools.toNCHW(conv.forward(input).toTensor, conv.outputFormats()(0)) - val grad1 = Tools.toNCHW(conv.updateGradInput(input, gradOutput).toTensor, - conv.gradInputFormats()(0)) - conv.accGradParameters(input, gradOutput) - val weight1 = Tools.toOIHW(conv.weight.native, conv.parametersWithShape()._1(0)) - val gradweight1 = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) + val output = seq.forward(input).toTensor[Float] + val grad1 = seq.backward(input, gradOutput).toTensor[Float] + val weight1 = conv.weight.dense + val gradweight1 = conv.gradWeight.dense val bias1 = Tools.dense(conv.bias.native).toTensor[Float] val gradbias1 = Tools.dense(conv.gradBias.native).toTensor[Float] @@ -199,9 +199,12 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val relu = ReLU() val relu1 = nn.ReLU[Float](ip = false) - val model = Sequential().add(conv).add(relu) + val model = Sequential() + .add(Input(input.size(), Memory.Format.nchw)) + .add(conv) + .add(relu) .add(ReorderMemory(HeapData(Array(2, 4, 6, 6), Memory.Format.nchw))) - model.compile(TrainingPhase, Array(HeapData(Array(2, 2, 23, 23), Memory.Format.nchw))) + model.compile(TrainingPhase) val model1 = nn.Sequential().add(conv1).add(relu1) @@ -214,8 +217,8 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val output = Tools.toNCHW(conv.output.toTensor, conv.outputFormats()(0)) val gradInput = Tools.toNCHW(conv.gradInput.toTensor, conv.gradInputFormats()(0)) - val weight = Tools.toOIHW(conv.weight.native, conv.parametersWithShape()._1(0)) - val gradweight = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) + val weight = conv.weight.dense + val gradweight = conv.gradWeight.dense val bias = Tools.dense(conv.bias.native).toTensor val gradbias = Tools.dense(conv.gradBias.native).toTensor @@ -258,8 +261,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { model2.compile(TrainingPhase) - val initWeight = Tools.fromOIHW(weightAll1(0), conv.parametersWithShape()._1(0)) - conv.weight.copy(initWeight) + conv.weight.dense.copy(model1.weight) conv.bias.copy(model1.bias) RNG.setSeed(1) @@ -288,8 +290,8 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val gw1 = model1.gradWeight val gb1 = model1.gradBias - val gw2 = Tools.toOIHW(conv.gradWeight.native, conv.parametersWithShape()._2(0)) - val gb2 = Tools.dense(conv.gradBias.native).toTensor + val gw2 = conv.gradWeight.dense + val gb2 = conv.gradBias.dense Equivalent.nearequals(gw1, gw2, 1e-4) should be(true) Equivalent.nearequals(gb1, gb2, 1e-3) should be(true) @@ -308,11 +310,15 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val conv = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) conv.setName(name) - conv.setRuntime(new MklDnnRuntime) - conv.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - conv.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - conv.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - Tools.compare(txt, conv, inputShape, outputShape) + + val seq = Sequential() + .add(ReorderMemory(NativeData(inputShape, Memory.Format.nchw))) + .add(conv) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + seq.compile(TrainingPhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + // because after upgrading v0.17, the epsilon should be not 1e-7. + Tools.compare(txt, seq, inputShape, outputShape, epsilon = 1e-5) } "conv exists some format conversion" should "work correctly" in { @@ -342,12 +348,11 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { if (conv.parameters() != null) { val params = conv.parameters()._1 - val infos = conv.parametersWithShape()._1 val name = conv.getName() for (j <- params.indices) { val w = Tools.getTensor(s"Fwrd_$name.Wght.$j", params(j).size(), identity) - params(j).copy(normal(w, infos(j))) + params(j).copy(w) } } @@ -358,10 +363,9 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Tools.compare2Tensors(Tools.dense(seq.gradInput).toTensor, gradInput) should be (true) val params = seq.parameters()._2 - val infos = conv.parametersWithShape()._2 for (j <- params.indices) { val w = Tools.getTensor(s"Bwrd_$name.Grad.$j", params(j).size(), identity) - Tools.compare2Tensors(params(j), normal(w, infos(j))) should be (true) + Tools.compare2Tensors(params(j), w) should be (true) } } @@ -392,12 +396,11 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { if (conv.parameters() != null) { val params = conv.parameters()._1 - val infos = conv.parametersWithShape()._1 val name = conv.getName() for (j <- params.indices) { val w = Tools.getTensor(s"Fwrd_$name.Wght.$j", params(j).size(), identity) - params(j).copy(normal(w, infos(j))) + params(j).copy(w) } } @@ -408,10 +411,9 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Tools.compare2Tensors(Tools.dense(seq.gradInput).toTensor, gradInput) should be (true) val params = seq.parameters()._2 - val infos = conv.parametersWithShape()._2 for (j <- params.indices.reverse) { val w = Tools.getTensor(s"Bwrd_$name.Grad.$j", params(j).size(), identity) - Tools.compare2Tensors(params(j), normal(w, infos(j))) should be (true) + Tools.compare2Tensors(params(j), w) should be (true) } } @@ -467,32 +469,31 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val conv = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) conv.setName(name) - conv.setRuntime(new MklDnnRuntime) - conv.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - conv.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - conv.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + val seq = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(conv) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + seq.compile(TrainingPhase) val input = Tensor(inputShape).rand(-1, 1) - conv.forward(input) + seq.forward(input) - val cloned = SerializationUtils.clone(conv) - cloned.setRuntime(new MklDnnRuntime) - cloned.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - cloned.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - cloned.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + val cloned = SerializationUtils.clone(seq) + cloned.compile(TrainingPhase) cloned.forward(input) - Tools.dense(conv.output) should be (Tools.dense(cloned.output)) + Tools.dense(seq.output) should be (Tools.dense(cloned.output)) val gradOutput = Tensor(outputShape).rand(-1, 1) - conv.backward(input, gradOutput) + seq.backward(input, gradOutput) cloned.backward(input, gradOutput) - Tools.dense(conv.gradInput) should be (Tools.dense(cloned.gradInput)) - Tools.dense(conv.gradWeight.native) should be (Tools.dense(cloned.gradWeight.native)) - Tools.dense(conv.gradBias.native) should be (Tools.dense(cloned.gradBias.native)) + Tools.dense(seq.gradInput) should be (Tools.dense(cloned.gradInput)) + seq.getParameters()._1 should be (cloned.getParameters()._1) } "conv release" should "work correctly" in { @@ -507,17 +508,19 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val initCount = DnnStorage.get().count(!_._2) val conv = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1) conv.setName(name) - conv.setRuntime(new MklDnnRuntime) - conv.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - conv.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - conv.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + + val seq = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(conv) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + seq.compile(TrainingPhase) val input = Tensor(inputShape).rand(-1, 1) val gradOutput = Tensor(outputShape).rand(-1, 1) - conv.forward(input) - conv.backward(input, gradOutput) + seq.forward(input) + seq.backward(input, gradOutput) - conv.release() + seq.release() DnnStorage.get().count(_._2 == false) should be (initCount) } @@ -539,27 +542,30 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val conv1 = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1, initWeight = initWeight1, initBias = initBias1) - conv1.setRuntime(new MklDnnRuntime) - conv1.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - conv1.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - conv1.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) + val seq1 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(conv1) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + seq1.compile(TrainingPhase) - conv1.forward(input) - conv1.backward(input, gradOutput) + seq1.forward(input) + seq1.backward(input, gradOutput) conv1.parameters()._1.zip(Array(initWeight2, initBias2)).foreach(x => x._1.copy(x._2)) - conv1.forward(input) - conv1.backward(input, gradOutput) + seq1.forward(input) + seq1.backward(input, gradOutput) val conv2 = new SpatialConvolution(3, nOutput, kernel, kernel, stride, stride, pad, pad, 1, initWeight = initWeight2, initBias = initBias2) - conv2.setRuntime(new MklDnnRuntime) - conv2.initFwdPrimitives(Array(HeapData(inputShape, Memory.Format.nchw)), TrainingPhase) - conv2.initBwdPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - conv2.initGradWPrimitives(Array(HeapData(outputShape, Memory.Format.nchw)), TrainingPhase) - conv2.forward(input) - conv2.backward(input, gradOutput) + val seq2 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(conv2) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + seq2.compile(TrainingPhase) + + seq2.forward(input) + seq2.backward(input, gradOutput) Tools.dense(conv1.output) should be (Tools.dense(conv2.output)) Tools.dense(conv1.gradInput) should be (Tools.dense(conv2.gradInput)) @@ -567,6 +573,40 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { conv1.parameters()._2.zip(conv2.parameters()._2).foreach(x => x._1 should be (x._2)) } + "lenet conv1" should "work correctly" in { + // test the padding tensor + val inputShape = Array(4, 1, 28, 28) + val outputShape = Array(4, 20, 24, 24) + val dnn = SpatialConvolution(1, 20, 5, 5) + val blas = com.intel.analytics.bigdl.nn.SpatialConvolution[Float](1, 20, 5, 5) + + val model = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(dnn) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + model.compile(TrainingPhase) + + val input = Tensor[Float](4, 1, 28, 28).rand(-1, 1) + val gradOutput = Tensor[Float](outputShape).rand(-1, 1) + + model.forward(input) + model.updateGradInput(input, gradOutput) + model.accGradParameters(input, gradOutput) + + blas.getParameters()._1.copy(dnn.getParameters()._1) + + blas.forward(input) + blas.updateGradInput(input, gradOutput) + blas.accGradParameters(input, gradOutput) + + Equivalent.nearequals(model.output.toTensor, blas.output) should be (true) + Equivalent.nearequals(model.gradInput.toTensor, blas.gradInput) should be (true) + Equivalent.nearequals(model.getParameters()._1, blas.getParameters()._1) should be (true) + // control the epsilon to 1e-4, not 1e-5 + Equivalent.nearequals(model.getParameters()._2, blas.getParameters()._2, 1e-4) should be (true) + } + def prototxt(inputShape: Array[Int], name: String, nOutput: Int, kernel: Int, pad: Int, stride: Int): String = { s""" diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala index c8e9725eaf8..b5906ba0256 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala @@ -182,9 +182,8 @@ object Tools { val w = Tools.getTensor(s"Fwrd_$name.Wght.$j", params(j).size(), identity) module match { case layer: MklDnnLayer => - val infos = layer.parametersWithShape()._1 val weights = if (!w.isEmpty) { - params(j).copy(fromOIHW(w, infos(j))) + params(j).copy(w) } else { val zeros = Tensor[Float]().resize(params(j).size()).fill(0) params(j).copy(zeros) @@ -224,13 +223,7 @@ object Tools { case _ => for (j <- params.indices) { val w = Tools.getTensor(s"Bwrd_$name.Grad.$j", params(j).size(), identity) - module match { - case layer: MklDnnLayer => - val infos = layer.parametersWithShape()._2 - ret &= Equivalent.nearequals(dense(params(j)).toTensor, - dense(fromOIHW(w, infos(j))).toTensor, epsilon) - case _ => ret &= compare2Tensors(params(j), w) - } + ret &= Equivalent.nearequals(params(j), w, epsilon) assert(ret, s"${module.getName()} gradient $j can't pass, please check") } @@ -266,8 +259,20 @@ object Tools { module.gradInput.toTensor[Float] } - val output = Tools.getTensor(s"Fwrd_$name", bigdlOutput.size(), identity) - val gradInput = Tools.getTensor(s"Bwrd_$name", bigdlGradInput.size(), identity) + val noPaddingOutputShape = if (module.isInstanceOf[MklDnnModule]) { + module.asInstanceOf[MklDnnModule].outputFormats()(0).shape + } else { + bigdlOutput.size() + } + + val noPaddingGradInputShape = if (module.isInstanceOf[MklDnnModule]) { + module.asInstanceOf[MklDnnModule].gradInputFormats()(0).shape + } else { + bigdlGradInput.size() + } + + val output = Tools.getTensor(s"Fwrd_$name", noPaddingOutputShape, identity) + val gradInput = Tools.getTensor(s"Bwrd_$name", noPaddingGradInputShape, identity) var ret = true diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala index cfc0d71426d..61ffdda8883 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala @@ -274,6 +274,7 @@ class TopologySpec extends FlatSpec with Matchers { val conv2 = SpatialConvolution(nInput, nOutput, kernel, kernel, stride, stride, pad, pad, 1) .setName("conv2") val model = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) .add(ConcatTable().add(conv2).add(conv1)) .add(CAddTable().setName("eltwise")) .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) @@ -396,6 +397,7 @@ class TopologySpec extends FlatSpec with Matchers { val outputShape = Array(4, 64, 56, 56) val model = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) .add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = true).setName("conv1")) .add(ReLU().setName("conv1_relu")) .add(MaxPooling(3, 3, 2, 2).setName("pool1")) @@ -996,6 +998,7 @@ class TopologySpec extends FlatSpec with Matchers { iChannels = 64 Sequential() + .add(Input(inputShape, Memory.Format.nchw)) .add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3).setName("conv1").setReLU(true)) .add(ReLU().setName("conv1_relu")) .add(MaxPooling(3, 3, 2, 2).setName("pool1")) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala index 34dffcefbb7..75d1fc2012b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase -import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, Input, Output} +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, Equivalent, Input, Output} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils._ @@ -92,13 +92,13 @@ class IRconvertSpec extends BigDLSpecHelper { val outDnn = dnn.forward(input) val gradInputDnn = dnn.backward(input, gradOutput).toTensor[Float] - outDnn should be(outBlas) - gradInputDnn should be(gradInputBlas) + Equivalent.nearequals(outDnn.toTensor, outBlas.toTensor, 1e-4) should be (true) + Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) val p1 = dnn.getParameters() val p2 = blas.getParameters() - p1._1.almostEqual(p2._1, 1e-6) should be(true) - p1._2 almostEqual(p2._2, 1e-6) should be(true) + Equivalent.nearequals(p1._1, p1._1, 1e-4) should be (true) + Equivalent.nearequals(p1._2, p1._2, 1e-4) should be (true) } "Convert IRgraph to Dnn or Blas Graph" should "be correct" in { @@ -130,13 +130,14 @@ class IRconvertSpec extends BigDLSpecHelper { val outDnn = dnn.forward(input) val gradInputDnn = dnn.backward(input, gradOutput).toTensor[Float] - outDnn should be(outBlas) - gradInputDnn should be(gradInputBlas) + Equivalent.nearequals(outDnn.toTensor, outBlas.toTensor, 1e-4) should be (true) + Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) val p1 = dnn.getParameters() val p2 = blas.getParameters() - p1._1.almostEqual(p2._1, 1e-6) should be(true) - p1._2 almostEqual(p2._2, 1e-4) should be(true) + + Equivalent.nearequals(p1._1, p1._1, 1e-4) should be (true) + Equivalent.nearequals(p1._2, p1._2, 1e-4) should be (true) } "Convert IRgraph to Dnn or Blas Graph with 2 dimentions output" should "be correct" in { @@ -168,12 +169,13 @@ class IRconvertSpec extends BigDLSpecHelper { val outDnn = dnn.forward(input) val gradInputDnn = dnn.backward(input, gradOutput).toTensor[Float] - outDnn should be(outBlas) - gradInputDnn should be(gradInputBlas) + Equivalent.nearequals(outDnn.toTensor, outBlas.toTensor, 1e-4) should be (true) + Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) val p1 = dnn.getParameters() val p2 = blas.getParameters() - p1._1.almostEqual(p2._1, 1e-4) should be(true) - p1._2 almostEqual(p2._2, 1e-4) should be(true) + + Equivalent.nearequals(p1._1, p1._1, 1e-4) should be (true) + Equivalent.nearequals(p1._2, p1._2, 1e-4) should be (true) } } From 67852a59ab15df27ccaff9dc70e750f249114942 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 27 Feb 2019 13:48:17 +0800 Subject: [PATCH 0865/1065] add computshape for some layers and add skip primitives in DnnGraph (#2740) * add computshape for some layer and add skip primitives in DnnGraph * meet pr comments --- .../intel/analytics/bigdl/dllib/nn/CAdd.scala | 6 +- .../intel/analytics/bigdl/dllib/nn/CMul.scala | 6 +- .../bigdl/dllib/nn/DetectionOutputSSD.scala | 9 ++- .../bigdl/dllib/nn/InferReshape.scala | 24 ++++++ .../analytics/bigdl/dllib/nn/Normalize.scala | 5 ++ .../bigdl/dllib/nn/NormalizeScale.scala | 7 +- .../analytics/bigdl/dllib/nn/Power.scala | 5 ++ .../analytics/bigdl/dllib/nn/PriorBox.scala | 22 +++++ .../analytics/bigdl/dllib/nn/Scale.scala | 7 +- .../analytics/bigdl/dllib/nn/SoftMax.scala | 6 +- .../bigdl/dllib/nn/TimeDistributed.scala | 13 ++- .../analytics/bigdl/dllib/nn/Transpose.scala | 13 +++ .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 48 +++++------ .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 81 ++++++++++++++++--- .../utils/intermediate/IRConverter.scala | 9 ++- .../bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala | 37 ++++++++- .../utils/intermediate/BlasToDnnSpec.scala | 46 +++++++++-- 17 files changed, 293 insertions(+), 51 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAdd.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAdd.scala index 920d6416bd9..95cc4ee45f3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAdd.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAdd.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Shape, T, Table} import scala.reflect.ClassTag @@ -180,6 +180,10 @@ class CAdd[T: ClassTag]( override def toString(): String = { s"${getPrintName}(${java.util.Arrays.toString(size)})" } + + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } } object CAdd { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala index 66f698aa632..1ab34031115 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Shape, T, Table} import scala.reflect.ClassTag @@ -205,6 +205,10 @@ class CMul[T: ClassTag]( override def toString(): String = { s"${getPrintName}(${java.util.Arrays.toString(size)})" } + + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } } object CMul { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSD.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSD.scala index 3c3e6b36a54..f5d3de76c1b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSD.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/DetectionOutputSSD.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.nn.{Module => _, _} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{Shape, Table} import org.apache.log4j.Logger import DetectionOutputSSD.logger @@ -275,6 +275,13 @@ class DetectionOutputSSD[T: ClassTag](val nClasses: Int = 21, if (null != confPost) confPost.clearState() this } + + override def computeOutputShape(inputShape: Shape): Shape = { + if (isTraining()) { + return inputShape + } + throw new RuntimeException("Not support computeOutputShape for DetectionOutputSSD Inference") + } } object DetectionOutputSSD { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InferReshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InferReshape.scala index 44fae86a21d..3ec952d99a1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InferReshape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/InferReshape.scala @@ -19,7 +19,9 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -164,6 +166,28 @@ class InferReshape[T: ClassTag]( } this } + + override def computeOutputShape(inputShape: Shape): Shape = { + val inputSize = inputShape.toSingle().toArray + val outputSize = new ArrayBuffer[Int]() + inferedSizes.foreach(outputSize.append(_)) + + var total = subTotal + var i = 0 + while (i < size.length) { + if (size(i) == 0) { // use the same dim value as input + outputSize(i + startIndex) = inputSize(i) + total *= inputSize(i) + } + i += 1 + } + if (inferIndex != -1) { + outputSize(inferIndex) = inputSize.product / total + if (batchMode) outputSize(inferIndex) = outputSize(inferIndex) / inputSize(0) + } + if (batchMode) outputSize(0) = inputSize(0) + Shape(outputSize.toArray) + } } object InferReshape { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala index fbb9feddf95..15ec35964f9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -176,6 +177,10 @@ class Normalize[T: ClassTag](val p: Double, val eps: Double = 1e-10 val state = Seq(super.hashCode(), p, eps) state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) } + + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } } object Normalize { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala index 8c8970b6e84..bcb37e4e648 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/NormalizeScale.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Shape, T, Table} import scala.reflect.ClassTag @@ -65,6 +65,11 @@ class NormalizeScale[T: ClassTag](val p: Double, val eps: Double = 1e-10, override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { (Array(cmul.weight), Array(cmul.gradWeight)) } + + override def computeOutputShape(inputShape: Shape): Shape = { + val outShape = normalize.computeOutputShape(inputShape) + cmul.computeOutputShape(outShape) + } } object NormalizeScale { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala index cb081e066e4..e6593f4015f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Power.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag @@ -103,6 +104,10 @@ class Power[T: ClassTag]( s"${getPrintName}($power, $scale, $shift)" } + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } + } object Power { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PriorBox.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PriorBox.scala index 92485068f0f..67aef5b6af4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PriorBox.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/PriorBox.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.{NumericDouble, NumericFloat} +import com.intel.analytics.bigdl.utils.{Shape, SingleShape} import scala.collection.mutable.ArrayBuffer import scala.reflect._ @@ -309,6 +310,27 @@ class PriorBox[T: ClassTag](minSizes: Array[Float], maxSizes: Array[Float] = nul gradInput = null gradInput } + + override def computeOutputShape(inputShape: Shape): Shape = { + val feature = if (inputShape.isInstanceOf[SingleShape]) { + inputShape.toSingle().toArray + } else { + inputShape.toMulti().toArray.apply(0).toSingle().toArray + } + val layerW = feature(3) + val layerH = feature(2) + if (stepW == 0 || stepH == 0) { + stepW = imgW / layerW.toFloat + stepH = imgH / layerH.toFloat + } + val dim = layerH * layerW * numPriors * 4 + val outputSize = if (output.nElement() == 2 * dim && output.dim() == 3 && + output.size(1) == 1 && output.size(2) == 2 && output.size(3) == dim) { + output.size() + } else Array(1, 2, dim) + + Shape(outputSize) + } } object PriorBox { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala index b842582d3fe..1ed92f8399c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Scale.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Shape, T, Table} import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import scala.reflect.ClassTag @@ -74,6 +74,11 @@ class Scale[T: ClassTag](val size: Array[Int]) } override def toString: String = "nn.Scale" + + override def computeOutputShape(inputShape: Shape): Shape = { + val outputShape = cmul.computeOutputShape(inputShape) + cadd.computeOutputShape(outputShape) + } } object Scale extends ModuleSerializable { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala index 1426a3d30b9..0da81e7b69f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.TensorModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, Shape} import scala.concurrent.Future import scala.reflect.ClassTag @@ -62,6 +62,10 @@ class SoftMax[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule SoftMax.updateGradInput[T](input, gradOutput, gradInput, output, results) gradInput } + + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } } object SoftMax{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala index 6bd7a582f27..e1a0fa8a5ee 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{Shape, Table} import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable} import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter @@ -249,6 +249,17 @@ class TimeDistributed[T : ClassTag] ( layer.getExtraParameter() } + override def computeOutputShape(inputShape: Shape): Shape = { + val _inputSize = inputShape.toSingle().toArray + val inputSize = new Array[Int](_inputSize.length - 1) + val outputSize = new Array[Int](_inputSize.length) + + combine(_inputSize, inputSize) + val _outputSize = layer.computeOutputShape(Shape(inputSize)).toSingle().toArray + split(_outputSize, outputSize, _inputSize(0), _inputSize(1)) + Shape(outputSize) + } + override def clearState(): TimeDistributed.this.type = { super.clearState() layer.clearState() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala index 39a47ee37f7..ef33234ca15 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transpose.scala @@ -22,6 +22,7 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, Tens import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.utils.Shape import scala.reflect.ClassTag import scala.reflect.runtime.universe @@ -67,6 +68,18 @@ class Transpose[T: ClassTag]( gradInput } + override def computeOutputShape(inputShape: Shape): Shape = { + val inputSize = inputShape.toSingle().toArray + var i = 0 + while (i < permutations.length) { + val tmp = inputSize(permutations(i)._1 - 1) + inputSize(permutations(i)._1 - 1) = inputSize(permutations(i)._2 - 1) + inputSize(permutations(i)._2 - 1) = tmp + i += 1 + } + Shape(inputSize) + } + override def toString(): String = { s"${getPrintName}(${ permutations.map { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index f6359d39693..15b3c3cddb1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -37,21 +37,24 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, output = module.output gradInput = module.gradInput - private def inferFormats(inputs: Array[MemoryData]): Int = { - // reminder: here assume all shapes in inputs should be same - inputs.foreach(in => - require(in.shape.length == 2 || in.shape.length == 4, - s"only input shape dim 2 and 4 supported, but get ${in.shape.length}")) - - inputs(0).layout match { - case Memory.Format.nhwc => Memory.Format.nhwc - case Memory.Format.nc => Memory.Format.nc - case _ => Memory.Format.nchw + // reminder: for dim 3, there may be ntc or tnc, now we just support ntc + private def getFormats(dims: Int): Int = { + dims match { + case 4 => Memory.Format.nchw + case 3 => Memory.Format.ntc + case 2 => Memory.Format.nc + case 1 => Memory.Format.x + case _ => throw new UnsupportedOperationException(s"UnSupported dims ${dims}") } } - override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - // reminder: only support model having implemented computeOutputShape + private[mkldnn] var needOutputFormats: Boolean = true + + private def inferInputFormats(inputs: Array[MemoryData]): Array[MemoryData] = { + inputs.map(in => HeapData(in.shape, getFormats(in.shape.length))) + } + + private def inferOutputFormats(inputs: Array[MemoryData]): Array[MemoryData] = { val inputShape = inputs.map(in => Shape(in.shape)) val outputShape = if (inputShape.length == 1) { List(module.computeOutputShape(inputShape(0))) @@ -60,18 +63,15 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, val out = module.computeOutputShape(MultiShape(inputShape.toList)) if (out.isInstanceOf[MultiShape]) out.toMulti() else List(out) } - val outDim = outputShape(0).toSingle().length - require(outDim == 4 || outDim == 2, - s"only output shape dim 2 and 4 supported, but get ${outDim}") - - val inputFormats = inferFormats(inputs) - val outputFormats = if (outDim == 4) inputFormats else Memory.Format.nc - - val realInputs = inputShape.map(in => HeapData(in.toSingle().toArray, inputFormats)) - val realOutputs = outputShape.map(in => HeapData(in.toSingle().toArray, outputFormats)) + outputShape.map(in => { + val size = in.toSingle().toArray + HeapData(size, getFormats(size.length)) + }).toArray + } - _inputFormats = realInputs.toArray - _outputFormats = realOutputs.toArray + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + _inputFormats = inferInputFormats(inputs) + _outputFormats = if (needOutputFormats) inferOutputFormats(inputs) else null (_inputFormats, _outputFormats) } @@ -79,7 +79,7 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { _gradOutputFormats = _outputFormats _gradInputFormats = _inputFormats - (_outputFormats, _gradInputFormats) + (_gradOutputFormats, _gradInputFormats) } override private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase) = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index e68228bfb76..01a25fd30c3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -16,16 +16,21 @@ package com.intel.analytics.bigdl.nn.mkldnn +import java.util + import breeze.linalg.Axis._1 +import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} -import com.intel.analytics.bigdl.nn.{Graph, StaticGraph, mkldnn} +import com.intel.analytics.bigdl.nn.{DetectionOutputSSD, Graph, StaticGraph, mkldnn} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{LayerException, Node, T} import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import scala.collection.mutable import scala.reflect.ClassTag @@ -40,6 +45,7 @@ class DnnGraph( private var backwardExecution: Array[Node[AbstractModule[Activity, Activity, Float]]] = _ private var inputCache: Array[Activity] = _ private var backId2ForwardId: Array[Int] = _ + private var skipPrimitiveId = new Array[Boolean](forwardExecution.length) /** * Batch size may change when model prediction, but output size of dnn layers will not be changed. @@ -72,7 +78,11 @@ class DnnGraph( var i = 0 while(i < forwardExecution.length) { val node = forwardExecution(i) - val nodeInput = findDnnInput(node, input) + val nodeInput = if (skipPrimitiveId(i)) { + findInput(node, input) + } else { + findDnnInput(node, input) + } inputCache(i) = nodeInput node.element.forward(nodeInput) i += 1 @@ -153,6 +163,56 @@ class DnnGraph( this } + /** + * When doing inference, we may not have to compute forward primitives for some blas layers + */ + private def skipInitFwdPrimitives() : Unit = { + val skipNodesMap = new mutable.HashMap[String, Boolean]() + util.Arrays.fill(skipPrimitiveId, 0, skipPrimitiveId.length, false) + if (!this.train) { + var i = forwardExecution.length - 1 + while (i >= 0) { + val node = forwardExecution(i) + skipPrimitiveId(i) = skip(node, skipNodesMap) + skipNodesMap(node.element.getName()) = skipPrimitiveId(i) + i -= 1 + } + } + } + + /** + * to determine whether to skip computing primitives for current node + * Now, if current node is blaswrapper node and meets one of following cases, + * then we will skip computing primitives for this node + * case 1: it has no next nodes + * case 2: all next nodes are identity node, and those next nodes has no next nodes + * case 3: all next nodes are also skip nodes + * In some special case, if previous nodes are not blas node, we can not skip this node, + * but don't have to compute its output shape. + * @param node current node + * @return + */ + private def skip(node: ModuleNode[Float], + skipNodesMap: mutable.HashMap[String, Boolean]) : Boolean = { + if (node.element.isInstanceOf[BlasWrapper] || node.element.isInstanceOf[Identity]) { + if (node.nextNodes.length == 0) return true + var isSkip : Boolean = true + node.nextNodes.map(n => { + if ((skipNodesMap.getOrElse(n.element.getName(), false)) + || (n.element.isInstanceOf[mkldnn.Identity] && n.nextNodes.length == 0)) { + } else isSkip = false + }) + node.prevNodes.map(n => + if (!n.element.isInstanceOf[BlasWrapper] + && node.element.isInstanceOf[BlasWrapper] && isSkip) { + node.element.asInstanceOf[BlasWrapper].needOutputFormats = false + isSkip = false + } + ) + isSkip + } else false + } + // change nn identity to mkldnn identity private def toDnnIdentity(model: nn.Identity[Float]) : AbstractModule[Activity, Activity, Float] = { @@ -333,17 +393,20 @@ class DnnGraph( // init forward primitives override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) : (Array[MemoryData], Array[MemoryData]) = { + skipInitFwdPrimitives() var lastOutputFormats = inputs var firstRealInputFormats: Array[MemoryData] = null for (i <- 0 until forwardExecution.length) { - val m = forwardExecution(i) - lastOutputFormats = findInputFormats(m, inputs) - val realInputAndOutputFormats = - m.element.asInstanceOf[MklDnnModule].initFwdPrimitives(lastOutputFormats, phase) - lastOutputFormats.zip(realInputAndOutputFormats._1).foreach { - case (o, i) => reorderManager.register(o, i) + if (!skipPrimitiveId(i)) { + val m = forwardExecution(i) + lastOutputFormats = findInputFormats(m, inputs) + val realInputAndOutputFormats = + m.element.asInstanceOf[MklDnnModule].initFwdPrimitives(lastOutputFormats, phase) + lastOutputFormats.zip(realInputAndOutputFormats._1).foreach { + case (o, i) => reorderManager.register(o, i) + } + if (i == 0) firstRealInputFormats = realInputAndOutputFormats._1 } - if (i == 0) firstRealInputFormats = realInputAndOutputFormats._1 } _inputFormats = firstRealInputFormats _outputFormats = lastOutputFormats diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala index 237b1ed0057..a138e56dad1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRConverter.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.utils.intermediate import com.intel.analytics.bigdl.nn.Graph import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, InputWrapper, Output} +import com.intel.analytics.bigdl.nn.mkldnn._ import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.{Module, utils} @@ -91,8 +91,11 @@ private[bigdl] class IRConverter[T: ClassTag](IRgraph: IRGraph[T])(implicit ev: // add output node for graph val realOutputs = outputs.zipWithIndex.map { case (model: Node[Module[Float]], index: Int) => - val node = new Node[Module[Float]](Output(IRgraph.outputFormats(index))) - model.add(node) + val node = if (model.element.isInstanceOf[BlasWrapper]) { + model + } else { + model.add(new Node[Module[Float]](Output(IRgraph.outputFormats(index)))) + } node } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala index db3769cf6ef..99e2a72bd64 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala @@ -22,16 +22,34 @@ import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase -import com.intel.analytics.bigdl.nn.{Module => _, _} +import com.intel.analytics.bigdl.nn.{Graph, Module => _, _} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils._ import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.models.resnet - +import com.intel.analytics.bigdl.utils.intermediate._ +import com.intel.analytics.bigdl.numeric.NumericFloat class DnnGraphSpec extends FlatSpec with Matchers { + def model(size: Array[Int]) : Module[Float] = { + val input = mkldnn.Input(size, Memory.Format.nchw).inputs() + val conv1 = mkldnn.SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5").inputs(input) + val tanh1 = mkldnn.BlasWrapper(Tanh[Float]()).inputs(conv1) + val pool1 = mkldnn.MaxPooling(2, 2, 2, 2).inputs(tanh1) + val conv2 = BlasWrapper( + nn.SpatialConvolution[Float](6, 12, 5, 5)).setName("conv2_5x5").inputs(pool1) + val tanh2 = mkldnn.BlasWrapper(Tanh[Float]()).inputs(conv2) + val pool2 = mkldnn.MaxPooling(2, 2, 2, 2).inputs(tanh2) + val fc1 = mkldnn.Linear(12 * 4 * 4, 100).setName("fc1").inputs(pool2) + val tanh3 = mkldnn.BlasWrapper(Tanh[Float]()).inputs(fc1) + val fc2 = mkldnn.BlasWrapper(nn.Linear[Float](100, 10)).setName("fc2").inputs(tanh3) + val output = mkldnn.BlasWrapper(LogSoftMax[Float]()).inputs(fc2) + + DnnGraph(Seq(input), Seq(output)) + } + "Dnn vgg16 graph model" should "be correct" in { val batchSize = 2 val seed = 1 @@ -155,4 +173,17 @@ class DnnGraphSpec extends FlatSpec with Matchers { t1 should be(t2) } } + + "DnnGraph skip primitives" should "be correct" in { + Engine.setEngineType(MklDnn) + val batchSize = 2 + val inputShape = Array(batchSize, 1, 28, 28) + val input = Tensor[Float](inputShape).rand() + + val dnn = model(inputShape).asInstanceOf[DnnGraph] + dnn.evaluate() + dnn.compile(Phase.InferencePhase) + + dnn.forward(input).toTensor[Float] + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala index 2c3f8f246f9..3757e0d0656 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala @@ -23,18 +23,25 @@ import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.resnet.ResNet import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} import com.intel.analytics.bigdl.models.vgg.Vgg_16 -import com.intel.analytics.bigdl.nn.StaticGraph +import com.intel.analytics.bigdl.nn.{Module, StaticGraph} import com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.mkldnn.Equivalent +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, Equivalent} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, RandomGenerator, T} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils._ import scala.util.Random class BlasToDnnSpec extends BigDLSpecHelper { - "vgg16 blas to dnn" should "work properly" in { + override def doBefore(): Unit = { System.setProperty("bigdl.engineType", "mkldnn") + } + + override def doAfter(): Unit = { + System.setProperty("bigdl.engineType", "mklblas") + } + "vgg16 blas to dnn" should "work properly" in { val batchSize = 2 val classNum = 1000 RandomGenerator.RNG.setSeed(1000) @@ -59,7 +66,6 @@ class BlasToDnnSpec extends BigDLSpecHelper { } "lenet5 blas to dnn" should "work properly" in { - System.setProperty("bigdl.engineType", "mkldnn") val batchSize = 2 val seed = 1 val inputFormat = Memory.Format.nchw @@ -82,4 +88,34 @@ class BlasToDnnSpec extends BigDLSpecHelper { Equivalent.nearequals(outDnn, outBlas, 1e-6) should be(true) Equivalent.nearequals(gradInputDnn, gradInputBlas, 1e-6) should be(true) } + + "resnet50 blas to dnn" should "work properly" in { + val batchSize = 2 + val classNum = 1000 + RandomGenerator.RNG.setSeed(1000) + val input = Tensor[Float](Array(batchSize, 3, 224, 224)).apply1(_ => + RandomGenerator.RNG.uniform(0.1, 1.0).toFloat) + var gradOutput = Tensor[Float](batchSize, classNum).apply1(_ => + RandomGenerator.RNG.uniform(1.0, 1000.0).toFloat) + + val blas = ResNet.graph(classNum, + T("shortcutType" -> ShortcutType.B, "depth" -> 50, + "optnet" -> false, "dataset" -> DatasetType.ImageNet)).asInstanceOf[StaticGraph[Float]] + val irBlas = blas.toIRgraph() + + irBlas.build() + val outBlas = blas.forward(input).toTensor[Float] + val outDnn = irBlas.forward(input).toTensor[Float] + + + gradOutput.resizeAs(outBlas).apply1(_ => + RandomGenerator.RNG.uniform(1.0, 1000.0).toFloat) + + val gradInputBlas = blas.backward(input, gradOutput).toTensor[Float] + + val gradInputDnn = irBlas.backward(input, gradOutput).toTensor[Float] + val gradInputTensor = Tensor[Float]().resize(gradInputDnn.size()).copy(gradInputDnn) + + Equivalent.nearequals(outDnn, outBlas, 1e-6) should be(true) + } } From 3622a4c7e17a1b65e4f100b5b97759966e54e3ed Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Fri, 8 Mar 2019 10:45:55 +0800 Subject: [PATCH 0866/1065] include edge case to cover all the data types (#2742) --- .../scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala | 1 + 1 file changed, 1 insertion(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala index 30428633e26..58803edd348 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/tf/ImageOps.scala @@ -182,6 +182,7 @@ private[bigdl] class DecodeRaw[T: ClassTag](val outType: DataType, case DataType.DT_INT64 => decodeInt64(input, buffer.asLongBuffer().capacity()) case DataType.DT_FLOAT => decodeFloat(input, buffer.asFloatBuffer().capacity()) case DataType.DT_DOUBLE => decodeDouble(input, buffer.asDoubleBuffer().capacity()) + case _ => throw new IllegalArgumentException(s"$outType are not supported") } output } From 6f1518029ae247d0f84e367c3549813efcebbd17 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 8 Mar 2019 13:40:42 +0800 Subject: [PATCH 0867/1065] layer auto fusion for dnn graph (#2746) * add auto fusion in dnn graph --- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 19 ++ .../bigdl/dllib/nn/mkldnn/Fusion.scala | 177 ++++++++++++++++++ .../bigdl/dllib/nn/mkldnn/Perf.scala | 4 +- .../nn/mkldnn/SpatialBatchNormalization.scala | 4 +- .../dllib/nn/mkldnn/SpatialConvolution.scala | 48 ++++- .../bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala | 81 +++++++- 6 files changed, 318 insertions(+), 15 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 01a25fd30c3..01217bfaaf2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -390,10 +390,29 @@ class DnnGraph( } } + /** + * fuse some layers when doing inference + * first fuse layers in sequence, mainly relu with bn/conv, conv with bn. + * after that, fuse sum operation. + */ + private def fusion(): Unit = { + if (!this.train) { + for (j <- 0 to 1) { + var i = forwardExecution.length - 1 + while (i >= 0) { + if (j == 0) Fusion.fuseModule(forwardExecution(i)) + if (j == 1) Fusion.fuseCAdd(forwardExecution(i)) + i -= 1 + } + } + } + } + // init forward primitives override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) : (Array[MemoryData], Array[MemoryData]) = { skipInitFwdPrimitives() + fusion() var lastOutputFormats = inputs var firstRealInputFormats: Array[MemoryData] = null for (i <- 0 until forwardExecution.length) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala new file mode 100644 index 00000000000..2505eab6943 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -0,0 +1,177 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Node + +/** + * Add fusion operation for dnn graph node, there are three cases about fusion: + * case 1: fuse relu with conv(SpatialConvolution) or bn(SpatialBatchNormalization) + * case 2: fuse conv with bn + * case 3: sum conv output with another layer output + * If you want to use fusion for inference, please set property "bigdl.mkldnn.fusion" to true + */ +private[mkldnn] object Fusion { + + private val fuse = System.getProperty("bigdl.mkldnn.fusion", "false").toBoolean + + def fuseModule(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { + if (!fuse) return; + node.element match { + case relu: ReLU => fusionRelu(node) + case bn: SpatialBatchNormalization => fusionBN(node) + case _ => + } + } + + def fuseCAdd(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { + if (!fuse) return; + node.element match { + case cadd: CAddTable => fusionCAddTable(node) + case _ => + } + } + + /** + * fuse conv(without relu or bn fusion) with bn + * if bn has fused with relu, then fuse relu and bn with conv + * @param node + */ + private def fusionBN(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { + val bn = node.element.asInstanceOf[SpatialBatchNormalization] + node.prevNodes.foreach(n => { + n.element match { + case conv : SpatialConvolution => + // reminder: may be conv can fuse with two bn + if (!conv.relu && !conv.batchNorm) { + if (bn.relu) conv.setReLU(true) + fusionConvBn(conv, bn) + node.element = Identity[Float]().asInstanceOf[AbstractModule[Activity, Activity, Float]] + } + case _ => null + }}) + } + + /** + * fuse relu with conv or bn + * @param node + */ + private def fusionRelu(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { + node.prevNodes.foreach(n => { + n.element match { + case conv: SpatialConvolution => + if (!conv.relu) { + conv.setReLU(true) + node.element = Identity[Float]().asInstanceOf[AbstractModule[Activity, Activity, Float]] + } + case bn: SpatialBatchNormalization => + if (!bn.relu) { + bn.setReLU(true) + node.element = Identity[Float]().asInstanceOf[AbstractModule[Activity, Activity, Float]] + } + case _ => null + }}) + } + + private def findPrevious(node: Node[AbstractModule[Activity, Activity, Float]]) + : Node[AbstractModule[Activity, Activity, Float]] = { + if (node.element.isInstanceOf[Identity] && node.prevNodes.length == 1) { + findPrevious(node.prevNodes(0)) + } else node + } + + /** + * If previous layers number of CAddTable is two, and one of it is conv layer. + * then fuse output of the other layer in conv layer. + * @param node + */ + private def fusionCAddTable(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { + if (node.element.isInstanceOf[CAddTable] && node.prevNodes.length == 2) { + val previousNodes = node.prevNodes.toArray + val node1 = findPrevious(previousNodes(0)) + val node2 = findPrevious(previousNodes(1)) + + var conv : Node[Module[Float]] = null + var otherNumber: Int = 0 + + if (node1.element.isInstanceOf[SpatialConvolution]) { + if (requirements(node1)) conv = node1 + otherNumber = 1 + } else if (node2.element.isInstanceOf[SpatialConvolution]) { + if (requirements(node2)) conv = node2 + otherNumber = 0 + } + // meet fuse requirements + if (conv != null) { + node.element = conv.element + val element = node.element.asInstanceOf[SpatialConvolution] + element.setSumOp(previousNodes(otherNumber - 1).element, otherNumber) + conv.element = Identity[Float]().asInstanceOf[AbstractModule[Activity, Activity, Float]] + + val nexts = node.nextNodes(0) + if (nexts.element.isInstanceOf[ReLU] && !element.relu) { + node.element.asInstanceOf[SpatialConvolution].setReLU(true) + nexts.element = new Identity() + } + } + } + } + + private def requirements(node: Node[AbstractModule[Activity, Activity, Float]]): Boolean = { + val conv = node.element.asInstanceOf[SpatialConvolution] + if (conv.sum) false else true + } + + private def fusionConvBn(conv: SpatialConvolution, + bn: SpatialBatchNormalization): Unit = { + conv.setBatchNorm(true) + val originVar = Tensor[Float].resize(bn.runningVariance.size()).copy(bn.runningVariance.dense) + val originMean = Tensor[Float].resize(bn.runningMean.size()).copy(bn.runningMean.dense) + + val convWeight = Tensor[Float].resize(conv.weight.size()).copy(conv.weight.dense) + val convBias = Tensor[Float].resize(conv.bias.size()).copy(conv.bias.dense) + + val bnWeight = Tensor[Float].resizeAs(bn.weightAndBias.dense).copy(bn.weightAndBias.dense) + + (0 until bn.nOutput).foreach { j => + val variance = originVar.storage().array()(j + originVar.storageOffset() - 1) + val base = Math.sqrt(variance.asInstanceOf[Float] + bn.eps).toFloat + require(base != 0.0, s"the eps of ${bn.getName()} should be more than 0") + + val alpha = bnWeight.storage().array()(bnWeight.storageOffset() - 1 + j) + val beta = bnWeight.storage().array()(bnWeight.storageOffset() - 1 + bn.nOutput + j) + + val weight = if (conv.nGroup == 1) { + convWeight.select(1, j + 1) + } else { + convWeight.select(2, j + 1) + } + weight.div(base) + weight.mul(alpha) + + val bias = convBias.storage().array()(j) + val mean = originMean.storage().array()(j) + convBias.storage().array()(j) = alpha / base * bias + beta - (alpha * mean) / base + } + + conv.weight.copy(convWeight) + conv.bias.copy(convBias) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala index 67c25dc527b..76b6dfa1f0b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Perf.scala @@ -88,21 +88,21 @@ object Perf { Engine.dnnComputing.invokeAndWait2(Array(1).map(_ => () => { if (training) { + model.training() if (model.isInstanceOf[MklDnnContainer]) { model.asInstanceOf[MklDnnContainer] .compile(TrainingPhase, Array(HeapData(inputShape, inputFormat))) } else if (model.isInstanceOf[DnnGraph]) { model.asInstanceOf[DnnGraph].compile(TrainingPhase) } - model.training() } else { + model.evaluate() if (model.isInstanceOf[MklDnnContainer]) { model.asInstanceOf[MklDnnContainer] .compile(InferencePhase, Array(HeapData(inputShape, inputFormat))) } else if (model.isInstanceOf[DnnGraph]) { model.asInstanceOf[DnnGraph].compile(InferencePhase) } - model.evaluate() } })) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index d014a8dc7c9..0603a98b63a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -58,8 +58,8 @@ class SpatialBatchNormalization( val weightAndBias = new TensorMMap(Array(nOutput * 2)) val gradWeightAndBias = new TensorMMap(Array(nOutput * 2)) - var scaleFactor: Float = 0.0f - var biasFactor: Float = 0.0f + var scaleFactor: Float = 1.0f + var biasFactor: Float = 1.0f { val wInit = Ones // RandomUniform(0, 1) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 3642e45c179..8dc61609ae8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -85,6 +85,9 @@ class SpatialConvolution( private var _relu = false private var _sum = false + private var _batchNorm = false + private var _dim = 1 + private var _sumInput = false def relu: Boolean = _relu def setReLU(value: Boolean = true): this.type = { @@ -92,6 +95,12 @@ class SpatialConvolution( this } + def batchNorm: Boolean = _batchNorm + def setBatchNorm(value: Boolean = true): this.type = { + _batchNorm = value + this + } + def sum: Boolean = _sum def setSum(value: Boolean = true): this.type = { _sum = value @@ -99,8 +108,10 @@ class SpatialConvolution( } var sumOp: MklDnnLayer = null - def setSumOp(conv: Module[Float]): this.type = { + def setSumOp(conv: Module[Float], number: Int = 1): this.type = { sumOp = conv.asInstanceOf[MklDnnLayer] + _dim = number + _sum = true this } @@ -151,8 +162,14 @@ class SpatialConvolution( override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { reorderManager.setRuntime(runtime) - val inputHeight = inputs(0).shape(2) // TODO only supports 4-D and nchw - val inputWidth = inputs(0).shape(3) + if (_sum && inputs.length > 1) { + _sumInput = true + require(inputs.length == 2, + s"inputs length should be 2 when having sum operation, but get ${inputs.length}") + } + val inputMemoryData = inputs(_dim - 1) + val inputHeight = inputMemoryData.shape(2) // TODO only supports 4-D and nchw + val inputWidth = inputMemoryData.shape(3) val sizes = if (padW == -1 && padH == -1) { Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW) @@ -170,8 +187,8 @@ class SpatialConvolution( paddingTL = Array(padTop, padLeft) paddingBR = Array(padBottom, padRight) - val inputShape = inputs(0).shape - val outputShape = Array(inputs(0).shape(0), nOutputPlane, outputHeight, outputWidth) + val inputShape = inputMemoryData.shape + val outputShape = Array(inputMemoryData.shape(0), nOutputPlane, outputHeight, outputWidth) val src = NativeData(inputShape, Memory.Format.any) val wei = NativeData(weightShape, Memory.Format.any) @@ -235,25 +252,31 @@ class SpatialConvolution( updateOutputPrimitives = Array(primitive) output = initTensor(realDst) - _inputFormats = Array(realSrc) + _inputFormats = if (_sumInput) Array(realSrc, realSrc) else Array(realSrc) _outputFormats = Array(realDst) (_inputFormats, _outputFormats) } override def updateOutput(input: Activity): Activity = { + val inputTensor = if (input.isTensor) { + input.toTensor[Float] + } else { + output = input.toTable.get[Tensor[Float]](3 - _dim).get + input.toTable.get[Tensor[Float]](_dim).get + } if (updateOutputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() - buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(inputTensor.asInstanceOf[Tensor[Float]]) buffer.append(weight.native) buffer.append(bias.native) - if (sum) { + if (sum && input.isTensor) { output = sumOp.output } buffer.append(output.asInstanceOf[Tensor[Float]]) updateOutputTensors = buffer.toArray } - updateWithNewTensor(updateOutputTensors, 0, input) + updateWithNewTensor(updateOutputTensors, 0, inputTensor) if (isTraining()) { weight.sync() @@ -391,8 +414,13 @@ class SpatialConvolution( override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { // if needed, reorder manager will reorder input to mkldnn wants + val inputTensor = if (input.isTensor) { + input.toTensor[Float] + } else { + input.toTable.get[Tensor[Float]](_dim).get + } inputForAcc = reorderManager.infer(Array(inputFormats()(0)), - Array(inputForAccMemoryData), input).asInstanceOf[DnnTensor[Float]] + Array(inputForAccMemoryData), inputTensor).asInstanceOf[DnnTensor[Float]] if (updateGradWTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala index 99e2a72bd64..0a21bcc1f03 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.nn.{Graph, Module => _, _} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ @@ -186,4 +186,83 @@ class DnnGraphSpec extends FlatSpec with Matchers { dnn.forward(input).toTensor[Float] } + + "Dnn graph fusion operation for resnet50" should "be correct" in { + System.setProperty("bigdl.mkldnn.fusion.convbn", "true") + System.setProperty("bigdl.mkldnn.fusion.bnrelu", "true") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + System.setProperty("bigdl.mkldnn.fusion.convsum", "true") + System.setProperty("bigdl.mkldnn.fusion", "true") + + val batchSize = 2 + val seed = 1 + val inputFormat = Memory.Format.nchw + val inputShape = Array(batchSize, 3, 224, 224) + + RNG.setSeed(seed) + val seqModel = mkldnn.ResNet(batchSize, 1000, T("depth" -> 50, + "dataSet" -> ResNet.DatasetType.ImageNet)) + RNG.setSeed(seed) + val graphFuse = mkldnn.ResNet.graph(batchSize, 1000, T("depth" -> 50, + "dataSet" -> ResNet.DatasetType.ImageNet)) + + seqModel.getExtraParameter().map(_.fill(1.0f)) + graphFuse.getExtraParameter().map(_.fill(1.0f)) + + seqModel.evaluate() + seqModel.asInstanceOf[MklDnnContainer].compile( + Phase.InferencePhase, Array(HeapData(inputShape, inputFormat))) + graphFuse.evaluate() + graphFuse.asInstanceOf[DnnGraph].compile(Phase.InferencePhase) + + RNG.setSeed(100) + val input = Tensor[Float](inputShape).rand() + + val output = seqModel.forward(input).toTensor[Float] + val outputFuse = graphFuse.forward(input).toTensor[Float] + + output.almostEqual(outputFuse, 1e-4) should be(true) + + System.clearProperty("bigdl.mkldnn.fusion.convbn") + System.clearProperty("bigdl.mkldnn.fusion.bnrelu") + System.clearProperty("bigdl.mkldnn.fusion.convrelu") + System.clearProperty("bigdl.mkldnn.fusion.convsum") + System.clearProperty("bigdl.mkldnn.fusion") + } + + "Dnn graph fusion operation for vgg16" should "be correct" in { + System.setProperty("bigdl.mkldnn.fusion.convbn", "true") + System.setProperty("bigdl.mkldnn.fusion.bnrelu", "true") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + System.setProperty("bigdl.mkldnn.fusion.convsum", "true") + System.setProperty("bigdl.mkldnn.fusion", "true") + + val batchSize = 2 + val seed = 1 + val inputFormat = Memory.Format.nchw + val inputShape = Array(batchSize, 3, 224, 224) + + RNG.setSeed(seed) + val seqModel = models.Vgg_16(batchSize, 1000, false) + RNG.setSeed(seed) + val graphFuse = models.Vgg_16.graph(batchSize, 1000, false) + + seqModel.evaluate() + graphFuse.evaluate() + graphFuse.asInstanceOf[DnnGraph].compile(Phase.InferencePhase) + seqModel.compile(Phase.InferencePhase) + + val input = Tensor[Float](inputShape).rand() + + val output = Tools.dense(graphFuse.forward(input)).toTensor[Float] + val outputDnn = Tools.dense(seqModel.forward(input)).toTensor[Float] + + output.almostEqual(outputDnn, 1e-4) should be(true) + + System.clearProperty("bigdl.mkldnn.fusion.convbn") + System.clearProperty("bigdl.mkldnn.fusion.bnrelu") + System.clearProperty("bigdl.mkldnn.fusion.convrelu") + System.clearProperty("bigdl.mkldnn.fusion.convsum") + System.clearProperty("bigdl.mkldnn.fusion") + } } From 92c3617fd1170ecabe18b2d478ccf88fe9c3164d Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 12 Mar 2019 13:21:26 +0800 Subject: [PATCH 0868/1065] refactor predict for dnn model (#2737) * refactor predict for dnn model --- .../dllib/models/resnet/TestImageNet.scala | 61 +++++ .../dllib/models/utils/ModelBroadcast.scala | 9 + .../dllib/nn/abstractnn/AbstractModule.scala | 16 +- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 14 +- .../bigdl/dllib/optim/AbstractOptimizer.scala | 3 +- .../bigdl/dllib/optim/DistriOptimizer.scala | 5 +- .../bigdl/dllib/optim/Evaluator.scala | 45 +++- .../bigdl/dllib/optim/LocalPredictor.scala | 15 +- .../bigdl/dllib/optim/Predictor.scala | 36 +-- .../utils/intermediate/ConversionUtils.scala | 63 ++++++ .../dllib/utils/intermediate/IRGraph.scala | 36 ++- .../dllib/utils/intermediate/IRToDnn.scala | 6 +- .../bigdl/dllib/optim/EvaluatorSpec.scala | 29 ++- .../utils/intermediate/BlasToDnnSpec.scala | 27 ++- .../utils/intermediate/DnnPredictorSpec.scala | 212 ++++++++++++++++++ 15 files changed, 526 insertions(+), 51 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TestImageNet.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TestImageNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TestImageNet.scala new file mode 100644 index 00000000000..81e921336cf --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TestImageNet.scala @@ -0,0 +1,61 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.models.resnet + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.image.CropCenter +import com.intel.analytics.bigdl.models.resnet.ResNet.DatasetType +import com.intel.analytics.bigdl.nn.{Module, StaticGraph} +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._ +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, MTImageFeatureToBatch, MatToTensor, PixelBytesToMat} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{ChannelScaledNormalizer, RandomCropper, RandomResize} +import com.intel.analytics.bigdl.utils._ +import org.apache.log4j.{Level, Logger} +import org.apache.spark.SparkContext + +/** + * This example is to evaluate trained resnet50 with imagenet data and get top1 and top5 accuracy + */ +object TestImageNet { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + val logger = Logger.getLogger(getClass) + + import Utils._ + + def main(args: Array[String]): Unit = { + testParser.parse(args, new TestParams()).map(param => { + val conf = Engine.createSparkConf().setAppName("Test model on ImageNet2012") + .set("spark.rpc.message.maxSize", "200") + val sc = new SparkContext(conf) + Engine.init + + val model = Module.loadModule[Float](param.model) + val evaluationSet = ImageNetDataSet.valDataSet(param.folder, + sc, 224, param.batchSize).toDistributed().data(train = false) + + val result = model.evaluate(evaluationSet, + Array(new Top1Accuracy[Float], new Top5Accuracy[Float])) + result.foreach(r => println(s"${r._2} is ${r._1}")) + + sc.stop() + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index bb2d2a347c1..327334e8760 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Container import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.Util._ import org.apache.commons.lang3.SerializationUtils import org.apache.spark.SparkContext @@ -83,7 +84,13 @@ private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = private var broadcastModel: Broadcast[ModelInfo[T]] = _ private var broadcastConsts: Broadcast[Map[String, Tensor[_]]] = _ private var broadcastParameters: Broadcast[Array[Tensor[T]]] = _ + private var nodeNumber : Int = _ + private var coreNumber : Int = _ + private def setNodeAndCore(): Unit = { + nodeNumber = Engine.nodeNumber() + coreNumber = Engine.coreNumber() + } /** * broadcast the model * first get and clear Const values from the model @@ -115,6 +122,7 @@ private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = putWeightBias(SerializationUtils.clone(weightsBias), model) initGradWeightBias(weightsBias, model) } + setNodeAndCore() this } @@ -127,6 +135,7 @@ private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = * @return model */ override def value(initGradient: Boolean = false, shareWeight: Boolean = true): Module[T] = { + Engine.setNodeAndCore(nodeNumber, coreNumber) CachedModels.deleteAll(uuid) if (applyProtoBuffer) { val localModel = broadcastModel.value.model.clone(false) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 3dc2e4832b0..70d6e44ea22 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn.abstractnn import java.nio.ByteOrder import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{LocalDataSet, MiniBatch, PaddingParam, Sample} +import com.intel.analytics.bigdl.dataset._ import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.quantized.Quantization import com.intel.analytics.bigdl.nn.{Module, _} @@ -859,6 +859,20 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, Evaluator(this).test(dataset, vMethods.map(v => v), batchSize) } + + /** + * use ValidationMethod to evaluate module on the given rdd dataset + * @param dataset + * @param vMethods + * @return + */ + final def evaluate( + dataset: RDD[MiniBatch[T]], + vMethods: Array[_ <:ValidationMethod[T]] + ): Array[(ValidationResult, ValidationMethod[T])] = { + Evaluator(this).testMiniBatch(dataset, vMethods.map(v => v)) + } + /** * use ValidationMethod to evaluate module on the given ImageFrame * @param imageFrame ImageFrame for valudation diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 01217bfaaf2..89893336682 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -87,8 +87,8 @@ class DnnGraph( node.element.forward(nodeInput) i += 1 } - output = dummyOutput.element.output - getRealOutput(input, output) + output = getRealOutput(input, dummyOutput.element.output) + output } override def backward(input: Activity, gradOutput: Activity): Activity = { @@ -112,8 +112,8 @@ class DnnGraph( } i += 1 } - gradInput = fetchModelGradInput() - getRealOutput(input, gradInput) + gradInput = getRealOutput(input, fetchModelGradInput()) + gradInput } override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { @@ -409,7 +409,7 @@ class DnnGraph( } // init forward primitives - override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) + override private[bigdl] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) : (Array[MemoryData], Array[MemoryData]) = { skipInitFwdPrimitives() fusion() @@ -433,7 +433,7 @@ class DnnGraph( } // init updateGradInput primitives - override private[mkldnn] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) + override private[bigdl] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) : (Array[MemoryData], Array[MemoryData]) = { var lastGradInputFormats = grads var firstRealGradOutputFormats: Array[MemoryData] = null @@ -453,7 +453,7 @@ class DnnGraph( } // init acc primitives - override private[mkldnn] def initGradWPrimitives(grads: Array[MemoryData], phase: Phase) + override private[bigdl] def initGradWPrimitives(grads: Array[MemoryData], phase: Phase) : Array[MemoryData] = { var lastGradInputFormats = grads var firstRealGradOutputFormats: Array[MemoryData] = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala index fd13980dd02..fa28f463093 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.optim.Optimizer.{saveModel, saveOptimMethod} import com.intel.analytics.bigdl.parameters.AllReduceParameter import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.intermediate.IRGraph import com.intel.analytics.bigdl.utils.{Engine, MklBlas, MklDnn, Table} import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.spark.rdd.{RDD, ZippedPartitionsWithLocalityRDD} @@ -140,7 +141,7 @@ abstract class AbstractOptimizer { val miniBatch = batch.slice(offset, length) val input = miniBatch.getInput() val target = miniBatch.getTarget() - if (Engine.getEngineType() == MklDnn) { + if (Engine.getEngineType() == MklDnn && !workingModels(b).isInstanceOf[IRGraph[T]]) { Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { workingModels(b).forward(input) })) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 0c342fb4a02..31b8113387d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -33,6 +33,7 @@ import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.utils.intermediate.IRGraph import org.apache.commons.lang.exception.ExceptionUtils import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.Logger @@ -256,7 +257,7 @@ object DistriOptimizer extends AbstractOptimizer { val input = miniBatchBuffer(i).getInput() val target = miniBatchBuffer(i).getTarget() - if (Engine.getEngineType() == MklBlas) { + if (Engine.getEngineType() == MklBlas || localModel.isInstanceOf[IRGraph[T]]) { val output = localModel.forward(input) lossArray(i) = ev.toType[Double](localCriterion.forward(output, target)) val errors = localCriterion.backward(output, target) @@ -580,7 +581,7 @@ object DistriOptimizer extends AbstractOptimizer { Engine.setNodeAndCore(nExecutor, executorCores) val cached = (0 until _subModelNumber).map { _ => val localModel = modelBroadcast.value(true) - if (Engine.getEngineType() == MklDnn) { + if (Engine.getEngineType() == MklDnn && !localModel.isInstanceOf[IRGraph[T]]) { Engine.dnnComputing.invokeAndWait2((0 until _subModelNumber).map(i => () => { localModel match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala index b5f8f4e5620..8e30f3f2a68 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala @@ -17,9 +17,12 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.dataset.{Sample, SampleToMiniBatch} +import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, SampleToMiniBatch} import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{Engine, MklDnn} +import com.intel.analytics.bigdl.utils.intermediate.ConversionUtils +import org.apache.spark.rdd import org.apache.spark.rdd.RDD import scala.reflect.ClassTag @@ -49,14 +52,16 @@ class Evaluator[T: ClassTag] private[optim](model: Module[T])(implicit ev: Tenso vMethods: Array[ValidationMethod[T]], batchSize: Option[Int] = None): Array[(ValidationResult, ValidationMethod[T])] = { - val modelBroad = ModelBroadcast[T]().broadcast(dataset.sparkContext, model.evaluate()) + val modelBroad = ModelBroadcast[T]().broadcast(dataset.sparkContext, + ConversionUtils.convert(model.evaluate())) val partitionNum = dataset.partitions.length val totalBatch = batchSize.getOrElse(batchPerPartition * partitionNum) - val otherBroad = dataset.sparkContext.broadcast(vMethods, SampleToMiniBatch( - batchSize = totalBatch, partitionNum = Some(partitionNum))) + val rdd = ConversionUtils.coalesce(dataset) + val otherBroad = rdd.sparkContext.broadcast(vMethods, SampleToMiniBatch( + batchSize = totalBatch, partitionNum = Some(rdd.partitions.length))) - dataset.mapPartitions(partition => { + rdd.mapPartitions(partition => { val localModel = modelBroad.value() val localMethod = otherBroad.value._1.map(_.clone()) val localTransformer = otherBroad.value._2.cloneTransformer() @@ -71,4 +76,34 @@ class Evaluator[T: ClassTag] private[optim](model: Module[T])(implicit ev: Tenso left.zip(right).map { case (l, r) => l + r } }).zip(vMethods) } + + /** + * Apply ValidationMethod to the model and rdd dataset. + * @param vMethods + * @return + */ + private[bigdl] def testMiniBatch(dataset: RDD[MiniBatch[T]], + vMethods: Array[ValidationMethod[T]] + ): Array[(ValidationResult, ValidationMethod[T])] = { + + val rdd = ConversionUtils.coalesce(dataset) + val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, + ConversionUtils.convert(model.evaluate())) + val otherBroad = rdd.sparkContext.broadcast(vMethods) + + + rdd.mapPartitions(miniBatch => { + val localModel = modelBroad.value() + val localMethod = otherBroad.value + miniBatch.map(batch => { + val output = localModel.forward(batch.getInput()) + localMethod.map(validation => { + validation(output, batch.getTarget()) + }) + }) + }).reduce((left, right) => { + left.zip(right).map { case (l, r) => l + r } + }).zip(vMethods) + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala index 7affec852fa..24e5d10f276 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalPredictor.scala @@ -24,6 +24,7 @@ import com.intel.analytics.bigdl.nn.quantized.QuantizedModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, LocalImageFrame} import com.intel.analytics.bigdl.utils.Util._ +import com.intel.analytics.bigdl.utils.intermediate.ConversionUtils import com.intel.analytics.bigdl.utils.{Engine, MklBlas, MklDnn, Util} import org.apache.log4j.Logger @@ -59,8 +60,10 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], case MklDnn => 1 } + private val batchPerModel = batchPerCore * coreNumber / subModelNumber + // we should clone a new model which has no impact to origin model - private val clonedModel = model.cloneModule() + private val clonedModel = ConversionUtils.convert(model.cloneModule()) private val workingModels = { @@ -77,7 +80,7 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], val workingToBatch = { val toBatch = SampleToMiniBatch[T]( - batchSize = batchPerCore * subModelNumber, + batchSize = batchPerModel * subModelNumber, partitionNum = Some(subModelNumber), featurePaddingParam = featurePaddingParam) (1 to subModelNumber).map(_ => { @@ -132,10 +135,10 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], } def predict(dataSet: Array[Sample[T]]): Array[Activity] = { - val dataIter = dataSet.grouped(batchPerCore * subModelNumber) + val dataIter = dataSet.grouped(batchPerModel * subModelNumber) dataIter.map(batch => { - val groupedSamples = batch.grouped(batchPerCore).toArray + val groupedSamples = batch.grouped(batchPerModel).toArray Engine.default.invokeAndWait( groupedSamples.indices.map(b => () => { @@ -162,10 +165,10 @@ class LocalPredictor[T: ClassTag] private[optim](model: Module[T], shareBuffer: Boolean = false, predictKey: String = ImageFeature.predict): LocalImageFrame = { - val dataIter = imageFrame.array.grouped(batchPerCore * subModelNumber) + val dataIter = imageFrame.array.grouped(batchPerModel * subModelNumber) val result = dataIter.map(batch => { - val groupedImages = batch.grouped(batchPerCore).toArray + val groupedImages = batch.grouped(batchPerModel).toArray Engine.default.invokeAndWait( groupedImages.indices.map(b => () => { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index a8a3f8a8e3d..61ad801c45b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -19,11 +19,15 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{MiniBatch, PaddingParam, Sample, SampleToMiniBatch, Transformer, DataSet => _} import com.intel.analytics.bigdl.models.utils.ModelBroadcast +import com.intel.analytics.bigdl.nn.{Graph, StaticGraph} import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame} -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.intermediate.{ConversionUtils, IRGraph} import org.apache.spark.rdd.RDD import scala.reflect.ClassTag @@ -120,15 +124,20 @@ object Predictor { model: Module[T], featurePaddingParam: Option[PaddingParam[T]])( implicit ev: TensorNumeric[T]): DistributedImageFrame = { - val localBatchPerPartition = batchPerPartition - val rdd = imageFrame.asInstanceOf[DistributedImageFrame].rdd - val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, model.evaluate()) - val partitionNum = rdd.partitions.length + val rdd = ConversionUtils.coalesce(imageFrame.asInstanceOf[DistributedImageFrame].rdd) + val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, + ConversionUtils.convert(model.evaluate())) + val totalBatch = imageFrame.rdd.partitions.length * batchPerPartition + + val realPartitionLength = rdd.partitions.length val toBatchBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( - batchSize = partitionNum * batchPerPartition, - partitionNum = Some(partitionNum), + batchSize = totalBatch, + partitionNum = Some(realPartitionLength), featurePaddingParam = featurePaddingParam), shareBuffer) + + val localBatchPerPartition = totalBatch / realPartitionLength + val result = rdd.mapPartitions(partition => { val localModel = modelBroad.value() val localToBatch = toBatchBroad.value._1.cloneTransformer() @@ -145,7 +154,8 @@ object Predictor { def predict[T: ClassTag](dataSet: RDD[Sample[T]], batchSize: Int = -1, shareBuffer: Boolean = false, model: Module[T], batchPerPartition: Int, featurePaddingParam: Option[PaddingParam[T]])(implicit ev: TensorNumeric[T]): RDD[Activity] = { - val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext, model.evaluate()) + val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext, + ConversionUtils.convert(model.evaluate())) val partitionNum = dataSet.partitions.length val totalBatch = if (batchSize > 0) { require(batchSize % partitionNum == 0, s"Predictor.predict: total batch size $batchSize " + @@ -154,18 +164,18 @@ object Predictor { } else { batchPerPartition * partitionNum } - val otherBroad = dataSet.sparkContext.broadcast(SampleToMiniBatch( + val rdd = ConversionUtils.coalesce(dataSet) + val otherBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( batchSize = totalBatch, - partitionNum = Some(partitionNum), + partitionNum = Some(rdd.partitions.length), featurePaddingParam = featurePaddingParam)) - dataSet.mapPartitions { partition => + rdd.mapPartitions { partition => val localModel = modelBroad.value() val localTransformer = otherBroad.value.cloneTransformer() val miniBatch = localTransformer(partition) miniBatch.flatMap(batch => { val output = localModel.forward(batch.getInput) splitBatch(output, shareBuffer, batch.size()) - }) } } @@ -175,8 +185,6 @@ object Predictor { implicit ev: TensorNumeric[T]): RDD[Int] = { val result = Predictor.predict(dataSet, batchSize, true, model, batchPerPartition, featurePaddingParam) - val res = Predictor.predict(dataSet, batchSize, true, model, - batchPerPartition, featurePaddingParam).collect() result.mapPartitions { partition => partition.map(output => { val _output = output.toTensor[T] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala new file mode 100644 index 00000000000..c8973eb8200 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala @@ -0,0 +1,63 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.mkldnn.DnnGraph +import com.intel.analytics.bigdl.utils.{Engine, MklDnn, T} +import org.apache.spark.rdd.RDD +import com.intel.analytics.bigdl.nn.Graph +import com.intel.analytics.bigdl.nn.StaticGraph + +import scala.reflect.ClassTag + +private[bigdl] object ConversionUtils { + /** + * convert model to ir graph and build + * @param model + * @return + */ + def convert[T: ClassTag](model: Module[T]): Module[T] = { + if (model.isInstanceOf[IRGraph[T]]) { + val g = model.asInstanceOf[IRGraph[T]] + if (g.isBuild) g else g.build() + } else if (!model.isInstanceOf[DnnGraph] && Engine.getEngineType() == MklDnn) { + val m = if (!model.isInstanceOf[Graph[T]]) model.toGraph() else model + if (!m.isInstanceOf[StaticGraph[T]]) return model + val ir = m.asInstanceOf[StaticGraph[T]].toIRgraph().asInstanceOf[Module[T]] + if (model.isTraining()) ir.training() else ir.evaluate() + ir + } else { + model + } + } + + /** + * For dnn backend, it is recommended to run single model on each node. + * So when partition number of dataset is not equal to node number, + * there will be coalesce operation. + * @param dataset + * @tparam T + * @return + */ + def coalesce[T: ClassTag](dataset: RDD[T]): RDD[T] = { + if (dataset.partitions.length != Engine.nodeNumber() + && Engine.getEngineType() == MklDnn) { + dataset.coalesce(Engine.nodeNumber(), false) + } else dataset + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index 62c16547b56..c3b2e1d4e98 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -22,7 +22,8 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFo import com.intel.analytics.bigdl.nn.mkldnn._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{Engine, MklBlas, Node, T} +import com.intel.analytics.bigdl.utils._ + import scala.reflect.ClassTag /** @@ -55,12 +56,19 @@ private[bigdl] class IRGraph[T: ClassTag]( private var graph: Graph[T] = null + private[bigdl] def isBuild(): Boolean = graph != null + override def updateOutput(input: Activity): Activity = { if (graph == null) { throw new UnsupportedOperationException("forward not supported, Please build graph first") } - initPrimitives(input) - output = graph.updateOutput(input) + if (graph.isInstanceOf[DnnGraph]) { + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + initPrimitives(input) + graph.updateOutput(input) + })) + } else graph.updateOutput(input) + output = graph.output output } @@ -68,7 +76,12 @@ private[bigdl] class IRGraph[T: ClassTag]( if (graph == null) { throw new UnsupportedOperationException("backward not supported, Please build graph first") } - gradInput = graph.updateGradInput(input, gradOutput) + if (graph.isInstanceOf[DnnGraph]) { + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + graph.updateGradInput(input, gradOutput) + })) + } else graph.updateGradInput(input, gradOutput) + gradInput = graph.gradInput gradInput } @@ -76,7 +89,11 @@ private[bigdl] class IRGraph[T: ClassTag]( if (graph == null) { throw new UnsupportedOperationException("backward not supported, Please build graph first") } - graph.accGradParameters(input, gradOutput) + if (graph.isInstanceOf[DnnGraph]) { + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + graph.accGradParameters(input, gradOutput) + })) + } else graph.accGradParameters(input, gradOutput) } def build(): this.type = { @@ -88,6 +105,8 @@ private[bigdl] class IRGraph[T: ClassTag]( graph.parameters() } + override def getParametersTable(): Table = graph.getParametersTable() + override def training(): this.type = { train = true graph.training() @@ -137,11 +156,12 @@ private[bigdl] class IRGraph[T: ClassTag]( }) } val dnnGraph = graph.asInstanceOf[DnnGraph] + val phase = if (dnnGraph.isTraining()) Phase.TrainingPhase else Phase.InferencePhase dnnGraph.setRuntime(new MklDnnRuntime()) - dnnGraph.initFwdPrimitives(inputMemory) + dnnGraph.initFwdPrimitives(inputMemory, phase) if (dnnGraph.isTraining()) { - dnnGraph.initBwdPrimitives(dnnGraph.outputFormats()) - dnnGraph.initGradWPrimitives(dnnGraph.outputFormats()) + dnnGraph.initBwdPrimitives(dnnGraph.outputFormats(), phase) + dnnGraph.initGradWPrimitives(dnnGraph.outputFormats(), phase) } initPrim = true } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index c4e47ba820b..da1d900ff41 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -145,8 +145,8 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def fromJoinTable(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRJoinTable[Float]] - require(t.nInputDims == 0, - s"Dnn JoinTable only supports nInputDims = 0, but get ${t.nInputDims}") + require(t.nInputDims <= 0, + s"Dnn JoinTable only supports nInputDims <= 0, but get ${t.nInputDims}") mkldnn.JoinTable(t.dimension) } @@ -197,7 +197,7 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] case lrn: IRSpatialCrossMapLRN[Float] => require(lrn.format == DataFormat.NCHW) case join: IRJoinTable[Float] => - require(join.nInputDims == 0) + require(join.nInputDims <= 0) case _ => null } true diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala index 524b5fdd63a..2883a9f016a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/EvaluatorSpec.scala @@ -16,13 +16,12 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.dataset.{DataSet, Sample} +import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch, Sample} import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.nn.CrossEntropyCriterion import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, MklDnn, SparkContextLifeCycle} import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.SparkContextLifeCycle import org.apache.spark.{SparkConf, SparkContext} import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import com.intel.analytics.bigdl._ @@ -64,4 +63,28 @@ class EvaluatorSpec extends SparkContextLifeCycle with Matchers { result(1)._1.result()._1 should be (1f) result(2)._1.result()._1 should be (2.3044279f+-0.000001f) } + + "Evaluator MiniBatch" should "be correct" in { + RNG.setSeed(100) + val tmp = new Array[MiniBatch[Float]](25) + var i = 0 + while (i < tmp.length) { + val input = Tensor[Float](4, 28, 28).fill(0.8f) + val label = Tensor[Float](4).fill(1.0f) + tmp(i) = MiniBatch(input, label) + i += 1 + } + val model = LeNet5(classNum = 10) + val dataSet = DataSet.array(tmp, sc).toDistributed().data(train = false) + + val result = model.evaluate(dataSet, Array(new Top1Accuracy[Float](), new Top5Accuracy[Float](), + new Loss[Float](CrossEntropyCriterion[Float]()))) + + result(0)._1 should be (new AccuracyResult(0, 100)) + result(1)._1 should be (new AccuracyResult(100, 100)) + result(2)._1 should be (new LossResult(57.610695f, 25)) + result(0)._1.result()._1 should be (0f) + result(1)._1.result()._1 should be (1f) + result(2)._1.result()._1 should be (2.3044279f+-0.000001f) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala index 3757e0d0656..5b565412241 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.utils.intermediate +import breeze.numerics._ import com.intel.analytics.bigdl.example.loadmodel.AlexNet import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.models.inception.{Inception_Layer_v1, Inception_v1_NoAuxClassifier} @@ -39,7 +40,7 @@ class BlasToDnnSpec extends BigDLSpecHelper { } override def doAfter(): Unit = { - System.setProperty("bigdl.engineType", "mklblas") + System.clearProperty("bigdl.engineType") } "vgg16 blas to dnn" should "work properly" in { val batchSize = 2 @@ -89,6 +90,30 @@ class BlasToDnnSpec extends BigDLSpecHelper { Equivalent.nearequals(gradInputDnn, gradInputBlas, 1e-6) should be(true) } + "inception_v1 blas to dnn" should "work properly" in { + val batchSize = 2 + val classNum = 1000 + RandomGenerator.RNG.setSeed(1) + + val input = Tensor[Float](Array(batchSize, 3, 224, 224)).apply1(_ => + RandomGenerator.RNG.uniform(0.1, 1.0).toFloat) + val gradOutput = Tensor[Float](batchSize, classNum).apply1(_ => + RandomGenerator.RNG.uniform(1, 10).toFloat) + + val blas = Inception_v1_NoAuxClassifier.graph(classNum, false).asInstanceOf[StaticGraph[Float]] + blas.setInputFormats(Seq(Memory.Format.nchw)) + blas.setOutputFormats(Seq(Memory.Format.nc)) + val irBlas = blas.cloneModule().toIRgraph() + + val outBlas = blas.forward(input).toTensor[Float] + val gradInputBlas = blas.backward(input, gradOutput).toTensor[Float] + + val outDnn = irBlas.forward(input).toTensor[Float] + val gradInputDnn = irBlas.backward(input, gradOutput).toTensor[Float] + + Equivalent.nearequals(outDnn, outBlas, 1e-6) should be(true) + } + "resnet50 blas to dnn" should "work properly" in { val batchSize = 2 val classNum = 1000 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala new file mode 100644 index 00000000000..c1e697b60fa --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala @@ -0,0 +1,212 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.utils.intermediate + +import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch, Sample} +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier +import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, ImageFrameToSample, MatToTensor} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.apache.spark.SparkContext +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class DnnPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { + private val coreNumber = 4 + private val nodeNumber = 1 + private var sc: SparkContext = _ + + before { + System.setProperty("bigdl.engineType", "mkldnn") + Engine.init(nodeNumber, coreNumber, true) + val conf = Engine.createSparkConf().setMaster(s"local[$coreNumber]").setAppName("dnn predictor") + sc = SparkContext.getOrCreate(conf) + Engine.init + } + + after { + if (sc != null) sc.stop() + System.clearProperty("bigdl.engineType") + } + + "predict image for dnn" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageRead = ImageFrame.read(resource.getFile, sc) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val imageFrame = ImageFrame.rdd(imageRead.toDistributed().rdd.repartition(coreNumber)) + val inception = Inception_v1_NoAuxClassifier(classNum = 20, false) + val model = inception.cloneModule() + val detection = model.predictImage(imageFrame).toDistributed() + val feature = detection.rdd.first() + println(feature(ImageFeature.predict)) + + imageFrame.rdd.partitions.length should be(coreNumber) + detection.rdd.partitions.length should be(1) + + val imageFeatures = detection.rdd.collect() + val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) + val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) + + prob(0) should be (model.forward(data(0).feature.reshape(Array(1, 3, 224, 224))) + .toTensor[Float].squeeze) + } + + "predict for dnn" should "work properly" in { + RNG.setSeed(100) + val data = new Array[Sample[Float]](97) + var i = 0 + while (i < data.length) { + val input = Tensor[Float](1, 28, 28).apply1(_ => + RNG.uniform(0.130660 + i, 0.3081078).toFloat) + val label = Tensor[Float](1).fill(1.0f) + data(i) = Sample(input, label) + i += 1 + } + val lenet = LeNet5(classNum = 10) + val model = lenet.cloneModule() + val dataSet = sc.parallelize(data, coreNumber).repartition(1) + var result = model.predict(dataSet) + var prob = result.collect() + + dataSet.partitions.length should be(1) + result.partitions.length should be(1) + + prob(0) should be (model.forward(data(0).feature).toTensor[Float].squeeze()) + prob(11) should be (model.forward(data(11).feature).toTensor[Float].squeeze()) + prob(31) should be (model.forward(data(31).feature).toTensor[Float].squeeze()) + prob(51) should be (model.forward(data(51).feature).toTensor[Float].squeeze()) + prob(71) should be (model.forward(data(71).feature).toTensor[Float].squeeze()) + prob(91) should be (model.forward(data(91).feature).toTensor[Float].squeeze()) + + val resultClass = model.predictClass(dataSet) + + val probClass = resultClass.collect() + probClass(0) should be + (model.forward(data(0).feature + ).toTensor[Float].squeeze().max(1)._2.valueAt(1).toInt) + probClass(11) should be + (model.forward(data(11).feature + ).toTensor[Float].squeeze().max(1)._2.valueAt(1).toInt) + probClass(31) should be + (model.forward(data(31).feature + ).toTensor[Float].squeeze().max(1)._2.valueAt(1).toInt) + probClass(51) should be + (model.forward(data(51).feature + ).toTensor[Float].squeeze().max(1)._2.valueAt(1).toInt) + probClass(71) should be + (model.forward(data(71).feature + ).toTensor[Float].squeeze().max(1)._2.valueAt(1).toInt) + probClass(91) should be + (model.forward(data(91).feature + ).toTensor[Float].squeeze().max(1)._2.valueAt(1).toInt) + } + + "Evaluator with dnn backend" should "be correct" in { + RNG.setSeed(100) + val tmp = new Array[Sample[Float]](100) + var i = 0 + while (i < tmp.length) { + val input = Tensor[Float](28, 28).fill(0.8f) + val label = Tensor[Float](1).fill(1.0f) + tmp(i) = Sample(input, label) + i += 1 + } + val model = LeNet5(classNum = 10) + val dataSet = DataSet.array(tmp, sc).toDistributed().data(train = false).repartition(4) + + val result = model.evaluate(dataSet, Array(new Top1Accuracy[Float](), new Top5Accuracy[Float](), + new Loss[Float](CrossEntropyCriterion[Float]()))) + + dataSet.partitions.length should be(4) + result(0)._1 should be (new AccuracyResult(0, 100)) + result(1)._1 should be (new AccuracyResult(100, 100)) + result(2)._1 should be (new LossResult(16.130993f, 7)) + result(0)._1.result()._1 should be (0f) + result(1)._1.result()._1 should be (1f) + result(2)._1.result()._1 should be (2.3044279f+-0.000001f) + } + + "Evaluator MiniBatch with dnn backend" should "be correct" in { + RNG.setSeed(100) + val tmp = new Array[MiniBatch[Float]](25) + var i = 0 + while (i < tmp.length) { + val input = Tensor[Float](4, 28, 28).fill(0.8f) + val label = Tensor[Float](4).fill(1.0f) + tmp(i) = MiniBatch(input, label) + i += 1 + } + val model = LeNet5(classNum = 10) + val dataSet = DataSet.array(tmp, sc).toDistributed().data(train = false).repartition(4) + + val result = model.evaluate(dataSet, Array(new Top1Accuracy[Float](), new Top5Accuracy[Float](), + new Loss[Float](CrossEntropyCriterion[Float]()))) + + dataSet.partitions.length should be(4) + result(0)._1 should be (new AccuracyResult(0, 100)) + result(1)._1 should be (new AccuracyResult(100, 100)) + result(2)._1 should be (new LossResult(57.610695f, 25)) + result(0)._1.result()._1 should be (0f) + result(1)._1.result()._1 should be (1f) + result(2)._1.result()._1 should be (2.3044279f+-0.000001f) + } + + "Local predictor with dnn backend" should "work properly" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val ims = (1 to 50).map(x => { + val im = ImageFeature() + im(ImageFeature.uri) = x.toString + im(ImageFeature.imageTensor) = Tensor[Float](3, 24, 24).randn() + im + }) + + val imageFrame = ImageFrame.array(ims.toArray) -> ImageFrameToSample() + val m = Sequential() + m.add(SpatialConvolution(3, 6, 5, 5)) + m.add(Tanh()) + val model = m.toGraph().asInstanceOf[StaticGraph[Float]] + model.setInputFormats(Seq(Memory.Format.nchw)) + model.setOutputFormats(Seq(Memory.Format.nchw)) + + val detection = model.predictImage(imageFrame).toLocal() + val feature = detection.array.head + + val imageFeatures = detection.array + val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) + val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) + val tmp1 = prob(0) + val tmp2 = model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 24, 24))) + .toTensor[Float].split(1)(0) + prob(0) should be(model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 24, 24))) + .toTensor[Float].split(1)(0)) + (1 to 20).foreach(x => { + imageFeatures(x - 1).uri() should be (x.toString) + if (imageFeatures(x - 1).predict() == null) println(x, imageFeatures(x - 1).predict()) + assert(imageFeatures(x - 1).predict() != null) + }) + } +} From 111f34fae66b4c352a97bc170e1ccae04e2be103 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 12 Mar 2019 23:16:48 +0800 Subject: [PATCH 0869/1065] remove some unit tests (#2752) --- .../bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala | 5 ----- 1 file changed, 5 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala index c1e697b60fa..6ef11db7778 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/DnnPredictorSpec.scala @@ -198,11 +198,6 @@ class DnnPredictorSpec extends FlatSpec with Matchers with BeforeAndAfter { val imageFeatures = detection.array val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) - val tmp1 = prob(0) - val tmp2 = model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 24, 24))) - .toTensor[Float].split(1)(0) - prob(0) should be(model.evaluate().forward(data(0).feature.reshape(Array(1, 3, 24, 24))) - .toTensor[Float].split(1)(0)) (1 to 20).foreach(x => { imageFeatures(x - 1).uri() should be (x.toString) if (imageFeatures(x - 1).predict() == null) println(x, imageFeatures(x - 1).predict()) From 641ec164c2a8a9af264652ea6278e2f3be89cbe4 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 13 Mar 2019 13:51:25 +0800 Subject: [PATCH 0870/1065] remove some conflict tests (#2753) --- .../bigdl/dllib/python/api/PythonSpec.scala | 271 +++++++++--------- 1 file changed, 137 insertions(+), 134 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala index eb4e9a78eae..a564e9fde5a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/python/api/PythonSpec.scala @@ -159,140 +159,143 @@ class PythonSpec extends FlatSpec with Matchers with BeforeAndAfter { require(tensorBack == tensor) } - "Double prototype" should "be test" in { - TestUtils.cancelOnWindows() - - Logger.getLogger("org").setLevel(Level.WARN) - Logger.getLogger("akka").setLevel(Level.WARN) - - import collection.JavaConverters._ - - val featuresShape = util.Arrays.asList(100) - val labelShape = util.Arrays.asList(1) - - val data = sc.parallelize(0 to 100).map {i => - val label = JTensor(Array(i % 2 + 1.0f), Array(1), "double") - val feature = JTensor(Range(0, 100).map(_ => Random.nextFloat()).toArray, - Array(100), "double") - val features = new JArrayList[JTensor]() - features.add(feature) - val labels = new JArrayList[JTensor]() - labels.add(label) - Sample(features, labels, "double") - } - - BigDLSerDe.javaToPython(data.toJavaRDD().asInstanceOf[JavaRDD[Any]]) - - val model = Sequential[Double]() - model.add(Linear[Double](100, 100)) - model.add(ReLU[Double]()) - - val m2 = Sequential[Double]() - m2.add(Linear[Double](100, 10)) - m2.add(ReLU[Double]()) - - model.add(m2) - - model.add(LogSoftMax[Double]()) - val batchSize = 32 - val pp = PythonBigDL.ofDouble() - val sgd = new SGD[Double]() - val optimMethod: Map[String, OptimMethod[Double]] = - Map(model.getName -> sgd) - sgd.learningRateSchedule = - SGD.Poly(0.5, math.ceil(1281167.toDouble / batchSize).toInt) - val optimizer = pp.createDistriOptimizer( - model, - data.toJavaRDD(), - ClassNLLCriterion[Double](), - optimMethod.asJava, - Trigger.maxEpoch(2), - 32) - pp.setValidation(optimizer = optimizer, - batchSize = batchSize, - trigger = Trigger.severalIteration(10), - valRdd = data.toJavaRDD(), - vMethods = util.Arrays.asList(new Top1Accuracy(), new Loss())) - - val logdir = com.google.common.io.Files.createTempDir() - val trainSummary = TrainSummary(logdir.getPath, "lenet") - .setSummaryTrigger("LearningRate", Trigger.severalIteration(1)) - .setSummaryTrigger("Loss", Trigger.severalIteration(1)) - .setSummaryTrigger("Throughput", Trigger.severalIteration(1)) - .setSummaryTrigger("Parameters", Trigger.severalIteration(20)) - val validationSummary = ValidationSummary(logdir.getPath, "lenet") - - pp.setTrainSummary(optimizer, trainSummary) - pp.setValSummary(optimizer, validationSummary) - - val trainedModel = optimizer.optimize() - - val lrResult = pp.summaryReadScalar(trainSummary, "LearningRate") - - // add modelPredictRDD unit test - val preRDD = pp.modelPredictRDD(trainedModel, data.toJavaRDD) - val preResult = preRDD.collect() - - val localData = data.collect() - pp.toTensor(preResult.get(0)) should be - (trainedModel.forward(pp.toJSample(localData(0)).feature)) - - pp.toTensor(preResult.get(25)) should be - (trainedModel.forward(pp.toJSample(localData(25)).feature)) - - pp.toTensor(preResult.get(55)) should be - (trainedModel.forward(pp.toJSample(localData(55)).feature)) - - pp.toTensor(preResult.get(75)) should be - (trainedModel.forward(pp.toJSample(localData(75)).feature)) - - // TODO: verify the parameters result - val parameters = pp.modelGetParameters(trainedModel) -// println(parameters) - val testResult = pp.modelEvaluate(trainedModel, - data.toJavaRDD(), - batchSize = 32, - valMethods = util.Arrays.asList(new Top1Accuracy())) - println(testResult) - } - - "local optimizer" should "be test" in { - - TestUtils.cancelOnWindows() - - Logger.getLogger("org").setLevel(Level.WARN) - Logger.getLogger("akka").setLevel(Level.WARN) - - import collection.JavaConverters._ - - val featuresShape = util.Arrays.asList(100) - val labelShape = util.Arrays.asList(1) - val pp = PythonBigDL.ofDouble() - - val X = pp.toJTensor(Tensor[Double](Array(100, 100)).randn()) - val y = pp.toJTensor(Tensor[Double](Array(100, 1)).zero().add(1)) - - val model = Sequential[Double]() - model.add(Linear[Double](100, 10)) - model.add(ReLU[Double]()) - model.add(LogSoftMax[Double]()) - val batchSize = 32 - val optimMethod: Map[String, OptimMethod[Double]] = - Map(model.getName() -> new SGD[Double]()) - val optimizer = pp.createLocalOptimizer( - List(X).asJava, - y, - model, - ClassNLLCriterion[Double](), - optimMethod.asJava, - Trigger.maxEpoch(2), - 32, - 2) - val trainedModel = optimizer.optimize() - val predictedResult = pp.predictLocal( - trainedModel, List(pp.toJTensor(Tensor[Double](Array(34, 100)).randn())).asJava) - println(predictedResult) - } + // todo: failed when running with mkldnn tests in parallelism + // and have to recover those tests after fix this issue + +// "Double prototype" should "be test" in { +// TestUtils.cancelOnWindows() +// +// Logger.getLogger("org").setLevel(Level.WARN) +// Logger.getLogger("akka").setLevel(Level.WARN) +// +// import collection.JavaConverters._ +// +// val featuresShape = util.Arrays.asList(100) +// val labelShape = util.Arrays.asList(1) +// +// val data = sc.parallelize(0 to 100).map {i => +// val label = JTensor(Array(i % 2 + 1.0f), Array(1), "double") +// val feature = JTensor(Range(0, 100).map(_ => Random.nextFloat()).toArray, +// Array(100), "double") +// val features = new JArrayList[JTensor]() +// features.add(feature) +// val labels = new JArrayList[JTensor]() +// labels.add(label) +// Sample(features, labels, "double") +// } +// +// BigDLSerDe.javaToPython(data.toJavaRDD().asInstanceOf[JavaRDD[Any]]) +// +// val model = Sequential[Double]() +// model.add(Linear[Double](100, 100)) +// model.add(ReLU[Double]()) +// +// val m2 = Sequential[Double]() +// m2.add(Linear[Double](100, 10)) +// m2.add(ReLU[Double]()) +// +// model.add(m2) +// +// model.add(LogSoftMax[Double]()) +// val batchSize = 32 +// val pp = PythonBigDL.ofDouble() +// val sgd = new SGD[Double]() +// val optimMethod: Map[String, OptimMethod[Double]] = +// Map(model.getName -> sgd) +// sgd.learningRateSchedule = +// SGD.Poly(0.5, math.ceil(1281167.toDouble / batchSize).toInt) +// val optimizer = pp.createDistriOptimizer( +// model, +// data.toJavaRDD(), +// ClassNLLCriterion[Double](), +// optimMethod.asJava, +// Trigger.maxEpoch(2), +// 32) +// pp.setValidation(optimizer = optimizer, +// batchSize = batchSize, +// trigger = Trigger.severalIteration(10), +// valRdd = data.toJavaRDD(), +// vMethods = util.Arrays.asList(new Top1Accuracy(), new Loss())) +// +// val logdir = com.google.common.io.Files.createTempDir() +// val trainSummary = TrainSummary(logdir.getPath, "lenet") +// .setSummaryTrigger("LearningRate", Trigger.severalIteration(1)) +// .setSummaryTrigger("Loss", Trigger.severalIteration(1)) +// .setSummaryTrigger("Throughput", Trigger.severalIteration(1)) +// .setSummaryTrigger("Parameters", Trigger.severalIteration(20)) +// val validationSummary = ValidationSummary(logdir.getPath, "lenet") +// +// pp.setTrainSummary(optimizer, trainSummary) +// pp.setValSummary(optimizer, validationSummary) +// +// val trainedModel = optimizer.optimize() +// +// val lrResult = pp.summaryReadScalar(trainSummary, "LearningRate") +// +// // add modelPredictRDD unit test +// val preRDD = pp.modelPredictRDD(trainedModel, data.toJavaRDD) +// val preResult = preRDD.collect() +// +// val localData = data.collect() +// pp.toTensor(preResult.get(0)) should be +// (trainedModel.forward(pp.toJSample(localData(0)).feature)) +// +// pp.toTensor(preResult.get(25)) should be +// (trainedModel.forward(pp.toJSample(localData(25)).feature)) +// +// pp.toTensor(preResult.get(55)) should be +// (trainedModel.forward(pp.toJSample(localData(55)).feature)) +// +// pp.toTensor(preResult.get(75)) should be +// (trainedModel.forward(pp.toJSample(localData(75)).feature)) +// +// // TODO: verify the parameters result +// val parameters = pp.modelGetParameters(trainedModel) +// // println(parameters) +// val testResult = pp.modelEvaluate(trainedModel, +// data.toJavaRDD(), +// batchSize = 32, +// valMethods = util.Arrays.asList(new Top1Accuracy())) +// println(testResult) +// } +// +// "local optimizer" should "be test" in { +// +// TestUtils.cancelOnWindows() +// +// Logger.getLogger("org").setLevel(Level.WARN) +// Logger.getLogger("akka").setLevel(Level.WARN) +// +// import collection.JavaConverters._ +// +// val featuresShape = util.Arrays.asList(100) +// val labelShape = util.Arrays.asList(1) +// val pp = PythonBigDL.ofDouble() +// +// val X = pp.toJTensor(Tensor[Double](Array(100, 100)).randn()) +// val y = pp.toJTensor(Tensor[Double](Array(100, 1)).zero().add(1)) +// +// val model = Sequential[Double]() +// model.add(Linear[Double](100, 10)) +// model.add(ReLU[Double]()) +// model.add(LogSoftMax[Double]()) +// val batchSize = 32 +// val optimMethod: Map[String, OptimMethod[Double]] = +// Map(model.getName() -> new SGD[Double]()) +// val optimizer = pp.createLocalOptimizer( +// List(X).asJava, +// y, +// model, +// ClassNLLCriterion[Double](), +// optimMethod.asJava, +// Trigger.maxEpoch(2), +// 32, +// 2) +// val trainedModel = optimizer.optimize() +// val predictedResult = pp.predictLocal( +// trainedModel, List(pp.toJTensor(Tensor[Double](Array(34, 100)).randn())).asJava) +// println(predictedResult) +// } "train with imageFrame" should "work" in { val images = (1 to 10).map(x => { From a37007b05efc2af3b5833d2bba5e7d0e55f62e14 Mon Sep 17 00:00:00 2001 From: Emiliano Martinez Date: Wed, 13 Mar 2019 08:23:43 +0100 Subject: [PATCH 0871/1065] Fix Add operation error when type is Double importing Tensorflow graph (#2721) --- .../com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala index ececcd1d4d5..c16dd743e4b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Add.scala @@ -29,12 +29,13 @@ import scala.reflect.ClassTag class Add extends TensorflowOpsLoader { override def build[T: ClassTag](nodeDef: NodeDef, byteOrder: ByteOrder , context: Context[T])(implicit ev: TensorNumeric[T]): Module[T] = { - val t = getType(nodeDef.getAttrMap, "T") if (t == DataType.DT_FLOAT) { new CAddTable[T, Float]() } else if (t == DataType.DT_INT32) { new CAddTable[T, Int]() + } else if (t == DataType.DT_DOUBLE) { + new CAddTable[T, Double]() } else { throw new UnsupportedOperationException(s"Not support numeric type $t") } From 096c82f916e73dedc715a94c6697f90cf38cbba8 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Wed, 13 Mar 2019 15:54:22 +0800 Subject: [PATCH 0872/1065] feature: add byte supports for DnnTensor (#2751) * feat: add byte supports for DnnTensor --- .../bigdl/dllib/tensor/Convertable.scala | 44 +++++++++++++++++++ .../bigdl/dllib/tensor/DnnStorage.scala | 27 +++++++++--- .../bigdl/dllib/tensor/DnnTensor.scala | 41 ++++++++++++++++- .../analytics/bigdl/dllib/tensor/Tensor.scala | 2 + .../bigdl/dllib/tensor/TensorNumeric.scala | 26 +++++++++++ .../bigdl/dllib/tensor/DnnTensorSpec.scala | 4 +- 6 files changed, 134 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala index 27735c2360d..271e812a72f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Convertable.scala @@ -156,6 +156,8 @@ trait ConvertableFrom[@spec A] { implicit def toChar(a: A): Char + implicit def toByte(a: A): Byte + implicit def toBoolean(a: A): Boolean } @@ -174,6 +176,8 @@ trait ConvertableFromFloat extends ConvertableFrom[Float] { implicit def toChar(a: Float): Char = a.toChar + implicit def toByte(a: Float): Byte = a.toByte + implicit def toBoolean(a: Float): Boolean = throw new UnsupportedOperationException("Float cannot be cast to Boolean type") } @@ -193,6 +197,8 @@ trait ConvertableFromDouble extends ConvertableFrom[Double] { implicit def toChar(a: Double): Char = a.toChar + implicit def toByte(a: Double): Byte = a.toByte + implicit def toBoolean(a: Double): Boolean = throw new UnsupportedOperationException("Float cannot be cast to Boolean type") } @@ -212,6 +218,8 @@ trait ConvertableFromInt extends ConvertableFrom[Int] { implicit def toChar(a: Int): Char = a.toChar + implicit def toByte(a: Int): Byte = a.toByte + implicit def toBoolean(a: Int): Boolean = throw new UnsupportedOperationException("Float cannot be cast to Boolean type") } @@ -231,6 +239,8 @@ trait ConvertableFromShort extends ConvertableFrom[Short] { implicit def toChar(a: Short): Char = a.toChar + implicit def toByte(a: Short): Byte = a.toByte + implicit def toBoolean(a: Short): Boolean = throw new UnsupportedOperationException("Float cannot be cast to Boolean type") } @@ -250,6 +260,8 @@ trait ConvertableFromLong extends ConvertableFrom[Long] { implicit def toChar(a: Long): Char = a.toChar + implicit def toByte(a: Long): Byte = a.toByte + implicit def toBoolean(a: Long): Boolean = throw new UnsupportedOperationException("Float cannot be cast to Boolean type") } @@ -275,6 +287,9 @@ trait ConvertableFromBoolean extends ConvertableFrom[Boolean] { implicit def toChar(a: Boolean): Char = throw new UnsupportedOperationException("Float cannot be cast to Boolean") + implicit def toByte(a: Boolean): Byte = + throw new UnsupportedOperationException("Boolean cannot be cast to Byte") + implicit def toBoolean(a: Boolean): Boolean = a } @@ -300,6 +315,9 @@ trait ConvertableFromString extends ConvertableFrom[String] { implicit def toBoolean(a: String): Boolean = throw new UnsupportedOperationException("Boolean cannot be cast to String") + implicit def toByte(a: String): Byte = + throw new UnsupportedOperationException("String cannot be cast to Byte type") + implicit def toString(a: String): String = a } @@ -320,6 +338,30 @@ trait ConvertableFromChar extends ConvertableFrom[Char] { implicit def toString(a: Char): String = a.toString implicit def toChar(a: Char): Char = a + + implicit def toByte(a: Char): Byte = a.toByte + +} + +trait ConvertableFromByte extends ConvertableFrom[Byte] { + implicit def toFloat(a: Byte): Float = a.toFloat + + implicit def toDouble(a: Byte): Double = a.toDouble + + implicit def toInt(a: Byte): Int = a.toInt + + implicit def toShort(a: Byte): Short = a.toShort + + implicit def toLong(a: Byte): Long = a.toLong + + implicit def toBoolean(a: Byte): Boolean = + throw new UnsupportedOperationException("Byte cannot be cast to boolean type") + + implicit def toString(a: Byte): String = a.toString + + implicit def toByte(a: Byte): Byte = a + + implicit def toChar(a: Byte): Char = a.toChar } object ConvertableFrom { @@ -339,5 +381,7 @@ object ConvertableFrom { implicit object ConvertableFromString extends ConvertableFromString implicit object ConvertableFromBoolean extends ConvertableFromBoolean + + implicit object ConvertableFromByte extends ConvertableFromByte } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala index ab9ed146a57..bd1665980e7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala @@ -28,7 +28,19 @@ import scala.reflect._ */ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { - require(classTag[T] == ClassTag.Float, "DnnStorage only support float") + private def checkIsInstanceOf(that: Any): Boolean = { + scala.reflect.classTag[T] == that + } + + private val bytes = if (checkIsInstanceOf(ClassTag.Float)) { + DnnStorage.FLOAT_BYTES + } else if (checkIsInstanceOf(ClassTag.Byte)) { + DnnStorage.INT8_BYTES + } else if (checkIsInstanceOf(ClassTag.Int)) { + DnnStorage.INT_BYTES + } else { + throw new UnsupportedOperationException(s"Unsupported type for storage") + } private var _isReleased: Boolean = false @@ -53,11 +65,12 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { : this.type = { source match { case s: ArrayStorage[T] => + require(checkIsInstanceOf(ClassTag.Float), s"copy from float storage not supported") Memory.CopyArray2Ptr(s.array().asInstanceOf[Array[Float]], sourceOffset, - ptr.address, offset, length, DnnStorage.FLOAT_BYTES) + ptr.address, offset, length, bytes) case s: DnnStorage[T] => Memory.CopyPtr2Ptr(s.ptr.address, sourceOffset, ptr.address, offset, length, - DnnStorage.FLOAT_BYTES) + bytes) case _ => throw new UnsupportedOperationException("Only support copy from ArrayStorage or DnnStorage") } @@ -83,7 +96,7 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { * Release the native array, the storage object is useless */ def release(): Unit = synchronized { - if (!this.isReleased()) { + if (!this.isReleased() && ptr.address != 0L) { Memory.AlignedFree(ptr.address) DnnStorage.checkAndSet(ptr.address) _isReleased = true @@ -94,8 +107,8 @@ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { def isReleased(): Boolean = _isReleased private def allocate(capacity: Int): Long = { - require(capacity > 0, s"capacity should not be larger than 0") - val ptr = Memory.AlignedMalloc(capacity * DnnStorage.FLOAT_BYTES, DnnStorage.CACHE_LINE_SIZE) + require(capacity > 0, s"capacity should be larger than 0") + val ptr = Memory.AlignedMalloc(capacity * bytes, DnnStorage.CACHE_LINE_SIZE) require(ptr != 0L, s"allocate native aligned memory failed") _isReleased = false DnnStorage.add(ptr) @@ -132,6 +145,8 @@ private[bigdl] class Pointer(val address: Long) object DnnStorage { private[tensor] val CACHE_LINE_SIZE = System.getProperty("bigdl.cache.line", "64").toInt private[tensor] val FLOAT_BYTES: Int = 4 + private[tensor] val INT8_BYTES: Int = 1 + private[tensor] val INT_BYTES: Int = 4 import java.util.concurrent.ConcurrentHashMap private val nativeStorages: ConcurrentHashMap[Long, Boolean] = new ConcurrentHashMap() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala index 6f23da77f6e..50e75409e5e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -134,6 +134,10 @@ class DnnTensor[T: ClassTag]( true } + override def getType(): TensorDataType = { + ev.getType() + } + override def hashCode(): Int = { val seed = 37 var hash = 1 @@ -149,8 +153,35 @@ class DnnTensor[T: ClassTag]( hash } - override def toString(): String = { - Tensor[Float]().resize(this.size()).copy(this.asInstanceOf[Tensor[Float]]).toString + override def set(): Tensor[T] = { + // TODO we will do nothing. the behavior is not the same with DenseTensor + this + } + + override def toString: String = { + ev.getType() match { + case FloatType => + if (size().product != this.nElement()) { + val dense = Tensor[Float](Array(this.nElement())) + Memory.CopyPtr2Array(this.storageAddress(), 0, dense.storage().array(), + 0, nElement(), 4) + dense.toString + } else { + val dense = Tensor[Float](size()) + dense.copy(this.asInstanceOf[DnnTensor[Float]]) + dense.toString + } + case ByteType => + val array = new Array[Byte](nElement()) + Memory.CopyPtr2ByteArray(this.asInstanceOf[DnnTensor[Byte]].storageAddress(), 0, + array, 0, nElement(), 1) + array.mkString("\t") + case IntType => + val array = new Array[Int](nElement()) + Memory.CopyPtr2IntArray(this.storageAddress(), 0, array, 0, nElement(), 4) + array.mkString("\t") + case _ => "unknown type" + } } } @@ -177,6 +208,12 @@ object DnnTensor { new DnnTensor[T](storage, sizes) } + def apply[T: ClassTag](sizes: Array[Int], realSize: Long)( + implicit ev: TensorNumeric[T]): DnnTensor[T] = { + val storage = new DnnStorage[T](realSize.toInt) // FIXME if size more than int ? + new DnnTensor[T](storage, sizes) + } + def apply[T: ClassTag](d1: Int)(implicit ev: TensorNumeric[T]): DnnTensor[T] = { val storage = new DnnStorage[T](d1) new DnnTensor[T](storage, Array(d1)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index e0017ca69de..5a384a7d581 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -817,6 +817,8 @@ object BooleanType extends TensorDataType object CharType extends TensorDataType +object ByteType extends TensorDataType + object StringType extends TensorDataType object IntType extends TensorDataType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala index 7c6be529b62..eea1fc96c7a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/TensorNumeric.scala @@ -1451,5 +1451,31 @@ object TensorNumericMath { a == b } } + + implicit object NumericByte extends UndefinedTensorNumeric[Byte]("Byte") { + override def getType(): TensorDataType = ByteType + + override def plus(x: Byte, y: Byte): Byte = (x + y).toByte + + override def minus(x: Byte, y: Byte): Byte = (x - y).toByte + + override def fromType[K](k: K)( + implicit c: ConvertableFrom[K]): Byte = + c.toByte(k) + + override def axpy(n: Int, da: Byte, dx: Array[Byte], _dx_offset: Int, + incx: Int, dy: Array[Byte], + _dy_offset: Int, incy: Int): Unit = { + var i = 0 + while (i < n) { + dy(i + _dy_offset) = (dx(_dx_offset + i) + dy(_dy_offset + i)).toByte + i += 1 + } + } + + override def nearlyEqual(a: Byte, b: Byte, epsilon: Double): Boolean = { + a == b + } + } } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala index a4503d30afd..bf2d289d8e0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala @@ -25,8 +25,8 @@ class DnnTensorSpec extends BigDLSpecHelper { tensor.nElement() should be(3 * 4 * 5) } - "DnnTensor" should "only support float" in { - intercept[IllegalArgumentException] { + "DnnTensor" should "does not support double" in { + intercept[UnsupportedOperationException] { val t = DnnTensor[Double](3, 4, 5) } } From c4b0a88954fe8aea8a9534ca109d46765efb8409 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Wed, 13 Mar 2019 16:38:07 +0800 Subject: [PATCH 0873/1065] [New Feature] Calculating Scales (#2750) * [New Feature]Calculating Scales --- .../dllib/utils/serialization/Bigdl.java | 2060 ++++++++++++++--- .../main/resources/serialization/bigdl.proto | 7 + .../bigdl/dllib/nn/ConcatTable.scala | 3 +- .../analytics/bigdl/dllib/nn/Graph.scala | 5 +- .../analytics/bigdl/dllib/nn/Linear.scala | 3 +- .../bigdl/dllib/nn/MklInt8Convertible.scala | 454 ++++ .../analytics/bigdl/dllib/nn/Sequential.scala | 3 +- .../bigdl/dllib/nn/SpatialConvolution.scala | 3 +- .../bigdl/dllib/nn/mkldnn/ConcatTable.scala | 3 +- .../bigdl/dllib/nn/mkldnn/Linear.scala | 6 +- .../bigdl/dllib/nn/mkldnn/Sequential.scala | 5 +- .../dllib/nn/mkldnn/SpatialConvolution.scala | 3 +- .../bigdl/dllib/optim/PredictionService.scala | 1 + .../analytics/bigdl/dllib/utils/Table.scala | 2 + .../utils/serializer/ModuleSerializable.scala | 116 +- .../serializer/converters/DataConverter.scala | 3 +- .../bigdl/dllib/utils/tf/loaders/Mean.scala | 4 + .../bigdl/dllib/nn/ScaleCalculatorSpec.scala | 461 ++++ 18 files changed, 2779 insertions(+), 363 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala diff --git a/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java b/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java index c889ed291b1..be8d509ef8e 100644 --- a/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java +++ b/scala/dllib/src/main/java/com/intel/analytics/bigdl/dllib/utils/serialization/Bigdl.java @@ -1313,6 +1313,98 @@ com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getAttrOrThrow( */ com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getParametersOrBuilder( int index); + + /** + * bool isMklInt8Enabled = 17; + */ + boolean getIsMklInt8Enabled(); + + /** + * int32 inputDimMasks = 18; + */ + int getInputDimMasks(); + + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + java.util.List + getInputScalesList(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getInputScales(int index); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + int getInputScalesCount(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + java.util.List + getInputScalesOrBuilderList(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getInputScalesOrBuilder( + int index); + + /** + * int32 outputDimMasks = 20; + */ + int getOutputDimMasks(); + + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + java.util.List + getOutputScalesList(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getOutputScales(int index); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + int getOutputScalesCount(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + java.util.List + getOutputScalesOrBuilderList(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getOutputScalesOrBuilder( + int index); + + /** + * int32 weightDimMasks = 22; + */ + int getWeightDimMasks(); + + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + java.util.List + getWeightScalesList(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getWeightScales(int index); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + int getWeightScalesCount(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + java.util.List + getWeightScalesOrBuilderList(); + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getWeightScalesOrBuilder( + int index); } /** * Protobuf type {@code com.intel.analytics.bigdl.serialization.BigDLModule} @@ -1338,6 +1430,13 @@ private BigDLModule() { id_ = 0; hasParameters_ = false; parameters_ = java.util.Collections.emptyList(); + isMklInt8Enabled_ = false; + inputDimMasks_ = 0; + inputScales_ = java.util.Collections.emptyList(); + outputDimMasks_ = 0; + outputScales_ = java.util.Collections.emptyList(); + weightDimMasks_ = 0; + weightScales_ = java.util.Collections.emptyList(); } @java.lang.Override @@ -1508,6 +1607,53 @@ private BigDLModule( input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.parser(), extensionRegistry)); break; } + case 136: { + + isMklInt8Enabled_ = input.readBool(); + break; + } + case 144: { + + inputDimMasks_ = input.readInt32(); + break; + } + case 154: { + if (!((mutable_bitField0_ & 0x00040000) == 0x00040000)) { + inputScales_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00040000; + } + inputScales_.add( + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.parser(), extensionRegistry)); + break; + } + case 160: { + + outputDimMasks_ = input.readInt32(); + break; + } + case 170: { + if (!((mutable_bitField0_ & 0x00100000) == 0x00100000)) { + outputScales_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00100000; + } + outputScales_.add( + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.parser(), extensionRegistry)); + break; + } + case 176: { + + weightDimMasks_ = input.readInt32(); + break; + } + case 186: { + if (!((mutable_bitField0_ & 0x00400000) == 0x00400000)) { + weightScales_ = new java.util.ArrayList(); + mutable_bitField0_ |= 0x00400000; + } + weightScales_.add( + input.readMessage(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.parser(), extensionRegistry)); + break; + } } } } catch (com.google.protobuf.InvalidProtocolBufferException e) { @@ -1528,6 +1674,15 @@ private BigDLModule( if (((mutable_bitField0_ & 0x00008000) == 0x00008000)) { parameters_ = java.util.Collections.unmodifiableList(parameters_); } + if (((mutable_bitField0_ & 0x00040000) == 0x00040000)) { + inputScales_ = java.util.Collections.unmodifiableList(inputScales_); + } + if (((mutable_bitField0_ & 0x00100000) == 0x00100000)) { + outputScales_ = java.util.Collections.unmodifiableList(outputScales_); + } + if (((mutable_bitField0_ & 0x00400000) == 0x00400000)) { + weightScales_ = java.util.Collections.unmodifiableList(weightScales_); + } this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } @@ -2187,6 +2342,147 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getPar return parameters_.get(index); } + public static final int ISMKLINT8ENABLED_FIELD_NUMBER = 17; + private boolean isMklInt8Enabled_; + /** + * bool isMklInt8Enabled = 17; + */ + public boolean getIsMklInt8Enabled() { + return isMklInt8Enabled_; + } + + public static final int INPUTDIMMASKS_FIELD_NUMBER = 18; + private int inputDimMasks_; + /** + * int32 inputDimMasks = 18; + */ + public int getInputDimMasks() { + return inputDimMasks_; + } + + public static final int INPUTSCALES_FIELD_NUMBER = 19; + private java.util.List inputScales_; + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public java.util.List getInputScalesList() { + return inputScales_; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public java.util.List + getInputScalesOrBuilderList() { + return inputScales_; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public int getInputScalesCount() { + return inputScales_.size(); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getInputScales(int index) { + return inputScales_.get(index); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getInputScalesOrBuilder( + int index) { + return inputScales_.get(index); + } + + public static final int OUTPUTDIMMASKS_FIELD_NUMBER = 20; + private int outputDimMasks_; + /** + * int32 outputDimMasks = 20; + */ + public int getOutputDimMasks() { + return outputDimMasks_; + } + + public static final int OUTPUTSCALES_FIELD_NUMBER = 21; + private java.util.List outputScales_; + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public java.util.List getOutputScalesList() { + return outputScales_; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public java.util.List + getOutputScalesOrBuilderList() { + return outputScales_; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public int getOutputScalesCount() { + return outputScales_.size(); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getOutputScales(int index) { + return outputScales_.get(index); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getOutputScalesOrBuilder( + int index) { + return outputScales_.get(index); + } + + public static final int WEIGHTDIMMASKS_FIELD_NUMBER = 22; + private int weightDimMasks_; + /** + * int32 weightDimMasks = 22; + */ + public int getWeightDimMasks() { + return weightDimMasks_; + } + + public static final int WEIGHTSCALES_FIELD_NUMBER = 23; + private java.util.List weightScales_; + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + public java.util.List getWeightScalesList() { + return weightScales_; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + public java.util.List + getWeightScalesOrBuilderList() { + return weightScales_; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + public int getWeightScalesCount() { + return weightScales_.size(); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getWeightScales(int index) { + return weightScales_.get(index); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getWeightScalesOrBuilder( + int index) { + return weightScales_.get(index); + } + private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; @@ -2250,6 +2546,27 @@ public void writeTo(com.google.protobuf.CodedOutputStream output) for (int i = 0; i < parameters_.size(); i++) { output.writeMessage(16, parameters_.get(i)); } + if (isMklInt8Enabled_ != false) { + output.writeBool(17, isMklInt8Enabled_); + } + if (inputDimMasks_ != 0) { + output.writeInt32(18, inputDimMasks_); + } + for (int i = 0; i < inputScales_.size(); i++) { + output.writeMessage(19, inputScales_.get(i)); + } + if (outputDimMasks_ != 0) { + output.writeInt32(20, outputDimMasks_); + } + for (int i = 0; i < outputScales_.size(); i++) { + output.writeMessage(21, outputScales_.get(i)); + } + if (weightDimMasks_ != 0) { + output.writeInt32(22, weightDimMasks_); + } + for (int i = 0; i < weightScales_.size(); i++) { + output.writeMessage(23, weightScales_.get(i)); + } unknownFields.writeTo(output); } @@ -2332,6 +2649,34 @@ public int getSerializedSize() { size += com.google.protobuf.CodedOutputStream .computeMessageSize(16, parameters_.get(i)); } + if (isMklInt8Enabled_ != false) { + size += com.google.protobuf.CodedOutputStream + .computeBoolSize(17, isMklInt8Enabled_); + } + if (inputDimMasks_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(18, inputDimMasks_); + } + for (int i = 0; i < inputScales_.size(); i++) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(19, inputScales_.get(i)); + } + if (outputDimMasks_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(20, outputDimMasks_); + } + for (int i = 0; i < outputScales_.size(); i++) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(21, outputScales_.get(i)); + } + if (weightDimMasks_ != 0) { + size += com.google.protobuf.CodedOutputStream + .computeInt32Size(22, weightDimMasks_); + } + for (int i = 0; i < weightScales_.size(); i++) { + size += com.google.protobuf.CodedOutputStream + .computeMessageSize(23, weightScales_.get(i)); + } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; @@ -2392,6 +2737,20 @@ public boolean equals(final java.lang.Object obj) { == other.getHasParameters()); result = result && getParametersList() .equals(other.getParametersList()); + result = result && (getIsMklInt8Enabled() + == other.getIsMklInt8Enabled()); + result = result && (getInputDimMasks() + == other.getInputDimMasks()); + result = result && getInputScalesList() + .equals(other.getInputScalesList()); + result = result && (getOutputDimMasks() + == other.getOutputDimMasks()); + result = result && getOutputScalesList() + .equals(other.getOutputScalesList()); + result = result && (getWeightDimMasks() + == other.getWeightDimMasks()); + result = result && getWeightScalesList() + .equals(other.getWeightScalesList()); result = result && unknownFields.equals(other.unknownFields); return result; } @@ -2455,6 +2814,27 @@ public int hashCode() { hash = (37 * hash) + PARAMETERS_FIELD_NUMBER; hash = (53 * hash) + getParametersList().hashCode(); } + hash = (37 * hash) + ISMKLINT8ENABLED_FIELD_NUMBER; + hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( + getIsMklInt8Enabled()); + hash = (37 * hash) + INPUTDIMMASKS_FIELD_NUMBER; + hash = (53 * hash) + getInputDimMasks(); + if (getInputScalesCount() > 0) { + hash = (37 * hash) + INPUTSCALES_FIELD_NUMBER; + hash = (53 * hash) + getInputScalesList().hashCode(); + } + hash = (37 * hash) + OUTPUTDIMMASKS_FIELD_NUMBER; + hash = (53 * hash) + getOutputDimMasks(); + if (getOutputScalesCount() > 0) { + hash = (37 * hash) + OUTPUTSCALES_FIELD_NUMBER; + hash = (53 * hash) + getOutputScalesList().hashCode(); + } + hash = (37 * hash) + WEIGHTDIMMASKS_FIELD_NUMBER; + hash = (53 * hash) + getWeightDimMasks(); + if (getWeightScalesCount() > 0) { + hash = (37 * hash) + WEIGHTSCALES_FIELD_NUMBER; + hash = (53 * hash) + getWeightScalesList().hashCode(); + } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; @@ -2604,6 +2984,9 @@ private void maybeForceBuilderInitialization() { .alwaysUseFieldBuilders) { getSubModulesFieldBuilder(); getParametersFieldBuilder(); + getInputScalesFieldBuilder(); + getOutputScalesFieldBuilder(); + getWeightScalesFieldBuilder(); } } public Builder clear() { @@ -2663,6 +3046,32 @@ public Builder clear() { } else { parametersBuilder_.clear(); } + isMklInt8Enabled_ = false; + + inputDimMasks_ = 0; + + if (inputScalesBuilder_ == null) { + inputScales_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00040000); + } else { + inputScalesBuilder_.clear(); + } + outputDimMasks_ = 0; + + if (outputScalesBuilder_ == null) { + outputScales_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00100000); + } else { + outputScalesBuilder_.clear(); + } + weightDimMasks_ = 0; + + if (weightScalesBuilder_ == null) { + weightScales_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00400000); + } else { + weightScalesBuilder_.clear(); + } return this; } @@ -2744,6 +3153,37 @@ public com.intel.analytics.bigdl.serialization.Bigdl.BigDLModule buildPartial() } else { result.parameters_ = parametersBuilder_.build(); } + result.isMklInt8Enabled_ = isMklInt8Enabled_; + result.inputDimMasks_ = inputDimMasks_; + if (inputScalesBuilder_ == null) { + if (((bitField0_ & 0x00040000) == 0x00040000)) { + inputScales_ = java.util.Collections.unmodifiableList(inputScales_); + bitField0_ = (bitField0_ & ~0x00040000); + } + result.inputScales_ = inputScales_; + } else { + result.inputScales_ = inputScalesBuilder_.build(); + } + result.outputDimMasks_ = outputDimMasks_; + if (outputScalesBuilder_ == null) { + if (((bitField0_ & 0x00100000) == 0x00100000)) { + outputScales_ = java.util.Collections.unmodifiableList(outputScales_); + bitField0_ = (bitField0_ & ~0x00100000); + } + result.outputScales_ = outputScales_; + } else { + result.outputScales_ = outputScalesBuilder_.build(); + } + result.weightDimMasks_ = weightDimMasks_; + if (weightScalesBuilder_ == null) { + if (((bitField0_ & 0x00400000) == 0x00400000)) { + weightScales_ = java.util.Collections.unmodifiableList(weightScales_); + bitField0_ = (bitField0_ & ~0x00400000); + } + result.weightScales_ = weightScales_; + } else { + result.weightScales_ = weightScalesBuilder_.build(); + } result.bitField0_ = to_bitField0_; onBuilt(); return result; @@ -2897,6 +3337,96 @@ public Builder mergeFrom(com.intel.analytics.bigdl.serialization.Bigdl.BigDLModu } } } + if (other.getIsMklInt8Enabled() != false) { + setIsMklInt8Enabled(other.getIsMklInt8Enabled()); + } + if (other.getInputDimMasks() != 0) { + setInputDimMasks(other.getInputDimMasks()); + } + if (inputScalesBuilder_ == null) { + if (!other.inputScales_.isEmpty()) { + if (inputScales_.isEmpty()) { + inputScales_ = other.inputScales_; + bitField0_ = (bitField0_ & ~0x00040000); + } else { + ensureInputScalesIsMutable(); + inputScales_.addAll(other.inputScales_); + } + onChanged(); + } + } else { + if (!other.inputScales_.isEmpty()) { + if (inputScalesBuilder_.isEmpty()) { + inputScalesBuilder_.dispose(); + inputScalesBuilder_ = null; + inputScales_ = other.inputScales_; + bitField0_ = (bitField0_ & ~0x00040000); + inputScalesBuilder_ = + com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? + getInputScalesFieldBuilder() : null; + } else { + inputScalesBuilder_.addAllMessages(other.inputScales_); + } + } + } + if (other.getOutputDimMasks() != 0) { + setOutputDimMasks(other.getOutputDimMasks()); + } + if (outputScalesBuilder_ == null) { + if (!other.outputScales_.isEmpty()) { + if (outputScales_.isEmpty()) { + outputScales_ = other.outputScales_; + bitField0_ = (bitField0_ & ~0x00100000); + } else { + ensureOutputScalesIsMutable(); + outputScales_.addAll(other.outputScales_); + } + onChanged(); + } + } else { + if (!other.outputScales_.isEmpty()) { + if (outputScalesBuilder_.isEmpty()) { + outputScalesBuilder_.dispose(); + outputScalesBuilder_ = null; + outputScales_ = other.outputScales_; + bitField0_ = (bitField0_ & ~0x00100000); + outputScalesBuilder_ = + com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? + getOutputScalesFieldBuilder() : null; + } else { + outputScalesBuilder_.addAllMessages(other.outputScales_); + } + } + } + if (other.getWeightDimMasks() != 0) { + setWeightDimMasks(other.getWeightDimMasks()); + } + if (weightScalesBuilder_ == null) { + if (!other.weightScales_.isEmpty()) { + if (weightScales_.isEmpty()) { + weightScales_ = other.weightScales_; + bitField0_ = (bitField0_ & ~0x00400000); + } else { + ensureWeightScalesIsMutable(); + weightScales_.addAll(other.weightScales_); + } + onChanged(); + } + } else { + if (!other.weightScales_.isEmpty()) { + if (weightScalesBuilder_.isEmpty()) { + weightScalesBuilder_.dispose(); + weightScalesBuilder_ = null; + weightScales_ = other.weightScales_; + bitField0_ = (bitField0_ & ~0x00400000); + weightScalesBuilder_ = + com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? + getWeightScalesFieldBuilder() : null; + } else { + weightScalesBuilder_.addAllMessages(other.weightScales_); + } + } + } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; @@ -4638,408 +5168,1232 @@ public Builder clearOutputShape() { outputShape_ = null; onChanged(); } else { - outputShape_ = null; - outputShapeBuilder_ = null; + outputShape_ = null; + outputShapeBuilder_ = null; + } + + return this; + } + /** + * 
+       *output shape
+       *
+ * + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getOutputShapeBuilder() { + + onChanged(); + return getOutputShapeFieldBuilder().getBuilder(); + } + /** + * 
+       *output shape
+       *
+ * + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder() { + if (outputShapeBuilder_ != null) { + return outputShapeBuilder_.getMessageOrBuilder(); + } else { + return outputShape_ == null ? + com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance() : outputShape_; + } + } + /** + * 
+       *output shape
+       *
+ * + * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + */ + private com.google.protobuf.SingleFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> + getOutputShapeFieldBuilder() { + if (outputShapeBuilder_ == null) { + outputShapeBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder>( + getOutputShape(), + getParentForChildren(), + isClean()); + outputShape_ = null; + } + return outputShapeBuilder_; + } + + private boolean hasParameters_ ; + /** + * 
+       * indicator if module has parameters
+       *
+ * + * bool hasParameters = 15; + */ + public boolean getHasParameters() { + return hasParameters_; + } + /** + * 
+       * indicator if module has parameters
+       *
+ * + * bool hasParameters = 15; + */ + public Builder setHasParameters(boolean value) { + + hasParameters_ = value; + onChanged(); + return this; + } + /** + * 
+       * indicator if module has parameters
+       *
+ * + * bool hasParameters = 15; + */ + public Builder clearHasParameters() { + + hasParameters_ = false; + onChanged(); + return this; + } + + private java.util.List parameters_ = + java.util.Collections.emptyList(); + private void ensureParametersIsMutable() { + if (!((bitField0_ & 0x00008000) == 0x00008000)) { + parameters_ = new java.util.ArrayList(parameters_); + bitField0_ |= 0x00008000; + } + } + + private com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> parametersBuilder_; + + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public java.util.List getParametersList() { + if (parametersBuilder_ == null) { + return java.util.Collections.unmodifiableList(parameters_); + } else { + return parametersBuilder_.getMessageList(); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public int getParametersCount() { + if (parametersBuilder_ == null) { + return parameters_.size(); + } else { + return parametersBuilder_.getCount(); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getParameters(int index) { + if (parametersBuilder_ == null) { + return parameters_.get(index); + } else { + return parametersBuilder_.getMessage(index); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder setParameters( + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { + if (parametersBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureParametersIsMutable(); + parameters_.set(index, value); + onChanged(); + } else { + parametersBuilder_.setMessage(index, value); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder setParameters( + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + parameters_.set(index, builderForValue.build()); + onChanged(); + } else { + parametersBuilder_.setMessage(index, builderForValue.build()); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addParameters(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { + if (parametersBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureParametersIsMutable(); + parameters_.add(value); + onChanged(); + } else { + parametersBuilder_.addMessage(value); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addParameters( + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { + if (parametersBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureParametersIsMutable(); + parameters_.add(index, value); + onChanged(); + } else { + parametersBuilder_.addMessage(index, value); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addParameters( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + parameters_.add(builderForValue.build()); + onChanged(); + } else { + parametersBuilder_.addMessage(builderForValue.build()); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addParameters( + int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + parameters_.add(index, builderForValue.build()); + onChanged(); + } else { + parametersBuilder_.addMessage(index, builderForValue.build()); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder addAllParameters( + java.lang.Iterable values) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, parameters_); + onChanged(); + } else { + parametersBuilder_.addAllMessages(values); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder clearParameters() { + if (parametersBuilder_ == null) { + parameters_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00008000); + onChanged(); + } else { + parametersBuilder_.clear(); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public Builder removeParameters(int index) { + if (parametersBuilder_ == null) { + ensureParametersIsMutable(); + parameters_.remove(index); + onChanged(); + } else { + parametersBuilder_.remove(index); + } + return this; + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getParametersBuilder( + int index) { + return getParametersFieldBuilder().getBuilder(index); + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getParametersOrBuilder( + int index) { + if (parametersBuilder_ == null) { + return parameters_.get(index); } else { + return parametersBuilder_.getMessageOrBuilder(index); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public java.util.List + getParametersOrBuilderList() { + if (parametersBuilder_ != null) { + return parametersBuilder_.getMessageOrBuilderList(); + } else { + return java.util.Collections.unmodifiableList(parameters_); + } + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder addParametersBuilder() { + return getParametersFieldBuilder().addBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()); + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder addParametersBuilder( + int index) { + return getParametersFieldBuilder().addBuilder( + index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()); + } + /** + * 
+       * parameters, e.g., weight and bias
+       *
+ * + * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + */ + public java.util.List + getParametersBuilderList() { + return getParametersFieldBuilder().getBuilderList(); + } + private com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> + getParametersFieldBuilder() { + if (parametersBuilder_ == null) { + parametersBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder>( + parameters_, + ((bitField0_ & 0x00008000) == 0x00008000), + getParentForChildren(), + isClean()); + parameters_ = null; + } + return parametersBuilder_; + } + + private boolean isMklInt8Enabled_ ; + /** + * bool isMklInt8Enabled = 17; + */ + public boolean getIsMklInt8Enabled() { + return isMklInt8Enabled_; + } + /** + * bool isMklInt8Enabled = 17; + */ + public Builder setIsMklInt8Enabled(boolean value) { + + isMklInt8Enabled_ = value; + onChanged(); + return this; + } + /** + * bool isMklInt8Enabled = 17; + */ + public Builder clearIsMklInt8Enabled() { + + isMklInt8Enabled_ = false; + onChanged(); + return this; + } + + private int inputDimMasks_ ; + /** + * int32 inputDimMasks = 18; + */ + public int getInputDimMasks() { + return inputDimMasks_; + } + /** + * int32 inputDimMasks = 18; + */ + public Builder setInputDimMasks(int value) { + + inputDimMasks_ = value; + onChanged(); + return this; + } + /** + * int32 inputDimMasks = 18; + */ + public Builder clearInputDimMasks() { + + inputDimMasks_ = 0; + onChanged(); + return this; + } + + private java.util.List inputScales_ = + java.util.Collections.emptyList(); + private void ensureInputScalesIsMutable() { + if (!((bitField0_ & 0x00040000) == 0x00040000)) { + inputScales_ = new java.util.ArrayList(inputScales_); + bitField0_ |= 0x00040000; + } + } + + private com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder> inputScalesBuilder_; + + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public java.util.List getInputScalesList() { + if (inputScalesBuilder_ == null) { + return java.util.Collections.unmodifiableList(inputScales_); + } else { + return inputScalesBuilder_.getMessageList(); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public int getInputScalesCount() { + if (inputScalesBuilder_ == null) { + return inputScales_.size(); + } else { + return inputScalesBuilder_.getCount(); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getInputScales(int index) { + if (inputScalesBuilder_ == null) { + return inputScales_.get(index); + } else { + return inputScalesBuilder_.getMessage(index); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder setInputScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (inputScalesBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureInputScalesIsMutable(); + inputScales_.set(index, value); + onChanged(); + } else { + inputScalesBuilder_.setMessage(index, value); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder setInputScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (inputScalesBuilder_ == null) { + ensureInputScalesIsMutable(); + inputScales_.set(index, builderForValue.build()); + onChanged(); + } else { + inputScalesBuilder_.setMessage(index, builderForValue.build()); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder addInputScales(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (inputScalesBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureInputScalesIsMutable(); + inputScales_.add(value); + onChanged(); + } else { + inputScalesBuilder_.addMessage(value); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder addInputScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (inputScalesBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureInputScalesIsMutable(); + inputScales_.add(index, value); + onChanged(); + } else { + inputScalesBuilder_.addMessage(index, value); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder addInputScales( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (inputScalesBuilder_ == null) { + ensureInputScalesIsMutable(); + inputScales_.add(builderForValue.build()); + onChanged(); + } else { + inputScalesBuilder_.addMessage(builderForValue.build()); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder addInputScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (inputScalesBuilder_ == null) { + ensureInputScalesIsMutable(); + inputScales_.add(index, builderForValue.build()); + onChanged(); + } else { + inputScalesBuilder_.addMessage(index, builderForValue.build()); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder addAllInputScales( + java.lang.Iterable values) { + if (inputScalesBuilder_ == null) { + ensureInputScalesIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, inputScales_); + onChanged(); + } else { + inputScalesBuilder_.addAllMessages(values); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder clearInputScales() { + if (inputScalesBuilder_ == null) { + inputScales_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00040000); + onChanged(); + } else { + inputScalesBuilder_.clear(); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public Builder removeInputScales(int index) { + if (inputScalesBuilder_ == null) { + ensureInputScalesIsMutable(); + inputScales_.remove(index); + onChanged(); + } else { + inputScalesBuilder_.remove(index); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder getInputScalesBuilder( + int index) { + return getInputScalesFieldBuilder().getBuilder(index); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getInputScalesOrBuilder( + int index) { + if (inputScalesBuilder_ == null) { + return inputScales_.get(index); } else { + return inputScalesBuilder_.getMessageOrBuilder(index); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public java.util.List + getInputScalesOrBuilderList() { + if (inputScalesBuilder_ != null) { + return inputScalesBuilder_.getMessageOrBuilderList(); + } else { + return java.util.Collections.unmodifiableList(inputScales_); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder addInputScalesBuilder() { + return getInputScalesFieldBuilder().addBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder addInputScalesBuilder( + int index) { + return getInputScalesFieldBuilder().addBuilder( + index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue inputScales = 19; + */ + public java.util.List + getInputScalesBuilderList() { + return getInputScalesFieldBuilder().getBuilderList(); + } + private com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder> + getInputScalesFieldBuilder() { + if (inputScalesBuilder_ == null) { + inputScalesBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder>( + inputScales_, + ((bitField0_ & 0x00040000) == 0x00040000), + getParentForChildren(), + isClean()); + inputScales_ = null; + } + return inputScalesBuilder_; + } + + private int outputDimMasks_ ; + /** + * int32 outputDimMasks = 20; + */ + public int getOutputDimMasks() { + return outputDimMasks_; + } + /** + * int32 outputDimMasks = 20; + */ + public Builder setOutputDimMasks(int value) { + + outputDimMasks_ = value; + onChanged(); + return this; + } + /** + * int32 outputDimMasks = 20; + */ + public Builder clearOutputDimMasks() { + + outputDimMasks_ = 0; + onChanged(); + return this; + } + + private java.util.List outputScales_ = + java.util.Collections.emptyList(); + private void ensureOutputScalesIsMutable() { + if (!((bitField0_ & 0x00100000) == 0x00100000)) { + outputScales_ = new java.util.ArrayList(outputScales_); + bitField0_ |= 0x00100000; + } + } + + private com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder> outputScalesBuilder_; + + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public java.util.List getOutputScalesList() { + if (outputScalesBuilder_ == null) { + return java.util.Collections.unmodifiableList(outputScales_); + } else { + return outputScalesBuilder_.getMessageList(); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public int getOutputScalesCount() { + if (outputScalesBuilder_ == null) { + return outputScales_.size(); + } else { + return outputScalesBuilder_.getCount(); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getOutputScales(int index) { + if (outputScalesBuilder_ == null) { + return outputScales_.get(index); + } else { + return outputScalesBuilder_.getMessage(index); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder setOutputScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (outputScalesBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureOutputScalesIsMutable(); + outputScales_.set(index, value); + onChanged(); + } else { + outputScalesBuilder_.setMessage(index, value); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder setOutputScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (outputScalesBuilder_ == null) { + ensureOutputScalesIsMutable(); + outputScales_.set(index, builderForValue.build()); + onChanged(); + } else { + outputScalesBuilder_.setMessage(index, builderForValue.build()); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder addOutputScales(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (outputScalesBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureOutputScalesIsMutable(); + outputScales_.add(value); + onChanged(); + } else { + outputScalesBuilder_.addMessage(value); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder addOutputScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (outputScalesBuilder_ == null) { + if (value == null) { + throw new NullPointerException(); + } + ensureOutputScalesIsMutable(); + outputScales_.add(index, value); + onChanged(); + } else { + outputScalesBuilder_.addMessage(index, value); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder addOutputScales( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (outputScalesBuilder_ == null) { + ensureOutputScalesIsMutable(); + outputScales_.add(builderForValue.build()); + onChanged(); + } else { + outputScalesBuilder_.addMessage(builderForValue.build()); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder addOutputScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (outputScalesBuilder_ == null) { + ensureOutputScalesIsMutable(); + outputScales_.add(index, builderForValue.build()); + onChanged(); + } else { + outputScalesBuilder_.addMessage(index, builderForValue.build()); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder addAllOutputScales( + java.lang.Iterable values) { + if (outputScalesBuilder_ == null) { + ensureOutputScalesIsMutable(); + com.google.protobuf.AbstractMessageLite.Builder.addAll( + values, outputScales_); + onChanged(); + } else { + outputScalesBuilder_.addAllMessages(values); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder clearOutputScales() { + if (outputScalesBuilder_ == null) { + outputScales_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00100000); + onChanged(); + } else { + outputScalesBuilder_.clear(); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public Builder removeOutputScales(int index) { + if (outputScalesBuilder_ == null) { + ensureOutputScalesIsMutable(); + outputScales_.remove(index); + onChanged(); + } else { + outputScalesBuilder_.remove(index); + } + return this; + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder getOutputScalesBuilder( + int index) { + return getOutputScalesFieldBuilder().getBuilder(index); + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getOutputScalesOrBuilder( + int index) { + if (outputScalesBuilder_ == null) { + return outputScales_.get(index); } else { + return outputScalesBuilder_.getMessageOrBuilder(index); + } + } + /** + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; + */ + public java.util.List + getOutputScalesOrBuilderList() { + if (outputScalesBuilder_ != null) { + return outputScalesBuilder_.getMessageOrBuilderList(); + } else { + return java.util.Collections.unmodifiableList(outputScales_); } - - return this; } /** - * 
-       *output shape
-       *
- * - * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; */ - public com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder getOutputShapeBuilder() { - - onChanged(); - return getOutputShapeFieldBuilder().getBuilder(); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder addOutputScalesBuilder() { + return getOutputScalesFieldBuilder().addBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()); } /** - * 
-       *output shape
-       *
- * - * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; */ - public com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder getOutputShapeOrBuilder() { - if (outputShapeBuilder_ != null) { - return outputShapeBuilder_.getMessageOrBuilder(); - } else { - return outputShape_ == null ? - com.intel.analytics.bigdl.serialization.Bigdl.Shape.getDefaultInstance() : outputShape_; - } + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder addOutputScalesBuilder( + int index) { + return getOutputScalesFieldBuilder().addBuilder( + index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()); } /** - * 
-       *output shape
-       *
- * - * .com.intel.analytics.bigdl.serialization.Shape outputShape = 14; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue outputScales = 21; */ - private com.google.protobuf.SingleFieldBuilderV3< - com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder> - getOutputShapeFieldBuilder() { - if (outputShapeBuilder_ == null) { - outputShapeBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - com.intel.analytics.bigdl.serialization.Bigdl.Shape, com.intel.analytics.bigdl.serialization.Bigdl.Shape.Builder, com.intel.analytics.bigdl.serialization.Bigdl.ShapeOrBuilder>( - getOutputShape(), + public java.util.List + getOutputScalesBuilderList() { + return getOutputScalesFieldBuilder().getBuilderList(); + } + private com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder> + getOutputScalesFieldBuilder() { + if (outputScalesBuilder_ == null) { + outputScalesBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder>( + outputScales_, + ((bitField0_ & 0x00100000) == 0x00100000), getParentForChildren(), isClean()); - outputShape_ = null; + outputScales_ = null; } - return outputShapeBuilder_; + return outputScalesBuilder_; } - private boolean hasParameters_ ; + private int weightDimMasks_ ; /** - * 
-       * indicator if module has parameters
-       *
- * - * bool hasParameters = 15; + * int32 weightDimMasks = 22; */ - public boolean getHasParameters() { - return hasParameters_; + public int getWeightDimMasks() { + return weightDimMasks_; } /** - * 
-       * indicator if module has parameters
-       *
- * - * bool hasParameters = 15; + * int32 weightDimMasks = 22; */ - public Builder setHasParameters(boolean value) { + public Builder setWeightDimMasks(int value) { - hasParameters_ = value; + weightDimMasks_ = value; onChanged(); return this; } /** - * 
-       * indicator if module has parameters
-       *
- * - * bool hasParameters = 15; + * int32 weightDimMasks = 22; */ - public Builder clearHasParameters() { + public Builder clearWeightDimMasks() { - hasParameters_ = false; + weightDimMasks_ = 0; onChanged(); return this; } - private java.util.List parameters_ = + private java.util.List weightScales_ = java.util.Collections.emptyList(); - private void ensureParametersIsMutable() { - if (!((bitField0_ & 0x00008000) == 0x00008000)) { - parameters_ = new java.util.ArrayList(parameters_); - bitField0_ |= 0x00008000; + private void ensureWeightScalesIsMutable() { + if (!((bitField0_ & 0x00400000) == 0x00400000)) { + weightScales_ = new java.util.ArrayList(weightScales_); + bitField0_ |= 0x00400000; } } private com.google.protobuf.RepeatedFieldBuilderV3< - com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> parametersBuilder_; + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder> weightScalesBuilder_; /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public java.util.List getParametersList() { - if (parametersBuilder_ == null) { - return java.util.Collections.unmodifiableList(parameters_); + public java.util.List getWeightScalesList() { + if (weightScalesBuilder_ == null) { + return java.util.Collections.unmodifiableList(weightScales_); } else { - return parametersBuilder_.getMessageList(); + return weightScalesBuilder_.getMessageList(); } } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public int getParametersCount() { - if (parametersBuilder_ == null) { - return parameters_.size(); + public int getWeightScalesCount() { + if (weightScalesBuilder_ == null) { + return weightScales_.size(); } else { - return parametersBuilder_.getCount(); + return weightScalesBuilder_.getCount(); } } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor getParameters(int index) { - if (parametersBuilder_ == null) { - return parameters_.get(index); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue getWeightScales(int index) { + if (weightScalesBuilder_ == null) { + return weightScales_.get(index); } else { - return parametersBuilder_.getMessage(index); + return weightScalesBuilder_.getMessage(index); } } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder setParameters( - int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { - if (parametersBuilder_ == null) { + public Builder setWeightScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (weightScalesBuilder_ == null) { if (value == null) { throw new NullPointerException(); } - ensureParametersIsMutable(); - parameters_.set(index, value); + ensureWeightScalesIsMutable(); + weightScales_.set(index, value); onChanged(); } else { - parametersBuilder_.setMessage(index, value); + weightScalesBuilder_.setMessage(index, value); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder setParameters( - int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { - if (parametersBuilder_ == null) { - ensureParametersIsMutable(); - parameters_.set(index, builderForValue.build()); + public Builder setWeightScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (weightScalesBuilder_ == null) { + ensureWeightScalesIsMutable(); + weightScales_.set(index, builderForValue.build()); onChanged(); } else { - parametersBuilder_.setMessage(index, builderForValue.build()); + weightScalesBuilder_.setMessage(index, builderForValue.build()); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder addParameters(com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { - if (parametersBuilder_ == null) { + public Builder addWeightScales(com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (weightScalesBuilder_ == null) { if (value == null) { throw new NullPointerException(); } - ensureParametersIsMutable(); - parameters_.add(value); + ensureWeightScalesIsMutable(); + weightScales_.add(value); onChanged(); } else { - parametersBuilder_.addMessage(value); + weightScalesBuilder_.addMessage(value); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder addParameters( - int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor value) { - if (parametersBuilder_ == null) { + public Builder addWeightScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue value) { + if (weightScalesBuilder_ == null) { if (value == null) { throw new NullPointerException(); } - ensureParametersIsMutable(); - parameters_.add(index, value); + ensureWeightScalesIsMutable(); + weightScales_.add(index, value); onChanged(); } else { - parametersBuilder_.addMessage(index, value); + weightScalesBuilder_.addMessage(index, value); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder addParameters( - com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { - if (parametersBuilder_ == null) { - ensureParametersIsMutable(); - parameters_.add(builderForValue.build()); + public Builder addWeightScales( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (weightScalesBuilder_ == null) { + ensureWeightScalesIsMutable(); + weightScales_.add(builderForValue.build()); onChanged(); } else { - parametersBuilder_.addMessage(builderForValue.build()); + weightScalesBuilder_.addMessage(builderForValue.build()); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder addParameters( - int index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder builderForValue) { - if (parametersBuilder_ == null) { - ensureParametersIsMutable(); - parameters_.add(index, builderForValue.build()); + public Builder addWeightScales( + int index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder builderForValue) { + if (weightScalesBuilder_ == null) { + ensureWeightScalesIsMutable(); + weightScales_.add(index, builderForValue.build()); onChanged(); } else { - parametersBuilder_.addMessage(index, builderForValue.build()); + weightScalesBuilder_.addMessage(index, builderForValue.build()); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder addAllParameters( - java.lang.Iterable values) { - if (parametersBuilder_ == null) { - ensureParametersIsMutable(); + public Builder addAllWeightScales( + java.lang.Iterable values) { + if (weightScalesBuilder_ == null) { + ensureWeightScalesIsMutable(); com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, parameters_); + values, weightScales_); onChanged(); } else { - parametersBuilder_.addAllMessages(values); + weightScalesBuilder_.addAllMessages(values); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder clearParameters() { - if (parametersBuilder_ == null) { - parameters_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00008000); + public Builder clearWeightScales() { + if (weightScalesBuilder_ == null) { + weightScales_ = java.util.Collections.emptyList(); + bitField0_ = (bitField0_ & ~0x00400000); onChanged(); } else { - parametersBuilder_.clear(); + weightScalesBuilder_.clear(); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public Builder removeParameters(int index) { - if (parametersBuilder_ == null) { - ensureParametersIsMutable(); - parameters_.remove(index); + public Builder removeWeightScales(int index) { + if (weightScalesBuilder_ == null) { + ensureWeightScalesIsMutable(); + weightScales_.remove(index); onChanged(); } else { - parametersBuilder_.remove(index); + weightScalesBuilder_.remove(index); } return this; } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder getParametersBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder getWeightScalesBuilder( int index) { - return getParametersFieldBuilder().getBuilder(index); + return getWeightScalesFieldBuilder().getBuilder(index); } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder getParametersOrBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder getWeightScalesOrBuilder( int index) { - if (parametersBuilder_ == null) { - return parameters_.get(index); } else { - return parametersBuilder_.getMessageOrBuilder(index); + if (weightScalesBuilder_ == null) { + return weightScales_.get(index); } else { + return weightScalesBuilder_.getMessageOrBuilder(index); } } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public java.util.List - getParametersOrBuilderList() { - if (parametersBuilder_ != null) { - return parametersBuilder_.getMessageOrBuilderList(); + public java.util.List + getWeightScalesOrBuilderList() { + if (weightScalesBuilder_ != null) { + return weightScalesBuilder_.getMessageOrBuilderList(); } else { - return java.util.Collections.unmodifiableList(parameters_); + return java.util.Collections.unmodifiableList(weightScales_); } } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder addParametersBuilder() { - return getParametersFieldBuilder().addBuilder( - com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()); + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder addWeightScalesBuilder() { + return getWeightScalesFieldBuilder().addBuilder( + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()); } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder addParametersBuilder( + public com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder addWeightScalesBuilder( int index) { - return getParametersFieldBuilder().addBuilder( - index, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.getDefaultInstance()); + return getWeightScalesFieldBuilder().addBuilder( + index, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.getDefaultInstance()); } /** - * 
-       * parameters, e.g., weight and bias
-       *
- * - * repeated .com.intel.analytics.bigdl.serialization.BigDLTensor parameters = 16; + * repeated .com.intel.analytics.bigdl.serialization.AttrValue weightScales = 23; */ - public java.util.List - getParametersBuilderList() { - return getParametersFieldBuilder().getBuilderList(); + public java.util.List + getWeightScalesBuilderList() { + return getWeightScalesFieldBuilder().getBuilderList(); } private com.google.protobuf.RepeatedFieldBuilderV3< - com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder> - getParametersFieldBuilder() { - if (parametersBuilder_ == null) { - parametersBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensor.Builder, com.intel.analytics.bigdl.serialization.Bigdl.BigDLTensorOrBuilder>( - parameters_, - ((bitField0_ & 0x00008000) == 0x00008000), + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder> + getWeightScalesFieldBuilder() { + if (weightScalesBuilder_ == null) { + weightScalesBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< + com.intel.analytics.bigdl.serialization.Bigdl.AttrValue, com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.Builder, com.intel.analytics.bigdl.serialization.Bigdl.AttrValueOrBuilder>( + weightScales_, + ((bitField0_ & 0x00400000) == 0x00400000), getParentForChildren(), isClean()); - parameters_ = null; + weightScales_ = null; } - return parametersBuilder_; + return weightScalesBuilder_; } public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { @@ -22144,7 +23498,7 @@ public com.intel.analytics.bigdl.serialization.Bigdl.Shape getDefaultInstanceFor java.lang.String[] descriptorData = { "\n\013bigdl.proto\022\'com.intel.analytics.bigdl" + ".serialization\032\031google/protobuf/any.prot" + - "o\"\206\006\n\013BigDLModule\022\014\n\004name\030\001 \001(\t\022H\n\nsubMo" + + "o\"\304\010\n\013BigDLModule\022\014\n\004name\030\001 \001(\t\022H\n\nsubMo" + "dules\030\002 \003(\01324.com.intel.analytics.bigdl." + "serialization.BigDLModule\022D\n\006weight\030\003 \001(" + "\01324.com.intel.analytics.bigdl.serializat" + @@ -22161,105 +23515,113 @@ public com.intel.analytics.bigdl.serialization.Bigdl.Shape getDefaultInstanceFor "igdl.serialization.Shape\022\025\n\rhasParameter" + "s\030\017 \001(\010\022H\n\nparameters\030\020 \003(\01324.com.intel." + "analytics.bigdl.serialization.BigDLTenso" + - "r\032_\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022A\n\005value\030\002 \001", - "(\01322.com.intel.analytics.bigdl.serializa" + - "tion.AttrValue:\0028\001\"g\n\nInitMethod\022K\n\nmeth" + - "odType\030\001 \001(\01627.com.intel.analytics.bigdl" + - ".serialization.InitMethodType\022\014\n\004data\030\002 " + - "\003(\001\"\326\002\n\013BigDLTensor\022C\n\010datatype\030\001 \001(\01621." + - "com.intel.analytics.bigdl.serialization." + - "DataType\022\014\n\004size\030\002 \003(\005\022\016\n\006stride\030\003 \003(\005\022\016" + - "\n\006offset\030\004 \001(\005\022\021\n\tdimension\030\005 \001(\005\022\021\n\tnEl" + - "ements\030\006 \001(\005\022\020\n\010isScalar\030\007 \001(\010\022G\n\007storag" + - "e\030\010 \001(\01326.com.intel.analytics.bigdl.seri", - "alization.TensorStorage\022\n\n\002id\030\t \001(\005\022G\n\nt" + - "ensorType\030\n \001(\01623.com.intel.analytics.bi" + - "gdl.serialization.TensorType\"\352\001\n\rTensorS" + - "torage\022C\n\010datatype\030\001 \001(\01621.com.intel.ana" + - "lytics.bigdl.serialization.DataType\022\022\n\nf" + - "loat_data\030\002 \003(\002\022\023\n\013double_data\030\003 \003(\001\022\021\n\t" + - "bool_data\030\004 \003(\010\022\023\n\013string_data\030\005 \003(\t\022\020\n\010" + - "int_data\030\006 \003(\005\022\021\n\tlong_data\030\007 \003(\003\022\022\n\nbyt" + - "es_data\030\010 \003(\014\022\n\n\002id\030\t \001(\005\"u\n\013Regularizer" + - "\022Q\n\017regularizerType\030\001 \001(\01628.com.intel.an", - "alytics.bigdl.serialization.RegularizerT" + - "ype\022\023\n\013regularData\030\002 \003(\001\"\224\016\n\tAttrValue\022C" + - "\n\010dataType\030\001 \001(\01621.com.intel.analytics.b" + - "igdl.serialization.DataType\022\017\n\007subType\030\002" + - " \001(\t\022\024\n\nint32Value\030\003 \001(\005H\000\022\024\n\nint64Value" + - "\030\004 \001(\003H\000\022\024\n\nfloatValue\030\005 \001(\002H\000\022\025\n\013double" + - "Value\030\006 \001(\001H\000\022\025\n\013stringValue\030\007 \001(\tH\000\022\023\n\t" + - "boolValue\030\010 \001(\010H\000\022P\n\020regularizerValue\030\t " + + "r\022\030\n\020isMklInt8Enabled\030\021 \001(\010\022\025\n\rinputDimM", + "asks\030\022 \001(\005\022G\n\013inputScales\030\023 \003(\01322.com.in" + + "tel.analytics.bigdl.serialization.AttrVa" + + "lue\022\026\n\016outputDimMasks\030\024 \001(\005\022H\n\014outputSca" + + "les\030\025 \003(\01322.com.intel.analytics.bigdl.se" + + "rialization.AttrValue\022\026\n\016weightDimMasks\030" + + "\026 \001(\005\022H\n\014weightScales\030\027 \003(\01322.com.intel." + + "analytics.bigdl.serialization.AttrValue\032" + + "_\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022A\n\005value\030\002 \001(\013" + + "22.com.intel.analytics.bigdl.serializati" + + "on.AttrValue:\0028\001\"g\n\nInitMethod\022K\n\nmethod", + "Type\030\001 \001(\01627.com.intel.analytics.bigdl.s" + + "erialization.InitMethodType\022\014\n\004data\030\002 \003(" + + "\001\"\326\002\n\013BigDLTensor\022C\n\010datatype\030\001 \001(\01621.co" + + "m.intel.analytics.bigdl.serialization.Da" + + "taType\022\014\n\004size\030\002 \003(\005\022\016\n\006stride\030\003 \003(\005\022\016\n\006" + + "offset\030\004 \001(\005\022\021\n\tdimension\030\005 \001(\005\022\021\n\tnElem" + + "ents\030\006 \001(\005\022\020\n\010isScalar\030\007 \001(\010\022G\n\007storage\030" + + "\010 \001(\01326.com.intel.analytics.bigdl.serial" + + "ization.TensorStorage\022\n\n\002id\030\t \001(\005\022G\n\nten" + + "sorType\030\n \001(\01623.com.intel.analytics.bigd", + "l.serialization.TensorType\"\352\001\n\rTensorSto" + + "rage\022C\n\010datatype\030\001 \001(\01621.com.intel.analy" + + "tics.bigdl.serialization.DataType\022\022\n\nflo" + + "at_data\030\002 \003(\002\022\023\n\013double_data\030\003 \003(\001\022\021\n\tbo" + + "ol_data\030\004 \003(\010\022\023\n\013string_data\030\005 \003(\t\022\020\n\010in" + + "t_data\030\006 \003(\005\022\021\n\tlong_data\030\007 \003(\003\022\022\n\nbytes" + + "_data\030\010 \003(\014\022\n\n\002id\030\t \001(\005\"u\n\013Regularizer\022Q" + + "\n\017regularizerType\030\001 \001(\01628.com.intel.anal" + + "ytics.bigdl.serialization.RegularizerTyp" + + "e\022\023\n\013regularData\030\002 \003(\001\"\224\016\n\tAttrValue\022C\n\010", + "dataType\030\001 \001(\01621.com.intel.analytics.big" + + "dl.serialization.DataType\022\017\n\007subType\030\002 \001" + + "(\t\022\024\n\nint32Value\030\003 \001(\005H\000\022\024\n\nint64Value\030\004" + + " \001(\003H\000\022\024\n\nfloatValue\030\005 \001(\002H\000\022\025\n\013doubleVa" + + "lue\030\006 \001(\001H\000\022\025\n\013stringValue\030\007 \001(\tH\000\022\023\n\tbo" + + "olValue\030\010 \001(\010H\000\022P\n\020regularizerValue\030\t \001(" + + "\01324.com.intel.analytics.bigdl.serializat" + + "ion.RegularizerH\000\022K\n\013tensorValue\030\n \001(\01324" + + ".com.intel.analytics.bigdl.serialization" + + ".BigDLTensorH\000\022Q\n\023variableFormatValue\030\013 ", + "\001(\01622.com.intel.analytics.bigdl.serializ" + + "ation.VarFormatH\000\022N\n\017initMethodValue\030\014 \001" + + "(\01323.com.intel.analytics.bigdl.serializa" + + "tion.InitMethodH\000\022P\n\020bigDLModuleValue\030\r " + "\001(\01324.com.intel.analytics.bigdl.serializ" + - 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"\022\014\n\004size\030\001 \001(\005\022C\n\010datatype\030\002 \001(\01621.com.i" + - "ntel.analytics.bigdl.serialization.DataT" + - "ype\022\013\n\003i32\030\003 \003(\005\022\013\n\003i64\030\004 \003(\003\022\013\n\003flt\030\005 \003", - "(\002\022\013\n\003dbl\030\006 \003(\001\022\013\n\003str\030\007 \003(\t\022\017\n\007boolean\030" + - "\010 \003(\010\022I\n\013Regularizer\030\t \003(\01324.com.intel.a" + - "nalytics.bigdl.serialization.Regularizer" + - "\022D\n\006tensor\030\n \003(\01324.com.intel.analytics.b" + - "igdl.serialization.BigDLTensor\022J\n\016variab" + - "leFormat\030\013 \003(\01622.com.intel.analytics.big" + - "dl.serialization.VarFormat\022G\n\ninitMethod" + - "\030\014 \003(\01323.com.intel.analytics.bigdl.seria" + - "lization.InitMethod\022I\n\013bigDLModule\030\r \003(\013" + - "24.com.intel.analytics.bigdl.serializati", - "on.BigDLModule\022K\n\014nameAttrList\030\016 \003(\01325.c" + - "om.intel.analytics.bigdl.serialization.N" + - "ameAttrList\022L\n\ndataFormat\030\017 \003(\01628.com.in" + - 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InternalDescriptorAssigner() { @@ -22279,7 +23641,7 @@ public com.google.protobuf.ExtensionRegistry assignDescriptors( internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_fieldAccessorTable = new com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor, - new java.lang.String[] { "Name", "SubModules", "Weight", "Bias", "PreModules", "NextModules", "ModuleType", "Attr", "Version", "Train", "NamePostfix", "Id", "InputShape", "OutputShape", "HasParameters", "Parameters", }); + new java.lang.String[] { "Name", "SubModules", "Weight", "Bias", "PreModules", "NextModules", "ModuleType", "Attr", "Version", "Train", "NamePostfix", "Id", "InputShape", "OutputShape", "HasParameters", "Parameters", "IsMklInt8Enabled", "InputDimMasks", "InputScales", "OutputDimMasks", "OutputScales", "WeightDimMasks", "WeightScales", }); internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_descriptor = internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_descriptor.getNestedTypes().get(0); internal_static_com_intel_analytics_bigdl_serialization_BigDLModule_AttrEntry_fieldAccessorTable = new diff --git a/scala/dllib/src/main/resources/serialization/bigdl.proto b/scala/dllib/src/main/resources/serialization/bigdl.proto index 9f0e276304d..638a5dca43d 100644 --- a/scala/dllib/src/main/resources/serialization/bigdl.proto +++ b/scala/dllib/src/main/resources/serialization/bigdl.proto @@ -21,6 +21,13 @@ message BigDLModule Shape outputShape = 14; //output shape bool hasParameters = 15; // indicator if module has parameters repeated BigDLTensor parameters = 16; // parameters, e.g., weight and bias + bool isMklInt8Enabled = 17; + int32 inputDimMasks = 18; + repeated AttrValue inputScales = 19; + int32 outputDimMasks = 20; + repeated AttrValue outputScales = 21; + int32 weightDimMasks = 22; + repeated AttrValue weightScales = 23; } enum VarFormat { EMPTY_FORMAT = 0; diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala index 71856b6f2fd..dbc4d7dfa81 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ConcatTable.scala @@ -34,7 +34,8 @@ import scala.reflect.ClassTag */ @SerialVersionUID(- 704681653938468956L) class ConcatTable[T : ClassTag] - (implicit ev: TensorNumeric[T]) extends DynamicContainer[Activity, Table, T] { + (implicit ev: TensorNumeric[T]) + extends DynamicContainer[Activity, Table, T] with MklInt8Convertible { override def updateOutput(input: Activity): Table = { require(modules.length > 0, "empty modules of concat table") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala index f11d1267bd2..48f1878bd54 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Graph.scala @@ -73,7 +73,8 @@ abstract class Graph[T: ClassTag]( val inputs : Seq[ModuleNode[T]], private[bigdl] val outputs : Seq[ModuleNode[T]], private[bigdl] val variables: Option[(Array[Tensor[T]], Array[Tensor[T]])] = None -)(implicit ev: TensorNumeric[T]) extends Container[Activity, Activity, T]{ +)(implicit ev: TensorNumeric[T]) + extends Container[Activity, Activity, T] with MklInt8Convertible { /** * For a multi-tensor output module, some output tensors may not contributed to the final forward @@ -293,7 +294,7 @@ abstract class Graph[T: ClassTag]( } } - protected def findInput(node: ModuleNode[T], input: Activity): Activity = { + def findInput(node: ModuleNode[T], input: Activity): Activity = { if (node.element.isInstanceOf[WithoutInput]) return null val nodeInput = if (node.prevNodes.isEmpty) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala index 986369dc605..f3497cce49b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala @@ -51,7 +51,8 @@ class Linear[T: ClassTag]( private val initBias: Tensor[T] = null, private val initGradWeight: Tensor[T] = null, private val initGradBias: Tensor[T] = null -)(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { +)(implicit ev: TensorNumeric[T]) + extends TensorModule[T] with Initializable with MklInt8Convertible { val weight: Tensor[T] = if (initWeight != null) initWeight else Tensor[T](outputSize, inputSize) val bias: Tensor[T] = diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala new file mode 100644 index 00000000000..cc72fdaafa0 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala @@ -0,0 +1,454 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table + +import scala.collection.mutable.ArrayBuffer + + +/** + * Trait which provides MKL-DNN functionality to convert from FP32 to INT8 + */ +trait MklInt8Convertible { + // input dimension mask + protected var inputDimMask: Int = 0 + // output dimension mask + protected var outputDimMask: Int = 0 + // weight dimension mask + protected var weightDimMask: Int = 0 + // input activation scales + private[nn] var inputScalesBuffer: ArrayBuffer[Array[Float]] = ArrayBuffer.empty[Array[Float]] + // output scales + private[nn] var outputScalesBuffer: ArrayBuffer[Array[Float]] = ArrayBuffer.empty[Array[Float]] + // weight scales + private[nn] var weightScalesBuffer: ArrayBuffer[Array[Float]] = ArrayBuffer.empty[Array[Float]] + + + /** + * Calculate the required scales for converting int8 modules + * Currently there are four type of modules should be supported: + * 1) Graph: calculate scales for input and output + * 2) Linear: calculate scales for input, output and weight + * 3) Spatial Convolution: calculate scales for input, output and weight + * 4) Sequential: calculate scales for input, output as well as the scales of submodules + * 5) ConcatTable: calculate scales for input, output as well as the scales of submodules + * @param inActivity input activity + */ + private[bigdl]def calcScales(inputActvt: Activity): Unit = { + + if (inputActvt != null) { + val module = this.asInstanceOf[AbstractModule[_, _, Float]] + val outputActvt = module.forward(inputActvt) + + module match { + case graph: Graph[Float] => calcGraphScales(inputActvt, outputActvt) + // handlers for BLAS modules + case linear: Linear[Float@unchecked] => + calcModuleScales(inputActvt, outputActvt, linear.weight) + case spatialConv: SpatialConvolution[Float@unchecked] => + calcModuleScales(inputActvt, outputActvt, spatialConv.weight) + case sequential: Sequential[Float@unchecked] => + calcSequentialScales(inputActvt, outputActvt) + case concatTable: ConcatTable[Float@unchecked] => + calcConcatTableScales(inputActvt, outputActvt) + // handlers for DNN modules + case dnnLinear: mkldnn.Linear => + calcModuleScales(inputActvt, outputActvt, getWeight(dnnLinear)) + case dnnSpatialConv: mkldnn.SpatialConvolution => + calcModuleScales(inputActvt, outputActvt, getWeight(dnnSpatialConv)) + case dnnSequential: mkldnn.Sequential => + calcSequentialScales(inputActvt, outputActvt) + case dnnConcatTable: mkldnn.ConcatTable => + calcConcatTableScales(inputActvt, outputActvt) + case _ => throw new UnsupportedOperationException( + "Int8 conversion is not supported for module: " + module.getName() + ) + } + } + } + + /** + * Calculate module's scales given its input and output + * Store calculated scales in array buffers + * @param inActivity input activity + * @param outActivity output activity + */ + private def calcModuleScales(inputActvt: Activity, outputActvt: Activity): Unit = { + if (inputActvt != null) { + calcActivityScales(inputActvt, inputDimMask).foreach(appendInputScales) + } + + if (outputActvt != null) { + calcActivityScales(outputActvt, outputDimMask).foreach(appendOutputScales) + } + } + + /** + * Calculate module's scales given its input, output and weight + * @param inActivity input activity + * @param outActivity output activity + * @param weightTensor weight + */ + private def calcModuleScales(inActivity: Activity, outActivity: Activity, + weightTensor: Tensor[Float]): Unit = { + // calculate scales for input and output + calcModuleScales(inActivity, outActivity) + // calculate scales for weight + appendWeightScales(calcTensorScale(weightTensor, weightDimMask)) + + } + + /** + * Calculate scales given activity, mask and update method + * @param activity target activity to get scales + * @param mask dimension mask associated with target activity + * @param appendFunc update method for scales + */ + private def calcActivityScales(activity: Activity, mask: Int): Array[Array[Float]] = { + activity match { + case tensor: Tensor[Float@unchecked] => Array(calcTensorScale(activity.toTensor[Float], mask)) + case table: Table => activity.toTable.map[Array[Float]](elem => { + val index: Any = elem._1 + val tensor: Tensor[Float] = elem._2.asInstanceOf[Tensor[Float]] + calcTensorScale(tensor, mask) + }).toArray + case _ => throw new IllegalArgumentException("Invalid activity " + activity) + } + } + + /** Given a tensor and a dimension mask, calculate the scales of this tensor + * @param tensor tensor of float, stores high dimension data + * @param mask dimension mask + * @return scalesBuffer Array, an array stores scales + */ + private def calcTensorScale(tensor: Tensor[Float], mask: Int): Array[Float] = { + if (mask == 0) { // no mask performed, return max of tensor storage + Array(tensor.abs().max()) + } else if (scala.math.pow(2, tensor.dim()) - 1 == mask) { + // mask bits are ON for all dimensions + // return the abs value of tensor as an array + tensor.abs().storage().toArray[Float] + } else { + // mask bits are ON for some of dimensions + // slice storage according to the dimension if its mask bit is ON + // find and store the max for each subset + val scalesBuffer = ArrayBuffer.empty[Float] + val binStrMask: String = mask.toBinaryString + val binStrLen = binStrMask.length + val bitMask: Array[Int] = new Array(binStrLen) + + for(i <- 1 to binStrLen) { + bitMask(binStrLen - i) = binStrMask(binStrLen - i).asDigit + if (bitMask(binStrLen - i) == 1) { + val dimSize = tensor.size(i) + for (j <- 1 to dimSize) { + scalesBuffer.append(tensor.select(i, j).abs().max()) + } + } + } + scalesBuffer.toArray[Float] + } + } + + /** + * Scales calculator for Sequential Module + * @param inActivity input of the Sequential Module + */ + private def calcSequentialScales(inputActvt: Activity, outputActvt: Activity): Unit = { + require(this.isInstanceOf[Sequential[Float@unchecked]] || this.isInstanceOf[mkldnn.Sequential], + this.getClass.getName + " is not an instance of Sequential.") + + val module: DynamicContainer[_, _, Float] = this.asInstanceOf[DynamicContainer[_, _, Float]] + + // output of previous module is the input of current module + var prevOutputActivity: Activity = inputActvt + + // calc scales for main module + this.calcModuleScales(inputActvt, outputActvt) + + // Iterator of Sequential modules + val moduleIter = module.modules.iterator + // calc scales for sub-module + while (moduleIter.hasNext) { + val currModule = moduleIter.next() + if (currModule.isInstanceOf[MklInt8Convertible]) { + val cvtbModule = currModule.asInstanceOf[MklInt8Convertible] + cvtbModule.calcScales(prevOutputActivity) + } + // update previous output + prevOutputActivity = currModule.output + } + } + + /** + * Scales calculator for ConcatTable module + * Submodules inside ConcatTable share the same input + * @param inActivity + */ + private def calcConcatTableScales(inputActvt: Activity, outputActvt: Activity): Unit = { + require(this.isInstanceOf[ConcatTable[Float@unchecked]] || this.isInstanceOf[mkldnn.ConcatTable] + , this.getClass.getName + " is not an instance of ConcatTable.") + + val module: DynamicContainer[_, _, Float] = this.asInstanceOf[DynamicContainer[_, _, Float]] + + // calc scales for main module + this.calcModuleScales(inputActvt, outputActvt) + + // calc scales for sub-module + val moduleIter = module.modules.iterator + while (moduleIter.hasNext) { + val currModule = moduleIter.next() + if (currModule.isInstanceOf[MklInt8Convertible]) { + val cvtbModule = currModule.asInstanceOf[MklInt8Convertible] + cvtbModule.calcScales(inputActvt) + } + } + } + + /** + * Scales calculator for Graph module + * Submodules inside Graph are traversed based on its topological sort + * The order can obtain from by calling getForwardExecutions + * @param inputActvt input activity of the graph module + * @param outputActvt output activity of the graph module + */ + private def calcGraphScales(inputActvt: Activity, outputActvt: Activity): Unit = { + require(this.isInstanceOf[Graph[Float@unchecked]], this.getClass.getName + + " is not an instance of Graph[Float]") + + // calc scales for main module + calcModuleScales(inputActvt, outputActvt) + + // calc scales for sub-module + val module: Graph[Float] = this.asInstanceOf[Graph[Float]] + val outputNodes = module.getForwardExecutions() + var i = 0 + // traverse through all the sub-modules + while(i < outputNodes.length) { + // get current sub-module + val currNode = outputNodes(i) + // get the input activity of current sub-module + val currInputActvt = module.findInput(currNode, inputActvt) + // calculate scales if current sub-module is int8 convertible + if (currNode.element.isInstanceOf[MklInt8Convertible]) { + currNode.element.asInstanceOf[MklInt8Convertible].calcScales(currInputActvt) + } + i += 1 + } + } + + /** + * Helper function to get weight from module parameter + * @param module the module to get weight from + * @return a tensor contains weight + */ + private def getWeight(module: AbstractModule[_, _, Float]): Tensor[Float] = { + if (module != null) { + module.getParameters()._1 + } else { + null + } + } + + /** + * Get dimension mask of input + * @return inputDimMask field which stores value of input dimension mask + */ + def getInputDimMask(): Int = { + inputDimMask + } + + /** + * Set dimension mask of input + * @param mask value of input dimension mask to be set + * @return Unit + */ + def setInputDimMask(mask: Int) : Unit = { + inputDimMask = mask + } + + /** + * Get dimension mask of output + * @return outputDimMask field which stores value of output dimension mask + */ + def getOutputDimMask(): Int = { + outputDimMask + } + + /** + * Set dimension mask of output + * @param mask value of output dimension mask to be set + * @return Unit + */ + def setOutputDimMask(mask: Int): Unit = { + outputDimMask = mask + } + + /** + * Get dimension mask of weight + * @return weightDimMask which stores value of weight mask + */ + def getWeightDimMask(): Int = { + weightDimMask + } + + /** + * Set dimension mask of weight + * @param mask value of weight mask to be set + * @return Unit + */ + def setWeightDimMask(mask: Int): Unit = { + weightDimMask = mask + } + + /** + * Get input scales + * @return field which stores value of input scales + */ + def getInputScales(): Array[Array[Float]] = { + inputScalesBuffer.toArray + } + + /** + * Set input scales + * Clear existing buffer of input scales, and place updated scales into the cleared buffer + * @param inScales value of input scales to be set + * @return Unit + */ + def setInputScales(inScales: Array[Array[Float]]): Unit = { + inputScalesBuffer.clear() + inScales.foreach(appendInputScales) + } + + /** + * Get output scales + * @return field which stores value of output scales + */ + def getOutputScales(): Array[Array[Float]] = { + outputScalesBuffer.toArray + } + + /** + * Set output scales + * Clear existing buffer of output scales, and place updated scales into the cleared buffer + * @param outScales value of output scales to be set + * @return Unit + */ + def setOutputScales(outScales: Array[Array[Float]]): Unit = { + outputScalesBuffer.clear() + outScales.foreach(appendOutputScales) + } + + /** + * Get weight scales + * @return field which stores value of weight scales + */ + def getWeightScales(): Array[Array[Float]] = { + weightScalesBuffer.toArray + } + + /** + * Set weight scales + * Clear existing buffer of weight scales, and place updated scales into the cleared buffer + * @param weightScales value of weight scales to be set + * @return Unit + */ + def setWeightScales(weightScales: Array[Array[Float]]): Unit = { + weightScalesBuffer.clear() + weightScales.foreach(appendWeightScales) + } + + /** + * Append a scale, an array of float, into input scales buffer + * @param scale value of an input scale to be appended + * @return Unit + */ + private def appendInputScales(scale: Array[Float]): Unit = { + inputScalesBuffer.append(scale) + } + + /** + * Append a scale, an array of float, into output scales buffer + * @param scale value of an output scale to be appended + * @return Unit + */ + private def appendOutputScales(scale: Array[Float]): Unit = { + outputScalesBuffer.append(scale) + } + + /** + * Append a scale, an array of float, into weight scales buffer + * @param scale value of an weight scale to be appended + * @return Unit + */ + private def appendWeightScales(scale: Array[Float]): Unit = { + weightScalesBuffer.append(scale) + } + + /** + * Update input scales at specific index with provided new scale + * @param scale the new scale + * @param index the index of which the scale need to be updated + * @return Unit + */ + def updateInputScales(scale: Array[Float], index: Int): Unit = { + updateScalesHelper(inputScalesBuffer, scale, index) + } + + /** + * Update output scales at specific index with provided new scale + * @param scale the new scale + * @param index the index of which the scale need to be updated + * @return Unit + */ + def updateOutputScales(scale: Array[Float], index: Int): Unit = { + updateScalesHelper(outputScalesBuffer, scale, index) + } + + /** + * Update weight scales at specific index with provided new scale + * @param scale the new scale + * @param index the index of which the scale need to be updated + * @return Unit + */ + def updateWeightScales(scale: Array[Float], index: Int): Unit = { + updateScalesHelper(weightScalesBuffer, scale, index) + } + + /** + * Scales update helper. Replace scale at specific index with provided new scale + * @param scales the scales arrayBuffer to be updated + * @param scale the new scale + * @param index the index of which the scale need to be updated + * @return Unit + */ + private def updateScalesHelper(scales: ArrayBuffer[Array[Float]], + scale: Array[Float], index: Int): Unit = { + if (scales.length - 1 < index) { + scales.append(scale) + } + + scales(index).indices.foreach(i => + if (scale(i) > scales(index)(i)) { + scales(index)(i) = scale(i) + }) + } + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala index 01d128b7321..ebfdce13e47 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Sequential.scala @@ -29,7 +29,8 @@ import scala.reflect.ClassTag @SerialVersionUID(5375403296928513267L) class Sequential[T: ClassTag] -(implicit ev: TensorNumeric[T]) extends DynamicContainer[Activity, Activity, T] { +(implicit ev: TensorNumeric[T]) + extends DynamicContainer[Activity, Activity, T] with MklInt8Convertible { override def updateOutput(input: Activity): Activity = { var i = 0 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala index 72a01f800f7..09b5a8e887e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialConvolution.scala @@ -70,7 +70,8 @@ class SpatialConvolution[T: ClassTag]( val initGradBias: Tensor[T] = null, val withBias: Boolean = true, val format: DataFormat = DataFormat.NCHW -)(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { +)(implicit ev: TensorNumeric[T]) + extends TensorModule[T] with Initializable with MklInt8Convertible { require(nOutputPlane % nGroup == 0, s"Number of input channels " + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala index 3887c011fba..929fbb1a835 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala @@ -15,13 +15,14 @@ */ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn} +import com.intel.analytics.bigdl.nn.MklInt8Convertible import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} import com.intel.analytics.bigdl.utils.{T, Table} import scala.collection.mutable.ArrayBuffer -class ConcatTable extends MklDnnContainer { +class ConcatTable extends MklDnnContainer with MklInt8Convertible { output = T() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 9613453c2fd..1cb69ca1e01 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} -import com.intel.analytics.bigdl.nn.{InitializationMethod, RandomUniform, VariableFormat} +import com.intel.analytics.bigdl.nn.{InitializationMethod, MklInt8Convertible, RandomUniform, VariableFormat} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor._ @@ -32,7 +32,8 @@ class Linear( private val initWeight: Tensor[Float] = null, private val initBias: Tensor[Float] = null, private val initGradWeight: Tensor[Float] = null, - private val initGradBias: Tensor[Float] = null) extends MklDnnLayer with Initializable { + private val initGradBias: Tensor[Float] = null) + extends MklDnnLayer with Initializable with MklInt8Convertible { private[mkldnn] val weight: TensorMMap = new TensorMMap(Array(outputSize, inputSize)) private[mkldnn] val bias: TensorMMap = new TensorMMap(Array(outputSize)) @@ -299,6 +300,7 @@ class Linear( super.release() List(weight, bias, gradWeight, gradBias).foreach(_.release()) } + } object Linear { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala index d0c45f38d52..a50f31fe719 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala @@ -18,12 +18,12 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} -import com.intel.analytics.bigdl.nn.{Sequential => Seq} +import com.intel.analytics.bigdl.nn.{MklInt8Convertible, Sequential => Seq} import com.intel.analytics.bigdl.tensor.Tensor import scala.collection.mutable.ArrayBuffer -class Sequential extends MklDnnContainer { +class Sequential extends MklDnnContainer with MklInt8Convertible { val fuseConvBn = System.getProperty("bigdl.mkldnn.fusion.convbn", "false").toBoolean val fuseBnRelu = System.getProperty("bigdl.mkldnn.fusion.bnrelu", "false").toBoolean @@ -369,6 +369,7 @@ class Sequential extends MklDnnContainer { } } + override def toString(): String = { val tab = " " diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 8dc61609ae8..a460acc9397 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -46,7 +46,7 @@ class SpatialConvolution( val initGradBias: Tensor[Float] = null, val withBias: Boolean = true, val format: DataFormat = DataFormat.NCHW -) extends MklDnnLayer with Initializable with Serializable { +) extends MklDnnLayer with Initializable with Serializable with MklInt8Convertible { private val weightShape = if (nGroup == 1) { Array(nOutputPlane, nInputPlane, kernelH, kernelW) } else { @@ -466,6 +466,7 @@ class SpatialConvolution( List(weight, bias, gradWeight, gradBias).foreach(_.release()) if (weightForBackward != null) { weightForBackward.release() } } + } object SpatialConvolution { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PredictionService.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PredictionService.scala index 3ba8b279287..b027f2346a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PredictionService.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PredictionService.scala @@ -347,6 +347,7 @@ object PredictionService { DataConverter.getAttributeValue[Boolean](dsc, attr) case DataType.CHAR => DataConverter.getAttributeValue[Char](dsc, attr) + case _ => throw new UnsupportedOperationException(s"Unsupported DataType($dataType)!") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala index 33b875ea937..38ce1d2a687 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala @@ -75,6 +75,8 @@ class Table private[bigdl]( def foreach[U](f: ((Any, Any)) => U): Unit = state.foreach(f) + def map[U](func: ((Any, Any)) => U): Iterable[U] = state.map(func) + def get[T](key: Any): Option[T] = { state.get(key).map(_.asInstanceOf[T]) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 56bb2ea1686..47bd3dbb3bc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.utils.serializer import java.lang.reflect.Field -import com.intel.analytics.bigdl.nn.Container +import com.intel.analytics.bigdl.nn.{Container, MklInt8Convertible} import scala.collection.JavaConverters._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} @@ -237,6 +237,14 @@ trait ModuleSerializable extends Loadable with Savable{ }) } + /** + * Re-create BigDL module by deserializing protobuf context. + * @param context Deserialization context + * @param module The BigDL module to be re-created + * @param ev + * @tparam T + * @return Tuple3 contains information of current module and modules adjacent to it + */ protected def createBigDLModule[T: ClassTag](context: DeserializeContext, module : AbstractModule[Activity, Activity, T]) (implicit ev: TensorNumeric[T]) @@ -261,6 +269,14 @@ trait ModuleSerializable extends Loadable with Savable{ if (_copyWeightAndBias && context.bigdlModule.getSubModulesCount == 0) { copy2BigDL(context, bigDLModule) } + + // Load MKL-DNN INT8 attributes (scales&mask of input&output) into + // BigDL Module from protobuf definition if the MKL-DNN INT8 flag is ON + if (model.getIsMklInt8Enabled) { + loadMklInt8Attr(context, module.asInstanceOf[MklInt8Convertible]) + + } + bigDLModule } @@ -288,6 +304,16 @@ trait ModuleSerializable extends Loadable with Savable{ if (_copyWeightAndBias && !module.isInstanceOf[Container[_, _, _]]) { copyFromBigDL(context, modelBuilder) } + + // Save MKL-DNN attributes (scales and masks) into model of protobuf definition if + // the module is with trait of MklInt8COnvertible, and set the MKL-DNN INT8 flag to true + if (module.module.isInstanceOf[MklInt8Convertible]) { + saveMklInt8Attr(context.moduleData.module.asInstanceOf[MklInt8Convertible], modelBuilder) + modelBuilder.setIsMklInt8Enabled(true) + } else { + modelBuilder.setIsMklInt8Enabled(false) + } + SerializeResult(modelBuilder, context.storages) } @@ -307,6 +333,43 @@ trait ModuleSerializable extends Loadable with Savable{ } } + /** + * Deserialize MKL-DNN INT8 attributes from protobuf context + * and load them into BigDL Module object + * @param context deserialized context + * @param module bigDL Module with relationships + */ + private def loadMklInt8Attr[T: ClassTag](context: DeserializeContext, + module: MklInt8Convertible) + (implicit ev: TensorNumeric[T]): Unit = { + val protobufModel = context.bigdlModule + // Extract ArrayValue for each AttrValue, and then get FltList as input scales + val inputScales = protobufModel.getInputScalesList.iterator().asScala + .map(attrValueToFloatArray) + // Extract ArrayValue for each AttrValue, and then get FltList as output scales + val outputScales = protobufModel.getOutputScalesList.iterator().asScala + .map(attrValueToFloatArray) + // Extract ArrayValue for each AttrValue, and then get FltList as weight scales + val weightScales = protobufModel.getWeightScalesList.iterator().asScala + .map(attrValueToFloatArray) + + module.setInputDimMask(protobufModel.getInputDimMasks) + module.setInputScales(inputScales.toArray) + module.setOutputDimMask(protobufModel.getOutputDimMasks) + module.setOutputScales(outputScales.toArray) + module.setWeightDimMask(protobufModel.getWeightDimMasks) + module.setWeightScales(weightScales.toArray) + } + + /** + * Convert Attr Value object to Array of Float + * @param AttrValue + * @return Array[Float] + */ + protected def attrValueToFloatArray(attr: AttrValue): Array[Float] = { + attr.getArrayValue.getFltList.asScala.toArray.map(_.asInstanceOf[Float]) + } + private def copyParameters2BigDL[T: ClassTag] (context: DeserializeContext, module : ModuleData[T]) (implicit ev: TensorNumeric[T]): Unit = { @@ -332,6 +395,9 @@ trait ModuleSerializable extends Loadable with Savable{ } } + + + // to keep compatible with models saved by release <= 0.5.0 private def copyWeightAndBias[T: ClassTag](context: DeserializeContext, module : ModuleData[T]) (implicit ev: TensorNumeric[T]): Unit = { @@ -355,6 +421,54 @@ trait ModuleSerializable extends Loadable with Savable{ } } + /** + * Serialize and save MKL DNN INT8 attributes into BigDL Model of protobuf definition + * @param modelBuilder serialized module builder + * @param context serialization context + */ + protected def saveMklInt8Attr[T: ClassTag](module : MklInt8Convertible, + modelBuilder : BigDLModule.Builder) + (implicit ev : TensorNumeric[T]) : Unit = { + // Save scale and mask of input into BigDL model builder + val inputScales : Array[Array[Float]] = module.getInputScales() + val inputMasks : Int = module.getInputDimMask() + val inputScalesAttrList = inputScales.map(floatArrayToAttrValue) + modelBuilder.addAllInputScales(inputScalesAttrList.toIterable.asJava) + modelBuilder.setInputDimMasks(inputMasks) + + // Save scale and mask of output into BigDL model builder + val outputScales : Array[Array[Float]] = module.getOutputScales() + val outputMasks : Int = module.getOutputDimMask() + val outputScalesAttrList = outputScales.map(floatArrayToAttrValue) + modelBuilder.addAllOutputScales(outputScalesAttrList.toIterable.asJava) + modelBuilder.setOutputDimMasks(outputMasks) + + // Save scale and mask of weight into BigDL model builder + val weightScales: Array[Array[Float]] = module.getWeightScales() + val weightMasks: Int = module.getWeightDimMask() + val weightScalesAttrList = weightScales.map(floatArrayToAttrValue) + modelBuilder.addAllWeightScales(weightScalesAttrList.toIterable.asJava) + modelBuilder.setWeightDimMasks(weightMasks) + } + + + /** + * Convert an array of float into an attr value object + * @param Array[Float] + * @return AttrValue + */ + private def floatArrayToAttrValue(arry : Array[Float]) : AttrValue = { + val tempAttrValBuilder = AttrValue.newBuilder() + tempAttrValBuilder.setDataType(DataType.ARRAY_VALUE) + + val tempArryValBuilder = ArrayValue.newBuilder() + tempArryValBuilder.setSize(arry.length) + tempArryValBuilder.setDatatype(DataType.FLOAT) + + arry.foreach(tempArryValBuilder.addFlt) + tempAttrValBuilder.setArrayValue(tempArryValBuilder).build() + } + /** * copy BigDL module data (weight and bias if exist) to BigDL Model to be persisted * @param modelBuilder serialized module builder diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala index edeb4b85a54..11aee0ea88a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala @@ -166,7 +166,8 @@ object DataConverter extends DataConverter{ VariableFormatConverter.setAttributeValue(context, attributeBuilder, value) } else if (valueType =:= universe.typeOf[InitializationMethod]) { InitMethodConverter.setAttributeValue(context, attributeBuilder, value) - } else if (valueType.toString == ModuleSerializer.regularizerType.toString) { + } else if (valueType.toString == ModuleSerializer.regularizerType.toString + || valueType <:< universe.typeOf[Regularizer[_]]) { RegularizerConverter.setAttributeValue(context, attributeBuilder, value) } else if (valueType <:< universe.typeOf[Tensor[_]]) { TensorConverter.setAttributeValue(context, attributeBuilder, value) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala index 6525ca770e1..8924c5ad160 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Mean.scala @@ -55,6 +55,8 @@ class Mean extends TensorflowOpsLoader { "Float" case DataType.DT_DOUBLE => "Double" + case _ => throw new UnsupportedOperationException("Data Type: " + dataType + + " is not Unsupported yet.") } new MeanLoadTF[T](dt, squeeze) } @@ -79,6 +81,8 @@ class MeanLoadTF[T: ClassTag](val dataType: String, dim.foreach(i => mean.add(Mean[T, Float](i, squeeze = squeeze))) case "Double" => dim.foreach(i => mean.add(Mean[T, Double](i, squeeze = squeeze))) + case _ => throw new UnsupportedOperationException("Data Type: " + dataType + + " is not Unsupported yet.") } mean } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala new file mode 100644 index 00000000000..db903b47a7f --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala @@ -0,0 +1,461 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package com.intel.analytics.bigdl.nn + +import java.io.File +import java.util.UUID + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.numeric.NumericFloat +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { + + val modelPath: String = "myTestModel" + UUID.randomUUID().toString + val weightPath: String = "myTestModelWeight" + UUID.randomUUID().toString + + + "Calculating scales" should "work correct for BLAS Linear Module" in { + + val sampleMax = 999 + val inputSize = 120 + val outputSize = 1 + var inputMask = 0 + var outputMask = 0 + val inputTensor = make1DTensor(inputSize, sampleMax) + + // Global mask, null input + val linear0 = Linear[Float](inputSize, outputSize) + linear0.calcScales(null) + linear0.output.isEmpty should be (true) + linear0.getInputScales().isEmpty should be (true) + linear0.getOutputScales().isEmpty should be (true) + linear0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val linear1 = Linear[Float](inputSize, outputSize) + linear1.calcScales(inputTensor) + linear1.getInputScales() should be (Array(Array[Float](sampleMax))) + linear1.getOutputScales().length should be (1) + linear1.getOutputScales()(0).length should be (1) + linear1.getWeightScales().length should be (1) + linear1.getWeightScales()(0).length should be (1) + + // Single dimension mask, non-null input + val linear2 = Linear[Float](inputSize, outputSize) + inputMask = Math.pow(2, 0).toInt + outputMask = Math.pow(2, 0).toInt + linear2.setInputDimMask(inputMask) + linear2.setOutputDimMask(outputMask) + + linear2.calcScales(inputTensor) + val output2 = linear2.output + linear2.getInputScales() should be (Array(getScalesFromTensor(inputTensor, inputMask))) + linear2.getOutputScales() should be (Array(getScalesFromTensor(output2, outputMask))) + + linear2.saveModule(modelPath, weightPath, true) + + val loadedModule2 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(linear2, loadedModule2) + } + + + private def compareModules(modX: MklInt8Convertible, modY: MklInt8Convertible): Unit = { + modX.getInputDimMask() should be (modY.getInputDimMask()) + modX.getOutputDimMask() should be (modY.getOutputDimMask()) + modX.getWeightDimMask() should be (modY.getWeightDimMask()) + modX.getInputScales() should be (modY.getInputScales()) + modX.getOutputScales() should be (modY.getOutputScales()) + modX.getWeightScales() should be (modY.getWeightScales()) + } + + + "Calculating scales" should "work correct for BLAS Spatial Convolution Module" in { + val inputSize = 1 + val outputSize = 1 + val sampleMax = 999 + var dimMaskIdx = 0 + val inputTensor = make2DTensor().reshape(Array(inputSize, 3, 4)) + + // Global mask, null input + val spatialConv0 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv0.calcScales(null) + spatialConv0.output.isEmpty should be (true) + spatialConv0.getInputScales().isEmpty should be (true) + spatialConv0.getOutputScales().isEmpty should be (true) + spatialConv0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val spatialConv1 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv1.calcScales(inputTensor) + spatialConv1.getInputScales() should be (Array(Array[Float](12))) + spatialConv1.getOutputScales().length should be (1) + spatialConv1.getOutputScales()(0).length should be (1) + spatialConv1.getWeightScales().length should be (1) + spatialConv1.getWeightScales()(0).length should be (1) + + // Single input dimension mask, non-null input + dimMaskIdx = 1 + val spatialConv2 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv2.calcScales(inputTensor) + val inputScales2 = Array(Array(inputTensor.select(dimMaskIdx, 1).max())) + spatialConv2.getInputScales() should be (inputScales2) + + dimMaskIdx = 2 + val spatialConv3 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv3.calcScales(inputTensor) + val inputScales3 = Array((1 to inputTensor.size(dimMaskIdx)).map( + idx => inputTensor.select(dimMaskIdx, idx).max() + ).toArray) + spatialConv3.getInputScales() should be (inputScales3) + + dimMaskIdx = 3 + val spatialConv4 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv4.calcScales(inputTensor) + val inputScales4 = Array((1 to inputTensor.size(dimMaskIdx)).map( + idx => inputTensor.select(dimMaskIdx, idx).max() + ).toArray) + spatialConv4.getInputScales() should be (inputScales4) + + spatialConv4.saveModule(modelPath, weightPath, true) + + val loadedModule4 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(spatialConv4, loadedModule4) + } + + + "Calculating scales" should "work correct for BLAS Sequential Module" in { + var dimMaskIdx = 0 + var inputDimMask = 0 + var outputDimMask = 0 + + def makeSequential(): Sequential[Float] = { + val sequential = Sequential[Float]() + sequential.add(Reshape[Float](Array(1, 28, 28))) + .add(SpatialConvolution[Float](1, 6, 5, 5).setName("conv1_5x5")) + .add(Tanh()) + .add(SpatialMaxPooling[Float](2, 2, 2, 2)) + .add(SpatialConvolution[Float](6, 12, 5, 5).setName("conv2_5x5")) + .add(Tanh()) + .add(SpatialMaxPooling[Float](2, 2, 2, 2)) + .add(Reshape[Float](Array(12 * 4 * 4))) + .add(Linear[Float](12 * 4 * 4, 100).setName("fc1")) + .add(Tanh()) + .add(Linear[Float](100, 10).setName("fc2")) + .add(LogSoftMax[Float]()) + sequential + } + + val inputTensor = Tensor[Float](1, 28, 28).rand() + + // Global mask, null input + val sequential0 = makeSequential() + sequential0.calcScales(null) + sequential0.output should be (null) + sequential0.getInputScales().isEmpty should be (true) + sequential0.getOutputScales().isEmpty should be (true) + sequential0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val sequential1 = makeSequential() + sequential1.calcScales(inputTensor) + sequential1.getInputScales().isEmpty should be (false) + sequential1.getInputScales().length should be (1) + sequential1.getInputScales()(0).length should be (1) + sequential1.getOutputScales().isEmpty should be (false) + sequential1.getOutputScales().length should be (1) + sequential1.getOutputScales()(0).length should be (1) + sequential1.getWeightScales().isEmpty should be (true) + val inputScales1 = Array(Array(inputTensor.abs().max())) + val outputScales1 = Array(Array(sequential1.output.toTensor[Float].abs().max())) + sequential1.getInputScales() should be (inputScales1) + sequential1.getOutputScales() should be (outputScales1) + sequentialValidationHelper(sequential1) + + sequential1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(sequential1, loadedModule1) + } + + + "Calculating scales" should "work correct for BLAS ConcatTable Module" in { + val sampleMax = 999 + val numElem = 12 + val inputTensor = make1DTensor(numElem, sampleMax) + + def makeConcatTable(): ConcatTable[Float] = { + val concatTable = new ConcatTable[Float]().setName("concatTable") + concatTable.add(Linear[Float](numElem, 1).setName("A")) + concatTable.add(Linear[Float](numElem, 1).setName("B")) + concatTable + } + + // Global mask, null input + val concatTable0 = makeConcatTable() + concatTable0.setInputDimMask(0) + concatTable0.setOutputDimMask(0) + concatTable0.setWeightDimMask(0) + + concatTable0.calcScales(null) + concatTable0.getInputScales().isEmpty should be (true) + concatTable0.getOutputScales().isEmpty should be (true) + concatTable0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val concatTable1 = makeConcatTable() + + concatTable1.calcScales(inputTensor) + concatTable1.getInputScales() should be (Array(Array[Float](sampleMax))) + concatTable1.getOutputScales() should be ( + concatTable1.output.toTable.map((pair: (Any, Any)) => { + val key = pair._1 + val value: Tensor[Float] = pair._2.asInstanceOf[Tensor[Float]] + Array(value.abs().max()) + }).toArray + ) + concatTableValidationHelper(inputTensor, concatTable1, 0) + + concatTable1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(concatTable1, loadedModule1) + } + + + "Calculating scales" should "work correct for Graph Module" in { + def makeTestingGraph(): Graph[Float] = { + val input = Reshape(Array(1, 28, 28)).inputs() + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5").inputs(input) + val tanh1 = Tanh().inputs(conv1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1) + val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5").inputs(pool1) + val tanh2 = Tanh().inputs(conv2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh2) + val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2) + val fc1 = Linear(12 * 4 * 4, 100).setName("fc1").inputs(reshape) + val tanh3 = Tanh().inputs(fc1) + val fc2 = Linear(100, 10).setName("fc2").inputs(tanh3) + val output = LogSoftMax().inputs(fc2) + + Graph(input, output) + } + + val inputTensor = Tensor(1, 28, 28).rand() + + // global mask, null input + val graph0 = makeTestingGraph() + graph0.setInputDimMask(0) + graph0.setOutputDimMask(0) + graph0.calcScales(null) + graph0.getInputDimMask() should be (0) + graph0.getOutputDimMask() should be (0) + graph0.getInputScales().isEmpty should be (true) + graph0.getOutputScales().isEmpty should be (true) + + // global mask, non-null input + val graph1 = makeTestingGraph() + graph1.setInputDimMask(0) + graph1.setOutputDimMask(0) + graph1.calcScales(inputTensor) + val graphOutput1 = graph1.output + + graph1.getInputDimMask() should be (0) + graph1.getOutputDimMask() should be (0) + graphOutput1 should not be (null) + graph1.getInputScales() should be (Array(Array(inputTensor.abs().max()))) + graph1.getOutputScales() should be (Array(Array(graphOutput1.toTensor.abs().max()))) + graphValidationHelper(graph1, inputTensor) + + graph1.saveModule(modelPath, weightPath, true) + + val loadedGraph1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(graph1, loadedGraph1) + } + + + private def graphValidationHelper(graph: Graph[Float], inputActvt: Activity): Unit = { + val nextNodes = graph.getForwardExecutions() + var i = 0 + while (i < nextNodes.length) { + val currNode = nextNodes(i) + val currInputActvt = graph.findInput(currNode, inputActvt) + val currOutputActvt = currNode.element.output + if (currNode.element.isInstanceOf[MklInt8Convertible]) { + val currNodeInt8 = currNode.element.asInstanceOf[MklInt8Convertible] + val currInputScales = currNodeInt8.getInputScales() + val currOutputScales = currNodeInt8.getOutputScales() + currNodeInt8.getInputDimMask() should be (0) + currNodeInt8.getOutputDimMask() should be (0) + currNodeInt8.getInputScales() should be (Array(Array(currInputActvt.toTensor.abs().max()))) + currNodeInt8.getOutputScales() should be ( + Array(Array(currOutputActvt.toTensor.abs().max())) + ) + } + i += 1 + } + } + + + /** + * Iterate over modules inside the Sequential module, verify their calculated scales + * @param sequential the sequential to be verified + */ + private def sequentialValidationHelper(sequential: Sequential[Float]): Unit = { + + var prevModule: AbstractModule[_, _, Float] = null + val moduleIter = sequential.modules.iterator + + while (moduleIter.hasNext) { + val currModule = moduleIter.next() + if (currModule.isInstanceOf[MklInt8Convertible]) { + val currInputMask = currModule.asInstanceOf[MklInt8Convertible].getInputDimMask() + val currOutputMask = currModule.asInstanceOf[MklInt8Convertible].getOutputDimMask() + val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() + val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() + if (prevModule != null) { + val prevOutput = prevModule.output.asInstanceOf[Tensor[Float]] + Array(getScalesFromTensor(prevOutput, currInputMask)) should be (currInputScales) + } + Array(getScalesFromTensor(currModule.output.toTensor[Float], currOutputMask)) should + be (currOutputScales) + } + prevModule = currModule + } + } + + + /** + * Iterate over modules inside the ConcatTable module, verify their calculated scales + * @param inputTensor input of the ConcatTable + * @param concatTable the ConcatTable to be verified + */ + private def concatTableValidationHelper(inputTensor: Tensor[Float], + concatTable: ConcatTable[Float], + mask: Int): Unit = { + + val moduleIter = concatTable.modules.iterator + if (mask == 0) { + while (moduleIter.hasNext) { + val currModule = moduleIter.next() + val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() + val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() + currModule.asInstanceOf[MklInt8Convertible].getInputDimMask() should be (0) + currModule.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be (0) + inputTensor.max() should be (currInputScales(0)(0)) + currModule.output.toTensor[Float].max() should be (currOutputScales(0)(0)) + } + } else { + while (moduleIter.hasNext) { + val currModule = moduleIter.next() + val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() + val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() + val inputDimSize = inputTensor.size(mask) + val outputDimSize = currModule.output.toTensor[Float].size(mask) + + (1 to inputDimSize).map(idx => { + inputTensor.select(mask, idx).abs().max() + }).toArray should be (currInputScales) + + (1 to outputDimSize).map(idx => { + currModule.output.toTensor[Float].select(mask, idx).abs().max() + }).toArray should be (currOutputScales) + } + + } + } + + + /** + * Calculate the scales based on the input tensor and dimension mask + * @param tensor input tensor + * @param mask dimension mask + * @return an Array contains scales + */ + private def getScalesFromTensor(tensor: Tensor[Float], mask: Int): Array[Float] = { + + if (mask == 0) { + Array(tensor.abs().max()) + } else { + val dimSize = tensor.size(mask) + + (1 to dimSize).map(idx => { + tensor.select(mask, idx).abs().max() + }).toArray + } + + } + + + /** + * Helper method to make testing 2 dimensional tensor + * tensor = + * 01 10 03 12 + * 09 07 11 08 + * 05 02 06 04 + * + * @return a 2D tensor of float + */ + private def make2DTensor(): Tensor[Float] = { + val tensor = Tensor[Float](3, 4) + tensor.setValue(1, 1, 1) + tensor.setValue(1, 2, 10) + tensor.setValue(1, 3, 3) + tensor.setValue(1, 4, 12) + tensor.setValue(2, 1, 9) + tensor.setValue(2, 2, 7) + tensor.setValue(2, 3, 11) + tensor.setValue(2, 4, 8) + tensor.setValue(3, 1, 5) + tensor.setValue(3, 2, 2) + tensor.setValue(3, 3, 6) + tensor.setValue(3, 4, 4) + + tensor + } + + + /** + * Helper method to make testing 1 dimensional tensor + * @param n tensor size + * @param max max value of the random generated tensor + * @return a tensor of float + */ + private def make1DTensor(n: Int, max: Float): Tensor[Float] = { + val tensor = Tensor[Float](n) + tensor.rand(0, 100) + tensor.setValue(1, max) + tensor + } + + + after { + new File(modelPath).delete() + new File(weightPath).delete() + } + +} From a17db5dc896f27bc78e5dace8d9728f73c22f35d Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Wed, 13 Mar 2019 19:23:54 +0800 Subject: [PATCH 0874/1065] recursively update mask for container module (#2754) * recursively update mask for container module --- .../bigdl/dllib/nn/MklInt8Convertible.scala | 27 +++++++++++++++++++ .../bigdl/dllib/nn/ScaleCalculatorSpec.scala | 23 ++++++++++++++++ 2 files changed, 50 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala index cc72fdaafa0..71cbdb69726 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala @@ -282,6 +282,15 @@ trait MklInt8Convertible { */ def setInputDimMask(mask: Int) : Unit = { inputDimMask = mask + if (this.isInstanceOf[Container[_, _, Float@unchecked]]) { + val container = this.asInstanceOf[Container[_, _, Float@unchecked]] + val modules = container.modules + modules.foreach(module => { + if (module.isInstanceOf[MklInt8Convertible]) { + module.asInstanceOf[MklInt8Convertible].setInputDimMask(mask) + } + }) + } } /** @@ -299,6 +308,15 @@ trait MklInt8Convertible { */ def setOutputDimMask(mask: Int): Unit = { outputDimMask = mask + if (this.isInstanceOf[Container[_, _, Float@unchecked]]) { + val container = this.asInstanceOf[Container[_, _, Float@unchecked]] + val modules = container.modules + modules.foreach(module => { + if (module.isInstanceOf[MklInt8Convertible]) { + module.asInstanceOf[MklInt8Convertible].setOutputDimMask(mask) + } + }) + } } /** @@ -316,6 +334,15 @@ trait MklInt8Convertible { */ def setWeightDimMask(mask: Int): Unit = { weightDimMask = mask + if (this.isInstanceOf[Container[_, _, Float@unchecked]]) { + val container = this.asInstanceOf[Container[_, _, Float@unchecked]] + val modules = container.modules + modules.foreach(module => { + if (module.isInstanceOf[MklInt8Convertible]) { + module.asInstanceOf[MklInt8Convertible].setWeightDimMask(mask) + } + }) + } } /** diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala index db903b47a7f..8eb6aab14cc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala @@ -198,6 +198,29 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) .asInstanceOf[MklInt8Convertible] compareModules(sequential1, loadedModule1) + + val sequential2 = makeSequential() + sequential2.getInputDimMask() should be (0) + sequential2.getOutputDimMask() should be (0) + sequential2.getWeightDimMask() should be (0) + sequential2.modules.filter(_.isInstanceOf[MklInt8Convertible]).foreach(x => { + x.asInstanceOf[MklInt8Convertible].getInputDimMask() should be(0) + x.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be(0) + x.asInstanceOf[MklInt8Convertible].getWeightDimMask() should be(0) + }) + + sequential2.setInputDimMask(2) + sequential2.setOutputDimMask(2) + sequential2.setWeightDimMask(2) + + sequential2.getInputDimMask() should be (2) + sequential2.getOutputDimMask() should be (2) + sequential2.getWeightDimMask() should be (2) + sequential2.modules.filter(_.isInstanceOf[MklInt8Convertible]).foreach(x => { + x.asInstanceOf[MklInt8Convertible].getInputDimMask() should be(2) + x.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be(2) + x.asInstanceOf[MklInt8Convertible].getWeightDimMask() should be(2) + }) } From a0f7a206d801264c8d2f366c9005351db2096e3c Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Thu, 14 Mar 2019 10:22:29 +0800 Subject: [PATCH 0875/1065] [Enhancement] - Speed up BlasWrapper performance under MKL-DNN (#2748) * add parallel in Blaswrapper * refactor to support ssd * meet pr comments * fix logger serialize --- .../intel/analytics/bigdl/utils/Engine.scala | 9 + .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 157 +++++++++++++++++- .../bigdl/dllib/nn/mkldnn/Fusion.scala | 2 +- .../dllib/nn/mkldnn/BlasWrapperSpec.scala | 29 +++- 4 files changed, 188 insertions(+), 9 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 2d41125b559..f79e091d573 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -220,6 +220,9 @@ object Engine { // Thread pool for layer use @volatile private var _model: ThreadPool = new ThreadPool(1) + // Thread pool for blas wrapper layer + private[bigdl] var wrapperComputing: ThreadPool = null + // This thread is mainly for mkldnn library. // Because if we use the parent thread directly, there will be two bugs, // 1. The child threads forked from parent thread will be bound to core 0 @@ -339,6 +342,9 @@ object Engine { if(_default == null || _default.getPoolSize != defaultPoolSize) { _default = new ThreadPool(defaultPoolSize) } + if (wrapperComputing == null || wrapperComputing.getPoolSize != defaultPoolSize) { + wrapperComputing = new ThreadPool(defaultPoolSize) + } // for dnn model we should set the pool size to 1 also. // otherwise, it will downgrade the performance and @@ -357,6 +363,9 @@ object Engine { if (engineType == MklDnn) { dnnComputing.setMKLThreadOfMklDnnBackend(MKL.getMklNumThreads) } + if (System.getProperty("multiThread", "false").toBoolean) { + wrapperComputing.setMKLThread(1) + } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index 15b3c3cddb1..aedf974f034 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -16,16 +16,22 @@ package com.intel.analytics.bigdl.nn.mkldnn +import breeze.linalg.Axis._1 +import breeze.linalg.dim import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.dataset.MiniBatch +import com.intel.analytics.bigdl.mkl.{MKL, Memory} +import com.intel.analytics.bigdl.nn.{DetectionOutputSSD, PriorBox} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{MultiShape, Shape} -import spire.syntax.module +import com.intel.analytics.bigdl.utils.Engine._ +import com.intel.analytics.bigdl.utils._ +import org.apache.log4j.Logger /** - * wrap blas module to be dnn module, + * wrap blas module to dnn module, * and the module should have implemented "computeOutputShape" func. * @param module */ @@ -49,6 +55,15 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, } private[mkldnn] var needOutputFormats: Boolean = true + @transient private val logger = Logger.getLogger(getClass) + + @transient private var subModels: Array[Module[Float]] = _ + @transient private var subModelNumber : Int = 1 + @transient private var withMultiThread: Boolean = false + @transient private var inputBuffer : Array[Activity] = _ + @transient private var tensorBuffer : Array[Tensor[Float]] = _ + @transient private var batchSize : Int = _ + @transient private var initEnv: Boolean = false private def inferInputFormats(inputs: Array[MemoryData]): Array[MemoryData] = { inputs.map(in => HeapData(in.shape, getFormats(in.shape.length))) @@ -69,10 +84,84 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, }).toArray } + /** + * Blas layers normally do not have competitive performance when running under mkldnn. + * So we can leverage multi-threading to resolve bottleneck introduced by one model only + * for mkl-dnn backend. The parallelism is determined by both bath size and core number, + * with restrictions that both input and output format must be batched. + */ + private def setMultiThreadEnv(input: Activity): Unit = { + initEnv = true + val multiThread = System.getProperty("multiThread", "false").toBoolean + if (this.train && multiThread) { + throw new IllegalArgumentException("Please not set multiThread to true for model training") + } + if (this.train + || !multiThread + || (_outputFormats != null && _outputFormats.length != 1) + || (_outputFormats != null && _inputFormats != null + && _inputFormats(0).shape(0) != _outputFormats(0).shape(0)) + || !flattenInput(input) + ) { + return + } + batchSize = tensorBuffer(0).size(1) + val residue = batchSize % Engine.coreNumber() + if (residue != 0 || batchSize < 2 || Engine.coreNumber() < 2) { + logger.warn("If you want to use multiThread property to speed up, " + + "please attention core number should be greater than 1, " + + s"batch size should be greater than 1 and divided by core number, " + + s"but now get core number ${Engine.coreNumber()} batch size ${batchSize}") + return + } + subModelNumber = Engine.coreNumber() + initModules() + withMultiThread = true + } + private def flattenInput(input: Activity): Boolean = { + val inputDepth = if (input.isTensor) 1 else input.toTable.length() + if (tensorBuffer == null) tensorBuffer = new Array[Tensor[Float]](inputDepth) + var batch : Int = 0 + if (inputDepth == 1) { + tensorBuffer(0) = input.toTensor[Float] + } else { + val in = input.toTable + for (i <- 1 to in.length()) { + if (in.get(i).get.isInstanceOf[Table]) return false + tensorBuffer(i - 1) = in.get[Tensor[Float]](i).get + if (i == 1) batch = tensorBuffer(i - 1).size(1) + // reminder: inputs for DetectionOutputSSD are not all in batch, + // but the non-batched input can be shared in all batch. So this layer can be paralleled. + if (batch != tensorBuffer(i - 1).size(1) + && !module.isInstanceOf[DetectionOutputSSD[Float]]) { + return false + } + } + } + true + } + private def initModules(): Unit = { + subModels = if (module.parameters() != null) { + val wb = Util.getAndClearWeightBias(module.parameters()) + val models = (1 to subModelNumber).map(i => { + val m = module.cloneModule() + Util.putWeightBias(wb, m) + m.asInstanceOf[Module[Float]] + }).toArray + Util.putWeightBias(wb, module) + models + } else { + val models = (1 to subModelNumber).map(i => { + val m = module.cloneModule() + m.asInstanceOf[Module[Float]] + }).toArray + models + } + } + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = inferInputFormats(inputs) _outputFormats = if (needOutputFormats) inferOutputFormats(inputs) else null - (_inputFormats, _outputFormats) } @@ -87,8 +176,64 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, _gradOutputFormatsForWeight } + private def getInput(dim: Int, index: Int, size: Int): Activity = { + if (tensorBuffer.length == 1) { + tensorBuffer(0).narrow(dim, index, size) + } else { + // the third tensor of inputs for DetectionOutputSSD is not in batch, + // but it can be shared with all batch. + if (module.isInstanceOf[DetectionOutputSSD[Float]]) { + T(tensorBuffer(0).narrow(dim, index, size), + tensorBuffer(1).narrow(dim, index, size), tensorBuffer(2)) + } else { + T.array(tensorBuffer.map(_.narrow(dim, index, size))) + } + } + } + private def forwardInParallel(input: Activity): Activity = { + if (inputBuffer == null) inputBuffer = new Array[Activity](subModelNumber) + val stackSize = batchSize / subModelNumber + + val tasks = Engine.wrapperComputing.invoke((0 until subModelNumber).map(i => + () => inputBuffer(i) = getInput(1, i * stackSize + 1, stackSize))) + Engine.wrapperComputing.sync(tasks) + + val forwardThreads = Engine.wrapperComputing.invoke((0 until subModelNumber).map(i => + () => subModels(i).forward(inputBuffer(i)).toTensor[Float])) + Engine.wrapperComputing.sync(forwardThreads) + + if (subModels(0).output.isTable) { + withMultiThread = false + module.forward(input) + } else { + val subOutSize = subModels(0).output.toTensor[Float].size() + if (subOutSize(0) != stackSize) { + withMultiThread = false + module.forward(input) + } else { + subOutSize(0) = batchSize + if (output == null || output.toTensor[Float].isEmpty) { + output = Tensor[Float]().resize(subOutSize) + } + val copyThreads = Engine.wrapperComputing.invoke((0 until subModelNumber).map(i => + () => { + output.toTensor[Float].narrow(1, i * stackSize + 1, stackSize) + .copy(subModels(i).output.toTensor[Float]) + })) + Engine.wrapperComputing.sync(copyThreads) + + output + } + } + } + override def updateOutput(input: Activity): Activity = { - output = module.forward(input) + if (!initEnv) setMultiThreadEnv(input) + output = if (withMultiThread) { + forwardInParallel(input) + } else { + module.forward(input) + } output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala index 2505eab6943..3d2244c5dc7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -122,7 +122,7 @@ private[mkldnn] object Fusion { if (conv != null) { node.element = conv.element val element = node.element.asInstanceOf[SpatialConvolution] - element.setSumOp(previousNodes(otherNumber - 1).element, otherNumber) + element.setSumOp(previousNodes(otherNumber).element, otherNumber + 1) conv.element = Identity[Float]().asInstanceOf[AbstractModule[Activity, Activity, Float]] val nexts = node.nextNodes(0) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapperSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapperSpec.scala index b3026672bb8..e611e4e7208 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapperSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapperSpec.scala @@ -20,16 +20,20 @@ import breeze.linalg.reshape import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.{Module, nn} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.nn.{Graph, Squeeze, mkldnn} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, RandomGenerator, T} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Engine, RandomGenerator, T} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.numeric.NumericFloat class BlasWrapperSpec extends BigDLSpecHelper { + override def doBefore(): Unit = { + Engine.init(1, 4, true) + } + def modelBlas(format: DataFormat = DataFormat("NCHW")) : Module[Float] = { val conv1 = nn.SpatialConvolution(1, 20, 5, 5, format = format).inputs() val pool1 = nn.SpatialMaxPooling(2, 2, 2, 2, format = format).setName("pool").inputs(conv1) @@ -87,4 +91,25 @@ class BlasWrapperSpec extends BigDLSpecHelper { weight1 should be(weight2) gradWeight1 should be(gradWeight2) } + + "wrapper model run with blas multithread" should "be correct" in { + val inputShape = Array(4, 1, 28, 28) + val input = Tensor[Float](inputShape).rand() + + RandomGenerator.RNG.setSeed(1) + val wrapper = modelWrapper(Memory.Format.nchw, inputShape) + wrapper.evaluate() + wrapper.compile(InferencePhase) + val out1 = wrapper.forward(input) + + RandomGenerator.RNG.setSeed(1) + System.setProperty("multiThread", "true") + val wrapperMulti = modelWrapper(Memory.Format.nchw, inputShape) + wrapperMulti.evaluate() + wrapperMulti.compile(InferencePhase) + val out2 = wrapperMulti.forward(input) + + out1 should be(out2) + System.clearProperty("multiThread") + } } From 9ca91afb95ac95781e3327006f7e74c58b8a1a49 Mon Sep 17 00:00:00 2001 From: Xin Qiu Date: Sat, 16 Mar 2019 15:48:41 +0800 Subject: [PATCH 0876/1065] change asInstanceOf to toDistirbuted in optimizer (#2755) * change asInstanceOf to toDistirbuted * change asInstanceOf to toDistirbuted --- .../bigdl/dllib/optim/DistriOptimizer.scala | 4 ++-- .../analytics/bigdl/dllib/optim/Optimizer.scala | 14 +++++++------- .../bigdl/dllib/optim/ParallelOptimizer.scala | 4 ++-- .../bigdl/dllib/optim/DistriOptimizerSpec.scala | 2 +- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 31b8113387d..e9ef002aa0c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -777,7 +777,7 @@ class DistriOptimizer[T: ClassTag] ( } override def prepareInput(): Unit = { - if (!dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].isCached) { + if (!dataset.toDistributed().isCached) { DistriOptimizer.logger.info("caching training rdd ...") DistriOptimizer.prepareInput(this.dataset, this.validationDataSet) } @@ -785,7 +785,7 @@ class DistriOptimizer[T: ClassTag] ( override def optimize(): Module[T] = { - val distDataset = dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]] + val distDataset = dataset.toDistributed() optimMethods.values.foreach { optimMethod => optimMethod.clearHistory() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala index 77a0c1f0849..fa9e7d91132 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Optimizer.scala @@ -140,7 +140,7 @@ abstract class Optimizer[T: ClassTag, D]( val dataSet = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(batchSize, Some(featurePaddingParam), Some(labelPaddingParam))) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + .toDistributed() this.validationDataSet = Some(dataSet) this.validationMethods = Some(vMethods) this @@ -161,7 +161,7 @@ abstract class Optimizer[T: ClassTag, D]( this.validationTrigger = Some(trigger) val dataSet = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(batchSize)) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + .toDistributed() this.validationDataSet = Some(dataSet) this.validationMethods = Some(vMethods) this @@ -182,7 +182,7 @@ abstract class Optimizer[T: ClassTag, D]( this.validationTrigger = Some(trigger) val dataSet = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(miniBatch, batchSize, None)) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]] + .toDistributed() this.validationDataSet = Some(dataSet) this.validationMethods = Some(vMethods) this @@ -615,7 +615,7 @@ object Optimizer { _model = model, _dataset = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(batchSize, _featurePaddingParam, _labelPaddingParam)) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]], + .toDistributed(), _criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] } @@ -644,7 +644,7 @@ object Optimizer { _model = model, _dataset = (DataSet.rdd(sampleRDD) -> SampleToMiniBatch(miniBatchImpl, batchSize, None)) - .asInstanceOf[DistributedDataSet[MiniBatch[T]]], + .toDistributed(), _criterion = criterion ).asInstanceOf[Optimizer[T, MiniBatch[T]]] } @@ -666,13 +666,13 @@ object Optimizer { case d: DistributedDataSet[_] => new DistriOptimizer[T]( _model = model, - _dataset = d.asInstanceOf[DistributedDataSet[MiniBatch[T]]], + _dataset = d.toDistributed().asInstanceOf[DistributedDataSet[MiniBatch[T]]], _criterion = criterion ).asInstanceOf[Optimizer[T, D]] case d: LocalDataSet[_] => new LocalOptimizer[T]( model = model, - dataset = d.asInstanceOf[LocalDataSet[MiniBatch[T]]], + dataset = d.toLocal().asInstanceOf[LocalDataSet[MiniBatch[T]]], criterion = criterion ).asInstanceOf[Optimizer[T, D]] case _ => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala index efbb2b1001c..4ee468a4a57 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala @@ -634,7 +634,7 @@ class ParallelOptimizer[T: ClassTag] ( override def prepareInput(): Unit = { - if (!dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]].isCached) { + if (!dataset.toDistributed().isCached) { ParallelOptimizer.logger.info("caching training rdd ...") ParallelOptimizer.prepareInput(this.dataset, this.validationDataSet) } @@ -685,7 +685,7 @@ class ParallelOptimizer[T: ClassTag] ( override def optimize(): Module[T] = { - val distDataset = dataset.asInstanceOf[DistributedDataSet[MiniBatch[T]]] + val distDataset = dataset.toDistributed() optimMethods.values.foreach { optimMethod => optimMethod.clearHistory() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index fd20c877a9f..b944392f2c9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -737,7 +737,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { val myOpt = new DistriOptimizer[Double](Identity[Double](), dataSet, null) { override def optimize(): Module[Double] = { - val dds = this.dataset.asInstanceOf[DistributedDataSet[MiniBatch[Double]]] + val dds = this.dataset.toDistributed() val rdd = dds.data(train = false) // flatmap to break minibatches into single tensors val input = rdd.flatMap[Tensor[Double]]{ From 84a9b5723632ac45f75fec870211959b30f75c7c Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Sat, 16 Mar 2019 22:31:11 +0800 Subject: [PATCH 0877/1065] convert scale in blas to dnn (#2758) * convert scale in blas to dnn * meet pr comment --- .../dllib/utils/intermediate/IRElement.scala | 3 +- .../dllib/utils/intermediate/IRGraph.scala | 2 +- .../utils/intermediate/ReflectionUtils.scala | 20 ++++++ .../utils/intermediate/IRconvertSpec.scala | 63 +++++++++++++++++++ 4 files changed, 86 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala index 733b9b22cb6..645813fcd1c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.utils.intermediate +import com.intel.analytics.bigdl.nn.MklInt8Convertible import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.{Tensor, TensorNumericMath} @@ -132,7 +133,7 @@ private[bigdl] class IRElement[T: ClassTag]( val name: String, val op: IROperator[T], private var weights: Tensor[T] = null, - private var gradWeights: Tensor[T] = null) extends Serializable { + private var gradWeights: Tensor[T] = null) extends Serializable with MklInt8Convertible { /** * set weight and bias diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index c3b2e1d4e98..bcdc0c9b4c9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -54,7 +54,7 @@ private[bigdl] class IRGraph[T: ClassTag]( require(outputFormats.length == outputs.length, s"IRGraph: outputFormats" + s"length ${inputFormats.length} should be same with input nodes length ${outputs.length}") - private var graph: Graph[T] = null + private[bigdl] var graph: Graph[T] = null private[bigdl] def isBuild(): Boolean = graph != null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala index 072c3b03c8c..7ade039a25f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala @@ -17,8 +17,10 @@ package com.intel.analytics.bigdl.utils.intermediate import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.MklInt8Convertible import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._ + import scala.collection.mutable import scala.reflect.{ClassTag, ManifestFactory} import scala.reflect.runtime._ @@ -92,6 +94,9 @@ private[bigdl] object ReflectionUtils { } if (layer.getName() != "") blasLayer.setName(layer.getName()) + if (blasLayer.isInstanceOf[MklInt8Convertible]) { + setScales(layer, blasLayer.asInstanceOf[MklInt8Convertible]) + } blasLayer } @@ -104,9 +109,24 @@ private[bigdl] object ReflectionUtils { if (layer.parameters() != null) layer.getParameters() else (null, null) val element = IRElement[T]( layer.getName(), op, weights = weightsAndBias._1, gradWeights = weightsAndBias._2) + if (layer.isInstanceOf[MklInt8Convertible]) { + setScales(layer.asInstanceOf[MklInt8Convertible], element) + } element } + // put scales in fromEle to toELe + private def setScales[T: ClassTag](fromEle: MklInt8Convertible, + toELe: MklInt8Convertible): Unit = { + toELe.setInputScales(fromEle.getInputScales()) + toELe.setOutputScales(fromEle.getOutputScales()) + toELe.setWeightScales(fromEle.getWeightScales()) + + toELe.setInputDimMask(fromEle.getInputDimMask()) + toELe.setOutputDimMask(fromEle.getOutputDimMask()) + toELe.setWeightDimMask(fromEle.getWeightDimMask()) + } + def findClass(name: String): Class[_] = { try { Class.forName(name) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala index 75d1fc2012b..c104cc78609 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala @@ -66,6 +66,31 @@ class IRconvertSpec extends BigDLSpecHelper { Graph(conv1, output) } + def modelWithScale(format: DataFormat = DataFormat("NCHW")) : Module[Float] = { + val convElement = nn.SpatialConvolution(1, 20, 5, 5, format = format) + convElement.setInputDimMask(1) + convElement.setWeightDimMask(2) + convElement.setOutputDimMask(3) + convElement.setInputScales(Array(Array(1, 2, 3))) + convElement.setWeightScales(Array(Array(4, 5, 6))) + val conv1 = convElement.setName("input").inputs() + val pool1 = nn.SpatialMaxPooling(2, 2, 2, 2, format = format).setName("pool").inputs(conv1) + val conv2 = nn.SpatialConvolution(20, 50, 5, 5, format = format).inputs(pool1) + val pool2 = nn.SpatialMaxPooling(2, 2, 2, 2, format = format).inputs(conv2) + val reshape = nn.Reshape(Array(50 * 4 * 4)).inputs(pool2) + val fc = nn.Linear(50 * 4 * 4, 500).inputs(reshape) + val relu = nn.ReLU().setName("relu1").inputs(fc) + + val linearElement = nn.Linear(500, 10) + linearElement.setInputDimMask(1) + linearElement.setOutputDimMask(2) + linearElement.setInputScales(Array(Array(0, 1, 2))) + linearElement.setOutputScales(Array(Array(7, 8, 9))) + val fc2 = linearElement.setName("output").inputs(relu) + val output = fc2 + Graph(conv1, output) + } + "Convert Blas with NCHW to Dnn" should "be correct" in { System.setProperty("bigdl.engineType", "mkldnn") val input = Tensor[Float](2, 1, 28, 28).rand() @@ -178,4 +203,42 @@ class IRconvertSpec extends BigDLSpecHelper { Equivalent.nearequals(p1._1, p1._1, 1e-4) should be (true) Equivalent.nearequals(p1._2, p1._2, 1e-4) should be (true) } + + "Convert Blas with scale to Dnn" should "be correct" in { + System.setProperty("bigdl.engineType", "mkldnn") + val input = Tensor[Float](2, 1, 28, 28).rand() + val gradOutput = Tensor[Float](2, 10).rand() + + val blasModel = modelWithScale().asInstanceOf[StaticGraph[Float]] + val irModel = blasModel.cloneModule().toIRgraph() + + val blasExecutions = blasModel.getSortedForwardExecutions() + val irExecutions = irModel.graph.getSortedForwardExecutions() + + val blasInputs = blasExecutions.filter(_.element.getName() == "input")(0) + .element.asInstanceOf[MklInt8Convertible] + val blasOutputs = blasExecutions.filter(_.element.getName() == "output")(0) + .element.asInstanceOf[MklInt8Convertible] + + val inputs = irExecutions.filter(_.element.getName() == "input")(0) + .element.asInstanceOf[MklInt8Convertible] + val outputs = irExecutions.filter(_.element.getName() == "output")(0) + .element.asInstanceOf[MklInt8Convertible] + + blasInputs.getWeightDimMask() should be(inputs.getWeightDimMask()) + blasInputs.getInputDimMask() should be(inputs.getInputDimMask()) + blasInputs.getOutputDimMask() should be(inputs.getOutputDimMask()) + + blasInputs.getWeightScales() should be(inputs.getWeightScales()) + blasInputs.getInputScales() should be(inputs.getInputScales()) + blasInputs.getOutputScales() should be(inputs.getOutputScales()) + + val outBlas = blasModel.forward(input).toTensor[Float] + val gradInputBlas = blasModel.backward(input, gradOutput) + val outDnn = irModel.forward(input).toTensor[Float] + val gradInputDnn = irModel.backward(input, gradOutput).toTensor[Float] + + Equivalent.nearequals(outDnn, outBlas, 1e-4) should be (true) + Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) + } } From 4f7aa88080340d873cba04ab24139fd77466f97e Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Sun, 17 Mar 2019 22:21:01 +0800 Subject: [PATCH 0878/1065] feat: reorder for int8 supports (#2756) 1. Because the new data type, we should add a new attribute called dataType to the `MemoryData`. 2. Because we should transfer the scales between FP32->int8 and Int8->FP32. we should add two new attributes called `mask` and `scales`. --- .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 2 +- .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 3 + .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 16 +- .../bigdl/dllib/nn/mkldnn/Dropout.scala | 2 + .../analytics/bigdl/dllib/nn/mkldnn/LRN.scala | 8 +- .../bigdl/dllib/nn/mkldnn/Linear.scala | 6 +- .../bigdl/dllib/nn/mkldnn/MaxPooling.scala | 6 +- .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 79 ++++++---- .../bigdl/dllib/nn/mkldnn/ReLU.scala | 2 +- .../dllib/nn/mkldnn/ReorderManager.scala | 8 +- .../bigdl/dllib/nn/mkldnn/ReorderMemory.scala | 58 +++++-- .../bigdl/dllib/tensor/DnnTensor.scala | 2 +- .../bigdl/dllib/nn/mkldnn/DropoutSpec.scala | 2 + .../dllib/nn/mkldnn/ReorderMemorySpec.scala | 145 ++++++++++++++++++ 14 files changed, 277 insertions(+), 62 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala index c7b6bd1b71e..ee7b25a3f8f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -102,7 +102,7 @@ class AvgPooling( strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) val pd = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD) - _gradInputFormats = Array(MemoryData.primitiveGradInput(pd)) + _gradInputFormats = Array(MemoryData.operationWant(pd, Query.DiffSrcPd)) updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(pd, _gradOutputFormats.map(_.getPrimitive(runtime)), Array(0, 0), 2, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index aedf974f034..f7e437880c8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -162,6 +162,9 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = inferInputFormats(inputs) _outputFormats = if (needOutputFormats) inferOutputFormats(inputs) else null + if (_outputFormats != null) { + _outputFormats.map(_.getPrimitive(runtime)) + } (_inputFormats, _outputFormats) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index 679ccc18cee..0834012950e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{Memory, MklDnn} +import com.intel.analytics.bigdl.mkl.DataType import com.intel.analytics.bigdl.nn.DynamicContainer import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.{DenseType, DnnTensor, MklDnnType, Tensor} @@ -80,12 +80,20 @@ trait MklDnnModuleHelper { } } - protected def initTensor(format: MemoryData): Tensor[Float] = { + protected def initTensor(format: MemoryData): Tensor[_] = { + val paddingShape = format.getPaddingShape + val realSize = format.getRealSize + format match { case d: NativeData => - DnnTensor[Float](Memory.GetPaddingShape(format.getMemoryDescription())) + d.dataType match { + case DataType.S8 => DnnTensor[Byte](paddingShape, realSize) + case DataType.U8 => DnnTensor[Byte](paddingShape, realSize) + case DataType.S32 => DnnTensor[Int](paddingShape, realSize) + case DataType.F32 => DnnTensor[Float](paddingShape, realSize) + } case d: HeapData => - Tensor[Float](Memory.GetPaddingShape(format.getMemoryDescription())) + Tensor[Float](paddingShape) case _ => throw new UnsupportedOperationException("memory format is not supported") } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala index db3bce93df6..7e881efd24f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala @@ -39,6 +39,8 @@ class Dropout( override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) _outputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) + // we should genereate the primitives here, otherwise the initTensor can't get the padding shape + _outputFormats.map(_.getPrimitive(runtime)) output = initTensor(_outputFormats.head) (_inputFormats, _outputFormats) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala index 0db5509d88a..11500322c40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{AlgKind, MklDnn, PropKind} +import com.intel.analytics.bigdl.mkl.{AlgKind, MklDnn, PropKind, Query} import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor @@ -40,8 +40,8 @@ class LRN( _inputFormats(0).getMemoryDescription(), size, alpha.toFloat, beta.toFloat, k.toFloat) fwdPrimDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) _outputFormats = Array(MemoryData.primitiveOutput(fwdPrimDesc)) - workSpaceFormat = MemoryData.primitiveWorkSpace(fwdPrimDesc) - workSpace = initTensor(workSpaceFormat) + workSpaceFormat = MemoryData.operationWant(fwdPrimDesc, Query.WorkspacePd) + workSpace = initTensor(workSpaceFormat).asInstanceOf[Tensor[Float]] updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPrimDesc, _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, Array(_outputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), 2)) @@ -59,7 +59,7 @@ class LRN( _gradOutputFormats(0).getMemoryDescription(), size, alpha.toFloat, beta.toFloat, k.toFloat) require(fwdPrimDesc != UNDEFINED, "You should call initFwdPrimitives first") val primDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPrimDesc) - _gradInputFormats = Array(MemoryData.primitiveGradInput(primDesc)) + _gradInputFormats = Array(MemoryData.operationWant(primDesc, Query.DiffSrcPd)) updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(primDesc, Array(_inputFormats(0), _gradOutputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), Array(0, 0, 0), 3, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 1cb69ca1e01..3b07a18b9e6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -272,8 +272,9 @@ class Linear( updateGradWTensors = buffer.toArray } - updateWithNewTensor(updateGradInputTensors, 0, input) - updateWithNewTensor(updateGradInputTensors, 1, gradOutput) + // do not use the updateGradInputTensors for acc + updateWithNewTensor(updateGradWTensors, 0, input) + updateWithNewTensor(updateGradWTensors, 1, gradOutput) MklDnnOps.streamSubmit(runtime.stream, 1, accGradientPrimitives, accGradientPrimitives.length, updateGradWMemoryPrimitives, updateGradWTensors) @@ -300,7 +301,6 @@ class Linear( super.release() List(weight, bias, gradWeight, gradBias).foreach(_.release()) } - } object Linear { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala index 97b88b4b89e..965b72afd16 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala @@ -84,8 +84,8 @@ class MaxPooling( fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) _outputFormats = Array(MemoryData.primitiveOutput(fwdPD)) output = initTensor(_outputFormats(0)) - workSpaceFormat = MemoryData.primitiveWorkSpace(fwdPD) - workSpace = initTensor(workSpaceFormat) + workSpaceFormat = MemoryData.operationWant(fwdPD, Query.WorkspacePd) + workSpace = initTensor(workSpaceFormat).asInstanceOf[Tensor[Float]] updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPD, _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, Array(_outputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), 2)) @@ -105,7 +105,7 @@ class MaxPooling( strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) val pd = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD) - _gradInputFormats = Array(MemoryData.primitiveGradInput(pd)) + _gradInputFormats = Array(MemoryData.operationWant(pd, Query.DiffSrcPd)) updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(pd, Array(_gradOutputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), Array(0, 0), 2, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index a372a46fe20..dba3d4cb9a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -15,14 +15,23 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn, Query} -import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} +import com.intel.analytics.bigdl.mkl._ sealed trait MemoryData extends Serializable { def shape: Array[Int] def layout: Int + def dataType: Int def setShape(shape: Array[Int]): Unit def setLayout(layout: Int): Unit + def setDataType(dataType: Int): Unit + + private var _mask: Int = -1 + private var _scales: Array[Float] = Array.emptyFloatArray + + def mask: Int = _mask + def setMask(s: Int): Unit = _mask = s + def scales: Array[Float] = _scales + def setScales(f: Array[Float]): Unit = _scales = f def isLayoutFixed(): Boolean = { layout != Memory.Format.format_undef && layout != Memory.Format.any @@ -39,7 +48,7 @@ sealed trait MemoryData extends Serializable { def getMemoryDescription(): Long = { if (description == UNDEFINED || description == ERROR) { - description = MklDnn.MemoryDescInit(shape.length, shape, DataType.F32, layout) + description = MklDnn.MemoryDescInit(shape.length, shape, dataType, layout) } description } @@ -69,9 +78,24 @@ sealed trait MemoryData extends Serializable { def setMemoryDescription(desc: Long): Unit = { description = desc } + + def getRealSize: Long = { + require(primitiveDesc != UNDEFINED && primitiveDesc != ERROR) + MklDnn.PrimitiveDescGetSize(primitiveDesc) + } + + def getPaddingShape: Array[Int] = { + require(description != UNDEFINED && description != ERROR) + Memory.GetPaddingShape(description) + } } -case class HeapData(private var _shape: Array[Int], private var _layout: Int) extends MemoryData { +case class HeapData(private var _shape: Array[Int], private var _layout: Int, + private var _dataType: Int = DataType.F32) extends MemoryData { + + override def dataType: Int = _dataType + + override def setDataType(dataType: Int): Unit = _dataType = dataType override def setShape(shape: Array[Int]): Unit = _shape = shape.clone() @@ -128,14 +152,16 @@ case class HeapData(private var _shape: Array[Int], private var _layout: Int) ex s"HeapData([${shape.mkString("x")}], ${layout})" } - override def cloneFormat(): MemoryData = new HeapData(_shape, _layout) + override def cloneFormat(): MemoryData = new HeapData(_shape, _layout, _dataType) def toNative(): NativeData = { NativeData(shape, layout) } } -case class NativeData(private var _shape: Array[Int], private var _layout: Int) extends MemoryData { +case class NativeData(private var _shape: Array[Int], private var _layout: Int, + private var _dataType: Int = DataType.F32) extends MemoryData { + override def shape: Array[Int] = _shape.clone() override def layout: Int = _layout @@ -188,10 +214,14 @@ case class NativeData(private var _shape: Array[Int], private var _layout: Int) } override def toString: String = { - s"NativeData([${shape.mkString("x")}], ${layout})" + s"NativeData([${shape.mkString("x")}], ${layout}, ${dataType}, ${mask}, ${scales})" } - override def cloneFormat(): MemoryData = new NativeData(_shape, _layout) + override def cloneFormat(): MemoryData = new NativeData(_shape, _layout, _dataType) + + override def dataType: Int = _dataType + + override def setDataType(dataType: Int): Unit = _dataType = dataType } private[mkldnn] object MemoryData { @@ -213,47 +243,28 @@ private[mkldnn] object MemoryData { val outputPD = MklDnn.PrimitiveDescQueryPd(pd, Query.DstPd, 0) val memoryDesc = MklDnn.PrimitiveDescQueryMemory(outputPD) val shape = Memory.GetShape(memoryDesc) + val paddingShape = Memory.GetPaddingShape(memoryDesc) val layout = Memory.GetLayout(memoryDesc) + val dataType = Memory.GetDataType(memoryDesc) + val size = MklDnn.PrimitiveDescGetSize(outputPD) - val memory = NativeData(shape, layout) + val memory = NativeData(shape, layout, dataType) memory.setMemoryDescription(memoryDesc) memory.setPrimitiveDescription(outputPD) memory } - def primitiveGradInput(pd: Long): NativeData = { - val gradInputPD = MklDnn.PrimitiveDescQueryPd(pd, Query.DiffSrcPd, 0) - val memoryDesc = MklDnn.PrimitiveDescQueryMemory(gradInputPD) - val shape = Memory.GetShape(memoryDesc) - val layout = Memory.GetLayout(memoryDesc) - - val memory = NativeData(shape, layout) - memory.setMemoryDescription(memoryDesc) - memory.setPrimitiveDescription(gradInputPD) - memory - } - def operationWant(primDesc: Long, queryType: Int): NativeData = { val memoryPrimDesc = MklDnn.PrimitiveDescQueryPd(primDesc, queryType, 0) val memoryDesc = MklDnn.PrimitiveDescQueryMemory(memoryPrimDesc) val shape = Memory.GetShape(memoryDesc) + val paddingShape = Memory.GetPaddingShape(memoryDesc) val layout = Memory.GetLayout(memoryDesc) + val dataType = Memory.GetDataType(memoryDesc) - val memory = NativeData(shape, layout) + val memory = NativeData(shape, layout, dataType) memory.setMemoryDescription(memoryDesc) memory.setPrimitiveDescription(memoryPrimDesc) memory } - - def primitiveWorkSpace(pd: Long): NativeData = { - val workspacePD = MklDnn.PrimitiveDescQueryPd(pd, Query.WorkspacePd, 0) - val memoryDesc = MklDnn.PrimitiveDescQueryMemory(workspacePD) - val shape = Memory.GetShape(memoryDesc) - val layout = Memory.GetLayout(memoryDesc) - - val memory = NativeData(shape, layout) - memory.setMemoryDescription(memoryDesc) - memory.setPrimitiveDescription(workspacePD) - memory - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala index a2bf3e62899..660b15dd397 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala @@ -45,7 +45,7 @@ class ReLU(value: Float = 0.0f) extends MklDnnLayer { value, 0) require(fwdPrimDesc != UNDEFINED, "You should call initFwdPrimitives first") val primDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPrimDesc) - _gradInputFormats = Array(MemoryData.primitiveGradInput(primDesc)) + _gradInputFormats = Array(MemoryData.operationWant(primDesc, Query.DiffSrcPd)) updateGradInputPrimitives = Array( MklDnn.PrimitiveCreate2(primDesc, Array(_inputFormats(0), _gradOutputFormats(0)).map(_.getPrimitive(runtime)), Array(0), 2, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala index d534a4b269d..6c5f6eb0944 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala @@ -15,6 +15,7 @@ */ package com.intel.analytics.bigdl.nn.mkldnn +import com.intel.analytics.bigdl.mkl.DataType import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T @@ -103,7 +104,12 @@ private[mkldnn] class ReorderManager() { to match { case hh: HeapData => true case nn: NativeData => - nn.layout != n.layout + // we will skip the S8 to U8 reorder + val doNotReorderIt = n.layout == nn.layout && ( + n.dataType == nn.dataType || // the same data type + (n.dataType == DataType.S8 && nn.dataType == DataType.U8)) // skip the s8->u8 + + !doNotReorderIt case _ => throw new UnsupportedOperationException("Not support such memory format") } case _ => throw new UnsupportedOperationException("Not support such memory format") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala index e38d4d7d5fa..05b52df615c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala @@ -33,11 +33,15 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, private def initMemory(src: MemoryData, shape: Array[Int], layout: Int) : Array[MemoryData] = { - src match { - case h: HeapData => Array(HeapData(shape, layout)) - case n: NativeData => Array(NativeData(shape, layout)) + val ret = src match { + case h: HeapData => Array(HeapData(shape, layout, src.dataType)) + case n: NativeData => Array(NativeData(shape, layout, src.dataType)) case _ => throw new UnsupportedOperationException("Not support such memory format") } + + ret(0).setMask(src.mask) + ret(0).setScales(src.scales) + ret.asInstanceOf[Array[MemoryData]] } private def shapeToString(shape: Array[Int]): String = { @@ -57,6 +61,36 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, } } + private def createInt8PrimDesc(): Long = { + val attr = MklDnn.CreateAttr() + MklDnn.AttrSetIntOutputRoundMode(attr, 1) + + if (realOutput(0).scales == null || realOutput(0).scales.isEmpty) { + realOutput(0).setMask(realInput(0).mask) + realOutput(0).setScales(realInput(0).scales) + } + + // if convert s8/u8 to f32, we should set the scale factor to 1.0f/x + if (realOutput(0).dataType == DataType.F32) { + realOutput(0).setScales(realOutput(0).scales.map(1.0f / _)) + } + + // copy the scales back to outputFormats if not equal + if (realOutput(0) ne _outputFormats(0)) { + _outputFormats(0).setMask(realOutput(0).mask) + _outputFormats(0).setScales(realOutput(0).scales) + } + + require(realOutput(0).scales.nonEmpty) + MklDnn.AttrSetOutputScales(attr, realOutput(0).scales.length, realOutput(0).mask, + realOutput(0).scales) + + MklDnn.ReorderPrimitiveDescCreateV2( + realInput(0).getPrimitiveDescription(runtime), + realOutput(0).getPrimitiveDescription(runtime), + attr) + } + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = if (inputFormat == null) inputs else Array(inputFormat) require(_inputFormats.length == 1, "Only accept one tensor as input") @@ -85,9 +119,17 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, } } - val fwdReorderPrimDesc = MklDnn.ReorderPrimitiveDescCreate( - realInput(0).getPrimitiveDescription(runtime), - realOutput(0).getPrimitiveDescription(runtime)) + val noInt8Formats = inputFormats()(0).dataType == DataType.F32 && + outputFormats()(0).dataType == DataType.F32 + + val fwdReorderPrimDesc = if (noInt8Formats) { + MklDnn.ReorderPrimitiveDescCreate( + realInput(0).getPrimitiveDescription(runtime), + realOutput(0).getPrimitiveDescription(runtime)) + } else { + createInt8PrimDesc() + } + val fwdReorderPrim = MklDnn.PrimitiveCreate2(fwdReorderPrimDesc, Array(realInput(0).getPrimitive(runtime)), Array(0), 1, Array(realOutput(0).getPrimitive(runtime)), 1) @@ -109,10 +151,6 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, override def getUpdateOutputMemoryPrimitives(): Array[Long] = { realInput.map(_.getPrimitive(runtime)) ++ realOutput.map(_.getPrimitive(runtime)) } - override def updateOutput(input: Activity): Activity = { - output = super.updateOutput(input) - output - } override private[bigdl] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { _gradInputFormats = (gradInputFormat, inputFormat) match { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala index 50e75409e5e..7955c417ca4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -31,7 +31,7 @@ class DnnTensor[T: ClassTag]( ) (implicit ev: TensorNumeric[T]) extends DnnTensorUnsupportOperations[T]{ - override def nElement(): Int = storage.length() + override def nElement(): Int = sizes.product override def copy(other: Tensor[T]): Tensor[T] = { other match { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala index 2eb0a62c4f3..5a45c236e7e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala @@ -27,6 +27,7 @@ class DropoutSpec extends FlatSpec with Matchers { val zeros = Tensor[Float](Array(2, 3, 4, 4)).fill(0) val dropout = Dropout() + dropout.setRuntime(new MklDnnRuntime) dropout.initFwdPrimitives(Array(HeapData(Array(2, 3, 4, 4), Memory.Format.nchw)), TrainingPhase) { @@ -54,6 +55,7 @@ class DropoutSpec extends FlatSpec with Matchers { val zeros = Tensor[Float](Array(2, 3, 4, 4)).fill(0) val dropout = Dropout() + dropout.setRuntime(new MklDnnRuntime) dropout.initFwdPrimitives(Array(HeapData(Array(2, 3, 4, 4), Memory.Format.nchw)), InferencePhase) dropout.evaluate() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala index 1f452328fd6..5eb60ca8b5b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala @@ -15,6 +15,10 @@ */ package com.intel.analytics.bigdl.nn.mkldnn +import com.intel.analytics.bigdl.mkl.{DataType, Memory} +import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.tensor.{DnnTensor, Storage, Tensor} import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} @@ -253,4 +257,145 @@ class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { reorder2.output.toTensor[Float] should be (t1) } + + "F32 to S8" should "work correctly" in { + val shape = Array[Int](2, 2) + val input = Tensor[Float](Array[Float](15, 14, 8, 10), shape).rand(0, 1) + val nativeData = NativeData(shape, Memory.Format.nc, DataType.S8) + val heapData = HeapData(shape, Memory.Format.nc) + heapData.setMask(0) + heapData.setScales(Array(127.0f / input.max())) + val f32ToS2 = ReorderMemory(nativeData) + + f32ToS2.setRuntime(new MklDnnRuntime) + f32ToS2.initFwdPrimitives(Array(heapData), Phase.InferencePhase) + + f32ToS2.forward(input) + + val srcAddress = f32ToS2.output.asInstanceOf[DnnTensor[Byte]].storageAddress() + + val len = shape.product + val output = new Array[Byte](len) + Memory.CopyPtr2ByteArray(srcAddress, 0, output, 0, len, 1) + + output.foreach(println) + + println(input) + + val S8ToF32 = ReorderMemory(HeapData(shape, Memory.Format.nc)) + S8ToF32.setRuntime(new MklDnnRuntime) + S8ToF32.initFwdPrimitives(Array(nativeData), Phase.InferencePhase) + + S8ToF32.forward(f32ToS2.output) + + // the int part should be the same + S8ToF32.output.toTensor[Float].storage().array().map(_.toInt) should be ( + input.storage().array().map(_.toInt)) + } + + "F32 to S8 NCHW" should "work correctly" in { + val shape = Array[Int](4, 3, 2, 2) + val input = Tensor[Float](shape).rand(0, 1) + val inputScales = input.max(1)._1.max(3)._1.max(4)._1.storage().array() + val nativeData = NativeData(shape, Memory.Format.nhwc, DataType.U8) + val heapData = HeapData(shape, Memory.Format.nchw, DataType.F32) + heapData.setMask(2) + heapData.setScales(inputScales.map(x => 255.0f / x)) + val f32ToS2 = ReorderMemory(nativeData) + println(Memory.Format.nchw) + + f32ToS2.setRuntime(new MklDnnRuntime) + f32ToS2.initFwdPrimitives(Array(heapData), Phase.InferencePhase) + + f32ToS2.forward(input) + + val srcAddress = f32ToS2.output.asInstanceOf[DnnTensor[Byte]].storageAddress() + + val len = shape.product + val output = new Array[Byte](len) + Memory.CopyPtr2ByteArray(srcAddress, 0, output, 0, len, 1) + + output.foreach(println) + + println(input) + + val S8ToF32 = ReorderMemory(HeapData(shape, Memory.Format.nchw, DataType.F32)) + S8ToF32.setRuntime(new MklDnnRuntime) + S8ToF32.initFwdPrimitives(Array(f32ToS2.outputFormats()(0)), Phase.InferencePhase) + + S8ToF32.forward(f32ToS2.output) + println(S8ToF32.output) + + // the int part should be the same + S8ToF32.output.toTensor[Float].storage().array().map(_.toInt) should be ( + input.storage().array().map(_.toInt)) + } + + "F32 to S32 Memory.Format.x" should "work correctly" in { + val shape = Array[Int](2) + val inputData = Array[Float](10, 12) + val input = Tensor[Float](inputData, shape).rand(0, 1) + val nativeData = NativeData(shape, Memory.Format.x, DataType.S32) + val heapData = HeapData(shape, Memory.Format.x) + heapData.setMask(1) + heapData.setScales(inputData.map(x => 100 / inputData.max)) + + println(Integer.MAX_VALUE) + + val f32ToS32 = ReorderMemory(nativeData) + + f32ToS32.setRuntime(new MklDnnRuntime) + f32ToS32.initFwdPrimitives(Array(heapData), Phase.InferencePhase) + + f32ToS32.forward(input) + + println(input) + println(f32ToS32.output) + + nativeData.setMask(1) + nativeData.setScales(inputData.map(x => 100 / inputData.max)) + val S32ToF32 = ReorderMemory(HeapData(shape, Memory.Format.x)) + S32ToF32.setRuntime(new MklDnnRuntime) + S32ToF32.initFwdPrimitives(Array(nativeData), Phase.InferencePhase) + + S32ToF32.forward(f32ToS32.output) + + println(S32ToF32.output) + + // the int part should be the same + S32ToF32.output.toTensor[Float].storage().array().map(_.toInt) should be ( + input.storage().array().map(_.toInt)) + } + + "oihw" should "work correctly" in { + // this test case is used to test oihw -> hwio_s8s8 reordering. + // the hwio_s8s8 will need more space than padding shape, which is called additional space + // called by mkldnn. + + // we will convert the hwio_s8s8 back to oihw, because mkldnn has not implemented it yet. + val shape = Array(50, 24, 5, 5) + val from = Tensor[Float](shape).rand(-1, 1) + + val heap = HeapData(shape, Memory.Format.oihw, DataType.F32) + val native = NativeData(shape, Memory.Format.hwio_s8s8, DataType.S8) + + val mask = 0 + // (1 to 50).map(i => from.select(1, i).max()).toArray + val scales = Array(from.clone().abs().max() / 127.0f) + + heap.setMask(mask) + heap.setScales(scales) + native.setMask(mask) + native.setScales(scales) + + val runtime = new MklDnnRuntime + val reorder = ReorderMemory(native) + reorder.setRuntime(runtime) + reorder.initFwdPrimitives(Array(heap), InferencePhase) + + (0 to 10).foreach ( i => { + println(s"do forward ${i}") + reorder.forward(from) + }) + } } From 52216874983d6a0d65cbfcca778efedee115fac8 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 18 Mar 2019 16:23:01 +0800 Subject: [PATCH 0879/1065] fix conversion accuracy (#2760) * fix accuracy for saved model * exclude mkldnn model when conversion --- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 5 +++ .../nn/mkldnn/SpatialBatchNormalization.scala | 15 +++++++- .../bigdl/dllib/optim/AbstractOptimizer.scala | 6 +++- .../bigdl/dllib/optim/DistriOptimizer.scala | 35 ++++++++++-------- .../utils/intermediate/ConversionUtils.scala | 4 +-- .../dllib/utils/intermediate/IRElement.scala | 22 ++++++++---- .../dllib/utils/intermediate/IRGraph.scala | 4 +++ .../dllib/utils/intermediate/IRToDnn.scala | 2 ++ .../{utils => optim}/LoggerFilterSpec.scala | 19 +++++----- .../dlframes/DLClassifierSpec.scala | 0 .../dlframes/DLEstimatorSpec.scala | 0 .../dlframes/DLImageReaderSpec.scala | 0 .../dlframes/DLImageTransformerSpec.scala | 0 .../utils/intermediate/BlasToDnnSpec.scala | 36 +++++++++++++++++++ 14 files changed, 114 insertions(+), 34 deletions(-) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{utils => optim}/LoggerFilterSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => optim}/dlframes/DLClassifierSpec.scala (100%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => optim}/dlframes/DLEstimatorSpec.scala (100%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => optim}/dlframes/DLImageReaderSpec.scala (100%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => optim}/dlframes/DLImageTransformerSpec.scala (100%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 89893336682..88a5ddd8140 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -481,6 +481,11 @@ class DnnGraph( ) checkDuplicate() } + + override def release(): Unit = { + super.release() + reorderManager.release() + } } object DnnGraph { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 0603a98b63a..a8dfa94ad87 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -43,6 +43,10 @@ class SpatialBatchNormalization( } def relu: Boolean = _relu + // reminder: runningMean/runningVariance in blas batch_norm is + // same to scaled runningMean/runningVariance in dnn. + private[bigdl] var needScale = false + @transient private var updateOutputTensors: Array[Tensor[Float]] = _ @transient private var updateOutputMemoryPrimitives: Array[Long] = _ @transient private var updateGradInputTensors: Array[Tensor[Float]] = _ @@ -61,6 +65,9 @@ class SpatialBatchNormalization( var scaleFactor: Float = 1.0f var biasFactor: Float = 1.0f + private val runningMeanScaled = Tensor[Float].resizeAs(runningMean.dense) + private val runningVarianceScaled = Tensor[Float].resizeAs(runningVariance.dense) + { val wInit = Ones // RandomUniform(0, 1) val bInit = Zeros @@ -348,7 +355,13 @@ class SpatialBatchNormalization( } override def getExtraParameter(): Array[Tensor[Float]] = { - Array(runningMean.dense, runningVariance.dense) + if (needScale) { + runningMeanScaled.copy(runningMean.dense).div(scaleFactor) + runningVarianceScaled.copy(runningVariance.dense).div(scaleFactor) + Array(runningMeanScaled, runningVarianceScaled) + } else { + Array(runningMean.dense, runningVariance.dense) + } } override def toString(): String = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala index fa28f463093..49df1177698 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala @@ -128,7 +128,11 @@ abstract class AbstractOptimizer { weightsResults.foreach(_.waitResult()) } - workingModels.foreach(_.evaluate()) + if (Engine.getEngineType() == MklDnn) { + if (dataIter.hasNext) workingModels.foreach(_.evaluate()) + } else { + workingModels.foreach(_.evaluate()) + } dataIter.map(batch => { val stackSize = batch.size() / _subModelNumber val extraSize = batch.size() % _subModelNumber diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index e9ef002aa0c..3bce315e3d4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -31,8 +31,8 @@ import java.util.Calendar import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer} -import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer, MklDnnLayer, MklDnnModule} import com.intel.analytics.bigdl.utils.intermediate.IRGraph import org.apache.commons.lang.exception.ExceptionUtils import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} @@ -45,6 +45,9 @@ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.concurrent.Future import scala.reflect.ClassTag +import com.intel.analytics.bigdl.nn.{Container, Graph, Module, Utils} +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer, MklDnnLayer} +import com.intel.analytics.bigdl.utils.intermediate.{ConversionUtils, IRGraph} object DistriOptimizer extends AbstractOptimizer { import Optimizer._ @@ -546,7 +549,7 @@ object DistriOptimizer extends AbstractOptimizer { // ModelBroadcast to clone model here. // Notes: All models returned by modelBroadcast.value() share the same weight&bias, while // gradWeight&gradBias is unshared. - val modelBroadcast = ModelBroadcast[T]().broadcast(sc, model) + val modelBroadcast = ModelBroadcast[T]().broadcast(sc, ConversionUtils.convert(model)) val _subModelNumber = Engine.getEngineType match { case MklBlas => coresPerNode case MklDnn => 1 @@ -786,6 +789,10 @@ class DistriOptimizer[T: ClassTag] ( override def optimize(): Module[T] = { val distDataset = dataset.toDistributed() + val trainingModel = if (Engine.getEngineType() == MklDnn && !model.isInstanceOf[MklDnnModule] + && !model.isInstanceOf[IRGraph[T]] && !model.isInstanceOf[Graph[T]]) { + model.toGraph().setName(model.getName()) + } else model optimMethods.values.foreach { optimMethod => optimMethod.clearHistory() @@ -806,11 +813,11 @@ class DistriOptimizer[T: ClassTag] ( val coresPerNode = Engine.coreNumber() val partitionNum = distDataset.originRDD().partitions.length - val modelParameters = model.getParameters() + val modelParameters = trainingModel.getParameters() // subModuleName -> (storageOffset, length, AllReduceParameter) val parameters = if (optimMethods.size != 1) { val p = optimMethods.map{case (subModuleName, optimMethods) => - val subModule = model(subModuleName) + val subModule = trainingModel(subModuleName) require(subModule.isDefined, s"Optimizer couldn't find $subModuleName in $model") val subModuleWeights = subModule.get.getParameters()._1 (subModuleName, subModuleWeights) @@ -823,18 +830,18 @@ class DistriOptimizer[T: ClassTag] ( (subModuleName, AllReduceParameter.newParameter[T]( partitionNum, weights.nElement(), weights.storageOffset())) } - } else if (optimMethods.contains(model.getName())) { - Map(model.getName() -> AllReduceParameter.newParameter[T]( + } else if (optimMethods.contains(trainingModel.getName())) { + Map(trainingModel.getName() -> AllReduceParameter.newParameter[T]( partitionNum, modelParameters._1.nElement())) } else { - throw new IllegalArgumentException(s"${model.getName()} doesn't " + + throw new IllegalArgumentException(s"${trainingModel.getName()} doesn't " + s"have corresponding OptimMethod") } prepareInput() - val modelsAndBroadcast = DistriOptimizer.initThreadModels(model, distDataset, criterion, state, - nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, + val modelsAndBroadcast = DistriOptimizer.initThreadModels(trainingModel, distDataset, criterion, + state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, optimMethods, parameterProcessors) models = if (reserveOptimMethod && previousOptim != null) { @@ -860,7 +867,7 @@ class DistriOptimizer[T: ClassTag] ( while (retryNum < maxRetry) { try { DistriOptimizer.optimize( - model, + trainingModel, distDataset, coresPerNode, state, @@ -909,7 +916,7 @@ class DistriOptimizer[T: ClassTag] ( Module.load[T](modelFile) } else { DistriOptimizer.logger.info("Model recover from origin model") - model + trainingModel } optimMethods = optimMethods.map { case (moduleName, optimMethod) => val methodFile = getLatestFile(checkpointPath.get, s"optimMethod-$moduleName") @@ -935,7 +942,7 @@ class DistriOptimizer[T: ClassTag] ( } } - DistriOptimizer.getModel(models, parameters, model) + DistriOptimizer.getModel(models, parameters, trainingModel) // Reset some internal states, so this or other optimizers can run optimize again clearState() @@ -953,7 +960,7 @@ class DistriOptimizer[T: ClassTag] ( } models.unpersist() - model + trainingModel } private def getLatestFile(path: String, fileName: String): String = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala index c8973eb8200..bbef32584a0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.utils.intermediate import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn.mkldnn.DnnGraph +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnLayer, MklDnnModule} import com.intel.analytics.bigdl.utils.{Engine, MklDnn, T} import org.apache.spark.rdd.RDD import com.intel.analytics.bigdl.nn.Graph @@ -35,7 +35,7 @@ private[bigdl] object ConversionUtils { if (model.isInstanceOf[IRGraph[T]]) { val g = model.asInstanceOf[IRGraph[T]] if (g.isBuild) g else g.build() - } else if (!model.isInstanceOf[DnnGraph] && Engine.getEngineType() == MklDnn) { + } else if (!model.isInstanceOf[MklDnnModule] && Engine.getEngineType() == MklDnn) { val m = if (!model.isInstanceOf[Graph[T]]) model.toGraph() else model if (!m.isInstanceOf[StaticGraph[T]]) return model val ir = m.asInstanceOf[StaticGraph[T]].toIRgraph().asInstanceOf[Module[T]] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala index 645813fcd1c..ad9b2641ed5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala @@ -25,11 +25,13 @@ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag sealed class IROperator[T: ClassTag] extends Serializable { - val tag: ClassTag[T] = scala.reflect.classTag[T] - val numerics: TensorNumeric[T] = tag match { - case ClassTag.Float => TensorNumeric.NumericFloat.asInstanceOf[TensorNumeric[T]] - case ClassTag.Double => TensorNumeric.NumericDouble.asInstanceOf[TensorNumeric[T]] - case _ => throw new IllegalArgumentException(s"not supported class tag: ${tag}") + val numerics: TensorNumeric[T] = getNumerics(scala.reflect.classTag[T]) + final def getNumerics[T](tag: ClassTag[T]) : TensorNumeric[T] = { + tag match { + case ClassTag.Float => TensorNumeric.NumericFloat.asInstanceOf[TensorNumeric[T]] + case ClassTag.Double => TensorNumeric.NumericDouble.asInstanceOf[TensorNumeric[T]] + case _ => throw new IllegalArgumentException(s"not supported class tag: ${tag}") + } } def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { (Array(scala.reflect.classTag[T]), Array(numerics)) @@ -112,7 +114,15 @@ case class IRSoftMax[T: ClassTag]() extends IROperator[T] case class IRSelectTable[T: ClassTag](dimension: Int) extends IROperator[T] -case class IRCAddTable[T: ClassTag, D: ClassTag](inplace: Boolean = false) extends IROperator[T] +case class IRCAddTable[T: ClassTag, D: ClassTag](inplace: Boolean = false) extends IROperator[T] { + private val ev = getNumerics(scala.reflect.classTag[T]) + private val ev2 = getNumerics(scala.reflect.classTag[D]) + + override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { + (Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]), + Array[TensorNumeric[_]](ev, ev2)) + } +} case class IRJoinTable[T: ClassTag](dimension: Int, nInputDims: Int = 0) extends IROperator[T] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index bcdc0c9b4c9..6b080158133 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -135,6 +135,10 @@ private[bigdl] class IRGraph[T: ClassTag]( graph.resetTimes() } + override def release(): Unit = { + graph.release() + } + private def initPrimitives(input: Activity): Unit = { if (!initPrim && graph.isInstanceOf[DnnGraph]) { val inputMemory = new Array[MemoryData](inputFormats.length) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index da1d900ff41..c6dad441321 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -172,6 +172,8 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] if (t.runningMean != null) extraParams(0).copy(t.runningMean.toTensor[Float]) if (t.runningVar != null) extraParams(1).copy(t.runningVar.toTensor[Float]) + // reminder: assume batch_norm is converted from blas + layer.needScale = true layer } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LoggerFilterSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LoggerFilterSpec.scala index 224860932b3..c1106071201 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/LoggerFilterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LoggerFilterSpec.scala @@ -14,24 +14,23 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.utils - -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.numeric.NumericDouble -import com.intel.analytics.bigdl.optim.{Optimizer, SGD, Trigger} -import com.intel.analytics.bigdl.nn.{Linear, MSECriterion, Sequential} -import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch, Sample, SampleToMiniBatch} +package com.intel.analytics.bigdl.optim import java.io.StringWriter import java.nio.charset.StandardCharsets import java.nio.file.{Files, Paths} -import org.apache.spark.SparkContext +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.{DataSet, Sample, SampleToMiniBatch} +import com.intel.analytics.bigdl.nn.{Linear, MSECriterion, Sequential} +import com.intel.analytics.bigdl.numeric.NumericDouble +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter, T, TestUtils} import org.apache.log4j.{Level, Logger, PatternLayout, WriterAppender} +import org.apache.spark.SparkContext +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.collection.JavaConverters._ -import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @com.intel.analytics.bigdl.tags.Serial class LoggerFilterSpec extends FlatSpec with BeforeAndAfter with Matchers { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/dlframes/DLClassifierSpec.scala similarity index 100% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLClassifierSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/dlframes/DLClassifierSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/dlframes/DLEstimatorSpec.scala similarity index 100% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLEstimatorSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/dlframes/DLEstimatorSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageReaderSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/dlframes/DLImageReaderSpec.scala similarity index 100% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageReaderSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/dlframes/DLImageReaderSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageTransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/dlframes/DLImageTransformerSpec.scala similarity index 100% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dlframes/DLImageTransformerSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/dlframes/DLImageTransformerSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala index 5b565412241..9dc06c783ad 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToDnnSpec.scala @@ -143,4 +143,40 @@ class BlasToDnnSpec extends BigDLSpecHelper { Equivalent.nearequals(outDnn, outBlas, 1e-6) should be(true) } + + "resnet50 dnn to blas" should "work properly" in { + val batchSize = 2 + val classNum = 1000 + RandomGenerator.RNG.setSeed(1000) + val blas = ResNet.graph(classNum, + T("shortcutType" -> ShortcutType.B, "depth" -> 50, + "optnet" -> false, "dataset" -> DatasetType.ImageNet)).asInstanceOf[StaticGraph[Float]] + val irBlas = blas.toIRgraph() + + for (i <- 0 to 3) { + val input = Tensor[Float](2, 3, 224, 224).rand() + val gradOutput = Tensor[Float](2, 1000).rand() + irBlas.training() + irBlas.forward(input) + irBlas.backward(input, gradOutput) + } + val input = Tensor[Float](2, 3, 224, 224).rand() + irBlas.evaluate() + irBlas.forward(input) + + val p1 = blas.getParameters() + val p2 = irBlas.getParameters() + p1._1.copy(p2._1) + p1._2.copy(p2._2) + blas.setExtraParameter(irBlas.getExtraParameter()) + + val in = Tensor[Float](2, 3, 224, 224).rand() + blas.evaluate() + irBlas.evaluate() + + val out1 = blas.forward(in).toTensor[Float] + val out2 = irBlas.forward(in).toTensor[Float] + + Equivalent.getunequals(out1, out2, 1e-4) should be(true) + } } From 21220ef6de54fca960cb85f2ed22ceede2a934f3 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 18 Mar 2019 18:36:00 +0800 Subject: [PATCH 0880/1065] feature: layer wise supports of int8 (#2762) Enable the int8 data type in layers, especially for convolutions. So for a specific layer, it can accept a int8 input. If you want to the fp32 output, should add a reorder. --- .../bigdl/dllib/nn/MklInt8Convertible.scala | 35 ++++- .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 19 ++- .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 8 +- .../bigdl/dllib/nn/mkldnn/CAddTable.scala | 17 +- .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 17 ++ .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 52 +++++-- .../bigdl/dllib/nn/mkldnn/JoinTable.scala | 2 +- .../analytics/bigdl/dllib/nn/mkldnn/LRN.scala | 47 ++++-- .../bigdl/dllib/nn/mkldnn/MaxPooling.scala | 49 ++++-- .../bigdl/dllib/nn/mkldnn/MklDnnOps.scala | 4 +- .../bigdl/dllib/nn/mkldnn/SelectTable.scala | 10 +- .../bigdl/dllib/nn/mkldnn/Sequential.scala | 24 ++- .../dllib/nn/mkldnn/SpatialConvolution.scala | 144 +++++++++++++++-- .../bigdl/dllib/nn/mkldnn/Utils.scala | 141 +++++++++++++++++ .../bigdl/dllib/tensor/DnnTensor.scala | 5 +- .../bigdl/dllib/nn/ScaleCalculatorSpec.scala | 147 +++++++++++++++++- .../dllib/nn/mkldnn/AvgPoolingSpec.scala | 53 ++++++- .../bigdl/dllib/nn/mkldnn/CAddTableSpec.scala | 49 +++++- .../dllib/nn/mkldnn/ConcatTableSpec.scala | 44 +++++- .../bigdl/dllib/nn/mkldnn/LRNSpec.scala | 42 ++++- .../dllib/nn/mkldnn/MaxPoolingSpec.scala | 55 ++++++- .../SpatialBatchNormalizationSpec.scala | 4 +- .../nn/mkldnn/SpatialConvolutionSpec.scala | 67 +++++++- .../bigdl/dllib/nn/mkldnn/TestUtils.scala | 2 +- 24 files changed, 934 insertions(+), 103 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala index 71cbdb69726..55259620c68 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala @@ -55,7 +55,7 @@ trait MklInt8Convertible { if (inputActvt != null) { val module = this.asInstanceOf[AbstractModule[_, _, Float]] - val outputActvt = module.forward(inputActvt) + val outputActvt = mkldnn.Utils.getOutput(module, inputActvt) module match { case graph: Graph[Float] => calcGraphScales(inputActvt, outputActvt) @@ -64,6 +64,12 @@ trait MklInt8Convertible { calcModuleScales(inputActvt, outputActvt, linear.weight) case spatialConv: SpatialConvolution[Float@unchecked] => calcModuleScales(inputActvt, outputActvt, spatialConv.weight) + case relu: ReLU[Float@unchecked] => + calcModuleScales(inputActvt, outputActvt) + case caddTable: CAddTable[Float@unchecked, Float@unchecked] => + calcModuleScales(inputActvt, outputActvt) + case bn: SpatialBatchNormalization[Float@unchecked] => + calcModuleScales(inputActvt, outputActvt) case sequential: Sequential[Float@unchecked] => calcSequentialScales(inputActvt, outputActvt) case concatTable: ConcatTable[Float@unchecked] => @@ -77,6 +83,12 @@ trait MklInt8Convertible { calcSequentialScales(inputActvt, outputActvt) case dnnConcatTable: mkldnn.ConcatTable => calcConcatTableScales(inputActvt, outputActvt) + case relu: mkldnn.ReLU => + calcModuleScales(inputActvt, outputActvt) + case bn: mkldnn.SpatialBatchNormalization => + calcModuleScales(inputActvt, outputActvt) + case caddTable: mkldnn.CAddTable => + calcModuleScales(inputActvt, outputActvt) case _ => throw new UnsupportedOperationException( "Int8 conversion is not supported for module: " + module.getName() ) @@ -84,6 +96,11 @@ trait MklInt8Convertible { } } + private[bigdl] def flushWeightScales(weight: Tensor[Float]): Unit = { + weightScalesBuffer.clear() + appendWeightScales(calcTensorScale(weight, weightDimMask)) + } + /** * Calculate module's scales given its input and output * Store calculated scales in array buffers @@ -92,11 +109,13 @@ trait MklInt8Convertible { */ private def calcModuleScales(inputActvt: Activity, outputActvt: Activity): Unit = { if (inputActvt != null) { - calcActivityScales(inputActvt, inputDimMask).foreach(appendInputScales) + val denseIn = mkldnn.Utils.getDenseIn(this, inputActvt) + calcActivityScales(denseIn, inputDimMask).foreach(appendInputScales) } if (outputActvt != null) { - calcActivityScales(outputActvt, outputDimMask).foreach(appendOutputScales) + val denseOut = mkldnn.Utils.getDenseOut(this, outputActvt) + calcActivityScales(denseOut, outputDimMask).foreach(appendOutputScales) } } @@ -139,12 +158,13 @@ trait MklInt8Convertible { * @return scalesBuffer Array, an array stores scales */ private def calcTensorScale(tensor: Tensor[Float], mask: Int): Array[Float] = { + // we must clone the tensor, the abs will change the original tensor's value if (mask == 0) { // no mask performed, return max of tensor storage - Array(tensor.abs().max()) + Array(tensor.clone().abs().max()) } else if (scala.math.pow(2, tensor.dim()) - 1 == mask) { // mask bits are ON for all dimensions // return the abs value of tensor as an array - tensor.abs().storage().toArray[Float] + tensor.clone().abs().storage().toArray[Float] } else { // mask bits are ON for some of dimensions // slice storage according to the dimension if its mask bit is ON @@ -159,7 +179,7 @@ trait MklInt8Convertible { if (bitMask(binStrLen - i) == 1) { val dimSize = tensor.size(i) for (j <- 1 to dimSize) { - scalesBuffer.append(tensor.select(i, j).abs().max()) + scalesBuffer.append(tensor.select(i, j).clone().abs().max()) } } } @@ -261,7 +281,8 @@ trait MklInt8Convertible { */ private def getWeight(module: AbstractModule[_, _, Float]): Tensor[Float] = { if (module != null) { - module.getParameters()._1 + // the getParameters will flatten the weight and bias, it's wrong + module.parameters()._1(0) } else { null } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala index ee7b25a3f8f..f8ecf6c641e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -16,8 +16,9 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ -import com.intel.analytics.bigdl.nn.Utils +import com.intel.analytics.bigdl.nn.{Utils => NNUtils} import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import com.intel.analytics.bigdl.tensor.Tensor class AvgPooling( @@ -69,17 +70,25 @@ class AvgPooling( val h = _inputFormats(0).shape(2) val w = _inputFormats(0).shape(3) val (pt, pb, pl, pr, oh, ow) = if (padH == -1 && padW == -1) { - val sizes = Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) + val sizes = NNUtils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) (sizes(0), sizes(1), sizes(2), sizes(3), sizes(4), sizes(5)) } else { - Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW, ceilMode) + NNUtils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW, ceilMode) } paddingTL = Array(pt, pl) paddingBR = Array(pb, pr) - val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), DataType.F32, Memory.Format.any) + val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), inputs(0).dataType, + Memory.Format.any) + + val kind = if (phase == InferencePhase) { + PropKind.ForwardScoring + } else { + PropKind.ForwardTraining + } + val description = MklDnn.PoolingForwardDescInit( - PropKind.Forward, algKind, + kind, algKind, _inputFormats(0).getMemoryDescription(), outputMD, strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index f7e437880c8..b1507492689 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -27,7 +27,7 @@ import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Engine._ -import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.{Util => NNUtils, _} import org.apache.log4j.Logger /** @@ -142,13 +142,13 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, } private def initModules(): Unit = { subModels = if (module.parameters() != null) { - val wb = Util.getAndClearWeightBias(module.parameters()) + val wb = NNUtils.getAndClearWeightBias(module.parameters()) val models = (1 to subModelNumber).map(i => { val m = module.cloneModule() - Util.putWeightBias(wb, m) + NNUtils.putWeightBias(wb, m) m.asInstanceOf[Module[Float]] }).toArray - Util.putWeightBias(wb, module) + NNUtils.putWeightBias(wb, module) models } else { val models = (1 to subModelNumber).map(i => { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala index 465ab1fba0c..59f335aaf33 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala @@ -16,10 +16,11 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn} +import com.intel.analytics.bigdl.nn.MklInt8Convertible import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.utils.T -class CAddTable extends MklDnnLayer { +class CAddTable extends MklDnnLayer with MklInt8Convertible { override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = nativeData(inputs) val shape = inputs(0).shape.clone() @@ -30,8 +31,18 @@ class CAddTable extends MklDnnLayer { } } - val outputMD = MklDnn.MemoryDescInit(shape.length, shape, DataType.F32, Memory.Format.any) - val scales = inputs.map(_ => 1f) + val outputMD = MklDnn.MemoryDescInit(shape.length, shape, inputs(0).dataType, Memory.Format.any) + + val scales = inputs.map { x => + if (x.dataType != DataType.F32 && x.scales.nonEmpty) { + // here only supports 1 scale for cadd + val max = inputs.flatMap(_.scales).max + x.scales.head / max + } else { + 1.0f + } + } + val pd = MklDnn.SumPrimitiveDescCreate(outputMD, inputs.length, scales, inputs.map(_.getPrimitiveDescription(runtime))) _outputFormats = Array(MemoryData.primitiveOutput(pd)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index 0834012950e..197745f3e2b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -38,6 +38,11 @@ trait MklDnnModule extends MklDnnModuleHelper { this.runtime = runtime } + private[bigdl] def getRuntime: MklDnnRuntime = { + require(runtime != null, s"you should init the mkldnn runtime first") + runtime + } + /** * Init the MKL-DNN primitives for the layer. Note that these primitives will be erased when * sent to a remote worker. @@ -69,6 +74,8 @@ trait MklDnnModule extends MklDnnModuleHelper { private[mkldnn] def gradOutputFormats(): Array[MemoryData] private[mkldnn] def gradOutputWeightFormats(): Array[MemoryData] + + def setQuantize(value: Boolean): this.type } trait MklDnnModuleHelper { @@ -287,6 +294,8 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM tensors.foreach(_.release()) } + + override def setQuantize(value: Boolean): MklDnnLayer.this.type = this } /** @@ -378,4 +387,12 @@ trait MklDnnContainer extends DynamicContainer[Activity, Activity, Float] with M super.release() reorderManager.release() } + + override def setQuantize(value: Boolean): this.type = { + this.modules.foreach { + case mkldnnModule: MklDnnModule => mkldnnModule.setQuantize(value) + case _ => + } + this + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 88a5ddd8140..3dd5a3e5c4b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -18,20 +18,15 @@ package com.intel.analytics.bigdl.nn.mkldnn import java.util -import breeze.linalg.Axis._1 -import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} -import com.intel.analytics.bigdl.nn.{DetectionOutputSSD, Graph, StaticGraph, mkldnn} +import com.intel.analytics.bigdl.nn.{Graph, mkldnn, MklInt8Convertible} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{LayerException, Node, T} -import com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.utils.{Node, T} import scala.collection.mutable -import scala.reflect.ClassTag class DnnGraph( @@ -40,7 +35,7 @@ class DnnGraph( private val _variables: Option[(Array[Tensor[Float]], Array[Tensor[Float]])] = None, private val enableExcludeChecking: Boolean = true) extends Graph[Float](_inputs, _outputs, _variables) - with MklDnnLayer { + with MklDnnLayer with MklInt8Convertible { private val forwardExecution = forwardGraph.topologySort.reverse private var backwardExecution: Array[Node[AbstractModule[Activity, Activity, Float]]] = _ private var inputCache: Array[Activity] = _ @@ -422,8 +417,15 @@ class DnnGraph( val realInputAndOutputFormats = m.element.asInstanceOf[MklDnnModule].initFwdPrimitives(lastOutputFormats, phase) lastOutputFormats.zip(realInputAndOutputFormats._1).foreach { - case (o, i) => reorderManager.register(o, i) + case (o, i) => + Utils.copyMaskAndScales(o, i) + reorderManager.register(o, i) } + + // copy the scales from the input formats to output formats, for some layers, + // it will not copy the mask and scales automatically or generate the scales themselves + Utils.copyMaskAndScales(realInputAndOutputFormats._1, realInputAndOutputFormats._2) + if (i == 0) firstRealInputFormats = realInputAndOutputFormats._1 } } @@ -486,6 +488,36 @@ class DnnGraph( super.release() reorderManager.release() } + + override def calcScales(input: Activity): Unit = { + if (input == null) return + + var i = 0 + while(i < forwardExecution.length) { + val node = forwardExecution(i) + val nodeInput = if (skipPrimitiveId(i)) { + findInput(node, input) + } else { + findDnnInput(node, input) + } + + node.element match { + case convertible: MklInt8Convertible => + convertible.calcScales(nodeInput) + case _ => + } + i += 1 + } + } + + override def setQuantize(value: Boolean): DnnGraph.this.type = { + this.forwardExecution.foreach { node => + if (node.element.isInstanceOf[MklDnnModule]) { + node.element.asInstanceOf[MklDnnModule].setQuantize(value) + } + } + this + } } object DnnGraph { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala index ac7c5638f97..c612860bdc2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala @@ -49,7 +49,7 @@ class JoinTable(val dimension: Int) extends MklDnnLayer { i += 1 } val primDesc = MklDnn.ConcatPrimitiveDescCreate( - MklDnn.MemoryDescInit(totalShape.length, totalShape, DataType.F32, Memory.Format.any), + MklDnn.MemoryDescInit(totalShape.length, totalShape, inputs(0).dataType, Memory.Format.any), inputs.length, dimension - 1, _inputFormats.map(_.getPrimitiveDescription(runtime))) _outputFormats = Array(MemoryData.primitiveOutput(primDesc)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala index 11500322c40..2e0f8264dff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala @@ -15,8 +15,9 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{AlgKind, MklDnn, PropKind, Query} +import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import com.intel.analytics.bigdl.tensor.Tensor class LRN( @@ -34,20 +35,36 @@ class LRN( @transient private var bwdMemPrims: Array[Long] = _ override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - _inputFormats = singleNativeData(inputs) + // the lrn only support f32 + _inputFormats = Array(NativeData(inputs(0).shape, inputs(0).layout, DataType.F32)) + + val kind = if (phase == InferencePhase) { + PropKind.ForwardScoring + } else { + PropKind.ForwardTraining + } + val description = MklDnn.LRNForwardDescInit( - PropKind.ForwardTraining, AlgKind.LrnAcrossChannels, + kind, AlgKind.LrnAcrossChannels, _inputFormats(0).getMemoryDescription(), size, alpha.toFloat, beta.toFloat, k.toFloat) fwdPrimDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) _outputFormats = Array(MemoryData.primitiveOutput(fwdPrimDesc)) - workSpaceFormat = MemoryData.operationWant(fwdPrimDesc, Query.WorkspacePd) - workSpace = initTensor(workSpaceFormat).asInstanceOf[Tensor[Float]] - updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPrimDesc, - _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, Array(_outputFormats(0), - workSpaceFormat).map(_.getPrimitive(runtime)), 2)) + output = initTensor(_outputFormats(0)) - fwdMemPrims = Array(_inputFormats(0), _outputFormats(0), workSpaceFormat) - .map(_.getPrimitive(runtime)) + + fwdMemPrims = if (phase == InferencePhase) { + Array(_inputFormats(0), _outputFormats(0)).map(_.getPrimitive(runtime)) + } else { + // we only create the workspace when the phase is training + workSpaceFormat = MemoryData.operationWant(fwdPrimDesc, Query.WorkspacePd) + workSpace = initTensor(workSpaceFormat).asInstanceOf[Tensor[Float]] + Array(_inputFormats(0), _outputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)) + } + + updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPrimDesc, + _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, + fwdMemPrims.drop(1), fwdMemPrims.length - 1)) + (_inputFormats, _outputFormats) } @@ -70,9 +87,13 @@ class LRN( } override def updateOutput(input: Activity): Activity = { - val buffer = Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], - workSpace) - MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, 1, fwdMemPrims, buffer) + val buffer = if (fwdMemPrims.length == 3) { + Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], workSpace) + } else { + Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]]) + } + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, 1, fwdMemPrims, + buffer) output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala index 965b72afd16..cf51a0361f2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala @@ -16,8 +16,9 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ -import com.intel.analytics.bigdl.nn.Utils +import com.intel.analytics.bigdl.nn.{Utils => NNUtils} import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor class MaxPooling( @@ -68,29 +69,45 @@ class MaxPooling( val w = _inputFormats(0).shape(3) val (pt, pb, pl, pr, oh, ow) = if (padH == -1 && padW == -1) { - val sizes = Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) + val sizes = NNUtils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) (sizes(0), sizes(1), sizes(2), sizes(3), sizes(4), sizes(5)) } else { - Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW, ceilMode) + NNUtils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW, ceilMode) } paddingTL = Array(pt, pl) paddingBR = Array(pb, pr) - val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), DataType.F32, Memory.Format.any) + val kind = if (InferencePhase == phase) { + PropKind.ForwardScoring + } else { + PropKind.ForwardTraining + } + + val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), inputs(0).dataType, + Memory.Format.any) val description = MklDnn.PoolingForwardDescInit( - PropKind.Forward, AlgKind.PoolingMax, + kind, AlgKind.PoolingMax, _inputFormats(0).getMemoryDescription(), outputMD, strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + _outputFormats = Array(MemoryData.primitiveOutput(fwdPD)) output = initTensor(_outputFormats(0)) - workSpaceFormat = MemoryData.operationWant(fwdPD, Query.WorkspacePd) - workSpace = initTensor(workSpaceFormat).asInstanceOf[Tensor[Float]] + if (phase == TrainingPhase) { + workSpaceFormat = MemoryData.operationWant(fwdPD, Query.WorkspacePd) + workSpace = initTensor(workSpaceFormat).asInstanceOf[Tensor[Float]] + fwdMemPrims = Array(_inputFormats(0), _outputFormats(0), workSpaceFormat) + .map(_.getPrimitive(runtime)) + } else { + fwdMemPrims = Array(_inputFormats(0), _outputFormats(0)).map(_.getPrimitive(runtime)) + } + updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPD, _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, - Array(_outputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), 2)) - fwdMemPrims = Array(_inputFormats(0), _outputFormats(0), workSpaceFormat) - .map(_.getPrimitive(runtime)) + fwdMemPrims.drop(1), fwdMemPrims.length - 1)) + // if it's training, should have output and workspace primitive memory + // otherwise, only need the output memory + (_inputFormats, _outputFormats) } @@ -116,9 +133,15 @@ class MaxPooling( } override def updateOutput(input: Activity): Activity = { - val buffer = Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], - workSpace) - MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, 1, fwdMemPrims, buffer) + val buffer = if (fwdMemPrims.length == 3) { // only for training. + Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]], + workSpace) + } else { + Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]]) + } + + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, 1, fwdMemPrims, + buffer) output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnOps.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnOps.scala index 1984bd255e8..faad4b2c8b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnOps.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnOps.scala @@ -30,7 +30,9 @@ private[mkldnn] object MklDnnOps { } def streamSubmit(loc: Long, block: Int, primitives: Array[Long], length: Int, - memory_primitives: Array[Long], buffers: Array[Tensor[Float]]): Unit = { + memory_primitives: Array[Long], + buffers: Array[Tensor[Float@unchecked]]): Unit = { + // the tensor maybe Tensor[Byte]. so use the unchecked to handle this require(MklDnn.isLoaded, "mkldnn isn't loaded") require(memory_primitives.length == buffers.length) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SelectTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SelectTable.scala index c25e5b5bc85..c6f7172a8c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SelectTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SelectTable.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.nn.Utils +import com.intel.analytics.bigdl.nn.{Utils => NNUtils} import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} @@ -46,14 +46,14 @@ class SelectTable(val index: Int)(implicit ev: TensorNumeric[Float]) extends Mkl override def updateGradInput(in: Activity, gradOutput: Activity): Table = { val input = in.asInstanceOf[Table] gradInput = T() - Utils.zeroTableCopy(gradInput.asInstanceOf[Table], input) + NNUtils.zeroTableCopy(gradInput.asInstanceOf[Table], input) val index = if (this.index < 0) { input.length() + this.index + 1 } else { this.index } - Utils.recursiveCopy(gradInput.asInstanceOf[Table](index), gradOutput) + NNUtils.recursiveCopy(gradInput.asInstanceOf[Table](index), gradOutput) require(gradInput.asInstanceOf[Table].contains(index), "Index exceeds the size of input table") @@ -80,12 +80,12 @@ class SelectTable(val index: Int)(implicit ev: TensorNumeric[Float]) extends Mkl override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = inputs - _outputFormats = Array(inputs(index)) + _outputFormats = Array(inputs(index - 1)) (inputs, _outputFormats) } override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { - _gradInputFormats = Array(grad(index)) + _gradInputFormats = Array(grad(index - 1)) _gradOutputFormats = grad (grad, _gradInputFormats) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala index a50f31fe719..a5243e8a229 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala @@ -51,8 +51,11 @@ class Sequential extends MklDnnContainer with MklInt8Convertible { val m = mklDnnModules(i) val (realInputFormats, outputFormats) = m.initFwdPrimitives(lastOutputFormats, phase) lastOutputFormats.zip(realInputFormats).foreach { - case (o, i) => reorderManager.register(o, i) + case (o, i) => + Utils.copyMaskAndScales(o, i) + reorderManager.register(o, i) } + Utils.copyMaskAndScales(realInputFormats, outputFormats) if (i == 0) firstRealInputFormats = realInputFormats lastOutputFormats = outputFormats } @@ -369,6 +372,25 @@ class Sequential extends MklDnnContainer with MklInt8Convertible { } } + override def calcScales(input: Activity): Unit = { + var i = 0 + var lastOutput = input + while (i < this.modules.length - 1) { + Utils.calcScales(this.modules(i), lastOutput) + + val curOutput = this.modules(i).output + require(mklDnnModules(i).outputFormats().length == mklDnnModules(i + 1).inputFormats().length) + lastOutput = reorderManager.infer( + mklDnnModules(i).outputFormats(), + mklDnnModules(i + 1).inputFormats(), + curOutput + ) + + i += 1 + } + + Utils.calcScales(this.modules(i), lastOutput) + } override def toString(): String = { val tab = " " diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index a460acc9397..9c82e72ed85 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -16,14 +16,12 @@ package com.intel.analytics.bigdl.nn.mkldnn -import java.io.{IOException, ObjectOutputStream} - import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.mkl._ -import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.{Utils => NNUtils, _} import com.intel.analytics.bigdl.nn.abstractnn._ import com.intel.analytics.bigdl.optim.Regularizer -import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} +import com.intel.analytics.bigdl.tensor.{DenseTensorMath, DnnTensor, Tensor} import scala.collection.mutable.ArrayBuffer @@ -83,6 +81,9 @@ class SpatialConvolution( @transient private var paddingTL: Array[Int] = _ @transient private var paddingBR: Array[Int] = _ + var needQuantize: Boolean = false + var negativeInput: Boolean = true + private var _relu = false private var _sum = false private var _batchNorm = false @@ -159,9 +160,28 @@ class SpatialConvolution( } } + private def setScalesOutForAttr(scaleIn: Array[Float], scaleOut: Array[Float], + attr: Long): Unit = { + require(this.getWeightScales() != null, s"you should use a model contains scales") + val scales = this.getWeightScales().flatten.map(w => + if (Math.abs(w - 0.0f) < DenseTensorMath.floatEpsilon) { + 0.0f + } else { + scaleOut(0) / (scaleIn(0) * 127.0f / w) + }).toArray + MklDnn.AttrSetOutputScales(attr, scales.length, 2, scales) + MklDnn.AttrSetIntOutputRoundMode(attr, 1) + } + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { reorderManager.setRuntime(runtime) + // maybe the model has no scales, we can't do quantize here. + if (getInputScales().flatten.isEmpty || getOutputScales().flatten.isEmpty || + getWeightScales().flatten.isEmpty) { + needQuantize = false + } + if (_sum && inputs.length > 1) { _sumInput = true require(inputs.length == 2, @@ -172,9 +192,10 @@ class SpatialConvolution( val inputWidth = inputMemoryData.shape(3) val sizes = if (padW == -1 && padH == -1) { - Utils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW) + NNUtils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, + kernelW) } else { - Utils.getOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW, + NNUtils.getOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW, padH, padW, ceilMode = false) } @@ -190,11 +211,52 @@ class SpatialConvolution( val inputShape = inputMemoryData.shape val outputShape = Array(inputMemoryData.shape(0), nOutputPlane, outputHeight, outputWidth) - val src = NativeData(inputShape, Memory.Format.any) - val wei = NativeData(weightShape, Memory.Format.any) - val bis = NativeData(Array(nOutputPlane), Memory.Format.x) - val dst = NativeData(outputShape, Memory.Format.any) + val inputDataType = if (needQuantize) { + if (negativeInput) { + DataType.S8 + } else { + DataType.U8 + } + } else { + DataType.F32 + } + val weightDataType = if (needQuantize) DataType.S8 else DataType.F32 + val biasDataType = if (needQuantize) DataType.S32 else DataType.F32 + val outputDataType = if (needQuantize) { + // must use the same datatype with the sumOp, otherwise the result will be wrong. + if (!relu || (sum && sumOp.outputFormats()(0).dataType == DataType.S8)) { + DataType.S8 + } else { + DataType.U8 + } + } else { + DataType.F32 + } + + val src = NativeData(inputShape, Memory.Format.any, inputDataType) + val wei = NativeData(weightShape, Memory.Format.any, weightDataType) + val bis = NativeData(Array(nOutputPlane), Memory.Format.x, biasDataType) + val dst = NativeData(outputShape, Memory.Format.any, outputDataType) + + val scaleIn = this.getInputScales().flatten.map { x => + if (negativeInput) { + Scale.S8_MAX / x + } else { + Scale.U8_MAX / x + } + } + val scaleOut = this.getOutputScales().flatten.map { x => + if (relu) { + Scale.U8_MAX / x + } else { + Scale.S8_MAX / x + } + } + + val scaleWeight = this.getWeightScales().flatten.map { w => Scale.S8_MAX / w } + + // TODO check wether ForwardInference and ForwardTraining is the same val desc = MklDnn.ConvForwardDescInit( PropKind.ForwardTraining, AlgKind.ConvolutionDirect, src.getMemoryDescription(), @@ -205,16 +267,37 @@ class SpatialConvolution( MklDnn.PaddingKind.mkldnnPaddingZero) forwardPrimDesc = if (relu || sum) { + val attr = MklDnn.CreateAttr() + + // create output scales for s8/u8 output + if (needQuantize) { + setScalesOutForAttr(scaleIn, scaleOut, attr) + } + val postOps = MklDnn.CreatePostOps() if (sum) { - MklDnn.PostOpsAppendSum(postOps, 1.0f) + val sumScale = if (needQuantize) { + require(scaleOut.length == sumOp.outputFormats()(0).scales.length, + s"the output scales should be the same between ${getName()} and ${sumOp.getName()}") + scaleOut(0) / sumOp.outputFormats()(0).scales(0) + } else { + 1.0f + } + MklDnn.PostOpsAppendSum(postOps, sumScale) } + if (relu) { MklDnn.PostOpsAppendEltwise(postOps, 1.0f, AlgKind.EltwiseRelu, 0.0f, 0.0f) } - val attr = MklDnn.CreateAttr() MklDnn.AttrSetPostOps(attr, postOps) + MklDnn.PrimitiveDescCreateV2(desc, attr, runtime.engine, 0) + // TODO we should destroy these ops + } else if (needQuantize) { + val attr = MklDnn.CreateAttr() + + setScalesOutForAttr(scaleIn, scaleOut, attr) + MklDnn.PrimitiveDescCreateV2(desc, attr, runtime.engine, 0) // TODO we should destroy these ops } else { @@ -232,10 +315,19 @@ class SpatialConvolution( Memory.Format.goihw } - weight.setMemoryData(HeapData(weight.dense.size(), defaultWeightLayout), - realWei, runtime) - bias.setMemoryData(HeapData(bias.dense.size(), Memory.Format.x), - bis, runtime) + val defaultWeight = HeapData(weight.dense.size(), defaultWeightLayout) + val defaultBias = HeapData(bias.dense.size(), Memory.Format.x) + + if (needQuantize) { + defaultWeight.setMask(getWeightDimMask()) + defaultWeight.setScales(scaleWeight) + + defaultBias.setMask(getWeightDimMask()) + defaultBias.setScales(scaleWeight.map(w => w * scaleIn(0))) + } + + weight.setMemoryData(defaultWeight, realWei, runtime) + bias.setMemoryData(defaultBias, bis, runtime) weight.sync() bias.sync() @@ -252,6 +344,17 @@ class SpatialConvolution( updateOutputPrimitives = Array(primitive) output = initTensor(realDst) + // quantize weight from fp32 to int8 + if (needQuantize) { + realSrc.setMask(this.getInputDimMask()) + realSrc.setScales(scaleIn) + } + + if (needQuantize) { + realDst.setMask(this.getOutputDimMask()) + realDst.setScales(scaleOut) + } + _inputFormats = if (_sumInput) Array(realSrc, realSrc) else Array(realSrc) _outputFormats = Array(realDst) (_inputFormats, _outputFormats) @@ -467,6 +570,10 @@ class SpatialConvolution( if (weightForBackward != null) { weightForBackward.release() } } + override def setQuantize(value: Boolean): this.type = { + needQuantize = value + this + } } object SpatialConvolution { @@ -494,3 +601,8 @@ object SpatialConvolution { initWeight, initBias, initGradWeight, initGradBias, withBias, format) } } + +object Scale { + val S8_MAX = 127.0f + val U8_MAX = 255.0f +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala new file mode 100644 index 00000000000..c17f433b243 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala @@ -0,0 +1,141 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.MklInt8Convertible +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T + +private[bigdl] object Utils { + def copyMaskAndScales(from: MemoryData, to: MemoryData): Unit = { + if (to.scales.isEmpty) { + to.setScales(from.scales.clone()) + to.setMask(from.mask) + } + } + + def copyMaskAndScales(from: Array[MemoryData], to: Array[MemoryData]): Unit = { + val valid = (from.length == 1 || to.length == 1) || // the ConcatTable or JoinTable + (from.length == to.length) // the same length of from and to. + + // if from has scales but to has no, copy them + val needCopy = from.ne(to) && from.forall(_.scales.nonEmpty) && to.forall(_.scales.isEmpty) + + if (valid && needCopy) { + if (from.length == to.length) { + to.zip(from).foreach(x => if (x._1.scales.isEmpty) { + x._1.setScales(x._2.scales) + x._1.setMask(x._2.mask) + }) + } else if (to.length == 1) { + to.head.setScales(from.map(_.scales).transpose.map(_.max)) + require(from.map(_.mask).distinct.length == 1, s"only support the same mask") + to.head.setMask(from.map(_.mask).distinct.head) + } else if (to.length > 1) { + to.foreach(_.setScales(from.head.scales)) + to.foreach(_.setMask(from.head.mask)) + } + } + } + + def getDefaultFormat(memoryData: MemoryData, isInOrOut: Boolean = true): Int = { + memoryData.shape.length match { + case 2 => if (isInOrOut) Memory.Format.nc else Memory.Format.oi + case 4 => if (isInOrOut) Memory.Format.nchw else Memory.Format.oihw + case _ => throw new UnsupportedOperationException("Linear only supports 2-D or 4-D") + } + } + + private def denseTensor(format: MemoryData, tensor: Tensor[Float], + isInOrOut: Boolean = true, runtime: MklDnnRuntime): Tensor[Float] = { + val reorder = ReorderMemory(HeapData(format.shape, getDefaultFormat(format, isInOrOut))) + reorder.setRuntime(runtime) + reorder.initFwdPrimitives(Array(format), InferencePhase) + reorder.forward(tensor).toTensor[Float] + } + + private def denseActivity(formats: Array[MemoryData], activity: Activity, + isInOrOut: Boolean = true, runtime: MklDnnRuntime): Activity = { + val ret = if (formats.length > 1) { // table + require(formats.length == activity.toTable.length(), + s"formats should be the same as activity") + val table = T() + + var i = 1 + while (i <= formats.length) { + val format = formats(i - 1) + val tensor = activity.toTable.get[Tensor[Float]](i).get + table(i) = denseTensor(format, tensor, isInOrOut, runtime) + i += 1 + } + + table + } else { // tensor + denseTensor(formats(0), activity.toTensor[Float], isInOrOut, runtime) + } + + ret + } + + def getDenseIn(module: MklInt8Convertible, input: Activity): Activity = { + if (module.isInstanceOf[MklDnnModule]) { + val mklDnnLayer = module.asInstanceOf[MklDnnModule] + Utils.denseActivity(mklDnnLayer.inputFormats(), input, true, mklDnnLayer.getRuntime) + } else { + input + } + } + + def getDenseOut(module: MklInt8Convertible, output: Activity): Activity = { + if (module.isInstanceOf[MklDnnModule]) { + val mklDnnLayer = module.asInstanceOf[MklDnnModule] + Utils.denseActivity(mklDnnLayer.outputFormats(), output, true, mklDnnLayer.getRuntime) + } else { + output + } + } + + private def setConvNegativeInput(module: MklInt8Convertible, input: Activity): Unit = { + if (module.isInstanceOf[SpatialConvolution]) { + val conv = module.asInstanceOf[SpatialConvolution] + val denseIn = getDenseIn(module, input) + val min = denseIn.toTensor[Float].min() + if (min >= 0.0f) { + conv.negativeInput = false + } + } + } + + def calcScales(module: AbstractModule[_, _, _], input: Activity): Unit = { + module match { + case mkldnnModule: MklInt8Convertible => + mkldnnModule.calcScales(input) + Utils.setConvNegativeInput(mkldnnModule, input) + case _ => + } + } + + def getOutput(module: AbstractModule[_, _, _], input: Activity): Activity = { + module match { + case mklDnnModule: MklDnnModule => module.output.asInstanceOf[Activity] + case _ => module.forward(input) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala index 7955c417ca4..a8e43c9b476 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -31,7 +31,10 @@ class DnnTensor[T: ClassTag]( ) (implicit ev: TensorNumeric[T]) extends DnnTensorUnsupportOperations[T]{ - override def nElement(): Int = sizes.product + // performance regression, the sizes.product will make the performance downgrade. + private val _nElement: Int = sizes.product + + override def nElement(): Int = _nElement override def copy(other: Tensor[T]): Tensor[T] = { other match { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala index 8eb6aab14cc..8ac920989a5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala @@ -21,8 +21,9 @@ import java.io.File import java.util.UUID import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { @@ -76,6 +77,62 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { compareModules(linear2, loadedModule2) } + "Calculating scales" should "work correct for DNN Linear Module" in { + import com.intel.analytics.bigdl.mkl.Memory + + val sampleMax = 999 + val inputSize = 2 + val outputSize = 2 + var inputMask = 0 + var outputMask = 0 + val inputTensor = Tensor[Float](Array(4, inputSize)).rand(-1, 1) + + // Global mask, null input + val linear0 = mkldnn.Linear(inputSize, outputSize) + linear0.calcScales(null) + + linear0.getInputScales().isEmpty should be (true) + linear0.getOutputScales().isEmpty should be (true) + linear0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val linear1 = mkldnn.Linear(inputSize, outputSize) + val seq1 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, inputSize), Memory.Format.nc)) + .add(linear1) + .add(mkldnn.Output(Memory.Format.nc)) + + seq1.compile(InferencePhase) + seq1.forward(inputTensor) + seq1.calcScales(inputTensor) + linear1.getInputScales() should be (Array(Array[Float](inputTensor.abs().max()))) + linear1.getOutputScales().length should be (1) + linear1.getOutputScales()(0).length should be (1) + linear1.getWeightScales().length should be (1) + linear1.getWeightScales()(0).length should be (1) + + // Single dimension mask, non-null input + val linear2 = mkldnn.Linear(inputSize, outputSize) + val seq2 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, inputSize), Memory.Format.nc)) + .add(linear2) + .add(mkldnn.Output(Memory.Format.nc)) + seq2.compile(InferencePhase) + + inputMask = Math.pow(2, 0).toInt + outputMask = Math.pow(2, 0).toInt + linear2.setInputDimMask(inputMask) + linear2.setOutputDimMask(outputMask) + + seq2.forward(inputTensor) + seq2.calcScales(inputTensor) + + val output2 = seq2.output.toTensor[Float] + linear2.getInputScales() should be (Array(getScalesFromTensor(inputTensor, inputMask))) + linear2.getOutputScales() should be (Array(getScalesFromTensor(output2, outputMask))) + + // for dnn linear, we skip the saveModule, because we do not support + } private def compareModules(modX: MklInt8Convertible, modY: MklInt8Convertible): Unit = { modX.getInputDimMask() should be (modY.getInputDimMask()) @@ -144,6 +201,92 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { compareModules(spatialConv4, loadedModule4) } + "Calculating scales" should "work correct for DNN Spatial Convolution Module" in { + import com.intel.analytics.bigdl.mkl.Memory + val inputSize = 8 + val outputSize = 8 + var dimMaskIdx = 0 + val input = Tensor[Float](4, 8, 8, 8).rand(-1, 1) + + // Global mask, null input + val spatialConv0 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + spatialConv0.calcScales(null) + spatialConv0.getInputScales().isEmpty should be (true) + spatialConv0.getOutputScales().isEmpty should be (true) + spatialConv0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val spatialConv1 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + val seq1 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) + .add(spatialConv1) + .add(mkldnn.Output(Memory.Format.nchw)) + + seq1.compile(InferencePhase) + seq1.forward(input) + spatialConv1.calcScales(input) + + spatialConv1.getInputScales() should be (Array(Array[Float](input.clone().abs().max()))) + spatialConv1.getOutputScales().length should be (1) + spatialConv1.getOutputScales()(0).length should be (1) + spatialConv1.getWeightScales().length should be (1) + spatialConv1.getWeightScales()(0).length should be (1) + + seq1.release() + + // Single input dimension mask, non-null input + dimMaskIdx = 1 + val spatialConv2 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + val seq2 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) + .add(spatialConv2) + .add(mkldnn.Output(Memory.Format.nchw)) + seq2.compile(InferencePhase) + seq2.forward(input) + + seq2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + seq2.calcScales(input) + + spatialConv2.getInputScales().length should be (1) + spatialConv2.getInputScales().flatten.length should be (4) + + seq2.release() + + dimMaskIdx = 2 + val spatialConv3 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + val seq3 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) + .add(spatialConv3) + .add(mkldnn.Output(Memory.Format.nchw)) + seq3.compile(InferencePhase) + seq3.forward(input) + + seq3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + seq3.calcScales(input) + + val inputScales3 = Array((1 to input.size(dimMaskIdx)).map( + idx => input.select(dimMaskIdx, idx).abs().max() + ).toArray) + spatialConv3.getInputScales() should be (inputScales3) + + seq3.release() + + dimMaskIdx = 3 + val spatialConv4 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + val seq4 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) + .add(spatialConv4) + .add(mkldnn.Output(Memory.Format.nchw)) + seq4.compile(InferencePhase) + seq4.forward(input) + + seq4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + seq4.calcScales(input) + val inputScales4 = Array((1 to input.size(dimMaskIdx)).map( + idx => input.select(dimMaskIdx, idx).abs().max() + ).toArray) + spatialConv4.getInputScales() should be (inputScales4) + } "Calculating scales" should "work correct for BLAS Sequential Module" in { var dimMaskIdx = 0 @@ -268,7 +411,6 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { compareModules(concatTable1, loadedModule1) } - "Calculating scales" should "work correct for Graph Module" in { def makeTestingGraph(): Graph[Float] = { val input = Reshape(Array(1, 28, 28)).inputs() @@ -320,7 +462,6 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { compareModules(graph1, loadedGraph1) } - private def graphValidationHelper(graph: Graph[Float], inputActvt: Activity): Unit = { val nextNodes = graph.getForwardExecutions() var i = 0 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala index c07be3ba7ac..3784c94959e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala @@ -15,11 +15,11 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.mkl.{DataType, Memory} import com.intel.analytics.bigdl.nn.SpatialAveragePooling -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Engine} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.intermediate.{BlasToIR, IRToDnn} import org.apache.commons.lang3.SerializationUtils @@ -191,4 +191,49 @@ class AvgPoolingSpec extends BigDLSpecHelper { Tools.dense(pool.gradInput) should be (Tools.dense(cloned.gradInput)) } + + "avg pooling with int8" should "be correct" in { + val inputShape = Array(4, 3, 5, 5) + val outputShape = Array(4, 3, 2, 2) + + val kernel = 3 + val pad = 1 + + val runtime = new MklDnnRuntime + + val input = Tensor[Float](inputShape).rand(0, 1) + + val heapData = HeapData(inputShape, Memory.Format.nchw, DataType.F32) + val nativeData = NativeData(inputShape, Memory.Format.nhwc, DataType.U8) + val inputScales = Array(input.max()) + + nativeData.setMask(0) + nativeData.setScales(inputScales.map(x => 255.0f / x)) + + val reorder = ReorderMemory(nativeData) + val pool = AvgPooling(3, 3, 2, 2) + val heapData2 = HeapData(outputShape, Memory.Format.nchw, DataType.F32) + val reorder2 = ReorderMemory(heapData2) + + val seq = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(reorder) + .add(pool) + .add(reorder2) + + seq.evaluate() + seq.compile(InferencePhase) + seq.forward(input) + + val seq2 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(AvgPooling(3, 3, 2, 2)) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + seq2.evaluate() + seq2.compile(InferencePhase) + seq2.forward(input) + + Equivalent.nearequals(seq.output.toTensor, seq2.output.toTensor, 1e-2) should be (true) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala index 078c0bdf1b1..94ff412d7a2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala @@ -15,8 +15,8 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.Memory -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.mkl.{DataType, Memory} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} @@ -94,4 +94,49 @@ class CAddTableSpec extends BigDLSpecHelper { Tools.dense(cat.gradInput.toTable(1)) should be (Tools.dense(cloned.gradInput.toTable(1))) Tools.dense(cat.gradInput.toTable(2)) should be (Tools.dense(cloned.gradInput.toTable(2))) } + + "CAddTable u8" should "be correct" in { + val shape = Array(4, 3, 5, 5) + val model = Sequential() + val concat = ConcatTable() + val cadd = CAddTable() + + model.add(Input(shape, Memory.Format.nchw)) + model.add(concat).add(cadd) + + val input = Tensor[Float](shape).rand(0, 1) + + val nativeData1 = NativeData(shape, Memory.Format.nhwc, DataType.U8) + val nativeData2 = NativeData(shape, Memory.Format.nhwc, DataType.U8) + + nativeData1.setMask(0) + nativeData1.setScales(Array(255.0f / input.clone().abs().max())) + + nativeData2.setMask(0) + nativeData2.setScales(Array(255.0f / input.clone().abs().max())) + + concat.add(ReorderMemory(nativeData1)) + concat.add(ReorderMemory(nativeData2)) + + model.add(ReorderMemory(HeapData(shape, Memory.Format.nchw))) + + model.evaluate() + model.compile(InferencePhase) + model.forward(input) + + + val seq2 = Sequential() + .add(Input(shape, Memory.Format.nchw)) + .add(ConcatTable() + .add(ReorderMemory(NativeData(shape, Memory.Format.nhwc))) + .add(ReorderMemory(NativeData(shape, Memory.Format.nchw)))) + .add(CAddTable()) + .add(ReorderMemory(HeapData(shape, Memory.Format.nchw))) + + seq2.evaluate() + seq2.compile(InferencePhase) + seq2.forward(input) + + println() + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala index befdaefc74d..eefe96e97d8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala @@ -15,10 +15,10 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.Memory -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.mkl.{DataType, Memory} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} import org.apache.commons.lang3.SerializationUtils @@ -98,4 +98,42 @@ class ConcatTableSpec extends BigDLSpecHelper { Tools.dense(ct.gradInput) should be(Tools.dense(cloned.gradInput)) } + + "ConcatTable with U8" should "be good" in { + val shape = Array(4, 3, 5, 5) + val input = Tensor[Float](shape).rand(0, 1) + + println(input) + + val nativeData1 = NativeData(shape, Memory.Format.nchw, DataType.U8) + val nativeData2 = NativeData(shape, Memory.Format.nchw, DataType.U8) + val heapData = HeapData(shape, Memory.Format.nchw, DataType.F32) + heapData.setMask(0) + val inputScales = Array(input.clone().abs().max()) + heapData.setScales(inputScales.map(x => 255f / x)) + + val concat = ConcatTable() + + concat.add(ReorderMemory(nativeData1)) + concat.add(ReorderMemory(nativeData2)) + concat.compile(Phase.InferencePhase, Array(heapData)) + + concat.forward(input) + + val output1 = new Array[Byte](shape.product) + Memory.CopyPtr2ByteArray( + concat.output.toTable[Tensor[Float]](1) + .asInstanceOf[DnnTensor[Byte]].storageAddress(), + 0, output1, 0, shape.product, 1) + output1.foreach(println) + + val output2 = new Array[Byte](shape.product) + Memory.CopyPtr2ByteArray( + concat.output.toTable[Tensor[Float]](1) + .asInstanceOf[DnnTensor[Byte]].storageAddress(), + 0, output2, 0, shape.product, 1) + output2.foreach(println) + + output1 should be (output2) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala index 573acb8a87b..37b11061216 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRNSpec.scala @@ -15,9 +15,9 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.mkl.{DataType, Memory} import com.intel.analytics.bigdl.nn.SpatialCrossMapLRN -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.BigDLSpecHelper import com.intel.analytics.bigdl.utils.RandomGenerator.RNG @@ -76,4 +76,42 @@ class LRNSpec extends BigDLSpecHelper { Tools.dense(lrn.output) should be (Tools.dense(cloned.output)) } + + "LRN in int8 model" should "work correctly" in { + RNG.setSeed(100) + + val inputShape = Array(4, 8, 3, 3) + val input = Tensor[Float](inputShape).rand(-1, 1) + + val int8NativeData = NativeData(inputShape, Memory.Format.nhwc, DataType.S8) + int8NativeData.setMask(0) + int8NativeData.setScales(Array(127.0f / input.clone().abs().max())) + val reorderToInt8 = ReorderMemory(int8NativeData) + + val seqInt8 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(reorderToInt8) + .add(LRN(8, 0.0001, 0.75, 1.0)) + .add(ReorderMemory(HeapData(inputShape, Memory.Format.nchw))) + + seqInt8.compile(InferencePhase) + + seqInt8.forward(input) + + val seqFP32 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(LRN(8, 0.0001, 0.75, 1.0)) + .add(ReorderMemory(HeapData(inputShape, Memory.Format.nchw))) + + seqFP32.compile(InferencePhase) + seqFP32.forward(input) + + // here, the 1e-2 is experience value + Equivalent.nearequals(seqInt8.output.toTensor, seqFP32.output.toTensor, 1e-2) should be (true) + + seqInt8.release() + seqFP32.release() + + println() + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala index 7d38763813f..91d4ae0e0b4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala @@ -15,11 +15,11 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{AlgKind, Memory} -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.mkl.{AlgKind, DataType, Memory} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, SpatialMaxPooling} -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.BigDLSpecHelper +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Engine} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.intermediate.{BlasToIR, IRToDnn} import org.apache.commons.lang3.SerializationUtils @@ -165,4 +165,51 @@ class MaxPoolingSpec extends BigDLSpecHelper { Tools.dense(pool.gradInput) should be (Tools.dense(cloned.gradInput)) } + + + "max pooling with int8" should "be correct" in { + val inputShape = Array(4, 3, 5, 5) + val outputShape = Array(4, 3, 2, 2) + + val kernel = 3 + val pad = 1 + + val runtime = new MklDnnRuntime + + val input = Tensor[Float](inputShape).rand(0, 1) + + val heapData = HeapData(inputShape, Memory.Format.nchw, DataType.F32) + val nativeData = NativeData(inputShape, Memory.Format.nhwc, DataType.U8) + val inputScales = Array(input.max()) + + nativeData.setMask(0) + nativeData.setScales(inputScales.map(x => 255.0f / x)) + + val reorder = ReorderMemory(nativeData) + val pool = MaxPooling(3, 3, 2, 2) + val heapData2 = HeapData(outputShape, Memory.Format.nchw, DataType.F32) + val reorder2 = ReorderMemory(heapData2) + + val seq = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(reorder) + .add(pool) + .add(reorder2) + + seq.evaluate() + seq.compile(InferencePhase) + seq.forward(input) + + val seq2 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(MaxPooling(3, 3, 2, 2)) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + seq2.evaluate() + seq2.compile(InferencePhase) + seq2.forward(input) + + Equivalent.nearequals(seq.output.toTensor, seq2.output.toTensor, 1e-2) should be (true) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala index 265d2414b9c..1033a59798e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalizationSpec.scala @@ -213,12 +213,12 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers { bn.initBwdPrimitives(Array(defaultFormat), TrainingPhase) bn.initGradWPrimitives(Array(defaultFormat), TrainingPhase) - Utils.manyTimes(bn.forward(input))(10) + TestUtils.manyTimes(bn.forward(input))(10) val nnBn = nn.SpatialBatchNormalization(channel, epsilon, initWeight = initWeight, initBias = initBias) - Utils.manyTimes(nnBn.forward(input))(10) + TestUtils.manyTimes(nnBn.forward(input))(10) val output = Tools.toNCHW(bn.output.toTensor, bn.outputFormats()(0)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index a45c17096ed..e432d1f1f80 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.mkl._ -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.nn.{Xavier, Zeros} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.{DnnStorage, Tensor} @@ -107,7 +107,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val weight1 = conv.weight.dense val gradweight1 = conv.gradWeight.dense - val bias1 = Tools.dense(conv.bias.native).toTensor[Float] + val bias1 = Tools.dense(conv.bias.native[Float]).toTensor[Float] val gradbias1 = Tools.dense(conv.gradBias.dense).toTensor val output2 = layer.forward(input) @@ -607,6 +607,69 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Equivalent.nearequals(model.getParameters()._2, blas.getParameters()._2, 1e-4) should be (true) } + "unsigned input quantization" should "work correctly" in { + RNG.setSeed(1) + + val inputShape = Array(1, 2, 12, 12) + val outputShape = Array(1, 4, 8, 8) + + val initBias = Tensor[Float](4).fill(1.0f) + + val model = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(SpatialConvolution(2, 4, 5, 5, initBias = initBias)).setName("conv2") + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + model.evaluate() + val input = Tensor[Float](inputShape).rand(-1, 1) + model.compile(InferencePhase) + val output = model.forward(input).toTensor.clone() + model.calcScales(input) + + val quantized = model.quantize() + quantized.asInstanceOf[Sequential].compile(InferencePhase) + quantized.forward(input) + Equivalent.nearequals(output, quantized.output.toTensor, 1e-1) should be (true) + } + + "generate the convolution scales with random" should "work correctly" in { + RNG.setSeed(1) + val inputShape = Array(1, 1, 2, 2) + val outputShape = Array(1, 2, 1, 1) + + val inputData = Array[Float](-100, 12, 14, 67) + val input = Tensor[Float](inputShape).rand(-100, 100) + + val initWeight = Tensor[Float](Array(2, 1, 2, 2)).rand(-10, 10) + val initBias = Tensor[Float](Array(2)).rand(-1, 1) + + val conv = SpatialConvolution(1, 2, 2, 2) + + val seq = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(conv) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) + + seq.compile(InferencePhase) + seq.forward(input) + + val outputFP32Model = seq.forward(input).toTensor.clone() + + seq.calcScales(input) + + val quantizedModel = seq.quantize() + quantizedModel.asInstanceOf[Sequential].compile(InferencePhase) + + val outputInt8Model = quantizedModel.forward(input).toTensor.clone() + + println(outputFP32Model) + println(outputInt8Model) + + outputFP32Model.storage().array().zip(outputInt8Model.storage().array()).foreach { x => + (Math.abs(x._1 - x._2) / Math.max(Math.abs(x._1), Math.abs(x._2)) <= 1e-1) should be (true) + } + } + def prototxt(inputShape: Array[Int], name: String, nOutput: Int, kernel: Int, pad: Int, stride: Int): String = { s""" diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala index b5906ba0256..b5c7066d926 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala @@ -425,7 +425,7 @@ object Collect { } } -object Utils { +object TestUtils { def time[R](block: => R): (Double, R) = { val t0 = System.nanoTime() val result = block From 9efa5c7bd66b323f50d2493e9f6ba8e22f5979cc Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 19 Mar 2019 09:46:30 +0800 Subject: [PATCH 0881/1065] feature: mkldnn int8 layer wise supports (#2759) including 3 steps. 1. generate scales of model. need an api like `generateScalesWithMask` to generate the scales of fp32 model. and the model returned is an fp32 model too. 2. quantize the model the `quantize()` api will be compatible with the `bigquant` backend, which will set the quantize flag. And when doing compile, the quantized weight, output, input will be generated by mkldnn at runtime. 3. do the inference (forward). --- .../bigdl/dllib/nn/BatchNormalization.scala | 3 +- .../analytics/bigdl/dllib/nn/CAddTable.scala | 2 +- .../intel/analytics/bigdl/dllib/nn/ReLU.scala | 2 +- .../dllib/nn/abstractnn/AbstractModule.scala | 3 +- .../bigdl/dllib/nn/mkldnn/ConcatTable.scala | 4 + .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 9 +- .../bigdl/dllib/nn/mkldnn/Fusion.scala | 70 +++++- .../bigdl/dllib/nn/mkldnn/ReLU.scala | 5 +- .../bigdl/dllib/nn/mkldnn/Sequential.scala | 57 +++-- .../nn/mkldnn/SpatialBatchNormalization.scala | 15 +- .../dllib/nn/mkldnn/SpatialConvolution.scala | 8 +- .../utils/intermediate/ConversionUtils.scala | 32 +++ .../dllib/utils/intermediate/IRGraph.scala | 20 +- .../dllib/utils/intermediate/IRToDnn.scala | 8 +- .../utils/intermediate/ReflectionUtils.scala | 2 +- .../bigdl/dllib/nn/ScaleCalculatorSpec.scala | 173 ++++++++++++++- .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 185 ++++++++++++++++ .../nn/mkldnn/SpatialConvolutionSpec.scala | 34 +++ .../bigdl/dllib/nn/mkldnn/TopologySpec.scala | 205 +++++++++++++++++- .../dllib/nn/quantized/QuantizableSpec.scala | 15 +- 20 files changed, 807 insertions(+), 45 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala index 61f17be08c1..7e5fc50b540 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BatchNormalization.scala @@ -57,7 +57,8 @@ class BatchNormalization[T: ClassTag]( private val initBias: Tensor[T] = null, private val initGradWeight: Tensor[T] = null, private val initGradBias: Tensor[T] = null -)(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable { +)(implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable + with MklInt8Convertible { require(nOutput > 0, "output feature map number must be greater than zero") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala index abf4a38c002..a2047007c9a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala @@ -34,7 +34,7 @@ import scala.reflect._ @SerialVersionUID(7959261460060075605L) class CAddTable[T: ClassTag, D: ClassTag](val inplace: Boolean = false)( implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) - extends AbstractModule[Table, Tensor[D], T] { + extends AbstractModule[Table, Tensor[D], T] with MklInt8Convertible { output = Tensor[D]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala index 4206f585b1b..2e82605898b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ReLU.scala @@ -31,7 +31,7 @@ import scala.reflect.ClassTag */ @SerialVersionUID(1208478077576570643L) class ReLU[T: ClassTag](ip: Boolean = false)( - implicit ev: TensorNumeric[T]) extends Threshold[T](0, 0, ip) { + implicit ev: TensorNumeric[T]) extends Threshold[T](0, 0, ip) with MklInt8Convertible { } object ReLU { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 70d6e44ea22..df2ace40563 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -30,6 +30,7 @@ import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, import com.intel.analytics.bigdl.utils.TorchObject.TYPE_MODULE import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.utils.caffe.CaffePersister +import com.intel.analytics.bigdl.utils.intermediate.ConversionUtils import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.tf.{TensorflowDataFormat, TensorflowSaver} import org.apache.commons.lang3.SerializationUtils @@ -916,7 +917,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @return */ final def quantize(): Module[T] = { - Quantization.quantize(this) + ConversionUtils.convert[T](this, true) } // ================================= Internal APIs =========================================== diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala index 929fbb1a835..1a6204e1987 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala @@ -130,6 +130,10 @@ class ConcatTable extends MklDnnContainer with MklInt8Convertible { _gradOutputWeightFormats } + private[mkldnn] def reconstruct(): Unit = { + mklDnnModules = modules.map(_.asInstanceOf[MklDnnModule]).toArray + } + override private[mkldnn] def inputFormats() = { require(_inputFormats != null, "You should call initFwdPrimitives first") _inputFormats diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 3dd5a3e5c4b..e385fa580ad 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -392,11 +392,13 @@ class DnnGraph( */ private def fusion(): Unit = { if (!this.train) { - for (j <- 0 to 1) { + for (j <- 0 to 2) { var i = forwardExecution.length - 1 while (i >= 0) { if (j == 0) Fusion.fuseModule(forwardExecution(i)) - if (j == 1) Fusion.fuseCAdd(forwardExecution(i)) + // we should do this before sum fusion, because it will change the structure of graph + if (j == 1) Fusion.setNegativeInputOfConv(forwardExecution(i)) + if (j == 2) Fusion.fuseCAdd(forwardExecution(i)) i -= 1 } } @@ -485,7 +487,8 @@ class DnnGraph( } override def release(): Unit = { - super.release() + // do not call super.release, it will call MklDnnLayer.release() + modules.foreach(_.release()) reorderManager.release() } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala index 3d2244c5dc7..32237784c3e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.MklInt8Convertible import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Node @@ -30,7 +31,7 @@ import com.intel.analytics.bigdl.utils.Node */ private[mkldnn] object Fusion { - private val fuse = System.getProperty("bigdl.mkldnn.fusion", "false").toBoolean + private def fuse = System.getProperty("bigdl.mkldnn.fusion", "false").toBoolean def fuseModule(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { if (!fuse) return; @@ -79,11 +80,13 @@ private[mkldnn] object Fusion { case conv: SpatialConvolution => if (!conv.relu) { conv.setReLU(true) + conv.setOutputScales(node.element.asInstanceOf[ReLU].getOutputScales()) node.element = Identity[Float]().asInstanceOf[AbstractModule[Activity, Activity, Float]] } case bn: SpatialBatchNormalization => if (!bn.relu) { bn.setReLU(true) + bn.setOutputScales(node.element.asInstanceOf[ReLU].getOutputScales()) node.element = Identity[Float]().asInstanceOf[AbstractModule[Activity, Activity, Float]] } case _ => null @@ -97,6 +100,15 @@ private[mkldnn] object Fusion { } else node } + private def findNext(node: Node[AbstractModule[Activity, Activity, Float]]) + : Seq[Node[AbstractModule[Activity, Activity, Float]]] = { + if (node.element.isInstanceOf[Identity]) { + node.nextNodes.flatMap(n => findNext(n)) + } else { + Seq(node) + } + } + /** * If previous layers number of CAddTable is two, and one of it is conv layer. * then fuse output of the other layer in conv layer. @@ -128,8 +140,24 @@ private[mkldnn] object Fusion { val nexts = node.nextNodes(0) if (nexts.element.isInstanceOf[ReLU] && !element.relu) { node.element.asInstanceOf[SpatialConvolution].setReLU(true) + node.element.asInstanceOf[SpatialConvolution].setOutputScales( + nexts.element.asInstanceOf[ReLU].getOutputScales()) nexts.element = new Identity() } + + val prevIsNotIdentity = findPrevious(previousNodes(otherNumber)) + + prevIsNotIdentity.element match { + case conv: SpatialConvolution => + conv.setOutputScales(node.element.asInstanceOf[SpatialConvolution].getOutputScales()) + case relu: ReLU => + relu.setOutputScales(node.element.asInstanceOf[SpatialConvolution].getOutputScales()) + prevIsNotIdentity.nextNodes.flatMap(x => findNext(x)) + .filter(x => x != node && x.element.isInstanceOf[MklInt8Convertible]) + .foreach(_.element.asInstanceOf[MklInt8Convertible].setInputScales( + node.element.asInstanceOf[SpatialConvolution].getOutputScales())) + case _ => + } } } } @@ -173,5 +201,45 @@ private[mkldnn] object Fusion { conv.weight.copy(convWeight) conv.bias.copy(convBias) + + // regenerate the weight scales and output scales + conv.flushWeightScales(conv.weight.dense) + conv.setOutputScales(bn.getOutputScales()) + } + + def setNegativeInputOfConv(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { + + def findAllNonIdentityPrevs(node: Node[AbstractModule[Activity, Activity, Float]]) + : Seq[Node[AbstractModule[Activity, Activity, Float]]] = { + // TODO currently, it will only skip the Identity, MaxPooling, AvgPooling + // becase if the output of layer/op previous of the three, they will output + // nonnegative too. it's not an elegant impl. + if (node.element.isInstanceOf[Identity] || + node.element.isInstanceOf[MaxPooling] || + node.element.isInstanceOf[AvgPooling]) { + node.prevNodes.flatMap(findAllNonIdentityPrevs) + } else { + Seq(node) + } + } + + if (!fuse || !node.element.isInstanceOf[SpatialConvolution]) return + + val successFromReLU = node.prevNodes.flatMap(x => findAllNonIdentityPrevs(x)) + .map { x => + x.element match { + case _: SpatialConvolution => + x.element.asInstanceOf[SpatialConvolution].relu + case _: ReLU => + true + case _ => + false + } + }.forall(_ == true) + + + if (successFromReLU) { + node.element.asInstanceOf[SpatialConvolution].negativeInput = false + } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala index 660b15dd397..7981d08a757 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala @@ -15,9 +15,10 @@ */ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{AlgKind, MklDnn, PropKind, Query} +import com.intel.analytics.bigdl.mkl._ +import com.intel.analytics.bigdl.nn.MklInt8Convertible -class ReLU(value: Float = 0.0f) extends MklDnnLayer { +class ReLU(value: Float = 0.0f) extends MklDnnLayer with MklInt8Convertible { private val UNDEFINED: Long = 0 @transient private var fwdPrimDesc: Long = UNDEFINED diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala index a5243e8a229..b695eb255f0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Sequential.scala @@ -25,10 +25,25 @@ import scala.collection.mutable.ArrayBuffer class Sequential extends MklDnnContainer with MklInt8Convertible { - val fuseConvBn = System.getProperty("bigdl.mkldnn.fusion.convbn", "false").toBoolean - val fuseBnRelu = System.getProperty("bigdl.mkldnn.fusion.bnrelu", "false").toBoolean - val fuseConvRelu = System.getProperty("bigdl.mkldnn.fusion.convrelu", "false").toBoolean - val fuseConvSum = System.getProperty("bigdl.mkldnn.fusion.convsum", "false").toBoolean + def fuse: Boolean = { + System.getProperty("bigdl.mkldnn.fusion", "false").toBoolean + } + + private def fuseConvBn: Boolean = { + fuse || System.getProperty("bigdl.mkldnn.fusion.convbn", "false").toBoolean + } + + private def fuseBnRelu: Boolean = { + fuse || System.getProperty("bigdl.mkldnn.fusion.bnrelu", "false").toBoolean + } + + private def fuseConvRelu: Boolean = { + fuse || System.getProperty("bigdl.mkldnn.fusion.convrelu", "false").toBoolean + } + + private def fuseConvSum: Boolean = { + fuse || System.getProperty("bigdl.mkldnn.fusion.convsum", "false").toBoolean + } override def add(module: AbstractModule[_ <: Activity, _ <: Activity, Float]): this.type = { require(mklDnnModules == null, "You should not call add after compilation") @@ -208,6 +223,7 @@ class Sequential extends MklDnnContainer with MklInt8Convertible { case (conv: SpatialConvolution, relu: ReLU) => newModules.append(conv) conv.setReLU() + conv.setOutputScales(relu.getOutputScales()) lastReLU = relu case (f: MklDnnContainer, s) => f.fusion(phase) @@ -261,9 +277,11 @@ class Sequential extends MklDnnContainer with MklInt8Convertible { if (sbt != null) { newModules.append(sbt) } + conv.setOutputScales(s.getOutputScales()) case (f: MklDnnContainer, s) => f.fusion(phase); newModules.append(f) case (f: CAddTable, s: ReLU) => if (lastConv != null) { lastConv.setReLU() + lastConv.setOutputScales(s.getOutputScales()) lastReLU = s lastConv = null } else { @@ -292,41 +310,50 @@ class Sequential extends MklDnnContainer with MklInt8Convertible { val convWeight = Tensor[Float].resize(conv.weight.size()).copy(conv.weight.dense) val convBias = Tensor[Float].resize(conv.bias.size()).copy(conv.bias.dense) + val bnWeight = Tensor[Float].resizeAs(bn.weightAndBias.dense).copy(bn.weightAndBias.dense) + (0 until bn.nOutput).foreach { j => val variance = originVar.storage().array()(j + originVar.storageOffset() - 1) val base = Math.sqrt(variance.asInstanceOf[Float] + bn.eps).toFloat require(base != 0.0, s"the eps of ${bn.getName()} should be more than 0") + val alpha = bnWeight.storage().array()(bnWeight.storageOffset() - 1 + j) + val beta = bnWeight.storage().array()(bnWeight.storageOffset() - 1 + bn.nOutput + j) + val weight = if (conv.nGroup == 1) { convWeight.select(1, j + 1) } else { convWeight.select(2, j + 1) } weight.div(base) + weight.mul(alpha) val bias = convBias.storage().array()(j) val mean = originMean.storage().array()(j) - convBias.storage().array()(j) = (bias - mean) / base + convBias.storage().array()(j) = alpha / base * bias + beta - (alpha * mean) / base } conv.weight.copy(convWeight) conv.bias.copy(convBias) + + conv.flushWeightScales(conv.weight.dense) + conv.setOutputScales(bn.getOutputScales()) } - private def getLast( - module: AbstractModule[Activity, Activity, Float]): AbstractModule[Activity, Activity, Any] = { + private type FloatActivityModule = AbstractModule[Activity, Activity, Float] + private def getLast(module: FloatActivityModule): FloatActivityModule = { val ret = module match { case sequential: Sequential => sequential.modules.last case _ => module } - ret.asInstanceOf[AbstractModule[Activity, Activity, Any]] + ret.asInstanceOf[FloatActivityModule] } private def convSum(concatTable: ConcatTable, cAddTable: CAddTable): (SpatialConvolution, SelectTable) = { - var branch1: AbstractModule[Activity, Activity, Any] = null - var branch2: AbstractModule[Activity, Activity, Any] = null + var branch1: FloatActivityModule = null + var branch2: FloatActivityModule = null var continue = concatTable.modules.length == 2 @@ -334,7 +361,7 @@ class Sequential extends MklDnnContainer with MklInt8Convertible { branch1 = getLast(concatTable.modules(0)) branch2 = getLast(concatTable.modules(1)) - def isConvOrIdentity(module: AbstractModule[Activity, Activity, Any]): Boolean = { + def isConvOrIdentity(module: AbstractModule[Activity, Activity, Float]): Boolean = { module.isInstanceOf[SpatialConvolution] || module.isInstanceOf[Identity] } @@ -351,13 +378,15 @@ class Sequential extends MklDnnContainer with MklInt8Convertible { concatTable.modules(0) = concatTable.modules(1) concatTable.modules(1) = tmp - tmp = branch1.asInstanceOf[AbstractModule[Activity, Activity, Float]] + concatTable.reconstruct() + + tmp = branch1 branch1 = branch2 - branch2 = tmp.asInstanceOf[AbstractModule[Activity, Activity, Any]] + branch2 = tmp } // get the index of conv, by default the output should be the first conv. - val (convIndex, conv, theOther) = (1, branch2.asInstanceOf[SpatialConvolution], branch1) + val (convIndex, conv, theOther) = (2, branch2.asInstanceOf[SpatialConvolution], branch1) conv.setSum() // delete CAddTable diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index a8dfa94ad87..3259bf54b49 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} -import com.intel.analytics.bigdl.nn.{Ones, VariableFormat, Zeros} +import com.intel.analytics.bigdl.nn.{MklInt8Convertible, Ones, VariableFormat, Zeros} import com.intel.analytics.bigdl.tensor._ import scala.collection.mutable.ArrayBuffer @@ -32,7 +32,7 @@ class SpatialBatchNormalization( private val initBias: Tensor[Float] = null, private val initGradWeight: Tensor[Float] = null, private val initGradBias: Tensor[Float] = null -) extends MklDnnLayer with Initializable { +) extends MklDnnLayer with Initializable with MklInt8Convertible { @transient private var forwardDesc: Long = 0L private var _relu: Boolean = false @@ -62,12 +62,14 @@ class SpatialBatchNormalization( val weightAndBias = new TensorMMap(Array(nOutput * 2)) val gradWeightAndBias = new TensorMMap(Array(nOutput * 2)) + // TODO the two should be learnable parameters var scaleFactor: Float = 1.0f var biasFactor: Float = 1.0f private val runningMeanScaled = Tensor[Float].resizeAs(runningMean.dense) private val runningVarianceScaled = Tensor[Float].resizeAs(runningVariance.dense) + // the blank shoud be here, otherwise the runningVarianceScaled will be a method { val wInit = Ones // RandomUniform(0, 1) val bInit = Zeros @@ -137,17 +139,20 @@ class SpatialBatchNormalization( // weight and bias should be combined val weightAndBias: NativeData = NativeData(Array(nOutput * 2), Memory.Format.x) + // the bn only accept F32 as input, like lrn + val src = NativeData(inputs.head.shape, inputs.head.layout, DataType.F32) + // init phase status initPhase(phase) forwardDesc = modelPhase match { case TrainingPhase => MklDnn.BatchNormForwardDescInit(PropKind.Forward, - inputs(0).getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_scaleshift) + src.getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_scaleshift) case InferencePhase => // we always use the weight and bias / scale and offset. So the flags should be combined // with use_scaleshift and use_global_stats. MklDnn.BatchNormForwardDescInit(PropKind.ForwardInference, - inputs(0).getMemoryDescription(), eps.toFloat, + src.getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_global_stats | MklDnn.BatchNormFlag.mkldnn_use_scaleshift) case _ => throw new UnsupportedOperationException } @@ -192,6 +197,8 @@ class SpatialBatchNormalization( updateOutputMemoryPrimitives = srcs ++ dsts updateOutputPrimitives = Array(primitive) + // if the output is not null, it means we have initialized the primitives before. + // so we do not need create weightAndBias native space again. if (output == null || output.isInstanceOf[DnnTensor[_]] && output.toTensor[Float].size().deep != outputFormats()(0).shape.deep) { output = initTensor(outputFormats()(0)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 9c82e72ed85..1c59fdb73d0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -187,7 +187,8 @@ class SpatialConvolution( require(inputs.length == 2, s"inputs length should be 2 when having sum operation, but get ${inputs.length}") } - val inputMemoryData = inputs(_dim - 1) + // we should not use output branch + val inputMemoryData = inputs(inputs.length - _dim) val inputHeight = inputMemoryData.shape(2) // TODO only supports 4-D and nchw val inputWidth = inputMemoryData.shape(3) @@ -364,8 +365,9 @@ class SpatialConvolution( val inputTensor = if (input.isTensor) { input.toTensor[Float] } else { - output = input.toTable.get[Tensor[Float]](3 - _dim).get - input.toTable.get[Tensor[Float]](_dim).get + // here we should not use the output branch + output = input.toTable.get[Tensor[Float]](_dim).get + input.toTable.get[Tensor[Float]](3 - _dim).get } if (updateOutputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala index bbef32584a0..4df8891ac7e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala @@ -17,11 +17,14 @@ package com.intel.analytics.bigdl.utils.intermediate import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer} import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnLayer, MklDnnModule} import com.intel.analytics.bigdl.utils.{Engine, MklDnn, T} import org.apache.spark.rdd.RDD import com.intel.analytics.bigdl.nn.Graph import com.intel.analytics.bigdl.nn.StaticGraph +import com.intel.analytics.bigdl.nn.quantized.Quantization +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag @@ -46,6 +49,12 @@ private[bigdl] object ConversionUtils { } } + def convert[T: ClassTag](model: Module[T], needQuantize: Boolean)( + implicit ev: TensorNumeric[T]): Module[T] = { + val convertedModel = convert(model) + getInt8ModelIfNeeded(convertedModel, needQuantize) + } + /** * For dnn backend, it is recommended to run single model on each node. * So when partition number of dataset is not equal to node number, @@ -60,4 +69,27 @@ private[bigdl] object ConversionUtils { dataset.coalesce(Engine.nodeNumber(), false) } else dataset } + + private def getInt8ModelIfNeeded[T: ClassTag](model: Module[T], + needQuantize: Boolean)(implicit ev: TensorNumeric[T]): Module[T] = { + // we will not set the model's quantize flag with `needQuantize`. + // because Evaluator will always has the `false` of it. + + // TODO we should handle different types of model. We need refactor here later + model match { + case ir: IRGraph[T] => if (needQuantize) ir.setQuantize(true) else ir + case dnnGraph: DnnGraph => if (needQuantize) { + dnnGraph.cloneModule().setQuantize(true) + } else { + dnnGraph + } + case dnnContainer: MklDnnContainer => + if (needQuantize) { + dnnContainer.cloneModule().setQuantize(true) + } else { + dnnContainer + } + case _ => if (needQuantize) Quantization.quantize[T](model) else model + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index 6b080158133..a366a29e0cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -135,10 +135,6 @@ private[bigdl] class IRGraph[T: ClassTag]( graph.resetTimes() } - override def release(): Unit = { - graph.release() - } - private def initPrimitives(input: Activity): Unit = { if (!initPrim && graph.isInstanceOf[DnnGraph]) { val inputMemory = new Array[MemoryData](inputFormats.length) @@ -170,6 +166,22 @@ private[bigdl] class IRGraph[T: ClassTag]( initPrim = true } } + + def setQuantize(value: Boolean): this.type = { + require(graph != null, s"you should build the graph first") + if (graph.isInstanceOf[DnnGraph]) { + graph.asInstanceOf[DnnGraph].setQuantize(value) + } + this + } + + override def release(): Unit = { + if (graph.isInstanceOf[DnnGraph]) { + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + graph.release() + })) + } + } } object IRGraph { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index c6dad441321..d21e0825f1a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -105,7 +105,11 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] nodeMap } - private def fromReLU(node: IRElement[Float]) : Module[Float] = mkldnn.ReLU() + private def fromReLU(node: IRElement[Float]) : Module[Float] = { + val layer = mkldnn.ReLU() + ReflectionUtils.setScales(node, layer) + layer + } private def fromSpatialConvolution(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialConvolution[Float]] @@ -172,6 +176,8 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] if (t.runningMean != null) extraParams(0).copy(t.runningMean.toTensor[Float]) if (t.runningVar != null) extraParams(1).copy(t.runningVar.toTensor[Float]) + ReflectionUtils.setScales(node, layer) + // reminder: assume batch_norm is converted from blas layer.needScale = true layer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala index 7ade039a25f..d83e2abe936 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala @@ -116,7 +116,7 @@ private[bigdl] object ReflectionUtils { } // put scales in fromEle to toELe - private def setScales[T: ClassTag](fromEle: MklInt8Convertible, + private[intermediate] def setScales[T: ClassTag](fromEle: MklInt8Convertible, toELe: MklInt8Convertible): Unit = { toELe.setInputScales(fromEle.getInputScales()) toELe.setOutputScales(fromEle.getOutputScales()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala index 8ac920989a5..9560c990041 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala @@ -21,9 +21,11 @@ import java.io.File import java.util.UUID import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.mkldnn.DnnGraph import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase -import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.T import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { @@ -411,6 +413,103 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { compareModules(concatTable1, loadedModule1) } + "Calculating scales" should "work correct for BLAS CAddTable Module" in { + val sampleMax = 999 + val numElem = 12 + val inputTable = T( + Tensor[Float](Array(1.0f, 2.0f), Array(2)), + Tensor[Float](Array(3.0f, 1.0f), Array(2))) + + val caddTable0 = CAddTable[Float]() + caddTable0.setInputDimMask(0) + caddTable0.setOutputDimMask(0) + caddTable0.setWeightDimMask(0) + + caddTable0.calcScales(null) + + caddTable0.getInputScales().isEmpty should be (true) + caddTable0.getOutputScales().isEmpty should be (true) + caddTable0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val caddTable1 = CAddTable() + + caddTable1.calcScales(inputTable) + caddTable1.getOutputScales() should be (Array(Array[Float](4.0f))) + caddTable1.getInputScales() should be ( + inputTable.toTable.map((pair: (Any, Any)) => { + val key = pair._1 + val value: Tensor[Float] = pair._2.asInstanceOf[Tensor[Float]] + Array(value.abs().max()) + }).toArray + ) + + caddTable1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(caddTable1, loadedModule1) + } + + "Calculating scales" should "work correct for BLAS ReLU Module" in { + val sampleMax = 999 + val numElem = 12 + val inputTensor = make1DTensor(numElem, sampleMax) + + val relu0 = ReLU[Float]() + relu0.setInputDimMask(0) + relu0.setOutputDimMask(0) + relu0.setWeightDimMask(0) + + relu0.calcScales(null) + + relu0.getInputScales().isEmpty should be (true) + relu0.getOutputScales().isEmpty should be (true) + relu0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val relu1 = ReLU[Float]() + + relu1.calcScales(inputTensor) + relu1.getInputScales() should be (Array(Array[Float](sampleMax))) + relu1.getOutputScales() should be (Array(Array[Float](relu1.output.max()))) + + relu1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(relu1, loadedModule1) + } + + "Calculating scales" should "work correct for BLAS SpatialBatchNormalization Module" in { + val numElem = 12 + val inputTensor = Tensor[Float](4, 2, 4, 4).rand(-100, 100) + + val bn0 = SpatialBatchNormalization[Float](2) + bn0.setInputDimMask(0) + bn0.setOutputDimMask(0) + bn0.setWeightDimMask(0) + + bn0.calcScales(null) + + bn0.getInputScales().isEmpty should be (true) + bn0.getOutputScales().isEmpty should be (true) + bn0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val bn1 = SpatialBatchNormalization[Float](2) + + bn1.calcScales(inputTensor) + bn1.getInputScales() should be (Array(Array[Float](inputTensor.abs().max()))) + bn1.getOutputScales() should be (Array(Array[Float](bn1.output.abs().max()))) + + bn1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(bn1, loadedModule1) + } + "Calculating scales" should "work correct for Graph Module" in { def makeTestingGraph(): Graph[Float] = { val input = Reshape(Array(1, 28, 28)).inputs() @@ -462,6 +561,78 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { compareModules(graph1, loadedGraph1) } + "Calculating scales" should "work correct for DNN Graph Module" in { + import com.intel.analytics.bigdl.mkl.Memory + + def dnnGraph(batchSize: Int, classNum: Int): mkldnn.DnnGraph = { + val inputShape = Array(batchSize, 1, 28, 28) + val outputShape = Array(batchSize, 10) + + val input = mkldnn.Input(inputShape, Memory.Format.nchw).inputs() + val conv1 = mkldnn.SpatialConvolution(1, 20, 5, 5).setName("conv1").inputs(input) + val bn1 = mkldnn.SpatialBatchNormalization(20).setName("bn1").inputs(conv1) + val pool1 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool1").inputs(bn1) + val conv2 = mkldnn.SpatialConvolution(20, 50, 5, 5).setName("conv2").inputs(pool1) + val pool2 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool2").inputs(conv2) + val ip1 = mkldnn.Linear(50 * 4 * 4, 500).setName("ip1").inputs(pool2) + val relu1 = mkldnn.ReLU().setName("relu1").inputs(ip1) + val ip2 = mkldnn.Linear(500, 10).setName("ip2").inputs(relu1) + val output = mkldnn.ReorderMemory(mkldnn.HeapData(outputShape, Memory.Format.nc)).inputs(ip2) + + val graph = DnnGraph(Array(input), Array(output)) + graph.evaluate() + graph.compile(InferencePhase) + graph + } + + val inputTensor = Tensor(4, 1, 28, 28).rand() + + // global mask, null input + val graph0 = dnnGraph(4, 10) + graph0.setInputDimMask(0) + graph0.setOutputDimMask(0) + graph0.calcScales(null) + graph0.getInputDimMask() should be (0) + graph0.getOutputDimMask() should be (0) + graph0.getInputScales().isEmpty should be (true) + graph0.getOutputScales().isEmpty should be (true) + graph0.release() + + // global mask, non-null input + val graph1 = dnnGraph(4, 10) + graph1.setInputDimMask(0) + graph1.setOutputDimMask(0) + graph1.setWeightDimMask(1) + graph1.forward(inputTensor) + graph1.calcScales(inputTensor) + val graphOutput1 = graph1.output + + graph1.getInputDimMask() should be (0) + graph1.getOutputDimMask() should be (0) + graphOutput1 should not be (null) + + graph1.getForwardExecutions() + .filter(_.element.isInstanceOf[mkldnn.SpatialConvolution]) + .map(_.element.asInstanceOf[mkldnn.SpatialConvolution]) + .map(x => x.nOutputPlane == x.getWeightScales().flatten.length) + .exists(_ == false) should be (false) + + graph1.getForwardExecutions() + .filter(_.element.isInstanceOf[mkldnn.SpatialBatchNormalization]) + .map(_.element.asInstanceOf[mkldnn.SpatialBatchNormalization]) + .map(x => x.getOutputScales().flatten.length == 1 && x.getInputScales().flatten.length == 1) + .exists(_ == false) should be (false) + + graph1.getForwardExecutions() + .filter(_.element.isInstanceOf[mkldnn.ReLU]) + .map(_.element.asInstanceOf[mkldnn.ReLU]) + .map(x => x.getOutputScales().flatten.length == 1 && x.getInputScales().flatten.length == 1) + .exists(_ == false) should be (false) + + graph1.release() + } + + private def graphValidationHelper(graph: Graph[Float], inputActvt: Activity): Unit = { val nextNodes = graph.getForwardExecutions() var i = 0 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala index e7149d99c08..29a4edb1f4e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -156,4 +156,189 @@ class FusionSpec extends FlatSpec with Matchers { model1.output should be (model2.output) } + "Conv sum fusion quantize" should "work correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + RandomGenerator.RNG.setSeed(1000) + + val input = Tensor[Float](2, 1, 6, 6).rand(-100, 100) + val inputShape = Array(2, 1, 6, 6) + val outputShape = Array(2, 3, 4, 4) + + val initWeight = Tensor[Float](3, 1, 2, 2).fill(1) + val initBias = Tensor[Float](3).fill(0) + + val conv1 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv2 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv3 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv4 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + + val reorder1 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val reorder2 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + + val model1 = Sequential() + .add(ConcatTable() + .add(Sequential().add(conv1)) + .add(Sequential().add(conv2))) + .add(CAddTable()) + .add(ReLU()) + .add(reorder1) + model1.evaluate() + model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + val model2 = Sequential() + .add(ConcatTable() + .add(Sequential().add(conv3)) + .add(Sequential().add(conv4))) + .add(CAddTable()) + .add(ReLU()) + .add(reorder2) + + model2.evaluate() + + System.setProperty("bigdl.mkldnn.fusion.convsum", "true") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + model2.forward(input) + model2.setWeightDimMask(1) + model2.calcScales(input) + model2.release() + println(model2) + val quantized = model2.quantize() + quantized.asInstanceOf[Sequential].compile(InferencePhase, + Array(HeapData(inputShape, Memory.Format.nchw))) + println(quantized) + System.setProperty("bigdl.mkldnn.fusion.convsum", "false") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "false") + + model1.forward(input) + quantized.forward(input) + println(model1.output) + println("=" * 80) + println(quantized.output) + + model1.output should be (model2.output) + } + + "Conv Bn merge quantize" should "work correctly" in { + RandomGenerator.RNG.setSeed(1) + val batchSize = 4 + val inputShape = Array(batchSize, 3, 224, 224) + val outputShape = Array(batchSize, 64, 112, 112) + val input = Tensor[Float](batchSize, 3, 224, 224).rand(-1, 1) + + val runningMean = Tensor[Float](64).rand(-1, 1) + val runningVar = Tensor[Float](64).fill(100) + val initWeight = Tensor[Float]().resize(Array(64, 3, 7, 7)).rand(-1, 1) + val initBias = Tensor[Float]().resize(Array(64)).fill(0) + + val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, initWeight = initWeight, + initBias = initBias) + val bn1 = SpatialBatchNormalization(64) + bn1.runningMean.copy(runningMean) + bn1.runningVariance.copy(runningVar) + bn1.scaleFactor = 1.0f + val reorder1 = ReorderMemory(HeapData(inputShape, Memory.Format.nchw)) + val reorder11 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val model1 = Sequential().add(reorder1).add(conv1).add(bn1).add(reorder11) + model1.evaluate() + + model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + System.setProperty("bigdl.mkldnn.fusion.convbn", "true") + val conv2 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false, + initWeight = initWeight, initBias = initBias) + val bn2 = SpatialBatchNormalization(64) + bn2.runningMean.copy(runningMean) + bn2.runningVariance.copy(runningVar) + bn2.scaleFactor = 1.0f + val reorder2 = ReorderMemory(NativeData(inputShape, Memory.Format.nchw)) + val reorder22 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val model2 = Sequential().add(reorder2).add(conv2).add(bn2).add(reorder22) + model2.evaluate() + model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + model2.forward(input) + model2.setWeightDimMask(1) + model2.calcScales(input) + model2.release() + val quantize = model2.quantize() + quantize.asInstanceOf[Sequential] + .compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + System.setProperty("bigdl.mkldnn.fusion.convbn", "false") + + model1.forward(input) + quantize.forward(input) + + Equivalent.nearequals(model1.output.toTensor, quantize.output.toTensor, 1e-1) should be (true) + } + + "Conv sum fusion quantize 2" should "work correctly" in { + import com.intel.analytics.bigdl.numeric.NumericFloat + RandomGenerator.RNG.setSeed(1000) + + val input = Tensor[Float](2, 1, 6, 6).rand(-1, 1) + val inputShape = Array(2, 1, 6, 6) + val outputShape = Array(2, 3, 4, 4) + + val initWeight = Tensor[Float](3, 1, 2, 2).rand(-1, 1) + val initBias = Tensor[Float](3).fill(0) + + val conv1 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv2 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv3 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + val conv4 = SpatialConvolution(1, 3, 2, 2, 2, 2, 1, 1, 1, initWeight = initWeight, + initBias = initBias) + + val reorder1 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + val reorder2 = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)) + + val model1 = Sequential() + .add(ConcatTable() + .add(Sequential().add(conv1)) + .add(Sequential().add(conv2))) + .add(CAddTable()) + .add(ReLU()) + .add(reorder1) + model1.evaluate() + model1.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + + val model2 = Sequential() + .add(ConcatTable() + .add(Sequential().add(conv3)) + .add(Sequential().add(conv4))) + .add(CAddTable()) + .add(ReLU()) + .add(reorder2) + + model2.evaluate() + + System.setProperty("bigdl.mkldnn.fusion.convsum", "true") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) + model2.forward(input) + model2.setWeightDimMask(1) + model2.calcScales(input) + model2.release() + println(model2) + val quantized = model2.quantize() + quantized.asInstanceOf[Sequential].compile(InferencePhase, + Array(HeapData(inputShape, Memory.Format.nchw))) + println(quantized) + System.setProperty("bigdl.mkldnn.fusion.convsum", "false") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "false") + + model1.forward(input) + quantized.forward(input) + println(model1.output) + println("=" * 80) + println(quantized.output) + + model1.output should be (model2.output) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index e432d1f1f80..14ac11b9c73 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -607,6 +607,40 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { Equivalent.nearequals(model.getParameters()._2, blas.getParameters()._2, 1e-4) should be (true) } + "conv quantization" should "work correctly" in { + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + RNG.setSeed(1) + val inputShape = Array(1, 2, 12, 12) + val outputShape = Array(1, 8) + val model = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(SpatialConvolution(2, 4, 5, 5).setName("conv2")) + .add(ReLU()).setName("relu") + .add(MaxPooling(2, 2, 2, 2).setName("pool2")) + .add(Linear(4 * 4 * 4, 8).setName("ip1")) + .add(ReorderMemory(HeapData(outputShape, Memory.Format.nc))) + model.evaluate() + + val input = Tensor[Float](inputShape).rand(-100, 100) + model.compile(InferencePhase) + println(model.forward(input)) + + val output = model.output.toTensor[Float].clone() + + model.setInputDimMask(1) + model.calcScales(input) + model.release() + + val quantized = model.cloneModule().quantize() + quantized.asInstanceOf[Sequential].compile(InferencePhase) + + quantized.forward(input) + println(quantized.output) + System.clearProperty("bigdl.mkldnn.fusion.convrelu") + + Equivalent.nearequals(output, quantized.output.toTensor, 1e-1) should be (true) + } + "unsigned input quantization" should "work correctly" in { RNG.setSeed(1) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala index 61ffdda8883..ac10ab9fa5d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala @@ -18,8 +18,14 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.mkl.Memory -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.{Module, Zeros} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.nn.mkldnn.ResNet.DatasetType.ImageNet import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{RandomGenerator, T} import org.scalatest.{FlatSpec, Matchers} class TopologySpec extends FlatSpec with Matchers { @@ -937,6 +943,122 @@ class TopologySpec extends FlatSpec with Matchers { Tools.compare(prototxt, model, inputShape, outputShape, 1e-5) } + "resnet50 bottleneck quantize" should "work correctly" in { + System.setProperty("bigdl.mkldnn.fusion.convsum", "true") + System.setProperty("bigdl.mkldnn.fusion.convbn", "true") + System.setProperty("bigdl.mkldnn.fusion.bnrelu", "true") + System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") + RandomGenerator.RNG.setSeed(1) + val inputShape = Array(4, 3, 224, 224) + val outputShape = Array(4, 256, 56, 56) + val model = ResNet50.getModel(inputShape, outputShape) + + model.evaluate() + model.compile(InferencePhase) + + val input = Tensor[Float](inputShape).rand(-1, 1) + model.forward(input) + model.asInstanceOf[Sequential].setWeightDimMask(1) + model.asInstanceOf[Sequential].calcScales(input) + + val output = model.output.toTensor[Float].clone() + + val quant = model.quantize() + println(quant) + quant.evaluate() + quant.asInstanceOf[Sequential].compile(InferencePhase) + println(quant) + System.clearProperty("bigdl.mkldnn.fusion.convbn") + System.clearProperty("bigdl.mkldnn.fusion.bnrelu") + System.clearProperty("bigdl.mkldnn.fusion.convrelu") + System.clearProperty("bigdl.mkldnn.fusion.convsum") + quant.forward(input) + + // we just compare the first three. because i can't static + quant.output.toTensor.storage().array().slice(0, 3) should be ( + Array(0.23977348f, 0.3023231f, 0.19286129f)) + output.storage().array().slice(0, 3) should be ( + Array(0.24132696f, 0.29746482f, 0.19848186f)) + println() + } + + "resnet-50 block graph" should "work correctly" in { + RandomGenerator.RNG.setSeed(1) + val inputShape = Array(4, 3, 224, 224) + val outputShape = Array(4, 256, 56, 56) + val model = ResNet50.graph(inputShape, outputShape) + + model.evaluate() + model.compile(InferencePhase) + + val input = Tensor[Float](inputShape).rand(-1, 1) + model.forward(input) + val output = model.output.toTensor[Float].clone() + + model.setWeightDimMask(1) + model.calcScales(input) + model.release() + + val quant = model.cloneModule().setQuantize(true) + System.setProperty("bigdl.mkldnn.fusion", "true") + val fusion = model.cloneModule() + fusion.asInstanceOf[DnnGraph].compile(InferencePhase) + quant.asInstanceOf[DnnGraph].compile(InferencePhase) + fusion.forward(input) + quant.forward(input) + + fusion.output.toTensor.storage().array().slice(0, 3) should be ( + Array(0.40521193f, 0.25312302f, 0.3346515f)) + quant.output.toTensor.storage().array.slice(0, 3) should be ( + Array(0.41136017f, 0.23250793f, 0.30404884f)) + System.clearProperty("bigdl.mkldnn.fusion") + } + + "resnet50 model" should "work correctly" in { + def setRuningMeanAndVariance(model: DnnGraph): Unit = { + model.getForwardExecutions() + .filter(_.element.isInstanceOf[SpatialBatchNormalization]) + .map(_.element.asInstanceOf[SpatialBatchNormalization]) + .foreach(bn => { + bn.runningMean.dense.rand() + bn.runningVariance.dense.rand() + }) + } + + RandomGenerator.RNG.setSeed(1) + val model = ResNet.graph(4, 1000, T("depth" -> 50, "dataSet" -> ImageNet)) + setRuningMeanAndVariance(model) + val inputShape = Array(4, 3, 224, 224) + val input = Tensor[Float](inputShape).rand(-1, 1) + + model.evaluate() + model.compile(InferencePhase) + + model.forward(input) + model.setWeightDimMask(1) + model.calcScales(input) + val output = model.output.toTensor[Float].clone() + model.release() + + val quant = model.cloneModule().setQuantize(true) + + System.setProperty("bigdl.mkldnn.fusion", "true") + val fusion = model.cloneModule() + fusion.asInstanceOf[DnnGraph].compile(InferencePhase) + quant.asInstanceOf[DnnGraph].compile(InferencePhase) + fusion.forward(input) + quant.forward(input) + + val tmp = fusion.output.toTensor.max(1) + + val softmax = SoftMax() + + softmax.forward(fusion.output).toTensor.max(2) should be ( + softmax.forward(quant.output).toTensor.max(2)) + + System.clearProperty("bigdl.mkldnn.fusion") + } + object ResNet50 { var iChannels = 64 @@ -966,9 +1088,9 @@ class TopologySpec extends FlatSpec with Matchers { .add(SpatialConvolution(n, n*4, 1, 1, 1, 1, 0, 0).setName(s"res${name}_branch2c")) val model = Sequential() - .add(ConcatTable(). - add(s). - add(shortcut(nInputPlane, n*4, stride, name)).setName(s"$name/concatTable")) + .add(ConcatTable() + .add(shortcut(nInputPlane, n*4, stride, name)).setName(s"$name/concatTable") + .add(s)) .add(CAddTable().setName(s"res$name")) .add(ReLU().setName(s"res${name}_relu")) model @@ -1005,6 +1127,81 @@ class TopologySpec extends FlatSpec with Matchers { .add(layer(bottleneck, 64, 3, name = "2")) .add(ReorderMemory(HeapData(outputShape, Memory.Format.nchw))) } + + def graph(inputShape: Array[Int], outputShape: Array[Int]): DnnGraph = { + + def shortcut(input: ModuleNode[Float], nInputPlane: Int, nOutputPlane: Int, + stride: Int, name: String): ModuleNode[Float] = { + val useConv = nInputPlane != nOutputPlane + + if (useConv) { + Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride) + .setName(s"res${name}_branch1").inputs(input) + } else if (nInputPlane != nOutputPlane) { + throw new IllegalArgumentException(s"useConv false") + } else { + Identity().inputs(input) + } + } + + def bottleneck(input: ModuleNode[Float], n: Int, stride: Int, name: String = "") + : ModuleNode[Float] = { + val nInputPlane = iChannels + iChannels = n * 4 + + val conv1 = Convolution(nInputPlane, n, 1, 1, 1, 1, 0, 0) + .setName(s"res${name}_branch2a").inputs(input) + val relu1 = ReLU().setName(s"res${name}_branch2a_relu").inputs(conv1) + val conv2 = Convolution(n, n, 3, 3, stride, stride, 1, 1).setName( + s"res${name}_branch2b").inputs(relu1) + val relu3 = ReLU().setName(s"res${name}_branch2b_relu").inputs(conv2) + val conv3 = Convolution(n, n*4, 1, 1, 1, 1, 0, 0).setName( + s"res${name}_branch2c").inputs(relu3) + + val short = shortcut(input, nInputPlane, n*4, stride, name) + val cadd = CAddTable().setName(s"res$name"). + inputs(Array(conv3.asInstanceOf[ModuleNode[Float]], short)) + val relu = ReLU().setName(s"res${name}_relu").inputs(cadd) + relu + } + + def getName(i: Int, name: String): String = { + val name1 = i match { + case 1 => name + "a" + case 2 => name + "b" + case 3 => name + "c" + case 4 => name + "d" + case 5 => name + "e" + case 6 => name + "f" + } + return name1 + } + + def layer(input: ModuleNode[Float], + block: (ModuleNode[Float], Int, Int, String) => ModuleNode[Float], + features: Int, + count: Int, stride: Int = 1, name : String): ModuleNode[Float] = { + var in = input + for (i <- 1 to count) { + val res = block(in, features, if (i == 1) stride else 1, getName(i, name)) + in = res + } + in + } + + iChannels = 64 + + val input = Input(inputShape, Memory.Format.nchw).inputs() + val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = false) + .setName("conv1").inputs(input) + val relu1 = ReLU().setName("conv1_relu").inputs(conv1) + val pool1 = MaxPooling(3, 3, 2, 2).setName("pool1").inputs(relu1) + val layer1 = layer(pool1, bottleneck, 64, 3, name = "2") + val output = ReorderMemory(HeapData(outputShape, Memory.Format.nchw)).inputs(layer1) + + val model = DnnGraph(Array(input), Array(output)) + model + } } private def shape2Dim(shape: Array[Int]): String = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizableSpec.scala index dee2ad5de22..670cc4a3300 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/quantized/QuantizableSpec.scala @@ -25,11 +25,20 @@ import com.intel.analytics.bigdl.nn.{Linear => NNLinear, SpatialConvolution => N import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator.RNG -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Engine, MklBlas, T, Table} import org.apache.log4j.Logger -import org.scalatest.{FlatSpec, Matchers} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class QuantizableSpec extends FlatSpec with Matchers with BeforeAndAfter { + before { + System.setProperty("bigdl.engineType", "mklblas") + Engine.setEngineType(MklBlas) + } + + after { + System.clearProperty("bigdl.engineType") + } -class QuantizableSpec extends FlatSpec with Matchers { val logger: Logger = Logger.getLogger(getClass) "Sequential LeNet5" should "work correctly" in { From 224ab0d820e9bff3fc4f0bfbbf98e751b7aed398 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 19 Mar 2019 16:36:40 +0800 Subject: [PATCH 0882/1065] change some docs about mkldnn (#2765) * add comments about mkldnn * meet pr comments --- .../analytics/bigdl/dllib/example/loadmodel/README.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/README.md index 754053652b0..021d3847dde 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/README.md @@ -4,6 +4,8 @@ This example demonstrates how to use BigDL to load pre-trained [Torch](http://to **ModelValidator** provides an integrated example to load models, and test over imagenet validation dataset on Spark. +For most CNN models, it's recommended to enable MKL-DNN acceleration by specifying `bigdl.engineType` as `mkldnn` for model validation. + ## Preparation To start with this example, you need prepare your model, dataset. @@ -116,6 +118,7 @@ For example, following the steps below will load BVLC GoogLeNet. - Execute command for Spark standalone mode. ```shell master=spark://xxx.xxx.xxx.xxx:xxxx # please set your own spark master + engine=... # mklblas/mkldnn. For most cnn models, you can set bigdl.engineType as mkldnn to get better performance. modelType=caffe folder=hdfs://... modelName=inception @@ -125,6 +128,8 @@ For example, following the steps below will load BVLC GoogLeNet. spark-submit --driver-memory 20g --master $master --executor-memory 100g \ --executor-cores 28 \ --total-executor-cores 112 \ + --conf "spark.driver.extraJavaOptions=-Dbigdl.engineType=$engine" \ + --conf "spark.executor.extraJavaOptions=-Dbigdl.engineType=$engine" \ --driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --class com.intel.analytics.bigdl.example.loadmodel.ModelValidator \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ @@ -136,12 +141,15 @@ For example, following the steps below will load BVLC GoogLeNet. ```shell modelType=caffe folder=hdfs://... + engine=... # mklblas/mkldnn. For most cnn models, you can set bigdl.engineType as mkldnn to get better performance. modelName=inception pathToCaffePrototxt=data/model/bvlc_googlenet/deploy.prototxt pathToModel=data/model/bvlc_googlenet/bvlc_googlenet.caffemodel batchSize=448 spark-submit --driver-memory 20g --master yarn --executor-memory 100g \ - --deploy-mode client \ + --deploy-mode client + --conf "spark.yarn.am.extraJavaOptions=-Dbigdl.engineType=$engine" \ + --conf "spark.executor.extraJavaOptions=-Dbigdl.engineType=$engine" \ --executor-cores 28 \ --num-executors 4 \ --driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ From bf21a5ff66e9cf135f49ae5d0da2a7cec3389e15 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 19 Mar 2019 19:45:40 +0800 Subject: [PATCH 0883/1065] examples for int8 (#2761) This is an example of how to use mkldnn int8. There're two steps, use GenInt8Scales to generate the scales first and save the new model. And than you can use the quantized model as usual. --- .../mkldnn/int8/GenerateInt8Scales.scala | 96 +++++++++++++++++ .../mkldnn/int8/ImageNetInference.scala | 60 +++++++++++ .../bigdl/dllib/example/mkldnn/int8/README.md | 102 ++++++++++++++++++ .../dllib/example/mkldnn/int8/Utils.scala | 67 ++++++++++++ 4 files changed, 325 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/ImageNetInference.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/Utils.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala new file mode 100644 index 00000000000..06a317752c3 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala @@ -0,0 +1,96 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.mkldnn.int8 + +import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch} +import com.intel.analytics.bigdl.models.resnet.ImageNetDataSet +import com.intel.analytics.bigdl.nn.{Graph, Module} +import com.intel.analytics.bigdl.utils.Engine +import org.apache.log4j.{Level, Logger} +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD + +/** + * GenerateInt8Scales will generate a model with scales information, + * which will be used with mkldnn int8. You can pass a model trained from BigDL + * and will genereate a model whose name is the same except including "quantized" + */ +object GenerateInt8Scales { + val logger: Logger = Logger.getLogger(getClass) + Logger.getLogger("org").setLevel(Level.ERROR) + Logger.getLogger("akka").setLevel(Level.ERROR) + Logger.getLogger("breeze").setLevel(Level.ERROR) + + import Utils._ + + def genereateInt8Scales(model: Graph[Float], modelName: String, + evaluationSet: RDD[MiniBatch[Float]]): Unit = { + model.evaluate() + + model.setInputDimMask(0) + model.setOutputDimMask(0) + model.setWeightDimMask(1) + + logger.info(s"Generate the scales for $modelName ...") + val samples = evaluationSet + .repartition(1) // repartition (shuffle) will have better accuracy + .take(1) // only split one batch to sample + .map(_.getInput().toTensor[Float]) + + samples.foreach { sample => + model.calcScales(sample) + } + + // we should clean the state, such as output + model.clearState() + + logger.info(s"Generate the scales for $modelName done.") + } + + def saveQuantizedModel(model: Graph[Float], modelName: String): Unit = { + val suffix = ".bigdl" + val prefix = modelName.stripSuffix(suffix) + val name = prefix.concat(".quantized").concat(suffix) + logger.info(s"Save the quantized model $name ...") + // it will force overWrite the existed model file + model.saveModule(name, overWrite = true) + logger.info(s"Save the quantized model $name done.") + } + + def main(args: Array[String]): Unit = { + genInt8ScalesParser.parse(args, GenInt8ScalesParams()).foreach { param => + val conf = Engine.createSparkConf().setAppName("Quantize the model") + .set("spark.akka.frameSize", 64.toString) + .set("spark.task.maxFailures", "1") + val sc = new SparkContext(conf) + Engine.init + + val partitionNum = Engine.nodeNumber() + val imageFrame = DataSet.SeqFileFolder.filesToImageFrame(param.folder, sc, 1000, + partitionNum = Option(partitionNum)) + + // the transformer is the same as as that in validation during training + val evaluationSet = ImageNetDataSet.valDataSet(param.folder, + sc, 224, param.batchSize).toDistributed().data(train = false) + // Currently, we only support the graph model, so we add a `toGraph` + // if the model is already graph, you can need not to it. + val model = Module.loadModule[Float](param.model).toGraph() + genereateInt8Scales(model, param.model, evaluationSet) + saveQuantizedModel(model, param.model) + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/ImageNetInference.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/ImageNetInference.scala new file mode 100644 index 00000000000..9f46534b708 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/ImageNetInference.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.mkldnn.int8 + +import com.intel.analytics.bigdl.models.resnet.ImageNetDataSet +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.optim._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric._ +import com.intel.analytics.bigdl.utils._ +import org.apache.log4j.{Level, Logger} +import org.apache.spark.SparkContext + +/** + * This example demonstrates how to evaluate pre-trained resnet-50 with ImageNet dataset using Int8 + */ +object ImageNetInference { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + + val logger: Logger = Logger.getLogger(getClass) + + import Utils._ + + def main(args: Array[String]): Unit = { + testParser.parse(args, TestParams()).foreach(param => { + val conf = Engine.createSparkConf() + .setAppName("Test model on ImageNet2012 with Int8") + .set("spark.rpc.message.maxSize", "200") + val sc = new SparkContext(conf) + Engine.init + + val evaluationSet = ImageNetDataSet.valDataSet(param.folder, + sc, 224, param.batchSize).toDistributed().data(train = false) + + val model = Module.loadModule[Float](param.model).quantize() + model.evaluate() + + val result = model.evaluate(evaluationSet, Array(new Top1Accuracy[Float], + new Top5Accuracy[Float])) + + result.foreach(r => println(s"${r._2} is ${r._1}")) + + sc.stop() + }) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/README.md new file mode 100644 index 00000000000..ce79755c5ff --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/README.md @@ -0,0 +1,102 @@ +# Inference acceleration with MKL-DNN Low Numerical Precision (Int8) Computing + +You can use the mkldnn version of low numerical precision inference by the +API `quantize()` which will give you better performance on Intel Xeon Scalable +processors. There're only two steps, scale generation and quantize the model. +Often you can combine them into one, but for demonstrate the process of how to +use it. + +## Generate the Scales of Pretrained Model + +If you use a BigDL model which is trained by yourself or converted from other +frameworks. You should generate the scales first. It needs some sample images +to do the `forward` which can be the test or validation dataset. And because +it's the sample images, you need no to pass the whole validate dataset. And of +cause you can use spark local mode to generate scales. + +After that, you can call `GenerateInt8Scales`, it will generate a model with +a `quantized` in the name. It's the original model combined with scales information. + +```bash +#!/bin/bash + +MASTER="local[1]" + +EXECUTOR_CORES=32 +DRIVER_MEMORY=50G +EXECUTOR_MEMORY=100G + +EXECUTOR_CORES=32 +TOTAL_EXECUTOR_CORES=${EXECUTOR_CORES} +BATCH_SIZE=128 + +BIGDL_VERSION=0.8.0 + +VAL_FOLDER=hdfs://xxx.xxx.xxx.xxx:xxxx/imagenet-noresize/val +MODEL=./resnet-50.bigdl + +spark-submit \ + --master ${MASTER} \ + --driver-memory ${DRIVER_MEMORY} \ + --executor-memory ${EXECUTOR_MEMORY} \ + --executor-cores ${EXECUTOR_CORES} \ + --total-executor-cores ${TOTAL_EXECUTOR_CORES} \ + --class com.intel.analytics.bigdl.example.mkldnn.int8.GenerateInt8Scales \ + ./dist/lib/bigdl-${BIGDL_VERSION}-jar-with-dependencies.jar \ + -f ${VAL_FOLDER} \ + --batchSize ${BATCH_SIZE} \ + --model ${MODEL} +``` + +## Do the Evaluation on the Quantized Model + +When you prepared the relative quantized model, it's very simple to use int8 based +on mkldnn. On your loaded model, to call `quantize()`, it will return a new +quantized model. Now, you can do inference like other models. You could enable the +model fusion by java property, `-Dbigdl.mkldnn.fusion=true`, which works for most +CNN models and you can normally get performance benifit. + +## Use different engine to quantize the model + +You can use `bigdl.engineType` to set different engine to do the quantize. If you +set the engine to `mklblas`, it will use bigquant to quantize the model, otherwise +will use the mkldnn int8 + +## Command to startup + +```bash +#!/bin/bash + +MASTER=spark://xxx.xxx.xxx.xxx:xxxx + +EXECUTOR_CORES=32 +DRIVER_MEMORY=50G +EXECUTOR_MEMORY=100G + +EXECUTOR_CORES=32 +EXECUTOR_NUMBER=4 # executor number you want +TOTAL_EXECUTOR_CORES=$((EXECUTOR_CORES * EXECUTOR_NUMBER)) +BATCH_SIZE=$((TOTAL_EXECUTOR_CORES * 4)) + +BIGDL_VERSION=0.8.0 + +VAL_FOLDER=hdfs://xxx.xxx.xxx.xxx:xxxx/imagenet-noresize/val +MODEL=./resnet-50.bigdl.quantized + +spark-submit \ + --master ${MASTER} \ + --driver-memory ${DRIVER_MEMORY} \ + --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" \ + --conf "spark.network.timeout=1000000" \ + --conf "spark.driver.extraJavaOptions=-Dbigdl.engineType=mkldnn -Dbigdl.mkldnn.fusion=true" \ + --conf "spark.executor.extraJavaOptions=-Dbigdl.engineType=mkldnn -Dbigdl.mkldnn.fusion=true" \ + --executor-memory ${EXECUTOR_MEMORY} \ + --executor-cores ${EXECUTOR_CORES} \ + --num-executors ${EXECUTOR_NUMBER} \ + --total-executor-cores ${TOTAL_EXECUTOR_CORES} \ + --class com.intel.analytics.bigdl.example.mkldnn.int8.TestImageNet \ + ./dist/lib/bigdl-${BIGDL_VERSION}-jar-with-dependencies.jar \ + -f ${VAL_FOLDER} \ + --batchSize ${BATCH_SIZE} \ + --model ${MODEL} +``` diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/Utils.scala new file mode 100644 index 00000000000..8cf05290155 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/Utils.scala @@ -0,0 +1,67 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.example.mkldnn.int8 + +import scopt.OptionParser + +object Utils { + case class TestParams( + folder: String = "./", + model: String = "", + batchSize: Int = 128 + ) + + val testParser: OptionParser[TestParams] = new OptionParser[TestParams]( + "BigDL ResNet-50 with mkldnn int8 on ImageNet Test Example") { + opt[String]('f', "folder") + .text("the location of imagenet dataset") + .action((x, c) => c.copy(folder = x)) + opt[String]('m', "model") + .text("the location of model snapshot") + .action((x, c) => c.copy(model = x)) + .required() + .required() + opt[Int]('b', "batchSize") + .text("batch size") + .action((x, c) => c.copy(batchSize = x)) + } + + case class GenInt8ScalesParams( + folder: String = "./", + model: String = "", + batchSize: Int = 128, + numOfBatch: Int = 1 + ) + + val genInt8ScalesParser: OptionParser[GenInt8ScalesParams] = + new OptionParser[GenInt8ScalesParams]( + "BigDL ResNet-50 generate scales on ImageNet Test Example") { + opt[String]('f', "folder") + .text("the location of imagenet dataset") + .action((x, c) => c.copy(folder = x)) + opt[String]('m', "model") + .text("the location of model snapshot") + .action((x, c) => c.copy(model = x)) + .required() + .required() + opt[Int]('b', "batchSize") + .text("batch size") + .action((x, c) => c.copy(batchSize = x)) + opt[Int]('n', "numOfBatch") + .action((x, c) => c.copy(batchSize = x)) + } +} From 173494a1e51894fb52b62f5de5d151c6f5670fa0 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 20 Mar 2019 10:01:10 +0800 Subject: [PATCH 0884/1065] enable fustion by default (#2766) --- .../com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala index 32237784c3e..b67c8b74cea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -31,7 +31,7 @@ import com.intel.analytics.bigdl.utils.Node */ private[mkldnn] object Fusion { - private def fuse = System.getProperty("bigdl.mkldnn.fusion", "false").toBoolean + private def fuse = System.getProperty("bigdl.mkldnn.fusion", "true").toBoolean def fuseModule(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { if (!fuse) return; From 5159c20e542bf1e5a5c04ce13303e939e08b4e2b Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 21 Mar 2019 08:26:12 +0800 Subject: [PATCH 0885/1065] fix: the influence of default value of fusion (#2768) --- .../intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala | 2 ++ .../intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala | 1 + 2 files changed, 3 insertions(+) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala index 9560c990041..30573d43aeb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala @@ -563,6 +563,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { "Calculating scales" should "work correct for DNN Graph Module" in { import com.intel.analytics.bigdl.mkl.Memory + System.setProperty("bigdl.mkldnn.fusion", "false") def dnnGraph(batchSize: Int, classNum: Int): mkldnn.DnnGraph = { val inputShape = Array(batchSize, 1, 28, 28) @@ -630,6 +631,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { .exists(_ == false) should be (false) graph1.release() + System.clearProperty("bigdl.mkldnn.fusion") } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala index ac10ab9fa5d..07b283d8c83 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala @@ -983,6 +983,7 @@ class TopologySpec extends FlatSpec with Matchers { } "resnet-50 block graph" should "work correctly" in { + System.setProperty("bigdl.mkldnn.fusion", "false") RandomGenerator.RNG.setSeed(1) val inputShape = Array(4, 3, 224, 224) val outputShape = Array(4, 256, 56, 56) From e2ccee287f27780bae9d888bfb891b45815a5a68 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 25 Mar 2019 14:27:49 +0800 Subject: [PATCH 0886/1065] fix: use too much memory of mkldnn models (#2783) --- .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 13 ++++++++++++- .../analytics/bigdl/dllib/tensor/DnnStorage.scala | 6 +++--- 2 files changed, 15 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index dba3d4cb9a4..084b0298c96 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ +import com.intel.analytics.bigdl.tensor.DnnStorage sealed trait MemoryData extends Serializable { def shape: Array[Int] @@ -81,13 +82,23 @@ sealed trait MemoryData extends Serializable { def getRealSize: Long = { require(primitiveDesc != UNDEFINED && primitiveDesc != ERROR) - MklDnn.PrimitiveDescGetSize(primitiveDesc) + MklDnn.PrimitiveDescGetSize(primitiveDesc) / getDataTypeBytes } def getPaddingShape: Array[Int] = { require(description != UNDEFINED && description != ERROR) Memory.GetPaddingShape(description) } + + private def getDataTypeBytes: Int = { + dataType match { + case DataType.F32 => DnnStorage.FLOAT_BYTES + case DataType.S32 => DnnStorage.INT_BYTES + case DataType.S8 => DnnStorage.INT8_BYTES + case DataType.U8 => DnnStorage.INT8_BYTES + case _ => throw new UnsupportedOperationException(s"unsupported data type") + } + } } case class HeapData(private var _shape: Array[Int], private var _layout: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala index bd1665980e7..7881a5e2f33 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala @@ -144,9 +144,9 @@ private[bigdl] class Pointer(val address: Long) object DnnStorage { private[tensor] val CACHE_LINE_SIZE = System.getProperty("bigdl.cache.line", "64").toInt - private[tensor] val FLOAT_BYTES: Int = 4 - private[tensor] val INT8_BYTES: Int = 1 - private[tensor] val INT_BYTES: Int = 4 + private[bigdl] val FLOAT_BYTES: Int = 4 + private[bigdl] val INT8_BYTES: Int = 1 + private[bigdl] val INT_BYTES: Int = 4 import java.util.concurrent.ConcurrentHashMap private val nativeStorages: ConcurrentHashMap[Long, Boolean] = new ConcurrentHashMap() From b10f87ddc914345d400a78c0324bc063179658a8 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 25 Mar 2019 15:45:24 +0800 Subject: [PATCH 0887/1065] fix: inplace of input/output and weight dimension error (#2779) Some layer's input and output use the same memory. We can't do forward in the `calcScales`. Because at that time, the input has been changed, its scales maybe not right. Such as, Seqeuntail().add(Conv).add(ReLU) it will do two steps, seq.forward(input) first. and when go into the ReLU, it will do another forward, so the input will be the output. And scales will be wrong. For convolution's weight, the dimension always is 5, although the group number is 1. But for dnn convolution, if there's no group, the weight's dimension should be 4. --- .../mkldnn/int8/GenerateInt8Scales.scala | 1 + .../bigdl/dllib/nn/MklInt8Convertible.scala | 20 ++++++++++++++----- .../bigdl/dllib/nn/mkldnn/Utils.scala | 2 +- .../bigdl/dllib/nn/ScaleCalculatorSpec.scala | 12 +++++++++++ 4 files changed, 29 insertions(+), 6 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala index 06a317752c3..fe9fd6f376d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala @@ -52,6 +52,7 @@ object GenerateInt8Scales { .map(_.getInput().toTensor[Float]) samples.foreach { sample => + model.forward(sample) model.calcScales(sample) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala index 55259620c68..75bc1c72fee 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala @@ -55,15 +55,17 @@ trait MklInt8Convertible { if (inputActvt != null) { val module = this.asInstanceOf[AbstractModule[_, _, Float]] - val outputActvt = mkldnn.Utils.getOutput(module, inputActvt) + // do not forward here, because the input maybe not the real input + // such as th ReLU(true) will do the computing inplace + val outputActvt = module.output.asInstanceOf[Activity] module match { case graph: Graph[Float] => calcGraphScales(inputActvt, outputActvt) // handlers for BLAS modules case linear: Linear[Float@unchecked] => - calcModuleScales(inputActvt, outputActvt, linear.weight) + calcModuleScales(inputActvt, outputActvt, getWeight(linear)) case spatialConv: SpatialConvolution[Float@unchecked] => - calcModuleScales(inputActvt, outputActvt, spatialConv.weight) + calcModuleScales(inputActvt, outputActvt, getWeight(spatialConv)) case relu: ReLU[Float@unchecked] => calcModuleScales(inputActvt, outputActvt) case caddTable: CAddTable[Float@unchecked, Float@unchecked] => @@ -131,7 +133,6 @@ trait MklInt8Convertible { calcModuleScales(inActivity, outActivity) // calculate scales for weight appendWeightScales(calcTensorScale(weightTensor, weightDimMask)) - } /** @@ -282,7 +283,16 @@ trait MklInt8Convertible { private def getWeight(module: AbstractModule[_, _, Float]): Tensor[Float] = { if (module != null) { // the getParameters will flatten the weight and bias, it's wrong - module.parameters()._1(0) + val weight = module.parameters()._1(0) + // If the weight is came from nn.SpatialConvolution and the nGroup is 1, + // we need to skip the first dimension. Because if the group is 1, mkldnn thinks + // it's 4-D tensor weight. But for original nn.SpatialConvolution, for convenience, + // it always use 5-D tensor weight although the nGroup is 1. + if (module.isInstanceOf[SpatialConvolution[Float]] && weight.size(1) == 1) { + weight.select(1, 1) + } else { + weight + } } else { null } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala index c17f433b243..793a95595fd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala @@ -135,7 +135,7 @@ private[bigdl] object Utils { def getOutput(module: AbstractModule[_, _, _], input: Activity): Activity = { module match { case mklDnnModule: MklDnnModule => module.output.asInstanceOf[Activity] - case _ => module.forward(input) + case _ => module.output.asInstanceOf[Activity] } } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala index 30573d43aeb..62be507daec 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala @@ -53,6 +53,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Global mask, non-null input val linear1 = Linear[Float](inputSize, outputSize) + linear1.forward(inputTensor) linear1.calcScales(inputTensor) linear1.getInputScales() should be (Array(Array[Float](sampleMax))) linear1.getOutputScales().length should be (1) @@ -67,6 +68,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { linear2.setInputDimMask(inputMask) linear2.setOutputDimMask(outputMask) + linear2.forward(inputTensor) linear2.calcScales(inputTensor) val output2 = linear2.output linear2.getInputScales() should be (Array(getScalesFromTensor(inputTensor, inputMask))) @@ -163,6 +165,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Global mask, non-null input val spatialConv1 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv1.forward(inputTensor) spatialConv1.calcScales(inputTensor) spatialConv1.getInputScales() should be (Array(Array[Float](12))) spatialConv1.getOutputScales().length should be (1) @@ -174,6 +177,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { dimMaskIdx = 1 val spatialConv2 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) spatialConv2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv2.forward(inputTensor) spatialConv2.calcScales(inputTensor) val inputScales2 = Array(Array(inputTensor.select(dimMaskIdx, 1).max())) spatialConv2.getInputScales() should be (inputScales2) @@ -181,6 +185,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { dimMaskIdx = 2 val spatialConv3 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) spatialConv3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv3.forward(inputTensor) spatialConv3.calcScales(inputTensor) val inputScales3 = Array((1 to inputTensor.size(dimMaskIdx)).map( idx => inputTensor.select(dimMaskIdx, idx).max() @@ -190,6 +195,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { dimMaskIdx = 3 val spatialConv4 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) spatialConv4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv4.forward(inputTensor) spatialConv4.calcScales(inputTensor) val inputScales4 = Array((1 to inputTensor.size(dimMaskIdx)).map( idx => inputTensor.select(dimMaskIdx, idx).max() @@ -324,6 +330,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Global mask, non-null input val sequential1 = makeSequential() + sequential1.forward(inputTensor) sequential1.calcScales(inputTensor) sequential1.getInputScales().isEmpty should be (false) sequential1.getInputScales().length should be (1) @@ -395,6 +402,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Global mask, non-null input val concatTable1 = makeConcatTable() + concatTable1.forward(inputTensor) concatTable1.calcScales(inputTensor) concatTable1.getInputScales() should be (Array(Array[Float](sampleMax))) concatTable1.getOutputScales() should be ( @@ -434,6 +442,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Global mask, non-null input val caddTable1 = CAddTable() + caddTable1.forward(inputTable) caddTable1.calcScales(inputTable) caddTable1.getOutputScales() should be (Array(Array[Float](4.0f))) caddTable1.getInputScales() should be ( @@ -470,6 +479,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Global mask, non-null input val relu1 = ReLU[Float]() + relu1.forward(inputTensor) relu1.calcScales(inputTensor) relu1.getInputScales() should be (Array(Array[Float](sampleMax))) relu1.getOutputScales() should be (Array(Array[Float](relu1.output.max()))) @@ -499,6 +509,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Global mask, non-null input val bn1 = SpatialBatchNormalization[Float](2) + bn1.forward(inputTensor) bn1.calcScales(inputTensor) bn1.getInputScales() should be (Array(Array[Float](inputTensor.abs().max()))) bn1.getOutputScales() should be (Array(Array[Float](bn1.output.abs().max()))) @@ -544,6 +555,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val graph1 = makeTestingGraph() graph1.setInputDimMask(0) graph1.setOutputDimMask(0) + graph1.forward(inputTensor) graph1.calcScales(inputTensor) val graphOutput1 = graph1.output From 19f484239b00102ee8cbda9d416a254643e3e916 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 25 Mar 2019 16:26:56 +0800 Subject: [PATCH 0888/1065] fix: the blas wrapper has no scales (#2778) --- .../com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala index 793a95595fd..489b0afe2d6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Utils.scala @@ -25,13 +25,19 @@ import com.intel.analytics.bigdl.utils.T private[bigdl] object Utils { def copyMaskAndScales(from: MemoryData, to: MemoryData): Unit = { - if (to.scales.isEmpty) { + // here we check the from and to, they should not be null ideally + // but if the model is mixin with blas layer, it may be null + if (from != null && to != null && to.scales.isEmpty) { to.setScales(from.scales.clone()) to.setMask(from.mask) } } def copyMaskAndScales(from: Array[MemoryData], to: Array[MemoryData]): Unit = { + // here we check the from and to, they should not be null ideally + // but if the model is mixin with blas layer, it may be null + if (from == null || to == null) return + val valid = (from.length == 1 || to.length == 1) || // the ConcatTable or JoinTable (from.length == to.length) // the same length of from and to. From ddbf39010c991333284fff8aa6473ae5c98fd3b6 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 25 Mar 2019 16:45:57 +0800 Subject: [PATCH 0889/1065] fix softmax (#2777) --- .../intel/analytics/bigdl/dllib/nn/SoftMax.scala | 3 ++- .../bigdl/dllib/torch/SoftMaxSpec.scala | 16 ++++++++++++++++ 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala index 0da81e7b69f..69f6825c26f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala @@ -94,12 +94,13 @@ object SoftMax{ } else { input.contiguous().storage().array() } + val storageOffset = input.storageOffset() - 1 var t = 0 while (t < stride * nFrame) { val _t = t results(_t) = Engine.model.invoke(() => { - val inputOffset = (_t / stride) * dim * stride + _t % stride + val inputOffset = (_t / stride) * dim * stride + _t % stride + storageOffset val outputOffset = (_t / stride) * dim * stride + _t % stride var inputMax = ev.fromType[Float](Float.MinValue) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala index f41f9b80dc8..6eb125c3f97 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala @@ -138,4 +138,20 @@ class SoftMaxSpec extends TorchSpec { println("Test case : SoftMax, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } + + "A SoftMax with narrowed input" should "generate correct output" in { + val layer = new SoftMax[Double]() + val input = Tensor[Double](4, 6).apply1(_ => Random.nextDouble()) + + val in1 = input.narrow(1, 1, 2) + val in2 = input.narrow(1, 3, 2) + + val output = layer.forward(input).clone() + val output1 = layer.forward(in1).clone() + val output2 = layer.forward(in2).clone() + + output.narrow(1, 1, 2) should be(output1) + output.narrow(1, 3, 2) should be(output2) + println("done") + } } From dafb8c9c059ee3561cbd47fbda9e6476de8b55bc Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 25 Mar 2019 17:58:42 +0800 Subject: [PATCH 0890/1065] fix: performance regression on resnet50 (#2774) the u8 to s8 or s8 to u8 needs no reorder on this case. --- .../intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala index 6c5f6eb0944..9ad4076806e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala @@ -107,7 +107,8 @@ private[mkldnn] class ReorderManager() { // we will skip the S8 to U8 reorder val doNotReorderIt = n.layout == nn.layout && ( n.dataType == nn.dataType || // the same data type - (n.dataType == DataType.S8 && nn.dataType == DataType.U8)) // skip the s8->u8 + (n.dataType == DataType.S8 && nn.dataType == DataType.U8) || // skip the u8 -> s8 + (n.dataType == DataType.U8 && nn.dataType == DataType.S8)) // skip the s8->u8 !doNotReorderIt case _ => throw new UnsupportedOperationException("Not support such memory format") From 95484f449739e346862955fbc4bd8386e2dffc15 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 26 Mar 2019 09:41:34 +0800 Subject: [PATCH 0891/1065] fix log init (#2781) --- .../com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index b1507492689..1903600b7dc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -55,7 +55,7 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, } private[mkldnn] var needOutputFormats: Boolean = true - @transient private val logger = Logger.getLogger(getClass) + @transient private lazy val logger = Logger.getLogger(getClass) @transient private var subModels: Array[Module[Float]] = _ @transient private var subModelNumber : Int = 1 From d6d1d82e4beb2b5a946cb06b2cebe7ea2138b045 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 26 Mar 2019 21:35:35 +0800 Subject: [PATCH 0892/1065] fix: dropout should init primitive (#2789) --- .../analytics/bigdl/dllib/nn/mkldnn/Dropout.scala | 1 + .../bigdl/dllib/nn/mkldnn/DropoutSpec.scala | 14 ++++++++++++++ 2 files changed, 15 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala index 7e881efd24f..67f178e2bb3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala @@ -49,6 +49,7 @@ class Dropout( _gradOutputFormats = grad.map(x => HeapData(x.shape, format(x.shape))) _gradOutputFormatsForWeight = grad.map(x => HeapData(x.shape, format(x.shape))) _gradInputFormats = grad.map(x => HeapData(x.shape, format(x.shape))) + _gradInputFormats.map(_.getPrimitive(runtime)) gradInput = initTensor(_gradInputFormats.head) (_gradOutputFormats, _gradInputFormats) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala index 5a45c236e7e..8fdf58b3e3e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DropoutSpec.scala @@ -67,4 +67,18 @@ class DropoutSpec extends FlatSpec with Matchers { val ratio = notEqZeros.toDouble / total ratio should be (1.0) } + + "dropout in sequential" should "work correctly" in { + val shape = Array(2, 3, 4, 4) + val dropout = Dropout() + val seq = Sequential().add(Input(shape, Memory.Format.nchw)) + .add(dropout) + .add(Output(Memory.Format.nchw)) + + seq.compile(TrainingPhase) + + val input = Tensor[Float](shape).rand(-1, 1) + seq.forward(input) + seq.backward(input, seq.output) + } } From bd9ede24f64d69e54f54f09574d36a3b86f3e32f Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 4 Apr 2019 13:50:14 +0800 Subject: [PATCH 0893/1065] flip to 0.9.0 (#2792) --- dist/pom.xml | 2 +- dl/pom.xml | 6 +++--- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- .../dllib/src/main/resources/bigdl-version-info.properties | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index 1ba4b689181..2c6d4b7f515 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.8.0-SNAPSHOT + 0.9.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index d11a80498f9..ab304e6aa05 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.8.0-SNAPSHOT + 0.9.0-SNAPSHOT 4.0.0 @@ -79,7 +79,7 @@ com.intel.analytics.bigdl.core.dist all - 0.8.0-SNAPSHOT + 0.9.0-SNAPSHOT ${bigdl-core-all-scope} @@ -314,7 +314,7 @@ com.intel.analytics.bigdl.core.dist ${os-flag} - 0.8.0-SNAPSHOT + 0.9.0-SNAPSHOT pom diff --git a/pom.xml b/pom.xml index 4bfa14b4887..bc29089d259 100644 --- a/pom.xml +++ b/pom.xml @@ -5,7 +5,7 @@ bigdl-parent com.intel.analytics.bigdl - 0.8.0-SNAPSHOT + 0.9.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/1.5-plus/pom.xml b/scala/common/spark-version/1.5-plus/pom.xml index fa713405e43..4a6398aa41c 100644 --- a/scala/common/spark-version/1.5-plus/pom.xml +++ b/scala/common/spark-version/1.5-plus/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.8.0-SNAPSHOT + 0.9.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/2.0/pom.xml b/scala/common/spark-version/2.0/pom.xml index d6c0e6b7c2d..c9fae4dd865 100644 --- a/scala/common/spark-version/2.0/pom.xml +++ b/scala/common/spark-version/2.0/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.8.0-SNAPSHOT + 0.9.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/pom.xml b/scala/common/spark-version/pom.xml index 91943d19268..e22f5b37a43 100644 --- a/scala/common/spark-version/pom.xml +++ b/scala/common/spark-version/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.8.0-SNAPSHOT + 0.9.0-SNAPSHOT 4.0.0 diff --git a/scala/dllib/src/main/resources/bigdl-version-info.properties b/scala/dllib/src/main/resources/bigdl-version-info.properties index 2d9aa85b43e..1a483922a9e 100644 --- a/scala/dllib/src/main/resources/bigdl-version-info.properties +++ b/scala/dllib/src/main/resources/bigdl-version-info.properties @@ -16,4 +16,4 @@ #BigDL version info config -version=0.8.0-SNAPSHOT \ No newline at end of file +version=0.9.0-SNAPSHOT \ No newline at end of file From 28d2a878b30f29a16fe2f1d1879fb84462d6935d Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 19 Apr 2019 10:14:17 +0800 Subject: [PATCH 0894/1065] test: should compare the right grad input (#2794) --- .../analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala index 2eece2f0bec..6ddc703bb7e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala @@ -71,7 +71,7 @@ class ReflectionUtilsSpec extends BigDLSpecHelper { Equivalent.nearequals(weight1, weight2) should be (true) Equivalent.nearequals(gradWeight1, gradWeight2) should be (true) - Equivalent.nearequals(Tools.dense(modelDnn.gradInput).toTensor, + Equivalent.nearequals(Tools.dense(seq.gradInput).toTensor, modelBlas.gradInput.toTensor[Float]) should be (true) } From 6362f913b28fb329268a2f40f58f52a33e52af4a Mon Sep 17 00:00:00 2001 From: yaochi Date: Wed, 24 Apr 2019 20:56:11 +0800 Subject: [PATCH 0895/1065] fix the wrong error message (#2800) --- .../scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala index 1583c045398..7e89414fc9c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BCECriterion.scala @@ -48,7 +48,7 @@ class BCECriterion[@specialized(Float, Double) T: ClassTag] override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { require(input.size().sameElements(target.size()), s"input size should be equal to target size, but got input size: ${input.size().toList}," + - s" target size: ${input.size().toList}") + s" target size: ${target.size().toList}") if (weights != null) { if (weights.nDimension() < input.nDimension()) { From 10ace9728bed5a0480d1d1003838607d1463c1a0 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 26 Apr 2019 09:49:44 +0800 Subject: [PATCH 0896/1065] [New feature] Add attention layer and ffn layer (#2795) * add attention layer * add ffn layer and more unit tests * refactor according to pr comments * add SerializationTest * fix unit tests * add python api --- .../analytics/bigdl/dllib/keras/SoftMax.scala | 16 +- .../analytics/bigdl/dllib/nn/Attention.scala | 158 ++++++++++ .../analytics/bigdl/dllib/nn/BaseModule.scala | 90 ++++++ .../bigdl/dllib/nn/FeedForwardNetwork.scala | 58 ++++ .../intel/analytics/bigdl/dllib/nn/MM.scala | 85 ++++-- .../bigdl/dllib/nn/TransformerOperation.scala | 58 ++++ .../bigdl/dllib/nn/ops/BatchMatMul.scala | 2 +- .../dllib/utils/python/api/PythonBigDL.scala | 9 + .../bigdl/dllib/nn/AttentionSpec.scala | 282 ++++++++++++++++++ .../bigdl/dllib/nn/CombineHeadsSpec.scala | 125 ++++++++ .../dllib/nn/FeedForwardNetworkSpec.scala | 158 ++++++++++ .../bigdl/dllib/nn/SplitHeadsSpec.scala | 184 ++++++++++++ 12 files changed, 1194 insertions(+), 31 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AttentionSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CombineHeadsSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetworkSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SplitHeadsSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala index ea3004b61ca..80268d037b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/SoftMax.scala @@ -33,8 +33,8 @@ class SoftMax[T: ClassTag]( override def computeOutputShape(inputShape: Shape): Shape = { val input = inputShape.toSingle().toArray - require(input.length == 2 || input.length == 3, - s"SoftMax requires 2D or 3D input, but got input dim ${input.length}") + require(input.length == 2 || input.length == 3 || input.length == 4, + s"SoftMax requires 2D or 3D or 4D input, but got input dim ${input.length}") inputShape } @@ -43,13 +43,21 @@ class SoftMax[T: ClassTag]( val layer = com.intel.analytics.bigdl.nn.SoftMax() if (input.length <= 2) { layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] - } - else { + } else if (input.length == 3) { val model = TSequential[T]() model.add(Transpose(Array((1, 3)))) model.add(layer) model.add(Transpose(Array((1, 3)))) model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } else if (input.length == 4) { + val model = TSequential[T]() + model.add(Transpose(Array((2, 4)))) + model.add(layer) + model.add(Transpose(Array((2, 4)))) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } else { + throw new IllegalArgumentException(s"SoftMax requires 2D or 3D or 4D input, " + + s"but got input dim ${input.length}") } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala new file mode 100644 index 00000000000..7f5633df26f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala @@ -0,0 +1,158 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Implementation of multiheaded attention and self-attention layers. + * @param hiddenSize hidden size + * @param numHeads heads number + * @param attentionDropout + */ +class Attention[T: ClassTag]( + val hiddenSize: Int, val numHeads: Int, val attentionDropout: Float) + (implicit ev: TensorNumeric[T]) extends BaseModule[T] { + + override def buildModel(): Module[T] = { + // InputX with shape (batch_size, length_x, hidden_size). + // InputY with shape (batch_size, length_x, hidden_size) + // for self attention, InputX and InputY should be the same. + // Bias is attention bias that will be added to the result of the dot product. + val inputX = Input() + val inputY = Input() + val inputBias = Input() + + // Layers for linearly projecting the queries, keys, and values. + val queryLayer = TransformerOperation.dense( + hiddenSize, hiddenSize, false, name = "q").inputs(inputX) + val keyLayer = TransformerOperation.dense( + hiddenSize, hiddenSize, false, name = "k").inputs(inputY) + val valueLayer = TransformerOperation.dense( + hiddenSize, hiddenSize, false, name = "v").inputs(inputY) + + val querySplit = new SplitHeads(hiddenSize, numHeads, true).inputs(queryLayer) + val keySplit = new SplitHeads(hiddenSize, numHeads).inputs(keyLayer) + val valueSplit = new SplitHeads(hiddenSize, numHeads).inputs(valueLayer) + + val contiguousQ = new Contiguous[T]().inputs(querySplit) + val contiguousK = new Contiguous[T]().inputs(keySplit) + val contiguousV = new Contiguous[T]().inputs(valueSplit) + + val matmul = MM(transB = true).inputs(contiguousQ, contiguousK) + val cadd = CAddTable().inputs(matmul, inputBias) + val softMax = TransformerOperation.softMax[T]().inputs(cadd) + + val drop = if (train) { + Dropout(initP = (1.0 - attentionDropout)).inputs(softMax) + } else softMax + val matmulNoTrans = MM().inputs(drop, contiguousV) + // Recombine heads --> (batch_size, length, hidden_size) + val combineHeads = new CombineHeads().inputs(matmulNoTrans) + // Run the combined outputs through another linear projection layer. + val outputLayer = TransformerOperation.dense( + hiddenSize, hiddenSize, false, name = "output_transform").inputs(combineHeads) + val graph = Graph(Array(inputX, inputY, inputBias), Array(outputLayer)) + if (this.train) graph.training() else graph.evaluate() + graph + } +} +// Combine tensor that has been splitted. +// input should be tensor with shape (batch_size, num_heads, length, hidden_size/num_heads) +// output should be tensor with shape (batch_size, length, hidden_size) +private[nn] class CombineHeads[T: ClassTag](implicit ev: TensorNumeric[T]) + extends TensorModule[T] { + + private val permutations: (Int, Int) = (2, 3) + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + val batchSize = input.size(1) + val length = input.size(3) + val hiddenSize = input.size(2) * input.size(4) + + output.resizeAs(input).copy(input) + output = output.transpose(permutations._1, permutations._2) + .reshape(Array(batchSize, length, hiddenSize)) + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val size = Array(input.size(1), input.size(3), input.size(2), input.size(4)) + if (gradOutput.isContiguous()) { + gradInput = gradOutput.view(size) + } else { + gradInput = gradOutput.contiguous().view(size) + } + gradInput = gradInput.transpose(permutations._1, permutations._2).contiguous() + gradInput + } +} + +/** + * Split x into different heads, and transpose the resulting value. + * The tensor is transposed to insure the inner dimensions hold the correct + * values during the matrix multiplication. + * input with shape (batch_size, length, hidden_size) + * output with shape (batch_size, num_heads, length, hidden_size/num_heads) + * @param hiddenSize + * @param numHeads + * @param mul + * @tparam T The numeric type in this module parameters + */ +private[nn] class SplitHeads[T: ClassTag](val hiddenSize: Int, val numHeads: Int, + val mul: Boolean = false)(implicit ev: TensorNumeric[T]) + extends TensorModule[T] { + + private val depth = hiddenSize / numHeads + private val value = ev.fromType(math.pow(depth, -0.5)) + private val permutations: (Int, Int) = (2, 3) + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + val batchSize = input.size(1) + val length = input.size(2) + + output.resizeAs(input).copy(input) + output = output.reshape(Array(batchSize, length, numHeads, depth)) + .transpose(permutations._1, permutations._2) + if (mul) { + output.mul(value) + } + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + if (mul) { + gradInput.resizeAs(gradOutput).zero().add(value, gradOutput) + } else { + gradInput.resizeAs(gradOutput).copy(gradOutput) + } + gradInput = gradInput.transpose(permutations._1, permutations._2).contiguous() + gradInput.resize(input.size()) + gradInput + } +} + +object Attention { + def apply[@specialized(Float, Double) T: ClassTag] + (hiddenSize: Int, numHeads: Int, attentionDropout: Float) + (implicit ev: TensorNumeric[T]): Attention[T] = + new Attention(hiddenSize: Int, numHeads: Int, attentionDropout: Float) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala new file mode 100644 index 00000000000..d441bb1483f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala @@ -0,0 +1,90 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + + +private[nn] abstract class BaseModule[T: ClassTag]()(implicit ev: TensorNumeric[T]) + extends AbstractModule[Activity, Activity, T] { + + val model : Module[T] = buildModel() + + def buildModel(): Module[T] + + override def updateOutput(input: Activity): Activity = { + output = model.updateOutput(input) + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = model.updateGradInput(input, gradOutput) + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + model.accGradParameters(input, gradOutput) + } + + override def backward(input: Activity, gradOutput: Activity): Activity = { + gradInput = model.backward(input, gradOutput) + gradInput + } + + override def training(): this.type = { + train = true + model.training() + this + } + + override def evaluate(): this.type = { + train = false + model.evaluate() + this + } + + override def getExtraParameter(): Array[Tensor[T]] = { + model.getExtraParameter() + } + + override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { + model.getTimes() + } + + override def resetTimes(): Unit = { + model.resetTimes() + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + model.parameters() + } + + override def getParametersTable(): Table = { + model.getParametersTable() + } + + override def clearState(): this.type = { + model.clearState() + this + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala new file mode 100644 index 00000000000..c184159e017 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.{Module => _} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Implementation FeedForwardNetwork constructed with fully connected network. + * Input with shape (batch_size, length, hidden_size) + * Output with shape (batch_size, length, hidden_size) + * @param hiddenSize hidden_size + * @param filterSize + * @param reluDropout + */ +class FeedForwardNetwork[T: ClassTag](val hiddenSize: Int, val filterSize: Int, + val reluDropout: Float)(implicit ev: TensorNumeric[T]) + extends BaseModule[T]{ + + override def buildModel(): Module[T] = { + val input = Input() + val filterLayer = TransformerOperation.dense( + hiddenSize, filterSize, bias = true, activation = ReLU[T]()).inputs(input) + val drop = if (train) { + Dropout(initP = (1.0 - reluDropout)).inputs(filterLayer) + } else filterLayer + val output_dense_layer = TransformerOperation.dense( + filterSize, hiddenSize, bias = true).inputs(drop) + val graph = Graph(Array(input), Array(output_dense_layer)) + if (this.train) graph.training() else graph.evaluate() + graph + } +} + +object FeedForwardNetwork { + def apply[@specialized(Float, Double) T: ClassTag]( + hiddenSize: Int, + filterSize: Int, + reluDropout: Float) + (implicit ev: TensorNumeric[T]): FeedForwardNetwork[T] = + new FeedForwardNetwork[T](hiddenSize, filterSize, reluDropout) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MM.scala index 0ca9a8dd263..e249b86fc19 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MM.scala @@ -42,9 +42,9 @@ class MM[T: ClassTag]( input(2).isInstanceOf[Tensor[T]], "Input must be two tensors") val m1: Tensor[T] = input(1) val m2: Tensor[T] = input(2) - require(m1.dim() == 2 || m1.dim() == 3, "input matrix must be 2D or 3D" + + require(m1.dim() == 2 || m1.dim() == 3 || m1.dim() == 4, "input matrix must be 2D or 3D or 4D" + s"input dim ${m1.dim()}") - require(m2.dim() == 2 || m2.dim() == 3, "input matrix must be 2D or 3D" + + require(m2.dim() == 2 || m2.dim() == 3 || m2.dim() == 4, "input matrix must be 2D or 3D or 4D" + s"input dim ${m2.dim()}") (m1, m2) @@ -69,36 +69,43 @@ class MM[T: ClassTag]( output.resize(ma.size(1), mb.size(2)) output.mm(ma, mb) } else { - require(mb.dim() == 3, "second input tensor must be 3D" + - s"second input dim ${mb.dim()}") - require(ma.size(1) == mb.size(1), "inputs must contain the same number of minibatches" + - s"The minibatces of each are ${ma.size(1)} and ${mb.size(1)}") + require(ma.dim() == mb.dim(), s"input tensors should be with same dimension," + + s"but get ${ma.dim()} ${mb.dim()}") + require(mb.dim() == 3 || mb.dim() == 4, "input tensor must be 3D or 4D, but get " + + s"input dim ${mb.dim()}") + + val dimNum = ma.dim() + val batchSizeX = ma.size().slice(0, dimNum - 2).product + val batchSizeY = mb.size().slice(0, dimNum - 2).product + require(batchSizeX == batchSizeY, "inputs must contain the same number of minibatches" + + s"The minibatches of each are ${batchSizeX} and ${batchSizeY}") + + var reshapedX = ma.view(Array(batchSizeX, ma.size(dimNum - 1), ma.size(dimNum))) + var reshapedY = mb.view(Array(batchSizeX, mb.size(dimNum - 1), mb.size(dimNum))) if (transA) { - ma = ma.transpose(2, 3) + reshapedX = reshapedX.transpose(2, 3) } if (transB) { - mb = mb.transpose(2, 3) + reshapedY = reshapedY.transpose(2, 3) } - require(ma.size(3) == mb.size(2), "matrix sizes do not match" + - s"the matrix sizes are ${ma.size(3)} and ${mb.size(2)}") + require(reshapedX.size(3) == reshapedY.size(2), "matrix sizes do not match" + + s"the matrix sizes are ${reshapedX.size(3)} and ${reshapedY.size(2)}") - output.resize(ma.size(1), ma.size(2), mb.size(3)) - output.baddbmm(ev.fromType[Float](0.0f), ev.fromType[Float](1.0f), ma, mb) + output.resize(batchSizeX, reshapedX.size(2), reshapedY.size(3)).zero() + output.bmm(reshapedX, reshapedY) + val outputSize = ma.size().slice(0, dimNum - 2) ++ Array(reshapedX.size(2), reshapedY.size(3)) + output.resize(outputSize) } output } override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + val (ma, mb) = checkInputFormat(input) - var (ma, mb) = checkInputFormat(input) - - gradInput[Tensor[T]](1).resizeAs(ma) - gradInput[Tensor[T]](2).resizeAs(mb) - - require(gradOutput.dim() == 2 || gradOutput.dim() == 3, - "arguments must be a 2D or 3D Tensor" + + require(gradOutput.dim() == 2 || gradOutput.dim() == 3 || gradOutput.dim() == 4, + "arguments must be a 2D or 3D or 4D Tensor" + s"arguments dim ${gradOutput.dim()}") @@ -110,7 +117,7 @@ class MM[T: ClassTag]( s"second input dim ${mb.dim()}") (1, 2, t => m1 => m2 => t.mm(m1, m2)) - } else { + } else if (gradOutput.dim() == 3) { require(ma.dim() == 3, "first input tensor must be 3D" + s"first input dim ${ma.dim()}") require(mb.dim() == 3, "second input tensor must be 3D" + @@ -118,26 +125,52 @@ class MM[T: ClassTag]( (2, 3, t => m1 => m2 => t.baddbmm(ev.fromType[Float](0.0f), ev.fromType[Float](1.0f), m1, m2)) + } else { + require(ma.dim() == 4, "first input tensor must be 4D" + + s"first input dim ${ma.dim()}") + require(mb.dim() == 4, "second input tensor must be 4D" + + s"second input dim ${mb.dim()}") + + (2, 3, t => m1 => m2 => t.bmm(m1, m2)) } + val dimNum = ma.dim() + val batchSize = mb.size().slice(0, dimNum - 2).product + val batchSizeGrad = gradOutput.size().slice(0, dimNum - 2).product + + var reshapedX = if (ma.dim() == 4) { + ma.view(Array(batchSize, ma.size(dimNum - 1), ma.size(dimNum))) + } else ma + var reshapedY = if (mb.dim() == 4) { + mb.view(Array(batchSize, mb.size(dimNum - 1), mb.size(dimNum))) + } else mb + val reshapeGradOutput = if (gradOutput.dim() == 4) { + gradOutput.contiguous().view(batchSizeGrad, + gradOutput.size(dimNum - 1), gradOutput.size(dimNum)) + } else gradOutput.contiguous() + + gradInput[Tensor[T]](1).resizeAs(reshapedX).zero() + gradInput[Tensor[T]](2).resizeAs(reshapedY).zero() if (transA == transB) { - ma = ma.transpose(hDim, wDim) - mb = mb.transpose(hDim, wDim) + reshapedX = reshapedX.transpose(hDim, wDim) + reshapedY = reshapedY.transpose(hDim, wDim) } if (transA) { - f (gradInput[Tensor[T]](1)) (mb) (gradOutput.clone().transpose(hDim, wDim)) + f (gradInput[Tensor[T]](1)) (reshapedY) (reshapeGradOutput.clone().transpose(hDim, wDim)) } else { - f (gradInput[Tensor[T]](1)) (gradOutput) (mb) + f (gradInput[Tensor[T]](1)) (reshapeGradOutput) (reshapedY) } if (transB) { - f (gradInput[Tensor[T]](2)) (gradOutput.clone().transpose(hDim, wDim)) (ma) + f (gradInput[Tensor[T]](2)) (reshapeGradOutput.clone().transpose(hDim, wDim)) (reshapedX) } else { - f (gradInput[Tensor[T]](2)) (ma) (gradOutput) + f (gradInput[Tensor[T]](2)) (reshapedX) (reshapeGradOutput) } + gradInput[Tensor[T]](1).resizeAs(ma) + gradInput[Tensor[T]](2).resizeAs(mb) gradInput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala new file mode 100644 index 00000000000..7ff93ad2ff1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} +import com.intel.analytics.bigdl.optim.Regularizer +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +private[nn] object TransformerOperation { + def dense[T: ClassTag]( + inputSize: Int, + outputSize: Int, + bias: Boolean = true, + activation: TensorModule[T] = null, + wRegularizer: Regularizer[T] = null, + bRegularizer: Regularizer[T] = null, + name: String = "")(implicit ev: TensorNumeric[T]): Module[T] = { + val seq = new Sequential[T]() + val layer = Linear[T]( + inputSize = inputSize, + outputSize = outputSize, + withBias = bias, + wRegularizer = wRegularizer, + bRegularizer = bRegularizer) + + layer.setInitMethod(weightInitMethod = Xavier, biasInitMethod = Zeros) + if (name != "") layer.setName(name) + seq.add(TimeDistributed[T](layer)) + if (activation != null) seq.add(activation) + seq + } + + def softMax[T: ClassTag]()(implicit ev: TensorNumeric[T]): Module[T] = { + val layer = SoftMax[T]() + val model = Sequential[T]() + model.add(Transpose[T](Array((2, 4)))) + model.add(layer) + model.add(Transpose[T](Array((2, 4)))) + model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala index 2cfc9c16c0b..44a7751e2ff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/BatchMatMul.scala @@ -79,7 +79,7 @@ class BatchMatMul[T: ClassTag, D: ClassTag]( if (adjY) { reshapedY = reshapedY.transpose(2, 3) } - require(reshapedX.size(2) == reshapedY.size(3), "matrix sizes do not match" + + require(reshapedX.size(3) == reshapedY.size(2), "matrix sizes do not match" + s"the matrix sizes are ${reshapedX.size(2)} and ${reshapedY.size(3)}") output.resize(batchSize, reshapedX.size(2), reshapedY.size(3)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 2b0262c7248..a8f8c1ed39d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -270,6 +270,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Sequential[T]() } + def createAttention(hiddenSize: Int, numHeads: Int, attentionDropout: Float): Attention[T] = { + Attention(hiddenSize, numHeads, attentionDropout) + } + + def createFeedForwardNetwork(hiddenSize: Int, + filterSize: Int, reluDropout: Float): FeedForwardNetwork[T] = { + FeedForwardNetwork(hiddenSize, filterSize, reluDropout) + } + def createLinear(inputSize: Int, outputSize: Int, withBias: Boolean, wRegularizer: Regularizer[T] = null, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AttentionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AttentionSpec.scala new file mode 100644 index 00000000000..0f0c564254e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AttentionSpec.scala @@ -0,0 +1,282 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{T, Table} +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class AttentionSpec extends FlatSpec with Matchers { + + + val inputX : Tensor[Float] = Tensor(T( + T(T( 2.43651805, -0.91763462, -0.79225763, -1.60945293, 1.29811144, + -3.45230805, 2.61721765, -1.14181035), + T( 0.47855864, -0.37405556, 2.19316191, -3.09021106, -0.48362581, + -0.57608153, 1.70065416, -1.6498369), + T(-0.25864231, -1.31678763, 0.06332062, 0.87422282, -1.65092877, + 1.71708556, 1.35238608, 0.75374151)), + T(T( 1.35128392, -1.02559179, -0.18433534, -1.40365415, -0.40183212, + 0.7955332, -1.03749113, -0.59513029), + T(-1.03075905, -1.26780846, -1.0068692, -0.0189969, -1.67596552, + 0.35162355, 2.48970327, 1.11306624), + T(-0.28775333, -1.33144345, -1.12073744, 2.5386819, 0.07621163, + -0.95549347, 0.28637323, 3.1503827)))) + + val inputY : Tensor[Float] = inputX.clone() + + val inputBias : Tensor[Float] = Tensor(T( + T(T(T( 0.06007948, 0.30860155, 0.15008516), + T(-0.17612492, -0.5712591, -0.17467136), + T(-0.10444712, 0.2933116, 0.41949171)), + + T(T( 0.46555104, 0.14279366, 0.44257058), + T(-0.37719897, 0.62643408, 0.25646491), + T(-0.14904642, 0.24425907, -0.03778586)), + + T(T( 0.56581469, 0.75990841, 1.0927877), + T(-0.69824817, -0.7220569, -0.25223293), + T( 0.08001853, 0.43808446, 0.15781747)), + + T(T(-1.01110061, -0.15310201, 0.41398732), + T( 0.11504737, 0.38100559, -0.11116407), + T(-0.10037903, 0.0932807, 0.20502582))), + + + T(T(T( 0.09914986, 0.05950432, -0.33533114), + T( 0.18878189, 0.06091064, 0.56474195), + T( 0.59945894, 0.09257821, -0.18764248)), + + T(T(-0.3193652, 0.21174718, 0.03867003), + T(-0.17192684, 0.02179843, -0.31000042), + T( 0.34901602, -0.22356428, 0.61225385)), + + T(T( 0.20174582, 0.29678926, -0.54745592), + T( 0.08469122, 0.37027823, -0.4768503), + T(-0.13310925, 0.01630727, -0.68655866)), + + T(T( 0.1575797, 0.42308032, -0.42975797), + T( 0.17527299, -0.65614171, -0.01934775), + T(-0.80788618, 0.56070885, 0.20445027))))) + + val outputExpected : Tensor[Float] = Tensor[Float]( + T(T(T(-1.3136294, -2.3003874, -1.8152907, -1.2017354, -0.30692226, + 0.7014533, -0.48527908, 1.2008696), + T(-0.80872196, -1.8982431, -3.7982664, -1.5464412, -2.128195, + 0.921171, -0.514083, 0.7338527), + T(-0.6878601, -2.1513283, 0.06790769, -1.8393003, 0.18802914, + 0.32452816, -0.63091534, -1.6523509)), + T(T( 1.0437143, -0.5991106, -1.8075838, -1.3340924, -1.9741716, + 2.2952275, -0.7159063, -0.56103015), + T( 0.12556843, -1.1148375, -1.1061573, -0.76132846, -1.6811743, + 1.434186, -0.5047271, 0.513326), + T(-0.4049306, -1.1523883, -1.3060606, -0.78532106, -1.1865962, + 1.1612856, -0.4876815, 0.57233703)))) + + val weights: Table = T( + "q" -> Tensor[Float]( + T(T(-0.372805, -0.57580054, -0.16542524, -0.29865405, 0.35401803, 0.15270126, + -0.54465574, 0.15932709), + T( 0.24691772, 0.30155098, 0.4186222, 0.2167002, 0.30048692, 0.27184665, + 0.39705545, -0.23575303), + T( 0.00388521, 0.20807374, -0.378344, -0.30214158, -0.34708476, 0.04026955, + -0.55643994, -0.5794907), + T( 0.49563867, -0.20237926, -0.46280175, 0.28509408, 0.54167503, -0.3143271, + -0.12728554, 0.38375044), + T( 0.32280642, -0.5431511, 0.09327781, 0.26422644, -0.1516226, -0.592104, + -0.4920348, -0.06154263), + T(-0.3427992, -0.28234676, 0.60987645, -0.04226011, -0.4681016, -0.1708524, + 0.14569217, -0.08339447), + T( 0.22560287, 0.35561, -0.50295657, 0.13627058, -0.3947954, 0.5856554, + -0.4278072, -0.20018426), + T(-0.262408, -0.21194538, -0.5646615, -0.50292665, -0.47206333, -0.5250014, + 0.26842934, 0.28272492))), + "k" -> Tensor[Float](T( + T(-0.343275, -0.5302577, 0.22225219, 0.22917205, -0.35248256, -0.52561647, + -0.49496183, 0.19416988), + T( 0.59556, 0.15709078, -0.5260543, 0.3003326, -0.4924144, 0.19770503, + 0.18886334, -0.4183287), + T(-0.14076799, 0.20558482, -0.44356102, 0.3057044, -0.0961917, -0.41457063, + -0.25426582, -0.43088654), + T( 0.00211596, 0.5313905, 0.38138926, -0.53933024, 0.25935173, -0.4545771, + -0.5513677, -0.42323098), + T( 0.60221463, 0.46009654, -0.3742085, 0.30695522, -0.14824063, 0.08633447, + 0.5154777, -0.31166738), + T( 0.5757794, -0.00155389, -0.27291873, 0.01211369, 0.10273433, -0.5679398, + -0.4605189, -0.60379565), + T(-0.2338264, -0.40447962, -0.20583275, 0.12039971, -0.4886889, -0.26302016, + 0.56051654, 0.0246914), + T(-0.0083527, 0.07543635, 0.6011241, 0.5061092, -0.17393082, -0.02609855, + -0.03866196, -0.47378802))), + "v" -> Tensor[Float]( + T(T(-0.27888697, -0.3508993, 0.00061786, -0.05899942, -0.4096707, -0.59099805, + 0.00982529, 0.05359054), + T( 0.3683961, -0.05546927, -0.2827503, 0.43347543, 0.1822511, -0.16377908, + -0.5162845, -0.43161902), + T( 0.46889406, 0.59701246, 0.48150903, 0.4334857, 0.486095, 0.53306824, + 0.27221018, 0.5941089), + T( 0.12607813, -0.5313994, -0.57173353, -0.12448379, -0.11713088, -0.4439688, + -0.527298, -0.37749383), + T(-0.3919587, 0.05043119, 0.18434244, -0.01674193, -0.20570382, -0.21749035, + -0.2891266, 0.12637317), + T( 0.52648765, -0.07314765, 0.48385805, -0.03910315, 0.22911525, 0.01771665, + -0.02246779, -0.40268806), + T(-0.54250515, -0.31025118, -0.03211451, -0.12393585, -0.4777977, 0.18552327, + -0.3151345, -0.5560428), + T( 0.38067168, 0.45435983, 0.46077865, -0.10283256, -0.3396571, 0.26476836, + -0.25029647, -0.5956288))), + "output_transform" -> Tensor[Float]( + T(T(-0.22421107, 0.350811, 0.05354661, 0.6110292, -0.3909106, -0.5944199, + 0.10645795, 0.57871825), + T(-0.5649649, -0.23917922, 0.3865885, 0.44473797, 0.29352474, -0.50426036, + -0.3379699, 0.00927532), + T(-0.37847292, -0.4825884, -0.05675334, -0.01127535, 0.08974767, -0.06169283, + 0.15506953, -0.02398986), + T(-0.34070057, 0.12476408, 0.5375441, 0.2504276, 0.5667407, -0.599416, + 0.09187245, 0.5948525), + T( 0.16609788, 0.55267304, 0.54386073, 0.18300432, 0.59399253, 0.02860391, + 0.26716715, -0.14422473), + T( 0.41911787, -0.19523674, 0.4970067, 0.15865183, -0.46091762, 0.5183502, + -0.2546733, 0.37238264), + T(-0.23758182, 0.2648332, 0.14880872, -0.41371652, -0.52281517, 0.3087402, + -0.4304452, -0.12153107), + T( 0.02987367, 0.01645315, 0.58394355, 0.16796988, 0.23654258, -0.50470173, + 0.07536042, -0.5896087)))) + + val gradWeightsExpected = T( + "q" -> Tensor[Float]( + T(T(-2.8356311, -2.4773571, 1.4626868, -0.6618118, -4.628455, + 5.1103063, -0.08714008, 0.17469034), + T(9.355147, 8.278282, -2.5292795, 1.2974377, 4.203867, + -3.8572924, -6.996191, 2.9874544), + T(-1.8837652, -1.9733994, 6.421815, -4.012224, 1.106437, + -0.727377, -4.41868, 1.7946866), + T(1.5535867, 1.5127493, -9.956939, 5.752798, 3.6158886, + -3.8260145, 2.7087438, -0.7301127), + T(7.9238024, 7.238671, -2.934513, 1.6450198, 0.14482632, + 0.48197156, -3.8312237, 2.020553), + T(-4.122905, -3.6462588, -0.68954813, 0.63985145, 4.7653265, + -5.5613956, 0.35840988, -0.5148314), + T(-12.359057, -11.95616, 6.206677, -4.027071, -7.648021, + 8.011374, 3.4324074, -1.9123353), + T(-0.78880894, -0.2865741, -4.623139, 2.743837, 1.5203985, + -2.1541567, 7.4303446, -2.7613451))), + "k" -> Tensor[Float]( + T(T(-4.8302217, 7.3123174, 1.6504537, 0.9700441, 2.3480177, + 16.103554, -2.2903576, -8.238806), + T(-0.32502297, 0.57444924, 0.14206292, 0.68621343, 0.9674345, + -3.0744767, -0.19433197, -0.7038229), + T(3.8866534, -2.5272706, 0.31954706, 1.4289378, 4.1573987, + -18.83666, -0.6341135, -2.324824), + T(3.2729623, -4.4220643, -0.4427252, -3.3440435, -4.4044204, + 4.3912477, 1.4300863, 4.571388), + T( -5.8958073, 6.9818015, 1.2565684, 0.95525885, 0.454724, + 12.518224, -1.0987915, -4.2782307), + T( 12.0007305, -10.336534, -0.4299186, -1.6812495, 3.2675288, + -18.79188, -0.5088088, -1.5055372), + T( -5.3735647, 0.367167, -2.2015429, 0.1204319, -6.43669, + 4.354989, 3.1718657, 11.734762), + T( 2.4331138, -4.1149745, -0.68130356, -2.264487, -3.9137423, + -1.6834034, 1.7719116, 5.8460846))), + "v" -> Tensor[Float]( + T(T( -2.5827155, 1.3582011, 2.75234, -5.447733, -19.511236, + -2.1552057, 1.0299364, 2.464095), + T( 9.669751, 9.907997, -2.63263, 16.046946, 17.104738, + -3.7717283, -7.977087, 16.455389), + T( 1.4447857, 4.726976, 6.355788, -0.3261361, 8.890461, + -2.7009814, -2.7735417, 4.597517), + T( 3.7391737, -2.652472, -10.343583, 3.4428883, 17.676163, + 1.3461249, -0.89123046, -5.0282454), + T( 6.218705, 8.004267, -2.7794306, 7.9928293, -2.384931, + -4.3906345, -6.2733536, 17.570385), + T( 1.4547603, -2.6338978, -0.1248658, -0.18013573, 26.240273, + 3.4490743, 2.5249462, -14.606245), + T(-13.57461, -9.627181, 5.8085995, -9.885314, -30.600082, + 1.5869138, 7.419276, -13.439232), + T( -2.4042547, -8.270382, -5.8752723, -7.9662743, 4.8289614, + 4.279446, 3.5127287, -10.48413))), + "output_transform" -> Tensor[Float]( + T(T( 2.8776762e+00, 9.0993910e+00, 4.4741273e+00, 7.2155852e+00, + 2.1099086e+00, -3.7240963e+00, 2.8145332e+00, 2.2895024e+00), + T(-1.4586559e-02, 2.0312614e+00, 2.9373944e+00, 2.0424197e+00, + 2.5295980e+00, -2.0257745e+00, 8.9276981e-01, 2.4247412e-01), + T(-4.7104540e+00, -1.0320190e+01, -7.1374278e+00, -6.6327205e+00, + -3.4700847e+00, 3.6055198e+00, -2.6972079e+00, 1.5516624e+00), + T(-3.3305209e+00, -1.6513400e+00, 3.6665101e+00, 6.0572940e-01, + 5.1936049e+00, -4.1640496e+00, 7.2286522e-01, 1.1406650e-01), + T( 5.9342289e+00, 1.7988518e+01, 1.9854633e+01, 1.3903372e+01, + 1.1869378e+01, -9.9151783e+00, 5.5996485e+00, -2.1639798e+00), + T( 1.5641862e+00, 1.1334980e+00, 2.3218460e+00, 6.1690319e-01, + -2.1347943e-01, 1.2895620e+00, -1.5324564e-01, -5.2227557e-01), + T(-5.4851645e-01, -3.2748022e+00, -1.7621651e+00, -2.8479974e+00, + -1.6886721e+00, 2.1033278e+00, -1.2398324e+00, -1.1378943e+00), + T( 2.3364418e+00, 7.2459126e+00, 1.4526119e+01, 4.9446974e+00, + 9.3395023e+00, -7.0863304e+00, 2.4963975e+00, -6.7891836e+00))) + ) + "attention layer" should "work correctly" in { + // compare with tensorflow 1.13.1 + val attention = new Attention[Float](8, 4, 1.0f) + + val paramsTable = attention.getParametersTable() + val w1 = weights.get[Tensor[Float]]("q").get + val w2 = weights.get[Tensor[Float]]("k").get + val w3 = weights.get[Tensor[Float]]("v").get + val w4 = weights.get[Tensor[Float]]("output_transform").get + for (i <- paramsTable.keySet) { + val params = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get + if (i == "q") { + params.copy(w1.t()) + } else if (i == "k") { + params.copy(w2.t()) + } else if (i == "v") { + params.copy(w3.t()) + } else if (i == "output_transform") { + params.copy(w4.t()) + } + } + + val output = attention.forward(T(inputX, inputY, inputBias)) + val gradInput = attention.backward(T(inputX, inputY, inputBias), output) + + output should be(outputExpected) + // gradInput should be(gradInputExpected) + + val gw1 = gradWeightsExpected.get[Tensor[Float]]("q").get + val gw2 = gradWeightsExpected.get[Tensor[Float]]("k").get + val gw3 = gradWeightsExpected.get[Tensor[Float]]("v").get + val gw4 = gradWeightsExpected.get[Tensor[Float]]("output_transform").get + for (i <- paramsTable.keySet) { + val params = paramsTable.get[Table](i).get.get[Tensor[Float]]("gradWeight").get + if (i == "q") params should be(gw1.t()) + if (i == "k") params should be(gw2.t()) + if (i == "v") params should be(gw3.t()) + if (i == "output_transform") params should be(gw4.t()) + } + } +} + +class AttentionSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val attention = new Attention[Float](8, 4, 1.0f).setName("attention") + val inputX = Tensor[Float](2, 3, 8).apply1(_ => Random.nextFloat()) + val inputY = inputX.clone() + val inputBias = Tensor[Float](2, 4, 3, 3).apply1(_ => Random.nextFloat()) + runSerializationTest(attention, T(inputX, inputY, inputBias)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CombineHeadsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CombineHeadsSpec.scala new file mode 100644 index 00000000000..12774f0c785 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CombineHeadsSpec.scala @@ -0,0 +1,125 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class CombineHeadsSpec extends FlatSpec with Matchers { + + val inputX = Tensor[Float]( + T(T(T(T( 0.06007948, 0.30860155), + T( 0.15008516, -0.17612492), + T(-0.5712591, -0.17467136)), + T(T(-0.10444712, 0.2933116), + T( 0.41949171, 0.46555104), + T( 0.14279366, 0.44257058)), + T(T(-0.37719897, 0.62643408), + T( 0.25646491, -0.14904642), + T( 0.24425907, -0.03778586)), + T(T( 0.56581469, 0.75990841), + T( 1.0927877, -0.69824817), + T(-0.7220569, -0.25223293))), + T(T(T( 0.08001853, 0.43808446), + T( 0.15781747, -1.01110061), + T(-0.15310201, 0.41398732)), + T(T( 0.11504737, 0.38100559), + T(-0.11116407, -0.10037903), + T( 0.0932807, 0.20502582)), + T(T( 0.09914986, 0.05950432), + T(-0.33533114, 0.18878189), + T( 0.06091064, 0.56474195)), + T(T( 0.59945894, 0.09257821), + T(-0.18764248, -0.3193652), + T( 0.21174718, 0.03867003)))) + ) + + val gradOutput = Tensor[Float]( + T(T(T( 0.81217268, -0.30587821, -0.26408588, -0.53648431, 0.43270381, + -1.15076935, 0.87240588, -0.38060345), + T( 0.15951955, -0.12468519, 0.73105397, -1.03007035, -0.1612086, + -0.19202718, 0.56688472, -0.54994563), + T(-0.0862141, -0.43892921, 0.02110687, 0.29140761, -0.55030959, + 0.57236185, 0.45079536, 0.25124717)), + T(T( 0.45042797, -0.34186393, -0.06144511, -0.46788472, -0.13394404, + 0.26517773, -0.34583038, -0.19837676), + T(-0.34358635, -0.42260282, -0.33562307, -0.0063323, -0.55865517, + 0.11720785, 0.82990109, 0.37102208), + T(-0.09591778, -0.44381448, -0.37357915, 0.8462273, 0.02540388, + -0.31849782, 0.09545774, 1.05012757)))) + + val gradInputExpected = Tensor[Float]( + T(T(T(T( 0.8121727, -0.3058782), + T( 0.15951955, -0.12468519), + T(-0.0862141, -0.4389292)), + T(T(-0.2640859, -0.5364843), + T( 0.73105395, -1.0300703), + T( 0.02110687, 0.29140761)), + T(T( 0.43270382, -1.1507694), + T(-0.1612086, -0.19202718), + T(-0.5503096, 0.5723618)), + T(T( 0.8724059, -0.38060346), + T( 0.5668847, -0.54994565), + T( 0.45079535, 0.25124717))), + T(T(T( 0.45042798, -0.34186393), + T(-0.34358636, -0.42260283), + T(-0.09591778, -0.4438145)), + T(T(-0.06144511, -0.46788472), + T(-0.33562306, -0.0063323), + T(-0.37357914, 0.8462273)), + T(T(-0.13394403, 0.26517773), + T(-0.5586552, 0.11720785), + T( 0.02540388, -0.31849784)), + T(T(-0.34583038, -0.19837676), + T( 0.8299011, 0.37102208), + T( 0.09545774, 1.0501276))))) + + val outputExpected = Tensor[Float]( + T(T(T( 0.06007948, 0.30860156, -0.10444712, 0.2933116, -0.37719896, + 0.6264341, 0.5658147, 0.75990844), + T( 0.15008517, -0.17612493, 0.4194917, 0.46555105, 0.2564649, + -0.14904642, 1.0927877, -0.69824815), + T(-0.5712591, -0.17467137, 0.14279366, 0.4425706, 0.24425907, + -0.03778586, -0.7220569, -0.25223294)), + T(T( 0.08001854, 0.43808445, 0.11504737, 0.3810056, 0.09914986, + 0.05950432, 0.59945893, 0.09257821), + T( 0.15781747, -1.0111006, -0.11116407, -0.10037903, -0.33533114, + 0.18878189, -0.18764247, -0.3193652), + T(-0.15310201, 0.4139873, 0.0932807, 0.20502582, 0.06091063, + 0.56474197, 0.21174718, 0.03867003)))) + + "Combine heads layer" should "work correctly" in { + val layer = new CombineHeads[Float]() + + val output = layer.forward(inputX) + val gradInput = layer.backward(inputX, gradOutput) + + output should be(outputExpected) + gradInput should be(gradInputExpected) + } +} + +class CombineHeadsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = new CombineHeads[Float]().setName("combine_heads") + val input = Tensor[Float](2, 4, 3, 2).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetworkSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetworkSpec.scala new file mode 100644 index 00000000000..759ef4aa65b --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetworkSpec.scala @@ -0,0 +1,158 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{T, Table} +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class FeedForwardNetworkSpec extends FlatSpec with Matchers { + val input : Tensor[Float] = Tensor(T(T( + T(1.62434536, -0.61175641, -0.52817175, -1.07296862, 0.86540763, -2.3015387, + 1.74481176, -0.7612069), + T(0.3190391, -0.24937038, 1.46210794, -2.06014071, -0.3224172, -0.38405435, + 1.13376944, -1.09989127), + T(-0.17242821, -0.87785842, 0.04221375, 0.58281521, -1.10061918, 1.14472371, + 0.90159072, 0.50249434)), + + T(T(0.90085595, -0.68372786, -0.12289023, -0.93576943, -0.26788808, 0.53035547, + -0.69166075, -0.39675353), + T(-0.6871727, -0.84520564, -0.67124613, -0.0126646, -1.11731035, 0.2344157, + 1.65980218, 0.74204416), + T(-0.19183555, -0.88762896, -0.74715829, 1.6924546, 0.05080775, -0.63699565, + 0.19091548, 2.10025514)) + )) + + val outputExpected : Tensor[Float] = Tensor[Float]( + T(T(T(-1.8375108, 1.2966242, -0.7180478, -0.23646069, -0.26609686, 1.5588356, + 0.660595, -0.31564748), + T(-0.19088337, 0.3883139, -0.07755771, 0.4170658, 0.26731488, -0.17202748, + -0.09847746, -0.48390502), + T(-0.83966494, 0.9178331, -1.5419102, -1.1216922, -0.71579254, -0.8785725, + 1.228107, -0.8411994)), + T(T(-0.05918283, 0.11268676, -0.14456667, -0.07148183, -0.08459917, -0.12416959, + 0.14727916, -0.04476965), + T(-0.8799405, 0.8714211, -0.9089507, -0.48824388, -0.44684958, -0.03341737, + 0.83358747, -0.3849794), + T(-1.6615174, 1.5758176, -1.3603796, -0.5994581, -0.6806528, 0.39317507, + 1.3247881, -0.49895704))) + ) + + val gradInputExpected : Tensor[Float] = Tensor[Float]( + T(T(T( 1.5185112, -1.227324, -0.5093635, 1.1931186, 0.3033546, -1.7162423, + 1.6165192, 0.7531797), + T(-0.34178314, 0.43916014, 0.2897479, -0.10748425, -0.23153473, -0.02656112, + 0.00322444, -0.1791711), + T( 0.224675, -2.402936, -0.20401537, 2.6879046, -0.9117198, 2.6889753, + 0.36316222, 1.2847843)), + T(T(-0.04712493, -0.18667392, -0.04214612, 0.22826877, -0.10985573, 0.33849096, + -0.04582711, 0.07289039), + T( 0.47275004, -1.6686311, -0.37326545, 1.8608568, -0.47097835, 1.1310074, + 0.54947317, 0.8439914), + T( 1.0096599, -2.7125025, -0.75489235, 3.013646, -0.6398741, 1.2858341, + 1.1247286, 1.3493999))) + ) + + val gradWeights: Table = T( + "filter_layer" -> Tensor[Float](T( + T( 3.0687852, -1.2373898, 0.26932785, -2.203119), + T(-6.725154, -4.244735, -0.21051459, -7.5355434), + T(-4.565733, -1.7183428, 1.2342887, -3.5032144), + T( 2.267335, 3.520794, -1.7391386, 6.6742306), + T(-0.43024874, -3.5453463, -0.27217957, -5.8415084), + T(-6.704142, 1.8187529, -0.3242127, 2.5449739), + T( 9.400441, 3.943234, 0.9571104, 6.649495), + T( 5.318757, 4.9089594, -0.9285111, 9.345928)) + ), + "output_layer" -> Tensor[Float]( + T(T(-9.917135, 8.18558, -6.3063927, -2.7818725, + -2.8578176, 5.0143423, 5.885993, -2.608803), + T(-2.1594772, 2.2862027, -3.000262, -1.8476318, + -1.3436246, -1.1266284, 2.4819474, -1.6156014), + T(-0.15865958, 0.32276106, -0.06446488, 0.34665924, + 0.22218838, -0.14298683, -0.08185308, -0.40221506), + T(-4.2312956, 4.2519608, -5.0156603, -2.9951196, + -2.4163678, -0.96183157, 4.4039025, -2.3232994)) + ) + ) + + val weights: Table = T( + "filter_layer" -> Tensor[Float]( + T(T( 0.5093561, -0.07532924, -0.40125486, -0.09511733), + T(-0.4116828, -0.20966673, 0.53027445, -0.41794384), + T(-0.17085642, 0.70417756, 0.3094539, -0.44296354), + T( 0.40020925, 0.07376623, -0.13086122, 0.59578115), + T( 0.10175461, -0.07514799, -0.27066603, -0.26833212), + T(-0.57568127, 0.6374385, -0.06203693, 0.6385146), + T( 0.542231, 0.06174886, 0.00085795, -0.15512145), + T( 0.25264, 0.4526841, -0.23395362, -0.00881493)) + ), + "output_layer" -> Tensor[Float]( + T(T(-0.63213325, 0.44605953, -0.24701998, -0.08134627, -0.09154159, 0.5362645, + 0.22725528, -0.1085878), + T(-0.198219, -0.06108004, -0.41906577, -0.5969921, -0.06956118, -0.16365921, + 0.07787138, -0.49795625), + T(-0.16827011, 0.4860949, 0.03646076, 0.6866401, 0.34314734, -0.1562866, + -0.14259237, -0.42798606), + T(-0.2204194, 0.5318032, -0.5550728, -0.17479968, -0.36973962, -0.52694166, + 0.6547342, -0.0777601))) + ) + + "FeedForwardNetwork layer" should "work correctly" in { + // compare with tensorflow 1.13.1 + val ffn = new FeedForwardNetwork[Float](8, 4, 1.0f) + + val paramsTable = ffn.getParametersTable() + val w1 = weights.get[Tensor[Float]]("filter_layer").get + val w2 = weights.get[Tensor[Float]]("output_layer").get + for (i <- paramsTable.keySet) { + val params = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get + if (params.size(1) == w1.size(2)) { + params.copy(w1.transpose(1, 2)) + } else if (params.size(1) == w2.size(2)) { + params.copy(w2.transpose(1, 2)) + } + } + + val output = ffn.forward(input) + val gradInput = ffn.backward(input, output) + + output should be(outputExpected) + gradInput should be(gradInputExpected) + + val gw1 = gradWeights.get[Tensor[Float]]("filter_layer").get + val gw2 = gradWeights.get[Tensor[Float]]("output_layer").get + for (i <- paramsTable.keySet) { + val params = paramsTable.get[Table](i).get.get[Tensor[Float]]("gradWeight").get + if (params.size(1) == gw1.size(2)) { + params should be(gw1.transpose(1, 2)) + } else if (params.size(1) == gw2.size(2)) { + params should be(gw2.transpose(1, 2)) + } + } + } +} + +class FeedForwardNetworkSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val ffn = new FeedForwardNetwork[Float](8, 4, 1.0f) + val input = Tensor[Float](2, 3, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(ffn, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SplitHeadsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SplitHeadsSpec.scala new file mode 100644 index 00000000000..d5c0cf6b19a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SplitHeadsSpec.scala @@ -0,0 +1,184 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class SplitHeadsSpec extends FlatSpec with Matchers { + val inputX : Tensor[Float] = Tensor( + T(T(T( 0.81217268, -0.30587821, -0.26408588, -0.53648431, 0.43270381, + -1.15076935, 0.87240588, -0.38060345), + T( 0.15951955, -0.12468519, 0.73105397, -1.03007035, -0.1612086, + -0.19202718, 0.56688472, -0.54994563), + T(-0.0862141, -0.43892921, 0.02110687, 0.29140761, -0.55030959, + 0.57236185, 0.45079536, 0.25124717)), + T(T( 0.45042797, -0.34186393, -0.06144511, -0.46788472, -0.13394404, + 0.26517773, -0.34583038, -0.19837676), + T(-0.34358635, -0.42260282, -0.33562307, -0.0063323, -0.55865517, + 0.11720785, 0.82990109, 0.37102208), + T(-0.09591778, -0.44381448, -0.37357915, 0.8462273, 0.02540388, + -0.31849782, 0.09545774, 1.05012757)))) + + val outputExpected : Tensor[Float] = Tensor[Float]( + T(T(T(T( 0.8121727, -0.3058782), + T( 0.15951955, -0.12468519), + T(-0.0862141, -0.4389292)), + T(T(-0.2640859, -0.5364843), + T( 0.73105395, -1.0300703), + T( 0.02110687, 0.29140761)), + T(T( 0.43270382, -1.1507694), + T(-0.1612086, -0.19202718), + T(-0.5503096, 0.5723618)), + T(T( 0.8724059, -0.38060346), + T( 0.5668847, -0.54994565), + T( 0.45079535, 0.25124717))), + T(T(T( 0.45042798, -0.34186393), + T(-0.34358636, -0.42260283), + T(-0.09591778, -0.4438145)), + T(T(-0.06144511, -0.46788472), + T(-0.33562306, -0.0063323), + T(-0.37357914, 0.8462273)), + T(T(-0.13394403, 0.26517773), + T(-0.5586552, 0.11720785), + T( 0.02540388, -0.31849784)), + T(T(-0.34583038, -0.19837676), + T( 0.8299011, 0.37102208), + T( 0.09545774, 1.0501276)))) + ) + + val gradInputExpected : Tensor[Float] = Tensor[Float]( + T(T(T( 0.06007948, 0.30860156, -0.10444712, 0.2933116, -0.37719896, + 0.6264341, 0.5658147, 0.75990844), + T( 0.15008517, -0.17612493, 0.4194917, 0.46555105, 0.2564649, + -0.14904642, 1.0927877, -0.69824815), + T(-0.5712591, -0.17467137, 0.14279366, 0.4425706, 0.24425907, + -0.03778586, -0.7220569, -0.25223294)), + + T(T( 0.08001854, 0.43808445, 0.11504737, 0.3810056, 0.09914986, + 0.05950432, 0.59945893, 0.09257821), + T(0.15781747, -1.0111006, -0.11116407, -0.10037903, -0.33533114, + 0.18878189, -0.18764247, -0.3193652), + T(-0.15310201, 0.4139873, 0.0932807, 0.20502582, 0.06091063, + 0.56474197, 0.21174718, 0.03867003))) + ) + + val gradOutput = Tensor[Float]( + T(T(T(T( 0.06007948, 0.30860155), + T( 0.15008516, -0.17612492), + T(-0.5712591, -0.17467136)), + + T(T(-0.10444712, 0.2933116), + T( 0.41949171, 0.46555104), + T( 0.14279366, 0.44257058)), + + T(T(-0.37719897, 0.62643408), + T( 0.25646491, -0.14904642), + T( 0.24425907, -0.03778586)), + + T(T( 0.56581469, 0.75990841), + T( 1.0927877, -0.69824817), + T(-0.7220569, -0.25223293))), + + T(T(T( 0.08001853, 0.43808446), + T( 0.15781747, -1.01110061), + T(-0.15310201, 0.41398732)), + + T(T( 0.11504737, 0.38100559), + T(-0.11116407, -0.10037903), + T( 0.0932807, 0.20502582)), + + T(T( 0.09914986, 0.05950432), + T(-0.33533114, 0.18878189), + T( 0.06091064, 0.56474195)), + + T(T( 0.59945894, 0.09257821), + T(-0.18764248, -0.3193652), + T( 0.21174718, 0.03867003)))) + ) + + "split heads layer" should "work correctly" in { + val layer = new SplitHeads[Float](8, 4) + + val output = layer.forward(inputX) + val gradInput = layer.backward(inputX, gradOutput) + + output should be(outputExpected) + gradInput should be(gradInputExpected) + } + + "split heads layer with mul" should "work correctly" in { + val layer = new SplitHeads[Float](8, 4, mul = true) + + val output = layer.forward(inputX) + val gradInput = layer.backward(inputX, gradOutput) + + val outputExpected = Tensor[Float]( + T(T(T(T( 0.57429284, -0.21628854), + T( 0.11279736, -0.08816575), + T(-0.06096258, -0.31036982)), + T(T(-0.18673693, -0.37935168), + T( 0.5169332, -0.7283697), + T( 0.01492481, 0.2060563)), + T(T( 0.3059678, -0.8137168), + T(-0.11399169, -0.13578372), + T(-0.38912764, 0.40472093)), + T(T( 0.6168841, -0.26912728), + T( 0.400848, -0.3888703), + T( 0.31876045, 0.17765857))), + T(T(T( 0.31850067, -0.2417343), + T(-0.24295224, -0.29882532), + T(-0.06782411, -0.31382424)), + T(T(-0.04344825, -0.33084446), + T(-0.23732133, -0.00447761), + T(-0.26416034, 0.59837306)), + T(T(-0.09471273, 0.18750897), + T(-0.3950289, 0.08287846), + T( 0.01796325, -0.22521198)), + T(T(-0.24453901, -0.14027356), + T( 0.5868287, 0.26235223), + T( 0.06749881, 0.74255234)))) + ) + val gradInputExpected = Tensor[Float]( + T(T(T( 0.04248261, 0.21821424, -0.07385527, 0.20740262, -0.26671994, + 0.4429558, 0.40009138, 0.5373364), + T( 0.10612623, -0.12453913, 0.29662544, 0.3291943, 0.18134807, + -0.10539173, 0.7727176, -0.493736), + T(-0.40394115, -0.12351131, 0.10097036, 0.31294465, 0.17271724, + -0.02671864, -0.51057136, -0.17835562)), + T(T( 0.05658165, 0.3097725, 0.08135077, 0.26941162, 0.07010954, + 0.04207591, 0.42388147, 0.06546268), + T( 0.1115938, -0.7149561, -0.07860487, -0.07097869, -0.23711492, + 0.13348895, -0.13268326, -0.2258253), + T(-0.10825947, 0.29273322, 0.06595941, 0.14497514, 0.04307032, + 0.39933288, 0.14972787, 0.02734384))) + ) + output should be(outputExpected) + gradInput should be(gradInputExpected) + } +} + +class SplitHeadsSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = new SplitHeads[Float](8, 4, mul = true).setName("splitheads") + val input = Tensor[Float](2, 3, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(layer, input) + } +} From 5d7704fff9c079858c17d26adf9a6541396b4e62 Mon Sep 17 00:00:00 2001 From: Menooker Date: Sun, 5 May 2019 19:20:10 +0800 Subject: [PATCH 0897/1065] [New feature & fix] Add layer-wise adaptive rate scaling optimizer (#2802) * [New feature & fix] Add layer-wise adaptive rate scaling optimizer: Add LARS optimizer: Layer-wise scaled. Also with utility functions to build a set of LARS optim for a container. Bug fix: The gradient block id of AllReduceParameter is originally composed of {id}{pidTo}gradientBytes{pidFrom}. But the combination of {id}{pidTo} will cause ambiguity. e.g., "112" can be {1}{12} or {11}{2}. Now a "_" is added to separate id from pidTo * refine documents, correctly set the lrSchedulerOwner bit * format the added code * make Lars inherit SGD * rename Lars -> LarsSGD and reformat * style changes --- .../analytics/bigdl/dllib/optim/LarsSGD.scala | 245 ++++++++++++++++++ .../optim/parameters/AllReduceParameter.scala | 2 +- .../bigdl/dllib/optim/LarsSGDSpec.scala | 121 +++++++++ 3 files changed, 367 insertions(+), 1 deletion(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGD.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGDSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGD.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGD.scala new file mode 100644 index 00000000000..00fe974dd3c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGD.scala @@ -0,0 +1,245 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Container +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.optim.SGD.{Default, LearningRateSchedule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table +import org.apache.log4j.{Level, Logger} + +import scala.reflect.ClassTag + + +/** + * An implementation of LARS https://arxiv.org/abs/1708.03888 + * Lars.createOptimForModule is recommended to be used to create LARS optim methods for multiple + * layers + * + * @param lrScheduleOwner if this optim method owns the learning rate scheduler. + * A scheduler may be shared by multiple LARS scheduler + * @param trust the trust on the learning rate scale, should be in 0 to 1 + * @param _learningRate learning rate + * @param _learningRateDecay learning rate decay + * @param _weightDecay weight decay + * @param _momentum momentum + * @param _learningRateSchedule the learning rate scheduler + * @tparam T + */ +class LarsSGD[T: ClassTag]( + lrScheduleOwner: Boolean, + trust: Double = 1.0, + _learningRate: Double = 1e-3, + _learningRateDecay: Double = 0.01, + _weightDecay: Double = 0.0005, + _momentum: Double = 0.5, + _learningRateSchedule: LearningRateSchedule + = Default() + )(implicit ev: TensorNumeric[T]) + extends SGD[T](_learningRate, _learningRateDecay, _weightDecay, _momentum, + learningRateSchedule = _learningRateSchedule) { + @transient + private var buffer: Tensor[T] = null + /** + * @param feval a function that takes a single input (X), the point of a evaluation, and + * returns f(X) and df/dX + * @param parameter the initial point + * @return the new x vector and the function list {fx}, evaluated before the update + */ + override def optimize(feval: Tensor[T] => (T, Tensor[T]), + parameter: Tensor[T]): (Tensor[T], Array[T]) = { + val weightDecay = this.weightDecay + val momentum = this.momentum + val (fx, dfdx) = feval(parameter) + if (buffer == null) buffer = Tensor[T]().resizeAs(dfdx) + val _v = + if (state.get[Tensor[T]]("v").isDefined) { + state.get[Tensor[T]]("v").get + } else { + Tensor[T]().resizeAs(dfdx).zero() + } + learningRateSchedule.updateHyperParameter(this) + val globalLr = -learningRateSchedule.currentRate * trust + val normGradient = ev.sqrt(dfdx.sumSquare()) + val normParam = ev.sqrt(parameter.sumSquare()) + // scale = (normGradient + weightDecay * normParam) / normParam + val scale = Tensor.scalar[T](normParam) + scale.mul(ev.fromType[Double](weightDecay)).add(normGradient).div(normParam) + val raw_scale_value = scale.value() + val scale_value = if (ev.isInf(raw_scale_value)) { + ev.fromType[Double](10000.0) + } else if (ev.nearlyEqual(raw_scale_value, ev.fromType[Double](0.0), 0.0001)) { + ev.fromType[Double](1e-4) + } else if (ev.isNan(raw_scale_value)) { + ev.fromType[Double](1.0) + } else { + raw_scale_value + } + // rate = globalLr / scale + val rate = ev.divide(ev.fromType[Double](globalLr), scale_value) + // _v = momentum * _v + rate * (dfdx + weightDecay * parameter) + _v.mul(ev.fromType[Double](momentum)) + buffer.mul(parameter, ev.fromType[Double](weightDecay)).add(dfdx).mul(rate) + _v.add(buffer) + parameter.sub(_v) + state("v") = _v + (parameter, Array(fx)) + } + + /** + * return an string of current hyperParameter. + */ + override def getHyperParameter(): String = { + if (lrScheduleOwner) { + val clr = -this.learningRateSchedule.currentRate + s"Current learning rate is $clr. " + } + else { + "" + } + } + + /** + * return an string of current hyperParameter. + */ + override def getHyperParameter(config: Table): String = { + if (lrScheduleOwner) { + val clr = -config[Double]("clr") + s"Current learning rate is $clr. " + } + else { + "" + } + } + + override def updateHyperParameter(config: Table, state: Table): Unit = { + val lrSchedule = config.get[LearningRateSchedule]("learningRateSchedule").getOrElse(Default()) + lrSchedule.updateHyperParameter(config, state) + } + + override def getLearningRate(): Double = this.learningRateSchedule.currentRate +} + +object LarsSGD { + /** + * Create a Map(String, OptimMethod) for a container. For each submodule in the container, + * generate (module.getName(), new Lars[T]) pair in the returned map. The resulting map can be + * used in setOptimMethods. + * Note: each Lars optim uses the same LearningRateSchedule + * + * @param model the container to build LARS optim method for + * @param trust the trust on the learning rate scale, should be in 0 to 1 + * @param learningRate learning rate + * @param learningRateDecay learning rate decay + * @param weightDecay weight decay + * @param momentum momentum + * @param learningRateSchedule the learning rate scheduler + * + */ + def createOptimForModule[T: ClassTag](model: Module[T], + trust: Double = 1.0, + learningRate: Double = 1e-3, + learningRateDecay: Double = 0.01, + weightDecay: Double = 0.005, + momentum: Double = 0.5, + learningRateSchedule: LearningRateSchedule = Default()) + (implicit ev: TensorNumeric[T]): Map[String, + OptimMethod[T]] = { + var isOwner = true + // lrScheGenerator generates the same learningRateSchedule for each module + // But it only returns isOwner = true for the first module + val lrScheGenerator = (_: AbstractModule[Activity, Activity, T]) => { + val _isOwner = isOwner + isOwner = false + (learningRateSchedule, _isOwner) + } + createOptimSeqForModule(model, lrScheGenerator, + trust, learningRate, learningRateDecay, weightDecay, momentum).toMap + } + + + /** + * Create a Map(String, OptimMethod) for a container. For each submodule in the container, + * generate (module.getName(), new Lars[T]) pair in the returned map. The resulting map can be + * used in setOptimMethods. + * This function sets different LearningRateSchedules for different submodules + * + * @param model the container to build LARS optim method for + * @param lrScheGenerator the learning rate schedule generator for each sub-module. + * Generator accepts the sub-module that the schedule is linked to. + * It should return a tuple (learningRateSchedule, isOwner), where + * isOwner indicates whether the corresponding LARS optim method is + * responsible for showing the learning rate in getHyperParameter + * (multiple LARS optim methods may share one learning rate scheduler) + * @param trust the trust on the learning rate scale, should be in 0 to 1 + * @param learningRate learning rate + * @param learningRateDecay learning rate decay + * @param weightDecay weight decay + * @param momentum momentum + * + */ + def createOptimLRSchedulerForModule[A <: Activity, B <: Activity, T: ClassTag] + (model: Container[A, B, T], + lrScheGenerator: AbstractModule[Activity, Activity, T] => (LearningRateSchedule, Boolean), + trust: Double = 1.0, + learningRate: Double = 1e-3, + learningRateDecay: Double = 0.01, + weightDecay: Double = 0.005, + momentum: Double = 0.5) + (implicit ev: TensorNumeric[T]): Map[String, OptimMethod[T]] = { + createOptimSeqForModule(model, lrScheGenerator, trust, learningRate, learningRateDecay, + weightDecay, momentum).toMap + } + + /** + * Create a Seq of (name,Lars) pair for the model + * + * @see createOptimLRSchedulerForModule + */ + private def createOptimSeqForModule[T: ClassTag](model: Module[T], + lrScheGenerator: AbstractModule[Activity, + Activity, T] => (LearningRateSchedule, + Boolean), + trust: Double, + learningRate: Double, + learningRateDecay: Double, + weightDecay: Double, + momentum: Double) + (implicit ev: TensorNumeric[T]): Seq[(String, + OptimMethod[T])] = { + model match { + case container: Container[_, _, T] => + container.modules.filter(mod => mod.parameters() != null).flatMap(mod => { + // generate Seq for each sub-module + createOptimSeqForModule(mod, lrScheGenerator, trust, learningRate, learningRateDecay, + weightDecay, momentum) + }) + case _ => + if (model.parameters() != null) { + val (lrSche, isOwner) = lrScheGenerator(model) + Seq((model.getName(), new LarsSGD[T](isOwner, trust, learningRate, learningRateDecay, + weightDecay, momentum, lrSche))) + } + else { + Seq() + } + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala index 8fa9e68b28c..52f028618db 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala @@ -179,7 +179,7 @@ class AllReduceParameter[T: ClassTag]( } private def getGradientBlockId(pidFrom: Int, pidTo: Int): BlockId = { - SparkExtension.getLocalBlockId(id.toString + pidTo + "gradientBytes" + pidFrom) + SparkExtension.getLocalBlockId(id.toString + "_" + pidTo + "gradientBytes" + pidFrom) } /** diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGDSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGDSpec.scala new file mode 100644 index 00000000000..333a100d9c7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGDSpec.scala @@ -0,0 +1,121 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + + +import com.intel.analytics.bigdl.DataSet +import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch} +import com.intel.analytics.bigdl.nn.{Linear, MSECriterion, ReLU, Sequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{Engine, T, TestUtils} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} +import com.intel.analytics.bigdl.optim._ + +import scala.collection.mutable.ArrayBuffer + +@com.intel.analytics.bigdl.tags.Parallel +class LarsSGDSpec extends FlatSpec with Matchers with BeforeAndAfter { + + before { + System.setProperty("bigdl.localMode", "true") + System.setProperty("spark.master", "local[2]") + Engine.init + } + + after { + System.clearProperty("bigdl.localMode") + System.clearProperty("spark.master") + } + + + val start = System.currentTimeMillis() + "lars" should "perform well on rosenbrock function" in { + val x = Tensor[Double](2).fill(0) + val optm = new LarsSGD[Double](true, _learningRate = 0.1, _learningRateDecay = 0.09) + var fx = new ArrayBuffer[Double] + for (i <- 1 to 10001) { + val result = optm.optimize(TestUtils.rosenBrock, x) + if ((i - 1) % 1000 == 0) { + fx += result._2(0) + } + } + + println(s"x is \n$x") + println("fx is") + for (i <- 1 to fx.length) { + println(s"${(i - 1) * 1000 + 1}, ${fx(i - 1)}") + } + + val spend = System.currentTimeMillis() - start + println("Time Cost: " + spend + "ms") + + (fx.last < 1e-5) should be(true) + x(Array(1)) should be(1.0 +- 0.01) + x(Array(2)) should be(1.0 +- 0.01) + } + + "lars" should "perform well on multi-layer network" in { + def thefunction(x1: Float, x2: Float): Float = { + x1 * x2 + Math.sin(x1).toFloat + Math.sin(x2).toFloat + Math.pow(x1, x2).toFloat + } + + val numSamples = 200000 / 64 + + def generateData(): DataSet[MiniBatch[Float]] = { + val features = Array.tabulate(numSamples) { i => { + val v = Tensor[Float](Array(32, 2)).rand() + val wrapped_tag = Tensor[Float](Array(32, 1)) + (1 to 32).map(i => + wrapped_tag.setValue(i, 1, thefunction(v.valueAt(i, 1), v.valueAt(i, 2))) + ) + MiniBatch(v, wrapped_tag) + } + } + DataSet.array(features) + } + + val module = Sequential[Float]() + module.add(Linear(2, 25)) + module.add(ReLU()) + module.add(Linear(25, 25)) + module.add(ReLU()) + module.add(Linear(25, 25)) + module.add(ReLU()) + module.add(Linear(25, 25)) + module.add(ReLU()) + module.add(Linear(25, 1)) + val optimizer = Optimizer(module, generateData(), MSECriterion[Float]()) + val epochs = 6 + optimizer + .setOptimMethods(LarsSGD.createOptimForModule(module, learningRate = 0.02, learningRateDecay + = 0.1)) + .setEndWhen(Trigger.maxEpoch(epochs)) + .optimize() + (1 to 10).foreach(i => { + val tensor = Tensor[Float](2).rand() + val v1 = tensor.valueAt(1) + val v2 = tensor.valueAt(2) + val realv = thefunction(v1, v2) + val difference = realv - module.forward(tensor).toTensor[Float].value() + val deviation = difference / realv + println(deviation) + deviation should be(0.0f +- 0.1f) + }) + } + +} + From fc6fe9bb4d1dd07b6e8024f756b2a92f4e4468e7 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Fri, 10 May 2019 10:28:03 +0800 Subject: [PATCH 0898/1065] bugfix - set mask for container (#2807) * bugfix - set mask for container * bugfix #2805: set dimension mask * Update Graph.scala * Update Graph.scala * change set mask indicator's name * rename set mask params --- .../mkldnn/int8/GenerateInt8Scales.scala | 6 +- .../bigdl/dllib/nn/MklInt8Convertible.scala | 56 ++++--- .../utils/intermediate/ReflectionUtils.scala | 6 +- .../utils/serializer/ModuleSerializable.scala | 6 +- .../dllib/nn/MklInt8ConvertibleSpec.scala | 143 ++++++++++++++++++ .../bigdl/dllib/nn/ScaleCalculatorSpec.scala | 68 ++++----- .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 6 +- .../nn/mkldnn/SpatialConvolutionSpec.scala | 2 +- .../bigdl/dllib/nn/mkldnn/TopologySpec.scala | 6 +- .../utils/intermediate/IRconvertSpec.scala | 10 +- 10 files changed, 230 insertions(+), 79 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8ConvertibleSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala index fe9fd6f376d..1b2532b02f6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/mkldnn/int8/GenerateInt8Scales.scala @@ -41,9 +41,9 @@ object GenerateInt8Scales { evaluationSet: RDD[MiniBatch[Float]]): Unit = { model.evaluate() - model.setInputDimMask(0) - model.setOutputDimMask(0) - model.setWeightDimMask(1) + model.setInputDimMask(0, true) + model.setOutputDimMask(0, true) + model.setWeightDimMask(1, true) logger.info(s"Generate the scales for $modelName ...") val samples = evaluationSet diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala index 75bc1c72fee..cbc096a5bf1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala @@ -40,7 +40,6 @@ trait MklInt8Convertible { // weight scales private[nn] var weightScalesBuffer: ArrayBuffer[Array[Float]] = ArrayBuffer.empty[Array[Float]] - /** * Calculate the required scales for converting int8 modules * Currently there are four type of modules should be supported: @@ -49,9 +48,9 @@ trait MklInt8Convertible { * 3) Spatial Convolution: calculate scales for input, output and weight * 4) Sequential: calculate scales for input, output as well as the scales of submodules * 5) ConcatTable: calculate scales for input, output as well as the scales of submodules - * @param inActivity input activity + * @param inputActvt input activity */ - private[bigdl]def calcScales(inputActvt: Activity): Unit = { + def calcScales(inputActvt: Activity): Unit = { if (inputActvt != null) { val module = this.asInstanceOf[AbstractModule[_, _, Float]] @@ -106,8 +105,8 @@ trait MklInt8Convertible { /** * Calculate module's scales given its input and output * Store calculated scales in array buffers - * @param inActivity input activity - * @param outActivity output activity + * @param inputActvt input activity + * @param outputActvt output activity */ private def calcModuleScales(inputActvt: Activity, outputActvt: Activity): Unit = { if (inputActvt != null) { @@ -136,10 +135,9 @@ trait MklInt8Convertible { } /** - * Calculate scales given activity, mask and update method + * Calculate scales given activity and mask * @param activity target activity to get scales * @param mask dimension mask associated with target activity - * @param appendFunc update method for scales */ private def calcActivityScales(activity: Activity, mask: Int): Array[Array[Float]] = { activity match { @@ -190,7 +188,8 @@ trait MklInt8Convertible { /** * Scales calculator for Sequential Module - * @param inActivity input of the Sequential Module + * @param inputActvt input of the Sequential Module + * @param outputActvt output of the Sequential Module */ private def calcSequentialScales(inputActvt: Activity, outputActvt: Activity): Unit = { require(this.isInstanceOf[Sequential[Float@unchecked]] || this.isInstanceOf[mkldnn.Sequential], @@ -221,7 +220,8 @@ trait MklInt8Convertible { /** * Scales calculator for ConcatTable module * Submodules inside ConcatTable share the same input - * @param inActivity + * @param inputActvt input of the ConcatTable Module + * @param outputActvt output of the ConcatTable Module */ private def calcConcatTableScales(inputActvt: Activity, outputActvt: Activity): Unit = { require(this.isInstanceOf[ConcatTable[Float@unchecked]] || this.isInstanceOf[mkldnn.ConcatTable] @@ -309,16 +309,19 @@ trait MklInt8Convertible { /** * Set dimension mask of input * @param mask value of input dimension mask to be set + * @param overrideSubmodules when set it to true, + * update mask including itself and submodules, + * otherwise only update mask to module itself. * @return Unit */ - def setInputDimMask(mask: Int) : Unit = { + def setInputDimMask(mask: Int, overrideSubmodules: Boolean = false) : Unit = { inputDimMask = mask - if (this.isInstanceOf[Container[_, _, Float@unchecked]]) { + if (this.isInstanceOf[Container[_, _, Float@unchecked]] && overrideSubmodules == true) { val container = this.asInstanceOf[Container[_, _, Float@unchecked]] val modules = container.modules modules.foreach(module => { if (module.isInstanceOf[MklInt8Convertible]) { - module.asInstanceOf[MklInt8Convertible].setInputDimMask(mask) + module.asInstanceOf[MklInt8Convertible].setInputDimMask(mask, overrideSubmodules) } }) } @@ -328,23 +331,26 @@ trait MklInt8Convertible { * Get dimension mask of output * @return outputDimMask field which stores value of output dimension mask */ - def getOutputDimMask(): Int = { + private[bigdl] def getOutputDimMask(): Int = { outputDimMask } /** * Set dimension mask of output * @param mask value of output dimension mask to be set + * @param overrideSubmodules when set it to true, + * update mask in full scope including itself and submodules, + * otherwise only update mask to module itself. * @return Unit */ - def setOutputDimMask(mask: Int): Unit = { + def setOutputDimMask(mask: Int, overrideSubmodules: Boolean = false): Unit = { outputDimMask = mask - if (this.isInstanceOf[Container[_, _, Float@unchecked]]) { + if (this.isInstanceOf[Container[_, _, Float@unchecked]] && overrideSubmodules == true) { val container = this.asInstanceOf[Container[_, _, Float@unchecked]] val modules = container.modules modules.foreach(module => { if (module.isInstanceOf[MklInt8Convertible]) { - module.asInstanceOf[MklInt8Convertible].setOutputDimMask(mask) + module.asInstanceOf[MklInt8Convertible].setOutputDimMask(mask, overrideSubmodules) } }) } @@ -359,18 +365,21 @@ trait MklInt8Convertible { } /** - * Set dimension mask of weight + * Set dimension mask for weight * @param mask value of weight mask to be set + * @param overrideSubmodules when set it to true, + * update mask in full scope including itself and submodules, + * otherwise only update mask to module itself. * @return Unit */ - def setWeightDimMask(mask: Int): Unit = { + def setWeightDimMask(mask: Int, overrideSubmodules: Boolean = false): Unit = { weightDimMask = mask - if (this.isInstanceOf[Container[_, _, Float@unchecked]]) { + if (this.isInstanceOf[Container[_, _, Float@unchecked]] && overrideSubmodules == true) { val container = this.asInstanceOf[Container[_, _, Float@unchecked]] val modules = container.modules modules.foreach(module => { if (module.isInstanceOf[MklInt8Convertible]) { - module.asInstanceOf[MklInt8Convertible].setWeightDimMask(mask) + module.asInstanceOf[MklInt8Convertible].setWeightDimMask(mask, overrideSubmodules) } }) } @@ -466,7 +475,7 @@ trait MklInt8Convertible { * @param index the index of which the scale need to be updated * @return Unit */ - def updateInputScales(scale: Array[Float], index: Int): Unit = { + private def updateInputScales(scale: Array[Float], index: Int): Unit = { updateScalesHelper(inputScalesBuffer, scale, index) } @@ -476,7 +485,7 @@ trait MklInt8Convertible { * @param index the index of which the scale need to be updated * @return Unit */ - def updateOutputScales(scale: Array[Float], index: Int): Unit = { + private def updateOutputScales(scale: Array[Float], index: Int): Unit = { updateScalesHelper(outputScalesBuffer, scale, index) } @@ -486,7 +495,7 @@ trait MklInt8Convertible { * @param index the index of which the scale need to be updated * @return Unit */ - def updateWeightScales(scale: Array[Float], index: Int): Unit = { + private def updateWeightScales(scale: Array[Float], index: Int): Unit = { updateScalesHelper(weightScalesBuffer, scale, index) } @@ -508,5 +517,4 @@ trait MklInt8Convertible { scales(index)(i) = scale(i) }) } - } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala index d83e2abe936..b0c789f02e4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala @@ -122,9 +122,9 @@ private[bigdl] object ReflectionUtils { toELe.setOutputScales(fromEle.getOutputScales()) toELe.setWeightScales(fromEle.getWeightScales()) - toELe.setInputDimMask(fromEle.getInputDimMask()) - toELe.setOutputDimMask(fromEle.getOutputDimMask()) - toELe.setWeightDimMask(fromEle.getWeightDimMask()) + toELe.setInputDimMask(fromEle.getInputDimMask(), true) + toELe.setOutputDimMask(fromEle.getOutputDimMask(), true) + toELe.setWeightDimMask(fromEle.getWeightDimMask(), true) } def findClass(name: String): Class[_] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 47bd3dbb3bc..c54f65f29bf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -363,7 +363,7 @@ trait ModuleSerializable extends Loadable with Savable{ /** * Convert Attr Value object to Array of Float - * @param AttrValue + * @param attr * @return Array[Float] */ protected def attrValueToFloatArray(attr: AttrValue): Array[Float] = { @@ -423,8 +423,8 @@ trait ModuleSerializable extends Loadable with Savable{ /** * Serialize and save MKL DNN INT8 attributes into BigDL Model of protobuf definition + * @param module * @param modelBuilder serialized module builder - * @param context serialization context */ protected def saveMklInt8Attr[T: ClassTag](module : MklInt8Convertible, modelBuilder : BigDLModule.Builder) @@ -454,7 +454,7 @@ trait ModuleSerializable extends Loadable with Savable{ /** * Convert an array of float into an attr value object - * @param Array[Float] + * @param arry * @return AttrValue */ private def floatArrayToAttrValue(arry : Array[Float]) : AttrValue = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8ConvertibleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8ConvertibleSpec.scala new file mode 100644 index 00000000000..9740c38a8ec --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8ConvertibleSpec.scala @@ -0,0 +1,143 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import java.util.UUID +import com.intel.analytics.bigdl.numeric.NumericFloat +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + + +class MklInt8ConvertibleSpec extends FlatSpec with Matchers with BeforeAndAfter { + + + "Unit test setInputDimMask" should "work properly" in { + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1") + val conv2 = SpatialConvolution(1, 6, 5, 5).setName("conv2") + val seq = Sequential().add(conv1).add(conv2) + + // Input mask default to 0 + seq.getInputDimMask() should be (0) + conv1.getInputDimMask() should be (0) + conv2.getInputDimMask() should be (0) + + // Sequential sets input dimension mask to 1 with recursive flag off + // the submodules conv1 & conv2 should not be effected + seq.setInputDimMask(1, false) + seq.getInputDimMask() should be (1) + conv1.getInputDimMask() should be (0) + conv2.getInputDimMask() should be (0) + + // Sequential sets input dimension mask to 1 with recursive flag on + // the submodules conv1 & conv2 should be effected + seq.setInputDimMask(2, true) + seq.getInputDimMask() should be (2) + conv1.getInputDimMask() should be (2) + conv2.getInputDimMask() should be (2) + + // change conv1's input dimension mask + conv1.setInputDimMask(4, false) + seq.getInputDimMask() should be (2) + conv1.getInputDimMask() should be (4) + conv2.getInputDimMask() should be (2) + + // change conv2's input dimension mask + conv2.setInputDimMask(8, false) + seq.getInputDimMask() should be (2) + conv1.getInputDimMask() should be (4) + conv2.getInputDimMask() should be (8) + + } + + + "Unit test setOutputDimMask" should "work properly" in { + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1") + val conv2 = SpatialConvolution(1, 6, 5, 5).setName("conv2") + val seq = Sequential().add(conv1).add(conv2) + + // Input mask default to 0 + seq.getOutputDimMask() should be (0) + conv1.getOutputDimMask() should be (0) + conv2.getOutputDimMask() should be (0) + + // Sequential sets input dimension mask to 1 with recursive flag off + // the submodules conv1 & conv2 should not be effected + seq.setOutputDimMask(1, false) + seq.getOutputDimMask() should be (1) + conv1.getOutputDimMask() should be (0) + conv2.getOutputDimMask() should be (0) + + // Sequential sets input dimension mask to 1 with recursive flag on + // the submodules conv1 & conv2 should be effected + seq.setOutputDimMask(2, true) + seq.getOutputDimMask() should be (2) + conv1.getOutputDimMask() should be (2) + conv2.getOutputDimMask() should be (2) + + // change conv1's input dimension mask + conv1.setOutputDimMask(4, false) + seq.getOutputDimMask() should be (2) + conv1.getOutputDimMask() should be (4) + conv2.getOutputDimMask() should be (2) + + // change conv2's input dimension mask + conv2.setOutputDimMask(8, false) + seq.getOutputDimMask() should be (2) + conv1.getOutputDimMask() should be (4) + conv2.getOutputDimMask() should be (8) + + } + + "Unit test setWeightDimMask" should "work properly" in { + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1") + val conv2 = SpatialConvolution(1, 6, 5, 5).setName("conv2") + val seq = Sequential().add(conv1).add(conv2) + + // Input mask default to 0 + seq.getWeightDimMask() should be (0) + conv1.getWeightDimMask() should be (0) + conv2.getWeightDimMask() should be (0) + + // Sequential sets input dimension mask to 1 with recursive flag off + // the submodules conv1 & conv2 should not be effected + seq.setWeightDimMask(1, false) + seq.getWeightDimMask() should be (1) + conv1.getWeightDimMask() should be (0) + conv2.getWeightDimMask() should be (0) + + // Sequential sets input dimension mask to 1 with recursive flag on + // the submodules conv1 & conv2 should be effected + seq.setWeightDimMask(2, true) + seq.getWeightDimMask() should be (2) + conv1.getWeightDimMask() should be (2) + conv2.getWeightDimMask() should be (2) + + // change conv1's input dimension mask + conv1.setWeightDimMask(4, false) + seq.getWeightDimMask() should be (2) + conv1.getWeightDimMask() should be (4) + conv2.getWeightDimMask() should be (2) + + // change conv2's input dimension mask + conv2.setWeightDimMask(8, false) + seq.getWeightDimMask() should be (2) + conv1.getWeightDimMask() should be (4) + conv2.getWeightDimMask() should be (8) + + } + +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala index 62be507daec..ed34892f910 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala @@ -65,8 +65,8 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val linear2 = Linear[Float](inputSize, outputSize) inputMask = Math.pow(2, 0).toInt outputMask = Math.pow(2, 0).toInt - linear2.setInputDimMask(inputMask) - linear2.setOutputDimMask(outputMask) + linear2.setInputDimMask(inputMask, true) + linear2.setOutputDimMask(outputMask, true) linear2.forward(inputTensor) linear2.calcScales(inputTensor) @@ -125,8 +125,8 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { inputMask = Math.pow(2, 0).toInt outputMask = Math.pow(2, 0).toInt - linear2.setInputDimMask(inputMask) - linear2.setOutputDimMask(outputMask) + linear2.setInputDimMask(inputMask, true) + linear2.setOutputDimMask(outputMask, true) seq2.forward(inputTensor) seq2.calcScales(inputTensor) @@ -176,7 +176,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Single input dimension mask, non-null input dimMaskIdx = 1 val spatialConv2 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) - spatialConv2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) spatialConv2.forward(inputTensor) spatialConv2.calcScales(inputTensor) val inputScales2 = Array(Array(inputTensor.select(dimMaskIdx, 1).max())) @@ -184,7 +184,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { dimMaskIdx = 2 val spatialConv3 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) - spatialConv3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) spatialConv3.forward(inputTensor) spatialConv3.calcScales(inputTensor) val inputScales3 = Array((1 to inputTensor.size(dimMaskIdx)).map( @@ -194,7 +194,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { dimMaskIdx = 3 val spatialConv4 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) - spatialConv4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + spatialConv4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) spatialConv4.forward(inputTensor) spatialConv4.calcScales(inputTensor) val inputScales4 = Array((1 to inputTensor.size(dimMaskIdx)).map( @@ -252,7 +252,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { seq2.compile(InferencePhase) seq2.forward(input) - seq2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + seq2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) seq2.calcScales(input) spatialConv2.getInputScales().length should be (1) @@ -269,7 +269,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { seq3.compile(InferencePhase) seq3.forward(input) - seq3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + seq3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) seq3.calcScales(input) val inputScales3 = Array((1 to input.size(dimMaskIdx)).map( @@ -288,7 +288,7 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { seq4.compile(InferencePhase) seq4.forward(input) - seq4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt) + seq4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) seq4.calcScales(input) val inputScales4 = Array((1 to input.size(dimMaskIdx)).map( idx => input.select(dimMaskIdx, idx).abs().max() @@ -361,9 +361,9 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { x.asInstanceOf[MklInt8Convertible].getWeightDimMask() should be(0) }) - sequential2.setInputDimMask(2) - sequential2.setOutputDimMask(2) - sequential2.setWeightDimMask(2) + sequential2.setInputDimMask(2, true) + sequential2.setOutputDimMask(2, true) + sequential2.setWeightDimMask(2, true) sequential2.getInputDimMask() should be (2) sequential2.getOutputDimMask() should be (2) @@ -390,9 +390,9 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // Global mask, null input val concatTable0 = makeConcatTable() - concatTable0.setInputDimMask(0) - concatTable0.setOutputDimMask(0) - concatTable0.setWeightDimMask(0) + concatTable0.setInputDimMask(0, true) + concatTable0.setOutputDimMask(0, true) + concatTable0.setWeightDimMask(0, true) concatTable0.calcScales(null) concatTable0.getInputScales().isEmpty should be (true) @@ -429,9 +429,9 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { Tensor[Float](Array(3.0f, 1.0f), Array(2))) val caddTable0 = CAddTable[Float]() - caddTable0.setInputDimMask(0) - caddTable0.setOutputDimMask(0) - caddTable0.setWeightDimMask(0) + caddTable0.setInputDimMask(0, true) + caddTable0.setOutputDimMask(0, true) + caddTable0.setWeightDimMask(0, true) caddTable0.calcScales(null) @@ -466,9 +466,9 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val inputTensor = make1DTensor(numElem, sampleMax) val relu0 = ReLU[Float]() - relu0.setInputDimMask(0) - relu0.setOutputDimMask(0) - relu0.setWeightDimMask(0) + relu0.setInputDimMask(0, true) + relu0.setOutputDimMask(0, true) + relu0.setWeightDimMask(0, true) relu0.calcScales(null) @@ -496,9 +496,9 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { val inputTensor = Tensor[Float](4, 2, 4, 4).rand(-100, 100) val bn0 = SpatialBatchNormalization[Float](2) - bn0.setInputDimMask(0) - bn0.setOutputDimMask(0) - bn0.setWeightDimMask(0) + bn0.setInputDimMask(0, true) + bn0.setOutputDimMask(0, true) + bn0.setWeightDimMask(0, true) bn0.calcScales(null) @@ -543,8 +543,8 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // global mask, null input val graph0 = makeTestingGraph() - graph0.setInputDimMask(0) - graph0.setOutputDimMask(0) + graph0.setInputDimMask(0, true) + graph0.setOutputDimMask(0, true) graph0.calcScales(null) graph0.getInputDimMask() should be (0) graph0.getOutputDimMask() should be (0) @@ -553,8 +553,8 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // global mask, non-null input val graph1 = makeTestingGraph() - graph1.setInputDimMask(0) - graph1.setOutputDimMask(0) + graph1.setInputDimMask(0, true) + graph1.setOutputDimMask(0, true) graph1.forward(inputTensor) graph1.calcScales(inputTensor) val graphOutput1 = graph1.output @@ -602,8 +602,8 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // global mask, null input val graph0 = dnnGraph(4, 10) - graph0.setInputDimMask(0) - graph0.setOutputDimMask(0) + graph0.setInputDimMask(0, true) + graph0.setOutputDimMask(0, true) graph0.calcScales(null) graph0.getInputDimMask() should be (0) graph0.getOutputDimMask() should be (0) @@ -613,9 +613,9 @@ class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { // global mask, non-null input val graph1 = dnnGraph(4, 10) - graph1.setInputDimMask(0) - graph1.setOutputDimMask(0) - graph1.setWeightDimMask(1) + graph1.setInputDimMask(0, true) + graph1.setOutputDimMask(0, true) + graph1.setWeightDimMask(1, true) graph1.forward(inputTensor) graph1.calcScales(inputTensor) val graphOutput1 = graph1.output diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala index 29a4edb1f4e..a5e1b785a83 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -203,7 +203,7 @@ class FusionSpec extends FlatSpec with Matchers { System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) model2.forward(input) - model2.setWeightDimMask(1) + model2.setWeightDimMask(1, true) model2.calcScales(input) model2.release() println(model2) @@ -261,7 +261,7 @@ class FusionSpec extends FlatSpec with Matchers { model2.evaluate() model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) model2.forward(input) - model2.setWeightDimMask(1) + model2.setWeightDimMask(1, true) model2.calcScales(input) model2.release() val quantize = model2.quantize() @@ -322,7 +322,7 @@ class FusionSpec extends FlatSpec with Matchers { System.setProperty("bigdl.mkldnn.fusion.convrelu", "true") model2.compile(InferencePhase, Array(HeapData(inputShape, Memory.Format.nchw))) model2.forward(input) - model2.setWeightDimMask(1) + model2.setWeightDimMask(1, true) model2.calcScales(input) model2.release() println(model2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index 14ac11b9c73..4446d10adf7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -627,7 +627,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { val output = model.output.toTensor[Float].clone() - model.setInputDimMask(1) + model.setInputDimMask(1, true) model.calcScales(input) model.release() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala index 07b283d8c83..0e3d87e91e4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala @@ -958,7 +958,7 @@ class TopologySpec extends FlatSpec with Matchers { val input = Tensor[Float](inputShape).rand(-1, 1) model.forward(input) - model.asInstanceOf[Sequential].setWeightDimMask(1) + model.asInstanceOf[Sequential].setWeightDimMask(1, true) model.asInstanceOf[Sequential].calcScales(input) val output = model.output.toTensor[Float].clone() @@ -996,7 +996,7 @@ class TopologySpec extends FlatSpec with Matchers { model.forward(input) val output = model.output.toTensor[Float].clone() - model.setWeightDimMask(1) + model.setWeightDimMask(1, true) model.calcScales(input) model.release() @@ -1036,7 +1036,7 @@ class TopologySpec extends FlatSpec with Matchers { model.compile(InferencePhase) model.forward(input) - model.setWeightDimMask(1) + model.setWeightDimMask(1, true) model.calcScales(input) val output = model.output.toTensor[Float].clone() model.release() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala index c104cc78609..918e8eade24 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala @@ -68,9 +68,9 @@ class IRconvertSpec extends BigDLSpecHelper { def modelWithScale(format: DataFormat = DataFormat("NCHW")) : Module[Float] = { val convElement = nn.SpatialConvolution(1, 20, 5, 5, format = format) - convElement.setInputDimMask(1) - convElement.setWeightDimMask(2) - convElement.setOutputDimMask(3) + convElement.setInputDimMask(1, true) + convElement.setWeightDimMask(2, true) + convElement.setOutputDimMask(3, true) convElement.setInputScales(Array(Array(1, 2, 3))) convElement.setWeightScales(Array(Array(4, 5, 6))) val conv1 = convElement.setName("input").inputs() @@ -82,8 +82,8 @@ class IRconvertSpec extends BigDLSpecHelper { val relu = nn.ReLU().setName("relu1").inputs(fc) val linearElement = nn.Linear(500, 10) - linearElement.setInputDimMask(1) - linearElement.setOutputDimMask(2) + linearElement.setInputDimMask(1, true) + linearElement.setOutputDimMask(2, true) linearElement.setInputScales(Array(Array(0, 1, 2))) linearElement.setOutputScales(Array(Array(7, 8, 9))) val fc2 = linearElement.setName("output").inputs(relu) From 1b73a78170dc7ef809e402663361ba3dfb9ca310 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Mon, 13 May 2019 17:01:30 +0800 Subject: [PATCH 0899/1065] [Enhancement]: Scala Reflection: get default value for constructor parameters (#2808) * reflection: get param's default value when instantiating a class * reflection: get param's default value when instantiating a class * reflection: get param's default value when instantiating a class * reflection: get param's default value when instantiating a class * reflection: get param's default value when instantiating a class * resolve conflict * resolve conflict * code style check * remove print * fix typos fix typos --- .../utils/serializer/ModuleSerializable.scala | 106 ++++++++++++++---- 1 file changed, 86 insertions(+), 20 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index c54f65f29bf..cce2b031a6c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -32,7 +32,7 @@ import com.intel.analytics.bigdl.serialization.Bigdl._ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag -import scala.reflect.runtime.universe +import scala.reflect.runtime.{currentMirror, universe} /** * [[ModuleSerializable]] trait inherits [[Loadable]] and [[Savable]] @@ -121,26 +121,48 @@ trait ModuleSerializable extends Loadable with Savable{ val constructorFullParams = constructorMirror.symbol.paramss val args = new Array[Object](constructorFullParams.map(_.size).sum) var i = 0 - constructorFullParams.foreach(map => { - map.foreach(param => { - val name = param.name.decodedName.toString - val ptype = param.typeSignature - if (ptype <:< universe.typeOf[ClassTag[_]]|| - ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { - require(tagIter.hasNext, "If your module contains multiple class tags, " + - "do you forget to override getClassTagNumerics method") - args(i) = tagIter.next - } else if (ptype <:< universe.typeOf[TensorNumeric[_]] - || ptype.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { - args(i) = numericIter.next - } else { - require(modelAttributes.containsKey(name), s"$name value cannot be found") - val attribute = modelAttributes.get(name) - val value = DataConverter.getAttributeValue(context, attribute) - args(i) = value + + val clsMirror = universe.runtimeMirror(cls.getClassLoader) + val clsSymbol = clsMirror.classSymbol(cls) + + /* + https://www.scala-lang.org/api/2.10.7/#scala.reflect.api.Symbols$Symbol + this line tries to get companion object of the class; + through the companion, default values can be accessed by calling + some static methods created by scala compiler, however it does not work when + the class is not a case class or has not defined a companion, which in this case, + calling companionSymbol returns universe.NoSymbol + */ + val companionSymbol = clsSymbol.companionSymbol + + val instanceMirror = companionSymbol match { + case universe.NoSymbol => null + case _ => + val compnInst = currentMirror.reflectModule(clsSymbol.companionSymbol.asModule).instance + clsMirror.reflect(compnInst) + } + + constructorFullParams.flatten.foreach(param => { + val pname = param.name.decodedName.toString + val ptypesig = param.typeSignature + if (ptypesig <:< universe.typeOf[ClassTag[_]]|| + ptypesig.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { + require(tagIter.hasNext, "If your module contains multiple class tags, " + + "do you forget to override getClassTagNumerics method") + args(i) = tagIter.next + } else if (ptypesig <:< universe.typeOf[TensorNumeric[_]] + || ptypesig.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { + args(i) = numericIter.next + } else { + val pvalue = if (modelAttributes.containsKey(pname)) { // for existing parameters + val attrValue = modelAttributes.get(pname) + DataConverter.getAttributeValue(context, attrValue) + } else { // parameter not found, get its default value + getPrimCtorDefaultParamValue(instanceMirror, param, i) } - i += 1 - }) + args(i) = pvalue + } + i += 1 }) constructorMirror.apply(args : _*). asInstanceOf[AbstractModule[Activity, Activity, T]] @@ -487,6 +509,50 @@ trait ModuleSerializable extends Loadable with Savable{ } } + + /** + * Get class primary consturctor's default parameter value by index + * @param instMirror instance mirror object of the class companion object + * @param paramSymbol symbol object of the target parameter with default value + * @param index the index of parameter in the class primary constructor + * @return AnyRef which is compatible with java Object + */ + private def getPrimCtorDefaultParamValue(instMirror: universe.InstanceMirror, + paramSymbol: universe.Symbol, + index: Int): AnyRef = { + if (paramSymbol == null || paramSymbol == universe.NoSymbol || + instMirror == null || index < 0) { + return None + } + + if (!paramSymbol.asTerm.isParamWithDefault) { // param has no default value + None + } else { + val instTypeSig = instMirror.symbol.typeSignature + val methodName = getCtorDefaultParamMethodByIndex(index) + val methodSymbol = instTypeSig.member(universe.newTermName(methodName)) + if (methodSymbol == universe.NoSymbol) { // method not found + None + } + else { + // make the method call using reflection + // need to cast it as AnyRef to be compatible with Java Object type + instMirror.reflectMethod(methodSymbol.asMethod).apply().asInstanceOf[AnyRef] + } + } + } + + /** + * get string name of the method, which returns default value of the i-th parameter + * Reference: + * https://stackoverflow.com/questions/39657211/scala-class-constructors-default-argument-naming + * @param i parameter index in primary constructor + * @return method name in string, calling this method returns default value of i-th parameter + */ + private def getCtorDefaultParamMethodByIndex(i: Int): String = { + s"$$lessinit$$greater$$default$$${i + 1}" + } + } trait ContainerSerializable extends ModuleSerializable { From 45d94359101ca29a7cd1837431150b68edf00302 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Wed, 15 May 2019 14:42:39 +0800 Subject: [PATCH 0900/1065] replace randomcropper with centercrop for better performance (#2818) --- .../analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala | 2 +- .../com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala index 462b636c446..a8586eeb990 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/loadmodel/DatasetUtil.scala @@ -115,7 +115,7 @@ object ResNetPreprocessor { val data = DataSet.SeqFileFolder.filesToImageFrame(path, sc, 1000) val transfomer = PixelBytesToMat() -> RandomResize(256, 256) -> - RandomCropper(224, 224, false, CropCenter) -> + CenterCrop(224, 224) -> ChannelScaledNormalizer(104, 117, 123, 0.0078125) -> MatToTensor[Float]() -> ImageFrameToSample[Float](targetKeys = Array(ImageFeature.label)) val imgFrame = data -> transfomer diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala index 6b1a63c05d1..ef9859f3f11 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/DataSet.scala @@ -121,7 +121,7 @@ object ImageNetDataSet extends ResNetDataSet { batchSize = batchSize, transformer = PixelBytesToMat() -> RandomResize(256, 256) -> - RandomCropper(224, 224, false, CropCenter) -> + CenterCrop(224, 224) -> ChannelScaledNormalizer(104, 117, 123, 0.0078125) -> MatToTensor[Float](), toRGB = false ) From 2d69d3b0db191e07dbbd14682fe7d1cbc838fbf3 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 20 May 2019 16:04:26 +0800 Subject: [PATCH 0901/1065] fix: memory data hash code should contain data type (#2821) --- .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 4 ++ .../dllib/nn/mkldnn/MemoryDataSpec.scala | 43 +++++++++++++++++++ 2 files changed, 47 insertions(+) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryDataSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index 084b0298c96..084856ce6bd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -126,6 +126,8 @@ case class HeapData(private var _shape: Array[Int], private var _layout: Int, d += 1 } + hash = hash * seed + this.dataType + hash } @@ -191,6 +193,8 @@ case class NativeData(private var _shape: Array[Int], private var _layout: Int, d += 1 } + hash = hash * seed + this.dataType + hash } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryDataSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryDataSpec.scala new file mode 100644 index 00000000000..84bf2615572 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryDataSpec.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.{DataType, Memory} +import org.scalatest.{FlatSpec, Matchers} + +class MemoryDataSpec extends FlatSpec with Matchers { + "memory data hashCode comparison data" should "work correctly" in { + val fp32 = HeapData(Array(4, 3), Memory.Format.nc, DataType.F32) + val int8 = HeapData(Array(4, 3), Memory.Format.nc, DataType.S8) + + fp32.hashCode() == int8.hashCode() should not be (true) + } + + "memory data hashCode comparison native" should "work correctly" in { + val fp32 = NativeData(Array(3, 3), Memory.Format.nc, DataType.F32) + val int8 = NativeData(Array(3, 3), Memory.Format.nc, DataType.S8) + + fp32.hashCode() == int8.hashCode() should not be (true) + } + + "memory data hashCode comparison heap and native" should "work correctly" in { + val heap = HeapData(Array(3, 3), Memory.Format.nc, DataType.F32) + val native = NativeData(Array(3, 3), Memory.Format.nc, DataType.F32) + + heap.hashCode() == native.hashCode() should not be (true) + } +} From 247704516cc8a362c1c3ac0bf628a0361fa5ef44 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 21 May 2019 12:26:35 +0800 Subject: [PATCH 0902/1065] Optimize backward graph generation and CAddTable (#2817) * Optimize backward graph generation and caddtable * refine add table * change api name --- .../analytics/bigdl/dllib/nn/CAddTable.scala | 44 ++++++++++++++++++- .../bigdl/dllib/utils/DirectedGraph.scala | 17 ++++++- .../bigdl/dllib/nn/CAddTableSpec.scala | 22 ++++++++++ .../bigdl/dllib/utils/DirectedGraphSpec.scala | 23 ++++++++++ 4 files changed, 104 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala index a2047007c9a..77f474f50fd 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala @@ -16,11 +16,14 @@ package com.intel.analytics.bigdl.nn +import javax.print.attribute.standard.MediaSize.Other + import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.utils.{T, Table} import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable} +import org.apache.hadoop.yarn.webapp.hamlet.HamletSpec.VAR import scala.reflect._ @@ -38,6 +41,42 @@ class CAddTable[T: ClassTag, D: ClassTag](val inplace: Boolean = false)( output = Tensor[D]() + @transient + private var bufferSumInput: Tensor[D] = null + @transient + private var bufferSumOutput: Tensor[D] = null + + private def expandWithDims(smallSize: Array[Int], otherSize: Array[Int]): Boolean = { + if (smallSize.length != otherSize.length) { + return false + } + var d = otherSize.length - 1 + while(d >= 0) { + if (smallSize(d) != 1 && smallSize(d) != otherSize(d)) { + return false + } + d -= 1 + } + return true + } + + private def sumAlongDims(tensor: Tensor[D], other: Tensor[D]): Tensor[D] = { + val size = tensor.size() + var target: Tensor[D] = other + if (bufferSumOutput == null) bufferSumOutput = Tensor[D]() + if (bufferSumInput == null) bufferSumInput = Tensor[D]() + + var i = 0 + while (i < size.length) { + if (size(i) == 1) { + bufferSumOutput.sum(target, i + 1) + target = bufferSumInput.resizeAs(bufferSumOutput).copy(bufferSumOutput) + } + i += 1 + } + target + } + override def updateOutput(input: Table): Tensor[D] = { var scalar = ev2.zero var hasTensor = false @@ -91,6 +130,9 @@ class CAddTable[T: ClassTag, D: ClassTag](val inplace: Boolean = false)( } else { if (input[Tensor[D]](i).isSameSizeAs(gradOutput)) { gradInput[Tensor[D]](i).resizeAs(gradOutput).copy(gradOutput) + } else if (expandWithDims(input[Tensor[D]](i).size(), gradOutput.size())) { + gradInput[Tensor[D]](i).resizeAs(input[Tensor[D]](i)).copy( + sumAlongDims(input[Tensor[D]](i), gradOutput)) } else { require(input[Tensor[D]](i).isScalar, "Only support scalar broadcast backward now") if (!calculateSum) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala index 3c6551de270..dd7f73f4700 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraph.scala @@ -154,7 +154,14 @@ class DirectedGraph[T](val source : Node[T], val reverse : Boolean = false) exte node.nextNodesAndEdges.foreach(nextNodeAndEdge => { // Some next nodes may be not included in the graph if (oldToNew.containsKey(nextNodeAndEdge._1)) { - oldToNew.get(nextNodeAndEdge._1).add(oldToNew.get(node), nextNodeAndEdge._2) + oldToNew.get(node).addPrevious( + oldToNew.get(nextNodeAndEdge._1), nextNodeAndEdge._2) + } + }) + node.prevNodesAndEdges.foreach(prevNodeAndEdge => { + if (oldToNew.containsKey(prevNodeAndEdge._1)) { + oldToNew.get(node).addNexts( + oldToNew.get(prevNodeAndEdge._1), prevNodeAndEdge._2) } }) } else { @@ -241,6 +248,14 @@ class Node[T](var element: T) extends Serializable { node } + def addPrevious(node: Node[T], e: Edge = Edge()): Unit = { + if (!this.prevs.contains((node, e))) this.prevs.append((node, e)) + } + + def addNexts(node: Node[T], e: Edge = Edge()): Unit = { + if (!this.nexts.contains((node, e))) this.nexts.append((node, e)) + } + def from(node: Node[T], e: Edge = Edge()): Node[T] = { if (!node.nexts.contains((this, e))) node.nexts.append((this, e)) if (!this.prevs.contains((node, e))) this.prevs.append((node, e)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala index 68060ea01d8..647806fcde8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CAddTableSpec.scala @@ -43,6 +43,28 @@ class CAddTableSpec extends FlatSpec with Matchers { grads[Tensor[Float]](1) should be(Tensor[Float](T(1, 2, 3))) grads[Tensor[Float]](2).value() should be(6) } + + "CAddTable with different size" should "be correct" in { + val input1 = Tensor[Float](T(T(-0.52817175, -1.07296862, 0.86540763, -2.3015387, + 1.74481176, -0.7612069, 0.3190391, -0.24937038), + T( 1.46210794, -2.06014071, -0.3224172, -0.38405435, 1.13376944, -1.09989127, + -0.17242821, -0.87785842))) + val input2 = Tensor[Float](T(T(1.62434536), T(-0.61175641))) + val input3 = Tensor[Float](T(T(1.62434536, 1.62434536, 1.62434536, 1.62434536, + 1.62434536, 1.62434536, 1.62434536, 1.62434536), + T(-0.61175641, -0.61175641, -0.61175641, -0.61175641, -0.61175641, + -0.61175641, -0.61175641, -0.61175641))) + val layer = CAddTable[Float]() + val output = layer.forward(T(input1, input2)) + val output2 = layer.forward(T(input1, input3)) + output should be(output2) + + val gradInput = layer.backward(T(input1, input2), output) + val gradInput2 = layer.backward(T(input1, input3), output2) + + gradInput[Tensor[Float]](1) should be(gradInput2[Tensor[Float]](1)) + gradInput[Tensor[Float]](2) should be(gradInput2[Tensor[Float]](2)) + } } class CAddTableSerialTest extends ModuleSerializationTest { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala index 7e909819865..e818449863b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/DirectedGraphSpec.scala @@ -15,6 +15,9 @@ */ package com.intel.analytics.bigdl.utils +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.{CAddTable, Graph, Input, Reshape} +import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} class DirectedGraphSpec extends FlatSpec with Matchers { @@ -404,4 +407,24 @@ class DirectedGraphSpec extends FlatSpec with Matchers { nodeA.nextEdges.length should be(1) nodeB.nextEdges.length should be(0) } + + "keep backward topology" should "be correct" in { + val input1 = Tensor[Float](2, 2, 3, 3).rand() + val input2 = Tensor[Float](1, 1, 3, 3).rand() + + def modelDef(): Module[Float] = { + val input1 = Input[Float]() + val input2 = Input[Float]() + + val add1 = CAddTable[Float]().inputs(input1, input2) + val add2 = CAddTable[Float]().inputs(add1, input2) + val add3 = CAddTable[Float]().inputs(add2, input2) + val add4 = CAddTable[Float]().inputs(add3, input2) + Graph[Float](Array(input1, input2), Array(add4)) + } + + val model = modelDef() + val output = model.forward(T(input1, input2)) + val gradInput = model.backward(T(input1, input2), output) + } } From fd1e43f9e65b41ae045e59bda561b0ab80dc78e7 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 22 May 2019 11:05:59 +0800 Subject: [PATCH 0903/1065] add layer norm and expand size layers (#2819) * add layer norm and expand size * meet pr comments --- .../analytics/bigdl/dllib/nn/ExpandSize.scala | 102 +++++++ .../bigdl/dllib/nn/LayerNormalization.scala | 172 +++++++++++ .../bigdl/dllib/nn/TableOperation.scala | 98 +++++++ .../dllib/utils/python/api/PythonBigDL.scala | 13 + .../dllib/nn/LayerNormalizationSpec.scala | 268 ++++++++++++++++++ 5 files changed, 653 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ExpandSize.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LayerNormalization.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TableOperation.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LayerNormalizationSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ExpandSize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ExpandSize.scala new file mode 100644 index 00000000000..1c5bcf36deb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ExpandSize.scala @@ -0,0 +1,102 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +/** + * Expand tensor to configured size + * @param targetSizes target tensor sizes, dim whose size is -1 will be ignored + * @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now. + */ +class ExpandSize[T: ClassTag](targetSizes: Array[Int]) + (implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[T], Tensor[T], T] { + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + require(targetSizes.length == input.dim(), + s"the number of dimensions provided must equal ${input.dim()}") + val tensorDim = input.dim() + val tensorStride = input.stride() + val tensorSize = input.size() + + var i = 0 + while (i < tensorDim) { + if (targetSizes(i) != -1) { + if (tensorSize(i) == 1) { + tensorSize(i) = targetSizes(i) + tensorStride(i) = 0 + } else if (tensorSize(i) != targetSizes(i)) { + throw new UnsupportedOperationException( + "incorrect size: only supporting singleton expansion (size=1)") + } + } + i += 1 + } + + output.set(input.storage(), input.storageOffset(), tensorSize, tensorStride) + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val tensorDim = input.dim() + val tensorSize = input.size() + + gradInput = Tensor[T](tensorSize) + val expandDim = new ArrayBuffer[Int]() + var i = 0 + while (i < tensorDim) { + if (targetSizes(i) != -1) { + if (tensorSize(i) == 1 && targetSizes(i) != 1) { + expandDim.append(i + 1) + } + } + i += 1 + } + + i = expandDim.size - 1 + val sizes = gradOutput.size() + var _gradOutput = gradOutput + while (i >= 0) { + var start = 1 + sizes(expandDim(i) - 1) = 1 + val _gradInput = Tensor[T](sizes) + while (start <= gradOutput.size(expandDim(i))) { + val x = _gradOutput.narrow(expandDim(i), start, 1) + _gradInput.add(x) + start += 1 + } + _gradOutput = _gradInput + i -= 1 + } + gradInput = _gradOutput + gradInput + } + + override def toString: String = s"ExpandSize" +} + +object ExpandSize { + def apply[@specialized(Float, Double) T: ClassTag](targetSizes: Array[Int]) + (implicit ev: TensorNumeric[T]) : ExpandSize[T] = { + new ExpandSize[T](targetSizes) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LayerNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LayerNormalization.scala new file mode 100644 index 00000000000..2cb0c575170 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LayerNormalization.scala @@ -0,0 +1,172 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, TensorModule} +import com.intel.analytics.bigdl.nn.{Module => _} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Applies layer normalization. + * @param hiddenSize + * @param ev$1 + * @param ev + * @tparam T + */ +class LayerNormalization[T: ClassTag](hiddenSize: Int) + (implicit ev: TensorNumeric[T]) extends BaseModule[T] { + override def buildModel(): Module[T] = { + val input = Input() + val mean = Mean(-1, squeeze = false).inputs(input) + val sub = CSubTableExpand().inputs(input, mean) + val square = Square().inputs(sub) + val mean2 = Mean(-1, squeeze = false).inputs(square) + val add = AddConstant(1e-6).inputs(mean2) + val sqrt = Power(-0.5, 1, 0).inputs(add) + val mul = CMulTableExpand().inputs(sub, sqrt) + val linear = new VectorProduct[T](hiddenSize).inputs(mul) + Graph(input, linear) + } + override def updateOutput(input: Activity): Activity = { + output = model.updateOutput(input) + output + } +} + +/** + * Implement x * weight vector + bias vector + * @param hiddenSize + * @param ev$1 + * @param ev + * @tparam T The numeric type in this module parameters + */ +private[nn] class VectorProduct[T: ClassTag](val hiddenSize: Int) + (implicit ev: TensorNumeric[T]) extends TensorModule[T] { + + var weight = Tensor[T](hiddenSize).fill(ev.one) + var bias = Tensor[T](hiddenSize).fill(ev.zero) + var gradWeight = Tensor[T](hiddenSize) + var gradBias = Tensor[T](hiddenSize) + + private val buffer = Tensor[T]() + private var inputSize: Array[Int] = _ + private var gradOutputSize: Array[Int] = _ + private var outputSize: Array[Int] = _ + + private def combine(src: Array[Int], target: Array[Int]): Array[Int] = { + if (src.length <= 2) return src + val targetArr = if (target == null) new Array[Int](src.length - 1) else target + require(src.length == targetArr.length + 1, + "combine method requires src.length == target.length + 1" + + s" Current src.length = ${src.length}" + + s" Current target.length = ${targetArr.length}") + targetArr(0) = src(0) * src(1) + var j = 1 + while (j < targetArr.length) { + targetArr(j) = src(j + 1) + j += 1 + } + targetArr + } + + private def split(src: Array[Int], target: Array[Int], srcInput: Array[Int]): Array[Int] = { + if (src.length == srcInput.length) return src + val dim1 = srcInput(0) + val dim2 = srcInput(1) + val targetArr = if (target == null) new Array[Int](srcInput.length) else target + require(src.length == targetArr.length - 1, + "split method requires src.length == target.length - 1" + + s" Current src.length = ${src.length}" + + s" Current target.length = ${targetArr.length}") + require(dim1 * dim2 == src(0), + "split method requires dim1 * dim2 == src(0), " + + s"Current dim1 = ${dim1}, dim2 = ${dim2}, src(0) = ${src(0)}") + + targetArr(0) = dim1 + targetArr(1) = dim2 + var j = 1 + while (j < src.length) { + targetArr(j + 1) = src(j) + j += 1 + } + targetArr + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + val _inputSize = input.size + inputSize = combine(_inputSize, inputSize) + + input.resize(inputSize) + output.resizeAs(input).copy(input) + val size = output.size(1) + var i = 1 + while(i <= size) { + output.select(1, i).cmul(weight).add(bias) + i += 1 + } + + outputSize = split(output.size, outputSize, _inputSize) + input.resize(_inputSize) + output.resize(outputSize) + + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + val _inputSize = input.size + val _gradOutputSize = gradOutput.size + gradOutputSize = combine(_gradOutputSize, gradOutputSize) + input.resize(inputSize) + gradOutput.resize(gradOutputSize) + gradInput.resizeAs(input).zero() + + val size = gradInput.size(1) + var i = 1 + while(i <= size) { + gradInput.select(1, i).addcmul(gradOutput.select(1, i), weight) + i += 1 + } + + gradInput.resize(_inputSize) + input.resize(_inputSize) + gradOutput.resize(_gradOutputSize) + + gradInput + } + + override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = { + val _inputSize = input.size + val _gradOutputSize = gradOutput.size + input.resize(inputSize) + gradOutput.resize(gradOutputSize) + + buffer.resizeAs(input).zero() + buffer.addcmul(input, gradOutput) + gradWeight = buffer.sum(1).squeeze() + gradBias = gradOutput.sum(1).squeeze() + + input.resize(_inputSize) + gradOutput.resize(_gradOutputSize) + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + (Array(this.weight, this.bias), Array(this.gradWeight, this.gradBias)) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TableOperation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TableOperation.scala new file mode 100644 index 00000000000..475b127bedb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TableOperation.scala @@ -0,0 +1,98 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.augmentation.Expand +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + +/** + * When two tensors have different size, firstly expand small size tensor to large size tensor, + * and then do table operation. + * @param operationLayer layer that handles table operation, such as CSubTable, CMulTable, etc. + * @param ev$1 + * @param ev + * @tparam T + */ +class TableOperation[T: ClassTag]( + val operationLayer: AbstractModule[Table, Tensor[T], T]) + (implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T] { + + @transient + private var expandLayer: AbstractModule[Tensor[T], Tensor[T], T] = null + + // small tensor position in input table + private var smallPos = 1 + + override def updateOutput(input: Table): Tensor[T] = { + // only support table with two tensors + require(input.length() == 2, s"Only support input two tensors, but get ${input.length()}") + // get small tensor position in table + val input1 = input[Tensor[T]](1) + val input2 = input[Tensor[T]](2) + if (input1.nElement() > input2.nElement()) { + smallPos = 2 + } + + val inputSmall = input[Tensor[T]](smallPos) + val inputLarge = input[Tensor[T]](3 - smallPos) + + if (expandLayer == null) expandLayer = ExpandSize(inputLarge.size()) + val inputExpand = expandLayer.forward(inputSmall) + + output = operationLayer.updateOutput(T(inputLarge, inputExpand)) + return output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + val inputSmall = input[Tensor[T]](smallPos) + val inputLarge = input[Tensor[T]](3 - smallPos) + + val inputExpand = expandLayer.output + gradInput = operationLayer.updateGradInput(T(inputLarge, inputExpand), gradOutput) + gradInput(2) = expandLayer.backward(inputSmall, gradInput[Tensor[T]](2)) + gradInput + } + + override def toString: String = s"TableOperationExpand" + + override def clearState(): this.type = { + if (expandLayer != null) expandLayer.clearState() + operationLayer.clearState() + this + } +} + +object CMulTableExpand { + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : TableOperation[T] = { + new TableOperation[T](CMulTable[T]) + } +} + +object CSubTableExpand { + def apply[@specialized(Float, Double) T: ClassTag]() + (implicit ev: TensorNumeric[T]) : TableOperation[T] = { + new TableOperation[T](CSubTable[T] + .asInstanceOf[AbstractModule[Table, Tensor[T], T]]) + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index a8f8c1ed39d..58e03252b02 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -279,6 +279,19 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab FeedForwardNetwork(hiddenSize, filterSize, reluDropout) } + def createExpandSize(targetSizes: JList[Int]): ExpandSize[T] = { + ExpandSize(targetSizes.asScala.toArray) + } + + def createTableOperation( + operationLayer: AbstractModule[Table, Tensor[T], T]): TableOperation[T] = { + new TableOperation(operationLayer) + } + + def createLayerNormalization(hiddenSize: Int): LayerNormalization[T] = { + new LayerNormalization[T](hiddenSize) + } + def createLinear(inputSize: Int, outputSize: Int, withBias: Boolean, wRegularizer: Regularizer[T] = null, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LayerNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LayerNormalizationSpec.scala new file mode 100644 index 00000000000..8ad6dc2d20a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LayerNormalizationSpec.scala @@ -0,0 +1,268 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.mkldnn.Equivalent +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class LayerNormalizationSpec extends FlatSpec with Matchers { + + val input = Tensor[Float]( + T(T(1.62434536, -0.61175641, -0.52817175, -1.07296862, 0.86540763, -2.3015387, + 1.74481176, -0.7612069), + T( 0.3190391, -0.24937038, 1.46210794, -2.06014071, -0.3224172, -0.38405435, + 1.13376944, -1.09989127)) + ) + val weightsExpected = Tensor[Float](T(-0.14037117, -0.16902402, -0.06451887, + -0.5642037, 0.24212438, 0.44951588, -0.4296978, 0.423163)) + + val biasExpected = Tensor[Float](T(0.44111532, -0.06523705, -0.3474969, -0.08237404, + -0.3565278, -0.18157673, 0.4592312, -0.36194998)) + + val outputExpected = Tensor[Float]( + T(T( 0.2547953, -0.00365025, -0.32806823, 0.32006884, -0.17416702, -0.92002285, + -0.15028489, -0.56398183), + T( 0.37940904, -0.04951846, -0.444961, 0.9273568, -0.39561632, -0.28010735, + -0.05768752, -0.73853105))) + + val gradInputExpected = Tensor[Float]( + T(T(-0.0655726, 0.11039984, 0.12039759, 0.00393196, -0.02003431, -0.09076728, + 0.00234376, -0.06069893), + T(0.00566998, 0.14491531, -0.08142705, -0.09353723, 0.05779467, 0.03840649, + -0.03802159, -0.03380056))) + + val gradWeightExpected = Tensor[Float]( + T( 0.5049854, 0.00593506, -0.5733794, -1.8879533, -0.06730913, 1.5727731, + -0.28257257, 0.9264967, 0.6342044, -0.05316871, -0.7730292, 1.2474256, + -0.56978333, -1.2001302, -0.2079724, -1.3025129)) + + "LayerNormalization layer" should "work correct" in { + val layerNorm = new LayerNormalization[Float](8) + val params = layerNorm.parameters() + params._1.apply(0).copy(weightsExpected) + params._1.apply(1).copy(biasExpected) + + val output = layerNorm.forward(input) + output should be(outputExpected) + + val gradInput = layerNorm.backward(input, output) + Equivalent.nearequals(gradInput.toTensor[Float], gradInputExpected) + + val gradWeights = layerNorm.getParameters()._2 + + gradWeights should be(gradWeightExpected) + } + + "LayerNormalization layer for 3 dims" should "work correct" in { + val layerNorm = new LayerNormalization[Float](8) + val params = layerNorm.parameters() + params._1.apply(0).copy(weightsExpected) + params._1.apply(1).copy(biasExpected) + + val input = Tensor[Float](T(T( + T( 1.62434536, -0.61175641, -0.52817175, -1.07296862, 0.86540763, + -2.3015387, 1.74481176, -0.7612069), + T( 0.3190391, -0.24937038, 1.46210794, -2.06014071, -0.3224172, + -0.38405435, 1.13376944, -1.09989127), + T(-0.17242821, -0.87785842, 0.04221375, 0.58281521, -1.10061918, + 1.14472371, 0.90159072, 0.50249434)), + T(T( 0.90085595, -0.68372786, -0.12289023, -0.93576943, -0.26788808, + 0.53035547, -0.69166075, -0.39675353), + T(-0.6871727, -0.84520564, -0.67124613, -0.0126646, -1.11731035, + 0.2344157, 1.65980218, 0.74204416), + T(-0.19183555, -0.88762896, -0.74715829, 1.6924546, 0.05080775, + -0.63699565, 0.19091548, 2.10025514)))) + + val outputExpected = Tensor[Float]( + T(T(T( 0.2547953, -0.00365025, -0.32806823, 0.32006884, -0.17416702, + -0.92002285, -0.15028489, -0.56398183), + T( 0.37940904, -0.04951846, -0.444961, 0.9273568, -0.39561632, + -0.28010735, -0.05768752, -0.73853105), + T( 0.49671584, 0.15898634, -0.34020767, -0.42094654, -0.74886715, + 0.4213413, 0.02069786, -0.15284662)), + T(T( 0.17843285, 0.07028739, -0.3568077, 0.60989976, -0.3808119, + 0.37866306, 0.80951583, -0.4963839), + T( 0.53689927, 0.08047631, -0.30464026, -0.13017833, -0.6401863, + -0.0171783, -0.3944744, 0.0371049), + T( 0.49308524, 0.1095071, -0.28943837, -0.8874372, -0.39013758, + -0.5388527, 0.46145913, 0.40644628)))) + + val gradInputExpected = Tensor[Float]( + T(T(T(-0.0655726, 0.11039984, 0.12039759, .00393196, -0.02003431, + -0.09076728, 0.00234376, -0.06069893), + T( 0.00566998, 0.14491531, -0.08142705, -0.09353723, 0.05779467, + 0.03840649, -0.03802159, -0.03380056), + T(-0.06164519, 0.10382065, 0.02612757, 0.22695568, -0.06549627, + 0.07673246, -0.14727353, -0.15922137)), + T(T(-0.2443429, 0.31894156, 0.18631479, -0.15545437, 0.04144509, + 0.21156964, -0.24511626, -0.11335772), + T(-0.03258525, 0.05264378, 0.07396686, 0.07267879, -0.08235938, + -0.04306597, 0.02329509, -0.06457391), + T(-0.01852474, 0.12962818, 0.14494516, 0.2547746, -0.07740442, + -0.11968417, -0.19652283, -0.11721179)))) + + val gradWeightExpected = Tensor[Float]( + T( 0.09323123, -0.44392002, -0.12362102, -4.166217, 1.9885124, 3.0318348, + -1.7074748, 1.7816961, 2.3393373, 0.36608845, -2.0641232, 0.41876316, + -2.7297864, -0.95615685, 0.68922603, -1.5081923) + ) + + val output = layerNorm.forward(input) + output should be(outputExpected) + + val gradInput = layerNorm.backward(input, output) + Equivalent.nearequals(gradInput.toTensor[Float], gradInputExpected) + + val gradWeights = layerNorm.getParameters()._2 + + gradWeights should be(gradWeightExpected) + } + + "vector linear with 2 dims" should "work correct" in { + val weight = Tensor[Float](T(-0.14037117, -0.16902402, -0.06451887, + -0.5642037, 0.24212438, 0.44951588, -0.4296978, 0.423163)) + val bias = Tensor[Float](T(0.44111532, -0.06523705, -0.3474969, + -0.08237404, -0.3565278, -0.18157673, 0.4592312, -0.36194998)) + + val outputExpected = Tensor[Float]( + T(T( 0.21310404, 0.03816448, -0.31341985, 0.5229988, -0.14699152, -1.2161549, + -0.2905106, -0.6840646), + T( 0.39633143, -0.02308746, -0.44183046, 1.0799649, -0.43459287, -0.35421526, + -0.02794704, -0.8273833))) + + val gradInputExpected = Tensor[Float]( + T(T(-0.02991366, -0.00645071, 0.02022149, -0.29507786, -0.03559023, -0.5466809, + 0.12483177, -0.28947085), + T(-0.05563351, 0.00390234, 0.0285064, -0.60932016, -0.10522553, -0.15922539, + 0.01200878, -0.35011798))) + + val gradWeightExpected = Tensor[Float](T(0.4725998, -0.01759003, -0.48046428, + -2.7860408, 0.01291263, 2.9350655, -0.5385718, 1.4307464, 0.60943544, + 0.01507702, -0.75525033, 1.6029637, -0.5815844, -1.5703702, -0.31845763, -1.5114479)) + + val layer = new VectorProduct[Float](8) + layer.setWeightsBias(Array(weight, bias)) + + val output = layer.forward(input) + output should be(outputExpected) + + val gradInput = layer.backward(input, output) + gradInput should be(gradInputExpected) + + val gradWeights = layer.getParameters()._2 + gradWeights should be(gradWeightExpected) + } + + "CMulTableExpand" should "work correctly" in { + val input1 = Tensor[Float](T(T(-0.52817175, -1.07296862, 0.86540763, -2.3015387, + 1.74481176, -0.7612069, 0.3190391, -0.24937038), + T( 1.46210794, -2.06014071, -0.3224172, -0.38405435, 1.13376944, -1.09989127, + -0.17242821, -0.87785842))) + val input2 = Tensor[Float](T(T(1.62434536), T(-0.61175641))) + val input3 = Tensor[Float](T(T(1.62434536, 1.62434536, 1.62434536, 1.62434536, + 1.62434536, 1.62434536, 1.62434536, 1.62434536), + T(-0.61175641, -0.61175641, -0.61175641, -0.61175641, -0.61175641, + -0.61175641, -0.61175641, -0.61175641))) + val layer = CMulTableExpand[Float]() + val output = layer.forward(T(input1, input2)) + val output2 = layer.forward(T(input1, input3)) + output should be(output2) + + val gradInput = layer.backward(T(input1, input2), output) + val gradInput2 = layer.backward(T(input1, input3), output2) + + gradInput[Tensor[Float]](1) should be(gradInput2[Tensor[Float]](1)) + gradInput[Tensor[Float]](2) should be(gradInput2[Tensor[Float]](2)) + } + + "CSubTableExpand" should "work correctly" in { + val input1 = Tensor[Float](T(T(-0.52817175, -1.07296862, 0.86540763, -2.3015387, + 1.74481176, -0.7612069, 0.3190391, -0.24937038), + T( 1.46210794, -2.06014071, -0.3224172, -0.38405435, 1.13376944, -1.09989127, + -0.17242821, -0.87785842))) + val input2 = Tensor[Float](T(T(1.62434536), T(-0.61175641))) + val input3 = Tensor[Float](T(T(1.62434536, 1.62434536, 1.62434536, 1.62434536, + 1.62434536, 1.62434536, 1.62434536, 1.62434536), + T(-0.61175641, -0.61175641, -0.61175641, -0.61175641, -0.61175641, + -0.61175641, -0.61175641, -0.61175641))) + val layer = CSubTableExpand[Float]() + val output = layer.forward(T(input1, input2)) + val output2 = layer.forward(T(input1, input3)) + output should be(output2) + + val gradInput = layer.backward(T(input1, input2), output) + val gradInput2 = layer.backward(T(input1, input3), output2) + + gradInput[Tensor[Float]](1) should be(gradInput2[Tensor[Float]](1)) + gradInput[Tensor[Float]](2) should be(gradInput2[Tensor[Float]](2)) + } +} + +class LayerNormalizationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = new LayerNormalization[Float](8).setName("LayerNormalization") + val input = Tensor[Float](2, 3, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input) + } +} + +class CMulTableExpandSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = CMulTableExpand[Float]().setName("CMulTableExpand") + val input1 = Tensor[Float](2, 8).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](2, 1).apply1(_ => Random.nextFloat()) + runSerializationTest(model, T(input1, input2)) + } +} + +class CSubTableExpandSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = CSubTableExpand[Float]().setName("CSubTableExpand") + val input1 = Tensor[Float](2, 8).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](2, 1).apply1(_ => Random.nextFloat()) + runSerializationTest(model, T(input1, input2)) + } +} + +class ExpandSizeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = ExpandSize[Float](Array(2, 8)).setName("ExpandSize") + val input = Tensor[Float](2, 1).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input) + } +} + +class VectorProductSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = new VectorProduct[Float](8).setName("VectorProduct") + val input = Tensor[Float](2, 8).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input) + } +} + +class TableOperationSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = new TableOperation[Float](CMulTable()).setName("TableOperation") + val input1 = Tensor[Float](2, 8).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](2, 1).apply1(_ => Random.nextFloat()) + runSerializationTest(model, T(input1, input2)) + } +} From 407467a365deb07deedc32e559a2a42393fe42dd Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 24 May 2019 10:14:29 +0800 Subject: [PATCH 0904/1065] feat: enable global average pooling (#2823) * feat: enable global average pooling * test: add more unit tests --- .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 24 ++++++--- .../dllib/nn/mkldnn/AvgPoolingSpec.scala | 50 +++++++++++++++++++ .../utils/intermediate/IRconvertSpec.scala | 25 ++++++++++ 3 files changed, 92 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala index f8ecf6c641e..228d16d548b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -22,12 +22,13 @@ import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import com.intel.analytics.bigdl.tensor.Tensor class AvgPooling( - kW: Int, - kH: Int, + var kW: Int, + var kH: Int, dW: Int = 1, dH: Int = 1, padW: Int = 0, - padH: Int = 0 + padH: Int = 0, + globalPooling: Boolean = false ) extends MklDnnLayer { @transient private var paddingTL: Array[Int] = _ @transient private var paddingBR: Array[Int] = _ @@ -63,12 +64,20 @@ class AvgPooling( override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = singleNativeData(inputs) - val strides = Array(dW, dH) - val kernel = Array(kH, kW) val n = _inputFormats(0).shape(0) val c = _inputFormats(0).shape(1) val h = _inputFormats(0).shape(2) val w = _inputFormats(0).shape(3) + + // global average pooling reduce each feature map to a single average value + if (globalPooling) { + kH = h + kW = w + } + + val strides = Array(dW, dH) + val kernel = Array(kH, kW) + val (pt, pb, pl, pr, oh, ow) = if (padH == -1 && padW == -1) { val sizes = NNUtils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW) (sizes(0), sizes(1), sizes(2), sizes(3), sizes(4), sizes(5)) @@ -127,6 +136,7 @@ object AvgPooling { dW: Int = 1, dH: Int = 1, padW: Int = 0, - padH: Int = 0 - ): AvgPooling = new AvgPooling(kW, kH, dW, dH, padW, padH) + padH: Int = 0, + globalPooling: Boolean = false + ): AvgPooling = new AvgPooling(kW, kH, dW, dH, padW, padH, globalPooling) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala index 3784c94959e..613c9d13086 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala @@ -236,4 +236,54 @@ class AvgPoolingSpec extends BigDLSpecHelper { Equivalent.nearequals(seq.output.toTensor, seq2.output.toTensor, 1e-2) should be (true) } + + "global average pooling" should "work correctly" in { + val gap = AvgPooling(2, 2, globalPooling = true) + val ap = AvgPooling(3, 3) + + val inputShape = Array(4, 2, 3, 3) + val input = Tensor[Float](inputShape).rand(-1, 1) + + val seq1 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(ap) + .add(Output(Memory.Format.nchw)) + + val seq2 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(gap) + .add(Output(Memory.Format.nchw)) + + seq1.evaluate() + seq2.evaluate() + + seq1.compile(InferencePhase) + seq2.compile(InferencePhase) + + seq1.forward(input) + seq2.forward(input) + + seq1.output should be (seq2.output) + } + + "global average pooling" should "has same behavior with nn" in { + val gap = AvgPooling(2, 2, globalPooling = true) + + val inputShape = Array(4, 2, 3, 3) + val input = Tensor[Float](inputShape).rand(-1, 1) + + val seq1 = Sequential() + .add(Input(inputShape, Memory.Format.nchw)) + .add(gap) + .add(Output(Memory.Format.nchw)) + + seq1.evaluate() + seq1.compile(InferencePhase) + seq1.forward(input) + + val nngap = SpatialAveragePooling[Float](2, 2, globalPooling = true) + nngap.forward(input) + + seq1.output should be (nngap.output) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala index 918e8eade24..ee6b549dd04 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala @@ -240,5 +240,30 @@ class IRconvertSpec extends BigDLSpecHelper { Equivalent.nearequals(outDnn, outBlas, 1e-4) should be (true) Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) + + System.clearProperty("bigdl.engineType") + } + + "convert blas gap to dnn" should "work correctly" in { + System.setProperty("bigdl.engineType", "mkldnn") + val graph = Sequential() + .add(SpatialAveragePooling[Float](2, 2, globalPooling = true)) + .toGraph() + + graph.asInstanceOf[StaticGraph[Float]].setOutputFormats(Seq(Memory.Format.nchw)) + val dnn = ConversionUtils.convert(graph.cloneModule()) + + graph.evaluate() + dnn.evaluate() + + val input = Tensor[Float](4, 2, 3, 3).rand(-1, 1) + + graph.forward(input) + dnn.forward(input) + + graph.output should be (dnn.output) + + dnn.release() + System.clearProperty("bigdl.engineType") } } From d8020cc039653d5625069ac669d86529a21bc500 Mon Sep 17 00:00:00 2001 From: Menooker Date: Fri, 24 May 2019 16:33:57 +0800 Subject: [PATCH 0905/1065] Optimizers: use member variable in parent class --- .../bigdl/dllib/optim/DistriOptimizer.scala | 2 +- .../bigdl/dllib/optim/LocalOptimizer.scala | 19 ++++++++++--------- .../bigdl/dllib/optim/ParallelOptimizer.scala | 6 +++--- 3 files changed, 14 insertions(+), 13 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 3bce315e3d4..8794a6d873c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -807,7 +807,7 @@ class DistriOptimizer[T: ClassTag] ( state("warmupIterationNum") = warmupIterationNum state("computeThresholdbatchSize") = computeThresholdbatchSize state("maxDropPercentage") = maxDropPercentage - state("isLayerwiseScaled") = Utils.isLayerwiseScaled(_model) + state("isLayerwiseScaled") = Utils.isLayerwiseScaled(model) val nodeNumber = Engine.nodeNumber() val coresPerNode = Engine.coreNumber() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index e8844cb82a8..ebf60846990 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -38,17 +38,17 @@ object LocalOptimizer { /** * Optimize a model on a single machine * - * @param model model to be optimized - * @param dataset data set - * @param criterion criterion to be used + * @param _model model to be optimized + * @param _dataset data set + * @param _criterion criterion to be used */ class LocalOptimizer[T: ClassTag] ( - model: Module[T], - dataset: LocalDataSet[MiniBatch[T]], - criterion: Criterion[T] + _model: Module[T], + _dataset: LocalDataSet[MiniBatch[T]], + _criterion: Criterion[T] )(implicit ev: TensorNumeric[T]) extends Optimizer[T, MiniBatch[T]]( - model, dataset, criterion) { + _model, _dataset, _criterion) { import LocalOptimizer._ import Optimizer.{header, saveModel, saveState, checkSubModules, getHyperParameterLog} @@ -114,8 +114,9 @@ class LocalOptimizer[T: ClassTag] ( state("isLayerwiseScaled") = Utils.isLayerwiseScaled(model) dataset.shuffle() - val numSamples = dataset.data(train = false).map(_.size()).reduce(_ + _) - var iter = dataset.data(train = true) + val _dataset = dataset.asInstanceOf[LocalDataSet[MiniBatch[T]]] + val numSamples = _dataset.data(train = false).map(_.size()).reduce(_ + _) + var iter = _dataset.data(train = true) logger.info("model thread pool size is " + Engine.model.getPoolSize) while (!endWhen(state)) { val start = System.nanoTime() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala index 4ee468a4a57..d5f217503cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala @@ -646,7 +646,7 @@ class ParallelOptimizer[T: ClassTag] ( asInstanceOf[Container[_, _, T]].modules, optimMethodMap(this.model.getName), optimMethodMap) } else { - require(optimMethodMap.contains(this._model.getName), + require(optimMethodMap.contains(this.model.getName), "single layer model should have optim method set") } @@ -675,7 +675,7 @@ class ParallelOptimizer[T: ClassTag] ( private def defaultPrioritize(): mutable.HashMap[String, Int] = { val priorities = new mutable.HashMap[String, Int] - val orders = ParallelOptimizer.getExecutionOrder(this._model) + val orders = ParallelOptimizer.getExecutionOrder(this.model) val len = orders.size orders.zipWithIndex.foreach(order => { priorities.put(order._1.getName, len - order._2) @@ -709,7 +709,7 @@ class ParallelOptimizer[T: ClassTag] ( state("warmupIterationNum") = warmupIterationNum state("computeThresholdbatchSize") = computeThresholdbatchSize state("maxDropPercentage") = maxDropPercentage - state("isLayerwiseScaled") = Utils.isLayerwiseScaled(_model) + state("isLayerwiseScaled") = Utils.isLayerwiseScaled(model) val nodeNumber = Engine.nodeNumber() val coresPerNode = Engine.coreNumber() From 78696531f412ce8cd30bf9b54a997aa893be536c Mon Sep 17 00:00:00 2001 From: Menooker Date: Fri, 24 May 2019 16:46:32 +0800 Subject: [PATCH 0906/1065] Revert "Optimizers: use member variable in parent class" This reverts commit 7e47204d --- .../bigdl/dllib/optim/DistriOptimizer.scala | 2 +- .../bigdl/dllib/optim/LocalOptimizer.scala | 19 +++++++++---------- .../bigdl/dllib/optim/ParallelOptimizer.scala | 6 +++--- 3 files changed, 13 insertions(+), 14 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 8794a6d873c..3bce315e3d4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -807,7 +807,7 @@ class DistriOptimizer[T: ClassTag] ( state("warmupIterationNum") = warmupIterationNum state("computeThresholdbatchSize") = computeThresholdbatchSize state("maxDropPercentage") = maxDropPercentage - state("isLayerwiseScaled") = Utils.isLayerwiseScaled(model) + state("isLayerwiseScaled") = Utils.isLayerwiseScaled(_model) val nodeNumber = Engine.nodeNumber() val coresPerNode = Engine.coreNumber() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala index ebf60846990..e8844cb82a8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LocalOptimizer.scala @@ -38,17 +38,17 @@ object LocalOptimizer { /** * Optimize a model on a single machine * - * @param _model model to be optimized - * @param _dataset data set - * @param _criterion criterion to be used + * @param model model to be optimized + * @param dataset data set + * @param criterion criterion to be used */ class LocalOptimizer[T: ClassTag] ( - _model: Module[T], - _dataset: LocalDataSet[MiniBatch[T]], - _criterion: Criterion[T] + model: Module[T], + dataset: LocalDataSet[MiniBatch[T]], + criterion: Criterion[T] )(implicit ev: TensorNumeric[T]) extends Optimizer[T, MiniBatch[T]]( - _model, _dataset, _criterion) { + model, dataset, criterion) { import LocalOptimizer._ import Optimizer.{header, saveModel, saveState, checkSubModules, getHyperParameterLog} @@ -114,9 +114,8 @@ class LocalOptimizer[T: ClassTag] ( state("isLayerwiseScaled") = Utils.isLayerwiseScaled(model) dataset.shuffle() - val _dataset = dataset.asInstanceOf[LocalDataSet[MiniBatch[T]]] - val numSamples = _dataset.data(train = false).map(_.size()).reduce(_ + _) - var iter = _dataset.data(train = true) + val numSamples = dataset.data(train = false).map(_.size()).reduce(_ + _) + var iter = dataset.data(train = true) logger.info("model thread pool size is " + Engine.model.getPoolSize) while (!endWhen(state)) { val start = System.nanoTime() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala index d5f217503cc..4ee468a4a57 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala @@ -646,7 +646,7 @@ class ParallelOptimizer[T: ClassTag] ( asInstanceOf[Container[_, _, T]].modules, optimMethodMap(this.model.getName), optimMethodMap) } else { - require(optimMethodMap.contains(this.model.getName), + require(optimMethodMap.contains(this._model.getName), "single layer model should have optim method set") } @@ -675,7 +675,7 @@ class ParallelOptimizer[T: ClassTag] ( private def defaultPrioritize(): mutable.HashMap[String, Int] = { val priorities = new mutable.HashMap[String, Int] - val orders = ParallelOptimizer.getExecutionOrder(this.model) + val orders = ParallelOptimizer.getExecutionOrder(this._model) val len = orders.size orders.zipWithIndex.foreach(order => { priorities.put(order._1.getName, len - order._2) @@ -709,7 +709,7 @@ class ParallelOptimizer[T: ClassTag] ( state("warmupIterationNum") = warmupIterationNum state("computeThresholdbatchSize") = computeThresholdbatchSize state("maxDropPercentage") = maxDropPercentage - state("isLayerwiseScaled") = Utils.isLayerwiseScaled(model) + state("isLayerwiseScaled") = Utils.isLayerwiseScaled(_model) val nodeNumber = Engine.nodeNumber() val coresPerNode = Engine.coreNumber() From 501a5770202cec0cc9beb17d9ecd0f66143d4314 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Mon, 27 May 2019 13:46:57 +0800 Subject: [PATCH 0907/1065] Dilation in MKL-DNN Convolution (#2815) * mkldnn-dilatedconv * mkldnn-dilatedconv * mkldnn-dilatedconv * mkldnn-dilatedconv * mkldnn-dilatedconv * mkldnn-dilatedconv * fix typos fix typos * make todo all uppercase --- .../intel/analytics/bigdl/utils/Engine.scala | 2 +- .../feature/dataset/image/ColorJitter.scala | 2 +- .../intel/analytics/bigdl/dllib/nn/ELU.scala | 2 +- .../dllib/nn/HingeEmbeddingCriterion.scala | 2 +- .../analytics/bigdl/dllib/nn/LogSigmoid.scala | 4 +- .../bigdl/dllib/nn/ResizeBilinear.scala | 2 +- .../bigdl/dllib/nn/SoftMarginCriterion.scala | 4 +- .../dllib/nn/mkldnn/SpatialConvolution.scala | 256 ++++++++++++++++-- .../utils/intermediate/ReflectionUtils.scala | 109 ++++++-- .../utils/serializer/ModuleSerializable.scala | 48 +--- .../nn/mkldnn/SpatialConvolutionSpec.scala | 73 +++++ 11 files changed, 407 insertions(+), 97 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index f79e091d573..1eea57289a9 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -253,7 +253,7 @@ object Engine { val coreNum = Runtime.getRuntime().availableProcessors() require(coreNum > 0, "Get a non-positive core number") // We assume the HT is enabled - // Todo: check the Hyper threading + // TODO: check the Hyper threading if (coreNum > 1) coreNum / 2 else 1 } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/ColorJitter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/ColorJitter.scala index 135ba795931..12b07408e29 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/ColorJitter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/image/ColorJitter.scala @@ -34,7 +34,7 @@ object ColorJitter { * Process an image with brightness, contrast, saturation in a random order */ class ColorJitter extends Transformer[LabeledBGRImage, LabeledBGRImage] { - // Todo: make the bcs parameter configurable + // TODO: make the bcs parameter configurable private val bcsParameters = Map("brightness" -> 0.4f, "contrast" -> 0.4f, "saturation" -> 0.4f) private var gs: Array[Float] = null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala index 2f65129e0b6..7c31496537b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ELU.scala @@ -36,7 +36,7 @@ class ELU[T: ClassTag]( val _alpha = ev.fromType[Double](alpha) - // Todo: Improve the performance of contiguous tensor + // TODO: Improve the performance of contiguous tensor override def updateOutput(input: Tensor[T]): Tensor[T] = { if (inplace) { input.apply1(in => { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HingeEmbeddingCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HingeEmbeddingCriterion.scala index f98b9e08cb9..ae41cd2121c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HingeEmbeddingCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/HingeEmbeddingCriterion.scala @@ -84,7 +84,7 @@ class HingeEmbeddingCriterion[@specialized(Float, Double) T: ClassTag]( output } - // Todo: Optimize performance to substitute apply3 + // TODO: Optimize performance to substitute apply3 override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { gradInput.resizeAs(input).copy(target) val func = new TensorFunc6[T] { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSigmoid.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSigmoid.scala index 93b3520bbfb..a6b7365d622 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSigmoid.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LogSigmoid.scala @@ -39,7 +39,7 @@ class LogSigmoid[T: ClassTag] (implicit ev: TensorNumeric[T]) output.resizeAs(input) buffer.resizeAs(input) - // Todo: Replace apply to get a better performance + // TODO: Replace apply to get a better performance val func = new TensorFunc6[T] { override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, data3: Array[T], offset3: Int): Unit = { @@ -59,7 +59,7 @@ class LogSigmoid[T: ClassTag] (implicit ev: TensorNumeric[T]) gradInput .resizeAs(buffer) - // Todo: Replace apply to get a better performance + // TODO: Replace apply to get a better performance val func = new TensorFunc6[T] { override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int, data3: Array[T], offset3: Int): Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala index 4c31a5366d5..4264572d7b0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ResizeBilinear.scala @@ -311,7 +311,7 @@ object ResizeBilinear { var _imageOffset = imageOffset var _outputOffset = outputOffset - // Todo: use multiple thread to speed up this + // TODO: use multiple thread to speed up this var b = 0 while(b < batchSize) { var y = 0 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMarginCriterion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMarginCriterion.scala index 56546dca6e4..60e2c16f598 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMarginCriterion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMarginCriterion.scala @@ -42,7 +42,7 @@ class SoftMarginCriterion[@specialized(Float, Double) T: ClassTag](var sizeAvera this } - // Todo: replace apply for performance optimization + // TODO: replace apply for performance optimization override def updateOutput(input: Tensor[T], target: Tensor[T]): T = { require(input.isSameSizeAs(target), "The input should have the same size as target" + s"input size ${input.nElement()}, target size ${target.nElement()}") @@ -63,7 +63,7 @@ class SoftMarginCriterion[@specialized(Float, Double) T: ClassTag](var sizeAvera output } - // Todo: replace apply for performance optimization + // TODO: replace apply for performance optimization override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = { require(input.isSameSizeAs(target), "The input should have the same size as target" + s"input size ${input.nElement()}, target size ${target.nElement()}") diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 1c59fdb73d0..e9e96f179fb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -25,6 +25,51 @@ import com.intel.analytics.bigdl.tensor.{DenseTensorMath, DnnTensor, Tensor} import scala.collection.mutable.ArrayBuffer +/** + * Applies a 2D convolution over an input image composed of several input planes. + * The input tensor in forward(input) is expected to be + * a 3D tensor (nInputPlane x height x width). + * + * nInputPlane The number of expected input planes in the image given into forward() + * nOutputPlane: The number of output planes the convolution layer will produce. + * kernelW: the kernel width of the convolution + * kernelH: The kernel height of the convolution + * strideW: Int = 1, The step of the convolution in the width dimension. + * strideH: Int = 1, The step of the convolution in the height dimension + * padW: Int = 0, The additional zeros added per width to the input planes. + * padH: Int = 0, The additional zeros added per height to the input planes. + * nGroup: Int = 1, Kernel group number + * propagateBack: Boolean = true, propagate gradient back + * wRegularizer: Regularizer[Float] = null, + * bRegularizer: Regularizer[Float] = null, + * initWeight: Tensor[Float] = null, + * initBias: Tensor[Float] = null, + * initGradWeight: Tensor[Float] = null, + * initGradBias: Tensor[Float] = null, + * withBias: Boolean = true, + * format: DataFormat = DataFormat.NCHW, + * dilationW: Int = 1, + * dilationH: Int = 1 + * + * When padW and padH are both -1, we use a padding algorithm similar to the "SAME" + * padding of tensorflow. That is + * + * outHeight = Math.ceil(inHeight.toFloat/strideH.toFloat) + * outWidth = Math.ceil(inWidth.toFloat/strideW.toFloat) + * + * padAlongHeight = Math.max(0, (outHeight - 1) * strideH + kernelH - inHeight) + * padAlongWidth = Math.max(0, (outWidth - 1) * strideW + kernelW - inWidth) + * + * padTop = padAlongHeight / 2 + * padLeft = padAlongWidth / 2 + * + * @param wRegularizer: instance of [[Regularizer]] + * (eg. L1 or L2 regularization), applied to the input weights matrices. + * @param bRegularizer: instance of [[Regularizer]] + * applied to the bias. + * @param dilationW: dilation width, default to 1 + * @param dilationH: dilation height, default to 1 + */ class SpatialConvolution( val nInputPlane: Int, val nOutputPlane: Int, @@ -43,7 +88,9 @@ class SpatialConvolution( val initGradWeight: Tensor[Float] = null, val initGradBias: Tensor[Float] = null, val withBias: Boolean = true, - val format: DataFormat = DataFormat.NCHW + val format: DataFormat = DataFormat.NCHW, + val dilationW: Int = 1, + val dilationH: Int = 1 ) extends MklDnnLayer with Initializable with Serializable with MklInt8Convertible { private val weightShape = if (nGroup == 1) { Array(nOutputPlane, nInputPlane, kernelH, kernelW) @@ -90,6 +137,7 @@ class SpatialConvolution( private var _dim = 1 private var _sumInput = false + def relu: Boolean = _relu def setReLU(value: Boolean = true): this.type = { _relu = value @@ -116,6 +164,22 @@ class SpatialConvolution( this } + // get padding type + private val paddingType: PaddingType.Value = getPaddingType() + + /* + Parameters for Dilated Convolution + In most deep learning frameworks, + the default value of dilation which defines regular convolution module is 1 + BigDL follows this convention + However the default value used by mkl-dnn is 0; + in order to keep consistensy, we internally transform them with deducting by 1. + */ + private val dilationW_mkldnn: Int = dilationW - 1 + private val dilationH_mkldnn: Int = dilationH - 1 + + + private def getOutputShape(oh: Int, ow: Int, batchSize: Int = -1): Array[Int] = { format match { case DataFormat.NCHW => @@ -192,20 +256,16 @@ class SpatialConvolution( val inputHeight = inputMemoryData.shape(2) // TODO only supports 4-D and nchw val inputWidth = inputMemoryData.shape(3) - val sizes = if (padW == -1 && padH == -1) { - NNUtils.getSAMEOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, - kernelW) - } else { - NNUtils.getOutSizeAndPadding(inputHeight, inputWidth, strideH, strideW, kernelH, kernelW, - padH, padW, ceilMode = false) - } + val convPaddingShape = getConvPaddingShape(inputHeight, inputWidth, paddingType) + val convOutputShape = getConvOutputShape(inputHeight, inputWidth, convPaddingShape) + + val padTop = convPaddingShape.top + val padBottom = convPaddingShape.bottom + val padLeft = convPaddingShape.left + val padRight = convPaddingShape.right + val outputHeight = convOutputShape.height + val outputWidth = convOutputShape.width - val padTop = sizes(0) - val padBottom = sizes(1) - val padLeft = sizes(2) - val padRight = sizes(3) - val outputHeight = sizes(4) - val outputWidth = sizes(5) paddingTL = Array(padTop, padLeft) paddingBR = Array(padBottom, padRight) @@ -258,13 +318,14 @@ class SpatialConvolution( val scaleWeight = this.getWeightScales().flatten.map { w => Scale.S8_MAX / w } // TODO check wether ForwardInference and ForwardTraining is the same - val desc = MklDnn.ConvForwardDescInit( + val desc = MklDnn.DilatedConvForwardDescInit( PropKind.ForwardTraining, AlgKind.ConvolutionDirect, src.getMemoryDescription(), wei.getMemoryDescription(), bis.getMemoryDescription(), dst.getMemoryDescription(), - Array(strideW, strideH), paddingTL, paddingBR, + Array(strideW, strideH), Array(dilationW_mkldnn, dilationH_mkldnn), + paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) forwardPrimDesc = if (relu || sum) { @@ -407,12 +468,15 @@ class SpatialConvolution( val bis = NativeData(Array(nOutputPlane), Memory.Format.x) val dst = NativeData(outputShape, Memory.Format.any) - val desc = MklDnn.ConvBackwardDataDescInit( + val desc = MklDnn.DilatedConvBackwardDataDescInit( AlgKind.ConvolutionDirect, src.getMemoryDescription(), wei.getMemoryDescription(), // TODO check correctness of strides and padding - dst.getMemoryDescription(), Array(strideW, strideH), paddingTL, paddingBR, + dst.getMemoryDescription(), + Array(strideW, strideH), Array(dilationW_mkldnn, dilationH_mkldnn), + paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) + val backwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) val List(realDiffSrc, realWei, realDiffDst) = @@ -472,13 +536,16 @@ class SpatialConvolution( val wei = NativeData(weightShape, Memory.Format.any) val bis = NativeData(Array(nOutputPlane), Memory.Format.x) - val desc = MklDnn.ConvBackwardWeightsDescInit( + val desc = MklDnn.DilatedConvBackwardWeightsDescInit( AlgKind.ConvolutionDirect, src.getMemoryDescription(), wei.getMemoryDescription(), bis.getMemoryDescription(), - grad(0).getMemoryDescription(), Array(strideW, strideH), paddingTL, paddingBR, + grad(0).getMemoryDescription(), + Array(strideW, strideH), Array(dilationW_mkldnn, dilationH_mkldnn), + paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) + val gradWeightPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) // TODO here seems some errors ?????? check the realSrc format. @@ -576,6 +643,114 @@ class SpatialConvolution( needQuantize = value this } + + /** + * TODO: + * (1) add calculation logic for Full padding + * (2) abstract and design an object type for return value, insteaof returnning + * the result as an int array + * Calculate padding size + * @param inputHeight height of input + * @param inputWidth width of input + * @param paddingType one of Same, Valid, Full + * @return an int array of length 4 representing padding sizes + * (top, bottom, left, right) + */ + private def getConvPaddingShape(inputHeight: Int, inputWidth: Int, + paddingType: PaddingType.Value): ConvPaddingShape = { + paddingType match { + case PaddingType.Same => + getConvPaddingShape(inputHeight, inputWidth, kernelH, kernelW, + strideH, strideW, dilationH, dilationW) + case PaddingType.Custom => ConvPaddingShape(padH, padH, padW, padW) + case _ => throw new IllegalArgumentException() + } + } + + /** + * Helper function to get convolution padding shape for Same Padding + * Steps: + * 1). calculate the dimension of dilated kernel + * dilated kernel = kernel + (kernel - 1) * (dilation - 1) + * 2). calculate the number of stride it would make + * number of stride = (input + stride - 1) / stride + * 3). calculate the number of pad needed to make + * number of pad = start of last stride + dilated kernel - input + * start of last stride = (number of stride - 1) * stride + * 4). assign the number of pad to left & right side + * @param inputHeight height of input + * @param inputWidth width of input + * @param kernelHeight height of kernel + * @param kernelWidth width of kernel + * @param strideHeight height of stride + * @param strideWidth width of stride + * @param dilationHeight height of dilation + * @param dilationWidth width of dilation + * @return ConvPaddingShape + */ + private def getConvPaddingShape(inputHeight: Int, inputWidth: Int, + kernelHeight: Int, kernelWidth: Int, + strideHeight: Int, strideWidth: Int, + dilationHeight: Int, dilationWidth: Int + ): ConvPaddingShape = { + val dilatedKernelHeight = kernelHeight + (kernelHeight - 1) * (dilationHeight - 1) + val dilatedKernelWidth = kernelWidth + (kernelWidth - 1) * (dilationWidth - 1) + val numOfStrideHeight = (inputHeight + strideHeight - 1) / strideHeight + val numOfStrideWidth = (inputWidth + strideWidth - 1) / strideWidth + val padAlongHeight = Math.max(0, + (numOfStrideHeight - 1) * strideHeight + dilatedKernelHeight - inputHeight) + val padAlongWidth = Math.max(0, + (numOfStrideWidth - 1) * strideWidth + dilatedKernelWidth - inputWidth) + val (padTop, padBottom) = (padAlongHeight / 2, (padAlongHeight + 1) / 2) + val (padLeft, padRight) = (padAlongWidth / 2, (padAlongWidth + 1) / 2) + ConvPaddingShape(padTop, padBottom, padLeft, padRight) + } + + + + /** + * Calculate convolution output shape + * Please try to keep the logic in consistent with MKL-DNN + * Reffernce: https://github.com/intel/mkl-dnn/blob/master/src/common/convolution.cpp#L117 + * @param inputH height of input + * @param inputW width of input + * @return a ConvOutputShape object + */ + private def getConvOutputShape(inputH: Int, inputW: Int, + paddingShape: ConvPaddingShape): ConvOutputShape = { + def getOutputLength(inputLength: Int, padLeft: Int, padRight: Int, + dilation: Int, kernelSize: Int, stride: Int): Int = { + val kernelRange = 1 + (kernelSize - 1) * (dilation + 1) + val outputLength = (inputLength - kernelRange + padLeft + padRight) / stride + 1 + return outputLength + } + val (padTop, padBottom, padLeft, padRight) = ( + paddingShape.top, paddingShape.bottom, + paddingShape.left, paddingShape.right + ) + val outputHeight = getOutputLength(inputH, padTop, padBottom, + dilationH_mkldnn, kernelH, strideH) + val outputWidth = getOutputLength(inputW, padLeft, padRight, + dilationW_mkldnn, kernelW, strideH) + + ConvOutputShape(outputHeight, outputWidth) + } + + + /** + * Get padding type + * @return PaddingType + */ + private def getPaddingType(): PaddingType.Value = { + if (padH == -1 && padW == -1) { + PaddingType.Same + } else if (padH >= 0 && padW >= 0) { + PaddingType.Custom + } else { + throw new IllegalArgumentException("Invalid padding") + } + } + } object SpatialConvolution { @@ -597,10 +772,13 @@ object SpatialConvolution { initGradWeight: Tensor[Float] = null, initGradBias: Tensor[Float] = null, withBias: Boolean = true, - format: DataFormat = DataFormat.NCHW): SpatialConvolution = { + format: DataFormat = DataFormat.NCHW, + dilationW: Int = 1, + dilationH: Int = 1): SpatialConvolution = { new SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, nGroup, propagateBack, wRegularizer, bRegularizer, - initWeight, initBias, initGradWeight, initGradBias, withBias, format) + initWeight, initBias, initGradWeight, + initGradBias, withBias, format, dilationW, dilationH) } } @@ -608,3 +786,37 @@ object Scale { val S8_MAX = 127.0f val U8_MAX = 255.0f } + + +/** + * Enum for padding type + * currently only support Same and Custom + */ +private[mkldnn] object PaddingType extends Enumeration { + val Same, Custom = Value +} + + +/** + * object to store meta for convolution padding shape + * @param top + * @param bottom + * @param left + * @param right + */ +private[mkldnn] case class ConvPaddingShape ( + top: Int, + bottom: Int, + left: Int, + right: Int +) + +/** + * case class to store meta for convolution output shape + * @param height + * @param width + */ +private[mkldnn] case class ConvOutputShape ( + height: Int, + width: Int +) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala index b0c789f02e4..f591583359c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala @@ -42,38 +42,61 @@ private[bigdl] object ReflectionUtils { } // create layer2 object form layer1 - private def reflection(layer1: Object, layer2: Class[_], - tags: Array[ClassTag[_]], numerics: Array[TensorNumeric[_]]) : Object = { - val nameAndValues = getFieldNameAndValues(layer1) - val constructorMirror = getCostructorMirror(layer2) + private def reflection(source: Object, target: Class[_], + tags: Array[ClassTag[_]], numerics: Array[TensorNumeric[_]]) : Object = { + val nameAndValues = getFieldNameAndValues(source) + val constructorMirror = getCostructorMirror(target) val constructorFullParams = constructorMirror.symbol.paramss val args = new Array[Object](constructorFullParams.map(_.size).sum) val tagIter = tags.iterator val numericIter = numerics.iterator - var i = 0 - constructorFullParams.foreach(map => { - map.foreach(param => { - val name = param.name.decodedName.toString - val ptype = param.typeSignature - if (ptype <:< universe.typeOf[ClassTag[_]]|| - ptype.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { + + val clsMirror = universe.runtimeMirror(target.getClassLoader) + val clsSymbol = clsMirror.classSymbol(target) + + /* + https://www.scala-lang.org/api/2.10.7/#scala.reflect.api.Symbols$Symbol + this line tries to get companion object of the class; + through the companion, default values can be accessed by calling + some static methods created by scala compiler, however it does not work when + the class is not a case class or has not defined a companion, which in this case, + calling companionSymbol returns universe.NoSymbol + */ + val companionSymbol = clsSymbol.companionSymbol + + val instanceMirror = companionSymbol match { + case universe.NoSymbol => null + case _ => + val compnInst = currentMirror.reflectModule(clsSymbol.companionSymbol.asModule).instance + clsMirror.reflect(compnInst) + } + + constructorFullParams.flatten.zipWithIndex.map { + case (param, idx) => + val pname = param.name.decodedName.toString + val ptypesig = param.typeSignature + if (ptypesig <:< universe.typeOf[ClassTag[_]]|| + ptypesig.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { require(tagIter.hasNext, "If your module contains multiple class tags, " + - s"do you forget to override getClassTagNumerics method ${layer1}") - args(i) = tagIter.next() - } else if (ptype <:< universe.typeOf[TensorNumeric[_]] - || ptype.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { - args(i) = numericIter.next() + "do you forget to override getClassTagNumerics method") + args(idx) = tagIter.next + } else if (ptypesig <:< universe.typeOf[TensorNumeric[_]] + || ptypesig.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { + args(idx) = numericIter.next } else { - val value = nameAndValues.get(name).getOrElse(null) - args(i) = value + val pvalue = if (nameAndValues.contains(pname)) { // for existing parameters + nameAndValues.get(pname).getOrElse(null) + } else { // parameter not found, get its default value + getPrimCtorDefaultParamValue(instanceMirror, param, idx) + } + args(idx) = pvalue } - i += 1 - }) - }) + } constructorMirror.apply(args : _*).asInstanceOf[Object] } + // create Module form IRElement def reflectFromIR[T : ClassTag](layer: IRElement[T], cls: Class[_]) : Module[T] = { val (tags, numerics) = layer.getOp().getClassTagNumerics() @@ -135,4 +158,48 @@ private[bigdl] object ReflectionUtils { case e: Throwable => throw e } } + + /** + * Get class primary consturctor's default parameter value by index + * @param instMirror instance mirror object of the class companion object + * @param paramSymbol symbol object of the target parameter with default value + * @param index the index of parameter in the class primary constructor + * @return AnyRef which is compatible with java Object + */ + def getPrimCtorDefaultParamValue(instMirror: universe.InstanceMirror, + paramSymbol: universe.Symbol, + index: Int): AnyRef = { + if (paramSymbol == null || paramSymbol == universe.NoSymbol || + instMirror == null || index < 0) { + return None + } + + if (!paramSymbol.asTerm.isParamWithDefault) { // param has no default value + None + } else { + val instTypeSig = instMirror.symbol.typeSignature + val methodName = getCtorDefaultParamMethodByIndex(index) + val methodSymbol = instTypeSig.member(universe.newTermName(methodName)) + if (methodSymbol == universe.NoSymbol) { // method not found + None + } + else { + // make the method call using reflection + // need to cast it as AnyRef to be compatible with Java Object type + instMirror.reflectMethod(methodSymbol.asMethod).apply().asInstanceOf[AnyRef] + } + } + } + + /** + * get string name of the method, which returns default value of the i-th parameter + * Reference: + * https://stackoverflow.com/questions/39657211/scala-class-constructors-default-argument-naming + * @param i parameter index in primary constructor + * @return method name in string, calling this method returns default value of i-th parameter + */ + def getCtorDefaultParamMethodByIndex(i: Int): String = { + s"$$lessinit$$greater$$default$$${i + 1}" + } + } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index cce2b031a6c..834ccf09b15 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -28,6 +28,7 @@ import com.intel.analytics.bigdl.utils.{Table, Shape => BigDLShape} import com.intel.analytics.bigdl.utils.serializer.converters.{DataConverter, ShapeConverter, TensorConverter} import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._ import com.intel.analytics.bigdl.serialization.Bigdl._ +import com.intel.analytics.bigdl.utils.intermediate.ReflectionUtils import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -142,6 +143,7 @@ trait ModuleSerializable extends Loadable with Savable{ clsMirror.reflect(compnInst) } + // Todo: to be replaced with ReflectionUtils.reflect constructorFullParams.flatten.foreach(param => { val pname = param.name.decodedName.toString val ptypesig = param.typeSignature @@ -158,7 +160,7 @@ trait ModuleSerializable extends Loadable with Savable{ val attrValue = modelAttributes.get(pname) DataConverter.getAttributeValue(context, attrValue) } else { // parameter not found, get its default value - getPrimCtorDefaultParamValue(instanceMirror, param, i) + ReflectionUtils.getPrimCtorDefaultParamValue(instanceMirror, param, i) } args(i) = pvalue } @@ -509,50 +511,6 @@ trait ModuleSerializable extends Loadable with Savable{ } } - - /** - * Get class primary consturctor's default parameter value by index - * @param instMirror instance mirror object of the class companion object - * @param paramSymbol symbol object of the target parameter with default value - * @param index the index of parameter in the class primary constructor - * @return AnyRef which is compatible with java Object - */ - private def getPrimCtorDefaultParamValue(instMirror: universe.InstanceMirror, - paramSymbol: universe.Symbol, - index: Int): AnyRef = { - if (paramSymbol == null || paramSymbol == universe.NoSymbol || - instMirror == null || index < 0) { - return None - } - - if (!paramSymbol.asTerm.isParamWithDefault) { // param has no default value - None - } else { - val instTypeSig = instMirror.symbol.typeSignature - val methodName = getCtorDefaultParamMethodByIndex(index) - val methodSymbol = instTypeSig.member(universe.newTermName(methodName)) - if (methodSymbol == universe.NoSymbol) { // method not found - None - } - else { - // make the method call using reflection - // need to cast it as AnyRef to be compatible with Java Object type - instMirror.reflectMethod(methodSymbol.asMethod).apply().asInstanceOf[AnyRef] - } - } - } - - /** - * get string name of the method, which returns default value of the i-th parameter - * Reference: - * https://stackoverflow.com/questions/39657211/scala-class-constructors-default-argument-naming - * @param i parameter index in primary constructor - * @return method name in string, calling this method returns default value of i-th parameter - */ - private def getCtorDefaultParamMethodByIndex(i: Int): String = { - s"$$lessinit$$greater$$default$$${i + 1}" - } - } trait ContainerSerializable extends ModuleSerializable { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index 4446d10adf7..85b7d07b4a5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -29,6 +29,79 @@ import org.scalatest.{FlatSpec, Matchers} import scala.util.Random class SpatialConvolutionSpec extends FlatSpec with Matchers { + + "MKL-DNN Dilated Convolution compared with BLAS Dilated Convolution" should "work correctly" in { + val nInputPlane = 2 + val nOutputPlane = 4 + val kW = 3 + val kH = 3 + val dW = 4 + val dH = 4 + val padW = 0 + val padH = 0 + var (dilationH, dilationW) = (1, 1) + + var input = Tensor[Float](2, 2, 23, 23).apply1(e => Random.nextFloat()) + var gradOutput = Tensor[Float](2, 4, 6, 6).apply1(e => Random.nextFloat()) + + + def compareHelper(input: Tensor[Float], gradOutput: Tensor[Float], + dilationHeight: Int, dilationWidth: Int): Unit = { + RNG.setSeed(100) + var mkldnnConv = SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, + dilationH = dilationH, dilationW = dilationW) + + + RNG.setSeed(100) + + val blasConv = nn.SpatialDilatedConvolution[Float](nInputPlane, nOutputPlane, kW, kH, dW, dH, + padW, padH, dilationH = dilationH, dilationW = dilationW) + + val mkldnnSeq = Sequential() + .add(Input(input.size(), Memory.Format.nchw)) + .add(mkldnnConv) + .add(ReorderMemory(HeapData(gradOutput.size(), Memory.Format.nchw))) + + mkldnnSeq.compile(TrainingPhase) + + val output = mkldnnSeq.forward(input) + val grad1 = mkldnnSeq.backward(input, gradOutput) + + val weight1 = mkldnnConv.weight.dense + val gradweight1 = mkldnnConv.gradWeight.dense + val bias1 = mkldnnConv.bias.dense + val gradbias1 = mkldnnConv.gradBias.dense + + val output2 = blasConv.forward(input) + val grad2 = blasConv.updateGradInput(input, gradOutput) + blasConv.accGradParameters(input, gradOutput) + + val weight2 = blasConv.weight + val gradweight2 = blasConv.gradWeight + val bias2 = blasConv.bias + val gradbias2 = blasConv.gradBias + + Equivalent.nearequals(weight1, weight2.resizeAs(weight1)) should be(true) + Equivalent.nearequals(gradweight1, gradweight2.resizeAs(gradweight1)) should be(true) + Equivalent.nearequals(bias1, bias2) should be(true) + Equivalent.nearequals(gradbias1, gradbias2) should be(true) + Equivalent.nearequals(output.toTensor, output2) should be(true) + Equivalent.nearequals(grad1.toTensor, grad2) should be(true) + } + + compareHelper(input, gradOutput, dilationH, dilationW) + + + + dilationH = 2 + dilationW = 2 + input = Tensor[Float](2, 2, 23, 23).apply1(e => Random.nextFloat()) + gradOutput = Tensor[Float](2, 4, 5, 5).apply1(e => Random.nextFloat()) + + compareHelper(input, gradOutput, dilationH, dilationW) + + } + "ConvolutionDnn with format=nchw and ngroup=1" should "work correctly" in { val nInputPlane = 2 val nOutputPlane = 4 From 89bfe23dee45c5690e30dd1fd792967410f72584 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 27 May 2019 15:43:30 +0800 Subject: [PATCH 0908/1065] fix: calculate arbitrary mask of scales (#2822) --- .../bigdl/dllib/nn/MklInt8Convertible.scala | 19 +- .../analytics/bigdl/dllib/nn/Utils.scala | 51 ++ .../dllib/nn/MklInt8ConvertibleSpec.scala | 810 ++++++++++++++++++ .../bigdl/dllib/nn/ScaleCalculatorSpec.scala | 810 ------------------ 4 files changed, 862 insertions(+), 828 deletions(-) delete mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala index cbc096a5bf1..76aba62b3cc 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8Convertible.scala @@ -165,24 +165,7 @@ trait MklInt8Convertible { // return the abs value of tensor as an array tensor.clone().abs().storage().toArray[Float] } else { - // mask bits are ON for some of dimensions - // slice storage according to the dimension if its mask bit is ON - // find and store the max for each subset - val scalesBuffer = ArrayBuffer.empty[Float] - val binStrMask: String = mask.toBinaryString - val binStrLen = binStrMask.length - val bitMask: Array[Int] = new Array(binStrLen) - - for(i <- 1 to binStrLen) { - bitMask(binStrLen - i) = binStrMask(binStrLen - i).asDigit - if (bitMask(binStrLen - i) == 1) { - val dimSize = tensor.size(i) - for (j <- 1 to dimSize) { - scalesBuffer.append(tensor.select(i, j).clone().abs().max()) - } - } - } - scalesBuffer.toArray[Float] + Utils.calcScales(tensor, mask) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala index 66dd2a6fd3d..4b9ad40163f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Utils.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.{T, Table} +import scala.collection.mutable import scala.reflect.ClassTag object Utils { @@ -570,4 +571,54 @@ object Utils { times: Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)]): (Long, Long) = { times.map(t => (t._2, t._3)).reduce((a, b) => (a._1 + b._1, a._2 + b._2)) } + + /** + * calculate scales of tensor based on the mask + * + * The mask parameter determines the dimension to which the scales array is applied to. + * If the ith bit of mask is set, it will select that dimension and calc scales on that. + * For a 5-dimensional tensor T[g0, o1,i2,h3,w4] where the numbering indicates the bit-index: + * + A mask = 3 = $2^0 | 2^1$ selects the group (g0) and output channels (o1). + * + A mask = 2 = $2^1$ selects the output channels (o1). + * For a [4, 3, 2, 2] tensor and 3 ( $2^0|2^1$ ) as the mask, it will generate 4*3=12 max values. + * + * @param tensor the tensor want to be caculated + * @param mask the mask value. You can construct it with math.pow(2, ?). + * @return the scales of tensor relevant with mask + */ + private[nn] def calcScales(tensor: Tensor[Float], mask: Int): Array[Float] = { + // inner helper function + def calcScalesHelper(tensor: Tensor[Float], maskStr: String, + result: mutable.ListBuffer[Float], index: Int): Unit = { + if (index < maskStr.length) { + if (maskStr(index).asDigit == 1) { // mask bit is ON at this dimension + (1 to tensor.size(index + 1)).foreach( + i => { // split the tensor based on its size + calcScalesHelper(tensor.narrow(index + 1, i, 1), maskStr, result, index + 1) + } + ) + } else { + calcScalesHelper(tensor, maskStr, result, index + 1) + } + + } else { // finished splitting tensor based on its mask bit, aggregate and append the result + result.append(tensor.clone().abs().max()) + } + + } + + def maskInterval: String = { + val start = 0 + val end = (math.pow(2, tensor.size().length) - 1).toInt + + s"mask should between [$start, $end]" + } + require(mask.toBinaryString.length <= tensor.size().length, s"$maskInterval") + + val result = mutable.ListBuffer[Float]() + + calcScalesHelper(tensor, mask.toBinaryString.reverse, result, 0 /* start dimension */) + + result.toArray + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8ConvertibleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8ConvertibleSpec.scala index 9740c38a8ec..dd2e3ec7851 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8ConvertibleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MklInt8ConvertibleSpec.scala @@ -16,13 +16,22 @@ package com.intel.analytics.bigdl.nn +import java.io.File import java.util.UUID + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.mkldnn.DnnGraph +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class MklInt8ConvertibleSpec extends FlatSpec with Matchers with BeforeAndAfter { + val modelPath: String = "myTestModel" + UUID.randomUUID().toString + val weightPath: String = "myTestModelWeight" + UUID.randomUUID().toString "Unit test setInputDimMask" should "work properly" in { val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1") @@ -139,5 +148,806 @@ class MklInt8ConvertibleSpec extends FlatSpec with Matchers with BeforeAndAfter } + "Calculating scales" should "work correct for BLAS Linear Module" in { + + val sampleMax = 999 + val inputSize = 120 + val outputSize = 1 + var inputMask = 0 + var outputMask = 0 + val inputTensor = make1DTensor(inputSize, sampleMax) + + // Global mask, null input + val linear0 = Linear[Float](inputSize, outputSize) + linear0.calcScales(null) + linear0.output.isEmpty should be (true) + linear0.getInputScales().isEmpty should be (true) + linear0.getOutputScales().isEmpty should be (true) + linear0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val linear1 = Linear[Float](inputSize, outputSize) + linear1.forward(inputTensor) + linear1.calcScales(inputTensor) + linear1.getInputScales() should be (Array(Array[Float](sampleMax))) + linear1.getOutputScales().length should be (1) + linear1.getOutputScales()(0).length should be (1) + linear1.getWeightScales().length should be (1) + linear1.getWeightScales()(0).length should be (1) + + // Single dimension mask, non-null input + val linear2 = Linear[Float](inputSize, outputSize) + inputMask = Math.pow(2, 0).toInt + outputMask = Math.pow(2, 0).toInt + linear2.setInputDimMask(inputMask, true) + linear2.setOutputDimMask(outputMask, true) + + linear2.forward(inputTensor) + linear2.calcScales(inputTensor) + val output2 = linear2.output + linear2.getInputScales() should be (Array(getScalesFromTensor(inputTensor, inputMask))) + linear2.getOutputScales() should be (Array(getScalesFromTensor(output2, outputMask))) + + linear2.saveModule(modelPath, weightPath, true) + + val loadedModule2 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(linear2, loadedModule2) + } + + "Calculating scales" should "work correct for DNN Linear Module" in { + import com.intel.analytics.bigdl.mkl.Memory + + val sampleMax = 999 + val inputSize = 2 + val outputSize = 2 + var inputMask = 0 + var outputMask = 0 + val inputTensor = Tensor[Float](Array(4, inputSize)).rand(-1, 1) + + // Global mask, null input + val linear0 = mkldnn.Linear(inputSize, outputSize) + linear0.calcScales(null) + + linear0.getInputScales().isEmpty should be (true) + linear0.getOutputScales().isEmpty should be (true) + linear0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val linear1 = mkldnn.Linear(inputSize, outputSize) + val seq1 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, inputSize), Memory.Format.nc)) + .add(linear1) + .add(mkldnn.Output(Memory.Format.nc)) + + seq1.compile(InferencePhase) + seq1.forward(inputTensor) + seq1.calcScales(inputTensor) + linear1.getInputScales() should be (Array(Array[Float](inputTensor.abs().max()))) + linear1.getOutputScales().length should be (1) + linear1.getOutputScales()(0).length should be (1) + linear1.getWeightScales().length should be (1) + linear1.getWeightScales()(0).length should be (1) + + // Single dimension mask, non-null input + val linear2 = mkldnn.Linear(inputSize, outputSize) + val seq2 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, inputSize), Memory.Format.nc)) + .add(linear2) + .add(mkldnn.Output(Memory.Format.nc)) + seq2.compile(InferencePhase) + + inputMask = Math.pow(2, 0).toInt + outputMask = Math.pow(2, 0).toInt + linear2.setInputDimMask(inputMask, true) + linear2.setOutputDimMask(outputMask, true) + + seq2.forward(inputTensor) + seq2.calcScales(inputTensor) + + val output2 = seq2.output.toTensor[Float] + linear2.getInputScales() should be (Array(getScalesFromTensor(inputTensor, inputMask))) + linear2.getOutputScales() should be (Array(getScalesFromTensor(output2, outputMask))) + + // for dnn linear, we skip the saveModule, because we do not support + } + + private def compareModules(modX: MklInt8Convertible, modY: MklInt8Convertible): Unit = { + modX.getInputDimMask() should be (modY.getInputDimMask()) + modX.getOutputDimMask() should be (modY.getOutputDimMask()) + modX.getWeightDimMask() should be (modY.getWeightDimMask()) + modX.getInputScales() should be (modY.getInputScales()) + modX.getOutputScales() should be (modY.getOutputScales()) + modX.getWeightScales() should be (modY.getWeightScales()) + } + + + "Calculating scales" should "work correct for BLAS Spatial Convolution Module" in { + val inputSize = 1 + val outputSize = 1 + val sampleMax = 999 + var dimMaskIdx = 0 + val inputTensor = make2DTensor().reshape(Array(inputSize, 3, 4)) + + // Global mask, null input + val spatialConv0 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv0.calcScales(null) + spatialConv0.output.isEmpty should be (true) + spatialConv0.getInputScales().isEmpty should be (true) + spatialConv0.getOutputScales().isEmpty should be (true) + spatialConv0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val spatialConv1 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv1.forward(inputTensor) + spatialConv1.calcScales(inputTensor) + spatialConv1.getInputScales() should be (Array(Array[Float](12))) + spatialConv1.getOutputScales().length should be (1) + spatialConv1.getOutputScales()(0).length should be (1) + spatialConv1.getWeightScales().length should be (1) + spatialConv1.getWeightScales()(0).length should be (1) + + // Single input dimension mask, non-null input + dimMaskIdx = 1 + val spatialConv2 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) + spatialConv2.forward(inputTensor) + spatialConv2.calcScales(inputTensor) + val inputScales2 = Array(Array(inputTensor.select(dimMaskIdx, 1).max())) + spatialConv2.getInputScales() should be (inputScales2) + + dimMaskIdx = 2 + val spatialConv3 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) + spatialConv3.forward(inputTensor) + spatialConv3.calcScales(inputTensor) + val inputScales3 = Array((1 to inputTensor.size(dimMaskIdx)).map( + idx => inputTensor.select(dimMaskIdx, idx).max() + ).toArray) + spatialConv3.getInputScales() should be (inputScales3) + + dimMaskIdx = 3 + val spatialConv4 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) + spatialConv4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) + spatialConv4.forward(inputTensor) + spatialConv4.calcScales(inputTensor) + val inputScales4 = Array((1 to inputTensor.size(dimMaskIdx)).map( + idx => inputTensor.select(dimMaskIdx, idx).max() + ).toArray) + spatialConv4.getInputScales() should be (inputScales4) + + spatialConv4.saveModule(modelPath, weightPath, true) + + val loadedModule4 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(spatialConv4, loadedModule4) + } + + "Calculating scales" should "work correct for DNN Spatial Convolution Module" in { + import com.intel.analytics.bigdl.mkl.Memory + val inputSize = 8 + val outputSize = 8 + var dimMaskIdx = 0 + val input = Tensor[Float](4, 8, 8, 8).rand(-1, 1) + + // Global mask, null input + val spatialConv0 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + spatialConv0.calcScales(null) + spatialConv0.getInputScales().isEmpty should be (true) + spatialConv0.getOutputScales().isEmpty should be (true) + spatialConv0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val spatialConv1 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + val seq1 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) + .add(spatialConv1) + .add(mkldnn.Output(Memory.Format.nchw)) + + seq1.compile(InferencePhase) + seq1.forward(input) + spatialConv1.calcScales(input) + + spatialConv1.getInputScales() should be (Array(Array[Float](input.clone().abs().max()))) + spatialConv1.getOutputScales().length should be (1) + spatialConv1.getOutputScales()(0).length should be (1) + spatialConv1.getWeightScales().length should be (1) + spatialConv1.getWeightScales()(0).length should be (1) + + seq1.release() + + // Single input dimension mask, non-null input + dimMaskIdx = 1 + val spatialConv2 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + val seq2 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) + .add(spatialConv2) + .add(mkldnn.Output(Memory.Format.nchw)) + seq2.compile(InferencePhase) + seq2.forward(input) + + seq2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) + seq2.calcScales(input) + + spatialConv2.getInputScales().length should be (1) + spatialConv2.getInputScales().flatten.length should be (4) + + seq2.release() + + dimMaskIdx = 2 + val spatialConv3 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + val seq3 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) + .add(spatialConv3) + .add(mkldnn.Output(Memory.Format.nchw)) + seq3.compile(InferencePhase) + seq3.forward(input) + + seq3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) + seq3.calcScales(input) + + val inputScales3 = Array((1 to input.size(dimMaskIdx)).map( + idx => input.select(dimMaskIdx, idx).abs().max() + ).toArray) + spatialConv3.getInputScales() should be (inputScales3) + + seq3.release() + + dimMaskIdx = 3 + val spatialConv4 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) + val seq4 = mkldnn.Sequential() + .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) + .add(spatialConv4) + .add(mkldnn.Output(Memory.Format.nchw)) + seq4.compile(InferencePhase) + seq4.forward(input) + + seq4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) + seq4.calcScales(input) + val inputScales4 = Array((1 to input.size(dimMaskIdx)).map( + idx => input.select(dimMaskIdx, idx).abs().max() + ).toArray) + spatialConv4.getInputScales() should be (inputScales4) + } + + "Calculating scales" should "work correct for BLAS Sequential Module" in { + var dimMaskIdx = 0 + var inputDimMask = 0 + var outputDimMask = 0 + + def makeSequential(): Sequential[Float] = { + val sequential = Sequential[Float]() + sequential.add(Reshape[Float](Array(1, 28, 28))) + .add(SpatialConvolution[Float](1, 6, 5, 5).setName("conv1_5x5")) + .add(Tanh()) + .add(SpatialMaxPooling[Float](2, 2, 2, 2)) + .add(SpatialConvolution[Float](6, 12, 5, 5).setName("conv2_5x5")) + .add(Tanh()) + .add(SpatialMaxPooling[Float](2, 2, 2, 2)) + .add(Reshape[Float](Array(12 * 4 * 4))) + .add(Linear[Float](12 * 4 * 4, 100).setName("fc1")) + .add(Tanh()) + .add(Linear[Float](100, 10).setName("fc2")) + .add(LogSoftMax[Float]()) + sequential + } + + val inputTensor = Tensor[Float](1, 28, 28).rand() + + // Global mask, null input + val sequential0 = makeSequential() + sequential0.calcScales(null) + sequential0.output should be (null) + sequential0.getInputScales().isEmpty should be (true) + sequential0.getOutputScales().isEmpty should be (true) + sequential0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val sequential1 = makeSequential() + sequential1.forward(inputTensor) + sequential1.calcScales(inputTensor) + sequential1.getInputScales().isEmpty should be (false) + sequential1.getInputScales().length should be (1) + sequential1.getInputScales()(0).length should be (1) + sequential1.getOutputScales().isEmpty should be (false) + sequential1.getOutputScales().length should be (1) + sequential1.getOutputScales()(0).length should be (1) + sequential1.getWeightScales().isEmpty should be (true) + val inputScales1 = Array(Array(inputTensor.abs().max())) + val outputScales1 = Array(Array(sequential1.output.toTensor[Float].abs().max())) + sequential1.getInputScales() should be (inputScales1) + sequential1.getOutputScales() should be (outputScales1) + sequentialValidationHelper(sequential1) + + sequential1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(sequential1, loadedModule1) + + val sequential2 = makeSequential() + sequential2.getInputDimMask() should be (0) + sequential2.getOutputDimMask() should be (0) + sequential2.getWeightDimMask() should be (0) + sequential2.modules.filter(_.isInstanceOf[MklInt8Convertible]).foreach(x => { + x.asInstanceOf[MklInt8Convertible].getInputDimMask() should be(0) + x.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be(0) + x.asInstanceOf[MklInt8Convertible].getWeightDimMask() should be(0) + }) + + sequential2.setInputDimMask(2, true) + sequential2.setOutputDimMask(2, true) + sequential2.setWeightDimMask(2, true) + + sequential2.getInputDimMask() should be (2) + sequential2.getOutputDimMask() should be (2) + sequential2.getWeightDimMask() should be (2) + sequential2.modules.filter(_.isInstanceOf[MklInt8Convertible]).foreach(x => { + x.asInstanceOf[MklInt8Convertible].getInputDimMask() should be(2) + x.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be(2) + x.asInstanceOf[MklInt8Convertible].getWeightDimMask() should be(2) + }) + } + + + "Calculating scales" should "work correct for BLAS ConcatTable Module" in { + val sampleMax = 999 + val numElem = 12 + val inputTensor = make1DTensor(numElem, sampleMax) + + def makeConcatTable(): ConcatTable[Float] = { + val concatTable = new ConcatTable[Float]().setName("concatTable") + concatTable.add(Linear[Float](numElem, 1).setName("A")) + concatTable.add(Linear[Float](numElem, 1).setName("B")) + concatTable + } + + // Global mask, null input + val concatTable0 = makeConcatTable() + concatTable0.setInputDimMask(0, true) + concatTable0.setOutputDimMask(0, true) + concatTable0.setWeightDimMask(0, true) + + concatTable0.calcScales(null) + concatTable0.getInputScales().isEmpty should be (true) + concatTable0.getOutputScales().isEmpty should be (true) + concatTable0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val concatTable1 = makeConcatTable() + + concatTable1.forward(inputTensor) + concatTable1.calcScales(inputTensor) + concatTable1.getInputScales() should be (Array(Array[Float](sampleMax))) + concatTable1.getOutputScales() should be ( + concatTable1.output.toTable.map((pair: (Any, Any)) => { + val key = pair._1 + val value: Tensor[Float] = pair._2.asInstanceOf[Tensor[Float]] + Array(value.abs().max()) + }).toArray + ) + concatTableValidationHelper(inputTensor, concatTable1, 0) + + concatTable1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(concatTable1, loadedModule1) + } + + "Calculating scales" should "work correct for BLAS CAddTable Module" in { + val sampleMax = 999 + val numElem = 12 + val inputTable = T( + Tensor[Float](Array(1.0f, 2.0f), Array(2)), + Tensor[Float](Array(3.0f, 1.0f), Array(2))) + + val caddTable0 = CAddTable[Float]() + caddTable0.setInputDimMask(0, true) + caddTable0.setOutputDimMask(0, true) + caddTable0.setWeightDimMask(0, true) + + caddTable0.calcScales(null) + + caddTable0.getInputScales().isEmpty should be (true) + caddTable0.getOutputScales().isEmpty should be (true) + caddTable0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val caddTable1 = CAddTable() + + caddTable1.forward(inputTable) + caddTable1.calcScales(inputTable) + caddTable1.getOutputScales() should be (Array(Array[Float](4.0f))) + caddTable1.getInputScales() should be ( + inputTable.toTable.map((pair: (Any, Any)) => { + val key = pair._1 + val value: Tensor[Float] = pair._2.asInstanceOf[Tensor[Float]] + Array(value.abs().max()) + }).toArray + ) + + caddTable1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(caddTable1, loadedModule1) + } + + "Calculating scales" should "work correct for BLAS ReLU Module" in { + val sampleMax = 999 + val numElem = 12 + val inputTensor = make1DTensor(numElem, sampleMax) + + val relu0 = ReLU[Float]() + relu0.setInputDimMask(0, true) + relu0.setOutputDimMask(0, true) + relu0.setWeightDimMask(0, true) + + relu0.calcScales(null) + + relu0.getInputScales().isEmpty should be (true) + relu0.getOutputScales().isEmpty should be (true) + relu0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val relu1 = ReLU[Float]() + + relu1.forward(inputTensor) + relu1.calcScales(inputTensor) + relu1.getInputScales() should be (Array(Array[Float](sampleMax))) + relu1.getOutputScales() should be (Array(Array[Float](relu1.output.max()))) + + relu1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(relu1, loadedModule1) + } + + "Calculating scales" should "work correct for BLAS SpatialBatchNormalization Module" in { + val numElem = 12 + val inputTensor = Tensor[Float](4, 2, 4, 4).rand(-100, 100) + + val bn0 = SpatialBatchNormalization[Float](2) + bn0.setInputDimMask(0, true) + bn0.setOutputDimMask(0, true) + bn0.setWeightDimMask(0, true) + + bn0.calcScales(null) + + bn0.getInputScales().isEmpty should be (true) + bn0.getOutputScales().isEmpty should be (true) + bn0.getWeightScales().isEmpty should be (true) + + // Global mask, non-null input + val bn1 = SpatialBatchNormalization[Float](2) + + bn1.forward(inputTensor) + bn1.calcScales(inputTensor) + bn1.getInputScales() should be (Array(Array[Float](inputTensor.abs().max()))) + bn1.getOutputScales() should be (Array(Array[Float](bn1.output.abs().max()))) + + bn1.saveModule(modelPath, weightPath, true) + + val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(bn1, loadedModule1) + } + + "Calculating scales" should "work correct for Graph Module" in { + def makeTestingGraph(): Graph[Float] = { + val input = Reshape(Array(1, 28, 28)).inputs() + val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5").inputs(input) + val tanh1 = Tanh().inputs(conv1) + val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1) + val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5").inputs(pool1) + val tanh2 = Tanh().inputs(conv2) + val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh2) + val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2) + val fc1 = Linear(12 * 4 * 4, 100).setName("fc1").inputs(reshape) + val tanh3 = Tanh().inputs(fc1) + val fc2 = Linear(100, 10).setName("fc2").inputs(tanh3) + val output = LogSoftMax().inputs(fc2) + + Graph(input, output) + } + + val inputTensor = Tensor(1, 28, 28).rand() + + // global mask, null input + val graph0 = makeTestingGraph() + graph0.setInputDimMask(0, true) + graph0.setOutputDimMask(0, true) + graph0.calcScales(null) + graph0.getInputDimMask() should be (0) + graph0.getOutputDimMask() should be (0) + graph0.getInputScales().isEmpty should be (true) + graph0.getOutputScales().isEmpty should be (true) + + // global mask, non-null input + val graph1 = makeTestingGraph() + graph1.setInputDimMask(0, true) + graph1.setOutputDimMask(0, true) + graph1.forward(inputTensor) + graph1.calcScales(inputTensor) + val graphOutput1 = graph1.output + + graph1.getInputDimMask() should be (0) + graph1.getOutputDimMask() should be (0) + graphOutput1 should not be (null) + graph1.getInputScales() should be (Array(Array(inputTensor.abs().max()))) + graph1.getOutputScales() should be (Array(Array(graphOutput1.toTensor.abs().max()))) + graphValidationHelper(graph1, inputTensor) + + graph1.saveModule(modelPath, weightPath, true) + + val loadedGraph1 = Module.loadModule[Float](modelPath, weightPath) + .asInstanceOf[MklInt8Convertible] + compareModules(graph1, loadedGraph1) + } + + "Calculating scales" should "work correct for DNN Graph Module" in { + import com.intel.analytics.bigdl.mkl.Memory + System.setProperty("bigdl.mkldnn.fusion", "false") + + def dnnGraph(batchSize: Int, classNum: Int): mkldnn.DnnGraph = { + val inputShape = Array(batchSize, 1, 28, 28) + val outputShape = Array(batchSize, 10) + + val input = mkldnn.Input(inputShape, Memory.Format.nchw).inputs() + val conv1 = mkldnn.SpatialConvolution(1, 20, 5, 5).setName("conv1").inputs(input) + val bn1 = mkldnn.SpatialBatchNormalization(20).setName("bn1").inputs(conv1) + val pool1 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool1").inputs(bn1) + val conv2 = mkldnn.SpatialConvolution(20, 50, 5, 5).setName("conv2").inputs(pool1) + val pool2 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool2").inputs(conv2) + val ip1 = mkldnn.Linear(50 * 4 * 4, 500).setName("ip1").inputs(pool2) + val relu1 = mkldnn.ReLU().setName("relu1").inputs(ip1) + val ip2 = mkldnn.Linear(500, 10).setName("ip2").inputs(relu1) + val output = mkldnn.ReorderMemory(mkldnn.HeapData(outputShape, Memory.Format.nc)).inputs(ip2) + + val graph = DnnGraph(Array(input), Array(output)) + graph.evaluate() + graph.compile(InferencePhase) + graph + } + + val inputTensor = Tensor(4, 1, 28, 28).rand() + + // global mask, null input + val graph0 = dnnGraph(4, 10) + graph0.setInputDimMask(0, true) + graph0.setOutputDimMask(0, true) + graph0.calcScales(null) + graph0.getInputDimMask() should be (0) + graph0.getOutputDimMask() should be (0) + graph0.getInputScales().isEmpty should be (true) + graph0.getOutputScales().isEmpty should be (true) + graph0.release() + + // global mask, non-null input + val graph1 = dnnGraph(4, 10) + graph1.setInputDimMask(0, true) + graph1.setOutputDimMask(0, true) + graph1.setWeightDimMask(1, true) + graph1.forward(inputTensor) + graph1.calcScales(inputTensor) + val graphOutput1 = graph1.output + + graph1.getInputDimMask() should be (0) + graph1.getOutputDimMask() should be (0) + graphOutput1 should not be (null) + + graph1.getForwardExecutions() + .filter(_.element.isInstanceOf[mkldnn.SpatialConvolution]) + .map(_.element.asInstanceOf[mkldnn.SpatialConvolution]) + .map(x => x.nOutputPlane == x.getWeightScales().flatten.length) + .exists(_ == false) should be (false) + + graph1.getForwardExecutions() + .filter(_.element.isInstanceOf[mkldnn.SpatialBatchNormalization]) + .map(_.element.asInstanceOf[mkldnn.SpatialBatchNormalization]) + .map(x => x.getOutputScales().flatten.length == 1 && x.getInputScales().flatten.length == 1) + .exists(_ == false) should be (false) + + graph1.getForwardExecutions() + .filter(_.element.isInstanceOf[mkldnn.ReLU]) + .map(_.element.asInstanceOf[mkldnn.ReLU]) + .map(x => x.getOutputScales().flatten.length == 1 && x.getInputScales().flatten.length == 1) + .exists(_ == false) should be (false) + + graph1.release() + System.clearProperty("bigdl.mkldnn.fusion") + } + + "calc scales with mask 3" should "work correctly" in { + var i = 0f + val tensor = Tensor(Array(4, 2, 2)).apply1(_ => { + i = i + 1 + i + }) + + println(tensor) + + val mask0 = Utils.calcScales(tensor, 0) + val mask1 = Utils.calcScales(tensor, 1) + val mask2 = Utils.calcScales(tensor, 2) + val mask3 = Utils.calcScales(tensor, 3) + val mask4 = Utils.calcScales(tensor, 4) + val mask5 = Utils.calcScales(tensor, 5) + val mask6 = Utils.calcScales(tensor, 6) + val mask7 = Utils.calcScales(tensor, 7) + + mask0 should be (Array(16.0)) + mask1 should be (Array(4, 8, 12, 16)) + mask2 should be (Array(14, 16)) + mask3 should be (Array(2, 4, 6, 8, 10, 12, 14, 16)) + mask4 should be (Array(15, 16)) + mask5 should be (Array(3, 4, 7, 8, 11, 12, 15, 16)) + mask6 should be (Array(13, 14, 15, 16)) + mask7 should be (Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)) + } + + + private def graphValidationHelper(graph: Graph[Float], inputActvt: Activity): Unit = { + val nextNodes = graph.getForwardExecutions() + var i = 0 + while (i < nextNodes.length) { + val currNode = nextNodes(i) + val currInputActvt = graph.findInput(currNode, inputActvt) + val currOutputActvt = currNode.element.output + if (currNode.element.isInstanceOf[MklInt8Convertible]) { + val currNodeInt8 = currNode.element.asInstanceOf[MklInt8Convertible] + val currInputScales = currNodeInt8.getInputScales() + val currOutputScales = currNodeInt8.getOutputScales() + currNodeInt8.getInputDimMask() should be (0) + currNodeInt8.getOutputDimMask() should be (0) + currNodeInt8.getInputScales() should be (Array(Array(currInputActvt.toTensor.abs().max()))) + currNodeInt8.getOutputScales() should be ( + Array(Array(currOutputActvt.toTensor.abs().max())) + ) + } + i += 1 + } + } + + + /** + * Iterate over modules inside the Sequential module, verify their calculated scales + * @param sequential the sequential to be verified + */ + private def sequentialValidationHelper(sequential: Sequential[Float]): Unit = { + + var prevModule: AbstractModule[_, _, Float] = null + val moduleIter = sequential.modules.iterator + + while (moduleIter.hasNext) { + val currModule = moduleIter.next() + if (currModule.isInstanceOf[MklInt8Convertible]) { + val currInputMask = currModule.asInstanceOf[MklInt8Convertible].getInputDimMask() + val currOutputMask = currModule.asInstanceOf[MklInt8Convertible].getOutputDimMask() + val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() + val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() + if (prevModule != null) { + val prevOutput = prevModule.output.asInstanceOf[Tensor[Float]] + Array(getScalesFromTensor(prevOutput, currInputMask)) should be (currInputScales) + } + Array(getScalesFromTensor(currModule.output.toTensor[Float], currOutputMask)) should + be (currOutputScales) + } + prevModule = currModule + } + } + + + /** + * Iterate over modules inside the ConcatTable module, verify their calculated scales + * @param inputTensor input of the ConcatTable + * @param concatTable the ConcatTable to be verified + */ + private def concatTableValidationHelper(inputTensor: Tensor[Float], + concatTable: ConcatTable[Float], + mask: Int): Unit = { + + val moduleIter = concatTable.modules.iterator + if (mask == 0) { + while (moduleIter.hasNext) { + val currModule = moduleIter.next() + val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() + val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() + currModule.asInstanceOf[MklInt8Convertible].getInputDimMask() should be (0) + currModule.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be (0) + inputTensor.max() should be (currInputScales(0)(0)) + currModule.output.toTensor[Float].max() should be (currOutputScales(0)(0)) + } + } else { + while (moduleIter.hasNext) { + val currModule = moduleIter.next() + val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() + val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() + val inputDimSize = inputTensor.size(mask) + val outputDimSize = currModule.output.toTensor[Float].size(mask) + + (1 to inputDimSize).map(idx => { + inputTensor.select(mask, idx).abs().max() + }).toArray should be (currInputScales) + + (1 to outputDimSize).map(idx => { + currModule.output.toTensor[Float].select(mask, idx).abs().max() + }).toArray should be (currOutputScales) + } + + } + } + + + /** + * Calculate the scales based on the input tensor and dimension mask + * @param tensor input tensor + * @param mask dimension mask + * @return an Array contains scales + */ + private def getScalesFromTensor(tensor: Tensor[Float], mask: Int): Array[Float] = { + + if (mask == 0) { + Array(tensor.abs().max()) + } else { + val dimSize = tensor.size(mask) + + (1 to dimSize).map(idx => { + tensor.select(mask, idx).abs().max() + }).toArray + } + + } + + + /** + * Helper method to make testing 2 dimensional tensor + * tensor = + * 01 10 03 12 + * 09 07 11 08 + * 05 02 06 04 + * + * @return a 2D tensor of float + */ + private def make2DTensor(): Tensor[Float] = { + val tensor = Tensor[Float](3, 4) + tensor.setValue(1, 1, 1) + tensor.setValue(1, 2, 10) + tensor.setValue(1, 3, 3) + tensor.setValue(1, 4, 12) + tensor.setValue(2, 1, 9) + tensor.setValue(2, 2, 7) + tensor.setValue(2, 3, 11) + tensor.setValue(2, 4, 8) + tensor.setValue(3, 1, 5) + tensor.setValue(3, 2, 2) + tensor.setValue(3, 3, 6) + tensor.setValue(3, 4, 4) + + tensor + } + + + /** + * Helper method to make testing 1 dimensional tensor + * @param n tensor size + * @param max max value of the random generated tensor + * @return a tensor of float + */ + private def make1DTensor(n: Int, max: Float): Tensor[Float] = { + val tensor = Tensor[Float](n) + tensor.rand(0, 100) + tensor.setValue(1, max) + tensor + } + + + after { + new File(modelPath).delete() + new File(weightPath).delete() + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala deleted file mode 100644 index ed34892f910..00000000000 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ScaleCalculatorSpec.scala +++ /dev/null @@ -1,810 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - - -package com.intel.analytics.bigdl.nn - -import java.io.File -import java.util.UUID - -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} -import com.intel.analytics.bigdl.nn.mkldnn.DnnGraph -import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.utils.T -import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} - -class ScaleCalculatorSpec extends FlatSpec with Matchers with BeforeAndAfter { - - val modelPath: String = "myTestModel" + UUID.randomUUID().toString - val weightPath: String = "myTestModelWeight" + UUID.randomUUID().toString - - - "Calculating scales" should "work correct for BLAS Linear Module" in { - - val sampleMax = 999 - val inputSize = 120 - val outputSize = 1 - var inputMask = 0 - var outputMask = 0 - val inputTensor = make1DTensor(inputSize, sampleMax) - - // Global mask, null input - val linear0 = Linear[Float](inputSize, outputSize) - linear0.calcScales(null) - linear0.output.isEmpty should be (true) - linear0.getInputScales().isEmpty should be (true) - linear0.getOutputScales().isEmpty should be (true) - linear0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val linear1 = Linear[Float](inputSize, outputSize) - linear1.forward(inputTensor) - linear1.calcScales(inputTensor) - linear1.getInputScales() should be (Array(Array[Float](sampleMax))) - linear1.getOutputScales().length should be (1) - linear1.getOutputScales()(0).length should be (1) - linear1.getWeightScales().length should be (1) - linear1.getWeightScales()(0).length should be (1) - - // Single dimension mask, non-null input - val linear2 = Linear[Float](inputSize, outputSize) - inputMask = Math.pow(2, 0).toInt - outputMask = Math.pow(2, 0).toInt - linear2.setInputDimMask(inputMask, true) - linear2.setOutputDimMask(outputMask, true) - - linear2.forward(inputTensor) - linear2.calcScales(inputTensor) - val output2 = linear2.output - linear2.getInputScales() should be (Array(getScalesFromTensor(inputTensor, inputMask))) - linear2.getOutputScales() should be (Array(getScalesFromTensor(output2, outputMask))) - - linear2.saveModule(modelPath, weightPath, true) - - val loadedModule2 = Module.loadModule[Float](modelPath, weightPath) - .asInstanceOf[MklInt8Convertible] - compareModules(linear2, loadedModule2) - } - - "Calculating scales" should "work correct for DNN Linear Module" in { - import com.intel.analytics.bigdl.mkl.Memory - - val sampleMax = 999 - val inputSize = 2 - val outputSize = 2 - var inputMask = 0 - var outputMask = 0 - val inputTensor = Tensor[Float](Array(4, inputSize)).rand(-1, 1) - - // Global mask, null input - val linear0 = mkldnn.Linear(inputSize, outputSize) - linear0.calcScales(null) - - linear0.getInputScales().isEmpty should be (true) - linear0.getOutputScales().isEmpty should be (true) - linear0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val linear1 = mkldnn.Linear(inputSize, outputSize) - val seq1 = mkldnn.Sequential() - .add(mkldnn.Input(Array(4, inputSize), Memory.Format.nc)) - .add(linear1) - .add(mkldnn.Output(Memory.Format.nc)) - - seq1.compile(InferencePhase) - seq1.forward(inputTensor) - seq1.calcScales(inputTensor) - linear1.getInputScales() should be (Array(Array[Float](inputTensor.abs().max()))) - linear1.getOutputScales().length should be (1) - linear1.getOutputScales()(0).length should be (1) - linear1.getWeightScales().length should be (1) - linear1.getWeightScales()(0).length should be (1) - - // Single dimension mask, non-null input - val linear2 = mkldnn.Linear(inputSize, outputSize) - val seq2 = mkldnn.Sequential() - .add(mkldnn.Input(Array(4, inputSize), Memory.Format.nc)) - .add(linear2) - .add(mkldnn.Output(Memory.Format.nc)) - seq2.compile(InferencePhase) - - inputMask = Math.pow(2, 0).toInt - outputMask = Math.pow(2, 0).toInt - linear2.setInputDimMask(inputMask, true) - linear2.setOutputDimMask(outputMask, true) - - seq2.forward(inputTensor) - seq2.calcScales(inputTensor) - - val output2 = seq2.output.toTensor[Float] - linear2.getInputScales() should be (Array(getScalesFromTensor(inputTensor, inputMask))) - linear2.getOutputScales() should be (Array(getScalesFromTensor(output2, outputMask))) - - // for dnn linear, we skip the saveModule, because we do not support - } - - private def compareModules(modX: MklInt8Convertible, modY: MklInt8Convertible): Unit = { - modX.getInputDimMask() should be (modY.getInputDimMask()) - modX.getOutputDimMask() should be (modY.getOutputDimMask()) - modX.getWeightDimMask() should be (modY.getWeightDimMask()) - modX.getInputScales() should be (modY.getInputScales()) - modX.getOutputScales() should be (modY.getOutputScales()) - modX.getWeightScales() should be (modY.getWeightScales()) - } - - - "Calculating scales" should "work correct for BLAS Spatial Convolution Module" in { - val inputSize = 1 - val outputSize = 1 - val sampleMax = 999 - var dimMaskIdx = 0 - val inputTensor = make2DTensor().reshape(Array(inputSize, 3, 4)) - - // Global mask, null input - val spatialConv0 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) - spatialConv0.calcScales(null) - spatialConv0.output.isEmpty should be (true) - spatialConv0.getInputScales().isEmpty should be (true) - spatialConv0.getOutputScales().isEmpty should be (true) - spatialConv0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val spatialConv1 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) - spatialConv1.forward(inputTensor) - spatialConv1.calcScales(inputTensor) - spatialConv1.getInputScales() should be (Array(Array[Float](12))) - spatialConv1.getOutputScales().length should be (1) - spatialConv1.getOutputScales()(0).length should be (1) - spatialConv1.getWeightScales().length should be (1) - spatialConv1.getWeightScales()(0).length should be (1) - - // Single input dimension mask, non-null input - dimMaskIdx = 1 - val spatialConv2 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) - spatialConv2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) - spatialConv2.forward(inputTensor) - spatialConv2.calcScales(inputTensor) - val inputScales2 = Array(Array(inputTensor.select(dimMaskIdx, 1).max())) - spatialConv2.getInputScales() should be (inputScales2) - - dimMaskIdx = 2 - val spatialConv3 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) - spatialConv3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) - spatialConv3.forward(inputTensor) - spatialConv3.calcScales(inputTensor) - val inputScales3 = Array((1 to inputTensor.size(dimMaskIdx)).map( - idx => inputTensor.select(dimMaskIdx, idx).max() - ).toArray) - spatialConv3.getInputScales() should be (inputScales3) - - dimMaskIdx = 3 - val spatialConv4 = SpatialConvolution[Float](inputSize, outputSize, 1, 1) - spatialConv4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) - spatialConv4.forward(inputTensor) - spatialConv4.calcScales(inputTensor) - val inputScales4 = Array((1 to inputTensor.size(dimMaskIdx)).map( - idx => inputTensor.select(dimMaskIdx, idx).max() - ).toArray) - spatialConv4.getInputScales() should be (inputScales4) - - spatialConv4.saveModule(modelPath, weightPath, true) - - val loadedModule4 = Module.loadModule[Float](modelPath, weightPath) - .asInstanceOf[MklInt8Convertible] - compareModules(spatialConv4, loadedModule4) - } - - "Calculating scales" should "work correct for DNN Spatial Convolution Module" in { - import com.intel.analytics.bigdl.mkl.Memory - val inputSize = 8 - val outputSize = 8 - var dimMaskIdx = 0 - val input = Tensor[Float](4, 8, 8, 8).rand(-1, 1) - - // Global mask, null input - val spatialConv0 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) - spatialConv0.calcScales(null) - spatialConv0.getInputScales().isEmpty should be (true) - spatialConv0.getOutputScales().isEmpty should be (true) - spatialConv0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val spatialConv1 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) - val seq1 = mkldnn.Sequential() - .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) - .add(spatialConv1) - .add(mkldnn.Output(Memory.Format.nchw)) - - seq1.compile(InferencePhase) - seq1.forward(input) - spatialConv1.calcScales(input) - - spatialConv1.getInputScales() should be (Array(Array[Float](input.clone().abs().max()))) - spatialConv1.getOutputScales().length should be (1) - spatialConv1.getOutputScales()(0).length should be (1) - spatialConv1.getWeightScales().length should be (1) - spatialConv1.getWeightScales()(0).length should be (1) - - seq1.release() - - // Single input dimension mask, non-null input - dimMaskIdx = 1 - val spatialConv2 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) - val seq2 = mkldnn.Sequential() - .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) - .add(spatialConv2) - .add(mkldnn.Output(Memory.Format.nchw)) - seq2.compile(InferencePhase) - seq2.forward(input) - - seq2.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) - seq2.calcScales(input) - - spatialConv2.getInputScales().length should be (1) - spatialConv2.getInputScales().flatten.length should be (4) - - seq2.release() - - dimMaskIdx = 2 - val spatialConv3 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) - val seq3 = mkldnn.Sequential() - .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) - .add(spatialConv3) - .add(mkldnn.Output(Memory.Format.nchw)) - seq3.compile(InferencePhase) - seq3.forward(input) - - seq3.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) - seq3.calcScales(input) - - val inputScales3 = Array((1 to input.size(dimMaskIdx)).map( - idx => input.select(dimMaskIdx, idx).abs().max() - ).toArray) - spatialConv3.getInputScales() should be (inputScales3) - - seq3.release() - - dimMaskIdx = 3 - val spatialConv4 = mkldnn.SpatialConvolution(inputSize, outputSize, 1, 1) - val seq4 = mkldnn.Sequential() - .add(mkldnn.Input(Array(4, 8, 8, 8), Memory.Format.nchw)) - .add(spatialConv4) - .add(mkldnn.Output(Memory.Format.nchw)) - seq4.compile(InferencePhase) - seq4.forward(input) - - seq4.setInputDimMask(Math.pow(2, dimMaskIdx - 1).toInt, true) - seq4.calcScales(input) - val inputScales4 = Array((1 to input.size(dimMaskIdx)).map( - idx => input.select(dimMaskIdx, idx).abs().max() - ).toArray) - spatialConv4.getInputScales() should be (inputScales4) - } - - "Calculating scales" should "work correct for BLAS Sequential Module" in { - var dimMaskIdx = 0 - var inputDimMask = 0 - var outputDimMask = 0 - - def makeSequential(): Sequential[Float] = { - val sequential = Sequential[Float]() - sequential.add(Reshape[Float](Array(1, 28, 28))) - .add(SpatialConvolution[Float](1, 6, 5, 5).setName("conv1_5x5")) - .add(Tanh()) - .add(SpatialMaxPooling[Float](2, 2, 2, 2)) - .add(SpatialConvolution[Float](6, 12, 5, 5).setName("conv2_5x5")) - .add(Tanh()) - .add(SpatialMaxPooling[Float](2, 2, 2, 2)) - .add(Reshape[Float](Array(12 * 4 * 4))) - .add(Linear[Float](12 * 4 * 4, 100).setName("fc1")) - .add(Tanh()) - .add(Linear[Float](100, 10).setName("fc2")) - .add(LogSoftMax[Float]()) - sequential - } - - val inputTensor = Tensor[Float](1, 28, 28).rand() - - // Global mask, null input - val sequential0 = makeSequential() - sequential0.calcScales(null) - sequential0.output should be (null) - sequential0.getInputScales().isEmpty should be (true) - sequential0.getOutputScales().isEmpty should be (true) - sequential0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val sequential1 = makeSequential() - sequential1.forward(inputTensor) - sequential1.calcScales(inputTensor) - sequential1.getInputScales().isEmpty should be (false) - sequential1.getInputScales().length should be (1) - sequential1.getInputScales()(0).length should be (1) - sequential1.getOutputScales().isEmpty should be (false) - sequential1.getOutputScales().length should be (1) - sequential1.getOutputScales()(0).length should be (1) - sequential1.getWeightScales().isEmpty should be (true) - val inputScales1 = Array(Array(inputTensor.abs().max())) - val outputScales1 = Array(Array(sequential1.output.toTensor[Float].abs().max())) - sequential1.getInputScales() should be (inputScales1) - sequential1.getOutputScales() should be (outputScales1) - sequentialValidationHelper(sequential1) - - sequential1.saveModule(modelPath, weightPath, true) - - val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) - .asInstanceOf[MklInt8Convertible] - compareModules(sequential1, loadedModule1) - - val sequential2 = makeSequential() - sequential2.getInputDimMask() should be (0) - sequential2.getOutputDimMask() should be (0) - sequential2.getWeightDimMask() should be (0) - sequential2.modules.filter(_.isInstanceOf[MklInt8Convertible]).foreach(x => { - x.asInstanceOf[MklInt8Convertible].getInputDimMask() should be(0) - x.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be(0) - x.asInstanceOf[MklInt8Convertible].getWeightDimMask() should be(0) - }) - - sequential2.setInputDimMask(2, true) - sequential2.setOutputDimMask(2, true) - sequential2.setWeightDimMask(2, true) - - sequential2.getInputDimMask() should be (2) - sequential2.getOutputDimMask() should be (2) - sequential2.getWeightDimMask() should be (2) - sequential2.modules.filter(_.isInstanceOf[MklInt8Convertible]).foreach(x => { - x.asInstanceOf[MklInt8Convertible].getInputDimMask() should be(2) - x.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be(2) - x.asInstanceOf[MklInt8Convertible].getWeightDimMask() should be(2) - }) - } - - - "Calculating scales" should "work correct for BLAS ConcatTable Module" in { - val sampleMax = 999 - val numElem = 12 - val inputTensor = make1DTensor(numElem, sampleMax) - - def makeConcatTable(): ConcatTable[Float] = { - val concatTable = new ConcatTable[Float]().setName("concatTable") - concatTable.add(Linear[Float](numElem, 1).setName("A")) - concatTable.add(Linear[Float](numElem, 1).setName("B")) - concatTable - } - - // Global mask, null input - val concatTable0 = makeConcatTable() - concatTable0.setInputDimMask(0, true) - concatTable0.setOutputDimMask(0, true) - concatTable0.setWeightDimMask(0, true) - - concatTable0.calcScales(null) - concatTable0.getInputScales().isEmpty should be (true) - concatTable0.getOutputScales().isEmpty should be (true) - concatTable0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val concatTable1 = makeConcatTable() - - concatTable1.forward(inputTensor) - concatTable1.calcScales(inputTensor) - concatTable1.getInputScales() should be (Array(Array[Float](sampleMax))) - concatTable1.getOutputScales() should be ( - concatTable1.output.toTable.map((pair: (Any, Any)) => { - val key = pair._1 - val value: Tensor[Float] = pair._2.asInstanceOf[Tensor[Float]] - Array(value.abs().max()) - }).toArray - ) - concatTableValidationHelper(inputTensor, concatTable1, 0) - - concatTable1.saveModule(modelPath, weightPath, true) - - val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) - .asInstanceOf[MklInt8Convertible] - compareModules(concatTable1, loadedModule1) - } - - "Calculating scales" should "work correct for BLAS CAddTable Module" in { - val sampleMax = 999 - val numElem = 12 - val inputTable = T( - Tensor[Float](Array(1.0f, 2.0f), Array(2)), - Tensor[Float](Array(3.0f, 1.0f), Array(2))) - - val caddTable0 = CAddTable[Float]() - caddTable0.setInputDimMask(0, true) - caddTable0.setOutputDimMask(0, true) - caddTable0.setWeightDimMask(0, true) - - caddTable0.calcScales(null) - - caddTable0.getInputScales().isEmpty should be (true) - caddTable0.getOutputScales().isEmpty should be (true) - caddTable0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val caddTable1 = CAddTable() - - caddTable1.forward(inputTable) - caddTable1.calcScales(inputTable) - caddTable1.getOutputScales() should be (Array(Array[Float](4.0f))) - caddTable1.getInputScales() should be ( - inputTable.toTable.map((pair: (Any, Any)) => { - val key = pair._1 - val value: Tensor[Float] = pair._2.asInstanceOf[Tensor[Float]] - Array(value.abs().max()) - }).toArray - ) - - caddTable1.saveModule(modelPath, weightPath, true) - - val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) - .asInstanceOf[MklInt8Convertible] - compareModules(caddTable1, loadedModule1) - } - - "Calculating scales" should "work correct for BLAS ReLU Module" in { - val sampleMax = 999 - val numElem = 12 - val inputTensor = make1DTensor(numElem, sampleMax) - - val relu0 = ReLU[Float]() - relu0.setInputDimMask(0, true) - relu0.setOutputDimMask(0, true) - relu0.setWeightDimMask(0, true) - - relu0.calcScales(null) - - relu0.getInputScales().isEmpty should be (true) - relu0.getOutputScales().isEmpty should be (true) - relu0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val relu1 = ReLU[Float]() - - relu1.forward(inputTensor) - relu1.calcScales(inputTensor) - relu1.getInputScales() should be (Array(Array[Float](sampleMax))) - relu1.getOutputScales() should be (Array(Array[Float](relu1.output.max()))) - - relu1.saveModule(modelPath, weightPath, true) - - val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) - .asInstanceOf[MklInt8Convertible] - compareModules(relu1, loadedModule1) - } - - "Calculating scales" should "work correct for BLAS SpatialBatchNormalization Module" in { - val numElem = 12 - val inputTensor = Tensor[Float](4, 2, 4, 4).rand(-100, 100) - - val bn0 = SpatialBatchNormalization[Float](2) - bn0.setInputDimMask(0, true) - bn0.setOutputDimMask(0, true) - bn0.setWeightDimMask(0, true) - - bn0.calcScales(null) - - bn0.getInputScales().isEmpty should be (true) - bn0.getOutputScales().isEmpty should be (true) - bn0.getWeightScales().isEmpty should be (true) - - // Global mask, non-null input - val bn1 = SpatialBatchNormalization[Float](2) - - bn1.forward(inputTensor) - bn1.calcScales(inputTensor) - bn1.getInputScales() should be (Array(Array[Float](inputTensor.abs().max()))) - bn1.getOutputScales() should be (Array(Array[Float](bn1.output.abs().max()))) - - bn1.saveModule(modelPath, weightPath, true) - - val loadedModule1 = Module.loadModule[Float](modelPath, weightPath) - .asInstanceOf[MklInt8Convertible] - compareModules(bn1, loadedModule1) - } - - "Calculating scales" should "work correct for Graph Module" in { - def makeTestingGraph(): Graph[Float] = { - val input = Reshape(Array(1, 28, 28)).inputs() - val conv1 = SpatialConvolution(1, 6, 5, 5).setName("conv1_5x5").inputs(input) - val tanh1 = Tanh().inputs(conv1) - val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh1) - val conv2 = SpatialConvolution(6, 12, 5, 5).setName("conv2_5x5").inputs(pool1) - val tanh2 = Tanh().inputs(conv2) - val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(tanh2) - val reshape = Reshape(Array(12 * 4 * 4)).inputs(pool2) - val fc1 = Linear(12 * 4 * 4, 100).setName("fc1").inputs(reshape) - val tanh3 = Tanh().inputs(fc1) - val fc2 = Linear(100, 10).setName("fc2").inputs(tanh3) - val output = LogSoftMax().inputs(fc2) - - Graph(input, output) - } - - val inputTensor = Tensor(1, 28, 28).rand() - - // global mask, null input - val graph0 = makeTestingGraph() - graph0.setInputDimMask(0, true) - graph0.setOutputDimMask(0, true) - graph0.calcScales(null) - graph0.getInputDimMask() should be (0) - graph0.getOutputDimMask() should be (0) - graph0.getInputScales().isEmpty should be (true) - graph0.getOutputScales().isEmpty should be (true) - - // global mask, non-null input - val graph1 = makeTestingGraph() - graph1.setInputDimMask(0, true) - graph1.setOutputDimMask(0, true) - graph1.forward(inputTensor) - graph1.calcScales(inputTensor) - val graphOutput1 = graph1.output - - graph1.getInputDimMask() should be (0) - graph1.getOutputDimMask() should be (0) - graphOutput1 should not be (null) - graph1.getInputScales() should be (Array(Array(inputTensor.abs().max()))) - graph1.getOutputScales() should be (Array(Array(graphOutput1.toTensor.abs().max()))) - graphValidationHelper(graph1, inputTensor) - - graph1.saveModule(modelPath, weightPath, true) - - val loadedGraph1 = Module.loadModule[Float](modelPath, weightPath) - .asInstanceOf[MklInt8Convertible] - compareModules(graph1, loadedGraph1) - } - - "Calculating scales" should "work correct for DNN Graph Module" in { - import com.intel.analytics.bigdl.mkl.Memory - System.setProperty("bigdl.mkldnn.fusion", "false") - - def dnnGraph(batchSize: Int, classNum: Int): mkldnn.DnnGraph = { - val inputShape = Array(batchSize, 1, 28, 28) - val outputShape = Array(batchSize, 10) - - val input = mkldnn.Input(inputShape, Memory.Format.nchw).inputs() - val conv1 = mkldnn.SpatialConvolution(1, 20, 5, 5).setName("conv1").inputs(input) - val bn1 = mkldnn.SpatialBatchNormalization(20).setName("bn1").inputs(conv1) - val pool1 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool1").inputs(bn1) - val conv2 = mkldnn.SpatialConvolution(20, 50, 5, 5).setName("conv2").inputs(pool1) - val pool2 = mkldnn.MaxPooling(2, 2, 2, 2).setName("pool2").inputs(conv2) - val ip1 = mkldnn.Linear(50 * 4 * 4, 500).setName("ip1").inputs(pool2) - val relu1 = mkldnn.ReLU().setName("relu1").inputs(ip1) - val ip2 = mkldnn.Linear(500, 10).setName("ip2").inputs(relu1) - val output = mkldnn.ReorderMemory(mkldnn.HeapData(outputShape, Memory.Format.nc)).inputs(ip2) - - val graph = DnnGraph(Array(input), Array(output)) - graph.evaluate() - graph.compile(InferencePhase) - graph - } - - val inputTensor = Tensor(4, 1, 28, 28).rand() - - // global mask, null input - val graph0 = dnnGraph(4, 10) - graph0.setInputDimMask(0, true) - graph0.setOutputDimMask(0, true) - graph0.calcScales(null) - graph0.getInputDimMask() should be (0) - graph0.getOutputDimMask() should be (0) - graph0.getInputScales().isEmpty should be (true) - graph0.getOutputScales().isEmpty should be (true) - graph0.release() - - // global mask, non-null input - val graph1 = dnnGraph(4, 10) - graph1.setInputDimMask(0, true) - graph1.setOutputDimMask(0, true) - graph1.setWeightDimMask(1, true) - graph1.forward(inputTensor) - graph1.calcScales(inputTensor) - val graphOutput1 = graph1.output - - graph1.getInputDimMask() should be (0) - graph1.getOutputDimMask() should be (0) - graphOutput1 should not be (null) - - graph1.getForwardExecutions() - .filter(_.element.isInstanceOf[mkldnn.SpatialConvolution]) - .map(_.element.asInstanceOf[mkldnn.SpatialConvolution]) - .map(x => x.nOutputPlane == x.getWeightScales().flatten.length) - .exists(_ == false) should be (false) - - graph1.getForwardExecutions() - .filter(_.element.isInstanceOf[mkldnn.SpatialBatchNormalization]) - .map(_.element.asInstanceOf[mkldnn.SpatialBatchNormalization]) - .map(x => x.getOutputScales().flatten.length == 1 && x.getInputScales().flatten.length == 1) - .exists(_ == false) should be (false) - - graph1.getForwardExecutions() - .filter(_.element.isInstanceOf[mkldnn.ReLU]) - .map(_.element.asInstanceOf[mkldnn.ReLU]) - .map(x => x.getOutputScales().flatten.length == 1 && x.getInputScales().flatten.length == 1) - .exists(_ == false) should be (false) - - graph1.release() - System.clearProperty("bigdl.mkldnn.fusion") - } - - - private def graphValidationHelper(graph: Graph[Float], inputActvt: Activity): Unit = { - val nextNodes = graph.getForwardExecutions() - var i = 0 - while (i < nextNodes.length) { - val currNode = nextNodes(i) - val currInputActvt = graph.findInput(currNode, inputActvt) - val currOutputActvt = currNode.element.output - if (currNode.element.isInstanceOf[MklInt8Convertible]) { - val currNodeInt8 = currNode.element.asInstanceOf[MklInt8Convertible] - val currInputScales = currNodeInt8.getInputScales() - val currOutputScales = currNodeInt8.getOutputScales() - currNodeInt8.getInputDimMask() should be (0) - currNodeInt8.getOutputDimMask() should be (0) - currNodeInt8.getInputScales() should be (Array(Array(currInputActvt.toTensor.abs().max()))) - currNodeInt8.getOutputScales() should be ( - Array(Array(currOutputActvt.toTensor.abs().max())) - ) - } - i += 1 - } - } - - - /** - * Iterate over modules inside the Sequential module, verify their calculated scales - * @param sequential the sequential to be verified - */ - private def sequentialValidationHelper(sequential: Sequential[Float]): Unit = { - - var prevModule: AbstractModule[_, _, Float] = null - val moduleIter = sequential.modules.iterator - - while (moduleIter.hasNext) { - val currModule = moduleIter.next() - if (currModule.isInstanceOf[MklInt8Convertible]) { - val currInputMask = currModule.asInstanceOf[MklInt8Convertible].getInputDimMask() - val currOutputMask = currModule.asInstanceOf[MklInt8Convertible].getOutputDimMask() - val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() - val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() - if (prevModule != null) { - val prevOutput = prevModule.output.asInstanceOf[Tensor[Float]] - Array(getScalesFromTensor(prevOutput, currInputMask)) should be (currInputScales) - } - Array(getScalesFromTensor(currModule.output.toTensor[Float], currOutputMask)) should - be (currOutputScales) - } - prevModule = currModule - } - } - - - /** - * Iterate over modules inside the ConcatTable module, verify their calculated scales - * @param inputTensor input of the ConcatTable - * @param concatTable the ConcatTable to be verified - */ - private def concatTableValidationHelper(inputTensor: Tensor[Float], - concatTable: ConcatTable[Float], - mask: Int): Unit = { - - val moduleIter = concatTable.modules.iterator - if (mask == 0) { - while (moduleIter.hasNext) { - val currModule = moduleIter.next() - val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() - val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() - currModule.asInstanceOf[MklInt8Convertible].getInputDimMask() should be (0) - currModule.asInstanceOf[MklInt8Convertible].getOutputDimMask() should be (0) - inputTensor.max() should be (currInputScales(0)(0)) - currModule.output.toTensor[Float].max() should be (currOutputScales(0)(0)) - } - } else { - while (moduleIter.hasNext) { - val currModule = moduleIter.next() - val currInputScales = currModule.asInstanceOf[MklInt8Convertible].getInputScales() - val currOutputScales = currModule.asInstanceOf[MklInt8Convertible].getOutputScales() - val inputDimSize = inputTensor.size(mask) - val outputDimSize = currModule.output.toTensor[Float].size(mask) - - (1 to inputDimSize).map(idx => { - inputTensor.select(mask, idx).abs().max() - }).toArray should be (currInputScales) - - (1 to outputDimSize).map(idx => { - currModule.output.toTensor[Float].select(mask, idx).abs().max() - }).toArray should be (currOutputScales) - } - - } - } - - - /** - * Calculate the scales based on the input tensor and dimension mask - * @param tensor input tensor - * @param mask dimension mask - * @return an Array contains scales - */ - private def getScalesFromTensor(tensor: Tensor[Float], mask: Int): Array[Float] = { - - if (mask == 0) { - Array(tensor.abs().max()) - } else { - val dimSize = tensor.size(mask) - - (1 to dimSize).map(idx => { - tensor.select(mask, idx).abs().max() - }).toArray - } - - } - - - /** - * Helper method to make testing 2 dimensional tensor - * tensor = - * 01 10 03 12 - * 09 07 11 08 - * 05 02 06 04 - * - * @return a 2D tensor of float - */ - private def make2DTensor(): Tensor[Float] = { - val tensor = Tensor[Float](3, 4) - tensor.setValue(1, 1, 1) - tensor.setValue(1, 2, 10) - tensor.setValue(1, 3, 3) - tensor.setValue(1, 4, 12) - tensor.setValue(2, 1, 9) - tensor.setValue(2, 2, 7) - tensor.setValue(2, 3, 11) - tensor.setValue(2, 4, 8) - tensor.setValue(3, 1, 5) - tensor.setValue(3, 2, 2) - tensor.setValue(3, 3, 6) - tensor.setValue(3, 4, 4) - - tensor - } - - - /** - * Helper method to make testing 1 dimensional tensor - * @param n tensor size - * @param max max value of the random generated tensor - * @return a tensor of float - */ - private def make1DTensor(n: Int, max: Float): Tensor[Float] = { - val tensor = Tensor[Float](n) - tensor.rand(0, 100) - tensor.setValue(1, max) - tensor - } - - - after { - new File(modelPath).delete() - new File(weightPath).delete() - } - -} From 3ade4047bfae6136d3b9e5d2e46e0fa13f05b570 Mon Sep 17 00:00:00 2001 From: Menooker Date: Tue, 4 Jun 2019 15:23:21 +0800 Subject: [PATCH 0909/1065] Use one AllReduceParameter for multi-optim method training (#2814) * enhancement: use one shared allreduceparameter * update localPartitionRange * change random seed in UT --- .../bigdl/dllib/optim/AbstractOptimizer.scala | 15 +- .../bigdl/dllib/optim/DistriOptimizer.scala | 274 +++++++++--------- .../bigdl/dllib/optim/ParallelOptimizer.scala | 55 ++-- .../optim/parameters/AllReduceParameter.scala | 22 +- .../dllib/optim/DistriOptimizerSpec.scala | 2 +- 5 files changed, 191 insertions(+), 177 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala index 49df1177698..96d905ac1d4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/AbstractOptimizer.scala @@ -34,7 +34,7 @@ abstract class AbstractOptimizer { protected def getModel[T: ClassTag]( models: RDD[Cache[T]], - parameters: Map[String, AllReduceParameter[T]], + parameters: AllReduceParameter[T], trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Module[T] /** @@ -48,7 +48,7 @@ abstract class AbstractOptimizer { trainSummary: TrainSummary, models: RDD[Cache[T]], driverState: Table, - parameters: Map[String, AllReduceParameter[T]], + parameters: AllReduceParameter[T], trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { val currentIteration = driverState[Int]("neval") - 1 val parametersTrigger = trainSummary.getSummaryTrigger("Parameters") @@ -98,7 +98,7 @@ abstract class AbstractOptimizer { state: Table, validationSummary: Option[ValidationSummary], header: String, - parameters: Map[String, AllReduceParameter[T]] = null): Unit = { + parameters: AllReduceParameter[T] = null): Unit = { if (validationTrigger.isEmpty || validationDataSet.isEmpty) { return } @@ -122,10 +122,9 @@ abstract class AbstractOptimizer { // update with latest weight for validation if (parameters != null) { - val weightsResults = parameters.values.map(p => - p.getWeights(cached.modelWeights.head.narrow(1, p.paramOffset, p.size)) - ).toArray - weightsResults.foreach(_.waitResult()) + parameters.getWeights(cached.modelWeights.head.narrow(1, + parameters.paramOffset, parameters.size)) + .waitResult() } if (Engine.getEngineType() == MklDnn) { @@ -210,7 +209,7 @@ abstract class AbstractOptimizer { wallClockTime: Long, models: RDD[Cache[T]], state: Table, - parameters: Map[String, AllReduceParameter[T]], + parameters: AllReduceParameter[T], optimMethods: Map[String, OptimMethod[T]], trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Unit = { cacheTrigger.foreach { trigger => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 3bce315e3d4..ae17674731b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -16,40 +16,32 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.{Module, _} -import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, PaddingParam, Sample, SampleToMiniBatch} -import com.intel.analytics.bigdl.nn.{Container, Module, Utils} +import com.intel.analytics.bigdl.dataset.{DistributedDataSet, MiniBatch, PaddingParam, Sample} +import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer, MklDnnModule} +import com.intel.analytics.bigdl.nn.{Container, Graph, Module, Utils} import com.intel.analytics.bigdl.parameters.{AllReduceParameter, ParameterProcessor} -import com.intel.analytics.bigdl.nn.{Container, Module, Utils} -import com.intel.analytics.bigdl.parameters.AllReduceParameter -import com.intel.analytics.bigdl.tensor.{FloatType, Tensor} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.intermediate.{ConversionUtils, IRGraph} +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} +import com.intel.analytics.bigdl.{Module, _} import java.io.{File, FilenameFilter} import java.text.SimpleDateFormat import java.util.Calendar - -import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} -import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase -import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer, MklDnnLayer, MklDnnModule} -import com.intel.analytics.bigdl.utils.intermediate.IRGraph import org.apache.commons.lang.exception.ExceptionUtils -import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} import org.apache.log4j.Logger -import org.apache.spark.network.netty.SparkTransportConf -import org.apache.spark.{SparkContext, TaskContext} -import org.apache.spark.rdd.{RDD, ZippedPartitionsWithLocalityRDD} - +import org.apache.spark.TaskContext +import org.apache.spark.rdd.RDD import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.concurrent.Future import scala.reflect.ClassTag -import com.intel.analytics.bigdl.nn.{Container, Graph, Module, Utils} -import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer, MklDnnLayer} -import com.intel.analytics.bigdl.utils.intermediate.{ConversionUtils, IRGraph} object DistriOptimizer extends AbstractOptimizer { + import Optimizer._ val logger: Logger = Logger.getLogger(getClass) @@ -91,6 +83,7 @@ object DistriOptimizer extends AbstractOptimizer { * @param models cached models * @param optimMethods optimization methods * @param parameters [[AllReduceParameter]] + * @param parameterSplits the segments of parameters (offset, length) * @param validationTrigger validation trigger * @param validationDataSet validation dataset * @param validationMethods validation methods @@ -110,7 +103,8 @@ object DistriOptimizer extends AbstractOptimizer { metrics: Metrics, models: RDD[Cache[T]], optimMethods: Map[String, OptimMethod[T]], - parameters: Map[String, AllReduceParameter[T]], + parameters: AllReduceParameter[T], + parameterSplits: Map[String, (Int, Int)], validationTrigger: Option[Trigger], validationDataSet: Option[DataSet[MiniBatch[T]]], validationMethods: Option[Array[ValidationMethod[T]]], @@ -127,7 +121,7 @@ object DistriOptimizer extends AbstractOptimizer { var lastEpochTime = 0L // driverState is needed to prevent serializing the whole optimizer - optimMethods.values.foreach{ optimMethod => + optimMethods.values.foreach { optimMethod => if (!optimMethod.state.contains("epoch")) optimMethod.state.update("epoch", 1) if (!optimMethod.state.contains("neval")) optimMethod.state.update("neval", 1) if (!optimMethod.state.contains("Loss")) { @@ -203,7 +197,6 @@ object DistriOptimizer extends AbstractOptimizer { val driverMetrics = metrics val start = System.nanoTime() - /* Run the forwards/backwards pass using multiple threads in each partition, and track the number of model updates that finished before the thread timeout mechanism. @@ -216,9 +209,8 @@ object DistriOptimizer extends AbstractOptimizer { Note: All models in `cached` share the same storage for weights, so we only need to copy the weights from parameter server into the first model's weights. */ - val weightsResults = parameters.values.map(p => - p.getWeights(cached.modelWeights.head.narrow(1, p.paramOffset, p.size)) - ).toArray + val weightsResults = parameters.getWeights(cached.modelWeights.head.narrow(1, + parameters.paramOffset, parameters.size)) val miniBatchBuffer = new Array[MiniBatch[T]](_subModelNumber) val batch = data.next() val stackSize = batch.size() / _subModelNumber @@ -238,7 +230,7 @@ object DistriOptimizer extends AbstractOptimizer { } }) Engine.default.sync(tasks) - weightsResults.foreach(_.waitResult()) + weightsResults.waitResult() val weightSyncTime = System.nanoTime() - syWStart driverMetrics.add("get weights average", weightSyncTime) driverMetrics.add("get weights for each node", weightSyncTime) @@ -291,41 +283,40 @@ object DistriOptimizer extends AbstractOptimizer { if (finishedThreads.nonEmpty) { val finishedGradients = finishedThreads.map(cached.modelGradients(_)) - parameters.values.foreach { p => - time = System.nanoTime() - val pOffset = p.paramOffset - val pLength = p.size - val taskSize = pLength / _subModelNumber - val extraTask = pLength % _subModelNumber - - // Aggregate multi-model's gradient to the first model's gradient - val parallelNum = if (taskSize == 0) extraTask else _subModelNumber - if (parallelNum != 1) { - Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { - val offset = pOffset + tid * taskSize + math.min(tid, extraTask) - val length = taskSize + (if (tid < extraTask) 1 else 0) - var i = 1 - while (i < finishedGradients.length) { - finishedGradients(0).narrow(1, offset, length) - .add(finishedGradients(i).narrow(1, offset, length)) - i += 1 - } - })) - driverMetrics.add("aggregate gradient time", System.nanoTime() - time) - } - val putG = System.nanoTime() - // Put first finished model's gradient who aggregated - // all other models' gradient to AllReduceParameter - p.putGradients(finishedGradients(0).narrow(1, pOffset, pLength)) - driverMetrics.add("put gradient", System.nanoTime() - putG) + + time = System.nanoTime() + val pOffset = parameters.paramOffset + val pLength = parameters.size + val taskSize = pLength / _subModelNumber + val extraTask = pLength % _subModelNumber + + // Aggregate multi-model's gradient to the first model's gradient + val parallelNum = if (taskSize == 0) extraTask else _subModelNumber + if (parallelNum != 1) { + Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { + val offset = pOffset + tid * taskSize + math.min(tid, extraTask) + val length = taskSize + (if (tid < extraTask) 1 else 0) + var i = 1 + while (i < finishedGradients.length) { + finishedGradients(0).narrow(1, offset, length) + .add(finishedGradients(i).narrow(1, offset, length)) + i += 1 + } + })) + driverMetrics.add("aggregate gradient time", System.nanoTime() - time) } + val putG = System.nanoTime() + // Put first finished model's gradient who aggregated + // all other models' gradient to AllReduceParameter + parameters.putGradients(finishedGradients(0).narrow(1, pOffset, pLength)) + driverMetrics.add("put gradient", System.nanoTime() - putG) + } else { val putG = System.nanoTime() // zero gradient in BlockManager when no thread finished. cached.modelGradients(0).zero() - parameters.values.foreach{p => - p.putGradients(cached.modelGradients(0).narrow(1, p.paramOffset, p.size)) - } + parameters.putGradients(cached.modelGradients(0).narrow(1, parameters.paramOffset, + parameters.size)) driverMetrics.add("put gradient", System.nanoTime() - putG) } @@ -339,7 +330,7 @@ object DistriOptimizer extends AbstractOptimizer { } Iterator.single(finishedThreads.size) } - }.reduce(_ + _) + }.reduce(_ + _) dropModelNumBatch += (driverSubModelNum - numFinishedModelUpdates) if (dropPercentage == 0.0 || @@ -352,41 +343,48 @@ object DistriOptimizer extends AbstractOptimizer { driverState("isGradientUpdated") = false // parameterProcesser like L2NormClippingProcessor may aggregate gradient, // and change the value of isGradientUpdated in driverState. - parameters.foreach { p => - parameterProcessers.foreach(_.collectGlobalData(models, p._2, metrics, driverState)) - } + parameterProcessers.foreach(_.collectGlobalData(models, parameters, metrics, driverState)) + val isGradientUpdated = driverState[Boolean]("isGradientUpdated") val stateBroadcast = sc.broadcast(driverState) models.mapPartitions { modelIter => + val (paramLocalStart, paramLocalLen) = parameters.localPartitionRange val modelCache = modelIter.next() // if parameterProcesser has aggregated gradient, we can skip this aggregation. if (!isGradientUpdated) { val getG = System.nanoTime() - parameters.values.foreach(_.aggregateGradientPartition(numFinishedModelUpdates)) + parameters.aggregateGradientPartition(numFinishedModelUpdates) driverMetrics.add("aggregrateGradientParition average executor", System.nanoTime() - getG) } - parameters.foreach { p => - parameterProcessers.foreach(_.processParameters(p._2, modelCache, driverState)) - } - modelCache.optimMethods.foreach{ case (name, optimMethod) => - var time = System.nanoTime() + parameterProcessers.foreach(_.processParameters(parameters, modelCache, driverState)) + + modelCache.optimMethods.foreach { case (name, optimMethod) => + optimMethod.state.update("epoch", driverState[Int]("epoch")) optimMethod.state.update("neval", driverState[Int]("neval")) optimMethod.state.update("Loss", driverState[Float]("Loss")) if (validationMethods.isDefined) { optimMethod.state.update("score", driverState[Float]("score")) } + val p = parameterSplits(name) + val startIdx = Math.max(paramLocalStart, p._1) + val endIdx = Math.min(paramLocalStart + paramLocalLen, p._1 + p._2) + if (endIdx > startIdx) { + + optimMethod.optimize(_ => (ev.fromType(value), parameters.gradientPartition.narrow(1, + startIdx - paramLocalStart + 1, endIdx - startIdx)), + parameters.weightPartition.narrow(1, + startIdx - paramLocalStart + 1, endIdx - startIdx)) + } - val p = parameters(name) - optimMethod.optimize(_ => (ev.fromType(value), p.gradientPartition), - p.weightPartition) - driverMetrics.add("compute weight average", System.nanoTime() - time) - time = System.nanoTime() - p.sendWeightPartition() - driverMetrics.add("send weights average", System.nanoTime() - time) } + var time = System.nanoTime() + driverMetrics.add("compute weight average", System.nanoTime() - time) + parameters.sendWeightPartition() + time = System.nanoTime() + driverMetrics.add("send weights average", System.nanoTime() - time) Iterator.empty }.count() @@ -396,7 +394,7 @@ object DistriOptimizer extends AbstractOptimizer { wallClockTime += end - start driverState("isGradientUpdated") = true driverState("Loss") = lossSum.value.toFloat / numFinishedModelUpdates - optimMethods.foreach{ v => + optimMethods.foreach { v => v._2.updateHyperParameter() } // TODO: Support show learningrate for multiOptimMethod @@ -407,7 +405,8 @@ object DistriOptimizer extends AbstractOptimizer { driverState[Int]("neval"), wallClockTime) logger.info(s"${_header} Trained ${recordsNum.value} records in ${(end - start) / 1e9} " + s"seconds. Throughput is ${driverState("Throughput")} records/second. Loss is ${ - driverState("Loss")}. ${getHyperParameterLog(optimMethods)}") + driverState("Loss") + }. ${getHyperParameterLog(optimMethods)}") logger.debug("\n" + metrics.summary()) logger.debug("Dropped modules: " + (driverSubModelNum - numFinishedModelUpdates)) lossArray = new Array[Double](_subModelNumber) @@ -422,7 +421,7 @@ object DistriOptimizer extends AbstractOptimizer { val k = (dropPercentage * computeThresholdbatchSize * driverSubModelNum).toInt if (k > dropModelNumBatch) { - threshold = Util.kthLargest(moduleTimeList, 0, moduleTimeList.length-1, + threshold = Util.kthLargest(moduleTimeList, 0, moduleTimeList.length - 1, k - dropModelNumBatch) } else { threshold = (threshold * 1.01).toLong @@ -521,7 +520,9 @@ object DistriOptimizer extends AbstractOptimizer { * @param nodeNumber node number * @param coresPerNode cores per node * @param checkSingleton if checkSingleton - * @param parameters all reduce parameter instance + * @param allReduceParameter all reduce parameter instance + * @param parameterSplits the mapping from module names to the parameter segments (offset, + * length) * @param validationMethods validation methods * @param optimMethod optimization method * @param parameterProcessors a list of ParameterProcessor used to process parameters @@ -535,11 +536,13 @@ object DistriOptimizer extends AbstractOptimizer { nodeNumber: Int, coresPerNode: Int, checkSingleton: Boolean, - parameters: Map[String, AllReduceParameter[T]], + allReduceParameter: AllReduceParameter[T], + parameterSplits: Map[String, (Int, Int)], validationMethods: Option[Array[ValidationMethod[T]]], optimMethod: Map[String, OptimMethod[T]], parameterProcessors: ArrayBuffer[ParameterProcessor] - )(implicit ev: TensorNumeric[T]): (RDD[DistriOptimizer.Cache[T]], ModelBroadcast[T]) = { + )(implicit ev: TensorNumeric[T]): (RDD[DistriOptimizer + .Cache[T]], ModelBroadcast[T]) = { val sc = dataset.originRDD().sparkContext val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod)) // ensure model's parameter is compacted for getting a better performance when broadcasting @@ -572,12 +575,14 @@ object DistriOptimizer extends AbstractOptimizer { if (!Engine.checkSingleton()) { if (checkSingleton) { require(Engine.checkSingleton(), "Partitions of the training data are not evenly" + - "distributed across the executors in the Spark cluster; are there sufficient training" + + "distributed across the executors in the Spark cluster; are there sufficient " + + "training" + "data to be distributed? Set property \"bigdl.check.singleton\" to false to skip " + "this check") } else { logger.warn("Partitions of the training data are not evenly" + - "distributed across the executors in the Spark cluster; are there sufficient training" + + "distributed across the executors in the Spark cluster; are there sufficient " + + "training" + "data to be distributed?") } } @@ -605,9 +610,8 @@ object DistriOptimizer extends AbstractOptimizer { logger.info("model thread pool size is " + Engine.model.getPoolSize) val weights = cached.head._2 - parameters.foreach(v => - v._2.init(weights.narrow(1, v._2.paramOffset, v._2.size)) - ) + allReduceParameter.init(weights.narrow(1, allReduceParameter.paramOffset, + allReduceParameter.size)) Iterator.single(Cache( cached.map(_._1), // models @@ -645,8 +649,10 @@ object DistriOptimizer extends AbstractOptimizer { */ override protected def getModel[T: ClassTag]( models: RDD[Cache[T]], - parameters: Map[String, AllReduceParameter[T]], - trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = { + parameters: AllReduceParameter[T], + trainingModel: Module[T])(implicit + ev: TensorNumeric[T]) + : Module[T] = { val partitionNum = models.partitions.length val extraState = models.map(_.localModels.head.getExtraParameter()).first() trainingModel.setExtraParameter(extraState) @@ -659,27 +665,25 @@ object DistriOptimizer extends AbstractOptimizer { val (parameter, gradientParameter) = trainingModel.getParameters() - parameters.foreach { case (moduleName, p) => - val currentModule = trainingModel(moduleName) - require(currentModule.isDefined, s"Couldn't find $moduleName in $trainingModel") - val (weights, gradients) = models.mapPartitions(iter => { - val cached = iter.next() - val curPartitionId = TaskContext.getPartitionId() - Iterator.single((Map(curPartitionId -> p.weightPartition), - Map(curPartitionId -> p.gradientPartition))) - }).reduce((a, b) => (a._1 ++ b._1, a._2 ++ b._2)) - - val taskSize = p.size / partitionNum - require(taskSize != 0, "parameter length should not less than partition number") - val extraSize = p.size % partitionNum - - (0 until partitionNum).map(pid => { - val start = p.paramOffset + pid * taskSize + math.min(pid, extraSize) - val length = taskSize + (if (pid < extraSize) 1 else 0) - parameter.narrow(1, start, length).copy(weights(pid)) - gradientParameter.narrow(1, start, length).copy(gradients(pid)) - }) - } + + val (weights, gradients) = models.mapPartitions(iter => { + val cached = iter.next() + val curPartitionId = TaskContext.getPartitionId() + Iterator.single((Map(curPartitionId -> parameters.weightPartition), + Map(curPartitionId -> parameters.gradientPartition))) + }).reduce((a, b) => (a._1 ++ b._1, a._2 ++ b._2)) + + val taskSize = parameters.size / partitionNum + require(taskSize != 0, "parameter length should not less than partition number") + val extraSize = parameters.size % partitionNum + + (0 until partitionNum).map(pid => { + val start = parameters.paramOffset + pid * taskSize + math.min(pid, extraSize) + val length = taskSize + (if (pid < extraSize) 1 else 0) + parameter.narrow(1, start, length).copy(weights(pid)) + gradientParameter.narrow(1, start, length).copy(gradients(pid)) + }) + trainingModel } @@ -693,11 +697,11 @@ object DistriOptimizer extends AbstractOptimizer { * @param _dataset train dataset * @param _criterion loss function */ -class DistriOptimizer[T: ClassTag] ( +class DistriOptimizer[T: ClassTag]( _model: Module[T], _dataset: DistributedDataSet[MiniBatch[T]], _criterion: Criterion[T] - )(implicit ev: TensorNumeric[T]) +)(implicit ev: TensorNumeric[T]) extends Optimizer[T, MiniBatch[T]]( _model, _dataset, _criterion) { val metrics = new Metrics @@ -713,8 +717,8 @@ class DistriOptimizer[T: ClassTag] ( * This method will be called at the end of optimize. You need not call it if optimize succeed. * If the optimize fails, you may call it before next optimize. */ - def clearState() : Unit = { - DistriOptimizer.clearState(models) + def clearState(): Unit = { + DistriOptimizer.clearState(models) } @@ -730,6 +734,7 @@ class DistriOptimizer[T: ClassTag] ( * If you want to reserve optimMethod for each worker and reuse those methods in * next training task, please set reserve = true * Otherwise, if just using optimMethod you set in optimizer, please set reserve = false + * * @param reserve whether to reserve optim method for each worker * @return */ @@ -741,13 +746,14 @@ class DistriOptimizer[T: ClassTag] ( // replace optim methods with previous private def resetOptimMethods[T: ClassTag]( models: RDD[DistriOptimizer.Cache[T]], - previousOptimMethods: RDD[Map[String, OptimMethod[T]]]): - RDD[DistriOptimizer.Cache[T]] = { - models.zipPartitions(previousOptimMethods) { (m1, m2) => { - val cache = m1.next() - cache.optimMethods = m2.next() - Iterator(cache) - } + previousOptimMethods: RDD[Map[String, + OptimMethod[T]]]): + RDD[DistriOptimizer.Cache[T]] = { + models.zipPartitions(previousOptimMethods) { (m1, m2) => { + val cache = m1.next() + cache.optimMethods = m2.next() + Iterator(cache) + } } } @@ -768,7 +774,7 @@ class DistriOptimizer[T: ClassTag] ( override def setTrainData(sampleRDD: RDD[Sample[T]], batchSize: Int, featurePaddingParam: PaddingParam[T] = null, - labelPaddingParam: PaddingParam[T] = null) : this.type = { + labelPaddingParam: PaddingParam[T] = null): this.type = { val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None this.dataset = DistriOptimizer.setTrainData(sampleRDD, batchSize, @@ -814,9 +820,11 @@ class DistriOptimizer[T: ClassTag] ( val partitionNum = distDataset.originRDD().partitions.length val modelParameters = trainingModel.getParameters() + val allReduceParameter = AllReduceParameter.newParameter[T](partitionNum, + modelParameters._1.nElement()) // subModuleName -> (storageOffset, length, AllReduceParameter) - val parameters = if (optimMethods.size != 1) { - val p = optimMethods.map{case (subModuleName, optimMethods) => + val parameterSplits = if (optimMethods.size != 1) { + val p = optimMethods.map { case (subModuleName, optimMethod) => val subModule = trainingModel(subModuleName) require(subModule.isDefined, s"Optimizer couldn't find $subModuleName in $model") val subModuleWeights = subModule.get.getParameters()._1 @@ -826,13 +834,11 @@ class DistriOptimizer[T: ClassTag] ( val compactWeights = Module.isCompact(sortedWeights) require(modelParameters._1 == compactWeights, s"DistriOptimizer: All subModules should have an OptimMethod.") - p.map{case (subModuleName, weights) => - (subModuleName, AllReduceParameter.newParameter[T]( - partitionNum, weights.nElement(), weights.storageOffset())) + p.map { case (subModuleName, weights) => + (subModuleName, (weights.storageOffset(), weights.nElement())) } } else if (optimMethods.contains(trainingModel.getName())) { - Map(trainingModel.getName() -> AllReduceParameter.newParameter[T]( - partitionNum, modelParameters._1.nElement())) + Map(trainingModel.getName() -> (1, modelParameters._1.nElement())) } else { throw new IllegalArgumentException(s"${trainingModel.getName()} doesn't " + s"have corresponding OptimMethod") @@ -841,7 +847,8 @@ class DistriOptimizer[T: ClassTag] ( prepareInput() val modelsAndBroadcast = DistriOptimizer.initThreadModels(trainingModel, distDataset, criterion, - state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods, + state, nodeNumber, coresPerNode, checkSingleton, allReduceParameter, parameterSplits, + validationMethods, optimMethods, parameterProcessors) models = if (reserveOptimMethod && previousOptim != null) { @@ -875,7 +882,8 @@ class DistriOptimizer[T: ClassTag] ( metrics, models, optimMethods, - parameters, + allReduceParameter, + parameterSplits, validationTrigger, validationDataSet, validationMethods, @@ -932,8 +940,8 @@ class DistriOptimizer[T: ClassTag] ( (moduleName, newOptimMethod) } val modelsAndBroadcast = DistriOptimizer.initThreadModels(newModel, distDataset, - criterion, state, nodeNumber, coresPerNode, checkSingleton, parameters, - validationMethods, optimMethods, parameterProcessors) + criterion, state, nodeNumber, coresPerNode, checkSingleton, allReduceParameter, + parameterSplits, validationMethods, optimMethods, parameterProcessors) models = modelsAndBroadcast._1 modelBroadcast = modelsAndBroadcast._2 } else { @@ -942,7 +950,7 @@ class DistriOptimizer[T: ClassTag] ( } } - DistriOptimizer.getModel(models, parameters, trainingModel) + DistriOptimizer.getModel(models, allReduceParameter, trainingModel) // Reset some internal states, so this or other optimizers can run optimize again clearState() @@ -973,7 +981,7 @@ class DistriOptimizer[T: ClassTag] ( var lastMod = Long.MinValue var choice: String = null - files.map {file => + files.map { file => if (file.lastModified() > lastMod) { choice = file.getPath; lastMod = file.lastModified(); diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala index 4ee468a4a57..f3c05aebc77 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala @@ -16,33 +16,31 @@ package com.intel.analytics.bigdl.optim -import com.intel.analytics.bigdl.{Module, _} import com.intel.analytics.bigdl.dataset._ -import com.intel.analytics.bigdl.nn.{Container, Graph, Module, Sequential, Utils} -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils._ -import java.io.{File, FilenameFilter} -import java.text.SimpleDateFormat -import java.util.Calendar - import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.nn.mkldnn.MklDnnContainer import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.nn.{Container, Utils} import com.intel.analytics.bigdl.optim.DistriOptimizer._ import com.intel.analytics.bigdl.parameters.AllReduceParameter -import org.apache.commons.lang.exception.ExceptionUtils +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils._ import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} +import com.intel.analytics.bigdl.{Module, _} +import java.io.{File, FilenameFilter} +import java.text.SimpleDateFormat +import java.util.Calendar import org.apache.log4j.Logger import org.apache.spark.TaskContext -import org.apache.spark.rdd.{RDD, ZippedPartitionsWithLocalityRDD} - +import org.apache.spark.rdd.RDD import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.concurrent.Future import scala.reflect.{ClassTag, classTag} object ParallelOptimizer extends AbstractOptimizer { + import Optimizer._ val logger: Logger = Logger.getLogger(getClass) @@ -90,7 +88,7 @@ object ParallelOptimizer extends AbstractOptimizer { var lastEpochTime = 0L // driverState is needed to prevent serializing the whole optimizer - optimMethods.values.foreach{ optimMethod => + optimMethods.values.foreach { optimMethod => if (!optimMethod.state.contains("epoch")) optimMethod.state.update("epoch", 1) if (!optimMethod.state.contains("neval")) optimMethod.state.update("neval", 1) if (!optimMethod.state.contains("Loss")) { @@ -172,7 +170,7 @@ object ParallelOptimizer extends AbstractOptimizer { var count = 0 var finishedThreadSize = 0 val cached = modelIter.next() - // val miniBatchBuffer = new Array[MiniBatch[T]](_subModelNumber) + // val miniBatchBuffer = new Array[MiniBatch[T]](_subModelNumber) var miniBatch: MiniBatch[T] = null while (count < iterationPerTime) { val syWStart = System.nanoTime() @@ -211,13 +209,13 @@ object ParallelOptimizer extends AbstractOptimizer { lossSum += lossArray(finishedThreads(i)) i += 1 } - count +=1 + count += 1 } val end = System.nanoTime() wallClockTime += end - start Iterator.single(finishedThreadSize, lossSum, recordsNum) } - }.reduce((a, b) => (a._1 + b._1, a._2 + b._2, a._3 + b._3)) + }.reduce((a, b) => (a._1 + b._1, a._2 + b._2, a._3 + b._3)) dropModelNumBatch += (driverSubModelNum - numFinishedModelUpdates) @@ -228,7 +226,7 @@ object ParallelOptimizer extends AbstractOptimizer { val end = System.nanoTime() wallClockTime += end - start driverState("Loss") = localLossSum / numFinishedModelUpdates - optimMethods.foreach{ v => + optimMethods.foreach { v => v._2.updateHyperParameter() } @@ -239,7 +237,8 @@ object ParallelOptimizer extends AbstractOptimizer { driverState[Int]("neval"), wallClockTime) logger.info(s"${_header} Trained ${localRecordsNum} records in ${(end - start) / 1e9} " + s"seconds. Throughput is ${driverState("Throughput")} records/second. Loss is ${ - driverState("Loss")}.") + driverState("Loss") + }.") logger.debug("\n" + metrics.summary()) logger.debug("Dropped modules: " + (driverSubModelNum - numFinishedModelUpdates)) lossArray = new Array[Double](_subModelNumber) @@ -253,7 +252,7 @@ object ParallelOptimizer extends AbstractOptimizer { val k = (dropPercentage * computeThresholdbatchSize * driverSubModelNum).toInt if (k > dropModelNumBatch) { - threshold = Util.kthLargest(moduleTimeList, 0, moduleTimeList.length-1, + threshold = Util.kthLargest(moduleTimeList, 0, moduleTimeList.length - 1, k - dropModelNumBatch) } else { threshold = (threshold * 1.01).toLong @@ -464,7 +463,7 @@ object ParallelOptimizer extends AbstractOptimizer { models } - private def getExecutionOrder[T: ClassTag](module : Module[T]): ArrayBuffer[Module[T]] = { + private def getExecutionOrder[T: ClassTag](module: Module[T]): ArrayBuffer[Module[T]] = { val res = new ArrayBuffer[Module[T]] if (module.isInstanceOf[Container[_, _, T]]) { val subModules = module.asInstanceOf[Container[_, _, T]].modules @@ -480,7 +479,7 @@ object ParallelOptimizer extends AbstractOptimizer { } private def setDistriPartitionsynchronizer[T: ClassTag](model: Module[T], - parameterSynchronizer: DistriParameterSynchronizer[T], + parameterSynchronizer: DistriParameterSynchronizer[T], barrierLayers: mutable.Map[Int, Int], slices: Int): Unit = { val globalWeights = model.getParameters()._1 val globalGrads = model.getParameters()._2 @@ -529,9 +528,9 @@ object ParallelOptimizer extends AbstractOptimizer { * @param trainingModel the model is trained by optimizer * @return trained model */ - override protected def getModel[T: ClassTag]( + override protected def getModel[T: ClassTag]( models: RDD[Cache[T]], - parameters: Map[String, AllReduceParameter[T]], + parameters: AllReduceParameter[T], trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = { val partitionNum = models.partitions.length val extraState = models.map(_.localModels.head.getExtraParameter()).first() @@ -577,11 +576,11 @@ object ParallelOptimizer extends AbstractOptimizer { * @param _dataset train dataset * @param _criterion loss function */ -class ParallelOptimizer[T: ClassTag] ( +class ParallelOptimizer[T: ClassTag]( _model: Module[T], _dataset: DistributedDataSet[MiniBatch[T]], _criterion: Criterion[T] - )(implicit ev: TensorNumeric[T]) +)(implicit ev: TensorNumeric[T]) extends Optimizer[T, MiniBatch[T]]( _model, _dataset, _criterion) { val metrics = new Metrics @@ -600,7 +599,7 @@ class ParallelOptimizer[T: ClassTag] ( * This method will be called at the end of optimize. You need not call it if optimize succeed. * If the optimize fails, you may call it before next optimize. */ - def clearState() : Unit = { + def clearState(): Unit = { ParallelOptimizer.clearState(models) } @@ -621,7 +620,7 @@ class ParallelOptimizer[T: ClassTag] ( override def setTrainData(sampleRDD: RDD[Sample[T]], batchSize: Int, featurePaddingParam: PaddingParam[T] = null, - labelPaddingParam: PaddingParam[T] = null) : this.type = { + labelPaddingParam: PaddingParam[T] = null): this.type = { val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None this.dataset = ParallelOptimizer.setTrainData(sampleRDD, batchSize, @@ -781,7 +780,7 @@ class ParallelOptimizer[T: ClassTag] ( var lastMod = Long.MinValue var choice: String = null - files.map {file => + files.map { file => if (file.lastModified() > lastMod) { choice = file.getPath; lastMod = file.lastModified(); diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala index 52f028618db..06eb0692e08 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala @@ -15,21 +15,17 @@ */ package com.intel.analytics.bigdl.parameters -import java.util.concurrent.atomic.AtomicLong -import java.util.concurrent.{Callable, ExecutorService, Executors, Future, ThreadFactory} - import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Engine +import java.util.concurrent._ +import java.util.concurrent.atomic.AtomicLong import org.apache.commons.lang.exception.ExceptionUtils import org.apache.log4j.Logger +import org.apache.spark.TaskContext import org.apache.spark.sparkExtension.SparkExtension import org.apache.spark.storage.{BlockId, BlockManagerWrapper, StorageLevel} -import org.apache.spark.TaskContext - import scala.collection.JavaConverters._ -import scala.collection.mutable -import scala.collection.mutable.ArrayBuffer import scala.reflect._ object AllReduceParameter { @@ -135,6 +131,18 @@ class AllReduceParameter[T: ClassTag]( .getOrElse(throw new IllegalStateException("Please initialize AllReduceParameter first!")) } + /** + * Returns the start index (starting from 1, within the whole origin parameter) + * and length of the current local partition + */ + private[bigdl] def localPartitionRange: (Int, Int) = { + // add paramOffset to the starting index + (paramOffset + partitionId * taskSize + math.min(partitionId, extraSize), + taskSize + (if (partitionId < extraSize) 1 else 0)) + } + + + /** * This method should be called on each RDD partition before parameter synchronization begins. * An empty gradient tensor is placed in the block manager that can be used to store gradients. diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index b944392f2c9..cae7d31cf69 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -282,7 +282,7 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } "Train with MSE with two LBFGS after set a new Model" should "be good" in { - RandomGenerator.RNG.setSeed(10) + RandomGenerator.RNG.setSeed(11) val optimizer = new DistriOptimizer[Double]( mse, dataSet, From eeefe914eab4e13fe4a776cf2f344072a726cc16 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 4 Jun 2019 16:55:47 +0800 Subject: [PATCH 0910/1065] [New feature] add transformer layer (#2825) * add transformer * refactor class name * use same embedding for translation * fix pr comments --- .../analytics/bigdl/dllib/nn/Attention.scala | 9 +- .../analytics/bigdl/dllib/nn/BaseModule.scala | 2 +- .../analytics/bigdl/dllib/nn/CAddTable.scala | 20 +- .../bigdl/dllib/nn/FeedForwardNetwork.scala | 5 +- .../bigdl/dllib/nn/Transformer.scala | 488 ++++++++++++++ .../bigdl/dllib/nn/TransformerOperation.scala | 94 +++ .../dllib/utils/python/api/PythonBigDL.scala | 14 + .../utils/serializer/ModuleSerializer.scala | 2 + .../bigdl/dllib/nn/TransformerSpec.scala | 618 ++++++++++++++++++ 9 files changed, 1236 insertions(+), 16 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala index 7f5633df26f..a71566a8fd9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala @@ -43,11 +43,11 @@ class Attention[T: ClassTag]( // Layers for linearly projecting the queries, keys, and values. val queryLayer = TransformerOperation.dense( - hiddenSize, hiddenSize, false, name = "q").inputs(inputX) + hiddenSize, hiddenSize, false, name = s"${this.getName()}_q").inputs(inputX) val keyLayer = TransformerOperation.dense( - hiddenSize, hiddenSize, false, name = "k").inputs(inputY) + hiddenSize, hiddenSize, false, name = s"${this.getName()}_k").inputs(inputY) val valueLayer = TransformerOperation.dense( - hiddenSize, hiddenSize, false, name = "v").inputs(inputY) + hiddenSize, hiddenSize, false, name = s"${this.getName()}_v").inputs(inputY) val querySplit = new SplitHeads(hiddenSize, numHeads, true).inputs(queryLayer) val keySplit = new SplitHeads(hiddenSize, numHeads).inputs(keyLayer) @@ -69,7 +69,8 @@ class Attention[T: ClassTag]( val combineHeads = new CombineHeads().inputs(matmulNoTrans) // Run the combined outputs through another linear projection layer. val outputLayer = TransformerOperation.dense( - hiddenSize, hiddenSize, false, name = "output_transform").inputs(combineHeads) + hiddenSize, hiddenSize, false, name = s"${this.getName()}_output_transform") + .inputs(combineHeads) val graph = Graph(Array(inputX, inputY, inputBias), Array(outputLayer)) if (this.train) graph.training() else graph.evaluate() graph diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala index d441bb1483f..3ed6b4206c5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala @@ -28,7 +28,7 @@ import scala.reflect.ClassTag private[nn] abstract class BaseModule[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Activity, T] { - val model : Module[T] = buildModel() + private[bigdl] var model : Module[T] = buildModel() def buildModel(): Module[T] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala index 77f474f50fd..0d8b053368b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CAddTable.scala @@ -46,13 +46,11 @@ class CAddTable[T: ClassTag, D: ClassTag](val inplace: Boolean = false)( @transient private var bufferSumOutput: Tensor[D] = null - private def expandWithDims(smallSize: Array[Int], otherSize: Array[Int]): Boolean = { - if (smallSize.length != otherSize.length) { - return false - } - var d = otherSize.length - 1 + private def canExpand(inputSize: Array[Int], targetSize: Array[Int]): Boolean = { + var d = inputSize.length - 1 + val diff = targetSize.length - inputSize.length while(d >= 0) { - if (smallSize(d) != 1 && smallSize(d) != otherSize(d)) { + if (inputSize(d) != 1 && inputSize(d) != targetSize(d + diff)) { return false } d -= 1 @@ -61,14 +59,18 @@ class CAddTable[T: ClassTag, D: ClassTag](val inplace: Boolean = false)( } private def sumAlongDims(tensor: Tensor[D], other: Tensor[D]): Tensor[D] = { + val diff = other.nDimension() - tensor.nDimension() val size = tensor.size() var target: Tensor[D] = other if (bufferSumOutput == null) bufferSumOutput = Tensor[D]() if (bufferSumInput == null) bufferSumInput = Tensor[D]() var i = 0 - while (i < size.length) { - if (size(i) == 1) { + while (i < other.nDimension()) { + if (i < diff) { + bufferSumOutput.sum(target, i + 1) + target = bufferSumInput.resizeAs(bufferSumOutput).copy(bufferSumOutput) + } else if (size(i - diff) == 1) { bufferSumOutput.sum(target, i + 1) target = bufferSumInput.resizeAs(bufferSumOutput).copy(bufferSumOutput) } @@ -130,7 +132,7 @@ class CAddTable[T: ClassTag, D: ClassTag](val inplace: Boolean = false)( } else { if (input[Tensor[D]](i).isSameSizeAs(gradOutput)) { gradInput[Tensor[D]](i).resizeAs(gradOutput).copy(gradOutput) - } else if (expandWithDims(input[Tensor[D]](i).size(), gradOutput.size())) { + } else if (canExpand(input[Tensor[D]](i).size(), gradOutput.size())) { gradInput[Tensor[D]](i).resizeAs(input[Tensor[D]](i)).copy( sumAlongDims(input[Tensor[D]](i), gradOutput)) } else { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala index c184159e017..3ff4a954083 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala @@ -36,12 +36,13 @@ class FeedForwardNetwork[T: ClassTag](val hiddenSize: Int, val filterSize: Int, override def buildModel(): Module[T] = { val input = Input() val filterLayer = TransformerOperation.dense( - hiddenSize, filterSize, bias = true, activation = ReLU[T]()).inputs(input) + hiddenSize, filterSize, bias = true, activation = ReLU[T](), + name = s"${this.getName()}_filter_layer").inputs(input) val drop = if (train) { Dropout(initP = (1.0 - reluDropout)).inputs(filterLayer) } else filterLayer val output_dense_layer = TransformerOperation.dense( - filterSize, hiddenSize, bias = true).inputs(drop) + filterSize, hiddenSize, bias = true, name = s"${this.getName()}_output_layer").inputs(drop) val graph = Graph(Array(input), Array(output_dense_layer)) if (this.train) graph.training() else graph.evaluate() graph diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala new file mode 100644 index 00000000000..f79d4de01eb --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala @@ -0,0 +1,488 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import breeze.linalg.* +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, ModuleSerializer, SerializeContext} +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag +import scala.reflect.runtime._ + +/** + * Transformer model from "Attention Is All You Need". + * The Transformer model consists of an encoder and a decoder, both are stacks + * of self-attention layers followed by feed-forward layers. This model yields + * good results on a number of problems, especially in NLP and machine translation. + * See "Attention Is All You Need" (https://arxiv.org/abs/1706.03762) for the full + * description of the model and the results obtained with its early version. + * @param hiddenSize + * @param numHeads + * @param filterSize + * @param numHiddenlayers + * @param embeddingDropout + * @param attentionDropout + * @param ffnDropout + * @tparam T The numeric type in this module parameters. + */ +class Transformer[T: ClassTag]( + val vocabSize: Int, + val hiddenSize: Int, + val numHeads: Int, + val filterSize: Int, + val numHiddenlayers: Int, + val embeddingDropout: Float, + val attentionDropout: Float, + val ffnDropout: Float, + val transformerType: TransformerType = LanguageModel) + (implicit ev: TensorNumeric[T]) extends BaseModule[T] { + + override def buildModel(): Module[T] = { + transformerType match { + case LanguageModel => buildLM() + case Translation => buildTranslation() + } + } + + private def buildTranslation(): Module[T] = { + // input: int tensor with shape [batch_size, input_length]. + val inputNode = Input() + // target: int tensor with shape [batch_size, target_length]. + val targetNode = Input() + val attentionBias = new PaddingMask().inputs(inputNode) + + val join = JoinTable(1, -1).inputs(inputNode, targetNode) + val constantValue = math.sqrt(hiddenSize) + val embedding = MulConstant(constantValue).inputs( + LookupTable[T](vocabSize, hiddenSize).inputs(join)) + val split = new SplitTensor(1, 2).inputs(embedding) + val embeddingInput = SelectTable(1).inputs(split) + val embeddingOutput = SelectTable(2).inputs(split) + + val encoderOutput = encode(embeddingInput, attentionBias) + val outputNode = decode(embeddingOutput, encoderOutput, attentionBias) + Graph(Array(inputNode, targetNode), outputNode) + } + + private def buildLM(): Module[T] = { + val inputNode = Input() + val constantValue = math.sqrt(hiddenSize) + val embeddingInput = MulConstant(constantValue).inputs( + LookupTable[T](vocabSize, hiddenSize).inputs(inputNode)) + val outputNode = decode(embeddingInput) + Graph(inputNode, outputNode) + } + + private[nn] def encode(inputs: ModuleNode[T], attentionBias: ModuleNode[T]): ModuleNode[T] = { + // Prepare inputs to the layer stack by adding positional encodings and + // applying dropout. + val position = new PositionEncode().inputs(inputs) + val encoderInput = CAddTable().inputs(inputs, position) + val encoderInputDrop = if (train) { + val postDropOut = Dropout(1- embeddingDropout) + postDropOut.inputs(encoderInput) + } else encoderInput + + block(numHiddenlayers, encoderInputDrop, attentionBias, blockType = "encode") + } + + private[nn] def decode(targets: ModuleNode[T], + encoderOutput: ModuleNode[T] = null, + attentionBias: ModuleNode[T] = null): ModuleNode[T] = { + val decoderInput = new PositionEncodeWithShift().inputs(targets) + val decoderSelfAttentionBias = new SelfAttentionMask().inputs(targets) + + val decoderInputDrop = if (train) { + val postDropOut = Dropout(1- embeddingDropout) + postDropOut.inputs(decoderInput) + } else decoderInput + + block(numHiddenlayers, decoderInputDrop, + decoderSelfAttentionBias, encoderOutput, attentionBias, blockType = "decode") + } + + private[nn] def block(numLayers: Int, + decoderInput: ModuleNode[T], + decoderSelfAttentionBias: ModuleNode[T], + encoderOutput: ModuleNode[T] = null, + encoderAttentionBias: ModuleNode[T] = null, + blockType: String): ModuleNode[T] = { + + var input = decoderInput + var i = 0 + while (i < numLayers) { + val selfAttention = new Attention[T](hiddenSize, numHeads, attentionDropout) + val selfAttentionModel = processSelfAttention( + selfAttention, input, decoderSelfAttentionBias, + s"${blockType}_self_attention_${i}") + input = selfAttentionModel + + if (encoderOutput != null && encoderAttentionBias != null) { + val encdecAttention = new Attention[T](hiddenSize, numHeads, attentionDropout) + val encdecAttentionModel = processEncDecAttention( + encdecAttention, input, encoderOutput, encoderAttentionBias, + s"${blockType}_encdec_attention_${i}") + input = encdecAttentionModel + } + + val ffn = new FeedForwardNetwork[T](hiddenSize, filterSize, ffnDropout) + val ffnModel = processFFN(ffn, input, s"${blockType}_ffn_${i}") + input = ffnModel + + i += 1 + } + new LayerNormalization[T](hiddenSize).inputs(input) + } + + private def processSelfAttention(layer: Module[T], decoderInput: ModuleNode[T], + decoderSelfAttentionBias: ModuleNode[T], preName: String): ModuleNode[T] = { + val norm = new LayerNormalization[T](hiddenSize).setName(preName + "/norm") + .inputs(decoderInput) + val drop = Dropout[T](1 - embeddingDropout).setName(preName + "/dropout") + .inputs(layer.setName(preName + "/self_attention") + .inputs(norm, norm, decoderSelfAttentionBias)) + CAddTable().inputs(decoderInput, drop) + } + + private def processEncDecAttention( + layer: Module[T], + decoderInput: ModuleNode[T], + encoderOutput: ModuleNode[T], + attentionBias: ModuleNode[T], preName: String): ModuleNode[T] = { + val norm = new LayerNormalization[T](hiddenSize).setName(preName + "/norm") + .inputs(decoderInput) + val drop = Dropout[T](1 - embeddingDropout).setName(preName + "/dropout") + .inputs(layer.setName(preName + "/encdec_attention") + .inputs(norm, encoderOutput, attentionBias)) + CAddTable().inputs(decoderInput, drop) + } + + private def processFFN(layer: Module[T], + decoderInput: ModuleNode[T], preName: String): ModuleNode[T] = { + val norm = new LayerNormalization[T](hiddenSize).setName(preName + "/norm") + .inputs(decoderInput) + val drop = Dropout[T](1 - embeddingDropout).setName(preName + "/dropout") + .inputs(layer.setName(preName + "/ffn").inputs(norm)) + CAddTable().inputs(decoderInput, drop) + } +} + +object Transformer extends ModuleSerializable { + def apply[T: ClassTag]( + vocabSize: Int, + hiddenSize: Int, + numHeads: Int, + filterSize: Int, + numHiddenlayers: Int, + embeddingDropout: Float, + attentionDropout: Float, + ffnDropout: Float, + transformerType: TransformerType = LanguageModel) + (implicit ev: TensorNumeric[T]): Transformer[T] = + new Transformer(vocabSize, hiddenSize, numHeads, + filterSize, numHiddenlayers, + embeddingDropout, attentionDropout, ffnDropout, transformerType) + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val attrMap = context.bigdlModule.getAttrMap + + val model = DataConverter + .getAttributeValue(context, attrMap.get("model")). + asInstanceOf[Module[T]] + + val vocabSize = DataConverter + .getAttributeValue(context, attrMap.get("vocabSize")) + .asInstanceOf[Int] + + val hiddenSize = DataConverter + .getAttributeValue(context, attrMap.get("hiddenSize")) + .asInstanceOf[Int] + + val numHeads = DataConverter + .getAttributeValue(context, attrMap.get("numHeads")) + .asInstanceOf[Int] + + val filterSize = DataConverter + .getAttributeValue(context, attrMap.get("filterSize")) + .asInstanceOf[Int] + + val numHiddenlayers = DataConverter + .getAttributeValue(context, attrMap.get("numHiddenlayers")) + .asInstanceOf[Int] + + val embeddingDropout = DataConverter + .getAttributeValue(context, attrMap.get("embeddingDropout")) + .asInstanceOf[Float] + + val attentionDropout = DataConverter + .getAttributeValue(context, attrMap.get("attentionDropout")) + .asInstanceOf[Float] + + val ffnDropout = DataConverter + .getAttributeValue(context, attrMap.get("ffnDropout")) + .asInstanceOf[Float] + + val tag = DataConverter + .getAttributeValue(context, attrMap.get("transformerType")) + .asInstanceOf[Int] + + val transformerType = tag match { + case 1 => LanguageModel + case 2 => Translation + case _ => throw new UnsupportedOperationException( + s"Only support transformer tag 1 and 2, but get ${tag}") + } + + val transformer = Transformer(vocabSize, hiddenSize, numHeads, filterSize, + numHiddenlayers, embeddingDropout, attentionDropout, ffnDropout, transformerType) + + transformer.model = model + transformer + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + transformerBuilder : BigDLModule.Builder)(implicit ev: TensorNumeric[T]) : Unit = { + + val transformer = context.moduleData.module.asInstanceOf[Transformer[T]] + + val modelBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, modelBuilder, transformer.model, + ModuleSerializer.abstractModuleType) + transformerBuilder.putAttr("model", modelBuilder.build) + + val vocabSizeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, vocabSizeBuilder, + transformer.vocabSize, universe.typeOf[Int]) + transformerBuilder.putAttr("vocabSize", vocabSizeBuilder.build) + + val hiddenSizeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, hiddenSizeBuilder, + transformer.hiddenSize, universe.typeOf[Int]) + transformerBuilder.putAttr("hiddenSize", hiddenSizeBuilder.build) + + val numHeadsBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, numHeadsBuilder, + transformer.numHeads, universe.typeOf[Int]) + transformerBuilder.putAttr("numHeads", numHeadsBuilder.build) + + val filterSizeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, filterSizeBuilder, + transformer.filterSize, universe.typeOf[Int]) + transformerBuilder.putAttr("filterSize", filterSizeBuilder.build) + + val numHiddenlayersBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, numHiddenlayersBuilder, + transformer.numHiddenlayers, universe.typeOf[Int]) + transformerBuilder.putAttr("numHiddenlayers", numHiddenlayersBuilder.build) + + val embeddingDropoutBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, embeddingDropoutBuilder, + transformer.embeddingDropout, universe.typeOf[Float]) + transformerBuilder.putAttr("embeddingDropout", embeddingDropoutBuilder.build) + + val attentionDropoutBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, attentionDropoutBuilder, + transformer.attentionDropout, universe.typeOf[Float]) + transformerBuilder.putAttr("attentionDropout", attentionDropoutBuilder.build) + + val ffnDropoutBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, ffnDropoutBuilder, + transformer.ffnDropout, universe.typeOf[Float]) + transformerBuilder.putAttr("ffnDropout", embeddingDropoutBuilder.build) + + // for language model, marked as 1 + // for translation model, marked as 2 + val tag = transformer.transformerType match { + case LanguageModel => 1 + case Translation => 2 + case _ => throw new UnsupportedOperationException(s"Only support LanguageModel" + + s"and Translation transformer type, but get ${transformer.transformerType}") + } + val transformerTypeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, transformerTypeBuilder, + tag, universe.typeOf[Int]) + transformerBuilder.putAttr("transformerType", transformerTypeBuilder.build) + } +} + +/** + * Return positional encoding. + * Calculates the position encoding as a mix of sine and cosine functions with + * geometrically increasing wavelengths. + * Defined and formulized in Attention is All You Need, section 3.5. + * @param ev$1 + * @param ev + * @tparam T The numeric type in this module parameters + */ +private[nn] class PositionEncode[T: ClassTag](implicit ev: TensorNumeric[T]) + extends TensorModule[T] { + @transient private var rangeBuffer : Tensor[T] = null + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + if (!output.isEmpty && output.nElement() == input.nElement()) return output + val length = input.size(2) + val channel = input.size(3) + + if (rangeBuffer == null) { + rangeBuffer = Tensor[T]() + TransformerOperation.initRangeTensor(length, rangeBuffer) + } + + output.resize(length, channel) + TransformerOperation.addTimingSignal1D(length, channel, + rangeBuffer = rangeBuffer, timeBuffer = output) + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + if (!gradInput.isEmpty && gradInput.nElement() == input.nElement()) return gradInput + gradInput.resizeAs(input).zero() + gradInput + } +} + +// Return postition encoding with input shift right +private[nn] class PositionEncodeWithShift[T: ClassTag](implicit ev: TensorNumeric[T]) + extends TensorModule[T] { + + @transient private var rangeBuffer : Tensor[T] = null + @transient private var timeBuffer : Tensor[T] = null + + // input a Tensor with shape [batch, length, channels] + override def updateOutput(input: Tensor[T]): Tensor[T] = { + TransformerOperation.shiftRight3D(input, output) + val length = output.size(2) + val channel = output.size(3) + + if (rangeBuffer == null) { + rangeBuffer = Tensor[T]() + TransformerOperation.initRangeTensor(length, rangeBuffer) + } + if (timeBuffer == null) { + timeBuffer = Tensor[T]().resize(length, channel) + TransformerOperation.addTimingSignal1D(length, channel, + rangeBuffer = rangeBuffer, timeBuffer = timeBuffer) + } + val batchSize = input.size(1) + var i = 1 + while (i <= batchSize) { + output.select(1, i).add(timeBuffer) + i += 1 + } + return output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + if (gradInput == null) gradInput = Tensor[T]() + gradInput.resizeAs(gradOutput).zero() + val size = gradOutput.size(2) + var i = 1 + while (i < size) { + gradInput.select(2, i).copy(gradOutput.select(2, i + 1)) + i += 1 + } + gradInput + } +} + +/** + * Calculate bias tensor from padding values in tensor. + * Bias tensor that is added to the pre-softmax multi-headed attention logits, + * which has shape [batch_size, num_heads, length, length]. The tensor is zero at + * non-padding locations, and -1e9 (negative infinity) at padding locations. + * @param ev$1 + * @param ev + * @tparam T The numeric type in this module parameters + */ +private[nn] class PaddingMask[T: ClassTag](implicit ev: TensorNumeric[T]) + extends TensorModule[T] { + override def updateOutput(input: Tensor[T]): Tensor[T] = { + output.resizeAs(input).copy(input) + output = TransformerOperation.getPaddingBias(output) + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + gradInput.resizeAs(input).zero() + gradInput + } +} + +// This mask is to hide both and future words. Used in decode +private[nn] class SelfAttentionMask[T: ClassTag](implicit ev: TensorNumeric[T]) + extends TensorModule[T] { + + private val maskValue = -1e9 + + /** + * Create an bias tensor to be added to attention logits. + * Returns tensor with shape (1, 1, length, length) + * @param length + * @tparam T + * @return + */ + private def attentionBiasLowerTriangle[T: ClassTag]( + length: Int, output: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + val arr = output.storage().array() + for (i <- 0 to (length - 1)) { + var j = length - 1 + while (j > i) { + // reminder: here not 1 + arr(i * length + j) = ev.fromType(maskValue) + j -= 1 + } + } + output.resize(Array(1, 1, length, length)) + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + if (!output.isEmpty && output.nElement() == input.nElement()) return output + output.resize(input.size(2), input.size(2)).zero() + attentionBiasLowerTriangle[T](input.size(2), output) + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + if (!gradInput.isEmpty && gradInput.nElement() == input.nElement()) return gradInput + gradInput.resizeAs(input).zero() + gradInput + } +} + +private[nn] class SplitTensor[T: ClassTag](dimension: Int, num: Int) + (implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[T], Table, T] { + + private val innerLayer = new JoinTable[T](dimension, -1) + + override def updateOutput(input: Tensor[T]): Table = { + output = T.array(input.split(input.size(dimension) / num, dimension)) + output + } + + override def updateGradInput(input: Tensor[T], gradOutput: Table): Tensor[T] = { + gradInput = innerLayer.forward(gradOutput).toTensor[T] + gradInput + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala index 7ff93ad2ff1..6942d0778de 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala @@ -20,6 +20,8 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.{EngineType, T} import scala.reflect.ClassTag @@ -55,4 +57,96 @@ private[nn] object TransformerOperation { model.add(Transpose[T](Array((2, 4)))) model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]] } + + /** + * Calculate bias tensor from padding values in tensor. + * Bias tensor that is added to the pre-softmax multi-headed attention logits, + * which has shape [batch_size, num_heads, length, length]. The tensor is zero at + * non-padding locations, and -1e9 (negative infinity) at padding locations. + * Args: x: int tensor with shape [batch_size, length] + * Returns: Attention bias tensor of shape [batch_size, 1, 1, length]. + * @param input + * @tparam T + * @return + */ + def getPaddingBias[T: ClassTag](input: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + val res = getPadding[T](input).mul(ev.fromType(-1e9)) + res.addSingletonDimension(res, 2) + res.addSingletonDimension(res, 3) + } + + /** + * Return float tensor representing the padding values in x. + * Args: + * x: int tensor with any shape + * padding_value: int value that + * Returns:float tensor with same shape as x containing values 0 or 1. + * 0 -> non-padding, 1 -> padding + */ + def getPadding[T: ClassTag](input: Tensor[T], paddingValue: Float = 0.0f) + (implicit ev: TensorNumeric[T]): Tensor[T] = { + input.apply1(e => {if (e == paddingValue) ev.one else ev.zero}) + } + + // Shift the second dimension of x right by one. + def shiftRight3D[T: ClassTag](input: Tensor[T], output: Tensor[T]) + (implicit ev: TensorNumeric[T]): Tensor[T] = { + output.resizeAs(input).zero() + val index = input.size(2) + output.narrow(2, 2, index - 1).copy(input.narrow(2, 1, index - 1)) + output + } + + def initRangeTensor[T: ClassTag](length: Int, rangeBuffer: Tensor[T]) + (implicit ev: TensorNumeric[T]): Unit = { + rangeBuffer.resize(Array(length, 2)) + val arr = rangeBuffer.select(2, 1).storage().array() + for (i <- 0 to (length - 1)) { + arr(i * 2) = ev.fromType(i) + arr(i * 2 + 1) = ev.fromType(i) + } + } + + /** + * Args:length: Sequence length. + * channels: Size of the hidden + * min_timescale: Minimum scale that will be applied at each position + * max_timescale: Maximum scale that will be applied at each position + * Returns: Tensor with shape [length, hidden_size] + */ + def addTimingSignal1D[T: ClassTag]( + length: Int, + channels: Int, + min_timescale : Float = 1.0f, + max_timescale: Float = 1.0e4f, + rangeBuffer: Tensor[T], + timeBuffer: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + // get_timing_signal_1d, return (1, length, channels) + val num_timescales = channels / 2 + val log_timescale_increment = math.log(max_timescale / min_timescale) / + math.max(num_timescales - 1, 1) + // tf.range(num_timescales) + val inv_timescales = new Array[Double](num_timescales) + var i = 0 + while (i < inv_timescales.length) { + inv_timescales(i) = min_timescale * math.exp(i * - log_timescale_increment) + i += 1 + } + rangeBuffer.select(2, 1).mul(ev.fromType[Double](inv_timescales(0))) + rangeBuffer.select(2, 2).mul(ev.fromType[Double](inv_timescales(1))) + + val sinRes = rangeBuffer.clone().apply1(e => + ev.fromType(math.sin(ev.toType[Float](e)))) + val cosRes = rangeBuffer.clone().apply1(e => + ev.fromType(math.cos(ev.toType[Float](e)))) + + timeBuffer.narrow(2, 1, sinRes.size(2)).copy(sinRes) + timeBuffer.narrow(2, sinRes.size(2) + 1, cosRes.size(2)).copy(cosRes) + timeBuffer + } } + +sealed trait TransformerType + +case object Translation extends TransformerType +case object LanguageModel extends TransformerType diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 58e03252b02..ba1d47beac5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -292,6 +292,20 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new LayerNormalization[T](hiddenSize) } + def createTransformer( + vocabSize: Int, + hiddenSize: Int, + numHeads: Int, + filterSize: Int, + numHiddenlayers: Int, + postprocessDropout: Double, + attentionDropout: Double, + reluDropout: Double): nn.Transformer[T] = { + Transformer(vocabSize, hiddenSize, numHeads, + filterSize, numHiddenlayers, postprocessDropout.toFloat, + attentionDropout.toFloat, reluDropout.toFloat) + } + def createLinear(inputSize: Int, outputSize: Int, withBias: Boolean, wRegularizer: Regularizer[T] = null, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 734c1a9369a..72df7fcddac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -278,6 +278,8 @@ object ModuleSerializer extends ModuleSerializable{ registerModule("com.intel.analytics.bigdl.nn.MultiRNNCell", MultiRNNCell) registerModule("com.intel.analytics.bigdl.nn.SpatialSeparableConvolution", SpatialSeparableConvolution) + registerModule("com.intel.analytics.bigdl.nn.Transformer", + Transformer) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala new file mode 100644 index 00000000000..6a926d46c32 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala @@ -0,0 +1,618 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{T, Table} +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class TransformerLayerSpec extends FlatSpec with Matchers { + "tranformer decode stack" should "work correctly" in { + val vocabSize = 10 + val hiddenSize = 4 + val numHeads = 2 + val filterSize = 3 + val num_hidden_layers = 1 + val postprocessDropout = 1.0f + val attentionDropout = 1.0f + val reluDropout = 1.0f + val transformer = new Transformer[Float](vocabSize, + hiddenSize, numHeads, filterSize, num_hidden_layers, + postprocessDropout, attentionDropout, reluDropout) + + val input1 = Input[Float]() + val input2 = Input[Float]() + + val blockOutput = transformer.block(num_hidden_layers, input1, input2, blockType = "encode") + val block = Graph(Array(input1, input2), blockOutput) + val paramsTable = block.getParametersTable() + + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.12254566, -0.3492695, 0.6760147, 0.4690166), + T(-0.70616156, -0.7172935, -0.70902413, -0.7268282), + T(-0.17867321, 0.03752673, 0.21406537, -0.84105927), + T(-0.40054652, 0.01422167, 0.49654406, -0.62966037))).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.80201703, 0.29880065, 0.8191585, 0.393151), + T(-0.43785518, 0.02502167, -0.85530514, 0.86387163), + T( 0.07737422, 0.34640843, 0.5547114, 0.12658376), + T( 0.6287202, -0.7140273, -0.08061278, -0.3983137))).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.14568096, 0.8488055, -0.38585222, -0.42583144), + T(-0.35776895, 0.00440949, 0.76952034, 0.7039148), + T(-0.4635923, -0.5273898, 0.36311775, 0.21081167), + T(-0.04171634, 0.24859089, 0.03242427, -0.01675642))).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.8254406, 0.7399195, -0.76593506, -0.38950253), + T( 0.51289314, 0.1285783, -0.24543494, -0.7138509), + T(-0.34158242, -0.37842813, -0.5111934, 0.5966528), + T( 0.39076942, -0.7022542, 0.8254971, -0.50844))).t()) + } else if (i.toString contains "_filter_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.4929167, -0.5465611, 0.4262464), + T( 0.5161569, -0.6786176, 0.37465477), + T( 0.35582626, 0.43647707, -0.23218763), + T( 0.7624726, 0.28653884, 0.20991063))).transpose(1, 2)) + } else if (i.toString contains "_output_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.9037433, 0.6076299, 0.6593666, -0.06372046), + T( 0.58014977, 0.6601094, -0.72481453, 0.89943814), + T( 0.02975523, -0.4040287, 0.6437061, -0.2594086))).transpose(1, 2)) + } + } + + val input = Tensor[Float](Tensor[Float]( + T(T(T( 2.43651805, -0.91763462, -0.79225763, -1.60945293), + T( 1.29811144, -3.45230805, 2.61721765, -1.14181035), + T( 0.47855864, -0.37405556, 2.19316191, -3.09021106), + T(-0.48362581, -0.57608153, 1.70065416, -1.6498369), + T(-0.25864231, -1.31678763, 0.06332062, 0.87422282), + T(-1.65092877, 1.71708556, 1.35238608, 0.75374151)), + T(T( 1.35128392, -1.02559179, -0.18433534, -1.40365415), + T(-0.40183212, 0.7955332, -1.03749113, -0.59513029), + T(-1.03075905, -1.26780846, -1.0068692, -0.0189969), + T(-1.67596552, 0.35162355, 2.48970327, 1.11306624), + T(-0.28775333, -1.33144345, -1.12073744, 2.5386819), + T( 0.07621163, -0.95549347, 0.28637323, 3.1503827))))) + val bias = Tensor[Float](Tensor[Float]( + T(T(T(T( 0.12015895, 0.61720311, 0.30017032, -0.35224985, -1.1425182, -0.34934272), + T(-0.20889423, 0.58662319, 0.83898341, 0.93110208, 0.28558733, 0.88514116), + T(-0.75439794, 1.25286816, 0.51292982, -0.29809284, 0.48851815, -0.07557171), + T( 1.13162939, 1.51981682, 2.18557541, -1.39649634, -1.44411381, -0.50446586), + T( 0.16003707, 0.87616892, 0.31563495, -2.02220122, -0.30620401, 0.82797464), + T( 0.23009474, 0.76201118, -0.22232814, -0.20075807, 0.18656139, 0.41005165)))) + )) + + val expectedOutput = Tensor[Float]( + T(T(T( 1.6739436, -0.5742816, -0.18686886, -0.91279316), + T( 0.56332755, -1.6895478, 0.8744801, 0.25174013), + T( 0.18294929, -0.03678755, 1.333065, -1.4792268), + T(-0.83871794, 0.09105678, 1.6003608, -0.8526995), + T(-0.6227458, 0.06268612, -1.0336334, 1.593693), + T(-1.6069404, 0.70157117, -0.05510008, 0.9604694)), + T(T( 1.500092, -0.12251449, -0.06195105, -1.3156266), + T( 0.88058877, 0.88686943, -0.2218959, -1.5455623), + T(-1.73186, 0.59709984, 0.5559552, 0.5788053), + T(-1.7018749, 0.8331325, 0.30757982, 0.56116235), + T(-0.5026365, -0.1983719, -0.96522677, 1.6662351), + T(-0.56770575, -0.17644365, -0.92594254, 1.6700919))) + ) + + val output = block.forward(T(input, bias)) + output should be(expectedOutput) + + val gradInput = block.backward(T(input, bias), output).toTable + + val expectedGradInput1 = Tensor[Float]( + T(T(T( 9.1339905e-07, -4.4728981e-07, -3.1617617e-07, -1.2013072e-07), + T( 6.3113339e-07, -5.3439135e-07, -3.3880960e-07, 2.1226521e-07), + T( 1.7116510e-06, -3.8029987e-07, -5.2847190e-07, -7.6935152e-07), + T(-1.0739063e-06, 3.4577083e-06, 5.1119628e-06, -7.4957657e-06), + T(-3.0945554e-07, -2.9928319e-07, -2.0712267e-07, 8.1958660e-07), + T(-1.0444269e-06, 1.6913917e-07, 3.5171459e-07, 4.8632023e-07)), + T(T(-5.9887736e-07, 1.0681491e-06, 3.0668511e-06, -3.5249473e-06), + T( 8.1442304e-06, 1.8526016e-05, 7.9063993e-06, -3.4815068e-05), + T(-3.3653050e-05, 6.2354911e-07, 1.1947766e-05, 2.1081711e-05), + T( 8.8148295e-07, -1.5203238e-06, 4.0907385e-06, -3.4518978e-06), + T(-1.8306933e-06, 1.3375227e-06, -1.5494516e-06, 1.9532151e-06), + T(1.1980649e-06, 9.9704266e-07, -3.0255887e-06, 8.8263494e-07))) + ) + + val expectedGradInput2 = Tensor[Float]( + T(T(T(T(1.92614536e-07, 8.18386638e-08, 1.83079862e-07, -5.29573754e-07, + 2.14264446e-07, -1.42223712e-07), + T( 9.00455632e-07, -4.55583267e-06, 4.20768583e-06, -8.96842812e-06, + 5.02361490e-06, 3.39250482e-06), + T(-3.51306221e-06, 1.35622076e-06, -2.57200622e-06, 1.08205404e-05, + -4.62260732e-06, -1.46908474e-06), + T(1.44854653e-06, 1.00405509e-06, -1.88945739e-06, -8.24743935e-08, + 1.16377095e-07, -5.97047006e-07), + T(-5.35773211e-07, 1.24227370e-07, 1.73641411e-07, 1.35646133e-07, + -1.13603612e-07, 2.15861803e-07), + T(-6.30813645e-07, 6.52564225e-08, 1.47730645e-07, 3.11057221e-07, + 7.64788126e-08, 3.02906109e-08)))) + ) + + val expectedGradWeights = Tensor[Float]( + T(-9.4019833e-06, -1.5453285e-06, -9.4909010e-06, 5.2547603e-07, + 4.0047512e-06, -7.0249803e-06, 1.3278475e-05, 4.4464814e-06, + -3.8477524e-06, -8.1469705e-07, -1.3136616e-06, 1.5246084e-06, + -3.6292959e-06, -1.3310753e-05, 1.0742175e-05, -1.3015128e-05, + -2.8296442e-06, 4.6112955e-06, -2.7704493e-06, 6.8603067e-06, + 1.0306692e-05, 9.5141559e-06, -6.6580633e-06, 4.6302143e-06, + -5.6733006e-06, -2.0463798e-05, -2.8769139e-06, -9.0087809e-07, + 4.6731147e-06, 1.3545281e-05, 1.2833587e-05, 1.6316061e-06, + 6.7491310e-06, 1.9667668e-05, 1.9997810e-07, 2.7255970e-07, + -5.7489456e-06, -1.2749153e-05, -1.0156651e-05, -1.0032876e-06, + -6.4571459e-06, -4.5748075e-06, 5.2935420e-06, 1.7019968e-06, + -2.8230164e-05, -2.3874696e-05, -3.1409923e-05, 1.4136816e-05, + 1.3851404e-05, 1.3069550e-05, 3.1755158e-06, 4.1450826e-06, + 2.0835905e-05, 1.5379959e-05, 2.2940867e-05, -1.9983896e-05, + 3.9784982e-07, 2.6731566e-06, -5.9224215e-07, -2.5417473e-06, + -7.5010930e-06, 1.6819112e-06, 1.4458296e-06, 4.3033779e-06, + 3.2009964e-05, 7.8872072e-06, -1.2185321e-05, -2.7866208e-05, + 3.1262254e-05, 8.2735351e-06, -1.2112221e-05, -2.7589167e-05, + -1.0220035e-05, 8.3456416e-06, -3.1375414e-05, 4.9415255e-05, + 2.3259896e-05, 2.5363222e-05, 1.2638515e-05, 2.9357281e-05, + -1.6661495e-05, 4.0918521e-06, 5.3757998e-07, + 1.8960893e-05, 8.4753447e-07, -2.3114646e-06, + -6.3083702e-05, -1.7887363e-05, 4.8286256e-06, + 6.0784321e-05, 1.2947977e-05, -3.0547415e-06, + 4.2135795e-05, -7.1919526e-06, -3.3792276e-06, + -2.0067891e-05, 6.7602373e-06, 5.6371910e-06, 7.6476235e-06, + -6.6570569e-06, 1.3790517e-06, 5.3389158e-06, -9.7211682e-08, + -1.1374552e-05, 2.0630792e-05, 4.2232737e-06, -1.3708518e-05, + -2.71759927e-05, 2.76453793e-05, 1.34781003e-05, -1.42119825e-05, + 16.322777, 5.6128163, 8.455086, 17.609234, + -2.7715797, 0.37446928, 1.2208222, 1.176289) + ) + + require(gradInput[Tensor[Float]](1).almostEqual(expectedGradInput1, 1e-6) == true) + require(gradInput[Tensor[Float]](2).almostEqual(expectedGradInput2, 1e-6) == true) + } + + "tranformer for translation" should "work correctly" in { + val vocabSize = 16 + val hiddenSize = 4 + val filterSize = 8 + val numHeads = 1 + val num_hidden_layers = 1 + val postprocessDropout = 1.0f + val attentionDropout = 1.0f + val reluDropout = 1.0f + val transformer = new Transformer[Float](vocabSize, + hiddenSize, numHeads, filterSize, num_hidden_layers, + postprocessDropout, attentionDropout, reluDropout, Translation) + + val attention0 = transformer.model("encode_self_attention_0/self_attention").get + val ffn0 = transformer.model("encode_ffn_0/ffn").get + + val attention1 = transformer.model("decode_self_attention_0/self_attention").get + val ffn1 = transformer.model("decode_ffn_0/ffn").get + val attention2 = transformer.model("decode_encdec_attention_0/encdec_attention").get + + var paramsTable = attention0.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(0.6719899, 0.29741684, -0.6073703, 0.58373296), + T(0.28487056, 0.12325107, -0.18469666, -0.3146433), + T(0.60392314, 0.65988046, 0.50996345, -0.19420744), + T(0.40057203, -0.9149872, 0.10390836, 0.97260743))).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(0.33549386, 0.88536686, -0.30634838, 0.05587747), + T(0.61110026, -0.66457653, -0.34049615, -0.14537863), + T(0.653832, 0.74835855, 0.76725274, -0.6947307), + T(0.49148628, -0.07944908, -0.845008, 0.6068878))).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(0.24006118, 0.6792713, 0.22704636, 0.49668023), + T(0.53909445, -0.32836607, 0.25972122, 0.5554116), + T(-0.4319324, 0.43911168, 0.20273127, -0.24734582), + T(0.23329619, -0.3165343, 0.40053207, -0.34865358))).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.5211139, -0.3813012, 0.34638476, -0.21196833), + T(0.1121366, -0.3850857, 0.15838127, -0.46018872), + T(0.42922392, 0.49836066, -0.00889128, -0.20409666), + T(-0.0800805, 0.6680052, 0.11346864, -0.3564058))).t()) + } + } + + paramsTable = ffn0.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_filter_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.42055795, -0.345141, -0.77015144, 1.0128733, -0.2070824, + 0.41457736, -0.27325338, 0.37545303), + T(0.83861953, 0.49639514, 0.10912374, 0.4054078, 0.01117581, + 0.4838021, 0.47710165, 0.23820893), + T(-0.37739983, -0.3799013, 0.26106557, -0.02527841, -0.09814293, + 0.15995328, 0.76590466, -0.38680843), + T(0.22057502, 0.4438025, 0.18568423, 0.2206358, -0.5293094, + -0.07671213, -0.5392774, -0.26026365))).transpose(1, 2)) + } else if (i.toString contains "_output_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.15800391, 0.00911217, 0.5716306, -0.4307602), + T(-0.17119521, 0.45397595, -0.15994692, 0.1173245), + T(0.02792565, 0.1785465, 0.03194377, -0.2635249), + T(-0.5619625, 0.34994912, 0.2134058, 0.17008546), + T(-0.16928878, -0.04155388, -0.00634552, 0.10220164), + T(-0.19378763, 0.60514146, 0.31211597, 0.32819757), + T(-0.12504072, -0.5004057, -0.53571004, -0.6392757), + T(-0.06203287, 0.25287995, 0.32892716, 0.11961207))).transpose(1, 2)) + } + } + + paramsTable = attention1.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.58024985, -0.48674917, -0.1278461, -0.1681186), + T(1.0511181, 0.50676775, -0.49831128, -0.13611957), + T(0.4512829, 0.00988893, 0.35473365, -0.4541598), + T(-0.01564673, -0.06611676, 0.20534483, -0.13249157))).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(0.25792515, 0.8091696, -1.1157143, -0.48759258), + T(0.2797681, -0.61634296, 0.29310933, 0.3868902), + T(-0.22521666, -0.08918925, 0.17066494, 0.06447314), + T(-0.14935619, -0.05546288, -1.134581, 0.33467665))).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.05646669, 0.2533887, 0.9146523, 0.09979013), + T(-0.03409033, 0.9656157, -0.00790233, 0.22394712), + T(0.44499645, -0.41030893, -0.40253338, -0.541713), + T(0.63082635, 0.05910337, 0.26689664, 0.06098993))).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(0.07528905, -0.6294302, -0.47716418, -0.3372765), + T(-0.4738406, -0.09567301, -0.21502851, 0.07263356), + T(0.21500742, -0.09957578, 0.05073479, 0.5063499), + T(-0.95140356, -0.19597691, 0.3108005, 0.3067237))).t()) + } + } + + paramsTable = attention2.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.09555588, 0.16374706, -0.81079763, 0.18353464), + T(0.72976017, -0.6785369, -0.1633139, -0.1220759), + T(-0.47357813, 0.19808318, 0.63312566, -0.14370666), + T( 0.11398887, 0.7884044, -0.36504376, -0.17514746))).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.19676681, -0.24631989, -1.1253904, -0.2751462), + T(-0.17718858, 0.06754616, 0.5731753, -0.8507766), + T( 0.06555229, -0.04867446, -0.05025194, -0.5535116), + T(-0.5346166, 0.23926297, -0.4628236, -0.3947385))).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T( 0.92687607, -0.545517, -0.05255984, 0.28678837), + T( 0.34195843, 0.3929567, 0.51847, 0.7892322), + T( 0.90397906, -0.9298378, 0.8783962, 0.2852646), + T( 0.6237778, 0.3783044, 0.37894192, 0.42552295))).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-1.9982174e-01, 1.4843611e-01, 4.4536388e-01, -3.4881935e-01), + T(6.5677509e-02, 7.3198605e-01, 4.1394565e-01, 3.6246496e-01), + T(3.8297844e-01, -2.0218496e-01, -6.0479283e-01, -8.4035518e-04), + T(8.8539845e-01, 8.1015706e-02, -2.0919992e-01, -3.2815292e-01))).t()) + } + } + paramsTable = ffn1.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_filter_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.3522124, -0.51549995, -0.67411846, 0.27011815, 0.6126283, -0.5052634, + 0.88756555, 0.47037336), + T( 0.15704805, -0.11248052, 0.45173776, 1.0609885, -0.02032901, -0.272949, + -0.27566594, 0.45384774), + T( 0.6470523, -0.6543102, -0.21736439, -0.43480754, -0.13311917, -1.1141537, + -0.59988606, -0.24346256), + T( 0.11163724, -0.03015788, 0.38666677, -0.39999688, -0.53780854, -0.09386043, + -0.09019023, 0.28964663))).transpose(1, 2)) + } else if (i.toString contains "_output_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.28514335, -0.5174819, -0.3048153, 0.16713372), + T(-0.2276286, -0.31804547, 0.269992, 0.03182783), + T(-0.26096576, -0.49425197, -0.23944728, 0.28338984), + T( 0.260591, -0.17206982, -0.14490226, -0.20425473), + T( 0.38700444, -0.5851576, 0.309289, -0.28129402), + T(-0.03296154, -0.47809625, 0.43516076, 0.21953852), + T(-0.38866428, 0.52283365, -0.60793763, 0.33401495), + T(-0.29918984, 0.6243824, -0.21915461, -0.14608558))).transpose(1, 2)) + } + } + + val expectedOutput = Tensor[Float]( + T(T(T(1.5693761, -1.0056276, 0.14640914, -0.71015745), + T(1.4049922, -1.1252292, 0.46041852, -0.74018157), + T(-0.7806267, -0.13584259, -0.75671536, 1.6731848), + T(-0.3983218, -0.9217702, -0.36959055, 1.6896812), + T(-0.62736577, -1.1783588, 0.36084852, 1.4448758), + T(-0.29645187, -1.3115996, 0.1336132, 1.4744384)), + T(T(1.281556, -1.111587, 0.65917075, -0.82913977), + T(1.3174573, -1.1678243, 0.59200275, -0.74163586), + T(0.68878394, -0.01719818, -1.6202699, 0.9486842), + T(1.706251, -0.6772593, -0.29021385, -0.738778), + T(-0.47597468, -0.88766754, -0.33201644, 1.6956586), + T(-0.82912564, -0.8601543, 0.08772265, 1.6015573))) + ) + + paramsTable = transformer.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "LookupTable") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float](T(T( 0.0901417, -0.25920567, 0.35886005, -0.79801846), + T( 0.7101189, -0.5279109, 0.24793072, 0.07292826), + T(-0.00906177, 0.6962627, 0.37465635, 0.15718417), + T(-0.11258064, -0.3311236, -0.3180385, 0.58970255), + T( 0.17320412, -0.49935055, 0.7124023, -0.28340986), + T(-0.33200186, 1.0381325, -0.18797834, 0.5976197), + T(-0.06744625, -0.23964763, 0.37403554, -0.4539435), + T(-0.39824682, 0.18769431, 0.02896992, -0.7393345), + T( 0.5590472, -0.7522993, -0.44121778, -0.1815617), + T( 0.7071572, 0.27919358, 0.23945637, -0.17475012), + T(-0.36576417, -1.6407981, -0.5480189, 0.00637588), + T( 0.3870772, 0.5724747, -0.6339975, -0.6118532), + T(-0.08697614, -0.21675488, -0.13310283, 0.19130157), + T( 0.06459922, -0.57852674, 0.9070809, 0.09887356), + T( 0.8016945, -0.09532502, -0.6059104, 0.74728966), + T( 0.24903144, 0.06780083, 0.16405171, -0.29252014)) + ) + ) + } + } + + val input1 = Tensor[Float](T(T(3, 1, 2, 3, 4, 5), T(6, 7, 8, 9, 10, 11))).add(1.0f) + val input2 = Tensor[Float](T(T(4, 5, 7, 9, 10, 11), T(4, 12, 6, 3, 2, 15))).add(1.0f) + val output = transformer.forward(T(input1, input2)) + output should be(expectedOutput) + + val gradInput = transformer.backward(T(input1, input2), output) + } + + "AttentionBiasConstant" should "work correctly" in { + val layer = new PositionEncode[Float]() + + val input = Tensor[Float](T(T( + T(1.5575712, 1.6023955, 1.4487493, 0.46178865), + T(1.4542825, 0.36078143, 1.0112681, 1.7850459), + T(1.0922418, 1.8467345, 0.17114377, 1.5875602), + T(1.3181713, 1.1110513, 0.31925488, 0.61749554), + T(0.30953693, 0.93909645, 1.9877799, 1.2225482), + T(1.3529022, 0.3599646, 1.3499286, 0.4491992)), + T(T(0.10186243, 0.9201369, 1.6568646, 0.47073865), + T(1.950448, 1.6722536, 0.5169549, 0.83770823), + T(1.4055192, 1.535857, 1.0745583, 1.4468269), + T(0.53809, 0.01234245, 0.06532454, 0.1288917), + T(1.6856189, 1.4987106, 0.1509037, 1.2490149), + T(0.6981592, 1.1585901, 1.1459568, 0.3643551)))) + val output = layer.forward(input) + + val outputExpected = Tensor[Float]( + T(T( 0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.0000000e+00), + T( 8.4147096e-01, 9.9999990e-05, 5.4030228e-01, 1.0000000e+00), + T( 9.0929741e-01, 1.9999998e-04, -4.1614681e-01, 1.0000000e+00), + T( 1.4112000e-01, 2.9999996e-04, -9.8999250e-01, 9.9999994e-01), + T(-7.5680250e-01, 3.9999996e-04, -6.5364361e-01, 9.9999994e-01), + T(-9.5892429e-01, 4.9999997e-04, 2.8366220e-01, 9.9999988e-01)) + ) + + output should be(outputExpected) + } + + "transformer prepare decode layer" should "work correctly" in { + val prepare = new PositionEncodeWithShift[Float]() + + val input = Tensor[Float]( + T(T(T( 16.24345364, -6.11756414, -5.28171752, -10.72968622), + T(8.65407629, -23.01538697, 17.44811764, -7.61206901), + T(3.19039096, -2.49370375, 14.62107937, -20.60140709), + T(-3.22417204, -3.84054355, 11.33769442, -10.99891267), + T(-1.72428208, -8.77858418, 0.42213747, 5.82815214), + T(-11.00619177, 11.4472371, 9.01590721, 5.02494339)), + T(T(9.00855949, -6.83727859, -1.22890226, -9.35769434), + T(-2.6788808, 5.30355467, -6.91660752, -3.96753527), + T(-6.871727, -8.45205641, -6.71246131, -0.12664599), + T(-11.17310349, 2.34415698, 16.59802177, 7.42044161), + T(-1.91835552, -8.87628964, -7.47158294, 16.92454601), + T(0.50807755, -6.36995647, 1.90915485, 21.00255136)))) + + val expectedOutput = Tensor[Float]( + T(T(T(0, 0, 1, 1), + T(17.084925, -6.117464, -4.741415, -9.729686), + T(9.563374, -23.015186, 17.031971, -6.612069), + T(3.331511, -2.493404, 13.631087, -19.601408), + T(-3.9809747, -3.8401434, 10.684051, -9.998913), + T(-2.6832063, -8.778085, 0.7057997, 6.828152)), + T(T(0, 0, 1, 1), + T(9.85003, -6.837178, -0.68859994, -8.357695), + T(-1.7695832, 5.3037543, -7.332754, -2.9675353), + T(-6.730607, -8.4517565, -7.702454, 0.87335396), + T(-11.929906, 2.344557, 15.944379, 8.420442), + T(-2.8772798, -8.87579, -7.1879206, 17.924545)))) + + val expectedGradInput = Tensor[Float]( + T(T(T(17.084925, -6.117464, -4.741415, -9.729686), + T(9.563374, -23.015186, 17.031971, -6.612069), + T(3.331511, -2.493404, 13.631087, -19.601408), + T(-3.9809747, -3.8401434, 10.684051, -9.998913), + T(-2.6832063, -8.778085, 0.7057997, 6.828152), + T(0, 0, 0, 0)), + T(T(9.85003, -6.837178, -0.68859994, -8.357695), + T(-1.7695832, 5.3037543, -7.332754, -2.9675353), + T(-6.730607, -8.4517565, -7.702454, 0.87335396), + T(-11.929906, 2.344557, 15.944379, 8.420442), + T(-2.8772798, -8.87579, -7.1879206, 17.924545), + T(0, 0, 0, 0)))) + + val out = prepare.forward(input) + out should be(expectedOutput) + + val out2 = prepare.backward(input, out) + out2 should be(expectedGradInput) + + } + + "SelfAttentionBiasConstant layer" should "work correctly" in { + val prepare = new SelfAttentionMask[Float]() + val input = Tensor[Float](T(T( + T( 16.24345364, -6.11756414, -5.28171752, -10.72968622), + T( 8.65407629, -23.01538697, 17.44811764, -7.61206901), + T( 3.19039096, -2.49370375, 14.62107937, -20.60140709), + T( -3.22417204, -3.84054355, 11.33769442, -10.99891267), + T( -1.72428208, -8.77858418, 0.42213747, 5.82815214), + T(-11.00619177, 11.4472371, 9.01590721, 5.02494339)), + T(T( 9.00855949, -6.83727859, -1.22890226, -9.35769434), + T( -2.6788808, 5.30355467, -6.91660752, -3.96753527), + T( -6.871727, -8.45205641, -6.71246131, -0.12664599), + T(-11.17310349, 2.34415698, 16.59802177, 7.42044161), + T( -1.91835552, -8.87628964, -7.47158294, 16.92454601), + T( 0.50807755, -6.36995647, 1.90915485, 21.00255136)))) + + val expectedOutput = Tensor[Float]( + T(T(T(T(0.0f, -1e9f, -1e9f, -1e9f, -1e9f, -1e9f), + T(0.0f, 0.0f, -1e9f, -1e9f, -1e9f, -1e9f), + T(0.0f, 0.0f, 0.0f, -1e9f, -1e9f, -1e9f), + T(0.0f, 0.0f, 0.0f, 0.0f, -1e9f, -1e9f), + T(0.0f, 0.0f, 0.0f, 0.0f, 0.0f, -1e9f), + T(0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f))))) + + val out = prepare.forward(input) + out should be(expectedOutput) + + val out2 = prepare.backward(input, out) + } + + "TransformerOperation getPaddingBias" should "work good" in { + val input = Tensor[Float](T(0, 1, 2, 3, 4, 5, 6, 7)).resize(Array(2, 4)) + val ops = TransformerOperation.getPaddingBias(input) + val opsExpected = Tensor[Float](T(-1e9f, 0.0f, 0f, 0f, 0f, 0f, 0f, 0f)) + .resize(Array(2, 1, 1, 4)) + ops should be(opsExpected) + } + + "Split tensor" should "be ok" in { + val l1 = Tensor[Float](Array[Float](1, 2, 3, 4, 5, 6, + 1.2f, 2.2f, 3.2f, 4.2f, 5.2f, 6.2f), Array(2, 6)) + val l2 = Tensor[Float](Array[Float](1.1f, 2.1f, 3.1f, 4.1f, 5.1f, 6.1f, + 1.3f, 2.3f, 3.3f, 4.3f, 5.3f, 6.3f), Array(2, 6)) + val input = T(l1, l2) + + val layer = new JoinTable[Float](1, -1) + val output = layer.forward(input).toTensor[Float] + + val layer2 = new SplitTensor[Float](1, 2) + val o2 = layer2.forward(output) + + val g1 = o2[Tensor[Float]](1) + val g2 = o2[Tensor[Float]](2) + assert(g1.almostEqual(l1, 1e-8) == true) + assert(g2.almostEqual(l2, 1e-8) == true) + + val gradInput = layer2.backward(output, o2) + assert(output.almostEqual(gradInput, 1e-8) == true) + } + + +} + +class SelfAttentionMaskSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = new SelfAttentionMask[Float]().setName("SelfAttentionMask") + val input = Tensor[Float](2, 6, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input) + } +} + +class PaddingMaskSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = new PaddingMask[Float]().setName("PaddingMask") + val input = Tensor[Float](2, 6, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input) + } +} + +class PositionEncodeWithShiftSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = new PositionEncodeWithShift[Float]().setName("PositionEncodeWithShift") + val input = Tensor[Float](2, 6, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input) + } +} + +class PositionEncodeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val model = new PositionEncode[Float]().setName("PositionEncode") + val input = Tensor[Float](2, 6, 4).apply1(_ => Random.nextFloat()) + runSerializationTest(model, input) + } +} + +class SplitTensorSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val l1 = Tensor[Float](Array[Float](1, 2, 3, 4, 5, 6, + 1.2f, 2.2f, 3.2f, 4.2f, 5.2f, 6.2f), Array(2, 6)) + val l2 = Tensor[Float](Array[Float](1.1f, 2.1f, 3.1f, 4.1f, 5.1f, 6.1f, + 1.3f, 2.3f, 3.3f, 4.3f, 5.3f, 6.3f), Array(2, 6)) + val input = T(l1, l2) + + val layer = new JoinTable[Float](1, -1) + val output = layer.forward(input).toTensor[Float] + + val model = new SplitTensor[Float](1, 2) + runSerializationTest(model, output) + } +} + +class TransformerSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val hiddenSize = 4 + val numHeads = 2 + val filterSize = 3 + val num_hidden_layers = 2 + val postprocessDropout = 1.0f + val attentionDropout = 1.0f + val reluDropout = 1.0f + val model = Transformer[Float](20, + hiddenSize, numHeads, filterSize, num_hidden_layers, + postprocessDropout, attentionDropout, reluDropout).setName("Transformer") + val input = Tensor[Float](2, 6).apply1(_ => Random.nextInt(10) + 1) + runSerializationTest(model, input) + } +} From c0d1b9a2a348dc691c7874c5da8383a274c47d36 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Thu, 6 Jun 2019 12:04:59 +0800 Subject: [PATCH 0911/1065] [Bug Fix] Fix Issue 2734 (#2816) * fix issue 2734 * fix issue 2734 * fix issue 2734 --- .../spark/rdd/ZippedPartitionsWithLocalityRDD.scala | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala index 31322c8d415..d2643079f58 100644 --- a/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala +++ b/scala/common/spark-version/2.0/src/main/scala/org/apache/spark/rdd/ZippedPartitionsWithLocalityRDD.scala @@ -72,13 +72,15 @@ class ZippedPartitionsWithLocalityRDD[A: ClassTag, B: ClassTag, V: ClassTag]( val candidateLocs = new ArrayBuffer[(Int, Seq[String])]() (0 until numParts).foreach(p => { - candidateLocs.append((p, rdds(1).context.getPreferredLocs(rdds(1), p).map(_.host).distinct)) + candidateLocs.append((p, rdds(1) + .context.getPreferredLocs(rdds(1), p) + .map(_.toString).distinct)) }) val nonmatchPartitionId = new ArrayBuffer[Int]() val parts = new Array[Partition](numParts) (0 until numParts).foreach { i => - val curPrefs = rdds(0).context.getPreferredLocs(rdds(0), i).map(_.host).distinct + val curPrefs = rdds(0).context.getPreferredLocs(rdds(0), i).map(_.toString).distinct var p = 0 var matchPartition: (Int, Seq[String]) = null var locs: Seq[String] = null @@ -104,7 +106,7 @@ class ZippedPartitionsWithLocalityRDD[A: ClassTag, B: ClassTag, V: ClassTag]( require(nonmatchPartitionId.size == candidateLocs.size, "unmatched partition size should be the same with candidateLocs size") nonmatchPartitionId.foreach { i => - val locs = rdds(0).context.getPreferredLocs(rdds(0), i).map(_.host).distinct + val locs = rdds(0).context.getPreferredLocs(rdds(0), i).map(_.toString).distinct val matchPartition = candidateLocs.remove(0) parts(i) = new ZippedPartitionsLocalityPartition(i, Array(i, matchPartition._1), rdds, locs) } From b5b7bbd999ba38bf8f11e9e30154b057182b7b5e Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Mon, 10 Jun 2019 11:00:46 +0800 Subject: [PATCH 0912/1065] [Refactor] Reflection Utilization (#2831) * refactor reflection utils * refactor reflection utils --- .../bigdl/dllib/utils/ReflectionUtils.scala | 316 ++++++++++++++++++ .../dllib/utils/intermediate/BlasToIR.scala | 2 + .../dllib/utils/intermediate/IRToBlas.scala | 2 +- .../dllib/utils/intermediate/IRToDnn.scala | 2 +- .../utils/intermediate/ReflectionUtils.scala | 205 ------------ .../utils/serializer/ModuleSerializable.scala | 71 +--- .../utils/serializer/ModuleSerializer.scala | 18 +- .../dllib/nn/mkldnn/ReflectionUtilsSpec.scala | 4 +- 8 files changed, 338 insertions(+), 282 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ReflectionUtils.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ReflectionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ReflectionUtils.scala new file mode 100644 index 00000000000..fb166a197b4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/ReflectionUtils.scala @@ -0,0 +1,316 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.utils + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.MklInt8Convertible +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.intermediate.{IRElement, IROperator} + +import scala.reflect.ClassTag +import scala.reflect.runtime.{currentMirror, universe} + + + +private[bigdl] object ReflectionUtils { + + private val runtimeMirror = universe.runtimeMirror(getClass.getClassLoader) + + /* -------------------------- External API -------------------------- */ + /** + * Instantiate an object from the target Class with given parameter values, + * assign default values if parameters' value is missing. + * @param target target class + * @param valueMap param's name as key, param's value as value + * @param tags class tag + * @param numerics type of tensor numeric the class is using + * @return + */ + def reflection(target: Class[_], valueMap: Map[String, AnyRef], + tags: Array[ClassTag[_]], numerics: Array[TensorNumeric[_]]): Object = { + + val ctor = getPrimCtorMirror(target) + val paramList = getPrimCtorParamList(target) + val instanceMirror = getInstanceMirror(target) + val (tagsIter, numericsIter) = (tags.toIterator, numerics.toIterator) + + val args = paramList.map { + param => + val typeSig = param.symbol.typeSignature + if (isClassTag(typeSig)) { + tagsIter.next() + } else if (isTensorNumeric(typeSig)) { + numericsIter.next() + } else { + val pname = param.symbol.name.decodedName.toString + // + valueMap.getOrElse(pname, getPrimCtorDefaultParamValue( + instanceMirror, + param.symbol, + param.index + )) + } + } + + ctor.apply(args : _*).asInstanceOf[Object] + } + + + // TODO: to be refined, naming is confusing + /** + * Create a Module object of target Class by mirroring the given IR element + * @param source the source IR element + * @param target the target class we want to make an object from + * @tparam T + * @return the target instance of type Module + */ + def reflectFromIR[T : ClassTag](source: IRElement[T], target: Class[_]): Module[T] = { + val nameAndValues = getFieldNameAndValues(source.getOp()) + val (tags, numerics) = source.getOp().getClassTagNumerics() + + val blasLayer = reflection(target, nameAndValues, tags, numerics) + .asInstanceOf[Module[T]] + + if (blasLayer.parameters() != null) { + val params = blasLayer.getParameters() + val params2 = source.getParameters() + if (params2._1 != null) { + params._1.copy(params2._1) + source.setWeights(params._1) + } + if (params2._2 != null) { + params._2.copy(params2._2) + source.setGradWeights(params._2) + } + } + + if (source.getName() != "") blasLayer.setName(source.getName()) + if (blasLayer.isInstanceOf[MklInt8Convertible]) { + setScales(source, blasLayer.asInstanceOf[MklInt8Convertible]) + } + + blasLayer + } + + + /** + * Create an IR element object of target Class by mirroring given source Module + * @param source the source Module we want to mirror + * @param target the class of target IR element we want to create + * @tparam T + * @return + */ + def reflectToIR[T: ClassTag](source: Module[T], target: Class[_]): IRElement[T] = { + val nameAndValues = getFieldNameAndValues(source) + val (tags, numerics) = source.getClassTagNumerics() + val op = ReflectionUtils.reflection(target, nameAndValues, + tags, numerics).asInstanceOf[IROperator[T]] + val weightsAndBias = + if (source.parameters() != null) source.getParameters() else (null, null) + val element = IRElement[T]( + source.getName(), op, weights = weightsAndBias._1, gradWeights = weightsAndBias._2) + if (source.isInstanceOf[MklInt8Convertible]) { + setScales(source.asInstanceOf[MklInt8Convertible], element) + } + element + } + + + /** + * Get the primary class constructor of input class + * @param cls + * @tparam T + * @return + */ + def getPrimCtorMirror[T : ClassTag](cls : Class[_]): universe.MethodMirror = { + + val clsSymbol = runtimeMirror.classSymbol(cls) + val cm = runtimeMirror.reflectClass(clsSymbol) + // to make it compatible with both 2.11 and 2.10 + val ctorCs = clsSymbol.toType.declaration(universe.nme.CONSTRUCTOR) + val primary: Option[universe.MethodSymbol] = ctorCs.asTerm.alternatives.collectFirst { + case cstor if cstor.asInstanceOf[universe.MethodSymbol].isPrimaryConstructor => + cstor.asInstanceOf[universe.MethodSymbol] + } + cm.reflectConstructor(primary.get) + + } + + + def findClass(name: String): Class[_] = { + try { + Class.forName(name) + } catch { + case ex: ClassNotFoundException => null + case e: Throwable => throw e + } + } + + + // TODO: this method should be moved to a more appropriate place + // put scales in fromEle to toELe + def setScales[T: ClassTag](fromEle: MklInt8Convertible, + toELe: MklInt8Convertible): Unit = { + toELe.setInputScales(fromEle.getInputScales()) + toELe.setOutputScales(fromEle.getOutputScales()) + toELe.setWeightScales(fromEle.getWeightScales()) + + toELe.setInputDimMask(fromEle.getInputDimMask(), true) + toELe.setOutputDimMask(fromEle.getOutputDimMask(), true) + toELe.setWeightDimMask(fromEle.getWeightDimMask(), true) + } + + + /* -------------------------- Internal API -------------------------- */ + /** + * Get key value map from input object, + * field name of the object as key, its reference as value + * @param o input object + * @return A map which field name as key and field refernece as value + */ + private def getFieldNameAndValues(o: Object): Map[String, AnyRef] = { + val c = o.getClass + var fields = c.getDeclaredFields + val superFields = c.getSuperclass.getDeclaredFields + + fields = fields ++ superFields + + val values = fields.map { + field => + field.setAccessible(true) + (field.getName, field.get(o)) + }.toMap + + values + } + + + /** + * Get instance mirror of the input target Class if it has been defined as a case class or + * has a companion object, otherwise it returns universe.NoSymbol + * @param target + * @return InstanceMirror, if the class has no companion then universe.NoSymbol + */ + private def getInstanceMirror(target: Class[_]): universe.InstanceMirror = { + val clsMirror = universe.runtimeMirror(target.getClassLoader) + val clsSymbol = clsMirror.classSymbol(target) + /* + https://www.scala-lang.org/api/2.10.7/#scala.reflect.api.Symbols$Symbol + this line tries to get companion object of the class; + through the companion, default values can be accessed by calling + some static methods created by scala compiler, however it does not work when + the class is not a case class or has not defined a companion, which in this case, + calling companionSymbol returns universe.NoSymbol + */ + val companionSymbol = clsSymbol.companionSymbol + + val instanceMirror = companionSymbol match { + case universe.NoSymbol => null + case _ => + val compnInst = currentMirror.reflectModule(clsSymbol.companionSymbol.asModule).instance + clsMirror.reflect(compnInst) + } + + instanceMirror + } + + /** + * Get primary constructor parameter list of the target Class + * @param target + * @return + */ + private def getPrimCtorParamList(target: Class[_]): List[CtorParam] = { + + val ctorMirror = getPrimCtorMirror(target) + val ctorParamSymbols = ctorMirror.symbol.paramss + + val ctorParamList = ctorParamSymbols.flatten.zipWithIndex.map { + case (param, index) => + CtorParam(index, param) + } + + ctorParamList + } + + /** + * Check given type signature is of ClassTag or not + * @param typeSig + * @return + */ + private def isClassTag(typeSig: universe.Type): Boolean = { + typeSig <:< universe.typeOf[ClassTag[_]] || + typeSig.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol + } + + /** + * Check given type signature is of TensorNumeric or not + * @param typeSig + * @return + */ + private def isTensorNumeric(typeSig: universe.Type): Boolean = { + typeSig <:< universe.typeOf[TensorNumeric[_]] || + typeSig.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol + } + + + /** + * Get class primary consturctor's default parameter value by index + * @param instMirror instance mirror object of the class companion object + * @param paramSymbol symbol object of the target parameter with default value + * @param index the index of parameter in the class primary constructor + * @return AnyRef which is compatible with java Object + */ + private def getPrimCtorDefaultParamValue(instMirror: universe.InstanceMirror, + paramSymbol: universe.Symbol, + index: Int): AnyRef = { + if (paramSymbol == null || paramSymbol == universe.NoSymbol || + instMirror == null || index < 0) { + return None + } + + if (!paramSymbol.asTerm.isParamWithDefault) { // param has no default value + None + } else { + val instTypeSig = instMirror.symbol.typeSignature + val methodName = getCtorDefaultParamMethodByIndex(index) + val methodSymbol = instTypeSig.member(universe.newTermName(methodName)) + if (methodSymbol == universe.NoSymbol) { // method not found + None + } + else { + // make the method call using reflection + // need to cast it as AnyRef to be compatible with Java Object type + instMirror.reflectMethod(methodSymbol.asMethod).apply().asInstanceOf[AnyRef] + } + } + } + + /** + * get string name of the method, which returns default value of the i-th parameter + * Reference: + * https://stackoverflow.com/questions/39657211/scala-class-constructors-default-argument-naming + * @param i parameter index in primary constructor + * @return method name in string, calling this method returns default value of i-th parameter + */ + private def getCtorDefaultParamMethodByIndex(i: Int): String = { + s"$$lessinit$$greater$$default$$${i + 1}" + } + +} + +private case class CtorParam(index: Int, symbol: universe.Symbol) + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToIR.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToIR.scala index 3075ad386c5..f042125dc40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToIR.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/BlasToIR.scala @@ -19,6 +19,8 @@ package com.intel.analytics.bigdl.utils.intermediate import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.ReflectionUtils + import scala.reflect.ClassTag private[bigdl] class BlasToIR[T: ClassTag] extends ConvertBase[Module[T], IRElement[T]]{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToBlas.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToBlas.scala index 727e320ecad..b69ef2432c4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToBlas.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToBlas.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.optim.DistriOptimizer._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{Node, T} +import com.intel.analytics.bigdl.utils.{Node, ReflectionUtils, T} import scala.collection.mutable import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index d21e0825f1a..1e78f36df43 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.nn.mkldnn._ import com.intel.analytics.bigdl.optim.DistriOptimizer._ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{DirectedGraph, Node, T} +import com.intel.analytics.bigdl.utils.{DirectedGraph, Node, ReflectionUtils, T} import scala.collection.mutable import scala.reflect.ClassTag diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala deleted file mode 100644 index f591583359c..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ReflectionUtils.scala +++ /dev/null @@ -1,205 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package com.intel.analytics.bigdl.utils.intermediate - -import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.nn.MklInt8Convertible -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._ - -import scala.collection.mutable -import scala.reflect.{ClassTag, ManifestFactory} -import scala.reflect.runtime._ - -private[bigdl] object ReflectionUtils { - - private def getFieldNameAndValues(o: Object): mutable.HashMap[String, AnyRef] = { - val c = o.getClass - var fields = c.getDeclaredFields - val superFields = c.getSuperclass.getDeclaredFields - fields = fields ++ superFields - - val values = new mutable.HashMap[String, AnyRef]() - fields.foreach(field => { - field.setAccessible(true) - values(field.getName) = field.get(o) - }) - values - } - - // create layer2 object form layer1 - private def reflection(source: Object, target: Class[_], - tags: Array[ClassTag[_]], numerics: Array[TensorNumeric[_]]) : Object = { - val nameAndValues = getFieldNameAndValues(source) - val constructorMirror = getCostructorMirror(target) - val constructorFullParams = constructorMirror.symbol.paramss - val args = new Array[Object](constructorFullParams.map(_.size).sum) - - val tagIter = tags.iterator - val numericIter = numerics.iterator - - val clsMirror = universe.runtimeMirror(target.getClassLoader) - val clsSymbol = clsMirror.classSymbol(target) - - /* - https://www.scala-lang.org/api/2.10.7/#scala.reflect.api.Symbols$Symbol - this line tries to get companion object of the class; - through the companion, default values can be accessed by calling - some static methods created by scala compiler, however it does not work when - the class is not a case class or has not defined a companion, which in this case, - calling companionSymbol returns universe.NoSymbol - */ - val companionSymbol = clsSymbol.companionSymbol - - val instanceMirror = companionSymbol match { - case universe.NoSymbol => null - case _ => - val compnInst = currentMirror.reflectModule(clsSymbol.companionSymbol.asModule).instance - clsMirror.reflect(compnInst) - } - - constructorFullParams.flatten.zipWithIndex.map { - case (param, idx) => - val pname = param.name.decodedName.toString - val ptypesig = param.typeSignature - if (ptypesig <:< universe.typeOf[ClassTag[_]]|| - ptypesig.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { - require(tagIter.hasNext, "If your module contains multiple class tags, " + - "do you forget to override getClassTagNumerics method") - args(idx) = tagIter.next - } else if (ptypesig <:< universe.typeOf[TensorNumeric[_]] - || ptypesig.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { - args(idx) = numericIter.next - } else { - val pvalue = if (nameAndValues.contains(pname)) { // for existing parameters - nameAndValues.get(pname).getOrElse(null) - } else { // parameter not found, get its default value - getPrimCtorDefaultParamValue(instanceMirror, param, idx) - } - args(idx) = pvalue - } - } - constructorMirror.apply(args : _*).asInstanceOf[Object] - } - - - // create Module form IRElement - def reflectFromIR[T : ClassTag](layer: IRElement[T], cls: Class[_]) : Module[T] = { - val (tags, numerics) = layer.getOp().getClassTagNumerics() - val blasLayer = ReflectionUtils.reflection(layer.getOp(), cls, tags, numerics) - .asInstanceOf[Module[T]] - - if (blasLayer.parameters() != null) { - val params = blasLayer.getParameters() - val params2 = layer.getParameters() - if (params2._1 != null) { - params._1.copy(params2._1) - layer.setWeights(params._1) - } - if (params2._2 != null) { - params._2.copy(params2._2) - layer.setGradWeights(params._2) - } - } - - if (layer.getName() != "") blasLayer.setName(layer.getName()) - if (blasLayer.isInstanceOf[MklInt8Convertible]) { - setScales(layer, blasLayer.asInstanceOf[MklInt8Convertible]) - } - - blasLayer - } - - // create IRElement form Module - def reflectToIR[T: ClassTag](layer: Module[T], cls: Class[_]) : IRElement[T] = { - val (tags, numerics) = layer.getClassTagNumerics() - val op = ReflectionUtils.reflection(layer, cls, tags, numerics).asInstanceOf[IROperator[T]] - val weightsAndBias = - if (layer.parameters() != null) layer.getParameters() else (null, null) - val element = IRElement[T]( - layer.getName(), op, weights = weightsAndBias._1, gradWeights = weightsAndBias._2) - if (layer.isInstanceOf[MklInt8Convertible]) { - setScales(layer.asInstanceOf[MklInt8Convertible], element) - } - element - } - - // put scales in fromEle to toELe - private[intermediate] def setScales[T: ClassTag](fromEle: MklInt8Convertible, - toELe: MklInt8Convertible): Unit = { - toELe.setInputScales(fromEle.getInputScales()) - toELe.setOutputScales(fromEle.getOutputScales()) - toELe.setWeightScales(fromEle.getWeightScales()) - - toELe.setInputDimMask(fromEle.getInputDimMask(), true) - toELe.setOutputDimMask(fromEle.getOutputDimMask(), true) - toELe.setWeightDimMask(fromEle.getWeightDimMask(), true) - } - - def findClass(name: String): Class[_] = { - try { - Class.forName(name) - } catch { - case ex: ClassNotFoundException => null - case e: Throwable => throw e - } - } - - /** - * Get class primary consturctor's default parameter value by index - * @param instMirror instance mirror object of the class companion object - * @param paramSymbol symbol object of the target parameter with default value - * @param index the index of parameter in the class primary constructor - * @return AnyRef which is compatible with java Object - */ - def getPrimCtorDefaultParamValue(instMirror: universe.InstanceMirror, - paramSymbol: universe.Symbol, - index: Int): AnyRef = { - if (paramSymbol == null || paramSymbol == universe.NoSymbol || - instMirror == null || index < 0) { - return None - } - - if (!paramSymbol.asTerm.isParamWithDefault) { // param has no default value - None - } else { - val instTypeSig = instMirror.symbol.typeSignature - val methodName = getCtorDefaultParamMethodByIndex(index) - val methodSymbol = instTypeSig.member(universe.newTermName(methodName)) - if (methodSymbol == universe.NoSymbol) { // method not found - None - } - else { - // make the method call using reflection - // need to cast it as AnyRef to be compatible with Java Object type - instMirror.reflectMethod(methodSymbol.asMethod).apply().asInstanceOf[AnyRef] - } - } - } - - /** - * get string name of the method, which returns default value of the i-th parameter - * Reference: - * https://stackoverflow.com/questions/39657211/scala-class-constructors-default-argument-naming - * @param i parameter index in primary constructor - * @return method name in string, calling this method returns default value of i-th parameter - */ - def getCtorDefaultParamMethodByIndex(i: Int): String = { - s"$$lessinit$$greater$$default$$${i + 1}" - } - -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 834ccf09b15..417fe3f6431 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -24,16 +24,14 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.serialization.Bigdl.AttrValue.ArrayValue import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.{Table, Shape => BigDLShape} +import com.intel.analytics.bigdl.utils.{ReflectionUtils, Table, Shape => BigDLShape} import com.intel.analytics.bigdl.utils.serializer.converters.{DataConverter, ShapeConverter, TensorConverter} import com.intel.analytics.bigdl.utils.serializer.ModuleSerializer._ import com.intel.analytics.bigdl.serialization.Bigdl._ -import com.intel.analytics.bigdl.utils.intermediate.ReflectionUtils -import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag -import scala.reflect.runtime.{currentMirror, universe} +import scala.reflect.runtime.universe /** * [[ModuleSerializable]] trait inherits [[Loadable]] and [[Savable]] @@ -108,68 +106,25 @@ trait ModuleSerializable extends Loadable with Savable{ * @return BigDL module */ protected def doLoadModule[T: ClassTag](context: DeserializeContext) - (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + (implicit ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T] = { val (tags, numerics) = getTypes(context) - val tagIter = tags.iterator - val numericIter = numerics.iterator - val evidence = scala.reflect.classTag[T] val model = context.bigdlModule - val modelAttributes = model.getAttrMap val moduleType = model.getModuleType val cls = Class.forName(moduleType) - val constructorMirror = getCostructorMirror(cls) - val constructorFullParams = constructorMirror.symbol.paramss - val args = new Array[Object](constructorFullParams.map(_.size).sum) - var i = 0 - val clsMirror = universe.runtimeMirror(cls.getClassLoader) - val clsSymbol = clsMirror.classSymbol(cls) - - /* - https://www.scala-lang.org/api/2.10.7/#scala.reflect.api.Symbols$Symbol - this line tries to get companion object of the class; - through the companion, default values can be accessed by calling - some static methods created by scala compiler, however it does not work when - the class is not a case class or has not defined a companion, which in this case, - calling companionSymbol returns universe.NoSymbol - */ - val companionSymbol = clsSymbol.companionSymbol - - val instanceMirror = companionSymbol match { - case universe.NoSymbol => null - case _ => - val compnInst = currentMirror.reflectModule(clsSymbol.companionSymbol.asModule).instance - clsMirror.reflect(compnInst) - } + val modelAttributes = model.getAttrMap.asScala.map{ + case (key, value) => + (key, DataConverter.getAttributeValue(context, value) + )}.toMap - // Todo: to be replaced with ReflectionUtils.reflect - constructorFullParams.flatten.foreach(param => { - val pname = param.name.decodedName.toString - val ptypesig = param.typeSignature - if (ptypesig <:< universe.typeOf[ClassTag[_]]|| - ptypesig.typeSymbol == universe.typeOf[ClassTag[_]].typeSymbol) { - require(tagIter.hasNext, "If your module contains multiple class tags, " + - "do you forget to override getClassTagNumerics method") - args(i) = tagIter.next - } else if (ptypesig <:< universe.typeOf[TensorNumeric[_]] - || ptypesig.typeSymbol == universe.typeOf[TensorNumeric[_]].typeSymbol) { - args(i) = numericIter.next - } else { - val pvalue = if (modelAttributes.containsKey(pname)) { // for existing parameters - val attrValue = modelAttributes.get(pname) - DataConverter.getAttributeValue(context, attrValue) - } else { // parameter not found, get its default value - ReflectionUtils.getPrimCtorDefaultParamValue(instanceMirror, param, i) - } - args(i) = pvalue - } - i += 1 - }) - constructorMirror.apply(args : _*). - asInstanceOf[AbstractModule[Activity, Activity, T]] + val module = ReflectionUtils.reflection(cls, modelAttributes, + tags, numerics) + + module.asInstanceOf[AbstractModule[Activity, Activity, T]] } + protected def getTypes(context: DeserializeContext): (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { val attrMap = context.bigdlModule.getAttrMap @@ -239,7 +194,7 @@ trait ModuleSerializable extends Loadable with Savable{ (implicit ev: TensorNumeric[T]) : Unit = { val module = context.moduleData.module val cls = module.getClass - val fullParams = getCostructorMirror(cls).symbol.paramss + val fullParams = ReflectionUtils.getPrimCtorMirror(cls).symbol.paramss val constructorParams = fullParams(0) constructorParams.foreach(param => { val paramName = param.name.decodedName.toString diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index 72df7fcddac..d797e5267e8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -24,6 +24,7 @@ import com.intel.analytics.bigdl.nn.tf.{DecodeRawSerializer, ParseExample, Parse import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.ReflectionUtils import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import scala.collection.mutable @@ -181,20 +182,7 @@ object ModuleSerializer extends ModuleSerializable{ groupSerializerMaps(superModuleType) = groupSerializer } - private[bigdl] def getCostructorMirror[T : ClassTag](cls : Class[_]): - universe.MethodMirror = { - getLock.synchronized { - val clsSymbol = runtimeMirror.classSymbol(cls) - val cm = runtimeMirror.reflectClass(clsSymbol) - // to make it compatible with both 2.11 and 2.10 - val ctorCs = clsSymbol.toType.declaration(universe.nme.CONSTRUCTOR) - val primary: Option[universe.MethodSymbol] = ctorCs.asTerm.alternatives.collectFirst { - case cstor if cstor.asInstanceOf[universe.MethodSymbol].isPrimaryConstructor => - cstor.asInstanceOf[universe.MethodSymbol] - } - cm.reflectConstructor(primary.get) - } - } + private def init() : Unit = { initializeDeclaredTypes @@ -204,7 +192,7 @@ object ModuleSerializer extends ModuleSerializable{ private def initializeDeclaredTypes() : Unit = { var wrapperCls = Class.forName("com.intel.analytics.bigdl.utils.serializer.GenericTypeWrapper") - val fullParams = getCostructorMirror(wrapperCls).symbol.paramss + val fullParams = ReflectionUtils.getPrimCtorMirror(wrapperCls).symbol.paramss fullParams.foreach(map => { map.foreach(param => { val name = param.name.decodedName.toString diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala index 6ddc703bb7e..86cea044512 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReflectionUtilsSpec.scala @@ -21,8 +21,8 @@ import com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.BigDLSpecHelper -import com.intel.analytics.bigdl.utils.intermediate.{IRToBlas, IRToDnn, ReflectionUtils} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, ReflectionUtils} +import com.intel.analytics.bigdl.utils.intermediate.{IRToBlas, IRToDnn} import com.intel.analytics.bigdl.{Module, nn} class ReflectionUtilsSpec extends BigDLSpecHelper { From 07bc5e17026ae696822549ea69f38f45cb3a63d8 Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Tue, 11 Jun 2019 13:32:16 +0800 Subject: [PATCH 0913/1065] feat: MKLDNN LSTM unidirectional/bidirectional inference support (#2806) * LSTM draft * MKLDNN LSTM fixed MD * added hiddenSize * setMemoryData NativeData * weights NativeData format set to ldigo, all 1 test passed * fixed format any problem * LSTM weights bias initialisation * add LSTM2 in nn * Bidirectional LSTM inference enabled * modified Bidirectional test * LSTMSpec input format conversion bug between bigdl and mkldnn fixed, not support random weights, bias * fixed the last problem 1 3 2 4 * Three inference tests with randomly generated parameters * Added comments and modified the LSTMSpec (tests using Equivalent.nearequals) * Deleted nn/LSTM2. Renamed methods. Added a requirement in nn/TimeDistributed * combined initMemoryDescs() into initFwdPrimitives() * Add require for input size and hidden size matching if layers of LSTM is more than one * Refactor RNN * Add comment on gate order to mkldnn/RNN * Add unidirectional multilayer test * add comments/ modify UTs * phase is not used anymore/ use isTraining() in stead * operationWant enhanced/ weight init/ release() parameters() * remove input format check and change some variables names * input format check / throw exception print info / release code * comment style and RNNSerialTest * remove unnecessary comments --- .../bigdl/dllib/nn/TimeDistributed.scala | 1 + .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 4 +- .../analytics/bigdl/dllib/nn/mkldnn/RNN.scala | 395 +++++++++++++++ .../bigdl/dllib/nn/mkldnn/RNNSpec.scala | 465 ++++++++++++++++++ .../utils/serializer/SerializerSpec.scala | 3 +- 5 files changed, 865 insertions(+), 3 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala index e1a0fa8a5ee..ce51a69027f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TimeDistributed.scala @@ -105,6 +105,7 @@ class TimeDistributed[T : ClassTag] ( val _inputSize = input.size combine(_inputSize, inputSize) + require(input.isContiguous(), "Input tensor to TimeDistributed should be contiguous") input.resize(inputSize) val _output = layer.forward(input).toTensor[T] split(_output.size, outputSize, _inputSize(0), _inputSize(1)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index 084856ce6bd..e6302f3a793 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -269,8 +269,8 @@ private[mkldnn] object MemoryData { memory } - def operationWant(primDesc: Long, queryType: Int): NativeData = { - val memoryPrimDesc = MklDnn.PrimitiveDescQueryPd(primDesc, queryType, 0) + def operationWant(primDesc: Long, queryType: Int, index: Int = 0): NativeData = { + val memoryPrimDesc = MklDnn.PrimitiveDescQueryPd(primDesc, queryType, index) val memoryDesc = MklDnn.PrimitiveDescQueryMemory(memoryPrimDesc) val shape = Memory.GetShape(memoryDesc) val paddingShape = Memory.GetPaddingShape(memoryDesc) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala new file mode 100644 index 00000000000..c7b96aa5339 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala @@ -0,0 +1,395 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl._ +import com.intel.analytics.bigdl.nn.{InitializationMethod, RandomUniform, VariableFormat, Zeros} +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} +import com.intel.analytics.bigdl.tensor.Tensor + +import scala.collection.mutable.ArrayBuffer + +/** + * @param mode : the type of RNN cell + * @param inputSize : the size of input vector + * @param hiddenSize : the size of hidden state + * @param f : the type of output activation function + * (AlgKind.EltwiseTanh or AlgKind.EltwiseRelu) + * @param direction : the direction to run RNN + * (e.g. Direction.UnidirectionalLeft2Right or Direction.BidirectionalConcat) + * @param layers : the number of RNN layers + */ + +class RNN( + val mode: Int, + val inputSize: Int, + val hiddenSize: Int, + val f: Int, + val direction: Int, + val layers: Int = 1, + val flags: Int = RNNCellFlags.RNNCellWithRelu, + val alpha: Float = 0F, + val clipping: Float = 0F, + private val initWeight: Tensor[Float] = null, + private val initWeightIter: Tensor[Float] = null, + private val initBias: Tensor[Float] = null +) extends MklDnnLayer with Initializable { + private var src_layer_MD: Long = _ + private var src_iter_MD: Long = _ + private var weights_layer_MD: Long = _ + private var weights_iter_MD: Long = _ + private var bis_MD: Long = _ + private var dist_layer_MD: Long = _ + private var dist_iter_MD: Long = _ + + private var fwdPD: Long = _ + + private var updateOutputMemoryPrimitives: Array[Long] = _ + private var updateOutputTensors: Array[Tensor[Float]] = _ + + private val common_n_layers: Int = layers + private var ngates: Int = _ + private var nstates: Int = _ + + private[mkldnn] var weight: TensorMMap = _ + private[mkldnn] var weight_i: TensorMMap = _ + private[mkldnn] var bias: TensorMMap = _ + private[mkldnn] var src_i: TensorMMap = _ + private[mkldnn] var dst_i: TensorMMap = _ + + if(layers > 1) { + require(inputSize == hiddenSize, + "If layers of LSTM is more than 1, the input size and the hidden size should equal.\n" + + "inputSize: " + inputSize + '\n' + + "hiddenSize: " + hiddenSize) + } + + mode match { + case AlgKind.VanillaLstm => + ngates = 4 + nstates = 2 + case _ => + throw new UnsupportedOperationException("Not support such RNN Cell. Cell type: " + mode) + } + + direction match { + case Direction.UnidirectionalLeft2Right + | Direction.UnidirectionalRight2Left => + + /** + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + */ + weight = new TensorMMap(Array(common_n_layers, 1, inputSize, ngates, hiddenSize)) + weight_i = new TensorMMap(Array(common_n_layers, 1, hiddenSize, ngates, hiddenSize)) + bias = new TensorMMap(Array(common_n_layers, 1, ngates, hiddenSize)) + + case Direction.BidirectionalConcat => + require(layers == 1, "Bidirectional Concat LSTM does not support multiple layers. " + + "layers = " + layers) + + weight = new TensorMMap(Array(common_n_layers, 2, inputSize, ngates, hiddenSize)) + weight_i = new TensorMMap(Array(common_n_layers, 2, hiddenSize, ngates, hiddenSize)) + bias = new TensorMMap(Array(common_n_layers, 2, ngates, hiddenSize)) + + case Direction.BidirectionalSum => + + /** TODO: Multi-layer Bidirectional LSTM is available in MKLDNN, + * but it is not supported in current version BigDL BLAS. + */ + + weight = new TensorMMap(Array(common_n_layers, 2, inputSize, ngates, hiddenSize)) + weight_i = new TensorMMap(Array(common_n_layers, 2, hiddenSize, ngates, hiddenSize)) + bias = new TensorMMap(Array(common_n_layers, 2, ngates, hiddenSize)) + } + + { + val stdv = 1.0 / math.sqrt(hiddenSize) + val wInit: InitializationMethod = RandomUniform(-stdv, stdv) + val bInit: InitializationMethod = Zeros + setInitMethod(wInit, bInit) + } + + override def reset(): Unit = { + if (initWeight == null) { + weightInitMethod.init(weight.dense, VariableFormat.Default) + } else { + weight.dense.copy(initWeight) + } + + if (initWeightIter == null) { + weightInitMethod.init(weight_i.dense, VariableFormat.Default) + } else { + weight_i.dense.copy(initWeightIter) + } + + if (initBias == null) { + biasInitMethod.init(bias.dense, VariableFormat.Default) + } else { + bias.dense.copy(initBias) + } + } + + private def initMemoryDescs(inputs: Array[MemoryData]) = { + // TODO: The default format of input is TNC + /** + * The default format of input is TNC. + * Batch size of input is needed by creating memory descriptors of src iter and dst iter. + * Step size of input is needed by creating memory descriptor of dst layer. + * By default, batch size of input is the second element of inputShape + * and step size is the first element of inputShape. + */ + val(inputShape, inputLayout) = inputs(0).layout match { + case Memory.Format.tnc => /* tnc */ + (inputs(0).shape, Memory.Format.tnc) + case _ => + throw new UnsupportedOperationException("Not support such input format. " + + "The input format is: " + inputs(0).layout) + } + + direction match { + case Direction.UnidirectionalLeft2Right + | Direction.UnidirectionalRight2Left => + val weightShape = weight.size() /* ldigo */ + val biasShape = bias.size() /* ldgo */ + val outputShape = Array(inputShape(0), inputShape(1), hiddenSize) /* tnc */ + + val inputShape_iter = Array(common_n_layers, 1, nstates, + inputShape(1), hiddenSize) /* ldsnc */ + val weightShape_iter = weight_i.size() /* ldigo */ + val outputShape_iter = inputShape_iter /* ldsnc */ + + val src_layer = NativeData(inputShape, Memory.Format.any) + val src_iter = NativeData(inputShape_iter, Memory.Format.any) + val wei_layer = NativeData(weightShape, Memory.Format.any) + val wei_iter = NativeData(weightShape_iter, Memory.Format.any) + val bis = NativeData(biasShape, Memory.Format.any) + val dst_layer = NativeData(outputShape, Memory.Format.any) + val dst_iter = NativeData(outputShape_iter, Memory.Format.any) + + src_layer_MD = src_layer.getMemoryDescription() + src_iter_MD = src_iter.getMemoryDescription() + weights_layer_MD = wei_layer.getMemoryDescription() + weights_iter_MD = wei_iter.getMemoryDescription() + bis_MD = bis.getMemoryDescription() + dist_layer_MD = dst_layer.getMemoryDescription() + dist_iter_MD = dst_iter.getMemoryDescription() + + src_i = new TensorMMap(inputShape_iter) + src_i.dense.copy(Tensor[Float]().resize(inputShape_iter).zero()) + dst_i = new TensorMMap(outputShape_iter) + dst_i.dense.copy(Tensor[Float]().resize(outputShape_iter).zero()) + + case Direction.BidirectionalConcat => + val weightShape = weight.size() /* ldigo */ + val biasShape = bias.size() /* ldgo */ + val outputShape = Array(inputShape(0), inputShape(1), 2 * hiddenSize) /* tnc */ + + val inputShape_iter = Array(common_n_layers, 2, nstates, + inputShape(1), hiddenSize) /* ldsnc */ + val weightShape_iter = weight_i.size() /* ldigo */ + val outputShape_iter = inputShape_iter /* ldsnc */ + + val src_layer = NativeData(inputShape, Memory.Format.any) + val src_iter = NativeData(inputShape_iter, Memory.Format.any) + val wei_layer = NativeData(weightShape, Memory.Format.any) + val wei_iter = NativeData(weightShape_iter, Memory.Format.any) + val bis = NativeData(biasShape, Memory.Format.any) + val dst_layer = NativeData(outputShape, Memory.Format.any) + val dst_iter = NativeData(outputShape_iter, Memory.Format.any) + + src_layer_MD = src_layer.getMemoryDescription() + src_iter_MD = src_iter.getMemoryDescription() + weights_layer_MD = wei_layer.getMemoryDescription() + weights_iter_MD = wei_iter.getMemoryDescription() + bis_MD = bis.getMemoryDescription() + dist_layer_MD = dst_layer.getMemoryDescription() + dist_iter_MD = dst_iter.getMemoryDescription() + + /** TODO: user-defined initial hidden state is not supported currently. + * The default initial hidden state is all zero. + */ + src_i = new TensorMMap(inputShape_iter) + src_i.dense.copy(Tensor[Float]().resize(inputShape_iter).zero()) + dst_i = new TensorMMap(outputShape_iter) + dst_i.dense.copy(Tensor[Float]().resize(outputShape_iter).zero()) + + case Direction.BidirectionalSum => + val weightShape = weight.size() /* ldigo */ + val biasShape = bias.size() /* ldgo */ + val outputShape = Array(inputShape(0), inputShape(1), hiddenSize) /* tnc */ + + val inputShape_iter = Array(common_n_layers, 2, nstates, + inputShape(1), hiddenSize) /* ldsnc */ + val weightShape_iter = weight_i.size() /* ldigo */ + val outputShape_iter = inputShape_iter /* ldsnc */ + + val src_layer = NativeData(inputShape, Memory.Format.any) + val src_iter = NativeData(inputShape_iter, Memory.Format.any) + val wei_layer = NativeData(weightShape, Memory.Format.any) + val wei_iter = NativeData(weightShape_iter, Memory.Format.any) + val bis = NativeData(biasShape, Memory.Format.any) + val dst_layer = NativeData(outputShape, Memory.Format.any) + val dst_iter = NativeData(outputShape_iter, Memory.Format.any) + + src_layer_MD = src_layer.getMemoryDescription() + src_iter_MD = src_iter.getMemoryDescription() + weights_layer_MD = wei_layer.getMemoryDescription() + weights_iter_MD = wei_iter.getMemoryDescription() + bis_MD = bis.getMemoryDescription() + dist_layer_MD = dst_layer.getMemoryDescription() + dist_iter_MD = dst_iter.getMemoryDescription() + + src_i = new TensorMMap(inputShape_iter) + src_i.dense.copy(Tensor[Float]().resize(inputShape_iter).zero()) + dst_i = new TensorMMap(outputShape_iter) + dst_i.dense.copy(Tensor[Float]().resize(outputShape_iter).zero()) + + case _ => throw new UnsupportedOperationException("Not support such direction") + } + } + + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + val kind = if (!isTraining()) { + PropKind.ForwardInference + } else { + throw new UnsupportedOperationException("Not support training") + } + + val rnnCellDesc = mode match { + case AlgKind.VanillaLstm => + MklDnn.RNNCellDescInit(AlgKind.VanillaLstm, f, flags, alpha, clipping) + case _ => throw new UnsupportedOperationException("Not support such RNN cell. " + + "Cell type: " + mode) + } + + initMemoryDescs(inputs) + + val description = MklDnn.RNNForwardDescInit(kind, rnnCellDesc, direction, src_layer_MD, + src_iter_MD, weights_layer_MD, weights_iter_MD, bis_MD, dist_layer_MD, dist_iter_MD) + + fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + + val realSrc = MemoryData.operationWant(fwdPD, Query.SrcPd, 0) + val realSrc_iter = MemoryData.operationWant(fwdPD, Query.SrcPd, 1) + val realWei = MemoryData.operationWant(fwdPD, Query.WeightsPd, 0) + val realWei_iter = MemoryData.operationWant(fwdPD, Query.WeightsPd, 1) + val realBias = MemoryData.operationWant(fwdPD, Query.WeightsPd, 2) + + val realDst = MemoryData.operationWant(fwdPD, Query.DstPd, 0) + val realDst_iter = MemoryData.operationWant(fwdPD, Query.DstPd, 1) + + require(src_i.size().product == realSrc_iter.shape.product, + s"${getName} src iter shape is not correct.") + require(dst_i.size().product == realDst_iter.shape.product, + s"${getName} dst iter shape is not correct.") + require(weight.size().product == realWei.shape.product, + s"${getName} weight shape is not correct.") + require(weight_i.size().product == realWei_iter.shape.product, + s"${getName} weight iter shape is not correct.") + require(bias.size().product == realBias.shape.product, + s"${getName} bias shape is not correct.") + + weight.setMemoryData(HeapData(weight.size(), Memory.Format.ldigo), realWei, runtime) + weight_i.setMemoryData(HeapData(weight_i.size(), Memory.Format.ldigo), realWei_iter, runtime) + bias.setMemoryData(HeapData(bias.size(), Memory.Format.ldgo), realBias, runtime) + src_i.setMemoryData(HeapData(src_i.size(), Memory.Format.ldsnc), realSrc_iter, runtime) + dst_i.setMemoryData(HeapData(dst_i.size(), Memory.Format.ldsnc), realDst_iter, runtime) + + weight.sync() + weight_i.sync() + bias.sync() + src_i.sync() + dst_i.sync() + + val srcs = Array(realSrc.getPrimitive(runtime), realSrc_iter.getPrimitive(runtime), + realWei.getPrimitive(runtime), realWei_iter.getPrimitive(runtime), + realBias.getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + + val dsts = Array(realDst.getPrimitive(runtime), realDst_iter.getPrimitive(runtime)) + + val primitive = MklDnn.PrimitiveCreate2(fwdPD, srcs, indexes, srcs.length, dsts, dsts.length) + + updateOutputMemoryPrimitives = srcs ++ dsts + updateOutputPrimitives = Array(primitive) + output = initTensor(realDst) + + _inputFormats = Array(realSrc) + _outputFormats = Array(realDst) + + (_inputFormats, _outputFormats) + } + + override def updateOutput(input: Activity): Activity = { + if (updateOutputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(src_i.native) + buffer.append(weight.native) + buffer.append(weight_i.native) + buffer.append(bias.native) + buffer.append(output.asInstanceOf[Tensor[Float]]) + buffer.append(dst_i.native) + + updateOutputTensors = buffer.toArray + } + + updateWithNewTensor(updateOutputTensors, 0, input) + + MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, + updateOutputMemoryPrimitives, updateOutputTensors) + + output + } + + override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { + throw new UnsupportedOperationException("Not support backward propagation") + } + + override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { + (Array(weight.dense, bias.dense, weight_i.dense), Array()) + } + + override def zeroGradParameters(): Unit = { + } + + override def release(): Unit = { + super.release() + List(weight, bias, weight_i, src_i, dst_i).foreach(_.release()) + } +} + +object RNN{ + def apply( + mode: Int, + inputSize: Int, + hiddenSize: Int, + f: Int, + direction: Int, + layers: Int = 1, + flags: Int = RNNCellFlags.RNNCellWithRelu, + alpha: Float = 0F, + clipping: Float = 0F, + initWeight: Tensor[Float] = null, + initWeightIter: Tensor[Float] = null, + initBias: Tensor[Float] = null + ): RNN = new RNN(mode, inputSize, hiddenSize, f, direction, layers, flags, alpha, + clipping, initWeight, initWeightIter, initBias) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala new file mode 100644 index 00000000000..640f7ad8b06 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala @@ -0,0 +1,465 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.mkl.{AlgKind, Direction, Memory} +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.utils.{T, Table} + +class RNNSpec extends FlatSpec with Matchers{ + "LSTM UnidirectionalInference updateOutput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.UnidirectionalLeft2Right + + val common_n_layers = 1 + val lstm_n_gates = 4 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + + var initWeight = Tensor[Float]( + Array(common_n_layers, 1, + inputSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 1, + hiddenSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 1, + lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + val mkldnnLSTM1 = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias)) + mkldnnLSTM1.evaluate() + mkldnnLSTM1.compile(InferencePhase) + val mkldnn_output1 = mkldnnLSTM1.forward(input) + println("MKLDNN output LSTM Uni Left2Right \n" + mkldnn_output1) + + direction = Direction.UnidirectionalRight2Left + val mkldnnLSTM2 = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias)) + mkldnnLSTM2.evaluate() + mkldnnLSTM2.compile(InferencePhase) + val mkldnn_output2 = mkldnnLSTM2.forward(input) + println("MKLDNN output LSTM Uni Right2Left \n" + mkldnn_output2) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + var inputt = input.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(inputSize, lstm_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + initWeightIter = initWeightIter.resize(Array(hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + initBias = initBias.resize(Array(lstm_n_gates, hiddenSize)) + + /** + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * uniParams(0) -> input weights + * uniParams(1) -> bias + * uniParams(2) -> hidden weights + */ + + var initWeight0 = Tensor[Float](Array(hiddenSize * lstm_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(hiddenSize * lstm_n_gates, hiddenSize)) + var initBias0 = Tensor[Float](Array(lstm_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 4) + initWeight0 = concat.forward(T(initWeight(1), initWeight(3), + initWeight(2), initWeight(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0 = concat.forward(T(initWeightIter(1), initWeightIter(3), + initWeightIter(2), initWeightIter(4))).asInstanceOf[Tensor[Float]].clone() + initBias0 = concat.forward(T(initBias(1), initBias(3), initBias(2), initBias(4))) + .asInstanceOf[Tensor[Float]].clone() + + val blasLSTM = nn.Recurrent().add(nn.LSTM(inputSize, hiddenSize)) + + val uniParams = blasLSTM.parameters()._1 + initWeight0 = initWeight0.resizeAs(uniParams(0)) + initBias0 = initBias0.resizeAs(uniParams(1)) + initWeightIter0 = initWeightIter0.resizeAs(uniParams(2)) + + uniParams(0).copy(initWeight0) + uniParams(1).copy(initBias0) + uniParams(2).copy(initWeightIter0) + + val blas_output1 = blasLSTM.forward(inputt).toTensor.transpose(1, 2) + println("BLAS output LSTM Uni Left2Right \n" + blas_output1) + + Equivalent.nearequals(Tools.dense(mkldnn_output1).asInstanceOf[Tensor[Float]], + blas_output1) should be(true) + + /** + * nn/LSTM Right2Left + */ + val reverse = nn.Reverse(2) + inputt = reverse.forward(inputt) + + var blas_output2 = blasLSTM.forward(inputt) + blas_output2 = reverse.forward(blas_output2).toTensor.transpose(1, 2) + println("BLAS output LSTM Uni Right2Left \n" + blas_output2) + println("==================================================================== \n\n\n") + + Equivalent.nearequals(Tools.dense(mkldnn_output2).asInstanceOf[Tensor[Float]], + blas_output2) should be(true) + } + + "LSTM BidirectionalConcatInference updateOutput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalConcat + + val common_n_layers = 1 + val lstm_n_gates = 4 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + + var initWeight = Tensor[Float]( + Array(common_n_layers, 2, + inputSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 2, + hiddenSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 2, + lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + val mkldnnLSTM = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias)) + mkldnnLSTM.evaluate() + mkldnnLSTM.compile(InferencePhase) + val mkldnn_output = mkldnnLSTM.forward(input) + println("MKLDNN output LSTM Bi Concat \n" + mkldnn_output) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + val inputt = input.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(2, inputSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter.resize(Array(2, hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(2, lstm_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, hiddenSize)) + var initBias0 = Tensor[Float](Array(2, lstm_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 4) + initWeight0(1) = concat.forward(T(initWeight(1)(1), initWeight(1)(3), + initWeight(1)(2), initWeight(1)(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(1) = concat.forward(T(initWeightIter(1)(1), initWeightIter(1)(3), + initWeightIter(1)(2), initWeightIter(1)(4))).asInstanceOf[Tensor[Float]].clone() + initBias0(1) = concat.forward(T(initBias(1)(1), initBias(1)(3), + initBias(1)(2), initBias(1)(4))).asInstanceOf[Tensor[Float]].clone() + + initWeight0(2) = concat.forward(T(initWeight(2)(1), initWeight(2)(3), + initWeight(2)(2), initWeight(2)(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(2) = concat.forward(T(initWeightIter(2)(1), initWeightIter(2)(3), + initWeightIter(2)(2), initWeightIter(2)(4))).asInstanceOf[Tensor[Float]].clone() + initBias0(2) = concat.forward(T(initBias(2)(1), initBias(2)(3), + initBias(2)(2), initBias(2)(4))).asInstanceOf[Tensor[Float]].clone() + + val blasLSTM = nn.BiRecurrent[Float](nn.JoinTable[Float](3, 0) + .asInstanceOf[AbstractModule[Table, Tensor[Float], Float]]) + .add(nn.LSTM(inputSize, hiddenSize)) + + /** + * biParams(0 - 2) and (3 - 5) are for the two directions respectively + * + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * biParams(0) -> input weights + * biParams(1) -> bias + * biParams(2) -> hidden weights + * biParams(3) -> input weights + * biParams(4) -> bias + * biParams(5) -> hidden weights + */ + + val biParams = blasLSTM.parameters()._1 + initWeight0(1).resizeAs(biParams(0)) + initBias0(1).resizeAs(biParams(1)) + initWeightIter0(1).resizeAs(biParams(2)) + initWeight0(2).resizeAs(biParams(3)) + initBias0(2).resizeAs(biParams(4)) + initWeightIter0(2).resizeAs(biParams(5)) + + biParams(0).copy(initWeight0(1)) + biParams(1).copy(initBias0(1)) + biParams(2).copy(initWeightIter0(1)) + biParams(3).copy(initWeight0(2)) + biParams(4).copy(initBias0(2)) + biParams(5).copy(initWeightIter0(2)) + + val blas_output = blasLSTM.forward(inputt).toTensor.transpose(1, 2) + println("BLAS output LSTM Bi Concat \n" + blas_output) + println("==================================================================== \n\n\n") + + Equivalent.nearequals(Tools.dense(mkldnn_output).asInstanceOf[Tensor[Float]], + blas_output) should be(true) + } + + "LSTM BidirectionalSumInference updateOutput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalSum + + val common_n_layers = 1 + val lstm_n_gates = 4 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + + var initWeight = Tensor[Float]( + Array(common_n_layers, 2, + inputSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 2, + hiddenSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 2, + lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + val mkldnnLSTM = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias)) + mkldnnLSTM.evaluate() + mkldnnLSTM.compile(InferencePhase) + val mkldnn_output = mkldnnLSTM.forward(input) + println("MKLDNN output LSTM Bi Sum \n" + mkldnn_output) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + val inputt = input.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(2, inputSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter.resize(Array(2, hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(2, lstm_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, hiddenSize)) + var initBias0 = Tensor[Float](Array(2, lstm_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 4) + initWeight0(1) = concat.forward(T(initWeight(1)(1), initWeight(1)(3), + initWeight(1)(2), initWeight(1)(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(1) = concat.forward(T(initWeightIter(1)(1), initWeightIter(1)(3), + initWeightIter(1)(2), initWeightIter(1)(4))).asInstanceOf[Tensor[Float]].clone() + initBias0(1) = concat.forward(T(initBias(1)(1), initBias(1)(3), + initBias(1)(2), initBias(1)(4))).asInstanceOf[Tensor[Float]].clone() + + initWeight0(2) = concat.forward(T(initWeight(2)(1), initWeight(2)(3), + initWeight(2)(2), initWeight(2)(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(2) = concat.forward(T(initWeightIter(2)(1), initWeightIter(2)(3), + initWeightIter(2)(2), initWeightIter(2)(4))).asInstanceOf[Tensor[Float]].clone() + initBias0(2) = concat.forward(T(initBias(2)(1), initBias(2)(3), + initBias(2)(2), initBias(2)(4))).asInstanceOf[Tensor[Float]].clone() + + val blasLSTM = nn.BiRecurrent[Float](nn.CAddTable() + .asInstanceOf[AbstractModule[Table, Tensor[Float], Float]]) + .add(nn.LSTM(inputSize, hiddenSize)) + + /** + * biParams(0 - 2) and (3 - 5) are for the two directions respectively + * + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * biParams(0) -> input weights + * biParams(1) -> bias + * biParams(2) -> hidden weights + * biParams(3) -> input weights + * biParams(4) -> bias + * biParams(5) -> hidden weights + */ + + val biParams = blasLSTM.parameters()._1 + initWeight0(1).resizeAs(biParams(0)) + initBias0(1).resizeAs(biParams(1)) + initWeightIter0(1).resizeAs(biParams(2)) + initWeight0(2).resizeAs(biParams(3)) + initBias0(2).resizeAs(biParams(4)) + initWeightIter0(2).resizeAs(biParams(5)) + + biParams(0).copy(initWeight0(1)) + biParams(1).copy(initBias0(1)) + biParams(2).copy(initWeightIter0(1)) + biParams(3).copy(initWeight0(2)) + biParams(4).copy(initBias0(2)) + biParams(5).copy(initWeightIter0(2)) + + val blas_output = blasLSTM.forward(inputt).toTensor.transpose(1, 2) + println("BLAS output LSTM Bi Sum \n" + blas_output) + println("==================================================================== \n\n\n") + + Equivalent.nearequals(Tools.dense(mkldnn_output).asInstanceOf[Tensor[Float]], + blas_output) should be(true) + } + + "LSTM UnidirectionalInference Multilayers updateOutput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val commonSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.UnidirectionalLeft2Right + + val common_n_layers = 3 + val lstm_n_gates = 4 + + val inputFormat = HeapData(Array(seqLength, batchSize, commonSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, commonSize)).rand() + + var initWeight = Tensor[Float]( + Array(common_n_layers, 1, + commonSize, lstm_n_gates, commonSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 1, + commonSize, lstm_n_gates, commonSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 1, + lstm_n_gates, commonSize)).rand(-1.0, 1.0) + + val mkldnnLSTM = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaLstm, commonSize, commonSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, + initBias = initBias, layers = common_n_layers)) + mkldnnLSTM.evaluate() + mkldnnLSTM.compile(InferencePhase) + val output = mkldnnLSTM.forward(input) + println("MKLDNN output LSTM Uni Multilayers Left2Right \n" + output) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + var inputt = input.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(common_n_layers, commonSize, lstm_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter + .resize(Array(common_n_layers, commonSize, lstm_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(common_n_layers, lstm_n_gates, commonSize)) + + /** + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * uniParams(0) -> input weights + * uniParams(1) -> bias + * uniParams(2) -> hidden weights + */ + + var initWeight0 = Tensor[Float](Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) + var initWeightIter0 = + Tensor[Float](Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) + var initBias0 = Tensor[Float](Array(common_n_layers, lstm_n_gates * commonSize)) + + val concat = nn.JoinTable(1, 4) + for(l <- 1 to common_n_layers) { + initWeight0(l).copy(concat.forward(T(initWeight(l)(1), initWeight(l)(3), + initWeight(l)(2), initWeight(l)(4))).asInstanceOf[Tensor[Float]].clone()) + initWeightIter0(l).copy(concat.forward(T(initWeightIter(l)(1), initWeightIter(l)(3), + initWeightIter(l)(2), initWeightIter(l)(4))).asInstanceOf[Tensor[Float]].clone()) + initBias0(l).copy(concat.forward(T(initBias(l)(1), initBias(l)(3), + initBias(l)(2), initBias(l)(4))) + .asInstanceOf[Tensor[Float]].clone()) + } + + val nn_input = nn.Input() + var nn_lstm = nn.Recurrent().add(nn.LSTM(commonSize, commonSize)).inputs(nn_input) + + for(i <- 1 until common_n_layers) { + nn_lstm = nn.Recurrent().add(nn.LSTM(commonSize, commonSize)).inputs(nn_lstm) + } + + val blasLSTM = nn.Graph(nn_input, nn_lstm) + + val uniParams = blasLSTM.parameters()._1 + + for(l <- 0 until common_n_layers) { + initWeight0(l + 1) = initWeight0(l + 1).resizeAs(uniParams(3 * l)) + initBias0(l + 1) = initBias0(l + 1).resizeAs(uniParams(3 * l + 1)) + initWeightIter0(l + 1) = initWeightIter0(l + 1).resizeAs(uniParams(3 * l + 2)) + + uniParams(3 * l).copy(initWeight0(l + 1)) + uniParams(3 * l + 1).copy(initBias0(l + 1)) + uniParams(3 * l + 2).copy(initWeightIter0(l + 1)) + } + + val blas_output = blasLSTM.forward(inputt).toTensor.transpose(1, 2) + println("BLAS output LSTM Uni Multilayers Left2Right \n" + blas_output) + println("==================================================================== \n\n\n") + + Equivalent.nearequals(Tools.dense(output).asInstanceOf[Tensor[Float]], + blas_output) should be(true) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala index 38075586564..79e1c9ca465 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/serializer/SerializerSpec.scala @@ -67,7 +67,8 @@ class SerializerSpec extends BigDLSpecHelper { "com.intel.analytics.bigdl.nn.mkldnn.BlasWrapper", "com.intel.analytics.bigdl.nn.mkldnn.Output", "com.intel.analytics.bigdl.nn.mkldnn.InputWrapper", - "com.intel.analytics.bigdl.utils.intermediate.IRGraph" + "com.intel.analytics.bigdl.utils.intermediate.IRGraph", + "com.intel.analytics.bigdl.nn.mkldnn.RNN" ) // Maybe one serial test class contains multiple module test From e45cbbc5e8b26e4065638affc3c3c081be78d668 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 12 Jun 2019 16:31:12 +0800 Subject: [PATCH 0914/1065] bug fix for cmul (#2836) * bug fix for cmul * meet pr comments --- .../intel/analytics/bigdl/dllib/nn/CMul.scala | 22 ++++++++++++------- .../analytics/bigdl/dllib/nn/CMulSpec.scala | 17 ++++++++++++++ 2 files changed, 31 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala index 1ab34031115..e9d2271ce93 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/CMul.scala @@ -82,15 +82,21 @@ class CMul[T: ClassTag]( } private def mulOneDimWeight(dim: Int, expand: Tensor[T], output: Tensor[T]): Unit = { - val multiplyDimSize = expand.size(dim) - var dimOfOutput : Int = 1 - while(output.size(dimOfOutput) != multiplyDimSize) { - - dimOfOutput += 1 - require(dimOfOutput <= output.dim(), s"OutOfBound : " + - s"Output does not have a dimension of $multiplyDimSize elements") + var outputDim : Int = dim + if (expand.dim() > output.dim()) { + val multiplyDimSize = expand.size(dim) + var dimTemp : Int = 1 + while(output.size(dimTemp) != multiplyDimSize) { + dimTemp += 1 + require(dimTemp <= output.dim(), s"OutOfBound : " + + s"Output does not have a dimension of $multiplyDimSize elements") + } + outputDim = dimTemp + } else { + require(output.size(dim) == expand.size(dim), s"OutOfBound : " + + s"Output does not have a dimension of ${expand.size(dim)} elements") } - val (innerNum, outerNum) = Utils.getInnerOuterNum(dimOfOutput, output) + val (innerNum, outerNum) = Utils.getInnerOuterNum(outputDim, output) val weightData = expand.storage().array() val weightOffset = expand.storageOffset() - 1 var outer = 0 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulSpec.scala index 1f21f990c63..5856005b9c1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/CMulSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.utils.RandomGenerator._ @@ -65,6 +66,22 @@ class CMulSpec extends FlatSpec with Matchers { gradInput3 should be (gradInput4) layer2.gradWeight should be (layer1.gradWeight.mul(0.5)) } + + "CMUl" should "works well on batch input" in { + val model = nn.CMul[Float](Array(1, 64, 1, 1)) + val model2 = model.cloneModule() + + val batchInput = Tensor[Float](64, 64, 112, 112).rand() + val input = batchInput.select(1, 1).resize(Array(1, 64, 112, 112)) + + model.evaluate() + model2.evaluate() + + val out1 = model.forward(batchInput) + val out2 = model2.forward(input).resize(Array(64, 112, 112)) + + out2 should be(out1.select(1, 1)) + } } class CMulSerialTest extends ModuleSerializationTest { From 6303757338a5f0c059ab6e771d1f5672af62cfb7 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 17 Jun 2019 15:09:07 +0800 Subject: [PATCH 0915/1065] set new storage to weight and bias for weight fusion (#2839) --- .../bigdl/dllib/nn/mkldnn/Fusion.scala | 7 ++- .../bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala | 50 ++++++++++++++++++- 2 files changed, 53 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala index b67c8b74cea..4eb758b5ddf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -199,8 +199,11 @@ private[mkldnn] object Fusion { convBias.storage().array()(j) = alpha / base * bias + beta - (alpha * mean) / base } - conv.weight.copy(convWeight) - conv.bias.copy(convBias) + // We will change model structure and weights when doing conv and bn fusion + // In order to not influence broadcast model and weights, + // we set new storage to weight and bias. + conv.weight.dense.set(convWeight) + conv.bias.dense.set(convBias) // regenerate the weight scales and output scales conv.flushWeightScales(conv.weight.dense) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala index 0a21bcc1f03..ee5c46602dd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala @@ -26,12 +26,29 @@ import com.intel.analytics.bigdl.nn.{Graph, Module => _, _} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils._ -import org.scalatest.{FlatSpec, Matchers} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import com.intel.analytics.bigdl.models.resnet +import com.intel.analytics.bigdl.models.utils.ModelBroadcast import com.intel.analytics.bigdl.utils.intermediate._ import com.intel.analytics.bigdl.numeric.NumericFloat +import org.apache.spark.SparkContext -class DnnGraphSpec extends FlatSpec with Matchers { +class DnnGraphSpec extends FlatSpec with Matchers with BeforeAndAfter { + + private var sc: SparkContext = _ + + before { + val nodeNumber = 1 + val coreNumber = 4 + Engine.init(nodeNumber, coreNumber, onSpark = true) + sc = new SparkContext("local[1]", "DnnGraphSpec") + } + + after { + if (sc != null) { + sc.stop() + } + } def model(size: Array[Int]) : Module[Float] = { val input = mkldnn.Input(size, Memory.Format.nchw).inputs() @@ -265,4 +282,33 @@ class DnnGraphSpec extends FlatSpec with Matchers { System.clearProperty("bigdl.mkldnn.fusion.convsum") System.clearProperty("bigdl.mkldnn.fusion") } + + "DnnGraph fusion" should "not change model parameters" in { + Engine.setEngineType(MklDnn) + import com.intel.analytics.bigdl.models.resnet + RNG.setSeed(100) + val module = resnet.ResNet(1000, T("shortcutType" -> ShortcutType.B, "depth" -> 50, + "optnet" -> false, "dataSet" -> DatasetType.ImageNet)) + .toGraph().asInstanceOf[StaticGraph[Float]] + .toIRgraph() + + val bcast = ModelBroadcast[Float]().broadcast(sc, module.evaluate()) + for(i <- 1 to 3) { + val data = sc.parallelize(0 to 10, 1) + data.mapPartitions(i => { + val tensor = Tensor[Float](2, 3, 224, 224).rand() + val mod = bcast.value() + Iterator(mod.forward(tensor).toTensor[Float]) + }).count() + + sc.parallelize(1 to 1, 1).mapPartitions(i => { + val weightSum = bcast.value().getWeightsBias().map(f => f.sum()).sum + require(weightSum == 11759.763f, s"sum of model weight " + + s"parameters should be 11759.764, but get ${weightSum}") + i + }).count() + + Engine.setEngineType(MklBlas) + } + } } From 97856e0ddc2afacde01203ab5b7c4055c7cd0968 Mon Sep 17 00:00:00 2001 From: Menooker Date: Fri, 21 Jun 2019 08:08:48 +0800 Subject: [PATCH 0916/1065] Add parameter processor for LARS (#2832) * enhancement: use one shared allreduceparameter * update localPartitionRange * implement lars whole layer gradient norm calculation * change random seed in UT * add limitation on "trust" of LARS, remove debug output * reformat * add tests in DirtriOptimizer for LARS * reformat * update parameters in UT * update parameters in UT --- .../bigdl/dllib/optim/DistriOptimizer.scala | 4 + .../analytics/bigdl/dllib/optim/LarsSGD.scala | 164 +++++++++++++++--- .../dllib/optim/DistriOptimizerSpec.scala | 20 +++ .../bigdl/dllib/optim/LarsSGDSpec.scala | 10 +- .../bigdl/dllib/optim/MetricsSpec.scala | 4 +- 5 files changed, 172 insertions(+), 30 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index ae17674731b..e37b1aa38ce 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -844,6 +844,10 @@ class DistriOptimizer[T: ClassTag]( s"have corresponding OptimMethod") } + LarsSGD.containsLarsSGD(optimMethods).foreach(weightDecay => + parameterProcessors.append(new LarsProcessor(parameterSplits, weightDecay)) + ) + prepareInput() val modelsAndBroadcast = DistriOptimizer.initThreadModels(trainingModel, distDataset, criterion, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGD.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGD.scala index 00fe974dd3c..9931af7c371 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGD.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGD.scala @@ -19,12 +19,13 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Container import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.optim.DistriOptimizer.Cache import com.intel.analytics.bigdl.optim.SGD.{Default, LearningRateSchedule} +import com.intel.analytics.bigdl.parameters.{AllReduceParameter, ParameterProcessor, Util} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table -import org.apache.log4j.{Level, Logger} - +import org.apache.spark.rdd.RDD import scala.reflect.ClassTag @@ -57,6 +58,12 @@ class LarsSGD[T: ClassTag]( learningRateSchedule = _learningRateSchedule) { @transient private var buffer: Tensor[T] = null + @transient + private[bigdl] var calculatedTrust: Option[T] = None + + require(trust > 0.0 && trust <= 1.0, + s"the trust for LARS is $trust, which should be greater than 0 and less than 1") + /** * @param feval a function that takes a single input (X), the point of a evaluation, and * returns f(X) and df/dX @@ -77,23 +84,9 @@ class LarsSGD[T: ClassTag]( } learningRateSchedule.updateHyperParameter(this) val globalLr = -learningRateSchedule.currentRate * trust - val normGradient = ev.sqrt(dfdx.sumSquare()) - val normParam = ev.sqrt(parameter.sumSquare()) - // scale = (normGradient + weightDecay * normParam) / normParam - val scale = Tensor.scalar[T](normParam) - scale.mul(ev.fromType[Double](weightDecay)).add(normGradient).div(normParam) - val raw_scale_value = scale.value() - val scale_value = if (ev.isInf(raw_scale_value)) { - ev.fromType[Double](10000.0) - } else if (ev.nearlyEqual(raw_scale_value, ev.fromType[Double](0.0), 0.0001)) { - ev.fromType[Double](1e-4) - } else if (ev.isNan(raw_scale_value)) { - ev.fromType[Double](1.0) - } else { - raw_scale_value - } + // rate = globalLr / scale - val rate = ev.divide(ev.fromType[Double](globalLr), scale_value) + val rate = ev.divide(ev.fromType[Double](globalLr), getGradientScale(dfdx, parameter)) // _v = momentum * _v + rate * (dfdx + weightDecay * parameter) _v.mul(ev.fromType[Double](momentum)) buffer.mul(parameter, ev.fromType[Double](weightDecay)).add(dfdx).mul(rate) @@ -103,6 +96,36 @@ class LarsSGD[T: ClassTag]( (parameter, Array(fx)) } + private[bigdl] def setGradientScale[T](scale: Double): Unit = { + calculatedTrust = Some(ev.fromType[Double](scale)) + } + + private[bigdl] def getGradientScale(dfdx: Tensor[T], parameter: Tensor[T]): T = { + val rawScaleValue = if (calculatedTrust.isDefined) { + val ret = calculatedTrust.get + calculatedTrust = None + ret + } else { + val normGradient = ev.sqrt(dfdx.sumSquare()) + val normParam = ev.sqrt(parameter.sumSquare()) + // scale = (normGradient + weightDecay * normParam) / normParam + val scale = Tensor.scalar[T](normParam) + scale.mul(ev.fromType[Double](weightDecay)).add(normGradient).div(normParam) + scale.value() + } + + if (ev.isInf(rawScaleValue)) { + ev.fromType[Double](10000.0) + } else if (ev.nearlyEqual(rawScaleValue, ev.fromType[Double](0.0), 0.0001)) { + ev.fromType[Double](1e-4) + } else if (ev.isNan(rawScaleValue)) { + ev.fromType[Double](1.0) + } else { + rawScaleValue + } + } + + /** * return an string of current hyperParameter. */ @@ -175,6 +198,20 @@ object LarsSGD { } + /** + * Check if there is LarsSGD in optimMethods. If so, return the weight decay of the first found + * LarsSGD. Else, return None + * @param optimMethods + * @tparam T + * @return The weight decay of the first found LarsSGD in the optimMethods. + * Or None if there is not one + */ + def containsLarsSGD[T](optimMethods: Map[String, OptimMethod[T]]): Option[Double] = { + optimMethods.find({ case (name, optim) => optim.isInstanceOf[LarsSGD[T]]}) + .map({case (name, optim) => optim.asInstanceOf[LarsSGD[T]].weightDecay}) + } + + /** * Create a Map(String, OptimMethod) for a container. For each submodule in the container, * generate (module.getName(), new Lars[T]) pair in the returned map. The resulting map can be @@ -196,14 +233,14 @@ object LarsSGD { * */ def createOptimLRSchedulerForModule[A <: Activity, B <: Activity, T: ClassTag] - (model: Container[A, B, T], - lrScheGenerator: AbstractModule[Activity, Activity, T] => (LearningRateSchedule, Boolean), - trust: Double = 1.0, - learningRate: Double = 1e-3, - learningRateDecay: Double = 0.01, - weightDecay: Double = 0.005, - momentum: Double = 0.5) - (implicit ev: TensorNumeric[T]): Map[String, OptimMethod[T]] = { + (model: Container[A, B, T], + lrScheGenerator: AbstractModule[Activity, Activity, T] => (LearningRateSchedule, Boolean), + trust: Double = 1.0, + learningRate: Double = 1e-3, + learningRateDecay: Double = 0.01, + weightDecay: Double = 0.005, + momentum: Double = 0.5) + (implicit ev: TensorNumeric[T]): Map[String, OptimMethod[T]] = { createOptimSeqForModule(model, lrScheGenerator, trust, learningRate, learningRateDecay, weightDecay, momentum).toMap } @@ -243,3 +280,78 @@ object LarsSGD { } } } + + +/** + * Process layer-wise l2 norm to scale the gradients + */ +private[bigdl] class LarsProcessor(paramaterSplits: Map[String, (Int, Int)], + weightDecay: Double +) extends ParameterProcessor { + override def collectGlobalData[T](models: RDD[Cache[T]], + parameters: AllReduceParameter[T], + metrics: Metrics, + state: Table)(implicit ev: TensorNumeric[T]): Unit = { + val numFinishedModel = state.get[Int]("numFinishedModel").get + val parallelism = state.get[Int]("parallelism").get + val isGradientUpdated = state.get[Boolean]("isGradientUpdated").get + + val scales = models.mapPartitions(modelIter => { + if (!isGradientUpdated) { + val getG = System.nanoTime() + parameters.aggregateGradientPartition(numFinishedModel) + metrics.add("aggregrateGradientParition average executor", + System.nanoTime() - getG) + } + val (paramLocalStart, paramLocalLen) = parameters.localPartitionRange + paramaterSplits.flatMap { case (name, p) => + val startIdx = Math.max(paramLocalStart, p._1) + val endIdx = Math.min(paramLocalStart + paramLocalLen, p._1 + p._2) + if (endIdx > startIdx) { + val grad = parameters.gradientPartition.narrow(1, + startIdx - paramLocalStart + 1, endIdx - startIdx) + val weight = parameters.weightPartition.narrow(1, + startIdx - paramLocalStart + 1, endIdx - startIdx) + val sumGrad = Util.getSumsquareInParallel(grad, parallelism) + val sumWeight = Util.getSumsquareInParallel(weight, parallelism) + Iterator((name, (sumWeight, sumGrad))) + } else { + Iterator.empty + } + }.toIterator + }) + .reduceByKey((weightGrad1, weightGrad2) => + (weightGrad1._1 + weightGrad2._1, weightGrad1._2 + weightGrad2._2)) + .map { case (name, data) => + val normGradient = Math.sqrt(data._2) + val normParam = Math.sqrt(data._1) + (name, (normGradient + weightDecay * normParam) / normParam) + } + .collect().toMap + state("isGradientUpdated") = true + state("larsScale") = scales + } + + override def processParameters[T](parameters: AllReduceParameter[T], + modelCache: Cache[T], + state: Table)(implicit ev: TensorNumeric[T]): Unit = { + val larsScale = state.get[Map[String, Double]]("larsScale").get + val (paramLocalStart, paramLocalLen) = parameters.localPartitionRange + paramaterSplits.foreach { case (name, p) => + val startIdx = Math.max(paramLocalStart, p._1) + val endIdx = Math.min(paramLocalStart + paramLocalLen, p._1 + p._2) + // if the layer is in the current partition, set the LarsSGD's gradient scale + if (endIdx > startIdx) { + modelCache.optimMethods(name) match { + case optim: LarsSGD[T] => + optim.setGradientScale(larsScale(name)) + } + } + } + } + + override def processParameters[T](model: Module[T], + state: Table)(implicit ev: TensorNumeric[T]): Unit = { + // LARS optim will calculate the scale, just leave LarsSGD.calculatedScale = None + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala index cae7d31cf69..a5f9c0a78f5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerSpec.scala @@ -247,6 +247,26 @@ class DistriOptimizerSpec extends FlatSpec with Matchers with BeforeAndAfter { } } + "Train with MSE with LARS" should "be good with LARS parameter processor" in { + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizer( + mse, + dataSet, + new MSECriterion[Double]()) + .setOptimMethods( + Map("fc_1" -> new LarsSGD[Double](true, _learningRate = 0.1, _learningRateDecay = 0, + _momentum = 0, _weightDecay = 0), + "fc_2" -> new LarsSGD[Double](false, _learningRate = 0.1, _learningRateDecay = 0, + _momentum = 0, _weightDecay = 0))) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 1e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 1e-2) + } + "Train with MSE and LBFGS" should "be good" in { LoggerFilter.redirectSparkInfoLogs() RandomGenerator.RNG.setSeed(10) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGDSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGDSpec.scala index 333a100d9c7..c79a0be50c6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGDSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/LarsSGDSpec.scala @@ -101,8 +101,8 @@ class LarsSGDSpec extends FlatSpec with Matchers with BeforeAndAfter { val optimizer = Optimizer(module, generateData(), MSECriterion[Float]()) val epochs = 6 optimizer - .setOptimMethods(LarsSGD.createOptimForModule(module, learningRate = 0.02, learningRateDecay - = 0.1)) + .setOptimMethods(LarsSGD.createOptimForModule(module, learningRate = 0.0001, + learningRateDecay = 0, momentum = 0, weightDecay = 0)) .setEndWhen(Trigger.maxEpoch(epochs)) .optimize() (1 to 10).foreach(i => { @@ -117,5 +117,11 @@ class LarsSGDSpec extends FlatSpec with Matchers with BeforeAndAfter { }) } + "lars" should "be found in multi-optims" in { + val optim = Map("1" -> new SGD[Float](), "2" -> new LarsSGD[Float](false, + _weightDecay = 1.23)) + LarsSGD.containsLarsSGD(optim).getOrElse(0.0) should be(1.23) + } + } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/MetricsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/MetricsSpec.scala index 1657d6e919e..5b1dcb4e617 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/MetricsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/MetricsSpec.scala @@ -110,7 +110,7 @@ class MetricsSpec extends FlatSpec with Matchers with BeforeAndAfter { result._2 should be(5) } - it should "throw exception when the local metric isn't exsited" in { + it should "throw exception when the local metric doesn't exist" in { val metric = new Metrics intercept[IllegalArgumentException] { metric.add("test", 10.0) @@ -128,7 +128,7 @@ class MetricsSpec extends FlatSpec with Matchers with BeforeAndAfter { result._2 should be(5) } - it should "throw exception when the distributed metric isn't exsited" in { + it should "throw exception when the distributed metric doesn't exist" in { val metric = new Metrics val conf = new SparkConf().setMaster("local[5]").setAppName("MetricsSpec") sc = new SparkContext(conf) From 3c5499d2a73a4577026072f753817b02c900dc49 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 24 Jun 2019 12:36:13 +0800 Subject: [PATCH 0917/1065] Add transformer to LM example (#2835) * add transformer to LM example * refactor dropout in Transformer * meet pr comments --- .../example/languagemodel/PTBModel.scala | 19 ++++- .../example/languagemodel/PTBWordLM.scala | 11 ++- .../dllib/example/languagemodel/README.md | 9 ++- .../dllib/example/languagemodel/Utils.scala | 7 +- .../analytics/bigdl/dllib/nn/Attention.scala | 5 +- .../analytics/bigdl/dllib/nn/BaseModule.scala | 5 -- .../bigdl/dllib/nn/FeedForwardNetwork.scala | 5 +- .../bigdl/dllib/nn/TableOperation.scala | 5 +- .../bigdl/dllib/nn/Transformer.scala | 81 +++++++++++++++---- .../bigdl/dllib/nn/TransformerSpec.scala | 72 +++++++++++++---- 10 files changed, 166 insertions(+), 53 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala index 2f81ddf23d6..df0ed8b185b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala @@ -21,7 +21,24 @@ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.{TimeDistributed, _} object PTBModel { - def apply( + def transformer( + inputSize: Int = 10000, + hiddenSize: Int = 256, + outputSize: Int = 10000, + numLayers: Int = 2, + keepProb: Float = 2.0f) + : Module[Float] = { + val input = Input[Float]() + val transformer = Transformer[Float](vocabSize = inputSize, + hiddenSize = hiddenSize, numHeads = 4, filterSize = hiddenSize*4, + numHiddenlayers = numLayers, embeddingDropout = 1- keepProb, + attentionDropout = 0.1f, ffnDropout = 0.1f).inputs(input) + val linear = Linear[Float](hiddenSize, outputSize) + val output = TimeDistributed[Float](linear).inputs(transformer) + Graph(input, output) + } + + def lstm( inputSize: Int, hiddenSize: Int, outputSize: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBWordLM.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBWordLM.scala index 523c955f01d..ad4077d7295 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBWordLM.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBWordLM.scala @@ -66,15 +66,20 @@ object PTBWordLM { val model = if (param.modelSnapshot.isDefined) { Module.loadModule[Float](param.modelSnapshot.get) + } else if (param.withTransformerModel) { + PTBModel.transformer( + inputSize = param.vocabSize, + hiddenSize = param.hiddenSize, + outputSize = param.vocabSize, + numLayers = param.numLayers, + keepProb = param.keepProb) } else { - val curModel = PTBModel( + PTBModel.lstm( inputSize = param.vocabSize, hiddenSize = param.hiddenSize, outputSize = param.vocabSize, numLayers = param.numLayers, keepProb = param.keepProb) - curModel.reset() - curModel } val optimMethod = if (param.stateSnapshot.isDefined) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md index 60d9cbc3185..7f813287534 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/README.md @@ -2,7 +2,9 @@ This example refers to [tensorflow ptb example](https://www.tensorflow.org/tutorials/recurrent#language_modeling), which shows how to train a recurrent neural network on a challenging task of language modeling. -The core of our model consists of LSTM cells that process one word at a time and computes probabilities of the possible values for the next word in the sentence. +We provide two types of model: multi-layer LSTM model and Transformer model. + +The core of our model is to process one word at a time and computes probabilities of the possible values for the next word in the sentence. Here we use [Penn Tree Bank (PTB)](https://catalog.ldc.upenn.edu/ldc99t42) as training dataset, which is a popular benchmark for measuring the quality of these models, whilst being small and relatively fast to train. @@ -29,7 +31,7 @@ spark-submit \ --total-executor-cores total_cores_for_the_job \ --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM \ dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ --f $HOME/simple-examples/data -b 40 --checkpoint $HOME/model --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 20 --learningRateDecay 0.001 --keepProb 0.5 --overWrite +-f $HOME/simple-examples/data -b 40 --checkpoint $HOME/model --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 20 --learningRateDecay 0.001 --keepProb 0.5 --overWrite --withTransformerModel ``` In the above commands: @@ -44,4 +46,5 @@ In the above commands: ```--numLayers```: numbers of lstm cell, default 2 lstm cells ```--numSteps```: number of words per record in LM ```--overWrite```: do overwrite when saving checkpoint -```--keepProb```: the probability to do dropout \ No newline at end of file +```--keepProb```: the probability to do dropout +```--withTransformerModel```: use transformer model in this LM \ No newline at end of file diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/Utils.scala index 127bd6d6ba0..e3e2cf96e2e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/Utils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/Utils.scala @@ -50,7 +50,8 @@ object Utils { numLayers: Int = 2, numSteps: Int = 20, overWriteCheckpoint: Boolean = false, - keepProb: Float = 2.0f) + keepProb: Float = 2.0f, + withTransformerModel: Boolean = false) val trainParser = new OptionParser[TrainParams]("BigDL ptbModel Train Example") { opt[String]('f', "dataFolder") @@ -109,5 +110,9 @@ object Utils { opt[Double]("keepProb") .text("the probability p to do dropout") .action((x, c) => c.copy(keepProb = x.toFloat)) + + opt[Boolean]("withTransformerModel") + .text("Use transformer model in this LM") + .action((x, c) => c.copy(withTransformerModel = true)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala index a71566a8fd9..b8bb0d6981e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala @@ -61,9 +61,7 @@ class Attention[T: ClassTag]( val cadd = CAddTable().inputs(matmul, inputBias) val softMax = TransformerOperation.softMax[T]().inputs(cadd) - val drop = if (train) { - Dropout(initP = (1.0 - attentionDropout)).inputs(softMax) - } else softMax + val drop = Dropout(initP = (1.0 - attentionDropout)).inputs(softMax) val matmulNoTrans = MM().inputs(drop, contiguousV) // Recombine heads --> (batch_size, length, hidden_size) val combineHeads = new CombineHeads().inputs(matmulNoTrans) @@ -72,7 +70,6 @@ class Attention[T: ClassTag]( hiddenSize, hiddenSize, false, name = s"${this.getName()}_output_transform") .inputs(combineHeads) val graph = Graph(Array(inputX, inputY, inputBias), Array(outputLayer)) - if (this.train) graph.training() else graph.evaluate() graph } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala index 3ed6b4206c5..e18fd28157c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BaseModule.scala @@ -46,11 +46,6 @@ private[nn] abstract class BaseModule[T: ClassTag]()(implicit ev: TensorNumeric[ model.accGradParameters(input, gradOutput) } - override def backward(input: Activity, gradOutput: Activity): Activity = { - gradInput = model.backward(input, gradOutput) - gradInput - } - override def training(): this.type = { train = true model.training() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala index 3ff4a954083..55597c271d1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FeedForwardNetwork.scala @@ -38,13 +38,10 @@ class FeedForwardNetwork[T: ClassTag](val hiddenSize: Int, val filterSize: Int, val filterLayer = TransformerOperation.dense( hiddenSize, filterSize, bias = true, activation = ReLU[T](), name = s"${this.getName()}_filter_layer").inputs(input) - val drop = if (train) { - Dropout(initP = (1.0 - reluDropout)).inputs(filterLayer) - } else filterLayer + val drop = Dropout(initP = (1.0 - reluDropout)).inputs(filterLayer) val output_dense_layer = TransformerOperation.dense( filterSize, hiddenSize, bias = true, name = s"${this.getName()}_output_layer").inputs(drop) val graph = Graph(Array(input), Array(output_dense_layer)) - if (this.train) graph.training() else graph.evaluate() graph } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TableOperation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TableOperation.scala index 475b127bedb..112e991b2c9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TableOperation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TableOperation.scala @@ -55,7 +55,10 @@ class TableOperation[T: ClassTag]( val inputSmall = input[Tensor[T]](smallPos) val inputLarge = input[Tensor[T]](3 - smallPos) - if (expandLayer == null) expandLayer = ExpandSize(inputLarge.size()) + val largeSize = inputLarge.size() + // batchSize may be not same for model inference + largeSize(0) = -1 + if (expandLayer == null) expandLayer = ExpandSize(largeSize) val inputExpand = expandLayer.forward(inputSmall) output = operationLayer.updateOutput(T(inputLarge, inputExpand)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala index f79d4de01eb..0b63732bad7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala @@ -44,6 +44,9 @@ import scala.reflect.runtime._ * @param embeddingDropout * @param attentionDropout * @param ffnDropout + * @param paddingValue padding value for word embedding, default 0, which means no padding. + * @param withShareWeightsLinear whether to add linear that sharing weights with embedding layer. + * @param transformerType transformer type, support LanguageModel and Translation. * @tparam T The numeric type in this module parameters. */ class Transformer[T: ClassTag]( @@ -55,9 +58,14 @@ class Transformer[T: ClassTag]( val embeddingDropout: Float, val attentionDropout: Float, val ffnDropout: Float, + val paddingValue: Double = 0, + val withShareWeightsLinear: Boolean = false, val transformerType: TransformerType = LanguageModel) (implicit ev: TensorNumeric[T]) extends BaseModule[T] { + private val linearSharedWeigths = TimeDistributed( + new Linear(inputSize = hiddenSize, outputSize = vocabSize, withBias = false)) + override def buildModel(): Module[T] = { transformerType match { case LanguageModel => buildLM() @@ -75,7 +83,8 @@ class Transformer[T: ClassTag]( val join = JoinTable(1, -1).inputs(inputNode, targetNode) val constantValue = math.sqrt(hiddenSize) val embedding = MulConstant(constantValue).inputs( - LookupTable[T](vocabSize, hiddenSize).inputs(join)) + LookupTable[T](vocabSize, hiddenSize, paddingValue = paddingValue, + maskZero = true).setName("embedding").inputs(join)) val split = new SplitTensor(1, 2).inputs(embedding) val embeddingInput = SelectTable(1).inputs(split) val embeddingOutput = SelectTable(2).inputs(split) @@ -89,20 +98,40 @@ class Transformer[T: ClassTag]( val inputNode = Input() val constantValue = math.sqrt(hiddenSize) val embeddingInput = MulConstant(constantValue).inputs( - LookupTable[T](vocabSize, hiddenSize).inputs(inputNode)) + LookupTable[T](vocabSize, hiddenSize, paddingValue = paddingValue, + maskZero = true).setName("embedding").inputs(inputNode)) val outputNode = decode(embeddingInput) Graph(inputNode, outputNode) } + override def updateOutput(input: Activity): Activity = { + output = model.updateOutput(input) + + // sharing weight between embedding and linear + if (withShareWeightsLinear) { + val embeddingLayer = model.apply("embedding").get + val embeddingParams = embeddingLayer.getParameters() + val linearParams = linearSharedWeigths.getParameters() + linearParams._1.copy(embeddingParams._1) + output = linearSharedWeigths.updateOutput(model.output.toTensor[T]) + } + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + val grad = if (withShareWeightsLinear) { + linearSharedWeigths.updateGradInput(model.output.toTensor[T], gradOutput.toTensor[T]) + } else gradOutput + gradInput = model.updateGradInput(input, grad) + gradInput + } + private[nn] def encode(inputs: ModuleNode[T], attentionBias: ModuleNode[T]): ModuleNode[T] = { // Prepare inputs to the layer stack by adding positional encodings and // applying dropout. val position = new PositionEncode().inputs(inputs) val encoderInput = CAddTable().inputs(inputs, position) - val encoderInputDrop = if (train) { - val postDropOut = Dropout(1- embeddingDropout) - postDropOut.inputs(encoderInput) - } else encoderInput + val encoderInputDrop = Dropout(1- embeddingDropout).inputs(encoderInput) block(numHiddenlayers, encoderInputDrop, attentionBias, blockType = "encode") } @@ -113,11 +142,7 @@ class Transformer[T: ClassTag]( val decoderInput = new PositionEncodeWithShift().inputs(targets) val decoderSelfAttentionBias = new SelfAttentionMask().inputs(targets) - val decoderInputDrop = if (train) { - val postDropOut = Dropout(1- embeddingDropout) - postDropOut.inputs(decoderInput) - } else decoderInput - + val decoderInputDrop = Dropout(1- embeddingDropout).inputs(decoderInput) block(numHiddenlayers, decoderInputDrop, decoderSelfAttentionBias, encoderOutput, attentionBias, blockType = "decode") } @@ -186,6 +211,11 @@ class Transformer[T: ClassTag]( .inputs(layer.setName(preName + "/ffn").inputs(norm)) CAddTable().inputs(decoderInput, drop) } + + override def clearState(): this.type = { + if (withShareWeightsLinear) linearSharedWeigths.clearState() + super.clearState() + } } object Transformer extends ModuleSerializable { @@ -198,11 +228,15 @@ object Transformer extends ModuleSerializable { embeddingDropout: Float, attentionDropout: Float, ffnDropout: Float, + paddingValue: Double = 0, + withShareWeightsLinear: Boolean = false, transformerType: TransformerType = LanguageModel) - (implicit ev: TensorNumeric[T]): Transformer[T] = + (implicit ev: TensorNumeric[T]): Transformer[T] = { new Transformer(vocabSize, hiddenSize, numHeads, filterSize, numHiddenlayers, - embeddingDropout, attentionDropout, ffnDropout, transformerType) + embeddingDropout, attentionDropout, ffnDropout, paddingValue, + withShareWeightsLinear = withShareWeightsLinear, transformerType = transformerType) + } override def doLoadModule[T: ClassTag](context: DeserializeContext) (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { @@ -244,10 +278,18 @@ object Transformer extends ModuleSerializable { .getAttributeValue(context, attrMap.get("ffnDropout")) .asInstanceOf[Float] + val paddingValue = DataConverter + .getAttributeValue(context, attrMap.get("paddingValue")) + .asInstanceOf[Double] + val tag = DataConverter .getAttributeValue(context, attrMap.get("transformerType")) .asInstanceOf[Int] + val withShareWeightsLinear = DataConverter + .getAttributeValue(context, attrMap.get("withShareWeightsLinear")) + .asInstanceOf[Boolean] + val transformerType = tag match { case 1 => LanguageModel case 2 => Translation @@ -256,7 +298,8 @@ object Transformer extends ModuleSerializable { } val transformer = Transformer(vocabSize, hiddenSize, numHeads, filterSize, - numHiddenlayers, embeddingDropout, attentionDropout, ffnDropout, transformerType) + numHiddenlayers, embeddingDropout, attentionDropout, ffnDropout, paddingValue, + withShareWeightsLinear = withShareWeightsLinear, transformerType) transformer.model = model transformer @@ -312,6 +355,16 @@ object Transformer extends ModuleSerializable { transformer.ffnDropout, universe.typeOf[Float]) transformerBuilder.putAttr("ffnDropout", embeddingDropoutBuilder.build) + val paddingValueBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, paddingValueBuilder, + transformer.paddingValue, universe.typeOf[Double]) + transformerBuilder.putAttr("paddingValue", paddingValueBuilder.build) + + val shareWeightsBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, shareWeightsBuilder, + transformer.withShareWeightsLinear, universe.typeOf[Boolean]) + transformerBuilder.putAttr("withShareWeightsLinear", shareWeightsBuilder.build) + // for language model, marked as 1 // for translation model, marked as 2 val tag = transformer.transformerType match { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala index 6a926d46c32..587bb5e2155 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala @@ -206,7 +206,8 @@ class TransformerLayerSpec extends FlatSpec with Matchers { val reluDropout = 1.0f val transformer = new Transformer[Float](vocabSize, hiddenSize, numHeads, filterSize, num_hidden_layers, - postprocessDropout, attentionDropout, reluDropout, Translation) + postprocessDropout, attentionDropout, reluDropout, withShareWeightsLinear = true, + transformerType = Translation) val attention0 = transformer.model("encode_self_attention_0/self_attention").get val ffn0 = transformer.model("encode_ffn_0/ffn").get @@ -359,23 +360,59 @@ class TransformerLayerSpec extends FlatSpec with Matchers { } val expectedOutput = Tensor[Float]( - T(T(T(1.5693761, -1.0056276, 0.14640914, -0.71015745), - T(1.4049922, -1.1252292, 0.46041852, -0.74018157), - T(-0.7806267, -0.13584259, -0.75671536, 1.6731848), - T(-0.3983218, -0.9217702, -0.36959055, 1.6896812), - T(-0.62736577, -1.1783588, 0.36084852, 1.4448758), - T(-0.29645187, -1.3115996, 0.1336132, 1.4744384)), - T(T(1.281556, -1.111587, 0.65917075, -0.82913977), - T(1.3174573, -1.1678243, 0.59200275, -0.74163586), - T(0.68878394, -0.01719818, -1.6202699, 0.9486842), - T(1.706251, -0.6772593, -0.29021385, -0.738778), - T(-0.47597468, -0.88766754, -0.33201644, 1.6956586), - T(-0.82912564, -0.8601543, 0.08772265, 1.6015573))) - ) + T(T(T(1.0213897, 1.6298342, -0.77117467, -0.30903974, + 1.079551, -2.0169363, 0.5122813, -0.28446424, + 1.6982273, 0.98818946, 0.9912475, 0.3734624, + -0.07386526, 0.7457521, 0.7346176, 0.5543957), + T(1.1742185, 1.6519041, -0.74003303, -0.36850277, + 1.3430122, -2.163493, 0.6831105, -0.21015275, + 1.56321, 0.9189906, 1.0753409, 0.06065345, + -0.08118278, 1.0861892, 0.40153468, 0.5656462), + T(-1.641943, -0.54821557, -0.10801831, 1.3602101, + -1.0806575, 1.2603211, -0.95736504, -0.97358, + -0.3041229, -1.0635418, 0.9337779, -0.92391706, + 0.51814425, -0.49280763, 1.0959804, -0.8171915), + T(-1.2780054, 0.23534934, -0.5110631, 1.4640164, + -0.35087395, 0.25458562, -0.65749437, -1.2743273, + 0.32705495, -0.92280126, 1.8714464, -1.4813888, + 0.60687494, 0.33935368, 1.2551553, -0.71658915), + T(-0.77465796, 0.3714019, -0.45245644, 1.1980948, + 0.32732904, -0.21935141, -0.19621742, -1.0291177, + 0.11420453, -0.9387212, 1.9743775, -2.0302484, + 0.5383579, 1.1113635, 0.4704703, -0.599585), + T(-0.8154292, 0.6225467, -0.6287141, 1.2946622, + 0.2809173, -0.40715468, -0.28501934, -1.2143513, + 0.49433085, -0.80149204, 2.1966798, -1.8524576, + 0.574358, 1.0066259, 0.9082394, -0.5721369)), + T( + T(1.3018701, 1.599837, -0.66893494, -0.4747932, + 1.4816235, -2.1988738, 0.80288893, -0.08690637, + 1.4123986, 0.8986485, 0.9886181, -0.05089509, + -0.1168761, 1.2418115, 0.11437249, 0.59445935), + T(1.2257497, 1.644748, -0.7198268, -0.38725024, + 1.4432738, -2.2042546, 0.7490996, -0.17839986, + 1.4885247, 0.8769602, 1.1051279, -0.08014816, + -0.08212907, 1.2243906, 0.2546038, 0.5629713), + T(-1.2719716, 0.16566879, -0.47614235, 1.0029007, + -1.2952622, 0.62499654, -1.0790219, -1.025868, + 0.94064814, -0.07148974, 0.6702734, 0.7035586, + 0.3409673, -1.3214715, 2.244514, -0.3729545), + T(0.81476617, 1.4433428, -0.71186674, -0.31119436, + 0.63634753, -1.6565179, 0.27403653, -0.26883018, + 1.7255578, 1.0771092, 0.64149255, 0.90875554, + -0.10430496, 0.16574204, 1.0562141, 0.5474888), + T(-1.285131, 0.17195511, -0.47159803, 1.4530414, + -0.35627913, 0.3122788, -0.64908904, -1.2403321, + 0.24032426, -0.96024, 1.8233403, -1.5194026, + 0.6023792, 0.34928182, 1.171351, -0.7291994), + T(-1.098374, 0.00385495, -0.30677474, 1.2947041, + -0.10549277, 0.3229493, -0.43214977, -1.0127946, + -0.14591314, -1.0853384, 1.6767416, -1.848886, + 0.55326104, 0.6819846, 0.56096387, -0.71889424)))) paramsTable = transformer.getParametersTable() for (i <- paramsTable.keySet) { - if (i.toString contains "LookupTable") { + if (i.toString contains "embedding") { paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( Tensor[Float](T(T( 0.0901417, -0.25920567, 0.35886005, -0.79801846), T( 0.7101189, -0.5279109, 0.24793072, 0.07292826), @@ -400,8 +437,9 @@ class TransformerLayerSpec extends FlatSpec with Matchers { val input1 = Tensor[Float](T(T(3, 1, 2, 3, 4, 5), T(6, 7, 8, 9, 10, 11))).add(1.0f) val input2 = Tensor[Float](T(T(4, 5, 7, 9, 10, 11), T(4, 12, 6, 3, 2, 15))).add(1.0f) - val output = transformer.forward(T(input1, input2)) - output should be(expectedOutput) + val output = transformer.forward(T(input1, input2)).toTensor[Float] + + require(output.almostEqual(expectedOutput, 1e-5) == true) val gradInput = transformer.backward(T(input1, input2), output) } From 518048717c0632dc5428a882b289f869c5e31613 Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Thu, 27 Jun 2019 10:14:30 +0800 Subject: [PATCH 0918/1065] feat: MKLDNN LSTM unidirectional/bidirectional backward support (#2840) * MKLDNN LSTM backward support with accuracy testing --- .../analytics/bigdl/dllib/nn/mkldnn/RNN.scala | 474 ++++++++----- .../bigdl/dllib/nn/mkldnn/RNNSpec.scala | 669 +++++++++++++++++- 2 files changed, 939 insertions(+), 204 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala index c7b96aa5339..eaee7ec8b65 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.{InitializationMethod, RandomUniform, VariableFormat, Zeros} import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} -import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} import scala.collection.mutable.ArrayBuffer @@ -47,28 +47,41 @@ class RNN( private val initWeightIter: Tensor[Float] = null, private val initBias: Tensor[Float] = null ) extends MklDnnLayer with Initializable { - private var src_layer_MD: Long = _ - private var src_iter_MD: Long = _ - private var weights_layer_MD: Long = _ - private var weights_iter_MD: Long = _ - private var bis_MD: Long = _ - private var dist_layer_MD: Long = _ - private var dist_iter_MD: Long = _ - - private var fwdPD: Long = _ - private var updateOutputMemoryPrimitives: Array[Long] = _ private var updateOutputTensors: Array[Tensor[Float]] = _ - - private val common_n_layers: Int = layers - private var ngates: Int = _ - private var nstates: Int = _ + private var updateGradInputMemoryPrimitives: Array[Long] = _ + private var updateGradInputTensors: Array[Tensor[Float]] = _ + private var fwdPD: Long = _ private[mkldnn] var weight: TensorMMap = _ private[mkldnn] var weight_i: TensorMMap = _ private[mkldnn] var bias: TensorMMap = _ - private[mkldnn] var src_i: TensorMMap = _ - private[mkldnn] var dst_i: TensorMMap = _ + private[mkldnn] var gradWeight: TensorMMap = _ + private[mkldnn] var gradWeight_i: TensorMMap = _ + private[mkldnn] var gradBias: TensorMMap = _ + + private var workSpaceFormat: MemoryData = _ + private var workSpace : Tensor[Float] = _ + + @transient private lazy val reorderManager = new ReorderManager + private var weightForBackward: DnnTensor[Float] = _ + private var weightForBackwardMemoryData: MemoryData = _ + private var weightIterForBackward: DnnTensor[Float] = _ + private var weightIterForBackwardMemoryData: MemoryData = _ + + private var batchSize: Int = _ + private var stepSize: Int = _ + private var inputShape: Array[Int] = _ + private var outputShape: Array[Int] = _ + private var weightShape: Array[Int] = _ + private var weightIterShape: Array[Int] = _ + private var biasShape: Array[Int] = _ + private var commonIterShape: Array[Int] = _ + + private var src_i: DnnTensor[Float] = _ + private var dst_i: DnnTensor[Float] = _ + private var gradsrc_i: DnnTensor[Float] = _ + private var graddst_i: DnnTensor[Float] = _ if(layers > 1) { require(inputSize == hiddenSize, @@ -77,46 +90,51 @@ class RNN( + "hiddenSize: " + hiddenSize) } - mode match { - case AlgKind.VanillaLstm => - ngates = 4 - nstates = 2 + var (ngates, nstates) = mode match { + case AlgKind.VanillaLstm => (4, 2) case _ => throw new UnsupportedOperationException("Not support such RNN Cell. Cell type: " + mode) } - direction match { - case Direction.UnidirectionalLeft2Right - | Direction.UnidirectionalRight2Left => - - /** - * Gate order matching between MKLDNN LSTM and nn/LSTM: - * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) - * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) - * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) - * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) - */ - weight = new TensorMMap(Array(common_n_layers, 1, inputSize, ngates, hiddenSize)) - weight_i = new TensorMMap(Array(common_n_layers, 1, hiddenSize, ngates, hiddenSize)) - bias = new TensorMMap(Array(common_n_layers, 1, ngates, hiddenSize)) + /** TODO: Multi-layer Bidirectional Sum LSTM is available in MKLDNN, + * TODO: but the current version of BigDL BLAS does not support it. + */ + val (numOfDirections, outputSizeFactor) = direction match { + case Direction.UnidirectionalLeft2Right + | Direction.UnidirectionalRight2Left => (1, 1) case Direction.BidirectionalConcat => require(layers == 1, "Bidirectional Concat LSTM does not support multiple layers. " + "layers = " + layers) + (2, 2) + case Direction.BidirectionalSum => (2, 1) + case _ => throw new UnsupportedOperationException("Not support such direction") + } - weight = new TensorMMap(Array(common_n_layers, 2, inputSize, ngates, hiddenSize)) - weight_i = new TensorMMap(Array(common_n_layers, 2, hiddenSize, ngates, hiddenSize)) - bias = new TensorMMap(Array(common_n_layers, 2, ngates, hiddenSize)) - - case Direction.BidirectionalSum => - - /** TODO: Multi-layer Bidirectional LSTM is available in MKLDNN, - * but it is not supported in current version BigDL BLAS. - */ - - weight = new TensorMMap(Array(common_n_layers, 2, inputSize, ngates, hiddenSize)) - weight_i = new TensorMMap(Array(common_n_layers, 2, hiddenSize, ngates, hiddenSize)) - bias = new TensorMMap(Array(common_n_layers, 2, ngates, hiddenSize)) + /** + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + */ + + weightShape = Array(layers, numOfDirections, inputSize, ngates, hiddenSize) + weightIterShape = Array(layers, numOfDirections, hiddenSize, ngates, hiddenSize) + biasShape = Array(layers, numOfDirections, ngates, hiddenSize) + + weight = new TensorMMap(weightShape) + weight_i = new TensorMMap(weightIterShape) + bias = new TensorMMap(biasShape) + gradWeight = new TensorMMap(weightShape) + gradWeight_i = new TensorMMap(weightIterShape) + gradBias = new TensorMMap(biasShape) + + val rnnCellDesc = mode match { + case AlgKind.VanillaLstm => + MklDnn.RNNCellDescInit(AlgKind.VanillaLstm, f, flags, alpha, clipping) + case _ => throw new UnsupportedOperationException("Not support such RNN cell. " + + "Cell type: " + mode) } { @@ -146,140 +164,52 @@ class RNN( } } - private def initMemoryDescs(inputs: Array[MemoryData]) = { - // TODO: The default format of input is TNC + override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { + val kind = if (!isTraining()) { + PropKind.ForwardInference + } else { + PropKind.ForwardTraining + } + /** - * The default format of input is TNC. + * TODO: The default format of input is TNC * Batch size of input is needed by creating memory descriptors of src iter and dst iter. * Step size of input is needed by creating memory descriptor of dst layer. * By default, batch size of input is the second element of inputShape * and step size is the first element of inputShape. */ - val(inputShape, inputLayout) = inputs(0).layout match { - case Memory.Format.tnc => /* tnc */ - (inputs(0).shape, Memory.Format.tnc) + + inputs(0).layout match { + case Memory.Format.tnc => + batchSize = inputs(0).shape(1) + stepSize = inputs(0).shape(0) + case Memory.Format.ntc => + batchSize = inputs(0).shape(0) + stepSize = inputs(0).shape(1) case _ => throw new UnsupportedOperationException("Not support such input format. " + "The input format is: " + inputs(0).layout) } - direction match { - case Direction.UnidirectionalLeft2Right - | Direction.UnidirectionalRight2Left => - val weightShape = weight.size() /* ldigo */ - val biasShape = bias.size() /* ldgo */ - val outputShape = Array(inputShape(0), inputShape(1), hiddenSize) /* tnc */ - - val inputShape_iter = Array(common_n_layers, 1, nstates, - inputShape(1), hiddenSize) /* ldsnc */ - val weightShape_iter = weight_i.size() /* ldigo */ - val outputShape_iter = inputShape_iter /* ldsnc */ - - val src_layer = NativeData(inputShape, Memory.Format.any) - val src_iter = NativeData(inputShape_iter, Memory.Format.any) - val wei_layer = NativeData(weightShape, Memory.Format.any) - val wei_iter = NativeData(weightShape_iter, Memory.Format.any) - val bis = NativeData(biasShape, Memory.Format.any) - val dst_layer = NativeData(outputShape, Memory.Format.any) - val dst_iter = NativeData(outputShape_iter, Memory.Format.any) - - src_layer_MD = src_layer.getMemoryDescription() - src_iter_MD = src_iter.getMemoryDescription() - weights_layer_MD = wei_layer.getMemoryDescription() - weights_iter_MD = wei_iter.getMemoryDescription() - bis_MD = bis.getMemoryDescription() - dist_layer_MD = dst_layer.getMemoryDescription() - dist_iter_MD = dst_iter.getMemoryDescription() - - src_i = new TensorMMap(inputShape_iter) - src_i.dense.copy(Tensor[Float]().resize(inputShape_iter).zero()) - dst_i = new TensorMMap(outputShape_iter) - dst_i.dense.copy(Tensor[Float]().resize(outputShape_iter).zero()) - - case Direction.BidirectionalConcat => - val weightShape = weight.size() /* ldigo */ - val biasShape = bias.size() /* ldgo */ - val outputShape = Array(inputShape(0), inputShape(1), 2 * hiddenSize) /* tnc */ - - val inputShape_iter = Array(common_n_layers, 2, nstates, - inputShape(1), hiddenSize) /* ldsnc */ - val weightShape_iter = weight_i.size() /* ldigo */ - val outputShape_iter = inputShape_iter /* ldsnc */ - - val src_layer = NativeData(inputShape, Memory.Format.any) - val src_iter = NativeData(inputShape_iter, Memory.Format.any) - val wei_layer = NativeData(weightShape, Memory.Format.any) - val wei_iter = NativeData(weightShape_iter, Memory.Format.any) - val bis = NativeData(biasShape, Memory.Format.any) - val dst_layer = NativeData(outputShape, Memory.Format.any) - val dst_iter = NativeData(outputShape_iter, Memory.Format.any) - - src_layer_MD = src_layer.getMemoryDescription() - src_iter_MD = src_iter.getMemoryDescription() - weights_layer_MD = wei_layer.getMemoryDescription() - weights_iter_MD = wei_iter.getMemoryDescription() - bis_MD = bis.getMemoryDescription() - dist_layer_MD = dst_layer.getMemoryDescription() - dist_iter_MD = dst_iter.getMemoryDescription() - - /** TODO: user-defined initial hidden state is not supported currently. - * The default initial hidden state is all zero. - */ - src_i = new TensorMMap(inputShape_iter) - src_i.dense.copy(Tensor[Float]().resize(inputShape_iter).zero()) - dst_i = new TensorMMap(outputShape_iter) - dst_i.dense.copy(Tensor[Float]().resize(outputShape_iter).zero()) - - case Direction.BidirectionalSum => - val weightShape = weight.size() /* ldigo */ - val biasShape = bias.size() /* ldgo */ - val outputShape = Array(inputShape(0), inputShape(1), hiddenSize) /* tnc */ - - val inputShape_iter = Array(common_n_layers, 2, nstates, - inputShape(1), hiddenSize) /* ldsnc */ - val weightShape_iter = weight_i.size() /* ldigo */ - val outputShape_iter = inputShape_iter /* ldsnc */ - - val src_layer = NativeData(inputShape, Memory.Format.any) - val src_iter = NativeData(inputShape_iter, Memory.Format.any) - val wei_layer = NativeData(weightShape, Memory.Format.any) - val wei_iter = NativeData(weightShape_iter, Memory.Format.any) - val bis = NativeData(biasShape, Memory.Format.any) - val dst_layer = NativeData(outputShape, Memory.Format.any) - val dst_iter = NativeData(outputShape_iter, Memory.Format.any) - - src_layer_MD = src_layer.getMemoryDescription() - src_iter_MD = src_iter.getMemoryDescription() - weights_layer_MD = wei_layer.getMemoryDescription() - weights_iter_MD = wei_iter.getMemoryDescription() - bis_MD = bis.getMemoryDescription() - dist_layer_MD = dst_layer.getMemoryDescription() - dist_iter_MD = dst_iter.getMemoryDescription() - - src_i = new TensorMMap(inputShape_iter) - src_i.dense.copy(Tensor[Float]().resize(inputShape_iter).zero()) - dst_i = new TensorMMap(outputShape_iter) - dst_i.dense.copy(Tensor[Float]().resize(outputShape_iter).zero()) - - case _ => throw new UnsupportedOperationException("Not support such direction") - } - } - - override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - val kind = if (!isTraining()) { - PropKind.ForwardInference - } else { - throw new UnsupportedOperationException("Not support training") - } - - val rnnCellDesc = mode match { - case AlgKind.VanillaLstm => - MklDnn.RNNCellDescInit(AlgKind.VanillaLstm, f, flags, alpha, clipping) - case _ => throw new UnsupportedOperationException("Not support such RNN cell. " + - "Cell type: " + mode) - } - - initMemoryDescs(inputs) + inputShape = Array(stepSize, batchSize, inputSize) + outputShape = Array(stepSize, batchSize, outputSizeFactor * hiddenSize) + commonIterShape = Array(layers, numOfDirections, nstates, batchSize, hiddenSize) + + val src_layer = NativeData(inputShape, Memory.Format.any) + val src_iter = NativeData(commonIterShape, Memory.Format.any) + val wei_layer = NativeData(weightShape, Memory.Format.any) + val wei_iter = NativeData(weightIterShape, Memory.Format.any) + val bis = NativeData(biasShape, Memory.Format.any) + val dst_layer = NativeData(outputShape, Memory.Format.any) + val dst_iter = NativeData(commonIterShape, Memory.Format.any) + + val src_layer_MD = src_layer.getMemoryDescription() + val src_iter_MD = src_iter.getMemoryDescription() + val weights_layer_MD = wei_layer.getMemoryDescription() + val weights_iter_MD = wei_iter.getMemoryDescription() + val bis_MD = bis.getMemoryDescription() + val dist_layer_MD = dst_layer.getMemoryDescription() + val dist_iter_MD = dst_iter.getMemoryDescription() val description = MklDnn.RNNForwardDescInit(kind, rnnCellDesc, direction, src_layer_MD, src_iter_MD, weights_layer_MD, weights_iter_MD, bis_MD, dist_layer_MD, dist_iter_MD) @@ -291,14 +221,9 @@ class RNN( val realWei = MemoryData.operationWant(fwdPD, Query.WeightsPd, 0) val realWei_iter = MemoryData.operationWant(fwdPD, Query.WeightsPd, 1) val realBias = MemoryData.operationWant(fwdPD, Query.WeightsPd, 2) - val realDst = MemoryData.operationWant(fwdPD, Query.DstPd, 0) val realDst_iter = MemoryData.operationWant(fwdPD, Query.DstPd, 1) - require(src_i.size().product == realSrc_iter.shape.product, - s"${getName} src iter shape is not correct.") - require(dst_i.size().product == realDst_iter.shape.product, - s"${getName} dst iter shape is not correct.") require(weight.size().product == realWei.shape.product, s"${getName} weight shape is not correct.") require(weight_i.size().product == realWei_iter.shape.product, @@ -306,24 +231,38 @@ class RNN( require(bias.size().product == realBias.shape.product, s"${getName} bias shape is not correct.") - weight.setMemoryData(HeapData(weight.size(), Memory.Format.ldigo), realWei, runtime) - weight_i.setMemoryData(HeapData(weight_i.size(), Memory.Format.ldigo), realWei_iter, runtime) - bias.setMemoryData(HeapData(bias.size(), Memory.Format.ldgo), realBias, runtime) - src_i.setMemoryData(HeapData(src_i.size(), Memory.Format.ldsnc), realSrc_iter, runtime) - dst_i.setMemoryData(HeapData(dst_i.size(), Memory.Format.ldsnc), realDst_iter, runtime) + weight.setMemoryData(HeapData(weightShape, Memory.Format.ldigo), realWei, runtime) + weight_i.setMemoryData(HeapData(weightIterShape, Memory.Format.ldigo), realWei_iter, runtime) + bias.setMemoryData(HeapData(biasShape, Memory.Format.ldgo), realBias, runtime) weight.sync() weight_i.sync() bias.sync() - src_i.sync() - dst_i.sync() + + src_i = initTensor(realSrc_iter).asInstanceOf[DnnTensor[Float]] + dst_i = initTensor(realDst_iter).asInstanceOf[DnnTensor[Float]] + src_i.zero() + dst_i.zero() val srcs = Array(realSrc.getPrimitive(runtime), realSrc_iter.getPrimitive(runtime), realWei.getPrimitive(runtime), realWei_iter.getPrimitive(runtime), realBias.getPrimitive(runtime)) val indexes = Array.fill(srcs.length)(0) - val dsts = Array(realDst.getPrimitive(runtime), realDst_iter.getPrimitive(runtime)) + if (isTraining()) { + workSpaceFormat = MemoryData.operationWant(fwdPD, Query.WorkspacePd, 0) + workSpace = initTensor(workSpaceFormat).asInstanceOf[Tensor[Float]] + } + + val dsts = if (isTraining()) { + Array(realDst.getPrimitive(runtime), + realDst_iter.getPrimitive(runtime), + workSpaceFormat.getPrimitive(runtime)) + } + else { + Array(realDst.getPrimitive(runtime), + realDst_iter.getPrimitive(runtime)) + } val primitive = MklDnn.PrimitiveCreate2(fwdPD, srcs, indexes, srcs.length, dsts, dsts.length) @@ -341,12 +280,15 @@ class RNN( if (updateOutputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() buffer.append(input.asInstanceOf[Tensor[Float]]) - buffer.append(src_i.native) + buffer.append(src_i) buffer.append(weight.native) buffer.append(weight_i.native) buffer.append(bias.native) buffer.append(output.asInstanceOf[Tensor[Float]]) - buffer.append(dst_i.native) + buffer.append(dst_i) + if (isTraining()) { + buffer.append(workSpace) + } updateOutputTensors = buffer.toArray } @@ -360,11 +302,163 @@ class RNN( } override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { - throw new UnsupportedOperationException("Not support backward propagation") + reorderManager.setRuntime(runtime) + + val src_layer_bw = NativeData(inputShape, Memory.Format.any) + val src_iter_bw = NativeData(commonIterShape, Memory.Format.any) + val wei_layer_bw = NativeData(weightShape, Memory.Format.any) + val wei_iter_bw = NativeData(weightIterShape, Memory.Format.any) + val bis_bw = NativeData(biasShape, Memory.Format.any) + val dst_layer_bw = NativeData(outputShape, Memory.Format.any) + val dst_iter_bw = NativeData(commonIterShape, Memory.Format.any) + val diff_src_layer = NativeData(inputShape, Memory.Format.any) + val diff_src_iter = NativeData(commonIterShape, Memory.Format.any) + val diff_weights_layer = NativeData(weightShape, Memory.Format.ldigo) + // IMPORTANT : it has to be ldigo + val diff_weights_iter = NativeData(weightIterShape, Memory.Format.ldigo) + // IMPORTANT : it has to be ldigo + val diff_bias = NativeData(biasShape, Memory.Format.any) + val diff_dist_layer = NativeData(outputShape, Memory.Format.any) + val diff_dist_iter = NativeData(commonIterShape, Memory.Format.any) + + val src_layer_bw_MD = src_layer_bw.getMemoryDescription() + val src_iter_bw_MD = src_iter_bw.getMemoryDescription() + val weights_layer_bw_MD = wei_layer_bw.getMemoryDescription() + val weights_iter_bw_MD = wei_iter_bw.getMemoryDescription() + val bis_bw_MD = bis_bw.getMemoryDescription() + val dist_layer_bw_MD = dst_layer_bw.getMemoryDescription() + val dist_iter_bw_MD = dst_iter_bw.getMemoryDescription() + val diff_src_layer_MD = diff_src_layer.getMemoryDescription() + val diff_src_iter_MD = diff_src_iter.getMemoryDescription() + val diff_weights_layer_MD = diff_weights_layer.getMemoryDescription() + val diff_weights_iter_MD = diff_weights_iter.getMemoryDescription() + val diff_bis_MD = diff_bias.getMemoryDescription() + val diff_dist_layer_MD = diff_dist_layer.getMemoryDescription() + val diff_dist_iter_MD = diff_dist_iter.getMemoryDescription() + + val description = MklDnn.RNNBackwardDescInit(PropKind.Backward, rnnCellDesc, + direction, src_layer_bw_MD, + src_iter_bw_MD, weights_layer_bw_MD, + weights_iter_bw_MD, bis_bw_MD, + dist_layer_bw_MD, dist_iter_bw_MD, + + diff_src_layer_MD, diff_src_iter_MD, + diff_weights_layer_MD, diff_weights_iter_MD, + diff_bis_MD, diff_dist_layer_MD, + diff_dist_iter_MD + ) + + val bwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD) + + val realSrc = MemoryData.operationWant(bwdPD, Query.SrcPd, 0) + val realSrc_iter = MemoryData.operationWant(bwdPD, Query.SrcPd, 1) + val realWei = MemoryData.operationWant(bwdPD, Query.WeightsPd, 0) + val realWei_iter = MemoryData.operationWant(bwdPD, Query.WeightsPd, 1) + val realBias = MemoryData.operationWant(bwdPD, Query.WeightsPd, 2) + val realDst = MemoryData.operationWant(bwdPD, Query.DstPd, 0) + val realDst_iter = MemoryData.operationWant(bwdPD, Query.DstPd, 1) + val realDiffDst = MemoryData.operationWant(bwdPD, Query.DiffDstPd, 0) + val realDiffDst_iter = MemoryData.operationWant(bwdPD, Query.DiffDstPd, 1) + + val realDiffSrc = MemoryData.operationWant(bwdPD, Query.DiffSrcPd, 0) + val realDiffSrc_iter = MemoryData.operationWant(bwdPD, Query.DiffSrcPd, 1) + val realDiffWei = MemoryData.operationWant(bwdPD, Query.DiffWeightsPd, 0) + val realDiffWei_iter = MemoryData.operationWant(bwdPD, Query.DiffWeightsPd, 1) + val realDiffBias = MemoryData.operationWant(bwdPD, Query.DiffWeightsPd, 2) + + weightForBackwardMemoryData = realWei + reorderManager.register(weight.heapData, realWei) + weightForBackward = reorderManager + .infer(Array(weight.heapData), Array(weightForBackwardMemoryData), weight.dense) + .asInstanceOf[DnnTensor[Float]] + + weightIterForBackwardMemoryData = realWei_iter + reorderManager.register(weight_i.heapData, realWei_iter) + weightIterForBackward = reorderManager + .infer(Array(weight_i.heapData), Array(weightIterForBackwardMemoryData), weight_i.dense) + .asInstanceOf[DnnTensor[Float]] + + gradWeight.setMemoryData(realDiffWei, HeapData(weightShape, Memory.Format.ldigo), runtime) + gradWeight_i.setMemoryData(realDiffWei_iter, HeapData(weightIterShape, Memory.Format.ldigo), + runtime) + gradBias.setMemoryData(realDiffBias, HeapData(biasShape, Memory.Format.ldgo), runtime) + + gradWeight.zero() + gradWeight_i.zero() + gradBias.zero() + + gradsrc_i = initTensor(realDiffSrc_iter).asInstanceOf[DnnTensor[Float]] + graddst_i = initTensor(realDiffDst_iter).asInstanceOf[DnnTensor[Float]] + gradsrc_i.zero() + graddst_i.zero() + + val srcs = Array(realSrc.getPrimitive(runtime), realSrc_iter.getPrimitive(runtime), + realWei.getPrimitive(runtime), realWei_iter.getPrimitive(runtime), + realBias.getPrimitive(runtime), realDst.getPrimitive(runtime), + realDst_iter.getPrimitive(runtime), realDiffDst.getPrimitive(runtime), + realDiffDst_iter.getPrimitive(runtime), workSpaceFormat.getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + + val dsts = Array(realDiffSrc.getPrimitive(runtime), realDiffSrc_iter.getPrimitive(runtime), + realDiffWei.getPrimitive(runtime), realDiffWei_iter.getPrimitive(runtime), + realDiffBias.getPrimitive(runtime) + ) + + val primitive = MklDnn.PrimitiveCreate2(bwdPD, srcs, indexes, srcs.length, + dsts, dsts.length) + + updateGradInputMemoryPrimitives = srcs ++ dsts + updateGradInputPrimitives = Array(primitive) + gradInput = initTensor(realDiffSrc) + + _gradInputFormats = Array(realDiffSrc) + _gradOutputFormats = Array(realDiffDst) + (_gradOutputFormats, _gradInputFormats) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + if (updateGradInputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(src_i) + buffer.append(weightForBackward) + buffer.append(weightIterForBackward) + buffer.append(bias.native) + buffer.append(output.asInstanceOf[Tensor[Float]]) + buffer.append(dst_i) + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) + buffer.append(graddst_i) + buffer.append(workSpace) + + buffer.append(gradInput.asInstanceOf[Tensor[Float]]) + buffer.append(gradsrc_i) + buffer.append(gradWeight.native) + buffer.append(gradWeight_i.native) + buffer.append(gradBias.native) + + updateGradInputTensors = buffer.toArray + } + + updateWithNewTensor(updateGradInputTensors, 0, input) + updateWithNewTensor(updateGradInputTensors, 7, gradOutput) + + MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, + updateGradInputPrimitives.length, updateGradInputMemoryPrimitives, updateGradInputTensors) + + gradWeight.sync() + gradWeight_i.sync() + gradBias.sync() + + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + // Do nothing } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { - (Array(weight.dense, bias.dense, weight_i.dense), Array()) + (Array(weight.dense, bias.dense, weight_i.dense), + Array(gradWeight.dense, gradBias.dense, gradWeight_i.dense)) } override def zeroGradParameters(): Unit = { @@ -372,7 +466,9 @@ class RNN( override def release(): Unit = { super.release() - List(weight, bias, weight_i, src_i, dst_i).foreach(_.release()) + List(weight, bias, weight_i, gradWeight, gradBias, gradWeight_i).foreach(_.release()) + List(src_i, dst_i, gradsrc_i, graddst_i).foreach(_.release()) + reorderManager.release() } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala index 640f7ad8b06..6e3913f4789 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala @@ -17,9 +17,10 @@ package com.intel.analytics.bigdl.nn.mkldnn import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.mkl.{AlgKind, Direction, Memory} -import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase +import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.{Recurrent, StaticGraph} import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.utils.{T, Table} @@ -59,7 +60,6 @@ class RNNSpec extends FlatSpec with Matchers{ mkldnnLSTM1.evaluate() mkldnnLSTM1.compile(InferencePhase) val mkldnn_output1 = mkldnnLSTM1.forward(input) - println("MKLDNN output LSTM Uni Left2Right \n" + mkldnn_output1) direction = Direction.UnidirectionalRight2Left val mkldnnLSTM2 = Sequential() @@ -69,7 +69,6 @@ class RNNSpec extends FlatSpec with Matchers{ mkldnnLSTM2.evaluate() mkldnnLSTM2.compile(InferencePhase) val mkldnn_output2 = mkldnnLSTM2.forward(input) - println("MKLDNN output LSTM Uni Right2Left \n" + mkldnn_output2) /** * Reorder to formats of BLAS. @@ -118,7 +117,6 @@ class RNNSpec extends FlatSpec with Matchers{ uniParams(2).copy(initWeightIter0) val blas_output1 = blasLSTM.forward(inputt).toTensor.transpose(1, 2) - println("BLAS output LSTM Uni Left2Right \n" + blas_output1) Equivalent.nearequals(Tools.dense(mkldnn_output1).asInstanceOf[Tensor[Float]], blas_output1) should be(true) @@ -131,8 +129,6 @@ class RNNSpec extends FlatSpec with Matchers{ var blas_output2 = blasLSTM.forward(inputt) blas_output2 = reverse.forward(blas_output2).toTensor.transpose(1, 2) - println("BLAS output LSTM Uni Right2Left \n" + blas_output2) - println("==================================================================== \n\n\n") Equivalent.nearequals(Tools.dense(mkldnn_output2).asInstanceOf[Tensor[Float]], blas_output2) should be(true) @@ -172,7 +168,6 @@ class RNNSpec extends FlatSpec with Matchers{ mkldnnLSTM.evaluate() mkldnnLSTM.compile(InferencePhase) val mkldnn_output = mkldnnLSTM.forward(input) - println("MKLDNN output LSTM Bi Concat \n" + mkldnn_output) /** * Reorder to formats of BLAS. @@ -241,8 +236,6 @@ class RNNSpec extends FlatSpec with Matchers{ biParams(5).copy(initWeightIter0(2)) val blas_output = blasLSTM.forward(inputt).toTensor.transpose(1, 2) - println("BLAS output LSTM Bi Concat \n" + blas_output) - println("==================================================================== \n\n\n") Equivalent.nearequals(Tools.dense(mkldnn_output).asInstanceOf[Tensor[Float]], blas_output) should be(true) @@ -282,7 +275,6 @@ class RNNSpec extends FlatSpec with Matchers{ mkldnnLSTM.evaluate() mkldnnLSTM.compile(InferencePhase) val mkldnn_output = mkldnnLSTM.forward(input) - println("MKLDNN output LSTM Bi Sum \n" + mkldnn_output) /** * Reorder to formats of BLAS. @@ -351,8 +343,6 @@ class RNNSpec extends FlatSpec with Matchers{ biParams(5).copy(initWeightIter0(2)) val blas_output = blasLSTM.forward(inputt).toTensor.transpose(1, 2) - println("BLAS output LSTM Bi Sum \n" + blas_output) - println("==================================================================== \n\n\n") Equivalent.nearequals(Tools.dense(mkldnn_output).asInstanceOf[Tensor[Float]], blas_output) should be(true) @@ -392,7 +382,6 @@ class RNNSpec extends FlatSpec with Matchers{ mkldnnLSTM.evaluate() mkldnnLSTM.compile(InferencePhase) val output = mkldnnLSTM.forward(input) - println("MKLDNN output LSTM Uni Multilayers Left2Right \n" + output) /** * Reorder to formats of BLAS. @@ -456,10 +445,660 @@ class RNNSpec extends FlatSpec with Matchers{ } val blas_output = blasLSTM.forward(inputt).toTensor.transpose(1, 2) - println("BLAS output LSTM Uni Multilayers Left2Right \n" + blas_output) - println("==================================================================== \n\n\n") Equivalent.nearequals(Tools.dense(output).asInstanceOf[Tensor[Float]], blas_output) should be(true) } + + "LSTM UnidirectionalTraining updateGradInput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.UnidirectionalLeft2Right + + val common_n_layers = 1 + val lstm_n_gates = 4 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + val gradOutputFormat = HeapData(Array(seqLength, batchSize, hiddenSize), Memory.Format.tnc) + val input = Tensor(Array(seqLength, batchSize, inputSize)).rand(-1.0, 1.0) + val gradOutput = Tensor(Array(seqLength, batchSize, hiddenSize)).rand(1.0, 1.0) + + var initWeight = Tensor[Float]( + Array(common_n_layers, 1, + inputSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 1, + hiddenSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 1, + lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + val rnn = RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias) + + val mkldnnLSTM = Sequential() + .add(Input(inputFormat.shape, inputFormat.layout)) + .add(rnn) + + mkldnnLSTM.compile(TrainingPhase) + mkldnnLSTM.forward(input) + val mkldnn_gradInput = mkldnnLSTM.backward(input, gradOutput) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + var inputt = input.transpose(1, 2).clone() + var gradOutputt = gradOutput.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(inputSize, lstm_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + initWeightIter = initWeightIter.resize(Array(hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + initBias = initBias.resize(Array(lstm_n_gates, hiddenSize)) + + /** + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * uniParams(0) -> input weights + * uniParams(1) -> bias + * uniParams(2) -> hidden weights + */ + + var initWeight0 = Tensor[Float](Array(hiddenSize * lstm_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(hiddenSize * lstm_n_gates, hiddenSize)) + var initBias0 = Tensor[Float](Array(lstm_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 4) + initWeight0 = concat.forward(T(initWeight(1), initWeight(3), + initWeight(2), initWeight(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0 = concat.forward(T(initWeightIter(1), initWeightIter(3), + initWeightIter(2), initWeightIter(4))).asInstanceOf[Tensor[Float]].clone() + initBias0 = concat.forward(T(initBias(1), initBias(3), initBias(2), initBias(4))) + .asInstanceOf[Tensor[Float]].clone() + + val blasrnn = nn.LSTM(inputSize, hiddenSize) + val blasLSTM = nn.Recurrent().add(blasrnn) + + val uniParams = blasLSTM.parameters()._1 + initWeight0 = initWeight0.resizeAs(uniParams(0)) + initBias0 = initBias0.resizeAs(uniParams(1)) + initWeightIter0 = initWeightIter0.resizeAs(uniParams(2)) + + uniParams(0).copy(initWeight0) + uniParams(1).copy(initBias0) + uniParams(2).copy(initWeightIter0) + + blasLSTM.forward(inputt).toTensor.transpose(1, 2) + + val blas_gradInput = blasLSTM.backward(inputt, gradOutputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_gradInput).asInstanceOf[Tensor[Float]], + blas_gradInput) should be(true) + + var mkldnn_gradWeight = Tools.dense(rnn.gradWeight.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradWeight_i = Tools.dense(rnn.gradWeight_i.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradBias = Tools.dense(rnn.gradBias.native).asInstanceOf[Tensor[Float]] + + var blas_gradWeight = blasrnn.preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + var blas_gradBias = blasrnn.preTopology.asInstanceOf[nn.Linear[Float]].gradBias + var blas_gradWeight_i = blasrnn.cell.asInstanceOf[nn.Sequential[Float]].modules(1) + .asInstanceOf[nn.StaticGraph[Float]].modules(1).asInstanceOf[nn.Linear[Float]].gradWeight + + mkldnn_gradWeight = mkldnn_gradWeight.resize(Array(inputSize, lstm_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + mkldnn_gradWeight_i = mkldnn_gradWeight_i.resize(Array(hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + mkldnn_gradBias = mkldnn_gradBias.resize(Array(lstm_n_gates, hiddenSize)) + + var mkldnn_gradWeight0 = Tensor[Float](Array(hiddenSize * lstm_n_gates, inputSize)) + var mkldnn_gradWeight_i0 = Tensor[Float](Array(hiddenSize * lstm_n_gates, hiddenSize)) + var mkldnn_gradBias0 = Tensor[Float](Array(lstm_n_gates * hiddenSize)) + + mkldnn_gradWeight0 = concat.forward(T(mkldnn_gradWeight(1), mkldnn_gradWeight(3), + mkldnn_gradWeight(2), mkldnn_gradWeight(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0 = concat.forward(T(mkldnn_gradWeight_i(1), mkldnn_gradWeight_i(3), + mkldnn_gradWeight_i(2), mkldnn_gradWeight_i(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradBias0 = concat.forward(T(mkldnn_gradBias(1), mkldnn_gradBias(3), + mkldnn_gradBias(2), mkldnn_gradBias(4))).asInstanceOf[Tensor[Float]].clone() + + Equivalent.nearequals(mkldnn_gradWeight0, blas_gradWeight) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0, blas_gradWeight_i) should be(true) + Equivalent.nearequals(mkldnn_gradBias0, blas_gradBias) should be(true) + } + + "LSTM BidirectionalConcatTraining updateGradInput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalConcat + + val common_n_layers = 1 + val lstm_n_gates = 4 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + val gradOutputFormat = HeapData(Array(seqLength, batchSize, 2 * hiddenSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + val gradOutput = Tensor(Array(seqLength, batchSize, 2 * hiddenSize)).rand(1.0, 1.0) + + var initWeight = Tensor[Float]( + Array(common_n_layers, 2, + inputSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 2, + hiddenSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 2, + lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + val rnn = RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias) + val mkldnnLSTM = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(rnn) + + mkldnnLSTM.compile(TrainingPhase) + mkldnnLSTM.forward(input) + val mkldnn_gradInput = mkldnnLSTM.backward(input, gradOutput) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + val inputt = input.transpose(1, 2).clone() + val gradOutputt = gradOutput.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(2, inputSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter.resize(Array(2, hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(2, lstm_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, hiddenSize)) + var initBias0 = Tensor[Float](Array(2, lstm_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 4) + initWeight0(1) = concat.forward(T(initWeight(1)(1), initWeight(1)(3), + initWeight(1)(2), initWeight(1)(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(1) = concat.forward(T(initWeightIter(1)(1), initWeightIter(1)(3), + initWeightIter(1)(2), initWeightIter(1)(4))).asInstanceOf[Tensor[Float]].clone() + initBias0(1) = concat.forward(T(initBias(1)(1), initBias(1)(3), + initBias(1)(2), initBias(1)(4))).asInstanceOf[Tensor[Float]].clone() + + initWeight0(2) = concat.forward(T(initWeight(2)(1), initWeight(2)(3), + initWeight(2)(2), initWeight(2)(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(2) = concat.forward(T(initWeightIter(2)(1), initWeightIter(2)(3), + initWeightIter(2)(2), initWeightIter(2)(4))).asInstanceOf[Tensor[Float]].clone() + initBias0(2) = concat.forward(T(initBias(2)(1), initBias(2)(3), + initBias(2)(2), initBias(2)(4))).asInstanceOf[Tensor[Float]].clone() + + val blasLSTM = nn.BiRecurrent[Float](nn.JoinTable[Float](3, 0) + .asInstanceOf[AbstractModule[Table, Tensor[Float], Float]]) + .add(nn.LSTM(inputSize, hiddenSize)) + + /** + * biParams(0 - 2) and (3 - 5) are for the two directions respectively + * + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * biParams(0) -> input weights + * biParams(1) -> bias + * biParams(2) -> hidden weights + * biParams(3) -> input weights + * biParams(4) -> bias + * biParams(5) -> hidden weights + */ + + val biParams = blasLSTM.parameters()._1 + initWeight0(1).resizeAs(biParams(0)) + initBias0(1).resizeAs(biParams(1)) + initWeightIter0(1).resizeAs(biParams(2)) + initWeight0(2).resizeAs(biParams(3)) + initBias0(2).resizeAs(biParams(4)) + initWeightIter0(2).resizeAs(biParams(5)) + + biParams(0).copy(initWeight0(1)) + biParams(1).copy(initBias0(1)) + biParams(2).copy(initWeightIter0(1)) + biParams(3).copy(initWeight0(2)) + biParams(4).copy(initBias0(2)) + biParams(5).copy(initWeightIter0(2)) + + blasLSTM.forward(inputt).toTensor.transpose(1, 2) + + val blas_gradInput = blasLSTM.backward(inputt, gradOutputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_gradInput).asInstanceOf[Tensor[Float]], + blas_gradInput) should be(true) + + var mkldnn_gradWeight = Tools.dense(rnn.gradWeight.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradWeight_i = Tools.dense(rnn.gradWeight_i.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradBias = Tools.dense(rnn.gradBias.native).asInstanceOf[Tensor[Float]] + + mkldnn_gradWeight = mkldnn_gradWeight.resize(Array(2, inputSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradWeight_i = mkldnn_gradWeight_i.resize(Array(2, hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradBias = mkldnn_gradBias.resize(Array(2, lstm_n_gates, hiddenSize)) + + var mkldnn_gradWeight0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, inputSize)) + var mkldnn_gradWeight_i0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, hiddenSize)) + var mkldnn_gradBias0 = Tensor[Float](Array(2, lstm_n_gates * hiddenSize)) + + mkldnn_gradWeight0(1) = concat.forward(T(mkldnn_gradWeight(1)(1), mkldnn_gradWeight(1)(3), + mkldnn_gradWeight(1)(2), mkldnn_gradWeight(1)(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0(1) = concat.forward(T(mkldnn_gradWeight_i(1)(1), mkldnn_gradWeight_i(1)(3), + mkldnn_gradWeight_i(1)(2), mkldnn_gradWeight_i(1)(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradBias0(1) = concat.forward(T(mkldnn_gradBias(1)(1), mkldnn_gradBias(1)(3), + mkldnn_gradBias(1)(2), mkldnn_gradBias(1)(4))).asInstanceOf[Tensor[Float]].clone() + + mkldnn_gradWeight0(2) = concat.forward(T(mkldnn_gradWeight(2)(1), mkldnn_gradWeight(2)(3), + mkldnn_gradWeight(2)(2), mkldnn_gradWeight(2)(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0(2) = concat.forward(T(mkldnn_gradWeight_i(2)(1), mkldnn_gradWeight_i(2)(3), + mkldnn_gradWeight_i(2)(2), mkldnn_gradWeight_i(2)(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradBias0(2) = concat.forward(T(mkldnn_gradBias(2)(1), mkldnn_gradBias(2)(3), + mkldnn_gradBias(2)(2), mkldnn_gradBias(2)(4))).asInstanceOf[Tensor[Float]].clone() + + val blas_gradWeight_1 = blasLSTM + .layer.modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_2 = blasLSTM + .revLayer.modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i_1 = blasLSTM + .layer.modules(1).asInstanceOf[nn.LSTM[Float]] + .cell.asInstanceOf[nn.Sequential[Float]].modules(1) + .asInstanceOf[nn.StaticGraph[Float]].modules(1).asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i_2 = blasLSTM + .revLayer.modules(1).asInstanceOf[nn.LSTM[Float]] + .cell.asInstanceOf[nn.Sequential[Float]].modules(1) + .asInstanceOf[nn.StaticGraph[Float]].modules(1).asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradBias_1 = blasLSTM + .layer.modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradBias + + val blas_gradBias_2 = blasLSTM + .revLayer.modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradBias + + Equivalent.nearequals(mkldnn_gradWeight0(1), blas_gradWeight_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight0(2), blas_gradWeight_2) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(1), blas_gradWeight_i_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(2), blas_gradWeight_i_2) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(1), blas_gradBias_1) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(2), blas_gradBias_2) should be(true) + } + + "LSTM BidirectionalSumTraining updateGradInput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalSum + + val common_n_layers = 1 + val lstm_n_gates = 4 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + val gradOutputFormat = HeapData(Array(seqLength, batchSize, hiddenSize), Memory.Format.tnc) + val input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + val gradOutput = Tensor(Array(seqLength, batchSize, hiddenSize)).rand(1.0, 1.0) + + var initWeight = Tensor[Float]( + Array(common_n_layers, 2, + inputSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 2, + hiddenSize, lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 2, + lstm_n_gates, hiddenSize)).rand(-1.0, 1.0) + + val rnn = RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias) + val mkldnnLSTM = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(rnn) + + mkldnnLSTM.compile(TrainingPhase) + mkldnnLSTM.forward(input) + val mkldnn_gradInput = mkldnnLSTM.backward(input, gradOutput) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + val inputt = input.transpose(1, 2).clone() + val gradOutputt = gradOutput.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(2, inputSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter.resize(Array(2, hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(2, lstm_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, hiddenSize)) + var initBias0 = Tensor[Float](Array(2, lstm_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 4) + initWeight0(1) = concat.forward(T(initWeight(1)(1), initWeight(1)(3), + initWeight(1)(2), initWeight(1)(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(1) = concat.forward(T(initWeightIter(1)(1), initWeightIter(1)(3), + initWeightIter(1)(2), initWeightIter(1)(4))).asInstanceOf[Tensor[Float]].clone() + initBias0(1) = concat.forward(T(initBias(1)(1), initBias(1)(3), + initBias(1)(2), initBias(1)(4))).asInstanceOf[Tensor[Float]].clone() + + initWeight0(2) = concat.forward(T(initWeight(2)(1), initWeight(2)(3), + initWeight(2)(2), initWeight(2)(4))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(2) = concat.forward(T(initWeightIter(2)(1), initWeightIter(2)(3), + initWeightIter(2)(2), initWeightIter(2)(4))).asInstanceOf[Tensor[Float]].clone() + initBias0(2) = concat.forward(T(initBias(2)(1), initBias(2)(3), + initBias(2)(2), initBias(2)(4))).asInstanceOf[Tensor[Float]].clone() + + val blasLSTM = nn.BiRecurrent[Float](nn.CAddTable() + .asInstanceOf[AbstractModule[Table, Tensor[Float], Float]]) + .add(nn.LSTM(inputSize, hiddenSize)) + + /** + * biParams(0 - 2) and (3 - 5) are for the two directions respectively + * + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * biParams(0) -> input weights + * biParams(1) -> bias + * biParams(2) -> hidden weights + * biParams(3) -> input weights + * biParams(4) -> bias + * biParams(5) -> hidden weights + */ + + val biParams = blasLSTM.parameters()._1 + initWeight0(1).resizeAs(biParams(0)) + initBias0(1).resizeAs(biParams(1)) + initWeightIter0(1).resizeAs(biParams(2)) + initWeight0(2).resizeAs(biParams(3)) + initBias0(2).resizeAs(biParams(4)) + initWeightIter0(2).resizeAs(biParams(5)) + + biParams(0).copy(initWeight0(1)) + biParams(1).copy(initBias0(1)) + biParams(2).copy(initWeightIter0(1)) + biParams(3).copy(initWeight0(2)) + biParams(4).copy(initBias0(2)) + biParams(5).copy(initWeightIter0(2)) + + blasLSTM.forward(inputt).toTensor.transpose(1, 2) + + val blas_gradInput = blasLSTM.backward(inputt, gradOutputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_gradInput).asInstanceOf[Tensor[Float]], + blas_gradInput) should be(true) + + var mkldnn_gradWeight = Tools.dense(rnn.gradWeight.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradWeight_i = Tools.dense(rnn.gradWeight_i.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradBias = Tools.dense(rnn.gradBias.native).asInstanceOf[Tensor[Float]] + + mkldnn_gradWeight = mkldnn_gradWeight.resize(Array(2, inputSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradWeight_i = mkldnn_gradWeight_i.resize(Array(2, hiddenSize, lstm_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradBias = mkldnn_gradBias.resize(Array(2, lstm_n_gates, hiddenSize)) + + var mkldnn_gradWeight0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, inputSize)) + var mkldnn_gradWeight_i0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, hiddenSize)) + var mkldnn_gradBias0 = Tensor[Float](Array(2, lstm_n_gates * hiddenSize)) + + mkldnn_gradWeight0(1) = concat.forward(T(mkldnn_gradWeight(1)(1), mkldnn_gradWeight(1)(3), + mkldnn_gradWeight(1)(2), mkldnn_gradWeight(1)(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0(1) = concat.forward(T(mkldnn_gradWeight_i(1)(1), mkldnn_gradWeight_i(1)(3), + mkldnn_gradWeight_i(1)(2), mkldnn_gradWeight_i(1)(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradBias0(1) = concat.forward(T(mkldnn_gradBias(1)(1), mkldnn_gradBias(1)(3), + mkldnn_gradBias(1)(2), mkldnn_gradBias(1)(4))).asInstanceOf[Tensor[Float]].clone() + + mkldnn_gradWeight0(2) = concat.forward(T(mkldnn_gradWeight(2)(1), mkldnn_gradWeight(2)(3), + mkldnn_gradWeight(2)(2), mkldnn_gradWeight(2)(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0(2) = concat.forward(T(mkldnn_gradWeight_i(2)(1), mkldnn_gradWeight_i(2)(3), + mkldnn_gradWeight_i(2)(2), mkldnn_gradWeight_i(2)(4))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradBias0(2) = concat.forward(T(mkldnn_gradBias(2)(1), mkldnn_gradBias(2)(3), + mkldnn_gradBias(2)(2), mkldnn_gradBias(2)(4))).asInstanceOf[Tensor[Float]].clone() + + val blas_gradWeight_1 = blasLSTM + .layer.modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_2 = blasLSTM + .revLayer.modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i_1 = blasLSTM + .layer.modules(1).asInstanceOf[nn.LSTM[Float]] + .cell.asInstanceOf[nn.Sequential[Float]].modules(1) + .asInstanceOf[nn.StaticGraph[Float]].modules(1).asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i_2 = blasLSTM + .revLayer.modules(1).asInstanceOf[nn.LSTM[Float]] + .cell.asInstanceOf[nn.Sequential[Float]].modules(1) + .asInstanceOf[nn.StaticGraph[Float]].modules(1).asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradBias_1 = blasLSTM + .layer.modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradBias + + val blas_gradBias_2 = blasLSTM + .revLayer.modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradBias + + Equivalent.nearequals(mkldnn_gradWeight0(1), blas_gradWeight_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight0(2), blas_gradWeight_2) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(1), blas_gradWeight_i_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(2), blas_gradWeight_i_2) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(1), blas_gradBias_1) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(2), blas_gradBias_2) should be(true) + } + + "LSTM UnidirectionalInference Multilayers updateGradInput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val commonSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.UnidirectionalLeft2Right + + val common_n_layers = 3 + val lstm_n_gates = 4 + + val inputFormat = HeapData(Array(seqLength, batchSize, commonSize), Memory.Format.tnc) + val gradOutputFormat = HeapData(Array(seqLength, batchSize, commonSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, commonSize)).rand() + val gradOutput = Tensor(Array(seqLength, batchSize, commonSize)).rand(1.0, 1.0) + + var initWeight = Tensor[Float]( + Array(common_n_layers, 1, + commonSize, lstm_n_gates, commonSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 1, + commonSize, lstm_n_gates, commonSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 1, + lstm_n_gates, commonSize)).rand(-1.0, 1.0) + + val rnn = RNN(AlgKind.VanillaLstm, commonSize, commonSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, + initBias = initBias, layers = common_n_layers) + val mkldnnLSTM = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(rnn) + + mkldnnLSTM.compile(TrainingPhase) + mkldnnLSTM.forward(input) + val mkldnn_gradInput = mkldnnLSTM.backward(input, gradOutput) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + var inputt = input.transpose(1, 2).clone() + var gradOutputt = gradOutput.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(common_n_layers, commonSize, lstm_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter + .resize(Array(common_n_layers, commonSize, lstm_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(common_n_layers, lstm_n_gates, commonSize)) + + /** + * Gate order matching between MKLDNN LSTM and nn/LSTM: + * MKLDNN Gate 1 -> nn/LSTM Gate 1 (input gate) + * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) + * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) + * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * uniParams(0) -> input weights + * uniParams(1) -> bias + * uniParams(2) -> hidden weights + */ + + var initWeight0 = Tensor[Float](Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) + var initWeightIter0 = + Tensor[Float](Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) + var initBias0 = Tensor[Float](Array(common_n_layers, lstm_n_gates * commonSize)) + + val concat = nn.JoinTable(1, 4) + for(l <- 1 to common_n_layers) { + initWeight0(l).copy(concat.forward(T(initWeight(l)(1), initWeight(l)(3), + initWeight(l)(2), initWeight(l)(4))).asInstanceOf[Tensor[Float]].clone()) + initWeightIter0(l).copy(concat.forward(T(initWeightIter(l)(1), initWeightIter(l)(3), + initWeightIter(l)(2), initWeightIter(l)(4))).asInstanceOf[Tensor[Float]].clone()) + initBias0(l).copy(concat.forward(T(initBias(l)(1), initBias(l)(3), + initBias(l)(2), initBias(l)(4))) + .asInstanceOf[Tensor[Float]].clone()) + } + + val nn_input = nn.Input() + var nn_lstm = nn.Recurrent().add(nn.LSTM(commonSize, commonSize)).inputs(nn_input) + + for(i <- 1 until common_n_layers) { + nn_lstm = nn.Recurrent().add(nn.LSTM(commonSize, commonSize)).inputs(nn_lstm) + } + + val blasLSTM = nn.Graph(nn_input, nn_lstm) + + val uniParams = blasLSTM.parameters()._1 + + for(l <- 0 until common_n_layers) { + initWeight0(l + 1) = initWeight0(l + 1).resizeAs(uniParams(3 * l)) + initBias0(l + 1) = initBias0(l + 1).resizeAs(uniParams(3 * l + 1)) + initWeightIter0(l + 1) = initWeightIter0(l + 1).resizeAs(uniParams(3 * l + 2)) + + uniParams(3 * l).copy(initWeight0(l + 1)) + uniParams(3 * l + 1).copy(initBias0(l + 1)) + uniParams(3 * l + 2).copy(initWeightIter0(l + 1)) + } + + blasLSTM.forward(inputt).toTensor.transpose(1, 2) + + val blas_gradInput = blasLSTM.backward(inputt, gradOutputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_gradInput).asInstanceOf[Tensor[Float]], + blas_gradInput) should be(true) + + var mkldnn_gradWeight = Tools.dense(rnn.gradWeight.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradWeight_i = Tools.dense(rnn.gradWeight_i.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradBias = Tools.dense(rnn.gradBias.native).asInstanceOf[Tensor[Float]] + + mkldnn_gradWeight = mkldnn_gradWeight + .resize(Array(common_n_layers, commonSize, lstm_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradWeight_i = mkldnn_gradWeight_i + .resize(Array(common_n_layers, commonSize, lstm_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradBias = mkldnn_gradBias.resize(Array(common_n_layers, lstm_n_gates, commonSize)) + + var mkldnn_gradWeight0 = Tensor[Float]( + Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) + var mkldnn_gradWeight_i0 = + Tensor[Float](Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) + var mkldnn_gradBias0 = Tensor[Float](Array(common_n_layers, lstm_n_gates * commonSize)) + + for(l <- 1 to common_n_layers) { + mkldnn_gradWeight0(l).copy( + concat + .forward(T( + mkldnn_gradWeight(l)(1), mkldnn_gradWeight(l)(3), + mkldnn_gradWeight(l)(2), mkldnn_gradWeight(l)(4))) + .asInstanceOf[Tensor[Float]].clone()) + mkldnn_gradWeight_i0(l).copy( + concat + .forward(T( + mkldnn_gradWeight_i(l)(1), mkldnn_gradWeight_i(l)(3), + mkldnn_gradWeight_i(l)(2), mkldnn_gradWeight_i(l)(4))) + .asInstanceOf[Tensor[Float]].clone()) + mkldnn_gradBias0(l).copy(concat.forward(T(mkldnn_gradBias(l)(1), mkldnn_gradBias(l)(3), + mkldnn_gradBias(l)(2), mkldnn_gradBias(l)(4))) + .asInstanceOf[Tensor[Float]].clone()) + } + + val blas_gradWeight = Tensor[Float]( + Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) + val blas_gradWeight_i = Tensor[Float]( + Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) + val blas_gradBias = Tensor[Float]( + Array(common_n_layers, lstm_n_gates * commonSize)) + + for (l <- 1 to common_n_layers) { + blas_gradWeight(l).copy(blasLSTM.modules(l).asInstanceOf[nn.Recurrent[Float]] + .modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]] + .gradWeight) + + blas_gradWeight_i(l).copy(blasLSTM.modules(l).asInstanceOf[nn.Recurrent[Float]] + .modules(1).asInstanceOf[nn.LSTM[Float]] + .cell.asInstanceOf[nn.Sequential[Float]].modules(1) + .asInstanceOf[nn.StaticGraph[Float]].modules(1).asInstanceOf[nn.Linear[Float]] + .gradWeight) + + blas_gradBias(l).copy(blasLSTM.modules(l).asInstanceOf[nn.Recurrent[Float]] + .modules(1).asInstanceOf[nn.LSTM[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]] + .gradBias) + } + + for (l <- 1 to common_n_layers) { + Equivalent.nearequals(mkldnn_gradWeight0(l), blas_gradWeight(l)) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(l), blas_gradWeight_i(l)) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(l), blas_gradBias(l)) should be(true) + } + } } From 97493dc305d93e147d1415b3deac43c26ec97044 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 28 Jun 2019 16:45:14 +0800 Subject: [PATCH 0919/1065] fix: require consistent between shape and layout of mkldnn (#2824) --- .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index e6302f3a793..18cf5d5a525 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -49,6 +49,7 @@ sealed trait MemoryData extends Serializable { def getMemoryDescription(): Long = { if (description == UNDEFINED || description == ERROR) { + checkConsistency(shape, layout) description = MklDnn.MemoryDescInit(shape.length, shape, dataType, layout) } description @@ -99,6 +100,19 @@ sealed trait MemoryData extends Serializable { case _ => throw new UnsupportedOperationException(s"unsupported data type") } } + + private def checkConsistency(shape: Array[Int], layout: Int): Unit = { + val isConsistency = Memory.Format.any == layout || (shape.length match { + case 1 => layout == Memory.Format.x + case 2 => layout == Memory.Format.nc || layout == Memory.Format.io || + layout == Memory.Format.oi + case 3 | 4 | 5 => layout != Memory.Format.nc || layout != Memory.Format.x + case _ => false + }) + + require(isConsistency, + s"the shape([${shape.mkString(",")}]) of tensor is different from layout(${layout})") + } } case class HeapData(private var _shape: Array[Int], private var _layout: Int, From 51a89bb546ea9a35b4d3a4c6618485677e987b6b Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Sun, 30 Jun 2019 23:14:30 +0800 Subject: [PATCH 0920/1065] fix: fusion for multi-group of convolution (#2826) --- .../bigdl/dllib/nn/mkldnn/Fusion.scala | 5 ++- .../dllib/nn/mkldnn/SpatialConvolution.scala | 10 ++++++ .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 31 +++++++++++++++++++ 3 files changed, 45 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala index 4eb758b5ddf..20ad29d8cab 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -189,7 +189,10 @@ private[mkldnn] object Fusion { val weight = if (conv.nGroup == 1) { convWeight.select(1, j + 1) } else { - convWeight.select(2, j + 1) + val channelPerGroup = conv.nOutputPlane / conv.nGroup + val group = j / channelPerGroup + 1 + val channel = j % channelPerGroup + 1 + convWeight.select(1, group).select(2, channel) } weight.div(base) weight.mul(alpha) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index e9e96f179fb..91d9dbdaf58 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -92,6 +92,16 @@ class SpatialConvolution( val dilationW: Int = 1, val dilationH: Int = 1 ) extends MklDnnLayer with Initializable with Serializable with MklInt8Convertible { + require(nInputPlane % nGroup == 0, s"Number of input channels " + + s"should be multiples of group " + + s"number of input channels ${nInputPlane}, " + + s"group ${nGroup}.") + require(nOutputPlane % nGroup == 0, + "Number of output channels " + + "should be multiples of group " + + s"(number of output channels ${nOutputPlane}, " + + s"group ${nGroup}).") + private val weightShape = if (nGroup == 1) { Array(nOutputPlane, nInputPlane, kernelH, kernelW) } else { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala index a5e1b785a83..3fbc3165a42 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -341,4 +341,35 @@ class FusionSpec extends FlatSpec with Matchers { model1.output should be (model2.output) } + + "multi-group conv fusion with bn" should "work correctly" in { + val inputShape = Array(4, 1024, 7, 7) + val input = Input(inputShape, Memory.Format.nchw).inputs() + val conv1 = SpatialConvolution(1024, 1024, 3, 3, 1, 1, 1, 1, nGroup = 1024).inputs(input) + val bn1 = SpatialBatchNormalization(1024).inputs(conv1) + val output = Output(Memory.Format.nchw).inputs(bn1) + + // the running mean and running variance should be 1. + bn1.element.getExtraParameter().foreach(_.fill(1)) + + val model = DnnGraph(Seq(input), Seq(output)) + val fused = model.cloneModule() + + model.evaluate() + fused.evaluate() + + val tensor = Tensor[Float](inputShape).rand(-1, 1) + + System.setProperty("bigdl.mkldnn.fusion", "false") + model.compile(InferencePhase) + model.forward(tensor) + + System.setProperty("bigdl.mkldnn.fusion", "true") + fused.compile(InferencePhase) + fused.forward(tensor) + + model.output should be (fused.output) + + System.clearProperty("bigdl.mkldnn.fusion") + } } From ae15852faedf88cfcaec7bcfab59a80caca4ae60 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 1 Jul 2019 13:55:41 +0800 Subject: [PATCH 0921/1065] fix: support int8 of jointable (#2827) * fix: support int8 of jointable * doc: add more docs --- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 8 +- .../bigdl/dllib/nn/mkldnn/Fusion.scala | 78 +++++++++++++++---- .../bigdl/dllib/nn/mkldnn/JoinTable.scala | 10 ++- .../bigdl/dllib/nn/mkldnn/Output.scala | 3 - .../bigdl/dllib/nn/mkldnn/JoinTableSpec.scala | 42 ++++++++++ 5 files changed, 120 insertions(+), 21 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index e385fa580ad..88b7d2e8002 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -327,8 +327,9 @@ class DnnGraph( private def getInputMemoryData(node: ModuleNode[Float], memoryData: Array[MemoryData]) : Array[MemoryData] = { - if (inputs.length == 1) { - require(inputs(0).eq(node), "input node is not in the input list") + // the model may contain two inputs and all of them is Input. + if (inputs.length == 1 || memoryData.isEmpty) { + require(inputs.contains(node), "input node must be in the input list") memoryData } else { val i = inputs.indexOf(node) @@ -392,13 +393,14 @@ class DnnGraph( */ private def fusion(): Unit = { if (!this.train) { - for (j <- 0 to 2) { + for (j <- 0 to 3) { var i = forwardExecution.length - 1 while (i >= 0) { if (j == 0) Fusion.fuseModule(forwardExecution(i)) // we should do this before sum fusion, because it will change the structure of graph if (j == 1) Fusion.setNegativeInputOfConv(forwardExecution(i)) if (j == 2) Fusion.fuseCAdd(forwardExecution(i)) + if (j == 3) Fusion.setScalesPrevousJoinTable(forwardExecution(i)) i -= 1 } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala index 20ad29d8cab..0533f741ced 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -215,20 +215,6 @@ private[mkldnn] object Fusion { def setNegativeInputOfConv(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { - def findAllNonIdentityPrevs(node: Node[AbstractModule[Activity, Activity, Float]]) - : Seq[Node[AbstractModule[Activity, Activity, Float]]] = { - // TODO currently, it will only skip the Identity, MaxPooling, AvgPooling - // becase if the output of layer/op previous of the three, they will output - // nonnegative too. it's not an elegant impl. - if (node.element.isInstanceOf[Identity] || - node.element.isInstanceOf[MaxPooling] || - node.element.isInstanceOf[AvgPooling]) { - node.prevNodes.flatMap(findAllNonIdentityPrevs) - } else { - Seq(node) - } - } - if (!fuse || !node.element.isInstanceOf[SpatialConvolution]) return val successFromReLU = node.prevNodes.flatMap(x => findAllNonIdentityPrevs(x)) @@ -248,4 +234,68 @@ private[mkldnn] object Fusion { node.element.asInstanceOf[SpatialConvolution].negativeInput = false } } + + /** + * set the layers' scales which is previous nodes of JoinTable. + * + * For a graph structure like below, + * + * conv1 --+ + * |--> JoinTable --> conv3 + * conv2 --+ + * + * we should set the conv1's and conv2's output scales to the conv3's input scales. + * + * If the operation next JoinTable has no input scales like below. We should set + * the scales to the max values of input scales of conv1 and conv2. + * + * conv1 --+ + * |--> JoinTable --> [Layer/Op has no input scales] + * conv2 --+ + * + * @param node current node + */ + def setScalesPrevousJoinTable(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { + // case 1, need not do fusion + if (!fuse || !node.element.isInstanceOf[JoinTable]) return + + val preConvs = node.prevNodes.flatMap(x => findAllNonIdentityPrevs(x)) + .filter(_.element.isInstanceOf[SpatialConvolution]) + .map(_.element.asInstanceOf[SpatialConvolution]) + + // case 2, there's one node need not quantize + if (!preConvs.exists(_.needQuantize)) return + + // case 3, the output dimension mask should be the same + val masks = preConvs.map(_.getOutputDimMask()).toSet + require(masks.size == 1, s"all preceding convolutions must have the same mask") + + val nextConvs = node.nextNodes.flatMap(findNext) + .filter(_.element.isInstanceOf[SpatialConvolution]) + + val scales = if (nextConvs.isEmpty) { + Array(preConvs.map(_.getOutputScales().flatten).transpose.map(_.max).toArray) + } else { + nextConvs.map(_.element.asInstanceOf[SpatialConvolution]).head.getInputScales() + } + + preConvs.foreach { conv => + conv.setOutputScales(scales) + } + } + + private def findAllNonIdentityPrevs(node: Node[AbstractModule[Activity, Activity, Float]]) + : Seq[Node[AbstractModule[Activity, Activity, Float]]] = { + // TODO currently, it will only skip the Identity, MaxPooling, AvgPooling, JoinTable + // becase if the output of layer/op previous of the four, they will output + // nonnegative too. it's not an elegant impl. + if (node.element.isInstanceOf[Identity] || + node.element.isInstanceOf[MaxPooling] || + node.element.isInstanceOf[AvgPooling] || + node.element.isInstanceOf[JoinTable]) { + node.prevNodes.flatMap(findAllNonIdentityPrevs) + } else { + Seq(node) + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala index c612860bdc2..69de6d28833 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala @@ -34,7 +34,6 @@ class JoinTable(val dimension: Int) extends MklDnnLayer { var i = 1 while(i < inputs.length) { val curShape = inputs(i).shape - require(layout == inputs(i).layout, "layout not match") require(totalShape.length == curShape.length, "tensor dimension not match") // require(inputs(i).isInstanceOf[NativeData], "memory should be native") var j = 0 @@ -46,6 +45,10 @@ class JoinTable(val dimension: Int) extends MklDnnLayer { } j += 1 } + + if (layout != inputs(i).layout || inputs(0).dataType != inputs(i).dataType) { + _inputFormats(i) = NativeData(inputs(i).shape, layout, inputs(0).dataType) + } i += 1 } val primDesc = MklDnn.ConcatPrimitiveDescCreate( @@ -108,6 +111,11 @@ class JoinTable(val dimension: Int) extends MklDnnLayer { } gradInput } + + private def isSameDataType(formats: Array[MemoryData]): Boolean = { + formats.map(_.dataType).toSet.size == 1 + } + } object JoinTable { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala index 368787739bd..10904342686 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala @@ -37,9 +37,6 @@ class Output(outputLayOut: Int = Memory.Format.nc, if (outLayout == Memory.Format.nhwc && inLayout != Memory.Format.nhwc) { // nchw* -> nhwc Array(inShape(0), inShape(2), inShape(3), inShape(1)) - } else if ((outLayout != Memory.Format.nhwc) && (inLayout == Memory.Format.nhwc)) { - // nhwc -> nchw* - Array(inShape(0), inShape(3), inShape(1), inShape(2)) } else inShape outputShape } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala index 7d4b7c8162d..ed75c054566 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} @@ -56,4 +57,45 @@ class JoinTableSpec extends BigDLSpecHelper { Tensor[Float](T(T(5, 8), T(10, 9))) ) } + + "int8 of join table" should "work correctly" in { + val inputShape1 = Array[Int](4, 2, 4, 4) + val inputShape2 = Array[Int](4, 4, 4, 4) + + val input1 = Input(inputShape1, Memory.Format.nchw).inputs() + val input2 = Input(inputShape2, Memory.Format.nchw).inputs() + val conv1 = SpatialConvolution(2, 4, 5, 5, 1, 1, 2, 2).inputs(input1) + val conv2 = SpatialConvolution(4, 4, 1, 1, 1, 1, 0, 0).inputs(input2) + val joinTable = JoinTable(2).inputs(conv1, conv2) + val output = Output(Memory.Format.nchw).inputs(joinTable) + + val model = DnnGraph(Seq(input1, input2), Seq(output)) + model.evaluate() + + val tensor1 = Tensor[Float](inputShape1).rand(-1, 1) + val tensor2 = Tensor[Float](inputShape2).rand(-0.1, 0.1) + val tableInput = T(tensor1, tensor2) + + model.setWeightDimMask(1, overrideSubmodules = true) + model.compile(InferencePhase) + model.forward(tableInput) + + model.calcScales(tableInput) + + val outputOfModel = Tensor[Float]() + .resizeAs(model.output.toTensor[Float]) + .copy(model.output.toTensor[Float]) + + + model.clearState() + + val quant = model.quantize().asInstanceOf[DnnGraph] + quant.compile(InferencePhase) + quant.forward(tableInput) + + Equivalent.nearequals(outputOfModel, quant.output.toTensor[Float], 1e-1) should be (true) + + model.release() + quant.release() + } } From 975442fb8236906d2e67e603a943fa2005f275db Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 1 Jul 2019 14:27:52 +0800 Subject: [PATCH 0922/1065] fix: invokeAndWait2 should throw the exception in the tasks (#2843) --- .../intel/analytics/bigdl/utils/Engine.scala | 21 ++++++- .../analytics/bigdl/utils/ThreadPool.scala | 49 +++++++++++----- .../bigdl/utils/ThreadPoolSpec.scala | 58 +++++++++++++++++-- 3 files changed, 107 insertions(+), 21 deletions(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 1eea57289a9..75c62d430e7 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -23,7 +23,7 @@ import java.util.concurrent.atomic.AtomicBoolean import org.apache.log4j.Logger import org.apache.spark._ import com.intel.analytics.bigdl.mkl.MKL -import com.intel.analytics.bigdl.mkl.hardware.CpuInfo +import com.intel.analytics.bigdl.mkl.hardware.{Affinity, CpuInfo} import org.apache.spark.utils.SparkUtils import py4j.GatewayServer @@ -556,7 +556,24 @@ object Engine { } private def setMklDnnEnvironments(): Unit = { - val default = Math.ceil(Runtime.getRuntime.availableProcessors().toFloat / 2).toInt + import com.intel.analytics.bigdl.mkl.hardware.CpuInfo + val affinityCores = Affinity.getAffinity + val physicalCoreNum = CpuInfo.getPhysicalProcessorCount + val affinityCoreNum = affinityCores.length + + // 1. this library in docker/cgroup env, which sets cpu affinity fist. so we can't use + // resources exceeding limits. + // 2. this library is in a hyper threading envs, so we should set the mkl num threads + // to physical core number for performance + + val default = if (affinityCores.min > 0 && affinityCores.max >= physicalCoreNumber) { + affinityCoreNum + } else if (physicalCoreNum > affinityCoreNum ) { + affinityCoreNum + } else { + physicalCoreNum + } + val threadsNumber = System.getProperty("bigdl.mklNumThreads", default.toString) System.setProperty("bigdl.mklNumThreads", s"$threadsNumber") diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index fb38210adf5..a4d9fc33c26 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -18,15 +18,15 @@ package com.intel.analytics.bigdl.utils import java.util.concurrent._ -import com.intel.analytics.bigdl.mkl.MKL import com.intel.analytics.bigdl.mkl.hardware.Affinity -import com.intel.analytics.bigdl.mkl.{MklDnn => BackendMklDnn} +import com.intel.analytics.bigdl.mkl.{MKL, MklDnn => BackendMklDnn} import org.apache.commons.lang.exception.ExceptionUtils import org.apache.log4j.Logger +import scala.collection.JavaConverters._ +import scala.collection.mutable import scala.concurrent.duration.Duration import scala.concurrent.{Await, ExecutionContext, Future} -import scala.collection.JavaConverters._ /** * A thread pool wrapper, provide some helper functions for multi-threading @@ -135,24 +135,45 @@ class ThreadPool(private var poolSize: Int) { }) } + private type JavaFuture[T] = java.util.concurrent.Future[T] + + /** + * Use java future to execute the tasks. It will be blocking until tasks completed. + * If any task throws an exception, it will throw that exception in caller. + * + * @param tasks task sequence. each task's return type is T + * @param timeout the maximum time to wait + * @param timeUnit the time unit for the timeout + * @tparam T return type of tasks + * @return a sequence of Futures representing the tasks. + */ def invokeAndWait2[T](tasks: Seq[() => T], timeout: Long = Long.MaxValue, - timeUnit: TimeUnit = TimeUnit.NANOSECONDS): - scala.collection.mutable.Buffer[java.util.concurrent.Future[T]] = { + timeUnit: TimeUnit = TimeUnit.NANOSECONDS): mutable.Buffer[JavaFuture[T]] = { val callables = tasks.map(task => new Callable[T] { override def call(): T = { - try { - task() - } catch { - case t : Throwable => - logger.error("Error: " + ExceptionUtils.getStackTrace(t)) - throw t - } + task() } }) - threadPool.invokeAll(callables.asJava, timeout, timeUnit).asScala + + val resultFutures = threadPool.invokeAll(callables.asJava, timeout, timeUnit) + + // we should check all the future in the list, if any task has an exception, + // we should throw it. + var i = 0 + while (i < resultFutures.size()) { + try { + resultFutures.get(i).get() + } catch { + case t: ExecutionException => throw t.getCause + case i: InterruptedException => throw i.getCause + } + i += 1 + } + + resultFutures.asScala } - def invoke2[T](tasks: Seq[() => T]): Seq[java.util.concurrent.Future[T]] = { + def invoke2[T](tasks: Seq[() => T]): Seq[JavaFuture[T]] = { tasks.map(task => new Callable[T] { override def call(): T = { try { diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala index 34fae59f5ea..46107a06565 100644 --- a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala @@ -16,10 +16,11 @@ package com.intel.analytics.bigdl.utils -import com.intel.analytics.bigdl.mkl.MKL import com.intel.analytics.bigdl.mkl.hardware.Affinity import org.scalatest.{FlatSpec, Matchers} +import scala.concurrent.ExecutionException + class ThreadPoolSpec extends FlatSpec with Matchers { "mkldnn backend" should "create omp threads and bind correctly" in { @@ -36,10 +37,11 @@ class ThreadPoolSpec extends FlatSpec with Matchers { threadPool.setMKLThreadOfMklDnnBackend(ompSize) + // the first core can be used maybe not the 0, it depends on the affinity settings. threadPool.invokeAndWait2( (0 until poolSize).map( i => () => { Affinity.getAffinity.length should be (1) - Affinity.getAffinity.head should be (0) + Affinity.getAffinity.head should be (affinities.head.head) })) // set back the affinities @@ -49,7 +51,7 @@ class ThreadPoolSpec extends FlatSpec with Matchers { threadPool.invokeAndWait2( (0 until poolSize).map( i => () => { - Affinity.getAffinity.zipWithIndex.foreach(ai => ai._1 should be (ai._2)) + Affinity.getAffinity.zip(affinities.head).foreach(ai => ai._1 should be (ai._2)) })) } @@ -59,18 +61,64 @@ class ThreadPoolSpec extends FlatSpec with Matchers { val ompSize = 4 val threadPool = new ThreadPool(poolSize) + // backup the affinities + val affinities = threadPool.invokeAndWait2( (0 until poolSize).map(i => + () => { + Affinity.getAffinity() + })).map(_.get()).toArray threadPool.setMKLThreadOfMklDnnBackend(ompSize) + // the thread in thread pool will be set affinity to one core, which is + // the first core can be used. threadPool.invokeAndWait2( (0 until poolSize).map( i => () => { Affinity.getAffinity.length should be (1) - Affinity.getAffinity.head should be (0) + Affinity.getAffinity.head should be (affinities.head.head) })) val threadPool2 = new ThreadPool(poolSize) + // the thread has not been set any affinities, so it should return all + // cores can be used. threadPool2.invokeAndWait2( (0 until poolSize).map(i => () => { println(Affinity.getAffinity.mkString("\t")) - Affinity.getAffinity.length should not be (1) + Affinity.getAffinity.length should be (affinities.head.length) })) } + + "invokeAndWait2" should "catch the unsupported exception" in { + val threadPool = new ThreadPool(1) + val task = () => { throw new UnsupportedOperationException(s"test invokeAndWait2") } + + intercept[UnsupportedOperationException] { + threadPool.invokeAndWait2( (0 until 1).map( i => task )) + } + } + + "invokeAndWait2" should "catch the interrupt exception" in { + val threadPool = new ThreadPool(1) + val task = () => { throw new InterruptedException(s"test invokeAndWait2")} + + intercept[InterruptedException] { + threadPool.invokeAndWait2( (0 until 1).map( i => task )) + } + } + + "invokeAndWait" should "catch the exception" in { + val threadPool = new ThreadPool(1) + val task = () => { throw new InterruptedException(s"test invokeAndWait")} + + intercept[InterruptedException] { + threadPool.invokeAndWait( (0 until 1).map( i => task )) + } + } + + "invoke" should "catch the exception" in { + val threadPool = new ThreadPool(1) + val task = () => { throw new UnsupportedOperationException(s"test invoke2") } + + intercept[ExecutionException] { + val results = threadPool.invoke2( (0 until 1).map( i => task )) + results.foreach(_.get()) + } + } } From 171557215e71c873b46657481f39850e49f5b8cc Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 1 Jul 2019 15:09:30 +0800 Subject: [PATCH 0923/1065] fix acc bug & init dnn thread (#2841) --- .../intel/analytics/bigdl/utils/Engine.scala | 8 +++++ .../dllib/nn/mkldnn/SpatialConvolution.scala | 4 ++- .../utils/intermediate/IRconvertSpec.scala | 36 +++++++++++++++++++ 3 files changed, 47 insertions(+), 1 deletion(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index 75c62d430e7..b6084d52b82 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -237,6 +237,8 @@ object Engine { // For `context` in ThreadPool, it is the called thread when poolSize is 1. // So many usages of that thread, we will not change it for now. val dnnComputing: ThreadPool = new ThreadPool(1) + // We need to init dnn thread in case that users directly call model operation in java local + initDnnThread() /** * If user undefine the property bigdl.coreNumber, it will return physical core number @@ -580,4 +582,10 @@ object Engine { System.setProperty("bigdl.disable.mklBlockTime", "true") System.setProperty("bigdl.coreNumber", "1") } + + private def initDnnThread(): Unit = { + if (engineType == MklDnn) { + dnnComputing.setMKLThreadOfMklDnnBackend(MKL.getMklNumThreads) + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 91d9dbdaf58..7f4a9b14f1f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -545,13 +545,15 @@ class SpatialConvolution( val src = NativeData(inputShape, Memory.Format.any) val wei = NativeData(weightShape, Memory.Format.any) val bis = NativeData(Array(nOutputPlane), Memory.Format.x) + // Use format "any" to init weight desc, otherwise maybe poor performance + val gradMemoryData = NativeData(grad(0).shape, Memory.Format.any) val desc = MklDnn.DilatedConvBackwardWeightsDescInit( AlgKind.ConvolutionDirect, src.getMemoryDescription(), wei.getMemoryDescription(), bis.getMemoryDescription(), - grad(0).getMemoryDescription(), + gradMemoryData.getMemoryDescription(), Array(strideW, strideH), Array(dilationW_mkldnn, dilationH_mkldnn), paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala index ee6b549dd04..d957a29a6b2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala @@ -266,4 +266,40 @@ class IRconvertSpec extends BigDLSpecHelper { dnn.release() System.clearProperty("bigdl.engineType") } + + "dnn resnet50" should "work correctly with dnn computing thread" in { + System.setProperty("bigdl.engineType", "mkldnn") + val batchSize = 2 + + RandomGenerator.RNG.setSeed(1) + val dnnThread = mkldnn.ResNet.graph(batchSize, classNum = 1000, + T("depth" -> 50, "optnet" -> false, "dataSet" -> mkldnn.ResNet.DatasetType.ImageNet)) + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + dnnThread.compile(mkldnn.Phase.TrainingPhase) + })) + + RandomGenerator.RNG.setSeed(1) + val dnn = mkldnn.ResNet.graph(batchSize, classNum = 1000, + T("depth" -> 50, "optnet" -> false, "dataSet" -> mkldnn.ResNet.DatasetType.ImageNet)) + dnn.compile(mkldnn.Phase.TrainingPhase) + + RandomGenerator.RNG.setSeed(100) + val in = Tensor[Float](batchSize, 3, 224, 224).rand(-1, 1) + + val out = dnn.forward(in).toTensor[Float] + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + dnnThread.forward(in).toTensor[Float] + })) + val outThread = dnnThread.output.toTensor[Float] + + val gradOutput = out.clone() + val grad = dnn.backward(in, gradOutput).toTensor[Float] + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + dnnThread.backward(in, gradOutput).toTensor[Float] + })) + val gradThread = dnnThread.gradInput.toTensor[Float] + + Equivalent.nearequals(out, outThread) should be(true) + Equivalent.nearequals(grad, gradThread) should be(true) + } } From 37c326c16d1f989feec18ed389ccc54047c6e310 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 1 Jul 2019 16:42:01 +0800 Subject: [PATCH 0924/1065] support tnc and ntc conversion (#2844) --- .../bigdl/dllib/nn/mkldnn/ReorderMemory.scala | 24 ++++++--- .../dllib/nn/mkldnn/ReorderMemorySpec.scala | 54 +++++++++++++++++++ 2 files changed, 70 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala index 05b52df615c..10053faa714 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala @@ -59,6 +59,10 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, if (format.layout == Memory.Format.nhwc && format.isInstanceOf[HeapData]) { tensor.toTensor[Float].resize(format.shape) } + // for mkldnn, it always use tnc format shape even though format is ntc + if (format.layout == Memory.Format.ntc && format.isInstanceOf[HeapData]) { + tensor.toTensor[Float].resize(format.shape) + } } private def createInt8PrimDesc(): Long = { @@ -110,11 +114,13 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, realOutput = _outputFormats if (inputLayout != outputLayout) { - if (inputLayout == Memory.Format.nhwc) { - // remind: if format of input MemoryData is nhwc, its shape should be output shape + if (inputLayout == Memory.Format.nhwc || inputLayout == Memory.Format.ntc) { + // remind: if format of input MemoryData is nhwc or ntc, + // its shape should be output shape realInput = initMemory(_inputFormats(0), outputShape, inputLayout) - } else if (outputLayout == Memory.Format.nhwc) { - // remind: if format of output MemoryData is nhwc, its shape should be input shape + } else if (outputLayout == Memory.Format.nhwc || outputLayout == Memory.Format.ntc) { + // remind: if format of output MemoryData is nhwc or ntc, + // its shape should be input shape realOutput = initMemory(_outputFormats(0), inputShape, outputLayout) } } @@ -174,11 +180,13 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, realgradOutput = _gradOutputFormats if (gradInputLayout != gradOutputLayout) { - if (gradOutputLayout == Memory.Format.nhwc) { - // remind: if format of gradOutput MemoryData is nhwc, its shape should be gradInput shape + if (gradOutputLayout == Memory.Format.nhwc || gradOutputLayout == Memory.Format.ntc) { + // remind: if format of gradOutput MemoryData is nhwc or ntc, + // its shape should be gradInput shape realgradOutput = initMemory(_gradOutputFormats(0), gradInputShape, gradOutputLayout) - } else if (gradInputLayout == Memory.Format.nhwc) { - // remind: if format of gradInput MemoryData is nhwc, its shape should be gradOutput shape + } else if (gradInputLayout == Memory.Format.nhwc || gradInputLayout == Memory.Format.ntc) { + // remind: if format of gradInput MemoryData is nhwc or ntc, + // its shape should be gradOutput shape realgradInput = initMemory(_gradInputFormats(0), gradOutputShape, gradInputLayout) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala index 5eb60ca8b5b..b9a369e580d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala @@ -146,6 +146,60 @@ class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { inputNHWC should be(grad) } + "Reorder from ntc to tnc" should "be correct" in { + val shapeNTC = Array(4, 3, 7) + val shapeTNC = Array(3, 4, 7) + + // for tnc case, users + val inputFormats = HeapData(shapeNTC, Memory.Format.ntc) + val outputFormats = HeapData(shapeTNC, Memory.Format.tnc) + val gradInputFormats = HeapData(shapeTNC, Memory.Format.tnc) + val gradOutputFormats = HeapData(shapeNTC, Memory.Format.ntc) + + val layer = ReorderMemory(inputFormat = inputFormats, outputFormat = outputFormats, + gradInputFormat = gradInputFormats, gradOutputFomat = gradOutputFormats) + + layer.setRuntime(new MklDnnRuntime()) + layer.initFwdPrimitives(Array(inputFormats), Phase.TrainingPhase) + layer.initBwdPrimitives(Array(gradOutputFormats), Phase.TrainingPhase) + + val input = Tensor[Float](4, 3, 7).rand() + val gradOutput = input.clone() + val output = layer.forward(input).toTensor[Float] + val grad = layer.backward(input, gradOutput) + + val inputTNC = input.transpose(1, 2).contiguous().clone() + inputTNC should be(output) + inputTNC should be(grad) + } + + "Reorder from tnc to ntc" should "be correct" in { + val shapeTNC = Array(4, 3, 7) + val shapeNTC = Array(3, 4, 7) + + // for tnc case, users + val inputFormats = HeapData(shapeTNC, Memory.Format.tnc) + val outputFormats = HeapData(shapeNTC, Memory.Format.ntc) + val gradInputFormats = HeapData(shapeNTC, Memory.Format.ntc) + val gradOutputFormats = HeapData(shapeTNC, Memory.Format.tnc) + + val layer = ReorderMemory(inputFormat = inputFormats, outputFormat = outputFormats, + gradInputFormat = gradInputFormats, gradOutputFomat = gradOutputFormats) + + layer.setRuntime(new MklDnnRuntime()) + layer.initFwdPrimitives(Array(inputFormats), Phase.TrainingPhase) + layer.initBwdPrimitives(Array(gradOutputFormats), Phase.TrainingPhase) + + val input = Tensor[Float](4, 3, 7).rand() + val gradOutput = input.clone() + val output = layer.forward(input).toTensor[Float] + val grad = layer.backward(input, gradOutput) + + val inputNTC = input.transpose(1, 2).contiguous().clone() + inputNTC should be(output) + inputNTC should be(grad) + } + "Reorder from nchw to nhwc" should "be correct" in { val shapeNCHW = Array(4, 3, 7, 7) val shapeNHWC = Array(4, 7, 7, 3) From 5f9970456f669a97c4b0a4c4a672ec8cb25cff3c Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 3 Jul 2019 16:15:18 +0800 Subject: [PATCH 0925/1065] support ntc in dnn layer (#2847) * support ntc in dnn layer * meet pr comments --- .../analytics/bigdl/dllib/nn/Linear.scala | 7 ++++ .../bigdl/dllib/nn/LookupTable.scala | 8 +++- .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 7 +++- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 3 +- .../bigdl/dllib/nn/mkldnn/Dropout.scala | 14 ++++--- .../bigdl/dllib/nn/mkldnn/Output.scala | 16 ++++++-- .../dllib/utils/intermediate/IRGraph.scala | 5 ++- .../bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala | 37 ++++++++++++++++++- 8 files changed, 82 insertions(+), 15 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala index f3497cce49b..792c20ae2ea 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Linear.scala @@ -215,6 +215,13 @@ class Linear[T: ClassTag]( override def toString(): String = { s"${getPrintName}($inputSize -> $outputSize)" } + + override def computeOutputShape(inputShape: Shape): Shape = { + val _inputSize = inputShape.toSingle().toArray + if (_inputSize.length == 1) { + Shape(outputSize) + } else Shape(_inputSize(0), outputSize) + } } object Linear extends quantized.Quantizable { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala index cee2de7a970..bbf76473c88 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/LookupTable.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.optim.Regularizer import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils.{Shape, T, Table} import scala.reflect.ClassTag @@ -297,6 +297,12 @@ class LookupTable[T: ClassTag] paddingValue, maxNorm, normType) state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b) } + override def computeOutputShape(inputShape: Shape): Shape = { + val _inputSize = inputShape.toSingle().toArray + if (_inputSize.length == 2) { + Shape(Array(_inputSize(0), _inputSize(1), nOutput)) + } else Shape(Array(_inputSize(0), nOutput)) + } } object LookupTable { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index 1903600b7dc..7af195bc8a1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -66,7 +66,12 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, @transient private var initEnv: Boolean = false private def inferInputFormats(inputs: Array[MemoryData]): Array[MemoryData] = { - inputs.map(in => HeapData(in.shape, getFormats(in.shape.length))) + inputs.map(in => { + if (in.layout == Memory.Format.tnc) { + val size = in.shape + HeapData(Array(size(1), size(0), size(2)), Memory.Format.ntc) + } else HeapData(in.shape, getFormats(in.shape.length)) + }) } private def inferOutputFormats(inputs: Array[MemoryData]): Array[MemoryData] = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 88b7d2e8002..37f9f979bd3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -55,7 +55,8 @@ class DnnGraph( val out = output.toTensor[Float] // for grey image, input should be 3 dims and the first dim should be batch size // for non grey image, input should be 4 dims and the first dim should be batch size - require(in.nDimension() == 4 || in.nDimension() == 3, + // for rnn model, input should be 2 dims and the first dim should be batch size + require(in.nDimension() == 4 || in.nDimension() == 3 || in.nDimension() == 2, s"only support input with 4 dimension or 3 dimension, but get ${in.nDimension()}") if (in.size(1) != out.size(1)) out.narrow(1, 1, in.size(1)) else output } else output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala index 67f178e2bb3..fae07f0ab9a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Dropout.scala @@ -28,17 +28,19 @@ class Dropout( private val dropout = NNDropout[Float](initP, inplace, scale) private var mask: DnnTensor[Float] = _ - private def format(shape: Array[Int]): Int = { + private def format(shape: Array[Int], layout: Int): Int = { shape.length match { case 2 => Memory.Format.nc + // reminder: for 3 dimension, we should keep original layout (ntc or tnc) + case 3 => layout case 4 => Memory.Format.nchw case _ => throw new UnsupportedOperationException(s"${getName()} unsupported input shape") } } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - _inputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) - _outputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) + _inputFormats = inputs.map(x => HeapData(x.shape, format(x.shape, x.layout))) + _outputFormats = inputs.map(x => HeapData(x.shape, format(x.shape, x.layout))) // we should genereate the primitives here, otherwise the initTensor can't get the padding shape _outputFormats.map(_.getPrimitive(runtime)) output = initTensor(_outputFormats.head) @@ -46,9 +48,9 @@ class Dropout( } override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { - _gradOutputFormats = grad.map(x => HeapData(x.shape, format(x.shape))) - _gradOutputFormatsForWeight = grad.map(x => HeapData(x.shape, format(x.shape))) - _gradInputFormats = grad.map(x => HeapData(x.shape, format(x.shape))) + _gradOutputFormats = grad.map(x => HeapData(x.shape, format(x.shape, x.layout))) + _gradOutputFormatsForWeight = grad.map(x => HeapData(x.shape, format(x.shape, x.layout))) + _gradInputFormats = grad.map(x => HeapData(x.shape, format(x.shape, x.layout))) _gradInputFormats.map(_.getPrimitive(runtime)) gradInput = initTensor(_gradInputFormats.head) (_gradOutputFormats, _gradInputFormats) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala index 10904342686..42765b0ea90 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala @@ -37,14 +37,21 @@ class Output(outputLayOut: Int = Memory.Format.nc, if (outLayout == Memory.Format.nhwc && inLayout != Memory.Format.nhwc) { // nchw* -> nhwc Array(inShape(0), inShape(2), inShape(3), inShape(1)) + } else if (outLayout == Memory.Format.tnc && inLayout == Memory.Format.ntc) { + // ntc -> tnc + Array(inShape(1), inShape(0), inShape(2)) + } else if (outLayout == Memory.Format.ntc && inLayout == Memory.Format.tnc) { + // tnc -> ntc + Array(inShape(1), inShape(0), inShape(2)) } else inShape outputShape } override private[bigdl] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { require(inputs.length == 1, "Only accept one tensor as input") - require(inputs(0).shape.length == 4 || inputs(0).shape.length == 2, - s"Only support input with 2 or 4 dimentions, but get ${inputs(0).shape.length}") + require(inputs(0).shape.length == 4 || inputs(0).shape.length == 2 + || inputs(0).shape.length == 3, + s"Only support input with 2 or 3 or 4 dimentions, but get ${inputs(0).shape.length}") val outputShape = getShape(inputs(0).layout, inputs(0).shape, _outputLayOut) // remind: output memory storage should be heapData @@ -61,8 +68,9 @@ class Output(outputLayOut: Int = Memory.Format.nc, override private[bigdl] def initBwdPrimitives(grads: Array[MemoryData], phase: Phase) = { require(grads.length == 1, "Only accept one tensor as input") - require(grads(0).shape.length == 4 || grads(0).shape.length == 2, - s"Only support gradOutput with 2 or 4 dimentions, but get ${grads(0).shape.length}") + require(grads(0).shape.length == 4 || grads(0).shape.length == 2 + || grads(0).shape.length == 3, + s"Only support gradOutput with 2 or 3 or 4 dimentions, but get ${grads(0).shape.length}") val outputShape = getShape(grads(0).layout, grads(0).shape, _gradOutputLayout) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index a366a29e0cc..5528b884d74 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -141,7 +141,10 @@ private[bigdl] class IRGraph[T: ClassTag]( if (input.isInstanceOf[Tensor[T]]) { // todo: handle for 3 dimensions, expand 3 dims to 4 dims val size = input.toTensor[T].size() - val sizeNew = if (size.length == 3) Array(size(0), 1, size(1), size(2)) else size + val sizeNew = if (size.length == 3 && inputFormats(0) != Memory.Format.ntc + && inputFormats(0) != Memory.Format.tnc) { + Array(size(0), 1, size(1), size(2)) + } else size inputMemory(0) = HeapData(sizeNew, inputFormats(0)) } else { val tensors = input.toTable diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala index ee5c46602dd..b8b8c1ed370 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraphSpec.scala @@ -18,7 +18,8 @@ package com.intel.analytics.bigdl.nn.mkldnn import breeze.linalg.Axis._1 import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.example.languagemodel.PTBModel +import com.intel.analytics.bigdl.mkl.{AlgKind, Direction, Memory} import com.intel.analytics.bigdl.models.lenet.LeNet5 import com.intel.analytics.bigdl.models.resnet.ResNet.{DatasetType, ShortcutType} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} @@ -311,4 +312,38 @@ class DnnGraphSpec extends FlatSpec with Matchers with BeforeAndAfter { Engine.setEngineType(MklBlas) } } + + "DnnGraph with ntc" should "work correct" in { + val vocabSize = 10001 + val hiddenSize = 256 + val numLayers = 1 + val batchSize = 8 + val seqLength = 16 + val inputSize = vocabSize + val outputSize = vocabSize + val f = AlgKind.EltwiseTanh + val direction = Direction.UnidirectionalLeft2Right + var i = 2 + + val inputShape = Array[Int](batchSize, seqLength) + val input = mkldnn.Input(inputShape, Memory.Format.nc).inputs() + val embeddingLookup = BlasWrapper(LookupTable[Float](inputSize, hiddenSize)).inputs(input) + val lstm = mkldnn.RNN(AlgKind.VanillaLstm, hiddenSize, hiddenSize, f = f, direction = direction) + .inputs(embeddingLookup) + val linear = BlasWrapper(TimeDistributed[Float](nn.Linear[Float](hiddenSize, outputSize))) + .inputs(lstm) + val output = mkldnn.Output(Memory.Format.ntc).inputs(linear) + + val dnn = DnnGraph(Array(input), Array(output)) + dnn.compile(Phase.TrainingPhase) + + val inputTensor = Tensor[Float](batchSize, seqLength).apply1(n => { + i += 1 + i + }) + val gradOutput = Tensor[Float](batchSize, seqLength, outputSize).rand() + + dnn.forward(inputTensor) + dnn.backward(inputTensor, gradOutput) + } } From 0db8fff344c8f68224c5eb6a833f052c9028c3a6 Mon Sep 17 00:00:00 2001 From: majing921201 <1834475657@qq.com> Date: Sun, 7 Jul 2019 07:54:02 +0800 Subject: [PATCH 0926/1065] [WIP]Add beam search feature in transformer model (#2834) * add beam search feature * Update beam search feature and unit test * add symbolToLogits function set check * update clearState and add serial test * add SequenceBeamSearch to python layers * add createSequenceBeamSearch method to python api --- .../bigdl/dllib/nn/SequenceBeamSearch.scala | 585 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 17 + .../dllib/nn/SequenceBeamSearchSpec.scala | 83 +++ 3 files changed, 685 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearch.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearchSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearch.scala new file mode 100644 index 00000000000..d258cb3199f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearch.scala @@ -0,0 +1,585 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.tensor._ +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + +/** + * Beam search to find the translated sequence with the highest probability. + * @param vocabSize size of tokens + * @param beamSize number of beams + * @param alpha defining the strength of length normalization + * @param maxDecodeLength maximum length to decoded sequence + * @param eosID id of eos token, used to determine when a sequence has finished + * @param numHiddenLayers number of hidden layers + * @param hiddenSize size of hidden layer + */ +class SequenceBeamSearch[T: ClassTag]( + val vocabSize: Int, + val beamSize: Int, + val alpha: Float, + val maxDecodeLength: Int, + val eosID: Float, + val numHiddenLayers: Int, + val hiddenSize: Int)(implicit ev: TensorNumeric[T]) + extends AbstractModule[Table, Activity, T] { + + private val inf = 1e7f * (-1) + private var batchSize = 0 + private val newFinishedFlags = Tensor[T] + private var aliveLogProbs = Tensor[T] + private var finishedSeq = Tensor[T] + private var aliveSeq = Tensor[T] + private var finishedFlags = Tensor[Boolean] + private val finishedFlagsSeq = Tensor[T] + private var finishedScores = Tensor[T] + private val gatherTensor = Tensor[T] + private val topkSeq = Tensor[T] + private val topkLogProbs = Tensor[T] + private val topkScore = Tensor[T] + private val topkFlags = Tensor[T] + private var symbolToLogits: (Tensor[T], Int, Int, Tensor[T], Tensor[T], List[Tensor[T]], + List[Tensor[T]]) => (Tensor[T], Tensor[T], Tensor[T], List[Tensor[T]], List[Tensor[T]]) = null + private val topkEncoder = Tensor[T] + private val topkAttentionBias = Tensor[T] + private var topkLayerK: List[Tensor[T]] = List() + private var topkLayerV: List[Tensor[T]] = List() + for (i <- 1 to numHiddenLayers) { + val tensor1 = Tensor[T] + val tensor2 = Tensor[T] + topkLayerK ++= List(tensor1) + topkLayerV ++= List(tensor2) + } + + private def expandDim(tensor: Tensor[T], axis: Int): Tensor[T] = { + val shape = tensor.size() + val newShape = shape.toBuffer + newShape.insert(axis, 1) + tensor.reshape(newShape.toArray) + } + + // Tiles a given tensor by beam_size. + private def extendBeamSize(t: Tensor[T], beamSize: Int): Tensor[T] = { + val tensor = expandDim(t, 1) + val tileDim = new Array[Int](tensor.dim()).map(a => a + 1) + tileDim(1) = beamSize + tensor.repeatTensor(tileDim) + } + + private def lengthNormalization(alpha: Float, length: Int): T = { + ev.pow(ev.fromType[Double](5.0 + length / 6.0), ev.fromType[Float](alpha)) + } + + private def boolToFloat(b: Boolean): T = { + if (b) ev.one + else ev.zero + } + + private def floatToBool(f: T): Boolean = { + if (f == 1.0) true + else false + } + + // Computes the "logical or" of elements across dimensions of a tensor. + private def reduceAny(tensor: Tensor[Boolean]): Tensor[T] = { + val tSize = tensor.size() + val outputs = Tensor[T](tSize(0)) + for (i <- 1 to tSize(0)) { + var valueAny = false + for (j <- 1 to tSize(1)) { + valueAny = valueAny || tensor.valueAt(i, j) + } + outputs.setValue(i, boolToFloat(valueAny)) + } + outputs + } + + // Computes the "logical and" of elements across dimensions of a tensor. + private def reduceAll(tensor1: Tensor[T], tensor2: Tensor[T]): Boolean = { + val sizeT = tensor1.size() + var outputs = true + for (i <- 1 to sizeT(0)) { + outputs &&= ev.isGreater(tensor1.valueAt(i), tensor2.valueAt(i)) + } + outputs + } + + /** + * Return whether to continue the search loop. + * The loops should terminate when + * 1) when decode length has been reached, or + * 2) when the worst score in the finished sequences is better than the best + * score in the alive sequences (i.e. the finished sequences are provably + * unchanging) + * + * @param state A map with the current loop state. + * @return Boolean value with value True if loop should continue, False if loop should + * terminate. + */ + private def continueSearch(state: Map[String, Any]): Boolean = { + val i = state("CUR_INDEX").asInstanceOf[Int] + finishedFlags = state("FINISHED_FLAGS").asInstanceOf[Tensor[Boolean]] + aliveLogProbs.copy(state("ALIVE_LOG_PROBS").asInstanceOf[Tensor[T]]) + finishedScores.resizeAs(state("FINISHED_SCORES").asInstanceOf[Tensor[T]]) + .copy(state("FINISHED_SCORES").asInstanceOf[Tensor[T]]) + var notAtMaxDecodeLength = true + if (i < maxDecodeLength) { + notAtMaxDecodeLength = true + } else { + notAtMaxDecodeLength = false + } + val maxLengthNorm = lengthNormalization(alpha, maxDecodeLength) + // Get the best possible scores from alive sequences. + val bestAliveScores = aliveLogProbs.select(2, 1) / maxLengthNorm + newFinishedFlags.applyFun[Boolean](finishedFlags, x => boolToFloat(x)) + finishedScores.cmul(newFinishedFlags) + // Compute worst score in finished sequences for each batch element + var lowestFinishedScores = finishedScores.min(2)._1 + lowestFinishedScores += (reduceAny(finishedFlags) * ev.fromType[Double](-1.0) + + ev.fromType[Double](1.0)) * ev.fromType[Double](inf) + lowestFinishedScores = lowestFinishedScores.reshape(Array(lowestFinishedScores.size()(0))) + val worstFinishedScoreBetterThanBestAliveScore = + reduceAll(lowestFinishedScores, bestAliveScores) + notAtMaxDecodeLength && (!worstFinishedScoreBetterThanBestAliveScore) + } + + // Reshapes first two dimensions in to single dimension. + private def flattenBeamDim(tensor: Tensor[T]): Tensor[T] = { + val shape = tensor.size() + val newShape = shape.toBuffer + newShape(0) = shape(0) * shape(1) + newShape.remove(1) + tensor.reshape(newShape.toArray) + } + + // Reshapes first dimension back to [batch_size, beam_size]. + private def unFlattenBeamDim(tensor: Tensor[T], batchSize: Int, beamSize: Int): Tensor[T] = { + val shape = tensor.size() + val newShape = shape.toBuffer + newShape(0) = batchSize + newShape.insert(1, beamSize) + tensor.reshape(newShape.toArray) + } + + // logits - log(sum(exp(logits))) + private def logProbFromLogits(logits: Tensor[T]): Tensor[T] = { + val shape = logits.size() + val getExp = Tensor[T](shape) + getExp.applyFun[T](logits, x => ev.exp(x)) + val getSumExp = getExp.sum(3) + val getLogSumExp = Tensor[T](getSumExp.size()) + getLogSumExp.applyFun[T](getSumExp, x => ev.log(x)) + logits - getLogSumExp.repeatTensor(Array(1, 1, shape(2))) + } + + // Gather slices from tensor into outputs with shape specified by indices. + private def gatherNd(tensor: Tensor[T], indices: Tensor[T], outputs: Tensor[T]): Tensor[T] = { + val shape1 = tensor.size() + val shape2 = indices.size() + var slices = new Array[T](0) + if (shape1.length == 2) { + outputs.resize(shape2(0), shape2(1)) + slices = new Array[T](shape2(0) * shape2(1)) + for (i <- 1 to shape2(0)) { + for (j <- 1 to shape2(1)) { + slices((i - 1) * shape2(1) + j - 1) = tensor.valueAt(ev.toType[Int](ev.plus + (indices.valueAt(i, j, 1), ev.fromType[Float](1.0f))), ev.toType[Int] + (ev.plus(indices.valueAt(i, j, 2), ev.fromType[Float](1.0f)))) + } + } + } else if (shape1.length == 3) { + outputs.resize(shape2(0), shape2(1), shape1(2)) + for (i <- 1 to shape2(0)) { + for (j <- 1 to shape2(1)) { + slices ++= tensor + .select(2, ev.toType[Int](ev.plus(indices.valueAt(i, j, 2), ev.fromType[Float](1.0f)))) + .select(1, ev.toType[Int](ev.plus(indices.valueAt(i, j, 1), ev.fromType[Float](1.0f)))) + .toArray() + } + } + } else if (shape1.length == 4) { + outputs.resize(shape2(0), shape2(1), shape1(2), shape1(3)) + for (i <- 1 to shape2(0)) { + for (j <- 1 to shape2(1)) { + slices ++= tensor + .select(2, ev.toType[Int](ev.plus(indices.valueAt(i, j, 2), ev.fromType[Float](1.0f)))) + .select(1, ev.toType[Int](ev.plus(indices.valueAt(i, j, 1), ev.fromType[Float](1.0f)))) + .reshape(Array(shape1(2) * shape1(3)))toArray() + } + } + } else if (shape1.length == 5) { + outputs.resize(shape2(0), shape2(1), shape1(2), shape1(3), shape1(4)) + for (i <- 1 to shape2(0)) { + for (j <- 1 to shape2(1)) { + slices ++= tensor + .select(2, ev.toType[Int](ev.plus(indices.valueAt(i, j, 2), ev.fromType[Float](1.0f)))) + .select(1, ev.toType[Int](ev.plus(indices.valueAt(i, j, 1), ev.fromType[Float](1.0f)))) + .reshape(Array(shape1(2) * shape1(3) * shape1(4)))toArray() + } + } + } + val outputData = outputs.storage().array() + val outputOffset = outputs.storageOffset() - 1 + for(i <- slices.indices) { + outputData(outputOffset + i) = slices(i) + } + shape1(0) = shape2(0) + shape1(1) = shape2(1) + outputs + } + + // Concatenates tensor1 and tensor2 along one dimension. + private def concat(tensor1: Tensor[T], tensor2: Tensor[T], dim: Int): Tensor[T] = { + val shape1 = tensor1.size() + val shape2 = tensor2.size() + val array1 = tensor1.reshape(Array(shape1.product)).toArray() + val array2 = tensor2.reshape(Array(shape2.product)).toArray() + var outputsArray = new Array[T](0) + var concatLength1 = 1 + var concatLength2 = 1 + for (i <- dim - 1 until shape1.length) { + concatLength1 *= shape1(i) + } + for (i <- dim - 1 until shape2.length) { + concatLength2 *= shape2(i) + } + val group1 = array1.grouped(concatLength1) + val group2 = array2.grouped(concatLength2) + while (group1.hasNext) { + + outputsArray ++= group1.next() + outputsArray ++= group2.next() + } + val newShape = shape1 + newShape(dim - 1) = shape1(dim - 1) + shape2(dim - 1) + Tensor(outputsArray, newShape) + } + + // Gather beams from tensors. + private def gatherBeams(nested: Tensor[T], beamIndices: Tensor[T], + batchSize: Int, newBeamSize: Int): Tensor[T] = { + val batchPos = (Tensor.range(0, batchSize * newBeamSize - 1, 1) / ev.fromType[Int](newBeamSize)) + .reshape(Array(batchSize, newBeamSize)) + val newBatchPos = batchPos.apply1(e => ev.floor(e)) + val coordinates = Tensor[T](batchSize, newBeamSize, 2) + for (i <- 1 to batchSize) { + for (j <- 1 to newBeamSize) { + coordinates.setValue(i, j, 1, newBatchPos.valueAt(i, j)) + coordinates.setValue(i, j, 2, beamIndices.valueAt(i, j)) + } + } + gatherNd(nested, coordinates.asInstanceOf[Tensor[T]], gatherTensor) + } + + // Gather top beams from nested structure. + private def gatherTopkBeams(tensor: Tensor[T], scoreOrLogProb: Tensor[T], + batchSize: Int, beamSize: Int): Tensor[T] = { + val (_, topkIndexes) = scoreOrLogProb.topk(beamSize, -1, false) + topkIndexes.apply1(e => ev.minus(e, ev.fromType[Float](1.0f))) + gatherBeams(tensor, topkIndexes, batchSize, beamSize) + } + + def setLogitFn(fn: (Tensor[T], Int, Int, Tensor[T], Tensor[T], List[Tensor[T]], + List[Tensor[T]]) => (Tensor[T], Tensor[T], Tensor[T], List[Tensor[T]], List[Tensor[T]])): + SequenceBeamSearch[T] = { + symbolToLogits = fn + this + } + + /** + * Grow alive sequences by one token, and collect top 2*beam_size sequences. + * 2*beam_size sequences are collected because some sequences may have reached + * the EOS token. 2*beam_size ensures that at least beam_size sequences are + * still alive. + * @param state A map with the current loop state. + * @return newSeq Top 2*beam_size sequences [batch_size, 2 * beam_size, cur_index + 1] + * topkLogProbs probabilities of returned sequences [batch_size, 2 * beam_size] + */ + private def growAliveSeq(state: Map[String, Any]): (Tensor[T], Tensor[T]) = { + val i = state("CUR_INDEX").asInstanceOf[Int] + aliveSeq = state("ALIVE_SEQ").asInstanceOf[Tensor[T]] + aliveLogProbs = state("ALIVE_LOG_PROBS").asInstanceOf[Tensor[T]] + val aliveEncoder = state("ENCODER").asInstanceOf[Tensor[T]] + val aliveAttentionsBias = state("ATTENTION_BIAS").asInstanceOf[Tensor[T]] + val aliveLayerK = state("LAYERK").asInstanceOf[List[Tensor[T]]] + val aliveLayerV = state("LAYERV").asInstanceOf[List[Tensor[T]]] + val beamsToKeep = 2 * beamSize + val flatIds = flattenBeamDim(aliveSeq) + val flatEncoder = flattenBeamDim(aliveEncoder) + val flatAttentionBias = flattenBeamDim(aliveAttentionsBias) + val flatLayerK = aliveLayerK.map(e => flattenBeamDim(e)) + val flatLayerV = aliveLayerV.map(e => flattenBeamDim(e)) + var (flatLogits, newFlatEncoder, newAttentionBias, newFlatLayerK, + newFlatLayerV) = symbolToLogits(flatIds, i, maxDecodeLength, flatEncoder, + flatAttentionBias, flatLayerK, flatLayerV) + newFlatEncoder = unFlattenBeamDim(newFlatEncoder, batchSize, beamSize) + newAttentionBias = unFlattenBeamDim(newAttentionBias, batchSize, beamSize) + newFlatLayerK = newFlatLayerK.map(e => unFlattenBeamDim(e, batchSize, beamSize)) + newFlatLayerV = newFlatLayerV.map(e => unFlattenBeamDim(e, batchSize, beamSize)) + val logits = unFlattenBeamDim(flatLogits, batchSize, beamSize) + val candidateLogProbs = logProbFromLogits(logits) + val logProbs = candidateLogProbs + expandDim(aliveLogProbs, 2) + .repeatTensor(Array(1, 1, vocabSize)) + val flatLogProbs = logProbs.reshape(Array(logProbs.size().product + / (beamSize * vocabSize), beamSize * vocabSize)) + val (topkLogProbs, topkIndices) = flatLogProbs.topk(beamsToKeep, -1, false) + topkIndices.apply1(e => ev.minus(e, ev.fromType[Float](1.0f))) + val topkBeamIndices = (topkIndices / ev.fromType[Int](vocabSize)).apply1(e => ev.floor(e)) + // Extract the alive sequences that generate the highest log probabilities + var gatherTmp = gatherBeams(aliveSeq, topkBeamIndices, batchSize, beamsToKeep) + topkSeq.resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherBeams(newFlatEncoder, topkBeamIndices, batchSize, beamsToKeep) + topkEncoder.resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherBeams(newAttentionBias, topkBeamIndices, batchSize, beamsToKeep) + topkAttentionBias.resizeAs(gatherTmp).copy(gatherTmp) + for (i <- 0 until numHiddenLayers) { + gatherTmp = gatherBeams(newFlatLayerK(i), topkBeamIndices, batchSize, beamsToKeep) + topkLayerK(i).resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherBeams(newFlatLayerV(i), topkBeamIndices, batchSize, beamsToKeep) + topkLayerV(i).resizeAs(gatherTmp).copy(gatherTmp) + } + var topkIds = topkIndices.apply1(e => ev.fromType[Int](ev.toType[Int](e) % vocabSize)) + topkIds = expandDim(topkIds, 2) + val newSeq = concat(topkSeq, topkIds, 3) + (newSeq, topkLogProbs) + } + + /** + * Gather the top k sequences that are still alive. + * @param newSeq New sequences generated by growing the current alive sequences + * @param newLogProbs Log probabilities of new sequences + * @return map with alive keys + */ + private def growNewAliveState(newSeq: Tensor[T], newLogProbs: Tensor[T]): Map[String, Any] = { + finishedFlagsSeq.copy(newSeq.select(3, newSeq.size()(2))) + finishedFlagsSeq.apply1(x => boolToFloat(ev.toType[Float](x) == eosID)) + val newLogProbs1 = newLogProbs + finishedFlagsSeq * ev.fromType[Double](inf) + var gatherTmp = gatherTopkBeams(newSeq, newLogProbs1, batchSize, beamSize) + aliveSeq.resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherTopkBeams(newLogProbs1, newLogProbs1, batchSize, beamSize) + topkLogProbs.resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherTopkBeams(topkEncoder, newLogProbs1, batchSize, beamSize) + topkEncoder.resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherTopkBeams(topkAttentionBias, newLogProbs1, batchSize, beamSize) + topkAttentionBias.resizeAs(gatherTmp).copy(gatherTmp) + for (i <- 0 until numHiddenLayers) { + gatherTmp = gatherTopkBeams(topkLayerK(i), newLogProbs1, batchSize, beamSize) + topkLayerK(i).resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherTopkBeams(topkLayerV(i), newLogProbs1, batchSize, beamSize) + topkLayerV(i).resizeAs(gatherTmp).copy(gatherTmp) + } + Map("ALIVE_SEQ" -> aliveSeq, "ALIVE_LOG_PROBS" -> topkLogProbs, + "ENCODER" -> topkEncoder, "ATTENTION_BIAS" -> topkAttentionBias, + "LAYERK" -> topkLayerK, "LAYERV" -> topkLayerV) + } + + /** + * Combine new and old finished sequences, and gather the top k sequences. + * @param state A map with the current loop state. + * @param newSeq New sequences generated by growing the current alive sequences + * @param newLogProbs Log probabilities of new sequences + * @return map with finished keys + */ + private def getNewFinishedState(state: Map[String, Any], newSeq: Tensor[T], + newLogProbs: Tensor[T]): Map[String, Any] = { + val i = state("CUR_INDEX").asInstanceOf[Int] + finishedSeq = state("FINISHED_SEQ").asInstanceOf[Tensor[T]] + finishedScores = state("FINISHED_SCORES").asInstanceOf[Tensor[T]] + finishedFlags = state("FINISHED_FLAGS").asInstanceOf[Tensor[Boolean]] + // append a column of 0-ids to finished_seq to increment the length. + finishedSeq = concat(finishedSeq, Tensor[T](batchSize, beamSize, 1), 3) + val lengthNorm = lengthNormalization(alpha, i) + var newScores = newLogProbs / lengthNorm + // Set the scores of the still-alive seq in new_seq to large negative values. + newScores += (Tensor(finishedFlagsSeq.size()).fill(ev.fromType[Float](1.0f)) + - finishedFlagsSeq) * ev.fromType[Float](inf) + // Combine sequences, scores, and flags. + finishedSeq = concat(finishedSeq, newSeq, 2) + finishedScores = concat(finishedScores, newScores, 2) + var finishedFlags1 = Tensor[T](finishedFlags.size()) + finishedFlags1.applyFun[Boolean](finishedFlags, x => boolToFloat(x)) + finishedFlags1 = concat(finishedFlags1, finishedFlagsSeq, 2) + var gatherTmp = gatherTopkBeams(finishedSeq, finishedScores, batchSize, beamSize) + topkSeq.resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherTopkBeams(finishedScores, finishedScores, batchSize, beamSize) + topkScore.resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherTopkBeams(finishedFlags1, finishedScores, batchSize, beamSize) + topkFlags.resizeAs(gatherTmp).copy(gatherTmp) + val topFinishedFlags1 = topkFlags.reshape(Array(topkFlags.size().product)) + .toArray() + val outputFlag = ArrayBuffer[Boolean]() + for (ele <- topFinishedFlags1) { + outputFlag.append(floatToBool(ele)) + } + finishedFlags = Tensor(outputFlag.toArray, topkFlags.size()) + finishedSeq.resizeAs(topkSeq).copy(topkSeq) + Map("FINISHED_SEQ" -> finishedSeq, "FINISHED_SCORES" -> topkScore, + "FINISHED_FLAGS" -> finishedFlags) + } + + /** + * Grow alive sequences by a single ID. Sequences that have reached the EOS + * token are marked as finished. The alive and finished sequences with the + * highest log probabilities and scores are returned. + */ + private def searchStep(state: Map[String, Any]): Map[String, Any] = { + val (newSeq, newLogProbs) = growAliveSeq(state) + val aliveState = growNewAliveState(newSeq, newLogProbs) + val finishedState = getNewFinishedState(state, newSeq, newLogProbs) + val newState: Map[String, Any] = Map("CUR_INDEX" -> (state("CUR_INDEX") + .asInstanceOf[Int] + 1)) ++ aliveState ++ finishedState + newState + } + + // return initial state map + private def createInitialState(encoderOutputs: Tensor[T], encoderDecoderAttentionBias: Tensor[T]): + Map[String, Any] = { + batchSize = encoderOutputs.size()(0) + newFinishedFlags.resize(batchSize, beamSize) + aliveLogProbs.resize(batchSize, beamSize) + finishedFlags.resize(batchSize, beamSize) + finishedFlagsSeq.resize(batchSize, beamSize * 2) + finishedScores.resize(batchSize, beamSize) + val curIndex = 0 + val initialID = Tensor[T](Array(batchSize)) + var initialAliveSeq = extendBeamSize(initialID, beamSize) + initialAliveSeq = expandDim(initialAliveSeq, 2) + var initialLogProbs = Tensor[T](beamSize).apply1(e => ev.fromType[Float](inf)) + initialLogProbs.setValue(1, ev.fromType[Float](0.0f)) + initialLogProbs = initialLogProbs.repeatTensor(Array(batchSize, 1)) + val aliveEncoder = extendBeamSize(encoderOutputs, beamSize) + val aliveAttentionsBias = extendBeamSize(encoderDecoderAttentionBias, beamSize) + var aliveLayerK: List[Tensor[T]] = List() + var aliveLayerV: List[Tensor[T]] = List() + for (i <- 1 to numHiddenLayers) { + val tensor1 = Tensor[T](batchSize, beamSize, 0, hiddenSize) + val tensor2 = Tensor[T](batchSize, beamSize, 0, hiddenSize) + aliveLayerK ++= List(tensor1) + aliveLayerV ++= List(tensor2) + } + val initialFinishedSeq = Tensor[T](initialAliveSeq.size()) + val initialFinishedScores = Tensor.ones[T](batchSize, beamSize) * ev.fromType[Float](inf) + val initialFinishedFlags = Tensor[Boolean](batchSize, beamSize) + + val state = Map("CUR_INDEX" -> curIndex, + "ALIVE_SEQ" -> initialAliveSeq, + "ALIVE_LOG_PROBS" -> initialLogProbs, + "ENCODER" -> aliveEncoder, + "ATTENTION_BIAS" -> aliveAttentionsBias, + "LAYERK" -> aliveLayerK, + "LAYERV" -> aliveLayerV, + "FINISHED_SEQ" -> initialFinishedSeq, + "FINISHED_SCORES" -> initialFinishedScores, + "FINISHED_FLAGS" -> initialFinishedFlags) + state + } + + // replace value in a with b according to tensor value + private def where(tensor: Tensor[T], a: Tensor[T], b: Tensor[T]): Tensor[T] = { + val arrayBool = tensor.toArray() + val shape = a.size() + for (i <- arrayBool.indices) { + if (arrayBool(i) == 0) { + if (shape.length == 3) { + for (j <- 1 to shape(1)) { + for (k <- 1 to shape(2)) { + a.setValue(i + 1, j, k, b.valueAt(i + 1, j, k)) + } + } + } else { + for (j <- 1 to shape(1)) { + a.setValue(i + 1, j, b.valueAt(i + 1, j)) + } + } + } + } + a + } + + override def updateOutput(input: Table): Activity = { + val encoderOutputs = input[Tensor[T]](1) + val encoderDecoderAttentionBias = input[Tensor[T]](2) + require(symbolToLogits != null, "symbolToLogits function is null, please set this function") + var state = createInitialState(encoderOutputs, encoderDecoderAttentionBias) + while (continueSearch(state)) { + state = searchStep(state) + } + val finishedState = state + val aliveSeq = finishedState("ALIVE_SEQ").asInstanceOf[Tensor[T]] + val aliveLogProbs = finishedState("ALIVE_LOG_PROBS").asInstanceOf[Tensor[T]] + var finishedSeq = finishedState("FINISHED_SEQ").asInstanceOf[Tensor[T]] + var finishedScores = finishedState("FINISHED_SCORES").asInstanceOf[Tensor[T]] + val finishedFlags = finishedState("FINISHED_FLAGS").asInstanceOf[Tensor[Boolean]] + finishedSeq = where(reduceAny(finishedFlags), finishedSeq, aliveSeq) + finishedScores = where(reduceAny(finishedFlags), finishedScores, aliveLogProbs) + output = T(finishedSeq, finishedScores) + output + } + + override def updateGradInput(input: Table, gradOutput: Activity): Table = { + gradInput = gradOutput.toTable + gradInput + } + + override def clearState(): this.type = { + super.clearState() + batchSize = 0 + newFinishedFlags.set() + aliveLogProbs.set() + finishedSeq.set() + aliveSeq.set() + finishedFlags.set() + finishedFlagsSeq.set() + finishedScores.set() + gatherTensor.set() + topkSeq.set() + topkLogProbs.set() + topkScore.set() + topkFlags.set() + topkEncoder.set() + topkAttentionBias.set() + topkLayerK.foreach(e => e.set()) + topkLayerV.foreach(e => e.set()) + this + } +} + +object SequenceBeamSearch { + def apply[@specialized(Float, Double) T: ClassTag]( + vocabSize: Int, + beamSize: Int, + alpha: Float, + maxDecodeLength: Int, + eosID: Float, + numHiddenLayers: Int, + hiddenSize: Int) + (implicit ev: TensorNumeric[T]): SequenceBeamSearch[T] = { + new SequenceBeamSearch[T]( + vocabSize, + beamSize, + alpha, + maxDecodeLength, + eosID, + numHiddenLayers, + hiddenSize) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index ba1d47beac5..66bf3ace72e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1165,6 +1165,23 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab SelectTable[T](dimension) } + def createSequenceBeamSearch(vocabSize: Int, + beamSize: Int, + alpha: Float, + decodeLength: Int, + eosId: Float, + numHiddenLayers: Int, + hiddenSize: Int) + : SequenceBeamSearch[T] = { + SequenceBeamSearch[T](vocabSize, + beamSize, + alpha, + decodeLength, + eosId, + numHiddenLayers, + hiddenSize) + } + def createSigmoid() : Sigmoid[T] = { Sigmoid[T]() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearchSpec.scala new file mode 100644 index 00000000000..ed38cb524e8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearchSpec.scala @@ -0,0 +1,83 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{T, Table} +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +class SequenceBeamSearchSpec extends FlatSpec with Matchers{ + "beam search" should "work correctly" in { + val batchSize = 2 + val beamSize = 3 + val vocabSize = 4 + val decodeLength = 10 + val alpha: Float = 0.0f + val eosId = 1.0f + val numHiddenLayers = 2 + val hiddenSize = 5 + val inputLength = 6 + + def symbolsToLogitsFn(Ids: Tensor[Float], i: Int, maxDecoderLen: Int, + encoder: Tensor[Float], Bias: Tensor[Float], list1: List[Tensor[Float]], + list2: List[Tensor[Float]]): + (Tensor[Float], Tensor[Float], Tensor[Float], List[Tensor[Float]], List[Tensor[Float]]) = { + val tensor = Tensor(Array(0.14f, 0.62f, 0.02f, 0.93f, + 0.59f, 0.48f, 0.27f, 0.70f, + 0.11f, 0.30f, 0.35f, 0.15f, + 0.67f, 0.39f, 0.33f, 0.01f, + 0.44f, 0.52f, 0.45f, 0.23f, + 0.75f, 0.79f, 0.26f, 0.47f), Array(6, 4)) + val encoder1 = encoder + Tensor[Float](encoder.size()).rand() + val Bias1 = Bias + Tensor[Float](Bias.size()).rand() + val batch_beam = encoder.size()(0) + list1.map(e => Tensor[Float](batch_beam, 1, hiddenSize).rand()) + list2.map(e => Tensor[Float](batch_beam, 1, hiddenSize).rand()) + (tensor, encoder1, Bias1, list1, list2) + } + + val beamSearch = new SequenceBeamSearch[Float](vocabSize, + beamSize, alpha, decodeLength, eosId, numHiddenLayers, hiddenSize) + beamSearch.setLogitFn(symbolsToLogitsFn) + val encodeOutputs = Tensor[Float](batchSize, inputLength, hiddenSize).rand() + val encoderDecoderAttentionBias = Tensor[Float](batchSize, 1, 1, inputLength).rand() + val output = beamSearch.forward(T(encodeOutputs, encoderDecoderAttentionBias)) + .asInstanceOf[Table] + val outputSeq = Tensor[Float].resizeAs(output[Tensor[Float]](1)).copy(output[Tensor[Float]](1)) + val outputScore = Tensor[Float].resizeAs(output[Tensor[Float]](2)) + .copy(output[Tensor[Float]](2)) + beamSearch.clearState() + val expectedOutputSeq = Tensor[Float]( + T(T(T(0.0, 1.0, 0.0, 0.0, 0.0), + T(0.0, 3.0, 1.0, 0.0, 0.0), + T(0.0, 3.0, 3.0, 1.0, 0.0)), + T(T(0.0, 1.0, 0.0, 0.0, 0.0), + T(0.0, 0.0, 1.0, 0.0, 0.0), + T(0.0, 2.0, 1.0, 0.0, 0.0)))) + val expectedOutputScore = Tensor[Float]( + T(T(-1.2615868, -2.2131736, -3.1647604), + T(-1.3734006, -2.4668012, -2.715382))) + outputSeq should be(expectedOutputSeq) + outputScore should be(expectedOutputScore) + } +} + +class SequenceBeamSearchSerialTest extends ModuleSerializationTest{ + override def test(): Unit = { + // skip serial test + } +} From f2d17465a71d8d1cb5cae4da5f5ef2084ed04b8e Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Wed, 10 Jul 2019 10:25:03 +0800 Subject: [PATCH 0927/1065] feat: add a property to disable omp thread affinity (#2849) --- .../scala/com/intel/analytics/bigdl/utils/ThreadPool.scala | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index a4d9fc33c26..5f6ec57449d 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -107,7 +107,9 @@ class ThreadPool(private var poolSize: Int) { MKL.setNumThreads(size) BackendMklDnn.setNumThreads(size) - Affinity.setOmpAffinity() + if (System.getProperty("bigdl.disableOmpAffinity", "false").toBoolean) { + Affinity.setOmpAffinity() + } })) this From be8232d2f1bc368afb70cfa0cae64e6ed7189c5e Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Wed, 10 Jul 2019 14:37:29 +0800 Subject: [PATCH 0928/1065] fix: use treeset to calc topk to upgrade the performance of DetectionOutputSSD (#2853) --- .../bigdl/dllib/tensor/DenseTensor.scala | 62 +++++++++++++++---- .../dllib/tensor/DenseTensorMathSpec.scala | 32 ++++++++++ 2 files changed, 81 insertions(+), 13 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 6604e148d4e..51762d04f05 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -16,6 +16,8 @@ package com.intel.analytics.bigdl.tensor +import java.util.Comparator + import breeze.linalg.{DenseMatrix => BrzDenseMatrix, DenseVector => BrzDenseVector} import com.intel.analytics.bigdl.mkl.MKL import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -25,6 +27,7 @@ import org.apache.spark.mllib.linalg.{DenseMatrix, DenseVector, Matrix, Vector} import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag +import scala.collection.JavaConverters._ @SerialVersionUID(5876322619614900645L) private[tensor] class DenseTensor[@specialized T: ClassTag]( @@ -962,7 +965,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } else if (DenseTensor.canFastBroadcast(this, x)) { // recursive add var i = 0 - while(i < this.size(1)) { + while (i < this.size(1)) { this.select(1, i + 1).add(x) i += 1 } @@ -1016,7 +1019,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this.storage().array(), this.storageOffset() - 1) } else { val func = new TensorFunc6[T] { - override def apply (data: Array[T], offset: Int, data1: Array[T], + override def apply(data: Array[T], offset: Int, data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = { data(offset1) = ev.plus(data1(offset1), data2(offset2)) } @@ -1028,7 +1031,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( // Puts the result of x + value * y in current tensor override def add(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] = - DenseTensorMath.cadd(this, x, value, y) + DenseTensorMath.cadd(this, x, value, y) override def add(value: T): Tensor[T] = { if (this.isContiguous()) { @@ -1053,8 +1056,8 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } else { val func = new TensorFunc4[T] { - override def apply (data1: Array[T], offset1: Int, - data2: Array[T], offset2: Int): Unit = { + override def apply(data1: Array[T], offset1: Int, + data2: Array[T], offset2: Int): Unit = { data1(offset1) = ev.minus(data1(offset1), data2(offset2)) } } @@ -1063,7 +1066,7 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( } else if (DenseTensor.canFastBroadcast(this, x)) { // recursive add var i = 0 - while(i < this.size(1)) { + while (i < this.size(1)) { this.select(1, i + 1).sub(x) i += 1 } @@ -1717,24 +1720,57 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( val indicesTensor = if (indices == null) Tensor[T]() else indices indicesTensor.resize(topKSize) + @inline + def compare(a: T, b: T): Boolean = ev.isGreater(b, a) ^ !increase + DenseTensorDimApply.dimApply3[T](this, resultTensor, indicesTensor, selectDim, (tdata, toffset, tstride, tsize, vdata, voffset, vstride, vsize, idata, ioffset, istride, isize) => { + val set = new java.util.TreeSet[(T, Int)](new Comparator[(T, Int)] { + override def compare(o1: (T, Int), o2: (T, Int)): Int = { + val ret = if (ev.isGreaterEq(o1._1, o2._1)) { + if (o1._2 == o2._2) { + 0 + } else { + 1 + } + } else { + -1 + } + + if (increase) { + -ret + } else { + ret + } + + } + }) + var i = 0 while (i < tsize) { - tmpResult(i) = (tdata(toffset + i * tstride), i + 1) + val v = tdata(toffset + i * tstride) + if (set.size() < k) { + set.add((v, i + 1)) + } else if (compare(v, set.first()._1)) { + set.remove(set.first()) + set.add((v, i + 1)) + } + i += 1 } - val sorted = tmpResult.sortWith((l, r) => - if (increase) ev.isGreater(r._1, l._1) else ev.isGreater(l._1, r._1)) + + val sorted = set.descendingIterator().asScala + i = 0 while (i < k) { + val current = sorted.next() if (sortedResult) { - vdata(voffset + i * vstride) = sorted(i)._1 - idata(ioffset + i * istride) = ev.fromType(sorted(i)._2) + vdata(voffset + i * vstride) = current._1 + idata(ioffset + i * istride) = ev.fromType(current._2) } else { - vdata(voffset + (k - i - 1) * vstride) = sorted(i)._1 - idata(ioffset + (k - i - 1) * istride) = ev.fromType(sorted(i)._2) + vdata(voffset + (k - i - 1) * vstride) = current._1 + idata(ioffset + (k - i - 1) * istride) = ev.fromType(current._2) } i += 1 } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala index 7fe820e3a81..0b556eb17d3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensorMathSpec.scala @@ -584,6 +584,13 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { i should be(Tensor(Storage(Array(1.0, 6.0, 2.0, 4.0, 3.0)))) } + "topk without increase" should "be correct for 1d tensor" in { + val t = Tensor(Storage(Array(0.0, 1.0, 5.0, 3.0, 9.0, 0.8, 6.3))) + val (v, i) = t.topk(5, increase = false) + v should be(Tensor(Storage(Array(9.0, 6.3, 5.0, 3.0, 1.0)))) + i should be(Tensor(Storage(Array(5.0, 7.0, 3.0, 4.0, 2.0)))) + } + "topk" should "be correct for 2d tensor" in { val t = Tensor(Storage(Array( 0.0, 1.0, 5.0, 3.0, 9.0, 0.8, 6.3, @@ -609,6 +616,31 @@ class DenseTensorMathSpec extends FlatSpec with Matchers { )), 1, Array(5, 5))) } + "topk without increase" should "be correct for 2d tensor" in { + val t = Tensor(Storage(Array( + 0.0, 1.0, 5.0, 3.0, 9.0, 0.8, 6.3, + 0.0, 1.0, 5.0, 3.0, 9.0, 0.8, 6.3, + 0.0, 1.0, 5.0, 3.0, 9.0, 0.8, 6.3, + 0.0, 1.0, 5.0, 3.0, 9.0, 0.8, 6.3, + 0.0, 1.0, 5.0, 3.0, 9.0, 0.8, 6.3 + )), 1, Array(5, 7)) + val (v, i) = t.topk(5, increase = false) + v should be(Tensor(Storage(Array( + 9.0, 6.3, 5.0, 3.0, 1.0, + 9.0, 6.3, 5.0, 3.0, 1.0, + 9.0, 6.3, 5.0, 3.0, 1.0, + 9.0, 6.3, 5.0, 3.0, 1.0, + 9.0, 6.3, 5.0, 3.0, 1.0 + )), 1, Array(5, 5))) + i should be(Tensor(Storage(Array( + 5.0, 7.0, 3.0, 4.0, 2.0, + 5.0, 7.0, 3.0, 4.0, 2.0, + 5.0, 7.0, 3.0, 4.0, 2.0, + 5.0, 7.0, 3.0, 4.0, 2.0, + 5.0, 7.0, 3.0, 4.0, 2.0 + )), 1, Array(5, 5))) + } + "powx(x,a)" should "return correct value" in { val t: Tensor[Double] = Tensor(Storage(Array(2.0, 3.0, 4.0))) val r: Tensor[Double] = Tensor(Storage(Array(0.0, 0.0, 0.0))) From dd4867c2a7d53c3c9af862d0aa19916b8de66f53 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Wed, 10 Jul 2019 16:36:30 +0800 Subject: [PATCH 0929/1065] fix: wrong affinity settings (#2857) --- .../main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 5f6ec57449d..7fe3512d978 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -107,7 +107,7 @@ class ThreadPool(private var poolSize: Int) { MKL.setNumThreads(size) BackendMklDnn.setNumThreads(size) - if (System.getProperty("bigdl.disableOmpAffinity", "false").toBoolean) { + if (!System.getProperty("bigdl.disableOmpAffinity", "false").toBoolean) { Affinity.setOmpAffinity() } })) From 9e7d51fa4a63722db736cef86bfced8a8aff26d7 Mon Sep 17 00:00:00 2001 From: majing921201 <1834475657@qq.com> Date: Wed, 10 Jul 2019 19:01:56 +0800 Subject: [PATCH 0930/1065] update beam search feature for interface with transformer model (#2855) * update beam search for padding value and cache structure * update python API for beam search * add comments and update python layer * modify comments format * modify comments format --- .../bigdl/dllib/nn/SequenceBeamSearch.scala | 138 ++++++++++++------ .../dllib/utils/python/api/PythonBigDL.scala | 2 + .../dllib/nn/SequenceBeamSearchSpec.scala | 36 ++--- 3 files changed, 113 insertions(+), 63 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearch.scala index d258cb3199f..727511dfeb7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearch.scala @@ -30,6 +30,7 @@ import scala.reflect.ClassTag * @param alpha defining the strength of length normalization * @param maxDecodeLength maximum length to decoded sequence * @param eosID id of eos token, used to determine when a sequence has finished + * @param paddingValue id of padding token, used at the beginning and the end * @param numHiddenLayers number of hidden layers * @param hiddenSize size of hidden layer */ @@ -39,6 +40,7 @@ class SequenceBeamSearch[T: ClassTag]( val alpha: Float, val maxDecodeLength: Int, val eosID: Float, + val paddingValue: Float, val numHiddenLayers: Int, val hiddenSize: Int)(implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Activity, T] { @@ -57,17 +59,16 @@ class SequenceBeamSearch[T: ClassTag]( private val topkLogProbs = Tensor[T] private val topkScore = Tensor[T] private val topkFlags = Tensor[T] - private var symbolToLogits: (Tensor[T], Int, Int, Tensor[T], Tensor[T], List[Tensor[T]], - List[Tensor[T]]) => (Tensor[T], Tensor[T], Tensor[T], List[Tensor[T]], List[Tensor[T]]) = null + private var symbolToLogits: (Tensor[T], Int, Int, Tensor[T], Tensor[T], Table) + => (Tensor[T], Table) = null private val topkEncoder = Tensor[T] private val topkAttentionBias = Tensor[T] - private var topkLayerK: List[Tensor[T]] = List() - private var topkLayerV: List[Tensor[T]] = List() + private var topkLayer = T() for (i <- 1 to numHiddenLayers) { val tensor1 = Tensor[T] val tensor2 = Tensor[T] - topkLayerK ++= List(tensor1) - topkLayerV ++= List(tensor2) + topkLayer("layer_" ++ i.toString ++ "_k") = tensor1 + topkLayer("layer_" ++ i.toString ++ "_v") = tensor2 } private def expandDim(tensor: Tensor[T], axis: Int): Tensor[T] = { @@ -298,8 +299,14 @@ class SequenceBeamSearch[T: ClassTag]( gatherBeams(tensor, topkIndexes, batchSize, beamSize) } - def setLogitFn(fn: (Tensor[T], Int, Int, Tensor[T], Tensor[T], List[Tensor[T]], - List[Tensor[T]]) => (Tensor[T], Tensor[T], Tensor[T], List[Tensor[T]], List[Tensor[T]])): + /** + * symbolToLogits is a function apply to compute decoding probability with all vocabulary + * it has 6 inputs: current decoding sequence, current decoding length, + * max decoding length, encoder outputs, encoder decoder attention bias, + * decoder attention values for each layer + * it returns: probability, decoder attention values for each layer + */ + def setLogitFn(fn: (Tensor[T], Int, Int, Tensor[T], Tensor[T], Table) => (Tensor[T], Table)): SequenceBeamSearch[T] = { symbolToLogits = fn this @@ -320,27 +327,40 @@ class SequenceBeamSearch[T: ClassTag]( aliveLogProbs = state("ALIVE_LOG_PROBS").asInstanceOf[Tensor[T]] val aliveEncoder = state("ENCODER").asInstanceOf[Tensor[T]] val aliveAttentionsBias = state("ATTENTION_BIAS").asInstanceOf[Tensor[T]] - val aliveLayerK = state("LAYERK").asInstanceOf[List[Tensor[T]]] - val aliveLayerV = state("LAYERV").asInstanceOf[List[Tensor[T]]] + val aliveLayer = state("LAYER").asInstanceOf[Table] val beamsToKeep = 2 * beamSize + // flatten following variables with first dimension (batchSize * beamSize) val flatIds = flattenBeamDim(aliveSeq) val flatEncoder = flattenBeamDim(aliveEncoder) val flatAttentionBias = flattenBeamDim(aliveAttentionsBias) - val flatLayerK = aliveLayerK.map(e => flattenBeamDim(e)) - val flatLayerV = aliveLayerV.map(e => flattenBeamDim(e)) - var (flatLogits, newFlatEncoder, newAttentionBias, newFlatLayerK, - newFlatLayerV) = symbolToLogits(flatIds, i, maxDecodeLength, flatEncoder, - flatAttentionBias, flatLayerK, flatLayerV) - newFlatEncoder = unFlattenBeamDim(newFlatEncoder, batchSize, beamSize) - newAttentionBias = unFlattenBeamDim(newAttentionBias, batchSize, beamSize) - newFlatLayerK = newFlatLayerK.map(e => unFlattenBeamDim(e, batchSize, beamSize)) - newFlatLayerV = newFlatLayerV.map(e => unFlattenBeamDim(e, batchSize, beamSize)) + if (i > 0) { + for (j <- 1 to numHiddenLayers) { + val tensor1 = aliveLayer("layer_" ++ j.toString ++ "_k").asInstanceOf[Tensor[T]] + val tensor2 = aliveLayer("layer_" ++ j.toString ++ "_v").asInstanceOf[Tensor[T]] + aliveLayer("layer_" ++ j.toString ++ "_k") = flattenBeamDim(tensor1) + aliveLayer("layer_" ++ j.toString ++ "_v") = flattenBeamDim(tensor2) + } + } + // get logits for the next candidate IDs for the alive sequences. + val (flatLogits, newLayer) = symbolToLogits(flatIds, i, maxDecodeLength, flatEncoder, + flatAttentionBias, aliveLayer) + // unflatten following variables with first dimension batchSize + val newFlatEncoder = unFlattenBeamDim(flatEncoder, batchSize, beamSize) + val newAttentionBias = unFlattenBeamDim(flatAttentionBias, batchSize, beamSize) + for (j <- 1 to numHiddenLayers) { + val tensor1 = newLayer("layer_" ++ j.toString ++ "_k").asInstanceOf[Tensor[T]] + val tensor2 = newLayer("layer_" ++ j.toString ++ "_v").asInstanceOf[Tensor[T]] + newLayer("layer_" ++ j.toString ++ "_k") = unFlattenBeamDim(tensor1, batchSize, beamSize) + newLayer("layer_" ++ j.toString ++ "_v") = unFlattenBeamDim(tensor2, batchSize, beamSize) + } val logits = unFlattenBeamDim(flatLogits, batchSize, beamSize) val candidateLogProbs = logProbFromLogits(logits) + // add new logProbs value to current alive sequence logProbs val logProbs = candidateLogProbs + expandDim(aliveLogProbs, 2) .repeatTensor(Array(1, 1, vocabSize)) val flatLogProbs = logProbs.reshape(Array(logProbs.size().product / (beamSize * vocabSize), beamSize * vocabSize)) + // for each batch item, get the k candidates with the highest log probabilities. val (topkLogProbs, topkIndices) = flatLogProbs.topk(beamsToKeep, -1, false) topkIndices.apply1(e => ev.minus(e, ev.fromType[Float](1.0f))) val topkBeamIndices = (topkIndices / ev.fromType[Int](vocabSize)).apply1(e => ev.floor(e)) @@ -351,13 +371,17 @@ class SequenceBeamSearch[T: ClassTag]( topkEncoder.resizeAs(gatherTmp).copy(gatherTmp) gatherTmp = gatherBeams(newAttentionBias, topkBeamIndices, batchSize, beamsToKeep) topkAttentionBias.resizeAs(gatherTmp).copy(gatherTmp) - for (i <- 0 until numHiddenLayers) { - gatherTmp = gatherBeams(newFlatLayerK(i), topkBeamIndices, batchSize, beamsToKeep) - topkLayerK(i).resizeAs(gatherTmp).copy(gatherTmp) - gatherTmp = gatherBeams(newFlatLayerV(i), topkBeamIndices, batchSize, beamsToKeep) - topkLayerV(i).resizeAs(gatherTmp).copy(gatherTmp) + for (j <- 1 to numHiddenLayers) { + val tensor1 = newLayer("layer_" ++ j.toString ++ "_k").asInstanceOf[Tensor[T]] + val tensor2 = newLayer("layer_" ++ j.toString ++ "_v").asInstanceOf[Tensor[T]] + gatherTmp = gatherBeams(tensor1, topkBeamIndices, batchSize, beamsToKeep) + topkLayer("layer_" ++ j.toString ++ "_k").asInstanceOf[Tensor[T]] + .resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherBeams(tensor2, topkBeamIndices, batchSize, beamsToKeep) + topkLayer("layer_" ++ j.toString ++ "_v").asInstanceOf[Tensor[T]] + .resizeAs(gatherTmp).copy(gatherTmp) } - var topkIds = topkIndices.apply1(e => ev.fromType[Int](ev.toType[Int](e) % vocabSize)) + var topkIds = topkIndices.apply1(e => ev.fromType[Int](ev.toType[Int](e) % vocabSize + 1)) topkIds = expandDim(topkIds, 2) val newSeq = concat(topkSeq, topkIds, 3) (newSeq, topkLogProbs) @@ -381,15 +405,17 @@ class SequenceBeamSearch[T: ClassTag]( topkEncoder.resizeAs(gatherTmp).copy(gatherTmp) gatherTmp = gatherTopkBeams(topkAttentionBias, newLogProbs1, batchSize, beamSize) topkAttentionBias.resizeAs(gatherTmp).copy(gatherTmp) - for (i <- 0 until numHiddenLayers) { - gatherTmp = gatherTopkBeams(topkLayerK(i), newLogProbs1, batchSize, beamSize) - topkLayerK(i).resizeAs(gatherTmp).copy(gatherTmp) - gatherTmp = gatherTopkBeams(topkLayerV(i), newLogProbs1, batchSize, beamSize) - topkLayerV(i).resizeAs(gatherTmp).copy(gatherTmp) + for (i <- 1 to numHiddenLayers) { + val tensor1 = topkLayer("layer_" ++ i.toString ++ "_k").asInstanceOf[Tensor[T]] + val tensor2 = topkLayer("layer_" ++ i.toString ++ "_v").asInstanceOf[Tensor[T]] + gatherTmp = gatherTopkBeams(tensor1, newLogProbs1, batchSize, beamSize) + tensor1.resizeAs(gatherTmp).copy(gatherTmp) + gatherTmp = gatherTopkBeams(tensor2, newLogProbs1, batchSize, beamSize) + tensor2.resizeAs(gatherTmp).copy(gatherTmp) } Map("ALIVE_SEQ" -> aliveSeq, "ALIVE_LOG_PROBS" -> topkLogProbs, "ENCODER" -> topkEncoder, "ATTENTION_BIAS" -> topkAttentionBias, - "LAYERK" -> topkLayerK, "LAYERV" -> topkLayerV) + "LAYER" -> topkLayer) } /** @@ -406,8 +432,9 @@ class SequenceBeamSearch[T: ClassTag]( finishedScores = state("FINISHED_SCORES").asInstanceOf[Tensor[T]] finishedFlags = state("FINISHED_FLAGS").asInstanceOf[Tensor[Boolean]] // append a column of 0-ids to finished_seq to increment the length. - finishedSeq = concat(finishedSeq, Tensor[T](batchSize, beamSize, 1), 3) - val lengthNorm = lengthNormalization(alpha, i) + finishedSeq = concat(finishedSeq, Tensor[T](batchSize, beamSize, 1) + .fill(ev.fromType[Float](paddingValue)), 3) + val lengthNorm = lengthNormalization(alpha, i + 1) var newScores = newLogProbs / lengthNorm // Set the scores of the still-alive seq in new_seq to large negative values. newScores += (Tensor(finishedFlagsSeq.size()).fill(ev.fromType[Float](1.0f)) @@ -450,7 +477,24 @@ class SequenceBeamSearch[T: ClassTag]( newState } - // return initial state map + /** + * return initial state map + * + * @param encoderOutputs Sequences after encoding + * @param encoderDecoderAttentionBias encoder decoder attention bias + * @return map with states + * CUR_INDEX: Variable storing the loop index. + * ALIVE_SEQ: Top sequences that are alive for each batch item. Alive sequences are ones + * that have not generated an EOS token. + * ALIVE_LOG_PROBS: Log probabilities of each alive sequence. + * ENCODER: Sequences after encoding + * ATTENTION_BIAS: encoder decoder attention bias + * LAYER: decoder attention values for each layer. + * FINISHED_SEQ: Top finished sequences for each batch item. + * FINISHED_SCORES: Scores for each finished sequence. Score=log probability/length norm + * FINISHED_FLAGS: Flags indicating which sequences in the finished sequences + * are finished. + */ private def createInitialState(encoderOutputs: Tensor[T], encoderDecoderAttentionBias: Tensor[T]): Map[String, Any] = { batchSize = encoderOutputs.size()(0) @@ -460,7 +504,7 @@ class SequenceBeamSearch[T: ClassTag]( finishedFlagsSeq.resize(batchSize, beamSize * 2) finishedScores.resize(batchSize, beamSize) val curIndex = 0 - val initialID = Tensor[T](Array(batchSize)) + val initialID = Tensor[T](Array(batchSize)).fill(ev.fromType[Float](paddingValue)) var initialAliveSeq = extendBeamSize(initialID, beamSize) initialAliveSeq = expandDim(initialAliveSeq, 2) var initialLogProbs = Tensor[T](beamSize).apply1(e => ev.fromType[Float](inf)) @@ -468,25 +512,23 @@ class SequenceBeamSearch[T: ClassTag]( initialLogProbs = initialLogProbs.repeatTensor(Array(batchSize, 1)) val aliveEncoder = extendBeamSize(encoderOutputs, beamSize) val aliveAttentionsBias = extendBeamSize(encoderDecoderAttentionBias, beamSize) - var aliveLayerK: List[Tensor[T]] = List() - var aliveLayerV: List[Tensor[T]] = List() + // Create aliveLayer storing decoder attention values for each layer. + val aliveLayer = T() for (i <- 1 to numHiddenLayers) { - val tensor1 = Tensor[T](batchSize, beamSize, 0, hiddenSize) - val tensor2 = Tensor[T](batchSize, beamSize, 0, hiddenSize) - aliveLayerK ++= List(tensor1) - aliveLayerV ++= List(tensor2) + val tensor1 = Tensor[T]() + val tensor2 = Tensor[T]() + aliveLayer("layer_" ++ i.toString ++ "_k") = tensor1 + aliveLayer("layer_" ++ i.toString ++ "_v") = tensor2 } val initialFinishedSeq = Tensor[T](initialAliveSeq.size()) val initialFinishedScores = Tensor.ones[T](batchSize, beamSize) * ev.fromType[Float](inf) val initialFinishedFlags = Tensor[Boolean](batchSize, beamSize) - val state = Map("CUR_INDEX" -> curIndex, "ALIVE_SEQ" -> initialAliveSeq, "ALIVE_LOG_PROBS" -> initialLogProbs, "ENCODER" -> aliveEncoder, "ATTENTION_BIAS" -> aliveAttentionsBias, - "LAYERK" -> aliveLayerK, - "LAYERV" -> aliveLayerV, + "LAYER" -> aliveLayer, "FINISHED_SEQ" -> initialFinishedSeq, "FINISHED_SCORES" -> initialFinishedScores, "FINISHED_FLAGS" -> initialFinishedFlags) @@ -557,8 +599,10 @@ class SequenceBeamSearch[T: ClassTag]( topkFlags.set() topkEncoder.set() topkAttentionBias.set() - topkLayerK.foreach(e => e.set()) - topkLayerV.foreach(e => e.set()) + for (i <- 1 to numHiddenLayers) { + topkLayer("layer_" ++ i.toString ++ "_k").asInstanceOf[Tensor[T]].set() + topkLayer("layer_" ++ i.toString ++ "_v").asInstanceOf[Tensor[T]].set() + } this } } @@ -570,6 +614,7 @@ object SequenceBeamSearch { alpha: Float, maxDecodeLength: Int, eosID: Float, + paddingValue: Float, numHiddenLayers: Int, hiddenSize: Int) (implicit ev: TensorNumeric[T]): SequenceBeamSearch[T] = { @@ -579,6 +624,7 @@ object SequenceBeamSearch { alpha, maxDecodeLength, eosID, + paddingValue, numHiddenLayers, hiddenSize) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 66bf3ace72e..0d37eec50d1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1170,6 +1170,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab alpha: Float, decodeLength: Int, eosId: Float, + paddingValue: Float, numHiddenLayers: Int, hiddenSize: Int) : SequenceBeamSearch[T] = { @@ -1178,6 +1179,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab alpha, decodeLength, eosId, + paddingValue, numHiddenLayers, hiddenSize) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearchSpec.scala index ed38cb524e8..6e7b1f2a658 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SequenceBeamSearchSpec.scala @@ -27,31 +27,33 @@ class SequenceBeamSearchSpec extends FlatSpec with Matchers{ val vocabSize = 4 val decodeLength = 10 val alpha: Float = 0.0f - val eosId = 1.0f + val eosId = 2.0f val numHiddenLayers = 2 val hiddenSize = 5 val inputLength = 6 + val paddingValue = 1.0f def symbolsToLogitsFn(Ids: Tensor[Float], i: Int, maxDecoderLen: Int, - encoder: Tensor[Float], Bias: Tensor[Float], list1: List[Tensor[Float]], - list2: List[Tensor[Float]]): - (Tensor[Float], Tensor[Float], Tensor[Float], List[Tensor[Float]], List[Tensor[Float]]) = { + encoder: Tensor[Float], Bias: Tensor[Float], Layer: Table): + (Tensor[Float], Table) = { val tensor = Tensor(Array(0.14f, 0.62f, 0.02f, 0.93f, 0.59f, 0.48f, 0.27f, 0.70f, 0.11f, 0.30f, 0.35f, 0.15f, 0.67f, 0.39f, 0.33f, 0.01f, 0.44f, 0.52f, 0.45f, 0.23f, 0.75f, 0.79f, 0.26f, 0.47f), Array(6, 4)) - val encoder1 = encoder + Tensor[Float](encoder.size()).rand() - val Bias1 = Bias + Tensor[Float](Bias.size()).rand() - val batch_beam = encoder.size()(0) - list1.map(e => Tensor[Float](batch_beam, 1, hiddenSize).rand()) - list2.map(e => Tensor[Float](batch_beam, 1, hiddenSize).rand()) - (tensor, encoder1, Bias1, list1, list2) + val outputLayer = T() + for (j <- 1 to numHiddenLayers) { + val tensor1 = Tensor[Float](batchSize*beamSize, i + 1, hiddenSize).rand() + val tensor2 = Tensor[Float](batchSize*beamSize, i + 1, hiddenSize).rand() + outputLayer("layer_" ++ j.toString ++ "_k") = tensor1 + outputLayer("layer_" ++ j.toString ++ "_v") = tensor2 + } + (tensor, outputLayer) } val beamSearch = new SequenceBeamSearch[Float](vocabSize, - beamSize, alpha, decodeLength, eosId, numHiddenLayers, hiddenSize) + beamSize, alpha, decodeLength, eosId, paddingValue, numHiddenLayers, hiddenSize) beamSearch.setLogitFn(symbolsToLogitsFn) val encodeOutputs = Tensor[Float](batchSize, inputLength, hiddenSize).rand() val encoderDecoderAttentionBias = Tensor[Float](batchSize, 1, 1, inputLength).rand() @@ -62,12 +64,12 @@ class SequenceBeamSearchSpec extends FlatSpec with Matchers{ .copy(output[Tensor[Float]](2)) beamSearch.clearState() val expectedOutputSeq = Tensor[Float]( - T(T(T(0.0, 1.0, 0.0, 0.0, 0.0), - T(0.0, 3.0, 1.0, 0.0, 0.0), - T(0.0, 3.0, 3.0, 1.0, 0.0)), - T(T(0.0, 1.0, 0.0, 0.0, 0.0), - T(0.0, 0.0, 1.0, 0.0, 0.0), - T(0.0, 2.0, 1.0, 0.0, 0.0)))) + T(T(T(1.0, 2.0, 1.0, 1.0, 1.0), + T(1.0, 4.0, 2.0, 1.0, 1.0), + T(1.0, 4.0, 4.0, 2.0, 1.0)), + T(T(1.0, 2.0, 1.0, 1.0, 1.0), + T(1.0, 1.0, 2.0, 1.0, 1.0), + T(1.0, 3.0, 2.0, 1.0, 1.0)))) val expectedOutputScore = Tensor[Float]( T(T(-1.2615868, -2.2131736, -3.1647604), T(-1.3734006, -2.4668012, -2.715382))) From d7c3b3aeaac42c4bb9f73d1b23ffd424463e5152 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 10 Jul 2019 21:52:09 +0800 Subject: [PATCH 0931/1065] Support converting blas lstm to dnn lstm (#2846) * convert from blas lstm to dnn lstm * meet pr comments --- .../example/languagemodel/PTBModel.scala | 8 +- .../bigdl/dllib/nn/BiRecurrent.scala | 8 +- .../analytics/bigdl/dllib/nn/Recurrent.scala | 2 + .../analytics/bigdl/dllib/nn/mkldnn/RNN.scala | 6 + .../bigdl/dllib/optim/DistriOptimizer.scala | 5 +- .../dllib/utils/intermediate/IRToDnn.scala | 170 ++++++++++++++++++ .../bigdl/dllib/nn/mkldnn/RNNSpec.scala | 77 ++++++++ .../utils/intermediate/IRGraphSpec.scala | 44 ++++- 8 files changed, 313 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala index df0ed8b185b..bf056a0d91b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/languagemodel/PTBModel.scala @@ -17,8 +17,10 @@ package com.intel.analytics.bigdl.example.languagemodel import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.{TimeDistributed, _} +import com.intel.analytics.bigdl.utils.{Engine, MklDnn} object PTBModel { def transformer( @@ -56,7 +58,11 @@ object PTBModel { val linear = Linear[Float](hiddenSize, outputSize) val output = TimeDistributed[Float](linear).inputs(lstm) - Graph(input, output) + val model = Graph(input, output) + model.asInstanceOf[StaticGraph[Float]].setInputFormats(Seq(Memory.Format.nc)) + model.asInstanceOf[StaticGraph[Float]].setOutputFormats(Seq(Memory.Format.ntc)) + if (Engine.getEngineType() == MklDnn) model.asInstanceOf[StaticGraph[Float]].toIRgraph() + else model } private def addLayer(inputSize: Int, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala index 5385fd7ffa6..88ee59d8116 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BiRecurrent.scala @@ -35,7 +35,7 @@ import scala.reflect.runtime._ * @tparam T numeric type */ class BiRecurrent[T : ClassTag] ( - private val merge: AbstractModule[Table, Tensor[T], T] = null, + private var merge: AbstractModule[Table, Tensor[T], T] = null, val batchNormParams: BatchNormParams[T] = null, val isSplitInput: Boolean = false) (implicit ev: TensorNumeric[T]) extends DynamicContainer[Tensor[T], Tensor[T], T] { @@ -62,8 +62,10 @@ class BiRecurrent[T : ClassTag] ( .add(Reverse[T](timeDim)) .add(revLayer) .add(Reverse[T](timeDim)))) - if (merge == null) birnn.add(CAddTable[T](true)) - else birnn.add(merge) + if (merge == null) merge = CAddTable[T](true) + birnn.add(merge) + + def getMerge(): AbstractModule[Table, Tensor[T], T] = merge override def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): this.type = { layer.add(module) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala index 456f3a41a4f..7d8dbf50f16 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Recurrent.scala @@ -77,6 +77,8 @@ class Recurrent[T : ClassTag]( private var outputBuffers: ArrayBuffer[Tensor[T]] = ArrayBuffer(Tensor()) private var minLength: Int = 0 + def getCell(): Cell[T] = topology + /** * * modules: -- preTopology diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala index eaee7ec8b65..5569e51c87f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala @@ -293,6 +293,12 @@ class RNN( updateOutputTensors = buffer.toArray } + if (isTraining()) { + weight.sync() + weight_i.sync() + bias.sync() + } + updateWithNewTensor(updateOutputTensors, 0, input) MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, updateOutputPrimitives.length, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index e37b1aa38ce..23567b2a6db 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -545,14 +545,15 @@ object DistriOptimizer extends AbstractOptimizer { .Cache[T]], ModelBroadcast[T]) = { val sc = dataset.originRDD().sparkContext val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod)) + val convertedModel = ConversionUtils.convert(model) // ensure model's parameter is compacted for getting a better performance when broadcasting - model.getParameters() + convertedModel.getParameters() // As cloneModel is using Serialization to implement deep copy, and will throw OOMError // when model's size is bigger than SerializationUtils' buffer size. So we can use // ModelBroadcast to clone model here. // Notes: All models returned by modelBroadcast.value() share the same weight&bias, while // gradWeight&gradBias is unshared. - val modelBroadcast = ModelBroadcast[T]().broadcast(sc, ConversionUtils.convert(model)) + val modelBroadcast = ModelBroadcast[T]().broadcast(sc, convertedModel) val _subModelNumber = Engine.getEngineType match { case MklBlas => coresPerNode case MklDnn => 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index 1e78f36df43..594343ede59 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.utils.intermediate import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.mkl.{AlgKind, Direction} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} import com.intel.analytics.bigdl.nn.{Module => _, _} import com.intel.analytics.bigdl.nn.mkldnn._ @@ -184,6 +185,175 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] } private def fromBlasModule(node: IRElement[Float]) : Module[Float] = { + val model = node.getOp().asInstanceOf[IRGeneralModule[Float]].model + if (model.isInstanceOf[BiRecurrent[Float]]) { + fromBiRecurrent(node) + } else if (model.isInstanceOf[Recurrent[Float]]) { + fromRecurrent(node) + } else BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) + } + + private def fromRecurrent(node: IRElement[Float]): Module[Float] = { + val model = node.getOp().asInstanceOf[IRGeneralModule[Float]] + .model.asInstanceOf[Recurrent[Float]] + val layer = model.getCell() + if (layer.isInstanceOf[LSTM[Float]] && model.batchNormParams == null) { + val lstm = layer.asInstanceOf[LSTM[Float]] + if (lstm.activation.isInstanceOf[Tanh[Float]] && + lstm.innerActivation.isInstanceOf[Sigmoid[Float]] && + lstm.p == 0.0f && + lstm.wRegularizer == null && + lstm.bRegularizer == null && + lstm.uRegularizer == null) { + val f = AlgKind.EltwiseTanh + val direction = Direction.UnidirectionalLeft2Right + val inputSize = lstm.inputSize + val hiddenSize = lstm.hiddenSize + val lstmDnn = nn.mkldnn.RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, + f, direction, layers = 1) + + // copy weight from blas lstm to dnn lstm + val lstm_n_gates = 4 + + val blasParams = model.parameters()._1 + val initWeight0 = blasParams(0) + val initBias0 = blasParams(1) + val initWeightIter0 = blasParams(2) + + var num = initWeight0.size(1) / lstm_n_gates + var gate1 = initWeight0.narrow(1, 1, num) + var gate3 = initWeight0.narrow(1, num + 1, num) + var gate2 = initWeight0.narrow(1, num * 2 + 1, num) + var gate4 = initWeight0.narrow(1, num * 3 + 1, num) + + var initWeight = Tensor[Float](lstm_n_gates, hiddenSize, inputSize) + initWeight.select(1, 1).copy(gate1) + initWeight.select(1, 2).copy(gate2) + initWeight.select(1, 3).copy(gate3) + initWeight.select(1, 4).copy(gate4) + // original Array(inputSize, lstm_n_gates, hiddenSize) + initWeight = initWeight.transpose(1, 3).transpose(2, 3) + + num = initBias0.size(1) / lstm_n_gates + gate1 = initBias0.narrow(1, 1, num) + gate3 = initBias0.narrow(1, num + 1, num) + gate2 = initBias0.narrow(1, num * 2 + 1, num) + gate4 = initBias0.narrow(1, num * 3 + 1, num) + + val initBias = Tensor[Float](lstm_n_gates, hiddenSize) + initBias.select(1, 1).copy(gate1) + initBias.select(1, 2).copy(gate2) + initBias.select(1, 3).copy(gate3) + initBias.select(1, 4).copy(gate4) + + num = initWeightIter0.size(1) / lstm_n_gates + gate1 = initWeightIter0.narrow(1, 1, num) + gate3 = initWeightIter0.narrow(1, num + 1, num) + gate2 = initWeightIter0.narrow(1, num * 2 + 1, num) + gate4 = initWeightIter0.narrow(1, num * 3 + 1, num) + + var initIterWeight = Tensor[Float](lstm_n_gates, hiddenSize, hiddenSize) + initIterWeight.select(1, 1).copy(gate1) + initIterWeight.select(1, 2).copy(gate2) + initIterWeight.select(1, 3).copy(gate3) + initIterWeight.select(1, 4).copy(gate4) + // original Array(hiddenSize, lstm_n_gates, hiddenSize) + initIterWeight = initIterWeight.transpose(1, 3).transpose(2, 3) + + val weights = lstmDnn.parameters()._1 + weights(0).copy(initWeight) + weights(1).copy(initBias) + weights(2).copy(initIterWeight) + + return lstmDnn + } + } + BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) + } + + private def fromBiRecurrent(node: IRElement[Float]): Module[Float] = { + val model = node.getOp().asInstanceOf[IRGeneralModule[Float]] + .model.asInstanceOf[BiRecurrent[Float]] + val layer = model.layer.getCell() + val revLayer = model.revLayer.getCell() + val merge = model.getMerge() + if ((layer equals revLayer) && layer.isInstanceOf[LSTM[Float]] && + model.batchNormParams == null && model.isSplitInput == false && + (merge.isInstanceOf[nn.CAddTable[Float, _]] || merge.isInstanceOf[nn.ConcatTable[Float]])) { + val lstm = layer.asInstanceOf[LSTM[Float]] + if (lstm.activation.isInstanceOf[Tanh[Float]] && + lstm.innerActivation.isInstanceOf[Sigmoid[Float]] && + lstm.p == 0.0f && + lstm.wRegularizer == null && + lstm.bRegularizer == null && + lstm.uRegularizer == null) { + val f = AlgKind.EltwiseTanh + val direction = if (merge.isInstanceOf[nn.CAddTable[Float, _]]) { + Direction.BidirectionalSum + } else Direction.BidirectionalConcat + val inputSize = lstm.inputSize + val hiddenSize = lstm.hiddenSize + val lstmDnn = nn.mkldnn.RNN(AlgKind.VanillaLstm, inputSize, hiddenSize, + f, direction, layers = 1) + + // copy weight from blas lstm to dnn lstm + val lstm_n_gates = 4 + + val blasParams = model.parameters()._1 + val initWeight0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, inputSize)) + val initWeightIter0 = Tensor[Float](Array(2, hiddenSize * lstm_n_gates, hiddenSize)) + val initBias0 = Tensor[Float](Array(2, lstm_n_gates * hiddenSize)) + + initWeight0(1).resizeAs(blasParams(0)).copy(blasParams(0)) + initBias0(1).resizeAs(blasParams(1)).copy(blasParams(1)) + initWeightIter0(1).resizeAs(blasParams(2)).copy(blasParams(2)) + initWeight0(2).resizeAs(blasParams(3)).copy(blasParams(3)) + initBias0(2).resizeAs(blasParams(4)).copy(blasParams(4)) + initWeightIter0(2).resizeAs(blasParams(5)).copy(blasParams(5)) + + val initWeight = Tensor[Float](Array(2, lstm_n_gates, hiddenSize, inputSize)) + val initWeightIter = Tensor[Float](Array(2, lstm_n_gates, hiddenSize, hiddenSize)) + val initBias = Tensor[Float](Array(2, lstm_n_gates, hiddenSize)) + + for (i <- 1 to 2) { + var num = initWeight0(i).size(1) / lstm_n_gates + var gate1 = initWeight0(i).narrow(1, 1, num) + var gate3 = initWeight0(i).narrow(1, num + 1, num) + var gate2 = initWeight0(i).narrow(1, num * 2 + 1, num) + var gate4 = initWeight0(i).narrow(1, num * 3 + 1, num) + initWeight(i).select(1, 1).copy(gate1) + initWeight(i).select(1, 2).copy(gate2) + initWeight(i).select(1, 3).copy(gate3) + initWeight(i).select(1, 4).copy(gate4) + + num = initWeightIter0(i).size(1) / 4 + gate1 = initWeightIter0(i).narrow(1, 1, num) + gate3 = initWeightIter0(i).narrow(1, num + 1, num) + gate2 = initWeightIter0(i).narrow(1, num * 2 + 1, num) + gate4 = initWeightIter0(i).narrow(1, num * 3 + 1, num) + initWeightIter(i).select(1, 1).copy(gate1) + initWeightIter(i).select(1, 2).copy(gate2) + initWeightIter(i).select(1, 3).copy(gate3) + initWeightIter(i).select(1, 4).copy(gate4) + + num = initBias0(i).size(1) / 4 + gate1 = initBias0(i).narrow(1, 1, num) + gate3 = initBias0(i).narrow(1, num + 1, num) + gate2 = initBias0(i).narrow(1, num * 2 + 1, num) + gate4 = initBias0(i).narrow(1, num * 3 + 1, num) + initBias(i).select(1, 1).copy(gate1) + initBias(i).select(1, 2).copy(gate2) + initBias(i).select(1, 3).copy(gate3) + initBias(i).select(1, 4).copy(gate4) + } + val weights = lstmDnn.parameters()._1 + weights(0).copy(initWeight.transpose(2, 4).transpose(3, 4)) + weights(1).copy(initBias) + weights(2).copy(initWeightIter.transpose(2, 4).transpose(3, 4)) + + return lstmDnn + } + } BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala index 6e3913f4789..b478ec9a0aa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala @@ -21,8 +21,10 @@ import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.{Recurrent, StaticGraph} +import com.intel.analytics.bigdl.nn.StaticGraph import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.{T, Table} class RNNSpec extends FlatSpec with Matchers{ @@ -1101,4 +1103,79 @@ class RNNSpec extends FlatSpec with Matchers{ Equivalent.nearequals(mkldnn_gradBias0(l), blas_gradBias(l)) should be(true) } } + + "Converting Blas LSTM to Dnn LSTM" should "work correctly" in { + System.setProperty("bigdl.engineType", "mkldnn") + RNG.setSeed(100) + + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.UnidirectionalLeft2Right + + var inputNTC = Tensor(Array(batchSize, seqLength, inputSize)).rand() + + val inputNode = nn.Input[Float]() + val outputNode = nn.Recurrent[Float]().add( + nn.LSTM[Float](inputSize, hiddenSize)).inputs(inputNode) + val blasLSTM = nn.Graph[Float](Array(inputNode), Array(outputNode)) + + val dnnLSTM = blasLSTM.asInstanceOf[StaticGraph[Float]] + .setInputFormats(Seq(Memory.Format.ntc)) + .setOutputFormats(Seq(Memory.Format.ntc)) + .toIRgraph() + + val mkldnn_output = dnnLSTM.forward(inputNTC).toTensor + val blas_output = blasLSTM.forward(inputNTC).toTensor + + Equivalent.nearequals(mkldnn_output, blas_output) should be(true) + + val gradOutput = Tensor[Float].resize(blas_output.size()).rand() + val mkldnn_gradInput = dnnLSTM.backward(inputNTC, gradOutput).toTensor + val blas_gradInput = blasLSTM.backward(inputNTC, gradOutput).toTensor + + Equivalent.nearequals(mkldnn_gradInput, blas_gradInput) should be(true) + + System.clearProperty("bigdl.engineType") + } + + "Converting Blas BiRecurrent LSTM to Dnn LSTM" should "work correctly" in { + System.setProperty("bigdl.engineType", "mkldnn") + RNG.setSeed(100) + + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalSum + + var inputNTC = Tensor(Array(batchSize, seqLength, inputSize)).rand() + val inputNode = nn.Input[Float]() + val outputNode = nn.BiRecurrent[Float]().add( + nn.LSTM[Float](inputSize, hiddenSize)).inputs(inputNode) + val blasLSTM = nn.Graph[Float](Array(inputNode), Array(outputNode)) + + val dnnLSTM = blasLSTM.asInstanceOf[StaticGraph[Float]] + .setInputFormats(Seq(Memory.Format.ntc)) + .setOutputFormats(Seq(Memory.Format.ntc)) + .toIRgraph() + + val mkldnn_output = dnnLSTM.forward(inputNTC).toTensor + val blas_output = blasLSTM.forward(inputNTC).toTensor + + Equivalent.nearequals(mkldnn_output, blas_output) should be(true) + + val gradOutput = Tensor[Float].resize(blas_output.size()).rand() + val mkldnn_gradInput = dnnLSTM.backward(inputNTC, gradOutput).toTensor + val blas_gradInput = blasLSTM.backward(inputNTC, gradOutput).toTensor + + Equivalent.nearequals(mkldnn_gradInput, blas_gradInput) should be(true) + + System.clearProperty("bigdl.engineType") + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala index ee265bf24d2..a6bff9c4500 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala @@ -16,9 +16,10 @@ package com.intel.analytics.bigdl.utils.intermediate +import com.intel.analytics.bigdl.example.languagemodel.PTBModel import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} -import com.intel.analytics.bigdl.nn.mkldnn.{Equivalent, HeapData} +import com.intel.analytics.bigdl.nn.mkldnn._ import com.intel.analytics.bigdl.{Module, nn, utils} import com.intel.analytics.bigdl.nn.{Graph, Reshape, StaticGraph} import com.intel.analytics.bigdl.tensor.Tensor @@ -155,4 +156,45 @@ class IRGraphSpec extends BigDLSpecHelper { Equivalent.nearequals(gradInputDnn.get[Tensor[Float]](2).get, gradInputBlas.get[Tensor[Float]](2).get, 1e-4) should be (true) } + + "PTB LSTM model running with mkldnn" should "work correctly" in { + Engine.init(1, 1, true) + RandomGenerator.RNG.setSeed(1000) + + val vocabSize = 10001 + val hiddenSize = 256 + val numLayers = 1 + val batchSize = 8 + val seqLength = 16 + var i = 2 + + Engine.setEngineType(MklBlas) + val blas = PTBModel.lstm( + inputSize = vocabSize, + hiddenSize = hiddenSize, + outputSize = vocabSize, + numLayers = numLayers, + keepProb = 1.0F) + + Engine.setEngineType(MklDnn) + val dnn = blas.cloneModule().asInstanceOf[StaticGraph[Float]].toIRgraph() + + val input = Tensor[Float](batchSize, seqLength).apply1(n => { + i += 1 + i + }) + + val outBlas = blas.forward(input).toTensor[Float] + val outDnn = dnn.forward(input).toTensor[Float] + + Equivalent.nearequals(outBlas, outDnn, 1e-6) should be(true) + + + val gradOutput = Tensor[Float](outBlas.size()).rand() + + val grad1 = blas.backward(input, gradOutput).toTensor[Float] + val grad2 = dnn.backward(input, gradOutput).toTensor[Float] + + Equivalent.nearequals(grad1, grad2, 1e-6) should be(true) + } } From b7f259eec5bb3f5888699e225aad05cb1ebc0224 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 10 Jul 2019 21:55:13 +0800 Subject: [PATCH 0932/1065] fix load lstm error bug (#2858) --- .../analytics/bigdl/dllib/nn/mkldnn/RNN.scala | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala index 5569e51c87f..71a68416213 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala @@ -52,6 +52,7 @@ class RNN( private var updateGradInputMemoryPrimitives: Array[Long] = _ private var updateGradInputTensors: Array[Tensor[Float]] = _ private var fwdPD: Long = _ + private var rnnCellDesc : Long = 0L private[mkldnn] var weight: TensorMMap = _ private[mkldnn] var weight_i: TensorMMap = _ @@ -130,13 +131,6 @@ class RNN( gradWeight_i = new TensorMMap(weightIterShape) gradBias = new TensorMMap(biasShape) - val rnnCellDesc = mode match { - case AlgKind.VanillaLstm => - MklDnn.RNNCellDescInit(AlgKind.VanillaLstm, f, flags, alpha, clipping) - case _ => throw new UnsupportedOperationException("Not support such RNN cell. " + - "Cell type: " + mode) - } - { val stdv = 1.0 / math.sqrt(hiddenSize) val wInit: InitializationMethod = RandomUniform(-stdv, stdv) @@ -211,6 +205,13 @@ class RNN( val dist_layer_MD = dst_layer.getMemoryDescription() val dist_iter_MD = dst_iter.getMemoryDescription() + rnnCellDesc = mode match { + case AlgKind.VanillaLstm => + MklDnn.RNNCellDescInit(AlgKind.VanillaLstm, f, flags, alpha, clipping) + case _ => throw new UnsupportedOperationException("Not support such RNN cell. " + + "Cell type: " + mode) + } + val description = MklDnn.RNNForwardDescInit(kind, rnnCellDesc, direction, src_layer_MD, src_iter_MD, weights_layer_MD, weights_iter_MD, bis_MD, dist_layer_MD, dist_iter_MD) From c063327e783083634ccb883325463c73b24d1cf5 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Thu, 11 Jul 2019 13:15:13 +0800 Subject: [PATCH 0933/1065] Add beam search in transformer (#2856) * Add beam search in transformer * meet pr comments --- .../analytics/bigdl/dllib/nn/Attention.scala | 200 +++++-- .../bigdl/dllib/nn/Transformer.scala | 300 +++++++++-- .../bigdl/dllib/nn/TransformerOperation.scala | 72 ++- .../bigdl/dllib/nn/AttentionSpec.scala | 16 +- .../bigdl/dllib/nn/TransformerSpec.scala | 497 +++++++++++++++++- 5 files changed, 966 insertions(+), 119 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala index b8bb0d6981e..5f7caf4251c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Attention.scala @@ -15,24 +15,56 @@ */ package com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.abstractnn.TensorModule +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} +import scala.language.existentials import scala.reflect.ClassTag /** * Implementation of multiheaded attention and self-attention layers. + * * @param hiddenSize hidden size * @param numHeads heads number * @param attentionDropout */ -class Attention[T: ClassTag]( - val hiddenSize: Int, val numHeads: Int, val attentionDropout: Float) - (implicit ev: TensorNumeric[T]) extends BaseModule[T] { +class Attention[T: ClassTag](val hiddenSize: Int, val numHeads: Int, val attentionDropout: Float) + (implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Activity, T] { + + // for prediction + private val joinK = nn.JoinTable[T](dimension = 2, nInputDims = -1) + private val joinV = nn.JoinTable[T](dimension = 2, nInputDims = -1) + + private val queryLayer = TransformerOperation.dense( + hiddenSize, hiddenSize, false, name = s"${this.getName()}_q") + private val keyLayer = TransformerOperation.dense( + hiddenSize, hiddenSize, false, name = s"${this.getName()}_k") + private val valueLayer = TransformerOperation.dense( + hiddenSize, hiddenSize, false, name = s"${this.getName()}_v") - override def buildModel(): Module[T] = { + private val querySplitLayer = new SplitHeads(hiddenSize, numHeads, true) + private val keySplitLayer = new SplitHeads(hiddenSize, numHeads) + private val valueSplitLayer = new SplitHeads(hiddenSize, numHeads) + + private val contiguousQLayer = new Contiguous[T]() + private val contiguousKLayer = new Contiguous[T]() + private val contiguousVLayer = new Contiguous[T]() + private val matmulLayer = MM(transB = true) + private val caddLayer = CAddTable() + private val softMaxLayer = TransformerOperation.softMax[T]() + private val dropLayer = Dropout(initP = (1.0 - attentionDropout)) + private val matmulNoTransLayer = MM() + // Recombine heads --> (batch_size, length, hidden_size) + private val combineHeadsLayer = new CombineHeads() + // Run the combined outputs through another linear projection layer. + private val outputLayer = TransformerOperation.dense( + hiddenSize, hiddenSize, false, name = s"${this.getName()}_output_transform") + + private[bigdl] val model : Module[T] = { // InputX with shape (batch_size, length_x, hidden_size). // InputY with shape (batch_size, length_x, hidden_size) // for self attention, InputX and InputY should be the same. @@ -41,38 +73,144 @@ class Attention[T: ClassTag]( val inputY = Input() val inputBias = Input() - // Layers for linearly projecting the queries, keys, and values. - val queryLayer = TransformerOperation.dense( - hiddenSize, hiddenSize, false, name = s"${this.getName()}_q").inputs(inputX) - val keyLayer = TransformerOperation.dense( - hiddenSize, hiddenSize, false, name = s"${this.getName()}_k").inputs(inputY) - val valueLayer = TransformerOperation.dense( - hiddenSize, hiddenSize, false, name = s"${this.getName()}_v").inputs(inputY) + val queryNode = queryLayer.inputs(inputX) + val keyNode = keyLayer.inputs(inputY) + val valueNode = valueLayer.inputs(inputY) + + val model = Graph(Array(inputX, inputY, inputBias), + Array(createModule(queryNode, keyNode, valueNode, inputBias))) + if (this.train) model.training() else model.evaluate() + } + + private val graph: Module[T] = { + val queryNode = Input() + val keyNode = Input() + val valueNode = Input() + val inputBias = Input() + + Graph(Array(queryNode, keyNode, valueNode, inputBias), + Array(createModule(queryNode, keyNode, valueNode, inputBias))) + } - val querySplit = new SplitHeads(hiddenSize, numHeads, true).inputs(queryLayer) - val keySplit = new SplitHeads(hiddenSize, numHeads).inputs(keyLayer) - val valueSplit = new SplitHeads(hiddenSize, numHeads).inputs(valueLayer) + private def createModule(inputQuery: ModuleNode[T], inputKey: ModuleNode[T], + inputValue: ModuleNode[T], inputBias: ModuleNode[T]) : ModuleNode[T] = { + val querySplit = querySplitLayer.inputs(inputQuery) + val keySplit = keySplitLayer.inputs(inputKey) + val valueSplit = valueSplitLayer.inputs(inputValue) - val contiguousQ = new Contiguous[T]().inputs(querySplit) - val contiguousK = new Contiguous[T]().inputs(keySplit) - val contiguousV = new Contiguous[T]().inputs(valueSplit) + val contiguousQ = contiguousQLayer.inputs(querySplit) + val contiguousK = contiguousKLayer.inputs(keySplit) + val contiguousV = contiguousVLayer.inputs(valueSplit) - val matmul = MM(transB = true).inputs(contiguousQ, contiguousK) - val cadd = CAddTable().inputs(matmul, inputBias) - val softMax = TransformerOperation.softMax[T]().inputs(cadd) + val matmul = matmulLayer.inputs(contiguousQ, contiguousK) + val cadd = caddLayer.inputs(matmul, inputBias) + val softMax = softMaxLayer.inputs(cadd) - val drop = Dropout(initP = (1.0 - attentionDropout)).inputs(softMax) - val matmulNoTrans = MM().inputs(drop, contiguousV) + val drop = dropLayer.inputs(softMax) + val matmulNoTrans = matmulNoTransLayer.inputs(drop, contiguousV) // Recombine heads --> (batch_size, length, hidden_size) - val combineHeads = new CombineHeads().inputs(matmulNoTrans) - // Run the combined outputs through another linear projection layer. - val outputLayer = TransformerOperation.dense( - hiddenSize, hiddenSize, false, name = s"${this.getName()}_output_transform") - .inputs(combineHeads) - val graph = Graph(Array(inputX, inputY, inputBias), Array(outputLayer)) - graph + val combineHeads = combineHeadsLayer.inputs(matmulNoTrans) + outputLayer.inputs(combineHeads) + } + + + private def updateOutputCache(input: Activity): Activity = { + require(!this.isTraining(), "Only support input cache for model inference") + val inputTable = input.toTable + val inputX = inputTable[Tensor[T]](1) + val inputY = inputTable[Tensor[T]](2) + val inputBias = inputTable[Table](3).apply[Tensor[T]](1) + /** + * cache: (Used during prediction) dictionary with tensors containing results of + * previous attentions. The dictionary must have the items: + * {"k": tensor with shape [batch_size, i, key_channels], + * "v": tensor with shape [batch_size, i, value_channels]} + * where i is the current decoded length. + */ + val cache = inputTable[Table](3).apply[Table](2) + + val query = queryLayer.forward(inputX).toTensor[T] + + val (inputK, inputV) = if (cache.length() > 0) { + (cache.apply[Tensor[T]](this.getName() + "_k"), + cache.apply[Tensor[T]](this.getName() + "_v")) + } else (null, null) + + val key = if (inputK != null && !inputK.isEmpty) { + joinK.forward(T(keyLayer.forward(inputY).toTensor[T], inputK)) + } else keyLayer.forward(inputY).toTensor[T] + val value = if (inputV != null && !inputV.isEmpty) { + joinV.forward(T(valueLayer.forward(inputY).toTensor[T], inputV)) + } else valueLayer.forward(inputY).toTensor[T] + + // update cache + if (cache.length() > 0) { + cache.update(this.getName() + "_k", key) + cache.update(this.getName() + "_v", value) + } + output = graph.updateOutput(T(query, key, value, inputBias)) + output + } + override def updateOutput(input: Activity): Activity = { + require(input.toTable.length() == 3, + s"only support 3 inputs, but get ${input.toTable.length()}") + + val cache = input.toTable.apply[Activity](3) + if (cache.isInstanceOf[Tensor[T]]) { + output = model.updateOutput(input) + } else if (cache.isInstanceOf[Table]) { + output = updateOutputCache(input) + } + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + gradInput = model.updateGradInput(input, gradOutput) + gradInput + } + + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + model.accGradParameters(input, gradOutput) + } + + override def training(): this.type = { + train = true + model.training() + this + } + + override def evaluate(): this.type = { + train = false + model.evaluate() + this + } + + override def getExtraParameter(): Array[Tensor[T]] = { + model.getExtraParameter() + } + + override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { + model.getTimes() + } + + override def resetTimes(): Unit = { + model.resetTimes() + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + model.parameters() + } + + override def getParametersTable(): Table = { + model.getParametersTable() + } + + override def clearState(): this.type = { + model.clearState() + this } } + // Combine tensor that has been splitted. // input should be tensor with shape (batch_size, num_heads, length, hidden_size/num_heads) // output should be tensor with shape (batch_size, length, hidden_size) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala index 0b63732bad7..a282a8e0280 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Transformer.scala @@ -19,12 +19,13 @@ import breeze.linalg.* import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} -import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule, DataType} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.serializer.{DeserializeContext, ModuleSerializable, ModuleSerializer, SerializeContext} import com.intel.analytics.bigdl.utils.{T, Table} +import org.apache.zookeeper.ZooDefs.Ids import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -60,37 +61,91 @@ class Transformer[T: ClassTag]( val ffnDropout: Float, val paddingValue: Double = 0, val withShareWeightsLinear: Boolean = false, - val transformerType: TransformerType = LanguageModel) - (implicit ev: TensorNumeric[T]) extends BaseModule[T] { - - private val linearSharedWeigths = TimeDistributed( - new Linear(inputSize = hiddenSize, outputSize = vocabSize, withBias = false)) - - override def buildModel(): Module[T] = { + val transformerType: TransformerType = LanguageModel, + val beamSearch: SequenceBeamSearch[T] = null) + (implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Activity, T] { + + // for translation layers + private[bigdl] var decoderStack: Module[T] = null + private[bigdl] var encoderStack: Module[T] = null + private[bigdl] var predictModel: Module[T] = null + private var linearSharedWeigths : Module[T] = null + // for symbols + private val rangeBuffer = Tensor[T]() + private val timeBuffer = Tensor[T]() + private var decoderBiasBuffer = Tensor[T]() + + private val embeddingLayer = Sequential[T]().add( + LookupTable[T](vocabSize, hiddenSize, paddingValue = paddingValue, + maskZero = true).setName("embedding")).add(MulConstant(math.sqrt(hiddenSize))) + + private[bigdl] var model : Module[T] = { transformerType match { case LanguageModel => buildLM() case Translation => buildTranslation() } } + private def createDecoder(): Module[T] = { + val decoderInputNode = Input() + val decoderSelfAttentionBiasNode = Input() + val encoderOutputNode = Input() + val encoderAttentionBiasNode = Input() + + Graph(Array(decoderInputNode, decoderSelfAttentionBiasNode, + encoderOutputNode, encoderAttentionBiasNode), + Array(block(numHiddenlayers, decoderInputNode, decoderSelfAttentionBiasNode, + encoderOutputNode, encoderAttentionBiasNode, blockType = "decoder"))) + } + + private def createEncoder(): Module[T] = { + val encoderInputNode = Input() + val encoderAttentionBiasNode = Input() + Graph(Array(encoderInputNode, encoderAttentionBiasNode), + Array(block(numHiddenlayers, encoderInputNode, encoderAttentionBiasNode, + blockType = "encoder"))) + } + private def buildTranslation(): Module[T] = { + // init layers + val mask = new PaddingMask() + if (linearSharedWeigths == null) { + linearSharedWeigths = TimeDistributed(new Linear( + inputSize = hiddenSize, outputSize = vocabSize, withBias = false)).asInstanceOf[Module[T]] + } + if (decoderStack == null) decoderStack = createDecoder() + if (encoderStack == null) encoderStack = createEncoder() + // input: int tensor with shape [batch_size, input_length]. val inputNode = Input() // target: int tensor with shape [batch_size, target_length]. val targetNode = Input() - val attentionBias = new PaddingMask().inputs(inputNode) - + val attentionBias = mask.inputs(inputNode) val join = JoinTable(1, -1).inputs(inputNode, targetNode) - val constantValue = math.sqrt(hiddenSize) - val embedding = MulConstant(constantValue).inputs( - LookupTable[T](vocabSize, hiddenSize, paddingValue = paddingValue, - maskZero = true).setName("embedding").inputs(join)) - val split = new SplitTensor(1, 2).inputs(embedding) + val embeddingForTrain = embeddingLayer.inputs(join) + val split = new SplitTensor(1, 2).inputs(embeddingForTrain) val embeddingInput = SelectTable(1).inputs(split) val embeddingOutput = SelectTable(2).inputs(split) - val encoderOutput = encode(embeddingInput, attentionBias) - val outputNode = decode(embeddingOutput, encoderOutput, attentionBias) + // create encode + val embeddingNode = Input() + val paddingNode = Input() + val encoderGraph = Graph(Array(embeddingNode, paddingNode), + encode(embeddingNode, paddingNode)) + + // create predict model + val predictNode = Input() + val attentionMask = mask.inputs(predictNode) + val embeddingForPredict = embeddingLayer.inputs(predictNode) + predictModel = Graph(predictNode, + Array(encoderGraph.inputs(embeddingForPredict, attentionMask), attentionMask)) + + // init beam search + if (beamSearch != null) beamSearch.setLogitFn(symbols) + + // create training model + val outputNode = decode(embeddingOutput, + encoderGraph.inputs(embeddingInput, attentionBias), attentionBias) Graph(Array(inputNode, targetNode), outputNode) } @@ -100,24 +155,131 @@ class Transformer[T: ClassTag]( val embeddingInput = MulConstant(constantValue).inputs( LookupTable[T](vocabSize, hiddenSize, paddingValue = paddingValue, maskZero = true).setName("embedding").inputs(inputNode)) - val outputNode = decode(embeddingInput) + + val decoderInput = new PositionEncodeWithShift().inputs(embeddingInput) + val decoderSelfAttentionBias = new SelfAttentionMask().inputs(embeddingInput) + val decoderInputDrop = Dropout(1- embeddingDropout).inputs(decoderInput) + + val outputNode = block(numHiddenlayers, decoderInputDrop, + decoderSelfAttentionBias, blockType = "decode") Graph(inputNode, outputNode) } - override def updateOutput(input: Activity): Activity = { - output = model.updateOutput(input) - - // sharing weight between embedding and linear + private def updateOutputLM(input: Tensor[T]): Tensor[T] = { + output = model.forward(input) if (withShareWeightsLinear) { - val embeddingLayer = model.apply("embedding").get + shareWeights(true) + output = linearSharedWeigths.updateOutput(model.output.toTensor[T]) + } + output.toTensor[T] + } + + private def shareWeights(share: Boolean): Unit = { + if (share) { val embeddingParams = embeddingLayer.getParameters() val linearParams = linearSharedWeigths.getParameters() linearParams._1.copy(embeddingParams._1) - output = linearSharedWeigths.updateOutput(model.output.toTensor[T]) + } + } + + /** + * Pass this function to beam search + * @param Ids + * @param i index + * @param maxDecodeLength max decode length + * @param encoder_outputs output from encoder + * @param encoder_decoder_attention_bias attention bias + * @param cacheValue k and v values for attention layers + * @return + */ + def symbols(Ids: Tensor[T], i: Int, maxDecodeLength: Int, + encoder_outputs: Tensor[T], encoder_decoder_attention_bias: Tensor[T], + cacheValue: Table): (Tensor[T], Table) = { + val cache = T() // pass to attention layer + for(m <- 1 to hiddenSize) { + if (cacheValue.contains(s"layer_${m}_k")) { + cache.update(s"decoder_self_attention_${m - 1}/self_attention_k", + cacheValue(s"layer_${m}_k")) + cache.update(s"decoder_self_attention_${m - 1}/self_attention_v", + cacheValue(s"layer_${m}_v")) + } + } + + val length = maxDecodeLength + 1 + TransformerOperation.initRangeTensor(length, rangeBuffer) + timeBuffer.resize(length, hiddenSize) + TransformerOperation.getPositionEncode(length, hiddenSize, + rangeBuffer = rangeBuffer, outBuffer = timeBuffer) + val timeSignal = TransformerOperation.getPositionEncode(length, hiddenSize, + rangeBuffer = rangeBuffer, outBuffer = timeBuffer) + + // size (1, 1, maxDecodeLength, maxDecodeLength) + if (decoderBiasBuffer == null + || decoderBiasBuffer.nElement() != maxDecodeLength * maxDecodeLength) { + decoderBiasBuffer = Tensor[T](1, 1, maxDecodeLength, maxDecodeLength) + } + TransformerOperation.attentionBiasLowerTriangle(maxDecodeLength, decoderBiasBuffer) + + val decoder_input = Ids.narrow(2, i + 1, 1) + val decoder_input_embedding = embeddingLayer.forward(decoder_input).toTensor[T] + + val timeSize = timeSignal.size() + val timingTemp = timeSignal.select(1, i + 1) + val decoder_input_add = decoder_input_embedding.add(timingTemp) + + val self_attention_bias = decoderBiasBuffer.select(3, i + 1) + .select(3, i + 1).resize(Array(1, 1, 1, i + 1)) + + val decoder_outputs = decoderStack.forward(T(decoder_input_add, + T(self_attention_bias, cache), encoder_outputs, encoder_decoder_attention_bias)).toTensor[T] + + shareWeights(withShareWeightsLinear) + val logits = this.linearSharedWeigths.forward(decoder_outputs).toTensor[T] + + for(m <- 1 to hiddenSize) { + if (cacheValue.contains(s"layer_${m}_k")) { + cacheValue.update(s"layer_${m}_k", + cache(s"decoder_self_attention_${m - 1}/self_attention_k")) + cacheValue.update(s"layer_${m}_v", + cache(s"decoder_self_attention_${m - 1}/self_attention_v")) + } + } + + (logits.squeeze(2), cacheValue) + } + + private def updateOutputTranslation(input: Activity): Activity = { + if (input.isTensor) { + require(!this.isTraining(), + "Input for Transformer should be tensor when doing translation prediction") + // inference case, first tensor is encoder_outputs, another is attention_bias + val res = predictModel.forward(input).toTable + beamSearch.forward(T(res[Tensor[T]](1), res[Tensor[T]](2))) + // output for beamsearch is table, and first tensor is decoder_ids, another is scores + val decodedIds = beamSearch.output.toTable.apply[Tensor[T]](1).select(2, 1) + val scores = beamSearch.output.toTable.apply[Tensor[T]](2).select(2, 1) + output = T(decodedIds.narrow(2, 2, decodedIds.size(2) - 1), scores) + } else { + require(input.toTable.length() == 2, s"Input should be two tensors when doing " + + s"translation training, but get ${input.toTable.length()}") + // training case + output = model.forward(input) + if (withShareWeightsLinear) { + shareWeights(true) + output = linearSharedWeigths.updateOutput(model.output.toTensor[T]) + } } output } + override def updateOutput(input: Activity): Activity = { + if (transformerType == Translation) { + updateOutputTranslation(input) + } else { + updateOutputLM(input.toTensor[T]) + } + } + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { val grad = if (withShareWeightsLinear) { linearSharedWeigths.updateGradInput(model.output.toTensor[T], gradOutput.toTensor[T]) @@ -126,14 +288,17 @@ class Transformer[T: ClassTag]( gradInput } + override def accGradParameters(input: Activity, gradOutput: Activity): Unit = { + model.accGradParameters(input, gradOutput) + } + private[nn] def encode(inputs: ModuleNode[T], attentionBias: ModuleNode[T]): ModuleNode[T] = { // Prepare inputs to the layer stack by adding positional encodings and // applying dropout. val position = new PositionEncode().inputs(inputs) val encoderInput = CAddTable().inputs(inputs, position) val encoderInputDrop = Dropout(1- embeddingDropout).inputs(encoderInput) - - block(numHiddenlayers, encoderInputDrop, attentionBias, blockType = "encode") + encoderStack.inputs(encoderInputDrop, attentionBias) } private[nn] def decode(targets: ModuleNode[T], @@ -141,10 +306,10 @@ class Transformer[T: ClassTag]( attentionBias: ModuleNode[T] = null): ModuleNode[T] = { val decoderInput = new PositionEncodeWithShift().inputs(targets) val decoderSelfAttentionBias = new SelfAttentionMask().inputs(targets) - val decoderInputDrop = Dropout(1- embeddingDropout).inputs(decoderInput) - block(numHiddenlayers, decoderInputDrop, - decoderSelfAttentionBias, encoderOutput, attentionBias, blockType = "decode") + + decoderStack.inputs(Array(decoderInputDrop, + decoderSelfAttentionBias, encoderOutput, attentionBias)) } private[nn] def block(numLayers: Int, @@ -214,7 +379,40 @@ class Transformer[T: ClassTag]( override def clearState(): this.type = { if (withShareWeightsLinear) linearSharedWeigths.clearState() - super.clearState() + model.clearState() + this + } + + override def training(): this.type = { + train = true + model.training() + this + } + + override def evaluate(): this.type = { + train = false + model.evaluate() + this + } + + override def getExtraParameter(): Array[Tensor[T]] = { + model.getExtraParameter() + } + + override def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)] = { + model.getTimes() + } + + override def resetTimes(): Unit = { + model.resetTimes() + } + + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + model.parameters() + } + + override def getParametersTable(): Table = { + model.getParametersTable() } } @@ -230,12 +428,13 @@ object Transformer extends ModuleSerializable { ffnDropout: Float, paddingValue: Double = 0, withShareWeightsLinear: Boolean = false, - transformerType: TransformerType = LanguageModel) + transformerType: TransformerType = LanguageModel, + beamSearch: SequenceBeamSearch[T] = null) (implicit ev: TensorNumeric[T]): Transformer[T] = { new Transformer(vocabSize, hiddenSize, numHeads, filterSize, numHiddenlayers, embeddingDropout, attentionDropout, ffnDropout, paddingValue, - withShareWeightsLinear = withShareWeightsLinear, transformerType = transformerType) + withShareWeightsLinear, transformerType = transformerType, beamSearch) } override def doLoadModule[T: ClassTag](context: DeserializeContext) @@ -278,6 +477,10 @@ object Transformer extends ModuleSerializable { .getAttributeValue(context, attrMap.get("ffnDropout")) .asInstanceOf[Float] + val beamSearch = DataConverter + .getAttributeValue(context, attrMap.get("beamSearch")) + .asInstanceOf[Module[T]] + val paddingValue = DataConverter .getAttributeValue(context, attrMap.get("paddingValue")) .asInstanceOf[Double] @@ -299,7 +502,7 @@ object Transformer extends ModuleSerializable { val transformer = Transformer(vocabSize, hiddenSize, numHeads, filterSize, numHiddenlayers, embeddingDropout, attentionDropout, ffnDropout, paddingValue, - withShareWeightsLinear = withShareWeightsLinear, transformerType) + withShareWeightsLinear, transformerType, beamSearch.asInstanceOf[SequenceBeamSearch[T]]) transformer.model = model transformer @@ -377,6 +580,11 @@ object Transformer extends ModuleSerializable { DataConverter.setAttributeValue(context, transformerTypeBuilder, tag, universe.typeOf[Int]) transformerBuilder.putAttr("transformerType", transformerTypeBuilder.build) + + val beamSearchBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, beamSearchBuilder, + transformer.beamSearch, universe.typeOf[Module[_]]) + transformerBuilder.putAttr("beamSearch", beamSearchBuilder.build) } } @@ -394,18 +602,17 @@ private[nn] class PositionEncode[T: ClassTag](implicit ev: TensorNumeric[T]) @transient private var rangeBuffer : Tensor[T] = null override def updateOutput(input: Tensor[T]): Tensor[T] = { - if (!output.isEmpty && output.nElement() == input.nElement()) return output val length = input.size(2) val channel = input.size(3) - if (rangeBuffer == null) { - rangeBuffer = Tensor[T]() - TransformerOperation.initRangeTensor(length, rangeBuffer) - } + if (!output.isEmpty && output.nElement() == length * channel) return output + + if (rangeBuffer == null) rangeBuffer = Tensor[T]() + TransformerOperation.initRangeTensor(length, rangeBuffer) output.resize(length, channel) - TransformerOperation.addTimingSignal1D(length, channel, - rangeBuffer = rangeBuffer, timeBuffer = output) + TransformerOperation.getPositionEncode(length, channel, + rangeBuffer = rangeBuffer, outBuffer = output) output } @@ -429,15 +636,16 @@ private[nn] class PositionEncodeWithShift[T: ClassTag](implicit ev: TensorNumeri val length = output.size(2) val channel = output.size(3) - if (rangeBuffer == null) { - rangeBuffer = Tensor[T]() + if (rangeBuffer == null) rangeBuffer = Tensor[T]() + if (timeBuffer == null) timeBuffer = Tensor[T]() + + if (timeBuffer.nElement() != length * channel) { TransformerOperation.initRangeTensor(length, rangeBuffer) - } - if (timeBuffer == null) { timeBuffer = Tensor[T]().resize(length, channel) - TransformerOperation.addTimingSignal1D(length, channel, - rangeBuffer = rangeBuffer, timeBuffer = timeBuffer) + TransformerOperation.getPositionEncode(length, channel, + rangeBuffer = rangeBuffer, outBuffer = timeBuffer) } + val batchSize = input.size(1) var i = 1 while (i <= batchSize) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala index 6942d0778de..11ea93566ae 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/TransformerOperation.scala @@ -15,6 +15,8 @@ */ package com.intel.analytics.bigdl.nn +import breeze.linalg.* +import breeze.numerics.exp import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.optim.Regularizer @@ -99,50 +101,70 @@ private[nn] object TransformerOperation { def initRangeTensor[T: ClassTag](length: Int, rangeBuffer: Tensor[T]) (implicit ev: TensorNumeric[T]): Unit = { - rangeBuffer.resize(Array(length, 2)) - val arr = rangeBuffer.select(2, 1).storage().array() + rangeBuffer.resize(Array(length)) + val arr = rangeBuffer.storage().array() for (i <- 0 to (length - 1)) { - arr(i * 2) = ev.fromType(i) - arr(i * 2 + 1) = ev.fromType(i) + arr(i) = ev.fromType(i) } } /** * Args:length: Sequence length. * channels: Size of the hidden - * min_timescale: Minimum scale that will be applied at each position - * max_timescale: Maximum scale that will be applied at each position + * minTimescale: Minimum scale that will be applied at each position + * maxTimescale: Maximum scale that will be applied at each position * Returns: Tensor with shape [length, hidden_size] */ - def addTimingSignal1D[T: ClassTag]( + def getPositionEncode[T: ClassTag]( length: Int, channels: Int, - min_timescale : Float = 1.0f, - max_timescale: Float = 1.0e4f, + minTimescale : Float = 1.0f, + maxTimescale: Float = 1.0e4f, rangeBuffer: Tensor[T], - timeBuffer: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + outBuffer: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { // get_timing_signal_1d, return (1, length, channels) - val num_timescales = channels / 2 - val log_timescale_increment = math.log(max_timescale / min_timescale) / - math.max(num_timescales - 1, 1) + val numTimescales = channels / 2 + val logTimescale = math.log(maxTimescale / minTimescale) / + math.max(numTimescales - 1, 1) // tf.range(num_timescales) - val inv_timescales = new Array[Double](num_timescales) + val invTensor = Tensor[T](1, numTimescales) + val inv_timescales = invTensor.storage().array() + val offset = invTensor.storageOffset() - 1 var i = 0 - while (i < inv_timescales.length) { - inv_timescales(i) = min_timescale * math.exp(i * - log_timescale_increment) + while (i < numTimescales) { + inv_timescales(i + offset) = ev.fromType(minTimescale * math.exp(i * - logTimescale)) i += 1 } - rangeBuffer.select(2, 1).mul(ev.fromType[Double](inv_timescales(0))) - rangeBuffer.select(2, 2).mul(ev.fromType[Double](inv_timescales(1))) - val sinRes = rangeBuffer.clone().apply1(e => - ev.fromType(math.sin(ev.toType[Float](e)))) - val cosRes = rangeBuffer.clone().apply1(e => - ev.fromType(math.cos(ev.toType[Float](e)))) + val outSin = outBuffer.narrow(2, 1, numTimescales) + outSin.addmm(ev.zero, ev.one, rangeBuffer.resize(length, 1), invTensor) + val outCos = outBuffer.narrow(2, numTimescales + 1, numTimescales).copy(outSin) + outSin.apply1(e => ev.fromType(math.sin(ev.toType[Float](e)))) + outCos.apply1(e => ev.fromType(math.cos(ev.toType[Float](e)))) - timeBuffer.narrow(2, 1, sinRes.size(2)).copy(sinRes) - timeBuffer.narrow(2, sinRes.size(2) + 1, cosRes.size(2)).copy(cosRes) - timeBuffer + outBuffer + } + + private val maskValue = -1e9 + /** + * Create an bias tensor to be added to attention logits. + * Returns tensor with shape (1, 1, length, length) + * @param length + * @tparam T + * @return + */ + def attentionBiasLowerTriangle[T: ClassTag]( + length: Int, output: Tensor[T])(implicit ev: TensorNumeric[T]): Tensor[T] = { + val arr = output.storage().array() + for (i <- 0 to (length - 1)) { + var j = length - 1 + while (j > i) { + // reminder: here not 1 + arr(i * length + j) = ev.fromType(maskValue) + j -= 1 + } + } + output.resize(Array(1, 1, length, length)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AttentionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AttentionSpec.scala index 0f0c564254e..bcc13dbc16d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AttentionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/AttentionSpec.scala @@ -240,13 +240,13 @@ class AttentionSpec extends FlatSpec with Matchers { val w4 = weights.get[Tensor[Float]]("output_transform").get for (i <- paramsTable.keySet) { val params = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get - if (i == "q") { + if (i.toString contains "_q") { params.copy(w1.t()) - } else if (i == "k") { + } else if (i.toString contains "_k") { params.copy(w2.t()) - } else if (i == "v") { + } else if (i.toString contains "_v") { params.copy(w3.t()) - } else if (i == "output_transform") { + } else if (i.toString contains "_output_transform") { params.copy(w4.t()) } } @@ -263,10 +263,10 @@ class AttentionSpec extends FlatSpec with Matchers { val gw4 = gradWeightsExpected.get[Tensor[Float]]("output_transform").get for (i <- paramsTable.keySet) { val params = paramsTable.get[Table](i).get.get[Tensor[Float]]("gradWeight").get - if (i == "q") params should be(gw1.t()) - if (i == "k") params should be(gw2.t()) - if (i == "v") params should be(gw3.t()) - if (i == "output_transform") params should be(gw4.t()) + if (i.toString contains "_q") params should be(gw1.t()) + if (i.toString contains "_k") params should be(gw2.t()) + if (i.toString contains "_v") params should be(gw3.t()) + if (i.toString contains "_output_transform") params should be(gw4.t()) } } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala index 587bb5e2155..a1f3b0f54bb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/TransformerSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{T, Table} +import org.apache.zookeeper.ZooDefs.Ids import org.scalatest.{FlatSpec, Matchers} import scala.util.Random @@ -209,12 +210,12 @@ class TransformerLayerSpec extends FlatSpec with Matchers { postprocessDropout, attentionDropout, reluDropout, withShareWeightsLinear = true, transformerType = Translation) - val attention0 = transformer.model("encode_self_attention_0/self_attention").get - val ffn0 = transformer.model("encode_ffn_0/ffn").get + val attention0 = transformer.encoderStack("encoder_self_attention_0/self_attention").get + val ffn0 = transformer.encoderStack("encoder_ffn_0/ffn").get - val attention1 = transformer.model("decode_self_attention_0/self_attention").get - val ffn1 = transformer.model("decode_ffn_0/ffn").get - val attention2 = transformer.model("decode_encdec_attention_0/encdec_attention").get + val attention1 = transformer.decoderStack("decoder_self_attention_0/self_attention").get + val ffn1 = transformer.decoderStack("decoder_ffn_0/ffn").get + val attention2 = transformer.decoderStack("decoder_encdec_attention_0/encdec_attention").get var paramsTable = attention0.getParametersTable() for (i <- paramsTable.keySet) { @@ -437,11 +438,481 @@ class TransformerLayerSpec extends FlatSpec with Matchers { val input1 = Tensor[Float](T(T(3, 1, 2, 3, 4, 5), T(6, 7, 8, 9, 10, 11))).add(1.0f) val input2 = Tensor[Float](T(T(4, 5, 7, 9, 10, 11), T(4, 12, 6, 3, 2, 15))).add(1.0f) + + transformer.forward(T(input1, input2)).toTensor[Float] + transformer.forward(T(input1, input2)).toTensor[Float] val output = transformer.forward(T(input1, input2)).toTensor[Float] require(output.almostEqual(expectedOutput, 1e-5) == true) val gradInput = transformer.backward(T(input1, input2), output) + + // check model consistence + val p1 = transformer.getParametersTable() + val p2 = transformer.predictModel.getParametersTable() + + for (i <- p2.keySet) { + val k = i.asInstanceOf[String] + val t1 = p1[Table](k) + val t2 = p2[Table](k) + t1 should be(t2) + } + } + + "translation symbols" should "work correctly" in { + val vocabSize = 4 + val hiddenSize = 6 + val filterSize = 8 + val numHeads = 1 + val num_hidden_layers = 1 + val postprocessDropout = 1.0f + val attentionDropout = 1.0f + val reluDropout = 1.0f + val beamSize = 3 + val alpha = 0.0 + + val transformer = new Transformer[Float](vocabSize, + hiddenSize, numHeads, filterSize, num_hidden_layers, + postprocessDropout, attentionDropout, reluDropout, withShareWeightsLinear = true, + transformerType = Translation) + + val attention0 = transformer.decoderStack("decoder_self_attention_0/self_attention").get + val ffn0 = transformer.decoderStack("decoder_ffn_0/ffn").get + val attention1 = transformer.decoderStack("decoder_encdec_attention_0/encdec_attention").get + + var paramsTable = attention0.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(0.09277314, -0.68820494, 0.16522777, -0.40581715, 0.06890422, 0.6565147), + T(0.49753946, -0.23763505, -0.10428226, -0.70136553, -0.5193925, -0.59029204), + T(-0.29122207, -0.1562357, -0.62931496, -0.27149835, 0.37125605, -0.09454733), + T(0.6886762, 0.21375972, 0.45840162, -0.38501668, 0.2418403, -0.17058176), + T(0.6508581, -0.20488912, 0.28981203, -0.2225352, 0.08715159, -0.08225363), + T(0.55958265, 0.12384564, -0.5891212, -0.5097943, 0.6749435, 0.54095346)) + ).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T( 0.35639137, -0.44010565, 0.69782406, 0.6712149, 0.6719194, -0.2073448), + T(-0.63924193, 0.47828382, 0.12184739, -0.0820483, -0.12530881, -0.31531507), + T(-0.23265177, 0.37762922, -0.7022769, -0.17911112, -0.5113645, -0.0036366), + T(-0.6591283, -0.5369195, 0.6824694, -0.68680227, 0.04187173, 0.48412377), + T(-0.10710287, -0.4410376, -0.31272674, 0.70627254, -0.6859787, -0.39919707), + T(-0.07416499, -0.13264036, -0.27625945, 0.68312794, 0.66134506, -0.04135364)) + ).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.34640983, -0.39100304, -0.18671203, -0.374746, 0.6314725, 0.6213555), + T(-0.06722593, 0.303369, -0.28636643, 0.04942727, -0.38919258, -0.26426342), + T(0.61537296, 0.28254843, 0.2740779, 0.38493305, -0.02474982, 0.5261882), + T(0.57718307, -0.5356672, 0.4199949, -0.06718278, 0.6942496, -0.63103056), + T(0.6798324, 0.09108728, -0.1643722, 0.05537456, -0.1276812, 0.18927693), + T(0.43600184, -0.12343174, 0.6414538, -0.5595496, -0.6388051, -0.1141628)) + ).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(1.0320681e-01, -4.7727546e-01, -1.8181628e-01, -2.2450918e-01, + 2.7059507e-01, -8.4132671e-02), + T( 5.8403677e-01, -1.5362275e-01, -7.6330304e-02, -6.9537580e-02, + -7.0423132e-01, 3.7879902e-01), + T(4.1482288e-01, -1.8996698e-01, 8.4956586e-02, -2.6389879e-01, + 3.5741490e-01, -3.7853482e-01), + T( 6.9471496e-01, 5.6738526e-01, 2.5773436e-01, 6.5494078e-01, + 4.1179293e-01, 2.4906039e-01), + T(5.4722553e-01, 3.1094271e-01, -3.8972145e-01, -2.4953604e-02, + 4.8401952e-02, -6.5118074e-02), + T(6.4152962e-01, 7.0548886e-01, -4.5418739e-04, 4.3950146e-01, + -5.3979152e-01, -6.9785893e-02)) + ).t()) + } + } + + paramsTable = ffn0.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_filter_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.6179467, 0.3898517, -0.53441167, -0.5707529, -0.15171456, 0.05913043, + 0.46225762, 0.57643664), + T( 0.2704727, 0.6383722, 0.29635727, -0.01696265, -0.24925217, 0.26989943, + 0.5874231, 0.39121008), + T(-0.44762594, -0.5994233, -0.5535429, -0.29463243, 0.49637818, -0.6225333, + -0.15982115, -0.00393605), + T( 0.04142767, 0.05573893, -0.07914865, -0.40308526, 0.38239682, -0.0798682, + 0.0601086, -0.44918442), + T( 0.07068157, -0.3035642, 0.55209386, 0.5801885, -0.38348204, 0.0491367, + -0.42336383, -0.42711353), + T(-0.54204327, 0.1572792, 0.3544749, -0.6479292, 0.436494, -0.23446563, + -0.0068211, -0.24168485)) + ).transpose(1, 2)) + } else if (i.toString contains "_output_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.2906804, -0.09650522, -0.51867586, 0.12019378, 0.04821312, 0.46652162), + T(-0.64988756, 0.02888119, .57290435, 0.3135442, 0.5520284, -0.5747767), + T(-0.09171528, -0.48300105, 0.09677291, 0.32325137, 0.5582509, -0.22009549), + T( 0.4888966, 0.27174932, 0.01399601, 0.3048225, 0.137241, 0.63783765), + T(-0.5621009, -0.37397432, -0.5940767, -0.23252288, -0.04682523, -0.35494938), + T(-0.34885955, -0.3917929, 0.11447227, -0.02745694, -0.10696101, -0.05186653), + T(-0.62620497, -0.36984035, 0.46339476, -0.5078753, 0.5395007, 0.03173214), + T( 0.6108756, -0.60984766, 0.362139, -0.5178795, -0.41857153, -0.20534298)) + ).transpose(1, 2)) + } + } + + paramsTable = attention1.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T( 0.3788157, -0.47771007, -0.2948789, 0.44290048, 0.37867445, 0.10330296), + T(-0.02101845, -0.19019842, -0.6287322, 0.0833388, -0.27369624, -0.08059168), + T(-0.03180993, -0.3726304, 0.30198818, 0.20495892, -0.38850272, -0.56752044), + T(-0.32829174, -0.17950934, 0.34590167, -0.14291608, -0.44545525, -0.41910926), + T(-0.6859364, -0.23764163, 0.0205223, -0.0020116, -0.6546056, -0.05751479), + T( 0.5593212, -0.45211565, -0.02765071, -0.676903, -0.11695242, -0.35845473)) + ).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T( 0.1622994, -0.64048725, -0.1577012, 0.5608987, -0.37632567, -0.5385455), + T( 0.31227297, 0.07187706, -0.33549297, 0.16784662, -0.07089436, 0.1894666), + T(-0.3069575, -0.4485674, 0.05343717, 0.05603844, -0.48447698, -0.45832017), + T( 0.129848, -0.11816221, -0.6020765, 0.24656898, 0.08551663, 0.15609998), + T( 0.5702588, 0.18114346, -0.06966603, 0.05247587, -0.35869226, -0.44106457), + T(-0.065126, 0.00342298, 0.3578952, -0.49916536, 0.2206322, 0.41099173)) + ).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T( 0.03114265, 0.2907918, -0.6444297, -0.1507507, 0.12634379, -0.665304), + T( 0.00952369, 0.5148153, 0.6418976, 0.23740274, -0.25036457, 0.33283466), + T( 0.30420464, 0.39165932, -0.07576096, -0.30266157, 0.6692197, 0.6492025), + T( 0.6242575, 0.41898304, 0.0172168, 0.19018292, -0.27464908, 0.22606927), + T(-0.3250402, 0.42158836, -0.4064593, 0.60533947, 0.33799917, -0.39806417), + T(-0.3696555, 0.59799665, 0.23027265, -0.53981984, 0.42475563, -0.3312717)) + ).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.6188926, -0.57150096, -0.41241905, 0.56527156, -0.22010231, -0.05784655), + T(-0.4110496, -0.59531057, 0.19572222, -0.70158654, 0.40615624, -0.06082886), + T( 0.4586776, 0.42834383, -0.0283308, -0.4419498, -0.691649, -0.05940056), + T(-0.35758752, -0.28089678, -0.03836983, 0.03994679, -0.08562958, -0.46829447), + T( 0.15341502, -0.48834035, 0.07678533, 0.21668231, 0.4941885, -0.50629056), + T( 0.10157996, -0.04297733, -0.16132396, -0.45030114, 0.26740336, -0.20009905)) + ).t()) + } + } + + paramsTable = transformer.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "embedding") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.65356344, 0.05228455, -0.28268468, -0.35488975, 0.28668904, 0.27116764), + T( 0.34086248, 0.13249631, 0.31554696, -0.2706043, 0.46191368, -0.43256745), + T(-0.38036123, -0.05285205, -0.02012104, 0.43764764, 0.10273226, 0.2986628), + T(-0.35866106, 0.13071355, 0.68658495, 0.6402194, -0.00890927, 0.45173198)) + ) + ) + } + } + + val encoder_outputs = Tensor[Float]( + T(T(T( 2.436518, -0.9176346, -0.7922576, -1.609453, 1.2981114, -3.452308), + T( 2.6172175, -1.1418103, 0.47855863, -0.37405556, 2.193162, -3.0902112), + T(-0.4836258, -0.5760815, 1.7006541, -1.6498369, -0.25864232, -1.3167876), + T( 0.06332062, 0.8742228, -1.6509287, 1.7170856, 1.3523861, 0.7537415), + T( 1.3512839, -1.0255917, -0.18433534, -1.4036541, -0.40183213, 0.7955332), + T(-1.0374911, -0.59513026, -1.0307591, -1.2678084, -1.0068692, -0.0189969)), + T(T(-1.6759655, 0.35162354, 2.4897032, 1.1130662, -0.2877533, -1.3314434), + T(-1.1207374, 2.538682, 0.07621163, -0.95549345, 0.28637323, 3.1503828), + T( 0.18023843, 0.9258047, 0.45025548, -0.5283748, -1.7137773, -0.52401406), + T(-0.31334135, 0.8799348, 1.2584751, 1.3966532, 0.428381, 1.3277117), + T(-1.1315969, 1.8793023, 0.76939476, -0.44713926, 0.73277724, -0.11335757), + T( 1.6974441, 2.2797253, 3.2783632, -2.0947444, -2.1661706, -0.7566988)), + T(T( 0.2400556, 1.3142533, 0.47345242, -3.0333018, -0.45930603, 1.241962), + T( 0.3451421, 1.1430168, -0.33349222, -0.3011371, 0.27984208, 0.6150775), + T( 0.2974496, 0.17851298, -1.0059935, 0.5663457, 0.18273191, 1.6942259), + T( 1.7983768, 0.27773464, -0.5629274, -0.9580956, 0.6352415, 0.1160101), + T(-0.5157805, 0.06539529, -0.93000126, 1.0470481, -0.67069286, 1.8367616), + T( 0.6052375, 0.8903678, -1.6423677, 0.25407365, 1.1108347, -1.4305509)), + T(T(-0.39932775, 0.04892182, -2.059676, 0.4727391, 1.269241, -1.2892739), + T( 0.52581894, -1.9684252, -0.05804326, -2.4236586, 1.6821265, 0.6133508), + T(-0.03692544, -1.1627424, 1.9106339, 2.9506526, -2.7869728, 1.854246), + T( 2.441476, 0.50701755, -1.798902, 1.295018, -0.27138045, -0.9058809), + T(-1.8450872, 0.82580626, 1.1892103, -0.9352961, 0.7808645, -1.7165121), + T( 1.2027916, 0.06985094, -0.27985466, -0.15261881, 1.3033292, 1.1256175)), + T(T( 0.794198, 0.20655182, 0.11673169, 0.9275704, 0.34874183, 1.0238271), + T(-0.46517515, -3.6522567, 1.558237, 3.2804694, 0.6620467, -0.15023285), + T(-0.20466712, -0.17858128, 0.02611411, -1.6830281, -0.7756417, -1.4955403), + T( 0.37319875, -0.44496173, 0.742817, -0.26205474, 1.4795028, 0.32030085), + T( 3.2860496, -2.8445413, -0.97037506, 1.3522303, 3.7924886, -0.37295216), + T( 0.06550349, -0.33947137, 1.9971857, -0.4309618, 1.0201048, -0.4797024)), + T(T(-1.9088382, 0.47032157, 0.7547772, 1.9398388, -0.16567054, -0.9260431), + T( 0.84414166, 0.36110565, 0.42099762, -0.10966905, 1.7405078, 0.5542391), + T( 2.856988, 1.6665851, 0.9885747, -2.4411576, 0.9034789, 0.6304233), + T( 1.2164276, 1.5666631, -0.6013173, 1.2360084, -0.8434582, 2.932317), + T(-1.9979275, -2.641033, -2.4760818, -1.3358334, -1.6786731, 2.9341183), + T(-0.48974925, -2.0140138, 1.6715745, -0.8797859, -1.85528, 1.3137584))) + ) + + val encoder_decoder_attention_bias = Tensor[Float]( + T(T(T(T( 6.23362184e-01, -4.34956670e-01, 1.40753996e+00, 1.29101574e-01, + 1.61694956e+00, 5.02740860e-01))), + T(T(T( 1.55880558e+00, 1.09402694e-01, -1.21974444e+00, 2.44936872e+00, + -5.45774162e-01, -1.98837861e-01))), + T(T(T(-7.00398505e-01, -2.03394443e-01, 2.42669448e-01, 2.01830178e-01, + 6.61020279e-01, 1.79215825e+00))), + T(T(T(-1.20464571e-01, -1.23312068e+00, -1.18231809e+00, -6.65754497e-01, + -1.67419577e+00, 8.25029850e-01))), + T(T(T(-4.98213559e-01, -3.10984969e-01, -1.89148285e-03, -1.39662039e+00, + -8.61316383e-01, 6.74711525e-01))), + T(T(T( 6.18539155e-01, -4.43171918e-01, 1.81053495e+00, -1.30572689e+00, + -3.44987214e-01, -2.30839744e-01)))) + ) + val ids = Tensor[Float](T(T(2), T(1), T(3), T(2), T(3), T(1))).add(1.0f) + val maxDecodeLength = 10 + + transformer.evaluate() + val logits = transformer.symbols(ids, 0, + maxDecodeLength, encoder_outputs, + encoder_decoder_attention_bias, T()) + + val expectedOutput = Tensor[Float]( + T(T(-0.15447775, -0.11302006, 0.9264371, 0.38503933), + T( 1.5546308, 1.3066487, -1.2042707, -1.7090195), + T(-0.5342786, 0.02837384, 0.84436226, 0.93868625), + T(-0.35054666, -0.01141939, 0.92066, 0.610096), + T(-0.3374302, 0.15798312, 0.7623861, 0.5607704), + T( 0.42308486, 1.684835, -0.98940516, -0.6840595))) + + require(logits._1.almostEqual(expectedOutput, 1e-6) == true) + } + + "tranformer for translation prediction" should "work correctly" in { + val vocabSize = 6 + val hiddenSize = 4 + val filterSize = 8 + val numHeads = 1 + val num_hidden_layers = 1 + val postprocessDropout = 1.0f + val attentionDropout = 1.0f + val reluDropout = 1.0f + val beamSize = 3 + val alpha = 0.0f + val padding = 1.0 + + val beamSearch = new SequenceBeamSearch[Float](vocabSize, beamSize, alpha, + maxDecodeLength = 6, eosID = 2.0f, numHiddenLayers = num_hidden_layers, + hiddenSize = hiddenSize, paddingValue = padding.toFloat) + + val transformer = new Transformer[Float](vocabSize, + hiddenSize, numHeads, filterSize, num_hidden_layers, + postprocessDropout, attentionDropout, reluDropout, withShareWeightsLinear = true, + transformerType = Translation, beamSearch = beamSearch, paddingValue = padding) + + val attention0 = transformer.encoderStack("encoder_self_attention_0/self_attention").get + val ffn0 = transformer.encoderStack("encoder_ffn_0/ffn").get + + val attention1 = transformer.decoderStack("decoder_self_attention_0/self_attention").get + val ffn1 = transformer.decoderStack("decoder_ffn_0/ffn").get + val attention2 = transformer.decoderStack("decoder_encdec_attention_0/encdec_attention").get + + + var paramsTable = transformer.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "embedding") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.10626597, 0.23630716, -0.21521048, 0.2920521), + T(0.29631686, 0.25602844, -0.7230936, 0.44407833), + T(0.21172947, -0.69782203, 0.22524196, -1.0138164), + T(0.06197122, 0.24819946, 0.98074985, -0.45509085), + T(0.5263322, -0.40988722, 0.42144877, -0.604839), + T(-0.87161285, -0.9569873, 0.50618875, 0.42917752)) + ) + ) + } + } + + paramsTable = attention0.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T( 0.60471255, 0.54166526, 0.39841092, -0.44462326), + T( 0.02691836, -0.43647325, 0.16844122, 0.9925212), + T(-0.544705, -0.8566129, -0.6552941, 0.01295163), + T(-0.14695838, 0.23456065, -0.37129694, 0.31026325))).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.20041183, -0.4891488, -0.5085714, 0.02489659), + T(-0.8556601, 0.5424248, -0.6202357, -0.05780638), + T( 0.20420216, -0.6134986, -0.4326076, 0.30206147), + T( 0.19872786, 0.2613737, -0.92315775, 0.22687511))).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.4871333, 0.06103574, -0.6497405, 0.2223501), + T(-0.9120996, 0.16089165, 0.33262423, -0.19571291), + T(-0.23360679, -0.50078744, 0.41204292, -0.02241471), + T(-0.89100724, -0.67421687, -0.34987354, -0.60579276))).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.47020948, 0.5356578, 0.3193113, -0.09291445), + T(-0.19973904, -0.49609232, -0.33667022, -0.17671368), + T(-0.8060424, 0.5186156, 0.7493292, 0.11365094), + T( 1.0632801, -0.00599942, 0.8937121, -0.38923746))).t()) + } + } + + paramsTable = ffn0.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_filter_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.35298067, -0.674392, 0.2591158, -0.39853054, -0.37949187, 0.02309175, + -0.65644926, 0.8986522), + T( 0.19363914, -0.28986388, -0.257797, -0.15664439, 0.6596536, 0.12020741, + -0.17007843, -0.5501685), + T(-0.41227788, -0.35933533, -0.45497704, -0.25715417, 0.60992306, -0.19668543, + -0.77470696, 0.24892384), + T(0.12124976, 0.8671879, -0.44282717, -0.27072555, 0.2076582, -0.53506184, + 0.39133728, 0.35779685))).transpose(1, 2)) + } else if (i.toString contains "_output_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.27241132, 0.3341913, -0.41847667, 0.02148553), + T(-0.01241873, -0.26167524, -0.03825141, 0.23399451), + T(-0.17590803, 0.3214216, 0.03090363, 0.13343768), + T( 0.5105916, 0.47454977, -0.33296397, -0.30425498), + T( 0.08448114, 0.4124062, -0.44084966, 0.49881822), + T( 0.65130925, -0.3216592, 0.65844834, 0.48436818), + T(-0.25914645, -0.20871995, 0.32968223, -0.17965943), + T(-0.06768182, 0.057122, 0.36694843, 0.31433672))).transpose(1, 2)) + } + } + + paramsTable = attention1.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T( 0.77750987, 0.4158453, -0.6369134, 0.3423233), + T(-0.23027276, 0.14109705, 0.06633862, -0.10180788), + T(-0.2759771, 0.03831478, -0.28692466, -0.34104064), + T( 0.17564411, -0.46582016, -0.07349259, 0.6100675))).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.09555588, 0.16374706, -0.81079763, 0.18353464), + T( 0.72976017, -0.6785369, -0.1633139, -0.1220759), + T(-0.47357813, 0.19808318, 0.63312566, -0.14370666), + T( 0.11398887, 0.7884044, -0.36504376, -0.17514746))).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.19676681, -0.24631989, -1.1253904, -0.2751462), + T(-0.17718858, 0.06754616, 0.5731753, -0.8507766), + T( 0.06555229, -0.04867446, -0.05025194, -0.5535116), + T(-0.5346166, 0.23926297, -0.4628236, -0.3947385))).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T(-0.9045557, -0.24963744, 0.15128663, 0.3981787), + T( 0.27012536, 0.68673694, -0.5106513, -0.403161), + T( 0.4013973, 0.56175864, 0.07839457, 0.12541114), + T( 1.0649085, -0.36113226, 0.62341034, 0.40576163))).t()) + } + } + + paramsTable = attention2.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_q") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.30996466, 0.18248673, 0.8567455, 0.28524998), + T(-0.14087993, 0.89362335, 0.508464, 0.11154915), + T(-0.57769585, -0.05840808, -0.03983077, -0.18773204), + T( 0.01721322, -0.98023546, 0.45239854, 0.36473998))).t()) + } else if (i.toString contains "_k") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.899124, 0.2551926, -0.44861552, -0.03521642), + T( 0.11243621, 0.3853058, 0.2681699, 0.92443305), + T(-0.95154715, 0.05751022, -0.78881997, -0.1350401), + T(-0.37174794, 0.40753722, 0.29484457, 0.19494648))).t()) + } else if (i.toString contains "_v") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy(Tensor[Float]( + T(T(-0.4060595, 0.16372263, 0.023297, 0.36362377), + T(-0.2023103, 0.21245559, 0.8890398, -0.3310149), + T(-0.43833923, 0.3280986, 0.12780678, -0.03614727), + T(-0.13311541, 0.606744, 0.8235367, -0.7286466))).t()) + } else if (i.toString contains "_output_transform") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.04250056, 0.22854215, 0.53206336, 0.4422663), + T( 0.33508906, -0.60044575, 0.6112323, -0.455282), + T(-0.2171716, 0.1193755, -0.20559259, -0.4482319), + T(-0.9909119, -0.42189652, 0.4596743, 0.54179823))).t()) + } + } + paramsTable = ffn1.getParametersTable() + for (i <- paramsTable.keySet) { + if (i.toString contains "_filter_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.02586995, 0.18380801, -0.4366081, 0.04554708, 0.502605, -0.0638033, + -0.02207431, 0.886901), + T( 0.71397907, -0.9562222, 0.5745853, -0.4594066, -0.03152881, -0.5183125, + -0.08600679, 0.29023197), + T( 0.45509827, -0.1206222, -0.00919442, -0.41815665, -0.10291624, 0.5107678, + -0.41308904, 0.5330143), + T( 0.15338193, 1.0132387, -0.7641731, 0.586552, -0.21082902, 0.06093456, + -0.13935581, -0.22199476))).transpose(1, 2)) + } else if (i.toString contains "_output_layer") { + paramsTable.get[Table](i).get[Tensor[Float]]("weight").copy( + Tensor[Float]( + T(T( 0.02742703, 0.4962597, -0.2972874, 0.19024311), + T(-0.2363084, -0.20939663, -0.19179073, 0.2456342), + T( 0.28929684, -0.08173832, 0.509045, 0.09438979), + T(-0.01957564, 0.46127063, 0.30457073, -0.05371396), + T( 0.44164342, -0.43652987, 0.25199357, 0.50121784), + T( 0.06327879, -0.41891155, 0.07631836, -0.1247088), + T(-0.31286344, -0.30700997, -0.4626535, 0.1554678), + T(-0.0544433, -0.5776823, -0.01178843, 0.02590635))).transpose(1, 2)) + } + } + + val input1 = Tensor[Float](T(T(3, 1, 2, 3, 4, 5), T(3, 2, 1, 4, 2, 1))).add(1.0f) + + transformer.evaluate() + + val output = transformer.forward(input1).toTable + val expectedOutput = Tensor[Float]( + T(T(2, 1, 1, 1, 1, 1), T(2, 1, 1, 1, 1, 1))) + + expectedOutput should be(output.apply[Tensor[Float]](1)) + + println("done") + } + + "PositionEncode" should "work correctly with hidden size = 8" in { + val layer = new PositionEncode[Float]() + + val input = Tensor[Float](2, 6, 8) + val output = layer.forward(input) + + val outputExpected = Tensor[Float]( + T(T(0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, + 1.0000000e+00, 1.0000000e+00, 1.0000000e+00, 1.0000000e+00), + T(8.4147096e-01, 4.6399228e-02, 2.1544332e-03, 9.9999990e-05, + 5.4030228e-01, 9.9892300e-01, 9.9999768e-01, 1.0000000e+00), + T(9.0929741e-01, 9.2698507e-02, 4.3088561e-03, 1.9999998e-04, + -4.1614681e-01, 9.9569422e-01, 9.9999070e-01, 1.0000000e+00), + T(1.4112000e-01, 1.3879810e-01, 6.4632590e-03, 2.9999996e-04, + -9.8999250e-01, 9.9032068e-01, 9.9997914e-01, 9.9999994e-01), + T(-7.5680250e-01, 1.8459874e-01, 8.6176321e-03, 3.9999996e-04, + -6.5364361e-01, 9.8281395e-01, 9.9996287e-01, 9.9999994e-01), + T(-9.5892429e-01, 2.3000173e-01, 1.0771966e-02, 4.9999997e-04, + 2.8366217e-01, 9.7319025e-01, 9.9994200e-01, 9.9999988e-01)) + ) + + output should be(outputExpected) } "AttentionBiasConstant" should "work correctly" in { @@ -586,8 +1057,6 @@ class TransformerLayerSpec extends FlatSpec with Matchers { val gradInput = layer2.backward(output, o2) assert(output.almostEqual(gradInput, 1e-8) == true) } - - } class SelfAttentionMaskSerialTest extends ModuleSerializationTest { @@ -640,6 +1109,7 @@ class SplitTensorSerialTest extends ModuleSerializationTest { class TransformerSerialTest extends ModuleSerializationTest { override def test(): Unit = { + val vocabSize = 20 val hiddenSize = 4 val numHeads = 2 val filterSize = 3 @@ -647,9 +1117,18 @@ class TransformerSerialTest extends ModuleSerializationTest { val postprocessDropout = 1.0f val attentionDropout = 1.0f val reluDropout = 1.0f - val model = Transformer[Float](20, + val beamSize = 3 + val alpha = 0.0f + + val beamSearch = new SequenceBeamSearch[Float](vocabSize, beamSize, alpha, + maxDecodeLength = 6, eosID = 2.0f, numHiddenLayers = num_hidden_layers, + hiddenSize = hiddenSize, paddingValue = 0.0f) + + val model = Transformer[Float](vocabSize, hiddenSize, numHeads, filterSize, num_hidden_layers, - postprocessDropout, attentionDropout, reluDropout).setName("Transformer") + postprocessDropout, attentionDropout, reluDropout, + transformerType = LanguageModel, beamSearch = beamSearch).setName("Transformer") + val input = Tensor[Float](2, 6).apply1(_ => Random.nextInt(10) + 1) runSerializationTest(model, input) } From 047a0c7aaa28669427220afcc43052c179371a40 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 11 Jul 2019 15:32:15 +0800 Subject: [PATCH 0934/1065] fix: upgrade the performance of normalize (#2854) --- .../analytics/bigdl/dllib/nn/Normalize.scala | 44 ++++++++++++++++++- 1 file changed, 42 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala index 15ec35964f9..00ed47a19a6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala @@ -69,13 +69,53 @@ class Normalize[T: ClassTag](val p: Double, val eps: Double = 1e-10 } else { buffer.resizeAs(inputBuffer).pow(inputBuffer, ev.fromType(p)) } - normp.sum(buffer, 2).add(ev.fromType(eps)) + // normp.sum(buffer, 2).add(ev.fromType(eps)) + // perf fix: the sum operation of tensor will call Java + element wise + // perf fix: start + if (buffer.nDimension() <= 2) { + normp.sum(buffer, 2).add(ev.fromType(eps)) + } else { + normp.resize(Array(buffer.size(1), 1, buffer.size(3), buffer.size(4))) + var batchSize = 0 + while (batchSize < normp.size(1)) { + val batchOfNormp = normp.select(1, batchSize + 1).zero + val batchOfInput = buffer.narrow(1, batchSize + 1, 1) + + var channel = 0 + while (channel < buffer.size(2)) { + batchOfNormp.add(batchOfInput.select(2, channel + 1)) + channel += 1 + } + batchSize += 1 + } + normp.add(ev.fromType(eps)) + } + // perf fix: end norm.resizeAs(normp).pow(normp, ev.fromType(1.0 / p)) } if (norm.dim() <= 2) { output.cdiv(inputBuffer, norm.view(norm.nElement(), 1).expandAs(inputBuffer)) } else if (norm.dim() == 4) { - output.cdiv(inputBuffer, norm.view(norm.size()).expandAs(inputBuffer)) + // output.cdiv(inputBuffer, norm.view(norm.size()).expandAs(inputBuffer)) + // perf fix: after expand, the tensor will be not contiguous. + // perf fix: start + var batchSize = 0 + while (batchSize < output.size(1)) { + + val oneBatchOutput = output.narrow(1, batchSize + 1, 1) + val oneBatchNorm = norm.select(1, batchSize + 1) + val oneBatchInput = inputBuffer.narrow(1, batchSize + 1, 1) + + var channel = 0 + while (channel < output.size(2)) { + oneBatchOutput.select(2, channel + 1) + .cdiv(oneBatchInput.select(2, channel + 1), oneBatchNorm) + channel += 1 + } + + batchSize += 1 + } + // perf fix: end } output = output.view(input.size()) From 1454ccd20f399710488d86dbdef4a531c68a07f5 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 11 Jul 2019 16:46:54 +0800 Subject: [PATCH 0935/1065] feat: add axis to softmax (#2859) --- .../analytics/bigdl/dllib/nn/Normalize.scala | 16 +++--- .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 39 ++++++++++--- .../dllib/utils/intermediate/IRToDnn.scala | 16 +++++- .../bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala | 56 +++++++++++++++++++ .../utils/intermediate/IRGraphSpec.scala | 47 +++++++++++++++- 5 files changed, 156 insertions(+), 18 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala index 00ed47a19a6..09741e56a3f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Normalize.scala @@ -78,12 +78,12 @@ class Normalize[T: ClassTag](val p: Double, val eps: Double = 1e-10 normp.resize(Array(buffer.size(1), 1, buffer.size(3), buffer.size(4))) var batchSize = 0 while (batchSize < normp.size(1)) { - val batchOfNormp = normp.select(1, batchSize + 1).zero - val batchOfInput = buffer.narrow(1, batchSize + 1, 1) + val normpPerBatch = normp.select(1, batchSize + 1).zero + val inputPerBatch = buffer.narrow(1, batchSize + 1, 1) var channel = 0 while (channel < buffer.size(2)) { - batchOfNormp.add(batchOfInput.select(2, channel + 1)) + normpPerBatch.add(inputPerBatch.select(2, channel + 1)) channel += 1 } batchSize += 1 @@ -102,14 +102,14 @@ class Normalize[T: ClassTag](val p: Double, val eps: Double = 1e-10 var batchSize = 0 while (batchSize < output.size(1)) { - val oneBatchOutput = output.narrow(1, batchSize + 1, 1) - val oneBatchNorm = norm.select(1, batchSize + 1) - val oneBatchInput = inputBuffer.narrow(1, batchSize + 1, 1) + val outputPerBatch = output.narrow(1, batchSize + 1, 1) + val normPerBatch = norm.select(1, batchSize + 1) + val inputPerBatch = inputBuffer.narrow(1, batchSize + 1, 1) var channel = 0 while (channel < output.size(2)) { - oneBatchOutput.select(2, channel + 1) - .cdiv(oneBatchInput.select(2, channel + 1), oneBatchNorm) + outputPerBatch.select(2, channel + 1) + .cdiv(inputPerBatch.select(2, channel + 1), normPerBatch) channel += 1 } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index 9f88fb18392..e9fdc1afca4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -20,11 +20,13 @@ import com.intel.analytics.bigdl.mkl.{Memory, MklDnn, PropKind, Stream => DnnStr import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{DenseType, Tensor} +import com.intel.analytics.bigdl.utils.Shape import scala.collection.mutable.ArrayBuffer -class SoftMax() extends MklDnnLayer { +class SoftMax(val axis: Int = -1) extends MklDnnLayer { private val nnSoftMax = nn.SoftMax[Float]() @transient private var updateOutputTensors: Array[Tensor[Float]] = _ @@ -58,20 +60,37 @@ class SoftMax() extends MklDnnLayer { (_inputFormats, _outputFormats) case InferencePhase => - val axis = inputs(0).shape.length match { + val defaultAxis = inputs(0).shape.length match { case 1 => 0 case 2 => 1 -// case 3 => 1 // TODO should support this? + case 3 => 0 case 4 => 1 - case _ => throw new UnsupportedOperationException("1D, 2D, or 4D tensor expected") + case _ => throw new UnsupportedOperationException("1D, 2D, 3D or 4D tensor expected") } _inputFormats = singleNativeData(inputs) + + val localInputFormat = if (inputs(0).shape.length == 3 && + inputs(0).layout == Memory.Format.ntc) { + // note: here, the format and the true memory layout is not consistent. + // for ntc input, we should reshape the `shape` and make the format to tnc + val shape = Array(inputs(0).shape(1), inputs(0).shape(0), inputs(0).shape(2)) + NativeData(shape, Memory.Format.tnc) + } else { + _inputFormats(0) + } + val desc = MklDnn.SoftMaxForwardDescInit(PropKind.ForwardInference, - inputFormats()(0).getMemoryDescription(), axis) + localInputFormat.getMemoryDescription(), if (axis == -1) defaultAxis else axis) val forwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, 0L) - _outputFormats = Array(MemoryData.primitiveOutput(forwardPrimDesc)) + _outputFormats = if (inputs(0).shape.length ==3 && + inputs(0).layout == Memory.Format.ntc) { + // because set the input format as tnc first, we should set the output to ntc. + Array(NativeData(inputs(0).shape, Memory.Format.ntc)) + } else { + Array(MemoryData.primitiveOutput(forwardPrimDesc)) + } val srcs = Array(inputs(0).getPrimitive(runtime)) val indexes = Array(0) @@ -126,10 +145,14 @@ class SoftMax() extends MklDnnLayer { gradInput = nnSoftMax.backward(input, gradOutput) gradInput } + + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } } object SoftMax { - def apply(): SoftMax = { - new SoftMax() + def apply(axis: Int = -1)(implicit ev: TensorNumeric[Float]): SoftMax = { + new SoftMax(axis) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index 594343ede59..e7c32a671c7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -48,6 +48,7 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] IR2DnnMap("IRJoinTable") = fromJoinTable IR2DnnMap("IRGeneralModule") = fromBlasModule IR2DnnMap("IRInput") = fromInput + IR2DnnMap("IRSoftMax") = fromSoftMax } override def convertLayerCheck(layer: IRElement[Float]): Boolean = { @@ -184,13 +185,22 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] layer } + private def fromSoftMax(node: IRElement[Float]): Module[Float] = { + mkldnn.SoftMax() + } + private def fromBlasModule(node: IRElement[Float]) : Module[Float] = { val model = node.getOp().asInstanceOf[IRGeneralModule[Float]].model if (model.isInstanceOf[BiRecurrent[Float]]) { fromBiRecurrent(node) } else if (model.isInstanceOf[Recurrent[Float]]) { fromRecurrent(node) - } else BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) + } else if (model.isInstanceOf[TimeDistributed[Float]] && + model.asInstanceOf[TimeDistributed[Float]].layer.isInstanceOf[nn.SoftMax[Float]]) { + fromTimeDistributedWithSoftMax(node) + } else { + BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) + } } private def fromRecurrent(node: IRElement[Float]): Module[Float] = { @@ -357,6 +367,10 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) } + private def fromTimeDistributedWithSoftMax(node: IRElement[Float]): Module[Float] = { + mkldnn.SoftMax(axis = 2) + } + private def fromInput(node: IRElement[Float]) : Module[Float] = { mkldnn.Identity[Float]() } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala index 8f548f5d444..02cfc3a87ba 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala @@ -100,6 +100,35 @@ class SoftMaxSpec extends FlatSpec with Matchers { } } + "SoftMax forward 3-D" should "work correctly" in { + // we should test the cases which contain 1 + val tests = List( + (3, 4, 4), + (3, 4, 4), + (3, 1, 1), + (1, 1, 1), + (1, 3, 3), + (1, 3, 3), + (2, 1, 1)) + + for ((i, j, k) <- tests) { + val sm = SoftMax() + sm.setRuntime(new MklDnnRuntime) + sm.initFwdPrimitives(Array(HeapData(Array(i, j, k), + Memory.Format.ncw)), InferencePhase) + sm.evaluate() + + val input = Tensor(i, j, k).rand() + + val output = sm.forward(input) + + val nnSm = nn.SoftMax() + val nnOutput = nnSm.forward(input) + + Tools.dense(output) should be (nnOutput) + } + } + "SoftMax backward" should "work correctly" in { val (batchSize, channel, height, width) = (2, 3, 4, 4) val sm = SoftMax() @@ -141,6 +170,33 @@ class SoftMaxSpec extends FlatSpec with Matchers { } } + "axis" should "work correctly" in { + val input = Tensor[Float](2, 24564, 21).rand(-1, 1) + + val sm1 = SoftMax(axis = 2) + val seq1 = Sequential() + .add(Input(Array(2, 24564, 21), Memory.Format.ntc)) + .add(sm1) + .add(Output(Memory.Format.ntc)) + seq1.asInstanceOf[MklDnnContainer].compile(InferencePhase) + seq1.evaluate() + + seq1.forward(input) + + input.resize(Array(2 * 24564, 21)) + + val sm2 = SoftMax() + val seq2 = Sequential().add(Input(Array(2 * 24564, 21), Memory.Format.nc)) + .add(sm2) + .add(Output()) + seq2.asInstanceOf[MklDnnContainer].compile(InferencePhase) + sm2.evaluate() + + seq2.forward(input) + + seq1.output.toTensor.view(Array(2 * 24564, 21)) should be (seq2.output) + } + "softmax with java serialization" should "work correctly" in { val inputShape = Array(2, 3, 4, 4) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala index a6bff9c4500..aa0b8f86014 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraphSpec.scala @@ -21,7 +21,7 @@ import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} import com.intel.analytics.bigdl.nn.mkldnn._ import com.intel.analytics.bigdl.{Module, nn, utils} -import com.intel.analytics.bigdl.nn.{Graph, Reshape, StaticGraph} +import com.intel.analytics.bigdl.nn.{Graph, Reshape, StaticGraph, TimeDistributed} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils._ @@ -197,4 +197,49 @@ class IRGraphSpec extends BigDLSpecHelper { Equivalent.nearequals(grad1, grad2, 1e-6) should be(true) } + + "timedistributed with softmax" should "work correctly" in { + Engine.setEngineType(MklBlas) + val input = nn.Input[Float]() + val softMax = nn.SoftMax[Float]() + val timeDistri = nn.TimeDistributed[Float](softMax).inputs(input) + val blas = nn.Graph(input, timeDistri).evaluate() + + Engine.setEngineType(MklDnn) + val dnn = blas.cloneModule() + .asInstanceOf[StaticGraph[Float]] + .setInputFormats(Seq(Memory.Format.ntc)) + .setOutputFormats(Seq(Memory.Format.ntc)) + .toIRgraph() + .evaluate() + + val data = Tensor[Float](2, 255, 21) + + val outBlas = blas.forward(data).toTensor[Float] + val outDnn = dnn.forward(data).toTensor[Float] + + Equivalent.nearequals(outBlas, outDnn, 1e-6) should be (true) + } + + "convert softmax" should "work correctly" in { + Engine.setEngineType(MklBlas) + val input = nn.Input[Float]() + val softMax = nn.SoftMax[Float]().inputs(input) + val blas = nn.Graph(input, softMax).evaluate() + + Engine.setEngineType(MklDnn) + val dnn = blas.cloneModule() + .asInstanceOf[StaticGraph[Float]] + .setInputFormats(Seq(Memory.Format.nc)) + .setOutputFormats(Seq(Memory.Format.nc)) + .toIRgraph() + .evaluate() + + val data = Tensor[Float](255, 21) + + val outBlas = blas.forward(data).toTensor[Float] + val outDnn = dnn.forward(data).toTensor[Float] + + Equivalent.nearequals(outBlas, outDnn, 1e-6) should be (true) + } } From 2b84fd7560c136b96f40f962e2cec9fb139bf637 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Mon, 22 Jul 2019 18:07:35 +0800 Subject: [PATCH 0936/1065] flip version to 0.10.0 (#2869) --- dist/pom.xml | 2 +- dl/pom.xml | 6 +++--- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- .../dllib/src/main/resources/bigdl-version-info.properties | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index 2c6d4b7f515..ca71c6eb333 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.9.0-SNAPSHOT + 0.10.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index ab304e6aa05..4f831b0e3bf 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.9.0-SNAPSHOT + 0.10.0-SNAPSHOT 4.0.0 @@ -79,7 +79,7 @@ com.intel.analytics.bigdl.core.dist all - 0.9.0-SNAPSHOT + 0.10.0-SNAPSHOT ${bigdl-core-all-scope} @@ -314,7 +314,7 @@ com.intel.analytics.bigdl.core.dist ${os-flag} - 0.9.0-SNAPSHOT + 0.10.0-SNAPSHOT pom diff --git a/pom.xml b/pom.xml index bc29089d259..cc02193121f 100644 --- a/pom.xml +++ b/pom.xml @@ -5,7 +5,7 @@ bigdl-parent com.intel.analytics.bigdl - 0.9.0-SNAPSHOT + 0.10.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/1.5-plus/pom.xml b/scala/common/spark-version/1.5-plus/pom.xml index 4a6398aa41c..da409bd709c 100644 --- a/scala/common/spark-version/1.5-plus/pom.xml +++ b/scala/common/spark-version/1.5-plus/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.9.0-SNAPSHOT + 0.10.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/2.0/pom.xml b/scala/common/spark-version/2.0/pom.xml index c9fae4dd865..027850121b1 100644 --- a/scala/common/spark-version/2.0/pom.xml +++ b/scala/common/spark-version/2.0/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.9.0-SNAPSHOT + 0.10.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/pom.xml b/scala/common/spark-version/pom.xml index e22f5b37a43..8b91a40e011 100644 --- a/scala/common/spark-version/pom.xml +++ b/scala/common/spark-version/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.9.0-SNAPSHOT + 0.10.0-SNAPSHOT 4.0.0 diff --git a/scala/dllib/src/main/resources/bigdl-version-info.properties b/scala/dllib/src/main/resources/bigdl-version-info.properties index 1a483922a9e..4c2ae8f6684 100644 --- a/scala/dllib/src/main/resources/bigdl-version-info.properties +++ b/scala/dllib/src/main/resources/bigdl-version-info.properties @@ -16,4 +16,4 @@ #BigDL version info config -version=0.9.0-SNAPSHOT \ No newline at end of file +version=0.10.0-SNAPSHOT \ No newline at end of file From c32969bf0356f8b682708fa7bb2392e177c2d694 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Fri, 26 Jul 2019 16:15:21 +0800 Subject: [PATCH 0937/1065] [Bug Fix] - Fix module version comparison (#2871) * update serialization * update serialization --- .../utils/serializer/ModuleSerializable.scala | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala index 417fe3f6431..e9285d6c851 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializable.scala @@ -52,11 +52,18 @@ trait ModuleSerializable extends Loadable with Savable{ // Separate this two methods for reuse in sub-classes protected def checkVersion[T: ClassTag](module : BigDLModule) (implicit ev: TensorNumeric[T]) : Unit = { - val version = module.getVersion - require(version <= bigDLVersion, s"bigDL version mismatch," + - s"module version $version," + - s"bigdl version $bigDLVersion, you cannot use low version bigdl" + - s" to load a higher version module") + val moduleVersion = module.getVersion + val modelVersionSplits = moduleVersion.split(".") + val bigdlVersionSplits = bigDLVersion.split(".") + require(modelVersionSplits.length == bigdlVersionSplits.length, + s"model version ${moduleVersion} has different format as BigDL version ${bigDLVersion}") + (0 until modelVersionSplits.length).foreach(idx => { + require(modelVersionSplits(idx).toInt <= bigdlVersionSplits(idx).toInt, + s"bigDL version mismatch," + + s"module version $moduleVersion," + + s"bigdl version $bigDLVersion, you cannot use low version bigdl" + + s" to load a higher version module") + }) } protected def setVersion[T: ClassTag](modelBuilder : BigDLModule.Builder) From 00f270d73634a637e66cb7cf66ba137ce01f78f4 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Thu, 8 Aug 2019 11:06:29 +0800 Subject: [PATCH 0938/1065] convert IRgraph momentum to mkldnn (#2872) --- .../analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index e7c32a671c7..bc2e2350e6e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -161,7 +161,7 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] require(t.dataFormat == DataFormat.NCHW, "Dnn SpatialBatchNormalization only supports NCHW") val nOutput = t.nOutput val eps = t.eps - val momentum = t.momentum + val momentum = 1 - t.momentum // meaning not same with mkldnn val initWeight = t.initWeight val initBias = t.initBias val initGradWeight = t.initGradWeight From ccc6a4f6d5aa68b50dda603182fa9ac26f391589 Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Fri, 16 Aug 2019 13:00:44 +0800 Subject: [PATCH 0939/1065] feat: RoiAlign Forward (#2874) --- .../analytics/bigdl/dllib/nn/Pooler.scala | 162 ++++++ .../analytics/bigdl/dllib/nn/RoiAlign.scala | 500 ++++++++++++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 8 + .../analytics/bigdl/dllib/nn/PoolerSpec.scala | 233 ++++++++ .../bigdl/dllib/nn/RoiAlignSpec.scala | 191 +++++++ 5 files changed, 1094 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala new file mode 100644 index 00000000000..bc2ee7e6b2e --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala @@ -0,0 +1,162 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect._ + +/** + * Pooler selects the feature map which matches the size of RoI for RoIAlign + * @param resolution The resolution of pooled feature maps. Height equals width. + * @param scales Spatial scales of each feature map + * @param samplingRatio Sampling ratio + */ + +class Pooler[T: ClassTag] ( + val resolution: Int, + val scales: Array[Float], + val samplingRatio: Int +) (implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{ + private val num_levels = scales.length + private val poolers = new Array[RoiAlign[T]](num_levels) + + for (i <- 0 until num_levels) { + poolers(i) = RoiAlign[T](scales(i), samplingRatio, resolution, resolution) + } + + private val lvl_min = if (classTag[T] == classTag[Float]) { + (-Math.log(scales(0))/Math.log(2.0)).toInt + } else if (classTag[T] == classTag[Double]) { + (-Math.log(scales(0))/Math.log(2.0)).toInt + } else { + throw new IllegalArgumentException("currently only Double and Float types are supported") + } + + private val lvl_max = if (classTag[T] == classTag[Float]) { + (-Math.log(scales(num_levels - 1))/Math.log(2.0)).toInt + } else if (classTag[T] == classTag[Double]) { + (-Math.log(scales(num_levels - 1))/Math.log(2.0)).toInt + } else { + throw new IllegalArgumentException("currently only Double and Float types are supported") + } + + private def levelMapping( + k_min: Int, + k_max: Int, + rois: Tensor[T], + canonical_scale: Int = 224, + canonical_level: Int = 4, + eps: Float = 1e-6f + ): Array[Int] = { + val s0 = canonical_scale + val lvl0 = canonical_level + + val target_lvls = new Array[Int](rois.size(1)) + for (i <- 1 to rois.size(1)) { + val a = if (classTag[T] == classTag[Float]) { + area(rois(i)).asInstanceOf[Float].toDouble + } else if (classTag[T] == classTag[Double]) { + area(rois(i)).asInstanceOf[Double] + } else { + throw new IllegalArgumentException("currently only Double and Float types are supported") + } + + val s = Math.sqrt(a) + var target_lvl = Math.floor(lvl0 + Math.log(s / s0 + eps) / Math.log(2)) + target_lvl = Math.min(Math.max(target_lvl, k_min), k_max) + target_lvls(i - 1) = (target_lvl - k_min).toInt + } + + target_lvls + } + + private def area(roi: Tensor[T]): T = { + require(roi.size().length == 1 && roi.size(1) == 4, + s"ROI bounding box should be 1 dimensional and have 4 elements " + + s"(xlow, ylow, xhigh, yhigh)") + val xlow = roi.valueAt(1) + val ylow = roi.valueAt(2) + val xhigh = roi.valueAt(3) + val yhigh = roi.valueAt(4) + + val area = ev.times(ev.plus(ev.minus(xhigh, xlow), ev.fromType(1)), + ev.plus(ev.minus(yhigh, ylow), ev.fromType(1))) + area + } + + override def updateOutput(input: Table): Tensor[T] = { + val featureMaps = input[Table](1) + val rois = input[Tensor[T]](2) + + require(featureMaps.length() == num_levels, + "The number of feature maps should be same as the size of scales") + + val roi_levels = levelMapping(lvl_min, lvl_max, rois) + val num_rois = rois.size(1) + val num_channels = featureMaps.get[Tensor[T]](1).get.size(2) + + output.resize(num_rois, num_channels, resolution, resolution) + .fill(ev.fromType[Float](Float.MinValue)) + + for (level <- 0 until num_levels) { + val feature_per_level = featureMaps.get[Tensor[T]](level + 1).get + val rois_ind_per_level = roi_levels.zipWithIndex.filter(_._1 == level).map(_._2) + val num_rois_per_level = rois_ind_per_level.length + + if (num_rois_per_level > 0) { + val rois_per_level = Tensor[T](Array(num_rois_per_level, 4)) // bbox has 4 elements + for (i <- 0 until num_rois_per_level) { + rois_per_level(i + 1) = rois(rois_ind_per_level(i) + 1) + } + + val res = poolers(level).forward(T(feature_per_level, rois_per_level)) + for (i <- 0 until num_rois_per_level) { + output(rois_ind_per_level(i) + 1) = res(i + 1) + } + } + } + + output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + throw new UnsupportedOperationException("Not support backward propagation") + } + + override def toString: String = "nn.Pooler" + + override def clearState(): this.type = { + super.clearState() + for (i <- 0 until num_levels) { + poolers(i).clearState() + } + this + } +} + +object Pooler { + def apply[@specialized(Float, Double) T: ClassTag]( + resolution: Int, + scales: Array[Float], + samplingRatio: Int) (implicit ev: TensorNumeric[T]): Pooler[T] = + new Pooler[T](resolution, scales, samplingRatio) +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala new file mode 100644 index 00000000000..ce56d67e122 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala @@ -0,0 +1,500 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.Table +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect._ + +/** + * Region of interest aligning (RoIAlign) for Mask-RCNN + * + * The RoIAlign uses average pooling on bilinear-interpolated sub-windows to convert + * the features inside any valid region of interest into a small feature map with a + * fixed spatial extent of pooledH * pooledW (e.g., 7 * 7). + * An RoI is a rectangular window into a conv feature map. + * Each RoI is defined by a four-tuple (x1, y1, x2, y2) that specifies its + * top-left corner (x1, y1) and its bottom-right corner (x2, y2). + * RoIAlign works by dividing the h * w RoI window into an pooledH * pooledW grid of + * sub-windows of approximate size h/H * w/W. In each sub-window, compute exact values + * of input features at four regularly sampled locations, and then do average pooling on + * the values in each sub-window. + * Pooling is applied independently to each feature map channel + * @param spatialScale Spatial scale + * @param samplingRatio Sampling ratio + * @param pooledH spatial extent in height + * @param pooledW spatial extent in width + */ +class RoiAlign[T: ClassTag] ( + val spatialScale: Float, + val samplingRatio: Int, + val pooledH: Int, + val pooledW: Int +) (implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{ + override def updateOutput(input: Table): Tensor[T] = { + if (classTag[T] == classTag[Float]) { + val data = input[Tensor[Float]](1) + val rois = input[Tensor[Float]](2) + + val num_rois = rois.size(1) + val channels = data.size(2) + val height = data.size(3) + val width = data.size(4) + + output.resize(num_rois, channels, pooledH, pooledW) + .fill(ev.fromType[Float](Float.MinValue)) + require(output.nElement() != 0, "Output contains no elements") + + val inputData = data.storage().array() + val outputData = output.storage().array().asInstanceOf[Array[Float]] + val roisFloat = rois.storage().array() + + poolOneRoiFloat( + inputData, + outputData, + roisFloat, + num_rois, + channels, + height, + width, + spatialScale) + } else if (classTag[T] == classTag[Double]) { + val data = input[Tensor[Double]](1) + val rois = input[Tensor[Double]](2) + + val num_rois = rois.size(1) + val channels = data.size(2) + val height = data.size(3) + val width = data.size(4) + + output.resize(num_rois, channels, pooledH, pooledW) + .fill(ev.fromType[Double](Float.MinValue)) + require(output.nElement() != 0, "Output contains no elements") + + val inputData = data.storage().array() + val outputData = output.storage().array().asInstanceOf[Array[Double]] + val roisFloat = rois.storage().array() + + poolOneRoiDouble( + inputData, + outputData, + roisFloat, + num_rois, + channels, + height, + width, + spatialScale) + } else { + throw new IllegalArgumentException("currently only Double and Float types are supported") + } + + output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { + throw new UnsupportedOperationException("Not support backward propagation") + } + + private def poolOneRoiFloat( + inputData: Array[Float], + outputData: Array[Float], + roisFloat: Array[Float], + num_rois: Int, + channels: Int, + height: Int, + width: Int, + spatialScale: Float + ): Unit = { + val roi_cols = 4 // bbox has 4 elements + + for (n <- 0 until num_rois) { + val index_n = n * channels * pooledW * pooledH + var offset_rois = n * roi_cols + val roi_batch_ind = 0 // bbox has 4 elements + + val roi_start_w = roisFloat(offset_rois) * spatialScale + val roi_start_h = roisFloat(offset_rois + 1) * spatialScale + val roi_end_w = roisFloat(offset_rois + 2) * spatialScale + val roi_end_h = roisFloat(offset_rois + 3) * spatialScale + + val roi_width = Math.max(roi_end_w - roi_start_w, 1.0f) + val roi_height = Math.max(roi_end_h - roi_start_h, 1.0f) + val bin_size_h = roi_height/ pooledH + val bin_size_w = roi_width / pooledW + + val roi_bin_grid_h = if (samplingRatio > 0) { + samplingRatio + } else { + Math.ceil(roi_height / pooledH).toInt + } + + val roi_bin_grid_w = if (samplingRatio > 0) { + samplingRatio + } else { + Math.ceil(roi_width / pooledW).toInt + } + + val count: Float = roi_bin_grid_h * roi_bin_grid_w + + val pre_cal = Tensor[Float]( + Array(pooledH * pooledW * roi_bin_grid_h * roi_bin_grid_w, 8)) + + preCalcForBilinearInterpolateFloat( + height, + width, + roi_bin_grid_h, + roi_bin_grid_w, + roi_start_h, + roi_start_w, + bin_size_h, + bin_size_w, + roi_bin_grid_h, + roi_bin_grid_w, + pre_cal + ) + + for (c <- 0 until channels) { + val index_n_c = index_n + c * pooledW * pooledH + val offset_data = (roi_batch_ind * channels + c) * height * width + var pre_calc_index: Int = 1 + + for (ph <- 0 until pooledH) { + for (pw <- 0 until pooledW) { + val index = index_n_c + ph * pooledW + pw + + var output_val: Float = 0.0f + for (iy <- 0 until roi_bin_grid_h) { + for (ix <- 0 until roi_bin_grid_w) { + val pc = pre_cal(pre_calc_index) + val pos1 = pc.valueAt(1).toInt + val pos2 = pc.valueAt(2).toInt + val pos3 = pc.valueAt(3).toInt + val pos4 = pc.valueAt(4).toInt + val w1 = pc.valueAt(5) + val w2 = pc.valueAt(6) + val w3 = pc.valueAt(7) + val w4 = pc.valueAt(8) + + output_val = output_val + w1 * inputData(offset_data.toInt + pos1) + + w2 * inputData(offset_data.toInt + pos2) + + w3 * inputData(offset_data.toInt + pos3) + + w4 * inputData(offset_data.toInt + pos4) + + pre_calc_index += 1 + } + } + output_val /= count + + outputData(index) = output_val + } + } + } + } + } + + private def preCalcForBilinearInterpolateFloat( + height: Int, + width: Int, + iy_upper: Int, + ix_upper: Int, + roi_start_h: Float, + roi_start_w: Float, + bin_size_h: Float, + bin_size_w: Float, + roi_bin_grid_h: Int, + roi_bin_grid_w: Int, + pre_cal: Tensor[Float] + ) : Unit = { + var pre_calc_index: Int = 1 + + for (ph <- 0 until pooledH) { + for (pw <- 0 until pooledW) { + for (iy <- 0 until iy_upper) { + val yy = roi_start_h + ph * bin_size_h + (iy + 0.5f) * bin_size_h / roi_bin_grid_h + for (ix <- 0 until ix_upper) { + val xx = roi_start_w + pw * bin_size_w + (ix + 0.5f) * bin_size_w / roi_bin_grid_w + var x = xx + var y = yy + if (y < -1.0 || y > height || x < -1.0 || x > width) { + pre_cal.setValue(pre_calc_index, 1, 0.0f) // pos1 + pre_cal.setValue(pre_calc_index, 2, 0.0f) // pos2 + pre_cal.setValue(pre_calc_index, 3, 0.0f) // pos3 + pre_cal.setValue(pre_calc_index, 4, 0.0f) // pos4 + pre_cal.setValue(pre_calc_index, 5, 0.0f) // w1 + pre_cal.setValue(pre_calc_index, 6, 0.0f) // w2 + pre_cal.setValue(pre_calc_index, 7, 0.0f) // w3 + pre_cal.setValue(pre_calc_index, 8, 0.0f) // w4 + pre_calc_index += 1 + } + + else { + if (y <= 0) { + y = 0 + } + + if (x <= 0) { + x = 0 + } + + var y_low = y.toInt + var x_low = x.toInt + + val y_high = if (y_low >= height - 1) { + y_low = height -1 + y = y_low.toFloat + y_low + } else { + y_low + 1 + } + + val x_high = if (x_low >= width - 1) { + x_low = width -1 + x = x_low.toFloat + x_low + } else { + x_low + 1 + } + + val ly = y - y_low + val lx = x - x_low + val hy = 1.0f - ly + val hx = 1.0f - lx + val w1 = hy * hx + val w2 = hy * lx + val w3 = ly * hx + val w4 = ly * lx + + pre_cal.setValue(pre_calc_index, 1, y_low * width + x_low) + pre_cal.setValue(pre_calc_index, 2, y_low * width + x_high) + pre_cal.setValue(pre_calc_index, 3, y_high * width + x_low) + pre_cal.setValue(pre_calc_index, 4, y_high * width + x_high) + pre_cal.setValue(pre_calc_index, 5, w1) + pre_cal.setValue(pre_calc_index, 6, w2) + pre_cal.setValue(pre_calc_index, 7, w3) + pre_cal.setValue(pre_calc_index, 8, w4) + pre_calc_index += 1 + } + } + } + } + } + } + + private def poolOneRoiDouble( + inputData: Array[Double], + outputData: Array[Double], + roisDouble: Array[Double], + num_rois: Int, + channels: Int, + height: Int, + width: Int, + spatialScale: Float + ): Unit = { + val roi_cols = 4 // bbox has 4 elements + + for (n <- 0 until num_rois) { + val index_n = n * channels * pooledW * pooledH + var offset_rois = n * roi_cols + val roi_batch_ind = 0 + + val roi_start_w = roisDouble(offset_rois) * spatialScale.toDouble + val roi_start_h = roisDouble(offset_rois + 1) * spatialScale.toDouble + val roi_end_w = roisDouble(offset_rois + 2) * spatialScale.toDouble + val roi_end_h = roisDouble(offset_rois + 3) * spatialScale.toDouble + + val roi_width = Math.max(roi_end_w - roi_start_w, 1.0) + val roi_height = Math.max(roi_end_h - roi_start_h, 1.0) + val bin_size_h = roi_height/ pooledH + val bin_size_w = roi_width / pooledW + + val roi_bin_grid_h = if (samplingRatio > 0) { + samplingRatio + } else { + Math.ceil(roi_height / pooledH).toInt + } + + val roi_bin_grid_w = if (samplingRatio > 0) { + samplingRatio + } else { + Math.ceil(roi_width / pooledW).toInt + } + + val count: Double = roi_bin_grid_h * roi_bin_grid_w + + val pre_cal = Tensor[Double]( + Array(pooledH * pooledW * roi_bin_grid_h * roi_bin_grid_w, 8)) + + preCalcForBilinearInterpolateDouble( + height, + width, + roi_bin_grid_h, + roi_bin_grid_w, + roi_start_h, + roi_start_w, + bin_size_h, + bin_size_w, + roi_bin_grid_h, + roi_bin_grid_w, + pre_cal + ) + + for (c <- 0 until channels) { + val index_n_c = index_n + c * pooledW * pooledH + val offset_data = (roi_batch_ind * channels + c) * height * width + var pre_calc_index: Int = 1 + + for (ph <- 0 until pooledH) { + for (pw <- 0 until pooledW) { + val index = index_n_c + ph * pooledW + pw + + var output_val: Double = 0.0 + for (iy <- 0 until roi_bin_grid_h) { + for (ix <- 0 until roi_bin_grid_w) { + val pc = pre_cal(pre_calc_index) + val pos1 = pc.valueAt(1).toInt + val pos2 = pc.valueAt(2).toInt + val pos3 = pc.valueAt(3).toInt + val pos4 = pc.valueAt(4).toInt + val w1 = pc.valueAt(5) + val w2 = pc.valueAt(6) + val w3 = pc.valueAt(7) + val w4 = pc.valueAt(8) + + output_val = output_val + w1 * inputData(offset_data.toInt + pos1) + + w2 * inputData(offset_data.toInt + pos2) + + w3 * inputData(offset_data.toInt + pos3) + + w4 * inputData(offset_data.toInt + pos4) + + pre_calc_index += 1 + } + } + output_val /= count + + outputData(index) = output_val + } + } + } + } + } + + private def preCalcForBilinearInterpolateDouble( + height: Int, + width: Int, + iy_upper: Int, + ix_upper: Int, + roi_start_h: Double, + roi_start_w: Double, + bin_size_h: Double, + bin_size_w: Double, + roi_bin_grid_h: Int, + roi_bin_grid_w: Int, + pre_cal: Tensor[Double] + ) : Unit = { + var pre_calc_index: Int = 1 + + for (ph <- 0 until pooledH) { + for (pw <- 0 until pooledW) { + for (iy <- 0 until iy_upper) { + val yy = roi_start_h + ph * bin_size_h + (iy + 0.5) * bin_size_h / roi_bin_grid_h + for (ix <- 0 until ix_upper) { + val xx = roi_start_w + pw * bin_size_w + (ix + 0.5) * bin_size_w / roi_bin_grid_w + var x = xx + var y = yy + if (y < -1.0 || y > height || x < -1.0 || x > width) { + pre_cal.setValue(pre_calc_index, 1, 0.0) // pos1 + pre_cal.setValue(pre_calc_index, 2, 0.0) // pos2 + pre_cal.setValue(pre_calc_index, 3, 0.0) // pos3 + pre_cal.setValue(pre_calc_index, 4, 0.0) // pos4 + pre_cal.setValue(pre_calc_index, 5, 0.0) // w1 + pre_cal.setValue(pre_calc_index, 6, 0.0) // w2 + pre_cal.setValue(pre_calc_index, 7, 0.0) // w3 + pre_cal.setValue(pre_calc_index, 8, 0.0) // w4 + pre_calc_index += 1 + } + + else { + if (y <= 0) { + y = 0 + } + + if (x <= 0) { + x = 0 + } + + var y_low = y.toInt + var x_low = x.toInt + + val y_high = if (y_low >= height - 1) { + y_low = height -1 + y = y_low.toDouble + y_low + } else { + y_low + 1 + } + + val x_high = if (x_low >= width - 1) { + x_low = width -1 + x = x_low.toDouble + x_low + } else { + x_low + 1 + } + + val ly = y - y_low + val lx = x - x_low + val hy = 1.0f - ly + val hx = 1.0f - lx + val w1 = hy * hx + val w2 = hy * lx + val w3 = ly * hx + val w4 = ly * lx + + pre_cal.setValue(pre_calc_index, 1, y_low * width + x_low) + pre_cal.setValue(pre_calc_index, 2, y_low * width + x_high) + pre_cal.setValue(pre_calc_index, 3, y_high * width + x_low) + pre_cal.setValue(pre_calc_index, 4, y_high * width + x_high) + pre_cal.setValue(pre_calc_index, 5, w1) + pre_cal.setValue(pre_calc_index, 6, w2) + pre_cal.setValue(pre_calc_index, 7, w3) + pre_cal.setValue(pre_calc_index, 8, w4) + pre_calc_index += 1 + } + } + } + } + } + } + + override def toString: String = "nn.RoiAlign" + + override def clearState(): this.type = { + super.clearState() + this + } +} + +object RoiAlign { + def apply[@specialized(Float, Double) T: ClassTag]( + spatialScale: Float, + samplingRatio: Int, + pooledH: Int, + pooledW: Int) (implicit ev: TensorNumeric[T]): RoiAlign[T] = + new RoiAlign[T](spatialScale, samplingRatio, pooledH, pooledW) +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0d37eec50d1..489bb5ac3ae 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1148,6 +1148,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ev.fromType(spatial_scale)) } + def createRoiAlign(spatial_scale: Double, sampling_ratio: Int, pooled_h: Int, pooled_w: Int) + : RoiAlign[T] = { + RoiAlign[T](spatial_scale.toFloat, + sampling_ratio, + pooled_h, + pooled_w) + } + def createScale(size: JList[Int]) : Scale[T] = { Scale[T](size.asScala.toArray) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala new file mode 100644 index 00000000000..32c966e8323 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala @@ -0,0 +1,233 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.util.Random + +class PoolerSpec extends FlatSpec with Matchers { + "updateOutput Float type" should "work properly" in { + val feature0 = Array( + 0.883362829685211182, 0.017709493637084961, 0.740627527236938477, + 0.975574254989624023, 0.904063880443572998, 0.293959677219390869, + 0.301572918891906738, 0.235482156276702881) + + val feature1 = Array( + 0.873747766017913818, 0.145658850669860840, 0.256294071674346924, + 0.280913352966308594, 0.062630355358123779, 0.272662281990051270, + 0.524160504341125488, 0.110454082489013672, 0.619955241680145264, + 0.568557560443878174, 0.214293479919433594, 0.648296296596527100, + 0.165463507175445557, 0.419352889060974121, 0.852317929267883301, + 0.628634154796600342, 0.678495228290557861, 0.896998584270477295, + 0.890723347663879395, 0.488525688648223877, 0.384370744228363037, + 0.571207761764526367, 0.788873314857482910, 0.954643964767456055, + 0.969983577728271484, 0.203537940979003906, 0.782353222370147705, + 0.848326086997985840, 0.304318606853485107, 0.800064325332641602, + 0.424848318099975586, 0.603751122951507568) + + val feature2 = Array( + 0.023863613605499268, 0.100520193576812744, 0.579659581184387207, + 0.491799056529998779, 0.695049762725830078, 0.174113810062408447, + 0.514802277088165283, 0.645381748676300049, 0.610754907131195068, + 0.642783403396606445, 0.261436760425567627, 0.865309834480285645, + 0.779586195945739746, 0.805720150470733643, 0.039021611213684082, + 0.052066206932067871, 0.859684348106384277, 0.286012887954711914, + 0.183007895946502686, 0.657920598983764648, 0.486495614051818848, + 0.339991390705108643, 0.349600136280059814, 0.292829811573028564, + 0.874850273132324219, 0.923728287220001221, 0.853209257125854492, + 0.078126728534698486, 0.975298523902893066, 0.889039456844329834, + 0.757552802562713623, 0.009770631790161133, 0.639949500560760498, + 0.384162366390228271, 0.993775784969329834, 0.225636243820190430, + 0.152042329311370850, 0.518522977828979492, 0.346138358116149902, + 0.560805261135101318, 0.197446644306182861, 0.270632088184356689, + 0.537619173526763916, 0.282237291336059570, 0.418838739395141602, + 0.348786175251007080, 0.827486872673034668, 0.671141088008880615, + 0.734223365783691406, 0.461709976196289062, 0.463822364807128906, + 0.256826639175415039, 0.187998294830322266, 0.387186825275421143, + 0.027970135211944580, 0.336534321308135986, 0.078408479690551758, + 0.748133420944213867, 0.996697187423706055, 0.590924799442291260, + 0.363863050937652588, 0.244512259960174561, 0.605456709861755371, + 0.989919960498809814, 0.998104333877563477, 0.318823933601379395, + 0.293298780918121338, 0.240437865257263184, 0.269145488739013672, + 0.321916043758392334, 0.241542100906372070, 0.097301602363586426, + 0.139740049839019775, 0.727295756340026855, 0.735020518302917480, + 0.977046966552734375, 0.562069535255432129, 0.962157845497131348, + 0.896494269371032715, 0.919544279575347900, 0.769982337951660156, + 0.902598083019256592, 0.699079096317291260, 0.970299720764160156, + 0.877977848052978516, 0.445257008075714111, 0.903108179569244385, + 0.029258608818054199, 0.953712522983551025, 0.740538537502288818, + 0.229142010211944580, 0.324616789817810059, 0.546005189418792725, + 0.471910834312438965, 0.479964077472686768, 0.404208302497863770, + 0.816056787967681885, 0.116290867328643799, 0.845461726188659668, + 0.313867926597595215, 0.281320571899414062, 0.693770170211791992, + 0.623112499713897705, 0.370123684406280518, 0.595665276050567627, + 0.433298051357269287, 0.971214890480041504, 0.087709188461303711, + 0.069373369216918945, 0.274347186088562012, 0.470574259757995605, + 0.883642554283142090, 0.518250524997711182, 0.118440926074981689, + 0.606658637523651123, 0.529120385646820068, 0.991135418415069580, + 0.020969033241271973, 0.601271688938140869, 0.031737148761749268, + 0.699844896793365479, 0.006896257400512695, 0.478346049785614014, + 0.267558634281158447, 0.762180626392364502, 0.907826840877532959, + 0.316000878810882568, 0.405982732772827148) + + val features = new Table() + features.insert(Tensor(Storage(feature0.map(x => x.toFloat))).resize(1, 2, 2, 2)) + features.insert(Tensor(Storage(feature1.map(x => x.toFloat))).resize(1, 2, 4, 4)) + features.insert(Tensor(Storage(feature2.map(x => x.toFloat))).resize(1, 2, 8, 8)) + val rois = Tensor[Float]( + T(T(0, 0, 3, 3), + T(2, 2, 50, 50), + T(50, 50, 500, 500))).resize(3, 4) + val input = T(features, rois) + + val pooler = Pooler[Float](resolution = 2, scales = Array(1.0f, 0.5f, 0.25f), samplingRatio = 2) + val res = pooler.forward(input) + val expectedRes = Array( + 0.710301160812377930, 0.338120758533477783, + 0.451076686382293701, 0.243893563747406006, + 0.327536046504974365, 0.126878187060356140, + 0.128067761659622192, 0.058870539069175720, + 0.157158538699150085, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.150937780737876892, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000) + + for (i <- expectedRes.indices) { + assert(Math.abs(res.storage().array()(i) - expectedRes(i)) < 1e-6) + } + } + + "updateOutput Double type" should "work properly" in { + val feature0 = Array( + 0.883362829685211182, 0.017709493637084961, 0.740627527236938477, + 0.975574254989624023, 0.904063880443572998, 0.293959677219390869, + 0.301572918891906738, 0.235482156276702881) + + val feature1 = Array( + 0.873747766017913818, 0.145658850669860840, 0.256294071674346924, + 0.280913352966308594, 0.062630355358123779, 0.272662281990051270, + 0.524160504341125488, 0.110454082489013672, 0.619955241680145264, + 0.568557560443878174, 0.214293479919433594, 0.648296296596527100, + 0.165463507175445557, 0.419352889060974121, 0.852317929267883301, + 0.628634154796600342, 0.678495228290557861, 0.896998584270477295, + 0.890723347663879395, 0.488525688648223877, 0.384370744228363037, + 0.571207761764526367, 0.788873314857482910, 0.954643964767456055, + 0.969983577728271484, 0.203537940979003906, 0.782353222370147705, + 0.848326086997985840, 0.304318606853485107, 0.800064325332641602, + 0.424848318099975586, 0.603751122951507568) + + val feature2 = Array( + 0.023863613605499268, 0.100520193576812744, 0.579659581184387207, + 0.491799056529998779, 0.695049762725830078, 0.174113810062408447, + 0.514802277088165283, 0.645381748676300049, 0.610754907131195068, + 0.642783403396606445, 0.261436760425567627, 0.865309834480285645, + 0.779586195945739746, 0.805720150470733643, 0.039021611213684082, + 0.052066206932067871, 0.859684348106384277, 0.286012887954711914, + 0.183007895946502686, 0.657920598983764648, 0.486495614051818848, + 0.339991390705108643, 0.349600136280059814, 0.292829811573028564, + 0.874850273132324219, 0.923728287220001221, 0.853209257125854492, + 0.078126728534698486, 0.975298523902893066, 0.889039456844329834, + 0.757552802562713623, 0.009770631790161133, 0.639949500560760498, + 0.384162366390228271, 0.993775784969329834, 0.225636243820190430, + 0.152042329311370850, 0.518522977828979492, 0.346138358116149902, + 0.560805261135101318, 0.197446644306182861, 0.270632088184356689, + 0.537619173526763916, 0.282237291336059570, 0.418838739395141602, + 0.348786175251007080, 0.827486872673034668, 0.671141088008880615, + 0.734223365783691406, 0.461709976196289062, 0.463822364807128906, + 0.256826639175415039, 0.187998294830322266, 0.387186825275421143, + 0.027970135211944580, 0.336534321308135986, 0.078408479690551758, + 0.748133420944213867, 0.996697187423706055, 0.590924799442291260, + 0.363863050937652588, 0.244512259960174561, 0.605456709861755371, + 0.989919960498809814, 0.998104333877563477, 0.318823933601379395, + 0.293298780918121338, 0.240437865257263184, 0.269145488739013672, + 0.321916043758392334, 0.241542100906372070, 0.097301602363586426, + 0.139740049839019775, 0.727295756340026855, 0.735020518302917480, + 0.977046966552734375, 0.562069535255432129, 0.962157845497131348, + 0.896494269371032715, 0.919544279575347900, 0.769982337951660156, + 0.902598083019256592, 0.699079096317291260, 0.970299720764160156, + 0.877977848052978516, 0.445257008075714111, 0.903108179569244385, + 0.029258608818054199, 0.953712522983551025, 0.740538537502288818, + 0.229142010211944580, 0.324616789817810059, 0.546005189418792725, + 0.471910834312438965, 0.479964077472686768, 0.404208302497863770, + 0.816056787967681885, 0.116290867328643799, 0.845461726188659668, + 0.313867926597595215, 0.281320571899414062, 0.693770170211791992, + 0.623112499713897705, 0.370123684406280518, 0.595665276050567627, + 0.433298051357269287, 0.971214890480041504, 0.087709188461303711, + 0.069373369216918945, 0.274347186088562012, 0.470574259757995605, + 0.883642554283142090, 0.518250524997711182, 0.118440926074981689, + 0.606658637523651123, 0.529120385646820068, 0.991135418415069580, + 0.020969033241271973, 0.601271688938140869, 0.031737148761749268, + 0.699844896793365479, 0.006896257400512695, 0.478346049785614014, + 0.267558634281158447, 0.762180626392364502, 0.907826840877532959, + 0.316000878810882568, 0.405982732772827148) + + val features = new Table() + features.insert(Tensor(Storage(feature0.map(x => x))).resize(1, 2, 2, 2)) + features.insert(Tensor(Storage(feature1.map(x => x))).resize(1, 2, 4, 4)) + features.insert(Tensor(Storage(feature2.map(x => x))).resize(1, 2, 8, 8)) + val rois = Tensor[Double]( + T(T(0, 0, 3, 3), + T(2, 2, 50, 50), + T(50, 50, 500, 500))).resize(3, 4) + val input = T(features, rois) + + val pooler = Pooler[Double](resolution = 2, scales = Array(1.0f, 0.5f, 0.25f), + samplingRatio = 2) + val res = pooler.forward(input) + val expectedRes = Array( + 0.710301160812377930, 0.338120758533477783, + 0.451076686382293701, 0.243893563747406006, + 0.327536046504974365, 0.126878187060356140, + 0.128067761659622192, 0.058870539069175720, + 0.157158538699150085, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.150937780737876892, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000, + 0.000000000000000000, 0.000000000000000000) + + for (i <- expectedRes.indices) { + assert(Math.abs(res.storage().array()(i) - expectedRes(i)) < 1e-6) + } + } +} + +class PoolerSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = T() + val feature0 = Tensor[Float](1, 1, 2, 2).apply1(_ => Random.nextFloat()) + val feature1 = Tensor[Float](1, 1, 4, 4).apply1(_ => Random.nextFloat()) + val feature2 = Tensor[Float](1, 1, 8, 8).apply1(_ => Random.nextFloat()) + val features = T(feature0, feature1, feature2) + val rois = Tensor[Float](1, 4).apply1(_ => Random.nextFloat()) + input(1.0f) = features + input(2.0f) = rois + val pooler = new Pooler[Float](resolution = 2, scales = Array(1.0f, 0.5f, 0.25f), + samplingRatio = 2).setName("pooler") + runSerializationTest(pooler, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala new file mode 100644 index 00000000000..a6d29da1230 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala @@ -0,0 +1,191 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{T, Table} +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class RoiAlignSpec extends FlatSpec with Matchers { + "updateOutput Float type" should "work properly" in { + val spatio_scale: Float = 1.0f + val sampling_ratio: Int = 3 + val pooled_height: Int = 2 + val pooled_width: Int = 2 + + val data = Array( + 0.327660024166107178, 0.783334434032440186, 0.359168112277984619, + 0.934897661209106445, 0.650066614151000977, 0.834474444389343262, + 0.424300372600555420, 0.149160504341125488, 0.730795919895172119, + 0.484096407890319824, 0.994338274002075195, 0.250495135784149170, + 0.259522974491119385, 0.887678027153015137, 0.194342792034149170, + 0.610941588878631592, 0.416747927665710449, 0.705707132816314697, + 0.435783147811889648, 0.778170645236968994, 0.193895518779754639, + 0.849628329277038574, 0.882959723472595215, 0.721439063549041748, + 0.832545340061187744, 0.774163544178009033, 0.781816542148590088, + 0.729343354701995850, 0.203778445720672607, 0.198633491992950439, + 0.781321287155151367, 0.118729889392852783, 0.643143951892852783, + 0.760397315025329590, 0.285254061222076416, 0.553620159626007080, + 0.232052326202392578, 0.728380799293518066, 0.775489747524261475, + 0.928656220436096191, 0.163158237934112549, 0.718611896038055420, + 0.744661569595336914, 0.593953251838684082, 0.372228324413299561, + 0.902524888515472412, 0.278600215911865234, 0.506435513496398926, + 0.818576753139495850, 0.757465600967407227, 0.705808222293853760, + 0.710981726646423340, 0.963726997375488281, 0.164355456829071045, + 0.780107796192169189, 0.850457072257995605, 0.839718520641326904, + 0.593321025371551514, 0.280547201633453369, 0.348339796066284180, + 0.423507034778594971, 0.949673593044281006, 0.518748283386230469, + 0.845408916473388672, 0.901987016201019287, 0.058945477008819580, + 0.631618440151214600, 0.488164126873016357, 0.698010146617889404, + 0.215178430080413818, 0.665156781673431396, 0.499578237533569336, + 0.863550186157226562, 0.088476061820983887, 0.177395820617675781, + 0.397035181522369385, 0.484034657478332520, 0.105176448822021484, + 0.095181167125701904, 0.111114203929901123, 0.715093195438385010, + 0.993503451347351074, 0.484178066253662109, 0.422980725765228271, + 0.192607104778289795, 0.983097016811370850, 0.638218641281127930, + 0.158814728260040283, 0.990248799324035645, 0.539387941360473633, + 0.657688558101654053, 0.316274046897888184, 0.851949751377105713, + 0.227342128753662109, 0.238007068634033203, 0.980791330337524414) + + val rois = Array( + 0, 0, 7, 5, + 6, 2, 7, 5, + 3, 1, 6, 4, + 3, 3, 3, 3) + + val input = new Table + input.insert(Tensor(Storage(data.map(x => x.toFloat))).resize(1, 2, 6, 8)) + input.insert(Tensor(Storage(rois.map(x => x.toFloat))).resize(4, 4)) + + val roiAlign = RoiAlign[Float](spatio_scale, sampling_ratio, pooled_height, pooled_width) + val res = roiAlign.forward(input) + val expectedRes = Array( + 0.614743709564208984, 0.550280153751373291, + 0.648947238922119141, 0.494060248136520386, + 0.514606714248657227, 0.596958041191101074, + 0.494195610284805298, 0.408652573823928833, + 0.707817792892456055, 0.494023799896240234, + 0.637864947319030762, 0.692903101444244385, + 0.308963924646377563, 0.266039490699768066, + 0.451879233121871948, 0.436514317989349365, + 0.393088847398757935, 0.704402685165405273, + 0.384622871875762939, 0.530835568904876709, + 0.525619626045227051, 0.501667082309722900, + 0.407763212919235229, 0.379031181335449219, + 0.566771149635314941, 0.329488337039947510, + 0.504409193992614746, 0.318125635385513306, + 0.405435621738433838, 0.409263730049133301, + 0.378736764192581177, 0.303221583366394043 + ) + + for (i <- expectedRes.indices) { + assert(Math.abs(res.storage().array()(i) - expectedRes(i)) < 1e-6) + } + } + + "updateOutput Double type" should "work properly" in { + val spatio_scale: Float = 1.0f + val sampling_ratio: Int = 3 + val pooled_height: Int = 2 + val pooled_width: Int = 2 + + val data = Array( + 0.327660024166107178, 0.783334434032440186, 0.359168112277984619, + 0.934897661209106445, 0.650066614151000977, 0.834474444389343262, + 0.424300372600555420, 0.149160504341125488, 0.730795919895172119, + 0.484096407890319824, 0.994338274002075195, 0.250495135784149170, + 0.259522974491119385, 0.887678027153015137, 0.194342792034149170, + 0.610941588878631592, 0.416747927665710449, 0.705707132816314697, + 0.435783147811889648, 0.778170645236968994, 0.193895518779754639, + 0.849628329277038574, 0.882959723472595215, 0.721439063549041748, + 0.832545340061187744, 0.774163544178009033, 0.781816542148590088, + 0.729343354701995850, 0.203778445720672607, 0.198633491992950439, + 0.781321287155151367, 0.118729889392852783, 0.643143951892852783, + 0.760397315025329590, 0.285254061222076416, 0.553620159626007080, + 0.232052326202392578, 0.728380799293518066, 0.775489747524261475, + 0.928656220436096191, 0.163158237934112549, 0.718611896038055420, + 0.744661569595336914, 0.593953251838684082, 0.372228324413299561, + 0.902524888515472412, 0.278600215911865234, 0.506435513496398926, + 0.818576753139495850, 0.757465600967407227, 0.705808222293853760, + 0.710981726646423340, 0.963726997375488281, 0.164355456829071045, + 0.780107796192169189, 0.850457072257995605, 0.839718520641326904, + 0.593321025371551514, 0.280547201633453369, 0.348339796066284180, + 0.423507034778594971, 0.949673593044281006, 0.518748283386230469, + 0.845408916473388672, 0.901987016201019287, 0.058945477008819580, + 0.631618440151214600, 0.488164126873016357, 0.698010146617889404, + 0.215178430080413818, 0.665156781673431396, 0.499578237533569336, + 0.863550186157226562, 0.088476061820983887, 0.177395820617675781, + 0.397035181522369385, 0.484034657478332520, 0.105176448822021484, + 0.095181167125701904, 0.111114203929901123, 0.715093195438385010, + 0.993503451347351074, 0.484178066253662109, 0.422980725765228271, + 0.192607104778289795, 0.983097016811370850, 0.638218641281127930, + 0.158814728260040283, 0.990248799324035645, 0.539387941360473633, + 0.657688558101654053, 0.316274046897888184, 0.851949751377105713, + 0.227342128753662109, 0.238007068634033203, 0.980791330337524414) + + val rois = Array( + 0, 0, 7, 5, + 6, 2, 7, 5, + 3, 1, 6, 4, + 3, 3, 3, 3) + + val input = new Table + input.insert(Tensor(Storage(data.map(x => x))).resize(1, 2, 6, 8)) + input.insert(Tensor(Storage(rois.map(x => x.toDouble))).resize(4, 4)) + + val roiAlign = RoiAlign[Double](spatio_scale, sampling_ratio, pooled_height, pooled_width) + val res = roiAlign.forward(input) + val expectedRes = Array( + 0.614743709564208984, 0.550280153751373291, + 0.648947238922119141, 0.494060248136520386, + 0.514606714248657227, 0.596958041191101074, + 0.494195610284805298, 0.408652573823928833, + 0.707817792892456055, 0.494023799896240234, + 0.637864947319030762, 0.692903101444244385, + 0.308963924646377563, 0.266039490699768066, + 0.451879233121871948, 0.436514317989349365, + 0.393088847398757935, 0.704402685165405273, + 0.384622871875762939, 0.530835568904876709, + 0.525619626045227051, 0.501667082309722900, + 0.407763212919235229, 0.379031181335449219, + 0.566771149635314941, 0.329488337039947510, + 0.504409193992614746, 0.318125635385513306, + 0.405435621738433838, 0.409263730049133301, + 0.378736764192581177, 0.303221583366394043 + ) + + for (i <- expectedRes.indices) { + assert(Math.abs(res.storage().array()(i) - expectedRes(i)) < 1e-6) + } + } +} + +class RoiAlignSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = T() + val input1 = Tensor[Float](1, 1, 2, 2).apply1(_ => Random.nextFloat()) + val input2 = Tensor[Float](1, 4).apply1(_ => Random.nextFloat()) + input(1.0f) = input1 + input(2.0f) = input2 + val roiAlign = new RoiAlign[Float](spatialScale = 1.0f, samplingRatio = 1, + pooledW = 1, pooledH = 1).setName("roiAlign") + runSerializationTest(roiAlign, input) + } +} From 29883632aba25268283a0d150d6b2bf925db37b3 Mon Sep 17 00:00:00 2001 From: jenniew Date: Thu, 15 Aug 2019 22:59:05 -0700 Subject: [PATCH 0940/1065] Add set input output format API in Python (#2880) * add set input output format * add static graph check --- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 489bb5ac3ae..c93eedd38eb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2597,6 +2597,14 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab model.saveGraphTopology(logPath) } + def setInputFormats(graph: StaticGraph[T], inputFormat: JList[Int]): StaticGraph[T] = { + graph.setInputFormats(inputFormat.asScala.toList) + } + + def setOutputFormats(graph: StaticGraph[T], outputFormat: JList[Int]): StaticGraph[T] = { + graph.setOutputFormats(outputFormat.asScala.toList) + } + def createResizeBilinear( outputHeight: Int, outputWidth: Int, From 9a912cd65378157615de614ff20757186e0cdbfd Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Wed, 21 Aug 2019 16:31:29 +0800 Subject: [PATCH 0941/1065] feat: Feature Pyramid Networks Forward (#2870) --- .../intel/analytics/bigdl/dllib/nn/FPN.scala | 120 +++++++++ .../dllib/utils/python/api/PythonBigDL.scala | 6 + .../analytics/bigdl/dllib/nn/FPNSpec.scala | 238 ++++++++++++++++++ 3 files changed, 364 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FPNSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala new file mode 100644 index 00000000000..76a2d7c1c2a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala @@ -0,0 +1,120 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.Graph.ModuleNode +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + +/** + * Feature Pyramid Network. + * @param inChannels number of channels of feature maps + * @param outChannels number of channels of FPN output + */ + +class FPN[T : ClassTag]( + val inChannels: Array[Int], + val outChannels: Int +) + (implicit ev: TensorNumeric[T]) + extends BaseModule[T]{ + override def buildModel(): Module[T] = { + val featureMapsNum = inChannels.length + val innerBlockModules = new Array[SpatialConvolution[T]](featureMapsNum) + val layerBlockModules = new Array[SpatialConvolution[T]](featureMapsNum) + + for (i <- 0 to featureMapsNum - 1) { + if (inChannels(i) != 0) { + val innerBlockModule = + SpatialConvolution[T](inChannels(i), outChannels, 1, 1, 1, 1) + val layerBlockModule = + SpatialConvolution[T](outChannels, outChannels, 3, 3, 1, 1, 1, 1) + innerBlockModules(i) = innerBlockModule + layerBlockModules(i) = layerBlockModule + } + } + + val inputs = new Array[ModuleNode[T]](featureMapsNum) + for (i <- 0 to featureMapsNum - 1) { + inputs(i) = Input[T]() + } + + val innerBlocks = new Array[ModuleNode[T]](featureMapsNum) + for (i <- 0 to featureMapsNum - 1) { + innerBlocks(i) = innerBlockModules(i).inputs(inputs(i)) + } + + var count = 0 + val results = new Array[ModuleNode[T]](featureMapsNum) + var lastInner = innerBlocks(featureMapsNum - 1) + results(count) = layerBlockModules(featureMapsNum - 1).inputs(lastInner) + + for(i <- featureMapsNum - 2 to 0 by -1) { + val layerBlock = layerBlockModules(i) + if (layerBlock != null) { + val innerTopDown = UpSampling2D[T](Array(2, 2)).inputs(lastInner) + val innerLateral = innerBlocks(i) + lastInner = CAddTable[T]().inputs(innerLateral, innerTopDown) + count += 1 + results(count) = layerBlock.inputs(lastInner) + } + } + + Graph(inputs, results) + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + throw new UnsupportedOperationException("Not support backward propagation") + } + + override def canEqual(other: Any): Boolean = other.isInstanceOf[FPN[T]] + + override def equals(other: Any): Boolean = other match { + case that: FPN[T] => + super.equals(that) && + (that canEqual this) && + inChannels.deep == that.inChannels.deep && + outChannels == that.outChannels + case _ => false + } + + override def hashCode(): Int = { + val state = Seq(super.hashCode(), inChannels, outChannels) + state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b) + } + + override def reset(): Unit = { + super.reset() + model.reset() + } + + override def toString: String = s"FPN($outChannels)" +} + +object FPN { + def apply[@specialized(Float, Double) T: ClassTag]( + inChannels: Array[Int], + outChannels: Int + )(implicit ev: TensorNumeric[T]): FPN[T] = { + new FPN[T](inChannels, outChannels) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index c93eedd38eb..129b8b1125f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1156,6 +1156,12 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab pooled_w) } + def createFPN(in_channels_list: JList[Int], out_channels: Int) + : FPN[T] = { + FPN[T](in_channels_list.asScala.toArray, + out_channels) + } + def createScale(size: JList[Int]) : Scale[T] = { Scale[T](size.asScala.toArray) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FPNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FPNSpec.scala new file mode 100644 index 00000000000..26901d95ed0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FPNSpec.scala @@ -0,0 +1,238 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.mkldnn.Equivalent +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest + +import scala.util.Random + +class FPNSpec extends FlatSpec with Matchers { + "FPN updateOutput" should "work correctly" in { + val in_channels_list = Array(1, 2, 4) + val out_channels = 2 + val model = FPN[Float](in_channels_list, out_channels) + + val feature1 = Tensor( + T(T(0.10110152, 0.10345000, 0.04320979, 0.84362656, + 0.59594363, 0.97288179, 0.34699517, 0.54275155), + T(0.93956870, 0.07543808, 0.50965708, 0.26184946, + 0.92378283, 0.83272308, 0.54440099, 0.56682664), + T(0.53608388, 0.74091697, 0.53824615, 0.12760854, + 0.70029002, 0.85137993, 0.01918983, 0.10134047), + T(0.61024511, 0.11725241, 0.46950370, 0.15163177, + 0.99792290, 0.50036842, 0.65618765, 0.76569498), + T(0.31238246, 0.96460360, 0.23587847, 0.94086981, + 0.15270233, 0.44916826, 0.53412461, 0.19992995), + T(0.14841199, 0.95466810, 0.89249784, 0.10235202, + 0.24293590, 0.83814293, 0.78163254, 0.94990700), + T(0.50397956, 0.23095572, 0.12026519, 0.70295823, + 0.80230796, 0.31913465, 0.86270124, 0.67926580), + T(0.93120003, 0.08011329, 0.30662805, 0.97467756, + 0.32988423, 0.90689850, 0.46856666, 0.66390038))) + .reshape(Array(1, 1, 8, 8)) + + val feature2 = Tensor( + T(T(T(0.30143285, 0.63111430, 0.45092928, 0.22753167), + T(0.80318344, 0.67537767, 0.14698678, 0.45962620), + T(0.21663177, 0.89086282, 0.92865956, 0.89360029), + T(0.49615270, 0.46269470, 0.73047608, 0.12438315)), + T(T(0.75820625, 0.59779423, 0.61585987, 0.35782731), + T(0.36951083, 0.35381025, 0.64314663, 0.75517660), + T(0.30200917, 0.69998586, 0.29572868, 0.46342885), + T(0.41677684, 0.26154006, 0.16909349, 0.94081402)))) + .reshape(Array(1, 2, 4, 4)) + + val feature3 = Tensor( + T(T(T(0.57270211, 0.25789189), + T(0.79134840, 0.62564188)), + T(T(0.27365083, 0.43420678), + T(0.61281836, 0.23570287)), + T(T(0.21393263, 0.50206852), + T(0.50650394, 0.73282623)), + T(T(0.20319027, 0.06753725), + T(0.18215942, 0.36703324)))) + .reshape(Array(1, 4, 2, 2)) + + val inner1_w = Tensor( + T(T(T(T(0.25616819)), + T(T(-0.74193102)), + T(T(0.22137421)), + T(T(0.53996474))), + T(T(T(-0.30102068)), + T(T(0.24491900)), + T(T(-0.84143710)), + T(T(-0.73395455))))) + .reshape(Array(1, 2, 4, 1, 1)) + val inner1_b = Tensor(T(0, 0)) + + val inner2_w = Tensor( + T(T(T(T(0.04691243)), + T(T(-0.90420955))), + T(T(T(1.09895408)), + T(T(0.51624501))))) + .reshape(Array(1, 2, 2, 1, 1)) + val inner2_b = Tensor(T(0, 0)) + + val inner3_w = Tensor( + T(T(T(T(0.24687862))), + T(T(T(-0.56227243))))) + .reshape(Array(1, 2, 1, 1, 1)) + val inner3_b = Tensor(T(0, 0)) + + val layer1_w = Tensor( + T(T(T(T(-0.04048228, 0.16222215, 0.10794550), + T(-0.34169874, -0.25080314, 0.11539066), + T(-0.27039635, 0.19380659, 0.19993830)), + T(T(0.12585402, -0.38708800, 0.09077036), + T(0.12301302, -0.29949811, 0.12835038), + T(-0.32869643, 0.37100095, -0.26665413))), + T(T(T(-0.23543328, -0.24697217, 0.15786803), + T(0.19520867, -0.06484443, 0.39382762), + T(-0.09158209, -0.22267270, 0.23828101)), + T(T(0.16857922, -0.26403868, -0.07582438), + T(0.31187642, -0.14743957, 0.19229126), + T(-0.00750843, -0.21541777, -0.04269919))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer1_b = Tensor(T(0, 0)) + + val layer2_w = Tensor( + T(T(T(T(-0.14214972, -0.17213514, -0.32127398), + T(-0.23303765, -0.27284676, -0.05630624), + T(-0.03209409, -0.16349350, -0.13884634)), + T(T(0.05150193, -0.01451367, 0.29302871), + T(0.38110715, 0.21102744, -0.01252702), + T(-0.14486188, 0.39937240, 0.26671016))), + T(T(T(-0.20462120, -0.03479487, -0.01640993), + T(0.34504193, 0.11599201, 0.40438360), + T(-0.17013551, 0.00606328, -0.14445123)), + T(T(0.15805143, -0.06925225, -0.24366492), + T(-0.16341771, -0.31556514, 0.03696010), + T(0.07415351, -0.08760622, -0.17086124))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer2_b = Tensor(T(0, 0)) + + val layer3_w = Tensor( + T(T(T(T(-0.21088375, 0.39961314, 0.28634924), + T(-0.09605905, -0.09238201, 0.29169798), + T(-0.16913360, 0.34432471, 0.12923980)), + T(T(0.15992212, 0.11829317, -0.08958191), + T(0.29556727, 0.28719366, 0.35837567), + T(0.35775679, 0.13369364, 0.22401685))), + T(T(T(0.23750001, -0.26816195, -0.33834153), + T(0.02364820, -0.28069261, -0.31661153), + T(-0.05442283, 0.30038035, 0.23050475)), + T(T(0.24013102, -0.04941136, -0.01676598), + T(0.36672127, -0.14019510, -0.18527937), + T(-0.21643242, -0.06160817, 0.14386815))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer3_b = Tensor(T(0, 0)) + + val result1 = Tensor( + T(T(T(T(-0.60857159, -0.49706429), + T(-0.44821957, -0.69798434)), + T(T(0.11003723, 0.24464746), + T(0.21994369, -0.22257896))))) + + val result2 = Tensor( + T(T(T(T(0.67646873, 0.75461042, 0.88370752, 0.72522950), + T(0.80561060, 1.40666068, 0.81269693, 0.72721291), + T(0.42856935, 0.57526082, 0.84400183, 0.24381584), + T(0.60819602, 0.32838598, 0.17468216, -0.05505963)), + T(T(-0.41587284, -0.59085888, -0.50279200, -0.25322908), + T(-0.42020139, -0.64106256, -0.23952308, -0.29740968), + T(-0.31366453, -0.12451494, -0.13788190, 0.07498236), + T(-0.31522152, -0.13974780, -0.06333419, 0.15230046))))) + + val result3 = Tensor( + T(T(T(T(-0.29643691, 0.32930288, 0.07719041, 0.20329267, -0.11702696, + 0.33030477, 0.19752777, 0.26074126), + T(-0.04022884, -0.04050549, -0.17072679, 0.05824373, -0.18035993, + -0.10781585, 0.21838233, 0.35475171), + T(-0.14252800, -0.16825707, -0.28704056, -0.26278189, -0.19001812, + 0.20092483, 0.17245048, 0.46969670), + T(-0.14943303, -0.45888224, 0.33286753, -0.42771903, 0.47255370, + 0.24915743, -0.21637592, 0.21200535), + T(0.00808068, -0.16809230, -0.14534889, 0.29852685, 0.36068499, + -0.19606119, -0.18463834, -0.19501874), + T(-0.06999602, 0.55371714, -0.33532500, 0.29894528, 0.44789663, + 0.21802102, -0.32107252, -0.07110818), + T(-0.19171244, 0.50532514, 0.00852559, -0.05432931, 0.56445789, + -0.21175916, 0.01788443, 0.39967728), + T(0.11412182, -0.05338766, 0.11950107, 0.33978215, 0.17466278, + -0.22752701, 0.06036017, 0.51162905)), + T(T(-0.18407047, -0.06274336, -0.19927005, -0.18067920, -0.12339569, + -0.10210013, -0.13622473, 0.09764731), + T(-0.21372095, -0.12506956, -0.10981269, -0.22901297, 0.15182146, + 0.01927174, -0.11695608, 0.25842062), + T(-0.08454411, 0.00893094, 0.06784435, -0.36769092, 0.24231599, + -0.07395025, -0.20645590, 0.32848105), + T(0.07287200, 0.06812082, 0.00125982, -0.20824122, 0.26192454, + -0.27801457, -0.43661070, 0.24346380), + T(-0.08816936, -0.14699535, -0.50232911, 0.17301719, 0.39865568, + 0.21348065, 0.22505483, 0.28257197), + T(0.12479763, -0.03339935, -0.48426947, 0.55722409, 0.36770806, + -0.01681852, 0.11375013, 0.19888467), + T(0.14368367, 0.01942967, -0.23314725, 0.41997516, 0.39273715, + -0.40041974, -0.07516777, 0.04501504), + T(-0.00356270, -0.15851222, 0.04203597, 0.33169088, -0.02303683, + -0.42069232, -0.08245742, 0.06082898))))) + + val input = T(feature1, feature2, feature3) + val expectedOutput = T(result1, result2, result3) + + model.parameters()._1(0).copy(inner1_w) + model.parameters()._1(1).copy(inner1_b) + model.parameters()._1(2).copy(inner2_w) + model.parameters()._1(3).copy(inner2_b) + model.parameters()._1(4).copy(inner3_w) + model.parameters()._1(5).copy(inner3_b) + model.parameters()._1(6).copy(layer1_w) + model.parameters()._1(7).copy(layer1_b) + model.parameters()._1(8).copy(layer2_w) + model.parameters()._1(9).copy(layer2_b) + model.parameters()._1(10).copy(layer3_w) + model.parameters()._1(11).copy(layer3_b) + + val output = model.forward(input) + + Equivalent.nearequals(output.toTable.get[Tensor[Float]](1).get, + expectedOutput.get[Tensor[Float]](1).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](2).get, + expectedOutput.get[Tensor[Float]](2).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](3).get, + expectedOutput.get[Tensor[Float]](3).get) should be(true) + } +} + +class FPNSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val input = T() + val feature1 = Tensor[Float](1, 1, 8, 8).apply1(_ => Random.nextFloat()) + val feature2 = Tensor[Float](1, 2, 4, 4).apply1(_ => Random.nextFloat()) + val feature3 = Tensor[Float](1, 4, 2, 2).apply1(_ => Random.nextFloat()) + input(1.0f) = feature1 + input(2.0f) = feature2 + input(3.0f) = feature3 + + val fpn = new FPN[Float](inChannels = Array(1, 2, 4), outChannels = 2).setName("FPN") + runSerializationTest(fpn, input) + } +} From baf985af7b353cca0e2f3b228e8be7b97d76d0c0 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 26 Aug 2019 10:40:42 +0800 Subject: [PATCH 0942/1065] fix memory leak for ir graph training (#2895) --- .../dllib/models/resnet/TrainImageNet.scala | 40 ++++++++----------- .../bigdl/dllib/optim/DistriOptimizer.scala | 9 ++++- 2 files changed, 24 insertions(+), 25 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala index 4443163b7c7..0a3828f3b7d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/resnet/TrainImageNet.scala @@ -68,31 +68,23 @@ object TrainImageNet { val model = if (param.modelSnapshot.isDefined) { Module.load[Float](param.modelSnapshot.get) } else { - Engine.getEngineType() match { - case MklBlas => - val curModel = - ResNet(classNum = param.classes, T("shortcutType" -> shortcut, "depth" -> param.depth, - "optnet" -> param.optnet, "dataSet" -> dataSetType)) - if (param.optnet) { - ResNet.shareGradInput(curModel) - } - ResNet.modelInit(curModel) - - /* Here we set parallism specificall for BatchNormalization and its Sub Layers, this is - very useful especially when you want to leverage more computing resources like you want - to use as many cores as possible but you cannot set batch size too big for each core due - to the memory limitation, so you can set batch size per core smaller, but the smaller - batch size will increase the instability of convergence, the synchronization among BN - layers basically do the parameters synchronization among cores and thus will avoid the - instability while improves the performance a lot. */ - val parallisim = Engine.coreNumber - setParallism(curModel, parallisim) - - curModel - case MklDnn => - nn.mkldnn.ResNet.graph(param.batchSize / Engine.nodeNumber(), param.classes, - T("depth" -> 50, "dataSet" -> ImageNet)) + val curModel = + ResNet(classNum = param.classes, T("shortcutType" -> shortcut, "depth" -> param.depth, + "optnet" -> param.optnet, "dataSet" -> dataSetType)) + if (param.optnet) { + ResNet.shareGradInput(curModel) } + ResNet.modelInit(curModel) + + /* Here we set parallism specificall for BatchNormalization and its Sub Layers, this is + very useful especially when you want to leverage more computing resources like you want + to use as many cores as possible but you cannot set batch size too big for each core due + to the memory limitation, so you can set batch size per core smaller, but the smaller + batch size will increase the instability of convergence, the synchronization among BN + layers basically do the parameters synchronization among cores and thus will avoid the + instability while improves the performance a lot. */ + if (Engine.getEngineType() == MklBlas) setParallism(curModel, Engine.coreNumber) + curModel } println(model) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 23567b2a6db..21824f70b11 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -252,11 +252,18 @@ object DistriOptimizer extends AbstractOptimizer { val input = miniBatchBuffer(i).getInput() val target = miniBatchBuffer(i).getTarget() - if (Engine.getEngineType() == MklBlas || localModel.isInstanceOf[IRGraph[T]]) { + if (Engine.getEngineType() == MklBlas) { val output = localModel.forward(input) lossArray(i) = ev.toType[Double](localCriterion.forward(output, target)) val errors = localCriterion.backward(output, target) localModel.backward(input, errors) + } else if (localModel.isInstanceOf[IRGraph[T]]) { + val output = localModel.forward(input) + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + lossArray(i) = ev.toType[Double](localCriterion.forward(output, target)) + localCriterion.backward(output, target) + })) + localModel.backward(input, localCriterion.gradInput) } else { Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { val output = localModel.forward(input) From 41ad87be04cccbeca5544a3f373b06fe5434ef09 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Mon, 26 Aug 2019 16:13:32 +0800 Subject: [PATCH 0943/1065] add gemm layer (#2882) * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add gemm layer * add transpose in gemm layer * add transpose in gemm layer * add transpose in gemm layer * add gemm layer * add gemm layer --- .../intel/analytics/bigdl/dllib/nn/Gemm.scala | 82 ++++++++++ .../analytics/bigdl/dllib/nn/GemmSpec.scala | 147 ++++++++++++++++++ 2 files changed, 229 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Gemm.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GemmSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Gemm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Gemm.scala new file mode 100644 index 00000000000..5d41a1b6624 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Gemm.scala @@ -0,0 +1,82 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.ops.{BatchMatMul, Operation} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + +import scala.reflect.ClassTag + + +/** + * General Matrix multiplication + * + * Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), + * input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), + * and output tensor Y has shape (M, N). + * + * @param alpha Scalar multiplier for the product of input tensors A * B. + * @param beta Scalar multiplier for input tensor C. + * @param transA Whether A should be transposed + * @param transB Whether B should be transposed + * @param ev + * @tparam T The numeric type in this module parameters. + */ +class Gemm[T: ClassTag]( + val alpha: Float = 1, val beta: Float = 1, + val transA: Boolean = false, val transB: Boolean = false +)(implicit ev: TensorNumeric[T]) +extends Operation[Table, Tensor[T], T] { + + private val internalModel: Module[T] = { + val tensorA = Input() + val tensorB = Input() + val tensorC = Input() + val alphaMul = MulConstant(scalar = alpha, inplace = true).inputs( + BatchMatMul(adjX = transA, adjY = transB).inputs(Array(tensorA, tensorB)) + ) + val betaAdd = CAddTable().inputs(Array(alphaMul, + MulConstant(scalar = beta, inplace = true).inputs(tensorC))) + + Graph(Array(tensorA, tensorB, tensorC), betaAdd) + } + + override def updateOutput(input: Table): Tensor[T] = { + require(input.length() == 3, "Input should be a table contains 3 tensors, actually size is: " + + input.toTable.length()) + internalModel.forward(input) + output = internalModel.output.asInstanceOf[Tensor[T]] + output + } + + override def release(): Unit = { + internalModel.release() + } + +} + +object Gemm { + def apply[@specialized(Float, Double) T: ClassTag]( + alpha: Float = 1, beta: Float = 1, + transA: Boolean = false, transB: Boolean = false + )(implicit ev: TensorNumeric[T]): Gemm[T] = { + new Gemm[T](alpha, beta, transA, transB) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GemmSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GemmSpec.scala new file mode 100644 index 00000000000..6f1dace7eac --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GemmSpec.scala @@ -0,0 +1,147 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.ops.BatchMatMul +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} + + +class GemmSpec extends FlatSpec with Matchers { + + "Gemm forward" should "work" in { + val transA = false + val transB = false + + val inputA = Tensor[Float](4, 2).rand() + val inputB = Tensor[Float](2, 7).rand() + val inputC = Tensor[Float](4, 7).rand() + + val tensorA = Input() + val tensorB = Input() + val tensorC = Input() + + val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(tensorA, tensorB)) + val add = CAddTable().inputs(Array(mul, tensorC)) + var model = Graph(Array(tensorA, tensorB, tensorC), add) + + var myGemm = new Gemm() + val myInput = T(inputA, inputB, inputC) + + val out1 = model.forward(myInput) + val out2 = myGemm.forward(myInput) + + out1 should be(out2) + + } + + + "Gemm with transA forward" should "work" in { + val transA = true + val transB = false + + var inputA = Tensor[Float](2, 4).rand() + var transInputA = inputA.t() + var inputB = Tensor[Float](2, 7).rand() + var transInputB = inputB.t() + var inputC = Tensor[Float](4, 7).rand() + + val tensorA = Input() + val tensorB = Input() + val tensorC = Input() + val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(tensorA, tensorB)) + val add = CAddTable().inputs(Array(mul, tensorC)) + var model = Graph(Array(tensorA, tensorB, tensorC), add) + + var myGemm = new Gemm(transA = true) + + val out1 = model.forward(T(inputA, inputB, inputC)) + val out2 = myGemm.forward(T(inputA, inputB, inputC)) + + out1 should be(out2) + + } + + + "Gemm with transB forward" should "work" in { + val transA = false + val transB = true + + var inputA = Tensor[Float](4, 2).rand() + var transInputA = inputA.t() + var inputB = Tensor[Float](7, 2).rand() + var transInputB = inputB.t() + var inputC = Tensor[Float](4, 7).rand() + + val tensorA = Input() + val tensorB = Input() + val tensorC = Input() + val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(tensorA, tensorB)) + val add = CAddTable().inputs(Array(mul, tensorC)) + var model = Graph(Array(tensorA, tensorB, tensorC), add) + + var myGemm = new Gemm(transB = true) + + val out1 = model.forward(T(inputA, inputB, inputC)) + val out2 = myGemm.forward(T(inputA, inputB, inputC)) + + out1 should be(out2) + + } + + + "Gemm with transA & transB forward" should "work" in { + val transA = true + val transB = true + + var tensorA = Tensor[Float](2, 4).rand() + var tensorB = Tensor[Float](7, 2).rand() + var tensorC = Tensor[Float](4, 7).rand() + + val inputA = Input() + val inputB = Input() + val inputC = Input() + + val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(inputA, inputB)) + val add = CAddTable().inputs(Array(mul, inputC)) + var model = Graph(Array(inputA, inputB, inputC), add) + + var myGemm = new Gemm(transA = transA, transB = transB) + + val out1 = model.forward(T(tensorA, tensorB, tensorC)) + val out2 = myGemm.forward(T(tensorA, tensorB, tensorC)) + + out1 should be(out2) + + } + +} + +class GemmSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val gemm = Gemm[Float]().setName("Gemm") + + val inputA = Tensor(2, 2).rand() + val inputB = Tensor(2, 2).rand() + val inputC = Tensor(2, 2).rand() + val input = T(inputA, inputB, inputC) + + runSerializationTest(gemm, input) + } +} From 2adcaf10bfa3dccb9113495f7f5e10dcfc8a0836 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Mon, 26 Aug 2019 16:17:12 +0800 Subject: [PATCH 0944/1065] add Shape layer (#2885) * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer * add shape layer --- .../analytics/bigdl/dllib/nn/onnx/Shape.scala | 52 ++++++++++++++++++ .../utils/python/api/PythonBigDLOnnx.scala | 39 ++++++++++++++ .../serializer/converters/DataConverter.scala | 2 + .../bigdl/dllib/nn/onnx/ShapeSpec.scala | 54 +++++++++++++++++++ 4 files changed, 147 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ShapeSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala new file mode 100644 index 00000000000..8e49ca0974a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.onnx + +import com.intel.analytics.bigdl.nn.ops.Operation +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + + +/** + * A layer which takes a tensor as input and outputs an 1D tensor containing the shape of the input. + * @param `classTag$T` + * @param ev + * @tparam T The numeric type in this module parameters + */ +class Shape[T: ClassTag](implicit ev: TensorNumeric[T]) + extends Operation[Tensor[T], Tensor[T], T] { + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + val dimSize = input.nDimension() + output = Tensor[T](dimSize) + (1 to dimSize).foreach(i => { + output.setValue(i, ev.fromType(input.size(i))) + }) + output + } + +} + +object Shape { + def apply[T: ClassTag]()( + implicit ev: TensorNumeric[T]): Shape[T] = { + new Shape[T]() + } +} + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala new file mode 100644 index 00000000000..be44718f293 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala @@ -0,0 +1,39 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.python.api + +import com.intel.analytics.bigdl.nn.onnx.Shape +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import scala.reflect.ClassTag + + +class PythonBigDLOnnx[T: ClassTag](implicit ev: TensorNumeric[T]) extends PythonBigDL[T] { + + def createShape(): Shape[T] = { + Shape() + } + +} + + +object PythonBigDLOnnx { + + def ofFloat(): PythonBigDLOnnx[Float] = new PythonBigDLOnnx[Float]() + + def ofDouble(): PythonBigDLOnnx[Double] = new PythonBigDLOnnx[Double]() + +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala index 11aee0ea88a..7970a4b4c4f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/converters/DataConverter.scala @@ -398,6 +398,8 @@ object DataConverter extends DataConverter{ attrValue.setShape(shape) ShapeConverter.getAttributeValue(context, attrValue.build).asInstanceOf[BigDLShape] }).toArray + + case _ => throw new UnsupportedOperationException("Unsupported data type: " + listType) } arr } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ShapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ShapeSpec.scala new file mode 100644 index 00000000000..3836d35a01a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ShapeSpec.scala @@ -0,0 +1,54 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.onnx + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} +import scala.util.Random + + +class ShapeSpec extends FlatSpec with Matchers { + + "Shape" should "work" in { + val inputTensor = Tensor[Float](20, 1, 9).rand() + val shape = Shape[Float]() + val output = shape.forward(inputTensor) + val ans = Tensor[Float](3) + ans.setValue(1, 20) + ans.setValue(2, 1) + ans.setValue(3, 9) + + output.nDimension() should be (1) + output.nDimension() should be (ans.nDimension()) + output.size(1) should be (ans.size(1)) + + (1 to output.size(1)).foreach(i => { + output.valueAt(i) should be (ans.valueAt(i)) + }) + + } +} + +class ShapeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val shape = Shape[Float]() + val input = Tensor[Float](5).rand() + runSerializationTest(shape, input) + } + +} + From 8b4a29c14f512de847a2b6f74f873a7cbd77207b Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Mon, 26 Aug 2019 17:31:50 +0800 Subject: [PATCH 0945/1065] add Gather layer (#2897) * add gather layer --- .../bigdl/dllib/nn/onnx/Gather.scala | 28 +++++++++++++++++++ .../analytics/bigdl/dllib/nn/onnx/Shape.scala | 1 - .../analytics/bigdl/dllib/nn/ops/Gather.scala | 2 +- 3 files changed, 29 insertions(+), 2 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gather.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gather.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gather.scala new file mode 100644 index 00000000000..1eb0adad33a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gather.scala @@ -0,0 +1,28 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package com.intel.analytics.bigdl.nn.onnx + +import com.intel.analytics.bigdl.nn.ops +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import scala.reflect.ClassTag + + +object Gather { + def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): + ops.Gather[T, D] = new ops.Gather() +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala index 8e49ca0974a..52a30ee4ccf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala @@ -49,4 +49,3 @@ object Shape { new Shape[T]() } } - diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala index f0ec3a6fc97..c98d79b16b1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala @@ -15,7 +15,7 @@ */ package com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.tensor.{IntType, Tensor, TensorDataType} +import com.intel.analytics.bigdl.tensor.{IntType, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table From ef30ddde8d70367399b82f1e1caea23358ddd10b Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 30 Aug 2019 13:32:28 +0800 Subject: [PATCH 0946/1065] [New feature] Add maskhead (#2892) * support for maskhead --- .../analytics/bigdl/dllib/nn/MaskHead.scala | 175 ++++ .../bigdl/dllib/nn/MaskHeadSpec.scala | 882 ++++++++++++++++++ .../dllib/nn/MaskPostProcessorSpec.scala | 532 +++++++++++ 3 files changed, 1589 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala new file mode 100644 index 00000000000..7a804ed5c34 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala @@ -0,0 +1,175 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +class MaskHead( + val inChannels: Int, + val resolution: Int, + val scales: Array[Float], + val samplingRratio: Float, + val layers: Array[Int], + val dilation: Int, + val numClasses: Int, + val useGn: Boolean = false)(implicit ev: TensorNumeric[Float]) + extends BaseModule[Float] { + + override def buildModel(): Module[Float] = { + val featureExtractor = this.maskFeatureExtractor( + inChannels, resolution, scales, samplingRratio, layers, dilation, useGn) + val dimReduced = layers(layers.length - 1) + val predictor = this.maskPredictor(dimReduced, numClasses, dimReduced) + val postProcessor = new MaskPostProcessor() + + /** + * input: feature-maps from possibly several levels and proposal boxes + * return: + * first tensor: the result of the feature extractor + * second tensor: proposals (list[BoxList]): during training, the original proposals + * are returned. During testing, the predicted boxlists are returned + * with the `mask` field set + */ + val features = Input() + val proposals = Input() + val labels = Input() + + val maskFeatures = featureExtractor.inputs(features, proposals) + val maskLogits = predictor.inputs(maskFeatures) + val result = postProcessor.inputs(maskLogits, proposals, labels) + + Graph(Array(features, proposals, labels), Array(maskFeatures, result)) + } + + private[nn] def maskPredictor(inChannels: Int, + numClasses: Int, + dimReduced: Int): Module[Float] = { + val convMask = SpatialFullConvolution(inChannels, dimReduced, + kW = 2, kH = 2, dW = 2, dH = 2) + val maskLogits = SpatialConvolution(nInputPlane = dimReduced, + nOutputPlane = numClasses, kernelW = 1, kernelH = 1, strideH = 1, strideW = 1) + + // init weight & bias, MSRAFill by default + convMask.setInitMethod(MsraFiller(false), Zeros) + maskLogits.setInitMethod(MsraFiller(false), Zeros) + + val model = Sequential[Float]() + model.add(convMask).add(ReLU[Float]()).add(maskLogits) + model + } + + private[nn] def maskFeatureExtractor(inChannels: Int, + resolution: Int, + scales: Array[Float], + samplingRatio: Float, + layers: Array[Int], + dilation: Int, + useGn: Boolean = false): Module[Float] = { + + require(dilation == 1, s"Only support dilation = 1, but got ${dilation}") + + val model = Sequential[Float]() + model.add(Pooler(resolution, scales, samplingRatio.toInt)) + + var nextFeatures = inChannels + var i = 0 + while (i < layers.length) { + val features = layers(i) + // todo: support dilation convolution with no bias + val module = SpatialConvolution[Float]( + nextFeatures, + features, + kernelW = 3, + kernelH = 3, + strideW = 1, + strideH = 1, + padW = dilation, + padH = dilation, + withBias = if (useGn) false else true + ).setName(s"mask_fcn{${i}}") + + // weight init + module.setInitMethod(MsraFiller(false), Zeros) + model.add(module) + nextFeatures = features + i += 1 + } + model.add(ReLU[Float]()) + } +} + +private[nn] class MaskPostProcessor()(implicit ev: TensorNumeric[Float]) + extends AbstractModule[Table, Tensor[Float], Float] { + + @transient var rangeBuffer: Tensor[Float] = null + private val sigmoid = Sigmoid[Float]() + + /** + * @param input feature-maps from possibly several levels, proposal boxes and labels + * @return the predicted boxlists are returned with the `mask` field set + */ + override def updateOutput(input: Table): Tensor[Float] = { + val maskLogits = input[Tensor[Float]](1) + val bbox = input[Tensor[Float]](2) // N * 4 + val labels = input[Tensor[Float]](3) + + val num_masks = maskLogits.size(1) + if (rangeBuffer == null || rangeBuffer.nElement() != num_masks) { + rangeBuffer = Tensor[Float](num_masks) + rangeBuffer.range(0, num_masks - 1, 1) + } + + val mask_prob = sigmoid.forward(maskLogits) + require(labels.nDimension() == 1, s"Labels should be tensor with one dimension," + + s"but get ${labels.nDimension()}") + require(rangeBuffer.nElement() == labels.nElement(), s"number of masks should be same" + + s"with labels, but get ${rangeBuffer.nElement()} ${labels.nElement()}") + + output.resize(rangeBuffer.nElement(), 1, mask_prob.size(3), mask_prob.size(4)) + + var i = 1 + while (i <= rangeBuffer.nElement()) { + val dim = rangeBuffer.valueAt(i).toInt + 1 + val index = labels.valueAt(i).toInt // start from 1 + output.narrow(1, i, 1).copy(mask_prob.narrow(1, i, 1).narrow(2, index, 1)) + i += 1 + } + output + } + + override def updateGradInput(input: Table, gradOutput: Tensor[Float]): Table = { + throw new UnsupportedOperationException("MaskPostProcessor only support inference") + } +} + +object MaskHead { + def apply(inChannels: Int, + resolution: Int = 14, + scales: Array[Float] = Array[Float](0.25f, 0.125f, 0.0625f, 0.03125f), + samplingRratio: Float = 0.1f, + layers: Array[Int] = Array[Int](256, 256, 256, 256), + dilation: Int = 1, + numClasses: Int = 81, + useGn: Boolean = false)(implicit ev: TensorNumeric[Float]): Module[Float] = { + new MaskHead(inChannels, resolution, scales, samplingRratio, + layers, dilation, numClasses, useGn) + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala new file mode 100644 index 00000000000..f9191f86346 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala @@ -0,0 +1,882 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} +import org.dmg.pmml.False +import org.scalatest.{FlatSpec, Matchers} + +class MaskHeadSpec extends FlatSpec with Matchers { + "MaskHead" should "be ok" in { + val inChannels: Int = 6 + val resolution: Int = 14 + val scales: Array[Float] = Array[Float](0.25f, 0.125f) + val samplingRratio: Float = 2.0f + val layers: Array[Int] = Array[Int](4, 4) + val dilation: Int = 1 + val numClasses: Int = 81 + val useGn: Boolean = false + + val layer = new MaskHead(inChannels, resolution, scales, + samplingRratio, layers, dilation, numClasses, useGn) + + val params = layer.getParameters() + params._1.fill(0.001f) + + val features1 = Tensor[Float](T(T(T(T(0.5381, 0.0856, 0.1124, 0.7493), + T(0.4624, 0.2182, 0.7364, 0.3522), + T(0.7552, 0.7117, 0.2715, 0.9082)), + T(T(0.0928, 0.2735, 0.7539, 0.7539), + T(0.4777, 0.1525, 0.8279, 0.6481), + T(0.6019, 0.4803, 0.5869, 0.7459)), + T(T(0.1924, 0.2795, 0.4463, 0.3887), + T(0.5791, 0.9832, 0.8752, 0.4598), + T(0.2278, 0.0758, 0.4988, 0.3742)), + T(T(0.1762, 0.6499, 0.2534, 0.9842), + T(0.0908, 0.8676, 0.1700, 0.1887), + T(0.7138, 0.9559, 0.0119, 0.7799)), + T(T(0.8200, 0.6767, 0.3637, 0.9771), + T(0.1217, 0.5645, 0.2574, 0.6729), + T(0.6140, 0.5333, 0.4425, 0.1740)), + T(T(0.3994, 0.9148, 0.0123, 0.0125), + T(0.5663, 0.9951, 0.8143, 0.9906), + T(0.0923, 0.8285, 0.2992, 0.2221))))) + + val features2 = Tensor[Float](T(T(T(T(0.0492, 0.1234), + T(0.3291, 0.0613), + T(0.4260, 0.1422), + T(0.2282, 0.4258), + T(0.7426, 0.9476)), + T(T(0.6662, 0.7015), + T(0.4598, 0.6378), + T(0.9571, 0.4947), + T(0.1659, 0.3034), + T(0.8583, 0.1369)), + T(T(0.1711, 0.6440), + T(0.2099, 0.4468), + T(0.9518, 0.3877), + T(0.4058, 0.6630), + T(0.9056, 0.4054)), + T(T(0.4562, 0.0277), + T(0.2358, 0.3938), + T(0.9187, 0.4067), + T(0.0445, 0.4171), + T(0.3434, 0.1964)), + T(T(0.9473, 0.7239), + T(0.1732, 0.5352), + T(0.8276, 0.6435), + T(0.3516, 0.3760), + T(0.3437, 0.0198)), + T(T(0.7811, 0.5682), + T(0.5121, 0.9655), + T(0.3496, 0.7632), + T(0.4267, 0.4533), + T(0.8624, 0.3172))))) + + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 10.0f))) + val labels = Tensor[Float](T(1, 3)) + + val output = layer.forward(T(T(features1, features2), bbox, labels)).toTable + + val expectedOutput = Tensor[Float](T(T(T( + T(0.0013, 0.0015, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0018, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 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0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013)), + + T(T(0.0013, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0018, 0.0018, 0.0017, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0019, 0.0018, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013))))) + + output[Tensor[Float]](1).almostEqual(expectedOutput, 1e-3) should be(true) + output[Tensor[Float]](2).apply1(a => { + a should be(0.5003f +- 1e-3f) + a + }) + } + + "MaskRCNNFPNFeatureExtractor" should "be ok" in { + val resolution = 14 + val scales = Array[Float](0.25f, 0.125f) + val sampling_ratio = 2 + val in_channels = 6 + val use_gn = false + val layers = Array[Int](4, 4) + val dilation = 1 + + val mask = new MaskHead(in_channels, resolution, scales, + sampling_ratio, layers, dilation, 81, use_gn) + val layer = mask.maskFeatureExtractor(in_channels, resolution, scales, + sampling_ratio, layers, dilation, use_gn) + + val paramsTable = layer.getParametersTable() + for (i <- paramsTable.keySet) { + val params = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get + params.fill(0.01f) + } + + val features1 = Tensor[Float](T(T(T(T(0.5381, 0.0856, 0.1124, 0.7493), + T(0.4624, 0.2182, 0.7364, 0.3522), + T(0.7552, 0.7117, 0.2715, 0.9082)), + T(T(0.0928, 0.2735, 0.7539, 0.7539), + T(0.4777, 0.1525, 0.8279, 0.6481), + T(0.6019, 0.4803, 0.5869, 0.7459)), + T(T(0.1924, 0.2795, 0.4463, 0.3887), + T(0.5791, 0.9832, 0.8752, 0.4598), + T(0.2278, 0.0758, 0.4988, 0.3742)), + T(T(0.1762, 0.6499, 0.2534, 0.9842), + T(0.0908, 0.8676, 0.1700, 0.1887), + T(0.7138, 0.9559, 0.0119, 0.7799)), + T(T(0.8200, 0.6767, 0.3637, 0.9771), + T(0.1217, 0.5645, 0.2574, 0.6729), + T(0.6140, 0.5333, 0.4425, 0.1740)), + T(T(0.3994, 0.9148, 0.0123, 0.0125), + T(0.5663, 0.9951, 0.8143, 0.9906), + T(0.0923, 0.8285, 0.2992, 0.2221))))) + + val features2 = Tensor[Float](T(T(T(T(0.0492, 0.1234), + T(0.3291, 0.0613), + T(0.4260, 0.1422), + T(0.2282, 0.4258), + T(0.7426, 0.9476)), + T(T(0.6662, 0.7015), + T(0.4598, 0.6378), + T(0.9571, 0.4947), + T(0.1659, 0.3034), + T(0.8583, 0.1369)), + T(T(0.1711, 0.6440), + T(0.2099, 0.4468), + T(0.9518, 0.3877), + T(0.4058, 0.6630), + T(0.9056, 0.4054)), + T(T(0.4562, 0.0277), + T(0.2358, 0.3938), + T(0.9187, 0.4067), + T(0.0445, 0.4171), + T(0.3434, 0.1964)), + T(T(0.9473, 0.7239), + T(0.1732, 0.5352), + T(0.8276, 0.6435), + T(0.3516, 0.3760), + T(0.3437, 0.0198)), + T(T(0.7811, 0.5682), + T(0.5121, 0.9655), + T(0.3496, 0.7632), + T(0.4267, 0.4533), + T(0.8624, 0.3172))))) + + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 10.0f))) + + val expectedOutput = Tensor[Float](T(T(T( + T(0.0275, 0.0447, 0.0516, 0.0533, 0.0551, 0.0568, 0.0585, 0.0603, + 0.0620, 0.0635, 0.0645, 0.0649, 0.0577, 0.0360), + T(0.0442, 0.0718, 0.0829, 0.0858, 0.0886, 0.0914, 0.0943, 0.0971, + 0.0999, 0.1023, 0.1040, 0.1047, 0.0932, 0.0582), + T(0.0501, 0.0814, 0.0940, 0.0973, 0.1005, 0.1038, 0.1070, 0.1103, + 0.1135, 0.1163, 0.1183, 0.1191, 0.1060, 0.0662), + T(0.0505, 0.0822, 0.0949, 0.0982, 0.1015, 0.1048, 0.1081, 0.1114, + 0.1146, 0.1175, 0.1195, 0.1204, 0.1071, 0.0669), + T(0.0510, 0.0830, 0.0958, 0.0990, 0.1022, 0.1055, 0.1087, 0.1120, + 0.1152, 0.1179, 0.1199, 0.1207, 0.1073, 0.0670), + T(0.0515, 0.0837, 0.0965, 0.0997, 0.1028, 0.1059, 0.1090, 0.1122, + 0.1153, 0.1179, 0.1197, 0.1203, 0.1069, 0.0667), + T(0.0521, 0.0845, 0.0973, 0.1003, 0.1032, 0.1062, 0.1092, 0.1122, + 0.1151, 0.1176, 0.1193, 0.1197, 0.1062, 0.0662), + T(0.0526, 0.0852, 0.0980, 0.1008, 0.1037, 0.1065, 0.1094, 0.1122, + 0.1150, 0.1173, 0.1188, 0.1190, 0.1055, 0.0657), + T(0.0531, 0.0860, 0.0988, 0.1014, 0.1041, 0.1068, 0.1095, 0.1122, + 0.1148, 0.1170, 0.1183, 0.1183, 0.1047, 0.0651), + T(0.0536, 0.0867, 0.0995, 0.1020, 0.1046, 0.1071, 0.1096, 0.1122, + 0.1147, 0.1167, 0.1178, 0.1176, 0.1039, 0.0646), + T(0.0541, 0.0875, 0.1002, 0.1026, 0.1050, 0.1074, 0.1098, 0.1122, + 0.1145, 0.1164, 0.1173, 0.1169, 0.1032, 0.0641), + T(0.0546, 0.0883, 0.1010, 0.1032, 0.1055, 0.1077, 0.1099, 0.1122, + 0.1143, 0.1161, 0.1168, 0.1162, 0.1024, 0.0635), + T(0.0489, 0.0790, 0.0903, 0.0921, 0.0940, 0.0959, 0.0978, 0.0997, + 0.1015, 0.1029, 0.1035, 0.1029, 0.0905, 0.0561), + T(0.0307, 0.0495, 0.0566, 0.0577, 0.0589, 0.0600, 0.0612, 0.0623, + 0.0634, 0.0643, 0.0646, 0.0641, 0.0564, 0.0349)), + + T(T(0.0275, 0.0447, 0.0516, 0.0533, 0.0551, 0.0568, 0.0585, 0.0603, + 0.0620, 0.0635, 0.0645, 0.0649, 0.0577, 0.0360), + T(0.0442, 0.0718, 0.0829, 0.0858, 0.0886, 0.0914, 0.0943, 0.0971, + 0.0999, 0.1023, 0.1040, 0.1047, 0.0932, 0.0582), + T(0.0501, 0.0814, 0.0940, 0.0973, 0.1005, 0.1038, 0.1070, 0.1103, + 0.1135, 0.1163, 0.1183, 0.1191, 0.1060, 0.0662), + T(0.0505, 0.0822, 0.0949, 0.0982, 0.1015, 0.1048, 0.1081, 0.1114, + 0.1146, 0.1175, 0.1195, 0.1204, 0.1071, 0.0669), + T(0.0510, 0.0830, 0.0958, 0.0990, 0.1022, 0.1055, 0.1087, 0.1120, + 0.1152, 0.1179, 0.1199, 0.1207, 0.1073, 0.0670), + T(0.0515, 0.0837, 0.0965, 0.0997, 0.1028, 0.1059, 0.1090, 0.1122, + 0.1153, 0.1179, 0.1197, 0.1203, 0.1069, 0.0667), + T(0.0521, 0.0845, 0.0973, 0.1003, 0.1032, 0.1062, 0.1092, 0.1122, + 0.1151, 0.1176, 0.1193, 0.1197, 0.1062, 0.0662), + T(0.0526, 0.0852, 0.0980, 0.1008, 0.1037, 0.1065, 0.1094, 0.1122, + 0.1150, 0.1173, 0.1188, 0.1190, 0.1055, 0.0657), + T(0.0531, 0.0860, 0.0988, 0.1014, 0.1041, 0.1068, 0.1095, 0.1122, + 0.1148, 0.1170, 0.1183, 0.1183, 0.1047, 0.0651), + T(0.0536, 0.0867, 0.0995, 0.1020, 0.1046, 0.1071, 0.1096, 0.1122, + 0.1147, 0.1167, 0.1178, 0.1176, 0.1039, 0.0646), + T(0.0541, 0.0875, 0.1002, 0.1026, 0.1050, 0.1074, 0.1098, 0.1122, + 0.1145, 0.1164, 0.1173, 0.1169, 0.1032, 0.0641), + T(0.0546, 0.0883, 0.1010, 0.1032, 0.1055, 0.1077, 0.1099, 0.1122, + 0.1143, 0.1161, 0.1168, 0.1162, 0.1024, 0.0635), + T(0.0489, 0.0790, 0.0903, 0.0921, 0.0940, 0.0959, 0.0978, 0.0997, + 0.1015, 0.1029, 0.1035, 0.1029, 0.0905, 0.0561), + T(0.0307, 0.0495, 0.0566, 0.0577, 0.0589, 0.0600, 0.0612, 0.0623, + 0.0634, 0.0643, 0.0646, 0.0641, 0.0564, 0.0349)), + + T(T(0.0275, 0.0447, 0.0516, 0.0533, 0.0551, 0.0568, 0.0585, 0.0603, + 0.0620, 0.0635, 0.0645, 0.0649, 0.0577, 0.0360), + T(0.0442, 0.0718, 0.0829, 0.0858, 0.0886, 0.0914, 0.0943, 0.0971, + 0.0999, 0.1023, 0.1040, 0.1047, 0.0932, 0.0582), + T(0.0501, 0.0814, 0.0940, 0.0973, 0.1005, 0.1038, 0.1070, 0.1103, + 0.1135, 0.1163, 0.1183, 0.1191, 0.1060, 0.0662), + T(0.0505, 0.0822, 0.0949, 0.0982, 0.1015, 0.1048, 0.1081, 0.1114, + 0.1146, 0.1175, 0.1195, 0.1204, 0.1071, 0.0669), + T(0.0510, 0.0830, 0.0958, 0.0990, 0.1022, 0.1055, 0.1087, 0.1120, + 0.1152, 0.1179, 0.1199, 0.1207, 0.1073, 0.0670), + T(0.0515, 0.0837, 0.0965, 0.0997, 0.1028, 0.1059, 0.1090, 0.1122, + 0.1153, 0.1179, 0.1197, 0.1203, 0.1069, 0.0667), + T(0.0521, 0.0845, 0.0973, 0.1003, 0.1032, 0.1062, 0.1092, 0.1122, + 0.1151, 0.1176, 0.1193, 0.1197, 0.1062, 0.0662), + T(0.0526, 0.0852, 0.0980, 0.1008, 0.1037, 0.1065, 0.1094, 0.1122, + 0.1150, 0.1173, 0.1188, 0.1190, 0.1055, 0.0657), + T(0.0531, 0.0860, 0.0988, 0.1014, 0.1041, 0.1068, 0.1095, 0.1122, + 0.1148, 0.1170, 0.1183, 0.1183, 0.1047, 0.0651), + T(0.0536, 0.0867, 0.0995, 0.1020, 0.1046, 0.1071, 0.1096, 0.1122, + 0.1147, 0.1167, 0.1178, 0.1176, 0.1039, 0.0646), + T(0.0541, 0.0875, 0.1002, 0.1026, 0.1050, 0.1074, 0.1098, 0.1122, + 0.1145, 0.1164, 0.1173, 0.1169, 0.1032, 0.0641), + T(0.0546, 0.0883, 0.1010, 0.1032, 0.1055, 0.1077, 0.1099, 0.1122, + 0.1143, 0.1161, 0.1168, 0.1162, 0.1024, 0.0635), + T(0.0489, 0.0790, 0.0903, 0.0921, 0.0940, 0.0959, 0.0978, 0.0997, + 0.1015, 0.1029, 0.1035, 0.1029, 0.0905, 0.0561), + T(0.0307, 0.0495, 0.0566, 0.0577, 0.0589, 0.0600, 0.0612, 0.0623, + 0.0634, 0.0643, 0.0646, 0.0641, 0.0564, 0.0349)), + + T(T(0.0275, 0.0447, 0.0516, 0.0533, 0.0551, 0.0568, 0.0585, 0.0603, + 0.0620, 0.0635, 0.0645, 0.0649, 0.0577, 0.0360), + T(0.0442, 0.0718, 0.0829, 0.0858, 0.0886, 0.0914, 0.0943, 0.0971, + 0.0999, 0.1023, 0.1040, 0.1047, 0.0932, 0.0582), + T(0.0501, 0.0814, 0.0940, 0.0973, 0.1005, 0.1038, 0.1070, 0.1103, + 0.1135, 0.1163, 0.1183, 0.1191, 0.1060, 0.0662), + T(0.0505, 0.0822, 0.0949, 0.0982, 0.1015, 0.1048, 0.1081, 0.1114, + 0.1146, 0.1175, 0.1195, 0.1204, 0.1071, 0.0669), + T(0.0510, 0.0830, 0.0958, 0.0990, 0.1022, 0.1055, 0.1087, 0.1120, + 0.1152, 0.1179, 0.1199, 0.1207, 0.1073, 0.0670), + T(0.0515, 0.0837, 0.0965, 0.0997, 0.1028, 0.1059, 0.1090, 0.1122, + 0.1153, 0.1179, 0.1197, 0.1203, 0.1069, 0.0667), + T(0.0521, 0.0845, 0.0973, 0.1003, 0.1032, 0.1062, 0.1092, 0.1122, + 0.1151, 0.1176, 0.1193, 0.1197, 0.1062, 0.0662), + T(0.0526, 0.0852, 0.0980, 0.1008, 0.1037, 0.1065, 0.1094, 0.1122, + 0.1150, 0.1173, 0.1188, 0.1190, 0.1055, 0.0657), + T(0.0531, 0.0860, 0.0988, 0.1014, 0.1041, 0.1068, 0.1095, 0.1122, + 0.1148, 0.1170, 0.1183, 0.1183, 0.1047, 0.0651), + T(0.0536, 0.0867, 0.0995, 0.1020, 0.1046, 0.1071, 0.1096, 0.1122, + 0.1147, 0.1167, 0.1178, 0.1176, 0.1039, 0.0646), + T(0.0541, 0.0875, 0.1002, 0.1026, 0.1050, 0.1074, 0.1098, 0.1122, + 0.1145, 0.1164, 0.1173, 0.1169, 0.1032, 0.0641), + T(0.0546, 0.0883, 0.1010, 0.1032, 0.1055, 0.1077, 0.1099, 0.1122, + 0.1143, 0.1161, 0.1168, 0.1162, 0.1024, 0.0635), + T(0.0489, 0.0790, 0.0903, 0.0921, 0.0940, 0.0959, 0.0978, 0.0997, + 0.1015, 0.1029, 0.1035, 0.1029, 0.0905, 0.0561), + T(0.0307, 0.0495, 0.0566, 0.0577, 0.0589, 0.0600, 0.0612, 0.0623, + 0.0634, 0.0643, 0.0646, 0.0641, 0.0564, 0.0349))), + + + T(T(T(0.0353, 0.0570, 0.0650, 0.0657, 0.0657, 0.0652, 0.0644, 0.0637, + 0.0629, 0.0621, 0.0613, 0.0606, 0.0533, 0.0331), + T(0.0564, 0.0911, 0.1038, 0.1048, 0.1047, 0.1038, 0.1025, 0.1012, + 0.0998, 0.0985, 0.0971, 0.0958, 0.0842, 0.0523), + T(0.0634, 0.1024, 0.1165, 0.1175, 0.1172, 0.1160, 0.1143, 0.1126, + 0.1108, 0.1091, 0.1074, 0.1056, 0.0927, 0.0575), + T(0.0633, 0.1022, 0.1161, 0.1169, 0.1163, 0.1148, 0.1129, 0.1109, + 0.1088, 0.1068, 0.1048, 0.1028, 0.0900, 0.0557), + T(0.0632, 0.1019, 0.1157, 0.1162, 0.1155, 0.1137, 0.1115, 0.1092, + 0.1069, 0.1045, 0.1022, 0.0999, 0.0873, 0.0540), + T(0.0631, 0.1017, 0.1153, 0.1156, 0.1146, 0.1126, 0.1100, 0.1075, + 0.1049, 0.1023, 0.0997, 0.0971, 0.0846, 0.0522), + T(0.0631, 0.1015, 0.1149, 0.1150, 0.1138, 0.1115, 0.1087, 0.1058, + 0.1029, 0.1000, 0.0972, 0.0943, 0.0819, 0.0505), + T(0.0630, 0.1013, 0.1146, 0.1145, 0.1130, 0.1105, 0.1074, 0.1043, + 0.1012, 0.0981, 0.0950, 0.0919, 0.0796, 0.0490), + T(0.0629, 0.1012, 0.1143, 0.1141, 0.1125, 0.1098, 0.1065, 0.1033, + 0.1000, 0.0967, 0.0934, 0.0901, 0.0779, 0.0479), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1122, 0.1094, 0.1061, 0.1027, + 0.0993, 0.0960, 0.0926, 0.0892, 0.0770, 0.0473), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1121, 0.1093, 0.1059, 0.1025, + 0.0991, 0.0957, 0.0923, 0.0889, 0.0767, 0.0471), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1121, 0.1093, 0.1059, 0.1025, + 0.0991, 0.0957, 0.0923, 0.0889, 0.0767, 0.0471), + T(0.0559, 0.0899, 0.1015, 0.1012, 0.0996, 0.0971, 0.0942, 0.0911, + 0.0881, 0.0851, 0.0820, 0.0790, 0.0682, 0.0419), + T(0.0349, 0.0562, 0.0634, 0.0633, 0.0623, 0.0607, 0.0588, 0.0570, + 0.0551, 0.0532, 0.0513, 0.0494, 0.0426, 0.0262)), + + T(T(0.0353, 0.0570, 0.0650, 0.0657, 0.0657, 0.0652, 0.0644, 0.0637, + 0.0629, 0.0621, 0.0613, 0.0606, 0.0533, 0.0331), + T(0.0564, 0.0911, 0.1038, 0.1048, 0.1047, 0.1038, 0.1025, 0.1012, + 0.0998, 0.0985, 0.0971, 0.0958, 0.0842, 0.0523), + T(0.0634, 0.1024, 0.1165, 0.1175, 0.1172, 0.1160, 0.1143, 0.1126, + 0.1108, 0.1091, 0.1074, 0.1056, 0.0927, 0.0575), + T(0.0633, 0.1022, 0.1161, 0.1169, 0.1163, 0.1148, 0.1129, 0.1109, + 0.1088, 0.1068, 0.1048, 0.1028, 0.0900, 0.0557), + T(0.0632, 0.1019, 0.1157, 0.1162, 0.1155, 0.1137, 0.1115, 0.1092, + 0.1069, 0.1045, 0.1022, 0.0999, 0.0873, 0.0540), + T(0.0631, 0.1017, 0.1153, 0.1156, 0.1146, 0.1126, 0.1100, 0.1075, + 0.1049, 0.1023, 0.0997, 0.0971, 0.0846, 0.0522), + T(0.0631, 0.1015, 0.1149, 0.1150, 0.1138, 0.1115, 0.1087, 0.1058, + 0.1029, 0.1000, 0.0972, 0.0943, 0.0819, 0.0505), + T(0.0630, 0.1013, 0.1146, 0.1145, 0.1130, 0.1105, 0.1074, 0.1043, + 0.1012, 0.0981, 0.0950, 0.0919, 0.0796, 0.0490), + T(0.0629, 0.1012, 0.1143, 0.1141, 0.1125, 0.1098, 0.1065, 0.1033, + 0.1000, 0.0967, 0.0934, 0.0901, 0.0779, 0.0479), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1122, 0.1094, 0.1061, 0.1027, + 0.0993, 0.0960, 0.0926, 0.0892, 0.0770, 0.0473), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1121, 0.1093, 0.1059, 0.1025, + 0.0991, 0.0957, 0.0923, 0.0889, 0.0767, 0.0471), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1121, 0.1093, 0.1059, 0.1025, + 0.0991, 0.0957, 0.0923, 0.0889, 0.0767, 0.0471), + T(0.0559, 0.0899, 0.1015, 0.1012, 0.0996, 0.0971, 0.0942, 0.0911, + 0.0881, 0.0851, 0.0820, 0.0790, 0.0682, 0.0419), + T(0.0349, 0.0562, 0.0634, 0.0633, 0.0623, 0.0607, 0.0588, 0.0570, + 0.0551, 0.0532, 0.0513, 0.0494, 0.0426, 0.0262)), + + T(T(0.0353, 0.0570, 0.0650, 0.0657, 0.0657, 0.0652, 0.0644, 0.0637, + 0.0629, 0.0621, 0.0613, 0.0606, 0.0533, 0.0331), + T(0.0564, 0.0911, 0.1038, 0.1048, 0.1047, 0.1038, 0.1025, 0.1012, + 0.0998, 0.0985, 0.0971, 0.0958, 0.0842, 0.0523), + T(0.0634, 0.1024, 0.1165, 0.1175, 0.1172, 0.1160, 0.1143, 0.1126, + 0.1108, 0.1091, 0.1074, 0.1056, 0.0927, 0.0575), + T(0.0633, 0.1022, 0.1161, 0.1169, 0.1163, 0.1148, 0.1129, 0.1109, + 0.1088, 0.1068, 0.1048, 0.1028, 0.0900, 0.0557), + T(0.0632, 0.1019, 0.1157, 0.1162, 0.1155, 0.1137, 0.1115, 0.1092, + 0.1069, 0.1045, 0.1022, 0.0999, 0.0873, 0.0540), + T(0.0631, 0.1017, 0.1153, 0.1156, 0.1146, 0.1126, 0.1100, 0.1075, + 0.1049, 0.1023, 0.0997, 0.0971, 0.0846, 0.0522), + T(0.0631, 0.1015, 0.1149, 0.1150, 0.1138, 0.1115, 0.1087, 0.1058, + 0.1029, 0.1000, 0.0972, 0.0943, 0.0819, 0.0505), + T(0.0630, 0.1013, 0.1146, 0.1145, 0.1130, 0.1105, 0.1074, 0.1043, + 0.1012, 0.0981, 0.0950, 0.0919, 0.0796, 0.0490), + T(0.0629, 0.1012, 0.1143, 0.1141, 0.1125, 0.1098, 0.1065, 0.1033, + 0.1000, 0.0967, 0.0934, 0.0901, 0.0779, 0.0479), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1122, 0.1094, 0.1061, 0.1027, + 0.0993, 0.0960, 0.0926, 0.0892, 0.0770, 0.0473), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1121, 0.1093, 0.1059, 0.1025, + 0.0991, 0.0957, 0.0923, 0.0889, 0.0767, 0.0471), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1121, 0.1093, 0.1059, 0.1025, + 0.0991, 0.0957, 0.0923, 0.0889, 0.0767, 0.0471), + T(0.0559, 0.0899, 0.1015, 0.1012, 0.0996, 0.0971, 0.0942, 0.0911, + 0.0881, 0.0851, 0.0820, 0.0790, 0.0682, 0.0419), + T(0.0349, 0.0562, 0.0634, 0.0633, 0.0623, 0.0607, 0.0588, 0.0570, + 0.0551, 0.0532, 0.0513, 0.0494, 0.0426, 0.0262)), + + T(T(0.0353, 0.0570, 0.0650, 0.0657, 0.0657, 0.0652, 0.0644, 0.0637, + 0.0629, 0.0621, 0.0613, 0.0606, 0.0533, 0.0331), + T(0.0564, 0.0911, 0.1038, 0.1048, 0.1047, 0.1038, 0.1025, 0.1012, + 0.0998, 0.0985, 0.0971, 0.0958, 0.0842, 0.0523), + T(0.0634, 0.1024, 0.1165, 0.1175, 0.1172, 0.1160, 0.1143, 0.1126, + 0.1108, 0.1091, 0.1074, 0.1056, 0.0927, 0.0575), + T(0.0633, 0.1022, 0.1161, 0.1169, 0.1163, 0.1148, 0.1129, 0.1109, + 0.1088, 0.1068, 0.1048, 0.1028, 0.0900, 0.0557), + T(0.0632, 0.1019, 0.1157, 0.1162, 0.1155, 0.1137, 0.1115, 0.1092, + 0.1069, 0.1045, 0.1022, 0.0999, 0.0873, 0.0540), + T(0.0631, 0.1017, 0.1153, 0.1156, 0.1146, 0.1126, 0.1100, 0.1075, + 0.1049, 0.1023, 0.0997, 0.0971, 0.0846, 0.0522), + T(0.0631, 0.1015, 0.1149, 0.1150, 0.1138, 0.1115, 0.1087, 0.1058, + 0.1029, 0.1000, 0.0972, 0.0943, 0.0819, 0.0505), + T(0.0630, 0.1013, 0.1146, 0.1145, 0.1130, 0.1105, 0.1074, 0.1043, + 0.1012, 0.0981, 0.0950, 0.0919, 0.0796, 0.0490), + T(0.0629, 0.1012, 0.1143, 0.1141, 0.1125, 0.1098, 0.1065, 0.1033, + 0.1000, 0.0967, 0.0934, 0.0901, 0.0779, 0.0479), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1122, 0.1094, 0.1061, 0.1027, + 0.0993, 0.0960, 0.0926, 0.0892, 0.0770, 0.0473), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1121, 0.1093, 0.1059, 0.1025, + 0.0991, 0.0957, 0.0923, 0.0889, 0.0767, 0.0471), + T(0.0629, 0.1011, 0.1142, 0.1139, 0.1121, 0.1093, 0.1059, 0.1025, + 0.0991, 0.0957, 0.0923, 0.0889, 0.0767, 0.0471), + T(0.0559, 0.0899, 0.1015, 0.1012, 0.0996, 0.0971, 0.0942, 0.0911, + 0.0881, 0.0851, 0.0820, 0.0790, 0.0682, 0.0419), + T(0.0349, 0.0562, 0.0634, 0.0633, 0.0623, 0.0607, 0.0588, 0.0570, + 0.0551, 0.0532, 0.0513, 0.0494, 0.0426, 0.0262))))) + + val output = layer.forward(T(T(features1, features2), bbox)).toTensor[Float] + + output.almostEqual(expectedOutput, 1e-3) should be(true) + } + + "MaskRCNNC4Predictor" should "be ok" in { + RandomGenerator.RNG.setSeed(100) + + val input = Tensor[Float](T(T(T( + T(0.1117, 0.8158, 0.2626, 0.4839, 0.6765, 0.7539, 0.2627, 0.0428), + T(0.2080, 0.1180, 0.1217, 0.7356, 0.7118, 0.7876, 0.4183, 0.9014), + T(0.9969, 0.7565, 0.2239, 0.3023, 0.1784, 0.8238, 0.5557, 0.9770), + T(0.4440, 0.9478, 0.7445, 0.4892, 0.2426, 0.7003, 0.5277, 0.2472), + T(0.7909, 0.4235, 0.0169, 0.2209, 0.9535, 0.7064, 0.1629, 0.8902), + T(0.5163, 0.0359, 0.6476, 0.3430, 0.3182, 0.5261, 0.0447, 0.5123), + T(0.9051, 0.5989, 0.4450, 0.7278, 0.4563, 0.3389, 0.6211, 0.5530), + T(0.6896, 0.3687, 0.9053, 0.8356, 0.3039, 0.6726, 0.5740, 0.9233)), + T(T(0.9178, 0.7590, 0.7775, 0.6179, 0.3379, 0.2170, 0.9454, 0.7116), + T(0.1157, 0.6574, 0.3451, 0.0453, 0.9798, 0.5548, 0.6868, 0.4920), + T(0.0748, 0.9605, 0.3271, 0.0103, 0.9516, 0.2855, 0.2324, 0.9141), + T(0.7668, 0.1659, 0.4393, 0.2243, 0.8935, 0.0497, 0.1780, 0.3011), + T(0.1893, 0.9186, 0.2131, 0.3957, 0.6017, 0.4234, 0.5224, 0.4175), + T(0.0340, 0.9157, 0.3079, 0.6269, 0.8277, 0.6594, 0.0887, 0.4890), + T(0.5887, 0.7340, 0.8497, 0.9112, 0.4847, 0.9436, 0.3904, 0.2499), + T(0.3206, 0.9753, 0.7582, 0.6688, 0.2651, 0.2336, 0.5057, 0.5688)), + T(T(0.0634, 0.8993, 0.2732, 0.3397, 0.1879, 0.5534, 0.2682, 0.9556), + T(0.9761, 0.5934, 0.3124, 0.9431, 0.8519, 0.9815, 0.1132, 0.4783), + T(0.4436, 0.3847, 0.4521, 0.5569, 0.9952, 0.0015, 0.0813, 0.4907), + T(0.2130, 0.4603, 0.1386, 0.0277, 0.5662, 0.3503, 0.6555, 0.7667), + T(0.2269, 0.7555, 0.6458, 0.3673, 0.1770, 0.2966, 0.9925, 0.2103), + T(0.1292, 0.1719, 0.9127, 0.6818, 0.1953, 0.9991, 0.1133, 0.0135), + T(0.1450, 0.7819, 0.3134, 0.2983, 0.3436, 0.2028, 0.9792, 0.4947), + T(0.3617, 0.9687, 0.0359, 0.3041, 0.9867, 0.1290, 0.6887, 0.1637))), + T(T(T(0.0899, 0.3139, 0.1219, 0.3516, 0.2316, 0.2847, 0.3520, 0.2828), + T(0.2420, 0.4928, 0.5772, 0.3771, 0.2440, 0.8994, 0.1041, 0.9193), + T(0.6201, 0.3658, 0.0623, 0.5967, 0.0829, 0.8185, 0.4964, 0.0589), + T(0.9840, 0.5836, 0.6737, 0.4738, 0.9336, 0.2557, 0.1506, 0.7856), + T(0.4152, 0.5809, 0.1088, 0.7065, 0.0105, 0.4602, 0.2945, 0.0475), + T(0.6401, 0.3784, 0.5887, 0.0720, 0.9140, 0.0085, 0.2174, 0.1890), + T(0.0911, 0.6344, 0.3142, 0.7052, 0.6447, 0.9517, 0.3581, 0.3411), + T(0.0433, 0.4373, 0.9947, 0.1748, 0.1374, 0.8005, 0.7004, 0.8803)), + T(T(0.1573, 0.3343, 0.9652, 0.1862, 0.1508, 0.3183, 0.0321, 0.3290), + T(0.5301, 0.6401, 0.7954, 0.3066, 0.2397, 0.1156, 0.4839, 0.3944), + T(0.0801, 0.7782, 0.6686, 0.2312, 0.1164, 0.1921, 0.2380, 0.1643), + T(0.1724, 0.8462, 0.1072, 0.7113, 0.1406, 0.2950, 0.3264, 0.4708), + T(0.3978, 0.7055, 0.9162, 0.8060, 0.7267, 0.8054, 0.1696, 0.2023), + T(0.9194, 0.0151, 0.0324, 0.9538, 0.5564, 0.7567, 0.1573, 0.3969), + T(0.2381, 0.1268, 0.4460, 0.0370, 0.6442, 0.8108, 0.2550, 0.8608), + T(0.8250, 0.2236, 0.0772, 0.4818, 0.0776, 0.0531, 0.2610, 0.1068)), + T(T(0.3011, 0.4587, 0.5222, 0.0683, 0.9118, 0.8286, 0.1635, 0.1775), + T(0.7163, 0.9355, 0.1430, 0.3933, 0.1124, 0.3087, 0.9973, 0.4257), + T(0.6890, 0.9657, 0.0257, 0.4205, 0.0656, 0.4508, 0.0553, 0.3140), + T(0.7460, 0.9357, 0.8925, 0.1370, 0.1803, 0.4023, 0.4296, 0.3692), + T(0.1611, 0.9422, 0.8777, 0.5321, 0.5392, 0.1580, 0.6420, 0.6931), + T(0.0031, 0.6751, 0.1537, 0.5281, 0.1162, 0.4431, 0.2135, 0.2118), + T(0.6561, 0.3722, 0.3653, 0.7055, 0.0839, 0.1767, 0.7989, 0.9738), + T(0.2665, 0.1409, 0.7630, 0.9691, 0.3708, 0.0624, 0.5867, 0.7174))))) + + val in_channels: Int = 3 + val num_classes: Int = 2 + val dim_reduced: Int = 10 + val resolution: Int = 14 + val scales: Array[Float] = Array[Float](0.25f, 0.125f) + val samplingRratio: Float = 2.0f + val layers: Array[Int] = Array[Int](4, 4) + val dilation: Int = 1 + val numClasses: Int = 81 + val useGn: Boolean = false + + val mask = new MaskHead(in_channels, resolution, scales, + samplingRratio, layers, dilation, numClasses, useGn) + val layer = mask.maskPredictor(in_channels, num_classes, dim_reduced) + val params = layer.getParameters() + params._1.fill(0.01f) + + val output = layer.forward(input).toTensor[Float] + + require(output.size(1) == 2 && output.size(2) == 2 && + output.size(3) == 16 && output.size(4) == 16) + + val expectedOutput = Tensor[Float](T(T(T( + T(0.0121, 0.0121, 0.0135, 0.0135, 0.0123, 0.0123, 0.0124, 0.0124, + 0.0122, 0.0122, 0.0125, 0.0125, 0.0125, 0.0125, 0.0127, 0.0127), + T(0.0121, 0.0121, 0.0135, 0.0135, 0.0123, 0.0123, 0.0124, 0.0124, + 0.0122, 0.0122, 0.0125, 0.0125, 0.0125, 0.0125, 0.0127, 0.0127), + T(0.0123, 0.0123, 0.0124, 0.0124, 0.0118, 0.0118, 0.0127, 0.0127, + 0.0135, 0.0135, 0.0133, 0.0133, 0.0122, 0.0122, 0.0129, 0.0129), + T(0.0123, 0.0123, 0.0124, 0.0124, 0.0118, 0.0118, 0.0127, 0.0127, + 0.0135, 0.0135, 0.0133, 0.0133, 0.0122, 0.0122, 0.0129, 0.0129), + T(0.0125, 0.0125, 0.0131, 0.0131, 0.0120, 0.0120, 0.0119, 0.0119, + 0.0131, 0.0131, 0.0121, 0.0121, 0.0119, 0.0119, 0.0134, 0.0134), + T(0.0125, 0.0125, 0.0131, 0.0131, 0.0120, 0.0120, 0.0119, 0.0119, + 0.0131, 0.0131, 0.0121, 0.0121, 0.0119, 0.0119, 0.0134, 0.0134), + T(0.0124, 0.0124, 0.0126, 0.0126, 0.0123, 0.0123, 0.0117, 0.0117, + 0.0127, 0.0127, 0.0121, 0.0121, 0.0124, 0.0124, 0.0123, 0.0123), + T(0.0124, 0.0124, 0.0126, 0.0126, 0.0123, 0.0123, 0.0117, 0.0117, + 0.0127, 0.0127, 0.0121, 0.0121, 0.0124, 0.0124, 0.0123, 0.0123), + T(0.0122, 0.0122, 0.0131, 0.0131, 0.0119, 0.0119, 0.0120, 0.0120, + 0.0127, 0.0127, 0.0124, 0.0124, 0.0127, 0.0127, 0.0125, 0.0125), + T(0.0122, 0.0122, 0.0131, 0.0131, 0.0119, 0.0119, 0.0120, 0.0120, + 0.0127, 0.0127, 0.0124, 0.0124, 0.0127, 0.0127, 0.0125, 0.0125), + T(0.0117, 0.0117, 0.0121, 0.0121, 0.0129, 0.0129, 0.0127, 0.0127, + 0.0123, 0.0123, 0.0132, 0.0132, 0.0112, 0.0112, 0.0120, 0.0120), + T(0.0117, 0.0117, 0.0121, 0.0121, 0.0129, 0.0129, 0.0127, 0.0127, + 0.0123, 0.0123, 0.0132, 0.0132, 0.0112, 0.0112, 0.0120, 0.0120), + T(0.0126, 0.0126, 0.0131, 0.0131, 0.0126, 0.0126, 0.0129, 0.0129, + 0.0123, 0.0123, 0.0125, 0.0125, 0.0130, 0.0130, 0.0123, 0.0123), + T(0.0126, 0.0126, 0.0131, 0.0131, 0.0126, 0.0126, 0.0129, 0.0129, + 0.0123, 0.0123, 0.0125, 0.0125, 0.0130, 0.0130, 0.0123, 0.0123), + T(0.0124, 0.0124, 0.0133, 0.0133, 0.0127, 0.0127, 0.0128, 0.0128, + 0.0126, 0.0126, 0.0120, 0.0120, 0.0128, 0.0128, 0.0127, 0.0127), + T(0.0124, 0.0124, 0.0133, 0.0133, 0.0127, 0.0127, 0.0128, 0.0128, + 0.0126, 0.0126, 0.0120, 0.0120, 0.0128, 0.0128, 0.0127, 0.0127)), + T(T(0.0121, 0.0121, 0.0135, 0.0135, 0.0123, 0.0123, 0.0124, 0.0124, + 0.0122, 0.0122, 0.0125, 0.0125, 0.0125, 0.0125, 0.0127, 0.0127), + T(0.0121, 0.0121, 0.0135, 0.0135, 0.0123, 0.0123, 0.0124, 0.0124, + 0.0122, 0.0122, 0.0125, 0.0125, 0.0125, 0.0125, 0.0127, 0.0127), + T(0.0123, 0.0123, 0.0124, 0.0124, 0.0118, 0.0118, 0.0127, 0.0127, + 0.0135, 0.0135, 0.0133, 0.0133, 0.0122, 0.0122, 0.0129, 0.0129), + T(0.0123, 0.0123, 0.0124, 0.0124, 0.0118, 0.0118, 0.0127, 0.0127, + 0.0135, 0.0135, 0.0133, 0.0133, 0.0122, 0.0122, 0.0129, 0.0129), + T(0.0125, 0.0125, 0.0131, 0.0131, 0.0120, 0.0120, 0.0119, 0.0119, + 0.0131, 0.0131, 0.0121, 0.0121, 0.0119, 0.0119, 0.0134, 0.0134), + T(0.0125, 0.0125, 0.0131, 0.0131, 0.0120, 0.0120, 0.0119, 0.0119, + 0.0131, 0.0131, 0.0121, 0.0121, 0.0119, 0.0119, 0.0134, 0.0134), + T(0.0124, 0.0124, 0.0126, 0.0126, 0.0123, 0.0123, 0.0117, 0.0117, + 0.0127, 0.0127, 0.0121, 0.0121, 0.0124, 0.0124, 0.0123, 0.0123), + T(0.0124, 0.0124, 0.0126, 0.0126, 0.0123, 0.0123, 0.0117, 0.0117, + 0.0127, 0.0127, 0.0121, 0.0121, 0.0124, 0.0124, 0.0123, 0.0123), + T(0.0122, 0.0122, 0.0131, 0.0131, 0.0119, 0.0119, 0.0120, 0.0120, + 0.0127, 0.0127, 0.0124, 0.0124, 0.0127, 0.0127, 0.0125, 0.0125), + T(0.0122, 0.0122, 0.0131, 0.0131, 0.0119, 0.0119, 0.0120, 0.0120, + 0.0127, 0.0127, 0.0124, 0.0124, 0.0127, 0.0127, 0.0125, 0.0125), + T(0.0117, 0.0117, 0.0121, 0.0121, 0.0129, 0.0129, 0.0127, 0.0127, + 0.0123, 0.0123, 0.0132, 0.0132, 0.0112, 0.0112, 0.0120, 0.0120), + T(0.0117, 0.0117, 0.0121, 0.0121, 0.0129, 0.0129, 0.0127, 0.0127, + 0.0123, 0.0123, 0.0132, 0.0132, 0.0112, 0.0112, 0.0120, 0.0120), + T(0.0126, 0.0126, 0.0131, 0.0131, 0.0126, 0.0126, 0.0129, 0.0129, + 0.0123, 0.0123, 0.0125, 0.0125, 0.0130, 0.0130, 0.0123, 0.0123), + T(0.0126, 0.0126, 0.0131, 0.0131, 0.0126, 0.0126, 0.0129, 0.0129, + 0.0123, 0.0123, 0.0125, 0.0125, 0.0130, 0.0130, 0.0123, 0.0123), + T(0.0124, 0.0124, 0.0133, 0.0133, 0.0127, 0.0127, 0.0128, 0.0128, + 0.0126, 0.0126, 0.0120, 0.0120, 0.0128, 0.0128, 0.0127, 0.0127), + T(0.0124, 0.0124, 0.0133, 0.0133, 0.0127, 0.0127, 0.0128, 0.0128, + 0.0126, 0.0126, 0.0120, 0.0120, 0.0128, 0.0128, 0.0127, 0.0127))), + T(T(T(0.0115, 0.0115, 0.0121, 0.0121, 0.0126, 0.0126, 0.0116, 0.0116, + 0.0123, 0.0123, 0.0124, 0.0124, 0.0115, 0.0115, 0.0118, 0.0118), + T(0.0115, 0.0115, 0.0121, 0.0121, 0.0126, 0.0126, 0.0116, 0.0116, + 0.0123, 0.0123, 0.0124, 0.0124, 0.0115, 0.0115, 0.0118, 0.0118), + T(0.0125, 0.0125, 0.0131, 0.0131, 0.0125, 0.0125, 0.0121, 0.0121, + 0.0116, 0.0116, 0.0123, 0.0123, 0.0126, 0.0126, 0.0127, 0.0127), + T(0.0125, 0.0125, 0.0131, 0.0131, 0.0125, 0.0125, 0.0121, 0.0121, + 0.0116, 0.0116, 0.0123, 0.0123, 0.0126, 0.0126, 0.0127, 0.0127), + T(0.0124, 0.0124, 0.0131, 0.0131, 0.0118, 0.0118, 0.0122, 0.0122, + 0.0113, 0.0113, 0.0125, 0.0125, 0.0118, 0.0118, 0.0115, 0.0115), + T(0.0124, 0.0124, 0.0131, 0.0131, 0.0118, 0.0118, 0.0122, 0.0122, + 0.0113, 0.0113, 0.0125, 0.0125, 0.0118, 0.0118, 0.0115, 0.0115), + T(0.0129, 0.0129, 0.0134, 0.0134, 0.0127, 0.0127, 0.0123, 0.0123, + 0.0123, 0.0123, 0.0120, 0.0120, 0.0119, 0.0119, 0.0126, 0.0126), + T(0.0129, 0.0129, 0.0134, 0.0134, 0.0127, 0.0127, 0.0123, 0.0123, + 0.0123, 0.0123, 0.0120, 0.0120, 0.0119, 0.0119, 0.0126, 0.0126), + T(0.0120, 0.0120, 0.0132, 0.0132, 0.0129, 0.0129, 0.0130, 0.0130, + 0.0123, 0.0123, 0.0124, 0.0124, 0.0121, 0.0121, 0.0119, 0.0119), + T(0.0120, 0.0120, 0.0132, 0.0132, 0.0129, 0.0129, 0.0130, 0.0130, + 0.0123, 0.0123, 0.0124, 0.0124, 0.0121, 0.0121, 0.0119, 0.0119), + T(0.0126, 0.0126, 0.0121, 0.0121, 0.0118, 0.0118, 0.0126, 0.0126, + 0.0126, 0.0126, 0.0122, 0.0122, 0.0116, 0.0116, 0.0118, 0.0118), + T(0.0126, 0.0126, 0.0121, 0.0121, 0.0118, 0.0118, 0.0126, 0.0126, + 0.0126, 0.0126, 0.0122, 0.0122, 0.0116, 0.0116, 0.0118, 0.0118), + T(0.0120, 0.0120, 0.0121, 0.0121, 0.0121, 0.0121, 0.0124, 0.0124, + 0.0124, 0.0124, 0.0129, 0.0129, 0.0124, 0.0124, 0.0132, 0.0132), + T(0.0120, 0.0120, 0.0121, 0.0121, 0.0121, 0.0121, 0.0124, 0.0124, + 0.0124, 0.0124, 0.0129, 0.0129, 0.0124, 0.0124, 0.0132, 0.0132), + T(0.0121, 0.0121, 0.0118, 0.0118, 0.0128, 0.0128, 0.0126, 0.0126, + 0.0116, 0.0116, 0.0119, 0.0119, 0.0125, 0.0125, 0.0127, 0.0127), + T(0.0121, 0.0121, 0.0118, 0.0118, 0.0128, 0.0128, 0.0126, 0.0126, + 0.0116, 0.0116, 0.0119, 0.0119, 0.0125, 0.0125, 0.0127, 0.0127)), + T(T(0.0115, 0.0115, 0.0121, 0.0121, 0.0126, 0.0126, 0.0116, 0.0116, + 0.0123, 0.0123, 0.0124, 0.0124, 0.0115, 0.0115, 0.0118, 0.0118), + T(0.0115, 0.0115, 0.0121, 0.0121, 0.0126, 0.0126, 0.0116, 0.0116, + 0.0123, 0.0123, 0.0124, 0.0124, 0.0115, 0.0115, 0.0118, 0.0118), + T(0.0125, 0.0125, 0.0131, 0.0131, 0.0125, 0.0125, 0.0121, 0.0121, + 0.0116, 0.0116, 0.0123, 0.0123, 0.0126, 0.0126, 0.0127, 0.0127), + T(0.0125, 0.0125, 0.0131, 0.0131, 0.0125, 0.0125, 0.0121, 0.0121, + 0.0116, 0.0116, 0.0123, 0.0123, 0.0126, 0.0126, 0.0127, 0.0127), + T(0.0124, 0.0124, 0.0131, 0.0131, 0.0118, 0.0118, 0.0122, 0.0122, + 0.0113, 0.0113, 0.0125, 0.0125, 0.0118, 0.0118, 0.0115, 0.0115), + T(0.0124, 0.0124, 0.0131, 0.0131, 0.0118, 0.0118, 0.0122, 0.0122, + 0.0113, 0.0113, 0.0125, 0.0125, 0.0118, 0.0118, 0.0115, 0.0115), + T(0.0129, 0.0129, 0.0134, 0.0134, 0.0127, 0.0127, 0.0123, 0.0123, + 0.0123, 0.0123, 0.0120, 0.0120, 0.0119, 0.0119, 0.0126, 0.0126), + T(0.0129, 0.0129, 0.0134, 0.0134, 0.0127, 0.0127, 0.0123, 0.0123, + 0.0123, 0.0123, 0.0120, 0.0120, 0.0119, 0.0119, 0.0126, 0.0126), + T(0.0120, 0.0120, 0.0132, 0.0132, 0.0129, 0.0129, 0.0130, 0.0130, + 0.0123, 0.0123, 0.0124, 0.0124, 0.0121, 0.0121, 0.0119, 0.0119), + T(0.0120, 0.0120, 0.0132, 0.0132, 0.0129, 0.0129, 0.0130, 0.0130, + 0.0123, 0.0123, 0.0124, 0.0124, 0.0121, 0.0121, 0.0119, 0.0119), + T(0.0126, 0.0126, 0.0121, 0.0121, 0.0118, 0.0118, 0.0126, 0.0126, + 0.0126, 0.0126, 0.0122, 0.0122, 0.0116, 0.0116, 0.0118, 0.0118), + T(0.0126, 0.0126, 0.0121, 0.0121, 0.0118, 0.0118, 0.0126, 0.0126, + 0.0126, 0.0126, 0.0122, 0.0122, 0.0116, 0.0116, 0.0118, 0.0118), + T(0.0120, 0.0120, 0.0121, 0.0121, 0.0121, 0.0121, 0.0124, 0.0124, + 0.0124, 0.0124, 0.0129, 0.0129, 0.0124, 0.0124, 0.0132, 0.0132), + T(0.0120, 0.0120, 0.0121, 0.0121, 0.0121, 0.0121, 0.0124, 0.0124, + 0.0124, 0.0124, 0.0129, 0.0129, 0.0124, 0.0124, 0.0132, 0.0132), + T(0.0121, 0.0121, 0.0118, 0.0118, 0.0128, 0.0128, 0.0126, 0.0126, + 0.0116, 0.0116, 0.0119, 0.0119, 0.0125, 0.0125, 0.0127, 0.0127), + T(0.0121, 0.0121, 0.0118, 0.0118, 0.0128, 0.0128, 0.0126, 0.0126, + 0.0116, 0.0116, 0.0119, 0.0119, 0.0125, 0.0125, 0.0127, 0.0127))))) + + output.almostEqual(expectedOutput, 1e-4) should be(true) + } +} + +class MaskHeadSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val inChannels: Int = 6 + val resolution: Int = 14 + val scales: Array[Float] = Array[Float](0.25f, 0.125f) + val samplingRratio: Float = 2.0f + val layers: Array[Int] = Array[Int](4, 4) + val dilation: Int = 1 + val numClasses: Int = 81 + val useGn: Boolean = false + + val layer = new MaskHead(inChannels, resolution, scales, + samplingRratio, layers, dilation, numClasses, useGn).setName("MaskHead") + + val features1 = Tensor[Float](1, 6, 3, 4).rand() + val features2 = Tensor[Float](1, 6, 5, 2).rand() + + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 10.0f))) + val labels = Tensor[Float](T(1, 3)) + + runSerializationTest(layer, T(T(features1, features2), bbox, labels)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala new file mode 100644 index 00000000000..ba6f1ce6760 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala @@ -0,0 +1,532 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} + +class MaskPostProcessorSpec extends FlatSpec with Matchers { + "MaskPostProcessor" should "be ok" in { + val inputMaskLogits = Tensor[Float](T(T(T( + T(8.9266e-01, 5.2952e-02, 8.8993e-01, 2.1737e-01, 4.3262e-01, + 9.1971e-01, 7.6453e-01, 2.1937e-01, 2.1589e-01, 2.5001e-01, + 8.6919e-01, 8.0406e-01), + T(7.0763e-01, 7.2727e-01, 4.8777e-02, 2.7696e-01, 7.0037e-01, + 8.1201e-01, 2.4355e-01, 5.4897e-02, 4.8910e-01, 7.1544e-02, + 3.4121e-01, 2.0984e-01), + T(5.2887e-01, 1.3025e-01, 6.7321e-02, 4.2251e-01, 3.9152e-01, + 1.7274e-01, 5.1561e-01, 6.2375e-01, 7.6026e-01, 1.4813e-01, + 4.5966e-01, 1.4207e-01), + T(2.7198e-01, 7.6154e-01, 3.3004e-01, 9.7574e-01, 2.6676e-01, + 3.4621e-01, 2.5595e-02, 9.9852e-03, 9.4801e-01, 4.2668e-01, + 6.0820e-01, 4.8713e-01), + T(8.9514e-01, 2.4535e-01, 5.4862e-01, 7.2516e-01, 6.0752e-01, + 8.6299e-01, 6.3599e-02, 3.3235e-01, 7.3556e-01, 3.1016e-01, + 5.1763e-01, 8.0128e-01), + T(9.1450e-01, 7.9486e-02, 6.6234e-01, 3.0249e-01, 3.8758e-01, + 6.8105e-02, 4.2100e-01, 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2.6683e-01, 3.0752e-01, 6.4153e-01, 7.2429e-01, + 9.1566e-01, 5.0017e-01), + T(7.2460e-01, 2.2245e-01, 1.2162e-03, 7.8895e-01, 2.5927e-01, + 7.7986e-01, 9.5457e-01, 1.7036e-01, 8.7724e-01, 5.7733e-01, + 7.3935e-01, 5.9932e-01), + T(6.9864e-01, 8.1021e-01, 7.4214e-02, 5.2962e-01, 2.5611e-01, + 8.7787e-01, 4.4532e-01, 8.9145e-01, 3.0780e-01, 2.8806e-01, + 2.1132e-01, 8.4222e-01), + T(8.0758e-01, 4.2134e-01, 2.8893e-01, 4.0820e-01, 5.3765e-01, + 4.5207e-01, 1.6186e-01, 9.4840e-01, 8.5380e-01, 9.8164e-02, + 5.2123e-01, 3.9020e-01), + T(9.6101e-01, 5.2674e-01, 5.4770e-01, 1.5045e-01, 7.0950e-01, + 3.9006e-01, 3.1002e-01, 1.7049e-01, 7.6328e-01, 1.9298e-01, + 7.5822e-01, 6.1642e-01), + T(9.9112e-01, 9.4758e-01, 7.0602e-03, 3.6465e-02, 9.4971e-01, + 5.1071e-01, 7.0599e-01, 2.8420e-01, 2.8210e-01, 7.1894e-01, + 7.7572e-01, 3.6888e-01), + T(8.7262e-01, 3.1759e-01, 7.7522e-01, 4.4778e-01, 2.7879e-01, + 4.2009e-01, 5.5572e-01, 9.5537e-01, 5.5494e-01, 1.5535e-01, + 1.1270e-01, 6.7605e-01), + T(3.7349e-01, 7.3304e-01, 5.8971e-01, 1.7675e-01, 9.4888e-01, + 8.3404e-02, 8.1588e-01, 9.1072e-01, 1.3691e-01, 7.5133e-01, + 7.1789e-01, 3.4329e-01), + T(8.2344e-01, 3.9210e-01, 2.0472e-01, 2.2641e-01, 2.8660e-01, + 3.3381e-01, 3.0478e-01, 1.1062e-01, 4.9216e-01, 3.3757e-02, + 1.9666e-01, 3.8566e-01))))) + + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), T(3.0f, 5.0f, 6.0f, 10.0f))) + val imageInfo = Tensor[Float](T(10, 15)) + val labels = Tensor[Float](T(2, 4)) + + val layer = new MaskPostProcessor() + + val output = layer.forward(T(inputMaskLogits, bbox, imageInfo, labels)) + + val expectedOutput = Tensor[Float](T(T(T( + T(0.5913, 0.5344, 0.6753, 0.5105, 0.5563, 0.5300, 0.6150, 0.6773, + 0.5334, 0.6796, 0.7197, 0.5047), + T(0.5079, 0.6325, 0.7249, 0.5010, 0.5237, 0.6543, 0.7238, 0.6993, + 0.5108, 0.5587, 0.6112, 0.6501), + T(0.5421, 0.7194, 0.5869, 0.5258, 0.6283, 0.6375, 0.5559, 0.6452, + 0.7038, 0.6460, 0.5651, 0.5759), + T(0.7247, 0.6702, 0.5494, 0.6430, 0.6619, 0.6262, 0.5516, 0.6720, + 0.6903, 0.6959, 0.7302, 0.5791), + T(0.7149, 0.6512, 0.7298, 0.5762, 0.6969, 0.5219, 0.5281, 0.5075, + 0.6563, 0.6873, 0.6791, 0.6138), + T(0.6892, 0.7255, 0.6853, 0.6685, 0.6638, 0.6407, 0.6019, 0.6871, + 0.6110, 0.5590, 0.5176, 0.5724), + T(0.6760, 0.6018, 0.6482, 0.6103, 0.5305, 0.6986, 0.5503, 0.5701, + 0.6220, 0.6769, 0.5751, 0.6787), + T(0.7105, 0.5102, 0.6404, 0.5744, 0.6111, 0.6538, 0.6311, 0.6877, + 0.6722, 0.5690, 0.5388, 0.6383), + T(0.5098, 0.5370, 0.6413, 0.6156, 0.5352, 0.6644, 0.6144, 0.6202, + 0.5670, 0.5420, 0.6689, 0.6006), + T(0.6803, 0.6049, 0.5806, 0.6732, 0.6295, 0.6162, 0.5395, 0.5491, + 0.5303, 0.6862, 0.6423, 0.6691), + T(0.7209, 0.5530, 0.5283, 0.6029, 0.5130, 0.5965, 0.5773, 0.5366, + 0.6282, 0.7149, 0.5041, 0.6715), + T(0.6751, 0.5801, 0.5841, 0.5236, 0.6276, 0.5339, 0.7290, 0.6393, + 0.5756, 0.7272, 0.5186, 0.6639))), + T(T(T(0.6209, 0.6525, 0.6033, 0.6315, 0.7057, 0.5001, 0.7230, 0.6290, + 0.5184, 0.6671, 0.5102, 0.6651), + T(0.6746, 0.6800, 0.5059, 0.5868, 0.7056, 0.6932, 0.6162, 0.7241, + 0.5055, 0.5603, 0.5682, 0.7147), + T(0.7303, 0.6846, 0.6485, 0.6219, 0.6246, 0.6223, 0.5161, 0.6901, + 0.5796, 0.6148, 0.6862, 0.5876), + T(0.6692, 0.6088, 0.6315, 0.6924, 0.5012, 0.5716, 0.5774, 0.6632, + 0.5330, 0.5665, 0.5456, 0.6654), + T(0.5462, 0.6693, 0.7155, 0.7074, 0.7147, 0.5031, 0.5367, 0.5257, + 0.6106, 0.7218, 0.5089, 0.6641), + T(0.5038, 0.6329, 0.5158, 0.5029, 0.7091, 0.6387, 0.6275, 0.6501, + 0.5698, 0.5514, 0.6040, 0.7041), + T(0.7290, 0.5159, 0.5661, 0.5445, 0.5491, 0.6063, 0.5921, 0.5004, + 0.6967, 0.7287, 0.6618, 0.6697), + T(0.6723, 0.6402, 0.6213, 0.7132, 0.5597, 0.6533, 0.6269, 0.5155, + 0.6174, 0.5908, 0.7038, 0.5038), + T(0.5585, 0.6790, 0.5427, 0.6505, 0.5750, 0.5556, 0.6094, 0.5234, + 0.7198, 0.5915, 0.5314, 0.6720), + T(0.5738, 0.5940, 0.6906, 0.6065, 0.5045, 0.5683, 0.6348, 0.6655, + 0.7301, 0.6856, 0.7273, 0.5842), + T(0.6944, 0.6791, 0.6710, 0.6560, 0.6275, 0.6213, 0.6668, 0.6005, + 0.6449, 0.6558, 0.5863, 0.7029), + T(0.5035, 0.6440, 0.5290, 0.6693, 0.6677, 0.5698, 0.7180, 0.5952, + 0.6821, 0.5830, 0.6005, 0.6017))))) + + + output.almostEqual(expectedOutput, 1e-3) should be(true) + } +} + +class MaskPostProcessorSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val proposal = new MaskPostProcessor().setName("MaskPostProcessor") + + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 10.0f))) + val labels = Tensor[Float](T(1, 3)) + val logits = Tensor[Float](2, 81, 18, 18).rand() + runSerializationTest(proposal, T(logits, bbox, labels)) + } +} From 06f59dea55bd440f77e42b96ff0c183df0b3dcf4 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 2 Sep 2019 11:24:37 +0800 Subject: [PATCH 0947/1065] fix unit tests (#2905) --- .../scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala | 1 - 1 file changed, 1 deletion(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala index f9191f86346..c65ea3637ae 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala @@ -18,7 +18,6 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} -import org.dmg.pmml.False import org.scalatest.{FlatSpec, Matchers} class MaskHeadSpec extends FlatSpec with Matchers { From 80fccde6ecf87ca91c1378122273d47793e8e72d Mon Sep 17 00:00:00 2001 From: Xiao Date: Fri, 6 Sep 2019 11:04:42 +0800 Subject: [PATCH 0948/1065] modify predict/predictClass function (#2868) * predictClass output modification * predict/predictClass function modification in Beta Api * predict/predictClass function modification * predict/predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification * predictClass function modification --- .../bigdl/dllib/feature/dataset/Sample.scala | 1 + .../dllib/nn/abstractnn/AbstractModule.scala | 3 +- .../bigdl/dllib/optim/Predictor.scala | 41 +++++++++++++------ .../dllib/utils/python/api/PythonBigDL.scala | 8 ++-- 4 files changed, 36 insertions(+), 17 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala index 370f37c539d..8ff957b6aa5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/Sample.scala @@ -496,6 +496,7 @@ class TensorSample[T: ClassTag] private[bigdl] ( require(index < this.numFeature, "Index out of range") if (index < this.numLabel) this.labels(index) else null } + } object TensorSample { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index df2ace40563..cec55896e55 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -646,7 +646,8 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @param batchSize total batchSize for all partitions. * if -1, default is 4 * partitionNumber of dataset */ - final def predictClass(dataset: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { + + final def predictClass(dataset: RDD[Sample[T]], batchSize: Int = -1): RDD[Sample[T]] = { Predictor(this).predictClass(dataset, batchSize) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index 61ad801c45b..5b18d86c6d5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -135,13 +135,11 @@ object Predictor { batchSize = totalBatch, partitionNum = Some(realPartitionLength), featurePaddingParam = featurePaddingParam), shareBuffer) - val localBatchPerPartition = totalBatch / realPartitionLength val result = rdd.mapPartitions(partition => { val localModel = modelBroad.value() val localToBatch = toBatchBroad.value._1.cloneTransformer() - partition.grouped(localBatchPerPartition).flatMap(imageFeatures => { Predictor.predictImageBatch[T](localModel, imageFeatures, outputLayer, predictKey, localToBatch, shareBuffer) @@ -182,15 +180,34 @@ object Predictor { def predictClass[T: ClassTag](dataSet: RDD[Sample[T]], batchSize: Int = -1, model: Module[T], batchPerPartition: Int, featurePaddingParam: Option[PaddingParam[T]])( - implicit ev: TensorNumeric[T]): RDD[Int] = { - val result = Predictor.predict(dataSet, batchSize, true, model, - batchPerPartition, featurePaddingParam) - result.mapPartitions { partition => - partition.map(output => { - val _output = output.toTensor[T] - require(_output.dim() == 1, s"Predictor.predictClass:" + - s"Only support one sample has one label, but got ${_output.dim()} label") - ev.toType[Int](_output.max(1)._2.valueAt(1)) + implicit ev: TensorNumeric[T]): RDD[Sample[T]] = { + val shareBuffer = false + val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext, + ConversionUtils.convert(model.evaluate())) + val partitionNum = dataSet.partitions.length + val totalBatch = if (batchSize > 0) { + require(batchSize % partitionNum == 0, s"Predictor.predict: total batch size $batchSize " + + s"should be divided by partitionNum ${partitionNum}") + batchSize + } else { + batchPerPartition * partitionNum + } + val rdd = ConversionUtils.coalesce(dataSet) + val realPartitionLength = rdd.partitions.length + val otherBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( + batchSize = totalBatch, + partitionNum = Some(realPartitionLength), + featurePaddingParam = featurePaddingParam)) + val localBatchPerPartition = totalBatch / realPartitionLength + rdd.mapPartitions { partition => + val localModel = modelBroad.value() + val localTransformer = otherBroad.value.cloneTransformer() + partition.grouped(localBatchPerPartition).flatMap(samples => { + val batchOut = predictSamples(localModel, samples, localTransformer, shareBuffer) + samples.toIterator.zip(batchOut).foreach(tuple => { + Sample(tuple._1.feature(), tuple._2.toTensor) + }) + samples }) } } @@ -216,7 +233,7 @@ class Predictor[T: ClassTag] private[optim]( batchPerPartition: Int = 4) (implicit ev: TensorNumeric[T]) extends Serializable { - def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { + def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Sample[T]] = { Predictor.predictClass(dataSet, batchSize, model, batchPerPartition, featurePaddingParam) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 129b8b1125f..17bbe417003 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2037,10 +2037,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelPredictClass(model: AbstractModule[Activity, Activity, T], - dataRdd: JavaRDD[Sample]): JavaRDD[Int] = { - val sampleRdd = toJSample(dataRdd) - val tensorRDD = model.predictClass(sampleRdd) - new JavaRDD[Int](tensorRDD) + dataRdd: JavaRDD[Sample]): JavaRDD[Sample] = { + val sampleRDD = toJSample(dataRdd) + val pySampleRDD = model.predictClass(sampleRDD).map(toPySample(_)) + new JavaRDD[Sample](pySampleRDD) } def modelForward(model: AbstractModule[Activity, Activity, T], From fde4aff962f17aac382cd3341d909137fd0c9307 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 9 Sep 2019 14:11:22 +0800 Subject: [PATCH 0949/1065] [New feature] Add Boxhead (#2894) * add boxhead * add SerialTest * meet pr comments --- .../vision/image/util/BboxUtil.scala | 104 +++ .../analytics/bigdl/dllib/nn/BoxHead.scala | 328 ++++++++ .../bigdl/dllib/nn/BoxHeadSpec.scala | 762 ++++++++++++++++++ 3 files changed, 1194 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala index 5cddd22f4b3..e302887eebf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala @@ -477,4 +477,108 @@ object BboxUtil { } indices } + + private def decodeSignalBoxWithWeight(encodeBox: Tensor[Float], bbox: Tensor[Float], + weight: Array[Float], decodeBox: Tensor[Float]): Unit = { + require(bbox.nDimension() == 1 && encodeBox.nDimension() == 1 && decodeBox.dim() == 1, + s"Only support decode single bbox, but " + + s"get ${bbox.nDimension()}, ${encodeBox.nDimension()}, ${decodeBox.dim()}") + + require(encodeBox.nElement() == decodeBox.nElement(), s"element number of encode tensor" + + s" and decode tensor should be same, but get ${encodeBox.nElement()} ${decodeBox.nElement()}") + + val TO_REMOVE = 1 // refer to pytorch, maybe it will be removed in future + val x1 = bbox.valueAt(1) + val y1 = bbox.valueAt(2) + val x2 = bbox.valueAt(3) + val y2 = bbox.valueAt(4) + val priorWidth = x2 - x1 + TO_REMOVE + val priorHight = y2 - y1 + TO_REMOVE + val pCenterX = x1 + priorWidth/ 2 + val pCenterY = y1 + priorHight / 2 + + val wx = weight(0) + val wy = weight(1) + val ww = weight(2) + val wh = weight(3) + + encodeBox.resize(Array(encodeBox.nElement() / 4, 4)) + decodeBox.resize(Array(4, decodeBox.nElement() / 4)) + + // copy for contigious + val dx = decodeBox.select(1, 1).copy(encodeBox.select(2, 1)).div(wx) + val dy = decodeBox.select(1, 2).copy(encodeBox.select(2, 2)).div(wy) + val dw = decodeBox.select(1, 3).copy(encodeBox.select(2, 3)).div(ww) + val dh = decodeBox.select(1, 4).copy(encodeBox.select(2, 4)).div(wh) + + // not change original input + encodeBox.resize(encodeBox.nElement()) + + // clamp, dw, dh + val bboxClip = 62.5f + clamp(dw, 0.0f, bboxClip) + clamp(dh, 0.0f, bboxClip) + + val pred_ctr_x = dx * priorWidth + pCenterX + val pred_ctr_y = dy * priorHight + pCenterY + + val pred_w = dw.exp().mul(priorWidth).mul(0.5f) + val pred_h = dh.exp().mul(priorHight).mul(0.5f) + + // todo: memory optimzation + val buffer1 = Tensor[Float]().resizeAs(pred_ctr_x).copy(pred_ctr_x).sub(pred_w) + val buffer2 = Tensor[Float]().resizeAs(pred_ctr_y).copy(pred_ctr_y).sub(pred_h) + val buffer3 = Tensor[Float]().resizeAs(pred_ctr_x).copy(pred_ctr_x).add(pred_w).add(-1.0f) + val buffer4 = Tensor[Float]().resizeAs(pred_ctr_y).copy(pred_ctr_y).add(pred_h).add(-1.0f) + decodeBox.resize(decodeBox.nElement()) + + val arrBuffer1 = buffer1.storage().array() + val arrBuffer2 = buffer2.storage().array() + val arrBuffer3 = buffer3.storage().array() + val arrBuffer4 = buffer4.storage().array() + val arrBox = decodeBox.storage().array() + val offset = decodeBox.storageOffset() - 1 + + var i = 0 + var j = 0 + while (i < arrBuffer1.length) { + arrBox(j + offset) = arrBuffer1(i) + arrBox(j + 1 + offset) = arrBuffer2(i) + arrBox(j + 2 + offset) = arrBuffer3(i) + arrBox(j + 3 + offset) = arrBuffer4(i) + i += 1 + j += 4 + } + } + + def decodeWithWeight(encodeBox: Tensor[Float], bbox: Tensor[Float], + weight: Array[Float], decodeBox: Tensor[Float]): Unit = { + require(encodeBox.size(1) == bbox.size(1)) + require(encodeBox.size(1) == decodeBox.size(1)) + + val numBboxes = bbox.size(1) + if (numBboxes > 0) { + require(bbox.size(2) == 4) + } + + var i = 1 + while (i <= numBboxes) { + decodeSignalBoxWithWeight(encodeBox.select(1, i), bbox.select(1, i), + weight, decodeBox.select(1, i)) + i += 1 + } + } + + private def clamp(input: Tensor[Float], min: Float, max: Float): Unit = { + require(input.isContiguous(), "input for clamp should be contiguous") + val arr = input.storage().array() + val offset = input.storageOffset() - 1 + var i = 0 + while (i < arr.length) { + val value = arr(i) + if (value < min) arr(i) = value + if (value > max) arr(i) = max + i += 1 + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala new file mode 100644 index 00000000000..ec797911750 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala @@ -0,0 +1,328 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import breeze.linalg.dim +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.collection.mutable.ArrayBuffer + +class BoxHead( + val inChannels: Int, + val resolution: Int, + val scales: Array[Float], + val samplingRatio: Int, + val scoreThresh: Float, + val nmsThresh: Float, + val maxPerImage: Int, + val outputSize: Int, + val numClasses: Int + )(implicit ev: TensorNumeric[Float]) + extends BaseModule[Float] { + + override def buildModel(): Module[Float] = { + val featureExtractor = this.featureExtractor( + inChannels, resolution, scales, samplingRatio, outputSize) + + val clsPredictor = this.clsPredictor(numClasses, outputSize) + val bboxPredictor = this.bboxPredictor(numClasses, outputSize) + + val weight = Array(10.0f, 10.0f, 5.0f, 5.0f) + val postProcessor = new BoxPostProcessor(scoreThresh, nmsThresh, + maxPerImage, numClasses, weight = weight) + + val features = Input() + val proposals = Input() + + val boxFeatures = featureExtractor.inputs(features, proposals) + val classLogits = clsPredictor.inputs(boxFeatures) + val boxRegression = bboxPredictor.inputs(boxFeatures) + val result = postProcessor.inputs(classLogits, boxRegression, proposals) + + Graph(Array(features, proposals), Array(boxFeatures, result)) + } + + private[nn] def clsPredictor(numClass: Int, + inChannels: Int): Module[Float] = { + val clsScore = Linear[Float](inChannels, numClass) + clsScore.weight.apply1(_ => RNG.normal(0, 0.01).toFloat) + clsScore.bias.fill(0.0f) + clsScore.asInstanceOf[Module[Float]] + } + + private[nn] def bboxPredictor(numClass: Int, + inChannels: Int): Module[Float] = { + val bboxRegression = Linear[Float](inChannels, numClass * 4) + bboxRegression.weight.apply1(_ => RNG.normal(0, 0.001).toFloat) + bboxRegression.bias.fill(0.0f) + bboxRegression.asInstanceOf[Module[Float]] + } + + private[nn] def featureExtractor(inChannels: Int, + resolution: Int, + scales: Array[Float], samplingRatio: Int, + representationSize: Int): Module[Float] = { + val pooler = new Pooler(resolution, scales, samplingRatio) + val inputSize = inChannels * math.pow(resolution, 2).toInt + + val fc1 = Linear[Float](inputSize, representationSize, withBias = true) + .setInitMethod(Xavier, Zeros) + val fc2 = Linear[Float](representationSize, representationSize, withBias = true) + .setInitMethod(Xavier, Zeros) + + val model = Sequential[Float]() + .add(pooler) + .add(InferReshape(Array(0, -1))) + .add(fc1) + .add(ReLU[Float]()) + .add(fc2) + .add(ReLU[Float]()) + + model + } +} + +private[nn] class BoxPostProcessor( + val scoreThresh: Float, + val nmsThresh: Float, + val maxPerImage: Int, + val nClasses: Int, + val weight: Array[Float] = Array(10.0f, 10.0f, 5.0f, 5.0f) + ) (implicit ev: TensorNumeric[Float]) extends AbstractModule[Table, Table, Float] { + + private val softMax = SoftMax[Float]() + private val nmsTool: Nms = new Nms + @transient private var boxesBuf: Tensor[Float] = null + + /** + * Returns bounding-box detection results by thresholding on scores and + * applying non-maximum suppression (NMS). + */ + private[nn] def filterResults(boxes: Tensor[Float], scores: Tensor[Float], + numOfClasses: Int): Array[RoiLabel] = { + val dim = numOfClasses * 4 + boxes.resize(Array(boxes.nElement() / dim, dim)) + scores.resize(Array(scores.nElement() / numOfClasses, numOfClasses)) + + val results = new Array[RoiLabel](numOfClasses) + // skip clsInd = 0, because it's the background class + var clsInd = 1 + while (clsInd < numOfClasses) { + results(clsInd) = postProcessOneClass(scores, boxes, clsInd) + clsInd += 1 + } + // Limit to max_per_image detections *over all classes* + if (maxPerImage > 0) { + limitMaxPerImage(results) + } + results + } + + private def postProcessOneClass(scores: Tensor[Float], boxes: Tensor[Float], + clsInd: Int): RoiLabel = { + val inds = (1 to scores.size(1)).filter(ind => + scores.valueAt(ind, clsInd + 1) > scoreThresh).toArray + if (inds.length == 0) return null + val clsScores = selectTensor(scores.select(2, clsInd + 1), inds, 1) + val clsBoxes = selectTensor(boxes.narrow(2, clsInd * 4 + 1, 4), inds, 1) + + val keepN = nmsTool.nms(clsScores, clsBoxes, nmsThresh, inds) + + val bboxNms = selectTensor(clsBoxes, inds, 1, keepN) + val scoresNms = selectTensor(clsScores, inds, 1, keepN) + + RoiLabel(scoresNms, bboxNms) + } + + private def selectTensor(matrix: Tensor[Float], indices: Array[Int], + dim: Int, indiceLen: Int = -1, out: Tensor[Float] = null): Tensor[Float] = { + require(dim == 1 || dim == 2, s"dim should be 1 or 2, but get ${dim}") + var i = 1 + val n = if (indiceLen == -1) indices.length else indiceLen + if (matrix.nDimension() == 1) { + val res = if (out == null) { + Tensor[Float](n) + } else { + out.resize(n) + } + while (i <= n) { + res.update(i, matrix.valueAt(indices(i - 1))) + i += 1 + } + return res + } + // select rows + if (dim == 1) { + val res = if (out == null) { + Tensor[Float](n, matrix.size(2)) + } else { + out.resize(n, matrix.size(2)) + } + while (i <= n) { + res.update(i, matrix(indices(i - 1))) + i += 1 + } + res + } else { + val res = if (out == null) { + Tensor[Float](matrix.size(1), n) + } else { + out.resize(matrix.size(1), n) + } + while (i <= n) { + var rid = 1 + val value = matrix.select(2, indices(i - 1)) + while (rid <= res.size(1)) { + res.setValue(rid, i, value.valueAt(rid)) + rid += 1 + } + i += 1 + } + res + } + } + + private def resultToTensor(results: Array[RoiLabel], labels: Tensor[Float], bbox: Tensor[Float]) + : Unit = { + var maxDetection = 0 + results.foreach(res => { + if (null != res) { + maxDetection += res.size() + } + }) + + labels.resize(maxDetection) + bbox.resize(maxDetection, 4) + + var offset = 1 + (0 until nClasses).foreach(c => { + val label = results(c) + if (null != label) { + (1 to label.size()).foreach(j => { + labels.setValue(offset, c) + bbox.setValue(offset, 1, label.bboxes.valueAt(j, 1)) + bbox.setValue(offset, 2, label.bboxes.valueAt(j, 2)) + bbox.setValue(offset, 3, label.bboxes.valueAt(j, 3)) + bbox.setValue(offset, 4, label.bboxes.valueAt(j, 4)) + offset += 1 + }) + } + }) + } + + private def limitMaxPerImage(results: Array[RoiLabel]): Unit = { + val nImageScores = (1 until nClasses).map(j => if (results(j) == null) 0 + else results(j).classes.size(1)).sum + if (nImageScores > maxPerImage) { + val imageScores = ArrayBuffer[Float]() + var j = 1 + while (j < nClasses) { + if (results(j) != null) { + val res = results(j).classes + if (res.nElement() > 0) { + res.apply1(x => { + imageScores.append(x) + x + }) + } + } + j += 1 + } + val imageThresh = imageScores.sortWith(_ < _)(imageScores.length - maxPerImage) + j = 1 + while (j < nClasses) { + if (results(j) != null) { + val box = results(j).classes + val keep = (1 to box.size(1)).filter(x => + box.valueAt(x) >= imageThresh).toArray + val selectedScores = selectTensor(results(j).classes, keep, 1) + val selectedBoxes = selectTensor(results(j).bboxes, keep, 1) + if (selectedScores.nElement() == 0) { + results(j).classes.set() + results(j).bboxes.set() + } else { + results(j).classes.resizeAs(selectedScores).copy(selectedScores) + results(j).bboxes.resizeAs(selectedBoxes).copy(selectedBoxes) + } + } + j += 1 + } + } + } + + /** + * input contains:the class logits, the box_regression and + * bounding boxes that are used as reference, one for ech image + * @param input + * @return labels and bbox + */ + override def updateOutput(input: Table): Table = { + if (isTraining()) { + output = input + return output + } + val classLogits = input[Tensor[Float]](1) + val boxRegression = input[Tensor[Float]](2) + val bbox = input[Tensor[Float]](3) + + if (boxesBuf == null) boxesBuf = Tensor[Float] + boxesBuf.resizeAs(boxRegression) + + val classProb = softMax.forward(classLogits) + BboxUtil.decodeWithWeight(boxRegression, bbox, weight, boxesBuf) + + val boxesInImage = bbox.size(1) + val proposalSplit = boxesBuf.split(boxesInImage, dim = 1) + val classProbSplit = classProb.split(boxesInImage, dim = 1) + + val roilabels = filterResults(proposalSplit(0), classProbSplit(0), nClasses) + + if (output.toTable.length() == 0) { + output.toTable(1) = Tensor[Float]() // for labels + output.toTable(2) = Tensor[Float]() // for bbox + } + + resultToTensor(roilabels, output.toTable(1), output.toTable(2)) + output + } + + override def updateGradInput(input: Table, gradOutput: Table): Table = { + gradInput = gradOutput.toTable + gradInput + } +} + +object BoxHead { + def apply(inChannels: Int, + resolution: Int = 7, + scales: Array[Float] = Array[Float](0.25f, 0.125f, 0.0625f, 0.03125f), + samplingRatio: Int = 2, + scoreThresh: Float = 0.05f, + nmsThresh: Float = 0.5f, + maxPerImage: Int = 100, + outputSize: Int = 1024, + numClasses: Int = 81 // coco dataset class number + ) ( implicit ev: TensorNumeric[Float]): BoxHead = + new BoxHead(inChannels, resolution, scales, samplingRatio, + scoreThresh, nmsThresh, maxPerImage, outputSize, numClasses) +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala new file mode 100644 index 00000000000..3130a574ab8 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala @@ -0,0 +1,762 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{T, Table} +import org.dmg.pmml.False +import org.scalatest.{FlatSpec, Matchers} + +import scala.math._ + +class BoxHeadSpec extends FlatSpec with Matchers { + "BoxHead" should "be ok" in { + val inChannels: Int = 6 + val resolution: Int = 7 + val scales: Array[Float] = Array[Float](0.25f, 0.125f) + val samplingRratio: Int = 2 + val scoreThresh: Float = 0.012f + val nmsThresh: Float = 0.5f + val detections_per_img: Int = 100 + val representation_size: Int = 1024 + val numClasses: Int = 81 // coco dataset class number + + val layer = new BoxHead(inChannels, resolution, scales, samplingRratio, scoreThresh, + nmsThresh, detections_per_img, representation_size, numClasses) + + val params = layer.getParameters() + params._1.fill(0.001f) + + val features1 = Tensor[Float](T(T(T( + T(0.4225, 0.6287, 0.4108, 0.5272), + T(0.6714, 0.0680, 0.2866, 0.1765), + T(0.8650, 0.4987, 0.9253, 0.7914)), + + T(T(0.0264, 0.2524, 0.1257, 0.5725), + T(0.6423, 0.1356, 0.3944, 0.0141), + T(0.9096, 0.2509, 0.5605, 0.8632)), + + T(T(0.9683, 0.0549, 0.6259, 0.3762), + T(0.3638, 0.8891, 0.2664, 0.2837), + T(0.9326, 0.1827, 0.7227, 0.2481)), + + T(T(0.6557, 0.9165, 0.8756, 0.5103), + T(0.4360, 0.8133, 0.0823, 0.2113), + T(0.2167, 0.9266, 0.9105, 0.1651)), + + T(T(0.8999, 0.8347, 0.5532, 0.2879), + T(0.1027, 0.0516, 0.9670, 0.2939), + T(0.8113, 0.5250, 0.0378, 0.2784)), + + T(T(0.2387, 0.5709, 0.2917, 0.5493), + T(0.9709, 0.3801, 0.7908, 0.4004), + T(0.5152, 0.7003, 0.5848, 0.2894))))) + + val features2 = Tensor[Float](T(T(T( + T(0.4946, 0.2608), + T(0.4005, 0.2644), + T(0.8069, 0.8160), + T(0.9803, 0.1142), + T(0.3023, 0.1687)), + + T(T(0.1729, 0.7137), + T(0.2192, 0.2045), + T(0.4112, 0.4602), + T(0.8264, 0.4080), + T(0.9286, 0.2458)), + + T(T(0.0585, 0.9190), + T(0.4231, 0.3296), + T(0.0760, 0.2377), + T(0.0743, 0.4729), + T(0.2597, 0.5092)), + + T(T(0.9204, 0.1691), + T(0.2999, 0.5060), + T(0.0182, 0.2920), + T(0.0119, 0.3593), + T(0.9800, 0.4025)), + + T(T(0.9874, 0.8074), + T(0.3378, 0.7128), + T(0.3650, 0.8991), + T(0.4262, 0.8433), + T(0.5001, 0.3274)), + + T(T(0.7418, 0.2529), + T(0.0263, 0.3555), + T(0.9085, 0.9952), + T(0.3573, 0.5961), + T(0.8601, 0.7605))))) + + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 7.0f))) + val labels = Tensor[Float](T(1, 3)) + + layer.evaluate() + + val output = layer.forward(T(T(features1, features2), bbox, labels)).toTable + + val expectedBbox = Tensor[Float](T( + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203))) + val expectedLable = Tensor[Float]( + T( 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, + 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, + 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, + 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 35, 35, 36, 36, + 37, 37, 38, 38, 39, 39, 40, 40, 41, 41, 42, 42, 43, 43, 44, 44, 45, 45, + 46, 46, 47, 47, 48, 48, 49, 49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 54, + 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 60, 61, 61, 62, 62, 63, 63, + 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, + 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, 80, 80)) + + output.apply[Table](2)[Tensor[Float]](2).map(expectedBbox, (v1, v2) => { + assert(abs(v1 - v2) < 1e-3) + v1 + }) + output.apply[Table](2)[Tensor[Float]](1) should be (expectedLable) + } + + "FeatureExtractor in BoxHead" should "be ok" in { + val inChannels: Int = 6 + val resolution: Int = 7 + val scales: Array[Float] = Array[Float](0.25f, 0.125f) + val samplingRratio: Int = 2 + val scoreThresh: Float = 0.012f + val nmsThresh: Float = 0.5f + val detections_per_img: Int = 100 + val representation_size: Int = 1024 + val numClasses: Int = 81 // coco dataset class number + + val boxhead = new BoxHead(inChannels, resolution, scales, samplingRratio, scoreThresh, + nmsThresh, detections_per_img, representation_size, numClasses) + + val layer = boxhead.featureExtractor(inChannels, resolution, scales, + samplingRratio.toInt, representation_size) + + val params = layer.getParameters() + params._1.fill(0.001f) + + val input = + T(Tensor[Float](T(T(T(T(0.2652, 0.0117, 0.7870, 0.7771), + T(0.8887, 0.1465, 0.0116, 0.7175), + T(0.1002, 0.2846, 0.6837, 0.8163)), + T(T(0.4443, 0.5314, 0.6855, 0.7886), + T(0.0127, 0.4880, 0.5673, 0.0363), + T(0.9980, 0.5475, 0.3907, 0.1388)), + T(T(0.0759, 0.3783, 0.7572, 0.9239), + T(0.4756, 0.9104, 0.0507, 0.6461), + T(0.0820, 0.7182, 0.0194, 0.7705)), + T(T(0.6381, 0.8038, 0.5847, 0.0681), + T(0.2012, 0.3092, 0.9843, 0.2165), + T(0.9428, 0.2162, 0.0829, 0.4050)), + T(T(0.4453, 0.2616, 0.6108, 0.6562), + T(0.7900, 0.4529, 0.8934, 0.2507), + T(0.1853, 0.8457, 0.8730, 0.6635)), + T(T(0.1257, 0.4139, 0.3218, 0.1551), + T(0.2408, 0.8716, 0.7023, 0.3724), + T(0.7436, 0.3309, 0.3117, 0.8723))))), + Tensor[Float](T(T(T(T(0.4907, 0.3940), + T(0.5765, 0.6642), + T(0.8227, 0.4993), + T(0.7554, 0.4512), + T(0.6055, 0.3898)), + T(T(0.4105, 0.1730), + T(0.2907, 0.8862), + T(0.4373, 0.5254), + T(0.4349, 0.5054), + T(0.8567, 0.7000)), + T(T(0.5932, 0.0901), + T(0.1488, 0.8821), + T(0.1665, 0.3353), + T(0.3736, 0.6871), + T(0.3214, 0.8596)), + T(T(0.1705, 0.9588), + T(0.3957, 0.4588), + T(0.3426, 0.1921), + T(0.3661, 0.1462), + T(0.6675, 0.5090)), + T(T(0.7188, 0.7556), + T(0.5221, 0.7814), + T(0.9024, 0.0615), + T(0.7277, 0.5004), + T(0.5957, 0.1157)), + T(T(0.8777, 0.7517), + T(0.5792, 0.2707), + T(0.6614, 0.4466), + T(0.6752, 0.2068), + T(0.7227, 0.6391)))))) + + val proposals = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), T(3.0f, 5.0f, 6.0f, 10.0f))) + + val imageInfo = Tensor[Float](T(10, 15)) + + val expectedOutput = Tensor[Float](T( + T(2.2933e-01, 4.7398e-01, 0.0000e+00, 0.0000e+00, 3.0377e-01, 0.0000e+00, + 1.5809e-01, 0.0000e+00, 3.4213e-03, 2.0427e-02, 0.0000e+00, 0.0000e+00, + 1.8175e-01, 3.0648e-01, 3.0486e-01, 4.8535e-01, 1.0736e-01, 0.0000e+00, + 0.0000e+00, 1.1959e-01, 4.1052e-01, 0.0000e+00, 0.0000e+00, 2.6293e-01, + 4.3437e-01, 7.2744e-01, 2.8527e-01, 0.0000e+00, 5.7330e-01, 3.2637e-02, + 3.6768e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 4.2849e-01, + 0.0000e+00, 7.6325e-01, 5.8331e-01, 1.9046e-01, 0.0000e+00, 6.1702e-01, + 0.0000e+00, 0.0000e+00, 4.2936e-01, 2.5023e-01, 1.5075e-02, 8.7771e-02, + 0.0000e+00, 5.7307e-01, 0.0000e+00, 8.0079e-01, 0.0000e+00, 0.0000e+00, + 2.6381e-03, 0.0000e+00, 1.1355e-01, 3.3714e-01, 1.6419e-01, 0.0000e+00, + 1.2210e-01, 0.0000e+00, 3.1064e-01, 2.6534e-01, 0.0000e+00, 9.3856e-01, + 0.0000e+00, 2.0916e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.8734e-01, + 0.0000e+00, 4.2838e-01, 4.2797e-01, 0.0000e+00, 0.0000e+00, 1.0801e-01, + 0.0000e+00, 7.2708e-02, 6.4211e-02, 2.4386e-01, 4.8236e-01, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.1248e-01, 0.0000e+00, + 3.6260e-01, 0.0000e+00, 2.6733e-01, 4.3891e-01, 0.0000e+00, 9.4399e-01, + 4.3039e-01, 1.9963e-01, 5.5950e-01, 0.0000e+00, 5.7901e-01, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 3.4324e-02, 0.0000e+00, 5.7276e-02, 5.4953e-01, + 1.1287e-01, 7.1539e-02, 6.8938e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 7.2902e-02, 1.1641e-01, 4.0944e-02, 1.0786e-01, 7.0635e-01, 0.0000e+00, + 6.2662e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 4.1090e-01, 0.0000e+00, 0.0000e+00, 3.8734e-01, 0.0000e+00, 9.4146e-02, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 3.6706e-01, 0.0000e+00, 0.0000e+00, + 9.5533e-01, 2.9218e-01, 7.2061e-01, 0.0000e+00, 3.2076e-01, 0.0000e+00, + 1.9556e-01, 0.0000e+00, 7.7142e-01, 2.0086e-01, 3.6382e-02, 0.0000e+00, + 2.4287e-01, 5.7229e-01, 0.0000e+00, 0.0000e+00, 2.8146e-01, 0.0000e+00, + 2.1619e-01, 0.0000e+00, 5.3419e-01, 0.0000e+00, 3.7420e-01, 0.0000e+00, + 3.9476e-03, 0.0000e+00, 0.0000e+00, 2.9974e-01, 2.0722e-01, 1.1056e-01, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.5141e-01, 0.0000e+00, + 0.0000e+00, 8.1091e-01, 6.0005e-01, 5.3662e-03, 5.5893e-02, 0.0000e+00, + 9.1367e-01, 3.9175e-01, 4.4933e-01, 3.6535e-01, 3.2467e-01, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 4.4286e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.5753e-01, 0.0000e+00, + 0.0000e+00, 4.3492e-02, 0.0000e+00, 3.9084e-01, 4.5749e-01, 0.0000e+00, + 8.8619e-02, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 4.8256e-01, 4.3740e-01, + 5.4134e-01, 8.1222e-01, 1.1236e+00, 3.8259e-01, 0.0000e+00, 4.0677e-01, + 0.0000e+00, 6.1217e-01, 0.0000e+00, 0.0000e+00, 9.0240e-02, 0.0000e+00, + 0.0000e+00, 3.7207e-01, 3.4780e-01, 0.0000e+00, 8.2428e-02, 0.0000e+00, + 0.0000e+00, 6.4101e-02, 6.2217e-01, 0.0000e+00, 0.0000e+00, 1.4846e-01, + 1.4339e-01, 0.0000e+00, 0.0000e+00, 1.4410e-01, 9.8182e-01, 6.2625e-01, + 0.0000e+00, 5.8311e-01, 3.7540e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 2.7168e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.5780e-01, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.5917e-01, 3.9157e-01, 2.2357e-01, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0621e-01, 0.0000e+00, 0.0000e+00, + 1.5849e-01, 0.0000e+00, 2.8027e-02, 1.3950e-01, 0.0000e+00, 4.5800e-01, + 4.8431e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 3.2327e-01, 3.9075e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 2.7243e-01, 6.9952e-01, 3.3709e-01, 7.4156e-02, + 0.0000e+00, 0.0000e+00, 4.3161e-01, 0.0000e+00, 5.5968e-01, 5.1705e-02, + 0.0000e+00, 0.0000e+00, 3.0565e-01, 4.5431e-01, 0.0000e+00, 4.4742e-02, + 2.3583e-01, 8.6285e-01, 0.0000e+00, 6.5763e-02, 0.0000e+00, 7.2151e-01, + 7.7982e-01, 5.3851e-01, 0.0000e+00, 0.0000e+00, 3.1895e-01, 0.0000e+00, + 0.0000e+00, 8.2074e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 5.9684e-01, 0.0000e+00, 0.0000e+00, 2.1435e-03, + 0.0000e+00, 4.0795e-02, 6.0204e-01, 5.3779e-02, 3.4706e-01, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 3.0793e-01, + 0.0000e+00, 2.8389e-01, 9.6737e-02, 0.0000e+00, 4.0340e-01, 5.9990e-01, + 9.6217e-01, 1.7721e-01, 0.0000e+00, 0.0000e+00, 2.1650e-01, 0.0000e+00, + 3.6605e-01, 1.4687e-01, 0.0000e+00, 0.0000e+00, 3.9823e-01, 3.9444e-01, + 0.0000e+00, 0.0000e+00, 2.0717e-01, 3.5793e-01, 0.0000e+00, 0.0000e+00, + 5.3949e-01, 3.0678e-01, 0.0000e+00, 5.6074e-01, 0.0000e+00, 5.5505e-01, + 0.0000e+00, 6.2500e-01, 1.4662e-01, 1.7523e-01, 1.1858e-01, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 4.8805e-01, 0.0000e+00, 0.0000e+00, 2.7573e-01, + 7.1308e-02, 0.0000e+00, 5.8064e-01, 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1.4573e-02, + 0.0000e+00, 7.9193e-02, 0.0000e+00, 0.0000e+00, 9.3218e-02, 4.6077e-01, + 4.5760e-01, 4.3444e-01, 1.9278e-01, 0.0000e+00, 7.7304e-01, 0.0000e+00, + 5.4956e-01, 5.2741e-01, 0.0000e+00, 1.9542e-01, 0.0000e+00, 5.5032e-02, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 3.7545e-01, 6.4552e-01, 0.0000e+00, + 2.8725e-01, 1.2046e-01, 6.8067e-02, 0.0000e+00, 5.6154e-01, 0.0000e+00, + 5.8774e-02, 0.0000e+00, 3.0693e-02, 3.3855e-01, 0.0000e+00, 0.0000e+00, + 9.2428e-02, 4.1654e-01, 0.0000e+00, 0.0000e+00))) + + val output = layer.forward(T(input, proposals, imageInfo)).toTensor[Float] + + output.select(1, 1).apply1(a => { + a should be(0.1516f +- 1e-3f) + a + }) + + output.select(1, 2).apply1(a => { + a should be(0.1460f +- 1e-3f) + a + }) + } +} + +class BoxHeadSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val inChannels: Int = 6 + val resolution: Int = 7 + val scales: Array[Float] = Array[Float](0.25f, 0.125f) + val samplingRratio: Int = 2 + val scoreThresh: Float = 0.012f + val nmsThresh: Float = 0.5f + val detections_per_img: Int = 100 + val representation_size: Int = 1024 + val numClasses: Int = 81 + + val layer = new BoxHead(inChannels, resolution, scales, samplingRratio, scoreThresh, + nmsThresh, detections_per_img, representation_size, numClasses).setName("BoxHead") + + val feature1 = Tensor[Float](1, 6, 3, 4).rand() + val feature2 = Tensor[Float](1, 6, 5, 2).rand() + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 7.0f))) + val labels = Tensor[Float](T(1, 3)) + + runSerializationTest(layer, T(T(feature1, feature2), bbox, labels)) + } +} + +class BoxPostProcessorSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = new BoxPostProcessor(0.012f, 0.5f, 100, 81).setName("BoxPostProcessor") + + val classLogits = Tensor[Float](2, 81).rand() + val boxRegression = Tensor[Float](2, 324).rand() + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 10.0f))) + + runSerializationTest(layer, T(classLogits, boxRegression, bbox)) + } +} From 8b73c4752c9f9036009220f4d54359cb1e01e4e4 Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Tue, 10 Sep 2019 09:43:02 +0800 Subject: [PATCH 0950/1065] fix: Add TopBlocks to Feature Pyramid Networks (FPN) (#2899) --- .../intel/analytics/bigdl/dllib/nn/FPN.scala | 50 +- .../analytics/bigdl/dllib/nn/Pooler.scala | 3 - .../dllib/utils/python/api/PythonBigDL.scala | 7 +- .../analytics/bigdl/dllib/nn/FPNSpec.scala | 779 +++++++++++++++++- .../analytics/bigdl/dllib/nn/PoolerSpec.scala | 143 ++-- 5 files changed, 858 insertions(+), 124 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala index 76a2d7c1c2a..5ab782cbdb1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala @@ -19,21 +19,31 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Graph.ModuleNode import com.intel.analytics.bigdl.nn.abstractnn.Activity -import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table import scala.reflect.ClassTag /** * Feature Pyramid Network. - * @param inChannels number of channels of feature maps - * @param outChannels number of channels of FPN output + * @param inChannels number of channels of feature maps + * @param outChannels number of channels of FPN output + * @param topBlocks Top Blocks option + * Extra operation to be performed on the smallest + * resolution FPN output, whose result is appended + * to the result list + * 0 for null, + * 1 for using max pooling on the last level + * 2 for extra layers P6 and P7 in RetinaNet + * @param inChannelsOfP6P7 number of input channels of P6 P7 in RetinaNet + * @param outChannelsOfP6P7 number of output channels of P6 P7 in RetinaNet */ class FPN[T : ClassTag]( val inChannels: Array[Int], - val outChannels: Int + val outChannels: Int, + val topBlocks: Int = 0, + val inChannelsOfP6P7: Int = 0, + val outChannelsOfP6P7: Int = 0 ) (implicit ev: TensorNumeric[T]) extends BaseModule[T]{ @@ -63,8 +73,9 @@ class FPN[T : ClassTag]( innerBlocks(i) = innerBlockModules(i).inputs(inputs(i)) } - var count = 0 - val results = new Array[ModuleNode[T]](featureMapsNum) + val results = new Array[ModuleNode[T]](featureMapsNum + topBlocks) + var count = results.length - 1 - topBlocks + var lastInner = innerBlocks(featureMapsNum - 1) results(count) = layerBlockModules(featureMapsNum - 1).inputs(lastInner) @@ -74,11 +85,27 @@ class FPN[T : ClassTag]( val innerTopDown = UpSampling2D[T](Array(2, 2)).inputs(lastInner) val innerLateral = innerBlocks(i) lastInner = CAddTable[T]().inputs(innerLateral, innerTopDown) - count += 1 + count -= 1 results(count) = layerBlock.inputs(lastInner) } } + if (topBlocks == 1) { + results(results.length - 1) = SpatialMaxPooling(1, 1, 2, 2) + .inputs(results(featureMapsNum - 1)) + } + + if (topBlocks == 2) { + val p6_module = SpatialConvolution[T](inChannelsOfP6P7, outChannelsOfP6P7, 3, 3, 2, 2, 1, 1) + val p7_module = SpatialConvolution[T](outChannelsOfP6P7, outChannelsOfP6P7, 3, 3, 2, 2, 1, 1) + results(results.length - 2) = if (inChannelsOfP6P7 == outChannelsOfP6P7) { + p6_module.inputs(results(featureMapsNum - 1)) + } else { + p6_module.inputs(inputs(featureMapsNum - 1)) + } + results(results.length - 1) = p7_module.inputs(ReLU[T]().inputs(results(results.length - 2))) + } + Graph(inputs, results) } @@ -113,8 +140,11 @@ class FPN[T : ClassTag]( object FPN { def apply[@specialized(Float, Double) T: ClassTag]( inChannels: Array[Int], - outChannels: Int + outChannels: Int, + topBlocks: Int = 0, + inChannelsOfP6P7: Int = 0, + outChannelsOfP6P7: Int = 0 )(implicit ev: TensorNumeric[T]): FPN[T] = { - new FPN[T](inChannels, outChannels) + new FPN[T](inChannels, outChannels, topBlocks, inChannelsOfP6P7, outChannelsOfP6P7) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala index bc2ee7e6b2e..59a05ba4b0b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala @@ -106,9 +106,6 @@ class Pooler[T: ClassTag] ( val featureMaps = input[Table](1) val rois = input[Tensor[T]](2) - require(featureMaps.length() == num_levels, - "The number of feature maps should be same as the size of scales") - val roi_levels = levelMapping(lvl_min, lvl_max, rois) val num_rois = rois.size(1) val num_channels = featureMaps.get[Tensor[T]](1).get.size(2) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 17bbe417003..52ee4f787ae 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1156,10 +1156,11 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab pooled_w) } - def createFPN(in_channels_list: JList[Int], out_channels: Int) + def createFPN(in_channels_list: JList[Int], out_channels: Int, + top_blocks: Int = 0, in_channels_of_p6p7: Int = 0, out_channels_of_p6p7: Int = 0) : FPN[T] = { - FPN[T](in_channels_list.asScala.toArray, - out_channels) + FPN[T](in_channels_list.asScala.toArray, out_channels, + top_blocks, in_channels_of_p6p7, out_channels_of_p6p7) } def createScale(size: JList[Int]) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FPNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FPNSpec.scala index 26901d95ed0..b2281b0ca53 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FPNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/FPNSpec.scala @@ -25,10 +25,10 @@ import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random class FPNSpec extends FlatSpec with Matchers { - "FPN updateOutput" should "work correctly" in { + "FPN updateOutput with None TopBlocks" should "work correctly" in { val in_channels_list = Array(1, 2, 4) val out_channels = 2 - val model = FPN[Float](in_channels_list, out_channels) + val model = FPN[Float](in_channels_list, out_channels, topBlocks = 0) val feature1 = Tensor( T(T(0.10110152, 0.10345000, 0.04320979, 0.84362656, @@ -72,15 +72,9 @@ class FPNSpec extends FlatSpec with Matchers { .reshape(Array(1, 4, 2, 2)) val inner1_w = Tensor( - T(T(T(T(0.25616819)), - T(T(-0.74193102)), - T(T(0.22137421)), - T(T(0.53996474))), - T(T(T(-0.30102068)), - T(T(0.24491900)), - T(T(-0.84143710)), - T(T(-0.73395455))))) - .reshape(Array(1, 2, 4, 1, 1)) + T(T(T(T(0.24687862))), + T(T(T(-0.56227243))))) + .reshape(Array(1, 2, 1, 1, 1)) val inner1_b = Tensor(T(0, 0)) val inner2_w = Tensor( @@ -92,9 +86,15 @@ class FPNSpec extends FlatSpec with Matchers { val inner2_b = Tensor(T(0, 0)) val inner3_w = Tensor( - T(T(T(T(0.24687862))), - T(T(T(-0.56227243))))) - .reshape(Array(1, 2, 1, 1, 1)) + T(T(T(T(0.25616819)), + T(T(-0.74193102)), + T(T(0.22137421)), + T(T(0.53996474))), + T(T(T(-0.30102068)), + T(T(0.24491900)), + T(T(-0.84143710)), + T(T(-0.73395455))))) + .reshape(Array(1, 2, 4, 1, 1)) val inner3_b = Tensor(T(0, 0)) val layer1_w = Tensor( @@ -146,22 +146,6 @@ class FPNSpec extends FlatSpec with Matchers { val layer3_b = Tensor(T(0, 0)) val result1 = Tensor( - T(T(T(T(-0.60857159, -0.49706429), - T(-0.44821957, -0.69798434)), - T(T(0.11003723, 0.24464746), - T(0.21994369, -0.22257896))))) - - val result2 = Tensor( - T(T(T(T(0.67646873, 0.75461042, 0.88370752, 0.72522950), - T(0.80561060, 1.40666068, 0.81269693, 0.72721291), - T(0.42856935, 0.57526082, 0.84400183, 0.24381584), - T(0.60819602, 0.32838598, 0.17468216, -0.05505963)), - T(T(-0.41587284, -0.59085888, -0.50279200, -0.25322908), - T(-0.42020139, -0.64106256, -0.23952308, -0.29740968), - T(-0.31366453, -0.12451494, -0.13788190, 0.07498236), - T(-0.31522152, -0.13974780, -0.06333419, 0.15230046))))) - - val result3 = Tensor( T(T(T(T(-0.29643691, 0.32930288, 0.07719041, 0.20329267, -0.11702696, 0.33030477, 0.19752777, 0.26074126), T(-0.04022884, -0.04050549, -0.17072679, 0.05824373, -0.18035993, @@ -195,21 +179,737 @@ class FPNSpec extends FlatSpec with Matchers { T(-0.00356270, -0.15851222, 0.04203597, 0.33169088, -0.02303683, -0.42069232, -0.08245742, 0.06082898))))) + val result2 = Tensor( + T(T(T(T(0.67646873, 0.75461042, 0.88370752, 0.72522950), + T(0.80561060, 1.40666068, 0.81269693, 0.72721291), + T(0.42856935, 0.57526082, 0.84400183, 0.24381584), + T(0.60819602, 0.32838598, 0.17468216, -0.05505963)), + T(T(-0.41587284, -0.59085888, -0.50279200, -0.25322908), + T(-0.42020139, -0.64106256, -0.23952308, -0.29740968), + T(-0.31366453, -0.12451494, -0.13788190, 0.07498236), + T(-0.31522152, -0.13974780, -0.06333419, 0.15230046))))) + + val result3 = Tensor( + T(T(T(T(-0.60857159, -0.49706429), + T(-0.44821957, -0.69798434)), + T(T(0.11003723, 0.24464746), + T(0.21994369, -0.22257896))))) + val input = T(feature1, feature2, feature3) val expectedOutput = T(result1, result2, result3) - model.parameters()._1(0).copy(inner1_w) - model.parameters()._1(1).copy(inner1_b) + model.parameters()._1(0).copy(inner3_w) + model.parameters()._1(1).copy(inner3_b) model.parameters()._1(2).copy(inner2_w) model.parameters()._1(3).copy(inner2_b) - model.parameters()._1(4).copy(inner3_w) - model.parameters()._1(5).copy(inner3_b) - model.parameters()._1(6).copy(layer1_w) - model.parameters()._1(7).copy(layer1_b) + model.parameters()._1(4).copy(inner1_w) + model.parameters()._1(5).copy(inner1_b) + model.parameters()._1(6).copy(layer3_w) + model.parameters()._1(7).copy(layer3_b) model.parameters()._1(8).copy(layer2_w) model.parameters()._1(9).copy(layer2_b) + model.parameters()._1(10).copy(layer1_w) + model.parameters()._1(11).copy(layer1_b) + + val output = model.forward(input) + + Equivalent.nearequals(output.toTable.get[Tensor[Float]](1).get, + expectedOutput.get[Tensor[Float]](1).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](2).get, + expectedOutput.get[Tensor[Float]](2).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](3).get, + expectedOutput.get[Tensor[Float]](3).get) should be(true) + } + + "FPN updateOutput with MaxPooling TopBlocks" should "work correctly" in { + val in_channels_list = Array(1, 2, 4) + val out_channels = 2 + val model = FPN[Float](in_channels_list, out_channels, topBlocks = 1) + + val feature1 = Tensor( + T(T(0.10110152, 0.10345000, 0.04320979, 0.84362656, + 0.59594363, 0.97288179, 0.34699517, 0.54275155), + T(0.93956870, 0.07543808, 0.50965708, 0.26184946, + 0.92378283, 0.83272308, 0.54440099, 0.56682664), + T(0.53608388, 0.74091697, 0.53824615, 0.12760854, + 0.70029002, 0.85137993, 0.01918983, 0.10134047), + T(0.61024511, 0.11725241, 0.46950370, 0.15163177, + 0.99792290, 0.50036842, 0.65618765, 0.76569498), + T(0.31238246, 0.96460360, 0.23587847, 0.94086981, + 0.15270233, 0.44916826, 0.53412461, 0.19992995), + T(0.14841199, 0.95466810, 0.89249784, 0.10235202, + 0.24293590, 0.83814293, 0.78163254, 0.94990700), + T(0.50397956, 0.23095572, 0.12026519, 0.70295823, + 0.80230796, 0.31913465, 0.86270124, 0.67926580), + T(0.93120003, 0.08011329, 0.30662805, 0.97467756, + 0.32988423, 0.90689850, 0.46856666, 0.66390038))) + .reshape(Array(1, 1, 8, 8)) + + val feature2 = Tensor( + T(T(T(0.30143285, 0.63111430, 0.45092928, 0.22753167), + T(0.80318344, 0.67537767, 0.14698678, 0.45962620), + T(0.21663177, 0.89086282, 0.92865956, 0.89360029), + T(0.49615270, 0.46269470, 0.73047608, 0.12438315)), + T(T(0.75820625, 0.59779423, 0.61585987, 0.35782731), + T(0.36951083, 0.35381025, 0.64314663, 0.75517660), + T(0.30200917, 0.69998586, 0.29572868, 0.46342885), + T(0.41677684, 0.26154006, 0.16909349, 0.94081402)))) + .reshape(Array(1, 2, 4, 4)) + + val feature3 = Tensor( + T(T(T(0.57270211, 0.25789189), + T(0.79134840, 0.62564188)), + T(T(0.27365083, 0.43420678), + T(0.61281836, 0.23570287)), + T(T(0.21393263, 0.50206852), + T(0.50650394, 0.73282623)), + T(T(0.20319027, 0.06753725), + T(0.18215942, 0.36703324)))) + .reshape(Array(1, 4, 2, 2)) + + val inner1_w = Tensor( + T(T(T(T(1.00586259))), + T(T(T(0.53887093))))) + .reshape(Array(1, 2, 1, 1, 1)) + val inner1_b = Tensor(T(0, 0)) + + val inner2_w = Tensor( + T(T(T(T(-0.57429278)), + T(T(-0.24179715))), + T(T(T(0.67793036)), + T(T(-0.94123614))))) + .reshape(Array(1, 2, 2, 1, 1)) + val inner2_b = Tensor(T(0, 0)) + + val inner3_w = Tensor( + T(T(T(T(0.17291552)), + T(T(-0.05612940)), + T(T(0.36356455)), + T(T(-0.79740608))), + T(T(T(0.72361153)), + T(T(-0.31787324)), + T(T(0.04836881)), + T(T(0.45409185))))) + .reshape(Array(1, 2, 4, 1, 1)) + val inner3_b = Tensor(T(0, 0)) + + val layer1_w = Tensor( + T(T(T(T(0.06878856, -0.35743117, 0.31631619), + T(-0.14119744, 0.30255783, 0.14926106), + T(-0.38726792, 0.04510748, -0.36082375)), + T(T(-0.23815951, -0.38959473, 0.05021074), + T(0.19526446, -0.35286927, -0.39654526), + T(-0.00148910, -0.24063437, -0.29699990))), + T(T(T(0.07476860, -0.02564883, 0.09487671), + T(-0.01090044, 0.23407942, 0.24647915), + T(-0.38014463, 0.33695221, 0.40465516)), + T(T(-0.00955230, 0.37457061, 0.10492092), + T(-0.12585542, 0.21253753, 0.10564721), + T(0.07659015, -0.03546333, -0.07322484))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer1_b = Tensor(T(0, 0)) + + val layer2_w = Tensor( + T(T(T(T(0.32807761, -0.33899420, 0.06800264), + T(0.07076809, 0.14122516, 0.10424459), + T(-0.03563347, -0.04193285, 0.26541936)), + T(T(-0.33386642, 0.38784570, -0.05316493), + T(-0.37846458, 0.03199247, -0.04221478), + T(-0.38094023, 0.21109033, 0.18027461))), + T(T(T(0.08262184, 0.38594717, 0.33632153), + T(-0.24012834, -0.19122560, 0.35697746), + T(-0.18635783, -0.16684684, -0.17575860)), + T(T(-0.24746780, -0.08889309, 0.01367763), + T(-0.12756592, -0.38951454, -0.28759271), + T(0.29410106, 0.03703991, 0.06836116))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer2_b = Tensor(T(0, 0)) + + val layer3_w = Tensor( + T(T(T(T(-0.37369999, -0.19362454, -0.32376695), + T(0.27765042, -0.03229478, -0.27471265), + T(0.11516148, 0.22647744, -0.15064511)), + T(T(0.23695397, 0.32747757, -0.08015823), + T(0.20880389, 0.34441620, 0.06963590), + T(-0.18623261, -0.23078077, -0.24822637))), + T(T(T(-0.00328833, -0.06870756, 0.37950665), + T(0.39529461, -0.23882844, 0.33771485), + T(-0.37432045, -0.18209046, 0.07186159)), + T(T(0.23758322, 0.39008999, -0.22646688), + T(-0.02726471, 0.03744176, 0.02614474), + T(0.23741430, -0.14411601, 0.32169968))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer3_b = Tensor(T(0, 0)) + + val result1 = Tensor( + T(T(T(T(-0.06027000, -0.63544929, -0.53757948, -0.50052786, -0.13087437, + -0.46421096, -0.50183028, -0.16715574), + T(-0.57532835, -0.84933513, -0.53883237, -0.96058822, -0.03360630, + -0.31204289, -0.08640554, -0.05677760), + T(-1.44774520, -0.81390339, -0.79381657, -0.59526253, 0.18857586, + -0.44416034, -0.48765731, -0.42349899), + T(-1.59604609, -1.46630657, -1.98059797, -0.45855987, -0.36292380, + -0.69029158, -0.14087702, 0.05812502), + T(-1.48957527, -1.13170886, -1.67776370, -1.03186679, -1.87643552, + -1.18758655, -1.37157834, -0.78431809), + T(-0.60699046, -1.50136328, -1.11882496, -2.10094833, -2.03609610, + -1.95528400, -1.52000046, -0.84520984), + T(-0.53370321, -1.59671521, -1.87052989, -1.35319352, -2.15164757, + -1.38132536, -0.71602869, -0.96002018), + T(-0.67362547, -0.74905103, -0.54873347, -0.96106482, -1.61241412, + -0.56977570, -0.16225342, -0.17618783)), + T(T(-0.05780253, -0.46292540, 0.08270258, 0.49520209, 0.78262019, + 0.43522203, 0.31832328, 0.13581192), + T(0.09274495, -0.03541034, 0.15171435, 0.59660333, 1.20995283, + 0.39199278, -0.01640499, 0.24762793), + T(0.22356896, 0.16624556, 0.27169892, 0.64676547, 1.07555902, + 0.24264205, 0.10081939, 0.06938417), + T(1.02827179, 0.52947670, 0.12263671, 0.52516317, -0.09253020, + 0.19734971, 0.02325941, -0.04252122), + T(1.19363809, 1.01430440, 0.02628537, 0.15349422, 0.21920983, + 0.51211888, 0.26795861, 0.11892501), + T(0.80922467, 0.72818476, 0.66089183, 0.58932924, 0.70654577, + 1.04901230, 1.27366579, 0.45431411), + T(0.69929636, 0.23726486, 1.02899837, 0.91582721, 0.73064196, + 1.17346644, 0.96129149, 0.62078124), + T(0.68208826, 0.32198429, 0.80620545, 0.92930055, 0.98262572, + 0.85965669, 0.12026373, 0.21281539))))) + + val result2 = Tensor( + T(T(T(T(0.01130757, -0.25607330, -0.60133040, 0.14051536), + T(-0.08047363, -0.12201009, -0.46985039, -0.09533735), + T(0.44763428, -0.26126349, -0.90198398, -0.72634822), + T(0.03062394, -0.13872625, -0.11850480, -0.42137370)), + T(T(0.06946735, 0.40965801, 0.48238945, 0.06515967), + T(-0.61845332, 0.06515414, 0.47206032, 0.74106240), + T(-0.73383999, -0.66845274, -0.55763161, 0.20043206), + T(-0.57443708, -1.08336210, -0.94348121, -0.44871095))))) + + val result3 = Tensor( + T(T(T(T(-0.11979339, -0.07183948), + T(0.26843292, 0.44522521)), + T(T(0.16508943, -0.07747011), + T(0.22362739, 0.18027946))))) + + val result4 = Tensor( + T(T(T(T(-0.11979339)), + T(T(0.16508943))))) + + val input = T(feature1, feature2, feature3) + val expectedOutput = T(result1, result2, result3, result4) + + model.parameters()._1(0).copy(inner3_w) + model.parameters()._1(1).copy(inner3_b) + model.parameters()._1(2).copy(inner2_w) + model.parameters()._1(3).copy(inner2_b) + model.parameters()._1(4).copy(inner1_w) + model.parameters()._1(5).copy(inner1_b) + model.parameters()._1(6).copy(layer3_w) + model.parameters()._1(7).copy(layer3_b) + model.parameters()._1(8).copy(layer2_w) + model.parameters()._1(9).copy(layer2_b) + model.parameters()._1(10).copy(layer1_w) + model.parameters()._1(11).copy(layer1_b) + + val output = model.forward(input) + + Equivalent.nearequals(output.toTable.get[Tensor[Float]](1).get, + expectedOutput.get[Tensor[Float]](1).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](2).get, + expectedOutput.get[Tensor[Float]](2).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](3).get, + expectedOutput.get[Tensor[Float]](3).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](4).get, + expectedOutput.get[Tensor[Float]](4).get) should be(true) + } + + "FPN updateOutput with P6P7 TopBlocks not use P5" should "work correctly" in { + val in_channels_list = Array(1, 2, 4) + val out_channels = 2 + val model = FPN[Float](in_channels_list, out_channels, topBlocks = 2, + inChannelsOfP6P7 = 4, outChannelsOfP6P7 = 2) // inChannelsP6P7 != outChannelsP6P7 + + val feature1 = Tensor( + T(T(0.10110152, 0.10345000, 0.04320979, 0.84362656, + 0.59594363, 0.97288179, 0.34699517, 0.54275155), + T(0.93956870, 0.07543808, 0.50965708, 0.26184946, + 0.92378283, 0.83272308, 0.54440099, 0.56682664), + T(0.53608388, 0.74091697, 0.53824615, 0.12760854, + 0.70029002, 0.85137993, 0.01918983, 0.10134047), + T(0.61024511, 0.11725241, 0.46950370, 0.15163177, + 0.99792290, 0.50036842, 0.65618765, 0.76569498), + T(0.31238246, 0.96460360, 0.23587847, 0.94086981, + 0.15270233, 0.44916826, 0.53412461, 0.19992995), + T(0.14841199, 0.95466810, 0.89249784, 0.10235202, + 0.24293590, 0.83814293, 0.78163254, 0.94990700), + T(0.50397956, 0.23095572, 0.12026519, 0.70295823, + 0.80230796, 0.31913465, 0.86270124, 0.67926580), + T(0.93120003, 0.08011329, 0.30662805, 0.97467756, + 0.32988423, 0.90689850, 0.46856666, 0.66390038))) + .reshape(Array(1, 1, 8, 8)) + + val feature2 = Tensor( + T(T(T(0.30143285, 0.63111430, 0.45092928, 0.22753167), + T(0.80318344, 0.67537767, 0.14698678, 0.45962620), + T(0.21663177, 0.89086282, 0.92865956, 0.89360029), + T(0.49615270, 0.46269470, 0.73047608, 0.12438315)), + T(T(0.75820625, 0.59779423, 0.61585987, 0.35782731), + T(0.36951083, 0.35381025, 0.64314663, 0.75517660), + T(0.30200917, 0.69998586, 0.29572868, 0.46342885), + T(0.41677684, 0.26154006, 0.16909349, 0.94081402)))) + .reshape(Array(1, 2, 4, 4)) + + val feature3 = Tensor( + T(T(T(0.57270211, 0.25789189), + T(0.79134840, 0.62564188)), + T(T(0.27365083, 0.43420678), + T(0.61281836, 0.23570287)), + T(T(0.21393263, 0.50206852), + T(0.50650394, 0.73282623)), + T(T(0.20319027, 0.06753725), + T(0.18215942, 0.36703324)))) + .reshape(Array(1, 4, 2, 2)) + + val inner1_w = Tensor( + T(T(T(T(1.47257316))), + T(T(T(0.57414114))))) + .reshape(Array(1, 2, 1, 1, 1)) + val inner1_b = Tensor(T(0, 0)) + + val inner2_w = Tensor( + T(T(T(T(0.45074105)), + T(T(-0.30885106))), + T(T(T(-0.08952701)), + T(T(-0.26140732))))) + .reshape(Array(1, 2, 2, 1, 1)) + val inner2_b = Tensor(T(0, 0)) + + val inner3_w = Tensor( + T(T(T(T(-0.30031908)), + T(T(-0.58480197)), + T(T(0.59235269)), + T(T(-0.13991892))), + T(T(T(0.62555033)), + T(T(0.72914702)), + T(T(-0.44170576)), + T(T(0.49929196))))) + .reshape(Array(1, 2, 4, 1, 1)) + val inner3_b = Tensor(T(0, 0)) + + val layer1_w = Tensor( + T(T(T(T(-0.04400888, -0.35957703, -0.02164334), + T(0.40402526, 0.36285782, 0.31368673), + T(-0.35616416, -0.21952458, 0.37052453)), + T(T(0.13778913, -0.30064595, -0.36663383), + T(0.37170672, 0.32204062, -0.07368714), + T(0.19972658, -0.39074513, -0.38521481))), + T(T(T(0.05121413, 0.23705125, 0.13029754), + T(-0.29272887, 0.08022153, -0.16771419), + T(-0.38660547, -0.30105561, -0.17050056)), + T(T(-0.38432136, 0.04626641, 0.20397991), + T(-0.24799925, -0.34601510, 0.23324311), + T(0.39426655, -0.28500557, 0.33542544))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer1_b = Tensor(T(0, 0)) + + val layer2_w = Tensor( + T(T(T(T(-0.26440758, -0.40462878, 0.35458815), + T(-0.27700549, -0.24707370, 0.14012802), + T(-0.02187592, 0.12944663, 0.15989727)), + T(T(0.25460601, 0.33005655, 0.19840294), + T(0.08936363, -0.01533994, -0.10784483), + T(0.14462578, -0.32323214, -0.31677228))), + T(T(T(0.24838877, -0.30633825, -0.14952859), + T(-0.10827839, -0.09704661, 0.01009622), + T(-0.17448114, 0.40084583, 0.25651050)), + T(T(0.02460378, 0.31060696, 0.29154462), + T(0.04250652, 0.06705299, 0.10902947), + T(-0.21223937, 0.02931285, -0.20978554))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer2_b = Tensor(T(0, 0)) + + val layer3_w = Tensor( + T(T(T(T(0.28868508, 0.34335995, -0.21298549), + T(0.13598031, 0.14855188, 0.16282564), + T(0.24104220, 0.19631046, 0.28864717)), + T(T(0.17355555, 0.17067927, 0.34322286), + T(-0.32470348, -0.15039983, -0.37904710), + T(-0.32140541, -0.31889421, -0.34283394))), + T(T(T(-0.27881464, 0.32479310, 0.33741760), + T(-0.04920617, 0.38263774, 0.37934089), + T(-0.07421857, 0.28872919, -0.24625073)), + T(T(-0.07631743, 0.15071201, 0.20164257), + T(-0.02279785, 0.24347979, -0.33499616), + T(0.25867003, 0.11343688, -0.39765364))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer3_b = Tensor(T(0, 0)) + + val p6_w = Tensor( + T(T(T(T(0.18047711, 0.27739489, -0.09207258), + T(-0.06993897, 0.10390741, 0.16100934), + T(0.09704280, -0.11835672, 0.11216679)), + T(T(0.07162413, -0.03068006, 0.21995866), + T(-0.01902044, -0.23496029, 0.08649853), + T(0.26076275, 0.12215102, -0.24565969)), + T(T(-0.26359978, 0.28385252, -0.08561571), + T(0.08719349, -0.03602475, -0.14762157), + T(0.04393122, 0.15552819, -0.19104180)), + T(T(-0.26122716, -0.23169036, 0.04371125), + T(0.17757964, 0.18492216, 0.18820083), + T(-0.18676189, 0.02983525, -0.04895349))), + T(T(T(-0.13829032, 0.28245789, -0.10234657), + T(0.14773294, 0.28724921, -0.09669375), + T(0.11997268, -0.19171268, 0.17503896)), + T(T(-0.01335889, 0.27340567, 0.15419030), + T(-0.27378151, 0.08404601, 0.20571443), + T(0.03300169, -0.07807332, 0.27800083)), + T(T(0.22714883, 0.10564631, 0.10429975), + T(-0.15422256, 0.12877643, -0.07962382), + T(0.05750173, 0.24986815, -0.24631210)), + T(T(0.14758101, -0.14909469, -0.02427217), + T(-0.22774965, 0.24656773, -0.09009914), + T(-0.08819377, -0.14353877, 0.02373797))))) + val p6_b = Tensor(T(0, 0)) + + val p7_w = Tensor( + T(T(T(T(-0.00312749, 0.15891045, -0.06029734), + T(0.32925665, -0.28568161, -0.22913636), + T(0.25732505, -0.02756864, 0.22088635)), + T(T(-0.19972500, -0.35011724, 0.36097509), + T(0.13380224, 0.31481904, -0.34110975), + T(0.00228858, -0.30160201, 0.39911568))), + T(T(T(0.10945880, 0.04096296, 0.34124666), + T(0.21367294, -0.40180174, 0.02459040), + T(0.01582986, -0.35805190, 0.19427061)), + T(T(-0.05247149, 0.03913751, -0.30283454), + T(-0.06808761, 0.30844611, -0.25382966), + T(0.39491993, -0.16227409, -0.33975506))))) + val p7_b = Tensor(T(0, 0)) + + val result1 = Tensor( + T(T(T(T(-0.82784057, -0.23263724, 0.12529419, 0.92917746, 1.29870367, + 0.59191561, 0.72206885, 0.21600738), + T(0.01256093, 0.10397450, -0.63768029, 0.11551863, 0.49406019, + 0.04690269, 0.59401810, 0.51324034), + T(-0.57496190, 0.80619323, 0.21891174, 0.46305850, -0.29128370, + -0.10264862, -0.19434255, -0.98540932), + T(-0.68715960, -0.63924152, -0.28786534, 0.21412316, 0.14116248, + 0.42578453, 0.50156069, 0.45927033), + T(-0.50619870, 0.29627720, -0.08331068, 0.55051923, 0.87432826, + 0.22587368, 0.05705506, -0.60149169), + T(-1.08125985, -0.19702393, 0.76295900, 0.24722506, 0.03166249, + 0.35292828, 0.89928788, 0.76004601), + T(-1.24697328, -0.93874627, -0.32030576, 0.52993482, 0.88237661, + -0.27623445, -0.30513218, -0.13993274), + T(-0.04807660, 0.65625536, 0.34513134, 0.66153854, 1.02909780, + 0.85668772, 0.74089944, 0.41518468)), + T(T(-0.43007800, -0.08928002, -0.18057445, -0.53732187, -1.18706334, + -1.21151233, -1.24502730, -0.81248140), + T(-0.41591629, -1.23449159, -0.37417489, -0.38646969, -0.35627022, + -0.87439328, -0.63093376, -0.38696021), + T(-0.19757175, -0.80025971, -0.61968642, -0.58690047, -0.29969466, + -0.66096216, -0.69664645, -0.61171681), + T(0.36569861, -0.43666506, -0.23078403, -1.02591038, -0.34318402, + -1.12366092, -1.22326660, -0.95382887), + T(0.22266409, -0.61877912, -1.04867685, -0.58774620, -0.58317888, + -1.11619925, -1.20713544, -1.40455294), + T(0.12496996, -0.14055842, -0.44808233, -0.85750657, -0.82033932, + -0.74288636, -1.17979848, -1.27777565), + T(-0.13870570, 0.01701410, -0.15212707, -1.16827607, -0.73849547, + -0.94292432, -0.49970376, -0.77397305), + T(0.20336530, -0.83974218, -0.26997119, -0.33915856, -0.64899278, + -0.23277763, -0.45086405, -0.36021605))))) + + val result2 = Tensor( + T(T(T(T(-0.24274831, -0.00741440, 0.02368479, 0.02028912), + T(-0.25623968, 0.24358158, 0.28456029, 0.18310754), + T(-0.31125316, 0.21291766, 0.18210757, -0.08399948), + T(0.36639184, 0.86005092, 0.24853867, -0.11300255)), + T(T(0.01302569, -0.06152091, -0.11953454, 0.00511467), + T(0.14370050, 0.07275833, 0.22634801, 0.10956798), + T(0.11319179, 0.22101365, 0.10727698, -0.24114035), + T(0.63779628, 0.32476583, 0.01623568, 0.07922356))))) + + val result3 = Tensor( + T(T(T(T(-0.68622679, -0.72637987), + T(-0.19554541, -0.29644468)), + T(T(-0.24871959, 0.35015780), + T(0.00696990, 0.17378137))))) + + val result4 = Tensor( + T(T(T(T(-0.03753495)), + T(T(0.18758345))))) + + val result5 = Tensor( + T(T(T(T(0.05905484)), + T(T(0.05785938))))) + + val input = T(feature1, feature2, feature3) + val expectedOutput = T(result1, result2, result3, result4, result5) + + model.parameters()._1(0).copy(inner3_w) + model.parameters()._1(1).copy(inner3_b) + model.parameters()._1(2).copy(inner2_w) + model.parameters()._1(3).copy(inner2_b) + model.parameters()._1(4).copy(inner1_w) + model.parameters()._1(5).copy(inner1_b) + + model.parameters()._1(6).copy(p6_w) + model.parameters()._1(7).copy(p6_b) + model.parameters()._1(8).copy(p7_w) + model.parameters()._1(9).copy(p7_b) + model.parameters()._1(10).copy(layer3_w) model.parameters()._1(11).copy(layer3_b) + model.parameters()._1(12).copy(layer2_w) + model.parameters()._1(13).copy(layer2_b) + model.parameters()._1(14).copy(layer1_w) + model.parameters()._1(15).copy(layer1_b) + + val output = model.forward(input) + + Equivalent.nearequals(output.toTable.get[Tensor[Float]](1).get, + expectedOutput.get[Tensor[Float]](1).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](2).get, + expectedOutput.get[Tensor[Float]](2).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](3).get, + expectedOutput.get[Tensor[Float]](3).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](4).get, + expectedOutput.get[Tensor[Float]](4).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](5).get, + expectedOutput.get[Tensor[Float]](5).get) should be(true) + } + + "FPN updateOutput with P6P7 TopBlocks use P5" should "work correctly" in { + val in_channels_list = Array(1, 2, 4) + val out_channels = 2 + val model = FPN[Float](in_channels_list, out_channels, topBlocks = 2, + inChannelsOfP6P7 = 2, outChannelsOfP6P7 = 2) // inChannelsP6P7 == outChannelsP6P7 + + val feature1 = Tensor( + T(T(0.10110152, 0.10345000, 0.04320979, 0.84362656, + 0.59594363, 0.97288179, 0.34699517, 0.54275155), + T(0.93956870, 0.07543808, 0.50965708, 0.26184946, + 0.92378283, 0.83272308, 0.54440099, 0.56682664), + T(0.53608388, 0.74091697, 0.53824615, 0.12760854, + 0.70029002, 0.85137993, 0.01918983, 0.10134047), + T(0.61024511, 0.11725241, 0.46950370, 0.15163177, + 0.99792290, 0.50036842, 0.65618765, 0.76569498), + T(0.31238246, 0.96460360, 0.23587847, 0.94086981, + 0.15270233, 0.44916826, 0.53412461, 0.19992995), + T(0.14841199, 0.95466810, 0.89249784, 0.10235202, + 0.24293590, 0.83814293, 0.78163254, 0.94990700), + T(0.50397956, 0.23095572, 0.12026519, 0.70295823, + 0.80230796, 0.31913465, 0.86270124, 0.67926580), + T(0.93120003, 0.08011329, 0.30662805, 0.97467756, + 0.32988423, 0.90689850, 0.46856666, 0.66390038))) + .reshape(Array(1, 1, 8, 8)) + + val feature2 = Tensor( + T(T(T(0.30143285, 0.63111430, 0.45092928, 0.22753167), + T(0.80318344, 0.67537767, 0.14698678, 0.45962620), + T(0.21663177, 0.89086282, 0.92865956, 0.89360029), + T(0.49615270, 0.46269470, 0.73047608, 0.12438315)), + T(T(0.75820625, 0.59779423, 0.61585987, 0.35782731), + T(0.36951083, 0.35381025, 0.64314663, 0.75517660), + T(0.30200917, 0.69998586, 0.29572868, 0.46342885), + T(0.41677684, 0.26154006, 0.16909349, 0.94081402)))) + .reshape(Array(1, 2, 4, 4)) + + val feature3 = Tensor( + T(T(T(0.57270211, 0.25789189), + T(0.79134840, 0.62564188)), + T(T(0.27365083, 0.43420678), + T(0.61281836, 0.23570287)), + T(T(0.21393263, 0.50206852), + T(0.50650394, 0.73282623)), + T(T(0.20319027, 0.06753725), + T(0.18215942, 0.36703324)))) + .reshape(Array(1, 4, 2, 2)) + + val inner1_w = Tensor( + T(T(T(T(-0.12393522))), + T(T(T(-0.49485075))))) + .reshape(Array(1, 2, 1, 1, 1)) + val inner1_b = Tensor(T(0, 0)) + + val inner2_w = Tensor( + T(T(T(T(-0.95695794)), + T(T(0.55932796))), + T(T(T(0.22264385)), + T(T(0.64771581))))) + .reshape(Array(1, 2, 2, 1, 1)) + val inner2_b = Tensor(T(0, 0)) + + val inner3_w = Tensor( + T(T(T(T(0.47477275)), + T(T(0.04092562)), + T(T(-0.01725465)), + T(T(-0.34568024))), + T(T(T(-0.79893148)), + T(T(-0.66726011)), + T(T(-0.14056665)), + T(T(-0.75817424))))) + .reshape(Array(1, 2, 4, 1, 1)) + val inner3_b = Tensor(T(0, 0)) + + val layer1_w = Tensor( + T(T(T(T(-0.32906294, -0.08600309, -0.38722333), + T(-0.29580453, 0.40037566, -0.16175754), + T(0.25444168, 0.11281389, -0.07697448)), + T(T(-0.20765188, -0.30854949, 0.33915347), + T(-0.05911121, -0.20772298, 0.36908209), + T(0.39145410, 0.07839337, 0.09654927))), + T(T(T(-0.26997358, -0.21366502, -0.14226845), + T(-0.05312893, -0.10671085, 0.37542689), + T(-0.28042397, 0.02129859, 0.33310878)), + T(T(-0.39731082, -0.22968259, 0.31097382), + T(0.24397695, -0.38017231, 0.40436870), + T(0.25588512, -0.12146497, 0.10941350))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer1_b = Tensor(T(0, 0)) + + val layer2_w = Tensor( + T(T(T(T(-0.13642263, 0.21656078, -0.01871455), + T(-0.20130268, -0.25516552, 0.34926140), + T(0.13896102, -0.37103790, 0.23734450)), + T(T(0.08139789, 0.37057930, 0.38387370), + T(0.34906447, 0.30327201, 0.23043340), + T(0.04161811, -0.07575810, 0.25803828))), + T(T(T(-0.34966460, -0.22834912, 0.01767731), + T(-0.16592246, -0.36947623, -0.01893327), + T(0.18922144, -0.23139042, -0.28582191)), + T(T(-0.02167633, -0.23346797, 0.00187096), + T(0.14594424, 0.39863366, 0.11338776), + T(-0.33135366, -0.30160487, -0.29802644))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer2_b = Tensor(T(0, 0)) + + val layer3_w = Tensor( + T(T(T(T(-0.00817868, 0.29565400, -0.03227356), + T(-0.11617559, 0.20846748, -0.03688866), + T(-0.05434576, -0.04842332, 0.02425647)), + T(T(0.18390912, -0.00540081, 0.29155219), + T(0.23329431, -0.11891335, 0.24823219), + T(0.23775083, -0.04294857, -0.34929958))), + T(T(T(0.12607539, 0.23896956, 0.01926240), + T(-0.09790298, -0.30780315, 0.14969867), + T(0.00337335, -0.31408104, -0.37880355)), + T(T(-0.25468409, 0.14823782, 0.40019959), + T(-0.03427723, -0.04853129, 0.02510184), + T(0.25904632, 0.34354115, 0.10385382))))) + .reshape(Array(1, 2, 2, 3, 3)) + val layer3_b = Tensor(T(0, 0)) + + val p6_w = Tensor( + T(T(T(T(-0.29818240, -0.22222301, -0.27881575), + T(0.19775295, -0.04910746, -0.11908785), + T(0.27843529, 0.29707819, 0.30488032)), + T(T(-0.12720180, 0.08535665, -0.33813587), + T(-0.02545372, -0.38678339, 0.11843002), + T(-0.06442717, -0.00726947, 0.14210951))), + T(T(T(0.09151843, -0.08247298, 0.07027003), + T(0.23849773, 0.26687491, -0.03779584), + T(0.35821044, 0.17980134, -0.07383940)), + T(T(0.31192303, 0.35286152, -0.00549397), + T(-0.33862600, -0.27117044, 0.37536448), + T(-0.23844577, -0.23443575, -0.35118651))))) + val p6_b = Tensor(T(0, 0)) + + val p7_w = Tensor( + T(T(T(T(0.27193576, 0.09538496, 0.09932923), + T(0.23505199, -0.08323237, -0.35580242), + T(0.06272587, -0.19060957, 0.32343888)), + T(T(0.24513763, -0.01483554, 0.25192779), + T(0.26561451, 0.05530944, 0.30232042), + T(0.30819184, 0.09326428, 0.12093598))), + T(T(T(0.32881397, -0.17656034, -0.26700664), + T(-0.16808785, 0.38506639, -0.15014803), + T(0.21106857, 0.21199214, -0.31056783)), + T(T(-0.16920617, 0.12196451, 0.08281082), + T(0.22818404, -0.17261851, -0.29054090), + T(-0.21099238, -0.04546800, -0.15372574))))) + val p7_b = Tensor(T(0, 0)) + + val result1 = Tensor( + T(T(T(T(-0.05875708, -0.24495505, -0.40095121, -0.36195034, -0.34048805, + -0.42995015, -0.37165138, -0.12609129), + T(-0.23046799, -0.59160781, -0.81221211, -0.36748683, -0.22889382, + -0.15329736, -0.12485854, 0.45016751), + T(-0.38892403, -0.57008761, -0.31188434, -0.44806325, -0.14688066, + 0.00538448, -0.08428087, 0.16207692), + T(-0.20208777, -0.19664873, -0.20870245, -0.44272959, -0.46557492, + -0.41775605, -0.76812929, -0.44619679), + T(-0.20316558, -0.05055160, -0.55165714, -0.40639529, -0.49637964, + -0.66946077, -0.75888383, -0.29708627), + T(-0.65783459, -0.16802897, -0.41265154, -0.02700083, -0.49787420, + -0.34201804, -0.01878840, 0.55896097), + T(-0.59010309, -0.48106664, -0.34888858, -0.17606093, -0.57338041, + -0.27389777, 0.12463056, 0.86562246), + T(-0.09638648, 0.05499656, -0.18625061, 0.50743264, 0.32407704, + 0.19390954, 0.34793308, 0.43689337)), + T(T(0.25326055, -0.47114417, -0.50304997, -0.12190270, -0.38302276, + -0.16330689, -0.28812358, 0.01487039), + T(-0.04750883, -0.58049947, -0.28602204, -0.01689222, 0.16504316, + -0.08511922, -0.14781611, 0.38237956), + T(-0.29861927, -0.31464750, -0.18262222, 0.11181816, 0.16474791, + 0.07716653, -0.16424689, 0.33493862), + T(0.09711685, -0.16085583, -0.12101643, -0.05731618, -0.25519797, + -0.15982063, -0.20263793, 0.06042653), + T(0.07343097, -0.05079371, -0.65273732, -0.35203332, -0.65474921, + -0.31770957, -0.47713327, 0.37339261), + T(-0.37637535, -0.02805071, -0.12414282, -0.43823543, -0.33148623, + 0.35421544, 0.32711336, 0.69742543), + T(-0.16029462, -0.29591912, -0.06338350, -0.28330535, -0.57328767, + 0.75277287, 0.65203953, 0.65192145), + T(0.10750079, -0.11812737, -0.21442677, 0.17212176, 0.05892290, + 0.85415667, 0.09521016, 0.31057179))))) + + val result2 = Tensor( + T(T(T(T(-0.26799202, -0.16586389, -0.32381740, -0.16350438), + T(-0.57322353, -0.51086164, -0.43073779, -0.17672254), + T(-1.16777086, -1.43536067, -0.29853198, -0.48934227), + T(-1.03757823, -1.56285250, -0.94800329, -0.71664643)), + T(T(0.13909623, 0.00816379, -0.06897804, -0.08329182), + T(0.35677588, 0.80284458, 0.61186469, 0.29867059), + T(0.12059141, 0.79457754, 0.50915569, -0.02383049), + T(-0.32545245, -0.54294991, -0.02146160, 0.02067354))))) + + val result3 = Tensor( + T(T(T(T(0.38887578, -0.39928365), + T(-0.16387880, -0.28960186)), + T(T(-0.72778845, -0.72761118), + T(-0.35890821, 0.18019901))))) + + val result4 = Tensor( + T(T(T(T(0.11501697)), + T(T(0.05588362))))) + + val result5 = Tensor( + T(T(T(T(-0.00648224)), + T(T(0.03464263))))) + + val input = T(feature1, feature2, feature3) + val expectedOutput = T(result1, result2, result3, result4, result5) + + model.parameters()._1(0).copy(inner3_w) + model.parameters()._1(1).copy(inner3_b) + model.parameters()._1(2).copy(inner2_w) + model.parameters()._1(3).copy(inner2_b) + model.parameters()._1(4).copy(layer3_w) + model.parameters()._1(5).copy(layer3_b) + + model.parameters()._1(6).copy(inner1_w) + model.parameters()._1(7).copy(inner1_b) + model.parameters()._1(8).copy(p6_w) + model.parameters()._1(9).copy(p6_b) + + model.parameters()._1(10).copy(p7_w) + model.parameters()._1(11).copy(p7_b) + model.parameters()._1(12).copy(layer2_w) + model.parameters()._1(13).copy(layer2_b) + model.parameters()._1(14).copy(layer1_w) + model.parameters()._1(15).copy(layer1_b) val output = model.forward(input) @@ -219,6 +919,10 @@ class FPNSpec extends FlatSpec with Matchers { expectedOutput.get[Tensor[Float]](2).get) should be(true) Equivalent.nearequals(output.toTable.get[Tensor[Float]](3).get, expectedOutput.get[Tensor[Float]](3).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](4).get, + expectedOutput.get[Tensor[Float]](4).get) should be(true) + Equivalent.nearequals(output.toTable.get[Tensor[Float]](5).get, + expectedOutput.get[Tensor[Float]](5).get) should be(true) } } @@ -232,7 +936,8 @@ class FPNSerialTest extends ModuleSerializationTest { input(2.0f) = feature2 input(3.0f) = feature3 - val fpn = new FPN[Float](inChannels = Array(1, 2, 4), outChannels = 2).setName("FPN") + val fpn = new FPN[Float](inChannels = Array(1, 2, 4), outChannels = 2, topBlocks = 0) + .setName("FPN") runSerializationTest(fpn, input) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala index 32c966e8323..9f928c6763e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala @@ -25,25 +25,7 @@ import scala.util.Random class PoolerSpec extends FlatSpec with Matchers { "updateOutput Float type" should "work properly" in { - val feature0 = Array( - 0.883362829685211182, 0.017709493637084961, 0.740627527236938477, - 0.975574254989624023, 0.904063880443572998, 0.293959677219390869, - 0.301572918891906738, 0.235482156276702881) - val feature1 = Array( - 0.873747766017913818, 0.145658850669860840, 0.256294071674346924, - 0.280913352966308594, 0.062630355358123779, 0.272662281990051270, - 0.524160504341125488, 0.110454082489013672, 0.619955241680145264, - 0.568557560443878174, 0.214293479919433594, 0.648296296596527100, - 0.165463507175445557, 0.419352889060974121, 0.852317929267883301, - 0.628634154796600342, 0.678495228290557861, 0.896998584270477295, - 0.890723347663879395, 0.488525688648223877, 0.384370744228363037, - 0.571207761764526367, 0.788873314857482910, 0.954643964767456055, - 0.969983577728271484, 0.203537940979003906, 0.782353222370147705, - 0.848326086997985840, 0.304318606853485107, 0.800064325332641602, - 0.424848318099975586, 0.603751122951507568) - - val feature2 = Array( 0.023863613605499268, 0.100520193576812744, 0.579659581184387207, 0.491799056529998779, 0.695049762725830078, 0.174113810062408447, 0.514802277088165283, 0.645381748676300049, 0.610754907131195068, @@ -88,30 +70,49 @@ class PoolerSpec extends FlatSpec with Matchers { 0.267558634281158447, 0.762180626392364502, 0.907826840877532959, 0.316000878810882568, 0.405982732772827148) + val feature2 = Array( + 0.873747766017913818, 0.145658850669860840, 0.256294071674346924, + 0.280913352966308594, 0.062630355358123779, 0.272662281990051270, + 0.524160504341125488, 0.110454082489013672, 0.619955241680145264, + 0.568557560443878174, 0.214293479919433594, 0.648296296596527100, + 0.165463507175445557, 0.419352889060974121, 0.852317929267883301, + 0.628634154796600342, 0.678495228290557861, 0.896998584270477295, + 0.890723347663879395, 0.488525688648223877, 0.384370744228363037, + 0.571207761764526367, 0.788873314857482910, 0.954643964767456055, + 0.969983577728271484, 0.203537940979003906, 0.782353222370147705, + 0.848326086997985840, 0.304318606853485107, 0.800064325332641602, + 0.424848318099975586, 0.603751122951507568) + + val feature3 = Array( + 0.883362829685211182, 0.017709493637084961, 0.740627527236938477, + 0.975574254989624023, 0.904063880443572998, 0.293959677219390869, + 0.301572918891906738, 0.235482156276702881) + val features = new Table() - features.insert(Tensor(Storage(feature0.map(x => x.toFloat))).resize(1, 2, 2, 2)) - features.insert(Tensor(Storage(feature1.map(x => x.toFloat))).resize(1, 2, 4, 4)) - features.insert(Tensor(Storage(feature2.map(x => x.toFloat))).resize(1, 2, 8, 8)) + features.insert(Tensor(Storage(feature1.map(x => x.toFloat))).resize(1, 2, 8, 8)) + features.insert(Tensor(Storage(feature2.map(x => x.toFloat))).resize(1, 2, 4, 4)) + features.insert(Tensor(Storage(feature3.map(x => x.toFloat))).resize(1, 2, 2, 2)) val rois = Tensor[Float]( - T(T(0, 0, 3, 3), - T(2, 2, 50, 50), - T(50, 50, 500, 500))).resize(3, 4) + T(T(0, 0, 10, 10), + T(0, 0, 60, 60), + T(0, 0, 500, 500))).resize(3, 4) val input = T(features, rois) - val pooler = Pooler[Float](resolution = 2, scales = Array(1.0f, 0.5f, 0.25f), samplingRatio = 2) + val pooler = Pooler[Float]( + resolution = 2, scales = Array(0.125f, 0.0625f, 0.03125f), samplingRatio = 2) val res = pooler.forward(input) val expectedRes = Array( - 0.710301160812377930, 0.338120758533477783, - 0.451076686382293701, 0.243893563747406006, - 0.327536046504974365, 0.126878187060356140, - 0.128067761659622192, 0.058870539069175720, - 0.157158538699150085, 0.000000000000000000, - 0.000000000000000000, 0.000000000000000000, - 0.150937780737876892, 0.000000000000000000, - 0.000000000000000000, 0.000000000000000000, - 0.000000000000000000, 0.000000000000000000, - 0.000000000000000000, 0.000000000000000000, + 0.226864114403724670, 0.272973388433456421, + 0.560893952846527100, 0.552965760231018066, + 0.641304850578308105, 0.476758182048797607, + 0.396813184022903442, 0.643787503242492676, + 0.610044836997985840, 0.499720931053161621, + 0.440425604581832886, 0.471624016761779785, + 0.690218806266784668, 0.652535200119018555, + 0.289968341588973999, 0.477649390697479248, + 0.243893563747406006, 0.000000000000000000, 0.000000000000000000, 0.000000000000000000, + 0.058870539069175720, 0.000000000000000000, 0.000000000000000000, 0.000000000000000000) for (i <- expectedRes.indices) { @@ -120,25 +121,7 @@ class PoolerSpec extends FlatSpec with Matchers { } "updateOutput Double type" should "work properly" in { - val feature0 = Array( - 0.883362829685211182, 0.017709493637084961, 0.740627527236938477, - 0.975574254989624023, 0.904063880443572998, 0.293959677219390869, - 0.301572918891906738, 0.235482156276702881) - val feature1 = Array( - 0.873747766017913818, 0.145658850669860840, 0.256294071674346924, - 0.280913352966308594, 0.062630355358123779, 0.272662281990051270, - 0.524160504341125488, 0.110454082489013672, 0.619955241680145264, - 0.568557560443878174, 0.214293479919433594, 0.648296296596527100, - 0.165463507175445557, 0.419352889060974121, 0.852317929267883301, - 0.628634154796600342, 0.678495228290557861, 0.896998584270477295, - 0.890723347663879395, 0.488525688648223877, 0.384370744228363037, - 0.571207761764526367, 0.788873314857482910, 0.954643964767456055, - 0.969983577728271484, 0.203537940979003906, 0.782353222370147705, - 0.848326086997985840, 0.304318606853485107, 0.800064325332641602, - 0.424848318099975586, 0.603751122951507568) - - val feature2 = Array( 0.023863613605499268, 0.100520193576812744, 0.579659581184387207, 0.491799056529998779, 0.695049762725830078, 0.174113810062408447, 0.514802277088165283, 0.645381748676300049, 0.610754907131195068, @@ -183,31 +166,49 @@ class PoolerSpec extends FlatSpec with Matchers { 0.267558634281158447, 0.762180626392364502, 0.907826840877532959, 0.316000878810882568, 0.405982732772827148) + val feature2 = Array( + 0.873747766017913818, 0.145658850669860840, 0.256294071674346924, + 0.280913352966308594, 0.062630355358123779, 0.272662281990051270, + 0.524160504341125488, 0.110454082489013672, 0.619955241680145264, + 0.568557560443878174, 0.214293479919433594, 0.648296296596527100, + 0.165463507175445557, 0.419352889060974121, 0.852317929267883301, + 0.628634154796600342, 0.678495228290557861, 0.896998584270477295, + 0.890723347663879395, 0.488525688648223877, 0.384370744228363037, + 0.571207761764526367, 0.788873314857482910, 0.954643964767456055, + 0.969983577728271484, 0.203537940979003906, 0.782353222370147705, + 0.848326086997985840, 0.304318606853485107, 0.800064325332641602, + 0.424848318099975586, 0.603751122951507568) + + val feature3 = Array( + 0.883362829685211182, 0.017709493637084961, 0.740627527236938477, + 0.975574254989624023, 0.904063880443572998, 0.293959677219390869, + 0.301572918891906738, 0.235482156276702881) + val features = new Table() - features.insert(Tensor(Storage(feature0.map(x => x))).resize(1, 2, 2, 2)) - features.insert(Tensor(Storage(feature1.map(x => x))).resize(1, 2, 4, 4)) - features.insert(Tensor(Storage(feature2.map(x => x))).resize(1, 2, 8, 8)) + features.insert(Tensor(Storage(feature1.map(x => x))).resize(1, 2, 8, 8)) + features.insert(Tensor(Storage(feature2.map(x => x))).resize(1, 2, 4, 4)) + features.insert(Tensor(Storage(feature3.map(x => x))).resize(1, 2, 2, 2)) val rois = Tensor[Double]( - T(T(0, 0, 3, 3), - T(2, 2, 50, 50), - T(50, 50, 500, 500))).resize(3, 4) + T(T(0, 0, 10, 10), + T(0, 0, 60, 60), + T(0, 0, 500, 500))).resize(3, 4) val input = T(features, rois) - val pooler = Pooler[Double](resolution = 2, scales = Array(1.0f, 0.5f, 0.25f), + val pooler = Pooler[Double](resolution = 2, scales = Array(0.125f, 0.0625f, 0.03125f), samplingRatio = 2) val res = pooler.forward(input) val expectedRes = Array( - 0.710301160812377930, 0.338120758533477783, - 0.451076686382293701, 0.243893563747406006, - 0.327536046504974365, 0.126878187060356140, - 0.128067761659622192, 0.058870539069175720, - 0.157158538699150085, 0.000000000000000000, - 0.000000000000000000, 0.000000000000000000, - 0.150937780737876892, 0.000000000000000000, - 0.000000000000000000, 0.000000000000000000, - 0.000000000000000000, 0.000000000000000000, - 0.000000000000000000, 0.000000000000000000, + 0.226864114403724670, 0.272973388433456421, + 0.560893952846527100, 0.552965760231018066, + 0.641304850578308105, 0.476758182048797607, + 0.396813184022903442, 0.643787503242492676, + 0.610044836997985840, 0.499720931053161621, + 0.440425604581832886, 0.471624016761779785, + 0.690218806266784668, 0.652535200119018555, + 0.289968341588973999, 0.477649390697479248, + 0.243893563747406006, 0.000000000000000000, 0.000000000000000000, 0.000000000000000000, + 0.058870539069175720, 0.000000000000000000, 0.000000000000000000, 0.000000000000000000) for (i <- expectedRes.indices) { @@ -226,7 +227,7 @@ class PoolerSerialTest extends ModuleSerializationTest { val rois = Tensor[Float](1, 4).apply1(_ => Random.nextFloat()) input(1.0f) = features input(2.0f) = rois - val pooler = new Pooler[Float](resolution = 2, scales = Array(1.0f, 0.5f, 0.25f), + val pooler = new Pooler[Float](resolution = 2, scales = Array(0.25f, 0.125f, 0.0625f), samplingRatio = 2).setName("pooler") runSerializationTest(pooler, input) } From 59e842c7b5b9de1a76dc75146a3803c7e132c6c8 Mon Sep 17 00:00:00 2001 From: Menooker Date: Tue, 10 Sep 2019 10:36:09 +0800 Subject: [PATCH 0951/1065] Add Mean Average Precision validation method (#2906) * add MeanAveragePrecision validation method * Add MAP basic code for object detection * update tests * bug fixes based on results of former MAP validation method * update documents * add python binding * typo fix, style change, change calculateAP to private * update comments --- .../bigdl/dllib/optim/ValidationMethod.scala | 324 +++++++++++++++++- .../dllib/utils/python/api/PythonBigDL.scala | 9 + .../bigdl/dllib/optim/ValidationSpec.scala | 181 ++++++++++ 3 files changed, 512 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala index 24cbdc080c8..cde2a866196 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import org.apache.commons.lang3.SerializationUtils - +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag /** @@ -219,7 +219,327 @@ class Top1Accuracy[T: ClassTag]( } /** - * Caculate the percentage that target in output's top5 probability indexes + * Calculate the Mean Average Precision (MAP). The algorithm follows VOC Challenge after 2007 + * Require class label beginning with 0 + * @param k Take top-k confident predictions into account. If k=-1, calculate on all predictions + * @param classes The number of classes + */ +class MeanAveragePrecision[T: ClassTag](k: Int, classes: Int)( + implicit ev: TensorNumeric[T]) extends ValidationMethod[T] { + + require(classes > 0 && classes <= classes, s"The number of classes should be " + + s"> 0 and <= $classes, but got $classes") + require(k > 0, s"k should be > 0, but got $k") + + override def apply(output: Activity, target: Activity): ValidationResult = { + var _target = target.asInstanceOf[Tensor[T]].squeezeNewTensor() + + val outTensor = output.toTensor[T] + val _output = if (outTensor.nDimension() != 1 && + outTensor.size(1) != _target.size(1)) { + outTensor.narrow(1, 1, _target.size().head) + } else { + outTensor + } + + require(_output.dim()==1 && _target.nElement() == 1 || + _output.size(1) == _target.nElement(), "The number of samples in the output should " + + "be the same as in the target") + + val posCnt = new Array[Int](classes) + for (i <- 1 to _target.nElement()) { + val clazz = ev.toType[Float](_target.valueAt(i)) + require(clazz == math.ceil(clazz), s"The class for $i-th test sample should be an integer, " + + s"got $clazz") + val intClazz = clazz.toInt + require(intClazz >= 0 && intClazz < classes, s"The class for $i-th test sample should be " + + s">= 0 and < $classes, but got $intClazz") + posCnt(intClazz) += 1 + } + + val confidenceArr = (0 until classes).map(_ => new ArrayBuffer[(Float, Boolean)]).toArray + if (_output.nDimension() == 2) { + (1 to _output.size(1)).foreach(i => { + val row = _output.select(1, i) + val gtClz = ev.toType[Float](_target.valueAt(i)) + for(clz <- 0 until classes) { + confidenceArr(clz) += ((ev.toType[Float](row.valueAt(clz + 1)), gtClz == clz)) + } + }) + } else { + require(_output.dim() == 1, "The output should have 1 or 2 dimensions") + val row = _output + val gtClz = ev.toType[Float](_target.valueAt(1)) + for(clz <- 0 until classes) { + confidenceArr(clz) += ((ev.toType[Float](row.valueAt(clz + 1)), gtClz == clz)) + } + } + new MAPValidationResult(classes, k, confidenceArr, posCnt) + } + + override def format(): String = s"MAP@$k" +} + +object MAPUtil { + + // find top k values & indices in a column of a matrix + def findTopK(k: Int, arr: Array[Array[Float]], column: Int): Array[(Int, Float)] = { + val q = collection.mutable.PriorityQueue[(Int, Float)]()(Ordering.by[(Int, Float), Float](_._2)) + arr.indices.foreach(i => { + q.enqueue((i, arr(i)(column))) + }) + val end = Math.min(k, q.size) + (1 to end).map(_ => q.dequeue()).toArray + } +} + +/** + * The MAP Validation Result. The results are not calculated until result() or format() is called + * require class label beginning with 0 + */ +class MAPValidationResult( + private val nClass: Int, + // take the first k samples, or -1 for all samples + private val k: Int, + // the predicts for each classes. (Confidence, GT) + private var predictForClass: Array[ArrayBuffer[(Float, Boolean)]], + private var gtCntForClass: Array[Int], + private val useVoc2007: Boolean = false, + private val skipClass: Int = -1 +) + extends ValidationResult { + + if (skipClass < 0) { + require(skipClass == -1, s"Invalid skipClass $skipClass") + } else { + require(skipClass >= 0 && skipClass < nClass, s"Invalid skipClass $skipClass") + } + + private[bigdl] def calculateClassAP(clz: Int): Float = { + val posCnt = gtCntForClass + // for each class, first find top k confident samples + val sorted = predictForClass(clz).sortBy(v => v._1)(Ordering.Float.reverse) // decending order + var tp = 0 + val refinedK = if (k > 0) k else sorted.size + // calculate the max precision for each different recall + // for each top-j items, calculate the (precision, recall) + val PnR = sorted.take(refinedK).zipWithIndex.flatMap { case (predict, j) => + if (predict._2) { + // if it is a hit + tp += 1 + // j + 1 is the total number of samples marked positive by the model + val precision = tp.toFloat / (j + 1) + val recall = tp.toFloat / posCnt(clz) + Iterator.single(recall, precision) + } else { + Iterator.empty + } + } + + // get Average precision over each different recall + if (useVoc2007) { + (0 to 10).map(r => { + val recall = 0.1f * r + // for every (R,P), where R>=recall, get max(P) + PnR.filter(_._1 >= recall).map(_._2).reduceOption(_ max _).getOrElse(0f) + }) + .reduceOption(_ + _) + .map(_ / 11) + .getOrElse(0f) + } else { + (1 to posCnt(clz)).map(r => { + val recall = r.toFloat / posCnt(clz) + // for every (R,P), where R>=recall, get max(P) + PnR.filter(_._1 >= recall).map(_._2).reduceOption(_ max _).getOrElse(0f) + }) + .reduceOption(_ + _) + .map(_ / posCnt(clz)) + .getOrElse(0f) + } + } + + + override def result(): (Float, Int) = { + // get the indices of top-k confident samples + val AP = (0 until nClass).filter(_ != skipClass).map { clz => calculateClassAP(clz) } + // APs are got. Now we get MAP + val result = AP.sum / (nClass - (if (skipClass == -1) 0 else 1)) + (result, 1) + } + // scalastyle:off methodName + override def +(other: ValidationResult): ValidationResult = { + val o = other.asInstanceOf[MAPValidationResult] + require(predictForClass.length == o.predictForClass.length) + require(gtCntForClass.length == o.gtCntForClass.length) + predictForClass.zip(o.predictForClass).foreach { + case (left, right) => left ++= right + } + gtCntForClass.indices.foreach( i => gtCntForClass(i) += o.gtCntForClass(i)) + this + } + // scalastyle:on methodName + + override protected def format(): String = { + val resultStr = (0 until nClass).map { clz => calculateClassAP(clz) }.zipWithIndex + .map { t => s"AP of class ${t._2} = ${t._1}\n"}.reduceOption( _ + _).getOrElse("") + s"MeanAveragePrecision@$k(${result()._1})\n $resultStr" + } +} + +private[bigdl] class GroundTruthBBox(val label: Int, val diff: Float, + val xmin: Float, val ymin: Float, val xmax: Float, val ymax: Float) { + private val area = (xmax - xmin) * (ymax - ymin) + + // if is false, the bbox is not matched with any predictions + private var isOccupied = false + + /** + * Returns if any previous prediction is matched with the current bbox + * @return + */ + def canOccupy: Boolean = !isOccupied + def occupy(): Unit = { + isOccupied = true + } + + /** get the IOU rate of another bbox with the current bbox + * + * @param x1 the min x + * @param y1 the min y + * @param x2 the max x + * @param y2 the max y + * @return + */ + def getIOURate(x1: Float, y1: Float, x2: Float, y2: Float): Float = { + val ixmin = Math.max(xmin, x1) + val iymin = Math.max(ymin, y1) + val ixmax = Math.min(xmax, x2) + val iymax = Math.min(ymax, y2) + val inter = Math.max(ixmax - ixmin, 0) * Math.max(iymax - iymin, 0) + inter / ((x2 - x1) * (y2 - y1) + area - inter) + } +} + +/** MeanAveragePrecision for Object Detection + * IMPORTANT: The labels in the target vector (Ground truth) begin with 0. BUT in the + * NN output, the labels begins with 1 + * + * The expected output from the last layer should be [num_of_batch X (1 + maxDetection * 6)] matrix + * The format of the matrix should be [, , ...], where each row vector is + * = [, ,...]. Each sample has format: + * = the labels begins with 1 + * imgId is the batch number of the sample. imgId begins with 0. + * Multiple samples may share one imgId + * + * The target vector (Ground truth) is a [num_of_gt X 7] matrix + * having format [, , , ...] + * where = the labels begins with 0 + * + * @param iou the IOU threshold + * @param classes the number of classes + * @param useVoc2007 use validation method before voc2010 (i.e. voc2007) + * @param skipClass skip calculating on a specific class (e.g. background) + * the class index starts from 0, or is -1 if no skipping + */ +class MeanAveragePrecisionObjectDetection[T: ClassTag]( + classes: Int, iou: Float = 0.5f, useVoc2007: Boolean = false, skipClass: Int = -1)( + implicit ev: TensorNumeric[T]) extends ValidationMethod[T] { + override def apply(output: Activity, target: Activity): ValidationResult = { + val gtTensor = target.toTensor[Float] + require(gtTensor.dim() == 2 && gtTensor.size(2) == 7, + "the ground truth tensor should have 2 dimensions " + + "and the second dimension should have size of 7") + + // the number of GT bboxes for each class + val gtCntByClass = new Array[Int](classes) + + // one image may contain multiple Ground truth bboxes + val gtImages = new ArrayBuffer[ArrayBuffer[GroundTruthBBox]] + // this converts the image-id in target tensor to the index within the image array + // imgId is for output tensor and target tensor. imgIdx is for gtImages + // the imgId should start from 0 + val imgId2imgIdx = scala.collection.mutable.Map[Int, Int]() + for(i <- 1 to gtTensor.size(1)) { + // the tensor is: (imgId, label, diff, bbox x4) + val imgId = gtTensor.valueAt(i, 1).toInt + val label = gtTensor.valueAt(i, 2).toInt - 1 + val diff = gtTensor.valueAt(i, 3).toInt + + val imgIdx = if (!imgId2imgIdx.contains(imgId)) { + val sz = gtImages.size + imgId2imgIdx(imgId) = sz + gtImages += new ArrayBuffer[GroundTruthBBox]() + sz + } else { + imgId2imgIdx(imgId) + } + gtImages(imgIdx) += new GroundTruthBBox(label, diff, gtTensor.valueAt(i, 4), + gtTensor.valueAt(i, 5), gtTensor.valueAt(i, 6), gtTensor.valueAt(i, 7)) + require(label >= 0 && label < classes, s"Bad label id $label") + + if (diff == 0) { + gtCntByClass(label) += 1 + } + } + + // the predicted bboxes for each classes + // predictByClass(classIdx)(bboxNum) is (Confidence, GT) + val predictByClass = new Array[ArrayBuffer[(Float, Boolean)]](classes) + for (i <- predictByClass.indices) { + predictByClass(i) = new ArrayBuffer[(Float, Boolean)] + } + + val outTensor = output.toTensor[Float] + require(outTensor.dim() == 2, "the output tensor should have 2 dimensions") + for (imgId <- 0 until outTensor.size(1)) { + // for each image + if (imgId2imgIdx.contains(imgId)) { + val imgIdx = imgId2imgIdx(imgId) // index within gtImages + val gtBbox = gtImages(imgIdx) + val batch = outTensor.select(1, imgId + 1) + val batchSize = batch.valueAt(1).toInt + var offset = 2 + for (bboxIdx <- 0 until batchSize) { + // for each predicted bboxes + val label = batch.valueAt(offset).toInt + require(label >= 0 && label < classes, s"Bad label id $label") + val score = batch.valueAt(offset + 1) + val x1 = batch.valueAt(offset + 2) + val y1 = batch.valueAt(offset + 3) + val x2 = batch.valueAt(offset + 4) + val y2 = batch.valueAt(offset + 5) + // for each GT boxes, try to find a matched one with current prediction + val matchedGt = gtBbox.filter(gt => label == gt.label && gt.canOccupy) + .flatMap(gt => { // calculate and filter out the bbox + val iouRate = gt.getIOURate(x1, y1, x2, y2) + if (iouRate >= iou) Iterator.single((gt, iouRate)) else Iterator.empty + }) + .reduceOption( (gtArea1, gtArea2) => { // find max IOU bbox + if (gtArea1._2 > gtArea2._2) gtArea1 else gtArea2 + }) + .map(bbox => { // occupy the bbox + bbox._1.occupy() + bbox._1 + }) + if (matchedGt.isEmpty || matchedGt.get.diff == 0) { + predictByClass(label).append((score, matchedGt.isDefined)) + } + // else: when the prediction matches a "difficult" GT, do nothing + // it is neither TP nor FP + // what is "difficult"? I have no idea... + offset += 6 + } + } + // if the image id does not have ground truth, do nothing + } + new MAPValidationResult(classes, -1, predictByClass, gtCntByClass, useVoc2007, skipClass) + } + + override protected def format(): String = s"MAPObjectDetection" +} + +/** + * Calculate the percentage that target in output's top5 probability indexes */ class Top5Accuracy[T: ClassTag]( implicit ev: TensorNumeric[T]) extends ValidationMethod[T] { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 52ee4f787ae..3b60e723fc6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2167,6 +2167,15 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab new Top5Accuracy() } + def createMeanAveragePrecision(k: Int, classes: Int): ValidationMethod[T] = { + new MeanAveragePrecision(k, classes) + } + + def createMeanAveragePrecisionObjectDetection(classes: Int, iou: Float, useVoc2007: Boolean, + skipClass: Int): ValidationMethod[T] = { + new MeanAveragePrecisionObjectDetection(classes, iou, useVoc2007, skipClass) + } + def createLoss(criterion: Criterion[T]): ValidationMethod[T] = { new Loss(criterion) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index e97e6147132..cae2f6b7e36 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -16,12 +16,193 @@ package com.intel.analytics.bigdl.optim +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} +import scala.collection.mutable.ArrayBuffer @com.intel.analytics.bigdl.tags.Parallel class ValidationSpec extends FlatSpec with Matchers { + "MAPUtil" should "be correct for find top k" in { + val arr = Array( + Array(1f, 2f, 3f, 4f, 5f), // 0 + Array(0f, 0f, 4f, 6f, 7f), // 1 + Array(6f, 4f, 1f, 5f, 2f), // 2 + Array(3f, 5f, 0f, 1f, 9f), // 3 + Array(1f, 2f, 3f, 2f, 5f), // 4 + Array(0f, 0f, 4f, 9f, 7f), // 5 + Array(6f, 4f, 1f, 8f, 2f), // 6 + Array(3f, 5f, 0f, 3f, 9f), // 7 + Array(6f, 4f, 1f, 7f, 2f) // 8 + ) + val result = MAPUtil.findTopK(16, arr, 3) + val test = Array((5, 9f), (6, 8f), (8, 7f), (1, 6f), (2, 5f), (0, 4f), (7, 3f), (4, 2f), + (3, 1f)) + result should be(test) + + val result2 = MAPUtil.findTopK(5, arr, 3) + val test2 = Array((5, 9f), (6, 8f), (8, 7f), (1, 6f), (2, 5f)) + result2 should be(test2) + } + + "MAPValidationResult" should "function well" in { + val confidence = Array( + Array(1f, 2f, 3f, 4f, 5f), // 0 + Array(0f, 0f, 4f, 6f, 7f), // 1 + Array(6f, 4f, 1f, 5f, 2f), // 2 + Array(3f, 5f, 0f, 1f, 9.1f), // 3 + Array(1f, 3f, 3f, 2f, 5f), // 4 + Array(1f, 0f, 4f, 9f, 7f), // 5 + Array(6f, 4f, 1f, 8f, 2.1f), // 6 + Array(3f, 5f, 0f, 3f, 9f), // 7 + Array(6f, 4f, 1f, 7f, 2.1f) // 8 + ) + val gt = Array( + 1f, // 0 + 0f, // 1 + 4f, // 2 + 4f, // 3 + 3f, // 4 + 3f, // 5 + 3f, // 6 + 2f, // 7 + 3f // 8 + ) + + def mkArray(data: (Float, Boolean)*): ArrayBuffer[(Float, Boolean)] = { + val ret = new ArrayBuffer[(Float, Boolean)]() + ret.appendAll(data) + ret + } + val result = new MAPValidationResult(5, 8, + Array( + mkArray((1f, false), (0f, true), (6f, false), (3f, false), (1f, false), (1f, false), (6f, + false), (3f, false), (6f, false)), + mkArray((2f, true), (0f, false), (4f, false), (5f, false), (3f, false), (0f, false), (4f, + false), (5f, false), (4f, false)), + mkArray((3f, false), (4f, false), (1f, false), (0f, false), (3f, false), (4f, false), + (1f, false), (0f, true), (1f, false)), + mkArray((4f, false), (6f, false), (5f, false), (1f, false), (2f, true), (9f, true), (8f, + true), (3f, false), (7f, true)), + mkArray((5f, false), (7f, false), (2f, true), (9.1f, true), (5f, false), (7f, false), + (2.1f, false), (9f, false), (2.1f, false)) + ), Array(1, 1, 1, 4, 2)) + val ap1 = result.calculateClassAP(0) + ap1 should be (0f) + val ap2 = result.calculateClassAP(1) + ap2 should be (1f/7f) + val ap3 = result.calculateClassAP(2) + ap3 should be (0f) + val ap4 = result.calculateClassAP(3) + ap4 should be (0.875f) + val ap5 = result.calculateClassAP(4) + ap5 should be (0.5f) + + result.result()._1 should be(0.303571429f +- 1e-5f) + } + + "MeanAveragePrecision" should "be correct on 1d tensor" in { + implicit val numeric = TensorNumeric.NumericFloat + val output = Tensor[Float]( + T( + T(6f, 4f, 1f, 5f, 2f), // 2 + T(3f, 5f, 0f, 1f, 9.1f), // 3 + T(1f, 3f, 3f, 2f, 5f), // 4 + T(1f, 0f, 4f, 9f, 7f), // 5 + T(6f, 4f, 1f, 8f, 2.1f), // 6 + T(3f, 5f, 0f, 3f, 9f), // 7 + T(6f, 4f, 1f, 7f, 2.1f) // 8 + )) + + val target = Tensor[Float]( + T(T( + 4f, // 2 + 4f, // 3 + 3f, // 4 + 3f, // 5 + 3f, // 6 + 2f, // 7 + 3f // 8 + ))) + + val r0 = new MeanAveragePrecision(8, 5).apply(output, target) + val r1 = new MeanAveragePrecision(8, 5).apply(Tensor[Float](T(1f, 2f, 3f, 4f, 5f)), + Tensor[Float](T(1f))) + val r2 = new MeanAveragePrecision(8, 5).apply(Tensor[Float](T(0f, 0f, 4f, 6f, 7f)), + Tensor[Float](T(0f))) + (r0 + r1 + r2).result()._1 should be(0.303571429f +- 1e-5f) + } + + "MeanAveragePrecision" should "be correct on 2d tensor" in { + implicit val numeric = TensorNumeric.NumericFloat + val output = Tensor[Float]( + T( + T(1f, 2f, 3f, 4f, 5f), // 0 + T(0f, 0f, 4f, 6f, 7f), // 1 + T(6f, 4f, 1f, 5f, 2f), // 2 + T(3f, 5f, 0f, 1f, 9.1f), // 3 + T(1f, 3f, 3f, 2f, 5f), // 4 + T(1f, 0f, 4f, 9f, 7f), // 5 + T(6f, 4f, 1f, 8f, 2.1f), // 6 + T(3f, 5f, 0f, 3f, 9f), // 7 + T(6f, 4f, 1f, 7f, 2.1f) // 8 + )) + + val target = Tensor[Float]( + T(T( + 1f, // 0 + 0f, // 1 + 4f, // 2 + 4f, // 3 + 3f, // 4 + 3f, // 5 + 3f, // 6 + 2f, // 7 + 3f // 8 + ))) + val v = new MeanAveragePrecision(8, 5) + val result = v(output, target) + result.result()._1 should be(0.303571429f +- 1e-5f) + } + + "MeanAveragePrecisionObjectDetection" should "be correct on 2d tensor" in { + implicit val numeric = TensorNumeric.NumericFloat + val output = Tensor[Float]( + T( + T(8f, + // label score bbox + 0, 1, 110, 90, 210, 190, + 0, 2, 310, 110, 410, 210, + 0, 4, 320, 290, 420, 390, + 0, 3, 210, 310, 290, 410, + 1, 1, 1110, 1090, 1210, 1190, + 1, 3, 1310, 1110, 1410, 1210, + 1, 4, 1320, 1290, 1420, 1390, + 1, 2, 1210, 1310, 1290, 1410 + ) + )) + + // + val target = Tensor[Float]( + T( + T(0, 1, 0, 100, 100, 200, 200), + T(0, 1, 0, 300, 100, 400, 200), + T(0, 1, 0, 100, 300, 200, 400), + T(0, 1, 0, 300, 300, 400, 400), + T(0, 1, 0, 210, 210, 230, 290), + T(0, 2, 0, 1100, 1100, 1200, 1200), + T(0, 2, 0, 1300, 1100, 1400, 1200), + T(0, 2, 0, 1100, 1300, 1200, 1400), + T(0, 2, 0, 1300, 1300, 1400, 1400), + T(0, 2, 0, 1210, 1210, 1230, 1290) + )) + val v = new MeanAveragePrecisionObjectDetection(3, 0.5f) + val result = v(output, target) + // 0.5f and 0.55f + result.result()._1 should be(0.35f +- 1e-5f) + } + "treeNN accuracy" should "be correct on 2d tensor" in { val output = Tensor[Double]( T( From 1e8aad907f944bb26b3bef16f1898a782e2788c8 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 10 Sep 2019 14:07:15 +0800 Subject: [PATCH 0952/1065] fix boxhead unit tests (#2912) --- .../scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala | 1 - 1 file changed, 1 deletion(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala index 3130a574ab8..be933cbbe90 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala @@ -19,7 +19,6 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{T, Table} -import org.dmg.pmml.False import org.scalatest.{FlatSpec, Matchers} import scala.math._ From 713a9f065e5cc20d86731f21aceefe3a5aa07c59 Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Thu, 12 Sep 2019 13:26:47 +0800 Subject: [PATCH 0953/1065] python api nested list input and pooler python api (#2900) --- .../dllib/utils/python/api/PythonBigDL.scala | 34 +++++++++++++------ 1 file changed, 23 insertions(+), 11 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 3b60e723fc6..0c5c7e76ea4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -96,18 +96,23 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab cls.getSimpleName } - private def toTable(input: JList[JTensor]): Table = { - input.asScala.foldLeft(new Table())((t, jtensor) => t.insert(toTensor(jtensor))) + private def toTable(input: JList[_ <: Object]): Table = { + input.asScala.foldLeft(new Table())((t, e) => + if (e.isInstanceOf[JTensor]) { + t.insert(toTensor(e.asInstanceOf[JTensor])) + } else { + t.insert(toTable(e.asInstanceOf[JList[Object]])) + }) } - def jTensorsToActivity(input: JList[JTensor], isTable: Boolean): Activity = { + def jTensorsToActivity(input: JList[_ <: Object], isTable: Boolean): Activity = { if (input.isEmpty) { throw new IllegalArgumentException("Empty input") } if (isTable) { toTable(input) } else { - toTensor(input.iterator().next()) + toTensor(input.asInstanceOf[JList[JTensor]].iterator().next()) } } @@ -1163,6 +1168,13 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab top_blocks, in_channels_of_p6p7, out_channels_of_p6p7) } + def createPooler(resolution: Int, scales: JList[Double], sampling_ratio: Int) + : Pooler[T] = { + Pooler[T](resolution, + scales.asScala.toArray.map(_.toFloat), + sampling_ratio) + } + def createScale(size: JList[Int]) : Scale[T] = { Scale[T](size.asScala.toArray) @@ -2045,7 +2057,7 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelForward(model: AbstractModule[Activity, Activity, T], - input: JList[JTensor], + input: JList[_ <: Object], inputIsTable: Boolean): JList[JTensor] = { val inputActivity = jTensorsToActivity(input, inputIsTable) val outputActivity = model.forward(inputActivity) @@ -2053,9 +2065,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelBackward(model: AbstractModule[Activity, Activity, T], - input: JList[JTensor], + input: JList[_ <: Object], inputIsTable: Boolean, - gradOutput: JList[JTensor], + gradOutput: JList[_ <: Object], gradOutputIsTable: Boolean): JList[JTensor] = { val inputActivity = jTensorsToActivity(input, inputIsTable) val gradOutputActivity = jTensorsToActivity(gradOutput, gradOutputIsTable) @@ -2081,9 +2093,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def criterionForward(criterion: AbstractCriterion[Activity, Activity, T], - input: JList[JTensor], + input: JList[_ <: Object], inputIsTable: Boolean, - target: JList[JTensor], + target: JList[_ <: Object], targetIsTable: Boolean): T = { val inputActivity = jTensorsToActivity(input, inputIsTable) val targetActivity = jTensorsToActivity(target, targetIsTable) @@ -2091,9 +2103,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def criterionBackward(criterion: AbstractCriterion[Activity, Activity, T], - input: JList[JTensor], + input: JList[_ <: Object], inputIsTable: Boolean, - target: JList[JTensor], + target: JList[_ <: Object], targetIsTable: Boolean): JList[JTensor] = { val inputActivity = jTensorsToActivity(input, inputIsTable) val targetActivity = jTensorsToActivity(target, targetIsTable) From b217459e27c24a0d8ccadfa88eb655b9e38a2a18 Mon Sep 17 00:00:00 2001 From: Menooker Date: Mon, 16 Sep 2019 13:26:39 +0800 Subject: [PATCH 0954/1065] Auto memory management for MKLDNN (#2867) * add memory owner * Add DnnTensor to MemoryOwner * delete unused file * style fix * Move ReorderManager to MemoryOwner * Fix compiling errors * use Releasable as a general management type. release input layer. * remove redundant null checking * style fixes * change _implicitMemoryOwner -> _this --- .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 14 +- .../bigdl/dllib/nn/mkldnn/CAddTable.scala | 7 +- .../bigdl/dllib/nn/mkldnn/ConcatTable.scala | 6 +- .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 28 +- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 3 +- .../bigdl/dllib/nn/mkldnn/InputWrapper.scala | 7 + .../bigdl/dllib/nn/mkldnn/JoinTable.scala | 13 +- .../analytics/bigdl/dllib/nn/mkldnn/LRN.scala | 12 +- .../bigdl/dllib/nn/mkldnn/Linear.scala | 24 +- .../bigdl/dllib/nn/mkldnn/MaxPooling.scala | 14 +- .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 12 +- .../bigdl/dllib/nn/mkldnn/MemoryOwner.scala | 51 +++ .../bigdl/dllib/nn/mkldnn/MklDnnMemory.scala | 320 ++++++++++++++++++ .../analytics/bigdl/dllib/nn/mkldnn/RNN.scala | 21 +- .../bigdl/dllib/nn/mkldnn/ReLU.scala | 12 +- .../dllib/nn/mkldnn/ReorderManager.scala | 8 +- .../bigdl/dllib/nn/mkldnn/ReorderMemory.scala | 44 ++- .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 8 +- .../nn/mkldnn/SpatialBatchNormalization.scala | 27 +- .../dllib/nn/mkldnn/SpatialConvolution.scala | 34 +- .../bigdl/dllib/nn/mkldnn/TensorMMap.scala | 8 +- .../bigdl/dllib/tensor/DnnStorage.scala | 4 +- .../bigdl/dllib/tensor/DnnTensor.scala | 25 +- .../bigdl/dllib/nn/mkldnn/CAddTableSpec.scala | 9 +- .../dllib/nn/mkldnn/ConcatTableSpec.scala | 4 +- .../bigdl/dllib/nn/mkldnn/JoinTableSpec.scala | 6 +- .../dllib/nn/mkldnn/ReorderMemorySpec.scala | 10 +- .../dllib/nn/mkldnn/SequentialSpec.scala | 12 +- .../nn/mkldnn/SpatialConvolutionSpec.scala | 2 +- .../bigdl/dllib/nn/mkldnn/TestUtils.scala | 8 +- .../bigdl/dllib/tensor/DnnTensorSpec.scala | 6 +- 31 files changed, 560 insertions(+), 199 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryOwner.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnMemory.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala index 228d16d548b..0b270af250b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -87,7 +87,7 @@ class AvgPooling( paddingTL = Array(pt, pl) paddingBR = Array(pb, pr) - val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), inputs(0).dataType, + val outputMD = MklDnnMemory.MemoryDescInit(4, Array(n, c, oh, ow), inputs(0).dataType, Memory.Format.any) val kind = if (phase == InferencePhase) { @@ -96,14 +96,14 @@ class AvgPooling( PropKind.ForwardTraining } - val description = MklDnn.PoolingForwardDescInit( + val description = MklDnnMemory.PoolingForwardDescInit( kind, algKind, _inputFormats(0).getMemoryDescription(), outputMD, strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) - fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + fwdPD = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, 0L) _outputFormats = Array(MemoryData.primitiveOutput(fwdPD)) output = initTensor(_outputFormats(0)) - updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPD, + updateOutputPrimitives = Array(MklDnnMemory.PrimitiveCreate2(fwdPD, _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, _outputFormats.map(_.getPrimitive(runtime)), 2)) (_inputFormats, _outputFormats) @@ -114,14 +114,14 @@ class AvgPooling( _gradOutputFormatsForWeight = _gradOutputFormats val strides = Array(dW, dH) val kernel = Array(kH, kW) - val description = MklDnn.PoolingBackwardDescInit(algKind, + val description = MklDnnMemory.PoolingBackwardDescInit(algKind, _inputFormats(0).getMemoryDescription(), _gradOutputFormats(0).getMemoryDescription(), strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) - val pd = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD) + val pd = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, fwdPD) _gradInputFormats = Array(MemoryData.operationWant(pd, Query.DiffSrcPd)) - updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(pd, + updateGradInputPrimitives = Array(MklDnnMemory.PrimitiveCreate2(pd, _gradOutputFormats.map(_.getPrimitive(runtime)), Array(0, 0), 2, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) gradInput = initTensor(_gradInputFormats(0)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala index 59f335aaf33..058b00b5ea1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTable.scala @@ -31,7 +31,8 @@ class CAddTable extends MklDnnLayer with MklInt8Convertible { } } - val outputMD = MklDnn.MemoryDescInit(shape.length, shape, inputs(0).dataType, Memory.Format.any) + val outputMD = MklDnnMemory.MemoryDescInit(shape.length, shape, + inputs(0).dataType, Memory.Format.any) val scales = inputs.map { x => if (x.dataType != DataType.F32 && x.scales.nonEmpty) { @@ -43,10 +44,10 @@ class CAddTable extends MklDnnLayer with MklInt8Convertible { } } - val pd = MklDnn.SumPrimitiveDescCreate(outputMD, inputs.length, scales, + val pd = MklDnnMemory.SumPrimitiveDescCreate(outputMD, inputs.length, scales, inputs.map(_.getPrimitiveDescription(runtime))) _outputFormats = Array(MemoryData.primitiveOutput(pd)) - updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(pd, + updateOutputPrimitives = Array(MklDnnMemory.PrimitiveCreate2(pd, _inputFormats.map(_.getPrimitive(runtime)), new Array[Int](inputs.length), _inputFormats.length, _outputFormats.map(_.getPrimitive(runtime)), 1)) output = initTensor(_outputFormats(0)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala index 1a6204e1987..31324b7332b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTable.scala @@ -102,13 +102,13 @@ class ConcatTable extends MklDnnContainer with MklInt8Convertible { } } } - val outputMD = MklDnn.MemoryDescInit(shape.length, shape, DataType.F32, Memory.Format.any) + val outputMD = MklDnnMemory.MemoryDescInit(shape.length, shape, DataType.F32, Memory.Format.any) val scales = grads.map(_ => 1f) - val pd = MklDnn.SumPrimitiveDescCreate(outputMD, grads.length, scales, + val pd = MklDnnMemory.SumPrimitiveDescCreate(outputMD, grads.length, scales, subGradInputs.map(_.getPrimitiveDescription(runtime))) _gradInputFormats = Array(MemoryData.primitiveOutput(pd)) tensorPrimitives(grads.length) = _gradInputFormats(0).getPrimitive(runtime) - sumPrimitive = Array(MklDnn.PrimitiveCreate2(pd, + sumPrimitive = Array(MklDnnMemory.PrimitiveCreate2(pd, subGradInputs.map(_.getPrimitive(runtime)), new Array[Int](grads.length), grads.length, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) gradInput = initTensor(_gradInputFormats(0)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index 197745f3e2b..bcc18df8b77 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -78,7 +78,10 @@ trait MklDnnModule extends MklDnnModuleHelper { def setQuantize(value: Boolean): this.type } -trait MklDnnModuleHelper { +trait MklDnnModuleHelper extends MemoryOwner { + + @transient protected implicit lazy val _this : MemoryOwner = this + protected def initActivity(formats: Array[MemoryData]): Activity = { if (formats.length == 1) { initTensor(formats(0)) @@ -162,7 +165,8 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM } def getUpdateOutputMemoryPrimitives(): Array[Long] = { - inputFormats().map(_.getPrimitive(runtime)) ++ outputFormats().map(_.getPrimitive(runtime)) + inputFormats().map(_.getPrimitive(runtime)) ++ + outputFormats().map(_.getPrimitive(runtime)) } def getUpdateGradInputMemoryPrimitives(): Array[Long] = { gradOutputFormats().map(_.getPrimitive(runtime)) ++ @@ -276,23 +280,7 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM } override def release(): Unit = { - val tensors: ArrayBuffer[DnnTensor[Float]] = ArrayBuffer.empty - List(output, gradInput).filter(_ != null).foreach { t => - if (t.isTensor && t.toTensor[Float].getTensorType == MklDnnType) { - tensors.append(t.asInstanceOf[DnnTensor[Float]]) - } - - if (t.isTable) { - val table = t.toTable - var i = 1 - while (i <= table.length()) { - tensors.append(table(i)) - i += 1 - } - } - } - - tensors.foreach(_.release()) + this.releaseResources() } override def setQuantize(value: Boolean): MklDnnLayer.this.type = this @@ -385,7 +373,7 @@ trait MklDnnContainer extends DynamicContainer[Activity, Activity, Float] with M override def release(): Unit = { super.release() - reorderManager.release() + this.releaseResources() } override def setQuantize(value: Boolean): this.type = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 37f9f979bd3..61c9aa8a951 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -492,7 +492,8 @@ class DnnGraph( override def release(): Unit = { // do not call super.release, it will call MklDnnLayer.release() modules.foreach(_.release()) - reorderManager.release() + // we need to call releaseResources here because super.release will never be called + this.releaseResources() } override def calcScales(input: Activity): Unit = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala index 58834860a96..3097e1497d7 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/InputWrapper.scala @@ -52,4 +52,11 @@ private[bigdl] class InputWrapper extends MklDnnLayer { override def toString(): String = { s"nn.mkl.InputWrapper" } + + override def release(): Unit = { + super.release() + if(inputLayer != null) { + inputLayer.release() + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala index 69de6d28833..7d0c3b91f39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTable.scala @@ -51,12 +51,13 @@ class JoinTable(val dimension: Int) extends MklDnnLayer { } i += 1 } - val primDesc = MklDnn.ConcatPrimitiveDescCreate( - MklDnn.MemoryDescInit(totalShape.length, totalShape, inputs(0).dataType, Memory.Format.any), + val primDesc = MklDnnMemory.ConcatPrimitiveDescCreate( + MklDnnMemory.MemoryDescInit(totalShape.length, totalShape, + inputs(0).dataType, Memory.Format.any), inputs.length, dimension - 1, _inputFormats.map(_.getPrimitiveDescription(runtime))) _outputFormats = Array(MemoryData.primitiveOutput(primDesc)) - updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(primDesc, + updateOutputPrimitives = Array(MklDnnMemory.PrimitiveCreate2(primDesc, _inputFormats.map(_.getPrimitive(runtime)), new Array[Int](inputs.length), inputs.length, _outputFormats.map(_.getPrimitive(runtime)), 1) @@ -75,13 +76,13 @@ class JoinTable(val dimension: Int) extends MklDnnLayer { val buffer = new ArrayBuffer[Array[Long]]() val offset = new Array[Int](_gradOutputFormats(0).shape.length) for(i <- 0 until _gradInputFormats.length) { - val viewPD = MklDnn.ViewPrimitiveDescCreate( + val viewPD = MklDnnMemory.ViewPrimitiveDescCreate( _gradOutputFormats(0).getPrimitiveDescription(runtime), _gradInputFormats(i).shape, offset) val viewFormat = MemoryData.primitiveOutput(viewPD) - val reorderPD = MklDnn.ReorderPrimitiveDescCreate( + val reorderPD = MklDnnMemory.ReorderPrimitiveDescCreate( viewFormat.getPrimitiveDescription(runtime), _gradInputFormats(i).getPrimitiveDescription(runtime)) - val reorderPrim = MklDnn.PrimitiveCreate2(reorderPD, + val reorderPrim = MklDnnMemory.PrimitiveCreate2(reorderPD, Array(viewFormat.getPrimitive(runtime)), Array(0), 1, Array(_gradInputFormats(i).getPrimitive(runtime)), 1) prims.append(reorderPrim) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala index 2e0f8264dff..2bd7cbb3ae5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala @@ -44,10 +44,10 @@ class LRN( PropKind.ForwardTraining } - val description = MklDnn.LRNForwardDescInit( + val description = MklDnnMemory.LRNForwardDescInit( kind, AlgKind.LrnAcrossChannels, _inputFormats(0).getMemoryDescription(), size, alpha.toFloat, beta.toFloat, k.toFloat) - fwdPrimDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + fwdPrimDesc = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, 0L) _outputFormats = Array(MemoryData.primitiveOutput(fwdPrimDesc)) output = initTensor(_outputFormats(0)) @@ -61,7 +61,7 @@ class LRN( Array(_inputFormats(0), _outputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)) } - updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPrimDesc, + updateOutputPrimitives = Array(MklDnnMemory.PrimitiveCreate2(fwdPrimDesc, _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, fwdMemPrims.drop(1), fwdMemPrims.length - 1)) @@ -71,13 +71,13 @@ class LRN( override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { _gradOutputFormats = singleNativeData(grad) _gradOutputFormatsForWeight = _gradOutputFormats - val description = MklDnn.LRNBackwardDescInit(AlgKind.LrnAcrossChannels, + val description = MklDnnMemory.LRNBackwardDescInit(AlgKind.LrnAcrossChannels, _inputFormats(0).getMemoryDescription(), _gradOutputFormats(0).getMemoryDescription(), size, alpha.toFloat, beta.toFloat, k.toFloat) require(fwdPrimDesc != UNDEFINED, "You should call initFwdPrimitives first") - val primDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPrimDesc) + val primDesc = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, fwdPrimDesc) _gradInputFormats = Array(MemoryData.operationWant(primDesc, Query.DiffSrcPd)) - updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(primDesc, + updateGradInputPrimitives = Array(MklDnnMemory.PrimitiveCreate2(primDesc, Array(_inputFormats(0), _gradOutputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), Array(0, 0, 0), 3, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) gradInput = initTensor(_gradInputFormats(0)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 3b07a18b9e6..d9366162681 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -84,7 +84,7 @@ class Linear( val outputShape = Array(inputs(0).shape(0), outputSize) - MklDnn.MemoryDescInit(inputShape.length, inputShape, + MklDnnMemory.MemoryDescInit(inputShape.length, inputShape, DataType.F32, Memory.Format.any) val src = NativeData(inputShape, Memory.Format.any) @@ -92,13 +92,13 @@ class Linear( val bis = NativeData(bias.size(), Memory.Format.x) val dst = NativeData(outputShape, Memory.Format.any) - val desc = MklDnn.LinearForwardDescInit( + val desc = MklDnnMemory.LinearForwardDescInit( PropKind.Forward, src.getMemoryDescription(), wei.getMemoryDescription(), bis.getMemoryDescription(), dst.getMemoryDescription()) - forwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, 0) + forwardPrimDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, 0) val List(realSrc, realWei, realDst) = List(Query.SrcPd, Query.WeightsPd, Query.DstPd).map {x => MemoryData.operationWant(forwardPrimDesc, x) @@ -118,7 +118,7 @@ class Linear( val indexes = Array.fill(srcs.length)(0) val dsts = Array(realDst.getPrimitive(runtime)) - val primitive = MklDnn.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, srcs.length, + val primitive = MklDnnMemory.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, srcs.length, dsts, dsts.length) updateOutputMemoryPrimitives = srcs ++ dsts @@ -168,11 +168,11 @@ class Linear( val bis = NativeData(bias.size(), Memory.Format.x) val dst = NativeData(outputShape, Memory.Format.any) - val desc = MklDnn.LinearBackwardDataDescInit( + val desc = MklDnnMemory.LinearBackwardDataDescInit( src.getMemoryDescription(), wei.getMemoryDescription(), grad(0).getMemoryDescription()) - val backwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) + val backwardPrimDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) val List(realDiffSrc, realWei, realDiffDst) = List(Query.DiffSrcPd, Query.WeightsPd, Query.DiffDstPd).map { x => @@ -183,7 +183,7 @@ class Linear( val indexes = Array.fill(srcs.length)(0) val dsts = Array(realDiffSrc.getPrimitive(runtime)) - val primitive = MklDnn.PrimitiveCreate2(backwardPrimDesc, srcs, indexes, srcs.length, + val primitive = MklDnnMemory.PrimitiveCreate2(backwardPrimDesc, srcs, indexes, srcs.length, dsts, dsts.length) updateGradInputMemoryPrimitives = srcs ++ dsts @@ -215,10 +215,10 @@ class Linear( val bis = NativeData(bias.size(), Memory.Format.x) val dst = NativeData(outputShape, Memory.Format.any) - val desc = MklDnn.LinearBackwardWeightsDescInit( + val desc = MklDnnMemory.LinearBackwardWeightsDescInit( src.getMemoryDescription(), wei.getMemoryDescription(), bis.getMemoryDescription(), dst.getMemoryDescription()) - val gradWeightPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) + val gradWeightPrimDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) val List(realWei, realDiffDst) = List(Query.DiffWeightsPd, Query.DiffDstPd).map { x => MemoryData.operationWant(gradWeightPrimDesc, x) @@ -235,7 +235,7 @@ class Linear( val indexes = Array.fill(srcs.length)(0) val dsts = Array(realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) - val primitive = MklDnn.PrimitiveCreate2(gradWeightPrimDesc, srcs, indexes, srcs.length, + val primitive = MklDnnMemory.PrimitiveCreate2(gradWeightPrimDesc, srcs, indexes, srcs.length, dsts, dsts.length) updateGradWMemoryPrimitives = srcs ++ dsts @@ -297,10 +297,6 @@ class Linear( override def zeroGradParameters(): Unit = { } - override def release(): Unit = { - super.release() - List(weight, bias, gradWeight, gradBias).foreach(_.release()) - } } object Linear { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala index cf51a0361f2..945353019f0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala @@ -83,13 +83,13 @@ class MaxPooling( PropKind.ForwardTraining } - val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), inputs(0).dataType, + val outputMD = MklDnnMemory.MemoryDescInit(4, Array(n, c, oh, ow), inputs(0).dataType, Memory.Format.any) - val description = MklDnn.PoolingForwardDescInit( + val description = MklDnnMemory.PoolingForwardDescInit( kind, AlgKind.PoolingMax, _inputFormats(0).getMemoryDescription(), outputMD, strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) - fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + fwdPD = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, 0L) _outputFormats = Array(MemoryData.primitiveOutput(fwdPD)) output = initTensor(_outputFormats(0)) @@ -102,7 +102,7 @@ class MaxPooling( fwdMemPrims = Array(_inputFormats(0), _outputFormats(0)).map(_.getPrimitive(runtime)) } - updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPD, + updateOutputPrimitives = Array(MklDnnMemory.PrimitiveCreate2(fwdPD, _inputFormats.map(_.getPrimitive(runtime)), Array(0), 1, fwdMemPrims.drop(1), fwdMemPrims.length - 1)) // if it's training, should have output and workspace primitive memory @@ -116,14 +116,14 @@ class MaxPooling( _gradOutputFormatsForWeight = _gradOutputFormats val strides = Array(dW, dH) val kernel = Array(kH, kW) - val description = MklDnn.PoolingBackwardDescInit(AlgKind.PoolingMax, + val description = MklDnnMemory.PoolingBackwardDescInit(AlgKind.PoolingMax, _inputFormats(0).getMemoryDescription(), _gradOutputFormats(0).getMemoryDescription(), strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) - val pd = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD) + val pd = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, fwdPD) _gradInputFormats = Array(MemoryData.operationWant(pd, Query.DiffSrcPd)) - updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(pd, + updateGradInputPrimitives = Array(MklDnnMemory.PrimitiveCreate2(pd, Array(_gradOutputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), Array(0, 0), 2, _gradInputFormats.map(_.getPrimitive(runtime)), 1)) gradInput = initTensor(_gradInputFormats(0)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index 18cf5d5a525..295cb7c813b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -47,28 +47,28 @@ sealed trait MemoryData extends Serializable { @transient private var primitiveDesc: Long = UNDEFINED @transient private var description: Long = UNDEFINED - def getMemoryDescription(): Long = { + def getMemoryDescription()(implicit owner: MemoryOwner): Long = { if (description == UNDEFINED || description == ERROR) { checkConsistency(shape, layout) - description = MklDnn.MemoryDescInit(shape.length, shape, dataType, layout) + description = MklDnnMemory.MemoryDescInit(shape.length, shape, dataType, layout) } description } - def getPrimitiveDescription(runtime: MklDnnRuntime): Long = { + def getPrimitiveDescription(runtime: MklDnnRuntime)(implicit owner: MemoryOwner): Long = { require(runtime != null, s"Have you initialized the MklDnnRuntime?") if (primitiveDesc == UNDEFINED || primitiveDesc == ERROR) { primitiveDesc = - MklDnn.MemoryPrimitiveDescCreate(getMemoryDescription(), runtime.engine) + MklDnnMemory.MemoryPrimitiveDescCreate(getMemoryDescription(), runtime.engine) } primitiveDesc } - def getPrimitive(runtime: MklDnnRuntime): Long = { + def getPrimitive(runtime: MklDnnRuntime)(implicit owner: MemoryOwner): Long = { require(runtime != null, s"Have you initialized the MklDnnRuntime?") if (primitive == UNDEFINED || primitive == ERROR) { primitive = - MklDnn.PrimitiveCreate0(getPrimitiveDescription(runtime)) + MklDnnMemory.PrimitiveCreate0(getPrimitiveDescription(runtime)) } primitive } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryOwner.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryOwner.scala new file mode 100644 index 00000000000..4a580358939 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryOwner.scala @@ -0,0 +1,51 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.mkldnn + +import scala.collection.mutable.ArrayBuffer + + +/** + * The trait for resources that need to be released + */ +private[bigdl] trait Releasable { + def release(): Unit +} + +/** + * This trait is a owner of the resources that need to be released. + * It will track all Releasable resources (Primitives, tensor, ReorderMemory). + * You can call releaseResources to release all the + * resources at once. These resources will require an implicit MemoryOwner at + * the constructors. The constructors of the resources will register themselves to the MemoryOwner. + * For DNN Layer classes, they extends MemoryOwner and have a implicit value of "this" as a + * MemoryOwner. ReorderMemory is a kind of special resource. They can be a normal layer or a + * resource of another layer. + */ +private[bigdl] trait MemoryOwner { + @transient + private lazy val _resources: ArrayBuffer[Releasable] = + new ArrayBuffer[Releasable]() + + def registerResource(m: Releasable): Unit = { + _resources.append(m) + } + + def releaseResources(): Unit = { + _resources.foreach(_.release()) + _resources.clear() + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnMemory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnMemory.scala new file mode 100644 index 00000000000..18363940893 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnMemory.scala @@ -0,0 +1,320 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.mkl.MklDnn + +abstract class MklDnnNativeMemory(protected var __ptr: Long)(implicit owner: MemoryOwner) +extends Releasable { + private val UNDEFINED: Long = -1 + private val ERROR: Long = 0 + + owner.registerResource(this) + + def isUndefOrError : Boolean = __ptr == UNDEFINED || __ptr == ERROR + def release(): Unit = { + if (!isUndefOrError) { + doRelease() + reset() + } + } + + def doRelease(): Unit + def ptr: Long = __ptr + def reset(): Unit = { + __ptr = ERROR + } + +} +class MklMemoryPrimitiveDesc(_ptr: Long)(implicit owner: MemoryOwner) + extends MklDnnNativeMemory(_ptr) { + def doRelease(): Unit = MklDnn.PrimitiveDescDestroy(ptr) +} + +class MklMemoryAttr(_ptr: Long)(implicit owner: MemoryOwner) + extends MklDnnNativeMemory(_ptr) { + def doRelease(): Unit = MklDnn.DestroyAttr(ptr) +} + +class MklMemoryPostOps(_ptr: Long)(implicit owner: MemoryOwner) + extends MklDnnNativeMemory(_ptr) { + def doRelease(): Unit = MklDnn.DestroyPostOps(ptr) +} + +// All *DescInit memory objects share the same dealloactor +class MklMemoryDescInit(_ptr: Long)(implicit owner: MemoryOwner) + extends MklDnnNativeMemory(_ptr) { + def doRelease(): Unit = MklDnn.FreeMemoryDescInit(ptr) +} + +class MklMemoryPrimitive(_ptr: Long)(implicit owner: MemoryOwner) + extends MklDnnNativeMemory(_ptr) { + def doRelease(): Unit = MklDnn.PrimitiveDestroy(ptr) +} + + +object MklDnnMemory { + + // scalastyle:off + def MemoryDescInit(ndims: Int, dims: Array[Int], dataType: Int, dataFormat: Int) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.MemoryDescInit(ndims, dims, dataType, dataFormat)).ptr + } + + def EltwiseForwardDescInit(propKind: Int, algKind: Int, srcDesc: Long, alpha: Float, + beta: Float)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.EltwiseForwardDescInit(propKind, algKind, srcDesc, alpha, beta)).ptr + } + + def EltwiseBackwardDescInit(algKind: Int, diffDataDesc: Long, dataDesc: Long, alpha: Float, + beta: Float)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.EltwiseBackwardDescInit(algKind, diffDataDesc, dataDesc, alpha, beta)).ptr + } + + def LinearForwardDescInit(propKind: Int, srcMemDesc: Long, weightMemDesc: Long, + biasMemDesc: Long, dstMemDesc: Long)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.LinearForwardDescInit(propKind, srcMemDesc, weightMemDesc, biasMemDesc, dstMemDesc)).ptr + } + + def LinearBackwardDataDescInit(diffSrcMemDesc: Long, weightMemDesc: Long, diffDstMemDesc: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.LinearBackwardDataDescInit(diffSrcMemDesc, weightMemDesc, diffDstMemDesc)).ptr + } + + def LinearBackwardWeightsDescInit(srcMemDesc: Long, diffWeightMemDesc: Long, + diffBiasMemDesc: Long, diffDstMemDesc: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.LinearBackwardWeightsDescInit(srcMemDesc, + diffWeightMemDesc, diffBiasMemDesc, diffDstMemDesc)).ptr + } + + def BatchNormForwardDescInit(propKind: Int, srcMemDesc: Long, epsilon: Float, flags: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.BatchNormForwardDescInit(propKind, srcMemDesc, epsilon, flags)).ptr + } + + def BatchNormBackwardDescInit(prop_kind: Int, diffDstMemDesc: Long, srcMemDesc: Long, + epsilon: Float, flags: Long)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.BatchNormBackwardDescInit(prop_kind, diffDstMemDesc, srcMemDesc, epsilon, flags)).ptr + } + + def SoftMaxForwardDescInit(prop_kind: Int, dataDesc: Long, axis: Int) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.SoftMaxForwardDescInit(prop_kind, dataDesc, axis)).ptr + } + + def ConvForwardDescInit(prop_kind: Int, alg_kind: Int, src_desc: Long, weights_desc: Long, + bias_desc: Long, dst_desc: Long, strides: Array[Int], padding_l: Array[Int], + padding_r: Array[Int], padding_kind: Int)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.ConvForwardDescInit(prop_kind, alg_kind, src_desc, weights_desc, + bias_desc, dst_desc, strides, padding_l, + padding_r, padding_kind)).ptr + } + + def DilatedConvForwardDescInit(prop_kind: Int, alg_kind: Int, src_desc: Long, + weights_desc: Long, bias_desc: Long, dst_desc: Long, strides: Array[Int], + dilates: Array[Int], padding_l: Array[Int], padding_r: Array[Int], padding_kind: Int) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.DilatedConvForwardDescInit(prop_kind, alg_kind, src_desc, + weights_desc, bias_desc, dst_desc, strides, + dilates, padding_l, padding_r, padding_kind)).ptr + } + + def ConvBackwardWeightsDescInit(alg_kind: Int, src_desc: Long, diff_weights_desc: Long, + diff_bias_desc: Long, diff_dst_desc: Long, strides: Array[Int], padding_l: Array[Int], + padding_r: Array[Int], padding_kind: Int)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.ConvBackwardWeightsDescInit(alg_kind, src_desc, diff_weights_desc, + diff_bias_desc, diff_dst_desc, strides, padding_l, + padding_r, padding_kind)).ptr + } + + def DilatedConvBackwardWeightsDescInit(alg_kind: Int, src_desc: Long, diff_weights_desc: Long, + diff_bias_desc: Long, diff_dst_desc: Long, strides: Array[Int], dilates: Array[Int], + padding_l: Array[Int], padding_r: Array[Int], padding_kind: Int) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.DilatedConvBackwardWeightsDescInit(alg_kind: Int, src_desc: Long, + diff_weights_desc: Long, + diff_bias_desc: Long, diff_dst_desc: Long, strides: Array[Int], dilates: Array[Int], + padding_l: Array[Int], padding_r: Array[Int], padding_kind: Int)).ptr + } + + def ConvBackwardDataDescInit(alg_kind: Int, diff_src_desc: Long, weights_desc: Long, + diff_dst_desc: Long, strides: Array[Int], padding_l: Array[Int], padding_r: Array[Int], + padding_kind: Int)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.ConvBackwardDataDescInit(alg_kind: Int, diff_src_desc: Long, weights_desc: Long, + diff_dst_desc: Long, strides: Array[Int], padding_l: Array[Int], padding_r: Array[Int], + padding_kind: Int)).ptr + } + + def DilatedConvBackwardDataDescInit(alg_kind: Int, diff_src_desc: Long, weights_desc: Long, + diff_dst_desc: Long, strides: Array[Int], padding_l: Array[Int], dilates: Array[Int], + padding_r: Array[Int], padding_kind: Int)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.DilatedConvBackwardDataDescInit(alg_kind: Int, diff_src_desc: Long, weights_desc: Long, + diff_dst_desc: Long, strides: Array[Int], padding_l: Array[Int], dilates: Array[Int], + padding_r: Array[Int], padding_kind: Int)).ptr + } + + def PoolingForwardDescInit(prop_kind: Int, alg_kind: Int, src_desc: Long, dst_desc: Long, + strides: Array[Int], kernel: Array[Int], padding_l: Array[Int], padding_r: Array[Int], + padding_kind: Int)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.PoolingForwardDescInit(prop_kind: Int, alg_kind: Int, src_desc: Long, dst_desc: Long, + strides: Array[Int], kernel: Array[Int], padding_l: Array[Int], padding_r: Array[Int], + padding_kind: Int)).ptr + } + + def PoolingBackwardDescInit(alg_kind: Int, diff_src_desc: Long, diff_dst_desc: Long, + strides: Array[Int], kernel: Array[Int], padding_l: Array[Int], padding_r: Array[Int], + padding_kind: Int)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.PoolingBackwardDescInit(alg_kind: Int, diff_src_desc: Long, diff_dst_desc: Long, + strides: Array[Int], kernel: Array[Int], padding_l: Array[Int], padding_r: Array[Int], + padding_kind: Int)).ptr + } + + def LRNForwardDescInit(prop_kind: Int, alg_kind: Int, data_desc: Long, local_size: Int, + alpha: Float, beta: Float, k: Float)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.LRNForwardDescInit(prop_kind: Int, alg_kind: Int, data_desc: Long, local_size: Int, + alpha: Float, beta: Float, k: Float)).ptr + } + + def LRNBackwardDescInit(alg_kind: Int, diff_data_desc: Long, data_desc: Long, local_size: Int, + alpha: Float, beta: Float, k: Float)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.LRNBackwardDescInit(alg_kind: Int, diff_data_desc: Long, data_desc: Long, + local_size: Int, alpha: Float, beta: Float, k: Float)).ptr + } + + def RNNCellDescInit(kind: Int, f: Int, flags: Int, alpha: Float, clipping: Float) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.RNNCellDescInit(kind: Int, f: Int, flags: Int, alpha: Float, clipping: Float)).ptr + } + + def RNNForwardDescInit(prop_kind: Int, rnn_cell_desc: Long, direction: Int, + src_layer_desc: Long, src_iter_desc: Long, weights_layer_desc: Long, weights_iter_desc: Long, + bias_desc: Long, dst_layer_desc: Long, dst_iter_desc: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.RNNForwardDescInit(prop_kind: Int, rnn_cell_desc: Long, direction: Int, + src_layer_desc: Long, src_iter_desc: Long, weights_layer_desc: Long, weights_iter_desc: Long, + bias_desc: Long, dst_layer_desc: Long, dst_iter_desc: Long)).ptr + } + + def RNNBackwardDescInit(prop_kind: Int, rnn_cell_desc: Long, direction: Int, + src_layer_desc: Long, src_iter_desc: Long, weights_layer_desc: Long, weights_iter_desc: Long, + bias_desc: Long, dst_layer_desc: Long, dst_iter_desc: Long, diff_src_layer_desc: Long, + diff_src_iter_desc: Long, diff_weights_layer_desc: Long, diff_weights_iter_desc: Long, + diff_bias_desc: Long, diff_dst_layer_desc: Long, diff_dst_iter_desc: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit( + MklDnn.RNNBackwardDescInit(prop_kind: Int, rnn_cell_desc: Long, direction: Int, + src_layer_desc: Long, src_iter_desc: Long, weights_layer_desc: Long, weights_iter_desc: Long, + bias_desc: Long, dst_layer_desc: Long, dst_iter_desc: Long, diff_src_layer_desc: Long, + diff_src_iter_desc: Long, diff_weights_layer_desc: Long, diff_weights_iter_desc: Long, + diff_bias_desc: Long, diff_dst_layer_desc: Long, diff_dst_iter_desc: Long)).ptr + } + + def ReorderPrimitiveDescCreate(input: Long, output: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitiveDesc( + MklDnn.ReorderPrimitiveDescCreate(input, output)).ptr + } + + def ReorderPrimitiveDescCreateV2(input: Long, output: Long, attr: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitiveDesc( + MklDnn.ReorderPrimitiveDescCreateV2(input, output, attr)).ptr + } + + def PrimitiveCreate0(desc: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitive( + MklDnn.PrimitiveCreate0(desc)).ptr + } + + def PrimitiveCreate2(desc: Long, inputs: Array[Long], indexes: Array[Int], inputLen: Int, + outputs: Array[Long], outputLen: Int)(implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitive( + MklDnn.PrimitiveCreate2(desc: Long, inputs: Array[Long], indexes: Array[Int], inputLen: Int, + outputs: Array[Long], outputLen: Int)).ptr + } + + def PrimitiveDescCreate(opDesc: Long, engine: Long, hingForwardPrimitiveDesc: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitiveDesc(MklDnn.PrimitiveDescCreate(opDesc, engine, hingForwardPrimitiveDesc)).ptr + } + + def PrimitiveDescCreateV2(opDesc: Long, attr: Long, engine: Long, + hingForwardPrimitiveDesc: Long)(implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitiveDesc( + MklDnn.PrimitiveDescCreateV2(opDesc: Long, attr: Long, engine: Long, + hingForwardPrimitiveDesc: Long)).ptr + } + + def MemoryPrimitiveDescCreate(desc: Long, engine: Long) + (implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitiveDesc( + MklDnn.MemoryPrimitiveDescCreate(desc, engine)).ptr + } + + def ConcatPrimitiveDescCreate(output_desc: Long, n: Int, concat_dimension: Int, + input_pds: Array[Long])(implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitiveDesc( + MklDnn.ConcatPrimitiveDescCreate(output_desc: Long, n: Int, concat_dimension: Int, + input_pds: Array[Long])).ptr + } + + def ViewPrimitiveDescCreate(memory_primitive_desc: Long, dims: Array[Int], offsets: Array[Int]) + (implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitiveDesc( + MklDnn.ViewPrimitiveDescCreate(memory_primitive_desc: Long, dims: Array[Int], offsets: Array[Int])).ptr + } + + def SumPrimitiveDescCreate(output_mem_desc: Long, n: Int, scales: Array[Float], + input_pds: Array[Long])(implicit owner: MemoryOwner): Long = { + new MklMemoryPrimitiveDesc( + MklDnn.SumPrimitiveDescCreate(output_mem_desc: Long, n: Int, scales: Array[Float], + input_pds: Array[Long])).ptr + } + + def CreateAttr()(implicit owner: MemoryOwner): Long = { + new MklMemoryAttr( + MklDnn.CreateAttr()).ptr + } + def CreatePostOps()(implicit owner: MemoryOwner): Long = { + new MklMemoryPostOps( + MklDnn.CreatePostOps()).ptr + } +// scalastyle:on +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala index 71a68416213..2e581570527 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala @@ -207,15 +207,15 @@ class RNN( rnnCellDesc = mode match { case AlgKind.VanillaLstm => - MklDnn.RNNCellDescInit(AlgKind.VanillaLstm, f, flags, alpha, clipping) + MklDnnMemory.RNNCellDescInit(AlgKind.VanillaLstm, f, flags, alpha, clipping) case _ => throw new UnsupportedOperationException("Not support such RNN cell. " + "Cell type: " + mode) } - val description = MklDnn.RNNForwardDescInit(kind, rnnCellDesc, direction, src_layer_MD, + val description = MklDnnMemory.RNNForwardDescInit(kind, rnnCellDesc, direction, src_layer_MD, src_iter_MD, weights_layer_MD, weights_iter_MD, bis_MD, dist_layer_MD, dist_iter_MD) - fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + fwdPD = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, 0L) val realSrc = MemoryData.operationWant(fwdPD, Query.SrcPd, 0) val realSrc_iter = MemoryData.operationWant(fwdPD, Query.SrcPd, 1) @@ -265,7 +265,8 @@ class RNN( realDst_iter.getPrimitive(runtime)) } - val primitive = MklDnn.PrimitiveCreate2(fwdPD, srcs, indexes, srcs.length, dsts, dsts.length) + val primitive = MklDnnMemory.PrimitiveCreate2(fwdPD, srcs, indexes, + srcs.length, dsts, dsts.length) updateOutputMemoryPrimitives = srcs ++ dsts updateOutputPrimitives = Array(primitive) @@ -343,7 +344,7 @@ class RNN( val diff_dist_layer_MD = diff_dist_layer.getMemoryDescription() val diff_dist_iter_MD = diff_dist_iter.getMemoryDescription() - val description = MklDnn.RNNBackwardDescInit(PropKind.Backward, rnnCellDesc, + val description = MklDnnMemory.RNNBackwardDescInit(PropKind.Backward, rnnCellDesc, direction, src_layer_bw_MD, src_iter_bw_MD, weights_layer_bw_MD, weights_iter_bw_MD, bis_bw_MD, @@ -355,7 +356,7 @@ class RNN( diff_dist_iter_MD ) - val bwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD) + val bwdPD = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, fwdPD) val realSrc = MemoryData.operationWant(bwdPD, Query.SrcPd, 0) val realSrc_iter = MemoryData.operationWant(bwdPD, Query.SrcPd, 1) @@ -411,7 +412,7 @@ class RNN( realDiffBias.getPrimitive(runtime) ) - val primitive = MklDnn.PrimitiveCreate2(bwdPD, srcs, indexes, srcs.length, + val primitive = MklDnnMemory.PrimitiveCreate2(bwdPD, srcs, indexes, srcs.length, dsts, dsts.length) updateGradInputMemoryPrimitives = srcs ++ dsts @@ -471,12 +472,6 @@ class RNN( override def zeroGradParameters(): Unit = { } - override def release(): Unit = { - super.release() - List(weight, bias, weight_i, gradWeight, gradBias, gradWeight_i).foreach(_.release()) - List(src_i, dst_i, gradsrc_i, graddst_i).foreach(_.release()) - reorderManager.release() - } } object RNN{ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala index 7981d08a757..65dfa67fa40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReLU.scala @@ -25,12 +25,12 @@ class ReLU(value: Float = 0.0f) extends MklDnnLayer with MklInt8Convertible { override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = singleNativeData(inputs) - val description = MklDnn.EltwiseForwardDescInit( + val description = MklDnnMemory.EltwiseForwardDescInit( PropKind.Forward, AlgKind.EltwiseRelu, _inputFormats(0).getMemoryDescription(), value, 0) - fwdPrimDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L) + fwdPrimDesc = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, 0L) _outputFormats = Array(MemoryData.primitiveOutput(fwdPrimDesc)) updateOutputPrimitives = Array( - MklDnn.PrimitiveCreate2(fwdPrimDesc, + MklDnnMemory.PrimitiveCreate2(fwdPrimDesc, Array(_inputFormats(0).getPrimitive(runtime)), Array(0), _inputFormats.length, _outputFormats.map(_.getPrimitive(runtime)), _outputFormats.length) ) @@ -41,14 +41,14 @@ class ReLU(value: Float = 0.0f) extends MklDnnLayer with MklInt8Convertible { override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { _gradOutputFormats = singleNativeData(grad) _gradOutputFormatsForWeight = _gradOutputFormats - val description = MklDnn.EltwiseBackwardDescInit(AlgKind.EltwiseRelu, + val description = MklDnnMemory.EltwiseBackwardDescInit(AlgKind.EltwiseRelu, _gradOutputFormats(0).getMemoryDescription(), _inputFormats(0).getMemoryDescription(), value, 0) require(fwdPrimDesc != UNDEFINED, "You should call initFwdPrimitives first") - val primDesc = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPrimDesc) + val primDesc = MklDnnMemory.PrimitiveDescCreate(description, runtime.engine, fwdPrimDesc) _gradInputFormats = Array(MemoryData.operationWant(primDesc, Query.DiffSrcPd)) updateGradInputPrimitives = Array( - MklDnn.PrimitiveCreate2(primDesc, Array(_inputFormats(0), + MklDnnMemory.PrimitiveCreate2(primDesc, Array(_inputFormats(0), _gradOutputFormats(0)).map(_.getPrimitive(runtime)), Array(0), 2, _gradInputFormats.map(_.getPrimitive(runtime)), _gradInputFormats.length)) gradInput = initTensor(_gradInputFormats(0)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala index 9ad4076806e..d46d2e8f569 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderManager.scala @@ -19,10 +19,10 @@ import com.intel.analytics.bigdl.mkl.DataType import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.T - import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer -private[mkldnn] class ReorderManager() { +private[mkldnn] class ReorderManager() (implicit owner: MemoryOwner) { // (MemoryFormatId, TargetFormat) -> Reorder val reorders = mutable.HashMap[(Int, MemoryData), ReorderMemory]() // ReorderId -> RefCount @@ -117,7 +117,5 @@ private[mkldnn] class ReorderManager() { } } - def release(): Unit = { - reorders.values.foreach(_.release()) - } + def release(): Unit = { } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala index 10053faa714..2199001e6b2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemory.scala @@ -20,9 +20,16 @@ import com.intel.analytics.bigdl.nn.abstractnn.{Activity, TensorModule} import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, - gradInputFormat: MemoryData, gradOutputFormat: MemoryData -) extends MklDnnLayer { - + gradInputFormat: MemoryData, gradOutputFormat: MemoryData, + memoryOwner: MemoryOwner = null) extends MklDnnLayer with Releasable { + + // ReorderMemory is a special layer. It can be owned by other layers. + // So there is an optional MemoryOwner that can be null. + // If it is null, this means the ReorderMemory is a normal layer. + // If it is not null, it means ReorderMemory is owned by another layer + if (memoryOwner != null) { + memoryOwner.registerResource(this) + } _outputFormats = Array(outputFormat) _gradInputFormats = Array(gradInputFormat) @@ -66,7 +73,7 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, } private def createInt8PrimDesc(): Long = { - val attr = MklDnn.CreateAttr() + val attr = MklDnnMemory.CreateAttr() MklDnn.AttrSetIntOutputRoundMode(attr, 1) if (realOutput(0).scales == null || realOutput(0).scales.isEmpty) { @@ -89,7 +96,7 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, MklDnn.AttrSetOutputScales(attr, realOutput(0).scales.length, realOutput(0).mask, realOutput(0).scales) - MklDnn.ReorderPrimitiveDescCreateV2( + MklDnnMemory.ReorderPrimitiveDescCreateV2( realInput(0).getPrimitiveDescription(runtime), realOutput(0).getPrimitiveDescription(runtime), attr) @@ -129,14 +136,14 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, outputFormats()(0).dataType == DataType.F32 val fwdReorderPrimDesc = if (noInt8Formats) { - MklDnn.ReorderPrimitiveDescCreate( + MklDnnMemory.ReorderPrimitiveDescCreate( realInput(0).getPrimitiveDescription(runtime), realOutput(0).getPrimitiveDescription(runtime)) } else { createInt8PrimDesc() } - val fwdReorderPrim = MklDnn.PrimitiveCreate2(fwdReorderPrimDesc, + val fwdReorderPrim = MklDnnMemory.PrimitiveCreate2(fwdReorderPrimDesc, Array(realInput(0).getPrimitive(runtime)), Array(0), 1, Array(realOutput(0).getPrimitive(runtime)), 1) @@ -191,10 +198,10 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, } } - val bwdReorderPrimDesc = MklDnn.ReorderPrimitiveDescCreate( + val bwdReorderPrimDesc = MklDnnMemory.ReorderPrimitiveDescCreate( realgradOutput(0).getPrimitiveDescription(runtime), realgradInput(0).getPrimitiveDescription(runtime)) - val bwdReorderPrim = MklDnn.PrimitiveCreate2(bwdReorderPrimDesc, + val bwdReorderPrim = MklDnnMemory.PrimitiveCreate2(bwdReorderPrimDesc, realgradOutput.map(_.getPrimitive(runtime)), Array(0), 1, realgradInput.map(_.getPrimitive(runtime)), 1) @@ -221,16 +228,17 @@ class ReorderMemory(inputFormat: MemoryData, outputFormat: MemoryData, } object ReorderMemory { - def apply(inputFormat: MemoryData, outputFormat: MemoryData, gradInputFormat: MemoryData, - gradOutputFomat: MemoryData): ReorderMemory = { - new ReorderMemory(inputFormat, outputFormat, gradInputFormat, gradOutputFomat) - } - - def apply(outputFormat: MemoryData, gradInputFormat: MemoryData): ReorderMemory = { - new ReorderMemory(null, outputFormat, gradInputFormat, null) + // We don't use "apply" as the function name here. The reason is that scala does not + // allow overloaded function (functions having the same name) with default parameters + // Hence, we bypass this issue by defining two functions. + def create(inputFormat: MemoryData, outputFormat: MemoryData, gradInputFormat: MemoryData, + gradOutputFomat: MemoryData)(implicit memoryOwner: MemoryOwner = null): ReorderMemory = { + new ReorderMemory(inputFormat, outputFormat, gradInputFormat, gradOutputFomat, memoryOwner) } - def apply(outputFormat: MemoryData): ReorderMemory = { - new ReorderMemory(null, outputFormat, null, null) + def apply(outputFormat: MemoryData, gradInputFormat: MemoryData = null) + (implicit memoryOwner: MemoryOwner = null): ReorderMemory = { + new ReorderMemory(null, outputFormat, gradInputFormat, null, + memoryOwner) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index e9fdc1afca4..2d1591631c4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -80,9 +80,9 @@ class SoftMax(val axis: Int = -1) extends MklDnnLayer { _inputFormats(0) } - val desc = MklDnn.SoftMaxForwardDescInit(PropKind.ForwardInference, + val desc = MklDnnMemory.SoftMaxForwardDescInit(PropKind.ForwardInference, localInputFormat.getMemoryDescription(), if (axis == -1) defaultAxis else axis) - val forwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, 0L) + val forwardPrimDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, 0L) _outputFormats = if (inputs(0).shape.length ==3 && inputs(0).layout == Memory.Format.ntc) { @@ -96,8 +96,8 @@ class SoftMax(val axis: Int = -1) extends MklDnnLayer { val indexes = Array(0) val dsts = Array(_outputFormats(0).getPrimitive(runtime)) - val primitive = MklDnn.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, srcs.length, dsts, - dsts.length) + val primitive = MklDnnMemory.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, + srcs.length, dsts, dsts.length) updateOutputPrimitives = Array(primitive) updateOutputMemoryPrimitives = srcs ++ dsts diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 3259bf54b49..06580eb7445 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -146,26 +146,26 @@ class SpatialBatchNormalization( initPhase(phase) forwardDesc = modelPhase match { case TrainingPhase => - MklDnn.BatchNormForwardDescInit(PropKind.Forward, + MklDnnMemory.BatchNormForwardDescInit(PropKind.Forward, src.getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_scaleshift) case InferencePhase => // we always use the weight and bias / scale and offset. So the flags should be combined // with use_scaleshift and use_global_stats. - MklDnn.BatchNormForwardDescInit(PropKind.ForwardInference, + MklDnnMemory.BatchNormForwardDescInit(PropKind.ForwardInference, src.getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_global_stats | MklDnn.BatchNormFlag.mkldnn_use_scaleshift) case _ => throw new UnsupportedOperationException } val primDesc = if (relu) { - val postOps = MklDnn.CreatePostOps() + val postOps = MklDnnMemory.CreatePostOps() MklDnn.PostOpsAppendEltwise(postOps, 1.0f, AlgKind.EltwiseRelu, 0.0f, 0.0f) - val attr = MklDnn.CreateAttr() + val attr = MklDnnMemory.CreateAttr() MklDnn.AttrSetPostOps(attr, postOps) - MklDnn.PrimitiveDescCreateV2(forwardDesc, attr, runtime.engine, 0) + MklDnnMemory.PrimitiveDescCreateV2(forwardDesc, attr, runtime.engine, 0) // TODO we should destroy these ops } else { - MklDnn.PrimitiveDescCreate(forwardDesc, runtime.engine, 0) + MklDnnMemory.PrimitiveDescCreate(forwardDesc, runtime.engine, 0) } if (_inputFormats == null) { @@ -192,7 +192,8 @@ class SpatialBatchNormalization( } val indexes = Array.fill(srcs.length)(0) - val primitive = MklDnn.PrimitiveCreate2(primDesc, srcs, indexes, srcs.length, dsts, dsts.length) + val primitive = MklDnnMemory.PrimitiveCreate2(primDesc, srcs, indexes, + srcs.length, dsts, dsts.length) updateOutputMemoryPrimitives = srcs ++ dsts updateOutputPrimitives = Array(primitive) @@ -289,7 +290,7 @@ class SpatialBatchNormalization( // [PERF] the format of gradInput should be the same as input val backwardDesc = modelPhase match { case TrainingPhase => - MklDnn.BatchNormBackwardDescInit(PropKind.Backward, + MklDnnMemory.BatchNormBackwardDescInit(PropKind.Backward, inputFormats()(0).getMemoryDescription(), inputFormats()(0).getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_scaleshift) @@ -299,7 +300,7 @@ class SpatialBatchNormalization( val gradWeightAndBias: NativeData = NativeData(Array(nOutput * 2), Memory.Format.x) val gradWeightPrimitive = gradWeightAndBias.getPrimitive(runtime) - val primDesc = MklDnn.PrimitiveDescCreate(backwardDesc, runtime.engine, 0) + val primDesc = MklDnnMemory.PrimitiveDescCreate(backwardDesc, runtime.engine, 0) _gradInputFormats = Array(MemoryData.operationWant(primDesc, Query.DiffSrcPd)) @@ -312,7 +313,7 @@ class SpatialBatchNormalization( val indexes = Array.fill(srcs.length)(0) val dsts = Array(gradInputFormats()(0), gradWeightAndBias).map(_.getPrimitive(runtime)) - val primitive = MklDnn.PrimitiveCreate2(primDesc, srcs, indexes, srcs.length, + val primitive = MklDnnMemory.PrimitiveCreate2(primDesc, srcs, indexes, srcs.length, dsts, dsts.length) updateGradInputMemoryPrimitives = srcs ++ dsts @@ -388,12 +389,6 @@ class SpatialBatchNormalization( } this } - - override def release(): Unit = { - super.release() - List(weightAndBias, gradWeightAndBias, runningMean, runningVariance).foreach(_.release()) - List(mean, variance).foreach(_.release()) - } } object SpatialBatchNormalization { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 7f4a9b14f1f..97a0f3e3441 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -328,7 +328,7 @@ class SpatialConvolution( val scaleWeight = this.getWeightScales().flatten.map { w => Scale.S8_MAX / w } // TODO check wether ForwardInference and ForwardTraining is the same - val desc = MklDnn.DilatedConvForwardDescInit( + val desc = MklDnnMemory.DilatedConvForwardDescInit( PropKind.ForwardTraining, AlgKind.ConvolutionDirect, src.getMemoryDescription(), wei.getMemoryDescription(), @@ -339,14 +339,14 @@ class SpatialConvolution( MklDnn.PaddingKind.mkldnnPaddingZero) forwardPrimDesc = if (relu || sum) { - val attr = MklDnn.CreateAttr() + val attr = MklDnnMemory.CreateAttr() // create output scales for s8/u8 output if (needQuantize) { setScalesOutForAttr(scaleIn, scaleOut, attr) } - val postOps = MklDnn.CreatePostOps() + val postOps = MklDnnMemory.CreatePostOps() if (sum) { val sumScale = if (needQuantize) { require(scaleOut.length == sumOp.outputFormats()(0).scales.length, @@ -363,17 +363,17 @@ class SpatialConvolution( } MklDnn.AttrSetPostOps(attr, postOps) - MklDnn.PrimitiveDescCreateV2(desc, attr, runtime.engine, 0) + MklDnnMemory.PrimitiveDescCreateV2(desc, attr, runtime.engine, 0) // TODO we should destroy these ops } else if (needQuantize) { - val attr = MklDnn.CreateAttr() + val attr = MklDnnMemory.CreateAttr() setScalesOutForAttr(scaleIn, scaleOut, attr) - MklDnn.PrimitiveDescCreateV2(desc, attr, runtime.engine, 0) + MklDnnMemory.PrimitiveDescCreateV2(desc, attr, runtime.engine, 0) // TODO we should destroy these ops } else { - MklDnn.PrimitiveDescCreate(desc, runtime.engine, 0) + MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, 0) } val List(realSrc, realWei, realDst) = List(Query.SrcPd, Query.WeightsPd, Query.DstPd).map {x => @@ -409,7 +409,7 @@ class SpatialConvolution( val indexes = Array.fill(srcs.length)(0) val dsts = Array(realDst.getPrimitive(runtime)) - val primitive = MklDnn.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, srcs.length, + val primitive = MklDnnMemory.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, srcs.length, dsts, dsts.length) updateOutputMemoryPrimitives = srcs ++ dsts @@ -478,7 +478,7 @@ class SpatialConvolution( val bis = NativeData(Array(nOutputPlane), Memory.Format.x) val dst = NativeData(outputShape, Memory.Format.any) - val desc = MklDnn.DilatedConvBackwardDataDescInit( + val desc = MklDnnMemory.DilatedConvBackwardDataDescInit( AlgKind.ConvolutionDirect, src.getMemoryDescription(), wei.getMemoryDescription(), // TODO check correctness of strides and padding @@ -487,7 +487,7 @@ class SpatialConvolution( paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) - val backwardPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) + val backwardPrimDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) val List(realDiffSrc, realWei, realDiffDst) = List(Query.DiffSrcPd, Query.WeightsPd, Query.DiffDstPd).map {x => @@ -503,7 +503,7 @@ class SpatialConvolution( val indexes = Array.fill(srcs.length)(0) val dsts = Array(realDiffSrc.getPrimitive(runtime)) - val primitive = MklDnn.PrimitiveCreate2(backwardPrimDesc, srcs, indexes, srcs.length, + val primitive = MklDnnMemory.PrimitiveCreate2(backwardPrimDesc, srcs, indexes, srcs.length, dsts, dsts.length) updateGradInputMemoryPrimitives = srcs ++ dsts @@ -548,7 +548,7 @@ class SpatialConvolution( // Use format "any" to init weight desc, otherwise maybe poor performance val gradMemoryData = NativeData(grad(0).shape, Memory.Format.any) - val desc = MklDnn.DilatedConvBackwardWeightsDescInit( + val desc = MklDnnMemory.DilatedConvBackwardWeightsDescInit( AlgKind.ConvolutionDirect, src.getMemoryDescription(), wei.getMemoryDescription(), @@ -558,7 +558,7 @@ class SpatialConvolution( paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero) - val gradWeightPrimDesc = MklDnn.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) + val gradWeightPrimDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, forwardPrimDesc) // TODO here seems some errors ?????? check the realSrc format. val List(realSrc, realWei, realDiffDst) = @@ -586,7 +586,7 @@ class SpatialConvolution( val indexes = Array.fill(srcs.length)(0) val dsts = Array(realWei.getPrimitive(runtime), bis.getPrimitive(runtime)) - val primitive = MklDnn.PrimitiveCreate2(gradWeightPrimDesc, srcs, indexes, srcs.length, + val primitive = MklDnnMemory.PrimitiveCreate2(gradWeightPrimDesc, srcs, indexes, srcs.length, dsts, dsts.length) updateGradWMemoryPrimitives = srcs ++ dsts @@ -645,12 +645,6 @@ class SpatialConvolution( override def zeroGradParameters(): Unit = { } - override def release(): Unit = { - super.release() - List(weight, bias, gradWeight, gradBias).foreach(_.release()) - if (weightForBackward != null) { weightForBackward.release() } - } - override def setQuantize(value: Boolean): this.type = { needQuantize = value this diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala index c8d635d8b7b..364242911cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala @@ -29,7 +29,8 @@ import com.intel.analytics.bigdl.tensor.{DnnTensor, FloatType, Tensor} * * @param _size the shape of Tensor, such as Array(4, 3, 224, 224) */ -private[mkldnn] class TensorMMap(_size: Array[Int]) extends Serializable { +private[mkldnn] class TensorMMap(_size: Array[Int])(implicit owner: MemoryOwner) + extends Serializable { // dense weight on heap is used to optimizer and so on, which is exposed to // AbstractModule level. val dense: Tensor[Float] = Tensor[Float](_size) @@ -114,9 +115,4 @@ private[mkldnn] class TensorMMap(_size: Array[Int]) extends Serializable { dense.size(index) } - def release(): Unit = { - if (native != null) { - native.release() - } - } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala index 7881a5e2f33..e4ccc72e759 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnStorage.scala @@ -16,9 +16,8 @@ package com.intel.analytics.bigdl.tensor import java.io.{IOException, ObjectInputStream, ObjectOutputStream} - import com.intel.analytics.bigdl.mkl.Memory - +import com.intel.analytics.bigdl.nn.mkldnn.MemoryOwner import scala.reflect._ /** @@ -27,7 +26,6 @@ import scala.reflect._ * @tparam T data type, only support float now */ private[tensor] class DnnStorage[T: ClassTag](size: Int) extends Storage[T] { - private def checkIsInstanceOf(that: Any): Boolean = { scala.reflect.classTag[T] == that } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala index a8e43c9b476..c5aa0a2c40d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -17,20 +17,21 @@ package com.intel.analytics.bigdl.tensor import breeze.linalg.{DenseMatrix, DenseVector} import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.mkldnn.{MemoryOwner, Releasable} import com.intel.analytics.bigdl.tensor.DnnTensor.DnnTensorUnsupportOperations import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.utils.Table import org.apache.spark.mllib.linalg import org.apache.spark.mllib.linalg.Matrix - import scala.reflect.ClassTag class DnnTensor[T: ClassTag]( private var _storage: DnnStorage[T], private var sizes: Array[Int] -) (implicit ev: TensorNumeric[T]) - extends DnnTensorUnsupportOperations[T]{ +) (implicit ev: TensorNumeric[T], owner: MemoryOwner) + extends DnnTensorUnsupportOperations[T] with Releasable { + owner.registerResource(this) // performance regression, the sizes.product will make the performance downgrade. private val _nElement: Int = sizes.product @@ -206,40 +207,44 @@ object DnnTensor { return true } - def apply[T: ClassTag](sizes: Array[Int])(implicit ev: TensorNumeric[T]): DnnTensor[T] = { + def apply[T: ClassTag](sizes: Array[Int])( + implicit ev: TensorNumeric[T], owner: MemoryOwner): DnnTensor[T] = { val storage = new DnnStorage[T](sizes.product) new DnnTensor[T](storage, sizes) } def apply[T: ClassTag](sizes: Array[Int], realSize: Long)( - implicit ev: TensorNumeric[T]): DnnTensor[T] = { + implicit ev: TensorNumeric[T], owner: MemoryOwner): DnnTensor[T] = { val storage = new DnnStorage[T](realSize.toInt) // FIXME if size more than int ? new DnnTensor[T](storage, sizes) } - def apply[T: ClassTag](d1: Int)(implicit ev: TensorNumeric[T]): DnnTensor[T] = { + def apply[T: ClassTag](d1: Int)( + implicit ev: TensorNumeric[T], owner: MemoryOwner): DnnTensor[T] = { val storage = new DnnStorage[T](d1) new DnnTensor[T](storage, Array(d1)) } - def apply[T: ClassTag](d1: Int, d2: Int)(implicit ev: TensorNumeric[T]): DnnTensor[T] = { + def apply[T: ClassTag](d1: Int, d2: Int)( + implicit ev: TensorNumeric[T], owner: MemoryOwner): DnnTensor[T] = { val storage = new DnnStorage[T](d1 * d2) new DnnTensor[T](storage, Array(d1, d2)) } - def apply[T: ClassTag](d1: Int, d2: Int, d3: Int)(implicit ev: TensorNumeric[T]): DnnTensor[T] = { + def apply[T: ClassTag](d1: Int, d2: Int, d3: Int)( + implicit ev: TensorNumeric[T], owner: MemoryOwner): DnnTensor[T] = { val storage = new DnnStorage[T](d1 * d2 * d3) new DnnTensor[T](storage, Array(d1, d2, d3)) } def apply[T: ClassTag](d1: Int, d2: Int, d3: Int, d4: Int)( - implicit ev: TensorNumeric[T]): DnnTensor[T] = { + implicit ev: TensorNumeric[T], owner: MemoryOwner): DnnTensor[T] = { val storage = new DnnStorage[T](d1 * d2 * d3 * d4) new DnnTensor[T](storage, Array(d1, d2, d3, d4)) } def apply[T: ClassTag](d1: Int, d2: Int, d3: Int, d4: Int, d5: Int)( - implicit ev: TensorNumeric[T]): DnnTensor[T] = { + implicit ev: TensorNumeric[T], owner: MemoryOwner): DnnTensor[T] = { val storage = new DnnStorage[T](d1 * d2 * d3 * d4 * d5) new DnnTensor[T](storage, Array(d1, d2, d3, d4, d5)) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala index 94ff412d7a2..619351d10d8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/CAddTableSpec.scala @@ -27,15 +27,15 @@ class CAddTableSpec extends BigDLSpecHelper { val layer = CAddTable() val model = Sequential() val concat = ConcatTable() - concat.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + concat.add(ReorderMemory.create(HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc))) - concat.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + concat.add(ReorderMemory.create(HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc))) model.add(concat) model.add(layer) - model.add(ReorderMemory(NativeData(Array(2, 2), Memory.Format.nc), + model.add(ReorderMemory.create(NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc))) model.compile(Phase.TrainingPhase, Array(HeapData(Array(2, 2), Memory.Format.nc))) @@ -57,6 +57,8 @@ class CAddTableSpec extends BigDLSpecHelper { } "caddtable with java serialization" should "work correctly" in { + implicit object Owner extends MemoryOwner { + } val shape = Array(2, 3, 4, 4) val _1 = Tensor(shape).rand(-1, 1) val _2 = Tensor(shape).rand(-1, 1) @@ -93,6 +95,7 @@ class CAddTableSpec extends BigDLSpecHelper { Tools.dense(cat.gradInput.toTable(1)) should be (Tools.dense(cloned.gradInput.toTable(1))) Tools.dense(cat.gradInput.toTable(2)) should be (Tools.dense(cloned.gradInput.toTable(2))) + Owner.releaseResources() } "CAddTable u8" should "be correct" in { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala index eefe96e97d8..d2324bec0b8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ConcatTableSpec.scala @@ -35,13 +35,13 @@ class ConcatTableSpec extends BigDLSpecHelper { "ConcatTable" should "be good" in { val container = ConcatTable() - container.add(ReorderMemory( + container.add(ReorderMemory.create( HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc))) val subcontainer = Sequential() - subcontainer.add(ReorderMemory( + subcontainer.add(ReorderMemory.create( HeapData(Array(3, 4), Memory.Format.nc), NativeData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala index ed75c054566..b39110203f5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/JoinTableSpec.scala @@ -25,15 +25,15 @@ class JoinTableSpec extends BigDLSpecHelper { val layer = JoinTable(1) val model = Sequential() val concat = ConcatTable() - concat.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + concat.add(ReorderMemory.create(HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc))) - concat.add(ReorderMemory(HeapData(Array(2, 2), Memory.Format.nc), + concat.add(ReorderMemory.create(HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc), HeapData(Array(2, 2), Memory.Format.nc), NativeData(Array(2, 2), Memory.Format.nc))) model.add(concat) model.add(layer) - model.add(ReorderMemory(NativeData(Array(4, 2), Memory.Format.nc), + model.add(ReorderMemory.create(NativeData(Array(4, 2), Memory.Format.nc), HeapData(Array(4, 2), Memory.Format.nc), NativeData(Array(4, 2), Memory.Format.nc), HeapData(Array(4, 2), Memory.Format.nc))) model.compile(Phase.TrainingPhase, Array(HeapData(Array(2, 2), Memory.Format.nc))) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala index b9a369e580d..c72a81de87d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/ReorderMemorySpec.scala @@ -104,7 +104,7 @@ class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { } "From heap to heap" should "be correct" in { - val layer = ReorderMemory( + val layer = ReorderMemory.create( HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), @@ -127,7 +127,7 @@ class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { val gradInputFormats = HeapData(shapeNCHW, Memory.Format.nchw) val gradOutputFormats = HeapData(shapeNHWC, Memory.Format.nhwc) - val layer = ReorderMemory(inputFormat = inputFormats, outputFormat = outputFormats, + val layer = ReorderMemory.create(inputFormat = inputFormats, outputFormat = outputFormats, gradInputFormat = gradInputFormats, gradOutputFomat = gradOutputFormats) layer.setRuntime(new MklDnnRuntime()) @@ -156,7 +156,7 @@ class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { val gradInputFormats = HeapData(shapeTNC, Memory.Format.tnc) val gradOutputFormats = HeapData(shapeNTC, Memory.Format.ntc) - val layer = ReorderMemory(inputFormat = inputFormats, outputFormat = outputFormats, + val layer = ReorderMemory.create(inputFormat = inputFormats, outputFormat = outputFormats, gradInputFormat = gradInputFormats, gradOutputFomat = gradOutputFormats) layer.setRuntime(new MklDnnRuntime()) @@ -183,7 +183,7 @@ class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { val gradInputFormats = HeapData(shapeNTC, Memory.Format.ntc) val gradOutputFormats = HeapData(shapeTNC, Memory.Format.tnc) - val layer = ReorderMemory(inputFormat = inputFormats, outputFormat = outputFormats, + val layer = ReorderMemory.create(inputFormat = inputFormats, outputFormat = outputFormats, gradInputFormat = gradInputFormats, gradOutputFomat = gradOutputFormats) layer.setRuntime(new MklDnnRuntime()) @@ -208,7 +208,7 @@ class ReorderMemorySpec extends FlatSpec with Matchers with BeforeAndAfter { val gradInputFormats = HeapData(shapeNHWC, Memory.Format.nhwc) val gradOutputFormats = HeapData(shapeNCHW, Memory.Format.nchw) - val layer = ReorderMemory(inputFormat = inputFormats, outputFormat = outputFormats, + val layer = ReorderMemory.create(inputFormat = inputFormats, outputFormat = outputFormats, gradInputFormat = gradInputFormats, gradOutputFomat = gradOutputFormats) layer.setRuntime(new MklDnnRuntime()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala index ed2301ce055..197bcc86da3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SequentialSpec.scala @@ -64,17 +64,17 @@ class SequentialSpec extends BigDLSpecHelper { } "Sequential" should "be correct when auto add memory reorder" in { - val layer1 = ReorderMemory( + val layer1 = ReorderMemory.create( HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc)) - val layer2 = ReorderMemory( + val layer2 = ReorderMemory.create( NativeData(Array(3, 4), Memory.Format.nc), NativeData(Array(3, 4), Memory.Format.io), NativeData(Array(3, 4), Memory.Format.nc), NativeData(Array(3, 4), Memory.Format.io)) - val layer3 = ReorderMemory( + val layer3 = ReorderMemory.create( HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), @@ -103,17 +103,17 @@ class SequentialSpec extends BigDLSpecHelper { } "seq with java serialization" should "work correctly" in { - val layer1 = ReorderMemory( + val layer1 = ReorderMemory.create( HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc)) - val layer2 = ReorderMemory( + val layer2 = ReorderMemory.create( NativeData(Array(3, 4), Memory.Format.nc), NativeData(Array(3, 4), Memory.Format.io), NativeData(Array(3, 4), Memory.Format.nc), NativeData(Array(3, 4), Memory.Format.io)) - val layer3 = ReorderMemory( + val layer3 = ReorderMemory.create( HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), HeapData(Array(3, 4), Memory.Format.nc), diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala index 85b7d07b4a5..25fc5943815 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolutionSpec.scala @@ -872,7 +872,7 @@ class SpatialConvolutionSpec extends FlatSpec with Matchers { if (defaultFormat != outputFormat.layout) { val inputFormat = HeapData(src.size(), defaultFormat) - val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + val reorder = ReorderMemory.create(inputFormat, outputFormat, null, null) reorder.setRuntime(new MklDnnRuntime) reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) reorder.updateOutput(src).toTensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala index b5c7066d926..74f71fedba9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TestUtils.scala @@ -320,7 +320,7 @@ object Tools { def toNCHW(src: Tensor[Float], inputFormat: MemoryData): Tensor[Float] = { val outputFormat = HeapData(inputFormat.shape, if (src.size().length == 2) { Memory.Format.nc } else { Memory.Format.nchw }) - val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + val reorder = ReorderMemory.create(inputFormat, outputFormat, null, null) reorder.setRuntime(new MklDnnRuntime) reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) @@ -335,7 +335,7 @@ object Tools { } val inputFormat = HeapData(src.size(), defaultFormat) - val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + val reorder = ReorderMemory.create(inputFormat, outputFormat, null, null) reorder.setRuntime(new MklDnnRuntime) reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) reorder.forward(src).toTensor @@ -349,7 +349,7 @@ object Tools { } val inputFormat = HeapData(outputFormat.shape, defaultFormat) - val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + val reorder = ReorderMemory.create(inputFormat, outputFormat, null, null) reorder.setRuntime(new MklDnnRuntime) reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) reorder.updateOutput(src).toTensor @@ -364,7 +364,7 @@ object Tools { } val outputFormat = HeapData(inputFormat.shape, defaultFormat) - val reorder = ReorderMemory(inputFormat, outputFormat, null, null) + val reorder = ReorderMemory.create(inputFormat, outputFormat, null, null) reorder.setRuntime(new MklDnnRuntime) reorder.initFwdPrimitives(Array(inputFormat), TrainingPhase) reorder.updateOutput(src).toTensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala index bf2d289d8e0..162288148ad 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensorSpec.scala @@ -16,10 +16,14 @@ package com.intel.analytics.bigdl.tensor import com.intel.analytics.bigdl.mkl.MklDnn +import com.intel.analytics.bigdl.nn.mkldnn.MemoryOwner import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, T} import org.apache.commons.lang3.SerializationUtils +import org.scalatest.BeforeAndAfter -class DnnTensorSpec extends BigDLSpecHelper { +class DnnTensorSpec extends BigDLSpecHelper{ + implicit object Owner extends MemoryOwner { + } "nElement" should "be correct" in { val tensor = DnnTensor[Float](3, 4, 5) tensor.nElement() should be(3 * 4 * 5) From 7987cc7f7395175950abac70aa5c3024db62b56d Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 17 Sep 2019 12:51:28 +0800 Subject: [PATCH 0955/1065] [New feature] Add region proposal (#2896) * add Regionproposal --- .../vision/image/util/BboxUtil.scala | 7 +- .../analytics/bigdl/dllib/nn/Anchor.scala | 7 +- .../bigdl/dllib/nn/RegionRroposal.scala | 344 +++++ .../bigdl/dllib/nn/RegionRroposalSpec.scala | 1125 +++++++++++++++++ 4 files changed, 1479 insertions(+), 4 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposal.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposalSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala index e302887eebf..b8062044a83 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala @@ -50,7 +50,8 @@ object BboxUtil { * @param deltas (N, 4a) * @return */ - def bboxTransformInv(boxes: Tensor[Float], deltas: Tensor[Float]): Tensor[Float] = { + def bboxTransformInv(boxes: Tensor[Float], deltas: Tensor[Float], + normalized: Boolean = false): Tensor[Float] = { if (boxes.size(1) == 0) { return boxes } @@ -69,8 +70,8 @@ object BboxUtil { while (i < boxes.size(1)) { val x1 = boxesArr(offset) val y1 = boxesArr(offset + 1) - val width = boxesArr(offset + 2) - x1 + 1 - val height = boxesArr(offset + 3) - y1 + 1 + val width = if (!normalized) boxesArr(offset + 2) - x1 + 1 else boxesArr(offset + 2) - x1 + val height = if (!normalized) boxesArr(offset + 3) - y1 + 1 else boxesArr(offset + 3) - y1 var j = 0 while (j < repeat) { j += 1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala index 8010cc4c761..9be31e5640b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala @@ -24,7 +24,8 @@ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} */ class Anchor(ratios: Array[Float], scales: Array[Float]) extends Serializable { - private val basicAnchors: Tensor[Float] = generateBasicAnchors(ratios, scales) + private var baseSize = 16 + private var basicAnchors: Tensor[Float] = generateBasicAnchors(ratios, scales, baseSize) val anchorNum = ratios.length * scales.length /** @@ -37,6 +38,10 @@ class Anchor(ratios: Array[Float], scales: Array[Float]) extends Serializable { */ def generateAnchors(width: Int, height: Int, featStride: Float = 16): Tensor[Float] = { val (shiftX, shiftY) = generateShifts(width, height, featStride) + if (featStride != baseSize) { + basicAnchors = generateBasicAnchors(ratios, scales, featStride) + baseSize = featStride.toInt + } getAllAnchors(shiftX, shiftY, basicAnchors) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposal.scala new file mode 100644 index 00000000000..9ec18e7b0c7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposal.scala @@ -0,0 +1,344 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import java.util +import breeze.linalg.{dim, min} +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil +import com.intel.analytics.bigdl.utils.{LayerException, T, Table} +import scala.collection.mutable.ArrayBuffer + +/** + * Layer for RPN computation. Takes feature maps from the backbone and + * outputs RPN proposals and losses. + * @param inChannels + * @param anchorSizes + * @param aspectRatios + * @param anchorStride + * @param preNmsTopNTest + * @param postNmsTopNTest + * @param nmsThread + * @param minSize + */ +class RegionRroposal( + val inChannels: Int, + val anchorSizes: Array[Float], + val aspectRatios: Array[Float], + val anchorStride: Array[Float], + val preNmsTopNTest: Int = 1000, + val postNmsTopNTest: Int = 1000, + val preNmsTopNTrain: Int = 2000, + val postNmsTopNTrain: Int = 2000, + val nmsThread: Float = 0.7f, + val minSize: Int = 0)(implicit ev: TensorNumeric[Float]) + extends AbstractModule[Table, Tensor[Float], Float] { + + // for anchor generation + require(anchorSizes.length == anchorStride.length, + s"length of anchor size and stride should be same, " + + s"but get size length ${anchorSizes.length}, stride length ${anchorStride.length}") + + private val scalesForStride = new Array[Float](1) + private val anchors = new ArrayBuffer[Anchor] + for (i <- 0 to anchorSizes.length - 1) { + scalesForStride(0) = anchorSizes(i) / anchorStride(i) + anchors.append(Anchor(aspectRatios, scalesForStride)) + } + + private val numAnchors = anchors(0).anchorNum + private val head = rpnHead(inChannels, numAnchors) + private val boxSelector = new ProposalPostProcessor(preNmsTopNTest, postNmsTopNTest, + preNmsTopNTrain, postNmsTopNTrain, nmsThread, minSize) + private val selectorRes = T() + + private[nn] def anchorGenerator(featuresMap: Table): Table = { + val res = T() + val length = Math.min(anchorSizes.length, featuresMap.length()) + for (i <- 0 to length - 1) { + val size = anchorSizes(i) + val stride = anchorStride(i) + val feature = featuresMap[Tensor[Float]](i + 1) + val height = feature.size(3) + val width = feature.size(4) + res(i + 1) = anchors(i).generateAnchors(width, height, stride) + } + res + } + + /** + * Add a simple RPN Head with classification and regression heads + */ + private[nn] def rpnHead(inChannels: Int, numAnchors: Int): Module[Float] = { + val conv = SpatialConvolution[Float](inChannels, inChannels, + kernelH = 3, kernelW = 3, strideH = 1, strideW = 1, padH = 1, padW = 1) + conv.setInitMethod(RandomNormal(0.0, 0.01), Zeros) + val clsLogits = SpatialConvolution[Float](inChannels, numAnchors, + kernelH = 1, kernelW = 1, strideH = 1, strideW = 1).setName(this.getName() + "_cls_logits") + clsLogits.setInitMethod(RandomNormal(0.0, 0.01), Zeros) + val bboxPred = SpatialConvolution[Float](inChannels, numAnchors * 4, + kernelH = 1, kernelW = 1, strideH = 1, strideW = 1).setName(this.getName() + "_bbox_pred") + bboxPred.setInitMethod(RandomNormal(0.0, 0.01), Zeros) + + val input = Input() + val node1 = conv.inputs(input) + val node2 = ReLU[Float]().inputs(node1) + val node3 = clsLogits.inputs(node2) + val node4 = bboxPred.inputs(node2) + + Graph(input, Array(node3, node4)) + } + + /** + * input is a table and contains: + * first tensor: images: images for which we want to compute the predictions + * second tensor: features: features computed from the images that are used for + * computing the predictions. + */ + override def updateOutput(input: Table): Tensor[Float] = { + val features = input[Table](1) + val images = input[Tensor[Float]](2) + val anchors = this.anchorGenerator(features) + + var bboxNumber = 0 + var i = 1 + while (i <= anchors.length()) { + val headOutput = head.forward(features(i)).toTable + val objectness = headOutput.apply[Tensor[Float]](1) + val boxRegression = headOutput.apply[Tensor[Float]](2) + + val out = boxSelector.forward(T(anchors[Tensor[Float]](i), objectness, + boxRegression, images)) + + if (!selectorRes.contains(i)) selectorRes(i) = T(Tensor[Float](), Tensor[Float]()) + selectorRes(i).asInstanceOf[Table].apply[Tensor[Float]](1).resizeAs(out[Tensor[Float]](1)) + .copy(out[Tensor[Float]](1)) + selectorRes(i).asInstanceOf[Table].apply[Tensor[Float]](2).resizeAs(out[Tensor[Float]](2)) + .copy(out[Tensor[Float]](2)) + + bboxNumber += selectorRes[Table](i)[Tensor[Float]](1).size(1) + i += 1 + } + + val postNmsTopN = if (this.isTraining()) min(postNmsTopNTrain, bboxNumber) + else min(postNmsTopNTest, bboxNumber) + output.resize(postNmsTopN, 4) + + // sort + selectOverAllLevels(selectorRes, postNmsTopN, bboxNumber, output) + output + } + + private def selectOverAllLevels(res: Table, postNmsTopN: Int, totalNumber: Int, + output: Tensor[Float]): Unit = { + val scoreResult = Tensor[Float]().resize(totalNumber) + val bboxResult = Tensor[Float]().resize(totalNumber, 4) + var i = 1 + var startOffset = 1 + while (i <= res.length()) { + val tmpScore = res[Table](i)[Tensor[Float]](2) + val tmpBbox = res[Table](i)[Tensor[Float]](1) + scoreResult.narrow(1, startOffset, tmpScore.size(1)).copy(tmpScore) + bboxResult.narrow(1, startOffset, tmpBbox.size(1)).copy(tmpBbox) + startOffset = startOffset + tmpScore.size(1) + i += 1 + } + + val inds = scoreResult.topk(postNmsTopN, dim = 1, sortedResult = true, increase = false) + + i = 1 + while (i <= inds._2.nElement()) { + val index = inds._2.valueAt(i).toInt + output.narrow(1, i, 1).copy(bboxResult.narrow(1, index, 1)) + i += 1 + } + } + + override def updateGradInput(input: Table, gradOutput: Tensor[Float]): Table = { + throw new UnsupportedOperationException("RegionRroposal only support inference") + } + + override def accGradParameters(input: Table, gradOutput: Tensor[Float]): Unit = { + throw new UnsupportedOperationException("RegionRroposal only support inference") + } + + override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { + head.parameters() + } + + override def getParametersTable(): Table = { + head.getParametersTable() + } + + override def clearState(): this.type = { + super.clearState() + head.clearState() + boxSelector.clearState() + this + } + + override def release(): Unit = { + super.release() + head.release() + boxSelector.release() + } + + override def training(): RegionRroposal.this.type = { + train = true + head.training() + boxSelector.training() + super.training() + } + + override def evaluate(): this.type = { + head.evaluate() + boxSelector.evaluate() + train = false + super.evaluate() + } +} + +object RegionRroposal { + def apply(inChannels: Int, + anchorSizes: Array[Float] = Array[Float](32, 64, 128, 256, 512), + aspectRatios: Array[Float] = Array[Float](0.5f, 1.0f, 2.0f), + anchorStride: Array[Float] = Array[Float](4, 8, 16, 32, 64), + preNmsTopNTest: Int = 1000, + postNmsTopNTest: Int = 1000, + preNmsTopNTrain: Int = 2000, + postNmsTopNTrain: Int = 2000, + nmsThread: Float = 0.7f, + minSize: Int = 0)(implicit ev: TensorNumeric[Float]): RegionRroposal = + new RegionRroposal(inChannels, anchorSizes, aspectRatios, anchorStride, + preNmsTopNTest, postNmsTopNTest, preNmsTopNTrain, postNmsTopNTrain, nmsThread, + minSize) +} + +private[nn] class ProposalPostProcessor( + val preNmsTopNTest: Int = 1000, + val postNmsTopNTest: Int = 1000, + val preNmsTopNTrain: Int = 2000, + val postNmsTopNTrain: Int = 2000, + val nmsThread: Float = 0.7f, + val minSize: Int = 0) + (implicit ev: TensorNumeric[Float]) extends AbstractModule[Table, Table, Float]{ + + @transient private var sortedScores: Tensor[Float] = null + @transient private var sortedInds: Tensor[Float] = null + @transient private var boxRegressionIndex: Tensor[Float] = null + @transient private var anchorsIndex: Tensor[Float] = null + + private val nms = new Nms() + private val arr = new Array[Int](10000) + private val sigmoid = Sigmoid[Float]() + + /** + * Arguments: + * anchors: Tensor with shape (batchsize, nums, 4) + * objectness: Tensor of size (batchsize, anchornumber, height, width) + * box_regression: Tensor of size (batchsize, anchornumber * 4, height, width) + * img_info: image size + * @param input + * @return + */ + override def updateOutput(input: Table): Table = { + // for memory case, input may be changed + val anchors = input[Tensor[Float]](1) + var objectness = input[Tensor[Float]](2) + var boxRegression = input[Tensor[Float]](3) + val imageSize = input[Tensor[Float]](4) // original image height & width + + val N = objectness.size(1) // batch size + val A = objectness.size(2) // anchor number + val H = objectness.size(3) // height + val W = objectness.size(4) // width + + // permute_and_flatten + objectness = objectness.transpose(3, 1).transpose(2, 4).contiguous() + // view[N, -1] + objectness.resize(Array(N, A * H * W)) + // sigmoid + objectness = sigmoid.forward(objectness) + + // permute_and_flatten + boxRegression = boxRegression.transpose(3, 1).transpose(2, 4) + .contiguous().resize(Array(N, A * H * W, 4)) + + val numAnchors = A * H * W + val topNum = if (this.isTraining()) { + Math.min(preNmsTopNTrain, numAnchors) + } else Math.min(preNmsTopNTest, numAnchors) + // scores ==> objectness + // sortedScores ===> objectness, sortedInds = topk_idx + 1 + // initial + if (sortedScores == null) sortedScores = Tensor[Float]() + if (sortedInds == null) sortedInds = Tensor[Float]() + + objectness.topk(topNum, dim = 2, increase = false, + result = sortedScores, indices = sortedInds) + + objectness.resizeAs(sortedScores).copy(sortedScores) + + if (boxRegressionIndex == null) boxRegressionIndex = Tensor[Float]() + boxRegressionIndex.resizeAs(boxRegression) + boxRegressionIndex.index(2, sortedInds.squeeze(1), boxRegression) + // view (-1, 4) + boxRegressionIndex.resize(boxRegressionIndex.nElement() / 4, 4) + + // view (-1, 4) + if (anchorsIndex == null) anchorsIndex = Tensor[Float]() + anchorsIndex.resizeAs(anchors) + anchorsIndex.index(1, sortedInds.squeeze(1), anchors) + anchorsIndex.resize(anchorsIndex.nElement() / 4, 4) + + val proposals = BboxUtil.bboxTransformInv(anchorsIndex, + boxRegressionIndex, normalized = true) + // remove _small box + val minBoxH = minSize + val minBoxW = minSize + var keepN = BboxUtil.clipBoxes(proposals, imageSize.valueAt(1), imageSize.valueAt(2), minBoxH + , minBoxW, sortedScores) + + util.Arrays.fill(arr, 0, arr.length, 0) + nms.nms(sortedScores, proposals, thresh = nmsThread, arr, sorted = true) + val arrFilter = arr.filter(_ > 0).map(_.toFloat) + + val indices = Tensor[Float]().set(Storage(arrFilter), 1, Array(arrFilter.length)) + + // initial output tensors + if (output.length() == 0) { + output(1) = Tensor[Float]() + output(2) = Tensor[Float]() + } + output[Tensor[Float]](1).resize(indices.nElement(), 4).zero() + output[Tensor[Float]](2).resize(indices.nElement()).zero() + + output[Tensor[Float]](1).index(1, indices, proposals) + objectness.resize(objectness.nElement()) + output[Tensor[Float]](2).index(1, indices, objectness) + + output + } + + override def updateGradInput(input: Table, gradOutput: Table): Table = { + throw new UnsupportedOperationException("ProposalPostProcessor only support inference") + } +} + + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposalSpec.scala new file mode 100644 index 00000000000..aed808461d0 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposalSpec.scala @@ -0,0 +1,1125 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn + +import com.intel.analytics.bigdl.nn.mkldnn.Equivalent +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{T, Table} +import org.scalatest.{FlatSpec, Matchers} + +import scala.util.Random + +class RegionRroposalSpec extends FlatSpec with Matchers { + "RegionRroposal" should "be ok" in { + val layer = new RegionRroposal(6, + Array[Float](32, 64, 128, 256, 512), + Array[Float](0.5f, 1.0f, 2.0f), + Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) + + val images = Tensor[Float](T(20, 38)) + + val features = Tensor[Float](T(T(T(T(0.7668, 0.1659, 0.4393, 0.2243), + T(0.8935, 0.0497, 0.1780, 0.3011), + T(0.1893, 0.9186, 0.2131, 0.3957)), + T(T(0.6017, 0.4234, 0.5224, 0.4175), + T(0.0340, 0.9157, 0.3079, 0.6269), + T(0.8277, 0.6594, 0.0887, 0.4890)), + T(T(0.5887, 0.7340, 0.8497, 0.9112), + T(0.4847, 0.9436, 0.3904, 0.2499), + T(0.3206, 0.9753, 0.7582, 0.6688)), + T(T(0.2651, 0.2336, 0.5057, 0.5688), + T(0.0634, 0.8993, 0.2732, 0.3397), + T(0.1879, 0.5534, 0.2682, 0.9556)), + T(T(0.9761, 0.5934, 0.3124, 0.9431), + T(0.8519, 0.9815, 0.1132, 0.4783), + T(0.4436, 0.3847, 0.4521, 0.5569)), + T(T(0.9952, 0.0015, 0.0813, 0.4907), + T(0.2130, 0.4603, 0.1386, 0.0277), + T(0.5662, 0.3503, 0.6555, 0.7667))))) + + val weight_conv = Tensor[Float](T(T(T(T(1.2685e-03, 1.3564e-02, 5.6322e-03), + T(-1.0393e-03, -3.5746e-03, 3.9174e-03), + T(-6.8009e-03, 2.4094e-03, 4.6981e-03)), + T(T( 1.2426e-02, 5.4030e-03, -1.1454e-02), + T(-1.4592e-02, -1.6281e-02, 3.8337e-03), + T(-1.7180e-03, -3.1896e-02, 1.5914e-02)), + T(T(-2.4669e-04, -8.4661e-03, 2.9301e-04), + T(-5.7207e-03, -1.2546e-02, 4.8611e-04), + T( 1.1705e-02, -5.4102e-03, -7.1156e-03)), + T(T( 5.7526e-04, 6.2625e-03, -1.7736e-02), + T(-2.2050e-03, 2.7467e-02, -1.7599e-02), + T( 1.0230e-02, -1.1073e-03, -3.8986e-03)), + T(T(-1.0300e-02, -1.5446e-02, 5.7298e-03), + T(-2.0956e-02, -1.8055e-03, 2.3464e-03), + T(-1.4774e-03, 5.8926e-03, 2.2533e-02)), + T(T(-2.5548e-03, 1.6513e-03, -1.6292e-03), + T(-8.0389e-03, -9.1740e-03, 8.9856e-03), + T( 8.2623e-03, -3.6677e-03, -4.2506e-03))), + T(T(T(-1.2455e-02, 1.1245e-02, -2.0157e-02), + T( 9.9260e-03, -6.0842e-03, -1.3856e-02), + T( 1.0412e-02, -8.0432e-03, -6.2443e-03)), + T(T(-5.8823e-03, 1.6700e-02, -9.2747e-03), + T(-9.7585e-03, 1.3312e-02, 9.0074e-03), + T(-6.5847e-03, -9.3275e-03, -1.5749e-02)), + T(T( 1.4861e-02, -1.4092e-02, 1.4330e-02), + T( 3.8986e-03, -1.1516e-03, -2.3609e-03), + T(-2.2235e-02, 7.8841e-04, 4.1560e-04)), + T(T( 1.2813e-02, -8.2621e-03, 2.3098e-04), + T( 1.9301e-02, 7.8028e-03, 3.1802e-03), + T(-6.9918e-03, -3.9213e-03, 2.1955e-02)), + T(T( 3.3116e-03, 1.4171e-03, -1.5268e-02), + T( 2.5214e-03, 6.5413e-03, 2.1024e-02), + T( 6.3311e-03, 1.9332e-02, -2.4634e-03)), + T(T(-7.0092e-03, 6.3621e-03, -5.6589e-03), + T( 1.0318e-02, -1.0371e-02, 1.3739e-03), + T(-1.1312e-02, 6.4710e-03, -7.1830e-03))), + T(T(T(-1.1984e-02, -8.8376e-03, 6.4301e-03), + T( 7.2037e-04, -5.7234e-03, 1.6078e-02), + T( 1.0007e-03, -1.0746e-02, -1.0924e-03)), + T(T( 2.4635e-03, -9.9438e-03, -6.8856e-03), + T( 1.2039e-02, -2.5186e-03, -1.9443e-02), + T(-1.9203e-02, 1.1464e-02, 2.3850e-02)), + T(T(-3.5508e-04, -3.1787e-03, 3.5779e-03), + T(-1.7844e-02, -3.0524e-03, 8.5366e-03), + T( 3.8534e-03, 1.2612e-02, 5.9866e-03)), + T(T(-2.4725e-02, -5.4071e-04, -1.1862e-02), + T( 7.3836e-03, -3.1864e-03, -5.1757e-03), + T(-1.4699e-03, 5.1577e-03, 3.3928e-03)), + T(T( 2.4955e-03, -9.5512e-03, 7.0652e-03), + T( 1.2566e-02, -2.9903e-02, -3.2173e-04), + T(-2.3036e-03, 1.2172e-03, 1.0538e-02)), + T(T( 2.4320e-03, 8.3910e-03, 2.2082e-03), + T(-1.3217e-02, 4.4108e-04, -3.4124e-03), + T(-1.1553e-02, 4.9376e-03, 7.9121e-03))), + T(T(T( 1.2293e-02, -3.9778e-03, 2.1020e-03), + T( 8.3877e-03, 2.3666e-02, 6.8477e-03), + T( 5.2052e-03, 1.4803e-02, -7.5135e-03)), + T(T(-8.7030e-03, 5.8776e-03, -4.8942e-05), + T( 2.0430e-02, 5.8311e-04, -3.6140e-03), + T( 1.7116e-02, 8.4518e-03, -2.8076e-03)), + T(T( 9.1432e-03, 4.6386e-03, -1.0463e-02), + T( 6.0311e-03, 4.2746e-03, -3.4131e-03), + T( 1.9404e-03, 7.9359e-03, -7.6828e-04)), + T(T( 4.8792e-03, -2.5789e-02, 1.0007e-02), + T( 2.1705e-04, -8.6712e-03, -4.5113e-03), + T(-6.6698e-03, 2.7328e-04, 6.6046e-03)), + T(T( 7.3924e-03, 7.1265e-03, 4.3357e-03), + T( 3.9357e-04, -2.3774e-03, 6.4933e-03), + T( 7.2543e-03, -4.8372e-03, 5.6666e-03)), + T(T(-3.9601e-03, 1.3844e-02, -8.2588e-03), + T(-1.6542e-03, -1.3295e-02, 3.8030e-03), + T(-6.6701e-04, 6.8487e-03, 7.7284e-04))), + T(T(T(-1.3936e-03, -4.7838e-03, -3.1820e-03), + T( 2.2058e-03, -1.6855e-03, 1.8463e-02), + T( 9.5022e-03, -3.3961e-03, -6.5992e-03)), + T(T(-9.5200e-03, -4.0727e-03, 1.4081e-02), + T( 1.2446e-03, 1.1088e-02, 1.7009e-03), + T( 1.1670e-03, -7.9839e-03, 9.1257e-03)), + T(T(-2.5381e-03, 6.8467e-03, -7.4647e-04), + T( 5.9466e-04, 8.1772e-03, 2.8940e-03), + T( 4.2105e-03, -1.3101e-02, 8.6801e-03)), + T(T( 7.1093e-03, 9.3525e-03, 7.6763e-03), + T(-2.8895e-03, 6.6717e-03, 1.1738e-03), + T( 5.4419e-03, -2.8676e-04, 1.3919e-02)), + T(T( 1.0932e-02, -2.3391e-02, -8.9627e-03), + T(-6.2398e-03, -5.7453e-03, -5.7471e-03), + T( 7.2978e-03, -2.2365e-03, 3.7101e-04)), + T(T( 6.5447e-03, -2.5417e-03, -7.0376e-03), + T(-1.1011e-03, -6.9527e-03, -2.4869e-02), + T( 6.0163e-03, 5.7055e-03, 5.8137e-03))), + T(T(T( 2.5749e-04, 5.5009e-03, 1.9151e-03), + T( 9.8616e-03, 1.1613e-02, -1.7455e-03), + T( 3.1561e-03, -1.8205e-03, -3.4044e-03)), + T(T(-5.8910e-03, 3.6100e-03, -1.4282e-02), + T( 9.2737e-03, -7.0391e-03, 3.8460e-03), + T( 6.2735e-03, 6.5410e-03, 1.0932e-03)), + T(T( 8.8084e-03, 1.5566e-02, 2.1806e-02), + T( 1.7355e-02, -1.5105e-02, 7.6660e-04), + T( 3.3541e-03, -5.3618e-03, -4.8840e-03)), + T(T( 1.4804e-03, 4.5057e-03, -5.1785e-03), + T(-5.5912e-03, -1.8077e-02, 5.0915e-03), + T( 4.0559e-03, 3.3477e-03, 8.6055e-04)), + T(T( 9.6151e-03, -2.7296e-03, 1.6761e-02), + T(-6.7708e-03, 5.9753e-03, -5.5834e-03), + T(-5.9345e-03, 2.2870e-02, 5.4827e-03)), + T(T(-8.7740e-03, 1.4306e-02, 1.7519e-02), + T(-1.0057e-04, 2.8130e-03, -1.4722e-02), + T(-5.0060e-03, 8.9401e-04, 4.7907e-03))))) + val weight_logits = Tensor[Float](T(T(T(T( 0.0013f)), + T(T( 0.0136f)), + T(T(-0.0002f)), + T(T(-0.0085f)), + T(T( 0.0003f)), + T(T(-0.0057f))), + T(T(T(-0.0125f)), + T(T( 0.0005f)), + T(T( 0.0028f)), + T(T(-0.0215f)), + T(T(-0.0071f)), + T(T( 0.0006f))), + T(T(T( 0.0063f)), + T(T(-0.0177f)), + T(T(-0.0022f)), + T(T( 0.0275f)), + T(T(-0.0105f)), + T(T( 0.0112f))))) + val weight_pred = Tensor[Float](T(T(T(T( 0.0013f)), + T(T( 0.0136f)), + T(T( 0.0056f)), + T(T(-0.0010f)), + T(T(-0.0036f)), + T(T( 0.0039f))), + T(T(T(-0.0068f)), + T(T( 0.0024f)), + T(T( 0.0047f)), + T(T( 0.0124f)), + T(T( 0.0054f)), + T(T(-0.0115f))), + T(T(T(-0.0146f)), + T(T(-0.0163f)), + T(T( 0.0038f)), + T(T(-0.0017f)), + T(T(-0.0319f)), + T(T( 0.0159f))), + T(T(T(-0.0002f)), + T(T(-0.0085f)), + T(T( 0.0003f)), + T(T(-0.0057f)), + T(T(-0.0125f)), + T(T( 0.0005f))), + T(T(T( 0.0117f)), + T(T(-0.0054f)), + T(T(-0.0071f)), + T(T( 0.0006f)), + T(T( 0.0063f)), + T(T(-0.0177f))), + T(T(T(-0.0022f)), + T(T( 0.0275f)), + T(T(-0.0176f)), + T(T( 0.0102f)), + T(T(-0.0011f)), + T(T(-0.0039f))), + T(T(T(-0.0103f)), + T(T(-0.0154f)), + T(T( 0.0057f)), + T(T(-0.0210f)), + T(T(-0.0018f)), + T(T( 0.0023f))), + T(T(T(-0.0015f)), + T(T( 0.0059f)), + T(T( 0.0225f)), + T(T(-0.0026f)), + T(T( 0.0017f)), + T(T(-0.0016f))), + T(T(T(-0.0080f)), + T(T(-0.0092f)), + T(T( 0.0090f)), + T(T( 0.0083f)), + T(T(-0.0037f)), + T(T(-0.0043f))), + T(T(T(-0.0125f)), + T(T( 0.0112f)), + T(T( 0.0044f)), + T(T( 0.0142f)), + T(T(-0.0043f)), + T(T( 0.0030f))), + T(T(T( 0.0266f)), + T(T(-0.0028f)), + T(T( 0.0017f)), + T(T( 0.0100f)), + T(T( 0.0022f)), + T(T(-0.0036f))), + T(T(T( 0.0081f)), + T(T( 0.0002f)), + T(T(-0.0084f)), + T(T( 0.0124f)), + T(T( 0.0151f)), + T(T(-0.0060f))))) + + val paramsTable = layer.getParametersTable() + for (i <- paramsTable.keySet) { + val weight = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get + if (i.toString contains "_cls_logits") { + weight.copy(weight_logits) + } else if (i.toString contains "_bbox_pred") { + weight.copy(weight_pred) + } else { + weight.copy(weight_conv) + } + } + + layer.evaluate() + val output = layer.forward(T(T(features), images)) + val outputExpected = Tensor[Float]( + T(T(0.0f, 0.0f, 20.999596f, 19.0f), + T(0.0f, 0.0f, 12.995603f, 19.0f), + T(0.0f, 0.0f, 37.0f, 19.0f), + T(0.0f, 0.0f, 29.011127f, 13.003019f) + )) + + output should be(outputExpected) + } + + "RegionRroposal with multi features" should "be ok" in { + val layer = new RegionRroposal(6, + Array[Float](32, 64, 128, 256, 512), + Array[Float](0.5f, 1.0f, 2.0f), + Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) + + val images = Tensor[Float](T(20, 38)) + + val features1 = Tensor[Float](T(T(T( + T(0.7668, 0.1659, 0.4393, 0.2243), + T(0.8935, 0.0497, 0.1780, 0.3011), + T(0.1893, 0.9186, 0.2131, 0.3957)), + T(T(0.6017, 0.4234, 0.5224, 0.4175), + T(0.0340, 0.9157, 0.3079, 0.6269), + T(0.8277, 0.6594, 0.0887, 0.4890)), + T(T(0.5887, 0.7340, 0.8497, 0.9112), + T(0.4847, 0.9436, 0.3904, 0.2499), + T(0.3206, 0.9753, 0.7582, 0.6688)), + T(T(0.2651, 0.2336, 0.5057, 0.5688), + T(0.0634, 0.8993, 0.2732, 0.3397), + T(0.1879, 0.5534, 0.2682, 0.9556)), + T(T(0.9761, 0.5934, 0.3124, 0.9431), + T(0.8519, 0.9815, 0.1132, 0.4783), + T(0.4436, 0.3847, 0.4521, 0.5569)), + T(T(0.9952, 0.0015, 0.0813, 0.4907), + T(0.2130, 0.4603, 0.1386, 0.0277), + T(0.5662, 0.3503, 0.6555, 0.7667))))) + + val features3 = Tensor[Float](T(T(T( + T(0.9336, 0.2557, 0.1506, 0.7856)), + T(T(0.4152, 0.5809, 0.1088, 0.7065)), + T(T(0.0105, 0.4602, 0.2945, 0.0475)), + T(T(0.6401, 0.3784, 0.5887, 0.0720)), + T(T(0.9140, 0.0085, 0.2174, 0.1890)), + T(T(0.0911, 0.6344, 0.3142, 0.7052))))) + + val features2 = Tensor[Float](T(T(T(T(0.2269, 0.7555), + T(0.6458, 0.3673), + T(0.1770, 0.2966), + T(0.9925, 0.2103), + T(0.1292, 0.1719)), + T(T(0.9127, 0.6818), + T(0.1953, 0.9991), + T(0.1133, 0.0135), + T(0.1450, 0.7819), + T(0.3134, 0.2983)), + T(T(0.3436, 0.2028), + T(0.9792, 0.4947), + T(0.3617, 0.9687), + T(0.0359, 0.3041), + T(0.9867, 0.1290)), + T(T(0.6887, 0.1637), + T(0.0899, 0.3139), + T(0.1219, 0.3516), + T(0.2316, 0.2847), + T(0.3520, 0.2828)), + T(T(0.2420, 0.4928), + T(0.5772, 0.3771), + T(0.2440, 0.8994), + T(0.1041, 0.9193), + T(0.6201, 0.3658)), + T(T(0.0623, 0.5967), + T(0.0829, 0.8185), + T(0.4964, 0.0589), + T(0.9840, 0.5836), + T(0.6737, 0.4738))))) + + val weight_conv = Tensor[Float](T(T(T(T(1.2685e-03, 1.3564e-02, 5.6322e-03), + T(-1.0393e-03, -3.5746e-03, 3.9174e-03), + T(-6.8009e-03, 2.4094e-03, 4.6981e-03)), + T(T( 1.2426e-02, 5.4030e-03, -1.1454e-02), + T(-1.4592e-02, -1.6281e-02, 3.8337e-03), + T(-1.7180e-03, -3.1896e-02, 1.5914e-02)), + T(T(-2.4669e-04, -8.4661e-03, 2.9301e-04), + T(-5.7207e-03, -1.2546e-02, 4.8611e-04), + T( 1.1705e-02, -5.4102e-03, -7.1156e-03)), + T(T( 5.7526e-04, 6.2625e-03, -1.7736e-02), + T(-2.2050e-03, 2.7467e-02, -1.7599e-02), + T( 1.0230e-02, -1.1073e-03, -3.8986e-03)), + T(T(-1.0300e-02, -1.5446e-02, 5.7298e-03), + T(-2.0956e-02, -1.8055e-03, 2.3464e-03), + T(-1.4774e-03, 5.8926e-03, 2.2533e-02)), + T(T(-2.5548e-03, 1.6513e-03, -1.6292e-03), + T(-8.0389e-03, -9.1740e-03, 8.9856e-03), + T( 8.2623e-03, -3.6677e-03, -4.2506e-03))), + T(T(T(-1.2455e-02, 1.1245e-02, -2.0157e-02), + T( 9.9260e-03, -6.0842e-03, -1.3856e-02), + T( 1.0412e-02, -8.0432e-03, -6.2443e-03)), + T(T(-5.8823e-03, 1.6700e-02, -9.2747e-03), + T(-9.7585e-03, 1.3312e-02, 9.0074e-03), + T(-6.5847e-03, -9.3275e-03, -1.5749e-02)), + T(T( 1.4861e-02, -1.4092e-02, 1.4330e-02), + T( 3.8986e-03, -1.1516e-03, -2.3609e-03), + T(-2.2235e-02, 7.8841e-04, 4.1560e-04)), + T(T( 1.2813e-02, -8.2621e-03, 2.3098e-04), + T( 1.9301e-02, 7.8028e-03, 3.1802e-03), + T(-6.9918e-03, -3.9213e-03, 2.1955e-02)), + T(T( 3.3116e-03, 1.4171e-03, -1.5268e-02), + T( 2.5214e-03, 6.5413e-03, 2.1024e-02), + T( 6.3311e-03, 1.9332e-02, -2.4634e-03)), + T(T(-7.0092e-03, 6.3621e-03, -5.6589e-03), + T( 1.0318e-02, -1.0371e-02, 1.3739e-03), + T(-1.1312e-02, 6.4710e-03, -7.1830e-03))), + T(T(T(-1.1984e-02, -8.8376e-03, 6.4301e-03), + T( 7.2037e-04, -5.7234e-03, 1.6078e-02), + T( 1.0007e-03, -1.0746e-02, -1.0924e-03)), + T(T( 2.4635e-03, -9.9438e-03, -6.8856e-03), + T( 1.2039e-02, -2.5186e-03, -1.9443e-02), + T(-1.9203e-02, 1.1464e-02, 2.3850e-02)), + T(T(-3.5508e-04, -3.1787e-03, 3.5779e-03), + T(-1.7844e-02, -3.0524e-03, 8.5366e-03), + T( 3.8534e-03, 1.2612e-02, 5.9866e-03)), + T(T(-2.4725e-02, -5.4071e-04, -1.1862e-02), + T( 7.3836e-03, -3.1864e-03, -5.1757e-03), + T(-1.4699e-03, 5.1577e-03, 3.3928e-03)), + T(T( 2.4955e-03, -9.5512e-03, 7.0652e-03), + T( 1.2566e-02, -2.9903e-02, -3.2173e-04), + T(-2.3036e-03, 1.2172e-03, 1.0538e-02)), + T(T( 2.4320e-03, 8.3910e-03, 2.2082e-03), + T(-1.3217e-02, 4.4108e-04, -3.4124e-03), + T(-1.1553e-02, 4.9376e-03, 7.9121e-03))), + T(T(T( 1.2293e-02, -3.9778e-03, 2.1020e-03), + T( 8.3877e-03, 2.3666e-02, 6.8477e-03), + T( 5.2052e-03, 1.4803e-02, -7.5135e-03)), + T(T(-8.7030e-03, 5.8776e-03, -4.8942e-05), + T( 2.0430e-02, 5.8311e-04, -3.6140e-03), + T( 1.7116e-02, 8.4518e-03, -2.8076e-03)), + T(T( 9.1432e-03, 4.6386e-03, -1.0463e-02), + T( 6.0311e-03, 4.2746e-03, -3.4131e-03), + T( 1.9404e-03, 7.9359e-03, -7.6828e-04)), + T(T( 4.8792e-03, -2.5789e-02, 1.0007e-02), + T( 2.1705e-04, -8.6712e-03, -4.5113e-03), + T(-6.6698e-03, 2.7328e-04, 6.6046e-03)), + T(T( 7.3924e-03, 7.1265e-03, 4.3357e-03), + T( 3.9357e-04, -2.3774e-03, 6.4933e-03), + T( 7.2543e-03, -4.8372e-03, 5.6666e-03)), + T(T(-3.9601e-03, 1.3844e-02, -8.2588e-03), + T(-1.6542e-03, -1.3295e-02, 3.8030e-03), + T(-6.6701e-04, 6.8487e-03, 7.7284e-04))), + T(T(T(-1.3936e-03, -4.7838e-03, -3.1820e-03), + T( 2.2058e-03, -1.6855e-03, 1.8463e-02), + T( 9.5022e-03, -3.3961e-03, -6.5992e-03)), + T(T(-9.5200e-03, -4.0727e-03, 1.4081e-02), + T( 1.2446e-03, 1.1088e-02, 1.7009e-03), + T( 1.1670e-03, -7.9839e-03, 9.1257e-03)), + T(T(-2.5381e-03, 6.8467e-03, -7.4647e-04), + T( 5.9466e-04, 8.1772e-03, 2.8940e-03), + T( 4.2105e-03, -1.3101e-02, 8.6801e-03)), + T(T( 7.1093e-03, 9.3525e-03, 7.6763e-03), + T(-2.8895e-03, 6.6717e-03, 1.1738e-03), + T( 5.4419e-03, -2.8676e-04, 1.3919e-02)), + T(T( 1.0932e-02, -2.3391e-02, -8.9627e-03), + T(-6.2398e-03, -5.7453e-03, -5.7471e-03), + T( 7.2978e-03, -2.2365e-03, 3.7101e-04)), + T(T( 6.5447e-03, -2.5417e-03, -7.0376e-03), + T(-1.1011e-03, -6.9527e-03, -2.4869e-02), + T( 6.0163e-03, 5.7055e-03, 5.8137e-03))), + T(T(T( 2.5749e-04, 5.5009e-03, 1.9151e-03), + T( 9.8616e-03, 1.1613e-02, -1.7455e-03), + T( 3.1561e-03, -1.8205e-03, -3.4044e-03)), + T(T(-5.8910e-03, 3.6100e-03, -1.4282e-02), + T( 9.2737e-03, -7.0391e-03, 3.8460e-03), + T( 6.2735e-03, 6.5410e-03, 1.0932e-03)), + T(T( 8.8084e-03, 1.5566e-02, 2.1806e-02), + T( 1.7355e-02, -1.5105e-02, 7.6660e-04), + T( 3.3541e-03, -5.3618e-03, -4.8840e-03)), + T(T( 1.4804e-03, 4.5057e-03, -5.1785e-03), + T(-5.5912e-03, -1.8077e-02, 5.0915e-03), + T( 4.0559e-03, 3.3477e-03, 8.6055e-04)), + T(T( 9.6151e-03, -2.7296e-03, 1.6761e-02), + T(-6.7708e-03, 5.9753e-03, -5.5834e-03), + T(-5.9345e-03, 2.2870e-02, 5.4827e-03)), + T(T(-8.7740e-03, 1.4306e-02, 1.7519e-02), + T(-1.0057e-04, 2.8130e-03, -1.4722e-02), + T(-5.0060e-03, 8.9401e-04, 4.7907e-03))))) + + val weight_logits = Tensor[Float](T(T(T(T( 0.0013f)), + T(T( 0.0136f)), + T(T(-0.0002f)), + T(T(-0.0085f)), + T(T( 0.0003f)), + T(T(-0.0057f))), + T(T(T(-0.0125f)), + T(T( 0.0005f)), + T(T( 0.0028f)), + T(T(-0.0215f)), + T(T(-0.0071f)), + T(T( 0.0006f))), + T(T(T( 0.0063f)), + T(T(-0.0177f)), + T(T(-0.0022f)), + T(T( 0.0275f)), + T(T(-0.0105f)), + T(T( 0.0112f))))) + + val weight_pred = Tensor[Float](T(T(T(T( 0.0013f)), + T(T( 0.0136f)), + T(T( 0.0056f)), + T(T(-0.0010f)), + T(T(-0.0036f)), + T(T( 0.0039f))), + T(T(T(-0.0068f)), + T(T( 0.0024f)), + T(T( 0.0047f)), + T(T( 0.0124f)), + T(T( 0.0054f)), + T(T(-0.0115f))), + T(T(T(-0.0146f)), + T(T(-0.0163f)), + T(T( 0.0038f)), + T(T(-0.0017f)), + T(T(-0.0319f)), + T(T( 0.0159f))), + T(T(T(-0.0002f)), + T(T(-0.0085f)), + T(T( 0.0003f)), + T(T(-0.0057f)), + T(T(-0.0125f)), + T(T( 0.0005f))), + T(T(T( 0.0117f)), + T(T(-0.0054f)), + T(T(-0.0071f)), + T(T( 0.0006f)), + T(T( 0.0063f)), + T(T(-0.0177f))), + T(T(T(-0.0022f)), + T(T( 0.0275f)), + T(T(-0.0176f)), + T(T( 0.0102f)), + T(T(-0.0011f)), + T(T(-0.0039f))), + T(T(T(-0.0103f)), + T(T(-0.0154f)), + T(T( 0.0057f)), + T(T(-0.0210f)), + T(T(-0.0018f)), + T(T( 0.0023f))), + T(T(T(-0.0015f)), + T(T( 0.0059f)), + T(T( 0.0225f)), + T(T(-0.0026f)), + T(T( 0.0017f)), + T(T(-0.0016f))), + T(T(T(-0.0080f)), + T(T(-0.0092f)), + T(T( 0.0090f)), + T(T( 0.0083f)), + T(T(-0.0037f)), + T(T(-0.0043f))), + T(T(T(-0.0125f)), + T(T( 0.0112f)), + T(T( 0.0044f)), + T(T( 0.0142f)), + T(T(-0.0043f)), + T(T( 0.0030f))), + T(T(T( 0.0266f)), + T(T(-0.0028f)), + T(T( 0.0017f)), + T(T( 0.0100f)), + T(T( 0.0022f)), + T(T(-0.0036f))), + T(T(T( 0.0081f)), + T(T( 0.0002f)), + T(T(-0.0084f)), + T(T( 0.0124f)), + T(T( 0.0151f)), + T(T(-0.0060f))))) + + val paramsTable = layer.getParametersTable() + for (i <- paramsTable.keySet) { + val weight = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get + if (i.toString contains "_cls_logits") { + weight.copy(weight_logits) + } else if (i.toString contains "_bbox_pred") { + weight.copy(weight_pred) + } else { + weight.copy(weight_conv) + } + } + + layer.evaluate() + val output = layer.forward(T(T(features1, features2, features3), images)) + val outputExpected = Tensor[Float](T( + T( 0.0000, 0.0000, 35.0363, 19.0000), + T( 0.0000, 0.0000, 20.9997, 19.0000), + T( 0.0000, 0.0000, 12.9955, 19.0000), + T( 0.0000, 0.0000, 37.0000, 19.0000), + T( 0.0000, 0.0000, 37.0000, 19.0000), + T(11.9914, 0.0000, 37.0000, 19.0000), + T( 0.0000, 0.0000, 29.0113, 13.0032), + T( 0.0000, 11.9920, 37.0000, 19.0000))) + + output should be(outputExpected) + } + + "RPNPostProcessor" should "be ok" in { + val anchors = Tensor[Float]( + T(T(-22, -10, 25, 13), + T(-14, -14, 17, 17), + T(-10, -22, 13, 25), + T(-18, -10, 29, 13), + T(-10, -14, 21, 17), + T(-6, -22, 17, 25), + T(-14, -10, 33, 13), + T(-6, -14, 25, 17), + T(-2, -22, 21, 25), + T(-10, -10, 37, 13), + T(-2, -14, 29, 17), + T(2, -22, 25, 25), + T(-6, -10, 41, 13), + T(2, -14, 33, 17), + T(6, -22, 29, 25), + T(-2, -10, 45, 13), + T(6, -14, 37, 17), + T(10, -22, 33, 25), + T(2, -10, 49, 13), + T(10, -14, 41, 17), + T(14, -22, 37, 25), + T(-22, -6, 25, 17), + T(-14, -10, 17, 21), + T(-10, -18, 13, 29), + T(-18, -6, 29, 17), + T(-10, -10, 21, 21), + T( -6, -18, 17, 29), + T(-14, -6, 33, 17), + T(-6, -10, 25, 21), + T(-2, -18, 21, 29), + T(-10, -6, 37, 17), + T(-2, -10, 29, 21), + T(2, -18, 25, 29), + T(-6, -6, 41, 17), + T(2, -10, 33, 21), + T(6, -18, 29, 29), + T(-2, -6, 45, 17), + T(6, -10, 37, 21), + T(10, -18, 33, 29), + T(2, -6, 49, 17), + T(10, -10, 41, 21), + T(14, -18, 37, 29), + T(-22, -2, 25, 21), + T(-14, -6, 17, 25), + T(-10, -14, 13, 33), + T(-18, -2, 29, 21), + T(-10, -6, 21, 25), + T(-6, -14, 17, 33), + T(-14, -2, 33, 21), + T(-6, -6, 25, 25), + T(-2, -14, 21, 33), + T(-10, -2, 37, 21), + T(-2, -6, 29, 25), + T(2, -14, 25, 33), + T(-6, -2, 41, 21), + T(2, -6, 33, 25), + T(6, -14, 29, 33), + T(-2, -2, 45, 21), + T(6, -6, 37, 25), + T(10, -14, 33, 33), + T(2, -2, 49, 21), + T(10, -6, 41, 25), + T(14, -14, 37, 33))) + val box_regression = Tensor[Float]( + T(T(T(T(-1.6730e-02, -2.5040e-02, -3.8669e-02, -2.5333e-02, -1.4004e-02, + -2.5377e-02, -1.2593e-02), + T(-3.6522e-02, -1.0507e-02, -2.6155e-02, -3.6207e-02, -2.4963e-02, + -2.1895e-02, -1.5993e-02), + T(-1.6325e-02, -2.7535e-02, -1.6704e-02, -1.4899e-02, -1.1344e-02, + -3.0802e-03, -1.2747e-02)), + T(T(-7.5157e-03, -2.8978e-02, -2.8847e-02, -4.5879e-02, -3.0130e-02, + -3.3889e-02, -5.1871e-02), + T(-2.1900e-02, -2.2046e-02, -2.7110e-02, -3.2612e-02, -2.8986e-02, + -6.6867e-02, -7.1081e-02), + T( 3.0462e-03, -2.0255e-02, -3.9770e-02, -3.5203e-02, -4.7388e-02, + -2.4220e-02, -4.6222e-02)), + T(T( 5.7844e-04, -1.6412e-04, 9.7524e-03, -6.9274e-03, 1.7444e-06, + 5.4107e-03, -2.1182e-02), + T(-1.5361e-02, 2.2865e-02, 1.7374e-02, 2.8522e-03, 3.3781e-02, + 1.0332e-02, 1.0356e-02), + T( 3.3926e-03, 3.6011e-02, 1.8886e-02, 2.5415e-02, 2.0812e-02, + 2.1618e-02, 2.0776e-02)), + T(T( 5.3066e-02, 5.4734e-02, 5.1326e-02, 3.5983e-02, 5.5721e-02, + 5.8108e-02, 3.7270e-02), + T( 7.3613e-02, 5.4528e-02, 6.9086e-02, 5.8593e-02, 3.3255e-02, + 7.0331e-02, 3.9792e-02), + T( 4.0440e-02, 4.5344e-02, 3.0102e-02, 3.9423e-02, 3.7462e-02, + 1.9178e-02, 3.4250e-02)), + T(T( 9.3921e-03, -6.3640e-03, 6.6344e-03, -2.9477e-02, 2.8380e-03, + 2.4094e-04, -3.8125e-02), + T( 1.3277e-02, 3.2003e-02, 9.2812e-03, 3.1793e-02, 3.5682e-02, + 5.4143e-03, -2.7538e-02), + T(-1.4505e-02, 4.2906e-03, -5.5038e-03, 1.1895e-02, -8.9942e-03, + 9.1047e-03, -5.2846e-03)), + T(T(-2.4140e-02, -4.9850e-02, -8.1354e-03, -4.0075e-02, -2.3858e-02, + -1.0505e-02, -1.8872e-03), + T(-5.3244e-02, -5.0973e-02, -5.3102e-02, -3.2843e-02, -4.9433e-02, + -2.6899e-02, -2.1426e-02), + T(-3.8070e-02, -3.4148e-02, -2.2365e-02, -1.0786e-02, -2.1428e-03, + -2.9661e-02, 6.5642e-03)), + T(T( 7.1718e-03, -1.8317e-02, -1.9746e-02, 3.5586e-04, 5.8551e-04, + 1.3969e-02, -2.5201e-03), + T(-1.3888e-02, -9.6641e-03, -3.8934e-02, -2.8148e-02, -2.5934e-02, + -1.8294e-02, -2.0061e-02), + T( 1.0523e-02, 2.6551e-02, -2.9795e-02, -9.7123e-03, -1.4083e-03, + -2.3482e-02, -1.5405e-02)), + T(T( 2.5275e-02, 1.6022e-02, 2.1474e-02, 2.3938e-02, 1.6918e-02, + 2.9566e-02, 1.6430e-02), + T(-8.9619e-03, -1.5747e-02, 2.2626e-02, 9.3860e-03, -2.7444e-03, + 1.0630e-02, 4.0585e-03), + T(-2.6552e-02, -4.6460e-02, -1.1829e-02, -5.0394e-02, -2.1685e-02, + -1.0684e-02, -3.7224e-02)), + T(T( 8.2827e-03, 1.7244e-02, 2.7117e-02, 9.7096e-05, 3.1359e-02, + 4.6453e-03, 9.5188e-03), + T( 4.0039e-02, 4.7410e-02, 9.9494e-03, 2.4956e-02, 2.7872e-02, + 2.4829e-02, 1.5199e-02), + T( 2.1342e-02, 3.1655e-02, 2.1581e-02, 2.5497e-02, 5.2575e-02, + 2.4982e-02, 2.5912e-02)), + T(T(-3.8185e-02, -3.9303e-02, -4.1358e-02, -4.0111e-02, -1.3078e-02, + -2.2576e-02, -2.8542e-02), + T(-3.6325e-02, -4.7150e-02, -1.7211e-02, -1.9650e-02, 5.6505e-04, + -4.6043e-03, -4.4149e-02), + T( 1.2474e-03, -2.1102e-02, -2.4141e-02, 9.8825e-03, -2.2259e-02, + -1.1524e-02, -1.6652e-04)), + T(T(-1.6188e-02, -2.3977e-02, 1.8660e-02, -1.5378e-02, -2.7290e-02, + -2.5314e-02, -1.1265e-02), + T(-2.8503e-02, -1.7718e-02, -5.1043e-03, -3.6894e-02, -1.6136e-02, + -3.3021e-02, -1.9824e-02), + T(-2.8551e-02, -3.7279e-02, -2.3878e-02, -2.9096e-02, -2.2290e-02, + -2.6733e-02, -2.2998e-02)), + T(T( 5.0010e-03, -8.0676e-03, -1.4430e-02, -1.5388e-02, 1.0738e-02, + 3.8478e-03, 2.1696e-03), + T(-2.3630e-03, -4.0806e-02, -2.7923e-02, -1.1444e-02, 3.1605e-03, + -1.7883e-02, -3.3700e-02), + T( 5.6951e-03, 1.8676e-02, -2.4579e-03, 1.0234e-02, 3.3008e-03, + 3.0289e-03, 3.3703e-02)))) + ) + val objectness = Tensor[Float](T(T(T( + T(-0.0429, -0.0315, -0.0317, -0.0458, -0.0145, -0.0326, -0.0305), + T(-0.0361, -0.0716, -0.0414, -0.0237, -0.0399, -0.0334, -0.0345), + T(-0.0168, -0.0163, -0.0441, -0.0193, -0.0388, -0.0227, -0.0345)), + T(T( 0.0194, -0.0012, 0.0251, -0.0154, -0.0265, -0.0014, 0.0094), + T( 0.0443, 0.0278, 0.0358, 0.0061, 0.0576, 0.0287, 0.0263), + T(-0.0037, -0.0024, 0.0217, 0.0264, 0.0165, 0.0058, 0.0382)), + T(T(-0.0011, -0.0058, -0.0089, -0.0017, -0.0266, -0.0007, -0.0156), + T( 0.0087, 0.0164, -0.0103, 0.0014, -0.0262, 0.0151, 0.0157), + T(-0.0223, 0.0009, -0.0051, -0.0074, -0.0148, -0.0156, -0.0043))))) + + val preNmsTopN: Int = 2000 + val postNmsTopN: Int = 2000 + val rpnPreNmsTopNTrain: Int = 2000 + + val proposal = new ProposalPostProcessor(2000, 2000, 2000, 2000, 0.7f, 0) + val output = proposal.forward(T(anchors, objectness, box_regression, Tensor[Float](T(20, 38)))) + + val expectOutput = Tensor[Float](T( + T(3.5029516f, 0.0, 33.70933f, 19.0f), + T(0.0, 0.0, 17.197811, 19.0), + T(0.0, 0.0, 24.902605, 17.08425), + T(14.575309, 0.0, 37.0, 19.0), + T(0.0, 0.0, 37.0f, 12.965991f)) + ) + + output[Tensor[Float]](1) should be(expectOutput) + } + + "AnchorGenerate" should "be ok" in { + val layer = new RegionRroposal(6, + Array[Float](32, 64, 128, 256, 512), + Array[Float](0.5f, 1.0f, 2.0f), + Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) + + val input = Tensor[Float](T(T(T(T(0.7668, 0.1659, 0.4393, 0.2243), + T(0.8935, 0.0497, 0.1780, 0.3011), + T(0.1893, 0.9186, 0.2131, 0.3957)), + T(T(0.6017, 0.4234, 0.5224, 0.4175), + T(0.0340, 0.9157, 0.3079, 0.6269), + T(0.8277, 0.6594, 0.0887, 0.4890)), + T(T(0.5887, 0.7340, 0.8497, 0.9112), + T(0.4847, 0.9436, 0.3904, 0.2499), + T(0.3206, 0.9753, 0.7582, 0.6688)), + T(T(0.2651, 0.2336, 0.5057, 0.5688), + T(0.0634, 0.8993, 0.2732, 0.3397), + T(0.1879, 0.5534, 0.2682, 0.9556)), + T(T(0.9761, 0.5934, 0.3124, 0.9431), + T(0.8519, 0.9815, 0.1132, 0.4783), + T(0.4436, 0.3847, 0.4521, 0.5569)), + T(T(0.9952, 0.0015, 0.0813, 0.4907), + T(0.2130, 0.4603, 0.1386, 0.0277), + T(0.5662, 0.3503, 0.6555, 0.7667))))) + + val expectedOutput = Tensor[Float](T(T(-22, -10, 25, 13), + T(-14, -14, 17, 17), + T(-10, -22, 13, 25), + T(-18, -10, 29, 13), + T(-10, -14, 21, 17), + T(-6, -22, 17, 25), + T(-14, -10, 33, 13), + T(-6, -14, 25, 17), + T(-2, -22, 21, 25), + T(-10, -10, 37, 13), + T(-2, -14, 29, 17), + T(2, -22, 25, 25), + T(-22, -6, 25, 17), + T(-14, -10, 17, 21), + T(-10, -18, 13, 29), + T(-18, -6, 29, 17), + T(-10, -10, 21, 21), + T(-6, -18, 17, 29), + T(-14, -6, 33, 17), + T(-6, -10, 25, 21), + T(-2, -18, 21, 29), + T(-10, -6, 37, 17), + T(-2, -10, 29, 21), + T(2, -18, 25, 29), + T(-22, -2, 25, 21), + T(-14, -6, 17, 25), + T(-10, -14, 13, 33), + T(-18, -2, 29, 21), + T(-10, -6, 21, 25), + T(-6, -14, 17, 33), + T(-14, -2, 33, 21), + T(-6, -6, 25, 25), + T(-2, -14, 21, 33), + T(-10, -2, 37, 21), + T(-2, -6, 29, 25), + T(2, -14, 25, 33))) + + val output = layer.anchorGenerator(T(input)) + + output.apply[Tensor[Float]](1) should be(expectedOutput) + } + "RPNHead" should "be ok" in { + val layer = new RegionRroposal(6, + Array[Float](32, 64, 128, 256, 512), + Array[Float](0.5f, 1.0f, 2.0f), + Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) + + val proposal = layer.rpnHead(6, 3) + val input = Tensor[Float](T(T(T(T(0.7668, 0.1659, 0.4393, 0.2243), + T(0.8935, 0.0497, 0.1780, 0.3011), + T(0.1893, 0.9186, 0.2131, 0.3957)), + T(T(0.6017, 0.4234, 0.5224, 0.4175), + T(0.0340, 0.9157, 0.3079, 0.6269), + T(0.8277, 0.6594, 0.0887, 0.4890)), + T(T(0.5887, 0.7340, 0.8497, 0.9112), + T(0.4847, 0.9436, 0.3904, 0.2499), + T(0.3206, 0.9753, 0.7582, 0.6688)), + T(T(0.2651, 0.2336, 0.5057, 0.5688), + T(0.0634, 0.8993, 0.2732, 0.3397), + T(0.1879, 0.5534, 0.2682, 0.9556)), + T(T(0.9761, 0.5934, 0.3124, 0.9431), + T(0.8519, 0.9815, 0.1132, 0.4783), + T(0.4436, 0.3847, 0.4521, 0.5569)), + T(T(0.9952, 0.0015, 0.0813, 0.4907), + T(0.2130, 0.4603, 0.1386, 0.0277), + T(0.5662, 0.3503, 0.6555, 0.7667))))) + + val weight_conv = Tensor[Float](T(T(T( + T( 1.2685e-03, 1.3564e-02, 5.6322e-03), + T(-1.0393e-03, -3.5746e-03, 3.9174e-03), + T(-6.8009e-03, 2.4094e-03, 4.6981e-03)), + T(T( 1.2426e-02, 5.4030e-03, -1.1454e-02), + T(-1.4592e-02, -1.6281e-02, 3.8337e-03), + T(-1.7180e-03, -3.1896e-02, 1.5914e-02)), + T(T(-2.4669e-04, -8.4661e-03, 2.9301e-04), + T(-5.7207e-03, -1.2546e-02, 4.8611e-04), + T( 1.1705e-02, -5.4102e-03, -7.1156e-03)), + T(T( 5.7526e-04, 6.2625e-03, -1.7736e-02), + T(-2.2050e-03, 2.7467e-02, -1.7599e-02), + T( 1.0230e-02, -1.1073e-03, -3.8986e-03)), + T(T(-1.0300e-02, -1.5446e-02, 5.7298e-03), + T(-2.0956e-02, -1.8055e-03, 2.3464e-03), + T(-1.4774e-03, 5.8926e-03, 2.2533e-02)), + T(T(-2.5548e-03, 1.6513e-03, -1.6292e-03), + T(-8.0389e-03, -9.1740e-03, 8.9856e-03), + T( 8.2623e-03, -3.6677e-03, -4.2506e-03))), + T(T(T(-1.2455e-02, 1.1245e-02, -2.0157e-02), + T( 9.9260e-03, -6.0842e-03, -1.3856e-02), + T( 1.0412e-02, -8.0432e-03, -6.2443e-03)), + T(T(-5.8823e-03, 1.6700e-02, -9.2747e-03), + T(-9.7585e-03, 1.3312e-02, 9.0074e-03), + T(-6.5847e-03, -9.3275e-03, -1.5749e-02)), + T(T( 1.4861e-02, -1.4092e-02, 1.4330e-02), + T( 3.8986e-03, -1.1516e-03, -2.3609e-03), + T(-2.2235e-02, 7.8841e-04, 4.1560e-04)), + T(T( 1.2813e-02, -8.2621e-03, 2.3098e-04), + T( 1.9301e-02, 7.8028e-03, 3.1802e-03), + T(-6.9918e-03, -3.9213e-03, 2.1955e-02)), + T(T( 3.3116e-03, 1.4171e-03, -1.5268e-02), + T( 2.5214e-03, 6.5413e-03, 2.1024e-02), + T( 6.3311e-03, 1.9332e-02, -2.4634e-03)), + T(T(-7.0092e-03, 6.3621e-03, -5.6589e-03), + T( 1.0318e-02, -1.0371e-02, 1.3739e-03), + T(-1.1312e-02, 6.4710e-03, -7.1830e-03))), + T(T(T(-1.1984e-02, -8.8376e-03, 6.4301e-03), + T( 7.2037e-04, -5.7234e-03, 1.6078e-02), + T( 1.0007e-03, -1.0746e-02, -1.0924e-03)), + T(T( 2.4635e-03, -9.9438e-03, -6.8856e-03), + T( 1.2039e-02, -2.5186e-03, -1.9443e-02), + T(-1.9203e-02, 1.1464e-02, 2.3850e-02)), + T(T(-3.5508e-04, -3.1787e-03, 3.5779e-03), + T(-1.7844e-02, -3.0524e-03, 8.5366e-03), + T( 3.8534e-03, 1.2612e-02, 5.9866e-03)), + T(T(-2.4725e-02, -5.4071e-04, -1.1862e-02), + T( 7.3836e-03, -3.1864e-03, -5.1757e-03), + T(-1.4699e-03, 5.1577e-03, 3.3928e-03)), + T(T( 2.4955e-03, -9.5512e-03, 7.0652e-03), + T( 1.2566e-02, -2.9903e-02, -3.2173e-04), + T(-2.3036e-03, 1.2172e-03, 1.0538e-02)), + T(T( 2.4320e-03, 8.3910e-03, 2.2082e-03), + T(-1.3217e-02, 4.4108e-04, -3.4124e-03), + T(-1.1553e-02, 4.9376e-03, 7.9121e-03))), + T(T(T( 1.2293e-02, -3.9778e-03, 2.1020e-03), + T( 8.3877e-03, 2.3666e-02, 6.8477e-03), + T( 5.2052e-03, 1.4803e-02, -7.5135e-03)), + T(T(-8.7030e-03, 5.8776e-03, -4.8942e-05), + T( 2.0430e-02, 5.8311e-04, -3.6140e-03), + T( 1.7116e-02, 8.4518e-03, -2.8076e-03)), + T(T( 9.1432e-03, 4.6386e-03, -1.0463e-02), + T( 6.0311e-03, 4.2746e-03, -3.4131e-03), + T( 1.9404e-03, 7.9359e-03, -7.6828e-04)), + T(T( 4.8792e-03, -2.5789e-02, 1.0007e-02), + T( 2.1705e-04, -8.6712e-03, -4.5113e-03), + T(-6.6698e-03, 2.7328e-04, 6.6046e-03)), + T(T( 7.3924e-03, 7.1265e-03, 4.3357e-03), + T( 3.9357e-04, -2.3774e-03, 6.4933e-03), + T( 7.2543e-03, -4.8372e-03, 5.6666e-03)), + T(T(-3.9601e-03, 1.3844e-02, -8.2588e-03), + T(-1.6542e-03, -1.3295e-02, 3.8030e-03), + T(-6.6701e-04, 6.8487e-03, 7.7284e-04))), + T(T(T(-1.3936e-03, -4.7838e-03, -3.1820e-03), + T( 2.2058e-03, -1.6855e-03, 1.8463e-02), + T( 9.5022e-03, -3.3961e-03, -6.5992e-03)), + T(T(-9.5200e-03, -4.0727e-03, 1.4081e-02), + T( 1.2446e-03, 1.1088e-02, 1.7009e-03), + T( 1.1670e-03, -7.9839e-03, 9.1257e-03)), + T(T(-2.5381e-03, 6.8467e-03, -7.4647e-04), + T( 5.9466e-04, 8.1772e-03, 2.8940e-03), + T( 4.2105e-03, -1.3101e-02, 8.6801e-03)), + T(T( 7.1093e-03, 9.3525e-03, 7.6763e-03), + T(-2.8895e-03, 6.6717e-03, 1.1738e-03), + T( 5.4419e-03, -2.8676e-04, 1.3919e-02)), + T(T( 1.0932e-02, -2.3391e-02, -8.9627e-03), + T(-6.2398e-03, -5.7453e-03, -5.7471e-03), + T( 7.2978e-03, -2.2365e-03, 3.7101e-04)), + T(T( 6.5447e-03, -2.5417e-03, -7.0376e-03), + T(-1.1011e-03, -6.9527e-03, -2.4869e-02), + T( 6.0163e-03, 5.7055e-03, 5.8137e-03))), + T(T(T( 2.5749e-04, 5.5009e-03, 1.9151e-03), + T( 9.8616e-03, 1.1613e-02, -1.7455e-03), + T( 3.1561e-03, -1.8205e-03, -3.4044e-03)), + T(T(-5.8910e-03, 3.6100e-03, -1.4282e-02), + T( 9.2737e-03, -7.0391e-03, 3.8460e-03), + T( 6.2735e-03, 6.5410e-03, 1.0932e-03)), + T(T( 8.8084e-03, 1.5566e-02, 2.1806e-02), + T( 1.7355e-02, -1.5105e-02, 7.6660e-04), + T( 3.3541e-03, -5.3618e-03, -4.8840e-03)), + T(T( 1.4804e-03, 4.5057e-03, -5.1785e-03), + T(-5.5912e-03, -1.8077e-02, 5.0915e-03), + T( 4.0559e-03, 3.3477e-03, 8.6055e-04)), + T(T( 9.6151e-03, -2.7296e-03, 1.6761e-02), + T(-6.7708e-03, 5.9753e-03, -5.5834e-03), + T(-5.9345e-03, 2.2870e-02, 5.4827e-03)), + T(T(-8.7740e-03, 1.4306e-02, 1.7519e-02), + T(-1.0057e-04, 2.8130e-03, -1.4722e-02), + T(-5.0060e-03, 8.9401e-04, 4.7907e-03))))) + + val weight_logits = Tensor[Float](T(T(T(T(0.0013f)), + T(T(0.0136f)), + T(T(-0.0002f)), + T(T(-0.0085f)), + T(T(0.0003f)), + T(T(-0.0057f))), + T(T(T(-0.0125f)), + T(T(0.0005f)), + T(T(0.0028f)), + T(T(-0.0215f)), + T(T(-0.0071f)), + T(T(0.0006f))), + T(T(T(0.0063f)), + T(T(-0.0177f)), + T(T(-0.0022f)), + T(T(0.0275f)), + T(T(-0.0105f)), + T(T(0.0112f))))) + + val weight_pred = Tensor[Float](T(T(T(T( 0.0013f)), + T(T( 0.0136f)), + T(T( 0.0056f)), + T(T(-0.0010f)), + T(T(-0.0036f)), + T(T( 0.0039f))), + T(T(T(-0.0068f)), + T(T( 0.0024f)), + T(T( 0.0047f)), + T(T( 0.0124f)), + T(T( 0.0054f)), + T(T(-0.0115f))), + T(T(T(-0.0146f)), + T(T(-0.0163f)), + T(T( 0.0038f)), + T(T(-0.0017f)), + T(T(-0.0319f)), + T(T( 0.0159f))), + T(T(T(-0.0002f)), + T(T(-0.0085f)), + T(T( 0.0003f)), + T(T(-0.0057f)), + T(T(-0.0125f)), + T(T( 0.0005f))), + T(T(T( 0.0117f)), + T(T(-0.0054f)), + T(T(-0.0071f)), + T(T( 0.0006f)), + T(T( 0.0063f)), + T(T(-0.0177f))), + T(T(T(-0.0022f)), + T(T( 0.0275f)), + T(T(-0.0176f)), + T(T( 0.0102f)), + T(T(-0.0011f)), + T(T(-0.0039f))), + T(T(T(-0.0103f)), + T(T(-0.0154f)), + T(T( 0.0057f)), + T(T(-0.0210f)), + T(T(-0.0018f)), + T(T( 0.0023f))), + T(T(T(-0.0015f)), + T(T( 0.0059f)), + T(T( 0.0225f)), + T(T(-0.0026f)), + T(T( 0.0017f)), + T(T(-0.0016f))), + T(T(T(-0.0080f)), + T(T(-0.0092f)), + T(T( 0.0090f)), + T(T( 0.0083f)), + T(T(-0.0037f)), + T(T(-0.0043f))), + T(T(T(-0.0125f)), + T(T( 0.0112f)), + T(T( 0.0044f)), + T(T( 0.0142f)), + T(T(-0.0043f)), + T(T( 0.0030f))), + T(T(T( 0.0266f)), + T(T(-0.0028f)), + T(T( 0.0017f)), + T(T( 0.0100f)), + T(T( 0.0022f)), + T(T(-0.0036f))), + T(T(T( 0.0081f)), + T(T( 0.0002f)), + T(T(-0.0084f)), + T(T( 0.0124f)), + T(T( 0.0151f)), + T(T(-0.0060f))))) + + val paramsTable = proposal.getParametersTable() + for (i <- paramsTable.keySet) { + val weight = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get + if (i.toString contains "_cls_logits") { + weight.copy(weight_logits) + } else if (i.toString contains "_bbox_pred") { + weight.copy(weight_pred) + } else { + weight.copy(weight_conv) + } + } + + val out = proposal.forward(input).toTable + + val expectOut1 = Tensor[Float](T(T(T( + T(1.8060e-06, 4.4266e-05, -4.4478e-04, -2.2285e-04), + T(-2.5498e-04, -6.5022e-04, -7.0122e-04, -6.0636e-04), + T(-1.8476e-04, -4.8722e-04, -5.2533e-06, 4.6438e-04)), + T(T(-7.3554e-04, -1.1268e-03, -1.3215e-03, -6.6221e-04), + T(-8.3318e-04, -1.5036e-03, -1.5799e-03, -1.1557e-03), + T(-8.6738e-05, -1.2133e-03, -7.8615e-04, -5.4828e-05)), + T(T( 2.7506e-04, 6.1878e-04, 1.2134e-03, 8.5279e-04), + T( 1.2438e-03, 1.7112e-03, 1.8907e-03, 1.8733e-03), + T( 3.9310e-04, 1.6892e-03, 6.9088e-04, -5.4726e-04))))) + + val expectOut2 = Tensor[Float]( + T(T(T(T( 3.2872e-04, 7.1260e-04, -1.4158e-04, 1.8721e-04), + T( 6.5671e-04, -3.3774e-05, 5.2606e-04, 2.5664e-04), + T( 1.2875e-04, 2.2438e-04, 5.7091e-04, 5.0023e-04)), + T(T(2.8230e-04, 5.8781e-04, 7.9334e-04, 1.9734e-04), + T(-1.2963e-04, 6.1144e-04, 6.2610e-05, 1.6840e-04), + T(-2.8223e-04, 4.0351e-04, 1.8795e-04, 1.1847e-04)), + T(T(-9.0362e-04, -9.5532e-04, -8.6006e-04, 1.9125e-05), + T( 3.8717e-04, -5.2082e-04, -3.2978e-04, 4.9921e-04), + T( 3.6314e-04, 1.7121e-04, -2.6632e-04, -5.8792e-04)), + T(T(-6.3682e-04, -9.0824e-04, -6.0754e-04, -2.8349e-04), + T(-4.8721e-04, -6.7283e-04, -1.0342e-03, -3.7313e-04), + T(-5.7329e-05, -4.1867e-04, -5.5699e-04, -3.3070e-04)), + T(T(-3.1610e-04, -7.2690e-04, 1.8156e-04, -3.7575e-04), + T(-1.2730e-03, -2.7875e-04, -1.4982e-03, -8.5495e-04), + T(-5.3218e-04, -5.5933e-04, -7.3757e-04, -2.1671e-04)), + T(T( 7.1007e-04, 1.3441e-03, 5.1928e-04, 5.4101e-04), + T( 1.0922e-03, 5.4261e-04, 8.5880e-04, 6.4227e-04), + T(-3.5457e-05, 8.1159e-04, 1.2434e-03, 1.0420e-03)), + T(T(-9.1579e-04, -1.6261e-03, -1.1619e-03, -7.7540e-04), + T(-1.2293e-03, -1.3072e-03, -1.5620e-03, -1.1916e-03), + T(-4.2265e-05, -1.3241e-03, -1.2636e-03, -6.3755e-04)), + T(T( 2.0630e-04, 5.1606e-04, -9.6566e-05, -4.2667e-05), + T(-1.0744e-05, -1.5150e-04, 3.6849e-05, -1.6123e-04), + T(-4.5339e-05, -1.2186e-04, 7.3991e-05, 2.0650e-04)), + T(T(-1.5414e-04, -5.6530e-05, 3.5260e-04, 6.4689e-05), + T(-2.3425e-04, 2.9584e-04, -1.8360e-04, 1.6757e-04), + T(-1.2330e-04, 2.3454e-04, -1.8758e-04, -3.1435e-04)), + T(T(5.7598e-04, 1.3014e-03, 6.5395e-04, 6.1371e-04), + T(1.1498e-03, 8.6787e-04, 1.3262e-03, 1.0322e-03), + T(1.5616e-04, 1.0433e-03, 1.0140e-03, 4.6570e-04)), + T(T(2.8315e-04, 3.3587e-04, 5.8697e-04, 2.1150e-04), + T(8.9972e-05, 5.7891e-04, 3.4025e-04, 3.5393e-04), + T(-6.9086e-05, 4.3144e-04, 1.5475e-04, -7.3657e-05)), + T(T(5.4750e-04, 6.1437e-04, 1.0269e-03, 2.9660e-04), + T(1.3999e-04, 9.9833e-04, 8.0928e-04, 4.0381e-04), + T(-9.1188e-05, 5.3704e-04, 3.3011e-04, 4.2898e-05))))) + + Equivalent.nearequals(out.apply[Tensor[Float]](1), expectOut1, 1e-4) should be(true) + Equivalent.nearequals(out.apply[Tensor[Float]](2), expectOut2, 1e-4) should be(true) + } +} + +class RegionRroposalSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val layer = new RegionRroposal(6, + Array[Float](32, 64, 128, 256, 512), + Array[Float](0.5f, 1.0f, 2.0f), + Array[Float](4, 8, 16, 32, 64), + 2000, 2000, 2000, 2000, 0.7f, 0).setName("RegionRroposal") + + val features = Tensor[Float](1, 6, 3, 4).rand() + val imgInfo = Tensor[Float](T(20, 38)) + runSerializationTest(layer, T(T(features), imgInfo)) + } +} + +class ProposalPostProcessorSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val proposal = new ProposalPostProcessor(2000, 2000, 2000, 2000, 0.7f, 0) + .setName("ProposalPostProcessor") + val anchors = Tensor[Float](63, 4).rand() + val objectness = Tensor[Float](1, 3, 3, 7).rand() + val box_regression = Tensor[Float](1, 12, 3, 7).rand() + runSerializationTest(proposal, T(anchors, objectness, box_regression, Tensor[Float](T(20, 38)))) + } +} From d519375a773a7a1a4fc0ae0085639feea926bc92 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 24 Sep 2019 08:34:36 +0800 Subject: [PATCH 0956/1065] [New feature] add maskrcnn (#2908) * add maskrcnn * fix mask head * move maskrcnn to models * add maskrcnn serialTest --- .../dllib/models/maskrcnn/MaskRCNN.scala | 373 ++++++++++++++++++ .../analytics/bigdl/dllib/nn/MaskHead.scala | 14 +- .../utils/serializer/ModuleSerializer.scala | 3 + .../dllib/models/maskrcnn/MaskRCNNSpec.scala | 174 ++++++++ .../bigdl/dllib/nn/MaskHeadSpec.scala | 6 +- 5 files changed, 560 insertions(+), 10 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala new file mode 100644 index 00000000000..9df6fa02c87 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala @@ -0,0 +1,373 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.models.maskrcnn + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.models.resnet.{Convolution, Sbn} +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.serializer._ +import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag +import scala.reflect.runtime._ + +case class MaskRCNNParams( + anchorSizes: Array[Float] = Array[Float](32, 64, 128, 256, 512), + aspectRatios: Array[Float] = Array[Float](0.5f, 1.0f, 2.0f), + anchorStride: Array[Float] = Array[Float](4, 8, 16, 32, 64), + preNmsTopNTest: Int = 1000, + postNmsTopNTest: Int = 1000, + preNmsTopNTrain: Int = 2000, + postNmsTopNTrain: Int = 2000, + rpnNmsThread: Float = 0.7f, + minSize: Int = 0, + boxResolution: Int = 7, + maskResolution: Int = 14, + scales: Array[Float] = Array[Float](0.25f, 0.125f, 0.0625f, 0.03125f), + samplingRatio: Int = 2, + boxScoreThresh: Float = 0.05f, + boxNmsThread: Float = 0.5f, + maxPerImage: Int = 100, + outputSize: Int = 1024, + layers: Array[Int] = Array[Int](256, 256, 256, 256), + dilation: Int = 1, + useGn: Boolean = false) + +class MaskRCNN(val inChannels: Int, + val outChannels: Int, + val numClasses: Int = 81, + val config: MaskRCNNParams = new MaskRCNNParams)(implicit ev: TensorNumeric[Float]) + extends Container[Activity, Activity, Float] { + + private val ImageInfo : Tensor[Float] = Tensor[Float](2) + private val backbone = buildBackbone(inChannels, outChannels) + private val rpn = RegionRroposal(inChannels, config.anchorSizes, config.aspectRatios, + config.anchorStride, config.preNmsTopNTest, config.postNmsTopNTest, config.preNmsTopNTrain, + config.postNmsTopNTrain, config.minSize) + private val boxHead = BoxHead(inChannels, config.boxResolution, config.scales, + config.samplingRatio, config.boxScoreThresh, config.boxNmsThread, config.maxPerImage, + config.outputSize, numClasses) + private val maskHead = MaskHead(inChannels, config.maskResolution, config.scales, + config.samplingRatio, config.layers, config.dilation, numClasses) + + // add layer to modules + modules.append(backbone.asInstanceOf[Module[Float]]) + modules.append(rpn.asInstanceOf[Module[Float]]) + modules.append(boxHead.asInstanceOf[Module[Float]]) + modules.append(maskHead.asInstanceOf[Module[Float]]) + + private def buildResNet50(): Module[Float] = { + + def shortcut(nInputPlane: Int, nOutputPlane: Int, stride: Int, + useConv: Boolean = false): Module[Float] = { + if (useConv) { + Sequential() + .add(Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride)) + .add(Sbn(nOutputPlane)) + } else { + Identity() + } + } + + def bottleneck(nInputPlane: Int, internalPlane: Int, nOutputPlane: Int, + stride: Int, useConv: Boolean = false): Module[Float] = { + val s = Sequential() + .add(Convolution(nInputPlane, internalPlane, 1, 1, stride, stride, 0, 0)) + .add(Sbn(internalPlane)) + .add(ReLU(true)) + .add(Convolution(internalPlane, internalPlane, 3, 3, 1, 1, 1, 1)) + .add(Sbn(internalPlane)) + .add(ReLU(true)) + .add(Convolution(internalPlane, nOutputPlane, 1, 1, 1, 1, 0, 0)) + .add(Sbn(nOutputPlane)) + + val m = Sequential() + .add(ConcatTable() + .add(s) + .add(shortcut(nInputPlane, nOutputPlane, stride, useConv))) + .add(CAddTable(true)) + .add(ReLU(true)) + m + } + + def layer(count: Int, nInputPlane: Int, nOutputPlane: Int, + downOutputPlane: Int, stride: Int = 1): Module[Float] = { + val s = Sequential() + .add(bottleneck(nInputPlane, nOutputPlane, downOutputPlane, stride, true)) + for (i <- 2 to count) { + s.add(bottleneck(downOutputPlane, nOutputPlane, downOutputPlane, 1, false)) + } + s + } + + val model = Sequential[Float]() + .add(Convolution(3, 64, 7, 7, 2, 2, 3, 3, optnet = false, propagateBack = false)) + .add(Sbn(64)) + .add(ReLU(true)) + .add(SpatialMaxPooling(3, 3, 2, 2, 1, 1)) + + val input = Input() + val node0 = model.inputs(input) + + val startChannels = 64 + val node1 = layer(3, startChannels, 64, inChannels, 1).inputs(node0) + val node2 = layer(4, inChannels, 128, inChannels * 2, 2).inputs(node1) + val node3 = layer(6, inChannels * 2, 256, inChannels * 4, 2).inputs(node2) + val node4 = layer(3, inChannels * 4, 512, inChannels * 8, 2).inputs(node3) + + Graph(input, Array(node1, node2, node3, node4)) + } + + private def buildBackbone(inChannels: Int, outChannels: Int): Module[Float] = { + val resnet = buildResNet50() + val inChannelList = Array(inChannels, inChannels*2, inChannels * 4, inChannels * 8) + val fpn = FPN(inChannelList, outChannels, topBlocks = 1) + val model = Sequential[Float]().add(resnet).add(fpn) + model + } + + override def updateOutput(input: Activity): Activity = { + val inputWidth = input.toTensor[Float].size(3) + val inputHeight = input.toTensor[Float].size(4) + ImageInfo.setValue(1, inputWidth) + ImageInfo.setValue(2, inputHeight) + + val features = this.backbone.forward(input) + val proposals = this.rpn.forward(T(features, ImageInfo)) + val boxOutput = this.boxHead.forward(T(features, proposals)).toTable + val postProcessorBox = boxOutput[Table](2) + val proposalsBox = postProcessorBox[Tensor[Float]](2) + val labelsBox = postProcessorBox[Tensor[Float]](1) + val mask = this.maskHead.forward(T(features, proposalsBox, labelsBox)) + output = T(proposalsBox, labelsBox, mask) + output + } + + override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { + throw new UnsupportedOperationException("MaskRCNN model only support inference now") + } +} + +object MaskRCNN extends ContainerSerializable { + def apply(inChannels: Int, outChannels: Int, numClasses: Int = 81, + config: MaskRCNNParams = new MaskRCNNParams)(implicit ev: TensorNumeric[Float]): MaskRCNN = + new MaskRCNN(inChannels, outChannels, numClasses, config) + + override def doLoadModule[T: ClassTag](context: DeserializeContext) + (implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = { + val attrMap = context.bigdlModule.getAttrMap + + val inChannels = DataConverter + .getAttributeValue(context, attrMap.get("inChannels")). + asInstanceOf[Int] + + val outChannels = DataConverter + .getAttributeValue(context, attrMap.get("outChannels")) + .asInstanceOf[Int] + + val numClasses = DataConverter + .getAttributeValue(context, attrMap.get("numClasses")) + .asInstanceOf[Int] + + // get MaskRCNNParams + val config = MaskRCNNParams( + anchorSizes = DataConverter + .getAttributeValue(context, attrMap.get("anchorSizes")) + .asInstanceOf[Array[Float]], + aspectRatios = DataConverter + .getAttributeValue(context, attrMap.get("aspectRatios")) + .asInstanceOf[Array[Float]], + anchorStride = DataConverter + .getAttributeValue(context, attrMap.get("anchorStride")) + .asInstanceOf[Array[Float]], + preNmsTopNTest = DataConverter + .getAttributeValue(context, attrMap.get("preNmsTopNTest")) + .asInstanceOf[Int], + postNmsTopNTest = DataConverter + .getAttributeValue(context, attrMap.get("postNmsTopNTest")) + .asInstanceOf[Int], + preNmsTopNTrain = DataConverter + .getAttributeValue(context, attrMap.get("preNmsTopNTrain")) + .asInstanceOf[Int], + postNmsTopNTrain = DataConverter + .getAttributeValue(context, attrMap.get("postNmsTopNTrain")) + .asInstanceOf[Int], + rpnNmsThread = DataConverter + .getAttributeValue(context, attrMap.get("rpnNmsThread")) + .asInstanceOf[Float], + minSize = DataConverter + .getAttributeValue(context, attrMap.get("minSize")) + .asInstanceOf[Int], + boxResolution = DataConverter + .getAttributeValue(context, attrMap.get("boxResolution")) + .asInstanceOf[Int], + maskResolution = DataConverter + .getAttributeValue(context, attrMap.get("maskResolution")) + .asInstanceOf[Int], + scales = DataConverter + .getAttributeValue(context, attrMap.get("scales")) + .asInstanceOf[Array[Float]], + samplingRatio = DataConverter + .getAttributeValue(context, attrMap.get("samplingRatio")) + .asInstanceOf[Int], + boxScoreThresh = DataConverter + .getAttributeValue(context, attrMap.get("boxScoreThresh")) + .asInstanceOf[Float], + maxPerImage = DataConverter + .getAttributeValue(context, attrMap.get("maxPerImage")) + .asInstanceOf[Int], + outputSize = DataConverter + .getAttributeValue(context, attrMap.get("outputSize")) + .asInstanceOf[Int], + layers = DataConverter + .getAttributeValue(context, attrMap.get("layers")) + .asInstanceOf[Array[Int]], + dilation = DataConverter + .getAttributeValue(context, attrMap.get("dilation")) + .asInstanceOf[Int], + useGn = DataConverter + .getAttributeValue(context, attrMap.get("useGn")) + .asInstanceOf[Boolean]) + + MaskRCNN(inChannels, outChannels, numClasses, config) + .asInstanceOf[AbstractModule[Activity, Activity, T]] + } + + override def doSerializeModule[T: ClassTag](context: SerializeContext[T], + maskrcnnBuilder : BigDLModule.Builder)(implicit ev: TensorNumeric[T]) : Unit = { + + val maskrcnn = context.moduleData.module.asInstanceOf[MaskRCNN] + + val inChannelsBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, inChannelsBuilder, maskrcnn.inChannels, + universe.typeOf[Int]) + maskrcnnBuilder.putAttr("inChannels", inChannelsBuilder.build) + + val outChannelsBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, outChannelsBuilder, maskrcnn.outChannels, + universe.typeOf[Int]) + maskrcnnBuilder.putAttr("outChannels", outChannelsBuilder.build) + + val numClassesBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, numClassesBuilder, maskrcnn.numClasses, + universe.typeOf[Int]) + maskrcnnBuilder.putAttr("numClasses", numClassesBuilder.build) + + // put MaskRCNNParams + val config = maskrcnn.config + + val anchorSizesBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, anchorSizesBuilder, + config.anchorSizes, universe.typeOf[Array[Float]]) + maskrcnnBuilder.putAttr("anchorSizes", anchorSizesBuilder.build) + + val aspectRatiosBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, aspectRatiosBuilder, + config.aspectRatios, universe.typeOf[Array[Float]]) + maskrcnnBuilder.putAttr("aspectRatios", aspectRatiosBuilder.build) + + val anchorStrideBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, anchorStrideBuilder, + config.anchorStride, universe.typeOf[Array[Float]]) + maskrcnnBuilder.putAttr("anchorStride", anchorStrideBuilder.build) + + val preNmsTopNTestBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, preNmsTopNTestBuilder, + config.preNmsTopNTest, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("preNmsTopNTest", preNmsTopNTestBuilder.build) + + val postNmsTopNTestBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, postNmsTopNTestBuilder, + config.postNmsTopNTest, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("postNmsTopNTest", postNmsTopNTestBuilder.build) + + val preNmsTopNTrainBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, preNmsTopNTrainBuilder, + config.preNmsTopNTrain, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("preNmsTopNTrain", preNmsTopNTrainBuilder.build) + + val postNmsTopNTrainBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, postNmsTopNTrainBuilder, + config.postNmsTopNTrain, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("postNmsTopNTrain", postNmsTopNTrainBuilder.build) + + val rpnNmsThreadBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, rpnNmsThreadBuilder, + config.rpnNmsThread, universe.typeOf[Float]) + maskrcnnBuilder.putAttr("rpnNmsThread", rpnNmsThreadBuilder.build) + + val minSizeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, minSizeBuilder, + config.minSize, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("minSize", minSizeBuilder.build) + + val boxResolutionBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, boxResolutionBuilder, + config.boxResolution, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("boxResolution", boxResolutionBuilder.build) + + val maskResolutionBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, maskResolutionBuilder, + config.maskResolution, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("maskResolution", maskResolutionBuilder.build) + + val scalesBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, scalesBuilder, + config.scales, universe.typeOf[Array[Float]]) + maskrcnnBuilder.putAttr("scales", scalesBuilder.build) + + val samplingRatioBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, samplingRatioBuilder, + config.samplingRatio, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("samplingRatio", samplingRatioBuilder.build) + + val boxScoreThreshBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, boxScoreThreshBuilder, + config.boxScoreThresh, universe.typeOf[Float]) + maskrcnnBuilder.putAttr("boxScoreThresh", boxScoreThreshBuilder.build) + + val maxPerImageBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, maxPerImageBuilder, + config.maxPerImage, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("maxPerImage", maxPerImageBuilder.build) + + val outputSizeBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, outputSizeBuilder, + config.outputSize, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("outputSize", outputSizeBuilder.build) + + val layersBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, layersBuilder, + config.layers, universe.typeOf[Array[Int]]) + maskrcnnBuilder.putAttr("layers", layersBuilder.build) + + val dilationBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, dilationBuilder, + config.dilation, universe.typeOf[Int]) + maskrcnnBuilder.putAttr("dilation", dilationBuilder.build) + + val useGnBuilder = AttrValue.newBuilder + DataConverter.setAttributeValue(context, useGnBuilder, + config.useGn, universe.typeOf[Boolean]) + maskrcnnBuilder.putAttr("useGn", useGnBuilder.build) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala index 7a804ed5c34..08f863b3c07 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala @@ -25,7 +25,7 @@ class MaskHead( val inChannels: Int, val resolution: Int, val scales: Array[Float], - val samplingRratio: Float, + val samplingRratio: Int, val layers: Array[Int], val dilation: Int, val numClasses: Int, @@ -78,7 +78,7 @@ class MaskHead( private[nn] def maskFeatureExtractor(inChannels: Int, resolution: Int, scales: Array[Float], - samplingRatio: Float, + samplingRatio: Int, layers: Array[Int], dilation: Int, useGn: Boolean = false): Module[Float] = { @@ -86,7 +86,7 @@ class MaskHead( require(dilation == 1, s"Only support dilation = 1, but got ${dilation}") val model = Sequential[Float]() - model.add(Pooler(resolution, scales, samplingRatio.toInt)) + model.add(Pooler(resolution, scales, samplingRatio)) var nextFeatures = inChannels var i = 0 @@ -103,15 +103,15 @@ class MaskHead( padW = dilation, padH = dilation, withBias = if (useGn) false else true - ).setName(s"mask_fcn{${i}}") + ).setName(s"mask_fcn${i + 1}") // weight init module.setInitMethod(MsraFiller(false), Zeros) - model.add(module) + model.add(module).add(ReLU[Float]()) nextFeatures = features i += 1 } - model.add(ReLU[Float]()) + model } } @@ -163,7 +163,7 @@ object MaskHead { def apply(inChannels: Int, resolution: Int = 14, scales: Array[Float] = Array[Float](0.25f, 0.125f, 0.0625f, 0.03125f), - samplingRratio: Float = 0.1f, + samplingRratio: Int = 2, layers: Array[Int] = Array[Int](256, 256, 256, 256), dilation: Int = 1, numClasses: Int = 81, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala index d797e5267e8..eb5ee69e4a3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleSerializer.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.utils.serializer import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.models.maskrcnn.MaskRCNN import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} import com.intel.analytics.bigdl.nn.keras.{KerasLayer, KerasLayerSerializer, Model, Sequential => KSequential} @@ -268,6 +269,8 @@ object ModuleSerializer extends ModuleSerializable{ SpatialSeparableConvolution) registerModule("com.intel.analytics.bigdl.nn.Transformer", Transformer) + registerModule("com.intel.analytics.bigdl.models.maskrcnn.MaskRCNN", + MaskRCNN) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala new file mode 100644 index 00000000000..bac36b5fed7 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala @@ -0,0 +1,174 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.models.maskrcnn + +import com.intel.analytics.bigdl.nn.Nms +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import com.intel.analytics.bigdl.utils.{RandomGenerator, T} +import org.scalatest.{FlatSpec, Matchers} + +class MaskRCNNSpec extends FlatSpec with Matchers { + "build maskrcnn" should "be ok" in { + RandomGenerator.RNG.setSeed(100) + val resNetOutChannels = 32 + val backboneOutChannels = 32 + val mask = new MaskRCNN(resNetOutChannels, backboneOutChannels) + mask.evaluate() + val input = Tensor[Float](1, 3, 224, 256).rand() + val output = mask.forward(input) + } + + "NMS" should "be ok" in { + val boxes = Tensor[Float](T( + T(18.0357, 0.0000, 41.2893, 37.1173), + T(30.0285, 6.2588, 53.1850, 39.0000), + T(26.0422, 0.0000, 49.1954, 39.0000), + T( 5.9485, 14.0573, 29.1708, 39.0000), + T(42.0456, 0.0000, 57.0000, 37.1553), + T(21.9588, 14.0357, 45.1161, 39.0000), + T( 6.0533, 0.0000, 29.4083, 39.0000), + T( 2.0541, 2.3791, 25.4243, 39.0000), + T(14.0495, 2.3053, 37.3108, 39.0000), + T(46.0309, 6.4025, 57.0000, 39.0000), + T(22.0302, 2.4089, 45.1933, 39.0000), + T(13.9671, 14.0175, 37.1495, 39.0000), + T(10.0404, 0.0000, 33.3284, 33.2829), + T(34.0374, 0.0000, 57.0000, 36.9072), + T(38.0379, 6.2769, 57.0000, 39.0000), + T(41.9751, 14.0583, 57.0000, 39.0000), + T( 0.0000, 0.0000, 13.2693, 33.3124), + T(38.0422, 0.0000, 57.0000, 28.9761), + T( 0.0000, 14.0690, 17.1186, 39.0000), + T( 0.0000, 6.0356, 13.2223, 39.0000), + T( 0.0000, 0.0000, 17.3122, 39.0000), + T(22.0270, 0.0000, 45.1928, 25.2032), + T(46.0094, 0.0000, 57.0000, 33.0826), + T( 0.0000, 0.0000, 33.7101, 13.0355), + T( 2.0302, 0.0000, 25.4260, 25.4481), + T(42.0226, 0.0000, 57.0000, 25.1449), + T(30.0364, 0.0000, 53.0853, 25.0766), + T(14.0171, 0.0000, 37.2881, 25.2999), + T(34.0521, 0.0000, 57.0000, 12.9051), + T( 0.0000, 3.8999, 57.0000, 39.0000), + T( 2.0133, 0.0000, 49.6427, 12.9898), + T(28.0456, 0.0000, 57.0000, 39.0000), + T( 0.0000, 11.8925, 47.3868, 39.0000), + T( 8.0708, 11.9606, 57.0000, 39.0000), + T( 0.0000, 0.0000, 27.2810, 39.0000), + T( 0.0000, 0.0000, 47.4577, 35.2592), + T( 0.0000, 0.0000, 57.0000, 39.0000), + T( 0.0000, 0.0000, 57.0000, 39.0000), + T(21.9457, 0.0000, 57.0000, 12.8811), + T( 0.0000, 0.0000, 57.0000, 39.0000), + T( 0.0000, 0.0000, 57.0000, 27.0690), + T(13.8674, 22.0563, 44.9398, 39.0000), + T(33.8700, 25.9730, 57.0000, 39.0000), + T( 0.0000, 22.0516, 20.9330, 39.0000), + T(41.9213, 21.9873, 57.0000, 39.0000), + T(17.8165, 0.0000, 57.0000, 16.8779), + T( 1.7646, 18.1004, 32.9480, 39.0000), + T(11.8512, 0.0000, 57.0000, 35.4317), + T(29.8503, 22.0435, 57.0000, 39.0000), + T( 9.7594, 18.0566, 40.9166, 39.0000), + T(33.7746, 1.9632, 57.0000, 24.9071), + T( 0.0000, 14.0776, 24.9558, 39.0000), + T(21.7241, 18.0735, 52.8998, 39.0000), + T( 0.0000, 0.0000, 29.2906, 29.5339), + T(41.8249, 0.0000, 57.0000, 17.0812), + T( 0.0000, 0.0000, 17.3257, 17.4717), + T( 0.0000, 0.0000, 17.1572, 25.5946), + T( 0.0000, 0.0000, 45.4454, 17.0065), + T( 0.0000, 2.0042, 21.2122, 33.4895), + T(37.8946, 18.1178, 57.0000, 39.0000), + T( 0.0000, 5.9850, 25.1862, 29.1060), + T( 1.7353, 6.0499, 33.1671, 37.4231), + T(21.6518, 26.0054, 57.0000, 39.0000), + T( 5.7049, 0.0000, 37.2819, 29.4436), + T(29.7011, 14.0272, 57.0000, 39.0000), + T(17.7255, 0.0000, 49.0772, 29.2946), + T(29.6133, 9.9153, 57.0000, 32.7949), + T( 0.0000, 26.0193, 32.8463, 39.0000), + T(17.6348, 10.0788, 48.9423, 39.0000), + T(21.6906, 2.1241, 52.9483, 33.3707), + T( 5.6194, 0.0000, 53.3307, 21.0163), + T(13.8104, 0.0000, 45.2210, 17.3200), + T(13.5956, 9.9687, 57.0000, 32.8566), + T( 5.7003, 10.0389, 37.0897, 39.0000), + T(13.7149, 2.0202, 45.0843, 33.2768), + T( 9.7322, 5.9888, 41.1038, 37.3045), + T( 5.5910, 26.0368, 52.8697, 39.0000), + T(29.7840, 0.0000, 57.0000, 17.1027), + T( 5.7736, 0.0000, 37.3917, 17.4214), + T( 0.0000, 13.9622, 36.9701, 36.8555), + T( 0.0000, 9.9967, 45.0663, 32.9533), + T( 0.0000, 0.0000, 33.2938, 21.2008), + T( 0.0000, 0.0000, 25.3888, 17.4817), + T(21.7062, 0.0000, 53.0319, 21.2508), + T( 9.6736, 0.0000, 41.2481, 21.3898), + T( 0.0000, 1.9933, 37.2186, 25.1230), + T( 5.5202, 5.9523, 53.1432, 28.9392), + T(25.5138, 5.9795, 57.0000, 28.8653), + T( 0.0000, 10.0011, 28.9181, 33.0324), + T( 5.5488, 14.0092, 52.8771, 36.8956), + T( 9.5096, 1.9473, 57.0000, 24.9822), + T(17.5084, 13.9728, 57.0000, 36.8385), + T( 0.0000, 22.0156, 40.7790, 39.0000), + T(17.5165, 22.0209, 57.0000, 39.0000), + T( 9.5040, 17.9792, 56.7784, 39.0000), + T( 0.0000, 5.9792, 41.1165, 29.0066))) + + val scores = Tensor[Float]( + T(0.1117, 0.8158, 0.2626, 0.4839, 0.6765, 0.7539, 0.2627, 0.0428, 0.2080, + 0.1180, 0.1217, 0.7356, 0.7118, 0.7876, 0.4183, 0.9014, 0.9969, 0.7565, + 0.2239, 0.3023, 0.1784, 0.8238, 0.5557, 0.9770, 0.4440, 0.9478, 0.7445, + 0.4892, 0.2426, 0.7003, 0.5277, 0.2472, 0.7909, 0.4235, 0.0169, 0.2209, + 0.9535, 0.7064, 0.1629, 0.8902, 0.5163, 0.0359, 0.6476, 0.3430, 0.3182, + 0.5261, 0.0447, 0.5123, 0.9051, 0.5989, 0.4450, 0.7278, 0.4563, 0.3389, + 0.6211, 0.5530, 0.6896, 0.3687, 0.9053, 0.8356, 0.3039, 0.6726, 0.5740, + 0.9233, 0.9178, 0.7590, 0.7775, 0.6179, 0.3379, 0.2170, 0.9454, 0.7116, + 0.1157, 0.6574, 0.3451, 0.0453, 0.9798, 0.5548, 0.6868, 0.4920, 0.0748, + 0.9605, 0.3271, 0.0103, 0.9516, 0.2855, 0.2324, 0.9141, 0.7668, 0.1659, + 0.4393, 0.2243, 0.8935, 0.0497, 0.1780, 0.3011)) + + val thresh = 0.5f + val inds = new Array[Int](scores.nElement()) + val nms = new Nms + val keepN = nms.nms(scores, boxes, thresh, inds) + + val expectedOutput = Array[Float](2.0f, 5.0f, 8.0f, 9.0f, 16.0f, + 21.0f, 23.0f, 24.0f, 25.0f, 36.0f, 42.0f, 43.0f, 49.0f, 55.0f, + 64.0f, 76.0f, 77.0f, 84.0f, 87.0f, 88.0f) + + for (i <- 0 to keepN - 1) { + require(expectedOutput.contains(inds(i) - 1), s"${i} ${inds(i)}") + } + } +} + +class MaskRCNNSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val resNetOutChannels = 32 + val backboneOutChannels = 32 + val mask = new MaskRCNN(resNetOutChannels, backboneOutChannels).setName("MaskRCNN") + mask.evaluate() + val input = Tensor[Float](1, 3, 224, 256).rand() + val output = mask.forward(input) + + runSerializationTest(mask, input) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala index c65ea3637ae..70017ddeb67 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala @@ -25,7 +25,7 @@ class MaskHeadSpec extends FlatSpec with Matchers { val inChannels: Int = 6 val resolution: Int = 14 val scales: Array[Float] = Array[Float](0.25f, 0.125f) - val samplingRratio: Float = 2.0f + val samplingRratio: Int = 2 val layers: Array[Int] = Array[Int](4, 4) val dilation: Int = 1 val numClasses: Int = 81 @@ -704,7 +704,7 @@ class MaskHeadSpec extends FlatSpec with Matchers { val dim_reduced: Int = 10 val resolution: Int = 14 val scales: Array[Float] = Array[Float](0.25f, 0.125f) - val samplingRratio: Float = 2.0f + val samplingRratio: Int = 2 val layers: Array[Int] = Array[Int](4, 4) val dilation: Int = 1 val numClasses: Int = 81 @@ -860,7 +860,7 @@ class MaskHeadSerialTest extends ModuleSerializationTest { val inChannels: Int = 6 val resolution: Int = 14 val scales: Array[Float] = Array[Float](0.25f, 0.125f) - val samplingRratio: Float = 2.0f + val samplingRratio: Int = 2 val layers: Array[Int] = Array[Int](4, 4) val dilation: Int = 1 val numClasses: Int = 81 From bf7fd0096e8cb8e2c8971b30f9ed9819028e374a Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Tue, 24 Sep 2019 17:08:36 +0800 Subject: [PATCH 0957/1065] Add Onnx Supported Layers (#2902) * remove duplicated layers --- .../intel/analytics/bigdl/dllib/nn/Gemm.scala | 82 ---------- .../bigdl/dllib/nn/onnx/Gather.scala | 28 ---- .../analytics/bigdl/dllib/nn/onnx/Gemm.scala | 97 +++++++++++ .../analytics/bigdl/dllib/nn/onnx/Shape.scala | 16 +- .../utils/python/api/PythonBigDLOnnx.scala | 28 ++-- .../dllib/utils/serializer/ModuleLoader.scala | 1 + .../bigdl/dllib/utils/tf/loaders/Cast.scala | 2 + .../dllib/utils/tf/loaders/ParseExample.scala | 1 + .../utils/tf/loaders/ParseSingleExample.scala | 1 + .../bigdl/dllib/utils/tf/loaders/Sum.scala | 1 + .../analytics/bigdl/dllib/nn/GemmSpec.scala | 147 ----------------- .../bigdl/dllib/nn/onnx/GemmSpec.scala | 152 ++++++++++++++++++ .../bigdl/dllib/nn/onnx/ShapeSpec.scala | 1 - 13 files changed, 286 insertions(+), 271 deletions(-) delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Gemm.scala delete mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gather.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gemm.scala delete mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GemmSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/GemmSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Gemm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Gemm.scala deleted file mode 100644 index 5d41a1b6624..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Gemm.scala +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package com.intel.analytics.bigdl.nn - -import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.ops.{BatchMatMul, Operation} -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Table - -import scala.reflect.ClassTag - - -/** - * General Matrix multiplication - * - * Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), - * input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), - * and output tensor Y has shape (M, N). - * - * @param alpha Scalar multiplier for the product of input tensors A * B. - * @param beta Scalar multiplier for input tensor C. - * @param transA Whether A should be transposed - * @param transB Whether B should be transposed - * @param ev - * @tparam T The numeric type in this module parameters. - */ -class Gemm[T: ClassTag]( - val alpha: Float = 1, val beta: Float = 1, - val transA: Boolean = false, val transB: Boolean = false -)(implicit ev: TensorNumeric[T]) -extends Operation[Table, Tensor[T], T] { - - private val internalModel: Module[T] = { - val tensorA = Input() - val tensorB = Input() - val tensorC = Input() - val alphaMul = MulConstant(scalar = alpha, inplace = true).inputs( - BatchMatMul(adjX = transA, adjY = transB).inputs(Array(tensorA, tensorB)) - ) - val betaAdd = CAddTable().inputs(Array(alphaMul, - MulConstant(scalar = beta, inplace = true).inputs(tensorC))) - - Graph(Array(tensorA, tensorB, tensorC), betaAdd) - } - - override def updateOutput(input: Table): Tensor[T] = { - require(input.length() == 3, "Input should be a table contains 3 tensors, actually size is: " - + input.toTable.length()) - internalModel.forward(input) - output = internalModel.output.asInstanceOf[Tensor[T]] - output - } - - override def release(): Unit = { - internalModel.release() - } - -} - -object Gemm { - def apply[@specialized(Float, Double) T: ClassTag]( - alpha: Float = 1, beta: Float = 1, - transA: Boolean = false, transB: Boolean = false - )(implicit ev: TensorNumeric[T]): Gemm[T] = { - new Gemm[T](alpha, beta, transA, transB) - } -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gather.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gather.scala deleted file mode 100644 index 1eb0adad33a..00000000000 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gather.scala +++ /dev/null @@ -1,28 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - - -package com.intel.analytics.bigdl.nn.onnx - -import com.intel.analytics.bigdl.nn.ops -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import scala.reflect.ClassTag - - -object Gather { - def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): - ops.Gather[T, D] = new ops.Gather() -} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gemm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gemm.scala new file mode 100644 index 00000000000..869a8ded08f --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gemm.scala @@ -0,0 +1,97 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.onnx + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.ops.{BatchMatMul, Operation} +import com.intel.analytics.bigdl.nn.{CAddTable, Graph, Input, MulConstant, Sequential} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.{T, Table} + +import scala.reflect.ClassTag + + +/** + * General Matrix multiplication + * + * Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), + * input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), + * and output tensor Y has shape (M, N). + * + * @param alpha Scalar multiplier for the product of input tensors A * B. + * @param beta Scalar multiplier for input tensor C. + * @param transA Whether A should be transposed + * @param transB Whether B should be transposed + * @param matrixB matrix B + * @param matrixC matrix C + * @param ev + * @tparam T The numeric type in this module parameters. + */ +private[bigdl] class Gemm[T: ClassTag]( + val alpha: Float, val beta: Float, + val transA: Boolean, val transB: Boolean, + val matrixB: Tensor[T], + val matrixC: Tensor[T] +)(implicit ev: TensorNumeric[T]) +extends Operation[Tensor[T], Tensor[T], T] { + + require(matrixB.dim() == 2, "Matrix B should be 2D") + require(matrixC.dim() == 2, "Matrix C should be 2D") + + // alpha * B' + val transformedMatrixB = (if (transB == true) matrixB.t() else matrixB).mul(ev.fromType(alpha)) + // beta * C + val transformedMatrixC = matrixC.mul(ev.fromType(beta)) + + // alpha * A' * B' + beta * C + val gemmGraph: Module[T] = { + val inputA = Input() + val inputB = Input() + val inputC = Input() + // alpha * A' * B' + val alphaMul = BatchMatMul(adjX = transA).inputs(Array(inputA, inputB)) + // alpha * A' * B' + beta * C + val betaAdd = CAddTable().inputs(Array(alphaMul, inputC)) + Graph(Array(inputA, inputB, inputC), betaAdd) + } + + override def updateOutput(input: Tensor[T]): Tensor[T] = { + output = gemmGraph.forward(T(input, + transformedMatrixB, transformedMatrixC)).asInstanceOf[Tensor[T]] + output + } + + override def release(): Unit = { + gemmGraph.release() + release() + } + +} + + +object Gemm { + def apply[@specialized(Float, Double) T: ClassTag]( + alpha: Float, beta: Float, + transA: Boolean, transB: Boolean, + matrixB: Tensor[T], matrixC: Tensor[T] + )(implicit ev: TensorNumeric[T]): Gemm[T] = { + new Gemm[T](alpha = alpha, beta = beta, transA = transA, transB = transB, + matrixB = matrixB, matrixC = matrixC) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala index 52a30ee4ccf..e9e6e0a6d05 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Shape.scala @@ -16,12 +16,13 @@ package com.intel.analytics.bigdl.nn.onnx +import com.intel.analytics.bigdl.nn.abstractnn.TensorModule + +import scala.reflect.ClassTag import com.intel.analytics.bigdl.nn.ops.Operation import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import scala.reflect.ClassTag - /** * A layer which takes a tensor as input and outputs an 1D tensor containing the shape of the input. @@ -29,8 +30,8 @@ import scala.reflect.ClassTag * @param ev * @tparam T The numeric type in this module parameters */ -class Shape[T: ClassTag](implicit ev: TensorNumeric[T]) - extends Operation[Tensor[T], Tensor[T], T] { +private[bigdl] class Shape[T: ClassTag](implicit ev: TensorNumeric[T]) + extends TensorModule[T] { override def updateOutput(input: Tensor[T]): Tensor[T] = { val dimSize = input.nDimension() @@ -41,6 +42,13 @@ class Shape[T: ClassTag](implicit ev: TensorNumeric[T]) output } + // Shape if a constant layer, + // which means Output does not depend on Weight which gets updated by Gradient + override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { + gradInput.resizeAs(input) + gradInput.zero() + gradInput + } } object Shape { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala index be44718f293..65c4e4eda7c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala @@ -16,24 +16,34 @@ package com.intel.analytics.bigdl.python.api -import com.intel.analytics.bigdl.nn.onnx.Shape -import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect.ClassTag +import com.intel.analytics.bigdl.nn.onnx._ +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -class PythonBigDLOnnx[T: ClassTag](implicit ev: TensorNumeric[T]) extends PythonBigDL[T] { - def createShape(): Shape[T] = { - Shape() - } +private[bigdl] object PythonBigDLOnnx { + + def ofFloat(): PythonBigDLOnnx[Float] = new PythonBigDLOnnx[Float]() + + def ofDouble(): PythonBigDLOnnx[Double] = new PythonBigDLOnnx[Double]() } -object PythonBigDLOnnx { +class PythonBigDLOnnx[T: ClassTag](implicit ev: TensorNumeric[T]) extends PythonBigDL[T] { - def ofFloat(): PythonBigDLOnnx[Float] = new PythonBigDLOnnx[Float]() + def createGemm(alpha: Float, beta: Float, transA: Int, transB: Int, + matrixB: JTensor, matrixC: JTensor): Gemm[T] = { + Gemm(alpha, beta, + (if (transA == 0) false else true), + (if (transB == 0) false else true), + toTensor(matrixB), toTensor(matrixC)) + } - def ofDouble(): PythonBigDLOnnx[Double] = new PythonBigDLOnnx[Double]() + + def createShape(): Shape[T] = { + Shape[T]() + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala index 5e2c6c7f74b..7f475b5af3f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/serializer/ModuleLoader.scala @@ -130,6 +130,7 @@ object ModuleLoader { val tensorStorage = tensorValue.getTensorType match { case TensorType.DENSE => tensor.storage() case TensorType.QUANT => tensor.asInstanceOf[QuantizedTensor[_]].getStorage + case _ => throw new UnsupportedOperationException("Unsupported Tensor Type") } storages(tensorId) = tensor storages(storageId) = tensorStorage diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala index 70d50d38450..c9c6fc0f453 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Cast.scala @@ -45,6 +45,8 @@ class Cast extends TensorflowOpsLoader { case DataType.DT_STRING => CastOps[T, String]() case DataType.DT_FLOAT => CastOps[T, Float]() case DataType.DT_DOUBLE => CastOps[T, Double]() + case _ => throw new UnsupportedOperationException("Unsupported data type: " + + dataType.toString) } layer } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala index 031741860d2..3c1318f1088 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseExample.scala @@ -42,6 +42,7 @@ class ParseExample extends TensorflowOpsLoader { case DataType.DT_FLOAT => FloatType case DataType.DT_DOUBLE => DoubleType case DataType.DT_STRING => StringType + case _ => throw new UnsupportedOperationException("Unsupported data type") } val denseShapes = nodeDef.getAttrMap.get("dense_shapes") .getList.getShapeList.asScala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseSingleExample.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseSingleExample.scala index 20cdb4c08fe..8c3f898f243 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseSingleExample.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/ParseSingleExample.scala @@ -42,6 +42,7 @@ class ParseSingleExample extends TensorflowOpsLoader { case DataType.DT_FLOAT => FloatType case DataType.DT_DOUBLE => DoubleType case DataType.DT_STRING => StringType + case _ => throw new IllegalArgumentException() } val denseKeysByteArray = nodeDef.getAttrMap.get("dense_keys").getList. getSList.asScala.map(_.toByteArray) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala index cf818edfe47..a47fa6acf5c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/tf/loaders/Sum.scala @@ -51,6 +51,7 @@ class Sum extends TensorflowOpsLoader { SumOps[T, Float](keepDims, startFromZero = true) case DataType.DT_DOUBLE => SumOps[T, Double](keepDims, startFromZero = true) + case _ => throw new UnsupportedOperationException() } } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GemmSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GemmSpec.scala deleted file mode 100644 index 6f1dace7eac..00000000000 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GemmSpec.scala +++ /dev/null @@ -1,147 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.nn - -import com.intel.analytics.bigdl.nn.ops.BatchMatMul -import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.T -import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest -import org.scalatest.{FlatSpec, Matchers} - - -class GemmSpec extends FlatSpec with Matchers { - - "Gemm forward" should "work" in { - val transA = false - val transB = false - - val inputA = Tensor[Float](4, 2).rand() - val inputB = Tensor[Float](2, 7).rand() - val inputC = Tensor[Float](4, 7).rand() - - val tensorA = Input() - val tensorB = Input() - val tensorC = Input() - - val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(tensorA, tensorB)) - val add = CAddTable().inputs(Array(mul, tensorC)) - var model = Graph(Array(tensorA, tensorB, tensorC), add) - - var myGemm = new Gemm() - val myInput = T(inputA, inputB, inputC) - - val out1 = model.forward(myInput) - val out2 = myGemm.forward(myInput) - - out1 should be(out2) - - } - - - "Gemm with transA forward" should "work" in { - val transA = true - val transB = false - - var inputA = Tensor[Float](2, 4).rand() - var transInputA = inputA.t() - var inputB = Tensor[Float](2, 7).rand() - var transInputB = inputB.t() - var inputC = Tensor[Float](4, 7).rand() - - val tensorA = Input() - val tensorB = Input() - val tensorC = Input() - val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(tensorA, tensorB)) - val add = CAddTable().inputs(Array(mul, tensorC)) - var model = Graph(Array(tensorA, tensorB, tensorC), add) - - var myGemm = new Gemm(transA = true) - - val out1 = model.forward(T(inputA, inputB, inputC)) - val out2 = myGemm.forward(T(inputA, inputB, inputC)) - - out1 should be(out2) - - } - - - "Gemm with transB forward" should "work" in { - val transA = false - val transB = true - - var inputA = Tensor[Float](4, 2).rand() - var transInputA = inputA.t() - var inputB = Tensor[Float](7, 2).rand() - var transInputB = inputB.t() - var inputC = Tensor[Float](4, 7).rand() - - val tensorA = Input() - val tensorB = Input() - val tensorC = Input() - val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(tensorA, tensorB)) - val add = CAddTable().inputs(Array(mul, tensorC)) - var model = Graph(Array(tensorA, tensorB, tensorC), add) - - var myGemm = new Gemm(transB = true) - - val out1 = model.forward(T(inputA, inputB, inputC)) - val out2 = myGemm.forward(T(inputA, inputB, inputC)) - - out1 should be(out2) - - } - - - "Gemm with transA & transB forward" should "work" in { - val transA = true - val transB = true - - var tensorA = Tensor[Float](2, 4).rand() - var tensorB = Tensor[Float](7, 2).rand() - var tensorC = Tensor[Float](4, 7).rand() - - val inputA = Input() - val inputB = Input() - val inputC = Input() - - val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(inputA, inputB)) - val add = CAddTable().inputs(Array(mul, inputC)) - var model = Graph(Array(inputA, inputB, inputC), add) - - var myGemm = new Gemm(transA = transA, transB = transB) - - val out1 = model.forward(T(tensorA, tensorB, tensorC)) - val out2 = myGemm.forward(T(tensorA, tensorB, tensorC)) - - out1 should be(out2) - - } - -} - -class GemmSerialTest extends ModuleSerializationTest { - override def test(): Unit = { - val gemm = Gemm[Float]().setName("Gemm") - - val inputA = Tensor(2, 2).rand() - val inputB = Tensor(2, 2).rand() - val inputC = Tensor(2, 2).rand() - val input = T(inputA, inputB, inputC) - - runSerializationTest(gemm, input) - } -} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/GemmSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/GemmSpec.scala new file mode 100644 index 00000000000..25218c5a277 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/GemmSpec.scala @@ -0,0 +1,152 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.onnx + +import com.intel.analytics.bigdl.nn.ops.BatchMatMul +import com.intel.analytics.bigdl.nn.{CAddTable, Graph, Input} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} + + +class GemmSpec extends FlatSpec with Matchers { + + "Gemm forward" should "work" in { + val transA = false + val transB = false + + val tensorA = Tensor[Float](4, 2).rand() + val tensorB = Tensor[Float](2, 7).rand() + val tensorC = Tensor[Float](4, 7).rand() + + val inputA = Input() + val inputB = Input() + val inputC = Input() + val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(inputA, inputB)) + val add = CAddTable().inputs(Array(mul, inputC)) + var model = Graph(Array(inputA, inputB, inputC), add) + + var myGemm = new Gemm(alpha = 1, beta = 1, transA = false, transB = false, + matrixB = tensorB, matrixC = tensorC + ) + + val myInput = T(tensorA, tensorB, tensorC) + + val out1 = model.forward(myInput) + val out2 = myGemm.forward(tensorA) + + out1 should be(out2) + + } + + + "Gemm with transA forward" should "work" in { + val transA = true + val transB = false + + val tensorA = Tensor[Float](2, 4).rand() + val tensorB = Tensor[Float](2, 7).rand() + val tensorC = Tensor[Float](4, 7).rand() + + val inputA = Input() + val inputB = Input() + val inputC = Input() + val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(inputA, inputB)) + val add = CAddTable().inputs(Array(mul, inputC)) + var model = Graph(Array(inputA, inputB, inputC), add) + + var myGemm = new Gemm(alpha = 1, beta = 1, transA = transA, transB = transB, + matrixB = tensorB, matrixC = tensorC + ) + + val out1 = model.forward(T(tensorA, tensorB, tensorC)) + val out2 = myGemm.forward(tensorA) + + out1 should be(out2) + + } + + + "Gemm with transB forward" should "work" in { + val transA = false + val transB = true + + val tensorA = Tensor[Float](4, 2).rand() + val tensorB = Tensor[Float](7, 2).rand() + val tensorC = Tensor[Float](4, 7).rand() + + val inputA = Input() + val inputB = Input() + val inputC = Input() + val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(inputA, inputB)) + val add = CAddTable().inputs(Array(mul, inputC)) + var model = Graph(Array(inputA, inputB, inputC), add) + + var myGemm = new Gemm(alpha = 1, beta = 1, transA = transA, transB = transB, + matrixB = tensorB, matrixC = tensorC + ) + + val out1 = model.forward(T(tensorA, tensorB, tensorC)) + val out2 = myGemm.forward(tensorA) + + out1 should be(out2) + + } + + + "Gemm with transA & transB forward" should "work" in { + val transA = true + val transB = true + + val tensorA = Tensor[Float](2, 4).rand() + val tensorB = Tensor[Float](7, 2).rand() + val tensorC = Tensor[Float](4, 7).rand() + + val inputA = Input() + val inputB = Input() + val inputC = Input() + val mul = BatchMatMul(adjX = transA, adjY = transB).inputs(Array(inputA, inputB)) + val add = CAddTable().inputs(Array(mul, inputC)) + var model = Graph(Array(inputA, inputB, inputC), add) + + var myGemm = new Gemm(alpha = 1, beta = 1, transA = transA, transB = transB, + matrixB = tensorB, matrixC = tensorC + ) + + val out1 = model.forward(T(tensorA, tensorB, tensorC)) + val out2 = myGemm.forward(tensorA) + + out1 should be(out2) + + } + +} + +class GemmSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val tensorA = Tensor[Float](2, 2).rand() + val tensorB = Tensor[Float](2, 2).rand() + val tensorC = Tensor[Float](2, 2).rand() + + val gemm = Gemm[Float](alpha = 1, beta = 1, transA = false, transB = false, + matrixB = tensorB, matrixC = tensorC + ).setName("Gemm") + + runSerializationTest(gemm, tensorA) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ShapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ShapeSpec.scala index 3836d35a01a..b7f6d7200f7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ShapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ShapeSpec.scala @@ -18,7 +18,6 @@ package com.intel.analytics.bigdl.nn.onnx import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import org.scalatest.{FlatSpec, Matchers} -import scala.util.Random class ShapeSpec extends FlatSpec with Matchers { From 509ac6712e81c63498ba609522c1420cb7b97fdc Mon Sep 17 00:00:00 2001 From: Menooker Date: Wed, 25 Sep 2019 15:41:09 +0800 Subject: [PATCH 0958/1065] Update RoiLabel class and add RoiImageFeatureToBatch (#2913) * add MeanAveragePrecision validation method * Add MAP basic code for object detection * update tests * bug fixes based on results of former MAP validation method * update documents * add python binding * typo fix, style change, change calculateAP to private * update comments * update RoiLabel, add RoiImageFeatureToBatch * fix typo in class name * updates by suggestions * minor updates * Move RoiMiniBatch to MTImageFeatureToBatch.scala * mask in RoiLabel now have Floats not Bytes * use IndexedSeq for RoiLabel * style fix * add isCrowd and origSize to final target table * style fix * isCrowd change to float, add doc * add tests and bug fixes * add util getting RoiLabels from ImageFeatures * add util getting RoiLabels from Table * comment out the tests * rename utils in RoiLabel --- .../vision/image/MTImageFeatureToBatch.scala | 204 +++++++++++++++--- .../vision/image/label/roi/RoiLabel.scala | 47 +++- .../image/MTImageFeatureToBatchSpec.scala | 120 +++++++++++ 3 files changed, 334 insertions(+), 37 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala index 1f9f65acb3f..37c28cdb01a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -16,37 +16,55 @@ package com.intel.analytics.bigdl.transform.vision.image import java.util.concurrent.atomic.AtomicInteger - -import com.intel.analytics.bigdl.dataset.{MiniBatch, Transformer, Utils} +import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, Transformer, Utils} +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.utils.Engine - +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import com.intel.analytics.bigdl.utils.{Engine, T, Table} +import scala.collection.mutable.IndexedSeq import scala.reflect.ClassTag object MTImageFeatureToBatch { + /** + * The transformer from ImageFeature to mini-batches + * @param width width of the output images + * @param height height of the output images + * @param batchSize batch size + * @param transformer pipeline for pre-processing, finally outputting ImageFeature + * @param toRGB if converted to RGB, default format is BGR + * @param extractRoi if true, extract ROI labels for segmentation; else the labels are for + * classification + * @return + */ def apply(width: Int, height: Int, batchSize: Int, - transformer: FeatureTransformer, toRGB: Boolean = true) + transformer: FeatureTransformer, toRGB: Boolean = true, extractRoi: Boolean = false) : MTImageFeatureToBatch = { - new MTImageFeatureToBatch ( - width, height, batchSize, transformer, toRGB) + if (extractRoi) { + new RoiMTImageFeatureToBatch ( + width, height, batchSize, transformer, toRGB) + } else { + new ClassificationMTImageFeatureToBatch ( + width, height, batchSize, transformer, toRGB) + } } } /** - * A transformer pipleline wrapper to create Minibatch in multiple threads - * @param width final image width - * @param height final image height + * An abstract class to convert ImageFeature iterator to MiniBatches. This transformer will be run + * on each image feature. "processImageFeature" will be called to buffer the image features. When + * there are enough buffered image features to form a batch, "createBatch" will be called. + * You should override processImageFeature to buffer each image feature, and createBatch + * to convert the buffered data into a mini-batch * @param totalBatchSize global batch size - * @param transformer pipleline for pre-processing - * @param toRGB if converted to RGB, default format is BGR + * @param transformer pipeline for pre-processing */ -class MTImageFeatureToBatch private[bigdl](width: Int, height: Int, - totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = true) +abstract class MTImageFeatureToBatch private[bigdl]( + totalBatchSize: Int, transformer: FeatureTransformer) extends Transformer[ImageFeature, MiniBatch[Float]] { - private val batchSize = Utils.getBatchSize(totalBatchSize) + protected val batchSize: Int = Utils.getBatchSize(totalBatchSize) - private val parallelism = Engine.coreNumber() + protected val parallelism: Int = Engine.coreNumber() private def getPosition(count: AtomicInteger): Int = { val position = count.getAndIncrement() @@ -57,11 +75,9 @@ class MTImageFeatureToBatch private[bigdl](width: Int, height: Int, _ => new PreFetch -> transformer.cloneTransformer() ).toArray - private val frameLength = height * width - private val featureData: Array[Float] = new Array[Float](batchSize * frameLength * 3) - private val labelData: Array[Float] = new Array[Float](batchSize) - private val featureTensor: Tensor[Float] = Tensor[Float]() - private val labelTensor: Tensor[Float] = Tensor[Float]() + protected def processImageFeature(img: ImageFeature, position: Int) + + protected def createBatch(batchSize: Int): MiniBatch[Float] override def apply(prev: Iterator[ImageFeature]): Iterator[MiniBatch[Float]] = { val iterators = transformers.map(_.apply(prev)) @@ -81,26 +97,18 @@ class MTImageFeatureToBatch private[bigdl](width: Int, height: Int, position != -1 }) { val img = iterators(tid).next() - img.copyTo(featureData, position * frameLength * 3, toRGB = toRGB) - labelData(position) = img.getLabel.asInstanceOf[Tensor[Float]].valueAt(1) + processImageFeature(img, position) record += 1 } record })).sum - - if (labelTensor.nElement() != batch) { - featureTensor.set(Storage[Float](featureData), - storageOffset = 1, sizes = Array(batch, 3, height, width)) - labelTensor.set(Storage[Float](labelData), - storageOffset = 1, sizes = Array(batch)) - } - - MiniBatch(featureTensor, labelTensor) + createBatch(batch) } } } } + private class PreFetch extends Transformer[ImageFeature, ImageFeature] { override def apply(prev: Iterator[ImageFeature]): Iterator[ImageFeature] = { new Iterator[ImageFeature] { @@ -127,3 +135,135 @@ private class PreFetch extends Transformer[ImageFeature, ImageFeature] { } } } + +/** + * A transformer pipeline wrapper to create Minibatch in multiple threads for classification + * @param width final image width + * @param height final image height + * @param totalBatchSize global batch size + * @param transformer pipeline for pre-processing + * @param toRGB if converted to RGB, default format is BGR + */ +class ClassificationMTImageFeatureToBatch private[bigdl](width: Int, height: Int, + totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = true) + extends MTImageFeatureToBatch(totalBatchSize, transformer) { + + private val frameLength = height * width + private val featureData: Array[Float] = new Array[Float](batchSize * frameLength * 3) + private val labelData: Array[Float] = new Array[Float](batchSize) + private val featureTensor: Tensor[Float] = Tensor[Float]() + private val labelTensor: Tensor[Float] = Tensor[Float]() + + override protected def processImageFeature(img: ImageFeature, position: Int): Unit = { + img.copyTo(featureData, position * frameLength * 3, toRGB = toRGB) + labelData(position) = img.getLabel.asInstanceOf[Tensor[Float]].valueAt(1) + } + + override protected def createBatch(batch: Int): MiniBatch[Float] = { + if (labelTensor.nElement() != batch) { + featureTensor.set(Storage[Float](featureData), + storageOffset = 1, sizes = Array(batch, 3, height, width)) + labelTensor.set(Storage[Float](labelData), + storageOffset = 1, sizes = Array(batch)) + } + + MiniBatch(featureTensor, labelTensor) + } +} + +/** + * A batch of images with flattened RoiLabels + * the getTarget() returns a Table with key from 1 to batchSize. Each key in the table is mapped to + * a Table for the annotation of an image in the batch. The annotation table holds the annotation + * info for one image (assume the image has N detections). The annotation table has + * + * Key Value + * RoiLabel.CLASSES the categories for each detections (see RoiLabel.clasees field) + * (1 x N), or (2 x N) Tensor[Float] + * RoiLabel.BBOXES the bboxes, (N x 4) Tensor[Float] + * RoiLabel.MASKS (Optional) the mask data, Array[Tensor[Float]\]. The outer array has N + * elements. The inner tensor holds the data for segmentation + * RoiLabel.ISCROWD Whether each detection is crowd. (1 x N) Tensor[Float]. + * -1: unknown, 0: not crowd, 1: is crowd + * RoiLabel.ORIGSIZE The original size of the image, tuple of (height, width, channels) + */ +class RoiMiniBatch(val input: Tensor[Float], val target: IndexedSeq[RoiLabel], + val isCrowd: IndexedSeq[Tensor[Float]], val originalSizes: IndexedSeq[(Int, Int, Int)]) + extends MiniBatch[Float] { + + override def size(): Int = { + input.size(1) + } + + override def getInput(): Tensor[Float] = input + + override def getTarget(): Table = { + val tables = (target, isCrowd, originalSizes).zipped.map { case (roiLabel, crowd, size) => + roiLabel.toTable + .update(RoiLabel.ISCROWD, crowd) + .update(RoiLabel.ORIGSIZE, size) + } + T.seq(tables) + } + + override def slice(offset: Int, length: Int): MiniBatch[Float] = { + val subInput = input.narrow(1, offset, length) + val subTarget = target.view(offset - 1, length) // offset starts from 1 + val subIsCrowd = isCrowd.view(offset - 1, length) // offset starts from 1 + val subSize = originalSizes.view(offset - 1, length) // offset starts from 1 + RoiMiniBatch(subInput, subTarget, subIsCrowd, subSize) + } + + override def set(samples: Seq[Sample[Float]])(implicit ev: TensorNumeric[Float]) + : RoiMiniBatch.this.type = { + throw new NotImplementedError("do not use Sample here") + } +} + +object RoiMiniBatch { + def apply(data: Tensor[Float], target: IndexedSeq[RoiLabel], + isCrowd: IndexedSeq[Tensor[Float]], originalSizes: IndexedSeq[(Int, Int, Int)]): + RoiMiniBatch = new RoiMiniBatch(data, target, isCrowd, originalSizes) +} + + +/** + * A transformer pipeline wrapper to create RoiMiniBatch in multiple threads + * The output "target" is a Table. The keys are from 1 to sizeof(batch). The values are + * the tables for each RoiLabel. Each Roi label table, contains fields of RoiLabel class. + * @param width final image width + * @param height final image height + * @param totalBatchSize global batch size + * @param transformer pipeline for pre-processing + * @param toRGB if converted to RGB, default format is BGR + */ +class RoiMTImageFeatureToBatch private[bigdl](width: Int, height: Int, + totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = true) + extends MTImageFeatureToBatch(totalBatchSize, transformer) { + + private val frameLength = height * width + private val featureData: Array[Float] = new Array[Float](batchSize * frameLength * 3) + private val labelData: Array[RoiLabel] = new Array[RoiLabel](batchSize) + private val isCrowdData: Array[Tensor[Float]] = new Array[Tensor[Float]](batchSize) + private val origSizeData: Array[(Int, Int, Int)] = new Array[(Int, Int, Int)](batchSize) + private var featureTensor: Tensor[Float] = null + + override protected def processImageFeature(img: ImageFeature, position: Int): Unit = { + img.copyTo(featureData, position * frameLength * 3, toRGB = toRGB) + val isCrowd = img(RoiLabel.ISCROWD).asInstanceOf[Tensor[Float]] + val label = img.getLabel.asInstanceOf[RoiLabel] + require(label.bboxes.size(1) == isCrowd.size(1), "The number of detections" + + "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") + isCrowdData(position) = isCrowd + labelData(position) = label + origSizeData(position) = img.getOriginalSize + } + + override protected def createBatch(batchSize: Int): MiniBatch[Float] = { + if (featureTensor == null) { + featureTensor = Tensor(Storage[Float](featureData), + storageOffset = 1, size = Array(batchSize, 3, height, width)) + } + RoiMiniBatch(featureTensor, labelData.view, isCrowdData.view, origSizeData.view) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala index f6da79287d6..ee757225030 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -25,26 +25,41 @@ import com.intel.analytics.bigdl.utils.{T, Table} * @param classes N (class labels) or 2 * N, the first row is class labels, * the second line is difficults * @param bboxes N * 4 + * @param masks the array of annotation masks of the targets */ -case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float]) { +case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], + masks: Array[Tensor[Float]] = null) { def copy(target: RoiLabel): Unit = { classes.resizeAs(target.classes).copy(target.classes) bboxes.resizeAs(target.bboxes).copy(target.bboxes) + require(target.masks == null, "Copying RoiLabels with masks not supported") } if (classes.dim() == 1) { - require(classes.size(1) == bboxes.size(1), "the number of classes should be" + - " equal to the number of bounding box numbers") + require(classes.size(1) == bboxes.size(1), s"the number of classes ${classes.size(1)} should " + + s"be equal to the number of bounding box numbers ${bboxes.size(1)}") + if (masks != null) { + require(classes.size(1) == masks.length, s"the number of classes ${classes.size(1)} should " + + s"be equal to the number of mask array ${masks.length}") + } } else if (classes.nElement() > 0 && classes.dim() == 2) { require(classes.size(2) == bboxes.size(1), s"the number of classes ${ classes.size(2) }" + s"should be equal to the number of bounding box numbers ${ bboxes.size(1) }") + if (masks != null) { + require(classes.size(2) == masks.length, s"the number of classes ${classes.size(2)}" + + s"should be equal to the number of bounding box numbers ${masks.length}") + } } def toTable: Table = { val table = T() - table.insert(classes) - table.insert(bboxes) + if (masks != null) { + table(RoiLabel.MASKS) = masks + } + table(RoiLabel.CLASSES) = classes + table(RoiLabel.BBOXES) = bboxes + table } def size(): Int = { @@ -53,6 +68,28 @@ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float]) { } object RoiLabel { + val CLASSES = "classes" + val BBOXES = "bboxes" + val MASKS = "masks" + // ISCROWD and ORIGSIZE are stored in ImageFeature + val ISCROWD = "is_crowd" + val ORIGSIZE = "size" + + + def getClasses(tab: Table): Tensor[Float] = tab[Tensor[Float]](CLASSES) + def getBBoxes(tab: Table): Tensor[Float] = tab[Tensor[Float]](BBOXES) + def getMasks(tab: Table): Array[Tensor[Float]] = + tab[Array[Tensor[Float]]](MASKS) + def getIsCrowd(tab: Table): Tensor[Float] = + tab[Tensor[Float]](ISCROWD) + + /** + * @return (height, width, channel) + */ + def getOrigSize(tab: Table): (Int, Int, Int) = + tab[(Int, Int, Int)](ORIGSIZE) + + def fromTensor(tensor: Tensor[Float]): RoiLabel = { val label = tensor.narrow(2, 1, 2).transpose(1, 2).contiguous() val rois = tensor.narrow(2, 3, 4) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala new file mode 100644 index 00000000000..968e1603b84 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala @@ -0,0 +1,120 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import com.intel.analytics.bigdl.utils.{Engine, Table} +import org.apache.spark.SparkContext +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAfter { + + var sc: SparkContext = null + before { + val conf = Engine.createSparkConf().setAppName("MTImageFeatureToBatchSpec") + .setMaster("local[2]") + sc = new SparkContext(conf) + Engine.init + } + + after { + if (null != sc) sc.stop() + } + + // todo: There is a race-condition bug in MTImageFeatureToBatch + /* + "MTImageFeatureToBatch classification" should "work well" in { + // + val imgData = (0 to 1000).map(idx => (idx to (idx + 10*10*3)).map(_.toFloat).toArray) + .map(arr => { + val imf = ImageFeature() + imf(ImageFeature.floats) = arr + val lab = Tensor[Float](Array(arr(0)), Array(1)) + imf(ImageFeature.label) = lab + imf(ImageFeature.originalSize) = (10, 10, 3) + imf + }) + val transformer = MTImageFeatureToBatch(10, 10, 19, new FeatureTransformer {}, toRGB = false) + val miniBatch = transformer(imgData.toIterator) + // val imgCheck = new Array[Boolean](1000) + miniBatch + .take(5) + // .take(1000 / 19) + .foreach(batch => { + (batch.size() <= 19) should be (true) + val input = batch.getInput().asInstanceOf[Tensor[Float]] + val target = batch.getTarget().asInstanceOf[Tensor[Float]] + input.size() should be (Array(batch.size(), 3, 10, 10)) + target.size() should be (Array(batch.size())) + for(i <- 1 to batch.size()) { + // R + val idx = input.valueAt(i, 1, 1, 1).toInt + val G = input.valueAt(i, 2, 1, 1).toInt + val B = input.valueAt(i, 3, 1, 1).toInt + idx should be (G - 1) + B should be (G + 1) + input.valueAt(i, 3, 10, 10) should be((idx.toFloat + 10 * 10 * 3 - 1) +- 0.000001f) + target.valueAt(i) should be (idx.toFloat) + /* imgCheck(idx) should be (false) + imgCheck(idx) = true */ + } + + }) + + } + + "MTImageFeatureToBatch with ROI" should "work well" in { + // + val imgData = (0 to 1000).map(idx => (idx to (idx + 10*10*3)).map(_.toFloat).toArray) + .map(arr => { + val imf = ImageFeature() + imf(ImageFeature.floats) = arr + imf(ImageFeature.label) = RoiLabel( + Tensor(new Array[Float](2), Array(2)), + Tensor(new Array[Float](2*4), Array(2, 4)), + Array(Tensor[Float](), Tensor[Float]()) + ) + imf(RoiLabel.ISCROWD) = Tensor(Array(0f, 1f), Array(2)) + imf(ImageFeature.originalSize) = (10, 10, 3) + imf + }) + val transformer = MTImageFeatureToBatch(10, 10, 19, new FeatureTransformer {}, + toRGB = false, extractRoi = true) + val miniBatch = transformer(imgData.toIterator) + // val imgCheck = new Array[Boolean](1000) + miniBatch + .take(5) + // .take(1000 / 19) + .foreach(batch => { + (batch.size() <= 19) should be (true) + val target = batch.getTarget().asInstanceOf[Table] + target.length() should be (batch.size()) + for(i <- 1 to batch.size()) { + val t = target(i).asInstanceOf[Table] + t[Tensor[Float]](RoiLabel.ISCROWD) should be (Tensor(Array(0f, 1f), Array(2))) + t[(Int, Int, Int)](RoiLabel.ORIGSIZE) should be((10, 10, 3)) + t[Tensor[Float]](RoiLabel.BBOXES).size() should be (Array(2, 4)) + t[Tensor[Float]](RoiLabel.CLASSES).size() should be (Array(2)) + t[Array[Tensor[Float]]](RoiLabel.MASKS).length should be (2) + } + + }) + + } */ + +} From 9f86fa0813a3af23c89b006e32ee87f89f4eedd1 Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Thu, 26 Sep 2019 17:29:56 +0800 Subject: [PATCH 0959/1065] feat: MKLDNN GRU forward/backward support (#2893) --- .../analytics/bigdl/dllib/nn/mkldnn/RNN.scala | 20 +- .../bigdl/dllib/nn/mkldnn/RNNSpec.scala | 1169 ++++++++++++++++- 2 files changed, 1179 insertions(+), 10 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala index 2e581570527..b0f65a1c22d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNN.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} import scala.collection.mutable.ArrayBuffer /** - * @param mode : the type of RNN cell + * @param mode : the type of RNN cell (LSTM / GRU) * @param inputSize : the size of input vector * @param hiddenSize : the size of hidden state * @param f : the type of output activation function @@ -86,18 +86,19 @@ class RNN( if(layers > 1) { require(inputSize == hiddenSize, - "If layers of LSTM is more than 1, the input size and the hidden size should equal.\n" + "If layer number of RNN is more than 1, the input size and the hidden size should equal.\n" + "inputSize: " + inputSize + '\n' + "hiddenSize: " + hiddenSize) } var (ngates, nstates) = mode match { case AlgKind.VanillaLstm => (4, 2) + case AlgKind.VanillaGru => (3, 1) case _ => throw new UnsupportedOperationException("Not support such RNN Cell. Cell type: " + mode) } - /** TODO: Multi-layer Bidirectional Sum LSTM is available in MKLDNN, + /** TODO: Multi-layer Bidirectional Sum RNN is available in MKLDNN, * TODO: but the current version of BigDL BLAS does not support it. */ @@ -105,7 +106,7 @@ class RNN( case Direction.UnidirectionalLeft2Right | Direction.UnidirectionalRight2Left => (1, 1) case Direction.BidirectionalConcat => - require(layers == 1, "Bidirectional Concat LSTM does not support multiple layers. " + + require(layers == 1, "Bidirectional Concat RNN does not support multiple layers. " + "layers = " + layers) (2, 2) case Direction.BidirectionalSum => (2, 1) @@ -118,6 +119,11 @@ class RNN( * MKLDNN Gate 2 -> nn/LSTM Gate 3 (forget gate) * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) + * + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 */ weightShape = Array(layers, numOfDirections, inputSize, ngates, hiddenSize) @@ -181,8 +187,8 @@ class RNN( batchSize = inputs(0).shape(0) stepSize = inputs(0).shape(1) case _ => - throw new UnsupportedOperationException("Not support such input format. " + - "The input format is: " + inputs(0).layout) + throw new UnsupportedOperationException("Unsupported input format: " + + inputs(0).layout) } inputShape = Array(stepSize, batchSize, inputSize) @@ -208,6 +214,8 @@ class RNN( rnnCellDesc = mode match { case AlgKind.VanillaLstm => MklDnnMemory.RNNCellDescInit(AlgKind.VanillaLstm, f, flags, alpha, clipping) + case AlgKind.VanillaGru => + MklDnnMemory.RNNCellDescInit(AlgKind.VanillaGru, f, flags, alpha, clipping) case _ => throw new UnsupportedOperationException("Not support such RNN cell. " + "Cell type: " + mode) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala index b478ec9a0aa..9c24c683e0d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala @@ -404,9 +404,10 @@ class RNNSpec extends FlatSpec with Matchers{ * MKLDNN Gate 3 -> nn/LSTM Gate 2 (hidden) * MKLDNN Gate 4 -> nn/LSTM Gate 4 (output gate) * - * uniParams(0) -> input weights - * uniParams(1) -> bias - * uniParams(2) -> hidden weights + * l -> 0 until common_n_layers + * uniParams(3 * l) -> input weights + * uniParams(3 * l + 1) -> bias + * uniParams(3 * l + 2) -> hidden weights */ var initWeight0 = Tensor[Float](Array(common_n_layers, commonSize * lstm_n_gates, commonSize)) @@ -928,7 +929,7 @@ class RNNSpec extends FlatSpec with Matchers{ Equivalent.nearequals(mkldnn_gradBias0(2), blas_gradBias_2) should be(true) } - "LSTM UnidirectionalInference Multilayers updateGradInput" should "work correctly" in { + "LSTM UnidirectionalTraining Multilayers updateGradInput" should "work correctly" in { val seqLength = 3 val batchSize = 2 val commonSize = 5 @@ -1178,4 +1179,1164 @@ class RNNSpec extends FlatSpec with Matchers{ System.clearProperty("bigdl.engineType") } + + "GRU UnidirectionalInference updateOutput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.UnidirectionalLeft2Right + + val n_layers = 1 + val gru_n_gates = 3 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + + var initWeight = Tensor[Float]( + Array(n_layers, 1, inputSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(n_layers, 1, hiddenSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(n_layers, 1, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + /** + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 + * + * uniParams(0) -> input weights + * uniParams(1) -> bias + * uniParams(2) -> hidden weights Gate 2 and Gate 1 + * uniParams(3) -> hidden weights Gate 3 + */ + + val mkldnnGRU1 = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaGru, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias)) + mkldnnGRU1.evaluate() + mkldnnGRU1.compile(InferencePhase) + val mkldnn_output1 = mkldnnGRU1.forward(input) + + direction = Direction.UnidirectionalRight2Left + val mkldnnGRU2 = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaGru, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias)) + mkldnnGRU2.evaluate() + mkldnnGRU2.compile(InferencePhase) + val mkldnn_output2 = mkldnnGRU2.forward(input) + + var inputt = input.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(inputSize, gru_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + initWeightIter = initWeightIter.resize(Array(hiddenSize, gru_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + initBias = initBias.resize(Array(gru_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(hiddenSize * gru_n_gates, inputSize)) + var initBias0 = Tensor[Float](Array(gru_n_gates * hiddenSize)) + var initWeightIter0 = Tensor[Float](Array(hiddenSize * 2, hiddenSize)) + var initWeightIter1 = Tensor[Float](Array(hiddenSize * 1, hiddenSize)) + + val concat = nn.JoinTable(1, 0) + initWeight0 = concat.forward(T(initWeight(2), initWeight(1), + initWeight(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0 = concat.forward(T(initWeightIter(2), initWeightIter(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1 = initWeightIter(3) + initBias0 = concat.forward(T(initBias(2), initBias(1), initBias(3))) + .asInstanceOf[Tensor[Float]].clone() + + val blasGRU = nn.Recurrent().add(nn.GRU(inputSize, hiddenSize)) + + val uniParams = blasGRU.parameters()._1 + initWeight0 = initWeight0.resizeAs(uniParams(0)) + initBias0 = initBias0.resizeAs(uniParams(1)) + initWeightIter0 = initWeightIter0.resizeAs(uniParams(2)) + initWeightIter1 = initWeightIter1.resizeAs(uniParams(3)) + + uniParams(0).copy(initWeight0) + uniParams(1).copy(initBias0) + uniParams(2).copy(initWeightIter0) + uniParams(3).copy(initWeightIter1) + + val blas_output1 = blasGRU.forward(inputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_output1).asInstanceOf[Tensor[Float]], + blas_output1) should be(true) + + /** + * nn/GRU Right2Left + */ + val reverse = nn.Reverse(2) + inputt = reverse.forward(inputt) + + var blas_output2 = blasGRU.forward(inputt) + blas_output2 = reverse.forward(blas_output2).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_output2).asInstanceOf[Tensor[Float]], + blas_output2) should be(true) + } + + "GRU BidirectionalConcatInference updateOutput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalConcat + + val common_n_layers = 1 + val gru_n_gates = 3 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + + var initWeight = Tensor[Float]( + Array(common_n_layers, 2, + inputSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 2, + hiddenSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 2, + gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + /** + * biParams(0 - 3) and (4 - 7) are for the two directions respectively + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 + * + * biParams(0) -> input weights + * biParams(1) -> bias + * biParams(2) -> hidden weights Gate 2 and Gate 1 + * biParams(3) -> hidden weights Gate 3 + * biParams(4) -> input weights + * biParams(5) -> bias + * biParams(6) -> hidden weights Gate 2 and Gate 1 + * biParams(7) -> hidden weights Gate 3 + */ + + val mkldnnGRU = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaGru, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias)) + mkldnnGRU.evaluate() + mkldnnGRU.compile(InferencePhase) + val mkldnn_output = mkldnnGRU.forward(input) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + val inputt = input.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(2, inputSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter.resize(Array(2, hiddenSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(2, gru_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(2, hiddenSize * gru_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(2, hiddenSize * 2, hiddenSize)) + var initWeightIter1 = Tensor[Float](Array(2, hiddenSize * 1, hiddenSize)) + var initBias0 = Tensor[Float](Array(2, gru_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 0) + initWeight0(1) = concat.forward(T(initWeight(1)(2), initWeight(1)(1), + initWeight(1)(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(1) = concat.forward(T(initWeightIter(1)(2), initWeightIter(1)(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1(1) = initWeightIter(1)(3).clone() + initBias0(1) = concat.forward(T(initBias(1)(2), initBias(1)(1), initBias(1)(3))) + .asInstanceOf[Tensor[Float]].clone() + + initWeight0(2) = concat.forward(T(initWeight(2)(2), initWeight(2)(1), + initWeight(2)(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(2) = concat.forward(T(initWeightIter(2)(2), initWeightIter(2)(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1(2) = initWeightIter(2)(3).clone() + initBias0(2) = concat.forward(T(initBias(2)(2), initBias(2)(1), initBias(2)(3))) + .asInstanceOf[Tensor[Float]].clone() + + val blasGRU = nn.BiRecurrent[Float](nn.JoinTable[Float](3, 0) + .asInstanceOf[AbstractModule[Table, Tensor[Float], Float]]) + .add(nn.GRU(inputSize, hiddenSize)) + + val biParams = blasGRU.parameters()._1 + initWeight0(1).resizeAs(biParams(0)) + initBias0(1).resizeAs(biParams(1)) + initWeightIter0(1).resizeAs(biParams(2)) + initWeightIter1(1).resizeAs(biParams(3)) + initWeight0(2).resizeAs(biParams(4)) + initBias0(2).resizeAs(biParams(5)) + initWeightIter0(2).resizeAs(biParams(6)) + initWeightIter1(2).resizeAs(biParams(7)) + + biParams(0).copy(initWeight0(1)) + biParams(1).copy(initBias0(1)) + biParams(2).copy(initWeightIter0(1)) + biParams(3).copy(initWeightIter1(1)) + biParams(4).copy(initWeight0(2)) + biParams(5).copy(initBias0(2)) + biParams(6).copy(initWeightIter0(2)) + biParams(7).copy(initWeightIter1(2)) + + val blas_output = blasGRU.forward(inputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_output).asInstanceOf[Tensor[Float]], + blas_output) should be(true) + } + + "GRU BidirectionalSumInference updateOutput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalSum + + val common_n_layers = 1 + val gru_n_gates = 3 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + + var initWeight = Tensor[Float]( + Array(common_n_layers, 2, + inputSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 2, + hiddenSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 2, + gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + /** + * biParams(0 - 3) and (4 - 7) are for the two directions respectively + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 + * + * biParams(0) -> input weights + * biParams(1) -> bias + * biParams(2) -> hidden weights Gate 2 and Gate 1 + * biParams(3) -> hidden weights Gate 3 + * biParams(4) -> input weights + * biParams(5) -> bias + * biParams(6) -> hidden weights Gate 2 and Gate 1 + * biParams(7) -> hidden weights Gate 3 + */ + + val mkldnnGRU = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaGru, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias)) + mkldnnGRU.evaluate() + mkldnnGRU.compile(InferencePhase) + val mkldnn_output = mkldnnGRU.forward(input) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + val inputt = input.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(2, inputSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter.resize(Array(2, hiddenSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(2, gru_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(2, hiddenSize * gru_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(2, hiddenSize * 2, hiddenSize)) + var initWeightIter1 = Tensor[Float](Array(2, hiddenSize * 1, hiddenSize)) + var initBias0 = Tensor[Float](Array(2, gru_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 0) + initWeight0(1) = concat.forward(T(initWeight(1)(2), initWeight(1)(1), + initWeight(1)(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(1) = concat.forward(T(initWeightIter(1)(2), initWeightIter(1)(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1(1) = initWeightIter(1)(3).clone() + initBias0(1) = concat.forward(T(initBias(1)(2), initBias(1)(1), initBias(1)(3))) + .asInstanceOf[Tensor[Float]].clone() + + initWeight0(2) = concat.forward(T(initWeight(2)(2), initWeight(2)(1), + initWeight(2)(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(2) = concat.forward(T(initWeightIter(2)(2), initWeightIter(2)(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1(2) = initWeightIter(2)(3).clone() + initBias0(2) = concat.forward(T(initBias(2)(2), initBias(2)(1), initBias(2)(3))) + .asInstanceOf[Tensor[Float]].clone() + + val blasGRU = nn.BiRecurrent[Float](nn.CAddTable() + .asInstanceOf[AbstractModule[Table, Tensor[Float], Float]]) + .add(nn.GRU(inputSize, hiddenSize)) + + val biParams = blasGRU.parameters()._1 + initWeight0(1).resizeAs(biParams(0)) + initBias0(1).resizeAs(biParams(1)) + initWeightIter0(1).resizeAs(biParams(2)) + initWeightIter1(1).resizeAs(biParams(3)) + initWeight0(2).resizeAs(biParams(4)) + initBias0(2).resizeAs(biParams(5)) + initWeightIter0(2).resizeAs(biParams(6)) + initWeightIter1(2).resizeAs(biParams(7)) + + biParams(0).copy(initWeight0(1)) + biParams(1).copy(initBias0(1)) + biParams(2).copy(initWeightIter0(1)) + biParams(3).copy(initWeightIter1(1)) + biParams(4).copy(initWeight0(2)) + biParams(5).copy(initBias0(2)) + biParams(6).copy(initWeightIter0(2)) + biParams(7).copy(initWeightIter1(2)) + + val blas_output = blasGRU.forward(inputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_output).asInstanceOf[Tensor[Float]], + blas_output) should be(true) + } + + "GRU UnidirectionalInference Multilayers updateOutput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val commonSize = 5 + + val f = AlgKind.EltwiseTanh + val direction = Direction.UnidirectionalLeft2Right + + val common_n_layers = 3 + val gru_n_gates = 3 + + val inputFormat = HeapData(Array(seqLength, batchSize, commonSize), Memory.Format.tnc) + val input = Tensor(Array(seqLength, batchSize, commonSize)).rand() + + var initWeight = Tensor[Float]( + Array(common_n_layers, 1, + commonSize, gru_n_gates, commonSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 1, + commonSize, gru_n_gates, commonSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 1, + gru_n_gates, commonSize)).rand(-1.0, 1.0) + + /** + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 + * + * l -> 0 until common_n_layers + * uniParams(4 * l) -> input weights + * uniParams(4 * l + 1) -> bias + * uniParams(4 * l + 2) -> hidden weights Gate 2 and Gate 1 + * uniParams(4 * l + 3) -> hidden weights Gate 3 + */ + + val mkldnnGRU = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(RNN(AlgKind.VanillaGru, commonSize, commonSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, + initBias = initBias, layers = common_n_layers)) + mkldnnGRU.evaluate() + mkldnnGRU.compile(InferencePhase) + val output = mkldnnGRU.forward(input) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + var inputt = input.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(common_n_layers, commonSize, gru_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter + .resize(Array(common_n_layers, commonSize, gru_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(common_n_layers, gru_n_gates, commonSize)) + + val initWeight0 = Tensor[Float](Array(common_n_layers, commonSize * gru_n_gates, commonSize)) + val initWeightIter0 = + Tensor[Float](Array(common_n_layers, commonSize * 2, commonSize)) + val initWeightIter1 = + Tensor[Float](Array(common_n_layers, commonSize * 1, commonSize)) + val initBias0 = Tensor[Float](Array(common_n_layers, gru_n_gates * commonSize)) + + val concat = nn.JoinTable(1, 0) + for(l <- 1 to common_n_layers) { + initWeight0(l).copy(concat.forward(T(initWeight(l)(2), initWeight(l)(1), + initWeight(l)(3))).asInstanceOf[Tensor[Float]].clone()) + initWeightIter0(l).copy(concat.forward(T(initWeightIter(l)(2), initWeightIter(l)(1))) + .asInstanceOf[Tensor[Float]].clone()) + initWeightIter1(l).copy(initWeightIter(l)(3) + .asInstanceOf[Tensor[Float]].clone()) + initBias0(l).copy(concat.forward(T(initBias(l)(2), initBias(l)(1), + initBias(l)(3))).asInstanceOf[Tensor[Float]].clone()) + } + + val nn_input = nn.Input() + var nn_gru = nn.Recurrent().add(nn.GRU(commonSize, commonSize)).inputs(nn_input) + + for(i <- 1 until common_n_layers) { + nn_gru = nn.Recurrent().add(nn.GRU(commonSize, commonSize)).inputs(nn_gru) + } + + val blasGRU = nn.Graph(nn_input, nn_gru) + + val uniParams = blasGRU.parameters()._1 + + for(l <- 0 until common_n_layers) { + initWeight0(l + 1) = initWeight0(l + 1).resizeAs(uniParams(4 * l)) + initBias0(l + 1) = initBias0(l + 1).resizeAs(uniParams(4 * l + 1)) + initWeightIter0(l + 1) = initWeightIter0(l + 1).resizeAs(uniParams(4 * l + 2)) + initWeightIter1(l + 1) = initWeightIter1(l + 1).resizeAs(uniParams(4 * l + 3)) + + uniParams(4 * l).copy(initWeight0(l + 1)) + uniParams(4 * l + 1).copy(initBias0(l + 1)) + uniParams(4 * l + 2).copy(initWeightIter0(l + 1)) + uniParams(4 * l + 3).copy(initWeightIter1(l + 1)) + } + + val blas_output = blasGRU.forward(inputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(output).asInstanceOf[Tensor[Float]], + blas_output) should be(true) + } + + "GRU UnidirectionalTraining updateGradInput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.UnidirectionalLeft2Right + + val common_n_layers = 1 + val gru_n_gates = 3 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + val gradOutputFormat = HeapData(Array(seqLength, batchSize, hiddenSize), Memory.Format.tnc) + val input = Tensor(Array(seqLength, batchSize, inputSize)).rand(-1.0, 1.0) + val gradOutput = Tensor(Array(seqLength, batchSize, hiddenSize)).rand(1.0, 1.0) + + var initWeight = Tensor[Float]( + Array(common_n_layers, 1, + inputSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 1, + hiddenSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 1, + gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + /** + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 + * + * uniParams(0) -> input weights + * uniParams(1) -> bias + * uniParams(2) -> hidden weights Gate 2 and Gate 1 + * uniParams(3) -> hidden weights Gate 3 + */ + + val rnn = RNN(AlgKind.VanillaGru, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias) + + val mkldnnGRU = Sequential() + .add(Input(inputFormat.shape, inputFormat.layout)) + .add(rnn) + + mkldnnGRU.compile(TrainingPhase) + mkldnnGRU.forward(input) + val mkldnn_gradInput = mkldnnGRU.backward(input, gradOutput) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + var inputt = input.transpose(1, 2).clone() + var gradOutputt = gradOutput.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(inputSize, gru_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + initWeightIter = initWeightIter.resize(Array(hiddenSize, gru_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + initBias = initBias.resize(Array(gru_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(hiddenSize * gru_n_gates, inputSize)) + var initBias0 = Tensor[Float](Array(gru_n_gates * hiddenSize)) + var initWeightIter0 = Tensor[Float](Array(hiddenSize * 2, hiddenSize)) + var initWeightIter1 = Tensor[Float](Array(hiddenSize * 1, hiddenSize)) + + val concat = nn.JoinTable(1, 0) + initWeight0 = concat.forward(T(initWeight(2), initWeight(1), + initWeight(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0 = concat.forward(T(initWeightIter(2), initWeightIter(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1 = initWeightIter(3) + initBias0 = concat.forward(T(initBias(2), initBias(1), initBias(3))) + .asInstanceOf[Tensor[Float]].clone() + + val blasrnn = nn.GRU(inputSize, hiddenSize) + val blasGRU = nn.Recurrent().add(blasrnn) + + val uniParams = blasGRU.parameters()._1 + initWeight0 = initWeight0.resizeAs(uniParams(0)) + initBias0 = initBias0.resizeAs(uniParams(1)) + initWeightIter0 = initWeightIter0.resizeAs(uniParams(2)) + initWeightIter1 = initWeightIter1.resizeAs(uniParams(3)) + + uniParams(0).copy(initWeight0) + uniParams(1).copy(initBias0) + uniParams(2).copy(initWeightIter0) + uniParams(3).copy(initWeightIter1) + + blasGRU.forward(inputt).toTensor.transpose(1, 2) + + val blas_gradInput = blasGRU.backward(inputt, gradOutputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_gradInput).asInstanceOf[Tensor[Float]], + blas_gradInput) should be(true) + + var mkldnn_gradWeight = Tools.dense(rnn.gradWeight.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradWeight_i = Tools.dense(rnn.gradWeight_i.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradBias = Tools.dense(rnn.gradBias.native).asInstanceOf[Tensor[Float]] + + var blas_gradWeight = blasrnn.preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + var blas_gradBias = blasrnn.preTopology.asInstanceOf[nn.Linear[Float]].gradBias + var blas_gradWeight_i_0 = blasrnn.cell.asInstanceOf[nn.StaticGraph[Float]].modules(2) + .asInstanceOf[nn.Linear[Float]].gradWeight + var blas_gradWeight_i_1 = blasrnn.cell.asInstanceOf[nn.StaticGraph[Float]].modules(10) + .asInstanceOf[nn.Linear[Float]].gradWeight + + mkldnn_gradWeight = mkldnn_gradWeight.resize(Array(inputSize, gru_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + mkldnn_gradWeight_i = mkldnn_gradWeight_i.resize(Array(hiddenSize, gru_n_gates, hiddenSize)) + .transpose(1, 2).transpose(2, 3) + mkldnn_gradBias = mkldnn_gradBias.resize(Array(gru_n_gates, hiddenSize)) + + var mkldnn_gradWeight0 = Tensor[Float](Array(hiddenSize * gru_n_gates, inputSize)) + var mkldnn_gradWeight_i0 = Tensor[Float](Array(hiddenSize * 2, hiddenSize)) + var mkldnn_gradWeight_i1 = Tensor[Float](Array(hiddenSize * 1, hiddenSize)) + var mkldnn_gradBias0 = Tensor[Float](Array(gru_n_gates * hiddenSize)) + + mkldnn_gradWeight0 = concat.forward(T(mkldnn_gradWeight(2), mkldnn_gradWeight(1), + mkldnn_gradWeight(3))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0 = concat.forward(T(mkldnn_gradWeight_i(2), mkldnn_gradWeight_i(1))) + .asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i1 = mkldnn_gradWeight_i(3) + mkldnn_gradBias0 = concat.forward(T(mkldnn_gradBias(2), mkldnn_gradBias(1), + mkldnn_gradBias(3))).asInstanceOf[Tensor[Float]].clone() + + Equivalent.nearequals(mkldnn_gradWeight0, blas_gradWeight) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0, blas_gradWeight_i_0) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i1, blas_gradWeight_i_1) should be(true) + Equivalent.nearequals(mkldnn_gradBias0, blas_gradBias) should be(true) + } + + "GRU BidirectionalConcatTraining updateGradInput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalConcat + + val common_n_layers = 1 + val gru_n_gates = 3 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + val gradOutputFormat = HeapData(Array(seqLength, batchSize, 2 * hiddenSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + val gradOutput = Tensor(Array(seqLength, batchSize, 2 * hiddenSize)).rand(1.0, 1.0) + + var initWeight = Tensor[Float]( + Array(common_n_layers, 2, + inputSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 2, + hiddenSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 2, + gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + /** + * biParams(0 - 3) and (4 - 7) are for the two directions respectively + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 + * + * biParams(0) -> input weights + * biParams(1) -> bias + * biParams(2) -> hidden weights Gate 2 and Gate 1 + * biParams(3) -> hidden weights Gate 3 + * biParams(4) -> input weights + * biParams(5) -> bias + * biParams(6) -> hidden weights Gate 2 and Gate 1 + * biParams(7) -> hidden weights Gate 3 + */ + + val rnn = RNN(AlgKind.VanillaGru, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias) + val mkldnnGRU = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(rnn) + + mkldnnGRU.compile(TrainingPhase) + mkldnnGRU.forward(input) + val mkldnn_gradInput = mkldnnGRU.backward(input, gradOutput) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + val inputt = input.transpose(1, 2).clone() + val gradOutputt = gradOutput.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(2, inputSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter.resize(Array(2, hiddenSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(2, gru_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(2, hiddenSize * gru_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(2, hiddenSize * 2, hiddenSize)) + var initWeightIter1 = Tensor[Float](Array(2, hiddenSize * 1, hiddenSize)) + var initBias0 = Tensor[Float](Array(2, gru_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 0) + initWeight0(1) = concat.forward(T(initWeight(1)(2), initWeight(1)(1), + initWeight(1)(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(1) = concat.forward(T(initWeightIter(1)(2), initWeightIter(1)(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1(1) = initWeightIter(1)(3).clone() + initBias0(1) = concat.forward(T(initBias(1)(2), initBias(1)(1), initBias(1)(3))) + .asInstanceOf[Tensor[Float]].clone() + + initWeight0(2) = concat.forward(T(initWeight(2)(2), initWeight(2)(1), + initWeight(2)(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(2) = concat.forward(T(initWeightIter(2)(2), initWeightIter(2)(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1(2) = initWeightIter(2)(3).clone() + initBias0(2) = concat.forward(T(initBias(2)(2), initBias(2)(1), initBias(2)(3))) + .asInstanceOf[Tensor[Float]].clone() + + val blasGRU = nn.BiRecurrent[Float](nn.JoinTable[Float](3, 0) + .asInstanceOf[AbstractModule[Table, Tensor[Float], Float]]) + .add(nn.GRU(inputSize, hiddenSize)) + + val biParams = blasGRU.parameters()._1 + initWeight0(1).resizeAs(biParams(0)) + initBias0(1).resizeAs(biParams(1)) + initWeightIter0(1).resizeAs(biParams(2)) + initWeightIter1(1).resizeAs(biParams(3)) + initWeight0(2).resizeAs(biParams(4)) + initBias0(2).resizeAs(biParams(5)) + initWeightIter0(2).resizeAs(biParams(6)) + initWeightIter1(2).resizeAs(biParams(7)) + + biParams(0).copy(initWeight0(1)) + biParams(1).copy(initBias0(1)) + biParams(2).copy(initWeightIter0(1)) + biParams(3).copy(initWeightIter1(1)) + biParams(4).copy(initWeight0(2)) + biParams(5).copy(initBias0(2)) + biParams(6).copy(initWeightIter0(2)) + biParams(7).copy(initWeightIter1(2)) + + blasGRU.forward(inputt).toTensor.transpose(1, 2) + + val blas_gradInput = blasGRU.backward(inputt, gradOutputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_gradInput).asInstanceOf[Tensor[Float]], + blas_gradInput) should be(true) + + var mkldnn_gradWeight = Tools.dense(rnn.gradWeight.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradWeight_i = Tools.dense(rnn.gradWeight_i.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradBias = Tools.dense(rnn.gradBias.native).asInstanceOf[Tensor[Float]] + + mkldnn_gradWeight = mkldnn_gradWeight.resize(Array(2, inputSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradWeight_i = mkldnn_gradWeight_i.resize(Array(2, hiddenSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradBias = mkldnn_gradBias.resize(Array(2, gru_n_gates, hiddenSize)) + + var mkldnn_gradWeight0 = Tensor[Float](Array(2, hiddenSize * gru_n_gates, inputSize)) + var mkldnn_gradWeight_i0 = Tensor[Float](Array(2, hiddenSize * 2, hiddenSize)) + var mkldnn_gradWeight_i1 = Tensor[Float](Array(2, hiddenSize * 1, hiddenSize)) + var mkldnn_gradBias0 = Tensor[Float](Array(2, gru_n_gates * hiddenSize)) + + mkldnn_gradWeight0(1) = concat.forward(T(mkldnn_gradWeight(1)(2), mkldnn_gradWeight(1)(1), + mkldnn_gradWeight(1)(3))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0(1) = concat.forward(T(mkldnn_gradWeight_i(1)(2), + mkldnn_gradWeight_i(1)(1))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i1(1) = mkldnn_gradWeight_i(1)(3).clone() + mkldnn_gradBias0(1) = concat.forward(T(mkldnn_gradBias(1)(2), mkldnn_gradBias(1)(1), + mkldnn_gradBias(1)(3))).asInstanceOf[Tensor[Float]].clone() + + mkldnn_gradWeight0(2) = concat.forward(T(mkldnn_gradWeight(2)(2), mkldnn_gradWeight(2)(1), + mkldnn_gradWeight(2)(3))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0(2) = concat.forward(T(mkldnn_gradWeight_i(2)(2), + mkldnn_gradWeight_i(2)(1))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i1(2) = mkldnn_gradWeight_i(2)(3).clone() + mkldnn_gradBias0(2) = concat.forward(T(mkldnn_gradBias(2)(2), mkldnn_gradBias(2)(1), + mkldnn_gradBias(2)(3))).asInstanceOf[Tensor[Float]].clone() + + val blas_gradWeight_1 = blasGRU + .layer.modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_2 = blasGRU + .revLayer.modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i0_1 = blasGRU + .layer.modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(2) + .asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i0_2 = blasGRU + .revLayer.modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(2) + .asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i1_1 = blasGRU + .layer.modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(10) + .asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i1_2 = blasGRU + .revLayer.modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(10) + .asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradBias_1 = blasGRU + .layer.modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradBias + + val blas_gradBias_2 = blasGRU + .revLayer.modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradBias + + Equivalent.nearequals(mkldnn_gradWeight0(1), blas_gradWeight_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight0(2), blas_gradWeight_2) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(1), blas_gradWeight_i0_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(2), blas_gradWeight_i0_2) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i1(1), blas_gradWeight_i1_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i1(2), blas_gradWeight_i1_2) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(1), blas_gradBias_1) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(2), blas_gradBias_2) should be(true) + } + + "GRU BidirectionalSumTraining updateGradInput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalSum + + val common_n_layers = 1 + val gru_n_gates = 3 + + val inputFormat = HeapData(Array(seqLength, batchSize, inputSize), Memory.Format.tnc) + val gradOutputFormat = HeapData(Array(seqLength, batchSize, hiddenSize), Memory.Format.tnc) + var input = Tensor(Array(seqLength, batchSize, inputSize)).rand() + val gradOutput = Tensor(Array(seqLength, batchSize, hiddenSize)).rand(1.0, 1.0) + + var initWeight = Tensor[Float]( + Array(common_n_layers, 2, + inputSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 2, + hiddenSize, gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 2, + gru_n_gates, hiddenSize)).rand(-1.0, 1.0) + + /** + * biParams(0 - 3) and (4 - 7) are for the two directions respectively + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 + * + * biParams(0) -> input weights + * biParams(1) -> bias + * biParams(2) -> hidden weights Gate 2 and Gate 1 + * biParams(3) -> hidden weights Gate 3 + * biParams(4) -> input weights + * biParams(5) -> bias + * biParams(6) -> hidden weights Gate 2 and Gate 1 + * biParams(7) -> hidden weights Gate 3 + */ + + val rnn = RNN(AlgKind.VanillaGru, inputSize, hiddenSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, initBias = initBias) + val mkldnnGRU = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(rnn) + + mkldnnGRU.compile(TrainingPhase) + mkldnnGRU.forward(input) + val mkldnn_gradInput = mkldnnGRU.backward(input, gradOutput) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + val inputt = input.transpose(1, 2).clone() + val gradOutputt = gradOutput.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(2, inputSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter.resize(Array(2, hiddenSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(2, gru_n_gates, hiddenSize)) + + var initWeight0 = Tensor[Float](Array(2, hiddenSize * gru_n_gates, inputSize)) + var initWeightIter0 = Tensor[Float](Array(2, hiddenSize * 2, hiddenSize)) + var initWeightIter1 = Tensor[Float](Array(2, hiddenSize * 1, hiddenSize)) + var initBias0 = Tensor[Float](Array(2, gru_n_gates * hiddenSize)) + + val concat = nn.JoinTable(1, 0) + initWeight0(1) = concat.forward(T(initWeight(1)(2), initWeight(1)(1), + initWeight(1)(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(1) = concat.forward(T(initWeightIter(1)(2), initWeightIter(1)(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1(1) = initWeightIter(1)(3).clone() + initBias0(1) = concat.forward(T(initBias(1)(2), initBias(1)(1), initBias(1)(3))) + .asInstanceOf[Tensor[Float]].clone() + + initWeight0(2) = concat.forward(T(initWeight(2)(2), initWeight(2)(1), + initWeight(2)(3))).asInstanceOf[Tensor[Float]].clone() + initWeightIter0(2) = concat.forward(T(initWeightIter(2)(2), initWeightIter(2)(1))) + .asInstanceOf[Tensor[Float]].clone() + initWeightIter1(2) = initWeightIter(2)(3).clone() + initBias0(2) = concat.forward(T(initBias(2)(2), initBias(2)(1), initBias(2)(3))) + .asInstanceOf[Tensor[Float]].clone() + + val blasGRU = nn.BiRecurrent[Float](nn.CAddTable() + .asInstanceOf[AbstractModule[Table, Tensor[Float], Float]]) + .add(nn.GRU(inputSize, hiddenSize)) + + val biParams = blasGRU.parameters()._1 + initWeight0(1).resizeAs(biParams(0)) + initBias0(1).resizeAs(biParams(1)) + initWeightIter0(1).resizeAs(biParams(2)) + initWeightIter1(1).resizeAs(biParams(3)) + initWeight0(2).resizeAs(biParams(4)) + initBias0(2).resizeAs(biParams(5)) + initWeightIter0(2).resizeAs(biParams(6)) + initWeightIter1(2).resizeAs(biParams(7)) + + biParams(0).copy(initWeight0(1)) + biParams(1).copy(initBias0(1)) + biParams(2).copy(initWeightIter0(1)) + biParams(3).copy(initWeightIter1(1)) + biParams(4).copy(initWeight0(2)) + biParams(5).copy(initBias0(2)) + biParams(6).copy(initWeightIter0(2)) + biParams(7).copy(initWeightIter1(2)) + + blasGRU.forward(inputt).toTensor.transpose(1, 2) + + val blas_gradInput = blasGRU.backward(inputt, gradOutputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_gradInput).asInstanceOf[Tensor[Float]], + blas_gradInput) should be(true) + + var mkldnn_gradWeight = Tools.dense(rnn.gradWeight.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradWeight_i = Tools.dense(rnn.gradWeight_i.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradBias = Tools.dense(rnn.gradBias.native).asInstanceOf[Tensor[Float]] + + mkldnn_gradWeight = mkldnn_gradWeight.resize(Array(2, inputSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradWeight_i = mkldnn_gradWeight_i.resize(Array(2, hiddenSize, gru_n_gates, hiddenSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradBias = mkldnn_gradBias.resize(Array(2, gru_n_gates, hiddenSize)) + + var mkldnn_gradWeight0 = Tensor[Float](Array(2, hiddenSize * gru_n_gates, inputSize)) + var mkldnn_gradWeight_i0 = Tensor[Float](Array(2, hiddenSize * 2, hiddenSize)) + var mkldnn_gradWeight_i1 = Tensor[Float](Array(2, hiddenSize * 1, hiddenSize)) + var mkldnn_gradBias0 = Tensor[Float](Array(2, gru_n_gates * hiddenSize)) + + mkldnn_gradWeight0(1) = concat.forward(T(mkldnn_gradWeight(1)(2), mkldnn_gradWeight(1)(1), + mkldnn_gradWeight(1)(3))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0(1) = concat.forward(T(mkldnn_gradWeight_i(1)(2), + mkldnn_gradWeight_i(1)(1))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i1(1) = mkldnn_gradWeight_i(1)(3).clone() + mkldnn_gradBias0(1) = concat.forward(T(mkldnn_gradBias(1)(2), mkldnn_gradBias(1)(1), + mkldnn_gradBias(1)(3))).asInstanceOf[Tensor[Float]].clone() + + mkldnn_gradWeight0(2) = concat.forward(T(mkldnn_gradWeight(2)(2), mkldnn_gradWeight(2)(1), + mkldnn_gradWeight(2)(3))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i0(2) = concat.forward(T(mkldnn_gradWeight_i(2)(2), + mkldnn_gradWeight_i(2)(1))).asInstanceOf[Tensor[Float]].clone() + mkldnn_gradWeight_i1(2) = mkldnn_gradWeight_i(2)(3).clone() + mkldnn_gradBias0(2) = concat.forward(T(mkldnn_gradBias(2)(2), mkldnn_gradBias(2)(1), + mkldnn_gradBias(2)(3))).asInstanceOf[Tensor[Float]].clone() + + val blas_gradWeight_1 = blasGRU + .layer.modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_2 = blasGRU + .revLayer.modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i0_1 = blasGRU + .layer.modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(2) + .asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i0_2 = blasGRU + .revLayer.modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(2) + .asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i1_1 = blasGRU + .layer.modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(10) + .asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradWeight_i1_2 = blasGRU + .revLayer.modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(10) + .asInstanceOf[nn.Linear[Float]].gradWeight + + val blas_gradBias_1 = blasGRU + .layer.modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradBias + + val blas_gradBias_2 = blasGRU + .revLayer.modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]].gradBias + + Equivalent.nearequals(mkldnn_gradWeight0(1), blas_gradWeight_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight0(2), blas_gradWeight_2) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(1), blas_gradWeight_i0_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(2), blas_gradWeight_i0_2) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i1(1), blas_gradWeight_i1_1) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i1(2), blas_gradWeight_i1_2) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(1), blas_gradBias_1) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(2), blas_gradBias_2) should be(true) + } + + "GRU UnidirectionalTraining Multilayers updateGradInput" should "work correctly" in { + val seqLength = 3 + val batchSize = 2 + val commonSize = 5 + + val f = AlgKind.EltwiseTanh + val direction = Direction.UnidirectionalLeft2Right + + val common_n_layers = 3 + val gru_n_gates = 3 + + val inputFormat = HeapData(Array(seqLength, batchSize, commonSize), Memory.Format.tnc) + val gradOutputFormat = HeapData(Array(seqLength, batchSize, commonSize), Memory.Format.tnc) + val input = Tensor(Array(seqLength, batchSize, commonSize)).rand() + val gradOutput = Tensor(Array(seqLength, batchSize, commonSize)).rand(1.0, 1.0) + + var initWeight = Tensor[Float]( + Array(common_n_layers, 1, + commonSize, gru_n_gates, commonSize)).rand(-1.0, 1.0) + + var initWeightIter = Tensor[Float]( + Array(common_n_layers, 1, + commonSize, gru_n_gates, commonSize)).rand(-1.0, 1.0) + + var initBias = Tensor[Float]( + Array(common_n_layers, 1, + gru_n_gates, commonSize)).rand(-1.0, 1.0) + + /** + * Gate order matching between MKLDNN GRU and nn/GRU: + * MKLDNN Gate 1 -> nn/GRU Gate 2 + * MKLDNN Gate 2 -> nn/GRU Gate 1 + * MKLDNN Gate 3 -> nn/GRU Gate 3 + * + * l -> 0 until common_n_layers + * uniParams(4 * l) -> input weights + * uniParams(4 * l + 1) -> bias + * uniParams(4 * l + 2) -> hidden weights Gate 2 and Gate 1 + * uniParams(4 * l + 3) -> hidden weights Gate 3 + */ + + val rnn = RNN(AlgKind.VanillaGru, commonSize, commonSize, f, direction, + initWeight = initWeight, initWeightIter = initWeightIter, + initBias = initBias, layers = common_n_layers) + val mkldnnGRU = Sequential() + .add(Input(input.size(), Memory.Format.tnc)) + .add(rnn) + + mkldnnGRU.compile(TrainingPhase) + mkldnnGRU.forward(input) + val mkldnn_gradInput = mkldnnGRU.backward(input, gradOutput) + + /** + * Reorder to formats of BLAS. + * The input format of MKLDNN is TNC, while that of BLAS is NTC. + */ + var inputt = input.transpose(1, 2).clone() + var gradOutputt = gradOutput.transpose(1, 2).clone() + initWeight = initWeight.resize(Array(common_n_layers, commonSize, gru_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + initWeightIter = initWeightIter + .resize(Array(common_n_layers, commonSize, gru_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + initBias = initBias.resize(Array(common_n_layers, gru_n_gates, commonSize)) + + val initWeight0 = Tensor[Float](Array(common_n_layers, commonSize * gru_n_gates, commonSize)) + val initWeightIter0 = + Tensor[Float](Array(common_n_layers, commonSize * 2, commonSize)) + val initWeightIter1 = + Tensor[Float](Array(common_n_layers, commonSize * 1, commonSize)) + val initBias0 = Tensor[Float](Array(common_n_layers, gru_n_gates * commonSize)) + + val concat = nn.JoinTable(1, 0) + for(l <- 1 to common_n_layers) { + initWeight0(l).copy(concat.forward(T(initWeight(l)(2), initWeight(l)(1), + initWeight(l)(3))).asInstanceOf[Tensor[Float]].clone()) + initWeightIter0(l).copy(concat.forward(T(initWeightIter(l)(2), initWeightIter(l)(1))) + .asInstanceOf[Tensor[Float]].clone()) + initWeightIter1(l).copy(initWeightIter(l)(3) + .asInstanceOf[Tensor[Float]].clone()) + initBias0(l).copy(concat.forward(T(initBias(l)(2), initBias(l)(1), + initBias(l)(3))).asInstanceOf[Tensor[Float]].clone()) + } + + val nn_input = nn.Input() + var nn_gru = nn.Recurrent().add(nn.GRU(commonSize, commonSize)).inputs(nn_input) + + for(i <- 1 until common_n_layers) { + nn_gru = nn.Recurrent().add(nn.GRU(commonSize, commonSize)).inputs(nn_gru) + } + + val blasGRU = nn.Graph(nn_input, nn_gru) + + val uniParams = blasGRU.parameters()._1 + + for(l <- 0 until common_n_layers) { + initWeight0(l + 1) = initWeight0(l + 1).resizeAs(uniParams(4 * l)) + initBias0(l + 1) = initBias0(l + 1).resizeAs(uniParams(4 * l + 1)) + initWeightIter0(l + 1) = initWeightIter0(l + 1).resizeAs(uniParams(4 * l + 2)) + initWeightIter1(l + 1) = initWeightIter1(l + 1).resizeAs(uniParams(4 * l + 3)) + + uniParams(4 * l).copy(initWeight0(l + 1)) + uniParams(4 * l + 1).copy(initBias0(l + 1)) + uniParams(4 * l + 2).copy(initWeightIter0(l + 1)) + uniParams(4 * l + 3).copy(initWeightIter1(l + 1)) + } + + blasGRU.forward(inputt).toTensor.transpose(1, 2) + + val blas_gradInput = blasGRU.backward(inputt, gradOutputt).toTensor.transpose(1, 2) + + Equivalent.nearequals(Tools.dense(mkldnn_gradInput).asInstanceOf[Tensor[Float]], + blas_gradInput) should be(true) + + var mkldnn_gradWeight = Tools.dense(rnn.gradWeight.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradWeight_i = Tools.dense(rnn.gradWeight_i.native).asInstanceOf[Tensor[Float]] + var mkldnn_gradBias = Tools.dense(rnn.gradBias.native).asInstanceOf[Tensor[Float]] + + mkldnn_gradWeight = mkldnn_gradWeight + .resize(Array(common_n_layers, commonSize, gru_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradWeight_i = mkldnn_gradWeight_i + .resize(Array(common_n_layers, commonSize, gru_n_gates, commonSize)) + .transpose(2, 3).transpose(3, 4) + mkldnn_gradBias = mkldnn_gradBias.resize(Array(common_n_layers, gru_n_gates, commonSize)) + + val mkldnn_gradWeight0 = Tensor[Float]( + Array(common_n_layers, commonSize * gru_n_gates, commonSize)) + val mkldnn_gradWeight_i0 = + Tensor[Float](Array(common_n_layers, commonSize * 2, commonSize)) + val mkldnn_gradWeight_i1 = + Tensor[Float](Array(common_n_layers, commonSize * 1, commonSize)) + val mkldnn_gradBias0 = Tensor[Float](Array(common_n_layers, gru_n_gates * commonSize)) + + for(l <- 1 to common_n_layers) { + mkldnn_gradWeight0(l).copy( + concat + .forward(T( + mkldnn_gradWeight(l)(2), mkldnn_gradWeight(l)(1), mkldnn_gradWeight(l)(3))) + .asInstanceOf[Tensor[Float]].clone()) + mkldnn_gradWeight_i0(l).copy( + concat + .forward(T( + mkldnn_gradWeight_i(l)(2), mkldnn_gradWeight_i(l)(1))) + .asInstanceOf[Tensor[Float]].clone()) + mkldnn_gradWeight_i1(l).copy(mkldnn_gradWeight_i(l)(3).clone()) + mkldnn_gradBias0(l).copy(concat.forward(T(mkldnn_gradBias(l)(2), mkldnn_gradBias(l)(1), + mkldnn_gradBias(l)(3))) + .asInstanceOf[Tensor[Float]].clone()) + } + + val blas_gradWeight = Tensor[Float]( + Array(common_n_layers, commonSize * gru_n_gates, commonSize)) + val blas_gradWeight_i0 = Tensor[Float]( + Array(common_n_layers, commonSize * 2, commonSize)) + val blas_gradWeight_i1 = Tensor[Float]( + Array(common_n_layers, commonSize * 1, commonSize)) + val blas_gradBias = Tensor[Float]( + Array(common_n_layers, gru_n_gates * commonSize)) + + for (l <- 1 to common_n_layers) { + blas_gradWeight(l).copy(blasGRU.modules(l).asInstanceOf[nn.Recurrent[Float]] + .modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]] + .gradWeight) + + blas_gradWeight_i0(l).copy(blasGRU.modules(l).asInstanceOf[nn.Recurrent[Float]] + .modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(2).asInstanceOf[nn.Linear[Float]] + .gradWeight) + + blas_gradWeight_i1(l).copy(blasGRU.modules(l).asInstanceOf[nn.Recurrent[Float]] + .modules(1).asInstanceOf[nn.GRU[Float]] + .cell.asInstanceOf[nn.StaticGraph[Float]].modules(10).asInstanceOf[nn.Linear[Float]] + .gradWeight) + + blas_gradBias(l).copy(blasGRU.modules(l).asInstanceOf[nn.Recurrent[Float]] + .modules(1).asInstanceOf[nn.GRU[Float]] + .preTopology.asInstanceOf[nn.Linear[Float]] + .gradBias) + } + + for (l <- 1 to common_n_layers) { + Equivalent.nearequals(mkldnn_gradWeight0(l), blas_gradWeight(l)) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i0(l), blas_gradWeight_i0(l)) should be(true) + Equivalent.nearequals(mkldnn_gradWeight_i1(l), blas_gradWeight_i1(l)) should be(true) + Equivalent.nearequals(mkldnn_gradBias0(l), blas_gradBias(l)) should be(true) + } + } } From 6a9041e5bf6c64f6619aeefe5d6e5672b539779f Mon Sep 17 00:00:00 2001 From: Xiao Date: Mon, 30 Sep 2019 10:03:07 +0800 Subject: [PATCH 0960/1065] Onnx support: modify unsqueeze function (#2910) * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function * modeify unsqueeze function --- .../analytics/bigdl/dllib/nn/Unsqueeze.scala | 74 ++++++++++++------- .../bigdl/dllib/tensor/DenseTensor.scala | 38 ++++++++++ .../bigdl/dllib/tensor/DnnTensor.scala | 1 + .../tensor/QuantizedTensorUnsupported.scala | 10 +++ .../bigdl/dllib/tensor/SparseTensor.scala | 4 + .../analytics/bigdl/dllib/tensor/Tensor.scala | 9 +++ .../dllib/utils/python/api/PythonBigDL.scala | 4 +- .../dllib/torch/MultiCriterionSpec.scala | 4 +- .../bigdl/dllib/torch/TorchSpec.scala | 1 - .../bigdl/dllib/torch/UnsqueezeSpec.scala | 2 +- 10 files changed, 116 insertions(+), 31 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala index 1ed93ce4f4b..723c466e299 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Unsqueeze.scala @@ -23,43 +23,55 @@ import com.intel.analytics.bigdl.tensor._ import scala.reflect.ClassTag /** - * Insert singleton dim (i.e., dimension 1) at position pos. For an input with dim = input.dim(), + * Insert singleton dim (i.e., dimension 1) at position array pos. + * For an input with dim = input.dim(), * there are dim + 1 possible positions to insert the singleton dimension. + * Dimension index are 1-based. 0 and negative pos correspond to unsqueeze() applied at + * pos = pos + input.dim() + 1 * - * @param pos The position will be insert singleton. + * @param pos The array of position will insert singleton. * @param numInputDims Optional. If in a batch model, set to the inputDims. */ @SerialVersionUID(- 5180889605872472241L) class Unsqueeze[T: ClassTag]( - val pos: Int, + val pos: Array[Int], var numInputDims: Int = Int.MinValue )(implicit ev: TensorNumeric[T]) extends AbstractModule[Tensor[_], Tensor[_], T] { + def this( pos: Int, numInputDims: Int )(implicit ev: TensorNumeric[T]) = { + this(Array(pos), numInputDims) + } + + def this( pos: Int )(implicit ev: TensorNumeric[T]) = { + this(Array(pos)) + } def setNumInputDims(numInputDims: Int): Unit = { this.numInputDims = numInputDims } - private def getActualPosition(input: Tensor[_]) : Int = { - val dim = if (pos <= 0) { - input.dim() + pos + 1 - } else { - pos + private def getActualPosition(input: Tensor[_]) : Array[Int] = { + for (index <- 0 until pos.length) { + // dimension index are 1-based + pos(index) = if (pos(index) <= 0) { + input.dim() + pos(index) + 1 + } + else { + pos(index) + } + // get valid dimension offset for batchMode (if any) + val inputDim = input.dim() // data batch dim + numInputDims = if (numInputDims != Int.MinValue) numInputDims else inputDim // feature map dim + val offsetDim = inputDim - numInputDims + require(offsetDim >= 0, "input feature map dim (numInputDims) must be <= input:dim()," + + s" input feature map dim ${numInputDims}, inputdim ${inputDim}") + // the actual position; clearer error message for batchMode (if any) + val actualPos = pos(index) + offsetDim + require(actualPos >= 1 && actualPos <= (inputDim + 1), s"Invalid position: ${pos(index)}. " + + s"input:dim() is $input, input feature map dim (numInputDims) is $numInputDims.") + pos(index) = actualPos } - - // get valid dimension offset for batchMode (if any) - val inputDim = input.dim() // data batch dim - numInputDims = if (numInputDims != Int.MinValue) numInputDims else inputDim // feature map dim - val offsetDim = inputDim - numInputDims - require(offsetDim >= 0, "input feature map dim (numInputDims) must be <= input:dim()," + - s" input feature map dim ${numInputDims}, inputdim ${inputDim}") - - // the actual position; clearer error message for batchMode (if any) - val actualPos = dim + offsetDim - require(actualPos >= 1 && actualPos <= (inputDim + 1), s"Invalid position: $pos. " + - s"input:dim() is $input, input feature map dim (numInputDims) is $numInputDims.") - - actualPos + pos } override def updateOutput(input: Tensor[_]): Tensor[_] = { @@ -68,9 +80,8 @@ class Unsqueeze[T: ClassTag]( output = input.emptyInstance() } - output - .asInstanceOf[Tensor[NumericWildcard]] - .addSingletonDimension(input.asInstanceOf[Tensor[NumericWildcard]], actualPos) + output.asInstanceOf[Tensor[NumericWildcard]] + .addMultiDimension(input.asInstanceOf[Tensor[NumericWildcard]], actualPos) output } @@ -108,6 +119,17 @@ object Unsqueeze { def apply[@specialized(Float, Double) T: ClassTag]( pos: Int, numInputDims: Int = Int.MinValue)(implicit ev: TensorNumeric[T]) : Unsqueeze[T] = { - new Unsqueeze[T](pos, numInputDims) + new Unsqueeze[T](Array(pos), numInputDims) + } + + def apply[T: ClassTag]( + posList: Array[Int], + numInputDims: Int)(implicit ev: TensorNumeric[T]) : Unsqueeze[T] = { + new Unsqueeze[T](posList, numInputDims) + } + + def apply[T: ClassTag]( + posList: Array[Int])(implicit ev: TensorNumeric[T]) : Unsqueeze[T] = { + new Unsqueeze[T](posList) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 51762d04f05..666108e6892 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -2092,6 +2092,44 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( this.set(t.storage(), t.storageOffset(), size, stride) } + override def addMultiDimension( t: Tensor[T], dims: Array[Int] = Array(1)): Tensor[T] = { + // increase 1 to the following pos after a previous smaller pos have one dimension inserted. + for (i <- 0 until dims.length) { + for (j <- i + 1 until dims.length) { + if (dims(j) > dims(i)) { + dims(j) = dims(j) + 1 + } + } + } + var temp = t.clone() + var size = new Array[Int](t.dim()) + var stride = new Array[Int](t.dim()) + + for ( i <- 0 until dims.length) { + require(dims(i) > 0 && dims(i) <= temp.dim() + 1, s"invalid dimension: ${dims(i)}. " + + s"Tensor is of ${temp.dim()} dimensions.") + + size = new Array[Int](temp.dim() + 1) + stride = new Array[Int](temp.dim() + 1) + var d = 0 + while (d < dims(i) - 1) { + size(d) = temp.size(d + 1) + stride(d) = temp.stride(d + 1) + d += 1 + } + size(dims(i) - 1) = 1 + stride(dims(i) - 1) = 1 + d += 1 + while (d < temp.dim + 1) { + size(d) = temp.size(d) + stride(d) = temp.stride(d) + d += 1 + } + temp.set(temp.storage(), temp.storageOffset(), size, stride) + } + this.set(temp.storage(), temp.storageOffset(), size, stride) + } + /** * Implements >= operator comparing each element in x with value * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala index c5aa0a2c40d..840652ef5c1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -332,6 +332,7 @@ object DnnTensor { override def getType(): TensorDataType = ??? override def diff(other: Tensor[T], count: Int, reverse: Boolean): Boolean = ??? override def addSingletonDimension(t: Tensor[T], dim: Int): Tensor[T] = ??? + override def addMultiDimension(t: Tensor[T], dims: Array[Int]): Tensor[T] = ??? override def reshape(sizes: Array[Int]): Tensor[T] = ??? override def save(path: String, overWrite: Boolean): DnnTensorUnsupportOperations.this.type = ??? override def getTensorNumeric(): TensorNumeric[T] = ??? diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala index 317162353f0..2dfaa77704c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/QuantizedTensorUnsupported.scala @@ -525,6 +525,16 @@ abstract class QuantizedTensorUnsupported[T: ClassTag] extends Tensor[T] { override def addSingletonDimension(t: Tensor[T], dim: Int): Tensor[T] = throw new UnsupportedOperationException(errorString) + /** + * view this.tensor and add multiple Dimensions to `dim` dimension + * + * @param t source tensor + * @param dim the specific dimension array, default is [1] + * @return this + */ + override def addMultiDimension(t: Tensor[T], dims: Array[Int]): Tensor[T] = + throw new UnsupportedOperationException(errorString) + /** * create a new tensor without any change of the tensor * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala index 51f4594b660..9970ab922ef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/SparseTensor.scala @@ -445,6 +445,10 @@ private[tensor] class SparseTensor[@specialized(Float, Double) T: ClassTag]( throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } + override def addMultiDimension(t: Tensor[T], dims: Array[Int]): Tensor[T] = { + throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") + } + override def reshape(sizes: Array[Int]): Tensor[T] = { throw new UnsupportedOperationException(s"SparseTensor: Unimplemented method") } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala index 5a384a7d581..2a2d7e14da3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/Tensor.scala @@ -717,6 +717,15 @@ trait Tensor[T] extends Serializable with TensorMath[T] with Activity { */ def addSingletonDimension(t: Tensor[T] = this, dim: Int = 1): Tensor[T] + /** + * view this.tensor and add multiple Dimensions to `dim` dimension + * + * @param t source tensor + * @param dim the specific dimension array, default is [1] + * @return this + */ + def addMultiDimension(t: Tensor[T] = this, dims: Array[Int] = Array(1)): Tensor[T] + /** * create a new tensor without any change of the tensor * diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 0c5c7e76ea4..bcc62e6ff82 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1537,10 +1537,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab ip) } - def createUnsqueeze(pos: Int, + def createUnsqueeze(pos: JList[Int], numInputDims: Int = Int.MinValue) : Unsqueeze[T] = { - Unsqueeze[T](pos, + Unsqueeze[T](pos.asScala.toArray, numInputDims) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiCriterionSpec.scala index c078742182d..8b0102ed507 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiCriterionSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import scala.util.Random @@ -25,13 +26,14 @@ import scala.util.Random class MultiCriterionSpec extends TorchSpec { "A MultiCriterion Module " should "generate correct output and grad with Tensor input" in { torchCheck() + RNG.setSeed(10) val module = new MultiCriterion[Double]() val nll = new ClassNLLCriterion[Double]() val nll2 = new MSECriterion[Double]() module.add(nll, 0.5) module.add(nll2) - val input = Tensor[Double](5).apply1(e => Random.nextDouble()) + val input = Tensor[Double](5).rand() val target = Tensor[Double](5) target(Array(1)) = 1 target(Array(2)) = 2 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TorchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TorchSpec.scala index 6d040a4b647..4689db0eb71 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TorchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TorchSpec.scala @@ -22,5 +22,4 @@ class TorchSpec extends FlatSpec with BeforeAndAfter with Matchers { cancel("Torch is not installed") } } - } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala index ebf129d6807..99f8bd4dbee 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala @@ -134,6 +134,6 @@ class UnsqueezeSpec extends TorchSpec { "A Unsqueeze(0)" should "generate correct output and grad" in { val layer = new Unsqueeze[Double](0) val input = Tensor[Double](2, 2).rand() - layer.forward(input).size() should be(Array(2, 2, 1)) + layer.forward(input).size() should be (Array(2, 2, 1)) } } From 01f54ee61d91fdff0a3831317fe722d408433ad4 Mon Sep 17 00:00:00 2001 From: Menooker Date: Tue, 8 Oct 2019 11:05:12 +0800 Subject: [PATCH 0961/1065] add maskutils (#2921) * add maskutils * update tests & docs * fix typo in document --- .../dataset/segmentation/MaskUtils.scala | 362 ++++++++++++++++++ .../SegmentationDatasetSpec.scala | 154 ++++++++ 2 files changed, 516 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/SegmentationDatasetSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala new file mode 100644 index 00000000000..292863551d1 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala @@ -0,0 +1,362 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dataset.segmentation + +import com.intel.analytics.bigdl.tensor.Tensor +import scala.collection.mutable.ArrayBuffer + + +abstract class SegmentationMasks extends Serializable { + /** + * Convert to a RLE encoded tensor + */ + def toRLETensor: Tensor[Float] +} + +/** + * A mask of regions defined by one or more polygons. The masked object(s) should have the same + * label. + * @param poly An array of polygons. The inner array defines one polygon, with [x1,y1,x2,y2,...] + * @param height the height of the image + * @param width the width of the image + */ +class PolyMasks(val poly: Array[Array[Float]], val height: Int, val width: Int) extends + SegmentationMasks { + override def toRLETensor: Tensor[Float] = { + require(height > 0 && width > 0, "the height and width must > 0 for toRLETensor()") + MaskUtils.mergeRLEs(MaskUtils.poly2RLE(this, height, width), false).toRLETensor + } +} + +object PolyMasks { + def apply(poly: Array[Array[Float]], height: Int, width: Int): PolyMasks = + new PolyMasks(poly, height, width) +} + +/** + * A mask of regions defined by RLE. The masked object(s) should have the same label. + * This class corresponds to "uncompressed RLE" of COCO dataset. + * RLE is a compact format for binary masks. Binary masks defines the region by assigning a boolean + * to every pixel of the image. RLE compresses the binary masks by instead recording the runs of + * trues and falses in the binary masks. RLE is an array of integer. + * The first element is the length of run of falses staring from the first pixel. + * The second element of RLE is the is the length of first run of trues. + * e.g. binary masks: 00001110000011 + * RLE: ---4--3----5-2 ====> 4,3,5,2 + * + * Also note that we don't use COCO's "compact" RLE string here because this RLE class has better + * time & space performance. + * + * @param counts the RLE counts + * @param height height of the image + * @param width width of the image + */ +class RLEMasks(val counts: Array[Int], val height: Int, val width: Int) extends SegmentationMasks { + override def toRLETensor: Tensor[Float] = { + Tensor(counts.map(MaskUtils.uint2long(_).toFloat), Array(counts.length)) + } + + /** + * Get an element in the counts. Process the overflowed int + * + * @param idx + * @return + */ + def get(idx: Int): Long = { + MaskUtils.uint2long(counts(idx)) + } +} + +object RLEMasks { + def apply(counts: Array[Int], height: Int, width: Int): RLEMasks = + new RLEMasks(counts, height, width) +} + + +object MaskUtils { + + /** + * Convert an unsigned int to long (note: int may overflow) + * + * @param i + * @return + */ + def uint2long(i: Int): Long = { + if (i >= 0) { + i + } else { + i.toLong - Int.MinValue.toLong + Int.MaxValue.toLong + 1 + } + } + + /** + * Convert "uncompressed" RLE to "compact" RLE string of COCO + * Implementation based on COCO's MaskApi.c + * @param rle + * @return RLE string + */ + // scalastyle:off methodName + def RLE2String(rle: RLEMasks): String = { + // Similar to LEB128 but using 6 bits/char and ascii chars 48-111. + val m = rle.counts.length + val s = new ArrayBuffer[Char]() + for (i <- 0 until m) { + var x = rle.get(i) + if (i > 2) x -= rle.get(i - 2) + var more = true + while (more) { + var c = (x & 0x1f) + x >>= 5 + more = if ((c & 0x10) != 0) x != -1 else x != 0 + if (more) c |= 0x20 + c += 48 + s += c.toChar + } + } + new String(s.toArray) + } + // scalastyle:on methodName + + /** + * Convert "compact" RLE string of COCO to "uncompressed" RLE + * Implementation based on COCO's MaskApi.c + * @param s the RLE string + * @param h height of the image + * @param w width of the image + * @return RLE string + */ + def string2RLE(s: String, h: Int, w: Int): RLEMasks = { + val cnts = new ArrayBuffer[Int]() + var m = 0 + var p = 0 + while (p < s.length) { + var x = 0L + var k = 0 + var more = true + while (more) { + val c = s(p).toLong - 48 + x |= (c & 0x1f) << (5 * k) + more = (c & 0x20) != 0 + k += 1 + p += 1 + if (!more && (c & 0x10) != 0) x |= -1 << (5 * k) + } + if (m > 2) x += uint2long(cnts(m - 2)) + cnts += x.toInt + m += 1 + } + RLEMasks(cnts.toArray, h, w) + } + + /** + * Convert a PolyMasks to an array of RLEMasks. Note that a PolyMasks may have multiple + * polygons. This function does not merge them. Instead, it returns the RLE for each polygon. + * Implementation based on COCO's MaskApi.c + * @param poly + * @param height height of the image + * @param width width of the image + * @return The converted RLEs + */ + def poly2RLE(poly: PolyMasks, height: Int, width: Int): Array[RLEMasks] = { + poly.poly.map(xy => { + // upsample and get discrete points densely along entire boundary + val scale = 5d + val (u, v, upsamplePoints) = { + val nPoints = xy.length / 2 + val x = new Array[Long](nPoints + 1) + val y = new Array[Long](nPoints + 1) + for (j <- 0 until nPoints) { + x(j) = Math.floor(scale * xy(j * 2 + 0) + .5).toLong + y(j) = Math.floor(scale * xy(j * 2 + 1) + .5).toLong + } + x(nPoints) = x(0) + y(nPoints) = y(0) + val m1 = (0 until nPoints).map { case j => + Math.max(Math.abs(x(j) - x(j + 1)), Math.abs(y(j) - y(j + 1))) + 1 + }.sum.toInt + val u = new Array[Long](m1) + val v = new Array[Long](m1) + + var m = 0 + for (j <- 0 until nPoints) { + val (xs, xe, ys, ye, dx, dy, flip) = { + val _xs = x(j) + val _xe = x(j + 1) + val _ys = y(j) + val _ye = y(j + 1) + val _dx = Math.abs(_xe - _xs) + val _dy = Math.abs(_ys - _ye) + val _flip = (_dx >= _dy && _xs > _xe) || (_dx < _dy && _ys > _ye) + if (_flip) (_xe, _xs, _ye, _ys, _dx, _dy, _flip) + else (_xs, _xe, _ys, _ye, _dx, _dy, _flip) + } + + if (dx >= dy) { + for (d <- 0 to dx.toInt) { + val s = (ye - ys).toDouble / dx + val t = if (flip) dx - d else d + u(m) = t + xs + v(m) = Math.floor(ys + s * t + .5).toLong + m += 1 + } + } + else { + for (d <- 0 to dy.toInt) { + val s = (xe - xs).toDouble / dy + val t = if (flip) dy - d else d + v(m) = t + ys + u(m) = Math.floor(xs + s * t + .5).toLong + m += 1 + } + } + } + (u, v, m) + } + // get points along y-boundary and downsample + val (downsampleX, downsampleY, downsamplePoints) = { + // use an independent scope + val nPoints = upsamplePoints + var m = 0 + val x = new Array[Long](nPoints) + val y = new Array[Long](nPoints) + for (j <- 1 until nPoints) { + if (u(j) != u(j - 1)) { + // Should u(j) - 1 be u(j - 1) ???? + val _xd = if (u(j) < u(j - 1)) u(j) else u(j) - 1 + val xd = (_xd.toDouble + .5) / scale - .5 + if (Math.floor(xd) != xd || xd < 0 || xd > width - 1) { + // continue + } else { + var yd = (if (v(j) < v(j - 1)) v(j) else v(j - 1)).toDouble + yd = (yd + .5) / scale - .5 + if (yd < 0) { + yd = 0 + } else if (yd > height) { + yd = height + } + yd = Math.ceil(yd) + x(m) = xd.toInt + y(m) = yd.toInt + m += 1 + } + } + } + (x, y, m) + } + + { + // compute rle encoding given y-boundary points + val x = downsampleX + val y = downsampleY + val nPoints = downsamplePoints + 1 + val a = new Array[Long](nPoints) + for (j <- 0 until nPoints - 1) + a(j) = x(j) * height + y(j) + a(nPoints - 1) = height * width + scala.util.Sorting.quickSort(a) + + var p = 0L + for (j <- 0 until nPoints) { + val t = a(j) + a(j) -= p + p = t + } + val b = new ArrayBuffer[Int]() + var j = 1 + var m = 1 + b += a(0).toInt + while (j < nPoints) { + if (a(j) > 0) { + b += a(j).toInt + m += 1 + j += 1 + } + else { + j += 1 + if (j < nPoints) { + b(m - 1) += a(j).toInt + j += 1 + } + } + } + RLEMasks(b.toArray, height, width) + } + }) + } + + /** + * Merge multiple RLEs into one (union or intersect) + * Implementation based on COCO's MaskApi.c + * @param R the RLEs + * @param intersect if true, do intersection; else find union + * @return the merged RLE + */ + def mergeRLEs(R: Array[RLEMasks], intersect: Boolean): RLEMasks = { + val n = R.length + if (n == 1) return R(0) + val h = R(0).height + val w = R(0).width + val cnts = new ArrayBuffer[Int]() + cnts.appendAll(R(0).counts) + for(i <- 1 until n) { + val B = R(i) + require(B.height == h && B.width == w, "The height and width of the merged RLEs must" + + " be the same") + val acnt = cnts.toArray + val am = cnts.length + cnts.clear() + var ca = uint2long(acnt(0)) + var cb = uint2long(B.counts(0)) + var (v, va, vb) = (false, false, false) + var a = 1 + var b = 1 + var cc = 0L + var ct = 1L + + while (ct > 0) { + val c = Math.min(ca, cb) + cc += c + ct = 0 + ca -= c + if (ca == 0 && a < am) { + ca = uint2long(acnt(a)) + a += 1 + va = !va + } + ct += ca + cb -= c + if (cb == 0 && b < B.counts.length) { + cb = B.get(b) + b += 1 + vb = !vb + } + ct += cb + val vp = v + if (intersect) { + v = va && vb + } else { + v = va || vb + } + if (v != vp || ct == 0) { + cnts += cc.toInt + cc = 0 + } + } + } + RLEMasks(cnts.toArray, h, w) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/SegmentationDatasetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/SegmentationDatasetSpec.scala new file mode 100644 index 00000000000..9cc81d7810d --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/SegmentationDatasetSpec.scala @@ -0,0 +1,154 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dataset.segmentation + +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class SegmentationDatasetSpec extends FlatSpec with Matchers with BeforeAndAfter { + + val compressed1 = "Q:XX24Sm0e:jQ1]EajKSV69iZJV9T_OdF[>^NmB`2Z`0Y?a^OmR5lc6Zj2m[IckG0ZEXdl9l0j" + + "[SFT9_\\e1Z:XgZNh[OPM:d\\4O" + val compressed2 = "iaj0]TWZ2j9XZleMYG_oLf9U`]7mQbWg0b[gHlV[RYOQmZQ2TTU1oj]PNeVbEl[VNnZ" + + "]OkYLgfMja01fgW=\\1TofBY6c:Sheb0`n1Q[dol1PXc`0YQh]RNi^Z_OZeMOb?30nbR1^P^g0ShmNfPkYO" + + "^LWkkNXW3]_m0gUQ[2kdb?ZePeMhPZB^[NaQQMgZLkVlU54aUSjJ32" + + val arr1 = Array(321, 2312, 4, 3243, 345, 4325, 6, 54, 6345, 63, 546, 357, 6, 57, + 465, 7, + 46, 87, 568, 576, 9, 5789, 6789, 8679, 2, 346, 2, 4, 324234, 32, 4, 324, 54675, 654, 123, + 6, 27, 16, 4527, 15) + + val arr2 = Array(27193, 2432141, 314, 3541, 35, 452, 345, 243657, 24365462, 5435, + 325234, 2146524, 363254, 63547, 21451, 4535, 2345, 754, 0, 1324, 1, 435234, 45, 6, 246, + 345, 612345, 2345, 64563546, 546345, 2435, 2, 45, 1, 543, 4, 543, 35426, 768557, 357, + 42563, 243, 5546, 3547, 35735, 2462354, 546354, 5436, 97866, 3754, 635, 1, 5436246, + 5, 7, 8, 9) + "string2RLE" should "run well" in { + + val result = MaskUtils.string2RLE(compressed1, 100, 200) + result.counts should be(arr1) + result.height should be (100) + result.width should be (200) + + val result2 = MaskUtils.string2RLE(compressed2, 100, 200) + result2.counts should be (arr2) + result2.height should be (100) + result2.width should be (200) + } + + "RLE2String" should "run well" in { + MaskUtils.RLE2String(RLEMasks(arr2, 100, 200)) should be (compressed2) + MaskUtils.RLE2String(RLEMasks(arr1, 100, 200)) should be (compressed1) + } + + // real data in instances_val2014.json + // annId = 455475 + val poly1 = Array(426.91, 58.24, 434.49, 77.74, 467.0, 80.99, 485.42, 86.41, 493.0, 129.75, + 521.17, 128.67, 532.01, 144.92, 545.01, 164.42, 552.6, 170.93, 588.35, 178.51, 629.53, + 165.51, 629.53, 177.43, 578.6, 214.27, 558.01, 241.35, 526.59, 329.12, 512.51, 370.29, + 502.75, 415.8, 418.24, 409.3, 399.82, 414.72, 388.98, 420.14, 382.48, 424.47, 391.15, 430.97, + 414.99, 425.55, 447.49, 427.72, 449.66, 435.3, 431.24, 438.56, 421.49, 452.64, 422.57, + 456.98, 432.33, 464.56, 439.91, 458.06, 481.08, 465.64, 502.75, 464.56, 507.09, 473.23, + 639.28, 474.31, 639.28, 1.9, 431.24, 0.0 + ).map(_.toFloat) + + // annId = 692513 + val poly2 = Array( + 416.41, 449.28, 253.36, 422.87, 234.06, 412.2, 277.23, 406.61, 343.77, 411.69, 379.84, + 414.23, 384.41, 424.9, 397.11, 427.95, 410.31, 427.95, 445.36, 429.98, 454.0, 438.61, 431.65, + 438.61, 423.01, 449.28 + ).map(_.toFloat) + + "poly2RLE" should "run well" in { + val rle = MaskUtils.poly2RLE(PolyMasks(Array(poly1), 480, 640), 480, 640) + val targetRle = MaskUtils.string2RLE( + "Xnc51n>2N2O0O2N2O1N101N10O0100O100O01000O10O10O100000O010000O01000O1000000O1001O00ZBAk" + + "T1k9hNfE64_1S:[NhE84`1Q:X" + + "NjE95a1P:UNkE:5d1m9RNnE96e1l9RNnE87f1k9RNnE78g1j9RNmE7:g1i9RNmE6;h1h9RNmE5k6EUI;h6HXI8e6K[I5b6N^I2_61aI0[63eIOV64jINQ6" + + "5oILm57SJKh58XJIe59[JI`5:`JG\\5Z3BeL>\\3BdL>\\3BdL=]3CcL<^3DaL=_3CaL<`3D`L;a3E^L;c3E]" + + "L;c3E]L:d3F[L:f3FZL:f3FZL9g3GXL9i3GWL8j3HVL8j3HUL8l3HTL7m3ISL6n3JQL7o3IQL6P4JPL5Q4Ko" + + "K5Q4KnK5S4KmK4T4LlK3U4M_50000000000000000n>", 480, 640 + ) + rle(0).counts.length should be (targetRle.counts.length) + rle(0).counts.zip(targetRle.counts).foreach{case (rleCount, targetCount) => + rleCount should be (targetCount +- 1) + } + + val rle2 = MaskUtils.poly2RLE(PolyMasks(Array(poly2), 480, 640), 480, 640) + MaskUtils.RLE2String(rle2(0)) should be( + "la^31o>1O001N101O001O001O001N2O001O001O001O0O1000001O00000O10001O000000000O2O0000000000" + + "1O0000000000001O00000000010O00000000001O00000000001O01O00000001O00000000001O0001O0000" + + "0001O00000000001O0001O000001O00000000001O00000001O01O00000000001O0000000001O01O000000" + + "00001O000000000010O0000000001O00000002N2O1N3M2N000010O000001O0000010O0000000001O00000" + + "000001O00000000001O000000000001O0000000O1O1O1O1O1N2O1O1O10000000001O00000000000000001" + + "O1O1O1O1O1O1O1OZhf2") + + } + + "mergeRLEs" should "run well" in { + val rle1 = MaskUtils.poly2RLE(PolyMasks(Array(poly1), 480, 640), 480, 640)(0) + val rle2 = MaskUtils.poly2RLE(PolyMasks(Array(poly2), 480, 640), 480, 640)(0) + val merged = MaskUtils.mergeRLEs(Array(rle1, rle2), false) + val targetRle = MaskUtils.string2RLE( + "la^31o>1O001N101O001O001O001N2O001O001O001O0O1000001O00000O10001O000000000O2O0000000000" + + "1O0000000000001O00000000010O00000000001O00000000001O01O00000001O00000000001O0001O0000" + + "0001O00000000001O0001O000001O00000000001O00000001O01O00000000001O0000000001O01O000000" + + "00001O000000000010O0000000001O00000002N2O1N3M00O100O2N100O100O100O2O0O100O10001N10000" + + "O10001O[OTB6k=KUB5k=KUB5k=KUB5j=KWB5i=KWB6h=JXB6g=KYB5g=KYB5g=KYB5g=KYB5f=LZB4f=L[B3f" + + "=LZB4f=LZB40]Ob=?^B4OB_=:bB3OE^=8cB2NH_=6cB1NK^=4dB?T2YOh88TE`0e1IT9HVE?X1:_9WOYE`0j0" + + "h0k9hN[Ea0?T1S:[N^Eb0>V1Q:XNaEb0>X1P:UNbEc0>[1m9RNeEb0?\\1l9RNeEa0`0]1k9RNeE`0a0^1j9R" + + "NfE>a0`1i9RNfE=b0a1h9RNfEk6EUI;" + + "h6HXI8e6K[I5b6N^I2_61aI0[63eIOV64jINQ65oILm57SJKh58XJIe59[JI`5:`JG\\5Z3BeL>\\3BdL>\\3BdL=]3CcL" + + "<^3DaL=_3CaL<`3D`L;a3E^L;c3E]L;c3E]L:d3F[L:f3FZL:f3FZL9g3GXL9i3GWL8j3HVL8j3HUL8l3HTL7" + + "m3ISL6n3JQL7o3IQL6P4JPL5Q4KoK5Q4KnK5S4KmK4T4LlK3U4M_50000000000000000n>", 480, 640 + ) + merged.counts.length should be (targetRle.counts.length) + merged.counts.zip(targetRle.counts).foreach{case (rleCount, targetCount) => + rleCount should be (targetCount +- 1) + } + } + +} + From 1f3d5d30c327a6f90c88a474128b4eaebc7f5190 Mon Sep 17 00:00:00 2001 From: Menooker Date: Thu, 10 Oct 2019 10:41:45 +0800 Subject: [PATCH 0962/1065] Fix memory leaks on training (#2914) * add memory owner * Add DnnTensor to MemoryOwner * delete unused file * style fix * Move ReorderManager to MemoryOwner * Fix compiling errors * use Releasable as a general management type. release input layer. * remove redundant null checking * fix memory leak in batch norm * style fixes * change _implicitMemoryOwner -> _this * release submat * release opencv submats --- .../vision/image/augmentation/Crop.scala | 4 +- .../vision/image/augmentation/Expand.scala | 8 +- .../vision/image/augmentation/Filler.scala | 5 +- .../nn/mkldnn/SpatialBatchNormalization.scala | 145 ++++++++++++------ 4 files changed, 111 insertions(+), 51 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Crop.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Crop.scala index 5fbe3723cee..25217ba66e6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Crop.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Crop.scala @@ -69,7 +69,9 @@ object Crop { y2 = Math.max(Math.min(y2, height), 0f) } val rect = new Rect(x1.toInt, y1.toInt, (x2 - x1).toInt, (y2 - y1).toInt) - input.submat(rect).copyTo(output) + val submat = input.submat(rect) + submat.copyTo(output) + submat.release() } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala index 8c805a12cc0..4b2cfb98526 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Expand.scala @@ -65,7 +65,9 @@ class Expand(meansR: Int = 123, meansG: Int = 117, meansB: Int = 104, channels.get(1).setTo(new Scalar(meansG)) channels.get(2).setTo(new Scalar(meansR)) Core.merge(channels, output) - input.copyTo(output.submat(bboxRoi)) + val submat = output.submat(bboxRoi) + input.copyTo(submat) + submat.release() // release memory (0 to 2).foreach(channels.get(_).release()) expandBbox @@ -112,7 +114,9 @@ class FixExpand(expandHeight: Int, expandWidth: Int) extends FeatureTransformer val leftPad = ((expandWidth - input.width()) / 2).floor val bboxRoi = new Rect(leftPad.toInt, topPad.toInt, width, height) output.create(expandHeight, expandWidth, input.`type`()) - input.copyTo(output.submat(bboxRoi)) + val submat = output.submat(bboxRoi) + input.copyTo(submat) + submat.release() output.copyTo(input) feature(ImageFeature.boundingBox) = BoundingBox(leftPad, topPad, leftPad + width, topPad + height) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Filler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Filler.scala index bb0b727059e..b6e6813e30e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Filler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/Filler.scala @@ -41,6 +41,7 @@ class Filler(startX: Float, startY: Float, endX: Float, endY: Float, value: Int override def transformMat(feature: ImageFeature): Unit = { var fillMat: Mat = null + var submat: Mat = null try { val mat = feature.opencvMat() val x1 = (startX * mat.cols()).ceil.toInt @@ -48,9 +49,11 @@ class Filler(startX: Float, startY: Float, endX: Float, endY: Float, value: Int val y1 = (startY * mat.rows()).ceil.toInt val y2 = (endY * mat.rows()).ceil.toInt fillMat = new core.Mat(y2 - y1, x2 - x1, mat.`type`(), new core.Scalar(value, value, value)) - fillMat.copyTo(mat.submat(y1, y2, x1, x2)) + submat = mat.submat(y1, y2, x1, x2) + fillMat.copyTo(submat) } finally { if (null != fillMat) fillMat.release() + if (null != submat) submat.release() } } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 06580eb7445..0a4b0af2391 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -47,6 +47,61 @@ class SpatialBatchNormalization( // same to scaled runningMean/runningVariance in dnn. private[bigdl] var needScale = false + class SwitchablePrimitives() { + private var _forwardDesc: Long = 0 + private var _updateOutputMemoryPrimitives : Array[Long] = _ + private var _updateOutputPrimitives: Array[Long] = _ + private var _fwdPrimDesc: Long = 0 + private var _inputFormat: NativeData = _ + private var _outputFormat: NativeData = _ + + def switchInOutFormats(): Unit = { + if (_inputFormat == null) { + _inputFormat = MemoryData.operationWant(fwdPrimDesc, Query.SrcPd) + } + if (_outputFormat == null) { + _outputFormat = MemoryData.operationWant(fwdPrimDesc, Query.DstPd) + } + _inputFormats(0) = _inputFormat + _outputFormats(0) = _outputFormat + } + + def fwdPrimDesc: Long = { + if (_fwdPrimDesc == 0) { + _fwdPrimDesc = if (relu) { + val postOps = MklDnnMemory.CreatePostOps() + MklDnn.PostOpsAppendEltwise(postOps, 1.0f, AlgKind.EltwiseRelu, 0.0f, 0.0f) + val attr = MklDnnMemory.CreateAttr() + MklDnn.AttrSetPostOps(attr, postOps) + MklDnnMemory.PrimitiveDescCreateV2(_forwardDesc, attr, runtime.engine, 0) + } else { + MklDnnMemory.PrimitiveDescCreate(_forwardDesc, runtime.engine, 0) + } + } + _fwdPrimDesc + } + + def forwardDesc(gen: () => Long): Long = { + if (_forwardDesc == 0) { + _forwardDesc = gen() + } + _forwardDesc + } + + def switchUpdateOutputMemoryPrimitives(gen: () => (Array[Long], Array[Long])): Unit = { + if (_updateOutputMemoryPrimitives == null) { + val generated = gen() + _updateOutputMemoryPrimitives = generated._1 + _updateOutputPrimitives = generated._2 + } + updateOutputMemoryPrimitives = _updateOutputMemoryPrimitives + updateOutputPrimitives = _updateOutputPrimitives + } + } + + @transient private lazy val trainingPrimitives = new SwitchablePrimitives + @transient private lazy val inferencePrimitives = new SwitchablePrimitives + @transient private var updateOutputTensors: Array[Tensor[Float]] = _ @transient private var updateOutputMemoryPrimitives: Array[Long] = _ @transient private var updateGradInputTensors: Array[Tensor[Float]] = _ @@ -142,62 +197,57 @@ class SpatialBatchNormalization( // the bn only accept F32 as input, like lrn val src = NativeData(inputs.head.shape, inputs.head.layout, DataType.F32) + // init once + if (_inputFormats == null) { + _inputFormats = new Array[MemoryData](1) + require(_outputFormats == null) + _outputFormats = new Array[MemoryData](1) + } + // init phase status initPhase(phase) - forwardDesc = modelPhase match { + + modelPhase match { case TrainingPhase => - MklDnnMemory.BatchNormForwardDescInit(PropKind.Forward, - src.getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_scaleshift) + forwardDesc = trainingPrimitives.forwardDesc(() => MklDnnMemory.BatchNormForwardDescInit( + PropKind.Forward, + src.getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_scaleshift)) + val fwdPrimDesc = trainingPrimitives.fwdPrimDesc + trainingPrimitives.switchInOutFormats() + trainingPrimitives.switchUpdateOutputMemoryPrimitives(() => { + val srcs = Array(inputFormats()(0), weightAndBias).map(_.getPrimitive(runtime)) + val dsts = Array(outputFormats()(0), mean, variance).map(_.getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + val primitive = MklDnnMemory.PrimitiveCreate2(fwdPrimDesc, srcs, indexes, + srcs.length, dsts, dsts.length) + val _updateOutputMemoryPrimitives = srcs ++ dsts + val _updateOutputPrimitives = Array(primitive) + (_updateOutputMemoryPrimitives, _updateOutputPrimitives) + }) case InferencePhase => // we always use the weight and bias / scale and offset. So the flags should be combined // with use_scaleshift and use_global_stats. - MklDnnMemory.BatchNormForwardDescInit(PropKind.ForwardInference, - src.getMemoryDescription(), eps.toFloat, - MklDnn.BatchNormFlag.mkldnn_use_global_stats | MklDnn.BatchNormFlag.mkldnn_use_scaleshift) + forwardDesc = inferencePrimitives.forwardDesc(() => + MklDnnMemory.BatchNormForwardDescInit(PropKind.ForwardInference, + src.getMemoryDescription(), eps.toFloat, MklDnn.BatchNormFlag.mkldnn_use_global_stats + | MklDnn.BatchNormFlag.mkldnn_use_scaleshift)) + val fwdPrimDesc = inferencePrimitives.fwdPrimDesc + inferencePrimitives.switchInOutFormats() + inferencePrimitives.switchUpdateOutputMemoryPrimitives(() => { + val srcs = Array(inputFormats()(0), mean, variance, weightAndBias).map(_.getPrimitive + (runtime)) + val dsts = Array(outputFormats()(0).getPrimitive(runtime)) + val indexes = Array.fill(srcs.length)(0) + val primitive = MklDnnMemory.PrimitiveCreate2(fwdPrimDesc, srcs, indexes, + srcs.length, dsts, dsts.length) + val _updateOutputMemoryPrimitives = srcs ++ dsts + val _updateOutputPrimitives = Array(primitive) + (_updateOutputMemoryPrimitives, _updateOutputPrimitives) + }) case _ => throw new UnsupportedOperationException } - val primDesc = if (relu) { - val postOps = MklDnnMemory.CreatePostOps() - MklDnn.PostOpsAppendEltwise(postOps, 1.0f, AlgKind.EltwiseRelu, 0.0f, 0.0f) - val attr = MklDnnMemory.CreateAttr() - MklDnn.AttrSetPostOps(attr, postOps) - MklDnnMemory.PrimitiveDescCreateV2(forwardDesc, attr, runtime.engine, 0) - // TODO we should destroy these ops - } else { - MklDnnMemory.PrimitiveDescCreate(forwardDesc, runtime.engine, 0) - } - - if (_inputFormats == null) { - _inputFormats = new Array[MemoryData](1) - } - - if (_outputFormats == null) { - _outputFormats = new Array[MemoryData](1) - } - - _inputFormats(0) = MemoryData.operationWant(primDesc, Query.SrcPd) - _outputFormats(0) = MemoryData.operationWant(primDesc, Query.DstPd) - - val (srcs, dsts) = if (modelPhase == TrainingPhase) { - val srcs = Array(inputFormats()(0), weightAndBias).map(_.getPrimitive(runtime)) - val dsts = Array(outputFormats()(0), mean, variance).map(_.getPrimitive(runtime)) - (srcs, dsts) - } else { - val srcs = Array(inputFormats()(0), mean, variance, weightAndBias).map { x => - x.getPrimitive(runtime) - } - val dsts = Array(outputFormats()(0).getPrimitive(runtime)) - (srcs, dsts) - } - val indexes = Array.fill(srcs.length)(0) - - val primitive = MklDnnMemory.PrimitiveCreate2(primDesc, srcs, indexes, - srcs.length, dsts, dsts.length) - - updateOutputMemoryPrimitives = srcs ++ dsts - updateOutputPrimitives = Array(primitive) - + // init once // if the output is not null, it means we have initialized the primitives before. // so we do not need create weightAndBias native space again. if (output == null || output.isInstanceOf[DnnTensor[_]] && @@ -209,6 +259,7 @@ class SpatialBatchNormalization( updateOutputTensors = null } + // init once if (this.weightAndBias.native == null) { if (modelPhase == InferencePhase) { this.runningMean.setMemoryData( From bbf4672a5a28a936350a93a975b973eb060ec6b0 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 11 Oct 2019 09:45:44 +0800 Subject: [PATCH 0963/1065] support samples with different size to one mini batch (#2929) * add to batch with resize * meet comments --- .../vision/image/MTImageFeatureToBatch.scala | 77 +++++++++ .../image/MTImageFeatureToBatchSpec.scala | 152 +++++++++++++++++- 2 files changed, 228 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala index 37c28cdb01a..20f1fb409a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -49,6 +49,20 @@ object MTImageFeatureToBatch { } } +object MTImageFeatureToBatchWithResize { + /** + * The transformer from ImageFeature to mini-batches, and extract ROI labels for segmentation + * if roi labels are set. + * @param sizeDivisible when it's greater than 0, height and wide should be divisible by this size + * @param batchSize global batch size + * @param transformer pipeline for pre-processing + * @param toRGB if converted to RGB, default format is BGR + */ + def apply(sizeDivisible: Int = -1, batchSize: Int, transformer: FeatureTransformer, + toRGB : Boolean = false): MTImageFeatureToBatch = + new RoiImageFeatureToBatchWithResize(sizeDivisible, batchSize, transformer, toRGB) +} + /** * An abstract class to convert ImageFeature iterator to MiniBatches. This transformer will be run * on each image feature. "processImageFeature" will be called to buffer the image features. When @@ -267,3 +281,66 @@ class RoiMTImageFeatureToBatch private[bigdl](width: Int, height: Int, RoiMiniBatch(featureTensor, labelData.view, isCrowdData.view, origSizeData.view) } } + +/** + * A transformer pipeline wrapper to create RoiMiniBatch in multiple threads. + * Image features may have different sizes, so firstly we need to calculate max size in one batch, + * then padding all features to one batch with max size. + * @param sizeDivisible when it's greater than 0, + * height and wide will be round up to multiple of this divisible size + * @param totalBatchSize global batch size + * @param transformer pipeline for pre-processing + * @param toRGB + */ +class RoiImageFeatureToBatchWithResize private[bigdl](sizeDivisible: Int = -1, totalBatchSize: Int, + transformer: FeatureTransformer, toRGB: Boolean = false) + extends MTImageFeatureToBatch(totalBatchSize, transformer) { + + private val labelData: Array[RoiLabel] = new Array[RoiLabel](batchSize) + private val isCrowdData: Array[Tensor[Float]] = new Array[Tensor[Float]](batchSize) + private val origSizeData: Array[(Int, Int, Int)] = new Array[(Int, Int, Int)](batchSize) + private var featureTensor: Tensor[Float] = null + private val imageBuffer = new Array[Tensor[Float]](batchSize) + + private def getFrameSize(batchSize: Int): (Int, Int) = { + var maxHeight = 0 + var maxWide = 0 + for (i <- 0 until batchSize) { + maxHeight = math.max(maxHeight, imageBuffer(i).size(2)) + maxWide = math.max(maxWide, imageBuffer(i).size(3)) + } + + if (sizeDivisible > 0) { + maxHeight = (math.ceil(maxHeight.toFloat / sizeDivisible) * sizeDivisible).toInt + maxWide = (math.ceil(maxWide.toFloat / sizeDivisible) * sizeDivisible).toInt + } + (maxHeight, maxWide) + } + + override protected def processImageFeature(img: ImageFeature, position: Int): Unit = { + if (imageBuffer(position) == null) imageBuffer(position) = Tensor[Float]() + imageBuffer(position).resize(3, img.getHeight(), img.getWidth()) + // save img to buffer + img.copyTo(imageBuffer(position).storage().array(), 0, toRGB = toRGB) + val isCrowd = img(RoiLabel.ISCROWD).asInstanceOf[Tensor[Float]] + val label = img.getLabel.asInstanceOf[RoiLabel] + require(label.bboxes.size(1) == isCrowd.size(1), "The number of detections" + + "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") + isCrowdData(position) = isCrowd + labelData(position) = label + origSizeData(position) = img.getOriginalSize + } + + override protected def createBatch(batchSize: Int): MiniBatch[Float] = { + val (height, wide) = getFrameSize(batchSize) + if (featureTensor == null) featureTensor = Tensor() + featureTensor.resize(batchSize, 3, height, wide).fill(0.0f) + // copy img buffer to feature tensor + for (i <- 0 to (batchSize - 1)) { + featureTensor.select(1, i + 1).narrow(2, 1, imageBuffer(i).size(2)) + .narrow(3, 1, imageBuffer(i).size(3)).copy(imageBuffer(i)) + } + RoiMiniBatch(featureTensor, labelData.view(0, batchSize), + isCrowdData.view(0, batchSize), origSizeData.view(0, batchSize)) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala index 968e1603b84..adc9bd2e834 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala @@ -16,9 +16,10 @@ package com.intel.analytics.bigdl.transform.vision.image +import com.intel.analytics.bigdl.dataset.DataSet import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel -import com.intel.analytics.bigdl.utils.{Engine, Table} +import com.intel.analytics.bigdl.utils.{Engine, T, Table} import org.apache.spark.SparkContext import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} @@ -36,6 +37,155 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft if (null != sc) sc.stop() } + "RoiImageFeatureToBatchWithMaxSize" should "work well" in { + val imgData = (0 to 10).map(n => { + val data = Tensor[Float](T(T( + T(0.6336, 0.3563, 0.1053, 0.6912, 0.3791, 0.2707, 0.6270, 0.8446, + 0.2008, 0.3051, 0.6324, 0.4001, 0.6439, 0.2275, 0.8395, 0.6917), + T(0.5191, 0.6917, 0.3929, 0.8765, 0.6981, 0.2679, 0.5423, 0.8095, + 0.1022, 0.0215, 0.1976, 0.3040, 0.3436, 0.0894, 0.5207, 0.9173), + T(0.7829, 0.8493, 0.6865, 0.5468, 0.8769, 0.0055, 0.5274, 0.6638, + 0.5623, 0.6986, 0.9963, 0.9332, 0.3322, 0.2322, 0.7539, 0.1027), + T(0.8297, 0.7903, 0.7254, 0.2109, 0.4015, 0.7729, 0.7242, 0.6415, + 0.0452, 0.5547, 0.7091, 0.8217, 0.6968, 0.7594, 0.3986, 0.5862), + T(0.6075, 0.6215, 0.8243, 0.7298, 0.5886, 0.3655, 0.6750, 0.4722, + 0.1140, 0.2483, 0.8853, 0.4583, 0.2110, 0.8364, 0.2063, 0.4120), + T(0.3350, 0.3226, 0.9264, 0.3657, 0.1387, 0.9268, 0.8490, 0.3405, + 0.1999, 0.2797, 0.8620, 0.2984, 0.1121, 0.9285, 0.3487, 0.1860), + T(0.4850, 0.4671, 0.4069, 0.5200, 0.5928, 0.1164, 0.1781, 0.1367, + 0.0951, 0.8707, 0.8220, 0.3016, 0.8646, 0.9668, 0.7803, 0.1323), + T(0.3663, 0.6169, 0.6257, 0.8451, 0.1146, 0.5394, 0.5738, 0.7960, + 0.4786, 0.6590, 0.5803, 0.0800, 0.0975, 0.1009, 0.1835, 0.5978)), + T(T(0.6848, 0.7909, 0.0584, 0.5309, 0.5087, 0.3893, 0.5740, 0.8990, + 0.9438, 0.7067, 0.3653, 0.1513, 0.8279, 0.6395, 0.6875, 0.8965), + T(0.8340, 0.4398, 0.5573, 0.2817, 0.1441, 0.7729, 0.0940, 0.9943, + 0.9369, 0.3792, 0.1262, 0.7556, 0.5480, 0.6573, 0.5901, 0.0393), + T(0.1406, 0.5208, 0.4751, 0.6157, 0.5476, 0.9403, 0.0226, 0.6577, + 0.4105, 0.6823, 0.2789, 0.5607, 0.0228, 0.4178, 0.7816, 0.5339), + T(0.6371, 0.0603, 0.3195, 0.6144, 0.2042, 0.1585, 0.1249, 0.9442, + 0.9533, 0.1570, 0.8457, 0.1685, 0.2243, 0.3009, 0.2149, 0.1328), + T(0.7049, 0.6040, 0.5683, 0.3084, 0.2516, 0.1883, 0.0982, 0.7712, + 0.5637, 0.5811, 0.1678, 0.3323, 0.9634, 0.5855, 0.4315, 0.8492), + T(0.6626, 0.1401, 0.7042, 0.3153, 0.6940, 0.5070, 0.6723, 0.6993, + 0.7467, 0.6185, 0.8907, 0.3982, 0.6435, 0.5429, 0.2580, 0.7538), + T(0.3496, 0.3059, 0.1777, 0.7922, 0.9832, 0.5681, 0.6051, 0.1525, + 0.7647, 0.6433, 0.8886, 0.8596, 0.6976, 0.1161, 0.0092, 0.1787), + T(0.0386, 0.8511, 0.4545, 0.1208, 0.2020, 0.7471, 0.7825, 0.3376, + 0.5597, 0.6067, 0.8809, 0.6917, 0.1960, 0.4223, 0.9569, 0.6081)), + T(T(0.6848, 0.7909, 0.0584, 0.5309, 0.5087, 0.3893, 0.5740, 0.8990, + 0.9438, 0.7067, 0.3653, 0.1513, 0.8279, 0.6395, 0.6875, 0.8965), + T(0.8340, 0.4398, 0.5573, 0.2817, 0.1441, 0.7729, 0.0940, 0.9943, + 0.9369, 0.3792, 0.1262, 0.7556, 0.5480, 0.6573, 0.5901, 0.0393), + T(0.1406, 0.5208, 0.4751, 0.6157, 0.5476, 0.9403, 0.0226, 0.6577, + 0.4105, 0.6823, 0.2789, 0.5607, 0.0228, 0.4178, 0.7816, 0.5339), + T(0.6371, 0.0603, 0.3195, 0.6144, 0.2042, 0.1585, 0.1249, 0.9442, + 0.9533, 0.1570, 0.8457, 0.1685, 0.2243, 0.3009, 0.2149, 0.1328), + T(0.7049, 0.6040, 0.5683, 0.3084, 0.2516, 0.1883, 0.0982, 0.7712, + 0.5637, 0.5811, 0.1678, 0.3323, 0.9634, 0.5855, 0.4315, 0.8492), + T(0.6626, 0.1401, 0.7042, 0.3153, 0.6940, 0.5070, 0.6723, 0.6993, + 0.7467, 0.6185, 0.8907, 0.3982, 0.6435, 0.5429, 0.2580, 0.7538), + T(0.3496, 0.3059, 0.1777, 0.7922, 0.9832, 0.5681, 0.6051, 0.1525, + 0.7647, 0.6433, 0.8886, 0.8596, 0.6976, 0.1161, 0.0092, 0.1787), + T(0.0386, 0.8511, 0.4545, 0.1208, 0.2020, 0.7471, 0.7825, 0.3376, + 0.5597, 0.6067, 0.8809, 0.6917, 0.1960, 0.4223, 0.9569, 0.6081)))) + .transpose(1, 2).transpose(2, 3).contiguous() + + val imf = ImageFeature() + imf(ImageFeature.floats) = data.storage().array() + imf(ImageFeature.label) = RoiLabel( + Tensor(new Array[Float](2), Array(2)), + Tensor(new Array[Float](2*4), Array(2, 4)), + null + ) + imf(RoiLabel.ISCROWD) = Tensor(Array(0f, 1f), Array(2)) + imf(ImageFeature.originalSize) = (8, 16, 3) + imf + }).toArray + + val transformer = MTImageFeatureToBatchWithResize(10, 3, + new FeatureTransformer {}, toRGB = false) + val miniBatch = transformer(DataSet.array(imgData).data(false)) + + val expectedOutput = Tensor[Float](T(T( + T(0.6336, 0.3563, 0.1053, 0.6912, 0.3791, 0.2707, 0.6270, 0.8446, 0.2008, 0.3051, + 0.6324, 0.4001, 0.6439, 0.2275, 0.8395, 0.6917, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.5191, 0.6917, 0.3929, 0.8765, 0.6981, 0.2679, 0.5423, 0.8095, 0.1022, 0.0215, + 0.1976, 0.3040, 0.3436, 0.0894, 0.5207, 0.9173, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.7829, 0.8493, 0.6865, 0.5468, 0.8769, 0.0055, 0.5274, 0.6638, 0.5623, 0.6986, + 0.9963, 0.9332, 0.3322, 0.2322, 0.7539, 0.1027, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.8297, 0.7903, 0.7254, 0.2109, 0.4015, 0.7729, 0.7242, 0.6415, 0.0452, 0.5547, + 0.7091, 0.8217, 0.6968, 0.7594, 0.3986, 0.5862, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.6075, 0.6215, 0.8243, 0.7298, 0.5886, 0.3655, 0.6750, 0.4722, 0.1140, 0.2483, + 0.8853, 0.4583, 0.2110, 0.8364, 0.2063, 0.4120, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.3350, 0.3226, 0.9264, 0.3657, 0.1387, 0.9268, 0.8490, 0.3405, 0.1999, 0.2797, + 0.8620, 0.2984, 0.1121, 0.9285, 0.3487, 0.1860, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.4850, 0.4671, 0.4069, 0.5200, 0.5928, 0.1164, 0.1781, 0.1367, 0.0951, 0.8707, + 0.8220, 0.3016, 0.8646, 0.9668, 0.7803, 0.1323, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.3663, 0.6169, 0.6257, 0.8451, 0.1146, 0.5394, 0.5738, 0.7960, 0.4786, 0.6590, + 0.5803, 0.0800, 0.0975, 0.1009, 0.1835, 0.5978, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, + 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, + 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000)), + T(T(0.6848, 0.7909, 0.0584, 0.5309, 0.5087, 0.3893, 0.5740, 0.8990, 0.9438, 0.7067, + 0.3653, 0.1513, 0.8279, 0.6395, 0.6875, 0.8965, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.8340, 0.4398, 0.5573, 0.2817, 0.1441, 0.7729, 0.0940, 0.9943, 0.9369, 0.3792, + 0.1262, 0.7556, 0.5480, 0.6573, 0.5901, 0.0393, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.1406, 0.5208, 0.4751, 0.6157, 0.5476, 0.9403, 0.0226, 0.6577, 0.4105, 0.6823, + 0.2789, 0.5607, 0.0228, 0.4178, 0.7816, 0.5339, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.6371, 0.0603, 0.3195, 0.6144, 0.2042, 0.1585, 0.1249, 0.9442, 0.9533, 0.1570, + 0.8457, 0.1685, 0.2243, 0.3009, 0.2149, 0.1328, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.7049, 0.6040, 0.5683, 0.3084, 0.2516, 0.1883, 0.0982, 0.7712, 0.5637, 0.5811, + 0.1678, 0.3323, 0.9634, 0.5855, 0.4315, 0.8492, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.6626, 0.1401, 0.7042, 0.3153, 0.6940, 0.5070, 0.6723, 0.6993, 0.7467, 0.6185, + 0.8907, 0.3982, 0.6435, 0.5429, 0.2580, 0.7538, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.3496, 0.3059, 0.1777, 0.7922, 0.9832, 0.5681, 0.6051, 0.1525, 0.7647, 0.6433, + 0.8886, 0.8596, 0.6976, 0.1161, 0.0092, 0.1787, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.0386, 0.8511, 0.4545, 0.1208, 0.2020, 0.7471, 0.7825, 0.3376, 0.5597, 0.6067, + 0.8809, 0.6917, 0.1960, 0.4223, 0.9569, 0.6081, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, + 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, + 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000)), + T(T(0.6848, 0.7909, 0.0584, 0.5309, 0.5087, 0.3893, 0.5740, 0.8990, 0.9438, 0.7067, + 0.3653, 0.1513, 0.8279, 0.6395, 0.6875, 0.8965, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.8340, 0.4398, 0.5573, 0.2817, 0.1441, 0.7729, 0.0940, 0.9943, 0.9369, 0.3792, + 0.1262, 0.7556, 0.5480, 0.6573, 0.5901, 0.0393, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.1406, 0.5208, 0.4751, 0.6157, 0.5476, 0.9403, 0.0226, 0.6577, 0.4105, 0.6823, + 0.2789, 0.5607, 0.0228, 0.4178, 0.7816, 0.5339, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.6371, 0.0603, 0.3195, 0.6144, 0.2042, 0.1585, 0.1249, 0.9442, 0.9533, 0.1570, + 0.8457, 0.1685, 0.2243, 0.3009, 0.2149, 0.1328, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.7049, 0.6040, 0.5683, 0.3084, 0.2516, 0.1883, 0.0982, 0.7712, 0.5637, 0.5811, + 0.1678, 0.3323, 0.9634, 0.5855, 0.4315, 0.8492, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.6626, 0.1401, 0.7042, 0.3153, 0.6940, 0.5070, 0.6723, 0.6993, 0.7467, 0.6185, + 0.8907, 0.3982, 0.6435, 0.5429, 0.2580, 0.7538, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.3496, 0.3059, 0.1777, 0.7922, 0.9832, 0.5681, 0.6051, 0.1525, 0.7647, 0.6433, + 0.8886, 0.8596, 0.6976, 0.1161, 0.0092, 0.1787, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.0386, 0.8511, 0.4545, 0.1208, 0.2020, 0.7471, 0.7825, 0.3376, 0.5597, 0.6067, + 0.8809, 0.6917, 0.1960, 0.4223, 0.9569, 0.6081, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, + 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000), + T(0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, + 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000)))) + + miniBatch.foreach(batch => { + (batch.size() <= 3) should be (true) + val input = batch.getInput().asInstanceOf[Tensor[Float]] + val target = batch.getTarget().asInstanceOf[Table] + input.size() should be (Array(batch.size(), 3, 10, 20)) + target.length() should be (batch.size()) + for(i <- 1 to batch.size()) { + val in = input.select(1, i) + in should be(expectedOutput) + val t = target(i).asInstanceOf[Table] + t[Tensor[Float]](RoiLabel.ISCROWD) should be (Tensor(Array(0f, 1f), Array(2))) + t[(Int, Int, Int)](RoiLabel.ORIGSIZE) should be((8, 16, 3)) + t[Tensor[Float]](RoiLabel.BBOXES).size() should be (Array(2, 4)) + t[Tensor[Float]](RoiLabel.CLASSES).size() should be (Array(2)) + } + }) + } + // todo: There is a race-condition bug in MTImageFeatureToBatch /* "MTImageFeatureToBatch classification" should "work well" in { From 27f727e1784a708c836a8c28840ca99d01a4f841 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Sat, 12 Oct 2019 10:05:18 +0800 Subject: [PATCH 0964/1065] support batch for mask head and pooler (#2926) * support batch for mask head * meet comments --- .../intel/analytics/bigdl/dllib/nn/FPN.scala | 5 +- .../analytics/bigdl/dllib/nn/MaskHead.scala | 11 +- .../analytics/bigdl/dllib/nn/Pooler.scala | 67 ++-- .../bigdl/dllib/nn/MaskHeadSpec.scala | 332 +++++++++++++++++- .../dllib/nn/MaskPostProcessorSpec.scala | 5 +- 5 files changed, 384 insertions(+), 36 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala index 5ab782cbdb1..86e3548859c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/FPN.scala @@ -56,8 +56,10 @@ class FPN[T : ClassTag]( if (inChannels(i) != 0) { val innerBlockModule = SpatialConvolution[T](inChannels(i), outChannels, 1, 1, 1, 1) + .setName(s"fpn_inner${i + 1}") val layerBlockModule = SpatialConvolution[T](outChannels, outChannels, 3, 3, 1, 1, 1, 1) + .setName(s"fpn_layer${i + 1}") innerBlockModules(i) = innerBlockModule layerBlockModules(i) = layerBlockModule } @@ -84,7 +86,8 @@ class FPN[T : ClassTag]( if (layerBlock != null) { val innerTopDown = UpSampling2D[T](Array(2, 2)).inputs(lastInner) val innerLateral = innerBlocks(i) - lastInner = CAddTable[T]().inputs(innerLateral, innerTopDown) + lastInner = CAddTable[T]().setName(s"number_${i}_${featureMapsNum}") + .inputs(innerLateral, innerTopDown) count -= 1 results(count) = layerBlock.inputs(lastInner) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala index 08f863b3c07..5b027ac7f20 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/MaskHead.scala @@ -25,7 +25,7 @@ class MaskHead( val inChannels: Int, val resolution: Int, val scales: Array[Float], - val samplingRratio: Int, + val samplingRatio: Int, val layers: Array[Int], val dilation: Int, val numClasses: Int, @@ -34,7 +34,7 @@ class MaskHead( override def buildModel(): Module[Float] = { val featureExtractor = this.maskFeatureExtractor( - inChannels, resolution, scales, samplingRratio, layers, dilation, useGn) + inChannels, resolution, scales, samplingRatio, layers, dilation, useGn) val dimReduced = layers(layers.length - 1) val predictor = this.maskPredictor(dimReduced, numClasses, dimReduced) val postProcessor = new MaskPostProcessor() @@ -53,7 +53,7 @@ class MaskHead( val maskFeatures = featureExtractor.inputs(features, proposals) val maskLogits = predictor.inputs(maskFeatures) - val result = postProcessor.inputs(maskLogits, proposals, labels) + val result = postProcessor.inputs(maskLogits, labels) Graph(Array(features, proposals, labels), Array(maskFeatures, result)) } @@ -127,8 +127,7 @@ private[nn] class MaskPostProcessor()(implicit ev: TensorNumeric[Float]) */ override def updateOutput(input: Table): Tensor[Float] = { val maskLogits = input[Tensor[Float]](1) - val bbox = input[Tensor[Float]](2) // N * 4 - val labels = input[Tensor[Float]](3) + val labels = input[Tensor[Float]](2) val num_masks = maskLogits.size(1) if (rangeBuffer == null || rangeBuffer.nElement() != num_masks) { @@ -148,7 +147,7 @@ private[nn] class MaskPostProcessor()(implicit ev: TensorNumeric[Float]) while (i <= rangeBuffer.nElement()) { val dim = rangeBuffer.valueAt(i).toInt + 1 val index = labels.valueAt(i).toInt // start from 1 - output.narrow(1, i, 1).copy(mask_prob.narrow(1, i, 1).narrow(2, index, 1)) + output.narrow(1, i, 1).copy(mask_prob.narrow(1, i, 1).narrow(2, index + 1, 1)) i += 1 } output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala index 59a05ba4b0b..df2761888c9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala @@ -104,33 +104,56 @@ class Pooler[T: ClassTag] ( override def updateOutput(input: Table): Tensor[T] = { val featureMaps = input[Table](1) - val rois = input[Tensor[T]](2) - - val roi_levels = levelMapping(lvl_min, lvl_max, rois) - val num_rois = rois.size(1) + val roiBatch = if (input(2).isInstanceOf[Tensor[T]]) { + T(input[Tensor[T]](2)) + } else { // for batch support + input[Table](2) + } + var totalNum = 0 val num_channels = featureMaps.get[Tensor[T]](1).get.size(2) - - output.resize(num_rois, num_channels, resolution, resolution) - .fill(ev.fromType[Float](Float.MinValue)) - - for (level <- 0 until num_levels) { - val feature_per_level = featureMaps.get[Tensor[T]](level + 1).get - val rois_ind_per_level = roi_levels.zipWithIndex.filter(_._1 == level).map(_._2) - val num_rois_per_level = rois_ind_per_level.length - - if (num_rois_per_level > 0) { - val rois_per_level = Tensor[T](Array(num_rois_per_level, 4)) // bbox has 4 elements - for (i <- 0 until num_rois_per_level) { - rois_per_level(i + 1) = rois(rois_ind_per_level(i) + 1) - } - - val res = poolers(level).forward(T(feature_per_level, rois_per_level)) - for (i <- 0 until num_rois_per_level) { - output(rois_ind_per_level(i) + 1) = res(i + 1) + val out = T() + for (i <- 0 to roiBatch.length() - 1) { + val rois = roiBatch[Tensor[T]](i + 1) + + val roi_levels = levelMapping(lvl_min, lvl_max, rois) + val num_rois = rois.size(1) + totalNum += num_rois + + if (out.getOrElse(i + 1, null) == null) out(i + 1) = Tensor[T]() + val outROI = out[Tensor[T]](i + 1) + outROI.resize(num_rois, num_channels, resolution, resolution) + .fill(ev.fromType[Float](Float.MinValue)) + + for (level <- 0 until num_levels) { + val tmp = featureMaps.get[Tensor[T]](level + 1).get.narrow(1, i + 1, 1) + val feature_per_level = Tensor[T]().resizeAs(tmp).copy(tmp) + val rois_ind_per_level = roi_levels.zipWithIndex.filter(_._1 == level).map(_._2) + val num_rois_per_level = rois_ind_per_level.length + + if (num_rois_per_level > 0) { + val rois_per_level = Tensor[T](Array(num_rois_per_level, 4)) // bbox has 4 elements + for (i <- 0 until num_rois_per_level) { + rois_per_level(i + 1) = rois(rois_ind_per_level(i) + 1) + } + + val res = poolers(level).forward(T(feature_per_level, rois_per_level)) + for (i <- 0 until num_rois_per_level) { + outROI(rois_ind_per_level(i) + 1) = res(i + 1) + } } } } + // merge to one tensor + output.resize(totalNum, num_channels, resolution, resolution) + var start = 1 + for (i <- 0 to roiBatch.length() - 1) { + val tmp = out[Tensor[T]](i + 1) + val length = tmp.size(1) + output.narrow(1, start, length).copy(tmp) + start += length + } + output } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala index 70017ddeb67..ea96dc47b65 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala @@ -91,7 +91,7 @@ class MaskHeadSpec extends FlatSpec with Matchers { T(3.0f, 5.0f, 6.0f, 10.0f))) val labels = Tensor[Float](T(1, 3)) - val output = layer.forward(T(T(features1, features2), bbox, labels)).toTable + val output = layer.forward(T(T(features1, features2), T(bbox), labels)).toTable val expectedOutput = Tensor[Float](T(T(T( T(0.0013, 0.0015, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, @@ -334,6 +334,332 @@ class MaskHeadSpec extends FlatSpec with Matchers { }) } + "MaskHead with batch size > 1" should "be ok" in { + val inChannels: Int = 6 + val resolution: Int = 14 + val scales: Array[Float] = Array[Float](0.25f, 0.125f) + val samplingRatio: Int = 2 + val layers: Array[Int] = Array[Int](4, 4) + val dilation: Int = 1 + val numClasses: Int = 81 + val useGn: Boolean = false + + val layer = new MaskHead(inChannels, resolution, scales, + samplingRatio, layers, dilation, numClasses, useGn) + + val params = layer.getParameters() + params._1.fill(0.001f) + + val features1 = Tensor[Float](T(T(T(T(0.5381, 0.0856, 0.1124, 0.7493), + T(0.4624, 0.2182, 0.7364, 0.3522), + T(0.7552, 0.7117, 0.2715, 0.9082)), + T(T(0.0928, 0.2735, 0.7539, 0.7539), + T(0.4777, 0.1525, 0.8279, 0.6481), + T(0.6019, 0.4803, 0.5869, 0.7459)), + T(T(0.1924, 0.2795, 0.4463, 0.3887), + T(0.5791, 0.9832, 0.8752, 0.4598), + T(0.2278, 0.0758, 0.4988, 0.3742)), + T(T(0.1762, 0.6499, 0.2534, 0.9842), + T(0.0908, 0.8676, 0.1700, 0.1887), + T(0.7138, 0.9559, 0.0119, 0.7799)), + T(T(0.8200, 0.6767, 0.3637, 0.9771), + T(0.1217, 0.5645, 0.2574, 0.6729), + T(0.6140, 0.5333, 0.4425, 0.1740)), + T(T(0.3994, 0.9148, 0.0123, 0.0125), + T(0.5663, 0.9951, 0.8143, 0.9906), + T(0.0923, 0.8285, 0.2992, 0.2221))))) + + val features2 = Tensor[Float](T(T(T(T(0.0492, 0.1234), + T(0.3291, 0.0613), + T(0.4260, 0.1422), + T(0.2282, 0.4258), + T(0.7426, 0.9476)), + T(T(0.6662, 0.7015), + T(0.4598, 0.6378), + T(0.9571, 0.4947), + T(0.1659, 0.3034), + T(0.8583, 0.1369)), + T(T(0.1711, 0.6440), + T(0.2099, 0.4468), + T(0.9518, 0.3877), + T(0.4058, 0.6630), + T(0.9056, 0.4054)), + T(T(0.4562, 0.0277), + T(0.2358, 0.3938), + T(0.9187, 0.4067), + T(0.0445, 0.4171), + T(0.3434, 0.1964)), + T(T(0.9473, 0.7239), + T(0.1732, 0.5352), + T(0.8276, 0.6435), + T(0.3516, 0.3760), + T(0.3437, 0.0198)), + T(T(0.7811, 0.5682), + T(0.5121, 0.9655), + T(0.3496, 0.7632), + T(0.4267, 0.4533), + T(0.8624, 0.3172))))) + + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 10.0f))) + val labels = Tensor[Float](T(1, 3)) + + val features1Batch = Tensor[Float](2, features1.size(2), features1.size(3), features1.size(4)) + features1Batch.select(1, 1).copy(features1) + features1Batch.select(1, 2).copy(features1) + + val features2Batch = Tensor[Float](2, features2.size(2), features2.size(3), features2.size(4)) + features2Batch.select(1, 1).copy(features2) + features2Batch.select(1, 2).copy(features2) + + val bboxBatch = T(Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), T(3.0f, 5.0f, 6.0f, 7.0f))), + Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), T(3.0f, 5.0f, 6.0f, 7.0f)))) + + val labelsBatch = Tensor[Float](T(1, 3, 1, 3)) + + val output = layer.forward(T(T(features1Batch, features2Batch), bboxBatch, labelsBatch)).toTable + + val expectedOutput = Tensor[Float](T(T(T( + T(0.0013, 0.0015, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0018, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0021, 0.0021, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0018, 0.0018, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, + 0.0015, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013)), + + T(T(0.0013, 0.0015, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0018, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0021, 0.0021, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0018, 0.0018, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, + 0.0015, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013)), + + T(T(0.0013, 0.0015, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0018, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0021, 0.0021, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0018, 0.0018, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, + 0.0015, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013)), + + T(T(0.0013, 0.0015, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0018, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0021, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0021, 0.0021, 0.0021, 0.0021, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0021, 0.0021, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0018, 0.0018, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, + 0.0015, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013))), + + + T(T(T(0.0013, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0018, 0.0018, 0.0017, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0019, 0.0018, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013)), + + T(T(0.0013, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0018, 0.0018, 0.0017, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0019, 0.0018, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013)), + + T(T(0.0013, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0018, 0.0018, 0.0017, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0019, 0.0018, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013)), + + T(T(0.0013, 0.0015, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, 0.0016, + 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0013), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0018, 0.0018, 0.0017, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0019, 0.0018, 0.0015), + T(0.0016, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0015), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0018, 0.0019, 0.0019, 0.0020, 0.0020, 0.0020, 0.0020, + 0.0020, 0.0020, 0.0020, 0.0020, 0.0019, 0.0016), + T(0.0015, 0.0017, 0.0018, 0.0018, 0.0019, 0.0019, 0.0019, 0.0019, + 0.0019, 0.0019, 0.0019, 0.0019, 0.0018, 0.0015), + T(0.0013, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.0016, + 0.0016, 0.0016, 0.0016, 0.0016, 0.0015, 0.0013))))) + + output[Tensor[Float]](1).narrow(1, 1, 2).almostEqual(expectedOutput, 1e-3) should be(true) + output[Tensor[Float]](2).narrow(1, 1, 2).apply1(a => { + a should be(0.5003f +- 1e-3f) + a + }) + } + "MaskRCNNFPNFeatureExtractor" should "be ok" in { val resolution = 14 val scales = Array[Float](0.25f, 0.125f) @@ -641,7 +967,7 @@ class MaskHeadSpec extends FlatSpec with Matchers { T(0.0349, 0.0562, 0.0634, 0.0633, 0.0623, 0.0607, 0.0588, 0.0570, 0.0551, 0.0532, 0.0513, 0.0494, 0.0426, 0.0262))))) - val output = layer.forward(T(T(features1, features2), bbox)).toTensor[Float] + val output = layer.forward(T(T(features1, features2), T(bbox))).toTensor[Float] output.almostEqual(expectedOutput, 1e-3) should be(true) } @@ -876,6 +1202,6 @@ class MaskHeadSerialTest extends ModuleSerializationTest { T(3.0f, 5.0f, 6.0f, 10.0f))) val labels = Tensor[Float](T(1, 3)) - runSerializationTest(layer, T(T(features1, features2), bbox, labels)) + runSerializationTest(layer, T(T(features1, features2), T(bbox), labels)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala index ba6f1ce6760..069e730684d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala @@ -522,11 +522,8 @@ class MaskPostProcessorSpec extends FlatSpec with Matchers { class MaskPostProcessorSerialTest extends ModuleSerializationTest { override def test(): Unit = { val proposal = new MaskPostProcessor().setName("MaskPostProcessor") - - val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), - T(3.0f, 5.0f, 6.0f, 10.0f))) val labels = Tensor[Float](T(1, 3)) val logits = Tensor[Float](2, 81, 18, 18).rand() - runSerializationTest(proposal, T(logits, bbox, labels)) + runSerializationTest(proposal, T(logits, labels)) } } From cd53093b0c981658a2fb37768f45b91aa846df43 Mon Sep 17 00:00:00 2001 From: Xiao Date: Mon, 14 Oct 2019 14:18:08 +0800 Subject: [PATCH 0965/1065] Onnx support: add a dim parameter to ops.Gather (#2920) * add dim parameter to ops.Gather * improve and simplify code * improve and simplify code * improve and simplify code * improve and simplify code --- .../analytics/bigdl/dllib/nn/ops/Gather.scala | 63 ++++++++++--------- .../bigdl/dllib/nn/ops/GatherSpec.scala | 26 ++++++++ 2 files changed, 61 insertions(+), 28 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala index c98d79b16b1..ec6baeb221a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/Gather.scala @@ -26,7 +26,8 @@ import scala.reflect.ClassTag * Input should be two tensors, the first one is the tensor which to gather values; * the second one is Index tensor. */ -class Gather[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) +class Gather[T: ClassTag, D: ClassTag]( + var dim: Int = 1)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Table, Tensor[D], T]{ output = Tensor[D]() @@ -44,34 +45,39 @@ class Gather[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Ten intBuffer } val inputSizes = inputTensor.size() + val inputDim = inputTensor.dim() // data batch dim + dim = if (dim <= 0) { + inputDim + dim + } + else dim + require(dim >= 1 && dim <= inputDim, s"Invalid position: $dim. " + + s"input:dim() is $inputTensor, input feature map dim (numInputDims) is $inputDim.") - if (indices.isScalar) { - val index = indices.value() - require(index < inputSizes(0), - s"index should smaller than ${inputSizes(0)}, but got $index") - val theOutput = inputTensor.select(1, index + 1) - inputSizes(0) = 1 - this.output.resize(inputSizes).copy(theOutput) + // set output shape + val indicesSize = indices.size() + val outputSizes = if (indices.isScalar) { + inputSizes.slice(0, dim-1) ++ Array(1) ++ inputSizes.slice(dim, inputSizes.length) } else { - val indicesSize = indices.size() - val outputSizes = indicesSize ++ inputSizes.slice(1, inputSizes.length) - - output.resize(Array(indices.nElement()) ++ inputSizes.slice(1, inputSizes.length)) - indices.resize(indices.nElement()) - var i = 0 - while (i < indices.nElement()) { - val index = indices.valueAt(i + 1) - require(index < inputSizes(0), - s"index should smaller than ${inputSizes(0)}, but got $index") - output.select(1, i + 1).copy(inputTensor.select(1, index + 1)) - i += 1 - } - - indices.resize(indicesSize) - output.resize(outputSizes) + inputSizes.slice(0, dim-1) ++ indicesSize ++ inputSizes.slice(dim, inputSizes.length) } + // set the insert position in output to one-dim array + output.resize(inputSizes.slice(0, dim-1)++ + Array(indices.nElement())++ + inputSizes.slice(dim, inputSizes.length)) - output + // copy selected element to the insert position + indices.resize(indices.nElement()) + var i = 0 + while (i < indices.nElement()) { + val index = indices.valueAt(i + 1) + require(index < inputSizes(dim - 1), + s"index should smaller than ${inputSizes(dim - 1)}, but got $index") + output.select(dim, i + 1).copy(inputTensor.select(dim, index + 1)) + i += 1 + } + // resize the output to expected shape + indices.resize(indicesSize) + output.resize(outputSizes) } override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = { @@ -88,7 +94,8 @@ class Gather[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: Ten } object Gather { - def apply[T: ClassTag, D: ClassTag]()(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): - Gather[T, D] = new Gather() - + def apply[T: ClassTag, D: ClassTag]( + dim: Int = 1 + )(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): + Gather[T, D] = new Gather(dim) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala index b72421e41ff..9a2ca44bf4e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ops/GatherSpec.scala @@ -66,6 +66,32 @@ class GatherSpec extends FlatSpec with Matchers { output should be (exceptedOutput) } + "gather with scalar and specific dimension" should "works fine" in { + val gather = Gather[Float, Float](2) + val indices = Tensor[Int](Array(1), Array[Int]()) + val input = Tensor.range(1, 24).resize(2, 3, 4) + val output = gather.forward(T(input, indices)) + + output should be (input.narrow(2, 2, 1)) + } + + "gather with scalar from last dimension" should "works fine" in { + val gather = Gather[Float, Float](0) + val indices = Tensor[Int](Array(1), Array[Int]()) + val input = Tensor.range(1, 24).resize(2, 3, 4, 5) + val output = gather.forward(T(input, indices)) + + output should be (input.narrow(4, 2, 1)) + } + + "gather with scalar from negative dimension" should "works fine" in { + val gather = Gather[Float, Float](-2) + val indices = Tensor[Int](Array(1), Array[Int]()) + val input = Tensor.range(1, 24).resize(2, 3, 4) + val output = gather.forward(T(input, indices)) + + output should be (input.narrow(1, 2, 1)) + } } class GatherSerialTest extends ModuleSerializationTest { From 6494d2dd35ce7a7733a0f6b921bb7d4f316a73e3 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Wed, 16 Oct 2019 13:58:32 +0800 Subject: [PATCH 0966/1065] support batch for regionproposal (#2928) * support batch for regionproposal --- .../dllib/models/maskrcnn/MaskRCNN.scala | 2 +- .../analytics/bigdl/dllib/nn/BoxHead.scala | 4 +- ...ionRroposal.scala => RegionProposal.scala} | 98 ++--- ...salSpec.scala => RegionProposalSpec.scala} | 350 +++++++++++++++++- 4 files changed, 390 insertions(+), 64 deletions(-) rename scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/{RegionRroposal.scala => RegionProposal.scala} (81%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/{RegionRroposalSpec.scala => RegionProposalSpec.scala} (77%) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala index 9df6fa02c87..172118dd317 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala @@ -60,7 +60,7 @@ class MaskRCNN(val inChannels: Int, private val ImageInfo : Tensor[Float] = Tensor[Float](2) private val backbone = buildBackbone(inChannels, outChannels) - private val rpn = RegionRroposal(inChannels, config.anchorSizes, config.aspectRatios, + private val rpn = RegionProposal(inChannels, config.anchorSizes, config.aspectRatios, config.anchorStride, config.preNmsTopNTest, config.postNmsTopNTest, config.preNmsTopNTrain, config.postNmsTopNTrain, config.minSize) private val boxHead = BoxHead(inChannels, config.boxResolution, config.scales, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala index ec797911750..f64caaed210 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala @@ -283,7 +283,9 @@ private[nn] class BoxPostProcessor( } val classLogits = input[Tensor[Float]](1) val boxRegression = input[Tensor[Float]](2) - val bbox = input[Tensor[Float]](3) + val bbox = if (input(3).isInstanceOf[Tensor[Float]]) { + input[Tensor[Float]](3) + } else input[Table](3)[Tensor[Float]](1) if (boxesBuf == null) boxesBuf = Tensor[Float] boxesBuf.resizeAs(boxRegression) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala similarity index 81% rename from scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposal.scala rename to scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala index 9ec18e7b0c7..f86dd4b7225 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala @@ -37,7 +37,7 @@ import scala.collection.mutable.ArrayBuffer * @param nmsThread * @param minSize */ -class RegionRroposal( +class RegionProposal( val inChannels: Int, val anchorSizes: Array[Float], val aspectRatios: Array[Float], @@ -48,7 +48,7 @@ class RegionRroposal( val postNmsTopNTrain: Int = 2000, val nmsThread: Float = 0.7f, val minSize: Int = 0)(implicit ev: TensorNumeric[Float]) - extends AbstractModule[Table, Tensor[Float], Float] { + extends AbstractModule[Table, Table, Float] { // for anchor generation require(anchorSizes.length == anchorStride.length, @@ -90,10 +90,10 @@ class RegionRroposal( kernelH = 3, kernelW = 3, strideH = 1, strideW = 1, padH = 1, padW = 1) conv.setInitMethod(RandomNormal(0.0, 0.01), Zeros) val clsLogits = SpatialConvolution[Float](inChannels, numAnchors, - kernelH = 1, kernelW = 1, strideH = 1, strideW = 1).setName(this.getName() + "_cls_logits") + kernelH = 1, kernelW = 1, strideH = 1, strideW = 1).setName(this.getName() + "cls_logits") clsLogits.setInitMethod(RandomNormal(0.0, 0.01), Zeros) val bboxPred = SpatialConvolution[Float](inChannels, numAnchors * 4, - kernelH = 1, kernelW = 1, strideH = 1, strideW = 1).setName(this.getName() + "_bbox_pred") + kernelH = 1, kernelW = 1, strideH = 1, strideW = 1).setName(this.getName() + "bbox_pred") bboxPred.setInitMethod(RandomNormal(0.0, 0.01), Zeros) val input = Input() @@ -107,44 +107,58 @@ class RegionRroposal( /** * input is a table and contains: - * first tensor: images: images for which we want to compute the predictions - * second tensor: features: features computed from the images that are used for - * computing the predictions. + * first tensor: features: features computed from the images that are used for + * computing the predictions. + * second tensor: image height and image width */ - override def updateOutput(input: Table): Tensor[Float] = { + override def updateOutput(input: Table): Table = { val features = input[Table](1) - val images = input[Tensor[Float]](2) + val imageSize = input[Tensor[Float]](2) val anchors = this.anchorGenerator(features) - var bboxNumber = 0 - var i = 1 - while (i <= anchors.length()) { - val headOutput = head.forward(features(i)).toTable - val objectness = headOutput.apply[Tensor[Float]](1) - val boxRegression = headOutput.apply[Tensor[Float]](2) - - val out = boxSelector.forward(T(anchors[Tensor[Float]](i), objectness, - boxRegression, images)) - - if (!selectorRes.contains(i)) selectorRes(i) = T(Tensor[Float](), Tensor[Float]()) - selectorRes(i).asInstanceOf[Table].apply[Tensor[Float]](1).resizeAs(out[Tensor[Float]](1)) - .copy(out[Tensor[Float]](1)) - selectorRes(i).asInstanceOf[Table].apply[Tensor[Float]](2).resizeAs(out[Tensor[Float]](2)) - .copy(out[Tensor[Float]](2)) - - bboxNumber += selectorRes[Table](i)[Tensor[Float]](1).size(1) - i += 1 + // for batch + val batchSize = features[Tensor[Float]](1).size(1) + for (b <- 1 to batchSize) { + var bboxNumber = 0 + var i = 1 + while (i <= anchors.length()) { + val singleFeatures = features[Tensor[Float]](i).narrow(1, b, 1) + val headOutput = head.forward(singleFeatures).toTable + val objectness = headOutput.apply[Tensor[Float]](1) + val boxRegression = headOutput.apply[Tensor[Float]](2) + + val out = boxSelector.forward(T(anchors[Tensor[Float]](i), objectness, + boxRegression, imageSize)) + + if (!selectorRes.contains(i)) selectorRes(i) = T(Tensor[Float](), Tensor[Float]()) + selectorRes(i).asInstanceOf[Table].apply[Tensor[Float]](1).resizeAs(out[Tensor[Float]](1)) + .copy(out[Tensor[Float]](1)) + selectorRes(i).asInstanceOf[Table].apply[Tensor[Float]](2).resizeAs(out[Tensor[Float]](2)) + .copy(out[Tensor[Float]](2)) + + bboxNumber += selectorRes[Table](i)[Tensor[Float]](1).size(1) + i += 1 + } + + val postNmsTopN = if (this.isTraining()) min(postNmsTopNTrain, bboxNumber) + else min(postNmsTopNTest, bboxNumber) + + if (output.getOrElse(b, null) == null) { + output(b) = Tensor[Float]() + } + output[Tensor[Float]](b).resize(postNmsTopN, 4) + + // sort + selectOverAllLevels(selectorRes, postNmsTopN, bboxNumber, output[Tensor[Float]](b)) } - - val postNmsTopN = if (this.isTraining()) min(postNmsTopNTrain, bboxNumber) - else min(postNmsTopNTest, bboxNumber) - output.resize(postNmsTopN, 4) - - // sort - selectOverAllLevels(selectorRes, postNmsTopN, bboxNumber, output) output } + /** + * different behavior during training and during testing: + * during training, post_nms_top_n is over *all* the proposals combined, while + * during testing, it is over the proposals for each image + */ private def selectOverAllLevels(res: Table, postNmsTopN: Int, totalNumber: Int, output: Tensor[Float]): Unit = { val scoreResult = Tensor[Float]().resize(totalNumber) @@ -170,12 +184,12 @@ class RegionRroposal( } } - override def updateGradInput(input: Table, gradOutput: Tensor[Float]): Table = { - throw new UnsupportedOperationException("RegionRroposal only support inference") + override def updateGradInput(input: Table, gradOutput: Table): Table = { + throw new UnsupportedOperationException("RegionProposal only support inference") } - override def accGradParameters(input: Table, gradOutput: Tensor[Float]): Unit = { - throw new UnsupportedOperationException("RegionRroposal only support inference") + override def accGradParameters(input: Table, gradOutput: Table): Unit = { + throw new UnsupportedOperationException("RegionProposal only support inference") } override def parameters(): (Array[Tensor[Float]], Array[Tensor[Float]]) = { @@ -199,7 +213,7 @@ class RegionRroposal( boxSelector.release() } - override def training(): RegionRroposal.this.type = { + override def training(): RegionProposal.this.type = { train = true head.training() boxSelector.training() @@ -214,7 +228,7 @@ class RegionRroposal( } } -object RegionRroposal { +object RegionProposal { def apply(inChannels: Int, anchorSizes: Array[Float] = Array[Float](32, 64, 128, 256, 512), aspectRatios: Array[Float] = Array[Float](0.5f, 1.0f, 2.0f), @@ -224,8 +238,8 @@ object RegionRroposal { preNmsTopNTrain: Int = 2000, postNmsTopNTrain: Int = 2000, nmsThread: Float = 0.7f, - minSize: Int = 0)(implicit ev: TensorNumeric[Float]): RegionRroposal = - new RegionRroposal(inChannels, anchorSizes, aspectRatios, anchorStride, + minSize: Int = 0)(implicit ev: TensorNumeric[Float]): RegionProposal = + new RegionProposal(inChannels, anchorSizes, aspectRatios, anchorStride, preNmsTopNTest, postNmsTopNTest, preNmsTopNTrain, postNmsTopNTrain, nmsThread, minSize) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposalSpec.scala similarity index 77% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposalSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposalSpec.scala index aed808461d0..a58517b06d1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionRroposalSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposalSpec.scala @@ -23,9 +23,9 @@ import org.scalatest.{FlatSpec, Matchers} import scala.util.Random -class RegionRroposalSpec extends FlatSpec with Matchers { - "RegionRroposal" should "be ok" in { - val layer = new RegionRroposal(6, +class RegionProposalSpec extends FlatSpec with Matchers { + "RegionProposal" should "be ok" in { + val layer = new RegionProposal(6, Array[Float](32, 64, 128, 256, 512), Array[Float](0.5f, 1.0f, 2.0f), Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) @@ -253,9 +253,9 @@ class RegionRroposalSpec extends FlatSpec with Matchers { val paramsTable = layer.getParametersTable() for (i <- paramsTable.keySet) { val weight = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get - if (i.toString contains "_cls_logits") { + if (i.toString contains "cls_logits") { weight.copy(weight_logits) - } else if (i.toString contains "_bbox_pred") { + } else if (i.toString contains "bbox_pred") { weight.copy(weight_pred) } else { weight.copy(weight_conv) @@ -263,7 +263,7 @@ class RegionRroposalSpec extends FlatSpec with Matchers { } layer.evaluate() - val output = layer.forward(T(T(features), images)) + val output = layer.forward(T(T(features), images)).toTable val outputExpected = Tensor[Float]( T(T(0.0f, 0.0f, 20.999596f, 19.0f), T(0.0f, 0.0f, 12.995603f, 19.0f), @@ -271,11 +271,11 @@ class RegionRroposalSpec extends FlatSpec with Matchers { T(0.0f, 0.0f, 29.011127f, 13.003019f) )) - output should be(outputExpected) + output[Tensor[Float]](1) should be(outputExpected) } - "RegionRroposal with multi features" should "be ok" in { - val layer = new RegionRroposal(6, + "RegionProposal with multi features" should "be ok" in { + val layer = new RegionProposal(6, Array[Float](32, 64, 128, 256, 512), Array[Float](0.5f, 1.0f, 2.0f), Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) @@ -545,9 +545,9 @@ class RegionRroposalSpec extends FlatSpec with Matchers { val paramsTable = layer.getParametersTable() for (i <- paramsTable.keySet) { val weight = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get - if (i.toString contains "_cls_logits") { + if (i.toString contains "cls_logits") { weight.copy(weight_logits) - } else if (i.toString contains "_bbox_pred") { + } else if (i.toString contains "bbox_pred") { weight.copy(weight_pred) } else { weight.copy(weight_conv) @@ -555,7 +555,7 @@ class RegionRroposalSpec extends FlatSpec with Matchers { } layer.evaluate() - val output = layer.forward(T(T(features1, features2, features3), images)) + val output = layer.forward(T(T(features1, features2, features3), images)).toTable val outputExpected = Tensor[Float](T( T( 0.0000, 0.0000, 35.0363, 19.0000), T( 0.0000, 0.0000, 20.9997, 19.0000), @@ -566,7 +566,7 @@ class RegionRroposalSpec extends FlatSpec with Matchers { T( 0.0000, 0.0000, 29.0113, 13.0032), T( 0.0000, 11.9920, 37.0000, 19.0000))) - output should be(outputExpected) + output[Tensor[Float]](1) should be(outputExpected) } "RPNPostProcessor" should "be ok" in { @@ -738,7 +738,7 @@ class RegionRroposalSpec extends FlatSpec with Matchers { } "AnchorGenerate" should "be ok" in { - val layer = new RegionRroposal(6, + val layer = new RegionProposal(6, Array[Float](32, 64, 128, 256, 512), Array[Float](0.5f, 1.0f, 2.0f), Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) @@ -803,8 +803,9 @@ class RegionRroposalSpec extends FlatSpec with Matchers { output.apply[Tensor[Float]](1) should be(expectedOutput) } + "RPNHead" should "be ok" in { - val layer = new RegionRroposal(6, + val layer = new RegionProposal(6, Array[Float](32, 64, 128, 256, 512), Array[Float](0.5f, 1.0f, 2.0f), Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) @@ -1034,9 +1035,9 @@ class RegionRroposalSpec extends FlatSpec with Matchers { val paramsTable = proposal.getParametersTable() for (i <- paramsTable.keySet) { val weight = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get - if (i.toString contains "_cls_logits") { + if (i.toString contains "cls_logits") { weight.copy(weight_logits) - } else if (i.toString contains "_bbox_pred") { + } else if (i.toString contains "bbox_pred") { weight.copy(weight_pred) } else { weight.copy(weight_conv) @@ -1097,15 +1098,324 @@ class RegionRroposalSpec extends FlatSpec with Matchers { Equivalent.nearequals(out.apply[Tensor[Float]](1), expectOut1, 1e-4) should be(true) Equivalent.nearequals(out.apply[Tensor[Float]](2), expectOut2, 1e-4) should be(true) } + + "RegionProposal with batch size > 1" should "be ok" in { + val layer = new RegionProposal(6, + Array[Float](32, 64, 128, 256, 512), + Array[Float](0.5f, 1.0f, 2.0f), + Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) + + val images = Tensor[Float](T(20, 38)) + + val features1 = Tensor[Float](T(T(T( + T(0.7668, 0.1659, 0.4393, 0.2243), + T(0.8935, 0.0497, 0.1780, 0.3011), + T(0.1893, 0.9186, 0.2131, 0.3957)), + T(T(0.6017, 0.4234, 0.5224, 0.4175), + T(0.0340, 0.9157, 0.3079, 0.6269), + T(0.8277, 0.6594, 0.0887, 0.4890)), + T(T(0.5887, 0.7340, 0.8497, 0.9112), + T(0.4847, 0.9436, 0.3904, 0.2499), + T(0.3206, 0.9753, 0.7582, 0.6688)), + T(T(0.2651, 0.2336, 0.5057, 0.5688), + T(0.0634, 0.8993, 0.2732, 0.3397), + T(0.1879, 0.5534, 0.2682, 0.9556)), + T(T(0.9761, 0.5934, 0.3124, 0.9431), + T(0.8519, 0.9815, 0.1132, 0.4783), + T(0.4436, 0.3847, 0.4521, 0.5569)), + T(T(0.9952, 0.0015, 0.0813, 0.4907), + T(0.2130, 0.4603, 0.1386, 0.0277), + T(0.5662, 0.3503, 0.6555, 0.7667))))) + + val features3 = Tensor[Float](T(T(T( + T(0.9336, 0.2557, 0.1506, 0.7856)), + T(T(0.4152, 0.5809, 0.1088, 0.7065)), + T(T(0.0105, 0.4602, 0.2945, 0.0475)), + T(T(0.6401, 0.3784, 0.5887, 0.0720)), + T(T(0.9140, 0.0085, 0.2174, 0.1890)), + T(T(0.0911, 0.6344, 0.3142, 0.7052))))) + + val features2 = Tensor[Float](T(T(T(T(0.2269, 0.7555), + T(0.6458, 0.3673), + T(0.1770, 0.2966), + T(0.9925, 0.2103), + T(0.1292, 0.1719)), + T(T(0.9127, 0.6818), + T(0.1953, 0.9991), + T(0.1133, 0.0135), + T(0.1450, 0.7819), + T(0.3134, 0.2983)), + T(T(0.3436, 0.2028), + T(0.9792, 0.4947), + T(0.3617, 0.9687), + T(0.0359, 0.3041), + T(0.9867, 0.1290)), + T(T(0.6887, 0.1637), + T(0.0899, 0.3139), + T(0.1219, 0.3516), + T(0.2316, 0.2847), + T(0.3520, 0.2828)), + T(T(0.2420, 0.4928), + T(0.5772, 0.3771), + T(0.2440, 0.8994), + T(0.1041, 0.9193), + T(0.6201, 0.3658)), + T(T(0.0623, 0.5967), + T(0.0829, 0.8185), + T(0.4964, 0.0589), + T(0.9840, 0.5836), + T(0.6737, 0.4738))))) + + val weight_conv = Tensor[Float](T(T(T(T(1.2685e-03, 1.3564e-02, 5.6322e-03), + T(-1.0393e-03, -3.5746e-03, 3.9174e-03), + T(-6.8009e-03, 2.4094e-03, 4.6981e-03)), + T(T( 1.2426e-02, 5.4030e-03, -1.1454e-02), + T(-1.4592e-02, -1.6281e-02, 3.8337e-03), + T(-1.7180e-03, -3.1896e-02, 1.5914e-02)), + T(T(-2.4669e-04, -8.4661e-03, 2.9301e-04), + T(-5.7207e-03, -1.2546e-02, 4.8611e-04), + T( 1.1705e-02, -5.4102e-03, -7.1156e-03)), + T(T( 5.7526e-04, 6.2625e-03, -1.7736e-02), + T(-2.2050e-03, 2.7467e-02, -1.7599e-02), + T( 1.0230e-02, -1.1073e-03, -3.8986e-03)), + T(T(-1.0300e-02, -1.5446e-02, 5.7298e-03), + T(-2.0956e-02, -1.8055e-03, 2.3464e-03), + T(-1.4774e-03, 5.8926e-03, 2.2533e-02)), + T(T(-2.5548e-03, 1.6513e-03, -1.6292e-03), + T(-8.0389e-03, -9.1740e-03, 8.9856e-03), + T( 8.2623e-03, -3.6677e-03, -4.2506e-03))), + T(T(T(-1.2455e-02, 1.1245e-02, -2.0157e-02), + T( 9.9260e-03, -6.0842e-03, -1.3856e-02), + T( 1.0412e-02, -8.0432e-03, -6.2443e-03)), + T(T(-5.8823e-03, 1.6700e-02, -9.2747e-03), + T(-9.7585e-03, 1.3312e-02, 9.0074e-03), + T(-6.5847e-03, -9.3275e-03, -1.5749e-02)), + T(T( 1.4861e-02, -1.4092e-02, 1.4330e-02), + T( 3.8986e-03, -1.1516e-03, -2.3609e-03), + T(-2.2235e-02, 7.8841e-04, 4.1560e-04)), + T(T( 1.2813e-02, -8.2621e-03, 2.3098e-04), + T( 1.9301e-02, 7.8028e-03, 3.1802e-03), + T(-6.9918e-03, -3.9213e-03, 2.1955e-02)), + T(T( 3.3116e-03, 1.4171e-03, -1.5268e-02), + T( 2.5214e-03, 6.5413e-03, 2.1024e-02), + T( 6.3311e-03, 1.9332e-02, -2.4634e-03)), + T(T(-7.0092e-03, 6.3621e-03, -5.6589e-03), + T( 1.0318e-02, -1.0371e-02, 1.3739e-03), + T(-1.1312e-02, 6.4710e-03, -7.1830e-03))), + T(T(T(-1.1984e-02, -8.8376e-03, 6.4301e-03), + T( 7.2037e-04, -5.7234e-03, 1.6078e-02), + T( 1.0007e-03, -1.0746e-02, -1.0924e-03)), + T(T( 2.4635e-03, -9.9438e-03, -6.8856e-03), + T( 1.2039e-02, -2.5186e-03, -1.9443e-02), + T(-1.9203e-02, 1.1464e-02, 2.3850e-02)), + T(T(-3.5508e-04, -3.1787e-03, 3.5779e-03), + T(-1.7844e-02, -3.0524e-03, 8.5366e-03), + T( 3.8534e-03, 1.2612e-02, 5.9866e-03)), + T(T(-2.4725e-02, -5.4071e-04, -1.1862e-02), + T( 7.3836e-03, -3.1864e-03, -5.1757e-03), + T(-1.4699e-03, 5.1577e-03, 3.3928e-03)), + T(T( 2.4955e-03, -9.5512e-03, 7.0652e-03), + T( 1.2566e-02, -2.9903e-02, -3.2173e-04), + T(-2.3036e-03, 1.2172e-03, 1.0538e-02)), + T(T( 2.4320e-03, 8.3910e-03, 2.2082e-03), + T(-1.3217e-02, 4.4108e-04, -3.4124e-03), + T(-1.1553e-02, 4.9376e-03, 7.9121e-03))), + T(T(T( 1.2293e-02, -3.9778e-03, 2.1020e-03), + T( 8.3877e-03, 2.3666e-02, 6.8477e-03), + T( 5.2052e-03, 1.4803e-02, -7.5135e-03)), + T(T(-8.7030e-03, 5.8776e-03, -4.8942e-05), + T( 2.0430e-02, 5.8311e-04, -3.6140e-03), + T( 1.7116e-02, 8.4518e-03, -2.8076e-03)), + T(T( 9.1432e-03, 4.6386e-03, -1.0463e-02), + T( 6.0311e-03, 4.2746e-03, -3.4131e-03), + T( 1.9404e-03, 7.9359e-03, -7.6828e-04)), + T(T( 4.8792e-03, -2.5789e-02, 1.0007e-02), + T( 2.1705e-04, -8.6712e-03, -4.5113e-03), + T(-6.6698e-03, 2.7328e-04, 6.6046e-03)), + T(T( 7.3924e-03, 7.1265e-03, 4.3357e-03), + T( 3.9357e-04, -2.3774e-03, 6.4933e-03), + T( 7.2543e-03, -4.8372e-03, 5.6666e-03)), + T(T(-3.9601e-03, 1.3844e-02, -8.2588e-03), + T(-1.6542e-03, -1.3295e-02, 3.8030e-03), + T(-6.6701e-04, 6.8487e-03, 7.7284e-04))), + T(T(T(-1.3936e-03, -4.7838e-03, -3.1820e-03), + T( 2.2058e-03, -1.6855e-03, 1.8463e-02), + T( 9.5022e-03, -3.3961e-03, -6.5992e-03)), + T(T(-9.5200e-03, -4.0727e-03, 1.4081e-02), + T( 1.2446e-03, 1.1088e-02, 1.7009e-03), + T( 1.1670e-03, -7.9839e-03, 9.1257e-03)), + T(T(-2.5381e-03, 6.8467e-03, -7.4647e-04), + T( 5.9466e-04, 8.1772e-03, 2.8940e-03), + T( 4.2105e-03, -1.3101e-02, 8.6801e-03)), + T(T( 7.1093e-03, 9.3525e-03, 7.6763e-03), + T(-2.8895e-03, 6.6717e-03, 1.1738e-03), + T( 5.4419e-03, -2.8676e-04, 1.3919e-02)), + T(T( 1.0932e-02, -2.3391e-02, -8.9627e-03), + T(-6.2398e-03, -5.7453e-03, -5.7471e-03), + T( 7.2978e-03, -2.2365e-03, 3.7101e-04)), + T(T( 6.5447e-03, -2.5417e-03, -7.0376e-03), + T(-1.1011e-03, -6.9527e-03, -2.4869e-02), + T( 6.0163e-03, 5.7055e-03, 5.8137e-03))), + T(T(T( 2.5749e-04, 5.5009e-03, 1.9151e-03), + T( 9.8616e-03, 1.1613e-02, -1.7455e-03), + T( 3.1561e-03, -1.8205e-03, -3.4044e-03)), + T(T(-5.8910e-03, 3.6100e-03, -1.4282e-02), + T( 9.2737e-03, -7.0391e-03, 3.8460e-03), + T( 6.2735e-03, 6.5410e-03, 1.0932e-03)), + T(T( 8.8084e-03, 1.5566e-02, 2.1806e-02), + T( 1.7355e-02, -1.5105e-02, 7.6660e-04), + T( 3.3541e-03, -5.3618e-03, -4.8840e-03)), + T(T( 1.4804e-03, 4.5057e-03, -5.1785e-03), + T(-5.5912e-03, -1.8077e-02, 5.0915e-03), + T( 4.0559e-03, 3.3477e-03, 8.6055e-04)), + T(T( 9.6151e-03, -2.7296e-03, 1.6761e-02), + T(-6.7708e-03, 5.9753e-03, -5.5834e-03), + T(-5.9345e-03, 2.2870e-02, 5.4827e-03)), + T(T(-8.7740e-03, 1.4306e-02, 1.7519e-02), + T(-1.0057e-04, 2.8130e-03, -1.4722e-02), + T(-5.0060e-03, 8.9401e-04, 4.7907e-03))))) + + val weight_logits = Tensor[Float](T(T(T(T( 0.0013f)), + T(T( 0.0136f)), + T(T(-0.0002f)), + T(T(-0.0085f)), + T(T( 0.0003f)), + T(T(-0.0057f))), + T(T(T(-0.0125f)), + T(T( 0.0005f)), + T(T( 0.0028f)), + T(T(-0.0215f)), + T(T(-0.0071f)), + T(T( 0.0006f))), + T(T(T( 0.0063f)), + T(T(-0.0177f)), + T(T(-0.0022f)), + T(T( 0.0275f)), + T(T(-0.0105f)), + T(T( 0.0112f))))) + + val weight_pred = Tensor[Float](T(T(T(T( 0.0013f)), + T(T( 0.0136f)), + T(T( 0.0056f)), + T(T(-0.0010f)), + T(T(-0.0036f)), + T(T( 0.0039f))), + T(T(T(-0.0068f)), + T(T( 0.0024f)), + T(T( 0.0047f)), + T(T( 0.0124f)), + T(T( 0.0054f)), + T(T(-0.0115f))), + T(T(T(-0.0146f)), + T(T(-0.0163f)), + T(T( 0.0038f)), + T(T(-0.0017f)), + T(T(-0.0319f)), + T(T( 0.0159f))), + T(T(T(-0.0002f)), + T(T(-0.0085f)), + T(T( 0.0003f)), + T(T(-0.0057f)), + T(T(-0.0125f)), + T(T( 0.0005f))), + T(T(T( 0.0117f)), + T(T(-0.0054f)), + T(T(-0.0071f)), + T(T( 0.0006f)), + T(T( 0.0063f)), + T(T(-0.0177f))), + T(T(T(-0.0022f)), + T(T( 0.0275f)), + T(T(-0.0176f)), + T(T( 0.0102f)), + T(T(-0.0011f)), + T(T(-0.0039f))), + T(T(T(-0.0103f)), + T(T(-0.0154f)), + T(T( 0.0057f)), + T(T(-0.0210f)), + T(T(-0.0018f)), + T(T( 0.0023f))), + T(T(T(-0.0015f)), + T(T( 0.0059f)), + T(T( 0.0225f)), + T(T(-0.0026f)), + T(T( 0.0017f)), + T(T(-0.0016f))), + T(T(T(-0.0080f)), + T(T(-0.0092f)), + T(T( 0.0090f)), + T(T( 0.0083f)), + T(T(-0.0037f)), + T(T(-0.0043f))), + T(T(T(-0.0125f)), + T(T( 0.0112f)), + T(T( 0.0044f)), + T(T( 0.0142f)), + T(T(-0.0043f)), + T(T( 0.0030f))), + T(T(T( 0.0266f)), + T(T(-0.0028f)), + T(T( 0.0017f)), + T(T( 0.0100f)), + T(T( 0.0022f)), + T(T(-0.0036f))), + T(T(T( 0.0081f)), + T(T( 0.0002f)), + T(T(-0.0084f)), + T(T( 0.0124f)), + T(T( 0.0151f)), + T(T(-0.0060f))))) + + val paramsTable = layer.getParametersTable() + for (i <- paramsTable.keySet) { + val weight = paramsTable.get[Table](i).get.get[Tensor[Float]]("weight").get + if (i.toString contains "cls_logits") { + weight.copy(weight_logits) + } else if (i.toString contains "bbox_pred") { + weight.copy(weight_pred) + } else { + weight.copy(weight_conv) + } + } + + layer.evaluate() + + val features12 = Tensor[Float](2, features1.size(2), features1.size(3), features1.size(4)) + features12.select(1, 1).copy(features1) + features12.select(1, 2).copy(features1) + + val features22 = Tensor[Float](2, features2.size(2), features2.size(3), features2.size(4)) + features22.select(1, 1).copy(features2) + features22.select(1, 2).copy(features2) + + val features32 = Tensor[Float](2, features3.size(2), features3.size(3), features3.size(4)) + features32.select(1, 1).copy(features3) + features32.select(1, 2).copy(features3) + + val output = layer.forward(T(T(features12, features22, features32), images)).toTable + val outputExpected = Tensor[Float](T( + T( 0.0000, 0.0000, 35.0363, 19.0000), + T( 0.0000, 0.0000, 20.9997, 19.0000), + T( 0.0000, 0.0000, 12.9955, 19.0000), + T( 0.0000, 0.0000, 37.0000, 19.0000), + T( 0.0000, 0.0000, 37.0000, 19.0000), + T(11.9914, 0.0000, 37.0000, 19.0000), + T( 0.0000, 0.0000, 29.0113, 13.0032), + T( 0.0000, 11.9920, 37.0000, 19.0000))) + + output[Tensor[Float]](1) should be(outputExpected) + output[Tensor[Float]](2) should be(outputExpected) + } } -class RegionRroposalSerialTest extends ModuleSerializationTest { +class RegionProposalSerialTest extends ModuleSerializationTest { override def test(): Unit = { - val layer = new RegionRroposal(6, + val layer = new RegionProposal(6, Array[Float](32, 64, 128, 256, 512), Array[Float](0.5f, 1.0f, 2.0f), Array[Float](4, 8, 16, 32, 64), - 2000, 2000, 2000, 2000, 0.7f, 0).setName("RegionRroposal") + 2000, 2000, 2000, 2000, 0.7f, 0).setName("RegionProposal") val features = Tensor[Float](1, 6, 3, 4).rand() val imgInfo = Tensor[Float](T(20, 38)) From e8d98df4da696cfc92d2b2e7e95d96362e75d6fe Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Wed, 16 Oct 2019 17:51:04 +0800 Subject: [PATCH 0967/1065] enable gru blas-to-dnn conversion (#2930) --- .../dllib/utils/intermediate/IRToDnn.scala | 150 ++++++++++++++++++ .../bigdl/dllib/nn/mkldnn/RNNSpec.scala | 75 +++++++++ 2 files changed, 225 insertions(+) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index bc2e2350e6e..abbf002be9b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -278,6 +278,77 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] return lstmDnn } } + + if (layer.isInstanceOf[GRU[Float]] && model.batchNormParams == null) { + val gru = layer.asInstanceOf[GRU[Float]] + if (gru.activation.isInstanceOf[Tanh[Float]] && + gru.innerActivation.isInstanceOf[Sigmoid[Float]] && + gru.p == 0.0f && + gru.wRegularizer == null && + gru.bRegularizer == null && + gru.uRegularizer == null) { + val f = AlgKind.EltwiseTanh + val direction = Direction.UnidirectionalLeft2Right + val inputSize = gru.inputSize + val hiddenSize = gru.outputSize + val gruDnn = nn.mkldnn.RNN(AlgKind.VanillaGru, inputSize, hiddenSize, + f, direction, layers = 1) + + // copy weight from blas gru to dnn gru + val gru_n_gates = 3 + + val blasParams = model.parameters()._1 + val initWeight0 = blasParams(0) + val initBias0 = blasParams(1) + // blas gru splits weight iteration into 2 tensors + val initWeightIter0 = blasParams(2) + val initWeightIter1 = blasParams(3) + + var num = initWeight0.size(1) / gru_n_gates + var gate2 = initWeight0.narrow(1, 1, num) + var gate1 = initWeight0.narrow(1, num + 1, num) + var gate3 = initWeight0.narrow(1, num * 2 + 1, num) + + var initWeight = Tensor[Float](gru_n_gates, hiddenSize, inputSize) + initWeight.select(1, 1).copy(gate1) + initWeight.select(1, 2).copy(gate2) + initWeight.select(1, 3).copy(gate3) + // original Array(inputSize, gru_n_gates, hiddenSize) + initWeight = initWeight.transpose(1, 3).transpose(2, 3) + + num = initBias0.size(1) / gru_n_gates + gate2 = initBias0.narrow(1, 1, num) + gate1 = initBias0.narrow(1, num + 1, num) + gate3 = initBias0.narrow(1, num * 2 + 1, num) + + val initBias = Tensor[Float](gru_n_gates, hiddenSize) + initBias.select(1, 1).copy(gate1) + initBias.select(1, 2).copy(gate2) + initBias.select(1, 3).copy(gate3) + + num = initWeightIter0.size(1) / 2 + gate2 = initWeightIter0.narrow(1, 1, num) + gate1 = initWeightIter0.narrow(1, num + 1, num) + + num = initWeightIter1.size(1) / 1 + gate3 = initWeightIter1.narrow(1, 1, num) + + var initIterWeight = Tensor[Float](gru_n_gates, hiddenSize, hiddenSize) + initIterWeight.select(1, 1).copy(gate1) + initIterWeight.select(1, 2).copy(gate2) + initIterWeight.select(1, 3).copy(gate3) + // original Array(hiddenSize, gru_n_gates, hiddenSize) + initIterWeight = initIterWeight.transpose(1, 3).transpose(2, 3) + + val weights = gruDnn.parameters()._1 + weights(0).copy(initWeight) + weights(1).copy(initBias) + weights(2).copy(initIterWeight) + + return gruDnn + } + } + BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) } @@ -364,6 +435,85 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] return lstmDnn } } + + if ((layer equals revLayer) && layer.isInstanceOf[GRU[Float]] && + model.batchNormParams == null && model.isSplitInput == false && + (merge.isInstanceOf[nn.CAddTable[Float, _]] || merge.isInstanceOf[nn.ConcatTable[Float]])) { + val gru = layer.asInstanceOf[GRU[Float]] + if (gru.activation.isInstanceOf[Tanh[Float]] && + gru.innerActivation.isInstanceOf[Sigmoid[Float]] && + gru.p == 0.0f && + gru.wRegularizer == null && + gru.bRegularizer == null && + gru.uRegularizer == null) { + val f = AlgKind.EltwiseTanh + val direction = if (merge.isInstanceOf[nn.CAddTable[Float, _]]) { + Direction.BidirectionalSum + } else Direction.BidirectionalConcat + val inputSize = gru.inputSize + val hiddenSize = gru.outputSize + val gruDnn = nn.mkldnn.RNN(AlgKind.VanillaGru, inputSize, hiddenSize, + f, direction, layers = 1) + + // copy weight from blas gru to dnn gru + val gru_n_gates = 3 + + val blasParams = model.parameters()._1 + val initWeight0 = Tensor[Float](Array(2, hiddenSize * gru_n_gates, inputSize)) + // blas gru splits weight iteration into 2 tensors + val initWeightIter0 = Tensor[Float](Array(2, hiddenSize * 2, hiddenSize)) + val initWeightIter1 = Tensor[Float](Array(2, hiddenSize * 1, hiddenSize)) + val initBias0 = Tensor[Float](Array(2, gru_n_gates * hiddenSize)) + + initWeight0(1).resizeAs(blasParams(0)).copy(blasParams(0)) + initBias0(1).resizeAs(blasParams(1)).copy(blasParams(1)) + initWeightIter0(1).resizeAs(blasParams(2)).copy(blasParams(2)) + initWeightIter1(1).resizeAs(blasParams(3)).copy(blasParams(3)) + initWeight0(2).resizeAs(blasParams(4)).copy(blasParams(4)) + initBias0(2).resizeAs(blasParams(5)).copy(blasParams(5)) + initWeightIter0(2).resizeAs(blasParams(6)).copy(blasParams(6)) + initWeightIter1(2).resizeAs(blasParams(7)).copy(blasParams(7)) + + val initWeight = Tensor[Float](Array(2, gru_n_gates, hiddenSize, inputSize)) + val initWeightIter = Tensor[Float](Array(2, gru_n_gates, hiddenSize, hiddenSize)) + val initBias = Tensor[Float](Array(2, gru_n_gates, hiddenSize)) + + for (i <- 1 to 2) { + var num = initWeight0(i).size(1) / gru_n_gates + var gate2 = initWeight0(i).narrow(1, 1, num) + var gate1 = initWeight0(i).narrow(1, num + 1, num) + var gate3 = initWeight0(i).narrow(1, num * 2 + 1, num) + initWeight(i).select(1, 1).copy(gate1) + initWeight(i).select(1, 2).copy(gate2) + initWeight(i).select(1, 3).copy(gate3) + + num = initWeightIter0(i).size(1) / 2 + gate2 = initWeightIter0(i).narrow(1, 1, num) + gate1 = initWeightIter0(i).narrow(1, num + 1, num) + initWeightIter(i).select(1, 1).copy(gate1) + initWeightIter(i).select(1, 2).copy(gate2) + + num = initWeightIter1(i).size(1) / 1 + gate3 = initWeightIter1(i).narrow(1, 1, num) + initWeightIter(i).select(1, 3).copy(gate3) + + num = initBias0(i).size(1) / gru_n_gates + gate2 = initBias0(i).narrow(1, 1, num) + gate1 = initBias0(i).narrow(1, num + 1, num) + gate3 = initBias0(i).narrow(1, num * 2 + 1, num) + initBias(i).select(1, 1).copy(gate1) + initBias(i).select(1, 2).copy(gate2) + initBias(i).select(1, 3).copy(gate3) + } + val weights = gruDnn.parameters()._1 + weights(0).copy(initWeight.transpose(2, 4).transpose(3, 4)) + weights(1).copy(initBias) + weights(2).copy(initWeightIter.transpose(2, 4).transpose(3, 4)) + + return gruDnn + } + } + BlasWrapper(node.getOp().asInstanceOf[IRGeneralModule[Float]].model) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala index 9c24c683e0d..3cc1a619bea 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala @@ -2339,4 +2339,79 @@ class RNNSpec extends FlatSpec with Matchers{ Equivalent.nearequals(mkldnn_gradBias0(l), blas_gradBias(l)) should be(true) } } + + "Converting Blas GRU to Dnn GRU" should "work correctly" in { + System.setProperty("bigdl.engineType", "mkldnn") + RNG.setSeed(100) + + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.UnidirectionalLeft2Right + + var inputNTC = Tensor(Array(batchSize, seqLength, inputSize)).rand() + + val inputNode = nn.Input[Float]() + val outputNode = nn.Recurrent[Float]().add( + nn.GRU[Float](inputSize, hiddenSize)).inputs(inputNode) + val blasGRU = nn.Graph[Float](Array(inputNode), Array(outputNode)) + + val dnnGRU = blasGRU.asInstanceOf[StaticGraph[Float]] + .setInputFormats(Seq(Memory.Format.ntc)) + .setOutputFormats(Seq(Memory.Format.ntc)) + .toIRgraph() + + val mkldnn_output = dnnGRU.forward(inputNTC).toTensor + val blas_output = blasGRU.forward(inputNTC).toTensor + + Equivalent.nearequals(mkldnn_output, blas_output) should be(true) + + val gradOutput = Tensor[Float].resize(blas_output.size()).rand() + val mkldnn_gradInput = dnnGRU.backward(inputNTC, gradOutput).toTensor + val blas_gradInput = blasGRU.backward(inputNTC, gradOutput).toTensor + + Equivalent.nearequals(mkldnn_gradInput, blas_gradInput) should be(true) + + System.clearProperty("bigdl.engineType") + } + + "Converting Blas BiRecurrent GRU to Dnn GRU" should "work correctly" in { + System.setProperty("bigdl.engineType", "mkldnn") + RNG.setSeed(100) + + val seqLength = 3 + val batchSize = 2 + val inputSize = 3 + val hiddenSize = 5 + + val f = AlgKind.EltwiseTanh + var direction = Direction.BidirectionalSum + + var inputNTC = Tensor(Array(batchSize, seqLength, inputSize)).rand() + val inputNode = nn.Input[Float]() + val outputNode = nn.BiRecurrent[Float]().add( + nn.GRU[Float](inputSize, hiddenSize)).inputs(inputNode) + val blasGRU = nn.Graph[Float](Array(inputNode), Array(outputNode)) + + val dnnGRU = blasGRU.asInstanceOf[StaticGraph[Float]] + .setInputFormats(Seq(Memory.Format.ntc)) + .setOutputFormats(Seq(Memory.Format.ntc)) + .toIRgraph() + + val mkldnn_output = dnnGRU.forward(inputNTC).toTensor + val blas_output = blasGRU.forward(inputNTC).toTensor + + Equivalent.nearequals(mkldnn_output, blas_output) should be(true) + + val gradOutput = Tensor[Float].resize(blas_output.size()).rand() + val mkldnn_gradInput = dnnGRU.backward(inputNTC, gradOutput).toTensor + val blas_gradInput = blasGRU.backward(inputNTC, gradOutput).toTensor + + Equivalent.nearequals(mkldnn_gradInput, blas_gradInput) should be(true) + + System.clearProperty("bigdl.engineType") + } } From 94b914c3d0186724ec04a69982e4910832f7687a Mon Sep 17 00:00:00 2001 From: Xiao Date: Fri, 18 Oct 2019 15:02:34 +0800 Subject: [PATCH 0968/1065] Onnx support: add pos parameter to softmax (#2933) * add pos parameter to softmax * add pos parameter to softmax * add pos parameter to softmax * fix review problem * fix review problem --- .../analytics/bigdl/dllib/nn/SoftMax.scala | 79 ++++++++++--------- .../dllib/utils/python/api/PythonBigDL.scala | 4 +- .../bigdl/dllib/torch/SoftMaxSpec.scala | 29 +++++++ 3 files changed, 74 insertions(+), 38 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala index 69f6825c26f..a5d233f3d16 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala @@ -31,35 +31,46 @@ import scala.reflect.ClassTag * where shift = max_i(x_i). */ @SerialVersionUID(- 7842335603491194236L) -class SoftMax[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T] { +class SoftMax[T: ClassTag](var pos: Int = 1)(implicit ev: TensorNumeric[T]) + extends TensorModule[T] { @transient private var results: Array[Future[Unit]] = null + private def getPositiveDimension(input: Tensor[T]): Int = { + val inputDim = input.nDimension() // data batch dim + pos = if (pos <= 0) { + inputDim + pos + } + else pos + require(1 <= pos && pos <= input.nDimension(), + s"Invalid position: $pos ." + s"input dimension ${input.nDimension()}") + pos + } + override def updateOutput(input: Tensor[T]): Tensor[T] = { require(1 <= input.nDimension() && input.nDimension() <= 4, "1D, 2D, 3D or 4D tensor expected" + s"input dimension ${input.nDimension()}") - val (nFrame, stride) = if (input.nDimension() == 1) { - (1, 1) - } else if (input.nDimension() == 2) { - (input.size(1), 1) - } else if (input.nDimension() == 3) { - (1, input.size(2) * input.size(3)) - } else { - (input.size(1), input.size(3) * input.size(4)) + pos = getPositiveDimension(input) + // get nFrame and stride value based on the input + val (nFrame, stride) = input.nDimension() - pos match { + case 0 => (1, 1) + case 1 => (input.size(pos), 1) + case 2 => (1, input.size(pos + 1) * input.size(pos + 2)) + case _ => (input.size(pos), input.size(pos + 2) * input.size(pos + 3)) } + if (results == null || results.length != nFrame * stride) { results = new Array[Future[Unit]](nFrame * stride) } output.resizeAs(input) - SoftMax.updateOutput[T](input, output, results) + SoftMax.updateOutput[T](input, output, results, pos) output } - override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = { gradInput.resizeAs(output) - SoftMax.updateGradInput[T](input, gradOutput, gradInput, output, results) + SoftMax.updateGradInput[T](input, gradOutput, gradInput, output, results, pos) gradInput } @@ -67,25 +78,21 @@ class SoftMax[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule inputShape } } - object SoftMax{ - def apply[@specialized(Float, Double) T: ClassTag]() + def apply[@specialized(Float, Double) T: ClassTag](pos: Int = 1) (implicit ev: TensorNumeric[T]) : SoftMax[T] = { - new SoftMax[T]() + new SoftMax[T](pos) } // Notice: SoftMin will call this function private[nn] def updateOutput[T: ClassTag](input: Tensor[T], output: Tensor[T], - results: Array[Future[Unit]]) (implicit ev: TensorNumeric[T]): Tensor[T] = { - - val (nFrame, dim, stride) = if (input.nDimension() == 1) { - (1, input.size(1), 1) - } else if (input.nDimension() == 2) { - (input.size(1), input.size(2), 1) - } else if (input.nDimension() == 3) { - (1, input.size(1), input.size(2) * input.size(3)) - } else { - (input.size(1), input.size(2), input.size(3) * input.size(4)) + results: Array[Future[Unit]], pos: Int = 1) (implicit ev: TensorNumeric[T]): Tensor[T] = { + // get nFrame, dim and stride value based on the input tensor and pos + val (nFrame, dim, stride) = input.nDimension() - pos match { + case 0 => (1, input.size(pos), 1) + case 1 => (input.size(pos), input.size(pos + 1), 1) + case 2 => (1, input.size(pos), input.size(pos + 1) * input.size(pos + 2)) + case _ => (input.size(pos), input.size(pos + 1), input.size(pos + 2) * input.size(pos + 3)) } val outputArray = output.storage().array() @@ -95,7 +102,7 @@ object SoftMax{ input.contiguous().storage().array() } val storageOffset = input.storageOffset() - 1 - + // calculate softmax var t = 0 while (t < stride * nFrame) { val _t = t @@ -139,19 +146,19 @@ object SoftMax{ private[nn] def updateGradInput[T: ClassTag](input: Tensor[T], gradOutput: Tensor[T], gradInput: Tensor[T], output: Tensor[T], - results: Array[Future[Unit]])(implicit ev: TensorNumeric[T]): Tensor[T] = { + results: Array[Future[Unit]], pos: Int = 1 + )(implicit ev: TensorNumeric[T]): Tensor[T] = { require(input.size().deep == gradOutput.size().deep, "input should have the same size with gradOutput" + s"inputsize ${input.size().deep} gradOutput ${gradOutput.size().deep}") - val (nFrame, dim, stride) = if (output.nDimension() == 1) { - (1, output.size(1), 1) - } else if (output.nDimension() == 2) { - (output.size(1), output.size(2), 1) - } else if (output.nDimension() == 3) { - (1, output.size(1), output.size(2) * output.size(3)) - } else { - (output.size(1), output.size(2), output.size(3) * output.size(4)) + // get nFrame, dim and stride value based on the output tensor and pos + val (nFrame, dim, stride) = output.nDimension() - pos match { + case 0 => (1, output.size(pos), 1) + case 1 => (output.size(pos), output.size(pos + 1), 1) + case 2 => (1, output.size(pos), output.size(pos + 1) * output.size(pos + 2)) + case _ => + (output.size(pos), output.size(pos + 1), output.size(pos + 2) * output.size(pos + 3)) } val gradInputArray = gradInput.storage().array() @@ -165,7 +172,7 @@ object SoftMax{ } else { gradOutput.contiguous().storage().array() } - + // calculate softmax var t = 0 while (t < stride * nFrame) { val _t = t diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index bcc62e6ff82..3cdf55ebe95 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -1216,9 +1216,9 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Sigmoid[T]() } - def createSoftMax() + def createSoftMax(pos: Int = 1) : SoftMax[T] = { - SoftMax[T]() + SoftMax[T](pos) } def createSoftMin() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala index 6eb125c3f97..4a92954a1fd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala @@ -81,6 +81,35 @@ class SoftMaxSpec extends TorchSpec { println("Test case : SoftMax, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } + "A SoftMax 2D input with pos parameter" should "generate correct output and grad" in { + torchCheck() + val layer = new SoftMax[Double](-1) + val input = Tensor[Double](3, 5) + input.apply1(_ => Random.nextDouble()) + val gradOutput = Tensor[Double](3, 5) + gradOutput.apply1(_ => Random.nextDouble()) + + val start = System.nanoTime() + val output = layer.forward(input) + val gradInput = layer.backward(input, gradOutput) + val end = System.nanoTime() + val scalaTime = end - start + + val code = "module = nn.SoftMax()\n" + + "output = module:forward(input)\n" + + "gradInput = module:backward(input,gradOutput)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), + Array("output", "gradInput")) + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] + + output should be (luaOutput) + gradInput should be (luaGradInput) + + println("Test case : SoftMax, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") + } + "A SoftMax 3D input" should "generate correct output and grad" in { torchCheck() val layer = new SoftMax[Double]() From bc54507393f1575c872fad2f644fd72d42ff327d Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 22 Oct 2019 12:54:23 +0800 Subject: [PATCH 0969/1065] Add resize for segmentation (#2923) * add resize for segmentation * meet pr comments --- .../image/augmentation/ScaleResize.scala | 108 ++++++++++++++++++ .../vision/image/label/roi/RoiLabel.scala | 9 +- .../image/augmentation/ResizeSpec.scala | 11 +- 3 files changed, 125 insertions(+), 3 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ScaleResize.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ScaleResize.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ScaleResize.scala new file mode 100644 index 00000000000..d478841c4c9 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/augmentation/ScaleResize.scala @@ -0,0 +1,108 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.transform.vision.image.augmentation + +import com.intel.analytics.bigdl.dataset.segmentation.PolyMasks +import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature} +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil + + +object ScaleResize { + /** + * Scaling length and width of image feature to ensure that: + * if maxSize is not set, the smaller one between width and length will be scaled to minSize. + * if maxSize is set, the larger one will be scaled to maxSize or maxSize -1. + * e.g. image feature height = 375, width = 500 + * case 1: minSize=100, maxSize=120, then new size (90, 120) + * case 2: minSize=100, maxSize=-1, then new size (100, 133) + * @param minSize the minimal size after resize + * @param maxSize the maximal size after resize + * @param resizeROI whether to resize roi, default false + */ + def apply(minSize: Int, maxSize: Int = -1, resizeROI: Boolean = false): ScaleResize = + new ScaleResize(minSize, maxSize, resizeROI) +} + +class ScaleResize(minSize: Int, maxSize: Int = -1, resizeROI: Boolean = false) + extends FeatureTransformer { + private def getSize(sizeH: Int, sizeW: Int): (Int, Int) = { + var size = minSize + if (maxSize > 0) { + val (minOrigSize, maxOrigSize) = if (sizeW > sizeH) (sizeH, sizeW) else (sizeW, sizeH) + val thread = maxOrigSize.toFloat / minOrigSize * size + if (thread > maxSize) size = math.round(maxSize * minOrigSize / maxOrigSize) + } + if ((sizeW <= sizeH && sizeW == size) || (sizeH <= sizeW && sizeH == size)) { + (sizeH, sizeW) + } else if (sizeW < sizeH) { + (size * sizeH / sizeW, size) + } else { + (size, size * sizeW / sizeH) + } + } + + override def transformMat(feature: ImageFeature): Unit = { + val sizes = this.getSize(feature.getHeight(), feature.getWidth()) + val resizeH = sizes._1 + val resizeW = sizes._2 + Resize.transform(feature.opencvMat(), feature.opencvMat(), resizeW, resizeH, + useScaleFactor = false) + + // resize roi label + if (feature.hasLabel() && feature(ImageFeature.label).isInstanceOf[RoiLabel] && resizeROI) { + // bbox resize + resizeBbox(feature) + // mask resize + resizeMask(feature) + } + } + + private def resizeBbox(feature: ImageFeature): Unit = { + val scaledW = feature.getWidth().toFloat / feature.getOriginalWidth + val scaledH = feature.getHeight().toFloat / feature.getOriginalHeight + val target = feature.getLabel[RoiLabel] + BboxUtil.scaleBBox(target.bboxes, scaledH, scaledW) + } + + private def resizeMask(feature: ImageFeature): Unit = { + val scaledW = feature.getWidth().toFloat / feature.getOriginalWidth + val scaledH = feature.getHeight().toFloat / feature.getOriginalHeight + + val masks = feature.getLabel[RoiLabel].masks + if (masks == null) return + + for (i <- 0 until masks.length) { + val oneMask = masks(i) + require(oneMask.isInstanceOf[PolyMasks], + s"Only support poly mask resize, but get ${oneMask}") + if (oneMask.isInstanceOf[PolyMasks]) { + val polyMask = oneMask.asInstanceOf[PolyMasks] + val poly = polyMask.poly + for (i <- 0 to (poly.length - 1)) { + val p = poly(i) + for (j <- 0 to (p.length - 1)) { + if (j % 2 == 0) p(j) *= scaledW // for x + else p(j) *= scaledH // for y + } + } + // change to resized mask + masks(i) = PolyMasks(poly, feature.getHeight(), feature.getWidth()) + } + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala index ee757225030..3389bbdbeb2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.transform.vision.image.label.roi +import com.intel.analytics.bigdl.dataset.segmentation.SegmentationMasks import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{T, Table} @@ -28,7 +29,7 @@ import com.intel.analytics.bigdl.utils.{T, Table} * @param masks the array of annotation masks of the targets */ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], - masks: Array[Tensor[Float]] = null) { + masks: Array[SegmentationMasks] = null) { def copy(target: RoiLabel): Unit = { classes.resizeAs(target.classes).copy(target.classes) bboxes.resizeAs(target.bboxes).copy(target.bboxes) @@ -55,7 +56,11 @@ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], def toTable: Table = { val table = T() if (masks != null) { - table(RoiLabel.MASKS) = masks + val masksRLE = new Array[Tensor[Float]](masks.length) + for (i <- 0 to masks.length - 1) { + masksRLE(i) = masks(i).toRLETensor + } + table(RoiLabel.MASKS) = masksRLE } table(RoiLabel.CLASSES) = classes table(RoiLabel.BBOXES) = bboxes diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala index 1ca264e23f2..f5a905d740b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/augmentation/ResizeSpec.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.transform.vision.image.augmentation import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat -import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFrame, LocalImageFrame} +import com.intel.analytics.bigdl.transform.vision.image.{BytesToMat, ImageFeature, ImageFrame, LocalImageFrame} import org.opencv.imgcodecs.Imgcodecs import org.scalatest.{FlatSpec, Matchers} @@ -78,4 +78,13 @@ class ResizeSpec extends FlatSpec with Matchers { height should be (600) width should be (800) } + + "scaleResize without roi" should "be ok" in { + val data = ImageFrame.read(resource.getFile) + val transformer = ScaleResize(minSize = 100, maxSize = 120, resizeROI = false) + val transformed = transformer(data) + val imageFeature = transformed.asInstanceOf[LocalImageFrame].array(0) + imageFeature.getHeight() should be(90) + imageFeature.getWidth() should be(120) + } } From f44f7d83aee2c4f8fb96c23ed68eae91022c6839 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 22 Oct 2019 13:29:45 +0800 Subject: [PATCH 0970/1065] support batch input for boxhead (#2924) * boxhead support batch input * meet pr comments --- .../dllib/models/maskrcnn/MaskRCNN.scala | 2 +- .../analytics/bigdl/dllib/nn/BoxHead.scala | 111 ++++-- .../intel/analytics/bigdl/dllib/nn/Nms.scala | 16 +- .../bigdl/dllib/nn/RegionProposal.scala | 2 +- .../bigdl/dllib/nn/BoxHeadSpec.scala | 315 +++++++++++++++++- .../analytics/bigdl/dllib/nn/PoolerSpec.scala | 4 +- 6 files changed, 406 insertions(+), 44 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala index 172118dd317..eabdeac9565 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala @@ -155,7 +155,7 @@ class MaskRCNN(val inChannels: Int, val proposals = this.rpn.forward(T(features, ImageInfo)) val boxOutput = this.boxHead.forward(T(features, proposals)).toTable val postProcessorBox = boxOutput[Table](2) - val proposalsBox = postProcessorBox[Tensor[Float]](2) + val proposalsBox = postProcessorBox[Table](2) val labelsBox = postProcessorBox[Tensor[Float]](1) val mask = this.maskHead.forward(T(features, proposalsBox, labelsBox)) output = T(proposalsBox, labelsBox, mask) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala index f64caaed210..7603ce4f654 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala @@ -113,6 +113,7 @@ private[nn] class BoxPostProcessor( private val softMax = SoftMax[Float]() private val nmsTool: Nms = new Nms @transient private var boxesBuf: Tensor[Float] = null + @transient private var concatBoxes: Tensor[Float] = null /** * Returns bounding-box detection results by thresholding on scores and @@ -146,7 +147,7 @@ private[nn] class BoxPostProcessor( val clsScores = selectTensor(scores.select(2, clsInd + 1), inds, 1) val clsBoxes = selectTensor(boxes.narrow(2, clsInd * 4 + 1, 4), inds, 1) - val keepN = nmsTool.nms(clsScores, clsBoxes, nmsThresh, inds) + val keepN = nmsTool.nms(clsScores, clsBoxes, nmsThresh, inds, orderWithBBox = true) val bboxNms = selectTensor(clsBoxes, inds, 1, keepN) val scoresNms = selectTensor(clsScores, inds, 1, keepN) @@ -202,29 +203,29 @@ private[nn] class BoxPostProcessor( } } - private def resultToTensor(results: Array[RoiLabel], labels: Tensor[Float], bbox: Tensor[Float]) + private def resultToTensor(results: Array[RoiLabel], + labels: Array[Float], labelsOffset: Int, + bbox: Array[Float], bboxOffset : Int, + scores: Array[Float], scoresOffset: Int) : Unit = { - var maxDetection = 0 - results.foreach(res => { - if (null != res) { - maxDetection += res.size() - } - }) + var bboxPos = bboxOffset + var labelsPos = labelsOffset + var scoresPos = scoresOffset - labels.resize(maxDetection) - bbox.resize(maxDetection, 4) - - var offset = 1 (0 until nClasses).foreach(c => { val label = results(c) if (null != label) { (1 to label.size()).foreach(j => { - labels.setValue(offset, c) - bbox.setValue(offset, 1, label.bboxes.valueAt(j, 1)) - bbox.setValue(offset, 2, label.bboxes.valueAt(j, 2)) - bbox.setValue(offset, 3, label.bboxes.valueAt(j, 3)) - bbox.setValue(offset, 4, label.bboxes.valueAt(j, 4)) - offset += 1 + labels(labelsPos) = c + scores(scoresPos) = label.classes.valueAt(j) + bbox(bboxPos) = label.bboxes.valueAt(j, 1) + bbox(bboxPos + 1) = label.bboxes.valueAt(j, 2) + bbox(bboxPos + 2) = label.bboxes.valueAt(j, 3) + bbox(bboxPos + 3) = label.bboxes.valueAt(j, 4) + + bboxPos += 4 + scoresPos += 1 + labelsPos += 1 }) } }) @@ -284,27 +285,79 @@ private[nn] class BoxPostProcessor( val classLogits = input[Tensor[Float]](1) val boxRegression = input[Tensor[Float]](2) val bbox = if (input(3).isInstanceOf[Tensor[Float]]) { - input[Tensor[Float]](3) - } else input[Table](3)[Tensor[Float]](1) + T(input[Tensor[Float]](3)) + } else input[Table](3) + + val boxesInImage = new Array[Int](bbox.length()) + for (i <- 0 to boxesInImage.length - 1) { + boxesInImage(i) = bbox[Tensor[Float]](i + 1).size(1) + } if (boxesBuf == null) boxesBuf = Tensor[Float] boxesBuf.resizeAs(boxRegression) + if (concatBoxes == null) concatBoxes = Tensor[Float] + concatBoxes.resize(boxesInImage.sum, 4) + var start = 1 + for (i <- 0 to boxesInImage.length - 1) { + val length = boxesInImage(i) + concatBoxes.narrow(1, start, length).copy(bbox[Tensor[Float]](i + 1)) + start += length + } val classProb = softMax.forward(classLogits) - BboxUtil.decodeWithWeight(boxRegression, bbox, weight, boxesBuf) - - val boxesInImage = bbox.size(1) - val proposalSplit = boxesBuf.split(boxesInImage, dim = 1) - val classProbSplit = classProb.split(boxesInImage, dim = 1) - - val roilabels = filterResults(proposalSplit(0), classProbSplit(0), nClasses) + BboxUtil.decodeWithWeight(boxRegression, concatBoxes, weight, boxesBuf) if (output.toTable.length() == 0) { output.toTable(1) = Tensor[Float]() // for labels - output.toTable(2) = Tensor[Float]() // for bbox + output.toTable(2) = T() // for bbox, use table in case of batch + output.toTable(3) = Tensor[Float]() // for scores + } + + val outLabels = output.toTable[Tensor[Float]](1) + val outBBoxs = output.toTable[Table](2) + val outScores = output.toTable[Tensor[Float]](3) + + val totalROILables = T() + var totalDetections = 0 + start = 1 + for (i <- 0 to boxesInImage.length - 1) { + val boxNum = boxesInImage(i) + val proposalNarrow = boxesBuf.narrow(1, start, boxNum) + val classProbNarrow = classProb.narrow(1, start, boxNum) + start += boxNum + val roilabels = filterResults(proposalNarrow, classProbNarrow, nClasses) + if (outBBoxs.getOrElse[Tensor[Float]](i + 1, null) == null) { + outBBoxs(i + 1) = Tensor[Float]() + } + var maxDetection = 0 + roilabels.foreach(res => { + if (null != res) { + maxDetection += res.size() + } + }) + totalDetections += maxDetection + outBBoxs[Tensor[Float]](i + 1).resize(maxDetection, 4) + totalROILables(i + 1) = roilabels + } + + // resize labels and scores + outLabels.resize(totalDetections) + outScores.resize(totalDetections) + + val labels = outLabels.storage().array() + val scores = outScores.storage().array() + var labelsOffset = outLabels.storageOffset() - 1 + var scoresOffset = outScores.storageOffset() - 1 + for (i <- 0 to boxesInImage.length - 1) { + val roilabels = totalROILables[Array[RoiLabel]](i + 1) + val bbox = outBBoxs[Tensor[Float]](i + 1).storage().array() + val bboxOffset = outBBoxs[Tensor[Float]](i + 1).storageOffset() - 1 + + resultToTensor(roilabels, labels, labelsOffset, bbox, bboxOffset, scores, scoresOffset) + labelsOffset += outBBoxs[Tensor[Float]](i + 1).size(1) + scoresOffset += outBBoxs[Tensor[Float]](i + 1).size(1) } - resultToTensor(roilabels, output.toTable(1), output.toTable(2)) output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Nms.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Nms.scala index f1a27af7fbd..8cdc1737413 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Nms.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Nms.scala @@ -61,10 +61,12 @@ class Nms extends Serializable { * @param thresh overlap thresh * @param indices buffer to store indices after nms * @param sorted whether the scores are sorted + * @param orderWithBBox whether return indices in the order with original bbox index * @return the length of indices after nms */ def nms(scores: Tensor[Float], boxes: Tensor[Float], thresh: Float, - indices: Array[Int], sorted: Boolean = false): Int = { + indices: Array[Int], sorted: Boolean = false, + orderWithBBox: Boolean = false): Int = { if (scores.nElement() == 0) return 0 require(indices.length >= scores.nElement() && boxes.size(2) == 4) @@ -74,7 +76,6 @@ class Nms extends Serializable { val rowLength = boxes.stride(1) getAreas(boxArray, offset, rowLength, boxes.size(1), areas) // indices start from 0 - // indices start from 0 val orderLength = if (!sorted) { getSortedScoreInds(scores, sortIndBuffer) } else { @@ -107,6 +108,17 @@ class Nms extends Serializable { i += 1 } + + // use suppressed + if (orderWithBBox) { + var j = 0 + for (i <- 0 to (orderLength - 1)) { + if (suppressed(i) == 0) { + indices(j) = i + 1 + j += 1 + } + } + } indexLenth } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala index f86dd4b7225..45941e65ea9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala @@ -330,7 +330,7 @@ private[nn] class ProposalPostProcessor( , minBoxW, sortedScores) util.Arrays.fill(arr, 0, arr.length, 0) - nms.nms(sortedScores, proposals, thresh = nmsThread, arr, sorted = true) + nms.nms(sortedScores, proposals, thresh = nmsThread, arr, sorted = true, orderWithBBox = false) val arrFilter = arr.filter(_ > 0).map(_.toFloat) val indices = Tensor[Float]().set(Storage(arrFilter), 1, Array(arrFilter.length)) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala index be933cbbe90..d2a0a6df8a8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala @@ -103,16 +103,15 @@ class BoxHeadSpec extends FlatSpec with Matchers { T(0.3573, 0.5961), T(0.8601, 0.7605))))) - val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), - T(3.0f, 5.0f, 6.0f, 7.0f))) + val bbox = T(Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 7.0f)))) val labels = Tensor[Float](T(1, 3)) layer.evaluate() - val output = layer.forward(T(T(features1, features2), bbox, labels)).toTable + val output = layer.forward(T(T(features1, features2), bbox, labels)).toTable[Table](2) val expectedBbox = Tensor[Float](T( - T(2.9990, 4.9992, 6.1299, 7.0975), T(0.9995, 2.9991, 2.0602, 6.1203), T(2.9990, 4.9992, 6.1299, 7.0975), T(0.9995, 2.9991, 2.0602, 6.1203), @@ -271,8 +270,9 @@ class BoxHeadSpec extends FlatSpec with Matchers { T(2.9990, 4.9992, 6.1299, 7.0975), T(0.9995, 2.9991, 2.0602, 6.1203), T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203))) - val expectedLable = Tensor[Float]( + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975))) + val expectedLabel = Tensor[Float]( T( 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, @@ -283,11 +283,308 @@ class BoxHeadSpec extends FlatSpec with Matchers { 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, 80, 80)) - output.apply[Table](2)[Tensor[Float]](2).map(expectedBbox, (v1, v2) => { + output[Tensor[Float]](1) should be (expectedLabel) + + output[Table](2)[Tensor[Float]](1).map(expectedBbox, (v1, v2) => { + assert(abs(v1 - v2) < 1e-3) + v1 + }) + } + + "BoxHead with batch size > 1" should "be ok" in { + val inChannels: Int = 6 + val resolution: Int = 7 + val scales: Array[Float] = Array[Float](0.25f, 0.125f) + val samplingRratio: Int = 2 + val scoreThresh: Float = 0.012f + val nmsThresh: Float = 0.5f + val detections_per_img: Int = 100 + val representation_size: Int = 1024 + val numClasses: Int = 81 // coco dataset class number + + val layer = new BoxHead(inChannels, resolution, scales, samplingRratio, scoreThresh, + nmsThresh, detections_per_img, representation_size, numClasses) + + val params = layer.getParameters() + params._1.fill(0.001f) + + val features1 = Tensor[Float](T(T(T( + T(0.4225, 0.6287, 0.4108, 0.5272), + T(0.6714, 0.0680, 0.2866, 0.1765), + T(0.8650, 0.4987, 0.9253, 0.7914)), + + T(T(0.0264, 0.2524, 0.1257, 0.5725), + T(0.6423, 0.1356, 0.3944, 0.0141), + T(0.9096, 0.2509, 0.5605, 0.8632)), + + T(T(0.9683, 0.0549, 0.6259, 0.3762), + T(0.3638, 0.8891, 0.2664, 0.2837), + T(0.9326, 0.1827, 0.7227, 0.2481)), + + T(T(0.6557, 0.9165, 0.8756, 0.5103), + T(0.4360, 0.8133, 0.0823, 0.2113), + T(0.2167, 0.9266, 0.9105, 0.1651)), + + T(T(0.8999, 0.8347, 0.5532, 0.2879), + T(0.1027, 0.0516, 0.9670, 0.2939), + T(0.8113, 0.5250, 0.0378, 0.2784)), + + T(T(0.2387, 0.5709, 0.2917, 0.5493), + T(0.9709, 0.3801, 0.7908, 0.4004), + T(0.5152, 0.7003, 0.5848, 0.2894))))) + + val features2 = Tensor[Float](T(T(T( + T(0.4946, 0.2608), + T(0.4005, 0.2644), + T(0.8069, 0.8160), + T(0.9803, 0.1142), + T(0.3023, 0.1687)), + + T(T(0.1729, 0.7137), + T(0.2192, 0.2045), + T(0.4112, 0.4602), + T(0.8264, 0.4080), + T(0.9286, 0.2458)), + + T(T(0.0585, 0.9190), + T(0.4231, 0.3296), + T(0.0760, 0.2377), + T(0.0743, 0.4729), + T(0.2597, 0.5092)), + + T(T(0.9204, 0.1691), + T(0.2999, 0.5060), + T(0.0182, 0.2920), + T(0.0119, 0.3593), + T(0.9800, 0.4025)), + + T(T(0.9874, 0.8074), + T(0.3378, 0.7128), + T(0.3650, 0.8991), + T(0.4262, 0.8433), + T(0.5001, 0.3274)), + + T(T(0.7418, 0.2529), + T(0.0263, 0.3555), + T(0.9085, 0.9952), + T(0.3573, 0.5961), + T(0.8601, 0.7605))))) + + val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 7.0f))) + val labels = Tensor[Float](T(1, 3)) + + layer.evaluate() + + val features12 = Tensor[Float](2, features1.size(2), features1.size(3), features1.size(4)) + features12.select(1, 1).copy(features1) + features12.select(1, 2).copy(features1) + + val features22 = Tensor[Float](2, features2.size(2), features2.size(3), features2.size(4)) + features22.select(1, 1).copy(features2) + features22.select(1, 2).copy(features2) + + val bbox2 = T(Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 7.0f))), Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), + T(3.0f, 5.0f, 6.0f, 7.0f)))) + + val labels2 = T(Tensor[Float](T(1, 3)), Tensor[Float](T(1, 3))) + + // val output = layer.forward(T(T(features1, features2), T(bbox), T(labels))).toTable[Table](2) + + val output = layer.forward(T(T(features12, features22), bbox2, labels2)).toTable[Table](2) + + val expectedBbox = Tensor[Float](T( + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975), + T(0.9995, 2.9991, 2.0602, 6.1203), + T(2.9990, 4.9992, 6.1299, 7.0975))) + val expectedLable = Tensor[Float]( + T( 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, + 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, + 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, + 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 35, 35, 36, 36, + 37, 37, 38, 38, 39, 39, 40, 40, 41, 41, 42, 42, 43, 43, 44, 44, 45, 45, + 46, 46, 47, 47, 48, 48, 49, 49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 54, + 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 60, 61, 61, 62, 62, 63, 63, + 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, + 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, + 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, + 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, + 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, + 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 35, 35, 36, 36, + 37, 37, 38, 38, 39, 39, 40, 40, 41, 41, 42, 42, 43, 43, 44, 44, 45, 45, + 46, 46, 47, 47, 48, 48, 49, 49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 54, + 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 60, 61, 61, 62, 62, 63, 63, + 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, + 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79)) + + output[Tensor[Float]](1) should be (expectedLable) + + output[Table](2)[Tensor[Float]](1).map(expectedBbox, (v1, v2) => { + assert(abs(v1 - v2) < 1e-3) + v1 + }) + + output[Table](2)[Tensor[Float]](2).map(expectedBbox, (v1, v2) => { assert(abs(v1 - v2) < 1e-3) v1 }) - output.apply[Table](2)[Tensor[Float]](1) should be (expectedLable) + } "FeatureExtractor in BoxHead" should "be ok" in { @@ -708,7 +1005,7 @@ class BoxHeadSpec extends FlatSpec with Matchers { 5.8774e-02, 0.0000e+00, 3.0693e-02, 3.3855e-01, 0.0000e+00, 0.0000e+00, 9.2428e-02, 4.1654e-01, 0.0000e+00, 0.0000e+00))) - val output = layer.forward(T(input, proposals, imageInfo)).toTensor[Float] + val output = layer.forward(T(input, T(proposals), imageInfo)).toTensor[Float] output.select(1, 1).apply1(a => { a should be(0.1516f +- 1e-3f) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala index 9f928c6763e..9dac05e1c13 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala @@ -96,7 +96,7 @@ class PoolerSpec extends FlatSpec with Matchers { T(T(0, 0, 10, 10), T(0, 0, 60, 60), T(0, 0, 500, 500))).resize(3, 4) - val input = T(features, rois) + val input = T(features, T(rois)) val pooler = Pooler[Float]( resolution = 2, scales = Array(0.125f, 0.0625f, 0.03125f), samplingRatio = 2) @@ -192,7 +192,7 @@ class PoolerSpec extends FlatSpec with Matchers { T(T(0, 0, 10, 10), T(0, 0, 60, 60), T(0, 0, 500, 500))).resize(3, 4) - val input = T(features, rois) + val input = T(features, T(rois)) val pooler = Pooler[Double](resolution = 2, scales = Array(0.125f, 0.0625f, 0.03125f), samplingRatio = 2) From 5c8c56c22896d49d3a21cbbe9cba5e4c6df99751 Mon Sep 17 00:00:00 2001 From: Menooker Date: Tue, 22 Oct 2019 14:25:00 +0800 Subject: [PATCH 0971/1065] COCO SeqFile (#2927) * Move COCO SeqFile related updates into this branch * bbox * add UT * add UT * add UT * ignore non-existing images * updates based on GH comments --- dl/pom.xml | 5 + .../bigdl/dllib/feature/dataset/DataSet.scala | 51 ++- .../dataset/segmentation/COCODataset.scala | 343 ++++++++++++++++++ .../dataset/segmentation/MaskUtils.scala | 12 +- .../vision/image/label/roi/RoiLabel.scala | 14 +- .../models/utils/COCOSeqFileGenerator.scala | 116 ++++++ .../coco/COCO_val2014_000000091136.jpg | Bin 0 -> 211698 bytes .../coco/COCO_val2014_000000153344.jpg | Bin 0 -> 160151 bytes .../coco/COCO_val2014_000000200365.jpg | Bin 0 -> 141568 bytes .../coco/COCO_val2014_000000374530.jpg | Bin 0 -> 77369 bytes .../coco/COCO_val2014_000000558840.jpg | Bin 0 -> 173812 bytes .../src/test/resources/coco/cocomini.json | 1 + .../bigdl/dllib/dataset/DataSetSpec.scala | 100 ++++- 13 files changed, 618 insertions(+), 24 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/COCOSeqFileGenerator.scala create mode 100644 scala/dllib/src/test/resources/coco/COCO_val2014_000000091136.jpg create mode 100644 scala/dllib/src/test/resources/coco/COCO_val2014_000000153344.jpg create mode 100644 scala/dllib/src/test/resources/coco/COCO_val2014_000000200365.jpg create mode 100644 scala/dllib/src/test/resources/coco/COCO_val2014_000000374530.jpg create mode 100644 scala/dllib/src/test/resources/coco/COCO_val2014_000000558840.jpg create mode 100644 scala/dllib/src/test/resources/coco/cocomini.json diff --git a/dl/pom.xml b/dl/pom.xml index 4f831b0e3bf..7300d3f6e4a 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -147,6 +147,11 @@ log4j ${log4j.version} + + com.google.code.gson + gson + 2.8.5 + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index 1bc646457d1..2601a07d3a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -19,17 +19,20 @@ package com.intel.analytics.bigdl.dataset import java.nio.ByteBuffer import java.nio.file.{Files, Path, Paths} import java.util.concurrent.atomic.AtomicInteger - import com.intel.analytics.bigdl.DataSet import com.intel.analytics.bigdl.dataset.image.{LabeledBGRImage, _} +import com.intel.analytics.bigdl.dataset.segmentation.{COCODataset, COCODeserializer} import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame} import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T} -import org.apache.hadoop.io.Text +import java.awt.image.DataBufferByte +import java.io.ByteArrayInputStream +import javax.imageio.ImageIO +import org.apache.hadoop.io.{BytesWritable, Text} import org.apache.log4j.Logger import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD - import scala.reflect._ /** @@ -586,6 +589,48 @@ object DataSet { ImageFrame.rdd(rawData) } + /** + * Extract hadoop sequence files from an HDFS path as ImageFrame + * @param url sequence files folder path + * @param sc spark context + * @param partitionNum partition number, default: Engine.nodeNumber() * Engine.coreNumber() + * @return + */ + private[bigdl] def filesToRoiImageFrame(url: String, sc: SparkContext, + partitionNum: Option[Int] = None): DataSet[ImageFeature] = { + val num = partitionNum.getOrElse(Engine.nodeNumber() * Engine.coreNumber()) + val rawData = sc.sequenceFile(url, classOf[BytesWritable], classOf[BytesWritable], num) + .map { data => + val metaBytes = new COCODeserializer(ByteBuffer.wrap(data._1.getBytes)) + val fileName = metaBytes.getString + val (height, width, anno) = metaBytes.getAnnotations + + val labelClasses = Tensor(anno.map(_.categoryId.toFloat), Array(anno.length)) + val bboxes = Tensor( + anno.toIterator.flatMap(ann => { + val x1 = ann.bbox1 + val y1 = ann.bbox2 + val x2 = ann.bbox3 + val y2 = ann.bbox4 + Iterator(x1, y1, x2, y2) + }).toArray, + Array(anno.length, 4)) + val isCrowd = Tensor(anno.map(ann => if (ann.isCrowd) 1f else 0f), Array(anno.length)) + val masks = anno.map(ann => ann.masks) + require(metaBytes.getInt == COCODataset.MAGIC_NUM, "Corrupted metadata") + + val inputStream = new ByteArrayInputStream(data._2.getBytes) + val image = ImageIO.read(inputStream) + val rawdata = image.getRaster.getDataBuffer.asInstanceOf[DataBufferByte].getData() + val imf = ImageFeature(rawdata, RoiLabel(labelClasses, bboxes, masks), fileName) + imf(ImageFeature.originalSize) = (height, width, 3) + imf(RoiLabel.ISCROWD) = isCrowd + imf + } + .coalesce(num) + DataSet.rdd(rawData) + } + private[bigdl] def filesToImageFeatureDataset(url: String, sc: SparkContext, classNum: Int, partitionNum: Option[Int] = None): DistributedDataSet[ImageFeature] = { rdd[ImageFeature](filesToImageFrame(url, sc, classNum, partitionNum).toDistributed().rdd) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala new file mode 100644 index 00000000000..5f278458ee8 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala @@ -0,0 +1,343 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dataset.segmentation + +import com.google.gson.{Gson, GsonBuilder, JsonDeserializationContext, JsonDeserializer, JsonElement, TypeAdapter} +import com.google.gson.annotations.SerializedName +import com.google.gson.reflect.TypeToken +import com.google.gson.stream.{JsonReader, JsonWriter} +import java.io.{BufferedReader, FileReader} +import java.lang.reflect.Type +import java.nio.ByteBuffer +import java.nio.file.{Files, Path, Paths} +import scala.collection.mutable.ArrayBuffer + +private[bigdl] class COCOSerializeContext { + private val converter4 = ByteBuffer.allocate(4) + private val converter8 = ByteBuffer.allocate(8) + private val buffer = new ArrayBuffer[Byte]() + + def dump(v: Float): Unit = { + converter4.clear() + converter4.putFloat(v) + buffer.appendAll(converter4.array()) + } + + def dump(v: Double): Unit = { + converter8.clear() + converter8.putDouble(v) + buffer.appendAll(converter8.array()) + } + + + def dump(v: Int): Unit = { + converter4.clear() + converter4.putInt(v) + buffer.appendAll(converter4.array()) + } + + def dump(v: Long): Unit = { + converter8.clear() + converter8.putLong(v) + buffer.appendAll(converter8.array()) + } + + def dump(v: Boolean): Unit = { + val d: Byte = if (v) 1 else 0 + buffer.append(d) + } + + def dump(v: String): Unit = { + val bytes = v.getBytes + dump(bytes) + } + + def clear(): Unit = buffer.clear() + + def dump(v: Array[Byte]): Unit = { + dump(v.length) + buffer.appendAll(v) + } + + def toByteArray: Array[Byte] = buffer.toArray +} + + +private[bigdl] class COCODeserializer(buffer: ByteBuffer) { + private def getFloat: Float = buffer.getFloat + private def getDouble: Double = buffer.getDouble + def getInt: Int = buffer.getInt + private def getLong: Long = buffer.getLong + private def getBoolean: Boolean = buffer.get != 0 + + def getString: String = { + val len = getInt + val arr = new Array[Byte](len) + buffer.get(arr) + new String(arr) + } + case class SimpleAnnotation(categoryId: Int, area: Float, bbox1: Float, bbox2: Float, + bbox3: Float, bbox4: Float, isCrowd: Boolean, masks: SegmentationMasks) + + // returns an image's height, width, all annotations + def getAnnotations: (Int, Int, Array[SimpleAnnotation]) = { + val height = getInt + val width = getInt + val nAnnotations = getInt + val anno = (0 until nAnnotations).map(_ => getAnnotation(height, width)) + (height, width, anno.toArray) + } + + private def getAnnotation(height: Int, width: Int): SimpleAnnotation = { + val categoryId = getInt + val area = getFloat + val bbox1 = getFloat + val bbox2 = getFloat + val bbox3 = getFloat + val bbox4 = getFloat + val isCrowd = getBoolean + val masks = if (isCrowd) { + // is RLE + val countLen = getInt + val arr = new Array[Int](countLen) + for (i <- 0 until countLen) { + arr(i) = getInt + } + RLEMasks(arr, height, width) + } else { + val firstDimLen = getInt + val poly = new Array[Array[Float]](firstDimLen) + for (i <- 0 until firstDimLen) { + val secondDimLen = getInt + val inner = new Array[Float](secondDimLen) + for (j <- 0 until secondDimLen) { + inner(j) = getFloat + } + poly(i) = inner + } + PolyMasks(poly, height, width) + } + SimpleAnnotation(categoryId, area, bbox1, bbox2, bbox3, bbox4, isCrowd, masks) + } +} + +case class COCODataset(info: COCODatasetInfo, images: Array[COCOImage], + annotations: Array[COCOAnotationOD], + licenses: Array[COCOLicence], categories: Array[COCOCategory]) { + + private lazy val cateId2catIdx = scala.collection.mutable.Map[Long, Int]() + private[segmentation] def init(imgRoot: String): Unit = { + val id2img = images.toIterator.map(img => (img.id, img)).toMap + annotations.foreach(anno => { + require(id2img.contains(anno.imageId), s"Cannot find image_id ${anno.imageId}") + val img = id2img(anno.imageId) + anno.image = img + img.annotations += anno + anno.segmentation match { + case poly: COCOPoly => + anno.segmentation = COCOPoly(poly.poly, img.height, img.width) + case _ => + } + }) + images.foreach(img => img.imgRootPath = imgRoot) + categories.zipWithIndex.foreach { case (cate, idx) => + cateId2catIdx(cate.id) = idx + 1 // the ids starts from 1, because 0 is for background + } + } + + /** + * Convert COCO categoryId into category index. + * COCO dataset's categoryId is not continuous from 1 to number of categories. + * This function maps every categoryId to a number from 1 to number of categories - The result is + * called category index. The category index 0 is reserved for "background" class. + * @param id categoryId + * @return category index + */ + def categoryId2Idx(id: Long): Int = cateId2catIdx(id) + + /** + * Get the category data by the category index + * @param idx category index + * @return category data + */ + def getCategoryByIdx(idx: Int): COCOCategory = categories(idx - 1) +} + +case class COCODatasetInfo( + year: Int, + version: String, + description: String, + contributor: String, + url: String + ) { + @SerializedName("date_created") var dateCreated: String = _ +} + +case class COCOImage( + id: Long, + height: Int, + width : Int, + license: Int +) { + @transient lazy val annotations: ArrayBuffer[COCOAnotationOD] = new ArrayBuffer[COCOAnotationOD] + @transient private[segmentation] var imgRootPath: String = _ + @SerializedName("flickr_url") var flickrUrl: String = _ + @SerializedName("coco_url") var cocoUrl: String = _ + @SerializedName("date_captured") var dateCaptured: String = _ + @SerializedName("file_name") var fileName: String = _ + + def dumpTo(context: COCOSerializeContext, dataset: COCODataset): Unit = { + context.dump(height) + context.dump(width) + context.dump(annotations.size) + annotations.foreach(_.dumpTo(context, dataset)) + } + + /** + * Get the path of the image in local file system + * @return + */ + def path: Path = Paths.get(imgRootPath, fileName) + + /** + * Read the data from the image file + * @return + */ + def data: Array[Byte] = Files.readAllBytes(path) + +} + +/** + * An annotation for an image (OD in the name for Object Detection) + * @param id + * @param imageId the Id of the image + * @param categoryId the Id of the category. Note that categoryId is not continuous from 0 to + * the number of categories. You can use COCODataset.cateId2Idx to convert an + * categoryId to a compact category index. + * @param segmentation the segmentation data + * @param area area + * @param bbox the bounding box, (xmin, ymin, xmax, ymax) + * @param isCrowd if the annotation is a crowd. e.g. a crowd of people. If true, segmentation is + * an COCORLE object + * @param image the reference to the image + */ +case class COCOAnotationOD(id: Long, imageId: Long, categoryId: Long, + var segmentation: COCOSegmentation, area: Float, + bbox: (Float, Float, Float, Float), isCrowd: Boolean, @transient var image: COCOImage = null) { + + def dumpTo(context: COCOSerializeContext, dataSet: COCODataset): Unit = { + context.dump(dataSet.categoryId2Idx(categoryId)) + context.dump(area) + context.dump(bbox._1) + context.dump(bbox._2) + context.dump(bbox._3) + context.dump(bbox._4) + context.dump(isCrowd) + segmentation.dumpTo(context) + } +} + +case class COCOLicence( + id: Long, name: String, url: String +) + +case class COCOCategory( + id: Long, name: String) { + @SerializedName("supercategory") var superCategory: String = _ +} + +trait COCOSegmentation { + def dumpTo(context: COCOSerializeContext): Unit +} + +case class COCOPoly(_poly: Array[Array[Float]], _height: Int, _width: Int) + extends PolyMasks(_poly, _height, _width) with COCOSegmentation { + override def dumpTo(context: COCOSerializeContext): Unit = { + context.dump(poly.length) + poly.foreach(p => { + context.dump(p.length) + p.foreach(xy => { + context.dump(xy) + }) + }) + } +} + + case class COCORLE(_counts: Array[Int], _height: Int, _width: Int) + extends RLEMasks(_counts, _height, _width) with COCOSegmentation { + override def dumpTo(context: COCOSerializeContext): Unit = { + context.dump(counts.length) + counts.foreach(p => { + context.dump(p) + }) + } + } + +object COCODataset { + private[bigdl] val MAGIC_NUM = 0x1f3d4e5a + private[segmentation] class AnnotationDeserializer extends + JsonDeserializer[COCOAnotationOD] { + private lazy val intArrAdapter = COCODataset.gson.getAdapter(classOf[Array[Int]]) + private lazy val polyAdapter = COCODataset.gson.getAdapter(classOf[Array[Array[Float]]]) + override def deserialize(json: JsonElement, ty: Type, + context: JsonDeserializationContext): COCOAnotationOD = { + val obj = json.getAsJsonObject + val id = obj.get("id").getAsLong + val imageId = obj.get("image_id").getAsLong + val categoryId = obj.get("category_id").getAsLong + val area = obj.get("area").getAsFloat + val rawBbox = obj.get("bbox").getAsJsonArray + require(rawBbox.size() == 4, "The bbox in the COCO annotation data should have 4 elements") + val (x1, y1, w, h) = (rawBbox.get(0).getAsFloat, rawBbox.get(1).getAsFloat, + rawBbox.get(2).getAsFloat, rawBbox.get(3).getAsFloat) + val bbox = (x1, y1, x1 + w - 1, y1 + h - 1) + val isCrowd = if (obj.get("iscrowd").getAsInt == 1) true else false + val seg = if (isCrowd) { + val segJson = obj.getAsJsonObject("segmentation") + val cnts = intArrAdapter.fromJsonTree(segJson.get("counts")) + val size = intArrAdapter.fromJsonTree(segJson.get("size")) + require(size.length == 2, "The size in the COCO annotation data should have 2 elements") + COCORLE(cnts, size(0), size(1)) + } else { + val polys = polyAdapter.fromJsonTree(obj.get("segmentation")) + COCOPoly(polys, -1, -1) + } + COCOAnotationOD(id, imageId, categoryId, seg, area, bbox, isCrowd) + } + } + + private lazy val gson = { + val gsonBuilder = new GsonBuilder() + val theType = new TypeToken[COCOAnotationOD]() {}.getType + val deserializer = new AnnotationDeserializer + gsonBuilder.registerTypeAdapter(theType, deserializer) + gsonBuilder.create() + } + + /** + * Load COCO dataset + * @param jsonPath the JSON metadata file path + * @param imageRoot the root path of the image files + * @return + */ + def load(jsonPath: String, imageRoot: String = "."): COCODataset = { + val d = gson.fromJson( + new BufferedReader(new FileReader(jsonPath)), classOf[COCODataset]) + d.init(imageRoot) + d + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala index 292863551d1..a391ec34b67 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala @@ -24,7 +24,7 @@ abstract class SegmentationMasks extends Serializable { /** * Convert to a RLE encoded tensor */ - def toRLETensor: Tensor[Float] + def toRLE: RLEMasks } /** @@ -36,9 +36,9 @@ abstract class SegmentationMasks extends Serializable { */ class PolyMasks(val poly: Array[Array[Float]], val height: Int, val width: Int) extends SegmentationMasks { - override def toRLETensor: Tensor[Float] = { - require(height > 0 && width > 0, "the height and width must > 0 for toRLETensor()") - MaskUtils.mergeRLEs(MaskUtils.poly2RLE(this, height, width), false).toRLETensor + override def toRLE: RLEMasks = { + require(height > 0 && width > 0, "the height and width must > 0 for toRLE") + MaskUtils.mergeRLEs(MaskUtils.poly2RLE(this, height, width), false) } } @@ -66,9 +66,7 @@ object PolyMasks { * @param width width of the image */ class RLEMasks(val counts: Array[Int], val height: Int, val width: Int) extends SegmentationMasks { - override def toRLETensor: Tensor[Float] = { - Tensor(counts.map(MaskUtils.uint2long(_).toFloat), Array(counts.length)) - } + override def toRLE: RLEMasks = this /** * Get an element in the counts. Process the overflowed int diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala index 3389bbdbeb2..2b5e1af5967 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.transform.vision.image.label.roi -import com.intel.analytics.bigdl.dataset.segmentation.SegmentationMasks +import com.intel.analytics.bigdl.dataset.segmentation.{RLEMasks, SegmentationMasks} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{T, Table} @@ -56,11 +56,7 @@ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], def toTable: Table = { val table = T() if (masks != null) { - val masksRLE = new Array[Tensor[Float]](masks.length) - for (i <- 0 to masks.length - 1) { - masksRLE(i) = masks(i).toRLETensor - } - table(RoiLabel.MASKS) = masksRLE + table(RoiLabel.MASKS) = masks.map(_.toRLE) } table(RoiLabel.CLASSES) = classes table(RoiLabel.BBOXES) = bboxes @@ -83,10 +79,8 @@ object RoiLabel { def getClasses(tab: Table): Tensor[Float] = tab[Tensor[Float]](CLASSES) def getBBoxes(tab: Table): Tensor[Float] = tab[Tensor[Float]](BBOXES) - def getMasks(tab: Table): Array[Tensor[Float]] = - tab[Array[Tensor[Float]]](MASKS) - def getIsCrowd(tab: Table): Tensor[Float] = - tab[Tensor[Float]](ISCROWD) + def getMasks(tab: Table): Array[RLEMasks] = tab[Array[RLEMasks]](MASKS) + def getIsCrowd(tab: Table): Tensor[Float] = tab[Tensor[Float]](ISCROWD) /** * @return (height, width, channel) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/COCOSeqFileGenerator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/COCOSeqFileGenerator.scala new file mode 100644 index 00000000000..d181024e170 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/COCOSeqFileGenerator.scala @@ -0,0 +1,116 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.models.utils + +import com.intel.analytics.bigdl.dataset.segmentation.{COCODataset, COCOSerializeContext} +import java.io.File +import java.nio.file.{Files, Paths} +import java.util.concurrent.atomic.AtomicInteger +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path +import org.apache.hadoop.io.SequenceFile.Writer +import org.apache.hadoop.io.compress.BZip2Codec +import org.apache.hadoop.io.{BytesWritable, SequenceFile} +import scala.collection.parallel.ForkJoinTaskSupport +import scopt.OptionParser + +object COCOSeqFileGenerator { + + /** + * Configuration class for COCO sequence file + * generator + * + * @param folder the COCO image files location + * @param metaPath the metadata json file location + * @param output generated seq files location + * @param parallel number of parallel + * @param blockSize block size + */ + case class COCOSeqFileGeneratorParams( + folder: String = ".", + metaPath: String = "instances_val2014.json", + output: String = ".", + parallel: Int = 1, + blockSize: Int = 12800 + ) + + private val parser = new OptionParser[COCOSeqFileGeneratorParams]("BigDL COCO " + + "Sequence File Generator") { + head("BigDL COCO Sequence File Generator") + opt[String]('f', "folder") + .text("where you put the COCO image files") + .action((x, c) => c.copy(folder = x)) + opt[String]('o', "output folder") + .text("where you put the generated seq files") + .action((x, c) => c.copy(output = x)) + opt[Int]('p', "parallel") + .text("parallel num") + .action((x, c) => c.copy(parallel = x)) + opt[Int]('b', "blockSize") + .text("block size") + .action((x, c) => c.copy(blockSize = x)) + opt[String]('m', "metaPath") + .text("metadata json file path") + .action((x, c) => c.copy(metaPath = x)) + } + + def main(args: Array[String]): Unit = { + parser.parse(args, COCOSeqFileGeneratorParams()).foreach { param => + println("Loading COCO metadata") + val meta = COCODataset.load(param.metaPath, param.folder) + println("Metadata loaded") + val conf: Configuration = new Configuration + val doneCount = new AtomicInteger(0) + val tasks = meta.images.filter(img => { + val path = img.path + val valid = Files.exists(path) && !Files.isDirectory(path) + if (!valid) { + System.err.print(s"[Warning] The image file ${path.getFileName} does not exist.\n") + } + valid + }).grouped(param.blockSize).zipWithIndex.toArray.par + tasks.tasksupport = new ForkJoinTaskSupport( + new scala.concurrent.forkjoin.ForkJoinPool(param.parallel)) + tasks.foreach { case (imgs, blkId) => + val outFile = new Path(param.output, s"coco-seq-$blkId.seq") + val key = new BytesWritable + val value = new BytesWritable + val writer = SequenceFile.createWriter(conf, Writer.file(outFile), Writer.keyClass(key + .getClass), Writer.valueClass(value.getClass), Writer.compression(SequenceFile + .CompressionType.BLOCK, new BZip2Codec)) + val context = new COCOSerializeContext + imgs.foreach { img => + context.clear() + context.dump(img.fileName) + img.dumpTo(context, meta) + context.dump(COCODataset.MAGIC_NUM) + val keyBytes = context.toByteArray + key.set(keyBytes, 0, keyBytes.length) + val bytes = img.data + value.set(bytes, 0, bytes.length) + writer.append(key, value) + val cnt = doneCount.incrementAndGet() + if (cnt % 500 == 0) { + System.err.print(s"\r$cnt / ${meta.images.length} = ${cnt.toFloat/meta.images.length}") + } + } + writer.close() + } + System.err.print("\n") + } + } +} diff --git a/scala/dllib/src/test/resources/coco/COCO_val2014_000000091136.jpg b/scala/dllib/src/test/resources/coco/COCO_val2014_000000091136.jpg new file mode 100644 index 0000000000000000000000000000000000000000..5f9a43bb7a18576eb4cf0c62fad127ce85d596f6 GIT binary patch literal 211698 zcmb4qWmlX{6XpPeySoGihv4oI+}(n^TX5GPgUsL(B)H4q&VvVnJ3$g4I0ScC-n07$ zwz|J{-&J+`l-yl)_1ntZHULLSPEig32L}gGcprebb$|>20UrK;^PPb26A=Xw5eP&? zLqM8#0f5AXPb-B?ph?GVK`8Bk5)R_gf)H62 zH4@8s4$SiYCZX4k_;&$CW%%^H_?;0s9N_=T`F~>mCl?5Z2tY!9Ule-R=v@jN5)cXD zeH#D{5e^=Ji-7lmOA3fjt7+j84k`MF$lW+F`bb}5tp 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java.io.File.createTempFile("UnitTest", System.nanoTime().toString) + require(tmpFile.delete()) + require(tmpFile.mkdir()) + COCOSeqFileGenerator.main(Array("-f", dataSetFolder, "-o", tmpFile.getPath, "-p", "4", + "-b", "2", "-m", dataSetFolder + "cocomini.json")) + + // write done, now read and check + DataSet.SeqFileFolder.filesToRoiImageFrame(tmpFile.getPath, sc).toDistributed() + .data(false) + .map(imf => { + (imf(ImageFeature.uri).asInstanceOf[String], imf.getOriginalSize, imf.getLabel[RoiLabel], + imf[Tensor[Float]](RoiLabel.ISCROWD), imf[Array[Byte]](ImageFeature.bytes)) + }) + .collect() + .foreach({ case (uri, size, label, iscrowd, bytes) => + val img = index(uri) + require(size == (img.height, img.width, 3)) + require(label.masks.length == img.annotations.length) + require(java.util.Arrays.equals(iscrowd.toArray(), + img.annotations.map(a => if (a.isCrowd) 1f else 0f).toArray)) + img.annotations.zipWithIndex.foreach { case (ann, idx) => + label.masks(idx) match { + case rle: RLEMasks => + val realArr = ann.segmentation.asInstanceOf[COCORLE].counts + val seqArr = rle.counts + require(java.util.Arrays.equals(realArr, seqArr)) + case poly: PolyMasks => + val realArr = ann.segmentation.asInstanceOf[PolyMasks].poly.flatten + val seqArr = poly.poly.flatten + require(java.util.Arrays.equals(realArr, seqArr)) + } + + val bb = label.bboxes.narrow(1, idx + 1, 1).squeeze().toArray() + val annbb = Array(ann.bbox._1, ann.bbox._2, + ann.bbox._3, ann.bbox._4) + require(java.util.Arrays.equals(bb, annbb)) + } + + // label checking done, now check the image data + val inputStream = new FileInputStream(dataSetFolder + uri) + val image = ImageIO.read(inputStream) + val rawdata = image.getRaster.getDataBuffer.asInstanceOf[DataBufferByte].getData() + require(java.util.Arrays.equals(rawdata, bytes)) + }) + } + + "mnist data source" should "load image correct" in { val resource = getClass().getClassLoader().getResource("mnist") From 427e23859ed9530dd482aa6941bfe64347870322 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Tue, 22 Oct 2019 17:03:54 +0800 Subject: [PATCH 0972/1065] ONNX Support (#2918) * onnx dev * add onnx loader * clean up --- .../analytics/bigdl/dllib/nn/onnx/Gemm.scala | 4 +- .../bigdl/dllib/nn/onnx/Reshape.scala | 79 +++++++++++++++++++ .../utils/python/api/PythonBigDLOnnx.scala | 18 +++++ .../bigdl/dllib/nn/onnx/ReshapeSpec.scala | 55 +++++++++++++ 4 files changed, 154 insertions(+), 2 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Reshape.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ReshapeSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gemm.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gemm.scala index 869a8ded08f..12803d62d13 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gemm.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Gemm.scala @@ -51,8 +51,8 @@ private[bigdl] class Gemm[T: ClassTag]( )(implicit ev: TensorNumeric[T]) extends Operation[Tensor[T], Tensor[T], T] { - require(matrixB.dim() == 2, "Matrix B should be 2D") - require(matrixC.dim() == 2, "Matrix C should be 2D") +// require(matrixB.dim() == 2, "Matrix B should be 2D") +// require(matrixC.dim() == 2, "Matrix C should be 2D") // alpha * B' val transformedMatrixB = (if (transB == true) matrixB.t() else matrixB).mul(ev.fromType(alpha)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Reshape.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Reshape.scala new file mode 100644 index 00000000000..8f148e62e2a --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/onnx/Reshape.scala @@ -0,0 +1,79 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package com.intel.analytics.bigdl.nn.onnx + +import scala.reflect.ClassTag + +import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils.Table + + +/** + * Reshape the input tensor similar to numpy.reshape. + * First input is the data tensor, second input is a shape tensor which specifies the output shape. + * It outputs the reshaped tensor. + * @param `classTag$T` + * @param ev + * @tparam T The numeric type in this module parameters. + */ +class Reshape[T: ClassTag](var shape: Array[Int] = null)(implicit ev: TensorNumeric[T]) + extends AbstractModule[Activity, Tensor[T], T] { + + override def updateOutput(input: Activity): Tensor[T] = { + var dataTensor: Tensor[T] = null + + if (input.isTable) { + val inputTable = input.toTable + require(inputTable.length() == 2) + dataTensor = inputTable.get[Tensor[T]](1).get + shape = inputTable.get[Tensor[T]](2).get.squeeze().toArray().map(ev.toType[Int]) + } else if (input.isTensor) { + dataTensor = input.toTensor[T] + } else { + throw new IllegalArgumentException() + } + require(shape != null, "shape should not be null") + val innerReshaper = nn.Reshape(shape, batchMode = Option(false)) + output = innerReshaper.forward(dataTensor) + output + } + + override def updateGradInput(input: Activity, gradOutput: Tensor[T]): Activity = { + val inputTensor = if (input.isTable) { + input.toTable.get[Tensor[T]](1).get + } else if (input.isTensor) { + input.toTensor[T] + } else { + throw new IllegalArgumentException() + } + gradInput = inputTensor.zero() + gradInput + } + +} + + +object Reshape { + def apply[T: ClassTag](shape: Array[Int] = null) + (implicit ev: TensorNumeric[T]): Reshape[T] = { + new Reshape[T](shape) + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala index 65c4e4eda7c..37aabecbc3d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDLOnnx.scala @@ -17,7 +17,10 @@ package com.intel.analytics.bigdl.python.api import scala.reflect.ClassTag +import scala.collection.JavaConverters._ +import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, Map => JMap} +import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.onnx._ import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -33,6 +36,16 @@ private[bigdl] object PythonBigDLOnnx { class PythonBigDLOnnx[T: ClassTag](implicit ev: TensorNumeric[T]) extends PythonBigDL[T] { + def createConstant(value: JTensor): nn.tf.Const[T, T] = { + nn.tf.Const[T, T](toTensor(value)) + } + + + def createGather(): nn.ops.Gather[T, T] = { + nn.ops.Gather() + } + + def createGemm(alpha: Float, beta: Float, transA: Int, transB: Int, matrixB: JTensor, matrixC: JTensor): Gemm[T] = { Gemm(alpha, beta, @@ -42,6 +55,11 @@ class PythonBigDLOnnx[T: ClassTag](implicit ev: TensorNumeric[T]) extends Python } + def createReshape(shape: JArrayList[Int]): nn.onnx.Reshape[T] = { + nn.onnx.Reshape(if (shape == null) null else shape.asScala.toArray) + } + + def createShape(): Shape[T] = { Shape[T]() } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ReshapeSpec.scala new file mode 100644 index 00000000000..c3a8ba63285 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/onnx/ReshapeSpec.scala @@ -0,0 +1,55 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.nn.onnx + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{FlatSpec, Matchers} + + +class ReshapeSpec extends FlatSpec with Matchers { + + "Reshape" should "work" in { + // case when shape is an attribute + val inputTensor = Tensor[Float](4, 8).rand() + val shape = Array[Int](2, 2, 8) + var reshape = Reshape[Float](shape) + var output = reshape.forward(inputTensor) + + output.size() should be (shape) + + // case when shape is an input + reshape = Reshape() + val shapeTensor = Tensor[Float](3) + shapeTensor.setValue(1, 2) + shapeTensor.setValue(2, 2) + shapeTensor.setValue(3, 8) + output = reshape.forward(T(inputTensor, shapeTensor)) + output.size() should be (shape) + + } +} + +class ReshapeSerialTest extends ModuleSerializationTest { + override def test(): Unit = { + val reshape = Reshape[Float](Array(2, 2, 8)) + val input = Tensor[Float](4, 8).rand() + runSerializationTest(reshape, input) + } + +} + From 513ec30e238ea61b19a80cfe68ecab8717d51986 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 24 Oct 2019 13:30:49 +0800 Subject: [PATCH 0973/1065] feat: add precision recall auc (#2941) * feat: add precision recall auc --- .../dllib/optim/PrecisionRecallAUC.scala | 94 +++++++++++++++++++ .../bigdl/dllib/optim/ValidationSpec.scala | 30 ++++++ 2 files changed, 124 insertions(+) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PrecisionRecallAUC.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PrecisionRecallAUC.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PrecisionRecallAUC.scala new file mode 100644 index 00000000000..5369d6cff7c --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/PrecisionRecallAUC.scala @@ -0,0 +1,94 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric + +import scala.reflect.ClassTag + +/** + * Precision Recall Area Under Curve will compute the precision-recall pairs and + * get the area under the curve. + * + * Note: It will gather all output probabilities and targets to driver and will compute the + * precision, recall and the auc every calling of `result()` + * + * @param ev tensor numeric environments + * @tparam T class tag for tensor numeric + */ +class PrecisionRecallAUC[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends ValidationMethod[T] { + override def apply(output: Activity, target: Activity): ValidationResult = { + require(output.isTensor && target.isTensor, s"only support tensor output and tensor target") + require(!output.toTensor.isEmpty && !target.toTensor.isEmpty, + s"the output and target should not be empty") + val array = List(output, target).map(_.toTensor[Float].storage().array()) + val results = array.head.zip(array.last).toArray + new PRAUCResult(results) + } + + override protected def format(): String = s"PrecisionRecallAUC" +} + +class PRAUCResult(val results: Array[(Float, Float)]) extends ValidationResult { + override def result(): (Float, Int) = { + val sorted = results.sortBy(_._1).reverse + val totalPositive = sorted.count(_._2 == 1) + + var truePositive = 0.0f + var falsePositive = 0.0f + + var areaUnderCurve = 0.0f + var prevPrecision = 1.0f + var prevRecall = 0.0f + + var i = 0 + while (truePositive != totalPositive) { + val target = sorted(i)._2 + + if (target == 1.0f) { + truePositive += 1 + } else { + falsePositive += 1 + } + + val precision = truePositive / (truePositive + falsePositive) + val recall = truePositive / totalPositive + + areaUnderCurve += (recall - prevRecall) * (precision + prevPrecision) + + prevRecall = recall + prevPrecision = precision + + i += 1 + } + + (areaUnderCurve / 2, results.length) + } + + // scalastyle:off methodName + override def +(other: ValidationResult): ValidationResult = { + new PRAUCResult(results ++ other.asInstanceOf[PRAUCResult].results) + } + // scalastyle:on + + override protected def format(): String = { + val getResult = result() + s"Precision Recall AUC is ${getResult._1} on ${getResult._2}" + } +} + diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index cae2f6b7e36..763c1256480 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -518,4 +518,34 @@ class ValidationSpec extends FlatSpec with Matchers { result.result()._1 should be (0.1f) result.result()._2 should be (3) } + + "precision recall auc" should "work correctly" in { + val output = Tensor(Storage(Array[Float]( + 0.1f, 0.4f, 0.35f, 0.8f + ))) + + val target = Tensor(Storage(Array[Float]( + 0, 0, 1, 1 + ))) + + val validation = new PrecisionRecallAUC[Float]() + val result = validation(output, target) + + val auc = result.result()._1 + val num = result.result()._2 + + auc should be (0.7916667f) + num should be (4) + } + + "precision recall auc with empty tensor" should "work correctly" in { + val output = Tensor[Float]() + val target = Tensor[Float]() + + val validation = new PrecisionRecallAUC[Float]() + + val thrown = intercept[IllegalArgumentException] { + validation(output, target) + } + } } From 3e4684aa1a8412674d91203b4aa5bc2fe7ec61aa Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Thu, 24 Oct 2019 14:09:06 +0800 Subject: [PATCH 0974/1065] add post processing for maskrcnn model (#2931) * add mask postprocessing * put image info to mask model --- .../dataset/segmentation/MaskUtils.scala | 75 +- .../vision/image/label/roi/RoiLabel.scala | 3 + .../vision/image/util/BboxUtil.scala | 14 +- .../dllib/models/maskrcnn/MaskRCNN.scala | 96 +- .../bigdl/dllib/models/maskrcnn/Utils.scala | 213 +++ .../analytics/bigdl/dllib/nn/BoxHead.scala | 8 +- .../bigdl/dllib/nn/RegionProposal.scala | 4 +- .../analytics/bigdl/dllib/utils/Table.scala | 5 +- .../dllib/models/maskrcnn/MaskRCNNSpec.scala | 314 ++++- .../dllib/models/maskrcnn/UtilsSpec.scala | 1164 +++++++++++++++++ .../bigdl/dllib/nn/BoxHeadSpec.scala | 12 +- 11 files changed, 1870 insertions(+), 38 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Utils.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/UtilsSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala index a391ec34b67..c749b189e49 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala @@ -65,7 +65,8 @@ object PolyMasks { * @param height height of the image * @param width width of the image */ -class RLEMasks(val counts: Array[Int], val height: Int, val width: Int) extends SegmentationMasks { +class RLEMasks(val counts: Array[Int], val height: Int, val width: Int) + extends SegmentationMasks { override def toRLE: RLEMasks = this /** @@ -77,6 +78,34 @@ class RLEMasks(val counts: Array[Int], val height: Int, val width: Int) extends def get(idx: Int): Long = { MaskUtils.uint2long(counts(idx)) } + + override def equals(obj: Any): Boolean = { + if (obj == null) { + return false + } + if (!obj.isInstanceOf[RLEMasks]) { + return false + } + val other = obj.asInstanceOf[RLEMasks] + if (this.eq(other)) { + return true + } + + this.counts.deep == other.counts.deep && + this.height == other.height && + this.width == other.width + } + + override def hashCode() : Int = { + val seed = 37 + var hash = 1 + hash = hash * seed + height + hash = hash * seed + width + this.counts.foreach(key => { + hash = hash * seed + key.hashCode() + }) + hash + } } object RLEMasks { @@ -357,4 +386,48 @@ object MaskUtils { } RLEMasks(cnts.toArray, h, w) } + + def polyToSingleRLE(poly: PolyMasks, height: Int, width: Int): RLEMasks = { + val out = poly2RLE(poly, height, width) + mergeRLEs(out, false) + } + + // convert binary mask to rle with counts + def binaryToRLE(binaryMask: Tensor[Float]): RLEMasks = { + val countsBuffer = new ArrayBuffer[Int] + + val h = binaryMask.size(1) + val w = binaryMask.size(2) + val maskArr = binaryMask.storage().array() + val offset = binaryMask.storageOffset() - 1 + + val n = binaryMask.nElement() + var i = 0 + var p = -1 + var c = 0 + while (i < n) { + // the first one should be 0 + val iw = i / h + val ih = i % h + val ss = ih * w + iw + if (p == -1 && maskArr(ss + offset) == 1) { + countsBuffer.append(0) + p = 1 + c = 1 + } else if (p == -1 && maskArr(ss + offset) == 0) { + p = 0 + c = 1 + } else if (maskArr(ss + offset) == p) { + c += 1 + } else { + countsBuffer.append(c) + c = 1 + p = maskArr(ss + offset).toInt + } + i += 1 + } + countsBuffer.append(c) + + RLEMasks(countsBuffer.toArray, height = h, width = w) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala index 2b5e1af5967..7b5dae7b8b0 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.transform.vision.image.label.roi import com.intel.analytics.bigdl.dataset.segmentation.{RLEMasks, SegmentationMasks} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.dataset.segmentation.{RLEMasks, SegmentationMasks} /** * image target with classes and bounding boxes @@ -75,12 +76,14 @@ object RoiLabel { // ISCROWD and ORIGSIZE are stored in ImageFeature val ISCROWD = "is_crowd" val ORIGSIZE = "size" + val SCORES = "scores" def getClasses(tab: Table): Tensor[Float] = tab[Tensor[Float]](CLASSES) def getBBoxes(tab: Table): Tensor[Float] = tab[Tensor[Float]](BBOXES) def getMasks(tab: Table): Array[RLEMasks] = tab[Array[RLEMasks]](MASKS) def getIsCrowd(tab: Table): Tensor[Float] = tab[Tensor[Float]](ISCROWD) + def getScores(tab: Table): Tensor[Float] = tab[Tensor[Float]](SCORES) /** * @return (height, width, channel) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala index b8062044a83..8d8586fa11b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/util/BboxUtil.scala @@ -504,13 +504,12 @@ object BboxUtil { val wh = weight(3) encodeBox.resize(Array(encodeBox.nElement() / 4, 4)) - decodeBox.resize(Array(4, decodeBox.nElement() / 4)) // copy for contigious - val dx = decodeBox.select(1, 1).copy(encodeBox.select(2, 1)).div(wx) - val dy = decodeBox.select(1, 2).copy(encodeBox.select(2, 2)).div(wy) - val dw = decodeBox.select(1, 3).copy(encodeBox.select(2, 3)).div(ww) - val dh = decodeBox.select(1, 4).copy(encodeBox.select(2, 4)).div(wh) + val dx = encodeBox.select(2, 1).contiguous().div(wx) + val dy = encodeBox.select(2, 2).contiguous().div(wy) + val dw = encodeBox.select(2, 3).contiguous().div(ww) + val dh = encodeBox.select(2, 4).contiguous().div(wh) // not change original input encodeBox.resize(encodeBox.nElement()) @@ -531,7 +530,6 @@ object BboxUtil { val buffer2 = Tensor[Float]().resizeAs(pred_ctr_y).copy(pred_ctr_y).sub(pred_h) val buffer3 = Tensor[Float]().resizeAs(pred_ctr_x).copy(pred_ctr_x).add(pred_w).add(-1.0f) val buffer4 = Tensor[Float]().resizeAs(pred_ctr_y).copy(pred_ctr_y).add(pred_h).add(-1.0f) - decodeBox.resize(decodeBox.nElement()) val arrBuffer1 = buffer1.storage().array() val arrBuffer2 = buffer2.storage().array() @@ -558,9 +556,7 @@ object BboxUtil { require(encodeBox.size(1) == decodeBox.size(1)) val numBboxes = bbox.size(1) - if (numBboxes > 0) { - require(bbox.size(2) == 4) - } + if (numBboxes > 0) require(bbox.size(2) == 4) var i = 1 while (i <= numBboxes) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala index eabdeac9565..f7587cae4b1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala @@ -17,12 +17,15 @@ package com.intel.analytics.bigdl.models.maskrcnn import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.dataset.segmentation.{MaskUtils, RLEMasks} import com.intel.analytics.bigdl.models.resnet.{Convolution, Sbn} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} @@ -58,11 +61,11 @@ class MaskRCNN(val inChannels: Int, val config: MaskRCNNParams = new MaskRCNNParams)(implicit ev: TensorNumeric[Float]) extends Container[Activity, Activity, Float] { - private val ImageInfo : Tensor[Float] = Tensor[Float](2) + private val batchImgInfo : Tensor[Float] = Tensor[Float](2) private val backbone = buildBackbone(inChannels, outChannels) private val rpn = RegionProposal(inChannels, config.anchorSizes, config.aspectRatios, config.anchorStride, config.preNmsTopNTest, config.postNmsTopNTest, config.preNmsTopNTrain, - config.postNmsTopNTrain, config.minSize) + config.postNmsTopNTrain, config.rpnNmsThread, config.minSize) private val boxHead = BoxHead(inChannels, config.boxResolution, config.scales, config.samplingRatio, config.boxScoreThresh, config.boxNmsThread, config.maxPerImage, config.outputSize, numClasses) @@ -146,19 +149,86 @@ class MaskRCNN(val inChannels: Int, } override def updateOutput(input: Activity): Activity = { - val inputWidth = input.toTensor[Float].size(3) - val inputHeight = input.toTensor[Float].size(4) - ImageInfo.setValue(1, inputWidth) - ImageInfo.setValue(2, inputHeight) - - val features = this.backbone.forward(input) - val proposals = this.rpn.forward(T(features, ImageInfo)) - val boxOutput = this.boxHead.forward(T(features, proposals)).toTable + val inputFeatures = input.toTable[Tensor[Float]](1) + // image info with shape (batchSize, 4) + // contains all images info (height, width, original height, original width) + val imageInfo = input.toTable[Tensor[Float]](2) + + batchImgInfo.setValue(1, inputFeatures.size(3)) + batchImgInfo.setValue(2, inputFeatures.size(4)) + + val features = this.backbone.forward(inputFeatures) + val proposals = this.rpn.forward(T(features, batchImgInfo)) + val boxOutput = this.boxHead.forward(T(features, proposals, batchImgInfo)).toTable val postProcessorBox = boxOutput[Table](2) - val proposalsBox = postProcessorBox[Table](2) val labelsBox = postProcessorBox[Tensor[Float]](1) - val mask = this.maskHead.forward(T(features, proposalsBox, labelsBox)) - output = T(proposalsBox, labelsBox, mask) + val proposalsBox = postProcessorBox[Table](2) + val scores = postProcessorBox[Tensor[Float]](3) + val masks = this.maskHead.forward(T(features, proposalsBox, labelsBox)).toTable + if (this.isTraining()) { + output = T(proposalsBox, labelsBox, masks, scores) + } else { + output = postProcessorForMaskRCNN(proposalsBox, labelsBox, masks[Tensor[Float]](2), + scores, imageInfo) + } + + output + } + + @transient var binaryMask : Tensor[Float] = null + private def postProcessorForMaskRCNN(bboxes: Table, labels: Tensor[Float], + masks: Tensor[Float], scores: Tensor[Float], imageInfo: Tensor[Float]): Table = { + val batchSize = bboxes.length() + val boxesInImage = new Array[Int](batchSize) + for (i <- 0 to batchSize - 1) { + boxesInImage(i) = bboxes[Tensor[Float]](i + 1).size(1) + } + + if (binaryMask == null) binaryMask = Tensor[Float]() + val output = T() + var start = 1 + for (i <- 0 to batchSize - 1) { + val info = imageInfo.select(1, i + 1) + val height = info.valueAt(1).toInt // image height after scale, no padding + val width = info.valueAt(2).toInt // image width after scale, no padding + val originalHeight = info.valueAt(3).toInt // Original height + val originalWidth = info.valueAt(4).toInt // Original width + + binaryMask.resize(originalHeight, originalWidth) + + val boxNumber = boxesInImage(i) + val maskPerImg = masks.narrow(1, start, boxNumber) + val bboxPerImg = bboxes[Tensor[Float]](i + 1) + val classPerImg = labels.narrow(1, start, boxNumber) + val scorePerImg = scores.narrow(1, start, boxNumber) + + require(maskPerImg.size(1) == bboxPerImg.size(1), + s"mask number ${maskPerImg.size(1)} should be same with box number ${bboxPerImg.size(1)}") + + // bbox resize to original size + if (height != originalHeight || width != originalWidth) { + BboxUtil.scaleBBox(bboxPerImg, + originalHeight.toFloat / height, originalWidth.toFloat / width) + } + // mask decode to original size + val masksRLE = new Array[RLEMasks](boxNumber) + for (j <- 0 to boxNumber - 1) { + binaryMask.fill(0.0f) + Utils.decodeMaskInImage(maskPerImg.select(1, j + 1), bboxPerImg.select(1, j + 1), + binaryMask = binaryMask) + masksRLE(j) = MaskUtils.binaryToRLE(binaryMask) + } + start += boxNumber + + // prepare for evaluation + val postOutput = T() + postOutput.update(RoiLabel.MASKS, masksRLE) + postOutput.update(RoiLabel.BBOXES, bboxPerImg) + postOutput.update(RoiLabel.CLASSES, classPerImg) + postOutput.update(RoiLabel.SCORES, scorePerImg) + + output(i + 1) = postOutput + } output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Utils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Utils.scala new file mode 100644 index 00000000000..ab07b8da73b --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Utils.scala @@ -0,0 +1,213 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package com.intel.analytics.bigdl.models.maskrcnn + +import breeze.linalg.{*, dim, max} +import com.intel.analytics.bigdl.nn.ResizeBilinear +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.tensor.Tensor +import com.sun.xml.internal.bind.v2.TODO + +import scala.collection.mutable.ArrayBuffer + +private[bigdl] object Utils { + // box with 4 element (xyxy) + def expandBoxes(bbox: Tensor[Float], bboxExpand: Tensor[Float], scale: Float) + : Unit = { + require(bbox.nElement() == 4 && bboxExpand.nElement() == 4 + && bbox.dim() == 1 && bboxExpand.dim() == 1, + "Box and expanded box should have 4 elements with one dim") + + val box0 = bbox.valueAt(1) + val box1 = bbox.valueAt(2) + val box2 = bbox.valueAt(3) + val box3 = bbox.valueAt(4) + + var wHalf = (box2 - box0) * 0.5f + var hHalf = (box3 - box1) * 0.5f + val x_c = (box2 + box0) * 0.5f + val y_c = (box3 + box1) * 0.5f + + wHalf *= scale + hHalf *= scale + + bboxExpand.setValue(1, x_c - wHalf) + bboxExpand.setValue(3, x_c + wHalf) + bboxExpand.setValue(2, y_c - hHalf) + bboxExpand.setValue(4, y_c + hHalf) + } + + // mask with three dims (channel, height, wide) + def expandMasks(mask: Tensor[Float], padding: Int): (Tensor[Float], Float) = { + require(mask.isContiguous(), "Only support contiguous mask") + + val channel = mask.size(1) + val width = mask.size(mask.dim() - 1) // height equals to width + val expandPadding = 2 * padding + val scale = (width + expandPadding).toFloat / width + val paddedMask = Tensor[Float](channel, width + expandPadding, width + expandPadding) + + val maskHeight = mask.size(2) + val maskWidth = mask.size(3) + val padHeight = paddedMask.size(2) + val padWidth = paddedMask.size(3) + + for (i <- 1 to channel) { + val maskPart = mask.select(1, i) + val maskArray = maskPart.storage().array() + val maskOffset = maskPart.storageOffset() - 1 + + val padPart = paddedMask.select(1, i) + val padArray = padPart.storage().array() + val padOffset = padPart.storageOffset() - 1 + + val nElement = padPart.nElement() + for (j <- 0 until nElement) { + val tempHeight = j / padWidth + 1 + val tempWidth = j % padWidth + 1 + val tempMaskHeight = + if ((tempHeight > padding + maskHeight) || (tempHeight < padding)) -1 + else tempHeight - padding + + val tempMaskWidth = + if ((tempWidth > padding + maskWidth) || (tempWidth < padding)) -1 + else tempWidth - padding + + if (tempMaskHeight > 0 && tempMaskWidth > 0) { + val offset = (tempMaskHeight - 1) * maskWidth + tempMaskWidth - 1 + padArray(j + padOffset) = maskArray(offset + maskOffset) + } + } + } + (paddedMask, scale) + } + + // mask and box should be one by one + def decodeMaskInImage(mask: Tensor[Float], box: Tensor[Float], binaryMask: Tensor[Float], + thresh: Float = 0.5f, padding : Int = 1): Unit = { + + val (paddedMask, scale) = expandMasks(mask, padding) + val boxExpand = Tensor[Float]().resizeAs(box) + expandBoxes(box, boxExpand, scale) + + val TO_REMOVE = 1 + val w = math.max(boxExpand.valueAt(3).toInt - boxExpand.valueAt(1).toInt + TO_REMOVE, 1) + val h = math.max(boxExpand.valueAt(4).toInt - boxExpand.valueAt(2).toInt + TO_REMOVE, 1) + + paddedMask.resize(1, paddedMask.size(2), paddedMask.size(3)) + val interpMask = Tensor[Float](1, h, w) + bilinear(paddedMask, interpMask) + + if (thresh >= 0) { + interpMask.apply1(m => if (m > thresh) 1 else 0) + } else { + interpMask.mul(255.0f) + } + + val imgHeight = binaryMask.size(1) + val imgWide = binaryMask.size(2) + + val x_0 = math.max(boxExpand.valueAt(1).toInt, 0) + val x_1 = math.min(boxExpand.valueAt(3).toInt + 1, imgWide) + val y_0 = math.max(boxExpand.valueAt(2).toInt, 0) + val y_1 = math.min(boxExpand.valueAt(4).toInt + 1, imgHeight) + + val maskX0 = y_0 - boxExpand.valueAt(2).toInt + val maskX1 = y_1 - boxExpand.valueAt(2).toInt + val maskY0 = x_0 - boxExpand.valueAt(1).toInt + val maskY1 = x_1 - boxExpand.valueAt(1).toInt + + binaryMask.narrow(1, y_0 + 1, y_1 - y_0).narrow(2, x_0 + 1, x_1 - x_0).copy( + interpMask.narrow(2, maskX0 + 1, maskX1 - maskX0).narrow(3, maskY0 + 1, maskY1 - maskY0)) + } + + // input & output should be 3 dims with (n, height, width) + def bilinear(input: Tensor[Float], output: Tensor[Float], + alignCorners: Boolean = false): Unit = { + require(input.dim() == 3 && output.dim() == 3, s"Only support 3 dims bilinear," + + s"but get ${input.dim()} ${output.dim()}") + + val input_height = input.size(2) + val input_width = input.size(3) + val output_height = output.size(2) + val output_width = output.size(3) + + if (input_height == output_height && input_width == output_width) { + output.copy(input) + return + } + + require(input.isContiguous() && output.isContiguous(), + "Only support contiguous tensor for bilinear") + val channels = input.size(1) + val inputData = input.storage().array() + val outputData = output.storage().array() + val inputOffset = input.storageOffset() - 1 + val outputOffset = output.storageOffset() - 1 + + val realHeight = areaPixelComputeScale( + input_height, output_height, alignCorners) + val realWidth = areaPixelComputeScale( + input_width, output_width, alignCorners) + + for (h2 <- 0 until output_height) { + val h1r = areaPixelComputeSourceIndex(realHeight, h2, alignCorners) + val h1 = h1r.toInt + val h1p = if (h1 < input_height - 1) 1 else 0 + val h1lambda = h1r - h1 + val h0lambda = 1.0f - h1lambda + + for (w2 <- 0 until output_width) { + val w1r = areaPixelComputeSourceIndex(realWidth, w2, alignCorners) + val w1 = w1r.toInt + val w1p = if (w1 < input_width - 1) 1 else 0 + val w1lambda = w1r - w1 + val w0lambda = 1.0f - w1lambda + + val pos1 = h1 * input_width + w1 + inputOffset + val pos2 = h2 * output_width + w2 + outputOffset + + for (c <- 0 to (channels - 1)) { + outputData(pos2) = h0lambda * (w0lambda * inputData(pos1) + + w1lambda * inputData(pos1 + w1p)) + + h1lambda * (w0lambda * inputData(pos1 + h1p * input_width) + + w1lambda * inputData(pos1 + h1p * input_width + w1p)) + } + } + } + } + + private def areaPixelComputeScale( + inputSize: Int, outputSize: Int, alignCorners: Boolean): Float = { + if (alignCorners) { + (inputSize - 1).toFloat / (outputSize - 1) + } else { + (inputSize.toFloat) / outputSize + } + } + + private def areaPixelComputeSourceIndex( + scale: Float, dstIndex: Int, alignCorners : Boolean) : Float = { + if (alignCorners) { + scale * dstIndex + } else { + val srcIdx = scale * (dstIndex + 0.5f) - 0.5f + if (srcIdx < 0) 0.0f else srcIdx + } + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala index 7603ce4f654..4d8a591ebff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala @@ -53,13 +53,14 @@ class BoxHead( val features = Input() val proposals = Input() + val imageInfo = Input() val boxFeatures = featureExtractor.inputs(features, proposals) val classLogits = clsPredictor.inputs(boxFeatures) val boxRegression = bboxPredictor.inputs(boxFeatures) - val result = postProcessor.inputs(classLogits, boxRegression, proposals) + val result = postProcessor.inputs(classLogits, boxRegression, proposals, imageInfo) - Graph(Array(features, proposals), Array(boxFeatures, result)) + Graph(Array(features, proposals, imageInfo), Array(boxFeatures, result)) } private[nn] def clsPredictor(numClass: Int, @@ -287,6 +288,7 @@ private[nn] class BoxPostProcessor( val bbox = if (input(3).isInstanceOf[Tensor[Float]]) { T(input[Tensor[Float]](3)) } else input[Table](3) + val imageInfo = input[Tensor[Float]](4) // height & width val boxesInImage = new Array[Int](bbox.length()) for (i <- 0 to boxesInImage.length - 1) { @@ -306,6 +308,8 @@ private[nn] class BoxPostProcessor( val classProb = softMax.forward(classLogits) BboxUtil.decodeWithWeight(boxRegression, concatBoxes, weight, boxesBuf) + // clip to images + BboxUtil.clipBoxes(boxesBuf, imageInfo.valueAt(1), imageInfo.valueAt(2)) if (output.toTable.length() == 0) { output.toTable(1) = Tensor[Float]() // for labels diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala index 45941e65ea9..f4fba43f51d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala @@ -276,7 +276,7 @@ private[nn] class ProposalPostProcessor( val anchors = input[Tensor[Float]](1) var objectness = input[Tensor[Float]](2) var boxRegression = input[Tensor[Float]](3) - val imageSize = input[Tensor[Float]](4) // original image height & width + val imageSize = input[Tensor[Float]](4) // image height & width val N = objectness.size(1) // batch size val A = objectness.size(2) // anchor number @@ -323,7 +323,7 @@ private[nn] class ProposalPostProcessor( val proposals = BboxUtil.bboxTransformInv(anchorsIndex, boxRegressionIndex, normalized = true) - // remove _small box + // remove _small box and clip to images val minBoxH = minSize val minBoxW = minSize var keepN = BboxUtil.clipBoxes(proposals, imageSize.valueAt(1), imageSize.valueAt(2), minBoxH diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala index 38ce1d2a687..5f2c7f8c446 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/Table.scala @@ -135,7 +135,10 @@ class Table private[bigdl]( return false } this.state.keys.foreach(key => { - if (this.state(key) != other.state(key)) { + if (this.state(key).isInstanceOf[Array[_]] && other.state(key).isInstanceOf[Array[_]]) { + return (this.state(key).asInstanceOf[Array[_]].deep == + other.state(key).asInstanceOf[Array[_]].deep) + } else if (this.state(key) != other.state(key)) { return false } }) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala index bac36b5fed7..276962d6178 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala @@ -16,10 +16,13 @@ package com.intel.analytics.bigdl.models.maskrcnn +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.dataset.segmentation.RLEMasks import com.intel.analytics.bigdl.nn.Nms import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest -import com.intel.analytics.bigdl.utils.{RandomGenerator, T} +import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} import org.scalatest.{FlatSpec, Matchers} class MaskRCNNSpec extends FlatSpec with Matchers { @@ -30,7 +33,63 @@ class MaskRCNNSpec extends FlatSpec with Matchers { val mask = new MaskRCNN(resNetOutChannels, backboneOutChannels) mask.evaluate() val input = Tensor[Float](1, 3, 224, 256).rand() - val output = mask.forward(input) + val output = mask.forward(T(input, Tensor[Float](T(T(224f, 256f, 224f, 256f))))) + } + + "build maskrcnn with batch size > 1" should "be ok" in { + RandomGenerator.RNG.setSeed(1) + val resNetOutChannels = 32 + val backboneOutChannels = 32 + val mask = new MaskRCNN(resNetOutChannels, backboneOutChannels) + mask.evaluate() + val maskBatch = mask.asInstanceOf[Module[Float]].cloneModule() + maskBatch.evaluate() + val mask3 = mask.asInstanceOf[Module[Float]].cloneModule() + mask3.evaluate() + + val input1 = Tensor[Float](1, 3, 224, 256).rand() + val input2 = Tensor[Float](1, 3, 224, 256).rand() + + val input = Tensor[Float](2, 3, 224, 256) + input.narrow(1, 1, 1).copy(input1) + input.narrow(1, 2, 1).copy(input2) + + val output1 = mask.forward(T(input1, + Tensor[Float](T(T(224f, 256f, 224f, 256f))))).toTable[Table](1) + val output2 = mask3.forward(T(input2, + Tensor[Float](T(T(224f, 256f, 224f, 256f))))).toTable[Table](1) + val output = maskBatch.forward(T(input, + Tensor[Float](T(T(224f, 256f, 224f, 256f), T(224f, 256f, 224f, 256f))))).toTable + val first = output[Table](1) + val second = output[Table](2) + + first.get[Tensor[Float]](RoiLabel.BBOXES) should be( + output1.get[Tensor[Float]](RoiLabel.BBOXES)) + first.get[Tensor[Float]](RoiLabel.CLASSES) should be( + output1.get[Tensor[Float]](RoiLabel.CLASSES)) + first.get[Tensor[Float]](RoiLabel.SCORES) should be( + output1.get[Tensor[Float]](RoiLabel.SCORES)) + + second.get[Tensor[Float]](RoiLabel.BBOXES) should be( + output2.get[Tensor[Float]](RoiLabel.BBOXES)) + second.get[Tensor[Float]](RoiLabel.CLASSES) should be( + output2.get[Tensor[Float]](RoiLabel.CLASSES)) + second.get[Tensor[Float]](RoiLabel.SCORES) should be( + output2.get[Tensor[Float]](RoiLabel.SCORES)) + + // for masks + val firstMasks = first.get[Array[RLEMasks]](RoiLabel.MASKS).get + val expectedMasks = output1.get[Array[RLEMasks]](RoiLabel.MASKS).get + for (i <- 0 to firstMasks.length - 1) { + firstMasks(i).counts should be(expectedMasks(i).counts) + } + + val secondMasks = second.get[Array[RLEMasks]](RoiLabel.MASKS).get + val expectedMasks2 = output2.get[Array[RLEMasks]](RoiLabel.MASKS).get + + for (i <- 0 to secondMasks.length - 1) { + secondMasks(i).counts should be(expectedMasks2(i).counts) + } } "NMS" should "be ok" in { @@ -158,6 +217,253 @@ class MaskRCNNSpec extends FlatSpec with Matchers { require(expectedOutput.contains(inds(i) - 1), s"${i} ${inds(i)}") } } + + "NMS test with " should "be ok" in { + val bbox = Tensor[Float](T( + T(897.1850, 313.4036, 932.1763, 374.2394), + T(455.7833, 333.2753, 500.8198, 415.9607), + T(359.4648, 344.7227, 419.7825, 415.5826), + T(896.3477, 313.1893, 932.4266, 373.6151), + T(453.1522, 334.3315, 501.1705, 421.0176), + T(897.9015, 313.4834, 931.5834, 372.5941), + T(896.4880, 313.9242, 931.5134, 375.4740), + T(868.6584, 330.6160, 911.9927, 384.6833), + T(942.7654, 292.9069, 999.7523, 358.1204), + T(928.7173, 290.2841, 1019.7722, 345.7566), + T(993.7571, 297.5816, 1018.6810, 345.1978), + T(888.2090, 314.3195, 929.4616, 381.5802), + T(889.7837, 313.1184, 928.6500, 372.4649), + T(980.4796, 253.2323, 992.5759, 278.4875), + T(868.4745, 334.2823, 909.0101, 385.3784), + T(895.6448, 313.4649, 931.4463, 372.2554), + T(913.7177, 341.4454, 1000.9200, 385.1247), + T(984.8840, 252.4099, 994.6163, 279.6371), + T(940.8889, 296.0774, 997.9278, 359.2623), + T(894.1754, 314.3835, 931.2900, 378.0296), + T(932.7524, 291.2802, 997.4014, 358.1486), + T(946.0168, 294.9995, 988.2959, 353.5110), + T(974.5388, 254.3933, 988.2365, 277.1239), + T(925.1069, 338.8027, 997.5690, 384.6586), + T(995.1877, 297.3512, 1019.7518, 343.0744), + T(985.8417, 252.9171, 995.6226, 281.1850), + T(975.4414, 254.2575, 987.0934, 275.7443), + T(896.0717, 313.9886, 931.0652, 377.5052), + T(867.7359, 337.7741, 907.4892, 386.2584), + T(896.1373, 313.2400, 931.5672, 374.0488), + T(938.9003, 295.1502, 997.1716, 361.1665), + T(982.5619, 252.5668, 994.3489, 276.8817), + T(896.9540, 314.2319, 932.6215, 375.3799), + T(933.0647, 292.4633, 1006.3927, 353.1749), + T(987.3625, 252.8081, 996.3571, 280.9350), + T(455.1857, 334.0815, 502.3899, 415.9973), + T(974.3162, 254.5754, 989.2289, 277.5487), + T(873.7986, 333.4843, 909.5336, 384.7832), + T(994.9200, 297.4040, 1019.5212, 344.3844), + T(977.9858, 253.4250, 990.1346, 275.3441), + T(897.6171, 313.9396, 930.9672, 363.0933), + T(972.5175, 302.8481, 1003.7957, 352.4216), + T(952.8575, 314.9540, 996.2098, 360.9853), + T(897.7755, 312.8476, 932.1740, 375.4126), + T(935.5133, 308.0755, 999.0497, 368.1589), + T(896.4603, 314.3011, 933.0221, 374.9068), + T(946.8304, 296.2131, 984.3329, 345.4001), + T(974.9713, 254.1632, 985.7785, 273.4439), + T(921.4911, 284.3484, 988.0775, 343.6074), + T(453.4486, 334.5969, 501.3417, 416.7791), + T(879.2617, 324.7776, 913.9814, 380.1909), + T(896.1531, 315.2972, 929.5491, 377.5840), + T(976.2934, 254.3697, 992.0207, 281.3517), + T(359.7283, 345.4523, 422.3232, 416.0405), + T(987.8149, 253.1223, 996.3849, 282.5437), + T(977.9693, 308.9249, 1004.4815, 351.7527), + T(934.2255, 295.4119, 997.7376, 360.3417), + T(983.8524, 252.6548, 995.5056, 282.6053), + T(992.8503, 299.1681, 1019.4303, 346.0417), + T(926.0668, 288.1923, 1005.9279, 360.2895), + T(921.8798, 283.3901, 958.5684, 335.0197), + T(892.4288, 316.7297, 924.1523, 377.9731), + T(865.8591, 336.5531, 912.1065, 386.3830), + T(898.3209, 313.6562, 933.8464, 375.7697), + T(949.4941, 295.6840, 981.8075, 335.6603), + T(944.0931, 295.7336, 1000.0449, 358.7041), + T(893.5613, 314.3767, 929.3250, 361.8053), + T(897.1752, 314.2693, 930.7831, 370.4089), + T(986.4834, 252.4445, 996.8439, 281.0986), + T(966.5795, 303.4845, 999.5891, 344.3765), + T(359.3189, 344.2225, 422.1163, 416.8134), + T(980.1839, 252.5571, 993.9897, 272.0330), + T(985.8586, 252.1824, 997.0685, 282.9771), + T(950.0735, 304.3362, 997.5048, 360.0750), + T(949.5023, 298.7940, 999.4456, 359.3679), + T(984.8854, 251.3433, 995.3776, 277.1697), + T(878.3315, 323.2667, 920.9296, 384.7577), + T(866.8826, 337.7082, 907.4388, 386.9977), + T(930.4151, 286.1067, 996.6746, 346.9260), + T(449.7876, 335.0375, 501.5532, 424.3545), + T(970.6074, 296.2614, 1015.4398, 349.3472), + T(936.3362, 299.4994, 999.0040, 360.4084), + T(887.7698, 316.0671, 921.5828, 375.5379), + T(866.1887, 327.5018, 913.1649, 385.5780), + T(451.1341, 334.8470, 501.5725, 420.2753), + T(966.8165, 299.6295, 1001.6013, 350.6080), + T(929.5203, 292.7051, 961.2527, 342.3767), + T(985.9116, 252.4055, 995.6019, 278.5398), + T(928.5327, 317.6143, 998.8132, 375.1267), + T(924.2203, 286.4263, 964.5058, 340.0036), + T(993.9672, 298.4504, 1019.6760, 344.0639), + T(993.6530, 298.9571, 1018.6897, 344.1803), + T(357.6289, 347.2077, 427.7265, 416.1507), + T(975.6861, 309.5988, 1001.9472, 345.2947), + T(1052.2827, 306.5223, 1063.3223, 337.4540), + T(893.9320, 313.9812, 931.5121, 378.7151), + T(950.3990, 295.4264, 1002.1595, 355.8525), + T(927.2559, 289.3035, 998.7040, 362.0621), + T(973.4485, 307.2058, 998.9310, 339.6187), + T(865.3060, 335.3534, 912.1841, 386.1739), + T(872.6038, 336.4193, 910.7700, 385.0566), + T(871.1727, 318.4342, 923.3215, 383.5176), + T(923.0536, 282.2944, 972.0072, 336.2596), + T(985.0390, 308.6352, 1010.7421, 350.6218), + T(444.0601, 336.1603, 500.7296, 423.0992), + T(869.2928, 332.5573, 910.4033, 384.0341), + T(986.0456, 251.7105, 996.0561, 275.1382), + T(945.4684, 298.7881, 994.5966, 358.8904), + T(883.4898, 331.6453, 910.9511, 379.8238), + T(940.1200, 293.8811, 1005.2361, 354.3233), + T(954.6428, 296.6301, 979.5766, 326.1757), + T(964.2259, 293.8177, 1016.9499, 345.5342), + T(949.8438, 294.3049, 992.1930, 348.0120), + T(994.1414, 297.6946, 1019.7372, 344.3959), + T(944.2752, 296.6947, 983.6104, 344.6640), + T(922.5219, 285.9380, 957.7973, 338.0446), + T(354.8602, 349.5603, 429.7922, 416.0119), + T(359.9245, 345.8270, 424.7833, 416.7082), + T(896.8448, 313.5126, 930.7410, 371.4969), + T(899.2472, 311.8966, 931.7090, 367.2727), + T(916.7671, 314.6682, 996.5461, 384.1572), + T(897.3294, 313.7223, 930.9153, 366.3692), + T(955.1675, 296.3772, 995.6541, 341.4865), + T(988.6592, 254.4973, 997.6077, 283.3870), + T(958.2998, 318.5701, 996.8839, 360.5596), + T(878.6404, 312.4912, 939.2751, 382.1180), + T(942.3732, 299.2073, 996.5104, 347.8272), + T(945.8544, 305.3195, 998.3005, 360.8294), + T(867.2707, 336.2115, 910.3326, 386.3813), + T(989.5474, 255.2382, 999.1593, 282.3305), + T(948.8654, 297.4831, 1000.3220, 358.7383), + T(959.0654, 297.3557, 997.5435, 337.0765), + T(986.8717, 297.8730, 1021.5273, 346.7177), + T(923.0396, 284.0523, 967.0013, 338.6024), + T(920.8279, 283.3512, 966.9508, 337.6205), + T(975.0892, 253.6959, 987.7636, 270.7398), + T(983.1747, 252.4163, 993.9336, 280.0854), + T(897.1261, 312.8062, 931.5692, 365.5327), + T(925.8576, 282.2936, 989.2410, 340.9687), + T(457.6447, 333.8348, 502.1255, 419.0621), + T(929.3680, 317.9347, 1000.9109, 378.6516), + T(931.9888, 292.2040, 1014.3851, 351.3676), + T(939.6970, 325.0891, 1002.1588, 377.7377), + T(937.0275, 294.9764, 1000.0521, 359.9520), + T(361.4387, 344.3737, 418.4546, 417.0056), + T(935.3657, 295.6170, 1001.8279, 357.1074), + T(447.8221, 333.7355, 500.2914, 423.8980), + T(358.5627, 348.3210, 426.6114, 416.0293), + T(942.4774, 294.9196, 996.1514, 360.9478), + T(355.6061, 347.0658, 423.3835, 415.2331), + T(897.5903, 313.1249, 932.2655, 373.6089), + T(357.3052, 345.8806, 428.0344, 418.0151), + T(360.9688, 345.8139, 423.1559, 413.8298), + T(358.0542, 344.5368, 422.1435, 415.4480), + T(986.8827, 296.2814, 1030.8202, 344.9389), + T(869.0630, 334.7263, 913.7510, 386.4895), + T(449.1287, 333.6480, 505.2426, 424.2687), + T(921.8153, 329.4345, 992.2134, 385.6496), + T(359.5635, 344.9244, 423.2573, 415.3024), + T(878.1603, 312.8528, 928.2896, 383.3929), + T(872.1131, 324.2969, 917.3246, 384.7457), + T(897.4950, 318.9093, 940.0261, 381.3441), + T(448.2094, 334.0672, 501.8153, 423.0515), + T(929.2242, 293.8395, 1000.8837, 352.6609), + T(451.7765, 334.3492, 501.5195, 418.6037), + T(934.4990, 289.2999, 1014.6516, 348.2116), + T(889.9292, 312.7710, 935.9241, 376.5245), + T(357.8701, 345.1031, 418.5174, 415.8235), + T(454.7349, 333.8158, 500.2321, 414.6725), + T(926.7469, 295.1546, 1001.5960, 361.4129), + T(947.5048, 293.6343, 999.5144, 359.6602), + T(357.0127, 346.7641, 437.0735, 436.5526), + T(359.8571, 344.0298, 424.8551, 413.9603), + T(888.2206, 312.1265, 946.9496, 365.7358), + T(361.8871, 346.2571, 425.2443, 415.1584), + T(931.9264, 344.0161, 1001.4952, 384.5714), + T(935.9602, 307.9165, 1000.4966, 363.4359), + T(449.1622, 335.5356, 501.0027, 425.3539), + T(939.4246, 289.7769, 998.1415, 365.2235), + T(937.6185, 298.4802, 1001.5556, 360.3358), + T(913.2161, 300.2504, 997.0823, 371.8651), + T(925.2327, 286.6145, 998.6547, 360.5739), + T(452.0296, 333.0158, 502.7156, 423.5693), + T(956.8554, 294.1949, 1004.6817, 360.6414), + T(990.3675, 296.7340, 1020.4952, 347.9465), + T(436.7827, 333.2799, 499.7540, 428.1917), + T(354.7817, 344.2999, 422.8938, 429.8361), + T(445.9945, 332.3218, 504.5183, 419.6527), + T(356.9930, 345.0077, 422.6898, 416.8002), + T(359.9024, 347.1724, 447.2239, 438.6215), + T(930.1599, 288.2958, 1007.5668, 367.4672), + T(890.3512, 307.0296, 986.4042, 383.4467))) + + val scores = Tensor[Float]( + T(0.8895, 0.9511, 0.9799, 0.9506, 0.9808, 0.9182, 0.8226, 0.2990, 0.8350, + 0.3171, 0.8467, 0.6840, 0.2517, 0.2627, 0.3000, 0.8631, 0.0790, 0.5911, + 0.7802, 0.8842, 0.5869, 0.6082, 0.4752, 0.0886, 0.6948, 0.6305, 0.4881, + 0.7345, 0.1136, 0.9514, 0.6845, 0.1704, 0.8708, 0.5591, 0.6080, 0.9622, + 0.4447, 0.3963, 0.5799, 0.0939, 0.5659, 0.1663, 0.4193, 0.7579, 0.3835, + 0.9022, 0.4478, 0.4581, 0.2037, 0.8378, 0.2552, 0.3402, 0.0867, 0.9663, + 0.3352, 0.1342, 0.6891, 0.2075, 0.4518, 0.3642, 0.0553, 0.2398, 0.1638, + 0.4666, 0.4430, 0.7205, 0.0781, 0.9210, 0.4735, 0.0672, 0.9619, 0.0522, + 0.3523, 0.6908, 0.6146, 0.2338, 0.2402, 0.1276, 0.3867, 0.7665, 0.2867, + 0.6170, 0.3110, 0.5327, 0.9125, 0.1714, 0.0521, 0.5585, 0.1243, 0.0681, + 0.6715, 0.5854, 0.3556, 0.0916, 0.0519, 0.7547, 0.5319, 0.4566, 0.0615, + 0.2157, 0.1761, 0.5554, 0.0843, 0.0555, 0.5980, 0.4277, 0.1303, 0.8261, + 0.2421, 0.7401, 0.1352, 0.1726, 0.4677, 0.6657, 0.4990, 0.1112, 0.1743, + 0.9252, 0.8494, 0.4821, 0.3603, 0.7493, 0.3581, 0.0843, 0.1877, 0.0510, + 0.6207, 0.4427, 0.1903, 0.0574, 0.7567, 0.1311, 0.3934, 0.1065, 0.0734, + 0.1276, 0.3197, 0.7413, 0.0748, 0.8815, 0.1857, 0.1483, 0.0995, 0.7282, + 0.9192, 0.6015, 0.6803, 0.0685, 0.7498, 0.2033, 0.8497, 0.6608, 0.9190, + 0.8556, 0.1348, 0.1649, 0.4675, 0.0945, 0.9043, 0.0679, 0.3472, 0.0681, + 0.5856, 0.5952, 0.7874, 0.3340, 0.3464, 0.9608, 0.9078, 0.1791, 0.8079, + 0.0590, 0.1971, 0.0504, 0.8636, 0.0506, 0.2310, 0.5520, 0.5228, 0.2222, + 0.2537, 0.3059, 0.6870, 0.2897, 0.4688, 0.1099, 0.0970, 0.1799, 0.8663, + 0.0548, 0.0747, 0.1079)) + + val index = Array[Int]( + 1, 2, 6, 9, 11, 12, 14, 18, 20, 21, 22, 23, 24, 26, + 31, 33, 34, 38, 39, 40, 41, 45, 46, 51, 55, 57, 60, 61, + 63, 66, 70, 75, 76, 79, 82, 83, 84, 85, 87, 91, 94, 99, + 102, 105, 107, 110, 113, 121, 130, 134, 136, 140, 145, 146, 159, 161, + 165, 166, 169, 174, 175, 177, 178, 181, 190, 196, 197, 198, 207, 211, + 214, 215, 219, 222, 223, 224, 225, 228, 230, 244, 257, 260, 262, 266, + 270, 273, 277, 282, 284, 285, 293, 295, 297, 302, 308, 315, 316, 329, + 330, 333, 334, 337, 341, 342, 343, 346, 351, 360, 361, 362, 372, 377, + 383, 395, 401, 403, 405, 407, 408, 415, 417, 418, 422, 429, 437, 439, + 445, 449, 455, 457, 459, 476, 482, 485, 490, 492, 495, 498, 506, 512, + 531, 536, 538, 552, 554, 559, 561, 563, 564, 567, 568, 578, 579, 592, + 595, 598, 604, 608, 623, 631, 636, 637, 639, 646, 652, 659, 667, 680, + 696, 698, 713, 714, 739, 744, 755, 760, 773, 776, 780, 786, 804, 824, + 842, 850, 862, 878, 908, 923, 954, 957, 958, 995) + + val expectedOut = Array[Float](2, 4, 8, 10, 13, 25, 26, 29, 42, + 48, 55, 64, 77, 80, 94, 98, 101, 108, 115, 120, 129, 131, 175) + + val nmsTool: Nms = new Nms + val out = nmsTool.nms(scores, bbox, 0.5f, index, orderWithBBox = true) + + out should be(expectedOut.length) + for (i <- 0 to (out - 1)) { + index(i) should be(expectedOut(i) + 1) + } + } } class MaskRCNNSerialTest extends ModuleSerializationTest { @@ -166,8 +472,8 @@ class MaskRCNNSerialTest extends ModuleSerializationTest { val backboneOutChannels = 32 val mask = new MaskRCNN(resNetOutChannels, backboneOutChannels).setName("MaskRCNN") mask.evaluate() - val input = Tensor[Float](1, 3, 224, 256).rand() - val output = mask.forward(input) + val input = T(Tensor[Float](1, 3, 224, 256).rand(), + Tensor[Float](T(T(224f, 256f, 224f, 256f)))) runSerializationTest(mask, input) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/UtilsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/UtilsSpec.scala new file mode 100644 index 00000000000..079222e8939 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/UtilsSpec.scala @@ -0,0 +1,1164 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.models.maskrcnn + +import com.intel.analytics.bigdl.dataset.segmentation.MaskUtils +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} + +class UtilsSpec extends FlatSpec with Matchers { + "expandBoxes" should "be ok" in { + val bbox = Tensor[Float](T(415.3202, 176.3966, 441.3445, 251.7300)) + val bboxExpand = Tensor[Float](4) + Utils.expandBoxes(bbox, bboxExpand, 1.0714285714285714f) + val expectedBBox = Tensor[Float](T(414.3907, 173.7061, 442.2739, 254.4204)) + bboxExpand should be(expectedBBox) + } + + "expandMask" should "be ok" in { + val mask = Tensor[Float](T(T( + T(4.6341e-04, 3.0892e-04, 5.5805e-05, 5.3648e-05, 3.8348e-05, + 4.0923e-05, 4.0874e-05, 4.9342e-05, 1.4934e-04, 2.3377e-04, + 9.2455e-04, 1.9640e-03, 6.7012e-03, 1.1700e-02, 2.2868e-02, + 3.4102e-02, 5.0373e-02, 8.3922e-02, 1.3713e-01, 2.3499e-01, + 3.7590e-01, 4.8422e-01, 5.4733e-01, 4.8918e-01, 3.3834e-01, + 2.3584e-01, 1.9745e-01, 1.6000e-01), + T(4.2732e-04, 2.9206e-04, 6.0863e-05, 6.0809e-05, 4.2969e-05, + 4.7592e-05, 4.0687e-05, 5.4618e-05, 1.4295e-04, 2.6305e-04, + 8.7722e-04, 2.1579e-03, 6.4814e-03, 1.2199e-02, 2.1211e-02, + 3.3435e-02, 5.0712e-02, 9.8141e-02, 1.7841e-01, 3.3267e-01, + 4.7087e-01, 5.6134e-01, 5.4585e-01, 4.3256e-01, 2.2894e-01, + 1.4579e-01, 1.1617e-01, 9.4591e-02), + T(7.4229e-05, 4.9629e-05, 1.1812e-05, 1.1427e-05, 1.3852e-05, + 1.4571e-05, 1.6429e-05, 2.0885e-05, 7.9878e-05, 1.4078e-04, + 6.3389e-04, 1.6315e-03, 4.8276e-03, 9.6609e-03, 1.8357e-02, + 2.9629e-02, 6.4242e-02, 1.3947e-01, 2.9693e-01, 5.0719e-01, + 6.8341e-01, 7.3825e-01, 7.0431e-01, 5.7563e-01, 3.4927e-01, + 2.0407e-01, 1.4150e-01, 1.1583e-01), + T(8.0966e-05, 5.7955e-05, 1.3771e-05, 1.4560e-05, 1.7485e-05, + 2.0353e-05, 1.9813e-05, 3.0120e-05, 8.6005e-05, 1.9037e-04, + 6.0123e-04, 1.7777e-03, 4.5884e-03, 1.0188e-02, 1.9338e-02, + 3.4767e-02, 6.8911e-02, 1.6141e-01, 3.1799e-01, 5.5213e-01, + 6.9518e-01, 7.3970e-01, 6.8841e-01, 5.5608e-01, 3.4583e-01, + 2.1902e-01, 1.3583e-01, 1.0586e-01), + T(3.6981e-05, 2.3346e-05, 7.2523e-06, 6.9358e-06, 1.3612e-05, + 1.5485e-05, 2.5761e-05, 3.4787e-05, 1.2198e-04, 2.2105e-04, + 6.6305e-04, 1.7468e-03, 4.2893e-03, 9.8238e-03, 2.0705e-02, + 3.8232e-02, 8.9242e-02, 2.1058e-01, 4.1479e-01, 6.3925e-01, + 7.2720e-01, 7.2505e-01, 6.1903e-01, 5.4229e-01, 3.9588e-01, + 2.4779e-01, 1.7973e-01, 1.3406e-01), + T(4.8939e-05, 3.4500e-05, 1.0843e-05, 1.2283e-05, 2.0171e-05, + 2.7103e-05, 3.6343e-05, 6.3062e-05, 1.7241e-04, 4.1421e-04, + 8.6098e-04, 2.7469e-03, 5.3915e-03, 1.4367e-02, 2.7193e-02, + 5.7192e-02, 1.1829e-01, 2.8904e-01, 4.8999e-01, 6.8236e-01, + 6.9587e-01, 6.6666e-01, 5.4644e-01, 5.2509e-01, 4.7553e-01, + 3.8484e-01, 3.1775e-01, 2.3912e-01), + T(6.2084e-05, 3.9022e-05, 1.4060e-05, 1.4152e-05, 2.7991e-05, + 3.2111e-05, 6.1916e-05, 8.8266e-05, 3.1988e-04, 6.1125e-04, + 1.5244e-03, 3.8437e-03, 9.7983e-03, 2.1962e-02, 5.3569e-02, + 1.0839e-01, 2.3466e-01, 4.2930e-01, 6.5016e-01, 7.1807e-01, + 7.1723e-01, 6.6036e-01, 6.6802e-01, 6.3845e-01, 6.4161e-01, + 6.2048e-01, 5.2979e-01, 4.4045e-01), + T(9.1347e-05, 6.4619e-05, 2.5321e-05, 3.1816e-05, 5.1436e-05, + 7.6462e-05, 1.1214e-04, 2.2407e-04, 6.1354e-04, 1.5753e-03, + 2.8594e-03, 8.4160e-03, 1.7025e-02, 4.0972e-02, 9.4575e-02, + 1.9026e-01, 3.7013e-01, 5.5772e-01, 6.8750e-01, 7.2380e-01, + 6.9485e-01, 6.5762e-01, 6.7078e-01, 6.5950e-01, 6.4230e-01, + 6.2810e-01, 5.1840e-01, 4.1623e-01), + T(8.7828e-05, 6.6269e-05, 3.6323e-05, 4.4775e-05, 1.1606e-04, + 1.4916e-04, 3.7401e-04, 6.2966e-04, 2.2745e-03, 4.3958e-03, + 8.7915e-03, 1.6948e-02, 3.0861e-02, 6.1891e-02, 1.3839e-01, + 2.8432e-01, 4.8105e-01, 6.2043e-01, 7.0035e-01, 7.0787e-01, + 7.0818e-01, 6.6805e-01, 6.9580e-01, 6.7569e-01, 6.0427e-01, + 5.3689e-01, 4.2307e-01, 2.7888e-01), + T(1.3902e-04, 1.1468e-04, 7.6237e-05, 1.1698e-04, 2.3893e-04, + 4.1147e-04, 8.1630e-04, 1.9367e-03, 5.3219e-03, 1.3054e-02, + 1.7966e-02, 3.7152e-02, 5.5747e-02, 1.1024e-01, 2.4388e-01, + 4.2369e-01, 5.9124e-01, 6.9195e-01, 7.2445e-01, 7.2798e-01, + 7.3068e-01, 7.0324e-01, 7.3541e-01, 7.3432e-01, 6.6021e-01, + 5.4254e-01, 2.8755e-01, 1.6474e-01), + T(2.8135e-04, 2.1398e-04, 2.1649e-04, 2.7242e-04, 7.0205e-04, + 9.5946e-04, 2.1251e-03, 3.9960e-03, 1.1550e-02, 1.8662e-02, + 2.4886e-02, 3.7426e-02, 5.9397e-02, 1.1954e-01, 2.3302e-01, + 3.8706e-01, 4.9049e-01, 5.7261e-01, 5.8114e-01, 5.4481e-01, + 6.3964e-01, 6.6289e-01, 7.0789e-01, 6.9167e-01, 5.9608e-01, + 3.2595e-01, 1.3987e-01, 6.1413e-02), + T(5.2232e-04, 4.4520e-04, 4.3554e-04, 7.0351e-04, 1.4864e-03, + 2.7261e-03, 5.5355e-03, 1.2103e-02, 2.5020e-02, 4.1414e-02, + 4.1824e-02, 6.2823e-02, 9.2860e-02, 1.6066e-01, 2.9213e-01, + 4.1854e-01, 4.8238e-01, 5.5534e-01, 5.4968e-01, 4.9130e-01, + 6.0384e-01, 6.1030e-01, 6.5871e-01, 6.2841e-01, 4.4143e-01, + 1.9048e-01, 7.4018e-02, 3.5332e-02), + T(1.4604e-03, 1.2570e-03, 1.9961e-03, 2.3598e-03, 5.9267e-03, + 8.0931e-03, 1.4601e-02, 2.0643e-02, 3.7449e-02, 4.3391e-02, + 6.2745e-02, 7.7446e-02, 1.2635e-01, 1.9466e-01, 2.7487e-01, + 3.7428e-01, 4.5982e-01, 4.7172e-01, 4.4790e-01, 3.9635e-01, + 4.8646e-01, 4.6517e-01, 4.6141e-01, 3.2603e-01, 1.8215e-01, + 7.3791e-02, 3.6567e-02, 2.0175e-02), + T(5.9303e-03, 6.6179e-03, 1.1214e-02, 1.6247e-02, 3.0351e-02, + 4.4109e-02, 6.2478e-02, 7.9755e-02, 9.6626e-02, 9.4567e-02, + 1.0874e-01, 1.1744e-01, 1.6492e-01, 2.2267e-01, 3.1359e-01, + 3.9987e-01, 4.4468e-01, 4.4507e-01, 4.1509e-01, 3.5315e-01, + 3.8601e-01, 3.3181e-01, 2.4343e-01, 1.3579e-01, 7.4115e-02, + 3.4047e-02, 2.3351e-02, 1.4313e-02), + T(2.1295e-02, 2.8350e-02, 5.5673e-02, 6.9987e-02, 9.1978e-02, + 1.3653e-01, 1.4478e-01, 1.6779e-01, 1.9329e-01, 1.7208e-01, + 1.8805e-01, 1.9608e-01, 2.2581e-01, 2.9534e-01, 3.1215e-01, + 3.4894e-01, 2.7688e-01, 2.3820e-01, 2.2527e-01, 1.9959e-01, + 1.8015e-01, 1.4319e-01, 8.3902e-02, 5.3386e-02, 3.3750e-02, + 1.8162e-02, 1.8458e-02, 1.2847e-02), + T(3.0469e-02, 3.5307e-02, 7.7832e-02, 9.4903e-02, 1.5087e-01, + 2.1573e-01, 2.9154e-01, 3.1367e-01, 3.8083e-01, 3.6850e-01, + 4.0402e-01, 4.5041e-01, 4.5721e-01, 5.5601e-01, 5.2863e-01, + 5.4976e-01, 4.0354e-01, 3.3515e-01, 3.0731e-01, 2.5419e-01, + 2.1781e-01, 1.6374e-01, 1.2298e-01, 7.7298e-02, 4.6294e-02, + 2.3752e-02, 2.0401e-02, 1.2909e-02), + T(2.9071e-02, 4.1723e-02, 7.9149e-02, 1.4050e-01, 2.9306e-01, + 5.1104e-01, 6.6809e-01, 7.1677e-01, 7.6009e-01, 7.6077e-01, + 7.7978e-01, 8.0736e-01, 8.2089e-01, 8.5746e-01, 8.5850e-01, + 8.6628e-01, 8.0307e-01, 7.8278e-01, 6.9163e-01, 6.8256e-01, + 6.7340e-01, 5.7951e-01, 5.1666e-01, 3.2986e-01, 1.6927e-01, + 5.2513e-02, 3.0492e-02, 1.5463e-02), + T(3.4533e-02, 4.8020e-02, 1.2032e-01, 2.4137e-01, 6.0096e-01, + 8.0251e-01, 8.8464e-01, 9.0904e-01, 9.1911e-01, 9.2781e-01, + 9.3747e-01, 9.4583e-01, 9.5007e-01, 9.5779e-01, 9.6114e-01, + 9.6043e-01, 9.5404e-01, 9.4383e-01, 9.2775e-01, 9.2331e-01, + 9.0956e-01, 8.8537e-01, 8.2962e-01, 7.0801e-01, 2.9882e-01, + 9.1867e-02, 3.4787e-02, 1.6245e-02), + T(3.4896e-02, 5.6930e-02, 1.4412e-01, 3.6995e-01, 7.1463e-01, + 8.7814e-01, 9.2850e-01, 9.3653e-01, 9.4908e-01, 9.5386e-01, + 9.6158e-01, 9.6472e-01, 9.6320e-01, 9.6544e-01, 9.6580e-01, + 9.6449e-01, 9.5944e-01, 9.5484e-01, 9.5333e-01, 9.4281e-01, + 9.5893e-01, 9.4436e-01, 9.0984e-01, 7.7387e-01, 3.1403e-01, + 7.1007e-02, 2.1945e-02, 1.0809e-02), + T(4.4868e-02, 7.4989e-02, 2.1560e-01, 5.2291e-01, 8.1661e-01, + 9.1452e-01, 9.4504e-01, 9.5013e-01, 9.5396e-01, 9.5815e-01, + 9.6116e-01, 9.6114e-01, 9.5929e-01, 9.5765e-01, 9.6087e-01, + 9.5712e-01, 9.5256e-01, 9.4704e-01, 9.4440e-01, 9.3279e-01, + 9.6347e-01, 9.5107e-01, 9.1002e-01, 7.5917e-01, 2.1720e-01, + 5.6064e-02, 2.3498e-02, 1.1083e-02), + T(7.7550e-02, 1.3681e-01, 3.2038e-01, 6.3215e-01, 8.2809e-01, + 8.9066e-01, 8.9183e-01, 8.8996e-01, 7.7995e-01, 7.8508e-01, + 7.6737e-01, 7.9057e-01, 7.3225e-01, 7.7120e-01, 7.2414e-01, + 7.4957e-01, 6.6325e-01, 6.9354e-01, 6.3172e-01, 6.5299e-01, + 8.4520e-01, 8.1888e-01, 7.9047e-01, 4.1030e-01, 1.1323e-01, + 4.6366e-02, 1.8671e-02, 1.2663e-02), + T(9.8263e-02, 1.8164e-01, 4.3265e-01, 7.3340e-01, 8.4517e-01, + 8.8330e-01, 8.7223e-01, 8.4796e-01, 7.0618e-01, 6.7429e-01, + 6.8330e-01, 6.5807e-01, 6.3468e-01, 6.2422e-01, 6.1901e-01, + 6.0265e-01, 5.3608e-01, 5.3792e-01, 5.1256e-01, 5.1631e-01, + 7.7521e-01, 7.3053e-01, 5.6897e-01, 1.8825e-01, 5.7340e-02, + 2.7946e-02, 1.9292e-02, 1.3273e-02), + T(1.2515e-01, 2.6012e-01, 5.8105e-01, 7.9504e-01, 8.7019e-01, + 8.9241e-01, 8.8640e-01, 8.9073e-01, 7.4305e-01, 7.6426e-01, + 7.1546e-01, 7.3403e-01, 6.5637e-01, 6.7678e-01, 6.9244e-01, + 6.9171e-01, 6.7243e-01, 6.8381e-01, 6.6941e-01, 6.9703e-01, + 8.1285e-01, 6.9987e-01, 4.5697e-01, 1.2757e-01, 3.5441e-02, + 2.6368e-02, 2.3072e-02, 2.6452e-02), + T(1.6706e-01, 3.4245e-01, 6.8553e-01, 8.3924e-01, 8.8395e-01, + 8.8617e-01, 8.8806e-01, 8.7181e-01, 7.5056e-01, 7.3379e-01, + 7.3424e-01, 7.1409e-01, 6.6733e-01, 6.5400e-01, 6.6427e-01, + 6.4249e-01, 5.9861e-01, 5.9251e-01, 6.1464e-01, 6.2991e-01, + 7.7426e-01, 6.3643e-01, 3.5219e-01, 9.7489e-02, 3.0105e-02, + 2.2138e-02, 2.0397e-02, 2.0346e-02), + T(2.6283e-01, 4.9972e-01, 7.8121e-01, 8.6183e-01, 8.5866e-01, + 8.7330e-01, 8.2221e-01, 8.3016e-01, 6.3395e-01, 6.6521e-01, + 7.3249e-01, 7.3808e-01, 7.5634e-01, 7.4819e-01, 7.0494e-01, + 6.7100e-01, 5.3396e-01, 5.4660e-01, 5.3508e-01, 5.7070e-01, + 7.2617e-01, 5.1958e-01, 2.5431e-01, 8.5448e-02, 4.2543e-02, + 3.6853e-02, 2.6910e-02, 3.0950e-02), + T(3.1833e-01, 5.4807e-01, 7.9297e-01, 8.5295e-01, 8.4800e-01, + 8.4096e-01, 8.0479e-01, 7.7077e-01, 5.9931e-01, 5.6310e-01, + 6.8010e-01, 6.3572e-01, 6.9430e-01, 6.4745e-01, 6.4169e-01, + 5.7040e-01, 5.1369e-01, 4.9333e-01, 5.4481e-01, 5.4363e-01, + 6.7796e-01, 4.3270e-01, 1.4516e-01, 5.0843e-02, 2.2773e-02, + 1.8443e-02, 1.5043e-02, 1.4516e-02), + T(3.4615e-01, 5.0847e-01, 7.2972e-01, 8.0083e-01, 7.5170e-01, + 7.4097e-01, 6.0599e-01, 5.7648e-01, 4.0951e-01, 3.9575e-01, + 4.2222e-01, 4.0706e-01, 3.9518e-01, 3.8708e-01, 3.6376e-01, + 3.6302e-01, 3.2241e-01, 3.7725e-01, 5.5631e-01, 5.7414e-01, + 6.3659e-01, 3.2865e-01, 7.5898e-02, 2.6716e-02, 1.3944e-02, + 1.1860e-02, 1.4716e-02, 1.7905e-02), + T(3.2063e-01, 4.5822e-01, 6.7359e-01, 7.3385e-01, 6.9173e-01, + 6.3816e-01, 5.5046e-01, 4.6633e-01, 3.6612e-01, 3.0594e-01, + 3.6013e-01, 3.0995e-01, 3.4988e-01, 3.1621e-01, 3.6424e-01, + 3.3739e-01, 3.6879e-01, 3.7739e-01, 5.5614e-01, 5.2886e-01, + 5.3811e-01, 2.6981e-01, 6.9401e-02, 2.5807e-02, 1.4949e-02, + 1.1628e-02, 1.7100e-02, 1.7004e-02)))) + + val expectedMask = Tensor[Float]( + T(T(T(0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00), + T(0.0000e+00, 4.6341e-04, 3.0892e-04, 5.5805e-05, 5.3648e-05, + 3.8348e-05, 4.0923e-05, 4.0874e-05, 4.9342e-05, 1.4934e-04, + 2.3377e-04, 9.2455e-04, 1.9640e-03, 6.7012e-03, 1.1700e-02, + 2.2868e-02, 3.4102e-02, 5.0373e-02, 8.3922e-02, 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9.8257e-01, + 8.8775e-01, 5.2885e-01, 2.2996e-01, 9.2814e-02, 0.0000e+00), + T(0.0000e+00, 4.1577e-02, 4.8213e-02, 5.6518e-02, 8.2797e-02, + 1.8809e-01, 2.1246e-01, 2.6011e-01, 2.8504e-01, 3.0879e-01, + 3.0685e-01, 2.9604e-01, 4.3665e-01, 7.0377e-01, 9.2423e-01, + 9.8351e-01, 9.9270e-01, 9.9652e-01, 9.9714e-01, 9.9812e-01, + 9.9798e-01, 9.9726e-01, 9.9656e-01, 9.9363e-01, 9.8390e-01, + 9.2150e-01, 7.0945e-01, 5.2447e-01, 2.6249e-01, 0.0000e+00), + T(0.0000e+00, 4.3041e-02, 5.7549e-02, 1.0371e-01, 2.0892e-01, + 2.8414e-01, 2.6340e-01, 1.5407e-01, 1.4225e-01, 2.2257e-01, + 2.2046e-01, 1.1707e-01, 1.8171e-01, 3.1578e-01, 6.1651e-01, + 8.3786e-01, 9.2774e-01, 9.5711e-01, 9.7792e-01, 9.9360e-01, + 9.9497e-01, 9.9584e-01, 9.9477e-01, 9.8817e-01, 9.6667e-01, + 8.9339e-01, 6.8557e-01, 5.0704e-01, 3.2797e-01, 0.0000e+00), + T(0.0000e+00, 4.9168e-02, 7.9762e-02, 1.4255e-01, 3.1908e-01, + 3.2118e-01, 2.9509e-01, 1.2394e-01, 1.1111e-01, 1.9526e-01, + 2.1016e-01, 9.9896e-02, 1.4676e-01, 2.0443e-01, 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8.5937e-01, + 4.5496e-01, 2.0237e-01, 1.0152e-01, 7.2687e-02, 0.0000e+00), + T(0.0000e+00, 4.9442e-02, 7.6667e-02, 7.3187e-02, 8.2513e-02, + 9.7021e-02, 9.1034e-02, 6.4151e-02, 7.2570e-02, 1.2189e-01, + 1.4677e-01, 5.4870e-02, 1.0266e-01, 1.3705e-01, 2.3842e-01, + 2.6176e-01, 3.3107e-01, 3.4387e-01, 5.5684e-01, 7.8205e-01, + 9.4111e-01, 9.7452e-01, 9.8105e-01, 9.5689e-01, 8.7585e-01, + 5.4566e-01, 2.6537e-01, 1.1464e-01, 7.2341e-02, 0.0000e+00), + T(0.0000e+00, 1.9823e-02, 2.3113e-02, 1.7871e-02, 1.9827e-02, + 2.3113e-02, 2.1277e-02, 1.5889e-02, 1.7244e-02, 3.0173e-02, + 4.5981e-02, 5.3338e-02, 1.1286e-01, 1.7230e-01, 2.6860e-01, + 2.8171e-01, 3.6020e-01, 3.5139e-01, 6.1736e-01, 7.8975e-01, + 9.4838e-01, 9.6199e-01, 9.7300e-01, 9.5711e-01, 9.2493e-01, + 7.2215e-01, 3.8161e-01, 1.6386e-01, 8.9880e-02, 0.0000e+00), + T(0.0000e+00, 1.1020e-02, 1.1338e-02, 7.6316e-03, 8.8145e-03, + 9.9691e-03, 1.0634e-02, 6.8242e-03, 7.4463e-03, 1.1914e-02, + 1.9584e-02, 3.6592e-02, 9.0816e-02, 1.4934e-01, 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0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00)))) + + val output = Tensor[Float](1, 40, 50) + Utils.bilinear(input, output) + + val expectedOut = Tensor[Float](T(T( + T(0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00), + T(0.0000e+00, 3.2404e-05, 8.1010e-05, 9.2271e-05, 8.9279e-05, + 5.7781e-05, 5.9528e-05, 7.7896e-05, 2.3313e-04, 5.0080e-04, + 9.9333e-04, 4.7100e-03, 1.0039e-02, 4.8233e-02, 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5.9343e-01, + 3.9146e-01, 2.4952e-01, 1.3760e-01, 5.5041e-02, 0.0000e+00), + T(0.0000e+00, 2.7776e-03, 6.9440e-03, 6.7437e-03, 5.8149e-03, + 3.4293e-03, 2.7060e-03, 2.8137e-03, 3.0493e-03, 3.3013e-03, + 3.5858e-03, 3.6133e-03, 3.5122e-03, 3.8683e-03, 4.4780e-03, + 5.5947e-03, 8.1167e-03, 1.1341e-02, 1.8543e-02, 3.0087e-02, + 5.0315e-02, 6.5920e-02, 7.9212e-02, 1.1876e-01, 1.4804e-01, + 1.5680e-01, 1.9185e-01, 2.4003e-01, 2.9753e-01, 3.7289e-01, + 4.8399e-01, 6.2297e-01, 7.7588e-01, 8.6631e-01, 9.2720e-01, + 9.2901e-01, 9.3938e-01, 9.5403e-01, 9.3906e-01, 9.2536e-01, + 9.1422e-01, 8.7700e-01, 8.2675e-01, 7.4306e-01, 6.2674e-01, + 4.4514e-01, 2.9681e-01, 1.6511e-01, 6.6046e-02, 0.0000e+00), + T(0.0000e+00, 3.6061e-03, 9.0151e-03, 7.9336e-03, 6.2377e-03, + 3.3131e-03, 2.1923e-03, 1.9735e-03, 1.8405e-03, 1.7306e-03, + 1.6668e-03, 1.6675e-03, 1.7004e-03, 1.6748e-03, 1.8974e-03, + 2.6162e-03, 3.9657e-03, 5.6305e-03, 1.1128e-02, 2.0203e-02, + 3.6430e-02, 4.7712e-02, 5.6522e-02, 8.8533e-02, 1.1054e-01, + 1.1256e-01, 1.2836e-01, 1.5106e-01, 2.6408e-01, 3.5831e-01, + 4.1497e-01, 5.3884e-01, 6.9632e-01, 7.9904e-01, 8.7306e-01, + 8.8971e-01, 9.0715e-01, 9.2498e-01, 9.0809e-01, 8.9378e-01, + 8.8465e-01, 8.5042e-01, 8.0365e-01, 7.4199e-01, 6.4905e-01, + 4.9355e-01, 3.5272e-01, 2.1921e-01, 8.7683e-02, 0.0000e+00), + T(0.0000e+00, 3.3314e-03, 8.3284e-03, 7.1207e-03, 5.3804e-03, + 2.5749e-03, 1.5147e-03, 1.3272e-03, 1.0936e-03, 9.1981e-04, + 8.6574e-04, 8.5662e-04, 8.6996e-04, 8.5916e-04, 1.0008e-03, + 1.4473e-03, 2.3075e-03, 3.3746e-03, 7.9505e-03, 1.5339e-02, + 2.8353e-02, 3.9975e-02, 5.0901e-02, 8.2517e-02, 1.0348e-01, + 1.0315e-01, 1.1696e-01, 1.3783e-01, 2.3885e-01, 3.2172e-01, + 3.6826e-01, 4.6231e-01, 5.8012e-01, 6.8432e-01, 7.6104e-01, + 7.8282e-01, 8.0598e-01, 8.2982e-01, 8.1701e-01, 8.0116e-01, + 7.7922e-01, 7.4308e-01, 6.9985e-01, 6.4089e-01, 5.6191e-01, + 4.4289e-01, 3.2862e-01, 2.1672e-01, 8.6689e-02, 0.0000e+00), + T(0.0000e+00, 1.9273e-03, 4.8183e-03, 4.0713e-03, 3.0149e-03, + 1.3398e-03, 7.2467e-04, 6.3961e-04, 4.7892e-04, 3.6899e-04, + 3.6062e-04, 3.5292e-04, 3.4556e-04, 3.6821e-04, 4.5058e-04, + 6.5238e-04, 1.0798e-03, 1.6201e-03, 4.2407e-03, 8.3808e-03, + 1.5560e-02, 2.3221e-02, 3.1123e-02, 5.1207e-02, 6.4374e-02, + 6.3710e-02, 7.2789e-02, 8.6739e-02, 1.4191e-01, 1.8826e-01, + 2.1696e-01, 2.6551e-01, 3.2398e-01, 3.8888e-01, 4.3702e-01, + 4.5163e-01, 4.6781e-01, 4.8477e-01, 4.7842e-01, 4.6797e-01, + 4.4933e-01, 4.2584e-01, 3.9992e-01, 3.6271e-01, 3.1712e-01, + 2.5478e-01, 1.9286e-01, 1.3114e-01, 5.2456e-02, 0.0000e+00), + T(0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00)))) + + output.almostEqual(expectedOut, 1e-4) should be(true) + } + + "bilinear with binary mask" should "be ok" in { + val input = Tensor[Float](T(T( + T(1.0f, 0.0f, 1.0f, 1.0f, 0.0f, 1.0f), + T(0.0, 1.0, 1.0, 1.0, 1.0, 0.0), + T(1.0, 1.0, 1.0, 0.0, 0, 0.0), + T(0.0, 0.0, 0.0, 0.0, 0.0, 1.0)))) + + val output = Tensor[Float](1, 5, 8) + Utils.bilinear(input, output) + + val expectedOut = Tensor[Float](T(T(T( + T(1.0000, 0.3750, 0.3750, 1.0000, 1.0000, 0.3750, 0.3750, 1.0000), + T(0.3000, 0.5500, 0.8125, 1.0000, 1.0000, 0.8125, 0.5500, 0.30000), + T(0.5000, 0.8125, 1.0000, 0.9375, 0.5625, 0.5000, 0.3125, 0.0000), + T(0.7000, 0.7000, 0.7000, 0.6125, 0.0875, 0.0000, 0.1125, 0.3000), + T(0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3750, 1.0000))))) + + output.almostEqual(expectedOut, 1e-6) should be(true) + } + + "binary to rle" should "be ok" in { + val mask = Tensor[Float](T(T(1, 0, 1, 1, 1), T(1, 0, 1, 1, 0))) + val out = MaskUtils.binaryToRLE(mask) + val expectedOut = Array(0, 2, 2, 5, 1) + out.counts should be(expectedOut) + } + + "decodeMaskInImage" should "be ok" in { + val mask = Tensor[Float](T(T( + T(1.7928e-04, 2.0692e-04, 1.1419e-04, 2.0437e-04, 4.1835e-04, 9.4657e-04, + 4.6943e-03, 1.4078e-02, 6.6533e-02, 2.0986e-01, 4.7807e-01, 6.9845e-01, + 8.3367e-01, 8.1354e-01, 6.1018e-01, 2.9873e-01, 1.1466e-01, 4.9374e-02, + 1.5091e-02, 5.1253e-03, 1.1708e-03, 5.3059e-04, 2.9873e-04, 1.7227e-04, + 1.4657e-04, 1.1615e-04, 2.4343e-04, 3.3061e-04), + T(2.0778e-04, 2.3388e-04, 1.9466e-04, 3.5014e-04, 7.1720e-04, 1.6670e-03, + 8.4499e-03, 3.2401e-02, 1.6663e-01, 5.3996e-01, 7.9149e-01, 8.9112e-01, + 9.3543e-01, 9.3029e-01, 8.4267e-01, 5.8298e-01, 2.7246e-01, 1.0355e-01, + 3.4595e-02, 1.0017e-02, 1.7930e-03, 7.9974e-04, 3.4362e-04, 2.0355e-04, + 1.6328e-04, 1.1402e-04, 2.4033e-04, 2.7334e-04), + T(9.5185e-05, 1.5875e-04, 2.2014e-04, 4.5034e-04, 1.5089e-03, 3.8962e-03, + 1.8745e-02, 8.5752e-02, 3.6358e-01, 7.8782e-01, 9.3798e-01, 9.6312e-01, + 9.7033e-01, 9.6447e-01, 9.3477e-01, 7.9702e-01, 4.8619e-01, 2.3604e-01, + 8.9071e-02, 2.2121e-02, 3.9173e-03, 1.1309e-03, 4.0723e-04, 1.8781e-04, + 8.3546e-05, 5.9426e-05, 6.1382e-05, 5.9903e-05), + T(1.6401e-04, 2.6043e-04, 4.4878e-04, 9.2276e-04, 2.7423e-03, 8.2678e-03, + 4.0664e-02, 2.0156e-01, 6.4597e-01, 9.0380e-01, 9.6477e-01, 9.7386e-01, + 9.7921e-01, 9.7543e-01, 9.5784e-01, 9.0109e-01, 6.9198e-01, 4.5481e-01, + 2.1539e-01, 5.2143e-02, 7.8477e-03, 2.3775e-03, 5.2359e-04, 2.7515e-04, + 1.0030e-04, 6.8600e-05, 7.8394e-05, 6.1336e-05), + T(1.3665e-04, 2.7183e-04, 6.3078e-04, 1.3654e-03, 4.5380e-03, 1.4096e-02, + 8.9603e-02, 3.7293e-01, 7.9767e-01, 9.4614e-01, 9.7820e-01, 9.8513e-01, + 9.9180e-01, 9.9073e-01, 9.8490e-01, 9.6567e-01, 8.7173e-01, 6.4768e-01, + 3.2502e-01, 6.8274e-02, 1.2379e-02, 2.3228e-03, 6.0599e-04, 2.2455e-04, + 7.6128e-05, 4.5087e-05, 2.9849e-05, 2.4246e-05), + T(2.9395e-04, 5.2626e-04, 1.3691e-03, 3.0833e-03, 8.4784e-03, 2.4840e-02, + 1.0825e-01, 3.3781e-01, 7.2135e-01, 9.0736e-01, 9.7591e-01, 9.8762e-01, + 9.9407e-01, 9.9431e-01, 9.9110e-01, 9.8120e-01, 9.1879e-01, 7.4382e-01, + 3.6428e-01, 9.0287e-02, 1.5536e-02, 3.7618e-03, 7.5778e-04, 3.3695e-04, + 8.8488e-05, 5.0563e-05, 3.6956e-05, 2.5858e-05), + T(3.3709e-04, 7.3902e-04, 2.1546e-03, 5.1880e-03, 1.4869e-02, 3.6619e-02, + 1.1205e-01, 2.4008e-01, 4.7358e-01, 7.7270e-01, 9.4354e-01, 9.8724e-01, + 9.9541e-01, 9.9609e-01, 9.9417e-01, 9.8742e-01, 9.4030e-01, 7.3967e-01, + 3.2890e-01, 6.3649e-02, 1.0635e-02, 2.0180e-03, 6.2573e-04, 1.9897e-04, + 7.7560e-05, 3.7549e-05, 2.0955e-05, 1.5558e-05), + T(5.8491e-04, 1.1162e-03, 4.2690e-03, 1.3020e-02, 3.5157e-02, 8.7344e-02, + 1.5394e-01, 2.6169e-01, 3.9548e-01, 6.9451e-01, 9.2202e-01, 9.8254e-01, + 9.9533e-01, 9.9627e-01, 9.9226e-01, 9.7961e-01, 8.8020e-01, 5.1072e-01, + 1.6972e-01, 4.4443e-02, 8.6884e-03, 2.3970e-03, 7.7104e-04, 3.0885e-04, + 9.4350e-05, 4.3260e-05, 2.8203e-05, 1.8742e-05), + T(1.2834e-03, 3.1489e-03, 1.4341e-02, 5.5467e-02, 1.6788e-01, 3.4051e-01, + 5.4373e-01, 7.1655e-01, 8.4651e-01, 9.4097e-01, 9.8247e-01, 9.9247e-01, + 9.9560e-01, 9.9519e-01, 9.8764e-01, 9.4701e-01, 7.2466e-01, 3.1205e-01, + 1.0391e-01, 3.3326e-02, 7.6892e-03, 2.1175e-03, 9.4444e-04, 3.1132e-04, + 9.5933e-05, 4.4204e-05, 2.4406e-05, 1.7895e-05), + T(2.8443e-03, 8.0350e-03, 5.2463e-02, 2.4951e-01, 6.5350e-01, 8.9142e-01, + 9.6346e-01, 9.7836e-01, 9.8918e-01, 9.9353e-01, 9.9634e-01, 9.9751e-01, + 9.9703e-01, 9.9593e-01, 9.8269e-01, 9.3080e-01, 7.0195e-01, 3.2495e-01, + 1.5233e-01, 4.9156e-02, 1.3870e-02, 4.1192e-03, 1.5574e-03, 6.2875e-04, + 1.7067e-04, 7.4994e-05, 4.6740e-05, 2.8302e-05), + T(7.8200e-03, 2.9424e-02, 1.6984e-01, 5.7369e-01, 9.2385e-01, 9.8158e-01, + 9.9368e-01, 9.9577e-01, 9.9798e-01, 9.9834e-01, 9.9887e-01, 9.9874e-01, + 9.9875e-01, 9.9813e-01, 9.9522e-01, 9.8773e-01, 9.0437e-01, 6.1899e-01, + 3.1323e-01, 1.2139e-01, 2.5527e-02, 7.0457e-03, 2.4347e-03, 8.0314e-04, + 2.1590e-04, 9.6527e-05, 5.6101e-05, 3.5453e-05), + T(1.4658e-02, 6.5434e-02, 3.6357e-01, 8.3279e-01, 9.7732e-01, 9.9152e-01, + 9.9686e-01, 9.9773e-01, 9.9875e-01, 9.9897e-01, 9.9910e-01, 9.9905e-01, + 9.9904e-01, 9.9878e-01, 9.9812e-01, 9.9676e-01, 9.8603e-01, 9.4868e-01, + 7.3707e-01, 3.3155e-01, 7.9630e-02, 1.7003e-02, 4.4124e-03, 1.4714e-03, + 3.4973e-04, 1.4844e-04, 9.3148e-05, 5.0959e-05), + T(2.6411e-02, 1.2346e-01, 5.0025e-01, 9.0578e-01, 9.9005e-01, 9.9616e-01, + 9.9839e-01, 9.9872e-01, 9.9927e-01, 9.9929e-01, 9.9939e-01, 9.9931e-01, + 9.9930e-01, 9.9917e-01, 9.9902e-01, 9.9880e-01, 9.9740e-01, 9.9226e-01, + 9.3064e-01, 5.7227e-01, 1.4149e-01, 3.4371e-02, 7.0848e-03, 2.0274e-03, + 4.6336e-04, 2.1692e-04, 1.1053e-04, 7.0626e-05), + T(4.3833e-02, 2.0485e-01, 6.3357e-01, 9.4217e-01, 9.9302e-01, 9.9702e-01, + 9.9851e-01, 9.9892e-01, 9.9927e-01, 9.9931e-01, 9.9935e-01, 9.9928e-01, + 9.9929e-01, 9.9921e-01, 9.9915e-01, 9.9911e-01, 9.9859e-01, 9.9653e-01, + 9.6976e-01, 7.7589e-01, 2.4619e-01, 6.2726e-02, 1.4346e-02, 3.7506e-03, + 9.5355e-04, 3.7222e-04, 2.1213e-04, 1.1961e-04), + T(6.9732e-02, 2.6408e-01, 7.5521e-01, 9.6384e-01, 9.9468e-01, 9.9765e-01, + 9.9899e-01, 9.9923e-01, 9.9955e-01, 9.9958e-01, 9.9955e-01, 9.9954e-01, + 9.9944e-01, 9.9939e-01, 9.9931e-01, 9.9922e-01, 9.9892e-01, 9.9771e-01, + 9.8597e-01, 8.6076e-01, 4.2889e-01, 1.3940e-01, 3.2867e-02, 1.1953e-02, + 3.6591e-03, 1.7576e-03, 6.4271e-04, 4.0583e-04), + T(1.0522e-01, 3.7747e-01, 8.0611e-01, 9.6327e-01, 9.9445e-01, 9.9744e-01, + 9.9886e-01, 9.9920e-01, 9.9949e-01, 9.9951e-01, 9.9947e-01, 9.9942e-01, + 9.9928e-01, 9.9925e-01, 9.9912e-01, 9.9912e-01, 9.9890e-01, 9.9805e-01, + 9.9045e-01, 9.3178e-01, 6.2792e-01, 2.5893e-01, 1.0537e-01, 3.8054e-02, + 1.5579e-02, 6.1348e-03, 2.5883e-03, 1.2838e-03), + T(1.5055e-01, 4.2908e-01, 8.5477e-01, 9.6827e-01, 9.9417e-01, 9.9711e-01, + 9.9880e-01, 9.9909e-01, 9.9950e-01, 9.9950e-01, 9.9939e-01, 9.9935e-01, + 9.9924e-01, 9.9920e-01, 9.9911e-01, 9.9900e-01, 9.9880e-01, 9.9757e-01, + 9.9317e-01, 9.6565e-01, 7.8117e-01, 4.1305e-01, 1.8546e-01, 9.3902e-02, + 3.8758e-02, 2.3142e-02, 1.3436e-02, 7.6208e-03), + T(1.7158e-01, 4.6332e-01, 8.3340e-01, 9.5613e-01, 9.9301e-01, 9.9674e-01, + 9.9853e-01, 9.9894e-01, 9.9936e-01, 9.9933e-01, 9.9914e-01, 9.9911e-01, + 9.9889e-01, 9.9893e-01, 9.9850e-01, 9.9854e-01, 9.9785e-01, 9.9648e-01, + 9.9272e-01, 9.7598e-01, 8.8509e-01, 6.5937e-01, 4.1227e-01, 2.2791e-01, + 1.0047e-01, 5.6547e-02, 4.0775e-02, 2.1832e-02), + T(1.8097e-01, 4.3488e-01, 8.0906e-01, 9.5279e-01, 9.9178e-01, 9.9642e-01, + 9.9856e-01, 9.9889e-01, 9.9934e-01, 9.9930e-01, 9.9902e-01, 9.9895e-01, + 9.9885e-01, 9.9869e-01, 9.9817e-01, 9.9681e-01, 9.9531e-01, 9.9068e-01, + 9.9072e-01, 9.8457e-01, 9.5545e-01, 8.5326e-01, 5.7086e-01, 3.4321e-01, + 1.7797e-01, 1.1025e-01, 7.5957e-02, 4.4180e-02), + T(1.7015e-01, 4.0648e-01, 7.6375e-01, 9.3904e-01, 9.9015e-01, 9.9615e-01, + 9.9833e-01, 9.9879e-01, 9.9921e-01, 9.9915e-01, 9.9876e-01, 9.9872e-01, + 9.9846e-01, 9.9829e-01, 9.9665e-01, 9.9374e-01, 9.8525e-01, 9.7681e-01, + 9.8209e-01, 9.7998e-01, 9.6424e-01, 9.2269e-01, 7.9389e-01, 6.1845e-01, + 3.8921e-01, 2.5130e-01, 1.7380e-01, 9.7910e-02), + T(1.4442e-01, 3.6091e-01, 7.6619e-01, 9.4772e-01, 9.9076e-01, 9.9623e-01, + 9.9853e-01, 9.9882e-01, 9.9931e-01, 9.9924e-01, 9.9895e-01, 9.9884e-01, + 9.9854e-01, 9.9812e-01, 9.9617e-01, 9.8976e-01, 9.7394e-01, 9.5045e-01, + 9.7235e-01, 9.7631e-01, 9.6480e-01, 9.5421e-01, 8.9240e-01, 8.1617e-01, + 6.3785e-01, 4.9790e-01, 4.2863e-01, 2.3719e-01), + T(1.5188e-01, 3.8625e-01, 7.8543e-01, 9.4934e-01, 9.9114e-01, 9.9616e-01, + 9.9834e-01, 9.9878e-01, 9.9916e-01, 9.9913e-01, 9.9873e-01, 9.9861e-01, + 9.9805e-01, 9.9754e-01, 9.9534e-01, 9.8644e-01, 9.6005e-01, 9.2391e-01, + 9.4608e-01, 9.6079e-01, 9.5493e-01, 9.5188e-01, 9.2766e-01, 8.9211e-01, + 8.0528e-01, 7.2582e-01, 6.6401e-01, 4.6471e-01), + T(1.5518e-01, 4.1780e-01, 8.4351e-01, 9.6595e-01, 9.9181e-01, 9.9595e-01, + 9.9834e-01, 9.9861e-01, 9.9898e-01, 9.9890e-01, 9.9844e-01, 9.9808e-01, + 9.9727e-01, 9.9611e-01, 9.9267e-01, 9.8220e-01, 9.6024e-01, 9.1465e-01, + 8.8761e-01, 8.7385e-01, 9.0305e-01, 9.0890e-01, 9.0726e-01, 8.6719e-01, + 8.0290e-01, 7.2254e-01, 6.5225e-01, 4.8456e-01), + T(1.7700e-01, 4.8719e-01, 8.8348e-01, 9.6884e-01, 9.9176e-01, 9.9540e-01, + 9.9782e-01, 9.9816e-01, 9.9841e-01, 9.9822e-01, 9.9751e-01, 9.9700e-01, + 9.9570e-01, 9.9440e-01, 9.9135e-01, 9.8140e-01, 9.6939e-01, 9.2907e-01, + 8.6017e-01, 7.9329e-01, 7.6943e-01, 7.7082e-01, 7.9685e-01, 7.3216e-01, + 6.2691e-01, 5.3519e-01, 4.8933e-01, 3.5212e-01), + T(2.1136e-01, 5.5828e-01, 9.2765e-01, 9.7843e-01, 9.9036e-01, 9.9435e-01, + 9.9651e-01, 9.9712e-01, 9.9697e-01, 9.9682e-01, 9.9507e-01, 9.9468e-01, + 9.9311e-01, 9.9215e-01, 9.8835e-01, 9.8476e-01, 9.6908e-01, 9.5477e-01, + 8.9311e-01, 8.1060e-01, 7.0127e-01, 6.3071e-01, 6.9608e-01, 6.3639e-01, + 5.5570e-01, 4.6190e-01, 4.1898e-01, 2.9993e-01), + T(3.2360e-01, 7.0305e-01, 9.4440e-01, 9.7558e-01, 9.8877e-01, 9.9246e-01, + 9.9513e-01, 9.9546e-01, 9.9506e-01, 9.9401e-01, 9.9184e-01, 9.9088e-01, + 9.8992e-01, 9.8919e-01, 9.8544e-01, 9.8226e-01, 9.6682e-01, 9.5486e-01, + 9.1470e-01, 8.2646e-01, 6.9278e-01, 5.5555e-01, 5.1462e-01, 4.4804e-01, + 3.8711e-01, 3.1771e-01, 2.9688e-01, 2.1935e-01), + T(5.3141e-01, 8.2496e-01, 9.3611e-01, 9.6395e-01, 9.7501e-01, 9.8039e-01, + 9.8184e-01, 9.8229e-01, 9.7864e-01, 9.7243e-01, 9.6421e-01, 9.6312e-01, + 9.6111e-01, 9.6264e-01, 9.6137e-01, 9.5721e-01, 9.4593e-01, 9.3147e-01, + 8.7769e-01, 7.7395e-01, 5.9635e-01, 4.0803e-01, 3.0769e-01, 2.7948e-01, + 1.9219e-01, 1.7522e-01, 1.4135e-01, 1.1969e-01), + T(5.7194e-01, 7.6795e-01, 8.4733e-01, 8.7384e-01, 8.7540e-01, 8.8041e-01, + 8.7553e-01, 8.6469e-01, 8.2037e-01, 7.8384e-01, 7.3051e-01, 7.4341e-01, + 7.2965e-01, 7.5573e-01, 7.6070e-01, 7.6097e-01, 7.6178e-01, 7.4295e-01, + 6.5525e-01, 5.2254e-01, 3.7112e-01, 2.2125e-01, 1.5398e-01, 1.2773e-01, + 1.0090e-01, 9.3117e-02, 8.6961e-02, 7.8307e-02)))) + + + val bbox = Tensor[Float](T(215.8138, 206.8337, 252.0270, 249.3495)) + val binaryMask = Tensor[Float](480, 640) + Utils.decodeMaskInImage(mask, bbox, binaryMask = binaryMask) + + val str = "f_U33`>NhA6Q>`0M3O1O1O100" + + "O100HWOXBj0g=8N2O100000000001O1O2N1POYBe0R>O1O2N3L3M3M2O2M2O0O2N101Nbde5" + val expectedRLE = MaskUtils.string2RLE(str, 480, 640) + + val outRLE = MaskUtils.binaryToRLE(binaryMask) + outRLE.counts should be(expectedRLE.counts) + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala index d2a0a6df8a8..07108780818 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala @@ -105,11 +105,11 @@ class BoxHeadSpec extends FlatSpec with Matchers { val bbox = T(Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), T(3.0f, 5.0f, 6.0f, 7.0f)))) - val labels = Tensor[Float](T(1, 3)) + val imageInfo = Tensor[Float](T(10, 10)) layer.evaluate() - val output = layer.forward(T(T(features1, features2), bbox, labels)).toTable[Table](2) + val output = layer.forward(T(T(features1, features2), bbox, imageInfo)).toTable[Table](2) val expectedBbox = Tensor[Float](T( T(0.9995, 2.9991, 2.0602, 6.1203), @@ -388,7 +388,7 @@ class BoxHeadSpec extends FlatSpec with Matchers { T(3.0f, 5.0f, 6.0f, 7.0f))), Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), T(3.0f, 5.0f, 6.0f, 7.0f)))) - val labels2 = T(Tensor[Float](T(1, 3)), Tensor[Float](T(1, 3))) + val labels2 = Tensor[Float](T(10, 10)) // val output = layer.forward(T(T(features1, features2), T(bbox), T(labels))).toTable[Table](2) @@ -1038,9 +1038,9 @@ class BoxHeadSerialTest extends ModuleSerializationTest { val feature2 = Tensor[Float](1, 6, 5, 2).rand() val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), T(3.0f, 5.0f, 6.0f, 7.0f))) - val labels = Tensor[Float](T(1, 3)) + val imageInfo = Tensor[Float](T(10, 10)) - runSerializationTest(layer, T(T(feature1, feature2), bbox, labels)) + runSerializationTest(layer, T(T(feature1, feature2), bbox, imageInfo)) } } @@ -1053,6 +1053,6 @@ class BoxPostProcessorSerialTest extends ModuleSerializationTest { val bbox = Tensor[Float](T(T(1.0f, 3.0f, 2.0f, 6.0f), T(3.0f, 5.0f, 6.0f, 10.0f))) - runSerializationTest(layer, T(classLogits, boxRegression, bbox)) + runSerializationTest(layer, T(classLogits, boxRegression, bbox, Tensor[Float](T(10, 10)))) } } From 05f043fe89f797e19ea24f55bb7eda3aaa68a149 Mon Sep 17 00:00:00 2001 From: Xiao Date: Thu, 24 Oct 2019 14:46:16 +0800 Subject: [PATCH 0975/1065] fix TimeDistributedCriterion() lack of parameter of dimension issue (#2940) --- .../analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 3cdf55ebe95..feec0418c26 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -387,8 +387,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def createTimeDistributedCriterion(critrn: TensorCriterion[T], - sizeAverage: Boolean = false): TimeDistributedCriterion[T] = { - TimeDistributedCriterion[T](critrn, sizeAverage) + sizeAverage: Boolean = false, dimension: Int = 2): TimeDistributedCriterion[T] = { + TimeDistributedCriterion[T](critrn, sizeAverage, dimension) } def createGRU( From 4fea0422cd791882ac4a42928249610bb5575a56 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Thu, 24 Oct 2019 16:22:08 +0800 Subject: [PATCH 0976/1065] revert back api (#2943) --- .../dllib/nn/abstractnn/AbstractModule.scala | 2 +- .../bigdl/dllib/optim/Predictor.scala | 39 +++++-------------- .../dllib/utils/python/api/PythonBigDL.scala | 8 ++-- 3 files changed, 15 insertions(+), 34 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index cec55896e55..f218abb36cf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -647,7 +647,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * if -1, default is 4 * partitionNumber of dataset */ - final def predictClass(dataset: RDD[Sample[T]], batchSize: Int = -1): RDD[Sample[T]] = { + final def predictClass(dataset: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { Predictor(this).predictClass(dataset, batchSize) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index 5b18d86c6d5..86886b7fc01 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -180,34 +180,15 @@ object Predictor { def predictClass[T: ClassTag](dataSet: RDD[Sample[T]], batchSize: Int = -1, model: Module[T], batchPerPartition: Int, featurePaddingParam: Option[PaddingParam[T]])( - implicit ev: TensorNumeric[T]): RDD[Sample[T]] = { - val shareBuffer = false - val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext, - ConversionUtils.convert(model.evaluate())) - val partitionNum = dataSet.partitions.length - val totalBatch = if (batchSize > 0) { - require(batchSize % partitionNum == 0, s"Predictor.predict: total batch size $batchSize " + - s"should be divided by partitionNum ${partitionNum}") - batchSize - } else { - batchPerPartition * partitionNum - } - val rdd = ConversionUtils.coalesce(dataSet) - val realPartitionLength = rdd.partitions.length - val otherBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( - batchSize = totalBatch, - partitionNum = Some(realPartitionLength), - featurePaddingParam = featurePaddingParam)) - val localBatchPerPartition = totalBatch / realPartitionLength - rdd.mapPartitions { partition => - val localModel = modelBroad.value() - val localTransformer = otherBroad.value.cloneTransformer() - partition.grouped(localBatchPerPartition).flatMap(samples => { - val batchOut = predictSamples(localModel, samples, localTransformer, shareBuffer) - samples.toIterator.zip(batchOut).foreach(tuple => { - Sample(tuple._1.feature(), tuple._2.toTensor) - }) - samples + implicit ev: TensorNumeric[T]): RDD[Int] = { + val result = Predictor.predict(dataSet, batchSize, true, model, + batchPerPartition, featurePaddingParam) + result.mapPartitions { partition => + partition.map(output => { + val _output = output.toTensor[T] + require(_output.dim() == 1, s"Predictor.predictClass:" + + s"Only support one sample has one label, but got ${_output.dim()} label") + ev.toType[Int](_output.max(1)._2.valueAt(1)) }) } } @@ -233,7 +214,7 @@ class Predictor[T: ClassTag] private[optim]( batchPerPartition: Int = 4) (implicit ev: TensorNumeric[T]) extends Serializable { - def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Sample[T]] = { + def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = -1): RDD[Int] = { Predictor.predictClass(dataSet, batchSize, model, batchPerPartition, featurePaddingParam) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index feec0418c26..bb849fa12c1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2050,10 +2050,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab } def modelPredictClass(model: AbstractModule[Activity, Activity, T], - dataRdd: JavaRDD[Sample]): JavaRDD[Sample] = { - val sampleRDD = toJSample(dataRdd) - val pySampleRDD = model.predictClass(sampleRDD).map(toPySample(_)) - new JavaRDD[Sample](pySampleRDD) + dataRdd: JavaRDD[Sample]): JavaRDD[Int] = { + val sampleRdd = toJSample(dataRdd) + val tensorRDD = model.predictClass(sampleRdd) + new JavaRDD[Int](tensorRDD) } def modelForward(model: AbstractModule[Activity, Activity, T], From 9d5b1f64a6d9eb3cf95117a1c5a9b69ba365afa6 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 24 Oct 2019 17:26:06 +0800 Subject: [PATCH 0977/1065] fix: softmax and bn+scale fusion (#2937) --- .../bigdl/dllib/nn/mkldnn/Fusion.scala | 35 ++++++++++++++++-- .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 4 +-- .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 36 ++++++++++++++++++- 3 files changed, 70 insertions(+), 5 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala index 0533f741ced..87cb0145b81 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.Module -import com.intel.analytics.bigdl.nn.MklInt8Convertible +import com.intel.analytics.bigdl.nn.{MklInt8Convertible, Scale => ScaleLayer} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Node @@ -38,6 +38,8 @@ private[mkldnn] object Fusion { node.element match { case relu: ReLU => fusionRelu(node) case bn: SpatialBatchNormalization => fusionBN(node) + case blasWrapper: BlasWrapper if blasWrapper.module.isInstanceOf[ScaleLayer[Float]] => + fuseScale(node) case _ => } } @@ -76,7 +78,8 @@ private[mkldnn] object Fusion { */ private def fusionRelu(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { node.prevNodes.foreach(n => { - n.element match { + val notIdentity = findPrevious(n) + notIdentity.element match { case conv: SpatialConvolution => if (!conv.relu) { conv.setReLU(true) @@ -284,6 +287,34 @@ private[mkldnn] object Fusion { } } + def fuseScale(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { + // check all prevNodes are SpatialBatchNormalization + val isValid = node.prevNodes.forall(_.element.isInstanceOf[SpatialBatchNormalization]) + if (!isValid) { return } + + node.prevNodes.foreach { prevNode => + val bn = prevNode.element.asInstanceOf[SpatialBatchNormalization] + val weightAndBias = bn.weightAndBias.dense + val weight = weightAndBias.narrow(1, 1, bn.nOutput) + val bias = weightAndBias.narrow(1, bn.nOutput + 1, bn.nOutput) + + val scale = node.element.asInstanceOf[BlasWrapper].module.asInstanceOf[ScaleLayer[Float]] + val scaleWeight = scale.parameters()._1(0) + val scaleBias = scale.parameters()._1(1) + + weight.cmul(scaleWeight) + bias.cmul(scaleWeight) + bias.add(scaleBias) + + + // set the weight and bias to new tensor, we do not modify the original model's tensor. + // sometimes, the model need to be reused. + bn.weightAndBias.dense.set(weightAndBias) + } + + node.element = Identity[Float]() // set the BlasWrapper to Identity, we need no scale now + } + private def findAllNonIdentityPrevs(node: Node[AbstractModule[Activity, Activity, Float]]) : Seq[Node[AbstractModule[Activity, Activity, Float]]] = { // TODO currently, it will only skip the Identity, MaxPooling, AvgPooling, JoinTable diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index 2d1591631c4..336282488ab 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{Memory, MklDnn, PropKind, Stream => DnnStream} +import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn, PropKind, Stream => DnnStream} import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} @@ -68,7 +68,7 @@ class SoftMax(val axis: Int = -1) extends MklDnnLayer { case _ => throw new UnsupportedOperationException("1D, 2D, 3D or 4D tensor expected") } - _inputFormats = singleNativeData(inputs) + _inputFormats = Array(NativeData(inputs(0).shape, inputs(0).layout, DataType.F32)) val localInputFormat = if (inputs(0).shape.length == 3 && inputs(0).layout == Memory.Format.ntc) { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala index 3fbc3165a42..891c15141f9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -17,11 +17,13 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn.{Module, StaticGraph} import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.RandomGenerator +import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator} +import com.intel.analytics.bigdl.utils.intermediate.ConversionUtils class FusionSpec extends FlatSpec with Matchers { "Conv with relu" should "work correctly" in { @@ -372,4 +374,36 @@ class FusionSpec extends FlatSpec with Matchers { System.clearProperty("bigdl.mkldnn.fusion") } + + "bn and scale fusion" should "work correctly" in { + import com.intel.analytics.bigdl.nn.{Scale => NNScale} + val inputShape = Array(4, 64, 3, 3) + val input = Input(inputShape, Memory.Format.nchw).inputs() + val bn1 = SpatialBatchNormalization(64).inputs(input) + val scale1 = BlasWrapper(NNScale[Float](Array(1, 64, 1, 1))).inputs(bn1) + val output = Output(Memory.Format.nchw).inputs(scale1) + + // the running mean and running variance should be 1. + bn1.element.getExtraParameter().foreach(_.fill(1)) + + val model = DnnGraph(Seq(input), Seq(output)) + val fused = model.cloneModule() + + model.evaluate() + fused.evaluate() + + val tensor = Tensor[Float](inputShape).rand(-1, 1) + + System.setProperty("bigdl.mkldnn.fusion", "false") + model.compile(InferencePhase) + model.forward(tensor) + + System.setProperty("bigdl.mkldnn.fusion", "true") + fused.compile(InferencePhase) + fused.forward(tensor) + + Equivalent.nearequals(model.output.toTensor[Float], fused.output.toTensor[Float], 1e-7) + + System.clearProperty("bigdl.mkldnn.fusion") + } } From 5a76cc441a6be50fff5ed2248351e321dfd5ea02 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Sat, 26 Oct 2019 16:11:29 +0800 Subject: [PATCH 0978/1065] feat: multi models support with MKL-DNN backend (#2936) * feat: multi models support with MKL-DNN backend --- .../intel/analytics/bigdl/utils/Engine.scala | 10 +- .../analytics/bigdl/utils/ThreadPool.scala | 3 +- .../dllib/models/utils/ModelBroadcast.scala | 66 +++++++- .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 5 + .../bigdl/dllib/nn/mkldnn/Linear.scala | 4 + .../nn/mkldnn/SpatialBatchNormalization.scala | 4 + .../dllib/nn/mkldnn/SpatialConvolution.scala | 4 + .../bigdl/dllib/nn/mkldnn/TensorMMap.scala | 15 +- .../bigdl/dllib/optim/Evaluator.scala | 16 +- .../bigdl/dllib/optim/Predictor.scala | 28 +++- .../bigdl/dllib/tensor/DnnTensor.scala | 7 + .../utils/intermediate/ConversionUtils.scala | 2 +- .../dllib/utils/intermediate/IRGraph.scala | 11 +- .../dllib/nn/mkldnn/MultiModelsSpec.scala | 146 ++++++++++++++++++ 14 files changed, 300 insertions(+), 21 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MultiModelsSpec.scala diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index b6084d52b82..adefe180c76 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -42,7 +42,8 @@ object Engine { // Initialize some properties for mkldnn engine. We should call it at the beginning. // Otherwise some properties will have no effect. - if (System.getProperty("bigdl.engineType") == "mkldnn") { + if (System.getProperty("bigdl.engineType") == "mkldnn" && + System.getProperty("bigdl.multiModels", "false") == "false") { setMklDnnEnvironments() } @@ -326,6 +327,13 @@ object Engine { this.engineType } + private[bigdl] def isMultiModels: Boolean = { + getEngineType() match { + case MklBlas => true + case MklDnn => System.getProperty("bigdl.multiModels", "false").toBoolean + } + } + private[bigdl] def model: ThreadPool = { _model } diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 7fe3512d978..8e229209978 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -63,7 +63,7 @@ class ThreadPool(private var poolSize: Int) { threadPool = Executors.newFixedThreadPool(poolSize, new ThreadFactory { override def newThread(r: Runnable): Thread = { val t = Executors.defaultThreadFactory().newThread(r) - t.setName("default-thread-computing") + t.setName("default-thread-computing " + t.getId) t.setDaemon(true) t } @@ -91,6 +91,7 @@ class ThreadPool(private var poolSize: Int) { mklPoolSize = Some(size) (1 to poolSize).map(i => Future { MKL.setNumThreads(size) + BackendMklDnn.setNumThreads(size) val tid = Thread.currentThread().getId() logger.info(s"Set mkl threads to $size on thread $tid") }(context)).foreach(Await.result(_, Duration.Inf)) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala index 327334e8760..b42b8c2ad9e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/ModelBroadcast.scala @@ -21,13 +21,17 @@ import java.util.UUID import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.nn.Container +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.{MklDnnLayer, TensorMMap} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor._ -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, MklDnn} import com.intel.analytics.bigdl.utils.Util._ +import com.intel.analytics.bigdl.utils.intermediate.IRGraph import org.apache.commons.lang3.SerializationUtils import org.apache.spark.SparkContext import org.apache.spark.broadcast.Broadcast +import org.apache.zookeeper.KeeperException.UnimplementedException import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -46,6 +50,11 @@ trait ModelBroadcast[T] extends Serializable { */ def broadcast(sc: SparkContext, model: Module[T]): this.type + private[bigdl] def broadcast(sc: SparkContext, model: Module[T], + dummyInput: Activity): this.type = { + throw new UnimplementedException + } + /** * Get the broadcast model on worker * @@ -55,6 +64,11 @@ trait ModelBroadcast[T] extends Serializable { */ def value(initGradient: Boolean = false, shareWeight: Boolean = true): Module[T] + private[bigdl] def value(initGradient: Boolean, shareWeight: Boolean, + dummyInput: Activity): Module[T] = { + throw new UnimplementedException + } + def uuid(): String = _uuid } @@ -81,9 +95,11 @@ object ModelBroadcast { private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = false) (implicit ev: TensorNumeric[T]) extends ModelBroadcast[T] { + private type NativeType = (String, (Array[TensorMMap], Array[TensorMMap])) private var broadcastModel: Broadcast[ModelInfo[T]] = _ private var broadcastConsts: Broadcast[Map[String, Tensor[_]]] = _ private var broadcastParameters: Broadcast[Array[Tensor[T]]] = _ + private var broadcastParametersNative: Broadcast[Array[NativeType]] = _ private var nodeNumber : Int = _ private var coreNumber : Int = _ @@ -209,6 +225,54 @@ private[bigdl] class ModelBroadcastImp[T: ClassTag](applyProtoBuffer: Boolean = Array() } } + + private def getTensorMMaps(ir: IRGraph[T]) = { + ir.graph + .getSortedForwardExecutions() + .filter(_.element.isInstanceOf[MklDnnLayer]) + .map { node => + val element = node.element + val name = element.getName() + val tensorMMap = element.asInstanceOf[MklDnnLayer].paramsMMap() + (name, tensorMMap) + } + } + + override def broadcast(sc: SparkContext, model: Module[T], + dummyInput: Activity): this.type = { + if (model.isInstanceOf[IRGraph[T]] && Engine.getEngineType() == MklDnn && + Engine.isMultiModels) { + val clonedModel = model.asInstanceOf[IRGraph[T]].cloneModule() + clonedModel.forward(dummyInput) + + broadcastParametersNative = sc.broadcast(getTensorMMaps(clonedModel)) + } + + this.broadcast(sc, model) + this + } + + override def value(initGradient: Boolean, shareWeight: Boolean, + dummyInput: Activity): Module[T] = { + val model = value(initGradient, shareWeight) + + if (model.isInstanceOf[IRGraph[T]] && Engine.getEngineType() == MklDnn && + Engine.isMultiModels) { + model.forward(dummyInput) + + if (shareWeight) { + getTensorMMaps(model.asInstanceOf[IRGraph[T]]).zip(broadcastParametersNative.value) + .foreach { case (src, dst) => + if (src._1 == dst._1) { + src._2._1.zip(dst._2._1) + .filter(x => x._1 != null && x._2 != null) + .foreach{ case (x, y) => x.setNative(y) } + } + } + } + } + model + } } private[bigdl] class ModelInfo[T: ClassTag](val uuid: String, @transient var model: Module[T])( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index bcc18df8b77..669cd0d88af 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -284,6 +284,11 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM } override def setQuantize(value: Boolean): MklDnnLayer.this.type = this + + def paramsMMap(): (Array[TensorMMap], Array[TensorMMap]) = { + // return null for weight and gradWeight by default + (Array.empty[TensorMMap], Array.empty[TensorMMap]) + } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index d9366162681..6c899b41a09 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -294,6 +294,10 @@ class Linear( (Array(weight.dense, bias.dense), Array(gradWeight.dense, gradBias.dense)) } + override def paramsMMap(): (Array[TensorMMap], Array[TensorMMap]) = { + (Array(weight, bias), Array(gradWeight, gradBias)) + } + override def zeroGradParameters(): Unit = { } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 0a4b0af2391..29bb8a12111 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -413,6 +413,10 @@ class SpatialBatchNormalization( (Array(weightAndBias.dense), Array(gradWeightAndBias.dense)) } + override def paramsMMap(): (Array[TensorMMap], Array[TensorMMap]) = { + (Array(weightAndBias), Array(gradWeightAndBias)) + } + override def getExtraParameter(): Array[Tensor[Float]] = { if (needScale) { runningMeanScaled.copy(runningMean.dense).div(scaleFactor) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala index 97a0f3e3441..8aba3668a31 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialConvolution.scala @@ -641,6 +641,10 @@ class SpatialConvolution( } + override def paramsMMap(): (Array[TensorMMap], Array[TensorMMap]) = { + (Array(weight, bias), Array(gradWeight, gradBias)) + } + // we need not implement it, because the grad parameters will clean by mkldnn override def zeroGradParameters(): Unit = { } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala index 364242911cf..fef28cbfdc1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TensorMMap.scala @@ -21,6 +21,8 @@ import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.{DnnTensor, FloatType, Tensor} +import scala.reflect.ClassTag + /** * `TensorMMap` contains two tensors, dense and native, which are a map of each other. * It's used in the layer which contains weights. For the weight, we should sync the @@ -29,7 +31,7 @@ import com.intel.analytics.bigdl.tensor.{DnnTensor, FloatType, Tensor} * * @param _size the shape of Tensor, such as Array(4, 3, 224, 224) */ -private[mkldnn] class TensorMMap(_size: Array[Int])(implicit owner: MemoryOwner) +private[bigdl] class TensorMMap(_size: Array[Int])(implicit owner: MemoryOwner) extends Serializable { // dense weight on heap is used to optimizer and so on, which is exposed to // AbstractModule level. @@ -115,4 +117,15 @@ private[mkldnn] class TensorMMap(_size: Array[Int])(implicit owner: MemoryOwner) dense.size(index) } + def release(): Unit = { + if (native != null) { + native.release() + } + } + + def setNative(another: TensorMMap): Unit = { + if (native != null && another.native != null) { + native.set(another.native.asInstanceOf[Tensor[_]]) + } + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala index 8e30f3f2a68..6089286f5ee 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala @@ -52,17 +52,19 @@ class Evaluator[T: ClassTag] private[optim](model: Module[T])(implicit ev: Tenso vMethods: Array[ValidationMethod[T]], batchSize: Option[Int] = None): Array[(ValidationResult, ValidationMethod[T])] = { - val modelBroad = ModelBroadcast[T]().broadcast(dataset.sparkContext, - ConversionUtils.convert(model.evaluate())) val partitionNum = dataset.partitions.length - val totalBatch = batchSize.getOrElse(batchPerPartition * partitionNum) + + val dummyInput = Predictor.getDummyData(dataset, totalBatch / partitionNum) + + val modelBroad = ModelBroadcast[T]().broadcast(dataset.sparkContext, + ConversionUtils.convert(model.evaluate()), dummyInput) val rdd = ConversionUtils.coalesce(dataset) val otherBroad = rdd.sparkContext.broadcast(vMethods, SampleToMiniBatch( batchSize = totalBatch, partitionNum = Some(rdd.partitions.length))) rdd.mapPartitions(partition => { - val localModel = modelBroad.value() + val localModel = modelBroad.value(false, true, dummyInput) val localMethod = otherBroad.value._1.map(_.clone()) val localTransformer = otherBroad.value._2.cloneTransformer() val miniBatch = localTransformer(partition) @@ -86,14 +88,14 @@ class Evaluator[T: ClassTag] private[optim](model: Module[T])(implicit ev: Tenso vMethods: Array[ValidationMethod[T]] ): Array[(ValidationResult, ValidationMethod[T])] = { + val dummyInput = dataset.takeSample(withReplacement = false, num = 1).head.getInput() val rdd = ConversionUtils.coalesce(dataset) val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, - ConversionUtils.convert(model.evaluate())) + ConversionUtils.convert(model.evaluate()), dummyInput) val otherBroad = rdd.sparkContext.broadcast(vMethods) - rdd.mapPartitions(miniBatch => { - val localModel = modelBroad.value() + val localModel = modelBroad.value(false, true, dummyInput) val localMethod = otherBroad.value miniBatch.map(batch => { val output = localModel.forward(batch.getInput()) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index 86886b7fc01..09790717585 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -124,11 +124,11 @@ object Predictor { model: Module[T], featurePaddingParam: Option[PaddingParam[T]])( implicit ev: TensorNumeric[T]): DistributedImageFrame = { - + val dummyInput = getDummyData(imageFrame.rdd, batchPerPartition) + val totalBatch = imageFrame.rdd.partitions.length * batchPerPartition val rdd = ConversionUtils.coalesce(imageFrame.asInstanceOf[DistributedImageFrame].rdd) val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, - ConversionUtils.convert(model.evaluate())) - val totalBatch = imageFrame.rdd.partitions.length * batchPerPartition + ConversionUtils.convert(model.evaluate()), dummyInput) val realPartitionLength = rdd.partitions.length val toBatchBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( @@ -138,7 +138,7 @@ object Predictor { val localBatchPerPartition = totalBatch / realPartitionLength val result = rdd.mapPartitions(partition => { - val localModel = modelBroad.value() + val localModel = modelBroad.value(false, true, dummyInput) val localToBatch = toBatchBroad.value._1.cloneTransformer() partition.grouped(localBatchPerPartition).flatMap(imageFeatures => { Predictor.predictImageBatch[T](localModel, imageFeatures, outputLayer, predictKey, @@ -152,8 +152,6 @@ object Predictor { def predict[T: ClassTag](dataSet: RDD[Sample[T]], batchSize: Int = -1, shareBuffer: Boolean = false, model: Module[T], batchPerPartition: Int, featurePaddingParam: Option[PaddingParam[T]])(implicit ev: TensorNumeric[T]): RDD[Activity] = { - val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext, - ConversionUtils.convert(model.evaluate())) val partitionNum = dataSet.partitions.length val totalBatch = if (batchSize > 0) { require(batchSize % partitionNum == 0, s"Predictor.predict: total batch size $batchSize " + @@ -162,13 +160,16 @@ object Predictor { } else { batchPerPartition * partitionNum } + val dummyInput = getDummyData(dataSet, totalBatch / partitionNum) + val modelBroad = ModelBroadcast[T]().broadcast(dataSet.sparkContext, + ConversionUtils.convert(model.evaluate()), dummyInput) val rdd = ConversionUtils.coalesce(dataSet) val otherBroad = rdd.sparkContext.broadcast(SampleToMiniBatch( batchSize = totalBatch, partitionNum = Some(rdd.partitions.length), featurePaddingParam = featurePaddingParam)) rdd.mapPartitions { partition => - val localModel = modelBroad.value() + val localModel = modelBroad.value(false, true, dummyInput) val localTransformer = otherBroad.value.cloneTransformer() val miniBatch = localTransformer(partition) miniBatch.flatMap(batch => { @@ -192,6 +193,19 @@ object Predictor { }) } } + + // because Evaluator will use it too, we extend the scope out of Predictor + private[optim] def getDummyData[T: ClassTag, R](dataSet: RDD[R], + batchSize: Int)(implicit ev: TensorNumeric[T]): Activity = { + // here has an assumption, batchSizePerPar is not very large. + val samples = dataSet.takeSample(withReplacement = false, num = batchSize) + .map { + case feature: ImageFeature => feature[Sample[T]](ImageFeature.sample) + case sample => sample.asInstanceOf[Sample[T]] + } + val sampleToMiniBatch = SampleToMiniBatch(batchSize) + sampleToMiniBatch(samples.toIterator).toSeq.head.getInput() + } } /** diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala index 840652ef5c1..b63453ff7f4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DnnTensor.scala @@ -162,6 +162,13 @@ class DnnTensor[T: ClassTag]( this } + override def set(other: Tensor[T]): Tensor[T] = { + require(other.isInstanceOf[DnnTensor[T]], s"only support to set DnnTensor") + this._storage.release() + this._storage = other.storage().asInstanceOf[DnnStorage[T]] + this + } + override def toString: String = { ev.getType() match { case FloatType => diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala index 4df8891ac7e..e6479ae8c3f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/ConversionUtils.scala @@ -65,7 +65,7 @@ private[bigdl] object ConversionUtils { */ def coalesce[T: ClassTag](dataset: RDD[T]): RDD[T] = { if (dataset.partitions.length != Engine.nodeNumber() - && Engine.getEngineType() == MklDnn) { + && !Engine.isMultiModels) { dataset.coalesce(Engine.nodeNumber(), false) } else dataset } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index 5528b884d74..d3f8294633e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -63,10 +63,17 @@ private[bigdl] class IRGraph[T: ClassTag]( throw new UnsupportedOperationException("forward not supported, Please build graph first") } if (graph.isInstanceOf[DnnGraph]) { - Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + // if using multi MKL-DNN model, we just use current thread directly + // because it's in sequential mode of MKL and MKL-DNN + if (Engine.isMultiModels) { initPrimitives(input) graph.updateOutput(input) - })) + } else { + Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => { + initPrimitives(input) + graph.updateOutput(input) + })) + } } else graph.updateOutput(input) output = graph.output output diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MultiModelsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MultiModelsSpec.scala new file mode 100644 index 00000000000..17fc0548f8e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MultiModelsSpec.scala @@ -0,0 +1,146 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import com.intel.analytics.bigdl.dataset.Sample +import com.intel.analytics.bigdl.models.inception.Inception_v1_NoAuxClassifier +import com.intel.analytics.bigdl.models.lenet.LeNet5 +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, ImageFrame, ImageFrameToSample, MatToTensor} +import com.intel.analytics.bigdl.transform.vision.image.augmentation.{CenterCrop, ChannelNormalize, Resize} +import com.intel.analytics.bigdl.utils.{Engine, LoggerFilter} +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.apache.spark.SparkContext +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class MultiModelsSpec extends FlatSpec with BeforeAndAfter with Matchers { + var sc: SparkContext = _ + val nodeNumber = 1 + val coreNumber = 2 + + before { + System.setProperty("bigdl.engineType", "mkldnn") + System.setProperty("bigdl.multiModels", "true") + + LoggerFilter.redirectSparkInfoLogs() + + val conf = Engine.createSparkConf() + .setMaster(s"local[$coreNumber]") + .setAppName("multi mkl-dnn models") + sc = SparkContext.getOrCreate(conf) + + Engine.init + } + + after { + System.clearProperty("bigdl.engineType") + System.clearProperty("bigdl.multiModels") + + if (sc != null) { + sc.stop() + } + } + + "model.predict" should "be correct" in { + RNG.setSeed(100) + val data = new Array[Sample[Float]](97) + var i = 0 + while (i < data.length) { + val input = Tensor[Float](28, 28).apply1(_ => + RNG.uniform(0.130660 + i, 0.3081078).toFloat) + val label = Tensor[Float](1).fill(1.0f) + data(i) = Sample(input, label) + i += 1 + } + val model = LeNet5(classNum = 10) + val dataSet = sc.parallelize(data, coreNumber) + + var result = model.predict(dataSet) + var prob = result.collect() + + prob(0) should be (model.forward(data(0).feature)) + prob(11) should be (model.forward(data(11).feature)) + prob(31) should be (model.forward(data(31).feature)) + prob(51) should be (model.forward(data(51).feature)) + prob(71) should be (model.forward(data(71).feature)) + prob(91) should be (model.forward(data(91).feature)) + + result = model.predict(dataSet, 20, true) + prob = result.collect() + + prob(0) should be(prob(10)) + prob(5) should be(prob(15)) + prob(0) should be(prob(20)) + prob(8) should be(prob(38)) + } + + "model.predictClass" should "be correct" in { + RNG.setSeed(100) + val data = new Array[Sample[Float]](97) + var i = 0 + while (i < data.length) { + val input = Tensor[Float](28, 28).apply1(_ => + RNG.uniform(0.130660 + i, 0.3081078).toFloat) + val label = Tensor[Float](1).fill(1.0f) + data(i) = Sample(input, label) + i += 1 + } + val model = LeNet5(classNum = 10) + val dataSet = sc.parallelize(data, 2) + val result = model.predictClass(dataSet) + + val prob = result.collect() + prob(0) should be + (model.forward(data(0).feature + ).toTensor[Float].max(1)._2.valueAt(1).toInt) + prob(11) should be + (model.forward(data(11).feature + ).toTensor[Float].max(1)._2.valueAt(1).toInt) + prob(31) should be + (model.forward(data(31).feature + ).toTensor[Float].max(1)._2.valueAt(1).toInt) + prob(51) should be + (model.forward(data(51).feature + ).toTensor[Float].max(1)._2.valueAt(1).toInt) + prob(71) should be + (model.forward(data(71).feature + ).toTensor[Float].max(1)._2.valueAt(1).toInt) + prob(91) should be + (model.forward(data(91).feature + ).toTensor[Float].max(1)._2.valueAt(1).toInt) + } + + "model.predictImage" should "be correct" in { + import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat + RNG.setSeed(100) + val resource = getClass.getClassLoader.getResource("pascal/") + val imageFrame = ImageFrame.read(resource.getFile, sc) -> + Resize(256, 256) -> CenterCrop(224, 224) -> + ChannelNormalize(0.485f, 0.456f, 0.406f, 0.229f, 0.224f, 0.225f) -> + MatToTensor() -> ImageFrameToSample() + val model = Inception_v1_NoAuxClassifier(classNum = 20) + val detection = model.predictImage(imageFrame).toDistributed() + val feature = detection.rdd.first() + println(feature(ImageFeature.predict)) + + val imageFeatures = detection.rdd.collect() + val prob = imageFeatures.map(x => x[Tensor[Float]](ImageFeature.predict)) + val data = imageFeatures.map(_[Sample[Float]](ImageFeature.sample)) + prob(0) should be (model.forward(data(0).feature.reshape(Array(1, 3, 224, 224))) + .toTensor[Float].squeeze) + } +} From 3eb6203570eb635885620dbe977761326737c77d Mon Sep 17 00:00:00 2001 From: Menooker Date: Sun, 27 Oct 2019 21:10:05 +0800 Subject: [PATCH 0979/1065] add COCO MAP (#2935) * Move COCO SeqFile related updates into this branch * bbox * add UT * add UT * add UT * add COCO MAP * revert merge conflict * ignore non-existing images * add IOU related API. MAP now parses RLEs * BBox now inclusive * updates based on GH comments * add COCODataset.getImageById * COCO topK default => -1, remove height: Int, width: Int in GroundTruthRLE * update imageId2Image * rename MAPObjectDetection utils, add cocoSegmentationAndBBox, refine formatting * rename utils * update documents * check size of bbox & classes & scores & labels & iscrowd. Handle empty predictions * add gt and target image size checking, add support for empty target bbox, add UT * detection sorted before matching with GT. Optimize MAPResult merging. Add UT for merging --- .../dataset/segmentation/COCODataset.scala | 16 +- .../dataset/segmentation/MaskUtils.scala | 162 ++++- .../vision/image/label/roi/RoiLabel.scala | 3 +- .../bigdl/dllib/optim/ValidationMethod.scala | 553 +++++++++++++----- .../dllib/utils/python/api/PythonBigDL.scala | 3 +- .../SegmentationDatasetSpec.scala | 32 + .../bigdl/dllib/optim/ValidationSpec.scala | 183 +++++- 7 files changed, 793 insertions(+), 159 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala index 5f278458ee8..215ef29b56f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala @@ -140,11 +140,12 @@ case class COCODataset(info: COCODatasetInfo, images: Array[COCOImage], licenses: Array[COCOLicence], categories: Array[COCOCategory]) { private lazy val cateId2catIdx = scala.collection.mutable.Map[Long, Int]() + private lazy val imageId2Image = images.toIterator.map(img => (img.id, img)).toMap + private[segmentation] def init(imgRoot: String): Unit = { - val id2img = images.toIterator.map(img => (img.id, img)).toMap annotations.foreach(anno => { - require(id2img.contains(anno.imageId), s"Cannot find image_id ${anno.imageId}") - val img = id2img(anno.imageId) + require(imageId2Image.contains(anno.imageId), s"Cannot find image_id ${anno.imageId}") + val img = imageId2Image(anno.imageId) anno.image = img img.annotations += anno anno.segmentation match { @@ -153,12 +154,19 @@ case class COCODataset(info: COCODatasetInfo, images: Array[COCOImage], case _ => } }) - images.foreach(img => img.imgRootPath = imgRoot) + images.foreach(_.imgRootPath = imgRoot) categories.zipWithIndex.foreach { case (cate, idx) => cateId2catIdx(cate.id) = idx + 1 // the ids starts from 1, because 0 is for background } } + /** + * Find a COCOImage by the image id + * @param id image id + * @return the COCOImage with the given id + */ + def getImageById(id: Long): COCOImage = imageId2Image(id) + /** * Convert COCO categoryId into category index. * COCO dataset's categoryId is not continuous from 1 to number of categories. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala index c749b189e49..39264a64de3 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala @@ -69,6 +69,14 @@ class RLEMasks(val counts: Array[Int], val height: Int, val width: Int) extends SegmentationMasks { override def toRLE: RLEMasks = this + // cached bbox value + @transient + lazy val bbox: (Float, Float, Float, Float) = MaskUtils.rleToOneBbox(this) + + // cached area value + @transient + lazy val area: Long = MaskUtils.rleArea(this) + /** * Get an element in the counts. Process the overflowed int * @@ -347,7 +355,7 @@ object MaskUtils { val am = cnts.length cnts.clear() var ca = uint2long(acnt(0)) - var cb = uint2long(B.counts(0)) + var cb = B.get(0) var (v, va, vb) = (false, false, false) var a = 1 var b = 1 @@ -387,6 +395,158 @@ object MaskUtils { RLEMasks(cnts.toArray, h, w) } + private[segmentation] def rleArea(R: RLEMasks): Long = { + var a = 0L + for (j <- 1.until(R.counts.length, 2)) + a += R.get(j) + a.toInt + } + + /** + * Calculate the intersection over union (IOU) of two RLEs + * @param detection the detection RLE + * @param groundTruth the ground truth RLE + * @param isCrowd if groundTruth is isCrowd + * @return IOU + */ + def rleIOU(detection: RLEMasks, groundTruth: RLEMasks, isCrowd: Boolean): Float = { + val gtBbox = groundTruth.bbox + val dtBbox = detection.bbox + require((detection.width, detection.height) == (groundTruth.width, groundTruth.height), + "The sizes of RLEs must be the same to compute IOU") + val iou = bboxIOU(gtBbox, dtBbox, isCrowd) + + if (iou > 0) { + val crowd = isCrowd + + val dCnts = detection + val gCnts = groundTruth + + var a = 1 + var b = 1 + + var ca = dCnts.get(0) + val ka = dCnts.counts.length + var va: Boolean = false + var vb: Boolean = false + + var cb = gCnts.get(0) + val kb = gCnts.counts.length + var i = 0L + var u = 0L + var ct = 1L + + while (ct > 0) { + val c = math.min(ca, cb) + if (va || vb) { + u = u + c + if (va && vb) i += c + } + ct = 0 + + ca = ca - c + if (ca == 0 && a < ka) { + ca = dCnts.get(a) + a += 1 + va = !va + } + ct += ca + + cb = cb - c + if (cb == 0 && b < kb) { + cb = gCnts.get(b) + b += 1 + vb = !vb + } + ct += cb + } + if (i == 0) { + u = 1 + } else if (crowd) { + u = dCnts.area + } + i.toFloat / u + } else { + iou + } + } + + /** + * Get the iou of two bounding boxes + * @param gtx1 Ground truth x1 + * @param gty1 Ground truth y1 + * @param gtx2 Ground truth x2 + * @param gty2 Ground truth y2 + * @param dtx1 Detection x1 + * @param dty1 Detection y1 + * @param dtx2 Detection x2 + * @param dty2 Detection y2 + * @param isCrowd if ground truth is is crowd + * @return + */ + def bboxIOU(gtx1: Float, gty1: Float, gtx2: Float, gty2: Float, dtx1: Float, dty1: Float, + dtx2: Float, dty2: Float, isCrowd: Boolean): Float = { + val (xmin, ymin, xmax, ymax) = (gtx1, gty1, gtx2, gty2) + val (x1, y1, x2, y2) = (dtx1, dty1, dtx2, dty2) + val area = (xmax - xmin + 1) * (ymax - ymin + 1) + val ixmin = Math.max(xmin, x1) + val iymin = Math.max(ymin, y1) + val ixmax = Math.min(xmax, x2) + val iymax = Math.min(ymax, y2) + val inter = Math.max(ixmax - ixmin + 1, 0) * Math.max(iymax - iymin + 1, 0) + val detectionArea = (x2 - x1 + 1) * (y2 - y1 + 1) + val union = if (isCrowd) detectionArea else (detectionArea + area - inter) + inter / union + } + + /** + * Get the iou of two bounding boxes + * @param groundTruth + * @param detection + * @param isCrowd if groundTruth is isCrowd + * @return + */ + def bboxIOU(groundTruth: (Float, Float, Float, Float), + detection: (Float, Float, Float, Float), isCrowd: Boolean): Float = { + bboxIOU(groundTruth._1, groundTruth._2, groundTruth._3, groundTruth._4, + detection._1, detection._2, detection._3, detection._4, isCrowd) + } + + // convert one rle to one bbox + private[segmentation] def rleToOneBbox(rle: RLEMasks): (Float, Float, Float, Float) = { + val m = rle.counts.length / 2 * 2 + + val h = rle.height.toLong + var xp = 0.0f + var cc = 0L + var xs = rle.width.toLong + var ys = rle.height.toLong + var ye = 0.0f + var xe = 0.0f + + if(m == 0) { + (0, 0, 0, 0) + } else { + for (j <- 0 until m) { + cc += rle.get(j) + val t = cc - j % 2 + val y = t % h + val x = (t - y) / h + if (j % 2 == 0) { + xp = x + } else if (xp < x) { + ys = 0 + ye = h - 1 + } + xs = math.min(xs, x) + xe = math.max(xe, x) + ys = math.min(ys, y) + ye = math.max(ye, y) + } + (xs, ys, xe, ye) + } + } + def polyToSingleRLE(poly: PolyMasks, height: Int, width: Int): RLEMasks = { val out = poly2RLE(poly, height, width) mergeRLEs(out, false) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala index 7b5dae7b8b0..19d3eab5631 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -78,12 +78,11 @@ object RoiLabel { val ORIGSIZE = "size" val SCORES = "scores" - + def getScores(tab: Table): Tensor[Float] = tab[Tensor[Float]](SCORES) def getClasses(tab: Table): Tensor[Float] = tab[Tensor[Float]](CLASSES) def getBBoxes(tab: Table): Tensor[Float] = tab[Tensor[Float]](BBOXES) def getMasks(tab: Table): Array[RLEMasks] = tab[Array[RLEMasks]](MASKS) def getIsCrowd(tab: Table): Tensor[Float] = tab[Tensor[Float]](ISCROWD) - def getScores(tab: Table): Tensor[Float] = tab[Tensor[Float]](SCORES) /** * @return (height, width, channel) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala index cde2a866196..286b05bcfba 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala @@ -17,11 +17,14 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.segmentation.{MaskUtils, RLEMasks} import com.intel.analytics.bigdl.nn.ClassNLLCriterion import com.intel.analytics.bigdl.nn.AbsCriterion import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import com.intel.analytics.bigdl.utils.Table import org.apache.commons.lang3.SerializationUtils import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -291,8 +294,124 @@ object MAPUtil { val end = Math.min(k, q.size) (1 to end).map(_ => q.dequeue()).toArray } + + /** + * convert the ground truth into parsed GroundTruthRegions + * @param gtTable + * @param classes + * @param isCOCO if using COCO's algorithm for IOU computation + * @param isSegmentation + * @return (array of GT BBoxes of images, # of GT bboxes for each class) + */ + def gtTablesToGroundTruthRegions(gtTable: Table, classes: Int, numIOU: Int, isCOCO: Boolean, + isSegmentation: Boolean): (Array[ArrayBuffer[GroundTruthRegion]], Array[Int]) = { + // the number of GT bboxes for each class + val gtCntByClass = new Array[Int](classes) + + // one image may contain multiple Ground truth bboxes + val gtImages = (1 to gtTable.length()).map { i => + val gtImage = new ArrayBuffer[GroundTruthRegion]() + val roiLabel = gtTable[Table](i) + if (roiLabel.length() > 0) { + val bbox = RoiLabel.getBBoxes(roiLabel) + val tclasses = RoiLabel.getClasses(roiLabel) + val isCrowd = RoiLabel.getIsCrowd(roiLabel) + val masks = if (isSegmentation) RoiLabel.getMasks(roiLabel) else null + val bboxCnt = bbox.size(1) + require(bboxCnt == tclasses.size(1), "CLASSES of target tables should have the" + + "same size of the bbox counts") + require(bboxCnt == isCrowd.nElement(), "ISCROWD of target tables should have the" + + "same size of the bbox counts") + require(masks == null || bboxCnt == masks.length, "MASKS of target tables should have the" + + "same size of the bbox counts") + for (j <- 1 to bboxCnt) { + val (label, _diff) = if (tclasses.dim() == 2) { + (tclasses.valueAt(1, j).toInt, tclasses.valueAt(2, j)) + } else { + (tclasses.valueAt(j).toInt, 0f) + } + val diff = if (isCrowd.valueAt(j) != 0 || _diff != 0) 1f else 0f + val newGt = if (isSegmentation) { + new GroundTruthRLE(numIOU, label, diff, masks(j - 1)) + } else { + new GroundTruthBBox(isCOCO, numIOU, label, diff, bbox.valueAt(j, 1), + bbox.valueAt(j, 2), bbox.valueAt(j, 3), bbox.valueAt(j, 4)) + } + gtImage += newGt + require(label >= 0 && label < classes, s"Bad label id $label") + + if (diff == 0) { + gtCntByClass(label) += 1 + } + } + } + gtImage + }.toArray + (gtImages, gtCntByClass) + } + + /** + * For a detection, match it with all GT boxes. Record the match in "predictByClass" + */ + def parseDetection(gtBbox: ArrayBuffer[GroundTruthRegion], label: Int, score: Float, x1: Float, + y1: Float, x2: Float, y2: Float, mask: RLEMasks, classes: Int, iou: Array[Float], + predictByClasses: Array[Array[ArrayBuffer[(Float, Boolean)]]]): Unit = { + require(label >= 0 && label < classes, s"Bad label id $label") + for (i <- iou.indices) { + // for each GT boxes, try to find a matched one with current prediction + val matchedGt = gtBbox.toIterator.filter(gt => label == gt.label && gt.canOccupy(i)) + .flatMap(gt => { // calculate and filter out the bbox + val iouRate = gt.getIOURate(x1, y1, x2, y2, mask) + if (iouRate >= iou(i)) Iterator.single((gt, iouRate)) else Iterator.empty + }) + .reduceOption((gtArea1, gtArea2) => { // find max IOU bbox + if (gtArea1._1.diff != gtArea2._1.diff) { + if (gtArea1._1.diff > gtArea2._1.diff) gtArea2 else gtArea1 + } else { + if (gtArea1._2 > gtArea2._2) gtArea1 else gtArea2 + } + }) + .map(bbox => { // occupy the bbox + bbox._1.occupy(i) + bbox._1 + }) + if (matchedGt.isEmpty || matchedGt.get.diff == 0) { + predictByClasses(i)(label).append((score, matchedGt.isDefined)) + } + // else: when the prediction matches a "difficult" GT, do nothing + // it is neither TP nor FP + // "difficult" is defined in PASCAL VOC dataset, meaning the image is difficult to detect + } + } + + def parseSegmentationTensorResult(outTensor: Tensor[Float], + func: (Int, Int, Float, Float, Float, Float, Float) => Unit): Unit = { + require(outTensor.dim() == 2, "the output tensor should have 2 dimensions") + for (imgId <- 0 until outTensor.size(1)) { + // for each image + val batch = outTensor.select(1, imgId + 1) + val batchSize = batch.valueAt(1).toInt + var offset = 2 + for (bboxIdx <- 0 until batchSize) { + // for each predicted bboxes + val label = batch.valueAt(offset).toInt + val score = batch.valueAt(offset + 1) + val x1 = batch.valueAt(offset + 2) + val y1 = batch.valueAt(offset + 3) + val x2 = batch.valueAt(offset + 4) + val y2 = batch.valueAt(offset + 5) + func(imgId, label, score, x1, y1, x2, y2) + offset += 6 + } + } + } } +class MAPType extends Serializable +object MAPPascalVoc2007 extends MAPType +object MAPPascalVoc2010 extends MAPType +object MAPCOCO extends MAPType + /** * The MAP Validation Result. The results are not calculated until result() or format() is called * require class label beginning with 0 @@ -302,10 +421,11 @@ class MAPValidationResult( // take the first k samples, or -1 for all samples private val k: Int, // the predicts for each classes. (Confidence, GT) - private var predictForClass: Array[ArrayBuffer[(Float, Boolean)]], - private var gtCntForClass: Array[Int], - private val useVoc2007: Boolean = false, - private val skipClass: Int = -1 + private[bigdl] var predictForClass: Array[ArrayBuffer[(Float, Boolean)]], + private[bigdl] var gtCntForClass: Array[Int], + private val theType: MAPType = MAPPascalVoc2010, + private val skipClass: Int = -1, + private val isSegmentation: Boolean = false ) extends ValidationResult { @@ -315,10 +435,14 @@ class MAPValidationResult( require(skipClass >= 0 && skipClass < nClass, s"Invalid skipClass $skipClass") } + private def sortPredictions(p: ArrayBuffer[(Float, Boolean)]): ArrayBuffer[(Float, Boolean)] = { + p.sortBy(v => v._1)(Ordering.Float.reverse) // decending order + } + private[bigdl] def calculateClassAP(clz: Int): Float = { val posCnt = gtCntForClass // for each class, first find top k confident samples - val sorted = predictForClass(clz).sortBy(v => v._1)(Ordering.Float.reverse) // decending order + val sorted = sortPredictions(predictForClass(clz)) var tp = 0 val refinedK = if (k > 0) k else sorted.size // calculate the max precision for each different recall @@ -337,24 +461,38 @@ class MAPValidationResult( } // get Average precision over each different recall - if (useVoc2007) { - (0 to 10).map(r => { - val recall = 0.1f * r - // for every (R,P), where R>=recall, get max(P) - PnR.filter(_._1 >= recall).map(_._2).reduceOption(_ max _).getOrElse(0f) - }) - .reduceOption(_ + _) - .map(_ / 11) - .getOrElse(0f) - } else { - (1 to posCnt(clz)).map(r => { - val recall = r.toFloat / posCnt(clz) - // for every (R,P), where R>=recall, get max(P) - PnR.filter(_._1 >= recall).map(_._2).reduceOption(_ max _).getOrElse(0f) - }) - .reduceOption(_ + _) - .map(_ / posCnt(clz)) - .getOrElse(0f) + theType match { + case _: MAPPascalVoc2007.type => + (0 to 10).map(r => { + val recall = 0.1f * r + // for every (R,P), where R>=recall, get max(P) + PnR.filter(_._1 >= recall).map(_._2).reduceOption(_ max _).getOrElse(0f) + }) + .reduceOption(_ + _) + .map(_ / 11) + .getOrElse(0f) + case _: MAPPascalVoc2010.type => + (1 to posCnt(clz)).map(r => { + val recall = r.toFloat / posCnt(clz) + // for every (R,P), where R>=recall, get max(P) + PnR.filter(_._1 >= recall).map(_._2).reduceOption(_ max _).getOrElse(0f) + }) + .reduceOption(_ + _) + .map(_ / posCnt(clz)) + .getOrElse(0f) + case _: MAPCOCO.type => + if (posCnt(clz) == 0) { + -1f + } else { + (0 to 100).map(r => { + val recall = 0.01f * r + // for every (R,P), where R>=recall, get max(P) + PnR.filter(_._1 >= recall).map(_._2).reduceOption(_ max _).getOrElse(0f) + }) + .reduceOption(_ + _) + .map(_ / 101) + .getOrElse(0f) + } } } @@ -363,181 +501,326 @@ class MAPValidationResult( // get the indices of top-k confident samples val AP = (0 until nClass).filter(_ != skipClass).map { clz => calculateClassAP(clz) } // APs are got. Now we get MAP - val result = AP.sum / (nClass - (if (skipClass == -1) 0 else 1)) + val result = theType match { + case t: MAPCOCO.type => + val filtered = AP.filter(_ != -1f) + filtered.sum / filtered.length + case _ => AP.sum / (nClass - (if (skipClass == -1) 0 else 1)) + } (result, 1) } - // scalastyle:off methodName - override def +(other: ValidationResult): ValidationResult = { - val o = other.asInstanceOf[MAPValidationResult] + + private[optim] def mergeWithoutGtCnt(o: MAPValidationResult): MAPValidationResult = { require(predictForClass.length == o.predictForClass.length) require(gtCntForClass.length == o.gtCntForClass.length) - predictForClass.zip(o.predictForClass).foreach { - case (left, right) => left ++= right + for (i <- predictForClass.indices) { + val (left, right) = (predictForClass(i), o.predictForClass(i)) + left ++= right + predictForClass(i) = if (k < 0) { + left + } else { + val sorted = sortPredictions(left) + sorted.take(k) + } } + this + } + + // scalastyle:off methodName + override def +(other: ValidationResult): ValidationResult = { + val o = other.asInstanceOf[MAPValidationResult] + mergeWithoutGtCnt(o) gtCntForClass.indices.foreach( i => gtCntForClass(i) += o.gtCntForClass(i)) this } // scalastyle:on methodName override protected def format(): String = { + val segOrBbox = if (isSegmentation) "segm" else "bbox" val resultStr = (0 until nClass).map { clz => calculateClassAP(clz) }.zipWithIndex .map { t => s"AP of class ${t._2} = ${t._1}\n"}.reduceOption( _ + _).getOrElse("") - s"MeanAveragePrecision@$k(${result()._1})\n $resultStr" + s"MeanAveragePrecision_$segOrBbox@$k(${result()._1})\n $resultStr" } } -private[bigdl] class GroundTruthBBox(val label: Int, val diff: Float, - val xmin: Float, val ymin: Float, val xmax: Float, val ymax: Float) { - private val area = (xmax - xmin) * (ymax - ymin) - - // if is false, the bbox is not matched with any predictions - private var isOccupied = false +abstract private[bigdl] class GroundTruthRegion(isCOCO: Boolean, numIOU: Int, val label: Int, + val diff: Float) { + // if is false, the region is not matched with any predictions + // indexed by the IOU threshold index + private val isOccupied = new Array[Boolean](numIOU) /** - * Returns if any previous prediction is matched with the current bbox + * Returns if any previous prediction is matched with the current region + * * @return */ - def canOccupy: Boolean = !isOccupied - def occupy(): Unit = { - isOccupied = true + def canOccupy(iouIdx: Int): Boolean = (isCOCO && diff == 1) || !isOccupied(iouIdx) + + def occupy(iouIdx: Int): Unit = { + isOccupied(iouIdx) = true } - /** get the IOU rate of another bbox with the current bbox + /** get the IOU rate of another region with the current region * * @param x1 the min x * @param y1 the min y * @param x2 the max x * @param y2 the max y + * @param rle RLE mask data, can be null * @return */ - def getIOURate(x1: Float, y1: Float, x2: Float, y2: Float): Float = { + def getIOURate(x1: Float, y1: Float, x2: Float, y2: Float, rle: RLEMasks = null): Float +} + +private[bigdl] class GroundTruthBBox(isCOCO: Boolean, numIOU: Int, label: Int, diff: Float, + val xmin: Float, val ymin: Float, val xmax: Float, val ymax: Float) + extends GroundTruthRegion(isCOCO, numIOU, label, diff) { + private val area = (xmax - xmin + 1) * (ymax - ymin + 1) + + override def getIOURate(x1: Float, y1: Float, x2: Float, y2: Float, + rle: RLEMasks = null): Float = { val ixmin = Math.max(xmin, x1) val iymin = Math.max(ymin, y1) val ixmax = Math.min(xmax, x2) val iymax = Math.min(ymax, y2) - val inter = Math.max(ixmax - ixmin, 0) * Math.max(iymax - iymin, 0) - inter / ((x2 - x1) * (y2 - y1) + area - inter) + val inter = Math.max(ixmax - ixmin + 1, 0) * Math.max(iymax - iymin + 1, 0) + val detectionArea = (x2 - x1 + 1) * (y2 - y1 + 1) + val union = if (isCOCO && diff != 0) detectionArea else (detectionArea + area - inter) + inter / union + } +} + +private[bigdl] class GroundTruthRLE(numIOU: Int, label: Int, diff: Float, rle: RLEMasks) + extends GroundTruthRegion(true, numIOU, label, diff) { + + override def getIOURate(x1: Float, y1: Float, x2: Float, y2: Float, + detRLE: RLEMasks): Float = { + MaskUtils.rleIOU(detRLE, rle, diff != 0) + } +} + +class MAPMultiIOUValidationResult( + private val nClass: Int, + // take the first k samples, or -1 for all samples + private val k: Int, + // the predicts for each classes. + // predictForClassIOU(iouIdx)(cls) is an array of (Confidence, GT) + private val predictForClassIOU: Array[Array[ArrayBuffer[(Float, Boolean)]]], + private var gtCntForClass: Array[Int], + private val iouRange: (Float, Float), + private val theType: MAPType = MAPPascalVoc2010, + private val skipClass: Int = -1, + private val isSegmentation: Boolean = false) extends ValidationResult { + + val impl = predictForClassIOU.map(predictForClass => { + new MAPValidationResult(nClass, k, predictForClass, + gtCntForClass, theType, skipClass, isSegmentation) + }) + override def result(): (Float, Int) = (impl.map(_.result()._1).sum / impl.length, 1) + + // scalastyle:off methodName + override def +(other: ValidationResult): ValidationResult = { + val o = other.asInstanceOf[MAPMultiIOUValidationResult] + require(o.predictForClassIOU.length == predictForClassIOU.length, + "To merge MAPMultiIOUValidationResult, the length of predictForClassIOU should be" + + "the same") + impl.zip(o.impl).foreach { case (v1, v2) => v1.mergeWithoutGtCnt(v2) } + gtCntForClass.indices.foreach( i => gtCntForClass(i) += o.gtCntForClass(i)) + this + } + // scalastyle:on methodName + + override protected def format(): String = { + val step = (iouRange._2 - iouRange._1) / (predictForClassIOU.length - 1) + val results = impl.map(_.result()._1) + val resultStr = results.zipWithIndex + .map { t => s"\t IOU(${iouRange._1 + t._2 * step}) = ${t._1}\n"} + .reduceOption( _ + _).getOrElse("") + val segOrBbox = if (isSegmentation) "segm" else "bbox" + f"MAP_$segOrBbox@IOU(${iouRange._1}%1.3f:$step%1.3f:${iouRange._2}%1.3f)=" + + s"${results.sum / impl.length}\n$resultStr" } } /** MeanAveragePrecision for Object Detection - * IMPORTANT: The labels in the target vector (Ground truth) begin with 0. BUT in the - * NN output, the labels begins with 1 + * The class label begins with 0 * - * The expected output from the last layer should be [num_of_batch X (1 + maxDetection * 6)] matrix + * The expected output from the last layer should be a Tensor[Float] or a Table + * If output is a tensor, it should be [num_of_batch X (1 + maxDetection * 6)] matrix * The format of the matrix should be [, , ...], where each row vector is * = [, ,...]. Each sample has format: - * = the labels begins with 1 + * = * imgId is the batch number of the sample. imgId begins with 0. * Multiple samples may share one imgId * - * The target vector (Ground truth) is a [num_of_gt X 7] matrix - * having format [, , , ...] - * where = the labels begins with 0 + * If output is a table, it is a table of tables. + * output(i) is the results of the i-th image in the batch, where i = 1 to sizeof(batch) + * output(i) is a table, which contains the same keys (fields) of image info in the "target" + * Please refer to RoiMiniBatch/RoiImageInfo's documents. Besides, the inner tables also contain + * the scores for the detections in the image. + * + * The "target" (Ground truth) is a table with the same structure of "output", except that + * it does not have "score" field * - * @param iou the IOU threshold * @param classes the number of classes - * @param useVoc2007 use validation method before voc2010 (i.e. voc2007) + * @param topK only take topK confident predictions (-1 for all predictions) + * @param iouThres the IOU thresholds + * @param theType the type of MAP algorithm. (voc2007/voc2010/COCO) * @param skipClass skip calculating on a specific class (e.g. background) * the class index starts from 0, or is -1 if no skipping + * @param isSegmentation if check the IOU of segmentations instead of bounding boxes. If true, + * the output and target must have "masks" data */ class MeanAveragePrecisionObjectDetection[T: ClassTag]( - classes: Int, iou: Float = 0.5f, useVoc2007: Boolean = false, skipClass: Int = -1)( + classes: Int, topK: Int = -1, iouThres: Array[Float] = Array(0.5f), + theType: MAPType = MAPPascalVoc2010, skipClass: Int = -1, isSegmentation: Boolean = false)( implicit ev: TensorNumeric[T]) extends ValidationMethod[T] { override def apply(output: Activity, target: Activity): ValidationResult = { - val gtTensor = target.toTensor[Float] - require(gtTensor.dim() == 2 && gtTensor.size(2) == 7, - "the ground truth tensor should have 2 dimensions " + - "and the second dimension should have size of 7") - - // the number of GT bboxes for each class - val gtCntByClass = new Array[Int](classes) - // one image may contain multiple Ground truth bboxes - val gtImages = new ArrayBuffer[ArrayBuffer[GroundTruthBBox]] - // this converts the image-id in target tensor to the index within the image array - // imgId is for output tensor and target tensor. imgIdx is for gtImages - // the imgId should start from 0 - val imgId2imgIdx = scala.collection.mutable.Map[Int, Int]() - for(i <- 1 to gtTensor.size(1)) { - // the tensor is: (imgId, label, diff, bbox x4) - val imgId = gtTensor.valueAt(i, 1).toInt - val label = gtTensor.valueAt(i, 2).toInt - 1 - val diff = gtTensor.valueAt(i, 3).toInt - - val imgIdx = if (!imgId2imgIdx.contains(imgId)) { - val sz = gtImages.size - imgId2imgIdx(imgId) = sz - gtImages += new ArrayBuffer[GroundTruthBBox]() - sz - } else { - imgId2imgIdx(imgId) - } - gtImages(imgIdx) += new GroundTruthBBox(label, diff, gtTensor.valueAt(i, 4), - gtTensor.valueAt(i, 5), gtTensor.valueAt(i, 6), gtTensor.valueAt(i, 7)) - require(label >= 0 && label < classes, s"Bad label id $label") - - if (diff == 0) { - gtCntByClass(label) += 1 - } - } + val (gtImages, gtCntByClass) = + MAPUtil.gtTablesToGroundTruthRegions(target.toTable, classes, iouThres.length, + theType.isInstanceOf[MAPCOCO.type], isSegmentation) // the predicted bboxes for each classes - // predictByClass(classIdx)(bboxNum) is (Confidence, GT) - val predictByClass = new Array[ArrayBuffer[(Float, Boolean)]](classes) - for (i <- predictByClass.indices) { - predictByClass(i) = new ArrayBuffer[(Float, Boolean)] - } + // predictByClasses(iouIdx)(classIdx)(bboxNum) is (Confidence, GT) + val predictByClasses = iouThres.map(_iou => { + (0 until classes).map(_ => new ArrayBuffer[(Float, Boolean)]).toArray + }) - val outTensor = output.toTensor[Float] - require(outTensor.dim() == 2, "the output tensor should have 2 dimensions") - for (imgId <- 0 until outTensor.size(1)) { - // for each image - if (imgId2imgIdx.contains(imgId)) { - val imgIdx = imgId2imgIdx(imgId) // index within gtImages - val gtBbox = gtImages(imgIdx) - val batch = outTensor.select(1, imgId + 1) - val batchSize = batch.valueAt(1).toInt - var offset = 2 - for (bboxIdx <- 0 until batchSize) { - // for each predicted bboxes - val label = batch.valueAt(offset).toInt - require(label >= 0 && label < classes, s"Bad label id $label") - val score = batch.valueAt(offset + 1) - val x1 = batch.valueAt(offset + 2) - val y1 = batch.valueAt(offset + 3) - val x2 = batch.valueAt(offset + 4) - val y2 = batch.valueAt(offset + 5) - // for each GT boxes, try to find a matched one with current prediction - val matchedGt = gtBbox.filter(gt => label == gt.label && gt.canOccupy) - .flatMap(gt => { // calculate and filter out the bbox - val iouRate = gt.getIOURate(x1, y1, x2, y2) - if (iouRate >= iou) Iterator.single((gt, iouRate)) else Iterator.empty - }) - .reduceOption( (gtArea1, gtArea2) => { // find max IOU bbox - if (gtArea1._2 > gtArea2._2) gtArea1 else gtArea2 - }) - .map(bbox => { // occupy the bbox - bbox._1.occupy() - bbox._1 - }) - if (matchedGt.isEmpty || matchedGt.get.diff == 0) { - predictByClass(label).append((score, matchedGt.isDefined)) + output match { + case _outTensor: Tensor[_] => + require(!isSegmentation, "Cannot get segmentation data from tensor output for MAP") + val outTensor = _outTensor.asInstanceOf[Tensor[Float]] + MAPUtil.parseSegmentationTensorResult(outTensor, + (imgIdx, label, score, x1, y1, x2, y2) => { + val gtBbox = gtImages(imgIdx) + MAPUtil.parseDetection(gtBbox, label, score, x1, y1, x2, y2, null, classes, iouThres, + predictByClasses = predictByClasses) + }) + case outTable: Table => + require(gtImages.length == outTable.length(), "The number of images in the output and " + + "in the target should be the same") + for (imgId <- 1 to outTable.length()) { + val gtBbox = gtImages(imgId - 1) + val imgOut = outTable[Table](imgId) + // if the image contains empty predictions, do nothing + if (imgOut.length() > 0) { + val bboxes = RoiLabel.getBBoxes(imgOut) + val scores = RoiLabel.getScores(imgOut) + val labels = RoiLabel.getClasses(imgOut) + require(bboxes.dim() == 2, "the bbox tensor should have 2 dimensions") + val masks = if (isSegmentation) Some(RoiLabel.getMasks(imgOut)) else None + val batchSize = bboxes.size(1) + require(batchSize == labels.size(1), "CLASSES of target tables should have the" + + "same size of the bbox counts") + require(batchSize == scores.nElement(), "ISCROWD of target tables should have the" + + "same size of the bbox counts") + require(masks.isEmpty || batchSize == masks.get.length, "MASKS of target tables " + + "should have the same size of the bbox counts") + val detections = new ArrayBuffer[(Int, Float, Float, Float, Float, + Float, RLEMasks)]() + for (bboxIdx <- 1 to batchSize) { + val score = scores.valueAt(bboxIdx) + val x1 = bboxes.valueAt(bboxIdx, 1) + val y1 = bboxes.valueAt(bboxIdx, 2) + val x2 = bboxes.valueAt(bboxIdx, 3) + val y2 = bboxes.valueAt(bboxIdx, 4) + val label = labels.valueAt(bboxIdx).toInt + val mask = masks.map(_ (bboxIdx - 1)).orNull + detections.append((label, score, x1, y1, x2, y2, mask)) + } + detections.sortBy(v => v._2)(Ordering.Float.reverse).foreach { + case (label, score, x1, y1, x2, y2, mask) => + MAPUtil.parseDetection(gtBbox, label, score, x1, y1, x2, y2, mask, classes, + iouThres, predictByClasses) + } } - // else: when the prediction matches a "difficult" GT, do nothing - // it is neither TP nor FP - // what is "difficult"? I have no idea... - offset += 6 } - } - // if the image id does not have ground truth, do nothing } - new MAPValidationResult(classes, -1, predictByClass, gtCntByClass, useVoc2007, skipClass) + if (iouThres.length != 1) { + new MAPMultiIOUValidationResult(classes, topK, predictByClasses, gtCntByClass, + (iouThres.head, iouThres.last), theType, skipClass, isSegmentation) + } else { + new MAPValidationResult(classes, topK, predictByClasses.head, gtCntByClass, theType, + skipClass, isSegmentation) + } } override protected def format(): String = s"MAPObjectDetection" } +object MeanAveragePrecision { + /** + * Create MeanAveragePrecision validation method using COCO's algorithm for object detection. + * IOU computed by the segmentation masks + * + * @param nClasses the number of classes (including skipped class) + * @param topK only take topK confident predictions (-1 for all predictions) + * @param skipClass skip calculating on a specific class (e.g. background) + * the class index starts from 0, or is -1 if no skipping + * @param iouThres the IOU thresholds, (rangeStart, stepSize, numOfThres), inclusive + * @return MeanAveragePrecisionObjectDetection + */ + def cocoSegmentation(nClasses: Int, topK: Int = -1, skipClass: Int = 0, + iouThres: (Float, Float, Int) = (0.5f, 0.05f, 10)) + : MeanAveragePrecisionObjectDetection[Float] = { + createCOCOMAP(nClasses, topK, skipClass, iouThres, true) + } + + /** + * Create MeanAveragePrecision validation method using COCO's algorithm for object detection. + * IOU computed by the bounding boxes + * + * @param nClasses the number of classes (including skipped class) + * @param topK only take topK confident predictions (-1 for all predictions) + * @param skipClass skip calculating on a specific class (e.g. background) + * the class index starts from 0, or is -1 if no skipping + * @param iouThres the IOU thresholds, (rangeStart, stepSize, numOfThres), inclusive + * @return MeanAveragePrecisionObjectDetection + */ + def cocoBBox(nClasses: Int, topK: Int = -1, skipClass: Int = 0, + iouThres: (Float, Float, Int) = (0.5f, 0.05f, 10)) + : MeanAveragePrecisionObjectDetection[Float] = { + createCOCOMAP(nClasses, topK, skipClass, iouThres, false) + } + + /** + * Calculate the Mean Average Precision (MAP) for classification output and target + * The algorithm follows VOC Challenge after 2007 + * Require class label beginning with 0 + * + * @param nClasses The number of classes + * @param topK Take top-k confident predictions into account. If k=-1,calculate on all predictions + */ + def classification(nClasses: Int, topK: Int = -1) + : MeanAveragePrecision[Float] = new MeanAveragePrecision[Float](topK, nClasses) + + private def createCOCOMAP(nClasses: Int, topK: Int, skipClass: Int, + iouThres: (Float, Float, Int), isSegmentation: Boolean) + : MeanAveragePrecisionObjectDetection[Float] = { + new MeanAveragePrecisionObjectDetection[Float](nClasses, topK, + (0 until iouThres._3).map(iouThres._1 + _ * iouThres._2).toArray, + MAPCOCO, skipClass, isSegmentation) + } + + /** + * Create MeanAveragePrecision validation method using Pascal VOC's algorithm for object detection + * + * @param nClasses the number of classes + * @param useVoc2007 if using the algorithm in Voc2007 (11 points). Otherwise, use Voc2010 + * @param topK only take topK confident predictions (-1 for all predictions) + * @param skipClass skip calculating on a specific class (e.g. background) + * the class index starts from 0, or is -1 if no skipping + * @return MeanAveragePrecisionObjectDetection + */ + def pascalVOC(nClasses: Int, useVoc2007: Boolean = false, topK: Int = -1, + skipClass: Int = 0) : MeanAveragePrecisionObjectDetection[Float] = { + new MeanAveragePrecisionObjectDetection[Float](nClasses, topK, + theType = if (useVoc2007) MAPPascalVoc2007 else MAPPascalVoc2010, + skipClass = skipClass) + } +} + /** * Calculate the percentage that target in output's top5 probability indexes */ diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index bb849fa12c1..3caebcddd40 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -2185,7 +2185,8 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab def createMeanAveragePrecisionObjectDetection(classes: Int, iou: Float, useVoc2007: Boolean, skipClass: Int): ValidationMethod[T] = { - new MeanAveragePrecisionObjectDetection(classes, iou, useVoc2007, skipClass) + new MeanAveragePrecisionObjectDetection(classes, iouThres = Array(iou), + theType = if (useVoc2007) MAPPascalVoc2007 else MAPPascalVoc2010, skipClass = skipClass) } def createLoss(criterion: Criterion[T]): ValidationMethod[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/SegmentationDatasetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/SegmentationDatasetSpec.scala index 9cc81d7810d..26889ad4e3f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/SegmentationDatasetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/SegmentationDatasetSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.dataset.segmentation +import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} class SegmentationDatasetSpec extends FlatSpec with Matchers with BeforeAndAfter { @@ -150,5 +151,36 @@ class SegmentationDatasetSpec extends FlatSpec with Matchers with BeforeAndAfter } } + // scalastyle:off + // annId 902000091136 + val _rle1 = Array[Int](68483, 6, 473, 8, 471, 10, 469, 12, 467, 13, 456, 6, 5, 13, 455, 8, 1, 16, 454, 26, 453, 27, 453, 26, 454, 26, 453, 26, 455, 24, 456, 21, 459, 20, 461, 18, 463, 17, 465, 14, 467, 13, 467, 17, 462, 19, 459, 22, 457, 24, 455, 25, 454, 26, 454, 26, 454, 26, 453, 28, 453, 28, 452, 28, 452, 28, 453, 27, 454, 26, 456, 4, 3, 17, 463, 16, 464, 16, 464, 19, 460, 21, 459, 22, 458, 23, 456, 24, 456, 24, 457, 23, 457, 23, 457, 24, 456, 24, 456, 24, 456, 24, 455, 25, 456, 24, 456, 23, 457, 11, 2, 10, 458, 10, 3, 8, 474, 3, 17764, 6, 473, 8, 471, 10, 469, 12, 468, 19, 461, 20, 459, 22, 459, 22, 458, 22, 458, 22, 459, 21, 460, 20, 459, 21, 459, 20, 459, 21, 458, 21, 458, 19, 461, 20, 460, 21, 458, 22, 459, 21, 459, 21, 459, 21, 460, 20, 461, 18, 464, 3, 3, 10, 471, 8, 474, 3, 1897, 6, 473, 8, 471, 10, 469, 12, 468, 17, 463, 18, 461, 20, 461, 20, 460, 20, 460, 20, 461, 19, 462, 18, 464, 16, 465, 14, 466, 14, 465, 14, 467, 12, 468, 12, 468, 11, 470, 10, 471, 8, 474, 3, 7647, 4, 475, 6, 473, 7, 473, 7, 468, 12, 467, 13, 466, 14, 465, 15, 465, 15, 465, 14, 465, 15, 466, 14, 466, 14, 466, 14, 455, 4, 8, 13, 455, 5, 7, 13, 454, 7, 6, 13, 453, 9, 6, 12, 447, 1, 3, 11, 7, 10, 447, 2, 3, 11, 7, 10, 446, 3, 3, 11, 7, 9, 446, 5, 3, 10, 7, 10, 445, 5, 3, 10, 7, 10, 445, 5, 3, 9, 9, 9, 444, 6, 3, 9, 12, 6, 445, 5, 3, 8, 13, 7, 444, 6, 2, 5, 17, 6, 444, 6, 2, 3, 19, 6, 445, 6, 1, 3, 19, 7, 445, 8, 20, 7, 473, 7, 473, 7, 473, 8, 472, 8, 472, 8, 3, 2, 467, 8, 2, 5, 465, 8, 2, 8, 462, 8, 1, 11, 460, 8, 1, 13, 458, 24, 458, 23, 458, 22, 459, 21, 459, 21, 459, 20, 461, 19, 461, 19, 461, 18, 463, 17, 464, 16, 434, 1, 29, 16, 431, 4, 30, 14, 427, 9, 31, 13, 427, 9, 31, 13, 427, 9, 32, 11, 427, 11, 32, 10, 427, 12, 31, 10, 427, 13, 30, 9, 427, 14, 31, 8, 428, 14, 31, 6, 428, 16, 32, 4, 429, 16, 464, 16, 464, 16, 465, 15, 466, 14, 468, 12, 467, 12, 467, 13, 466, 13, 466, 12, 468, 12, 468, 12, 467, 13, 468, 13, 467, 14, 465, 15, 464, 16, 463, 17, 462, 18, 462, 18, 462, 17, 462, 18, 463, 18, 462, 18, 462, 18, 463, 17, 464, 16, 466, 14, 466, 13, 466, 14, 467, 12, 468, 12, 468, 11, 470, 10, 471, 8, 474, 3, 1917, 6, 473, 8, 471, 10, 469, 12, 468, 12, 468, 12, 467, 13, 467, 13, 468, 12, 468, 12, 468, 11, 468, 12, 468, 12, 468, 12, 467, 13, 468, 12, 468, 12, 468, 11, 467, 16, 463, 18, 461, 20, 459, 22, 458, 22, 458, 22, 457, 23, 457, 23, 456, 24, 456, 23, 457, 23, 456, 23, 458, 20, 460, 21, 459, 22, 459, 22, 459, 21, 460, 20, 460, 20, 459, 21, 459, 21, 459, 21, 458, 21, 460, 20, 460, 19, 461, 18, 463, 10, 1, 3, 467, 8, 474, 3, 24001, 4, 475, 6, 474, 6, 469, 11, 468, 12, 467, 13, 466, 14, 466, 14, 466, 14, 465, 18, 463, 18, 462, 19, 461, 20, 461, 19, 462, 18, 463, 17, 462, 18, 461, 20, 460, 21, 459, 22, 457, 23, 458, 22, 458, 22, 458, 23, 458, 23, 458, 23, 459, 3, 4, 14, 467, 13, 467, 13, 467, 13, 468, 12, 467, 13, 467, 12, 468, 12, 467, 13, 468, 12, 468, 12, 468, 11, 470, 10, 471, 8, 474, 3, 22057, 7, 1, 6, 465, 16, 463, 18, 461, 20, 460, 20, 460, 20, 459, 21, 460, 20, 460, 20, 460, 19, 462, 18, 463, 17, 462, 18, 461, 19, 461, 18, 462, 18, 461, 18, 462, 15, 465, 14, 465, 15, 465, 14, 467, 13, 467, 13, 467, 13, 4, 6, 452, 18, 3, 8, 450, 19, 2, 10, 448, 20, 1, 12, 446, 21, 1, 12, 446, 21, 1, 12, 446, 34, 445, 35, 445, 35, 446, 35, 445, 36, 444, 37, 444, 37, 444, 36, 445, 35, 445, 35, 444, 36, 445, 12, 4, 19, 445, 12, 4, 18, 446, 11, 5, 18, 447, 10, 6, 16, 449, 8, 8, 12, 454, 3, 13, 4, 5967) + // annId 900100218891 + val _rle2 = Array[Int](636, 27, 6, 31, 416, 30, 2, 35, 413, 68, 412, 70, 410, 71, 409, 72, 408, 73, 407, 74, 406, 74, 406, 75, 405, 75, 406, 75, 405, 75, 406, 74, 406, 74, 407, 74, 407, 73, 407, 74, 406, 74, 406, 74, 406, 74, 406, 74, 406, 74, 406, 74, 407, 73, 407, 73, 407, 73, 406, 74, 406, 74, 406, 74, 406, 74, 406, 75, 405, 75, 405, 75, 405, 75, 405, 75, 405, 76, 405, 75, 405, 76, 404, 76, 404, 79, 400, 83, 397, 84, 396, 86, 394, 87, 393, 88, 392, 89, 391, 90, 390, 90, 390, 91, 389, 91, 389, 92, 388, 92, 388, 92, 388, 92, 389, 91, 389, 91, 389, 91, 389, 91, 389, 91, 389, 91, 389, 91, 389, 91, 389, 90, 390, 90, 390, 90, 390, 89, 391, 98, 382, 101, 379, 102, 378, 104, 376, 105, 375, 106, 374, 108, 372, 111, 369, 112, 368, 115, 365, 116, 364, 118, 362, 125, 355, 128, 352, 129, 351, 131, 349, 132, 348, 133, 347, 138, 342, 141, 339, 142, 338, 144, 336, 145, 336, 145, 335, 146, 335, 146, 334, 61, 2, 83, 335, 60, 2, 84, 335, 58, 5, 82, 11, 14, 310, 26, 1, 30, 7, 82, 7, 20, 309, 23, 2, 29, 13, 77, 5, 25, 307, 20, 6, 27, 15, 75, 3, 28, 307, 18, 8, 23, 19, 74, 1, 32, 307, 14, 11, 19, 23, 107, 310, 6, 17, 14, 27, 107, 335, 9, 29, 108, 338, 3, 31, 109, 371, 119, 361, 122, 360, 123, 361, 10, 3, 107, 375, 107, 372, 109, 371, 110, 369, 112, 367, 114, 366, 115, 364, 117, 363, 118, 362, 118, 361, 120, 360, 120, 360, 121, 277, 8, 74, 121, 274, 12, 73, 121, 273, 13, 73, 122, 273, 12, 73, 122, 274, 11, 73, 122, 274, 11, 23, 1, 3, 2, 44, 123, 274, 10, 29, 1, 43, 123, 275, 9, 31, 1, 41, 123, 265, 1, 11, 7, 33, 1, 39, 124, 263, 2, 12, 6, 73, 124, 263, 3, 10, 7, 73, 124, 262, 4, 9, 14, 33, 1, 33, 124, 262, 5, 6, 22, 13, 17, 32, 123, 262, 36, 5, 22, 32, 123, 262, 38, 2, 24, 32, 122, 262, 64, 32, 122, 262, 65, 32, 122, 261, 65, 32, 122, 261, 65, 33, 122, 260, 65, 34, 121, 260, 65, 34, 2, 2, 117, 260, 65, 39, 117, 259, 65, 39, 117, 259, 65, 40, 116, 260, 64, 40, 116, 260, 64, 41, 116, 260, 63, 41, 116, 260, 63, 43, 115, 260, 61, 47, 112, 261, 60, 51, 108, 261, 60, 61, 99, 262, 11, 10, 24, 76, 97, 263, 7, 14, 19, 80, 97, 264, 5, 16, 15, 83, 97, 266, 2, 17, 12, 86, 98, 285, 8, 89, 98, 285, 6, 91, 99, 381, 99, 381, 99, 381, 100, 381, 100, 380, 100, 381, 100, 380, 100, 381, 100, 380, 100, 381, 99, 382, 99, 381, 99, 257, 10, 115, 98, 254, 23, 5, 4, 96, 98, 253, 42, 87, 98, 251, 45, 86, 98, 250, 48, 84, 98, 249, 50, 83, 98, 248, 52, 82, 98, 248, 53, 81, 98, 247, 55, 80, 98, 246, 56, 80, 98, 246, 57, 79, 98, 245, 58, 79, 98, 245, 59, 78, 98, 245, 59, 78, 97, 246, 59, 78, 97, 246, 59, 78, 96, 247, 59, 79, 95, 247, 59, 79, 95, 247, 59, 80, 93, 248, 59, 80, 92, 249, 59, 81, 91, 249, 59, 81, 90, 250, 58, 81, 90, 252, 53, 74, 100, 253, 49, 75, 102, 255, 44, 77, 103, 256, 42, 70, 110, 259, 40, 68, 85, 1, 26, 261, 37, 68, 86, 4, 22, 263, 21, 8, 5, 69, 88, 7, 16, 268, 17, 82, 89, 12, 8, 273, 13, 84, 90, 294, 12, 84, 90, 296, 10, 84, 89, 301, 5, 85, 88, 300, 7, 85, 88, 298, 9, 85, 87, 297, 11, 85, 86, 297, 12, 84, 86, 297, 13, 84, 85, 297, 14, 84, 84, 297, 15, 83, 83, 298, 17, 82, 82, 298, 18, 81, 81, 300, 18, 80, 82, 299, 19, 74, 88, 298, 22, 68, 92, 298, 24, 63, 95, 297, 27, 59, 97, 297, 34, 50, 98, 298, 39, 45, 98, 297, 42, 43, 97, 298, 43, 42, 97, 298, 44, 41, 97, 298, 45, 40, 96, 299, 52, 33, 95, 300, 57, 28, 95, 300, 58, 27, 94, 301, 58, 27, 93, 302, 59, 26, 92, 303, 59, 26, 91, 304, 59, 26, 90, 305, 60, 25, 88, 307, 60, 25, 87, 308, 60, 26, 84, 310, 60, 26, 81, 313, 60, 27, 76, 317, 60, 25, 69, 327, 59, 24, 69, 328, 59, 23, 68, 331, 58, 22, 68, 332, 58, 22, 66, 335, 57, 21, 64, 338, 57, 20, 61, 343, 56, 20, 55, 350, 55, 19, 56, 350, 55, 19, 56, 351, 53, 20, 56, 352, 52, 20, 56, 353, 41, 3, 6, 21, 56, 354, 36, 10, 3, 21, 56, 356, 34, 34, 56, 357, 20, 8, 4, 44, 47, 358, 19, 56, 47, 357, 20, 56, 47, 356, 21, 56, 47, 355, 22, 56, 46, 355, 23, 56, 46, 354, 24, 56, 46, 354, 25, 10, 2, 38, 50, 354, 26, 8, 4, 36, 51, 354, 39, 15, 4, 18, 49, 355, 39, 10, 15, 13, 48, 354, 66, 12, 47, 355, 66, 14, 43, 357, 67, 14, 20, 3, 18, 357, 68, 15, 18, 6, 14, 359, 68, 17, 14, 12, 6, 363, 69, 20, 6, 385, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 69, 411, 68, 412, 68, 412, 67, 413, 67, 413, 67, 413, 66, 414, 65, 415, 65, 415, 64, 416, 63, 417, 43, 3, 16, 418, 42, 6, 13, 419, 40, 10, 10, 421, 38, 12, 7, 423, 36, 14, 6, 425, 34, 15, 4, 426, 34, 446, 33, 446, 34, 446, 33, 447, 31, 448, 26, 454, 21, 459, 20, 460, 20, 460, 20, 460, 20, 460, 21, 459, 21, 459, 21, 459, 21, 459, 21, 459, 21, 459, 21, 459, 21, 459, 25, 455, 29, 451, 31, 450, 33, 447, 35, 446, 31, 1, 4, 444, 29, 7, 5, 440, 27, 9, 4, 440, 26, 11, 2, 442, 24, 457, 22, 458, 21, 460, 20, 461, 19, 462, 18, 463, 19, 463, 19, 462, 20, 462, 22, 461, 16, 468, 8, 2410, 3, 474, 6, 12, 1, 459, 8, 9, 7, 454, 16, 1, 10, 451, 31, 447, 34, 444, 37, 4, 12, 426, 57, 422, 61, 418, 63, 416, 66, 413, 68, 411, 70, 410, 71, 408, 73, 406, 75, 405, 76, 403, 78, 402, 78, 402, 79, 400, 80, 400, 81, 399, 81, 399, 81, 399, 82, 398, 82, 398, 82, 398, 82, 398, 82, 398, 82, 398, 82, 398, 82, 396, 84, 394, 86, 392, 88, 390, 90, 389, 91, 388, 92, 387, 93, 386, 94, 385, 94, 385, 95, 385, 94, 385, 95, 384, 96, 384, 97, 382, 98, 382, 98, 382, 99, 5, 13, 362, 100, 2, 19, 359, 124, 356, 125, 355, 127, 353, 128, 352, 129, 351, 130, 350, 131, 349, 132, 348, 133, 347, 134, 346, 134, 346, 135, 345, 135, 345, 136, 344, 136, 345, 135, 345, 136, 345, 46, 1, 88, 345, 135, 346, 134, 346, 134, 347, 133, 348, 132, 348, 132, 347, 133, 347, 133, 346, 134, 346, 134, 346, 134, 345, 135, 345, 135, 345, 134, 346, 134, 346, 133, 347, 133, 347, 133, 347, 132, 348, 131, 349, 131, 349, 130, 350, 129, 351, 128, 352, 127, 353, 124, 357, 119, 361, 114, 367, 110, 370, 108, 372, 102, 1, 2, 375, 100, 380, 97, 384, 92, 388, 92, 389, 91, 389, 92, 389, 91, 389, 91, 389, 91, 389, 91, 389, 91, 389, 91, 390, 90, 390, 90, 391, 89, 391, 89, 392, 88, 392, 88, 393, 87, 393, 87, 394, 85, 396, 84, 396, 83, 398, 82, 399, 81, 399, 80, 401, 78, 401, 79, 400, 79, 401, 78, 401, 78, 401, 79, 401, 80, 399, 88, 392, 89, 391, 89, 45, 3, 342, 90, 44, 11, 17, 2, 316, 91, 41, 20, 9, 4, 315, 94, 36, 29, 2, 4, 315, 98, 31, 36, 315, 102, 25, 38, 315, 105, 21, 39, 315, 109, 12, 44, 315, 164, 316, 162, 318, 161, 318, 152, 328, 137, 343, 133, 347, 130, 350, 129, 351, 129, 351, 128, 353, 126, 355, 125, 356, 123, 357, 121, 360, 119, 361, 117, 364, 114, 366, 113, 367, 112, 368, 112, 368, 111, 369, 111, 369, 111, 369, 110, 370, 110, 370, 110, 370, 110, 370, 109, 371, 109, 371, 109, 372, 108, 372, 108, 373, 107, 373, 108, 373, 107, 373, 48, 2, 58, 373, 47, 5, 56, 373, 46, 11, 51, 372, 45, 12, 51, 373, 43, 13, 52, 373, 42, 13, 19, 1, 31, 375, 40, 14, 17, 5, 29, 376, 38, 16, 14, 8, 27, 378, 36, 17, 12, 11, 26, 379, 34, 19, 10, 14, 23, 381, 30, 22, 8, 17, 24, 381, 22, 32, 3, 17, 28, 379, 18, 54, 33, 27, 2, 348, 14, 56, 33, 18, 14, 348, 4, 62, 35, 7, 26, 412, 70, 409, 72, 408, 73, 331, 6, 69, 75, 329, 8, 68, 76, 327, 10, 66, 78, 326, 11, 65, 79, 324, 12, 64, 81, 321, 14, 64, 81, 319, 15, 64, 83, 316, 17, 64, 83, 119, 15, 181, 18, 63, 85, 118, 18, 177, 19, 63, 85, 119, 20, 173, 20, 62, 86, 119, 23, 169, 21, 62, 87, 118, 24, 167, 22, 61, 88, 118, 26, 164, 24, 60, 88, 118, 26, 164, 25, 16, 2, 40, 89, 118, 26, 163, 27, 14, 3, 35, 94, 118, 26, 162, 31, 10, 5, 34, 94, 118, 26, 162, 34, 7, 5, 34, 94, 118, 26, 161, 48, 33, 97, 115, 26, 161, 48, 33, 100, 112, 26, 161, 48, 32, 104, 79, 6, 24, 26, 160, 50, 31, 105, 77, 21, 10, 26, 160, 50, 31, 107, 74, 27, 5, 26, 160, 50, 30, 109, 70, 61, 160, 50, 30, 110, 65, 65, 160, 50, 30, 111, 61, 68, 160, 50, 30, 112, 57, 71, 160, 50, 21, 122, 53, 74, 160, 50, 15, 129, 50, 76, 160, 50, 12, 133, 47, 78, 160, 50, 10, 135, 45, 80, 160, 50, 8, 138, 43, 81, 160, 50, 6, 140, 42, 82, 160, 35, 3, 12, 5, 142, 40, 83, 160, 22, 3, 7, 10, 7, 4, 144, 39, 84, 160, 14, 1, 6, 24, 1, 6, 145, 38, 85, 161, 10, 7, 1, 33, 146, 36, 86, 161, 11, 39, 147, 35, 87, 162, 11, 37, 148, 34, 88, 162, 11, 36, 149, 34, 88, 163, 10, 34, 151, 33, 89, 163, 10, 32, 153, 32, 90, 164, 9, 30, 155, 32, 90, 165, 8, 28, 157, 31, 91, 165, 9, 25, 159, 31, 91, 166, 9, 4, 6, 12, 161, 31, 91, 167, 22, 4, 165, 30, 92, 168, 190, 30, 92, 169, 189, 30, 92, 171, 187, 30, 92, 172, 186, 30, 92, 174, 183, 31, 92, 177, 180, 31, 92, 181, 8, 1, 166, 32, 92, 190, 166, 32, 92, 190, 166, 32, 92, 190, 165, 33, 92, 190, 164, 34, 92, 190, 164, 34, 92, 190, 163, 35, 92, 190, 162, 36, 92, 190, 161, 37, 92, 190, 160, 38, 92, 190, 159, 40, 91, 190, 157, 42, 91, 190, 156, 44, 90, 190, 154, 46, 90, 190, 153, 48, 89, 191, 152, 48, 89, 191, 152, 49, 88, 192, 152, 49, 87, 192, 152, 41, 95, 193, 151, 24, 112, 193, 151, 21, 115, 194, 150, 19, 117, 195, 149, 17, 119, 195, 149, 15, 121, 195, 149, 14, 122, 195, 149, 13, 123, 195, 149, 12, 124, 195, 149, 11, 125, 195, 149, 10, 126, 195, 149, 9, 127, 195, 149, 9, 127, 195, 149, 8, 128, 195, 148, 8, 129, 195, 148, 8, 129, 195, 147, 8, 130, 195, 147, 8, 130, 195, 147, 8, 130, 195, 146, 8, 131, 195, 145, 9, 131, 195, 145, 9, 131, 195, 144, 10, 131, 196, 142, 11, 131, 196, 141, 12, 131, 197, 139, 13, 131, 197, 138, 14, 130, 199, 135, 16, 129, 200, 134, 17, 128, 202, 131, 19, 126, 205, 110, 1, 16, 22, 125, 6) + // scalastyle:on + + val rle1 = RLEMasks(_rle1, 480, 640) + val rle2 = RLEMasks(_rle2, 480, 640) + + "rleToOneBbox" should "run well" in { + rle1.bbox should be (142f, 245f, 486f + 141, 111f + 244) + rle2.bbox should be (1f, 155f, 639f, 325f + 154) + } + + "bboxIOU" should "run well" in { + MaskUtils.bboxIOU((142f, 245f, 486f + 141, 111f + 244), (1f, 155f, 639f, 325f + 154), + false) should be (0.25976165f) + } + + "rleArea" should "run well" in { + MaskUtils.rleArea(rle1) should be (5976) + MaskUtils.rleArea(rle2) should be (77429) + } + + "rleIOU" should "run well" in { + MaskUtils.rleIOU(rle1, rle2, true) should be (0.58199465f) + MaskUtils.rleIOU(rle1, rle2, false) should be (0.04351471f +- 0.0000001f) + MaskUtils.rleIOU(rle2, rle1, false) should be (0.04351471f +- 0.0000001f) + MaskUtils.rleIOU(rle2, rle1, true) should be (0.04491857f) + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index 763c1256480..37423674a01 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} import scala.collection.mutable.ArrayBuffer @@ -102,6 +103,67 @@ class ValidationSpec extends FlatSpec with Matchers { result.result()._1 should be(0.303571429f +- 1e-5f) } + "MAPValidationResult" should "merge well" in { + def predictForClass1: Array[ArrayBuffer[(Float, Boolean)]] = (1 to 5).map(i => { + val p = new ArrayBuffer[(Float, Boolean)] + for (j <- 101 to 200) { + p.append((j.toFloat, true)) + } + p + }).toArray + + def predictForClass2: Array[ArrayBuffer[(Float, Boolean)]] = (1 to 5).map(i => { + val p = new ArrayBuffer[(Float, Boolean)] + for (j <- 201 to 210) { + p.append((j.toFloat, true)) + } + p + }).toArray + + def predictForClass3: Array[ArrayBuffer[(Float, Boolean)]] = (1 to 5).map(i => { + val p = new ArrayBuffer[(Float, Boolean)] + for (j <- 51 to 100) { + p.append((j.toFloat, true)) + } + for (j <- 211 to 260) { + p.append((j.toFloat, true)) + } + p + }).toArray + + { + val vr1 = new MAPValidationResult(5, -1, predictForClass1, Array(1, 2, 3, 4, 5)) + val vr2 = new MAPValidationResult(5, -1, predictForClass2, Array(6, 7, 8, 9, 10)) + val vr3 = new MAPValidationResult(5, -1, predictForClass3, Array(3, 2, 1, 0, 2)) + + val tmpv = vr1 + vr2 + tmpv.asInstanceOf[MAPValidationResult].predictForClass.foreach(p => { + p.zip(101 to 210).foreach(p => p._1._1 should be (p._2.toFloat)) + }) + vr1 + vr3 + vr1.asInstanceOf[MAPValidationResult].predictForClass.foreach(p => { + p.sortBy(_._1).zip(51 to 260).foreach(p => p._1._1 should be (p._2.toFloat)) + }) + } + + { + val vr1 = new MAPValidationResult(5, 150, predictForClass1, Array(1, 2, 3, 4, 5)) + val vr2 = new MAPValidationResult(5, 150, predictForClass2, Array(6, 7, 8, 9, 10)) + val vr3 = new MAPValidationResult(5, 150, predictForClass3, Array(3, 2, 1, 0, 2)) + + val tmpv = vr1 + vr2 + tmpv.asInstanceOf[MAPValidationResult].predictForClass.foreach(p => { + p.sortBy(_._1).zip(101 to 210).foreach(p => p._1._1 should be (p._2.toFloat)) + }) + vr1 + vr3 + vr1.asInstanceOf[MAPValidationResult].predictForClass.foreach(p => { + p.sortBy(_._1).zip(111 to 260).foreach(p => p._1._1 should be (p._2.toFloat)) + }) + vr1.gtCntForClass.zip(Array(10, 11, 12, 13, 17)).foreach(p => p._1 should be (p._2)) + } + + } + "MeanAveragePrecision" should "be correct on 1d tensor" in { implicit val numeric = TensorNumeric.NumericFloat val output = Tensor[Float]( @@ -166,7 +228,7 @@ class ValidationSpec extends FlatSpec with Matchers { result.result()._1 should be(0.303571429f +- 1e-5f) } - "MeanAveragePrecisionObjectDetection" should "be correct on 2d tensor" in { + "MeanAveragePrecisionObjectDetection" should "be correct" in { implicit val numeric = TensorNumeric.NumericFloat val output = Tensor[Float]( T( @@ -183,24 +245,113 @@ class ValidationSpec extends FlatSpec with Matchers { ) )) - // - val target = Tensor[Float]( - T( - T(0, 1, 0, 100, 100, 200, 200), - T(0, 1, 0, 300, 100, 400, 200), - T(0, 1, 0, 100, 300, 200, 400), - T(0, 1, 0, 300, 300, 400, 400), - T(0, 1, 0, 210, 210, 230, 290), - T(0, 2, 0, 1100, 1100, 1200, 1200), - T(0, 2, 0, 1300, 1100, 1400, 1200), - T(0, 2, 0, 1100, 1300, 1200, 1400), - T(0, 2, 0, 1300, 1300, 1400, 1400), - T(0, 2, 0, 1210, 1210, 1230, 1290) - )) - val v = new MeanAveragePrecisionObjectDetection(3, 0.5f) + val target = T( + T() + .update(RoiLabel.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0, 0, 0, 0, 0, 0))) + .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0, 1, 1, 1, 1, 1))) + .update(RoiLabel.BBOXES, Tensor[Float](T( + T(100, 100, 200, 200), + T(300, 100, 400, 200), + T(100, 300, 200, 400), + T(300, 300, 400, 400), + T(210, 210, 230, 290), + T(1100, 1100, 1200, 1200), + T(1300, 1100, 1400, 1200), + T(1100, 1300, 1200, 1400), + T(1300, 1300, 1400, 1400), + T(1210, 1210, 1230, 1290) + )) + ) + ) + + val v = new MeanAveragePrecisionObjectDetection(3) val result = v(output, target) // 0.5f and 0.55f result.result()._1 should be(0.35f +- 1e-5f) + + val outputTable = T( + T() + .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0, 1, 1, 1, 1))) + .update(RoiLabel.BBOXES, Tensor[Float](T( + T(110, 90, 210, 190), + T(310, 110, 410, 210), + T(320, 290, 420, 390), + T(210, 310, 290, 410), + T(1110, 1090, 1210, 1190), + T(1310, 1110, 1410, 1210), + T(1320, 1290, 1420, 1390), + T(1210, 1310, 1290, 1410) + )) + ) + .update(RoiLabel.SCORES, Tensor[Float](T(1, 2, 4, 3, 1, 3, 4, 2))) + ) + val v2 = new MeanAveragePrecisionObjectDetection(3) + val result2 = v2(outputTable, target) + // 0.5f and 0.55f + result2.result()._1 should be(0.35f +- 1e-5f) + } + + "MeanAveragePrecisionObjectDetection" should "be correct on empty detections" in { + val target = T( + T() + .update(RoiLabel.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0))) + .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0))) + .update(RoiLabel.BBOXES, Tensor[Float](T( + T(100, 100, 200, 200), + T(300, 100, 400, 200), + T(100, 300, 200, 400), + T(300, 300, 400, 400), + T(210, 210, 230, 290) + )) + ) + ) + val outputTable = T(T()) + val v = new MeanAveragePrecisionObjectDetection[Float](3) + val result = v(outputTable, target) + result.result()._1 should be(0f) + } + + "MeanAveragePrecisionObjectDetection" should "be correct on empty targets" in { + val target = T( + T() + .update(RoiLabel.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0))) + .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0))) + .update(RoiLabel.BBOXES, Tensor[Float](T( + T(100, 100, 200, 200), + T(300, 100, 400, 200), + T(100, 300, 200, 400), + T(300, 300, 400, 400), + T(210, 210, 230, 290) + )) + ), + // Empty target + T() + ) + val outputTable = T( + T() + .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0))) + .update(RoiLabel.BBOXES, Tensor[Float](T( + T(110, 90, 210, 190), + T(310, 110, 410, 210), + T(320, 290, 420, 390), + T(210, 310, 290, 410) + )) + ) + .update(RoiLabel.SCORES, Tensor[Float](T(1, 2, 9, 7))), + T() + .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0))) + .update(RoiLabel.BBOXES, Tensor[Float](T( + T(1110, 1090, 1210, 1190), + T(1310, 1110, 1410, 1210), + T(1320, 1290, 1420, 1390), + T(1210, 1310, 1290, 1410) + )) + ) + .update(RoiLabel.SCORES, Tensor[Float](T(0, 5, 4, 8))) + ) + val v = new MeanAveragePrecisionObjectDetection[Float](3) + val result = v(outputTable, target) + result.result()._1 should be(0.123809524f +- 0.00000001f) } "treeNN accuracy" should "be correct on 2d tensor" in { From 5248f993cc1749bb09502c599fdb81b8f1a302fb Mon Sep 17 00:00:00 2001 From: Menooker Date: Mon, 28 Oct 2019 10:07:45 +0800 Subject: [PATCH 0980/1065] COCO Seq file reader: grey to bgr (#2942) * grey to bgr * refactor isGrayScaleImage * simplify grey scale image checking --- .../bigdl/dllib/feature/dataset/DataSet.scala | 22 +++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index 2601a07d3a4..7c5217546d5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -26,7 +26,8 @@ import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame} import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T} -import java.awt.image.DataBufferByte +import java.awt.Color +import java.awt.image.{BufferedImage, DataBufferByte} import java.io.ByteArrayInputStream import javax.imageio.ImageIO import org.apache.hadoop.io.{BytesWritable, Text} @@ -589,6 +590,12 @@ object DataSet { ImageFrame.rdd(rawData) } + private def isSingleChannelImage(image: BufferedImage): Boolean = { + if (image.getType == BufferedImage.TYPE_BYTE_GRAY) return true + if (image.getType == BufferedImage.TYPE_USHORT_GRAY) return true + if (image.getRaster.getNumBands == 1) return true + false + } /** * Extract hadoop sequence files from an HDFS path as ImageFrame * @param url sequence files folder path @@ -620,8 +627,19 @@ object DataSet { require(metaBytes.getInt == COCODataset.MAGIC_NUM, "Corrupted metadata") val inputStream = new ByteArrayInputStream(data._2.getBytes) - val image = ImageIO.read(inputStream) + val image = { + val img = ImageIO.read(inputStream) + if (isSingleChannelImage(img)) { + val imageBuff: BufferedImage = + new BufferedImage(img.getWidth(), img.getHeight(), BufferedImage.TYPE_3BYTE_BGR) + imageBuff.getGraphics.drawImage(img, 0, 0, new Color(0, 0, 0), null) + imageBuff + } else { + img + } + } val rawdata = image.getRaster.getDataBuffer.asInstanceOf[DataBufferByte].getData() + require(rawdata.length == height * width * 3) val imf = ImageFeature(rawdata, RoiLabel(labelClasses, bboxes, masks), fileName) imf(ImageFeature.originalSize) = (height, width, 3) imf(RoiLabel.ISCROWD) = isCrowd From c08b8014d5dc150c976983d418259b84d8035acc Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Mon, 28 Oct 2019 12:50:07 +0800 Subject: [PATCH 0981/1065] Add the flushing denormal values option on BigDL side (#2934) --- .../analytics/bigdl/utils/ThreadPool.scala | 4 + .../bigdl/utils/ThreadPoolSpec.scala | 99 +++++++++++++++++++ 2 files changed, 103 insertions(+) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 8e229209978..7a8eea87427 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -103,6 +103,10 @@ class ThreadPool(private var poolSize: Int) { this.invokeAndWait2((0 until 1).map(_ => () => { + if (System.getProperty("bigdl.flushDenormalState", "true").toBoolean) { + BackendMklDnn.setFlushDenormalState() + } + require(MKL.isMKLLoaded) require(BackendMklDnn.isLoaded) diff --git a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala index 46107a06565..4f89cec2963 100644 --- a/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala +++ b/scala/common/utils/src/test/scala/com/intel/analytics/bigdl/utils/ThreadPoolSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.utils import com.intel.analytics.bigdl.mkl.hardware.Affinity +import com.intel.analytics.bigdl.tensor.Tensor import org.scalatest.{FlatSpec, Matchers} import scala.concurrent.ExecutionException @@ -121,4 +122,102 @@ class ThreadPoolSpec extends FlatSpec with Matchers { results.foreach(_.get()) } } + + "setFlushDenormalState" should "influence float arithmetic operations" in { + val poolSize = 1 + val ompSize = 4 + + val threadPool = new ThreadPool(poolSize) + + System.setProperty("bigdl.flushDenormalState", "false") + threadPool.setMKLThreadOfMklDnnBackend(ompSize) + + threadPool.invokeAndWait2( (0 until 1).map(i => () => { + // A denormalized value is in the range + // from 1.4E-45 to 1.18E-38, + // or from -1.18E-38 to -1.4E-45 + val denormal: Float = -1.234E-41F + val floatOne: Float = 1.0F + + val result: Float = denormal * floatOne + // The result should not be zero without setting + (result == 0.0F) should be(false) + })) + + System.setProperty("bigdl.flushDenormalState", "true") + threadPool.setMKLThreadOfMklDnnBackend(ompSize) + + threadPool.invokeAndWait2( (0 until 1).map(i => () => { + // A denormalized value is in the range + // from 1.4E-45 to 1.18E-38, + // or from -1.18E-38 to -1.4E-45 + val denormal: Float = -1.234E-41F + val floatOne: Float = 1.0F + + val result = denormal * floatOne + // The result should be zero with setting + (result == 0.0F) should be (true) + })) + + System.clearProperty("bigdl.flushDenormalState") + } + + "setFlushDenormalState" should "not influence other threads float arithmetic operations" in { + val poolSize = 1 + val ompSize = 4 + + val threadPool1 = new ThreadPool(poolSize) + System.setProperty("bigdl.flushDenormalState", "true") + threadPool1.setMKLThreadOfMklDnnBackend(ompSize) + + val threadPool2 = new ThreadPool(poolSize) + System.setProperty("bigdl.flushDenormalState", "false") + threadPool2.setMKLThreadOfMklDnnBackend(ompSize) + + // A denormalized value is in the range + // from 1.4E-45 to 1.18E-38, + // or from -1.18E-38 to -1.4E-45 + val denormal: Float = -1.234E-41F + val floatOne: Float = 1.0F + + threadPool1.invokeAndWait2( (0 until 1).map(i => () => { + val result = denormal * floatOne + // The result should be zero with setting + (result == 0.0F) should be (true) + })) + + threadPool2.invokeAndWait2( (0 until 1).map(i => () => { + val result = denormal * floatOne + // The result should not be zero without setting + (result == 0.0F) should be (false) + })) + + System.clearProperty("bigdl.flushDenormalState") + } + + "setFlushDenormalState" should "not influence other threads MKL operations" in { + val poolSize = 1 + val ompSize = 4 + + val threadPool1 = new ThreadPool(poolSize) + System.setProperty("bigdl.flushDenormalState", "false") + threadPool1.setMKLThreadOfMklDnnBackend(ompSize) + + val threadPool2 = new ThreadPool(poolSize) + System.setProperty("bigdl.flushDenormalState", "true") + threadPool2.setMKLThreadOfMklDnnBackend(ompSize) + + val tensor = Tensor[Float](1).fill(-1.234E-41F) + val floatOne = 1.0F + + threadPool1.invokeAndWait2( (0 until 1).map(i => () => { + val result = tensor.pow(floatOne) + + for (i <- 0 to result.storage().array().length -1) { + assert(result.storage().array()(i) != 0.0F) + } + })) + + System.clearProperty("bigdl.flushDenormalState") + } } From 9df21064f76202b8f226be45a8ce398375c1be50 Mon Sep 17 00:00:00 2001 From: Xiao Date: Mon, 28 Oct 2019 13:35:21 +0800 Subject: [PATCH 0982/1065] add no argument apply api for softmax (#2945) * add no argument apply api for softmax * add no argument apply api for softmax --- .../analytics/bigdl/dllib/nn/SoftMax.scala | 4 +++ .../bigdl/dllib/torch/SoftMaxSpec.scala | 29 ++++++++++++++++++- 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala index a5d233f3d16..74ceb8a8081 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SoftMax.scala @@ -84,6 +84,10 @@ object SoftMax{ (implicit ev: TensorNumeric[T]) : SoftMax[T] = { new SoftMax[T](pos) } + def apply[@specialized(Float, Double) T: ClassTag] + (implicit ev: TensorNumeric[T]) : SoftMax[T] = { + new SoftMax[T](1) + } // Notice: SoftMin will call this function private[nn] def updateOutput[T: ClassTag](input: Tensor[T], output: Tensor[T], results: Array[Future[Unit]], pos: Int = 1) (implicit ev: TensorNumeric[T]): Tensor[T] = { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala index 4a92954a1fd..71780722265 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala @@ -23,7 +23,7 @@ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial class SoftMaxSpec extends TorchSpec { - "A SoftMax 1D input" should "generate correct output and grad" in { + "A SoftMax 1D input" should "generate correct output and grad" in { torchCheck() val layer = new SoftMax[Double]() val input = Tensor[Double](10) @@ -52,6 +52,33 @@ class SoftMaxSpec extends TorchSpec { println("Test case : SoftMax, Torch : " + luaTime + " s, Scala : " + scalaTime / 1e9 + " s") } + "A SoftMax 1D input without argument" should "generate correct output and grad" in { + torchCheck() + val layer = new SoftMax[Double] + val input = Tensor[Double](10) + input.apply1(_ => Random.nextDouble()) + val gradOutput = Tensor[Double](10) + gradOutput.apply1(_ => Random.nextDouble()) + + val start = System.nanoTime() + val output = layer.forward(input) + val gradInput = layer.backward(input, gradOutput) + val end = System.nanoTime() + val scalaTime = end - start + + val code = "module = nn.SoftMax()\n" + + "output = module:forward(input)\n" + + "gradInput = module:backward(input,gradOutput)" + + val (luaTime, torchResult) = TH.run(code, Map("input" -> input, "gradOutput" -> gradOutput), + Array("output", "gradInput")) + val luaOutput = torchResult("output").asInstanceOf[Tensor[Double]] + val luaGradInput = torchResult("gradInput").asInstanceOf[Tensor[Double]] + + output should be (luaOutput) + gradInput should be (luaGradInput) + } + "A SoftMax 2D input" should "generate correct output and grad" in { torchCheck() val layer = new SoftMax[Double]() From 6ef00e6805163325fe0518d6c466d277cf0db3ba Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 28 Oct 2019 16:07:27 +0800 Subject: [PATCH 0983/1065] add maskrcnn inference example (#2944) * add maskrcnn inference example * meet pr comments * add model download url --- .../bigdl/dllib/feature/dataset/DataSet.scala | 9 +- .../vision/image/MTImageFeatureToBatch.scala | 47 +++++--- .../vision/image/label/roi/RoiLabel.scala | 12 +- .../dllib/models/maskrcnn/MaskRCNN.scala | 114 +++++++++++------- .../bigdl/dllib/models/maskrcnn/README.md | 71 +++++++++++ .../bigdl/dllib/models/maskrcnn/Test.scala | 89 ++++++++++++++ .../analytics/bigdl/dllib/nn/BoxHead.scala | 21 ++-- .../analytics/bigdl/dllib/nn/Pooler.scala | 8 +- .../bigdl/dllib/nn/RegionProposal.scala | 4 + .../bigdl/dllib/optim/Evaluator.scala | 6 +- .../image/MTImageFeatureToBatchSpec.scala | 5 +- 11 files changed, 294 insertions(+), 92 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/README.md create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index 7c5217546d5..0b491a63c51 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -356,13 +356,14 @@ object DataSet { /** * Wrap a RDD as a DataSet. * @param data + * @param partitionNum repartition data rdd to partition number, default node number. * @tparam T * @return */ - def rdd[T: ClassTag](data: RDD[T]): DistributedDataSet[T] = { - val nodeNumber = Engine.nodeNumber() + def rdd[T: ClassTag](data: RDD[T], partitionNum: Int = Engine.nodeNumber() + ): DistributedDataSet[T] = { new CachedDistriDataSet[T]( - data.coalesce(nodeNumber, true) + data.coalesce(partitionNum, true) .mapPartitions(iter => { Iterator.single(iter.toArray) }).setName("cached dataset") @@ -646,7 +647,7 @@ object DataSet { imf } .coalesce(num) - DataSet.rdd(rawData) + DataSet.rdd(rawData, num) } private[bigdl] def filesToImageFeatureDataset(url: String, sc: SparkContext, diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala index 20f1fb409a4..a88741aa6cb 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -16,11 +16,14 @@ package com.intel.analytics.bigdl.transform.vision.image import java.util.concurrent.atomic.AtomicInteger + import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, Transformer, Utils} +import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.{Engine, T, Table} + import scala.collection.mutable.IndexedSeq import scala.reflect.ClassTag @@ -199,23 +202,26 @@ class ClassificationMTImageFeatureToBatch private[bigdl](width: Int, height: Int * elements. The inner tensor holds the data for segmentation * RoiLabel.ISCROWD Whether each detection is crowd. (1 x N) Tensor[Float]. * -1: unknown, 0: not crowd, 1: is crowd - * RoiLabel.ORIGSIZE The original size of the image, tuple of (height, width, channels) + * RoiLabel.ImageInfo with shape (batchSize, 4), contains all images info + * (height, width, original height, original width) */ class RoiMiniBatch(val input: Tensor[Float], val target: IndexedSeq[RoiLabel], - val isCrowd: IndexedSeq[Tensor[Float]], val originalSizes: IndexedSeq[(Int, Int, Int)]) + val isCrowd: IndexedSeq[Tensor[Float]], val imageInfo: Tensor[Float] = null) extends MiniBatch[Float] { - override def size(): Int = { - input.size(1) - } + override def size(): Int = input.size(1) - override def getInput(): Tensor[Float] = input + override def getInput(): Activity = { + if (imageInfo == null) input else T(input, imageInfo) + } override def getTarget(): Table = { - val tables = (target, isCrowd, originalSizes).zipped.map { case (roiLabel, crowd, size) => + var i = 0 + val tables = (target, isCrowd).zipped.map { case (roiLabel, crowd) => + i += 1 roiLabel.toTable .update(RoiLabel.ISCROWD, crowd) - .update(RoiLabel.ORIGSIZE, size) + .update(RoiLabel.IMGINFO, imageInfo.select(1, i)) } T.seq(tables) } @@ -224,7 +230,7 @@ class RoiMiniBatch(val input: Tensor[Float], val target: IndexedSeq[RoiLabel], val subInput = input.narrow(1, offset, length) val subTarget = target.view(offset - 1, length) // offset starts from 1 val subIsCrowd = isCrowd.view(offset - 1, length) // offset starts from 1 - val subSize = originalSizes.view(offset - 1, length) // offset starts from 1 + val subSize = imageInfo.narrow(1, offset, length) RoiMiniBatch(subInput, subTarget, subIsCrowd, subSize) } @@ -236,8 +242,8 @@ class RoiMiniBatch(val input: Tensor[Float], val target: IndexedSeq[RoiLabel], object RoiMiniBatch { def apply(data: Tensor[Float], target: IndexedSeq[RoiLabel], - isCrowd: IndexedSeq[Tensor[Float]], originalSizes: IndexedSeq[(Int, Int, Int)]): - RoiMiniBatch = new RoiMiniBatch(data, target, isCrowd, originalSizes) + isCrowd: IndexedSeq[Tensor[Float]], imageInfo: Tensor[Float] = null): + RoiMiniBatch = new RoiMiniBatch(data, target, isCrowd, imageInfo) } @@ -259,7 +265,6 @@ class RoiMTImageFeatureToBatch private[bigdl](width: Int, height: Int, private val featureData: Array[Float] = new Array[Float](batchSize * frameLength * 3) private val labelData: Array[RoiLabel] = new Array[RoiLabel](batchSize) private val isCrowdData: Array[Tensor[Float]] = new Array[Tensor[Float]](batchSize) - private val origSizeData: Array[(Int, Int, Int)] = new Array[(Int, Int, Int)](batchSize) private var featureTensor: Tensor[Float] = null override protected def processImageFeature(img: ImageFeature, position: Int): Unit = { @@ -270,7 +275,6 @@ class RoiMTImageFeatureToBatch private[bigdl](width: Int, height: Int, "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") isCrowdData(position) = isCrowd labelData(position) = label - origSizeData(position) = img.getOriginalSize } override protected def createBatch(batchSize: Int): MiniBatch[Float] = { @@ -278,7 +282,7 @@ class RoiMTImageFeatureToBatch private[bigdl](width: Int, height: Int, featureTensor = Tensor(Storage[Float](featureData), storageOffset = 1, size = Array(batchSize, 3, height, width)) } - RoiMiniBatch(featureTensor, labelData.view, isCrowdData.view, origSizeData.view) + RoiMiniBatch(featureTensor, labelData.view, isCrowdData.view) } } @@ -298,7 +302,7 @@ class RoiImageFeatureToBatchWithResize private[bigdl](sizeDivisible: Int = -1, t private val labelData: Array[RoiLabel] = new Array[RoiLabel](batchSize) private val isCrowdData: Array[Tensor[Float]] = new Array[Tensor[Float]](batchSize) - private val origSizeData: Array[(Int, Int, Int)] = new Array[(Int, Int, Int)](batchSize) + private val imgInfoData: Tensor[Float] = Tensor[Float](batchSize, 4) private var featureTensor: Tensor[Float] = null private val imageBuffer = new Array[Tensor[Float]](batchSize) @@ -324,11 +328,16 @@ class RoiImageFeatureToBatchWithResize private[bigdl](sizeDivisible: Int = -1, t img.copyTo(imageBuffer(position).storage().array(), 0, toRGB = toRGB) val isCrowd = img(RoiLabel.ISCROWD).asInstanceOf[Tensor[Float]] val label = img.getLabel.asInstanceOf[RoiLabel] - require(label.bboxes.size(1) == isCrowd.size(1), "The number of detections" + - "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") + if (isCrowd != null && label != null) { + require(label.bboxes.size(1) == isCrowd.size(1), "The number of detections" + + "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") + } isCrowdData(position) = isCrowd labelData(position) = label - origSizeData(position) = img.getOriginalSize + imgInfoData.setValue(position + 1, 1, img.getHeight()) + imgInfoData.setValue(position + 1, 2, img.getWidth()) + imgInfoData.setValue(position + 1, 3, img.getOriginalHeight) + imgInfoData.setValue(position + 1, 4, img.getOriginalWidth) } override protected def createBatch(batchSize: Int): MiniBatch[Float] = { @@ -341,6 +350,6 @@ class RoiImageFeatureToBatchWithResize private[bigdl](sizeDivisible: Int = -1, t .narrow(3, 1, imageBuffer(i).size(3)).copy(imageBuffer(i)) } RoiMiniBatch(featureTensor, labelData.view(0, batchSize), - isCrowdData.view(0, batchSize), origSizeData.view(0, batchSize)) + isCrowdData.view(0, batchSize), imgInfoData.narrow(1, 1, batchSize)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala index 19d3eab5631..9bfd21a9c51 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -75,20 +75,16 @@ object RoiLabel { val MASKS = "masks" // ISCROWD and ORIGSIZE are stored in ImageFeature val ISCROWD = "is_crowd" - val ORIGSIZE = "size" + val IMGINFO = "imgInfo" val SCORES = "scores" - def getScores(tab: Table): Tensor[Float] = tab[Tensor[Float]](SCORES) + def getClasses(tab: Table): Tensor[Float] = tab[Tensor[Float]](CLASSES) def getBBoxes(tab: Table): Tensor[Float] = tab[Tensor[Float]](BBOXES) def getMasks(tab: Table): Array[RLEMasks] = tab[Array[RLEMasks]](MASKS) def getIsCrowd(tab: Table): Tensor[Float] = tab[Tensor[Float]](ISCROWD) - - /** - * @return (height, width, channel) - */ - def getOrigSize(tab: Table): (Int, Int, Int) = - tab[(Int, Int, Int)](ORIGSIZE) + def getScores(tab: Table): Tensor[Float] = tab[Tensor[Float]](SCORES) + def getImgInfo(tab: Table): Tensor[Float] = tab[Tensor[Float]](IMGINFO) def fromTensor(tensor: Tensor[Float]): RoiLabel = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala index f7587cae4b1..4d4f1d59fd2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala @@ -18,7 +18,6 @@ package com.intel.analytics.bigdl.models.maskrcnn import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.dataset.segmentation.{MaskUtils, RLEMasks} -import com.intel.analytics.bigdl.models.resnet.{Convolution, Sbn} import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} @@ -78,14 +77,28 @@ class MaskRCNN(val inChannels: Int, modules.append(boxHead.asInstanceOf[Module[Float]]) modules.append(maskHead.asInstanceOf[Module[Float]]) - private def buildResNet50(): Module[Float] = { + private def buildResNet50(): Module[Float] = { + + def convolution (nInputPlane: Int, nOutputPlane: Int, kernelW: Int, kernelH: Int, + strideW: Int = 1, strideH: Int = 1, padW: Int = 0, padH: Int = 0, + nGroup: Int = 1, propagateBack: Boolean = true): SpatialConvolution[Float] = { + val conv = SpatialConvolution[Float](nInputPlane, nOutputPlane, kernelW, kernelH, + strideW, strideH, padW, padH, nGroup, propagateBack, withBias = false) + conv.setInitMethod(MsraFiller(false), Zeros) + conv + } + + def sbn(nOutput: Int, eps: Double = 1e-3, momentum: Double = 0.1, affine: Boolean = true) + : SpatialBatchNormalization[Float] = { + SpatialBatchNormalization[Float](nOutput, eps, momentum, affine).setInitMethod(Ones, Zeros) + } def shortcut(nInputPlane: Int, nOutputPlane: Int, stride: Int, useConv: Boolean = false): Module[Float] = { if (useConv) { Sequential() - .add(Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride)) - .add(Sbn(nOutputPlane)) + .add(convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride)) + .add(sbn(nOutputPlane)) } else { Identity() } @@ -94,14 +107,14 @@ class MaskRCNN(val inChannels: Int, def bottleneck(nInputPlane: Int, internalPlane: Int, nOutputPlane: Int, stride: Int, useConv: Boolean = false): Module[Float] = { val s = Sequential() - .add(Convolution(nInputPlane, internalPlane, 1, 1, stride, stride, 0, 0)) - .add(Sbn(internalPlane)) + .add(convolution(nInputPlane, internalPlane, 1, 1, stride, stride, 0, 0)) + .add(sbn(internalPlane)) .add(ReLU(true)) - .add(Convolution(internalPlane, internalPlane, 3, 3, 1, 1, 1, 1)) - .add(Sbn(internalPlane)) + .add(convolution(internalPlane, internalPlane, 3, 3, 1, 1, 1, 1)) + .add(sbn(internalPlane)) .add(ReLU(true)) - .add(Convolution(internalPlane, nOutputPlane, 1, 1, 1, 1, 0, 0)) - .add(Sbn(nOutputPlane)) + .add(convolution(internalPlane, nOutputPlane, 1, 1, 1, 1, 0, 0)) + .add(sbn(nOutputPlane)) val m = Sequential() .add(ConcatTable() @@ -123,8 +136,8 @@ class MaskRCNN(val inChannels: Int, } val model = Sequential[Float]() - .add(Convolution(3, 64, 7, 7, 2, 2, 3, 3, optnet = false, propagateBack = false)) - .add(Sbn(64)) + .add(convolution(3, 64, 7, 7, 2, 2, 3, 3, propagateBack = false)) + .add(sbn(64)) .add(ReLU(true)) .add(SpatialMaxPooling(3, 3, 2, 2, 1, 1)) @@ -164,12 +177,18 @@ class MaskRCNN(val inChannels: Int, val labelsBox = postProcessorBox[Tensor[Float]](1) val proposalsBox = postProcessorBox[Table](2) val scores = postProcessorBox[Tensor[Float]](3) - val masks = this.maskHead.forward(T(features, proposalsBox, labelsBox)).toTable - if (this.isTraining()) { - output = T(proposalsBox, labelsBox, masks, scores) - } else { - output = postProcessorForMaskRCNN(proposalsBox, labelsBox, masks[Tensor[Float]](2), - scores, imageInfo) + if (labelsBox.size(1) > 0) { + val masks = this.maskHead.forward(T(features, proposalsBox, labelsBox)).toTable + if (this.isTraining()) { + output = T(proposalsBox, labelsBox, masks, scores) + } else { + output = postProcessorForMaskRCNN(proposalsBox, labelsBox, masks[Tensor[Float]](2), + scores, imageInfo) + } + } else { // detect nothing + for (i <- 1 to inputFeatures.size(1)) { + output.toTable(i) = T() + } } output @@ -196,36 +215,39 @@ class MaskRCNN(val inChannels: Int, binaryMask.resize(originalHeight, originalWidth) - val boxNumber = boxesInImage(i) - val maskPerImg = masks.narrow(1, start, boxNumber) - val bboxPerImg = bboxes[Tensor[Float]](i + 1) - val classPerImg = labels.narrow(1, start, boxNumber) - val scorePerImg = scores.narrow(1, start, boxNumber) - - require(maskPerImg.size(1) == bboxPerImg.size(1), - s"mask number ${maskPerImg.size(1)} should be same with box number ${bboxPerImg.size(1)}") - - // bbox resize to original size - if (height != originalHeight || width != originalWidth) { - BboxUtil.scaleBBox(bboxPerImg, - originalHeight.toFloat / height, originalWidth.toFloat / width) - } - // mask decode to original size - val masksRLE = new Array[RLEMasks](boxNumber) - for (j <- 0 to boxNumber - 1) { - binaryMask.fill(0.0f) - Utils.decodeMaskInImage(maskPerImg.select(1, j + 1), bboxPerImg.select(1, j + 1), - binaryMask = binaryMask) - masksRLE(j) = MaskUtils.binaryToRLE(binaryMask) - } - start += boxNumber - // prepare for evaluation val postOutput = T() - postOutput.update(RoiLabel.MASKS, masksRLE) - postOutput.update(RoiLabel.BBOXES, bboxPerImg) - postOutput.update(RoiLabel.CLASSES, classPerImg) - postOutput.update(RoiLabel.SCORES, scorePerImg) + + val boxNumber = boxesInImage(i) + if (boxNumber > 0) { + val maskPerImg = masks.narrow(1, start, boxNumber) + val bboxPerImg = bboxes[Tensor[Float]](i + 1) + val classPerImg = labels.narrow(1, start, boxNumber) + val scorePerImg = scores.narrow(1, start, boxNumber) + + require(maskPerImg.size(1) == bboxPerImg.size(1), s"mask number ${maskPerImg.size(1)} " + + s"should be the same with box number ${bboxPerImg.size(1)}") + + // resize bbox to original size + if (height != originalHeight || width != originalWidth) { + BboxUtil.scaleBBox(bboxPerImg, + originalHeight.toFloat / height, originalWidth.toFloat / width) + } + // decode mask to original size + val masksRLE = new Array[RLEMasks](boxNumber) + for (j <- 0 to boxNumber - 1) { + binaryMask.fill(0.0f) + Utils.decodeMaskInImage(maskPerImg.select(1, j + 1), bboxPerImg.select(1, j + 1), + binaryMask = binaryMask) + masksRLE(j) = MaskUtils.binaryToRLE(binaryMask) + } + start += boxNumber + + postOutput.update(RoiLabel.MASKS, masksRLE) + postOutput.update(RoiLabel.BBOXES, bboxPerImg) + postOutput.update(RoiLabel.CLASSES, classPerImg) + postOutput.update(RoiLabel.SCORES, scorePerImg) + } output(i + 1) = postOutput } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/README.md b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/README.md new file mode 100644 index 00000000000..13e2fb189a7 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/README.md @@ -0,0 +1,71 @@ +# MaskRCNN +This example demonstrates how to use BigDL to evaluate the [MaskRCNN](https://arxiv.org/abs/1703.06870) architecture on COCO data + +## Prepare the data +* You can download [COCO dataset]() firstly. +Extract the dataset and get images and annotations like (use **coco_2017_val** as example): +``` +coco +|_ coco_val2017 +| |_ .jpg +| |_ ... +| |_ .jpg +|_ annotations + |_ instances_train2017.json + |_ ... +``` + +* Generate the hadoop sequence files for COCO dataset +The following command will transform the images and annotations into hadoop sequence files. +```bash +java -cp com.intel.analytics.bigdl.models.utils.COCOSeqFileGenerator bigdl-VERSION-jar-with-dependencies.jar -f ./coco/coco_val2017 -m ./coco/annotations/instances_val2017.json -p 4 -o ./coco/output +``` +In the above commands: +-f: the COCO image files location +-m: the annotation json file location +-o: generated seq files location +-p: number of parallel + +## Data Processing +Input data are transformed by several pipeline classes, such as ScaleResize, ChannelNormalize, ImageFeatureToBatch, etc. + +## Model +You can download **preTrain-MaskRCNN model** for BigDL by running +```bash +wget https://bigdlmodels.s3-us-west-2.amazonaws.com/segmentation/bigdl_mask-rcnn_COCO_0.10.0.model +``` +This MaskRCNN model refers to [facebookresearch/maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark), and the model backbone is **R-50-FPN**. + +## Test the Model +* Spark standalone, example command +```bash +spark-submit \ +--master spark://xxx.xxx.xxx.xxx:xxxx \ +--executor-cores cores_per_executor \ +--total-executor-cores total_cores_for_the_job \ +--driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +--class com.intel.analytics.bigdl.models.maskrcnn.Test \ +dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +--batchSize batch_size \ +-f hdfs://.../coco/val \ +--model modelPath +``` +* Spark yarn client mode, example command +```bash +spark-submit \ +--master yarn \ +--deploy-mode client \ +--executor-cores cores_per_executor \ +--num-executors executors_number \ +--driver-class-path dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +--class com.intel.analytics.bigdl.models.inception.Test \ +dist/lib/bigdl-VERSION-jar-with-dependencies.jar \ +--batchSize batch_size \ +-f hdfs://.../coco/val \ +--model modelPath +``` +In the above command +* -f: where you put your COCO data, it should be a hdfs folder +* --model: the model snapshot file +* --batchSize: The mini-batch size. It is expected that the mini-batch size is a multiple of node_number * core_number. +* --partitionNum: the partition number, default is node_number * core_number. diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala new file mode 100644 index 00000000000..b4ab3d3c8d4 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala @@ -0,0 +1,89 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.models.maskrcnn + +import com.intel.analytics.bigdl.models.resnet.Utils.{TestParams, _} +import com.intel.analytics.bigdl.transform.vision.image._ +import com.intel.analytics.bigdl.transform.vision.image.augmentation._ +import com.intel.analytics.bigdl.utils.{Engine, T} +import scopt.OptionParser +import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch, segmentation} +import com.intel.analytics.bigdl.nn.Module +import com.intel.analytics.bigdl.optim.MeanAveragePrecision +import org.apache.spark.{SparkContext, rdd} +object Test { + case class TestParams( + folder: String = "./", + model: String = "", + batchSize: Int = 2, + partitionNum: Int = -1 + ) + + val testParser = new OptionParser[TestParams]("BigDL Mask-RCNN on COCO Test Example") { + opt[String]('f', "folder") + .text("the location of COCO dataset") + .action((x, c) => c.copy(folder = x)) + + opt[String]('m', "model") + .text("the location of model snapshot") + .action((x, c) => c.copy(model = x)) + + opt[Int]('b', "batchSize") + .text("total batch size") + .action((x, c) => c.copy(batchSize = x)) + + opt[Int]('p', "partitionNum") + .text("partition number") + .action((x, c) => c.copy(partitionNum = x)) + } + + def main(args: Array[String]): Unit = { + testParser.parse(args, TestParams()).foreach { param => { + val conf = Engine.createSparkConf().setAppName("Test MaskRCNN on COCO") + .set("spark.akka.frameSize", 64.toString) + .set("spark.task.maxFailures", "1") + val sc = new SparkContext(conf) + Engine.init + + val partitionNum = if (param.partitionNum > 0) param.partitionNum + else Engine.nodeNumber() * Engine.coreNumber() + + val rddData = DataSet.SeqFileFolder.filesToRoiImageFrame(param.folder, sc, Some(partitionNum)) + .toDistributed().data(train = false) + + val transformer = MTImageFeatureToBatchWithResize( + sizeDivisible = 32, + batchSize = param.batchSize / Engine.nodeNumber(), + transformer = + PixelBytesToMat() -> + ScaleResize(minSize = 800, maxSize = 1333) -> + ChannelNormalize(122.7717f, 115.9465f, 102.9801f) -> + MatToTensor[Float](), + toRGB = false + ) + val evaluationSet = transformer(rddData) + + val model = Module.loadModule[Float](param.model) + + val result = model.evaluate(evaluationSet, + Array(MeanAveragePrecision.cocoBBox(81), MeanAveragePrecision.cocoSegmentation(81))) + result.foreach(r => println(s"${r._2} is ${r._1}")) + + sc.stop() + }} + } +} diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala index 4d8a591ebff..b502bc7b753 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/BoxHead.scala @@ -342,6 +342,11 @@ private[nn] class BoxPostProcessor( totalDetections += maxDetection outBBoxs[Tensor[Float]](i + 1).resize(maxDetection, 4) totalROILables(i + 1) = roilabels + boxesInImage(i) = maxDetection + } + // clear others tensors in output + for (i <- (boxesInImage.length + 1) to outBBoxs.length()) { + outBBoxs.remove[Tensor[Float]](i) } // resize labels and scores @@ -353,13 +358,15 @@ private[nn] class BoxPostProcessor( var labelsOffset = outLabels.storageOffset() - 1 var scoresOffset = outScores.storageOffset() - 1 for (i <- 0 to boxesInImage.length - 1) { - val roilabels = totalROILables[Array[RoiLabel]](i + 1) - val bbox = outBBoxs[Tensor[Float]](i + 1).storage().array() - val bboxOffset = outBBoxs[Tensor[Float]](i + 1).storageOffset() - 1 - - resultToTensor(roilabels, labels, labelsOffset, bbox, bboxOffset, scores, scoresOffset) - labelsOffset += outBBoxs[Tensor[Float]](i + 1).size(1) - scoresOffset += outBBoxs[Tensor[Float]](i + 1).size(1) + if (boxesInImage(i) > 0) { + val roilabels = totalROILables[Array[RoiLabel]](i + 1) + val bbox = outBBoxs[Tensor[Float]](i + 1).storage().array() + val bboxOffset = outBBoxs[Tensor[Float]](i + 1).storageOffset() - 1 + + resultToTensor(roilabels, labels, labelsOffset, bbox, bboxOffset, scores, scoresOffset) + labelsOffset += outBBoxs[Tensor[Float]](i + 1).size(1) + scoresOffset += outBBoxs[Tensor[Float]](i + 1).size(1) + } } output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala index df2761888c9..19deb3b5039 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Pooler.scala @@ -109,10 +109,12 @@ class Pooler[T: ClassTag] ( } else { // for batch support input[Table](2) } + + val batchSize = featureMaps.get[Tensor[Float]](1).get.size(1) var totalNum = 0 val num_channels = featureMaps.get[Tensor[T]](1).get.size(2) val out = T() - for (i <- 0 to roiBatch.length() - 1) { + for (i <- 0 to batchSize - 1) { val rois = roiBatch[Tensor[T]](i + 1) val roi_levels = levelMapping(lvl_min, lvl_max, rois) @@ -147,10 +149,10 @@ class Pooler[T: ClassTag] ( // merge to one tensor output.resize(totalNum, num_channels, resolution, resolution) var start = 1 - for (i <- 0 to roiBatch.length() - 1) { + for (i <- 0 to batchSize - 1) { val tmp = out[Tensor[T]](i + 1) val length = tmp.size(1) - output.narrow(1, start, length).copy(tmp) + if (length > 0) output.narrow(1, start, length).copy(tmp) start += length } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala index f4fba43f51d..a79f7d058df 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposal.scala @@ -151,6 +151,10 @@ class RegionProposal( // sort selectOverAllLevels(selectorRes, postNmsTopN, bboxNumber, output[Tensor[Float]](b)) } + // clear others tensors in output + for (i <- (batchSize + 1) to output.length()) { + output.remove[Tensor[Float]](i) + } output } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala index 6089286f5ee..de091ecf689 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Evaluator.scala @@ -88,14 +88,14 @@ class Evaluator[T: ClassTag] private[optim](model: Module[T])(implicit ev: Tenso vMethods: Array[ValidationMethod[T]] ): Array[(ValidationResult, ValidationMethod[T])] = { - val dummyInput = dataset.takeSample(withReplacement = false, num = 1).head.getInput() val rdd = ConversionUtils.coalesce(dataset) val modelBroad = ModelBroadcast[T]().broadcast(rdd.sparkContext, - ConversionUtils.convert(model.evaluate()), dummyInput) + ConversionUtils.convert(model.evaluate())) val otherBroad = rdd.sparkContext.broadcast(vMethods) + rdd.mapPartitions(miniBatch => { - val localModel = modelBroad.value(false, true, dummyInput) + val localModel = modelBroad.value() val localMethod = otherBroad.value miniBatch.map(batch => { val output = localModel.forward(batch.getInput()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala index adc9bd2e834..191d0df86cc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala @@ -170,7 +170,8 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft miniBatch.foreach(batch => { (batch.size() <= 3) should be (true) - val input = batch.getInput().asInstanceOf[Tensor[Float]] + val inputAll = batch.getInput().asInstanceOf[Table] + val input = inputAll[Tensor[Float]](1) val target = batch.getTarget().asInstanceOf[Table] input.size() should be (Array(batch.size(), 3, 10, 20)) target.length() should be (batch.size()) @@ -179,7 +180,7 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft in should be(expectedOutput) val t = target(i).asInstanceOf[Table] t[Tensor[Float]](RoiLabel.ISCROWD) should be (Tensor(Array(0f, 1f), Array(2))) - t[(Int, Int, Int)](RoiLabel.ORIGSIZE) should be((8, 16, 3)) + // t[(Int, Int, Int)](RoiLabel.ORIGSIZE) should be((8, 16, 3)) t[Tensor[Float]](RoiLabel.BBOXES).size() should be (Array(2, 4)) t[Tensor[Float]](RoiLabel.CLASSES).size() should be (Array(2)) } From d729ee338c53a00da2ca878e0b77c2288373c1ac Mon Sep 17 00:00:00 2001 From: Menooker Date: Mon, 28 Oct 2019 18:58:12 +0800 Subject: [PATCH 0984/1065] Update the RoiLabel and MTImageFeatureToBatch (#2925) * Update the RoiLabel related files from Sequence-file related PR * var -> val * Bug fix for curBatchSize < batchSize. toRGB default to false * add ROISIZE * update documents * update documents * add UT * fix document --- .../bigdl/dllib/feature/dataset/DataSet.scala | 4 +- .../dataset/segmentation/MaskUtils.scala | 14 +- .../vision/image/MTImageFeatureToBatch.scala | 142 +++++++++++++----- .../vision/image/label/roi/RoiLabel.scala | 34 ++--- .../dllib/models/maskrcnn/MaskRCNN.scala | 10 +- .../bigdl/dllib/optim/ValidationMethod.scala | 17 ++- .../bigdl/dllib/dataset/DataSetSpec.scala | 4 +- .../dllib/models/maskrcnn/MaskRCNNSpec.scala | 33 ++-- .../bigdl/dllib/optim/ValidationSpec.scala | 37 ++--- .../image/MTImageFeatureToBatchSpec.scala | 71 +++++---- 10 files changed, 225 insertions(+), 141 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index 0b491a63c51..c6e28d5ff59 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -24,7 +24,7 @@ import com.intel.analytics.bigdl.dataset.image.{LabeledBGRImage, _} import com.intel.analytics.bigdl.dataset.segmentation.{COCODataset, COCODeserializer} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel -import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame} +import com.intel.analytics.bigdl.transform.vision.image.{DistributedImageFrame, ImageFeature, ImageFrame, LocalImageFrame, RoiImageInfo} import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T} import java.awt.Color import java.awt.image.{BufferedImage, DataBufferByte} @@ -643,7 +643,7 @@ object DataSet { require(rawdata.length == height * width * 3) val imf = ImageFeature(rawdata, RoiLabel(labelClasses, bboxes, masks), fileName) imf(ImageFeature.originalSize) = (height, width, 3) - imf(RoiLabel.ISCROWD) = isCrowd + imf(RoiImageInfo.ISCROWD) = isCrowd imf } .coalesce(num) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala index 39264a64de3..e7773a6e69f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/MaskUtils.scala @@ -22,9 +22,14 @@ import scala.collection.mutable.ArrayBuffer abstract class SegmentationMasks extends Serializable { /** - * Convert to a RLE encoded tensor + * Convert to a RLE encoded masks */ def toRLE: RLEMasks + + /** + * Get the height and width + */ + def size: (Int, Int) } /** @@ -40,6 +45,11 @@ class PolyMasks(val poly: Array[Array[Float]], val height: Int, val width: Int) require(height > 0 && width > 0, "the height and width must > 0 for toRLE") MaskUtils.mergeRLEs(MaskUtils.poly2RLE(this, height, width), false) } + + /** + * Get the height and width + */ + override def size: (Int, Int) = (height, width) } object PolyMasks { @@ -69,6 +79,8 @@ class RLEMasks(val counts: Array[Int], val height: Int, val width: Int) extends SegmentationMasks { override def toRLE: RLEMasks = this + override def size: (Int, Int) = (height, width) + // cached bbox value @transient lazy val bbox: (Float, Float, Float, Float) = MaskUtils.rleToOneBbox(this) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala index a88741aa6cb..d8fb0476613 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -15,8 +15,8 @@ */ package com.intel.analytics.bigdl.transform.vision.image +import com.intel.analytics.bigdl.dataset.segmentation.RLEMasks import java.util.concurrent.atomic.AtomicInteger - import com.intel.analytics.bigdl.dataset.{MiniBatch, Sample, Transformer, Utils} import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -24,9 +24,6 @@ import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.{Engine, T, Table} -import scala.collection.mutable.IndexedSeq -import scala.reflect.ClassTag - object MTImageFeatureToBatch { /** * The transformer from ImageFeature to mini-batches @@ -40,7 +37,7 @@ object MTImageFeatureToBatch { * @return */ def apply(width: Int, height: Int, batchSize: Int, - transformer: FeatureTransformer, toRGB: Boolean = true, extractRoi: Boolean = false) + transformer: FeatureTransformer, toRGB: Boolean = false, extractRoi: Boolean = false) : MTImageFeatureToBatch = { if (extractRoi) { new RoiMTImageFeatureToBatch ( @@ -162,7 +159,7 @@ private class PreFetch extends Transformer[ImageFeature, ImageFeature] { * @param toRGB if converted to RGB, default format is BGR */ class ClassificationMTImageFeatureToBatch private[bigdl](width: Int, height: Int, - totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = true) + totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = false) extends MTImageFeatureToBatch(totalBatchSize, transformer) { private val frameLength = height * width @@ -188,6 +185,73 @@ class ClassificationMTImageFeatureToBatch private[bigdl](width: Int, height: Int } } + +object RoiImageInfo { + // the keys in the target table + // fields from RoiLabel + val CLASSES = "classes" + val BBOXES = "bboxes" + val MASKS = "masks" + // ISCROWD and ORIGSIZE are stored in ImageFeature + val ISCROWD = "is_crowd" + val ORIGSIZE = "orig_size" + val SCORES = "scores" + val IMGINFO = "imginfo" + + /** + * Get the output score tensor from the table. + * (1 x N) tensor for N detections + * + * @param tab + * @return + */ + def getScores(tab: Table): Tensor[Float] = tab[Tensor[Float]](SCORES) + + /** + * Get the class label tensor from the table. See RoiLabel.classes + * the categories for each detections (see RoiLabel.clasees field) + * (1 x N), or (2 x N) Tensor[Float] + * + * @param tab + * @return + */ + def getClasses(tab: Table): Tensor[Float] = tab[Tensor[Float]](CLASSES) + + /** + * Get the bbox tensor from the table. See RoiLabel.bboxes + * @param tab + * @return + */ + def getBBoxes(tab: Table): Tensor[Float] = tab[Tensor[Float]](BBOXES) + + /** + * Get the (optional) mask data from the table. See RoiLabel.masks + * @param tab + * @return + */ + def getMasks(tab: Table): Array[RLEMasks] = tab[Array[RLEMasks]](MASKS) + + /** + * Get the isCrowd tensor from the table. Should be 1 x N vector (N is the # of detections) + * @param tab + * @return + */ + def getIsCrowd(tab: Table): Tensor[Float] = tab[Tensor[Float]](ISCROWD) + + /** + * Get the size of the image before resizing + * @return (height, width, channel) + */ + def getOrigSize(tab: Table): (Int, Int, Int) = tab[(Int, Int, Int)](ORIGSIZE) + + /** + * Get the isCrowd tensor from the table. Should be 1 x N vector (N is the # of detections) + * @param tab + * @return + */ + def getImageInfo(tab: Table): Tensor[Float] = tab[Tensor[Float]](IMGINFO) + +} /** * A batch of images with flattened RoiLabels * the getTarget() returns a Table with key from 1 to batchSize. Each key in the table is mapped to @@ -195,18 +259,18 @@ class ClassificationMTImageFeatureToBatch private[bigdl](width: Int, height: Int * info for one image (assume the image has N detections). The annotation table has * * Key Value - * RoiLabel.CLASSES the categories for each detections (see RoiLabel.clasees field) + * RoiImageInfo.CLASSES the categories for each detections (see RoiLabel.clasees field) * (1 x N), or (2 x N) Tensor[Float] - * RoiLabel.BBOXES the bboxes, (N x 4) Tensor[Float] - * RoiLabel.MASKS (Optional) the mask data, Array[Tensor[Float]\]. The outer array has N + * RoiImageInfo.BBOXES the bboxes, (N x 4) Tensor[Float] + * RoiImageInfo.MASKS (Optional) the mask data, Array[Tensor[Float]\]. The outer array has N * elements. The inner tensor holds the data for segmentation - * RoiLabel.ISCROWD Whether each detection is crowd. (1 x N) Tensor[Float]. + * RoiImageInfo.ISCROWD Whether each detection is crowd. (1 x N) Tensor[Float]. * -1: unknown, 0: not crowd, 1: is crowd - * RoiLabel.ImageInfo with shape (batchSize, 4), contains all images info + * RoiImageInfo.IMGINFO with shape (batchSize, 4), contains all images info * (height, width, original height, original width) */ -class RoiMiniBatch(val input: Tensor[Float], val target: IndexedSeq[RoiLabel], - val isCrowd: IndexedSeq[Tensor[Float]], val imageInfo: Tensor[Float] = null) +class RoiMiniBatch(val input: Tensor[Float], val target: Array[RoiLabel], + val isCrowd: Array[Tensor[Float]], val imageInfo: Tensor[Float] = null) extends MiniBatch[Float] { override def size(): Int = input.size(1) @@ -216,20 +280,18 @@ class RoiMiniBatch(val input: Tensor[Float], val target: IndexedSeq[RoiLabel], } override def getTarget(): Table = { - var i = 0 - val tables = (target, isCrowd).zipped.map { case (roiLabel, crowd) => - i += 1 + val tables = (target, isCrowd, 1 to isCrowd.length).zipped.map { case (roiLabel, crowd, i) => roiLabel.toTable - .update(RoiLabel.ISCROWD, crowd) - .update(RoiLabel.IMGINFO, imageInfo.select(1, i)) + .update(RoiImageInfo.ISCROWD, crowd) + .update(RoiImageInfo.IMGINFO, imageInfo.select(1, i)) } T.seq(tables) } override def slice(offset: Int, length: Int): MiniBatch[Float] = { val subInput = input.narrow(1, offset, length) - val subTarget = target.view(offset - 1, length) // offset starts from 1 - val subIsCrowd = isCrowd.view(offset - 1, length) // offset starts from 1 + val subTarget = target.slice(offset - 1, length) // offset starts from 1 + val subIsCrowd = isCrowd.slice(offset - 1, length) // offset starts from 1 val subSize = imageInfo.narrow(1, offset, length) RoiMiniBatch(subInput, subTarget, subIsCrowd, subSize) } @@ -241,8 +303,8 @@ class RoiMiniBatch(val input: Tensor[Float], val target: IndexedSeq[RoiLabel], } object RoiMiniBatch { - def apply(data: Tensor[Float], target: IndexedSeq[RoiLabel], - isCrowd: IndexedSeq[Tensor[Float]], imageInfo: Tensor[Float] = null): + def apply(data: Tensor[Float], target: Array[RoiLabel], + isCrowd: Array[Tensor[Float]], imageInfo: Tensor[Float] = null): RoiMiniBatch = new RoiMiniBatch(data, target, isCrowd, imageInfo) } @@ -258,31 +320,40 @@ object RoiMiniBatch { * @param toRGB if converted to RGB, default format is BGR */ class RoiMTImageFeatureToBatch private[bigdl](width: Int, height: Int, - totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = true) + totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = false) extends MTImageFeatureToBatch(totalBatchSize, transformer) { private val frameLength = height * width private val featureData: Array[Float] = new Array[Float](batchSize * frameLength * 3) private val labelData: Array[RoiLabel] = new Array[RoiLabel](batchSize) private val isCrowdData: Array[Tensor[Float]] = new Array[Tensor[Float]](batchSize) - private var featureTensor: Tensor[Float] = null + private val imgInfoData: Tensor[Float] = Tensor[Float](batchSize, 4) + private var featureTensor: Tensor[Float] = Tensor[Float]() override protected def processImageFeature(img: ImageFeature, position: Int): Unit = { img.copyTo(featureData, position * frameLength * 3, toRGB = toRGB) - val isCrowd = img(RoiLabel.ISCROWD).asInstanceOf[Tensor[Float]] + val isCrowd = img(RoiImageInfo.ISCROWD).asInstanceOf[Tensor[Float]] val label = img.getLabel.asInstanceOf[RoiLabel] require(label.bboxes.size(1) == isCrowd.size(1), "The number of detections" + "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") isCrowdData(position) = isCrowd labelData(position) = label + imgInfoData.setValue(position + 1, 1, img.getHeight()) + imgInfoData.setValue(position + 1, 2, img.getWidth()) + imgInfoData.setValue(position + 1, 3, img.getOriginalHeight) + imgInfoData.setValue(position + 1, 4, img.getOriginalWidth) } - override protected def createBatch(batchSize: Int): MiniBatch[Float] = { - if (featureTensor == null) { - featureTensor = Tensor(Storage[Float](featureData), - storageOffset = 1, size = Array(batchSize, 3, height, width)) + override protected def createBatch(curBatchSize: Int): MiniBatch[Float] = { + if (featureTensor.nElement() != curBatchSize) { + featureTensor.set(Storage[Float](featureData), + storageOffset = 1, sizes = Array(curBatchSize, 3, height, width)) + } + def arraySlice[T](array: Array[T]) = { + if (array.length == curBatchSize) array else array.slice(0, curBatchSize) } - RoiMiniBatch(featureTensor, labelData.view, isCrowdData.view) + RoiMiniBatch(featureTensor, arraySlice(labelData), arraySlice(isCrowdData), + imgInfoData.narrow(1, 1, curBatchSize)) } } @@ -326,7 +397,7 @@ class RoiImageFeatureToBatchWithResize private[bigdl](sizeDivisible: Int = -1, t imageBuffer(position).resize(3, img.getHeight(), img.getWidth()) // save img to buffer img.copyTo(imageBuffer(position).storage().array(), 0, toRGB = toRGB) - val isCrowd = img(RoiLabel.ISCROWD).asInstanceOf[Tensor[Float]] + val isCrowd = img(RoiImageInfo.ISCROWD).asInstanceOf[Tensor[Float]] val label = img.getLabel.asInstanceOf[RoiLabel] if (isCrowd != null && label != null) { require(label.bboxes.size(1) == isCrowd.size(1), "The number of detections" + @@ -345,11 +416,14 @@ class RoiImageFeatureToBatchWithResize private[bigdl](sizeDivisible: Int = -1, t if (featureTensor == null) featureTensor = Tensor() featureTensor.resize(batchSize, 3, height, wide).fill(0.0f) // copy img buffer to feature tensor - for (i <- 0 to (batchSize - 1)) { + for (i <- 0 until batchSize) { featureTensor.select(1, i + 1).narrow(2, 1, imageBuffer(i).size(2)) .narrow(3, 1, imageBuffer(i).size(3)).copy(imageBuffer(i)) } - RoiMiniBatch(featureTensor, labelData.view(0, batchSize), - isCrowdData.view(0, batchSize), imgInfoData.narrow(1, 1, batchSize)) + def arraySlice[T](array: Array[T]) = { + if (array.length == batchSize) array else array.slice(0, batchSize) + } + RoiMiniBatch(featureTensor, arraySlice(labelData), + arraySlice(isCrowdData), imgInfoData.narrow(1, 1, batchSize)) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala index 9bfd21a9c51..cb038780450 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -16,17 +16,17 @@ package com.intel.analytics.bigdl.transform.vision.image.label.roi -import com.intel.analytics.bigdl.dataset.segmentation.{RLEMasks, SegmentationMasks} +import com.intel.analytics.bigdl.dataset.segmentation.{MaskUtils, SegmentationMasks, RLEMasks} import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.RoiImageInfo import com.intel.analytics.bigdl.utils.{T, Table} -import com.intel.analytics.bigdl.dataset.segmentation.{RLEMasks, SegmentationMasks} /** * image target with classes and bounding boxes * * @param classes N (class labels) or 2 * N, the first row is class labels, * the second line is difficults - * @param bboxes N * 4 + * @param bboxes N * 4, (xmin, ymin, xmax, ymax) * @param masks the array of annotation masks of the targets */ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], @@ -45,8 +45,8 @@ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], s"be equal to the number of mask array ${masks.length}") } } else if (classes.nElement() > 0 && classes.dim() == 2) { - require(classes.size(2) == bboxes.size(1), s"the number of classes ${ classes.size(2) }" + - s"should be equal to the number of bounding box numbers ${ bboxes.size(1) }") + require(classes.size(2) == bboxes.size(1), s"the number of classes ${classes.size(2)}" + + s"should be equal to the number of bounding box numbers ${bboxes.size(1)}") if (masks != null) { require(classes.size(2) == masks.length, s"the number of classes ${classes.size(2)}" + s"should be equal to the number of bounding box numbers ${masks.length}") @@ -57,10 +57,11 @@ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], def toTable: Table = { val table = T() if (masks != null) { - table(RoiLabel.MASKS) = masks.map(_.toRLE) + require(masks.length > 0, "The masks can either be null or a non-empty array") + table(RoiImageInfo.MASKS) = masks.map(_.toRLE) } - table(RoiLabel.CLASSES) = classes - table(RoiLabel.BBOXES) = bboxes + table(RoiImageInfo.CLASSES) = classes + table(RoiImageInfo.BBOXES) = bboxes table } @@ -70,23 +71,6 @@ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], } object RoiLabel { - val CLASSES = "classes" - val BBOXES = "bboxes" - val MASKS = "masks" - // ISCROWD and ORIGSIZE are stored in ImageFeature - val ISCROWD = "is_crowd" - val IMGINFO = "imgInfo" - val SCORES = "scores" - - - def getClasses(tab: Table): Tensor[Float] = tab[Tensor[Float]](CLASSES) - def getBBoxes(tab: Table): Tensor[Float] = tab[Tensor[Float]](BBOXES) - def getMasks(tab: Table): Array[RLEMasks] = tab[Array[RLEMasks]](MASKS) - def getIsCrowd(tab: Table): Tensor[Float] = tab[Tensor[Float]](ISCROWD) - def getScores(tab: Table): Tensor[Float] = tab[Tensor[Float]](SCORES) - def getImgInfo(tab: Table): Tensor[Float] = tab[Tensor[Float]](IMGINFO) - - def fromTensor(tensor: Tensor[Float]): RoiLabel = { val label = tensor.narrow(2, 1, 2).transpose(1, 2).contiguous() val rois = tensor.narrow(2, 3, 4) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala index 4d4f1d59fd2..34bf3aa7cf4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala @@ -23,12 +23,12 @@ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.RoiImageInfo import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.transform.vision.image.util.BboxUtil import com.intel.analytics.bigdl.utils.serializer._ import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter import com.intel.analytics.bigdl.utils.{T, Table} - import scala.reflect.ClassTag import scala.reflect.runtime._ @@ -243,10 +243,10 @@ class MaskRCNN(val inChannels: Int, } start += boxNumber - postOutput.update(RoiLabel.MASKS, masksRLE) - postOutput.update(RoiLabel.BBOXES, bboxPerImg) - postOutput.update(RoiLabel.CLASSES, classPerImg) - postOutput.update(RoiLabel.SCORES, scorePerImg) + postOutput.update(RoiImageInfo.MASKS, masksRLE) + postOutput.update(RoiImageInfo.BBOXES, bboxPerImg) + postOutput.update(RoiImageInfo.CLASSES, classPerImg) + postOutput.update(RoiImageInfo.SCORES, scorePerImg) } output(i + 1) = postOutput diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala index 286b05bcfba..f4d344c1e81 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ValidationMethod.scala @@ -23,6 +23,7 @@ import com.intel.analytics.bigdl.nn.AbsCriterion import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.transform.vision.image.RoiImageInfo import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.Table import org.apache.commons.lang3.SerializationUtils @@ -313,10 +314,10 @@ object MAPUtil { val gtImage = new ArrayBuffer[GroundTruthRegion]() val roiLabel = gtTable[Table](i) if (roiLabel.length() > 0) { - val bbox = RoiLabel.getBBoxes(roiLabel) - val tclasses = RoiLabel.getClasses(roiLabel) - val isCrowd = RoiLabel.getIsCrowd(roiLabel) - val masks = if (isSegmentation) RoiLabel.getMasks(roiLabel) else null + val bbox = RoiImageInfo.getBBoxes(roiLabel) + val tclasses = RoiImageInfo.getClasses(roiLabel) + val isCrowd = RoiImageInfo.getIsCrowd(roiLabel) + val masks = if (isSegmentation) RoiImageInfo.getMasks(roiLabel) else null val bboxCnt = bbox.size(1) require(bboxCnt == tclasses.size(1), "CLASSES of target tables should have the" + "same size of the bbox counts") @@ -705,11 +706,11 @@ class MeanAveragePrecisionObjectDetection[T: ClassTag]( val imgOut = outTable[Table](imgId) // if the image contains empty predictions, do nothing if (imgOut.length() > 0) { - val bboxes = RoiLabel.getBBoxes(imgOut) - val scores = RoiLabel.getScores(imgOut) - val labels = RoiLabel.getClasses(imgOut) + val bboxes = RoiImageInfo.getBBoxes(imgOut) + val scores = RoiImageInfo.getScores(imgOut) + val labels = RoiImageInfo.getClasses(imgOut) require(bboxes.dim() == 2, "the bbox tensor should have 2 dimensions") - val masks = if (isSegmentation) Some(RoiLabel.getMasks(imgOut)) else None + val masks = if (isSegmentation) Some(RoiImageInfo.getMasks(imgOut)) else None val batchSize = bboxes.size(1) require(batchSize == labels.size(1), "CLASSES of target tables should have the" + "same size of the bbox counts") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala index 71413128b7c..f05ae64decd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala @@ -23,7 +23,7 @@ import com.intel.analytics.bigdl.dataset.image._ import com.intel.analytics.bigdl.dataset.segmentation.{COCODataset, COCOPoly, COCORLE, PolyMasks, RLEMasks} import com.intel.analytics.bigdl.models.utils.COCOSeqFileGenerator import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.transform.vision.image.ImageFeature +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, RoiImageInfo} import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, SparkContextLifeCycle, TestUtils} import java.awt.image.DataBufferByte @@ -99,7 +99,7 @@ class DataSetSpec extends SparkContextLifeCycle with Matchers { .data(false) .map(imf => { (imf(ImageFeature.uri).asInstanceOf[String], imf.getOriginalSize, imf.getLabel[RoiLabel], - imf[Tensor[Float]](RoiLabel.ISCROWD), imf[Array[Byte]](ImageFeature.bytes)) + imf[Tensor[Float]](RoiImageInfo.ISCROWD), imf[Array[Byte]](ImageFeature.bytes)) }) .collect() .foreach({ case (uri, size, label, iscrowd, bytes) => diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala index 276962d6178..0a8f147eb73 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala @@ -20,6 +20,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.dataset.segmentation.RLEMasks import com.intel.analytics.bigdl.nn.Nms import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.RoiImageInfo import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} @@ -63,29 +64,29 @@ class MaskRCNNSpec extends FlatSpec with Matchers { val first = output[Table](1) val second = output[Table](2) - first.get[Tensor[Float]](RoiLabel.BBOXES) should be( - output1.get[Tensor[Float]](RoiLabel.BBOXES)) - first.get[Tensor[Float]](RoiLabel.CLASSES) should be( - output1.get[Tensor[Float]](RoiLabel.CLASSES)) - first.get[Tensor[Float]](RoiLabel.SCORES) should be( - output1.get[Tensor[Float]](RoiLabel.SCORES)) + first.get[Tensor[Float]](RoiImageInfo.BBOXES) should be( + output1.get[Tensor[Float]](RoiImageInfo.BBOXES)) + first.get[Tensor[Float]](RoiImageInfo.CLASSES) should be( + output1.get[Tensor[Float]](RoiImageInfo.CLASSES)) + first.get[Tensor[Float]](RoiImageInfo.SCORES) should be( + output1.get[Tensor[Float]](RoiImageInfo.SCORES)) - second.get[Tensor[Float]](RoiLabel.BBOXES) should be( - output2.get[Tensor[Float]](RoiLabel.BBOXES)) - second.get[Tensor[Float]](RoiLabel.CLASSES) should be( - output2.get[Tensor[Float]](RoiLabel.CLASSES)) - second.get[Tensor[Float]](RoiLabel.SCORES) should be( - output2.get[Tensor[Float]](RoiLabel.SCORES)) + second.get[Tensor[Float]](RoiImageInfo.BBOXES) should be( + output2.get[Tensor[Float]](RoiImageInfo.BBOXES)) + second.get[Tensor[Float]](RoiImageInfo.CLASSES) should be( + output2.get[Tensor[Float]](RoiImageInfo.CLASSES)) + second.get[Tensor[Float]](RoiImageInfo.SCORES) should be( + output2.get[Tensor[Float]](RoiImageInfo.SCORES)) // for masks - val firstMasks = first.get[Array[RLEMasks]](RoiLabel.MASKS).get - val expectedMasks = output1.get[Array[RLEMasks]](RoiLabel.MASKS).get + val firstMasks = first.get[Array[RLEMasks]](RoiImageInfo.MASKS).get + val expectedMasks = output1.get[Array[RLEMasks]](RoiImageInfo.MASKS).get for (i <- 0 to firstMasks.length - 1) { firstMasks(i).counts should be(expectedMasks(i).counts) } - val secondMasks = second.get[Array[RLEMasks]](RoiLabel.MASKS).get - val expectedMasks2 = output2.get[Array[RLEMasks]](RoiLabel.MASKS).get + val secondMasks = second.get[Array[RLEMasks]](RoiImageInfo.MASKS).get + val expectedMasks2 = output2.get[Array[RLEMasks]](RoiImageInfo.MASKS).get for (i <- 0 to secondMasks.length - 1) { secondMasks(i).counts should be(expectedMasks2(i).counts) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala index 37423674a01..aac1b0cd141 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidationSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.optim import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.transform.vision.image.RoiImageInfo import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.T import org.scalatest.{FlatSpec, Matchers} @@ -247,9 +248,9 @@ class ValidationSpec extends FlatSpec with Matchers { val target = T( T() - .update(RoiLabel.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0, 0, 0, 0, 0, 0))) - .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0, 1, 1, 1, 1, 1))) - .update(RoiLabel.BBOXES, Tensor[Float](T( + .update(RoiImageInfo.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0, 0, 0, 0, 0, 0))) + .update(RoiImageInfo.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0, 1, 1, 1, 1, 1))) + .update(RoiImageInfo.BBOXES, Tensor[Float](T( T(100, 100, 200, 200), T(300, 100, 400, 200), T(100, 300, 200, 400), @@ -271,8 +272,8 @@ class ValidationSpec extends FlatSpec with Matchers { val outputTable = T( T() - .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0, 1, 1, 1, 1))) - .update(RoiLabel.BBOXES, Tensor[Float](T( + .update(RoiImageInfo.CLASSES, Tensor[Float](T(0, 0, 0, 0, 1, 1, 1, 1))) + .update(RoiImageInfo.BBOXES, Tensor[Float](T( T(110, 90, 210, 190), T(310, 110, 410, 210), T(320, 290, 420, 390), @@ -283,7 +284,7 @@ class ValidationSpec extends FlatSpec with Matchers { T(1210, 1310, 1290, 1410) )) ) - .update(RoiLabel.SCORES, Tensor[Float](T(1, 2, 4, 3, 1, 3, 4, 2))) + .update(RoiImageInfo.SCORES, Tensor[Float](T(1, 2, 4, 3, 1, 3, 4, 2))) ) val v2 = new MeanAveragePrecisionObjectDetection(3) val result2 = v2(outputTable, target) @@ -294,9 +295,9 @@ class ValidationSpec extends FlatSpec with Matchers { "MeanAveragePrecisionObjectDetection" should "be correct on empty detections" in { val target = T( T() - .update(RoiLabel.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0))) - .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0))) - .update(RoiLabel.BBOXES, Tensor[Float](T( + .update(RoiImageInfo.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0))) + .update(RoiImageInfo.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0))) + .update(RoiImageInfo.BBOXES, Tensor[Float](T( T(100, 100, 200, 200), T(300, 100, 400, 200), T(100, 300, 200, 400), @@ -314,9 +315,9 @@ class ValidationSpec extends FlatSpec with Matchers { "MeanAveragePrecisionObjectDetection" should "be correct on empty targets" in { val target = T( T() - .update(RoiLabel.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0))) - .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0))) - .update(RoiLabel.BBOXES, Tensor[Float](T( + .update(RoiImageInfo.ISCROWD, Tensor[Float](T(0, 0, 0, 0, 0))) + .update(RoiImageInfo.CLASSES, Tensor[Float](T(0, 0, 0, 0, 0))) + .update(RoiImageInfo.BBOXES, Tensor[Float](T( T(100, 100, 200, 200), T(300, 100, 400, 200), T(100, 300, 200, 400), @@ -329,25 +330,25 @@ class ValidationSpec extends FlatSpec with Matchers { ) val outputTable = T( T() - .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0))) - .update(RoiLabel.BBOXES, Tensor[Float](T( + .update(RoiImageInfo.CLASSES, Tensor[Float](T(0, 0, 0, 0))) + .update(RoiImageInfo.BBOXES, Tensor[Float](T( T(110, 90, 210, 190), T(310, 110, 410, 210), T(320, 290, 420, 390), T(210, 310, 290, 410) )) ) - .update(RoiLabel.SCORES, Tensor[Float](T(1, 2, 9, 7))), + .update(RoiImageInfo.SCORES, Tensor[Float](T(1, 2, 9, 7))), T() - .update(RoiLabel.CLASSES, Tensor[Float](T(0, 0, 0, 0))) - .update(RoiLabel.BBOXES, Tensor[Float](T( + .update(RoiImageInfo.CLASSES, Tensor[Float](T(0, 0, 0, 0))) + .update(RoiImageInfo.BBOXES, Tensor[Float](T( T(1110, 1090, 1210, 1190), T(1310, 1110, 1410, 1210), T(1320, 1290, 1420, 1390), T(1210, 1310, 1290, 1410) )) ) - .update(RoiLabel.SCORES, Tensor[Float](T(0, 5, 4, 8))) + .update(RoiImageInfo.SCORES, Tensor[Float](T(0, 5, 4, 8))) ) val v = new MeanAveragePrecisionObjectDetection[Float](3) val result = v(outputTable, target) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala index 191d0df86cc..e8e5c77e232 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.transform.vision.image import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.segmentation.RLEMasks import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel import com.intel.analytics.bigdl.utils.{Engine, T, Table} @@ -97,7 +98,7 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft Tensor(new Array[Float](2*4), Array(2, 4)), null ) - imf(RoiLabel.ISCROWD) = Tensor(Array(0f, 1f), Array(2)) + imf(RoiImageInfo.ISCROWD) = Tensor(Array(0f, 1f), Array(2)) imf(ImageFeature.originalSize) = (8, 16, 3) imf }).toArray @@ -179,18 +180,26 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft val in = input.select(1, i) in should be(expectedOutput) val t = target(i).asInstanceOf[Table] - t[Tensor[Float]](RoiLabel.ISCROWD) should be (Tensor(Array(0f, 1f), Array(2))) - // t[(Int, Int, Int)](RoiLabel.ORIGSIZE) should be((8, 16, 3)) - t[Tensor[Float]](RoiLabel.BBOXES).size() should be (Array(2, 4)) - t[Tensor[Float]](RoiLabel.CLASSES).size() should be (Array(2)) + t[Tensor[Float]](RoiImageInfo.ISCROWD) should be (Tensor(Array(0f, 1f), Array(2))) + // t[(Int, Int, Int)](RoiImageInfo.ORIGSIZE) should be((8, 16, 3)) + t[Tensor[Float]](RoiImageInfo.BBOXES).size() should be (Array(2, 4)) + t[Tensor[Float]](RoiImageInfo.CLASSES).size() should be (Array(2)) } }) } - // todo: There is a race-condition bug in MTImageFeatureToBatch - /* + "RoiMiniBatch" should "serialize well" in { + def batch: RoiMiniBatch = RoiMiniBatch( + Tensor[Float](), + Array[RoiLabel](RoiLabel(Tensor[Float](), Tensor[Float]())), + Array[Tensor[Float]](Tensor[Float]()), + Tensor()) + val result = sc.parallelize(Array(batch, batch, batch, batch, batch), 3) + .coalesce(2, true) + .takeSample(false, 3).head + } + "MTImageFeatureToBatch classification" should "work well" in { - // val imgData = (0 to 1000).map(idx => (idx to (idx + 10*10*3)).map(_.toFloat).toArray) .map(arr => { val imf = ImageFeature() @@ -199,13 +208,11 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft imf(ImageFeature.label) = lab imf(ImageFeature.originalSize) = (10, 10, 3) imf - }) + }).toArray val transformer = MTImageFeatureToBatch(10, 10, 19, new FeatureTransformer {}, toRGB = false) - val miniBatch = transformer(imgData.toIterator) - // val imgCheck = new Array[Boolean](1000) + val miniBatch = transformer(DataSet.array(imgData).data(false)) + val imgCheck = new Array[Boolean](1001) miniBatch - .take(5) - // .take(1000 / 19) .foreach(batch => { (batch.size() <= 19) should be (true) val input = batch.getInput().asInstanceOf[Tensor[Float]] @@ -221,16 +228,16 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft B should be (G + 1) input.valueAt(i, 3, 10, 10) should be((idx.toFloat + 10 * 10 * 3 - 1) +- 0.000001f) target.valueAt(i) should be (idx.toFloat) - /* imgCheck(idx) should be (false) - imgCheck(idx) = true */ + imgCheck(idx) should be (false) + imgCheck(idx) = true } - }) + imgCheck.count(!_) should be (0) } "MTImageFeatureToBatch with ROI" should "work well" in { - // + val imgCheck = new Array[Boolean](1001) val imgData = (0 to 1000).map(idx => (idx to (idx + 10*10*3)).map(_.toFloat).toArray) .map(arr => { val imf = ImageFeature() @@ -238,34 +245,38 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft imf(ImageFeature.label) = RoiLabel( Tensor(new Array[Float](2), Array(2)), Tensor(new Array[Float](2*4), Array(2, 4)), - Array(Tensor[Float](), Tensor[Float]()) + Array(new RLEMasks(Array(), 10, 10), + new RLEMasks(Array(), 10, 10) + ) ) - imf(RoiLabel.ISCROWD) = Tensor(Array(0f, 1f), Array(2)) + imf(RoiImageInfo.ISCROWD) = Tensor(Array(0f, 1f), Array(2)) imf(ImageFeature.originalSize) = (10, 10, 3) imf - }) + }).toArray val transformer = MTImageFeatureToBatch(10, 10, 19, new FeatureTransformer {}, toRGB = false, extractRoi = true) - val miniBatch = transformer(imgData.toIterator) - // val imgCheck = new Array[Boolean](1000) + val miniBatch = transformer(DataSet.array(imgData).data(false)) miniBatch - .take(5) - // .take(1000 / 19) .foreach(batch => { (batch.size() <= 19) should be (true) val target = batch.getTarget().asInstanceOf[Table] target.length() should be (batch.size()) for(i <- 1 to batch.size()) { val t = target(i).asInstanceOf[Table] - t[Tensor[Float]](RoiLabel.ISCROWD) should be (Tensor(Array(0f, 1f), Array(2))) - t[(Int, Int, Int)](RoiLabel.ORIGSIZE) should be((10, 10, 3)) - t[Tensor[Float]](RoiLabel.BBOXES).size() should be (Array(2, 4)) - t[Tensor[Float]](RoiLabel.CLASSES).size() should be (Array(2)) - t[Array[Tensor[Float]]](RoiLabel.MASKS).length should be (2) + RoiImageInfo.getIsCrowd(t) should be (Tensor(Array(0f, 1f), Array(2))) + RoiImageInfo.getImageInfo(t).size() should be(Array(4)) + RoiImageInfo.getBBoxes(t).size() should be (Array(2, 4)) + RoiImageInfo.getClasses(t).size() should be (Array(2)) + RoiImageInfo.getMasks(t).length should be (2) + val idx = batch.getInput().asInstanceOf[Table].apply[Tensor[Float]](1) + .valueAt(i, 1, 1, 1).toInt + imgCheck(idx) should be (false) + imgCheck(idx) = true } }) + imgCheck.count(!_) should be (0) - } */ + } } From ca74668ceea1b2cf1517ac1dcb6602ba4ee1bb9c Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Mon, 28 Oct 2019 21:46:59 +0800 Subject: [PATCH 0985/1065] Python MKLDNN examples for CNN(LeNet) and RNN(LSTM) (#2932) --- .../bigdl/dllib/utils/python/api/PythonBigDL.scala | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala index 3caebcddd40..36ac83aac7d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/python/api/PythonBigDL.scala @@ -20,7 +20,7 @@ import java.util.{ArrayList => JArrayList, HashMap => JHashMap, List => JList, M import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.dataset.{Identity => DIdentity, Sample => JSample, _} -import com.intel.analytics.bigdl.nn.{PGCriterion, Zeros, _} +import com.intel.analytics.bigdl.nn.{PGCriterion, Sequential, Zeros, _} import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, _} import com.intel.analytics.bigdl.numeric._ import com.intel.analytics.bigdl.optim.{Optimizer, _} @@ -275,6 +275,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Sequential[T]() } + def toGraph(sequential: Sequential[T]): StaticGraph[T] = { + sequential.toGraph().asInstanceOf[StaticGraph[T]] + } + def createAttention(hiddenSize: Int, numHeads: Int, attentionDropout: Float): Attention[T] = { Attention(hiddenSize, numHeads, attentionDropout) } @@ -2504,6 +2508,10 @@ class PythonBigDL[T: ClassTag](implicit ev: TensorNumeric[T]) extends Serializab Engine.init } + def getEngineType(): String = { + Engine.getEngineType().toString + } + def getNodeAndCoreNumber(): Array[Int] = { Array(Engine.nodeNumber(), Engine.coreNumber()) } From b91a591a7bf8b3d1ee3bdf26a2ab0a26b6fe8f2d Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 28 Oct 2019 23:57:21 +0800 Subject: [PATCH 0986/1065] fix: takeSample only works for dnn backend and get one batch (#2947) * fix: takeSample only works for dnn backend and get one batch --- .../bigdl/dllib/optim/Predictor.scala | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala index 09790717585..30cfb4fb0a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/Predictor.scala @@ -197,14 +197,19 @@ object Predictor { // because Evaluator will use it too, we extend the scope out of Predictor private[optim] def getDummyData[T: ClassTag, R](dataSet: RDD[R], batchSize: Int)(implicit ev: TensorNumeric[T]): Activity = { - // here has an assumption, batchSizePerPar is not very large. - val samples = dataSet.takeSample(withReplacement = false, num = batchSize) - .map { - case feature: ImageFeature => feature[Sample[T]](ImageFeature.sample) - case sample => sample.asInstanceOf[Sample[T]] - } - val sampleToMiniBatch = SampleToMiniBatch(batchSize) - sampleToMiniBatch(samples.toIterator).toSeq.head.getInput() + if (Engine.getEngineType() == MklDnn && Engine.isMultiModels) { + // here has an assumption, batchSizePerPar is not very large. + val samples = dataSet.takeSample(withReplacement = false, num = batchSize) + .map { + case feature: ImageFeature => feature[Sample[T]](ImageFeature.sample) + case sample => sample.asInstanceOf[Sample[T]] + } + val sampleToMiniBatch = SampleToMiniBatch(batchSize, partitionNum = Some(1)) + val miniBatch = sampleToMiniBatch(samples.toIterator).toSeq + miniBatch.head.getInput() + } else { + Tensor() + } } } From 7b750c54135fe2c441daab0e9d7e38a542bb9abd Mon Sep 17 00:00:00 2001 From: Menooker Date: Tue, 29 Oct 2019 11:13:44 +0800 Subject: [PATCH 0987/1065] Rename filesToRoiImageFrame to filesToRoiImageFeatures (#2949) * Update the RoiLabel related files from Sequence-file related PR * var -> val * Bug fix for curBatchSize < batchSize. toRGB default to false * add ROISIZE * update documents * update documents * add UT * fix document * filesToRoiImageFrame -> filesToRoiImageFeatures, to public --- .../intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala | 4 ++-- .../intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala | 3 ++- .../com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala | 2 +- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index c6e28d5ff59..1f81d40a9ec 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -598,13 +598,13 @@ object DataSet { false } /** - * Extract hadoop sequence files from an HDFS path as ImageFrame + * Extract hadoop sequence files from an HDFS path as ImageFeatures * @param url sequence files folder path * @param sc spark context * @param partitionNum partition number, default: Engine.nodeNumber() * Engine.coreNumber() * @return */ - private[bigdl] def filesToRoiImageFrame(url: String, sc: SparkContext, + def filesToRoiImageFeatures(url: String, sc: SparkContext, partitionNum: Option[Int] = None): DataSet[ImageFeature] = { val num = partitionNum.getOrElse(Engine.nodeNumber() * Engine.coreNumber()) val rawData = sc.sequenceFile(url, classOf[BytesWritable], classOf[BytesWritable], num) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala index b4ab3d3c8d4..9fb86c3ba11 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala @@ -62,7 +62,8 @@ object Test { val partitionNum = if (param.partitionNum > 0) param.partitionNum else Engine.nodeNumber() * Engine.coreNumber() - val rddData = DataSet.SeqFileFolder.filesToRoiImageFrame(param.folder, sc, Some(partitionNum)) + val rddData = DataSet.SeqFileFolder.filesToRoiImageFeatures(param.folder, + sc, Some(partitionNum)) .toDistributed().data(train = false) val transformer = MTImageFeatureToBatchWithResize( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala index f05ae64decd..bd51e41f873 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala @@ -95,7 +95,7 @@ class DataSetSpec extends SparkContextLifeCycle with Matchers { "-b", "2", "-m", dataSetFolder + "cocomini.json")) // write done, now read and check - DataSet.SeqFileFolder.filesToRoiImageFrame(tmpFile.getPath, sc).toDistributed() + DataSet.SeqFileFolder.filesToRoiImageFeatures(tmpFile.getPath, sc).toDistributed() .data(false) .map(imf => { (imf(ImageFeature.uri).asInstanceOf[String], imf.getOriginalSize, imf.getLabel[RoiLabel], From a2c88566cab212fbcc3222578cf3bd22795839b6 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 29 Oct 2019 15:07:08 +0800 Subject: [PATCH 0988/1065] fix: move out setMklThreads of MklDnn (#2950) --- .../src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala | 1 + .../main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala index adefe180c76..e0c880573b0 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/Engine.scala @@ -372,6 +372,7 @@ object Engine { // this thread and the omp threads forked from computing. if (engineType == MklDnn) { dnnComputing.setMKLThreadOfMklDnnBackend(MKL.getMklNumThreads) + _model.setMKLThreadOfMklDnnBackend(MKL.getMklNumThreads) } if (System.getProperty("multiThread", "false").toBoolean) { wrapperComputing.setMKLThread(1) diff --git a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala index 7a8eea87427..75b5e30f48b 100644 --- a/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala +++ b/scala/common/utils/src/main/scala/com/intel/analytics/bigdl/utils/ThreadPool.scala @@ -91,7 +91,6 @@ class ThreadPool(private var poolSize: Int) { mklPoolSize = Some(size) (1 to poolSize).map(i => Future { MKL.setNumThreads(size) - BackendMklDnn.setNumThreads(size) val tid = Thread.currentThread().getId() logger.info(s"Set mkl threads to $size on thread $tid") }(context)).foreach(Await.result(_, Duration.Inf)) From a445efaf0fceb5134f3c4dde0e66d0936fcc9735 Mon Sep 17 00:00:00 2001 From: LeicongLi Date: Tue, 12 Nov 2019 16:13:49 +0800 Subject: [PATCH 0989/1065] memory data cleanup (#2956) * memory data cleanup --- .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 47 ++----------------- 1 file changed, 5 insertions(+), 42 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index 295cb7c813b..e3ee74b2cef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -22,9 +22,6 @@ sealed trait MemoryData extends Serializable { def shape: Array[Int] def layout: Int def dataType: Int - def setShape(shape: Array[Int]): Unit - def setLayout(layout: Int): Unit - def setDataType(dataType: Int): Unit private var _mask: Int = -1 private var _scales: Array[Float] = Array.emptyFloatArray @@ -34,10 +31,6 @@ sealed trait MemoryData extends Serializable { def scales: Array[Float] = _scales def setScales(f: Array[Float]): Unit = _scales = f - def isLayoutFixed(): Boolean = { - layout != Memory.Format.format_undef && layout != Memory.Format.any - } - def cloneFormat(): MemoryData private val UNDEFINED: Long = -1 @@ -120,11 +113,11 @@ case class HeapData(private var _shape: Array[Int], private var _layout: Int, override def dataType: Int = _dataType - override def setDataType(dataType: Int): Unit = _dataType = dataType - - override def setShape(shape: Array[Int]): Unit = _shape = shape.clone() +// override def setDataType(dataType: Int): Unit = _dataType = dataType - override def setLayout(layout: Int): Unit = _layout = layout +// override def setShape(shape: Array[Int]): Unit = _shape = shape.clone() +// +// override def setLayout(layout: Int): Unit = _layout = layout override def shape: Array[Int] = _shape.clone() @@ -193,10 +186,6 @@ case class NativeData(private var _shape: Array[Int], private var _layout: Int, override def layout: Int = _layout - override def setShape(shape: Array[Int]): Unit = _shape = shape.clone() - - override def setLayout(layout: Int): Unit = _layout = layout - override def hashCode(): Int = { val seed = 41 var hash = 1 @@ -249,38 +238,12 @@ case class NativeData(private var _shape: Array[Int], private var _layout: Int, override def cloneFormat(): MemoryData = new NativeData(_shape, _layout, _dataType) override def dataType: Int = _dataType - - override def setDataType(dataType: Int): Unit = _dataType = dataType } private[mkldnn] object MemoryData { - def noUndef(formats: Array[MemoryData]): Boolean = { - if (formats == null || formats.length == 0) return true - formats.foreach(f => if (f.layout == Memory.Format.format_undef) return false) - return true - } - - def isSizeCompatible(actual: MemoryData, expect: MemoryData): Boolean = { - if (expect == null) return true - if (actual == null) return false - if (actual.shape.length != expect.shape.length) return false - actual.shape.zip(expect.shape).foreach {case (a, e) => if (a != e) return false} - return true - } def primitiveOutput(pd: Long): NativeData = { - val outputPD = MklDnn.PrimitiveDescQueryPd(pd, Query.DstPd, 0) - val memoryDesc = MklDnn.PrimitiveDescQueryMemory(outputPD) - val shape = Memory.GetShape(memoryDesc) - val paddingShape = Memory.GetPaddingShape(memoryDesc) - val layout = Memory.GetLayout(memoryDesc) - val dataType = Memory.GetDataType(memoryDesc) - val size = MklDnn.PrimitiveDescGetSize(outputPD) - - val memory = NativeData(shape, layout, dataType) - memory.setMemoryDescription(memoryDesc) - memory.setPrimitiveDescription(outputPD) - memory + operationWant(pd, Query.DstPd, 0) } def operationWant(primDesc: Long, queryType: Int, index: Int = 0): NativeData = { From 7ebb0059dbc3b5353be95a388e401b4ec03f8c3e Mon Sep 17 00:00:00 2001 From: Xiao Date: Fri, 15 Nov 2019 17:00:08 +0800 Subject: [PATCH 0990/1065] Onnx support: RoiAlign and TopK parameter update (#2957) * Topk add dim and increase parameter * RoiAlign add max pooling mode * add test cases * add test cases --- .../analytics/bigdl/dllib/nn/RoiAlign.scala | 224 ++++++++++++------ .../analytics/bigdl/dllib/nn/ops/TopK.scala | 25 +- .../bigdl/dllib/nn/RoiAlignSpec.scala | 5 +- 3 files changed, 177 insertions(+), 77 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala index ce56d67e122..71cb1d0a6ef 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala @@ -46,8 +46,9 @@ class RoiAlign[T: ClassTag] ( val spatialScale: Float, val samplingRatio: Int, val pooledH: Int, - val pooledW: Int -) (implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{ + val pooledW: Int, + val mode: String = "avg" +)(implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{ override def updateOutput(input: Table): Tensor[T] = { if (classTag[T] == classTag[Float]) { val data = input[Tensor[Float]](1) @@ -170,41 +171,83 @@ class RoiAlign[T: ClassTag] ( pre_cal ) - for (c <- 0 until channels) { - val index_n_c = index_n + c * pooledW * pooledH - val offset_data = (roi_batch_ind * channels + c) * height * width - var pre_calc_index: Int = 1 - - for (ph <- 0 until pooledH) { - for (pw <- 0 until pooledW) { - val index = index_n_c + ph * pooledW + pw - - var output_val: Float = 0.0f - for (iy <- 0 until roi_bin_grid_h) { - for (ix <- 0 until roi_bin_grid_w) { - val pc = pre_cal(pre_calc_index) - val pos1 = pc.valueAt(1).toInt - val pos2 = pc.valueAt(2).toInt - val pos3 = pc.valueAt(3).toInt - val pos4 = pc.valueAt(4).toInt - val w1 = pc.valueAt(5) - val w2 = pc.valueAt(6) - val w3 = pc.valueAt(7) - val w4 = pc.valueAt(8) - - output_val = output_val + w1 * inputData(offset_data.toInt + pos1) + - w2 * inputData(offset_data.toInt + pos2) + - w3 * inputData(offset_data.toInt + pos3) + - w4 * inputData(offset_data.toInt + pos4) - - pre_calc_index += 1 + mode match { + case "avg" => + for (c <- 0 until channels) { + val index_n_c = index_n + c * pooledW * pooledH + val offset_data = (roi_batch_ind * channels + c) * height * width + var pre_calc_index: Int = 1 + + for (ph <- 0 until pooledH) { + for (pw <- 0 until pooledW) { + val index = index_n_c + ph * pooledW + pw + + var output_val: Float = 0.0f + for (iy <- 0 until roi_bin_grid_h) { + for (ix <- 0 until roi_bin_grid_w) { + val pc = pre_cal(pre_calc_index) + val pos1 = pc.valueAt(1).toInt + val pos2 = pc.valueAt(2).toInt + val pos3 = pc.valueAt(3).toInt + val pos4 = pc.valueAt(4).toInt + val w1 = pc.valueAt(5) + val w2 = pc.valueAt(6) + val w3 = pc.valueAt(7) + val w4 = pc.valueAt(8) + + output_val = output_val + w1 * inputData(offset_data.toInt + pos1) + + w2 * inputData(offset_data.toInt + pos2) + + w3 * inputData(offset_data.toInt + pos3) + + w4 * inputData(offset_data.toInt + pos4) + + pre_calc_index += 1 + } + } + output_val /= count + + outputData(index) = output_val + } + } + } + case "max" => + for (c <- 0 until channels) { + val index_n_c = index_n + c * pooledW * pooledH + val offset_data = (roi_batch_ind * channels + c) * height * width + var pre_calc_index: Int = 1 + + for (ph <- 0 until pooledH) { + for (pw <- 0 until pooledW) { + val index = index_n_c + ph * pooledW + pw + + var output_val = Float.MinValue + for (iy <- 0 until roi_bin_grid_h) { + for (ix <- 0 until roi_bin_grid_w) { + val pc = pre_cal(pre_calc_index) + val pos1 = pc.valueAt(1).toInt + val pos2 = pc.valueAt(2).toInt + val pos3 = pc.valueAt(3).toInt + val pos4 = pc.valueAt(4).toInt + val w1 = pc.valueAt(5) + val w2 = pc.valueAt(6) + val w3 = pc.valueAt(7) + val w4 = pc.valueAt(8) + + val value = w1 * inputData(offset_data.toInt + pos1) + + w2 * inputData(offset_data.toInt + pos2) + + w3 * inputData(offset_data.toInt + pos3) + + w4 * inputData(offset_data.toInt + pos4) + + if (value > output_val) { + output_val = value + } + + pre_calc_index += 1 + } + } + outputData(index) = output_val } } - output_val /= count - - outputData(index) = output_val } - } } } } @@ -354,42 +397,83 @@ class RoiAlign[T: ClassTag] ( roi_bin_grid_w, pre_cal ) - - for (c <- 0 until channels) { - val index_n_c = index_n + c * pooledW * pooledH - val offset_data = (roi_batch_ind * channels + c) * height * width - var pre_calc_index: Int = 1 - - for (ph <- 0 until pooledH) { - for (pw <- 0 until pooledW) { - val index = index_n_c + ph * pooledW + pw - - var output_val: Double = 0.0 - for (iy <- 0 until roi_bin_grid_h) { - for (ix <- 0 until roi_bin_grid_w) { - val pc = pre_cal(pre_calc_index) - val pos1 = pc.valueAt(1).toInt - val pos2 = pc.valueAt(2).toInt - val pos3 = pc.valueAt(3).toInt - val pos4 = pc.valueAt(4).toInt - val w1 = pc.valueAt(5) - val w2 = pc.valueAt(6) - val w3 = pc.valueAt(7) - val w4 = pc.valueAt(8) - - output_val = output_val + w1 * inputData(offset_data.toInt + pos1) + - w2 * inputData(offset_data.toInt + pos2) + - w3 * inputData(offset_data.toInt + pos3) + - w4 * inputData(offset_data.toInt + pos4) - - pre_calc_index += 1 + mode match { + case "avg" => + for (c <- 0 until channels) { + val index_n_c = index_n + c * pooledW * pooledH + val offset_data = (roi_batch_ind * channels + c) * height * width + var pre_calc_index: Int = 1 + + for (ph <- 0 until pooledH) { + for (pw <- 0 until pooledW) { + val index = index_n_c + ph * pooledW + pw + + var output_val: Double = 0.0 + for (iy <- 0 until roi_bin_grid_h) { + for (ix <- 0 until roi_bin_grid_w) { + val pc = pre_cal(pre_calc_index) + val pos1 = pc.valueAt(1).toInt + val pos2 = pc.valueAt(2).toInt + val pos3 = pc.valueAt(3).toInt + val pos4 = pc.valueAt(4).toInt + val w1 = pc.valueAt(5) + val w2 = pc.valueAt(6) + val w3 = pc.valueAt(7) + val w4 = pc.valueAt(8) + + output_val = output_val + w1 * inputData(offset_data.toInt + pos1) + + w2 * inputData(offset_data.toInt + pos2) + + w3 * inputData(offset_data.toInt + pos3) + + w4 * inputData(offset_data.toInt + pos4) + + pre_calc_index += 1 + } + } + output_val /= count + + outputData(index) = output_val + } + } + } + case "max" => + for (c <- 0 until channels) { + val index_n_c = index_n + c * pooledW * pooledH + val offset_data = (roi_batch_ind * channels + c) * height * width + var pre_calc_index: Int = 1 + + for (ph <- 0 until pooledH) { + for (pw <- 0 until pooledW) { + val index = index_n_c + ph * pooledW + pw + + var output_val = Double.MinValue + for (iy <- 0 until roi_bin_grid_h) { + for (ix <- 0 until roi_bin_grid_w) { + val pc = pre_cal(pre_calc_index) + val pos1 = pc.valueAt(1).toInt + val pos2 = pc.valueAt(2).toInt + val pos3 = pc.valueAt(3).toInt + val pos4 = pc.valueAt(4).toInt + val w1 = pc.valueAt(5) + val w2 = pc.valueAt(6) + val w3 = pc.valueAt(7) + val w4 = pc.valueAt(8) + + val value = w1 * inputData(offset_data.toInt + pos1) + + w2 * inputData(offset_data.toInt + pos2) + + w3 * inputData(offset_data.toInt + pos3) + + w4 * inputData(offset_data.toInt + pos4) + + if (value > output_val) { + output_val = value + } + + pre_calc_index += 1 + } + } + outputData(index) = output_val } } - output_val /= count - - outputData(index) = output_val } - } } } } @@ -495,6 +579,8 @@ object RoiAlign { spatialScale: Float, samplingRatio: Int, pooledH: Int, - pooledW: Int) (implicit ev: TensorNumeric[T]): RoiAlign[T] = - new RoiAlign[T](spatialScale, samplingRatio, pooledH, pooledW) + pooledW: Int, + mode: String = "avg" + ) (implicit ev: TensorNumeric[T]): RoiAlign[T] = + new RoiAlign[T](spatialScale, samplingRatio, pooledH, pooledW, mode) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala index 159fec33248..ddef566fc61 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/ops/TopK.scala @@ -24,8 +24,10 @@ import scala.reflect.ClassTag class TopK[T: ClassTag, D: ClassTag]( val k: Int, val sorted: Boolean = true, - val startIndex: Int = 1 -)(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) + val startIndex: Int = 1, + val dim: Int = -1, + val increase: Boolean = false) +(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) extends Operation[Tensor[D], Table, T] { private val indices = Tensor[Int]() @@ -35,7 +37,13 @@ class TopK[T: ClassTag, D: ClassTag]( output = T(values, indices) override def updateOutput(input: Tensor[D]): Table = { - input.topk(k = k, increase = false, result = values, indices = indicesD, sortedResult = sorted) + input.topk( + k = k, + dim = dim, + increase = increase, + result = values, + indices = indicesD, + sortedResult = sorted) indices.resizeAs(indicesD) indices.zipWith[Int, D](indices, indicesD, (a, b) => { ev2.toType[Int](b) + startIndex - 1 @@ -50,7 +58,12 @@ class TopK[T: ClassTag, D: ClassTag]( } object TopK { - def apply[T: ClassTag, D: ClassTag](k: Int, sorted: Boolean = true, startIndex : Int = 1 - )(implicit ev: TensorNumeric[T], - ev2: TensorNumeric[D]): TopK[T, D] = new TopK(k, sorted, startIndex) + def apply[T: ClassTag, D: ClassTag]( + k: Int, + sorted: Boolean = true, + startIndex : Int = 1, + dim: Int = -1, + increase: Boolean = false) + (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]): + TopK[T, D] = new TopK(k, sorted, startIndex, dim, increase) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala index a6d29da1230..de3a127c741 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala @@ -74,7 +74,7 @@ class RoiAlignSpec extends FlatSpec with Matchers { input.insert(Tensor(Storage(data.map(x => x.toFloat))).resize(1, 2, 6, 8)) input.insert(Tensor(Storage(rois.map(x => x.toFloat))).resize(4, 4)) - val roiAlign = RoiAlign[Float](spatio_scale, sampling_ratio, pooled_height, pooled_width) + val roiAlign = RoiAlign[Float](spatio_scale, sampling_ratio, pooled_height, pooled_width, "avg") val res = roiAlign.forward(input) val expectedRes = Array( 0.614743709564208984, 0.550280153751373291, @@ -150,7 +150,8 @@ class RoiAlignSpec extends FlatSpec with Matchers { input.insert(Tensor(Storage(data.map(x => x))).resize(1, 2, 6, 8)) input.insert(Tensor(Storage(rois.map(x => x.toDouble))).resize(4, 4)) - val roiAlign = RoiAlign[Double](spatio_scale, sampling_ratio, pooled_height, pooled_width) + val roiAlign = RoiAlign[Double]( + spatio_scale, sampling_ratio, pooled_height, pooled_width, "avg") val res = roiAlign.forward(input) val expectedRes = Array( 0.614743709564208984, 0.550280153751373291, From 44b118f9703b83904f36b91fc220b2a7ce37a93a Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 18 Nov 2019 22:02:38 +0800 Subject: [PATCH 0991/1065] remove masks requirements (#2959) --- .../feature/transform/vision/image/label/roi/RoiLabel.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala index cb038780450..ae57a49b0a1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/label/roi/RoiLabel.scala @@ -57,7 +57,7 @@ case class RoiLabel(classes: Tensor[Float], bboxes: Tensor[Float], def toTable: Table = { val table = T() if (masks != null) { - require(masks.length > 0, "The masks can either be null or a non-empty array") + // masks may be empty array table(RoiImageInfo.MASKS) = masks.map(_.toRLE) } table(RoiImageInfo.CLASSES) = classes From 1cd740f442f96d8bdfbc7b699419aa72c9488201 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 19 Nov 2019 10:07:34 +0800 Subject: [PATCH 0992/1065] fix: the squeeze should not be included in IRElement (#2962) --- .../dllib/utils/intermediate/IRElement.scala | 2 -- .../bigdl/dllib/nn/mkldnn/SqueezeSpec.scala | 34 +++++++++++++++++++ 2 files changed, 34 insertions(+), 2 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SqueezeSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala index ad9b2641ed5..efeb813dad6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala @@ -101,8 +101,6 @@ case class IRLinear[T: ClassTag]( initGradWeight: Tensor[T] = null, initGradBias: Tensor[T] = null) extends IROperator[T] -case class IRSqueeze[T: ClassTag](dims: Array[Int], batchMode: Boolean) extends IROperator[T] - case class IRSpatialCrossMapLRN[T: ClassTag]( size: Int = 5, alpha: Double = 1.0, diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SqueezeSpec.scala new file mode 100644 index 00000000000..726db31fb8e --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SqueezeSpec.scala @@ -0,0 +1,34 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.nn.mkldnn + +import org.scalatest.{FlatSpec, Matchers} +import com.intel.analytics.bigdl.nn.{Squeeze, StaticGraph, Input => NNInput} + +class SqueezeSpec extends FlatSpec with Matchers { + "a graph with squeeze" should "convert correctly" in { + System.setProperty("bigdl.engineType", "mkldnn") + val input = NNInput[Float]() + val squeeze = Squeeze[Float]().inputs(input) + + val graph = new StaticGraph[Float](Array(input), Array(squeeze)) + + // if there's no exception here, means it's right. + graph.toIRgraph() + System.clearProperty("bigdl.engineType") + } +} From cc2e0308d501856f7fbbd44e60c10f3e4268eafc Mon Sep 17 00:00:00 2001 From: Menooker Date: Wed, 20 Nov 2019 09:01:02 +0800 Subject: [PATCH 0993/1065] enhance COCODataset (#2954) * enhance COCODataset: Add COCODataset.loadFromSeqFile Add COCODataset.toImageFeatures Add COCOImage.toTable * rename and polish doc * fix COCO serialize bug * fix typo in function name --- .../bigdl/dllib/feature/dataset/DataSet.scala | 41 +++--- .../dataset/segmentation/COCODataset.scala | 120 ++++++++++++++++-- .../models/utils/COCOSeqFileGenerator.scala | 2 +- .../bigdl/dllib/dataset/DataSetSpec.scala | 32 ----- .../segmentation/COCODatasetSpec.scala | 117 +++++++++++++++++ 5 files changed, 252 insertions(+), 60 deletions(-) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/COCODatasetSpec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala index 1f81d40a9ec..bf3ec476b1f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/DataSet.scala @@ -597,6 +597,29 @@ object DataSet { if (image.getRaster.getNumBands == 1) return true false } + + /** + * Decode raw bytes read from an image file into decoded bytes. If the file is 1 channel grey + * scale image, automatically convert to 3 channels + * @param in the input raw image bytes + * @return the decoded 3 channel bytes in BGR order + */ + private[bigdl] def decodeRawImageToBGR(in: Array[Byte]) : Array[Byte] = { + val inputStream = new ByteArrayInputStream(in) + val image = { + val img = ImageIO.read(inputStream) + if (isSingleChannelImage(img)) { + val imageBuff: BufferedImage = + new BufferedImage(img.getWidth(), img.getHeight(), BufferedImage.TYPE_3BYTE_BGR) + imageBuff.getGraphics.drawImage(img, 0, 0, new Color(0, 0, 0), null) + imageBuff + } else { + img + } + } + image.getRaster.getDataBuffer.asInstanceOf[DataBufferByte].getData() + } + /** * Extract hadoop sequence files from an HDFS path as ImageFeatures * @param url sequence files folder path @@ -604,7 +627,7 @@ object DataSet { * @param partitionNum partition number, default: Engine.nodeNumber() * Engine.coreNumber() * @return */ - def filesToRoiImageFeatures(url: String, sc: SparkContext, + private[bigdl] def filesToRoiImageFeatures(url: String, sc: SparkContext, partitionNum: Option[Int] = None): DataSet[ImageFeature] = { val num = partitionNum.getOrElse(Engine.nodeNumber() * Engine.coreNumber()) val rawData = sc.sequenceFile(url, classOf[BytesWritable], classOf[BytesWritable], num) @@ -613,7 +636,7 @@ object DataSet { val fileName = metaBytes.getString val (height, width, anno) = metaBytes.getAnnotations - val labelClasses = Tensor(anno.map(_.categoryId.toFloat), Array(anno.length)) + val labelClasses = Tensor(anno.map(_.categoryIdx.toFloat), Array(anno.length)) val bboxes = Tensor( anno.toIterator.flatMap(ann => { val x1 = ann.bbox1 @@ -627,19 +650,7 @@ object DataSet { val masks = anno.map(ann => ann.masks) require(metaBytes.getInt == COCODataset.MAGIC_NUM, "Corrupted metadata") - val inputStream = new ByteArrayInputStream(data._2.getBytes) - val image = { - val img = ImageIO.read(inputStream) - if (isSingleChannelImage(img)) { - val imageBuff: BufferedImage = - new BufferedImage(img.getWidth(), img.getHeight(), BufferedImage.TYPE_3BYTE_BGR) - imageBuff.getGraphics.drawImage(img, 0, 0, new Color(0, 0, 0), null) - imageBuff - } else { - img - } - } - val rawdata = image.getRaster.getDataBuffer.asInstanceOf[DataBufferByte].getData() + val rawdata = decodeRawImageToBGR(data._2.getBytes) require(rawdata.length == height * width * 3) val imf = ImageFeature(rawdata, RoiLabel(labelClasses, bboxes, masks), fileName) imf(ImageFeature.originalSize) = (height, width, 3) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala index 215ef29b56f..aaa538c4dac 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/dataset/segmentation/COCODataset.scala @@ -20,10 +20,18 @@ import com.google.gson.{Gson, GsonBuilder, JsonDeserializationContext, JsonDeser import com.google.gson.annotations.SerializedName import com.google.gson.reflect.TypeToken import com.google.gson.stream.{JsonReader, JsonWriter} +import com.intel.analytics.bigdl.DataSet +import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.DataSet.SeqFileFolder +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, RoiImageInfo} +import com.intel.analytics.bigdl.utils.{T, Table} import java.io.{BufferedReader, FileReader} import java.lang.reflect.Type import java.nio.ByteBuffer import java.nio.file.{Files, Path, Paths} +import org.apache.spark.SparkContext import scala.collection.mutable.ArrayBuffer private[bigdl] class COCOSerializeContext { @@ -90,7 +98,7 @@ private[bigdl] class COCODeserializer(buffer: ByteBuffer) { buffer.get(arr) new String(arr) } - case class SimpleAnnotation(categoryId: Int, area: Float, bbox1: Float, bbox2: Float, + case class SimpleAnnotation(categoryIdx: Int, area: Float, bbox1: Float, bbox2: Float, bbox3: Float, bbox4: Float, isCrowd: Boolean, masks: SegmentationMasks) // returns an image's height, width, all annotations @@ -103,7 +111,7 @@ private[bigdl] class COCODeserializer(buffer: ByteBuffer) { } private def getAnnotation(height: Int, width: Int): SimpleAnnotation = { - val categoryId = getInt + val categoryIdx = getInt val area = getFloat val bbox1 = getFloat val bbox2 = getFloat @@ -131,7 +139,7 @@ private[bigdl] class COCODeserializer(buffer: ByteBuffer) { } PolyMasks(poly, height, width) } - SimpleAnnotation(categoryId, area, bbox1, bbox2, bbox3, bbox4, isCrowd, masks) + SimpleAnnotation(categoryIdx, area, bbox1, bbox2, bbox3, bbox4, isCrowd, masks) } } @@ -143,9 +151,13 @@ case class COCODataset(info: COCODatasetInfo, images: Array[COCOImage], private lazy val imageId2Image = images.toIterator.map(img => (img.id, img)).toMap private[segmentation] def init(imgRoot: String): Unit = { + categories.zipWithIndex.foreach { case (cate, idx) => + cateId2catIdx(cate.id) = idx + 1 // the ids starts from 1, because 0 is for background + } annotations.foreach(anno => { require(imageId2Image.contains(anno.imageId), s"Cannot find image_id ${anno.imageId}") val img = imageId2Image(anno.imageId) + anno._categoryIdx = cateId2catIdx(anno.categoryId) anno.image = img img.annotations += anno anno.segmentation match { @@ -155,9 +167,7 @@ case class COCODataset(info: COCODatasetInfo, images: Array[COCOImage], } }) images.foreach(_.imgRootPath = imgRoot) - categories.zipWithIndex.foreach { case (cate, idx) => - cateId2catIdx(cate.id) = idx + 1 // the ids starts from 1, because 0 is for background - } + } /** @@ -183,6 +193,13 @@ case class COCODataset(info: COCODatasetInfo, images: Array[COCOImage], * @return category data */ def getCategoryByIdx(idx: Int): COCOCategory = categories(idx - 1) + + /** + * Convert the images & ground truths into ImageFeatures. + * The image feature is in the same format of what COCODataset.loadFromSeqFile returns + * @return + */ + def toImageFeatures: Iterator[ImageFeature] = images.toIterator.map(_.toImageFeature) } case class COCODatasetInfo( @@ -208,11 +225,11 @@ case class COCOImage( @SerializedName("date_captured") var dateCaptured: String = _ @SerializedName("file_name") var fileName: String = _ - def dumpTo(context: COCOSerializeContext, dataset: COCODataset): Unit = { + def dumpTo(context: COCOSerializeContext): Unit = { context.dump(height) context.dump(width) context.dump(annotations.size) - annotations.foreach(_.dumpTo(context, dataset)) + annotations.foreach(_.dumpTo(context)) } /** @@ -227,6 +244,64 @@ case class COCOImage( */ def data: Array[Byte] = Files.readAllBytes(path) + /** + * Convert the image's image data and ground truth into an image feature. + * The image feature is in the same format of what COCODataset.loadFromSeqFile returns + * @return an ImageFeature with ground truth & image data + */ + def toImageFeature: ImageFeature = { + val labelClasses = Tensor(annotations.map(_.categoryIdx.toFloat).toArray, + Array(annotations.length)) + val bboxes = Tensor( + annotations.toIterator.flatMap(ann => { + val x1 = ann.bbox._1 + val y1 = ann.bbox._2 + val x2 = ann.bbox._3 + val y2 = ann.bbox._4 + Iterator(x1, y1, x2, y2) + }).toArray, + Array(annotations.length, 4)) + val isCrowd = Tensor(annotations.map(ann => if (ann.isCrowd) 1f else 0f).toArray, + Array(annotations.length)) + val masks = annotations.map(ann => ann.segmentation.asInstanceOf[SegmentationMasks]).toArray + + val rawdata = SeqFileFolder.decodeRawImageToBGR(this.data) + require(rawdata.length == height * width * 3) + val imf = ImageFeature(rawdata, RoiLabel(labelClasses, bboxes, masks), fileName) + imf(ImageFeature.originalSize) = (height, width, 3) + imf(RoiImageInfo.ISCROWD) = isCrowd + imf + } + + /** + * Convert the image's ground truth label & masks into Table for RoiMiniBatch + * @return a table with ground truth label & masks for the image + */ + def toTable: Table = { + val img = this + val bboxes = Tensor( + img.annotations.toIterator.flatMap(ann => { + val x1 = ann.bbox._1 + val y1 = ann.bbox._2 + val x2 = ann.bbox._3 + val y2 = ann.bbox._4 + Iterator(x1, y1, x2, y2) + }).toArray, + Array(img.annotations.length, 4)) + + T() + .update(RoiImageInfo.ISCROWD, + Tensor(img.annotations.map(ann => if (ann.isCrowd) 1f else 0f).toArray, + Array(img.annotations.length)) + ) + .update(RoiImageInfo.ORIGSIZE, (img.height, img.width, 3)) + .update(RoiImageInfo.MASKS, + img.annotations.map(ann => ann.segmentation.asInstanceOf[SegmentationMasks].toRLE).toArray) + .update(RoiImageInfo.BBOXES, bboxes) + .update(RoiImageInfo.CLASSES, + Tensor(img.annotations.map(ann => ann.categoryIdx.toFloat).toArray, + Array(img.annotations.length))) + } } /** @@ -247,8 +322,12 @@ case class COCOAnotationOD(id: Long, imageId: Long, categoryId: Long, var segmentation: COCOSegmentation, area: Float, bbox: (Float, Float, Float, Float), isCrowd: Boolean, @transient var image: COCOImage = null) { - def dumpTo(context: COCOSerializeContext, dataSet: COCODataset): Unit = { - context.dump(dataSet.categoryId2Idx(categoryId)) + @transient private[segmentation] var _categoryIdx: Long = -1 + def categoryIdx: Long = _categoryIdx + + def dumpTo(context: COCOSerializeContext): Unit = { + require(_categoryIdx != -1, "COCOAnotationOD should be initialized") + context.dump(_categoryIdx.toInt) context.dump(area) context.dump(bbox._1) context.dump(bbox._2) @@ -337,10 +416,10 @@ object COCODataset { } /** - * Load COCO dataset + * Load COCO dataset from local file * @param jsonPath the JSON metadata file path * @param imageRoot the root path of the image files - * @return + * @return the loaded COCO dataset */ def load(jsonPath: String, imageRoot: String = "."): COCODataset = { val d = gson.fromJson( @@ -348,4 +427,21 @@ object COCODataset { d.init(imageRoot) d } + + /** + * Load COCO dataset from Hadoop sequence files + * @param url sequence files folder path on HDFS/Local + * @param sc spark context + * @param partitionNum partition number, default: Engine.nodeNumber() * Engine.coreNumber() + * @return ImageFeatures for the dataset. + * Key in ImageFeature Value Type + * ImageFeature.bytes decoded image data Array[Byte] + * ImageFeature.uri Image file name String + * ImageFeature.label Label & masks RoiLabel + * RoiImageInfo.ISCROWD isCrowd Tensor[Float] + */ + def loadFromSeqFile(url: String, sc: SparkContext, + partitionNum: Option[Int] = None): DataSet[ImageFeature] = { + SeqFileFolder.filesToRoiImageFeatures(url, sc, partitionNum) + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/COCOSeqFileGenerator.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/COCOSeqFileGenerator.scala index d181024e170..3019cc9e772 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/COCOSeqFileGenerator.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/utils/COCOSeqFileGenerator.scala @@ -96,7 +96,7 @@ object COCOSeqFileGenerator { imgs.foreach { img => context.clear() context.dump(img.fileName) - img.dumpTo(context, meta) + img.dumpTo(context) context.dump(COCODataset.MAGIC_NUM) val keyBytes = context.toByteArray key.set(keyBytes, 0, keyBytes.length) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala index bd51e41f873..085ef74ef35 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/DataSetSpec.scala @@ -47,38 +47,6 @@ class DataSetSpec extends SparkContextLifeCycle with Matchers { } } - "COCODataset" should "correctly be loaded" in { - val resource = getClass().getClassLoader().getResource("coco") - - val dataSet = COCODataset.load(processPath(resource.getPath()) - + File.separator + "cocomini.json") - dataSet.images.length should be (5) - dataSet.annotations.length should be (6) - val cateIdx = Array(53, 53, 53, 1, 19, 1).toIterator - val sizes = Array((428, 640), (480, 640), (427, 640), (480, 640), (427, 640)).toIterator - for (anno <- dataSet.annotations) { - anno.image.id should be (anno.imageId) - dataSet.categoryId2Idx(anno.categoryId) should be (cateIdx.next()) - if (anno.isCrowd) { - anno.segmentation.isInstanceOf[COCORLE] should be (true) - } else { - anno.segmentation.isInstanceOf[COCOPoly] should be (true) - val poly = anno.segmentation.asInstanceOf[COCOPoly] - poly.height should be (anno.image.height) - poly.width should be (anno.image.width) - } - } - for (img <- dataSet.images) { - val size = sizes.next() - img.height should be (size._1) - img.width should be (size._2) - } - for (i <- 1 to dataSet.categories.length) { - val cate = dataSet.getCategoryByIdx(i) - dataSet.categoryId2Idx(cate.id) should be (i) - } - } - "COCODataset" should "correctly transform into sequence file" in { val resource = getClass().getClassLoader().getResource("coco") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/COCODatasetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/COCODatasetSpec.scala new file mode 100644 index 00000000000..4fc95c8bd10 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/dataset/segmentation/COCODatasetSpec.scala @@ -0,0 +1,117 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.dataset.segmentation + +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.transform.vision.image.{ImageFeature, RoiImageInfo} +import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel +import java.awt.image.DataBufferByte +import java.io.{File, FileInputStream} +import javax.imageio.ImageIO +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +class COCODatasetSpec extends FlatSpec with Matchers with BeforeAndAfter { + + private def processPath(path: String): String = { + if (path.contains(":")) { + path.substring(1) + } else { + path + } + } + + val resourcePath: String = processPath(getClass.getClassLoader.getResource("coco").getPath) + val dataSet: COCODataset = COCODataset.load(resourcePath + + File.separator + "cocomini.json", resourcePath) + + "COCODataset" should "correctly be loaded" in { + dataSet.images.length should be (5) + dataSet.annotations.length should be (6) + val cateIdx = Array(53, 53, 53, 1, 19, 1).toIterator + val sizes = Array((428, 640), (480, 640), (427, 640), (480, 640), (427, 640)).toIterator + for (anno <- dataSet.annotations) { + anno.image.id should be (anno.imageId) + dataSet.categoryId2Idx(anno.categoryId) should be (cateIdx.next()) + anno.categoryIdx should be (dataSet.categoryId2Idx(anno.categoryId)) + if (anno.isCrowd) { + anno.segmentation.isInstanceOf[COCORLE] should be (true) + } else { + anno.segmentation.isInstanceOf[COCOPoly] should be (true) + val poly = anno.segmentation.asInstanceOf[COCOPoly] + poly.height should be (anno.image.height) + poly.width should be (anno.image.width) + } + } + for (img <- dataSet.images) { + val size = sizes.next() + img.height should be (size._1) + img.width should be (size._2) + } + for (i <- 1 to dataSet.categories.length) { + val cate = dataSet.getCategoryByIdx(i) + dataSet.categoryId2Idx(cate.id) should be (i) + } + } + + "COCODataset.toImageFeatures" should "correctly work" in { + val cateIdx = Array(1, 19, 53, 53, 53, 1).toIterator + val sizes = Array((428, 640, 3), (480, 640, 3), (427, 640, 3), (480, 640, 3), + (427, 640, 3)).toIterator + val uri = Array("COCO_val2014_000000153344.jpg", "COCO_val2014_000000091136.jpg", + "COCO_val2014_000000558840.jpg", "COCO_val2014_000000200365.jpg", + "COCO_val2014_000000374530.jpg" + ).toIterator + val isCrowd = Array(1f, 1f, 0f, 0f, 0f, 1f).toIterator + dataSet.toImageFeatures.foreach(imf => { + imf.getOriginalSize should be (sizes.next()) + val iscr = imf[Tensor[Float]](RoiImageInfo.ISCROWD) + + val roilabel = imf.getLabel[RoiLabel] + roilabel.classes.size() should be (iscr.size()) + for(i <- 1 to iscr.nElement()) { + iscr.valueAt(i) should be (isCrowd.next()) + roilabel.classes.valueAt(i) should be (cateIdx.next()) + } + roilabel.bboxes.size() should be (Array(roilabel.classes.size(1), 4)) + + val inputStream = new FileInputStream(resourcePath + File.separator + uri.next()) + val image = ImageIO.read(inputStream) + val rawdata = image.getRaster.getDataBuffer.asInstanceOf[DataBufferByte].getData() + require(java.util.Arrays.equals(rawdata, imf[Array[Byte]](ImageFeature.bytes))) + }) + } + + "COCOImage.toTable" should "correctly work" in { + val cateIdx = Array(1, 19, 53, 53, 53, 1).toIterator + val sizes = Array((428, 640, 3), (480, 640, 3), (427, 640, 3), (480, 640, 3), + (427, 640, 3)).toIterator + val isCrowd = Array(1f, 1f, 0f, 0f, 0f, 1f).toIterator + dataSet.images.map(_.toTable).foreach(tab => { + RoiImageInfo.getOrigSize(tab) should be (sizes.next()) + val iscr = RoiImageInfo.getIsCrowd(tab) + val classes = RoiImageInfo.getClasses(tab) + classes.size() should be (iscr.size()) + for(i <- 1 to iscr.nElement()) { + iscr.valueAt(i) should be (isCrowd.next()) + classes.valueAt(i) should be (cateIdx.next()) + } + RoiImageInfo.getBBoxes(tab).size() should be (Array(classes.size(1), 4)) + + }) + } + +} From b8d19465ae5328e655393e32bec17650a836948f Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Wed, 20 Nov 2019 09:33:29 +0800 Subject: [PATCH 0994/1065] typo fix (#2965) --- .../analytics/bigdl/dllib/utils/intermediate/IRGraph.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index d3f8294633e..0aaefc747a4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -52,7 +52,7 @@ private[bigdl] class IRGraph[T: ClassTag]( require(inputFormats.length == inputs.length, s"IRGraph: inputFormats" + s"length ${inputFormats.length} should be same with input nodes length ${inputs.length}") require(outputFormats.length == outputs.length, s"IRGraph: outputFormats" + - s"length ${inputFormats.length} should be same with input nodes length ${outputs.length}") + s"length ${outputFormats.length} should be same with output nodes length ${outputs.length}") private[bigdl] var graph: Graph[T] = null From acf3294061f0a600013a3c2413dae8f6aa102f49 Mon Sep 17 00:00:00 2001 From: Menooker Date: Mon, 25 Nov 2019 08:54:03 +0800 Subject: [PATCH 0995/1065] rename RoiImageFeatureToBatch APIs (#2964) --- .../vision/image/MTImageFeatureToBatch.scala | 40 ++++++++++++------- .../bigdl/dllib/models/maskrcnn/Test.scala | 2 +- .../image/MTImageFeatureToBatchSpec.scala | 8 ++-- 3 files changed, 31 insertions(+), 19 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala index d8fb0476613..c35ce7e4f1b 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -32,35 +32,46 @@ object MTImageFeatureToBatch { * @param batchSize batch size * @param transformer pipeline for pre-processing, finally outputting ImageFeature * @param toRGB if converted to RGB, default format is BGR - * @param extractRoi if true, extract ROI labels for segmentation; else the labels are for - * classification * @return */ def apply(width: Int, height: Int, batchSize: Int, - transformer: FeatureTransformer, toRGB: Boolean = false, extractRoi: Boolean = false) + transformer: FeatureTransformer, toRGB: Boolean = false) : MTImageFeatureToBatch = { - if (extractRoi) { - new RoiMTImageFeatureToBatch ( - width, height, batchSize, transformer, toRGB) - } else { new ClassificationMTImageFeatureToBatch ( width, height, batchSize, transformer, toRGB) - } } } -object MTImageFeatureToBatchWithResize { +object RoiImageFeatureToBatch { /** * The transformer from ImageFeature to mini-batches, and extract ROI labels for segmentation - * if roi labels are set. + * if roi labels are set. The sizes of the images can be different. + * @param batchSize global batch size + * @param transformer pipeline for pre-processing + * @param toRGB if converted to RGB, default format is BGR * @param sizeDivisible when it's greater than 0, height and wide should be divisible by this size + * + */ + def withResize(batchSize: Int, transformer: FeatureTransformer, + toRGB : Boolean = false, sizeDivisible: Int = -1) + : MTImageFeatureToBatch = + new RoiImageFeatureToBatchWithResize(sizeDivisible, batchSize, transformer, toRGB) + + + /** + * The transformer from ImageFeature to mini-batches, and extract ROI labels for segmentation + * if roi labels are set. The sizes of the images must be the same. + * @param width width of the output images + * @param height height of the output images * @param batchSize global batch size * @param transformer pipeline for pre-processing * @param toRGB if converted to RGB, default format is BGR + * */ - def apply(sizeDivisible: Int = -1, batchSize: Int, transformer: FeatureTransformer, - toRGB : Boolean = false): MTImageFeatureToBatch = - new RoiImageFeatureToBatchWithResize(sizeDivisible, batchSize, transformer, toRGB) + def apply(width: Int, height: Int, batchSize: Int, + transformer: FeatureTransformer, toRGB: Boolean = false) : MTImageFeatureToBatch = { + new RoiImageFeatureToBatch(width, height, batchSize, transformer, toRGB) + } } /** @@ -313,13 +324,14 @@ object RoiMiniBatch { * A transformer pipeline wrapper to create RoiMiniBatch in multiple threads * The output "target" is a Table. The keys are from 1 to sizeof(batch). The values are * the tables for each RoiLabel. Each Roi label table, contains fields of RoiLabel class. + * The sizes of the input images should be the same * @param width final image width * @param height final image height * @param totalBatchSize global batch size * @param transformer pipeline for pre-processing * @param toRGB if converted to RGB, default format is BGR */ -class RoiMTImageFeatureToBatch private[bigdl](width: Int, height: Int, +class RoiImageFeatureToBatch private[bigdl](width: Int, height: Int, totalBatchSize: Int, transformer: FeatureTransformer, toRGB: Boolean = false) extends MTImageFeatureToBatch(totalBatchSize, transformer) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala index 9fb86c3ba11..edd008d9bc9 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/Test.scala @@ -66,7 +66,7 @@ object Test { sc, Some(partitionNum)) .toDistributed().data(train = false) - val transformer = MTImageFeatureToBatchWithResize( + val transformer = RoiImageFeatureToBatch.withResize( sizeDivisible = 32, batchSize = param.batchSize / Engine.nodeNumber(), transformer = diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala index e8e5c77e232..1c18cf8b2d5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala @@ -103,8 +103,8 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft imf }).toArray - val transformer = MTImageFeatureToBatchWithResize(10, 3, - new FeatureTransformer {}, toRGB = false) + val transformer = RoiImageFeatureToBatch.withResize(3, + new FeatureTransformer {}, toRGB = false, 10) val miniBatch = transformer(DataSet.array(imgData).data(false)) val expectedOutput = Tensor[Float](T(T( @@ -253,8 +253,8 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft imf(ImageFeature.originalSize) = (10, 10, 3) imf }).toArray - val transformer = MTImageFeatureToBatch(10, 10, 19, new FeatureTransformer {}, - toRGB = false, extractRoi = true) + val transformer = RoiImageFeatureToBatch(10, 10, 19, new FeatureTransformer {}, + toRGB = false) val miniBatch = transformer(DataSet.array(imgData).data(false)) miniBatch .foreach(batch => { From 814b4d4256fcaf9f49b7c38ba8b65b2a9e1178ba Mon Sep 17 00:00:00 2001 From: Menooker Date: Mon, 25 Nov 2019 13:27:49 +0800 Subject: [PATCH 0996/1065] RoiMiniBatch enhancement (#2953) * SerializableIndexedSeq * allow empty target & image size info * rename RoiImageFeatureToBatch APIs * set as private * change back to array * MTImageFeatureToBatch without labels * handle iscrowd * remove duplication in merge --- .../vision/image/MTImageFeatureToBatch.scala | 75 +++++++++---- .../image/MTImageFeatureToBatchSpec.scala | 105 +++++++++++++++++- 2 files changed, 159 insertions(+), 21 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala index c35ce7e4f1b..5af2db3a41d 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/feature/transform/vision/image/MTImageFeatureToBatch.scala @@ -40,8 +40,28 @@ object MTImageFeatureToBatch { new ClassificationMTImageFeatureToBatch ( width, height, batchSize, transformer, toRGB) } + + private[image] def checkLabels[T](labelData: Array[T]): Array[T] = { + if (labelData.length == 0) { + labelData + } else { + val hasLabel = labelData.head != null + for (i <- 1 until labelData.length) { + val curHasLabel = labelData(i) != null + require(curHasLabel == hasLabel, "The input data must either be all labeled or" + + " be all unlabeled") + } + if (hasLabel) labelData else null + } + } + + private[image] def arraySlice[T](array: Array[T], batchSize: Int) = { + if (array.length == batchSize) array else array.slice(0, batchSize) + } } +import MTImageFeatureToBatch._ + object RoiImageFeatureToBatch { /** * The transformer from ImageFeature to mini-batches, and extract ROI labels for segmentation @@ -162,7 +182,7 @@ private class PreFetch extends Transformer[ImageFeature, ImageFeature] { } /** - * A transformer pipeline wrapper to create Minibatch in multiple threads for classification + * A transformer pipeline wrapper to create labeled Minibatch in multiple threads for classification * @param width final image width * @param height final image height * @param totalBatchSize global batch size @@ -291,19 +311,27 @@ class RoiMiniBatch(val input: Tensor[Float], val target: Array[RoiLabel], } override def getTarget(): Table = { + require(target != null, "The target should not be null") val tables = (target, isCrowd, 1 to isCrowd.length).zipped.map { case (roiLabel, crowd, i) => - roiLabel.toTable + val ret = roiLabel.toTable .update(RoiImageInfo.ISCROWD, crowd) - .update(RoiImageInfo.IMGINFO, imageInfo.select(1, i)) + if (imageInfo != null) { + ret.update(RoiImageInfo.IMGINFO, imageInfo.select(1, i)) + } + ret } T.seq(tables) } override def slice(offset: Int, length: Int): MiniBatch[Float] = { val subInput = input.narrow(1, offset, length) - val subTarget = target.slice(offset - 1, length) // offset starts from 1 - val subIsCrowd = isCrowd.slice(offset - 1, length) // offset starts from 1 - val subSize = imageInfo.narrow(1, offset, length) + val subTarget = if (target != null) { + target.slice(offset - 1, offset + length - 1) // offset starts from 1 + } else { + null + } + val subIsCrowd = isCrowd.slice(offset - 1, offset + length - 1) // offset starts from 1 + val subSize = if (imageInfo != null) imageInfo.narrow(1, offset, length) else null RoiMiniBatch(subInput, subTarget, subIsCrowd, subSize) } @@ -346,8 +374,13 @@ class RoiImageFeatureToBatch private[bigdl](width: Int, height: Int, img.copyTo(featureData, position * frameLength * 3, toRGB = toRGB) val isCrowd = img(RoiImageInfo.ISCROWD).asInstanceOf[Tensor[Float]] val label = img.getLabel.asInstanceOf[RoiLabel] - require(label.bboxes.size(1) == isCrowd.size(1), "The number of detections" + - "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") + if (label != null) { + require(isCrowd != null && label.bboxes.size(1) == isCrowd.size(1), "The number" + + " of detections " + + "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") + } else { + require(isCrowd == null, "ImageFeature's ISCROWD should be not be set if the label is empty") + } isCrowdData(position) = isCrowd labelData(position) = label imgInfoData.setValue(position + 1, 1, img.getHeight()) @@ -361,10 +394,10 @@ class RoiImageFeatureToBatch private[bigdl](width: Int, height: Int, featureTensor.set(Storage[Float](featureData), storageOffset = 1, sizes = Array(curBatchSize, 3, height, width)) } - def arraySlice[T](array: Array[T]) = { - if (array.length == curBatchSize) array else array.slice(0, curBatchSize) - } - RoiMiniBatch(featureTensor, arraySlice(labelData), arraySlice(isCrowdData), + + val labels = checkLabels(arraySlice(labelData, curBatchSize)) + val crowd = if (labels != null) arraySlice(isCrowdData, curBatchSize) else null + RoiMiniBatch(featureTensor, labels, crowd, imgInfoData.narrow(1, 1, curBatchSize)) } } @@ -411,9 +444,12 @@ class RoiImageFeatureToBatchWithResize private[bigdl](sizeDivisible: Int = -1, t img.copyTo(imageBuffer(position).storage().array(), 0, toRGB = toRGB) val isCrowd = img(RoiImageInfo.ISCROWD).asInstanceOf[Tensor[Float]] val label = img.getLabel.asInstanceOf[RoiLabel] - if (isCrowd != null && label != null) { - require(label.bboxes.size(1) == isCrowd.size(1), "The number of detections" + - "in ImageFeature's ISCROWD should be equal to the number of detections in the RoiLabel") + if (label != null) { + require(isCrowd != null && label.bboxes.size(1) == isCrowd.size(1), "The number of " + + "detections in ImageFeature's ISCROWD should be equal to the number of detections in the " + + "RoiLabel") + } else { + require(isCrowd == null, "ImageFeature's ISCROWD should be not be set if the label is empty") } isCrowdData(position) = isCrowd labelData(position) = label @@ -432,10 +468,9 @@ class RoiImageFeatureToBatchWithResize private[bigdl](sizeDivisible: Int = -1, t featureTensor.select(1, i + 1).narrow(2, 1, imageBuffer(i).size(2)) .narrow(3, 1, imageBuffer(i).size(3)).copy(imageBuffer(i)) } - def arraySlice[T](array: Array[T]) = { - if (array.length == batchSize) array else array.slice(0, batchSize) - } - RoiMiniBatch(featureTensor, arraySlice(labelData), - arraySlice(isCrowdData), imgInfoData.narrow(1, 1, batchSize)) + + val labels = checkLabels(arraySlice(labelData, batchSize)) + val crowd = if (labels != null) arraySlice(isCrowdData, batchSize) else null + RoiMiniBatch(featureTensor, labels, crowd, imgInfoData.narrow(1, 1, batchSize)) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala index 1c18cf8b2d5..aa6ee5ddab4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/transform/vision/image/MTImageFeatureToBatchSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.transform.vision.image -import com.intel.analytics.bigdl.dataset.DataSet +import com.intel.analytics.bigdl.dataset.{DataSet, MiniBatch} import com.intel.analytics.bigdl.dataset.segmentation.RLEMasks import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel @@ -234,6 +234,31 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft }) imgCheck.count(!_) should be (0) + } + "MTImageFeatureToBatch without labels" should "work well" in { + val imgData = (0 to 1000).map(idx => (idx to (idx + 10*10*3)).map(_.toFloat).toArray) + .map(arr => { + val imf = ImageFeature() + imf(ImageFeature.floats) = arr + imf(ImageFeature.originalSize) = (10, 10, 3) + imf + }).toArray + val transformer = RoiImageFeatureToBatch(10, 10, 19, new FeatureTransformer {}, + toRGB = false) + val miniBatch = transformer(DataSet.array(imgData).data(false)) + miniBatch.foreach(batch => { + batch.asInstanceOf[RoiMiniBatch].target should be (null) + batch.getInput().asInstanceOf[Table].get[Tensor[Float]](1) should not be (null) + }) + + val transformer2 = RoiImageFeatureToBatch.withResize(batchSize = 19, + transformer = new FeatureTransformer {}) + val miniBatch2 = transformer(DataSet.array(imgData).data(false)) + miniBatch2.foreach(batch => { + batch.asInstanceOf[RoiMiniBatch].target should be (null) + batch.getInput().asInstanceOf[Table].get[Tensor[Float]](1) should not be (null) + }) + } "MTImageFeatureToBatch with ROI" should "work well" in { @@ -279,4 +304,82 @@ class MTImageFeatureToBatchSpec extends FlatSpec with Matchers with BeforeAndAft } + def arrayToTensor(a: Array[Float]): Tensor[Float] = Tensor[Float](a, Array(a.length)) + + "RoiMiniBatch" should "correctly slice" in { + val dummyBBox = Tensor[Float](Array(1f, 2, 3, 4), Array(1, 4)) + val roiLables = (0 until 100).map(i => { + RoiLabel(arrayToTensor(Array(i.toFloat)), dummyBBox) + }).toArray + val isCrowds = (0 until 100).map(i => { + arrayToTensor(Array(i.toFloat)) + }).toArray + val b = RoiMiniBatch( + arrayToTensor((1 to 100).toArray.map(_.toFloat)), + roiLables, + isCrowds, + arrayToTensor((1 to 100).toArray.map(_.toFloat)) + ) + + val s1 = b.slice(3, 20) + + def checkSlice(s1: MiniBatch[Float], start: Int, len: Int, + checkTarget: Boolean = true, checkImgInfo: Boolean = true): Unit = { + + if (checkImgInfo) { + val input = s1.getInput().toTable + val imgData = input[Tensor[Float]](1) + imgData.nElement() should be(len) + imgData.valueAt(1) should be(start.toFloat) + imgData.valueAt(len) should be(start.toFloat + len - 1) + + val imgInfo = input[Tensor[Float]](2) + imgInfo.nElement() should be(len) + imgInfo.valueAt(1) should be(start.toFloat) + imgInfo.valueAt(len) should be(start.toFloat + len - 1) + } else { + val imgData = s1.getInput().toTensor[Float] + imgData.nElement() should be(len) + imgData.valueAt(1) should be(start.toFloat) + imgData.valueAt(len) should be(start.toFloat + len - 1) + } + if (checkTarget) { + val target = s1.getTarget().asInstanceOf[Table] + target.length() should be (len) + for (i <- 1 to target.length()) { + val imgTarget = target[Table](i) + RoiImageInfo.getBBoxes(imgTarget).size() should be (Array(1, 4)) + RoiImageInfo.getClasses(imgTarget).valueAt(1) should be (i.toFloat + start - 2) + RoiImageInfo.getIsCrowd(imgTarget).nElement() should be (1) + RoiImageInfo.getIsCrowd(imgTarget).valueAt(1) should be (i.toFloat + start - 2) + RoiImageInfo.getImageInfo(imgTarget).value() should be (i.toFloat + start - 1) + } + } + } + + checkSlice(s1, 3, 20) + + // check slice of slice + val s2 = s1.slice(3, 10) + checkSlice(s2, 5, 10) + + // this also checks empty target + val b2 = RoiMiniBatch( + arrayToTensor((1 to 100).toArray.map(_.toFloat)), + null, + isCrowds, + arrayToTensor((1 to 100).toArray.map(_.toFloat)) + ) + checkSlice(b2.slice(12, 80).slice(2, 20), + 13, 20, false) + + val b3 = RoiMiniBatch( + arrayToTensor((1 to 100).toArray.map(_.toFloat)), + null, + isCrowds + ) + checkSlice(b3.slice(12, 80).slice(2, 20), + 13, 20, false, false) + } + } From 2c6932bc78d34d1c0db61964bd8a1d2957edf55f Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 26 Nov 2019 16:32:38 +0800 Subject: [PATCH 0997/1065] feat: add softmax backward (#2967) * feat: add softmax backward --- .../bigdl/dllib/nn/mkldnn/MklDnnMemory.scala | 5 + .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 182 ++++++++++-------- .../bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala | 55 ++++-- .../bigdl/dllib/nn/mkldnn/TopologySpec.scala | 2 +- 4 files changed, 149 insertions(+), 95 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnMemory.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnMemory.scala index 18363940893..a9c82ab5b36 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnMemory.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MklDnnMemory.scala @@ -126,6 +126,11 @@ object MklDnnMemory { MklDnn.SoftMaxForwardDescInit(prop_kind, dataDesc, axis)).ptr } + def SoftMaxBackwardDescInit(propKind: Int, diffDesc: Long, dstDesc: Long, + axis: Int)(implicit owner: MemoryOwner): Long = { + new MklMemoryDescInit(MklDnn.SoftMaxBackwardDescInit(diffDesc, dstDesc, axis)).ptr + } + def ConvForwardDescInit(prop_kind: Int, alg_kind: Int, src_desc: Long, weights_desc: Long, bias_desc: Long, dst_desc: Long, strides: Array[Int], padding_l: Array[Int], padding_r: Array[Int], padding_kind: Int)(implicit owner: MemoryOwner): Long = { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index 336282488ab..62079435f78 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn -import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn, PropKind, Stream => DnnStream} +import com.intel.analytics.bigdl.mkl.{DataType, Memory, MklDnn, PropKind, Query, Stream => DnnStream} import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} @@ -27,12 +27,14 @@ import com.intel.analytics.bigdl.utils.Shape import scala.collection.mutable.ArrayBuffer class SoftMax(val axis: Int = -1) extends MklDnnLayer { - private val nnSoftMax = nn.SoftMax[Float]() - @transient private var updateOutputTensors: Array[Tensor[Float]] = _ @transient private var updateOutputMemoryPrimitives: Array[Long] = _ + @transient private var updateGradInputTensors: Array[Tensor[Float]] = _ + @transient private var updateGradInputMemoryPrimitives: Array[Long] = _ @transient private var modelPhase: Phase = null + private var defaultAxis = 0 + private def initPhase(phase: Phase): Unit = { if (phase != null) return modelPhase = phase isTraining() match { @@ -53,96 +55,118 @@ class SoftMax(val axis: Int = -1) extends MklDnnLayer { override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { initPhase(phase) - modelPhase match { - case TrainingPhase => - _inputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) - _outputFormats = inputs.map(x => HeapData(x.shape, format(x.shape))) - - (_inputFormats, _outputFormats) - case InferencePhase => - val defaultAxis = inputs(0).shape.length match { - case 1 => 0 - case 2 => 1 - case 3 => 0 - case 4 => 1 - case _ => throw new UnsupportedOperationException("1D, 2D, 3D or 4D tensor expected") - } - - _inputFormats = Array(NativeData(inputs(0).shape, inputs(0).layout, DataType.F32)) - - val localInputFormat = if (inputs(0).shape.length == 3 && - inputs(0).layout == Memory.Format.ntc) { - // note: here, the format and the true memory layout is not consistent. - // for ntc input, we should reshape the `shape` and make the format to tnc - val shape = Array(inputs(0).shape(1), inputs(0).shape(0), inputs(0).shape(2)) - NativeData(shape, Memory.Format.tnc) - } else { - _inputFormats(0) - } - - val desc = MklDnnMemory.SoftMaxForwardDescInit(PropKind.ForwardInference, - localInputFormat.getMemoryDescription(), if (axis == -1) defaultAxis else axis) - val forwardPrimDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, 0L) - - _outputFormats = if (inputs(0).shape.length ==3 && - inputs(0).layout == Memory.Format.ntc) { - // because set the input format as tnc first, we should set the output to ntc. - Array(NativeData(inputs(0).shape, Memory.Format.ntc)) - } else { - Array(MemoryData.primitiveOutput(forwardPrimDesc)) - } - - val srcs = Array(inputs(0).getPrimitive(runtime)) - val indexes = Array(0) - val dsts = Array(_outputFormats(0).getPrimitive(runtime)) - - val primitive = MklDnnMemory.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, - srcs.length, dsts, dsts.length) - - updateOutputPrimitives = Array(primitive) - updateOutputMemoryPrimitives = srcs ++ dsts - - output = initTensor(_outputFormats(0)) - - (_inputFormats, _outputFormats) - case _ => throw new UnsupportedOperationException + defaultAxis = inputs(0).shape.length match { + case 1 => 0 + case 2 => 1 + case 3 => 0 + case 4 => 1 + case _ => throw new UnsupportedOperationException("1D, 2D, 3D or 4D tensor expected") + } + + _inputFormats = Array(NativeData(inputs(0).shape, inputs(0).layout, DataType.F32)) + + val localInputFormat = if (inputs(0).shape.length == 3 && + inputs(0).layout == Memory.Format.ntc) { + // note: here, the format and the true memory layout is not consistent. + // for ntc input, we should reshape the `shape` and make the format to tnc + val shape = Array(inputs(0).shape(1), inputs(0).shape(0), inputs(0).shape(2)) + NativeData(shape, Memory.Format.tnc) + } else { + _inputFormats(0) + } + + val desc = MklDnnMemory.SoftMaxForwardDescInit(PropKind.Forward, + localInputFormat.getMemoryDescription(), if (axis == -1) defaultAxis else axis) + val forwardPrimDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, 0L) + + _outputFormats = if (inputs(0).shape.length ==3 && + inputs(0).layout == Memory.Format.ntc) { + // because set the input format as tnc first, we should set the output to ntc. + Array(NativeData(inputs(0).shape, Memory.Format.ntc)) + } else { + Array(MemoryData.primitiveOutput(forwardPrimDesc)) } + + val srcs = Array(inputs(0).getPrimitive(runtime)) + val indexes = Array(0) + val dsts = Array(_outputFormats(0).getPrimitive(runtime)) + + val primitive = MklDnnMemory.PrimitiveCreate2(forwardPrimDesc, srcs, indexes, + srcs.length, dsts, dsts.length) + + updateOutputPrimitives = Array(primitive) + updateOutputMemoryPrimitives = srcs ++ dsts + + output = initTensor(_outputFormats(0)) + + (_inputFormats, _outputFormats) } override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { - _gradInputFormats = grad.clone() - _gradOutputFormats = grad.clone() + val desc = MklDnnMemory.SoftMaxBackwardDescInit(PropKind.Backward, + grad(0).getMemoryDescription(), outputFormats()(0).getMemoryDescription(), + if (axis == -1) defaultAxis else axis) + val primDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, 0L) + + _gradOutputFormats = grad + _gradInputFormats = Array(MemoryData.operationWant(primDesc, Query.DiffSrcPd)) + + val srcs = Array(grad(0).getPrimitive(runtime), outputFormats()(0).getPrimitive(runtime)) + val indexes = Array(0) + val dsts = Array(_gradInputFormats(0).getPrimitive(runtime)) + + val primitive = MklDnnMemory.PrimitiveCreate2(primDesc, srcs, indexes, + srcs.length, dsts, dsts.length) + + updateGradInputPrimitives = Array(primitive) + updateGradInputMemoryPrimitives = srcs ++ dsts + + gradInput = initTensor(_gradInputFormats(0)) + (_gradInputFormats, _gradOutputFormats) } override def updateOutput(input: Activity): Activity = { - if (this.isTraining()) { - nnSoftMax.forward(input) - output = nnSoftMax.output - } else { - if (updateOutputTensors == null) { - val buffer = new ArrayBuffer[Tensor[Float]]() - buffer.append(input.asInstanceOf[Tensor[Float]]) - buffer.append(output.asInstanceOf[Tensor[Float]]) - updateOutputTensors = buffer.toArray - } - - input.toTensor[Float].getTensorType match { - case DenseType => updateOutputTensors(0) = input.toTensor - case _ => - } - - MklDnnOps.streamSubmit(runtime.stream, 1, - updateOutputPrimitives, - updateOutputPrimitives.length, - updateOutputMemoryPrimitives, updateOutputTensors) + if (updateOutputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(input.asInstanceOf[Tensor[Float]]) + buffer.append(output.asInstanceOf[Tensor[Float]]) + updateOutputTensors = buffer.toArray + } + + input.toTensor[Float].getTensorType match { + case DenseType => updateOutputTensors(0) = input.toTensor + case _ => } + MklDnnOps.streamSubmit(runtime.stream, 1, + updateOutputPrimitives, + updateOutputPrimitives.length, + updateOutputMemoryPrimitives, updateOutputTensors) output } override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { - gradInput = nnSoftMax.backward(input, gradOutput) + if (updateGradInputTensors == null) { + val buffer = new ArrayBuffer[Tensor[Float]]() + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) + buffer.append(output.asInstanceOf[Tensor[Float]]) + buffer.append(gradInput.asInstanceOf[Tensor[Float]]) + + updateGradInputTensors = buffer.toArray + } + + gradOutput.toTensor[Float].getTensorType match { + case DenseType => updateGradInputTensors(0) = gradOutput.toTensor + case _ => + } + + MklDnnOps.streamSubmit(runtime.stream, 1, + updateGradInputPrimitives, + updateGradInputPrimitives.length, + updateGradInputMemoryPrimitives, updateGradInputTensors + ) + gradInput } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala index 02cfc3a87ba..fc185e765d9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala @@ -31,9 +31,9 @@ class SoftMaxSpec extends FlatSpec with Matchers { for (x <- tests) { val sm = SoftMax() - sm.evaluate() sm.setRuntime(new MklDnnRuntime) - sm.initFwdPrimitives(Array(HeapData(Array(x), Memory.Format.x)), InferencePhase) + sm.initFwdPrimitives(Array(HeapData(Array(x), Memory.Format.x)), TrainingPhase) + sm.initBwdPrimitives(Array(HeapData(Array(x), Memory.Format.x)), TrainingPhase) val input = Tensor(x).rand() @@ -43,6 +43,11 @@ class SoftMaxSpec extends FlatSpec with Matchers { val nnOutput = nnSm.forward(input) Tools.dense(output) should be (nnOutput) + + sm.backward(input, nnOutput) + nnSm.backward(input, nnOutput) + + Tools.dense(sm.gradInput) should be (nnSm.gradInput) } } @@ -57,8 +62,9 @@ class SoftMaxSpec extends FlatSpec with Matchers { val sm = SoftMax() sm.setRuntime(new MklDnnRuntime) sm.initFwdPrimitives(Array(HeapData(Array(batchSize, channel), Memory.Format.nc)), - InferencePhase) - sm.evaluate() + TrainingPhase) + sm.initBwdPrimitives(Array(HeapData(Array(batchSize, channel), Memory.Format.nc)), + TrainingPhase) val input = Tensor(batchSize, channel).rand() @@ -68,6 +74,11 @@ class SoftMaxSpec extends FlatSpec with Matchers { val nnOutput = nnSm.forward(input) Tools.dense(output) shouldEqual nnOutput + + sm.backward(input, nnOutput) + nnSm.backward(input, nnOutput) + + Tools.dense(sm.gradInput) should be (nnSm.gradInput) } } @@ -86,8 +97,9 @@ class SoftMaxSpec extends FlatSpec with Matchers { val sm = SoftMax() sm.setRuntime(new MklDnnRuntime) sm.initFwdPrimitives(Array(HeapData(Array(batchSize, channel, height, width), - Memory.Format.nchw)), InferencePhase) - sm.evaluate() + Memory.Format.nchw)), TrainingPhase) + sm.initBwdPrimitives(Array(HeapData(Array(batchSize, channel, height, width), + Memory.Format.nchw)), TrainingPhase) val input = Tensor(batchSize, channel, height, width).rand() @@ -97,6 +109,12 @@ class SoftMaxSpec extends FlatSpec with Matchers { val nnOutput = nnSm.forward(input) Tools.dense(output) should be (nnOutput) + + sm.backward(input, nnOutput) + nnSm.backward(input, nnOutput) + + Equivalent.nearequals(Tools.dense(sm.gradInput).toTensor, nnSm.gradInput.toTensor, + epsilon = 10-5) } } @@ -114,9 +132,8 @@ class SoftMaxSpec extends FlatSpec with Matchers { for ((i, j, k) <- tests) { val sm = SoftMax() sm.setRuntime(new MklDnnRuntime) - sm.initFwdPrimitives(Array(HeapData(Array(i, j, k), - Memory.Format.ncw)), InferencePhase) - sm.evaluate() + sm.initFwdPrimitives(Array(HeapData(Array(i, j, k), Memory.Format.ncw)), TrainingPhase) + sm.initBwdPrimitives(Array(HeapData(Array(i, j, k), Memory.Format.ncw)), TrainingPhase) val input = Tensor(i, j, k).rand() @@ -126,6 +143,11 @@ class SoftMaxSpec extends FlatSpec with Matchers { val nnOutput = nnSm.forward(input) Tools.dense(output) should be (nnOutput) + sm.backward(input, nnOutput) + nnSm.backward(input, nnOutput) + + Equivalent.nearequals(Tools.dense(sm.gradInput).toTensor, nnSm.gradInput.toTensor, + epsilon = 10-5) } } @@ -134,7 +156,9 @@ class SoftMaxSpec extends FlatSpec with Matchers { val sm = SoftMax() sm.setRuntime(new MklDnnRuntime) sm.initFwdPrimitives(Array(HeapData(Array(batchSize, channel, height, width), - Memory.Format.nchw)), InferencePhase) + Memory.Format.nchw)), TrainingPhase) + sm.initBwdPrimitives(Array(HeapData(Array(batchSize, channel, height, width), + Memory.Format.nchw)), TrainingPhase) val nnSm = nn.SoftMax() @@ -147,8 +171,10 @@ class SoftMaxSpec extends FlatSpec with Matchers { sm.backward(input, gradOutput) nnSm.backward(input, gradOutput) - sm.output should be (nnSm.output) - sm.gradInput should be (nnSm.gradInput) + Equivalent.nearequals(Tools.dense(sm.output).toTensor, nnSm.output.toTensor, + epsilon = 10-4) + Equivalent.nearequals(Tools.dense(sm.gradInput).toTensor, nnSm.gradInput.toTensor, + epsilon = 10-4) } "SoftMax multi times forward" should "work correctly" in { @@ -178,8 +204,7 @@ class SoftMaxSpec extends FlatSpec with Matchers { .add(Input(Array(2, 24564, 21), Memory.Format.ntc)) .add(sm1) .add(Output(Memory.Format.ntc)) - seq1.asInstanceOf[MklDnnContainer].compile(InferencePhase) - seq1.evaluate() + seq1.asInstanceOf[MklDnnContainer].compile(TrainingPhase) seq1.forward(input) @@ -189,7 +214,7 @@ class SoftMaxSpec extends FlatSpec with Matchers { val seq2 = Sequential().add(Input(Array(2 * 24564, 21), Memory.Format.nc)) .add(sm2) .add(Output()) - seq2.asInstanceOf[MklDnnContainer].compile(InferencePhase) + seq2.asInstanceOf[MklDnnContainer].compile(TrainingPhase) sm2.evaluate() seq2.forward(input) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala index 0e3d87e91e4..3f1099ba6b8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/TopologySpec.scala @@ -1052,7 +1052,7 @@ class TopologySpec extends FlatSpec with Matchers { val tmp = fusion.output.toTensor.max(1) - val softmax = SoftMax() + val softmax = nn.SoftMax() softmax.forward(fusion.output).toTensor.max(2) should be ( softmax.forward(quant.output).toTensor.max(2)) From 534cfb5579570fccee3b29221d133abb3dcdfe19 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Wed, 27 Nov 2019 12:22:24 +0800 Subject: [PATCH 0998/1065] fix: fuse bn scale and relu to bn. (#2966) * fix: fuse bn scale and relu. --- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 11 ++--- .../bigdl/dllib/nn/mkldnn/Fusion.scala | 44 ++++++++++--------- .../bigdl/dllib/nn/mkldnn/FusionSpec.scala | 33 ++++++++++++++ 3 files changed, 62 insertions(+), 26 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 61c9aa8a951..60cee93d3c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -394,14 +394,15 @@ class DnnGraph( */ private def fusion(): Unit = { if (!this.train) { - for (j <- 0 to 3) { + for (j <- 0 to 4) { var i = forwardExecution.length - 1 while (i >= 0) { - if (j == 0) Fusion.fuseModule(forwardExecution(i)) + if (j == 0) Fusion.fuseScale(forwardExecution(i)) + if (j == 1) Fusion.fuseModule(forwardExecution(i)) // we should do this before sum fusion, because it will change the structure of graph - if (j == 1) Fusion.setNegativeInputOfConv(forwardExecution(i)) - if (j == 2) Fusion.fuseCAdd(forwardExecution(i)) - if (j == 3) Fusion.setScalesPrevousJoinTable(forwardExecution(i)) + if (j == 2) Fusion.setNegativeInputOfConv(forwardExecution(i)) + if (j == 3) Fusion.fuseCAdd(forwardExecution(i)) + if (j == 4) Fusion.setScalesPrevousJoinTable(forwardExecution(i)) i -= 1 } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala index 87cb0145b81..b99a7b19f95 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Fusion.scala @@ -38,8 +38,6 @@ private[mkldnn] object Fusion { node.element match { case relu: ReLU => fusionRelu(node) case bn: SpatialBatchNormalization => fusionBN(node) - case blasWrapper: BlasWrapper if blasWrapper.module.isInstanceOf[ScaleLayer[Float]] => - fuseScale(node) case _ => } } @@ -288,31 +286,35 @@ private[mkldnn] object Fusion { } def fuseScale(node: Node[AbstractModule[Activity, Activity, Float]]): Unit = { - // check all prevNodes are SpatialBatchNormalization - val isValid = node.prevNodes.forall(_.element.isInstanceOf[SpatialBatchNormalization]) - if (!isValid) { return } + node.element match { + case wrapper: BlasWrapper if wrapper.module.isInstanceOf[ScaleLayer[Float]] => + // check all prevNodes are SpatialBatchNormalization + val isValid = node.prevNodes.forall(_.element.isInstanceOf[SpatialBatchNormalization]) + if (!isValid) { return } - node.prevNodes.foreach { prevNode => - val bn = prevNode.element.asInstanceOf[SpatialBatchNormalization] - val weightAndBias = bn.weightAndBias.dense - val weight = weightAndBias.narrow(1, 1, bn.nOutput) - val bias = weightAndBias.narrow(1, bn.nOutput + 1, bn.nOutput) + node.prevNodes.foreach { prevNode => + val bn = prevNode.element.asInstanceOf[SpatialBatchNormalization] + val weightAndBias = bn.weightAndBias.dense + val weight = weightAndBias.narrow(1, 1, bn.nOutput) + val bias = weightAndBias.narrow(1, bn.nOutput + 1, bn.nOutput) - val scale = node.element.asInstanceOf[BlasWrapper].module.asInstanceOf[ScaleLayer[Float]] - val scaleWeight = scale.parameters()._1(0) - val scaleBias = scale.parameters()._1(1) + val scale = node.element.asInstanceOf[BlasWrapper].module.asInstanceOf[ScaleLayer[Float]] + val scaleWeight = scale.parameters()._1(0) + val scaleBias = scale.parameters()._1(1) - weight.cmul(scaleWeight) - bias.cmul(scaleWeight) - bias.add(scaleBias) + weight.cmul(scaleWeight) + bias.cmul(scaleWeight) + bias.add(scaleBias) - // set the weight and bias to new tensor, we do not modify the original model's tensor. - // sometimes, the model need to be reused. - bn.weightAndBias.dense.set(weightAndBias) - } + // set the weight and bias to new tensor, we do not modify the original model's tensor. + // sometimes, the model need to be reused. + bn.weightAndBias.dense.set(weightAndBias) + } - node.element = Identity[Float]() // set the BlasWrapper to Identity, we need no scale now + node.element = Identity[Float]() // set the BlasWrapper to Identity, we need no scale now + case _ => + } } private def findAllNonIdentityPrevs(node: Node[AbstractModule[Activity, Activity, Float]]) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala index 891c15141f9..29f69048c0a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/FusionSpec.scala @@ -406,4 +406,37 @@ class FusionSpec extends FlatSpec with Matchers { System.clearProperty("bigdl.mkldnn.fusion") } + + "bn + scale + relu fusion" should "work correctly" in { + import com.intel.analytics.bigdl.nn.{Scale => NNScale} + val inputShape = Array(4, 64, 3, 3) + val input = Input(inputShape, Memory.Format.nchw).inputs() + val bn1 = SpatialBatchNormalization(64).inputs(input) + val scale1 = BlasWrapper(NNScale[Float](Array(1, 64, 1, 1))).inputs(bn1) + val relu1 = ReLU() + val output = Output(Memory.Format.nchw).inputs(scale1) + + // the running mean and running variance should be 1. + bn1.element.getExtraParameter().foreach(_.fill(1)) + + val model = DnnGraph(Seq(input), Seq(output)) + val fused = model.cloneModule() + + model.evaluate() + fused.evaluate() + + val tensor = Tensor[Float](inputShape).rand(-1, 1) + + System.setProperty("bigdl.mkldnn.fusion", "false") + model.compile(InferencePhase) + model.forward(tensor) + + System.setProperty("bigdl.mkldnn.fusion", "true") + fused.compile(InferencePhase) + fused.forward(tensor) + + Equivalent.nearequals(model.output.toTensor[Float], fused.output.toTensor[Float], 1e-7) + + System.clearProperty("bigdl.mkldnn.fusion") + } } From 1b216029232a3bfad50282a8e9cf7fda1606c307 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 29 Nov 2019 18:29:07 +0800 Subject: [PATCH 0999/1065] fix mask unit tests (#2973) --- .../dllib/nn/MaskPostProcessorSpec.scala | 98 +++++++++---------- 1 file changed, 49 insertions(+), 49 deletions(-) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala index 069e730684d..ec8defe72d0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskPostProcessorSpec.scala @@ -462,57 +462,57 @@ class MaskPostProcessorSpec extends FlatSpec with Matchers { val layer = new MaskPostProcessor() - val output = layer.forward(T(inputMaskLogits, bbox, imageInfo, labels)) + val output = layer.forward(T(inputMaskLogits, labels)) val expectedOutput = Tensor[Float](T(T(T( - T(0.5913, 0.5344, 0.6753, 0.5105, 0.5563, 0.5300, 0.6150, 0.6773, - 0.5334, 0.6796, 0.7197, 0.5047), - T(0.5079, 0.6325, 0.7249, 0.5010, 0.5237, 0.6543, 0.7238, 0.6993, - 0.5108, 0.5587, 0.6112, 0.6501), - T(0.5421, 0.7194, 0.5869, 0.5258, 0.6283, 0.6375, 0.5559, 0.6452, - 0.7038, 0.6460, 0.5651, 0.5759), - T(0.7247, 0.6702, 0.5494, 0.6430, 0.6619, 0.6262, 0.5516, 0.6720, - 0.6903, 0.6959, 0.7302, 0.5791), - T(0.7149, 0.6512, 0.7298, 0.5762, 0.6969, 0.5219, 0.5281, 0.5075, - 0.6563, 0.6873, 0.6791, 0.6138), - T(0.6892, 0.7255, 0.6853, 0.6685, 0.6638, 0.6407, 0.6019, 0.6871, - 0.6110, 0.5590, 0.5176, 0.5724), - T(0.6760, 0.6018, 0.6482, 0.6103, 0.5305, 0.6986, 0.5503, 0.5701, - 0.6220, 0.6769, 0.5751, 0.6787), - T(0.7105, 0.5102, 0.6404, 0.5744, 0.6111, 0.6538, 0.6311, 0.6877, - 0.6722, 0.5690, 0.5388, 0.6383), - T(0.5098, 0.5370, 0.6413, 0.6156, 0.5352, 0.6644, 0.6144, 0.6202, - 0.5670, 0.5420, 0.6689, 0.6006), - T(0.6803, 0.6049, 0.5806, 0.6732, 0.6295, 0.6162, 0.5395, 0.5491, - 0.5303, 0.6862, 0.6423, 0.6691), - T(0.7209, 0.5530, 0.5283, 0.6029, 0.5130, 0.5965, 0.5773, 0.5366, - 0.6282, 0.7149, 0.5041, 0.6715), - T(0.6751, 0.5801, 0.5841, 0.5236, 0.6276, 0.5339, 0.7290, 0.6393, - 0.5756, 0.7272, 0.5186, 0.6639))), - T(T(T(0.6209, 0.6525, 0.6033, 0.6315, 0.7057, 0.5001, 0.7230, 0.6290, - 0.5184, 0.6671, 0.5102, 0.6651), - T(0.6746, 0.6800, 0.5059, 0.5868, 0.7056, 0.6932, 0.6162, 0.7241, - 0.5055, 0.5603, 0.5682, 0.7147), - T(0.7303, 0.6846, 0.6485, 0.6219, 0.6246, 0.6223, 0.5161, 0.6901, - 0.5796, 0.6148, 0.6862, 0.5876), - T(0.6692, 0.6088, 0.6315, 0.6924, 0.5012, 0.5716, 0.5774, 0.6632, - 0.5330, 0.5665, 0.5456, 0.6654), - T(0.5462, 0.6693, 0.7155, 0.7074, 0.7147, 0.5031, 0.5367, 0.5257, - 0.6106, 0.7218, 0.5089, 0.6641), - T(0.5038, 0.6329, 0.5158, 0.5029, 0.7091, 0.6387, 0.6275, 0.6501, - 0.5698, 0.5514, 0.6040, 0.7041), - T(0.7290, 0.5159, 0.5661, 0.5445, 0.5491, 0.6063, 0.5921, 0.5004, - 0.6967, 0.7287, 0.6618, 0.6697), - T(0.6723, 0.6402, 0.6213, 0.7132, 0.5597, 0.6533, 0.6269, 0.5155, - 0.6174, 0.5908, 0.7038, 0.5038), - T(0.5585, 0.6790, 0.5427, 0.6505, 0.5750, 0.5556, 0.6094, 0.5234, - 0.7198, 0.5915, 0.5314, 0.6720), - T(0.5738, 0.5940, 0.6906, 0.6065, 0.5045, 0.5683, 0.6348, 0.6655, - 0.7301, 0.6856, 0.7273, 0.5842), - T(0.6944, 0.6791, 0.6710, 0.6560, 0.6275, 0.6213, 0.6668, 0.6005, - 0.6449, 0.6558, 0.5863, 0.7029), - T(0.5035, 0.6440, 0.5290, 0.6693, 0.6677, 0.5698, 0.7180, 0.5952, - 0.6821, 0.5830, 0.6005, 0.6017))))) + T(0.5934, 0.5641569, 0.6431586, 0.6026766, 0.7236081, 0.5483138, 0.50372845, 0.67025316, + 0.68438125, 0.58940595, 0.6918666, 0.6811279), + T(0.7201, 0.53883165, 0.723842, 0.51308227, 0.6546382, 0.70471245, 0.51526153, 0.6907483, + 0.66083336, 0.6424974, 0.6473018, 0.578115), + T(0.7088, 0.5821584, 0.5356295, 0.5714085, 0.59446144, 0.66915154, 0.70744973, 0.5658036, + 0.507182, 0.5930311, 0.60385174, 0.60178304), + T(0.5896, 0.70458543, 0.71928346, 0.65614235, 0.7009027, 0.6174432, 0.6975462, 0.5695266, + 0.64458483, 0.69773394, 0.56289756, 0.57414913), + T(0.71855193, 0.68514967, 0.5350624, 0.6769565, 0.54047376, 0.7156327, 0.5531707, + 0.5676133, 0.51373154, 0.70830995, 0.6759344, 0.5094966), + T(0.51264906, 0.5449657, 0.69929224, 0.7196084, 0.6788409, 0.6048201, 0.5488066, + 0.5363483, 0.72345006, 0.5405681, 0.6089998, 0.70805573), + T(0.5928766, 0.63519526, 0.65843326, 0.6217353, 0.64459175, 0.5350151, 0.7062603, + 0.55394423, 0.6501412, 0.5486432, 0.7080888, 0.675643), + T(0.7090862, 0.61549497, 0.53033024, 0.7098962, 0.72101665, 0.7188572, 0.6981471, + 0.7136484, 0.5859967, 0.6871868, 0.65447533, 0.56007093), + T(0.58580256, 0.5502003, 0.6148747, 0.64277065, 0.6857534, 0.5011686, 0.54080904, + 0.65560967, 0.56999725, 0.54757565, 0.609852, 0.5351395), + T(0.7143652, 0.53502005, 0.70207745, 0.6875393, 0.7029301, 0.65189064, 0.70781386, + 0.5729237, 0.5160258, 0.69524366, 0.6165334, 0.5316028), + T(0.6367116, 0.5886046, 0.56053656, 0.64915115, 0.65535223, 0.58132136, 0.67581826, + 0.6921457, 0.64890057, 0.7144876, 0.6201936, 0.6979384), + T(0.57011, 0.6531807, 0.62503153, 0.6436198, 0.69175565, 0.6528884, 0.6487297, + 0.6395312, 0.6955445, 0.66967374, 0.64985913, 0.6532872)), + T(T(0.51045823, 0.7136831, 0.56406343, 0.56233406, 0.5221792, 0.6315658, 0.7002062, + 0.68388414, 0.7203382, 0.5339054, 0.62687886, 0.5190161), + T(0.6007161, 0.54905915, 0.5275296, 0.5509995, 0.6349218, 0.72120166, 0.59122944, + 0.56807214, 0.71899664, 0.6526436, 0.57267904, 0.67870134), + T(0.5151846, 0.54783577, 0.5851861, 0.68118656, 0.71166825, 0.631233, 0.71900266, + 0.52196133, 0.7111365, 0.61995095, 0.70218617, 0.5590531), + T(0.7129838, 0.6559077, 0.7258186, 0.5041189, 0.5232647, 0.7047582, 0.59610456, + 0.67488647, 0.71441, 0.5842877, 0.58227265, 0.5689087), + T(0.6220621, 0.5836973, 0.7299364, 0.5582246, 0.50226533, 0.6958726, 0.51557744, + 0.5727451, 0.52065825, 0.64763504, 0.55392694, 0.7093481), + T(0.6862833, 0.6981787, 0.60825187, 0.63988155, 0.5681973, 0.6164435, 0.5012094, + 0.5269813, 0.72196907, 0.672021, 0.5152003, 0.59994596), + T(0.6534933, 0.62057513, 0.5725102, 0.7126337, 0.69655156, 0.52507395, 0.6930016, + 0.7185013, 0.7277586, 0.50503635, 0.6303666, 0.7210629), + T(0.7096779, 0.72210556, 0.728121, 0.6906394, 0.53061163, 0.5837192, 0.59222233, + 0.65138894, 0.67794853, 0.5689136, 0.50981325, 0.54487646), + T(0.56297135, 0.62408656, 0.50655365, 0.6549637, 0.53530365, 0.5023403, 0.6510051, + 0.6869589, 0.5829244, 0.6573664, 0.54007137, 0.7282872), + T(0.6250854, 0.5283047, 0.5904026, 0.6528091, 0.55060863, 0.69158715, 0.538936, + 0.6800518, 0.6919966, 0.7258603, 0.6873867, 0.5875557), + T(0.67806643, 0.699513, 0.52257764, 0.65697443, 0.6783283, 0.64929, 0.54697603, + 0.6097425, 0.688104, 0.71832335, 0.6101874, 0.5322427), + T(0.5505245, 0.51022655, 0.72613287, 0.723738, 0.553964, 0.5327107, 0.59699506, + 0.58739096, 0.66101485, 0.6416125, 0.63273776, 0.61910945))))) output.almostEqual(expectedOutput, 1e-3) should be(true) From 4f0eb5648a8ddc17c05d198b2be8ccf3b0bcbe34 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Fri, 29 Nov 2019 23:01:51 +0800 Subject: [PATCH 1000/1065] fix: nms stability when using treeset. (#2972) --- .../com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala index 666108e6892..0873960315e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/tensor/DenseTensor.scala @@ -1729,8 +1729,8 @@ private[tensor] class DenseTensor[@specialized T: ClassTag]( val set = new java.util.TreeSet[(T, Int)](new Comparator[(T, Int)] { override def compare(o1: (T, Int), o2: (T, Int)): Int = { val ret = if (ev.isGreaterEq(o1._1, o2._1)) { - if (o1._2 == o2._2) { - 0 + if (o1._1 == o2._1 && o1._2 > o2._2) { + -1 } else { 1 } From 76646c504aaa69582363b323c56ad39a38779f83 Mon Sep 17 00:00:00 2001 From: Jerry Wu Date: Sat, 30 Nov 2019 14:21:51 +0800 Subject: [PATCH 1001/1065] flip version to 0.11 (#2974) --- dist/pom.xml | 2 +- dl/pom.xml | 6 +++--- pom.xml | 2 +- scala/common/spark-version/1.5-plus/pom.xml | 2 +- scala/common/spark-version/2.0/pom.xml | 2 +- scala/common/spark-version/pom.xml | 2 +- .../dllib/src/main/resources/bigdl-version-info.properties | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/dist/pom.xml b/dist/pom.xml index ca71c6eb333..55964259bfd 100644 --- a/dist/pom.xml +++ b/dist/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.10.0-SNAPSHOT + 0.11.0-SNAPSHOT 4.0.0 diff --git a/dl/pom.xml b/dl/pom.xml index 7300d3f6e4a..a5763700971 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.10.0-SNAPSHOT + 0.11.0-SNAPSHOT 4.0.0 @@ -79,7 +79,7 @@ com.intel.analytics.bigdl.core.dist all - 0.10.0-SNAPSHOT + 0.11.0-SNAPSHOT ${bigdl-core-all-scope} @@ -319,7 +319,7 @@ com.intel.analytics.bigdl.core.dist ${os-flag} - 0.10.0-SNAPSHOT + 0.11.0-SNAPSHOT pom diff --git a/pom.xml b/pom.xml index cc02193121f..6e6e625777e 100644 --- a/pom.xml +++ b/pom.xml @@ -5,7 +5,7 @@ bigdl-parent com.intel.analytics.bigdl - 0.10.0-SNAPSHOT + 0.11.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/1.5-plus/pom.xml b/scala/common/spark-version/1.5-plus/pom.xml index da409bd709c..31ef5447fdc 100644 --- a/scala/common/spark-version/1.5-plus/pom.xml +++ b/scala/common/spark-version/1.5-plus/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.10.0-SNAPSHOT + 0.11.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/2.0/pom.xml b/scala/common/spark-version/2.0/pom.xml index 027850121b1..7d57cca2294 100644 --- a/scala/common/spark-version/2.0/pom.xml +++ b/scala/common/spark-version/2.0/pom.xml @@ -5,7 +5,7 @@ spark-version com.intel.analytics.bigdl - 0.10.0-SNAPSHOT + 0.11.0-SNAPSHOT 4.0.0 diff --git a/scala/common/spark-version/pom.xml b/scala/common/spark-version/pom.xml index 8b91a40e011..76f38cc13a0 100644 --- a/scala/common/spark-version/pom.xml +++ b/scala/common/spark-version/pom.xml @@ -5,7 +5,7 @@ spark_bigdl com.intel.analytics.bigdl - 0.10.0-SNAPSHOT + 0.11.0-SNAPSHOT 4.0.0 diff --git a/scala/dllib/src/main/resources/bigdl-version-info.properties b/scala/dllib/src/main/resources/bigdl-version-info.properties index 4c2ae8f6684..d1a525406ab 100644 --- a/scala/dllib/src/main/resources/bigdl-version-info.properties +++ b/scala/dllib/src/main/resources/bigdl-version-info.properties @@ -16,4 +16,4 @@ #BigDL version info config -version=0.10.0-SNAPSHOT \ No newline at end of file +version=0.11.0-SNAPSHOT \ No newline at end of file From 95be1c8f7757625bd085eadfc0d3c9ed2a83a10f Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 2 Dec 2019 13:35:12 +0800 Subject: [PATCH 1002/1065] refactor anchor generator (#2963) * refactor anchor generator * meet pr comments * fix code style --- .../analytics/bigdl/dllib/nn/Anchor.scala | 57 ++++---- .../bigdl/dllib/nn/RegionProposalSpec.scala | 122 +++++++++--------- 2 files changed, 91 insertions(+), 88 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala index 9be31e5640b..f5cf38d3472 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Anchor.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import scala.collection.mutable.ArrayBuffer /** * Generates a regular grid of multi-scale, multi-aspect anchor boxes. @@ -45,6 +46,34 @@ class Anchor(ratios: Array[Float], scales: Array[Float]) extends Serializable { getAllAnchors(shiftX, shiftY, basicAnchors) } + /** + * Here, we generate anchors without change area. + * @param ratios + * @param scales + * @param baseSize stride to move + * @return anchors with shape (ratios number * scales number, 4). + * And element order is (-width / 2, -height / 2, width / 2, height / 2) + */ + private def generateBasicAnchors(ratios: Array[Float], scales: Array[Float], + baseSize: Float = 16): Tensor[Float] = { + val anchors = new ArrayBuffer[Float] + for (i <- 0 until scales.length) { + val area = math.pow(scales(i) * baseSize, 2) + for (j <- 0 until ratios.length) { + val w = math.sqrt(area / ratios(j)).toFloat + val h = ratios(j) * w.toFloat + val halfW = w / 2.0f + val halfH = h / 2.0f + + anchors.append(-halfW) + anchors.append(-halfH) + anchors.append(halfW) + anchors.append(halfH) + } + } + Tensor[Float](data = anchors.toArray, shape = Array[Int](ratios.length * scales.length, 4)) + } + @transient private var shiftX: Tensor[Float] = _ @transient private var shiftY: Tensor[Float] = _ @@ -117,34 +146,6 @@ class Anchor(ratios: Array[Float], scales: Array[Float]) extends Serializable { allAnchors } - /** - * Generate anchor (reference) windows by enumerating aspect ratios(M) X scales(N) - * wrt a reference (0, 0, 15, 15) window. - * 1. generate anchors for different ratios (N, 4) - * 2. for each anchors generated in 1, scale them to get scaled anchors (M*N, 4) - */ - private[nn] def generateBasicAnchors(_ratios: Array[Float], _scales: Array[Float], - baseSize: Float = 16): Tensor[Float] = { - val ratios = Tensor(Storage(_ratios)) - val scales = Tensor(Storage(_scales)) - val baseAnchor = Tensor(Storage(Array(0, 0, baseSize - 1, baseSize - 1))) - val ratioAnchors = ratioEnum(baseAnchor, ratios) - val anchors = Tensor(scales.size(1) * ratioAnchors.size(1), 4) - var idx = 1 - var i = 1 - while (i <= ratioAnchors.size(1)) { - val scaleAnchors = scaleEnum(ratioAnchors(i), scales) - var j = 1 - while (j <= scaleAnchors.size(1)) { - anchors.update(idx, scaleAnchors(j)) - idx = idx + 1 - j += 1 - } - i += 1 - } - anchors - } - /** * Given a vector of widths (ws) and heights (hs) around a center * (x_ctr, y_ctr), output a set of anchors (windows). diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposalSpec.scala index a58517b06d1..6824edc1f1b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposalSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RegionProposalSpec.scala @@ -265,10 +265,11 @@ class RegionProposalSpec extends FlatSpec with Matchers { layer.evaluate() val output = layer.forward(T(T(features), images)).toTable val outputExpected = Tensor[Float]( - T(T(0.0f, 0.0f, 20.999596f, 19.0f), - T(0.0f, 0.0f, 12.995603f, 19.0f), - T(0.0f, 0.0f, 37.0f, 19.0f), - T(0.0f, 0.0f, 29.011127f, 13.003019f) + T(T(0.0f, 0.0f, 19.313313f, 19.0f), + T(0.0f, 0.0f, 11.309382f, 19.0f), + T(0.0f, 0.0f, 34.63681, 19.0f), + T(0.0f, 0.0f, 26.63813f, 11.316678f), + T(0.0f, 0.0f, 27.975597f, 16.016605f) )) output[Tensor[Float]](1) should be(outputExpected) @@ -557,16 +558,16 @@ class RegionProposalSpec extends FlatSpec with Matchers { layer.evaluate() val output = layer.forward(T(T(features1, features2, features3), images)).toTable val outputExpected = Tensor[Float](T( - T( 0.0000, 0.0000, 35.0363, 19.0000), - T( 0.0000, 0.0000, 20.9997, 19.0000), - T( 0.0000, 0.0000, 12.9955, 19.0000), - T( 0.0000, 0.0000, 37.0000, 19.0000), - T( 0.0000, 0.0000, 37.0000, 19.0000), - T(11.9914, 0.0000, 37.0000, 19.0000), - T( 0.0000, 0.0000, 29.0113, 13.0032), - T( 0.0000, 11.9920, 37.0000, 19.0000))) + T(0.0, 0.0, 30.661697f, 19.0), + T(0.0, 0.0, 19.313313f, 19.0), + T(0.0, 0.0, 11.309382f, 19.0), + T(0.0, 0.0, 34.63681f, 19.0), + T(0.0, 0.0, 37.0f, 19.0f), + T(0.0, 0.0, 26.63813f, 11.316678), + T(0.0, 9.364996f, 37.0, 19.0), + T(0.0, 0.0, 27.975597f, 16.016605))) - output[Tensor[Float]](1) should be(outputExpected) + output[Tensor[Float]](1) should be(outputExpected) } "RPNPostProcessor" should "be ok" in { @@ -739,9 +740,9 @@ class RegionProposalSpec extends FlatSpec with Matchers { "AnchorGenerate" should "be ok" in { val layer = new RegionProposal(6, - Array[Float](32, 64, 128, 256, 512), - Array[Float](0.5f, 1.0f, 2.0f), - Array[Float](4, 8, 16, 32, 64), 2000, 2000, 2000, 2000, 0.7f, 0) + Array[Float](32, 64), + Array[Float](0.25f, 1.0f, 4.0f), + Array[Float](4, 8), 2000, 2000, 2000, 2000, 0.7f, 0) val input = Tensor[Float](T(T(T(T(0.7668, 0.1659, 0.4393, 0.2243), T(0.8935, 0.0497, 0.1780, 0.3011), @@ -762,42 +763,43 @@ class RegionProposalSpec extends FlatSpec with Matchers { T(0.2130, 0.4603, 0.1386, 0.0277), T(0.5662, 0.3503, 0.6555, 0.7667))))) - val expectedOutput = Tensor[Float](T(T(-22, -10, 25, 13), - T(-14, -14, 17, 17), - T(-10, -22, 13, 25), - T(-18, -10, 29, 13), - T(-10, -14, 21, 17), - T(-6, -22, 17, 25), - T(-14, -10, 33, 13), - T(-6, -14, 25, 17), - T(-2, -22, 21, 25), - T(-10, -10, 37, 13), - T(-2, -14, 29, 17), - T(2, -22, 25, 25), - T(-22, -6, 25, 17), - T(-14, -10, 17, 21), - T(-10, -18, 13, 29), - T(-18, -6, 29, 17), - T(-10, -10, 21, 21), - T(-6, -18, 17, 29), - T(-14, -6, 33, 17), - T(-6, -10, 25, 21), - T(-2, -18, 21, 29), - T(-10, -6, 37, 17), - T(-2, -10, 29, 21), - T(2, -18, 25, 29), - T(-22, -2, 25, 21), - T(-14, -6, 17, 25), - T(-10, -14, 13, 33), - T(-18, -2, 29, 21), - T(-10, -6, 21, 25), - T(-6, -14, 17, 33), - T(-14, -2, 33, 21), - T(-6, -6, 25, 25), - T(-2, -14, 21, 33), - T(-10, -2, 37, 21), - T(-2, -6, 29, 25), - T(2, -14, 25, 33))) + val expectedOutput = Tensor[Float](T( + T(-32.0, -8.0, 32.0, 8.0), + T(-16.0, -16.0, 16.0, 16.0), + T(-8.0, -32.0, 8.0, 32.0), + T(-28.0, -8.0, 36.0, 8.0), + T(-12.0, -16.0, 20.0, 16.0), + T(-4.0, -32.0, 12.0, 32.0), + T(-24.0, -8.0, 40.0, 8.0), + T(-8.0, -16.0, 24.0, 16.0), + T(0.0, -32.0, 16.0, 32.0), + T(-20.0, -8.0, 44.0, 8.0), + T(-4.0, -16.0, 28.0, 16.0), + T(4.0, -32.0, 20.0, 32.0), + T(-32.0, -4.0, 32.0, 12.0), + T(-16.0, -12.0, 16.0, 20.0), + T(-8.0, -28.0, 8.0, 36.0), + T(-28.0, -4.0, 36.0, 12.0), + T(-12.0, -12.0, 20.0, 20.0), + T(-4.0, -28.0, 12.0, 36.0), + T(-24.0, -4.0, 40.0, 12.0), + T(-8.0, -12.0, 24.0, 20.0), + T(0.0, -28.0, 16.0, 36.0), + T(-20.0, -4.0, 44.0, 12.0), + T(-4.0, -12.0, 28.0, 20.0), + T(4.0, -28.0, 20.0, 36.0), + T(-32.0, 0.0, 32.0, 16.0), + T(-16.0, -8.0, 16.0, 24.0), + T(-8.0, -24.0, 8.0, 40.0), + T(-28.0, 0.0, 36.0, 16.0), + T(-12.0, -8.0, 20.0, 24.0), + T(-4.0, -24.0, 12.0, 40.0), + T(-24.0, 0.0, 40.0, 16.0), + T(-8.0, -8.0, 24.0, 24.0), + T(0.0, -24.0, 16.0, 40.0), + T(-20.0, 0.0, 44.0, 16.0), + T(-4.0, -8.0, 28.0, 24.0), + T(4.0, -24.0, 20.0, 40.0))) val output = layer.anchorGenerator(T(input)) @@ -1395,14 +1397,14 @@ class RegionProposalSpec extends FlatSpec with Matchers { val output = layer.forward(T(T(features12, features22, features32), images)).toTable val outputExpected = Tensor[Float](T( - T( 0.0000, 0.0000, 35.0363, 19.0000), - T( 0.0000, 0.0000, 20.9997, 19.0000), - T( 0.0000, 0.0000, 12.9955, 19.0000), - T( 0.0000, 0.0000, 37.0000, 19.0000), - T( 0.0000, 0.0000, 37.0000, 19.0000), - T(11.9914, 0.0000, 37.0000, 19.0000), - T( 0.0000, 0.0000, 29.0113, 13.0032), - T( 0.0000, 11.9920, 37.0000, 19.0000))) + T(0.0, 0.0, 30.661697f, 19.0), + T(0.0, 0.0, 19.313313f, 19.0), + T(0.0, 0.0, 11.309382f, 19.0), + T(0.0, 0.0, 34.63681f, 19.0), + T(0.0, 0.0, 37.0f, 19.0), + T(0.0, 0.0, 26.63813f, 11.316678), + T(0.0, 9.364996f, 37.0f, 19.0), + T(0.0, 0.0, 27.975597f, 16.016605))) output[Tensor[Float]](1) should be(outputExpected) output[Tensor[Float]](2) should be(outputExpected) From 222f20ba880e2d35e2835a08895e8726811b1d4e Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 2 Dec 2019 14:40:10 +0800 Subject: [PATCH 1003/1065] ROIAlign refactor (#2960) * ROIAlign refactor * fix unit tests --- .../analytics/bigdl/dllib/nn/RoiAlign.scala | 65 ++- .../bigdl/dllib/nn/BoxHeadSpec.scala | 378 ++------------- .../bigdl/dllib/nn/MaskHeadSpec.scala | 456 +++++++++--------- .../analytics/bigdl/dllib/nn/PoolerSpec.scala | 45 +- .../bigdl/dllib/nn/RoiAlignSpec.scala | 42 +- 5 files changed, 368 insertions(+), 618 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala index 71cb1d0a6ef..a81ba967dd6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala @@ -47,7 +47,8 @@ class RoiAlign[T: ClassTag] ( val samplingRatio: Int, val pooledH: Int, val pooledW: Int, - val mode: String = "avg" + val mode: String = "avg", + val aligned: Boolean = true )(implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{ override def updateOutput(input: Table): Tensor[T] = { if (classTag[T] == classTag[Float]) { @@ -127,16 +128,24 @@ class RoiAlign[T: ClassTag] ( for (n <- 0 until num_rois) { val index_n = n * channels * pooledW * pooledH - var offset_rois = n * roi_cols + val offset_rois = n * roi_cols val roi_batch_ind = 0 // bbox has 4 elements + val alignedOffset = if (aligned) 0.5f else 0.0f + val roi_start_w = roisFloat(offset_rois) * spatialScale - alignedOffset + val roi_start_h = roisFloat(offset_rois + 1) * spatialScale - alignedOffset + val roi_end_w = roisFloat(offset_rois + 2) * spatialScale - alignedOffset + val roi_end_h = roisFloat(offset_rois + 3) * spatialScale - alignedOffset + var roi_width = roi_end_w - roi_start_w + var roi_height = roi_end_h - roi_start_h + + if (aligned) { + require(roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlign cannot have non-negative size!") + } else { + roi_width = math.max(roi_width, 1.0f) + roi_height = math.max(roi_height, 1.0f) + } - val roi_start_w = roisFloat(offset_rois) * spatialScale - val roi_start_h = roisFloat(offset_rois + 1) * spatialScale - val roi_end_w = roisFloat(offset_rois + 2) * spatialScale - val roi_end_h = roisFloat(offset_rois + 3) * spatialScale - - val roi_width = Math.max(roi_end_w - roi_start_w, 1.0f) - val roi_height = Math.max(roi_end_h - roi_start_h, 1.0f) val bin_size_h = roi_height/ pooledH val bin_size_w = roi_width / pooledW @@ -152,7 +161,7 @@ class RoiAlign[T: ClassTag] ( Math.ceil(roi_width / pooledW).toInt } - val count: Float = roi_bin_grid_h * roi_bin_grid_w + val count: Float = math.max(roi_bin_grid_h * roi_bin_grid_w, 1.0f) val pre_cal = Tensor[Float]( Array(pooledH * pooledW * roi_bin_grid_h * roi_bin_grid_w, 8)) @@ -285,9 +294,7 @@ class RoiAlign[T: ClassTag] ( pre_cal.setValue(pre_calc_index, 7, 0.0f) // w3 pre_cal.setValue(pre_calc_index, 8, 0.0f) // w4 pre_calc_index += 1 - } - - else { + } else { if (y <= 0) { y = 0 } @@ -354,16 +361,24 @@ class RoiAlign[T: ClassTag] ( for (n <- 0 until num_rois) { val index_n = n * channels * pooledW * pooledH - var offset_rois = n * roi_cols + val offset_rois = n * roi_cols val roi_batch_ind = 0 - val roi_start_w = roisDouble(offset_rois) * spatialScale.toDouble - val roi_start_h = roisDouble(offset_rois + 1) * spatialScale.toDouble - val roi_end_w = roisDouble(offset_rois + 2) * spatialScale.toDouble - val roi_end_h = roisDouble(offset_rois + 3) * spatialScale.toDouble - - val roi_width = Math.max(roi_end_w - roi_start_w, 1.0) - val roi_height = Math.max(roi_end_h - roi_start_h, 1.0) + val alignedOffset = if (aligned) 0.5f else 0.0f + val roi_start_w = roisDouble(offset_rois) * spatialScale - alignedOffset + val roi_start_h = roisDouble(offset_rois + 1) * spatialScale - alignedOffset + val roi_end_w = roisDouble(offset_rois + 2) * spatialScale - alignedOffset + val roi_end_h = roisDouble(offset_rois + 3) * spatialScale - alignedOffset + + var roi_width = roi_end_w - roi_start_w + var roi_height = roi_end_h - roi_start_h + if (aligned) { + require(roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlign cannot have non-negative size!") + } else { + roi_width = math.max(roi_width, 1.0f) + roi_height = math.max(roi_height, 1.0f) + } val bin_size_h = roi_height/ pooledH val bin_size_w = roi_width / pooledW @@ -379,8 +394,7 @@ class RoiAlign[T: ClassTag] ( Math.ceil(roi_width / pooledW).toInt } - val count: Double = roi_bin_grid_h * roi_bin_grid_w - + val count: Double = math.max(roi_bin_grid_h * roi_bin_grid_w, 1.0f) val pre_cal = Tensor[Double]( Array(pooledH * pooledW * roi_bin_grid_h * roi_bin_grid_w, 8)) @@ -580,7 +594,8 @@ object RoiAlign { samplingRatio: Int, pooledH: Int, pooledW: Int, - mode: String = "avg" + mode: String = "avg", + aligned: Boolean = true ) (implicit ev: TensorNumeric[T]): RoiAlign[T] = - new RoiAlign[T](spatialScale, samplingRatio, pooledH, pooledW, mode) + new RoiAlign[T](spatialScale, samplingRatio, pooledH, pooledW, mode, aligned) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala index 07108780818..9568e99e2ff 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/BoxHeadSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.mkldnn.{Equivalent, Tools} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{T, Table} @@ -110,168 +111,10 @@ class BoxHeadSpec extends FlatSpec with Matchers { layer.evaluate() val output = layer.forward(T(T(features1, features2), bbox, imageInfo)).toTable[Table](2) - val expectedBbox = Tensor[Float](T( - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975))) + T(0.9994, 2.9988, 2.0674, 6.1349), + T(2.9991, 4.9993, 6.1162, 7.0871))) + val expectedLabel = Tensor[Float]( T( 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, @@ -285,10 +128,13 @@ class BoxHeadSpec extends FlatSpec with Matchers { output[Tensor[Float]](1) should be (expectedLabel) - output[Table](2)[Tensor[Float]](1).map(expectedBbox, (v1, v2) => { - assert(abs(v1 - v2) < 1e-3) - v1 - }) + val outBbox = output[Table](2)[Tensor[Float]](1) + var n = 1 + while (n <= outBbox.size(1)) { + Equivalent.nearequals(outBbox.narrow(1, n, 2), expectedBbox, + epsilon = 10-5) + n += 2 + } } "BoxHead with batch size > 1" should "be ok" in { @@ -395,164 +241,9 @@ class BoxHeadSpec extends FlatSpec with Matchers { val output = layer.forward(T(T(features12, features22), bbox2, labels2)).toTable[Table](2) val expectedBbox = Tensor[Float](T( - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975), - T(0.9995, 2.9991, 2.0602, 6.1203), - T(2.9990, 4.9992, 6.1299, 7.0975))) + T(0.9994, 2.9988, 2.0674, 6.1349), + T(2.9991, 4.9993, 6.1162, 7.0871))) + val expectedLable = Tensor[Float]( T( 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, @@ -562,7 +253,7 @@ class BoxHeadSpec extends FlatSpec with Matchers { 46, 46, 47, 47, 48, 48, 49, 49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 54, 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 60, 61, 61, 62, 62, 63, 63, 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, - 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, + 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, 80, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, @@ -571,20 +262,33 @@ class BoxHeadSpec extends FlatSpec with Matchers { 46, 46, 47, 47, 48, 48, 49, 49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 54, 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 60, 61, 61, 62, 62, 63, 63, 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, - 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79)) + 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, 80)) output[Tensor[Float]](1) should be (expectedLable) - output[Table](2)[Tensor[Float]](1).map(expectedBbox, (v1, v2) => { - assert(abs(v1 - v2) < 1e-3) - v1 - }) - - output[Table](2)[Tensor[Float]](2).map(expectedBbox, (v1, v2) => { - assert(abs(v1 - v2) < 1e-3) - v1 - }) - + var outBbox = output[Table](2)[Tensor[Float]](1) + var n = 1 + while (n <= outBbox.size(1)) { + Equivalent.nearequals(outBbox.narrow(1, n, 1), expectedBbox.narrow(1, 1, 1), + epsilon = 10-5) + if (n + 2 <= outBbox.size(1)) { + Equivalent.nearequals(outBbox.narrow(1, n + 1, 1), expectedBbox.narrow(1, 2, 1), + epsilon = 10-5) + } + n += 2 + } + + outBbox = output[Table](2)[Tensor[Float]](2) + n = 1 + while (n <= outBbox.size(1)) { + Equivalent.nearequals(outBbox.narrow(1, n, 1), expectedBbox.narrow(1, 1, 1), + epsilon = 10-5) + if (n + 2 <= outBbox.size(1)) { + Equivalent.nearequals(outBbox.narrow(1, n + 1, 1), expectedBbox.narrow(1, 2, 1), + epsilon = 10-5) + } + n += 2 + } } "FeatureExtractor in BoxHead" should "be ok" in { @@ -1008,12 +712,12 @@ class BoxHeadSpec extends FlatSpec with Matchers { val output = layer.forward(T(input, T(proposals), imageInfo)).toTensor[Float] output.select(1, 1).apply1(a => { - a should be(0.1516f +- 1e-3f) + a should be(0.1213f +- 1e-3f) a }) output.select(1, 2).apply1(a => { - a should be(0.1460f +- 1e-3f) + a should be(0.1502f +- 1e-3f) a }) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala index ea96dc47b65..9dda6de6bb0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/MaskHeadSpec.scala @@ -734,238 +734,230 @@ class MaskHeadSpec extends FlatSpec with Matchers { T(3.0f, 5.0f, 6.0f, 10.0f))) val expectedOutput = Tensor[Float](T(T(T( - T(0.0275, 0.0447, 0.0516, 0.0533, 0.0551, 0.0568, 0.0585, 0.0603, - 0.0620, 0.0635, 0.0645, 0.0649, 0.0577, 0.0360), - T(0.0442, 0.0718, 0.0829, 0.0858, 0.0886, 0.0914, 0.0943, 0.0971, - 0.0999, 0.1023, 0.1040, 0.1047, 0.0932, 0.0582), - T(0.0501, 0.0814, 0.0940, 0.0973, 0.1005, 0.1038, 0.1070, 0.1103, - 0.1135, 0.1163, 0.1183, 0.1191, 0.1060, 0.0662), - T(0.0505, 0.0822, 0.0949, 0.0982, 0.1015, 0.1048, 0.1081, 0.1114, - 0.1146, 0.1175, 0.1195, 0.1204, 0.1071, 0.0669), - T(0.0510, 0.0830, 0.0958, 0.0990, 0.1022, 0.1055, 0.1087, 0.1120, - 0.1152, 0.1179, 0.1199, 0.1207, 0.1073, 0.0670), - T(0.0515, 0.0837, 0.0965, 0.0997, 0.1028, 0.1059, 0.1090, 0.1122, - 0.1153, 0.1179, 0.1197, 0.1203, 0.1069, 0.0667), - T(0.0521, 0.0845, 0.0973, 0.1003, 0.1032, 0.1062, 0.1092, 0.1122, - 0.1151, 0.1176, 0.1193, 0.1197, 0.1062, 0.0662), - T(0.0526, 0.0852, 0.0980, 0.1008, 0.1037, 0.1065, 0.1094, 0.1122, - 0.1150, 0.1173, 0.1188, 0.1190, 0.1055, 0.0657), - T(0.0531, 0.0860, 0.0988, 0.1014, 0.1041, 0.1068, 0.1095, 0.1122, - 0.1148, 0.1170, 0.1183, 0.1183, 0.1047, 0.0651), - T(0.0536, 0.0867, 0.0995, 0.1020, 0.1046, 0.1071, 0.1096, 0.1122, - 0.1147, 0.1167, 0.1178, 0.1176, 0.1039, 0.0646), - T(0.0541, 0.0875, 0.1002, 0.1026, 0.1050, 0.1074, 0.1098, 0.1122, - 0.1145, 0.1164, 0.1173, 0.1169, 0.1032, 0.0641), - T(0.0546, 0.0883, 0.1010, 0.1032, 0.1055, 0.1077, 0.1099, 0.1122, - 0.1143, 0.1161, 0.1168, 0.1162, 0.1024, 0.0635), - T(0.0489, 0.0790, 0.0903, 0.0921, 0.0940, 0.0959, 0.0978, 0.0997, - 0.1015, 0.1029, 0.1035, 0.1029, 0.0905, 0.0561), - T(0.0307, 0.0495, 0.0566, 0.0577, 0.0589, 0.0600, 0.0612, 0.0623, - 0.0634, 0.0643, 0.0646, 0.0641, 0.0564, 0.0349)), - - T(T(0.0275, 0.0447, 0.0516, 0.0533, 0.0551, 0.0568, 0.0585, 0.0603, - 0.0620, 0.0635, 0.0645, 0.0649, 0.0577, 0.0360), - T(0.0442, 0.0718, 0.0829, 0.0858, 0.0886, 0.0914, 0.0943, 0.0971, - 0.0999, 0.1023, 0.1040, 0.1047, 0.0932, 0.0582), - T(0.0501, 0.0814, 0.0940, 0.0973, 0.1005, 0.1038, 0.1070, 0.1103, - 0.1135, 0.1163, 0.1183, 0.1191, 0.1060, 0.0662), - T(0.0505, 0.0822, 0.0949, 0.0982, 0.1015, 0.1048, 0.1081, 0.1114, - 0.1146, 0.1175, 0.1195, 0.1204, 0.1071, 0.0669), - T(0.0510, 0.0830, 0.0958, 0.0990, 0.1022, 0.1055, 0.1087, 0.1120, - 0.1152, 0.1179, 0.1199, 0.1207, 0.1073, 0.0670), - T(0.0515, 0.0837, 0.0965, 0.0997, 0.1028, 0.1059, 0.1090, 0.1122, - 0.1153, 0.1179, 0.1197, 0.1203, 0.1069, 0.0667), - T(0.0521, 0.0845, 0.0973, 0.1003, 0.1032, 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0.1045, 0.1067, + 0.1089, 0.1111, 0.1133, 0.1155, 0.1041, 0.0656), + T(0.0524, 0.0846, 0.0968, 0.0988, 0.1009, 0.1029, 0.1050, 0.1070, + 0.1091, 0.1112, 0.1132, 0.1153, 0.1038, 0.0654), + T(0.0531, 0.0857, 0.0978, 0.0997, 0.1016, 0.1036, 0.1055, 0.1074, + 0.1093, 0.1112, 0.1131, 0.1151, 0.1036, 0.0652), + T(0.0537, 0.0867, 0.0988, 0.1006, 0.1024, 0.1042, 0.1060, 0.1077, + 0.1095, 0.1113, 0.1131, 0.1148, 0.1033, 0.0650), + T(0.0544, 0.0877, 0.0999, 0.1015, 0.1032, 0.1048, 0.1064, 0.1081, + 0.1097, 0.1113, 0.1130, 0.1146, 0.1030, 0.0648), + T(0.0551, 0.0887, 0.1009, 0.1024, 0.1039, 0.1054, 0.1069, 0.1084, + 0.1099, 0.1114, 0.1129, 0.1144, 0.1027, 0.0646), + T(0.0557, 0.0897, 0.1020, 0.1033, 0.1047, 0.1060, 0.1074, 0.1088, + 0.1101, 0.1115, 0.1128, 0.1142, 0.1024, 0.0643), + T(0.0500, 0.0804, 0.0913, 0.0925, 0.0936, 0.0947, 0.0958, 0.0969, + 0.0980, 0.0991, 0.1002, 0.1014, 0.0908, 0.0571), + T(0.0314, 0.0505, 0.0573, 0.0580, 0.0587, 0.0593, 0.0600, 0.0607, + 0.0613, 0.0620, 0.0626, 0.0633, 0.0567, 0.0356)), + T(T(0.0273, 0.0442, 0.0507, 0.0520, 0.0533, 0.0546, 0.0560, 0.0573, + 0.0586, 0.0599, 0.0613, 0.0626, 0.0565, 0.0356), + T(0.0438, 0.0710, 0.0815, 0.0837, 0.0858, 0.0880, 0.0901, 0.0923, + 0.0944, 0.0965, 0.0987, 0.1008, 0.0911, 0.0575), + T(0.0498, 0.0806, 0.0925, 0.0950, 0.0974, 0.0999, 0.1023, 0.1047, + 0.1072, 0.1096, 0.1121, 0.1145, 0.1034, 0.0653), + T(0.0504, 0.0816, 0.0936, 0.0960, 0.0985, 0.1009, 0.1033, 0.1057, + 0.1081, 0.1106, 0.1130, 0.1154, 0.1042, 0.0657), + T(0.0510, 0.0826, 0.0947, 0.0970, 0.0993, 0.1017, 0.1040, 0.1063, + 0.1086, 0.1110, 0.1133, 0.1156, 0.1043, 0.0658), + T(0.0517, 0.0836, 0.0957, 0.0979, 0.1001, 0.1023, 0.1045, 0.1067, + 0.1089, 0.1111, 0.1133, 0.1155, 0.1041, 0.0656), + T(0.0524, 0.0846, 0.0968, 0.0988, 0.1009, 0.1029, 0.1050, 0.1070, + 0.1091, 0.1112, 0.1132, 0.1153, 0.1038, 0.0654), + T(0.0531, 0.0857, 0.0978, 0.0997, 0.1016, 0.1036, 0.1055, 0.1074, + 0.1093, 0.1112, 0.1131, 0.1151, 0.1036, 0.0652), + T(0.0537, 0.0867, 0.0988, 0.1006, 0.1024, 0.1042, 0.1060, 0.1077, + 0.1095, 0.1113, 0.1131, 0.1148, 0.1033, 0.0650), + T(0.0544, 0.0877, 0.0999, 0.1015, 0.1032, 0.1048, 0.1064, 0.1081, + 0.1097, 0.1113, 0.1130, 0.1146, 0.1030, 0.0648), + T(0.0551, 0.0887, 0.1009, 0.1024, 0.1039, 0.1054, 0.1069, 0.1084, + 0.1099, 0.1114, 0.1129, 0.1144, 0.1027, 0.0646), + T(0.0557, 0.0897, 0.1020, 0.1033, 0.1047, 0.1060, 0.1074, 0.1088, + 0.1101, 0.1115, 0.1128, 0.1142, 0.1024, 0.0643), + T(0.0500, 0.0804, 0.0913, 0.0925, 0.0936, 0.0947, 0.0958, 0.0969, + 0.0980, 0.0991, 0.1002, 0.1014, 0.0908, 0.0571), + T(0.0314, 0.0505, 0.0573, 0.0580, 0.0587, 0.0593, 0.0600, 0.0607, + 0.0613, 0.0620, 0.0626, 0.0633, 0.0567, 0.0356)), + T(T(0.0273, 0.0442, 0.0507, 0.0520, 0.0533, 0.0546, 0.0560, 0.0573, + 0.0586, 0.0599, 0.0613, 0.0626, 0.0565, 0.0356), + T(0.0438, 0.0710, 0.0815, 0.0837, 0.0858, 0.0880, 0.0901, 0.0923, + 0.0944, 0.0965, 0.0987, 0.1008, 0.0911, 0.0575), + T(0.0498, 0.0806, 0.0925, 0.0950, 0.0974, 0.0999, 0.1023, 0.1047, + 0.1072, 0.1096, 0.1121, 0.1145, 0.1034, 0.0653), + T(0.0504, 0.0816, 0.0936, 0.0960, 0.0985, 0.1009, 0.1033, 0.1057, + 0.1081, 0.1106, 0.1130, 0.1154, 0.1042, 0.0657), + T(0.0510, 0.0826, 0.0947, 0.0970, 0.0993, 0.1017, 0.1040, 0.1063, + 0.1086, 0.1110, 0.1133, 0.1156, 0.1043, 0.0658), + T(0.0517, 0.0836, 0.0957, 0.0979, 0.1001, 0.1023, 0.1045, 0.1067, + 0.1089, 0.1111, 0.1133, 0.1155, 0.1041, 0.0656), + T(0.0524, 0.0846, 0.0968, 0.0988, 0.1009, 0.1029, 0.1050, 0.1070, + 0.1091, 0.1112, 0.1132, 0.1153, 0.1038, 0.0654), + T(0.0531, 0.0857, 0.0978, 0.0997, 0.1016, 0.1036, 0.1055, 0.1074, + 0.1093, 0.1112, 0.1131, 0.1151, 0.1036, 0.0652), + T(0.0537, 0.0867, 0.0988, 0.1006, 0.1024, 0.1042, 0.1060, 0.1077, + 0.1095, 0.1113, 0.1131, 0.1148, 0.1033, 0.0650), + T(0.0544, 0.0877, 0.0999, 0.1015, 0.1032, 0.1048, 0.1064, 0.1081, + 0.1097, 0.1113, 0.1130, 0.1146, 0.1030, 0.0648), + T(0.0551, 0.0887, 0.1009, 0.1024, 0.1039, 0.1054, 0.1069, 0.1084, + 0.1099, 0.1114, 0.1129, 0.1144, 0.1027, 0.0646), + T(0.0557, 0.0897, 0.1020, 0.1033, 0.1047, 0.1060, 0.1074, 0.1088, + 0.1101, 0.1115, 0.1128, 0.1142, 0.1024, 0.0643), + T(0.0500, 0.0804, 0.0913, 0.0925, 0.0936, 0.0947, 0.0958, 0.0969, + 0.0980, 0.0991, 0.1002, 0.1014, 0.0908, 0.0571), + T(0.0314, 0.0505, 0.0573, 0.0580, 0.0587, 0.0593, 0.0600, 0.0607, + 0.0613, 0.0620, 0.0626, 0.0633, 0.0567, 0.0356))))) val output = layer.forward(T(T(features1, features2), T(bbox))).toTensor[Float] diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala index 9dac05e1c13..0840c72a3b1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/PoolerSpec.scala @@ -18,6 +18,7 @@ package com.intel.analytics.bigdl.nn import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{T, Table} @@ -102,14 +103,14 @@ class PoolerSpec extends FlatSpec with Matchers { resolution = 2, scales = Array(0.125f, 0.0625f, 0.03125f), samplingRatio = 2) val res = pooler.forward(input) val expectedRes = Array( - 0.226864114403724670, 0.272973388433456421, - 0.560893952846527100, 0.552965760231018066, - 0.641304850578308105, 0.476758182048797607, - 0.396813184022903442, 0.643787503242492676, - 0.610044836997985840, 0.499720931053161621, - 0.440425604581832886, 0.471624016761779785, - 0.690218806266784668, 0.652535200119018555, - 0.289968341588973999, 0.477649390697479248, + 0.023863614098541271, 0.057400867850185459, + 0.280628564810485104, 0.305623784018421591, + 0.998104330194710485, 0.700919154915548130, + 0.622570158108509184, 0.567865130189475190, + 0.482630044810485019, 0.516544848104851085, + 0.549660193754091783, 0.254286142185043710, + 0.634043431284018491, 0.601322654816104865, + 0.528360197830765149, 0.564136290194751285, 0.243893563747406006, 0.000000000000000000, 0.000000000000000000, 0.000000000000000000, 0.058870539069175720, 0.000000000000000000, @@ -198,14 +199,14 @@ class PoolerSpec extends FlatSpec with Matchers { samplingRatio = 2) val res = pooler.forward(input) val expectedRes = Array( - 0.226864114403724670, 0.272973388433456421, - 0.560893952846527100, 0.552965760231018066, - 0.641304850578308105, 0.476758182048797607, - 0.396813184022903442, 0.643787503242492676, - 0.610044836997985840, 0.499720931053161621, - 0.440425604581832886, 0.471624016761779785, - 0.690218806266784668, 0.652535200119018555, - 0.289968341588973999, 0.477649390697479248, + 0.023863614098541271, 0.057400867850185459, + 0.280628564810485104, 0.305623784018421591, + 0.998104330194710485, 0.700919154915548130, + 0.622570158108509184, 0.567865130189475190, + 0.482630044810485019, 0.516544848104851085, + 0.549660193754091783, 0.254286142185043710, + 0.634043431284018491, 0.601322654816104865, + 0.528360197830765149, 0.564136290194751285, 0.243893563747406006, 0.000000000000000000, 0.000000000000000000, 0.000000000000000000, 0.058870539069175720, 0.000000000000000000, @@ -220,11 +221,15 @@ class PoolerSpec extends FlatSpec with Matchers { class PoolerSerialTest extends ModuleSerializationTest { override def test(): Unit = { val input = T() - val feature0 = Tensor[Float](1, 1, 2, 2).apply1(_ => Random.nextFloat()) - val feature1 = Tensor[Float](1, 1, 4, 4).apply1(_ => Random.nextFloat()) - val feature2 = Tensor[Float](1, 1, 8, 8).apply1(_ => Random.nextFloat()) + RNG.setSeed(10) + val feature0 = Tensor[Float](1, 2, 8, 8).apply1(_ => RNG.uniform(-1, 1).toFloat) + val feature1 = Tensor[Float](1, 2, 4, 4).apply1(_ => RNG.uniform(-1, 1).toFloat) + val feature2 = Tensor[Float](1, 2, 2, 2).apply1(_ => RNG.uniform(-1, 1).toFloat) val features = T(feature0, feature1, feature2) - val rois = Tensor[Float](1, 4).apply1(_ => Random.nextFloat()) + val rois = Tensor[Float]( + T(T(0, 0, 10, 10), + T(0, 0, 60, 60), + T(0, 0, 500, 500))).resize(3, 4) input(1.0f) = features input(2.0f) = rois val pooler = new Pooler[Float](resolution = 2, scales = Array(0.25f, 0.125f, 0.0625f), diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala index de3a127c741..219b9ef496c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala @@ -17,6 +17,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.{Storage, Tensor} +import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{T, Table} import org.scalatest.{FlatSpec, Matchers} @@ -74,7 +75,8 @@ class RoiAlignSpec extends FlatSpec with Matchers { input.insert(Tensor(Storage(data.map(x => x.toFloat))).resize(1, 2, 6, 8)) input.insert(Tensor(Storage(rois.map(x => x.toFloat))).resize(4, 4)) - val roiAlign = RoiAlign[Float](spatio_scale, sampling_ratio, pooled_height, pooled_width, "avg") + val roiAlign = RoiAlign[Float](spatio_scale, sampling_ratio, pooled_height, pooled_width, "avg", + aligned = false) val res = roiAlign.forward(input) val expectedRes = Array( 0.614743709564208984, 0.550280153751373291, @@ -151,7 +153,7 @@ class RoiAlignSpec extends FlatSpec with Matchers { input.insert(Tensor(Storage(rois.map(x => x.toDouble))).resize(4, 4)) val roiAlign = RoiAlign[Double]( - spatio_scale, sampling_ratio, pooled_height, pooled_width, "avg") + spatio_scale, sampling_ratio, pooled_height, pooled_width, "avg", aligned = false) val res = roiAlign.forward(input) val expectedRes = Array( 0.614743709564208984, 0.550280153751373291, @@ -176,13 +178,45 @@ class RoiAlignSpec extends FlatSpec with Matchers { assert(Math.abs(res.storage().array()(i) - expectedRes(i)) < 1e-6) } } + + "ROIAlign with aligned" should "be ok" in { + val rois = Tensor[Float](T(T(1.0f, 1.0f, 3.0f, 3.0f))) + val features = Tensor[Float](T(T(T( + T( 0.0f, 1.0f, 2.0f, 3.0f, 4.0f), + T( 5.0f, 6.0f, 7.0f, 8.0f, 9.0f), + T(10.0f, 11.0f, 12.0f, 13.0f, 14.0f), + T(15.0f, 16.0f, 17.0f, 18.0f, 19.0f), + T(20.0f, 21.0f, 22.0f, 23.0f, 24.0f))))) + + val expectedWithAlign = Tensor[Float](T(T(T( + T(4.5, 5.0, 5.5, 6.0), + T(7.0, 7.5, 8.0, 8.5), + T(9.5, 10.0, 10.5, 11.0), + T(12.0, 12.5, 13.0, 13.5))))) + + val expected = Tensor[Float](T(T(T( + T(7.5, 8, 8.5, 9), + T(10, 10.5, 11, 11.5), + T(12.5, 13, 13.5, 14), + T(15, 15.5, 16, 16.5))))) + + val roiAlign = RoiAlign[Float](1.0f, 0, 4, 4, "avg", aligned = true) + val roiNoAlign = RoiAlign[Float](1.0f, 0, 4, 4, "avg", aligned = false) + + val out = roiAlign.forward(T(features, rois)) + val out2 = roiNoAlign.forward(T(features, rois)) + + out should be(expectedWithAlign) + out2 should be(expected) + } } class RoiAlignSerialTest extends ModuleSerializationTest { override def test(): Unit = { val input = T() - val input1 = Tensor[Float](1, 1, 2, 2).apply1(_ => Random.nextFloat()) - val input2 = Tensor[Float](1, 4).apply1(_ => Random.nextFloat()) + RNG.setSeed(10) + val input1 = Tensor[Float](1, 2, 6, 8).apply1(_ => RNG.uniform(-1, 1).toFloat) + val input2 = Tensor[Float](T(T( 6, 2, 7, 5))) input(1.0f) = input1 input(2.0f) = input2 val roiAlign = new RoiAlign[Float](spatialScale = 1.0f, samplingRatio = 1, From 56b348c8dcf0f8b98492d043cb0421938cf9fcc3 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Mon, 2 Dec 2019 15:40:41 +0800 Subject: [PATCH 1004/1065] fix model load of maskrcnn (#2961) * fix maskrcnn model load * delete temp file * fix maskrcnn tests --- .../dllib/models/maskrcnn/MaskRCNN.scala | 60 ++++++++++++------- .../dllib/models/maskrcnn/MaskRCNNSpec.scala | 17 +++++- 2 files changed, 54 insertions(+), 23 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala index 34bf3aa7cf4..0a2966ee15f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNN.scala @@ -60,22 +60,26 @@ class MaskRCNN(val inChannels: Int, val config: MaskRCNNParams = new MaskRCNNParams)(implicit ev: TensorNumeric[Float]) extends Container[Activity, Activity, Float] { - private val batchImgInfo : Tensor[Float] = Tensor[Float](2) - private val backbone = buildBackbone(inChannels, outChannels) - private val rpn = RegionProposal(inChannels, config.anchorSizes, config.aspectRatios, - config.anchorStride, config.preNmsTopNTest, config.postNmsTopNTest, config.preNmsTopNTrain, - config.postNmsTopNTrain, config.rpnNmsThread, config.minSize) - private val boxHead = BoxHead(inChannels, config.boxResolution, config.scales, - config.samplingRatio, config.boxScoreThresh, config.boxNmsThread, config.maxPerImage, - config.outputSize, numClasses) - private val maskHead = MaskHead(inChannels, config.maskResolution, config.scales, - config.samplingRatio, config.layers, config.dilation, numClasses) - - // add layer to modules - modules.append(backbone.asInstanceOf[Module[Float]]) - modules.append(rpn.asInstanceOf[Module[Float]]) - modules.append(boxHead.asInstanceOf[Module[Float]]) - modules.append(maskHead.asInstanceOf[Module[Float]]) + private val batchImgInfo : Tensor[Float] = Tensor[Float](2) + initModules() + // add layer to modules + private def initModules(): Unit = { + modules.clear() + val backbone = buildBackbone(inChannels, outChannels) + val rpn = RegionProposal(inChannels, config.anchorSizes, config.aspectRatios, + config.anchorStride, config.preNmsTopNTest, config.postNmsTopNTest, config.preNmsTopNTrain, + config.postNmsTopNTrain, config.rpnNmsThread, config.minSize) + val boxHead = BoxHead(inChannels, config.boxResolution, config.scales, + config.samplingRatio, config.boxScoreThresh, config.boxNmsThread, config.maxPerImage, + config.outputSize, numClasses) + val maskHead = MaskHead(inChannels, config.maskResolution, config.scales, + config.samplingRatio, config.layers, config.dilation, numClasses) + + modules.append(backbone.asInstanceOf[Module[Float]]) + modules.append(rpn.asInstanceOf[Module[Float]]) + modules.append(boxHead.asInstanceOf[Module[Float]]) + modules.append(maskHead.asInstanceOf[Module[Float]]) + } private def buildResNet50(): Module[Float] = { @@ -167,18 +171,24 @@ class MaskRCNN(val inChannels: Int, // contains all images info (height, width, original height, original width) val imageInfo = input.toTable[Tensor[Float]](2) + // get each layer from modules + val backbone = modules(0) + val rpn = modules(1) + val boxHead = modules(2) + val maskHead = modules(3) + batchImgInfo.setValue(1, inputFeatures.size(3)) batchImgInfo.setValue(2, inputFeatures.size(4)) - val features = this.backbone.forward(inputFeatures) - val proposals = this.rpn.forward(T(features, batchImgInfo)) - val boxOutput = this.boxHead.forward(T(features, proposals, batchImgInfo)).toTable + val features = backbone.forward(inputFeatures) + val proposals = rpn.forward(T(features, batchImgInfo)) + val boxOutput = boxHead.forward(T(features, proposals, batchImgInfo)).toTable val postProcessorBox = boxOutput[Table](2) val labelsBox = postProcessorBox[Tensor[Float]](1) val proposalsBox = postProcessorBox[Table](2) val scores = postProcessorBox[Tensor[Float]](3) if (labelsBox.size(1) > 0) { - val masks = this.maskHead.forward(T(features, proposalsBox, labelsBox)).toTable + val masks = maskHead.forward(T(features, proposalsBox, labelsBox)).toTable if (this.isTraining()) { output = T(proposalsBox, labelsBox, masks, scores) } else { @@ -340,8 +350,12 @@ object MaskRCNN extends ContainerSerializable { .getAttributeValue(context, attrMap.get("useGn")) .asInstanceOf[Boolean]) - MaskRCNN(inChannels, outChannels, numClasses, config) - .asInstanceOf[AbstractModule[Activity, Activity, T]] + val maskrcnn = MaskRCNN(inChannels, outChannels, numClasses, config) + .asInstanceOf[Container[Activity, Activity, T]] + maskrcnn.modules.clear() + loadSubModules(context, maskrcnn) + + maskrcnn } override def doSerializeModule[T: ClassTag](context: SerializeContext[T], @@ -461,5 +475,7 @@ object MaskRCNN extends ContainerSerializable { DataConverter.setAttributeValue(context, useGnBuilder, config.useGn, universe.typeOf[Boolean]) maskrcnnBuilder.putAttr("useGn", useGnBuilder.build) + + serializeSubModules(context, maskrcnnBuilder) } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala index 0a8f147eb73..f65caa8e97e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/maskrcnn/MaskRCNNSpec.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.models.maskrcnn import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.dataset.segmentation.RLEMasks -import com.intel.analytics.bigdl.nn.Nms +import com.intel.analytics.bigdl.nn.{Module, Nms} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.transform.vision.image.RoiImageInfo import com.intel.analytics.bigdl.transform.vision.image.label.roi.RoiLabel @@ -26,6 +26,8 @@ import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import com.intel.analytics.bigdl.utils.{RandomGenerator, T, Table} import org.scalatest.{FlatSpec, Matchers} +import scala.reflect.io.File + class MaskRCNNSpec extends FlatSpec with Matchers { "build maskrcnn" should "be ok" in { RandomGenerator.RNG.setSeed(100) @@ -465,6 +467,19 @@ class MaskRCNNSpec extends FlatSpec with Matchers { index(i) should be(expectedOut(i) + 1) } } + + "MaskRCNN model load" should "be ok" in { + val resNetOutChannels = 32 + val backboneOutChannels = 32 + val mask = new MaskRCNN(resNetOutChannels, backboneOutChannels) + mask.getExtraParameter().foreach(_.fill(0.1f)) + + val tempFile = "/tmp/maskrcnn.model" + mask.saveModule(tempFile, overWrite = true) + val maskLoad = Module.loadModule[Float](tempFile) + maskLoad.getExtraParameter().foreach(t => require(t.valueAt(1) == 0.1f)) + File(tempFile).delete() + } } class MaskRCNNSerialTest extends ModuleSerializationTest { From cf170342e324ee19d2d5f11e5f479876f26adbfd Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Fri, 6 Dec 2019 12:17:40 +0800 Subject: [PATCH 1005/1065] support roialign backward (#2975) * support roialign backward * fix sparselinear unit test --- .../analytics/bigdl/dllib/nn/RoiAlign.scala | 190 +++++++++++++++++- .../bigdl/dllib/nn/RoiAlignSpec.scala | 87 ++++++++ .../bigdl/dllib/nn/SparseLinearSpec.scala | 8 +- 3 files changed, 273 insertions(+), 12 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala index a81ba967dd6..40a18757ec5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlign.scala @@ -18,7 +18,7 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Table -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric import scala.reflect._ @@ -49,11 +49,11 @@ class RoiAlign[T: ClassTag] ( val pooledW: Int, val mode: String = "avg", val aligned: Boolean = true -)(implicit ev: TensorNumeric[T]) extends AbstractModule[Table, Tensor[T], T]{ - override def updateOutput(input: Table): Tensor[T] = { +)(implicit ev: TensorNumeric[T]) extends AbstractModule[Activity, Tensor[T], T]{ + override def updateOutput(input: Activity): Tensor[T] = { if (classTag[T] == classTag[Float]) { - val data = input[Tensor[Float]](1) - val rois = input[Tensor[Float]](2) + val data = input.toTable[Tensor[Float]](1) + val rois = input.toTable[Tensor[Float]](2) val num_rois = rois.size(1) val channels = data.size(2) @@ -78,8 +78,8 @@ class RoiAlign[T: ClassTag] ( width, spatialScale) } else if (classTag[T] == classTag[Double]) { - val data = input[Tensor[Double]](1) - val rois = input[Tensor[Double]](2) + val data = input.toTable[Tensor[Double]](1) + val rois = input.toTable[Tensor[Double]](2) val num_rois = rois.size(1) val channels = data.size(2) @@ -110,8 +110,180 @@ class RoiAlign[T: ClassTag] ( output } - override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = { - throw new UnsupportedOperationException("Not support backward propagation") + + private def bilinearInterpolateGradient(height: Int, width: Int, y: Float, x: Float) + : (Float, Float, Float, Float, Int, Int, Int, Int) = { + var w1: Float = 0.0f + var w2: Float = 0.0f + var w3: Float = 0.0f + var w4: Float = 0.0f + var x_low : Int = 0 + var x_high: Int = 0 + var y_low: Int = 0 + var y_high: Int = 0 + + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + return (w1, w2, w3, w4, x_low, x_high, y_low, y_high) + } + + var realY = if (y <= 0) 0 else y + var realX = if (x <= 0) 0 else x + y_low = realY.toInt + x_low = realX.toInt + + if (y_low >= height - 1) { + y_high = height - 1 + y_low = height - 1 + realY = y_low + } else y_high = y_low + 1 + + if (x_low >= width - 1) { + x_high = width - 1 + x_low = width - 1 + realX = x_low + } else x_high = x_low + 1 + + val ly = realY - y_low + val lx = realX - x_low + val hy = 1.0 - ly + val hx = 1.0 - lx + + w1 = (hy * hx).toFloat + w2 = (hy * lx).toFloat + w3 = (ly * hx).toFloat + w4 = (ly * lx).toFloat + + return (w1, w2, w3, w4, x_low, x_high, y_low, y_high) + } + + private def roiAlignBackward( + nums: Int, + gradOutputArr: Array[T], + gradInputArr: Array[T], + gradInputOffset: Int, + rois: Array[T], + channels: Int, + height: Int, + width: Int, + pooled_height: Int, + pooled_width: Int, + sampling_ratio : Int, + n_stride : Int, + c_stride : Int, + h_stride : Int, + w_stride : Int, + spatial_scale: Float) { + val roi_cols = 4 + for (index <- 0 until nums) { + val pw = index % pooled_width + val ph = (index / pooled_width) % pooled_height + val c = (index / pooled_width / pooled_height) % channels + val n = index / pooled_width / pooled_height / channels + val offset_rois = n * roi_cols + + val offset = if (aligned) 0.5f else 0.0f + val roi_start_w = ev.toType[Float](rois(offset_rois)) * spatial_scale - offset + val roi_start_h = ev.toType[Float](rois(offset_rois + 1)) * spatial_scale - offset + val roi_end_w = ev.toType[Float](rois(offset_rois + 2)) * spatial_scale - offset + val roi_end_h = ev.toType[Float](rois(offset_rois + 3)) * spatial_scale - offset + + var roi_width = roi_end_w - roi_start_w + var roi_height = roi_end_h - roi_start_h + + if (aligned) { + require(roi_width >= 0 && roi_height >= 0, + s"ROIs in ROIAlign do not have non-negative size!" + + s"But get ${roi_height} ${roi_width}") + } else { + roi_width = math.max(roi_width, 1.0f) + roi_height = math.max(roi_height, 1.0f) + } + + val bin_size_h = roi_height / pooled_height + val bin_size_w = roi_width / pooled_width + val output_offset = n * n_stride + c * c_stride + val grad_output_value = gradOutputArr(output_offset + ph * h_stride + pw * w_stride) + + // We use roi_bin_grid to sample the grid and mimic integral + val roi_bin_grid_h = + if (sampling_ratio > 0) sampling_ratio else math.ceil(roi_height / pooled_height).toInt + val roi_bin_grid_w = + if (sampling_ratio > 0) sampling_ratio else math.ceil(roi_width / pooled_width).toInt + + // We do average (integral) pooling inside a bin + val count = roi_bin_grid_h * roi_bin_grid_w + + for (iy <- 0 until roi_bin_grid_h) { + val y = roi_start_h + ph * bin_size_h + (iy + 0.5) * bin_size_h / roi_bin_grid_h + for (ix <- 0 until roi_bin_grid_w) { + val x = roi_start_w + pw * bin_size_w + (ix + 0.5) * bin_size_w / roi_bin_grid_w + + val (w1, w2, w3, w4, x_low, x_high, y_low, y_high) = + bilinearInterpolateGradient(height, width, y.toFloat, x.toFloat) + + val g1 = ev.times(grad_output_value, ev.fromType(w1 / count)) + val g2 = ev.times(grad_output_value, ev.fromType(w2 / count)) + val g3 = ev.times(grad_output_value, ev.fromType(w3 / count)) + val g4 = ev.times(grad_output_value, ev.fromType(w4 / count)) + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + gradInputArr(gradInputOffset + y_low * width + x_low) = + ev.plus(gradInputArr(gradInputOffset + y_low * width + x_low), g1) + gradInputArr(gradInputOffset + y_low * width + x_high) = + ev.plus(gradInputArr(gradInputOffset + y_low * width + x_high), g2) + gradInputArr(gradInputOffset + y_high * width + x_low) = + ev.plus(gradInputArr(gradInputOffset + y_high * width + x_low), g3) + gradInputArr(gradInputOffset + y_high * width + x_high) = + ev.plus(gradInputArr(gradInputOffset + y_high * width + x_high), g4) + } + } + } + } + } + + override def updateGradInput(input: Activity, gradOutput: Tensor[T]): Activity = { + require(mode == "avg", s"Only support backward for average mode, but get ${mode}") + val data = input.toTable[Tensor[T]](1) + val rois = input.toTable[Tensor[T]](2) + val num_rois = rois.size(1) + val channels = data.size(2) + val height = data.size(3) + val width = data.size(4) + + require(gradOutput.isContiguous(), "gradOutput should be contiguous") + require(gradOutput.dim() == 4, s"gradOutput should be with 4 dims, but get ${gradOutput.dim()}") + + val n_stride = gradOutput.stride(1) + val c_stride = gradOutput.stride(2) + val h_stride = gradOutput.stride(3) + val w_stride = gradOutput.stride(4) + + if (gradInput == null) gradInput = Tensor[T]() + gradInput.toTensor[T].resize(channels, height, width) + val gradInputArr = gradInput.toTensor[T].storage().array() + val gradInputOffset = gradInput.toTensor[T].storageOffset() - 1 + + roiAlignBackward( + gradOutput.nElement(), + gradOutputArr = gradOutput.asInstanceOf[Tensor[T]].storage().array(), + gradInputArr = gradInputArr, + gradInputOffset = 0, + rois = rois.storage().array(), + channels = channels, + height = height, + width = width, + pooled_height = pooledH, + pooled_width = pooledW, + sampling_ratio = samplingRatio, + n_stride = n_stride, + c_stride = c_stride, + h_stride = h_stride, + w_stride = w_stride, + spatial_scale = spatialScale) + + gradInput } private def poolOneRoiFloat( diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala index 219b9ef496c..158459fcaec 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/RoiAlignSpec.scala @@ -16,6 +16,7 @@ package com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.mkldnn.Equivalent import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest @@ -209,6 +210,92 @@ class RoiAlignSpec extends FlatSpec with Matchers { out should be(expectedWithAlign) out2 should be(expected) } + + "backward" should "work correctly" in { + val input = Tensor[Float](T(T(T( + T(0.0611, 0.2246, 0.2343, 0.1771, 0.5561, 0.1094, 0.4609, 0.7084, + 0.5798, 0.4967), + T(0.5104, 0.3295, 0.7182, 0.3845, 0.0898, 0.1175, 0.6402, 0.1968, + 0.5124, 0.7118), + T(0.9249, 0.9997, 0.8927, 0.8767, 0.8450, 0.1544, 0.1705, 0.9842, + 0.8127, 0.4358), + T(0.4143, 0.4284, 0.7578, 0.9225, 0.9643, 0.1760, 0.9539, 0.3134, + 0.4544, 0.2956), + T(0.1875, 0.2433, 0.3493, 0.4441, 0.4069, 0.2859, 0.8036, 0.3218, + 0.3639, 0.2985), + T(0.6635, 0.2552, 0.4144, 0.8396, 0.7418, 0.2865, 0.7929, 0.5001, + 0.8977, 0.1051), + T(0.5809, 0.9867, 0.1315, 0.2391, 0.3047, 0.5158, 0.4514, 0.4929, + 0.5301, 0.2647), + T(0.1671, 0.5482, 0.2380, 0.5374, 0.4422, 0.6454, 0.5376, 0.2245, + 0.6632, 0.8439), + T(0.0109, 0.2807, 0.9301, 0.5438, 0.8123, 0.7750, 0.7308, 0.9924, + 0.7282, 0.2328), + T(0.9997, 0.5540, 0.4200, 0.5419, 0.8642, 0.4312, 0.1213, 0.8956, + 0.8784, 0.9128))))) + + val rois = Tensor[Float](T(T(0.0f, 0.0f, 9.0f, 9.0f), + T(0.0f, 5.0f, 4.0f, 9.0f), + T(5.0f, 5.0f, 9.0f, 9.0f))) + + val layer = RoiAlign[Float](spatialScale = 1, samplingRatio = 2, pooledH = 5, + pooledW = 5, aligned = true) + val out = layer.forward(T(input, rois)) + + val output = Tensor[Float](T(T(T( + T(0.2593, 0.3618, 0.2819, 0.3935, 0.5265), + T(0.7170, 0.8159, 0.6562, 0.4006, 0.6567), + T(0.3210, 0.4949, 0.5372, 0.5892, 0.4368), + T(0.6147, 0.3702, 0.4642, 0.5216, 0.5698), + T(0.2292, 0.5687, 0.6427, 0.6625, 0.6822))), + + T(T(T(0.5731, 0.3794, 0.3402, 0.4984, 0.7202), + T(0.6138, 0.7188, 0.4918, 0.2772, 0.4116), + T(0.3937, 0.6494, 0.4761, 0.2458, 0.3759), + T(0.1376, 0.3636, 0.4568, 0.4737, 0.5367), + T(0.1754, 0.2846, 0.5770, 0.7363, 0.5957))), + + T(T(T(0.3776, 0.6335, 0.6252, 0.5709, 0.6844), + T(0.4507, 0.5218, 0.5245, 0.5387, 0.5696), + T(0.5452, 0.5203, 0.4266, 0.4301, 0.5784), + T(0.6602, 0.6221, 0.5252, 0.5232, 0.6680), + T(0.7253, 0.6559, 0.7846, 0.8819, 0.6998))))) + + val gradOutput = Tensor[Float](T(T( + T(T(0.9688, 0.4150, 0.4094, 0.6885, 0.6800), + T(0.6415, 0.4019, 0.4875, 0.9569, 0.5172), + T(0.9534, 0.8540, 0.9555, 0.0836, 0.1684), + T(0.1883, 0.9384, 0.3543, 0.2027, 0.5069), + T(0.7145, 0.6801, 0.9717, 0.2403, 0.3372))), + T(T(T(0.5260, 0.1794, 0.4793, 0.3070, 0.7682), + T(0.6350, 0.7321, 0.9899, 0.1897, 0.6957), + T(0.1313, 0.9514, 0.3386, 0.5337, 0.1051), + T(0.1800, 0.4603, 0.7114, 0.5114, 0.2422), + T(0.1480, 0.2527, 0.2014, 0.3004, 0.7147))), + T(T(T(0.4033, 0.9819, 0.4697, 0.3446, 0.7631), + T(0.3554, 0.2396, 0.6231, 0.6009, 0.3054), + T(0.2082, 0.2404, 0.6693, 0.7529, 0.1088), + T(0.0441, 0.4054, 0.0348, 0.7627, 0.0077), + T(0.9582, 0.6859, 0.3182, 0.5291, 0.3420))))) + + Equivalent.nearequals(output, out, 1e-3) should be(true) + + val grad = layer.backward(T(input, rois), gradOutput).toTensor[Float] + + val expectedGrad = Tensor[Float](T(T( + T(0.3203, 0.2666, 0.1312, 0.1305, 0.1295, 0.1816, 0.2177, 0.2157, 0.2150, 0.0098), + T(0.2828, 0.2374, 0.1246, 0.1265, 0.1292, 0.1868, 0.2267, 0.2018, 0.1945, 0.0088), + T(0.2029, 0.1776, 0.1216, 0.1322, 0.1475, 0.2314, 0.2895, 0.1867, 0.1565, 0.0071), + T(0.2432, 0.2201, 0.1775, 0.1889, 0.2054, 0.1912, 0.1814, 0.1288, 0.1133, 0.0051), + T(0.3845, 0.3403, 0.3323, 0.3769, 0.3154, 0.2258, 0.1666, 0.1222, 0.1580, 0.0195), + T(0.8482, 0.8043, 0.8665, 0.9852, 0.3694, 0.7024, 0.9496, 0.7323, 0.8099, 0.1104), + T(0.8683, 1.2765, 1.0463, 0.7984, 0.2498, 0.4796, 0.7130, 1.1149, 0.6427, 0.0529), + T(0.6204, 1.1059, 1.0230, 0.6332, 0.3176, 0.4221, 0.5735, 0.9508, 0.4563, 0.0167), + T(0.4918, 0.6479, 0.7008, 0.8754, 0.5076, 0.9881, 0.7134, 0.6981, 0.5184, 0.0460), + T(0.0427, 0.0525, 0.0614, 0.1103, 0.0510, 0.1533, 0.1064, 0.0863, 0.0695, 0.0079)))) + + Equivalent.nearequals(grad, expectedGrad, 1e-3) should be(true) + } } class RoiAlignSerialTest extends ModuleSerializationTest { diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala index 4c1da650a73..da88ce1e365 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala @@ -19,7 +19,7 @@ package com.intel.analytics.bigdl.nn import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.{SparseTensor, Tensor} -import com.intel.analytics.bigdl.utils.T +import com.intel.analytics.bigdl.utils.{RandomGenerator, T} import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random @@ -143,9 +143,11 @@ class SparseLinearSpec extends FlatSpec with Matchers { } "Sparse Linear" should "return the same result with Linear 7" in { + RandomGenerator.RNG.setSeed(10) + val rnd = new Random(10) val gradOutput = Tensor(4, 2).rand() - val input = Tensor(4, 1023213).apply1(_ => Random.nextInt(100000) / 99999 * Random.nextFloat()) - val input2 = Tensor(4, 50).apply1(_ => Random.nextInt(2) * Random.nextFloat()) + val input = Tensor(4, 1023213).apply1(_ => rnd.nextInt(100000) / 99999 * rnd.nextFloat()) + val input2 = Tensor(4, 50).apply1(_ => rnd.nextInt(2) * rnd.nextFloat()) val sl = SparseLinear(1023263, 2, backwardStart = 1, backwardLength = 1023263) val sj = SparseJoinTable(2) val sparseModel = Sequential().add(ParallelTable().add(Identity()).add(Identity())) From 815f473a90ac061094c479e7e080bd581a923e41 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Thu, 12 Dec 2019 21:00:08 +0800 Subject: [PATCH 1006/1065] fix: bn nhwc error, the channel should be the last dim (#2981) --- .../dllib/nn/SpatialBatchNormalization.scala | 6 +++++- .../nn/SpatialBatchNormalizationSpec.scala | 21 +++++++++++++++++++ 2 files changed, 26 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala index 7e4d67c0595..9c86608bae1 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalization.scala @@ -73,7 +73,11 @@ class SpatialBatchNormalization[T: ClassTag]( _input.set(input) makeBatch(_input) - val nInput = _input.size(channelDim) + val nInput = if (dataFormat == DataFormat.NCHW) { + _input.size(2) + } else { + _input.size(4) + } if (runningMean.nElement == 0 || runningMean.nElement < nInput) { initializeBuffer(nInput) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala index a20727fc0ab..9e7ac7e53fe 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SpatialBatchNormalizationSpec.scala @@ -219,6 +219,27 @@ class SpatialBatchNormalizationSpec extends FlatSpec with Matchers with BeforeAn bnNCHW.gradWeight.almostEqual(bnNHWC.gradWeight, 1e-5) bnNCHW.gradBias.almostEqual(bnNHWC.gradBias, 1e-5) } + + "bn with NHWC" should "return correct extra parameters" in { + val bn1 = SpatialBatchNormalization[Float](16, dataFormat = DataFormat.NCHW) + val bn2 = SpatialBatchNormalization[Float](16, dataFormat = DataFormat.NHWC) + + bn2.parameters()._1.zip(bn1.parameters()._1).foreach { + case (bn2Para, bn1Para) => bn2Para.copy(bn1Para) + } + + val input1 = Tensor[Float](4, 16, 3, 3).rand(-1, 1) + bn1.forward(input1) + + val input2 = input1.transpose(2, 4).contiguous() + bn2.forward(input2) + + bn1.getExtraParameter().zip(bn2.getExtraParameter()).foreach { + case (p1, p2) => + p1.size() should be (p2.size()) + p1 should be (p2) + } + } } class SpatialBatchNormalizationSerialTest extends ModuleSerializationTest { From 642aa56a77cfb089f0e275e5aad5f24f460954ed Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 17 Dec 2019 10:35:14 +0800 Subject: [PATCH 1007/1065] refactor: move torch relevants unit tests to integration tests. (#2971) --- .../torch/AbsCriterionSpec.scala | 2 +- .../{ => integration}/torch/AbsSpec.scala | 2 +- .../{ => integration}/torch/AdagradSpec.scala | 2 +- .../torch/AddConstantSpec.scala | 2 +- .../{ => integration}/torch/AddSpec.scala | 2 +- .../torch/BCECriterionSpec.scala | 2 +- .../torch/BatchNormalizationSpec.scala | 2 +- .../torch/BiRecurrentSpec.scala | 22 +- .../torch/BilinearSpec.scala | 2 +- .../{ => integration}/torch/BottleSpec.scala | 2 +- .../{ => integration}/torch/CAddSpec.scala | 2 +- .../torch/CAddTableSpec.scala | 2 +- .../torch/CDivTableSpec.scala | 2 +- .../torch/CMaxTableSpec.scala | 2 +- .../torch/CMinTableSpec.scala | 2 +- .../{ => integration}/torch/CMulSpec.scala | 2 +- .../torch/CMulTableSpec.scala | 2 +- .../torch/CSubTableSpec.scala | 2 +- .../{ => integration}/torch/ClampSpec.scala | 2 +- .../torch/ClassNLLCriterionSpec.scala | 2 +- .../torch/ClassSimplexCriterionSpec.scala | 2 +- .../torch/ColorJitterSpec.scala | 4 +- .../{ => integration}/torch/ConcatSpec.scala | 2 +- .../torch/ConcatTableSpec.scala | 2 +- .../torch/ContiguousSpec.scala | 2 +- .../torch/ConvLSTMPeephole3DSpec.scala | 2 +- .../torch/CosineDistanceCriterionSpec.scala | 2 +- .../torch/CosineDistanceSpec.scala | 2 +- .../torch/CosineEmbeddingCriterionSpec.scala | 2 +- .../{ => integration}/torch/CosineSpec.scala | 2 +- .../torch/CrossEntropyCriterionSpec.scala | 2 +- .../torch/DenseTensorMathSpec.scala | 2 +- .../torch/DistKLDivCriterionSpec.scala | 2 +- .../{ => integration}/torch/DropoutSpec.scala | 2 +- .../{ => integration}/torch/ELUSpec.scala | 2 +- .../torch/EuclideanSpec.scala | 2 +- .../{ => integration}/torch/ExpSpec.scala | 2 +- .../{ => integration}/torch/GRUSpec.scala | 19 +- .../torch/GaussianCriterionSpec.scala | 2 +- .../torch/GradientReversalSpec.scala | 2 +- .../torch/HardShrinkSpec.scala | 2 +- .../torch/HardTanhSpec.scala | 2 +- .../torch/HingeEmbeddingCriterionSpec.scala | 2 +- .../{ => integration}/torch/IndexSpec.scala | 2 +- .../torch/JoinTableSpec.scala | 2 +- .../{ => integration}/torch/L1CostSpec.scala | 2 +- .../torch/L1HingeEmbeddingCriterionSpec.scala | 2 +- .../torch/L1PenaltySpec.scala | 2 +- .../torch/LSTMPeepholeSpec.scala | 19 +- .../{ => integration}/torch/LSTMSpec.scala | 20 +- .../torch/LeakyReLUSpec.scala | 2 +- .../{ => integration}/torch/LinearSpec.scala | 2 +- .../torch/LogSigmoidSpec.scala | 2 +- .../torch/LogSoftMaxSpec.scala | 2 +- .../{ => integration}/torch/LogSpec.scala | 2 +- .../torch/LookupTableSpec.scala | 2 +- .../{ => integration}/torch/MMSpec.scala | 2 +- .../torch/MSECriterionSpec.scala | 2 +- .../{ => integration}/torch/MVSpec.scala | 2 +- .../torch/MarginCriterionSpec.scala | 2 +- .../torch/MarginRankingCriterionSpec.scala | 2 +- .../torch/MaskedSelectSpec.scala | 2 +- .../{ => integration}/torch/MaxSpec.scala | 2 +- .../{ => integration}/torch/MeanSpec.scala | 2 +- .../{ => integration}/torch/MinSpec.scala | 2 +- .../torch/MixtureTableSpec.scala | 2 +- .../{ => integration}/torch/ModuleSpec.scala | 2 +- .../torch/MulConstantSpec.scala | 2 +- .../{ => integration}/torch/MulSpec.scala | 2 +- .../torch/MultiCriterionSpec.scala | 2 +- .../torch/MultiLabelMarginCriterionSpec.scala | 2 +- .../MultiLabelSoftMarginCriterionSpec.scala | 2 +- .../torch/MultiMarginCriterionSpec.scala | 2 +- .../{ => integration}/torch/NarrowSpec.scala | 2 +- .../torch/NarrowTableSpec.scala | 2 +- .../torch/NormalizeSpec.scala | 2 +- .../{ => integration}/torch/PReLUSpec.scala | 2 +- .../{ => integration}/torch/PaddingSpec.scala | 2 +- .../torch/PairwiseDistanceSpec.scala | 2 +- .../torch/ParallelCriterionSpec.scala | 2 +- .../{ => integration}/torch/PowerSpec.scala | 2 +- .../{ => integration}/torch/RReLUSpec.scala | 2 +- .../{ => integration}/torch/ReLU6Spec.scala | 6 +- .../{ => integration}/torch/ReLUSpec.scala | 2 +- .../torch/ReplicateSpec.scala | 2 +- .../{ => integration}/torch/ReshapeSpec.scala | 2 +- .../{ => integration}/torch/SamplerSpec.scala | 2 +- .../{ => integration}/torch/SelectSpec.scala | 2 +- .../torch/SelectTableSpec.scala | 2 +- .../torch/SequentialSpec.scala | 2 +- .../{ => integration}/torch/SigmoidSpec.scala | 2 +- .../torch/SmoothL1CriterionSpec.scala | 2 +- .../torch/SoftMarginCriterionSpec.scala | 2 +- .../{ => integration}/torch/SoftMaxSpec.scala | 2 +- .../{ => integration}/torch/SoftMinSpec.scala | 2 +- .../torch/SoftPlusSpec.scala | 2 +- .../torch/SoftShrinkSpec.scala | 2 +- .../torch/SoftSignSpec.scala | 2 +- .../torch/SpatialAveragePoolingSpec.scala | 2 +- .../torch/SpatialBatchNormalizationSpec.scala | 2 +- .../SpatialContrastiveNormalizationSpec.scala | 2 +- .../torch/SpatialConvolutionMapSpec.scala | 2 +- .../torch/SpatialConvolutionSpec.scala | 2 +- .../torch/SpatialCrossMapLRNSpec.scala | 4 +- .../torch/SpatialDilatedConvolutionSpec.scala | 2 +- .../SpatialDivisiveNormalizationSpec.scala | 2 +- .../torch/SpatialDropout1DSpec.scala | 2 +- .../torch/SpatialDropout2DSpec.scala | 2 +- .../torch/SpatialDropout3DSpec.scala | 2 +- .../torch/SpatialFullConvolutionSpec.scala | 2 +- .../torch/SpatialMaxPoolingSpec.scala | 2 +- .../SpatialSubtractiveNormalizationSpec.scala | 2 +- .../torch/SplitTableSpec.scala | 2 +- .../{ => integration}/torch/SqrtSpec.scala | 2 +- .../{ => integration}/torch/SquareSpec.scala | 2 +- .../{ => integration}/torch/SqueezeSpec.scala | 2 +- .../{ => integration}/torch/SumSpec.scala | 2 +- .../dllib/{ => integration}/torch/TH.scala | 122 ++- .../torch/TanhShrinkSpec.scala | 2 +- .../{ => integration}/torch/TanhSpec.scala | 2 +- .../torch/TemporalConvolutionSpec.scala | 2 +- .../torch/TemporalMaxPoolingSpec.scala | 2 +- .../{ => integration}/torch/TensorSpec.scala | 2 +- .../torch/ThresholdSpec.scala | 2 +- .../dllib/integration/torch/TorchSpec.scala | 65 ++ .../torch/TransposeSpec.scala | 2 +- .../torch/UnsqueezeSpec.scala | 2 +- .../{ => integration}/torch/ViewSpec.scala | 2 +- .../torch/VolumetricAveragePoolingSpec.scala | 2 +- .../torch/VolumetricConvolutionSpec.scala | 2 +- .../torch/VolumetricFullConvolutionSpec.scala | 2 +- .../torch/VolumetricMaxPoolingSpec.scala | 2 +- .../torch/models/AlexNetSpec.scala | 312 +++++++ .../torch/models/InceptionSpec.scala | 784 ++++++++++++++++++ .../bigdl/dllib/models/AlexNetSpec.scala | 291 +------ .../bigdl/dllib/models/InceptionSpec.scala | 762 +---------------- .../bigdl/dllib/models/ResNetSpec.scala | 18 +- .../bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala | 2 +- .../bigdl/dllib/nn/LogSoftMaxSpec.scala | 3 +- .../bigdl/dllib/torch/TorchSpec.scala | 25 - 140 files changed, 1414 insertions(+), 1310 deletions(-) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/AbsCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/AbsSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/AdagradSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/AddConstantSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/AddSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/BCECriterionSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/BatchNormalizationSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/BiRecurrentSpec.scala (93%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/BilinearSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/BottleSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CAddSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CAddTableSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CDivTableSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CMaxTableSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CMinTableSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CMulSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CMulTableSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CSubTableSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ClampSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ClassNLLCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ClassSimplexCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ColorJitterSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ConcatSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ConcatTableSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ContiguousSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ConvLSTMPeephole3DSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CosineDistanceCriterionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CosineDistanceSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CosineEmbeddingCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CosineSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/CrossEntropyCriterionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/DenseTensorMathSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/DistKLDivCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/DropoutSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ELUSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/EuclideanSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ExpSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/GRUSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/GaussianCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/GradientReversalSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/HardShrinkSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/HardTanhSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/HingeEmbeddingCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/IndexSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/JoinTableSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/L1CostSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/L1HingeEmbeddingCriterionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/L1PenaltySpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/LSTMPeepholeSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/LSTMSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/LeakyReLUSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/LinearSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/LogSigmoidSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/LogSoftMaxSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/LogSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/LookupTableSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MMSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MSECriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MVSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MarginCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MarginRankingCriterionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MaskedSelectSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MaxSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MeanSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MinSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MixtureTableSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ModuleSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MulConstantSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MulSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MultiCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MultiLabelMarginCriterionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MultiLabelSoftMarginCriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/MultiMarginCriterionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/NarrowSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/NarrowTableSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/NormalizeSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/PReLUSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/PaddingSpec.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/PairwiseDistanceSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ParallelCriterionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/PowerSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/RReLUSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ReLU6Spec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ReLUSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ReplicateSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ReshapeSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SamplerSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SelectSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SelectTableSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SequentialSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SigmoidSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SmoothL1CriterionSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SoftMarginCriterionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SoftMaxSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SoftMinSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SoftPlusSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SoftShrinkSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SoftSignSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialAveragePoolingSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialBatchNormalizationSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialContrastiveNormalizationSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialConvolutionMapSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialConvolutionSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialCrossMapLRNSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialDilatedConvolutionSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialDivisiveNormalizationSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialDropout1DSpec.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialDropout2DSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialDropout3DSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialFullConvolutionSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialMaxPoolingSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SpatialSubtractiveNormalizationSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SplitTableSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SqrtSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SquareSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SqueezeSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/SumSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/TH.scala (51%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/TanhShrinkSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/TanhSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/TemporalConvolutionSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/TemporalMaxPoolingSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/TensorSpec.scala (96%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ThresholdSpec.scala (98%) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TorchSpec.scala rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/TransposeSpec.scala (98%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/UnsqueezeSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/ViewSpec.scala (97%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/VolumetricAveragePoolingSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/VolumetricConvolutionSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/VolumetricFullConvolutionSpec.scala (99%) rename scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/{ => integration}/torch/VolumetricMaxPoolingSpec.scala (99%) create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/models/AlexNetSpec.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/models/InceptionSpec.scala delete mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TorchSpec.scala diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AbsCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AbsCriterionSpec.scala index 03a941e43a8..d5674c7e900 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AbsCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.AbsCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AbsSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AbsSpec.scala index f50b979e08d..1ce2bb9c180 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AbsSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AbsSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Abs import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AdagradSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AdagradSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AdagradSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AdagradSpec.scala index d54b381e4ab..708ac715efe 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AdagradSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AdagradSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.optim.Adagrad import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AddConstantSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AddConstantSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AddConstantSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AddConstantSpec.scala index 7caa9b48172..2bff48d4ee4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AddConstantSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AddConstantSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.AddConstant import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AddSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AddSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AddSpec.scala index 4aab0f81da3..20980aaa96d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/AddSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/AddSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Add import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BCECriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BCECriterionSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BCECriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BCECriterionSpec.scala index 05bca92c6a9..65da5bc59d7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BCECriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BCECriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.BCECriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BatchNormalizationSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BatchNormalizationSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BatchNormalizationSpec.scala index 273db78bb47..c732e67950d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BatchNormalizationSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import breeze.numerics.abs import com.intel.analytics.bigdl.nn.{BatchNormalization, GradientChecker} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BiRecurrentSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BiRecurrentSpec.scala similarity index 93% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BiRecurrentSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BiRecurrentSpec.scala index c75e043e657..e1ffe641d33 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BiRecurrentSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BiRecurrentSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import java.io.PrintWriter @@ -28,22 +28,10 @@ import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.{T, Table} import scala.sys.process._ +import scala.util.Random @com.intel.analytics.bigdl.tags.Serial -class BiRecurrentSpec extends TorchSpec { - override def torchCheck(): Unit = { - super.torchCheck() - val tmpFile = java.io.File.createTempFile("checkRNN", ".lua") - val writer = new PrintWriter(tmpFile) - writer.write("exist = (pcall(require, 'rnn'))\n print(exist)") - writer.close() - - val existsRNN = - Seq(System.getProperty("torch_location", "th"), tmpFile.getAbsolutePath).!!.trim - if (!existsRNN.contains("true")) { - cancel("Torch rnn is not installed") - } - } +class BiRecurrentSpec extends TorchRNNSpec { "A BiRecurrent" should "uses isSplitInput correctly" in { val inputSize = 4 @@ -119,8 +107,8 @@ class BiRecurrentSpec extends TorchSpec { val input = Tensor[Double](Array(1, seqLength, inputSize)) val labels = Tensor[Double](Array(1, seqLength)) for (i <- 1 to seqLength) { - val rdmLabel = Math.ceil(math.random * outputSize).toInt - val rdmInput = Math.ceil(math.random * inputSize).toInt + val rdmLabel = Math.ceil(Random.nextFloat * outputSize).toInt + val rdmInput = Math.ceil(Random.nextFloat * inputSize).toInt input.setValue(1, i, rdmInput, 1.0) labels.setValue(1, i, rdmLabel) } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BilinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BilinearSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BilinearSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BilinearSpec.scala index 335c707a5e7..a84df39767a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BilinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BilinearSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BottleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BottleSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BottleSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BottleSpec.scala index 6738a3211fc..70ca613e34b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/BottleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/BottleSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Bottle, Linear} import com.intel.analytics.bigdl._ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CAddSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CAddSpec.scala index 28e10170d6c..9051ef6b13f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CAddSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CAddTableSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CAddTableSpec.scala index 7086793d1c5..7b86cf2a802 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CAddTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CAddTableSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{CAddTable, ConcatTable, Linear, Sequential} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CDivTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CDivTableSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CDivTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CDivTableSpec.scala index f9ca5fe16eb..4749c9439a5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CDivTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CDivTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.CDivTable import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMaxTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMaxTableSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMaxTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMaxTableSpec.scala index 1201757beef..61d294ba26d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMaxTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMaxTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.CMaxTable import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMinTableSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMinTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMinTableSpec.scala index 923eac7107e..cb1372254cd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMinTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.CMinTable import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMulSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMulSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMulSpec.scala index d97180ce3d4..8033171e99f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMulSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMulSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMulTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMulTableSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMulTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMulTableSpec.scala index cab8454474e..24aa525dadd 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CMulTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CMulTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CSubTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CSubTableSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CSubTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CSubTableSpec.scala index a14ccbbc4cd..e4d49bd8561 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CSubTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CSubTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClampSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClampSpec.scala index 1fc0a49e751..8d0056e48f6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClampSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClampSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Clamp import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClassNLLCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClassNLLCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClassNLLCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClassNLLCriterionSpec.scala index 09a1de6233e..d3eed6f85bf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClassNLLCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClassNLLCriterionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.ClassNLLCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClassSimplexCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClassSimplexCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClassSimplexCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClassSimplexCriterionSpec.scala index 727d1ab80e4..a47f31e288a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ClassSimplexCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ClassSimplexCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.ClassSimplexCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ColorJitterSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ColorJitterSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ColorJitterSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ColorJitterSpec.scala index ea764c9c94e..ab12c58fde8 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ColorJitterSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ColorJitterSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl._ @@ -29,7 +29,7 @@ import com.intel.analytics.bigdl.dataset.image.{ColorJitter, LabeledBGRImage} import com.intel.analytics.bigdl.utils.RandomGenerator @com.intel.analytics.bigdl.tags.Serial -class ColorJitterSpec extends FlatSpec with BeforeAndAfter with Matchers { +class ColorJitterSpec extends TorchSpec { "A ColorJitter" should "blend image correctly" in { if (!TH.hasTorch()) { cancel("Torch is not installed") diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConcatSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConcatSpec.scala index 2d7c11bdf0c..ec089cf262e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConcatSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl._ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConcatTableSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConcatTableSpec.scala index d2c3ecf4666..6ef4537ba84 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConcatTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConcatTableSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{ConcatTable, Linear} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ContiguousSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ContiguousSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ContiguousSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ContiguousSpec.scala index bd72ee59b4a..59ae7e20dd0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ContiguousSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ContiguousSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Contiguous import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConvLSTMPeephole3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConvLSTMPeephole3DSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConvLSTMPeephole3DSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConvLSTMPeephole3DSpec.scala index dc7327e903c..7153f7f7b21 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ConvLSTMPeephole3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ConvLSTMPeephole3DSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineDistanceCriterionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineDistanceCriterionSpec.scala index 67ce1eaec2c..8ab990c9008 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineDistanceCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.CosineDistanceCriterion import com.intel.analytics.bigdl.tensor.{Storage, Tensor} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineDistanceSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineDistanceSpec.scala index fafb9edf318..c001bdc772e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineDistanceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineDistanceSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.CosineDistance import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineEmbeddingCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineEmbeddingCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineEmbeddingCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineEmbeddingCriterionSpec.scala index 1b0dca724de..779ddf08314 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineEmbeddingCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineEmbeddingCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.CosineEmbeddingCriterion import com.intel.analytics.bigdl.tensor.{Storage, Tensor} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineSpec.scala index 1de96c90f72..63da2f21202 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CosineSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CosineSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Cosine import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CrossEntropyCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CrossEntropyCriterionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CrossEntropyCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CrossEntropyCriterionSpec.scala index 7028ab444a2..db7441fa297 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/CrossEntropyCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/CrossEntropyCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.CrossEntropyCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DenseTensorMathSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DenseTensorMathSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DenseTensorMathSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DenseTensorMathSpec.scala index 33256505c60..39e009e8e83 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DenseTensorMathSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DenseTensorMathSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DistKLDivCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DistKLDivCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DistKLDivCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DistKLDivCriterionSpec.scala index 5e4e759483c..d89372f9bcb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DistKLDivCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DistKLDivCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.DistKLDivCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DropoutSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DropoutSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DropoutSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DropoutSpec.scala index fdfb08e2cc5..217d65ee7a6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/DropoutSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/DropoutSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Dropout import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ELUSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ELUSpec.scala index 7190f973b76..c907af14d1b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ELUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ELUSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.ELU import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/EuclideanSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/EuclideanSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/EuclideanSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/EuclideanSpec.scala index 647b0c1da45..d7ba2ca2125 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/EuclideanSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/EuclideanSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Euclidean import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ExpSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ExpSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ExpSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ExpSpec.scala index 4f35e94e336..590c4faa02f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ExpSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ExpSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Exp, Power} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GRUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GRUSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GRUSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GRUSpec.scala index 6fbed6dbdf8..ae196166ef6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GRUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GRUSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import java.io.PrintWriter @@ -29,24 +29,9 @@ import scala.collection.mutable.ArrayBuffer import scala.sys.process._ @com.intel.analytics.bigdl.tags.Serial -class GRUSpec extends TorchSpec { +class GRUSpec extends TorchRNNSpec { System.setProperty("bigdl.disableCheckSysEnv", "true") Engine.init(1, 1, true) - override def torchCheck(): Unit = { - if (!TH.hasTorch()) { - cancel("Torch is not installed") - } - val tmpFile = java.io.File.createTempFile("checkRNN", ".lua") - val writer = new PrintWriter(tmpFile) - writer.write("exist = (pcall(require, 'rnn'))\n print(exist)") - writer.close() - - val existsRNN = - Seq(System.getProperty("torch_location", "th"), tmpFile.getAbsolutePath).!!.trim - if (!existsRNN.contains("true")) { - cancel("Torch rnn is not installed") - } - } "A GRU" should " be fast" in { val inputSize = 1000 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GaussianCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GaussianCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GaussianCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GaussianCriterionSpec.scala index 4434973f995..88c4da8ba30 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GaussianCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GaussianCriterionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.GaussianCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GradientReversalSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GradientReversalSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GradientReversalSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GradientReversalSpec.scala index b14efbfcbb8..bce45ebade5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/GradientReversalSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/GradientReversalSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.GradientReversal import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardShrinkSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HardShrinkSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardShrinkSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HardShrinkSpec.scala index 6bf8245c3d8..339bbba090b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardShrinkSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HardShrinkSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.HardShrink import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HardTanhSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HardTanhSpec.scala index 98844446abe..b9f7eb04dd5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HardTanhSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HardTanhSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.HardTanh import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HingeEmbeddingCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HingeEmbeddingCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HingeEmbeddingCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HingeEmbeddingCriterionSpec.scala index 16a1bd672af..dbcfba58a99 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/HingeEmbeddingCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/HingeEmbeddingCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.HingeEmbeddingCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/IndexSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/IndexSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/IndexSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/IndexSpec.scala index 7288e95a9e3..7e487d30784 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/IndexSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/IndexSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Index import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/JoinTableSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/JoinTableSpec.scala index 2b38e906557..916d2b1d0f6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/JoinTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/JoinTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1CostSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1CostSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1CostSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1CostSpec.scala index 205fd5e81e3..50c3684b368 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1CostSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1CostSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.L1Cost import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1HingeEmbeddingCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1HingeEmbeddingCriterionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1HingeEmbeddingCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1HingeEmbeddingCriterionSpec.scala index e233224f4f1..8a476bbcad4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1HingeEmbeddingCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1HingeEmbeddingCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.L1HingeEmbeddingCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1PenaltySpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1PenaltySpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1PenaltySpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1PenaltySpec.scala index e3f82e1d903..b3db5f23c71 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/L1PenaltySpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/L1PenaltySpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.L1Penalty import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LSTMPeepholeSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LSTMPeepholeSpec.scala index f5c9d637c24..c618143c837 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMPeepholeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LSTMPeepholeSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import java.io.PrintWriter @@ -32,22 +32,7 @@ import scala.reflect.ClassTag import scala.sys.process._ @com.intel.analytics.bigdl.tags.Serial -class LSTMPeepholeSpec extends TorchSpec { - override def torchCheck(): Unit = { - if (!TH.hasTorch()) { - cancel("Torch is not installed") - } - val tmpFile = java.io.File.createTempFile("checkRNN", ".lua") - val writer = new PrintWriter(tmpFile) - writer.write("exist = (pcall(require, 'rnn'))\n print(exist)") - writer.close() - - val existsRNN = - Seq(System.getProperty("torch_location", "th"), tmpFile.getAbsolutePath).!!.trim - if (!existsRNN.contains("true")) { - cancel("Torch rnn is not installed") - } - } +class LSTMPeepholeSpec extends TorchRNNSpec { "A LSTMPeephole" should " be fast" in { val inputSize = 300 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LSTMSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LSTMSpec.scala index 63334b00a6c..69701faf9ae 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LSTMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LSTMSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import java.io.PrintWriter @@ -28,23 +28,7 @@ import com.intel.analytics.bigdl.utils.T import scala.sys.process._ @com.intel.analytics.bigdl.tags.Serial -class LSTMSpec extends TorchSpec { - override def torchCheck(): Unit = { - if (!TH.hasTorch()) { - cancel("Torch is not installed") - } - val tmpFile = java.io.File.createTempFile("checkRNN", ".lua") - val writer = new PrintWriter(tmpFile) - writer.write("exist = (pcall(require, 'rnn'))\n print(exist)") - writer.close() - - val existsRNN = - Seq(System.getProperty("torch_location", "th"), tmpFile.getAbsolutePath).!!.trim - if (!existsRNN.contains("true")) { - cancel("Torch rnn is not installed") - } - } - +class LSTMSpec extends TorchRNNSpec { "A LSTM dropout " should "works correctly" in { val inputSize = 6 val hiddenSize = 5 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LeakyReLUSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LeakyReLUSpec.scala index b911f0d31bb..48c55576d50 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LeakyReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LeakyReLUSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{LeakyReLU, RReLU} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LinearSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LinearSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LinearSpec.scala index 5b7002d8b6a..da1026ffdd4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LinearSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{GradientChecker, Linear, MSECriterion} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSigmoidSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSigmoidSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSigmoidSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSigmoidSpec.scala index 7efa335ceb7..1728dd222f6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSigmoidSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSigmoidSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.LogSigmoid import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSoftMaxSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSoftMaxSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSoftMaxSpec.scala index 1c8f265ca0f..878cca92400 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSoftMaxSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.{GradientChecker, LogSoftMax} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSpec.scala index 771918dbed6..6c588ada2cb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LogSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LogSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Log, Power} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LookupTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LookupTableSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LookupTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LookupTableSpec.scala index 6cceb7014ef..ef73a7b6d31 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/LookupTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/LookupTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MMSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MMSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MMSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MMSpec.scala index 85ba224f5d9..fb857f034b3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MMSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MMSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MM import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MSECriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MSECriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MSECriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MSECriterionSpec.scala index 59ebf175291..8f64332e68f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MSECriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MSECriterionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MSECriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MVSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MVSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MVSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MVSpec.scala index e7558584f24..6d20e25c17d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MVSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MVSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MV import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MarginCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MarginCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MarginCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MarginCriterionSpec.scala index 50f2f3c74fc..a06e2872fa2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MarginCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MarginCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MarginCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MarginRankingCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MarginRankingCriterionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MarginRankingCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MarginRankingCriterionSpec.scala index 8acd3edb99d..d590466add9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MarginRankingCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MarginRankingCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MarginRankingCriterion import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.utils.Table diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MaskedSelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MaskedSelectSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MaskedSelectSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MaskedSelectSpec.scala index 11dfbb59c1c..78baaac59b9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MaskedSelectSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MaskedSelectSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MaskedSelect import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MaxSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MaxSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MaxSpec.scala index 53383ef9f36..638f253e7cb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MaxSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Max import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MeanSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MeanSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MeanSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MeanSpec.scala index a9e270369a3..93f68074e3f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MeanSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MeanSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Mean import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MinSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MinSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MinSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MinSpec.scala index 5e7795aa715..5c096c5c1d9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MinSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MinSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Min import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MixtureTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MixtureTableSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MixtureTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MixtureTableSpec.scala index 7abd6fc6153..8bc6f457dbb 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MixtureTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MixtureTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MixtureTable import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ModuleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ModuleSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ModuleSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ModuleSpec.scala index d2d8450ce4e..e0dba5077dc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ModuleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ModuleSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Linear, Sequential} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MulConstantSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MulConstantSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MulConstantSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MulConstantSpec.scala index b9f358efaa1..830807e4770 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MulConstantSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MulConstantSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MulConstant import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MulSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MulSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MulSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MulSpec.scala index 331d1c188c9..33a031ab12d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MulSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MulSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Mul import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiCriterionSpec.scala index 8b0102ed507..b9bab2de80b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl._ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelMarginCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiLabelMarginCriterionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelMarginCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiLabelMarginCriterionSpec.scala index ffc3bce9bb3..f6ddbc9baaf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelMarginCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiLabelMarginCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MultiLabelMarginCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelSoftMarginCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiLabelSoftMarginCriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelSoftMarginCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiLabelSoftMarginCriterionSpec.scala index 1bb3f548851..87ed0175a00 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiLabelSoftMarginCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiLabelSoftMarginCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MultiLabelSoftMarginCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiMarginCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiMarginCriterionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiMarginCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiMarginCriterionSpec.scala index c281f58f118..ca15d251d83 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/MultiMarginCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/MultiMarginCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.MultiMarginCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NarrowSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NarrowSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NarrowSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NarrowSpec.scala index 733ef8fb02a..d7644425cc9 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NarrowSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NarrowSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Narrow import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NarrowTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NarrowTableSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NarrowTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NarrowTableSpec.scala index b39408a7384..00cc3929f9e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NarrowTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NarrowTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.NarrowTable import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NormalizeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NormalizeSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NormalizeSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NormalizeSpec.scala index ea959f6d54c..8a2d0b882fc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/NormalizeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/NormalizeSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Normalize import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PReLUSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PReLUSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PReLUSpec.scala index 350588b618c..1fec340c6d7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PReLUSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.PReLU import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PaddingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PaddingSpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PaddingSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PaddingSpec.scala index 1edb31bdac9..7ec621672dc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PaddingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PaddingSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Padding import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PairwiseDistanceSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PairwiseDistanceSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PairwiseDistanceSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PairwiseDistanceSpec.scala index 2f844f2a2c0..c5d5e1cfb29 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PairwiseDistanceSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PairwiseDistanceSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.PairwiseDistance import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ParallelCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ParallelCriterionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ParallelCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ParallelCriterionSpec.scala index d750fac69c2..8b0118a1344 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ParallelCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ParallelCriterionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, MSECriterion, ParallelCriterion} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PowerSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PowerSpec.scala index 345ca2e0182..64a02deb182 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/PowerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/PowerSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Power} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/RReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/RReLUSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/RReLUSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/RReLUSpec.scala index a123e4e42b5..f7907ee670a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/RReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/RReLUSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{RReLU, ReLU} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReLU6Spec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReLU6Spec.scala index 9a1fc1554c9..ec90c8ecc88 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLU6Spec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReLU6Spec.scala @@ -13,12 +13,16 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch + +import java.io.File import com.intel.analytics.bigdl.nn.ReLU6 import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.Engine +import org.scalatest._ +import scala.collection.mutable.ListBuffer import scala.math._ @com.intel.analytics.bigdl.tags.Serial diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLUSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReLUSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLUSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReLUSpec.scala index 674a116bad5..82df831a0ed 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReLUSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReLUSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{GradientChecker, ReLU} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReplicateSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReplicateSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReplicateSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReplicateSpec.scala index 0a1462bd606..4cd68f31e49 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReplicateSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReplicateSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Replicate import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReshapeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReshapeSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReshapeSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReshapeSpec.scala index 8e8bc9d1958..b553a0fa403 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ReshapeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ReshapeSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Reshape import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SamplerSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SamplerSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SamplerSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SamplerSpec.scala index 6f719ef8ea2..3eb16bf9f41 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SamplerSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SamplerSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{KLDCriterion, GaussianSampler} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SelectSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SelectSpec.scala index e13bcab4fe4..06ea0027fa4 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SelectSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Select import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SelectTableSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SelectTableSpec.scala index a8e09e2fe9d..3eb9e5ddaa7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SelectTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SelectTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SelectTable import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SequentialSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SequentialSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SequentialSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SequentialSpec.scala index f37c74aec1d..d81b740a863 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SequentialSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SequentialSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Linear, Sequential} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SigmoidSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SigmoidSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SigmoidSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SigmoidSpec.scala index 75b30aa5f06..fe3d6a32de1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SigmoidSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SigmoidSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Sigmoid import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SmoothL1CriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SmoothL1CriterionSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SmoothL1CriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SmoothL1CriterionSpec.scala index 1d2eb73e67e..72598958468 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SmoothL1CriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SmoothL1CriterionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SmoothL1Criterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMarginCriterionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMarginCriterionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMarginCriterionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMarginCriterionSpec.scala index 015fa75e6e8..a335df50a38 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMarginCriterionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMarginCriterionSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SoftMarginCriterion import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMaxSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMaxSpec.scala index 71780722265..374154dc8fa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMaxSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SoftMax import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMinSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMinSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMinSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMinSpec.scala index f31fa641060..734b9b7a4a6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftMinSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftMinSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SoftMin import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftPlusSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftPlusSpec.scala index 8c9f818390d..b4297159379 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftPlusSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftPlusSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SoftPlus import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftShrinkSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftShrinkSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftShrinkSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftShrinkSpec.scala index 03d3240caf1..31ccc255c77 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftShrinkSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftShrinkSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SoftShrink import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftSignSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftSignSpec.scala index 8ccb818462a..81025ca7280 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SoftSignSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SoftSignSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SoftSign import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialAveragePoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialAveragePoolingSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialAveragePoolingSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialAveragePoolingSpec.scala index 598fdbaf50f..4dd7b52262e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialAveragePoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialAveragePoolingSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{GradientChecker, SpatialAveragePooling} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialBatchNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialBatchNormalizationSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialBatchNormalizationSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialBatchNormalizationSpec.scala index 96caae78753..24e35c23290 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialBatchNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialBatchNormalizationSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import breeze.numerics.abs import com.intel.analytics.bigdl.nn.{BatchNormalization, GradientChecker, SpatialBatchNormalization} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialContrastiveNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialContrastiveNormalizationSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialContrastiveNormalizationSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialContrastiveNormalizationSpec.scala index 69d7c58cf37..e7be06686b0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialContrastiveNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialContrastiveNormalizationSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Sequential, SpatialContrastiveNormalization} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionMapSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialConvolutionMapSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionMapSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialConvolutionMapSpec.scala index d0cd9c1067c..1400c7fd69b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionMapSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialConvolutionMapSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialConvolutionSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialConvolutionSpec.scala index aced2ecf2d7..6d9cf2f38aa 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialConvolutionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.{Sequential, SpatialConvolution} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialCrossMapLRNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialCrossMapLRNSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialCrossMapLRNSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialCrossMapLRNSpec.scala index 05044260617..8db374a52c3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialCrossMapLRNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialCrossMapLRNSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.tensor.Tensor @@ -128,7 +128,7 @@ class SpatialCrossMapLRNSpec extends TorchSpec { val layer = new SpatialCrossMapLRN[Double](5, 1.0, 0.75, 1.0) - val tmpFile = java.io.File.createTempFile("module", ".t7") + val tmpFile = java.io.File.createTempFile("module", ".t7." + suffix, TH.resultsRoot.toFile) val absolutePath = tmpFile.getAbsolutePath layer.saveTorch(absolutePath, true) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDilatedConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDilatedConvolutionSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDilatedConvolutionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDilatedConvolutionSpec.scala index 90534bc9729..564aede48d5 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDilatedConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDilatedConvolutionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDivisiveNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDivisiveNormalizationSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDivisiveNormalizationSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDivisiveNormalizationSpec.scala index d5d34abbc83..e64bcfaa1a2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDivisiveNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDivisiveNormalizationSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Sequential, SpatialDivisiveNormalization} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout1DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout1DSpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout1DSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout1DSpec.scala index c42ee945ccc..a21d2e0d9c6 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout1DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout1DSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{SpatialDropout1D, SpatialDropout2D} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout2DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout2DSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout2DSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout2DSpec.scala index e9a338d1a19..219bada7302 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout2DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout2DSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SpatialDropout2D import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout3DSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout3DSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout3DSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout3DSpec.scala index 90a2bfc7dca..50dfe93d5ed 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialDropout3DSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialDropout3DSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SpatialDropout3D import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialFullConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialFullConvolutionSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialFullConvolutionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialFullConvolutionSpec.scala index 5675860b1c8..2f2704bef9a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialFullConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialFullConvolutionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Sequential, SpatialFullConvolution} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialMaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialMaxPoolingSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialMaxPoolingSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialMaxPoolingSpec.scala index 1139cdf9346..ec81550e96d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialMaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialMaxPoolingSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{GradientChecker, SpatialMaxPooling} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialSubtractiveNormalizationSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialSubtractiveNormalizationSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialSubtractiveNormalizationSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialSubtractiveNormalizationSpec.scala index be0025b4aab..1e997da470d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SpatialSubtractiveNormalizationSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SpatialSubtractiveNormalizationSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{Sequential, SpatialSubtractiveNormalization} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SplitTableSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SplitTableSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SplitTableSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SplitTableSpec.scala index a9ae8fbdfa5..6c9bd44d212 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SplitTableSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SplitTableSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.SplitTable import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SqrtSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SqrtSpec.scala index c1ead4637d7..3f47fde2dfc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqrtSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SqrtSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Sqrt import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SquareSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SquareSpec.scala index 569f44dab2a..8a3a4228ee3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SquareSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SquareSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Square import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SqueezeSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqueezeSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SqueezeSpec.scala index d669a9acd18..5e1afb608c0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SqueezeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SqueezeSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Squeeze import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SumSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SumSpec.scala index b7e40bf9800..17053002d5d 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/SumSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/SumSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Sum import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TH.scala similarity index 51% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TH.scala index f152afcd740..0f0562dad11 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TH.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TH.scala @@ -14,16 +14,18 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import java.io._ -import java.util.Random +import java.nio.file.{Files, Path, Paths} import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor._ import com.intel.analytics.bigdl.utils.TorchObject._ import com.intel.analytics.bigdl.utils.{File, Table} +import org.apache.commons.lang.SerializationUtils +import scala.collection.immutable.ListMap import scala.io.Source import scala.sys.process._ @@ -42,19 +44,57 @@ object TH { return exitValue == 0 } + def hasRNN: Boolean = { + val tmpFile = java.io.File.createTempFile("checkRNN", ".lua", scriptsRoot.toFile) + val writer = new PrintWriter(tmpFile) + writer.write("exist = (pcall(require, 'rnn'))\n print(exist)") + writer.close() + + val existsRNN = + Seq(System.getProperty("torch_location", "th"), tmpFile.getAbsolutePath).!!.trim + + existsRNN.contains("true") + } + + private def getRoot(subDir: String): Path = { + val tmpDir = System.getProperty("java.io.tmpdir") + val root = Paths.get(tmpDir, subDir) + if (Files.notExists(root)) { + Files.createDirectory(root) + } + root + } + + val resultsRoot: Path = { + getRoot("torch-results") + } + + val scriptsRoot: Path = { + getRoot("torch-scripts") + } + + val inputsRoot: Path = { + getRoot("torch-inputs") + } + + val timeSuffix: String = ".time" + // Run with map def run(code: String, parameters: Map[String, Any], - result: Array[String]): (Double, Map[String, Any]) = { - val suffix = ".t7" + (new Random()).nextLong() - val tmpFile = java.io.File.createTempFile("UnitTest", "lua") - val absolutePath = tmpFile.getAbsolutePath - val subPath = absolutePath.substring(0, absolutePath.lastIndexOf(java.io.File.separator) + 1) + result: Array[String])(implicit id: TestCaseIdentity): (Double, Map[String, Any]) = { + val suffix = id.suffix var resultMap: Map[String, Any] = Map() - val luaTime = runNM(code: String, parameters: Map[String, Any], result: Array[String], suffix) + val luaTime = if (isExists(result, id.suffix)) { + val path = Paths.get(resultsRoot.toAbsolutePath.toString, suffix + timeSuffix).toString + File.load[Array[Double]](path).head + } else { + runNM(code: String, parameters: Map[String, Any], result: Array[String], suffix) + } result.foreach { k => - val tmp: Any = File.loadTorch(subPath + k + suffix) + val subPath = Paths.get(resultsRoot.toAbsolutePath.toString, k + suffix).toString + val tmp: Any = File.loadTorch(subPath) resultMap += (k -> tmp) } @@ -62,7 +102,7 @@ object TH { } def run(path: java.nio.file.Path, parameters: Map[String, Tensor[Double]], - result: Array[String]): (Double, Map[String, Any]) = { + result: Array[String])(implicit id: TestCaseIdentity): (Double, Map[String, Any]) = { val code = new StringBuilder("") Source.fromFile(path.toString()).foreach { k => code.append(k) @@ -73,28 +113,46 @@ object TH { // Run without map def runNM(code: String, parameters: Map[String, Any], result: Array[String], suffix: String) : Double = { + if (isExists(result, suffix)) { + val luaTime = { + val path = Paths.get(resultsRoot.toString, suffix + timeSuffix) + File.load[Array[Double]](path.toString).head + } + + return luaTime // stop early + } + val varCode = new StringBuilder("require 'nn'\n" + "require 'optim'\n") val usrCode = new StringBuilder("") val resCode = new StringBuilder("") // Variable load code of lua parameters.keys.foreach { k => - val tmp = java.io.File.createTempFile(k + "Tmp", suffix) - val tmpPath = tmp.getAbsolutePath + // sometimes the k is too short, createTempFile will failed. + // so we just need to swap the k and suffix + val tmp = try { + java.io.File.createTempFile(k, suffix, inputsRoot.toFile) + } catch { + case illegalArgumentException: IllegalArgumentException => + java.io.File.createTempFile(suffix, k, inputsRoot.toFile) + case iOException: IOException => throw iOException + } + + val inputsPath = tmp.getAbsolutePath parameters(k) match { case _: Tensor[_] => if (parameters(k).asInstanceOf[Tensor[_]].getType() == FloatType) { - File.saveTorch(parameters(k), tmpPath, TYPE_FLOAT_TENSOR, true) + File.saveTorch(parameters(k), inputsPath, TYPE_FLOAT_TENSOR, true) } else { - File.saveTorch(parameters(k), tmpPath, TYPE_DOUBLE_TENSOR, true) + File.saveTorch(parameters(k), inputsPath, TYPE_DOUBLE_TENSOR, true) } case _: AbstractModule[_, _, _] => - File.saveTorch(parameters(k), tmpPath, TYPE_MODULE, true) + File.saveTorch(parameters(k), inputsPath, TYPE_MODULE, true) case _: Table => - File.saveTorch(parameters(k).asInstanceOf[Table], tmpPath, TYPE_TABLE, true) + File.saveTorch(parameters(k).asInstanceOf[Table], inputsPath, TYPE_TABLE, true) case _ => } - varCode.append(k + " = torch.load(\'" + tmpPath + "\')\n") + varCode.append(k + " = torch.load(\'" + inputsPath + "\')\n") } // Read from user`s code @@ -102,13 +160,12 @@ object TH { usrCode.append(code) usrCode.append("\nluaTime = Timer:time().real\nprint(luaTime)") - val tmpFile = java.io.File.createTempFile("UnitTest", "lua") - val absolutePath = tmpFile.getAbsolutePath - val subPath = absolutePath.substring(0, absolutePath.lastIndexOf(java.io.File.separator) + 1) + val tmpFile = java.io.File.createTempFile("UnitTest", "lua", scriptsRoot.toFile) // Result save code of lua result.foreach { k => - resCode.append("torch.save(\'" + subPath + k + suffix + "\', " + k + ")\n") + val savePath = Paths.get(resultsRoot.toAbsolutePath.toString, k + suffix) + resCode.append("torch.save(\'" + savePath + "\', " + k + ")\n") } val writer = new PrintWriter(tmpFile) println("\n============== lua code start ==============\n") @@ -121,8 +178,6 @@ object TH { writer.write(resCode.toString() + "\n\n") writer.close() - println(tmpFile.getAbsolutePath) - var luaTime = Seq(System.getProperty("torch_location", "th"), tmpFile.getAbsolutePath).!!.trim println("luaTime:" + luaTime) @@ -146,16 +201,29 @@ object TH { } } + val timePath = Paths.get(resultsRoot.toAbsolutePath.toString, suffix + timeSuffix) + File.save(Array[Double](luaTime.toDouble), timePath.toString) + luaTime.toDouble } // Single map def map(result: String, suffix: String): (Any) = { - val tmpFile = java.io.File.createTempFile("UnitTest", "lua") - val absolutePath = tmpFile.getAbsolutePath - val subPath = absolutePath.substring(0, absolutePath.lastIndexOf(java.io.File.separator) + 1) - val tmp: Any = File.loadTorch(subPath + result + suffix) + val subPath = Paths.get(resultsRoot.toAbsolutePath.toString, result + suffix) + val tmp: Any = File.loadTorch(subPath.toAbsolutePath.toString) tmp } + def isExists(results: Array[String], suffix: String): Boolean = { + val tensors = results.forall { result => + val path = Paths.get(resultsRoot.toAbsolutePath.toString, result + suffix) + Files.exists(path) + } + val time = { + val path = Paths.get(resultsRoot.toAbsolutePath.toString, suffix + timeSuffix) + Files.exists(path) + } + + tensors && time + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TanhShrinkSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TanhShrinkSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TanhShrinkSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TanhShrinkSpec.scala index 9a557756393..3a42c7a1314 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TanhShrinkSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TanhShrinkSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.TanhShrink import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TanhSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TanhSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TanhSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TanhSpec.scala index 8cc2b799205..d11f277e378 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TanhSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TanhSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Tanh import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TemporalConvolutionSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalConvolutionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TemporalConvolutionSpec.scala index eb0eed46842..0dda050312c 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TemporalConvolutionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.TemporalConvolution diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalMaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TemporalMaxPoolingSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalMaxPoolingSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TemporalMaxPoolingSpec.scala index e8604c5d98a..94b070bc635 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TemporalMaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TemporalMaxPoolingSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.{GradientChecker, TemporalMaxPooling} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TensorSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TensorSpec.scala similarity index 96% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TensorSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TensorSpec.scala index 03149e5e4b2..84070935227 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TensorSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TensorSpec.scala @@ -13,7 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ThresholdSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ThresholdSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ThresholdSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ThresholdSpec.scala index 4bb571bd576..978c420a786 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ThresholdSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ThresholdSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Threshold import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TorchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TorchSpec.scala new file mode 100644 index 00000000000..ad8d6d1b386 --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TorchSpec.scala @@ -0,0 +1,65 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package com.intel.analytics.bigdl.integration.torch +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import org.scalatest._ + +import scala.util.Random + +case class TestCaseIdentity(value: String) { + def suffix: String = List(".t7", value).mkString(".") +} + +class TorchSpec extends FlatSpec with BeforeAndAfter with Matchers { + + implicit var testCaseIdentity: TestCaseIdentity = _ + + before { + testCaseIdentity = null + } + + override def withFixture(test: NoArgTest): Outcome = { + Random.setSeed(1) + RNG.setSeed(100) + + // the identity name is class name + test case name + val id = List(this.getClass.getName, test.name.hashCode).mkString("_") + testCaseIdentity = TestCaseIdentity(id) + super.withFixture(test) + } + + protected def suffix: String = { + testCaseIdentity.suffix + } + + def torchCheck(): Unit = { + if (!TH.hasTorch()) { + cancel("Torch is not installed") + } + } +} + +class TorchRNNSpec extends TorchSpec { + override def torchCheck(): Unit = { + if (!TH.hasTorch()) { + cancel("Torch is not installed") + } + + if (!TH.hasRNN) { + cancel("Torch rnn is not installed") + } + } +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TransposeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TransposeSpec.scala similarity index 98% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TransposeSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TransposeSpec.scala index 7070b97f9eb..581b811302f 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TransposeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/TransposeSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Transpose import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/UnsqueezeSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/UnsqueezeSpec.scala index 99f8bd4dbee..6ca4b90932b 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/UnsqueezeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/UnsqueezeSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.Unsqueeze import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ViewSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ViewSpec.scala similarity index 97% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ViewSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ViewSpec.scala index f51509e8130..21f7346b79a 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/ViewSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/ViewSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.{GradientChecker, View} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricAveragePoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricAveragePoolingSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricAveragePoolingSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricAveragePoolingSpec.scala index 23e51e6d7d6..e1f0ee15805 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricAveragePoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricAveragePoolingSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.{GradientChecker, VolumetricAveragePooling} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricConvolutionSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricConvolutionSpec.scala index 734ded0d1d3..db6cffe13da 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricConvolutionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn._ diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricFullConvolutionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricFullConvolutionSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricFullConvolutionSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricFullConvolutionSpec.scala index 4ff715626f6..cfb72abf1ab 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricFullConvolutionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricFullConvolutionSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl.nn.{BilinearFiller, Sequential, VolumetricFullConvolution, Zeros} import com.intel.analytics.bigdl.tensor.Tensor diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricMaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricMaxPoolingSpec.scala similarity index 99% rename from scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricMaxPoolingSpec.scala rename to scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricMaxPoolingSpec.scala index ec6026553eb..a92822caaea 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/VolumetricMaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/VolumetricMaxPoolingSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.integration.torch import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.{GradientChecker, VolumetricMaxPooling} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/models/AlexNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/models/AlexNetSpec.scala new file mode 100644 index 00000000000..fca2515ab9c --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/models/AlexNetSpec.scala @@ -0,0 +1,312 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.integration.torch.models + +import com.intel.analytics.bigdl.Module +import com.intel.analytics.bigdl.example.loadmodel.AlexNet_OWT +import com.intel.analytics.bigdl.integration.torch.{TH, TorchSpec} +import com.intel.analytics.bigdl.nn.ClassNLLCriterion +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.optim.SGD +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.T + +import scala.math._ +import scala.util.Random + +class AlexNetSpec extends TorchSpec { + "AlexNet float" should "generate correct output" in { + torchCheck() + + Random.setSeed(1) + val input = Tensor[Double](8, 3, 224, 224).apply1(e => Random.nextDouble()) + val labels = Tensor[Double](8).apply1(e => Random.nextInt(100)) + + val seed = 100 + RNG.setSeed(seed) + val model = AlexNet_OWT(1000, false, true) + model.zeroGradParameters() + + + val code = "torch.manualSeed(" + seed + ")\n" + + """local nClasses = 1000 +local feature = nn.Sequential() +feature:add(nn.SpatialConvolutionMM(3,64,11,11,4,4,2,2)) -- 224 -> 55 +feature:add(nn.ReLU()) +feature:add(nn.SpatialMaxPooling(3,3,2,2)) -- 55 -> 27 +feature:add(nn.SpatialConvolutionMM(64,192,5,5,1,1,2,2)) -- 27 -> 27 +feature:add(nn.ReLU()) +feature:add(nn.SpatialMaxPooling(3,3,2,2)) -- 27 -> 13 +feature:add(nn.SpatialConvolutionMM(192,384,3,3,1,1,1,1)) -- 13 -> 13 +feature:add(nn.ReLU()) +feature:add(nn.SpatialConvolutionMM(384,256,3,3,1,1,1,1)) -- 13 -> 13 +feature:add(nn.ReLU()) +feature:add(nn.SpatialConvolutionMM(256,256,3,3,1,1,1,1)) -- 13 -> 13 +feature:add(nn.ReLU()) +feature:add(nn.SpatialMaxPooling(3,3,2,2)) -- 13 -> 6 + +-- 1.3. Create Classifier (fully connected layers) +local classifier = nn.Sequential() +classifier:add(nn.View(256*6*6)) +--classifier:add(nn.Dropout(0.5)) +classifier:add(nn.Linear(256*6*6, 4096)) +classifier:add(nn.ReLU()) +--classifier:add(nn.Dropout(0.5)) +classifier:add(nn.Linear(4096, 4096)) +classifier:add(nn.ReLU()) +classifier:add(nn.Linear(4096, nClasses)) +classifier:add(nn.LogSoftMax()) + + +-- 1.4. Combine 1.1 and 1.3 to produce final model +model = nn.Sequential():add(feature):add(classifier) + +local parameters, gradParameters = model:getParameters() +model:zeroGradParameters() +parameters_initial = parameters : clone() +gradParameters_initial = gradParameters : clone() + +local criterion = nn.ClassNLLCriterion() + +state = { + learningRate = 1e-2, + momentum = 0.9, + dampening = 0.0, + weightDecay = 5e-4 +} + +feval = function(x) +model:zeroGradParameters() +model_initial = model : clone() + +local output1 = model:forward(input) +local err1 = criterion:forward(output1, labels) +local gradOutput1 = criterion:backward(output1, labels) +model:backward(input, gradOutput1) +return err1, gradParameters +end + +for i = 1,1,1 do + optim.sgd(feval, parameters, state) +end + +output=model.output +err=criterion.output +gradOutput=criterion.gradInput +gradInput = model.gradInput + """ + + TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", + "parameters_initial", "gradParameters_initial", "gradInput", "model"), suffix) + + val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] + val parameters = model.getParameters()._1.asInstanceOf[Tensor[Float]] + + for (i <- 0 until parameters.nElement()) { + if (abs(parameters.storage().array()(i) - parameterTorch.storage().array()(i)) > 1e-8) { + println(s"${parameters.storage().array()(i)} ${parameterTorch.storage().array()(i)}") + } + } + + val criterion = new ClassNLLCriterion[Float]() + val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, + "dampening" -> 0.0) + val sgd = new SGD[Float] + + val floatInput = Tensor[Float](8, 3, 224, 224) + val floatLabel = Tensor[Float](8) + + for (i <- 0 until floatInput.nElement()) { + floatInput.storage().array()(i) = input.storage().array()(i).toFloat + } + for (i <- 0 until floatLabel.nElement()) { + floatLabel.storage().array()(i) = labels.storage().array()(i).toFloat + } + + model.zeroGradParameters() + val output = TH.map("output", suffix).asInstanceOf[Tensor[Double]] + val outputTest = model.forward(floatInput).toTensor + var abss = 0.0 + for (i <- 0 until outputTest.nElement()) { + val tmp = abs(outputTest.storage().array()(i) - output.storage().array()(i)) + abss += tmp + } + assert(abss < 1e-2) + println(s"outputAbs:$abss") + + val errTest = criterion.forward(outputTest, floatLabel) + val err = TH.map("err", suffix).asInstanceOf[Double] + println(s"${abs(errTest - err)}") + assert(abs(errTest - err) < 1e-6) + + val gradOutputTest = criterion.backward(outputTest, floatLabel).toTensor + val gradOutput = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] + abss = 0.0 + for (i <- 0 until gradOutputTest.nElement()) { + val tmp = abs(gradOutputTest.storage().array()(i) - gradOutput.storage().array()(i)) + abss += tmp + } + assert(abss == 0.0) + println(s"gradOutputTestAbs:$abss") + + val gradInput = model.backward(floatInput, gradOutputTest).toTensor[Float] + val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Double]] + + abss = 0.0 + for (i <- 0 until gradInputTorch.nElement()) { + val tmp = abs(gradInputTorch.storage().array()(i) - gradInput.storage().array()(i)) + abss += tmp + } + println(s"gradInputTestAbs:$abss") + + val (weights, grad) = model.getParameters() + val modelTorch = TH.map("model", suffix).asInstanceOf[Module[Double]] + val (weightsTorch, gradTorch) = modelTorch.getParameters() + sgd.optimize(_ => (errTest, grad), weights, state, state) + abss = 0.0 + for (i <- 0 until weights.nElement()) { + val tmp = abs(weights.storage().array()(i) - weightsTorch.storage().array()(i)) + abss += tmp + } + assert(abss < 2e-2) + } + + "AlexNet Float save to torch" should "generate correct output" in { + torchCheck() + + Random.setSeed(1) + val input = Tensor[Float](8, 3, 224, 224).apply1(e => Random.nextFloat()) + val labels = Tensor[Float](8).apply1(e => Random.nextInt(100)) + + val seed = 100 + RNG.setSeed(seed) + val model = AlexNet_OWT(1000, false, true) + model.zeroGradParameters() + + + val code = "torch.manualSeed(" + seed + ")\n" + + """local nClasses = 1000 +torch.setdefaulttensortype('torch.FloatTensor') + +local parameters, gradParameters = model:getParameters() +model:zeroGradParameters() +parameters_initial = parameters : clone() +gradParameters_initial = gradParameters : clone() + +local criterion = nn.ClassNLLCriterion() + +state = { + learningRate = 1e-2, + momentum = 0.9, + dampening = 0.0, + weightDecay = 5e-4 +} +feval = function(x) + model:zeroGradParameters() + model_initial = model : clone() + local output1 = model:forward(input) + local err1 = criterion:forward(output1, labels) + local gradOutput1 = criterion:backward(output1, labels) + model:backward(input, gradOutput1) + return err1, gradParameters +end + +for i = 1,5,1 do + optim.sgd(feval, parameters, state) +end + +output=model.output +err=criterion.output +gradOutput=criterion.gradInput +gradInput = model.gradInput + """ + + TH.runNM(code, Map("model" -> model, "input" -> input, "labels" -> labels), + Array("output", "gradOutput", "err", + "parameters_initial", "gradParameters_initial", "gradInput", "model"), suffix) + + val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Float]] + val parameters = model.getParameters()._1.asInstanceOf[Tensor[Float]] + + parameterTorch should be (parameters) + + val criterion = new ClassNLLCriterion[Float]() + val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, + "dampening" -> 0.0) + val sgd = new SGD[Float] + val (weights, grad) = model.getParameters() + + for (i <- 1 to 4) { + model.zeroGradParameters() + val outputtest = model.forward(input).toTensor[Float] + val loss = criterion.forward(outputtest, labels) + val gradoutputtest = criterion.backward(outputtest, labels) + model.backward(input, gradoutputtest) + sgd.optimize(_ => (loss, grad), weights, state, state) + } + + model.zeroGradParameters() + val output = TH.map("output", suffix).asInstanceOf[Tensor[Float]] + val outputTest = model.forward(input).toTensor + var abss = 0.0 + for (i <- 0 until outputTest.nElement()) { + val tmp = abs(outputTest.storage().array()(i) - output.storage().array()(i)) + abss += tmp + } + assert(abss < 1e-2) + println(s"outputAbs:$abss") + + val errTest = criterion.forward(outputTest, labels) + val err = TH.map("err", suffix).asInstanceOf[Double] + println(s"err:${abs(errTest - err)}") + assert(abs(errTest - err) < 1e-6) + + val gradOutputTest = criterion.backward(outputTest, labels).toTensor + val gradOutput = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Float]] + abss = 0.0 + for (i <- 0 until gradOutputTest.nElement()) { + val tmp = abs(gradOutputTest.storage().array()(i) - gradOutput.storage().array()(i)) + abss += tmp + } + assert(abss == 0.0) + println(s"gradOutputTestAbs:$abss") + + val gradInput = model.backward(input, gradOutputTest).toTensor[Float] + val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Float]] + + abss = 0.0 + for (i <- 0 until gradInputTorch.nElement()) { + val tmp = abs(gradInputTorch.storage().array()(i) - gradInput.storage().array()(i)) + abss += tmp + } + println(s"gradInputTestAbs:$abss") + + val modelTorch = TH.map("model", suffix).asInstanceOf[Module[Float]] + val (weightsTorch, gradTorch) = modelTorch.getParameters() + sgd.optimize(_ => (errTest, grad), weights, state, state) + abss = 0.0 + for (i <- 0 until weights.nElement()) { + val tmp = abs(weights.storage().array()(i) - weightsTorch.storage().array()(i)) + abss += tmp + } + assert(abss < 2e-2) + } + + + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/models/InceptionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/models/InceptionSpec.scala new file mode 100644 index 00000000000..6a9dced23aa --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/integration/torch/models/InceptionSpec.scala @@ -0,0 +1,784 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.integration.torch.models + +import com.intel.analytics.bigdl.models.inception._ +import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Graph, Input} +import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule +import com.intel.analytics.bigdl.optim.SGD +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.integration.torch.{TH, TorchSpec} +import com.intel.analytics.bigdl.models.Inception +import com.intel.analytics.bigdl.utils.RandomGenerator._ +import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.numeric.NumericFloat + +import scala.math._ +import scala.util.Random + +class InceptionSpec extends TorchSpec { + "Inception+bn" should "generate correct output" in { + torchCheck() + + Random.setSeed(4) + val input = Tensor[Double](4, 3, 224, 224).apply1(e => Random.nextDouble()) + val labels = Tensor[Double](4).apply1(e => Random.nextInt(1000)) + + val seed = 890 + RNG.setSeed(seed) + + val code = "torch.manualSeed(" + seed + ")\n" + + """ + local nClasses = 1000 + local function inception(input_size, config) + local concat = nn.Concat(2) + if config[1][1] ~= 0 then + local conv1 = nn.Sequential() + conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)) + conv1:add(nn.SpatialBatchNormalization(config[1][1],1e-3)) + conv1:add(nn.ReLU(true)) + concat:add(conv1) + end + local conv3 = nn.Sequential() + conv3:add(nn.SpatialConvolution(input_size, config[2][1],1,1,1,1)) + conv3:add(nn.SpatialBatchNormalization(config[2][1],1e-3)) + conv3:add(nn.ReLU(true)) + conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)) + conv3:add(nn.SpatialBatchNormalization(config[2][2],1e-3)) + conv3:add(nn.ReLU(true)) + concat:add(conv3) + local conv3xx = nn.Sequential() + conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][1],1e-3)) + conv3xx:add(nn.ReLU(true)) + conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) + conv3xx:add(nn.ReLU(true)) + conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) + conv3xx:add(nn.ReLU(true)) + concat:add(conv3xx) + local pool = nn.Sequential() + pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode + if config[4][1] == 'max' then + pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil()) + elseif config[4][1] == 'avg' then + pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil()) + else + error('Unknown pooling') + end + if config[4][2] ~= 0 then + pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)) + pool:add(nn.SpatialBatchNormalization(config[4][2],1e-3)) + pool:add(nn.ReLU(true)) + end + concat:add(pool) + return concat + end + local features = nn.Sequential() + features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)) + features:add(nn.SpatialBatchNormalization(64,1e-3)) + features:add(nn.ReLU(true)) + features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) + features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true)) + features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)) + features:add(nn.SpatialBatchNormalization(192,1e-3)) + features:add(nn.ReLU(true)) + features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) + features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a) + features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b) + features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c) + features:add(nn.SpatialConvolution(576,576,2,2,2,2)) + features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a) + features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b) + features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c) + features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d) + local main_branch = nn.Sequential() + main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e) + main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2)) + main_branch:add(nn.SpatialBatchNormalization(1024,1e-3)) + main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a) + main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b) + main_branch:add(nn.SpatialAveragePooling(7,7,1,1)) + main_branch:add(nn.View(1024):setNumInputDims(3)) + main_branch:add(nn.Linear(1024,nClasses)) + main_branch:add(nn.LogSoftMax()) + -- add auxillary classifier here (thanks to Christian Szegedy for the details) + local aux_classifier = nn.Sequential() + aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil()) + aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1)) + aux_classifier:add(nn.SpatialBatchNormalization(128,1e-3)) + aux_classifier:add(nn.View(128*4*4):setNumInputDims(3)) + aux_classifier:add(nn.Linear(128*4*4,768)) + aux_classifier:add(nn.ReLU(true)) + aux_classifier:add(nn.Linear(768,nClasses)) + aux_classifier:add(nn.LogSoftMax()) + local splitter = nn.Concat(2) + splitter:add(main_branch):add(aux_classifier) + local model = nn.Sequential():add(features):add(splitter) + parameters, gradParameters = model:getParameters() + model:zeroGradParameters() + parameters_initial = parameters : clone() + gradParameters_initial = gradParameters : clone() + criterion = nn.ClassNLLCriterion() + state = { + learningRate = 1e-2, + momentum = 0.9, + dampening = 0.0, + weightDecay = 5e-4 + } + feval = function(x) + model:zeroGradParameters() + model_initial = model : clone() + local output1 = model:forward(input) + local err1 = criterion:forward(output1, labels) + local gradOutput1 = criterion:backward(output1, labels) + model:backward(input, gradOutput1) + return err1, gradParameters + end + w, err = optim.sgd(feval, parameters, state) + output=model.output + gradOutput=criterion.gradInput + gradInput = model.gradInput + model2=model:get(2) + parameters, gradParameters = model:getParameters() + """ + + TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", + "parameters_initial", "gradParameters_initial", "gradParameters", "parameters", "model2"), + suffix) + + val model = Inception.getModel[Double](1000, "inception-bn") + + val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] + println(s"model size: ${parameters.nElement()}") + val parametersInitTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] + require(parameters == parametersInitTorch, "parameter compare failed") + + val gradGarametersInitTorch = TH.map("gradParameters_initial", suffix) + .asInstanceOf[Tensor[Double]] + val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] + require(gradparameters == gradGarametersInitTorch, "gradparameter compare failed") + + val (weights, grad) = model.getParameters() + val criterion = new ClassNLLCriterion[Double]() + val sgd = new SGD[Double] + val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, + "dampening" -> 0.0) + + model.zeroGradParameters() + val outputTest = model.forward(input).toTensor[Double] + val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] + outputTest shouldEqual outputTorch + + val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) + val errTest = criterion.forward(outputTest, labels) + println(s"err:${abs(errTest - errTorch)}") + assert(abs(errTest - errTorch) < 4e-15) + + val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] + val gradOutputTest = criterion.backward(outputTest, labels) + model.backward(input, gradOutputTest) + gradOutputTest shouldEqual gradOutputTorch + + sgd.optimize(_ => (errTest, grad), weights, state, state) + + val gradParametersTorch = TH.map("gradParameters", suffix).asInstanceOf[Tensor[Double]] + grad.equals(gradParametersTorch) should be (true) + val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] + parameters.equals(parametersTorch) should be (true) + } + + "Inception" should "generate correct output" in { + torchCheck() + + Random.setSeed(3) + val input = Tensor[Double](4, 3, 224, 224).apply1(e => Random.nextDouble()) + val labels = Tensor[Double](4).apply1(e => Random.nextInt(1000)) + + val seed = 100 + RNG.setSeed(seed) + val model = Inception.getModel[Double](1000, "inception") + + val code = "torch.manualSeed(" + seed + ")\n" + + """ + local nClasses = 1000 + local function inception(input_size, config) + local concat = nn.Concat(2) + if config[1][1] ~= 0 then + local conv1 = nn.Sequential() + conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)) + conv1:add(nn.ReLU(true)) + concat:add(conv1) + end + + local conv3 = nn.Sequential() + conv3:add(nn.SpatialConvolution( input_size, config[2][1],1,1,1,1)) + conv3:add(nn.ReLU(true)) + + conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)) + conv3:add(nn.ReLU(true)) + + concat:add(conv3) + + local conv3xx = nn.Sequential() + conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)) + conv3xx:add(nn.ReLU(true)) + + conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)) + conv3xx:add(nn.ReLU(true)) + + conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)) + conv3xx:add(nn.ReLU(true)) + concat:add(conv3xx) + + local pool = nn.Sequential() + pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode + if config[4][1] == 'max' then + pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil()) + elseif config[4][1] == 'avg' then + pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil()) + else + error('Unknown pooling') + end + if config[4][2] ~= 0 then + pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)) + pool:add(nn.ReLU(true)) + + end + concat:add(pool) + + return concat + end + + + local features = nn.Sequential() + features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)) + features:add(nn.ReLU(true)) + features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) + features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true)) + features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)) + features:add(nn.ReLU(true)) + features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) + features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a) + features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b) + features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c) + features:add(nn.SpatialConvolution(576,576,2,2,2,2)) + features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a) + features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b) + features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c) + features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d) + + local main_branch = nn.Sequential() + main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e) + main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2)) + --main_branch:add(nn.SpatialBatchNormalization(1024,1e-3)) + + main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a) + main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b) + main_branch:add(nn.SpatialAveragePooling(7,7,1,1)) + main_branch:add(nn.View(1024):setNumInputDims(3)) + main_branch:add(nn.Linear(1024,nClasses)) + main_branch:add(nn.LogSoftMax()) + + -- add auxillary classifier here (thanks to Christian Szegedy for the details) + local aux_classifier = nn.Sequential() + aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil()) + aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1)) + --aux_classifier:add(nn.SpatialBatchNormalization(128,1e-3)) + + aux_classifier:add(nn.View(128*4*4):setNumInputDims(3)) + aux_classifier:add(nn.Linear(128*4*4,768)) + aux_classifier:add(nn.ReLU(true)) + aux_classifier:add(nn.Linear(768,nClasses)) + aux_classifier:add(nn.LogSoftMax()) + + local splitter = nn.Concat(2) + splitter:add(main_branch):add(aux_classifier) + local model = nn.Sequential():add(features):add(splitter) + + local parameters, gradParameters = model:getParameters() + model:zeroGradParameters() + parameters_initial = parameters : clone() + gradParameters_initial = gradParameters : clone() + + criterion = nn.ClassNLLCriterion() + + state = { + learningRate = 1e-2, + momentum = 0.9, + dampening = 0.0, + weightDecay = 5e-4 + } + + feval = function(x) + model:zeroGradParameters() + model_initial = model : clone() + + local output1 = model:forward(input) + local err1 = criterion:forward(output1, labels) + local gradOutput1 = criterion:backward(output1, labels) + model:backward(input, gradOutput1) + return err1, gradParameters + end + + for i = 1,5,1 do + w, err = optim.sgd(feval, parameters, state) + end + + output=model.output + gradOutput=criterion.gradInput + gradInput = model.gradInput + + """ + + TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", + "parameters_initial", "gradParameters_initial", "gradInput", "parameters"), suffix) + + val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Double]] + + val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] + model.zeroGradParameters() + println(s"model size: ${parameters.nElement()}") + val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] + require(parameters == parameterTorch, "parameter compare failed") + + val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] + val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] + val gradparameterTorch = TH.map("gradParameters_initial", suffix).asInstanceOf[Tensor[Double]] + require(gradparameters == gradparameterTorch, "gradparameter compare failed") + + val (weights, grad) = model.getParameters() + val criterion = new ClassNLLCriterion[Double]() + + val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, + "dampening" -> 0.0) + val sgd = new SGD[Double] + val epsilon = System.getProperty("DoubleTensorEpsilon", "0.0000001").toDouble + + for (i <- 1 to 4) { + model.zeroGradParameters() + val outputtest = model.forward(input).toTensor[Double] + val loss = criterion.forward(outputtest, labels) + val gradoutputtest = criterion.backward(outputtest, labels) + model.backward(input, gradoutputtest) + sgd.optimize(_ => (loss, grad), weights, state, state) + } + + model.zeroGradParameters() + var outputAbs = 0.0 + val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] + val outputTest = model.forward(input).toTensor[Double] + outputTest.map(outputTorch, (v1, v2) => { + outputAbs += abs(v1 - v2) + v1 + }) + println(s"outputAbs:$outputAbs") + + val errTest = criterion.forward(outputTest, labels) + val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) + println(s"err:${abs(errTest - errTorch)}") + assert(abs(errTest - errTorch) == 0) + + val gradOutputTest = criterion.backward(outputTest, labels) + val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] + gradOutputTest shouldEqual gradOutputTorch + + val gradInput = model.backward(input, gradOutputTest) + gradInput shouldEqual gradInputTorch + sgd.optimize(_ => (errTest, grad), weights, state, state) + } + + "load torch's Inception+bn" should "generate correct output" in { + torchCheck() + + Random.setSeed(4) + val input = Tensor[Double](4, 3, 224, 224).apply1(e => Random.nextDouble()) + val labels = Tensor[Double](4).apply1(e => Random.nextInt(1000)) + + val seed = 890 + RNG.setSeed(seed) + + val code = "torch.manualSeed(" + seed + ")\n" + + """ + local nClasses = 1000 + local function inception(input_size, config) + local concat = nn.Concat(2) + if config[1][1] ~= 0 then + local conv1 = nn.Sequential() + conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)) + conv1:add(nn.SpatialBatchNormalization(config[1][1],1e-3)) + conv1:add(nn.ReLU(true)) + concat:add(conv1) + end + local conv3 = nn.Sequential() + conv3:add(nn.SpatialConvolution(input_size, config[2][1],1,1,1,1)) + conv3:add(nn.SpatialBatchNormalization(config[2][1],1e-3)) + conv3:add(nn.ReLU(true)) + conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)) + conv3:add(nn.SpatialBatchNormalization(config[2][2],1e-3)) + conv3:add(nn.ReLU(true)) + concat:add(conv3) + local conv3xx = nn.Sequential() + conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][1],1e-3)) + conv3xx:add(nn.ReLU(true)) + conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) + conv3xx:add(nn.ReLU(true)) + conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) + conv3xx:add(nn.ReLU(true)) + concat:add(conv3xx) + local pool = nn.Sequential() + pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode + if config[4][1] == 'max' then + pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil()) + elseif config[4][1] == 'avg' then + pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil()) + else + error('Unknown pooling') + end + if config[4][2] ~= 0 then + pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)) + pool:add(nn.SpatialBatchNormalization(config[4][2],1e-3)) + pool:add(nn.ReLU(true)) + end + concat:add(pool) + return concat + end + local features = nn.Sequential() + features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)) + features:add(nn.SpatialBatchNormalization(64,1e-3)) + features:add(nn.ReLU(true)) + features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) + features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true)) + features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)) + features:add(nn.SpatialBatchNormalization(192,1e-3)) + features:add(nn.ReLU(true)) + features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) + features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a) + features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b) + features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c) + features:add(nn.SpatialConvolution(576,576,2,2,2,2)) + features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a) + features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b) + features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c) + features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d) + local main_branch = nn.Sequential() + main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e) + main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2)) + main_branch:add(nn.SpatialBatchNormalization(1024,1e-3)) + main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a) + main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b) + main_branch:add(nn.SpatialAveragePooling(7,7,1,1)) + main_branch:add(nn.View(1024):setNumInputDims(3)) + main_branch:add(nn.Linear(1024,nClasses)) + main_branch:add(nn.LogSoftMax()) + -- add auxillary classifier here (thanks to Christian Szegedy for the details) + local aux_classifier = nn.Sequential() + aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil()) + aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1)) + aux_classifier:add(nn.SpatialBatchNormalization(128,1e-3)) + aux_classifier:add(nn.View(128*4*4):setNumInputDims(3)) + aux_classifier:add(nn.Linear(128*4*4,768)) + aux_classifier:add(nn.ReLU(true)) + aux_classifier:add(nn.Linear(768,nClasses)) + aux_classifier:add(nn.LogSoftMax()) + local splitter = nn.Concat(2) + splitter:add(main_branch):add(aux_classifier) + local model = nn.Sequential():add(features):add(splitter) + local initModel = model:clone() + parameters, gradParameters = model:getParameters() + model:zeroGradParameters() + parameters_initial = parameters : clone() + gradParameters_initial = gradParameters : clone() + criterion = nn.ClassNLLCriterion() + state = { + learningRate = 1e-2, + momentum = 0.9, + dampening = 0.0, + weightDecay = 5e-4 + } + feval = function(x) + model:zeroGradParameters() + model_initial = model : clone() + local output1 = model:forward(input) + local err1 = criterion:forward(output1, labels) + local gradOutput1 = criterion:backward(output1, labels) + model:backward(input, gradOutput1) + return err1, gradParameters + end + w, err = optim.sgd(feval, parameters, state) + output=model.output + gradOutput=criterion.gradInput + gradInput = model.gradInput + parameters, gradParameters = model:getParameters() + """ + + TH.runNM(code, + Map("input" -> input, "labels" -> labels), + Array("output", "gradOutput", "err", "parameters_initial", "gradParameters_initial", + "gradParameters", "parameters", "initModel"), suffix) + + val model = TH.map("initModel", suffix). + asInstanceOf[AbstractModule[Tensor[Double], Tensor[Double], Double]] + + val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] + println(s"model size: ${parameters.nElement()}") + val parametersInitTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] + require(parameters == parametersInitTorch, "parameter compare failed") + + val gradGarametersInitTorch = TH.map("gradParameters_initial", suffix) + .asInstanceOf[Tensor[Double]] + val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] + require(gradparameters == gradGarametersInitTorch, "gradparameter compare failed") + + val (weights, grad) = model.getParameters() + val criterion = new ClassNLLCriterion[Double]() + val sgd = new SGD[Double] + val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, + "dampening" -> 0.0) + + model.zeroGradParameters() + val outputTest = model.forward(input) + val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] + outputTest shouldEqual outputTorch + + val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) + val errTest = criterion.forward(outputTest, labels) + println(s"err:${abs(errTest - errTorch)}") + assert(abs(errTest - errTorch) < 4e-10) + + val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] + val gradOutputTest = criterion.backward(outputTest, labels) + model.backward(input, gradOutputTest) + gradOutputTest shouldEqual gradOutputTorch + + sgd.optimize(_ => (errTest, grad), weights, state, state) + val gradParametersTorch = TH.map("gradParameters", suffix).asInstanceOf[Tensor[Double]] + grad == gradParametersTorch should be (true) + val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] + parameters == parametersTorch should be (true) + } + + "load torch's Inception+bn float version" should "generate correct output" in { + torchCheck() + + Random.setSeed(3) + val input = Tensor[Float](4, 3, 224, 224).apply1(e => Random.nextFloat()) + val labels = Tensor[Float](4).apply1(e => Random.nextInt(1000)) + + val seed = 100 + RNG.setSeed(seed) + + val code = "torch.manualSeed(" + seed + ")\n" + + """ + torch.setdefaulttensortype('torch.FloatTensor') + local nClasses = 1000 + local function inception(input_size, config) + local concat = nn.Concat(2) + if config[1][1] ~= 0 then + local conv1 = nn.Sequential() + conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)) + conv1:add(nn.SpatialBatchNormalization(config[1][1],1e-3)) + conv1:add(nn.ReLU(true)) + concat:add(conv1) + end + local conv3 = nn.Sequential() + conv3:add(nn.SpatialConvolution(input_size, config[2][1],1,1,1,1)) + conv3:add(nn.SpatialBatchNormalization(config[2][1],1e-3)) + conv3:add(nn.ReLU(true)) + conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)) + conv3:add(nn.SpatialBatchNormalization(config[2][2],1e-3)) + conv3:add(nn.ReLU(true)) + concat:add(conv3) + local conv3xx = nn.Sequential() + conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][1],1e-3)) + conv3xx:add(nn.ReLU(true)) + conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) + conv3xx:add(nn.ReLU(true)) + conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)) + conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) + conv3xx:add(nn.ReLU(true)) + concat:add(conv3xx) + local pool = nn.Sequential() + pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode + if config[4][1] == 'max' then + pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil()) + elseif config[4][1] == 'avg' then + pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil()) + else + error('Unknown pooling') + end + if config[4][2] ~= 0 then + pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)) + pool:add(nn.SpatialBatchNormalization(config[4][2],1e-3)) + pool:add(nn.ReLU(true)) + end + concat:add(pool) + return concat + end + local features = nn.Sequential() + features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)) + features:add(nn.SpatialBatchNormalization(64,1e-3)) + features:add(nn.ReLU(true)) + features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) + features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true)) + features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)) + features:add(nn.SpatialBatchNormalization(192,1e-3)) + features:add(nn.ReLU(true)) + features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) + features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a) + features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b) + features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c) + features:add(nn.SpatialConvolution(576,576,2,2,2,2)) + features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a) + features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b) + features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c) + features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d) + local main_branch = nn.Sequential() + main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e) + main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2)) + main_branch:add(nn.SpatialBatchNormalization(1024,1e-3)) + main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a) + main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b) + main_branch:add(nn.SpatialAveragePooling(7,7,1,1)) + main_branch:add(nn.View(1024):setNumInputDims(3)) + main_branch:add(nn.Linear(1024,nClasses)) + main_branch:add(nn.LogSoftMax()) + -- add auxillary classifier here (thanks to Christian Szegedy for the details) + local aux_classifier = nn.Sequential() + aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil()) + aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1)) + aux_classifier:add(nn.SpatialBatchNormalization(128,1e-3)) + aux_classifier:add(nn.View(128*4*4):setNumInputDims(3)) + aux_classifier:add(nn.Linear(128*4*4,768)) + aux_classifier:add(nn.ReLU(true)) + aux_classifier:add(nn.Linear(768,nClasses)) + aux_classifier:add(nn.LogSoftMax()) + local splitter = nn.Concat(2) + splitter:add(main_branch):add(aux_classifier) + local model = nn.Sequential():add(features):add(splitter) + local initModel = model:clone() + parameters, gradParameters = model:getParameters() + model:zeroGradParameters() + parameters_initial = parameters : clone() + gradParameters_initial = gradParameters : clone() + criterion = nn.ClassNLLCriterion() + state = { + learningRate = 1e-2, + momentum = 0.9, + dampening = 0.0, + weightDecay = 5e-4 + } + feval = function(x) + model:zeroGradParameters() + model_initial = model : clone() + local output1 = model:forward(input) + local err1 = criterion:forward(output1, labels) + local gradOutput1 = criterion:backward(output1, labels) + model:backward(input, gradOutput1) + return err1, gradParameters + end + """ + + TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("initModel"), suffix) + + val model = Inception.getModel[Float](1000, "inception-bn") + val model2 = TH.map("initModel", suffix). + asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]] + model2 should be (model) + + val (weights, grad) = model.getParameters() + val (weights2, grad2) = model2.getParameters() + // Notice: as a very small different with torch's init parameter, we need to copy the weight. + weights2.copy(weights) + val criterion = new ClassNLLCriterion[Float]() + val sgd = new SGD[Float] + val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, + "dampening" -> 0.0) + val state2 = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, + "dampening" -> 0.0) + + for (i <- 1 to 5) { + model.zeroGradParameters() + val outputtest = model.forward(input).toTensor[Float] + val loss = criterion.forward(outputtest, labels) + val gradoutputtest = criterion.backward(outputtest, labels) + val gradInput = model.backward(input, gradoutputtest) + sgd.optimize(_ => (loss, grad), weights, state, state) + + model2.zeroGradParameters() + val outputtest2 = model2.forward(input) + val loss2 = criterion.forward(outputtest, labels) + val gradoutputtest2 = criterion.backward(outputtest, labels) + val gradInput2 = model2.backward(input, gradoutputtest2) + sgd.optimize(_ => (loss2, grad2), weights2, state2, state2) + loss should be (loss2) + gradInput should be (gradInput2) + grad.equals(grad2) should be (true) + outputtest should be (outputtest2) + gradoutputtest should be (gradoutputtest2) + weights.equals(weights2) should be (true) + } + } + + "Inception ModelCaffe" should "init right" in { + RNG.setSeed(1024) + + Random.setSeed(1024) + + val input = Tensor[Float](4, 3, 224, 224).apply1(e => Random.nextFloat()) + val labels = Tensor[Float](4).apply1(e => Random.nextInt(1000)) + + val model = Inception.getModelCaffe[Float](1000) + + val criterion = new ClassNLLCriterion[Float]() + + model.zeroGradParameters() + val output = model.forward(input).toTensor[Float] + val loss = criterion.forward(output, labels) + + // since we already set the seed, the loss should match exactly + loss should be (6.893043f) + } + + "InceptionV1 " should "init right" in { + RNG.setSeed(1024) + + Random.setSeed(1024) + + val input = Tensor[Float](4, 3, 224, 224).apply1(e => Random.nextFloat()) + val labels = Tensor[Float](4).apply1(e => Random.nextInt(1000)) + + val model = Inception_v1(1000) + + val criterion = new ClassNLLCriterion[Float]() + + model.zeroGradParameters() + val output = model.forward(input).toTensor[Float] + val loss = criterion.forward(output, labels) + + // since we already set the seed, the loss should match exactly + loss should be (6.6648364f) + } + + +} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/AlexNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/AlexNetSpec.scala index c4dcf634151..e8e7b3b9846 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/AlexNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/AlexNetSpec.scala @@ -16,301 +16,16 @@ package com.intel.analytics.bigdl.models -import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.example.loadmodel.{AlexNet, AlexNet_OWT} -import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.numeric.NumericFloat -import com.intel.analytics.bigdl.optim.SGD -import com.intel.analytics.bigdl.tensor.{Tensor, _} -import com.intel.analytics.bigdl.torch.{TH, TorchSpec} +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.T +import org.scalatest.{FlatSpec, Matchers} -import scala.math._ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial -class AlexNetSpec extends TorchSpec { - private val suffix = ".t7" + (new java.util.Random()).nextLong() - - "AlexNet float" should "generate correct output" in { - torchCheck() - - Random.setSeed(1) - val input = Tensor[Double](8, 3, 224, 224).apply1(e => Random.nextDouble()) - val labels = Tensor[Double](8).apply1(e => Random.nextInt(100)) - - val seed = 100 - RNG.setSeed(seed) - val model = AlexNet_OWT(1000, false, true) - model.zeroGradParameters() - - - val code = "torch.manualSeed(" + seed + ")\n" + - """local nClasses = 1000 -local feature = nn.Sequential() -feature:add(nn.SpatialConvolutionMM(3,64,11,11,4,4,2,2)) -- 224 -> 55 -feature:add(nn.ReLU()) -feature:add(nn.SpatialMaxPooling(3,3,2,2)) -- 55 -> 27 -feature:add(nn.SpatialConvolutionMM(64,192,5,5,1,1,2,2)) -- 27 -> 27 -feature:add(nn.ReLU()) -feature:add(nn.SpatialMaxPooling(3,3,2,2)) -- 27 -> 13 -feature:add(nn.SpatialConvolutionMM(192,384,3,3,1,1,1,1)) -- 13 -> 13 -feature:add(nn.ReLU()) -feature:add(nn.SpatialConvolutionMM(384,256,3,3,1,1,1,1)) -- 13 -> 13 -feature:add(nn.ReLU()) -feature:add(nn.SpatialConvolutionMM(256,256,3,3,1,1,1,1)) -- 13 -> 13 -feature:add(nn.ReLU()) -feature:add(nn.SpatialMaxPooling(3,3,2,2)) -- 13 -> 6 - --- 1.3. Create Classifier (fully connected layers) -local classifier = nn.Sequential() -classifier:add(nn.View(256*6*6)) ---classifier:add(nn.Dropout(0.5)) -classifier:add(nn.Linear(256*6*6, 4096)) -classifier:add(nn.ReLU()) ---classifier:add(nn.Dropout(0.5)) -classifier:add(nn.Linear(4096, 4096)) -classifier:add(nn.ReLU()) -classifier:add(nn.Linear(4096, nClasses)) -classifier:add(nn.LogSoftMax()) - - --- 1.4. Combine 1.1 and 1.3 to produce final model -model = nn.Sequential():add(feature):add(classifier) - -local parameters, gradParameters = model:getParameters() -model:zeroGradParameters() -parameters_initial = parameters : clone() -gradParameters_initial = gradParameters : clone() - -local criterion = nn.ClassNLLCriterion() - -state = { - learningRate = 1e-2, - momentum = 0.9, - dampening = 0.0, - weightDecay = 5e-4 -} - -feval = function(x) -model:zeroGradParameters() -model_initial = model : clone() - -local output1 = model:forward(input) -local err1 = criterion:forward(output1, labels) -local gradOutput1 = criterion:backward(output1, labels) -model:backward(input, gradOutput1) -return err1, gradParameters -end - -for i = 1,1,1 do - optim.sgd(feval, parameters, state) -end - -output=model.output -err=criterion.output -gradOutput=criterion.gradInput -gradInput = model.gradInput - """ - - TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", - "parameters_initial", "gradParameters_initial", "gradInput", "model"), suffix) - - val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] - val parameters = model.getParameters()._1.asInstanceOf[Tensor[Float]] - - for (i <- 0 until parameters.nElement()) { - if (abs(parameters.storage().array()(i) - parameterTorch.storage().array()(i)) > 1e-8) { - println(s"${parameters.storage().array()(i)} ${parameterTorch.storage().array()(i)}") - } - } - - val criterion = new ClassNLLCriterion[Float]() - val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - val sgd = new SGD[Float] - - val floatInput = Tensor[Float](8, 3, 224, 224) - val floatLabel = Tensor[Float](8) - - for (i <- 0 until floatInput.nElement()) { - floatInput.storage().array()(i) = input.storage().array()(i).toFloat - } - for (i <- 0 until floatLabel.nElement()) { - floatLabel.storage().array()(i) = labels.storage().array()(i).toFloat - } - - model.zeroGradParameters() - val output = TH.map("output", suffix).asInstanceOf[Tensor[Double]] - val outputTest = model.forward(floatInput).toTensor - var abss = 0.0 - for (i <- 0 until outputTest.nElement()) { - val tmp = abs(outputTest.storage().array()(i) - output.storage().array()(i)) - abss += tmp - } - assert(abss < 1e-2) - println(s"outputAbs:$abss") - - val errTest = criterion.forward(outputTest, floatLabel) - val err = TH.map("err", suffix).asInstanceOf[Double] - println(s"${abs(errTest - err)}") - assert(abs(errTest - err) < 1e-6) - - val gradOutputTest = criterion.backward(outputTest, floatLabel).toTensor - val gradOutput = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] - abss = 0.0 - for (i <- 0 until gradOutputTest.nElement()) { - val tmp = abs(gradOutputTest.storage().array()(i) - gradOutput.storage().array()(i)) - abss += tmp - } - assert(abss == 0.0) - println(s"gradOutputTestAbs:$abss") - - val gradInput = model.backward(floatInput, gradOutputTest).toTensor[Float] - val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Double]] - - abss = 0.0 - for (i <- 0 until gradInputTorch.nElement()) { - val tmp = abs(gradInputTorch.storage().array()(i) - gradInput.storage().array()(i)) - abss += tmp - } - println(s"gradInputTestAbs:$abss") - - val (weights, grad) = model.getParameters() - val modelTorch = TH.map("model", suffix).asInstanceOf[Module[Double]] - val (weightsTorch, gradTorch) = modelTorch.getParameters() - sgd.optimize(_ => (errTest, grad), weights, state, state) - abss = 0.0 - for (i <- 0 until weights.nElement()) { - val tmp = abs(weights.storage().array()(i) - weightsTorch.storage().array()(i)) - abss += tmp - } - assert(abss < 2e-2) - } - - "AlexNet Float save to torch" should "generate correct output" in { - torchCheck() - - Random.setSeed(1) - val input = Tensor[Float](8, 3, 224, 224).apply1(e => Random.nextFloat()) - val labels = Tensor[Float](8).apply1(e => Random.nextInt(100)) - - val seed = 100 - RNG.setSeed(seed) - val model = AlexNet_OWT(1000, false, true) - model.zeroGradParameters() - - - val code = "torch.manualSeed(" + seed + ")\n" + - """local nClasses = 1000 -torch.setdefaulttensortype('torch.FloatTensor') - -local parameters, gradParameters = model:getParameters() -model:zeroGradParameters() -parameters_initial = parameters : clone() -gradParameters_initial = gradParameters : clone() - -local criterion = nn.ClassNLLCriterion() - -state = { - learningRate = 1e-2, - momentum = 0.9, - dampening = 0.0, - weightDecay = 5e-4 -} -feval = function(x) - model:zeroGradParameters() - model_initial = model : clone() - local output1 = model:forward(input) - local err1 = criterion:forward(output1, labels) - local gradOutput1 = criterion:backward(output1, labels) - model:backward(input, gradOutput1) - return err1, gradParameters -end - -for i = 1,5,1 do - optim.sgd(feval, parameters, state) -end - -output=model.output -err=criterion.output -gradOutput=criterion.gradInput -gradInput = model.gradInput - """ - - TH.runNM(code, Map("model" -> model, "input" -> input, "labels" -> labels), - Array("output", "gradOutput", "err", - "parameters_initial", "gradParameters_initial", "gradInput", "model"), suffix) - - val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Float]] - val parameters = model.getParameters()._1.asInstanceOf[Tensor[Float]] - - parameterTorch should be (parameters) - - val criterion = new ClassNLLCriterion[Float]() - val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - val sgd = new SGD[Float] - val (weights, grad) = model.getParameters() - - for (i <- 1 to 4) { - model.zeroGradParameters() - val outputtest = model.forward(input).toTensor[Float] - val loss = criterion.forward(outputtest, labels) - val gradoutputtest = criterion.backward(outputtest, labels) - model.backward(input, gradoutputtest) - sgd.optimize(_ => (loss, grad), weights, state, state) - } - - model.zeroGradParameters() - val output = TH.map("output", suffix).asInstanceOf[Tensor[Float]] - val outputTest = model.forward(input).toTensor - var abss = 0.0 - for (i <- 0 until outputTest.nElement()) { - val tmp = abs(outputTest.storage().array()(i) - output.storage().array()(i)) - abss += tmp - } - assert(abss < 1e-2) - println(s"outputAbs:$abss") - - val errTest = criterion.forward(outputTest, labels) - val err = TH.map("err", suffix).asInstanceOf[Double] - println(s"err:${abs(errTest - err)}") - assert(abs(errTest - err) < 1e-6) - - val gradOutputTest = criterion.backward(outputTest, labels).toTensor - val gradOutput = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Float]] - abss = 0.0 - for (i <- 0 until gradOutputTest.nElement()) { - val tmp = abs(gradOutputTest.storage().array()(i) - gradOutput.storage().array()(i)) - abss += tmp - } - assert(abss == 0.0) - println(s"gradOutputTestAbs:$abss") - - val gradInput = model.backward(input, gradOutputTest).toTensor[Float] - val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Float]] - - abss = 0.0 - for (i <- 0 until gradInputTorch.nElement()) { - val tmp = abs(gradInputTorch.storage().array()(i) - gradInput.storage().array()(i)) - abss += tmp - } - println(s"gradInputTestAbs:$abss") - - val modelTorch = TH.map("model", suffix).asInstanceOf[Module[Float]] - val (weightsTorch, gradTorch) = modelTorch.getParameters() - sgd.optimize(_ => (errTest, grad), weights, state, state) - abss = 0.0 - for (i <- 0 until weights.nElement()) { - val tmp = abs(weights.storage().array()(i) - weightsTorch.storage().array()(i)) - abss += tmp - } - assert(abss < 2e-2) - } - - +class AlexNetSpec extends FlatSpec with Matchers { "ALexNet_OWT graph" should "be same with original one" in { Random.setSeed(1) val batchSize = 4 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala index 17f02b00acb..3ba5bd8f84e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/InceptionSpec.scala @@ -17,771 +17,17 @@ package com.intel.analytics.bigdl.models import com.intel.analytics.bigdl.models.inception._ -import com.intel.analytics.bigdl.nn.{ClassNLLCriterion, Graph, Input} -import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule -import com.intel.analytics.bigdl.optim.SGD +import com.intel.analytics.bigdl.nn.{Graph, Input} +import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.torch.{TH, TorchSpec} import com.intel.analytics.bigdl.utils.RandomGenerator._ import com.intel.analytics.bigdl.utils.{T, Table} -import com.intel.analytics.bigdl.numeric.NumericFloat +import org.scalatest.{FlatSpec, Matchers} -import scala.math._ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial -class InceptionSpec extends TorchSpec { - private val suffix = ".t7" + (new java.util.Random()).nextLong() - - "Inception+bn" should "generate correct output" in { - torchCheck() - - Random.setSeed(4) - val input = Tensor[Double](4, 3, 224, 224).apply1(e => Random.nextDouble()) - val labels = Tensor[Double](4).apply1(e => Random.nextInt(1000)) - - val seed = 890 - RNG.setSeed(seed) - - val code = "torch.manualSeed(" + seed + ")\n" + - """ - local nClasses = 1000 - local function inception(input_size, config) - local concat = nn.Concat(2) - if config[1][1] ~= 0 then - local conv1 = nn.Sequential() - conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)) - conv1:add(nn.SpatialBatchNormalization(config[1][1],1e-3)) - conv1:add(nn.ReLU(true)) - concat:add(conv1) - end - local conv3 = nn.Sequential() - conv3:add(nn.SpatialConvolution(input_size, config[2][1],1,1,1,1)) - conv3:add(nn.SpatialBatchNormalization(config[2][1],1e-3)) - conv3:add(nn.ReLU(true)) - conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)) - conv3:add(nn.SpatialBatchNormalization(config[2][2],1e-3)) - conv3:add(nn.ReLU(true)) - concat:add(conv3) - local conv3xx = nn.Sequential() - conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][1],1e-3)) - conv3xx:add(nn.ReLU(true)) - conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) - conv3xx:add(nn.ReLU(true)) - conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) - conv3xx:add(nn.ReLU(true)) - concat:add(conv3xx) - local pool = nn.Sequential() - pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode - if config[4][1] == 'max' then - pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil()) - elseif config[4][1] == 'avg' then - pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil()) - else - error('Unknown pooling') - end - if config[4][2] ~= 0 then - pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)) - pool:add(nn.SpatialBatchNormalization(config[4][2],1e-3)) - pool:add(nn.ReLU(true)) - end - concat:add(pool) - return concat - end - local features = nn.Sequential() - features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)) - features:add(nn.SpatialBatchNormalization(64,1e-3)) - features:add(nn.ReLU(true)) - features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) - features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true)) - features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)) - features:add(nn.SpatialBatchNormalization(192,1e-3)) - features:add(nn.ReLU(true)) - features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) - features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a) - features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b) - features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c) - features:add(nn.SpatialConvolution(576,576,2,2,2,2)) - features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a) - features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b) - features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c) - features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d) - local main_branch = nn.Sequential() - main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e) - main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2)) - main_branch:add(nn.SpatialBatchNormalization(1024,1e-3)) - main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a) - main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b) - main_branch:add(nn.SpatialAveragePooling(7,7,1,1)) - main_branch:add(nn.View(1024):setNumInputDims(3)) - main_branch:add(nn.Linear(1024,nClasses)) - main_branch:add(nn.LogSoftMax()) - -- add auxillary classifier here (thanks to Christian Szegedy for the details) - local aux_classifier = nn.Sequential() - aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil()) - aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1)) - aux_classifier:add(nn.SpatialBatchNormalization(128,1e-3)) - aux_classifier:add(nn.View(128*4*4):setNumInputDims(3)) - aux_classifier:add(nn.Linear(128*4*4,768)) - aux_classifier:add(nn.ReLU(true)) - aux_classifier:add(nn.Linear(768,nClasses)) - aux_classifier:add(nn.LogSoftMax()) - local splitter = nn.Concat(2) - splitter:add(main_branch):add(aux_classifier) - local model = nn.Sequential():add(features):add(splitter) - parameters, gradParameters = model:getParameters() - model:zeroGradParameters() - parameters_initial = parameters : clone() - gradParameters_initial = gradParameters : clone() - criterion = nn.ClassNLLCriterion() - state = { - learningRate = 1e-2, - momentum = 0.9, - dampening = 0.0, - weightDecay = 5e-4 - } - feval = function(x) - model:zeroGradParameters() - model_initial = model : clone() - local output1 = model:forward(input) - local err1 = criterion:forward(output1, labels) - local gradOutput1 = criterion:backward(output1, labels) - model:backward(input, gradOutput1) - return err1, gradParameters - end - w, err = optim.sgd(feval, parameters, state) - output=model.output - gradOutput=criterion.gradInput - gradInput = model.gradInput - model2=model:get(2) - parameters, gradParameters = model:getParameters() - """ - - TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", - "parameters_initial", "gradParameters_initial", "gradParameters", "parameters", "model2"), - suffix) - - val model = Inception.getModel[Double](1000, "inception-bn") - - val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] - println(s"model size: ${parameters.nElement()}") - val parametersInitTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] - require(parameters == parametersInitTorch, "parameter compare failed") - - val gradGarametersInitTorch = TH.map("gradParameters_initial", suffix) - .asInstanceOf[Tensor[Double]] - val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] - require(gradparameters == gradGarametersInitTorch, "gradparameter compare failed") - - val (weights, grad) = model.getParameters() - val criterion = new ClassNLLCriterion[Double]() - val sgd = new SGD[Double] - val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - - model.zeroGradParameters() - val outputTest = model.forward(input).toTensor[Double] - val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] - outputTest shouldEqual outputTorch - - val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) - val errTest = criterion.forward(outputTest, labels) - println(s"err:${abs(errTest - errTorch)}") - assert(abs(errTest - errTorch) < 4e-15) - - val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] - val gradOutputTest = criterion.backward(outputTest, labels) - model.backward(input, gradOutputTest) - gradOutputTest shouldEqual gradOutputTorch - - sgd.optimize(_ => (errTest, grad), weights, state, state) - - val gradParametersTorch = TH.map("gradParameters", suffix).asInstanceOf[Tensor[Double]] - grad.equals(gradParametersTorch) should be (true) - val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] - parameters.equals(parametersTorch) should be (true) - } - - "Inception" should "generate correct output" in { - torchCheck() - - Random.setSeed(3) - val input = Tensor[Double](4, 3, 224, 224).apply1(e => Random.nextDouble()) - val labels = Tensor[Double](4).apply1(e => Random.nextInt(1000)) - - val seed = 100 - RNG.setSeed(seed) - val model = Inception.getModel[Double](1000, "inception") - - val code = "torch.manualSeed(" + seed + ")\n" + - """ - local nClasses = 1000 - local function inception(input_size, config) - local concat = nn.Concat(2) - if config[1][1] ~= 0 then - local conv1 = nn.Sequential() - conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)) - conv1:add(nn.ReLU(true)) - concat:add(conv1) - end - - local conv3 = nn.Sequential() - conv3:add(nn.SpatialConvolution( input_size, config[2][1],1,1,1,1)) - conv3:add(nn.ReLU(true)) - - conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)) - conv3:add(nn.ReLU(true)) - - concat:add(conv3) - - local conv3xx = nn.Sequential() - conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)) - conv3xx:add(nn.ReLU(true)) - - conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)) - conv3xx:add(nn.ReLU(true)) - - conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)) - conv3xx:add(nn.ReLU(true)) - concat:add(conv3xx) - - local pool = nn.Sequential() - pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode - if config[4][1] == 'max' then - pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil()) - elseif config[4][1] == 'avg' then - pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil()) - else - error('Unknown pooling') - end - if config[4][2] ~= 0 then - pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)) - pool:add(nn.ReLU(true)) - - end - concat:add(pool) - - return concat - end - - - local features = nn.Sequential() - features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)) - features:add(nn.ReLU(true)) - features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) - features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true)) - features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)) - features:add(nn.ReLU(true)) - features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) - features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a) - features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b) - features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c) - features:add(nn.SpatialConvolution(576,576,2,2,2,2)) - features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a) - features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b) - features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c) - features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d) - - local main_branch = nn.Sequential() - main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e) - main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2)) - --main_branch:add(nn.SpatialBatchNormalization(1024,1e-3)) - - main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a) - main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b) - main_branch:add(nn.SpatialAveragePooling(7,7,1,1)) - main_branch:add(nn.View(1024):setNumInputDims(3)) - main_branch:add(nn.Linear(1024,nClasses)) - main_branch:add(nn.LogSoftMax()) - - -- add auxillary classifier here (thanks to Christian Szegedy for the details) - local aux_classifier = nn.Sequential() - aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil()) - aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1)) - --aux_classifier:add(nn.SpatialBatchNormalization(128,1e-3)) - - aux_classifier:add(nn.View(128*4*4):setNumInputDims(3)) - aux_classifier:add(nn.Linear(128*4*4,768)) - aux_classifier:add(nn.ReLU(true)) - aux_classifier:add(nn.Linear(768,nClasses)) - aux_classifier:add(nn.LogSoftMax()) - - local splitter = nn.Concat(2) - splitter:add(main_branch):add(aux_classifier) - local model = nn.Sequential():add(features):add(splitter) - - local parameters, gradParameters = model:getParameters() - model:zeroGradParameters() - parameters_initial = parameters : clone() - gradParameters_initial = gradParameters : clone() - - criterion = nn.ClassNLLCriterion() - - state = { - learningRate = 1e-2, - momentum = 0.9, - dampening = 0.0, - weightDecay = 5e-4 - } - - feval = function(x) - model:zeroGradParameters() - model_initial = model : clone() - - local output1 = model:forward(input) - local err1 = criterion:forward(output1, labels) - local gradOutput1 = criterion:backward(output1, labels) - model:backward(input, gradOutput1) - return err1, gradParameters - end - - for i = 1,5,1 do - w, err = optim.sgd(feval, parameters, state) - end - - output=model.output - gradOutput=criterion.gradInput - gradInput = model.gradInput - - """ - - TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("output", "gradOutput", "err", - "parameters_initial", "gradParameters_initial", "gradInput", "parameters"), suffix) - - val gradInputTorch = TH.map("gradInput", suffix).asInstanceOf[Tensor[Double]] - - val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] - model.zeroGradParameters() - println(s"model size: ${parameters.nElement()}") - val parameterTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] - require(parameters == parameterTorch, "parameter compare failed") - - val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] - val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] - val gradparameterTorch = TH.map("gradParameters_initial", suffix).asInstanceOf[Tensor[Double]] - require(gradparameters == gradparameterTorch, "gradparameter compare failed") - - val (weights, grad) = model.getParameters() - val criterion = new ClassNLLCriterion[Double]() - - val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - val sgd = new SGD[Double] - val epsilon = System.getProperty("DoubleTensorEpsilon", "0.0000001").toDouble - - for (i <- 1 to 4) { - model.zeroGradParameters() - val outputtest = model.forward(input).toTensor[Double] - val loss = criterion.forward(outputtest, labels) - val gradoutputtest = criterion.backward(outputtest, labels) - model.backward(input, gradoutputtest) - sgd.optimize(_ => (loss, grad), weights, state, state) - } - - model.zeroGradParameters() - var outputAbs = 0.0 - val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] - val outputTest = model.forward(input).toTensor[Double] - outputTest.map(outputTorch, (v1, v2) => { - outputAbs += abs(v1 - v2) - v1 - }) - println(s"outputAbs:$outputAbs") - - val errTest = criterion.forward(outputTest, labels) - val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) - println(s"err:${abs(errTest - errTorch)}") - assert(abs(errTest - errTorch) == 0) - - val gradOutputTest = criterion.backward(outputTest, labels) - val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] - gradOutputTest shouldEqual gradOutputTorch - - val gradInput = model.backward(input, gradOutputTest) - gradInput shouldEqual gradInputTorch - sgd.optimize(_ => (errTest, grad), weights, state, state) - } - - "load torch's Inception+bn" should "generate correct output" in { - torchCheck() - - Random.setSeed(4) - val input = Tensor[Double](4, 3, 224, 224).apply1(e => Random.nextDouble()) - val labels = Tensor[Double](4).apply1(e => Random.nextInt(1000)) - - val seed = 890 - RNG.setSeed(seed) - - val code = "torch.manualSeed(" + seed + ")\n" + - """ - local nClasses = 1000 - local function inception(input_size, config) - local concat = nn.Concat(2) - if config[1][1] ~= 0 then - local conv1 = nn.Sequential() - conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)) - conv1:add(nn.SpatialBatchNormalization(config[1][1],1e-3)) - conv1:add(nn.ReLU(true)) - concat:add(conv1) - end - local conv3 = nn.Sequential() - conv3:add(nn.SpatialConvolution(input_size, config[2][1],1,1,1,1)) - conv3:add(nn.SpatialBatchNormalization(config[2][1],1e-3)) - conv3:add(nn.ReLU(true)) - conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)) - conv3:add(nn.SpatialBatchNormalization(config[2][2],1e-3)) - conv3:add(nn.ReLU(true)) - concat:add(conv3) - local conv3xx = nn.Sequential() - conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][1],1e-3)) - conv3xx:add(nn.ReLU(true)) - conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) - conv3xx:add(nn.ReLU(true)) - conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) - conv3xx:add(nn.ReLU(true)) - concat:add(conv3xx) - local pool = nn.Sequential() - pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode - if config[4][1] == 'max' then - pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil()) - elseif config[4][1] == 'avg' then - pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil()) - else - error('Unknown pooling') - end - if config[4][2] ~= 0 then - pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)) - pool:add(nn.SpatialBatchNormalization(config[4][2],1e-3)) - pool:add(nn.ReLU(true)) - end - concat:add(pool) - return concat - end - local features = nn.Sequential() - features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)) - features:add(nn.SpatialBatchNormalization(64,1e-3)) - features:add(nn.ReLU(true)) - features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) - features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true)) - features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)) - features:add(nn.SpatialBatchNormalization(192,1e-3)) - features:add(nn.ReLU(true)) - features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) - features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a) - features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b) - features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c) - features:add(nn.SpatialConvolution(576,576,2,2,2,2)) - features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a) - features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b) - features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c) - features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d) - local main_branch = nn.Sequential() - main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e) - main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2)) - main_branch:add(nn.SpatialBatchNormalization(1024,1e-3)) - main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a) - main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b) - main_branch:add(nn.SpatialAveragePooling(7,7,1,1)) - main_branch:add(nn.View(1024):setNumInputDims(3)) - main_branch:add(nn.Linear(1024,nClasses)) - main_branch:add(nn.LogSoftMax()) - -- add auxillary classifier here (thanks to Christian Szegedy for the details) - local aux_classifier = nn.Sequential() - aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil()) - aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1)) - aux_classifier:add(nn.SpatialBatchNormalization(128,1e-3)) - aux_classifier:add(nn.View(128*4*4):setNumInputDims(3)) - aux_classifier:add(nn.Linear(128*4*4,768)) - aux_classifier:add(nn.ReLU(true)) - aux_classifier:add(nn.Linear(768,nClasses)) - aux_classifier:add(nn.LogSoftMax()) - local splitter = nn.Concat(2) - splitter:add(main_branch):add(aux_classifier) - local model = nn.Sequential():add(features):add(splitter) - local initModel = model:clone() - parameters, gradParameters = model:getParameters() - model:zeroGradParameters() - parameters_initial = parameters : clone() - gradParameters_initial = gradParameters : clone() - criterion = nn.ClassNLLCriterion() - state = { - learningRate = 1e-2, - momentum = 0.9, - dampening = 0.0, - weightDecay = 5e-4 - } - feval = function(x) - model:zeroGradParameters() - model_initial = model : clone() - local output1 = model:forward(input) - local err1 = criterion:forward(output1, labels) - local gradOutput1 = criterion:backward(output1, labels) - model:backward(input, gradOutput1) - return err1, gradParameters - end - w, err = optim.sgd(feval, parameters, state) - output=model.output - gradOutput=criterion.gradInput - gradInput = model.gradInput - parameters, gradParameters = model:getParameters() - """ - - TH.runNM(code, - Map("input" -> input, "labels" -> labels), - Array("output", "gradOutput", "err", "parameters_initial", "gradParameters_initial", - "gradParameters", "parameters", "initModel"), suffix) - - val model = TH.map("initModel", suffix). - asInstanceOf[AbstractModule[Tensor[Double], Tensor[Double], Double]] - - val parameters = model.getParameters()._1.asInstanceOf[Tensor[Double]] - println(s"model size: ${parameters.nElement()}") - val parametersInitTorch = TH.map("parameters_initial", suffix).asInstanceOf[Tensor[Double]] - require(parameters == parametersInitTorch, "parameter compare failed") - - val gradGarametersInitTorch = TH.map("gradParameters_initial", suffix) - .asInstanceOf[Tensor[Double]] - val gradparameters = model.getParameters()._2.asInstanceOf[Tensor[Double]] - require(gradparameters == gradGarametersInitTorch, "gradparameter compare failed") - - val (weights, grad) = model.getParameters() - val criterion = new ClassNLLCriterion[Double]() - val sgd = new SGD[Double] - val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - - model.zeroGradParameters() - val outputTest = model.forward(input) - val outputTorch = TH.map("output", suffix).asInstanceOf[Tensor[Double]] - outputTest shouldEqual outputTorch - - val errTorch = TH.map("err", suffix).asInstanceOf[Table][Double](1) - val errTest = criterion.forward(outputTest, labels) - println(s"err:${abs(errTest - errTorch)}") - assert(abs(errTest - errTorch) < 4e-10) - - val gradOutputTorch = TH.map("gradOutput", suffix).asInstanceOf[Tensor[Double]] - val gradOutputTest = criterion.backward(outputTest, labels) - model.backward(input, gradOutputTest) - gradOutputTest shouldEqual gradOutputTorch - - sgd.optimize(_ => (errTest, grad), weights, state, state) - val gradParametersTorch = TH.map("gradParameters", suffix).asInstanceOf[Tensor[Double]] - grad == gradParametersTorch should be (true) - val parametersTorch = TH.map("parameters", suffix).asInstanceOf[Tensor[Double]] - parameters == parametersTorch should be (true) - } - - "load torch's Inception+bn float version" should "generate correct output" in { - torchCheck() - - Random.setSeed(3) - val input = Tensor[Float](4, 3, 224, 224).apply1(e => Random.nextFloat()) - val labels = Tensor[Float](4).apply1(e => Random.nextInt(1000)) - - val seed = 100 - RNG.setSeed(seed) - - val code = "torch.manualSeed(" + seed + ")\n" + - """ - torch.setdefaulttensortype('torch.FloatTensor') - local nClasses = 1000 - local function inception(input_size, config) - local concat = nn.Concat(2) - if config[1][1] ~= 0 then - local conv1 = nn.Sequential() - conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)) - conv1:add(nn.SpatialBatchNormalization(config[1][1],1e-3)) - conv1:add(nn.ReLU(true)) - concat:add(conv1) - end - local conv3 = nn.Sequential() - conv3:add(nn.SpatialConvolution(input_size, config[2][1],1,1,1,1)) - conv3:add(nn.SpatialBatchNormalization(config[2][1],1e-3)) - conv3:add(nn.ReLU(true)) - conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)) - conv3:add(nn.SpatialBatchNormalization(config[2][2],1e-3)) - conv3:add(nn.ReLU(true)) - concat:add(conv3) - local conv3xx = nn.Sequential() - conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][1],1e-3)) - conv3xx:add(nn.ReLU(true)) - conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) - conv3xx:add(nn.ReLU(true)) - conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)) - conv3xx:add(nn.SpatialBatchNormalization(config[3][2],1e-3)) - conv3xx:add(nn.ReLU(true)) - concat:add(conv3xx) - local pool = nn.Sequential() - pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode - if config[4][1] == 'max' then - pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil()) - elseif config[4][1] == 'avg' then - pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil()) - else - error('Unknown pooling') - end - if config[4][2] ~= 0 then - pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)) - pool:add(nn.SpatialBatchNormalization(config[4][2],1e-3)) - pool:add(nn.ReLU(true)) - end - concat:add(pool) - return concat - end - local features = nn.Sequential() - features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)) - features:add(nn.SpatialBatchNormalization(64,1e-3)) - features:add(nn.ReLU(true)) - features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) - features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true)) - features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)) - features:add(nn.SpatialBatchNormalization(192,1e-3)) - features:add(nn.ReLU(true)) - features:add(nn.SpatialMaxPooling(3,3,2,2):ceil()) - features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a) - features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b) - features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c) - features:add(nn.SpatialConvolution(576,576,2,2,2,2)) - features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a) - features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b) - features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c) - features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d) - local main_branch = nn.Sequential() - main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e) - main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2)) - main_branch:add(nn.SpatialBatchNormalization(1024,1e-3)) - main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a) - main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b) - main_branch:add(nn.SpatialAveragePooling(7,7,1,1)) - main_branch:add(nn.View(1024):setNumInputDims(3)) - main_branch:add(nn.Linear(1024,nClasses)) - main_branch:add(nn.LogSoftMax()) - -- add auxillary classifier here (thanks to Christian Szegedy for the details) - local aux_classifier = nn.Sequential() - aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil()) - aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1)) - aux_classifier:add(nn.SpatialBatchNormalization(128,1e-3)) - aux_classifier:add(nn.View(128*4*4):setNumInputDims(3)) - aux_classifier:add(nn.Linear(128*4*4,768)) - aux_classifier:add(nn.ReLU(true)) - aux_classifier:add(nn.Linear(768,nClasses)) - aux_classifier:add(nn.LogSoftMax()) - local splitter = nn.Concat(2) - splitter:add(main_branch):add(aux_classifier) - local model = nn.Sequential():add(features):add(splitter) - local initModel = model:clone() - parameters, gradParameters = model:getParameters() - model:zeroGradParameters() - parameters_initial = parameters : clone() - gradParameters_initial = gradParameters : clone() - criterion = nn.ClassNLLCriterion() - state = { - learningRate = 1e-2, - momentum = 0.9, - dampening = 0.0, - weightDecay = 5e-4 - } - feval = function(x) - model:zeroGradParameters() - model_initial = model : clone() - local output1 = model:forward(input) - local err1 = criterion:forward(output1, labels) - local gradOutput1 = criterion:backward(output1, labels) - model:backward(input, gradOutput1) - return err1, gradParameters - end - """ - - TH.runNM(code, Map("input" -> input, "labels" -> labels), Array("initModel"), suffix) - - val model = Inception.getModel[Float](1000, "inception-bn") - val model2 = TH.map("initModel", suffix). - asInstanceOf[AbstractModule[Tensor[Float], Tensor[Float], Float]] - model2 should be (model) - - val (weights, grad) = model.getParameters() - val (weights2, grad2) = model2.getParameters() - // Notice: as a very small different with torch's init parameter, we need to copy the weight. - weights2.copy(weights) - val criterion = new ClassNLLCriterion[Float]() - val sgd = new SGD[Float] - val state = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - val state2 = T("learningRate" -> 1e-2, "momentum" -> 0.9, "weightDecay" -> 5e-4, - "dampening" -> 0.0) - - for (i <- 1 to 5) { - model.zeroGradParameters() - val outputtest = model.forward(input).toTensor[Float] - val loss = criterion.forward(outputtest, labels) - val gradoutputtest = criterion.backward(outputtest, labels) - val gradInput = model.backward(input, gradoutputtest) - sgd.optimize(_ => (loss, grad), weights, state, state) - - model2.zeroGradParameters() - val outputtest2 = model2.forward(input) - val loss2 = criterion.forward(outputtest, labels) - val gradoutputtest2 = criterion.backward(outputtest, labels) - val gradInput2 = model2.backward(input, gradoutputtest2) - sgd.optimize(_ => (loss2, grad2), weights2, state2, state2) - loss should be (loss2) - gradInput should be (gradInput2) - grad.equals(grad2) should be (true) - outputtest should be (outputtest2) - gradoutputtest should be (gradoutputtest2) - weights.equals(weights2) should be (true) - } - } - - "Inception ModelCaffe" should "init right" in { - RNG.setSeed(1024) - - Random.setSeed(1024) - - val input = Tensor[Float](4, 3, 224, 224).apply1(e => Random.nextFloat()) - val labels = Tensor[Float](4).apply1(e => Random.nextInt(1000)) - - val model = Inception.getModelCaffe[Float](1000) - - val criterion = new ClassNLLCriterion[Float]() - - model.zeroGradParameters() - val output = model.forward(input).toTensor[Float] - val loss = criterion.forward(output, labels) - - // since we already set the seed, the loss should match exactly - loss should be (6.893043f) - } - - "InceptionV1 " should "init right" in { - RNG.setSeed(1024) - - Random.setSeed(1024) - - val input = Tensor[Float](4, 3, 224, 224).apply1(e => Random.nextFloat()) - val labels = Tensor[Float](4).apply1(e => Random.nextInt(1000)) - - val model = Inception_v1(1000) - - val criterion = new ClassNLLCriterion[Float]() - - model.zeroGradParameters() - val output = model.forward(input).toTensor[Float] - val loss = criterion.forward(output, labels) - - // since we already set the seed, the loss should match exactly - loss should be (6.6648364f) - } - +class InceptionSpec extends FlatSpec with Matchers { "Inception_Layer_V1 graph" should "be correct" in { val batchSize = 8 RNG.setSeed(1000) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala index a0c38504446..b4c265f7918 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/models/ResNetSpec.scala @@ -17,27 +17,21 @@ package com.intel.analytics.bigdl.models import com.intel.analytics.bigdl._ -import com.intel.analytics.bigdl.models.resnet.{Convolution, ResNet} import com.intel.analytics.bigdl.models.resnet.ResNet._ +import com.intel.analytics.bigdl.models.resnet.{Convolution, ResNet} import com.intel.analytics.bigdl.nn.Graph.{apply => _, _} -import com.intel.analytics.bigdl.nn.abstractnn.Activity import com.intel.analytics.bigdl.nn.{Graph, _} -import com.intel.analytics.bigdl.optim.SGD -import com.intel.analytics.bigdl.tensor.{Storage, Tensor} -import com.intel.analytics.bigdl.torch.{TH, TorchSpec} +import com.intel.analytics.bigdl.numeric.NumericFloat +import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.utils.RandomGenerator.RNG -import com.intel.analytics.bigdl.utils.{Engine, RandomGenerator, T, Table} +import com.intel.analytics.bigdl.utils.{RandomGenerator, T} import org.apache.log4j.Logger -import com.intel.analytics.bigdl.numeric.NumericFloat +import org.scalatest.{FlatSpec, Matchers} -import scala.collection.{immutable, mutable} -import scala.math._ import scala.util.Random @com.intel.analytics.bigdl.tags.Serial -class ResNetSpec extends TorchSpec { - - private val suffix = ".t7" + (new java.util.Random()).nextLong() +class ResNetSpec extends FlatSpec with Matchers { "ResNet basicBlockFunc graph" should "be same with original one" in { val depth = 16 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala index 8df4cf72633..f2736d79bd0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/ConvLSTMPeepholeSpec.scala @@ -14,7 +14,7 @@ * limitations under the License. */ -package com.intel.analytics.bigdl.torch +package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.optim.{L2Regularizer, SGD} diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala index 13df53e8d43..9b60a8778cc 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/LogSoftMaxSpec.scala @@ -16,11 +16,10 @@ package com.intel.analytics.bigdl.nn -import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.torch.TH import com.intel.analytics.bigdl.utils.Engine import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import scala.util.Random diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TorchSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TorchSpec.scala deleted file mode 100644 index 4689db0eb71..00000000000 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/torch/TorchSpec.scala +++ /dev/null @@ -1,25 +0,0 @@ -/* - * Copyright 2016 The BigDL Authors. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package com.intel.analytics.bigdl.torch -import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} - -class TorchSpec extends FlatSpec with BeforeAndAfter with Matchers { - def torchCheck(): Unit = { - if (!TH.hasTorch()) { - cancel("Torch is not installed") - } - } -} From c0eca0357bcb9ccadef0d038b7f0664047fa3ef3 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 17 Dec 2019 20:35:43 +0800 Subject: [PATCH 1008/1065] fix: enable integration accuracy tests (#2976) --- dl/src/test/integration-accuracy-test.robot | 40 ++++++++++----------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/dl/src/test/integration-accuracy-test.robot b/dl/src/test/integration-accuracy-test.robot index 35c9a77f841..c92e34e0848 100644 --- a/dl/src/test/integration-accuracy-test.robot +++ b/dl/src/test/integration-accuracy-test.robot @@ -5,17 +5,15 @@ Suite Setup Prepare DataSource And Verticals Suite Teardown Delete All Sessions Test template BigDL Test -*** Variables *** -@{verticals} ${spark_200_3_vid} ${spark_210_3_vid} ${hdfs_264_3_vid} ${spark_tf_210_3_vid} ${spark_tf_163_3_vid} - -*** Test Cases *** SuiteName VerticalId -1 Spark2.0 Test Suite ${spark_200_3_vid} -2 Spark2.1 Test Suite ${spark_210_3_vid} -3 Hdfs Test Suite ${hdfs_264_3_vid} -4 Quantization Test Suite ${hdfs_264_3_vid} -5 PySpark2.1 Test Suite ${spark_tf_210_3_vid} -6 PySpark1.6 Test Suite ${spark_tf_163_3_vid} -7 Yarn Test Suite ${hdfs_264_3_vid} +*** Test Cases *** SuiteName +1 Spark2.0 Test Suite +2 Spark2.1 Test Suite +3 Hdfs Test Suite +4 Quantization Test Suite +5 PySpark2.1 Test Suite +6 PySpark1.6 Test Suite +7 Yarn Test Suite +8 Torch Test Suite # predefined service masters: # hdfs_264_3_master @@ -42,7 +40,7 @@ Build SparkJar Log To Console build jar finished DownLoad Input - ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.6.5/bin hadoop + ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.7.2/bin hadoop Run ${hadoop} fs -get ${mnist_data_source} ./ Log To Console got mnist data!! Run ${hadoop} fs -get ${cifar_data_source} ./ @@ -84,18 +82,18 @@ Run Spark Test Log To Console begin PTBWordLM Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 40g --executor-memory 100g --executor-cores 8 --total-executor-cores 8 --class com.intel.analytics.bigdl.example.languagemodel.PTBWordLM ${jar_path} -f ./simple-examples/data -b 120 --numLayers 2 --vocab 10001 --hidden 650 --numSteps 35 --learningRate 0.005 -e 1 --learningRateDecay 0.001 --keepProb 0.5 --overWrite > 3.txt Log To Console begin resnet Train - Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 5g --executor-memory 5g --executor-cores 8 --total-executor-cores 32 --class com.intel.analytics.bigdl.models.resnet.Train ${jar_path} -f ./cifar --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 > 4.txt + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 5g --executor-memory 5g --executor-cores 8 --total-executor-cores 32 --class com.intel.analytics.bigdl.models.resnet.TrainCIFAR10 ${jar_path} -f ./cifar --batchSize 448 --optnet true --depth 20 --classes 10 --shortcutType A --nEpochs 1 --learningRate 0.1 > 4.txt Log To Console begin DLClassifierLeNet - Run Shell ${submit} --master ${spark_master} --executor-cores 24 --total-executor-cores 24 --driver-memory 60g --executor-memory 200g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 1200 -f ./mnist --maxEpoch 1 > 5.txt + Run Shell ${submit} --master ${spark_master} --executor-cores 16 --total-executor-cores 16 --driver-memory 5g --executor-memory 30g --class com.intel.analytics.bigdl.example.MLPipeline.DLClassifierLeNet ${jar_path} -b 1200 -f ./mnist --maxEpoch 1 > 5.txt Log To Console begin rnn Train Run Shell ${submit} --master ${spark_master} --driver-memory 5g --executor-memory 5g --executor-cores 12 --total-executor-cores 12 --class com.intel.analytics.bigdl.models.rnn.Train ${jar_path} -f ./ -s ./models --nEpochs 1 --checkpoint ./model/ -b 12 > 6.txt Run Shell bash spark/dl/src/test/accuracy-judge.sh Log To Console begin inceptionV1 train - Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 60g --executor-memory 200g --executor-cores 24 --total-executor-cores 24 --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 24 -f ${imagenet_test_data_source} --learningRate 0.1 -e 1 + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 20g --executor-memory 40g --executor-cores 4 --total-executor-cores 8 --class com.intel.analytics.bigdl.models.inception.TrainInceptionV1 ${jar_path} -b 24 -f ${imagenet_test_data_source} --learningRate 0.1 -i 200 Log To Console begin googlenet - Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-class-path ${jar_path} --driver-memory 20g --executor-memory 100g --executor-cores 28 --total-executor-cores 112 --class com.intel.analytics.bigdl.example.loadmodel.ModelValidator ${jar_path} -b 448 -f ${imagenet_data_source}/val/ -t caffe -m inception --caffeDefPath ${public_hdfs_master}:9000/models/bvlc_googlenet/deploy.prototxt --modelPath ${public_hdfs_master}:9000/models/bvlc_googlenet.caffemodel + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-class-path ${jar_path} --driver-memory 20g --executor-memory 100g --executor-cores 10 --total-executor-cores 20 --class com.intel.analytics.bigdl.example.loadmodel.ModelValidator ${jar_path} -b 420 -f ${imagenet_data_source}/val/ -t caffe -m inception --caffeDefPath ${public_hdfs_master}:9000/models/bvlc_googlenet/deploy.prototxt --modelPath ${public_hdfs_master}:9000/models/bvlc_googlenet.caffemodel Log To Console begin alexnet - Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-class-path ${jar_path} --driver-memory 20g --executor-memory 100g --executor-cores 28 --total-executor-cores 112 --class com.intel.analytics.bigdl.example.loadmodel.ModelValidator ${jar_path} -b 448 -f ${imagenet_data_source}/val/ -t caffe -m inception --caffeDefPath ${public_hdfs_master}:9000/models/bvlc_alexnet/deploy.prototxt --modelPath ${public_hdfs_master}:9000/models/bvlc_alexnet.caffemodel + Run Shell ${submit} --master ${spark_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-class-path ${jar_path} --driver-memory 20g --executor-memory 100g --executor-cores 10 --total-executor-cores 20 --class com.intel.analytics.bigdl.example.loadmodel.ModelValidator ${jar_path} -b 420 -f ${imagenet_data_source}/val/ -t caffe -m inception --caffeDefPath ${public_hdfs_master}:9000/models/bvlc_alexnet/deploy.prototxt --modelPath ${public_hdfs_master}:9000/models/bvlc_alexnet.caffemodel Log To Console begin treeLSTM Run Shell ${submit} --master ${spark_master} --driver-memory 20g --executor-memory 10g --total-executor-cores 8 --executor-cores 8 --class com.intel.analytics.bigdl.example.treeLSTMSentiment.Train ${jar_path} --baseDir ${public_hdfs_master}:9000/dataset/ --epoch 1 Log To Console begin text classification @@ -125,7 +123,7 @@ Hdfs Test Suite Quantization Test Suite - ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.6.5/bin hadoop + ${hadoop}= Catenate SEPARATOR=/ /opt/work/hadoop-2.7.2/bin hadoop Run ${hadoop} fs -get ${mnist_data_source} /tmp/ Log To Console got mnist data!! Run ${hadoop} fs -get ${cifar_data_source} /tmp/ @@ -145,8 +143,6 @@ Yarn Test Suite Set Environment Variable https_proxy ${https_proxy} ${submit}= Catenate SEPARATOR=/ /opt/work/spark-2.0.0-bin-hadoop2.7/bin spark-submit Run Shell ${submit} --master yarn --deploy-mode client --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --executor-cores 10 --num-executors 3 --driver-memory 150g --class com.intel.analytics.bigdl.models.lenet.Train ${jar_path} -f ${mnist_data_source} -b 120 -e 3 - Set Environment Variable PYSPARK_DRIVER_PYTHON /var/jenkins_home/venv/bin/python - Set Environment Variable PYSPARK_PYTHON ./venv.zip/venv/bin/python Run Shell ${submit} --master yarn --deploy-mode client --executor-memory 2g --driver-memory 2g --executor-cores 10 --num-executors 2 --properties-file ${curdir}/dist/conf/spark-bigdl.conf --jars ${jar_path} --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --archives /var/jenkins_home/venv.zip --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 200 --action train --endTriggerType epoch --endTriggerNum 1 Remove Environment Variable http_proxy https_proxy PYSPARK_DRIVER_PYTHON PYSPARK_PYTHON @@ -162,3 +158,7 @@ PySpark1.6 Test Suite Set Environment Variable SPARK_HOME /opt/work/spark-1.6.3-bin-hadoop2.6 ${submit}= Catenate SEPARATOR=/ /opt/work/spark-1.6.3-bin-hadoop2.6/bin spark-submit Run Shell ${submit} --master ${spark_tf_163_3_master} --conf "spark.serializer=org.apache.spark.serializer.JavaSerializer" --driver-memory 150g --executor-cores 28 --total-executor-cores 56 --py-files ${curdir}/dist/lib/bigdl-${version}-python-api.zip --jars ${jar_path} --properties-file ${curdir}/dist/conf/spark-bigdl.conf --conf spark.driver.extraClassPath=${jar_path} --conf spark.executor.extraClassPath=bigdl-${version}-jar-with-dependencies.jar ${curdir}/pyspark/bigdl/models/lenet/lenet5.py -b 224 --action train --endTriggerType epoch --endTriggerNum 1 + +Torch Test Suite + Build SparkJar spark_1.6 + Run Shell mvn clean test -Dsuites=com.intel.analytics.bigdl.integration.torch From dde65141b1854dfa4f37ae9be4ef1289f36a9463 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Mon, 23 Dec 2019 15:54:54 +0800 Subject: [PATCH 1009/1065] fix: softmax dnn backend wrong order of primitive (#2986) --- .../bigdl/dllib/nn/mkldnn/SoftMax.scala | 8 ++--- .../bigdl/dllib/nn/SparseLinearSpec.scala | 9 ++++-- .../bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala | 29 +++++++++++-------- 3 files changed, 28 insertions(+), 18 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala index 62079435f78..8707359bc6c 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMax.scala @@ -108,10 +108,10 @@ class SoftMax(val axis: Int = -1) extends MklDnnLayer { if (axis == -1) defaultAxis else axis) val primDesc = MklDnnMemory.PrimitiveDescCreate(desc, runtime.engine, 0L) - _gradOutputFormats = grad + _gradOutputFormats = grad.clone() _gradInputFormats = Array(MemoryData.operationWant(primDesc, Query.DiffSrcPd)) - val srcs = Array(grad(0).getPrimitive(runtime), outputFormats()(0).getPrimitive(runtime)) + val srcs = Array(outputFormats()(0).getPrimitive(runtime), grad(0).getPrimitive(runtime)) val indexes = Array(0) val dsts = Array(_gradInputFormats(0).getPrimitive(runtime)) @@ -149,15 +149,15 @@ class SoftMax(val axis: Int = -1) extends MklDnnLayer { override def updateGradInput(input: Activity, gradOutput: Activity): Activity = { if (updateGradInputTensors == null) { val buffer = new ArrayBuffer[Tensor[Float]]() - buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) buffer.append(output.asInstanceOf[Tensor[Float]]) + buffer.append(gradOutput.asInstanceOf[Tensor[Float]]) buffer.append(gradInput.asInstanceOf[Tensor[Float]]) updateGradInputTensors = buffer.toArray } gradOutput.toTensor[Float].getTensorType match { - case DenseType => updateGradInputTensors(0) = gradOutput.toTensor + case DenseType => updateGradInputTensors(1) = gradOutput.toTensor case _ => } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala index da88ce1e365..abc98eb585e 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/SparseLinearSpec.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn -import org.scalatest.{FlatSpec, Matchers} +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.{SparseTensor, Tensor} import com.intel.analytics.bigdl.utils.{RandomGenerator, T} @@ -24,7 +24,12 @@ import com.intel.analytics.bigdl.utils.serializer.ModuleSerializationTest import scala.util.Random -class SparseLinearSpec extends FlatSpec with Matchers { +class SparseLinearSpec extends FlatSpec with Matchers with BeforeAndAfter { + + before { + RandomGenerator.RNG.setSeed(100) + } + "Sparse Linear" should "return the same result with Linear" in { val weight = Tensor.range(1, 8, 1).resize(2, 4) val bias = Tensor(2) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala index fc185e765d9..1431917e6e1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SoftMaxSpec.scala @@ -44,8 +44,9 @@ class SoftMaxSpec extends FlatSpec with Matchers { Tools.dense(output) should be (nnOutput) - sm.backward(input, nnOutput) - nnSm.backward(input, nnOutput) + val gradOutput = Tensor[Float]().resizeAs(nnOutput).rand(-10, 10) + sm.backward(input, gradOutput) + nnSm.backward(input, gradOutput) Tools.dense(sm.gradInput) should be (nnSm.gradInput) } @@ -75,8 +76,9 @@ class SoftMaxSpec extends FlatSpec with Matchers { Tools.dense(output) shouldEqual nnOutput - sm.backward(input, nnOutput) - nnSm.backward(input, nnOutput) + val gradOutput = Tensor[Float]().resizeAs(nnOutput).rand(-10, 10) + sm.backward(input, gradOutput) + nnSm.backward(input, gradOutput) Tools.dense(sm.gradInput) should be (nnSm.gradInput) } @@ -110,11 +112,12 @@ class SoftMaxSpec extends FlatSpec with Matchers { Tools.dense(output) should be (nnOutput) - sm.backward(input, nnOutput) - nnSm.backward(input, nnOutput) + val gradOutput = Tensor[Float]().resizeAs(nnOutput).rand(-10, 10) + sm.backward(input, gradOutput) + nnSm.backward(input, gradOutput) Equivalent.nearequals(Tools.dense(sm.gradInput).toTensor, nnSm.gradInput.toTensor, - epsilon = 10-5) + epsilon = 1e-5) should be (true) } } @@ -143,11 +146,13 @@ class SoftMaxSpec extends FlatSpec with Matchers { val nnOutput = nnSm.forward(input) Tools.dense(output) should be (nnOutput) - sm.backward(input, nnOutput) - nnSm.backward(input, nnOutput) + + val gradOutput = Tensor[Float]().resizeAs(nnOutput).rand(-10, 10) + sm.backward(input, gradOutput) + nnSm.backward(input, gradOutput) Equivalent.nearequals(Tools.dense(sm.gradInput).toTensor, nnSm.gradInput.toTensor, - epsilon = 10-5) + epsilon = 1e-5) should be (true) } } @@ -172,9 +177,9 @@ class SoftMaxSpec extends FlatSpec with Matchers { nnSm.backward(input, gradOutput) Equivalent.nearequals(Tools.dense(sm.output).toTensor, nnSm.output.toTensor, - epsilon = 10-4) + epsilon = 1e-5) should be (true) Equivalent.nearequals(Tools.dense(sm.gradInput).toTensor, nnSm.gradInput.toTensor, - epsilon = 10-4) + epsilon = 1e-5) should be (true) } "SoftMax multi times forward" should "work correctly" in { From bb078f00f1a5ff78ebf0ed0ea9f612c0fb1c0b9f Mon Sep 17 00:00:00 2001 From: Hui Li Date: Wed, 25 Dec 2019 09:35:26 +0800 Subject: [PATCH 1010/1065] modify TextClassifier.scala (#2987) --- .../bigdl/dllib/example/utils/TextClassifier.scala | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala index 1f3f6d13e5b..b47d893519a 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/utils/TextClassifier.scala @@ -128,7 +128,7 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab val frequencies = dataRdd.flatMap{case (text: String, label: Float) => SimpleTokenizer.toTokens(text) }.map(word => (word, 1)).reduceByKey(_ + _) - .sortBy(- _._2).collect().slice(10, param.maxWordsNum) + .sortBy(- _._2).persist().collect().slice(10, param.maxWordsNum) val indexes = Range(1, frequencies.length) val word2Meta = frequencies.zip(indexes).map{item => @@ -198,7 +198,7 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab val trainingSplit = param.trainingSplit // For large dataset, you might want to get such RDD[(String, Float)] from HDFS - val dataRdd = sc.parallelize(loadRawData(), param.partitionNum) + val dataRdd = sc.parallelize(loadRawData(), param.partitionNum).persist() val (word2Meta, word2Vec) = analyzeTexts(dataRdd) val word2MetaBC = sc.broadcast(word2Meta) val word2VecBC = sc.broadcast(word2Vec) @@ -211,7 +211,7 @@ class TextClassifier(param: AbstractTextClassificationParams) extends Serializab Sample( featureTensor = Tensor(input.flatten, Array(sequenceLen, embeddingDim)), label = label) - } + }.persist() val Array(trainingRDD, valRDD) = sampleRDD.randomSplit( Array(trainingSplit, 1 - trainingSplit)) From 15ecfb78b4775bf8973f54445b30384582a4608f Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Thu, 2 Jan 2020 10:51:19 +0800 Subject: [PATCH 1011/1065] Add a method to merge nested StaticGraphs (#2985) --- .../bigdl/dllib/nn/StaticGraph.scala | 56 +++++++++++++++++++ .../dllib/nn/abstractnn/AbstractModule.scala | 8 ++- .../analytics/bigdl/dllib/nn/GraphSpec.scala | 33 +++++++++++ 3 files changed, 96 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala index 5b62ecb8e2f..622cd1b3e39 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -208,4 +208,60 @@ class StaticGraph[T: ClassTag]( val model = IRGraph(inputsIR, outputsIR, variables, true, inFormats, outFormats) model.build() } + + // Merge a nested StaticGraph into a non-nested one + private[bigdl] def toSingleGraph(): StaticGraph[T] = { + if (this.isNestedGraph()) { + val graph = this.cloneModule() + val fwdExecution = graph.getSortedForwardExecutions() + val dmOutput = fwdExecution(fwdExecution.length - 1).nextNodes(0) + + var i = 0 + while (i < fwdExecution.length) { + if (fwdExecution(i).element.isInstanceOf[StaticGraph[T]]) { + var g = fwdExecution(i).element.asInstanceOf[StaticGraph[T]].toSingleGraph() + fwdExecution(i).element = g + + for (inputIndex <- 0 until fwdExecution(i).prevNodes.length) { + val inputNode = g.inputs(inputIndex) + inputNode.element = Identity() + + while (fwdExecution(i).prevNodes.length != 0) { + val preNode = fwdExecution(i).prevNodes(0) + preNode.delete(fwdExecution(i)) + preNode.add(inputNode) + } + } + + for (outputIndex <- 0 until g.outputs.length) { + val outputNode = g.outputs(outputIndex) + outputNode.removeNextEdges() + while (fwdExecution(i).nextNodes.length != 0) { + val nextNode = fwdExecution(i).nextNodes(0) + fwdExecution(i).delete(nextNode) + outputNode.add(nextNode) + } + } + } + i += 1 + } + + val resultOutputNodes = dmOutput.prevNodes + resultOutputNodes.foreach(_.delete(dmOutput)) + new StaticGraph[T](Array(graph.inputs(0)), resultOutputNodes, + enableExcludeChecking = this.enableExcludeChecking) + } else { + this + } + } + + private def isNestedGraph(): Boolean = { + for (i <- 0 until forwardExecution.length) { + if (forwardExecution(i).element.isInstanceOf[StaticGraph[T]]) { + return true + } + } + + false + } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index f218abb36cf..7a7207c9808 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -828,7 +828,13 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, def toGraph(startNodes: ModuleNode[T]*): Graph[T] = { val starts = if (startNodes.isEmpty) Array(Input[T]()) else startNodes.toArray val endNodes = this.getEndNodes(starts) - Graph(starts, endNodes) + val graph = Graph(starts, endNodes) + if (graph.isInstanceOf[StaticGraph[T]]) { + // Merge nested graphs inside to make the whole graph non-nested + graph.asInstanceOf[StaticGraph[T]].toSingleGraph() + } else { + graph + } } /** diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala index ea887a86d09..a25242376d3 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/GraphSpec.scala @@ -1336,6 +1336,39 @@ class StaticGraphSpec extends FlatSpec with Matchers { val model = Graph(Array(n1, n2), Array(n3, n4)) } } + + "Graph toSingleGraph" should "work correctly" in { + val input = Input() + + val linear1 = Linear[Float](2, 3).inputs(input) + + val inputg1 = Input() + val l1 = Linear[Float](3, 5).inputs(inputg1) + val inputg1nested = Input() + val l1nested = Linear[Float](5, 5).inputs(inputg1nested) + val g1nested = Graph(inputg1nested, l1nested).inputs(l1) + val g1 = Graph(inputg1, g1nested).inputs(linear1) + + val inputg2 = Input() + val l2 = Linear[Float](5, 3).inputs(inputg2) + val g2 = Graph(inputg2, l2).inputs(g1) + + val linear3 = Linear(3, 6).inputs(g2) + val linear4 = Linear(3, 5).inputs(g2) + + val graph = Graph(input, Array(linear3, linear4)).asInstanceOf[StaticGraph[Float]] + val toSingle = graph.toSingleGraph() + + val tensor = Tensor[Float](Array(3, 2)).rand() + val graphOutput = graph.forward(tensor) + val toSingleOutput = toSingle.forward(tensor) + graphOutput should equal(toSingleOutput) + + val fwdExecution = toSingle.asInstanceOf[StaticGraph[Float]].getForwardExecutions() + for (i <- 0 until fwdExecution.length) { + assert(!fwdExecution(i).element.isInstanceOf[StaticGraph[Float]]) + } + } } object ModelUntils { From abe8f78e3f6634c98477b85c8d85ae3ef38221d9 Mon Sep 17 00:00:00 2001 From: zhangxiaoli73 <380761639@qq.com> Date: Tue, 7 Jan 2020 10:35:45 +0800 Subject: [PATCH 1012/1065] NHWC support when running with MKL-DNN (#2989) * support NHWC for MKLDNN * fix unit tests --- .../analytics/bigdl/dllib/nn/Dropout.scala | 7 +- .../bigdl/dllib/nn/StaticGraph.scala | 6 - .../bigdl/dllib/nn/mkldnn/AvgPooling.scala | 10 +- .../bigdl/dllib/nn/mkldnn/BlasWrapper.scala | 27 +++- .../bigdl/dllib/nn/mkldnn/DnnBase.scala | 6 +- .../bigdl/dllib/nn/mkldnn/DnnGraph.scala | 63 +++++++- .../analytics/bigdl/dllib/nn/mkldnn/LRN.scala | 10 +- .../bigdl/dllib/nn/mkldnn/Linear.scala | 28 ++-- .../bigdl/dllib/nn/mkldnn/MaxPooling.scala | 10 +- .../bigdl/dllib/nn/mkldnn/MemoryData.scala | 11 ++ .../bigdl/dllib/nn/mkldnn/Output.scala | 5 +- .../nn/mkldnn/SpatialBatchNormalization.scala | 10 +- .../dllib/utils/intermediate/IRElement.scala | 3 - .../dllib/utils/intermediate/IRGraph.scala | 10 +- .../dllib/utils/intermediate/IRToDnn.scala | 58 ++++--- .../dllib/nn/mkldnn/AvgPoolingSpec.scala | 47 +++++- .../bigdl/dllib/nn/mkldnn/LinearSpec.scala | 34 +++++ .../dllib/nn/mkldnn/MaxPoolingSpec.scala | 37 ++++- .../bigdl/dllib/nn/mkldnn/RNNSpec.scala | 41 ++++- .../utils/intermediate/IRconvertSpec.scala | 143 +++++++++++++++++- 20 files changed, 475 insertions(+), 91 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala index 5c483e2bf6a..18ab0324a37 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Dropout.scala @@ -18,9 +18,10 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{IdentityOutputShape, TensorModule} import com.intel.analytics.bigdl.tensor.{Storage, Tensor} import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric -import com.intel.analytics.bigdl.utils.Engine +import com.intel.analytics.bigdl.utils.{Engine, Shape} import com.intel.analytics.bigdl.utils.RandomGenerator._ +import scala.collection.mutable.ArrayBuffer import scala.concurrent.Future import scala.reflect.ClassTag @@ -207,6 +208,10 @@ class Dropout[T: ClassTag]( override def toString(): String = { s"${getPrintName}($p)" } + + override def computeOutputShape(inputShape: Shape): Shape = { + inputShape + } } object Dropout { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala index 622cd1b3e39..e8061c60bcf 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -191,12 +191,6 @@ class StaticGraph[T: ClassTag]( val allNodes = forwardExecution if (!BlasToIR[T].convertingCheck(allNodes)) return null - inFormats.foreach(in => - if (in == Memory.Format.nhwc) { - logger.warn("Not support NHWC in IRGraph") - return null - } - ) val nodeMap = BlasToIR[T].convert(allNodes) val inputNodes = inputs.toArray.map(n => nodeMap.get(n).get) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala index 0b270af250b..d7d8e1e8f85 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPooling.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.{Utils => NNUtils} -import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import com.intel.analytics.bigdl.tensor.Tensor @@ -28,7 +28,8 @@ class AvgPooling( dH: Int = 1, padW: Int = 0, padH: Int = 0, - globalPooling: Boolean = false + globalPooling: Boolean = false, + val format: DataFormat = DataFormat.NCHW ) extends MklDnnLayer { @transient private var paddingTL: Array[Int] = _ @transient private var paddingBR: Array[Int] = _ @@ -137,6 +138,7 @@ object AvgPooling { dH: Int = 1, padW: Int = 0, padH: Int = 0, - globalPooling: Boolean = false - ): AvgPooling = new AvgPooling(kW, kH, dW, dH, padW, padH, globalPooling) + globalPooling: Boolean = false, + format: DataFormat = DataFormat.NCHW + ): AvgPooling = new AvgPooling(kW, kH, dW, dH, padW, padH, globalPooling, format = format) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala index 7af195bc8a1..57a8d92820e 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/BlasWrapper.scala @@ -22,7 +22,7 @@ import com.intel.analytics.bigdl.Module import com.intel.analytics.bigdl.dataset.MiniBatch import com.intel.analytics.bigdl.mkl.{MKL, Memory} import com.intel.analytics.bigdl.nn.{DetectionOutputSSD, PriorBox} -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat, TensorModule} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -54,6 +54,12 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, } } + private def getHeapFormats(in: MemoryData): Int = { + if (in.heapFormat == -1 || in.shape.length != 4) { + getFormats(in.shape.length) + } else in.heapFormat + } + private[mkldnn] var needOutputFormats: Boolean = true @transient private lazy val logger = Logger.getLogger(getClass) @@ -67,15 +73,18 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, private def inferInputFormats(inputs: Array[MemoryData]): Array[MemoryData] = { inputs.map(in => { - if (in.layout == Memory.Format.tnc) { + val heap = if (in.layout == Memory.Format.tnc) { val size = in.shape HeapData(Array(size(1), size(0), size(2)), Memory.Format.ntc) - } else HeapData(in.shape, getFormats(in.shape.length)) + } else { + HeapData(in.shape, getHeapFormats(in)) + } + heap.setHeapFormat(in.heapFormat) }) } private def inferOutputFormats(inputs: Array[MemoryData]): Array[MemoryData] = { - val inputShape = inputs.map(in => Shape(in.shape)) + val inputShape = inputs.map(in => Shape(in.getHeapShape())) val outputShape = if (inputShape.length == 1) { List(module.computeOutputShape(inputShape(0))) } else { @@ -85,7 +94,13 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, } outputShape.map(in => { val size = in.toSingle().toArray - HeapData(size, getFormats(size.length)) + val f = if (size.length == 4 && inputs(0).heapFormat == Memory.Format.nhwc) { + Memory.Format.nhwc + } else getFormats(size.length) + val outSize = if (f == Memory.Format.nhwc) { + Array(size(0), size(3), size(1), size(2)) + } else size + HeapData(outSize, f).setHeapFormat(f) }).toArray } @@ -166,7 +181,7 @@ private[bigdl] class BlasWrapper(val module: AbstractModule[Activity, Activity, override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { _inputFormats = inferInputFormats(inputs) - _outputFormats = if (needOutputFormats) inferOutputFormats(inputs) else null + _outputFormats = if (needOutputFormats) inferOutputFormats(_inputFormats) else null if (_outputFormats != null) { _outputFormats.map(_.getPrimitive(runtime)) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala index 669cd0d88af..c4b5c1becff 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnBase.scala @@ -247,12 +247,12 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM } - override private[mkldnn] def inputFormats() = { + override private[bigdl] def inputFormats() = { require(_inputFormats != null, "You should call initFwdPrimitives first") _inputFormats } - override private[mkldnn] def gradInputFormats() = { + override private[bigdl] def gradInputFormats() = { require(_gradInputFormats != null, "You should call initBwdPrimitives first") _gradInputFormats } @@ -262,7 +262,7 @@ trait MklDnnLayer extends AbstractModule[Activity, Activity, Float] with MklDnnM _outputFormats } - override private[mkldnn] def gradOutputFormats() = { + override private[bigdl] def gradOutputFormats() = { require(_gradOutputFormats != null, "You should call initBwdPrimitives first") _gradOutputFormats } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala index 60cee93d3c6..43a27b5f379 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/DnnGraph.scala @@ -18,9 +18,10 @@ package com.intel.analytics.bigdl.nn.mkldnn import java.util +import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.Graph.ModuleNode -import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} +import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, DataFormat} import com.intel.analytics.bigdl.nn.tf.{ControlDependency, WithoutInput} import com.intel.analytics.bigdl.nn.{Graph, mkldnn, MklInt8Convertible} import com.intel.analytics.bigdl.tensor.Tensor @@ -80,7 +81,13 @@ class DnnGraph( findDnnInput(node, input) } inputCache(i) = nodeInput - node.element.forward(nodeInput) + val output = node.element.forward(nodeInput) + // resize to heap size + if (!skipPrimitiveId(i) && output.isTensor && + !node.element.isInstanceOf[BlasWrapper]) { + output.toTensor[Float].resize( + node.element.asInstanceOf[MklDnnLayer].outputFormats()(0).getHeapShape()) + } i += 1 } output = getRealOutput(input, dummyOutput.element.output) @@ -104,7 +111,13 @@ class DnnGraph( // use input from forward val curInput = inputCache(backId2ForwardId(i)) if (!isStopGradient(curNode.element)) { - curNode.element.updateGradInput(curInput, curGradOutput) + val gradInput = curNode.element.updateGradInput(curInput, curGradOutput) + // resize to heap size + if (!skipPrimitiveId(i) && gradInput.isTensor && + !curNode.element.isInstanceOf[BlasWrapper]) { + gradInput.toTensor[Float].resize( + curNode.element.asInstanceOf[MklDnnLayer].gradInputFormats()(0).getHeapShape()) + } } i += 1 } @@ -409,6 +422,40 @@ class DnnGraph( } } + private def getHeapFormat(inputs: Array[MemoryData]): Int = { + var heapFormat: Int = -1 + inputs.foreach(m => { + if (m.shape.length == 4) { + return inputs(0).layout + } + }) + + @inline + def transferFormat(format: DataFormat): Int = { + if (format == DataFormat.NHWC) Memory.Format.nhwc else Memory.Format.nchw + } + + for (i <- 0 until forwardExecution.length) { + val m = forwardExecution(i).element + val format = m match { + case conv: mkldnn.SpatialConvolution => transferFormat(conv.format) + case maxPool: mkldnn.MaxPooling => transferFormat(maxPool.format) + case avgPool: mkldnn.AvgPooling => transferFormat(avgPool.format) + case sbn: mkldnn.SpatialBatchNormalization => transferFormat(sbn.format) + case lrn: mkldnn.LRN => transferFormat(lrn.format) + case _ => -1 + } + + if (heapFormat == -1) { + heapFormat = format + } else if (format != -1) { + require(heapFormat == format, + s"layer ${m} should use format ${heapFormat}, but get ${format}") + } + } + if (heapFormat == -1) Memory.Format.nchw else heapFormat + } + // init forward primitives override private[bigdl] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) : (Array[MemoryData], Array[MemoryData]) = { @@ -416,13 +463,15 @@ class DnnGraph( fusion() var lastOutputFormats = inputs var firstRealInputFormats: Array[MemoryData] = null + val heapFormat : Int = getHeapFormat(inputs) for (i <- 0 until forwardExecution.length) { if (!skipPrimitiveId(i)) { val m = forwardExecution(i) lastOutputFormats = findInputFormats(m, inputs) - val realInputAndOutputFormats = + val (realInputFormats, realOutputFormats) = m.element.asInstanceOf[MklDnnModule].initFwdPrimitives(lastOutputFormats, phase) - lastOutputFormats.zip(realInputAndOutputFormats._1).foreach { + if (realOutputFormats != null) realOutputFormats.foreach(_.setHeapFormat(heapFormat)) + lastOutputFormats.zip(realInputFormats).foreach { case (o, i) => Utils.copyMaskAndScales(o, i) reorderManager.register(o, i) @@ -430,9 +479,9 @@ class DnnGraph( // copy the scales from the input formats to output formats, for some layers, // it will not copy the mask and scales automatically or generate the scales themselves - Utils.copyMaskAndScales(realInputAndOutputFormats._1, realInputAndOutputFormats._2) + Utils.copyMaskAndScales(realInputFormats, realOutputFormats) - if (i == 0) firstRealInputFormats = realInputAndOutputFormats._1 + if (i == 0) firstRealInputFormats = realInputFormats } } _inputFormats = firstRealInputFormats diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala index 2bd7cbb3ae5..d5e813b23e8 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LRN.scala @@ -16,7 +16,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ -import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} import com.intel.analytics.bigdl.nn.mkldnn.Phase.InferencePhase import com.intel.analytics.bigdl.tensor.Tensor @@ -24,7 +24,8 @@ class LRN( size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75, - k: Double = 1.0 + k: Double = 1.0, + val format: DataFormat = DataFormat.NCHW ) extends MklDnnLayer { private val UNDEFINED = 0 @@ -108,6 +109,7 @@ class LRN( } object LRN { - def apply(size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75, k: Double = 1.0): LRN = - new LRN(size, alpha, beta, k) + def apply(size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75, k: Double = 1.0, + format: DataFormat = DataFormat.NCHW): LRN = + new LRN(size, alpha, beta, k, format) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala index 6c899b41a09..ac8274196c6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Linear.scala @@ -41,6 +41,8 @@ class Linear( private[mkldnn] val gradBias: TensorMMap = new TensorMMap(Array(outputSize)) @transient private var forwardPrimDesc: Long = 0L + @transient private var weightShape: Array[Int] = null + @transient private var weightLayout: Int = -1 @transient private var updateOutputMemoryPrimitives: Array[Long] = _ @transient private var updateOutputTensors: Array[Tensor[Float]] = _ @@ -71,13 +73,20 @@ class Linear( } override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = { - val (weightShape, weightLayout) = inputs(0).shape.length match { + val weightParams = inputs(0).shape.length match { case 4 => - (Array(weight.size(1)) ++ inputs(0).shape.slice(1, 4), - Memory.Format.oihw) + if (inputs(0).heapFormat == Memory.Format.nhwc) { + (Array(weight.size(1)) ++ inputs(0).shape.slice(1, 4), + Memory.Format.nhwc) // ohwi + } else { + (Array(weight.size(1)) ++ inputs(0).shape.slice(1, 4), + Memory.Format.nchw) // oihw + } case 2 => (weight.size(), Memory.Format.nc) case 1 => (weight.size(), Memory.Format.x) } + weightShape = weightParams._1 + weightLayout = weightParams._2 val inputShape = inputs(0).shape require(inputs(0).shape.length > 1, s"mkldnn linear unspported input dimension") @@ -154,11 +163,6 @@ class Linear( } override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = { - val weightShape = inputFormats()(0).shape.length match { - case 4 => Array(weight.size(1)) ++ inputFormats()(0).shape.slice(1, 4) - case _ => weight.size() - } - val inputShape = inputFormats()(0).shape val outputShape = Array(inputFormats()(0).shape(0), outputSize) @@ -197,14 +201,6 @@ class Linear( override private[mkldnn] def initGradWPrimitives(grad: Array[MemoryData], phase: Phase): Array[MemoryData] = { - val (weightShape, weightLayout) = inputFormats()(0).shape.length match { - case 4 => - (Array(weight.size(1)) ++ inputFormats()(0).shape.slice(1, 4), - Memory.Format.oihw) - case 2 => (weight.size(), Memory.Format.nc) - case 1 => (weight.size(), Memory.Format.x) - } - val inputShape = inputFormats()(0).shape val outputShape = Array(inputFormats()(0).shape(0), outputSize) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala index 945353019f0..c895bc2e4a5 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPooling.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ import com.intel.analytics.bigdl.nn.{Utils => NNUtils} -import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor @@ -27,7 +27,8 @@ class MaxPooling( dW: Int = 1, dH: Int = 1, padW: Int = 0, - padH: Int = 0 + padH: Int = 0, + val format: DataFormat = DataFormat.NCHW ) extends MklDnnLayer { @transient private var workSpaceFormat: MemoryData = _ @transient private var workSpace: Tensor[Float] = _ @@ -162,6 +163,7 @@ object MaxPooling { dW: Int = 1, dH: Int = 1, padW: Int = 0, - padH: Int = 0 - ): MaxPooling = new MaxPooling(kW, kH, dW, dH, padW, padH) + padH: Int = 0, + format: DataFormat = DataFormat.NCHW + ): MaxPooling = new MaxPooling(kW, kH, dW, dH, padW, padH, format) } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala index e3ee74b2cef..a9e21466731 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MemoryData.scala @@ -22,6 +22,7 @@ sealed trait MemoryData extends Serializable { def shape: Array[Int] def layout: Int def dataType: Int + var heapFormat : Int = -1 private var _mask: Int = -1 private var _scales: Array[Float] = Array.emptyFloatArray @@ -31,6 +32,16 @@ sealed trait MemoryData extends Serializable { def scales: Array[Float] = _scales def setScales(f: Array[Float]): Unit = _scales = f + def setHeapFormat(f: Int): this.type = { + heapFormat = f + this + } + def getHeapShape(): Array[Int] = { + if (layout == Memory.Format.nhwc) { // native shape is nchw + Array(shape(0), shape(2), shape(3), shape(1)) + } else shape + } + def cloneFormat(): MemoryData private val UNDEFINED: Long = -1 diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala index 42765b0ea90..6825de4ddc2 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/Output.scala @@ -34,10 +34,7 @@ class Output(outputLayOut: Int = Memory.Format.nc, private def getShape(inLayout: Int, inShape: Array[Int], outLayout: Int): Array[Int] = { val outputShape = - if (outLayout == Memory.Format.nhwc && inLayout != Memory.Format.nhwc) { - // nchw* -> nhwc - Array(inShape(0), inShape(2), inShape(3), inShape(1)) - } else if (outLayout == Memory.Format.tnc && inLayout == Memory.Format.ntc) { + if (outLayout == Memory.Format.tnc && inLayout == Memory.Format.ntc) { // ntc -> tnc Array(inShape(1), inShape(0), inShape(2)) } else if (outLayout == Memory.Format.ntc && inLayout == Memory.Format.tnc) { diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala index 29bb8a12111..f2fa0327cb6 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/SpatialBatchNormalization.scala @@ -17,7 +17,7 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl._ -import com.intel.analytics.bigdl.nn.abstractnn.{Activity, Initializable} +import com.intel.analytics.bigdl.nn.abstractnn.{Activity, DataFormat, Initializable} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.nn.{MklInt8Convertible, Ones, VariableFormat, Zeros} import com.intel.analytics.bigdl.tensor._ @@ -31,7 +31,8 @@ class SpatialBatchNormalization( private val initWeight: Tensor[Float] = null, private val initBias: Tensor[Float] = null, private val initGradWeight: Tensor[Float] = null, - private val initGradBias: Tensor[Float] = null + private val initGradBias: Tensor[Float] = null, + val format: DataFormat = DataFormat.NCHW ) extends MklDnnLayer with Initializable with MklInt8Convertible { @transient private var forwardDesc: Long = 0L @@ -455,8 +456,9 @@ object SpatialBatchNormalization { initWeight: Tensor[Float] = null, initBias: Tensor[Float] = null, initGradWeight: Tensor[Float] = null, - initGradBias: Tensor[Float] = null): SpatialBatchNormalization = { + initGradBias: Tensor[Float] = null, + format: DataFormat = DataFormat.NCHW): SpatialBatchNormalization = { new SpatialBatchNormalization(nOutput, eps, momentum, initWeight, initBias, initGradWeight, - initGradBias) + initGradBias, format = format) } } diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala index efeb813dad6..5b63ad4ec51 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRElement.scala @@ -85,9 +85,6 @@ case class IRSpatialBatchNormalization[T: ClassTag]( case class IRIdentity[T: ClassTag]() extends IROperator[T] -case class IRDropout[T: ClassTag](initP: Double = 0.5, inplace: Boolean = false, - scale: Boolean = true) extends IROperator[T] - case class IRReLU[T: ClassTag](ip: Boolean = false) extends IROperator[T] case class IRLinear[T: ClassTag]( diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala index 0aaefc747a4..0b365a58d6f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRGraph.scala @@ -151,6 +151,9 @@ private[bigdl] class IRGraph[T: ClassTag]( val sizeNew = if (size.length == 3 && inputFormats(0) != Memory.Format.ntc && inputFormats(0) != Memory.Format.tnc) { Array(size(0), 1, size(1), size(2)) + } else if (inputFormats(0) == Memory.Format.nhwc) { + // always use NCHW to create heap data + Array(size(0), size(3), size(1), size(2)) } else size inputMemory(0) = HeapData(sizeNew, inputFormats(0)) } else { @@ -162,7 +165,12 @@ private[bigdl] class IRGraph[T: ClassTag]( "Only support input with tensor type, table not supported") val t1 = t._1.asInstanceOf[Int] // starts from 1 val t2 = t._2.asInstanceOf[Tensor[T]] - inputMemory(t1 - 1) = HeapData(t2.size(), inputFormats(t1 - 1)) + if (inputFormats(t1 - 1 ) == Memory.Format.nhwc) { + val sizeNew = Array(t2.size(1), t2.size(4), t2.size(2), t2.size(3)) + inputMemory(t1 - 1) = HeapData(sizeNew, inputFormats(t1 - 1)) + } else { + inputMemory(t1 - 1) = HeapData(t2.size(), inputFormats(t1 - 1)) + } }) } val dnnGraph = graph.asInstanceOf[DnnGraph] diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala index abbf002be9b..9f3faeea858 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRToDnn.scala @@ -115,19 +115,54 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def fromSpatialConvolution(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialConvolution[Float]] - require(t.format == DataFormat.NCHW, "Dnn SpatialConvolution only supports NCHW") - ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) + if (t.format == DataFormat.NCHW) { + ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) + } else { + // special process for NHWC + require(t.nGroup == 1, "Only support nGroup is 1 for NHWC") + val layer = ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) + val p = layer.parameters() + val weight = p._1(0) + val gradWeight = p._2(0) + + val weightSize = weight.size() // for nchw + val newSize = Array(weightSize(2), weightSize(3), weightSize(1), weightSize(0))// for nhwc + val bufferNHWC = Tensor[Float]().resizeAs(weight).copy(weight).resize(newSize) + weight.copy(bufferNHWC.transpose(1, 4).transpose(2, 3).transpose(3, 4).contiguous()) + + bufferNHWC.copy(gradWeight) + gradWeight.copy(bufferNHWC.transpose(1, 4).transpose(2, 3).contiguous()) + + layer + } } private def fromSpatialShareConvolution(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialShareConvolution[Float]] - require(t.format == DataFormat.NCHW, "Dnn SpatialConvolution only supports NCHW") - ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) + if (t.format == DataFormat.NCHW) { + ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) + } else { + // special process for NHWC + require(t.nGroup == 1, "Only support nGroup is 1 for NHWC") + val layer = ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "SpatialConvolution")) + val p = layer.parameters() + val weight = p._1(0) + val gradWeight = p._2(0) + + val weightSize = weight.size() // for nchw + val newSize = Array(weightSize(2), weightSize(3), weightSize(1), weightSize(0)) // for nhwc + val bufferNHWC = Tensor[Float]().resizeAs(weight).copy(weight).resize(newSize) + weight.copy(bufferNHWC.transpose(1, 4).transpose(2, 3).transpose(3, 4).contiguous()) + + bufferNHWC.copy(gradWeight) + gradWeight.copy(bufferNHWC.transpose(1, 4).transpose(2, 3).transpose(3, 4).contiguous()) + + layer + } } private def fromSpatialMaxPooling(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialMaxPooling[Float]] - require(t.format == DataFormat.NCHW, "Dnn SpatialMaxPooling only supports NCHW") val layer = ReflectionUtils.reflectFromIR( node, Class.forName(prefix + "MaxPooling")).asInstanceOf[MaxPooling] if (t.ceilMode) layer.ceil() else layer.floor() @@ -136,7 +171,6 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def fromSpatialAveragePooling(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialAveragePooling[Float]] - require(t.format == DataFormat.NCHW, "Dnn SpatialAveragePooling only supports NCHW") val layer = ReflectionUtils.reflectFromIR( node, Class.forName(prefix + "AvgPooling")).asInstanceOf[AvgPooling] if (t.ceilMode) layer.ceil() else layer.floor() @@ -145,7 +179,6 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def fromSpatialCrossMapLRN(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialCrossMapLRN[Float]] - require(t.format == DataFormat.NCHW, "Dnn LRN only supports NCHW") ReflectionUtils.reflectFromIR(node, Class.forName(prefix + "LRN")) } @@ -158,7 +191,6 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def fromSpatialBatchNormalization(node: IRElement[Float]) : Module[Float] = { val t = node.getOp().asInstanceOf[IRSpatialBatchNormalization[Float]] - require(t.dataFormat == DataFormat.NCHW, "Dnn SpatialBatchNormalization only supports NCHW") val nOutput = t.nOutput val eps = t.eps val momentum = 1 - t.momentum // meaning not same with mkldnn @@ -528,16 +560,6 @@ private[bigdl] class IRToDnn extends ConvertBase[IRElement[Float], Module[Float] private def checkRequirement(layer: IRElement[Float]) : Boolean = { try { layer.getOp() match { - case conv: IRSpatialConvolution[Float] => - require(conv.format == DataFormat.NCHW) - case maxPool: IRSpatialMaxPooling[Float] => - require(maxPool.format == DataFormat.NCHW) - case avgPool: IRSpatialAveragePooling[Float] => - require(avgPool.format == DataFormat.NCHW) - case sbn: IRSpatialBatchNormalization[Float] => - require(sbn.dataFormat == DataFormat.NCHW) - case lrn: IRSpatialCrossMapLRN[Float] => - require(lrn.format == DataFormat.NCHW) case join: IRJoinTable[Float] => require(join.nInputDims <= 0) case _ => null diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala index 613c9d13086..2d679c273c7 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/AvgPoolingSpec.scala @@ -15,11 +15,13 @@ */ package com.intel.analytics.bigdl.nn.mkldnn +import breeze.numerics.ceil import com.intel.analytics.bigdl.mkl.{DataType, Memory} -import com.intel.analytics.bigdl.nn.SpatialAveragePooling +import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, SpatialMaxPooling, StaticGraph} +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} -import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Engine} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Engine, MklDnn} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.intermediate.{BlasToIR, IRToDnn} import org.apache.commons.lang3.SerializationUtils @@ -66,6 +68,47 @@ class AvgPoolingSpec extends BigDLSpecHelper { grad1 should be(grad2) } + "Avg Pooling with NHWC" should "be correct" in { + Engine.setEngineType(MklDnn) + RNG.setSeed(100) + val batchSize = 2 + val input = Tensor[Float](batchSize, 28, 28, 480).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 14, 14, 480).apply1(e => Random.nextFloat()) + + val pad = -1 + RNG.setSeed(100) + val layer = SpatialAveragePooling[Float](3, 3, 2, 2, + padH = pad, padW = pad, format = DataFormat.NHWC).ceil() + RNG.setSeed(100) + val layer2 = SpatialAveragePooling[Float](3, 3, 2, 2, + padH = pad, padW = pad).ceil() + + import com.intel.analytics.bigdl.nn + val static = nn.Sequential[Float]().add(layer2) + .toGraph().asInstanceOf[StaticGraph[Float]] + static.setInputFormats(Seq(Memory.Format.nhwc)) + static.setOutputFormats(Seq(Memory.Format.nhwc)) + val dnn = static.toIRgraph() + + for (i <- 0 to 3) { + input.rand() + gradOutput.rand() + + dnn.forward(input) + dnn.backward(input, gradOutput) + + layer.forward(input) + layer.backward(input, gradOutput) + } + val output1 = dnn.forward(input) + val output2 = layer.forward(input).toTensor[Float] + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = dnn.backward(input, output2) + grad1 should be(grad2) + } + "Convert average pooling with ceilMode to dnn layer" should "be correct" in { val batchSize = 2 val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala index e65bc142036..f4c1016c5bf 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/LinearSpec.scala @@ -18,15 +18,49 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn +import com.intel.analytics.bigdl.nn.StaticGraph import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.optim.L2Regularizer import com.intel.analytics.bigdl.tensor.{DnnStorage, Tensor} +import com.intel.analytics.bigdl.utils.{Engine, MklDnn, RandomGenerator} import com.intel.analytics.bigdl.utils.RandomGenerator._ import org.apache.commons.lang3.SerializationUtils import org.scalatest.{FlatSpec, Matchers} class LinearSpec extends FlatSpec with Matchers { + + "Convert blas linear to mkldnn with NHWC" should "be correct" in { + Engine.setEngineType(MklDnn) + RandomGenerator.RNG.setSeed(100) + val linear = nn.Linear(12, 3) + val linear2 = nn.Linear(12, 3) + + linear2.weight.copy(linear.weight) + linear2.bias.copy(linear.bias) + + val blas = nn.Sequential() + blas.add(nn.Reshape(Array(12)).setName("linear")) + blas.add(linear) + + val blas2 = nn.Sequential().add(linear2).toGraph(). + asInstanceOf[StaticGraph[Float]] + blas2.setInputFormats(Seq(Memory.Format.nhwc)) + blas2.setOutputFormats(Seq(Memory.Format.nc)) + val dnn = blas2.toIRgraph() + + val input = Tensor[Float](4, 2, 2, 3).rand() // nhwc + + val outBlas = blas.forward(input).toTensor[Float] + val outDnn = dnn.forward(input).toTensor[Float] + Equivalent.nearequals(outDnn.toTensor, outBlas.toTensor, 1e-5) should be (true) + + val gradOutput = Tensor[Float]().resizeAs(outBlas).copy(outBlas) + val gradInputBlas = blas.backward(input, gradOutput) + val gradInputDnn = dnn.backward(input, gradOutput) + Equivalent.nearequals(gradInputBlas.toTensor, gradInputDnn.toTensor, 1e-5) should be (true) + } + "linear updateOutput" should "work correctly" in { val inputSize = 2 val outputSize = 2 diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala index 91d4ae0e0b4..f017f06b051 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/MaxPoolingSpec.scala @@ -16,10 +16,11 @@ package com.intel.analytics.bigdl.nn.mkldnn import com.intel.analytics.bigdl.mkl.{AlgKind, DataType, Memory} +import com.intel.analytics.bigdl.nn.abstractnn.DataFormat import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} -import com.intel.analytics.bigdl.nn.{SpatialAveragePooling, SpatialMaxPooling} +import com.intel.analytics.bigdl.nn.{Graph, SpatialAveragePooling, SpatialMaxPooling, StaticGraph} import com.intel.analytics.bigdl.tensor.{DnnTensor, Tensor} -import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Engine} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Engine, MklDnn} import com.intel.analytics.bigdl.utils.RandomGenerator.RNG import com.intel.analytics.bigdl.utils.intermediate.{BlasToIR, IRToDnn} import org.apache.commons.lang3.SerializationUtils @@ -68,6 +69,38 @@ class MaxPoolingSpec extends BigDLSpecHelper { grad1 should be(grad2) } + "Max Pooling with NHWC format" should "be correct" in { + Engine.setEngineType(MklDnn) + val batchSize = 2 + val input = Tensor[Float](batchSize, 28, 28, 480).apply1(e => Random.nextFloat()) + val gradOutput = Tensor[Float](batchSize, 14, 14, 480).apply1(e => Random.nextFloat()) + + val pad = -1 + RNG.setSeed(100) + val pool = MaxPooling(3, 3, 2, 2, padH = pad, padW = pad) + RNG.setSeed(100) + val layer = SpatialMaxPooling[Float](3, 3, 2, 2, padH = pad, padW = pad, + format = DataFormat.NHWC).ceil() + val layer2 = SpatialMaxPooling[Float](3, 3, 2, 2, padH = pad, padW = pad).ceil() + + import com.intel.analytics.bigdl.nn + val static = nn.Sequential[Float]().add(layer2) + .toGraph().asInstanceOf[StaticGraph[Float]] + static.setInputFormats(Seq(Memory.Format.nhwc)) + static.setOutputFormats(Seq(Memory.Format.nhwc)) + + val dnn = static.toIRgraph() + + val output1 = dnn.forward(input) + val output2 = layer.forward(input).toTensor[Float] + + output1 should be(output2) + + val grad2 = layer.backward(input, output2).toTensor[Float] + val grad1 = dnn.backward(input, output2) + grad1 should be(grad2) + } + "Convert max pooling with ceilMode to dnn layer" should "be correct" in { val batchSize = 2 val input = Tensor[Float](batchSize, 480, 28, 28).apply1(e => Random.nextFloat()) diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala index 3cc1a619bea..f8088682ca1 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/nn/mkldnn/RNNSpec.scala @@ -15,17 +15,19 @@ */ package com.intel.analytics.bigdl.nn.mkldnn +import com.intel.analytics.bigdl.example.languagemodel.PTBModel import org.scalatest.{FlatSpec, Matchers} import com.intel.analytics.bigdl.mkl.{AlgKind, Direction, Memory} import com.intel.analytics.bigdl.nn.mkldnn.Phase.{InferencePhase, TrainingPhase} import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.nn -import com.intel.analytics.bigdl.nn.{Recurrent, StaticGraph} -import com.intel.analytics.bigdl.nn.StaticGraph +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat import com.intel.analytics.bigdl.utils.RandomGenerator._ -import com.intel.analytics.bigdl.utils.{T, Table} +import com.intel.analytics.bigdl.utils._ + +import scala.util.Random class RNNSpec extends FlatSpec with Matchers{ "LSTM UnidirectionalInference updateOutput" should "work correctly" in { @@ -2414,4 +2416,35 @@ class RNNSpec extends FlatSpec with Matchers{ System.clearProperty("bigdl.engineType") } + + "PTB model" should "work correctly" in { + System.setProperty("bigdl.engineType", "mkldnn") + Random.setSeed(10) + + val inputSize: Int = 1000 + val hiddenSize: Int = 128 + val outputSize: Int = 1000 + val numLayers: Int = 2 + val keepProb: Float = 1.0f + + Engine.setEngineType(MklBlas) + val model = PTBModel.lstm(inputSize, hiddenSize, outputSize, numLayers, keepProb) + Engine.setEngineType(MklDnn) + val modelDnn = model.asInstanceOf[StaticGraph[Float]].cloneModule().toIRgraph() + + val input = Tensor[Float](4, 35).apply1(n => Random.nextInt(999)) + + val output = model.forward(input).toTensor[Float] + val outDnn = modelDnn.forward(input).toTensor[Float] + + val gradOutput = Tensor[Float]().resizeAs(output).copy(output) + + val gradInput = model.backward(input, gradOutput).toTensor[Float] + val gradInputDnn = modelDnn.backward(input, gradOutput).toTensor[Float] + + Equivalent.nearequals(output, outDnn) should be(true) + Equivalent.nearequals(gradInput, gradInputDnn) should be(true) + + System.clearProperty("bigdl.engineType") + } } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala index d957a29a6b2..63229b4a3c0 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/utils/intermediate/IRconvertSpec.scala @@ -19,6 +19,7 @@ package com.intel.analytics.bigdl.utils.intermediate import com.intel.analytics.bigdl.mkl.Memory import com.intel.analytics.bigdl.nn._ import com.intel.analytics.bigdl.nn.abstractnn.DataFormat +import com.intel.analytics.bigdl.nn.keras._ import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, Equivalent, Input, Output} import com.intel.analytics.bigdl.numeric.NumericFloat @@ -43,7 +44,7 @@ class IRconvertSpec extends BigDLSpecHelper { val pool1 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) val conv2 = Node(IRElement[Float]("", IRSpatialConvolution[Float](20, 50, 5, 5))) val pool2 = Node(IRElement[Float]("", IRSpatialMaxPooling[Float](2, 2, 2, 2))) - val reshape = Node(IRElement("", IRGeneralModule[Float](Reshape[Float](Array(50*4*4))))) + val reshape = Node(IRElement("", IRGeneralModule[Float](nn.Reshape[Float](Array(50*4*4))))) val linear = Node(IRElement("", IRLinear[Float](50 * 4 * 4, 500))) val relu = Node(IRElement("", IRReLU[Float]())) val fc2 = Node(IRElement("output", IRLinear[Float](500, 10))) @@ -91,6 +92,144 @@ class IRconvertSpec extends BigDLSpecHelper { Graph(conv1, output) } + def keras(classNum: Int, shape: Shape = Shape(28, 28, 3)): nn.keras.Sequential[Float] = { + import com.intel.analytics.bigdl.nn.keras._ + import com.intel.analytics.bigdl.utils.Shape + + val model = Sequential() + model.add(Convolution2D(6, 5, 5, activation = "tanh", + dimOrdering = "tf", inputShape = shape).setName("conv1_5x5")) + model.add(BatchNormalization(dimOrdering = "tf")).setName("bnbn") + model.add(MaxPooling2D(dimOrdering = "tf")) + model.add(Convolution2D(12, 5, 5, activation = "tanh", dimOrdering = "tf") + .setName("conv2_5x5")) + model.add(BatchNormalization(dimOrdering = "tf")).setName("bnbn22") + model.add(MaxPooling2D(dimOrdering = "tf")) + model.add(Flatten()) + model.add(Dense(100, activation = "tanh").setName("fc1")) + model.add(Dense(classNum, activation = "softmax").setName("fc2")) + model + } + + "Convert Blas with NHWC" should "be correct" in { + System.setProperty("bigdl.engineType", "mkldnn") + val input = Tensor[Float](2, 28, 28, 1).rand() + val gradOutput = Tensor[Float](2, 10).rand() + + val blas = modelBlas(format = DataFormat("NHWC")).asInstanceOf[StaticGraph[Float]] + blas.setInputFormats(Seq(Memory.Format.nhwc)) + blas.setOutputFormats(Seq(Memory.Format.nc)) + val dnn = blas.cloneModule().toIRgraph() + + val outBlas = blas.forward(input) + val outDnn = dnn.forward(input) + Equivalent.nearequals(outDnn.toTensor, outBlas.toTensor, 1e-4) should be (true) + + val gradInputBlas = blas.backward(input, gradOutput) + val gradInputDnn = dnn.backward(input, gradOutput).toTensor[Float] + Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) + System.clearProperty("bigdl.engineType") + } + + // todo: use those unit tests after supporting keras conversion + +// "Running keras with mkldnn" should "be correct"in { +// System.setProperty("bigdl.engineType", "mkldnn") +// val model = keras(classNum = 10) +// +// val blas = model.toGraph().asInstanceOf[StaticGraph[Float]] +// blas.setInputFormats(Seq(Memory.Format.nhwc)) +// blas.setOutputFormats(Seq(Memory.Format.nc)) +// +// val dnn = blas.cloneModule().asInstanceOf[StaticGraph[Float]].toIRgraph() +// +// val input = Tensor[Float](2, 28, 28, 3).rand() +// +// val out1 = blas.forward(input) +// val out2 = dnn.forward(input) +// Equivalent.nearequals(out1.toTensor[Float], out2.toTensor[Float], 1e-5) should be (true) +// +// val gradOutput = Tensor[Float]().resizeAs(out1.toTensor[Float]) +// +// val gradInputBlas = blas.backward(input, gradOutput) +// val gradInputDnn = dnn.backward(input, gradOutput) +// Equivalent.nearequals(gradInputDnn.toTensor, gradInputBlas.toTensor, 1e-4) should be (true) +// System.clearProperty("bigdl.engineType") +// } +// +// "KSequential to IRGraph" should "work" in { +// System.setProperty("bigdl.engineType", "mkldnn") +// +// RandomGenerator.RNG.setSeed(10) +// import com.intel.analytics.bigdl.mkl.Memory +// +// val seq = nn.keras.Sequential[Float]() +// seq.add(InputLayer(inputShape = Shape(20, 100))) +// seq.add(Convolution1D(10, 5, activation = "relu")) +// seq.add(GlobalMaxPooling1D()) +// seq.add(Dense(128)) +// // seq.add(KDropout(0.2)) +// seq.add(Activation("relu")) +// seq.add(Dense(10, activation = "softmax")) +// +// // For such cases, toSingleGraph() is unnecessary +// val graph = seq.toGraph().asInstanceOf[StaticGraph[Float]] +// graph.asInstanceOf[StaticGraph[Float]].setInputFormats(Seq(Memory.Format.ntc)) +// graph.asInstanceOf[StaticGraph[Float]].setOutputFormats(Seq(Memory.Format.nc)) +// // set gradWeight +// graph.getParameters()._2.rand() +// +// val ir = graph.asInstanceOf[StaticGraph[Float]].cloneModule().toIRgraph() +// +// val tensor = Tensor[Float](Array(3, 20, 100)).rand() +// val outputBlas = graph.forward(tensor) +// val output = ir.forward(tensor) +// outputBlas should be(output) +// +// val gradOutput = Tensor[Float]().resizeAs(outputBlas.toTensor[Float]).rand() +// val gradInputBlas = graph.backward(tensor, gradOutput) +// val gradInput = ir.backward(tensor, gradOutput) +// +// Equivalent.nearequals(gradInput.toTensor[Float], +// gradInputBlas.toTensor[Float], 1e-5) should be(true) +// +// System.clearProperty("bigdl.engineType") +// } +// +// "KGraph to IRGraph" should "work" in { +// System.setProperty("bigdl.engineType", "mkldnn") +// RandomGenerator.RNG.setSeed(10) +// +// import com.intel.analytics.bigdl.mkl.Memory +// val input = nn.keras.Input[Float](inputShape = Shape(10)) +// val d = nn.keras.Dense[Float](20, activation = "relu").setName("dense1").inputs(input) +// val d2 = nn.keras.Dense[Float](5).setName("dense2").inputs(d) +// val model = nn.keras.Model[Float](input, d2) +// +// val graph = model.toGraph().asInstanceOf[StaticGraph[Float]] +// graph.asInstanceOf[StaticGraph[Float]].setInputFormats(Seq(Memory.Format.nc)) +// graph.asInstanceOf[StaticGraph[Float]].setOutputFormats(Seq(Memory.Format.nc)) +// +// // set gradWeight +// graph.getParameters()._2.rand() +// +// // graph.evaluate() +// val ir = graph.asInstanceOf[StaticGraph[Float]].cloneModule().toIRgraph() +// val tensor = Tensor[Float](Array(3, 10)).rand() +// +// val outputBlas = graph.forward(tensor) +// val output = ir.forward(tensor) +// +// val gradOutput = Tensor[Float]().resizeAs(outputBlas.toTensor[Float]).rand() +// val gradInputBlas = graph.backward(tensor, gradOutput) +// val gradInput = ir.backward(tensor, gradOutput) +// +// outputBlas should be(output) +// gradInputBlas should be(gradInput) +// +// System.clearProperty("bigdl.engineType") +// } + "Convert Blas with NCHW to Dnn" should "be correct" in { System.setProperty("bigdl.engineType", "mkldnn") val input = Tensor[Float](2, 1, 28, 28).rand() @@ -246,7 +385,7 @@ class IRconvertSpec extends BigDLSpecHelper { "convert blas gap to dnn" should "work correctly" in { System.setProperty("bigdl.engineType", "mkldnn") - val graph = Sequential() + val graph = nn.Sequential() .add(SpatialAveragePooling[Float](2, 2, globalPooling = true)) .toGraph() From ac48debc72953f0becbf9c0d7c9d513b1e86dbe0 Mon Sep 17 00:00:00 2001 From: Firecrackerxox Date: Wed, 8 Jan 2020 09:21:01 +0800 Subject: [PATCH 1013/1065] Keras with MKL-DNN backend support (#2990) --- .../bigdl/dllib/keras/KerasLayer.scala | 95 ++++++++++++- .../analytics/bigdl/dllib/nn/Squeeze.scala | 13 ++ .../bigdl/dllib/nn/StaticGraph.scala | 23 ---- .../dllib/nn/abstractnn/AbstractModule.scala | 38 +++++- .../bigdl/dllib/keras/nn/KerasStyleSpec.scala | 129 +++++++++++++++++- 5 files changed, 267 insertions(+), 31 deletions(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala index e1b3ffe4e4d..9e36beec698 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/keras/KerasLayer.scala @@ -20,7 +20,7 @@ import com.intel.analytics.bigdl._ import com.intel.analytics.bigdl.nn.Graph._ import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity} import com.intel.analytics.bigdl.nn.keras.{Sequential => KSequential} -import com.intel.analytics.bigdl.nn.{Container => TContainer} +import com.intel.analytics.bigdl.nn.{Graph, StaticGraph, Container => TContainer, Input => TInput, Sequential => TSequential} import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric @@ -167,6 +167,91 @@ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: C inputShapeValue = batchInputShape + override def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { + if (this.isKerasGraph()) { + this.toGraph().getEndNodes(startNodes) + } else if (labor.isKerasStyle() && labor.getName().equals(this.getName())) { + Array(this.processInputs(startNodes)) + } else { + labor.getEndNodes(startNodes) + } + } + + override def toGraph(startNodes: ModuleNode[T]*): Graph[T] = { + if (this.isKerasGraph()) { + val graph = labor.asInstanceOf[StaticGraph[T]] + val fwdExecutions = graph.getSortedForwardExecutions() + for (i <- 0 until fwdExecutions.length) { + val layer = fwdExecutions(i).element.asInstanceOf[KerasLayer[Activity, Activity, T]] + if (layer.isKerasContainer()) { + fwdExecutions(i).element = layer.toGraph() + } else if ((!layer.labor.isKerasStyle() + && layer.labor.isInstanceOf[TContainer[Activity, Activity, T]]) || + (layer.isKerasStyle() && layer.labor.isKerasStyle() && + layer.labor.asInstanceOf[KerasLayer[Activity, Activity, T]].isKerasContainer())) { + fwdExecutions(i).element = layer.labor.toGraph() + } else { + fwdExecutions(i).element = layer.labor + } + } + val result = graph.toSingleGraph() + if (inputsFormats != null) { + result.setInputFormats(inputsFormats) + } + + if (inputsFormats != null) { + result.setOutputFormats(outputsFormats) + } + result + } else if (this.isKerasSequential()) { + val starts = if (startNodes.isEmpty) Array(TInput[T]()) else startNodes.toArray + val endNodes = this.getEndNodes(starts) + // Disable excludeInvalidLayers to allow customized Keras layers + val result = new StaticGraph(starts, endNodes, enableExcludeChecking = false).toSingleGraph() + if (inputsFormats != null) { + result.setInputFormats(inputsFormats) + } + + if (outputsFormats != null) { + result.setOutputFormats(outputsFormats) + } + result + } else { + this.labor.toGraph() + } + } + + private def isKerasGraph(): Boolean = { + if (labor.isInstanceOf[StaticGraph[T]]) { + val fwdExecutions = labor.asInstanceOf[StaticGraph[T]].getForwardExecutions() + for (i <- 0 until fwdExecutions.length) { + if (!fwdExecutions(i).element.isKerasStyle()) { + return false + } + } + true + } else { + false + } + } + + private def isKerasSequential(): Boolean = { + if (labor.isInstanceOf[TSequential[T]]) { + for (i <- 0 until labor.asInstanceOf[TSequential[T]].modules.length) { + if (!labor.asInstanceOf[TSequential[T]].modules(i).isKerasStyle()) { + return false + } + } + true + } else { + false + } + } + + private def isKerasContainer(): Boolean = { + isKerasGraph() || isKerasSequential() + } + def labor: AbstractModule[A, B, T] = { if (this.modules.isEmpty) { throw new RuntimeException("This Layer hasn't been built") @@ -183,6 +268,14 @@ abstract class KerasLayer[A <: Activity: ClassTag, B <: Activity: ClassTag, T: C } // scalastyle:on + override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = { + if (isBuilt()) { + labor.parameters() + } else { + null + } + } + override def updateOutput(input: A): B = { output = labor.updateOutput(input) output diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala index b1c0a8bc8ad..95947f5bf94 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/Squeeze.scala @@ -18,6 +18,8 @@ package com.intel.analytics.bigdl.nn import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, TensorModule} import com.intel.analytics.bigdl.tensor.Tensor import com.intel.analytics.bigdl.tensor.TensorNumericMath.{NumericWildcard, TensorNumeric} +import com.intel.analytics.bigdl.utils.Shape +import scala.collection.mutable.ArrayBuffer import scala.reflect.ClassTag @@ -46,6 +48,17 @@ class Squeeze[T: ClassTag]( dims } + override def computeOutputShape(inputShape: Shape): Shape = { + val _inputSize = inputShape.toSingle().toArray + var resultSize = new ArrayBuffer[Int]() + for (i <- 1 to _inputSize.length) { + if (!dims.contains(i) || (dims.contains(i) && _inputSize(i - 1) != 1)) { + resultSize.append(_inputSize(i - 1)) + } + } + Shape(resultSize.toArray) + } + override def updateOutput(input: Tensor[_]): Tensor[_] = { if (output.getType() != input.getType()) { output = input.emptyInstance() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala index e8061c60bcf..4cd8a9090e4 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/StaticGraph.scala @@ -151,29 +151,6 @@ class StaticGraph[T: ClassTag]( gradInput } - private var inputsFormats: Seq[Int] = null - private var outputsFormats: Seq[Int] = null - - /** - * set input formats for graph - * @param formats - * @return - */ - def setInputFormats(formats: Seq[Int]): this.type = { - inputsFormats = formats - this - } - - /** - * set output formats for graph - * @param formats - * @return - */ - def setOutputFormats(formats: Seq[Int]): this.type = { - outputsFormats = formats - this - } - /** * convert static graph to ir graph and build according to engine type * @return return ir graph if converted successfully, otherwise null diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala index 7a7207c9808..9f0827bdd41 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nn/abstractnn/AbstractModule.scala @@ -72,6 +72,29 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, */ var gradInput: A = Activity.allocate[A, T]() + protected var inputsFormats: Seq[Int] = null + protected var outputsFormats: Seq[Int] = null + + /** + * set input formats for graph + * @param formats + * @return + */ + def setInputFormats(formats: Seq[Int]): this.type = { + inputsFormats = formats + this + } + + /** + * set output formats for graph + * @param formats + * @return + */ + def setOutputFormats(formats: Seq[Int]): this.type = { + outputsFormats = formats + this + } + /** * Get the scale of gradientWeight */ @@ -828,13 +851,18 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, def toGraph(startNodes: ModuleNode[T]*): Graph[T] = { val starts = if (startNodes.isEmpty) Array(Input[T]()) else startNodes.toArray val endNodes = this.getEndNodes(starts) - val graph = Graph(starts, endNodes) + var graph = Graph(starts, endNodes) if (graph.isInstanceOf[StaticGraph[T]]) { // Merge nested graphs inside to make the whole graph non-nested - graph.asInstanceOf[StaticGraph[T]].toSingleGraph() - } else { - graph + graph = graph.asInstanceOf[StaticGraph[T]].toSingleGraph() + } + if (inputsFormats != null) { + graph.setInputFormats(inputsFormats) + } + if (outputsFormats != null) { + graph.setOutputFormats(outputsFormats) } + graph } /** @@ -1109,7 +1137,7 @@ abstract class AbstractModule[A <: Activity: ClassTag, B <: Activity: ClassTag, * @return current end nodes */ private[bigdl] def getEndNodes(startNodes: Array[ModuleNode[T]]): Array[ModuleNode[T]] = { - val endNodes = Array(this.inputs(startNodes: _*)) + val endNodes = Array(this.processInputs(startNodes)) endNodes } diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala index bcff5090b45..36fd9671ff2 100644 --- a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/keras/nn/KerasStyleSpec.scala @@ -18,11 +18,12 @@ package com.intel.analytics.bigdl.keras.nn import com.intel.analytics.bigdl.example.loadmodel.AlexNet_OWT import com.intel.analytics.bigdl.nn.abstractnn.InvalidLayer -import com.intel.analytics.bigdl.nn.keras.{Activation, Dense, Input, InputLayer, KerasIdentityWrapper, Model, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.keras.{Activation, Convolution1D, Dense, GlobalMaxPooling1D, Input, InputLayer, KerasIdentityWrapper, Model, Sequential => KSequential} +import com.intel.analytics.bigdl.nn.mkldnn.Equivalent import com.intel.analytics.bigdl.nn.{Input => TInput, Sequential => TSequential, _} import com.intel.analytics.bigdl.numeric.NumericFloat import com.intel.analytics.bigdl.tensor.Tensor -import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, Shape, T, TestUtils} +import com.intel.analytics.bigdl.utils.{BigDLSpecHelper, RandomGenerator, Shape, T, TestUtils} class KerasStyleSpec extends BigDLSpecHelper { @@ -272,4 +273,128 @@ class KerasStyleSpec extends BigDLSpecHelper { seq.forward(output) TestUtils.compareOutputShape(seq, Shape(2, 3)) should be (true) } + + "KSequential to IRGraph" should "work" in { + System.setProperty("bigdl.engineType", "mkldnn") + + RandomGenerator.RNG.setSeed(10) + import com.intel.analytics.bigdl.mkl.Memory + + val seq = KSequential[Float]() + seq.add(InputLayer(inputShape = Shape(20, 100))) + seq.add(Convolution1D(10, 5, activation = "relu")) + seq.add(GlobalMaxPooling1D()) + seq.add(Dense(128)) + // seq.add(KDropout(0.2)) + seq.add(Activation("relu")) + seq.add(Dense(10, activation = "softmax")) + + val graph = seq.toGraph().asInstanceOf[StaticGraph[Float]] + graph.asInstanceOf[StaticGraph[Float]].setInputFormats(Seq(Memory.Format.ntc)) + graph.asInstanceOf[StaticGraph[Float]].setOutputFormats(Seq(Memory.Format.nc)) + val ir = graph.asInstanceOf[StaticGraph[Float]].cloneModule().toIRgraph() + + val tensor = Tensor[Float](Array(3, 20, 100)).rand() + val outputBlas = graph.forward(tensor) + val output = ir.forward(tensor) + outputBlas should be(output) + + val gradOutput = Tensor[Float]().resizeAs(outputBlas.toTensor[Float]).rand() + val gradInputBlas = graph.backward(tensor, gradOutput) + val gradInput = ir.backward(tensor, gradOutput) + + Equivalent.nearequals(gradInput.toTensor[Float], + gradInputBlas.toTensor[Float], 1e-5) should be(true) + + System.clearProperty("bigdl.engineType") + } + + "KGraph to IRGraph" should "work" in { + System.setProperty("bigdl.engineType", "mkldnn") + import com.intel.analytics.bigdl.mkl.Memory + val input = Input[Float](inputShape = Shape(10)) + val d = Dense[Float](20, activation = "relu").setName("dense1").inputs(input) + val d2 = Dense[Float](5).setName("dense2").inputs(d) + val model = Model[Float](input, d2) + + val graph = model.toGraph().asInstanceOf[StaticGraph[Float]] + graph.asInstanceOf[StaticGraph[Float]].setInputFormats(Seq(Memory.Format.nc)) + graph.asInstanceOf[StaticGraph[Float]].setOutputFormats(Seq(Memory.Format.nc)) + val ir = graph.asInstanceOf[StaticGraph[Float]].cloneModule().toIRgraph() + + val tensor = Tensor[Float](Array(3, 10)).rand() + val outputBlas = graph.forward(tensor) + val output = ir.forward(tensor) + + val gradOutput = Tensor[Float]().resizeAs(outputBlas.toTensor[Float]).rand() + val gradInputBlas = graph.backward(tensor, gradOutput) + val gradInput = ir.backward(tensor, gradOutput) + + outputBlas should be(output) + gradInputBlas should be(gradInput) + + System.clearProperty("bigdl.engineType") + } + + "KSequential with KGraph module to IRGraph" should "work" in { + System.setProperty("bigdl.engineType", "mkldnn") + import com.intel.analytics.bigdl.mkl.Memory + val input = Input[Float](inputShape = Shape(10)) + val d = Dense[Float](20, activation = "relu").setName("dense1").inputs(input) + val d2 = Dense[Float](5).setName("dense2").inputs(d) + val kgraph = Model[Float](input, d2) + + val seq = KSequential[Float]() + seq.add(kgraph) + + val graph = seq.toGraph().asInstanceOf[StaticGraph[Float]] + graph.asInstanceOf[StaticGraph[Float]].setInputFormats(Seq(Memory.Format.nc)) + graph.asInstanceOf[StaticGraph[Float]].setOutputFormats(Seq(Memory.Format.nc)) + val ir = graph.asInstanceOf[StaticGraph[Float]].toIRgraph() + + val tensor = Tensor[Float](Array(3, 10)).rand() + + val outputBlas = graph.forward(tensor) + val output = ir.forward(tensor) + + val gradOutput = Tensor[Float]().resizeAs(outputBlas.toTensor[Float]).rand() + val gradInputBlas = graph.backward(tensor, gradOutput) + val gradInput = ir.backward(tensor, gradOutput) + + outputBlas should be(output) + gradInputBlas should be(gradInput) + + System.clearProperty("bigdl.engineType") + } + + "KGraph with KGraph module to IRGraph" should "work" in { + System.setProperty("bigdl.engineType", "mkldnn") + import com.intel.analytics.bigdl.mkl.Memory + val input1 = Input[Float](inputShape = Shape(10)) + + val input2 = Input[Float](inputShape = Shape(10)) + val d = Dense[Float](20, activation = "relu").setName("dense1").inputs(input2) + val d2 = Dense[Float](5).setName("dense2").inputs(d) + val kgraph1 = Model[Float](input2, d2).inputs(input1) + + val kgraph2 = Model[Float](input1, kgraph1) + + val graph = kgraph2.toGraph().asInstanceOf[StaticGraph[Float]] + graph.asInstanceOf[StaticGraph[Float]].setInputFormats(Seq(Memory.Format.nc)) + graph.asInstanceOf[StaticGraph[Float]].setOutputFormats(Seq(Memory.Format.nc)) + val ir = graph.asInstanceOf[StaticGraph[Float]].toIRgraph() + + val tensor = Tensor[Float](Array(3, 10)).rand() + val outputBlas = graph.forward(tensor) + val output = ir.forward(tensor) + + val gradOutput = Tensor[Float]().resizeAs(outputBlas.toTensor[Float]).rand() + val gradInputBlas = graph.backward(tensor, gradOutput) + val gradInput = ir.backward(tensor, gradOutput) + + outputBlas should be(output) + gradInputBlas should be(gradInput) + + System.clearProperty("bigdl.engineType") + } } From ed05ebe54791e28e790d2e39467234f4b6887b97 Mon Sep 17 00:00:00 2001 From: Yanzhang Wang Date: Tue, 10 Mar 2020 11:16:57 +0800 Subject: [PATCH 1014/1065] feat: add distri optimizer v2 (#2992) --- .../bigdl/dllib/optim/DistriOptimizer.scala | 45 +- .../bigdl/dllib/optim/DistriOptimizerV2.scala | 1155 +++++++++++++++++ .../bigdl/dllib/optim/ParallelOptimizer.scala | 10 +- .../dllib/optim/DistriOptimizerV2Spec.scala | 893 +++++++++++++ 4 files changed, 2081 insertions(+), 22 deletions(-) create mode 100644 scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerV2.scala create mode 100644 scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerV2Spec.scala diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala index 21824f70b11..19fbfa57dca 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizer.scala @@ -60,7 +60,19 @@ object DistriOptimizer extends AbstractOptimizer { * @param parameterSynchronizer cached parameter synchronizer * @tparam T Tensor element type */ - case class Cache[T]( + + abstract class Cache[T] { + def localModels: Array[Module[T]] + def modelWeights: Array[Tensor[T]] + def modelGradients: Array[Tensor[T]] + def localCriterions: Array[Criterion[T]] + def localMethods: Array[Option[Array[ValidationMethod[T]]]] + def optimMethods: Map[String, OptimMethod[T]] + def moduleTimeList: Array[Long] + def parameterSynchronizer: DistriParameterSynchronizer[T] + } + + case class CacheV1[T]( localModels: Array[Module[T]], modelWeights: Array[Tensor[T]], modelGradients: Array[Tensor[T]], @@ -70,7 +82,7 @@ object DistriOptimizer extends AbstractOptimizer { localMethods: Array[Option[Array[ValidationMethod[T]]]], var optimMethods: Map[String, OptimMethod[T]], parameterSynchronizer: DistriParameterSynchronizer[T] = null - ) + ) extends Cache[T] /** * Train the model. @@ -101,7 +113,7 @@ object DistriOptimizer extends AbstractOptimizer { state: Table, endWhen: Trigger, metrics: Metrics, - models: RDD[Cache[T]], + models: RDD[CacheV1[T]], optimMethods: Map[String, OptimMethod[T]], parameters: AllReduceParameter[T], parameterSplits: Map[String, (Int, Int)], @@ -350,7 +362,8 @@ object DistriOptimizer extends AbstractOptimizer { driverState("isGradientUpdated") = false // parameterProcesser like L2NormClippingProcessor may aggregate gradient, // and change the value of isGradientUpdated in driverState. - parameterProcessers.foreach(_.collectGlobalData(models, parameters, metrics, driverState)) + parameterProcessers.foreach(_.collectGlobalData(models.asInstanceOf[RDD[Cache[T]]], + parameters, metrics, driverState)) val isGradientUpdated = driverState[Boolean]("isGradientUpdated") val stateBroadcast = sc.broadcast(driverState) @@ -478,7 +491,7 @@ object DistriOptimizer extends AbstractOptimizer { validationDataSet, validationMethods, coresPerNode, - models, + models.asInstanceOf[RDD[Cache[T]]], driverState, validationSummary, _header, @@ -488,7 +501,7 @@ object DistriOptimizer extends AbstractOptimizer { trainSummary.foreach { summary => saveSummary( summary, - models, + models.asInstanceOf[RDD[Cache[T]]], driverState, parameters, trainingModel @@ -500,7 +513,7 @@ object DistriOptimizer extends AbstractOptimizer { cachePath, isOverWrite, wallClockTime, - models, + models.asInstanceOf[RDD[Cache[T]]], driverState, parameters, optimMethods, @@ -548,8 +561,7 @@ object DistriOptimizer extends AbstractOptimizer { validationMethods: Option[Array[ValidationMethod[T]]], optimMethod: Map[String, OptimMethod[T]], parameterProcessors: ArrayBuffer[ParameterProcessor] - )(implicit ev: TensorNumeric[T]): (RDD[DistriOptimizer - .Cache[T]], ModelBroadcast[T]) = { + )(implicit ev: TensorNumeric[T]): (RDD[DistriOptimizer.CacheV1[T]], ModelBroadcast[T]) = { val sc = dataset.originRDD().sparkContext val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod)) val convertedModel = ConversionUtils.convert(model) @@ -621,7 +633,7 @@ object DistriOptimizer extends AbstractOptimizer { allReduceParameter.init(weights.narrow(1, allReduceParameter.paramOffset, allReduceParameter.size)) - Iterator.single(Cache( + Iterator.single(CacheV1( cached.map(_._1), // models cached.map(_._2), // weights cached.map(_._3), // gradients @@ -714,7 +726,7 @@ class DistriOptimizer[T: ClassTag]( _model, _dataset, _criterion) { val metrics = new Metrics - private var models: RDD[DistriOptimizer.Cache[T]] = null + private var models: RDD[DistriOptimizer.CacheV1[T]] = null // this variable is used to check the models cloned when broadcast, if there're native resources, // it will be deleted at the end of Optimizer. private var modelBroadcast: ModelBroadcast[T] = null @@ -726,7 +738,7 @@ class DistriOptimizer[T: ClassTag]( * If the optimize fails, you may call it before next optimize. */ def clearState(): Unit = { - DistriOptimizer.clearState(models) + DistriOptimizer.clearState(models.asInstanceOf[RDD[DistriOptimizer.Cache[T]]]) } @@ -753,10 +765,8 @@ class DistriOptimizer[T: ClassTag]( // replace optim methods with previous private def resetOptimMethods[T: ClassTag]( - models: RDD[DistriOptimizer.Cache[T]], - previousOptimMethods: RDD[Map[String, - OptimMethod[T]]]): - RDD[DistriOptimizer.Cache[T]] = { + models: RDD[DistriOptimizer.CacheV1[T]], + previousOptimMethods: RDD[Map[String, OptimMethod[T]]]): RDD[DistriOptimizer.CacheV1[T]] = { models.zipPartitions(previousOptimMethods) { (m1, m2) => { val cache = m1.next() cache.optimMethods = m2.next() @@ -962,7 +972,8 @@ class DistriOptimizer[T: ClassTag]( } } - DistriOptimizer.getModel(models, allReduceParameter, trainingModel) + DistriOptimizer.getModel(models.asInstanceOf[RDD[DistriOptimizer.Cache[T]]], + allReduceParameter, trainingModel) // Reset some internal states, so this or other optimizers can run optimize again clearState() diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerV2.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerV2.scala new file mode 100644 index 00000000000..2706c9d2680 --- /dev/null +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerV2.scala @@ -0,0 +1,1155 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import java.io.File +import java.text.SimpleDateFormat +import java.util.Calendar + +import com.intel.analytics.bigdl.dataset.{DistributedDataSet, MiniBatch, PaddingParam, Sample} +import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast} +import com.intel.analytics.bigdl.nn.{Container, Module} +import com.intel.analytics.bigdl.parameters.{AllReduceParameter, ParameterProcessor} +import com.intel.analytics.bigdl.tensor.Tensor +import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.utils.intermediate.ConversionUtils +import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary} +import com.intel.analytics.bigdl.{Module, _} +import org.apache.log4j.Logger +import org.apache.spark.rdd.RDD +import org.apache.spark.{SparkContext, TaskContext} + +import scala.collection.mutable +import scala.reflect.ClassTag + +object DistriOptimizerV2 extends AbstractOptimizer { + + case class Cache[T]( + localModels: Array[Module[T]], + modelWeights: Array[Tensor[T]], + modelGradients: Array[Tensor[T]], + localCriterions: Array[Criterion[T]], + localStates: Array[Table], + var moduleTimeList: Array[Long] = null, + localMethods: Array[Option[Array[ValidationMethod[T]]]], + var optimMethods: Map[String, OptimMethod[T]], + parameterSynchronizer: DistriParameterSynchronizer[T] = null, + parameter: AllReduceParameter[T] = null, + parameterSplits: Map[String, (Int, Int)] = null, + parameterProcessers: Array[ParameterProcessor] = null) extends DistriOptimizer.Cache[T] + + import Optimizer._ + + private[DistriOptimizerV2] var _logger: Option[OptimizerLogger] = None + + def logger: OptimizerLogger = { + if (_logger.isEmpty) { + _logger = Some(new DistriLogger) + } + + _logger.get + } + + private[optim] def optimize[T: ClassTag]( + cacheOfMaster: MasterCache[T], + cacheOfSlave: RDD[Cache[T]], + dataset: DistributedDataSet[MiniBatch[T]], + endWhen: Trigger, + validationTrigger: Option[Trigger], + validationDataSet: Option[DataSet[MiniBatch[T]]], + validationMethods: Option[Array[ValidationMethod[T]]], + cacheTrigger: Option[Trigger], + cachePath: Option[String], + trainSummary: Option[TrainSummary], + validationSummary: Option[ValidationSummary], + isOverWrite: Boolean, + context: TrainingContext[T] + )(implicit ev: TensorNumeric[T]): Unit = { + val headOptimMethod = cacheOfMaster.optimMethods.values.head + context.loadState(headOptimMethod.state) + logger.info(s"config ${context.state}") + + if (headOptimMethod.state[Int](StateEntry.RECORDS_PROCESSED) == 0) { + val shuffleBefore = System.nanoTime() + logger.info("Shuffle data") + dataset.shuffle() + val shuffleEnd = System.nanoTime() + logger.info(s"Shuffle data complete. Takes ${(shuffleEnd - shuffleBefore) / 1e9}s") + } + + val sc = dataset.originRDD().sparkContext + var trainingDataSet = dataset.data(train = true) + val trainingTrace = TrainingTrace(headOptimMethod.state) + + while (!endWhen(context.state)) { + iteration(sc, trainingDataSet, cacheOfSlave, cacheOfMaster, context, trainingTrace) + + if (context.hasCompleteAllSamples(trainingTrace.recordsOfEpoch, cacheOfMaster.model)) { + dataset.shuffle() + trainingDataSet = dataset.data(train = true) + } + + val _header = header( + trainingTrace.epochs, + trainingTrace.recordsOfEpoch, + context.numSamples, + trainingTrace.iterations, + trainingTrace.trainingTakes) + + validate( + validationTrigger, + validationDataSet, + validationMethods, + context.subModelNumber, + cacheOfSlave.asInstanceOf[RDD[DistriOptimizer.Cache[T]]], + context.state, + validationSummary, + _header, + cacheOfMaster.parameter + ) + + checkpoint( + cacheTrigger, + cachePath, + isOverWrite, + trainingTrace.trainingTakes, + cacheOfSlave.asInstanceOf[RDD[DistriOptimizer.Cache[T]]], + context.state, + cacheOfMaster.parameter, + cacheOfMaster.optimMethods, + cacheOfMaster.model + ) + + trainSummary.foreach { summary => + saveSummary( + summary, + cacheOfSlave.asInstanceOf[RDD[DistriOptimizer.Cache[T]]], + context.state, + cacheOfMaster.parameter, + cacheOfMaster.model + ) + } + } + } + + private def initMetrics(sc: SparkContext, metrics: Metrics, partitionNum: Int): Unit = { + metrics.set(COMPUTING_TIME_EACH_NODE.value, mutable.ArrayBuffer[Double](), sc) + metrics.set(GET_WEIGHTS_EACH_NODE.value, mutable.ArrayBuffer[Double](), sc) + metrics.set(COMPUTING_TIME_AVERAGE.value, 0.0, sc, partitionNum) + metrics.set(AGGREGATE_GRADIENT_TIME.value, 0.0, sc, partitionNum) + metrics.set(GET_WEIGHTS_AVERAGE.value, 0.0, sc, partitionNum) + metrics.set(PUT_GRADIENT.value, 0.0, sc, Engine.nodeNumber()) + metrics.set(AGGREGATE_PARTITION_GRADIENT.value, 0.0, sc, Engine.nodeNumber()) + metrics.set(COMPUTE_WEIGHT_AVERAGE.value, 0.0, sc, Engine.nodeNumber()) + metrics.set(SEND_WEIGHTS_AVERAGE.value, 0.0, sc, Engine.nodeNumber()) + } + + private def iteration[T: ClassTag]( + sc: SparkContext, + dataRDD: RDD[MiniBatch[T]], + models: RDD[Cache[T]], + cacheOfMaster: MasterCache[T], + context: TrainingContext[T], trainingTrace: TrainingTrace + )(implicit ev: TensorNumeric[T]): Unit = { + val lossSum = sc.accumulator(0.0, "loss sum") + val recordsNum = sc.accumulator(0, "record number") + val metrics = cacheOfMaster.metrics + val partitionNum = cacheOfMaster.partitionNum + initMetrics(sc, metrics, partitionNum) + + /* + Run the forwards/backwards pass using multiple threads in each partition, and track the + number of model updates that finished before the thread timeout mechanism. + */ + val training = dataRDD.zipPartitions(models, preservesPartitioning = true) { (data, iter) => + val cached = iter.next() + /* + Note: All models in `cached` share the same storage for weights, so we only need to + copy the weights from parameter server into the first model's weights. + */ + val offset = cached.parameter.paramOffset + val size = cached.parameter.size + val weights = cached.modelWeights.head.narrow(1, offset, size) + + TrainingTrace.time ( + cached.parameter.getWeights(weights).waitResult(), + metrics + )(Array(GET_WEIGHTS_AVERAGE, GET_WEIGHTS_EACH_NODE)) + + val results = train(cached, data, context, metrics) + + lossSum += results.loss + recordsNum += results.records + + Iterator.single(results.successed) + } + + val successModels = trainingTrace.traceIteration(training.reduce(_ + _)) + + parameterSync(lossSum.value, successModels, cacheOfMaster, models, context) + + driverStatesUpdate(cacheOfMaster, recordsNum.value, + context, trainingTrace, metrics) + } + + /** + * Init engine and cache models, weights, gradients, criterions, state tables + * and validation methods on worker nodes. + * + * @param model train model + * @param dataset train dataset + * @param criterion loss function + * @param state state table + * @param allReduceParameter all reduce parameter instance + * @param validationMethods validation methods + * @param optimMethod optimization method + * @return cached models + */ + private def initCacheOfSlave[T: ClassTag]( + cacheOfMaster: MasterCache[T], + dataset: DistributedDataSet[MiniBatch[T]], + context: TrainingContext[T])( + implicit ev: TensorNumeric[T]): (RDD[Cache[T]], ModelBroadcast[T]) = { + case class TrainingConfig[T](criterion: Criterion[T], + validationMethods: Option[Array[ValidationMethod[T]]], + optimMethods: Map[String, OptimMethod[T]], + parameterSplits: Map[String, (Int, Int)], + parameterProcessers: Array[ParameterProcessor] + ) + + case class Replica(model: Module[T], weights: Tensor[T], gradients: Tensor[T], + criterion: Criterion[T], state: Table, + validationMethods: Option[Array[ValidationMethod[T]]]) + + val config = TrainingConfig( + cacheOfMaster.criterion, + cacheOfMaster.validationMethods, + cacheOfMaster.optimMethods, + cacheOfMaster.parameterSplits, + cacheOfMaster.parameterProcessers) + + val sc = dataset.originRDD().sparkContext + val broadcast = sc.broadcast(config) + + val model = ConversionUtils.convert(cacheOfMaster.model) + // ensure model's parameter is compacted for getting a better performance when broadcasting + model.getParameters() + // As cloneModel is using Serialization to implement deep copy, and will throw OOMError + // when model's size is bigger than SerializationUtils' buffer size. So we can use + // ModelBroadcast to clone model here. + // Notes: All models returned by modelBroadcast.value() share the same weight&bias, while + // gradWeight&gradBias is unshared. + val modelBroadcast = ModelBroadcast[T]().broadcast(sc, model) + + val nExecutor = Engine.nodeNumber() + val executorCores = Engine.coreNumber() + val allReduceParameter = cacheOfMaster.parameter + + val subModelNumber = context.subModelNumber + val state = context.state + + val cache = dataset.originRDD().mapPartitions(_ => { + val partitionId = TaskContext.getPartitionId + val config = broadcast.value + Engine.setNodeAndCore(nExecutor, executorCores) + + val replicas = (0 until subModelNumber).map { _ => + val localModel = modelBroadcast.value(true) + val localCriterion = config.criterion.cloneCriterion() + val localState = state.clone() + val localMethod = if (config.validationMethods.isDefined) { + Some(config.validationMethods.get.map(_.clone())) + } else { + None + } + val (weights, grads) = localModel.getParameters() + + // at last, we bind the model to partition id + setModelId(localModel, partitionId) + + Replica(localModel, weights, grads, localCriterion, localState, localMethod) + }.toArray + + logger.info("model thread pool size is " + Engine.model.getPoolSize) + + // now we should init the all reduce parameters by the weights + // note: only get the head, because they are same in the array + val offset = allReduceParameter.paramOffset + val size = allReduceParameter.size + allReduceParameter.init(replicas.head.weights.narrow(1, offset, size)) + + Iterator.single(Cache( + replicas.map(_.model), + replicas.map(_.weights), + replicas.map(_.gradients), + replicas.map(_.criterion), + replicas.map(_.state), + new Array[Long](subModelNumber), + replicas.map(_.validationMethods), + config.optimMethods.map(v => (v._1, v._2.clone())), + null, + allReduceParameter, + config.parameterSplits, + config.parameterProcessers + )) + }).persist() + + cache.setName("Thread Model RDD") + logger.info("Cache thread models...") + cache.count() + logger.info("Cache thread models... done") + (cache, modelBroadcast) + } + + private def setModelId[T: ClassTag](model: Module[T], partitionId: Int): Unit = { + model.setId(partitionId) + if (model.isInstanceOf[Container[_, _, T]]) { + model.asInstanceOf[Container[_, _, T]].modules. + foreach(sub => setModelId(sub, partitionId)) + } + } + + /** + * Fetch current model parameters to driver, and copy to trainingModel. + * + * @param models cached models + * @param parameters [[AllReduceParameter]] + * @param trainingModel the model is trained by optimizer + * @return trained model + */ + override protected def getModel[T: ClassTag]( + models: RDD[DistriOptimizer.Cache[T]], + parameters: AllReduceParameter[T], + trainingModel: Module[T])(implicit + ev: TensorNumeric[T]) + : Module[T] = { + val partitionNum = models.partitions.length + val extraState = models.map(_.localModels.head.getExtraParameter()).first() + trainingModel.setExtraParameter(extraState) + + // make sure gradient is as the same length as weight + val parameterArray = trainingModel.parameters() + (0 until parameterArray._2.length).foreach(i => + parameterArray._2(i).resizeAs(parameterArray._1(i)) + ) + + val (parameter, gradientParameter) = trainingModel.getParameters() + + + val (weights, gradients) = models.mapPartitions(iter => { + val cached = iter.next() + val curPartitionId = TaskContext.getPartitionId() + Iterator.single((Map(curPartitionId -> parameters.weightPartition), + Map(curPartitionId -> parameters.gradientPartition))) + }).reduce((a, b) => (a._1 ++ b._1, a._2 ++ b._2)) + + val taskSize = parameters.size / partitionNum + require(taskSize != 0, "parameter length should not less than partition number") + val extraSize = parameters.size % partitionNum + + (0 until partitionNum).map(pid => { + val start = parameters.paramOffset + pid * taskSize + math.min(pid, extraSize) + val length = taskSize + (if (pid < extraSize) 1 else 0) + parameter.narrow(1, start, length).copy(weights(pid)) + gradientParameter.narrow(1, start, length).copy(gradients(pid)) + }) + + trainingModel + } + + + private case class TrainingResults(successed: Int, loss: Double, records: Int) + private def train[T: ClassTag]( + cached: Cache[T], + data: Iterator[MiniBatch[T]], + context: TrainingContext[T], + metrics: Metrics)(implicit ev: TensorNumeric[T]): TrainingResults = { + val miniBatchBuffer = context.preTrain(data) + val stackSize = miniBatchBuffer.head.size() + + // ======================Start train models=================================== + val modelsResult = TrainingTrace.time ( + context.train(miniBatchBuffer, cached.localModels, cached.localCriterions), + metrics + )(Array(COMPUTING_TIME_EACH_NODE, COMPUTING_TIME_AVERAGE)) + + var lossSum = 0.0 + var i = 0 + while (i < modelsResult.size) { + lossSum += modelsResult(i).loss + cached.moduleTimeList(i) = modelsResult(i).elapsed + i += 1 + } + + val gradients = TrainingTrace.time ( + { + if (modelsResult.nonEmpty) { + val successedGradients = modelsResult.map { + result => cached.modelGradients(result.index) + }.toArray + context.aggregate(successedGradients) + } else { + cached.modelGradients(0).zero() + cached.modelGradients(0) + } + }, metrics)(Array(AGGREGATE_GRADIENT_TIME)) + + TrainingTrace.time ( + cached.parameter.putGradients(gradients), + metrics + )(Array(PUT_GRADIENT)) + + (0 until context.subModelNumber).foreach { i => + cached.localModels(i).training() + cached.localModels(i).zeroGradParameters() + } + + TrainingResults(modelsResult.size, lossSum, modelsResult.size * stackSize) + } + + private def updateStates[T](optimMethods: Map[String, OptimMethod[T]], state: Table, + updateScore: Boolean): Unit = { + import StateEntry._ + optimMethods.map { case (moduleName, optimMethod) => + optimMethod.state.update(EPOCH, state[Int](EPOCH)) + optimMethod.state.update(NEVAL, state[Int](NEVAL)) + optimMethod.state.update(LOSS, state[Float](LOSS)) + if (updateScore) { + optimMethod.state.update(SCORE, state[Float](SCORE)) + } + + if (optimMethod.state.keySet.contains(RECORDS_PROCESSED)) { + optimMethod.state.update(RECORDS_PROCESSED, state[Int](RECORDS_PROCESSED)) + } + } + } + + private def driverStatesUpdate[T: ClassTag]( + cacheOfMaster: MasterCache[T], + recordsNum: Int, + context: TrainingContext[T], + trainingTrace: TrainingTrace, metrics: Metrics)( + implicit ev: TensorNumeric[T]): Unit = { + val optimMethods = cacheOfMaster.optimMethods + val updateScore = cacheOfMaster.validationMethods.isDefined + + optimMethods.foreach { v => + v._2.updateHyperParameter() + } + + val trainingTakes = trainingTrace.trainingTakes + val iterationTakes = trainingTrace.iterationTakes + val throughput = recordsNum.toFloat / (iterationTakes / 1e9f) + val records = trainingTrace.updateRecords(recordsNum).recordsOfEpoch + val _header = header(trainingTrace.epochs, records, context.numSamples, + trainingTrace.iterations, trainingTakes) + val loss = context.state[Float](StateEntry.LOSS) + logger.info(s"${_header} Trained $recordsNum records in $iterationTakes seconds. " + + s"Throughput is $throughput records/second. " + + s"Loss is $loss. " + + s"${getHyperParameterLog(optimMethods)}") + logger.debug("\n" + metrics.summary()) + + context.state(StateEntry.THROUGHPUT) = recordsNum.toFloat / (iterationTakes / 1e9f) + context.state(StateEntry.NEVAL) = trainingTrace.iterations + 1 + // for next iteration training + context.state(StateEntry.LEARNING_RATE) = optimMethods.head._2.getLearningRate().toFloat + + if (context.hasCompleteAllSamples(trainingTrace.recordsOfEpoch, cacheOfMaster.model)) { + // Epoch is finished + trainingTrace.startNewEpoch() + logger.info(s"${_header} Epoch finished. Wall clock time is ${trainingTakes / 1e6} ms") + } + context.state(StateEntry.EPOCH) = trainingTrace.epochs + context.state(StateEntry.RECORDS_PROCESSED) = trainingTrace.recordsOfEpoch + + updateStates(optimMethods, context.state, updateScore) + } + + private def parameterSync[T: ClassTag]( + lossSum: Double, + successedModels: Int, + cacheOfMaster: MasterCache[T], + cacheOfSlave: RDD[Cache[T]], + context: TrainingContext[T])(implicit ev: TensorNumeric[T]): Unit = { + val metrics = cacheOfMaster.metrics + val parameter = cacheOfMaster.parameter + val updateScore = cacheOfMaster.validationMethods.isDefined + + context.state(StateEntry.NUM_FINISHED_MODELS) = successedModels + context.state(StateEntry.IS_GRADIENT_UPDATED) = false + cacheOfMaster.parameterProcessers.foreach { processer => + processer.collectGlobalData(cacheOfSlave.asInstanceOf[RDD[DistriOptimizer.Cache[T]]], + parameter, metrics, context.state) + } + + val isGradientUpdated = context.state[Boolean](StateEntry.IS_GRADIENT_UPDATED) + cacheOfSlave.mapPartitions { iter => + val cache = iter.next() + val localOptimMethods = cache.optimMethods + val parameterProcessers = cache.parameterProcessers + val parameterSplits = cache.parameterSplits + val (paramLocalStart, paramLocalLen) = cache.parameter.localPartitionRange + + // if parameterProcesser has aggregated gradient, we can skip this aggregation. + if (!isGradientUpdated) { + TrainingTrace.time ( + cache.parameter.aggregateGradientPartition(successedModels), + metrics + )(Array(AGGREGATE_PARTITION_GRADIENT)) + } + + parameterProcessers.foreach(_.processParameters(parameter, cache, context.state)) + + updateStates(localOptimMethods, context.state, updateScore) + + val optimSegments = localOptimMethods.map { + case (name, method) => + val p = parameterSplits(name) + val startIdx = Math.max(paramLocalStart, p._1) + val endIdx = Math.min(paramLocalLen + paramLocalStart, p._1 + p._2) + (name, ParamSegments(startIdx - paramLocalStart + 1, endIdx - startIdx, method)) + } + + val weights = cache.parameter.weightPartition + val gradients = cache.parameter.gradientPartition + val loss = lossSum / successedModels + + TrainingTrace.time ( + context.update(optimSegments, weights, gradients, loss), + metrics + )(Array(COMPUTE_WEIGHT_AVERAGE)) + + TrainingTrace.time ( + cache.parameter.sendWeightPartition(), metrics + )(Array(SEND_WEIGHTS_AVERAGE)) + + Iterator.empty + }.count() + + context.state(StateEntry.IS_GRADIENT_UPDATED) = true + context.state(StateEntry.LOSS) = lossSum.toFloat / successedModels + } +} + +/** + * The optimizer run on a distributed cluster. + * + * @param _model train model + * @param _dataset train dataset + * @param _criterion loss function + */ +class DistriOptimizerV2[T: ClassTag]( + _model: Module[T], + _dataset: DistributedDataSet[MiniBatch[T]], + _criterion: Criterion[T] +)(implicit ev: TensorNumeric[T]) + extends Optimizer[T, MiniBatch[T]]( + _model, _dataset, _criterion) { + private var _context: Option[TrainingContext[T]] = None + private var _canBeReused: Boolean = false + + def setContext(context: TrainingContext[T]): Unit = _context = Some(context) + + def resetContext(): this.type = { _context = None + this + } + + def context: TrainingContext[T] = { + if (_context.isEmpty) { + val subModelNumber = Engine.getEngineType() match { + case MklBlas => Engine.coreNumber() + case MklDnn => 1 + } + + DistriOptimizer.logger.info("Count dataset") + val countBefore = System.nanoTime() + val numSamples = dataset.toDistributed().data(train = false).map(_.size()).reduce(_ + _) + val countAfter = System.nanoTime() + DistriOptimizer.logger.info( + s"Count dataset complete. Time elapsed: ${(countAfter - countBefore) / 1e9}s") + + if (numSamples != dataset.size()) { + DistriOptimizer.logger.warn(""" + If the dataset is built directly from RDD[Minibatch], the data in each + minibatch is fixed, and a single minibatch is randomly selected in each partition. If + the dataset is transformed from RDD[Sample], each minibatch will be constructed on the + fly from random samples, which is better for convergence.""") + } + + val state = T() + + _context = Some(new TrainingContext(subModelNumber, numSamples, state)) + } + + _context.get + } + + private var _allReduceParameter: AllReduceParameter[T] = _ + private var _parameterSplits: Map[String, (Int, Int)] = _ + private var cacheOfSlave: RDD[DistriOptimizerV2.Cache[T]] = null + // this variable is used to check the models cloned when broadcast, if there're native resources, + // it will be deleted at the end of Optimizer. + private var modelBroadcast: ModelBroadcast[T] = null + + /** + * Clean some internal states, so this or other optimizers can run optimize again + * + * This method will be called at the end of optimize. You need not call it if optimize succeed. + * If the optimize fails, you may call it before next optimize. + */ + def clearState(): Unit = { + DistriOptimizerV2.clearState(cacheOfSlave.asInstanceOf[RDD[DistriOptimizer.Cache[T]]]) + } + + + // By default, optimMethod internal state for each worker will not be reserved and reuse. + private var reserveOptimMethod = false + private[bigdl] var previousOptim: RDD[Map[String, OptimMethod[T]]] = null + /** + * If you want to reserve optimMethod for each worker, and reuse those methods in + * next training task, you can call it. + */ + + /** + * If you want to reserve optimMethod for each worker and reuse those methods in + * next training task, please set reserve = true + * Otherwise, if just using optimMethod you set in optimizer, please set reserve = false + * + * @param reserve whether to reserve optim method for each worker + * @return + */ + override def reserveOptim(reserve: Boolean): this.type = { + reserveOptimMethod = reserve + this + } + + // replace optim methods with previous + private def resetOptimMethods[T: ClassTag]( + models: RDD[DistriOptimizerV2.Cache[T]], + previousOptimMethods: RDD[Map[String, + OptimMethod[T]]]): + RDD[DistriOptimizerV2.Cache[T]] = { + models.zipPartitions(previousOptimMethods) { (m1, m2) => { + val cache = m1.next() + cache.optimMethods = m2.next() + Iterator(cache) + } + } + } + + private def endEpoch(): Unit = { + DistriOptimizer.endEpoch(optimMethods) + } + + override def setTrainData(sampleRDD: RDD[Sample[T]], + batchSize: Int, + miniBatch: MiniBatch[T]): this.type = { + this.dataset = DistriOptimizer.setTrainData(sampleRDD, batchSize, miniBatch) + // if current epoch is not finished, we will end the + // current epoch and start a new epoch when optimize is called + endEpoch() + this + } + + override def setTrainData(sampleRDD: RDD[Sample[T]], + batchSize: Int, + featurePaddingParam: PaddingParam[T] = null, + labelPaddingParam: PaddingParam[T] = null): this.type = { + val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None + val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None + this.dataset = DistriOptimizer.setTrainData(sampleRDD, batchSize, + featurePaddingParam, labelPaddingParam) + // if current epoch is not finished, we will end the + // current epoch and start a new epoch when optimize is called + endEpoch() + this + } + + override def prepareInput(): Unit = { + if (!dataset.toDistributed().isCached) { + DistriOptimizer.logger.info("caching training rdd ...") + DistriOptimizer.prepareInput(this.dataset, this.validationDataSet) + } + } + + override def optimize(): Module[T] = { + require(validArgs(), "please check the args you set, there's some wrong") + + val modelParameters = model.getParameters() + val size = modelParameters._1.nElement() + val partitionNum = dataset.toDistributed().originRDD().partitions.length + val reuse = _canBeReused && + _allReduceParameter != null && _parameterSplits != null + + if (!reuse) { + _allReduceParameter = AllReduceParameter.newParameter[T](partitionNum, size) + _parameterSplits = initOptimMethods(optimMethods, modelParameters._1) + } + + prepareInput() + + val cacheOfMaster = new MasterCache(model, + _allReduceParameter, + optimMethods, + _parameterSplits, + parameterProcessors.toArray, + new Metrics, + criterion, + validationMethods, + dataset.toDistributed().originRDD().partitions.length) + + if (!reuse) { + import DistriOptimizerV2.initCacheOfSlave + val modelsAndBroadcast = initCacheOfSlave(cacheOfMaster, dataset.toDistributed(), context) + cacheOfSlave = if (reserveOptimMethod && previousOptim != null) { + // replace optimMethods with previous ones + resetOptimMethods(modelsAndBroadcast._1, previousOptim) + } else { + modelsAndBroadcast._1 + } + modelBroadcast = modelsAndBroadcast._2 + } + + if (checkpointPath.isDefined) { + val file = checkpointPath.get + "/" + + new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()) + new File(file).mkdir() + checkpointPath = Some(file) + } + + DistriOptimizerV2.optimize( + cacheOfMaster, + cacheOfSlave, + dataset.toDistributed(), + endWhen, + validationTrigger, + validationDataSet, + validationMethods, + checkpointTrigger, + checkpointPath, + trainSummary, + validationSummary, + isOverWrite, + context + ) + + DistriOptimizerV2.getModel(cacheOfSlave.asInstanceOf[RDD[DistriOptimizer.Cache[T]]], + cacheOfMaster.parameter, cacheOfMaster.model) + // reserve optimMethod internal state for each worker if need + if (reserveOptimMethod) { + previousOptim = cacheOfSlave.map(m => m.optimMethods).cache() + previousOptim.count() + } else { + if (previousOptim != null) previousOptim.unpersist() + } + + // Reset some internal states, so this or other optimizers can run optimize again + clearState() + + // unpersist the model because the next time optimize is called, new `models` will be + // created + shutdown() + + if (!reuse) { + cacheOfSlave.unpersist() + } + + cacheOfMaster.model + } + + def setReuse(): this.type = { + _canBeReused = true + this + } + + private type ModuleOptimMethods[R] = Map[String, OptimMethod[R]] + private def initOptimMethods(optimMethods: ModuleOptimMethods[T], + parameters: Tensor[T]): Map[String, (Int, Int)] = { + import StateEntry._ + optimMethods.values.foreach { optimMethod => + optimMethod.clearHistory() + } + + // To be compatible with the old usage that user define hyperparameters in a table. + if (optimMethods.size == 1) { + optimMethods.head._2.loadFromTable(state) + } + + optimMethods.values.foreach { optimMethod => + if (!optimMethod.state.contains(EPOCH)) optimMethod.state.update(EPOCH, 1) + if (!optimMethod.state.contains(NEVAL)) optimMethod.state.update(NEVAL, 0) + if (!optimMethod.state.contains(LOSS)) { + optimMethod.state.update(LOSS, Float.PositiveInfinity) + } + if (!optimMethod.state.contains(SCORE)) optimMethod.state.update(SCORE, 0f) + if (!optimMethod.state.contains(RECORDS_PROCESSED)) { + optimMethod.state.update(RECORDS_PROCESSED, 0) + } + } + + // subModuleName -> (storageOffset, length, AllReduceParameter) + val parameterSplits = if (optimMethods.size != 1) { + val p = optimMethods.map { case (subModuleName, optimMethod) => + val subModule = model(subModuleName) + require(subModule.isDefined, s"Optimizer couldn't find $subModuleName in $model") + val subModuleWeights = subModule.get.getParameters()._1 + (subModuleName, subModuleWeights) + } + + // check the weights of submodule with whole model's weight, they should be the same + val sortedWeights = p.values.toArray.sortWith((a, b) => a.storageOffset() < b.storageOffset()) + val compactWeights = Module.isCompact(sortedWeights) + require(parameters == compactWeights, + s"DistriOptimizer: All subModules should have an OptimMethod.") + + p.map { case (subModuleName, weights) => + (subModuleName, (weights.storageOffset(), weights.nElement())) + } + } else if (optimMethods.contains(model.getName())) { + Map(model.getName() -> (1, parameters.nElement())) + } else { + throw new IllegalArgumentException(s"${model.getName()} doesn't " + + s"have corresponding OptimMethod") + } + + // LarsSSD will check the optimMethods and append LarsProcessor + // if there's no LarsSSD in optimMethods map, it will do nothing. + // should be refactored later if possible. + LarsSGD.containsLarsSGD(optimMethods).foreach(weightDecay => + parameterProcessors.append(new LarsProcessor(parameterSplits, weightDecay))) + + parameterSplits + } + + // this shutdown should not be called out of this scope. + private[optim] override def shutdown(): Unit = { + cacheOfSlave.mapPartitions { iter => + iter.foreach { arrayModels => + arrayModels.localModels.foreach(_.release()) + } + + iter + }.count() + CachedModels.deleteKey(modelBroadcast.uuid) + } + + def setLogger(logger: OptimizerLogger): Unit = { + DistriOptimizerV2._logger = Some(logger) + } + + private def validArgs(): Boolean = { + val checkSingleton = this.checkSingleton + val nodeNumber = Engine.nodeNumber() + val partitionNumber = dataset.toDistributed().originRDD().partitions.length + require(partitionNumber == nodeNumber, + s"Passed in rdd partition number $partitionNumber " + + s" is not equal to configured node number $nodeNumber") + + dataset.toDistributed().originRDD().foreachPartition { _ => + if (!Engine.checkSingleton()) { + if (checkSingleton) { + require(Engine.checkSingleton(), "Partitions of the training data are not evenly" + + "distributed across the executors in the Spark cluster; are there sufficient " + + "training" + + "data to be distributed? Set property \"bigdl.check.singleton\" to false to skip " + + "this check") + } else { + DistriOptimizer.logger.warn("Partitions of the training data are not evenly" + + "distributed across the executors in the Spark cluster; are there sufficient " + + "training" + + "data to be distributed?") + } + } + } + + true + } +} + +case class LossWithElapsedTime(index: Int, loss: Double, elapsed: Long) +case class ParamSegments[T](start: Int, length: Int, method: OptimMethod[T]) + +class TrainingContext[T: ClassTag]( + val subModelNumber: Int, + val numSamples: Int, + val state: Table) extends Serializable { + + def hasCompleteAllSamples(recordsProcessed: Int, model: Module[T]): Boolean = { + recordsProcessed >= numSamples + } + + def preTrain[T: ClassTag](data: Iterator[MiniBatch[T]]): Array[MiniBatch[T]] = { + val syWStart = System.nanoTime() + val miniBatchBuffer = new Array[MiniBatch[T]](subModelNumber) + val batch = data.next() + val stackSize = batch.size() / subModelNumber + // TODO performance call Engine.invoke + require((batch.size() >= subModelNumber) && + (batch.size() % subModelNumber == 0), "total batch size: " + + s"${batch.size()} should be divided by total core number: $subModelNumber") + + if (batch.size() < subModelNumber * 2) { + Logger.getLogger(this.getClass).warn( + s"Warning: for better training speed, total batch size is recommended to be " + + s"at least two times of core number $subModelNumber. " + + s"please tune your batch size accordingly") + } + + var b = 0 + while (b < subModelNumber) { + miniBatchBuffer(b) = batch.slice(b * stackSize + 1, stackSize) + b += 1 + } + + miniBatchBuffer + } + + def train[T: ClassTag](data: Array[MiniBatch[T]], models: Array[Module[T]], + criterion: Array[Criterion[T]])(implicit ev: TensorNumeric[T]): Seq[LossWithElapsedTime] = { + val trainingThreads = Engine.default.invokeAndWait2(models.indices.map(i => + () => { + val start = System.nanoTime() + + val localModel = models(i) + val localCriterion = criterion(i) + val input = data(i).getInput() + val target = data(i).getTarget() + var loss = 0.0 + + localModel.training() + + val output = localModel.forward(input) + loss = ev.toType[Double](localCriterion.forward(output, target)) + val errors = localCriterion.backward(output, target) + localModel.backward(input, errors) + + val end = System.nanoTime() + + LossWithElapsedTime(i, loss, end - start) + } + ), Long.MaxValue) + trainingThreads.filter(!_.isCancelled).map(_.get()) + } + + def update[T: ClassTag]( + optimSegments: Map[String, ParamSegments[T]], + weight: Tensor[T], gradient: Tensor[T], + averageLoss: Double + )(implicit ev: TensorNumeric[T]): Unit = { + optimSegments.foreach { case (name, ParamSegments(start, length, method)) => + if (length > 0) { + method.optimize( + _ => (ev.fromType(averageLoss), gradient.narrow(1, start, length)), + weight.narrow(1, start, length)) + } + } + } + + def aggregate[T: ClassTag](gradients: Array[Tensor[T]]): Tensor[T] = { + // NOTE: optimizer requires the gradients will be seprated with each other. + // so you should not merge them. + val start = gradients.head.storageOffset() + val length = gradients.head.nElement() + + val taskSize = length / this.subModelNumber + val extraTask = length % this.subModelNumber + + // Aggregate multi-model's gradient to the first model's gradient + val parallelNum = if (taskSize == 0) extraTask else this.subModelNumber + if (parallelNum != 1) { + Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => { + val offset = start + tid * taskSize + math.min(tid, extraTask) + val length = taskSize + (if (tid < extraTask) 1 else 0) + var i = 1 + while (i < gradients.length) { + val target = gradients(0).narrow(1, offset, length) + val source = gradients(i).narrow(1, offset, length) + target.add(source) + i += 1 + } + })) + } + + gradients(0) + } + + final def loadState(state: Table): this.type = { + this.state.update(StateEntry.EPOCH, state(StateEntry.EPOCH)) + this.state.update(StateEntry.NEVAL, state(StateEntry.NEVAL)) + this.state.update(StateEntry.LOSS, state(StateEntry.LOSS)) + this.state.update(StateEntry.SCORE, state(StateEntry.SCORE)) + this.state.update(StateEntry.PARALLELISM, subModelNumber) + this.state.update(StateEntry.RECORDS_PROCESSED, state(StateEntry.RECORDS_PROCESSED)) + this + } +} + +object StateEntry { + val NEVAL: String = "neval" + val EPOCH: String = "epoch" + val RECORDS_PROCESSED: String = "recordsProcessedThisEpoch" + val LOSS: String = "Loss" + val SCORE: String = "score" + val PARALLELISM: String = "parallelism" + val LEARNING_RATE: String = "LearningRate" + val THROUGHPUT: String = "Throughput" + val NUM_FINISHED_MODELS = "numFinishedModel" + + // for parameter processers, it's not a good design of that, but for compatible, keep it now. + // parameterProcesser like L2NormClippingProcessor may aggregate gradient, + // and change the value of isGradientUpdated in driverState. + val IS_GRADIENT_UPDATED = "isGradientUpdated" +} + +trait OptimizerLogger { + def info(message: String): Unit + + def debug(message: String): Unit + + def trace(message: String): Unit + + def warn(message: String): Unit + + def error(message: String): Unit +} + + +private class MetricEntry(val value: String) +private case object AGGREGATE_GRADIENT_TIME extends MetricEntry("aggregate gradient time") +private case object COMPUTING_TIME_EACH_NODE extends MetricEntry("computing time for each node") +private case object COMPUTING_TIME_AVERAGE extends MetricEntry("computing time average") +private case object COMPUTE_WEIGHT_AVERAGE extends MetricEntry("compute weight average") +private case object GET_WEIGHTS_EACH_NODE extends MetricEntry("get weights for each node") +private case object GET_WEIGHTS_AVERAGE extends MetricEntry("get weights average") +private case object PUT_GRADIENT extends MetricEntry("put gradient") +// scalastyle:off +private case object AGGREGATE_PARTITION_GRADIENT extends MetricEntry("aggregrateGradientParition average executor") +// scalastyle:on +private case object SEND_WEIGHTS_AVERAGE extends MetricEntry("send weights average") + +private class TrainingTrace( + private var _records: Int = 0, + private var _iterations: Int = 0, + private var _epochs: Int = 1) { + + private var _epochStart: Long = 0 + private var _iterationTakes: Long = 0 + private var _trainingStart: Long = System.nanoTime() + + def startNewEpoch(): Unit = { + _epochStart = System.nanoTime() + // we can't reset iterations to 0 for compatible +// _iterations = 0 + _records = 0 + _epochs += 1 + } + + def trainingTakes: Long = System.nanoTime() - _trainingStart + + def epochTakes: Long = System.nanoTime() - _epochStart + + def iterationTakes: Long = _iterationTakes + + def traceIteration[R](block: => R): R = { + val (ret, elapsed) = TrainingTrace.time(block) + _iterationTakes = elapsed + _iterations += 1 + ret + } + + def recordsOfEpoch: Int = _records + + def updateRecords(num: Int): this.type = { + _records += num + this + } + + def iterations: Int = _iterations + + def epochs: Int = _epochs +} + +private object TrainingTrace { + def apply(records: Int, iterations: Int, epochs: Int): TrainingTrace = { + new TrainingTrace(records, iterations, epochs) + } + + def apply(state: Table): TrainingTrace = { + val records = state[Int](StateEntry.RECORDS_PROCESSED) + val iterations = state[Int](StateEntry.NEVAL) - 1 // for compatible + val epochs = state[Int](StateEntry.EPOCH) + + new TrainingTrace(records, iterations, epochs) + } + + def time[R](block: => R): (R, Long) = { + val start = System.nanoTime() + val ret = block + val end = System.nanoTime() + val elapsed = end - start + + (ret, elapsed) + } + + def time[R](block: => R, metrics: Metrics)(entries: Array[MetricEntry]): R = { + val (ret, elapsed) = time(block) + + var i = 0 + while (i < entries.length) { + metrics.add(entries(0).value, elapsed) + i += 1 + } + + ret + } +} + +private class DistriLogger extends OptimizerLogger { + override def info(message: String): Unit = { + Logger.getLogger(getClass).info(message) + } + + override def debug(message: String): Unit = { + Logger.getLogger(getClass).debug(message) + } + + override def trace(message: String): Unit = { + Logger.getLogger(getClass).trace(message) + } + + override def warn(message: String): Unit = { + Logger.getLogger(getClass).warn(message) + } + + override def error(message: String): Unit = { + Logger.getLogger(getClass).error(message) + } +} + +private class MasterCache[T]( + val model: Module[T], + val parameter: AllReduceParameter[T], + val optimMethods: Map[String, OptimMethod[T]], + val parameterSplits: Map[String, (Int, Int)], + val parameterProcessers: Array[ParameterProcessor], + val metrics: Metrics, + val criterion: Criterion[T], + val validationMethods: Option[Array[ValidationMethod[T]]], + val partitionNum: Int) + diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala index f3c05aebc77..8c92eb45efa 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/ParallelOptimizer.scala @@ -444,7 +444,7 @@ object ParallelOptimizer extends AbstractOptimizer { logger.info("model thread pool size is " + Engine.model.getPoolSize) val weights = cached.head._2 - Iterator.single(Cache( + Iterator.single(CacheV1( cached.map(_._1), // models cached.map(_._2), // weights cached.map(_._3), // gradients @@ -585,7 +585,7 @@ class ParallelOptimizer[T: ClassTag]( _model, _dataset, _criterion) { val metrics = new Metrics - private var models: RDD[DistriOptimizer.Cache[T]] = null + private var models: RDD[DistriOptimizer.CacheV1[T]] = null private var _priorities: mutable.Map[String, Int] = null @@ -600,7 +600,7 @@ class ParallelOptimizer[T: ClassTag]( * If the optimize fails, you may call it before next optimize. */ def clearState(): Unit = { - ParallelOptimizer.clearState(models) + ParallelOptimizer.clearState(models.asInstanceOf[RDD[Cache[T]]]) } private def endEpoch(): Unit = { @@ -740,7 +740,7 @@ class ParallelOptimizer[T: ClassTag]( state, endWhen, metrics, - models, + models.asInstanceOf[RDD[Cache[T]]], optimMethods, validationTrigger, validationDataSet, @@ -752,7 +752,7 @@ class ParallelOptimizer[T: ClassTag]( isOverWrite ) - ParallelOptimizer.getModel(models, null, model) + ParallelOptimizer.getModel(models.asInstanceOf[RDD[Cache[T]]], null, model) // Reset some internal states, so this or other optimizers can run optimize again clearState() diff --git a/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerV2Spec.scala b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerV2Spec.scala new file mode 100644 index 00000000000..913fe863d4a --- /dev/null +++ b/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/DistriOptimizerV2Spec.scala @@ -0,0 +1,893 @@ +/* + * Copyright 2016 The BigDL Authors. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.intel.analytics.bigdl.optim + +import java.nio.file.{Files, Paths} + +import com.intel.analytics.bigdl._ +import com.intel.analytics.bigdl.dataset.image.{BGRImgToBatch, LabeledBGRImage} +import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, Sample} +import com.intel.analytics.bigdl.mkl.Memory +import com.intel.analytics.bigdl.nn._ +import com.intel.analytics.bigdl.nn.abstractnn.Activity +import com.intel.analytics.bigdl.nn.mkldnn.HeapData +import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase +import com.intel.analytics.bigdl.parameters.AllReduceParameter +import com.intel.analytics.bigdl.tensor.{DenseTensor, DnnStorage, Storage, Tensor} +import com.intel.analytics.bigdl.utils._ +import com.intel.analytics.bigdl.visualization.TrainSummary +import org.apache.log4j.{Level, Logger} +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.scalatest.{BeforeAndAfter, FlatSpec, Matchers} + +object DistriOptimizerV2Spec { + private val input1: Tensor[Double] = Tensor[Double](Storage[Double](Array(0.0, 1.0, 0.0, 1.0))) + private val output1 = 0.0 + private val input2: Tensor[Double] = Tensor[Double](Storage[Double](Array(1.0, 0.0, 1.0, 0.0))) + private val output2 = 1.0 + private var plusOne = 0.0 + private val nodeNumber = 4 + private val coreNumber = 4 + Engine.init(nodeNumber, coreNumber, onSpark = true) + + private val batchSize = 2 * coreNumber + + private val prepareData: Int => (MiniBatch[Double]) = index => { + val input = Tensor[Double]().resize(batchSize, 4) + val target = Tensor[Double]().resize(batchSize) + var i = 0 + while (i < batchSize) { + if (i % 2 == 0) { + target.setValue(i + 1, output1 + plusOne) + input.select(1, i + 1).copy(input1) + } else { + target.setValue(i + 1, output2 + plusOne) + input.select(1, i + 1).copy(input2) + } + i += 1 + } + MiniBatch(input, target) + } +} + +object DistriOptimizerV2SpecModel { + def mse: Module[Double] = { + Sequential[Double]().setName("mse") + .add(Linear[Double](4, 4).setName("fc_1")) + .add(Sigmoid()) + .add(Linear[Double](4, 1).setName("fc_2")) + .add(Sigmoid()) + } + + def mse2: Module[Double] = { + Sequential[Double]() + .add(Linear[Double](4, 8).setName("fc_1")) + .add(Sigmoid()) + .add(Linear[Double](8, 1).setName("fc_2")) + .add(Sigmoid()) + } + + def linear: Module[Double] = { + new Sequential[Double] + .add(new Linear(10, 5)) + .add(new Sigmoid) + .add(new Linear(5, 1)) + .add(new Sigmoid) + } + + def bn: Module[Double] = { + Sequential[Double] + .add(Linear(4, 2)) + .add(BatchNormalization(2)) + .add(ReLU()) + .add(Linear(2, 1)) + .add(Sigmoid()) + } + + def cre: Module[Double] = { + new Sequential[Double] + .add(new Linear(4, 2)) + .add(new LogSoftMax) + } + + def mserf(failCountNumberLists: Array[Int], sleep: Boolean = false): Module[Double] = { + new Sequential[Double] + .add(new Linear(4, 2)) + .add(new Sigmoid) + .add(new Linear(2, 1)) + .add(new Sigmoid) + .add(new ExceptionTest(failCountNumberLists, sleep)) + } + + def dnn: Module[Float] = { + new nn.mkldnn.Sequential() + .add(nn.mkldnn.Input(Array(8, 4), Memory.Format.nc)) + .add(nn.mkldnn.Linear(4, 2)) + .add(nn.mkldnn.ReorderMemory(HeapData(Array(8, 2), Memory.Format.nc))) + } +} + +@com.intel.analytics.bigdl.tags.Serial +class DistriOptimizerV2Spec extends FlatSpec with Matchers with BeforeAndAfter { + + import DistriOptimizerV2Spec._ + import DistriOptimizerV2SpecModel._ + + Logger.getLogger("org").setLevel(Level.WARN) + Logger.getLogger("akka").setLevel(Level.WARN) + + private var sc: SparkContext = _ + + private var dataSet: DistributedDataSet[MiniBatch[Double]] = _ + + before { + sc = new SparkContext("local[1]", "RDDOptimizerSpec") + + val rdd = sc.parallelize(1 to (256 * nodeNumber), nodeNumber).map(prepareData) + + dataSet = new DistributedDataSet[MiniBatch[Double]] { + override def originRDD(): RDD[_] = rdd + + override def data(train: Boolean): RDD[MiniBatch[Double]] = rdd + + override def size(): Long = rdd.count() + + override def shuffle(): Unit = {} + } + + plusOne = 0.0 + System.setProperty("bigdl.check.singleton", false.toString) + Engine.model.setPoolSize(1) + } + + after { + if (sc != null) { + sc.stop() + } + } + + "DistriOptimizer" should "train all minibatches per epoch" in { + val numSamples = 64 + val numClasses = 3 + val height = 32 + val width = 32 + val images = Array.tabulate(64) { i => + val image = new LabeledBGRImage(width, height) + image.setLabel((i % numClasses).toFloat + 1F) + val tensor = Tensor[Float](Storage[Float](image.content), 1, Array(3, width, height)) + tensor.rand() + image + } + + val numPartitions = 4 + val dataSet = DataSet.rdd(sc.parallelize(images, numPartitions)) + + val batchSize = 16 + val toTensor = new BGRImgToBatch(batchSize) + val nn = new Sequential[Float]() + .add(new Reshape(Array(3 * height * width))) + .add(new Linear(3 * height * width, numClasses)) + .add(new LogSoftMax[Float]()) + val sampleDataSet = (dataSet -> toTensor).asInstanceOf[DistributedDataSet[MiniBatch[Float]]] + val batchDataSet = DataSet.rdd(sampleDataSet.data(train = false)) + assert(sampleDataSet.size() == numSamples) + assert(batchDataSet.size() == numSamples / batchSize * numPartitions) + + Seq(sampleDataSet, batchDataSet).foreach { dataset => + RandomGenerator.RNG.setSeed(10) + val maxEpochs = 2 + val logdir = com.google.common.io.Files.createTempDir() + val trainSummary = TrainSummary(logdir.getPath, "minibatch-test") + val optimizer = new DistriOptimizerV2( + nn, + dataset, + ClassNLLCriterion[Float]()) + .setOptimMethod(new LBFGS) + .setTrainSummary(trainSummary) + .setEndWhen(Trigger.maxEpoch(maxEpochs)) + val model = optimizer.optimize() + val losses = trainSummary.readScalar("Loss") + trainSummary.close() + + losses should have length maxEpochs * (dataset.data(train = false).count() / nodeNumber) + } + } + + it should "not train model with duplicate layers" in { + val m = Sequential[Double]() + val l1 = Identity[Double]() + val l2 = Identity[Double]() + val c = Sequential[Double]() + m.add(l1).add(c) + c.add(l1).add(l2) + + intercept[IllegalArgumentException] { + val optimizer = new DistriOptimizerV2[Double]( + m, + dataSet, + ClassNLLCriterion[Double]() + ) + } + } + + it should "not set model with duplicate layers" in { + val m = Sequential[Double]() + val l1 = Identity[Double]() + val l2 = Identity[Double]() + val c = Sequential[Double]() + m.add(l1).add(c) + c.add(l1).add(l2) + + val optimizer = new DistriOptimizerV2[Double]( + c, + dataSet, + ClassNLLCriterion[Double]() + ) + intercept[IllegalArgumentException] { + val optimizer = new DistriOptimizerV2[Double]( + m, + dataSet, + ClassNLLCriterion[Double]() + ) + } + } + + "Train with MSE with LARS" should "be good with LARS parameter processor" in { + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizerV2( + mse, + dataSet, + new MSECriterion[Double]()) + .setOptimMethods( + Map("fc_1" -> new LarsSGD[Double](true, _learningRate = 0.1, _learningRateDecay = 0, + _momentum = 0, _weightDecay = 0), + "fc_2" -> new LarsSGD[Double](false, _learningRate = 0.1, _learningRateDecay = 0, + _momentum = 0, _weightDecay = 0))) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 1e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 1e-2) + } + + "Train with MSE and LBFGS" should "be good" in { + LoggerFilter.redirectSparkInfoLogs() + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizerV2( + mse, + dataSet, + new MSECriterion[Double]()) + .setOptimMethod(new LBFGS) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 1e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 1e-2) + } + + "Train with MSE with two LBFGS" should "be good" in { + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizerV2( + mse, + dataSet, + new MSECriterion[Double]()) + .setOptimMethods( + Map("fc_1" -> new LBFGS(), "fc_2" -> new LBFGS())) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 1e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 1e-2) + } + + "Train with MSE with two LBFGS after set a new Model" should "be good" in { + RandomGenerator.RNG.setSeed(11) + val optimizer = new DistriOptimizerV2[Double]( + mse, + dataSet, + new MSECriterion[Double]()) + .setOptimMethods( + Map("fc_1" -> new LBFGS(), "fc_2" -> new LBFGS())) + optimizer.optimize() + + Array(mse, mse2).foreach { mse => + optimizer.setModelAndOptimMethods(mse, Map("fc_1" -> new LBFGS(), "fc_2" -> new LBFGS())) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 1e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 1e-2) + } + } + + "Train with MSE and SGD" should "be trained with good result" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + "Train with MSE and two SGD" should "be trained with good result" in { + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setOptimMethods(Map("fc_1" -> new SGD(learningRate = 20), + "fc_2" -> new SGD(learningRate = 20))) + .setEndWhen(Trigger.maxEpoch(1)) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + "Train with MSE and SGD" should "be trained with good result after reset model" in { + LoggerFilter.redirectSparkInfoLogs() + var mm = bn + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + optimizer.optimize() + + mm = mse + mm.getParameters()._1.fill(0.125) + optimizer.setModel(mm) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + it should "be same compare to ref optimizer" in { + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizerV2( + mse, + dataSet, + new MSECriterion[Double]()) + val model = optimizer.optimize() + + RandomGenerator.RNG.setSeed(10) + val optimizerRef = new RefDistriOptimizer( + mse, + dataSet, + new MSECriterion[Double]() + ) + val modelRef = optimizerRef.optimize() + + model.getParameters()._1 should be(modelRef.getParameters()._1) + } + + "An Artificial Neural Network with Cross Entropy and LBFGS" should + "be trained with good result" in { + plusOne = 1.0 + val optimizer = new DistriOptimizerV2[Double](cre, dataSet, + new ClassNLLCriterion[Double]()) + .setEndWhen(Trigger.maxEpoch(1)).setOptimMethod(new LBFGS) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1.max(1)._2(Array(1)) should be(1.0) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2.max(1)._2(Array(1)) should be(2.0) + } + + "An Artificial Neural Network with Cross Entropy and SGD" should + "be trained with good result" in { + plusOne = 1.0 + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizerV2[Double](cre, dataSet, + new ClassNLLCriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1.max(1)._2(Array(1)) should be(1.0) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2.max(1)._2(Array(1)) should be(2.0) + } + + it should "be same compare to ref optimizer" in { + plusOne = 1.0 + RandomGenerator.RNG.setSeed(10) + val optimizer = new DistriOptimizerV2[Double]( + cre, + dataSet, + new ClassNLLCriterion[Double]() + ).setState(T("learningRate" -> 20.0)) + val model = optimizer.optimize() + + RandomGenerator.RNG.setSeed(10) + val optimizerRef = new RefDistriOptimizer( + cre, + dataSet, + new ClassNLLCriterion[Double]() + ).setState(T("learningRate" -> 20.0)) + val modelRef = optimizerRef.optimize() + + model.getParameters()._1 should be(modelRef.getParameters()._1) + + } + + "Train with BatchNormalization" should "return with state" in { + RandomGenerator.RNG.setSeed(10) + val mm = bn + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + val model = optimizer.optimize() + val batchNormalization = model.asInstanceOf[Sequential[Double]].modules(1). + asInstanceOf[BatchNormalization[Double]] + val expectedMeans = Array(0.37499998210083496, 0.37499998210083496) + val expectedVariances = Array(1188.2811870277535, 1188.2811870277535) + batchNormalization.runningMean.storage().array().zip(expectedMeans).foreach { + case (actual, expected) => actual should be(expected +- 1e-4) + } + batchNormalization.runningVar.storage().array().zip(expectedVariances).foreach { + case (actual, expected) => actual should be(expected +- 1e-4) + } + } + + "Train with one partition one executor" should "won't throw mult-task exception" in { + System.setProperty("bigdl.check.singleton", true.toString) + RandomGenerator.RNG.setSeed(10) + Engine.setNodeNumber(1) + val mm = bn + mm.getParameters()._1.fill(0.125) + val rdd = sc.parallelize(1 to (256 * nodeNumber), 1).map(prepareData) + val dataSet = new DistributedDataSet[MiniBatch[Double]] { + override def originRDD(): RDD[_] = rdd + + override def data(train: Boolean): RDD[MiniBatch[Double]] = rdd + + override def size(): Long = 256 * nodeNumber + + override def shuffle(): Unit = {} + } + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + .optimize() + + Engine.setNodeNumber(nodeNumber) + } + + "DistriOptimizer checkpoint" should "work correctly" in { + val filePath = java.io.File.createTempFile("OptimizerSpec", "model").getAbsolutePath + Files.delete(Paths.get(filePath)) + Files.createDirectory(Paths.get(filePath)) + + import com.intel.analytics.bigdl._ + plusOne = 1.0 + RandomGenerator.RNG.setSeed(10) + val model = cre + val optimizer = new DistriOptimizerV2[Double]( + model, + dataSet, + new ClassNLLCriterion[Double]() + ) + optimizer.setState(T("learningRate" -> 20.0)) + .setCheckpoint(filePath, Trigger.everyEpoch) + .setEndWhen(Trigger.maxEpoch(2)) + .optimize() + + val numIterations = dataSet.data(train = false).count() / nodeNumber + 1 + val optimMethod = OptimMethod.load[Double](optimizer.getCheckpointPath().get + + s"/optimMethod-${model.getName()}.$numIterations") + + optimMethod.state.get[Int]("epoch").get should be(2) + optimMethod.state.get[Int]("neval").get should be(numIterations) + } + + "TrainSummary with MSE and LBFGS" should "work correctly" in { + TestUtils.cancelOnWindows() + RandomGenerator.RNG.setSeed(10) + val logdir = com.google.common.io.Files.createTempDir() + val trainSummary = TrainSummary(logdir.getPath, "lbfgs") + val optimizer = new DistriOptimizerV2( + mse, + dataSet, + new MSECriterion[Double]()) + .setOptimMethod(new LBFGS) + .setTrainSummary(trainSummary) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 1e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 1e-2) + trainSummary.readScalar("Loss").last._2 should be(0.0f +- 1e-3f) + trainSummary.close() + } + + "TrainSummary with MSE and SGD" should "work correctly" in { + TestUtils.cancelOnWindows() + RandomGenerator.RNG.setSeed(10) + val logdir = com.google.common.io.Files.createTempDir() + val trainSummary = TrainSummary(logdir.getPath, "sgd") + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + .setTrainSummary(trainSummary) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + trainSummary.readScalar("Loss").last._2 should be(0.0f +- 1e-3f) + trainSummary.close() + } + + "TrainSummary with MSE and Adagrad" should "work correctly" in { + TestUtils.cancelOnWindows() + RandomGenerator.RNG.setSeed(10) + val logdir = com.google.common.io.Files.createTempDir() + val trainSummary = TrainSummary(logdir.getPath, "adagrad") + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 1.0)) + .setOptimMethod(new Adagrad[Double]()) + .setEndWhen(Trigger.maxEpoch(1)) + .setTrainSummary(trainSummary) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + trainSummary.readScalar("Loss").last._2 should be(0.0f +- 1e-3f) + trainSummary.close() + } + + "Train with Plateau" should "work properly" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) + + RandomGenerator.RNG.setSeed(10) + val logdir = com.google.common.io.Files.createTempDir() + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double]( + _model = mm, + _dataset = dataSet, + _criterion = new MSECriterion[Double]() + ) + + val optimMethod = new SGD[Double](learningRate = 20.0, learningRateSchedule = + SGD.Plateau("Loss", epsilon = 0, patience = 1, mode = "min")) + + optimizer.setOptimMethod(optimMethod) + .setEndWhen(Trigger.maxEpoch(1)) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + "Train with Plateau Score" should "work properly" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) + + RandomGenerator.RNG.setSeed(10) + val logdir = com.google.common.io.Files.createTempDir() + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double]( + _model = mm, + _dataset = dataSet, + _criterion = new MSECriterion[Double]() + ) + + val optimMethod = new SGD[Double](learningRate = 20.0, learningRateSchedule = + SGD.Plateau("score", epsilon = 0, patience = 1, mode = "max")) + + optimizer.setOptimMethod(optimMethod) + .setEndWhen(Trigger.maxEpoch(1)) + optimizer.setValidation(Trigger.everyEpoch, dataSet, + Array(new Top1Accuracy[Double]())) + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + "Train with L1Regularization" should "work properly in DistriOptimizer" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) + + RandomGenerator.RNG.setSeed(10) + val logdir = com.google.common.io.Files.createTempDir() + val mm = Sequential[Double]().add(Linear(4, 2, + wRegularizer = L1Regularizer(1), bRegularizer = L1Regularizer(1))) + .add(Sigmoid()) + .add(Linear(2, 1)) + .add(Sigmoid()) + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double]( + _model = mm, + _dataset = dataSet, + _criterion = new MSECriterion[Double]() + ) + + val optimMethod = new SGD[Double](learningRate = 20.0) + + optimizer.setOptimMethod(optimMethod) + .setEndWhen(Trigger.severalIteration(10)) + optimizer.setValidation(Trigger.everyEpoch, dataSet, + Array(new Top1Accuracy[Double]())) + val model = optimizer.optimize() + } + + "setTrainData" should "work properly" in { + + RandomGenerator.RNG.setSeed(10) + val rdd = sc.parallelize(1 to (2 * nodeNumber), nodeNumber) + .map(_ => Sample[Double](Tensor[Double](2, 3).fill(2.0), Tensor[Double](1).fill(1.0))) + + val inputOri = rdd.map{s => s.feature} + val targetOri = rdd.map{s => s.label} + val inputOriArr = inputOri.collect() + val targetOriArr = targetOri.collect() + + + val myOpt = new DistriOptimizerV2[Double](Identity[Double](), dataSet, null) { + override def optimize(): Module[Double] = { + val dds = this.dataset.toDistributed() + val rdd = dds.data(train = false) + // flatmap to break minibatches into single tensors + val input = rdd.flatMap[Tensor[Double]]{ + data => data.getInput().asInstanceOf[Tensor[Double]].split(dim = 1)} + val target = rdd.flatMap[Tensor[Double]]{ + data => data.getTarget().asInstanceOf[Tensor[Double]].split(dim = 1)} + val inputArr = input.collect() + val targetArr = target.collect() + + inputArr.sameElements(inputOriArr) should be (true) + targetArr.sameElements(targetOriArr) should be (true) + + model + } + } + + myOpt.setTrainData(rdd, 2*nodeNumber) + myOpt.optimize() + } + + + "Train with MSE " should "generate correct gradients with constant clipping" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + val oriW = mm.getParameters()._1.clone() + + val _learningRate = 20.0 + val optimizationMethod = new SGD[Double](learningRate = _learningRate) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setEndWhen(Trigger.maxEpoch(1)) + .setOptimMethod(optimizationMethod) + .setConstantGradientClipping(-0.0, 0.0) + + val model = optimizer.optimize() + val newW = model.getParameters()._1 + val newG = model.getParameters()._2 + + assert(newW.almostEqual(oriW, 0.0), "weight should keep the same") + assert(newG.almostEqual(oriW.fill(0.0), 0.0), "gradient should be 0") + } + + "Train with MSE" should "generate correct gradients with l2norm clipping" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + + val _learningRate = 20.0 + val optimizationMethod = new SGD[Double](learningRate = _learningRate) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setEndWhen(Trigger.maxIteration(1)) + .setOptimMethod(optimizationMethod) + + val model = optimizer.optimize() + val gradient = model.getParameters()._2.clone() + val scale = math.sqrt(gradient.sumSquare()) / 0.03 + val expectedG = gradient.clone().div(scale) + + val mm2 = mse + mm2.getParameters()._1.fill(0.125) + val optimizationMethod2 = new SGD[Double](learningRate = _learningRate) + val optimizer2 = new DistriOptimizerV2[Double](mm2, dataSet, new MSECriterion[Double]()) + .setEndWhen(Trigger.maxIteration(1)) + .setOptimMethod(optimizationMethod2) + .setGradientClippingByl2Norm(0.03) + + val model2 = optimizer2.optimize() + val newG = model2.getParameters()._2 + assert(expectedG.almostEqual(newG, 0.0), "clipbynorm2 should generate correct gradient") + } + + "Train with MSE and SGD with constant clipping" should "be trained with good result" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + .setConstantGradientClipping(-0.001, 0.001) + + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + "Train with MSE and SGD with l2 clipping" should "be trained with good result" in { + LoggerFilter.redirectSparkInfoLogs() + val mm = mse + mm.getParameters()._1.fill(0.125) + val optimizer = new DistriOptimizerV2[Double](mm, dataSet, new MSECriterion[Double]()) + .setState(T("learningRate" -> 20.0)) + .setEndWhen(Trigger.maxEpoch(1)) + .setGradientClippingByl2Norm(0.002) + + val model = optimizer.optimize() + + val result1 = model.forward(input1).asInstanceOf[Tensor[Double]] + result1(Array(1)) should be(0.0 +- 5e-2) + + val result2 = model.forward(input2).asInstanceOf[Tensor[Double]] + result2(Array(1)) should be(1.0 +- 5e-2) + } + + "optimMethod state" should "be updated correctly after optimize" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) + + val mm = Sequential[Double]().add(Linear(4, 1)) + .add(Sigmoid()) + + val optimizer = new DistriOptimizerV2[Double]( + _model = mm, + _dataset = dataSet, + _criterion = new MSECriterion[Double]() + ) + + val optimMethod = new SGD[Double](learningRate = 20.0) + + optimizer.setOptimMethod(optimMethod) + .setEndWhen(Trigger.maxIteration(10)) + val model = optimizer.optimize() + + optimMethod.state[Int]("epoch") should be(1) + optimMethod.state[Int]("neval") should be(11) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be(320) + + optimizer.setEndWhen(Trigger.maxIteration(20)) + optimizer.resetContext().optimize() + + optimMethod.state[Int]("epoch") should be(1) + optimMethod.state[Int]("neval") should be(21) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be(640) + + val rdd = sc.parallelize(1 to (160 * nodeNumber), nodeNumber) + .map(_ => Sample[Double](Tensor[Double](4).fill(2.0), Tensor[Double](1).fill(1.0))) + + optimizer.setTrainData(rdd, 16 * nodeNumber) + + optimMethod.state[Int]("epoch") should be(2) + optimMethod.state[Int]("neval") should be(21) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be(0) + + optimizer.setEndWhen(Trigger.maxEpoch(2)) + optimizer.resetContext().optimize() + + optimMethod.state[Int]("epoch") should be(3) + optimMethod.state[Int]("neval") should be(31) + optimMethod.state[Int]("recordsProcessedThisEpoch") should be(0) + } + + "reserve optimMethod for each worker" should "be correct" in { + LoggerFilter.redirectSparkInfoLogs() + Logger.getLogger("com.intel.analytics.bigdl.optim").setLevel(Level.INFO) + Logger.getLogger("com.intel.analytics.bigdl").setLevel(Level.INFO) + + val mm = Sequential[Double]().add(Linear(4, 1)) + .add(Sigmoid()) + + val optimizer = new DistriOptimizerV2[Double]( + _model = mm, + _dataset = dataSet, + _criterion = new MSECriterion[Double]() + ) + + val optimMethod = new Adam[Double](learningRate = 20.0) + + optimizer + .setOptimMethod(optimMethod) + .reserveOptim(true) + .setEndWhen(Trigger.maxIteration(3)) + val model = optimizer.optimize() + val optim1 = optimizer.previousOptim.collect() + + optimizer.setEndWhen(Trigger.maxEpoch(0)) + optimizer.optimize() + val optim2 = optimizer.previousOptim.collect() + + var i = 0 + while (i < optim1.length) { + val t1 = optim1(i).values.head.asInstanceOf[Adam[Double]] + val t2 = optim2(i).values.head.asInstanceOf[Adam[Double]] + + t1.beta1 should be(t2.beta1) + t1.beta2 should be(t2.beta2) + t1.learningRate should be(t2.learningRate) + t1.learningRateDecay should be(t2.learningRateDecay) + t1.Epsilon should be(t2.Epsilon) + + t2.state.contains("s") should be(true) + t2.state.contains("r") should be(true) + + t1.state should be(t2.state) + + i += 1 + } + } +} + From 88d7bc78f40c3a30ffe01b7f5ae4b5e0c7c9ba98 Mon Sep 17 00:00:00 2001 From: dding3 Date: Wed, 8 Apr 2020 20:03:18 -0700 Subject: [PATCH 1015/1065] update error message in AllReduceParameter (#2997) * update error message in AllReduceParameter --- .../bigdl/dllib/optim/parameters/AllReduceParameter.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala index 06eb0692e08..2a71233188f 100644 --- a/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala +++ b/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/optim/parameters/AllReduceParameter.scala @@ -207,7 +207,8 @@ class AllReduceParameter[T: ClassTag]( val blockId = getWeightBlockId(pid) val localBuffer = BlockManagerWrapper.getLocalOrRemoteBytes(blockId).getOrElse { throw new RuntimeException(s"Didn't find weight block $blockId in the block " + - s"manager. Did you initialize this AllReduceParameter on every executor?") + s"manager. This is usually because executor crashed. Please check your" + + s"executors' log see the error (usually an OutOfMemory error)") } val start = pid * taskSize + math.min(pid, extraSize) val length = taskSize + (if (pid < extraSize) 1 else 0) From 9eee11fb7f0fb4901f726d69a09377af2514591d Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 15 Apr 2020 21:14:16 +0800 Subject: [PATCH 1016/1065] use tensorflow proto jar (#2994) --- dl/pom.xml | 10 +- .../src/main/java/org/tensorflow/README.md | 23 - .../org/tensorflow/example/BytesList.java | 537 -- .../example/BytesListOrBuilder.java | 22 - .../java/org/tensorflow/example/Example.java | 567 -- .../tensorflow/example/ExampleOrBuilder.java | 22 - .../org/tensorflow/example/ExampleProtos.java | 74 - .../java/org/tensorflow/example/Feature.java | 1074 --- .../org/tensorflow/example/FeatureList.java | 747 --- .../example/FeatureListOrBuilder.java | 33 - .../org/tensorflow/example/FeatureLists.java | 704 -- .../example/FeatureListsOrBuilder.java | 63 - .../tensorflow/example/FeatureOrBuilder.java | 50 - .../org/tensorflow/example/FeatureProtos.java | 158 - .../java/org/tensorflow/example/Features.java | 704 -- .../tensorflow/example/FeaturesOrBuilder.java | 63 - .../org/tensorflow/example/FloatList.java | 544 -- .../example/FloatListOrBuilder.java | 22 - .../org/tensorflow/example/Int64List.java | 547 -- .../example/Int64ListOrBuilder.java | 22 - .../tensorflow/example/SequenceExample.java | 748 --- .../example/SequenceExampleOrBuilder.java | 35 - .../framework/AllocationDescription.java | 897 --- .../AllocationDescriptionOrBuilder.java | 72 - .../AllocationDescriptionProtos.java | 60 - .../framework/AllocatorMemoryUsed.java | 612 -- .../AllocatorMemoryUsedOrBuilder.java | 29 - .../org/tensorflow/framework/AttrValue.java | 5146 --------------- .../framework/AttrValueOrBuilder.java | 168 - .../tensorflow/framework/AttrValueProtos.java | 115 - .../org/tensorflow/framework/DataType.java | 553 -- .../framework/DeviceAttributes.java | 1227 ---- .../framework/DeviceAttributesOrBuilder.java | 110 - .../framework/DeviceAttributesProtos.java | 72 - .../tensorflow/framework/DeviceLocality.java | 445 -- .../framework/DeviceLocalityOrBuilder.java | 19 - .../tensorflow/framework/DeviceStepStats.java | 842 --- .../framework/DeviceStepStatsOrBuilder.java | 43 - .../org/tensorflow/framework/FunctionDef.java | 1722 ----- .../framework/FunctionDefLibrary.java | 1063 --- .../FunctionDefLibraryOrBuilder.java | 57 - .../framework/FunctionDefOrBuilder.java | 204 - .../tensorflow/framework/FunctionProtos.java | 121 - .../org/tensorflow/framework/GradientDef.java | 713 -- .../framework/GradientDefOrBuilder.java | 45 - .../org/tensorflow/framework/GraphDef.java | 1539 ----- .../framework/GraphDefOrBuilder.java | 163 - .../org/tensorflow/framework/GraphProtos.java | 69 - .../tensorflow/framework/HistogramProto.java | 1078 --- .../framework/HistogramProtoOrBuilder.java | 84 - .../org/tensorflow/framework/KernelDef.java | 2237 ------- .../framework/KernelDefOrBuilder.java | 130 - .../tensorflow/framework/KernelDefProtos.java | 75 - .../tensorflow/framework/LogMemoryProtos.java | 130 - .../framework/MemoryLogRawAllocation.java | 981 --- .../MemoryLogRawAllocationOrBuilder.java | 82 - .../framework/MemoryLogRawDeallocation.java | 910 --- .../MemoryLogRawDeallocationOrBuilder.java | 74 - .../tensorflow/framework/MemoryLogStep.java | 597 -- .../framework/MemoryLogStepOrBuilder.java | 36 - .../framework/MemoryLogTensorAllocation.java | 833 --- .../MemoryLogTensorAllocationOrBuilder.java | 63 - .../MemoryLogTensorDeallocation.java | 601 -- .../MemoryLogTensorDeallocationOrBuilder.java | 37 - .../framework/MemoryLogTensorOutput.java | 907 --- .../MemoryLogTensorOutputOrBuilder.java | 72 - .../tensorflow/framework/NameAttrList.java | 779 --- .../framework/NameAttrListOrBuilder.java | 53 - .../org/tensorflow/framework/NodeDef.java | 1686 ----- .../framework/NodeDefOrBuilder.java | 263 - .../tensorflow/framework/NodeExecStats.java | 2065 ------ .../framework/NodeExecStatsOrBuilder.java | 145 - .../org/tensorflow/framework/NodeOutput.java | 614 -- .../framework/NodeOutputOrBuilder.java | 27 - .../org/tensorflow/framework/NodeProto.java | 74 - .../java/org/tensorflow/framework/OpDef.java | 5878 ----------------- .../tensorflow/framework/OpDefOrBuilder.java | 252 - .../org/tensorflow/framework/OpDefProtos.java | 125 - .../tensorflow/framework/OpDeprecation.java | 604 -- .../framework/OpDeprecationOrBuilder.java | 36 - .../java/org/tensorflow/framework/OpList.java | 720 -- .../tensorflow/framework/OpListOrBuilder.java | 33 - .../tensorflow/framework/ResourceHandle.java | 1085 --- .../framework/ResourceHandleOrBuilder.java | 93 - .../framework/ResourceHandleProto.java | 59 - .../framework/SaveSliceInfoDef.java | 1122 ---- .../framework/SaveSliceInfoDefOrBuilder.java | 102 - .../org/tensorflow/framework/StepStats.java | 712 -- .../framework/StepStatsOrBuilder.java | 33 - .../tensorflow/framework/StepStatsProtos.java | 124 - .../org/tensorflow/framework/Summary.java | 4211 ------------ .../framework/SummaryDescription.java | 537 -- .../SummaryDescriptionOrBuilder.java | 29 - .../framework/SummaryOrBuilder.java | 53 - .../tensorflow/framework/SummaryProtos.java | 132 - .../framework/TensorDescription.java | 936 --- .../framework/TensorDescriptionOrBuilder.java | 76 - .../framework/TensorDescriptionProtos.java | 70 - .../org/tensorflow/framework/TensorProto.java | 3087 --------- .../framework/TensorProtoOrBuilder.java | 346 - .../tensorflow/framework/TensorProtos.java | 75 - .../framework/TensorShapeProto.java | 1752 ----- .../framework/TensorShapeProtoOrBuilder.java | 108 - .../framework/TensorShapeProtos.java | 70 - .../framework/TensorSliceProto.java | 1487 ----- .../framework/TensorSliceProtoOrBuilder.java | 68 - .../framework/TensorSliceProtos.java | 70 - .../org/tensorflow/framework/TypesProtos.java | 62 - .../org/tensorflow/framework/VariableDef.java | 1069 --- .../framework/VariableDefOrBuilder.java | 88 - .../tensorflow/framework/VariableProtos.java | 73 - .../org/tensorflow/framework/VersionDef.java | 743 --- .../framework/VersionDefOrBuilder.java | 52 - .../tensorflow/framework/VersionsProtos.java | 58 - .../main/java/org/tensorflow/util/Event.java | 1920 ------ .../org/tensorflow/util/EventOrBuilder.java | 141 - .../java/org/tensorflow/util/EventProtos.java | 109 - .../java/org/tensorflow/util/LogMessage.java | 719 -- .../tensorflow/util/LogMessageOrBuilder.java | 28 - .../java/org/tensorflow/util/SessionLog.java | 856 --- .../tensorflow/util/SessionLogOrBuilder.java | 46 - .../tensorflow/util/TaggedRunMetadata.java | 611 -- .../util/TaggedRunMetadataOrBuilder.java | 37 - 123 files changed, 5 insertions(+), 68200 deletions(-) delete mode 100644 scala/dllib/src/main/java/org/tensorflow/README.md delete mode 100644 scala/dllib/src/main/java/org/tensorflow/example/BytesList.java 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delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/StepStats.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/StepStatsOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/StepStatsProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/Summary.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/SummaryDescription.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/SummaryDescriptionOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/SummaryOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/SummaryProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorDescription.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorDescriptionOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorDescriptionProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorProto.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorProtoOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProto.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProtoOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorShapeProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorSliceProto.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorSliceProtoOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TensorSliceProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/TypesProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/VariableDef.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/VariableDefOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/VariableProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/VersionDef.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/VersionDefOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/framework/VersionsProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/Event.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/EventOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/EventProtos.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/LogMessage.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/LogMessageOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/SessionLog.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/SessionLogOrBuilder.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/TaggedRunMetadata.java delete mode 100644 scala/dllib/src/main/java/org/tensorflow/util/TaggedRunMetadataOrBuilder.java diff --git a/dl/pom.xml b/dl/pom.xml index a5763700971..40df8830ad0 100644 --- a/dl/pom.xml +++ b/dl/pom.xml @@ -152,6 +152,11 @@ gson 2.8.5 + + org.tensorflow + proto + 1.15.0 + @@ -200,10 +205,6 @@ com.google.protobuf com.intel.analytics.bigdl.shaded.protobuf - - org.tensorflow.framework - com.intel.analytics.bigdl.shaded.tensorflow.framework - @@ -217,7 +218,6 @@ com.google.protobuf - org.tensorflow.framework diff --git a/scala/dllib/src/main/java/org/tensorflow/README.md b/scala/dllib/src/main/java/org/tensorflow/README.md deleted file mode 100644 index 7b1abed7513..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# Tensorflow protobuf Java classes -This folder contains Tensorflow protobuf classes. So we can provide some features like -fine-tune/inference saved Tensorflow models on Spark and save BigDL model in format which can be -loaded by Tensorflow(e.g. for inference on mobile). - -## Why not use Tensorflow java API -We cannot just import Tensorflow java API jar from maven and use it. Tensorflow must be installed on -the machine. This brings unnecessary dependency. - -Tensorflow Java API is not so sufficient to parse the model graph. - -## Which version of Tensorflow are these codes generate from? -Tensorflow 1.0.0 - -## How to generate the classes? -Download protobuf binary from [here](https://github.com/google/protobuf/releases/download/v3.0.2/protoc-3.0.2-linux-x86_64.zip). - -After extract the package, go to the bin folder and run -```bash -protoc -I=$path_to_tensorflow --java_out=./ $path_to_tensorflow/tensorflow/core/framework/*.proto -``` - -Then you can see the generated Java class files in the current folder. diff --git a/scala/dllib/src/main/java/org/tensorflow/example/BytesList.java b/scala/dllib/src/main/java/org/tensorflow/example/BytesList.java deleted file mode 100644 index e8d4f3ace3c..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/BytesList.java +++ /dev/null @@ -1,537 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -/** - * 
- * Containers to hold repeated fundamental values.
- *
- * - * Protobuf type {@code tensorflow.BytesList} - */ -public final class BytesList extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.BytesList) - BytesListOrBuilder { -private static final long serialVersionUID = 0L; - // Use BytesList.newBuilder() to construct. - private BytesList(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private BytesList() { - value_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private BytesList( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - value_.add(input.readBytes()); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = java.util.Collections.unmodifiableList(value_); - } - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.BytesList.class, org.tensorflow.example.BytesList.Builder.class); - } - - public static final int VALUE_FIELD_NUMBER = 1; - private java.util.List value_; - /** - * repeated bytes value = 1; - */ - public java.util.List - getValueList() { - return value_; - } - /** - * repeated bytes value = 1; - */ - public int getValueCount() { - return value_.size(); - } - /** - * repeated bytes value = 1; - */ - public com.google.protobuf.ByteString getValue(int index) { - return value_.get(index); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - for (int i = 0; i < value_.size(); i++) { - output.writeBytes(1, value_.get(i)); - } - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - { - int dataSize = 0; - for (int i = 0; i < value_.size(); i++) { - dataSize += com.google.protobuf.CodedOutputStream - .computeBytesSizeNoTag(value_.get(i)); - } - size += dataSize; - size += 1 * getValueList().size(); - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.BytesList)) { - return super.equals(obj); - } - org.tensorflow.example.BytesList other = (org.tensorflow.example.BytesList) obj; - - boolean result = true; - result = result && getValueList() - .equals(other.getValueList()); - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - if (getValueCount() > 0) { - hash = (37 * hash) + VALUE_FIELD_NUMBER; - hash = (53 * hash) + getValueList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.BytesList parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.BytesList parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.BytesList parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.BytesList parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.BytesList parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.BytesList parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.BytesList parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.BytesList parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.BytesList parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.BytesList parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.BytesList parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.BytesList parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.BytesList prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Containers to hold repeated fundamental values.
-   *
- * - * Protobuf type {@code tensorflow.BytesList} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.BytesList) - org.tensorflow.example.BytesListOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.BytesList.class, org.tensorflow.example.BytesList.Builder.class); - } - - // Construct using org.tensorflow.example.BytesList.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - value_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_BytesList_descriptor; - } - - public org.tensorflow.example.BytesList getDefaultInstanceForType() { - return org.tensorflow.example.BytesList.getDefaultInstance(); - } - - public org.tensorflow.example.BytesList build() { - org.tensorflow.example.BytesList result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.BytesList buildPartial() { - org.tensorflow.example.BytesList result = new org.tensorflow.example.BytesList(this); - int from_bitField0_ = bitField0_; - if (((bitField0_ & 0x00000001) == 0x00000001)) { - value_ = java.util.Collections.unmodifiableList(value_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.value_ = value_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.BytesList) { - return mergeFrom((org.tensorflow.example.BytesList)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.BytesList other) { - if (other == org.tensorflow.example.BytesList.getDefaultInstance()) return this; - if (!other.value_.isEmpty()) { - if (value_.isEmpty()) { - value_ = other.value_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureValueIsMutable(); - value_.addAll(other.value_); - } - onChanged(); - } - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.BytesList parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.BytesList) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List value_ = java.util.Collections.emptyList(); - private void ensureValueIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - value_ = new java.util.ArrayList(value_); - bitField0_ |= 0x00000001; - } - } - /** - * repeated bytes value = 1; - */ - public java.util.List - getValueList() { - return java.util.Collections.unmodifiableList(value_); - } - /** - * repeated bytes value = 1; - */ - public int getValueCount() { - return value_.size(); - } - /** - * repeated bytes value = 1; - */ - public com.google.protobuf.ByteString getValue(int index) { - return value_.get(index); - } - /** - * repeated bytes value = 1; - */ - public Builder setValue( - int index, com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - ensureValueIsMutable(); - value_.set(index, value); - onChanged(); - return this; - } - /** - * repeated bytes value = 1; - */ - public Builder addValue(com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - ensureValueIsMutable(); - value_.add(value); - onChanged(); - return this; - } - /** - * repeated bytes value = 1; - */ - public Builder addAllValue( - java.lang.Iterable values) { - ensureValueIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, value_); - onChanged(); - return this; - } - /** - * repeated bytes value = 1; - */ - public Builder clearValue() { - value_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.BytesList) - } - - // @@protoc_insertion_point(class_scope:tensorflow.BytesList) - private static final org.tensorflow.example.BytesList DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.BytesList(); - } - - public static org.tensorflow.example.BytesList getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public BytesList parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new BytesList(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.BytesList getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/BytesListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/BytesListOrBuilder.java deleted file mode 100644 index d7f0d92093e..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/BytesListOrBuilder.java +++ /dev/null @@ -1,22 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -public interface BytesListOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.BytesList) - com.google.protobuf.MessageOrBuilder { - - /** - * repeated bytes value = 1; - */ - java.util.List getValueList(); - /** - * repeated bytes value = 1; - */ - int getValueCount(); - /** - * repeated bytes value = 1; - */ - com.google.protobuf.ByteString getValue(int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Example.java b/scala/dllib/src/main/java/org/tensorflow/example/Example.java deleted file mode 100644 index 719de2547fa..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/Example.java +++ /dev/null @@ -1,567 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tensorflow/core/example/example.proto - -package org.tensorflow.example; - -/** - * Protobuf type {@code tensorflow.Example} - */ -public final class Example extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.Example) - ExampleOrBuilder { -private static final long serialVersionUID = 0L; - // Use Example.newBuilder() to construct. - private Example(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private Example() { - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private Example( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 10: { - org.tensorflow.example.Features.Builder subBuilder = null; - if (features_ != null) { - subBuilder = features_.toBuilder(); - } - features_ = input.readMessage(org.tensorflow.example.Features.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(features_); - features_ = subBuilder.buildPartial(); - } - - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.Example.class, org.tensorflow.example.Example.Builder.class); - } - - public static final int FEATURES_FIELD_NUMBER = 1; - private org.tensorflow.example.Features features_; - /** - * .tensorflow.Features features = 1; - */ - public boolean hasFeatures() { - return features_ != null; - } - /** - * .tensorflow.Features features = 1; - */ - public org.tensorflow.example.Features getFeatures() { - return features_ == null ? org.tensorflow.example.Features.getDefaultInstance() : features_; - } - /** - * .tensorflow.Features features = 1; - */ - public org.tensorflow.example.FeaturesOrBuilder getFeaturesOrBuilder() { - return getFeatures(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (features_ != null) { - output.writeMessage(1, getFeatures()); - } - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (features_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, getFeatures()); - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.Example)) { - return super.equals(obj); - } - org.tensorflow.example.Example other = (org.tensorflow.example.Example) obj; - - boolean result = true; - result = result && (hasFeatures() == other.hasFeatures()); - if (hasFeatures()) { - result = result && getFeatures() - .equals(other.getFeatures()); - } - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - if (hasFeatures()) { - hash = (37 * hash) + FEATURES_FIELD_NUMBER; - hash = (53 * hash) + getFeatures().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.Example parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Example parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Example parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Example parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Example parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Example parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Example parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.Example parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.Example parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.Example parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.Example parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.Example parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.Example prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.Example} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.Example) - org.tensorflow.example.ExampleOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.Example.class, org.tensorflow.example.Example.Builder.class); - } - - // Construct using org.tensorflow.example.Example.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - if (featuresBuilder_ == null) { - features_ = null; - } else { - features_ = null; - featuresBuilder_ = null; - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_Example_descriptor; - } - - public org.tensorflow.example.Example getDefaultInstanceForType() { - return org.tensorflow.example.Example.getDefaultInstance(); - } - - public org.tensorflow.example.Example build() { - org.tensorflow.example.Example result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.Example buildPartial() { - org.tensorflow.example.Example result = new org.tensorflow.example.Example(this); - if (featuresBuilder_ == null) { - result.features_ = features_; - } else { - result.features_ = featuresBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.Example) { - return mergeFrom((org.tensorflow.example.Example)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.Example other) { - if (other == org.tensorflow.example.Example.getDefaultInstance()) return this; - if (other.hasFeatures()) { - mergeFeatures(other.getFeatures()); - } - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.Example parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.Example) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private org.tensorflow.example.Features features_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder> featuresBuilder_; - /** - * .tensorflow.Features features = 1; - */ - public boolean hasFeatures() { - return featuresBuilder_ != null || features_ != null; - } - /** - * .tensorflow.Features features = 1; - */ - public org.tensorflow.example.Features getFeatures() { - if (featuresBuilder_ == null) { - return features_ == null ? org.tensorflow.example.Features.getDefaultInstance() : features_; - } else { - return featuresBuilder_.getMessage(); - } - } - /** - * .tensorflow.Features features = 1; - */ - public Builder setFeatures(org.tensorflow.example.Features value) { - if (featuresBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - features_ = value; - onChanged(); - } else { - featuresBuilder_.setMessage(value); - } - - return this; - } - /** - * .tensorflow.Features features = 1; - */ - public Builder setFeatures( - org.tensorflow.example.Features.Builder builderForValue) { - if (featuresBuilder_ == null) { - features_ = builderForValue.build(); - onChanged(); - } else { - featuresBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * .tensorflow.Features features = 1; - */ - public Builder mergeFeatures(org.tensorflow.example.Features value) { - if (featuresBuilder_ == null) { - if (features_ != null) { - features_ = - org.tensorflow.example.Features.newBuilder(features_).mergeFrom(value).buildPartial(); - } else { - features_ = value; - } - onChanged(); - } else { - featuresBuilder_.mergeFrom(value); - } - - return this; - } - /** - * .tensorflow.Features features = 1; - */ - public Builder clearFeatures() { - if (featuresBuilder_ == null) { - features_ = null; - onChanged(); - } else { - features_ = null; - featuresBuilder_ = null; - } - - return this; - } - /** - * .tensorflow.Features features = 1; - */ - public org.tensorflow.example.Features.Builder getFeaturesBuilder() { - - onChanged(); - return getFeaturesFieldBuilder().getBuilder(); - } - /** - * .tensorflow.Features features = 1; - */ - public org.tensorflow.example.FeaturesOrBuilder getFeaturesOrBuilder() { - if (featuresBuilder_ != null) { - return featuresBuilder_.getMessageOrBuilder(); - } else { - return features_ == null ? - org.tensorflow.example.Features.getDefaultInstance() : features_; - } - } - /** - * .tensorflow.Features features = 1; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder> - getFeaturesFieldBuilder() { - if (featuresBuilder_ == null) { - featuresBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder>( - getFeatures(), - getParentForChildren(), - isClean()); - features_ = null; - } - return featuresBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.Example) - } - - // @@protoc_insertion_point(class_scope:tensorflow.Example) - private static final org.tensorflow.example.Example DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.Example(); - } - - public static org.tensorflow.example.Example getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public Example parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new Example(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.Example getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/ExampleOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/ExampleOrBuilder.java deleted file mode 100644 index 94aaff0d906..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/ExampleOrBuilder.java +++ /dev/null @@ -1,22 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tensorflow/core/example/example.proto - -package org.tensorflow.example; - -public interface ExampleOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.Example) - com.google.protobuf.MessageOrBuilder { - - /** - * .tensorflow.Features features = 1; - */ - boolean hasFeatures(); - /** - * .tensorflow.Features features = 1; - */ - org.tensorflow.example.Features getFeatures(); - /** - * .tensorflow.Features features = 1; - */ - org.tensorflow.example.FeaturesOrBuilder getFeaturesOrBuilder(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/ExampleProtos.java b/scala/dllib/src/main/java/org/tensorflow/example/ExampleProtos.java deleted file mode 100644 index d4f59778160..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/ExampleProtos.java +++ /dev/null @@ -1,74 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tensorflow/core/example/example.proto - -package org.tensorflow.example; - -public final class ExampleProtos { - private ExampleProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_Example_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_Example_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_SequenceExample_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_SequenceExample_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n%tensorflow/core/example/example.proto\022" + - "\ntensorflow\032%tensorflow/core/example/fea" + - "ture.proto\"1\n\007Example\022&\n\010features\030\001 \001(\0132" + - "\024.tensorflow.Features\"i\n\017SequenceExample" + - "\022%\n\007context\030\001 \001(\0132\024.tensorflow.Features\022" + - "/\n\rfeature_lists\030\002 \001(\0132\030.tensorflow.Feat" + - "ureListsB,\n\026org.tensorflow.exampleB\rExam" + - "pleProtosP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.example.FeatureProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_Example_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_Example_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_Example_descriptor, - new java.lang.String[] { "Features", }); - internal_static_tensorflow_SequenceExample_descriptor = - getDescriptor().getMessageTypes().get(1); - internal_static_tensorflow_SequenceExample_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_SequenceExample_descriptor, - new java.lang.String[] { "Context", "FeatureLists", }); - org.tensorflow.example.FeatureProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Feature.java b/scala/dllib/src/main/java/org/tensorflow/example/Feature.java deleted file mode 100644 index 72933d0fdd4..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/Feature.java +++ /dev/null @@ -1,1074 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -/** - * 
- * Containers for non-sequential data.
- *
- * - * Protobuf type {@code tensorflow.Feature} - */ -public final class Feature extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.Feature) - FeatureOrBuilder { -private static final long serialVersionUID = 0L; - // Use Feature.newBuilder() to construct. - private Feature(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private Feature() { - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private Feature( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 10: { - org.tensorflow.example.BytesList.Builder subBuilder = null; - if (kindCase_ == 1) { - subBuilder = ((org.tensorflow.example.BytesList) kind_).toBuilder(); - } - kind_ = - input.readMessage(org.tensorflow.example.BytesList.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom((org.tensorflow.example.BytesList) kind_); - kind_ = subBuilder.buildPartial(); - } - kindCase_ = 1; - break; - } - case 18: { - org.tensorflow.example.FloatList.Builder subBuilder = null; - if (kindCase_ == 2) { - subBuilder = ((org.tensorflow.example.FloatList) kind_).toBuilder(); - } - kind_ = - input.readMessage(org.tensorflow.example.FloatList.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom((org.tensorflow.example.FloatList) kind_); - kind_ = subBuilder.buildPartial(); - } - kindCase_ = 2; - break; - } - case 26: { - org.tensorflow.example.Int64List.Builder subBuilder = null; - if (kindCase_ == 3) { - subBuilder = ((org.tensorflow.example.Int64List) kind_).toBuilder(); - } - kind_ = - input.readMessage(org.tensorflow.example.Int64List.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom((org.tensorflow.example.Int64List) kind_); - kind_ = subBuilder.buildPartial(); - } - kindCase_ = 3; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.Feature.class, org.tensorflow.example.Feature.Builder.class); - } - - private int kindCase_ = 0; - private java.lang.Object kind_; - public enum KindCase - implements com.google.protobuf.Internal.EnumLite { - BYTES_LIST(1), - FLOAT_LIST(2), - INT64_LIST(3), - KIND_NOT_SET(0); - private final int value; - private KindCase(int value) { - this.value = value; - } - /** - * @deprecated Use {@link #forNumber(int)} instead. - */ - @java.lang.Deprecated - public static KindCase valueOf(int value) { - return forNumber(value); - } - - public static KindCase forNumber(int value) { - switch (value) { - case 1: return BYTES_LIST; - case 2: return FLOAT_LIST; - case 3: return INT64_LIST; - case 0: return KIND_NOT_SET; - default: return null; - } - } - public int getNumber() { - return this.value; - } - }; - - public KindCase - getKindCase() { - return KindCase.forNumber( - kindCase_); - } - - public static final int BYTES_LIST_FIELD_NUMBER = 1; - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public boolean hasBytesList() { - return kindCase_ == 1; - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public org.tensorflow.example.BytesList getBytesList() { - if (kindCase_ == 1) { - return (org.tensorflow.example.BytesList) kind_; - } - return org.tensorflow.example.BytesList.getDefaultInstance(); - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public org.tensorflow.example.BytesListOrBuilder getBytesListOrBuilder() { - if (kindCase_ == 1) { - return (org.tensorflow.example.BytesList) kind_; - } - return org.tensorflow.example.BytesList.getDefaultInstance(); - } - - public static final int FLOAT_LIST_FIELD_NUMBER = 2; - /** - * .tensorflow.FloatList float_list = 2; - */ - public boolean hasFloatList() { - return kindCase_ == 2; - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public org.tensorflow.example.FloatList getFloatList() { - if (kindCase_ == 2) { - return (org.tensorflow.example.FloatList) kind_; - } - return org.tensorflow.example.FloatList.getDefaultInstance(); - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public org.tensorflow.example.FloatListOrBuilder getFloatListOrBuilder() { - if (kindCase_ == 2) { - return (org.tensorflow.example.FloatList) kind_; - } - return org.tensorflow.example.FloatList.getDefaultInstance(); - } - - public static final int INT64_LIST_FIELD_NUMBER = 3; - /** - * .tensorflow.Int64List int64_list = 3; - */ - public boolean hasInt64List() { - return kindCase_ == 3; - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public org.tensorflow.example.Int64List getInt64List() { - if (kindCase_ == 3) { - return (org.tensorflow.example.Int64List) kind_; - } - return org.tensorflow.example.Int64List.getDefaultInstance(); - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public org.tensorflow.example.Int64ListOrBuilder getInt64ListOrBuilder() { - if (kindCase_ == 3) { - return (org.tensorflow.example.Int64List) kind_; - } - return org.tensorflow.example.Int64List.getDefaultInstance(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (kindCase_ == 1) { - output.writeMessage(1, (org.tensorflow.example.BytesList) kind_); - } - if (kindCase_ == 2) { - output.writeMessage(2, (org.tensorflow.example.FloatList) kind_); - } - if (kindCase_ == 3) { - output.writeMessage(3, (org.tensorflow.example.Int64List) kind_); - } - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (kindCase_ == 1) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, (org.tensorflow.example.BytesList) kind_); - } - if (kindCase_ == 2) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, (org.tensorflow.example.FloatList) kind_); - } - if (kindCase_ == 3) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(3, (org.tensorflow.example.Int64List) kind_); - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.Feature)) { - return super.equals(obj); - } - org.tensorflow.example.Feature other = (org.tensorflow.example.Feature) obj; - - boolean result = true; - result = result && getKindCase().equals( - other.getKindCase()); - if (!result) return false; - switch (kindCase_) { - case 1: - result = result && getBytesList() - .equals(other.getBytesList()); - break; - case 2: - result = result && getFloatList() - .equals(other.getFloatList()); - break; - case 3: - result = result && getInt64List() - .equals(other.getInt64List()); - break; - case 0: - default: - } - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - switch (kindCase_) { - case 1: - hash = (37 * hash) + BYTES_LIST_FIELD_NUMBER; - hash = (53 * hash) + getBytesList().hashCode(); - break; - case 2: - hash = (37 * hash) + FLOAT_LIST_FIELD_NUMBER; - hash = (53 * hash) + getFloatList().hashCode(); - break; - case 3: - hash = (37 * hash) + INT64_LIST_FIELD_NUMBER; - hash = (53 * hash) + getInt64List().hashCode(); - break; - case 0: - default: - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.Feature parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Feature parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Feature parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Feature parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Feature parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Feature parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Feature parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.Feature parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.Feature parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.Feature parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.Feature parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.Feature parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.Feature prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Containers for non-sequential data.
-   *
- * - * Protobuf type {@code tensorflow.Feature} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.Feature) - org.tensorflow.example.FeatureOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.Feature.class, org.tensorflow.example.Feature.Builder.class); - } - - // Construct using org.tensorflow.example.Feature.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - kindCase_ = 0; - kind_ = null; - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Feature_descriptor; - } - - public org.tensorflow.example.Feature getDefaultInstanceForType() { - return org.tensorflow.example.Feature.getDefaultInstance(); - } - - public org.tensorflow.example.Feature build() { - org.tensorflow.example.Feature result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.Feature buildPartial() { - org.tensorflow.example.Feature result = new org.tensorflow.example.Feature(this); - if (kindCase_ == 1) { - if (bytesListBuilder_ == null) { - result.kind_ = kind_; - } else { - result.kind_ = bytesListBuilder_.build(); - } - } - if (kindCase_ == 2) { - if (floatListBuilder_ == null) { - result.kind_ = kind_; - } else { - result.kind_ = floatListBuilder_.build(); - } - } - if (kindCase_ == 3) { - if (int64ListBuilder_ == null) { - result.kind_ = kind_; - } else { - result.kind_ = int64ListBuilder_.build(); - } - } - result.kindCase_ = kindCase_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.Feature) { - return mergeFrom((org.tensorflow.example.Feature)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.Feature other) { - if (other == org.tensorflow.example.Feature.getDefaultInstance()) return this; - switch (other.getKindCase()) { - case BYTES_LIST: { - mergeBytesList(other.getBytesList()); - break; - } - case FLOAT_LIST: { - mergeFloatList(other.getFloatList()); - break; - } - case INT64_LIST: { - mergeInt64List(other.getInt64List()); - break; - } - case KIND_NOT_SET: { - break; - } - } - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.Feature parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.Feature) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int kindCase_ = 0; - private java.lang.Object kind_; - public KindCase - getKindCase() { - return KindCase.forNumber( - kindCase_); - } - - public Builder clearKind() { - kindCase_ = 0; - kind_ = null; - onChanged(); - return this; - } - - - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.BytesList, org.tensorflow.example.BytesList.Builder, org.tensorflow.example.BytesListOrBuilder> bytesListBuilder_; - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public boolean hasBytesList() { - return kindCase_ == 1; - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public org.tensorflow.example.BytesList getBytesList() { - if (bytesListBuilder_ == null) { - if (kindCase_ == 1) { - return (org.tensorflow.example.BytesList) kind_; - } - return org.tensorflow.example.BytesList.getDefaultInstance(); - } else { - if (kindCase_ == 1) { - return bytesListBuilder_.getMessage(); - } - return org.tensorflow.example.BytesList.getDefaultInstance(); - } - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public Builder setBytesList(org.tensorflow.example.BytesList value) { - if (bytesListBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - kind_ = value; - onChanged(); - } else { - bytesListBuilder_.setMessage(value); - } - kindCase_ = 1; - return this; - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public Builder setBytesList( - org.tensorflow.example.BytesList.Builder builderForValue) { - if (bytesListBuilder_ == null) { - kind_ = builderForValue.build(); - onChanged(); - } else { - bytesListBuilder_.setMessage(builderForValue.build()); - } - kindCase_ = 1; - return this; - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public Builder mergeBytesList(org.tensorflow.example.BytesList value) { - if (bytesListBuilder_ == null) { - if (kindCase_ == 1 && - kind_ != org.tensorflow.example.BytesList.getDefaultInstance()) { - kind_ = org.tensorflow.example.BytesList.newBuilder((org.tensorflow.example.BytesList) kind_) - .mergeFrom(value).buildPartial(); - } else { - kind_ = value; - } - onChanged(); - } else { - if (kindCase_ == 1) { - bytesListBuilder_.mergeFrom(value); - } - bytesListBuilder_.setMessage(value); - } - kindCase_ = 1; - return this; - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public Builder clearBytesList() { - if (bytesListBuilder_ == null) { - if (kindCase_ == 1) { - kindCase_ = 0; - kind_ = null; - onChanged(); - } - } else { - if (kindCase_ == 1) { - kindCase_ = 0; - kind_ = null; - } - bytesListBuilder_.clear(); - } - return this; - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public org.tensorflow.example.BytesList.Builder getBytesListBuilder() { - return getBytesListFieldBuilder().getBuilder(); - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - public org.tensorflow.example.BytesListOrBuilder getBytesListOrBuilder() { - if ((kindCase_ == 1) && (bytesListBuilder_ != null)) { - return bytesListBuilder_.getMessageOrBuilder(); - } else { - if (kindCase_ == 1) { - return (org.tensorflow.example.BytesList) kind_; - } - return org.tensorflow.example.BytesList.getDefaultInstance(); - } - } - /** - * .tensorflow.BytesList bytes_list = 1; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.BytesList, org.tensorflow.example.BytesList.Builder, org.tensorflow.example.BytesListOrBuilder> - getBytesListFieldBuilder() { - if (bytesListBuilder_ == null) { - if (!(kindCase_ == 1)) { - kind_ = org.tensorflow.example.BytesList.getDefaultInstance(); - } - bytesListBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.BytesList, org.tensorflow.example.BytesList.Builder, org.tensorflow.example.BytesListOrBuilder>( - (org.tensorflow.example.BytesList) kind_, - getParentForChildren(), - isClean()); - kind_ = null; - } - kindCase_ = 1; - onChanged();; - return bytesListBuilder_; - } - - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.FloatList, org.tensorflow.example.FloatList.Builder, org.tensorflow.example.FloatListOrBuilder> floatListBuilder_; - /** - * .tensorflow.FloatList float_list = 2; - */ - public boolean hasFloatList() { - return kindCase_ == 2; - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public org.tensorflow.example.FloatList getFloatList() { - if (floatListBuilder_ == null) { - if (kindCase_ == 2) { - return (org.tensorflow.example.FloatList) kind_; - } - return org.tensorflow.example.FloatList.getDefaultInstance(); - } else { - if (kindCase_ == 2) { - return floatListBuilder_.getMessage(); - } - return org.tensorflow.example.FloatList.getDefaultInstance(); - } - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public Builder setFloatList(org.tensorflow.example.FloatList value) { - if (floatListBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - kind_ = value; - onChanged(); - } else { - floatListBuilder_.setMessage(value); - } - kindCase_ = 2; - return this; - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public Builder setFloatList( - org.tensorflow.example.FloatList.Builder builderForValue) { - if (floatListBuilder_ == null) { - kind_ = builderForValue.build(); - onChanged(); - } else { - floatListBuilder_.setMessage(builderForValue.build()); - } - kindCase_ = 2; - return this; - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public Builder mergeFloatList(org.tensorflow.example.FloatList value) { - if (floatListBuilder_ == null) { - if (kindCase_ == 2 && - kind_ != org.tensorflow.example.FloatList.getDefaultInstance()) { - kind_ = org.tensorflow.example.FloatList.newBuilder((org.tensorflow.example.FloatList) kind_) - .mergeFrom(value).buildPartial(); - } else { - kind_ = value; - } - onChanged(); - } else { - if (kindCase_ == 2) { - floatListBuilder_.mergeFrom(value); - } - floatListBuilder_.setMessage(value); - } - kindCase_ = 2; - return this; - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public Builder clearFloatList() { - if (floatListBuilder_ == null) { - if (kindCase_ == 2) { - kindCase_ = 0; - kind_ = null; - onChanged(); - } - } else { - if (kindCase_ == 2) { - kindCase_ = 0; - kind_ = null; - } - floatListBuilder_.clear(); - } - return this; - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public org.tensorflow.example.FloatList.Builder getFloatListBuilder() { - return getFloatListFieldBuilder().getBuilder(); - } - /** - * .tensorflow.FloatList float_list = 2; - */ - public org.tensorflow.example.FloatListOrBuilder getFloatListOrBuilder() { - if ((kindCase_ == 2) && (floatListBuilder_ != null)) { - return floatListBuilder_.getMessageOrBuilder(); - } else { - if (kindCase_ == 2) { - return (org.tensorflow.example.FloatList) kind_; - } - return org.tensorflow.example.FloatList.getDefaultInstance(); - } - } - /** - * .tensorflow.FloatList float_list = 2; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.FloatList, org.tensorflow.example.FloatList.Builder, org.tensorflow.example.FloatListOrBuilder> - getFloatListFieldBuilder() { - if (floatListBuilder_ == null) { - if (!(kindCase_ == 2)) { - kind_ = org.tensorflow.example.FloatList.getDefaultInstance(); - } - floatListBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.FloatList, org.tensorflow.example.FloatList.Builder, org.tensorflow.example.FloatListOrBuilder>( - (org.tensorflow.example.FloatList) kind_, - getParentForChildren(), - isClean()); - kind_ = null; - } - kindCase_ = 2; - onChanged();; - return floatListBuilder_; - } - - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Int64List, org.tensorflow.example.Int64List.Builder, org.tensorflow.example.Int64ListOrBuilder> int64ListBuilder_; - /** - * .tensorflow.Int64List int64_list = 3; - */ - public boolean hasInt64List() { - return kindCase_ == 3; - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public org.tensorflow.example.Int64List getInt64List() { - if (int64ListBuilder_ == null) { - if (kindCase_ == 3) { - return (org.tensorflow.example.Int64List) kind_; - } - return org.tensorflow.example.Int64List.getDefaultInstance(); - } else { - if (kindCase_ == 3) { - return int64ListBuilder_.getMessage(); - } - return org.tensorflow.example.Int64List.getDefaultInstance(); - } - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public Builder setInt64List(org.tensorflow.example.Int64List value) { - if (int64ListBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - kind_ = value; - onChanged(); - } else { - int64ListBuilder_.setMessage(value); - } - kindCase_ = 3; - return this; - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public Builder setInt64List( - org.tensorflow.example.Int64List.Builder builderForValue) { - if (int64ListBuilder_ == null) { - kind_ = builderForValue.build(); - onChanged(); - } else { - int64ListBuilder_.setMessage(builderForValue.build()); - } - kindCase_ = 3; - return this; - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public Builder mergeInt64List(org.tensorflow.example.Int64List value) { - if (int64ListBuilder_ == null) { - if (kindCase_ == 3 && - kind_ != org.tensorflow.example.Int64List.getDefaultInstance()) { - kind_ = org.tensorflow.example.Int64List.newBuilder((org.tensorflow.example.Int64List) kind_) - .mergeFrom(value).buildPartial(); - } else { - kind_ = value; - } - onChanged(); - } else { - if (kindCase_ == 3) { - int64ListBuilder_.mergeFrom(value); - } - int64ListBuilder_.setMessage(value); - } - kindCase_ = 3; - return this; - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public Builder clearInt64List() { - if (int64ListBuilder_ == null) { - if (kindCase_ == 3) { - kindCase_ = 0; - kind_ = null; - onChanged(); - } - } else { - if (kindCase_ == 3) { - kindCase_ = 0; - kind_ = null; - } - int64ListBuilder_.clear(); - } - return this; - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public org.tensorflow.example.Int64List.Builder getInt64ListBuilder() { - return getInt64ListFieldBuilder().getBuilder(); - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - public org.tensorflow.example.Int64ListOrBuilder getInt64ListOrBuilder() { - if ((kindCase_ == 3) && (int64ListBuilder_ != null)) { - return int64ListBuilder_.getMessageOrBuilder(); - } else { - if (kindCase_ == 3) { - return (org.tensorflow.example.Int64List) kind_; - } - return org.tensorflow.example.Int64List.getDefaultInstance(); - } - } - /** - * .tensorflow.Int64List int64_list = 3; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Int64List, org.tensorflow.example.Int64List.Builder, org.tensorflow.example.Int64ListOrBuilder> - getInt64ListFieldBuilder() { - if (int64ListBuilder_ == null) { - if (!(kindCase_ == 3)) { - kind_ = org.tensorflow.example.Int64List.getDefaultInstance(); - } - int64ListBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Int64List, org.tensorflow.example.Int64List.Builder, org.tensorflow.example.Int64ListOrBuilder>( - (org.tensorflow.example.Int64List) kind_, - getParentForChildren(), - isClean()); - kind_ = null; - } - kindCase_ = 3; - onChanged();; - return int64ListBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.Feature) - } - - // @@protoc_insertion_point(class_scope:tensorflow.Feature) - private static final org.tensorflow.example.Feature DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.Feature(); - } - - public static org.tensorflow.example.Feature getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public Feature parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new Feature(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.Feature getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureList.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureList.java deleted file mode 100644 index 43a957e2c88..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FeatureList.java +++ /dev/null @@ -1,747 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -/** - * 
- * Containers for sequential data.
- * A FeatureList contains lists of Features.  These may hold zero or more
- * Feature values.
- * FeatureLists are organized into categories by name.  The FeatureLists message
- * contains the mapping from name to FeatureList.
- *
- * - * Protobuf type {@code tensorflow.FeatureList} - */ -public final class FeatureList extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.FeatureList) - FeatureListOrBuilder { -private static final long serialVersionUID = 0L; - // Use FeatureList.newBuilder() to construct. - private FeatureList(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private FeatureList() { - feature_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private FeatureList( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - feature_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - feature_.add( - input.readMessage(org.tensorflow.example.Feature.parser(), extensionRegistry)); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - feature_ = java.util.Collections.unmodifiableList(feature_); - } - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.FeatureList.class, org.tensorflow.example.FeatureList.Builder.class); - } - - public static final int FEATURE_FIELD_NUMBER = 1; - private java.util.List feature_; - /** - * repeated .tensorflow.Feature feature = 1; - */ - public java.util.List getFeatureList() { - return feature_; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public java.util.List - getFeatureOrBuilderList() { - return feature_; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public int getFeatureCount() { - return feature_.size(); - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public org.tensorflow.example.Feature getFeature(int index) { - return feature_.get(index); - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public org.tensorflow.example.FeatureOrBuilder getFeatureOrBuilder( - int index) { - return feature_.get(index); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - for (int i = 0; i < feature_.size(); i++) { - output.writeMessage(1, feature_.get(i)); - } - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - for (int i = 0; i < feature_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, feature_.get(i)); - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.FeatureList)) { - return super.equals(obj); - } - org.tensorflow.example.FeatureList other = (org.tensorflow.example.FeatureList) obj; - - boolean result = true; - result = result && getFeatureList() - .equals(other.getFeatureList()); - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - if (getFeatureCount() > 0) { - hash = (37 * hash) + FEATURE_FIELD_NUMBER; - hash = (53 * hash) + getFeatureList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.FeatureList parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FeatureList parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FeatureList parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FeatureList parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FeatureList parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FeatureList parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FeatureList parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.FeatureList parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.FeatureList parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.FeatureList parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.FeatureList parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.FeatureList parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.FeatureList prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Containers for sequential data.
-   * A FeatureList contains lists of Features.  These may hold zero or more
-   * Feature values.
-   * FeatureLists are organized into categories by name.  The FeatureLists message
-   * contains the mapping from name to FeatureList.
-   *
- * - * Protobuf type {@code tensorflow.FeatureList} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.FeatureList) - org.tensorflow.example.FeatureListOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.FeatureList.class, org.tensorflow.example.FeatureList.Builder.class); - } - - // Construct using org.tensorflow.example.FeatureList.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getFeatureFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - if (featureBuilder_ == null) { - feature_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - } else { - featureBuilder_.clear(); - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureList_descriptor; - } - - public org.tensorflow.example.FeatureList getDefaultInstanceForType() { - return org.tensorflow.example.FeatureList.getDefaultInstance(); - } - - public org.tensorflow.example.FeatureList build() { - org.tensorflow.example.FeatureList result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.FeatureList buildPartial() { - org.tensorflow.example.FeatureList result = new org.tensorflow.example.FeatureList(this); - int from_bitField0_ = bitField0_; - if (featureBuilder_ == null) { - if (((bitField0_ & 0x00000001) == 0x00000001)) { - feature_ = java.util.Collections.unmodifiableList(feature_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.feature_ = feature_; - } else { - result.feature_ = featureBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.FeatureList) { - return mergeFrom((org.tensorflow.example.FeatureList)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.FeatureList other) { - if (other == org.tensorflow.example.FeatureList.getDefaultInstance()) return this; - if (featureBuilder_ == null) { - if (!other.feature_.isEmpty()) { - if (feature_.isEmpty()) { - feature_ = other.feature_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureFeatureIsMutable(); - feature_.addAll(other.feature_); - } - onChanged(); - } - } else { - if (!other.feature_.isEmpty()) { - if (featureBuilder_.isEmpty()) { - featureBuilder_.dispose(); - featureBuilder_ = null; - feature_ = other.feature_; - bitField0_ = (bitField0_ & ~0x00000001); - featureBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getFeatureFieldBuilder() : null; - } else { - featureBuilder_.addAllMessages(other.feature_); - } - } - } - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.FeatureList parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.FeatureList) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List feature_ = - java.util.Collections.emptyList(); - private void ensureFeatureIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - feature_ = new java.util.ArrayList(feature_); - bitField0_ |= 0x00000001; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.example.Feature, org.tensorflow.example.Feature.Builder, org.tensorflow.example.FeatureOrBuilder> featureBuilder_; - - /** - * repeated .tensorflow.Feature feature = 1; - */ - public java.util.List getFeatureList() { - if (featureBuilder_ == null) { - return java.util.Collections.unmodifiableList(feature_); - } else { - return featureBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public int getFeatureCount() { - if (featureBuilder_ == null) { - return feature_.size(); - } else { - return featureBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public org.tensorflow.example.Feature getFeature(int index) { - if (featureBuilder_ == null) { - return feature_.get(index); - } else { - return featureBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder setFeature( - int index, org.tensorflow.example.Feature value) { - if (featureBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFeatureIsMutable(); - feature_.set(index, value); - onChanged(); - } else { - featureBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder setFeature( - int index, org.tensorflow.example.Feature.Builder builderForValue) { - if (featureBuilder_ == null) { - ensureFeatureIsMutable(); - feature_.set(index, builderForValue.build()); - onChanged(); - } else { - featureBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder addFeature(org.tensorflow.example.Feature value) { - if (featureBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFeatureIsMutable(); - feature_.add(value); - onChanged(); - } else { - featureBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder addFeature( - int index, org.tensorflow.example.Feature value) { - if (featureBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFeatureIsMutable(); - feature_.add(index, value); - onChanged(); - } else { - featureBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder addFeature( - org.tensorflow.example.Feature.Builder builderForValue) { - if (featureBuilder_ == null) { - ensureFeatureIsMutable(); - feature_.add(builderForValue.build()); - onChanged(); - } else { - featureBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder addFeature( - int index, org.tensorflow.example.Feature.Builder builderForValue) { - if (featureBuilder_ == null) { - ensureFeatureIsMutable(); - feature_.add(index, builderForValue.build()); - onChanged(); - } else { - featureBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder addAllFeature( - java.lang.Iterable values) { - if (featureBuilder_ == null) { - ensureFeatureIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, feature_); - onChanged(); - } else { - featureBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder clearFeature() { - if (featureBuilder_ == null) { - feature_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - } else { - featureBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public Builder removeFeature(int index) { - if (featureBuilder_ == null) { - ensureFeatureIsMutable(); - feature_.remove(index); - onChanged(); - } else { - featureBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public org.tensorflow.example.Feature.Builder getFeatureBuilder( - int index) { - return getFeatureFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public org.tensorflow.example.FeatureOrBuilder getFeatureOrBuilder( - int index) { - if (featureBuilder_ == null) { - return feature_.get(index); } else { - return featureBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public java.util.List - getFeatureOrBuilderList() { - if (featureBuilder_ != null) { - return featureBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(feature_); - } - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public org.tensorflow.example.Feature.Builder addFeatureBuilder() { - return getFeatureFieldBuilder().addBuilder( - org.tensorflow.example.Feature.getDefaultInstance()); - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public org.tensorflow.example.Feature.Builder addFeatureBuilder( - int index) { - return getFeatureFieldBuilder().addBuilder( - index, org.tensorflow.example.Feature.getDefaultInstance()); - } - /** - * repeated .tensorflow.Feature feature = 1; - */ - public java.util.List - getFeatureBuilderList() { - return getFeatureFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.example.Feature, org.tensorflow.example.Feature.Builder, org.tensorflow.example.FeatureOrBuilder> - getFeatureFieldBuilder() { - if (featureBuilder_ == null) { - featureBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.example.Feature, org.tensorflow.example.Feature.Builder, org.tensorflow.example.FeatureOrBuilder>( - feature_, - ((bitField0_ & 0x00000001) == 0x00000001), - getParentForChildren(), - isClean()); - feature_ = null; - } - return featureBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.FeatureList) - } - - // @@protoc_insertion_point(class_scope:tensorflow.FeatureList) - private static final org.tensorflow.example.FeatureList DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.FeatureList(); - } - - public static org.tensorflow.example.FeatureList getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public FeatureList parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new FeatureList(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.FeatureList getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureListOrBuilder.java deleted file mode 100644 index 0926b311a9f..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FeatureListOrBuilder.java +++ /dev/null @@ -1,33 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -public interface FeatureListOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.FeatureList) - com.google.protobuf.MessageOrBuilder { - - /** - * repeated .tensorflow.Feature feature = 1; - */ - java.util.List - getFeatureList(); - /** - * repeated .tensorflow.Feature feature = 1; - */ - org.tensorflow.example.Feature getFeature(int index); - /** - * repeated .tensorflow.Feature feature = 1; - */ - int getFeatureCount(); - /** - * repeated .tensorflow.Feature feature = 1; - */ - java.util.List - getFeatureOrBuilderList(); - /** - * repeated .tensorflow.Feature feature = 1; - */ - org.tensorflow.example.FeatureOrBuilder getFeatureOrBuilder( - int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureLists.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureLists.java deleted file mode 100644 index c8ac05a7713..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FeatureLists.java +++ /dev/null @@ -1,704 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -/** - * Protobuf type {@code tensorflow.FeatureLists} - */ -public final class FeatureLists extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.FeatureLists) - FeatureListsOrBuilder { -private static final long serialVersionUID = 0L; - // Use FeatureLists.newBuilder() to construct. - private FeatureLists(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private FeatureLists() { - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private FeatureLists( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - featureList_ = com.google.protobuf.MapField.newMapField( - FeatureListDefaultEntryHolder.defaultEntry); - mutable_bitField0_ |= 0x00000001; - } - com.google.protobuf.MapEntry - featureList__ = input.readMessage( - FeatureListDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); - featureList_.getMutableMap().put( - featureList__.getKey(), featureList__.getValue()); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 1: - return internalGetFeatureList(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.FeatureLists.class, org.tensorflow.example.FeatureLists.Builder.class); - } - - public static final int FEATURE_LIST_FIELD_NUMBER = 1; - private static final class FeatureListDefaultEntryHolder { - static final com.google.protobuf.MapEntry< - java.lang.String, org.tensorflow.example.FeatureList> defaultEntry = - com.google.protobuf.MapEntry - .newDefaultInstance( - org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_FeatureListEntry_descriptor, - com.google.protobuf.WireFormat.FieldType.STRING, - "", - com.google.protobuf.WireFormat.FieldType.MESSAGE, - org.tensorflow.example.FeatureList.getDefaultInstance()); - } - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.example.FeatureList> featureList_; - private com.google.protobuf.MapField - internalGetFeatureList() { - if (featureList_ == null) { - return com.google.protobuf.MapField.emptyMapField( - FeatureListDefaultEntryHolder.defaultEntry); - } - return featureList_; - } - - public int getFeatureListCount() { - return internalGetFeatureList().getMap().size(); - } - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public boolean containsFeatureList( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetFeatureList().getMap().containsKey(key); - } - /** - * Use {@link #getFeatureListMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getFeatureList() { - return getFeatureListMap(); - } - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public java.util.Map getFeatureListMap() { - return internalGetFeatureList().getMap(); - } - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public org.tensorflow.example.FeatureList getFeatureListOrDefault( - java.lang.String key, - org.tensorflow.example.FeatureList defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetFeatureList().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public org.tensorflow.example.FeatureList getFeatureListOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetFeatureList().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - com.google.protobuf.GeneratedMessageV3 - .serializeStringMapTo( - output, - internalGetFeatureList(), - FeatureListDefaultEntryHolder.defaultEntry, - 1); - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - for (java.util.Map.Entry entry - : internalGetFeatureList().getMap().entrySet()) { - com.google.protobuf.MapEntry - featureList__ = FeatureListDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, featureList__); - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.FeatureLists)) { - return super.equals(obj); - } - org.tensorflow.example.FeatureLists other = (org.tensorflow.example.FeatureLists) obj; - - boolean result = true; - result = result && internalGetFeatureList().equals( - other.internalGetFeatureList()); - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - if (!internalGetFeatureList().getMap().isEmpty()) { - hash = (37 * hash) + FEATURE_LIST_FIELD_NUMBER; - hash = (53 * hash) + internalGetFeatureList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.FeatureLists parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FeatureLists parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FeatureLists parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FeatureLists parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FeatureLists parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FeatureLists parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FeatureLists parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.FeatureLists parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.FeatureLists parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.FeatureLists parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.FeatureLists parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.FeatureLists parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.FeatureLists prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.FeatureLists} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.FeatureLists) - org.tensorflow.example.FeatureListsOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 1: - return internalGetFeatureList(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMutableMapField( - int number) { - switch (number) { - case 1: - return internalGetMutableFeatureList(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.FeatureLists.class, org.tensorflow.example.FeatureLists.Builder.class); - } - - // Construct using org.tensorflow.example.FeatureLists.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - internalGetMutableFeatureList().clear(); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FeatureLists_descriptor; - } - - public org.tensorflow.example.FeatureLists getDefaultInstanceForType() { - return org.tensorflow.example.FeatureLists.getDefaultInstance(); - } - - public org.tensorflow.example.FeatureLists build() { - org.tensorflow.example.FeatureLists result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.FeatureLists buildPartial() { - org.tensorflow.example.FeatureLists result = new org.tensorflow.example.FeatureLists(this); - int from_bitField0_ = bitField0_; - result.featureList_ = internalGetFeatureList(); - result.featureList_.makeImmutable(); - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.FeatureLists) { - return mergeFrom((org.tensorflow.example.FeatureLists)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.FeatureLists other) { - if (other == org.tensorflow.example.FeatureLists.getDefaultInstance()) return this; - internalGetMutableFeatureList().mergeFrom( - other.internalGetFeatureList()); - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.FeatureLists parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.FeatureLists) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.example.FeatureList> featureList_; - private com.google.protobuf.MapField - internalGetFeatureList() { - if (featureList_ == null) { - return com.google.protobuf.MapField.emptyMapField( - FeatureListDefaultEntryHolder.defaultEntry); - } - return featureList_; - } - private com.google.protobuf.MapField - internalGetMutableFeatureList() { - onChanged();; - if (featureList_ == null) { - featureList_ = com.google.protobuf.MapField.newMapField( - FeatureListDefaultEntryHolder.defaultEntry); - } - if (!featureList_.isMutable()) { - featureList_ = featureList_.copy(); - } - return featureList_; - } - - public int getFeatureListCount() { - return internalGetFeatureList().getMap().size(); - } - /** - * 
-     * Map from feature name to feature list.
-     *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public boolean containsFeatureList( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetFeatureList().getMap().containsKey(key); - } - /** - * Use {@link #getFeatureListMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getFeatureList() { - return getFeatureListMap(); - } - /** - * 
-     * Map from feature name to feature list.
-     *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public java.util.Map getFeatureListMap() { - return internalGetFeatureList().getMap(); - } - /** - * 
-     * Map from feature name to feature list.
-     *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public org.tensorflow.example.FeatureList getFeatureListOrDefault( - java.lang.String key, - org.tensorflow.example.FeatureList defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetFeatureList().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-     * Map from feature name to feature list.
-     *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public org.tensorflow.example.FeatureList getFeatureListOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetFeatureList().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - public Builder clearFeatureList() { - internalGetMutableFeatureList().getMutableMap() - .clear(); - return this; - } - /** - * 
-     * Map from feature name to feature list.
-     *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public Builder removeFeatureList( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - internalGetMutableFeatureList().getMutableMap() - .remove(key); - return this; - } - /** - * Use alternate mutation accessors instead. - */ - @java.lang.Deprecated - public java.util.Map - getMutableFeatureList() { - return internalGetMutableFeatureList().getMutableMap(); - } - /** - * 
-     * Map from feature name to feature list.
-     *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - public Builder putFeatureList( - java.lang.String key, - org.tensorflow.example.FeatureList value) { - if (key == null) { throw new java.lang.NullPointerException(); } - if (value == null) { throw new java.lang.NullPointerException(); } - internalGetMutableFeatureList().getMutableMap() - .put(key, value); - return this; - } - /** - * 
-     * Map from feature name to feature list.
-     *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - public Builder putAllFeatureList( - java.util.Map values) { - internalGetMutableFeatureList().getMutableMap() - .putAll(values); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.FeatureLists) - } - - // @@protoc_insertion_point(class_scope:tensorflow.FeatureLists) - private static final org.tensorflow.example.FeatureLists DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.FeatureLists(); - } - - public static org.tensorflow.example.FeatureLists getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public FeatureLists parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new FeatureLists(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.FeatureLists getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureListsOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureListsOrBuilder.java deleted file mode 100644 index 2ecb197af86..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FeatureListsOrBuilder.java +++ /dev/null @@ -1,63 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -public interface FeatureListsOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.FeatureLists) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - int getFeatureListCount(); - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - boolean containsFeatureList( - java.lang.String key); - /** - * Use {@link #getFeatureListMap()} instead. - */ - @java.lang.Deprecated - java.util.Map - getFeatureList(); - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - java.util.Map - getFeatureListMap(); - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - org.tensorflow.example.FeatureList getFeatureListOrDefault( - java.lang.String key, - org.tensorflow.example.FeatureList defaultValue); - /** - * 
-   * Map from feature name to feature list.
-   *
- * - * map<string, .tensorflow.FeatureList> feature_list = 1; - */ - - org.tensorflow.example.FeatureList getFeatureListOrThrow( - java.lang.String key); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureOrBuilder.java deleted file mode 100644 index e59d6109887..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FeatureOrBuilder.java +++ /dev/null @@ -1,50 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -public interface FeatureOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.Feature) - com.google.protobuf.MessageOrBuilder { - - /** - * .tensorflow.BytesList bytes_list = 1; - */ - boolean hasBytesList(); - /** - * .tensorflow.BytesList bytes_list = 1; - */ - org.tensorflow.example.BytesList getBytesList(); - /** - * .tensorflow.BytesList bytes_list = 1; - */ - org.tensorflow.example.BytesListOrBuilder getBytesListOrBuilder(); - - /** - * .tensorflow.FloatList float_list = 2; - */ - boolean hasFloatList(); - /** - * .tensorflow.FloatList float_list = 2; - */ - org.tensorflow.example.FloatList getFloatList(); - /** - * .tensorflow.FloatList float_list = 2; - */ - org.tensorflow.example.FloatListOrBuilder getFloatListOrBuilder(); - - /** - * .tensorflow.Int64List int64_list = 3; - */ - boolean hasInt64List(); - /** - * .tensorflow.Int64List int64_list = 3; - */ - org.tensorflow.example.Int64List getInt64List(); - /** - * .tensorflow.Int64List int64_list = 3; - */ - org.tensorflow.example.Int64ListOrBuilder getInt64ListOrBuilder(); - - public org.tensorflow.example.Feature.KindCase getKindCase(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeatureProtos.java b/scala/dllib/src/main/java/org/tensorflow/example/FeatureProtos.java deleted file mode 100644 index 960a87ecce9..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FeatureProtos.java +++ /dev/null @@ -1,158 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -public final class FeatureProtos { - private FeatureProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_BytesList_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_BytesList_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_FloatList_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_FloatList_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_Int64List_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_Int64List_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_Feature_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_Feature_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_Features_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_Features_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_Features_FeatureEntry_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_Features_FeatureEntry_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_FeatureList_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_FeatureList_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_FeatureLists_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_FeatureLists_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_FeatureLists_FeatureListEntry_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_FeatureLists_FeatureListEntry_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\rfeature.proto\022\ntensorflow\"\032\n\tBytesList" + - "\022\r\n\005value\030\001 \003(\014\"\036\n\tFloatList\022\021\n\005value\030\001 " + - "\003(\002B\002\020\001\"\036\n\tInt64List\022\021\n\005value\030\001 \003(\003B\002\020\001\"" + - "\230\001\n\007Feature\022+\n\nbytes_list\030\001 \001(\0132\025.tensor" + - "flow.BytesListH\000\022+\n\nfloat_list\030\002 \001(\0132\025.t" + - "ensorflow.FloatListH\000\022+\n\nint64_list\030\003 \001(" + - "\0132\025.tensorflow.Int64ListH\000B\006\n\004kind\"\203\001\n\010F" + - "eatures\0222\n\007feature\030\001 \003(\0132!.tensorflow.Fe" + - "atures.FeatureEntry\032C\n\014FeatureEntry\022\013\n\003k" + - "ey\030\001 \001(\t\022\"\n\005value\030\002 \001(\0132\023.tensorflow.Fea", - "ture:\0028\001\"3\n\013FeatureList\022$\n\007feature\030\001 \003(\013" + - "2\023.tensorflow.Feature\"\234\001\n\014FeatureLists\022?" + - "\n\014feature_list\030\001 \003(\0132).tensorflow.Featur" + - "eLists.FeatureListEntry\032K\n\020FeatureListEn" + - "try\022\013\n\003key\030\001 \001(\t\022&\n\005value\030\002 \001(\0132\027.tensor" + - "flow.FeatureList:\0028\001B,\n\026org.tensorflow.e" + - "xampleB\rFeatureProtosP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - }, assigner); - internal_static_tensorflow_BytesList_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_BytesList_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_BytesList_descriptor, - new java.lang.String[] { "Value", }); - internal_static_tensorflow_FloatList_descriptor = - getDescriptor().getMessageTypes().get(1); - internal_static_tensorflow_FloatList_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_FloatList_descriptor, - new java.lang.String[] { "Value", }); - internal_static_tensorflow_Int64List_descriptor = - getDescriptor().getMessageTypes().get(2); - internal_static_tensorflow_Int64List_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_Int64List_descriptor, - new java.lang.String[] { "Value", }); - internal_static_tensorflow_Feature_descriptor = - getDescriptor().getMessageTypes().get(3); - internal_static_tensorflow_Feature_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_Feature_descriptor, - new java.lang.String[] { "BytesList", "FloatList", "Int64List", "Kind", }); - internal_static_tensorflow_Features_descriptor = - getDescriptor().getMessageTypes().get(4); - internal_static_tensorflow_Features_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_Features_descriptor, - new java.lang.String[] { "Feature", }); - internal_static_tensorflow_Features_FeatureEntry_descriptor = - internal_static_tensorflow_Features_descriptor.getNestedTypes().get(0); - internal_static_tensorflow_Features_FeatureEntry_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_Features_FeatureEntry_descriptor, - new java.lang.String[] { "Key", "Value", }); - internal_static_tensorflow_FeatureList_descriptor = - getDescriptor().getMessageTypes().get(5); - internal_static_tensorflow_FeatureList_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_FeatureList_descriptor, - new java.lang.String[] { "Feature", }); - internal_static_tensorflow_FeatureLists_descriptor = - getDescriptor().getMessageTypes().get(6); - internal_static_tensorflow_FeatureLists_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_FeatureLists_descriptor, - new java.lang.String[] { "FeatureList", }); - internal_static_tensorflow_FeatureLists_FeatureListEntry_descriptor = - internal_static_tensorflow_FeatureLists_descriptor.getNestedTypes().get(0); - internal_static_tensorflow_FeatureLists_FeatureListEntry_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_FeatureLists_FeatureListEntry_descriptor, - new java.lang.String[] { "Key", "Value", }); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Features.java b/scala/dllib/src/main/java/org/tensorflow/example/Features.java deleted file mode 100644 index 3faedff284f..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/Features.java +++ /dev/null @@ -1,704 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -/** - * Protobuf type {@code tensorflow.Features} - */ -public final class Features extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.Features) - FeaturesOrBuilder { -private static final long serialVersionUID = 0L; - // Use Features.newBuilder() to construct. - private Features(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private Features() { - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private Features( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - feature_ = com.google.protobuf.MapField.newMapField( - FeatureDefaultEntryHolder.defaultEntry); - mutable_bitField0_ |= 0x00000001; - } - com.google.protobuf.MapEntry - feature__ = input.readMessage( - FeatureDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); - feature_.getMutableMap().put( - feature__.getKey(), feature__.getValue()); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 1: - return internalGetFeature(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.Features.class, org.tensorflow.example.Features.Builder.class); - } - - public static final int FEATURE_FIELD_NUMBER = 1; - private static final class FeatureDefaultEntryHolder { - static final com.google.protobuf.MapEntry< - java.lang.String, org.tensorflow.example.Feature> defaultEntry = - com.google.protobuf.MapEntry - .newDefaultInstance( - org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_FeatureEntry_descriptor, - com.google.protobuf.WireFormat.FieldType.STRING, - "", - com.google.protobuf.WireFormat.FieldType.MESSAGE, - org.tensorflow.example.Feature.getDefaultInstance()); - } - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.example.Feature> feature_; - private com.google.protobuf.MapField - internalGetFeature() { - if (feature_ == null) { - return com.google.protobuf.MapField.emptyMapField( - FeatureDefaultEntryHolder.defaultEntry); - } - return feature_; - } - - public int getFeatureCount() { - return internalGetFeature().getMap().size(); - } - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public boolean containsFeature( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetFeature().getMap().containsKey(key); - } - /** - * Use {@link #getFeatureMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getFeature() { - return getFeatureMap(); - } - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public java.util.Map getFeatureMap() { - return internalGetFeature().getMap(); - } - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public org.tensorflow.example.Feature getFeatureOrDefault( - java.lang.String key, - org.tensorflow.example.Feature defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetFeature().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public org.tensorflow.example.Feature getFeatureOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetFeature().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - com.google.protobuf.GeneratedMessageV3 - .serializeStringMapTo( - output, - internalGetFeature(), - FeatureDefaultEntryHolder.defaultEntry, - 1); - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - for (java.util.Map.Entry entry - : internalGetFeature().getMap().entrySet()) { - com.google.protobuf.MapEntry - feature__ = FeatureDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, feature__); - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.Features)) { - return super.equals(obj); - } - org.tensorflow.example.Features other = (org.tensorflow.example.Features) obj; - - boolean result = true; - result = result && internalGetFeature().equals( - other.internalGetFeature()); - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - if (!internalGetFeature().getMap().isEmpty()) { - hash = (37 * hash) + FEATURE_FIELD_NUMBER; - hash = (53 * hash) + internalGetFeature().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.Features parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Features parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Features parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Features parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Features parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Features parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Features parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.Features parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.Features parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.Features parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.Features parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.Features parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.Features prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.Features} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.Features) - org.tensorflow.example.FeaturesOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 1: - return internalGetFeature(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMutableMapField( - int number) { - switch (number) { - case 1: - return internalGetMutableFeature(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.Features.class, org.tensorflow.example.Features.Builder.class); - } - - // Construct using org.tensorflow.example.Features.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - internalGetMutableFeature().clear(); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Features_descriptor; - } - - public org.tensorflow.example.Features getDefaultInstanceForType() { - return org.tensorflow.example.Features.getDefaultInstance(); - } - - public org.tensorflow.example.Features build() { - org.tensorflow.example.Features result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.Features buildPartial() { - org.tensorflow.example.Features result = new org.tensorflow.example.Features(this); - int from_bitField0_ = bitField0_; - result.feature_ = internalGetFeature(); - result.feature_.makeImmutable(); - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.Features) { - return mergeFrom((org.tensorflow.example.Features)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.Features other) { - if (other == org.tensorflow.example.Features.getDefaultInstance()) return this; - internalGetMutableFeature().mergeFrom( - other.internalGetFeature()); - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.Features parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.Features) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.example.Feature> feature_; - private com.google.protobuf.MapField - internalGetFeature() { - if (feature_ == null) { - return com.google.protobuf.MapField.emptyMapField( - FeatureDefaultEntryHolder.defaultEntry); - } - return feature_; - } - private com.google.protobuf.MapField - internalGetMutableFeature() { - onChanged();; - if (feature_ == null) { - feature_ = com.google.protobuf.MapField.newMapField( - FeatureDefaultEntryHolder.defaultEntry); - } - if (!feature_.isMutable()) { - feature_ = feature_.copy(); - } - return feature_; - } - - public int getFeatureCount() { - return internalGetFeature().getMap().size(); - } - /** - * 
-     * Map from feature name to feature.
-     *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public boolean containsFeature( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetFeature().getMap().containsKey(key); - } - /** - * Use {@link #getFeatureMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getFeature() { - return getFeatureMap(); - } - /** - * 
-     * Map from feature name to feature.
-     *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public java.util.Map getFeatureMap() { - return internalGetFeature().getMap(); - } - /** - * 
-     * Map from feature name to feature.
-     *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public org.tensorflow.example.Feature getFeatureOrDefault( - java.lang.String key, - org.tensorflow.example.Feature defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetFeature().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-     * Map from feature name to feature.
-     *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public org.tensorflow.example.Feature getFeatureOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetFeature().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - public Builder clearFeature() { - internalGetMutableFeature().getMutableMap() - .clear(); - return this; - } - /** - * 
-     * Map from feature name to feature.
-     *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public Builder removeFeature( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - internalGetMutableFeature().getMutableMap() - .remove(key); - return this; - } - /** - * Use alternate mutation accessors instead. - */ - @java.lang.Deprecated - public java.util.Map - getMutableFeature() { - return internalGetMutableFeature().getMutableMap(); - } - /** - * 
-     * Map from feature name to feature.
-     *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - public Builder putFeature( - java.lang.String key, - org.tensorflow.example.Feature value) { - if (key == null) { throw new java.lang.NullPointerException(); } - if (value == null) { throw new java.lang.NullPointerException(); } - internalGetMutableFeature().getMutableMap() - .put(key, value); - return this; - } - /** - * 
-     * Map from feature name to feature.
-     *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - public Builder putAllFeature( - java.util.Map values) { - internalGetMutableFeature().getMutableMap() - .putAll(values); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.Features) - } - - // @@protoc_insertion_point(class_scope:tensorflow.Features) - private static final org.tensorflow.example.Features DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.Features(); - } - - public static org.tensorflow.example.Features getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public Features parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new Features(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.Features getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FeaturesOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FeaturesOrBuilder.java deleted file mode 100644 index 2856c3f7d91..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FeaturesOrBuilder.java +++ /dev/null @@ -1,63 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -public interface FeaturesOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.Features) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - int getFeatureCount(); - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - boolean containsFeature( - java.lang.String key); - /** - * Use {@link #getFeatureMap()} instead. - */ - @java.lang.Deprecated - java.util.Map - getFeature(); - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - java.util.Map - getFeatureMap(); - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - org.tensorflow.example.Feature getFeatureOrDefault( - java.lang.String key, - org.tensorflow.example.Feature defaultValue); - /** - * 
-   * Map from feature name to feature.
-   *
- * - * map<string, .tensorflow.Feature> feature = 1; - */ - - org.tensorflow.example.Feature getFeatureOrThrow( - java.lang.String key); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FloatList.java b/scala/dllib/src/main/java/org/tensorflow/example/FloatList.java deleted file mode 100644 index 44fa7e98f97..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FloatList.java +++ /dev/null @@ -1,544 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -/** - * Protobuf type {@code tensorflow.FloatList} - */ -public final class FloatList extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.FloatList) - FloatListOrBuilder { -private static final long serialVersionUID = 0L; - // Use FloatList.newBuilder() to construct. - private FloatList(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private FloatList() { - value_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private FloatList( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 13: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - value_.add(input.readFloat()); - break; - } - case 10: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001) && input.getBytesUntilLimit() > 0) { - value_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - while (input.getBytesUntilLimit() > 0) { - value_.add(input.readFloat()); - } - input.popLimit(limit); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = java.util.Collections.unmodifiableList(value_); - } - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.FloatList.class, org.tensorflow.example.FloatList.Builder.class); - } - - public static final int VALUE_FIELD_NUMBER = 1; - private java.util.List value_; - /** - * repeated float value = 1 [packed = true]; - */ - public java.util.List - getValueList() { - return value_; - } - /** - * repeated float value = 1 [packed = true]; - */ - public int getValueCount() { - return value_.size(); - } - /** - * repeated float value = 1 [packed = true]; - */ - public float getValue(int index) { - return value_.get(index); - } - private int valueMemoizedSerializedSize = -1; - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - getSerializedSize(); - if (getValueList().size() > 0) { - output.writeUInt32NoTag(10); - output.writeUInt32NoTag(valueMemoizedSerializedSize); - } - for (int i = 0; i < value_.size(); i++) { - output.writeFloatNoTag(value_.get(i)); - } - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - { - int dataSize = 0; - dataSize = 4 * getValueList().size(); - size += dataSize; - if (!getValueList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - valueMemoizedSerializedSize = dataSize; - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.FloatList)) { - return super.equals(obj); - } - org.tensorflow.example.FloatList other = (org.tensorflow.example.FloatList) obj; - - boolean result = true; - result = result && getValueList() - .equals(other.getValueList()); - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - if (getValueCount() > 0) { - hash = (37 * hash) + VALUE_FIELD_NUMBER; - hash = (53 * hash) + getValueList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.FloatList parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FloatList parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FloatList parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FloatList parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FloatList parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.FloatList parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.FloatList parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.FloatList parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.FloatList parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.FloatList parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.FloatList parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.FloatList parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.FloatList prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.FloatList} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.FloatList) - org.tensorflow.example.FloatListOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.FloatList.class, org.tensorflow.example.FloatList.Builder.class); - } - - // Construct using org.tensorflow.example.FloatList.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - value_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_FloatList_descriptor; - } - - public org.tensorflow.example.FloatList getDefaultInstanceForType() { - return org.tensorflow.example.FloatList.getDefaultInstance(); - } - - public org.tensorflow.example.FloatList build() { - org.tensorflow.example.FloatList result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.FloatList buildPartial() { - org.tensorflow.example.FloatList result = new org.tensorflow.example.FloatList(this); - int from_bitField0_ = bitField0_; - if (((bitField0_ & 0x00000001) == 0x00000001)) { - value_ = java.util.Collections.unmodifiableList(value_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.value_ = value_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.FloatList) { - return mergeFrom((org.tensorflow.example.FloatList)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.FloatList other) { - if (other == org.tensorflow.example.FloatList.getDefaultInstance()) return this; - if (!other.value_.isEmpty()) { - if (value_.isEmpty()) { - value_ = other.value_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureValueIsMutable(); - value_.addAll(other.value_); - } - onChanged(); - } - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.FloatList parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.FloatList) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List value_ = java.util.Collections.emptyList(); - private void ensureValueIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - value_ = new java.util.ArrayList(value_); - bitField0_ |= 0x00000001; - } - } - /** - * repeated float value = 1 [packed = true]; - */ - public java.util.List - getValueList() { - return java.util.Collections.unmodifiableList(value_); - } - /** - * repeated float value = 1 [packed = true]; - */ - public int getValueCount() { - return value_.size(); - } - /** - * repeated float value = 1 [packed = true]; - */ - public float getValue(int index) { - return value_.get(index); - } - /** - * repeated float value = 1 [packed = true]; - */ - public Builder setValue( - int index, float value) { - ensureValueIsMutable(); - value_.set(index, value); - onChanged(); - return this; - } - /** - * repeated float value = 1 [packed = true]; - */ - public Builder addValue(float value) { - ensureValueIsMutable(); - value_.add(value); - onChanged(); - return this; - } - /** - * repeated float value = 1 [packed = true]; - */ - public Builder addAllValue( - java.lang.Iterable values) { - ensureValueIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, value_); - onChanged(); - return this; - } - /** - * repeated float value = 1 [packed = true]; - */ - public Builder clearValue() { - value_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.FloatList) - } - - // @@protoc_insertion_point(class_scope:tensorflow.FloatList) - private static final org.tensorflow.example.FloatList DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.FloatList(); - } - - public static org.tensorflow.example.FloatList getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public FloatList parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new FloatList(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.FloatList getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/FloatListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/FloatListOrBuilder.java deleted file mode 100644 index 3d89a4a4eb8..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/FloatListOrBuilder.java +++ /dev/null @@ -1,22 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -public interface FloatListOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.FloatList) - com.google.protobuf.MessageOrBuilder { - - /** - * repeated float value = 1 [packed = true]; - */ - java.util.List getValueList(); - /** - * repeated float value = 1 [packed = true]; - */ - int getValueCount(); - /** - * repeated float value = 1 [packed = true]; - */ - float getValue(int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Int64List.java b/scala/dllib/src/main/java/org/tensorflow/example/Int64List.java deleted file mode 100644 index 63af761d7f2..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/Int64List.java +++ /dev/null @@ -1,547 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -/** - * Protobuf type {@code tensorflow.Int64List} - */ -public final class Int64List extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.Int64List) - Int64ListOrBuilder { -private static final long serialVersionUID = 0L; - // Use Int64List.newBuilder() to construct. - private Int64List(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private Int64List() { - value_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private Int64List( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 8: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - value_.add(input.readInt64()); - break; - } - case 10: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001) && input.getBytesUntilLimit() > 0) { - value_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - while (input.getBytesUntilLimit() > 0) { - value_.add(input.readInt64()); - } - input.popLimit(limit); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = java.util.Collections.unmodifiableList(value_); - } - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.Int64List.class, org.tensorflow.example.Int64List.Builder.class); - } - - public static final int VALUE_FIELD_NUMBER = 1; - private java.util.List value_; - /** - * repeated int64 value = 1 [packed = true]; - */ - public java.util.List - getValueList() { - return value_; - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public int getValueCount() { - return value_.size(); - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public long getValue(int index) { - return value_.get(index); - } - private int valueMemoizedSerializedSize = -1; - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - getSerializedSize(); - if (getValueList().size() > 0) { - output.writeUInt32NoTag(10); - output.writeUInt32NoTag(valueMemoizedSerializedSize); - } - for (int i = 0; i < value_.size(); i++) { - output.writeInt64NoTag(value_.get(i)); - } - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - { - int dataSize = 0; - for (int i = 0; i < value_.size(); i++) { - dataSize += com.google.protobuf.CodedOutputStream - .computeInt64SizeNoTag(value_.get(i)); - } - size += dataSize; - if (!getValueList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - valueMemoizedSerializedSize = dataSize; - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.Int64List)) { - return super.equals(obj); - } - org.tensorflow.example.Int64List other = (org.tensorflow.example.Int64List) obj; - - boolean result = true; - result = result && getValueList() - .equals(other.getValueList()); - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - if (getValueCount() > 0) { - hash = (37 * hash) + VALUE_FIELD_NUMBER; - hash = (53 * hash) + getValueList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.Int64List parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Int64List parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Int64List parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Int64List parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Int64List parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.Int64List parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.Int64List parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.Int64List parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.Int64List parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.Int64List parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.Int64List parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.Int64List parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.Int64List prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.Int64List} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.Int64List) - org.tensorflow.example.Int64ListOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.Int64List.class, org.tensorflow.example.Int64List.Builder.class); - } - - // Construct using org.tensorflow.example.Int64List.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - value_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.FeatureProtos.internal_static_tensorflow_Int64List_descriptor; - } - - public org.tensorflow.example.Int64List getDefaultInstanceForType() { - return org.tensorflow.example.Int64List.getDefaultInstance(); - } - - public org.tensorflow.example.Int64List build() { - org.tensorflow.example.Int64List result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.Int64List buildPartial() { - org.tensorflow.example.Int64List result = new org.tensorflow.example.Int64List(this); - int from_bitField0_ = bitField0_; - if (((bitField0_ & 0x00000001) == 0x00000001)) { - value_ = java.util.Collections.unmodifiableList(value_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.value_ = value_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.Int64List) { - return mergeFrom((org.tensorflow.example.Int64List)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.Int64List other) { - if (other == org.tensorflow.example.Int64List.getDefaultInstance()) return this; - if (!other.value_.isEmpty()) { - if (value_.isEmpty()) { - value_ = other.value_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureValueIsMutable(); - value_.addAll(other.value_); - } - onChanged(); - } - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.Int64List parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.Int64List) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List value_ = java.util.Collections.emptyList(); - private void ensureValueIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - value_ = new java.util.ArrayList(value_); - bitField0_ |= 0x00000001; - } - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public java.util.List - getValueList() { - return java.util.Collections.unmodifiableList(value_); - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public int getValueCount() { - return value_.size(); - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public long getValue(int index) { - return value_.get(index); - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public Builder setValue( - int index, long value) { - ensureValueIsMutable(); - value_.set(index, value); - onChanged(); - return this; - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public Builder addValue(long value) { - ensureValueIsMutable(); - value_.add(value); - onChanged(); - return this; - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public Builder addAllValue( - java.lang.Iterable values) { - ensureValueIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, value_); - onChanged(); - return this; - } - /** - * repeated int64 value = 1 [packed = true]; - */ - public Builder clearValue() { - value_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.Int64List) - } - - // @@protoc_insertion_point(class_scope:tensorflow.Int64List) - private static final org.tensorflow.example.Int64List DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.Int64List(); - } - - public static org.tensorflow.example.Int64List getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public Int64List parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new Int64List(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.Int64List getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/Int64ListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/Int64ListOrBuilder.java deleted file mode 100644 index 667578cf1a2..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/Int64ListOrBuilder.java +++ /dev/null @@ -1,22 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: feature.proto - -package org.tensorflow.example; - -public interface Int64ListOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.Int64List) - com.google.protobuf.MessageOrBuilder { - - /** - * repeated int64 value = 1 [packed = true]; - */ - java.util.List getValueList(); - /** - * repeated int64 value = 1 [packed = true]; - */ - int getValueCount(); - /** - * repeated int64 value = 1 [packed = true]; - */ - long getValue(int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/example/SequenceExample.java b/scala/dllib/src/main/java/org/tensorflow/example/SequenceExample.java deleted file mode 100644 index 0e0eb6eb494..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/SequenceExample.java +++ /dev/null @@ -1,748 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tensorflow/core/example/example.proto - -package org.tensorflow.example; - -/** - * Protobuf type {@code tensorflow.SequenceExample} - */ -public final class SequenceExample extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.SequenceExample) - SequenceExampleOrBuilder { -private static final long serialVersionUID = 0L; - // Use SequenceExample.newBuilder() to construct. - private SequenceExample(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private SequenceExample() { - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return this.unknownFields; - } - private SequenceExample( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - com.google.protobuf.UnknownFieldSet.Builder unknownFields = - com.google.protobuf.UnknownFieldSet.newBuilder(); - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!parseUnknownFieldProto3( - input, unknownFields, extensionRegistry, tag)) { - done = true; - } - break; - } - case 10: { - org.tensorflow.example.Features.Builder subBuilder = null; - if (context_ != null) { - subBuilder = context_.toBuilder(); - } - context_ = input.readMessage(org.tensorflow.example.Features.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(context_); - context_ = subBuilder.buildPartial(); - } - - break; - } - case 18: { - org.tensorflow.example.FeatureLists.Builder subBuilder = null; - if (featureLists_ != null) { - subBuilder = featureLists_.toBuilder(); - } - featureLists_ = input.readMessage(org.tensorflow.example.FeatureLists.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(featureLists_); - featureLists_ = subBuilder.buildPartial(); - } - - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - this.unknownFields = unknownFields.build(); - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.SequenceExample.class, org.tensorflow.example.SequenceExample.Builder.class); - } - - public static final int CONTEXT_FIELD_NUMBER = 1; - private org.tensorflow.example.Features context_; - /** - * .tensorflow.Features context = 1; - */ - public boolean hasContext() { - return context_ != null; - } - /** - * .tensorflow.Features context = 1; - */ - public org.tensorflow.example.Features getContext() { - return context_ == null ? org.tensorflow.example.Features.getDefaultInstance() : context_; - } - /** - * .tensorflow.Features context = 1; - */ - public org.tensorflow.example.FeaturesOrBuilder getContextOrBuilder() { - return getContext(); - } - - public static final int FEATURE_LISTS_FIELD_NUMBER = 2; - private org.tensorflow.example.FeatureLists featureLists_; - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public boolean hasFeatureLists() { - return featureLists_ != null; - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public org.tensorflow.example.FeatureLists getFeatureLists() { - return featureLists_ == null ? org.tensorflow.example.FeatureLists.getDefaultInstance() : featureLists_; - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public org.tensorflow.example.FeatureListsOrBuilder getFeatureListsOrBuilder() { - return getFeatureLists(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (context_ != null) { - output.writeMessage(1, getContext()); - } - if (featureLists_ != null) { - output.writeMessage(2, getFeatureLists()); - } - unknownFields.writeTo(output); - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (context_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, getContext()); - } - if (featureLists_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, getFeatureLists()); - } - size += unknownFields.getSerializedSize(); - memoizedSize = size; - return size; - } - - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.example.SequenceExample)) { - return super.equals(obj); - } - org.tensorflow.example.SequenceExample other = (org.tensorflow.example.SequenceExample) obj; - - boolean result = true; - result = result && (hasContext() == other.hasContext()); - if (hasContext()) { - result = result && getContext() - .equals(other.getContext()); - } - result = result && (hasFeatureLists() == other.hasFeatureLists()); - if (hasFeatureLists()) { - result = result && getFeatureLists() - .equals(other.getFeatureLists()); - } - result = result && unknownFields.equals(other.unknownFields); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptor().hashCode(); - if (hasContext()) { - hash = (37 * hash) + CONTEXT_FIELD_NUMBER; - hash = (53 * hash) + getContext().hashCode(); - } - if (hasFeatureLists()) { - hash = (37 * hash) + FEATURE_LISTS_FIELD_NUMBER; - hash = (53 * hash) + getFeatureLists().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.example.SequenceExample parseFrom( - java.nio.ByteBuffer data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.SequenceExample parseFrom( - java.nio.ByteBuffer data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.SequenceExample parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.SequenceExample parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.SequenceExample parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.example.SequenceExample parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.example.SequenceExample parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.SequenceExample parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.SequenceExample parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.example.SequenceExample parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.example.SequenceExample parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.example.SequenceExample parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.example.SequenceExample prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.SequenceExample} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.SequenceExample) - org.tensorflow.example.SequenceExampleOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.example.SequenceExample.class, org.tensorflow.example.SequenceExample.Builder.class); - } - - // Construct using org.tensorflow.example.SequenceExample.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - if (contextBuilder_ == null) { - context_ = null; - } else { - context_ = null; - contextBuilder_ = null; - } - if (featureListsBuilder_ == null) { - featureLists_ = null; - } else { - featureLists_ = null; - featureListsBuilder_ = null; - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.example.ExampleProtos.internal_static_tensorflow_SequenceExample_descriptor; - } - - public org.tensorflow.example.SequenceExample getDefaultInstanceForType() { - return org.tensorflow.example.SequenceExample.getDefaultInstance(); - } - - public org.tensorflow.example.SequenceExample build() { - org.tensorflow.example.SequenceExample result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.example.SequenceExample buildPartial() { - org.tensorflow.example.SequenceExample result = new org.tensorflow.example.SequenceExample(this); - if (contextBuilder_ == null) { - result.context_ = context_; - } else { - result.context_ = contextBuilder_.build(); - } - if (featureListsBuilder_ == null) { - result.featureLists_ = featureLists_; - } else { - result.featureLists_ = featureListsBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, java.lang.Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - java.lang.Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.example.SequenceExample) { - return mergeFrom((org.tensorflow.example.SequenceExample)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.example.SequenceExample other) { - if (other == org.tensorflow.example.SequenceExample.getDefaultInstance()) return this; - if (other.hasContext()) { - mergeContext(other.getContext()); - } - if (other.hasFeatureLists()) { - mergeFeatureLists(other.getFeatureLists()); - } - this.mergeUnknownFields(other.unknownFields); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.example.SequenceExample parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.example.SequenceExample) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private org.tensorflow.example.Features context_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder> contextBuilder_; - /** - * .tensorflow.Features context = 1; - */ - public boolean hasContext() { - return contextBuilder_ != null || context_ != null; - } - /** - * .tensorflow.Features context = 1; - */ - public org.tensorflow.example.Features getContext() { - if (contextBuilder_ == null) { - return context_ == null ? org.tensorflow.example.Features.getDefaultInstance() : context_; - } else { - return contextBuilder_.getMessage(); - } - } - /** - * .tensorflow.Features context = 1; - */ - public Builder setContext(org.tensorflow.example.Features value) { - if (contextBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - context_ = value; - onChanged(); - } else { - contextBuilder_.setMessage(value); - } - - return this; - } - /** - * .tensorflow.Features context = 1; - */ - public Builder setContext( - org.tensorflow.example.Features.Builder builderForValue) { - if (contextBuilder_ == null) { - context_ = builderForValue.build(); - onChanged(); - } else { - contextBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * .tensorflow.Features context = 1; - */ - public Builder mergeContext(org.tensorflow.example.Features value) { - if (contextBuilder_ == null) { - if (context_ != null) { - context_ = - org.tensorflow.example.Features.newBuilder(context_).mergeFrom(value).buildPartial(); - } else { - context_ = value; - } - onChanged(); - } else { - contextBuilder_.mergeFrom(value); - } - - return this; - } - /** - * .tensorflow.Features context = 1; - */ - public Builder clearContext() { - if (contextBuilder_ == null) { - context_ = null; - onChanged(); - } else { - context_ = null; - contextBuilder_ = null; - } - - return this; - } - /** - * .tensorflow.Features context = 1; - */ - public org.tensorflow.example.Features.Builder getContextBuilder() { - - onChanged(); - return getContextFieldBuilder().getBuilder(); - } - /** - * .tensorflow.Features context = 1; - */ - public org.tensorflow.example.FeaturesOrBuilder getContextOrBuilder() { - if (contextBuilder_ != null) { - return contextBuilder_.getMessageOrBuilder(); - } else { - return context_ == null ? - org.tensorflow.example.Features.getDefaultInstance() : context_; - } - } - /** - * .tensorflow.Features context = 1; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder> - getContextFieldBuilder() { - if (contextBuilder_ == null) { - contextBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.Features, org.tensorflow.example.Features.Builder, org.tensorflow.example.FeaturesOrBuilder>( - getContext(), - getParentForChildren(), - isClean()); - context_ = null; - } - return contextBuilder_; - } - - private org.tensorflow.example.FeatureLists featureLists_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.FeatureLists, org.tensorflow.example.FeatureLists.Builder, org.tensorflow.example.FeatureListsOrBuilder> featureListsBuilder_; - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public boolean hasFeatureLists() { - return featureListsBuilder_ != null || featureLists_ != null; - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public org.tensorflow.example.FeatureLists getFeatureLists() { - if (featureListsBuilder_ == null) { - return featureLists_ == null ? org.tensorflow.example.FeatureLists.getDefaultInstance() : featureLists_; - } else { - return featureListsBuilder_.getMessage(); - } - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public Builder setFeatureLists(org.tensorflow.example.FeatureLists value) { - if (featureListsBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - featureLists_ = value; - onChanged(); - } else { - featureListsBuilder_.setMessage(value); - } - - return this; - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public Builder setFeatureLists( - org.tensorflow.example.FeatureLists.Builder builderForValue) { - if (featureListsBuilder_ == null) { - featureLists_ = builderForValue.build(); - onChanged(); - } else { - featureListsBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public Builder mergeFeatureLists(org.tensorflow.example.FeatureLists value) { - if (featureListsBuilder_ == null) { - if (featureLists_ != null) { - featureLists_ = - org.tensorflow.example.FeatureLists.newBuilder(featureLists_).mergeFrom(value).buildPartial(); - } else { - featureLists_ = value; - } - onChanged(); - } else { - featureListsBuilder_.mergeFrom(value); - } - - return this; - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public Builder clearFeatureLists() { - if (featureListsBuilder_ == null) { - featureLists_ = null; - onChanged(); - } else { - featureLists_ = null; - featureListsBuilder_ = null; - } - - return this; - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public org.tensorflow.example.FeatureLists.Builder getFeatureListsBuilder() { - - onChanged(); - return getFeatureListsFieldBuilder().getBuilder(); - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - public org.tensorflow.example.FeatureListsOrBuilder getFeatureListsOrBuilder() { - if (featureListsBuilder_ != null) { - return featureListsBuilder_.getMessageOrBuilder(); - } else { - return featureLists_ == null ? - org.tensorflow.example.FeatureLists.getDefaultInstance() : featureLists_; - } - } - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.FeatureLists, org.tensorflow.example.FeatureLists.Builder, org.tensorflow.example.FeatureListsOrBuilder> - getFeatureListsFieldBuilder() { - if (featureListsBuilder_ == null) { - featureListsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.example.FeatureLists, org.tensorflow.example.FeatureLists.Builder, org.tensorflow.example.FeatureListsOrBuilder>( - getFeatureLists(), - getParentForChildren(), - isClean()); - featureLists_ = null; - } - return featureListsBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.setUnknownFieldsProto3(unknownFields); - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return super.mergeUnknownFields(unknownFields); - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.SequenceExample) - } - - // @@protoc_insertion_point(class_scope:tensorflow.SequenceExample) - private static final org.tensorflow.example.SequenceExample DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.example.SequenceExample(); - } - - public static org.tensorflow.example.SequenceExample getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public SequenceExample parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new SequenceExample(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.example.SequenceExample getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/example/SequenceExampleOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/example/SequenceExampleOrBuilder.java deleted file mode 100644 index 9b35b76f50a..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/example/SequenceExampleOrBuilder.java +++ /dev/null @@ -1,35 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tensorflow/core/example/example.proto - -package org.tensorflow.example; - -public interface SequenceExampleOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.SequenceExample) - com.google.protobuf.MessageOrBuilder { - - /** - * .tensorflow.Features context = 1; - */ - boolean hasContext(); - /** - * .tensorflow.Features context = 1; - */ - org.tensorflow.example.Features getContext(); - /** - * .tensorflow.Features context = 1; - */ - org.tensorflow.example.FeaturesOrBuilder getContextOrBuilder(); - - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - boolean hasFeatureLists(); - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - org.tensorflow.example.FeatureLists getFeatureLists(); - /** - * .tensorflow.FeatureLists feature_lists = 2; - */ - org.tensorflow.example.FeatureListsOrBuilder getFeatureListsOrBuilder(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescription.java b/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescription.java deleted file mode 100644 index fd45dc8a30a..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescription.java +++ /dev/null @@ -1,897 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: allocation_description.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.AllocationDescription} - */ -public final class AllocationDescription extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.AllocationDescription) - AllocationDescriptionOrBuilder { - // Use AllocationDescription.newBuilder() to construct. - private AllocationDescription(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private AllocationDescription() { - requestedBytes_ = 0L; - allocatedBytes_ = 0L; - allocatorName_ = ""; - allocationId_ = 0L; - hasSingleReference_ = false; - ptr_ = 0L; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private AllocationDescription( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - requestedBytes_ = input.readInt64(); - break; - } - case 16: { - - allocatedBytes_ = input.readInt64(); - break; - } - case 26: { - java.lang.String s = input.readStringRequireUtf8(); - - allocatorName_ = s; - break; - } - case 32: { - - allocationId_ = input.readInt64(); - break; - } - case 40: { - - hasSingleReference_ = input.readBool(); - break; - } - case 48: { - - ptr_ = input.readUInt64(); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.AllocationDescriptionProtos.internal_static_tensorflow_AllocationDescription_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.AllocationDescriptionProtos.internal_static_tensorflow_AllocationDescription_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.AllocationDescription.class, org.tensorflow.framework.AllocationDescription.Builder.class); - } - - public static final int REQUESTED_BYTES_FIELD_NUMBER = 1; - private long requestedBytes_; - /** - * 
-   * Total number of bytes requested
-   *
- * - * optional int64 requested_bytes = 1; - */ - public long getRequestedBytes() { - return requestedBytes_; - } - - public static final int ALLOCATED_BYTES_FIELD_NUMBER = 2; - private long allocatedBytes_; - /** - * 
-   * Total number of bytes allocated if known
-   *
- * - * optional int64 allocated_bytes = 2; - */ - public long getAllocatedBytes() { - return allocatedBytes_; - } - - public static final int ALLOCATOR_NAME_FIELD_NUMBER = 3; - private volatile java.lang.Object allocatorName_; - /** - * 
-   * Name of the allocator used
-   *
- * - * optional string allocator_name = 3; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } - } - /** - * 
-   * Name of the allocator used
-   *
- * - * optional string allocator_name = 3; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int ALLOCATION_ID_FIELD_NUMBER = 4; - private long allocationId_; - /** - * 
-   * Identifier of the allocated buffer if known
-   *
- * - * optional int64 allocation_id = 4; - */ - public long getAllocationId() { - return allocationId_; - } - - public static final int HAS_SINGLE_REFERENCE_FIELD_NUMBER = 5; - private boolean hasSingleReference_; - /** - * 
-   * Set if this tensor only has one remaining reference
-   *
- * - * optional bool has_single_reference = 5; - */ - public boolean getHasSingleReference() { - return hasSingleReference_; - } - - public static final int PTR_FIELD_NUMBER = 6; - private long ptr_; - /** - * 
-   * Address of the allocation.
-   *
- * - * optional uint64 ptr = 6; - */ - public long getPtr() { - return ptr_; - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (requestedBytes_ != 0L) { - output.writeInt64(1, requestedBytes_); - } - if (allocatedBytes_ != 0L) { - output.writeInt64(2, allocatedBytes_); - } - if (!getAllocatorNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 3, allocatorName_); - } - if (allocationId_ != 0L) { - output.writeInt64(4, allocationId_); - } - if (hasSingleReference_ != false) { - output.writeBool(5, hasSingleReference_); - } - if (ptr_ != 0L) { - output.writeUInt64(6, ptr_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (requestedBytes_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(1, requestedBytes_); - } - if (allocatedBytes_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(2, allocatedBytes_); - } - if (!getAllocatorNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(3, allocatorName_); - } - if (allocationId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(4, allocationId_); - } - if (hasSingleReference_ != false) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize(5, hasSingleReference_); - } - if (ptr_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeUInt64Size(6, ptr_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.AllocationDescription)) { - return super.equals(obj); - } - org.tensorflow.framework.AllocationDescription other = (org.tensorflow.framework.AllocationDescription) obj; - - boolean result = true; - result = result && (getRequestedBytes() - == other.getRequestedBytes()); - result = result && (getAllocatedBytes() - == other.getAllocatedBytes()); - result = result && getAllocatorName() - .equals(other.getAllocatorName()); - result = result && (getAllocationId() - == other.getAllocationId()); - result = result && (getHasSingleReference() - == other.getHasSingleReference()); - result = result && (getPtr() - == other.getPtr()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + REQUESTED_BYTES_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getRequestedBytes()); - hash = (37 * hash) + ALLOCATED_BYTES_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getAllocatedBytes()); - hash = (37 * hash) + ALLOCATOR_NAME_FIELD_NUMBER; - hash = (53 * hash) + getAllocatorName().hashCode(); - hash = (37 * hash) + ALLOCATION_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getAllocationId()); - hash = (37 * hash) + HAS_SINGLE_REFERENCE_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getHasSingleReference()); - hash = (37 * hash) + PTR_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getPtr()); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.AllocationDescription parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.AllocationDescription parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.AllocationDescription parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.AllocationDescription parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.AllocationDescription parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AllocationDescription parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.AllocationDescription parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AllocationDescription parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.AllocationDescription parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AllocationDescription parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.AllocationDescription prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.AllocationDescription} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.AllocationDescription) - org.tensorflow.framework.AllocationDescriptionOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.AllocationDescriptionProtos.internal_static_tensorflow_AllocationDescription_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.AllocationDescriptionProtos.internal_static_tensorflow_AllocationDescription_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.AllocationDescription.class, org.tensorflow.framework.AllocationDescription.Builder.class); - } - - // Construct using org.tensorflow.framework.AllocationDescription.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - requestedBytes_ = 0L; - - allocatedBytes_ = 0L; - - allocatorName_ = ""; - - allocationId_ = 0L; - - hasSingleReference_ = false; - - ptr_ = 0L; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.AllocationDescriptionProtos.internal_static_tensorflow_AllocationDescription_descriptor; - } - - public org.tensorflow.framework.AllocationDescription getDefaultInstanceForType() { - return org.tensorflow.framework.AllocationDescription.getDefaultInstance(); - } - - public org.tensorflow.framework.AllocationDescription build() { - org.tensorflow.framework.AllocationDescription result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.AllocationDescription buildPartial() { - org.tensorflow.framework.AllocationDescription result = new org.tensorflow.framework.AllocationDescription(this); - result.requestedBytes_ = requestedBytes_; - result.allocatedBytes_ = allocatedBytes_; - result.allocatorName_ = allocatorName_; - result.allocationId_ = allocationId_; - result.hasSingleReference_ = hasSingleReference_; - result.ptr_ = ptr_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.AllocationDescription) { - return mergeFrom((org.tensorflow.framework.AllocationDescription)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.AllocationDescription other) { - if (other == org.tensorflow.framework.AllocationDescription.getDefaultInstance()) return this; - if (other.getRequestedBytes() != 0L) { - setRequestedBytes(other.getRequestedBytes()); - } - if (other.getAllocatedBytes() != 0L) { - setAllocatedBytes(other.getAllocatedBytes()); - } - if (!other.getAllocatorName().isEmpty()) { - allocatorName_ = other.allocatorName_; - onChanged(); - } - if (other.getAllocationId() != 0L) { - setAllocationId(other.getAllocationId()); - } - if (other.getHasSingleReference() != false) { - setHasSingleReference(other.getHasSingleReference()); - } - if (other.getPtr() != 0L) { - setPtr(other.getPtr()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.AllocationDescription parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.AllocationDescription) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private long requestedBytes_ ; - /** - * 
-     * Total number of bytes requested
-     *
- * - * optional int64 requested_bytes = 1; - */ - public long getRequestedBytes() { - return requestedBytes_; - } - /** - * 
-     * Total number of bytes requested
-     *
- * - * optional int64 requested_bytes = 1; - */ - public Builder setRequestedBytes(long value) { - - requestedBytes_ = value; - onChanged(); - return this; - } - /** - * 
-     * Total number of bytes requested
-     *
- * - * optional int64 requested_bytes = 1; - */ - public Builder clearRequestedBytes() { - - requestedBytes_ = 0L; - onChanged(); - return this; - } - - private long allocatedBytes_ ; - /** - * 
-     * Total number of bytes allocated if known
-     *
- * - * optional int64 allocated_bytes = 2; - */ - public long getAllocatedBytes() { - return allocatedBytes_; - } - /** - * 
-     * Total number of bytes allocated if known
-     *
- * - * optional int64 allocated_bytes = 2; - */ - public Builder setAllocatedBytes(long value) { - - allocatedBytes_ = value; - onChanged(); - return this; - } - /** - * 
-     * Total number of bytes allocated if known
-     *
- * - * optional int64 allocated_bytes = 2; - */ - public Builder clearAllocatedBytes() { - - allocatedBytes_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object allocatorName_ = ""; - /** - * 
-     * Name of the allocator used
-     *
- * - * optional string allocator_name = 3; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the allocator used
-     *
- * - * optional string allocator_name = 3; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the allocator used
-     *
- * - * optional string allocator_name = 3; - */ - public Builder setAllocatorName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - allocatorName_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the allocator used
-     *
- * - * optional string allocator_name = 3; - */ - public Builder clearAllocatorName() { - - allocatorName_ = getDefaultInstance().getAllocatorName(); - onChanged(); - return this; - } - /** - * 
-     * Name of the allocator used
-     *
- * - * optional string allocator_name = 3; - */ - public Builder setAllocatorNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - allocatorName_ = value; - onChanged(); - return this; - } - - private long allocationId_ ; - /** - * 
-     * Identifier of the allocated buffer if known
-     *
- * - * optional int64 allocation_id = 4; - */ - public long getAllocationId() { - return allocationId_; - } - /** - * 
-     * Identifier of the allocated buffer if known
-     *
- * - * optional int64 allocation_id = 4; - */ - public Builder setAllocationId(long value) { - - allocationId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Identifier of the allocated buffer if known
-     *
- * - * optional int64 allocation_id = 4; - */ - public Builder clearAllocationId() { - - allocationId_ = 0L; - onChanged(); - return this; - } - - private boolean hasSingleReference_ ; - /** - * 
-     * Set if this tensor only has one remaining reference
-     *
- * - * optional bool has_single_reference = 5; - */ - public boolean getHasSingleReference() { - return hasSingleReference_; - } - /** - * 
-     * Set if this tensor only has one remaining reference
-     *
- * - * optional bool has_single_reference = 5; - */ - public Builder setHasSingleReference(boolean value) { - - hasSingleReference_ = value; - onChanged(); - return this; - } - /** - * 
-     * Set if this tensor only has one remaining reference
-     *
- * - * optional bool has_single_reference = 5; - */ - public Builder clearHasSingleReference() { - - hasSingleReference_ = false; - onChanged(); - return this; - } - - private long ptr_ ; - /** - * 
-     * Address of the allocation.
-     *
- * - * optional uint64 ptr = 6; - */ - public long getPtr() { - return ptr_; - } - /** - * 
-     * Address of the allocation.
-     *
- * - * optional uint64 ptr = 6; - */ - public Builder setPtr(long value) { - - ptr_ = value; - onChanged(); - return this; - } - /** - * 
-     * Address of the allocation.
-     *
- * - * optional uint64 ptr = 6; - */ - public Builder clearPtr() { - - ptr_ = 0L; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.AllocationDescription) - } - - // @@protoc_insertion_point(class_scope:tensorflow.AllocationDescription) - private static final org.tensorflow.framework.AllocationDescription DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.AllocationDescription(); - } - - public static org.tensorflow.framework.AllocationDescription getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public AllocationDescription parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new AllocationDescription(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.AllocationDescription getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescriptionOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescriptionOrBuilder.java deleted file mode 100644 index caed542959a..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescriptionOrBuilder.java +++ /dev/null @@ -1,72 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: allocation_description.proto - -package org.tensorflow.framework; - -public interface AllocationDescriptionOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.AllocationDescription) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Total number of bytes requested
-   *
- * - * optional int64 requested_bytes = 1; - */ - long getRequestedBytes(); - - /** - * 
-   * Total number of bytes allocated if known
-   *
- * - * optional int64 allocated_bytes = 2; - */ - long getAllocatedBytes(); - - /** - * 
-   * Name of the allocator used
-   *
- * - * optional string allocator_name = 3; - */ - java.lang.String getAllocatorName(); - /** - * 
-   * Name of the allocator used
-   *
- * - * optional string allocator_name = 3; - */ - com.google.protobuf.ByteString - getAllocatorNameBytes(); - - /** - * 
-   * Identifier of the allocated buffer if known
-   *
- * - * optional int64 allocation_id = 4; - */ - long getAllocationId(); - - /** - * 
-   * Set if this tensor only has one remaining reference
-   *
- * - * optional bool has_single_reference = 5; - */ - boolean getHasSingleReference(); - - /** - * 
-   * Address of the allocation.
-   *
- * - * optional uint64 ptr = 6; - */ - long getPtr(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescriptionProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescriptionProtos.java deleted file mode 100644 index 78effd6048a..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/AllocationDescriptionProtos.java +++ /dev/null @@ -1,60 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: allocation_description.proto - -package org.tensorflow.framework; - -public final class AllocationDescriptionProtos { - private AllocationDescriptionProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_AllocationDescription_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_AllocationDescription_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\034allocation_description.proto\022\ntensorfl" + - "ow\"\243\001\n\025AllocationDescription\022\027\n\017requeste" + - "d_bytes\030\001 \001(\003\022\027\n\017allocated_bytes\030\002 \001(\003\022\026" + - "\n\016allocator_name\030\003 \001(\t\022\025\n\rallocation_id\030" + - "\004 \001(\003\022\034\n\024has_single_reference\030\005 \001(\010\022\013\n\003p" + - "tr\030\006 \001(\004B<\n\030org.tensorflow.frameworkB\033Al" + - "locationDescriptionProtosP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - }, assigner); - internal_static_tensorflow_AllocationDescription_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_AllocationDescription_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_AllocationDescription_descriptor, - new java.lang.String[] { "RequestedBytes", "AllocatedBytes", "AllocatorName", "AllocationId", "HasSingleReference", "Ptr", }); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/AllocatorMemoryUsed.java b/scala/dllib/src/main/java/org/tensorflow/framework/AllocatorMemoryUsed.java deleted file mode 100644 index 7ee36048bff..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/AllocatorMemoryUsed.java +++ /dev/null @@ -1,612 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.AllocatorMemoryUsed} - */ -public final class AllocatorMemoryUsed extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.AllocatorMemoryUsed) - AllocatorMemoryUsedOrBuilder { - // Use AllocatorMemoryUsed.newBuilder() to construct. - private AllocatorMemoryUsed(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private AllocatorMemoryUsed() { - allocatorName_ = ""; - totalBytes_ = 0L; - peakBytes_ = 0L; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private AllocatorMemoryUsed( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - allocatorName_ = s; - break; - } - case 16: { - - totalBytes_ = input.readInt64(); - break; - } - case 24: { - - peakBytes_ = input.readInt64(); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_AllocatorMemoryUsed_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_AllocatorMemoryUsed_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.AllocatorMemoryUsed.class, org.tensorflow.framework.AllocatorMemoryUsed.Builder.class); - } - - public static final int ALLOCATOR_NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object allocatorName_; - /** - * optional string allocator_name = 1; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } - } - /** - * optional string allocator_name = 1; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int TOTAL_BYTES_FIELD_NUMBER = 2; - private long totalBytes_; - /** - * optional int64 total_bytes = 2; - */ - public long getTotalBytes() { - return totalBytes_; - } - - public static final int PEAK_BYTES_FIELD_NUMBER = 3; - private long peakBytes_; - /** - * optional int64 peak_bytes = 3; - */ - public long getPeakBytes() { - return peakBytes_; - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getAllocatorNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, allocatorName_); - } - if (totalBytes_ != 0L) { - output.writeInt64(2, totalBytes_); - } - if (peakBytes_ != 0L) { - output.writeInt64(3, peakBytes_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getAllocatorNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, allocatorName_); - } - if (totalBytes_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(2, totalBytes_); - } - if (peakBytes_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(3, peakBytes_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.AllocatorMemoryUsed)) { - return super.equals(obj); - } - org.tensorflow.framework.AllocatorMemoryUsed other = (org.tensorflow.framework.AllocatorMemoryUsed) obj; - - boolean result = true; - result = result && getAllocatorName() - .equals(other.getAllocatorName()); - result = result && (getTotalBytes() - == other.getTotalBytes()); - result = result && (getPeakBytes() - == other.getPeakBytes()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + ALLOCATOR_NAME_FIELD_NUMBER; - hash = (53 * hash) + getAllocatorName().hashCode(); - hash = (37 * hash) + TOTAL_BYTES_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getTotalBytes()); - hash = (37 * hash) + PEAK_BYTES_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getPeakBytes()); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.AllocatorMemoryUsed parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AllocatorMemoryUsed parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.AllocatorMemoryUsed prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.AllocatorMemoryUsed} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.AllocatorMemoryUsed) - org.tensorflow.framework.AllocatorMemoryUsedOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_AllocatorMemoryUsed_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_AllocatorMemoryUsed_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.AllocatorMemoryUsed.class, org.tensorflow.framework.AllocatorMemoryUsed.Builder.class); - } - - // Construct using org.tensorflow.framework.AllocatorMemoryUsed.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - allocatorName_ = ""; - - totalBytes_ = 0L; - - peakBytes_ = 0L; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_AllocatorMemoryUsed_descriptor; - } - - public org.tensorflow.framework.AllocatorMemoryUsed getDefaultInstanceForType() { - return org.tensorflow.framework.AllocatorMemoryUsed.getDefaultInstance(); - } - - public org.tensorflow.framework.AllocatorMemoryUsed build() { - org.tensorflow.framework.AllocatorMemoryUsed result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.AllocatorMemoryUsed buildPartial() { - org.tensorflow.framework.AllocatorMemoryUsed result = new org.tensorflow.framework.AllocatorMemoryUsed(this); - result.allocatorName_ = allocatorName_; - result.totalBytes_ = totalBytes_; - result.peakBytes_ = peakBytes_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.AllocatorMemoryUsed) { - return mergeFrom((org.tensorflow.framework.AllocatorMemoryUsed)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.AllocatorMemoryUsed other) { - if (other == org.tensorflow.framework.AllocatorMemoryUsed.getDefaultInstance()) return this; - if (!other.getAllocatorName().isEmpty()) { - allocatorName_ = other.allocatorName_; - onChanged(); - } - if (other.getTotalBytes() != 0L) { - setTotalBytes(other.getTotalBytes()); - } - if (other.getPeakBytes() != 0L) { - setPeakBytes(other.getPeakBytes()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.AllocatorMemoryUsed parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.AllocatorMemoryUsed) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private java.lang.Object allocatorName_ = ""; - /** - * optional string allocator_name = 1; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * optional string allocator_name = 1; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * optional string allocator_name = 1; - */ - public Builder setAllocatorName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - allocatorName_ = value; - onChanged(); - return this; - } - /** - * optional string allocator_name = 1; - */ - public Builder clearAllocatorName() { - - allocatorName_ = getDefaultInstance().getAllocatorName(); - onChanged(); - return this; - } - /** - * optional string allocator_name = 1; - */ - public Builder setAllocatorNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - allocatorName_ = value; - onChanged(); - return this; - } - - private long totalBytes_ ; - /** - * optional int64 total_bytes = 2; - */ - public long getTotalBytes() { - return totalBytes_; - } - /** - * optional int64 total_bytes = 2; - */ - public Builder setTotalBytes(long value) { - - totalBytes_ = value; - onChanged(); - return this; - } - /** - * optional int64 total_bytes = 2; - */ - public Builder clearTotalBytes() { - - totalBytes_ = 0L; - onChanged(); - return this; - } - - private long peakBytes_ ; - /** - * optional int64 peak_bytes = 3; - */ - public long getPeakBytes() { - return peakBytes_; - } - /** - * optional int64 peak_bytes = 3; - */ - public Builder setPeakBytes(long value) { - - peakBytes_ = value; - onChanged(); - return this; - } - /** - * optional int64 peak_bytes = 3; - */ - public Builder clearPeakBytes() { - - peakBytes_ = 0L; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.AllocatorMemoryUsed) - } - - // @@protoc_insertion_point(class_scope:tensorflow.AllocatorMemoryUsed) - private static final org.tensorflow.framework.AllocatorMemoryUsed DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.AllocatorMemoryUsed(); - } - - public static org.tensorflow.framework.AllocatorMemoryUsed getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public AllocatorMemoryUsed parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new AllocatorMemoryUsed(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.AllocatorMemoryUsed getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/AllocatorMemoryUsedOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/AllocatorMemoryUsedOrBuilder.java deleted file mode 100644 index 1b51490cd6c..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/AllocatorMemoryUsedOrBuilder.java +++ /dev/null @@ -1,29 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -public interface AllocatorMemoryUsedOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.AllocatorMemoryUsed) - com.google.protobuf.MessageOrBuilder { - - /** - * optional string allocator_name = 1; - */ - java.lang.String getAllocatorName(); - /** - * optional string allocator_name = 1; - */ - com.google.protobuf.ByteString - getAllocatorNameBytes(); - - /** - * optional int64 total_bytes = 2; - */ - long getTotalBytes(); - - /** - * optional int64 peak_bytes = 3; - */ - long getPeakBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/AttrValue.java b/scala/dllib/src/main/java/org/tensorflow/framework/AttrValue.java deleted file mode 100644 index 28fb052b9de..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/AttrValue.java +++ /dev/null @@ -1,5146 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: attr_value.proto - -package org.tensorflow.framework; - -/** - * 
- * Protocol buffer representing the value for an attr used to configure an Op.
- * Comment indicates the corresponding attr type.  Only the field matching the
- * attr type may be filled.
- *
- * - * Protobuf type {@code tensorflow.AttrValue} - */ -public final class AttrValue extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.AttrValue) - AttrValueOrBuilder { - // Use AttrValue.newBuilder() to construct. - private AttrValue(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private AttrValue() { - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private AttrValue( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - org.tensorflow.framework.AttrValue.ListValue.Builder subBuilder = null; - if (valueCase_ == 1) { - subBuilder = ((org.tensorflow.framework.AttrValue.ListValue) value_).toBuilder(); - } - value_ = - input.readMessage(org.tensorflow.framework.AttrValue.ListValue.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom((org.tensorflow.framework.AttrValue.ListValue) value_); - value_ = subBuilder.buildPartial(); - } - valueCase_ = 1; - break; - } - case 18: { - valueCase_ = 2; - value_ = input.readBytes(); - break; - } - case 24: { - valueCase_ = 3; - value_ = input.readInt64(); - break; - } - case 37: { - valueCase_ = 4; - value_ = input.readFloat(); - break; - } - case 40: { - valueCase_ = 5; - value_ = input.readBool(); - break; - } - case 48: { - int rawValue = input.readEnum(); - valueCase_ = 6; - value_ = rawValue; - break; - } - case 58: { - org.tensorflow.framework.TensorShapeProto.Builder subBuilder = null; - if (valueCase_ == 7) { - subBuilder = ((org.tensorflow.framework.TensorShapeProto) value_).toBuilder(); - } - value_ = - input.readMessage(org.tensorflow.framework.TensorShapeProto.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom((org.tensorflow.framework.TensorShapeProto) value_); - value_ = subBuilder.buildPartial(); - } - valueCase_ = 7; - break; - } - case 66: { - org.tensorflow.framework.TensorProto.Builder subBuilder = null; - if (valueCase_ == 8) { - subBuilder = ((org.tensorflow.framework.TensorProto) value_).toBuilder(); - } - value_ = - input.readMessage(org.tensorflow.framework.TensorProto.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom((org.tensorflow.framework.TensorProto) value_); - value_ = subBuilder.buildPartial(); - } - valueCase_ = 8; - break; - } - case 74: { - java.lang.String s = input.readStringRequireUtf8(); - valueCase_ = 9; - value_ = s; - break; - } - case 82: { - org.tensorflow.framework.NameAttrList.Builder subBuilder = null; - if (valueCase_ == 10) { - subBuilder = ((org.tensorflow.framework.NameAttrList) value_).toBuilder(); - } - value_ = - input.readMessage(org.tensorflow.framework.NameAttrList.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom((org.tensorflow.framework.NameAttrList) value_); - value_ = subBuilder.buildPartial(); - } - valueCase_ = 10; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.AttrValue.class, org.tensorflow.framework.AttrValue.Builder.class); - } - - public interface ListValueOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.AttrValue.ListValue) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-     * "list(string)"
-     *
- * - * repeated bytes s = 2; - */ - java.util.List getSList(); - /** - * 
-     * "list(string)"
-     *
- * - * repeated bytes s = 2; - */ - int getSCount(); - /** - * 
-     * "list(string)"
-     *
- * - * repeated bytes s = 2; - */ - com.google.protobuf.ByteString getS(int index); - - /** - * 
-     * "list(int)"
-     *
- * - * repeated int64 i = 3 [packed = true]; - */ - java.util.List getIList(); - /** - * 
-     * "list(int)"
-     *
- * - * repeated int64 i = 3 [packed = true]; - */ - int getICount(); - /** - * 
-     * "list(int)"
-     *
- * - * repeated int64 i = 3 [packed = true]; - */ - long getI(int index); - - /** - * 
-     * "list(float)"
-     *
- * - * repeated float f = 4 [packed = true]; - */ - java.util.List getFList(); - /** - * 
-     * "list(float)"
-     *
- * - * repeated float f = 4 [packed = true]; - */ - int getFCount(); - /** - * 
-     * "list(float)"
-     *
- * - * repeated float f = 4 [packed = true]; - */ - float getF(int index); - - /** - * 
-     * "list(bool)"
-     *
- * - * repeated bool b = 5 [packed = true]; - */ - java.util.List getBList(); - /** - * 
-     * "list(bool)"
-     *
- * - * repeated bool b = 5 [packed = true]; - */ - int getBCount(); - /** - * 
-     * "list(bool)"
-     *
- * - * repeated bool b = 5 [packed = true]; - */ - boolean getB(int index); - - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - java.util.List getTypeList(); - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - int getTypeCount(); - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - org.tensorflow.framework.DataType getType(int index); - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - java.util.List - getTypeValueList(); - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - int getTypeValue(int index); - - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - java.util.List - getShapeList(); - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - org.tensorflow.framework.TensorShapeProto getShape(int index); - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - int getShapeCount(); - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - java.util.List - getShapeOrBuilderList(); - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - org.tensorflow.framework.TensorShapeProtoOrBuilder getShapeOrBuilder( - int index); - - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - java.util.List - getTensorList(); - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - org.tensorflow.framework.TensorProto getTensor(int index); - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - int getTensorCount(); - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - java.util.List - getTensorOrBuilderList(); - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - org.tensorflow.framework.TensorProtoOrBuilder getTensorOrBuilder( - int index); - - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - java.util.List - getFuncList(); - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - org.tensorflow.framework.NameAttrList getFunc(int index); - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - int getFuncCount(); - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - java.util.List - getFuncOrBuilderList(); - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - org.tensorflow.framework.NameAttrListOrBuilder getFuncOrBuilder( - int index); - } - /** - * 
-   * LINT.IfChange
-   *
- * - * Protobuf type {@code tensorflow.AttrValue.ListValue} - */ - public static final class ListValue extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.AttrValue.ListValue) - ListValueOrBuilder { - // Use ListValue.newBuilder() to construct. - private ListValue(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private ListValue() { - s_ = java.util.Collections.emptyList(); - i_ = java.util.Collections.emptyList(); - f_ = java.util.Collections.emptyList(); - b_ = java.util.Collections.emptyList(); - type_ = java.util.Collections.emptyList(); - shape_ = java.util.Collections.emptyList(); - tensor_ = java.util.Collections.emptyList(); - func_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private ListValue( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 18: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - s_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - s_.add(input.readBytes()); - break; - } - case 24: { - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - i_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000002; - } - i_.add(input.readInt64()); - break; - } - case 26: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002) && input.getBytesUntilLimit() > 0) { - i_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000002; - } - while (input.getBytesUntilLimit() > 0) { - i_.add(input.readInt64()); - } - input.popLimit(limit); - break; - } - case 37: { - if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - f_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000004; - } - f_.add(input.readFloat()); - break; - } - case 34: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000004) == 0x00000004) && input.getBytesUntilLimit() > 0) { - f_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000004; - } - while (input.getBytesUntilLimit() > 0) { - f_.add(input.readFloat()); - } - input.popLimit(limit); - break; - } - case 40: { - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - b_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000008; - } - b_.add(input.readBool()); - break; - } - case 42: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008) && input.getBytesUntilLimit() > 0) { - b_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000008; - } - while (input.getBytesUntilLimit() > 0) { - b_.add(input.readBool()); - } - input.popLimit(limit); - break; - } - case 48: { - int rawValue = input.readEnum(); - if (!((mutable_bitField0_ & 0x00000010) == 0x00000010)) { - type_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000010; - } - type_.add(rawValue); - break; - } - case 50: { - int length = input.readRawVarint32(); - int oldLimit = input.pushLimit(length); - while(input.getBytesUntilLimit() > 0) { - int rawValue = input.readEnum(); - if (!((mutable_bitField0_ & 0x00000010) == 0x00000010)) { - type_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000010; - } - type_.add(rawValue); - } - input.popLimit(oldLimit); - break; - } - case 58: { - if (!((mutable_bitField0_ & 0x00000020) == 0x00000020)) { - shape_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000020; - } - shape_.add( - input.readMessage(org.tensorflow.framework.TensorShapeProto.parser(), extensionRegistry)); - break; - } - case 66: { - if (!((mutable_bitField0_ & 0x00000040) == 0x00000040)) { - tensor_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000040; - } - tensor_.add( - input.readMessage(org.tensorflow.framework.TensorProto.parser(), extensionRegistry)); - break; - } - case 74: { - if (!((mutable_bitField0_ & 0x00000080) == 0x00000080)) { - func_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000080; - } - func_.add( - input.readMessage(org.tensorflow.framework.NameAttrList.parser(), extensionRegistry)); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - s_ = java.util.Collections.unmodifiableList(s_); - } - if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - i_ = java.util.Collections.unmodifiableList(i_); - } - if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - f_ = java.util.Collections.unmodifiableList(f_); - } - if (((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - b_ = java.util.Collections.unmodifiableList(b_); - } - if (((mutable_bitField0_ & 0x00000010) == 0x00000010)) { - type_ = java.util.Collections.unmodifiableList(type_); - } - if (((mutable_bitField0_ & 0x00000020) == 0x00000020)) { - shape_ = java.util.Collections.unmodifiableList(shape_); - } - if (((mutable_bitField0_ & 0x00000040) == 0x00000040)) { - tensor_ = java.util.Collections.unmodifiableList(tensor_); - } - if (((mutable_bitField0_ & 0x00000080) == 0x00000080)) { - func_ = java.util.Collections.unmodifiableList(func_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_ListValue_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_ListValue_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.AttrValue.ListValue.class, org.tensorflow.framework.AttrValue.ListValue.Builder.class); - } - - public static final int S_FIELD_NUMBER = 2; - private java.util.List s_; - /** - * 
-     * "list(string)"
-     *
- * - * repeated bytes s = 2; - */ - public java.util.List - getSList() { - return s_; - } - /** - * 
-     * "list(string)"
-     *
- * - * repeated bytes s = 2; - */ - public int getSCount() { - return s_.size(); - } - /** - * 
-     * "list(string)"
-     *
- * - * repeated bytes s = 2; - */ - public com.google.protobuf.ByteString getS(int index) { - return s_.get(index); - } - - public static final int I_FIELD_NUMBER = 3; - private java.util.List i_; - /** - * 
-     * "list(int)"
-     *
- * - * repeated int64 i = 3 [packed = true]; - */ - public java.util.List - getIList() { - return i_; - } - /** - * 
-     * "list(int)"
-     *
- * - * repeated int64 i = 3 [packed = true]; - */ - public int getICount() { - return i_.size(); - } - /** - * 
-     * "list(int)"
-     *
- * - * repeated int64 i = 3 [packed = true]; - */ - public long getI(int index) { - return i_.get(index); - } - private int iMemoizedSerializedSize = -1; - - public static final int F_FIELD_NUMBER = 4; - private java.util.List f_; - /** - * 
-     * "list(float)"
-     *
- * - * repeated float f = 4 [packed = true]; - */ - public java.util.List - getFList() { - return f_; - } - /** - * 
-     * "list(float)"
-     *
- * - * repeated float f = 4 [packed = true]; - */ - public int getFCount() { - return f_.size(); - } - /** - * 
-     * "list(float)"
-     *
- * - * repeated float f = 4 [packed = true]; - */ - public float getF(int index) { - return f_.get(index); - } - private int fMemoizedSerializedSize = -1; - - public static final int B_FIELD_NUMBER = 5; - private java.util.List b_; - /** - * 
-     * "list(bool)"
-     *
- * - * repeated bool b = 5 [packed = true]; - */ - public java.util.List - getBList() { - return b_; - } - /** - * 
-     * "list(bool)"
-     *
- * - * repeated bool b = 5 [packed = true]; - */ - public int getBCount() { - return b_.size(); - } - /** - * 
-     * "list(bool)"
-     *
- * - * repeated bool b = 5 [packed = true]; - */ - public boolean getB(int index) { - return b_.get(index); - } - private int bMemoizedSerializedSize = -1; - - public static final int TYPE_FIELD_NUMBER = 6; - private java.util.List type_; - private static final com.google.protobuf.Internal.ListAdapter.Converter< - java.lang.Integer, org.tensorflow.framework.DataType> type_converter_ = - new com.google.protobuf.Internal.ListAdapter.Converter< - java.lang.Integer, org.tensorflow.framework.DataType>() { - public org.tensorflow.framework.DataType convert(java.lang.Integer from) { - org.tensorflow.framework.DataType result = org.tensorflow.framework.DataType.valueOf(from); - return result == null ? org.tensorflow.framework.DataType.UNRECOGNIZED : result; - } - }; - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public java.util.List getTypeList() { - return new com.google.protobuf.Internal.ListAdapter< - java.lang.Integer, org.tensorflow.framework.DataType>(type_, type_converter_); - } - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public int getTypeCount() { - return type_.size(); - } - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public org.tensorflow.framework.DataType getType(int index) { - return type_converter_.convert(type_.get(index)); - } - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public java.util.List - getTypeValueList() { - return type_; - } - /** - * 
-     * "list(type)"
-     *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public int getTypeValue(int index) { - return type_.get(index); - } - private int typeMemoizedSerializedSize; - - public static final int SHAPE_FIELD_NUMBER = 7; - private java.util.List shape_; - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public java.util.List getShapeList() { - return shape_; - } - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public java.util.List - getShapeOrBuilderList() { - return shape_; - } - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public int getShapeCount() { - return shape_.size(); - } - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProto getShape(int index) { - return shape_.get(index); - } - /** - * 
-     * "list(shape)"
-     *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProtoOrBuilder getShapeOrBuilder( - int index) { - return shape_.get(index); - } - - public static final int TENSOR_FIELD_NUMBER = 8; - private java.util.List tensor_; - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public java.util.List getTensorList() { - return tensor_; - } - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public java.util.List - getTensorOrBuilderList() { - return tensor_; - } - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public int getTensorCount() { - return tensor_.size(); - } - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProto getTensor(int index) { - return tensor_.get(index); - } - /** - * 
-     * "list(tensor)"
-     *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProtoOrBuilder getTensorOrBuilder( - int index) { - return tensor_.get(index); - } - - public static final int FUNC_FIELD_NUMBER = 9; - private java.util.List func_; - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public java.util.List getFuncList() { - return func_; - } - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public java.util.List - getFuncOrBuilderList() { - return func_; - } - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public int getFuncCount() { - return func_.size(); - } - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public org.tensorflow.framework.NameAttrList getFunc(int index) { - return func_.get(index); - } - /** - * 
-     * "list(attr)"
-     *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public org.tensorflow.framework.NameAttrListOrBuilder getFuncOrBuilder( - int index) { - return func_.get(index); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - getSerializedSize(); - for (int i = 0; i < s_.size(); i++) { - output.writeBytes(2, s_.get(i)); - } - if (getIList().size() > 0) { - output.writeUInt32NoTag(26); - output.writeUInt32NoTag(iMemoizedSerializedSize); - } - for (int i = 0; i < i_.size(); i++) { - output.writeInt64NoTag(i_.get(i)); - } - if (getFList().size() > 0) { - output.writeUInt32NoTag(34); - output.writeUInt32NoTag(fMemoizedSerializedSize); - } - for (int i = 0; i < f_.size(); i++) { - output.writeFloatNoTag(f_.get(i)); - } - if (getBList().size() > 0) { - output.writeUInt32NoTag(42); - output.writeUInt32NoTag(bMemoizedSerializedSize); - } - for (int i = 0; i < b_.size(); i++) { - output.writeBoolNoTag(b_.get(i)); - } - if (getTypeList().size() > 0) { - output.writeUInt32NoTag(50); - output.writeUInt32NoTag(typeMemoizedSerializedSize); - } - for (int i = 0; i < type_.size(); i++) { - output.writeEnumNoTag(type_.get(i)); - } - for (int i = 0; i < shape_.size(); i++) { - output.writeMessage(7, shape_.get(i)); - } - for (int i = 0; i < tensor_.size(); i++) { - output.writeMessage(8, tensor_.get(i)); - } - for (int i = 0; i < func_.size(); i++) { - output.writeMessage(9, func_.get(i)); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - { - int dataSize = 0; - for (int i = 0; i < s_.size(); i++) { - dataSize += com.google.protobuf.CodedOutputStream - .computeBytesSizeNoTag(s_.get(i)); - } - size += dataSize; - size += 1 * getSList().size(); - } - { - int dataSize = 0; - for (int i = 0; i < i_.size(); i++) { - dataSize += com.google.protobuf.CodedOutputStream - .computeInt64SizeNoTag(i_.get(i)); - } - size += dataSize; - if (!getIList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - iMemoizedSerializedSize = dataSize; - } - { - int dataSize = 0; - dataSize = 4 * getFList().size(); - size += dataSize; - if (!getFList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - fMemoizedSerializedSize = dataSize; - } - { - int dataSize = 0; - dataSize = 1 * getBList().size(); - size += dataSize; - if (!getBList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - bMemoizedSerializedSize = dataSize; - } - { - int dataSize = 0; - for (int i = 0; i < type_.size(); i++) { - dataSize += com.google.protobuf.CodedOutputStream - .computeEnumSizeNoTag(type_.get(i)); - } - size += dataSize; - if (!getTypeList().isEmpty()) { size += 1; - size += com.google.protobuf.CodedOutputStream - .computeUInt32SizeNoTag(dataSize); - }typeMemoizedSerializedSize = dataSize; - } - for (int i = 0; i < shape_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(7, shape_.get(i)); - } - for (int i = 0; i < tensor_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(8, tensor_.get(i)); - } - for (int i = 0; i < func_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(9, func_.get(i)); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.AttrValue.ListValue)) { - return super.equals(obj); - } - org.tensorflow.framework.AttrValue.ListValue other = (org.tensorflow.framework.AttrValue.ListValue) obj; - - boolean result = true; - result = result && getSList() - .equals(other.getSList()); - result = result && getIList() - .equals(other.getIList()); - result = result && getFList() - .equals(other.getFList()); - result = result && getBList() - .equals(other.getBList()); - result = result && type_.equals(other.type_); - result = result && getShapeList() - .equals(other.getShapeList()); - result = result && getTensorList() - .equals(other.getTensorList()); - result = result && getFuncList() - .equals(other.getFuncList()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - if (getSCount() > 0) { - hash = (37 * hash) + S_FIELD_NUMBER; - hash = (53 * hash) + getSList().hashCode(); - } - if (getICount() > 0) { - hash = (37 * hash) + I_FIELD_NUMBER; - hash = (53 * hash) + getIList().hashCode(); - } - if (getFCount() > 0) { - hash = (37 * hash) + F_FIELD_NUMBER; - hash = (53 * hash) + getFList().hashCode(); - } - if (getBCount() > 0) { - hash = (37 * hash) + B_FIELD_NUMBER; - hash = (53 * hash) + getBList().hashCode(); - } - if (getTypeCount() > 0) { - hash = (37 * hash) + TYPE_FIELD_NUMBER; - hash = (53 * hash) + type_.hashCode(); - } - if (getShapeCount() > 0) { - hash = (37 * hash) + SHAPE_FIELD_NUMBER; - hash = (53 * hash) + getShapeList().hashCode(); - } - if (getTensorCount() > 0) { - hash = (37 * hash) + TENSOR_FIELD_NUMBER; - hash = (53 * hash) + getTensorList().hashCode(); - } - if (getFuncCount() > 0) { - hash = (37 * hash) + FUNC_FIELD_NUMBER; - hash = (53 * hash) + getFuncList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.AttrValue.ListValue parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.AttrValue.ListValue parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.AttrValue.ListValue parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.AttrValue.ListValue parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.AttrValue.ListValue parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AttrValue.ListValue parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.AttrValue.ListValue parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AttrValue.ListValue parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.AttrValue.ListValue parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AttrValue.ListValue parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.AttrValue.ListValue prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-     * LINT.IfChange
-     *
- * - * Protobuf type {@code tensorflow.AttrValue.ListValue} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.AttrValue.ListValue) - org.tensorflow.framework.AttrValue.ListValueOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_ListValue_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_ListValue_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.AttrValue.ListValue.class, org.tensorflow.framework.AttrValue.ListValue.Builder.class); - } - - // Construct using org.tensorflow.framework.AttrValue.ListValue.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getShapeFieldBuilder(); - getTensorFieldBuilder(); - getFuncFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - s_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - i_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - f_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - b_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000008); - type_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000010); - if (shapeBuilder_ == null) { - shape_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000020); - } else { - shapeBuilder_.clear(); - } - if (tensorBuilder_ == null) { - tensor_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000040); - } else { - tensorBuilder_.clear(); - } - if (funcBuilder_ == null) { - func_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000080); - } else { - funcBuilder_.clear(); - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_ListValue_descriptor; - } - - public org.tensorflow.framework.AttrValue.ListValue getDefaultInstanceForType() { - return org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance(); - } - - public org.tensorflow.framework.AttrValue.ListValue build() { - org.tensorflow.framework.AttrValue.ListValue result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.AttrValue.ListValue buildPartial() { - org.tensorflow.framework.AttrValue.ListValue result = new org.tensorflow.framework.AttrValue.ListValue(this); - int from_bitField0_ = bitField0_; - if (((bitField0_ & 0x00000001) == 0x00000001)) { - s_ = java.util.Collections.unmodifiableList(s_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.s_ = s_; - if (((bitField0_ & 0x00000002) == 0x00000002)) { - i_ = java.util.Collections.unmodifiableList(i_); - bitField0_ = (bitField0_ & ~0x00000002); - } - result.i_ = i_; - if (((bitField0_ & 0x00000004) == 0x00000004)) { - f_ = java.util.Collections.unmodifiableList(f_); - bitField0_ = (bitField0_ & ~0x00000004); - } - result.f_ = f_; - if (((bitField0_ & 0x00000008) == 0x00000008)) { - b_ = java.util.Collections.unmodifiableList(b_); - bitField0_ = (bitField0_ & ~0x00000008); - } - result.b_ = b_; - if (((bitField0_ & 0x00000010) == 0x00000010)) { - type_ = java.util.Collections.unmodifiableList(type_); - bitField0_ = (bitField0_ & ~0x00000010); - } - result.type_ = type_; - if (shapeBuilder_ == null) { - if (((bitField0_ & 0x00000020) == 0x00000020)) { - shape_ = java.util.Collections.unmodifiableList(shape_); - bitField0_ = (bitField0_ & ~0x00000020); - } - result.shape_ = shape_; - } else { - result.shape_ = shapeBuilder_.build(); - } - if (tensorBuilder_ == null) { - if (((bitField0_ & 0x00000040) == 0x00000040)) { - tensor_ = java.util.Collections.unmodifiableList(tensor_); - bitField0_ = (bitField0_ & ~0x00000040); - } - result.tensor_ = tensor_; - } else { - result.tensor_ = tensorBuilder_.build(); - } - if (funcBuilder_ == null) { - if (((bitField0_ & 0x00000080) == 0x00000080)) { - func_ = java.util.Collections.unmodifiableList(func_); - bitField0_ = (bitField0_ & ~0x00000080); - } - result.func_ = func_; - } else { - result.func_ = funcBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.AttrValue.ListValue) { - return mergeFrom((org.tensorflow.framework.AttrValue.ListValue)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.AttrValue.ListValue other) { - if (other == org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance()) return this; - if (!other.s_.isEmpty()) { - if (s_.isEmpty()) { - s_ = other.s_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureSIsMutable(); - s_.addAll(other.s_); - } - onChanged(); - } - if (!other.i_.isEmpty()) { - if (i_.isEmpty()) { - i_ = other.i_; - bitField0_ = (bitField0_ & ~0x00000002); - } else { - ensureIIsMutable(); - i_.addAll(other.i_); - } - onChanged(); - } - if (!other.f_.isEmpty()) { - if (f_.isEmpty()) { - f_ = other.f_; - bitField0_ = (bitField0_ & ~0x00000004); - } else { - ensureFIsMutable(); - f_.addAll(other.f_); - } - onChanged(); - } - if (!other.b_.isEmpty()) { - if (b_.isEmpty()) { - b_ = other.b_; - bitField0_ = (bitField0_ & ~0x00000008); - } else { - ensureBIsMutable(); - b_.addAll(other.b_); - } - onChanged(); - } - if (!other.type_.isEmpty()) { - if (type_.isEmpty()) { - type_ = other.type_; - bitField0_ = (bitField0_ & ~0x00000010); - } else { - ensureTypeIsMutable(); - type_.addAll(other.type_); - } - onChanged(); - } - if (shapeBuilder_ == null) { - if (!other.shape_.isEmpty()) { - if (shape_.isEmpty()) { - shape_ = other.shape_; - bitField0_ = (bitField0_ & ~0x00000020); - } else { - ensureShapeIsMutable(); - shape_.addAll(other.shape_); - } - onChanged(); - } - } else { - if (!other.shape_.isEmpty()) { - if (shapeBuilder_.isEmpty()) { - shapeBuilder_.dispose(); - shapeBuilder_ = null; - shape_ = other.shape_; - bitField0_ = (bitField0_ & ~0x00000020); - shapeBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getShapeFieldBuilder() : null; - } else { - shapeBuilder_.addAllMessages(other.shape_); - } - } - } - if (tensorBuilder_ == null) { - if (!other.tensor_.isEmpty()) { - if (tensor_.isEmpty()) { - tensor_ = other.tensor_; - bitField0_ = (bitField0_ & ~0x00000040); - } else { - ensureTensorIsMutable(); - tensor_.addAll(other.tensor_); - } - onChanged(); - } - } else { - if (!other.tensor_.isEmpty()) { - if (tensorBuilder_.isEmpty()) { - tensorBuilder_.dispose(); - tensorBuilder_ = null; - tensor_ = other.tensor_; - bitField0_ = (bitField0_ & ~0x00000040); - tensorBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getTensorFieldBuilder() : null; - } else { - tensorBuilder_.addAllMessages(other.tensor_); - } - } - } - if (funcBuilder_ == null) { - if (!other.func_.isEmpty()) { - if (func_.isEmpty()) { - func_ = other.func_; - bitField0_ = (bitField0_ & ~0x00000080); - } else { - ensureFuncIsMutable(); - func_.addAll(other.func_); - } - onChanged(); - } - } else { - if (!other.func_.isEmpty()) { - if (funcBuilder_.isEmpty()) { - funcBuilder_.dispose(); - funcBuilder_ = null; - func_ = other.func_; - bitField0_ = (bitField0_ & ~0x00000080); - funcBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getFuncFieldBuilder() : null; - } else { - funcBuilder_.addAllMessages(other.func_); - } - } - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.AttrValue.ListValue parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.AttrValue.ListValue) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List s_ = java.util.Collections.emptyList(); - private void ensureSIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - s_ = new java.util.ArrayList(s_); - bitField0_ |= 0x00000001; - } - } - /** - * 
-       * "list(string)"
-       *
- * - * repeated bytes s = 2; - */ - public java.util.List - getSList() { - return java.util.Collections.unmodifiableList(s_); - } - /** - * 
-       * "list(string)"
-       *
- * - * repeated bytes s = 2; - */ - public int getSCount() { - return s_.size(); - } - /** - * 
-       * "list(string)"
-       *
- * - * repeated bytes s = 2; - */ - public com.google.protobuf.ByteString getS(int index) { - return s_.get(index); - } - /** - * 
-       * "list(string)"
-       *
- * - * repeated bytes s = 2; - */ - public Builder setS( - int index, com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - ensureSIsMutable(); - s_.set(index, value); - onChanged(); - return this; - } - /** - * 
-       * "list(string)"
-       *
- * - * repeated bytes s = 2; - */ - public Builder addS(com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - ensureSIsMutable(); - s_.add(value); - onChanged(); - return this; - } - /** - * 
-       * "list(string)"
-       *
- * - * repeated bytes s = 2; - */ - public Builder addAllS( - java.lang.Iterable values) { - ensureSIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, s_); - onChanged(); - return this; - } - /** - * 
-       * "list(string)"
-       *
- * - * repeated bytes s = 2; - */ - public Builder clearS() { - s_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - return this; - } - - private java.util.List i_ = java.util.Collections.emptyList(); - private void ensureIIsMutable() { - if (!((bitField0_ & 0x00000002) == 0x00000002)) { - i_ = new java.util.ArrayList(i_); - bitField0_ |= 0x00000002; - } - } - /** - * 
-       * "list(int)"
-       *
- * - * repeated int64 i = 3 [packed = true]; - */ - public java.util.List - getIList() { - return java.util.Collections.unmodifiableList(i_); - } - /** - * 
-       * "list(int)"
-       *
- * - * repeated int64 i = 3 [packed = true]; - */ - public int getICount() { - return i_.size(); - } - /** - * 
-       * "list(int)"
-       *
- * - * repeated int64 i = 3 [packed = true]; - */ - public long getI(int index) { - return i_.get(index); - } - /** - * 
-       * "list(int)"
-       *
- * - * repeated int64 i = 3 [packed = true]; - */ - public Builder setI( - int index, long value) { - ensureIIsMutable(); - i_.set(index, value); - onChanged(); - return this; - } - /** - * 
-       * "list(int)"
-       *
- * - * repeated int64 i = 3 [packed = true]; - */ - public Builder addI(long value) { - ensureIIsMutable(); - i_.add(value); - onChanged(); - return this; - } - /** - * 
-       * "list(int)"
-       *
- * - * repeated int64 i = 3 [packed = true]; - */ - public Builder addAllI( - java.lang.Iterable values) { - ensureIIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, i_); - onChanged(); - return this; - } - /** - * 
-       * "list(int)"
-       *
- * - * repeated int64 i = 3 [packed = true]; - */ - public Builder clearI() { - i_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - onChanged(); - return this; - } - - private java.util.List f_ = java.util.Collections.emptyList(); - private void ensureFIsMutable() { - if (!((bitField0_ & 0x00000004) == 0x00000004)) { - f_ = new java.util.ArrayList(f_); - bitField0_ |= 0x00000004; - } - } - /** - * 
-       * "list(float)"
-       *
- * - * repeated float f = 4 [packed = true]; - */ - public java.util.List - getFList() { - return java.util.Collections.unmodifiableList(f_); - } - /** - * 
-       * "list(float)"
-       *
- * - * repeated float f = 4 [packed = true]; - */ - public int getFCount() { - return f_.size(); - } - /** - * 
-       * "list(float)"
-       *
- * - * repeated float f = 4 [packed = true]; - */ - public float getF(int index) { - return f_.get(index); - } - /** - * 
-       * "list(float)"
-       *
- * - * repeated float f = 4 [packed = true]; - */ - public Builder setF( - int index, float value) { - ensureFIsMutable(); - f_.set(index, value); - onChanged(); - return this; - } - /** - * 
-       * "list(float)"
-       *
- * - * repeated float f = 4 [packed = true]; - */ - public Builder addF(float value) { - ensureFIsMutable(); - f_.add(value); - onChanged(); - return this; - } - /** - * 
-       * "list(float)"
-       *
- * - * repeated float f = 4 [packed = true]; - */ - public Builder addAllF( - java.lang.Iterable values) { - ensureFIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, f_); - onChanged(); - return this; - } - /** - * 
-       * "list(float)"
-       *
- * - * repeated float f = 4 [packed = true]; - */ - public Builder clearF() { - f_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - onChanged(); - return this; - } - - private java.util.List b_ = java.util.Collections.emptyList(); - private void ensureBIsMutable() { - if (!((bitField0_ & 0x00000008) == 0x00000008)) { - b_ = new java.util.ArrayList(b_); - bitField0_ |= 0x00000008; - } - } - /** - * 
-       * "list(bool)"
-       *
- * - * repeated bool b = 5 [packed = true]; - */ - public java.util.List - getBList() { - return java.util.Collections.unmodifiableList(b_); - } - /** - * 
-       * "list(bool)"
-       *
- * - * repeated bool b = 5 [packed = true]; - */ - public int getBCount() { - return b_.size(); - } - /** - * 
-       * "list(bool)"
-       *
- * - * repeated bool b = 5 [packed = true]; - */ - public boolean getB(int index) { - return b_.get(index); - } - /** - * 
-       * "list(bool)"
-       *
- * - * repeated bool b = 5 [packed = true]; - */ - public Builder setB( - int index, boolean value) { - ensureBIsMutable(); - b_.set(index, value); - onChanged(); - return this; - } - /** - * 
-       * "list(bool)"
-       *
- * - * repeated bool b = 5 [packed = true]; - */ - public Builder addB(boolean value) { - ensureBIsMutable(); - b_.add(value); - onChanged(); - return this; - } - /** - * 
-       * "list(bool)"
-       *
- * - * repeated bool b = 5 [packed = true]; - */ - public Builder addAllB( - java.lang.Iterable values) { - ensureBIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, b_); - onChanged(); - return this; - } - /** - * 
-       * "list(bool)"
-       *
- * - * repeated bool b = 5 [packed = true]; - */ - public Builder clearB() { - b_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000008); - onChanged(); - return this; - } - - private java.util.List type_ = - java.util.Collections.emptyList(); - private void ensureTypeIsMutable() { - if (!((bitField0_ & 0x00000010) == 0x00000010)) { - type_ = new java.util.ArrayList(type_); - bitField0_ |= 0x00000010; - } - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public java.util.List getTypeList() { - return new com.google.protobuf.Internal.ListAdapter< - java.lang.Integer, org.tensorflow.framework.DataType>(type_, type_converter_); - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public int getTypeCount() { - return type_.size(); - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public org.tensorflow.framework.DataType getType(int index) { - return type_converter_.convert(type_.get(index)); - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public Builder setType( - int index, org.tensorflow.framework.DataType value) { - if (value == null) { - throw new NullPointerException(); - } - ensureTypeIsMutable(); - type_.set(index, value.getNumber()); - onChanged(); - return this; - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public Builder addType(org.tensorflow.framework.DataType value) { - if (value == null) { - throw new NullPointerException(); - } - ensureTypeIsMutable(); - type_.add(value.getNumber()); - onChanged(); - return this; - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public Builder addAllType( - java.lang.Iterable values) { - ensureTypeIsMutable(); - for (org.tensorflow.framework.DataType value : values) { - type_.add(value.getNumber()); - } - onChanged(); - return this; - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public Builder clearType() { - type_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000010); - onChanged(); - return this; - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public java.util.List - getTypeValueList() { - return java.util.Collections.unmodifiableList(type_); - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public int getTypeValue(int index) { - return type_.get(index); - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public Builder setTypeValue( - int index, int value) { - ensureTypeIsMutable(); - type_.set(index, value); - onChanged(); - return this; - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public Builder addTypeValue(int value) { - ensureTypeIsMutable(); - type_.add(value); - onChanged(); - return this; - } - /** - * 
-       * "list(type)"
-       *
- * - * repeated .tensorflow.DataType type = 6 [packed = true]; - */ - public Builder addAllTypeValue( - java.lang.Iterable values) { - ensureTypeIsMutable(); - for (int value : values) { - type_.add(value); - } - onChanged(); - return this; - } - - private java.util.List shape_ = - java.util.Collections.emptyList(); - private void ensureShapeIsMutable() { - if (!((bitField0_ & 0x00000020) == 0x00000020)) { - shape_ = new java.util.ArrayList(shape_); - bitField0_ |= 0x00000020; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.TensorShapeProto, org.tensorflow.framework.TensorShapeProto.Builder, org.tensorflow.framework.TensorShapeProtoOrBuilder> shapeBuilder_; - - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public java.util.List getShapeList() { - if (shapeBuilder_ == null) { - return java.util.Collections.unmodifiableList(shape_); - } else { - return shapeBuilder_.getMessageList(); - } - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public int getShapeCount() { - if (shapeBuilder_ == null) { - return shape_.size(); - } else { - return shapeBuilder_.getCount(); - } - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProto getShape(int index) { - if (shapeBuilder_ == null) { - return shape_.get(index); - } else { - return shapeBuilder_.getMessage(index); - } - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder setShape( - int index, org.tensorflow.framework.TensorShapeProto value) { - if (shapeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureShapeIsMutable(); - shape_.set(index, value); - onChanged(); - } else { - shapeBuilder_.setMessage(index, value); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder setShape( - int index, org.tensorflow.framework.TensorShapeProto.Builder builderForValue) { - if (shapeBuilder_ == null) { - ensureShapeIsMutable(); - shape_.set(index, builderForValue.build()); - onChanged(); - } else { - shapeBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder addShape(org.tensorflow.framework.TensorShapeProto value) { - if (shapeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureShapeIsMutable(); - shape_.add(value); - onChanged(); - } else { - shapeBuilder_.addMessage(value); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder addShape( - int index, org.tensorflow.framework.TensorShapeProto value) { - if (shapeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureShapeIsMutable(); - shape_.add(index, value); - onChanged(); - } else { - shapeBuilder_.addMessage(index, value); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder addShape( - org.tensorflow.framework.TensorShapeProto.Builder builderForValue) { - if (shapeBuilder_ == null) { - ensureShapeIsMutable(); - shape_.add(builderForValue.build()); - onChanged(); - } else { - shapeBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder addShape( - int index, org.tensorflow.framework.TensorShapeProto.Builder builderForValue) { - if (shapeBuilder_ == null) { - ensureShapeIsMutable(); - shape_.add(index, builderForValue.build()); - onChanged(); - } else { - shapeBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder addAllShape( - java.lang.Iterable values) { - if (shapeBuilder_ == null) { - ensureShapeIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, shape_); - onChanged(); - } else { - shapeBuilder_.addAllMessages(values); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder clearShape() { - if (shapeBuilder_ == null) { - shape_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000020); - onChanged(); - } else { - shapeBuilder_.clear(); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public Builder removeShape(int index) { - if (shapeBuilder_ == null) { - ensureShapeIsMutable(); - shape_.remove(index); - onChanged(); - } else { - shapeBuilder_.remove(index); - } - return this; - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProto.Builder getShapeBuilder( - int index) { - return getShapeFieldBuilder().getBuilder(index); - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProtoOrBuilder getShapeOrBuilder( - int index) { - if (shapeBuilder_ == null) { - return shape_.get(index); } else { - return shapeBuilder_.getMessageOrBuilder(index); - } - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public java.util.List - getShapeOrBuilderList() { - if (shapeBuilder_ != null) { - return shapeBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(shape_); - } - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProto.Builder addShapeBuilder() { - return getShapeFieldBuilder().addBuilder( - org.tensorflow.framework.TensorShapeProto.getDefaultInstance()); - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProto.Builder addShapeBuilder( - int index) { - return getShapeFieldBuilder().addBuilder( - index, org.tensorflow.framework.TensorShapeProto.getDefaultInstance()); - } - /** - * 
-       * "list(shape)"
-       *
- * - * repeated .tensorflow.TensorShapeProto shape = 7; - */ - public java.util.List - getShapeBuilderList() { - return getShapeFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.TensorShapeProto, org.tensorflow.framework.TensorShapeProto.Builder, org.tensorflow.framework.TensorShapeProtoOrBuilder> - getShapeFieldBuilder() { - if (shapeBuilder_ == null) { - shapeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.TensorShapeProto, org.tensorflow.framework.TensorShapeProto.Builder, org.tensorflow.framework.TensorShapeProtoOrBuilder>( - shape_, - ((bitField0_ & 0x00000020) == 0x00000020), - getParentForChildren(), - isClean()); - shape_ = null; - } - return shapeBuilder_; - } - - private java.util.List tensor_ = - java.util.Collections.emptyList(); - private void ensureTensorIsMutable() { - if (!((bitField0_ & 0x00000040) == 0x00000040)) { - tensor_ = new java.util.ArrayList(tensor_); - bitField0_ |= 0x00000040; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.TensorProto, org.tensorflow.framework.TensorProto.Builder, org.tensorflow.framework.TensorProtoOrBuilder> tensorBuilder_; - - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public java.util.List getTensorList() { - if (tensorBuilder_ == null) { - return java.util.Collections.unmodifiableList(tensor_); - } else { - return tensorBuilder_.getMessageList(); - } - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public int getTensorCount() { - if (tensorBuilder_ == null) { - return tensor_.size(); - } else { - return tensorBuilder_.getCount(); - } - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProto getTensor(int index) { - if (tensorBuilder_ == null) { - return tensor_.get(index); - } else { - return tensorBuilder_.getMessage(index); - } - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder setTensor( - int index, org.tensorflow.framework.TensorProto value) { - if (tensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureTensorIsMutable(); - tensor_.set(index, value); - onChanged(); - } else { - tensorBuilder_.setMessage(index, value); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder setTensor( - int index, org.tensorflow.framework.TensorProto.Builder builderForValue) { - if (tensorBuilder_ == null) { - ensureTensorIsMutable(); - tensor_.set(index, builderForValue.build()); - onChanged(); - } else { - tensorBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder addTensor(org.tensorflow.framework.TensorProto value) { - if (tensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureTensorIsMutable(); - tensor_.add(value); - onChanged(); - } else { - tensorBuilder_.addMessage(value); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder addTensor( - int index, org.tensorflow.framework.TensorProto value) { - if (tensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureTensorIsMutable(); - tensor_.add(index, value); - onChanged(); - } else { - tensorBuilder_.addMessage(index, value); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder addTensor( - org.tensorflow.framework.TensorProto.Builder builderForValue) { - if (tensorBuilder_ == null) { - ensureTensorIsMutable(); - tensor_.add(builderForValue.build()); - onChanged(); - } else { - tensorBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder addTensor( - int index, org.tensorflow.framework.TensorProto.Builder builderForValue) { - if (tensorBuilder_ == null) { - ensureTensorIsMutable(); - tensor_.add(index, builderForValue.build()); - onChanged(); - } else { - tensorBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder addAllTensor( - java.lang.Iterable values) { - if (tensorBuilder_ == null) { - ensureTensorIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, tensor_); - onChanged(); - } else { - tensorBuilder_.addAllMessages(values); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder clearTensor() { - if (tensorBuilder_ == null) { - tensor_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000040); - onChanged(); - } else { - tensorBuilder_.clear(); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public Builder removeTensor(int index) { - if (tensorBuilder_ == null) { - ensureTensorIsMutable(); - tensor_.remove(index); - onChanged(); - } else { - tensorBuilder_.remove(index); - } - return this; - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProto.Builder getTensorBuilder( - int index) { - return getTensorFieldBuilder().getBuilder(index); - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProtoOrBuilder getTensorOrBuilder( - int index) { - if (tensorBuilder_ == null) { - return tensor_.get(index); } else { - return tensorBuilder_.getMessageOrBuilder(index); - } - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public java.util.List - getTensorOrBuilderList() { - if (tensorBuilder_ != null) { - return tensorBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(tensor_); - } - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProto.Builder addTensorBuilder() { - return getTensorFieldBuilder().addBuilder( - org.tensorflow.framework.TensorProto.getDefaultInstance()); - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProto.Builder addTensorBuilder( - int index) { - return getTensorFieldBuilder().addBuilder( - index, org.tensorflow.framework.TensorProto.getDefaultInstance()); - } - /** - * 
-       * "list(tensor)"
-       *
- * - * repeated .tensorflow.TensorProto tensor = 8; - */ - public java.util.List - getTensorBuilderList() { - return getTensorFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.TensorProto, org.tensorflow.framework.TensorProto.Builder, org.tensorflow.framework.TensorProtoOrBuilder> - getTensorFieldBuilder() { - if (tensorBuilder_ == null) { - tensorBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.TensorProto, org.tensorflow.framework.TensorProto.Builder, org.tensorflow.framework.TensorProtoOrBuilder>( - tensor_, - ((bitField0_ & 0x00000040) == 0x00000040), - getParentForChildren(), - isClean()); - tensor_ = null; - } - return tensorBuilder_; - } - - private java.util.List func_ = - java.util.Collections.emptyList(); - private void ensureFuncIsMutable() { - if (!((bitField0_ & 0x00000080) == 0x00000080)) { - func_ = new java.util.ArrayList(func_); - bitField0_ |= 0x00000080; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NameAttrList, org.tensorflow.framework.NameAttrList.Builder, org.tensorflow.framework.NameAttrListOrBuilder> funcBuilder_; - - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public java.util.List getFuncList() { - if (funcBuilder_ == null) { - return java.util.Collections.unmodifiableList(func_); - } else { - return funcBuilder_.getMessageList(); - } - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public int getFuncCount() { - if (funcBuilder_ == null) { - return func_.size(); - } else { - return funcBuilder_.getCount(); - } - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public org.tensorflow.framework.NameAttrList getFunc(int index) { - if (funcBuilder_ == null) { - return func_.get(index); - } else { - return funcBuilder_.getMessage(index); - } - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder setFunc( - int index, org.tensorflow.framework.NameAttrList value) { - if (funcBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFuncIsMutable(); - func_.set(index, value); - onChanged(); - } else { - funcBuilder_.setMessage(index, value); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder setFunc( - int index, org.tensorflow.framework.NameAttrList.Builder builderForValue) { - if (funcBuilder_ == null) { - ensureFuncIsMutable(); - func_.set(index, builderForValue.build()); - onChanged(); - } else { - funcBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder addFunc(org.tensorflow.framework.NameAttrList value) { - if (funcBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFuncIsMutable(); - func_.add(value); - onChanged(); - } else { - funcBuilder_.addMessage(value); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder addFunc( - int index, org.tensorflow.framework.NameAttrList value) { - if (funcBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFuncIsMutable(); - func_.add(index, value); - onChanged(); - } else { - funcBuilder_.addMessage(index, value); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder addFunc( - org.tensorflow.framework.NameAttrList.Builder builderForValue) { - if (funcBuilder_ == null) { - ensureFuncIsMutable(); - func_.add(builderForValue.build()); - onChanged(); - } else { - funcBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder addFunc( - int index, org.tensorflow.framework.NameAttrList.Builder builderForValue) { - if (funcBuilder_ == null) { - ensureFuncIsMutable(); - func_.add(index, builderForValue.build()); - onChanged(); - } else { - funcBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder addAllFunc( - java.lang.Iterable values) { - if (funcBuilder_ == null) { - ensureFuncIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, func_); - onChanged(); - } else { - funcBuilder_.addAllMessages(values); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder clearFunc() { - if (funcBuilder_ == null) { - func_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000080); - onChanged(); - } else { - funcBuilder_.clear(); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public Builder removeFunc(int index) { - if (funcBuilder_ == null) { - ensureFuncIsMutable(); - func_.remove(index); - onChanged(); - } else { - funcBuilder_.remove(index); - } - return this; - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public org.tensorflow.framework.NameAttrList.Builder getFuncBuilder( - int index) { - return getFuncFieldBuilder().getBuilder(index); - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public org.tensorflow.framework.NameAttrListOrBuilder getFuncOrBuilder( - int index) { - if (funcBuilder_ == null) { - return func_.get(index); } else { - return funcBuilder_.getMessageOrBuilder(index); - } - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public java.util.List - getFuncOrBuilderList() { - if (funcBuilder_ != null) { - return funcBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(func_); - } - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public org.tensorflow.framework.NameAttrList.Builder addFuncBuilder() { - return getFuncFieldBuilder().addBuilder( - org.tensorflow.framework.NameAttrList.getDefaultInstance()); - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public org.tensorflow.framework.NameAttrList.Builder addFuncBuilder( - int index) { - return getFuncFieldBuilder().addBuilder( - index, org.tensorflow.framework.NameAttrList.getDefaultInstance()); - } - /** - * 
-       * "list(attr)"
-       *
- * - * repeated .tensorflow.NameAttrList func = 9; - */ - public java.util.List - getFuncBuilderList() { - return getFuncFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NameAttrList, org.tensorflow.framework.NameAttrList.Builder, org.tensorflow.framework.NameAttrListOrBuilder> - getFuncFieldBuilder() { - if (funcBuilder_ == null) { - funcBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NameAttrList, org.tensorflow.framework.NameAttrList.Builder, org.tensorflow.framework.NameAttrListOrBuilder>( - func_, - ((bitField0_ & 0x00000080) == 0x00000080), - getParentForChildren(), - isClean()); - func_ = null; - } - return funcBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.AttrValue.ListValue) - } - - // @@protoc_insertion_point(class_scope:tensorflow.AttrValue.ListValue) - private static final org.tensorflow.framework.AttrValue.ListValue DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.AttrValue.ListValue(); - } - - public static org.tensorflow.framework.AttrValue.ListValue getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public ListValue parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new ListValue(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.AttrValue.ListValue getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - - } - - private int valueCase_ = 0; - private java.lang.Object value_; - public enum ValueCase - implements com.google.protobuf.Internal.EnumLite { - S(2), - I(3), - F(4), - B(5), - TYPE(6), - SHAPE(7), - TENSOR(8), - LIST(1), - FUNC(10), - PLACEHOLDER(9), - VALUE_NOT_SET(0); - private final int value; - private ValueCase(int value) { - this.value = value; - } - /** - * @deprecated Use {@link #forNumber(int)} instead. - */ - @java.lang.Deprecated - public static ValueCase valueOf(int value) { - return forNumber(value); - } - - public static ValueCase forNumber(int value) { - switch (value) { - case 2: return S; - case 3: return I; - case 4: return F; - case 5: return B; - case 6: return TYPE; - case 7: return SHAPE; - case 8: return TENSOR; - case 1: return LIST; - case 10: return FUNC; - case 9: return PLACEHOLDER; - case 0: return VALUE_NOT_SET; - default: return null; - } - } - public int getNumber() { - return this.value; - } - }; - - public ValueCase - getValueCase() { - return ValueCase.forNumber( - valueCase_); - } - - public static final int S_FIELD_NUMBER = 2; - /** - * 
-   * "string"
-   *
- * - * optional bytes s = 2; - */ - public com.google.protobuf.ByteString getS() { - if (valueCase_ == 2) { - return (com.google.protobuf.ByteString) value_; - } - return com.google.protobuf.ByteString.EMPTY; - } - - public static final int I_FIELD_NUMBER = 3; - /** - * 
-   * "int"
-   *
- * - * optional int64 i = 3; - */ - public long getI() { - if (valueCase_ == 3) { - return (java.lang.Long) value_; - } - return 0L; - } - - public static final int F_FIELD_NUMBER = 4; - /** - * 
-   * "float"
-   *
- * - * optional float f = 4; - */ - public float getF() { - if (valueCase_ == 4) { - return (java.lang.Float) value_; - } - return 0F; - } - - public static final int B_FIELD_NUMBER = 5; - /** - * 
-   * "bool"
-   *
- * - * optional bool b = 5; - */ - public boolean getB() { - if (valueCase_ == 5) { - return (java.lang.Boolean) value_; - } - return false; - } - - public static final int TYPE_FIELD_NUMBER = 6; - /** - * 
-   * "type"
-   *
- * - * optional .tensorflow.DataType type = 6; - */ - public int getTypeValue() { - if (valueCase_ == 6) { - return (java.lang.Integer) value_; - } - return 0; - } - /** - * 
-   * "type"
-   *
- * - * optional .tensorflow.DataType type = 6; - */ - public org.tensorflow.framework.DataType getType() { - if (valueCase_ == 6) { - org.tensorflow.framework.DataType result = org.tensorflow.framework.DataType.valueOf( - (java.lang.Integer) value_); - return result == null ? org.tensorflow.framework.DataType.UNRECOGNIZED : result; - } - return org.tensorflow.framework.DataType.DT_INVALID; - } - - public static final int SHAPE_FIELD_NUMBER = 7; - /** - * 
-   * "shape"
-   *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProto getShape() { - if (valueCase_ == 7) { - return (org.tensorflow.framework.TensorShapeProto) value_; - } - return org.tensorflow.framework.TensorShapeProto.getDefaultInstance(); - } - /** - * 
-   * "shape"
-   *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProtoOrBuilder getShapeOrBuilder() { - if (valueCase_ == 7) { - return (org.tensorflow.framework.TensorShapeProto) value_; - } - return org.tensorflow.framework.TensorShapeProto.getDefaultInstance(); - } - - public static final int TENSOR_FIELD_NUMBER = 8; - /** - * 
-   * "tensor"
-   *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProto getTensor() { - if (valueCase_ == 8) { - return (org.tensorflow.framework.TensorProto) value_; - } - return org.tensorflow.framework.TensorProto.getDefaultInstance(); - } - /** - * 
-   * "tensor"
-   *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProtoOrBuilder getTensorOrBuilder() { - if (valueCase_ == 8) { - return (org.tensorflow.framework.TensorProto) value_; - } - return org.tensorflow.framework.TensorProto.getDefaultInstance(); - } - - public static final int LIST_FIELD_NUMBER = 1; - /** - * 
-   * any "list(...)"
-   *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public org.tensorflow.framework.AttrValue.ListValue getList() { - if (valueCase_ == 1) { - return (org.tensorflow.framework.AttrValue.ListValue) value_; - } - return org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance(); - } - /** - * 
-   * any "list(...)"
-   *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public org.tensorflow.framework.AttrValue.ListValueOrBuilder getListOrBuilder() { - if (valueCase_ == 1) { - return (org.tensorflow.framework.AttrValue.ListValue) value_; - } - return org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance(); - } - - public static final int FUNC_FIELD_NUMBER = 10; - /** - * 
-   * "func" represents a function. func.name is a function's name or
-   * a primitive op's name. func.attr.first is the name of an attr
-   * defined for that function. func.attr.second is the value for
-   * that attr in the instantiation.
-   *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public org.tensorflow.framework.NameAttrList getFunc() { - if (valueCase_ == 10) { - return (org.tensorflow.framework.NameAttrList) value_; - } - return org.tensorflow.framework.NameAttrList.getDefaultInstance(); - } - /** - * 
-   * "func" represents a function. func.name is a function's name or
-   * a primitive op's name. func.attr.first is the name of an attr
-   * defined for that function. func.attr.second is the value for
-   * that attr in the instantiation.
-   *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public org.tensorflow.framework.NameAttrListOrBuilder getFuncOrBuilder() { - if (valueCase_ == 10) { - return (org.tensorflow.framework.NameAttrList) value_; - } - return org.tensorflow.framework.NameAttrList.getDefaultInstance(); - } - - public static final int PLACEHOLDER_FIELD_NUMBER = 9; - /** - * 
-   * This is a placeholder only used in nodes defined inside a
-   * function.  It indicates the attr value will be supplied when
-   * the function is instantiated.  For example, let us suppose a
-   * node "N" in function "FN". "N" has an attr "A" with value
-   * placeholder = "foo". When FN is instantiated with attr "foo"
-   * set to "bar", the instantiated node N's attr A will have been
-   * given the value "bar".
-   *
- * - * optional string placeholder = 9; - */ - public java.lang.String getPlaceholder() { - java.lang.Object ref = ""; - if (valueCase_ == 9) { - ref = value_; - } - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - if (valueCase_ == 9) { - value_ = s; - } - return s; - } - } - /** - * 
-   * This is a placeholder only used in nodes defined inside a
-   * function.  It indicates the attr value will be supplied when
-   * the function is instantiated.  For example, let us suppose a
-   * node "N" in function "FN". "N" has an attr "A" with value
-   * placeholder = "foo". When FN is instantiated with attr "foo"
-   * set to "bar", the instantiated node N's attr A will have been
-   * given the value "bar".
-   *
- * - * optional string placeholder = 9; - */ - public com.google.protobuf.ByteString - getPlaceholderBytes() { - java.lang.Object ref = ""; - if (valueCase_ == 9) { - ref = value_; - } - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - if (valueCase_ == 9) { - value_ = b; - } - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (valueCase_ == 1) { - output.writeMessage(1, (org.tensorflow.framework.AttrValue.ListValue) value_); - } - if (valueCase_ == 2) { - output.writeBytes( - 2, (com.google.protobuf.ByteString)((com.google.protobuf.ByteString) value_)); - } - if (valueCase_ == 3) { - output.writeInt64( - 3, (long)((java.lang.Long) value_)); - } - if (valueCase_ == 4) { - output.writeFloat( - 4, (float)((java.lang.Float) value_)); - } - if (valueCase_ == 5) { - output.writeBool( - 5, (boolean)((java.lang.Boolean) value_)); - } - if (valueCase_ == 6) { - output.writeEnum(6, ((java.lang.Integer) value_)); - } - if (valueCase_ == 7) { - output.writeMessage(7, (org.tensorflow.framework.TensorShapeProto) value_); - } - if (valueCase_ == 8) { - output.writeMessage(8, (org.tensorflow.framework.TensorProto) value_); - } - if (valueCase_ == 9) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 9, value_); - } - if (valueCase_ == 10) { - output.writeMessage(10, (org.tensorflow.framework.NameAttrList) value_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (valueCase_ == 1) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, (org.tensorflow.framework.AttrValue.ListValue) value_); - } - if (valueCase_ == 2) { - size += com.google.protobuf.CodedOutputStream - .computeBytesSize( - 2, (com.google.protobuf.ByteString)((com.google.protobuf.ByteString) value_)); - } - if (valueCase_ == 3) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size( - 3, (long)((java.lang.Long) value_)); - } - if (valueCase_ == 4) { - size += com.google.protobuf.CodedOutputStream - .computeFloatSize( - 4, (float)((java.lang.Float) value_)); - } - if (valueCase_ == 5) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize( - 5, (boolean)((java.lang.Boolean) value_)); - } - if (valueCase_ == 6) { - size += com.google.protobuf.CodedOutputStream - .computeEnumSize(6, ((java.lang.Integer) value_)); - } - if (valueCase_ == 7) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(7, (org.tensorflow.framework.TensorShapeProto) value_); - } - if (valueCase_ == 8) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(8, (org.tensorflow.framework.TensorProto) value_); - } - if (valueCase_ == 9) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(9, value_); - } - if (valueCase_ == 10) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(10, (org.tensorflow.framework.NameAttrList) value_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.AttrValue)) { - return super.equals(obj); - } - org.tensorflow.framework.AttrValue other = (org.tensorflow.framework.AttrValue) obj; - - boolean result = true; - result = result && getValueCase().equals( - other.getValueCase()); - if (!result) return false; - switch (valueCase_) { - case 2: - result = result && getS() - .equals(other.getS()); - break; - case 3: - result = result && (getI() - == other.getI()); - break; - case 4: - result = result && ( - java.lang.Float.floatToIntBits(getF()) - == java.lang.Float.floatToIntBits( - other.getF())); - break; - case 5: - result = result && (getB() - == other.getB()); - break; - case 6: - result = result && getTypeValue() - == other.getTypeValue(); - break; - case 7: - result = result && getShape() - .equals(other.getShape()); - break; - case 8: - result = result && getTensor() - .equals(other.getTensor()); - break; - case 1: - result = result && getList() - .equals(other.getList()); - break; - case 10: - result = result && getFunc() - .equals(other.getFunc()); - break; - case 9: - result = result && getPlaceholder() - .equals(other.getPlaceholder()); - break; - case 0: - default: - } - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - switch (valueCase_) { - case 2: - hash = (37 * hash) + S_FIELD_NUMBER; - hash = (53 * hash) + getS().hashCode(); - break; - case 3: - hash = (37 * hash) + I_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getI()); - break; - case 4: - hash = (37 * hash) + F_FIELD_NUMBER; - hash = (53 * hash) + java.lang.Float.floatToIntBits( - getF()); - break; - case 5: - hash = (37 * hash) + B_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getB()); - break; - case 6: - hash = (37 * hash) + TYPE_FIELD_NUMBER; - hash = (53 * hash) + getTypeValue(); - break; - case 7: - hash = (37 * hash) + SHAPE_FIELD_NUMBER; - hash = (53 * hash) + getShape().hashCode(); - break; - case 8: - hash = (37 * hash) + TENSOR_FIELD_NUMBER; - hash = (53 * hash) + getTensor().hashCode(); - break; - case 1: - hash = (37 * hash) + LIST_FIELD_NUMBER; - hash = (53 * hash) + getList().hashCode(); - break; - case 10: - hash = (37 * hash) + FUNC_FIELD_NUMBER; - hash = (53 * hash) + getFunc().hashCode(); - break; - case 9: - hash = (37 * hash) + PLACEHOLDER_FIELD_NUMBER; - hash = (53 * hash) + getPlaceholder().hashCode(); - break; - case 0: - default: - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.AttrValue parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.AttrValue parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.AttrValue parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.AttrValue parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.AttrValue parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AttrValue parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.AttrValue parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AttrValue parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.AttrValue parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.AttrValue parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.AttrValue prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Protocol buffer representing the value for an attr used to configure an Op.
-   * Comment indicates the corresponding attr type.  Only the field matching the
-   * attr type may be filled.
-   *
- * - * Protobuf type {@code tensorflow.AttrValue} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.AttrValue) - org.tensorflow.framework.AttrValueOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.AttrValue.class, org.tensorflow.framework.AttrValue.Builder.class); - } - - // Construct using org.tensorflow.framework.AttrValue.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - valueCase_ = 0; - value_ = null; - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_AttrValue_descriptor; - } - - public org.tensorflow.framework.AttrValue getDefaultInstanceForType() { - return org.tensorflow.framework.AttrValue.getDefaultInstance(); - } - - public org.tensorflow.framework.AttrValue build() { - org.tensorflow.framework.AttrValue result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.AttrValue buildPartial() { - org.tensorflow.framework.AttrValue result = new org.tensorflow.framework.AttrValue(this); - if (valueCase_ == 2) { - result.value_ = value_; - } - if (valueCase_ == 3) { - result.value_ = value_; - } - if (valueCase_ == 4) { - result.value_ = value_; - } - if (valueCase_ == 5) { - result.value_ = value_; - } - if (valueCase_ == 6) { - result.value_ = value_; - } - if (valueCase_ == 7) { - if (shapeBuilder_ == null) { - result.value_ = value_; - } else { - result.value_ = shapeBuilder_.build(); - } - } - if (valueCase_ == 8) { - if (tensorBuilder_ == null) { - result.value_ = value_; - } else { - result.value_ = tensorBuilder_.build(); - } - } - if (valueCase_ == 1) { - if (listBuilder_ == null) { - result.value_ = value_; - } else { - result.value_ = listBuilder_.build(); - } - } - if (valueCase_ == 10) { - if (funcBuilder_ == null) { - result.value_ = value_; - } else { - result.value_ = funcBuilder_.build(); - } - } - if (valueCase_ == 9) { - result.value_ = value_; - } - result.valueCase_ = valueCase_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.AttrValue) { - return mergeFrom((org.tensorflow.framework.AttrValue)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.AttrValue other) { - if (other == org.tensorflow.framework.AttrValue.getDefaultInstance()) return this; - switch (other.getValueCase()) { - case S: { - setS(other.getS()); - break; - } - case I: { - setI(other.getI()); - break; - } - case F: { - setF(other.getF()); - break; - } - case B: { - setB(other.getB()); - break; - } - case TYPE: { - setTypeValue(other.getTypeValue()); - break; - } - case SHAPE: { - mergeShape(other.getShape()); - break; - } - case TENSOR: { - mergeTensor(other.getTensor()); - break; - } - case LIST: { - mergeList(other.getList()); - break; - } - case FUNC: { - mergeFunc(other.getFunc()); - break; - } - case PLACEHOLDER: { - valueCase_ = 9; - value_ = other.value_; - onChanged(); - break; - } - case VALUE_NOT_SET: { - break; - } - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.AttrValue parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.AttrValue) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int valueCase_ = 0; - private java.lang.Object value_; - public ValueCase - getValueCase() { - return ValueCase.forNumber( - valueCase_); - } - - public Builder clearValue() { - valueCase_ = 0; - value_ = null; - onChanged(); - return this; - } - - - /** - * 
-     * "string"
-     *
- * - * optional bytes s = 2; - */ - public com.google.protobuf.ByteString getS() { - if (valueCase_ == 2) { - return (com.google.protobuf.ByteString) value_; - } - return com.google.protobuf.ByteString.EMPTY; - } - /** - * 
-     * "string"
-     *
- * - * optional bytes s = 2; - */ - public Builder setS(com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - valueCase_ = 2; - value_ = value; - onChanged(); - return this; - } - /** - * 
-     * "string"
-     *
- * - * optional bytes s = 2; - */ - public Builder clearS() { - if (valueCase_ == 2) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - return this; - } - - /** - * 
-     * "int"
-     *
- * - * optional int64 i = 3; - */ - public long getI() { - if (valueCase_ == 3) { - return (java.lang.Long) value_; - } - return 0L; - } - /** - * 
-     * "int"
-     *
- * - * optional int64 i = 3; - */ - public Builder setI(long value) { - valueCase_ = 3; - value_ = value; - onChanged(); - return this; - } - /** - * 
-     * "int"
-     *
- * - * optional int64 i = 3; - */ - public Builder clearI() { - if (valueCase_ == 3) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - return this; - } - - /** - * 
-     * "float"
-     *
- * - * optional float f = 4; - */ - public float getF() { - if (valueCase_ == 4) { - return (java.lang.Float) value_; - } - return 0F; - } - /** - * 
-     * "float"
-     *
- * - * optional float f = 4; - */ - public Builder setF(float value) { - valueCase_ = 4; - value_ = value; - onChanged(); - return this; - } - /** - * 
-     * "float"
-     *
- * - * optional float f = 4; - */ - public Builder clearF() { - if (valueCase_ == 4) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - return this; - } - - /** - * 
-     * "bool"
-     *
- * - * optional bool b = 5; - */ - public boolean getB() { - if (valueCase_ == 5) { - return (java.lang.Boolean) value_; - } - return false; - } - /** - * 
-     * "bool"
-     *
- * - * optional bool b = 5; - */ - public Builder setB(boolean value) { - valueCase_ = 5; - value_ = value; - onChanged(); - return this; - } - /** - * 
-     * "bool"
-     *
- * - * optional bool b = 5; - */ - public Builder clearB() { - if (valueCase_ == 5) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - return this; - } - - /** - * 
-     * "type"
-     *
- * - * optional .tensorflow.DataType type = 6; - */ - public int getTypeValue() { - if (valueCase_ == 6) { - return ((java.lang.Integer) value_).intValue(); - } - return 0; - } - /** - * 
-     * "type"
-     *
- * - * optional .tensorflow.DataType type = 6; - */ - public Builder setTypeValue(int value) { - valueCase_ = 6; - value_ = value; - onChanged(); - return this; - } - /** - * 
-     * "type"
-     *
- * - * optional .tensorflow.DataType type = 6; - */ - public org.tensorflow.framework.DataType getType() { - if (valueCase_ == 6) { - org.tensorflow.framework.DataType result = org.tensorflow.framework.DataType.valueOf( - (java.lang.Integer) value_); - return result == null ? org.tensorflow.framework.DataType.UNRECOGNIZED : result; - } - return org.tensorflow.framework.DataType.DT_INVALID; - } - /** - * 
-     * "type"
-     *
- * - * optional .tensorflow.DataType type = 6; - */ - public Builder setType(org.tensorflow.framework.DataType value) { - if (value == null) { - throw new NullPointerException(); - } - valueCase_ = 6; - value_ = value.getNumber(); - onChanged(); - return this; - } - /** - * 
-     * "type"
-     *
- * - * optional .tensorflow.DataType type = 6; - */ - public Builder clearType() { - if (valueCase_ == 6) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - return this; - } - - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorShapeProto, org.tensorflow.framework.TensorShapeProto.Builder, org.tensorflow.framework.TensorShapeProtoOrBuilder> shapeBuilder_; - /** - * 
-     * "shape"
-     *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProto getShape() { - if (shapeBuilder_ == null) { - if (valueCase_ == 7) { - return (org.tensorflow.framework.TensorShapeProto) value_; - } - return org.tensorflow.framework.TensorShapeProto.getDefaultInstance(); - } else { - if (valueCase_ == 7) { - return shapeBuilder_.getMessage(); - } - return org.tensorflow.framework.TensorShapeProto.getDefaultInstance(); - } - } - /** - * 
-     * "shape"
-     *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public Builder setShape(org.tensorflow.framework.TensorShapeProto value) { - if (shapeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - value_ = value; - onChanged(); - } else { - shapeBuilder_.setMessage(value); - } - valueCase_ = 7; - return this; - } - /** - * 
-     * "shape"
-     *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public Builder setShape( - org.tensorflow.framework.TensorShapeProto.Builder builderForValue) { - if (shapeBuilder_ == null) { - value_ = builderForValue.build(); - onChanged(); - } else { - shapeBuilder_.setMessage(builderForValue.build()); - } - valueCase_ = 7; - return this; - } - /** - * 
-     * "shape"
-     *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public Builder mergeShape(org.tensorflow.framework.TensorShapeProto value) { - if (shapeBuilder_ == null) { - if (valueCase_ == 7 && - value_ != org.tensorflow.framework.TensorShapeProto.getDefaultInstance()) { - value_ = org.tensorflow.framework.TensorShapeProto.newBuilder((org.tensorflow.framework.TensorShapeProto) value_) - .mergeFrom(value).buildPartial(); - } else { - value_ = value; - } - onChanged(); - } else { - if (valueCase_ == 7) { - shapeBuilder_.mergeFrom(value); - } - shapeBuilder_.setMessage(value); - } - valueCase_ = 7; - return this; - } - /** - * 
-     * "shape"
-     *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public Builder clearShape() { - if (shapeBuilder_ == null) { - if (valueCase_ == 7) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - } else { - if (valueCase_ == 7) { - valueCase_ = 0; - value_ = null; - } - shapeBuilder_.clear(); - } - return this; - } - /** - * 
-     * "shape"
-     *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProto.Builder getShapeBuilder() { - return getShapeFieldBuilder().getBuilder(); - } - /** - * 
-     * "shape"
-     *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - public org.tensorflow.framework.TensorShapeProtoOrBuilder getShapeOrBuilder() { - if ((valueCase_ == 7) && (shapeBuilder_ != null)) { - return shapeBuilder_.getMessageOrBuilder(); - } else { - if (valueCase_ == 7) { - return (org.tensorflow.framework.TensorShapeProto) value_; - } - return org.tensorflow.framework.TensorShapeProto.getDefaultInstance(); - } - } - /** - * 
-     * "shape"
-     *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorShapeProto, org.tensorflow.framework.TensorShapeProto.Builder, org.tensorflow.framework.TensorShapeProtoOrBuilder> - getShapeFieldBuilder() { - if (shapeBuilder_ == null) { - if (!(valueCase_ == 7)) { - value_ = org.tensorflow.framework.TensorShapeProto.getDefaultInstance(); - } - shapeBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorShapeProto, org.tensorflow.framework.TensorShapeProto.Builder, org.tensorflow.framework.TensorShapeProtoOrBuilder>( - (org.tensorflow.framework.TensorShapeProto) value_, - getParentForChildren(), - isClean()); - value_ = null; - } - valueCase_ = 7; - onChanged();; - return shapeBuilder_; - } - - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorProto, org.tensorflow.framework.TensorProto.Builder, org.tensorflow.framework.TensorProtoOrBuilder> tensorBuilder_; - /** - * 
-     * "tensor"
-     *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProto getTensor() { - if (tensorBuilder_ == null) { - if (valueCase_ == 8) { - return (org.tensorflow.framework.TensorProto) value_; - } - return org.tensorflow.framework.TensorProto.getDefaultInstance(); - } else { - if (valueCase_ == 8) { - return tensorBuilder_.getMessage(); - } - return org.tensorflow.framework.TensorProto.getDefaultInstance(); - } - } - /** - * 
-     * "tensor"
-     *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public Builder setTensor(org.tensorflow.framework.TensorProto value) { - if (tensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - value_ = value; - onChanged(); - } else { - tensorBuilder_.setMessage(value); - } - valueCase_ = 8; - return this; - } - /** - * 
-     * "tensor"
-     *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public Builder setTensor( - org.tensorflow.framework.TensorProto.Builder builderForValue) { - if (tensorBuilder_ == null) { - value_ = builderForValue.build(); - onChanged(); - } else { - tensorBuilder_.setMessage(builderForValue.build()); - } - valueCase_ = 8; - return this; - } - /** - * 
-     * "tensor"
-     *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public Builder mergeTensor(org.tensorflow.framework.TensorProto value) { - if (tensorBuilder_ == null) { - if (valueCase_ == 8 && - value_ != org.tensorflow.framework.TensorProto.getDefaultInstance()) { - value_ = org.tensorflow.framework.TensorProto.newBuilder((org.tensorflow.framework.TensorProto) value_) - .mergeFrom(value).buildPartial(); - } else { - value_ = value; - } - onChanged(); - } else { - if (valueCase_ == 8) { - tensorBuilder_.mergeFrom(value); - } - tensorBuilder_.setMessage(value); - } - valueCase_ = 8; - return this; - } - /** - * 
-     * "tensor"
-     *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public Builder clearTensor() { - if (tensorBuilder_ == null) { - if (valueCase_ == 8) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - } else { - if (valueCase_ == 8) { - valueCase_ = 0; - value_ = null; - } - tensorBuilder_.clear(); - } - return this; - } - /** - * 
-     * "tensor"
-     *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProto.Builder getTensorBuilder() { - return getTensorFieldBuilder().getBuilder(); - } - /** - * 
-     * "tensor"
-     *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - public org.tensorflow.framework.TensorProtoOrBuilder getTensorOrBuilder() { - if ((valueCase_ == 8) && (tensorBuilder_ != null)) { - return tensorBuilder_.getMessageOrBuilder(); - } else { - if (valueCase_ == 8) { - return (org.tensorflow.framework.TensorProto) value_; - } - return org.tensorflow.framework.TensorProto.getDefaultInstance(); - } - } - /** - * 
-     * "tensor"
-     *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorProto, org.tensorflow.framework.TensorProto.Builder, org.tensorflow.framework.TensorProtoOrBuilder> - getTensorFieldBuilder() { - if (tensorBuilder_ == null) { - if (!(valueCase_ == 8)) { - value_ = org.tensorflow.framework.TensorProto.getDefaultInstance(); - } - tensorBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorProto, org.tensorflow.framework.TensorProto.Builder, org.tensorflow.framework.TensorProtoOrBuilder>( - (org.tensorflow.framework.TensorProto) value_, - getParentForChildren(), - isClean()); - value_ = null; - } - valueCase_ = 8; - onChanged();; - return tensorBuilder_; - } - - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue.ListValue, org.tensorflow.framework.AttrValue.ListValue.Builder, org.tensorflow.framework.AttrValue.ListValueOrBuilder> listBuilder_; - /** - * 
-     * any "list(...)"
-     *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public org.tensorflow.framework.AttrValue.ListValue getList() { - if (listBuilder_ == null) { - if (valueCase_ == 1) { - return (org.tensorflow.framework.AttrValue.ListValue) value_; - } - return org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance(); - } else { - if (valueCase_ == 1) { - return listBuilder_.getMessage(); - } - return org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance(); - } - } - /** - * 
-     * any "list(...)"
-     *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public Builder setList(org.tensorflow.framework.AttrValue.ListValue value) { - if (listBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - value_ = value; - onChanged(); - } else { - listBuilder_.setMessage(value); - } - valueCase_ = 1; - return this; - } - /** - * 
-     * any "list(...)"
-     *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public Builder setList( - org.tensorflow.framework.AttrValue.ListValue.Builder builderForValue) { - if (listBuilder_ == null) { - value_ = builderForValue.build(); - onChanged(); - } else { - listBuilder_.setMessage(builderForValue.build()); - } - valueCase_ = 1; - return this; - } - /** - * 
-     * any "list(...)"
-     *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public Builder mergeList(org.tensorflow.framework.AttrValue.ListValue value) { - if (listBuilder_ == null) { - if (valueCase_ == 1 && - value_ != org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance()) { - value_ = org.tensorflow.framework.AttrValue.ListValue.newBuilder((org.tensorflow.framework.AttrValue.ListValue) value_) - .mergeFrom(value).buildPartial(); - } else { - value_ = value; - } - onChanged(); - } else { - if (valueCase_ == 1) { - listBuilder_.mergeFrom(value); - } - listBuilder_.setMessage(value); - } - valueCase_ = 1; - return this; - } - /** - * 
-     * any "list(...)"
-     *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public Builder clearList() { - if (listBuilder_ == null) { - if (valueCase_ == 1) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - } else { - if (valueCase_ == 1) { - valueCase_ = 0; - value_ = null; - } - listBuilder_.clear(); - } - return this; - } - /** - * 
-     * any "list(...)"
-     *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public org.tensorflow.framework.AttrValue.ListValue.Builder getListBuilder() { - return getListFieldBuilder().getBuilder(); - } - /** - * 
-     * any "list(...)"
-     *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - public org.tensorflow.framework.AttrValue.ListValueOrBuilder getListOrBuilder() { - if ((valueCase_ == 1) && (listBuilder_ != null)) { - return listBuilder_.getMessageOrBuilder(); - } else { - if (valueCase_ == 1) { - return (org.tensorflow.framework.AttrValue.ListValue) value_; - } - return org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance(); - } - } - /** - * 
-     * any "list(...)"
-     *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue.ListValue, org.tensorflow.framework.AttrValue.ListValue.Builder, org.tensorflow.framework.AttrValue.ListValueOrBuilder> - getListFieldBuilder() { - if (listBuilder_ == null) { - if (!(valueCase_ == 1)) { - value_ = org.tensorflow.framework.AttrValue.ListValue.getDefaultInstance(); - } - listBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue.ListValue, org.tensorflow.framework.AttrValue.ListValue.Builder, org.tensorflow.framework.AttrValue.ListValueOrBuilder>( - (org.tensorflow.framework.AttrValue.ListValue) value_, - getParentForChildren(), - isClean()); - value_ = null; - } - valueCase_ = 1; - onChanged();; - return listBuilder_; - } - - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.NameAttrList, org.tensorflow.framework.NameAttrList.Builder, org.tensorflow.framework.NameAttrListOrBuilder> funcBuilder_; - /** - * 
-     * "func" represents a function. func.name is a function's name or
-     * a primitive op's name. func.attr.first is the name of an attr
-     * defined for that function. func.attr.second is the value for
-     * that attr in the instantiation.
-     *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public org.tensorflow.framework.NameAttrList getFunc() { - if (funcBuilder_ == null) { - if (valueCase_ == 10) { - return (org.tensorflow.framework.NameAttrList) value_; - } - return org.tensorflow.framework.NameAttrList.getDefaultInstance(); - } else { - if (valueCase_ == 10) { - return funcBuilder_.getMessage(); - } - return org.tensorflow.framework.NameAttrList.getDefaultInstance(); - } - } - /** - * 
-     * "func" represents a function. func.name is a function's name or
-     * a primitive op's name. func.attr.first is the name of an attr
-     * defined for that function. func.attr.second is the value for
-     * that attr in the instantiation.
-     *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public Builder setFunc(org.tensorflow.framework.NameAttrList value) { - if (funcBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - value_ = value; - onChanged(); - } else { - funcBuilder_.setMessage(value); - } - valueCase_ = 10; - return this; - } - /** - * 
-     * "func" represents a function. func.name is a function's name or
-     * a primitive op's name. func.attr.first is the name of an attr
-     * defined for that function. func.attr.second is the value for
-     * that attr in the instantiation.
-     *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public Builder setFunc( - org.tensorflow.framework.NameAttrList.Builder builderForValue) { - if (funcBuilder_ == null) { - value_ = builderForValue.build(); - onChanged(); - } else { - funcBuilder_.setMessage(builderForValue.build()); - } - valueCase_ = 10; - return this; - } - /** - * 
-     * "func" represents a function. func.name is a function's name or
-     * a primitive op's name. func.attr.first is the name of an attr
-     * defined for that function. func.attr.second is the value for
-     * that attr in the instantiation.
-     *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public Builder mergeFunc(org.tensorflow.framework.NameAttrList value) { - if (funcBuilder_ == null) { - if (valueCase_ == 10 && - value_ != org.tensorflow.framework.NameAttrList.getDefaultInstance()) { - value_ = org.tensorflow.framework.NameAttrList.newBuilder((org.tensorflow.framework.NameAttrList) value_) - .mergeFrom(value).buildPartial(); - } else { - value_ = value; - } - onChanged(); - } else { - if (valueCase_ == 10) { - funcBuilder_.mergeFrom(value); - } - funcBuilder_.setMessage(value); - } - valueCase_ = 10; - return this; - } - /** - * 
-     * "func" represents a function. func.name is a function's name or
-     * a primitive op's name. func.attr.first is the name of an attr
-     * defined for that function. func.attr.second is the value for
-     * that attr in the instantiation.
-     *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public Builder clearFunc() { - if (funcBuilder_ == null) { - if (valueCase_ == 10) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - } else { - if (valueCase_ == 10) { - valueCase_ = 0; - value_ = null; - } - funcBuilder_.clear(); - } - return this; - } - /** - * 
-     * "func" represents a function. func.name is a function's name or
-     * a primitive op's name. func.attr.first is the name of an attr
-     * defined for that function. func.attr.second is the value for
-     * that attr in the instantiation.
-     *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public org.tensorflow.framework.NameAttrList.Builder getFuncBuilder() { - return getFuncFieldBuilder().getBuilder(); - } - /** - * 
-     * "func" represents a function. func.name is a function's name or
-     * a primitive op's name. func.attr.first is the name of an attr
-     * defined for that function. func.attr.second is the value for
-     * that attr in the instantiation.
-     *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - public org.tensorflow.framework.NameAttrListOrBuilder getFuncOrBuilder() { - if ((valueCase_ == 10) && (funcBuilder_ != null)) { - return funcBuilder_.getMessageOrBuilder(); - } else { - if (valueCase_ == 10) { - return (org.tensorflow.framework.NameAttrList) value_; - } - return org.tensorflow.framework.NameAttrList.getDefaultInstance(); - } - } - /** - * 
-     * "func" represents a function. func.name is a function's name or
-     * a primitive op's name. func.attr.first is the name of an attr
-     * defined for that function. func.attr.second is the value for
-     * that attr in the instantiation.
-     *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.NameAttrList, org.tensorflow.framework.NameAttrList.Builder, org.tensorflow.framework.NameAttrListOrBuilder> - getFuncFieldBuilder() { - if (funcBuilder_ == null) { - if (!(valueCase_ == 10)) { - value_ = org.tensorflow.framework.NameAttrList.getDefaultInstance(); - } - funcBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.NameAttrList, org.tensorflow.framework.NameAttrList.Builder, org.tensorflow.framework.NameAttrListOrBuilder>( - (org.tensorflow.framework.NameAttrList) value_, - getParentForChildren(), - isClean()); - value_ = null; - } - valueCase_ = 10; - onChanged();; - return funcBuilder_; - } - - /** - * 
-     * This is a placeholder only used in nodes defined inside a
-     * function.  It indicates the attr value will be supplied when
-     * the function is instantiated.  For example, let us suppose a
-     * node "N" in function "FN". "N" has an attr "A" with value
-     * placeholder = "foo". When FN is instantiated with attr "foo"
-     * set to "bar", the instantiated node N's attr A will have been
-     * given the value "bar".
-     *
- * - * optional string placeholder = 9; - */ - public java.lang.String getPlaceholder() { - java.lang.Object ref = ""; - if (valueCase_ == 9) { - ref = value_; - } - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - if (valueCase_ == 9) { - value_ = s; - } - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * This is a placeholder only used in nodes defined inside a
-     * function.  It indicates the attr value will be supplied when
-     * the function is instantiated.  For example, let us suppose a
-     * node "N" in function "FN". "N" has an attr "A" with value
-     * placeholder = "foo". When FN is instantiated with attr "foo"
-     * set to "bar", the instantiated node N's attr A will have been
-     * given the value "bar".
-     *
- * - * optional string placeholder = 9; - */ - public com.google.protobuf.ByteString - getPlaceholderBytes() { - java.lang.Object ref = ""; - if (valueCase_ == 9) { - ref = value_; - } - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - if (valueCase_ == 9) { - value_ = b; - } - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * This is a placeholder only used in nodes defined inside a
-     * function.  It indicates the attr value will be supplied when
-     * the function is instantiated.  For example, let us suppose a
-     * node "N" in function "FN". "N" has an attr "A" with value
-     * placeholder = "foo". When FN is instantiated with attr "foo"
-     * set to "bar", the instantiated node N's attr A will have been
-     * given the value "bar".
-     *
- * - * optional string placeholder = 9; - */ - public Builder setPlaceholder( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - valueCase_ = 9; - value_ = value; - onChanged(); - return this; - } - /** - * 
-     * This is a placeholder only used in nodes defined inside a
-     * function.  It indicates the attr value will be supplied when
-     * the function is instantiated.  For example, let us suppose a
-     * node "N" in function "FN". "N" has an attr "A" with value
-     * placeholder = "foo". When FN is instantiated with attr "foo"
-     * set to "bar", the instantiated node N's attr A will have been
-     * given the value "bar".
-     *
- * - * optional string placeholder = 9; - */ - public Builder clearPlaceholder() { - if (valueCase_ == 9) { - valueCase_ = 0; - value_ = null; - onChanged(); - } - return this; - } - /** - * 
-     * This is a placeholder only used in nodes defined inside a
-     * function.  It indicates the attr value will be supplied when
-     * the function is instantiated.  For example, let us suppose a
-     * node "N" in function "FN". "N" has an attr "A" with value
-     * placeholder = "foo". When FN is instantiated with attr "foo"
-     * set to "bar", the instantiated node N's attr A will have been
-     * given the value "bar".
-     *
- * - * optional string placeholder = 9; - */ - public Builder setPlaceholderBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - valueCase_ = 9; - value_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.AttrValue) - } - - // @@protoc_insertion_point(class_scope:tensorflow.AttrValue) - private static final org.tensorflow.framework.AttrValue DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.AttrValue(); - } - - public static org.tensorflow.framework.AttrValue getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public AttrValue parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new AttrValue(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.AttrValue getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/AttrValueOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/AttrValueOrBuilder.java deleted file mode 100644 index b3395e00195..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/AttrValueOrBuilder.java +++ /dev/null @@ -1,168 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: attr_value.proto - -package org.tensorflow.framework; - -public interface AttrValueOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.AttrValue) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * "string"
-   *
- * - * optional bytes s = 2; - */ - com.google.protobuf.ByteString getS(); - - /** - * 
-   * "int"
-   *
- * - * optional int64 i = 3; - */ - long getI(); - - /** - * 
-   * "float"
-   *
- * - * optional float f = 4; - */ - float getF(); - - /** - * 
-   * "bool"
-   *
- * - * optional bool b = 5; - */ - boolean getB(); - - /** - * 
-   * "type"
-   *
- * - * optional .tensorflow.DataType type = 6; - */ - int getTypeValue(); - /** - * 
-   * "type"
-   *
- * - * optional .tensorflow.DataType type = 6; - */ - org.tensorflow.framework.DataType getType(); - - /** - * 
-   * "shape"
-   *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - org.tensorflow.framework.TensorShapeProto getShape(); - /** - * 
-   * "shape"
-   *
- * - * optional .tensorflow.TensorShapeProto shape = 7; - */ - org.tensorflow.framework.TensorShapeProtoOrBuilder getShapeOrBuilder(); - - /** - * 
-   * "tensor"
-   *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - org.tensorflow.framework.TensorProto getTensor(); - /** - * 
-   * "tensor"
-   *
- * - * optional .tensorflow.TensorProto tensor = 8; - */ - org.tensorflow.framework.TensorProtoOrBuilder getTensorOrBuilder(); - - /** - * 
-   * any "list(...)"
-   *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - org.tensorflow.framework.AttrValue.ListValue getList(); - /** - * 
-   * any "list(...)"
-   *
- * - * optional .tensorflow.AttrValue.ListValue list = 1; - */ - org.tensorflow.framework.AttrValue.ListValueOrBuilder getListOrBuilder(); - - /** - * 
-   * "func" represents a function. func.name is a function's name or
-   * a primitive op's name. func.attr.first is the name of an attr
-   * defined for that function. func.attr.second is the value for
-   * that attr in the instantiation.
-   *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - org.tensorflow.framework.NameAttrList getFunc(); - /** - * 
-   * "func" represents a function. func.name is a function's name or
-   * a primitive op's name. func.attr.first is the name of an attr
-   * defined for that function. func.attr.second is the value for
-   * that attr in the instantiation.
-   *
- * - * optional .tensorflow.NameAttrList func = 10; - */ - org.tensorflow.framework.NameAttrListOrBuilder getFuncOrBuilder(); - - /** - * 
-   * This is a placeholder only used in nodes defined inside a
-   * function.  It indicates the attr value will be supplied when
-   * the function is instantiated.  For example, let us suppose a
-   * node "N" in function "FN". "N" has an attr "A" with value
-   * placeholder = "foo". When FN is instantiated with attr "foo"
-   * set to "bar", the instantiated node N's attr A will have been
-   * given the value "bar".
-   *
- * - * optional string placeholder = 9; - */ - java.lang.String getPlaceholder(); - /** - * 
-   * This is a placeholder only used in nodes defined inside a
-   * function.  It indicates the attr value will be supplied when
-   * the function is instantiated.  For example, let us suppose a
-   * node "N" in function "FN". "N" has an attr "A" with value
-   * placeholder = "foo". When FN is instantiated with attr "foo"
-   * set to "bar", the instantiated node N's attr A will have been
-   * given the value "bar".
-   *
- * - * optional string placeholder = 9; - */ - com.google.protobuf.ByteString - getPlaceholderBytes(); - - public org.tensorflow.framework.AttrValue.ValueCase getValueCase(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/AttrValueProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/AttrValueProtos.java deleted file mode 100644 index c9b86e12382..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/AttrValueProtos.java +++ /dev/null @@ -1,115 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: attr_value.proto - -package org.tensorflow.framework; - -public final class AttrValueProtos { - private AttrValueProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_AttrValue_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_AttrValue_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_AttrValue_ListValue_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_AttrValue_ListValue_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_NameAttrList_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_NameAttrList_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_NameAttrList_AttrEntry_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_NameAttrList_AttrEntry_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\020attr_value.proto\022\ntensorflow\032&tensorfl" + - "ow/core/framework/tensor.proto\032,tensorfl" + - "ow/core/framework/tensor_shape.proto\032%te" + - "nsorflow/core/framework/types.proto\"\246\004\n\t" + - "AttrValue\022\013\n\001s\030\002 \001(\014H\000\022\013\n\001i\030\003 \001(\003H\000\022\013\n\001f" + - "\030\004 \001(\002H\000\022\013\n\001b\030\005 \001(\010H\000\022$\n\004type\030\006 \001(\0162\024.te" + - "nsorflow.DataTypeH\000\022-\n\005shape\030\007 \001(\0132\034.ten" + - "sorflow.TensorShapeProtoH\000\022)\n\006tensor\030\010 \001" + - "(\0132\027.tensorflow.TensorProtoH\000\022/\n\004list\030\001 " + - "\001(\0132\037.tensorflow.AttrValue.ListValueH\000\022(", - "\n\004func\030\n \001(\0132\030.tensorflow.NameAttrListH\000" + - "\022\025\n\013placeholder\030\t \001(\tH\000\032\351\001\n\tListValue\022\t\n" + - "\001s\030\002 \003(\014\022\r\n\001i\030\003 \003(\003B\002\020\001\022\r\n\001f\030\004 \003(\002B\002\020\001\022\r" + - "\n\001b\030\005 \003(\010B\002\020\001\022&\n\004type\030\006 \003(\0162\024.tensorflow" + - ".DataTypeB\002\020\001\022+\n\005shape\030\007 \003(\0132\034.tensorflo" + - "w.TensorShapeProto\022\'\n\006tensor\030\010 \003(\0132\027.ten" + - "sorflow.TensorProto\022&\n\004func\030\t \003(\0132\030.tens" + - "orflow.NameAttrListB\007\n\005value\"\222\001\n\014NameAtt" + - "rList\022\014\n\004name\030\001 \001(\t\0220\n\004attr\030\002 \003(\0132\".tens" + - "orflow.NameAttrList.AttrEntry\032B\n\tAttrEnt", - "ry\022\013\n\003key\030\001 \001(\t\022$\n\005value\030\002 \001(\0132\025.tensorf" + - "low.AttrValue:\0028\001B0\n\030org.tensorflow.fram" + - "eworkB\017AttrValueProtosP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.framework.TensorProtos.getDescriptor(), - org.tensorflow.framework.TensorShapeProtos.getDescriptor(), - org.tensorflow.framework.TypesProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_AttrValue_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_AttrValue_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_AttrValue_descriptor, - new java.lang.String[] { "S", "I", "F", "B", "Type", "Shape", "Tensor", "List", "Func", "Placeholder", "Value", }); - internal_static_tensorflow_AttrValue_ListValue_descriptor = - internal_static_tensorflow_AttrValue_descriptor.getNestedTypes().get(0); - internal_static_tensorflow_AttrValue_ListValue_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_AttrValue_ListValue_descriptor, - new java.lang.String[] { "S", "I", "F", "B", "Type", "Shape", "Tensor", "Func", }); - internal_static_tensorflow_NameAttrList_descriptor = - getDescriptor().getMessageTypes().get(1); - internal_static_tensorflow_NameAttrList_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_NameAttrList_descriptor, - new java.lang.String[] { "Name", "Attr", }); - internal_static_tensorflow_NameAttrList_AttrEntry_descriptor = - internal_static_tensorflow_NameAttrList_descriptor.getNestedTypes().get(0); - internal_static_tensorflow_NameAttrList_AttrEntry_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_NameAttrList_AttrEntry_descriptor, - new java.lang.String[] { "Key", "Value", }); - org.tensorflow.framework.TensorProtos.getDescriptor(); - org.tensorflow.framework.TensorShapeProtos.getDescriptor(); - org.tensorflow.framework.TypesProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DataType.java b/scala/dllib/src/main/java/org/tensorflow/framework/DataType.java deleted file mode 100644 index a9280ac9111..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DataType.java +++ /dev/null @@ -1,553 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: types.proto - -package org.tensorflow.framework; - -/** - * 
- * LINT.IfChange
- *
- * - * Protobuf enum {@code tensorflow.DataType} - */ -public enum DataType - implements com.google.protobuf.ProtocolMessageEnum { - /** - * 
-   * Not a legal value for DataType.  Used to indicate a DataType field
-   * has not been set.
-   *
- * - * DT_INVALID = 0; - */ - DT_INVALID(0), - /** - * 
-   * Data types that all computation devices are expected to be
-   * capable to support.
-   *
- * - * DT_FLOAT = 1; - */ - DT_FLOAT(1), - /** - * DT_DOUBLE = 2; - */ - DT_DOUBLE(2), - /** - * DT_INT32 = 3; - */ - DT_INT32(3), - /** - * DT_UINT8 = 4; - */ - DT_UINT8(4), - /** - * DT_INT16 = 5; - */ - DT_INT16(5), - /** - * DT_INT8 = 6; - */ - DT_INT8(6), - /** - * DT_STRING = 7; - */ - DT_STRING(7), - /** - * 
-   * Single-precision complex
-   *
- * - * DT_COMPLEX64 = 8; - */ - DT_COMPLEX64(8), - /** - * DT_INT64 = 9; - */ - DT_INT64(9), - /** - * DT_BOOL = 10; - */ - DT_BOOL(10), - /** - * 
-   * Quantized int8
-   *
- * - * DT_QINT8 = 11; - */ - DT_QINT8(11), - /** - * 
-   * Quantized uint8
-   *
- * - * DT_QUINT8 = 12; - */ - DT_QUINT8(12), - /** - * 
-   * Quantized int32
-   *
- * - * DT_QINT32 = 13; - */ - DT_QINT32(13), - /** - * 
-   * Float32 truncated to 16 bits.  Only for cast ops.
-   *
- * - * DT_BFLOAT16 = 14; - */ - DT_BFLOAT16(14), - /** - * 
-   * Quantized int16
-   *
- * - * DT_QINT16 = 15; - */ - DT_QINT16(15), - /** - * 
-   * Quantized uint16
-   *
- * - * DT_QUINT16 = 16; - */ - DT_QUINT16(16), - /** - * DT_UINT16 = 17; - */ - DT_UINT16(17), - /** - * 
-   * Double-precision complex
-   *
- * - * DT_COMPLEX128 = 18; - */ - DT_COMPLEX128(18), - /** - * DT_HALF = 19; - */ - DT_HALF(19), - /** - * DT_RESOURCE = 20; - */ - DT_RESOURCE(20), - /** - * 
-   * Do not use!  These are only for parameters.  Every enum above
-   * should have a corresponding value below (verified by types_test).
-   *
- * - * DT_FLOAT_REF = 101; - */ - DT_FLOAT_REF(101), - /** - * DT_DOUBLE_REF = 102; - */ - DT_DOUBLE_REF(102), - /** - * DT_INT32_REF = 103; - */ - DT_INT32_REF(103), - /** - * DT_UINT8_REF = 104; - */ - DT_UINT8_REF(104), - /** - * DT_INT16_REF = 105; - */ - DT_INT16_REF(105), - /** - * DT_INT8_REF = 106; - */ - DT_INT8_REF(106), - /** - * DT_STRING_REF = 107; - */ - DT_STRING_REF(107), - /** - * DT_COMPLEX64_REF = 108; - */ - DT_COMPLEX64_REF(108), - /** - * DT_INT64_REF = 109; - */ - DT_INT64_REF(109), - /** - * DT_BOOL_REF = 110; - */ - DT_BOOL_REF(110), - /** - * DT_QINT8_REF = 111; - */ - DT_QINT8_REF(111), - /** - * DT_QUINT8_REF = 112; - */ - DT_QUINT8_REF(112), - /** - * DT_QINT32_REF = 113; - */ - DT_QINT32_REF(113), - /** - * DT_BFLOAT16_REF = 114; - */ - DT_BFLOAT16_REF(114), - /** - * DT_QINT16_REF = 115; - */ - DT_QINT16_REF(115), - /** - * DT_QUINT16_REF = 116; - */ - DT_QUINT16_REF(116), - /** - * DT_UINT16_REF = 117; - */ - DT_UINT16_REF(117), - /** - * DT_COMPLEX128_REF = 118; - */ - DT_COMPLEX128_REF(118), - /** - * DT_HALF_REF = 119; - */ - DT_HALF_REF(119), - /** - * DT_RESOURCE_REF = 120; - */ - DT_RESOURCE_REF(120), - UNRECOGNIZED(-1), - ; - - /** - * 
-   * Not a legal value for DataType.  Used to indicate a DataType field
-   * has not been set.
-   *
- * - * DT_INVALID = 0; - */ - public static final int DT_INVALID_VALUE = 0; - /** - * 
-   * Data types that all computation devices are expected to be
-   * capable to support.
-   *
- * - * DT_FLOAT = 1; - */ - public static final int DT_FLOAT_VALUE = 1; - /** - * DT_DOUBLE = 2; - */ - public static final int DT_DOUBLE_VALUE = 2; - /** - * DT_INT32 = 3; - */ - public static final int DT_INT32_VALUE = 3; - /** - * DT_UINT8 = 4; - */ - public static final int DT_UINT8_VALUE = 4; - /** - * DT_INT16 = 5; - */ - public static final int DT_INT16_VALUE = 5; - /** - * DT_INT8 = 6; - */ - public static final int DT_INT8_VALUE = 6; - /** - * DT_STRING = 7; - */ - public static final int DT_STRING_VALUE = 7; - /** - * 
-   * Single-precision complex
-   *
- * - * DT_COMPLEX64 = 8; - */ - public static final int DT_COMPLEX64_VALUE = 8; - /** - * DT_INT64 = 9; - */ - public static final int DT_INT64_VALUE = 9; - /** - * DT_BOOL = 10; - */ - public static final int DT_BOOL_VALUE = 10; - /** - * 
-   * Quantized int8
-   *
- * - * DT_QINT8 = 11; - */ - public static final int DT_QINT8_VALUE = 11; - /** - * 
-   * Quantized uint8
-   *
- * - * DT_QUINT8 = 12; - */ - public static final int DT_QUINT8_VALUE = 12; - /** - * 
-   * Quantized int32
-   *
- * - * DT_QINT32 = 13; - */ - public static final int DT_QINT32_VALUE = 13; - /** - * 
-   * Float32 truncated to 16 bits.  Only for cast ops.
-   *
- * - * DT_BFLOAT16 = 14; - */ - public static final int DT_BFLOAT16_VALUE = 14; - /** - * 
-   * Quantized int16
-   *
- * - * DT_QINT16 = 15; - */ - public static final int DT_QINT16_VALUE = 15; - /** - * 
-   * Quantized uint16
-   *
- * - * DT_QUINT16 = 16; - */ - public static final int DT_QUINT16_VALUE = 16; - /** - * DT_UINT16 = 17; - */ - public static final int DT_UINT16_VALUE = 17; - /** - * 
-   * Double-precision complex
-   *
- * - * DT_COMPLEX128 = 18; - */ - public static final int DT_COMPLEX128_VALUE = 18; - /** - * DT_HALF = 19; - */ - public static final int DT_HALF_VALUE = 19; - /** - * DT_RESOURCE = 20; - */ - public static final int DT_RESOURCE_VALUE = 20; - /** - * 
-   * Do not use!  These are only for parameters.  Every enum above
-   * should have a corresponding value below (verified by types_test).
-   *
- * - * DT_FLOAT_REF = 101; - */ - public static final int DT_FLOAT_REF_VALUE = 101; - /** - * DT_DOUBLE_REF = 102; - */ - public static final int DT_DOUBLE_REF_VALUE = 102; - /** - * DT_INT32_REF = 103; - */ - public static final int DT_INT32_REF_VALUE = 103; - /** - * DT_UINT8_REF = 104; - */ - public static final int DT_UINT8_REF_VALUE = 104; - /** - * DT_INT16_REF = 105; - */ - public static final int DT_INT16_REF_VALUE = 105; - /** - * DT_INT8_REF = 106; - */ - public static final int DT_INT8_REF_VALUE = 106; - /** - * DT_STRING_REF = 107; - */ - public static final int DT_STRING_REF_VALUE = 107; - /** - * DT_COMPLEX64_REF = 108; - */ - public static final int DT_COMPLEX64_REF_VALUE = 108; - /** - * DT_INT64_REF = 109; - */ - public static final int DT_INT64_REF_VALUE = 109; - /** - * DT_BOOL_REF = 110; - */ - public static final int DT_BOOL_REF_VALUE = 110; - /** - * DT_QINT8_REF = 111; - */ - public static final int DT_QINT8_REF_VALUE = 111; - /** - * DT_QUINT8_REF = 112; - */ - public static final int DT_QUINT8_REF_VALUE = 112; - /** - * DT_QINT32_REF = 113; - */ - public static final int DT_QINT32_REF_VALUE = 113; - /** - * DT_BFLOAT16_REF = 114; - */ - public static final int DT_BFLOAT16_REF_VALUE = 114; - /** - * DT_QINT16_REF = 115; - */ - public static final int DT_QINT16_REF_VALUE = 115; - /** - * DT_QUINT16_REF = 116; - */ - public static final int DT_QUINT16_REF_VALUE = 116; - /** - * DT_UINT16_REF = 117; - */ - public static final int DT_UINT16_REF_VALUE = 117; - /** - * DT_COMPLEX128_REF = 118; - */ - public static final int DT_COMPLEX128_REF_VALUE = 118; - /** - * DT_HALF_REF = 119; - */ - public static final int DT_HALF_REF_VALUE = 119; - /** - * DT_RESOURCE_REF = 120; - */ - public static final int DT_RESOURCE_REF_VALUE = 120; - - - public final int getNumber() { - if (this == UNRECOGNIZED) { - throw new java.lang.IllegalArgumentException( - "Can't get the number of an unknown enum value."); - } - return value; - } - - /** - * @deprecated Use {@link #forNumber(int)} instead. - */ - @java.lang.Deprecated - public static DataType valueOf(int value) { - return forNumber(value); - } - - public static DataType forNumber(int value) { - switch (value) { - case 0: return DT_INVALID; - case 1: return DT_FLOAT; - case 2: return DT_DOUBLE; - case 3: return DT_INT32; - case 4: return DT_UINT8; - case 5: return DT_INT16; - case 6: return DT_INT8; - case 7: return DT_STRING; - case 8: return DT_COMPLEX64; - case 9: return DT_INT64; - case 10: return DT_BOOL; - case 11: return DT_QINT8; - case 12: return DT_QUINT8; - case 13: return DT_QINT32; - case 14: return DT_BFLOAT16; - case 15: return DT_QINT16; - case 16: return DT_QUINT16; - case 17: return DT_UINT16; - case 18: return DT_COMPLEX128; - case 19: return DT_HALF; - case 20: return DT_RESOURCE; - case 101: return DT_FLOAT_REF; - case 102: return DT_DOUBLE_REF; - case 103: return DT_INT32_REF; - case 104: return DT_UINT8_REF; - case 105: return DT_INT16_REF; - case 106: return DT_INT8_REF; - case 107: return DT_STRING_REF; - case 108: return DT_COMPLEX64_REF; - case 109: return DT_INT64_REF; - case 110: return DT_BOOL_REF; - case 111: return DT_QINT8_REF; - case 112: return DT_QUINT8_REF; - case 113: return DT_QINT32_REF; - case 114: return DT_BFLOAT16_REF; - case 115: return DT_QINT16_REF; - case 116: return DT_QUINT16_REF; - case 117: return DT_UINT16_REF; - case 118: return DT_COMPLEX128_REF; - case 119: return DT_HALF_REF; - case 120: return DT_RESOURCE_REF; - default: return null; - } - } - - public static com.google.protobuf.Internal.EnumLiteMap - internalGetValueMap() { - return internalValueMap; - } - private static final com.google.protobuf.Internal.EnumLiteMap< - DataType> internalValueMap = - new com.google.protobuf.Internal.EnumLiteMap() { - public DataType findValueByNumber(int number) { - return DataType.forNumber(number); - } - }; - - public final com.google.protobuf.Descriptors.EnumValueDescriptor - getValueDescriptor() { - return getDescriptor().getValues().get(ordinal()); - } - public final com.google.protobuf.Descriptors.EnumDescriptor - getDescriptorForType() { - return getDescriptor(); - } - public static final com.google.protobuf.Descriptors.EnumDescriptor - getDescriptor() { - return org.tensorflow.framework.TypesProtos.getDescriptor() - .getEnumTypes().get(0); - } - - private static final DataType[] VALUES = values(); - - public static DataType valueOf( - com.google.protobuf.Descriptors.EnumValueDescriptor desc) { - if (desc.getType() != getDescriptor()) { - throw new java.lang.IllegalArgumentException( - "EnumValueDescriptor is not for this type."); - } - if (desc.getIndex() == -1) { - return UNRECOGNIZED; - } - return VALUES[desc.getIndex()]; - } - - private final int value; - - private DataType(int value) { - this.value = value; - } - - // @@protoc_insertion_point(enum_scope:tensorflow.DataType) -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributes.java b/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributes.java deleted file mode 100644 index 539a0df2c15..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributes.java +++ /dev/null @@ -1,1227 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: device_attributes.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.DeviceAttributes} - */ -public final class DeviceAttributes extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.DeviceAttributes) - DeviceAttributesOrBuilder { - // Use DeviceAttributes.newBuilder() to construct. - private DeviceAttributes(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private DeviceAttributes() { - name_ = ""; - deviceType_ = ""; - memoryLimit_ = 0L; - incarnation_ = 0L; - physicalDeviceDesc_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private DeviceAttributes( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - name_ = s; - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - deviceType_ = s; - break; - } - case 32: { - - memoryLimit_ = input.readInt64(); - break; - } - case 42: { - org.tensorflow.framework.DeviceLocality.Builder subBuilder = null; - if (locality_ != null) { - subBuilder = locality_.toBuilder(); - } - locality_ = input.readMessage(org.tensorflow.framework.DeviceLocality.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(locality_); - locality_ = subBuilder.buildPartial(); - } - - break; - } - case 49: { - - incarnation_ = input.readFixed64(); - break; - } - case 58: { - java.lang.String s = input.readStringRequireUtf8(); - - physicalDeviceDesc_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceAttributes_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceAttributes_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.DeviceAttributes.class, org.tensorflow.framework.DeviceAttributes.Builder.class); - } - - public static final int NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object name_; - /** - * 
-   * Fully specified name of the device within a cluster.
-   *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } - } - /** - * 
-   * Fully specified name of the device within a cluster.
-   *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int DEVICE_TYPE_FIELD_NUMBER = 2; - private volatile java.lang.Object deviceType_; - /** - * 
-   * String representation of device_type.
-   *
- * - * optional string device_type = 2; - */ - public java.lang.String getDeviceType() { - java.lang.Object ref = deviceType_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - deviceType_ = s; - return s; - } - } - /** - * 
-   * String representation of device_type.
-   *
- * - * optional string device_type = 2; - */ - public com.google.protobuf.ByteString - getDeviceTypeBytes() { - java.lang.Object ref = deviceType_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - deviceType_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int MEMORY_LIMIT_FIELD_NUMBER = 4; - private long memoryLimit_; - /** - * 
-   * Memory capacity of device in bytes.
-   *
- * - * optional int64 memory_limit = 4; - */ - public long getMemoryLimit() { - return memoryLimit_; - } - - public static final int LOCALITY_FIELD_NUMBER = 5; - private org.tensorflow.framework.DeviceLocality locality_; - /** - * 
-   * Platform-specific data about device that may be useful
-   * for supporting efficient data transfers.
-   *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public boolean hasLocality() { - return locality_ != null; - } - /** - * 
-   * Platform-specific data about device that may be useful
-   * for supporting efficient data transfers.
-   *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public org.tensorflow.framework.DeviceLocality getLocality() { - return locality_ == null ? org.tensorflow.framework.DeviceLocality.getDefaultInstance() : locality_; - } - /** - * 
-   * Platform-specific data about device that may be useful
-   * for supporting efficient data transfers.
-   *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public org.tensorflow.framework.DeviceLocalityOrBuilder getLocalityOrBuilder() { - return getLocality(); - } - - public static final int INCARNATION_FIELD_NUMBER = 6; - private long incarnation_; - /** - * 
-   * A device is assigned a global unique number each time it is
-   * initialized. "incarnation" should never be 0.
-   *
- * - * optional fixed64 incarnation = 6; - */ - public long getIncarnation() { - return incarnation_; - } - - public static final int PHYSICAL_DEVICE_DESC_FIELD_NUMBER = 7; - private volatile java.lang.Object physicalDeviceDesc_; - /** - * 
-   * String representation of the physical device that this device maps to.
-   *
- * - * optional string physical_device_desc = 7; - */ - public java.lang.String getPhysicalDeviceDesc() { - java.lang.Object ref = physicalDeviceDesc_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - physicalDeviceDesc_ = s; - return s; - } - } - /** - * 
-   * String representation of the physical device that this device maps to.
-   *
- * - * optional string physical_device_desc = 7; - */ - public com.google.protobuf.ByteString - getPhysicalDeviceDescBytes() { - java.lang.Object ref = physicalDeviceDesc_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - physicalDeviceDesc_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_); - } - if (!getDeviceTypeBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, deviceType_); - } - if (memoryLimit_ != 0L) { - output.writeInt64(4, memoryLimit_); - } - if (locality_ != null) { - output.writeMessage(5, getLocality()); - } - if (incarnation_ != 0L) { - output.writeFixed64(6, incarnation_); - } - if (!getPhysicalDeviceDescBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 7, physicalDeviceDesc_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_); - } - if (!getDeviceTypeBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, deviceType_); - } - if (memoryLimit_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(4, memoryLimit_); - } - if (locality_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(5, getLocality()); - } - if (incarnation_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeFixed64Size(6, incarnation_); - } - if (!getPhysicalDeviceDescBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(7, physicalDeviceDesc_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.DeviceAttributes)) { - return super.equals(obj); - } - org.tensorflow.framework.DeviceAttributes other = (org.tensorflow.framework.DeviceAttributes) obj; - - boolean result = true; - result = result && getName() - .equals(other.getName()); - result = result && getDeviceType() - .equals(other.getDeviceType()); - result = result && (getMemoryLimit() - == other.getMemoryLimit()); - result = result && (hasLocality() == other.hasLocality()); - if (hasLocality()) { - result = result && getLocality() - .equals(other.getLocality()); - } - result = result && (getIncarnation() - == other.getIncarnation()); - result = result && getPhysicalDeviceDesc() - .equals(other.getPhysicalDeviceDesc()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + NAME_FIELD_NUMBER; - hash = (53 * hash) + getName().hashCode(); - hash = (37 * hash) + DEVICE_TYPE_FIELD_NUMBER; - hash = (53 * hash) + getDeviceType().hashCode(); - hash = (37 * hash) + MEMORY_LIMIT_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getMemoryLimit()); - if (hasLocality()) { - hash = (37 * hash) + LOCALITY_FIELD_NUMBER; - hash = (53 * hash) + getLocality().hashCode(); - } - hash = (37 * hash) + INCARNATION_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getIncarnation()); - hash = (37 * hash) + PHYSICAL_DEVICE_DESC_FIELD_NUMBER; - hash = (53 * hash) + getPhysicalDeviceDesc().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.DeviceAttributes parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.DeviceAttributes parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.DeviceAttributes parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.DeviceAttributes parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.DeviceAttributes parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceAttributes parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.DeviceAttributes parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceAttributes parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.DeviceAttributes parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceAttributes parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.DeviceAttributes prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.DeviceAttributes} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.DeviceAttributes) - org.tensorflow.framework.DeviceAttributesOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceAttributes_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceAttributes_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.DeviceAttributes.class, org.tensorflow.framework.DeviceAttributes.Builder.class); - } - - // Construct using org.tensorflow.framework.DeviceAttributes.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - name_ = ""; - - deviceType_ = ""; - - memoryLimit_ = 0L; - - if (localityBuilder_ == null) { - locality_ = null; - } else { - locality_ = null; - localityBuilder_ = null; - } - incarnation_ = 0L; - - physicalDeviceDesc_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceAttributes_descriptor; - } - - public org.tensorflow.framework.DeviceAttributes getDefaultInstanceForType() { - return org.tensorflow.framework.DeviceAttributes.getDefaultInstance(); - } - - public org.tensorflow.framework.DeviceAttributes build() { - org.tensorflow.framework.DeviceAttributes result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.DeviceAttributes buildPartial() { - org.tensorflow.framework.DeviceAttributes result = new org.tensorflow.framework.DeviceAttributes(this); - result.name_ = name_; - result.deviceType_ = deviceType_; - result.memoryLimit_ = memoryLimit_; - if (localityBuilder_ == null) { - result.locality_ = locality_; - } else { - result.locality_ = localityBuilder_.build(); - } - result.incarnation_ = incarnation_; - result.physicalDeviceDesc_ = physicalDeviceDesc_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.DeviceAttributes) { - return mergeFrom((org.tensorflow.framework.DeviceAttributes)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.DeviceAttributes other) { - if (other == org.tensorflow.framework.DeviceAttributes.getDefaultInstance()) return this; - if (!other.getName().isEmpty()) { - name_ = other.name_; - onChanged(); - } - if (!other.getDeviceType().isEmpty()) { - deviceType_ = other.deviceType_; - onChanged(); - } - if (other.getMemoryLimit() != 0L) { - setMemoryLimit(other.getMemoryLimit()); - } - if (other.hasLocality()) { - mergeLocality(other.getLocality()); - } - if (other.getIncarnation() != 0L) { - setIncarnation(other.getIncarnation()); - } - if (!other.getPhysicalDeviceDesc().isEmpty()) { - physicalDeviceDesc_ = other.physicalDeviceDesc_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.DeviceAttributes parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.DeviceAttributes) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private java.lang.Object name_ = ""; - /** - * 
-     * Fully specified name of the device within a cluster.
-     *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Fully specified name of the device within a cluster.
-     *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Fully specified name of the device within a cluster.
-     *
- * - * optional string name = 1; - */ - public Builder setName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - name_ = value; - onChanged(); - return this; - } - /** - * 
-     * Fully specified name of the device within a cluster.
-     *
- * - * optional string name = 1; - */ - public Builder clearName() { - - name_ = getDefaultInstance().getName(); - onChanged(); - return this; - } - /** - * 
-     * Fully specified name of the device within a cluster.
-     *
- * - * optional string name = 1; - */ - public Builder setNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - name_ = value; - onChanged(); - return this; - } - - private java.lang.Object deviceType_ = ""; - /** - * 
-     * String representation of device_type.
-     *
- * - * optional string device_type = 2; - */ - public java.lang.String getDeviceType() { - java.lang.Object ref = deviceType_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - deviceType_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * String representation of device_type.
-     *
- * - * optional string device_type = 2; - */ - public com.google.protobuf.ByteString - getDeviceTypeBytes() { - java.lang.Object ref = deviceType_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - deviceType_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * String representation of device_type.
-     *
- * - * optional string device_type = 2; - */ - public Builder setDeviceType( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - deviceType_ = value; - onChanged(); - return this; - } - /** - * 
-     * String representation of device_type.
-     *
- * - * optional string device_type = 2; - */ - public Builder clearDeviceType() { - - deviceType_ = getDefaultInstance().getDeviceType(); - onChanged(); - return this; - } - /** - * 
-     * String representation of device_type.
-     *
- * - * optional string device_type = 2; - */ - public Builder setDeviceTypeBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - deviceType_ = value; - onChanged(); - return this; - } - - private long memoryLimit_ ; - /** - * 
-     * Memory capacity of device in bytes.
-     *
- * - * optional int64 memory_limit = 4; - */ - public long getMemoryLimit() { - return memoryLimit_; - } - /** - * 
-     * Memory capacity of device in bytes.
-     *
- * - * optional int64 memory_limit = 4; - */ - public Builder setMemoryLimit(long value) { - - memoryLimit_ = value; - onChanged(); - return this; - } - /** - * 
-     * Memory capacity of device in bytes.
-     *
- * - * optional int64 memory_limit = 4; - */ - public Builder clearMemoryLimit() { - - memoryLimit_ = 0L; - onChanged(); - return this; - } - - private org.tensorflow.framework.DeviceLocality locality_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.DeviceLocality, org.tensorflow.framework.DeviceLocality.Builder, org.tensorflow.framework.DeviceLocalityOrBuilder> localityBuilder_; - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public boolean hasLocality() { - return localityBuilder_ != null || locality_ != null; - } - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public org.tensorflow.framework.DeviceLocality getLocality() { - if (localityBuilder_ == null) { - return locality_ == null ? org.tensorflow.framework.DeviceLocality.getDefaultInstance() : locality_; - } else { - return localityBuilder_.getMessage(); - } - } - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public Builder setLocality(org.tensorflow.framework.DeviceLocality value) { - if (localityBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - locality_ = value; - onChanged(); - } else { - localityBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public Builder setLocality( - org.tensorflow.framework.DeviceLocality.Builder builderForValue) { - if (localityBuilder_ == null) { - locality_ = builderForValue.build(); - onChanged(); - } else { - localityBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public Builder mergeLocality(org.tensorflow.framework.DeviceLocality value) { - if (localityBuilder_ == null) { - if (locality_ != null) { - locality_ = - org.tensorflow.framework.DeviceLocality.newBuilder(locality_).mergeFrom(value).buildPartial(); - } else { - locality_ = value; - } - onChanged(); - } else { - localityBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public Builder clearLocality() { - if (localityBuilder_ == null) { - locality_ = null; - onChanged(); - } else { - locality_ = null; - localityBuilder_ = null; - } - - return this; - } - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public org.tensorflow.framework.DeviceLocality.Builder getLocalityBuilder() { - - onChanged(); - return getLocalityFieldBuilder().getBuilder(); - } - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - public org.tensorflow.framework.DeviceLocalityOrBuilder getLocalityOrBuilder() { - if (localityBuilder_ != null) { - return localityBuilder_.getMessageOrBuilder(); - } else { - return locality_ == null ? - org.tensorflow.framework.DeviceLocality.getDefaultInstance() : locality_; - } - } - /** - * 
-     * Platform-specific data about device that may be useful
-     * for supporting efficient data transfers.
-     *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.DeviceLocality, org.tensorflow.framework.DeviceLocality.Builder, org.tensorflow.framework.DeviceLocalityOrBuilder> - getLocalityFieldBuilder() { - if (localityBuilder_ == null) { - localityBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.DeviceLocality, org.tensorflow.framework.DeviceLocality.Builder, org.tensorflow.framework.DeviceLocalityOrBuilder>( - getLocality(), - getParentForChildren(), - isClean()); - locality_ = null; - } - return localityBuilder_; - } - - private long incarnation_ ; - /** - * 
-     * A device is assigned a global unique number each time it is
-     * initialized. "incarnation" should never be 0.
-     *
- * - * optional fixed64 incarnation = 6; - */ - public long getIncarnation() { - return incarnation_; - } - /** - * 
-     * A device is assigned a global unique number each time it is
-     * initialized. "incarnation" should never be 0.
-     *
- * - * optional fixed64 incarnation = 6; - */ - public Builder setIncarnation(long value) { - - incarnation_ = value; - onChanged(); - return this; - } - /** - * 
-     * A device is assigned a global unique number each time it is
-     * initialized. "incarnation" should never be 0.
-     *
- * - * optional fixed64 incarnation = 6; - */ - public Builder clearIncarnation() { - - incarnation_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object physicalDeviceDesc_ = ""; - /** - * 
-     * String representation of the physical device that this device maps to.
-     *
- * - * optional string physical_device_desc = 7; - */ - public java.lang.String getPhysicalDeviceDesc() { - java.lang.Object ref = physicalDeviceDesc_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - physicalDeviceDesc_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * String representation of the physical device that this device maps to.
-     *
- * - * optional string physical_device_desc = 7; - */ - public com.google.protobuf.ByteString - getPhysicalDeviceDescBytes() { - java.lang.Object ref = physicalDeviceDesc_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - physicalDeviceDesc_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * String representation of the physical device that this device maps to.
-     *
- * - * optional string physical_device_desc = 7; - */ - public Builder setPhysicalDeviceDesc( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - physicalDeviceDesc_ = value; - onChanged(); - return this; - } - /** - * 
-     * String representation of the physical device that this device maps to.
-     *
- * - * optional string physical_device_desc = 7; - */ - public Builder clearPhysicalDeviceDesc() { - - physicalDeviceDesc_ = getDefaultInstance().getPhysicalDeviceDesc(); - onChanged(); - return this; - } - /** - * 
-     * String representation of the physical device that this device maps to.
-     *
- * - * optional string physical_device_desc = 7; - */ - public Builder setPhysicalDeviceDescBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - physicalDeviceDesc_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.DeviceAttributes) - } - - // @@protoc_insertion_point(class_scope:tensorflow.DeviceAttributes) - private static final org.tensorflow.framework.DeviceAttributes DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.DeviceAttributes(); - } - - public static org.tensorflow.framework.DeviceAttributes getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public DeviceAttributes parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new DeviceAttributes(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.DeviceAttributes getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributesOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributesOrBuilder.java deleted file mode 100644 index fa070dfa703..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributesOrBuilder.java +++ /dev/null @@ -1,110 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: device_attributes.proto - -package org.tensorflow.framework; - -public interface DeviceAttributesOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.DeviceAttributes) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Fully specified name of the device within a cluster.
-   *
- * - * optional string name = 1; - */ - java.lang.String getName(); - /** - * 
-   * Fully specified name of the device within a cluster.
-   *
- * - * optional string name = 1; - */ - com.google.protobuf.ByteString - getNameBytes(); - - /** - * 
-   * String representation of device_type.
-   *
- * - * optional string device_type = 2; - */ - java.lang.String getDeviceType(); - /** - * 
-   * String representation of device_type.
-   *
- * - * optional string device_type = 2; - */ - com.google.protobuf.ByteString - getDeviceTypeBytes(); - - /** - * 
-   * Memory capacity of device in bytes.
-   *
- * - * optional int64 memory_limit = 4; - */ - long getMemoryLimit(); - - /** - * 
-   * Platform-specific data about device that may be useful
-   * for supporting efficient data transfers.
-   *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - boolean hasLocality(); - /** - * 
-   * Platform-specific data about device that may be useful
-   * for supporting efficient data transfers.
-   *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - org.tensorflow.framework.DeviceLocality getLocality(); - /** - * 
-   * Platform-specific data about device that may be useful
-   * for supporting efficient data transfers.
-   *
- * - * optional .tensorflow.DeviceLocality locality = 5; - */ - org.tensorflow.framework.DeviceLocalityOrBuilder getLocalityOrBuilder(); - - /** - * 
-   * A device is assigned a global unique number each time it is
-   * initialized. "incarnation" should never be 0.
-   *
- * - * optional fixed64 incarnation = 6; - */ - long getIncarnation(); - - /** - * 
-   * String representation of the physical device that this device maps to.
-   *
- * - * optional string physical_device_desc = 7; - */ - java.lang.String getPhysicalDeviceDesc(); - /** - * 
-   * String representation of the physical device that this device maps to.
-   *
- * - * optional string physical_device_desc = 7; - */ - com.google.protobuf.ByteString - getPhysicalDeviceDescBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributesProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributesProtos.java deleted file mode 100644 index 6a85bc930a1..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceAttributesProtos.java +++ /dev/null @@ -1,72 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: device_attributes.proto - -package org.tensorflow.framework; - -public final class DeviceAttributesProtos { - private DeviceAttributesProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_DeviceLocality_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_DeviceLocality_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_DeviceAttributes_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_DeviceAttributes_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\027device_attributes.proto\022\ntensorflow\" \n" + - "\016DeviceLocality\022\016\n\006bus_id\030\001 \001(\005\"\254\001\n\020Devi" + - "ceAttributes\022\014\n\004name\030\001 \001(\t\022\023\n\013device_typ" + - "e\030\002 \001(\t\022\024\n\014memory_limit\030\004 \001(\003\022,\n\010localit" + - "y\030\005 \001(\0132\032.tensorflow.DeviceLocality\022\023\n\013i" + - "ncarnation\030\006 \001(\006\022\034\n\024physical_device_desc" + - "\030\007 \001(\tB7\n\030org.tensorflow.frameworkB\026Devi" + - "ceAttributesProtosP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - }, assigner); - internal_static_tensorflow_DeviceLocality_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_DeviceLocality_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_DeviceLocality_descriptor, - new java.lang.String[] { "BusId", }); - internal_static_tensorflow_DeviceAttributes_descriptor = - getDescriptor().getMessageTypes().get(1); - internal_static_tensorflow_DeviceAttributes_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_DeviceAttributes_descriptor, - new java.lang.String[] { "Name", "DeviceType", "MemoryLimit", "Locality", "Incarnation", "PhysicalDeviceDesc", }); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceLocality.java b/scala/dllib/src/main/java/org/tensorflow/framework/DeviceLocality.java deleted file mode 100644 index d27d449da6d..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceLocality.java +++ /dev/null @@ -1,445 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: device_attributes.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.DeviceLocality} - */ -public final class DeviceLocality extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.DeviceLocality) - DeviceLocalityOrBuilder { - // Use DeviceLocality.newBuilder() to construct. - private DeviceLocality(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private DeviceLocality() { - busId_ = 0; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private DeviceLocality( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - busId_ = input.readInt32(); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceLocality_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceLocality_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.DeviceLocality.class, org.tensorflow.framework.DeviceLocality.Builder.class); - } - - public static final int BUS_ID_FIELD_NUMBER = 1; - private int busId_; - /** - * 
-   * Optional bus locality of device.  Default value of 0 means
-   * no specific locality.  Specific localities are indexed from 1.
-   *
- * - * optional int32 bus_id = 1; - */ - public int getBusId() { - return busId_; - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (busId_ != 0) { - output.writeInt32(1, busId_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (busId_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeInt32Size(1, busId_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.DeviceLocality)) { - return super.equals(obj); - } - org.tensorflow.framework.DeviceLocality other = (org.tensorflow.framework.DeviceLocality) obj; - - boolean result = true; - result = result && (getBusId() - == other.getBusId()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + BUS_ID_FIELD_NUMBER; - hash = (53 * hash) + getBusId(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.DeviceLocality parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.DeviceLocality parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.DeviceLocality parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.DeviceLocality parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.DeviceLocality parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceLocality parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.DeviceLocality parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceLocality parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.DeviceLocality parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceLocality parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.DeviceLocality prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.DeviceLocality} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.DeviceLocality) - org.tensorflow.framework.DeviceLocalityOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceLocality_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceLocality_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.DeviceLocality.class, org.tensorflow.framework.DeviceLocality.Builder.class); - } - - // Construct using org.tensorflow.framework.DeviceLocality.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - busId_ = 0; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.DeviceAttributesProtos.internal_static_tensorflow_DeviceLocality_descriptor; - } - - public org.tensorflow.framework.DeviceLocality getDefaultInstanceForType() { - return org.tensorflow.framework.DeviceLocality.getDefaultInstance(); - } - - public org.tensorflow.framework.DeviceLocality build() { - org.tensorflow.framework.DeviceLocality result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.DeviceLocality buildPartial() { - org.tensorflow.framework.DeviceLocality result = new org.tensorflow.framework.DeviceLocality(this); - result.busId_ = busId_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.DeviceLocality) { - return mergeFrom((org.tensorflow.framework.DeviceLocality)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.DeviceLocality other) { - if (other == org.tensorflow.framework.DeviceLocality.getDefaultInstance()) return this; - if (other.getBusId() != 0) { - setBusId(other.getBusId()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.DeviceLocality parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.DeviceLocality) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private int busId_ ; - /** - * 
-     * Optional bus locality of device.  Default value of 0 means
-     * no specific locality.  Specific localities are indexed from 1.
-     *
- * - * optional int32 bus_id = 1; - */ - public int getBusId() { - return busId_; - } - /** - * 
-     * Optional bus locality of device.  Default value of 0 means
-     * no specific locality.  Specific localities are indexed from 1.
-     *
- * - * optional int32 bus_id = 1; - */ - public Builder setBusId(int value) { - - busId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Optional bus locality of device.  Default value of 0 means
-     * no specific locality.  Specific localities are indexed from 1.
-     *
- * - * optional int32 bus_id = 1; - */ - public Builder clearBusId() { - - busId_ = 0; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.DeviceLocality) - } - - // @@protoc_insertion_point(class_scope:tensorflow.DeviceLocality) - private static final org.tensorflow.framework.DeviceLocality DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.DeviceLocality(); - } - - public static org.tensorflow.framework.DeviceLocality getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public DeviceLocality parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new DeviceLocality(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.DeviceLocality getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceLocalityOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/DeviceLocalityOrBuilder.java deleted file mode 100644 index 28e5b4ecf4a..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceLocalityOrBuilder.java +++ /dev/null @@ -1,19 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: device_attributes.proto - -package org.tensorflow.framework; - -public interface DeviceLocalityOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.DeviceLocality) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Optional bus locality of device.  Default value of 0 means
-   * no specific locality.  Specific localities are indexed from 1.
-   *
- * - * optional int32 bus_id = 1; - */ - int getBusId(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceStepStats.java b/scala/dllib/src/main/java/org/tensorflow/framework/DeviceStepStats.java deleted file mode 100644 index 4559300715c..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceStepStats.java +++ /dev/null @@ -1,842 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.DeviceStepStats} - */ -public final class DeviceStepStats extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.DeviceStepStats) - DeviceStepStatsOrBuilder { - // Use DeviceStepStats.newBuilder() to construct. - private DeviceStepStats(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private DeviceStepStats() { - device_ = ""; - nodeStats_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private DeviceStepStats( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - device_ = s; - break; - } - case 18: { - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - nodeStats_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000002; - } - nodeStats_.add( - input.readMessage(org.tensorflow.framework.NodeExecStats.parser(), extensionRegistry)); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - nodeStats_ = java.util.Collections.unmodifiableList(nodeStats_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_DeviceStepStats_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_DeviceStepStats_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.DeviceStepStats.class, org.tensorflow.framework.DeviceStepStats.Builder.class); - } - - private int bitField0_; - public static final int DEVICE_FIELD_NUMBER = 1; - private volatile java.lang.Object device_; - /** - * optional string device = 1; - */ - public java.lang.String getDevice() { - java.lang.Object ref = device_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - device_ = s; - return s; - } - } - /** - * optional string device = 1; - */ - public com.google.protobuf.ByteString - getDeviceBytes() { - java.lang.Object ref = device_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - device_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int NODE_STATS_FIELD_NUMBER = 2; - private java.util.List nodeStats_; - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public java.util.List getNodeStatsList() { - return nodeStats_; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public java.util.List - getNodeStatsOrBuilderList() { - return nodeStats_; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public int getNodeStatsCount() { - return nodeStats_.size(); - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public org.tensorflow.framework.NodeExecStats getNodeStats(int index) { - return nodeStats_.get(index); - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public org.tensorflow.framework.NodeExecStatsOrBuilder getNodeStatsOrBuilder( - int index) { - return nodeStats_.get(index); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getDeviceBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, device_); - } - for (int i = 0; i < nodeStats_.size(); i++) { - output.writeMessage(2, nodeStats_.get(i)); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getDeviceBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, device_); - } - for (int i = 0; i < nodeStats_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, nodeStats_.get(i)); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.DeviceStepStats)) { - return super.equals(obj); - } - org.tensorflow.framework.DeviceStepStats other = (org.tensorflow.framework.DeviceStepStats) obj; - - boolean result = true; - result = result && getDevice() - .equals(other.getDevice()); - result = result && getNodeStatsList() - .equals(other.getNodeStatsList()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + DEVICE_FIELD_NUMBER; - hash = (53 * hash) + getDevice().hashCode(); - if (getNodeStatsCount() > 0) { - hash = (37 * hash) + NODE_STATS_FIELD_NUMBER; - hash = (53 * hash) + getNodeStatsList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.DeviceStepStats parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.DeviceStepStats parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.DeviceStepStats parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.DeviceStepStats parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.DeviceStepStats parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceStepStats parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.DeviceStepStats parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceStepStats parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.DeviceStepStats parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.DeviceStepStats parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.DeviceStepStats prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.DeviceStepStats} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.DeviceStepStats) - org.tensorflow.framework.DeviceStepStatsOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_DeviceStepStats_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_DeviceStepStats_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.DeviceStepStats.class, org.tensorflow.framework.DeviceStepStats.Builder.class); - } - - // Construct using org.tensorflow.framework.DeviceStepStats.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getNodeStatsFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - device_ = ""; - - if (nodeStatsBuilder_ == null) { - nodeStats_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - } else { - nodeStatsBuilder_.clear(); - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_DeviceStepStats_descriptor; - } - - public org.tensorflow.framework.DeviceStepStats getDefaultInstanceForType() { - return org.tensorflow.framework.DeviceStepStats.getDefaultInstance(); - } - - public org.tensorflow.framework.DeviceStepStats build() { - org.tensorflow.framework.DeviceStepStats result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.DeviceStepStats buildPartial() { - org.tensorflow.framework.DeviceStepStats result = new org.tensorflow.framework.DeviceStepStats(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - result.device_ = device_; - if (nodeStatsBuilder_ == null) { - if (((bitField0_ & 0x00000002) == 0x00000002)) { - nodeStats_ = java.util.Collections.unmodifiableList(nodeStats_); - bitField0_ = (bitField0_ & ~0x00000002); - } - result.nodeStats_ = nodeStats_; - } else { - result.nodeStats_ = nodeStatsBuilder_.build(); - } - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.DeviceStepStats) { - return mergeFrom((org.tensorflow.framework.DeviceStepStats)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.DeviceStepStats other) { - if (other == org.tensorflow.framework.DeviceStepStats.getDefaultInstance()) return this; - if (!other.getDevice().isEmpty()) { - device_ = other.device_; - onChanged(); - } - if (nodeStatsBuilder_ == null) { - if (!other.nodeStats_.isEmpty()) { - if (nodeStats_.isEmpty()) { - nodeStats_ = other.nodeStats_; - bitField0_ = (bitField0_ & ~0x00000002); - } else { - ensureNodeStatsIsMutable(); - nodeStats_.addAll(other.nodeStats_); - } - onChanged(); - } - } else { - if (!other.nodeStats_.isEmpty()) { - if (nodeStatsBuilder_.isEmpty()) { - nodeStatsBuilder_.dispose(); - nodeStatsBuilder_ = null; - nodeStats_ = other.nodeStats_; - bitField0_ = (bitField0_ & ~0x00000002); - nodeStatsBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getNodeStatsFieldBuilder() : null; - } else { - nodeStatsBuilder_.addAllMessages(other.nodeStats_); - } - } - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.DeviceStepStats parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.DeviceStepStats) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.lang.Object device_ = ""; - /** - * optional string device = 1; - */ - public java.lang.String getDevice() { - java.lang.Object ref = device_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - device_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * optional string device = 1; - */ - public com.google.protobuf.ByteString - getDeviceBytes() { - java.lang.Object ref = device_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - device_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * optional string device = 1; - */ - public Builder setDevice( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - device_ = value; - onChanged(); - return this; - } - /** - * optional string device = 1; - */ - public Builder clearDevice() { - - device_ = getDefaultInstance().getDevice(); - onChanged(); - return this; - } - /** - * optional string device = 1; - */ - public Builder setDeviceBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - device_ = value; - onChanged(); - return this; - } - - private java.util.List nodeStats_ = - java.util.Collections.emptyList(); - private void ensureNodeStatsIsMutable() { - if (!((bitField0_ & 0x00000002) == 0x00000002)) { - nodeStats_ = new java.util.ArrayList(nodeStats_); - bitField0_ |= 0x00000002; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeExecStats, org.tensorflow.framework.NodeExecStats.Builder, org.tensorflow.framework.NodeExecStatsOrBuilder> nodeStatsBuilder_; - - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public java.util.List getNodeStatsList() { - if (nodeStatsBuilder_ == null) { - return java.util.Collections.unmodifiableList(nodeStats_); - } else { - return nodeStatsBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public int getNodeStatsCount() { - if (nodeStatsBuilder_ == null) { - return nodeStats_.size(); - } else { - return nodeStatsBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public org.tensorflow.framework.NodeExecStats getNodeStats(int index) { - if (nodeStatsBuilder_ == null) { - return nodeStats_.get(index); - } else { - return nodeStatsBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder setNodeStats( - int index, org.tensorflow.framework.NodeExecStats value) { - if (nodeStatsBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeStatsIsMutable(); - nodeStats_.set(index, value); - onChanged(); - } else { - nodeStatsBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder setNodeStats( - int index, org.tensorflow.framework.NodeExecStats.Builder builderForValue) { - if (nodeStatsBuilder_ == null) { - ensureNodeStatsIsMutable(); - nodeStats_.set(index, builderForValue.build()); - onChanged(); - } else { - nodeStatsBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder addNodeStats(org.tensorflow.framework.NodeExecStats value) { - if (nodeStatsBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeStatsIsMutable(); - nodeStats_.add(value); - onChanged(); - } else { - nodeStatsBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder addNodeStats( - int index, org.tensorflow.framework.NodeExecStats value) { - if (nodeStatsBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeStatsIsMutable(); - nodeStats_.add(index, value); - onChanged(); - } else { - nodeStatsBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder addNodeStats( - org.tensorflow.framework.NodeExecStats.Builder builderForValue) { - if (nodeStatsBuilder_ == null) { - ensureNodeStatsIsMutable(); - nodeStats_.add(builderForValue.build()); - onChanged(); - } else { - nodeStatsBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder addNodeStats( - int index, org.tensorflow.framework.NodeExecStats.Builder builderForValue) { - if (nodeStatsBuilder_ == null) { - ensureNodeStatsIsMutable(); - nodeStats_.add(index, builderForValue.build()); - onChanged(); - } else { - nodeStatsBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder addAllNodeStats( - java.lang.Iterable values) { - if (nodeStatsBuilder_ == null) { - ensureNodeStatsIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, nodeStats_); - onChanged(); - } else { - nodeStatsBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder clearNodeStats() { - if (nodeStatsBuilder_ == null) { - nodeStats_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - onChanged(); - } else { - nodeStatsBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public Builder removeNodeStats(int index) { - if (nodeStatsBuilder_ == null) { - ensureNodeStatsIsMutable(); - nodeStats_.remove(index); - onChanged(); - } else { - nodeStatsBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public org.tensorflow.framework.NodeExecStats.Builder getNodeStatsBuilder( - int index) { - return getNodeStatsFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public org.tensorflow.framework.NodeExecStatsOrBuilder getNodeStatsOrBuilder( - int index) { - if (nodeStatsBuilder_ == null) { - return nodeStats_.get(index); } else { - return nodeStatsBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public java.util.List - getNodeStatsOrBuilderList() { - if (nodeStatsBuilder_ != null) { - return nodeStatsBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(nodeStats_); - } - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public org.tensorflow.framework.NodeExecStats.Builder addNodeStatsBuilder() { - return getNodeStatsFieldBuilder().addBuilder( - org.tensorflow.framework.NodeExecStats.getDefaultInstance()); - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public org.tensorflow.framework.NodeExecStats.Builder addNodeStatsBuilder( - int index) { - return getNodeStatsFieldBuilder().addBuilder( - index, org.tensorflow.framework.NodeExecStats.getDefaultInstance()); - } - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - public java.util.List - getNodeStatsBuilderList() { - return getNodeStatsFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeExecStats, org.tensorflow.framework.NodeExecStats.Builder, org.tensorflow.framework.NodeExecStatsOrBuilder> - getNodeStatsFieldBuilder() { - if (nodeStatsBuilder_ == null) { - nodeStatsBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeExecStats, org.tensorflow.framework.NodeExecStats.Builder, org.tensorflow.framework.NodeExecStatsOrBuilder>( - nodeStats_, - ((bitField0_ & 0x00000002) == 0x00000002), - getParentForChildren(), - isClean()); - nodeStats_ = null; - } - return nodeStatsBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.DeviceStepStats) - } - - // @@protoc_insertion_point(class_scope:tensorflow.DeviceStepStats) - private static final org.tensorflow.framework.DeviceStepStats DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.DeviceStepStats(); - } - - public static org.tensorflow.framework.DeviceStepStats getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public DeviceStepStats parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new DeviceStepStats(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.DeviceStepStats getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceStepStatsOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/DeviceStepStatsOrBuilder.java deleted file mode 100644 index feea34b3e09..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/DeviceStepStatsOrBuilder.java +++ /dev/null @@ -1,43 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -public interface DeviceStepStatsOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.DeviceStepStats) - com.google.protobuf.MessageOrBuilder { - - /** - * optional string device = 1; - */ - java.lang.String getDevice(); - /** - * optional string device = 1; - */ - com.google.protobuf.ByteString - getDeviceBytes(); - - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - java.util.List - getNodeStatsList(); - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - org.tensorflow.framework.NodeExecStats getNodeStats(int index); - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - int getNodeStatsCount(); - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - java.util.List - getNodeStatsOrBuilderList(); - /** - * repeated .tensorflow.NodeExecStats node_stats = 2; - */ - org.tensorflow.framework.NodeExecStatsOrBuilder getNodeStatsOrBuilder( - int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDef.java b/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDef.java deleted file mode 100644 index 73b68eff6ee..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDef.java +++ /dev/null @@ -1,1722 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: function.proto - -package org.tensorflow.framework; - -/** - * 
- * A function can be instantiated when the runtime can bind every attr
- * with a value. When a GraphDef has a call to a function, it must
- * have binding for every attr defined in the signature.
- * TODO(zhifengc):
- *   * device spec, etc.
- *
- * - * Protobuf type {@code tensorflow.FunctionDef} - */ -public final class FunctionDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.FunctionDef) - FunctionDefOrBuilder { - // Use FunctionDef.newBuilder() to construct. - private FunctionDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private FunctionDef() { - nodeDef_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private FunctionDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - org.tensorflow.framework.OpDef.Builder subBuilder = null; - if (signature_ != null) { - subBuilder = signature_.toBuilder(); - } - signature_ = input.readMessage(org.tensorflow.framework.OpDef.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(signature_); - signature_ = subBuilder.buildPartial(); - } - - break; - } - case 26: { - if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - nodeDef_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000004; - } - nodeDef_.add( - input.readMessage(org.tensorflow.framework.NodeDef.parser(), extensionRegistry)); - break; - } - case 34: { - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - ret_ = com.google.protobuf.MapField.newMapField( - RetDefaultEntryHolder.defaultEntry); - mutable_bitField0_ |= 0x00000008; - } - com.google.protobuf.MapEntry - ret = input.readMessage( - RetDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); - ret_.getMutableMap().put(ret.getKey(), ret.getValue()); - break; - } - case 42: { - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - attr_ = com.google.protobuf.MapField.newMapField( - AttrDefaultEntryHolder.defaultEntry); - mutable_bitField0_ |= 0x00000002; - } - com.google.protobuf.MapEntry - attr = input.readMessage( - AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); - attr_.getMutableMap().put(attr.getKey(), attr.getValue()); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - nodeDef_ = java.util.Collections.unmodifiableList(nodeDef_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDef_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 5: - return internalGetAttr(); - case 4: - return internalGetRet(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.FunctionDef.class, org.tensorflow.framework.FunctionDef.Builder.class); - } - - private int bitField0_; - public static final int SIGNATURE_FIELD_NUMBER = 1; - private org.tensorflow.framework.OpDef signature_; - /** - * 
-   * The definition of the function's name, arguments, return values,
-   * attrs etc.
-   *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public boolean hasSignature() { - return signature_ != null; - } - /** - * 
-   * The definition of the function's name, arguments, return values,
-   * attrs etc.
-   *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public org.tensorflow.framework.OpDef getSignature() { - return signature_ == null ? org.tensorflow.framework.OpDef.getDefaultInstance() : signature_; - } - /** - * 
-   * The definition of the function's name, arguments, return values,
-   * attrs etc.
-   *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public org.tensorflow.framework.OpDefOrBuilder getSignatureOrBuilder() { - return getSignature(); - } - - public static final int ATTR_FIELD_NUMBER = 5; - private static final class AttrDefaultEntryHolder { - static final com.google.protobuf.MapEntry< - java.lang.String, org.tensorflow.framework.AttrValue> defaultEntry = - com.google.protobuf.MapEntry - .newDefaultInstance( - org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDef_AttrEntry_descriptor, - com.google.protobuf.WireFormat.FieldType.STRING, - "", - com.google.protobuf.WireFormat.FieldType.MESSAGE, - org.tensorflow.framework.AttrValue.getDefaultInstance()); - } - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.framework.AttrValue> attr_; - private com.google.protobuf.MapField - internalGetAttr() { - if (attr_ == null) { - return com.google.protobuf.MapField.emptyMapField( - AttrDefaultEntryHolder.defaultEntry); - } - return attr_; - } - - public int getAttrCount() { - return internalGetAttr().getMap().size(); - } - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public boolean containsAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetAttr().getMap().containsKey(key); - } - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getAttr() { - return getAttrMap(); - } - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public java.util.Map getAttrMap() { - return internalGetAttr().getMap(); - } - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - public static final int NODE_DEF_FIELD_NUMBER = 3; - private java.util.List nodeDef_; - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public java.util.List getNodeDefList() { - return nodeDef_; - } - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public java.util.List - getNodeDefOrBuilderList() { - return nodeDef_; - } - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public int getNodeDefCount() { - return nodeDef_.size(); - } - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public org.tensorflow.framework.NodeDef getNodeDef(int index) { - return nodeDef_.get(index); - } - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public org.tensorflow.framework.NodeDefOrBuilder getNodeDefOrBuilder( - int index) { - return nodeDef_.get(index); - } - - public static final int RET_FIELD_NUMBER = 4; - private static final class RetDefaultEntryHolder { - static final com.google.protobuf.MapEntry< - java.lang.String, java.lang.String> defaultEntry = - com.google.protobuf.MapEntry - .newDefaultInstance( - org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDef_RetEntry_descriptor, - com.google.protobuf.WireFormat.FieldType.STRING, - "", - com.google.protobuf.WireFormat.FieldType.STRING, - ""); - } - private com.google.protobuf.MapField< - java.lang.String, java.lang.String> ret_; - private com.google.protobuf.MapField - internalGetRet() { - if (ret_ == null) { - return com.google.protobuf.MapField.emptyMapField( - RetDefaultEntryHolder.defaultEntry); - } - return ret_; - } - - public int getRetCount() { - return internalGetRet().getMap().size(); - } - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - - public boolean containsRet( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetRet().getMap().containsKey(key); - } - /** - * Use {@link #getRetMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getRet() { - return getRetMap(); - } - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - - public java.util.Map getRetMap() { - return internalGetRet().getMap(); - } - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - - public java.lang.String getRetOrDefault( - java.lang.String key, - java.lang.String defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetRet().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - - public java.lang.String getRetOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetRet().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (signature_ != null) { - output.writeMessage(1, getSignature()); - } - for (int i = 0; i < nodeDef_.size(); i++) { - output.writeMessage(3, nodeDef_.get(i)); - } - for (java.util.Map.Entry entry - : internalGetRet().getMap().entrySet()) { - com.google.protobuf.MapEntry - ret = RetDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - output.writeMessage(4, ret); - } - for (java.util.Map.Entry entry - : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - output.writeMessage(5, attr); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (signature_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, getSignature()); - } - for (int i = 0; i < nodeDef_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(3, nodeDef_.get(i)); - } - for (java.util.Map.Entry entry - : internalGetRet().getMap().entrySet()) { - com.google.protobuf.MapEntry - ret = RetDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(4, ret); - } - for (java.util.Map.Entry entry - : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(5, attr); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.FunctionDef)) { - return super.equals(obj); - } - org.tensorflow.framework.FunctionDef other = (org.tensorflow.framework.FunctionDef) obj; - - boolean result = true; - result = result && (hasSignature() == other.hasSignature()); - if (hasSignature()) { - result = result && getSignature() - .equals(other.getSignature()); - } - result = result && internalGetAttr().equals( - other.internalGetAttr()); - result = result && getNodeDefList() - .equals(other.getNodeDefList()); - result = result && internalGetRet().equals( - other.internalGetRet()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - if (hasSignature()) { - hash = (37 * hash) + SIGNATURE_FIELD_NUMBER; - hash = (53 * hash) + getSignature().hashCode(); - } - if (!internalGetAttr().getMap().isEmpty()) { - hash = (37 * hash) + ATTR_FIELD_NUMBER; - hash = (53 * hash) + internalGetAttr().hashCode(); - } - if (getNodeDefCount() > 0) { - hash = (37 * hash) + NODE_DEF_FIELD_NUMBER; - hash = (53 * hash) + getNodeDefList().hashCode(); - } - if (!internalGetRet().getMap().isEmpty()) { - hash = (37 * hash) + RET_FIELD_NUMBER; - hash = (53 * hash) + internalGetRet().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.FunctionDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.FunctionDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.FunctionDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.FunctionDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.FunctionDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.FunctionDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.FunctionDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.FunctionDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.FunctionDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.FunctionDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.FunctionDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * A function can be instantiated when the runtime can bind every attr
-   * with a value. When a GraphDef has a call to a function, it must
-   * have binding for every attr defined in the signature.
-   * TODO(zhifengc):
-   *   * device spec, etc.
-   *
- * - * Protobuf type {@code tensorflow.FunctionDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.FunctionDef) - org.tensorflow.framework.FunctionDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDef_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 5: - return internalGetAttr(); - case 4: - return internalGetRet(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMutableMapField( - int number) { - switch (number) { - case 5: - return internalGetMutableAttr(); - case 4: - return internalGetMutableRet(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.FunctionDef.class, org.tensorflow.framework.FunctionDef.Builder.class); - } - - // Construct using org.tensorflow.framework.FunctionDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getNodeDefFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - if (signatureBuilder_ == null) { - signature_ = null; - } else { - signature_ = null; - signatureBuilder_ = null; - } - internalGetMutableAttr().clear(); - if (nodeDefBuilder_ == null) { - nodeDef_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - } else { - nodeDefBuilder_.clear(); - } - internalGetMutableRet().clear(); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDef_descriptor; - } - - public org.tensorflow.framework.FunctionDef getDefaultInstanceForType() { - return org.tensorflow.framework.FunctionDef.getDefaultInstance(); - } - - public org.tensorflow.framework.FunctionDef build() { - org.tensorflow.framework.FunctionDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.FunctionDef buildPartial() { - org.tensorflow.framework.FunctionDef result = new org.tensorflow.framework.FunctionDef(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - if (signatureBuilder_ == null) { - result.signature_ = signature_; - } else { - result.signature_ = signatureBuilder_.build(); - } - result.attr_ = internalGetAttr(); - result.attr_.makeImmutable(); - if (nodeDefBuilder_ == null) { - if (((bitField0_ & 0x00000004) == 0x00000004)) { - nodeDef_ = java.util.Collections.unmodifiableList(nodeDef_); - bitField0_ = (bitField0_ & ~0x00000004); - } - result.nodeDef_ = nodeDef_; - } else { - result.nodeDef_ = nodeDefBuilder_.build(); - } - result.ret_ = internalGetRet(); - result.ret_.makeImmutable(); - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.FunctionDef) { - return mergeFrom((org.tensorflow.framework.FunctionDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.FunctionDef other) { - if (other == org.tensorflow.framework.FunctionDef.getDefaultInstance()) return this; - if (other.hasSignature()) { - mergeSignature(other.getSignature()); - } - internalGetMutableAttr().mergeFrom( - other.internalGetAttr()); - if (nodeDefBuilder_ == null) { - if (!other.nodeDef_.isEmpty()) { - if (nodeDef_.isEmpty()) { - nodeDef_ = other.nodeDef_; - bitField0_ = (bitField0_ & ~0x00000004); - } else { - ensureNodeDefIsMutable(); - nodeDef_.addAll(other.nodeDef_); - } - onChanged(); - } - } else { - if (!other.nodeDef_.isEmpty()) { - if (nodeDefBuilder_.isEmpty()) { - nodeDefBuilder_.dispose(); - nodeDefBuilder_ = null; - nodeDef_ = other.nodeDef_; - bitField0_ = (bitField0_ & ~0x00000004); - nodeDefBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getNodeDefFieldBuilder() : null; - } else { - nodeDefBuilder_.addAllMessages(other.nodeDef_); - } - } - } - internalGetMutableRet().mergeFrom( - other.internalGetRet()); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.FunctionDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.FunctionDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private org.tensorflow.framework.OpDef signature_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.OpDef, org.tensorflow.framework.OpDef.Builder, org.tensorflow.framework.OpDefOrBuilder> signatureBuilder_; - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public boolean hasSignature() { - return signatureBuilder_ != null || signature_ != null; - } - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public org.tensorflow.framework.OpDef getSignature() { - if (signatureBuilder_ == null) { - return signature_ == null ? org.tensorflow.framework.OpDef.getDefaultInstance() : signature_; - } else { - return signatureBuilder_.getMessage(); - } - } - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public Builder setSignature(org.tensorflow.framework.OpDef value) { - if (signatureBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - signature_ = value; - onChanged(); - } else { - signatureBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public Builder setSignature( - org.tensorflow.framework.OpDef.Builder builderForValue) { - if (signatureBuilder_ == null) { - signature_ = builderForValue.build(); - onChanged(); - } else { - signatureBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public Builder mergeSignature(org.tensorflow.framework.OpDef value) { - if (signatureBuilder_ == null) { - if (signature_ != null) { - signature_ = - org.tensorflow.framework.OpDef.newBuilder(signature_).mergeFrom(value).buildPartial(); - } else { - signature_ = value; - } - onChanged(); - } else { - signatureBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public Builder clearSignature() { - if (signatureBuilder_ == null) { - signature_ = null; - onChanged(); - } else { - signature_ = null; - signatureBuilder_ = null; - } - - return this; - } - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public org.tensorflow.framework.OpDef.Builder getSignatureBuilder() { - - onChanged(); - return getSignatureFieldBuilder().getBuilder(); - } - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - public org.tensorflow.framework.OpDefOrBuilder getSignatureOrBuilder() { - if (signatureBuilder_ != null) { - return signatureBuilder_.getMessageOrBuilder(); - } else { - return signature_ == null ? - org.tensorflow.framework.OpDef.getDefaultInstance() : signature_; - } - } - /** - * 
-     * The definition of the function's name, arguments, return values,
-     * attrs etc.
-     *
- * - * optional .tensorflow.OpDef signature = 1; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.OpDef, org.tensorflow.framework.OpDef.Builder, org.tensorflow.framework.OpDefOrBuilder> - getSignatureFieldBuilder() { - if (signatureBuilder_ == null) { - signatureBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.OpDef, org.tensorflow.framework.OpDef.Builder, org.tensorflow.framework.OpDefOrBuilder>( - getSignature(), - getParentForChildren(), - isClean()); - signature_ = null; - } - return signatureBuilder_; - } - - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.framework.AttrValue> attr_; - private com.google.protobuf.MapField - internalGetAttr() { - if (attr_ == null) { - return com.google.protobuf.MapField.emptyMapField( - AttrDefaultEntryHolder.defaultEntry); - } - return attr_; - } - private com.google.protobuf.MapField - internalGetMutableAttr() { - onChanged();; - if (attr_ == null) { - attr_ = com.google.protobuf.MapField.newMapField( - AttrDefaultEntryHolder.defaultEntry); - } - if (!attr_.isMutable()) { - attr_ = attr_.copy(); - } - return attr_; - } - - public int getAttrCount() { - return internalGetAttr().getMap().size(); - } - /** - * 
-     * Attributes specific to this function definition.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public boolean containsAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetAttr().getMap().containsKey(key); - } - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getAttr() { - return getAttrMap(); - } - /** - * 
-     * Attributes specific to this function definition.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public java.util.Map getAttrMap() { - return internalGetAttr().getMap(); - } - /** - * 
-     * Attributes specific to this function definition.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-     * Attributes specific to this function definition.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - public Builder clearAttr() { - getMutableAttr().clear(); - return this; - } - /** - * 
-     * Attributes specific to this function definition.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public Builder removeAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().remove(key); - return this; - } - /** - * Use alternate mutation accessors instead. - */ - @java.lang.Deprecated - public java.util.Map - getMutableAttr() { - return internalGetMutableAttr().getMutableMap(); - } - /** - * 
-     * Attributes specific to this function definition.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - public Builder putAttr( - java.lang.String key, - org.tensorflow.framework.AttrValue value) { - if (key == null) { throw new java.lang.NullPointerException(); } - if (value == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().put(key, value); - return this; - } - /** - * 
-     * Attributes specific to this function definition.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public Builder putAllAttr( - java.util.Map values) { - getMutableAttr().putAll(values); - return this; - } - - private java.util.List nodeDef_ = - java.util.Collections.emptyList(); - private void ensureNodeDefIsMutable() { - if (!((bitField0_ & 0x00000004) == 0x00000004)) { - nodeDef_ = new java.util.ArrayList(nodeDef_); - bitField0_ |= 0x00000004; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder> nodeDefBuilder_; - - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public java.util.List getNodeDefList() { - if (nodeDefBuilder_ == null) { - return java.util.Collections.unmodifiableList(nodeDef_); - } else { - return nodeDefBuilder_.getMessageList(); - } - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public int getNodeDefCount() { - if (nodeDefBuilder_ == null) { - return nodeDef_.size(); - } else { - return nodeDefBuilder_.getCount(); - } - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public org.tensorflow.framework.NodeDef getNodeDef(int index) { - if (nodeDefBuilder_ == null) { - return nodeDef_.get(index); - } else { - return nodeDefBuilder_.getMessage(index); - } - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder setNodeDef( - int index, org.tensorflow.framework.NodeDef value) { - if (nodeDefBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeDefIsMutable(); - nodeDef_.set(index, value); - onChanged(); - } else { - nodeDefBuilder_.setMessage(index, value); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder setNodeDef( - int index, org.tensorflow.framework.NodeDef.Builder builderForValue) { - if (nodeDefBuilder_ == null) { - ensureNodeDefIsMutable(); - nodeDef_.set(index, builderForValue.build()); - onChanged(); - } else { - nodeDefBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder addNodeDef(org.tensorflow.framework.NodeDef value) { - if (nodeDefBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeDefIsMutable(); - nodeDef_.add(value); - onChanged(); - } else { - nodeDefBuilder_.addMessage(value); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder addNodeDef( - int index, org.tensorflow.framework.NodeDef value) { - if (nodeDefBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeDefIsMutable(); - nodeDef_.add(index, value); - onChanged(); - } else { - nodeDefBuilder_.addMessage(index, value); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder addNodeDef( - org.tensorflow.framework.NodeDef.Builder builderForValue) { - if (nodeDefBuilder_ == null) { - ensureNodeDefIsMutable(); - nodeDef_.add(builderForValue.build()); - onChanged(); - } else { - nodeDefBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder addNodeDef( - int index, org.tensorflow.framework.NodeDef.Builder builderForValue) { - if (nodeDefBuilder_ == null) { - ensureNodeDefIsMutable(); - nodeDef_.add(index, builderForValue.build()); - onChanged(); - } else { - nodeDefBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder addAllNodeDef( - java.lang.Iterable values) { - if (nodeDefBuilder_ == null) { - ensureNodeDefIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, nodeDef_); - onChanged(); - } else { - nodeDefBuilder_.addAllMessages(values); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder clearNodeDef() { - if (nodeDefBuilder_ == null) { - nodeDef_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - onChanged(); - } else { - nodeDefBuilder_.clear(); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public Builder removeNodeDef(int index) { - if (nodeDefBuilder_ == null) { - ensureNodeDefIsMutable(); - nodeDef_.remove(index); - onChanged(); - } else { - nodeDefBuilder_.remove(index); - } - return this; - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public org.tensorflow.framework.NodeDef.Builder getNodeDefBuilder( - int index) { - return getNodeDefFieldBuilder().getBuilder(index); - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public org.tensorflow.framework.NodeDefOrBuilder getNodeDefOrBuilder( - int index) { - if (nodeDefBuilder_ == null) { - return nodeDef_.get(index); } else { - return nodeDefBuilder_.getMessageOrBuilder(index); - } - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public java.util.List - getNodeDefOrBuilderList() { - if (nodeDefBuilder_ != null) { - return nodeDefBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(nodeDef_); - } - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public org.tensorflow.framework.NodeDef.Builder addNodeDefBuilder() { - return getNodeDefFieldBuilder().addBuilder( - org.tensorflow.framework.NodeDef.getDefaultInstance()); - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public org.tensorflow.framework.NodeDef.Builder addNodeDefBuilder( - int index) { - return getNodeDefFieldBuilder().addBuilder( - index, org.tensorflow.framework.NodeDef.getDefaultInstance()); - } - /** - * 
-     * By convention, "op" in node_def is resolved by consulting with a
-     * user-defined library first. If not resolved, "func" is assumed to
-     * be a builtin op.
-     *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - public java.util.List - getNodeDefBuilderList() { - return getNodeDefFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder> - getNodeDefFieldBuilder() { - if (nodeDefBuilder_ == null) { - nodeDefBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder>( - nodeDef_, - ((bitField0_ & 0x00000004) == 0x00000004), - getParentForChildren(), - isClean()); - nodeDef_ = null; - } - return nodeDefBuilder_; - } - - private com.google.protobuf.MapField< - java.lang.String, java.lang.String> ret_; - private com.google.protobuf.MapField - internalGetRet() { - if (ret_ == null) { - return com.google.protobuf.MapField.emptyMapField( - RetDefaultEntryHolder.defaultEntry); - } - return ret_; - } - private com.google.protobuf.MapField - internalGetMutableRet() { - onChanged();; - if (ret_ == null) { - ret_ = com.google.protobuf.MapField.newMapField( - RetDefaultEntryHolder.defaultEntry); - } - if (!ret_.isMutable()) { - ret_ = ret_.copy(); - } - return ret_; - } - - public int getRetCount() { - return internalGetRet().getMap().size(); - } - /** - * 
-     * A mapping from the output arg names from `signature` to the
-     * outputs from `node_def` that should be returned by the function.
-     *
- * - * map<string, string> ret = 4; - */ - - public boolean containsRet( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetRet().getMap().containsKey(key); - } - /** - * Use {@link #getRetMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getRet() { - return getRetMap(); - } - /** - * 
-     * A mapping from the output arg names from `signature` to the
-     * outputs from `node_def` that should be returned by the function.
-     *
- * - * map<string, string> ret = 4; - */ - - public java.util.Map getRetMap() { - return internalGetRet().getMap(); - } - /** - * 
-     * A mapping from the output arg names from `signature` to the
-     * outputs from `node_def` that should be returned by the function.
-     *
- * - * map<string, string> ret = 4; - */ - - public java.lang.String getRetOrDefault( - java.lang.String key, - java.lang.String defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetRet().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-     * A mapping from the output arg names from `signature` to the
-     * outputs from `node_def` that should be returned by the function.
-     *
- * - * map<string, string> ret = 4; - */ - - public java.lang.String getRetOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetRet().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - public Builder clearRet() { - getMutableRet().clear(); - return this; - } - /** - * 
-     * A mapping from the output arg names from `signature` to the
-     * outputs from `node_def` that should be returned by the function.
-     *
- * - * map<string, string> ret = 4; - */ - - public Builder removeRet( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - getMutableRet().remove(key); - return this; - } - /** - * Use alternate mutation accessors instead. - */ - @java.lang.Deprecated - public java.util.Map - getMutableRet() { - return internalGetMutableRet().getMutableMap(); - } - /** - * 
-     * A mapping from the output arg names from `signature` to the
-     * outputs from `node_def` that should be returned by the function.
-     *
- * - * map<string, string> ret = 4; - */ - public Builder putRet( - java.lang.String key, - java.lang.String value) { - if (key == null) { throw new java.lang.NullPointerException(); } - if (value == null) { throw new java.lang.NullPointerException(); } - getMutableRet().put(key, value); - return this; - } - /** - * 
-     * A mapping from the output arg names from `signature` to the
-     * outputs from `node_def` that should be returned by the function.
-     *
- * - * map<string, string> ret = 4; - */ - - public Builder putAllRet( - java.util.Map values) { - getMutableRet().putAll(values); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.FunctionDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.FunctionDef) - private static final org.tensorflow.framework.FunctionDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.FunctionDef(); - } - - public static org.tensorflow.framework.FunctionDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public FunctionDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new FunctionDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.FunctionDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefLibrary.java b/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefLibrary.java deleted file mode 100644 index f45455bbd46..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefLibrary.java +++ /dev/null @@ -1,1063 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: function.proto - -package org.tensorflow.framework; - -/** - * 
- * A library is a set of named functions.
- *
- * - * Protobuf type {@code tensorflow.FunctionDefLibrary} - */ -public final class FunctionDefLibrary extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.FunctionDefLibrary) - FunctionDefLibraryOrBuilder { - // Use FunctionDefLibrary.newBuilder() to construct. - private FunctionDefLibrary(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private FunctionDefLibrary() { - function_ = java.util.Collections.emptyList(); - gradient_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private FunctionDefLibrary( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - function_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - function_.add( - input.readMessage(org.tensorflow.framework.FunctionDef.parser(), extensionRegistry)); - break; - } - case 18: { - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - gradient_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000002; - } - gradient_.add( - input.readMessage(org.tensorflow.framework.GradientDef.parser(), extensionRegistry)); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - function_ = java.util.Collections.unmodifiableList(function_); - } - if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - gradient_ = java.util.Collections.unmodifiableList(gradient_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDefLibrary_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDefLibrary_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.FunctionDefLibrary.class, org.tensorflow.framework.FunctionDefLibrary.Builder.class); - } - - public static final int FUNCTION_FIELD_NUMBER = 1; - private java.util.List function_; - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public java.util.List getFunctionList() { - return function_; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public java.util.List - getFunctionOrBuilderList() { - return function_; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public int getFunctionCount() { - return function_.size(); - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public org.tensorflow.framework.FunctionDef getFunction(int index) { - return function_.get(index); - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public org.tensorflow.framework.FunctionDefOrBuilder getFunctionOrBuilder( - int index) { - return function_.get(index); - } - - public static final int GRADIENT_FIELD_NUMBER = 2; - private java.util.List gradient_; - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public java.util.List getGradientList() { - return gradient_; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public java.util.List - getGradientOrBuilderList() { - return gradient_; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public int getGradientCount() { - return gradient_.size(); - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public org.tensorflow.framework.GradientDef getGradient(int index) { - return gradient_.get(index); - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public org.tensorflow.framework.GradientDefOrBuilder getGradientOrBuilder( - int index) { - return gradient_.get(index); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - for (int i = 0; i < function_.size(); i++) { - output.writeMessage(1, function_.get(i)); - } - for (int i = 0; i < gradient_.size(); i++) { - output.writeMessage(2, gradient_.get(i)); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - for (int i = 0; i < function_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, function_.get(i)); - } - for (int i = 0; i < gradient_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, gradient_.get(i)); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.FunctionDefLibrary)) { - return super.equals(obj); - } - org.tensorflow.framework.FunctionDefLibrary other = (org.tensorflow.framework.FunctionDefLibrary) obj; - - boolean result = true; - result = result && getFunctionList() - .equals(other.getFunctionList()); - result = result && getGradientList() - .equals(other.getGradientList()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - if (getFunctionCount() > 0) { - hash = (37 * hash) + FUNCTION_FIELD_NUMBER; - hash = (53 * hash) + getFunctionList().hashCode(); - } - if (getGradientCount() > 0) { - hash = (37 * hash) + GRADIENT_FIELD_NUMBER; - hash = (53 * hash) + getGradientList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.FunctionDefLibrary parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.FunctionDefLibrary parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.FunctionDefLibrary parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.FunctionDefLibrary parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.FunctionDefLibrary parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.FunctionDefLibrary parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.FunctionDefLibrary parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.FunctionDefLibrary parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.FunctionDefLibrary parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.FunctionDefLibrary parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.FunctionDefLibrary prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * A library is a set of named functions.
-   *
- * - * Protobuf type {@code tensorflow.FunctionDefLibrary} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.FunctionDefLibrary) - org.tensorflow.framework.FunctionDefLibraryOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDefLibrary_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDefLibrary_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.FunctionDefLibrary.class, org.tensorflow.framework.FunctionDefLibrary.Builder.class); - } - - // Construct using org.tensorflow.framework.FunctionDefLibrary.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getFunctionFieldBuilder(); - getGradientFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - if (functionBuilder_ == null) { - function_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - } else { - functionBuilder_.clear(); - } - if (gradientBuilder_ == null) { - gradient_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - } else { - gradientBuilder_.clear(); - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_FunctionDefLibrary_descriptor; - } - - public org.tensorflow.framework.FunctionDefLibrary getDefaultInstanceForType() { - return org.tensorflow.framework.FunctionDefLibrary.getDefaultInstance(); - } - - public org.tensorflow.framework.FunctionDefLibrary build() { - org.tensorflow.framework.FunctionDefLibrary result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.FunctionDefLibrary buildPartial() { - org.tensorflow.framework.FunctionDefLibrary result = new org.tensorflow.framework.FunctionDefLibrary(this); - int from_bitField0_ = bitField0_; - if (functionBuilder_ == null) { - if (((bitField0_ & 0x00000001) == 0x00000001)) { - function_ = java.util.Collections.unmodifiableList(function_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.function_ = function_; - } else { - result.function_ = functionBuilder_.build(); - } - if (gradientBuilder_ == null) { - if (((bitField0_ & 0x00000002) == 0x00000002)) { - gradient_ = java.util.Collections.unmodifiableList(gradient_); - bitField0_ = (bitField0_ & ~0x00000002); - } - result.gradient_ = gradient_; - } else { - result.gradient_ = gradientBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.FunctionDefLibrary) { - return mergeFrom((org.tensorflow.framework.FunctionDefLibrary)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.FunctionDefLibrary other) { - if (other == org.tensorflow.framework.FunctionDefLibrary.getDefaultInstance()) return this; - if (functionBuilder_ == null) { - if (!other.function_.isEmpty()) { - if (function_.isEmpty()) { - function_ = other.function_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureFunctionIsMutable(); - function_.addAll(other.function_); - } - onChanged(); - } - } else { - if (!other.function_.isEmpty()) { - if (functionBuilder_.isEmpty()) { - functionBuilder_.dispose(); - functionBuilder_ = null; - function_ = other.function_; - bitField0_ = (bitField0_ & ~0x00000001); - functionBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getFunctionFieldBuilder() : null; - } else { - functionBuilder_.addAllMessages(other.function_); - } - } - } - if (gradientBuilder_ == null) { - if (!other.gradient_.isEmpty()) { - if (gradient_.isEmpty()) { - gradient_ = other.gradient_; - bitField0_ = (bitField0_ & ~0x00000002); - } else { - ensureGradientIsMutable(); - gradient_.addAll(other.gradient_); - } - onChanged(); - } - } else { - if (!other.gradient_.isEmpty()) { - if (gradientBuilder_.isEmpty()) { - gradientBuilder_.dispose(); - gradientBuilder_ = null; - gradient_ = other.gradient_; - bitField0_ = (bitField0_ & ~0x00000002); - gradientBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getGradientFieldBuilder() : null; - } else { - gradientBuilder_.addAllMessages(other.gradient_); - } - } - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.FunctionDefLibrary parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.FunctionDefLibrary) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List function_ = - java.util.Collections.emptyList(); - private void ensureFunctionIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - function_ = new java.util.ArrayList(function_); - bitField0_ |= 0x00000001; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.FunctionDef, org.tensorflow.framework.FunctionDef.Builder, org.tensorflow.framework.FunctionDefOrBuilder> functionBuilder_; - - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public java.util.List getFunctionList() { - if (functionBuilder_ == null) { - return java.util.Collections.unmodifiableList(function_); - } else { - return functionBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public int getFunctionCount() { - if (functionBuilder_ == null) { - return function_.size(); - } else { - return functionBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public org.tensorflow.framework.FunctionDef getFunction(int index) { - if (functionBuilder_ == null) { - return function_.get(index); - } else { - return functionBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder setFunction( - int index, org.tensorflow.framework.FunctionDef value) { - if (functionBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFunctionIsMutable(); - function_.set(index, value); - onChanged(); - } else { - functionBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder setFunction( - int index, org.tensorflow.framework.FunctionDef.Builder builderForValue) { - if (functionBuilder_ == null) { - ensureFunctionIsMutable(); - function_.set(index, builderForValue.build()); - onChanged(); - } else { - functionBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder addFunction(org.tensorflow.framework.FunctionDef value) { - if (functionBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFunctionIsMutable(); - function_.add(value); - onChanged(); - } else { - functionBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder addFunction( - int index, org.tensorflow.framework.FunctionDef value) { - if (functionBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureFunctionIsMutable(); - function_.add(index, value); - onChanged(); - } else { - functionBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder addFunction( - org.tensorflow.framework.FunctionDef.Builder builderForValue) { - if (functionBuilder_ == null) { - ensureFunctionIsMutable(); - function_.add(builderForValue.build()); - onChanged(); - } else { - functionBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder addFunction( - int index, org.tensorflow.framework.FunctionDef.Builder builderForValue) { - if (functionBuilder_ == null) { - ensureFunctionIsMutable(); - function_.add(index, builderForValue.build()); - onChanged(); - } else { - functionBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder addAllFunction( - java.lang.Iterable values) { - if (functionBuilder_ == null) { - ensureFunctionIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, function_); - onChanged(); - } else { - functionBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder clearFunction() { - if (functionBuilder_ == null) { - function_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - } else { - functionBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public Builder removeFunction(int index) { - if (functionBuilder_ == null) { - ensureFunctionIsMutable(); - function_.remove(index); - onChanged(); - } else { - functionBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public org.tensorflow.framework.FunctionDef.Builder getFunctionBuilder( - int index) { - return getFunctionFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public org.tensorflow.framework.FunctionDefOrBuilder getFunctionOrBuilder( - int index) { - if (functionBuilder_ == null) { - return function_.get(index); } else { - return functionBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public java.util.List - getFunctionOrBuilderList() { - if (functionBuilder_ != null) { - return functionBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(function_); - } - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public org.tensorflow.framework.FunctionDef.Builder addFunctionBuilder() { - return getFunctionFieldBuilder().addBuilder( - org.tensorflow.framework.FunctionDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public org.tensorflow.framework.FunctionDef.Builder addFunctionBuilder( - int index) { - return getFunctionFieldBuilder().addBuilder( - index, org.tensorflow.framework.FunctionDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - public java.util.List - getFunctionBuilderList() { - return getFunctionFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.FunctionDef, org.tensorflow.framework.FunctionDef.Builder, org.tensorflow.framework.FunctionDefOrBuilder> - getFunctionFieldBuilder() { - if (functionBuilder_ == null) { - functionBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.FunctionDef, org.tensorflow.framework.FunctionDef.Builder, org.tensorflow.framework.FunctionDefOrBuilder>( - function_, - ((bitField0_ & 0x00000001) == 0x00000001), - getParentForChildren(), - isClean()); - function_ = null; - } - return functionBuilder_; - } - - private java.util.List gradient_ = - java.util.Collections.emptyList(); - private void ensureGradientIsMutable() { - if (!((bitField0_ & 0x00000002) == 0x00000002)) { - gradient_ = new java.util.ArrayList(gradient_); - bitField0_ |= 0x00000002; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.GradientDef, org.tensorflow.framework.GradientDef.Builder, org.tensorflow.framework.GradientDefOrBuilder> gradientBuilder_; - - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public java.util.List getGradientList() { - if (gradientBuilder_ == null) { - return java.util.Collections.unmodifiableList(gradient_); - } else { - return gradientBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public int getGradientCount() { - if (gradientBuilder_ == null) { - return gradient_.size(); - } else { - return gradientBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public org.tensorflow.framework.GradientDef getGradient(int index) { - if (gradientBuilder_ == null) { - return gradient_.get(index); - } else { - return gradientBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder setGradient( - int index, org.tensorflow.framework.GradientDef value) { - if (gradientBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureGradientIsMutable(); - gradient_.set(index, value); - onChanged(); - } else { - gradientBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder setGradient( - int index, org.tensorflow.framework.GradientDef.Builder builderForValue) { - if (gradientBuilder_ == null) { - ensureGradientIsMutable(); - gradient_.set(index, builderForValue.build()); - onChanged(); - } else { - gradientBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder addGradient(org.tensorflow.framework.GradientDef value) { - if (gradientBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureGradientIsMutable(); - gradient_.add(value); - onChanged(); - } else { - gradientBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder addGradient( - int index, org.tensorflow.framework.GradientDef value) { - if (gradientBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureGradientIsMutable(); - gradient_.add(index, value); - onChanged(); - } else { - gradientBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder addGradient( - org.tensorflow.framework.GradientDef.Builder builderForValue) { - if (gradientBuilder_ == null) { - ensureGradientIsMutable(); - gradient_.add(builderForValue.build()); - onChanged(); - } else { - gradientBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder addGradient( - int index, org.tensorflow.framework.GradientDef.Builder builderForValue) { - if (gradientBuilder_ == null) { - ensureGradientIsMutable(); - gradient_.add(index, builderForValue.build()); - onChanged(); - } else { - gradientBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder addAllGradient( - java.lang.Iterable values) { - if (gradientBuilder_ == null) { - ensureGradientIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, gradient_); - onChanged(); - } else { - gradientBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder clearGradient() { - if (gradientBuilder_ == null) { - gradient_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - onChanged(); - } else { - gradientBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public Builder removeGradient(int index) { - if (gradientBuilder_ == null) { - ensureGradientIsMutable(); - gradient_.remove(index); - onChanged(); - } else { - gradientBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public org.tensorflow.framework.GradientDef.Builder getGradientBuilder( - int index) { - return getGradientFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public org.tensorflow.framework.GradientDefOrBuilder getGradientOrBuilder( - int index) { - if (gradientBuilder_ == null) { - return gradient_.get(index); } else { - return gradientBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public java.util.List - getGradientOrBuilderList() { - if (gradientBuilder_ != null) { - return gradientBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(gradient_); - } - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public org.tensorflow.framework.GradientDef.Builder addGradientBuilder() { - return getGradientFieldBuilder().addBuilder( - org.tensorflow.framework.GradientDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public org.tensorflow.framework.GradientDef.Builder addGradientBuilder( - int index) { - return getGradientFieldBuilder().addBuilder( - index, org.tensorflow.framework.GradientDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - public java.util.List - getGradientBuilderList() { - return getGradientFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.GradientDef, org.tensorflow.framework.GradientDef.Builder, org.tensorflow.framework.GradientDefOrBuilder> - getGradientFieldBuilder() { - if (gradientBuilder_ == null) { - gradientBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.GradientDef, org.tensorflow.framework.GradientDef.Builder, org.tensorflow.framework.GradientDefOrBuilder>( - gradient_, - ((bitField0_ & 0x00000002) == 0x00000002), - getParentForChildren(), - isClean()); - gradient_ = null; - } - return gradientBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.FunctionDefLibrary) - } - - // @@protoc_insertion_point(class_scope:tensorflow.FunctionDefLibrary) - private static final org.tensorflow.framework.FunctionDefLibrary DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.FunctionDefLibrary(); - } - - public static org.tensorflow.framework.FunctionDefLibrary getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public FunctionDefLibrary parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new FunctionDefLibrary(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.FunctionDefLibrary getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefLibraryOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefLibraryOrBuilder.java deleted file mode 100644 index cc1969aaad0..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefLibraryOrBuilder.java +++ /dev/null @@ -1,57 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: function.proto - -package org.tensorflow.framework; - -public interface FunctionDefLibraryOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.FunctionDefLibrary) - com.google.protobuf.MessageOrBuilder { - - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - java.util.List - getFunctionList(); - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - org.tensorflow.framework.FunctionDef getFunction(int index); - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - int getFunctionCount(); - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - java.util.List - getFunctionOrBuilderList(); - /** - * repeated .tensorflow.FunctionDef function = 1; - */ - org.tensorflow.framework.FunctionDefOrBuilder getFunctionOrBuilder( - int index); - - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - java.util.List - getGradientList(); - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - org.tensorflow.framework.GradientDef getGradient(int index); - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - int getGradientCount(); - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - java.util.List - getGradientOrBuilderList(); - /** - * repeated .tensorflow.GradientDef gradient = 2; - */ - org.tensorflow.framework.GradientDefOrBuilder getGradientOrBuilder( - int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefOrBuilder.java deleted file mode 100644 index cad766bd0ea..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionDefOrBuilder.java +++ /dev/null @@ -1,204 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: function.proto - -package org.tensorflow.framework; - -public interface FunctionDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.FunctionDef) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * The definition of the function's name, arguments, return values,
-   * attrs etc.
-   *
- * - * optional .tensorflow.OpDef signature = 1; - */ - boolean hasSignature(); - /** - * 
-   * The definition of the function's name, arguments, return values,
-   * attrs etc.
-   *
- * - * optional .tensorflow.OpDef signature = 1; - */ - org.tensorflow.framework.OpDef getSignature(); - /** - * 
-   * The definition of the function's name, arguments, return values,
-   * attrs etc.
-   *
- * - * optional .tensorflow.OpDef signature = 1; - */ - org.tensorflow.framework.OpDefOrBuilder getSignatureOrBuilder(); - - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - int getAttrCount(); - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - boolean containsAttr( - java.lang.String key); - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - java.util.Map - getAttr(); - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - java.util.Map - getAttrMap(); - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue); - /** - * 
-   * Attributes specific to this function definition.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key); - - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - java.util.List - getNodeDefList(); - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - org.tensorflow.framework.NodeDef getNodeDef(int index); - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - int getNodeDefCount(); - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - java.util.List - getNodeDefOrBuilderList(); - /** - * 
-   * By convention, "op" in node_def is resolved by consulting with a
-   * user-defined library first. If not resolved, "func" is assumed to
-   * be a builtin op.
-   *
- * - * repeated .tensorflow.NodeDef node_def = 3; - */ - org.tensorflow.framework.NodeDefOrBuilder getNodeDefOrBuilder( - int index); - - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - int getRetCount(); - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - boolean containsRet( - java.lang.String key); - /** - * Use {@link #getRetMap()} instead. - */ - @java.lang.Deprecated - java.util.Map - getRet(); - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - java.util.Map - getRetMap(); - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - - java.lang.String getRetOrDefault( - java.lang.String key, - java.lang.String defaultValue); - /** - * 
-   * A mapping from the output arg names from `signature` to the
-   * outputs from `node_def` that should be returned by the function.
-   *
- * - * map<string, string> ret = 4; - */ - - java.lang.String getRetOrThrow( - java.lang.String key); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/FunctionProtos.java deleted file mode 100644 index 46209bff0b5..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/FunctionProtos.java +++ /dev/null @@ -1,121 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: function.proto - -package org.tensorflow.framework; - -public final class FunctionProtos { - private FunctionProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_FunctionDefLibrary_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_FunctionDefLibrary_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_FunctionDef_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_FunctionDef_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_FunctionDef_AttrEntry_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_FunctionDef_AttrEntry_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_FunctionDef_RetEntry_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_FunctionDef_RetEntry_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_GradientDef_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_GradientDef_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\016function.proto\022\ntensorflow\032*tensorflow" + - "/core/framework/attr_value.proto\032(tensor" + - "flow/core/framework/node_def.proto\032&tens" + - "orflow/core/framework/op_def.proto\"j\n\022Fu" + - "nctionDefLibrary\022)\n\010function\030\001 \003(\0132\027.ten" + - "sorflow.FunctionDef\022)\n\010gradient\030\002 \003(\0132\027." + - "tensorflow.GradientDef\"\252\002\n\013FunctionDef\022$" + - "\n\tsignature\030\001 \001(\0132\021.tensorflow.OpDef\022/\n\004" + - "attr\030\005 \003(\0132!.tensorflow.FunctionDef.Attr" + - "Entry\022%\n\010node_def\030\003 \003(\0132\023.tensorflow.Nod", - "eDef\022-\n\003ret\030\004 \003(\0132 .tensorflow.FunctionD" + - "ef.RetEntry\032B\n\tAttrEntry\022\013\n\003key\030\001 \001(\t\022$\n" + - "\005value\030\002 \001(\0132\025.tensorflow.AttrValue:\0028\001\032" + - "*\n\010RetEntry\022\013\n\003key\030\001 \001(\t\022\r\n\005value\030\002 \001(\t:" + - "\0028\001\";\n\013GradientDef\022\025\n\rfunction_name\030\001 \001(" + - "\t\022\025\n\rgradient_func\030\002 \001(\tB/\n\030org.tensorfl" + - "ow.frameworkB\016FunctionProtosP\001\370\001\001b\006proto" + - "3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.framework.AttrValueProtos.getDescriptor(), - org.tensorflow.framework.NodeProto.getDescriptor(), - org.tensorflow.framework.OpDefProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_FunctionDefLibrary_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_FunctionDefLibrary_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_FunctionDefLibrary_descriptor, - new java.lang.String[] { "Function", "Gradient", }); - internal_static_tensorflow_FunctionDef_descriptor = - getDescriptor().getMessageTypes().get(1); - internal_static_tensorflow_FunctionDef_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_FunctionDef_descriptor, - new java.lang.String[] { "Signature", "Attr", "NodeDef", "Ret", }); - internal_static_tensorflow_FunctionDef_AttrEntry_descriptor = - internal_static_tensorflow_FunctionDef_descriptor.getNestedTypes().get(0); - internal_static_tensorflow_FunctionDef_AttrEntry_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_FunctionDef_AttrEntry_descriptor, - new java.lang.String[] { "Key", "Value", }); - internal_static_tensorflow_FunctionDef_RetEntry_descriptor = - internal_static_tensorflow_FunctionDef_descriptor.getNestedTypes().get(1); - internal_static_tensorflow_FunctionDef_RetEntry_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_FunctionDef_RetEntry_descriptor, - new java.lang.String[] { "Key", "Value", }); - internal_static_tensorflow_GradientDef_descriptor = - getDescriptor().getMessageTypes().get(2); - internal_static_tensorflow_GradientDef_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_GradientDef_descriptor, - new java.lang.String[] { "FunctionName", "GradientFunc", }); - org.tensorflow.framework.AttrValueProtos.getDescriptor(); - org.tensorflow.framework.NodeProto.getDescriptor(); - org.tensorflow.framework.OpDefProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/GradientDef.java b/scala/dllib/src/main/java/org/tensorflow/framework/GradientDef.java deleted file mode 100644 index 7e9c3040f8d..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/GradientDef.java +++ /dev/null @@ -1,713 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: function.proto - -package org.tensorflow.framework; - -/** - * 
- * GradientDef defines the gradient function of a function defined in
- * a function library.
- * A gradient function g (specified by gradient_func) for a function f
- * (specified by function_name) must follow the following:
- * The function 'f' must be a numerical function which takes N inputs
- * and produces M outputs. Its gradient function 'g', which is a
- * function taking N + M inputs and produces N outputs.
- * I.e. if we have
- *    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
- * then, g is
- *    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
- *                                      dL/dy1, dL/dy2, ..., dL/dy_M),
- * where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
- * loss function). dL/dx_i is the partial derivative of L with respect
- * to x_i.
- *
- * - * Protobuf type {@code tensorflow.GradientDef} - */ -public final class GradientDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.GradientDef) - GradientDefOrBuilder { - // Use GradientDef.newBuilder() to construct. - private GradientDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private GradientDef() { - functionName_ = ""; - gradientFunc_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private GradientDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - functionName_ = s; - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - gradientFunc_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_GradientDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_GradientDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.GradientDef.class, org.tensorflow.framework.GradientDef.Builder.class); - } - - public static final int FUNCTION_NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object functionName_; - /** - * 
-   * The function name.
-   *
- * - * optional string function_name = 1; - */ - public java.lang.String getFunctionName() { - java.lang.Object ref = functionName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - functionName_ = s; - return s; - } - } - /** - * 
-   * The function name.
-   *
- * - * optional string function_name = 1; - */ - public com.google.protobuf.ByteString - getFunctionNameBytes() { - java.lang.Object ref = functionName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - functionName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int GRADIENT_FUNC_FIELD_NUMBER = 2; - private volatile java.lang.Object gradientFunc_; - /** - * 
-   * The gradient function's name.
-   *
- * - * optional string gradient_func = 2; - */ - public java.lang.String getGradientFunc() { - java.lang.Object ref = gradientFunc_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - gradientFunc_ = s; - return s; - } - } - /** - * 
-   * The gradient function's name.
-   *
- * - * optional string gradient_func = 2; - */ - public com.google.protobuf.ByteString - getGradientFuncBytes() { - java.lang.Object ref = gradientFunc_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - gradientFunc_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getFunctionNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, functionName_); - } - if (!getGradientFuncBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, gradientFunc_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getFunctionNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, functionName_); - } - if (!getGradientFuncBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, gradientFunc_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.GradientDef)) { - return super.equals(obj); - } - org.tensorflow.framework.GradientDef other = (org.tensorflow.framework.GradientDef) obj; - - boolean result = true; - result = result && getFunctionName() - .equals(other.getFunctionName()); - result = result && getGradientFunc() - .equals(other.getGradientFunc()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + FUNCTION_NAME_FIELD_NUMBER; - hash = (53 * hash) + getFunctionName().hashCode(); - hash = (37 * hash) + GRADIENT_FUNC_FIELD_NUMBER; - hash = (53 * hash) + getGradientFunc().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.GradientDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.GradientDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.GradientDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.GradientDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.GradientDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.GradientDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.GradientDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.GradientDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.GradientDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.GradientDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.GradientDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * GradientDef defines the gradient function of a function defined in
-   * a function library.
-   * A gradient function g (specified by gradient_func) for a function f
-   * (specified by function_name) must follow the following:
-   * The function 'f' must be a numerical function which takes N inputs
-   * and produces M outputs. Its gradient function 'g', which is a
-   * function taking N + M inputs and produces N outputs.
-   * I.e. if we have
-   *    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
-   * then, g is
-   *    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
-   *                                      dL/dy1, dL/dy2, ..., dL/dy_M),
-   * where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
-   * loss function). dL/dx_i is the partial derivative of L with respect
-   * to x_i.
-   *
- * - * Protobuf type {@code tensorflow.GradientDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.GradientDef) - org.tensorflow.framework.GradientDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_GradientDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_GradientDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.GradientDef.class, org.tensorflow.framework.GradientDef.Builder.class); - } - - // Construct using org.tensorflow.framework.GradientDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - functionName_ = ""; - - gradientFunc_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_GradientDef_descriptor; - } - - public org.tensorflow.framework.GradientDef getDefaultInstanceForType() { - return org.tensorflow.framework.GradientDef.getDefaultInstance(); - } - - public org.tensorflow.framework.GradientDef build() { - org.tensorflow.framework.GradientDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.GradientDef buildPartial() { - org.tensorflow.framework.GradientDef result = new org.tensorflow.framework.GradientDef(this); - result.functionName_ = functionName_; - result.gradientFunc_ = gradientFunc_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.GradientDef) { - return mergeFrom((org.tensorflow.framework.GradientDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.GradientDef other) { - if (other == org.tensorflow.framework.GradientDef.getDefaultInstance()) return this; - if (!other.getFunctionName().isEmpty()) { - functionName_ = other.functionName_; - onChanged(); - } - if (!other.getGradientFunc().isEmpty()) { - gradientFunc_ = other.gradientFunc_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.GradientDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.GradientDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private java.lang.Object functionName_ = ""; - /** - * 
-     * The function name.
-     *
- * - * optional string function_name = 1; - */ - public java.lang.String getFunctionName() { - java.lang.Object ref = functionName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - functionName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * The function name.
-     *
- * - * optional string function_name = 1; - */ - public com.google.protobuf.ByteString - getFunctionNameBytes() { - java.lang.Object ref = functionName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - functionName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * The function name.
-     *
- * - * optional string function_name = 1; - */ - public Builder setFunctionName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - functionName_ = value; - onChanged(); - return this; - } - /** - * 
-     * The function name.
-     *
- * - * optional string function_name = 1; - */ - public Builder clearFunctionName() { - - functionName_ = getDefaultInstance().getFunctionName(); - onChanged(); - return this; - } - /** - * 
-     * The function name.
-     *
- * - * optional string function_name = 1; - */ - public Builder setFunctionNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - functionName_ = value; - onChanged(); - return this; - } - - private java.lang.Object gradientFunc_ = ""; - /** - * 
-     * The gradient function's name.
-     *
- * - * optional string gradient_func = 2; - */ - public java.lang.String getGradientFunc() { - java.lang.Object ref = gradientFunc_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - gradientFunc_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * The gradient function's name.
-     *
- * - * optional string gradient_func = 2; - */ - public com.google.protobuf.ByteString - getGradientFuncBytes() { - java.lang.Object ref = gradientFunc_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - gradientFunc_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * The gradient function's name.
-     *
- * - * optional string gradient_func = 2; - */ - public Builder setGradientFunc( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - gradientFunc_ = value; - onChanged(); - return this; - } - /** - * 
-     * The gradient function's name.
-     *
- * - * optional string gradient_func = 2; - */ - public Builder clearGradientFunc() { - - gradientFunc_ = getDefaultInstance().getGradientFunc(); - onChanged(); - return this; - } - /** - * 
-     * The gradient function's name.
-     *
- * - * optional string gradient_func = 2; - */ - public Builder setGradientFuncBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - gradientFunc_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.GradientDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.GradientDef) - private static final org.tensorflow.framework.GradientDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.GradientDef(); - } - - public static org.tensorflow.framework.GradientDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public GradientDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new GradientDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.GradientDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/GradientDefOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/GradientDefOrBuilder.java deleted file mode 100644 index 041259b96c2..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/GradientDefOrBuilder.java +++ /dev/null @@ -1,45 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: function.proto - -package org.tensorflow.framework; - -public interface GradientDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.GradientDef) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * The function name.
-   *
- * - * optional string function_name = 1; - */ - java.lang.String getFunctionName(); - /** - * 
-   * The function name.
-   *
- * - * optional string function_name = 1; - */ - com.google.protobuf.ByteString - getFunctionNameBytes(); - - /** - * 
-   * The gradient function's name.
-   *
- * - * optional string gradient_func = 2; - */ - java.lang.String getGradientFunc(); - /** - * 
-   * The gradient function's name.
-   *
- * - * optional string gradient_func = 2; - */ - com.google.protobuf.ByteString - getGradientFuncBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/GraphDef.java b/scala/dllib/src/main/java/org/tensorflow/framework/GraphDef.java deleted file mode 100644 index 2f9f18ff9f2..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/GraphDef.java +++ /dev/null @@ -1,1539 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: graph.proto - -package org.tensorflow.framework; - -/** - * 
- * Represents the graph of operations
- *
- * - * Protobuf type {@code tensorflow.GraphDef} - */ -public final class GraphDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.GraphDef) - GraphDefOrBuilder { - // Use GraphDef.newBuilder() to construct. - private GraphDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private GraphDef() { - node_ = java.util.Collections.emptyList(); - version_ = 0; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private GraphDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - node_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - node_.add( - input.readMessage(org.tensorflow.framework.NodeDef.parser(), extensionRegistry)); - break; - } - case 18: { - org.tensorflow.framework.FunctionDefLibrary.Builder subBuilder = null; - if (library_ != null) { - subBuilder = library_.toBuilder(); - } - library_ = input.readMessage(org.tensorflow.framework.FunctionDefLibrary.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(library_); - library_ = subBuilder.buildPartial(); - } - - break; - } - case 24: { - - version_ = input.readInt32(); - break; - } - case 34: { - org.tensorflow.framework.VersionDef.Builder subBuilder = null; - if (versions_ != null) { - subBuilder = versions_.toBuilder(); - } - versions_ = input.readMessage(org.tensorflow.framework.VersionDef.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(versions_); - versions_ = subBuilder.buildPartial(); - } - - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - node_ = java.util.Collections.unmodifiableList(node_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.GraphDef.class, org.tensorflow.framework.GraphDef.Builder.class); - } - - private int bitField0_; - public static final int NODE_FIELD_NUMBER = 1; - private java.util.List node_; - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public java.util.List getNodeList() { - return node_; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public java.util.List - getNodeOrBuilderList() { - return node_; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public int getNodeCount() { - return node_.size(); - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public org.tensorflow.framework.NodeDef getNode(int index) { - return node_.get(index); - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public org.tensorflow.framework.NodeDefOrBuilder getNodeOrBuilder( - int index) { - return node_.get(index); - } - - public static final int VERSIONS_FIELD_NUMBER = 4; - private org.tensorflow.framework.VersionDef versions_; - /** - * 
-   * Compatibility versions of the graph.  See core/public/version.h for version
-   * history.  The GraphDef version is distinct from the TensorFlow version, and
-   * each release of TensorFlow will support a range of GraphDef versions.
-   *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public boolean hasVersions() { - return versions_ != null; - } - /** - * 
-   * Compatibility versions of the graph.  See core/public/version.h for version
-   * history.  The GraphDef version is distinct from the TensorFlow version, and
-   * each release of TensorFlow will support a range of GraphDef versions.
-   *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public org.tensorflow.framework.VersionDef getVersions() { - return versions_ == null ? org.tensorflow.framework.VersionDef.getDefaultInstance() : versions_; - } - /** - * 
-   * Compatibility versions of the graph.  See core/public/version.h for version
-   * history.  The GraphDef version is distinct from the TensorFlow version, and
-   * each release of TensorFlow will support a range of GraphDef versions.
-   *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public org.tensorflow.framework.VersionDefOrBuilder getVersionsOrBuilder() { - return getVersions(); - } - - public static final int VERSION_FIELD_NUMBER = 3; - private int version_; - /** - * 
-   * Deprecated single version field; use versions above instead.  Since all
-   * GraphDef changes before "versions" was introduced were forward
-   * compatible, this field is entirely ignored.
-   *
- * - * optional int32 version = 3 [deprecated = true]; - */ - @java.lang.Deprecated public int getVersion() { - return version_; - } - - public static final int LIBRARY_FIELD_NUMBER = 2; - private org.tensorflow.framework.FunctionDefLibrary library_; - /** - * 
-   * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-   * "library" provides user-defined functions.
-   * Naming:
-   *   * library.function.name are in a flat namespace.
-   *     NOTE: We may need to change it to be hierarchical to support
-   *     different orgs. E.g.,
-   *     { "/google/nn", { ... }},
-   *     { "/google/vision", { ... }}
-   *     { "/org_foo/module_bar", { ... }}
-   *     map<string, FunctionDefLib> named_lib;
-   *   * If node[i].op is the name of one function in "library",
-   *     node[i] is deemed as a function call. Otherwise, node[i].op
-   *     must be a primitive operation supported by the runtime.
-   * Function call semantics:
-   *   * The callee may start execution as soon as some of its inputs
-   *     are ready. The caller may want to use Tuple() mechanism to
-   *     ensure all inputs are ready in the same time.
-   *   * The consumer of return values may start executing as soon as
-   *     the return values the consumer depends on are ready.  The
-   *     consumer may want to use Tuple() mechanism to ensure the
-   *     consumer does not start until all return values of the callee
-   *     function are ready.
-   *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public boolean hasLibrary() { - return library_ != null; - } - /** - * 
-   * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-   * "library" provides user-defined functions.
-   * Naming:
-   *   * library.function.name are in a flat namespace.
-   *     NOTE: We may need to change it to be hierarchical to support
-   *     different orgs. E.g.,
-   *     { "/google/nn", { ... }},
-   *     { "/google/vision", { ... }}
-   *     { "/org_foo/module_bar", { ... }}
-   *     map<string, FunctionDefLib> named_lib;
-   *   * If node[i].op is the name of one function in "library",
-   *     node[i] is deemed as a function call. Otherwise, node[i].op
-   *     must be a primitive operation supported by the runtime.
-   * Function call semantics:
-   *   * The callee may start execution as soon as some of its inputs
-   *     are ready. The caller may want to use Tuple() mechanism to
-   *     ensure all inputs are ready in the same time.
-   *   * The consumer of return values may start executing as soon as
-   *     the return values the consumer depends on are ready.  The
-   *     consumer may want to use Tuple() mechanism to ensure the
-   *     consumer does not start until all return values of the callee
-   *     function are ready.
-   *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public org.tensorflow.framework.FunctionDefLibrary getLibrary() { - return library_ == null ? org.tensorflow.framework.FunctionDefLibrary.getDefaultInstance() : library_; - } - /** - * 
-   * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-   * "library" provides user-defined functions.
-   * Naming:
-   *   * library.function.name are in a flat namespace.
-   *     NOTE: We may need to change it to be hierarchical to support
-   *     different orgs. E.g.,
-   *     { "/google/nn", { ... }},
-   *     { "/google/vision", { ... }}
-   *     { "/org_foo/module_bar", { ... }}
-   *     map<string, FunctionDefLib> named_lib;
-   *   * If node[i].op is the name of one function in "library",
-   *     node[i] is deemed as a function call. Otherwise, node[i].op
-   *     must be a primitive operation supported by the runtime.
-   * Function call semantics:
-   *   * The callee may start execution as soon as some of its inputs
-   *     are ready. The caller may want to use Tuple() mechanism to
-   *     ensure all inputs are ready in the same time.
-   *   * The consumer of return values may start executing as soon as
-   *     the return values the consumer depends on are ready.  The
-   *     consumer may want to use Tuple() mechanism to ensure the
-   *     consumer does not start until all return values of the callee
-   *     function are ready.
-   *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public org.tensorflow.framework.FunctionDefLibraryOrBuilder getLibraryOrBuilder() { - return getLibrary(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - for (int i = 0; i < node_.size(); i++) { - output.writeMessage(1, node_.get(i)); - } - if (library_ != null) { - output.writeMessage(2, getLibrary()); - } - if (version_ != 0) { - output.writeInt32(3, version_); - } - if (versions_ != null) { - output.writeMessage(4, getVersions()); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - for (int i = 0; i < node_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, node_.get(i)); - } - if (library_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, getLibrary()); - } - if (version_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeInt32Size(3, version_); - } - if (versions_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(4, getVersions()); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.GraphDef)) { - return super.equals(obj); - } - org.tensorflow.framework.GraphDef other = (org.tensorflow.framework.GraphDef) obj; - - boolean result = true; - result = result && getNodeList() - .equals(other.getNodeList()); - result = result && (hasVersions() == other.hasVersions()); - if (hasVersions()) { - result = result && getVersions() - .equals(other.getVersions()); - } - result = result && (getVersion() - == other.getVersion()); - result = result && (hasLibrary() == other.hasLibrary()); - if (hasLibrary()) { - result = result && getLibrary() - .equals(other.getLibrary()); - } - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - if (getNodeCount() > 0) { - hash = (37 * hash) + NODE_FIELD_NUMBER; - hash = (53 * hash) + getNodeList().hashCode(); - } - if (hasVersions()) { - hash = (37 * hash) + VERSIONS_FIELD_NUMBER; - hash = (53 * hash) + getVersions().hashCode(); - } - hash = (37 * hash) + VERSION_FIELD_NUMBER; - hash = (53 * hash) + getVersion(); - if (hasLibrary()) { - hash = (37 * hash) + LIBRARY_FIELD_NUMBER; - hash = (53 * hash) + getLibrary().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.GraphDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.GraphDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.GraphDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.GraphDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.GraphDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.GraphDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.GraphDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.GraphDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.GraphDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.GraphDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.GraphDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Represents the graph of operations
-   *
- * - * Protobuf type {@code tensorflow.GraphDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.GraphDef) - org.tensorflow.framework.GraphDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.GraphDef.class, org.tensorflow.framework.GraphDef.Builder.class); - } - - // Construct using org.tensorflow.framework.GraphDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getNodeFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - if (nodeBuilder_ == null) { - node_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - } else { - nodeBuilder_.clear(); - } - if (versionsBuilder_ == null) { - versions_ = null; - } else { - versions_ = null; - versionsBuilder_ = null; - } - version_ = 0; - - if (libraryBuilder_ == null) { - library_ = null; - } else { - library_ = null; - libraryBuilder_ = null; - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_descriptor; - } - - public org.tensorflow.framework.GraphDef getDefaultInstanceForType() { - return org.tensorflow.framework.GraphDef.getDefaultInstance(); - } - - public org.tensorflow.framework.GraphDef build() { - org.tensorflow.framework.GraphDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.GraphDef buildPartial() { - org.tensorflow.framework.GraphDef result = new org.tensorflow.framework.GraphDef(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - if (nodeBuilder_ == null) { - if (((bitField0_ & 0x00000001) == 0x00000001)) { - node_ = java.util.Collections.unmodifiableList(node_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.node_ = node_; - } else { - result.node_ = nodeBuilder_.build(); - } - if (versionsBuilder_ == null) { - result.versions_ = versions_; - } else { - result.versions_ = versionsBuilder_.build(); - } - result.version_ = version_; - if (libraryBuilder_ == null) { - result.library_ = library_; - } else { - result.library_ = libraryBuilder_.build(); - } - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.GraphDef) { - return mergeFrom((org.tensorflow.framework.GraphDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.GraphDef other) { - if (other == org.tensorflow.framework.GraphDef.getDefaultInstance()) return this; - if (nodeBuilder_ == null) { - if (!other.node_.isEmpty()) { - if (node_.isEmpty()) { - node_ = other.node_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureNodeIsMutable(); - node_.addAll(other.node_); - } - onChanged(); - } - } else { - if (!other.node_.isEmpty()) { - if (nodeBuilder_.isEmpty()) { - nodeBuilder_.dispose(); - nodeBuilder_ = null; - node_ = other.node_; - bitField0_ = (bitField0_ & ~0x00000001); - nodeBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getNodeFieldBuilder() : null; - } else { - nodeBuilder_.addAllMessages(other.node_); - } - } - } - if (other.hasVersions()) { - mergeVersions(other.getVersions()); - } - if (other.getVersion() != 0) { - setVersion(other.getVersion()); - } - if (other.hasLibrary()) { - mergeLibrary(other.getLibrary()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.GraphDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.GraphDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List node_ = - java.util.Collections.emptyList(); - private void ensureNodeIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - node_ = new java.util.ArrayList(node_); - bitField0_ |= 0x00000001; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder> nodeBuilder_; - - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public java.util.List getNodeList() { - if (nodeBuilder_ == null) { - return java.util.Collections.unmodifiableList(node_); - } else { - return nodeBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public int getNodeCount() { - if (nodeBuilder_ == null) { - return node_.size(); - } else { - return nodeBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public org.tensorflow.framework.NodeDef getNode(int index) { - if (nodeBuilder_ == null) { - return node_.get(index); - } else { - return nodeBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder setNode( - int index, org.tensorflow.framework.NodeDef value) { - if (nodeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeIsMutable(); - node_.set(index, value); - onChanged(); - } else { - nodeBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder setNode( - int index, org.tensorflow.framework.NodeDef.Builder builderForValue) { - if (nodeBuilder_ == null) { - ensureNodeIsMutable(); - node_.set(index, builderForValue.build()); - onChanged(); - } else { - nodeBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder addNode(org.tensorflow.framework.NodeDef value) { - if (nodeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeIsMutable(); - node_.add(value); - onChanged(); - } else { - nodeBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder addNode( - int index, org.tensorflow.framework.NodeDef value) { - if (nodeBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureNodeIsMutable(); - node_.add(index, value); - onChanged(); - } else { - nodeBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder addNode( - org.tensorflow.framework.NodeDef.Builder builderForValue) { - if (nodeBuilder_ == null) { - ensureNodeIsMutable(); - node_.add(builderForValue.build()); - onChanged(); - } else { - nodeBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder addNode( - int index, org.tensorflow.framework.NodeDef.Builder builderForValue) { - if (nodeBuilder_ == null) { - ensureNodeIsMutable(); - node_.add(index, builderForValue.build()); - onChanged(); - } else { - nodeBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder addAllNode( - java.lang.Iterable values) { - if (nodeBuilder_ == null) { - ensureNodeIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, node_); - onChanged(); - } else { - nodeBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder clearNode() { - if (nodeBuilder_ == null) { - node_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - } else { - nodeBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public Builder removeNode(int index) { - if (nodeBuilder_ == null) { - ensureNodeIsMutable(); - node_.remove(index); - onChanged(); - } else { - nodeBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public org.tensorflow.framework.NodeDef.Builder getNodeBuilder( - int index) { - return getNodeFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public org.tensorflow.framework.NodeDefOrBuilder getNodeOrBuilder( - int index) { - if (nodeBuilder_ == null) { - return node_.get(index); } else { - return nodeBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public java.util.List - getNodeOrBuilderList() { - if (nodeBuilder_ != null) { - return nodeBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(node_); - } - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public org.tensorflow.framework.NodeDef.Builder addNodeBuilder() { - return getNodeFieldBuilder().addBuilder( - org.tensorflow.framework.NodeDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public org.tensorflow.framework.NodeDef.Builder addNodeBuilder( - int index) { - return getNodeFieldBuilder().addBuilder( - index, org.tensorflow.framework.NodeDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.NodeDef node = 1; - */ - public java.util.List - getNodeBuilderList() { - return getNodeFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder> - getNodeFieldBuilder() { - if (nodeBuilder_ == null) { - nodeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder>( - node_, - ((bitField0_ & 0x00000001) == 0x00000001), - getParentForChildren(), - isClean()); - node_ = null; - } - return nodeBuilder_; - } - - private org.tensorflow.framework.VersionDef versions_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.VersionDef, org.tensorflow.framework.VersionDef.Builder, org.tensorflow.framework.VersionDefOrBuilder> versionsBuilder_; - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public boolean hasVersions() { - return versionsBuilder_ != null || versions_ != null; - } - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public org.tensorflow.framework.VersionDef getVersions() { - if (versionsBuilder_ == null) { - return versions_ == null ? org.tensorflow.framework.VersionDef.getDefaultInstance() : versions_; - } else { - return versionsBuilder_.getMessage(); - } - } - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public Builder setVersions(org.tensorflow.framework.VersionDef value) { - if (versionsBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - versions_ = value; - onChanged(); - } else { - versionsBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public Builder setVersions( - org.tensorflow.framework.VersionDef.Builder builderForValue) { - if (versionsBuilder_ == null) { - versions_ = builderForValue.build(); - onChanged(); - } else { - versionsBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public Builder mergeVersions(org.tensorflow.framework.VersionDef value) { - if (versionsBuilder_ == null) { - if (versions_ != null) { - versions_ = - org.tensorflow.framework.VersionDef.newBuilder(versions_).mergeFrom(value).buildPartial(); - } else { - versions_ = value; - } - onChanged(); - } else { - versionsBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public Builder clearVersions() { - if (versionsBuilder_ == null) { - versions_ = null; - onChanged(); - } else { - versions_ = null; - versionsBuilder_ = null; - } - - return this; - } - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public org.tensorflow.framework.VersionDef.Builder getVersionsBuilder() { - - onChanged(); - return getVersionsFieldBuilder().getBuilder(); - } - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - public org.tensorflow.framework.VersionDefOrBuilder getVersionsOrBuilder() { - if (versionsBuilder_ != null) { - return versionsBuilder_.getMessageOrBuilder(); - } else { - return versions_ == null ? - org.tensorflow.framework.VersionDef.getDefaultInstance() : versions_; - } - } - /** - * 
-     * Compatibility versions of the graph.  See core/public/version.h for version
-     * history.  The GraphDef version is distinct from the TensorFlow version, and
-     * each release of TensorFlow will support a range of GraphDef versions.
-     *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.VersionDef, org.tensorflow.framework.VersionDef.Builder, org.tensorflow.framework.VersionDefOrBuilder> - getVersionsFieldBuilder() { - if (versionsBuilder_ == null) { - versionsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.VersionDef, org.tensorflow.framework.VersionDef.Builder, org.tensorflow.framework.VersionDefOrBuilder>( - getVersions(), - getParentForChildren(), - isClean()); - versions_ = null; - } - return versionsBuilder_; - } - - private int version_ ; - /** - * 
-     * Deprecated single version field; use versions above instead.  Since all
-     * GraphDef changes before "versions" was introduced were forward
-     * compatible, this field is entirely ignored.
-     *
- * - * optional int32 version = 3 [deprecated = true]; - */ - @java.lang.Deprecated public int getVersion() { - return version_; - } - /** - * 
-     * Deprecated single version field; use versions above instead.  Since all
-     * GraphDef changes before "versions" was introduced were forward
-     * compatible, this field is entirely ignored.
-     *
- * - * optional int32 version = 3 [deprecated = true]; - */ - @java.lang.Deprecated public Builder setVersion(int value) { - - version_ = value; - onChanged(); - return this; - } - /** - * 
-     * Deprecated single version field; use versions above instead.  Since all
-     * GraphDef changes before "versions" was introduced were forward
-     * compatible, this field is entirely ignored.
-     *
- * - * optional int32 version = 3 [deprecated = true]; - */ - @java.lang.Deprecated public Builder clearVersion() { - - version_ = 0; - onChanged(); - return this; - } - - private org.tensorflow.framework.FunctionDefLibrary library_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.FunctionDefLibrary, org.tensorflow.framework.FunctionDefLibrary.Builder, org.tensorflow.framework.FunctionDefLibraryOrBuilder> libraryBuilder_; - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public boolean hasLibrary() { - return libraryBuilder_ != null || library_ != null; - } - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public org.tensorflow.framework.FunctionDefLibrary getLibrary() { - if (libraryBuilder_ == null) { - return library_ == null ? org.tensorflow.framework.FunctionDefLibrary.getDefaultInstance() : library_; - } else { - return libraryBuilder_.getMessage(); - } - } - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public Builder setLibrary(org.tensorflow.framework.FunctionDefLibrary value) { - if (libraryBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - library_ = value; - onChanged(); - } else { - libraryBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public Builder setLibrary( - org.tensorflow.framework.FunctionDefLibrary.Builder builderForValue) { - if (libraryBuilder_ == null) { - library_ = builderForValue.build(); - onChanged(); - } else { - libraryBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public Builder mergeLibrary(org.tensorflow.framework.FunctionDefLibrary value) { - if (libraryBuilder_ == null) { - if (library_ != null) { - library_ = - org.tensorflow.framework.FunctionDefLibrary.newBuilder(library_).mergeFrom(value).buildPartial(); - } else { - library_ = value; - } - onChanged(); - } else { - libraryBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public Builder clearLibrary() { - if (libraryBuilder_ == null) { - library_ = null; - onChanged(); - } else { - library_ = null; - libraryBuilder_ = null; - } - - return this; - } - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public org.tensorflow.framework.FunctionDefLibrary.Builder getLibraryBuilder() { - - onChanged(); - return getLibraryFieldBuilder().getBuilder(); - } - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - public org.tensorflow.framework.FunctionDefLibraryOrBuilder getLibraryOrBuilder() { - if (libraryBuilder_ != null) { - return libraryBuilder_.getMessageOrBuilder(); - } else { - return library_ == null ? - org.tensorflow.framework.FunctionDefLibrary.getDefaultInstance() : library_; - } - } - /** - * 
-     * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-     * "library" provides user-defined functions.
-     * Naming:
-     *   * library.function.name are in a flat namespace.
-     *     NOTE: We may need to change it to be hierarchical to support
-     *     different orgs. E.g.,
-     *     { "/google/nn", { ... }},
-     *     { "/google/vision", { ... }}
-     *     { "/org_foo/module_bar", { ... }}
-     *     map<string, FunctionDefLib> named_lib;
-     *   * If node[i].op is the name of one function in "library",
-     *     node[i] is deemed as a function call. Otherwise, node[i].op
-     *     must be a primitive operation supported by the runtime.
-     * Function call semantics:
-     *   * The callee may start execution as soon as some of its inputs
-     *     are ready. The caller may want to use Tuple() mechanism to
-     *     ensure all inputs are ready in the same time.
-     *   * The consumer of return values may start executing as soon as
-     *     the return values the consumer depends on are ready.  The
-     *     consumer may want to use Tuple() mechanism to ensure the
-     *     consumer does not start until all return values of the callee
-     *     function are ready.
-     *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.FunctionDefLibrary, org.tensorflow.framework.FunctionDefLibrary.Builder, org.tensorflow.framework.FunctionDefLibraryOrBuilder> - getLibraryFieldBuilder() { - if (libraryBuilder_ == null) { - libraryBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.FunctionDefLibrary, org.tensorflow.framework.FunctionDefLibrary.Builder, org.tensorflow.framework.FunctionDefLibraryOrBuilder>( - getLibrary(), - getParentForChildren(), - isClean()); - library_ = null; - } - return libraryBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.GraphDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.GraphDef) - private static final org.tensorflow.framework.GraphDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.GraphDef(); - } - - public static org.tensorflow.framework.GraphDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public GraphDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new GraphDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.GraphDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/GraphDefOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/GraphDefOrBuilder.java deleted file mode 100644 index c0f36596603..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/GraphDefOrBuilder.java +++ /dev/null @@ -1,163 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: graph.proto - -package org.tensorflow.framework; - -public interface GraphDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.GraphDef) - com.google.protobuf.MessageOrBuilder { - - /** - * repeated .tensorflow.NodeDef node = 1; - */ - java.util.List - getNodeList(); - /** - * repeated .tensorflow.NodeDef node = 1; - */ - org.tensorflow.framework.NodeDef getNode(int index); - /** - * repeated .tensorflow.NodeDef node = 1; - */ - int getNodeCount(); - /** - * repeated .tensorflow.NodeDef node = 1; - */ - java.util.List - getNodeOrBuilderList(); - /** - * repeated .tensorflow.NodeDef node = 1; - */ - org.tensorflow.framework.NodeDefOrBuilder getNodeOrBuilder( - int index); - - /** - * 
-   * Compatibility versions of the graph.  See core/public/version.h for version
-   * history.  The GraphDef version is distinct from the TensorFlow version, and
-   * each release of TensorFlow will support a range of GraphDef versions.
-   *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - boolean hasVersions(); - /** - * 
-   * Compatibility versions of the graph.  See core/public/version.h for version
-   * history.  The GraphDef version is distinct from the TensorFlow version, and
-   * each release of TensorFlow will support a range of GraphDef versions.
-   *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - org.tensorflow.framework.VersionDef getVersions(); - /** - * 
-   * Compatibility versions of the graph.  See core/public/version.h for version
-   * history.  The GraphDef version is distinct from the TensorFlow version, and
-   * each release of TensorFlow will support a range of GraphDef versions.
-   *
- * - * optional .tensorflow.VersionDef versions = 4; - */ - org.tensorflow.framework.VersionDefOrBuilder getVersionsOrBuilder(); - - /** - * 
-   * Deprecated single version field; use versions above instead.  Since all
-   * GraphDef changes before "versions" was introduced were forward
-   * compatible, this field is entirely ignored.
-   *
- * - * optional int32 version = 3 [deprecated = true]; - */ - @java.lang.Deprecated int getVersion(); - - /** - * 
-   * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-   * "library" provides user-defined functions.
-   * Naming:
-   *   * library.function.name are in a flat namespace.
-   *     NOTE: We may need to change it to be hierarchical to support
-   *     different orgs. E.g.,
-   *     { "/google/nn", { ... }},
-   *     { "/google/vision", { ... }}
-   *     { "/org_foo/module_bar", { ... }}
-   *     map<string, FunctionDefLib> named_lib;
-   *   * If node[i].op is the name of one function in "library",
-   *     node[i] is deemed as a function call. Otherwise, node[i].op
-   *     must be a primitive operation supported by the runtime.
-   * Function call semantics:
-   *   * The callee may start execution as soon as some of its inputs
-   *     are ready. The caller may want to use Tuple() mechanism to
-   *     ensure all inputs are ready in the same time.
-   *   * The consumer of return values may start executing as soon as
-   *     the return values the consumer depends on are ready.  The
-   *     consumer may want to use Tuple() mechanism to ensure the
-   *     consumer does not start until all return values of the callee
-   *     function are ready.
-   *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - boolean hasLibrary(); - /** - * 
-   * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-   * "library" provides user-defined functions.
-   * Naming:
-   *   * library.function.name are in a flat namespace.
-   *     NOTE: We may need to change it to be hierarchical to support
-   *     different orgs. E.g.,
-   *     { "/google/nn", { ... }},
-   *     { "/google/vision", { ... }}
-   *     { "/org_foo/module_bar", { ... }}
-   *     map<string, FunctionDefLib> named_lib;
-   *   * If node[i].op is the name of one function in "library",
-   *     node[i] is deemed as a function call. Otherwise, node[i].op
-   *     must be a primitive operation supported by the runtime.
-   * Function call semantics:
-   *   * The callee may start execution as soon as some of its inputs
-   *     are ready. The caller may want to use Tuple() mechanism to
-   *     ensure all inputs are ready in the same time.
-   *   * The consumer of return values may start executing as soon as
-   *     the return values the consumer depends on are ready.  The
-   *     consumer may want to use Tuple() mechanism to ensure the
-   *     consumer does not start until all return values of the callee
-   *     function are ready.
-   *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - org.tensorflow.framework.FunctionDefLibrary getLibrary(); - /** - * 
-   * EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-   * "library" provides user-defined functions.
-   * Naming:
-   *   * library.function.name are in a flat namespace.
-   *     NOTE: We may need to change it to be hierarchical to support
-   *     different orgs. E.g.,
-   *     { "/google/nn", { ... }},
-   *     { "/google/vision", { ... }}
-   *     { "/org_foo/module_bar", { ... }}
-   *     map<string, FunctionDefLib> named_lib;
-   *   * If node[i].op is the name of one function in "library",
-   *     node[i] is deemed as a function call. Otherwise, node[i].op
-   *     must be a primitive operation supported by the runtime.
-   * Function call semantics:
-   *   * The callee may start execution as soon as some of its inputs
-   *     are ready. The caller may want to use Tuple() mechanism to
-   *     ensure all inputs are ready in the same time.
-   *   * The consumer of return values may start executing as soon as
-   *     the return values the consumer depends on are ready.  The
-   *     consumer may want to use Tuple() mechanism to ensure the
-   *     consumer does not start until all return values of the callee
-   *     function are ready.
-   *
- * - * optional .tensorflow.FunctionDefLibrary library = 2; - */ - org.tensorflow.framework.FunctionDefLibraryOrBuilder getLibraryOrBuilder(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/GraphProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/GraphProtos.java deleted file mode 100644 index 0911af63bfa..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/GraphProtos.java +++ /dev/null @@ -1,69 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: graph.proto - -package org.tensorflow.framework; - -public final class GraphProtos { - private GraphProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_GraphDef_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_GraphDef_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\013graph.proto\022\ntensorflow\032(tensorflow/co" + - "re/framework/node_def.proto\032(tensorflow/" + - "core/framework/function.proto\032(tensorflo" + - "w/core/framework/versions.proto\"\235\001\n\010Grap" + - "hDef\022!\n\004node\030\001 \003(\0132\023.tensorflow.NodeDef\022" + - "(\n\010versions\030\004 \001(\0132\026.tensorflow.VersionDe" + - "f\022\023\n\007version\030\003 \001(\005B\002\030\001\022/\n\007library\030\002 \001(\0132" + - "\036.tensorflow.FunctionDefLibraryB,\n\030org.t" + - "ensorflow.frameworkB\013GraphProtosP\001\370\001\001b\006p" + - "roto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.framework.NodeProto.getDescriptor(), - org.tensorflow.framework.FunctionProtos.getDescriptor(), - org.tensorflow.framework.VersionsProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_GraphDef_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_GraphDef_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_GraphDef_descriptor, - new java.lang.String[] { "Node", "Versions", "Version", "Library", }); - org.tensorflow.framework.NodeProto.getDescriptor(); - org.tensorflow.framework.FunctionProtos.getDescriptor(); - org.tensorflow.framework.VersionsProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java deleted file mode 100644 index 22c9321f399..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProto.java +++ /dev/null @@ -1,1078 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: summary.proto - -package org.tensorflow.framework; - -/** - * 
- * Serialization format for histogram module in
- * core/lib/histogram/histogram.h
- *
- * - * Protobuf type {@code tensorflow.HistogramProto} - */ -public final class HistogramProto extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.HistogramProto) - HistogramProtoOrBuilder { - // Use HistogramProto.newBuilder() to construct. - private HistogramProto(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private HistogramProto() { - min_ = 0D; - max_ = 0D; - num_ = 0D; - sum_ = 0D; - sumSquares_ = 0D; - bucketLimit_ = java.util.Collections.emptyList(); - bucket_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private HistogramProto( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 9: { - - min_ = input.readDouble(); - break; - } - case 17: { - - max_ = input.readDouble(); - break; - } - case 25: { - - num_ = input.readDouble(); - break; - } - case 33: { - - sum_ = input.readDouble(); - break; - } - case 41: { - - sumSquares_ = input.readDouble(); - break; - } - case 49: { - if (!((mutable_bitField0_ & 0x00000020) == 0x00000020)) { - bucketLimit_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000020; - } - bucketLimit_.add(input.readDouble()); - break; - } - case 50: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000020) == 0x00000020) && input.getBytesUntilLimit() > 0) { - bucketLimit_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000020; - } - while (input.getBytesUntilLimit() > 0) { - bucketLimit_.add(input.readDouble()); - } - input.popLimit(limit); - break; - } - case 57: { - if (!((mutable_bitField0_ & 0x00000040) == 0x00000040)) { - bucket_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000040; - } - bucket_.add(input.readDouble()); - break; - } - case 58: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000040) == 0x00000040) && input.getBytesUntilLimit() > 0) { - bucket_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000040; - } - while (input.getBytesUntilLimit() > 0) { - bucket_.add(input.readDouble()); - } - input.popLimit(limit); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000020) == 0x00000020)) { - bucketLimit_ = java.util.Collections.unmodifiableList(bucketLimit_); - } - if (((mutable_bitField0_ & 0x00000040) == 0x00000040)) { - bucket_ = java.util.Collections.unmodifiableList(bucket_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_HistogramProto_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_HistogramProto_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.HistogramProto.class, org.tensorflow.framework.HistogramProto.Builder.class); - } - - private int bitField0_; - public static final int MIN_FIELD_NUMBER = 1; - private double min_; - /** - * optional double min = 1; - */ - public double getMin() { - return min_; - } - - public static final int MAX_FIELD_NUMBER = 2; - private double max_; - /** - * optional double max = 2; - */ - public double getMax() { - return max_; - } - - public static final int NUM_FIELD_NUMBER = 3; - private double num_; - /** - * optional double num = 3; - */ - public double getNum() { - return num_; - } - - public static final int SUM_FIELD_NUMBER = 4; - private double sum_; - /** - * optional double sum = 4; - */ - public double getSum() { - return sum_; - } - - public static final int SUM_SQUARES_FIELD_NUMBER = 5; - private double sumSquares_; - /** - * optional double sum_squares = 5; - */ - public double getSumSquares() { - return sumSquares_; - } - - public static final int BUCKET_LIMIT_FIELD_NUMBER = 6; - private java.util.List bucketLimit_; - /** - * 
-   * Parallel arrays encoding the bucket boundaries and the bucket values.
-   * bucket(i) is the count for the bucket i.  The range for
-   * a bucket is:
-   *   i == 0:  -DBL_MAX .. bucket_limit(0)
-   *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-   *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public java.util.List - getBucketLimitList() { - return bucketLimit_; - } - /** - * 
-   * Parallel arrays encoding the bucket boundaries and the bucket values.
-   * bucket(i) is the count for the bucket i.  The range for
-   * a bucket is:
-   *   i == 0:  -DBL_MAX .. bucket_limit(0)
-   *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-   *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public int getBucketLimitCount() { - return bucketLimit_.size(); - } - /** - * 
-   * Parallel arrays encoding the bucket boundaries and the bucket values.
-   * bucket(i) is the count for the bucket i.  The range for
-   * a bucket is:
-   *   i == 0:  -DBL_MAX .. bucket_limit(0)
-   *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-   *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public double getBucketLimit(int index) { - return bucketLimit_.get(index); - } - private int bucketLimitMemoizedSerializedSize = -1; - - public static final int BUCKET_FIELD_NUMBER = 7; - private java.util.List bucket_; - /** - * repeated double bucket = 7 [packed = true]; - */ - public java.util.List - getBucketList() { - return bucket_; - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public int getBucketCount() { - return bucket_.size(); - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public double getBucket(int index) { - return bucket_.get(index); - } - private int bucketMemoizedSerializedSize = -1; - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - getSerializedSize(); - if (min_ != 0D) { - output.writeDouble(1, min_); - } - if (max_ != 0D) { - output.writeDouble(2, max_); - } - if (num_ != 0D) { - output.writeDouble(3, num_); - } - if (sum_ != 0D) { - output.writeDouble(4, sum_); - } - if (sumSquares_ != 0D) { - output.writeDouble(5, sumSquares_); - } - if (getBucketLimitList().size() > 0) { - output.writeUInt32NoTag(50); - output.writeUInt32NoTag(bucketLimitMemoizedSerializedSize); - } - for (int i = 0; i < bucketLimit_.size(); i++) { - output.writeDoubleNoTag(bucketLimit_.get(i)); - } - if (getBucketList().size() > 0) { - output.writeUInt32NoTag(58); - output.writeUInt32NoTag(bucketMemoizedSerializedSize); - } - for (int i = 0; i < bucket_.size(); i++) { - output.writeDoubleNoTag(bucket_.get(i)); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (min_ != 0D) { - size += com.google.protobuf.CodedOutputStream - .computeDoubleSize(1, min_); - } - if (max_ != 0D) { - size += com.google.protobuf.CodedOutputStream - .computeDoubleSize(2, max_); - } - if (num_ != 0D) { - size += com.google.protobuf.CodedOutputStream - .computeDoubleSize(3, num_); - } - if (sum_ != 0D) { - size += com.google.protobuf.CodedOutputStream - .computeDoubleSize(4, sum_); - } - if (sumSquares_ != 0D) { - size += com.google.protobuf.CodedOutputStream - .computeDoubleSize(5, sumSquares_); - } - { - int dataSize = 0; - dataSize = 8 * getBucketLimitList().size(); - size += dataSize; - if (!getBucketLimitList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - bucketLimitMemoizedSerializedSize = dataSize; - } - { - int dataSize = 0; - dataSize = 8 * getBucketList().size(); - size += dataSize; - if (!getBucketList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - bucketMemoizedSerializedSize = dataSize; - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.HistogramProto)) { - return super.equals(obj); - } - org.tensorflow.framework.HistogramProto other = (org.tensorflow.framework.HistogramProto) obj; - - boolean result = true; - result = result && ( - java.lang.Double.doubleToLongBits(getMin()) - == java.lang.Double.doubleToLongBits( - other.getMin())); - result = result && ( - java.lang.Double.doubleToLongBits(getMax()) - == java.lang.Double.doubleToLongBits( - other.getMax())); - result = result && ( - java.lang.Double.doubleToLongBits(getNum()) - == java.lang.Double.doubleToLongBits( - other.getNum())); - result = result && ( - java.lang.Double.doubleToLongBits(getSum()) - == java.lang.Double.doubleToLongBits( - other.getSum())); - result = result && ( - java.lang.Double.doubleToLongBits(getSumSquares()) - == java.lang.Double.doubleToLongBits( - other.getSumSquares())); - result = result && getBucketLimitList() - .equals(other.getBucketLimitList()); - result = result && getBucketList() - .equals(other.getBucketList()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + MIN_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - java.lang.Double.doubleToLongBits(getMin())); - hash = (37 * hash) + MAX_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - java.lang.Double.doubleToLongBits(getMax())); - hash = (37 * hash) + NUM_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - java.lang.Double.doubleToLongBits(getNum())); - hash = (37 * hash) + SUM_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - java.lang.Double.doubleToLongBits(getSum())); - hash = (37 * hash) + SUM_SQUARES_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - java.lang.Double.doubleToLongBits(getSumSquares())); - if (getBucketLimitCount() > 0) { - hash = (37 * hash) + BUCKET_LIMIT_FIELD_NUMBER; - hash = (53 * hash) + getBucketLimitList().hashCode(); - } - if (getBucketCount() > 0) { - hash = (37 * hash) + BUCKET_FIELD_NUMBER; - hash = (53 * hash) + getBucketList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.HistogramProto parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.HistogramProto parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.HistogramProto parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.HistogramProto parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.HistogramProto parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.HistogramProto parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.HistogramProto parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.HistogramProto parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.HistogramProto parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.HistogramProto parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.HistogramProto prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Serialization format for histogram module in
-   * core/lib/histogram/histogram.h
-   *
- * - * Protobuf type {@code tensorflow.HistogramProto} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.HistogramProto) - org.tensorflow.framework.HistogramProtoOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_HistogramProto_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_HistogramProto_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.HistogramProto.class, org.tensorflow.framework.HistogramProto.Builder.class); - } - - // Construct using org.tensorflow.framework.HistogramProto.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - min_ = 0D; - - max_ = 0D; - - num_ = 0D; - - sum_ = 0D; - - sumSquares_ = 0D; - - bucketLimit_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000020); - bucket_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000040); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_HistogramProto_descriptor; - } - - public org.tensorflow.framework.HistogramProto getDefaultInstanceForType() { - return org.tensorflow.framework.HistogramProto.getDefaultInstance(); - } - - public org.tensorflow.framework.HistogramProto build() { - org.tensorflow.framework.HistogramProto result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.HistogramProto buildPartial() { - org.tensorflow.framework.HistogramProto result = new org.tensorflow.framework.HistogramProto(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - result.min_ = min_; - result.max_ = max_; - result.num_ = num_; - result.sum_ = sum_; - result.sumSquares_ = sumSquares_; - if (((bitField0_ & 0x00000020) == 0x00000020)) { - bucketLimit_ = java.util.Collections.unmodifiableList(bucketLimit_); - bitField0_ = (bitField0_ & ~0x00000020); - } - result.bucketLimit_ = bucketLimit_; - if (((bitField0_ & 0x00000040) == 0x00000040)) { - bucket_ = java.util.Collections.unmodifiableList(bucket_); - bitField0_ = (bitField0_ & ~0x00000040); - } - result.bucket_ = bucket_; - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.HistogramProto) { - return mergeFrom((org.tensorflow.framework.HistogramProto)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.HistogramProto other) { - if (other == org.tensorflow.framework.HistogramProto.getDefaultInstance()) return this; - if (other.getMin() != 0D) { - setMin(other.getMin()); - } - if (other.getMax() != 0D) { - setMax(other.getMax()); - } - if (other.getNum() != 0D) { - setNum(other.getNum()); - } - if (other.getSum() != 0D) { - setSum(other.getSum()); - } - if (other.getSumSquares() != 0D) { - setSumSquares(other.getSumSquares()); - } - if (!other.bucketLimit_.isEmpty()) { - if (bucketLimit_.isEmpty()) { - bucketLimit_ = other.bucketLimit_; - bitField0_ = (bitField0_ & ~0x00000020); - } else { - ensureBucketLimitIsMutable(); - bucketLimit_.addAll(other.bucketLimit_); - } - onChanged(); - } - if (!other.bucket_.isEmpty()) { - if (bucket_.isEmpty()) { - bucket_ = other.bucket_; - bitField0_ = (bitField0_ & ~0x00000040); - } else { - ensureBucketIsMutable(); - bucket_.addAll(other.bucket_); - } - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.HistogramProto parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.HistogramProto) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private double min_ ; - /** - * optional double min = 1; - */ - public double getMin() { - return min_; - } - /** - * optional double min = 1; - */ - public Builder setMin(double value) { - - min_ = value; - onChanged(); - return this; - } - /** - * optional double min = 1; - */ - public Builder clearMin() { - - min_ = 0D; - onChanged(); - return this; - } - - private double max_ ; - /** - * optional double max = 2; - */ - public double getMax() { - return max_; - } - /** - * optional double max = 2; - */ - public Builder setMax(double value) { - - max_ = value; - onChanged(); - return this; - } - /** - * optional double max = 2; - */ - public Builder clearMax() { - - max_ = 0D; - onChanged(); - return this; - } - - private double num_ ; - /** - * optional double num = 3; - */ - public double getNum() { - return num_; - } - /** - * optional double num = 3; - */ - public Builder setNum(double value) { - - num_ = value; - onChanged(); - return this; - } - /** - * optional double num = 3; - */ - public Builder clearNum() { - - num_ = 0D; - onChanged(); - return this; - } - - private double sum_ ; - /** - * optional double sum = 4; - */ - public double getSum() { - return sum_; - } - /** - * optional double sum = 4; - */ - public Builder setSum(double value) { - - sum_ = value; - onChanged(); - return this; - } - /** - * optional double sum = 4; - */ - public Builder clearSum() { - - sum_ = 0D; - onChanged(); - return this; - } - - private double sumSquares_ ; - /** - * optional double sum_squares = 5; - */ - public double getSumSquares() { - return sumSquares_; - } - /** - * optional double sum_squares = 5; - */ - public Builder setSumSquares(double value) { - - sumSquares_ = value; - onChanged(); - return this; - } - /** - * optional double sum_squares = 5; - */ - public Builder clearSumSquares() { - - sumSquares_ = 0D; - onChanged(); - return this; - } - - private java.util.List bucketLimit_ = java.util.Collections.emptyList(); - private void ensureBucketLimitIsMutable() { - if (!((bitField0_ & 0x00000020) == 0x00000020)) { - bucketLimit_ = new java.util.ArrayList(bucketLimit_); - bitField0_ |= 0x00000020; - } - } - /** - * 
-     * Parallel arrays encoding the bucket boundaries and the bucket values.
-     * bucket(i) is the count for the bucket i.  The range for
-     * a bucket is:
-     *   i == 0:  -DBL_MAX .. bucket_limit(0)
-     *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-     *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public java.util.List - getBucketLimitList() { - return java.util.Collections.unmodifiableList(bucketLimit_); - } - /** - * 
-     * Parallel arrays encoding the bucket boundaries and the bucket values.
-     * bucket(i) is the count for the bucket i.  The range for
-     * a bucket is:
-     *   i == 0:  -DBL_MAX .. bucket_limit(0)
-     *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-     *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public int getBucketLimitCount() { - return bucketLimit_.size(); - } - /** - * 
-     * Parallel arrays encoding the bucket boundaries and the bucket values.
-     * bucket(i) is the count for the bucket i.  The range for
-     * a bucket is:
-     *   i == 0:  -DBL_MAX .. bucket_limit(0)
-     *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-     *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public double getBucketLimit(int index) { - return bucketLimit_.get(index); - } - /** - * 
-     * Parallel arrays encoding the bucket boundaries and the bucket values.
-     * bucket(i) is the count for the bucket i.  The range for
-     * a bucket is:
-     *   i == 0:  -DBL_MAX .. bucket_limit(0)
-     *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-     *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public Builder setBucketLimit( - int index, double value) { - ensureBucketLimitIsMutable(); - bucketLimit_.set(index, value); - onChanged(); - return this; - } - /** - * 
-     * Parallel arrays encoding the bucket boundaries and the bucket values.
-     * bucket(i) is the count for the bucket i.  The range for
-     * a bucket is:
-     *   i == 0:  -DBL_MAX .. bucket_limit(0)
-     *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-     *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public Builder addBucketLimit(double value) { - ensureBucketLimitIsMutable(); - bucketLimit_.add(value); - onChanged(); - return this; - } - /** - * 
-     * Parallel arrays encoding the bucket boundaries and the bucket values.
-     * bucket(i) is the count for the bucket i.  The range for
-     * a bucket is:
-     *   i == 0:  -DBL_MAX .. bucket_limit(0)
-     *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-     *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public Builder addAllBucketLimit( - java.lang.Iterable values) { - ensureBucketLimitIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, bucketLimit_); - onChanged(); - return this; - } - /** - * 
-     * Parallel arrays encoding the bucket boundaries and the bucket values.
-     * bucket(i) is the count for the bucket i.  The range for
-     * a bucket is:
-     *   i == 0:  -DBL_MAX .. bucket_limit(0)
-     *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-     *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - public Builder clearBucketLimit() { - bucketLimit_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000020); - onChanged(); - return this; - } - - private java.util.List bucket_ = java.util.Collections.emptyList(); - private void ensureBucketIsMutable() { - if (!((bitField0_ & 0x00000040) == 0x00000040)) { - bucket_ = new java.util.ArrayList(bucket_); - bitField0_ |= 0x00000040; - } - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public java.util.List - getBucketList() { - return java.util.Collections.unmodifiableList(bucket_); - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public int getBucketCount() { - return bucket_.size(); - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public double getBucket(int index) { - return bucket_.get(index); - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public Builder setBucket( - int index, double value) { - ensureBucketIsMutable(); - bucket_.set(index, value); - onChanged(); - return this; - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public Builder addBucket(double value) { - ensureBucketIsMutable(); - bucket_.add(value); - onChanged(); - return this; - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public Builder addAllBucket( - java.lang.Iterable values) { - ensureBucketIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, bucket_); - onChanged(); - return this; - } - /** - * repeated double bucket = 7 [packed = true]; - */ - public Builder clearBucket() { - bucket_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000040); - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.HistogramProto) - } - - // @@protoc_insertion_point(class_scope:tensorflow.HistogramProto) - private static final org.tensorflow.framework.HistogramProto DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.HistogramProto(); - } - - public static org.tensorflow.framework.HistogramProto getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public HistogramProto parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new HistogramProto(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.HistogramProto getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProtoOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProtoOrBuilder.java deleted file mode 100644 index fa02cd9be57..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/HistogramProtoOrBuilder.java +++ /dev/null @@ -1,84 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: summary.proto - -package org.tensorflow.framework; - -public interface HistogramProtoOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.HistogramProto) - com.google.protobuf.MessageOrBuilder { - - /** - * optional double min = 1; - */ - double getMin(); - - /** - * optional double max = 2; - */ - double getMax(); - - /** - * optional double num = 3; - */ - double getNum(); - - /** - * optional double sum = 4; - */ - double getSum(); - - /** - * optional double sum_squares = 5; - */ - double getSumSquares(); - - /** - * 
-   * Parallel arrays encoding the bucket boundaries and the bucket values.
-   * bucket(i) is the count for the bucket i.  The range for
-   * a bucket is:
-   *   i == 0:  -DBL_MAX .. bucket_limit(0)
-   *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-   *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - java.util.List getBucketLimitList(); - /** - * 
-   * Parallel arrays encoding the bucket boundaries and the bucket values.
-   * bucket(i) is the count for the bucket i.  The range for
-   * a bucket is:
-   *   i == 0:  -DBL_MAX .. bucket_limit(0)
-   *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-   *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - int getBucketLimitCount(); - /** - * 
-   * Parallel arrays encoding the bucket boundaries and the bucket values.
-   * bucket(i) is the count for the bucket i.  The range for
-   * a bucket is:
-   *   i == 0:  -DBL_MAX .. bucket_limit(0)
-   *   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-   *
- * - * repeated double bucket_limit = 6 [packed = true]; - */ - double getBucketLimit(int index); - - /** - * repeated double bucket = 7 [packed = true]; - */ - java.util.List getBucketList(); - /** - * repeated double bucket = 7 [packed = true]; - */ - int getBucketCount(); - /** - * repeated double bucket = 7 [packed = true]; - */ - double getBucket(int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/KernelDef.java b/scala/dllib/src/main/java/org/tensorflow/framework/KernelDef.java deleted file mode 100644 index 561882603b7..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/KernelDef.java +++ /dev/null @@ -1,2237 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: kernel_def.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.KernelDef} - */ -public final class KernelDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.KernelDef) - KernelDefOrBuilder { - // Use KernelDef.newBuilder() to construct. - private KernelDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private KernelDef() { - op_ = ""; - deviceType_ = ""; - constraint_ = java.util.Collections.emptyList(); - hostMemoryArg_ = com.google.protobuf.LazyStringArrayList.EMPTY; - label_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private KernelDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - op_ = s; - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - deviceType_ = s; - break; - } - case 26: { - if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - constraint_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000004; - } - constraint_.add( - input.readMessage(org.tensorflow.framework.KernelDef.AttrConstraint.parser(), extensionRegistry)); - break; - } - case 34: { - java.lang.String s = input.readStringRequireUtf8(); - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - hostMemoryArg_ = new com.google.protobuf.LazyStringArrayList(); - mutable_bitField0_ |= 0x00000008; - } - hostMemoryArg_.add(s); - break; - } - case 42: { - java.lang.String s = input.readStringRequireUtf8(); - - label_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - constraint_ = java.util.Collections.unmodifiableList(constraint_); - } - if (((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - hostMemoryArg_ = hostMemoryArg_.getUnmodifiableView(); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.KernelDef.class, org.tensorflow.framework.KernelDef.Builder.class); - } - - public interface AttrConstraintOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.KernelDef.AttrConstraint) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-     * Name of an attr from the Op.
-     *
- * - * optional string name = 1; - */ - java.lang.String getName(); - /** - * 
-     * Name of an attr from the Op.
-     *
- * - * optional string name = 1; - */ - com.google.protobuf.ByteString - getNameBytes(); - - /** - * 
-     * A list of values that this kernel supports for this attr.
-     * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - boolean hasAllowedValues(); - /** - * 
-     * A list of values that this kernel supports for this attr.
-     * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - org.tensorflow.framework.AttrValue getAllowedValues(); - /** - * 
-     * A list of values that this kernel supports for this attr.
-     * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - org.tensorflow.framework.AttrValueOrBuilder getAllowedValuesOrBuilder(); - } - /** - * Protobuf type {@code tensorflow.KernelDef.AttrConstraint} - */ - public static final class AttrConstraint extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.KernelDef.AttrConstraint) - AttrConstraintOrBuilder { - // Use AttrConstraint.newBuilder() to construct. - private AttrConstraint(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private AttrConstraint() { - name_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private AttrConstraint( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - name_ = s; - break; - } - case 18: { - org.tensorflow.framework.AttrValue.Builder subBuilder = null; - if (allowedValues_ != null) { - subBuilder = allowedValues_.toBuilder(); - } - allowedValues_ = input.readMessage(org.tensorflow.framework.AttrValue.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(allowedValues_); - allowedValues_ = subBuilder.buildPartial(); - } - - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_AttrConstraint_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_AttrConstraint_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.KernelDef.AttrConstraint.class, org.tensorflow.framework.KernelDef.AttrConstraint.Builder.class); - } - - public static final int NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object name_; - /** - * 
-     * Name of an attr from the Op.
-     *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } - } - /** - * 
-     * Name of an attr from the Op.
-     *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int ALLOWED_VALUES_FIELD_NUMBER = 2; - private org.tensorflow.framework.AttrValue allowedValues_; - /** - * 
-     * A list of values that this kernel supports for this attr.
-     * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public boolean hasAllowedValues() { - return allowedValues_ != null; - } - /** - * 
-     * A list of values that this kernel supports for this attr.
-     * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public org.tensorflow.framework.AttrValue getAllowedValues() { - return allowedValues_ == null ? org.tensorflow.framework.AttrValue.getDefaultInstance() : allowedValues_; - } - /** - * 
-     * A list of values that this kernel supports for this attr.
-     * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public org.tensorflow.framework.AttrValueOrBuilder getAllowedValuesOrBuilder() { - return getAllowedValues(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_); - } - if (allowedValues_ != null) { - output.writeMessage(2, getAllowedValues()); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_); - } - if (allowedValues_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, getAllowedValues()); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.KernelDef.AttrConstraint)) { - return super.equals(obj); - } - org.tensorflow.framework.KernelDef.AttrConstraint other = (org.tensorflow.framework.KernelDef.AttrConstraint) obj; - - boolean result = true; - result = result && getName() - .equals(other.getName()); - result = result && (hasAllowedValues() == other.hasAllowedValues()); - if (hasAllowedValues()) { - result = result && getAllowedValues() - .equals(other.getAllowedValues()); - } - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + NAME_FIELD_NUMBER; - hash = (53 * hash) + getName().hashCode(); - if (hasAllowedValues()) { - hash = (37 * hash) + ALLOWED_VALUES_FIELD_NUMBER; - hash = (53 * hash) + getAllowedValues().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.KernelDef.AttrConstraint parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.KernelDef.AttrConstraint parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.KernelDef.AttrConstraint prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.KernelDef.AttrConstraint} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.KernelDef.AttrConstraint) - org.tensorflow.framework.KernelDef.AttrConstraintOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_AttrConstraint_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_AttrConstraint_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.KernelDef.AttrConstraint.class, org.tensorflow.framework.KernelDef.AttrConstraint.Builder.class); - } - - // Construct using org.tensorflow.framework.KernelDef.AttrConstraint.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - name_ = ""; - - if (allowedValuesBuilder_ == null) { - allowedValues_ = null; - } else { - allowedValues_ = null; - allowedValuesBuilder_ = null; - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_AttrConstraint_descriptor; - } - - public org.tensorflow.framework.KernelDef.AttrConstraint getDefaultInstanceForType() { - return org.tensorflow.framework.KernelDef.AttrConstraint.getDefaultInstance(); - } - - public org.tensorflow.framework.KernelDef.AttrConstraint build() { - org.tensorflow.framework.KernelDef.AttrConstraint result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.KernelDef.AttrConstraint buildPartial() { - org.tensorflow.framework.KernelDef.AttrConstraint result = new org.tensorflow.framework.KernelDef.AttrConstraint(this); - result.name_ = name_; - if (allowedValuesBuilder_ == null) { - result.allowedValues_ = allowedValues_; - } else { - result.allowedValues_ = allowedValuesBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.KernelDef.AttrConstraint) { - return mergeFrom((org.tensorflow.framework.KernelDef.AttrConstraint)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.KernelDef.AttrConstraint other) { - if (other == org.tensorflow.framework.KernelDef.AttrConstraint.getDefaultInstance()) return this; - if (!other.getName().isEmpty()) { - name_ = other.name_; - onChanged(); - } - if (other.hasAllowedValues()) { - mergeAllowedValues(other.getAllowedValues()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.KernelDef.AttrConstraint parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.KernelDef.AttrConstraint) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private java.lang.Object name_ = ""; - /** - * 
-       * Name of an attr from the Op.
-       *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * Name of an attr from the Op.
-       *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * Name of an attr from the Op.
-       *
- * - * optional string name = 1; - */ - public Builder setName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - name_ = value; - onChanged(); - return this; - } - /** - * 
-       * Name of an attr from the Op.
-       *
- * - * optional string name = 1; - */ - public Builder clearName() { - - name_ = getDefaultInstance().getName(); - onChanged(); - return this; - } - /** - * 
-       * Name of an attr from the Op.
-       *
- * - * optional string name = 1; - */ - public Builder setNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - name_ = value; - onChanged(); - return this; - } - - private org.tensorflow.framework.AttrValue allowedValues_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder> allowedValuesBuilder_; - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public boolean hasAllowedValues() { - return allowedValuesBuilder_ != null || allowedValues_ != null; - } - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public org.tensorflow.framework.AttrValue getAllowedValues() { - if (allowedValuesBuilder_ == null) { - return allowedValues_ == null ? org.tensorflow.framework.AttrValue.getDefaultInstance() : allowedValues_; - } else { - return allowedValuesBuilder_.getMessage(); - } - } - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public Builder setAllowedValues(org.tensorflow.framework.AttrValue value) { - if (allowedValuesBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - allowedValues_ = value; - onChanged(); - } else { - allowedValuesBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public Builder setAllowedValues( - org.tensorflow.framework.AttrValue.Builder builderForValue) { - if (allowedValuesBuilder_ == null) { - allowedValues_ = builderForValue.build(); - onChanged(); - } else { - allowedValuesBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public Builder mergeAllowedValues(org.tensorflow.framework.AttrValue value) { - if (allowedValuesBuilder_ == null) { - if (allowedValues_ != null) { - allowedValues_ = - org.tensorflow.framework.AttrValue.newBuilder(allowedValues_).mergeFrom(value).buildPartial(); - } else { - allowedValues_ = value; - } - onChanged(); - } else { - allowedValuesBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public Builder clearAllowedValues() { - if (allowedValuesBuilder_ == null) { - allowedValues_ = null; - onChanged(); - } else { - allowedValues_ = null; - allowedValuesBuilder_ = null; - } - - return this; - } - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public org.tensorflow.framework.AttrValue.Builder getAllowedValuesBuilder() { - - onChanged(); - return getAllowedValuesFieldBuilder().getBuilder(); - } - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - public org.tensorflow.framework.AttrValueOrBuilder getAllowedValuesOrBuilder() { - if (allowedValuesBuilder_ != null) { - return allowedValuesBuilder_.getMessageOrBuilder(); - } else { - return allowedValues_ == null ? - org.tensorflow.framework.AttrValue.getDefaultInstance() : allowedValues_; - } - } - /** - * 
-       * A list of values that this kernel supports for this attr.
-       * Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 2; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder> - getAllowedValuesFieldBuilder() { - if (allowedValuesBuilder_ == null) { - allowedValuesBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder>( - getAllowedValues(), - getParentForChildren(), - isClean()); - allowedValues_ = null; - } - return allowedValuesBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.KernelDef.AttrConstraint) - } - - // @@protoc_insertion_point(class_scope:tensorflow.KernelDef.AttrConstraint) - private static final org.tensorflow.framework.KernelDef.AttrConstraint DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.KernelDef.AttrConstraint(); - } - - public static org.tensorflow.framework.KernelDef.AttrConstraint getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public AttrConstraint parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new AttrConstraint(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.KernelDef.AttrConstraint getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - - } - - private int bitField0_; - public static final int OP_FIELD_NUMBER = 1; - private volatile java.lang.Object op_; - /** - * 
-   * Must match the name of an Op.
-   *
- * - * optional string op = 1; - */ - public java.lang.String getOp() { - java.lang.Object ref = op_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - op_ = s; - return s; - } - } - /** - * 
-   * Must match the name of an Op.
-   *
- * - * optional string op = 1; - */ - public com.google.protobuf.ByteString - getOpBytes() { - java.lang.Object ref = op_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - op_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int DEVICE_TYPE_FIELD_NUMBER = 2; - private volatile java.lang.Object deviceType_; - /** - * 
-   * Type of device this kernel runs on.
-   *
- * - * optional string device_type = 2; - */ - public java.lang.String getDeviceType() { - java.lang.Object ref = deviceType_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - deviceType_ = s; - return s; - } - } - /** - * 
-   * Type of device this kernel runs on.
-   *
- * - * optional string device_type = 2; - */ - public com.google.protobuf.ByteString - getDeviceTypeBytes() { - java.lang.Object ref = deviceType_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - deviceType_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int CONSTRAINT_FIELD_NUMBER = 3; - private java.util.List constraint_; - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public java.util.List getConstraintList() { - return constraint_; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public java.util.List - getConstraintOrBuilderList() { - return constraint_; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public int getConstraintCount() { - return constraint_.size(); - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public org.tensorflow.framework.KernelDef.AttrConstraint getConstraint(int index) { - return constraint_.get(index); - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public org.tensorflow.framework.KernelDef.AttrConstraintOrBuilder getConstraintOrBuilder( - int index) { - return constraint_.get(index); - } - - public static final int HOST_MEMORY_ARG_FIELD_NUMBER = 4; - private com.google.protobuf.LazyStringList hostMemoryArg_; - /** - * 
-   * Names of the Op's input_/output_args that reside in host memory
-   * instead of device memory.
-   *
- * - * repeated string host_memory_arg = 4; - */ - public com.google.protobuf.ProtocolStringList - getHostMemoryArgList() { - return hostMemoryArg_; - } - /** - * 
-   * Names of the Op's input_/output_args that reside in host memory
-   * instead of device memory.
-   *
- * - * repeated string host_memory_arg = 4; - */ - public int getHostMemoryArgCount() { - return hostMemoryArg_.size(); - } - /** - * 
-   * Names of the Op's input_/output_args that reside in host memory
-   * instead of device memory.
-   *
- * - * repeated string host_memory_arg = 4; - */ - public java.lang.String getHostMemoryArg(int index) { - return hostMemoryArg_.get(index); - } - /** - * 
-   * Names of the Op's input_/output_args that reside in host memory
-   * instead of device memory.
-   *
- * - * repeated string host_memory_arg = 4; - */ - public com.google.protobuf.ByteString - getHostMemoryArgBytes(int index) { - return hostMemoryArg_.getByteString(index); - } - - public static final int LABEL_FIELD_NUMBER = 5; - private volatile java.lang.Object label_; - /** - * 
-   * This allows experimental kernels to be registered for an op that
-   * won't be used unless the user specifies a "_kernel" attr with
-   * value matching this.
-   *
- * - * optional string label = 5; - */ - public java.lang.String getLabel() { - java.lang.Object ref = label_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - label_ = s; - return s; - } - } - /** - * 
-   * This allows experimental kernels to be registered for an op that
-   * won't be used unless the user specifies a "_kernel" attr with
-   * value matching this.
-   *
- * - * optional string label = 5; - */ - public com.google.protobuf.ByteString - getLabelBytes() { - java.lang.Object ref = label_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - label_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getOpBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, op_); - } - if (!getDeviceTypeBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, deviceType_); - } - for (int i = 0; i < constraint_.size(); i++) { - output.writeMessage(3, constraint_.get(i)); - } - for (int i = 0; i < hostMemoryArg_.size(); i++) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 4, hostMemoryArg_.getRaw(i)); - } - if (!getLabelBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 5, label_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getOpBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, op_); - } - if (!getDeviceTypeBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, deviceType_); - } - for (int i = 0; i < constraint_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(3, constraint_.get(i)); - } - { - int dataSize = 0; - for (int i = 0; i < hostMemoryArg_.size(); i++) { - dataSize += computeStringSizeNoTag(hostMemoryArg_.getRaw(i)); - } - size += dataSize; - size += 1 * getHostMemoryArgList().size(); - } - if (!getLabelBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(5, label_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.KernelDef)) { - return super.equals(obj); - } - org.tensorflow.framework.KernelDef other = (org.tensorflow.framework.KernelDef) obj; - - boolean result = true; - result = result && getOp() - .equals(other.getOp()); - result = result && getDeviceType() - .equals(other.getDeviceType()); - result = result && getConstraintList() - .equals(other.getConstraintList()); - result = result && getHostMemoryArgList() - .equals(other.getHostMemoryArgList()); - result = result && getLabel() - .equals(other.getLabel()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + OP_FIELD_NUMBER; - hash = (53 * hash) + getOp().hashCode(); - hash = (37 * hash) + DEVICE_TYPE_FIELD_NUMBER; - hash = (53 * hash) + getDeviceType().hashCode(); - if (getConstraintCount() > 0) { - hash = (37 * hash) + CONSTRAINT_FIELD_NUMBER; - hash = (53 * hash) + getConstraintList().hashCode(); - } - if (getHostMemoryArgCount() > 0) { - hash = (37 * hash) + HOST_MEMORY_ARG_FIELD_NUMBER; - hash = (53 * hash) + getHostMemoryArgList().hashCode(); - } - hash = (37 * hash) + LABEL_FIELD_NUMBER; - hash = (53 * hash) + getLabel().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.KernelDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.KernelDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.KernelDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.KernelDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.KernelDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.KernelDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.KernelDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.KernelDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.KernelDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.KernelDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.KernelDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.KernelDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.KernelDef) - org.tensorflow.framework.KernelDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.KernelDef.class, org.tensorflow.framework.KernelDef.Builder.class); - } - - // Construct using org.tensorflow.framework.KernelDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getConstraintFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - op_ = ""; - - deviceType_ = ""; - - if (constraintBuilder_ == null) { - constraint_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - } else { - constraintBuilder_.clear(); - } - hostMemoryArg_ = com.google.protobuf.LazyStringArrayList.EMPTY; - bitField0_ = (bitField0_ & ~0x00000008); - label_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.KernelDefProtos.internal_static_tensorflow_KernelDef_descriptor; - } - - public org.tensorflow.framework.KernelDef getDefaultInstanceForType() { - return org.tensorflow.framework.KernelDef.getDefaultInstance(); - } - - public org.tensorflow.framework.KernelDef build() { - org.tensorflow.framework.KernelDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.KernelDef buildPartial() { - org.tensorflow.framework.KernelDef result = new org.tensorflow.framework.KernelDef(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - result.op_ = op_; - result.deviceType_ = deviceType_; - if (constraintBuilder_ == null) { - if (((bitField0_ & 0x00000004) == 0x00000004)) { - constraint_ = java.util.Collections.unmodifiableList(constraint_); - bitField0_ = (bitField0_ & ~0x00000004); - } - result.constraint_ = constraint_; - } else { - result.constraint_ = constraintBuilder_.build(); - } - if (((bitField0_ & 0x00000008) == 0x00000008)) { - hostMemoryArg_ = hostMemoryArg_.getUnmodifiableView(); - bitField0_ = (bitField0_ & ~0x00000008); - } - result.hostMemoryArg_ = hostMemoryArg_; - result.label_ = label_; - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.KernelDef) { - return mergeFrom((org.tensorflow.framework.KernelDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.KernelDef other) { - if (other == org.tensorflow.framework.KernelDef.getDefaultInstance()) return this; - if (!other.getOp().isEmpty()) { - op_ = other.op_; - onChanged(); - } - if (!other.getDeviceType().isEmpty()) { - deviceType_ = other.deviceType_; - onChanged(); - } - if (constraintBuilder_ == null) { - if (!other.constraint_.isEmpty()) { - if (constraint_.isEmpty()) { - constraint_ = other.constraint_; - bitField0_ = (bitField0_ & ~0x00000004); - } else { - ensureConstraintIsMutable(); - constraint_.addAll(other.constraint_); - } - onChanged(); - } - } else { - if (!other.constraint_.isEmpty()) { - if (constraintBuilder_.isEmpty()) { - constraintBuilder_.dispose(); - constraintBuilder_ = null; - constraint_ = other.constraint_; - bitField0_ = (bitField0_ & ~0x00000004); - constraintBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getConstraintFieldBuilder() : null; - } else { - constraintBuilder_.addAllMessages(other.constraint_); - } - } - } - if (!other.hostMemoryArg_.isEmpty()) { - if (hostMemoryArg_.isEmpty()) { - hostMemoryArg_ = other.hostMemoryArg_; - bitField0_ = (bitField0_ & ~0x00000008); - } else { - ensureHostMemoryArgIsMutable(); - hostMemoryArg_.addAll(other.hostMemoryArg_); - } - onChanged(); - } - if (!other.getLabel().isEmpty()) { - label_ = other.label_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.KernelDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.KernelDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.lang.Object op_ = ""; - /** - * 
-     * Must match the name of an Op.
-     *
- * - * optional string op = 1; - */ - public java.lang.String getOp() { - java.lang.Object ref = op_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - op_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Must match the name of an Op.
-     *
- * - * optional string op = 1; - */ - public com.google.protobuf.ByteString - getOpBytes() { - java.lang.Object ref = op_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - op_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Must match the name of an Op.
-     *
- * - * optional string op = 1; - */ - public Builder setOp( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - op_ = value; - onChanged(); - return this; - } - /** - * 
-     * Must match the name of an Op.
-     *
- * - * optional string op = 1; - */ - public Builder clearOp() { - - op_ = getDefaultInstance().getOp(); - onChanged(); - return this; - } - /** - * 
-     * Must match the name of an Op.
-     *
- * - * optional string op = 1; - */ - public Builder setOpBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - op_ = value; - onChanged(); - return this; - } - - private java.lang.Object deviceType_ = ""; - /** - * 
-     * Type of device this kernel runs on.
-     *
- * - * optional string device_type = 2; - */ - public java.lang.String getDeviceType() { - java.lang.Object ref = deviceType_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - deviceType_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Type of device this kernel runs on.
-     *
- * - * optional string device_type = 2; - */ - public com.google.protobuf.ByteString - getDeviceTypeBytes() { - java.lang.Object ref = deviceType_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - deviceType_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Type of device this kernel runs on.
-     *
- * - * optional string device_type = 2; - */ - public Builder setDeviceType( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - deviceType_ = value; - onChanged(); - return this; - } - /** - * 
-     * Type of device this kernel runs on.
-     *
- * - * optional string device_type = 2; - */ - public Builder clearDeviceType() { - - deviceType_ = getDefaultInstance().getDeviceType(); - onChanged(); - return this; - } - /** - * 
-     * Type of device this kernel runs on.
-     *
- * - * optional string device_type = 2; - */ - public Builder setDeviceTypeBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - deviceType_ = value; - onChanged(); - return this; - } - - private java.util.List constraint_ = - java.util.Collections.emptyList(); - private void ensureConstraintIsMutable() { - if (!((bitField0_ & 0x00000004) == 0x00000004)) { - constraint_ = new java.util.ArrayList(constraint_); - bitField0_ |= 0x00000004; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.KernelDef.AttrConstraint, org.tensorflow.framework.KernelDef.AttrConstraint.Builder, org.tensorflow.framework.KernelDef.AttrConstraintOrBuilder> constraintBuilder_; - - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public java.util.List getConstraintList() { - if (constraintBuilder_ == null) { - return java.util.Collections.unmodifiableList(constraint_); - } else { - return constraintBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public int getConstraintCount() { - if (constraintBuilder_ == null) { - return constraint_.size(); - } else { - return constraintBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public org.tensorflow.framework.KernelDef.AttrConstraint getConstraint(int index) { - if (constraintBuilder_ == null) { - return constraint_.get(index); - } else { - return constraintBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder setConstraint( - int index, org.tensorflow.framework.KernelDef.AttrConstraint value) { - if (constraintBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureConstraintIsMutable(); - constraint_.set(index, value); - onChanged(); - } else { - constraintBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder setConstraint( - int index, org.tensorflow.framework.KernelDef.AttrConstraint.Builder builderForValue) { - if (constraintBuilder_ == null) { - ensureConstraintIsMutable(); - constraint_.set(index, builderForValue.build()); - onChanged(); - } else { - constraintBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder addConstraint(org.tensorflow.framework.KernelDef.AttrConstraint value) { - if (constraintBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureConstraintIsMutable(); - constraint_.add(value); - onChanged(); - } else { - constraintBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder addConstraint( - int index, org.tensorflow.framework.KernelDef.AttrConstraint value) { - if (constraintBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureConstraintIsMutable(); - constraint_.add(index, value); - onChanged(); - } else { - constraintBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder addConstraint( - org.tensorflow.framework.KernelDef.AttrConstraint.Builder builderForValue) { - if (constraintBuilder_ == null) { - ensureConstraintIsMutable(); - constraint_.add(builderForValue.build()); - onChanged(); - } else { - constraintBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder addConstraint( - int index, org.tensorflow.framework.KernelDef.AttrConstraint.Builder builderForValue) { - if (constraintBuilder_ == null) { - ensureConstraintIsMutable(); - constraint_.add(index, builderForValue.build()); - onChanged(); - } else { - constraintBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder addAllConstraint( - java.lang.Iterable values) { - if (constraintBuilder_ == null) { - ensureConstraintIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, constraint_); - onChanged(); - } else { - constraintBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder clearConstraint() { - if (constraintBuilder_ == null) { - constraint_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - onChanged(); - } else { - constraintBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public Builder removeConstraint(int index) { - if (constraintBuilder_ == null) { - ensureConstraintIsMutable(); - constraint_.remove(index); - onChanged(); - } else { - constraintBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public org.tensorflow.framework.KernelDef.AttrConstraint.Builder getConstraintBuilder( - int index) { - return getConstraintFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public org.tensorflow.framework.KernelDef.AttrConstraintOrBuilder getConstraintOrBuilder( - int index) { - if (constraintBuilder_ == null) { - return constraint_.get(index); } else { - return constraintBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public java.util.List - getConstraintOrBuilderList() { - if (constraintBuilder_ != null) { - return constraintBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(constraint_); - } - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public org.tensorflow.framework.KernelDef.AttrConstraint.Builder addConstraintBuilder() { - return getConstraintFieldBuilder().addBuilder( - org.tensorflow.framework.KernelDef.AttrConstraint.getDefaultInstance()); - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public org.tensorflow.framework.KernelDef.AttrConstraint.Builder addConstraintBuilder( - int index) { - return getConstraintFieldBuilder().addBuilder( - index, org.tensorflow.framework.KernelDef.AttrConstraint.getDefaultInstance()); - } - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - public java.util.List - getConstraintBuilderList() { - return getConstraintFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.KernelDef.AttrConstraint, org.tensorflow.framework.KernelDef.AttrConstraint.Builder, org.tensorflow.framework.KernelDef.AttrConstraintOrBuilder> - getConstraintFieldBuilder() { - if (constraintBuilder_ == null) { - constraintBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.KernelDef.AttrConstraint, org.tensorflow.framework.KernelDef.AttrConstraint.Builder, org.tensorflow.framework.KernelDef.AttrConstraintOrBuilder>( - constraint_, - ((bitField0_ & 0x00000004) == 0x00000004), - getParentForChildren(), - isClean()); - constraint_ = null; - } - return constraintBuilder_; - } - - private com.google.protobuf.LazyStringList hostMemoryArg_ = com.google.protobuf.LazyStringArrayList.EMPTY; - private void ensureHostMemoryArgIsMutable() { - if (!((bitField0_ & 0x00000008) == 0x00000008)) { - hostMemoryArg_ = new com.google.protobuf.LazyStringArrayList(hostMemoryArg_); - bitField0_ |= 0x00000008; - } - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public com.google.protobuf.ProtocolStringList - getHostMemoryArgList() { - return hostMemoryArg_.getUnmodifiableView(); - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public int getHostMemoryArgCount() { - return hostMemoryArg_.size(); - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public java.lang.String getHostMemoryArg(int index) { - return hostMemoryArg_.get(index); - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public com.google.protobuf.ByteString - getHostMemoryArgBytes(int index) { - return hostMemoryArg_.getByteString(index); - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public Builder setHostMemoryArg( - int index, java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - ensureHostMemoryArgIsMutable(); - hostMemoryArg_.set(index, value); - onChanged(); - return this; - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public Builder addHostMemoryArg( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - ensureHostMemoryArgIsMutable(); - hostMemoryArg_.add(value); - onChanged(); - return this; - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public Builder addAllHostMemoryArg( - java.lang.Iterable values) { - ensureHostMemoryArgIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, hostMemoryArg_); - onChanged(); - return this; - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public Builder clearHostMemoryArg() { - hostMemoryArg_ = com.google.protobuf.LazyStringArrayList.EMPTY; - bitField0_ = (bitField0_ & ~0x00000008); - onChanged(); - return this; - } - /** - * 
-     * Names of the Op's input_/output_args that reside in host memory
-     * instead of device memory.
-     *
- * - * repeated string host_memory_arg = 4; - */ - public Builder addHostMemoryArgBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - ensureHostMemoryArgIsMutable(); - hostMemoryArg_.add(value); - onChanged(); - return this; - } - - private java.lang.Object label_ = ""; - /** - * 
-     * This allows experimental kernels to be registered for an op that
-     * won't be used unless the user specifies a "_kernel" attr with
-     * value matching this.
-     *
- * - * optional string label = 5; - */ - public java.lang.String getLabel() { - java.lang.Object ref = label_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - label_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * This allows experimental kernels to be registered for an op that
-     * won't be used unless the user specifies a "_kernel" attr with
-     * value matching this.
-     *
- * - * optional string label = 5; - */ - public com.google.protobuf.ByteString - getLabelBytes() { - java.lang.Object ref = label_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - label_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * This allows experimental kernels to be registered for an op that
-     * won't be used unless the user specifies a "_kernel" attr with
-     * value matching this.
-     *
- * - * optional string label = 5; - */ - public Builder setLabel( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - label_ = value; - onChanged(); - return this; - } - /** - * 
-     * This allows experimental kernels to be registered for an op that
-     * won't be used unless the user specifies a "_kernel" attr with
-     * value matching this.
-     *
- * - * optional string label = 5; - */ - public Builder clearLabel() { - - label_ = getDefaultInstance().getLabel(); - onChanged(); - return this; - } - /** - * 
-     * This allows experimental kernels to be registered for an op that
-     * won't be used unless the user specifies a "_kernel" attr with
-     * value matching this.
-     *
- * - * optional string label = 5; - */ - public Builder setLabelBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - label_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.KernelDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.KernelDef) - private static final org.tensorflow.framework.KernelDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.KernelDef(); - } - - public static org.tensorflow.framework.KernelDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public KernelDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new KernelDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.KernelDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/KernelDefOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/KernelDefOrBuilder.java deleted file mode 100644 index 65e823af5e6..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/KernelDefOrBuilder.java +++ /dev/null @@ -1,130 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: kernel_def.proto - -package org.tensorflow.framework; - -public interface KernelDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.KernelDef) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Must match the name of an Op.
-   *
- * - * optional string op = 1; - */ - java.lang.String getOp(); - /** - * 
-   * Must match the name of an Op.
-   *
- * - * optional string op = 1; - */ - com.google.protobuf.ByteString - getOpBytes(); - - /** - * 
-   * Type of device this kernel runs on.
-   *
- * - * optional string device_type = 2; - */ - java.lang.String getDeviceType(); - /** - * 
-   * Type of device this kernel runs on.
-   *
- * - * optional string device_type = 2; - */ - com.google.protobuf.ByteString - getDeviceTypeBytes(); - - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - java.util.List - getConstraintList(); - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - org.tensorflow.framework.KernelDef.AttrConstraint getConstraint(int index); - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - int getConstraintCount(); - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - java.util.List - getConstraintOrBuilderList(); - /** - * repeated .tensorflow.KernelDef.AttrConstraint constraint = 3; - */ - org.tensorflow.framework.KernelDef.AttrConstraintOrBuilder getConstraintOrBuilder( - int index); - - /** - * 
-   * Names of the Op's input_/output_args that reside in host memory
-   * instead of device memory.
-   *
- * - * repeated string host_memory_arg = 4; - */ - java.util.List - getHostMemoryArgList(); - /** - * 
-   * Names of the Op's input_/output_args that reside in host memory
-   * instead of device memory.
-   *
- * - * repeated string host_memory_arg = 4; - */ - int getHostMemoryArgCount(); - /** - * 
-   * Names of the Op's input_/output_args that reside in host memory
-   * instead of device memory.
-   *
- * - * repeated string host_memory_arg = 4; - */ - java.lang.String getHostMemoryArg(int index); - /** - * 
-   * Names of the Op's input_/output_args that reside in host memory
-   * instead of device memory.
-   *
- * - * repeated string host_memory_arg = 4; - */ - com.google.protobuf.ByteString - getHostMemoryArgBytes(int index); - - /** - * 
-   * This allows experimental kernels to be registered for an op that
-   * won't be used unless the user specifies a "_kernel" attr with
-   * value matching this.
-   *
- * - * optional string label = 5; - */ - java.lang.String getLabel(); - /** - * 
-   * This allows experimental kernels to be registered for an op that
-   * won't be used unless the user specifies a "_kernel" attr with
-   * value matching this.
-   *
- * - * optional string label = 5; - */ - com.google.protobuf.ByteString - getLabelBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/KernelDefProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/KernelDefProtos.java deleted file mode 100644 index 96417e4e14e..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/KernelDefProtos.java +++ /dev/null @@ -1,75 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: kernel_def.proto - -package org.tensorflow.framework; - -public final class KernelDefProtos { - private KernelDefProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_KernelDef_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_KernelDef_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_KernelDef_AttrConstraint_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_KernelDef_AttrConstraint_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\020kernel_def.proto\022\ntensorflow\032*tensorfl" + - "ow/core/framework/attr_value.proto\"\335\001\n\tK" + - "ernelDef\022\n\n\002op\030\001 \001(\t\022\023\n\013device_type\030\002 \001(" + - "\t\0228\n\nconstraint\030\003 \003(\0132$.tensorflow.Kerne" + - "lDef.AttrConstraint\022\027\n\017host_memory_arg\030\004" + - " \003(\t\022\r\n\005label\030\005 \001(\t\032M\n\016AttrConstraint\022\014\n" + - "\004name\030\001 \001(\t\022-\n\016allowed_values\030\002 \001(\0132\025.te" + - "nsorflow.AttrValueB0\n\030org.tensorflow.fra" + - "meworkB\017KernelDefProtosP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.framework.AttrValueProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_KernelDef_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_KernelDef_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_KernelDef_descriptor, - new java.lang.String[] { "Op", "DeviceType", "Constraint", "HostMemoryArg", "Label", }); - internal_static_tensorflow_KernelDef_AttrConstraint_descriptor = - internal_static_tensorflow_KernelDef_descriptor.getNestedTypes().get(0); - internal_static_tensorflow_KernelDef_AttrConstraint_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_KernelDef_AttrConstraint_descriptor, - new java.lang.String[] { "Name", "AllowedValues", }); - org.tensorflow.framework.AttrValueProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/LogMemoryProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/LogMemoryProtos.java deleted file mode 100644 index 6e7493b82fa..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/LogMemoryProtos.java +++ /dev/null @@ -1,130 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -public final class LogMemoryProtos { - private LogMemoryProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_MemoryLogStep_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_MemoryLogStep_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_MemoryLogTensorAllocation_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_MemoryLogTensorAllocation_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_MemoryLogTensorDeallocation_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_MemoryLogTensorDeallocation_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_MemoryLogTensorOutput_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_MemoryLogTensorOutput_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_MemoryLogRawAllocation_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_MemoryLogRawAllocation_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_MemoryLogRawDeallocation_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_MemoryLogRawDeallocation_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\020log_memory.proto\022\ntensorflow\0322tensorfl" + - "ow/core/framework/tensor_description.pro" + - "to\"0\n\rMemoryLogStep\022\017\n\007step_id\030\001 \001(\003\022\016\n\006" + - "handle\030\002 \001(\t\"p\n\031MemoryLogTensorAllocatio" + - "n\022\017\n\007step_id\030\001 \001(\003\022\023\n\013kernel_name\030\002 \001(\t\022" + - "-\n\006tensor\030\003 \001(\0132\035.tensorflow.TensorDescr" + - "iption\"L\n\033MemoryLogTensorDeallocation\022\025\n" + - "\rallocation_id\030\001 \001(\003\022\026\n\016allocator_name\030\002" + - " \001(\t\"{\n\025MemoryLogTensorOutput\022\017\n\007step_id" + - "\030\001 \001(\003\022\023\n\013kernel_name\030\002 \001(\t\022\r\n\005index\030\003 \001", - "(\005\022-\n\006tensor\030\004 \001(\0132\035.tensorflow.TensorDe" + - "scription\"\213\001\n\026MemoryLogRawAllocation\022\017\n\007" + - "step_id\030\001 \001(\003\022\021\n\toperation\030\002 \001(\t\022\021\n\tnum_" + - "bytes\030\003 \001(\003\022\013\n\003ptr\030\004 \001(\004\022\025\n\rallocation_i" + - "d\030\005 \001(\003\022\026\n\016allocator_name\030\006 \001(\t\"\177\n\030Memor" + - "yLogRawDeallocation\022\017\n\007step_id\030\001 \001(\003\022\021\n\t" + - "operation\030\002 \001(\t\022\025\n\rallocation_id\030\003 \001(\003\022\026" + - "\n\016allocator_name\030\004 \001(\t\022\020\n\010deferred\030\005 \001(\010" + - "B0\n\030org.tensorflow.frameworkB\017LogMemoryP" + - "rotosP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.framework.TensorDescriptionProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_MemoryLogStep_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_MemoryLogStep_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_MemoryLogStep_descriptor, - new java.lang.String[] { "StepId", "Handle", }); - internal_static_tensorflow_MemoryLogTensorAllocation_descriptor = - getDescriptor().getMessageTypes().get(1); - internal_static_tensorflow_MemoryLogTensorAllocation_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_MemoryLogTensorAllocation_descriptor, - new java.lang.String[] { "StepId", "KernelName", "Tensor", }); - internal_static_tensorflow_MemoryLogTensorDeallocation_descriptor = - getDescriptor().getMessageTypes().get(2); - internal_static_tensorflow_MemoryLogTensorDeallocation_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_MemoryLogTensorDeallocation_descriptor, - new java.lang.String[] { "AllocationId", "AllocatorName", }); - internal_static_tensorflow_MemoryLogTensorOutput_descriptor = - getDescriptor().getMessageTypes().get(3); - internal_static_tensorflow_MemoryLogTensorOutput_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_MemoryLogTensorOutput_descriptor, - new java.lang.String[] { "StepId", "KernelName", "Index", "Tensor", }); - internal_static_tensorflow_MemoryLogRawAllocation_descriptor = - getDescriptor().getMessageTypes().get(4); - internal_static_tensorflow_MemoryLogRawAllocation_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_MemoryLogRawAllocation_descriptor, - new java.lang.String[] { "StepId", "Operation", "NumBytes", "Ptr", "AllocationId", "AllocatorName", }); - internal_static_tensorflow_MemoryLogRawDeallocation_descriptor = - getDescriptor().getMessageTypes().get(5); - internal_static_tensorflow_MemoryLogRawDeallocation_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_MemoryLogRawDeallocation_descriptor, - new java.lang.String[] { "StepId", "Operation", "AllocationId", "AllocatorName", "Deferred", }); - org.tensorflow.framework.TensorDescriptionProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawAllocation.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawAllocation.java deleted file mode 100644 index 627bb36914e..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawAllocation.java +++ /dev/null @@ -1,981 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.MemoryLogRawAllocation} - */ -public final class MemoryLogRawAllocation extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.MemoryLogRawAllocation) - MemoryLogRawAllocationOrBuilder { - // Use MemoryLogRawAllocation.newBuilder() to construct. - private MemoryLogRawAllocation(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private MemoryLogRawAllocation() { - stepId_ = 0L; - operation_ = ""; - numBytes_ = 0L; - ptr_ = 0L; - allocationId_ = 0L; - allocatorName_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private MemoryLogRawAllocation( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - stepId_ = input.readInt64(); - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - operation_ = s; - break; - } - case 24: { - - numBytes_ = input.readInt64(); - break; - } - case 32: { - - ptr_ = input.readUInt64(); - break; - } - case 40: { - - allocationId_ = input.readInt64(); - break; - } - case 50: { - java.lang.String s = input.readStringRequireUtf8(); - - allocatorName_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawAllocation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawAllocation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogRawAllocation.class, org.tensorflow.framework.MemoryLogRawAllocation.Builder.class); - } - - public static final int STEP_ID_FIELD_NUMBER = 1; - private long stepId_; - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - - public static final int OPERATION_FIELD_NUMBER = 2; - private volatile java.lang.Object operation_; - /** - * 
-   * Name of the operation making the allocation.
-   *
- * - * optional string operation = 2; - */ - public java.lang.String getOperation() { - java.lang.Object ref = operation_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - operation_ = s; - return s; - } - } - /** - * 
-   * Name of the operation making the allocation.
-   *
- * - * optional string operation = 2; - */ - public com.google.protobuf.ByteString - getOperationBytes() { - java.lang.Object ref = operation_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - operation_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int NUM_BYTES_FIELD_NUMBER = 3; - private long numBytes_; - /** - * 
-   * Number of bytes in the allocation.
-   *
- * - * optional int64 num_bytes = 3; - */ - public long getNumBytes() { - return numBytes_; - } - - public static final int PTR_FIELD_NUMBER = 4; - private long ptr_; - /** - * 
-   * Address of the allocation.
-   *
- * - * optional uint64 ptr = 4; - */ - public long getPtr() { - return ptr_; - } - - public static final int ALLOCATION_ID_FIELD_NUMBER = 5; - private long allocationId_; - /** - * 
-   * Id of the tensor buffer being allocated, used to match to a
-   * corresponding deallocation.
-   *
- * - * optional int64 allocation_id = 5; - */ - public long getAllocationId() { - return allocationId_; - } - - public static final int ALLOCATOR_NAME_FIELD_NUMBER = 6; - private volatile java.lang.Object allocatorName_; - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 6; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } - } - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 6; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (stepId_ != 0L) { - output.writeInt64(1, stepId_); - } - if (!getOperationBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, operation_); - } - if (numBytes_ != 0L) { - output.writeInt64(3, numBytes_); - } - if (ptr_ != 0L) { - output.writeUInt64(4, ptr_); - } - if (allocationId_ != 0L) { - output.writeInt64(5, allocationId_); - } - if (!getAllocatorNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 6, allocatorName_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (stepId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(1, stepId_); - } - if (!getOperationBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, operation_); - } - if (numBytes_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(3, numBytes_); - } - if (ptr_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeUInt64Size(4, ptr_); - } - if (allocationId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(5, allocationId_); - } - if (!getAllocatorNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(6, allocatorName_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.MemoryLogRawAllocation)) { - return super.equals(obj); - } - org.tensorflow.framework.MemoryLogRawAllocation other = (org.tensorflow.framework.MemoryLogRawAllocation) obj; - - boolean result = true; - result = result && (getStepId() - == other.getStepId()); - result = result && getOperation() - .equals(other.getOperation()); - result = result && (getNumBytes() - == other.getNumBytes()); - result = result && (getPtr() - == other.getPtr()); - result = result && (getAllocationId() - == other.getAllocationId()); - result = result && getAllocatorName() - .equals(other.getAllocatorName()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + STEP_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getStepId()); - hash = (37 * hash) + OPERATION_FIELD_NUMBER; - hash = (53 * hash) + getOperation().hashCode(); - hash = (37 * hash) + NUM_BYTES_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getNumBytes()); - hash = (37 * hash) + PTR_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getPtr()); - hash = (37 * hash) + ALLOCATION_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getAllocationId()); - hash = (37 * hash) + ALLOCATOR_NAME_FIELD_NUMBER; - hash = (53 * hash) + getAllocatorName().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.MemoryLogRawAllocation parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogRawAllocation parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.MemoryLogRawAllocation prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.MemoryLogRawAllocation} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.MemoryLogRawAllocation) - org.tensorflow.framework.MemoryLogRawAllocationOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawAllocation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawAllocation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogRawAllocation.class, org.tensorflow.framework.MemoryLogRawAllocation.Builder.class); - } - - // Construct using org.tensorflow.framework.MemoryLogRawAllocation.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - stepId_ = 0L; - - operation_ = ""; - - numBytes_ = 0L; - - ptr_ = 0L; - - allocationId_ = 0L; - - allocatorName_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawAllocation_descriptor; - } - - public org.tensorflow.framework.MemoryLogRawAllocation getDefaultInstanceForType() { - return org.tensorflow.framework.MemoryLogRawAllocation.getDefaultInstance(); - } - - public org.tensorflow.framework.MemoryLogRawAllocation build() { - org.tensorflow.framework.MemoryLogRawAllocation result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.MemoryLogRawAllocation buildPartial() { - org.tensorflow.framework.MemoryLogRawAllocation result = new org.tensorflow.framework.MemoryLogRawAllocation(this); - result.stepId_ = stepId_; - result.operation_ = operation_; - result.numBytes_ = numBytes_; - result.ptr_ = ptr_; - result.allocationId_ = allocationId_; - result.allocatorName_ = allocatorName_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.MemoryLogRawAllocation) { - return mergeFrom((org.tensorflow.framework.MemoryLogRawAllocation)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.MemoryLogRawAllocation other) { - if (other == org.tensorflow.framework.MemoryLogRawAllocation.getDefaultInstance()) return this; - if (other.getStepId() != 0L) { - setStepId(other.getStepId()); - } - if (!other.getOperation().isEmpty()) { - operation_ = other.operation_; - onChanged(); - } - if (other.getNumBytes() != 0L) { - setNumBytes(other.getNumBytes()); - } - if (other.getPtr() != 0L) { - setPtr(other.getPtr()); - } - if (other.getAllocationId() != 0L) { - setAllocationId(other.getAllocationId()); - } - if (!other.getAllocatorName().isEmpty()) { - allocatorName_ = other.allocatorName_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.MemoryLogRawAllocation parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.MemoryLogRawAllocation) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private long stepId_ ; - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder setStepId(long value) { - - stepId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder clearStepId() { - - stepId_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object operation_ = ""; - /** - * 
-     * Name of the operation making the allocation.
-     *
- * - * optional string operation = 2; - */ - public java.lang.String getOperation() { - java.lang.Object ref = operation_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - operation_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the operation making the allocation.
-     *
- * - * optional string operation = 2; - */ - public com.google.protobuf.ByteString - getOperationBytes() { - java.lang.Object ref = operation_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - operation_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the operation making the allocation.
-     *
- * - * optional string operation = 2; - */ - public Builder setOperation( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - operation_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the operation making the allocation.
-     *
- * - * optional string operation = 2; - */ - public Builder clearOperation() { - - operation_ = getDefaultInstance().getOperation(); - onChanged(); - return this; - } - /** - * 
-     * Name of the operation making the allocation.
-     *
- * - * optional string operation = 2; - */ - public Builder setOperationBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - operation_ = value; - onChanged(); - return this; - } - - private long numBytes_ ; - /** - * 
-     * Number of bytes in the allocation.
-     *
- * - * optional int64 num_bytes = 3; - */ - public long getNumBytes() { - return numBytes_; - } - /** - * 
-     * Number of bytes in the allocation.
-     *
- * - * optional int64 num_bytes = 3; - */ - public Builder setNumBytes(long value) { - - numBytes_ = value; - onChanged(); - return this; - } - /** - * 
-     * Number of bytes in the allocation.
-     *
- * - * optional int64 num_bytes = 3; - */ - public Builder clearNumBytes() { - - numBytes_ = 0L; - onChanged(); - return this; - } - - private long ptr_ ; - /** - * 
-     * Address of the allocation.
-     *
- * - * optional uint64 ptr = 4; - */ - public long getPtr() { - return ptr_; - } - /** - * 
-     * Address of the allocation.
-     *
- * - * optional uint64 ptr = 4; - */ - public Builder setPtr(long value) { - - ptr_ = value; - onChanged(); - return this; - } - /** - * 
-     * Address of the allocation.
-     *
- * - * optional uint64 ptr = 4; - */ - public Builder clearPtr() { - - ptr_ = 0L; - onChanged(); - return this; - } - - private long allocationId_ ; - /** - * 
-     * Id of the tensor buffer being allocated, used to match to a
-     * corresponding deallocation.
-     *
- * - * optional int64 allocation_id = 5; - */ - public long getAllocationId() { - return allocationId_; - } - /** - * 
-     * Id of the tensor buffer being allocated, used to match to a
-     * corresponding deallocation.
-     *
- * - * optional int64 allocation_id = 5; - */ - public Builder setAllocationId(long value) { - - allocationId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Id of the tensor buffer being allocated, used to match to a
-     * corresponding deallocation.
-     *
- * - * optional int64 allocation_id = 5; - */ - public Builder clearAllocationId() { - - allocationId_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object allocatorName_ = ""; - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 6; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 6; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 6; - */ - public Builder setAllocatorName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - allocatorName_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 6; - */ - public Builder clearAllocatorName() { - - allocatorName_ = getDefaultInstance().getAllocatorName(); - onChanged(); - return this; - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 6; - */ - public Builder setAllocatorNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - allocatorName_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.MemoryLogRawAllocation) - } - - // @@protoc_insertion_point(class_scope:tensorflow.MemoryLogRawAllocation) - private static final org.tensorflow.framework.MemoryLogRawAllocation DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.MemoryLogRawAllocation(); - } - - public static org.tensorflow.framework.MemoryLogRawAllocation getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public MemoryLogRawAllocation parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new MemoryLogRawAllocation(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.MemoryLogRawAllocation getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawAllocationOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawAllocationOrBuilder.java deleted file mode 100644 index 5820ecffabc..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawAllocationOrBuilder.java +++ /dev/null @@ -1,82 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -public interface MemoryLogRawAllocationOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.MemoryLogRawAllocation) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - long getStepId(); - - /** - * 
-   * Name of the operation making the allocation.
-   *
- * - * optional string operation = 2; - */ - java.lang.String getOperation(); - /** - * 
-   * Name of the operation making the allocation.
-   *
- * - * optional string operation = 2; - */ - com.google.protobuf.ByteString - getOperationBytes(); - - /** - * 
-   * Number of bytes in the allocation.
-   *
- * - * optional int64 num_bytes = 3; - */ - long getNumBytes(); - - /** - * 
-   * Address of the allocation.
-   *
- * - * optional uint64 ptr = 4; - */ - long getPtr(); - - /** - * 
-   * Id of the tensor buffer being allocated, used to match to a
-   * corresponding deallocation.
-   *
- * - * optional int64 allocation_id = 5; - */ - long getAllocationId(); - - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 6; - */ - java.lang.String getAllocatorName(); - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 6; - */ - com.google.protobuf.ByteString - getAllocatorNameBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawDeallocation.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawDeallocation.java deleted file mode 100644 index 6cb0744a28e..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawDeallocation.java +++ /dev/null @@ -1,910 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.MemoryLogRawDeallocation} - */ -public final class MemoryLogRawDeallocation extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.MemoryLogRawDeallocation) - MemoryLogRawDeallocationOrBuilder { - // Use MemoryLogRawDeallocation.newBuilder() to construct. - private MemoryLogRawDeallocation(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private MemoryLogRawDeallocation() { - stepId_ = 0L; - operation_ = ""; - allocationId_ = 0L; - allocatorName_ = ""; - deferred_ = false; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private MemoryLogRawDeallocation( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - stepId_ = input.readInt64(); - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - operation_ = s; - break; - } - case 24: { - - allocationId_ = input.readInt64(); - break; - } - case 34: { - java.lang.String s = input.readStringRequireUtf8(); - - allocatorName_ = s; - break; - } - case 40: { - - deferred_ = input.readBool(); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawDeallocation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawDeallocation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogRawDeallocation.class, org.tensorflow.framework.MemoryLogRawDeallocation.Builder.class); - } - - public static final int STEP_ID_FIELD_NUMBER = 1; - private long stepId_; - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - - public static final int OPERATION_FIELD_NUMBER = 2; - private volatile java.lang.Object operation_; - /** - * 
-   * Name of the operation making the deallocation.
-   *
- * - * optional string operation = 2; - */ - public java.lang.String getOperation() { - java.lang.Object ref = operation_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - operation_ = s; - return s; - } - } - /** - * 
-   * Name of the operation making the deallocation.
-   *
- * - * optional string operation = 2; - */ - public com.google.protobuf.ByteString - getOperationBytes() { - java.lang.Object ref = operation_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - operation_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int ALLOCATION_ID_FIELD_NUMBER = 3; - private long allocationId_; - /** - * 
-   * Id of the tensor buffer being deallocated, used to match to a
-   * corresponding allocation.
-   *
- * - * optional int64 allocation_id = 3; - */ - public long getAllocationId() { - return allocationId_; - } - - public static final int ALLOCATOR_NAME_FIELD_NUMBER = 4; - private volatile java.lang.Object allocatorName_; - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 4; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } - } - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 4; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int DEFERRED_FIELD_NUMBER = 5; - private boolean deferred_; - /** - * 
-   * True if the deallocation is queued and will be performed later,
-   * e.g. for GPU lazy freeing of buffers.
-   *
- * - * optional bool deferred = 5; - */ - public boolean getDeferred() { - return deferred_; - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (stepId_ != 0L) { - output.writeInt64(1, stepId_); - } - if (!getOperationBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, operation_); - } - if (allocationId_ != 0L) { - output.writeInt64(3, allocationId_); - } - if (!getAllocatorNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 4, allocatorName_); - } - if (deferred_ != false) { - output.writeBool(5, deferred_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (stepId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(1, stepId_); - } - if (!getOperationBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, operation_); - } - if (allocationId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(3, allocationId_); - } - if (!getAllocatorNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(4, allocatorName_); - } - if (deferred_ != false) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize(5, deferred_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.MemoryLogRawDeallocation)) { - return super.equals(obj); - } - org.tensorflow.framework.MemoryLogRawDeallocation other = (org.tensorflow.framework.MemoryLogRawDeallocation) obj; - - boolean result = true; - result = result && (getStepId() - == other.getStepId()); - result = result && getOperation() - .equals(other.getOperation()); - result = result && (getAllocationId() - == other.getAllocationId()); - result = result && getAllocatorName() - .equals(other.getAllocatorName()); - result = result && (getDeferred() - == other.getDeferred()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + STEP_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getStepId()); - hash = (37 * hash) + OPERATION_FIELD_NUMBER; - hash = (53 * hash) + getOperation().hashCode(); - hash = (37 * hash) + ALLOCATION_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getAllocationId()); - hash = (37 * hash) + ALLOCATOR_NAME_FIELD_NUMBER; - hash = (53 * hash) + getAllocatorName().hashCode(); - hash = (37 * hash) + DEFERRED_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getDeferred()); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.MemoryLogRawDeallocation parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogRawDeallocation parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.MemoryLogRawDeallocation prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.MemoryLogRawDeallocation} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.MemoryLogRawDeallocation) - org.tensorflow.framework.MemoryLogRawDeallocationOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawDeallocation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawDeallocation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogRawDeallocation.class, org.tensorflow.framework.MemoryLogRawDeallocation.Builder.class); - } - - // Construct using org.tensorflow.framework.MemoryLogRawDeallocation.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - stepId_ = 0L; - - operation_ = ""; - - allocationId_ = 0L; - - allocatorName_ = ""; - - deferred_ = false; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogRawDeallocation_descriptor; - } - - public org.tensorflow.framework.MemoryLogRawDeallocation getDefaultInstanceForType() { - return org.tensorflow.framework.MemoryLogRawDeallocation.getDefaultInstance(); - } - - public org.tensorflow.framework.MemoryLogRawDeallocation build() { - org.tensorflow.framework.MemoryLogRawDeallocation result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.MemoryLogRawDeallocation buildPartial() { - org.tensorflow.framework.MemoryLogRawDeallocation result = new org.tensorflow.framework.MemoryLogRawDeallocation(this); - result.stepId_ = stepId_; - result.operation_ = operation_; - result.allocationId_ = allocationId_; - result.allocatorName_ = allocatorName_; - result.deferred_ = deferred_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.MemoryLogRawDeallocation) { - return mergeFrom((org.tensorflow.framework.MemoryLogRawDeallocation)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.MemoryLogRawDeallocation other) { - if (other == org.tensorflow.framework.MemoryLogRawDeallocation.getDefaultInstance()) return this; - if (other.getStepId() != 0L) { - setStepId(other.getStepId()); - } - if (!other.getOperation().isEmpty()) { - operation_ = other.operation_; - onChanged(); - } - if (other.getAllocationId() != 0L) { - setAllocationId(other.getAllocationId()); - } - if (!other.getAllocatorName().isEmpty()) { - allocatorName_ = other.allocatorName_; - onChanged(); - } - if (other.getDeferred() != false) { - setDeferred(other.getDeferred()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.MemoryLogRawDeallocation parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.MemoryLogRawDeallocation) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private long stepId_ ; - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder setStepId(long value) { - - stepId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder clearStepId() { - - stepId_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object operation_ = ""; - /** - * 
-     * Name of the operation making the deallocation.
-     *
- * - * optional string operation = 2; - */ - public java.lang.String getOperation() { - java.lang.Object ref = operation_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - operation_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the operation making the deallocation.
-     *
- * - * optional string operation = 2; - */ - public com.google.protobuf.ByteString - getOperationBytes() { - java.lang.Object ref = operation_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - operation_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the operation making the deallocation.
-     *
- * - * optional string operation = 2; - */ - public Builder setOperation( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - operation_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the operation making the deallocation.
-     *
- * - * optional string operation = 2; - */ - public Builder clearOperation() { - - operation_ = getDefaultInstance().getOperation(); - onChanged(); - return this; - } - /** - * 
-     * Name of the operation making the deallocation.
-     *
- * - * optional string operation = 2; - */ - public Builder setOperationBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - operation_ = value; - onChanged(); - return this; - } - - private long allocationId_ ; - /** - * 
-     * Id of the tensor buffer being deallocated, used to match to a
-     * corresponding allocation.
-     *
- * - * optional int64 allocation_id = 3; - */ - public long getAllocationId() { - return allocationId_; - } - /** - * 
-     * Id of the tensor buffer being deallocated, used to match to a
-     * corresponding allocation.
-     *
- * - * optional int64 allocation_id = 3; - */ - public Builder setAllocationId(long value) { - - allocationId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Id of the tensor buffer being deallocated, used to match to a
-     * corresponding allocation.
-     *
- * - * optional int64 allocation_id = 3; - */ - public Builder clearAllocationId() { - - allocationId_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object allocatorName_ = ""; - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 4; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 4; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 4; - */ - public Builder setAllocatorName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - allocatorName_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 4; - */ - public Builder clearAllocatorName() { - - allocatorName_ = getDefaultInstance().getAllocatorName(); - onChanged(); - return this; - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 4; - */ - public Builder setAllocatorNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - allocatorName_ = value; - onChanged(); - return this; - } - - private boolean deferred_ ; - /** - * 
-     * True if the deallocation is queued and will be performed later,
-     * e.g. for GPU lazy freeing of buffers.
-     *
- * - * optional bool deferred = 5; - */ - public boolean getDeferred() { - return deferred_; - } - /** - * 
-     * True if the deallocation is queued and will be performed later,
-     * e.g. for GPU lazy freeing of buffers.
-     *
- * - * optional bool deferred = 5; - */ - public Builder setDeferred(boolean value) { - - deferred_ = value; - onChanged(); - return this; - } - /** - * 
-     * True if the deallocation is queued and will be performed later,
-     * e.g. for GPU lazy freeing of buffers.
-     *
- * - * optional bool deferred = 5; - */ - public Builder clearDeferred() { - - deferred_ = false; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.MemoryLogRawDeallocation) - } - - // @@protoc_insertion_point(class_scope:tensorflow.MemoryLogRawDeallocation) - private static final org.tensorflow.framework.MemoryLogRawDeallocation DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.MemoryLogRawDeallocation(); - } - - public static org.tensorflow.framework.MemoryLogRawDeallocation getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public MemoryLogRawDeallocation parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new MemoryLogRawDeallocation(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.MemoryLogRawDeallocation getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawDeallocationOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawDeallocationOrBuilder.java deleted file mode 100644 index 3a0e0474542..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogRawDeallocationOrBuilder.java +++ /dev/null @@ -1,74 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -public interface MemoryLogRawDeallocationOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.MemoryLogRawDeallocation) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - long getStepId(); - - /** - * 
-   * Name of the operation making the deallocation.
-   *
- * - * optional string operation = 2; - */ - java.lang.String getOperation(); - /** - * 
-   * Name of the operation making the deallocation.
-   *
- * - * optional string operation = 2; - */ - com.google.protobuf.ByteString - getOperationBytes(); - - /** - * 
-   * Id of the tensor buffer being deallocated, used to match to a
-   * corresponding allocation.
-   *
- * - * optional int64 allocation_id = 3; - */ - long getAllocationId(); - - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 4; - */ - java.lang.String getAllocatorName(); - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 4; - */ - com.google.protobuf.ByteString - getAllocatorNameBytes(); - - /** - * 
-   * True if the deallocation is queued and will be performed later,
-   * e.g. for GPU lazy freeing of buffers.
-   *
- * - * optional bool deferred = 5; - */ - boolean getDeferred(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogStep.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogStep.java deleted file mode 100644 index e6fa6d053e0..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogStep.java +++ /dev/null @@ -1,597 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.MemoryLogStep} - */ -public final class MemoryLogStep extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.MemoryLogStep) - MemoryLogStepOrBuilder { - // Use MemoryLogStep.newBuilder() to construct. - private MemoryLogStep(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private MemoryLogStep() { - stepId_ = 0L; - handle_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private MemoryLogStep( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - stepId_ = input.readInt64(); - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - handle_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogStep_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogStep_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogStep.class, org.tensorflow.framework.MemoryLogStep.Builder.class); - } - - public static final int STEP_ID_FIELD_NUMBER = 1; - private long stepId_; - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - - public static final int HANDLE_FIELD_NUMBER = 2; - private volatile java.lang.Object handle_; - /** - * 
-   * Handle describing the feeds and fetches of the step.
-   *
- * - * optional string handle = 2; - */ - public java.lang.String getHandle() { - java.lang.Object ref = handle_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - handle_ = s; - return s; - } - } - /** - * 
-   * Handle describing the feeds and fetches of the step.
-   *
- * - * optional string handle = 2; - */ - public com.google.protobuf.ByteString - getHandleBytes() { - java.lang.Object ref = handle_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - handle_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (stepId_ != 0L) { - output.writeInt64(1, stepId_); - } - if (!getHandleBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, handle_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (stepId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(1, stepId_); - } - if (!getHandleBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, handle_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.MemoryLogStep)) { - return super.equals(obj); - } - org.tensorflow.framework.MemoryLogStep other = (org.tensorflow.framework.MemoryLogStep) obj; - - boolean result = true; - result = result && (getStepId() - == other.getStepId()); - result = result && getHandle() - .equals(other.getHandle()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + STEP_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getStepId()); - hash = (37 * hash) + HANDLE_FIELD_NUMBER; - hash = (53 * hash) + getHandle().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.MemoryLogStep parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogStep parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogStep parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogStep parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogStep parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogStep parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogStep parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogStep parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogStep parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogStep parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.MemoryLogStep prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.MemoryLogStep} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.MemoryLogStep) - org.tensorflow.framework.MemoryLogStepOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogStep_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogStep_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogStep.class, org.tensorflow.framework.MemoryLogStep.Builder.class); - } - - // Construct using org.tensorflow.framework.MemoryLogStep.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - stepId_ = 0L; - - handle_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogStep_descriptor; - } - - public org.tensorflow.framework.MemoryLogStep getDefaultInstanceForType() { - return org.tensorflow.framework.MemoryLogStep.getDefaultInstance(); - } - - public org.tensorflow.framework.MemoryLogStep build() { - org.tensorflow.framework.MemoryLogStep result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.MemoryLogStep buildPartial() { - org.tensorflow.framework.MemoryLogStep result = new org.tensorflow.framework.MemoryLogStep(this); - result.stepId_ = stepId_; - result.handle_ = handle_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.MemoryLogStep) { - return mergeFrom((org.tensorflow.framework.MemoryLogStep)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.MemoryLogStep other) { - if (other == org.tensorflow.framework.MemoryLogStep.getDefaultInstance()) return this; - if (other.getStepId() != 0L) { - setStepId(other.getStepId()); - } - if (!other.getHandle().isEmpty()) { - handle_ = other.handle_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.MemoryLogStep parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.MemoryLogStep) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private long stepId_ ; - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder setStepId(long value) { - - stepId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder clearStepId() { - - stepId_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object handle_ = ""; - /** - * 
-     * Handle describing the feeds and fetches of the step.
-     *
- * - * optional string handle = 2; - */ - public java.lang.String getHandle() { - java.lang.Object ref = handle_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - handle_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Handle describing the feeds and fetches of the step.
-     *
- * - * optional string handle = 2; - */ - public com.google.protobuf.ByteString - getHandleBytes() { - java.lang.Object ref = handle_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - handle_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Handle describing the feeds and fetches of the step.
-     *
- * - * optional string handle = 2; - */ - public Builder setHandle( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - handle_ = value; - onChanged(); - return this; - } - /** - * 
-     * Handle describing the feeds and fetches of the step.
-     *
- * - * optional string handle = 2; - */ - public Builder clearHandle() { - - handle_ = getDefaultInstance().getHandle(); - onChanged(); - return this; - } - /** - * 
-     * Handle describing the feeds and fetches of the step.
-     *
- * - * optional string handle = 2; - */ - public Builder setHandleBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - handle_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.MemoryLogStep) - } - - // @@protoc_insertion_point(class_scope:tensorflow.MemoryLogStep) - private static final org.tensorflow.framework.MemoryLogStep DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.MemoryLogStep(); - } - - public static org.tensorflow.framework.MemoryLogStep getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public MemoryLogStep parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new MemoryLogStep(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.MemoryLogStep getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogStepOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogStepOrBuilder.java deleted file mode 100644 index ad496b65b94..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogStepOrBuilder.java +++ /dev/null @@ -1,36 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -public interface MemoryLogStepOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.MemoryLogStep) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - long getStepId(); - - /** - * 
-   * Handle describing the feeds and fetches of the step.
-   *
- * - * optional string handle = 2; - */ - java.lang.String getHandle(); - /** - * 
-   * Handle describing the feeds and fetches of the step.
-   *
- * - * optional string handle = 2; - */ - com.google.protobuf.ByteString - getHandleBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorAllocation.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorAllocation.java deleted file mode 100644 index 9e919ce788b..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorAllocation.java +++ /dev/null @@ -1,833 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.MemoryLogTensorAllocation} - */ -public final class MemoryLogTensorAllocation extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.MemoryLogTensorAllocation) - MemoryLogTensorAllocationOrBuilder { - // Use MemoryLogTensorAllocation.newBuilder() to construct. - private MemoryLogTensorAllocation(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private MemoryLogTensorAllocation() { - stepId_ = 0L; - kernelName_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private MemoryLogTensorAllocation( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - stepId_ = input.readInt64(); - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - kernelName_ = s; - break; - } - case 26: { - org.tensorflow.framework.TensorDescription.Builder subBuilder = null; - if (tensor_ != null) { - subBuilder = tensor_.toBuilder(); - } - tensor_ = input.readMessage(org.tensorflow.framework.TensorDescription.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(tensor_); - tensor_ = subBuilder.buildPartial(); - } - - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorAllocation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorAllocation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogTensorAllocation.class, org.tensorflow.framework.MemoryLogTensorAllocation.Builder.class); - } - - public static final int STEP_ID_FIELD_NUMBER = 1; - private long stepId_; - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - - public static final int KERNEL_NAME_FIELD_NUMBER = 2; - private volatile java.lang.Object kernelName_; - /** - * 
-   * Name of the kernel making the allocation as set in GraphDef,
-   * e.g., "affine2/weights/Assign".
-   *
- * - * optional string kernel_name = 2; - */ - public java.lang.String getKernelName() { - java.lang.Object ref = kernelName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - kernelName_ = s; - return s; - } - } - /** - * 
-   * Name of the kernel making the allocation as set in GraphDef,
-   * e.g., "affine2/weights/Assign".
-   *
- * - * optional string kernel_name = 2; - */ - public com.google.protobuf.ByteString - getKernelNameBytes() { - java.lang.Object ref = kernelName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - kernelName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int TENSOR_FIELD_NUMBER = 3; - private org.tensorflow.framework.TensorDescription tensor_; - /** - * 
-   * Allocated tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public boolean hasTensor() { - return tensor_ != null; - } - /** - * 
-   * Allocated tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public org.tensorflow.framework.TensorDescription getTensor() { - return tensor_ == null ? org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensor_; - } - /** - * 
-   * Allocated tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public org.tensorflow.framework.TensorDescriptionOrBuilder getTensorOrBuilder() { - return getTensor(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (stepId_ != 0L) { - output.writeInt64(1, stepId_); - } - if (!getKernelNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, kernelName_); - } - if (tensor_ != null) { - output.writeMessage(3, getTensor()); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (stepId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(1, stepId_); - } - if (!getKernelNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, kernelName_); - } - if (tensor_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(3, getTensor()); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.MemoryLogTensorAllocation)) { - return super.equals(obj); - } - org.tensorflow.framework.MemoryLogTensorAllocation other = (org.tensorflow.framework.MemoryLogTensorAllocation) obj; - - boolean result = true; - result = result && (getStepId() - == other.getStepId()); - result = result && getKernelName() - .equals(other.getKernelName()); - result = result && (hasTensor() == other.hasTensor()); - if (hasTensor()) { - result = result && getTensor() - .equals(other.getTensor()); - } - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + STEP_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getStepId()); - hash = (37 * hash) + KERNEL_NAME_FIELD_NUMBER; - hash = (53 * hash) + getKernelName().hashCode(); - if (hasTensor()) { - hash = (37 * hash) + TENSOR_FIELD_NUMBER; - hash = (53 * hash) + getTensor().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.MemoryLogTensorAllocation parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorAllocation parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.MemoryLogTensorAllocation prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.MemoryLogTensorAllocation} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.MemoryLogTensorAllocation) - org.tensorflow.framework.MemoryLogTensorAllocationOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorAllocation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorAllocation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogTensorAllocation.class, org.tensorflow.framework.MemoryLogTensorAllocation.Builder.class); - } - - // Construct using org.tensorflow.framework.MemoryLogTensorAllocation.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - stepId_ = 0L; - - kernelName_ = ""; - - if (tensorBuilder_ == null) { - tensor_ = null; - } else { - tensor_ = null; - tensorBuilder_ = null; - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorAllocation_descriptor; - } - - public org.tensorflow.framework.MemoryLogTensorAllocation getDefaultInstanceForType() { - return org.tensorflow.framework.MemoryLogTensorAllocation.getDefaultInstance(); - } - - public org.tensorflow.framework.MemoryLogTensorAllocation build() { - org.tensorflow.framework.MemoryLogTensorAllocation result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.MemoryLogTensorAllocation buildPartial() { - org.tensorflow.framework.MemoryLogTensorAllocation result = new org.tensorflow.framework.MemoryLogTensorAllocation(this); - result.stepId_ = stepId_; - result.kernelName_ = kernelName_; - if (tensorBuilder_ == null) { - result.tensor_ = tensor_; - } else { - result.tensor_ = tensorBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.MemoryLogTensorAllocation) { - return mergeFrom((org.tensorflow.framework.MemoryLogTensorAllocation)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.MemoryLogTensorAllocation other) { - if (other == org.tensorflow.framework.MemoryLogTensorAllocation.getDefaultInstance()) return this; - if (other.getStepId() != 0L) { - setStepId(other.getStepId()); - } - if (!other.getKernelName().isEmpty()) { - kernelName_ = other.kernelName_; - onChanged(); - } - if (other.hasTensor()) { - mergeTensor(other.getTensor()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.MemoryLogTensorAllocation parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.MemoryLogTensorAllocation) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private long stepId_ ; - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder setStepId(long value) { - - stepId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder clearStepId() { - - stepId_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object kernelName_ = ""; - /** - * 
-     * Name of the kernel making the allocation as set in GraphDef,
-     * e.g., "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public java.lang.String getKernelName() { - java.lang.Object ref = kernelName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - kernelName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the kernel making the allocation as set in GraphDef,
-     * e.g., "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public com.google.protobuf.ByteString - getKernelNameBytes() { - java.lang.Object ref = kernelName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - kernelName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the kernel making the allocation as set in GraphDef,
-     * e.g., "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public Builder setKernelName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - kernelName_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the kernel making the allocation as set in GraphDef,
-     * e.g., "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public Builder clearKernelName() { - - kernelName_ = getDefaultInstance().getKernelName(); - onChanged(); - return this; - } - /** - * 
-     * Name of the kernel making the allocation as set in GraphDef,
-     * e.g., "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public Builder setKernelNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - kernelName_ = value; - onChanged(); - return this; - } - - private org.tensorflow.framework.TensorDescription tensor_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder> tensorBuilder_; - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public boolean hasTensor() { - return tensorBuilder_ != null || tensor_ != null; - } - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public org.tensorflow.framework.TensorDescription getTensor() { - if (tensorBuilder_ == null) { - return tensor_ == null ? org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensor_; - } else { - return tensorBuilder_.getMessage(); - } - } - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public Builder setTensor(org.tensorflow.framework.TensorDescription value) { - if (tensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - tensor_ = value; - onChanged(); - } else { - tensorBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public Builder setTensor( - org.tensorflow.framework.TensorDescription.Builder builderForValue) { - if (tensorBuilder_ == null) { - tensor_ = builderForValue.build(); - onChanged(); - } else { - tensorBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public Builder mergeTensor(org.tensorflow.framework.TensorDescription value) { - if (tensorBuilder_ == null) { - if (tensor_ != null) { - tensor_ = - org.tensorflow.framework.TensorDescription.newBuilder(tensor_).mergeFrom(value).buildPartial(); - } else { - tensor_ = value; - } - onChanged(); - } else { - tensorBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public Builder clearTensor() { - if (tensorBuilder_ == null) { - tensor_ = null; - onChanged(); - } else { - tensor_ = null; - tensorBuilder_ = null; - } - - return this; - } - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public org.tensorflow.framework.TensorDescription.Builder getTensorBuilder() { - - onChanged(); - return getTensorFieldBuilder().getBuilder(); - } - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - public org.tensorflow.framework.TensorDescriptionOrBuilder getTensorOrBuilder() { - if (tensorBuilder_ != null) { - return tensorBuilder_.getMessageOrBuilder(); - } else { - return tensor_ == null ? - org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensor_; - } - } - /** - * 
-     * Allocated tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder> - getTensorFieldBuilder() { - if (tensorBuilder_ == null) { - tensorBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder>( - getTensor(), - getParentForChildren(), - isClean()); - tensor_ = null; - } - return tensorBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.MemoryLogTensorAllocation) - } - - // @@protoc_insertion_point(class_scope:tensorflow.MemoryLogTensorAllocation) - private static final org.tensorflow.framework.MemoryLogTensorAllocation DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.MemoryLogTensorAllocation(); - } - - public static org.tensorflow.framework.MemoryLogTensorAllocation getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public MemoryLogTensorAllocation parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new MemoryLogTensorAllocation(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.MemoryLogTensorAllocation getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorAllocationOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorAllocationOrBuilder.java deleted file mode 100644 index 501641fb72c..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorAllocationOrBuilder.java +++ /dev/null @@ -1,63 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -public interface MemoryLogTensorAllocationOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.MemoryLogTensorAllocation) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - long getStepId(); - - /** - * 
-   * Name of the kernel making the allocation as set in GraphDef,
-   * e.g., "affine2/weights/Assign".
-   *
- * - * optional string kernel_name = 2; - */ - java.lang.String getKernelName(); - /** - * 
-   * Name of the kernel making the allocation as set in GraphDef,
-   * e.g., "affine2/weights/Assign".
-   *
- * - * optional string kernel_name = 2; - */ - com.google.protobuf.ByteString - getKernelNameBytes(); - - /** - * 
-   * Allocated tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - boolean hasTensor(); - /** - * 
-   * Allocated tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - org.tensorflow.framework.TensorDescription getTensor(); - /** - * 
-   * Allocated tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 3; - */ - org.tensorflow.framework.TensorDescriptionOrBuilder getTensorOrBuilder(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorDeallocation.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorDeallocation.java deleted file mode 100644 index 5391dcac192..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorDeallocation.java +++ /dev/null @@ -1,601 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.MemoryLogTensorDeallocation} - */ -public final class MemoryLogTensorDeallocation extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.MemoryLogTensorDeallocation) - MemoryLogTensorDeallocationOrBuilder { - // Use MemoryLogTensorDeallocation.newBuilder() to construct. - private MemoryLogTensorDeallocation(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private MemoryLogTensorDeallocation() { - allocationId_ = 0L; - allocatorName_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private MemoryLogTensorDeallocation( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - allocationId_ = input.readInt64(); - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - allocatorName_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorDeallocation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorDeallocation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogTensorDeallocation.class, org.tensorflow.framework.MemoryLogTensorDeallocation.Builder.class); - } - - public static final int ALLOCATION_ID_FIELD_NUMBER = 1; - private long allocationId_; - /** - * 
-   * Id of the tensor buffer being deallocated, used to match to a
-   * corresponding allocation.
-   *
- * - * optional int64 allocation_id = 1; - */ - public long getAllocationId() { - return allocationId_; - } - - public static final int ALLOCATOR_NAME_FIELD_NUMBER = 2; - private volatile java.lang.Object allocatorName_; - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 2; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } - } - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 2; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (allocationId_ != 0L) { - output.writeInt64(1, allocationId_); - } - if (!getAllocatorNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, allocatorName_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (allocationId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(1, allocationId_); - } - if (!getAllocatorNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, allocatorName_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.MemoryLogTensorDeallocation)) { - return super.equals(obj); - } - org.tensorflow.framework.MemoryLogTensorDeallocation other = (org.tensorflow.framework.MemoryLogTensorDeallocation) obj; - - boolean result = true; - result = result && (getAllocationId() - == other.getAllocationId()); - result = result && getAllocatorName() - .equals(other.getAllocatorName()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + ALLOCATION_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getAllocationId()); - hash = (37 * hash) + ALLOCATOR_NAME_FIELD_NUMBER; - hash = (53 * hash) + getAllocatorName().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorDeallocation parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.MemoryLogTensorDeallocation prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.MemoryLogTensorDeallocation} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.MemoryLogTensorDeallocation) - org.tensorflow.framework.MemoryLogTensorDeallocationOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorDeallocation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorDeallocation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogTensorDeallocation.class, org.tensorflow.framework.MemoryLogTensorDeallocation.Builder.class); - } - - // Construct using org.tensorflow.framework.MemoryLogTensorDeallocation.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - allocationId_ = 0L; - - allocatorName_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorDeallocation_descriptor; - } - - public org.tensorflow.framework.MemoryLogTensorDeallocation getDefaultInstanceForType() { - return org.tensorflow.framework.MemoryLogTensorDeallocation.getDefaultInstance(); - } - - public org.tensorflow.framework.MemoryLogTensorDeallocation build() { - org.tensorflow.framework.MemoryLogTensorDeallocation result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.MemoryLogTensorDeallocation buildPartial() { - org.tensorflow.framework.MemoryLogTensorDeallocation result = new org.tensorflow.framework.MemoryLogTensorDeallocation(this); - result.allocationId_ = allocationId_; - result.allocatorName_ = allocatorName_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.MemoryLogTensorDeallocation) { - return mergeFrom((org.tensorflow.framework.MemoryLogTensorDeallocation)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.MemoryLogTensorDeallocation other) { - if (other == org.tensorflow.framework.MemoryLogTensorDeallocation.getDefaultInstance()) return this; - if (other.getAllocationId() != 0L) { - setAllocationId(other.getAllocationId()); - } - if (!other.getAllocatorName().isEmpty()) { - allocatorName_ = other.allocatorName_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.MemoryLogTensorDeallocation parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.MemoryLogTensorDeallocation) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private long allocationId_ ; - /** - * 
-     * Id of the tensor buffer being deallocated, used to match to a
-     * corresponding allocation.
-     *
- * - * optional int64 allocation_id = 1; - */ - public long getAllocationId() { - return allocationId_; - } - /** - * 
-     * Id of the tensor buffer being deallocated, used to match to a
-     * corresponding allocation.
-     *
- * - * optional int64 allocation_id = 1; - */ - public Builder setAllocationId(long value) { - - allocationId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Id of the tensor buffer being deallocated, used to match to a
-     * corresponding allocation.
-     *
- * - * optional int64 allocation_id = 1; - */ - public Builder clearAllocationId() { - - allocationId_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object allocatorName_ = ""; - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 2; - */ - public java.lang.String getAllocatorName() { - java.lang.Object ref = allocatorName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - allocatorName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 2; - */ - public com.google.protobuf.ByteString - getAllocatorNameBytes() { - java.lang.Object ref = allocatorName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - allocatorName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 2; - */ - public Builder setAllocatorName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - allocatorName_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 2; - */ - public Builder clearAllocatorName() { - - allocatorName_ = getDefaultInstance().getAllocatorName(); - onChanged(); - return this; - } - /** - * 
-     * Name of the allocator used.
-     *
- * - * optional string allocator_name = 2; - */ - public Builder setAllocatorNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - allocatorName_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.MemoryLogTensorDeallocation) - } - - // @@protoc_insertion_point(class_scope:tensorflow.MemoryLogTensorDeallocation) - private static final org.tensorflow.framework.MemoryLogTensorDeallocation DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.MemoryLogTensorDeallocation(); - } - - public static org.tensorflow.framework.MemoryLogTensorDeallocation getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public MemoryLogTensorDeallocation parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new MemoryLogTensorDeallocation(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.MemoryLogTensorDeallocation getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorDeallocationOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorDeallocationOrBuilder.java deleted file mode 100644 index 1841ade464e..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorDeallocationOrBuilder.java +++ /dev/null @@ -1,37 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -public interface MemoryLogTensorDeallocationOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.MemoryLogTensorDeallocation) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Id of the tensor buffer being deallocated, used to match to a
-   * corresponding allocation.
-   *
- * - * optional int64 allocation_id = 1; - */ - long getAllocationId(); - - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 2; - */ - java.lang.String getAllocatorName(); - /** - * 
-   * Name of the allocator used.
-   *
- * - * optional string allocator_name = 2; - */ - com.google.protobuf.ByteString - getAllocatorNameBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorOutput.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorOutput.java deleted file mode 100644 index caf78cdae59..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorOutput.java +++ /dev/null @@ -1,907 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.MemoryLogTensorOutput} - */ -public final class MemoryLogTensorOutput extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.MemoryLogTensorOutput) - MemoryLogTensorOutputOrBuilder { - // Use MemoryLogTensorOutput.newBuilder() to construct. - private MemoryLogTensorOutput(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private MemoryLogTensorOutput() { - stepId_ = 0L; - kernelName_ = ""; - index_ = 0; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private MemoryLogTensorOutput( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - stepId_ = input.readInt64(); - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - kernelName_ = s; - break; - } - case 24: { - - index_ = input.readInt32(); - break; - } - case 34: { - org.tensorflow.framework.TensorDescription.Builder subBuilder = null; - if (tensor_ != null) { - subBuilder = tensor_.toBuilder(); - } - tensor_ = input.readMessage(org.tensorflow.framework.TensorDescription.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(tensor_); - tensor_ = subBuilder.buildPartial(); - } - - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorOutput_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorOutput_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogTensorOutput.class, org.tensorflow.framework.MemoryLogTensorOutput.Builder.class); - } - - public static final int STEP_ID_FIELD_NUMBER = 1; - private long stepId_; - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - - public static final int KERNEL_NAME_FIELD_NUMBER = 2; - private volatile java.lang.Object kernelName_; - /** - * 
-   * Name of the kernel producing an output as set in GraphDef, e.g.,
-   * "affine2/weights/Assign".
-   *
- * - * optional string kernel_name = 2; - */ - public java.lang.String getKernelName() { - java.lang.Object ref = kernelName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - kernelName_ = s; - return s; - } - } - /** - * 
-   * Name of the kernel producing an output as set in GraphDef, e.g.,
-   * "affine2/weights/Assign".
-   *
- * - * optional string kernel_name = 2; - */ - public com.google.protobuf.ByteString - getKernelNameBytes() { - java.lang.Object ref = kernelName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - kernelName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int INDEX_FIELD_NUMBER = 3; - private int index_; - /** - * 
-   * Index of the output being set.
-   *
- * - * optional int32 index = 3; - */ - public int getIndex() { - return index_; - } - - public static final int TENSOR_FIELD_NUMBER = 4; - private org.tensorflow.framework.TensorDescription tensor_; - /** - * 
-   * Output tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public boolean hasTensor() { - return tensor_ != null; - } - /** - * 
-   * Output tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public org.tensorflow.framework.TensorDescription getTensor() { - return tensor_ == null ? org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensor_; - } - /** - * 
-   * Output tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public org.tensorflow.framework.TensorDescriptionOrBuilder getTensorOrBuilder() { - return getTensor(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (stepId_ != 0L) { - output.writeInt64(1, stepId_); - } - if (!getKernelNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, kernelName_); - } - if (index_ != 0) { - output.writeInt32(3, index_); - } - if (tensor_ != null) { - output.writeMessage(4, getTensor()); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (stepId_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(1, stepId_); - } - if (!getKernelNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, kernelName_); - } - if (index_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeInt32Size(3, index_); - } - if (tensor_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(4, getTensor()); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.MemoryLogTensorOutput)) { - return super.equals(obj); - } - org.tensorflow.framework.MemoryLogTensorOutput other = (org.tensorflow.framework.MemoryLogTensorOutput) obj; - - boolean result = true; - result = result && (getStepId() - == other.getStepId()); - result = result && getKernelName() - .equals(other.getKernelName()); - result = result && (getIndex() - == other.getIndex()); - result = result && (hasTensor() == other.hasTensor()); - if (hasTensor()) { - result = result && getTensor() - .equals(other.getTensor()); - } - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + STEP_ID_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getStepId()); - hash = (37 * hash) + KERNEL_NAME_FIELD_NUMBER; - hash = (53 * hash) + getKernelName().hashCode(); - hash = (37 * hash) + INDEX_FIELD_NUMBER; - hash = (53 * hash) + getIndex(); - if (hasTensor()) { - hash = (37 * hash) + TENSOR_FIELD_NUMBER; - hash = (53 * hash) + getTensor().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.MemoryLogTensorOutput parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.MemoryLogTensorOutput parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.MemoryLogTensorOutput prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.MemoryLogTensorOutput} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.MemoryLogTensorOutput) - org.tensorflow.framework.MemoryLogTensorOutputOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorOutput_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorOutput_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.MemoryLogTensorOutput.class, org.tensorflow.framework.MemoryLogTensorOutput.Builder.class); - } - - // Construct using org.tensorflow.framework.MemoryLogTensorOutput.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - stepId_ = 0L; - - kernelName_ = ""; - - index_ = 0; - - if (tensorBuilder_ == null) { - tensor_ = null; - } else { - tensor_ = null; - tensorBuilder_ = null; - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.LogMemoryProtos.internal_static_tensorflow_MemoryLogTensorOutput_descriptor; - } - - public org.tensorflow.framework.MemoryLogTensorOutput getDefaultInstanceForType() { - return org.tensorflow.framework.MemoryLogTensorOutput.getDefaultInstance(); - } - - public org.tensorflow.framework.MemoryLogTensorOutput build() { - org.tensorflow.framework.MemoryLogTensorOutput result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.MemoryLogTensorOutput buildPartial() { - org.tensorflow.framework.MemoryLogTensorOutput result = new org.tensorflow.framework.MemoryLogTensorOutput(this); - result.stepId_ = stepId_; - result.kernelName_ = kernelName_; - result.index_ = index_; - if (tensorBuilder_ == null) { - result.tensor_ = tensor_; - } else { - result.tensor_ = tensorBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.MemoryLogTensorOutput) { - return mergeFrom((org.tensorflow.framework.MemoryLogTensorOutput)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.MemoryLogTensorOutput other) { - if (other == org.tensorflow.framework.MemoryLogTensorOutput.getDefaultInstance()) return this; - if (other.getStepId() != 0L) { - setStepId(other.getStepId()); - } - if (!other.getKernelName().isEmpty()) { - kernelName_ = other.kernelName_; - onChanged(); - } - if (other.getIndex() != 0) { - setIndex(other.getIndex()); - } - if (other.hasTensor()) { - mergeTensor(other.getTensor()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.MemoryLogTensorOutput parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.MemoryLogTensorOutput) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private long stepId_ ; - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public long getStepId() { - return stepId_; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder setStepId(long value) { - - stepId_ = value; - onChanged(); - return this; - } - /** - * 
-     * Process-unique step id.
-     *
- * - * optional int64 step_id = 1; - */ - public Builder clearStepId() { - - stepId_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object kernelName_ = ""; - /** - * 
-     * Name of the kernel producing an output as set in GraphDef, e.g.,
-     * "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public java.lang.String getKernelName() { - java.lang.Object ref = kernelName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - kernelName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the kernel producing an output as set in GraphDef, e.g.,
-     * "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public com.google.protobuf.ByteString - getKernelNameBytes() { - java.lang.Object ref = kernelName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - kernelName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the kernel producing an output as set in GraphDef, e.g.,
-     * "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public Builder setKernelName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - kernelName_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the kernel producing an output as set in GraphDef, e.g.,
-     * "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public Builder clearKernelName() { - - kernelName_ = getDefaultInstance().getKernelName(); - onChanged(); - return this; - } - /** - * 
-     * Name of the kernel producing an output as set in GraphDef, e.g.,
-     * "affine2/weights/Assign".
-     *
- * - * optional string kernel_name = 2; - */ - public Builder setKernelNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - kernelName_ = value; - onChanged(); - return this; - } - - private int index_ ; - /** - * 
-     * Index of the output being set.
-     *
- * - * optional int32 index = 3; - */ - public int getIndex() { - return index_; - } - /** - * 
-     * Index of the output being set.
-     *
- * - * optional int32 index = 3; - */ - public Builder setIndex(int value) { - - index_ = value; - onChanged(); - return this; - } - /** - * 
-     * Index of the output being set.
-     *
- * - * optional int32 index = 3; - */ - public Builder clearIndex() { - - index_ = 0; - onChanged(); - return this; - } - - private org.tensorflow.framework.TensorDescription tensor_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder> tensorBuilder_; - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public boolean hasTensor() { - return tensorBuilder_ != null || tensor_ != null; - } - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public org.tensorflow.framework.TensorDescription getTensor() { - if (tensorBuilder_ == null) { - return tensor_ == null ? org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensor_; - } else { - return tensorBuilder_.getMessage(); - } - } - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public Builder setTensor(org.tensorflow.framework.TensorDescription value) { - if (tensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - tensor_ = value; - onChanged(); - } else { - tensorBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public Builder setTensor( - org.tensorflow.framework.TensorDescription.Builder builderForValue) { - if (tensorBuilder_ == null) { - tensor_ = builderForValue.build(); - onChanged(); - } else { - tensorBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public Builder mergeTensor(org.tensorflow.framework.TensorDescription value) { - if (tensorBuilder_ == null) { - if (tensor_ != null) { - tensor_ = - org.tensorflow.framework.TensorDescription.newBuilder(tensor_).mergeFrom(value).buildPartial(); - } else { - tensor_ = value; - } - onChanged(); - } else { - tensorBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public Builder clearTensor() { - if (tensorBuilder_ == null) { - tensor_ = null; - onChanged(); - } else { - tensor_ = null; - tensorBuilder_ = null; - } - - return this; - } - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public org.tensorflow.framework.TensorDescription.Builder getTensorBuilder() { - - onChanged(); - return getTensorFieldBuilder().getBuilder(); - } - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - public org.tensorflow.framework.TensorDescriptionOrBuilder getTensorOrBuilder() { - if (tensorBuilder_ != null) { - return tensorBuilder_.getMessageOrBuilder(); - } else { - return tensor_ == null ? - org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensor_; - } - } - /** - * 
-     * Output tensor details.
-     *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder> - getTensorFieldBuilder() { - if (tensorBuilder_ == null) { - tensorBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder>( - getTensor(), - getParentForChildren(), - isClean()); - tensor_ = null; - } - return tensorBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.MemoryLogTensorOutput) - } - - // @@protoc_insertion_point(class_scope:tensorflow.MemoryLogTensorOutput) - private static final org.tensorflow.framework.MemoryLogTensorOutput DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.MemoryLogTensorOutput(); - } - - public static org.tensorflow.framework.MemoryLogTensorOutput getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public MemoryLogTensorOutput parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new MemoryLogTensorOutput(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.MemoryLogTensorOutput getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorOutputOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorOutputOrBuilder.java deleted file mode 100644 index 6eb39a10109..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/MemoryLogTensorOutputOrBuilder.java +++ /dev/null @@ -1,72 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: log_memory.proto - -package org.tensorflow.framework; - -public interface MemoryLogTensorOutputOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.MemoryLogTensorOutput) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Process-unique step id.
-   *
- * - * optional int64 step_id = 1; - */ - long getStepId(); - - /** - * 
-   * Name of the kernel producing an output as set in GraphDef, e.g.,
-   * "affine2/weights/Assign".
-   *
- * - * optional string kernel_name = 2; - */ - java.lang.String getKernelName(); - /** - * 
-   * Name of the kernel producing an output as set in GraphDef, e.g.,
-   * "affine2/weights/Assign".
-   *
- * - * optional string kernel_name = 2; - */ - com.google.protobuf.ByteString - getKernelNameBytes(); - - /** - * 
-   * Index of the output being set.
-   *
- * - * optional int32 index = 3; - */ - int getIndex(); - - /** - * 
-   * Output tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - boolean hasTensor(); - /** - * 
-   * Output tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - org.tensorflow.framework.TensorDescription getTensor(); - /** - * 
-   * Output tensor details.
-   *
- * - * optional .tensorflow.TensorDescription tensor = 4; - */ - org.tensorflow.framework.TensorDescriptionOrBuilder getTensorOrBuilder(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NameAttrList.java b/scala/dllib/src/main/java/org/tensorflow/framework/NameAttrList.java deleted file mode 100644 index 691922998c4..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NameAttrList.java +++ /dev/null @@ -1,779 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: attr_value.proto - -package org.tensorflow.framework; - -/** - * 
- * A list of attr names and their values. The whole list is attached
- * with a string name.  E.g., MatMul[T=float].
- *
- * - * Protobuf type {@code tensorflow.NameAttrList} - */ -public final class NameAttrList extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.NameAttrList) - NameAttrListOrBuilder { - // Use NameAttrList.newBuilder() to construct. - private NameAttrList(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private NameAttrList() { - name_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private NameAttrList( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - name_ = s; - break; - } - case 18: { - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - attr_ = com.google.protobuf.MapField.newMapField( - AttrDefaultEntryHolder.defaultEntry); - mutable_bitField0_ |= 0x00000002; - } - com.google.protobuf.MapEntry - attr = input.readMessage( - AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); - attr_.getMutableMap().put(attr.getKey(), attr.getValue()); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_NameAttrList_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 2: - return internalGetAttr(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_NameAttrList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.NameAttrList.class, org.tensorflow.framework.NameAttrList.Builder.class); - } - - private int bitField0_; - public static final int NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object name_; - /** - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } - } - /** - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int ATTR_FIELD_NUMBER = 2; - private static final class AttrDefaultEntryHolder { - static final com.google.protobuf.MapEntry< - java.lang.String, org.tensorflow.framework.AttrValue> defaultEntry = - com.google.protobuf.MapEntry - .newDefaultInstance( - org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_NameAttrList_AttrEntry_descriptor, - com.google.protobuf.WireFormat.FieldType.STRING, - "", - com.google.protobuf.WireFormat.FieldType.MESSAGE, - org.tensorflow.framework.AttrValue.getDefaultInstance()); - } - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.framework.AttrValue> attr_; - private com.google.protobuf.MapField - internalGetAttr() { - if (attr_ == null) { - return com.google.protobuf.MapField.emptyMapField( - AttrDefaultEntryHolder.defaultEntry); - } - return attr_; - } - - public int getAttrCount() { - return internalGetAttr().getMap().size(); - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public boolean containsAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetAttr().getMap().containsKey(key); - } - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getAttr() { - return getAttrMap(); - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public java.util.Map getAttrMap() { - return internalGetAttr().getMap(); - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_); - } - for (java.util.Map.Entry entry - : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - output.writeMessage(2, attr); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_); - } - for (java.util.Map.Entry entry - : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, attr); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.NameAttrList)) { - return super.equals(obj); - } - org.tensorflow.framework.NameAttrList other = (org.tensorflow.framework.NameAttrList) obj; - - boolean result = true; - result = result && getName() - .equals(other.getName()); - result = result && internalGetAttr().equals( - other.internalGetAttr()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + NAME_FIELD_NUMBER; - hash = (53 * hash) + getName().hashCode(); - if (!internalGetAttr().getMap().isEmpty()) { - hash = (37 * hash) + ATTR_FIELD_NUMBER; - hash = (53 * hash) + internalGetAttr().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.NameAttrList parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.NameAttrList parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.NameAttrList parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.NameAttrList parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.NameAttrList parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NameAttrList parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.NameAttrList parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NameAttrList parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.NameAttrList parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NameAttrList parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.NameAttrList prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * A list of attr names and their values. The whole list is attached
-   * with a string name.  E.g., MatMul[T=float].
-   *
- * - * Protobuf type {@code tensorflow.NameAttrList} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.NameAttrList) - org.tensorflow.framework.NameAttrListOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_NameAttrList_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 2: - return internalGetAttr(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMutableMapField( - int number) { - switch (number) { - case 2: - return internalGetMutableAttr(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_NameAttrList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.NameAttrList.class, org.tensorflow.framework.NameAttrList.Builder.class); - } - - // Construct using org.tensorflow.framework.NameAttrList.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - name_ = ""; - - internalGetMutableAttr().clear(); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.AttrValueProtos.internal_static_tensorflow_NameAttrList_descriptor; - } - - public org.tensorflow.framework.NameAttrList getDefaultInstanceForType() { - return org.tensorflow.framework.NameAttrList.getDefaultInstance(); - } - - public org.tensorflow.framework.NameAttrList build() { - org.tensorflow.framework.NameAttrList result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.NameAttrList buildPartial() { - org.tensorflow.framework.NameAttrList result = new org.tensorflow.framework.NameAttrList(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - result.name_ = name_; - result.attr_ = internalGetAttr(); - result.attr_.makeImmutable(); - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.NameAttrList) { - return mergeFrom((org.tensorflow.framework.NameAttrList)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.NameAttrList other) { - if (other == org.tensorflow.framework.NameAttrList.getDefaultInstance()) return this; - if (!other.getName().isEmpty()) { - name_ = other.name_; - onChanged(); - } - internalGetMutableAttr().mergeFrom( - other.internalGetAttr()); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.NameAttrList parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.NameAttrList) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.lang.Object name_ = ""; - /** - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * optional string name = 1; - */ - public Builder setName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - name_ = value; - onChanged(); - return this; - } - /** - * optional string name = 1; - */ - public Builder clearName() { - - name_ = getDefaultInstance().getName(); - onChanged(); - return this; - } - /** - * optional string name = 1; - */ - public Builder setNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - name_ = value; - onChanged(); - return this; - } - - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.framework.AttrValue> attr_; - private com.google.protobuf.MapField - internalGetAttr() { - if (attr_ == null) { - return com.google.protobuf.MapField.emptyMapField( - AttrDefaultEntryHolder.defaultEntry); - } - return attr_; - } - private com.google.protobuf.MapField - internalGetMutableAttr() { - onChanged();; - if (attr_ == null) { - attr_ = com.google.protobuf.MapField.newMapField( - AttrDefaultEntryHolder.defaultEntry); - } - if (!attr_.isMutable()) { - attr_ = attr_.copy(); - } - return attr_; - } - - public int getAttrCount() { - return internalGetAttr().getMap().size(); - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public boolean containsAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetAttr().getMap().containsKey(key); - } - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getAttr() { - return getAttrMap(); - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public java.util.Map getAttrMap() { - return internalGetAttr().getMap(); - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - public Builder clearAttr() { - getMutableAttr().clear(); - return this; - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public Builder removeAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().remove(key); - return this; - } - /** - * Use alternate mutation accessors instead. - */ - @java.lang.Deprecated - public java.util.Map - getMutableAttr() { - return internalGetMutableAttr().getMutableMap(); - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - public Builder putAttr( - java.lang.String key, - org.tensorflow.framework.AttrValue value) { - if (key == null) { throw new java.lang.NullPointerException(); } - if (value == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().put(key, value); - return this; - } - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - public Builder putAllAttr( - java.util.Map values) { - getMutableAttr().putAll(values); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.NameAttrList) - } - - // @@protoc_insertion_point(class_scope:tensorflow.NameAttrList) - private static final org.tensorflow.framework.NameAttrList DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.NameAttrList(); - } - - public static org.tensorflow.framework.NameAttrList getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public NameAttrList parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new NameAttrList(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.NameAttrList getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NameAttrListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/NameAttrListOrBuilder.java deleted file mode 100644 index 4bfe0be1c2e..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NameAttrListOrBuilder.java +++ /dev/null @@ -1,53 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: attr_value.proto - -package org.tensorflow.framework; - -public interface NameAttrListOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.NameAttrList) - com.google.protobuf.MessageOrBuilder { - - /** - * optional string name = 1; - */ - java.lang.String getName(); - /** - * optional string name = 1; - */ - com.google.protobuf.ByteString - getNameBytes(); - - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - int getAttrCount(); - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - boolean containsAttr( - java.lang.String key); - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - java.util.Map - getAttr(); - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - java.util.Map - getAttrMap(); - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue); - /** - * map<string, .tensorflow.AttrValue> attr = 2; - */ - - org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NodeDef.java b/scala/dllib/src/main/java/org/tensorflow/framework/NodeDef.java deleted file mode 100644 index f1d031db712..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NodeDef.java +++ /dev/null @@ -1,1686 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: node_def.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.NodeDef} - */ -public final class NodeDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.NodeDef) - NodeDefOrBuilder { - // Use NodeDef.newBuilder() to construct. - private NodeDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private NodeDef() { - name_ = ""; - op_ = ""; - input_ = com.google.protobuf.LazyStringArrayList.EMPTY; - device_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private NodeDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - name_ = s; - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - op_ = s; - break; - } - case 26: { - java.lang.String s = input.readStringRequireUtf8(); - if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - input_ = new com.google.protobuf.LazyStringArrayList(); - mutable_bitField0_ |= 0x00000004; - } - input_.add(s); - break; - } - case 34: { - java.lang.String s = input.readStringRequireUtf8(); - - device_ = s; - break; - } - case 42: { - if (!((mutable_bitField0_ & 0x00000010) == 0x00000010)) { - attr_ = com.google.protobuf.MapField.newMapField( - AttrDefaultEntryHolder.defaultEntry); - mutable_bitField0_ |= 0x00000010; - } - com.google.protobuf.MapEntry - attr = input.readMessage( - AttrDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); - attr_.getMutableMap().put(attr.getKey(), attr.getValue()); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - input_ = input_.getUnmodifiableView(); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.NodeProto.internal_static_tensorflow_NodeDef_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 5: - return internalGetAttr(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.NodeProto.internal_static_tensorflow_NodeDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.NodeDef.class, org.tensorflow.framework.NodeDef.Builder.class); - } - - private int bitField0_; - public static final int NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object name_; - /** - * 
-   * The name given to this operator. Used for naming inputs,
-   * logging, visualization, etc.  Unique within a single GraphDef.
-   * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-   *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } - } - /** - * 
-   * The name given to this operator. Used for naming inputs,
-   * logging, visualization, etc.  Unique within a single GraphDef.
-   * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-   *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int OP_FIELD_NUMBER = 2; - private volatile java.lang.Object op_; - /** - * 
-   * The operation name.  There may be custom parameters in attrs.
-   * Op names starting with an underscore are reserved for internal use.
-   *
- * - * optional string op = 2; - */ - public java.lang.String getOp() { - java.lang.Object ref = op_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - op_ = s; - return s; - } - } - /** - * 
-   * The operation name.  There may be custom parameters in attrs.
-   * Op names starting with an underscore are reserved for internal use.
-   *
- * - * optional string op = 2; - */ - public com.google.protobuf.ByteString - getOpBytes() { - java.lang.Object ref = op_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - op_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int INPUT_FIELD_NUMBER = 3; - private com.google.protobuf.LazyStringList input_; - /** - * 
-   * Each input is "node:src_output" with "node" being a string name and
-   * "src_output" indicating which output tensor to use from "node". If
-   * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-   * may optionally be followed by control inputs that have the format
-   * "^node".
-   *
- * - * repeated string input = 3; - */ - public com.google.protobuf.ProtocolStringList - getInputList() { - return input_; - } - /** - * 
-   * Each input is "node:src_output" with "node" being a string name and
-   * "src_output" indicating which output tensor to use from "node". If
-   * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-   * may optionally be followed by control inputs that have the format
-   * "^node".
-   *
- * - * repeated string input = 3; - */ - public int getInputCount() { - return input_.size(); - } - /** - * 
-   * Each input is "node:src_output" with "node" being a string name and
-   * "src_output" indicating which output tensor to use from "node". If
-   * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-   * may optionally be followed by control inputs that have the format
-   * "^node".
-   *
- * - * repeated string input = 3; - */ - public java.lang.String getInput(int index) { - return input_.get(index); - } - /** - * 
-   * Each input is "node:src_output" with "node" being a string name and
-   * "src_output" indicating which output tensor to use from "node". If
-   * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-   * may optionally be followed by control inputs that have the format
-   * "^node".
-   *
- * - * repeated string input = 3; - */ - public com.google.protobuf.ByteString - getInputBytes(int index) { - return input_.getByteString(index); - } - - public static final int DEVICE_FIELD_NUMBER = 4; - private volatile java.lang.Object device_; - /** - * 
-   * A (possibly partial) specification for the device on which this
-   * node should be placed.
-   * The expected syntax for this string is as follows:
-   * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-   * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-   * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-   * CONSTRAINT ::= ("job:" JOB_NAME)
-   *              | ("replica:" [1-9][0-9]*)
-   *              | ("task:" [1-9][0-9]*)
-   *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-   * Valid values for this string include:
-   * * "@other/node"                         (colocate with "other/node")
-   * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-   * * "/job:worker/gpu:3"                   (partial specification)
-   * * ""                                    (no specification)
-   * If the constraints do not resolve to a single device (or if this
-   * field is empty or not present), the runtime will attempt to
-   * choose a device automatically.
-   *
- * - * optional string device = 4; - */ - public java.lang.String getDevice() { - java.lang.Object ref = device_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - device_ = s; - return s; - } - } - /** - * 
-   * A (possibly partial) specification for the device on which this
-   * node should be placed.
-   * The expected syntax for this string is as follows:
-   * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-   * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-   * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-   * CONSTRAINT ::= ("job:" JOB_NAME)
-   *              | ("replica:" [1-9][0-9]*)
-   *              | ("task:" [1-9][0-9]*)
-   *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-   * Valid values for this string include:
-   * * "@other/node"                         (colocate with "other/node")
-   * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-   * * "/job:worker/gpu:3"                   (partial specification)
-   * * ""                                    (no specification)
-   * If the constraints do not resolve to a single device (or if this
-   * field is empty or not present), the runtime will attempt to
-   * choose a device automatically.
-   *
- * - * optional string device = 4; - */ - public com.google.protobuf.ByteString - getDeviceBytes() { - java.lang.Object ref = device_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - device_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int ATTR_FIELD_NUMBER = 5; - private static final class AttrDefaultEntryHolder { - static final com.google.protobuf.MapEntry< - java.lang.String, org.tensorflow.framework.AttrValue> defaultEntry = - com.google.protobuf.MapEntry - .newDefaultInstance( - org.tensorflow.framework.NodeProto.internal_static_tensorflow_NodeDef_AttrEntry_descriptor, - com.google.protobuf.WireFormat.FieldType.STRING, - "", - com.google.protobuf.WireFormat.FieldType.MESSAGE, - org.tensorflow.framework.AttrValue.getDefaultInstance()); - } - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.framework.AttrValue> attr_; - private com.google.protobuf.MapField - internalGetAttr() { - if (attr_ == null) { - return com.google.protobuf.MapField.emptyMapField( - AttrDefaultEntryHolder.defaultEntry); - } - return attr_; - } - - public int getAttrCount() { - return internalGetAttr().getMap().size(); - } - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public boolean containsAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetAttr().getMap().containsKey(key); - } - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getAttr() { - return getAttrMap(); - } - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public java.util.Map getAttrMap() { - return internalGetAttr().getMap(); - } - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_); - } - if (!getOpBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, op_); - } - for (int i = 0; i < input_.size(); i++) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 3, input_.getRaw(i)); - } - if (!getDeviceBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 4, device_); - } - for (java.util.Map.Entry entry - : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - output.writeMessage(5, attr); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_); - } - if (!getOpBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, op_); - } - { - int dataSize = 0; - for (int i = 0; i < input_.size(); i++) { - dataSize += computeStringSizeNoTag(input_.getRaw(i)); - } - size += dataSize; - size += 1 * getInputList().size(); - } - if (!getDeviceBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(4, device_); - } - for (java.util.Map.Entry entry - : internalGetAttr().getMap().entrySet()) { - com.google.protobuf.MapEntry - attr = AttrDefaultEntryHolder.defaultEntry.newBuilderForType() - .setKey(entry.getKey()) - .setValue(entry.getValue()) - .build(); - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(5, attr); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.NodeDef)) { - return super.equals(obj); - } - org.tensorflow.framework.NodeDef other = (org.tensorflow.framework.NodeDef) obj; - - boolean result = true; - result = result && getName() - .equals(other.getName()); - result = result && getOp() - .equals(other.getOp()); - result = result && getInputList() - .equals(other.getInputList()); - result = result && getDevice() - .equals(other.getDevice()); - result = result && internalGetAttr().equals( - other.internalGetAttr()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + NAME_FIELD_NUMBER; - hash = (53 * hash) + getName().hashCode(); - hash = (37 * hash) + OP_FIELD_NUMBER; - hash = (53 * hash) + getOp().hashCode(); - if (getInputCount() > 0) { - hash = (37 * hash) + INPUT_FIELD_NUMBER; - hash = (53 * hash) + getInputList().hashCode(); - } - hash = (37 * hash) + DEVICE_FIELD_NUMBER; - hash = (53 * hash) + getDevice().hashCode(); - if (!internalGetAttr().getMap().isEmpty()) { - hash = (37 * hash) + ATTR_FIELD_NUMBER; - hash = (53 * hash) + internalGetAttr().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.NodeDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.NodeDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.NodeDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.NodeDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.NodeDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.NodeDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.NodeDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.NodeDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.NodeDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.NodeDef) - org.tensorflow.framework.NodeDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.NodeProto.internal_static_tensorflow_NodeDef_descriptor; - } - - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMapField( - int number) { - switch (number) { - case 5: - return internalGetAttr(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - @SuppressWarnings({"rawtypes"}) - protected com.google.protobuf.MapField internalGetMutableMapField( - int number) { - switch (number) { - case 5: - return internalGetMutableAttr(); - default: - throw new RuntimeException( - "Invalid map field number: " + number); - } - } - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.NodeProto.internal_static_tensorflow_NodeDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.NodeDef.class, org.tensorflow.framework.NodeDef.Builder.class); - } - - // Construct using org.tensorflow.framework.NodeDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - name_ = ""; - - op_ = ""; - - input_ = com.google.protobuf.LazyStringArrayList.EMPTY; - bitField0_ = (bitField0_ & ~0x00000004); - device_ = ""; - - internalGetMutableAttr().clear(); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.NodeProto.internal_static_tensorflow_NodeDef_descriptor; - } - - public org.tensorflow.framework.NodeDef getDefaultInstanceForType() { - return org.tensorflow.framework.NodeDef.getDefaultInstance(); - } - - public org.tensorflow.framework.NodeDef build() { - org.tensorflow.framework.NodeDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.NodeDef buildPartial() { - org.tensorflow.framework.NodeDef result = new org.tensorflow.framework.NodeDef(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - result.name_ = name_; - result.op_ = op_; - if (((bitField0_ & 0x00000004) == 0x00000004)) { - input_ = input_.getUnmodifiableView(); - bitField0_ = (bitField0_ & ~0x00000004); - } - result.input_ = input_; - result.device_ = device_; - result.attr_ = internalGetAttr(); - result.attr_.makeImmutable(); - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.NodeDef) { - return mergeFrom((org.tensorflow.framework.NodeDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.NodeDef other) { - if (other == org.tensorflow.framework.NodeDef.getDefaultInstance()) return this; - if (!other.getName().isEmpty()) { - name_ = other.name_; - onChanged(); - } - if (!other.getOp().isEmpty()) { - op_ = other.op_; - onChanged(); - } - if (!other.input_.isEmpty()) { - if (input_.isEmpty()) { - input_ = other.input_; - bitField0_ = (bitField0_ & ~0x00000004); - } else { - ensureInputIsMutable(); - input_.addAll(other.input_); - } - onChanged(); - } - if (!other.getDevice().isEmpty()) { - device_ = other.device_; - onChanged(); - } - internalGetMutableAttr().mergeFrom( - other.internalGetAttr()); - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.NodeDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.NodeDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.lang.Object name_ = ""; - /** - * 
-     * The name given to this operator. Used for naming inputs,
-     * logging, visualization, etc.  Unique within a single GraphDef.
-     * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-     *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * The name given to this operator. Used for naming inputs,
-     * logging, visualization, etc.  Unique within a single GraphDef.
-     * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-     *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * The name given to this operator. Used for naming inputs,
-     * logging, visualization, etc.  Unique within a single GraphDef.
-     * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-     *
- * - * optional string name = 1; - */ - public Builder setName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - name_ = value; - onChanged(); - return this; - } - /** - * 
-     * The name given to this operator. Used for naming inputs,
-     * logging, visualization, etc.  Unique within a single GraphDef.
-     * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-     *
- * - * optional string name = 1; - */ - public Builder clearName() { - - name_ = getDefaultInstance().getName(); - onChanged(); - return this; - } - /** - * 
-     * The name given to this operator. Used for naming inputs,
-     * logging, visualization, etc.  Unique within a single GraphDef.
-     * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-     *
- * - * optional string name = 1; - */ - public Builder setNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - name_ = value; - onChanged(); - return this; - } - - private java.lang.Object op_ = ""; - /** - * 
-     * The operation name.  There may be custom parameters in attrs.
-     * Op names starting with an underscore are reserved for internal use.
-     *
- * - * optional string op = 2; - */ - public java.lang.String getOp() { - java.lang.Object ref = op_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - op_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * The operation name.  There may be custom parameters in attrs.
-     * Op names starting with an underscore are reserved for internal use.
-     *
- * - * optional string op = 2; - */ - public com.google.protobuf.ByteString - getOpBytes() { - java.lang.Object ref = op_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - op_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * The operation name.  There may be custom parameters in attrs.
-     * Op names starting with an underscore are reserved for internal use.
-     *
- * - * optional string op = 2; - */ - public Builder setOp( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - op_ = value; - onChanged(); - return this; - } - /** - * 
-     * The operation name.  There may be custom parameters in attrs.
-     * Op names starting with an underscore are reserved for internal use.
-     *
- * - * optional string op = 2; - */ - public Builder clearOp() { - - op_ = getDefaultInstance().getOp(); - onChanged(); - return this; - } - /** - * 
-     * The operation name.  There may be custom parameters in attrs.
-     * Op names starting with an underscore are reserved for internal use.
-     *
- * - * optional string op = 2; - */ - public Builder setOpBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - op_ = value; - onChanged(); - return this; - } - - private com.google.protobuf.LazyStringList input_ = com.google.protobuf.LazyStringArrayList.EMPTY; - private void ensureInputIsMutable() { - if (!((bitField0_ & 0x00000004) == 0x00000004)) { - input_ = new com.google.protobuf.LazyStringArrayList(input_); - bitField0_ |= 0x00000004; - } - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public com.google.protobuf.ProtocolStringList - getInputList() { - return input_.getUnmodifiableView(); - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public int getInputCount() { - return input_.size(); - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public java.lang.String getInput(int index) { - return input_.get(index); - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public com.google.protobuf.ByteString - getInputBytes(int index) { - return input_.getByteString(index); - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public Builder setInput( - int index, java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - ensureInputIsMutable(); - input_.set(index, value); - onChanged(); - return this; - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public Builder addInput( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - ensureInputIsMutable(); - input_.add(value); - onChanged(); - return this; - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public Builder addAllInput( - java.lang.Iterable values) { - ensureInputIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, input_); - onChanged(); - return this; - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public Builder clearInput() { - input_ = com.google.protobuf.LazyStringArrayList.EMPTY; - bitField0_ = (bitField0_ & ~0x00000004); - onChanged(); - return this; - } - /** - * 
-     * Each input is "node:src_output" with "node" being a string name and
-     * "src_output" indicating which output tensor to use from "node". If
-     * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-     * may optionally be followed by control inputs that have the format
-     * "^node".
-     *
- * - * repeated string input = 3; - */ - public Builder addInputBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - ensureInputIsMutable(); - input_.add(value); - onChanged(); - return this; - } - - private java.lang.Object device_ = ""; - /** - * 
-     * A (possibly partial) specification for the device on which this
-     * node should be placed.
-     * The expected syntax for this string is as follows:
-     * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-     * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-     * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-     * CONSTRAINT ::= ("job:" JOB_NAME)
-     *              | ("replica:" [1-9][0-9]*)
-     *              | ("task:" [1-9][0-9]*)
-     *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-     * Valid values for this string include:
-     * * "@other/node"                         (colocate with "other/node")
-     * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-     * * "/job:worker/gpu:3"                   (partial specification)
-     * * ""                                    (no specification)
-     * If the constraints do not resolve to a single device (or if this
-     * field is empty or not present), the runtime will attempt to
-     * choose a device automatically.
-     *
- * - * optional string device = 4; - */ - public java.lang.String getDevice() { - java.lang.Object ref = device_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - device_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * A (possibly partial) specification for the device on which this
-     * node should be placed.
-     * The expected syntax for this string is as follows:
-     * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-     * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-     * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-     * CONSTRAINT ::= ("job:" JOB_NAME)
-     *              | ("replica:" [1-9][0-9]*)
-     *              | ("task:" [1-9][0-9]*)
-     *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-     * Valid values for this string include:
-     * * "@other/node"                         (colocate with "other/node")
-     * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-     * * "/job:worker/gpu:3"                   (partial specification)
-     * * ""                                    (no specification)
-     * If the constraints do not resolve to a single device (or if this
-     * field is empty or not present), the runtime will attempt to
-     * choose a device automatically.
-     *
- * - * optional string device = 4; - */ - public com.google.protobuf.ByteString - getDeviceBytes() { - java.lang.Object ref = device_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - device_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * A (possibly partial) specification for the device on which this
-     * node should be placed.
-     * The expected syntax for this string is as follows:
-     * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-     * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-     * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-     * CONSTRAINT ::= ("job:" JOB_NAME)
-     *              | ("replica:" [1-9][0-9]*)
-     *              | ("task:" [1-9][0-9]*)
-     *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-     * Valid values for this string include:
-     * * "@other/node"                         (colocate with "other/node")
-     * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-     * * "/job:worker/gpu:3"                   (partial specification)
-     * * ""                                    (no specification)
-     * If the constraints do not resolve to a single device (or if this
-     * field is empty or not present), the runtime will attempt to
-     * choose a device automatically.
-     *
- * - * optional string device = 4; - */ - public Builder setDevice( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - device_ = value; - onChanged(); - return this; - } - /** - * 
-     * A (possibly partial) specification for the device on which this
-     * node should be placed.
-     * The expected syntax for this string is as follows:
-     * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-     * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-     * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-     * CONSTRAINT ::= ("job:" JOB_NAME)
-     *              | ("replica:" [1-9][0-9]*)
-     *              | ("task:" [1-9][0-9]*)
-     *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-     * Valid values for this string include:
-     * * "@other/node"                         (colocate with "other/node")
-     * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-     * * "/job:worker/gpu:3"                   (partial specification)
-     * * ""                                    (no specification)
-     * If the constraints do not resolve to a single device (or if this
-     * field is empty or not present), the runtime will attempt to
-     * choose a device automatically.
-     *
- * - * optional string device = 4; - */ - public Builder clearDevice() { - - device_ = getDefaultInstance().getDevice(); - onChanged(); - return this; - } - /** - * 
-     * A (possibly partial) specification for the device on which this
-     * node should be placed.
-     * The expected syntax for this string is as follows:
-     * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-     * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-     * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-     * CONSTRAINT ::= ("job:" JOB_NAME)
-     *              | ("replica:" [1-9][0-9]*)
-     *              | ("task:" [1-9][0-9]*)
-     *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-     * Valid values for this string include:
-     * * "@other/node"                         (colocate with "other/node")
-     * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-     * * "/job:worker/gpu:3"                   (partial specification)
-     * * ""                                    (no specification)
-     * If the constraints do not resolve to a single device (or if this
-     * field is empty or not present), the runtime will attempt to
-     * choose a device automatically.
-     *
- * - * optional string device = 4; - */ - public Builder setDeviceBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - device_ = value; - onChanged(); - return this; - } - - private com.google.protobuf.MapField< - java.lang.String, org.tensorflow.framework.AttrValue> attr_; - private com.google.protobuf.MapField - internalGetAttr() { - if (attr_ == null) { - return com.google.protobuf.MapField.emptyMapField( - AttrDefaultEntryHolder.defaultEntry); - } - return attr_; - } - private com.google.protobuf.MapField - internalGetMutableAttr() { - onChanged();; - if (attr_ == null) { - attr_ = com.google.protobuf.MapField.newMapField( - AttrDefaultEntryHolder.defaultEntry); - } - if (!attr_.isMutable()) { - attr_ = attr_.copy(); - } - return attr_; - } - - public int getAttrCount() { - return internalGetAttr().getMap().size(); - } - /** - * 
-     * Operation-specific graph-construction-time configuration.
-     * Note that this should include all attrs defined in the
-     * corresponding OpDef, including those with a value matching
-     * the default -- this allows the default to change and makes
-     * NodeDefs easier to interpret on their own.  However, if
-     * an attr with a default is not specified in this list, the
-     * default will be used.
-     * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-     * one of the names from the corresponding OpDef's attr field).
-     * The values must have a type matching the corresponding OpDef
-     * attr's type field.
-     * TODO(josh11b): Add some examples here showing best practices.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public boolean containsAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - return internalGetAttr().getMap().containsKey(key); - } - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - public java.util.Map getAttr() { - return getAttrMap(); - } - /** - * 
-     * Operation-specific graph-construction-time configuration.
-     * Note that this should include all attrs defined in the
-     * corresponding OpDef, including those with a value matching
-     * the default -- this allows the default to change and makes
-     * NodeDefs easier to interpret on their own.  However, if
-     * an attr with a default is not specified in this list, the
-     * default will be used.
-     * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-     * one of the names from the corresponding OpDef's attr field).
-     * The values must have a type matching the corresponding OpDef
-     * attr's type field.
-     * TODO(josh11b): Add some examples here showing best practices.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public java.util.Map getAttrMap() { - return internalGetAttr().getMap(); - } - /** - * 
-     * Operation-specific graph-construction-time configuration.
-     * Note that this should include all attrs defined in the
-     * corresponding OpDef, including those with a value matching
-     * the default -- this allows the default to change and makes
-     * NodeDefs easier to interpret on their own.  However, if
-     * an attr with a default is not specified in this list, the
-     * default will be used.
-     * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-     * one of the names from the corresponding OpDef's attr field).
-     * The values must have a type matching the corresponding OpDef
-     * attr's type field.
-     * TODO(josh11b): Add some examples here showing best practices.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - return map.containsKey(key) ? map.get(key) : defaultValue; - } - /** - * 
-     * Operation-specific graph-construction-time configuration.
-     * Note that this should include all attrs defined in the
-     * corresponding OpDef, including those with a value matching
-     * the default -- this allows the default to change and makes
-     * NodeDefs easier to interpret on their own.  However, if
-     * an attr with a default is not specified in this list, the
-     * default will be used.
-     * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-     * one of the names from the corresponding OpDef's attr field).
-     * The values must have a type matching the corresponding OpDef
-     * attr's type field.
-     * TODO(josh11b): Add some examples here showing best practices.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - java.util.Map map = - internalGetAttr().getMap(); - if (!map.containsKey(key)) { - throw new java.lang.IllegalArgumentException(); - } - return map.get(key); - } - - public Builder clearAttr() { - getMutableAttr().clear(); - return this; - } - /** - * 
-     * Operation-specific graph-construction-time configuration.
-     * Note that this should include all attrs defined in the
-     * corresponding OpDef, including those with a value matching
-     * the default -- this allows the default to change and makes
-     * NodeDefs easier to interpret on their own.  However, if
-     * an attr with a default is not specified in this list, the
-     * default will be used.
-     * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-     * one of the names from the corresponding OpDef's attr field).
-     * The values must have a type matching the corresponding OpDef
-     * attr's type field.
-     * TODO(josh11b): Add some examples here showing best practices.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public Builder removeAttr( - java.lang.String key) { - if (key == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().remove(key); - return this; - } - /** - * Use alternate mutation accessors instead. - */ - @java.lang.Deprecated - public java.util.Map - getMutableAttr() { - return internalGetMutableAttr().getMutableMap(); - } - /** - * 
-     * Operation-specific graph-construction-time configuration.
-     * Note that this should include all attrs defined in the
-     * corresponding OpDef, including those with a value matching
-     * the default -- this allows the default to change and makes
-     * NodeDefs easier to interpret on their own.  However, if
-     * an attr with a default is not specified in this list, the
-     * default will be used.
-     * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-     * one of the names from the corresponding OpDef's attr field).
-     * The values must have a type matching the corresponding OpDef
-     * attr's type field.
-     * TODO(josh11b): Add some examples here showing best practices.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - public Builder putAttr( - java.lang.String key, - org.tensorflow.framework.AttrValue value) { - if (key == null) { throw new java.lang.NullPointerException(); } - if (value == null) { throw new java.lang.NullPointerException(); } - getMutableAttr().put(key, value); - return this; - } - /** - * 
-     * Operation-specific graph-construction-time configuration.
-     * Note that this should include all attrs defined in the
-     * corresponding OpDef, including those with a value matching
-     * the default -- this allows the default to change and makes
-     * NodeDefs easier to interpret on their own.  However, if
-     * an attr with a default is not specified in this list, the
-     * default will be used.
-     * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-     * one of the names from the corresponding OpDef's attr field).
-     * The values must have a type matching the corresponding OpDef
-     * attr's type field.
-     * TODO(josh11b): Add some examples here showing best practices.
-     *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - public Builder putAllAttr( - java.util.Map values) { - getMutableAttr().putAll(values); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.NodeDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.NodeDef) - private static final org.tensorflow.framework.NodeDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.NodeDef(); - } - - public static org.tensorflow.framework.NodeDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public NodeDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new NodeDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.NodeDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NodeDefOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/NodeDefOrBuilder.java deleted file mode 100644 index 3c182d2edcd..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NodeDefOrBuilder.java +++ /dev/null @@ -1,263 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: node_def.proto - -package org.tensorflow.framework; - -public interface NodeDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.NodeDef) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * The name given to this operator. Used for naming inputs,
-   * logging, visualization, etc.  Unique within a single GraphDef.
-   * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-   *
- * - * optional string name = 1; - */ - java.lang.String getName(); - /** - * 
-   * The name given to this operator. Used for naming inputs,
-   * logging, visualization, etc.  Unique within a single GraphDef.
-   * Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-   *
- * - * optional string name = 1; - */ - com.google.protobuf.ByteString - getNameBytes(); - - /** - * 
-   * The operation name.  There may be custom parameters in attrs.
-   * Op names starting with an underscore are reserved for internal use.
-   *
- * - * optional string op = 2; - */ - java.lang.String getOp(); - /** - * 
-   * The operation name.  There may be custom parameters in attrs.
-   * Op names starting with an underscore are reserved for internal use.
-   *
- * - * optional string op = 2; - */ - com.google.protobuf.ByteString - getOpBytes(); - - /** - * 
-   * Each input is "node:src_output" with "node" being a string name and
-   * "src_output" indicating which output tensor to use from "node". If
-   * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-   * may optionally be followed by control inputs that have the format
-   * "^node".
-   *
- * - * repeated string input = 3; - */ - java.util.List - getInputList(); - /** - * 
-   * Each input is "node:src_output" with "node" being a string name and
-   * "src_output" indicating which output tensor to use from "node". If
-   * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-   * may optionally be followed by control inputs that have the format
-   * "^node".
-   *
- * - * repeated string input = 3; - */ - int getInputCount(); - /** - * 
-   * Each input is "node:src_output" with "node" being a string name and
-   * "src_output" indicating which output tensor to use from "node". If
-   * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-   * may optionally be followed by control inputs that have the format
-   * "^node".
-   *
- * - * repeated string input = 3; - */ - java.lang.String getInput(int index); - /** - * 
-   * Each input is "node:src_output" with "node" being a string name and
-   * "src_output" indicating which output tensor to use from "node". If
-   * "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-   * may optionally be followed by control inputs that have the format
-   * "^node".
-   *
- * - * repeated string input = 3; - */ - com.google.protobuf.ByteString - getInputBytes(int index); - - /** - * 
-   * A (possibly partial) specification for the device on which this
-   * node should be placed.
-   * The expected syntax for this string is as follows:
-   * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-   * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-   * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-   * CONSTRAINT ::= ("job:" JOB_NAME)
-   *              | ("replica:" [1-9][0-9]*)
-   *              | ("task:" [1-9][0-9]*)
-   *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-   * Valid values for this string include:
-   * * "@other/node"                         (colocate with "other/node")
-   * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-   * * "/job:worker/gpu:3"                   (partial specification)
-   * * ""                                    (no specification)
-   * If the constraints do not resolve to a single device (or if this
-   * field is empty or not present), the runtime will attempt to
-   * choose a device automatically.
-   *
- * - * optional string device = 4; - */ - java.lang.String getDevice(); - /** - * 
-   * A (possibly partial) specification for the device on which this
-   * node should be placed.
-   * The expected syntax for this string is as follows:
-   * DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-   * COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-   * PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-   * CONSTRAINT ::= ("job:" JOB_NAME)
-   *              | ("replica:" [1-9][0-9]*)
-   *              | ("task:" [1-9][0-9]*)
-   *              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-   * Valid values for this string include:
-   * * "@other/node"                         (colocate with "other/node")
-   * * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-   * * "/job:worker/gpu:3"                   (partial specification)
-   * * ""                                    (no specification)
-   * If the constraints do not resolve to a single device (or if this
-   * field is empty or not present), the runtime will attempt to
-   * choose a device automatically.
-   *
- * - * optional string device = 4; - */ - com.google.protobuf.ByteString - getDeviceBytes(); - - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - int getAttrCount(); - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - boolean containsAttr( - java.lang.String key); - /** - * Use {@link #getAttrMap()} instead. - */ - @java.lang.Deprecated - java.util.Map - getAttr(); - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - java.util.Map - getAttrMap(); - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - org.tensorflow.framework.AttrValue getAttrOrDefault( - java.lang.String key, - org.tensorflow.framework.AttrValue defaultValue); - /** - * 
-   * Operation-specific graph-construction-time configuration.
-   * Note that this should include all attrs defined in the
-   * corresponding OpDef, including those with a value matching
-   * the default -- this allows the default to change and makes
-   * NodeDefs easier to interpret on their own.  However, if
-   * an attr with a default is not specified in this list, the
-   * default will be used.
-   * The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-   * one of the names from the corresponding OpDef's attr field).
-   * The values must have a type matching the corresponding OpDef
-   * attr's type field.
-   * TODO(josh11b): Add some examples here showing best practices.
-   *
- * - * map<string, .tensorflow.AttrValue> attr = 5; - */ - - org.tensorflow.framework.AttrValue getAttrOrThrow( - java.lang.String key); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NodeExecStats.java b/scala/dllib/src/main/java/org/tensorflow/framework/NodeExecStats.java deleted file mode 100644 index 0f826c48398..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NodeExecStats.java +++ /dev/null @@ -1,2065 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -/** - * 
- * Time/size stats recorded for a single execution of a graph node.
- *
- * - * Protobuf type {@code tensorflow.NodeExecStats} - */ -public final class NodeExecStats extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.NodeExecStats) - NodeExecStatsOrBuilder { - // Use NodeExecStats.newBuilder() to construct. - private NodeExecStats(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private NodeExecStats() { - nodeName_ = ""; - allStartMicros_ = 0L; - opStartRelMicros_ = 0L; - opEndRelMicros_ = 0L; - allEndRelMicros_ = 0L; - memory_ = java.util.Collections.emptyList(); - output_ = java.util.Collections.emptyList(); - timelineLabel_ = ""; - scheduledMicros_ = 0L; - threadId_ = 0; - referencedTensor_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private NodeExecStats( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - nodeName_ = s; - break; - } - case 16: { - - allStartMicros_ = input.readInt64(); - break; - } - case 24: { - - opStartRelMicros_ = input.readInt64(); - break; - } - case 32: { - - opEndRelMicros_ = input.readInt64(); - break; - } - case 40: { - - allEndRelMicros_ = input.readInt64(); - break; - } - case 50: { - if (!((mutable_bitField0_ & 0x00000020) == 0x00000020)) { - memory_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000020; - } - memory_.add( - input.readMessage(org.tensorflow.framework.AllocatorMemoryUsed.parser(), extensionRegistry)); - break; - } - case 58: { - if (!((mutable_bitField0_ & 0x00000040) == 0x00000040)) { - output_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000040; - } - output_.add( - input.readMessage(org.tensorflow.framework.NodeOutput.parser(), extensionRegistry)); - break; - } - case 66: { - java.lang.String s = input.readStringRequireUtf8(); - - timelineLabel_ = s; - break; - } - case 72: { - - scheduledMicros_ = input.readInt64(); - break; - } - case 80: { - - threadId_ = input.readUInt32(); - break; - } - case 90: { - if (!((mutable_bitField0_ & 0x00000400) == 0x00000400)) { - referencedTensor_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000400; - } - referencedTensor_.add( - input.readMessage(org.tensorflow.framework.AllocationDescription.parser(), extensionRegistry)); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000020) == 0x00000020)) { - memory_ = java.util.Collections.unmodifiableList(memory_); - } - if (((mutable_bitField0_ & 0x00000040) == 0x00000040)) { - output_ = java.util.Collections.unmodifiableList(output_); - } - if (((mutable_bitField0_ & 0x00000400) == 0x00000400)) { - referencedTensor_ = java.util.Collections.unmodifiableList(referencedTensor_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeExecStats_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeExecStats_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.NodeExecStats.class, org.tensorflow.framework.NodeExecStats.Builder.class); - } - - private int bitField0_; - public static final int NODE_NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object nodeName_; - /** - * 
-   * TODO(tucker): Use some more compact form of node identity than
-   * the full string name.  Either all processes should agree on a
-   * global id (cost_id?) for each node, or we should use a hash of
-   * the name.
-   *
- * - * optional string node_name = 1; - */ - public java.lang.String getNodeName() { - java.lang.Object ref = nodeName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - nodeName_ = s; - return s; - } - } - /** - * 
-   * TODO(tucker): Use some more compact form of node identity than
-   * the full string name.  Either all processes should agree on a
-   * global id (cost_id?) for each node, or we should use a hash of
-   * the name.
-   *
- * - * optional string node_name = 1; - */ - public com.google.protobuf.ByteString - getNodeNameBytes() { - java.lang.Object ref = nodeName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - nodeName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int ALL_START_MICROS_FIELD_NUMBER = 2; - private long allStartMicros_; - /** - * optional int64 all_start_micros = 2; - */ - public long getAllStartMicros() { - return allStartMicros_; - } - - public static final int OP_START_REL_MICROS_FIELD_NUMBER = 3; - private long opStartRelMicros_; - /** - * optional int64 op_start_rel_micros = 3; - */ - public long getOpStartRelMicros() { - return opStartRelMicros_; - } - - public static final int OP_END_REL_MICROS_FIELD_NUMBER = 4; - private long opEndRelMicros_; - /** - * optional int64 op_end_rel_micros = 4; - */ - public long getOpEndRelMicros() { - return opEndRelMicros_; - } - - public static final int ALL_END_REL_MICROS_FIELD_NUMBER = 5; - private long allEndRelMicros_; - /** - * optional int64 all_end_rel_micros = 5; - */ - public long getAllEndRelMicros() { - return allEndRelMicros_; - } - - public static final int MEMORY_FIELD_NUMBER = 6; - private java.util.List memory_; - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public java.util.List getMemoryList() { - return memory_; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public java.util.List - getMemoryOrBuilderList() { - return memory_; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public int getMemoryCount() { - return memory_.size(); - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public org.tensorflow.framework.AllocatorMemoryUsed getMemory(int index) { - return memory_.get(index); - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public org.tensorflow.framework.AllocatorMemoryUsedOrBuilder getMemoryOrBuilder( - int index) { - return memory_.get(index); - } - - public static final int OUTPUT_FIELD_NUMBER = 7; - private java.util.List output_; - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public java.util.List getOutputList() { - return output_; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public java.util.List - getOutputOrBuilderList() { - return output_; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public int getOutputCount() { - return output_.size(); - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public org.tensorflow.framework.NodeOutput getOutput(int index) { - return output_.get(index); - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public org.tensorflow.framework.NodeOutputOrBuilder getOutputOrBuilder( - int index) { - return output_.get(index); - } - - public static final int TIMELINE_LABEL_FIELD_NUMBER = 8; - private volatile java.lang.Object timelineLabel_; - /** - * optional string timeline_label = 8; - */ - public java.lang.String getTimelineLabel() { - java.lang.Object ref = timelineLabel_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - timelineLabel_ = s; - return s; - } - } - /** - * optional string timeline_label = 8; - */ - public com.google.protobuf.ByteString - getTimelineLabelBytes() { - java.lang.Object ref = timelineLabel_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - timelineLabel_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int SCHEDULED_MICROS_FIELD_NUMBER = 9; - private long scheduledMicros_; - /** - * optional int64 scheduled_micros = 9; - */ - public long getScheduledMicros() { - return scheduledMicros_; - } - - public static final int THREAD_ID_FIELD_NUMBER = 10; - private int threadId_; - /** - * optional uint32 thread_id = 10; - */ - public int getThreadId() { - return threadId_; - } - - public static final int REFERENCED_TENSOR_FIELD_NUMBER = 11; - private java.util.List referencedTensor_; - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public java.util.List getReferencedTensorList() { - return referencedTensor_; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public java.util.List - getReferencedTensorOrBuilderList() { - return referencedTensor_; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public int getReferencedTensorCount() { - return referencedTensor_.size(); - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public org.tensorflow.framework.AllocationDescription getReferencedTensor(int index) { - return referencedTensor_.get(index); - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public org.tensorflow.framework.AllocationDescriptionOrBuilder getReferencedTensorOrBuilder( - int index) { - return referencedTensor_.get(index); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getNodeNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, nodeName_); - } - if (allStartMicros_ != 0L) { - output.writeInt64(2, allStartMicros_); - } - if (opStartRelMicros_ != 0L) { - output.writeInt64(3, opStartRelMicros_); - } - if (opEndRelMicros_ != 0L) { - output.writeInt64(4, opEndRelMicros_); - } - if (allEndRelMicros_ != 0L) { - output.writeInt64(5, allEndRelMicros_); - } - for (int i = 0; i < memory_.size(); i++) { - output.writeMessage(6, memory_.get(i)); - } - for (int i = 0; i < output_.size(); i++) { - output.writeMessage(7, output_.get(i)); - } - if (!getTimelineLabelBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 8, timelineLabel_); - } - if (scheduledMicros_ != 0L) { - output.writeInt64(9, scheduledMicros_); - } - if (threadId_ != 0) { - output.writeUInt32(10, threadId_); - } - for (int i = 0; i < referencedTensor_.size(); i++) { - output.writeMessage(11, referencedTensor_.get(i)); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getNodeNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, nodeName_); - } - if (allStartMicros_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(2, allStartMicros_); - } - if (opStartRelMicros_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(3, opStartRelMicros_); - } - if (opEndRelMicros_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(4, opEndRelMicros_); - } - if (allEndRelMicros_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(5, allEndRelMicros_); - } - for (int i = 0; i < memory_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(6, memory_.get(i)); - } - for (int i = 0; i < output_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(7, output_.get(i)); - } - if (!getTimelineLabelBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(8, timelineLabel_); - } - if (scheduledMicros_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(9, scheduledMicros_); - } - if (threadId_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeUInt32Size(10, threadId_); - } - for (int i = 0; i < referencedTensor_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(11, referencedTensor_.get(i)); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.NodeExecStats)) { - return super.equals(obj); - } - org.tensorflow.framework.NodeExecStats other = (org.tensorflow.framework.NodeExecStats) obj; - - boolean result = true; - result = result && getNodeName() - .equals(other.getNodeName()); - result = result && (getAllStartMicros() - == other.getAllStartMicros()); - result = result && (getOpStartRelMicros() - == other.getOpStartRelMicros()); - result = result && (getOpEndRelMicros() - == other.getOpEndRelMicros()); - result = result && (getAllEndRelMicros() - == other.getAllEndRelMicros()); - result = result && getMemoryList() - .equals(other.getMemoryList()); - result = result && getOutputList() - .equals(other.getOutputList()); - result = result && getTimelineLabel() - .equals(other.getTimelineLabel()); - result = result && (getScheduledMicros() - == other.getScheduledMicros()); - result = result && (getThreadId() - == other.getThreadId()); - result = result && getReferencedTensorList() - .equals(other.getReferencedTensorList()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + NODE_NAME_FIELD_NUMBER; - hash = (53 * hash) + getNodeName().hashCode(); - hash = (37 * hash) + ALL_START_MICROS_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getAllStartMicros()); - hash = (37 * hash) + OP_START_REL_MICROS_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getOpStartRelMicros()); - hash = (37 * hash) + OP_END_REL_MICROS_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getOpEndRelMicros()); - hash = (37 * hash) + ALL_END_REL_MICROS_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getAllEndRelMicros()); - if (getMemoryCount() > 0) { - hash = (37 * hash) + MEMORY_FIELD_NUMBER; - hash = (53 * hash) + getMemoryList().hashCode(); - } - if (getOutputCount() > 0) { - hash = (37 * hash) + OUTPUT_FIELD_NUMBER; - hash = (53 * hash) + getOutputList().hashCode(); - } - hash = (37 * hash) + TIMELINE_LABEL_FIELD_NUMBER; - hash = (53 * hash) + getTimelineLabel().hashCode(); - hash = (37 * hash) + SCHEDULED_MICROS_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getScheduledMicros()); - hash = (37 * hash) + THREAD_ID_FIELD_NUMBER; - hash = (53 * hash) + getThreadId(); - if (getReferencedTensorCount() > 0) { - hash = (37 * hash) + REFERENCED_TENSOR_FIELD_NUMBER; - hash = (53 * hash) + getReferencedTensorList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.NodeExecStats parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.NodeExecStats parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.NodeExecStats parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.NodeExecStats parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.NodeExecStats parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeExecStats parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.NodeExecStats parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeExecStats parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.NodeExecStats parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeExecStats parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.NodeExecStats prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Time/size stats recorded for a single execution of a graph node.
-   *
- * - * Protobuf type {@code tensorflow.NodeExecStats} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.NodeExecStats) - org.tensorflow.framework.NodeExecStatsOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeExecStats_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeExecStats_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.NodeExecStats.class, org.tensorflow.framework.NodeExecStats.Builder.class); - } - - // Construct using org.tensorflow.framework.NodeExecStats.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getMemoryFieldBuilder(); - getOutputFieldBuilder(); - getReferencedTensorFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - nodeName_ = ""; - - allStartMicros_ = 0L; - - opStartRelMicros_ = 0L; - - opEndRelMicros_ = 0L; - - allEndRelMicros_ = 0L; - - if (memoryBuilder_ == null) { - memory_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000020); - } else { - memoryBuilder_.clear(); - } - if (outputBuilder_ == null) { - output_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000040); - } else { - outputBuilder_.clear(); - } - timelineLabel_ = ""; - - scheduledMicros_ = 0L; - - threadId_ = 0; - - if (referencedTensorBuilder_ == null) { - referencedTensor_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000400); - } else { - referencedTensorBuilder_.clear(); - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeExecStats_descriptor; - } - - public org.tensorflow.framework.NodeExecStats getDefaultInstanceForType() { - return org.tensorflow.framework.NodeExecStats.getDefaultInstance(); - } - - public org.tensorflow.framework.NodeExecStats build() { - org.tensorflow.framework.NodeExecStats result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.NodeExecStats buildPartial() { - org.tensorflow.framework.NodeExecStats result = new org.tensorflow.framework.NodeExecStats(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - result.nodeName_ = nodeName_; - result.allStartMicros_ = allStartMicros_; - result.opStartRelMicros_ = opStartRelMicros_; - result.opEndRelMicros_ = opEndRelMicros_; - result.allEndRelMicros_ = allEndRelMicros_; - if (memoryBuilder_ == null) { - if (((bitField0_ & 0x00000020) == 0x00000020)) { - memory_ = java.util.Collections.unmodifiableList(memory_); - bitField0_ = (bitField0_ & ~0x00000020); - } - result.memory_ = memory_; - } else { - result.memory_ = memoryBuilder_.build(); - } - if (outputBuilder_ == null) { - if (((bitField0_ & 0x00000040) == 0x00000040)) { - output_ = java.util.Collections.unmodifiableList(output_); - bitField0_ = (bitField0_ & ~0x00000040); - } - result.output_ = output_; - } else { - result.output_ = outputBuilder_.build(); - } - result.timelineLabel_ = timelineLabel_; - result.scheduledMicros_ = scheduledMicros_; - result.threadId_ = threadId_; - if (referencedTensorBuilder_ == null) { - if (((bitField0_ & 0x00000400) == 0x00000400)) { - referencedTensor_ = java.util.Collections.unmodifiableList(referencedTensor_); - bitField0_ = (bitField0_ & ~0x00000400); - } - result.referencedTensor_ = referencedTensor_; - } else { - result.referencedTensor_ = referencedTensorBuilder_.build(); - } - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.NodeExecStats) { - return mergeFrom((org.tensorflow.framework.NodeExecStats)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.NodeExecStats other) { - if (other == org.tensorflow.framework.NodeExecStats.getDefaultInstance()) return this; - if (!other.getNodeName().isEmpty()) { - nodeName_ = other.nodeName_; - onChanged(); - } - if (other.getAllStartMicros() != 0L) { - setAllStartMicros(other.getAllStartMicros()); - } - if (other.getOpStartRelMicros() != 0L) { - setOpStartRelMicros(other.getOpStartRelMicros()); - } - if (other.getOpEndRelMicros() != 0L) { - setOpEndRelMicros(other.getOpEndRelMicros()); - } - if (other.getAllEndRelMicros() != 0L) { - setAllEndRelMicros(other.getAllEndRelMicros()); - } - if (memoryBuilder_ == null) { - if (!other.memory_.isEmpty()) { - if (memory_.isEmpty()) { - memory_ = other.memory_; - bitField0_ = (bitField0_ & ~0x00000020); - } else { - ensureMemoryIsMutable(); - memory_.addAll(other.memory_); - } - onChanged(); - } - } else { - if (!other.memory_.isEmpty()) { - if (memoryBuilder_.isEmpty()) { - memoryBuilder_.dispose(); - memoryBuilder_ = null; - memory_ = other.memory_; - bitField0_ = (bitField0_ & ~0x00000020); - memoryBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getMemoryFieldBuilder() : null; - } else { - memoryBuilder_.addAllMessages(other.memory_); - } - } - } - if (outputBuilder_ == null) { - if (!other.output_.isEmpty()) { - if (output_.isEmpty()) { - output_ = other.output_; - bitField0_ = (bitField0_ & ~0x00000040); - } else { - ensureOutputIsMutable(); - output_.addAll(other.output_); - } - onChanged(); - } - } else { - if (!other.output_.isEmpty()) { - if (outputBuilder_.isEmpty()) { - outputBuilder_.dispose(); - outputBuilder_ = null; - output_ = other.output_; - bitField0_ = (bitField0_ & ~0x00000040); - outputBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getOutputFieldBuilder() : null; - } else { - outputBuilder_.addAllMessages(other.output_); - } - } - } - if (!other.getTimelineLabel().isEmpty()) { - timelineLabel_ = other.timelineLabel_; - onChanged(); - } - if (other.getScheduledMicros() != 0L) { - setScheduledMicros(other.getScheduledMicros()); - } - if (other.getThreadId() != 0) { - setThreadId(other.getThreadId()); - } - if (referencedTensorBuilder_ == null) { - if (!other.referencedTensor_.isEmpty()) { - if (referencedTensor_.isEmpty()) { - referencedTensor_ = other.referencedTensor_; - bitField0_ = (bitField0_ & ~0x00000400); - } else { - ensureReferencedTensorIsMutable(); - referencedTensor_.addAll(other.referencedTensor_); - } - onChanged(); - } - } else { - if (!other.referencedTensor_.isEmpty()) { - if (referencedTensorBuilder_.isEmpty()) { - referencedTensorBuilder_.dispose(); - referencedTensorBuilder_ = null; - referencedTensor_ = other.referencedTensor_; - bitField0_ = (bitField0_ & ~0x00000400); - referencedTensorBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getReferencedTensorFieldBuilder() : null; - } else { - referencedTensorBuilder_.addAllMessages(other.referencedTensor_); - } - } - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.NodeExecStats parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.NodeExecStats) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.lang.Object nodeName_ = ""; - /** - * 
-     * TODO(tucker): Use some more compact form of node identity than
-     * the full string name.  Either all processes should agree on a
-     * global id (cost_id?) for each node, or we should use a hash of
-     * the name.
-     *
- * - * optional string node_name = 1; - */ - public java.lang.String getNodeName() { - java.lang.Object ref = nodeName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - nodeName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * TODO(tucker): Use some more compact form of node identity than
-     * the full string name.  Either all processes should agree on a
-     * global id (cost_id?) for each node, or we should use a hash of
-     * the name.
-     *
- * - * optional string node_name = 1; - */ - public com.google.protobuf.ByteString - getNodeNameBytes() { - java.lang.Object ref = nodeName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - nodeName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * TODO(tucker): Use some more compact form of node identity than
-     * the full string name.  Either all processes should agree on a
-     * global id (cost_id?) for each node, or we should use a hash of
-     * the name.
-     *
- * - * optional string node_name = 1; - */ - public Builder setNodeName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - nodeName_ = value; - onChanged(); - return this; - } - /** - * 
-     * TODO(tucker): Use some more compact form of node identity than
-     * the full string name.  Either all processes should agree on a
-     * global id (cost_id?) for each node, or we should use a hash of
-     * the name.
-     *
- * - * optional string node_name = 1; - */ - public Builder clearNodeName() { - - nodeName_ = getDefaultInstance().getNodeName(); - onChanged(); - return this; - } - /** - * 
-     * TODO(tucker): Use some more compact form of node identity than
-     * the full string name.  Either all processes should agree on a
-     * global id (cost_id?) for each node, or we should use a hash of
-     * the name.
-     *
- * - * optional string node_name = 1; - */ - public Builder setNodeNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - nodeName_ = value; - onChanged(); - return this; - } - - private long allStartMicros_ ; - /** - * optional int64 all_start_micros = 2; - */ - public long getAllStartMicros() { - return allStartMicros_; - } - /** - * optional int64 all_start_micros = 2; - */ - public Builder setAllStartMicros(long value) { - - allStartMicros_ = value; - onChanged(); - return this; - } - /** - * optional int64 all_start_micros = 2; - */ - public Builder clearAllStartMicros() { - - allStartMicros_ = 0L; - onChanged(); - return this; - } - - private long opStartRelMicros_ ; - /** - * optional int64 op_start_rel_micros = 3; - */ - public long getOpStartRelMicros() { - return opStartRelMicros_; - } - /** - * optional int64 op_start_rel_micros = 3; - */ - public Builder setOpStartRelMicros(long value) { - - opStartRelMicros_ = value; - onChanged(); - return this; - } - /** - * optional int64 op_start_rel_micros = 3; - */ - public Builder clearOpStartRelMicros() { - - opStartRelMicros_ = 0L; - onChanged(); - return this; - } - - private long opEndRelMicros_ ; - /** - * optional int64 op_end_rel_micros = 4; - */ - public long getOpEndRelMicros() { - return opEndRelMicros_; - } - /** - * optional int64 op_end_rel_micros = 4; - */ - public Builder setOpEndRelMicros(long value) { - - opEndRelMicros_ = value; - onChanged(); - return this; - } - /** - * optional int64 op_end_rel_micros = 4; - */ - public Builder clearOpEndRelMicros() { - - opEndRelMicros_ = 0L; - onChanged(); - return this; - } - - private long allEndRelMicros_ ; - /** - * optional int64 all_end_rel_micros = 5; - */ - public long getAllEndRelMicros() { - return allEndRelMicros_; - } - /** - * optional int64 all_end_rel_micros = 5; - */ - public Builder setAllEndRelMicros(long value) { - - allEndRelMicros_ = value; - onChanged(); - return this; - } - /** - * optional int64 all_end_rel_micros = 5; - */ - public Builder clearAllEndRelMicros() { - - allEndRelMicros_ = 0L; - onChanged(); - return this; - } - - private java.util.List memory_ = - java.util.Collections.emptyList(); - private void ensureMemoryIsMutable() { - if (!((bitField0_ & 0x00000020) == 0x00000020)) { - memory_ = new java.util.ArrayList(memory_); - bitField0_ |= 0x00000020; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.AllocatorMemoryUsed, org.tensorflow.framework.AllocatorMemoryUsed.Builder, org.tensorflow.framework.AllocatorMemoryUsedOrBuilder> memoryBuilder_; - - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public java.util.List getMemoryList() { - if (memoryBuilder_ == null) { - return java.util.Collections.unmodifiableList(memory_); - } else { - return memoryBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public int getMemoryCount() { - if (memoryBuilder_ == null) { - return memory_.size(); - } else { - return memoryBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public org.tensorflow.framework.AllocatorMemoryUsed getMemory(int index) { - if (memoryBuilder_ == null) { - return memory_.get(index); - } else { - return memoryBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder setMemory( - int index, org.tensorflow.framework.AllocatorMemoryUsed value) { - if (memoryBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureMemoryIsMutable(); - memory_.set(index, value); - onChanged(); - } else { - memoryBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder setMemory( - int index, org.tensorflow.framework.AllocatorMemoryUsed.Builder builderForValue) { - if (memoryBuilder_ == null) { - ensureMemoryIsMutable(); - memory_.set(index, builderForValue.build()); - onChanged(); - } else { - memoryBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder addMemory(org.tensorflow.framework.AllocatorMemoryUsed value) { - if (memoryBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureMemoryIsMutable(); - memory_.add(value); - onChanged(); - } else { - memoryBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder addMemory( - int index, org.tensorflow.framework.AllocatorMemoryUsed value) { - if (memoryBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureMemoryIsMutable(); - memory_.add(index, value); - onChanged(); - } else { - memoryBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder addMemory( - org.tensorflow.framework.AllocatorMemoryUsed.Builder builderForValue) { - if (memoryBuilder_ == null) { - ensureMemoryIsMutable(); - memory_.add(builderForValue.build()); - onChanged(); - } else { - memoryBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder addMemory( - int index, org.tensorflow.framework.AllocatorMemoryUsed.Builder builderForValue) { - if (memoryBuilder_ == null) { - ensureMemoryIsMutable(); - memory_.add(index, builderForValue.build()); - onChanged(); - } else { - memoryBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder addAllMemory( - java.lang.Iterable values) { - if (memoryBuilder_ == null) { - ensureMemoryIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, memory_); - onChanged(); - } else { - memoryBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder clearMemory() { - if (memoryBuilder_ == null) { - memory_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000020); - onChanged(); - } else { - memoryBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public Builder removeMemory(int index) { - if (memoryBuilder_ == null) { - ensureMemoryIsMutable(); - memory_.remove(index); - onChanged(); - } else { - memoryBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public org.tensorflow.framework.AllocatorMemoryUsed.Builder getMemoryBuilder( - int index) { - return getMemoryFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public org.tensorflow.framework.AllocatorMemoryUsedOrBuilder getMemoryOrBuilder( - int index) { - if (memoryBuilder_ == null) { - return memory_.get(index); } else { - return memoryBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public java.util.List - getMemoryOrBuilderList() { - if (memoryBuilder_ != null) { - return memoryBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(memory_); - } - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public org.tensorflow.framework.AllocatorMemoryUsed.Builder addMemoryBuilder() { - return getMemoryFieldBuilder().addBuilder( - org.tensorflow.framework.AllocatorMemoryUsed.getDefaultInstance()); - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public org.tensorflow.framework.AllocatorMemoryUsed.Builder addMemoryBuilder( - int index) { - return getMemoryFieldBuilder().addBuilder( - index, org.tensorflow.framework.AllocatorMemoryUsed.getDefaultInstance()); - } - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - public java.util.List - getMemoryBuilderList() { - return getMemoryFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.AllocatorMemoryUsed, org.tensorflow.framework.AllocatorMemoryUsed.Builder, org.tensorflow.framework.AllocatorMemoryUsedOrBuilder> - getMemoryFieldBuilder() { - if (memoryBuilder_ == null) { - memoryBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.AllocatorMemoryUsed, org.tensorflow.framework.AllocatorMemoryUsed.Builder, org.tensorflow.framework.AllocatorMemoryUsedOrBuilder>( - memory_, - ((bitField0_ & 0x00000020) == 0x00000020), - getParentForChildren(), - isClean()); - memory_ = null; - } - return memoryBuilder_; - } - - private java.util.List output_ = - java.util.Collections.emptyList(); - private void ensureOutputIsMutable() { - if (!((bitField0_ & 0x00000040) == 0x00000040)) { - output_ = new java.util.ArrayList(output_); - bitField0_ |= 0x00000040; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeOutput, org.tensorflow.framework.NodeOutput.Builder, org.tensorflow.framework.NodeOutputOrBuilder> outputBuilder_; - - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public java.util.List getOutputList() { - if (outputBuilder_ == null) { - return java.util.Collections.unmodifiableList(output_); - } else { - return outputBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public int getOutputCount() { - if (outputBuilder_ == null) { - return output_.size(); - } else { - return outputBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public org.tensorflow.framework.NodeOutput getOutput(int index) { - if (outputBuilder_ == null) { - return output_.get(index); - } else { - return outputBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder setOutput( - int index, org.tensorflow.framework.NodeOutput value) { - if (outputBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOutputIsMutable(); - output_.set(index, value); - onChanged(); - } else { - outputBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder setOutput( - int index, org.tensorflow.framework.NodeOutput.Builder builderForValue) { - if (outputBuilder_ == null) { - ensureOutputIsMutable(); - output_.set(index, builderForValue.build()); - onChanged(); - } else { - outputBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder addOutput(org.tensorflow.framework.NodeOutput value) { - if (outputBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOutputIsMutable(); - output_.add(value); - onChanged(); - } else { - outputBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder addOutput( - int index, org.tensorflow.framework.NodeOutput value) { - if (outputBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOutputIsMutable(); - output_.add(index, value); - onChanged(); - } else { - outputBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder addOutput( - org.tensorflow.framework.NodeOutput.Builder builderForValue) { - if (outputBuilder_ == null) { - ensureOutputIsMutable(); - output_.add(builderForValue.build()); - onChanged(); - } else { - outputBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder addOutput( - int index, org.tensorflow.framework.NodeOutput.Builder builderForValue) { - if (outputBuilder_ == null) { - ensureOutputIsMutable(); - output_.add(index, builderForValue.build()); - onChanged(); - } else { - outputBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder addAllOutput( - java.lang.Iterable values) { - if (outputBuilder_ == null) { - ensureOutputIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, output_); - onChanged(); - } else { - outputBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder clearOutput() { - if (outputBuilder_ == null) { - output_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000040); - onChanged(); - } else { - outputBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public Builder removeOutput(int index) { - if (outputBuilder_ == null) { - ensureOutputIsMutable(); - output_.remove(index); - onChanged(); - } else { - outputBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public org.tensorflow.framework.NodeOutput.Builder getOutputBuilder( - int index) { - return getOutputFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public org.tensorflow.framework.NodeOutputOrBuilder getOutputOrBuilder( - int index) { - if (outputBuilder_ == null) { - return output_.get(index); } else { - return outputBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public java.util.List - getOutputOrBuilderList() { - if (outputBuilder_ != null) { - return outputBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(output_); - } - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public org.tensorflow.framework.NodeOutput.Builder addOutputBuilder() { - return getOutputFieldBuilder().addBuilder( - org.tensorflow.framework.NodeOutput.getDefaultInstance()); - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public org.tensorflow.framework.NodeOutput.Builder addOutputBuilder( - int index) { - return getOutputFieldBuilder().addBuilder( - index, org.tensorflow.framework.NodeOutput.getDefaultInstance()); - } - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - public java.util.List - getOutputBuilderList() { - return getOutputFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeOutput, org.tensorflow.framework.NodeOutput.Builder, org.tensorflow.framework.NodeOutputOrBuilder> - getOutputFieldBuilder() { - if (outputBuilder_ == null) { - outputBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.NodeOutput, org.tensorflow.framework.NodeOutput.Builder, org.tensorflow.framework.NodeOutputOrBuilder>( - output_, - ((bitField0_ & 0x00000040) == 0x00000040), - getParentForChildren(), - isClean()); - output_ = null; - } - return outputBuilder_; - } - - private java.lang.Object timelineLabel_ = ""; - /** - * optional string timeline_label = 8; - */ - public java.lang.String getTimelineLabel() { - java.lang.Object ref = timelineLabel_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - timelineLabel_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * optional string timeline_label = 8; - */ - public com.google.protobuf.ByteString - getTimelineLabelBytes() { - java.lang.Object ref = timelineLabel_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - timelineLabel_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * optional string timeline_label = 8; - */ - public Builder setTimelineLabel( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - timelineLabel_ = value; - onChanged(); - return this; - } - /** - * optional string timeline_label = 8; - */ - public Builder clearTimelineLabel() { - - timelineLabel_ = getDefaultInstance().getTimelineLabel(); - onChanged(); - return this; - } - /** - * optional string timeline_label = 8; - */ - public Builder setTimelineLabelBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - timelineLabel_ = value; - onChanged(); - return this; - } - - private long scheduledMicros_ ; - /** - * optional int64 scheduled_micros = 9; - */ - public long getScheduledMicros() { - return scheduledMicros_; - } - /** - * optional int64 scheduled_micros = 9; - */ - public Builder setScheduledMicros(long value) { - - scheduledMicros_ = value; - onChanged(); - return this; - } - /** - * optional int64 scheduled_micros = 9; - */ - public Builder clearScheduledMicros() { - - scheduledMicros_ = 0L; - onChanged(); - return this; - } - - private int threadId_ ; - /** - * optional uint32 thread_id = 10; - */ - public int getThreadId() { - return threadId_; - } - /** - * optional uint32 thread_id = 10; - */ - public Builder setThreadId(int value) { - - threadId_ = value; - onChanged(); - return this; - } - /** - * optional uint32 thread_id = 10; - */ - public Builder clearThreadId() { - - threadId_ = 0; - onChanged(); - return this; - } - - private java.util.List referencedTensor_ = - java.util.Collections.emptyList(); - private void ensureReferencedTensorIsMutable() { - if (!((bitField0_ & 0x00000400) == 0x00000400)) { - referencedTensor_ = new java.util.ArrayList(referencedTensor_); - bitField0_ |= 0x00000400; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.AllocationDescription, org.tensorflow.framework.AllocationDescription.Builder, org.tensorflow.framework.AllocationDescriptionOrBuilder> referencedTensorBuilder_; - - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public java.util.List getReferencedTensorList() { - if (referencedTensorBuilder_ == null) { - return java.util.Collections.unmodifiableList(referencedTensor_); - } else { - return referencedTensorBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public int getReferencedTensorCount() { - if (referencedTensorBuilder_ == null) { - return referencedTensor_.size(); - } else { - return referencedTensorBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public org.tensorflow.framework.AllocationDescription getReferencedTensor(int index) { - if (referencedTensorBuilder_ == null) { - return referencedTensor_.get(index); - } else { - return referencedTensorBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder setReferencedTensor( - int index, org.tensorflow.framework.AllocationDescription value) { - if (referencedTensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureReferencedTensorIsMutable(); - referencedTensor_.set(index, value); - onChanged(); - } else { - referencedTensorBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder setReferencedTensor( - int index, org.tensorflow.framework.AllocationDescription.Builder builderForValue) { - if (referencedTensorBuilder_ == null) { - ensureReferencedTensorIsMutable(); - referencedTensor_.set(index, builderForValue.build()); - onChanged(); - } else { - referencedTensorBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder addReferencedTensor(org.tensorflow.framework.AllocationDescription value) { - if (referencedTensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureReferencedTensorIsMutable(); - referencedTensor_.add(value); - onChanged(); - } else { - referencedTensorBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder addReferencedTensor( - int index, org.tensorflow.framework.AllocationDescription value) { - if (referencedTensorBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureReferencedTensorIsMutable(); - referencedTensor_.add(index, value); - onChanged(); - } else { - referencedTensorBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder addReferencedTensor( - org.tensorflow.framework.AllocationDescription.Builder builderForValue) { - if (referencedTensorBuilder_ == null) { - ensureReferencedTensorIsMutable(); - referencedTensor_.add(builderForValue.build()); - onChanged(); - } else { - referencedTensorBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder addReferencedTensor( - int index, org.tensorflow.framework.AllocationDescription.Builder builderForValue) { - if (referencedTensorBuilder_ == null) { - ensureReferencedTensorIsMutable(); - referencedTensor_.add(index, builderForValue.build()); - onChanged(); - } else { - referencedTensorBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder addAllReferencedTensor( - java.lang.Iterable values) { - if (referencedTensorBuilder_ == null) { - ensureReferencedTensorIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, referencedTensor_); - onChanged(); - } else { - referencedTensorBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder clearReferencedTensor() { - if (referencedTensorBuilder_ == null) { - referencedTensor_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000400); - onChanged(); - } else { - referencedTensorBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public Builder removeReferencedTensor(int index) { - if (referencedTensorBuilder_ == null) { - ensureReferencedTensorIsMutable(); - referencedTensor_.remove(index); - onChanged(); - } else { - referencedTensorBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public org.tensorflow.framework.AllocationDescription.Builder getReferencedTensorBuilder( - int index) { - return getReferencedTensorFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public org.tensorflow.framework.AllocationDescriptionOrBuilder getReferencedTensorOrBuilder( - int index) { - if (referencedTensorBuilder_ == null) { - return referencedTensor_.get(index); } else { - return referencedTensorBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public java.util.List - getReferencedTensorOrBuilderList() { - if (referencedTensorBuilder_ != null) { - return referencedTensorBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(referencedTensor_); - } - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public org.tensorflow.framework.AllocationDescription.Builder addReferencedTensorBuilder() { - return getReferencedTensorFieldBuilder().addBuilder( - org.tensorflow.framework.AllocationDescription.getDefaultInstance()); - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public org.tensorflow.framework.AllocationDescription.Builder addReferencedTensorBuilder( - int index) { - return getReferencedTensorFieldBuilder().addBuilder( - index, org.tensorflow.framework.AllocationDescription.getDefaultInstance()); - } - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - public java.util.List - getReferencedTensorBuilderList() { - return getReferencedTensorFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.AllocationDescription, org.tensorflow.framework.AllocationDescription.Builder, org.tensorflow.framework.AllocationDescriptionOrBuilder> - getReferencedTensorFieldBuilder() { - if (referencedTensorBuilder_ == null) { - referencedTensorBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.AllocationDescription, org.tensorflow.framework.AllocationDescription.Builder, org.tensorflow.framework.AllocationDescriptionOrBuilder>( - referencedTensor_, - ((bitField0_ & 0x00000400) == 0x00000400), - getParentForChildren(), - isClean()); - referencedTensor_ = null; - } - return referencedTensorBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.NodeExecStats) - } - - // @@protoc_insertion_point(class_scope:tensorflow.NodeExecStats) - private static final org.tensorflow.framework.NodeExecStats DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.NodeExecStats(); - } - - public static org.tensorflow.framework.NodeExecStats getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public NodeExecStats parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new NodeExecStats(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.NodeExecStats getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NodeExecStatsOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/NodeExecStatsOrBuilder.java deleted file mode 100644 index db671d34536..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NodeExecStatsOrBuilder.java +++ /dev/null @@ -1,145 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -public interface NodeExecStatsOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.NodeExecStats) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * TODO(tucker): Use some more compact form of node identity than
-   * the full string name.  Either all processes should agree on a
-   * global id (cost_id?) for each node, or we should use a hash of
-   * the name.
-   *
- * - * optional string node_name = 1; - */ - java.lang.String getNodeName(); - /** - * 
-   * TODO(tucker): Use some more compact form of node identity than
-   * the full string name.  Either all processes should agree on a
-   * global id (cost_id?) for each node, or we should use a hash of
-   * the name.
-   *
- * - * optional string node_name = 1; - */ - com.google.protobuf.ByteString - getNodeNameBytes(); - - /** - * optional int64 all_start_micros = 2; - */ - long getAllStartMicros(); - - /** - * optional int64 op_start_rel_micros = 3; - */ - long getOpStartRelMicros(); - - /** - * optional int64 op_end_rel_micros = 4; - */ - long getOpEndRelMicros(); - - /** - * optional int64 all_end_rel_micros = 5; - */ - long getAllEndRelMicros(); - - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - java.util.List - getMemoryList(); - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - org.tensorflow.framework.AllocatorMemoryUsed getMemory(int index); - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - int getMemoryCount(); - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - java.util.List - getMemoryOrBuilderList(); - /** - * repeated .tensorflow.AllocatorMemoryUsed memory = 6; - */ - org.tensorflow.framework.AllocatorMemoryUsedOrBuilder getMemoryOrBuilder( - int index); - - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - java.util.List - getOutputList(); - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - org.tensorflow.framework.NodeOutput getOutput(int index); - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - int getOutputCount(); - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - java.util.List - getOutputOrBuilderList(); - /** - * repeated .tensorflow.NodeOutput output = 7; - */ - org.tensorflow.framework.NodeOutputOrBuilder getOutputOrBuilder( - int index); - - /** - * optional string timeline_label = 8; - */ - java.lang.String getTimelineLabel(); - /** - * optional string timeline_label = 8; - */ - com.google.protobuf.ByteString - getTimelineLabelBytes(); - - /** - * optional int64 scheduled_micros = 9; - */ - long getScheduledMicros(); - - /** - * optional uint32 thread_id = 10; - */ - int getThreadId(); - - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - java.util.List - getReferencedTensorList(); - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - org.tensorflow.framework.AllocationDescription getReferencedTensor(int index); - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - int getReferencedTensorCount(); - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - java.util.List - getReferencedTensorOrBuilderList(); - /** - * repeated .tensorflow.AllocationDescription referenced_tensor = 11; - */ - org.tensorflow.framework.AllocationDescriptionOrBuilder getReferencedTensorOrBuilder( - int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NodeOutput.java b/scala/dllib/src/main/java/org/tensorflow/framework/NodeOutput.java deleted file mode 100644 index c73a66e17a4..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NodeOutput.java +++ /dev/null @@ -1,614 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -/** - * 
- * Output sizes recorded for a single execution of a graph node.
- *
- * - * Protobuf type {@code tensorflow.NodeOutput} - */ -public final class NodeOutput extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.NodeOutput) - NodeOutputOrBuilder { - // Use NodeOutput.newBuilder() to construct. - private NodeOutput(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private NodeOutput() { - slot_ = 0; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private NodeOutput( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - slot_ = input.readInt32(); - break; - } - case 26: { - org.tensorflow.framework.TensorDescription.Builder subBuilder = null; - if (tensorDescription_ != null) { - subBuilder = tensorDescription_.toBuilder(); - } - tensorDescription_ = input.readMessage(org.tensorflow.framework.TensorDescription.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(tensorDescription_); - tensorDescription_ = subBuilder.buildPartial(); - } - - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeOutput_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeOutput_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.NodeOutput.class, org.tensorflow.framework.NodeOutput.Builder.class); - } - - public static final int SLOT_FIELD_NUMBER = 1; - private int slot_; - /** - * optional int32 slot = 1; - */ - public int getSlot() { - return slot_; - } - - public static final int TENSOR_DESCRIPTION_FIELD_NUMBER = 3; - private org.tensorflow.framework.TensorDescription tensorDescription_; - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public boolean hasTensorDescription() { - return tensorDescription_ != null; - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public org.tensorflow.framework.TensorDescription getTensorDescription() { - return tensorDescription_ == null ? org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensorDescription_; - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public org.tensorflow.framework.TensorDescriptionOrBuilder getTensorDescriptionOrBuilder() { - return getTensorDescription(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (slot_ != 0) { - output.writeInt32(1, slot_); - } - if (tensorDescription_ != null) { - output.writeMessage(3, getTensorDescription()); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (slot_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeInt32Size(1, slot_); - } - if (tensorDescription_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(3, getTensorDescription()); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.NodeOutput)) { - return super.equals(obj); - } - org.tensorflow.framework.NodeOutput other = (org.tensorflow.framework.NodeOutput) obj; - - boolean result = true; - result = result && (getSlot() - == other.getSlot()); - result = result && (hasTensorDescription() == other.hasTensorDescription()); - if (hasTensorDescription()) { - result = result && getTensorDescription() - .equals(other.getTensorDescription()); - } - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + SLOT_FIELD_NUMBER; - hash = (53 * hash) + getSlot(); - if (hasTensorDescription()) { - hash = (37 * hash) + TENSOR_DESCRIPTION_FIELD_NUMBER; - hash = (53 * hash) + getTensorDescription().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.NodeOutput parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.NodeOutput parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.NodeOutput parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.NodeOutput parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.NodeOutput parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeOutput parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.NodeOutput parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeOutput parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.NodeOutput parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.NodeOutput parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.NodeOutput prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Output sizes recorded for a single execution of a graph node.
-   *
- * - * Protobuf type {@code tensorflow.NodeOutput} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.NodeOutput) - org.tensorflow.framework.NodeOutputOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeOutput_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeOutput_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.NodeOutput.class, org.tensorflow.framework.NodeOutput.Builder.class); - } - - // Construct using org.tensorflow.framework.NodeOutput.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - slot_ = 0; - - if (tensorDescriptionBuilder_ == null) { - tensorDescription_ = null; - } else { - tensorDescription_ = null; - tensorDescriptionBuilder_ = null; - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_NodeOutput_descriptor; - } - - public org.tensorflow.framework.NodeOutput getDefaultInstanceForType() { - return org.tensorflow.framework.NodeOutput.getDefaultInstance(); - } - - public org.tensorflow.framework.NodeOutput build() { - org.tensorflow.framework.NodeOutput result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.NodeOutput buildPartial() { - org.tensorflow.framework.NodeOutput result = new org.tensorflow.framework.NodeOutput(this); - result.slot_ = slot_; - if (tensorDescriptionBuilder_ == null) { - result.tensorDescription_ = tensorDescription_; - } else { - result.tensorDescription_ = tensorDescriptionBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.NodeOutput) { - return mergeFrom((org.tensorflow.framework.NodeOutput)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.NodeOutput other) { - if (other == org.tensorflow.framework.NodeOutput.getDefaultInstance()) return this; - if (other.getSlot() != 0) { - setSlot(other.getSlot()); - } - if (other.hasTensorDescription()) { - mergeTensorDescription(other.getTensorDescription()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.NodeOutput parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.NodeOutput) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private int slot_ ; - /** - * optional int32 slot = 1; - */ - public int getSlot() { - return slot_; - } - /** - * optional int32 slot = 1; - */ - public Builder setSlot(int value) { - - slot_ = value; - onChanged(); - return this; - } - /** - * optional int32 slot = 1; - */ - public Builder clearSlot() { - - slot_ = 0; - onChanged(); - return this; - } - - private org.tensorflow.framework.TensorDescription tensorDescription_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder> tensorDescriptionBuilder_; - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public boolean hasTensorDescription() { - return tensorDescriptionBuilder_ != null || tensorDescription_ != null; - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public org.tensorflow.framework.TensorDescription getTensorDescription() { - if (tensorDescriptionBuilder_ == null) { - return tensorDescription_ == null ? org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensorDescription_; - } else { - return tensorDescriptionBuilder_.getMessage(); - } - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public Builder setTensorDescription(org.tensorflow.framework.TensorDescription value) { - if (tensorDescriptionBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - tensorDescription_ = value; - onChanged(); - } else { - tensorDescriptionBuilder_.setMessage(value); - } - - return this; - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public Builder setTensorDescription( - org.tensorflow.framework.TensorDescription.Builder builderForValue) { - if (tensorDescriptionBuilder_ == null) { - tensorDescription_ = builderForValue.build(); - onChanged(); - } else { - tensorDescriptionBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public Builder mergeTensorDescription(org.tensorflow.framework.TensorDescription value) { - if (tensorDescriptionBuilder_ == null) { - if (tensorDescription_ != null) { - tensorDescription_ = - org.tensorflow.framework.TensorDescription.newBuilder(tensorDescription_).mergeFrom(value).buildPartial(); - } else { - tensorDescription_ = value; - } - onChanged(); - } else { - tensorDescriptionBuilder_.mergeFrom(value); - } - - return this; - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public Builder clearTensorDescription() { - if (tensorDescriptionBuilder_ == null) { - tensorDescription_ = null; - onChanged(); - } else { - tensorDescription_ = null; - tensorDescriptionBuilder_ = null; - } - - return this; - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public org.tensorflow.framework.TensorDescription.Builder getTensorDescriptionBuilder() { - - onChanged(); - return getTensorDescriptionFieldBuilder().getBuilder(); - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - public org.tensorflow.framework.TensorDescriptionOrBuilder getTensorDescriptionOrBuilder() { - if (tensorDescriptionBuilder_ != null) { - return tensorDescriptionBuilder_.getMessageOrBuilder(); - } else { - return tensorDescription_ == null ? - org.tensorflow.framework.TensorDescription.getDefaultInstance() : tensorDescription_; - } - } - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder> - getTensorDescriptionFieldBuilder() { - if (tensorDescriptionBuilder_ == null) { - tensorDescriptionBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.TensorDescription, org.tensorflow.framework.TensorDescription.Builder, org.tensorflow.framework.TensorDescriptionOrBuilder>( - getTensorDescription(), - getParentForChildren(), - isClean()); - tensorDescription_ = null; - } - return tensorDescriptionBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.NodeOutput) - } - - // @@protoc_insertion_point(class_scope:tensorflow.NodeOutput) - private static final org.tensorflow.framework.NodeOutput DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.NodeOutput(); - } - - public static org.tensorflow.framework.NodeOutput getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public NodeOutput parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new NodeOutput(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.NodeOutput getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NodeOutputOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/NodeOutputOrBuilder.java deleted file mode 100644 index 6f56e9f094c..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NodeOutputOrBuilder.java +++ /dev/null @@ -1,27 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -public interface NodeOutputOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.NodeOutput) - com.google.protobuf.MessageOrBuilder { - - /** - * optional int32 slot = 1; - */ - int getSlot(); - - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - boolean hasTensorDescription(); - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - org.tensorflow.framework.TensorDescription getTensorDescription(); - /** - * optional .tensorflow.TensorDescription tensor_description = 3; - */ - org.tensorflow.framework.TensorDescriptionOrBuilder getTensorDescriptionOrBuilder(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/NodeProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/NodeProto.java deleted file mode 100644 index 244f7430a46..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/NodeProto.java +++ /dev/null @@ -1,74 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: node_def.proto - -package org.tensorflow.framework; - -public final class NodeProto { - private NodeProto() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_NodeDef_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_NodeDef_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_NodeDef_AttrEntry_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_NodeDef_AttrEntry_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\016node_def.proto\022\ntensorflow\032*tensorflow" + - "/core/framework/attr_value.proto\"\263\001\n\007Nod" + - "eDef\022\014\n\004name\030\001 \001(\t\022\n\n\002op\030\002 \001(\t\022\r\n\005input\030" + - "\003 \003(\t\022\016\n\006device\030\004 \001(\t\022+\n\004attr\030\005 \003(\0132\035.te" + - "nsorflow.NodeDef.AttrEntry\032B\n\tAttrEntry\022" + - "\013\n\003key\030\001 \001(\t\022$\n\005value\030\002 \001(\0132\025.tensorflow" + - ".AttrValue:\0028\001B*\n\030org.tensorflow.framewo" + - "rkB\tNodeProtoP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.framework.AttrValueProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_NodeDef_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_NodeDef_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_NodeDef_descriptor, - new java.lang.String[] { "Name", "Op", "Input", "Device", "Attr", }); - internal_static_tensorflow_NodeDef_AttrEntry_descriptor = - internal_static_tensorflow_NodeDef_descriptor.getNestedTypes().get(0); - internal_static_tensorflow_NodeDef_AttrEntry_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_NodeDef_AttrEntry_descriptor, - new java.lang.String[] { "Key", "Value", }); - org.tensorflow.framework.AttrValueProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/OpDef.java b/scala/dllib/src/main/java/org/tensorflow/framework/OpDef.java deleted file mode 100644 index 1bb40519077..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/OpDef.java +++ /dev/null @@ -1,5878 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: op_def.proto - -package org.tensorflow.framework; - -/** - * 
- * Defines an operation. A NodeDef in a GraphDef specifies an Op by
- * using the "op" field which should match the name of a OpDef.
- *
- * - * Protobuf type {@code tensorflow.OpDef} - */ -public final class OpDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.OpDef) - OpDefOrBuilder { - // Use OpDef.newBuilder() to construct. - private OpDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private OpDef() { - name_ = ""; - inputArg_ = java.util.Collections.emptyList(); - outputArg_ = java.util.Collections.emptyList(); - attr_ = java.util.Collections.emptyList(); - summary_ = ""; - description_ = ""; - isCommutative_ = false; - isAggregate_ = false; - isStateful_ = false; - allowsUninitializedInput_ = false; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private OpDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - name_ = s; - break; - } - case 18: { - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - inputArg_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000002; - } - inputArg_.add( - input.readMessage(org.tensorflow.framework.OpDef.ArgDef.parser(), extensionRegistry)); - break; - } - case 26: { - if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - outputArg_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000004; - } - outputArg_.add( - input.readMessage(org.tensorflow.framework.OpDef.ArgDef.parser(), extensionRegistry)); - break; - } - case 34: { - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - attr_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000008; - } - attr_.add( - input.readMessage(org.tensorflow.framework.OpDef.AttrDef.parser(), extensionRegistry)); - break; - } - case 42: { - java.lang.String s = input.readStringRequireUtf8(); - - summary_ = s; - break; - } - case 50: { - java.lang.String s = input.readStringRequireUtf8(); - - description_ = s; - break; - } - case 66: { - org.tensorflow.framework.OpDeprecation.Builder subBuilder = null; - if (deprecation_ != null) { - subBuilder = deprecation_.toBuilder(); - } - deprecation_ = input.readMessage(org.tensorflow.framework.OpDeprecation.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(deprecation_); - deprecation_ = subBuilder.buildPartial(); - } - - break; - } - case 128: { - - isAggregate_ = input.readBool(); - break; - } - case 136: { - - isStateful_ = input.readBool(); - break; - } - case 144: { - - isCommutative_ = input.readBool(); - break; - } - case 152: { - - allowsUninitializedInput_ = input.readBool(); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - inputArg_ = java.util.Collections.unmodifiableList(inputArg_); - } - if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - outputArg_ = java.util.Collections.unmodifiableList(outputArg_); - } - if (((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - attr_ = java.util.Collections.unmodifiableList(attr_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpDef.class, org.tensorflow.framework.OpDef.Builder.class); - } - - public interface ArgDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.OpDef.ArgDef) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-     * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-     *
- * - * optional string name = 1; - */ - java.lang.String getName(); - /** - * 
-     * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-     *
- * - * optional string name = 1; - */ - com.google.protobuf.ByteString - getNameBytes(); - - /** - * 
-     * Human readable description.
-     *
- * - * optional string description = 2; - */ - java.lang.String getDescription(); - /** - * 
-     * Human readable description.
-     *
- * - * optional string description = 2; - */ - com.google.protobuf.ByteString - getDescriptionBytes(); - - /** - * 
-     * Describes the type of one or more tensors that are accepted/produced
-     * by this input/output arg.  The only legal combinations are:
-     * * For a single tensor: either the "type" field is set or the
-     *   "type_attr" field is set to the name of an attr with type "type".
-     * * For a sequence of tensors with the same type: the "number_attr"
-     *   field will be set to the name of an attr with type "int", and
-     *   either the "type" or "type_attr" field will be set as for
-     *   single tensors.
-     * * For a sequence of tensors, the "type_list_attr" field will be set
-     *   to the name of an attr with type "list(type)".
-     *
- * - * optional .tensorflow.DataType type = 3; - */ - int getTypeValue(); - /** - * 
-     * Describes the type of one or more tensors that are accepted/produced
-     * by this input/output arg.  The only legal combinations are:
-     * * For a single tensor: either the "type" field is set or the
-     *   "type_attr" field is set to the name of an attr with type "type".
-     * * For a sequence of tensors with the same type: the "number_attr"
-     *   field will be set to the name of an attr with type "int", and
-     *   either the "type" or "type_attr" field will be set as for
-     *   single tensors.
-     * * For a sequence of tensors, the "type_list_attr" field will be set
-     *   to the name of an attr with type "list(type)".
-     *
- * - * optional .tensorflow.DataType type = 3; - */ - org.tensorflow.framework.DataType getType(); - - /** - * 
-     * if specified, attr must have type "type"
-     *
- * - * optional string type_attr = 4; - */ - java.lang.String getTypeAttr(); - /** - * 
-     * if specified, attr must have type "type"
-     *
- * - * optional string type_attr = 4; - */ - com.google.protobuf.ByteString - getTypeAttrBytes(); - - /** - * 
-     * if specified, attr must have type "int"
-     *
- * - * optional string number_attr = 5; - */ - java.lang.String getNumberAttr(); - /** - * 
-     * if specified, attr must have type "int"
-     *
- * - * optional string number_attr = 5; - */ - com.google.protobuf.ByteString - getNumberAttrBytes(); - - /** - * 
-     * If specified, attr must have type "list(type)", and none of
-     * type, type_attr, and number_attr may be specified.
-     *
- * - * optional string type_list_attr = 6; - */ - java.lang.String getTypeListAttr(); - /** - * 
-     * If specified, attr must have type "list(type)", and none of
-     * type, type_attr, and number_attr may be specified.
-     *
- * - * optional string type_list_attr = 6; - */ - com.google.protobuf.ByteString - getTypeListAttrBytes(); - - /** - * 
-     * For inputs: if true, the inputs are required to be refs.
-     *   By default, inputs can be either refs or non-refs.
-     * For outputs: if true, outputs are refs, otherwise they are not.
-     *
- * - * optional bool is_ref = 16; - */ - boolean getIsRef(); - } - /** - * 
-   * For describing inputs and outputs.
-   *
- * - * Protobuf type {@code tensorflow.OpDef.ArgDef} - */ - public static final class ArgDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.OpDef.ArgDef) - ArgDefOrBuilder { - // Use ArgDef.newBuilder() to construct. - private ArgDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private ArgDef() { - name_ = ""; - description_ = ""; - type_ = 0; - typeAttr_ = ""; - numberAttr_ = ""; - typeListAttr_ = ""; - isRef_ = false; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private ArgDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - name_ = s; - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - description_ = s; - break; - } - case 24: { - int rawValue = input.readEnum(); - - type_ = rawValue; - break; - } - case 34: { - java.lang.String s = input.readStringRequireUtf8(); - - typeAttr_ = s; - break; - } - case 42: { - java.lang.String s = input.readStringRequireUtf8(); - - numberAttr_ = s; - break; - } - case 50: { - java.lang.String s = input.readStringRequireUtf8(); - - typeListAttr_ = s; - break; - } - case 128: { - - isRef_ = input.readBool(); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_ArgDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_ArgDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpDef.ArgDef.class, org.tensorflow.framework.OpDef.ArgDef.Builder.class); - } - - public static final int NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object name_; - /** - * 
-     * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-     *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } - } - /** - * 
-     * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-     *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int DESCRIPTION_FIELD_NUMBER = 2; - private volatile java.lang.Object description_; - /** - * 
-     * Human readable description.
-     *
- * - * optional string description = 2; - */ - public java.lang.String getDescription() { - java.lang.Object ref = description_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - description_ = s; - return s; - } - } - /** - * 
-     * Human readable description.
-     *
- * - * optional string description = 2; - */ - public com.google.protobuf.ByteString - getDescriptionBytes() { - java.lang.Object ref = description_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - description_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int TYPE_FIELD_NUMBER = 3; - private int type_; - /** - * 
-     * Describes the type of one or more tensors that are accepted/produced
-     * by this input/output arg.  The only legal combinations are:
-     * * For a single tensor: either the "type" field is set or the
-     *   "type_attr" field is set to the name of an attr with type "type".
-     * * For a sequence of tensors with the same type: the "number_attr"
-     *   field will be set to the name of an attr with type "int", and
-     *   either the "type" or "type_attr" field will be set as for
-     *   single tensors.
-     * * For a sequence of tensors, the "type_list_attr" field will be set
-     *   to the name of an attr with type "list(type)".
-     *
- * - * optional .tensorflow.DataType type = 3; - */ - public int getTypeValue() { - return type_; - } - /** - * 
-     * Describes the type of one or more tensors that are accepted/produced
-     * by this input/output arg.  The only legal combinations are:
-     * * For a single tensor: either the "type" field is set or the
-     *   "type_attr" field is set to the name of an attr with type "type".
-     * * For a sequence of tensors with the same type: the "number_attr"
-     *   field will be set to the name of an attr with type "int", and
-     *   either the "type" or "type_attr" field will be set as for
-     *   single tensors.
-     * * For a sequence of tensors, the "type_list_attr" field will be set
-     *   to the name of an attr with type "list(type)".
-     *
- * - * optional .tensorflow.DataType type = 3; - */ - public org.tensorflow.framework.DataType getType() { - org.tensorflow.framework.DataType result = org.tensorflow.framework.DataType.valueOf(type_); - return result == null ? org.tensorflow.framework.DataType.UNRECOGNIZED : result; - } - - public static final int TYPE_ATTR_FIELD_NUMBER = 4; - private volatile java.lang.Object typeAttr_; - /** - * 
-     * if specified, attr must have type "type"
-     *
- * - * optional string type_attr = 4; - */ - public java.lang.String getTypeAttr() { - java.lang.Object ref = typeAttr_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - typeAttr_ = s; - return s; - } - } - /** - * 
-     * if specified, attr must have type "type"
-     *
- * - * optional string type_attr = 4; - */ - public com.google.protobuf.ByteString - getTypeAttrBytes() { - java.lang.Object ref = typeAttr_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - typeAttr_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int NUMBER_ATTR_FIELD_NUMBER = 5; - private volatile java.lang.Object numberAttr_; - /** - * 
-     * if specified, attr must have type "int"
-     *
- * - * optional string number_attr = 5; - */ - public java.lang.String getNumberAttr() { - java.lang.Object ref = numberAttr_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - numberAttr_ = s; - return s; - } - } - /** - * 
-     * if specified, attr must have type "int"
-     *
- * - * optional string number_attr = 5; - */ - public com.google.protobuf.ByteString - getNumberAttrBytes() { - java.lang.Object ref = numberAttr_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - numberAttr_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int TYPE_LIST_ATTR_FIELD_NUMBER = 6; - private volatile java.lang.Object typeListAttr_; - /** - * 
-     * If specified, attr must have type "list(type)", and none of
-     * type, type_attr, and number_attr may be specified.
-     *
- * - * optional string type_list_attr = 6; - */ - public java.lang.String getTypeListAttr() { - java.lang.Object ref = typeListAttr_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - typeListAttr_ = s; - return s; - } - } - /** - * 
-     * If specified, attr must have type "list(type)", and none of
-     * type, type_attr, and number_attr may be specified.
-     *
- * - * optional string type_list_attr = 6; - */ - public com.google.protobuf.ByteString - getTypeListAttrBytes() { - java.lang.Object ref = typeListAttr_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - typeListAttr_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int IS_REF_FIELD_NUMBER = 16; - private boolean isRef_; - /** - * 
-     * For inputs: if true, the inputs are required to be refs.
-     *   By default, inputs can be either refs or non-refs.
-     * For outputs: if true, outputs are refs, otherwise they are not.
-     *
- * - * optional bool is_ref = 16; - */ - public boolean getIsRef() { - return isRef_; - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_); - } - if (!getDescriptionBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, description_); - } - if (type_ != org.tensorflow.framework.DataType.DT_INVALID.getNumber()) { - output.writeEnum(3, type_); - } - if (!getTypeAttrBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 4, typeAttr_); - } - if (!getNumberAttrBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 5, numberAttr_); - } - if (!getTypeListAttrBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 6, typeListAttr_); - } - if (isRef_ != false) { - output.writeBool(16, isRef_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_); - } - if (!getDescriptionBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, description_); - } - if (type_ != org.tensorflow.framework.DataType.DT_INVALID.getNumber()) { - size += com.google.protobuf.CodedOutputStream - .computeEnumSize(3, type_); - } - if (!getTypeAttrBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(4, typeAttr_); - } - if (!getNumberAttrBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(5, numberAttr_); - } - if (!getTypeListAttrBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(6, typeListAttr_); - } - if (isRef_ != false) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize(16, isRef_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.OpDef.ArgDef)) { - return super.equals(obj); - } - org.tensorflow.framework.OpDef.ArgDef other = (org.tensorflow.framework.OpDef.ArgDef) obj; - - boolean result = true; - result = result && getName() - .equals(other.getName()); - result = result && getDescription() - .equals(other.getDescription()); - result = result && type_ == other.type_; - result = result && getTypeAttr() - .equals(other.getTypeAttr()); - result = result && getNumberAttr() - .equals(other.getNumberAttr()); - result = result && getTypeListAttr() - .equals(other.getTypeListAttr()); - result = result && (getIsRef() - == other.getIsRef()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + NAME_FIELD_NUMBER; - hash = (53 * hash) + getName().hashCode(); - hash = (37 * hash) + DESCRIPTION_FIELD_NUMBER; - hash = (53 * hash) + getDescription().hashCode(); - hash = (37 * hash) + TYPE_FIELD_NUMBER; - hash = (53 * hash) + type_; - hash = (37 * hash) + TYPE_ATTR_FIELD_NUMBER; - hash = (53 * hash) + getTypeAttr().hashCode(); - hash = (37 * hash) + NUMBER_ATTR_FIELD_NUMBER; - hash = (53 * hash) + getNumberAttr().hashCode(); - hash = (37 * hash) + TYPE_LIST_ATTR_FIELD_NUMBER; - hash = (53 * hash) + getTypeListAttr().hashCode(); - hash = (37 * hash) + IS_REF_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getIsRef()); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.OpDef.ArgDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpDef.ArgDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpDef.ArgDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpDef.ArgDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpDef.ArgDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef.ArgDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpDef.ArgDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef.ArgDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpDef.ArgDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef.ArgDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.OpDef.ArgDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-     * For describing inputs and outputs.
-     *
- * - * Protobuf type {@code tensorflow.OpDef.ArgDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.OpDef.ArgDef) - org.tensorflow.framework.OpDef.ArgDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_ArgDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_ArgDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpDef.ArgDef.class, org.tensorflow.framework.OpDef.ArgDef.Builder.class); - } - - // Construct using org.tensorflow.framework.OpDef.ArgDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - name_ = ""; - - description_ = ""; - - type_ = 0; - - typeAttr_ = ""; - - numberAttr_ = ""; - - typeListAttr_ = ""; - - isRef_ = false; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_ArgDef_descriptor; - } - - public org.tensorflow.framework.OpDef.ArgDef getDefaultInstanceForType() { - return org.tensorflow.framework.OpDef.ArgDef.getDefaultInstance(); - } - - public org.tensorflow.framework.OpDef.ArgDef build() { - org.tensorflow.framework.OpDef.ArgDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.OpDef.ArgDef buildPartial() { - org.tensorflow.framework.OpDef.ArgDef result = new org.tensorflow.framework.OpDef.ArgDef(this); - result.name_ = name_; - result.description_ = description_; - result.type_ = type_; - result.typeAttr_ = typeAttr_; - result.numberAttr_ = numberAttr_; - result.typeListAttr_ = typeListAttr_; - result.isRef_ = isRef_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.OpDef.ArgDef) { - return mergeFrom((org.tensorflow.framework.OpDef.ArgDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.OpDef.ArgDef other) { - if (other == org.tensorflow.framework.OpDef.ArgDef.getDefaultInstance()) return this; - if (!other.getName().isEmpty()) { - name_ = other.name_; - onChanged(); - } - if (!other.getDescription().isEmpty()) { - description_ = other.description_; - onChanged(); - } - if (other.type_ != 0) { - setTypeValue(other.getTypeValue()); - } - if (!other.getTypeAttr().isEmpty()) { - typeAttr_ = other.typeAttr_; - onChanged(); - } - if (!other.getNumberAttr().isEmpty()) { - numberAttr_ = other.numberAttr_; - onChanged(); - } - if (!other.getTypeListAttr().isEmpty()) { - typeListAttr_ = other.typeListAttr_; - onChanged(); - } - if (other.getIsRef() != false) { - setIsRef(other.getIsRef()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.OpDef.ArgDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.OpDef.ArgDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private java.lang.Object name_ = ""; - /** - * 
-       * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-       *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-       *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-       *
- * - * optional string name = 1; - */ - public Builder setName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - name_ = value; - onChanged(); - return this; - } - /** - * 
-       * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-       *
- * - * optional string name = 1; - */ - public Builder clearName() { - - name_ = getDefaultInstance().getName(); - onChanged(); - return this; - } - /** - * 
-       * Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-       *
- * - * optional string name = 1; - */ - public Builder setNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - name_ = value; - onChanged(); - return this; - } - - private java.lang.Object description_ = ""; - /** - * 
-       * Human readable description.
-       *
- * - * optional string description = 2; - */ - public java.lang.String getDescription() { - java.lang.Object ref = description_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - description_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * Human readable description.
-       *
- * - * optional string description = 2; - */ - public com.google.protobuf.ByteString - getDescriptionBytes() { - java.lang.Object ref = description_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - description_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * Human readable description.
-       *
- * - * optional string description = 2; - */ - public Builder setDescription( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - description_ = value; - onChanged(); - return this; - } - /** - * 
-       * Human readable description.
-       *
- * - * optional string description = 2; - */ - public Builder clearDescription() { - - description_ = getDefaultInstance().getDescription(); - onChanged(); - return this; - } - /** - * 
-       * Human readable description.
-       *
- * - * optional string description = 2; - */ - public Builder setDescriptionBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - description_ = value; - onChanged(); - return this; - } - - private int type_ = 0; - /** - * 
-       * Describes the type of one or more tensors that are accepted/produced
-       * by this input/output arg.  The only legal combinations are:
-       * * For a single tensor: either the "type" field is set or the
-       *   "type_attr" field is set to the name of an attr with type "type".
-       * * For a sequence of tensors with the same type: the "number_attr"
-       *   field will be set to the name of an attr with type "int", and
-       *   either the "type" or "type_attr" field will be set as for
-       *   single tensors.
-       * * For a sequence of tensors, the "type_list_attr" field will be set
-       *   to the name of an attr with type "list(type)".
-       *
- * - * optional .tensorflow.DataType type = 3; - */ - public int getTypeValue() { - return type_; - } - /** - * 
-       * Describes the type of one or more tensors that are accepted/produced
-       * by this input/output arg.  The only legal combinations are:
-       * * For a single tensor: either the "type" field is set or the
-       *   "type_attr" field is set to the name of an attr with type "type".
-       * * For a sequence of tensors with the same type: the "number_attr"
-       *   field will be set to the name of an attr with type "int", and
-       *   either the "type" or "type_attr" field will be set as for
-       *   single tensors.
-       * * For a sequence of tensors, the "type_list_attr" field will be set
-       *   to the name of an attr with type "list(type)".
-       *
- * - * optional .tensorflow.DataType type = 3; - */ - public Builder setTypeValue(int value) { - type_ = value; - onChanged(); - return this; - } - /** - * 
-       * Describes the type of one or more tensors that are accepted/produced
-       * by this input/output arg.  The only legal combinations are:
-       * * For a single tensor: either the "type" field is set or the
-       *   "type_attr" field is set to the name of an attr with type "type".
-       * * For a sequence of tensors with the same type: the "number_attr"
-       *   field will be set to the name of an attr with type "int", and
-       *   either the "type" or "type_attr" field will be set as for
-       *   single tensors.
-       * * For a sequence of tensors, the "type_list_attr" field will be set
-       *   to the name of an attr with type "list(type)".
-       *
- * - * optional .tensorflow.DataType type = 3; - */ - public org.tensorflow.framework.DataType getType() { - org.tensorflow.framework.DataType result = org.tensorflow.framework.DataType.valueOf(type_); - return result == null ? org.tensorflow.framework.DataType.UNRECOGNIZED : result; - } - /** - * 
-       * Describes the type of one or more tensors that are accepted/produced
-       * by this input/output arg.  The only legal combinations are:
-       * * For a single tensor: either the "type" field is set or the
-       *   "type_attr" field is set to the name of an attr with type "type".
-       * * For a sequence of tensors with the same type: the "number_attr"
-       *   field will be set to the name of an attr with type "int", and
-       *   either the "type" or "type_attr" field will be set as for
-       *   single tensors.
-       * * For a sequence of tensors, the "type_list_attr" field will be set
-       *   to the name of an attr with type "list(type)".
-       *
- * - * optional .tensorflow.DataType type = 3; - */ - public Builder setType(org.tensorflow.framework.DataType value) { - if (value == null) { - throw new NullPointerException(); - } - - type_ = value.getNumber(); - onChanged(); - return this; - } - /** - * 
-       * Describes the type of one or more tensors that are accepted/produced
-       * by this input/output arg.  The only legal combinations are:
-       * * For a single tensor: either the "type" field is set or the
-       *   "type_attr" field is set to the name of an attr with type "type".
-       * * For a sequence of tensors with the same type: the "number_attr"
-       *   field will be set to the name of an attr with type "int", and
-       *   either the "type" or "type_attr" field will be set as for
-       *   single tensors.
-       * * For a sequence of tensors, the "type_list_attr" field will be set
-       *   to the name of an attr with type "list(type)".
-       *
- * - * optional .tensorflow.DataType type = 3; - */ - public Builder clearType() { - - type_ = 0; - onChanged(); - return this; - } - - private java.lang.Object typeAttr_ = ""; - /** - * 
-       * if specified, attr must have type "type"
-       *
- * - * optional string type_attr = 4; - */ - public java.lang.String getTypeAttr() { - java.lang.Object ref = typeAttr_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - typeAttr_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * if specified, attr must have type "type"
-       *
- * - * optional string type_attr = 4; - */ - public com.google.protobuf.ByteString - getTypeAttrBytes() { - java.lang.Object ref = typeAttr_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - typeAttr_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * if specified, attr must have type "type"
-       *
- * - * optional string type_attr = 4; - */ - public Builder setTypeAttr( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - typeAttr_ = value; - onChanged(); - return this; - } - /** - * 
-       * if specified, attr must have type "type"
-       *
- * - * optional string type_attr = 4; - */ - public Builder clearTypeAttr() { - - typeAttr_ = getDefaultInstance().getTypeAttr(); - onChanged(); - return this; - } - /** - * 
-       * if specified, attr must have type "type"
-       *
- * - * optional string type_attr = 4; - */ - public Builder setTypeAttrBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - typeAttr_ = value; - onChanged(); - return this; - } - - private java.lang.Object numberAttr_ = ""; - /** - * 
-       * if specified, attr must have type "int"
-       *
- * - * optional string number_attr = 5; - */ - public java.lang.String getNumberAttr() { - java.lang.Object ref = numberAttr_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - numberAttr_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * if specified, attr must have type "int"
-       *
- * - * optional string number_attr = 5; - */ - public com.google.protobuf.ByteString - getNumberAttrBytes() { - java.lang.Object ref = numberAttr_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - numberAttr_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * if specified, attr must have type "int"
-       *
- * - * optional string number_attr = 5; - */ - public Builder setNumberAttr( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - numberAttr_ = value; - onChanged(); - return this; - } - /** - * 
-       * if specified, attr must have type "int"
-       *
- * - * optional string number_attr = 5; - */ - public Builder clearNumberAttr() { - - numberAttr_ = getDefaultInstance().getNumberAttr(); - onChanged(); - return this; - } - /** - * 
-       * if specified, attr must have type "int"
-       *
- * - * optional string number_attr = 5; - */ - public Builder setNumberAttrBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - numberAttr_ = value; - onChanged(); - return this; - } - - private java.lang.Object typeListAttr_ = ""; - /** - * 
-       * If specified, attr must have type "list(type)", and none of
-       * type, type_attr, and number_attr may be specified.
-       *
- * - * optional string type_list_attr = 6; - */ - public java.lang.String getTypeListAttr() { - java.lang.Object ref = typeListAttr_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - typeListAttr_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * If specified, attr must have type "list(type)", and none of
-       * type, type_attr, and number_attr may be specified.
-       *
- * - * optional string type_list_attr = 6; - */ - public com.google.protobuf.ByteString - getTypeListAttrBytes() { - java.lang.Object ref = typeListAttr_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - typeListAttr_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * If specified, attr must have type "list(type)", and none of
-       * type, type_attr, and number_attr may be specified.
-       *
- * - * optional string type_list_attr = 6; - */ - public Builder setTypeListAttr( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - typeListAttr_ = value; - onChanged(); - return this; - } - /** - * 
-       * If specified, attr must have type "list(type)", and none of
-       * type, type_attr, and number_attr may be specified.
-       *
- * - * optional string type_list_attr = 6; - */ - public Builder clearTypeListAttr() { - - typeListAttr_ = getDefaultInstance().getTypeListAttr(); - onChanged(); - return this; - } - /** - * 
-       * If specified, attr must have type "list(type)", and none of
-       * type, type_attr, and number_attr may be specified.
-       *
- * - * optional string type_list_attr = 6; - */ - public Builder setTypeListAttrBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - typeListAttr_ = value; - onChanged(); - return this; - } - - private boolean isRef_ ; - /** - * 
-       * For inputs: if true, the inputs are required to be refs.
-       *   By default, inputs can be either refs or non-refs.
-       * For outputs: if true, outputs are refs, otherwise they are not.
-       *
- * - * optional bool is_ref = 16; - */ - public boolean getIsRef() { - return isRef_; - } - /** - * 
-       * For inputs: if true, the inputs are required to be refs.
-       *   By default, inputs can be either refs or non-refs.
-       * For outputs: if true, outputs are refs, otherwise they are not.
-       *
- * - * optional bool is_ref = 16; - */ - public Builder setIsRef(boolean value) { - - isRef_ = value; - onChanged(); - return this; - } - /** - * 
-       * For inputs: if true, the inputs are required to be refs.
-       *   By default, inputs can be either refs or non-refs.
-       * For outputs: if true, outputs are refs, otherwise they are not.
-       *
- * - * optional bool is_ref = 16; - */ - public Builder clearIsRef() { - - isRef_ = false; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.OpDef.ArgDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.OpDef.ArgDef) - private static final org.tensorflow.framework.OpDef.ArgDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.OpDef.ArgDef(); - } - - public static org.tensorflow.framework.OpDef.ArgDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public ArgDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new ArgDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.OpDef.ArgDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - - } - - public interface AttrDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.OpDef.AttrDef) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-     * A descriptive name for the argument.  May be used, e.g. by the
-     * Python client, as a keyword argument name, and so should match
-     * the regexp "[a-z][a-z0-9_]+".
-     *
- * - * optional string name = 1; - */ - java.lang.String getName(); - /** - * 
-     * A descriptive name for the argument.  May be used, e.g. by the
-     * Python client, as a keyword argument name, and so should match
-     * the regexp "[a-z][a-z0-9_]+".
-     *
- * - * optional string name = 1; - */ - com.google.protobuf.ByteString - getNameBytes(); - - /** - * 
-     * One of the type names from attr_value.proto ("string", "list(string)",
-     * "int", etc.).
-     *
- * - * optional string type = 2; - */ - java.lang.String getType(); - /** - * 
-     * One of the type names from attr_value.proto ("string", "list(string)",
-     * "int", etc.).
-     *
- * - * optional string type = 2; - */ - com.google.protobuf.ByteString - getTypeBytes(); - - /** - * 
-     * A reasonable default for this attribute if the user does not supply
-     * a value.  If not specified, the user must supply a value.
-     *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - boolean hasDefaultValue(); - /** - * 
-     * A reasonable default for this attribute if the user does not supply
-     * a value.  If not specified, the user must supply a value.
-     *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - org.tensorflow.framework.AttrValue getDefaultValue(); - /** - * 
-     * A reasonable default for this attribute if the user does not supply
-     * a value.  If not specified, the user must supply a value.
-     *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - org.tensorflow.framework.AttrValueOrBuilder getDefaultValueOrBuilder(); - - /** - * 
-     * Human-readable description.
-     *
- * - * optional string description = 4; - */ - java.lang.String getDescription(); - /** - * 
-     * Human-readable description.
-     *
- * - * optional string description = 4; - */ - com.google.protobuf.ByteString - getDescriptionBytes(); - - /** - * 
-     * For type == "int", this is a minimum value.  For "list(___)"
-     * types, this is the minimum length.
-     *
- * - * optional bool has_minimum = 5; - */ - boolean getHasMinimum(); - - /** - * optional int64 minimum = 6; - */ - long getMinimum(); - - /** - * 
-     * The set of allowed values.  Has type that is the "list" version
-     * of the "type" field above (uses the "list" field of AttrValue).
-     * If type == "type" or "list(type)" above, then the "type" field
-     * of "allowed_values.list" has the set of allowed DataTypes.
-     * If type == "string" or "list(string)", then the "s" field of
-     * "allowed_values.list" has the set of allowed strings.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - boolean hasAllowedValues(); - /** - * 
-     * The set of allowed values.  Has type that is the "list" version
-     * of the "type" field above (uses the "list" field of AttrValue).
-     * If type == "type" or "list(type)" above, then the "type" field
-     * of "allowed_values.list" has the set of allowed DataTypes.
-     * If type == "string" or "list(string)", then the "s" field of
-     * "allowed_values.list" has the set of allowed strings.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - org.tensorflow.framework.AttrValue getAllowedValues(); - /** - * 
-     * The set of allowed values.  Has type that is the "list" version
-     * of the "type" field above (uses the "list" field of AttrValue).
-     * If type == "type" or "list(type)" above, then the "type" field
-     * of "allowed_values.list" has the set of allowed DataTypes.
-     * If type == "string" or "list(string)", then the "s" field of
-     * "allowed_values.list" has the set of allowed strings.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - org.tensorflow.framework.AttrValueOrBuilder getAllowedValuesOrBuilder(); - } - /** - * 
-   * Description of the graph-construction-time configuration of this
-   * Op.  That is to say, this describes the attr fields that will
-   * be specified in the NodeDef.
-   *
- * - * Protobuf type {@code tensorflow.OpDef.AttrDef} - */ - public static final class AttrDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.OpDef.AttrDef) - AttrDefOrBuilder { - // Use AttrDef.newBuilder() to construct. - private AttrDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private AttrDef() { - name_ = ""; - type_ = ""; - description_ = ""; - hasMinimum_ = false; - minimum_ = 0L; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private AttrDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - name_ = s; - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - type_ = s; - break; - } - case 26: { - org.tensorflow.framework.AttrValue.Builder subBuilder = null; - if (defaultValue_ != null) { - subBuilder = defaultValue_.toBuilder(); - } - defaultValue_ = input.readMessage(org.tensorflow.framework.AttrValue.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(defaultValue_); - defaultValue_ = subBuilder.buildPartial(); - } - - break; - } - case 34: { - java.lang.String s = input.readStringRequireUtf8(); - - description_ = s; - break; - } - case 40: { - - hasMinimum_ = input.readBool(); - break; - } - case 48: { - - minimum_ = input.readInt64(); - break; - } - case 58: { - org.tensorflow.framework.AttrValue.Builder subBuilder = null; - if (allowedValues_ != null) { - subBuilder = allowedValues_.toBuilder(); - } - allowedValues_ = input.readMessage(org.tensorflow.framework.AttrValue.parser(), extensionRegistry); - if (subBuilder != null) { - subBuilder.mergeFrom(allowedValues_); - allowedValues_ = subBuilder.buildPartial(); - } - - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_AttrDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_AttrDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpDef.AttrDef.class, org.tensorflow.framework.OpDef.AttrDef.Builder.class); - } - - public static final int NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object name_; - /** - * 
-     * A descriptive name for the argument.  May be used, e.g. by the
-     * Python client, as a keyword argument name, and so should match
-     * the regexp "[a-z][a-z0-9_]+".
-     *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } - } - /** - * 
-     * A descriptive name for the argument.  May be used, e.g. by the
-     * Python client, as a keyword argument name, and so should match
-     * the regexp "[a-z][a-z0-9_]+".
-     *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int TYPE_FIELD_NUMBER = 2; - private volatile java.lang.Object type_; - /** - * 
-     * One of the type names from attr_value.proto ("string", "list(string)",
-     * "int", etc.).
-     *
- * - * optional string type = 2; - */ - public java.lang.String getType() { - java.lang.Object ref = type_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - type_ = s; - return s; - } - } - /** - * 
-     * One of the type names from attr_value.proto ("string", "list(string)",
-     * "int", etc.).
-     *
- * - * optional string type = 2; - */ - public com.google.protobuf.ByteString - getTypeBytes() { - java.lang.Object ref = type_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - type_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int DEFAULT_VALUE_FIELD_NUMBER = 3; - private org.tensorflow.framework.AttrValue defaultValue_; - /** - * 
-     * A reasonable default for this attribute if the user does not supply
-     * a value.  If not specified, the user must supply a value.
-     *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public boolean hasDefaultValue() { - return defaultValue_ != null; - } - /** - * 
-     * A reasonable default for this attribute if the user does not supply
-     * a value.  If not specified, the user must supply a value.
-     *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public org.tensorflow.framework.AttrValue getDefaultValue() { - return defaultValue_ == null ? org.tensorflow.framework.AttrValue.getDefaultInstance() : defaultValue_; - } - /** - * 
-     * A reasonable default for this attribute if the user does not supply
-     * a value.  If not specified, the user must supply a value.
-     *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public org.tensorflow.framework.AttrValueOrBuilder getDefaultValueOrBuilder() { - return getDefaultValue(); - } - - public static final int DESCRIPTION_FIELD_NUMBER = 4; - private volatile java.lang.Object description_; - /** - * 
-     * Human-readable description.
-     *
- * - * optional string description = 4; - */ - public java.lang.String getDescription() { - java.lang.Object ref = description_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - description_ = s; - return s; - } - } - /** - * 
-     * Human-readable description.
-     *
- * - * optional string description = 4; - */ - public com.google.protobuf.ByteString - getDescriptionBytes() { - java.lang.Object ref = description_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - description_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int HAS_MINIMUM_FIELD_NUMBER = 5; - private boolean hasMinimum_; - /** - * 
-     * For type == "int", this is a minimum value.  For "list(___)"
-     * types, this is the minimum length.
-     *
- * - * optional bool has_minimum = 5; - */ - public boolean getHasMinimum() { - return hasMinimum_; - } - - public static final int MINIMUM_FIELD_NUMBER = 6; - private long minimum_; - /** - * optional int64 minimum = 6; - */ - public long getMinimum() { - return minimum_; - } - - public static final int ALLOWED_VALUES_FIELD_NUMBER = 7; - private org.tensorflow.framework.AttrValue allowedValues_; - /** - * 
-     * The set of allowed values.  Has type that is the "list" version
-     * of the "type" field above (uses the "list" field of AttrValue).
-     * If type == "type" or "list(type)" above, then the "type" field
-     * of "allowed_values.list" has the set of allowed DataTypes.
-     * If type == "string" or "list(string)", then the "s" field of
-     * "allowed_values.list" has the set of allowed strings.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public boolean hasAllowedValues() { - return allowedValues_ != null; - } - /** - * 
-     * The set of allowed values.  Has type that is the "list" version
-     * of the "type" field above (uses the "list" field of AttrValue).
-     * If type == "type" or "list(type)" above, then the "type" field
-     * of "allowed_values.list" has the set of allowed DataTypes.
-     * If type == "string" or "list(string)", then the "s" field of
-     * "allowed_values.list" has the set of allowed strings.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public org.tensorflow.framework.AttrValue getAllowedValues() { - return allowedValues_ == null ? org.tensorflow.framework.AttrValue.getDefaultInstance() : allowedValues_; - } - /** - * 
-     * The set of allowed values.  Has type that is the "list" version
-     * of the "type" field above (uses the "list" field of AttrValue).
-     * If type == "type" or "list(type)" above, then the "type" field
-     * of "allowed_values.list" has the set of allowed DataTypes.
-     * If type == "string" or "list(string)", then the "s" field of
-     * "allowed_values.list" has the set of allowed strings.
-     *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public org.tensorflow.framework.AttrValueOrBuilder getAllowedValuesOrBuilder() { - return getAllowedValues(); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_); - } - if (!getTypeBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, type_); - } - if (defaultValue_ != null) { - output.writeMessage(3, getDefaultValue()); - } - if (!getDescriptionBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 4, description_); - } - if (hasMinimum_ != false) { - output.writeBool(5, hasMinimum_); - } - if (minimum_ != 0L) { - output.writeInt64(6, minimum_); - } - if (allowedValues_ != null) { - output.writeMessage(7, getAllowedValues()); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_); - } - if (!getTypeBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, type_); - } - if (defaultValue_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(3, getDefaultValue()); - } - if (!getDescriptionBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(4, description_); - } - if (hasMinimum_ != false) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize(5, hasMinimum_); - } - if (minimum_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(6, minimum_); - } - if (allowedValues_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(7, getAllowedValues()); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.OpDef.AttrDef)) { - return super.equals(obj); - } - org.tensorflow.framework.OpDef.AttrDef other = (org.tensorflow.framework.OpDef.AttrDef) obj; - - boolean result = true; - result = result && getName() - .equals(other.getName()); - result = result && getType() - .equals(other.getType()); - result = result && (hasDefaultValue() == other.hasDefaultValue()); - if (hasDefaultValue()) { - result = result && getDefaultValue() - .equals(other.getDefaultValue()); - } - result = result && getDescription() - .equals(other.getDescription()); - result = result && (getHasMinimum() - == other.getHasMinimum()); - result = result && (getMinimum() - == other.getMinimum()); - result = result && (hasAllowedValues() == other.hasAllowedValues()); - if (hasAllowedValues()) { - result = result && getAllowedValues() - .equals(other.getAllowedValues()); - } - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + NAME_FIELD_NUMBER; - hash = (53 * hash) + getName().hashCode(); - hash = (37 * hash) + TYPE_FIELD_NUMBER; - hash = (53 * hash) + getType().hashCode(); - if (hasDefaultValue()) { - hash = (37 * hash) + DEFAULT_VALUE_FIELD_NUMBER; - hash = (53 * hash) + getDefaultValue().hashCode(); - } - hash = (37 * hash) + DESCRIPTION_FIELD_NUMBER; - hash = (53 * hash) + getDescription().hashCode(); - hash = (37 * hash) + HAS_MINIMUM_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getHasMinimum()); - hash = (37 * hash) + MINIMUM_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getMinimum()); - if (hasAllowedValues()) { - hash = (37 * hash) + ALLOWED_VALUES_FIELD_NUMBER; - hash = (53 * hash) + getAllowedValues().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.OpDef.AttrDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpDef.AttrDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpDef.AttrDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpDef.AttrDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpDef.AttrDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef.AttrDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpDef.AttrDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef.AttrDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpDef.AttrDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef.AttrDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.OpDef.AttrDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-     * Description of the graph-construction-time configuration of this
-     * Op.  That is to say, this describes the attr fields that will
-     * be specified in the NodeDef.
-     *
- * - * Protobuf type {@code tensorflow.OpDef.AttrDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.OpDef.AttrDef) - org.tensorflow.framework.OpDef.AttrDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_AttrDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_AttrDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpDef.AttrDef.class, org.tensorflow.framework.OpDef.AttrDef.Builder.class); - } - - // Construct using org.tensorflow.framework.OpDef.AttrDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - name_ = ""; - - type_ = ""; - - if (defaultValueBuilder_ == null) { - defaultValue_ = null; - } else { - defaultValue_ = null; - defaultValueBuilder_ = null; - } - description_ = ""; - - hasMinimum_ = false; - - minimum_ = 0L; - - if (allowedValuesBuilder_ == null) { - allowedValues_ = null; - } else { - allowedValues_ = null; - allowedValuesBuilder_ = null; - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_AttrDef_descriptor; - } - - public org.tensorflow.framework.OpDef.AttrDef getDefaultInstanceForType() { - return org.tensorflow.framework.OpDef.AttrDef.getDefaultInstance(); - } - - public org.tensorflow.framework.OpDef.AttrDef build() { - org.tensorflow.framework.OpDef.AttrDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.OpDef.AttrDef buildPartial() { - org.tensorflow.framework.OpDef.AttrDef result = new org.tensorflow.framework.OpDef.AttrDef(this); - result.name_ = name_; - result.type_ = type_; - if (defaultValueBuilder_ == null) { - result.defaultValue_ = defaultValue_; - } else { - result.defaultValue_ = defaultValueBuilder_.build(); - } - result.description_ = description_; - result.hasMinimum_ = hasMinimum_; - result.minimum_ = minimum_; - if (allowedValuesBuilder_ == null) { - result.allowedValues_ = allowedValues_; - } else { - result.allowedValues_ = allowedValuesBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.OpDef.AttrDef) { - return mergeFrom((org.tensorflow.framework.OpDef.AttrDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.OpDef.AttrDef other) { - if (other == org.tensorflow.framework.OpDef.AttrDef.getDefaultInstance()) return this; - if (!other.getName().isEmpty()) { - name_ = other.name_; - onChanged(); - } - if (!other.getType().isEmpty()) { - type_ = other.type_; - onChanged(); - } - if (other.hasDefaultValue()) { - mergeDefaultValue(other.getDefaultValue()); - } - if (!other.getDescription().isEmpty()) { - description_ = other.description_; - onChanged(); - } - if (other.getHasMinimum() != false) { - setHasMinimum(other.getHasMinimum()); - } - if (other.getMinimum() != 0L) { - setMinimum(other.getMinimum()); - } - if (other.hasAllowedValues()) { - mergeAllowedValues(other.getAllowedValues()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.OpDef.AttrDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.OpDef.AttrDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private java.lang.Object name_ = ""; - /** - * 
-       * A descriptive name for the argument.  May be used, e.g. by the
-       * Python client, as a keyword argument name, and so should match
-       * the regexp "[a-z][a-z0-9_]+".
-       *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * A descriptive name for the argument.  May be used, e.g. by the
-       * Python client, as a keyword argument name, and so should match
-       * the regexp "[a-z][a-z0-9_]+".
-       *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * A descriptive name for the argument.  May be used, e.g. by the
-       * Python client, as a keyword argument name, and so should match
-       * the regexp "[a-z][a-z0-9_]+".
-       *
- * - * optional string name = 1; - */ - public Builder setName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - name_ = value; - onChanged(); - return this; - } - /** - * 
-       * A descriptive name for the argument.  May be used, e.g. by the
-       * Python client, as a keyword argument name, and so should match
-       * the regexp "[a-z][a-z0-9_]+".
-       *
- * - * optional string name = 1; - */ - public Builder clearName() { - - name_ = getDefaultInstance().getName(); - onChanged(); - return this; - } - /** - * 
-       * A descriptive name for the argument.  May be used, e.g. by the
-       * Python client, as a keyword argument name, and so should match
-       * the regexp "[a-z][a-z0-9_]+".
-       *
- * - * optional string name = 1; - */ - public Builder setNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - name_ = value; - onChanged(); - return this; - } - - private java.lang.Object type_ = ""; - /** - * 
-       * One of the type names from attr_value.proto ("string", "list(string)",
-       * "int", etc.).
-       *
- * - * optional string type = 2; - */ - public java.lang.String getType() { - java.lang.Object ref = type_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - type_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * One of the type names from attr_value.proto ("string", "list(string)",
-       * "int", etc.).
-       *
- * - * optional string type = 2; - */ - public com.google.protobuf.ByteString - getTypeBytes() { - java.lang.Object ref = type_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - type_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * One of the type names from attr_value.proto ("string", "list(string)",
-       * "int", etc.).
-       *
- * - * optional string type = 2; - */ - public Builder setType( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - type_ = value; - onChanged(); - return this; - } - /** - * 
-       * One of the type names from attr_value.proto ("string", "list(string)",
-       * "int", etc.).
-       *
- * - * optional string type = 2; - */ - public Builder clearType() { - - type_ = getDefaultInstance().getType(); - onChanged(); - return this; - } - /** - * 
-       * One of the type names from attr_value.proto ("string", "list(string)",
-       * "int", etc.).
-       *
- * - * optional string type = 2; - */ - public Builder setTypeBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - type_ = value; - onChanged(); - return this; - } - - private org.tensorflow.framework.AttrValue defaultValue_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder> defaultValueBuilder_; - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public boolean hasDefaultValue() { - return defaultValueBuilder_ != null || defaultValue_ != null; - } - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public org.tensorflow.framework.AttrValue getDefaultValue() { - if (defaultValueBuilder_ == null) { - return defaultValue_ == null ? org.tensorflow.framework.AttrValue.getDefaultInstance() : defaultValue_; - } else { - return defaultValueBuilder_.getMessage(); - } - } - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public Builder setDefaultValue(org.tensorflow.framework.AttrValue value) { - if (defaultValueBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - defaultValue_ = value; - onChanged(); - } else { - defaultValueBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public Builder setDefaultValue( - org.tensorflow.framework.AttrValue.Builder builderForValue) { - if (defaultValueBuilder_ == null) { - defaultValue_ = builderForValue.build(); - onChanged(); - } else { - defaultValueBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public Builder mergeDefaultValue(org.tensorflow.framework.AttrValue value) { - if (defaultValueBuilder_ == null) { - if (defaultValue_ != null) { - defaultValue_ = - org.tensorflow.framework.AttrValue.newBuilder(defaultValue_).mergeFrom(value).buildPartial(); - } else { - defaultValue_ = value; - } - onChanged(); - } else { - defaultValueBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public Builder clearDefaultValue() { - if (defaultValueBuilder_ == null) { - defaultValue_ = null; - onChanged(); - } else { - defaultValue_ = null; - defaultValueBuilder_ = null; - } - - return this; - } - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public org.tensorflow.framework.AttrValue.Builder getDefaultValueBuilder() { - - onChanged(); - return getDefaultValueFieldBuilder().getBuilder(); - } - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - public org.tensorflow.framework.AttrValueOrBuilder getDefaultValueOrBuilder() { - if (defaultValueBuilder_ != null) { - return defaultValueBuilder_.getMessageOrBuilder(); - } else { - return defaultValue_ == null ? - org.tensorflow.framework.AttrValue.getDefaultInstance() : defaultValue_; - } - } - /** - * 
-       * A reasonable default for this attribute if the user does not supply
-       * a value.  If not specified, the user must supply a value.
-       *
- * - * optional .tensorflow.AttrValue default_value = 3; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder> - getDefaultValueFieldBuilder() { - if (defaultValueBuilder_ == null) { - defaultValueBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder>( - getDefaultValue(), - getParentForChildren(), - isClean()); - defaultValue_ = null; - } - return defaultValueBuilder_; - } - - private java.lang.Object description_ = ""; - /** - * 
-       * Human-readable description.
-       *
- * - * optional string description = 4; - */ - public java.lang.String getDescription() { - java.lang.Object ref = description_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - description_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-       * Human-readable description.
-       *
- * - * optional string description = 4; - */ - public com.google.protobuf.ByteString - getDescriptionBytes() { - java.lang.Object ref = description_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - description_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-       * Human-readable description.
-       *
- * - * optional string description = 4; - */ - public Builder setDescription( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - description_ = value; - onChanged(); - return this; - } - /** - * 
-       * Human-readable description.
-       *
- * - * optional string description = 4; - */ - public Builder clearDescription() { - - description_ = getDefaultInstance().getDescription(); - onChanged(); - return this; - } - /** - * 
-       * Human-readable description.
-       *
- * - * optional string description = 4; - */ - public Builder setDescriptionBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - description_ = value; - onChanged(); - return this; - } - - private boolean hasMinimum_ ; - /** - * 
-       * For type == "int", this is a minimum value.  For "list(___)"
-       * types, this is the minimum length.
-       *
- * - * optional bool has_minimum = 5; - */ - public boolean getHasMinimum() { - return hasMinimum_; - } - /** - * 
-       * For type == "int", this is a minimum value.  For "list(___)"
-       * types, this is the minimum length.
-       *
- * - * optional bool has_minimum = 5; - */ - public Builder setHasMinimum(boolean value) { - - hasMinimum_ = value; - onChanged(); - return this; - } - /** - * 
-       * For type == "int", this is a minimum value.  For "list(___)"
-       * types, this is the minimum length.
-       *
- * - * optional bool has_minimum = 5; - */ - public Builder clearHasMinimum() { - - hasMinimum_ = false; - onChanged(); - return this; - } - - private long minimum_ ; - /** - * optional int64 minimum = 6; - */ - public long getMinimum() { - return minimum_; - } - /** - * optional int64 minimum = 6; - */ - public Builder setMinimum(long value) { - - minimum_ = value; - onChanged(); - return this; - } - /** - * optional int64 minimum = 6; - */ - public Builder clearMinimum() { - - minimum_ = 0L; - onChanged(); - return this; - } - - private org.tensorflow.framework.AttrValue allowedValues_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder> allowedValuesBuilder_; - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public boolean hasAllowedValues() { - return allowedValuesBuilder_ != null || allowedValues_ != null; - } - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public org.tensorflow.framework.AttrValue getAllowedValues() { - if (allowedValuesBuilder_ == null) { - return allowedValues_ == null ? org.tensorflow.framework.AttrValue.getDefaultInstance() : allowedValues_; - } else { - return allowedValuesBuilder_.getMessage(); - } - } - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public Builder setAllowedValues(org.tensorflow.framework.AttrValue value) { - if (allowedValuesBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - allowedValues_ = value; - onChanged(); - } else { - allowedValuesBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public Builder setAllowedValues( - org.tensorflow.framework.AttrValue.Builder builderForValue) { - if (allowedValuesBuilder_ == null) { - allowedValues_ = builderForValue.build(); - onChanged(); - } else { - allowedValuesBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public Builder mergeAllowedValues(org.tensorflow.framework.AttrValue value) { - if (allowedValuesBuilder_ == null) { - if (allowedValues_ != null) { - allowedValues_ = - org.tensorflow.framework.AttrValue.newBuilder(allowedValues_).mergeFrom(value).buildPartial(); - } else { - allowedValues_ = value; - } - onChanged(); - } else { - allowedValuesBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public Builder clearAllowedValues() { - if (allowedValuesBuilder_ == null) { - allowedValues_ = null; - onChanged(); - } else { - allowedValues_ = null; - allowedValuesBuilder_ = null; - } - - return this; - } - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public org.tensorflow.framework.AttrValue.Builder getAllowedValuesBuilder() { - - onChanged(); - return getAllowedValuesFieldBuilder().getBuilder(); - } - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - public org.tensorflow.framework.AttrValueOrBuilder getAllowedValuesOrBuilder() { - if (allowedValuesBuilder_ != null) { - return allowedValuesBuilder_.getMessageOrBuilder(); - } else { - return allowedValues_ == null ? - org.tensorflow.framework.AttrValue.getDefaultInstance() : allowedValues_; - } - } - /** - * 
-       * The set of allowed values.  Has type that is the "list" version
-       * of the "type" field above (uses the "list" field of AttrValue).
-       * If type == "type" or "list(type)" above, then the "type" field
-       * of "allowed_values.list" has the set of allowed DataTypes.
-       * If type == "string" or "list(string)", then the "s" field of
-       * "allowed_values.list" has the set of allowed strings.
-       *
- * - * optional .tensorflow.AttrValue allowed_values = 7; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder> - getAllowedValuesFieldBuilder() { - if (allowedValuesBuilder_ == null) { - allowedValuesBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.AttrValue, org.tensorflow.framework.AttrValue.Builder, org.tensorflow.framework.AttrValueOrBuilder>( - getAllowedValues(), - getParentForChildren(), - isClean()); - allowedValues_ = null; - } - return allowedValuesBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.OpDef.AttrDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.OpDef.AttrDef) - private static final org.tensorflow.framework.OpDef.AttrDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.OpDef.AttrDef(); - } - - public static org.tensorflow.framework.OpDef.AttrDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public AttrDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new AttrDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.OpDef.AttrDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - - } - - private int bitField0_; - public static final int NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object name_; - /** - * 
-   * Op names starting with an underscore are reserved for internal use.
-   * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-   *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } - } - /** - * 
-   * Op names starting with an underscore are reserved for internal use.
-   * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-   *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int INPUT_ARG_FIELD_NUMBER = 2; - private java.util.List inputArg_; - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public java.util.List getInputArgList() { - return inputArg_; - } - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public java.util.List - getInputArgOrBuilderList() { - return inputArg_; - } - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public int getInputArgCount() { - return inputArg_.size(); - } - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public org.tensorflow.framework.OpDef.ArgDef getInputArg(int index) { - return inputArg_.get(index); - } - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public org.tensorflow.framework.OpDef.ArgDefOrBuilder getInputArgOrBuilder( - int index) { - return inputArg_.get(index); - } - - public static final int OUTPUT_ARG_FIELD_NUMBER = 3; - private java.util.List outputArg_; - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public java.util.List getOutputArgList() { - return outputArg_; - } - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public java.util.List - getOutputArgOrBuilderList() { - return outputArg_; - } - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public int getOutputArgCount() { - return outputArg_.size(); - } - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public org.tensorflow.framework.OpDef.ArgDef getOutputArg(int index) { - return outputArg_.get(index); - } - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public org.tensorflow.framework.OpDef.ArgDefOrBuilder getOutputArgOrBuilder( - int index) { - return outputArg_.get(index); - } - - public static final int ATTR_FIELD_NUMBER = 4; - private java.util.List attr_; - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public java.util.List getAttrList() { - return attr_; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public java.util.List - getAttrOrBuilderList() { - return attr_; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public int getAttrCount() { - return attr_.size(); - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public org.tensorflow.framework.OpDef.AttrDef getAttr(int index) { - return attr_.get(index); - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public org.tensorflow.framework.OpDef.AttrDefOrBuilder getAttrOrBuilder( - int index) { - return attr_.get(index); - } - - public static final int DEPRECATION_FIELD_NUMBER = 8; - private org.tensorflow.framework.OpDeprecation deprecation_; - /** - * 
-   * Optional deprecation based on GraphDef versions.
-   *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public boolean hasDeprecation() { - return deprecation_ != null; - } - /** - * 
-   * Optional deprecation based on GraphDef versions.
-   *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public org.tensorflow.framework.OpDeprecation getDeprecation() { - return deprecation_ == null ? org.tensorflow.framework.OpDeprecation.getDefaultInstance() : deprecation_; - } - /** - * 
-   * Optional deprecation based on GraphDef versions.
-   *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public org.tensorflow.framework.OpDeprecationOrBuilder getDeprecationOrBuilder() { - return getDeprecation(); - } - - public static final int SUMMARY_FIELD_NUMBER = 5; - private volatile java.lang.Object summary_; - /** - * 
-   * One-line human-readable description of what the Op does.
-   *
- * - * optional string summary = 5; - */ - public java.lang.String getSummary() { - java.lang.Object ref = summary_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - summary_ = s; - return s; - } - } - /** - * 
-   * One-line human-readable description of what the Op does.
-   *
- * - * optional string summary = 5; - */ - public com.google.protobuf.ByteString - getSummaryBytes() { - java.lang.Object ref = summary_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - summary_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int DESCRIPTION_FIELD_NUMBER = 6; - private volatile java.lang.Object description_; - /** - * 
-   * Additional, longer human-readable description of what the Op does.
-   *
- * - * optional string description = 6; - */ - public java.lang.String getDescription() { - java.lang.Object ref = description_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - description_ = s; - return s; - } - } - /** - * 
-   * Additional, longer human-readable description of what the Op does.
-   *
- * - * optional string description = 6; - */ - public com.google.protobuf.ByteString - getDescriptionBytes() { - java.lang.Object ref = description_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - description_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int IS_COMMUTATIVE_FIELD_NUMBER = 18; - private boolean isCommutative_; - /** - * 
-   * True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-   *
- * - * optional bool is_commutative = 18; - */ - public boolean getIsCommutative() { - return isCommutative_; - } - - public static final int IS_AGGREGATE_FIELD_NUMBER = 16; - private boolean isAggregate_; - /** - * 
-   * If is_aggregate is true, then this operation accepts N >= 2
-   * inputs and produces 1 output all of the same type.  Should be
-   * associative and commutative, and produce output with the same
-   * shape as the input.  The optimizer may replace an aggregate op
-   * taking input from multiple devices with a tree of aggregate ops
-   * that aggregate locally within each device (and possibly within
-   * groups of nearby devices) before communicating.
-   * TODO(josh11b): Implement that optimization.
-   *
- * - * optional bool is_aggregate = 16; - */ - public boolean getIsAggregate() { - return isAggregate_; - } - - public static final int IS_STATEFUL_FIELD_NUMBER = 17; - private boolean isStateful_; - /** - * 
-   * By default Ops may be moved between devices.  Stateful ops should
-   * either not be moved, or should only be moved if that state can also
-   * be moved (e.g. via some sort of save / restore).
-   * Stateful ops are guaranteed to never be optimized away by Common
-   * Subexpression Elimination (CSE).
-   *
- * - * optional bool is_stateful = 17; - */ - public boolean getIsStateful() { - return isStateful_; - } - - public static final int ALLOWS_UNINITIALIZED_INPUT_FIELD_NUMBER = 19; - private boolean allowsUninitializedInput_; - /** - * 
-   * By default, all inputs to an Op must be initialized Tensors.  Ops
-   * that may initialize tensors for the first time should set this
-   * field to true, to allow the Op to take an uninitialized Tensor as
-   * input.
-   *
- * - * optional bool allows_uninitialized_input = 19; - */ - public boolean getAllowsUninitializedInput() { - return allowsUninitializedInput_; - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_); - } - for (int i = 0; i < inputArg_.size(); i++) { - output.writeMessage(2, inputArg_.get(i)); - } - for (int i = 0; i < outputArg_.size(); i++) { - output.writeMessage(3, outputArg_.get(i)); - } - for (int i = 0; i < attr_.size(); i++) { - output.writeMessage(4, attr_.get(i)); - } - if (!getSummaryBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 5, summary_); - } - if (!getDescriptionBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 6, description_); - } - if (deprecation_ != null) { - output.writeMessage(8, getDeprecation()); - } - if (isAggregate_ != false) { - output.writeBool(16, isAggregate_); - } - if (isStateful_ != false) { - output.writeBool(17, isStateful_); - } - if (isCommutative_ != false) { - output.writeBool(18, isCommutative_); - } - if (allowsUninitializedInput_ != false) { - output.writeBool(19, allowsUninitializedInput_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_); - } - for (int i = 0; i < inputArg_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(2, inputArg_.get(i)); - } - for (int i = 0; i < outputArg_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(3, outputArg_.get(i)); - } - for (int i = 0; i < attr_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(4, attr_.get(i)); - } - if (!getSummaryBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(5, summary_); - } - if (!getDescriptionBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(6, description_); - } - if (deprecation_ != null) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(8, getDeprecation()); - } - if (isAggregate_ != false) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize(16, isAggregate_); - } - if (isStateful_ != false) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize(17, isStateful_); - } - if (isCommutative_ != false) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize(18, isCommutative_); - } - if (allowsUninitializedInput_ != false) { - size += com.google.protobuf.CodedOutputStream - .computeBoolSize(19, allowsUninitializedInput_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.OpDef)) { - return super.equals(obj); - } - org.tensorflow.framework.OpDef other = (org.tensorflow.framework.OpDef) obj; - - boolean result = true; - result = result && getName() - .equals(other.getName()); - result = result && getInputArgList() - .equals(other.getInputArgList()); - result = result && getOutputArgList() - .equals(other.getOutputArgList()); - result = result && getAttrList() - .equals(other.getAttrList()); - result = result && (hasDeprecation() == other.hasDeprecation()); - if (hasDeprecation()) { - result = result && getDeprecation() - .equals(other.getDeprecation()); - } - result = result && getSummary() - .equals(other.getSummary()); - result = result && getDescription() - .equals(other.getDescription()); - result = result && (getIsCommutative() - == other.getIsCommutative()); - result = result && (getIsAggregate() - == other.getIsAggregate()); - result = result && (getIsStateful() - == other.getIsStateful()); - result = result && (getAllowsUninitializedInput() - == other.getAllowsUninitializedInput()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + NAME_FIELD_NUMBER; - hash = (53 * hash) + getName().hashCode(); - if (getInputArgCount() > 0) { - hash = (37 * hash) + INPUT_ARG_FIELD_NUMBER; - hash = (53 * hash) + getInputArgList().hashCode(); - } - if (getOutputArgCount() > 0) { - hash = (37 * hash) + OUTPUT_ARG_FIELD_NUMBER; - hash = (53 * hash) + getOutputArgList().hashCode(); - } - if (getAttrCount() > 0) { - hash = (37 * hash) + ATTR_FIELD_NUMBER; - hash = (53 * hash) + getAttrList().hashCode(); - } - if (hasDeprecation()) { - hash = (37 * hash) + DEPRECATION_FIELD_NUMBER; - hash = (53 * hash) + getDeprecation().hashCode(); - } - hash = (37 * hash) + SUMMARY_FIELD_NUMBER; - hash = (53 * hash) + getSummary().hashCode(); - hash = (37 * hash) + DESCRIPTION_FIELD_NUMBER; - hash = (53 * hash) + getDescription().hashCode(); - hash = (37 * hash) + IS_COMMUTATIVE_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getIsCommutative()); - hash = (37 * hash) + IS_AGGREGATE_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getIsAggregate()); - hash = (37 * hash) + IS_STATEFUL_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getIsStateful()); - hash = (37 * hash) + ALLOWS_UNINITIALIZED_INPUT_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean( - getAllowsUninitializedInput()); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.OpDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.OpDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Defines an operation. A NodeDef in a GraphDef specifies an Op by
-   * using the "op" field which should match the name of a OpDef.
-   *
- * - * Protobuf type {@code tensorflow.OpDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.OpDef) - org.tensorflow.framework.OpDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpDef.class, org.tensorflow.framework.OpDef.Builder.class); - } - - // Construct using org.tensorflow.framework.OpDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getInputArgFieldBuilder(); - getOutputArgFieldBuilder(); - getAttrFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - name_ = ""; - - if (inputArgBuilder_ == null) { - inputArg_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - } else { - inputArgBuilder_.clear(); - } - if (outputArgBuilder_ == null) { - outputArg_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - } else { - outputArgBuilder_.clear(); - } - if (attrBuilder_ == null) { - attr_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000008); - } else { - attrBuilder_.clear(); - } - if (deprecationBuilder_ == null) { - deprecation_ = null; - } else { - deprecation_ = null; - deprecationBuilder_ = null; - } - summary_ = ""; - - description_ = ""; - - isCommutative_ = false; - - isAggregate_ = false; - - isStateful_ = false; - - allowsUninitializedInput_ = false; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDef_descriptor; - } - - public org.tensorflow.framework.OpDef getDefaultInstanceForType() { - return org.tensorflow.framework.OpDef.getDefaultInstance(); - } - - public org.tensorflow.framework.OpDef build() { - org.tensorflow.framework.OpDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.OpDef buildPartial() { - org.tensorflow.framework.OpDef result = new org.tensorflow.framework.OpDef(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - result.name_ = name_; - if (inputArgBuilder_ == null) { - if (((bitField0_ & 0x00000002) == 0x00000002)) { - inputArg_ = java.util.Collections.unmodifiableList(inputArg_); - bitField0_ = (bitField0_ & ~0x00000002); - } - result.inputArg_ = inputArg_; - } else { - result.inputArg_ = inputArgBuilder_.build(); - } - if (outputArgBuilder_ == null) { - if (((bitField0_ & 0x00000004) == 0x00000004)) { - outputArg_ = java.util.Collections.unmodifiableList(outputArg_); - bitField0_ = (bitField0_ & ~0x00000004); - } - result.outputArg_ = outputArg_; - } else { - result.outputArg_ = outputArgBuilder_.build(); - } - if (attrBuilder_ == null) { - if (((bitField0_ & 0x00000008) == 0x00000008)) { - attr_ = java.util.Collections.unmodifiableList(attr_); - bitField0_ = (bitField0_ & ~0x00000008); - } - result.attr_ = attr_; - } else { - result.attr_ = attrBuilder_.build(); - } - if (deprecationBuilder_ == null) { - result.deprecation_ = deprecation_; - } else { - result.deprecation_ = deprecationBuilder_.build(); - } - result.summary_ = summary_; - result.description_ = description_; - result.isCommutative_ = isCommutative_; - result.isAggregate_ = isAggregate_; - result.isStateful_ = isStateful_; - result.allowsUninitializedInput_ = allowsUninitializedInput_; - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.OpDef) { - return mergeFrom((org.tensorflow.framework.OpDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.OpDef other) { - if (other == org.tensorflow.framework.OpDef.getDefaultInstance()) return this; - if (!other.getName().isEmpty()) { - name_ = other.name_; - onChanged(); - } - if (inputArgBuilder_ == null) { - if (!other.inputArg_.isEmpty()) { - if (inputArg_.isEmpty()) { - inputArg_ = other.inputArg_; - bitField0_ = (bitField0_ & ~0x00000002); - } else { - ensureInputArgIsMutable(); - inputArg_.addAll(other.inputArg_); - } - onChanged(); - } - } else { - if (!other.inputArg_.isEmpty()) { - if (inputArgBuilder_.isEmpty()) { - inputArgBuilder_.dispose(); - inputArgBuilder_ = null; - inputArg_ = other.inputArg_; - bitField0_ = (bitField0_ & ~0x00000002); - inputArgBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getInputArgFieldBuilder() : null; - } else { - inputArgBuilder_.addAllMessages(other.inputArg_); - } - } - } - if (outputArgBuilder_ == null) { - if (!other.outputArg_.isEmpty()) { - if (outputArg_.isEmpty()) { - outputArg_ = other.outputArg_; - bitField0_ = (bitField0_ & ~0x00000004); - } else { - ensureOutputArgIsMutable(); - outputArg_.addAll(other.outputArg_); - } - onChanged(); - } - } else { - if (!other.outputArg_.isEmpty()) { - if (outputArgBuilder_.isEmpty()) { - outputArgBuilder_.dispose(); - outputArgBuilder_ = null; - outputArg_ = other.outputArg_; - bitField0_ = (bitField0_ & ~0x00000004); - outputArgBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getOutputArgFieldBuilder() : null; - } else { - outputArgBuilder_.addAllMessages(other.outputArg_); - } - } - } - if (attrBuilder_ == null) { - if (!other.attr_.isEmpty()) { - if (attr_.isEmpty()) { - attr_ = other.attr_; - bitField0_ = (bitField0_ & ~0x00000008); - } else { - ensureAttrIsMutable(); - attr_.addAll(other.attr_); - } - onChanged(); - } - } else { - if (!other.attr_.isEmpty()) { - if (attrBuilder_.isEmpty()) { - attrBuilder_.dispose(); - attrBuilder_ = null; - attr_ = other.attr_; - bitField0_ = (bitField0_ & ~0x00000008); - attrBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getAttrFieldBuilder() : null; - } else { - attrBuilder_.addAllMessages(other.attr_); - } - } - } - if (other.hasDeprecation()) { - mergeDeprecation(other.getDeprecation()); - } - if (!other.getSummary().isEmpty()) { - summary_ = other.summary_; - onChanged(); - } - if (!other.getDescription().isEmpty()) { - description_ = other.description_; - onChanged(); - } - if (other.getIsCommutative() != false) { - setIsCommutative(other.getIsCommutative()); - } - if (other.getIsAggregate() != false) { - setIsAggregate(other.getIsAggregate()); - } - if (other.getIsStateful() != false) { - setIsStateful(other.getIsStateful()); - } - if (other.getAllowsUninitializedInput() != false) { - setAllowsUninitializedInput(other.getAllowsUninitializedInput()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.OpDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.OpDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.lang.Object name_ = ""; - /** - * 
-     * Op names starting with an underscore are reserved for internal use.
-     * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-     *
- * - * optional string name = 1; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Op names starting with an underscore are reserved for internal use.
-     * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-     *
- * - * optional string name = 1; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Op names starting with an underscore are reserved for internal use.
-     * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-     *
- * - * optional string name = 1; - */ - public Builder setName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - name_ = value; - onChanged(); - return this; - } - /** - * 
-     * Op names starting with an underscore are reserved for internal use.
-     * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-     *
- * - * optional string name = 1; - */ - public Builder clearName() { - - name_ = getDefaultInstance().getName(); - onChanged(); - return this; - } - /** - * 
-     * Op names starting with an underscore are reserved for internal use.
-     * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-     *
- * - * optional string name = 1; - */ - public Builder setNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - name_ = value; - onChanged(); - return this; - } - - private java.util.List inputArg_ = - java.util.Collections.emptyList(); - private void ensureInputArgIsMutable() { - if (!((bitField0_ & 0x00000002) == 0x00000002)) { - inputArg_ = new java.util.ArrayList(inputArg_); - bitField0_ |= 0x00000002; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.ArgDef, org.tensorflow.framework.OpDef.ArgDef.Builder, org.tensorflow.framework.OpDef.ArgDefOrBuilder> inputArgBuilder_; - - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public java.util.List getInputArgList() { - if (inputArgBuilder_ == null) { - return java.util.Collections.unmodifiableList(inputArg_); - } else { - return inputArgBuilder_.getMessageList(); - } - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public int getInputArgCount() { - if (inputArgBuilder_ == null) { - return inputArg_.size(); - } else { - return inputArgBuilder_.getCount(); - } - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public org.tensorflow.framework.OpDef.ArgDef getInputArg(int index) { - if (inputArgBuilder_ == null) { - return inputArg_.get(index); - } else { - return inputArgBuilder_.getMessage(index); - } - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder setInputArg( - int index, org.tensorflow.framework.OpDef.ArgDef value) { - if (inputArgBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureInputArgIsMutable(); - inputArg_.set(index, value); - onChanged(); - } else { - inputArgBuilder_.setMessage(index, value); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder setInputArg( - int index, org.tensorflow.framework.OpDef.ArgDef.Builder builderForValue) { - if (inputArgBuilder_ == null) { - ensureInputArgIsMutable(); - inputArg_.set(index, builderForValue.build()); - onChanged(); - } else { - inputArgBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder addInputArg(org.tensorflow.framework.OpDef.ArgDef value) { - if (inputArgBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureInputArgIsMutable(); - inputArg_.add(value); - onChanged(); - } else { - inputArgBuilder_.addMessage(value); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder addInputArg( - int index, org.tensorflow.framework.OpDef.ArgDef value) { - if (inputArgBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureInputArgIsMutable(); - inputArg_.add(index, value); - onChanged(); - } else { - inputArgBuilder_.addMessage(index, value); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder addInputArg( - org.tensorflow.framework.OpDef.ArgDef.Builder builderForValue) { - if (inputArgBuilder_ == null) { - ensureInputArgIsMutable(); - inputArg_.add(builderForValue.build()); - onChanged(); - } else { - inputArgBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder addInputArg( - int index, org.tensorflow.framework.OpDef.ArgDef.Builder builderForValue) { - if (inputArgBuilder_ == null) { - ensureInputArgIsMutable(); - inputArg_.add(index, builderForValue.build()); - onChanged(); - } else { - inputArgBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder addAllInputArg( - java.lang.Iterable values) { - if (inputArgBuilder_ == null) { - ensureInputArgIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, inputArg_); - onChanged(); - } else { - inputArgBuilder_.addAllMessages(values); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder clearInputArg() { - if (inputArgBuilder_ == null) { - inputArg_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - onChanged(); - } else { - inputArgBuilder_.clear(); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public Builder removeInputArg(int index) { - if (inputArgBuilder_ == null) { - ensureInputArgIsMutable(); - inputArg_.remove(index); - onChanged(); - } else { - inputArgBuilder_.remove(index); - } - return this; - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public org.tensorflow.framework.OpDef.ArgDef.Builder getInputArgBuilder( - int index) { - return getInputArgFieldBuilder().getBuilder(index); - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public org.tensorflow.framework.OpDef.ArgDefOrBuilder getInputArgOrBuilder( - int index) { - if (inputArgBuilder_ == null) { - return inputArg_.get(index); } else { - return inputArgBuilder_.getMessageOrBuilder(index); - } - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public java.util.List - getInputArgOrBuilderList() { - if (inputArgBuilder_ != null) { - return inputArgBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(inputArg_); - } - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public org.tensorflow.framework.OpDef.ArgDef.Builder addInputArgBuilder() { - return getInputArgFieldBuilder().addBuilder( - org.tensorflow.framework.OpDef.ArgDef.getDefaultInstance()); - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public org.tensorflow.framework.OpDef.ArgDef.Builder addInputArgBuilder( - int index) { - return getInputArgFieldBuilder().addBuilder( - index, org.tensorflow.framework.OpDef.ArgDef.getDefaultInstance()); - } - /** - * 
-     * Description of the input(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - public java.util.List - getInputArgBuilderList() { - return getInputArgFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.ArgDef, org.tensorflow.framework.OpDef.ArgDef.Builder, org.tensorflow.framework.OpDef.ArgDefOrBuilder> - getInputArgFieldBuilder() { - if (inputArgBuilder_ == null) { - inputArgBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.ArgDef, org.tensorflow.framework.OpDef.ArgDef.Builder, org.tensorflow.framework.OpDef.ArgDefOrBuilder>( - inputArg_, - ((bitField0_ & 0x00000002) == 0x00000002), - getParentForChildren(), - isClean()); - inputArg_ = null; - } - return inputArgBuilder_; - } - - private java.util.List outputArg_ = - java.util.Collections.emptyList(); - private void ensureOutputArgIsMutable() { - if (!((bitField0_ & 0x00000004) == 0x00000004)) { - outputArg_ = new java.util.ArrayList(outputArg_); - bitField0_ |= 0x00000004; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.ArgDef, org.tensorflow.framework.OpDef.ArgDef.Builder, org.tensorflow.framework.OpDef.ArgDefOrBuilder> outputArgBuilder_; - - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public java.util.List getOutputArgList() { - if (outputArgBuilder_ == null) { - return java.util.Collections.unmodifiableList(outputArg_); - } else { - return outputArgBuilder_.getMessageList(); - } - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public int getOutputArgCount() { - if (outputArgBuilder_ == null) { - return outputArg_.size(); - } else { - return outputArgBuilder_.getCount(); - } - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public org.tensorflow.framework.OpDef.ArgDef getOutputArg(int index) { - if (outputArgBuilder_ == null) { - return outputArg_.get(index); - } else { - return outputArgBuilder_.getMessage(index); - } - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder setOutputArg( - int index, org.tensorflow.framework.OpDef.ArgDef value) { - if (outputArgBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOutputArgIsMutable(); - outputArg_.set(index, value); - onChanged(); - } else { - outputArgBuilder_.setMessage(index, value); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder setOutputArg( - int index, org.tensorflow.framework.OpDef.ArgDef.Builder builderForValue) { - if (outputArgBuilder_ == null) { - ensureOutputArgIsMutable(); - outputArg_.set(index, builderForValue.build()); - onChanged(); - } else { - outputArgBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder addOutputArg(org.tensorflow.framework.OpDef.ArgDef value) { - if (outputArgBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOutputArgIsMutable(); - outputArg_.add(value); - onChanged(); - } else { - outputArgBuilder_.addMessage(value); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder addOutputArg( - int index, org.tensorflow.framework.OpDef.ArgDef value) { - if (outputArgBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOutputArgIsMutable(); - outputArg_.add(index, value); - onChanged(); - } else { - outputArgBuilder_.addMessage(index, value); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder addOutputArg( - org.tensorflow.framework.OpDef.ArgDef.Builder builderForValue) { - if (outputArgBuilder_ == null) { - ensureOutputArgIsMutable(); - outputArg_.add(builderForValue.build()); - onChanged(); - } else { - outputArgBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder addOutputArg( - int index, org.tensorflow.framework.OpDef.ArgDef.Builder builderForValue) { - if (outputArgBuilder_ == null) { - ensureOutputArgIsMutable(); - outputArg_.add(index, builderForValue.build()); - onChanged(); - } else { - outputArgBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder addAllOutputArg( - java.lang.Iterable values) { - if (outputArgBuilder_ == null) { - ensureOutputArgIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, outputArg_); - onChanged(); - } else { - outputArgBuilder_.addAllMessages(values); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder clearOutputArg() { - if (outputArgBuilder_ == null) { - outputArg_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - onChanged(); - } else { - outputArgBuilder_.clear(); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public Builder removeOutputArg(int index) { - if (outputArgBuilder_ == null) { - ensureOutputArgIsMutable(); - outputArg_.remove(index); - onChanged(); - } else { - outputArgBuilder_.remove(index); - } - return this; - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public org.tensorflow.framework.OpDef.ArgDef.Builder getOutputArgBuilder( - int index) { - return getOutputArgFieldBuilder().getBuilder(index); - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public org.tensorflow.framework.OpDef.ArgDefOrBuilder getOutputArgOrBuilder( - int index) { - if (outputArgBuilder_ == null) { - return outputArg_.get(index); } else { - return outputArgBuilder_.getMessageOrBuilder(index); - } - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public java.util.List - getOutputArgOrBuilderList() { - if (outputArgBuilder_ != null) { - return outputArgBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(outputArg_); - } - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public org.tensorflow.framework.OpDef.ArgDef.Builder addOutputArgBuilder() { - return getOutputArgFieldBuilder().addBuilder( - org.tensorflow.framework.OpDef.ArgDef.getDefaultInstance()); - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public org.tensorflow.framework.OpDef.ArgDef.Builder addOutputArgBuilder( - int index) { - return getOutputArgFieldBuilder().addBuilder( - index, org.tensorflow.framework.OpDef.ArgDef.getDefaultInstance()); - } - /** - * 
-     * Description of the output(s).
-     *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - public java.util.List - getOutputArgBuilderList() { - return getOutputArgFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.ArgDef, org.tensorflow.framework.OpDef.ArgDef.Builder, org.tensorflow.framework.OpDef.ArgDefOrBuilder> - getOutputArgFieldBuilder() { - if (outputArgBuilder_ == null) { - outputArgBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.ArgDef, org.tensorflow.framework.OpDef.ArgDef.Builder, org.tensorflow.framework.OpDef.ArgDefOrBuilder>( - outputArg_, - ((bitField0_ & 0x00000004) == 0x00000004), - getParentForChildren(), - isClean()); - outputArg_ = null; - } - return outputArgBuilder_; - } - - private java.util.List attr_ = - java.util.Collections.emptyList(); - private void ensureAttrIsMutable() { - if (!((bitField0_ & 0x00000008) == 0x00000008)) { - attr_ = new java.util.ArrayList(attr_); - bitField0_ |= 0x00000008; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.AttrDef, org.tensorflow.framework.OpDef.AttrDef.Builder, org.tensorflow.framework.OpDef.AttrDefOrBuilder> attrBuilder_; - - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public java.util.List getAttrList() { - if (attrBuilder_ == null) { - return java.util.Collections.unmodifiableList(attr_); - } else { - return attrBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public int getAttrCount() { - if (attrBuilder_ == null) { - return attr_.size(); - } else { - return attrBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public org.tensorflow.framework.OpDef.AttrDef getAttr(int index) { - if (attrBuilder_ == null) { - return attr_.get(index); - } else { - return attrBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder setAttr( - int index, org.tensorflow.framework.OpDef.AttrDef value) { - if (attrBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureAttrIsMutable(); - attr_.set(index, value); - onChanged(); - } else { - attrBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder setAttr( - int index, org.tensorflow.framework.OpDef.AttrDef.Builder builderForValue) { - if (attrBuilder_ == null) { - ensureAttrIsMutable(); - attr_.set(index, builderForValue.build()); - onChanged(); - } else { - attrBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder addAttr(org.tensorflow.framework.OpDef.AttrDef value) { - if (attrBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureAttrIsMutable(); - attr_.add(value); - onChanged(); - } else { - attrBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder addAttr( - int index, org.tensorflow.framework.OpDef.AttrDef value) { - if (attrBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureAttrIsMutable(); - attr_.add(index, value); - onChanged(); - } else { - attrBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder addAttr( - org.tensorflow.framework.OpDef.AttrDef.Builder builderForValue) { - if (attrBuilder_ == null) { - ensureAttrIsMutable(); - attr_.add(builderForValue.build()); - onChanged(); - } else { - attrBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder addAttr( - int index, org.tensorflow.framework.OpDef.AttrDef.Builder builderForValue) { - if (attrBuilder_ == null) { - ensureAttrIsMutable(); - attr_.add(index, builderForValue.build()); - onChanged(); - } else { - attrBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder addAllAttr( - java.lang.Iterable values) { - if (attrBuilder_ == null) { - ensureAttrIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, attr_); - onChanged(); - } else { - attrBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder clearAttr() { - if (attrBuilder_ == null) { - attr_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000008); - onChanged(); - } else { - attrBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public Builder removeAttr(int index) { - if (attrBuilder_ == null) { - ensureAttrIsMutable(); - attr_.remove(index); - onChanged(); - } else { - attrBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public org.tensorflow.framework.OpDef.AttrDef.Builder getAttrBuilder( - int index) { - return getAttrFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public org.tensorflow.framework.OpDef.AttrDefOrBuilder getAttrOrBuilder( - int index) { - if (attrBuilder_ == null) { - return attr_.get(index); } else { - return attrBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public java.util.List - getAttrOrBuilderList() { - if (attrBuilder_ != null) { - return attrBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(attr_); - } - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public org.tensorflow.framework.OpDef.AttrDef.Builder addAttrBuilder() { - return getAttrFieldBuilder().addBuilder( - org.tensorflow.framework.OpDef.AttrDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public org.tensorflow.framework.OpDef.AttrDef.Builder addAttrBuilder( - int index) { - return getAttrFieldBuilder().addBuilder( - index, org.tensorflow.framework.OpDef.AttrDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - public java.util.List - getAttrBuilderList() { - return getAttrFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.AttrDef, org.tensorflow.framework.OpDef.AttrDef.Builder, org.tensorflow.framework.OpDef.AttrDefOrBuilder> - getAttrFieldBuilder() { - if (attrBuilder_ == null) { - attrBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef.AttrDef, org.tensorflow.framework.OpDef.AttrDef.Builder, org.tensorflow.framework.OpDef.AttrDefOrBuilder>( - attr_, - ((bitField0_ & 0x00000008) == 0x00000008), - getParentForChildren(), - isClean()); - attr_ = null; - } - return attrBuilder_; - } - - private org.tensorflow.framework.OpDeprecation deprecation_ = null; - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.OpDeprecation, org.tensorflow.framework.OpDeprecation.Builder, org.tensorflow.framework.OpDeprecationOrBuilder> deprecationBuilder_; - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public boolean hasDeprecation() { - return deprecationBuilder_ != null || deprecation_ != null; - } - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public org.tensorflow.framework.OpDeprecation getDeprecation() { - if (deprecationBuilder_ == null) { - return deprecation_ == null ? org.tensorflow.framework.OpDeprecation.getDefaultInstance() : deprecation_; - } else { - return deprecationBuilder_.getMessage(); - } - } - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public Builder setDeprecation(org.tensorflow.framework.OpDeprecation value) { - if (deprecationBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - deprecation_ = value; - onChanged(); - } else { - deprecationBuilder_.setMessage(value); - } - - return this; - } - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public Builder setDeprecation( - org.tensorflow.framework.OpDeprecation.Builder builderForValue) { - if (deprecationBuilder_ == null) { - deprecation_ = builderForValue.build(); - onChanged(); - } else { - deprecationBuilder_.setMessage(builderForValue.build()); - } - - return this; - } - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public Builder mergeDeprecation(org.tensorflow.framework.OpDeprecation value) { - if (deprecationBuilder_ == null) { - if (deprecation_ != null) { - deprecation_ = - org.tensorflow.framework.OpDeprecation.newBuilder(deprecation_).mergeFrom(value).buildPartial(); - } else { - deprecation_ = value; - } - onChanged(); - } else { - deprecationBuilder_.mergeFrom(value); - } - - return this; - } - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public Builder clearDeprecation() { - if (deprecationBuilder_ == null) { - deprecation_ = null; - onChanged(); - } else { - deprecation_ = null; - deprecationBuilder_ = null; - } - - return this; - } - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public org.tensorflow.framework.OpDeprecation.Builder getDeprecationBuilder() { - - onChanged(); - return getDeprecationFieldBuilder().getBuilder(); - } - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - public org.tensorflow.framework.OpDeprecationOrBuilder getDeprecationOrBuilder() { - if (deprecationBuilder_ != null) { - return deprecationBuilder_.getMessageOrBuilder(); - } else { - return deprecation_ == null ? - org.tensorflow.framework.OpDeprecation.getDefaultInstance() : deprecation_; - } - } - /** - * 
-     * Optional deprecation based on GraphDef versions.
-     *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - private com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.OpDeprecation, org.tensorflow.framework.OpDeprecation.Builder, org.tensorflow.framework.OpDeprecationOrBuilder> - getDeprecationFieldBuilder() { - if (deprecationBuilder_ == null) { - deprecationBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< - org.tensorflow.framework.OpDeprecation, org.tensorflow.framework.OpDeprecation.Builder, org.tensorflow.framework.OpDeprecationOrBuilder>( - getDeprecation(), - getParentForChildren(), - isClean()); - deprecation_ = null; - } - return deprecationBuilder_; - } - - private java.lang.Object summary_ = ""; - /** - * 
-     * One-line human-readable description of what the Op does.
-     *
- * - * optional string summary = 5; - */ - public java.lang.String getSummary() { - java.lang.Object ref = summary_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - summary_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * One-line human-readable description of what the Op does.
-     *
- * - * optional string summary = 5; - */ - public com.google.protobuf.ByteString - getSummaryBytes() { - java.lang.Object ref = summary_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - summary_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * One-line human-readable description of what the Op does.
-     *
- * - * optional string summary = 5; - */ - public Builder setSummary( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - summary_ = value; - onChanged(); - return this; - } - /** - * 
-     * One-line human-readable description of what the Op does.
-     *
- * - * optional string summary = 5; - */ - public Builder clearSummary() { - - summary_ = getDefaultInstance().getSummary(); - onChanged(); - return this; - } - /** - * 
-     * One-line human-readable description of what the Op does.
-     *
- * - * optional string summary = 5; - */ - public Builder setSummaryBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - summary_ = value; - onChanged(); - return this; - } - - private java.lang.Object description_ = ""; - /** - * 
-     * Additional, longer human-readable description of what the Op does.
-     *
- * - * optional string description = 6; - */ - public java.lang.String getDescription() { - java.lang.Object ref = description_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - description_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Additional, longer human-readable description of what the Op does.
-     *
- * - * optional string description = 6; - */ - public com.google.protobuf.ByteString - getDescriptionBytes() { - java.lang.Object ref = description_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - description_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Additional, longer human-readable description of what the Op does.
-     *
- * - * optional string description = 6; - */ - public Builder setDescription( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - description_ = value; - onChanged(); - return this; - } - /** - * 
-     * Additional, longer human-readable description of what the Op does.
-     *
- * - * optional string description = 6; - */ - public Builder clearDescription() { - - description_ = getDefaultInstance().getDescription(); - onChanged(); - return this; - } - /** - * 
-     * Additional, longer human-readable description of what the Op does.
-     *
- * - * optional string description = 6; - */ - public Builder setDescriptionBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - description_ = value; - onChanged(); - return this; - } - - private boolean isCommutative_ ; - /** - * 
-     * True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-     *
- * - * optional bool is_commutative = 18; - */ - public boolean getIsCommutative() { - return isCommutative_; - } - /** - * 
-     * True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-     *
- * - * optional bool is_commutative = 18; - */ - public Builder setIsCommutative(boolean value) { - - isCommutative_ = value; - onChanged(); - return this; - } - /** - * 
-     * True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-     *
- * - * optional bool is_commutative = 18; - */ - public Builder clearIsCommutative() { - - isCommutative_ = false; - onChanged(); - return this; - } - - private boolean isAggregate_ ; - /** - * 
-     * If is_aggregate is true, then this operation accepts N >= 2
-     * inputs and produces 1 output all of the same type.  Should be
-     * associative and commutative, and produce output with the same
-     * shape as the input.  The optimizer may replace an aggregate op
-     * taking input from multiple devices with a tree of aggregate ops
-     * that aggregate locally within each device (and possibly within
-     * groups of nearby devices) before communicating.
-     * TODO(josh11b): Implement that optimization.
-     *
- * - * optional bool is_aggregate = 16; - */ - public boolean getIsAggregate() { - return isAggregate_; - } - /** - * 
-     * If is_aggregate is true, then this operation accepts N >= 2
-     * inputs and produces 1 output all of the same type.  Should be
-     * associative and commutative, and produce output with the same
-     * shape as the input.  The optimizer may replace an aggregate op
-     * taking input from multiple devices with a tree of aggregate ops
-     * that aggregate locally within each device (and possibly within
-     * groups of nearby devices) before communicating.
-     * TODO(josh11b): Implement that optimization.
-     *
- * - * optional bool is_aggregate = 16; - */ - public Builder setIsAggregate(boolean value) { - - isAggregate_ = value; - onChanged(); - return this; - } - /** - * 
-     * If is_aggregate is true, then this operation accepts N >= 2
-     * inputs and produces 1 output all of the same type.  Should be
-     * associative and commutative, and produce output with the same
-     * shape as the input.  The optimizer may replace an aggregate op
-     * taking input from multiple devices with a tree of aggregate ops
-     * that aggregate locally within each device (and possibly within
-     * groups of nearby devices) before communicating.
-     * TODO(josh11b): Implement that optimization.
-     *
- * - * optional bool is_aggregate = 16; - */ - public Builder clearIsAggregate() { - - isAggregate_ = false; - onChanged(); - return this; - } - - private boolean isStateful_ ; - /** - * 
-     * By default Ops may be moved between devices.  Stateful ops should
-     * either not be moved, or should only be moved if that state can also
-     * be moved (e.g. via some sort of save / restore).
-     * Stateful ops are guaranteed to never be optimized away by Common
-     * Subexpression Elimination (CSE).
-     *
- * - * optional bool is_stateful = 17; - */ - public boolean getIsStateful() { - return isStateful_; - } - /** - * 
-     * By default Ops may be moved between devices.  Stateful ops should
-     * either not be moved, or should only be moved if that state can also
-     * be moved (e.g. via some sort of save / restore).
-     * Stateful ops are guaranteed to never be optimized away by Common
-     * Subexpression Elimination (CSE).
-     *
- * - * optional bool is_stateful = 17; - */ - public Builder setIsStateful(boolean value) { - - isStateful_ = value; - onChanged(); - return this; - } - /** - * 
-     * By default Ops may be moved between devices.  Stateful ops should
-     * either not be moved, or should only be moved if that state can also
-     * be moved (e.g. via some sort of save / restore).
-     * Stateful ops are guaranteed to never be optimized away by Common
-     * Subexpression Elimination (CSE).
-     *
- * - * optional bool is_stateful = 17; - */ - public Builder clearIsStateful() { - - isStateful_ = false; - onChanged(); - return this; - } - - private boolean allowsUninitializedInput_ ; - /** - * 
-     * By default, all inputs to an Op must be initialized Tensors.  Ops
-     * that may initialize tensors for the first time should set this
-     * field to true, to allow the Op to take an uninitialized Tensor as
-     * input.
-     *
- * - * optional bool allows_uninitialized_input = 19; - */ - public boolean getAllowsUninitializedInput() { - return allowsUninitializedInput_; - } - /** - * 
-     * By default, all inputs to an Op must be initialized Tensors.  Ops
-     * that may initialize tensors for the first time should set this
-     * field to true, to allow the Op to take an uninitialized Tensor as
-     * input.
-     *
- * - * optional bool allows_uninitialized_input = 19; - */ - public Builder setAllowsUninitializedInput(boolean value) { - - allowsUninitializedInput_ = value; - onChanged(); - return this; - } - /** - * 
-     * By default, all inputs to an Op must be initialized Tensors.  Ops
-     * that may initialize tensors for the first time should set this
-     * field to true, to allow the Op to take an uninitialized Tensor as
-     * input.
-     *
- * - * optional bool allows_uninitialized_input = 19; - */ - public Builder clearAllowsUninitializedInput() { - - allowsUninitializedInput_ = false; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.OpDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.OpDef) - private static final org.tensorflow.framework.OpDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.OpDef(); - } - - public static org.tensorflow.framework.OpDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public OpDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new OpDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.OpDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/OpDefOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/OpDefOrBuilder.java deleted file mode 100644 index 44c6b212bbc..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/OpDefOrBuilder.java +++ /dev/null @@ -1,252 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: op_def.proto - -package org.tensorflow.framework; - -public interface OpDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.OpDef) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Op names starting with an underscore are reserved for internal use.
-   * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-   *
- * - * optional string name = 1; - */ - java.lang.String getName(); - /** - * 
-   * Op names starting with an underscore are reserved for internal use.
-   * Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-   *
- * - * optional string name = 1; - */ - com.google.protobuf.ByteString - getNameBytes(); - - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - java.util.List - getInputArgList(); - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - org.tensorflow.framework.OpDef.ArgDef getInputArg(int index); - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - int getInputArgCount(); - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - java.util.List - getInputArgOrBuilderList(); - /** - * 
-   * Description of the input(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef input_arg = 2; - */ - org.tensorflow.framework.OpDef.ArgDefOrBuilder getInputArgOrBuilder( - int index); - - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - java.util.List - getOutputArgList(); - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - org.tensorflow.framework.OpDef.ArgDef getOutputArg(int index); - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - int getOutputArgCount(); - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - java.util.List - getOutputArgOrBuilderList(); - /** - * 
-   * Description of the output(s).
-   *
- * - * repeated .tensorflow.OpDef.ArgDef output_arg = 3; - */ - org.tensorflow.framework.OpDef.ArgDefOrBuilder getOutputArgOrBuilder( - int index); - - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - java.util.List - getAttrList(); - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - org.tensorflow.framework.OpDef.AttrDef getAttr(int index); - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - int getAttrCount(); - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - java.util.List - getAttrOrBuilderList(); - /** - * repeated .tensorflow.OpDef.AttrDef attr = 4; - */ - org.tensorflow.framework.OpDef.AttrDefOrBuilder getAttrOrBuilder( - int index); - - /** - * 
-   * Optional deprecation based on GraphDef versions.
-   *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - boolean hasDeprecation(); - /** - * 
-   * Optional deprecation based on GraphDef versions.
-   *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - org.tensorflow.framework.OpDeprecation getDeprecation(); - /** - * 
-   * Optional deprecation based on GraphDef versions.
-   *
- * - * optional .tensorflow.OpDeprecation deprecation = 8; - */ - org.tensorflow.framework.OpDeprecationOrBuilder getDeprecationOrBuilder(); - - /** - * 
-   * One-line human-readable description of what the Op does.
-   *
- * - * optional string summary = 5; - */ - java.lang.String getSummary(); - /** - * 
-   * One-line human-readable description of what the Op does.
-   *
- * - * optional string summary = 5; - */ - com.google.protobuf.ByteString - getSummaryBytes(); - - /** - * 
-   * Additional, longer human-readable description of what the Op does.
-   *
- * - * optional string description = 6; - */ - java.lang.String getDescription(); - /** - * 
-   * Additional, longer human-readable description of what the Op does.
-   *
- * - * optional string description = 6; - */ - com.google.protobuf.ByteString - getDescriptionBytes(); - - /** - * 
-   * True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-   *
- * - * optional bool is_commutative = 18; - */ - boolean getIsCommutative(); - - /** - * 
-   * If is_aggregate is true, then this operation accepts N >= 2
-   * inputs and produces 1 output all of the same type.  Should be
-   * associative and commutative, and produce output with the same
-   * shape as the input.  The optimizer may replace an aggregate op
-   * taking input from multiple devices with a tree of aggregate ops
-   * that aggregate locally within each device (and possibly within
-   * groups of nearby devices) before communicating.
-   * TODO(josh11b): Implement that optimization.
-   *
- * - * optional bool is_aggregate = 16; - */ - boolean getIsAggregate(); - - /** - * 
-   * By default Ops may be moved between devices.  Stateful ops should
-   * either not be moved, or should only be moved if that state can also
-   * be moved (e.g. via some sort of save / restore).
-   * Stateful ops are guaranteed to never be optimized away by Common
-   * Subexpression Elimination (CSE).
-   *
- * - * optional bool is_stateful = 17; - */ - boolean getIsStateful(); - - /** - * 
-   * By default, all inputs to an Op must be initialized Tensors.  Ops
-   * that may initialize tensors for the first time should set this
-   * field to true, to allow the Op to take an uninitialized Tensor as
-   * input.
-   *
- * - * optional bool allows_uninitialized_input = 19; - */ - boolean getAllowsUninitializedInput(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/OpDefProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/OpDefProtos.java deleted file mode 100644 index b06f9e1d741..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/OpDefProtos.java +++ /dev/null @@ -1,125 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: op_def.proto - -package org.tensorflow.framework; - -public final class OpDefProtos { - private OpDefProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_OpDef_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_OpDef_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_OpDef_ArgDef_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_OpDef_ArgDef_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_OpDef_AttrDef_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_OpDef_AttrDef_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_OpDeprecation_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_OpDeprecation_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_OpList_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_OpList_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\014op_def.proto\022\ntensorflow\032*tensorflow/c" + - "ore/framework/attr_value.proto\032%tensorfl" + - "ow/core/framework/types.proto\"\270\005\n\005OpDef\022" + - "\014\n\004name\030\001 \001(\t\022+\n\tinput_arg\030\002 \003(\0132\030.tenso" + - "rflow.OpDef.ArgDef\022,\n\noutput_arg\030\003 \003(\0132\030" + - ".tensorflow.OpDef.ArgDef\022\'\n\004attr\030\004 \003(\0132\031" + - ".tensorflow.OpDef.AttrDef\022.\n\013deprecation" + - "\030\010 \001(\0132\031.tensorflow.OpDeprecation\022\017\n\007sum" + - "mary\030\005 \001(\t\022\023\n\013description\030\006 \001(\t\022\026\n\016is_co" + - "mmutative\030\022 \001(\010\022\024\n\014is_aggregate\030\020 \001(\010\022\023\n", - "\013is_stateful\030\021 \001(\010\022\"\n\032allows_uninitializ" + - "ed_input\030\023 \001(\010\032\237\001\n\006ArgDef\022\014\n\004name\030\001 \001(\t\022" + - "\023\n\013description\030\002 \001(\t\022\"\n\004type\030\003 \001(\0162\024.ten" + - "sorflow.DataType\022\021\n\ttype_attr\030\004 \001(\t\022\023\n\013n" + - "umber_attr\030\005 \001(\t\022\026\n\016type_list_attr\030\006 \001(\t" + - "\022\016\n\006is_ref\030\020 \001(\010\032\275\001\n\007AttrDef\022\014\n\004name\030\001 \001" + - "(\t\022\014\n\004type\030\002 \001(\t\022,\n\rdefault_value\030\003 \001(\0132" + - "\025.tensorflow.AttrValue\022\023\n\013description\030\004 " + - "\001(\t\022\023\n\013has_minimum\030\005 \001(\010\022\017\n\007minimum\030\006 \001(" + - "\003\022-\n\016allowed_values\030\007 \001(\0132\025.tensorflow.A", - "ttrValue\"5\n\rOpDeprecation\022\017\n\007version\030\001 \001" + - "(\005\022\023\n\013explanation\030\002 \001(\t\"\'\n\006OpList\022\035\n\002op\030" + - "\001 \003(\0132\021.tensorflow.OpDefB,\n\030org.tensorfl" + - "ow.frameworkB\013OpDefProtosP\001\370\001\001b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.framework.AttrValueProtos.getDescriptor(), - org.tensorflow.framework.TypesProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_OpDef_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_OpDef_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_OpDef_descriptor, - new java.lang.String[] { "Name", "InputArg", "OutputArg", "Attr", "Deprecation", "Summary", "Description", "IsCommutative", "IsAggregate", "IsStateful", "AllowsUninitializedInput", }); - internal_static_tensorflow_OpDef_ArgDef_descriptor = - internal_static_tensorflow_OpDef_descriptor.getNestedTypes().get(0); - internal_static_tensorflow_OpDef_ArgDef_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_OpDef_ArgDef_descriptor, - new java.lang.String[] { "Name", "Description", "Type", "TypeAttr", "NumberAttr", "TypeListAttr", "IsRef", }); - internal_static_tensorflow_OpDef_AttrDef_descriptor = - internal_static_tensorflow_OpDef_descriptor.getNestedTypes().get(1); - internal_static_tensorflow_OpDef_AttrDef_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_OpDef_AttrDef_descriptor, - new java.lang.String[] { "Name", "Type", "DefaultValue", "Description", "HasMinimum", "Minimum", "AllowedValues", }); - internal_static_tensorflow_OpDeprecation_descriptor = - getDescriptor().getMessageTypes().get(1); - internal_static_tensorflow_OpDeprecation_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_OpDeprecation_descriptor, - new java.lang.String[] { "Version", "Explanation", }); - internal_static_tensorflow_OpList_descriptor = - getDescriptor().getMessageTypes().get(2); - internal_static_tensorflow_OpList_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_OpList_descriptor, - new java.lang.String[] { "Op", }); - org.tensorflow.framework.AttrValueProtos.getDescriptor(); - org.tensorflow.framework.TypesProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/OpDeprecation.java b/scala/dllib/src/main/java/org/tensorflow/framework/OpDeprecation.java deleted file mode 100644 index 8c6ebdda445..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/OpDeprecation.java +++ /dev/null @@ -1,604 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: op_def.proto - -package org.tensorflow.framework; - -/** - * 
- * Information about version-dependent deprecation of an op
- *
- * - * Protobuf type {@code tensorflow.OpDeprecation} - */ -public final class OpDeprecation extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.OpDeprecation) - OpDeprecationOrBuilder { - // Use OpDeprecation.newBuilder() to construct. - private OpDeprecation(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private OpDeprecation() { - version_ = 0; - explanation_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private OpDeprecation( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - version_ = input.readInt32(); - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - explanation_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDeprecation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDeprecation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpDeprecation.class, org.tensorflow.framework.OpDeprecation.Builder.class); - } - - public static final int VERSION_FIELD_NUMBER = 1; - private int version_; - /** - * 
-   * First GraphDef version at which the op is disallowed.
-   *
- * - * optional int32 version = 1; - */ - public int getVersion() { - return version_; - } - - public static final int EXPLANATION_FIELD_NUMBER = 2; - private volatile java.lang.Object explanation_; - /** - * 
-   * Explanation of why it was deprecated and what to use instead.
-   *
- * - * optional string explanation = 2; - */ - public java.lang.String getExplanation() { - java.lang.Object ref = explanation_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - explanation_ = s; - return s; - } - } - /** - * 
-   * Explanation of why it was deprecated and what to use instead.
-   *
- * - * optional string explanation = 2; - */ - public com.google.protobuf.ByteString - getExplanationBytes() { - java.lang.Object ref = explanation_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - explanation_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (version_ != 0) { - output.writeInt32(1, version_); - } - if (!getExplanationBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, explanation_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (version_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeInt32Size(1, version_); - } - if (!getExplanationBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, explanation_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.OpDeprecation)) { - return super.equals(obj); - } - org.tensorflow.framework.OpDeprecation other = (org.tensorflow.framework.OpDeprecation) obj; - - boolean result = true; - result = result && (getVersion() - == other.getVersion()); - result = result && getExplanation() - .equals(other.getExplanation()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + VERSION_FIELD_NUMBER; - hash = (53 * hash) + getVersion(); - hash = (37 * hash) + EXPLANATION_FIELD_NUMBER; - hash = (53 * hash) + getExplanation().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.OpDeprecation parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpDeprecation parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpDeprecation parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpDeprecation parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpDeprecation parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDeprecation parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpDeprecation parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDeprecation parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpDeprecation parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpDeprecation parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.OpDeprecation prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Information about version-dependent deprecation of an op
-   *
- * - * Protobuf type {@code tensorflow.OpDeprecation} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.OpDeprecation) - org.tensorflow.framework.OpDeprecationOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDeprecation_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDeprecation_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpDeprecation.class, org.tensorflow.framework.OpDeprecation.Builder.class); - } - - // Construct using org.tensorflow.framework.OpDeprecation.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - version_ = 0; - - explanation_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpDeprecation_descriptor; - } - - public org.tensorflow.framework.OpDeprecation getDefaultInstanceForType() { - return org.tensorflow.framework.OpDeprecation.getDefaultInstance(); - } - - public org.tensorflow.framework.OpDeprecation build() { - org.tensorflow.framework.OpDeprecation result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.OpDeprecation buildPartial() { - org.tensorflow.framework.OpDeprecation result = new org.tensorflow.framework.OpDeprecation(this); - result.version_ = version_; - result.explanation_ = explanation_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.OpDeprecation) { - return mergeFrom((org.tensorflow.framework.OpDeprecation)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.OpDeprecation other) { - if (other == org.tensorflow.framework.OpDeprecation.getDefaultInstance()) return this; - if (other.getVersion() != 0) { - setVersion(other.getVersion()); - } - if (!other.getExplanation().isEmpty()) { - explanation_ = other.explanation_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.OpDeprecation parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.OpDeprecation) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private int version_ ; - /** - * 
-     * First GraphDef version at which the op is disallowed.
-     *
- * - * optional int32 version = 1; - */ - public int getVersion() { - return version_; - } - /** - * 
-     * First GraphDef version at which the op is disallowed.
-     *
- * - * optional int32 version = 1; - */ - public Builder setVersion(int value) { - - version_ = value; - onChanged(); - return this; - } - /** - * 
-     * First GraphDef version at which the op is disallowed.
-     *
- * - * optional int32 version = 1; - */ - public Builder clearVersion() { - - version_ = 0; - onChanged(); - return this; - } - - private java.lang.Object explanation_ = ""; - /** - * 
-     * Explanation of why it was deprecated and what to use instead.
-     *
- * - * optional string explanation = 2; - */ - public java.lang.String getExplanation() { - java.lang.Object ref = explanation_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - explanation_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Explanation of why it was deprecated and what to use instead.
-     *
- * - * optional string explanation = 2; - */ - public com.google.protobuf.ByteString - getExplanationBytes() { - java.lang.Object ref = explanation_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - explanation_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Explanation of why it was deprecated and what to use instead.
-     *
- * - * optional string explanation = 2; - */ - public Builder setExplanation( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - explanation_ = value; - onChanged(); - return this; - } - /** - * 
-     * Explanation of why it was deprecated and what to use instead.
-     *
- * - * optional string explanation = 2; - */ - public Builder clearExplanation() { - - explanation_ = getDefaultInstance().getExplanation(); - onChanged(); - return this; - } - /** - * 
-     * Explanation of why it was deprecated and what to use instead.
-     *
- * - * optional string explanation = 2; - */ - public Builder setExplanationBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - explanation_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.OpDeprecation) - } - - // @@protoc_insertion_point(class_scope:tensorflow.OpDeprecation) - private static final org.tensorflow.framework.OpDeprecation DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.OpDeprecation(); - } - - public static org.tensorflow.framework.OpDeprecation getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public OpDeprecation parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new OpDeprecation(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.OpDeprecation getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/OpDeprecationOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/OpDeprecationOrBuilder.java deleted file mode 100644 index 9cf5b85852e..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/OpDeprecationOrBuilder.java +++ /dev/null @@ -1,36 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: op_def.proto - -package org.tensorflow.framework; - -public interface OpDeprecationOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.OpDeprecation) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * First GraphDef version at which the op is disallowed.
-   *
- * - * optional int32 version = 1; - */ - int getVersion(); - - /** - * 
-   * Explanation of why it was deprecated and what to use instead.
-   *
- * - * optional string explanation = 2; - */ - java.lang.String getExplanation(); - /** - * 
-   * Explanation of why it was deprecated and what to use instead.
-   *
- * - * optional string explanation = 2; - */ - com.google.protobuf.ByteString - getExplanationBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/OpList.java b/scala/dllib/src/main/java/org/tensorflow/framework/OpList.java deleted file mode 100644 index 11a3336c9b5..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/OpList.java +++ /dev/null @@ -1,720 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: op_def.proto - -package org.tensorflow.framework; - -/** - * 
- * A collection of OpDefs
- *
- * - * Protobuf type {@code tensorflow.OpList} - */ -public final class OpList extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.OpList) - OpListOrBuilder { - // Use OpList.newBuilder() to construct. - private OpList(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private OpList() { - op_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private OpList( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - op_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - op_.add( - input.readMessage(org.tensorflow.framework.OpDef.parser(), extensionRegistry)); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - op_ = java.util.Collections.unmodifiableList(op_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpList_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpList.class, org.tensorflow.framework.OpList.Builder.class); - } - - public static final int OP_FIELD_NUMBER = 1; - private java.util.List op_; - /** - * repeated .tensorflow.OpDef op = 1; - */ - public java.util.List getOpList() { - return op_; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public java.util.List - getOpOrBuilderList() { - return op_; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public int getOpCount() { - return op_.size(); - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public org.tensorflow.framework.OpDef getOp(int index) { - return op_.get(index); - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public org.tensorflow.framework.OpDefOrBuilder getOpOrBuilder( - int index) { - return op_.get(index); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - for (int i = 0; i < op_.size(); i++) { - output.writeMessage(1, op_.get(i)); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - for (int i = 0; i < op_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, op_.get(i)); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.OpList)) { - return super.equals(obj); - } - org.tensorflow.framework.OpList other = (org.tensorflow.framework.OpList) obj; - - boolean result = true; - result = result && getOpList() - .equals(other.getOpList()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - if (getOpCount() > 0) { - hash = (37 * hash) + OP_FIELD_NUMBER; - hash = (53 * hash) + getOpList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.OpList parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpList parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpList parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.OpList parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.OpList parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpList parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpList parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpList parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.OpList parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.OpList parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.OpList prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * A collection of OpDefs
-   *
- * - * Protobuf type {@code tensorflow.OpList} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.OpList) - org.tensorflow.framework.OpListOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpList_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpList_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.OpList.class, org.tensorflow.framework.OpList.Builder.class); - } - - // Construct using org.tensorflow.framework.OpList.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getOpFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - if (opBuilder_ == null) { - op_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - } else { - opBuilder_.clear(); - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.OpDefProtos.internal_static_tensorflow_OpList_descriptor; - } - - public org.tensorflow.framework.OpList getDefaultInstanceForType() { - return org.tensorflow.framework.OpList.getDefaultInstance(); - } - - public org.tensorflow.framework.OpList build() { - org.tensorflow.framework.OpList result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.OpList buildPartial() { - org.tensorflow.framework.OpList result = new org.tensorflow.framework.OpList(this); - int from_bitField0_ = bitField0_; - if (opBuilder_ == null) { - if (((bitField0_ & 0x00000001) == 0x00000001)) { - op_ = java.util.Collections.unmodifiableList(op_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.op_ = op_; - } else { - result.op_ = opBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.OpList) { - return mergeFrom((org.tensorflow.framework.OpList)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.OpList other) { - if (other == org.tensorflow.framework.OpList.getDefaultInstance()) return this; - if (opBuilder_ == null) { - if (!other.op_.isEmpty()) { - if (op_.isEmpty()) { - op_ = other.op_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureOpIsMutable(); - op_.addAll(other.op_); - } - onChanged(); - } - } else { - if (!other.op_.isEmpty()) { - if (opBuilder_.isEmpty()) { - opBuilder_.dispose(); - opBuilder_ = null; - op_ = other.op_; - bitField0_ = (bitField0_ & ~0x00000001); - opBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getOpFieldBuilder() : null; - } else { - opBuilder_.addAllMessages(other.op_); - } - } - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.OpList parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.OpList) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List op_ = - java.util.Collections.emptyList(); - private void ensureOpIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - op_ = new java.util.ArrayList(op_); - bitField0_ |= 0x00000001; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef, org.tensorflow.framework.OpDef.Builder, org.tensorflow.framework.OpDefOrBuilder> opBuilder_; - - /** - * repeated .tensorflow.OpDef op = 1; - */ - public java.util.List getOpList() { - if (opBuilder_ == null) { - return java.util.Collections.unmodifiableList(op_); - } else { - return opBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public int getOpCount() { - if (opBuilder_ == null) { - return op_.size(); - } else { - return opBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public org.tensorflow.framework.OpDef getOp(int index) { - if (opBuilder_ == null) { - return op_.get(index); - } else { - return opBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder setOp( - int index, org.tensorflow.framework.OpDef value) { - if (opBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOpIsMutable(); - op_.set(index, value); - onChanged(); - } else { - opBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder setOp( - int index, org.tensorflow.framework.OpDef.Builder builderForValue) { - if (opBuilder_ == null) { - ensureOpIsMutable(); - op_.set(index, builderForValue.build()); - onChanged(); - } else { - opBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder addOp(org.tensorflow.framework.OpDef value) { - if (opBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOpIsMutable(); - op_.add(value); - onChanged(); - } else { - opBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder addOp( - int index, org.tensorflow.framework.OpDef value) { - if (opBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureOpIsMutable(); - op_.add(index, value); - onChanged(); - } else { - opBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder addOp( - org.tensorflow.framework.OpDef.Builder builderForValue) { - if (opBuilder_ == null) { - ensureOpIsMutable(); - op_.add(builderForValue.build()); - onChanged(); - } else { - opBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder addOp( - int index, org.tensorflow.framework.OpDef.Builder builderForValue) { - if (opBuilder_ == null) { - ensureOpIsMutable(); - op_.add(index, builderForValue.build()); - onChanged(); - } else { - opBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder addAllOp( - java.lang.Iterable values) { - if (opBuilder_ == null) { - ensureOpIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, op_); - onChanged(); - } else { - opBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder clearOp() { - if (opBuilder_ == null) { - op_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - } else { - opBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public Builder removeOp(int index) { - if (opBuilder_ == null) { - ensureOpIsMutable(); - op_.remove(index); - onChanged(); - } else { - opBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public org.tensorflow.framework.OpDef.Builder getOpBuilder( - int index) { - return getOpFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public org.tensorflow.framework.OpDefOrBuilder getOpOrBuilder( - int index) { - if (opBuilder_ == null) { - return op_.get(index); } else { - return opBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public java.util.List - getOpOrBuilderList() { - if (opBuilder_ != null) { - return opBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(op_); - } - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public org.tensorflow.framework.OpDef.Builder addOpBuilder() { - return getOpFieldBuilder().addBuilder( - org.tensorflow.framework.OpDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public org.tensorflow.framework.OpDef.Builder addOpBuilder( - int index) { - return getOpFieldBuilder().addBuilder( - index, org.tensorflow.framework.OpDef.getDefaultInstance()); - } - /** - * repeated .tensorflow.OpDef op = 1; - */ - public java.util.List - getOpBuilderList() { - return getOpFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef, org.tensorflow.framework.OpDef.Builder, org.tensorflow.framework.OpDefOrBuilder> - getOpFieldBuilder() { - if (opBuilder_ == null) { - opBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.OpDef, org.tensorflow.framework.OpDef.Builder, org.tensorflow.framework.OpDefOrBuilder>( - op_, - ((bitField0_ & 0x00000001) == 0x00000001), - getParentForChildren(), - isClean()); - op_ = null; - } - return opBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.OpList) - } - - // @@protoc_insertion_point(class_scope:tensorflow.OpList) - private static final org.tensorflow.framework.OpList DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.OpList(); - } - - public static org.tensorflow.framework.OpList getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public OpList parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new OpList(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.OpList getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/OpListOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/OpListOrBuilder.java deleted file mode 100644 index a6cba8488d3..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/OpListOrBuilder.java +++ /dev/null @@ -1,33 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: op_def.proto - -package org.tensorflow.framework; - -public interface OpListOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.OpList) - com.google.protobuf.MessageOrBuilder { - - /** - * repeated .tensorflow.OpDef op = 1; - */ - java.util.List - getOpList(); - /** - * repeated .tensorflow.OpDef op = 1; - */ - org.tensorflow.framework.OpDef getOp(int index); - /** - * repeated .tensorflow.OpDef op = 1; - */ - int getOpCount(); - /** - * repeated .tensorflow.OpDef op = 1; - */ - java.util.List - getOpOrBuilderList(); - /** - * repeated .tensorflow.OpDef op = 1; - */ - org.tensorflow.framework.OpDefOrBuilder getOpOrBuilder( - int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java deleted file mode 100644 index 5073a6a4bc9..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandle.java +++ /dev/null @@ -1,1085 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: resource_handle.proto - -package org.tensorflow.framework; - -/** - * 
- * Protocol buffer representing a handle to a tensorflow resource. Handles are
- * not valid across executions, but can be serialized back and forth from within
- * a single run.
- *
- * - * Protobuf type {@code tensorflow.ResourceHandle} - */ -public final class ResourceHandle extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.ResourceHandle) - ResourceHandleOrBuilder { - // Use ResourceHandle.newBuilder() to construct. - private ResourceHandle(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private ResourceHandle() { - device_ = ""; - container_ = ""; - name_ = ""; - hashCode_ = 0L; - maybeTypeName_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private ResourceHandle( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - device_ = s; - break; - } - case 18: { - java.lang.String s = input.readStringRequireUtf8(); - - container_ = s; - break; - } - case 26: { - java.lang.String s = input.readStringRequireUtf8(); - - name_ = s; - break; - } - case 32: { - - hashCode_ = input.readUInt64(); - break; - } - case 42: { - java.lang.String s = input.readStringRequireUtf8(); - - maybeTypeName_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorflow_ResourceHandle_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorflow_ResourceHandle_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.ResourceHandle.class, org.tensorflow.framework.ResourceHandle.Builder.class); - } - - public static final int DEVICE_FIELD_NUMBER = 1; - private volatile java.lang.Object device_; - /** - * 
-   * Unique name for the device containing the resource.
-   *
- * - * optional string device = 1; - */ - public java.lang.String getDevice() { - java.lang.Object ref = device_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - device_ = s; - return s; - } - } - /** - * 
-   * Unique name for the device containing the resource.
-   *
- * - * optional string device = 1; - */ - public com.google.protobuf.ByteString - getDeviceBytes() { - java.lang.Object ref = device_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - device_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int CONTAINER_FIELD_NUMBER = 2; - private volatile java.lang.Object container_; - /** - * 
-   * Container in which this resource is placed.
-   *
- * - * optional string container = 2; - */ - public java.lang.String getContainer() { - java.lang.Object ref = container_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - container_ = s; - return s; - } - } - /** - * 
-   * Container in which this resource is placed.
-   *
- * - * optional string container = 2; - */ - public com.google.protobuf.ByteString - getContainerBytes() { - java.lang.Object ref = container_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - container_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int NAME_FIELD_NUMBER = 3; - private volatile java.lang.Object name_; - /** - * 
-   * Unique name of this resource.
-   *
- * - * optional string name = 3; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } - } - /** - * 
-   * Unique name of this resource.
-   *
- * - * optional string name = 3; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int HASH_CODE_FIELD_NUMBER = 4; - private long hashCode_; - /** - * 
-   * Hash code for the type of the resource. Is only valid in the same device
-   * and in the same execution.
-   *
- * - * optional uint64 hash_code = 4; - */ - public long getHashCode() { - return hashCode_; - } - - public static final int MAYBE_TYPE_NAME_FIELD_NUMBER = 5; - private volatile java.lang.Object maybeTypeName_; - /** - * 
-   * For debug-only, the name of the type pointed to by this handle, if
-   * available.
-   *
- * - * optional string maybe_type_name = 5; - */ - public java.lang.String getMaybeTypeName() { - java.lang.Object ref = maybeTypeName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - maybeTypeName_ = s; - return s; - } - } - /** - * 
-   * For debug-only, the name of the type pointed to by this handle, if
-   * available.
-   *
- * - * optional string maybe_type_name = 5; - */ - public com.google.protobuf.ByteString - getMaybeTypeNameBytes() { - java.lang.Object ref = maybeTypeName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - maybeTypeName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (!getDeviceBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, device_); - } - if (!getContainerBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 2, container_); - } - if (!getNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 3, name_); - } - if (hashCode_ != 0L) { - output.writeUInt64(4, hashCode_); - } - if (!getMaybeTypeNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 5, maybeTypeName_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getDeviceBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, device_); - } - if (!getContainerBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, container_); - } - if (!getNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(3, name_); - } - if (hashCode_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeUInt64Size(4, hashCode_); - } - if (!getMaybeTypeNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(5, maybeTypeName_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.ResourceHandle)) { - return super.equals(obj); - } - org.tensorflow.framework.ResourceHandle other = (org.tensorflow.framework.ResourceHandle) obj; - - boolean result = true; - result = result && getDevice() - .equals(other.getDevice()); - result = result && getContainer() - .equals(other.getContainer()); - result = result && getName() - .equals(other.getName()); - result = result && (getHashCode() - == other.getHashCode()); - result = result && getMaybeTypeName() - .equals(other.getMaybeTypeName()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + DEVICE_FIELD_NUMBER; - hash = (53 * hash) + getDevice().hashCode(); - hash = (37 * hash) + CONTAINER_FIELD_NUMBER; - hash = (53 * hash) + getContainer().hashCode(); - hash = (37 * hash) + NAME_FIELD_NUMBER; - hash = (53 * hash) + getName().hashCode(); - hash = (37 * hash) + HASH_CODE_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getHashCode()); - hash = (37 * hash) + MAYBE_TYPE_NAME_FIELD_NUMBER; - hash = (53 * hash) + getMaybeTypeName().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.ResourceHandle parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.ResourceHandle parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.ResourceHandle parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.ResourceHandle parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.ResourceHandle parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.ResourceHandle parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.ResourceHandle parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.ResourceHandle parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.ResourceHandle parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.ResourceHandle parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.ResourceHandle prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * 
-   * Protocol buffer representing a handle to a tensorflow resource. Handles are
-   * not valid across executions, but can be serialized back and forth from within
-   * a single run.
-   *
- * - * Protobuf type {@code tensorflow.ResourceHandle} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.ResourceHandle) - org.tensorflow.framework.ResourceHandleOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorflow_ResourceHandle_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorflow_ResourceHandle_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.ResourceHandle.class, org.tensorflow.framework.ResourceHandle.Builder.class); - } - - // Construct using org.tensorflow.framework.ResourceHandle.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - device_ = ""; - - container_ = ""; - - name_ = ""; - - hashCode_ = 0L; - - maybeTypeName_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.ResourceHandleProto.internal_static_tensorflow_ResourceHandle_descriptor; - } - - public org.tensorflow.framework.ResourceHandle getDefaultInstanceForType() { - return org.tensorflow.framework.ResourceHandle.getDefaultInstance(); - } - - public org.tensorflow.framework.ResourceHandle build() { - org.tensorflow.framework.ResourceHandle result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.ResourceHandle buildPartial() { - org.tensorflow.framework.ResourceHandle result = new org.tensorflow.framework.ResourceHandle(this); - result.device_ = device_; - result.container_ = container_; - result.name_ = name_; - result.hashCode_ = hashCode_; - result.maybeTypeName_ = maybeTypeName_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.ResourceHandle) { - return mergeFrom((org.tensorflow.framework.ResourceHandle)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.ResourceHandle other) { - if (other == org.tensorflow.framework.ResourceHandle.getDefaultInstance()) return this; - if (!other.getDevice().isEmpty()) { - device_ = other.device_; - onChanged(); - } - if (!other.getContainer().isEmpty()) { - container_ = other.container_; - onChanged(); - } - if (!other.getName().isEmpty()) { - name_ = other.name_; - onChanged(); - } - if (other.getHashCode() != 0L) { - setHashCode(other.getHashCode()); - } - if (!other.getMaybeTypeName().isEmpty()) { - maybeTypeName_ = other.maybeTypeName_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.ResourceHandle parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.ResourceHandle) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private java.lang.Object device_ = ""; - /** - * 
-     * Unique name for the device containing the resource.
-     *
- * - * optional string device = 1; - */ - public java.lang.String getDevice() { - java.lang.Object ref = device_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - device_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Unique name for the device containing the resource.
-     *
- * - * optional string device = 1; - */ - public com.google.protobuf.ByteString - getDeviceBytes() { - java.lang.Object ref = device_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - device_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Unique name for the device containing the resource.
-     *
- * - * optional string device = 1; - */ - public Builder setDevice( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - device_ = value; - onChanged(); - return this; - } - /** - * 
-     * Unique name for the device containing the resource.
-     *
- * - * optional string device = 1; - */ - public Builder clearDevice() { - - device_ = getDefaultInstance().getDevice(); - onChanged(); - return this; - } - /** - * 
-     * Unique name for the device containing the resource.
-     *
- * - * optional string device = 1; - */ - public Builder setDeviceBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - device_ = value; - onChanged(); - return this; - } - - private java.lang.Object container_ = ""; - /** - * 
-     * Container in which this resource is placed.
-     *
- * - * optional string container = 2; - */ - public java.lang.String getContainer() { - java.lang.Object ref = container_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - container_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Container in which this resource is placed.
-     *
- * - * optional string container = 2; - */ - public com.google.protobuf.ByteString - getContainerBytes() { - java.lang.Object ref = container_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - container_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Container in which this resource is placed.
-     *
- * - * optional string container = 2; - */ - public Builder setContainer( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - container_ = value; - onChanged(); - return this; - } - /** - * 
-     * Container in which this resource is placed.
-     *
- * - * optional string container = 2; - */ - public Builder clearContainer() { - - container_ = getDefaultInstance().getContainer(); - onChanged(); - return this; - } - /** - * 
-     * Container in which this resource is placed.
-     *
- * - * optional string container = 2; - */ - public Builder setContainerBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - container_ = value; - onChanged(); - return this; - } - - private java.lang.Object name_ = ""; - /** - * 
-     * Unique name of this resource.
-     *
- * - * optional string name = 3; - */ - public java.lang.String getName() { - java.lang.Object ref = name_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - name_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Unique name of this resource.
-     *
- * - * optional string name = 3; - */ - public com.google.protobuf.ByteString - getNameBytes() { - java.lang.Object ref = name_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - name_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Unique name of this resource.
-     *
- * - * optional string name = 3; - */ - public Builder setName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - name_ = value; - onChanged(); - return this; - } - /** - * 
-     * Unique name of this resource.
-     *
- * - * optional string name = 3; - */ - public Builder clearName() { - - name_ = getDefaultInstance().getName(); - onChanged(); - return this; - } - /** - * 
-     * Unique name of this resource.
-     *
- * - * optional string name = 3; - */ - public Builder setNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - name_ = value; - onChanged(); - return this; - } - - private long hashCode_ ; - /** - * 
-     * Hash code for the type of the resource. Is only valid in the same device
-     * and in the same execution.
-     *
- * - * optional uint64 hash_code = 4; - */ - public long getHashCode() { - return hashCode_; - } - /** - * 
-     * Hash code for the type of the resource. Is only valid in the same device
-     * and in the same execution.
-     *
- * - * optional uint64 hash_code = 4; - */ - public Builder setHashCode(long value) { - - hashCode_ = value; - onChanged(); - return this; - } - /** - * 
-     * Hash code for the type of the resource. Is only valid in the same device
-     * and in the same execution.
-     *
- * - * optional uint64 hash_code = 4; - */ - public Builder clearHashCode() { - - hashCode_ = 0L; - onChanged(); - return this; - } - - private java.lang.Object maybeTypeName_ = ""; - /** - * 
-     * For debug-only, the name of the type pointed to by this handle, if
-     * available.
-     *
- * - * optional string maybe_type_name = 5; - */ - public java.lang.String getMaybeTypeName() { - java.lang.Object ref = maybeTypeName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - maybeTypeName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * For debug-only, the name of the type pointed to by this handle, if
-     * available.
-     *
- * - * optional string maybe_type_name = 5; - */ - public com.google.protobuf.ByteString - getMaybeTypeNameBytes() { - java.lang.Object ref = maybeTypeName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - maybeTypeName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * For debug-only, the name of the type pointed to by this handle, if
-     * available.
-     *
- * - * optional string maybe_type_name = 5; - */ - public Builder setMaybeTypeName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - maybeTypeName_ = value; - onChanged(); - return this; - } - /** - * 
-     * For debug-only, the name of the type pointed to by this handle, if
-     * available.
-     *
- * - * optional string maybe_type_name = 5; - */ - public Builder clearMaybeTypeName() { - - maybeTypeName_ = getDefaultInstance().getMaybeTypeName(); - onChanged(); - return this; - } - /** - * 
-     * For debug-only, the name of the type pointed to by this handle, if
-     * available.
-     *
- * - * optional string maybe_type_name = 5; - */ - public Builder setMaybeTypeNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - maybeTypeName_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.ResourceHandle) - } - - // @@protoc_insertion_point(class_scope:tensorflow.ResourceHandle) - private static final org.tensorflow.framework.ResourceHandle DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.ResourceHandle(); - } - - public static org.tensorflow.framework.ResourceHandle getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public ResourceHandle parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new ResourceHandle(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.ResourceHandle getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleOrBuilder.java deleted file mode 100644 index 2a726622c0e..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleOrBuilder.java +++ /dev/null @@ -1,93 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: resource_handle.proto - -package org.tensorflow.framework; - -public interface ResourceHandleOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.ResourceHandle) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Unique name for the device containing the resource.
-   *
- * - * optional string device = 1; - */ - java.lang.String getDevice(); - /** - * 
-   * Unique name for the device containing the resource.
-   *
- * - * optional string device = 1; - */ - com.google.protobuf.ByteString - getDeviceBytes(); - - /** - * 
-   * Container in which this resource is placed.
-   *
- * - * optional string container = 2; - */ - java.lang.String getContainer(); - /** - * 
-   * Container in which this resource is placed.
-   *
- * - * optional string container = 2; - */ - com.google.protobuf.ByteString - getContainerBytes(); - - /** - * 
-   * Unique name of this resource.
-   *
- * - * optional string name = 3; - */ - java.lang.String getName(); - /** - * 
-   * Unique name of this resource.
-   *
- * - * optional string name = 3; - */ - com.google.protobuf.ByteString - getNameBytes(); - - /** - * 
-   * Hash code for the type of the resource. Is only valid in the same device
-   * and in the same execution.
-   *
- * - * optional uint64 hash_code = 4; - */ - long getHashCode(); - - /** - * 
-   * For debug-only, the name of the type pointed to by this handle, if
-   * available.
-   *
- * - * optional string maybe_type_name = 5; - */ - java.lang.String getMaybeTypeName(); - /** - * 
-   * For debug-only, the name of the type pointed to by this handle, if
-   * available.
-   *
- * - * optional string maybe_type_name = 5; - */ - com.google.protobuf.ByteString - getMaybeTypeNameBytes(); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java b/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java deleted file mode 100644 index ae1248c7ebb..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/ResourceHandleProto.java +++ /dev/null @@ -1,59 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: resource_handle.proto - -package org.tensorflow.framework; - -public final class ResourceHandleProto { - private ResourceHandleProto() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_ResourceHandle_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_ResourceHandle_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\025resource_handle.proto\022\ntensorflow\"m\n\016R" + - "esourceHandle\022\016\n\006device\030\001 \001(\t\022\021\n\tcontain" + - "er\030\002 \001(\t\022\014\n\004name\030\003 \001(\t\022\021\n\thash_code\030\004 \001(" + - "\004\022\027\n\017maybe_type_name\030\005 \001(\tB4\n\030org.tensor" + - "flow.frameworkB\023ResourceHandleProtoP\001\370\001\001" + - "b\006proto3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - }, assigner); - internal_static_tensorflow_ResourceHandle_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_ResourceHandle_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_ResourceHandle_descriptor, - new java.lang.String[] { "Device", "Container", "Name", "HashCode", "MaybeTypeName", }); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/SaveSliceInfoDef.java b/scala/dllib/src/main/java/org/tensorflow/framework/SaveSliceInfoDef.java deleted file mode 100644 index 3603c2d2990..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/SaveSliceInfoDef.java +++ /dev/null @@ -1,1122 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: variable.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.SaveSliceInfoDef} - */ -public final class SaveSliceInfoDef extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.SaveSliceInfoDef) - SaveSliceInfoDefOrBuilder { - // Use SaveSliceInfoDef.newBuilder() to construct. - private SaveSliceInfoDef(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private SaveSliceInfoDef() { - fullName_ = ""; - fullShape_ = java.util.Collections.emptyList(); - varOffset_ = java.util.Collections.emptyList(); - varShape_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private SaveSliceInfoDef( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - java.lang.String s = input.readStringRequireUtf8(); - - fullName_ = s; - break; - } - case 16: { - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - fullShape_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000002; - } - fullShape_.add(input.readInt64()); - break; - } - case 18: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000002) == 0x00000002) && input.getBytesUntilLimit() > 0) { - fullShape_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000002; - } - while (input.getBytesUntilLimit() > 0) { - fullShape_.add(input.readInt64()); - } - input.popLimit(limit); - break; - } - case 24: { - if (!((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - varOffset_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000004; - } - varOffset_.add(input.readInt64()); - break; - } - case 26: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000004) == 0x00000004) && input.getBytesUntilLimit() > 0) { - varOffset_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000004; - } - while (input.getBytesUntilLimit() > 0) { - varOffset_.add(input.readInt64()); - } - input.popLimit(limit); - break; - } - case 32: { - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - varShape_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000008; - } - varShape_.add(input.readInt64()); - break; - } - case 34: { - int length = input.readRawVarint32(); - int limit = input.pushLimit(length); - if (!((mutable_bitField0_ & 0x00000008) == 0x00000008) && input.getBytesUntilLimit() > 0) { - varShape_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000008; - } - while (input.getBytesUntilLimit() > 0) { - varShape_.add(input.readInt64()); - } - input.popLimit(limit); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000002) == 0x00000002)) { - fullShape_ = java.util.Collections.unmodifiableList(fullShape_); - } - if (((mutable_bitField0_ & 0x00000004) == 0x00000004)) { - varOffset_ = java.util.Collections.unmodifiableList(varOffset_); - } - if (((mutable_bitField0_ & 0x00000008) == 0x00000008)) { - varShape_ = java.util.Collections.unmodifiableList(varShape_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.VariableProtos.internal_static_tensorflow_SaveSliceInfoDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.VariableProtos.internal_static_tensorflow_SaveSliceInfoDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.SaveSliceInfoDef.class, org.tensorflow.framework.SaveSliceInfoDef.Builder.class); - } - - private int bitField0_; - public static final int FULL_NAME_FIELD_NUMBER = 1; - private volatile java.lang.Object fullName_; - /** - * 
-   * Name of the full variable of which this is a slice.
-   *
- * - * optional string full_name = 1; - */ - public java.lang.String getFullName() { - java.lang.Object ref = fullName_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - fullName_ = s; - return s; - } - } - /** - * 
-   * Name of the full variable of which this is a slice.
-   *
- * - * optional string full_name = 1; - */ - public com.google.protobuf.ByteString - getFullNameBytes() { - java.lang.Object ref = fullName_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - fullName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - public static final int FULL_SHAPE_FIELD_NUMBER = 2; - private java.util.List fullShape_; - /** - * 
-   * Shape of the full variable.
-   *
- * - * repeated int64 full_shape = 2; - */ - public java.util.List - getFullShapeList() { - return fullShape_; - } - /** - * 
-   * Shape of the full variable.
-   *
- * - * repeated int64 full_shape = 2; - */ - public int getFullShapeCount() { - return fullShape_.size(); - } - /** - * 
-   * Shape of the full variable.
-   *
- * - * repeated int64 full_shape = 2; - */ - public long getFullShape(int index) { - return fullShape_.get(index); - } - private int fullShapeMemoizedSerializedSize = -1; - - public static final int VAR_OFFSET_FIELD_NUMBER = 3; - private java.util.List varOffset_; - /** - * 
-   * Offset of this variable into the full variable.
-   *
- * - * repeated int64 var_offset = 3; - */ - public java.util.List - getVarOffsetList() { - return varOffset_; - } - /** - * 
-   * Offset of this variable into the full variable.
-   *
- * - * repeated int64 var_offset = 3; - */ - public int getVarOffsetCount() { - return varOffset_.size(); - } - /** - * 
-   * Offset of this variable into the full variable.
-   *
- * - * repeated int64 var_offset = 3; - */ - public long getVarOffset(int index) { - return varOffset_.get(index); - } - private int varOffsetMemoizedSerializedSize = -1; - - public static final int VAR_SHAPE_FIELD_NUMBER = 4; - private java.util.List varShape_; - /** - * 
-   * Shape of this variable.
-   *
- * - * repeated int64 var_shape = 4; - */ - public java.util.List - getVarShapeList() { - return varShape_; - } - /** - * 
-   * Shape of this variable.
-   *
- * - * repeated int64 var_shape = 4; - */ - public int getVarShapeCount() { - return varShape_.size(); - } - /** - * 
-   * Shape of this variable.
-   *
- * - * repeated int64 var_shape = 4; - */ - public long getVarShape(int index) { - return varShape_.get(index); - } - private int varShapeMemoizedSerializedSize = -1; - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - getSerializedSize(); - if (!getFullNameBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 1, fullName_); - } - if (getFullShapeList().size() > 0) { - output.writeUInt32NoTag(18); - output.writeUInt32NoTag(fullShapeMemoizedSerializedSize); - } - for (int i = 0; i < fullShape_.size(); i++) { - output.writeInt64NoTag(fullShape_.get(i)); - } - if (getVarOffsetList().size() > 0) { - output.writeUInt32NoTag(26); - output.writeUInt32NoTag(varOffsetMemoizedSerializedSize); - } - for (int i = 0; i < varOffset_.size(); i++) { - output.writeInt64NoTag(varOffset_.get(i)); - } - if (getVarShapeList().size() > 0) { - output.writeUInt32NoTag(34); - output.writeUInt32NoTag(varShapeMemoizedSerializedSize); - } - for (int i = 0; i < varShape_.size(); i++) { - output.writeInt64NoTag(varShape_.get(i)); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (!getFullNameBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, fullName_); - } - { - int dataSize = 0; - for (int i = 0; i < fullShape_.size(); i++) { - dataSize += com.google.protobuf.CodedOutputStream - .computeInt64SizeNoTag(fullShape_.get(i)); - } - size += dataSize; - if (!getFullShapeList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - fullShapeMemoizedSerializedSize = dataSize; - } - { - int dataSize = 0; - for (int i = 0; i < varOffset_.size(); i++) { - dataSize += com.google.protobuf.CodedOutputStream - .computeInt64SizeNoTag(varOffset_.get(i)); - } - size += dataSize; - if (!getVarOffsetList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - varOffsetMemoizedSerializedSize = dataSize; - } - { - int dataSize = 0; - for (int i = 0; i < varShape_.size(); i++) { - dataSize += com.google.protobuf.CodedOutputStream - .computeInt64SizeNoTag(varShape_.get(i)); - } - size += dataSize; - if (!getVarShapeList().isEmpty()) { - size += 1; - size += com.google.protobuf.CodedOutputStream - .computeInt32SizeNoTag(dataSize); - } - varShapeMemoizedSerializedSize = dataSize; - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.SaveSliceInfoDef)) { - return super.equals(obj); - } - org.tensorflow.framework.SaveSliceInfoDef other = (org.tensorflow.framework.SaveSliceInfoDef) obj; - - boolean result = true; - result = result && getFullName() - .equals(other.getFullName()); - result = result && getFullShapeList() - .equals(other.getFullShapeList()); - result = result && getVarOffsetList() - .equals(other.getVarOffsetList()); - result = result && getVarShapeList() - .equals(other.getVarShapeList()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + FULL_NAME_FIELD_NUMBER; - hash = (53 * hash) + getFullName().hashCode(); - if (getFullShapeCount() > 0) { - hash = (37 * hash) + FULL_SHAPE_FIELD_NUMBER; - hash = (53 * hash) + getFullShapeList().hashCode(); - } - if (getVarOffsetCount() > 0) { - hash = (37 * hash) + VAR_OFFSET_FIELD_NUMBER; - hash = (53 * hash) + getVarOffsetList().hashCode(); - } - if (getVarShapeCount() > 0) { - hash = (37 * hash) + VAR_SHAPE_FIELD_NUMBER; - hash = (53 * hash) + getVarShapeList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.SaveSliceInfoDef parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.SaveSliceInfoDef parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.SaveSliceInfoDef prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.SaveSliceInfoDef} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.SaveSliceInfoDef) - org.tensorflow.framework.SaveSliceInfoDefOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.VariableProtos.internal_static_tensorflow_SaveSliceInfoDef_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.VariableProtos.internal_static_tensorflow_SaveSliceInfoDef_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.SaveSliceInfoDef.class, org.tensorflow.framework.SaveSliceInfoDef.Builder.class); - } - - // Construct using org.tensorflow.framework.SaveSliceInfoDef.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - fullName_ = ""; - - fullShape_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - varOffset_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - varShape_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000008); - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.VariableProtos.internal_static_tensorflow_SaveSliceInfoDef_descriptor; - } - - public org.tensorflow.framework.SaveSliceInfoDef getDefaultInstanceForType() { - return org.tensorflow.framework.SaveSliceInfoDef.getDefaultInstance(); - } - - public org.tensorflow.framework.SaveSliceInfoDef build() { - org.tensorflow.framework.SaveSliceInfoDef result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.SaveSliceInfoDef buildPartial() { - org.tensorflow.framework.SaveSliceInfoDef result = new org.tensorflow.framework.SaveSliceInfoDef(this); - int from_bitField0_ = bitField0_; - int to_bitField0_ = 0; - result.fullName_ = fullName_; - if (((bitField0_ & 0x00000002) == 0x00000002)) { - fullShape_ = java.util.Collections.unmodifiableList(fullShape_); - bitField0_ = (bitField0_ & ~0x00000002); - } - result.fullShape_ = fullShape_; - if (((bitField0_ & 0x00000004) == 0x00000004)) { - varOffset_ = java.util.Collections.unmodifiableList(varOffset_); - bitField0_ = (bitField0_ & ~0x00000004); - } - result.varOffset_ = varOffset_; - if (((bitField0_ & 0x00000008) == 0x00000008)) { - varShape_ = java.util.Collections.unmodifiableList(varShape_); - bitField0_ = (bitField0_ & ~0x00000008); - } - result.varShape_ = varShape_; - result.bitField0_ = to_bitField0_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.SaveSliceInfoDef) { - return mergeFrom((org.tensorflow.framework.SaveSliceInfoDef)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.SaveSliceInfoDef other) { - if (other == org.tensorflow.framework.SaveSliceInfoDef.getDefaultInstance()) return this; - if (!other.getFullName().isEmpty()) { - fullName_ = other.fullName_; - onChanged(); - } - if (!other.fullShape_.isEmpty()) { - if (fullShape_.isEmpty()) { - fullShape_ = other.fullShape_; - bitField0_ = (bitField0_ & ~0x00000002); - } else { - ensureFullShapeIsMutable(); - fullShape_.addAll(other.fullShape_); - } - onChanged(); - } - if (!other.varOffset_.isEmpty()) { - if (varOffset_.isEmpty()) { - varOffset_ = other.varOffset_; - bitField0_ = (bitField0_ & ~0x00000004); - } else { - ensureVarOffsetIsMutable(); - varOffset_.addAll(other.varOffset_); - } - onChanged(); - } - if (!other.varShape_.isEmpty()) { - if (varShape_.isEmpty()) { - varShape_ = other.varShape_; - bitField0_ = (bitField0_ & ~0x00000008); - } else { - ensureVarShapeIsMutable(); - varShape_.addAll(other.varShape_); - } - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.SaveSliceInfoDef parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.SaveSliceInfoDef) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.lang.Object fullName_ = ""; - /** - * 
-     * Name of the full variable of which this is a slice.
-     *
- * - * optional string full_name = 1; - */ - public java.lang.String getFullName() { - java.lang.Object ref = fullName_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - fullName_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * 
-     * Name of the full variable of which this is a slice.
-     *
- * - * optional string full_name = 1; - */ - public com.google.protobuf.ByteString - getFullNameBytes() { - java.lang.Object ref = fullName_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - fullName_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * 
-     * Name of the full variable of which this is a slice.
-     *
- * - * optional string full_name = 1; - */ - public Builder setFullName( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - fullName_ = value; - onChanged(); - return this; - } - /** - * 
-     * Name of the full variable of which this is a slice.
-     *
- * - * optional string full_name = 1; - */ - public Builder clearFullName() { - - fullName_ = getDefaultInstance().getFullName(); - onChanged(); - return this; - } - /** - * 
-     * Name of the full variable of which this is a slice.
-     *
- * - * optional string full_name = 1; - */ - public Builder setFullNameBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - fullName_ = value; - onChanged(); - return this; - } - - private java.util.List fullShape_ = java.util.Collections.emptyList(); - private void ensureFullShapeIsMutable() { - if (!((bitField0_ & 0x00000002) == 0x00000002)) { - fullShape_ = new java.util.ArrayList(fullShape_); - bitField0_ |= 0x00000002; - } - } - /** - * 
-     * Shape of the full variable.
-     *
- * - * repeated int64 full_shape = 2; - */ - public java.util.List - getFullShapeList() { - return java.util.Collections.unmodifiableList(fullShape_); - } - /** - * 
-     * Shape of the full variable.
-     *
- * - * repeated int64 full_shape = 2; - */ - public int getFullShapeCount() { - return fullShape_.size(); - } - /** - * 
-     * Shape of the full variable.
-     *
- * - * repeated int64 full_shape = 2; - */ - public long getFullShape(int index) { - return fullShape_.get(index); - } - /** - * 
-     * Shape of the full variable.
-     *
- * - * repeated int64 full_shape = 2; - */ - public Builder setFullShape( - int index, long value) { - ensureFullShapeIsMutable(); - fullShape_.set(index, value); - onChanged(); - return this; - } - /** - * 
-     * Shape of the full variable.
-     *
- * - * repeated int64 full_shape = 2; - */ - public Builder addFullShape(long value) { - ensureFullShapeIsMutable(); - fullShape_.add(value); - onChanged(); - return this; - } - /** - * 
-     * Shape of the full variable.
-     *
- * - * repeated int64 full_shape = 2; - */ - public Builder addAllFullShape( - java.lang.Iterable values) { - ensureFullShapeIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, fullShape_); - onChanged(); - return this; - } - /** - * 
-     * Shape of the full variable.
-     *
- * - * repeated int64 full_shape = 2; - */ - public Builder clearFullShape() { - fullShape_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000002); - onChanged(); - return this; - } - - private java.util.List varOffset_ = java.util.Collections.emptyList(); - private void ensureVarOffsetIsMutable() { - if (!((bitField0_ & 0x00000004) == 0x00000004)) { - varOffset_ = new java.util.ArrayList(varOffset_); - bitField0_ |= 0x00000004; - } - } - /** - * 
-     * Offset of this variable into the full variable.
-     *
- * - * repeated int64 var_offset = 3; - */ - public java.util.List - getVarOffsetList() { - return java.util.Collections.unmodifiableList(varOffset_); - } - /** - * 
-     * Offset of this variable into the full variable.
-     *
- * - * repeated int64 var_offset = 3; - */ - public int getVarOffsetCount() { - return varOffset_.size(); - } - /** - * 
-     * Offset of this variable into the full variable.
-     *
- * - * repeated int64 var_offset = 3; - */ - public long getVarOffset(int index) { - return varOffset_.get(index); - } - /** - * 
-     * Offset of this variable into the full variable.
-     *
- * - * repeated int64 var_offset = 3; - */ - public Builder setVarOffset( - int index, long value) { - ensureVarOffsetIsMutable(); - varOffset_.set(index, value); - onChanged(); - return this; - } - /** - * 
-     * Offset of this variable into the full variable.
-     *
- * - * repeated int64 var_offset = 3; - */ - public Builder addVarOffset(long value) { - ensureVarOffsetIsMutable(); - varOffset_.add(value); - onChanged(); - return this; - } - /** - * 
-     * Offset of this variable into the full variable.
-     *
- * - * repeated int64 var_offset = 3; - */ - public Builder addAllVarOffset( - java.lang.Iterable values) { - ensureVarOffsetIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, varOffset_); - onChanged(); - return this; - } - /** - * 
-     * Offset of this variable into the full variable.
-     *
- * - * repeated int64 var_offset = 3; - */ - public Builder clearVarOffset() { - varOffset_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000004); - onChanged(); - return this; - } - - private java.util.List varShape_ = java.util.Collections.emptyList(); - private void ensureVarShapeIsMutable() { - if (!((bitField0_ & 0x00000008) == 0x00000008)) { - varShape_ = new java.util.ArrayList(varShape_); - bitField0_ |= 0x00000008; - } - } - /** - * 
-     * Shape of this variable.
-     *
- * - * repeated int64 var_shape = 4; - */ - public java.util.List - getVarShapeList() { - return java.util.Collections.unmodifiableList(varShape_); - } - /** - * 
-     * Shape of this variable.
-     *
- * - * repeated int64 var_shape = 4; - */ - public int getVarShapeCount() { - return varShape_.size(); - } - /** - * 
-     * Shape of this variable.
-     *
- * - * repeated int64 var_shape = 4; - */ - public long getVarShape(int index) { - return varShape_.get(index); - } - /** - * 
-     * Shape of this variable.
-     *
- * - * repeated int64 var_shape = 4; - */ - public Builder setVarShape( - int index, long value) { - ensureVarShapeIsMutable(); - varShape_.set(index, value); - onChanged(); - return this; - } - /** - * 
-     * Shape of this variable.
-     *
- * - * repeated int64 var_shape = 4; - */ - public Builder addVarShape(long value) { - ensureVarShapeIsMutable(); - varShape_.add(value); - onChanged(); - return this; - } - /** - * 
-     * Shape of this variable.
-     *
- * - * repeated int64 var_shape = 4; - */ - public Builder addAllVarShape( - java.lang.Iterable values) { - ensureVarShapeIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, varShape_); - onChanged(); - return this; - } - /** - * 
-     * Shape of this variable.
-     *
- * - * repeated int64 var_shape = 4; - */ - public Builder clearVarShape() { - varShape_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000008); - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.SaveSliceInfoDef) - } - - // @@protoc_insertion_point(class_scope:tensorflow.SaveSliceInfoDef) - private static final org.tensorflow.framework.SaveSliceInfoDef DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.SaveSliceInfoDef(); - } - - public static org.tensorflow.framework.SaveSliceInfoDef getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public SaveSliceInfoDef parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new SaveSliceInfoDef(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.SaveSliceInfoDef getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/SaveSliceInfoDefOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/SaveSliceInfoDefOrBuilder.java deleted file mode 100644 index 0ae22300c81..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/SaveSliceInfoDefOrBuilder.java +++ /dev/null @@ -1,102 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: variable.proto - -package org.tensorflow.framework; - -public interface SaveSliceInfoDefOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.SaveSliceInfoDef) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-   * Name of the full variable of which this is a slice.
-   *
- * - * optional string full_name = 1; - */ - java.lang.String getFullName(); - /** - * 
-   * Name of the full variable of which this is a slice.
-   *
- * - * optional string full_name = 1; - */ - com.google.protobuf.ByteString - getFullNameBytes(); - - /** - * 
-   * Shape of the full variable.
-   *
- * - * repeated int64 full_shape = 2; - */ - java.util.List getFullShapeList(); - /** - * 
-   * Shape of the full variable.
-   *
- * - * repeated int64 full_shape = 2; - */ - int getFullShapeCount(); - /** - * 
-   * Shape of the full variable.
-   *
- * - * repeated int64 full_shape = 2; - */ - long getFullShape(int index); - - /** - * 
-   * Offset of this variable into the full variable.
-   *
- * - * repeated int64 var_offset = 3; - */ - java.util.List getVarOffsetList(); - /** - * 
-   * Offset of this variable into the full variable.
-   *
- * - * repeated int64 var_offset = 3; - */ - int getVarOffsetCount(); - /** - * 
-   * Offset of this variable into the full variable.
-   *
- * - * repeated int64 var_offset = 3; - */ - long getVarOffset(int index); - - /** - * 
-   * Shape of this variable.
-   *
- * - * repeated int64 var_shape = 4; - */ - java.util.List getVarShapeList(); - /** - * 
-   * Shape of this variable.
-   *
- * - * repeated int64 var_shape = 4; - */ - int getVarShapeCount(); - /** - * 
-   * Shape of this variable.
-   *
- * - * repeated int64 var_shape = 4; - */ - long getVarShape(int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/StepStats.java b/scala/dllib/src/main/java/org/tensorflow/framework/StepStats.java deleted file mode 100644 index 2779446d5ed..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/StepStats.java +++ /dev/null @@ -1,712 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -/** - * Protobuf type {@code tensorflow.StepStats} - */ -public final class StepStats extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.StepStats) - StepStatsOrBuilder { - // Use StepStats.newBuilder() to construct. - private StepStats(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private StepStats() { - devStats_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private StepStats( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - devStats_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - devStats_.add( - input.readMessage(org.tensorflow.framework.DeviceStepStats.parser(), extensionRegistry)); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - devStats_ = java.util.Collections.unmodifiableList(devStats_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_StepStats_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_StepStats_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.StepStats.class, org.tensorflow.framework.StepStats.Builder.class); - } - - public static final int DEV_STATS_FIELD_NUMBER = 1; - private java.util.List devStats_; - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public java.util.List getDevStatsList() { - return devStats_; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public java.util.List - getDevStatsOrBuilderList() { - return devStats_; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public int getDevStatsCount() { - return devStats_.size(); - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public org.tensorflow.framework.DeviceStepStats getDevStats(int index) { - return devStats_.get(index); - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public org.tensorflow.framework.DeviceStepStatsOrBuilder getDevStatsOrBuilder( - int index) { - return devStats_.get(index); - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - for (int i = 0; i < devStats_.size(); i++) { - output.writeMessage(1, devStats_.get(i)); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - for (int i = 0; i < devStats_.size(); i++) { - size += com.google.protobuf.CodedOutputStream - .computeMessageSize(1, devStats_.get(i)); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.StepStats)) { - return super.equals(obj); - } - org.tensorflow.framework.StepStats other = (org.tensorflow.framework.StepStats) obj; - - boolean result = true; - result = result && getDevStatsList() - .equals(other.getDevStatsList()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - if (getDevStatsCount() > 0) { - hash = (37 * hash) + DEV_STATS_FIELD_NUMBER; - hash = (53 * hash) + getDevStatsList().hashCode(); - } - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.StepStats parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.StepStats parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.StepStats parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.StepStats parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.StepStats parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.StepStats parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.StepStats parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.StepStats parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.StepStats parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.StepStats parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.StepStats prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.StepStats} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.StepStats) - org.tensorflow.framework.StepStatsOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_StepStats_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_StepStats_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.StepStats.class, org.tensorflow.framework.StepStats.Builder.class); - } - - // Construct using org.tensorflow.framework.StepStats.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - getDevStatsFieldBuilder(); - } - } - public Builder clear() { - super.clear(); - if (devStatsBuilder_ == null) { - devStats_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - } else { - devStatsBuilder_.clear(); - } - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.StepStatsProtos.internal_static_tensorflow_StepStats_descriptor; - } - - public org.tensorflow.framework.StepStats getDefaultInstanceForType() { - return org.tensorflow.framework.StepStats.getDefaultInstance(); - } - - public org.tensorflow.framework.StepStats build() { - org.tensorflow.framework.StepStats result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.StepStats buildPartial() { - org.tensorflow.framework.StepStats result = new org.tensorflow.framework.StepStats(this); - int from_bitField0_ = bitField0_; - if (devStatsBuilder_ == null) { - if (((bitField0_ & 0x00000001) == 0x00000001)) { - devStats_ = java.util.Collections.unmodifiableList(devStats_); - bitField0_ = (bitField0_ & ~0x00000001); - } - result.devStats_ = devStats_; - } else { - result.devStats_ = devStatsBuilder_.build(); - } - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.StepStats) { - return mergeFrom((org.tensorflow.framework.StepStats)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.StepStats other) { - if (other == org.tensorflow.framework.StepStats.getDefaultInstance()) return this; - if (devStatsBuilder_ == null) { - if (!other.devStats_.isEmpty()) { - if (devStats_.isEmpty()) { - devStats_ = other.devStats_; - bitField0_ = (bitField0_ & ~0x00000001); - } else { - ensureDevStatsIsMutable(); - devStats_.addAll(other.devStats_); - } - onChanged(); - } - } else { - if (!other.devStats_.isEmpty()) { - if (devStatsBuilder_.isEmpty()) { - devStatsBuilder_.dispose(); - devStatsBuilder_ = null; - devStats_ = other.devStats_; - bitField0_ = (bitField0_ & ~0x00000001); - devStatsBuilder_ = - com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? - getDevStatsFieldBuilder() : null; - } else { - devStatsBuilder_.addAllMessages(other.devStats_); - } - } - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.StepStats parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.StepStats) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - private int bitField0_; - - private java.util.List devStats_ = - java.util.Collections.emptyList(); - private void ensureDevStatsIsMutable() { - if (!((bitField0_ & 0x00000001) == 0x00000001)) { - devStats_ = new java.util.ArrayList(devStats_); - bitField0_ |= 0x00000001; - } - } - - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.DeviceStepStats, org.tensorflow.framework.DeviceStepStats.Builder, org.tensorflow.framework.DeviceStepStatsOrBuilder> devStatsBuilder_; - - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public java.util.List getDevStatsList() { - if (devStatsBuilder_ == null) { - return java.util.Collections.unmodifiableList(devStats_); - } else { - return devStatsBuilder_.getMessageList(); - } - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public int getDevStatsCount() { - if (devStatsBuilder_ == null) { - return devStats_.size(); - } else { - return devStatsBuilder_.getCount(); - } - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public org.tensorflow.framework.DeviceStepStats getDevStats(int index) { - if (devStatsBuilder_ == null) { - return devStats_.get(index); - } else { - return devStatsBuilder_.getMessage(index); - } - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder setDevStats( - int index, org.tensorflow.framework.DeviceStepStats value) { - if (devStatsBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureDevStatsIsMutable(); - devStats_.set(index, value); - onChanged(); - } else { - devStatsBuilder_.setMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder setDevStats( - int index, org.tensorflow.framework.DeviceStepStats.Builder builderForValue) { - if (devStatsBuilder_ == null) { - ensureDevStatsIsMutable(); - devStats_.set(index, builderForValue.build()); - onChanged(); - } else { - devStatsBuilder_.setMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder addDevStats(org.tensorflow.framework.DeviceStepStats value) { - if (devStatsBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureDevStatsIsMutable(); - devStats_.add(value); - onChanged(); - } else { - devStatsBuilder_.addMessage(value); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder addDevStats( - int index, org.tensorflow.framework.DeviceStepStats value) { - if (devStatsBuilder_ == null) { - if (value == null) { - throw new NullPointerException(); - } - ensureDevStatsIsMutable(); - devStats_.add(index, value); - onChanged(); - } else { - devStatsBuilder_.addMessage(index, value); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder addDevStats( - org.tensorflow.framework.DeviceStepStats.Builder builderForValue) { - if (devStatsBuilder_ == null) { - ensureDevStatsIsMutable(); - devStats_.add(builderForValue.build()); - onChanged(); - } else { - devStatsBuilder_.addMessage(builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder addDevStats( - int index, org.tensorflow.framework.DeviceStepStats.Builder builderForValue) { - if (devStatsBuilder_ == null) { - ensureDevStatsIsMutable(); - devStats_.add(index, builderForValue.build()); - onChanged(); - } else { - devStatsBuilder_.addMessage(index, builderForValue.build()); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder addAllDevStats( - java.lang.Iterable values) { - if (devStatsBuilder_ == null) { - ensureDevStatsIsMutable(); - com.google.protobuf.AbstractMessageLite.Builder.addAll( - values, devStats_); - onChanged(); - } else { - devStatsBuilder_.addAllMessages(values); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder clearDevStats() { - if (devStatsBuilder_ == null) { - devStats_ = java.util.Collections.emptyList(); - bitField0_ = (bitField0_ & ~0x00000001); - onChanged(); - } else { - devStatsBuilder_.clear(); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public Builder removeDevStats(int index) { - if (devStatsBuilder_ == null) { - ensureDevStatsIsMutable(); - devStats_.remove(index); - onChanged(); - } else { - devStatsBuilder_.remove(index); - } - return this; - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public org.tensorflow.framework.DeviceStepStats.Builder getDevStatsBuilder( - int index) { - return getDevStatsFieldBuilder().getBuilder(index); - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public org.tensorflow.framework.DeviceStepStatsOrBuilder getDevStatsOrBuilder( - int index) { - if (devStatsBuilder_ == null) { - return devStats_.get(index); } else { - return devStatsBuilder_.getMessageOrBuilder(index); - } - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public java.util.List - getDevStatsOrBuilderList() { - if (devStatsBuilder_ != null) { - return devStatsBuilder_.getMessageOrBuilderList(); - } else { - return java.util.Collections.unmodifiableList(devStats_); - } - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public org.tensorflow.framework.DeviceStepStats.Builder addDevStatsBuilder() { - return getDevStatsFieldBuilder().addBuilder( - org.tensorflow.framework.DeviceStepStats.getDefaultInstance()); - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public org.tensorflow.framework.DeviceStepStats.Builder addDevStatsBuilder( - int index) { - return getDevStatsFieldBuilder().addBuilder( - index, org.tensorflow.framework.DeviceStepStats.getDefaultInstance()); - } - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - public java.util.List - getDevStatsBuilderList() { - return getDevStatsFieldBuilder().getBuilderList(); - } - private com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.DeviceStepStats, org.tensorflow.framework.DeviceStepStats.Builder, org.tensorflow.framework.DeviceStepStatsOrBuilder> - getDevStatsFieldBuilder() { - if (devStatsBuilder_ == null) { - devStatsBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< - org.tensorflow.framework.DeviceStepStats, org.tensorflow.framework.DeviceStepStats.Builder, org.tensorflow.framework.DeviceStepStatsOrBuilder>( - devStats_, - ((bitField0_ & 0x00000001) == 0x00000001), - getParentForChildren(), - isClean()); - devStats_ = null; - } - return devStatsBuilder_; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.StepStats) - } - - // @@protoc_insertion_point(class_scope:tensorflow.StepStats) - private static final org.tensorflow.framework.StepStats DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.StepStats(); - } - - public static org.tensorflow.framework.StepStats getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public StepStats parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new StepStats(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.StepStats getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - -} - diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/StepStatsOrBuilder.java b/scala/dllib/src/main/java/org/tensorflow/framework/StepStatsOrBuilder.java deleted file mode 100644 index 5acc9989ae7..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/StepStatsOrBuilder.java +++ /dev/null @@ -1,33 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -public interface StepStatsOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.StepStats) - com.google.protobuf.MessageOrBuilder { - - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - java.util.List - getDevStatsList(); - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - org.tensorflow.framework.DeviceStepStats getDevStats(int index); - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - int getDevStatsCount(); - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - java.util.List - getDevStatsOrBuilderList(); - /** - * repeated .tensorflow.DeviceStepStats dev_stats = 1; - */ - org.tensorflow.framework.DeviceStepStatsOrBuilder getDevStatsOrBuilder( - int index); -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/StepStatsProtos.java b/scala/dllib/src/main/java/org/tensorflow/framework/StepStatsProtos.java deleted file mode 100644 index 3aaa5cd3534..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/StepStatsProtos.java +++ /dev/null @@ -1,124 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: step_stats.proto - -package org.tensorflow.framework; - -public final class StepStatsProtos { - private StepStatsProtos() {} - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistryLite registry) { - } - - public static void registerAllExtensions( - com.google.protobuf.ExtensionRegistry registry) { - registerAllExtensions( - (com.google.protobuf.ExtensionRegistryLite) registry); - } - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_AllocatorMemoryUsed_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_AllocatorMemoryUsed_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_NodeOutput_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_NodeOutput_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_NodeExecStats_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_NodeExecStats_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_DeviceStepStats_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_DeviceStepStats_fieldAccessorTable; - static final com.google.protobuf.Descriptors.Descriptor - internal_static_tensorflow_StepStats_descriptor; - static final - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internal_static_tensorflow_StepStats_fieldAccessorTable; - - public static com.google.protobuf.Descriptors.FileDescriptor - getDescriptor() { - return descriptor; - } - private static com.google.protobuf.Descriptors.FileDescriptor - descriptor; - static { - java.lang.String[] descriptorData = { - "\n\020step_stats.proto\022\ntensorflow\0326tensorfl" + - "ow/core/framework/allocation_description" + - ".proto\0322tensorflow/core/framework/tensor" + - "_description.proto\"V\n\023AllocatorMemoryUse" + - "d\022\026\n\016allocator_name\030\001 \001(\t\022\023\n\013total_bytes" + - "\030\002 \001(\003\022\022\n\npeak_bytes\030\003 \001(\003\"U\n\nNodeOutput" + - "\022\014\n\004slot\030\001 \001(\005\0229\n\022tensor_description\030\003 \001" + - "(\0132\035.tensorflow.TensorDescription\"\354\002\n\rNo" + - "deExecStats\022\021\n\tnode_name\030\001 \001(\t\022\030\n\020all_st" + - "art_micros\030\002 \001(\003\022\033\n\023op_start_rel_micros\030", - "\003 \001(\003\022\031\n\021op_end_rel_micros\030\004 \001(\003\022\032\n\022all_" + - "end_rel_micros\030\005 \001(\003\022/\n\006memory\030\006 \003(\0132\037.t" + - "ensorflow.AllocatorMemoryUsed\022&\n\006output\030" + - "\007 \003(\0132\026.tensorflow.NodeOutput\022\026\n\016timelin" + - "e_label\030\010 \001(\t\022\030\n\020scheduled_micros\030\t \001(\003\022" + - "\021\n\tthread_id\030\n \001(\r\022<\n\021referenced_tensor\030" + - "\013 \003(\0132!.tensorflow.AllocationDescription" + - "\"P\n\017DeviceStepStats\022\016\n\006device\030\001 \001(\t\022-\n\nn" + - "ode_stats\030\002 \003(\0132\031.tensorflow.NodeExecSta" + - "ts\";\n\tStepStats\022.\n\tdev_stats\030\001 \003(\0132\033.ten", - "sorflow.DeviceStepStatsB0\n\030org.tensorflo" + - "w.frameworkB\017StepStatsProtosP\001\370\001\001b\006proto" + - "3" - }; - com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner = - new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() { - public com.google.protobuf.ExtensionRegistry assignDescriptors( - com.google.protobuf.Descriptors.FileDescriptor root) { - descriptor = root; - return null; - } - }; - com.google.protobuf.Descriptors.FileDescriptor - .internalBuildGeneratedFileFrom(descriptorData, - new com.google.protobuf.Descriptors.FileDescriptor[] { - org.tensorflow.framework.AllocationDescriptionProtos.getDescriptor(), - org.tensorflow.framework.TensorDescriptionProtos.getDescriptor(), - }, assigner); - internal_static_tensorflow_AllocatorMemoryUsed_descriptor = - getDescriptor().getMessageTypes().get(0); - internal_static_tensorflow_AllocatorMemoryUsed_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_AllocatorMemoryUsed_descriptor, - new java.lang.String[] { "AllocatorName", "TotalBytes", "PeakBytes", }); - internal_static_tensorflow_NodeOutput_descriptor = - getDescriptor().getMessageTypes().get(1); - internal_static_tensorflow_NodeOutput_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_NodeOutput_descriptor, - new java.lang.String[] { "Slot", "TensorDescription", }); - internal_static_tensorflow_NodeExecStats_descriptor = - getDescriptor().getMessageTypes().get(2); - internal_static_tensorflow_NodeExecStats_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_NodeExecStats_descriptor, - new java.lang.String[] { "NodeName", "AllStartMicros", "OpStartRelMicros", "OpEndRelMicros", "AllEndRelMicros", "Memory", "Output", "TimelineLabel", "ScheduledMicros", "ThreadId", "ReferencedTensor", }); - internal_static_tensorflow_DeviceStepStats_descriptor = - getDescriptor().getMessageTypes().get(3); - internal_static_tensorflow_DeviceStepStats_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_DeviceStepStats_descriptor, - new java.lang.String[] { "Device", "NodeStats", }); - internal_static_tensorflow_StepStats_descriptor = - getDescriptor().getMessageTypes().get(4); - internal_static_tensorflow_StepStats_fieldAccessorTable = new - com.google.protobuf.GeneratedMessageV3.FieldAccessorTable( - internal_static_tensorflow_StepStats_descriptor, - new java.lang.String[] { "DevStats", }); - org.tensorflow.framework.AllocationDescriptionProtos.getDescriptor(); - org.tensorflow.framework.TensorDescriptionProtos.getDescriptor(); - } - - // @@protoc_insertion_point(outer_class_scope) -} diff --git a/scala/dllib/src/main/java/org/tensorflow/framework/Summary.java b/scala/dllib/src/main/java/org/tensorflow/framework/Summary.java deleted file mode 100644 index a3a5544f6d7..00000000000 --- a/scala/dllib/src/main/java/org/tensorflow/framework/Summary.java +++ /dev/null @@ -1,4211 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: summary.proto - -package org.tensorflow.framework; - -/** - * 
- * A Summary is a set of named values to be displayed by the
- * visualizer.
- * Summaries are produced regularly during training, as controlled by
- * the "summary_interval_secs" attribute of the training operation.
- * Summaries are also produced at the end of an evaluation.
- *
- * - * Protobuf type {@code tensorflow.Summary} - */ -public final class Summary extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.Summary) - SummaryOrBuilder { - // Use Summary.newBuilder() to construct. - private Summary(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private Summary() { - value_ = java.util.Collections.emptyList(); - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private Summary( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 10: { - if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = new java.util.ArrayList(); - mutable_bitField0_ |= 0x00000001; - } - value_.add( - input.readMessage(org.tensorflow.framework.Summary.Value.parser(), extensionRegistry)); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { - value_ = java.util.Collections.unmodifiableList(value_); - } - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.Summary.class, org.tensorflow.framework.Summary.Builder.class); - } - - public interface ImageOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.Summary.Image) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-     * Dimensions of the image.
-     *
- * - * optional int32 height = 1; - */ - int getHeight(); - - /** - * optional int32 width = 2; - */ - int getWidth(); - - /** - * 
-     * Valid colorspace values are
-     *   1 - grayscale
-     *   2 - grayscale + alpha
-     *   3 - RGB
-     *   4 - RGBA
-     *   5 - DIGITAL_YUV
-     *   6 - BGRA
-     *
- * - * optional int32 colorspace = 3; - */ - int getColorspace(); - - /** - * 
-     * Image data in encoded format.  All image formats supported by
-     * image_codec::CoderUtil can be stored here.
-     *
- * - * optional bytes encoded_image_string = 4; - */ - com.google.protobuf.ByteString getEncodedImageString(); - } - /** - * Protobuf type {@code tensorflow.Summary.Image} - */ - public static final class Image extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.Summary.Image) - ImageOrBuilder { - // Use Image.newBuilder() to construct. - private Image(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private Image() { - height_ = 0; - width_ = 0; - colorspace_ = 0; - encodedImageString_ = com.google.protobuf.ByteString.EMPTY; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private Image( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 8: { - - height_ = input.readInt32(); - break; - } - case 16: { - - width_ = input.readInt32(); - break; - } - case 24: { - - colorspace_ = input.readInt32(); - break; - } - case 34: { - - encodedImageString_ = input.readBytes(); - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Image_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Image_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.Summary.Image.class, org.tensorflow.framework.Summary.Image.Builder.class); - } - - public static final int HEIGHT_FIELD_NUMBER = 1; - private int height_; - /** - * 
-     * Dimensions of the image.
-     *
- * - * optional int32 height = 1; - */ - public int getHeight() { - return height_; - } - - public static final int WIDTH_FIELD_NUMBER = 2; - private int width_; - /** - * optional int32 width = 2; - */ - public int getWidth() { - return width_; - } - - public static final int COLORSPACE_FIELD_NUMBER = 3; - private int colorspace_; - /** - * 
-     * Valid colorspace values are
-     *   1 - grayscale
-     *   2 - grayscale + alpha
-     *   3 - RGB
-     *   4 - RGBA
-     *   5 - DIGITAL_YUV
-     *   6 - BGRA
-     *
- * - * optional int32 colorspace = 3; - */ - public int getColorspace() { - return colorspace_; - } - - public static final int ENCODED_IMAGE_STRING_FIELD_NUMBER = 4; - private com.google.protobuf.ByteString encodedImageString_; - /** - * 
-     * Image data in encoded format.  All image formats supported by
-     * image_codec::CoderUtil can be stored here.
-     *
- * - * optional bytes encoded_image_string = 4; - */ - public com.google.protobuf.ByteString getEncodedImageString() { - return encodedImageString_; - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (height_ != 0) { - output.writeInt32(1, height_); - } - if (width_ != 0) { - output.writeInt32(2, width_); - } - if (colorspace_ != 0) { - output.writeInt32(3, colorspace_); - } - if (!encodedImageString_.isEmpty()) { - output.writeBytes(4, encodedImageString_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (height_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeInt32Size(1, height_); - } - if (width_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeInt32Size(2, width_); - } - if (colorspace_ != 0) { - size += com.google.protobuf.CodedOutputStream - .computeInt32Size(3, colorspace_); - } - if (!encodedImageString_.isEmpty()) { - size += com.google.protobuf.CodedOutputStream - .computeBytesSize(4, encodedImageString_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.Summary.Image)) { - return super.equals(obj); - } - org.tensorflow.framework.Summary.Image other = (org.tensorflow.framework.Summary.Image) obj; - - boolean result = true; - result = result && (getHeight() - == other.getHeight()); - result = result && (getWidth() - == other.getWidth()); - result = result && (getColorspace() - == other.getColorspace()); - result = result && getEncodedImageString() - .equals(other.getEncodedImageString()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + HEIGHT_FIELD_NUMBER; - hash = (53 * hash) + getHeight(); - hash = (37 * hash) + WIDTH_FIELD_NUMBER; - hash = (53 * hash) + getWidth(); - hash = (37 * hash) + COLORSPACE_FIELD_NUMBER; - hash = (53 * hash) + getColorspace(); - hash = (37 * hash) + ENCODED_IMAGE_STRING_FIELD_NUMBER; - hash = (53 * hash) + getEncodedImageString().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.Summary.Image parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.Summary.Image parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.Summary.Image parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.Summary.Image parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.Summary.Image parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.Summary.Image parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.Summary.Image parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.Summary.Image parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.Summary.Image parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.Summary.Image parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.Summary.Image prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.Summary.Image} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.Summary.Image) - org.tensorflow.framework.Summary.ImageOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Image_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Image_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.Summary.Image.class, org.tensorflow.framework.Summary.Image.Builder.class); - } - - // Construct using org.tensorflow.framework.Summary.Image.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - height_ = 0; - - width_ = 0; - - colorspace_ = 0; - - encodedImageString_ = com.google.protobuf.ByteString.EMPTY; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Image_descriptor; - } - - public org.tensorflow.framework.Summary.Image getDefaultInstanceForType() { - return org.tensorflow.framework.Summary.Image.getDefaultInstance(); - } - - public org.tensorflow.framework.Summary.Image build() { - org.tensorflow.framework.Summary.Image result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.Summary.Image buildPartial() { - org.tensorflow.framework.Summary.Image result = new org.tensorflow.framework.Summary.Image(this); - result.height_ = height_; - result.width_ = width_; - result.colorspace_ = colorspace_; - result.encodedImageString_ = encodedImageString_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.Summary.Image) { - return mergeFrom((org.tensorflow.framework.Summary.Image)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.Summary.Image other) { - if (other == org.tensorflow.framework.Summary.Image.getDefaultInstance()) return this; - if (other.getHeight() != 0) { - setHeight(other.getHeight()); - } - if (other.getWidth() != 0) { - setWidth(other.getWidth()); - } - if (other.getColorspace() != 0) { - setColorspace(other.getColorspace()); - } - if (other.getEncodedImageString() != com.google.protobuf.ByteString.EMPTY) { - setEncodedImageString(other.getEncodedImageString()); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.Summary.Image parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.Summary.Image) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private int height_ ; - /** - * 
-       * Dimensions of the image.
-       *
- * - * optional int32 height = 1; - */ - public int getHeight() { - return height_; - } - /** - * 
-       * Dimensions of the image.
-       *
- * - * optional int32 height = 1; - */ - public Builder setHeight(int value) { - - height_ = value; - onChanged(); - return this; - } - /** - * 
-       * Dimensions of the image.
-       *
- * - * optional int32 height = 1; - */ - public Builder clearHeight() { - - height_ = 0; - onChanged(); - return this; - } - - private int width_ ; - /** - * optional int32 width = 2; - */ - public int getWidth() { - return width_; - } - /** - * optional int32 width = 2; - */ - public Builder setWidth(int value) { - - width_ = value; - onChanged(); - return this; - } - /** - * optional int32 width = 2; - */ - public Builder clearWidth() { - - width_ = 0; - onChanged(); - return this; - } - - private int colorspace_ ; - /** - * 
-       * Valid colorspace values are
-       *   1 - grayscale
-       *   2 - grayscale + alpha
-       *   3 - RGB
-       *   4 - RGBA
-       *   5 - DIGITAL_YUV
-       *   6 - BGRA
-       *
- * - * optional int32 colorspace = 3; - */ - public int getColorspace() { - return colorspace_; - } - /** - * 
-       * Valid colorspace values are
-       *   1 - grayscale
-       *   2 - grayscale + alpha
-       *   3 - RGB
-       *   4 - RGBA
-       *   5 - DIGITAL_YUV
-       *   6 - BGRA
-       *
- * - * optional int32 colorspace = 3; - */ - public Builder setColorspace(int value) { - - colorspace_ = value; - onChanged(); - return this; - } - /** - * 
-       * Valid colorspace values are
-       *   1 - grayscale
-       *   2 - grayscale + alpha
-       *   3 - RGB
-       *   4 - RGBA
-       *   5 - DIGITAL_YUV
-       *   6 - BGRA
-       *
- * - * optional int32 colorspace = 3; - */ - public Builder clearColorspace() { - - colorspace_ = 0; - onChanged(); - return this; - } - - private com.google.protobuf.ByteString encodedImageString_ = com.google.protobuf.ByteString.EMPTY; - /** - * 
-       * Image data in encoded format.  All image formats supported by
-       * image_codec::CoderUtil can be stored here.
-       *
- * - * optional bytes encoded_image_string = 4; - */ - public com.google.protobuf.ByteString getEncodedImageString() { - return encodedImageString_; - } - /** - * 
-       * Image data in encoded format.  All image formats supported by
-       * image_codec::CoderUtil can be stored here.
-       *
- * - * optional bytes encoded_image_string = 4; - */ - public Builder setEncodedImageString(com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - - encodedImageString_ = value; - onChanged(); - return this; - } - /** - * 
-       * Image data in encoded format.  All image formats supported by
-       * image_codec::CoderUtil can be stored here.
-       *
- * - * optional bytes encoded_image_string = 4; - */ - public Builder clearEncodedImageString() { - - encodedImageString_ = getDefaultInstance().getEncodedImageString(); - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.Summary.Image) - } - - // @@protoc_insertion_point(class_scope:tensorflow.Summary.Image) - private static final org.tensorflow.framework.Summary.Image DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.Summary.Image(); - } - - public static org.tensorflow.framework.Summary.Image getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser - PARSER = new com.google.protobuf.AbstractParser() { - public Image parsePartialFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return new Image(input, extensionRegistry); - } - }; - - public static com.google.protobuf.Parser parser() { - return PARSER; - } - - @java.lang.Override - public com.google.protobuf.Parser getParserForType() { - return PARSER; - } - - public org.tensorflow.framework.Summary.Image getDefaultInstanceForType() { - return DEFAULT_INSTANCE; - } - - } - - public interface AudioOrBuilder extends - // @@protoc_insertion_point(interface_extends:tensorflow.Summary.Audio) - com.google.protobuf.MessageOrBuilder { - - /** - * 
-     * Sample rate of the audio in Hz.
-     *
- * - * optional float sample_rate = 1; - */ - float getSampleRate(); - - /** - * 
-     * Number of channels of audio.
-     *
- * - * optional int64 num_channels = 2; - */ - long getNumChannels(); - - /** - * 
-     * Length of the audio in frames (samples per channel).
-     *
- * - * optional int64 length_frames = 3; - */ - long getLengthFrames(); - - /** - * 
-     * Encoded audio data and its associated RFC 2045 content type (e.g.
-     * "audio/wav").
-     *
- * - * optional bytes encoded_audio_string = 4; - */ - com.google.protobuf.ByteString getEncodedAudioString(); - - /** - * optional string content_type = 5; - */ - java.lang.String getContentType(); - /** - * optional string content_type = 5; - */ - com.google.protobuf.ByteString - getContentTypeBytes(); - } - /** - * Protobuf type {@code tensorflow.Summary.Audio} - */ - public static final class Audio extends - com.google.protobuf.GeneratedMessageV3 implements - // @@protoc_insertion_point(message_implements:tensorflow.Summary.Audio) - AudioOrBuilder { - // Use Audio.newBuilder() to construct. - private Audio(com.google.protobuf.GeneratedMessageV3.Builder builder) { - super(builder); - } - private Audio() { - sampleRate_ = 0F; - numChannels_ = 0L; - lengthFrames_ = 0L; - encodedAudioString_ = com.google.protobuf.ByteString.EMPTY; - contentType_ = ""; - } - - @java.lang.Override - public final com.google.protobuf.UnknownFieldSet - getUnknownFields() { - return com.google.protobuf.UnknownFieldSet.getDefaultInstance(); - } - private Audio( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - this(); - int mutable_bitField0_ = 0; - try { - boolean done = false; - while (!done) { - int tag = input.readTag(); - switch (tag) { - case 0: - done = true; - break; - default: { - if (!input.skipField(tag)) { - done = true; - } - break; - } - case 13: { - - sampleRate_ = input.readFloat(); - break; - } - case 16: { - - numChannels_ = input.readInt64(); - break; - } - case 24: { - - lengthFrames_ = input.readInt64(); - break; - } - case 34: { - - encodedAudioString_ = input.readBytes(); - break; - } - case 42: { - java.lang.String s = input.readStringRequireUtf8(); - - contentType_ = s; - break; - } - } - } - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - throw e.setUnfinishedMessage(this); - } catch (java.io.IOException e) { - throw new com.google.protobuf.InvalidProtocolBufferException( - e).setUnfinishedMessage(this); - } finally { - makeExtensionsImmutable(); - } - } - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Audio_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Audio_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.Summary.Audio.class, org.tensorflow.framework.Summary.Audio.Builder.class); - } - - public static final int SAMPLE_RATE_FIELD_NUMBER = 1; - private float sampleRate_; - /** - * 
-     * Sample rate of the audio in Hz.
-     *
- * - * optional float sample_rate = 1; - */ - public float getSampleRate() { - return sampleRate_; - } - - public static final int NUM_CHANNELS_FIELD_NUMBER = 2; - private long numChannels_; - /** - * 
-     * Number of channels of audio.
-     *
- * - * optional int64 num_channels = 2; - */ - public long getNumChannels() { - return numChannels_; - } - - public static final int LENGTH_FRAMES_FIELD_NUMBER = 3; - private long lengthFrames_; - /** - * 
-     * Length of the audio in frames (samples per channel).
-     *
- * - * optional int64 length_frames = 3; - */ - public long getLengthFrames() { - return lengthFrames_; - } - - public static final int ENCODED_AUDIO_STRING_FIELD_NUMBER = 4; - private com.google.protobuf.ByteString encodedAudioString_; - /** - * 
-     * Encoded audio data and its associated RFC 2045 content type (e.g.
-     * "audio/wav").
-     *
- * - * optional bytes encoded_audio_string = 4; - */ - public com.google.protobuf.ByteString getEncodedAudioString() { - return encodedAudioString_; - } - - public static final int CONTENT_TYPE_FIELD_NUMBER = 5; - private volatile java.lang.Object contentType_; - /** - * optional string content_type = 5; - */ - public java.lang.String getContentType() { - java.lang.Object ref = contentType_; - if (ref instanceof java.lang.String) { - return (java.lang.String) ref; - } else { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - contentType_ = s; - return s; - } - } - /** - * optional string content_type = 5; - */ - public com.google.protobuf.ByteString - getContentTypeBytes() { - java.lang.Object ref = contentType_; - if (ref instanceof java.lang.String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - contentType_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - - private byte memoizedIsInitialized = -1; - public final boolean isInitialized() { - byte isInitialized = memoizedIsInitialized; - if (isInitialized == 1) return true; - if (isInitialized == 0) return false; - - memoizedIsInitialized = 1; - return true; - } - - public void writeTo(com.google.protobuf.CodedOutputStream output) - throws java.io.IOException { - if (sampleRate_ != 0F) { - output.writeFloat(1, sampleRate_); - } - if (numChannels_ != 0L) { - output.writeInt64(2, numChannels_); - } - if (lengthFrames_ != 0L) { - output.writeInt64(3, lengthFrames_); - } - if (!encodedAudioString_.isEmpty()) { - output.writeBytes(4, encodedAudioString_); - } - if (!getContentTypeBytes().isEmpty()) { - com.google.protobuf.GeneratedMessageV3.writeString(output, 5, contentType_); - } - } - - public int getSerializedSize() { - int size = memoizedSize; - if (size != -1) return size; - - size = 0; - if (sampleRate_ != 0F) { - size += com.google.protobuf.CodedOutputStream - .computeFloatSize(1, sampleRate_); - } - if (numChannels_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(2, numChannels_); - } - if (lengthFrames_ != 0L) { - size += com.google.protobuf.CodedOutputStream - .computeInt64Size(3, lengthFrames_); - } - if (!encodedAudioString_.isEmpty()) { - size += com.google.protobuf.CodedOutputStream - .computeBytesSize(4, encodedAudioString_); - } - if (!getContentTypeBytes().isEmpty()) { - size += com.google.protobuf.GeneratedMessageV3.computeStringSize(5, contentType_); - } - memoizedSize = size; - return size; - } - - private static final long serialVersionUID = 0L; - @java.lang.Override - public boolean equals(final java.lang.Object obj) { - if (obj == this) { - return true; - } - if (!(obj instanceof org.tensorflow.framework.Summary.Audio)) { - return super.equals(obj); - } - org.tensorflow.framework.Summary.Audio other = (org.tensorflow.framework.Summary.Audio) obj; - - boolean result = true; - result = result && ( - java.lang.Float.floatToIntBits(getSampleRate()) - == java.lang.Float.floatToIntBits( - other.getSampleRate())); - result = result && (getNumChannels() - == other.getNumChannels()); - result = result && (getLengthFrames() - == other.getLengthFrames()); - result = result && getEncodedAudioString() - .equals(other.getEncodedAudioString()); - result = result && getContentType() - .equals(other.getContentType()); - return result; - } - - @java.lang.Override - public int hashCode() { - if (memoizedHashCode != 0) { - return memoizedHashCode; - } - int hash = 41; - hash = (19 * hash) + getDescriptorForType().hashCode(); - hash = (37 * hash) + SAMPLE_RATE_FIELD_NUMBER; - hash = (53 * hash) + java.lang.Float.floatToIntBits( - getSampleRate()); - hash = (37 * hash) + NUM_CHANNELS_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getNumChannels()); - hash = (37 * hash) + LENGTH_FRAMES_FIELD_NUMBER; - hash = (53 * hash) + com.google.protobuf.Internal.hashLong( - getLengthFrames()); - hash = (37 * hash) + ENCODED_AUDIO_STRING_FIELD_NUMBER; - hash = (53 * hash) + getEncodedAudioString().hashCode(); - hash = (37 * hash) + CONTENT_TYPE_FIELD_NUMBER; - hash = (53 * hash) + getContentType().hashCode(); - hash = (29 * hash) + unknownFields.hashCode(); - memoizedHashCode = hash; - return hash; - } - - public static org.tensorflow.framework.Summary.Audio parseFrom( - com.google.protobuf.ByteString data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.Summary.Audio parseFrom( - com.google.protobuf.ByteString data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.Summary.Audio parseFrom(byte[] data) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data); - } - public static org.tensorflow.framework.Summary.Audio parseFrom( - byte[] data, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws com.google.protobuf.InvalidProtocolBufferException { - return PARSER.parseFrom(data, extensionRegistry); - } - public static org.tensorflow.framework.Summary.Audio parseFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.Summary.Audio parseFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.Summary.Audio parseDelimitedFrom(java.io.InputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input); - } - public static org.tensorflow.framework.Summary.Audio parseDelimitedFrom( - java.io.InputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseDelimitedWithIOException(PARSER, input, extensionRegistry); - } - public static org.tensorflow.framework.Summary.Audio parseFrom( - com.google.protobuf.CodedInputStream input) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input); - } - public static org.tensorflow.framework.Summary.Audio parseFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - return com.google.protobuf.GeneratedMessageV3 - .parseWithIOException(PARSER, input, extensionRegistry); - } - - public Builder newBuilderForType() { return newBuilder(); } - public static Builder newBuilder() { - return DEFAULT_INSTANCE.toBuilder(); - } - public static Builder newBuilder(org.tensorflow.framework.Summary.Audio prototype) { - return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); - } - public Builder toBuilder() { - return this == DEFAULT_INSTANCE - ? new Builder() : new Builder().mergeFrom(this); - } - - @java.lang.Override - protected Builder newBuilderForType( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - Builder builder = new Builder(parent); - return builder; - } - /** - * Protobuf type {@code tensorflow.Summary.Audio} - */ - public static final class Builder extends - com.google.protobuf.GeneratedMessageV3.Builder implements - // @@protoc_insertion_point(builder_implements:tensorflow.Summary.Audio) - org.tensorflow.framework.Summary.AudioOrBuilder { - public static final com.google.protobuf.Descriptors.Descriptor - getDescriptor() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Audio_descriptor; - } - - protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable - internalGetFieldAccessorTable() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Audio_fieldAccessorTable - .ensureFieldAccessorsInitialized( - org.tensorflow.framework.Summary.Audio.class, org.tensorflow.framework.Summary.Audio.Builder.class); - } - - // Construct using org.tensorflow.framework.Summary.Audio.newBuilder() - private Builder() { - maybeForceBuilderInitialization(); - } - - private Builder( - com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { - super(parent); - maybeForceBuilderInitialization(); - } - private void maybeForceBuilderInitialization() { - if (com.google.protobuf.GeneratedMessageV3 - .alwaysUseFieldBuilders) { - } - } - public Builder clear() { - super.clear(); - sampleRate_ = 0F; - - numChannels_ = 0L; - - lengthFrames_ = 0L; - - encodedAudioString_ = com.google.protobuf.ByteString.EMPTY; - - contentType_ = ""; - - return this; - } - - public com.google.protobuf.Descriptors.Descriptor - getDescriptorForType() { - return org.tensorflow.framework.SummaryProtos.internal_static_tensorflow_Summary_Audio_descriptor; - } - - public org.tensorflow.framework.Summary.Audio getDefaultInstanceForType() { - return org.tensorflow.framework.Summary.Audio.getDefaultInstance(); - } - - public org.tensorflow.framework.Summary.Audio build() { - org.tensorflow.framework.Summary.Audio result = buildPartial(); - if (!result.isInitialized()) { - throw newUninitializedMessageException(result); - } - return result; - } - - public org.tensorflow.framework.Summary.Audio buildPartial() { - org.tensorflow.framework.Summary.Audio result = new org.tensorflow.framework.Summary.Audio(this); - result.sampleRate_ = sampleRate_; - result.numChannels_ = numChannels_; - result.lengthFrames_ = lengthFrames_; - result.encodedAudioString_ = encodedAudioString_; - result.contentType_ = contentType_; - onBuilt(); - return result; - } - - public Builder clone() { - return (Builder) super.clone(); - } - public Builder setField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.setField(field, value); - } - public Builder clearField( - com.google.protobuf.Descriptors.FieldDescriptor field) { - return (Builder) super.clearField(field); - } - public Builder clearOneof( - com.google.protobuf.Descriptors.OneofDescriptor oneof) { - return (Builder) super.clearOneof(oneof); - } - public Builder setRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - int index, Object value) { - return (Builder) super.setRepeatedField(field, index, value); - } - public Builder addRepeatedField( - com.google.protobuf.Descriptors.FieldDescriptor field, - Object value) { - return (Builder) super.addRepeatedField(field, value); - } - public Builder mergeFrom(com.google.protobuf.Message other) { - if (other instanceof org.tensorflow.framework.Summary.Audio) { - return mergeFrom((org.tensorflow.framework.Summary.Audio)other); - } else { - super.mergeFrom(other); - return this; - } - } - - public Builder mergeFrom(org.tensorflow.framework.Summary.Audio other) { - if (other == org.tensorflow.framework.Summary.Audio.getDefaultInstance()) return this; - if (other.getSampleRate() != 0F) { - setSampleRate(other.getSampleRate()); - } - if (other.getNumChannels() != 0L) { - setNumChannels(other.getNumChannels()); - } - if (other.getLengthFrames() != 0L) { - setLengthFrames(other.getLengthFrames()); - } - if (other.getEncodedAudioString() != com.google.protobuf.ByteString.EMPTY) { - setEncodedAudioString(other.getEncodedAudioString()); - } - if (!other.getContentType().isEmpty()) { - contentType_ = other.contentType_; - onChanged(); - } - onChanged(); - return this; - } - - public final boolean isInitialized() { - return true; - } - - public Builder mergeFrom( - com.google.protobuf.CodedInputStream input, - com.google.protobuf.ExtensionRegistryLite extensionRegistry) - throws java.io.IOException { - org.tensorflow.framework.Summary.Audio parsedMessage = null; - try { - parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); - } catch (com.google.protobuf.InvalidProtocolBufferException e) { - parsedMessage = (org.tensorflow.framework.Summary.Audio) e.getUnfinishedMessage(); - throw e.unwrapIOException(); - } finally { - if (parsedMessage != null) { - mergeFrom(parsedMessage); - } - } - return this; - } - - private float sampleRate_ ; - /** - * 
-       * Sample rate of the audio in Hz.
-       *
- * - * optional float sample_rate = 1; - */ - public float getSampleRate() { - return sampleRate_; - } - /** - * 
-       * Sample rate of the audio in Hz.
-       *
- * - * optional float sample_rate = 1; - */ - public Builder setSampleRate(float value) { - - sampleRate_ = value; - onChanged(); - return this; - } - /** - * 
-       * Sample rate of the audio in Hz.
-       *
- * - * optional float sample_rate = 1; - */ - public Builder clearSampleRate() { - - sampleRate_ = 0F; - onChanged(); - return this; - } - - private long numChannels_ ; - /** - * 
-       * Number of channels of audio.
-       *
- * - * optional int64 num_channels = 2; - */ - public long getNumChannels() { - return numChannels_; - } - /** - * 
-       * Number of channels of audio.
-       *
- * - * optional int64 num_channels = 2; - */ - public Builder setNumChannels(long value) { - - numChannels_ = value; - onChanged(); - return this; - } - /** - * 
-       * Number of channels of audio.
-       *
- * - * optional int64 num_channels = 2; - */ - public Builder clearNumChannels() { - - numChannels_ = 0L; - onChanged(); - return this; - } - - private long lengthFrames_ ; - /** - * 
-       * Length of the audio in frames (samples per channel).
-       *
- * - * optional int64 length_frames = 3; - */ - public long getLengthFrames() { - return lengthFrames_; - } - /** - * 
-       * Length of the audio in frames (samples per channel).
-       *
- * - * optional int64 length_frames = 3; - */ - public Builder setLengthFrames(long value) { - - lengthFrames_ = value; - onChanged(); - return this; - } - /** - * 
-       * Length of the audio in frames (samples per channel).
-       *
- * - * optional int64 length_frames = 3; - */ - public Builder clearLengthFrames() { - - lengthFrames_ = 0L; - onChanged(); - return this; - } - - private com.google.protobuf.ByteString encodedAudioString_ = com.google.protobuf.ByteString.EMPTY; - /** - * 
-       * Encoded audio data and its associated RFC 2045 content type (e.g.
-       * "audio/wav").
-       *
- * - * optional bytes encoded_audio_string = 4; - */ - public com.google.protobuf.ByteString getEncodedAudioString() { - return encodedAudioString_; - } - /** - * 
-       * Encoded audio data and its associated RFC 2045 content type (e.g.
-       * "audio/wav").
-       *
- * - * optional bytes encoded_audio_string = 4; - */ - public Builder setEncodedAudioString(com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - - encodedAudioString_ = value; - onChanged(); - return this; - } - /** - * 
-       * Encoded audio data and its associated RFC 2045 content type (e.g.
-       * "audio/wav").
-       *
- * - * optional bytes encoded_audio_string = 4; - */ - public Builder clearEncodedAudioString() { - - encodedAudioString_ = getDefaultInstance().getEncodedAudioString(); - onChanged(); - return this; - } - - private java.lang.Object contentType_ = ""; - /** - * optional string content_type = 5; - */ - public java.lang.String getContentType() { - java.lang.Object ref = contentType_; - if (!(ref instanceof java.lang.String)) { - com.google.protobuf.ByteString bs = - (com.google.protobuf.ByteString) ref; - java.lang.String s = bs.toStringUtf8(); - contentType_ = s; - return s; - } else { - return (java.lang.String) ref; - } - } - /** - * optional string content_type = 5; - */ - public com.google.protobuf.ByteString - getContentTypeBytes() { - java.lang.Object ref = contentType_; - if (ref instanceof String) { - com.google.protobuf.ByteString b = - com.google.protobuf.ByteString.copyFromUtf8( - (java.lang.String) ref); - contentType_ = b; - return b; - } else { - return (com.google.protobuf.ByteString) ref; - } - } - /** - * optional string content_type = 5; - */ - public Builder setContentType( - java.lang.String value) { - if (value == null) { - throw new NullPointerException(); - } - - contentType_ = value; - onChanged(); - return this; - } - /** - * optional string content_type = 5; - */ - public Builder clearContentType() { - - contentType_ = getDefaultInstance().getContentType(); - onChanged(); - return this; - } - /** - * optional string content_type = 5; - */ - public Builder setContentTypeBytes( - com.google.protobuf.ByteString value) { - if (value == null) { - throw new NullPointerException(); - } - checkByteStringIsUtf8(value); - - contentType_ = value; - onChanged(); - return this; - } - public final Builder setUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - public final Builder mergeUnknownFields( - final com.google.protobuf.UnknownFieldSet unknownFields) { - return this; - } - - - // @@protoc_insertion_point(builder_scope:tensorflow.Summary.Audio) - } - - // @@protoc_insertion_point(class_scope:tensorflow.Summary.Audio) - private static final org.tensorflow.framework.Summary.Audio DEFAULT_INSTANCE; - static { - DEFAULT_INSTANCE = new org.tensorflow.framework.Summary.Audio(); - } - - public static org.tensorflow.framework.Summary.Audio getDefaultInstance() { - return DEFAULT_INSTANCE; - } - - private static final com.google.protobuf.Parser